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t=r*c,n=r*h,i=o*c,s=o*h;e[0]=a*c,e[4]=-a*h,e[8]=l,e[1]=n+i*l,e[5]=t-s*l,e[9]=-o*a,e[2]=s-t*l,e[6]=i+n*l,e[10]=r*a}else if(\\\\\\\"YXZ\\\\\\\"===t.order){const t=a*c,n=a*h,i=l*c,s=l*h;e[0]=t+s*o,e[4]=i*o-n,e[8]=r*l,e[1]=r*h,e[5]=r*c,e[9]=-o,e[2]=n*o-i,e[6]=s+t*o,e[10]=r*a}else if(\\\\\\\"ZXY\\\\\\\"===t.order){const t=a*c,n=a*h,i=l*c,s=l*h;e[0]=t-s*o,e[4]=-r*h,e[8]=i+n*o,e[1]=n+i*o,e[5]=r*c,e[9]=s-t*o,e[2]=-r*l,e[6]=o,e[10]=r*a}else if(\\\\\\\"ZYX\\\\\\\"===t.order){const t=r*c,n=r*h,i=o*c,s=o*h;e[0]=a*c,e[4]=i*l-n,e[8]=t*l+s,e[1]=a*h,e[5]=s*l+t,e[9]=n*l-i,e[2]=-l,e[6]=o*a,e[10]=r*a}else if(\\\\\\\"YZX\\\\\\\"===t.order){const t=r*a,n=r*l,i=o*a,s=o*l;e[0]=a*c,e[4]=s-t*h,e[8]=i*h+n,e[1]=h,e[5]=r*c,e[9]=-o*c,e[2]=-l*c,e[6]=n*h+i,e[10]=t-s*h}else if(\\\\\\\"XZY\\\\\\\"===t.order){const t=r*a,n=r*l,i=o*a,s=o*l;e[0]=a*c,e[4]=-h,e[8]=l*c,e[1]=t*h+s,e[5]=r*c,e[9]=n*h-i,e[2]=i*h-n,e[6]=o*c,e[10]=s*h+t}return e[3]=0,e[7]=0,e[11]=0,e[12]=0,e[13]=0,e[14]=0,e[15]=1,this}makeRotationFromQuaternion(t){return this.compose(a,t,l)}lookAt(t,e,n){const i=this.elements;return u.subVectors(t,e),0===u.lengthSq()&&(u.z=1),u.normalize(),c.crossVectors(n,u),0===c.lengthSq()&&(1===Math.abs(n.z)?u.x+=1e-4:u.z+=1e-4,u.normalize(),c.crossVectors(n,u)),c.normalize(),h.crossVectors(u,c),i[0]=c.x,i[4]=h.x,i[8]=u.x,i[1]=c.y,i[5]=h.y,i[9]=u.y,i[2]=c.z,i[6]=h.z,i[10]=u.z,this}multiply(t,e){return void 0!==e?(console.warn(\\\\\\\"THREE.Matrix4: .multiply() now only accepts one argument. Use .multiplyMatrices( a, b ) instead.\\\\\\\"),this.multiplyMatrices(t,e)):this.multiplyMatrices(this,t)}premultiply(t){return this.multiplyMatrices(t,this)}multiplyMatrices(t,e){const n=t.elements,i=e.elements,s=this.elements,r=n[0],o=n[4],a=n[8],l=n[12],c=n[1],h=n[5],u=n[9],d=n[13],p=n[2],_=n[6],m=n[10],f=n[14],g=n[3],v=n[7],y=n[11],x=n[15],b=i[0],w=i[4],T=i[8],A=i[12],M=i[1],E=i[5],S=i[9],C=i[13],N=i[2],L=i[6],O=i[10],P=i[14],R=i[3],I=i[7],F=i[11],D=i[15];return s[0]=r*b+o*M+a*N+l*R,s[4]=r*w+o*E+a*L+l*I,s[8]=r*T+o*S+a*O+l*F,s[12]=r*A+o*C+a*P+l*D,s[1]=c*b+h*M+u*N+d*R,s[5]=c*w+h*E+u*L+d*I,s[9]=c*T+h*S+u*O+d*F,s[13]=c*A+h*C+u*P+d*D,s[2]=p*b+_*M+m*N+f*R,s[6]=p*w+_*E+m*L+f*I,s[10]=p*T+_*S+m*O+f*F,s[14]=p*A+_*C+m*P+f*D,s[3]=g*b+v*M+y*N+x*R,s[7]=g*w+v*E+y*L+x*I,s[11]=g*T+v*S+y*O+x*F,s[15]=g*A+v*C+y*P+x*D,this}multiplyScalar(t){const e=this.elements;return e[0]*=t,e[4]*=t,e[8]*=t,e[12]*=t,e[1]*=t,e[5]*=t,e[9]*=t,e[13]*=t,e[2]*=t,e[6]*=t,e[10]*=t,e[14]*=t,e[3]*=t,e[7]*=t,e[11]*=t,e[15]*=t,this}determinant(){const t=this.elements,e=t[0],n=t[4],i=t[8],s=t[12],r=t[1],o=t[5],a=t[9],l=t[13],c=t[2],h=t[6],u=t[10],d=t[14];return t[3]*(+s*a*h-i*l*h-s*o*u+n*l*u+i*o*d-n*a*d)+t[7]*(+e*a*d-e*l*u+s*r*u-i*r*d+i*l*c-s*a*c)+t[11]*(+e*l*h-e*o*d-s*r*h+n*r*d+s*o*c-n*l*c)+t[15]*(-i*o*c-e*a*h+e*o*u+i*r*h-n*r*u+n*a*c)}transpose(){const t=this.elements;let e;return e=t[1],t[1]=t[4],t[4]=e,e=t[2],t[2]=t[8],t[8]=e,e=t[6],t[6]=t[9],t[9]=e,e=t[3],t[3]=t[12],t[12]=e,e=t[7],t[7]=t[13],t[13]=e,e=t[11],t[11]=t[14],t[14]=e,this}setPosition(t,e,n){const i=this.elements;return t.isVector3?(i[12]=t.x,i[13]=t.y,i[14]=t.z):(i[12]=t,i[13]=e,i[14]=n),this}invert(){const t=this.elements,e=t[0],n=t[1],i=t[2],s=t[3],r=t[4],o=t[5],a=t[6],l=t[7],c=t[8],h=t[9],u=t[10],d=t[11],p=t[12],_=t[13],m=t[14],f=t[15],g=h*m*l-_*u*l+_*a*d-o*m*d-h*a*f+o*u*f,v=p*u*l-c*m*l-p*a*d+r*m*d+c*a*f-r*u*f,y=c*_*l-p*h*l+p*o*d-r*_*d-c*o*f+r*h*f,x=p*h*a-c*_*a-p*o*u+r*_*u+c*o*m-r*h*m,b=e*g+n*v+i*y+s*x;if(0===b)return this.set(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0);const w=1/b;return t[0]=g*w,t[1]=(_*u*s-h*m*s-_*i*d+n*m*d+h*i*f-n*u*f)*w,t[2]=(o*m*s-_*a*s+_*i*l-n*m*l-o*i*f+n*a*f)*w,t[3]=(h*a*s-o*u*s-h*i*l+n*u*l+o*i*d-n*a*d)*w,t[4]=v*w,t[5]=(c*m*s-p*u*s+p*i*d-e*m*d-c*i*f+e*u*f)*w,t[6]=(p*a*s-r*m*s-p*i*l+e*m*l+r*i*f-e*a*f)*w,t[7]=(r*u*s-c*a*s+c*i*l-e*u*l-r*i*d+e*a*d)*w,t[8]=y*w,t[9]=(p*h*s-c*_*s-p*n*d+e*_*d+c*n*f-e*h*f)*w,t[10]=(r*_*s-p*o*s+p*n*l-e*_*l-r*n*f+e*o*f)*w,t[11]=(c*o*s-r*h*s-c*n*l+e*h*l+r*n*d-e*o*d)*w,t[12]=x*w,t[13]=(c*_*i-p*h*i+p*n*u-e*_*u-c*n*m+e*h*m)*w,t[14]=(p*o*i-r*_*i-p*n*a+e*_*a+r*n*m-e*o*m)*w,t[15]=(r*h*i-c*o*i+c*n*a-e*h*a-r*n*u+e*o*u)*w,this}scale(t){const e=this.elements,n=t.x,i=t.y,s=t.z;return e[0]*=n,e[4]*=i,e[8]*=s,e[1]*=n,e[5]*=i,e[9]*=s,e[2]*=n,e[6]*=i,e[10]*=s,e[3]*=n,e[7]*=i,e[11]*=s,this}getMaxScaleOnAxis(){const t=this.elements,e=t[0]*t[0]+t[1]*t[1]+t[2]*t[2],n=t[4]*t[4]+t[5]*t[5]+t[6]*t[6],i=t[8]*t[8]+t[9]*t[9]+t[10]*t[10];return Math.sqrt(Math.max(e,n,i))}makeTranslation(t,e,n){return this.set(1,0,0,t,0,1,0,e,0,0,1,n,0,0,0,1),this}makeRotationX(t){const e=Math.cos(t),n=Math.sin(t);return this.set(1,0,0,0,0,e,-n,0,0,n,e,0,0,0,0,1),this}makeRotationY(t){const e=Math.cos(t),n=Math.sin(t);return this.set(e,0,n,0,0,1,0,0,-n,0,e,0,0,0,0,1),this}makeRotationZ(t){const e=Math.cos(t),n=Math.sin(t);return this.set(e,-n,0,0,n,e,0,0,0,0,1,0,0,0,0,1),this}makeRotationAxis(t,e){const n=Math.cos(e),i=Math.sin(e),s=1-n,r=t.x,o=t.y,a=t.z,l=s*r,c=s*o;return this.set(l*r+n,l*o-i*a,l*a+i*o,0,l*o+i*a,c*o+n,c*a-i*r,0,l*a-i*o,c*a+i*r,s*a*a+n,0,0,0,0,1),this}makeScale(t,e,n){return this.set(t,0,0,0,0,e,0,0,0,0,n,0,0,0,0,1),this}makeShear(t,e,n,i,s,r){return this.set(1,n,s,0,t,1,r,0,e,i,1,0,0,0,0,1),this}compose(t,e,n){const i=this.elements,s=e._x,r=e._y,o=e._z,a=e._w,l=s+s,c=r+r,h=o+o,u=s*l,d=s*c,p=s*h,_=r*c,m=r*h,f=o*h,g=a*l,v=a*c,y=a*h,x=n.x,b=n.y,w=n.z;return i[0]=(1-(_+f))*x,i[1]=(d+y)*x,i[2]=(p-v)*x,i[3]=0,i[4]=(d-y)*b,i[5]=(1-(u+f))*b,i[6]=(m+g)*b,i[7]=0,i[8]=(p+v)*w,i[9]=(m-g)*w,i[10]=(1-(u+_))*w,i[11]=0,i[12]=t.x,i[13]=t.y,i[14]=t.z,i[15]=1,this}decompose(t,e,n){const i=this.elements;let s=r.set(i[0],i[1],i[2]).length();const a=r.set(i[4],i[5],i[6]).length(),l=r.set(i[8],i[9],i[10]).length();this.determinant()<0&&(s=-s),t.x=i[12],t.y=i[13],t.z=i[14],o.copy(this);const c=1/s,h=1/a,u=1/l;return o.elements[0]*=c,o.elements[1]*=c,o.elements[2]*=c,o.elements[4]*=h,o.elements[5]*=h,o.elements[6]*=h,o.elements[8]*=u,o.elements[9]*=u,o.elements[10]*=u,e.setFromRotationMatrix(o),n.x=s,n.y=a,n.z=l,this}makePerspective(t,e,n,i,s,r){void 0===r&&console.warn(\\\\\\\"THREE.Matrix4: .makePerspective() has been redefined and has a new signature. Please check the docs.\\\\\\\");const o=this.elements,a=2*s/(e-t),l=2*s/(n-i),c=(e+t)/(e-t),h=(n+i)/(n-i),u=-(r+s)/(r-s),d=-2*r*s/(r-s);return o[0]=a,o[4]=0,o[8]=c,o[12]=0,o[1]=0,o[5]=l,o[9]=h,o[13]=0,o[2]=0,o[6]=0,o[10]=u,o[14]=d,o[3]=0,o[7]=0,o[11]=-1,o[15]=0,this}makeOrthographic(t,e,n,i,s,r){const o=this.elements,a=1/(e-t),l=1/(n-i),c=1/(r-s),h=(e+t)*a,u=(n+i)*l,d=(r+s)*c;return o[0]=2*a,o[4]=0,o[8]=0,o[12]=-h,o[1]=0,o[5]=2*l,o[9]=0,o[13]=-u,o[2]=0,o[6]=0,o[10]=-2*c,o[14]=-d,o[3]=0,o[7]=0,o[11]=0,o[15]=1,this}equals(t){const e=this.elements,n=t.elements;for(let t=0;t<16;t++)if(e[t]!==n[t])return!1;return!0}fromArray(t,e=0){for(let n=0;n<16;n++)this.elements[n]=t[n+e];return this}toArray(t=[],e=0){const n=this.elements;return t[e]=n[0],t[e+1]=n[1],t[e+2]=n[2],t[e+3]=n[3],t[e+4]=n[4],t[e+5]=n[5],t[e+6]=n[6],t[e+7]=n[7],t[e+8]=n[8],t[e+9]=n[9],t[e+10]=n[10],t[e+11]=n[11],t[e+12]=n[12],t[e+13]=n[13],t[e+14]=n[14],t[e+15]=n[15],t}}s.prototype.isMatrix4=!0;const r=new i.a,o=new s,a=new i.a(0,0,0),l=new i.a(1,1,1),c=new i.a,h=new i.a,u=new i.a},function(t,e,n){\\\\\\\"use strict\\\\\\\";n.d(e,\\\\\\\"a\\\\\\\",(function(){return h}));var i=n(3);const s={aliceblue:15792383,antiquewhite:16444375,aqua:65535,aquamarine:8388564,azure:15794175,beige:16119260,bisque:16770244,black:0,blanchedalmond:16772045,blue:255,blueviolet:9055202,brown:10824234,burlywood:14596231,cadetblue:6266528,chartreuse:8388352,chocolate:13789470,coral:16744272,cornflowerblue:6591981,cornsilk:16775388,crimson:14423100,cyan:65535,darkblue:139,darkcyan:35723,darkgoldenrod:12092939,darkgray:11119017,darkgreen:25600,darkgrey:11119017,darkkhaki:12433259,darkmagenta:9109643,darkolivegreen:5597999,darkorange:16747520,darkorchid:10040012,darkred:9109504,darksalmon:15308410,darkseagreen:9419919,darkslateblue:4734347,darkslategray:3100495,darkslategrey:3100495,darkturquoise:52945,darkviolet:9699539,deeppink:16716947,deepskyblue:49151,dimgray:6908265,dimgrey:6908265,dodgerblue:2003199,firebrick:11674146,floralwhite:16775920,forestgreen:2263842,fuchsia:16711935,gainsboro:14474460,ghostwhite:16316671,gold:16766720,goldenrod:14329120,gray:8421504,green:32768,greenyellow:11403055,grey:8421504,honeydew:15794160,hotpink:16738740,indianred:13458524,indigo:4915330,ivory:16777200,khaki:15787660,lavender:15132410,lavenderblush:16773365,lawngreen:8190976,lemonchiffon:16775885,lightblue:11393254,lightcoral:15761536,lightcyan:14745599,lightgoldenrodyellow:16448210,lightgray:13882323,lightgreen:9498256,lightgrey:13882323,lightpink:16758465,lightsalmon:16752762,lightseagreen:2142890,lightskyblue:8900346,lightslategray:7833753,lightslategrey:7833753,lightsteelblue:11584734,lightyellow:16777184,lime:65280,limegreen:3329330,linen:16445670,magenta:16711935,maroon:8388608,mediumaquamarine:6737322,mediumblue:205,mediumorchid:12211667,mediumpurple:9662683,mediumseagreen:3978097,mediumslateblue:8087790,mediumspringgreen:64154,mediumturquoise:4772300,mediumvioletred:13047173,midnightblue:1644912,mintcream:16121850,mistyrose:16770273,moccasin:16770229,navajowhite:16768685,navy:128,oldlace:16643558,olive:8421376,olivedrab:7048739,orange:16753920,orangered:16729344,orchid:14315734,palegoldenrod:15657130,palegreen:10025880,paleturquoise:11529966,palevioletred:14381203,papayawhip:16773077,peachpuff:16767673,peru:13468991,pink:16761035,plum:14524637,powderblue:11591910,purple:8388736,rebeccapurple:6697881,red:16711680,rosybrown:12357519,royalblue:4286945,saddlebrown:9127187,salmon:16416882,sandybrown:16032864,seagreen:3050327,seashell:16774638,sienna:10506797,silver:12632256,skyblue:8900331,slateblue:6970061,slategray:7372944,slategrey:7372944,snow:16775930,springgreen:65407,steelblue:4620980,tan:13808780,teal:32896,thistle:14204888,tomato:16737095,turquoise:4251856,violet:15631086,wheat:16113331,white:16777215,whitesmoke:16119285,yellow:16776960,yellowgreen:10145074},r={h:0,s:0,l:0},o={h:0,s:0,l:0};function a(t,e,n){return n<0&&(n+=1),n>1&&(n-=1),n<1/6?t+6*(e-t)*n:n<.5?e:n<2/3?t+6*(e-t)*(2/3-n):t}function l(t){return t<.04045?.0773993808*t:Math.pow(.9478672986*t+.0521327014,2.4)}function c(t){return t<.0031308?12.92*t:1.055*Math.pow(t,.41666)-.055}class h{constructor(t,e,n){return void 0===e&&void 0===n?this.set(t):this.setRGB(t,e,n)}set(t){return t&&t.isColor?this.copy(t):\\\\\\\"number\\\\\\\"==typeof t?this.setHex(t):\\\\\\\"string\\\\\\\"==typeof t&&this.setStyle(t),this}setScalar(t){return this.r=t,this.g=t,this.b=t,this}setHex(t){return t=Math.floor(t),this.r=(t>>16&255)/255,this.g=(t>>8&255)/255,this.b=(255&t)/255,this}setRGB(t,e,n){return this.r=t,this.g=e,this.b=n,this}setHSL(t,e,n){if(t=i.f(t,1),e=i.d(e,0,1),n=i.d(n,0,1),0===e)this.r=this.g=this.b=n;else{const i=n<=.5?n*(1+e):n+e-n*e,s=2*n-i;this.r=a(s,i,t+1/3),this.g=a(s,i,t),this.b=a(s,i,t-1/3)}return this}setStyle(t){function e(e){void 0!==e&&parseFloat(e)<1&&console.warn(\\\\\\\"THREE.Color: Alpha component of \\\\\\\"+t+\\\\\\\" will be ignored.\\\\\\\")}let n;if(n=/^((?:rgb|hsl)a?)\\\\(([^\\\\)]*)\\\\)/.exec(t)){let t;const i=n[1],s=n[2];switch(i){case\\\\\\\"rgb\\\\\\\":case\\\\\\\"rgba\\\\\\\":if(t=/^\\\\s*(\\\\d+)\\\\s*,\\\\s*(\\\\d+)\\\\s*,\\\\s*(\\\\d+)\\\\s*(?:,\\\\s*(\\\\d*\\\\.?\\\\d+)\\\\s*)?$/.exec(s))return this.r=Math.min(255,parseInt(t[1],10))/255,this.g=Math.min(255,parseInt(t[2],10))/255,this.b=Math.min(255,parseInt(t[3],10))/255,e(t[4]),this;if(t=/^\\\\s*(\\\\d+)\\\\%\\\\s*,\\\\s*(\\\\d+)\\\\%\\\\s*,\\\\s*(\\\\d+)\\\\%\\\\s*(?:,\\\\s*(\\\\d*\\\\.?\\\\d+)\\\\s*)?$/.exec(s))return this.r=Math.min(100,parseInt(t[1],10))/100,this.g=Math.min(100,parseInt(t[2],10))/100,this.b=Math.min(100,parseInt(t[3],10))/100,e(t[4]),this;break;case\\\\\\\"hsl\\\\\\\":case\\\\\\\"hsla\\\\\\\":if(t=/^\\\\s*(\\\\d*\\\\.?\\\\d+)\\\\s*,\\\\s*(\\\\d+)\\\\%\\\\s*,\\\\s*(\\\\d+)\\\\%\\\\s*(?:,\\\\s*(\\\\d*\\\\.?\\\\d+)\\\\s*)?$/.exec(s)){const n=parseFloat(t[1])/360,i=parseInt(t[2],10)/100,s=parseInt(t[3],10)/100;return e(t[4]),this.setHSL(n,i,s)}}}else if(n=/^\\\\#([A-Fa-f\\\\d]+)$/.exec(t)){const t=n[1],e=t.length;if(3===e)return this.r=parseInt(t.charAt(0)+t.charAt(0),16)/255,this.g=parseInt(t.charAt(1)+t.charAt(1),16)/255,this.b=parseInt(t.charAt(2)+t.charAt(2),16)/255,this;if(6===e)return this.r=parseInt(t.charAt(0)+t.charAt(1),16)/255,this.g=parseInt(t.charAt(2)+t.charAt(3),16)/255,this.b=parseInt(t.charAt(4)+t.charAt(5),16)/255,this}return t&&t.length>0?this.setColorName(t):this}setColorName(t){const e=s[t.toLowerCase()];return void 0!==e?this.setHex(e):console.warn(\\\\\\\"THREE.Color: Unknown color \\\\\\\"+t),this}clone(){return new this.constructor(this.r,this.g,this.b)}copy(t){return this.r=t.r,this.g=t.g,this.b=t.b,this}copyGammaToLinear(t,e=2){return this.r=Math.pow(t.r,e),this.g=Math.pow(t.g,e),this.b=Math.pow(t.b,e),this}copyLinearToGamma(t,e=2){const n=e>0?1/e:1;return this.r=Math.pow(t.r,n),this.g=Math.pow(t.g,n),this.b=Math.pow(t.b,n),this}convertGammaToLinear(t){return this.copyGammaToLinear(this,t),this}convertLinearToGamma(t){return this.copyLinearToGamma(this,t),this}copySRGBToLinear(t){return this.r=l(t.r),this.g=l(t.g),this.b=l(t.b),this}copyLinearToSRGB(t){return this.r=c(t.r),this.g=c(t.g),this.b=c(t.b),this}convertSRGBToLinear(){return this.copySRGBToLinear(this),this}convertLinearToSRGB(){return this.copyLinearToSRGB(this),this}getHex(){return 255*this.r<<16^255*this.g<<8^255*this.b<<0}getHexString(){return(\\\\\\\"000000\\\\\\\"+this.getHex().toString(16)).slice(-6)}getHSL(t){const e=this.r,n=this.g,i=this.b,s=Math.max(e,n,i),r=Math.min(e,n,i);let o,a;const l=(r+s)/2;if(r===s)o=0,a=0;else{const t=s-r;switch(a=l<=.5?t/(s+r):t/(2-s-r),s){case e:o=(n-i)/t+(n65535?a.i:a.h)(t,1):this.index=t,this}getAttribute(t){return this.attributes[t]}setAttribute(t,e){return this.attributes[t]=e,this}deleteAttribute(t){return delete this.attributes[t],this}hasAttribute(t){return void 0!==this.attributes[t]}addGroup(t,e,n=0){this.groups.push({start:t,count:e,materialIndex:n})}clearGroups(){this.groups=[]}setDrawRange(t,e){this.drawRange.start=t,this.drawRange.count=e}applyMatrix4(t){const e=this.attributes.position;void 0!==e&&(e.applyMatrix4(t),e.needsUpdate=!0);const n=this.attributes.normal;if(void 0!==n){const e=(new u.a).getNormalMatrix(t);n.applyNormalMatrix(e),n.needsUpdate=!0}const i=this.attributes.tangent;return void 0!==i&&(i.transformDirection(t),i.needsUpdate=!0),null!==this.boundingBox&&this.computeBoundingBox(),null!==this.boundingSphere&&this.computeBoundingSphere(),this}applyQuaternion(t){return m.makeRotationFromQuaternion(t),this.applyMatrix4(m),this}rotateX(t){return m.makeRotationX(t),this.applyMatrix4(m),this}rotateY(t){return m.makeRotationY(t),this.applyMatrix4(m),this}rotateZ(t){return m.makeRotationZ(t),this.applyMatrix4(m),this}translate(t,e,n){return m.makeTranslation(t,e,n),this.applyMatrix4(m),this}scale(t,e,n){return m.makeScale(t,e,n),this.applyMatrix4(m),this}lookAt(t){return f.lookAt(t),f.updateMatrix(),this.applyMatrix4(f.matrix),this}center(){return this.computeBoundingBox(),this.boundingBox.getCenter(g).negate(),this.translate(g.x,g.y,g.z),this}setFromPoints(t){const e=[];for(let n=0,i=t.length;n0&&(t.userData=this.userData),void 0!==this.parameters){const e=this.parameters;for(const n in e)void 0!==e[n]&&(t[n]=e[n]);return t}t.data={attributes:{}};const e=this.index;null!==e&&(t.data.index={type:e.array.constructor.name,array:Array.prototype.slice.call(e.array)});const n=this.attributes;for(const e in n){const i=n[e];t.data.attributes[e]=i.toJSON(t.data)}const i={};let s=!1;for(const e in this.morphAttributes){const n=this.morphAttributes[e],r=[];for(let e=0,i=n.length;e0&&(i[e]=r,s=!0)}s&&(t.data.morphAttributes=i,t.data.morphTargetsRelative=this.morphTargetsRelative);const r=this.groups;r.length>0&&(t.data.groups=JSON.parse(JSON.stringify(r)));const o=this.boundingSphere;return null!==o&&(t.data.boundingSphere={center:o.center.toArray(),radius:o.radius}),t}clone(){return(new this.constructor).copy(this)}copy(t){this.index=null,this.attributes={},this.morphAttributes={},this.groups=[],this.boundingBox=null,this.boundingSphere=null;const e={};this.name=t.name;const n=t.index;null!==n&&this.setIndex(n.clone(e));const i=t.attributes;for(const t in i){const n=i[t];this.setAttribute(t,n.clone(e))}const s=t.morphAttributes;for(const t in s){const n=[],i=s[t];for(let t=0,s=i.length;t=0?1:-1,i=1-e*e;if(i>Number.EPSILON){const s=Math.sqrt(i),r=Math.atan2(s,e*n);t=Math.sin(t*r)/s,o=Math.sin(o*r)/s}const s=o*n;if(a=a*t+u*s,l=l*t+d*s,c=c*t+p*s,h=h*t+_*s,t===1-o){const t=1/Math.sqrt(a*a+l*l+c*c+h*h);a*=t,l*=t,c*=t,h*=t}}t[e]=a,t[e+1]=l,t[e+2]=c,t[e+3]=h}static multiplyQuaternionsFlat(t,e,n,i,s,r){const o=n[i],a=n[i+1],l=n[i+2],c=n[i+3],h=s[r],u=s[r+1],d=s[r+2],p=s[r+3];return t[e]=o*p+c*h+a*d-l*u,t[e+1]=a*p+c*u+l*h-o*d,t[e+2]=l*p+c*d+o*u-a*h,t[e+3]=c*p-o*h-a*u-l*d,t}get x(){return this._x}set x(t){this._x=t,this._onChangeCallback()}get y(){return this._y}set y(t){this._y=t,this._onChangeCallback()}get z(){return this._z}set z(t){this._z=t,this._onChangeCallback()}get w(){return this._w}set w(t){this._w=t,this._onChangeCallback()}set(t,e,n,i){return this._x=t,this._y=e,this._z=n,this._w=i,this._onChangeCallback(),this}clone(){return new this.constructor(this._x,this._y,this._z,this._w)}copy(t){return this._x=t.x,this._y=t.y,this._z=t.z,this._w=t.w,this._onChangeCallback(),this}setFromEuler(t,e){if(!t||!t.isEuler)throw new Error(\\\\\\\"THREE.Quaternion: .setFromEuler() now expects an Euler rotation rather than a Vector3 and order.\\\\\\\");const n=t._x,i=t._y,s=t._z,r=t._order,o=Math.cos,a=Math.sin,l=o(n/2),c=o(i/2),h=o(s/2),u=a(n/2),d=a(i/2),p=a(s/2);switch(r){case\\\\\\\"XYZ\\\\\\\":this._x=u*c*h+l*d*p,this._y=l*d*h-u*c*p,this._z=l*c*p+u*d*h,this._w=l*c*h-u*d*p;break;case\\\\\\\"YXZ\\\\\\\":this._x=u*c*h+l*d*p,this._y=l*d*h-u*c*p,this._z=l*c*p-u*d*h,this._w=l*c*h+u*d*p;break;case\\\\\\\"ZXY\\\\\\\":this._x=u*c*h-l*d*p,this._y=l*d*h+u*c*p,this._z=l*c*p+u*d*h,this._w=l*c*h-u*d*p;break;case\\\\\\\"ZYX\\\\\\\":this._x=u*c*h-l*d*p,this._y=l*d*h+u*c*p,this._z=l*c*p-u*d*h,this._w=l*c*h+u*d*p;break;case\\\\\\\"YZX\\\\\\\":this._x=u*c*h+l*d*p,this._y=l*d*h+u*c*p,this._z=l*c*p-u*d*h,this._w=l*c*h-u*d*p;break;case\\\\\\\"XZY\\\\\\\":this._x=u*c*h-l*d*p,this._y=l*d*h-u*c*p,this._z=l*c*p+u*d*h,this._w=l*c*h+u*d*p;break;default:console.warn(\\\\\\\"THREE.Quaternion: .setFromEuler() encountered an unknown order: \\\\\\\"+r)}return!1!==e&&this._onChangeCallback(),this}setFromAxisAngle(t,e){const n=e/2,i=Math.sin(n);return this._x=t.x*i,this._y=t.y*i,this._z=t.z*i,this._w=Math.cos(n),this._onChangeCallback(),this}setFromRotationMatrix(t){const e=t.elements,n=e[0],i=e[4],s=e[8],r=e[1],o=e[5],a=e[9],l=e[2],c=e[6],h=e[10],u=n+o+h;if(u>0){const t=.5/Math.sqrt(u+1);this._w=.25/t,this._x=(c-a)*t,this._y=(s-l)*t,this._z=(r-i)*t}else if(n>o&&n>h){const t=2*Math.sqrt(1+n-o-h);this._w=(c-a)/t,this._x=.25*t,this._y=(i+r)/t,this._z=(s+l)/t}else if(o>h){const t=2*Math.sqrt(1+o-n-h);this._w=(s-l)/t,this._x=(i+r)/t,this._y=.25*t,this._z=(a+c)/t}else{const t=2*Math.sqrt(1+h-n-o);this._w=(r-i)/t,this._x=(s+l)/t,this._y=(a+c)/t,this._z=.25*t}return this._onChangeCallback(),this}setFromUnitVectors(t,e){let n=t.dot(e)+1;return nMath.abs(t.z)?(this._x=-t.y,this._y=t.x,this._z=0,this._w=n):(this._x=0,this._y=-t.z,this._z=t.y,this._w=n)):(this._x=t.y*e.z-t.z*e.y,this._y=t.z*e.x-t.x*e.z,this._z=t.x*e.y-t.y*e.x,this._w=n),this.normalize()}angleTo(t){return 2*Math.acos(Math.abs(i.d(this.dot(t),-1,1)))}rotateTowards(t,e){const n=this.angleTo(t);if(0===n)return this;const i=Math.min(1,e/n);return this.slerp(t,i),this}identity(){return this.set(0,0,0,1)}invert(){return this.conjugate()}conjugate(){return this._x*=-1,this._y*=-1,this._z*=-1,this._onChangeCallback(),this}dot(t){return this._x*t._x+this._y*t._y+this._z*t._z+this._w*t._w}lengthSq(){return this._x*this._x+this._y*this._y+this._z*this._z+this._w*this._w}length(){return Math.sqrt(this._x*this._x+this._y*this._y+this._z*this._z+this._w*this._w)}normalize(){let t=this.length();return 0===t?(this._x=0,this._y=0,this._z=0,this._w=1):(t=1/t,this._x=this._x*t,this._y=this._y*t,this._z=this._z*t,this._w=this._w*t),this._onChangeCallback(),this}multiply(t,e){return void 0!==e?(console.warn(\\\\\\\"THREE.Quaternion: .multiply() now only accepts one argument. Use .multiplyQuaternions( a, b ) instead.\\\\\\\"),this.multiplyQuaternions(t,e)):this.multiplyQuaternions(this,t)}premultiply(t){return this.multiplyQuaternions(t,this)}multiplyQuaternions(t,e){const n=t._x,i=t._y,s=t._z,r=t._w,o=e._x,a=e._y,l=e._z,c=e._w;return this._x=n*c+r*o+i*l-s*a,this._y=i*c+r*a+s*o-n*l,this._z=s*c+r*l+n*a-i*o,this._w=r*c-n*o-i*a-s*l,this._onChangeCallback(),this}slerp(t,e){if(0===e)return this;if(1===e)return this.copy(t);const n=this._x,i=this._y,s=this._z,r=this._w;let o=r*t._w+n*t._x+i*t._y+s*t._z;if(o<0?(this._w=-t._w,this._x=-t._x,this._y=-t._y,this._z=-t._z,o=-o):this.copy(t),o>=1)return this._w=r,this._x=n,this._y=i,this._z=s,this;const a=1-o*o;if(a<=Number.EPSILON){const t=1-e;return this._w=t*r+e*this._w,this._x=t*n+e*this._x,this._y=t*i+e*this._y,this._z=t*s+e*this._z,this.normalize(),this._onChangeCallback(),this}const l=Math.sqrt(a),c=Math.atan2(l,o),h=Math.sin((1-e)*c)/l,u=Math.sin(e*c)/l;return this._w=r*h+this._w*u,this._x=n*h+this._x*u,this._y=i*h+this._y*u,this._z=s*h+this._z*u,this._onChangeCallback(),this}slerpQuaternions(t,e,n){this.copy(t).slerp(e,n)}random(){const t=Math.random(),e=Math.sqrt(1-t),n=Math.sqrt(t),i=2*Math.PI*Math.random(),s=2*Math.PI*Math.random();return this.set(e*Math.cos(i),n*Math.sin(s),n*Math.cos(s),e*Math.sin(i))}equals(t){return t._x===this._x&&t._y===this._y&&t._z===this._z&&t._w===this._w}fromArray(t,e=0){return this._x=t[e],this._y=t[e+1],this._z=t[e+2],this._w=t[e+3],this._onChangeCallback(),this}toArray(t=[],e=0){return t[e]=this._x,t[e+1]=this._y,t[e+2]=this._z,t[e+3]=this._w,t}fromBufferAttribute(t,e){return this._x=t.getX(e),this._y=t.getY(e),this._z=t.getZ(e),this._w=t.getW(e),this}_onChange(t){return this._onChangeCallback=t,this}_onChangeCallback(){}}s.prototype.isQuaternion=!0},function(t,e,n){\\\\\\\"use strict\\\\\\\";n.d(e,\\\\\\\"a\\\\\\\",(function(){return i}));class i{constructor(t=0,e=0,n=0,i=1){this.x=t,this.y=e,this.z=n,this.w=i}get width(){return this.z}set width(t){this.z=t}get height(){return this.w}set height(t){this.w=t}set(t,e,n,i){return this.x=t,this.y=e,this.z=n,this.w=i,this}setScalar(t){return this.x=t,this.y=t,this.z=t,this.w=t,this}setX(t){return this.x=t,this}setY(t){return this.y=t,this}setZ(t){return this.z=t,this}setW(t){return this.w=t,this}setComponent(t,e){switch(t){case 0:this.x=e;break;case 1:this.y=e;break;case 2:this.z=e;break;case 3:this.w=e;break;default:throw new Error(\\\\\\\"index is out of range: \\\\\\\"+t)}return this}getComponent(t){switch(t){case 0:return this.x;case 1:return this.y;case 2:return this.z;case 3:return this.w;default:throw new Error(\\\\\\\"index is out of range: \\\\\\\"+t)}}clone(){return new this.constructor(this.x,this.y,this.z,this.w)}copy(t){return this.x=t.x,this.y=t.y,this.z=t.z,this.w=void 0!==t.w?t.w:1,this}add(t,e){return void 0!==e?(console.warn(\\\\\\\"THREE.Vector4: .add() now only accepts one argument. Use .addVectors( a, b ) instead.\\\\\\\"),this.addVectors(t,e)):(this.x+=t.x,this.y+=t.y,this.z+=t.z,this.w+=t.w,this)}addScalar(t){return this.x+=t,this.y+=t,this.z+=t,this.w+=t,this}addVectors(t,e){return this.x=t.x+e.x,this.y=t.y+e.y,this.z=t.z+e.z,this.w=t.w+e.w,this}addScaledVector(t,e){return this.x+=t.x*e,this.y+=t.y*e,this.z+=t.z*e,this.w+=t.w*e,this}sub(t,e){return void 0!==e?(console.warn(\\\\\\\"THREE.Vector4: .sub() now only accepts one argument. Use .subVectors( a, b ) instead.\\\\\\\"),this.subVectors(t,e)):(this.x-=t.x,this.y-=t.y,this.z-=t.z,this.w-=t.w,this)}subScalar(t){return this.x-=t,this.y-=t,this.z-=t,this.w-=t,this}subVectors(t,e){return this.x=t.x-e.x,this.y=t.y-e.y,this.z=t.z-e.z,this.w=t.w-e.w,this}multiply(t){return this.x*=t.x,this.y*=t.y,this.z*=t.z,this.w*=t.w,this}multiplyScalar(t){return this.x*=t,this.y*=t,this.z*=t,this.w*=t,this}applyMatrix4(t){const e=this.x,n=this.y,i=this.z,s=this.w,r=t.elements;return this.x=r[0]*e+r[4]*n+r[8]*i+r[12]*s,this.y=r[1]*e+r[5]*n+r[9]*i+r[13]*s,this.z=r[2]*e+r[6]*n+r[10]*i+r[14]*s,this.w=r[3]*e+r[7]*n+r[11]*i+r[15]*s,this}divideScalar(t){return this.multiplyScalar(1/t)}setAxisAngleFromQuaternion(t){this.w=2*Math.acos(t.w);const e=Math.sqrt(1-t.w*t.w);return e<1e-4?(this.x=1,this.y=0,this.z=0):(this.x=t.x/e,this.y=t.y/e,this.z=t.z/e),this}setAxisAngleFromRotationMatrix(t){let e,n,i,s;const r=.01,o=.1,a=t.elements,l=a[0],c=a[4],h=a[8],u=a[1],d=a[5],p=a[9],_=a[2],m=a[6],f=a[10];if(Math.abs(c-u)a&&t>g?tg?a1){for(let t=0;t1){for(let t=0;t0){i.children=[];for(let e=0;e0){i.animations=[];for(let e=0;e0&&(n.geometries=e),i.length>0&&(n.materials=i),s.length>0&&(n.textures=s),o.length>0&&(n.images=o),a.length>0&&(n.shapes=a),l.length>0&&(n.skeletons=l),c.length>0&&(n.animations=c)}return n.object=i,n;function r(t){const e=[];for(const n in t){const i=t[n];delete i.metadata,e.push(i)}return e}}clone(t){return(new this.constructor).copy(this,t)}copy(t,e=!0){if(this.name=t.name,this.up.copy(t.up),this.position.copy(t.position),this.rotation.order=t.rotation.order,this.quaternion.copy(t.quaternion),this.scale.copy(t.scale),this.matrix.copy(t.matrix),this.matrixWorld.copy(t.matrixWorld),this.matrixAutoUpdate=t.matrixAutoUpdate,this.matrixWorldNeedsUpdate=t.matrixWorldNeedsUpdate,this.layers.mask=t.layers.mask,this.visible=t.visible,this.castShadow=t.castShadow,this.receiveShadow=t.receiveShadow,this.frustumCulled=t.frustumCulled,this.renderOrder=t.renderOrder,this.userData=JSON.parse(JSON.stringify(t.userData)),!0===e)for(let e=0;e0&&console.error(\\\\\\\"THREE.Matrix3: the constructor no longer reads arguments. use .set() instead.\\\\\\\")}set(t,e,n,i,s,r,o,a,l){const c=this.elements;return c[0]=t,c[1]=i,c[2]=o,c[3]=e,c[4]=s,c[5]=a,c[6]=n,c[7]=r,c[8]=l,this}identity(){return this.set(1,0,0,0,1,0,0,0,1),this}copy(t){const e=this.elements,n=t.elements;return e[0]=n[0],e[1]=n[1],e[2]=n[2],e[3]=n[3],e[4]=n[4],e[5]=n[5],e[6]=n[6],e[7]=n[7],e[8]=n[8],this}extractBasis(t,e,n){return t.setFromMatrix3Column(this,0),e.setFromMatrix3Column(this,1),n.setFromMatrix3Column(this,2),this}setFromMatrix4(t){const e=t.elements;return this.set(e[0],e[4],e[8],e[1],e[5],e[9],e[2],e[6],e[10]),this}multiply(t){return this.multiplyMatrices(this,t)}premultiply(t){return this.multiplyMatrices(t,this)}multiplyMatrices(t,e){const n=t.elements,i=e.elements,s=this.elements,r=n[0],o=n[3],a=n[6],l=n[1],c=n[4],h=n[7],u=n[2],d=n[5],p=n[8],_=i[0],m=i[3],f=i[6],g=i[1],v=i[4],y=i[7],x=i[2],b=i[5],w=i[8];return s[0]=r*_+o*g+a*x,s[3]=r*m+o*v+a*b,s[6]=r*f+o*y+a*w,s[1]=l*_+c*g+h*x,s[4]=l*m+c*v+h*b,s[7]=l*f+c*y+h*w,s[2]=u*_+d*g+p*x,s[5]=u*m+d*v+p*b,s[8]=u*f+d*y+p*w,this}multiplyScalar(t){const e=this.elements;return e[0]*=t,e[3]*=t,e[6]*=t,e[1]*=t,e[4]*=t,e[7]*=t,e[2]*=t,e[5]*=t,e[8]*=t,this}determinant(){const t=this.elements,e=t[0],n=t[1],i=t[2],s=t[3],r=t[4],o=t[5],a=t[6],l=t[7],c=t[8];return e*r*c-e*o*l-n*s*c+n*o*a+i*s*l-i*r*a}invert(){const t=this.elements,e=t[0],n=t[1],i=t[2],s=t[3],r=t[4],o=t[5],a=t[6],l=t[7],c=t[8],h=c*r-o*l,u=o*a-c*s,d=l*s-r*a,p=e*h+n*u+i*d;if(0===p)return this.set(0,0,0,0,0,0,0,0,0);const _=1/p;return t[0]=h*_,t[1]=(i*l-c*n)*_,t[2]=(o*n-i*r)*_,t[3]=u*_,t[4]=(c*e-i*a)*_,t[5]=(i*s-o*e)*_,t[6]=d*_,t[7]=(n*a-l*e)*_,t[8]=(r*e-n*s)*_,this}transpose(){let t;const e=this.elements;return t=e[1],e[1]=e[3],e[3]=t,t=e[2],e[2]=e[6],e[6]=t,t=e[5],e[5]=e[7],e[7]=t,this}getNormalMatrix(t){return this.setFromMatrix4(t).invert().transpose()}transposeIntoArray(t){const e=this.elements;return t[0]=e[0],t[1]=e[3],t[2]=e[6],t[3]=e[1],t[4]=e[4],t[5]=e[7],t[6]=e[2],t[7]=e[5],t[8]=e[8],this}setUvTransform(t,e,n,i,s,r,o){const a=Math.cos(s),l=Math.sin(s);return this.set(n*a,n*l,-n*(a*r+l*o)+r+t,-i*l,i*a,-i*(-l*r+a*o)+o+e,0,0,1),this}scale(t,e){const n=this.elements;return n[0]*=t,n[3]*=t,n[6]*=t,n[1]*=e,n[4]*=e,n[7]*=e,this}rotate(t){const e=Math.cos(t),n=Math.sin(t),i=this.elements,s=i[0],r=i[3],o=i[6],a=i[1],l=i[4],c=i[7];return i[0]=e*s+n*a,i[3]=e*r+n*l,i[6]=e*o+n*c,i[1]=-n*s+e*a,i[4]=-n*r+e*l,i[7]=-n*o+e*c,this}translate(t,e){const n=this.elements;return n[0]+=t*n[2],n[3]+=t*n[5],n[6]+=t*n[8],n[1]+=e*n[2],n[4]+=e*n[5],n[7]+=e*n[8],this}equals(t){const e=this.elements,n=t.elements;for(let t=0;t<9;t++)if(e[t]!==n[t])return!1;return!0}fromArray(t,e=0){for(let n=0;n<9;n++)this.elements[n]=t[n+e];return this}toArray(t=[],e=0){const n=this.elements;return t[e]=n[0],t[e+1]=n[1],t[e+2]=n[2],t[e+3]=n[3],t[e+4]=n[4],t[e+5]=n[5],t[e+6]=n[6],t[e+7]=n[7],t[e+8]=n[8],t}clone(){return(new this.constructor).fromArray(this.elements)}}i.prototype.isMatrix3=!0},function(t,e,n){\\\\\\\"use strict\\\\\\\";n.d(e,\\\\\\\"a\\\\\\\",(function(){return a}));var i=n(15),s=n(1),r=n(3);let o=0;class a extends i.a{constructor(){super(),Object.defineProperty(this,\\\\\\\"id\\\\\\\",{value:o++}),this.uuid=r.h(),this.name=\\\\\\\"\\\\\\\",this.type=\\\\\\\"Material\\\\\\\",this.fog=!0,this.blending=s.xb,this.side=s.H,this.vertexColors=!1,this.opacity=1,this.format=s.Ib,this.transparent=!1,this.blendSrc=s.Nc,this.blendDst=s.Db,this.blendEquation=s.b,this.blendSrcAlpha=null,this.blendDstAlpha=null,this.blendEquationAlpha=null,this.depthFunc=s.T,this.depthTest=!0,this.depthWrite=!0,this.stencilWriteMask=255,this.stencilFunc=s.h,this.stencilRef=0,this.stencilFuncMask=255,this.stencilFail=s.R,this.stencilZFail=s.R,this.stencilZPass=s.R,this.stencilWrite=!1,this.clippingPlanes=null,this.clipIntersection=!1,this.clipShadows=!1,this.shadowSide=null,this.colorWrite=!0,this.precision=null,this.polygonOffset=!1,this.polygonOffsetFactor=0,this.polygonOffsetUnits=0,this.dithering=!1,this.alphaToCoverage=!1,this.premultipliedAlpha=!1,this.visible=!0,this.toneMapped=!0,this.userData={},this.version=0,this._alphaTest=0}get alphaTest(){return this._alphaTest}set alphaTest(t){this._alphaTest>0!=t>0&&this.version++,this._alphaTest=t}onBuild(){}onBeforeRender(){}onBeforeCompile(){}customProgramCacheKey(){return this.onBeforeCompile.toString()}setValues(t){if(void 0!==t)for(const e in t){const n=t[e];if(void 0===n){console.warn(\\\\\\\"THREE.Material: '\\\\\\\"+e+\\\\\\\"' parameter is undefined.\\\\\\\");continue}if(\\\\\\\"shading\\\\\\\"===e){console.warn(\\\\\\\"THREE.\\\\\\\"+this.type+\\\\\\\": .shading has been removed. Use the boolean .flatShading instead.\\\\\\\"),this.flatShading=n===s.F;continue}const i=this[e];void 0!==i?i&&i.isColor?i.set(n):i&&i.isVector3&&n&&n.isVector3?i.copy(n):this[e]=n:console.warn(\\\\\\\"THREE.\\\\\\\"+this.type+\\\\\\\": '\\\\\\\"+e+\\\\\\\"' is not a property of this material.\\\\\\\")}}toJSON(t){const e=void 0===t||\\\\\\\"string\\\\\\\"==typeof t;e&&(t={textures:{},images:{}});const n={metadata:{version:4.5,type:\\\\\\\"Material\\\\\\\",generator:\\\\\\\"Material.toJSON\\\\\\\"}};function i(t){const e=[];for(const n in t){const i=t[n];delete i.metadata,e.push(i)}return e}if(n.uuid=this.uuid,n.type=this.type,\\\\\\\"\\\\\\\"!==this.name&&(n.name=this.name),this.color&&this.color.isColor&&(n.color=this.color.getHex()),void 0!==this.roughness&&(n.roughness=this.roughness),void 0!==this.metalness&&(n.metalness=this.metalness),void 0!==this.sheen&&(n.sheen=this.sheen),this.sheenTint&&this.sheenTint.isColor&&(n.sheenTint=this.sheenTint.getHex()),void 0!==this.sheenRoughness&&(n.sheenRoughness=this.sheenRoughness),this.emissive&&this.emissive.isColor&&(n.emissive=this.emissive.getHex()),this.emissiveIntensity&&1!==this.emissiveIntensity&&(n.emissiveIntensity=this.emissiveIntensity),this.specular&&this.specular.isColor&&(n.specular=this.specular.getHex()),void 0!==this.specularIntensity&&(n.specularIntensity=this.specularIntensity),this.specularTint&&this.specularTint.isColor&&(n.specularTint=this.specularTint.getHex()),void 0!==this.shininess&&(n.shininess=this.shininess),void 0!==this.clearcoat&&(n.clearcoat=this.clearcoat),void 0!==this.clearcoatRoughness&&(n.clearcoatRoughness=this.clearcoatRoughness),this.clearcoatMap&&this.clearcoatMap.isTexture&&(n.clearcoatMap=this.clearcoatMap.toJSON(t).uuid),this.clearcoatRoughnessMap&&this.clearcoatRoughnessMap.isTexture&&(n.clearcoatRoughnessMap=this.clearcoatRoughnessMap.toJSON(t).uuid),this.clearcoatNormalMap&&this.clearcoatNormalMap.isTexture&&(n.clearcoatNormalMap=this.clearcoatNormalMap.toJSON(t).uuid,n.clearcoatNormalScale=this.clearcoatNormalScale.toArray()),this.map&&this.map.isTexture&&(n.map=this.map.toJSON(t).uuid),this.matcap&&this.matcap.isTexture&&(n.matcap=this.matcap.toJSON(t).uuid),this.alphaMap&&this.alphaMap.isTexture&&(n.alphaMap=this.alphaMap.toJSON(t).uuid),this.lightMap&&this.lightMap.isTexture&&(n.lightMap=this.lightMap.toJSON(t).uuid,n.lightMapIntensity=this.lightMapIntensity),this.aoMap&&this.aoMap.isTexture&&(n.aoMap=this.aoMap.toJSON(t).uuid,n.aoMapIntensity=this.aoMapIntensity),this.bumpMap&&this.bumpMap.isTexture&&(n.bumpMap=this.bumpMap.toJSON(t).uuid,n.bumpScale=this.bumpScale),this.normalMap&&this.normalMap.isTexture&&(n.normalMap=this.normalMap.toJSON(t).uuid,n.normalMapType=this.normalMapType,n.normalScale=this.normalScale.toArray()),this.displacementMap&&this.displacementMap.isTexture&&(n.displacementMap=this.displacementMap.toJSON(t).uuid,n.displacementScale=this.displacementScale,n.displacementBias=this.displacementBias),this.roughnessMap&&this.roughnessMap.isTexture&&(n.roughnessMap=this.roughnessMap.toJSON(t).uuid),this.metalnessMap&&this.metalnessMap.isTexture&&(n.metalnessMap=this.metalnessMap.toJSON(t).uuid),this.emissiveMap&&this.emissiveMap.isTexture&&(n.emissiveMap=this.emissiveMap.toJSON(t).uuid),this.specularMap&&this.specularMap.isTexture&&(n.specularMap=this.specularMap.toJSON(t).uuid),this.specularIntensityMap&&this.specularIntensityMap.isTexture&&(n.specularIntensityMap=this.specularIntensityMap.toJSON(t).uuid),this.specularTintMap&&this.specularTintMap.isTexture&&(n.specularTintMap=this.specularTintMap.toJSON(t).uuid),this.envMap&&this.envMap.isTexture&&(n.envMap=this.envMap.toJSON(t).uuid,void 0!==this.combine&&(n.combine=this.combine)),void 0!==this.envMapIntensity&&(n.envMapIntensity=this.envMapIntensity),void 0!==this.reflectivity&&(n.reflectivity=this.reflectivity),void 0!==this.refractionRatio&&(n.refractionRatio=this.refractionRatio),this.gradientMap&&this.gradientMap.isTexture&&(n.gradientMap=this.gradientMap.toJSON(t).uuid),void 0!==this.transmission&&(n.transmission=this.transmission),this.transmissionMap&&this.transmissionMap.isTexture&&(n.transmissionMap=this.transmissionMap.toJSON(t).uuid),void 0!==this.thickness&&(n.thickness=this.thickness),this.thicknessMap&&this.thicknessMap.isTexture&&(n.thicknessMap=this.thicknessMap.toJSON(t).uuid),void 0!==this.attenuationDistance&&(n.attenuationDistance=this.attenuationDistance),void 0!==this.attenuationTint&&(n.attenuationTint=this.attenuationTint.getHex()),void 0!==this.size&&(n.size=this.size),null!==this.shadowSide&&(n.shadowSide=this.shadowSide),void 0!==this.sizeAttenuation&&(n.sizeAttenuation=this.sizeAttenuation),this.blending!==s.xb&&(n.blending=this.blending),this.side!==s.H&&(n.side=this.side),this.vertexColors&&(n.vertexColors=!0),this.opacity<1&&(n.opacity=this.opacity),this.format!==s.Ib&&(n.format=this.format),!0===this.transparent&&(n.transparent=this.transparent),n.depthFunc=this.depthFunc,n.depthTest=this.depthTest,n.depthWrite=this.depthWrite,n.colorWrite=this.colorWrite,n.stencilWrite=this.stencilWrite,n.stencilWriteMask=this.stencilWriteMask,n.stencilFunc=this.stencilFunc,n.stencilRef=this.stencilRef,n.stencilFuncMask=this.stencilFuncMask,n.stencilFail=this.stencilFail,n.stencilZFail=this.stencilZFail,n.stencilZPass=this.stencilZPass,this.rotation&&0!==this.rotation&&(n.rotation=this.rotation),!0===this.polygonOffset&&(n.polygonOffset=!0),0!==this.polygonOffsetFactor&&(n.polygonOffsetFactor=this.polygonOffsetFactor),0!==this.polygonOffsetUnits&&(n.polygonOffsetUnits=this.polygonOffsetUnits),this.linewidth&&1!==this.linewidth&&(n.linewidth=this.linewidth),void 0!==this.dashSize&&(n.dashSize=this.dashSize),void 0!==this.gapSize&&(n.gapSize=this.gapSize),void 0!==this.scale&&(n.scale=this.scale),!0===this.dithering&&(n.dithering=!0),this.alphaTest>0&&(n.alphaTest=this.alphaTest),!0===this.alphaToCoverage&&(n.alphaToCoverage=this.alphaToCoverage),!0===this.premultipliedAlpha&&(n.premultipliedAlpha=this.premultipliedAlpha),!0===this.wireframe&&(n.wireframe=this.wireframe),this.wireframeLinewidth>1&&(n.wireframeLinewidth=this.wireframeLinewidth),\\\\\\\"round\\\\\\\"!==this.wireframeLinecap&&(n.wireframeLinecap=this.wireframeLinecap),\\\\\\\"round\\\\\\\"!==this.wireframeLinejoin&&(n.wireframeLinejoin=this.wireframeLinejoin),!0===this.flatShading&&(n.flatShading=this.flatShading),!1===this.visible&&(n.visible=!1),!1===this.toneMapped&&(n.toneMapped=!1),\\\\\\\"{}\\\\\\\"!==JSON.stringify(this.userData)&&(n.userData=this.userData),e){const e=i(t.textures),s=i(t.images);e.length>0&&(n.textures=e),s.length>0&&(n.images=s)}return n}clone(){return(new this.constructor).copy(this)}copy(t){this.name=t.name,this.fog=t.fog,this.blending=t.blending,this.side=t.side,this.vertexColors=t.vertexColors,this.opacity=t.opacity,this.format=t.format,this.transparent=t.transparent,this.blendSrc=t.blendSrc,this.blendDst=t.blendDst,this.blendEquation=t.blendEquation,this.blendSrcAlpha=t.blendSrcAlpha,this.blendDstAlpha=t.blendDstAlpha,this.blendEquationAlpha=t.blendEquationAlpha,this.depthFunc=t.depthFunc,this.depthTest=t.depthTest,this.depthWrite=t.depthWrite,this.stencilWriteMask=t.stencilWriteMask,this.stencilFunc=t.stencilFunc,this.stencilRef=t.stencilRef,this.stencilFuncMask=t.stencilFuncMask,this.stencilFail=t.stencilFail,this.stencilZFail=t.stencilZFail,this.stencilZPass=t.stencilZPass,this.stencilWrite=t.stencilWrite;const e=t.clippingPlanes;let n=null;if(null!==e){const t=e.length;n=new Array(t);for(let i=0;i!==t;++i)n[i]=e[i].clone()}return this.clippingPlanes=n,this.clipIntersection=t.clipIntersection,this.clipShadows=t.clipShadows,this.shadowSide=t.shadowSide,this.colorWrite=t.colorWrite,this.precision=t.precision,this.polygonOffset=t.polygonOffset,this.polygonOffsetFactor=t.polygonOffsetFactor,this.polygonOffsetUnits=t.polygonOffsetUnits,this.dithering=t.dithering,this.alphaTest=t.alphaTest,this.alphaToCoverage=t.alphaToCoverage,this.premultipliedAlpha=t.premultipliedAlpha,this.visible=t.visible,this.toneMapped=t.toneMapped,this.userData=JSON.parse(JSON.stringify(t.userData)),this}dispose(){this.dispatchEvent({type:\\\\\\\"dispose\\\\\\\"})}set needsUpdate(t){!0===t&&this.version++}}a.prototype.isMaterial=!0},function(t,e,n){\\\\\\\"use strict\\\\\\\";n.d(e,\\\\\\\"a\\\\\\\",(function(){return s}));var i=n(28);class s{constructor(t){this.manager=void 0!==t?t:i.a,this.crossOrigin=\\\\\\\"anonymous\\\\\\\",this.withCredentials=!1,this.path=\\\\\\\"\\\\\\\",this.resourcePath=\\\\\\\"\\\\\\\",this.requestHeader={}}load(){}loadAsync(t,e){const n=this;return new Promise((function(i,s){n.load(t,i,e,s)}))}parse(){}setCrossOrigin(t){return this.crossOrigin=t,this}setWithCredentials(t){return this.withCredentials=t,this}setPath(t){return this.path=t,this}setResourcePath(t){return this.resourcePath=t,this}setRequestHeader(t){return this.requestHeader=t,this}}},function(t,e,n){\\\\\\\"use strict\\\\\\\";n.d(e,\\\\\\\"a\\\\\\\",(function(){return L}));var i=n(0),s=n(2),r=n(18),o=n(39),a=n(5),l=n(10),c=n(40),h=n(1),u=n(29),d=n(7);const p=new a.a,_=new o.a,m=new r.a,f=new i.a,g=new i.a,v=new i.a,y=new i.a,x=new i.a,b=new i.a,w=new i.a,T=new i.a,A=new i.a,M=new s.a,E=new s.a,S=new s.a,C=new i.a,N=new i.a;class L extends l.a{constructor(t=new d.a,e=new u.a){super(),this.type=\\\\\\\"Mesh\\\\\\\",this.geometry=t,this.material=e,this.updateMorphTargets()}copy(t){return super.copy(t),void 0!==t.morphTargetInfluences&&(this.morphTargetInfluences=t.morphTargetInfluences.slice()),void 0!==t.morphTargetDictionary&&(this.morphTargetDictionary=Object.assign({},t.morphTargetDictionary)),this.material=t.material,this.geometry=t.geometry,this}updateMorphTargets(){const t=this.geometry;if(t.isBufferGeometry){const e=t.morphAttributes,n=Object.keys(e);if(n.length>0){const t=e[n[0]];if(void 0!==t){this.morphTargetInfluences=[],this.morphTargetDictionary={};for(let e=0,n=t.length;e0&&console.error(\\\\\\\"THREE.Mesh.updateMorphTargets() no longer supports THREE.Geometry. Use THREE.BufferGeometry instead.\\\\\\\")}}raycast(t,e){const n=this.geometry,i=this.material,s=this.matrixWorld;if(void 0===i)return;if(null===n.boundingSphere&&n.computeBoundingSphere(),m.copy(n.boundingSphere),m.applyMatrix4(s),!1===t.ray.intersectsSphere(m))return;if(p.copy(s).invert(),_.copy(t.ray).applyMatrix4(p),null!==n.boundingBox&&!1===_.intersectsBox(n.boundingBox))return;let r;if(n.isBufferGeometry){const s=n.index,o=n.attributes.position,a=n.morphAttributes.position,l=n.morphTargetsRelative,c=n.attributes.uv,h=n.attributes.uv2,u=n.groups,d=n.drawRange;if(null!==s)if(Array.isArray(i))for(let n=0,p=u.length;nn.far?null:{distance:c,point:N.clone(),object:t}}(t,e,n,r,f,g,v,C);if(O){u&&(M.fromBufferAttribute(u,p),E.fromBufferAttribute(u,_),S.fromBufferAttribute(u,m),O.uv=c.a.getUV(C,f,g,v,M,E,S,new s.a)),d&&(M.fromBufferAttribute(d,p),E.fromBufferAttribute(d,_),S.fromBufferAttribute(d,m),O.uv2=c.a.getUV(C,f,g,v,M,E,S,new s.a));const t={a:p,b:_,c:m,normal:new 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l>=0?(n=(t.min.x-u.x)*l,i=(t.max.x-u.x)*l):(n=(t.max.x-u.x)*l,i=(t.min.x-u.x)*l),c>=0?(s=(t.min.y-u.y)*c,r=(t.max.y-u.y)*c):(s=(t.max.y-u.y)*c,r=(t.min.y-u.y)*c),n>r||s>i?null:((s>n||n!=n)&&(n=s),(r=0?(o=(t.min.z-u.z)*h,a=(t.max.z-u.z)*h):(o=(t.max.z-u.z)*h,a=(t.min.z-u.z)*h),n>a||o>i?null:((o>n||n!=n)&&(n=o),(a=0?n:i,e)))}intersectsBox(t){return null!==this.intersectBox(t,s)}intersectTriangle(t,e,n,i,s){l.subVectors(e,t),c.subVectors(n,t),h.crossVectors(l,c);let r,o=this.direction.dot(h);if(o>0){if(i)return null;r=1}else{if(!(o<0))return null;r=-1,o=-o}a.subVectors(this.origin,t);const u=r*this.direction.dot(c.crossVectors(a,c));if(u<0)return null;const d=r*this.direction.dot(l.cross(a));if(d<0)return null;if(u+d>o)return null;const p=-r*a.dot(h);return p<0?null:this.at(p/o,s)}applyMatrix4(t){return this.origin.applyMatrix4(t),this.direction.transformDirection(t),this}equals(t){return t.origin.equals(this.origin)&&t.direction.equals(this.direction)}clone(){return(new this.constructor).copy(this)}}},function(t,e,n){\\\\\\\"use strict\\\\\\\";n.d(e,\\\\\\\"a\\\\\\\",(function(){return _}));var i=n(0);const s=new i.a,r=new i.a,o=new i.a,a=new i.a,l=new i.a,c=new i.a,h=new i.a,u=new i.a,d=new i.a,p=new i.a;class _{constructor(t=new i.a,e=new i.a,n=new i.a){this.a=t,this.b=e,this.c=n}static getNormal(t,e,n,i){i.subVectors(n,e),s.subVectors(t,e),i.cross(s);const r=i.lengthSq();return r>0?i.multiplyScalar(1/Math.sqrt(r)):i.set(0,0,0)}static getBarycoord(t,e,n,i,a){s.subVectors(i,e),r.subVectors(n,e),o.subVectors(t,e);const l=s.dot(s),c=s.dot(r),h=s.dot(o),u=r.dot(r),d=r.dot(o),p=l*u-c*c;if(0===p)return a.set(-2,-1,-1);const _=1/p,m=(u*h-c*d)*_,f=(l*d-c*h)*_;return a.set(1-m-f,f,m)}static containsPoint(t,e,n,i){return this.getBarycoord(t,e,n,i,a),a.x>=0&&a.y>=0&&a.x+a.y<=1}static getUV(t,e,n,i,s,r,o,l){return this.getBarycoord(t,e,n,i,a),l.set(0,0),l.addScaledVector(s,a.x),l.addScaledVector(r,a.y),l.addScaledVector(o,a.z),l}static isFrontFacing(t,e,n,i){return s.subVectors(n,e),r.subVectors(t,e),s.cross(r).dot(i)<0}set(t,e,n){return this.a.copy(t),this.b.copy(e),this.c.copy(n),this}setFromPointsAndIndices(t,e,n,i){return this.a.copy(t[e]),this.b.copy(t[n]),this.c.copy(t[i]),this}setFromAttributeAndIndices(t,e,n,i){return this.a.fromBufferAttribute(t,e),this.b.fromBufferAttribute(t,n),this.c.fromBufferAttribute(t,i),this}clone(){return(new this.constructor).copy(this)}copy(t){return this.a.copy(t.a),this.b.copy(t.b),this.c.copy(t.c),this}getArea(){return s.subVectors(this.c,this.b),r.subVectors(this.a,this.b),.5*s.cross(r).length()}getMidpoint(t){return t.addVectors(this.a,this.b).add(this.c).multiplyScalar(1/3)}getNormal(t){return _.getNormal(this.a,this.b,this.c,t)}getPlane(t){return t.setFromCoplanarPoints(this.a,this.b,this.c)}getBarycoord(t,e){return _.getBarycoord(t,this.a,this.b,this.c,e)}getUV(t,e,n,i,s){return _.getUV(t,this.a,this.b,this.c,e,n,i,s)}containsPoint(t){return _.containsPoint(t,this.a,this.b,this.c)}isFrontFacing(t){return _.isFrontFacing(this.a,this.b,this.c,t)}intersectsBox(t){return t.intersectsTriangle(this)}closestPointToPoint(t,e){const n=this.a,i=this.b,s=this.c;let r,o;l.subVectors(i,n),c.subVectors(s,n),u.subVectors(t,n);const a=l.dot(u),_=c.dot(u);if(a<=0&&_<=0)return e.copy(n);d.subVectors(t,i);const m=l.dot(d),f=c.dot(d);if(m>=0&&f<=m)return e.copy(i);const g=a*f-m*_;if(g<=0&&a>=0&&m<=0)return r=a/(a-m),e.copy(n).addScaledVector(l,r);p.subVectors(t,s);const v=l.dot(p),y=c.dot(p);if(y>=0&&v<=y)return e.copy(s);const x=v*_-a*y;if(x<=0&&_>=0&&y<=0)return o=_/(_-y),e.copy(n).addScaledVector(c,o);const b=m*y-v*f;if(b<=0&&f-m>=0&&v-y>=0)return h.subVectors(s,i),o=(f-m)/(f-m+(v-y)),e.copy(i).addScaledVector(h,o);const w=1/(b+x+g);return r=x*w,o=g*w,e.copy(n).addScaledVector(l,r).addScaledVector(c,o)}equals(t){return t.a.equals(this.a)&&t.b.equals(this.b)&&t.c.equals(this.c)}}},function(t,e,n){\\\\\\\"use 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i=t.ray.origin.distanceTo(d);it.far||e.push({distance:i,point:u.clone().applyMatrix4(this.matrixWorld),index:n,face:null,faceIndex:null,object:this})}}}else n.isGeometry&&console.error(\\\\\\\"THREE.Line.raycast() no longer supports THREE.Geometry. Use THREE.BufferGeometry instead.\\\\\\\")}updateMorphTargets(){const t=this.geometry;if(t.isBufferGeometry){const e=t.morphAttributes,n=Object.keys(e);if(n.length>0){const t=e[n[0]];if(void 0!==t){this.morphTargetInfluences=[],this.morphTargetDictionary={};for(let e=0,n=t.length;e0&&console.error(\\\\\\\"THREE.Line.updateMorphTargets() does not support THREE.Geometry. 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l{constructor(){this._next_id=0,this._successors=new Map,this._predecessors=new Map,this._nodes_by_id=new Map,this._nodesCount=0,this._debugging=!1,this._addedNodesDuringDebugging=new Map}startDebugging(){this._debugging=!0,console.log(\\\\\\\"CoreGraph.startDebugging\\\\\\\",this._next_id)}stopDebugging(){this._debugging=!1,console.log(\\\\\\\"CoreGraph.stopDebugging\\\\\\\",this._next_id)}printDebug(){this._addedNodesDuringDebugging.forEach(((t,e)=>{console.log(e,t,t.graphPredecessors(),t.graphSuccessors())}))}setScene(t){this._scene=t}scene(){return this._scene}nextId(){return this._next_id+=1,this._next_id}nodesFromIds(t){const e=[];for(let n of t){const t=this.nodeFromId(n);t&&e.push(t)}return e}nodeFromId(t){return this._nodes_by_id.get(t)}hasNode(t){return null!=this._nodes_by_id.get(t.graphNodeId())}addNode(t){this._nodes_by_id.set(t.graphNodeId(),t),this._nodesCount+=1,this._debugging&&this._addedNodesDuringDebugging.set(t.graphNodeId(),t)}removeNode(t){this._nodes_by_id.delete(t.graphNodeId()),this._successors.delete(t.graphNodeId()),this._predecessors.delete(t.graphNodeId()),this._nodesCount-=1,this._debugging&&this._addedNodesDuringDebugging.delete(t.graphNodeId())}nodesCount(){return this._nodesCount}connect(t,e,n=!0){const i=t.graphNodeId(),s=e.graphNodeId();if(this.hasNode(t)&&this.hasNode(e)){if(n){n=!(!this._scene||this._scene.loadingController.isLoading())}let e=!1;return n&&(e=this._hasPredecessor(i,s)),!e&&(this._createConnection(i,s),t.dirtyController.clearSuccessorsCacheWithPredecessors(),!0)}return console.warn(`attempt to connect non existing node ${i} or ${s}`),!1}disconnect(t,e){this._remove_connection(t.graphNodeId(),e.graphNodeId()),t.dirtyController.clearSuccessorsCacheWithPredecessors()}disconnectPredecessors(t){const e=this.predecessors(t);for(let n of e)this.disconnect(n,t)}disconnectSuccessors(t){const e=this.successors(t);for(let n of e)this.disconnect(t,n)}predecessorIds(t){const e=this._predecessors.get(t);if(e){const t=[];return e.forEach(((e,n)=>{t.push(n)})),t}return[]}predecessors(t){const e=this.predecessorIds(t.graphNodeId());return this.nodesFromIds(e)}successorIds(t){const e=this._successors.get(t);if(e){const t=[];return e.forEach(((e,n)=>{t.push(n)})),t}return[]}successors(t){const e=this.successorIds(t.graphNodeId())||[];return this.nodesFromIds(e)}allPredecessorIds(t){return this.allNextIds(t,\\\\\\\"predecessorIds\\\\\\\")}allSuccessorIds(t){return this.allNextIds(t,\\\\\\\"successorIds\\\\\\\")}allPredecessors(t){const e=this.allPredecessorIds(t);return this.nodesFromIds(e)}allSuccessors(t){const e=this.allSuccessorIds(t);return this.nodesFromIds(e)}_createConnection(t,e){let n=this._successors.get(t);if(n||(n=new Set,this._successors.set(t,n)),n.has(e))return;n.add(e);let i=this._predecessors.get(e);i||(i=new Set,this._predecessors.set(e,i)),i.add(t)}_remove_connection(t,e){let n=this._successors.get(t);n&&(n.delete(e),0==n.size&&this._successors.delete(t));let i=this._predecessors.get(e);i&&(i.delete(t),0==i.size&&this._predecessors.delete(e))}allNextIds(t,e){const n=new Map,i=[];let s=this[e](t.graphNodeId());for(;s.length>0;){const t=[];for(let n of s)for(let i of this[e](n))t.push(i);for(let t of s)n.set(t,!0);for(let e of t)s.push(e);s=t}return n.forEach(((t,e)=>{i.push(e)})),i}_hasPredecessor(t,e){const n=this.predecessorIds(t);if(n){if(n.includes(e))return!0;for(let t of n)return this._hasPredecessor(t,e)}return!1}}class c{constructor(t){this._node=t,this._cooks_count=0,this._total_cook_time=0,this._total_inputs_time=0,this._total_params_time=0}update_cook_data(t){this._cooks_count+=1,this._total_cook_time+=t.cookTime,this._total_inputs_time+=t.inputsTime,this._total_params_time+=t.paramsTime}total_time(){return this._total_cook_time+this._total_inputs_time+this._total_params_time}total_cook_time(){return this._total_cook_time}cook_time_per_iteration(){return this._cooks_count>0?this._total_cook_time/this._cooks_count:0}total_inputs_time(){return this._total_inputs_time}inputs_time_per_iteration(){return this._cooks_count>0?this._total_inputs_time/this._cooks_count:0}total_params_time2(){return this._total_params_time}params_time_per_iteration2(){return this._cooks_count>0?this._total_params_time/this._cooks_count:0}cooks_count(){return 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u.intersection(this.toSet(t),this.toSet(e)).forEach((t=>n.push(t))),n}static difference(t,e){const n=[];return u.difference(this.toSet(t),this.toSet(e)).forEach((t=>n.push(t))),n}static toSet(t){const e=new Set;for(let n of t)e.add(n);return e}static isEqual(t,e){if(t.length!=e.length)return!1;const n=t.length;for(let i=0;i{s[r]=t,r++})),m.isString(s[0])?s.sort():s.sort(((t,e)=>t-e));const o=new Array(t.length);r=0;for(let t of s){const e=n.get(t);if(e)for(let t of e)o[r]=t,r++}return o}static range(t,e,n=1){null==e&&(e=t,t=0);const i=Math.floor((e-t)/n),s=new Array(i);for(let e=0;e0?1:-1,c.push(C.x,C.y,C.z),h.push(a/f),h.push(1-r/g),E+=1}}for(let t=0;t0&&(e.defines=this.defines),e.vertexShader=this.vertexShader,e.fragmentShader=this.fragmentShader;const n={};for(const t in this.extensions)!0===this.extensions[t]&&(n[t]=!0);return Object.keys(n).length>0&&(e.extensions=n),e}}F.prototype.isShaderMaterial=!0;var D=n(6),B=n(14),z=\\\\\\\"\\\\n#ifdef USE_SHADOWMAP\\\\n\\\\n\\\\t#if NUM_DIR_LIGHT_SHADOWS > 0\\\\n\\\\n\\\\t\\\\tuniform sampler2D directionalShadowMap[ NUM_DIR_LIGHT_SHADOWS ];\\\\n\\\\t\\\\tvarying vec4 vDirectionalShadowCoord[ NUM_DIR_LIGHT_SHADOWS ];\\\\n\\\\n\\\\t\\\\tstruct DirectionalLightShadow {\\\\n\\\\t\\\\t\\\\tfloat shadowBias;\\\\n\\\\t\\\\t\\\\tfloat shadowNormalBias;\\\\n\\\\t\\\\t\\\\tfloat shadowRadius;\\\\n\\\\t\\\\t\\\\tvec2 shadowMapSize;\\\\n\\\\t\\\\t};\\\\n\\\\n\\\\t\\\\tuniform DirectionalLightShadow directionalLightShadows[ NUM_DIR_LIGHT_SHADOWS ];\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\t#if NUM_SPOT_LIGHT_SHADOWS > 0\\\\n\\\\n\\\\t\\\\tuniform sampler2D spotShadowMap[ NUM_SPOT_LIGHT_SHADOWS ];\\\\n\\\\t\\\\tvarying vec4 vSpotShadowCoord[ NUM_SPOT_LIGHT_SHADOWS ];\\\\n\\\\n\\\\t\\\\tstruct SpotLightShadow {\\\\n\\\\t\\\\t\\\\tfloat shadowBias;\\\\n\\\\t\\\\t\\\\tfloat shadowNormalBias;\\\\n\\\\t\\\\t\\\\tfloat shadowRadius;\\\\n\\\\t\\\\t\\\\tvec2 shadowMapSize;\\\\n\\\\t\\\\t};\\\\n\\\\n\\\\t\\\\tuniform SpotLightShadow spotLightShadows[ NUM_SPOT_LIGHT_SHADOWS ];\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\t#if NUM_POINT_LIGHT_SHADOWS > 0\\\\n\\\\n\\\\t\\\\tuniform sampler2D pointShadowMap[ NUM_POINT_LIGHT_SHADOWS ];\\\\n\\\\t\\\\tvarying vec4 vPointShadowCoord[ NUM_POINT_LIGHT_SHADOWS ];\\\\n\\\\n\\\\t\\\\tstruct PointLightShadow {\\\\n\\\\t\\\\t\\\\tfloat shadowBias;\\\\n\\\\t\\\\t\\\\tfloat shadowNormalBias;\\\\n\\\\t\\\\t\\\\tfloat shadowRadius;\\\\n\\\\t\\\\t\\\\tvec2 shadowMapSize;\\\\n\\\\t\\\\t\\\\tfloat shadowCameraNear;\\\\n\\\\t\\\\t\\\\tfloat shadowCameraFar;\\\\n\\\\t\\\\t};\\\\n\\\\n\\\\t\\\\tuniform PointLightShadow pointLightShadows[ NUM_POINT_LIGHT_SHADOWS ];\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\t/*\\\\n\\\\t#if NUM_RECT_AREA_LIGHTS > 0\\\\n\\\\n\\\\t\\\\t// TODO (abelnation): create uniforms for area light shadows\\\\n\\\\n\\\\t#endif\\\\n\\\\t*/\\\\n\\\\n\\\\tfloat texture2DCompare( sampler2D depths, vec2 uv, float compare ) {\\\\n\\\\n\\\\t\\\\treturn step( compare, unpackRGBAToDepth( texture2D( depths, uv ) ) );\\\\n\\\\n\\\\t}\\\\n\\\\n\\\\tvec2 texture2DDistribution( sampler2D shadow, vec2 uv ) {\\\\n\\\\n\\\\t\\\\treturn unpackRGBATo2Half( texture2D( shadow, uv ) );\\\\n\\\\n\\\\t}\\\\n\\\\n\\\\tfloat VSMShadow (sampler2D shadow, vec2 uv, float compare ){\\\\n\\\\n\\\\t\\\\tfloat occlusion = 1.0;\\\\n\\\\n\\\\t\\\\tvec2 distribution = texture2DDistribution( shadow, uv );\\\\n\\\\n\\\\t\\\\tfloat hard_shadow = step( compare , distribution.x ); // Hard Shadow\\\\n\\\\n\\\\t\\\\tif (hard_shadow != 1.0 ) {\\\\n\\\\n\\\\t\\\\t\\\\tfloat distance = compare - distribution.x ;\\\\n\\\\t\\\\t\\\\tfloat variance = max( 0.00000, distribution.y * distribution.y );\\\\n\\\\t\\\\t\\\\tfloat softness_probability = variance / (variance + distance * distance ); // Chebeyshevs inequality\\\\n\\\\t\\\\t\\\\tsoftness_probability = clamp( ( softness_probability - 0.3 ) / ( 0.95 - 0.3 ), 0.0, 1.0 ); // 0.3 reduces light bleed\\\\n\\\\t\\\\t\\\\tocclusion = clamp( max( hard_shadow, softness_probability ), 0.0, 1.0 );\\\\n\\\\n\\\\t\\\\t}\\\\n\\\\t\\\\treturn occlusion;\\\\n\\\\n\\\\t}\\\\n\\\\n\\\\tfloat getShadow( sampler2D shadowMap, vec2 shadowMapSize, float shadowBias, float shadowRadius, vec4 shadowCoord ) {\\\\n\\\\n\\\\t\\\\tfloat shadow = 1.0;\\\\n\\\\n\\\\t\\\\tshadowCoord.xyz /= shadowCoord.w;\\\\n\\\\t\\\\tshadowCoord.z += shadowBias;\\\\n\\\\n\\\\t\\\\t// if ( something && something ) breaks ATI OpenGL shader compiler\\\\n\\\\t\\\\t// if ( all( something, something ) ) using this instead\\\\n\\\\n\\\\t\\\\tbvec4 inFrustumVec = bvec4 ( shadowCoord.x >= 0.0, shadowCoord.x <= 1.0, shadowCoord.y >= 0.0, shadowCoord.y <= 1.0 );\\\\n\\\\t\\\\tbool inFrustum = all( inFrustumVec );\\\\n\\\\n\\\\t\\\\tbvec2 frustumTestVec = bvec2( inFrustum, shadowCoord.z <= 1.0 );\\\\n\\\\n\\\\t\\\\tbool frustumTest = all( frustumTestVec );\\\\n\\\\n\\\\t\\\\tif ( frustumTest ) {\\\\n\\\\n\\\\t\\\\t#if defined( SHADOWMAP_TYPE_PCF )\\\\n\\\\n\\\\t\\\\t\\\\tvec2 texelSize = vec2( 1.0 ) / shadowMapSize;\\\\n\\\\n\\\\t\\\\t\\\\tfloat dx0 = - texelSize.x * shadowRadius;\\\\n\\\\t\\\\t\\\\tfloat dy0 = - texelSize.y * shadowRadius;\\\\n\\\\t\\\\t\\\\tfloat dx1 = + texelSize.x * shadowRadius;\\\\n\\\\t\\\\t\\\\tfloat dy1 = + texelSize.y * shadowRadius;\\\\n\\\\t\\\\t\\\\tfloat dx2 = dx0 / 2.0;\\\\n\\\\t\\\\t\\\\tfloat dy2 = dy0 / 2.0;\\\\n\\\\t\\\\t\\\\tfloat dx3 = dx1 / 2.0;\\\\n\\\\t\\\\t\\\\tfloat dy3 = dy1 / 2.0;\\\\n\\\\n\\\\t\\\\t\\\\tshadow = (\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx0, dy0 ), shadowCoord.z ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( 0.0, dy0 ), shadowCoord.z ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx1, dy0 ), shadowCoord.z ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx2, dy2 ), shadowCoord.z ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( 0.0, dy2 ), shadowCoord.z ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx3, dy2 ), shadowCoord.z ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx0, 0.0 ), shadowCoord.z ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx2, 0.0 ), shadowCoord.z ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, shadowCoord.xy, shadowCoord.z ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx3, 0.0 ), shadowCoord.z ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx1, 0.0 ), shadowCoord.z ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx2, dy3 ), shadowCoord.z ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( 0.0, dy3 ), shadowCoord.z ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx3, dy3 ), shadowCoord.z ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx0, dy1 ), shadowCoord.z ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( 0.0, dy1 ), shadowCoord.z ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx1, dy1 ), shadowCoord.z )\\\\n\\\\t\\\\t\\\\t) * ( 1.0 / 17.0 );\\\\n\\\\n\\\\t\\\\t#elif defined( SHADOWMAP_TYPE_PCF_SOFT )\\\\n\\\\n\\\\t\\\\t\\\\tvec2 texelSize = vec2( 1.0 ) / shadowMapSize;\\\\n\\\\t\\\\t\\\\tfloat dx = texelSize.x;\\\\n\\\\t\\\\t\\\\tfloat dy = texelSize.y;\\\\n\\\\n\\\\t\\\\t\\\\tvec2 uv = shadowCoord.xy;\\\\n\\\\t\\\\t\\\\tvec2 f = fract( uv * shadowMapSize + 0.5 );\\\\n\\\\t\\\\t\\\\tuv -= f * texelSize;\\\\n\\\\n\\\\t\\\\t\\\\tshadow = (\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, uv, shadowCoord.z ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, uv + vec2( dx, 0.0 ), shadowCoord.z ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, uv + vec2( 0.0, dy ), shadowCoord.z ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, uv + texelSize, shadowCoord.z ) +\\\\n\\\\t\\\\t\\\\t\\\\tmix( texture2DCompare( shadowMap, uv + vec2( -dx, 0.0 ), shadowCoord.z ), \\\\n\\\\t\\\\t\\\\t\\\\t\\\\t texture2DCompare( shadowMap, uv + vec2( 2.0 * dx, 0.0 ), shadowCoord.z ),\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t f.x ) +\\\\n\\\\t\\\\t\\\\t\\\\tmix( texture2DCompare( shadowMap, uv + vec2( -dx, dy ), shadowCoord.z ), \\\\n\\\\t\\\\t\\\\t\\\\t\\\\t texture2DCompare( shadowMap, uv + vec2( 2.0 * dx, dy ), shadowCoord.z ),\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t f.x ) +\\\\n\\\\t\\\\t\\\\t\\\\tmix( texture2DCompare( shadowMap, uv + vec2( 0.0, -dy ), shadowCoord.z ), \\\\n\\\\t\\\\t\\\\t\\\\t\\\\t texture2DCompare( shadowMap, uv + vec2( 0.0, 2.0 * dy ), shadowCoord.z ),\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t f.y ) +\\\\n\\\\t\\\\t\\\\t\\\\tmix( texture2DCompare( shadowMap, uv + vec2( dx, -dy ), shadowCoord.z ), \\\\n\\\\t\\\\t\\\\t\\\\t\\\\t texture2DCompare( shadowMap, uv + vec2( dx, 2.0 * dy ), shadowCoord.z ),\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t f.y ) +\\\\n\\\\t\\\\t\\\\t\\\\tmix( mix( texture2DCompare( shadowMap, uv + vec2( -dx, -dy ), shadowCoord.z ), \\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t texture2DCompare( shadowMap, uv + vec2( 2.0 * dx, -dy ), shadowCoord.z ),\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t f.x ),\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t mix( texture2DCompare( shadowMap, uv + vec2( -dx, 2.0 * dy ), shadowCoord.z ), \\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t texture2DCompare( shadowMap, uv + vec2( 2.0 * dx, 2.0 * dy ), shadowCoord.z ),\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t f.x ),\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t f.y )\\\\n\\\\t\\\\t\\\\t) * ( 1.0 / 9.0 );\\\\n\\\\n\\\\t\\\\t#elif defined( SHADOWMAP_TYPE_VSM )\\\\n\\\\n\\\\t\\\\t\\\\tshadow = VSMShadow( shadowMap, shadowCoord.xy, shadowCoord.z );\\\\n\\\\n\\\\t\\\\t#else // no percentage-closer filtering:\\\\n\\\\n\\\\t\\\\t\\\\tshadow = texture2DCompare( shadowMap, shadowCoord.xy, shadowCoord.z );\\\\n\\\\n\\\\t\\\\t#endif\\\\n\\\\n\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\treturn shadow;\\\\n\\\\n\\\\t}\\\\n\\\\n\\\\t// cubeToUV() maps a 3D direction vector suitable for cube texture mapping to a 2D\\\\n\\\\t// vector suitable for 2D texture mapping. This code uses the following layout for the\\\\n\\\\t// 2D texture:\\\\n\\\\t//\\\\n\\\\t// xzXZ\\\\n\\\\t// y Y\\\\n\\\\t//\\\\n\\\\t// Y - Positive y direction\\\\n\\\\t// y - Negative y direction\\\\n\\\\t// X - Positive x direction\\\\n\\\\t// x - Negative x direction\\\\n\\\\t// Z - Positive z direction\\\\n\\\\t// z - Negative z direction\\\\n\\\\t//\\\\n\\\\t// Source and test bed:\\\\n\\\\t// https://gist.github.com/tschw/da10c43c467ce8afd0c4\\\\n\\\\n\\\\tvec2 cubeToUV( vec3 v, float texelSizeY ) {\\\\n\\\\n\\\\t\\\\t// Number of texels to avoid at the edge of each square\\\\n\\\\n\\\\t\\\\tvec3 absV = abs( v );\\\\n\\\\n\\\\t\\\\t// Intersect unit cube\\\\n\\\\n\\\\t\\\\tfloat scaleToCube = 1.0 / max( absV.x, max( absV.y, absV.z ) );\\\\n\\\\t\\\\tabsV *= scaleToCube;\\\\n\\\\n\\\\t\\\\t// Apply scale to avoid seams\\\\n\\\\n\\\\t\\\\t// two texels less per square (one texel will do for NEAREST)\\\\n\\\\t\\\\tv *= scaleToCube * ( 1.0 - 2.0 * texelSizeY );\\\\n\\\\n\\\\t\\\\t// Unwrap\\\\n\\\\n\\\\t\\\\t// space: -1 ... 1 range for each square\\\\n\\\\t\\\\t//\\\\n\\\\t\\\\t// #X##\\\\t\\\\tdim := ( 4 , 2 )\\\\n\\\\t\\\\t// # #\\\\t\\\\tcenter := ( 1 , 1 )\\\\n\\\\n\\\\t\\\\tvec2 planar = v.xy;\\\\n\\\\n\\\\t\\\\tfloat almostATexel = 1.5 * texelSizeY;\\\\n\\\\t\\\\tfloat almostOne = 1.0 - almostATexel;\\\\n\\\\n\\\\t\\\\tif ( absV.z >= almostOne ) {\\\\n\\\\n\\\\t\\\\t\\\\tif ( v.z > 0.0 )\\\\n\\\\t\\\\t\\\\t\\\\tplanar.x = 4.0 - v.x;\\\\n\\\\n\\\\t\\\\t} else if ( absV.x >= almostOne ) {\\\\n\\\\n\\\\t\\\\t\\\\tfloat signX = sign( v.x );\\\\n\\\\t\\\\t\\\\tplanar.x = v.z * signX + 2.0 * signX;\\\\n\\\\n\\\\t\\\\t} else if ( absV.y >= almostOne ) {\\\\n\\\\n\\\\t\\\\t\\\\tfloat signY = sign( v.y );\\\\n\\\\t\\\\t\\\\tplanar.x = v.x + 2.0 * signY + 2.0;\\\\n\\\\t\\\\t\\\\tplanar.y = v.z * signY - 2.0;\\\\n\\\\n\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\t// Transform to UV space\\\\n\\\\n\\\\t\\\\t// scale := 0.5 / dim\\\\n\\\\t\\\\t// translate := ( center + 0.5 ) / dim\\\\n\\\\t\\\\treturn vec2( 0.125, 0.25 ) * planar + vec2( 0.375, 0.75 );\\\\n\\\\n\\\\t}\\\\n\\\\n\\\\tfloat getPointShadow( sampler2D shadowMap, vec2 shadowMapSize, float shadowBias, float shadowRadius, vec4 shadowCoord, float shadowCameraNear, float shadowCameraFar ) {\\\\n\\\\n\\\\t\\\\tvec2 texelSize = vec2( 1.0 ) / ( shadowMapSize * vec2( 4.0, 2.0 ) );\\\\n\\\\n\\\\t\\\\t// for point lights, the uniform @vShadowCoord is re-purposed to hold\\\\n\\\\t\\\\t// the vector from the light to the world-space position of the fragment.\\\\n\\\\t\\\\tvec3 lightToPosition = shadowCoord.xyz;\\\\n\\\\n\\\\t\\\\t// dp = normalized distance from light to fragment position\\\\n\\\\t\\\\tfloat dp = ( length( lightToPosition ) - shadowCameraNear ) / ( shadowCameraFar - shadowCameraNear ); // need to clamp?\\\\n\\\\t\\\\tdp += shadowBias;\\\\n\\\\n\\\\t\\\\t// bd3D = base direction 3D\\\\n\\\\t\\\\tvec3 bd3D = normalize( lightToPosition );\\\\n\\\\n\\\\t\\\\t#if defined( SHADOWMAP_TYPE_PCF ) || defined( SHADOWMAP_TYPE_PCF_SOFT ) || defined( SHADOWMAP_TYPE_VSM )\\\\n\\\\n\\\\t\\\\t\\\\tvec2 offset = vec2( - 1, 1 ) * shadowRadius * texelSize.y;\\\\n\\\\n\\\\t\\\\t\\\\treturn (\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, cubeToUV( bd3D + offset.xyy, texelSize.y ), dp ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, cubeToUV( bd3D + offset.yyy, texelSize.y ), dp ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, cubeToUV( bd3D + offset.xyx, texelSize.y ), dp ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, cubeToUV( bd3D + offset.yyx, texelSize.y ), dp ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, cubeToUV( bd3D, texelSize.y ), dp ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, cubeToUV( bd3D + offset.xxy, texelSize.y ), dp ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, cubeToUV( bd3D + offset.yxy, texelSize.y ), dp ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, cubeToUV( bd3D + offset.xxx, texelSize.y ), dp ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, cubeToUV( bd3D + offset.yxx, texelSize.y ), dp )\\\\n\\\\t\\\\t\\\\t) * ( 1.0 / 9.0 );\\\\n\\\\n\\\\t\\\\t#else // no percentage-closer filtering\\\\n\\\\n\\\\t\\\\t\\\\treturn texture2DCompare( shadowMap, cubeToUV( bd3D, texelSize.y ), dp );\\\\n\\\\n\\\\t\\\\t#endif\\\\n\\\\n\\\\t}\\\\n\\\\n#endif\\\\n\\\\\\\",k=\\\\\\\"\\\\n#ifdef USE_TRANSMISSION\\\\n\\\\n\\\\tfloat transmissionAlpha = 1.0;\\\\n\\\\tfloat transmissionFactor = transmission;\\\\n\\\\tfloat thicknessFactor = thickness;\\\\n\\\\n\\\\t#ifdef USE_TRANSMISSIONMAP\\\\n\\\\n\\\\t\\\\ttransmissionFactor *= texture2D( transmissionMap, vUv ).r;\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\t#ifdef USE_THICKNESSMAP\\\\n\\\\n\\\\t\\\\tthicknessFactor *= texture2D( thicknessMap, vUv ).g;\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\tvec3 pos = vWorldPosition;\\\\n\\\\tvec3 v = normalize( cameraPosition - pos );\\\\n\\\\tvec3 n = inverseTransformDirection( normal, viewMatrix );\\\\n\\\\n\\\\tvec4 transmission = getIBLVolumeRefraction(\\\\n\\\\t\\\\tn, v, roughnessFactor, material.diffuseColor, material.specularColor, material.specularF90,\\\\n\\\\t\\\\tpos, modelMatrix, viewMatrix, projectionMatrix, ior, thicknessFactor,\\\\n\\\\t\\\\tattenuationTint, attenuationDistance );\\\\n\\\\n\\\\ttotalDiffuse = mix( totalDiffuse, transmission.rgb, transmissionFactor );\\\\n\\\\ttransmissionAlpha = mix( transmissionAlpha, transmission.a, transmissionFactor );\\\\n#endif\\\\n\\\\\\\";const U={alphamap_fragment:\\\\\\\"\\\\n#ifdef USE_ALPHAMAP\\\\n\\\\n\\\\tdiffuseColor.a *= texture2D( alphaMap, vUv ).g;\\\\n\\\\n#endif\\\\n\\\\\\\",alphamap_pars_fragment:\\\\\\\"\\\\n#ifdef USE_ALPHAMAP\\\\n\\\\n\\\\tuniform sampler2D alphaMap;\\\\n\\\\n#endif\\\\n\\\\\\\",alphatest_fragment:\\\\\\\"\\\\n#ifdef USE_ALPHATEST\\\\n\\\\n\\\\tif ( diffuseColor.a < alphaTest ) discard;\\\\n\\\\n#endif\\\\n\\\\\\\",alphatest_pars_fragment:\\\\\\\"\\\\n#ifdef USE_ALPHATEST\\\\n\\\\tuniform float alphaTest;\\\\n#endif\\\\n\\\\\\\",aomap_fragment:\\\\\\\"\\\\n#ifdef USE_AOMAP\\\\n\\\\n\\\\t// reads channel R, compatible with a combined OcclusionRoughnessMetallic (RGB) texture\\\\n\\\\tfloat ambientOcclusion = ( texture2D( aoMap, vUv2 ).r - 1.0 ) * aoMapIntensity + 1.0;\\\\n\\\\n\\\\treflectedLight.indirectDiffuse *= ambientOcclusion;\\\\n\\\\n\\\\t#if defined( USE_ENVMAP ) && defined( STANDARD )\\\\n\\\\n\\\\t\\\\tfloat dotNV = saturate( dot( geometry.normal, geometry.viewDir ) );\\\\n\\\\n\\\\t\\\\treflectedLight.indirectSpecular *= computeSpecularOcclusion( dotNV, ambientOcclusion, material.roughness );\\\\n\\\\n\\\\t#endif\\\\n\\\\n#endif\\\\n\\\\\\\",aomap_pars_fragment:\\\\\\\"\\\\n#ifdef USE_AOMAP\\\\n\\\\n\\\\tuniform sampler2D aoMap;\\\\n\\\\tuniform float aoMapIntensity;\\\\n\\\\n#endif\\\\n\\\\\\\",begin_vertex:\\\\\\\"\\\\nvec3 transformed = vec3( position );\\\\n\\\\\\\",beginnormal_vertex:\\\\\\\"\\\\nvec3 objectNormal = vec3( normal );\\\\n\\\\n#ifdef USE_TANGENT\\\\n\\\\n\\\\tvec3 objectTangent = vec3( tangent.xyz );\\\\n\\\\n#endif\\\\n\\\\\\\",bsdfs:'\\\\n\\\\nvec3 BRDF_Lambert( const in vec3 diffuseColor ) {\\\\n\\\\n\\\\treturn RECIPROCAL_PI * diffuseColor;\\\\n\\\\n} // validated\\\\n\\\\nvec3 F_Schlick( const in vec3 f0, const in float f90, const in float dotVH ) {\\\\n\\\\n\\\\t// Original approximation by Christophe Schlick \\\\'94\\\\n\\\\t// float fresnel = pow( 1.0 - dotVH, 5.0 );\\\\n\\\\n\\\\t// Optimized variant (presented by Epic at SIGGRAPH \\\\'13)\\\\n\\\\t// https://cdn2.unrealengine.com/Resources/files/2013SiggraphPresentationsNotes-26915738.pdf\\\\n\\\\tfloat fresnel = exp2( ( - 5.55473 * dotVH - 6.98316 ) * dotVH );\\\\n\\\\n\\\\treturn f0 * ( 1.0 - fresnel ) + ( f90 * fresnel );\\\\n\\\\n} // validated\\\\n\\\\n// Moving Frostbite to Physically Based Rendering 3.0 - page 12, listing 2\\\\n// https://seblagarde.files.wordpress.com/2015/07/course_notes_moving_frostbite_to_pbr_v32.pdf\\\\nfloat V_GGX_SmithCorrelated( const in float alpha, const in float dotNL, const in float dotNV ) {\\\\n\\\\n\\\\tfloat a2 = pow2( alpha );\\\\n\\\\n\\\\tfloat gv = dotNL * sqrt( a2 + ( 1.0 - a2 ) * pow2( dotNV ) );\\\\n\\\\tfloat gl = dotNV * sqrt( a2 + ( 1.0 - a2 ) * pow2( dotNL ) );\\\\n\\\\n\\\\treturn 0.5 / max( gv + gl, EPSILON );\\\\n\\\\n}\\\\n\\\\n// Microfacet Models for Refraction through Rough Surfaces - equation (33)\\\\n// http://graphicrants.blogspot.com/2013/08/specular-brdf-reference.html\\\\n// alpha is \\\\\\\"roughness squared\\\\\\\" in Disney’s reparameterization\\\\nfloat D_GGX( const in float alpha, const in float dotNH ) {\\\\n\\\\n\\\\tfloat a2 = pow2( alpha );\\\\n\\\\n\\\\tfloat denom = pow2( dotNH ) * ( a2 - 1.0 ) + 1.0; // avoid alpha = 0 with dotNH = 1\\\\n\\\\n\\\\treturn RECIPROCAL_PI * a2 / pow2( denom );\\\\n\\\\n}\\\\n\\\\n// GGX Distribution, Schlick Fresnel, GGX_SmithCorrelated Visibility\\\\nvec3 BRDF_GGX( const in vec3 lightDir, const in vec3 viewDir, const in vec3 normal, const in vec3 f0, const in float f90, const in float roughness ) {\\\\n\\\\n\\\\tfloat alpha = pow2( roughness ); // UE4\\\\'s roughness\\\\n\\\\n\\\\tvec3 halfDir = normalize( lightDir + viewDir );\\\\n\\\\n\\\\tfloat dotNL = saturate( dot( normal, lightDir ) );\\\\n\\\\tfloat dotNV = saturate( dot( normal, viewDir ) );\\\\n\\\\tfloat dotNH = saturate( dot( normal, halfDir ) );\\\\n\\\\tfloat dotVH = saturate( dot( viewDir, halfDir ) );\\\\n\\\\n\\\\tvec3 F = F_Schlick( f0, f90, dotVH );\\\\n\\\\n\\\\tfloat V = V_GGX_SmithCorrelated( alpha, dotNL, dotNV );\\\\n\\\\n\\\\tfloat D = D_GGX( alpha, dotNH );\\\\n\\\\n\\\\treturn F * ( V * D );\\\\n\\\\n}\\\\n\\\\n// Rect Area Light\\\\n\\\\n// Real-Time Polygonal-Light Shading with Linearly Transformed Cosines\\\\n// by Eric Heitz, Jonathan Dupuy, Stephen Hill and David Neubelt\\\\n// code: https://github.com/selfshadow/ltc_code/\\\\n\\\\nvec2 LTC_Uv( const in vec3 N, const in vec3 V, const in float roughness ) {\\\\n\\\\n\\\\tconst float LUT_SIZE = 64.0;\\\\n\\\\tconst float LUT_SCALE = ( LUT_SIZE - 1.0 ) / LUT_SIZE;\\\\n\\\\tconst float LUT_BIAS = 0.5 / LUT_SIZE;\\\\n\\\\n\\\\tfloat dotNV = saturate( dot( N, V ) );\\\\n\\\\n\\\\t// texture parameterized by sqrt( GGX alpha ) and sqrt( 1 - cos( theta ) )\\\\n\\\\tvec2 uv = vec2( roughness, sqrt( 1.0 - dotNV ) );\\\\n\\\\n\\\\tuv = uv * LUT_SCALE + LUT_BIAS;\\\\n\\\\n\\\\treturn uv;\\\\n\\\\n}\\\\n\\\\nfloat LTC_ClippedSphereFormFactor( const in vec3 f ) {\\\\n\\\\n\\\\t// Real-Time Area Lighting: a Journey from Research to Production (p.102)\\\\n\\\\t// An approximation of the form factor of a horizon-clipped rectangle.\\\\n\\\\n\\\\tfloat l = length( f );\\\\n\\\\n\\\\treturn max( ( l * l + f.z ) / ( l + 1.0 ), 0.0 );\\\\n\\\\n}\\\\n\\\\nvec3 LTC_EdgeVectorFormFactor( const in vec3 v1, const in vec3 v2 ) {\\\\n\\\\n\\\\tfloat x = dot( v1, v2 );\\\\n\\\\n\\\\tfloat y = abs( x );\\\\n\\\\n\\\\t// rational polynomial approximation to theta / sin( theta ) / 2PI\\\\n\\\\tfloat a = 0.8543985 + ( 0.4965155 + 0.0145206 * y ) * y;\\\\n\\\\tfloat b = 3.4175940 + ( 4.1616724 + y ) * y;\\\\n\\\\tfloat v = a / b;\\\\n\\\\n\\\\tfloat theta_sintheta = ( x > 0.0 ) ? v : 0.5 * inversesqrt( max( 1.0 - x * x, 1e-7 ) ) - v;\\\\n\\\\n\\\\treturn cross( v1, v2 ) * theta_sintheta;\\\\n\\\\n}\\\\n\\\\nvec3 LTC_Evaluate( const in vec3 N, const in vec3 V, const in vec3 P, const in mat3 mInv, const in vec3 rectCoords[ 4 ] ) {\\\\n\\\\n\\\\t// bail if point is on back side of plane of light\\\\n\\\\t// assumes ccw winding order of light vertices\\\\n\\\\tvec3 v1 = rectCoords[ 1 ] - rectCoords[ 0 ];\\\\n\\\\tvec3 v2 = rectCoords[ 3 ] - rectCoords[ 0 ];\\\\n\\\\tvec3 lightNormal = cross( v1, v2 );\\\\n\\\\n\\\\tif( dot( lightNormal, P - rectCoords[ 0 ] ) < 0.0 ) return vec3( 0.0 );\\\\n\\\\n\\\\t// construct orthonormal basis around N\\\\n\\\\tvec3 T1, T2;\\\\n\\\\tT1 = normalize( V - N * dot( V, N ) );\\\\n\\\\tT2 = - cross( N, T1 ); // negated from paper; possibly due to a different handedness of world coordinate system\\\\n\\\\n\\\\t// compute transform\\\\n\\\\tmat3 mat = mInv * transposeMat3( mat3( T1, T2, N ) );\\\\n\\\\n\\\\t// transform rect\\\\n\\\\tvec3 coords[ 4 ];\\\\n\\\\tcoords[ 0 ] = mat * ( rectCoords[ 0 ] - P );\\\\n\\\\tcoords[ 1 ] = mat * ( rectCoords[ 1 ] - P );\\\\n\\\\tcoords[ 2 ] = mat * ( rectCoords[ 2 ] - P );\\\\n\\\\tcoords[ 3 ] = mat * ( rectCoords[ 3 ] - P );\\\\n\\\\n\\\\t// project rect onto sphere\\\\n\\\\tcoords[ 0 ] = normalize( coords[ 0 ] );\\\\n\\\\tcoords[ 1 ] = normalize( coords[ 1 ] );\\\\n\\\\tcoords[ 2 ] = normalize( coords[ 2 ] );\\\\n\\\\tcoords[ 3 ] = normalize( coords[ 3 ] );\\\\n\\\\n\\\\t// calculate vector form factor\\\\n\\\\tvec3 vectorFormFactor = vec3( 0.0 );\\\\n\\\\tvectorFormFactor += LTC_EdgeVectorFormFactor( coords[ 0 ], coords[ 1 ] );\\\\n\\\\tvectorFormFactor += LTC_EdgeVectorFormFactor( coords[ 1 ], coords[ 2 ] );\\\\n\\\\tvectorFormFactor += LTC_EdgeVectorFormFactor( coords[ 2 ], coords[ 3 ] );\\\\n\\\\tvectorFormFactor += LTC_EdgeVectorFormFactor( coords[ 3 ], coords[ 0 ] );\\\\n\\\\n\\\\t// adjust for horizon clipping\\\\n\\\\tfloat result = LTC_ClippedSphereFormFactor( vectorFormFactor );\\\\n\\\\n/*\\\\n\\\\t// alternate method of adjusting for horizon clipping (see referece)\\\\n\\\\t// refactoring required\\\\n\\\\tfloat len = length( vectorFormFactor );\\\\n\\\\tfloat z = vectorFormFactor.z / len;\\\\n\\\\n\\\\tconst float LUT_SIZE = 64.0;\\\\n\\\\tconst float LUT_SCALE = ( LUT_SIZE - 1.0 ) / LUT_SIZE;\\\\n\\\\tconst float LUT_BIAS = 0.5 / LUT_SIZE;\\\\n\\\\n\\\\t// tabulated horizon-clipped sphere, apparently...\\\\n\\\\tvec2 uv = vec2( z * 0.5 + 0.5, len );\\\\n\\\\tuv = uv * LUT_SCALE + LUT_BIAS;\\\\n\\\\n\\\\tfloat scale = texture2D( ltc_2, uv ).w;\\\\n\\\\n\\\\tfloat result = len * scale;\\\\n*/\\\\n\\\\n\\\\treturn vec3( result );\\\\n\\\\n}\\\\n\\\\n// End Rect Area Light\\\\n\\\\n\\\\nfloat G_BlinnPhong_Implicit( /* const in float dotNL, const in float dotNV */ ) {\\\\n\\\\n\\\\t// geometry term is (n dot l)(n dot v) / 4(n dot l)(n dot v)\\\\n\\\\treturn 0.25;\\\\n\\\\n}\\\\n\\\\nfloat D_BlinnPhong( const in float shininess, const in float dotNH ) {\\\\n\\\\n\\\\treturn RECIPROCAL_PI * ( shininess * 0.5 + 1.0 ) * pow( dotNH, shininess );\\\\n\\\\n}\\\\n\\\\nvec3 BRDF_BlinnPhong( const in vec3 lightDir, const in vec3 viewDir, const in vec3 normal, const in vec3 specularColor, const in float shininess ) {\\\\n\\\\n\\\\tvec3 halfDir = normalize( lightDir + viewDir );\\\\n\\\\n\\\\tfloat dotNH = saturate( dot( normal, halfDir ) );\\\\n\\\\tfloat dotVH = saturate( dot( viewDir, halfDir ) );\\\\n\\\\n\\\\tvec3 F = F_Schlick( specularColor, 1.0, dotVH );\\\\n\\\\n\\\\tfloat G = G_BlinnPhong_Implicit( /* dotNL, dotNV */ );\\\\n\\\\n\\\\tfloat D = D_BlinnPhong( shininess, dotNH );\\\\n\\\\n\\\\treturn F * ( G * D );\\\\n\\\\n} // validated\\\\n\\\\n#if defined( USE_SHEEN )\\\\n\\\\n// https://github.com/google/filament/blob/master/shaders/src/brdf.fs\\\\nfloat D_Charlie( float roughness, float dotNH ) {\\\\n\\\\n\\\\tfloat alpha = pow2( roughness );\\\\n\\\\n\\\\t// Estevez and Kulla 2017, \\\\\\\"Production Friendly Microfacet Sheen BRDF\\\\\\\"\\\\n\\\\tfloat invAlpha = 1.0 / alpha;\\\\n\\\\tfloat cos2h = dotNH * dotNH;\\\\n\\\\tfloat sin2h = max( 1.0 - cos2h, 0.0078125 ); // 2^(-14/2), so sin2h^2 > 0 in fp16\\\\n\\\\n\\\\treturn ( 2.0 + invAlpha ) * pow( sin2h, invAlpha * 0.5 ) / ( 2.0 * PI );\\\\n\\\\n}\\\\n\\\\n// https://github.com/google/filament/blob/master/shaders/src/brdf.fs\\\\nfloat V_Neubelt( float dotNV, float dotNL ) {\\\\n\\\\n\\\\t// Neubelt and Pettineo 2013, \\\\\\\"Crafting a Next-gen Material Pipeline for The Order: 1886\\\\\\\"\\\\n\\\\treturn saturate( 1.0 / ( 4.0 * ( dotNL + dotNV - dotNL * dotNV ) ) );\\\\n\\\\n}\\\\n\\\\nvec3 BRDF_Sheen( const in vec3 lightDir, const in vec3 viewDir, const in vec3 normal, vec3 sheenTint, const in float sheenRoughness ) {\\\\n\\\\n\\\\tvec3 halfDir = normalize( lightDir + viewDir );\\\\n\\\\n\\\\tfloat dotNL = saturate( dot( normal, lightDir ) );\\\\n\\\\tfloat dotNV = saturate( dot( normal, viewDir ) );\\\\n\\\\tfloat dotNH = saturate( dot( normal, halfDir ) );\\\\n\\\\n\\\\tfloat D = D_Charlie( sheenRoughness, dotNH );\\\\n\\\\tfloat V = V_Neubelt( dotNV, dotNL );\\\\n\\\\n\\\\treturn sheenTint * ( D * V );\\\\n\\\\n}\\\\n\\\\n#endif\\\\n',bumpmap_pars_fragment:\\\\\\\"\\\\n#ifdef USE_BUMPMAP\\\\n\\\\n\\\\tuniform sampler2D bumpMap;\\\\n\\\\tuniform float bumpScale;\\\\n\\\\n\\\\t// Bump Mapping Unparametrized Surfaces on the GPU by Morten S. Mikkelsen\\\\n\\\\t// http://api.unrealengine.com/attachments/Engine/Rendering/LightingAndShadows/BumpMappingWithoutTangentSpace/mm_sfgrad_bump.pdf\\\\n\\\\n\\\\t// Evaluate the derivative of the height w.r.t. screen-space using forward differencing (listing 2)\\\\n\\\\n\\\\tvec2 dHdxy_fwd() {\\\\n\\\\n\\\\t\\\\tvec2 dSTdx = dFdx( vUv );\\\\n\\\\t\\\\tvec2 dSTdy = dFdy( vUv );\\\\n\\\\n\\\\t\\\\tfloat Hll = bumpScale * texture2D( bumpMap, vUv ).x;\\\\n\\\\t\\\\tfloat dBx = bumpScale * texture2D( bumpMap, vUv + dSTdx ).x - Hll;\\\\n\\\\t\\\\tfloat dBy = bumpScale * texture2D( bumpMap, vUv + dSTdy ).x - Hll;\\\\n\\\\n\\\\t\\\\treturn vec2( dBx, dBy );\\\\n\\\\n\\\\t}\\\\n\\\\n\\\\tvec3 perturbNormalArb( vec3 surf_pos, vec3 surf_norm, vec2 dHdxy, float faceDirection ) {\\\\n\\\\n\\\\t\\\\t// Workaround for Adreno 3XX dFd*( vec3 ) bug. See #9988\\\\n\\\\n\\\\t\\\\tvec3 vSigmaX = vec3( dFdx( surf_pos.x ), dFdx( surf_pos.y ), dFdx( surf_pos.z ) );\\\\n\\\\t\\\\tvec3 vSigmaY = vec3( dFdy( surf_pos.x ), dFdy( surf_pos.y ), dFdy( surf_pos.z ) );\\\\n\\\\t\\\\tvec3 vN = surf_norm;\\\\t\\\\t// normalized\\\\n\\\\n\\\\t\\\\tvec3 R1 = cross( vSigmaY, vN );\\\\n\\\\t\\\\tvec3 R2 = cross( vN, vSigmaX );\\\\n\\\\n\\\\t\\\\tfloat fDet = dot( vSigmaX, R1 ) * faceDirection;\\\\n\\\\n\\\\t\\\\tvec3 vGrad = sign( fDet ) * ( dHdxy.x * R1 + dHdxy.y * R2 );\\\\n\\\\t\\\\treturn normalize( abs( fDet ) * surf_norm - vGrad );\\\\n\\\\n\\\\t}\\\\n\\\\n#endif\\\\n\\\\\\\",clipping_planes_fragment:\\\\\\\"\\\\n#if NUM_CLIPPING_PLANES > 0\\\\n\\\\n\\\\tvec4 plane;\\\\n\\\\n\\\\t#pragma unroll_loop_start\\\\n\\\\tfor ( int i = 0; i < UNION_CLIPPING_PLANES; i ++ ) {\\\\n\\\\n\\\\t\\\\tplane = clippingPlanes[ i ];\\\\n\\\\t\\\\tif ( dot( vClipPosition, plane.xyz ) > plane.w ) discard;\\\\n\\\\n\\\\t}\\\\n\\\\t#pragma unroll_loop_end\\\\n\\\\n\\\\t#if UNION_CLIPPING_PLANES < NUM_CLIPPING_PLANES\\\\n\\\\n\\\\t\\\\tbool clipped = true;\\\\n\\\\n\\\\t\\\\t#pragma unroll_loop_start\\\\n\\\\t\\\\tfor ( int i = UNION_CLIPPING_PLANES; i < NUM_CLIPPING_PLANES; i ++ ) {\\\\n\\\\n\\\\t\\\\t\\\\tplane = clippingPlanes[ i ];\\\\n\\\\t\\\\t\\\\tclipped = ( dot( vClipPosition, plane.xyz ) > plane.w ) && clipped;\\\\n\\\\n\\\\t\\\\t}\\\\n\\\\t\\\\t#pragma unroll_loop_end\\\\n\\\\n\\\\t\\\\tif ( clipped ) discard;\\\\n\\\\n\\\\t#endif\\\\n\\\\n#endif\\\\n\\\\\\\",clipping_planes_pars_fragment:\\\\\\\"\\\\n#if NUM_CLIPPING_PLANES > 0\\\\n\\\\n\\\\tvarying vec3 vClipPosition;\\\\n\\\\n\\\\tuniform vec4 clippingPlanes[ NUM_CLIPPING_PLANES ];\\\\n\\\\n#endif\\\\n\\\\\\\",clipping_planes_pars_vertex:\\\\\\\"\\\\n#if NUM_CLIPPING_PLANES > 0\\\\n\\\\n\\\\tvarying vec3 vClipPosition;\\\\n\\\\n#endif\\\\n\\\\\\\",clipping_planes_vertex:\\\\\\\"\\\\n#if NUM_CLIPPING_PLANES > 0\\\\n\\\\n\\\\tvClipPosition = - mvPosition.xyz;\\\\n\\\\n#endif\\\\n\\\\\\\",color_fragment:\\\\\\\"\\\\n#if defined( USE_COLOR_ALPHA )\\\\n\\\\n\\\\tdiffuseColor *= vColor;\\\\n\\\\n#elif defined( USE_COLOR )\\\\n\\\\n\\\\tdiffuseColor.rgb *= vColor;\\\\n\\\\n#endif\\\\n\\\\\\\",color_pars_fragment:\\\\\\\"\\\\n#if defined( USE_COLOR_ALPHA )\\\\n\\\\n\\\\tvarying vec4 vColor;\\\\n\\\\n#elif defined( USE_COLOR )\\\\n\\\\n\\\\tvarying vec3 vColor;\\\\n\\\\n#endif\\\\n\\\\\\\",color_pars_vertex:\\\\\\\"\\\\n#if defined( USE_COLOR_ALPHA )\\\\n\\\\n\\\\tvarying vec4 vColor;\\\\n\\\\n#elif defined( USE_COLOR ) || defined( USE_INSTANCING_COLOR )\\\\n\\\\n\\\\tvarying vec3 vColor;\\\\n\\\\n#endif\\\\n\\\\\\\",color_vertex:\\\\\\\"\\\\n#if defined( USE_COLOR_ALPHA )\\\\n\\\\n\\\\tvColor = vec4( 1.0 );\\\\n\\\\n#elif defined( USE_COLOR ) || defined( USE_INSTANCING_COLOR )\\\\n\\\\n\\\\tvColor = vec3( 1.0 );\\\\n\\\\n#endif\\\\n\\\\n#ifdef USE_COLOR\\\\n\\\\n\\\\tvColor *= color;\\\\n\\\\n#endif\\\\n\\\\n#ifdef USE_INSTANCING_COLOR\\\\n\\\\n\\\\tvColor.xyz *= instanceColor.xyz;\\\\n\\\\n#endif\\\\n\\\\\\\",common:\\\\\\\"\\\\n#define PI 3.141592653589793\\\\n#define PI2 6.283185307179586\\\\n#define PI_HALF 1.5707963267948966\\\\n#define RECIPROCAL_PI 0.3183098861837907\\\\n#define RECIPROCAL_PI2 0.15915494309189535\\\\n#define EPSILON 1e-6\\\\n\\\\n#ifndef saturate\\\\n// may have defined saturate() already\\\\n#define saturate( a ) clamp( a, 0.0, 1.0 )\\\\n#endif\\\\n#define whiteComplement( a ) ( 1.0 - saturate( a ) )\\\\n\\\\nfloat pow2( const in float x ) { return x*x; }\\\\nfloat pow3( const in float x ) { return x*x*x; }\\\\nfloat pow4( const in float x ) { float x2 = x*x; return x2*x2; }\\\\nfloat max3( const in vec3 v ) { return max( max( v.x, v.y ), v.z ); }\\\\nfloat average( const in vec3 color ) { return dot( color, vec3( 0.3333 ) ); }\\\\n\\\\n// expects values in the range of [0,1]x[0,1], returns values in the [0,1] range.\\\\n// do not collapse into a single function per: http://byteblacksmith.com/improvements-to-the-canonical-one-liner-glsl-rand-for-opengl-es-2-0/\\\\nhighp float rand( const in vec2 uv ) {\\\\n\\\\n\\\\tconst highp float a = 12.9898, b = 78.233, c = 43758.5453;\\\\n\\\\thighp float dt = dot( uv.xy, vec2( a,b ) ), sn = mod( dt, PI );\\\\n\\\\n\\\\treturn fract( sin( sn ) * c );\\\\n\\\\n}\\\\n\\\\n#ifdef HIGH_PRECISION\\\\n\\\\tfloat precisionSafeLength( vec3 v ) { return length( v ); }\\\\n#else\\\\n\\\\tfloat precisionSafeLength( vec3 v ) {\\\\n\\\\t\\\\tfloat maxComponent = max3( abs( v ) );\\\\n\\\\t\\\\treturn length( v / maxComponent ) * maxComponent;\\\\n\\\\t}\\\\n#endif\\\\n\\\\nstruct IncidentLight {\\\\n\\\\tvec3 color;\\\\n\\\\tvec3 direction;\\\\n\\\\tbool visible;\\\\n};\\\\n\\\\nstruct ReflectedLight {\\\\n\\\\tvec3 directDiffuse;\\\\n\\\\tvec3 directSpecular;\\\\n\\\\tvec3 indirectDiffuse;\\\\n\\\\tvec3 indirectSpecular;\\\\n};\\\\n\\\\nstruct GeometricContext {\\\\n\\\\tvec3 position;\\\\n\\\\tvec3 normal;\\\\n\\\\tvec3 viewDir;\\\\n#ifdef USE_CLEARCOAT\\\\n\\\\tvec3 clearcoatNormal;\\\\n#endif\\\\n};\\\\n\\\\nvec3 transformDirection( in vec3 dir, in mat4 matrix ) {\\\\n\\\\n\\\\treturn normalize( ( matrix * vec4( dir, 0.0 ) ).xyz );\\\\n\\\\n}\\\\n\\\\nvec3 inverseTransformDirection( in vec3 dir, in mat4 matrix ) {\\\\n\\\\n\\\\t// dir can be either a direction vector or a normal vector\\\\n\\\\t// upper-left 3x3 of matrix is assumed to be orthogonal\\\\n\\\\n\\\\treturn normalize( ( vec4( dir, 0.0 ) * matrix ).xyz );\\\\n\\\\n}\\\\n\\\\nmat3 transposeMat3( const in mat3 m ) {\\\\n\\\\n\\\\tmat3 tmp;\\\\n\\\\n\\\\ttmp[ 0 ] = vec3( m[ 0 ].x, m[ 1 ].x, m[ 2 ].x );\\\\n\\\\ttmp[ 1 ] = vec3( m[ 0 ].y, m[ 1 ].y, m[ 2 ].y );\\\\n\\\\ttmp[ 2 ] = vec3( m[ 0 ].z, m[ 1 ].z, m[ 2 ].z );\\\\n\\\\n\\\\treturn tmp;\\\\n\\\\n}\\\\n\\\\n// https://en.wikipedia.org/wiki/Relative_luminance\\\\nfloat linearToRelativeLuminance( const in vec3 color ) {\\\\n\\\\n\\\\tvec3 weights = vec3( 0.2126, 0.7152, 0.0722 );\\\\n\\\\n\\\\treturn dot( weights, color.rgb );\\\\n\\\\n}\\\\n\\\\nbool isPerspectiveMatrix( mat4 m ) {\\\\n\\\\n\\\\treturn m[ 2 ][ 3 ] == - 1.0;\\\\n\\\\n}\\\\n\\\\nvec2 equirectUv( in vec3 dir ) {\\\\n\\\\n\\\\t// dir is assumed to be unit length\\\\n\\\\n\\\\tfloat u = atan( dir.z, dir.x ) * RECIPROCAL_PI2 + 0.5;\\\\n\\\\n\\\\tfloat v = asin( clamp( dir.y, - 1.0, 1.0 ) ) * RECIPROCAL_PI + 0.5;\\\\n\\\\n\\\\treturn vec2( u, v );\\\\n\\\\n}\\\\n\\\\\\\",cube_uv_reflection_fragment:\\\\\\\"\\\\n#ifdef ENVMAP_TYPE_CUBE_UV\\\\n\\\\n\\\\t#define cubeUV_maxMipLevel 8.0\\\\n\\\\t#define cubeUV_minMipLevel 4.0\\\\n\\\\t#define cubeUV_maxTileSize 256.0\\\\n\\\\t#define cubeUV_minTileSize 16.0\\\\n\\\\n\\\\t// These shader functions convert between the UV coordinates of a single face of\\\\n\\\\t// a cubemap, the 0-5 integer index of a cube face, and the direction vector for\\\\n\\\\t// sampling a textureCube (not generally normalized ).\\\\n\\\\n\\\\tfloat getFace( vec3 direction ) {\\\\n\\\\n\\\\t\\\\tvec3 absDirection = abs( direction );\\\\n\\\\n\\\\t\\\\tfloat face = - 1.0;\\\\n\\\\n\\\\t\\\\tif ( absDirection.x > absDirection.z ) {\\\\n\\\\n\\\\t\\\\t\\\\tif ( absDirection.x > absDirection.y )\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tface = direction.x > 0.0 ? 0.0 : 3.0;\\\\n\\\\n\\\\t\\\\t\\\\telse\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tface = direction.y > 0.0 ? 1.0 : 4.0;\\\\n\\\\n\\\\t\\\\t} else {\\\\n\\\\n\\\\t\\\\t\\\\tif ( absDirection.z > absDirection.y )\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tface = direction.z > 0.0 ? 2.0 : 5.0;\\\\n\\\\n\\\\t\\\\t\\\\telse\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tface = direction.y > 0.0 ? 1.0 : 4.0;\\\\n\\\\n\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\treturn face;\\\\n\\\\n\\\\t}\\\\n\\\\n\\\\t// RH coordinate system; PMREM face-indexing convention\\\\n\\\\tvec2 getUV( vec3 direction, float face ) {\\\\n\\\\n\\\\t\\\\tvec2 uv;\\\\n\\\\n\\\\t\\\\tif ( face == 0.0 ) {\\\\n\\\\n\\\\t\\\\t\\\\tuv = vec2( direction.z, direction.y ) / abs( direction.x ); // pos x\\\\n\\\\n\\\\t\\\\t} else if ( face == 1.0 ) {\\\\n\\\\n\\\\t\\\\t\\\\tuv = vec2( - direction.x, - direction.z ) / abs( direction.y ); // pos y\\\\n\\\\n\\\\t\\\\t} else if ( face == 2.0 ) {\\\\n\\\\n\\\\t\\\\t\\\\tuv = vec2( - direction.x, direction.y ) / abs( direction.z ); // pos z\\\\n\\\\n\\\\t\\\\t} else if ( face == 3.0 ) {\\\\n\\\\n\\\\t\\\\t\\\\tuv = vec2( - direction.z, direction.y ) / abs( direction.x ); // neg x\\\\n\\\\n\\\\t\\\\t} else if ( face == 4.0 ) {\\\\n\\\\n\\\\t\\\\t\\\\tuv = vec2( - direction.x, direction.z ) / abs( direction.y ); // neg y\\\\n\\\\n\\\\t\\\\t} else {\\\\n\\\\n\\\\t\\\\t\\\\tuv = vec2( direction.x, direction.y ) / abs( direction.z ); // neg z\\\\n\\\\n\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\treturn 0.5 * ( uv + 1.0 );\\\\n\\\\n\\\\t}\\\\n\\\\n\\\\tvec3 bilinearCubeUV( sampler2D envMap, vec3 direction, float mipInt ) {\\\\n\\\\n\\\\t\\\\tfloat face = getFace( direction );\\\\n\\\\n\\\\t\\\\tfloat filterInt = max( cubeUV_minMipLevel - mipInt, 0.0 );\\\\n\\\\n\\\\t\\\\tmipInt = max( mipInt, cubeUV_minMipLevel );\\\\n\\\\n\\\\t\\\\tfloat faceSize = exp2( mipInt );\\\\n\\\\n\\\\t\\\\tfloat texelSize = 1.0 / ( 3.0 * cubeUV_maxTileSize );\\\\n\\\\n\\\\t\\\\tvec2 uv = getUV( direction, face ) * ( faceSize - 1.0 );\\\\n\\\\n\\\\t\\\\tvec2 f = fract( uv );\\\\n\\\\n\\\\t\\\\tuv += 0.5 - f;\\\\n\\\\n\\\\t\\\\tif ( face > 2.0 ) {\\\\n\\\\n\\\\t\\\\t\\\\tuv.y += faceSize;\\\\n\\\\n\\\\t\\\\t\\\\tface -= 3.0;\\\\n\\\\n\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\tuv.x += face * faceSize;\\\\n\\\\n\\\\t\\\\tif ( mipInt < cubeUV_maxMipLevel ) {\\\\n\\\\n\\\\t\\\\t\\\\tuv.y += 2.0 * cubeUV_maxTileSize;\\\\n\\\\n\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\tuv.y += filterInt * 2.0 * cubeUV_minTileSize;\\\\n\\\\n\\\\t\\\\tuv.x += 3.0 * max( 0.0, cubeUV_maxTileSize - 2.0 * faceSize );\\\\n\\\\n\\\\t\\\\tuv *= texelSize;\\\\n\\\\n\\\\t\\\\tvec3 tl = envMapTexelToLinear( texture2D( envMap, uv ) ).rgb;\\\\n\\\\n\\\\t\\\\tuv.x += texelSize;\\\\n\\\\n\\\\t\\\\tvec3 tr = envMapTexelToLinear( texture2D( envMap, uv ) ).rgb;\\\\n\\\\n\\\\t\\\\tuv.y += texelSize;\\\\n\\\\n\\\\t\\\\tvec3 br = envMapTexelToLinear( texture2D( envMap, uv ) ).rgb;\\\\n\\\\n\\\\t\\\\tuv.x -= texelSize;\\\\n\\\\n\\\\t\\\\tvec3 bl = envMapTexelToLinear( texture2D( envMap, uv ) ).rgb;\\\\n\\\\n\\\\t\\\\tvec3 tm = mix( tl, tr, f.x );\\\\n\\\\n\\\\t\\\\tvec3 bm = mix( bl, br, f.x );\\\\n\\\\n\\\\t\\\\treturn mix( tm, bm, f.y );\\\\n\\\\n\\\\t}\\\\n\\\\n\\\\t// These defines must match with PMREMGenerator\\\\n\\\\n\\\\t#define r0 1.0\\\\n\\\\t#define v0 0.339\\\\n\\\\t#define m0 - 2.0\\\\n\\\\t#define r1 0.8\\\\n\\\\t#define v1 0.276\\\\n\\\\t#define m1 - 1.0\\\\n\\\\t#define r4 0.4\\\\n\\\\t#define v4 0.046\\\\n\\\\t#define m4 2.0\\\\n\\\\t#define r5 0.305\\\\n\\\\t#define v5 0.016\\\\n\\\\t#define m5 3.0\\\\n\\\\t#define r6 0.21\\\\n\\\\t#define v6 0.0038\\\\n\\\\t#define m6 4.0\\\\n\\\\n\\\\tfloat roughnessToMip( float roughness ) {\\\\n\\\\n\\\\t\\\\tfloat mip = 0.0;\\\\n\\\\n\\\\t\\\\tif ( roughness >= r1 ) {\\\\n\\\\n\\\\t\\\\t\\\\tmip = ( r0 - roughness ) * ( m1 - m0 ) / ( r0 - r1 ) + m0;\\\\n\\\\n\\\\t\\\\t} else if ( roughness >= r4 ) {\\\\n\\\\n\\\\t\\\\t\\\\tmip = ( r1 - roughness ) * ( m4 - m1 ) / ( r1 - r4 ) + m1;\\\\n\\\\n\\\\t\\\\t} else if ( roughness >= r5 ) {\\\\n\\\\n\\\\t\\\\t\\\\tmip = ( r4 - roughness ) * ( m5 - m4 ) / ( r4 - r5 ) + m4;\\\\n\\\\n\\\\t\\\\t} else if ( roughness >= r6 ) {\\\\n\\\\n\\\\t\\\\t\\\\tmip = ( r5 - roughness ) * ( m6 - m5 ) / ( r5 - r6 ) + m5;\\\\n\\\\n\\\\t\\\\t} else {\\\\n\\\\n\\\\t\\\\t\\\\tmip = - 2.0 * log2( 1.16 * roughness ); // 1.16 = 1.79^0.25\\\\n\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\treturn mip;\\\\n\\\\n\\\\t}\\\\n\\\\n\\\\tvec4 textureCubeUV( sampler2D envMap, vec3 sampleDir, float roughness ) {\\\\n\\\\n\\\\t\\\\tfloat mip = clamp( roughnessToMip( roughness ), m0, cubeUV_maxMipLevel );\\\\n\\\\n\\\\t\\\\tfloat mipF = fract( mip );\\\\n\\\\n\\\\t\\\\tfloat mipInt = floor( mip );\\\\n\\\\n\\\\t\\\\tvec3 color0 = bilinearCubeUV( envMap, sampleDir, mipInt );\\\\n\\\\n\\\\t\\\\tif ( mipF == 0.0 ) {\\\\n\\\\n\\\\t\\\\t\\\\treturn vec4( color0, 1.0 );\\\\n\\\\n\\\\t\\\\t} else {\\\\n\\\\n\\\\t\\\\t\\\\tvec3 color1 = bilinearCubeUV( envMap, sampleDir, mipInt + 1.0 );\\\\n\\\\n\\\\t\\\\t\\\\treturn vec4( mix( color0, color1, mipF ), 1.0 );\\\\n\\\\n\\\\t\\\\t}\\\\n\\\\n\\\\t}\\\\n\\\\n#endif\\\\n\\\\\\\",defaultnormal_vertex:\\\\\\\"\\\\nvec3 transformedNormal = objectNormal;\\\\n\\\\n#ifdef USE_INSTANCING\\\\n\\\\n\\\\t// this is in lieu of a per-instance normal-matrix\\\\n\\\\t// shear transforms in the instance matrix are not supported\\\\n\\\\n\\\\tmat3 m = mat3( instanceMatrix );\\\\n\\\\n\\\\ttransformedNormal /= vec3( dot( m[ 0 ], m[ 0 ] ), dot( m[ 1 ], m[ 1 ] ), dot( m[ 2 ], m[ 2 ] ) );\\\\n\\\\n\\\\ttransformedNormal = m * transformedNormal;\\\\n\\\\n#endif\\\\n\\\\ntransformedNormal = normalMatrix * transformedNormal;\\\\n\\\\n#ifdef FLIP_SIDED\\\\n\\\\n\\\\ttransformedNormal = - transformedNormal;\\\\n\\\\n#endif\\\\n\\\\n#ifdef USE_TANGENT\\\\n\\\\n\\\\tvec3 transformedTangent = ( modelViewMatrix * vec4( objectTangent, 0.0 ) ).xyz;\\\\n\\\\n\\\\t#ifdef FLIP_SIDED\\\\n\\\\n\\\\t\\\\ttransformedTangent = - transformedTangent;\\\\n\\\\n\\\\t#endif\\\\n\\\\n#endif\\\\n\\\\\\\",displacementmap_pars_vertex:\\\\\\\"\\\\n#ifdef USE_DISPLACEMENTMAP\\\\n\\\\n\\\\tuniform sampler2D displacementMap;\\\\n\\\\tuniform float displacementScale;\\\\n\\\\tuniform float displacementBias;\\\\n\\\\n#endif\\\\n\\\\\\\",displacementmap_vertex:\\\\\\\"\\\\n#ifdef USE_DISPLACEMENTMAP\\\\n\\\\n\\\\ttransformed += normalize( objectNormal ) * ( texture2D( displacementMap, vUv ).x * displacementScale + displacementBias );\\\\n\\\\n#endif\\\\n\\\\\\\",emissivemap_fragment:\\\\\\\"\\\\n#ifdef USE_EMISSIVEMAP\\\\n\\\\n\\\\tvec4 emissiveColor = texture2D( emissiveMap, vUv );\\\\n\\\\n\\\\temissiveColor.rgb = emissiveMapTexelToLinear( emissiveColor ).rgb;\\\\n\\\\n\\\\ttotalEmissiveRadiance *= emissiveColor.rgb;\\\\n\\\\n#endif\\\\n\\\\\\\",emissivemap_pars_fragment:\\\\\\\"\\\\n#ifdef USE_EMISSIVEMAP\\\\n\\\\n\\\\tuniform sampler2D emissiveMap;\\\\n\\\\n#endif\\\\n\\\\\\\",encodings_fragment:\\\\\\\"\\\\ngl_FragColor = linearToOutputTexel( gl_FragColor );\\\\n\\\\\\\",encodings_pars_fragment:\\\\\\\"\\\\n// For a discussion of what this is, please read this: http://lousodrome.net/blog/light/2013/05/26/gamma-correct-and-hdr-rendering-in-a-32-bits-buffer/\\\\n\\\\nvec4 LinearToLinear( in vec4 value ) {\\\\n\\\\treturn value;\\\\n}\\\\n\\\\nvec4 GammaToLinear( in vec4 value, in float gammaFactor ) {\\\\n\\\\treturn vec4( pow( value.rgb, vec3( gammaFactor ) ), value.a );\\\\n}\\\\n\\\\nvec4 LinearToGamma( in vec4 value, in float gammaFactor ) {\\\\n\\\\treturn vec4( pow( value.rgb, vec3( 1.0 / gammaFactor ) ), value.a );\\\\n}\\\\n\\\\nvec4 sRGBToLinear( in vec4 value ) {\\\\n\\\\treturn vec4( mix( pow( value.rgb * 0.9478672986 + vec3( 0.0521327014 ), vec3( 2.4 ) ), value.rgb * 0.0773993808, vec3( lessThanEqual( value.rgb, vec3( 0.04045 ) ) ) ), value.a );\\\\n}\\\\n\\\\nvec4 LinearTosRGB( in vec4 value ) {\\\\n\\\\treturn vec4( mix( pow( value.rgb, vec3( 0.41666 ) ) * 1.055 - vec3( 0.055 ), value.rgb * 12.92, vec3( lessThanEqual( value.rgb, vec3( 0.0031308 ) ) ) ), value.a );\\\\n}\\\\n\\\\nvec4 RGBEToLinear( in vec4 value ) {\\\\n\\\\treturn vec4( value.rgb * exp2( value.a * 255.0 - 128.0 ), 1.0 );\\\\n}\\\\n\\\\nvec4 LinearToRGBE( in vec4 value ) {\\\\n\\\\tfloat maxComponent = max( max( value.r, value.g ), value.b );\\\\n\\\\tfloat fExp = clamp( ceil( log2( maxComponent ) ), -128.0, 127.0 );\\\\n\\\\treturn vec4( value.rgb / exp2( fExp ), ( fExp + 128.0 ) / 255.0 );\\\\n\\\\t// return vec4( value.brg, ( 3.0 + 128.0 ) / 256.0 );\\\\n}\\\\n\\\\n// reference: http://iwasbeingirony.blogspot.ca/2010/06/difference-between-rgbm-and-rgbd.html\\\\nvec4 RGBMToLinear( in vec4 value, in float maxRange ) {\\\\n\\\\treturn vec4( value.rgb * value.a * maxRange, 1.0 );\\\\n}\\\\n\\\\nvec4 LinearToRGBM( in vec4 value, in float maxRange ) {\\\\n\\\\tfloat maxRGB = max( value.r, max( value.g, value.b ) );\\\\n\\\\tfloat M = clamp( maxRGB / maxRange, 0.0, 1.0 );\\\\n\\\\tM = ceil( M * 255.0 ) / 255.0;\\\\n\\\\treturn vec4( value.rgb / ( M * maxRange ), M );\\\\n}\\\\n\\\\n// reference: http://iwasbeingirony.blogspot.ca/2010/06/difference-between-rgbm-and-rgbd.html\\\\nvec4 RGBDToLinear( in vec4 value, in float maxRange ) {\\\\n\\\\treturn vec4( value.rgb * ( ( maxRange / 255.0 ) / value.a ), 1.0 );\\\\n}\\\\n\\\\nvec4 LinearToRGBD( in vec4 value, in float maxRange ) {\\\\n\\\\tfloat maxRGB = max( value.r, max( value.g, value.b ) );\\\\n\\\\tfloat D = max( maxRange / maxRGB, 1.0 );\\\\n\\\\t// NOTE: The implementation with min causes the shader to not compile on\\\\n\\\\t// a common Alcatel A502DL in Chrome 78/Android 8.1. Some research suggests \\\\n\\\\t// that the chipset is Mediatek MT6739 w/ IMG PowerVR GE8100 GPU.\\\\n\\\\t// D = min( floor( D ) / 255.0, 1.0 );\\\\n\\\\tD = clamp( floor( D ) / 255.0, 0.0, 1.0 );\\\\n\\\\treturn vec4( value.rgb * ( D * ( 255.0 / maxRange ) ), D );\\\\n}\\\\n\\\\n// LogLuv reference: http://graphicrants.blogspot.ca/2009/04/rgbm-color-encoding.html\\\\n\\\\n// M matrix, for encoding\\\\nconst mat3 cLogLuvM = mat3( 0.2209, 0.3390, 0.4184, 0.1138, 0.6780, 0.7319, 0.0102, 0.1130, 0.2969 );\\\\nvec4 LinearToLogLuv( in vec4 value ) {\\\\n\\\\tvec3 Xp_Y_XYZp = cLogLuvM * value.rgb;\\\\n\\\\tXp_Y_XYZp = max( Xp_Y_XYZp, vec3( 1e-6, 1e-6, 1e-6 ) );\\\\n\\\\tvec4 vResult;\\\\n\\\\tvResult.xy = Xp_Y_XYZp.xy / Xp_Y_XYZp.z;\\\\n\\\\tfloat Le = 2.0 * log2(Xp_Y_XYZp.y) + 127.0;\\\\n\\\\tvResult.w = fract( Le );\\\\n\\\\tvResult.z = ( Le - ( floor( vResult.w * 255.0 ) ) / 255.0 ) / 255.0;\\\\n\\\\treturn vResult;\\\\n}\\\\n\\\\n// Inverse M matrix, for decoding\\\\nconst mat3 cLogLuvInverseM = mat3( 6.0014, -2.7008, -1.7996, -1.3320, 3.1029, -5.7721, 0.3008, -1.0882, 5.6268 );\\\\nvec4 LogLuvToLinear( in vec4 value ) {\\\\n\\\\tfloat Le = value.z * 255.0 + value.w;\\\\n\\\\tvec3 Xp_Y_XYZp;\\\\n\\\\tXp_Y_XYZp.y = exp2( ( Le - 127.0 ) / 2.0 );\\\\n\\\\tXp_Y_XYZp.z = Xp_Y_XYZp.y / value.y;\\\\n\\\\tXp_Y_XYZp.x = value.x * Xp_Y_XYZp.z;\\\\n\\\\tvec3 vRGB = cLogLuvInverseM * Xp_Y_XYZp.rgb;\\\\n\\\\treturn vec4( max( vRGB, 0.0 ), 1.0 );\\\\n}\\\\n\\\\\\\",envmap_fragment:\\\\\\\"\\\\n#ifdef USE_ENVMAP\\\\n\\\\n\\\\t#ifdef ENV_WORLDPOS\\\\n\\\\n\\\\t\\\\tvec3 cameraToFrag;\\\\n\\\\n\\\\t\\\\tif ( isOrthographic ) {\\\\n\\\\n\\\\t\\\\t\\\\tcameraToFrag = normalize( vec3( - viewMatrix[ 0 ][ 2 ], - viewMatrix[ 1 ][ 2 ], - viewMatrix[ 2 ][ 2 ] ) );\\\\n\\\\n\\\\t\\\\t} else {\\\\n\\\\n\\\\t\\\\t\\\\tcameraToFrag = normalize( vWorldPosition - cameraPosition );\\\\n\\\\n\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\t// Transforming Normal Vectors with the Inverse Transformation\\\\n\\\\t\\\\tvec3 worldNormal = inverseTransformDirection( normal, viewMatrix );\\\\n\\\\n\\\\t\\\\t#ifdef ENVMAP_MODE_REFLECTION\\\\n\\\\n\\\\t\\\\t\\\\tvec3 reflectVec = reflect( cameraToFrag, worldNormal );\\\\n\\\\n\\\\t\\\\t#else\\\\n\\\\n\\\\t\\\\t\\\\tvec3 reflectVec = refract( cameraToFrag, worldNormal, refractionRatio );\\\\n\\\\n\\\\t\\\\t#endif\\\\n\\\\n\\\\t#else\\\\n\\\\n\\\\t\\\\tvec3 reflectVec = vReflect;\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\t#ifdef ENVMAP_TYPE_CUBE\\\\n\\\\n\\\\t\\\\tvec4 envColor = textureCube( envMap, vec3( flipEnvMap * reflectVec.x, reflectVec.yz ) );\\\\n\\\\n\\\\t\\\\tenvColor = envMapTexelToLinear( envColor );\\\\n\\\\n\\\\t#elif defined( ENVMAP_TYPE_CUBE_UV )\\\\n\\\\n\\\\t\\\\tvec4 envColor = textureCubeUV( envMap, reflectVec, 0.0 );\\\\n\\\\n\\\\t#else\\\\n\\\\n\\\\t\\\\tvec4 envColor = vec4( 0.0 );\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\t#ifdef ENVMAP_BLENDING_MULTIPLY\\\\n\\\\n\\\\t\\\\toutgoingLight = mix( outgoingLight, outgoingLight * envColor.xyz, specularStrength * reflectivity );\\\\n\\\\n\\\\t#elif defined( ENVMAP_BLENDING_MIX )\\\\n\\\\n\\\\t\\\\toutgoingLight = mix( outgoingLight, envColor.xyz, specularStrength * reflectivity );\\\\n\\\\n\\\\t#elif defined( ENVMAP_BLENDING_ADD )\\\\n\\\\n\\\\t\\\\toutgoingLight += envColor.xyz * specularStrength * reflectivity;\\\\n\\\\n\\\\t#endif\\\\n\\\\n#endif\\\\n\\\\\\\",envmap_common_pars_fragment:\\\\\\\"\\\\n#ifdef USE_ENVMAP\\\\n\\\\n\\\\tuniform float envMapIntensity;\\\\n\\\\tuniform float flipEnvMap;\\\\n\\\\tuniform int maxMipLevel;\\\\n\\\\n\\\\t#ifdef ENVMAP_TYPE_CUBE\\\\n\\\\t\\\\tuniform samplerCube envMap;\\\\n\\\\t#else\\\\n\\\\t\\\\tuniform sampler2D envMap;\\\\n\\\\t#endif\\\\n\\\\t\\\\n#endif\\\\n\\\\\\\",envmap_pars_fragment:\\\\\\\"\\\\n#ifdef USE_ENVMAP\\\\n\\\\n\\\\tuniform float reflectivity;\\\\n\\\\n\\\\t#if defined( USE_BUMPMAP ) || defined( USE_NORMALMAP ) || defined( PHONG )\\\\n\\\\n\\\\t\\\\t#define ENV_WORLDPOS\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\t#ifdef ENV_WORLDPOS\\\\n\\\\n\\\\t\\\\tvarying vec3 vWorldPosition;\\\\n\\\\t\\\\tuniform float refractionRatio;\\\\n\\\\t#else\\\\n\\\\t\\\\tvarying vec3 vReflect;\\\\n\\\\t#endif\\\\n\\\\n#endif\\\\n\\\\\\\",envmap_pars_vertex:\\\\\\\"\\\\n#ifdef USE_ENVMAP\\\\n\\\\n\\\\t#if defined( USE_BUMPMAP ) || defined( USE_NORMALMAP ) ||defined( PHONG )\\\\n\\\\n\\\\t\\\\t#define ENV_WORLDPOS\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\t#ifdef ENV_WORLDPOS\\\\n\\\\t\\\\t\\\\n\\\\t\\\\tvarying vec3 vWorldPosition;\\\\n\\\\n\\\\t#else\\\\n\\\\n\\\\t\\\\tvarying vec3 vReflect;\\\\n\\\\t\\\\tuniform float refractionRatio;\\\\n\\\\n\\\\t#endif\\\\n\\\\n#endif\\\\n\\\\\\\",envmap_physical_pars_fragment:\\\\\\\"\\\\n#if defined( USE_ENVMAP )\\\\n\\\\n\\\\t#ifdef ENVMAP_MODE_REFRACTION\\\\n\\\\n\\\\t\\\\tuniform float refractionRatio;\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\tvec3 getIBLIrradiance( const in vec3 normal ) {\\\\n\\\\n\\\\t\\\\t#if defined( ENVMAP_TYPE_CUBE_UV )\\\\n\\\\n\\\\t\\\\t\\\\tvec3 worldNormal = inverseTransformDirection( normal, viewMatrix );\\\\n\\\\n\\\\t\\\\t\\\\tvec4 envMapColor = textureCubeUV( envMap, worldNormal, 1.0 );\\\\n\\\\n\\\\t\\\\t\\\\treturn PI * envMapColor.rgb * envMapIntensity;\\\\n\\\\n\\\\t\\\\t#else\\\\n\\\\n\\\\t\\\\t\\\\treturn vec3( 0.0 );\\\\n\\\\n\\\\t\\\\t#endif\\\\n\\\\n\\\\t}\\\\n\\\\n\\\\tvec3 getIBLRadiance( const in vec3 viewDir, const in vec3 normal, const in float roughness ) {\\\\n\\\\n\\\\t\\\\t#if defined( ENVMAP_TYPE_CUBE_UV )\\\\n\\\\n\\\\t\\\\t\\\\tvec3 reflectVec;\\\\n\\\\n\\\\t\\\\t\\\\t#ifdef ENVMAP_MODE_REFLECTION\\\\n\\\\n\\\\t\\\\t\\\\t\\\\treflectVec = reflect( - viewDir, normal );\\\\n\\\\n\\\\t\\\\t\\\\t\\\\t// Mixing the reflection with the normal is more accurate and keeps rough objects from gathering light from behind their tangent plane.\\\\n\\\\t\\\\t\\\\t\\\\treflectVec = normalize( mix( reflectVec, normal, roughness * roughness) );\\\\n\\\\n\\\\t\\\\t\\\\t#else\\\\n\\\\n\\\\t\\\\t\\\\t\\\\treflectVec = refract( - viewDir, normal, refractionRatio );\\\\n\\\\n\\\\t\\\\t\\\\t#endif\\\\n\\\\n\\\\t\\\\t\\\\treflectVec = inverseTransformDirection( reflectVec, viewMatrix );\\\\n\\\\n\\\\t\\\\t\\\\tvec4 envMapColor = textureCubeUV( envMap, reflectVec, roughness );\\\\n\\\\n\\\\t\\\\t\\\\treturn envMapColor.rgb * envMapIntensity;\\\\n\\\\n\\\\t\\\\t#else\\\\n\\\\n\\\\t\\\\t\\\\treturn vec3( 0.0 );\\\\n\\\\n\\\\t\\\\t#endif\\\\n\\\\n\\\\t}\\\\n\\\\n#endif\\\\n\\\\\\\",envmap_vertex:\\\\\\\"\\\\n#ifdef USE_ENVMAP\\\\n\\\\n\\\\t#ifdef ENV_WORLDPOS\\\\n\\\\n\\\\t\\\\tvWorldPosition = worldPosition.xyz;\\\\n\\\\n\\\\t#else\\\\n\\\\n\\\\t\\\\tvec3 cameraToVertex;\\\\n\\\\n\\\\t\\\\tif ( isOrthographic ) {\\\\n\\\\n\\\\t\\\\t\\\\tcameraToVertex = normalize( vec3( - viewMatrix[ 0 ][ 2 ], - viewMatrix[ 1 ][ 2 ], - viewMatrix[ 2 ][ 2 ] ) );\\\\n\\\\n\\\\t\\\\t} else {\\\\n\\\\n\\\\t\\\\t\\\\tcameraToVertex = normalize( worldPosition.xyz - cameraPosition );\\\\n\\\\n\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\tvec3 worldNormal = inverseTransformDirection( transformedNormal, viewMatrix );\\\\n\\\\n\\\\t\\\\t#ifdef ENVMAP_MODE_REFLECTION\\\\n\\\\n\\\\t\\\\t\\\\tvReflect = reflect( cameraToVertex, worldNormal );\\\\n\\\\n\\\\t\\\\t#else\\\\n\\\\n\\\\t\\\\t\\\\tvReflect = refract( cameraToVertex, worldNormal, refractionRatio );\\\\n\\\\n\\\\t\\\\t#endif\\\\n\\\\n\\\\t#endif\\\\n\\\\n#endif\\\\n\\\\\\\",fog_vertex:\\\\\\\"\\\\n#ifdef USE_FOG\\\\n\\\\n\\\\tvFogDepth = - mvPosition.z;\\\\n\\\\n#endif\\\\n\\\\\\\",fog_pars_vertex:\\\\\\\"\\\\n#ifdef USE_FOG\\\\n\\\\n\\\\tvarying float vFogDepth;\\\\n\\\\n#endif\\\\n\\\\\\\",fog_fragment:\\\\\\\"\\\\n#ifdef USE_FOG\\\\n\\\\n\\\\t#ifdef FOG_EXP2\\\\n\\\\n\\\\t\\\\tfloat fogFactor = 1.0 - exp( - fogDensity * fogDensity * vFogDepth * vFogDepth );\\\\n\\\\n\\\\t#else\\\\n\\\\n\\\\t\\\\tfloat fogFactor = smoothstep( fogNear, fogFar, vFogDepth );\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\tgl_FragColor.rgb = mix( gl_FragColor.rgb, fogColor, fogFactor );\\\\n\\\\n#endif\\\\n\\\\\\\",fog_pars_fragment:\\\\\\\"\\\\n#ifdef USE_FOG\\\\n\\\\n\\\\tuniform vec3 fogColor;\\\\n\\\\tvarying float vFogDepth;\\\\n\\\\n\\\\t#ifdef FOG_EXP2\\\\n\\\\n\\\\t\\\\tuniform float fogDensity;\\\\n\\\\n\\\\t#else\\\\n\\\\n\\\\t\\\\tuniform float fogNear;\\\\n\\\\t\\\\tuniform float fogFar;\\\\n\\\\n\\\\t#endif\\\\n\\\\n#endif\\\\n\\\\\\\",gradientmap_pars_fragment:\\\\\\\"\\\\n\\\\n#ifdef USE_GRADIENTMAP\\\\n\\\\n\\\\tuniform sampler2D gradientMap;\\\\n\\\\n#endif\\\\n\\\\nvec3 getGradientIrradiance( vec3 normal, vec3 lightDirection ) {\\\\n\\\\n\\\\t// dotNL will be from -1.0 to 1.0\\\\n\\\\tfloat dotNL = dot( normal, lightDirection );\\\\n\\\\tvec2 coord = vec2( dotNL * 0.5 + 0.5, 0.0 );\\\\n\\\\n\\\\t#ifdef USE_GRADIENTMAP\\\\n\\\\n\\\\t\\\\treturn texture2D( gradientMap, coord ).rgb;\\\\n\\\\n\\\\t#else\\\\n\\\\n\\\\t\\\\treturn ( coord.x < 0.7 ) ? vec3( 0.7 ) : vec3( 1.0 );\\\\n\\\\n\\\\t#endif\\\\n\\\\n}\\\\n\\\\\\\",lightmap_fragment:\\\\\\\"\\\\n#ifdef USE_LIGHTMAP\\\\n\\\\n\\\\tvec4 lightMapTexel = texture2D( lightMap, vUv2 );\\\\n\\\\tvec3 lightMapIrradiance = lightMapTexelToLinear( lightMapTexel ).rgb * lightMapIntensity;\\\\n\\\\n\\\\t#ifndef PHYSICALLY_CORRECT_LIGHTS\\\\n\\\\n\\\\t\\\\tlightMapIrradiance *= PI;\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\treflectedLight.indirectDiffuse += lightMapIrradiance;\\\\n\\\\n#endif\\\\n\\\\\\\",lightmap_pars_fragment:\\\\\\\"\\\\n#ifdef USE_LIGHTMAP\\\\n\\\\n\\\\tuniform sampler2D lightMap;\\\\n\\\\tuniform float lightMapIntensity;\\\\n\\\\n#endif\\\\n\\\\\\\",lights_lambert_vertex:\\\\\\\"\\\\nvec3 diffuse = vec3( 1.0 );\\\\n\\\\nGeometricContext geometry;\\\\ngeometry.position = mvPosition.xyz;\\\\ngeometry.normal = normalize( transformedNormal );\\\\ngeometry.viewDir = ( isOrthographic ) ? vec3( 0, 0, 1 ) : normalize( -mvPosition.xyz );\\\\n\\\\nGeometricContext backGeometry;\\\\nbackGeometry.position = geometry.position;\\\\nbackGeometry.normal = -geometry.normal;\\\\nbackGeometry.viewDir = geometry.viewDir;\\\\n\\\\nvLightFront = vec3( 0.0 );\\\\nvIndirectFront = vec3( 0.0 );\\\\n#ifdef DOUBLE_SIDED\\\\n\\\\tvLightBack = vec3( 0.0 );\\\\n\\\\tvIndirectBack = vec3( 0.0 );\\\\n#endif\\\\n\\\\nIncidentLight directLight;\\\\nfloat dotNL;\\\\nvec3 directLightColor_Diffuse;\\\\n\\\\nvIndirectFront += getAmbientLightIrradiance( ambientLightColor );\\\\n\\\\nvIndirectFront += getLightProbeIrradiance( lightProbe, geometry.normal );\\\\n\\\\n#ifdef DOUBLE_SIDED\\\\n\\\\n\\\\tvIndirectBack += getAmbientLightIrradiance( ambientLightColor );\\\\n\\\\n\\\\tvIndirectBack += getLightProbeIrradiance( lightProbe, backGeometry.normal );\\\\n\\\\n#endif\\\\n\\\\n#if NUM_POINT_LIGHTS > 0\\\\n\\\\n\\\\t#pragma unroll_loop_start\\\\n\\\\tfor ( int i = 0; i < NUM_POINT_LIGHTS; i ++ ) {\\\\n\\\\n\\\\t\\\\tgetPointLightInfo( pointLights[ i ], geometry, directLight );\\\\n\\\\n\\\\t\\\\tdotNL = dot( geometry.normal, directLight.direction );\\\\n\\\\t\\\\tdirectLightColor_Diffuse = directLight.color;\\\\n\\\\n\\\\t\\\\tvLightFront += saturate( dotNL ) * directLightColor_Diffuse;\\\\n\\\\n\\\\t\\\\t#ifdef DOUBLE_SIDED\\\\n\\\\n\\\\t\\\\t\\\\tvLightBack += saturate( - dotNL ) * directLightColor_Diffuse;\\\\n\\\\n\\\\t\\\\t#endif\\\\n\\\\n\\\\t}\\\\n\\\\t#pragma unroll_loop_end\\\\n\\\\n#endif\\\\n\\\\n#if NUM_SPOT_LIGHTS > 0\\\\n\\\\n\\\\t#pragma unroll_loop_start\\\\n\\\\tfor ( int i = 0; i < NUM_SPOT_LIGHTS; i ++ ) {\\\\n\\\\n\\\\t\\\\tgetSpotLightInfo( spotLights[ i ], geometry, directLight );\\\\n\\\\n\\\\t\\\\tdotNL = dot( geometry.normal, directLight.direction );\\\\n\\\\t\\\\tdirectLightColor_Diffuse = directLight.color;\\\\n\\\\n\\\\t\\\\tvLightFront += saturate( dotNL ) * directLightColor_Diffuse;\\\\n\\\\n\\\\t\\\\t#ifdef DOUBLE_SIDED\\\\n\\\\n\\\\t\\\\t\\\\tvLightBack += saturate( - dotNL ) * directLightColor_Diffuse;\\\\n\\\\n\\\\t\\\\t#endif\\\\n\\\\t}\\\\n\\\\t#pragma unroll_loop_end\\\\n\\\\n#endif\\\\n\\\\n#if NUM_DIR_LIGHTS > 0\\\\n\\\\n\\\\t#pragma unroll_loop_start\\\\n\\\\tfor ( int i = 0; i < NUM_DIR_LIGHTS; i ++ ) {\\\\n\\\\n\\\\t\\\\tgetDirectionalLightInfo( directionalLights[ i ], geometry, directLight );\\\\n\\\\n\\\\t\\\\tdotNL = dot( geometry.normal, directLight.direction );\\\\n\\\\t\\\\tdirectLightColor_Diffuse = directLight.color;\\\\n\\\\n\\\\t\\\\tvLightFront += saturate( dotNL ) * directLightColor_Diffuse;\\\\n\\\\n\\\\t\\\\t#ifdef DOUBLE_SIDED\\\\n\\\\n\\\\t\\\\t\\\\tvLightBack += saturate( - dotNL ) * directLightColor_Diffuse;\\\\n\\\\n\\\\t\\\\t#endif\\\\n\\\\n\\\\t}\\\\n\\\\t#pragma unroll_loop_end\\\\n\\\\n#endif\\\\n\\\\n#if NUM_HEMI_LIGHTS > 0\\\\n\\\\n\\\\t#pragma unroll_loop_start\\\\n\\\\tfor ( int i = 0; i < NUM_HEMI_LIGHTS; i ++ ) {\\\\n\\\\n\\\\t\\\\tvIndirectFront += getHemisphereLightIrradiance( hemisphereLights[ i ], geometry.normal );\\\\n\\\\n\\\\t\\\\t#ifdef DOUBLE_SIDED\\\\n\\\\n\\\\t\\\\t\\\\tvIndirectBack += getHemisphereLightIrradiance( hemisphereLights[ i ], backGeometry.normal );\\\\n\\\\n\\\\t\\\\t#endif\\\\n\\\\n\\\\t}\\\\n\\\\t#pragma unroll_loop_end\\\\n\\\\n#endif\\\\n\\\\\\\",lights_pars_begin:\\\\\\\"\\\\nuniform bool receiveShadow;\\\\nuniform vec3 ambientLightColor;\\\\nuniform vec3 lightProbe[ 9 ];\\\\n\\\\n// get the irradiance (radiance convolved with cosine lobe) at the point 'normal' on the unit sphere\\\\n// source: https://graphics.stanford.edu/papers/envmap/envmap.pdf\\\\nvec3 shGetIrradianceAt( in vec3 normal, in vec3 shCoefficients[ 9 ] ) {\\\\n\\\\n\\\\t// normal is assumed to have unit length\\\\n\\\\n\\\\tfloat x = normal.x, y = normal.y, z = normal.z;\\\\n\\\\n\\\\t// band 0\\\\n\\\\tvec3 result = shCoefficients[ 0 ] * 0.886227;\\\\n\\\\n\\\\t// band 1\\\\n\\\\tresult += shCoefficients[ 1 ] * 2.0 * 0.511664 * y;\\\\n\\\\tresult += shCoefficients[ 2 ] * 2.0 * 0.511664 * z;\\\\n\\\\tresult += shCoefficients[ 3 ] * 2.0 * 0.511664 * x;\\\\n\\\\n\\\\t// band 2\\\\n\\\\tresult += shCoefficients[ 4 ] * 2.0 * 0.429043 * x * y;\\\\n\\\\tresult += shCoefficients[ 5 ] * 2.0 * 0.429043 * y * z;\\\\n\\\\tresult += shCoefficients[ 6 ] * ( 0.743125 * z * z - 0.247708 );\\\\n\\\\tresult += shCoefficients[ 7 ] * 2.0 * 0.429043 * x * z;\\\\n\\\\tresult += shCoefficients[ 8 ] * 0.429043 * ( x * x - y * y );\\\\n\\\\n\\\\treturn result;\\\\n\\\\n}\\\\n\\\\nvec3 getLightProbeIrradiance( const in vec3 lightProbe[ 9 ], const in vec3 normal ) {\\\\n\\\\n\\\\tvec3 worldNormal = inverseTransformDirection( normal, viewMatrix );\\\\n\\\\n\\\\tvec3 irradiance = shGetIrradianceAt( worldNormal, lightProbe );\\\\n\\\\n\\\\treturn irradiance;\\\\n\\\\n}\\\\n\\\\nvec3 getAmbientLightIrradiance( const in vec3 ambientLightColor ) {\\\\n\\\\n\\\\tvec3 irradiance = ambientLightColor;\\\\n\\\\n\\\\treturn irradiance;\\\\n\\\\n}\\\\n\\\\nfloat getDistanceAttenuation( const in float lightDistance, const in float cutoffDistance, const in float decayExponent ) {\\\\n\\\\n\\\\t#if defined ( PHYSICALLY_CORRECT_LIGHTS )\\\\n\\\\n\\\\t\\\\t// based upon Frostbite 3 Moving to Physically-based Rendering\\\\n\\\\t\\\\t// page 32, equation 26: E[window1]\\\\n\\\\t\\\\t// https://seblagarde.files.wordpress.com/2015/07/course_notes_moving_frostbite_to_pbr_v32.pdf\\\\n\\\\t\\\\tfloat distanceFalloff = 1.0 / max( pow( lightDistance, decayExponent ), 0.01 );\\\\n\\\\n\\\\t\\\\tif ( cutoffDistance > 0.0 ) {\\\\n\\\\n\\\\t\\\\t\\\\tdistanceFalloff *= pow2( saturate( 1.0 - pow4( lightDistance / cutoffDistance ) ) );\\\\n\\\\n\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\treturn distanceFalloff;\\\\n\\\\n\\\\t#else\\\\n\\\\n\\\\t\\\\tif ( cutoffDistance > 0.0 && decayExponent > 0.0 ) {\\\\n\\\\n\\\\t\\\\t\\\\treturn pow( saturate( - lightDistance / cutoffDistance + 1.0 ), decayExponent );\\\\n\\\\n\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\treturn 1.0;\\\\n\\\\n\\\\t#endif\\\\n\\\\n}\\\\n\\\\nfloat getSpotAttenuation( const in float coneCosine, const in float penumbraCosine, const in float angleCosine ) {\\\\n\\\\n\\\\treturn smoothstep( coneCosine, penumbraCosine, angleCosine );\\\\n\\\\n}\\\\n\\\\n#if NUM_DIR_LIGHTS > 0\\\\n\\\\n\\\\tstruct DirectionalLight {\\\\n\\\\t\\\\tvec3 direction;\\\\n\\\\t\\\\tvec3 color;\\\\n\\\\t};\\\\n\\\\n\\\\tuniform DirectionalLight directionalLights[ NUM_DIR_LIGHTS ];\\\\n\\\\n\\\\tvoid getDirectionalLightInfo( const in DirectionalLight directionalLight, const in GeometricContext geometry, out IncidentLight light ) {\\\\n\\\\n\\\\t\\\\tlight.color = directionalLight.color;\\\\n\\\\t\\\\tlight.direction = directionalLight.direction;\\\\n\\\\t\\\\tlight.visible = true;\\\\n\\\\n\\\\t}\\\\n\\\\n#endif\\\\n\\\\n\\\\n#if NUM_POINT_LIGHTS > 0\\\\n\\\\n\\\\tstruct PointLight {\\\\n\\\\t\\\\tvec3 position;\\\\n\\\\t\\\\tvec3 color;\\\\n\\\\t\\\\tfloat distance;\\\\n\\\\t\\\\tfloat decay;\\\\n\\\\t};\\\\n\\\\n\\\\tuniform PointLight pointLights[ NUM_POINT_LIGHTS ];\\\\n\\\\n\\\\t// light is an out parameter as having it as a return value caused compiler errors on some devices\\\\n\\\\tvoid getPointLightInfo( const in PointLight pointLight, const in GeometricContext geometry, out IncidentLight light ) {\\\\n\\\\n\\\\t\\\\tvec3 lVector = pointLight.position - geometry.position;\\\\n\\\\n\\\\t\\\\tlight.direction = normalize( lVector );\\\\n\\\\n\\\\t\\\\tfloat lightDistance = length( lVector );\\\\n\\\\n\\\\t\\\\tlight.color = pointLight.color;\\\\n\\\\t\\\\tlight.color *= getDistanceAttenuation( lightDistance, pointLight.distance, pointLight.decay );\\\\n\\\\t\\\\tlight.visible = ( light.color != vec3( 0.0 ) );\\\\n\\\\n\\\\t}\\\\n\\\\n#endif\\\\n\\\\n\\\\n#if NUM_SPOT_LIGHTS > 0\\\\n\\\\n\\\\tstruct SpotLight {\\\\n\\\\t\\\\tvec3 position;\\\\n\\\\t\\\\tvec3 direction;\\\\n\\\\t\\\\tvec3 color;\\\\n\\\\t\\\\tfloat distance;\\\\n\\\\t\\\\tfloat decay;\\\\n\\\\t\\\\tfloat coneCos;\\\\n\\\\t\\\\tfloat penumbraCos;\\\\n\\\\t};\\\\n\\\\n\\\\tuniform SpotLight spotLights[ NUM_SPOT_LIGHTS ];\\\\n\\\\n\\\\t// light is an out parameter as having it as a return value caused compiler errors on some devices\\\\n\\\\tvoid getSpotLightInfo( const in SpotLight spotLight, const in GeometricContext geometry, out IncidentLight light ) {\\\\n\\\\n\\\\t\\\\tvec3 lVector = spotLight.position - geometry.position;\\\\n\\\\n\\\\t\\\\tlight.direction = normalize( lVector );\\\\n\\\\n\\\\t\\\\tfloat angleCos = dot( light.direction, spotLight.direction );\\\\n\\\\n\\\\t\\\\tfloat spotAttenuation = getSpotAttenuation( spotLight.coneCos, spotLight.penumbraCos, angleCos );\\\\n\\\\n\\\\t\\\\tif ( spotAttenuation > 0.0 ) {\\\\n\\\\n\\\\t\\\\t\\\\tfloat lightDistance = length( lVector );\\\\n\\\\n\\\\t\\\\t\\\\tlight.color = spotLight.color * spotAttenuation;\\\\n\\\\t\\\\t\\\\tlight.color *= getDistanceAttenuation( lightDistance, spotLight.distance, spotLight.decay );\\\\n\\\\t\\\\t\\\\tlight.visible = ( light.color != vec3( 0.0 ) );\\\\n\\\\n\\\\t\\\\t} else {\\\\n\\\\n\\\\t\\\\t\\\\tlight.color = vec3( 0.0 );\\\\n\\\\t\\\\t\\\\tlight.visible = false;\\\\n\\\\n\\\\t\\\\t}\\\\n\\\\n\\\\t}\\\\n\\\\n#endif\\\\n\\\\n\\\\n#if NUM_RECT_AREA_LIGHTS > 0\\\\n\\\\n\\\\tstruct RectAreaLight {\\\\n\\\\t\\\\tvec3 color;\\\\n\\\\t\\\\tvec3 position;\\\\n\\\\t\\\\tvec3 halfWidth;\\\\n\\\\t\\\\tvec3 halfHeight;\\\\n\\\\t};\\\\n\\\\n\\\\t// Pre-computed values of LinearTransformedCosine approximation of BRDF\\\\n\\\\t// BRDF approximation Texture is 64x64\\\\n\\\\tuniform sampler2D ltc_1; // RGBA Float\\\\n\\\\tuniform sampler2D ltc_2; // RGBA Float\\\\n\\\\n\\\\tuniform RectAreaLight rectAreaLights[ NUM_RECT_AREA_LIGHTS ];\\\\n\\\\n#endif\\\\n\\\\n\\\\n#if NUM_HEMI_LIGHTS > 0\\\\n\\\\n\\\\tstruct HemisphereLight {\\\\n\\\\t\\\\tvec3 direction;\\\\n\\\\t\\\\tvec3 skyColor;\\\\n\\\\t\\\\tvec3 groundColor;\\\\n\\\\t};\\\\n\\\\n\\\\tuniform HemisphereLight hemisphereLights[ NUM_HEMI_LIGHTS ];\\\\n\\\\n\\\\tvec3 getHemisphereLightIrradiance( const in HemisphereLight hemiLight, const in vec3 normal ) {\\\\n\\\\n\\\\t\\\\tfloat dotNL = dot( normal, hemiLight.direction );\\\\n\\\\t\\\\tfloat hemiDiffuseWeight = 0.5 * dotNL + 0.5;\\\\n\\\\n\\\\t\\\\tvec3 irradiance = mix( hemiLight.groundColor, hemiLight.skyColor, hemiDiffuseWeight );\\\\n\\\\n\\\\t\\\\treturn irradiance;\\\\n\\\\n\\\\t}\\\\n\\\\n#endif\\\\n\\\\\\\",lights_toon_fragment:\\\\\\\"\\\\nToonMaterial material;\\\\nmaterial.diffuseColor = diffuseColor.rgb;\\\\n\\\\\\\",lights_toon_pars_fragment:\\\\\\\"\\\\nvarying vec3 vViewPosition;\\\\n\\\\nstruct ToonMaterial {\\\\n\\\\n\\\\tvec3 diffuseColor;\\\\n\\\\n};\\\\n\\\\nvoid RE_Direct_Toon( const in IncidentLight directLight, const in GeometricContext geometry, const in ToonMaterial material, inout ReflectedLight reflectedLight ) {\\\\n\\\\n\\\\tvec3 irradiance = getGradientIrradiance( geometry.normal, directLight.direction ) * directLight.color;\\\\n\\\\n\\\\treflectedLight.directDiffuse += irradiance * BRDF_Lambert( material.diffuseColor );\\\\n\\\\n}\\\\n\\\\nvoid RE_IndirectDiffuse_Toon( const in vec3 irradiance, const in GeometricContext geometry, const in ToonMaterial material, inout ReflectedLight reflectedLight ) {\\\\n\\\\n\\\\treflectedLight.indirectDiffuse += irradiance * BRDF_Lambert( material.diffuseColor );\\\\n\\\\n}\\\\n\\\\n#define RE_Direct\\\\t\\\\t\\\\t\\\\tRE_Direct_Toon\\\\n#define RE_IndirectDiffuse\\\\t\\\\tRE_IndirectDiffuse_Toon\\\\n\\\\n#define Material_LightProbeLOD( material )\\\\t(0)\\\\n\\\\\\\",lights_phong_fragment:\\\\\\\"\\\\nBlinnPhongMaterial material;\\\\nmaterial.diffuseColor = diffuseColor.rgb;\\\\nmaterial.specularColor = specular;\\\\nmaterial.specularShininess = shininess;\\\\nmaterial.specularStrength = specularStrength;\\\\n\\\\\\\",lights_phong_pars_fragment:\\\\\\\"\\\\nvarying vec3 vViewPosition;\\\\n\\\\nstruct BlinnPhongMaterial {\\\\n\\\\n\\\\tvec3 diffuseColor;\\\\n\\\\tvec3 specularColor;\\\\n\\\\tfloat specularShininess;\\\\n\\\\tfloat specularStrength;\\\\n\\\\n};\\\\n\\\\nvoid RE_Direct_BlinnPhong( const in IncidentLight directLight, const in GeometricContext geometry, const in BlinnPhongMaterial material, inout ReflectedLight reflectedLight ) {\\\\n\\\\n\\\\tfloat dotNL = saturate( dot( geometry.normal, directLight.direction ) );\\\\n\\\\tvec3 irradiance = dotNL * directLight.color;\\\\n\\\\n\\\\treflectedLight.directDiffuse += irradiance * BRDF_Lambert( material.diffuseColor );\\\\n\\\\n\\\\treflectedLight.directSpecular += irradiance * BRDF_BlinnPhong( directLight.direction, geometry.viewDir, geometry.normal, material.specularColor, material.specularShininess ) * material.specularStrength;\\\\n\\\\n}\\\\n\\\\nvoid RE_IndirectDiffuse_BlinnPhong( const in vec3 irradiance, const in GeometricContext geometry, const in BlinnPhongMaterial material, inout ReflectedLight reflectedLight ) {\\\\n\\\\n\\\\treflectedLight.indirectDiffuse += irradiance * BRDF_Lambert( material.diffuseColor );\\\\n\\\\n}\\\\n\\\\n#define RE_Direct\\\\t\\\\t\\\\t\\\\tRE_Direct_BlinnPhong\\\\n#define RE_IndirectDiffuse\\\\t\\\\tRE_IndirectDiffuse_BlinnPhong\\\\n\\\\n#define Material_LightProbeLOD( material )\\\\t(0)\\\\n\\\\\\\",lights_physical_fragment:\\\\\\\"\\\\nPhysicalMaterial material;\\\\nmaterial.diffuseColor = diffuseColor.rgb * ( 1.0 - metalnessFactor );\\\\n\\\\nvec3 dxy = max( abs( dFdx( geometryNormal ) ), abs( dFdy( geometryNormal ) ) );\\\\nfloat geometryRoughness = max( max( dxy.x, dxy.y ), dxy.z );\\\\n\\\\nmaterial.roughness = max( roughnessFactor, 0.0525 );// 0.0525 corresponds to the base mip of a 256 cubemap.\\\\nmaterial.roughness += geometryRoughness;\\\\nmaterial.roughness = min( material.roughness, 1.0 );\\\\n\\\\n#ifdef IOR\\\\n\\\\n\\\\t#ifdef SPECULAR\\\\n\\\\n\\\\t\\\\tfloat specularIntensityFactor = specularIntensity;\\\\n\\\\t\\\\tvec3 specularTintFactor = specularTint;\\\\n\\\\n\\\\t\\\\t#ifdef USE_SPECULARINTENSITYMAP\\\\n\\\\n\\\\t\\\\t\\\\tspecularIntensityFactor *= texture2D( specularIntensityMap, vUv ).a;\\\\n\\\\n\\\\t\\\\t#endif\\\\n\\\\n\\\\t\\\\t#ifdef USE_SPECULARTINTMAP\\\\n\\\\n\\\\t\\\\t\\\\tspecularTintFactor *= specularTintMapTexelToLinear( texture2D( specularTintMap, vUv ) ).rgb;\\\\n\\\\n\\\\t\\\\t#endif\\\\n\\\\n\\\\t\\\\tmaterial.specularF90 = mix( specularIntensityFactor, 1.0, metalnessFactor );\\\\n\\\\n\\\\t#else\\\\n\\\\n\\\\t\\\\tfloat specularIntensityFactor = 1.0;\\\\n\\\\t\\\\tvec3 specularTintFactor = vec3( 1.0 );\\\\n\\\\t\\\\tmaterial.specularF90 = 1.0;\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\tmaterial.specularColor = mix( min( pow2( ( ior - 1.0 ) / ( ior + 1.0 ) ) * specularTintFactor, vec3( 1.0 ) ) * specularIntensityFactor, diffuseColor.rgb, metalnessFactor );\\\\n\\\\n#else\\\\n\\\\n\\\\tmaterial.specularColor = mix( vec3( 0.04 ), diffuseColor.rgb, metalnessFactor );\\\\n\\\\tmaterial.specularF90 = 1.0;\\\\n\\\\n#endif\\\\n\\\\n#ifdef USE_CLEARCOAT\\\\n\\\\n\\\\tmaterial.clearcoat = clearcoat;\\\\n\\\\tmaterial.clearcoatRoughness = clearcoatRoughness;\\\\n\\\\tmaterial.clearcoatF0 = vec3( 0.04 );\\\\n\\\\tmaterial.clearcoatF90 = 1.0;\\\\n\\\\n\\\\t#ifdef USE_CLEARCOATMAP\\\\n\\\\n\\\\t\\\\tmaterial.clearcoat *= texture2D( clearcoatMap, vUv ).x;\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\t#ifdef USE_CLEARCOAT_ROUGHNESSMAP\\\\n\\\\n\\\\t\\\\tmaterial.clearcoatRoughness *= texture2D( clearcoatRoughnessMap, vUv ).y;\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\tmaterial.clearcoat = saturate( material.clearcoat ); // Burley clearcoat model\\\\n\\\\tmaterial.clearcoatRoughness = max( material.clearcoatRoughness, 0.0525 );\\\\n\\\\tmaterial.clearcoatRoughness += geometryRoughness;\\\\n\\\\tmaterial.clearcoatRoughness = min( material.clearcoatRoughness, 1.0 );\\\\n\\\\n#endif\\\\n\\\\n#ifdef USE_SHEEN\\\\n\\\\n\\\\tmaterial.sheenTint = sheenTint;\\\\n\\\\tmaterial.sheenRoughness = clamp( sheenRoughness, 0.07, 1.0 );\\\\n\\\\n#endif\\\\n\\\\\\\",lights_physical_pars_fragment:'\\\\nstruct PhysicalMaterial {\\\\n\\\\n\\\\tvec3 diffuseColor;\\\\n\\\\tfloat roughness;\\\\n\\\\tvec3 specularColor;\\\\n\\\\tfloat specularF90;\\\\n\\\\n\\\\t#ifdef USE_CLEARCOAT\\\\n\\\\t\\\\tfloat clearcoat;\\\\n\\\\t\\\\tfloat clearcoatRoughness;\\\\n\\\\t\\\\tvec3 clearcoatF0;\\\\n\\\\t\\\\tfloat clearcoatF90;\\\\n\\\\t#endif\\\\n\\\\n\\\\t#ifdef USE_SHEEN\\\\n\\\\t\\\\tvec3 sheenTint;\\\\n\\\\t\\\\tfloat sheenRoughness;\\\\n\\\\t#endif\\\\n\\\\n};\\\\n\\\\n// temporary\\\\nvec3 clearcoatSpecular = vec3( 0.0 );\\\\n\\\\n// Analytical approximation of the DFG LUT, one half of the\\\\n// split-sum approximation used in indirect specular lighting.\\\\n// via \\\\'environmentBRDF\\\\' from \\\\\\\"Physically Based Shading on Mobile\\\\\\\"\\\\n// https://www.unrealengine.com/blog/physically-based-shading-on-mobile\\\\nvec2 DFGApprox( const in vec3 normal, const in vec3 viewDir, const in float roughness ) {\\\\n\\\\n\\\\tfloat dotNV = saturate( dot( normal, viewDir ) );\\\\n\\\\n\\\\tconst vec4 c0 = vec4( - 1, - 0.0275, - 0.572, 0.022 );\\\\n\\\\n\\\\tconst vec4 c1 = vec4( 1, 0.0425, 1.04, - 0.04 );\\\\n\\\\n\\\\tvec4 r = roughness * c0 + c1;\\\\n\\\\n\\\\tfloat a004 = min( r.x * r.x, exp2( - 9.28 * dotNV ) ) * r.x + r.y;\\\\n\\\\n\\\\tvec2 fab = vec2( - 1.04, 1.04 ) * a004 + r.zw;\\\\n\\\\n\\\\treturn fab;\\\\n\\\\n}\\\\n\\\\nvec3 EnvironmentBRDF( const in vec3 normal, const in vec3 viewDir, const in vec3 specularColor, const in float specularF90, const in float roughness ) {\\\\n\\\\n\\\\tvec2 fab = DFGApprox( normal, viewDir, roughness );\\\\n\\\\n\\\\treturn specularColor * fab.x + specularF90 * fab.y;\\\\n\\\\n}\\\\n\\\\n// Fdez-Agüera\\\\'s \\\\\\\"Multiple-Scattering Microfacet Model for Real-Time Image Based Lighting\\\\\\\"\\\\n// Approximates multiscattering in order to preserve energy.\\\\n// http://www.jcgt.org/published/0008/01/03/\\\\nvoid computeMultiscattering( const in vec3 normal, const in vec3 viewDir, const in vec3 specularColor, const in float specularF90, const in float roughness, inout vec3 singleScatter, inout vec3 multiScatter ) {\\\\n\\\\n\\\\tvec2 fab = DFGApprox( normal, viewDir, roughness );\\\\n\\\\n\\\\tvec3 FssEss = specularColor * fab.x + specularF90 * fab.y;\\\\n\\\\n\\\\tfloat Ess = fab.x + fab.y;\\\\n\\\\tfloat Ems = 1.0 - Ess;\\\\n\\\\n\\\\tvec3 Favg = specularColor + ( 1.0 - specularColor ) * 0.047619; // 1/21\\\\n\\\\tvec3 Fms = FssEss * Favg / ( 1.0 - Ems * Favg );\\\\n\\\\n\\\\tsingleScatter += FssEss;\\\\n\\\\tmultiScatter += Fms * Ems;\\\\n\\\\n}\\\\n\\\\n#if NUM_RECT_AREA_LIGHTS > 0\\\\n\\\\n\\\\tvoid RE_Direct_RectArea_Physical( const in RectAreaLight rectAreaLight, const in GeometricContext geometry, const in PhysicalMaterial material, inout ReflectedLight reflectedLight ) {\\\\n\\\\n\\\\t\\\\tvec3 normal = geometry.normal;\\\\n\\\\t\\\\tvec3 viewDir = geometry.viewDir;\\\\n\\\\t\\\\tvec3 position = geometry.position;\\\\n\\\\t\\\\tvec3 lightPos = rectAreaLight.position;\\\\n\\\\t\\\\tvec3 halfWidth = rectAreaLight.halfWidth;\\\\n\\\\t\\\\tvec3 halfHeight = rectAreaLight.halfHeight;\\\\n\\\\t\\\\tvec3 lightColor = rectAreaLight.color;\\\\n\\\\t\\\\tfloat roughness = material.roughness;\\\\n\\\\n\\\\t\\\\tvec3 rectCoords[ 4 ];\\\\n\\\\t\\\\trectCoords[ 0 ] = lightPos + halfWidth - halfHeight; // counterclockwise; light shines in local neg z direction\\\\n\\\\t\\\\trectCoords[ 1 ] = lightPos - halfWidth - halfHeight;\\\\n\\\\t\\\\trectCoords[ 2 ] = lightPos - halfWidth + halfHeight;\\\\n\\\\t\\\\trectCoords[ 3 ] = lightPos + halfWidth + halfHeight;\\\\n\\\\n\\\\t\\\\tvec2 uv = LTC_Uv( normal, viewDir, roughness );\\\\n\\\\n\\\\t\\\\tvec4 t1 = texture2D( ltc_1, uv );\\\\n\\\\t\\\\tvec4 t2 = texture2D( ltc_2, uv );\\\\n\\\\n\\\\t\\\\tmat3 mInv = mat3(\\\\n\\\\t\\\\t\\\\tvec3( t1.x, 0, t1.y ),\\\\n\\\\t\\\\t\\\\tvec3( 0, 1, 0 ),\\\\n\\\\t\\\\t\\\\tvec3( t1.z, 0, t1.w )\\\\n\\\\t\\\\t);\\\\n\\\\n\\\\t\\\\t// LTC Fresnel Approximation by Stephen Hill\\\\n\\\\t\\\\t// http://blog.selfshadow.com/publications/s2016-advances/s2016_ltc_fresnel.pdf\\\\n\\\\t\\\\tvec3 fresnel = ( material.specularColor * t2.x + ( vec3( 1.0 ) - material.specularColor ) * t2.y );\\\\n\\\\n\\\\t\\\\treflectedLight.directSpecular += lightColor * fresnel * LTC_Evaluate( normal, viewDir, position, mInv, rectCoords );\\\\n\\\\n\\\\t\\\\treflectedLight.directDiffuse += lightColor * material.diffuseColor * LTC_Evaluate( normal, viewDir, position, mat3( 1.0 ), rectCoords );\\\\n\\\\n\\\\t}\\\\n\\\\n#endif\\\\n\\\\nvoid RE_Direct_Physical( const in IncidentLight directLight, const in GeometricContext geometry, const in PhysicalMaterial material, inout ReflectedLight reflectedLight ) {\\\\n\\\\n\\\\tfloat dotNL = saturate( dot( geometry.normal, directLight.direction ) );\\\\n\\\\n\\\\tvec3 irradiance = dotNL * directLight.color;\\\\n\\\\n\\\\t#ifdef USE_CLEARCOAT\\\\n\\\\n\\\\t\\\\tfloat dotNLcc = saturate( dot( geometry.clearcoatNormal, directLight.direction ) );\\\\n\\\\n\\\\t\\\\tvec3 ccIrradiance = dotNLcc * directLight.color;\\\\n\\\\n\\\\t\\\\tclearcoatSpecular += ccIrradiance * BRDF_GGX( directLight.direction, geometry.viewDir, geometry.clearcoatNormal, material.clearcoatF0, material.clearcoatF90, material.clearcoatRoughness );\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\t#ifdef USE_SHEEN\\\\n\\\\n\\\\t\\\\treflectedLight.directSpecular += irradiance * BRDF_Sheen( directLight.direction, geometry.viewDir, geometry.normal, material.sheenTint, material.sheenRoughness );\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\treflectedLight.directSpecular += irradiance * BRDF_GGX( directLight.direction, geometry.viewDir, geometry.normal, material.specularColor, material.specularF90, material.roughness );\\\\n\\\\n\\\\n\\\\treflectedLight.directDiffuse += irradiance * BRDF_Lambert( material.diffuseColor );\\\\n}\\\\n\\\\nvoid RE_IndirectDiffuse_Physical( const in vec3 irradiance, const in GeometricContext geometry, const in PhysicalMaterial material, inout ReflectedLight reflectedLight ) {\\\\n\\\\n\\\\treflectedLight.indirectDiffuse += irradiance * BRDF_Lambert( material.diffuseColor );\\\\n\\\\n}\\\\n\\\\nvoid RE_IndirectSpecular_Physical( const in vec3 radiance, const in vec3 irradiance, const in vec3 clearcoatRadiance, const in GeometricContext geometry, const in PhysicalMaterial material, inout ReflectedLight reflectedLight) {\\\\n\\\\n\\\\t#ifdef USE_CLEARCOAT\\\\n\\\\n\\\\t\\\\tclearcoatSpecular += clearcoatRadiance * EnvironmentBRDF( geometry.clearcoatNormal, geometry.viewDir, material.clearcoatF0, material.clearcoatF90, material.clearcoatRoughness );\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\t// Both indirect specular and indirect diffuse light accumulate here\\\\n\\\\n\\\\tvec3 singleScattering = vec3( 0.0 );\\\\n\\\\tvec3 multiScattering = vec3( 0.0 );\\\\n\\\\tvec3 cosineWeightedIrradiance = irradiance * RECIPROCAL_PI;\\\\n\\\\n\\\\tcomputeMultiscattering( geometry.normal, geometry.viewDir, material.specularColor, material.specularF90, material.roughness, singleScattering, multiScattering );\\\\n\\\\n\\\\tvec3 diffuse = material.diffuseColor * ( 1.0 - ( singleScattering + multiScattering ) );\\\\n\\\\n\\\\treflectedLight.indirectSpecular += radiance * singleScattering;\\\\n\\\\treflectedLight.indirectSpecular += multiScattering * cosineWeightedIrradiance;\\\\n\\\\n\\\\treflectedLight.indirectDiffuse += diffuse * cosineWeightedIrradiance;\\\\n\\\\n}\\\\n\\\\n#define RE_Direct\\\\t\\\\t\\\\t\\\\tRE_Direct_Physical\\\\n#define RE_Direct_RectArea\\\\t\\\\tRE_Direct_RectArea_Physical\\\\n#define RE_IndirectDiffuse\\\\t\\\\tRE_IndirectDiffuse_Physical\\\\n#define RE_IndirectSpecular\\\\t\\\\tRE_IndirectSpecular_Physical\\\\n\\\\n// ref: https://seblagarde.files.wordpress.com/2015/07/course_notes_moving_frostbite_to_pbr_v32.pdf\\\\nfloat computeSpecularOcclusion( const in float dotNV, const in float ambientOcclusion, const in float roughness ) {\\\\n\\\\n\\\\treturn saturate( pow( dotNV + ambientOcclusion, exp2( - 16.0 * roughness - 1.0 ) ) - 1.0 + ambientOcclusion );\\\\n\\\\n}\\\\n',lights_fragment_begin:\\\\\\\"\\\\n/**\\\\n * This is a template that can be used to light a material, it uses pluggable\\\\n * RenderEquations (RE)for specific lighting scenarios.\\\\n *\\\\n * Instructions for use:\\\\n * - Ensure that both RE_Direct, RE_IndirectDiffuse and RE_IndirectSpecular are defined\\\\n * - If you have defined an RE_IndirectSpecular, you need to also provide a Material_LightProbeLOD. <---- ???\\\\n * - Create a material parameter that is to be passed as the third parameter to your lighting functions.\\\\n *\\\\n * TODO:\\\\n * - Add area light support.\\\\n * - Add sphere light support.\\\\n * - Add diffuse light probe (irradiance cubemap) support.\\\\n */\\\\n\\\\nGeometricContext geometry;\\\\n\\\\ngeometry.position = - vViewPosition;\\\\ngeometry.normal = normal;\\\\ngeometry.viewDir = ( isOrthographic ) ? vec3( 0, 0, 1 ) : normalize( vViewPosition );\\\\n\\\\n#ifdef USE_CLEARCOAT\\\\n\\\\n\\\\tgeometry.clearcoatNormal = clearcoatNormal;\\\\n\\\\n#endif\\\\n\\\\nIncidentLight directLight;\\\\n\\\\n#if ( NUM_POINT_LIGHTS > 0 ) && defined( RE_Direct )\\\\n\\\\n\\\\tPointLight pointLight;\\\\n\\\\t#if defined( USE_SHADOWMAP ) && NUM_POINT_LIGHT_SHADOWS > 0\\\\n\\\\tPointLightShadow pointLightShadow;\\\\n\\\\t#endif\\\\n\\\\n\\\\t#pragma unroll_loop_start\\\\n\\\\tfor ( int i = 0; i < NUM_POINT_LIGHTS; i ++ ) {\\\\n\\\\n\\\\t\\\\tpointLight = pointLights[ i ];\\\\n\\\\n\\\\t\\\\tgetPointLightInfo( pointLight, geometry, directLight );\\\\n\\\\n\\\\t\\\\t#if defined( USE_SHADOWMAP ) && ( UNROLLED_LOOP_INDEX < NUM_POINT_LIGHT_SHADOWS )\\\\n\\\\t\\\\tpointLightShadow = pointLightShadows[ i ];\\\\n\\\\t\\\\tdirectLight.color *= all( bvec2( directLight.visible, receiveShadow ) ) ? getPointShadow( pointShadowMap[ i ], pointLightShadow.shadowMapSize, pointLightShadow.shadowBias, pointLightShadow.shadowRadius, vPointShadowCoord[ i ], pointLightShadow.shadowCameraNear, pointLightShadow.shadowCameraFar ) : 1.0;\\\\n\\\\t\\\\t#endif\\\\n\\\\n\\\\t\\\\tRE_Direct( directLight, geometry, material, reflectedLight );\\\\n\\\\n\\\\t}\\\\n\\\\t#pragma unroll_loop_end\\\\n\\\\n#endif\\\\n\\\\n#if ( NUM_SPOT_LIGHTS > 0 ) && defined( RE_Direct )\\\\n\\\\n\\\\tSpotLight spotLight;\\\\n\\\\t#if defined( USE_SHADOWMAP ) && NUM_SPOT_LIGHT_SHADOWS > 0\\\\n\\\\tSpotLightShadow spotLightShadow;\\\\n\\\\t#endif\\\\n\\\\n\\\\t#pragma unroll_loop_start\\\\n\\\\tfor ( int i = 0; i < NUM_SPOT_LIGHTS; i ++ ) {\\\\n\\\\n\\\\t\\\\tspotLight = spotLights[ i ];\\\\n\\\\n\\\\t\\\\tgetSpotLightInfo( spotLight, geometry, directLight );\\\\n\\\\n\\\\t\\\\t#if defined( USE_SHADOWMAP ) && ( UNROLLED_LOOP_INDEX < NUM_SPOT_LIGHT_SHADOWS )\\\\n\\\\t\\\\tspotLightShadow = spotLightShadows[ i ];\\\\n\\\\t\\\\tdirectLight.color *= all( bvec2( directLight.visible, receiveShadow ) ) ? getShadow( spotShadowMap[ i ], spotLightShadow.shadowMapSize, spotLightShadow.shadowBias, spotLightShadow.shadowRadius, vSpotShadowCoord[ i ] ) : 1.0;\\\\n\\\\t\\\\t#endif\\\\n\\\\n\\\\t\\\\tRE_Direct( directLight, geometry, material, reflectedLight );\\\\n\\\\n\\\\t}\\\\n\\\\t#pragma unroll_loop_end\\\\n\\\\n#endif\\\\n\\\\n#if ( NUM_DIR_LIGHTS > 0 ) && defined( RE_Direct )\\\\n\\\\n\\\\tDirectionalLight directionalLight;\\\\n\\\\t#if defined( USE_SHADOWMAP ) && NUM_DIR_LIGHT_SHADOWS > 0\\\\n\\\\tDirectionalLightShadow directionalLightShadow;\\\\n\\\\t#endif\\\\n\\\\n\\\\t#pragma unroll_loop_start\\\\n\\\\tfor ( int i = 0; i < NUM_DIR_LIGHTS; i ++ ) {\\\\n\\\\n\\\\t\\\\tdirectionalLight = directionalLights[ i ];\\\\n\\\\n\\\\t\\\\tgetDirectionalLightInfo( directionalLight, geometry, directLight );\\\\n\\\\n\\\\t\\\\t#if defined( USE_SHADOWMAP ) && ( UNROLLED_LOOP_INDEX < NUM_DIR_LIGHT_SHADOWS )\\\\n\\\\t\\\\tdirectionalLightShadow = directionalLightShadows[ i ];\\\\n\\\\t\\\\tdirectLight.color *= all( bvec2( directLight.visible, receiveShadow ) ) ? getShadow( directionalShadowMap[ i ], directionalLightShadow.shadowMapSize, directionalLightShadow.shadowBias, directionalLightShadow.shadowRadius, vDirectionalShadowCoord[ i ] ) : 1.0;\\\\n\\\\t\\\\t#endif\\\\n\\\\n\\\\t\\\\tRE_Direct( directLight, geometry, material, reflectedLight );\\\\n\\\\n\\\\t}\\\\n\\\\t#pragma unroll_loop_end\\\\n\\\\n#endif\\\\n\\\\n#if ( NUM_RECT_AREA_LIGHTS > 0 ) && defined( RE_Direct_RectArea )\\\\n\\\\n\\\\tRectAreaLight rectAreaLight;\\\\n\\\\n\\\\t#pragma unroll_loop_start\\\\n\\\\tfor ( int i = 0; i < NUM_RECT_AREA_LIGHTS; i ++ ) {\\\\n\\\\n\\\\t\\\\trectAreaLight = rectAreaLights[ i ];\\\\n\\\\t\\\\tRE_Direct_RectArea( rectAreaLight, geometry, material, reflectedLight );\\\\n\\\\n\\\\t}\\\\n\\\\t#pragma unroll_loop_end\\\\n\\\\n#endif\\\\n\\\\n#if defined( RE_IndirectDiffuse )\\\\n\\\\n\\\\tvec3 iblIrradiance = vec3( 0.0 );\\\\n\\\\n\\\\tvec3 irradiance = getAmbientLightIrradiance( ambientLightColor );\\\\n\\\\n\\\\tirradiance += getLightProbeIrradiance( lightProbe, geometry.normal );\\\\n\\\\n\\\\t#if ( NUM_HEMI_LIGHTS > 0 )\\\\n\\\\n\\\\t\\\\t#pragma unroll_loop_start\\\\n\\\\t\\\\tfor ( int i = 0; i < NUM_HEMI_LIGHTS; i ++ ) {\\\\n\\\\n\\\\t\\\\t\\\\tirradiance += getHemisphereLightIrradiance( hemisphereLights[ i ], geometry.normal );\\\\n\\\\n\\\\t\\\\t}\\\\n\\\\t\\\\t#pragma unroll_loop_end\\\\n\\\\n\\\\t#endif\\\\n\\\\n#endif\\\\n\\\\n#if defined( RE_IndirectSpecular )\\\\n\\\\n\\\\tvec3 radiance = vec3( 0.0 );\\\\n\\\\tvec3 clearcoatRadiance = vec3( 0.0 );\\\\n\\\\n#endif\\\\n\\\\\\\",lights_fragment_maps:\\\\\\\"\\\\n#if defined( RE_IndirectDiffuse )\\\\n\\\\n\\\\t#ifdef USE_LIGHTMAP\\\\n\\\\n\\\\t\\\\tvec4 lightMapTexel = texture2D( lightMap, vUv2 );\\\\n\\\\t\\\\tvec3 lightMapIrradiance = lightMapTexelToLinear( lightMapTexel ).rgb * lightMapIntensity;\\\\n\\\\n\\\\t\\\\t#ifndef PHYSICALLY_CORRECT_LIGHTS\\\\n\\\\n\\\\t\\\\t\\\\tlightMapIrradiance *= PI;\\\\n\\\\n\\\\t\\\\t#endif\\\\n\\\\n\\\\t\\\\tirradiance += lightMapIrradiance;\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\t#if defined( USE_ENVMAP ) && defined( STANDARD ) && defined( ENVMAP_TYPE_CUBE_UV )\\\\n\\\\n\\\\t\\\\tiblIrradiance += getIBLIrradiance( geometry.normal );\\\\n\\\\n\\\\t#endif\\\\n\\\\n#endif\\\\n\\\\n#if defined( USE_ENVMAP ) && defined( RE_IndirectSpecular )\\\\n\\\\n\\\\tradiance += getIBLRadiance( geometry.viewDir, geometry.normal, material.roughness );\\\\n\\\\n\\\\t#ifdef USE_CLEARCOAT\\\\n\\\\n\\\\t\\\\tclearcoatRadiance += getIBLRadiance( geometry.viewDir, geometry.clearcoatNormal, material.clearcoatRoughness );\\\\n\\\\n\\\\t#endif\\\\n\\\\n#endif\\\\n\\\\\\\",lights_fragment_end:\\\\\\\"\\\\n#if defined( RE_IndirectDiffuse )\\\\n\\\\n\\\\tRE_IndirectDiffuse( irradiance, geometry, material, reflectedLight );\\\\n\\\\n#endif\\\\n\\\\n#if defined( RE_IndirectSpecular )\\\\n\\\\n\\\\tRE_IndirectSpecular( radiance, iblIrradiance, clearcoatRadiance, geometry, material, reflectedLight );\\\\n\\\\n#endif\\\\n\\\\\\\",logdepthbuf_fragment:\\\\\\\"\\\\n#if defined( USE_LOGDEPTHBUF ) && defined( USE_LOGDEPTHBUF_EXT )\\\\n\\\\n\\\\t// Doing a strict comparison with == 1.0 can cause noise artifacts\\\\n\\\\t// on some platforms. See issue #17623.\\\\n\\\\tgl_FragDepthEXT = vIsPerspective == 0.0 ? gl_FragCoord.z : log2( vFragDepth ) * logDepthBufFC * 0.5;\\\\n\\\\n#endif\\\\n\\\\\\\",logdepthbuf_pars_fragment:\\\\\\\"\\\\n#if defined( USE_LOGDEPTHBUF ) && defined( USE_LOGDEPTHBUF_EXT )\\\\n\\\\n\\\\tuniform float logDepthBufFC;\\\\n\\\\tvarying float vFragDepth;\\\\n\\\\tvarying float vIsPerspective;\\\\n\\\\n#endif\\\\n\\\\\\\",logdepthbuf_pars_vertex:\\\\\\\"\\\\n#ifdef USE_LOGDEPTHBUF\\\\n\\\\n\\\\t#ifdef USE_LOGDEPTHBUF_EXT\\\\n\\\\n\\\\t\\\\tvarying float vFragDepth;\\\\n\\\\t\\\\tvarying float vIsPerspective;\\\\n\\\\n\\\\t#else\\\\n\\\\n\\\\t\\\\tuniform float logDepthBufFC;\\\\n\\\\n\\\\t#endif\\\\n\\\\n#endif\\\\n\\\\\\\",logdepthbuf_vertex:\\\\\\\"\\\\n#ifdef USE_LOGDEPTHBUF\\\\n\\\\n\\\\t#ifdef USE_LOGDEPTHBUF_EXT\\\\n\\\\n\\\\t\\\\tvFragDepth = 1.0 + gl_Position.w;\\\\n\\\\t\\\\tvIsPerspective = float( isPerspectiveMatrix( projectionMatrix ) );\\\\n\\\\n\\\\t#else\\\\n\\\\n\\\\t\\\\tif ( isPerspectiveMatrix( projectionMatrix ) ) {\\\\n\\\\n\\\\t\\\\t\\\\tgl_Position.z = log2( max( EPSILON, gl_Position.w + 1.0 ) ) * logDepthBufFC - 1.0;\\\\n\\\\n\\\\t\\\\t\\\\tgl_Position.z *= gl_Position.w;\\\\n\\\\n\\\\t\\\\t}\\\\n\\\\n\\\\t#endif\\\\n\\\\n#endif\\\\n\\\\\\\",map_fragment:\\\\\\\"\\\\n#ifdef USE_MAP\\\\n\\\\n\\\\tvec4 texelColor = texture2D( map, vUv );\\\\n\\\\n\\\\ttexelColor = mapTexelToLinear( texelColor );\\\\n\\\\tdiffuseColor *= texelColor;\\\\n\\\\n#endif\\\\n\\\\\\\",map_pars_fragment:\\\\\\\"\\\\n#ifdef USE_MAP\\\\n\\\\n\\\\tuniform sampler2D map;\\\\n\\\\n#endif\\\\n\\\\\\\",map_particle_fragment:\\\\\\\"\\\\n#if defined( USE_MAP ) || defined( USE_ALPHAMAP )\\\\n\\\\n\\\\tvec2 uv = ( uvTransform * vec3( gl_PointCoord.x, 1.0 - gl_PointCoord.y, 1 ) ).xy;\\\\n\\\\n#endif\\\\n\\\\n#ifdef USE_MAP\\\\n\\\\n\\\\tvec4 mapTexel = texture2D( map, uv );\\\\n\\\\tdiffuseColor *= mapTexelToLinear( mapTexel );\\\\n\\\\n#endif\\\\n\\\\n#ifdef USE_ALPHAMAP\\\\n\\\\n\\\\tdiffuseColor.a *= texture2D( alphaMap, uv ).g;\\\\n\\\\n#endif\\\\n\\\\\\\",map_particle_pars_fragment:\\\\\\\"\\\\n#if defined( USE_MAP ) || defined( USE_ALPHAMAP )\\\\n\\\\n\\\\tuniform mat3 uvTransform;\\\\n\\\\n#endif\\\\n\\\\n#ifdef USE_MAP\\\\n\\\\n\\\\tuniform sampler2D map;\\\\n\\\\n#endif\\\\n\\\\n#ifdef USE_ALPHAMAP\\\\n\\\\n\\\\tuniform sampler2D alphaMap;\\\\n\\\\n#endif\\\\n\\\\\\\",metalnessmap_fragment:\\\\\\\"\\\\nfloat metalnessFactor = metalness;\\\\n\\\\n#ifdef USE_METALNESSMAP\\\\n\\\\n\\\\tvec4 texelMetalness = texture2D( metalnessMap, vUv );\\\\n\\\\n\\\\t// reads channel B, compatible with a combined OcclusionRoughnessMetallic (RGB) texture\\\\n\\\\tmetalnessFactor *= texelMetalness.b;\\\\n\\\\n#endif\\\\n\\\\\\\",metalnessmap_pars_fragment:\\\\\\\"\\\\n#ifdef USE_METALNESSMAP\\\\n\\\\n\\\\tuniform sampler2D metalnessMap;\\\\n\\\\n#endif\\\\n\\\\\\\",morphnormal_vertex:\\\\\\\"\\\\n#ifdef USE_MORPHNORMALS\\\\n\\\\n\\\\t// morphTargetBaseInfluence is set based on BufferGeometry.morphTargetsRelative value:\\\\n\\\\t// When morphTargetsRelative is false, this is set to 1 - sum(influences); this results in normal = sum((target - base) * influence)\\\\n\\\\t// When morphTargetsRelative is true, this is set to 1; as a result, all morph targets are simply added to the base after weighting\\\\n\\\\tobjectNormal *= morphTargetBaseInfluence;\\\\n\\\\n\\\\t#ifdef MORPHTARGETS_TEXTURE\\\\n\\\\n\\\\t\\\\tfor ( int i = 0; i < MORPHTARGETS_COUNT; i ++ ) {\\\\n\\\\n\\\\t\\\\t\\\\tif ( morphTargetInfluences[ i ] > 0.0 ) objectNormal += getMorph( gl_VertexID, i, 1, 2 ) * morphTargetInfluences[ i ];\\\\n\\\\n\\\\t\\\\t}\\\\n\\\\n\\\\t#else\\\\n\\\\n\\\\t\\\\tobjectNormal += morphNormal0 * morphTargetInfluences[ 0 ];\\\\n\\\\t\\\\tobjectNormal += morphNormal1 * morphTargetInfluences[ 1 ];\\\\n\\\\t\\\\tobjectNormal += morphNormal2 * morphTargetInfluences[ 2 ];\\\\n\\\\t\\\\tobjectNormal += morphNormal3 * morphTargetInfluences[ 3 ];\\\\n\\\\n\\\\t#endif\\\\n\\\\n#endif\\\\n\\\\\\\",morphtarget_pars_vertex:\\\\\\\"\\\\n#ifdef USE_MORPHTARGETS\\\\n\\\\n\\\\tuniform float morphTargetBaseInfluence;\\\\n\\\\n\\\\t#ifdef MORPHTARGETS_TEXTURE\\\\n\\\\n\\\\t\\\\tuniform float morphTargetInfluences[ MORPHTARGETS_COUNT ];\\\\n\\\\t\\\\tuniform sampler2DArray morphTargetsTexture;\\\\n\\\\t\\\\tuniform vec2 morphTargetsTextureSize;\\\\n\\\\n\\\\t\\\\tvec3 getMorph( const in int vertexIndex, const in int morphTargetIndex, const in int offset, const in int stride ) {\\\\n\\\\n\\\\t\\\\t\\\\tfloat texelIndex = float( vertexIndex * stride + offset );\\\\n\\\\t\\\\t\\\\tfloat y = floor( texelIndex / morphTargetsTextureSize.x );\\\\n\\\\t\\\\t\\\\tfloat x = texelIndex - y * morphTargetsTextureSize.x;\\\\n\\\\n\\\\t\\\\t\\\\tvec3 morphUV = vec3( ( x + 0.5 ) / morphTargetsTextureSize.x, y / morphTargetsTextureSize.y, morphTargetIndex );\\\\n\\\\t\\\\t\\\\treturn texture( morphTargetsTexture, morphUV ).xyz;\\\\n\\\\n\\\\t\\\\t}\\\\n\\\\n\\\\t#else\\\\n\\\\n\\\\t\\\\t#ifndef USE_MORPHNORMALS\\\\n\\\\n\\\\t\\\\t\\\\tuniform float morphTargetInfluences[ 8 ];\\\\n\\\\n\\\\t\\\\t#else\\\\n\\\\n\\\\t\\\\t\\\\tuniform float morphTargetInfluences[ 4 ];\\\\n\\\\n\\\\t\\\\t#endif\\\\n\\\\n\\\\t#endif\\\\n\\\\n#endif\\\\n\\\\\\\",morphtarget_vertex:\\\\\\\"\\\\n#ifdef USE_MORPHTARGETS\\\\n\\\\n\\\\t// morphTargetBaseInfluence is set based on BufferGeometry.morphTargetsRelative value:\\\\n\\\\t// When morphTargetsRelative is false, this is set to 1 - sum(influences); this results in position = sum((target - base) * influence)\\\\n\\\\t// When morphTargetsRelative is true, this is set to 1; as a result, all morph targets are simply added to the base after weighting\\\\n\\\\ttransformed *= morphTargetBaseInfluence;\\\\n\\\\n\\\\t#ifdef MORPHTARGETS_TEXTURE\\\\n\\\\n\\\\t\\\\tfor ( int i = 0; i < MORPHTARGETS_COUNT; i ++ ) {\\\\n\\\\n\\\\t\\\\t\\\\t#ifndef USE_MORPHNORMALS\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tif ( morphTargetInfluences[ i ] > 0.0 ) transformed += getMorph( gl_VertexID, i, 0, 1 ) * morphTargetInfluences[ i ];\\\\n\\\\n\\\\t\\\\t\\\\t#else\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tif ( morphTargetInfluences[ i ] > 0.0 ) transformed += getMorph( gl_VertexID, i, 0, 2 ) * morphTargetInfluences[ i ];\\\\n\\\\n\\\\t\\\\t\\\\t#endif\\\\n\\\\n\\\\t\\\\t}\\\\n\\\\n\\\\t#else\\\\n\\\\n\\\\t\\\\ttransformed += morphTarget0 * morphTargetInfluences[ 0 ];\\\\n\\\\t\\\\ttransformed += morphTarget1 * morphTargetInfluences[ 1 ];\\\\n\\\\t\\\\ttransformed += morphTarget2 * morphTargetInfluences[ 2 ];\\\\n\\\\t\\\\ttransformed += morphTarget3 * morphTargetInfluences[ 3 ];\\\\n\\\\n\\\\t\\\\t#ifndef USE_MORPHNORMALS\\\\n\\\\n\\\\t\\\\t\\\\ttransformed += morphTarget4 * morphTargetInfluences[ 4 ];\\\\n\\\\t\\\\t\\\\ttransformed += morphTarget5 * morphTargetInfluences[ 5 ];\\\\n\\\\t\\\\t\\\\ttransformed += morphTarget6 * morphTargetInfluences[ 6 ];\\\\n\\\\t\\\\t\\\\ttransformed += morphTarget7 * morphTargetInfluences[ 7 ];\\\\n\\\\n\\\\t\\\\t#endif\\\\n\\\\n\\\\t#endif\\\\n\\\\n#endif\\\\n\\\\\\\",normal_fragment_begin:\\\\\\\"\\\\nfloat faceDirection = gl_FrontFacing ? 1.0 : - 1.0;\\\\n\\\\n#ifdef FLAT_SHADED\\\\n\\\\n\\\\t// Workaround for Adreno GPUs not able to do dFdx( vViewPosition )\\\\n\\\\n\\\\tvec3 fdx = vec3( dFdx( vViewPosition.x ), dFdx( vViewPosition.y ), dFdx( vViewPosition.z ) );\\\\n\\\\tvec3 fdy = vec3( dFdy( vViewPosition.x ), dFdy( vViewPosition.y ), dFdy( vViewPosition.z ) );\\\\n\\\\tvec3 normal = normalize( cross( fdx, fdy ) );\\\\n\\\\n#else\\\\n\\\\n\\\\tvec3 normal = normalize( vNormal );\\\\n\\\\n\\\\t#ifdef DOUBLE_SIDED\\\\n\\\\n\\\\t\\\\tnormal = normal * faceDirection;\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\t#ifdef USE_TANGENT\\\\n\\\\n\\\\t\\\\tvec3 tangent = normalize( vTangent );\\\\n\\\\t\\\\tvec3 bitangent = normalize( vBitangent );\\\\n\\\\n\\\\t\\\\t#ifdef DOUBLE_SIDED\\\\n\\\\n\\\\t\\\\t\\\\ttangent = tangent * faceDirection;\\\\n\\\\t\\\\t\\\\tbitangent = bitangent * faceDirection;\\\\n\\\\n\\\\t\\\\t#endif\\\\n\\\\n\\\\t\\\\t#if defined( TANGENTSPACE_NORMALMAP ) || defined( USE_CLEARCOAT_NORMALMAP )\\\\n\\\\n\\\\t\\\\t\\\\tmat3 vTBN = mat3( tangent, bitangent, normal );\\\\n\\\\n\\\\t\\\\t#endif\\\\n\\\\n\\\\t#endif\\\\n\\\\n#endif\\\\n\\\\n// non perturbed normal for clearcoat among others\\\\n\\\\nvec3 geometryNormal = normal;\\\\n\\\\n\\\\\\\",normal_fragment_maps:\\\\\\\"\\\\n\\\\n#ifdef OBJECTSPACE_NORMALMAP\\\\n\\\\n\\\\tnormal = texture2D( normalMap, vUv ).xyz * 2.0 - 1.0; // overrides both flatShading and attribute normals\\\\n\\\\n\\\\t#ifdef FLIP_SIDED\\\\n\\\\n\\\\t\\\\tnormal = - normal;\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\t#ifdef DOUBLE_SIDED\\\\n\\\\n\\\\t\\\\tnormal = normal * faceDirection;\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\tnormal = normalize( normalMatrix * normal );\\\\n\\\\n#elif defined( TANGENTSPACE_NORMALMAP )\\\\n\\\\n\\\\tvec3 mapN = texture2D( normalMap, vUv ).xyz * 2.0 - 1.0;\\\\n\\\\tmapN.xy *= normalScale;\\\\n\\\\n\\\\t#ifdef USE_TANGENT\\\\n\\\\n\\\\t\\\\tnormal = normalize( vTBN * mapN );\\\\n\\\\n\\\\t#else\\\\n\\\\n\\\\t\\\\tnormal = perturbNormal2Arb( - vViewPosition, normal, mapN, faceDirection );\\\\n\\\\n\\\\t#endif\\\\n\\\\n#elif defined( USE_BUMPMAP )\\\\n\\\\n\\\\tnormal = perturbNormalArb( - vViewPosition, normal, dHdxy_fwd(), faceDirection );\\\\n\\\\n#endif\\\\n\\\\\\\",normal_pars_fragment:\\\\\\\"\\\\n#ifndef FLAT_SHADED\\\\n\\\\n\\\\tvarying vec3 vNormal;\\\\n\\\\n\\\\t#ifdef USE_TANGENT\\\\n\\\\n\\\\t\\\\tvarying vec3 vTangent;\\\\n\\\\t\\\\tvarying vec3 vBitangent;\\\\n\\\\n\\\\t#endif\\\\n\\\\n#endif\\\\n\\\\\\\",normal_pars_vertex:\\\\\\\"\\\\n#ifndef FLAT_SHADED\\\\n\\\\n\\\\tvarying vec3 vNormal;\\\\n\\\\n\\\\t#ifdef USE_TANGENT\\\\n\\\\n\\\\t\\\\tvarying vec3 vTangent;\\\\n\\\\t\\\\tvarying vec3 vBitangent;\\\\n\\\\n\\\\t#endif\\\\n\\\\n#endif\\\\n\\\\\\\",normal_vertex:\\\\\\\"\\\\n#ifndef FLAT_SHADED // normal is computed with derivatives when FLAT_SHADED\\\\n\\\\n\\\\tvNormal = normalize( transformedNormal );\\\\n\\\\n\\\\t#ifdef USE_TANGENT\\\\n\\\\n\\\\t\\\\tvTangent = normalize( transformedTangent );\\\\n\\\\t\\\\tvBitangent = normalize( cross( vNormal, vTangent ) * tangent.w );\\\\n\\\\n\\\\t#endif\\\\n\\\\n#endif\\\\n\\\\\\\",normalmap_pars_fragment:\\\\\\\"\\\\n#ifdef USE_NORMALMAP\\\\n\\\\n\\\\tuniform sampler2D normalMap;\\\\n\\\\tuniform vec2 normalScale;\\\\n\\\\n#endif\\\\n\\\\n#ifdef OBJECTSPACE_NORMALMAP\\\\n\\\\n\\\\tuniform mat3 normalMatrix;\\\\n\\\\n#endif\\\\n\\\\n#if ! defined ( USE_TANGENT ) && ( defined ( TANGENTSPACE_NORMALMAP ) || defined ( USE_CLEARCOAT_NORMALMAP ) )\\\\n\\\\n\\\\t// Normal Mapping Without Precomputed Tangents\\\\n\\\\t// http://www.thetenthplanet.de/archives/1180\\\\n\\\\n\\\\tvec3 perturbNormal2Arb( vec3 eye_pos, vec3 surf_norm, vec3 mapN, float faceDirection ) {\\\\n\\\\n\\\\t\\\\t// Workaround for Adreno 3XX dFd*( vec3 ) bug. See #9988\\\\n\\\\n\\\\t\\\\tvec3 q0 = vec3( dFdx( eye_pos.x ), dFdx( eye_pos.y ), dFdx( eye_pos.z ) );\\\\n\\\\t\\\\tvec3 q1 = vec3( dFdy( eye_pos.x ), dFdy( eye_pos.y ), dFdy( eye_pos.z ) );\\\\n\\\\t\\\\tvec2 st0 = dFdx( vUv.st );\\\\n\\\\t\\\\tvec2 st1 = dFdy( vUv.st );\\\\n\\\\n\\\\t\\\\tvec3 N = surf_norm; // normalized\\\\n\\\\n\\\\t\\\\tvec3 q1perp = cross( q1, N );\\\\n\\\\t\\\\tvec3 q0perp = cross( N, q0 );\\\\n\\\\n\\\\t\\\\tvec3 T = q1perp * st0.x + q0perp * st1.x;\\\\n\\\\t\\\\tvec3 B = q1perp * st0.y + q0perp * st1.y;\\\\n\\\\n\\\\t\\\\tfloat det = max( dot( T, T ), dot( B, B ) );\\\\n\\\\t\\\\tfloat scale = ( det == 0.0 ) ? 0.0 : faceDirection * inversesqrt( det );\\\\n\\\\n\\\\t\\\\treturn normalize( T * ( mapN.x * scale ) + B * ( mapN.y * scale ) + N * mapN.z );\\\\n\\\\n\\\\t}\\\\n\\\\n#endif\\\\n\\\\\\\",clearcoat_normal_fragment_begin:\\\\\\\"\\\\n#ifdef USE_CLEARCOAT\\\\n\\\\n\\\\tvec3 clearcoatNormal = geometryNormal;\\\\n\\\\n#endif\\\\n\\\\\\\",clearcoat_normal_fragment_maps:\\\\\\\"\\\\n#ifdef USE_CLEARCOAT_NORMALMAP\\\\n\\\\n\\\\tvec3 clearcoatMapN = texture2D( clearcoatNormalMap, vUv ).xyz * 2.0 - 1.0;\\\\n\\\\tclearcoatMapN.xy *= clearcoatNormalScale;\\\\n\\\\n\\\\t#ifdef USE_TANGENT\\\\n\\\\n\\\\t\\\\tclearcoatNormal = normalize( vTBN * clearcoatMapN );\\\\n\\\\n\\\\t#else\\\\n\\\\n\\\\t\\\\tclearcoatNormal = perturbNormal2Arb( - vViewPosition, clearcoatNormal, clearcoatMapN, faceDirection );\\\\n\\\\n\\\\t#endif\\\\n\\\\n#endif\\\\n\\\\\\\",clearcoat_pars_fragment:\\\\\\\"\\\\n\\\\n#ifdef USE_CLEARCOATMAP\\\\n\\\\n\\\\tuniform sampler2D clearcoatMap;\\\\n\\\\n#endif\\\\n\\\\n#ifdef USE_CLEARCOAT_ROUGHNESSMAP\\\\n\\\\n\\\\tuniform sampler2D clearcoatRoughnessMap;\\\\n\\\\n#endif\\\\n\\\\n#ifdef USE_CLEARCOAT_NORMALMAP\\\\n\\\\n\\\\tuniform sampler2D clearcoatNormalMap;\\\\n\\\\tuniform vec2 clearcoatNormalScale;\\\\n\\\\n#endif\\\\n\\\\\\\",output_fragment:\\\\\\\"\\\\n#ifdef OPAQUE\\\\ndiffuseColor.a = 1.0;\\\\n#endif\\\\n\\\\n// https://github.com/mrdoob/three.js/pull/22425\\\\n#ifdef USE_TRANSMISSION\\\\ndiffuseColor.a *= transmissionAlpha + 0.1;\\\\n#endif\\\\n\\\\ngl_FragColor = vec4( outgoingLight, diffuseColor.a );\\\\n\\\\\\\",packing:\\\\\\\"\\\\nvec3 packNormalToRGB( const in vec3 normal ) {\\\\n\\\\treturn normalize( normal ) * 0.5 + 0.5;\\\\n}\\\\n\\\\nvec3 unpackRGBToNormal( const in vec3 rgb ) {\\\\n\\\\treturn 2.0 * rgb.xyz - 1.0;\\\\n}\\\\n\\\\nconst float PackUpscale = 256. / 255.; // fraction -> 0..1 (including 1)\\\\nconst float UnpackDownscale = 255. / 256.; // 0..1 -> fraction (excluding 1)\\\\n\\\\nconst vec3 PackFactors = vec3( 256. * 256. * 256., 256. * 256., 256. );\\\\nconst vec4 UnpackFactors = UnpackDownscale / vec4( PackFactors, 1. );\\\\n\\\\nconst float ShiftRight8 = 1. / 256.;\\\\n\\\\nvec4 packDepthToRGBA( const in float v ) {\\\\n\\\\tvec4 r = vec4( fract( v * PackFactors ), v );\\\\n\\\\tr.yzw -= r.xyz * ShiftRight8; // tidy overflow\\\\n\\\\treturn r * PackUpscale;\\\\n}\\\\n\\\\nfloat unpackRGBAToDepth( const in vec4 v ) {\\\\n\\\\treturn dot( v, UnpackFactors );\\\\n}\\\\n\\\\nvec4 pack2HalfToRGBA( vec2 v ) {\\\\n\\\\tvec4 r = vec4( v.x, fract( v.x * 255.0 ), v.y, fract( v.y * 255.0 ) );\\\\n\\\\treturn vec4( r.x - r.y / 255.0, r.y, r.z - r.w / 255.0, r.w );\\\\n}\\\\n\\\\nvec2 unpackRGBATo2Half( vec4 v ) {\\\\n\\\\treturn vec2( v.x + ( v.y / 255.0 ), v.z + ( v.w / 255.0 ) );\\\\n}\\\\n\\\\n// NOTE: viewZ/eyeZ is < 0 when in front of the camera per OpenGL conventions\\\\n\\\\nfloat viewZToOrthographicDepth( const in float viewZ, const in float near, const in float far ) {\\\\n\\\\treturn ( viewZ + near ) / ( near - far );\\\\n}\\\\nfloat orthographicDepthToViewZ( const in float linearClipZ, const in float near, const in float far ) {\\\\n\\\\treturn linearClipZ * ( near - far ) - near;\\\\n}\\\\n\\\\n// NOTE: https://twitter.com/gonnavis/status/1377183786949959682\\\\n\\\\nfloat viewZToPerspectiveDepth( const in float viewZ, const in float near, const in float far ) {\\\\n\\\\treturn ( ( near + viewZ ) * far ) / ( ( far - near ) * viewZ );\\\\n}\\\\nfloat perspectiveDepthToViewZ( const in float invClipZ, const in float near, const in float far ) {\\\\n\\\\treturn ( near * far ) / ( ( far - near ) * invClipZ - far );\\\\n}\\\\n\\\\\\\",premultiplied_alpha_fragment:\\\\\\\"\\\\n#ifdef PREMULTIPLIED_ALPHA\\\\n\\\\n\\\\t// Get get normal blending with premultipled, use with CustomBlending, OneFactor, OneMinusSrcAlphaFactor, AddEquation.\\\\n\\\\tgl_FragColor.rgb *= gl_FragColor.a;\\\\n\\\\n#endif\\\\n\\\\\\\",project_vertex:\\\\\\\"\\\\nvec4 mvPosition = vec4( transformed, 1.0 );\\\\n\\\\n#ifdef USE_INSTANCING\\\\n\\\\n\\\\tmvPosition = instanceMatrix * mvPosition;\\\\n\\\\n#endif\\\\n\\\\nmvPosition = modelViewMatrix * mvPosition;\\\\n\\\\ngl_Position = projectionMatrix * mvPosition;\\\\n\\\\\\\",dithering_fragment:\\\\\\\"\\\\n#ifdef DITHERING\\\\n\\\\n\\\\tgl_FragColor.rgb = dithering( gl_FragColor.rgb );\\\\n\\\\n#endif\\\\n\\\\\\\",dithering_pars_fragment:\\\\\\\"\\\\n#ifdef DITHERING\\\\n\\\\n\\\\t// based on https://www.shadertoy.com/view/MslGR8\\\\n\\\\tvec3 dithering( vec3 color ) {\\\\n\\\\t\\\\t//Calculate grid position\\\\n\\\\t\\\\tfloat grid_position = rand( gl_FragCoord.xy );\\\\n\\\\n\\\\t\\\\t//Shift the individual colors differently, thus making it even harder to see the dithering pattern\\\\n\\\\t\\\\tvec3 dither_shift_RGB = vec3( 0.25 / 255.0, -0.25 / 255.0, 0.25 / 255.0 );\\\\n\\\\n\\\\t\\\\t//modify shift acording to grid position.\\\\n\\\\t\\\\tdither_shift_RGB = mix( 2.0 * dither_shift_RGB, -2.0 * dither_shift_RGB, grid_position );\\\\n\\\\n\\\\t\\\\t//shift the color by dither_shift\\\\n\\\\t\\\\treturn color + dither_shift_RGB;\\\\n\\\\t}\\\\n\\\\n#endif\\\\n\\\\\\\",roughnessmap_fragment:\\\\\\\"\\\\nfloat roughnessFactor = roughness;\\\\n\\\\n#ifdef USE_ROUGHNESSMAP\\\\n\\\\n\\\\tvec4 texelRoughness = texture2D( roughnessMap, vUv );\\\\n\\\\n\\\\t// reads channel G, compatible with a combined OcclusionRoughnessMetallic (RGB) texture\\\\n\\\\troughnessFactor *= texelRoughness.g;\\\\n\\\\n#endif\\\\n\\\\\\\",roughnessmap_pars_fragment:\\\\\\\"\\\\n#ifdef USE_ROUGHNESSMAP\\\\n\\\\n\\\\tuniform sampler2D roughnessMap;\\\\n\\\\n#endif\\\\n\\\\\\\",shadowmap_pars_fragment:z,shadowmap_pars_vertex:\\\\\\\"\\\\n#ifdef USE_SHADOWMAP\\\\n\\\\n\\\\t#if NUM_DIR_LIGHT_SHADOWS > 0\\\\n\\\\n\\\\t\\\\tuniform mat4 directionalShadowMatrix[ NUM_DIR_LIGHT_SHADOWS ];\\\\n\\\\t\\\\tvarying vec4 vDirectionalShadowCoord[ NUM_DIR_LIGHT_SHADOWS ];\\\\n\\\\n\\\\t\\\\tstruct DirectionalLightShadow {\\\\n\\\\t\\\\t\\\\tfloat shadowBias;\\\\n\\\\t\\\\t\\\\tfloat shadowNormalBias;\\\\n\\\\t\\\\t\\\\tfloat shadowRadius;\\\\n\\\\t\\\\t\\\\tvec2 shadowMapSize;\\\\n\\\\t\\\\t};\\\\n\\\\n\\\\t\\\\tuniform DirectionalLightShadow directionalLightShadows[ NUM_DIR_LIGHT_SHADOWS ];\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\t#if NUM_SPOT_LIGHT_SHADOWS > 0\\\\n\\\\n\\\\t\\\\tuniform mat4 spotShadowMatrix[ NUM_SPOT_LIGHT_SHADOWS ];\\\\n\\\\t\\\\tvarying vec4 vSpotShadowCoord[ NUM_SPOT_LIGHT_SHADOWS ];\\\\n\\\\n\\\\t\\\\tstruct SpotLightShadow {\\\\n\\\\t\\\\t\\\\tfloat shadowBias;\\\\n\\\\t\\\\t\\\\tfloat shadowNormalBias;\\\\n\\\\t\\\\t\\\\tfloat shadowRadius;\\\\n\\\\t\\\\t\\\\tvec2 shadowMapSize;\\\\n\\\\t\\\\t};\\\\n\\\\n\\\\t\\\\tuniform SpotLightShadow spotLightShadows[ NUM_SPOT_LIGHT_SHADOWS ];\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\t#if NUM_POINT_LIGHT_SHADOWS > 0\\\\n\\\\n\\\\t\\\\tuniform mat4 pointShadowMatrix[ NUM_POINT_LIGHT_SHADOWS ];\\\\n\\\\t\\\\tvarying vec4 vPointShadowCoord[ NUM_POINT_LIGHT_SHADOWS ];\\\\n\\\\n\\\\t\\\\tstruct PointLightShadow {\\\\n\\\\t\\\\t\\\\tfloat shadowBias;\\\\n\\\\t\\\\t\\\\tfloat shadowNormalBias;\\\\n\\\\t\\\\t\\\\tfloat shadowRadius;\\\\n\\\\t\\\\t\\\\tvec2 shadowMapSize;\\\\n\\\\t\\\\t\\\\tfloat shadowCameraNear;\\\\n\\\\t\\\\t\\\\tfloat shadowCameraFar;\\\\n\\\\t\\\\t};\\\\n\\\\n\\\\t\\\\tuniform PointLightShadow pointLightShadows[ NUM_POINT_LIGHT_SHADOWS ];\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\t/*\\\\n\\\\t#if NUM_RECT_AREA_LIGHTS > 0\\\\n\\\\n\\\\t\\\\t// TODO (abelnation): uniforms for area light shadows\\\\n\\\\n\\\\t#endif\\\\n\\\\t*/\\\\n\\\\n#endif\\\\n\\\\\\\",shadowmap_vertex:\\\\\\\"\\\\n#ifdef USE_SHADOWMAP\\\\n\\\\n\\\\t#if NUM_DIR_LIGHT_SHADOWS > 0 || NUM_SPOT_LIGHT_SHADOWS > 0 || NUM_POINT_LIGHT_SHADOWS > 0\\\\n\\\\n\\\\t\\\\t// Offsetting the position used for querying occlusion along the world normal can be used to reduce shadow acne.\\\\n\\\\t\\\\tvec3 shadowWorldNormal = inverseTransformDirection( transformedNormal, viewMatrix );\\\\n\\\\t\\\\tvec4 shadowWorldPosition;\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\t#if NUM_DIR_LIGHT_SHADOWS > 0\\\\n\\\\n\\\\t#pragma unroll_loop_start\\\\n\\\\tfor ( int i = 0; i < NUM_DIR_LIGHT_SHADOWS; i ++ ) {\\\\n\\\\n\\\\t\\\\tshadowWorldPosition = worldPosition + vec4( shadowWorldNormal * directionalLightShadows[ i ].shadowNormalBias, 0 );\\\\n\\\\t\\\\tvDirectionalShadowCoord[ i ] = directionalShadowMatrix[ i ] * shadowWorldPosition;\\\\n\\\\n\\\\t}\\\\n\\\\t#pragma unroll_loop_end\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\t#if NUM_SPOT_LIGHT_SHADOWS > 0\\\\n\\\\n\\\\t#pragma unroll_loop_start\\\\n\\\\tfor ( int i = 0; i < NUM_SPOT_LIGHT_SHADOWS; i ++ ) {\\\\n\\\\n\\\\t\\\\tshadowWorldPosition = worldPosition + vec4( shadowWorldNormal * spotLightShadows[ i ].shadowNormalBias, 0 );\\\\n\\\\t\\\\tvSpotShadowCoord[ i ] = spotShadowMatrix[ i ] * shadowWorldPosition;\\\\n\\\\n\\\\t}\\\\n\\\\t#pragma unroll_loop_end\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\t#if NUM_POINT_LIGHT_SHADOWS > 0\\\\n\\\\n\\\\t#pragma unroll_loop_start\\\\n\\\\tfor ( int i = 0; i < NUM_POINT_LIGHT_SHADOWS; i ++ ) {\\\\n\\\\n\\\\t\\\\tshadowWorldPosition = worldPosition + vec4( shadowWorldNormal * pointLightShadows[ i ].shadowNormalBias, 0 );\\\\n\\\\t\\\\tvPointShadowCoord[ i ] = pointShadowMatrix[ i ] * shadowWorldPosition;\\\\n\\\\n\\\\t}\\\\n\\\\t#pragma unroll_loop_end\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\t/*\\\\n\\\\t#if NUM_RECT_AREA_LIGHTS > 0\\\\n\\\\n\\\\t\\\\t// TODO (abelnation): update vAreaShadowCoord with area light info\\\\n\\\\n\\\\t#endif\\\\n\\\\t*/\\\\n\\\\n#endif\\\\n\\\\\\\",shadowmask_pars_fragment:\\\\\\\"\\\\nfloat getShadowMask() {\\\\n\\\\n\\\\tfloat shadow = 1.0;\\\\n\\\\n\\\\t#ifdef USE_SHADOWMAP\\\\n\\\\n\\\\t#if NUM_DIR_LIGHT_SHADOWS > 0\\\\n\\\\n\\\\tDirectionalLightShadow directionalLight;\\\\n\\\\n\\\\t#pragma unroll_loop_start\\\\n\\\\tfor ( int i = 0; i < NUM_DIR_LIGHT_SHADOWS; i ++ ) {\\\\n\\\\n\\\\t\\\\tdirectionalLight = directionalLightShadows[ i ];\\\\n\\\\t\\\\tshadow *= receiveShadow ? getShadow( directionalShadowMap[ i ], directionalLight.shadowMapSize, directionalLight.shadowBias, directionalLight.shadowRadius, vDirectionalShadowCoord[ i ] ) : 1.0;\\\\n\\\\n\\\\t}\\\\n\\\\t#pragma unroll_loop_end\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\t#if NUM_SPOT_LIGHT_SHADOWS > 0\\\\n\\\\n\\\\tSpotLightShadow spotLight;\\\\n\\\\n\\\\t#pragma unroll_loop_start\\\\n\\\\tfor ( int i = 0; i < NUM_SPOT_LIGHT_SHADOWS; i ++ ) {\\\\n\\\\n\\\\t\\\\tspotLight = spotLightShadows[ i ];\\\\n\\\\t\\\\tshadow *= receiveShadow ? getShadow( spotShadowMap[ i ], spotLight.shadowMapSize, spotLight.shadowBias, spotLight.shadowRadius, vSpotShadowCoord[ i ] ) : 1.0;\\\\n\\\\n\\\\t}\\\\n\\\\t#pragma unroll_loop_end\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\t#if NUM_POINT_LIGHT_SHADOWS > 0\\\\n\\\\n\\\\tPointLightShadow pointLight;\\\\n\\\\n\\\\t#pragma unroll_loop_start\\\\n\\\\tfor ( int i = 0; i < NUM_POINT_LIGHT_SHADOWS; i ++ ) {\\\\n\\\\n\\\\t\\\\tpointLight = pointLightShadows[ i ];\\\\n\\\\t\\\\tshadow *= receiveShadow ? getPointShadow( pointShadowMap[ i ], pointLight.shadowMapSize, pointLight.shadowBias, pointLight.shadowRadius, vPointShadowCoord[ i ], pointLight.shadowCameraNear, pointLight.shadowCameraFar ) : 1.0;\\\\n\\\\n\\\\t}\\\\n\\\\t#pragma unroll_loop_end\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\t/*\\\\n\\\\t#if NUM_RECT_AREA_LIGHTS > 0\\\\n\\\\n\\\\t\\\\t// TODO (abelnation): update shadow for Area light\\\\n\\\\n\\\\t#endif\\\\n\\\\t*/\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\treturn shadow;\\\\n\\\\n}\\\\n\\\\\\\",skinbase_vertex:\\\\\\\"\\\\n#ifdef USE_SKINNING\\\\n\\\\n\\\\tmat4 boneMatX = getBoneMatrix( skinIndex.x );\\\\n\\\\tmat4 boneMatY = getBoneMatrix( skinIndex.y );\\\\n\\\\tmat4 boneMatZ = getBoneMatrix( skinIndex.z );\\\\n\\\\tmat4 boneMatW = getBoneMatrix( skinIndex.w );\\\\n\\\\n#endif\\\\n\\\\\\\",skinning_pars_vertex:\\\\\\\"\\\\n#ifdef USE_SKINNING\\\\n\\\\n\\\\tuniform mat4 bindMatrix;\\\\n\\\\tuniform mat4 bindMatrixInverse;\\\\n\\\\n\\\\t#ifdef BONE_TEXTURE\\\\n\\\\n\\\\t\\\\tuniform highp sampler2D boneTexture;\\\\n\\\\t\\\\tuniform int boneTextureSize;\\\\n\\\\n\\\\t\\\\tmat4 getBoneMatrix( const in float i ) {\\\\n\\\\n\\\\t\\\\t\\\\tfloat j = i * 4.0;\\\\n\\\\t\\\\t\\\\tfloat x = mod( j, float( boneTextureSize ) );\\\\n\\\\t\\\\t\\\\tfloat y = floor( j / float( boneTextureSize ) );\\\\n\\\\n\\\\t\\\\t\\\\tfloat dx = 1.0 / float( boneTextureSize );\\\\n\\\\t\\\\t\\\\tfloat dy = 1.0 / float( boneTextureSize );\\\\n\\\\n\\\\t\\\\t\\\\ty = dy * ( y + 0.5 );\\\\n\\\\n\\\\t\\\\t\\\\tvec4 v1 = texture2D( boneTexture, vec2( dx * ( x + 0.5 ), y ) );\\\\n\\\\t\\\\t\\\\tvec4 v2 = texture2D( boneTexture, vec2( dx * ( x + 1.5 ), y ) );\\\\n\\\\t\\\\t\\\\tvec4 v3 = texture2D( boneTexture, vec2( dx * ( x + 2.5 ), y ) );\\\\n\\\\t\\\\t\\\\tvec4 v4 = texture2D( boneTexture, vec2( dx * ( x + 3.5 ), y ) );\\\\n\\\\n\\\\t\\\\t\\\\tmat4 bone = mat4( v1, v2, v3, v4 );\\\\n\\\\n\\\\t\\\\t\\\\treturn bone;\\\\n\\\\n\\\\t\\\\t}\\\\n\\\\n\\\\t#else\\\\n\\\\n\\\\t\\\\tuniform mat4 boneMatrices[ MAX_BONES ];\\\\n\\\\n\\\\t\\\\tmat4 getBoneMatrix( const in float i ) {\\\\n\\\\n\\\\t\\\\t\\\\tmat4 bone = boneMatrices[ int(i) ];\\\\n\\\\t\\\\t\\\\treturn bone;\\\\n\\\\n\\\\t\\\\t}\\\\n\\\\n\\\\t#endif\\\\n\\\\n#endif\\\\n\\\\\\\",skinning_vertex:\\\\\\\"\\\\n#ifdef USE_SKINNING\\\\n\\\\n\\\\tvec4 skinVertex = bindMatrix * vec4( transformed, 1.0 );\\\\n\\\\n\\\\tvec4 skinned = vec4( 0.0 );\\\\n\\\\tskinned += boneMatX * skinVertex * skinWeight.x;\\\\n\\\\tskinned += boneMatY * skinVertex * skinWeight.y;\\\\n\\\\tskinned += boneMatZ * skinVertex * skinWeight.z;\\\\n\\\\tskinned += boneMatW * skinVertex * skinWeight.w;\\\\n\\\\n\\\\ttransformed = ( bindMatrixInverse * skinned ).xyz;\\\\n\\\\n#endif\\\\n\\\\\\\",skinnormal_vertex:\\\\\\\"\\\\n#ifdef USE_SKINNING\\\\n\\\\n\\\\tmat4 skinMatrix = mat4( 0.0 );\\\\n\\\\tskinMatrix += skinWeight.x * boneMatX;\\\\n\\\\tskinMatrix += skinWeight.y * boneMatY;\\\\n\\\\tskinMatrix += skinWeight.z * boneMatZ;\\\\n\\\\tskinMatrix += skinWeight.w * boneMatW;\\\\n\\\\tskinMatrix = bindMatrixInverse * skinMatrix * bindMatrix;\\\\n\\\\n\\\\tobjectNormal = vec4( skinMatrix * vec4( objectNormal, 0.0 ) ).xyz;\\\\n\\\\n\\\\t#ifdef USE_TANGENT\\\\n\\\\n\\\\t\\\\tobjectTangent = vec4( skinMatrix * vec4( objectTangent, 0.0 ) ).xyz;\\\\n\\\\n\\\\t#endif\\\\n\\\\n#endif\\\\n\\\\\\\",specularmap_fragment:\\\\\\\"\\\\nfloat specularStrength;\\\\n\\\\n#ifdef USE_SPECULARMAP\\\\n\\\\n\\\\tvec4 texelSpecular = texture2D( specularMap, vUv );\\\\n\\\\tspecularStrength = texelSpecular.r;\\\\n\\\\n#else\\\\n\\\\n\\\\tspecularStrength = 1.0;\\\\n\\\\n#endif\\\\n\\\\\\\",specularmap_pars_fragment:\\\\\\\"\\\\n#ifdef USE_SPECULARMAP\\\\n\\\\n\\\\tuniform sampler2D specularMap;\\\\n\\\\n#endif\\\\n\\\\\\\",tonemapping_fragment:\\\\\\\"\\\\n#if defined( TONE_MAPPING )\\\\n\\\\n\\\\tgl_FragColor.rgb = toneMapping( gl_FragColor.rgb );\\\\n\\\\n#endif\\\\n\\\\\\\",tonemapping_pars_fragment:\\\\\\\"\\\\n#ifndef saturate\\\\n// may have defined saturate() already\\\\n#define saturate( a ) clamp( a, 0.0, 1.0 )\\\\n#endif\\\\n\\\\nuniform float toneMappingExposure;\\\\n\\\\n// exposure only\\\\nvec3 LinearToneMapping( vec3 color ) {\\\\n\\\\n\\\\treturn toneMappingExposure * color;\\\\n\\\\n}\\\\n\\\\n// source: https://www.cs.utah.edu/~reinhard/cdrom/\\\\nvec3 ReinhardToneMapping( vec3 color ) {\\\\n\\\\n\\\\tcolor *= toneMappingExposure;\\\\n\\\\treturn saturate( color / ( vec3( 1.0 ) + color ) );\\\\n\\\\n}\\\\n\\\\n// source: http://filmicworlds.com/blog/filmic-tonemapping-operators/\\\\nvec3 OptimizedCineonToneMapping( vec3 color ) {\\\\n\\\\n\\\\t// optimized filmic operator by Jim Hejl and Richard Burgess-Dawson\\\\n\\\\tcolor *= toneMappingExposure;\\\\n\\\\tcolor = max( vec3( 0.0 ), color - 0.004 );\\\\n\\\\treturn pow( ( color * ( 6.2 * color + 0.5 ) ) / ( color * ( 6.2 * color + 1.7 ) + 0.06 ), vec3( 2.2 ) );\\\\n\\\\n}\\\\n\\\\n// source: https://github.com/selfshadow/ltc_code/blob/master/webgl/shaders/ltc/ltc_blit.fs\\\\nvec3 RRTAndODTFit( vec3 v ) {\\\\n\\\\n\\\\tvec3 a = v * ( v + 0.0245786 ) - 0.000090537;\\\\n\\\\tvec3 b = v * ( 0.983729 * v + 0.4329510 ) + 0.238081;\\\\n\\\\treturn a / b;\\\\n\\\\n}\\\\n\\\\n// this implementation of ACES is modified to accommodate a brighter viewing environment.\\\\n// the scale factor of 1/0.6 is subjective. see discussion in #19621.\\\\n\\\\nvec3 ACESFilmicToneMapping( vec3 color ) {\\\\n\\\\n\\\\t// sRGB => XYZ => D65_2_D60 => AP1 => RRT_SAT\\\\n\\\\tconst mat3 ACESInputMat = mat3(\\\\n\\\\t\\\\tvec3( 0.59719, 0.07600, 0.02840 ), // transposed from source\\\\n\\\\t\\\\tvec3( 0.35458, 0.90834, 0.13383 ),\\\\n\\\\t\\\\tvec3( 0.04823, 0.01566, 0.83777 )\\\\n\\\\t);\\\\n\\\\n\\\\t// ODT_SAT => XYZ => D60_2_D65 => sRGB\\\\n\\\\tconst mat3 ACESOutputMat = mat3(\\\\n\\\\t\\\\tvec3( 1.60475, -0.10208, -0.00327 ), // transposed from source\\\\n\\\\t\\\\tvec3( -0.53108, 1.10813, -0.07276 ),\\\\n\\\\t\\\\tvec3( -0.07367, -0.00605, 1.07602 )\\\\n\\\\t);\\\\n\\\\n\\\\tcolor *= toneMappingExposure / 0.6;\\\\n\\\\n\\\\tcolor = ACESInputMat * color;\\\\n\\\\n\\\\t// Apply RRT and ODT\\\\n\\\\tcolor = RRTAndODTFit( color );\\\\n\\\\n\\\\tcolor = ACESOutputMat * color;\\\\n\\\\n\\\\t// Clamp to [0, 1]\\\\n\\\\treturn saturate( color );\\\\n\\\\n}\\\\n\\\\nvec3 CustomToneMapping( vec3 color ) { return color; }\\\\n\\\\\\\",transmission_fragment:k,transmission_pars_fragment:\\\\\\\"\\\\n#ifdef USE_TRANSMISSION\\\\n\\\\n\\\\t// Transmission code is based on glTF-Sampler-Viewer\\\\n\\\\t// https://github.com/KhronosGroup/glTF-Sample-Viewer\\\\n\\\\n\\\\tuniform float transmission;\\\\n\\\\tuniform float thickness;\\\\n\\\\tuniform float attenuationDistance;\\\\n\\\\tuniform vec3 attenuationTint;\\\\n\\\\n\\\\t#ifdef USE_TRANSMISSIONMAP\\\\n\\\\n\\\\t\\\\tuniform sampler2D transmissionMap;\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\t#ifdef USE_THICKNESSMAP\\\\n\\\\n\\\\t\\\\tuniform sampler2D thicknessMap;\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\tuniform vec2 transmissionSamplerSize;\\\\n\\\\tuniform sampler2D transmissionSamplerMap;\\\\n\\\\n\\\\tuniform mat4 modelMatrix;\\\\n\\\\tuniform mat4 projectionMatrix;\\\\n\\\\n\\\\tvarying vec3 vWorldPosition;\\\\n\\\\n\\\\tvec3 getVolumeTransmissionRay( vec3 n, vec3 v, float thickness, float ior, mat4 modelMatrix ) {\\\\n\\\\n\\\\t\\\\t// Direction of refracted light.\\\\n\\\\t\\\\tvec3 refractionVector = refract( - v, normalize( n ), 1.0 / ior );\\\\n\\\\n\\\\t\\\\t// Compute rotation-independant scaling of the model matrix.\\\\n\\\\t\\\\tvec3 modelScale;\\\\n\\\\t\\\\tmodelScale.x = length( vec3( modelMatrix[ 0 ].xyz ) );\\\\n\\\\t\\\\tmodelScale.y = length( vec3( modelMatrix[ 1 ].xyz ) );\\\\n\\\\t\\\\tmodelScale.z = length( vec3( modelMatrix[ 2 ].xyz ) );\\\\n\\\\n\\\\t\\\\t// The thickness is specified in local space.\\\\n\\\\t\\\\treturn normalize( refractionVector ) * thickness * modelScale;\\\\n\\\\n\\\\t}\\\\n\\\\n\\\\tfloat applyIorToRoughness( float roughness, float ior ) {\\\\n\\\\n\\\\t\\\\t// Scale roughness with IOR so that an IOR of 1.0 results in no microfacet refraction and\\\\n\\\\t\\\\t// an IOR of 1.5 results in the default amount of microfacet refraction.\\\\n\\\\t\\\\treturn roughness * clamp( ior * 2.0 - 2.0, 0.0, 1.0 );\\\\n\\\\n\\\\t}\\\\n\\\\n\\\\tvec4 getTransmissionSample( vec2 fragCoord, float roughness, float ior ) {\\\\n\\\\n\\\\t\\\\tfloat framebufferLod = log2( transmissionSamplerSize.x ) * applyIorToRoughness( roughness, ior );\\\\n\\\\n\\\\t\\\\t#ifdef TEXTURE_LOD_EXT\\\\n\\\\n\\\\t\\\\t\\\\treturn texture2DLodEXT( transmissionSamplerMap, fragCoord.xy, framebufferLod );\\\\n\\\\n\\\\t\\\\t#else\\\\n\\\\n\\\\t\\\\t\\\\treturn texture2D( transmissionSamplerMap, fragCoord.xy, framebufferLod );\\\\n\\\\n\\\\t\\\\t#endif\\\\n\\\\n\\\\t}\\\\n\\\\n\\\\tvec3 applyVolumeAttenuation( vec3 radiance, float transmissionDistance, vec3 attenuationColor, float attenuationDistance ) {\\\\n\\\\n\\\\t\\\\tif ( attenuationDistance == 0.0 ) {\\\\n\\\\n\\\\t\\\\t\\\\t// Attenuation distance is +∞ (which we indicate by zero), i.e. the transmitted color is not attenuated at all.\\\\n\\\\t\\\\t\\\\treturn radiance;\\\\n\\\\n\\\\t\\\\t} else {\\\\n\\\\n\\\\t\\\\t\\\\t// Compute light attenuation using Beer's law.\\\\n\\\\t\\\\t\\\\tvec3 attenuationCoefficient = -log( attenuationColor ) / attenuationDistance;\\\\n\\\\t\\\\t\\\\tvec3 transmittance = exp( - attenuationCoefficient * transmissionDistance ); // Beer's law\\\\n\\\\t\\\\t\\\\treturn transmittance * radiance;\\\\n\\\\n\\\\t\\\\t}\\\\n\\\\n\\\\t}\\\\n\\\\n\\\\tvec4 getIBLVolumeRefraction( vec3 n, vec3 v, float roughness, vec3 diffuseColor, vec3 specularColor, float specularF90,\\\\n\\\\t\\\\tvec3 position, mat4 modelMatrix, mat4 viewMatrix, mat4 projMatrix, float ior, float thickness,\\\\n\\\\t\\\\tvec3 attenuationColor, float attenuationDistance ) {\\\\n\\\\n\\\\t\\\\tvec3 transmissionRay = getVolumeTransmissionRay( n, v, thickness, ior, modelMatrix );\\\\n\\\\t\\\\tvec3 refractedRayExit = position + transmissionRay;\\\\n\\\\n\\\\t\\\\t// Project refracted vector on the framebuffer, while mapping to normalized device coordinates.\\\\n\\\\t\\\\tvec4 ndcPos = projMatrix * viewMatrix * vec4( refractedRayExit, 1.0 );\\\\n\\\\t\\\\tvec2 refractionCoords = ndcPos.xy / ndcPos.w;\\\\n\\\\t\\\\trefractionCoords += 1.0;\\\\n\\\\t\\\\trefractionCoords /= 2.0;\\\\n\\\\n\\\\t\\\\t// Sample framebuffer to get pixel the refracted ray hits.\\\\n\\\\t\\\\tvec4 transmittedLight = getTransmissionSample( refractionCoords, roughness, ior );\\\\n\\\\n\\\\t\\\\tvec3 attenuatedColor = applyVolumeAttenuation( transmittedLight.rgb, length( transmissionRay ), attenuationColor, attenuationDistance );\\\\n\\\\n\\\\t\\\\t// Get the specular component.\\\\n\\\\t\\\\tvec3 F = EnvironmentBRDF( n, v, specularColor, specularF90, roughness );\\\\n\\\\n\\\\t\\\\treturn vec4( ( 1.0 - F ) * attenuatedColor * diffuseColor, transmittedLight.a );\\\\n\\\\n\\\\t}\\\\n#endif\\\\n\\\\\\\",uv_pars_fragment:\\\\\\\"\\\\n#if ( defined( USE_UV ) && ! defined( UVS_VERTEX_ONLY ) )\\\\n\\\\n\\\\tvarying vec2 vUv;\\\\n\\\\n#endif\\\\n\\\\\\\",uv_pars_vertex:\\\\\\\"\\\\n#ifdef USE_UV\\\\n\\\\n\\\\t#ifdef UVS_VERTEX_ONLY\\\\n\\\\n\\\\t\\\\tvec2 vUv;\\\\n\\\\n\\\\t#else\\\\n\\\\n\\\\t\\\\tvarying vec2 vUv;\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\tuniform mat3 uvTransform;\\\\n\\\\n#endif\\\\n\\\\\\\",uv_vertex:\\\\\\\"\\\\n#ifdef USE_UV\\\\n\\\\n\\\\tvUv = ( uvTransform * vec3( uv, 1 ) ).xy;\\\\n\\\\n#endif\\\\n\\\\\\\",uv2_pars_fragment:\\\\\\\"\\\\n#if defined( USE_LIGHTMAP ) || defined( USE_AOMAP )\\\\n\\\\n\\\\tvarying vec2 vUv2;\\\\n\\\\n#endif\\\\n\\\\\\\",uv2_pars_vertex:\\\\\\\"\\\\n#if defined( USE_LIGHTMAP ) || defined( USE_AOMAP )\\\\n\\\\n\\\\tattribute vec2 uv2;\\\\n\\\\tvarying vec2 vUv2;\\\\n\\\\n\\\\tuniform mat3 uv2Transform;\\\\n\\\\n#endif\\\\n\\\\\\\",uv2_vertex:\\\\\\\"\\\\n#if defined( USE_LIGHTMAP ) || defined( USE_AOMAP )\\\\n\\\\n\\\\tvUv2 = ( uv2Transform * vec3( uv2, 1 ) ).xy;\\\\n\\\\n#endif\\\\n\\\\\\\",worldpos_vertex:\\\\\\\"\\\\n#if defined( USE_ENVMAP ) || defined( DISTANCE ) || defined ( USE_SHADOWMAP ) || defined ( USE_TRANSMISSION )\\\\n\\\\n\\\\tvec4 worldPosition = vec4( transformed, 1.0 );\\\\n\\\\n\\\\t#ifdef USE_INSTANCING\\\\n\\\\n\\\\t\\\\tworldPosition = instanceMatrix * worldPosition;\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\tworldPosition = modelMatrix * worldPosition;\\\\n\\\\n#endif\\\\n\\\\\\\",background_vert:\\\\\\\"\\\\nvarying vec2 vUv;\\\\nuniform mat3 uvTransform;\\\\n\\\\nvoid main() {\\\\n\\\\n\\\\tvUv = ( uvTransform * vec3( uv, 1 ) ).xy;\\\\n\\\\n\\\\tgl_Position = vec4( position.xy, 1.0, 1.0 );\\\\n\\\\n}\\\\n\\\\\\\",background_frag:\\\\\\\"\\\\nuniform sampler2D t2D;\\\\n\\\\nvarying vec2 vUv;\\\\n\\\\nvoid main() {\\\\n\\\\n\\\\tvec4 texColor = texture2D( t2D, vUv );\\\\n\\\\n\\\\tgl_FragColor = mapTexelToLinear( texColor );\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n}\\\\n\\\\\\\",cube_vert:\\\\\\\"\\\\nvarying vec3 vWorldDirection;\\\\n\\\\n#include \\\\n\\\\nvoid main() {\\\\n\\\\n\\\\tvWorldDirection = transformDirection( position, modelMatrix );\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n\\\\tgl_Position.z = gl_Position.w; // set z to camera.far\\\\n\\\\n}\\\\n\\\\\\\",cube_frag:\\\\\\\"\\\\n#include \\\\nuniform float opacity;\\\\n\\\\nvarying vec3 vWorldDirection;\\\\n\\\\n#include \\\\n\\\\nvoid main() {\\\\n\\\\n\\\\tvec3 vReflect = vWorldDirection;\\\\n\\\\t#include \\\\n\\\\n\\\\tgl_FragColor = envColor;\\\\n\\\\tgl_FragColor.a *= opacity;\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n}\\\\n\\\\\\\",depth_vert:\\\\\\\"\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n\\\\n// This is used for computing an equivalent of gl_FragCoord.z that is as high precision as possible.\\\\n// Some platforms compute gl_FragCoord at a lower precision which makes the manually computed value better for\\\\n// depth-based postprocessing effects. Reproduced on iPad with A10 processor / iPadOS 13.3.1.\\\\nvarying vec2 vHighPrecisionZW;\\\\n\\\\nvoid main() {\\\\n\\\\n\\\\t#include \\\\n\\\\n\\\\t#include \\\\n\\\\n\\\\t#ifdef USE_DISPLACEMENTMAP\\\\n\\\\n\\\\t\\\\t#include \\\\n\\\\t\\\\t#include \\\\n\\\\t\\\\t#include \\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n\\\\tvHighPrecisionZW = gl_Position.zw;\\\\n\\\\n}\\\\n\\\\\\\",depth_frag:\\\\\\\"\\\\n#if DEPTH_PACKING == 3200\\\\n\\\\n\\\\tuniform float opacity;\\\\n\\\\n#endif\\\\n\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n\\\\nvarying vec2 vHighPrecisionZW;\\\\n\\\\nvoid main() {\\\\n\\\\n\\\\t#include \\\\n\\\\n\\\\tvec4 diffuseColor = vec4( 1.0 );\\\\n\\\\n\\\\t#if DEPTH_PACKING == 3200\\\\n\\\\n\\\\t\\\\tdiffuseColor.a = opacity;\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n\\\\t#include \\\\n\\\\n\\\\t// Higher precision equivalent of gl_FragCoord.z. This assumes depthRange has been left to its default values.\\\\n\\\\tfloat fragCoordZ = 0.5 * vHighPrecisionZW[0] / vHighPrecisionZW[1] + 0.5;\\\\n\\\\n\\\\t#if DEPTH_PACKING == 3200\\\\n\\\\n\\\\t\\\\tgl_FragColor = vec4( vec3( 1.0 - fragCoordZ ), opacity );\\\\n\\\\n\\\\t#elif DEPTH_PACKING == 3201\\\\n\\\\n\\\\t\\\\tgl_FragColor = packDepthToRGBA( fragCoordZ );\\\\n\\\\n\\\\t#endif\\\\n\\\\n}\\\\n\\\\\\\",distanceRGBA_vert:\\\\\\\"\\\\n#define DISTANCE\\\\n\\\\nvarying vec3 vWorldPosition;\\\\n\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n\\\\nvoid main() {\\\\n\\\\n\\\\t#include \\\\n\\\\n\\\\t#include \\\\n\\\\n\\\\t#ifdef USE_DISPLACEMENTMAP\\\\n\\\\n\\\\t\\\\t#include \\\\n\\\\t\\\\t#include \\\\n\\\\t\\\\t#include \\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n\\\\tvWorldPosition = worldPosition.xyz;\\\\n\\\\n}\\\\n\\\\\\\",distanceRGBA_frag:\\\\\\\"\\\\n#define DISTANCE\\\\n\\\\nuniform vec3 referencePosition;\\\\nuniform float nearDistance;\\\\nuniform float farDistance;\\\\nvarying vec3 vWorldPosition;\\\\n\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n\\\\nvoid main () {\\\\n\\\\n\\\\t#include \\\\n\\\\n\\\\tvec4 diffuseColor = vec4( 1.0 );\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n\\\\tfloat dist = length( vWorldPosition - referencePosition );\\\\n\\\\tdist = ( dist - nearDistance ) / ( farDistance - nearDistance );\\\\n\\\\tdist = saturate( dist ); // clamp to [ 0, 1 ]\\\\n\\\\n\\\\tgl_FragColor = packDepthToRGBA( dist );\\\\n\\\\n}\\\\n\\\\\\\",equirect_vert:\\\\\\\"\\\\nvarying vec3 vWorldDirection;\\\\n\\\\n#include \\\\n\\\\nvoid main() {\\\\n\\\\n\\\\tvWorldDirection = transformDirection( position, modelMatrix );\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n}\\\\n\\\\\\\",equirect_frag:\\\\\\\"\\\\nuniform sampler2D tEquirect;\\\\n\\\\nvarying vec3 vWorldDirection;\\\\n\\\\n#include \\\\n\\\\nvoid main() {\\\\n\\\\n\\\\tvec3 direction = normalize( vWorldDirection );\\\\n\\\\n\\\\tvec2 sampleUV = equirectUv( direction );\\\\n\\\\n\\\\tvec4 texColor = texture2D( tEquirect, sampleUV );\\\\n\\\\n\\\\tgl_FragColor = mapTexelToLinear( texColor );\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n}\\\\n\\\\\\\",linedashed_vert:\\\\\\\"\\\\nuniform float scale;\\\\nattribute float lineDistance;\\\\n\\\\nvarying float vLineDistance;\\\\n\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n\\\\nvoid main() {\\\\n\\\\n\\\\tvLineDistance = scale * lineDistance;\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n}\\\\n\\\\\\\",linedashed_frag:\\\\\\\"\\\\nuniform vec3 diffuse;\\\\nuniform float opacity;\\\\n\\\\nuniform float dashSize;\\\\nuniform float totalSize;\\\\n\\\\nvarying float vLineDistance;\\\\n\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n\\\\nvoid main() {\\\\n\\\\n\\\\t#include \\\\n\\\\n\\\\tif ( mod( vLineDistance, totalSize ) > dashSize ) {\\\\n\\\\n\\\\t\\\\tdiscard;\\\\n\\\\n\\\\t}\\\\n\\\\n\\\\tvec3 outgoingLight = vec3( 0.0 );\\\\n\\\\tvec4 diffuseColor = vec4( diffuse, opacity );\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n\\\\toutgoingLight = diffuseColor.rgb; // simple shader\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n}\\\\n\\\\\\\",meshbasic_vert:\\\\\\\"\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n\\\\nvoid main() {\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n\\\\t#if defined ( USE_ENVMAP ) || defined ( USE_SKINNING )\\\\n\\\\n\\\\t\\\\t#include \\\\n\\\\t\\\\t#include \\\\n\\\\t\\\\t#include \\\\n\\\\t\\\\t#include \\\\n\\\\t\\\\t#include \\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n}\\\\n\\\\\\\",meshbasic_frag:\\\\\\\"\\\\nuniform vec3 diffuse;\\\\nuniform float opacity;\\\\n\\\\n#ifndef FLAT_SHADED\\\\n\\\\n\\\\tvarying vec3 vNormal;\\\\n\\\\n#endif\\\\n\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n\\\\nvoid main() {\\\\n\\\\n\\\\t#include \\\\n\\\\n\\\\tvec4 diffuseColor = vec4( diffuse, opacity );\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n\\\\tReflectedLight reflectedLight = ReflectedLight( vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ) );\\\\n\\\\n\\\\t// accumulation (baked indirect lighting only)\\\\n\\\\t#ifdef USE_LIGHTMAP\\\\n\\\\n\\\\t\\\\tvec4 lightMapTexel= texture2D( lightMap, vUv2 );\\\\n\\\\t\\\\treflectedLight.indirectDiffuse += lightMapTexelToLinear( lightMapTexel ).rgb * lightMapIntensity;\\\\n\\\\n\\\\t#else\\\\n\\\\n\\\\t\\\\treflectedLight.indirectDiffuse += vec3( 1.0 );\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\t// modulation\\\\n\\\\t#include \\\\n\\\\n\\\\treflectedLight.indirectDiffuse *= diffuseColor.rgb;\\\\n\\\\n\\\\tvec3 outgoingLight = reflectedLight.indirectDiffuse;\\\\n\\\\n\\\\t#include \\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n}\\\\n\\\\\\\",meshlambert_vert:\\\\\\\"\\\\n#define LAMBERT\\\\n\\\\nvarying vec3 vLightFront;\\\\nvarying vec3 vIndirectFront;\\\\n\\\\n#ifdef DOUBLE_SIDED\\\\n\\\\tvarying vec3 vLightBack;\\\\n\\\\tvarying vec3 vIndirectBack;\\\\n#endif\\\\n\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n\\\\nvoid main() {\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n}\\\\n\\\\\\\",meshlambert_frag:\\\\\\\"\\\\nuniform vec3 diffuse;\\\\nuniform vec3 emissive;\\\\nuniform float opacity;\\\\n\\\\nvarying vec3 vLightFront;\\\\nvarying vec3 vIndirectFront;\\\\n\\\\n#ifdef DOUBLE_SIDED\\\\n\\\\tvarying vec3 vLightBack;\\\\n\\\\tvarying vec3 vIndirectBack;\\\\n#endif\\\\n\\\\n\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n\\\\nvoid main() {\\\\n\\\\n\\\\t#include \\\\n\\\\n\\\\tvec4 diffuseColor = vec4( diffuse, opacity );\\\\n\\\\tReflectedLight reflectedLight = ReflectedLight( vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ) );\\\\n\\\\tvec3 totalEmissiveRadiance = emissive;\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n\\\\t// accumulation\\\\n\\\\n\\\\t#ifdef DOUBLE_SIDED\\\\n\\\\n\\\\t\\\\treflectedLight.indirectDiffuse += ( gl_FrontFacing ) ? vIndirectFront : vIndirectBack;\\\\n\\\\n\\\\t#else\\\\n\\\\n\\\\t\\\\treflectedLight.indirectDiffuse += vIndirectFront;\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\t#include \\\\n\\\\n\\\\treflectedLight.indirectDiffuse *= BRDF_Lambert( diffuseColor.rgb );\\\\n\\\\n\\\\t#ifdef DOUBLE_SIDED\\\\n\\\\n\\\\t\\\\treflectedLight.directDiffuse = ( gl_FrontFacing ) ? vLightFront : vLightBack;\\\\n\\\\n\\\\t#else\\\\n\\\\n\\\\t\\\\treflectedLight.directDiffuse = vLightFront;\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\treflectedLight.directDiffuse *= BRDF_Lambert( diffuseColor.rgb ) * getShadowMask();\\\\n\\\\n\\\\t// modulation\\\\n\\\\n\\\\t#include \\\\n\\\\n\\\\tvec3 outgoingLight = reflectedLight.directDiffuse + reflectedLight.indirectDiffuse + totalEmissiveRadiance;\\\\n\\\\n\\\\t#include \\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n}\\\\n\\\\\\\",meshmatcap_vert:\\\\\\\"\\\\n#define MATCAP\\\\n\\\\nvarying vec3 vViewPosition;\\\\n\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n\\\\n#include \\\\n#include \\\\n\\\\nvoid main() {\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n\\\\tvViewPosition = - mvPosition.xyz;\\\\n\\\\n}\\\\n\\\\\\\",meshmatcap_frag:\\\\\\\"\\\\n#define MATCAP\\\\n\\\\nuniform vec3 diffuse;\\\\nuniform float opacity;\\\\nuniform sampler2D matcap;\\\\n\\\\nvarying vec3 vViewPosition;\\\\n\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n\\\\nvoid main() {\\\\n\\\\n\\\\t#include \\\\n\\\\n\\\\tvec4 diffuseColor = vec4( diffuse, opacity );\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n\\\\tvec3 viewDir = normalize( vViewPosition );\\\\n\\\\tvec3 x = normalize( vec3( viewDir.z, 0.0, - viewDir.x ) );\\\\n\\\\tvec3 y = cross( viewDir, x );\\\\n\\\\tvec2 uv = vec2( dot( x, normal ), dot( y, normal ) ) * 0.495 + 0.5; // 0.495 to remove artifacts caused by undersized matcap disks\\\\n\\\\n\\\\t#ifdef USE_MATCAP\\\\n\\\\n\\\\t\\\\tvec4 matcapColor = texture2D( matcap, uv );\\\\n\\\\t\\\\tmatcapColor = matcapTexelToLinear( matcapColor );\\\\n\\\\n\\\\t#else\\\\n\\\\n\\\\t\\\\tvec4 matcapColor = vec4( 1.0 );\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\tvec3 outgoingLight = diffuseColor.rgb * matcapColor.rgb;\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n}\\\\n\\\\\\\",meshnormal_vert:\\\\\\\"\\\\n#define NORMAL\\\\n\\\\n#if defined( FLAT_SHADED ) || defined( USE_BUMPMAP ) || defined( TANGENTSPACE_NORMALMAP )\\\\n\\\\n\\\\tvarying vec3 vViewPosition;\\\\n\\\\n#endif\\\\n\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n\\\\nvoid main() {\\\\n\\\\n\\\\t#include \\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n#if defined( FLAT_SHADED ) || defined( USE_BUMPMAP ) || defined( TANGENTSPACE_NORMALMAP )\\\\n\\\\n\\\\tvViewPosition = - mvPosition.xyz;\\\\n\\\\n#endif\\\\n\\\\n}\\\\n\\\\\\\",meshnormal_frag:\\\\\\\"\\\\n#define NORMAL\\\\n\\\\nuniform float opacity;\\\\n\\\\n#if defined( FLAT_SHADED ) || defined( USE_BUMPMAP ) || defined( TANGENTSPACE_NORMALMAP )\\\\n\\\\n\\\\tvarying vec3 vViewPosition;\\\\n\\\\n#endif\\\\n\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n\\\\nvoid main() {\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n\\\\tgl_FragColor = vec4( packNormalToRGB( normal ), opacity );\\\\n\\\\n}\\\\n\\\\\\\",meshphong_vert:\\\\\\\"\\\\n#define PHONG\\\\n\\\\nvarying vec3 vViewPosition;\\\\n\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n\\\\nvoid main() {\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n\\\\tvViewPosition = - mvPosition.xyz;\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n}\\\\n\\\\\\\",meshphong_frag:\\\\\\\"\\\\n#define PHONG\\\\n\\\\nuniform vec3 diffuse;\\\\nuniform vec3 emissive;\\\\nuniform vec3 specular;\\\\nuniform float shininess;\\\\nuniform float opacity;\\\\n\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n\\\\nvoid main() {\\\\n\\\\n\\\\t#include \\\\n\\\\n\\\\tvec4 diffuseColor = vec4( diffuse, opacity );\\\\n\\\\tReflectedLight reflectedLight = ReflectedLight( vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ) );\\\\n\\\\tvec3 totalEmissiveRadiance = emissive;\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n\\\\t// accumulation\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n\\\\t// modulation\\\\n\\\\t#include \\\\n\\\\n\\\\tvec3 outgoingLight = reflectedLight.directDiffuse + reflectedLight.indirectDiffuse + reflectedLight.directSpecular + reflectedLight.indirectSpecular + totalEmissiveRadiance;\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n}\\\\n\\\\\\\",meshphysical_vert:\\\\\\\"\\\\n#define STANDARD\\\\n\\\\nvarying vec3 vViewPosition;\\\\n\\\\n#ifdef USE_TRANSMISSION\\\\n\\\\n\\\\tvarying vec3 vWorldPosition;\\\\n\\\\n#endif\\\\n\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n\\\\nvoid main() {\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n\\\\tvViewPosition = - mvPosition.xyz;\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n#ifdef USE_TRANSMISSION\\\\n\\\\n\\\\tvWorldPosition = worldPosition.xyz;\\\\n\\\\n#endif\\\\n}\\\\n\\\\\\\",meshphysical_frag:\\\\\\\"\\\\n#define STANDARD\\\\n\\\\n#ifdef PHYSICAL\\\\n\\\\t#define IOR\\\\n\\\\t#define SPECULAR\\\\n#endif\\\\n\\\\nuniform vec3 diffuse;\\\\nuniform vec3 emissive;\\\\nuniform float roughness;\\\\nuniform float metalness;\\\\nuniform float opacity;\\\\n\\\\n#ifdef IOR\\\\n\\\\tuniform float ior;\\\\n#endif\\\\n\\\\n#ifdef SPECULAR\\\\n\\\\tuniform float specularIntensity;\\\\n\\\\tuniform vec3 specularTint;\\\\n\\\\n\\\\t#ifdef USE_SPECULARINTENSITYMAP\\\\n\\\\t\\\\tuniform sampler2D specularIntensityMap;\\\\n\\\\t#endif\\\\n\\\\n\\\\t#ifdef USE_SPECULARTINTMAP\\\\n\\\\t\\\\tuniform sampler2D specularTintMap;\\\\n\\\\t#endif\\\\n#endif\\\\n\\\\n#ifdef USE_CLEARCOAT\\\\n\\\\tuniform float clearcoat;\\\\n\\\\tuniform float clearcoatRoughness;\\\\n#endif\\\\n\\\\n#ifdef USE_SHEEN\\\\n\\\\tuniform vec3 sheenTint;\\\\n\\\\tuniform float sheenRoughness;\\\\n#endif\\\\n\\\\nvarying vec3 vViewPosition;\\\\n\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n\\\\nvoid main() {\\\\n\\\\n\\\\t#include \\\\n\\\\n\\\\tvec4 diffuseColor = vec4( diffuse, opacity );\\\\n\\\\tReflectedLight reflectedLight = ReflectedLight( vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ) );\\\\n\\\\tvec3 totalEmissiveRadiance = emissive;\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n\\\\t// accumulation\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n\\\\t// modulation\\\\n\\\\t#include \\\\n\\\\n\\\\tvec3 totalDiffuse = reflectedLight.directDiffuse + reflectedLight.indirectDiffuse;\\\\n\\\\tvec3 totalSpecular = reflectedLight.directSpecular + reflectedLight.indirectSpecular;\\\\n\\\\n\\\\t#include \\\\n\\\\n\\\\tvec3 outgoingLight = totalDiffuse + totalSpecular + totalEmissiveRadiance;\\\\n\\\\n\\\\t#ifdef USE_CLEARCOAT\\\\n\\\\n\\\\t\\\\tfloat dotNVcc = saturate( dot( geometry.clearcoatNormal, geometry.viewDir ) );\\\\n\\\\n\\\\t\\\\tvec3 Fcc = F_Schlick( material.clearcoatF0, material.clearcoatF90, dotNVcc );\\\\n\\\\n\\\\t\\\\toutgoingLight = outgoingLight * ( 1.0 - clearcoat * Fcc ) + clearcoatSpecular * clearcoat;\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n}\\\\n\\\\\\\",meshtoon_vert:\\\\\\\"\\\\n#define TOON\\\\n\\\\nvarying vec3 vViewPosition;\\\\n\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n\\\\nvoid main() {\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n\\\\tvViewPosition = - mvPosition.xyz;\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n}\\\\n\\\\\\\",meshtoon_frag:\\\\\\\"\\\\n#define TOON\\\\n\\\\nuniform vec3 diffuse;\\\\nuniform vec3 emissive;\\\\nuniform float opacity;\\\\n\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n\\\\nvoid main() {\\\\n\\\\n\\\\t#include \\\\n\\\\n\\\\tvec4 diffuseColor = vec4( diffuse, opacity );\\\\n\\\\tReflectedLight reflectedLight = ReflectedLight( vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ) );\\\\n\\\\tvec3 totalEmissiveRadiance = emissive;\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n\\\\t// accumulation\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n\\\\t// modulation\\\\n\\\\t#include \\\\n\\\\n\\\\tvec3 outgoingLight = reflectedLight.directDiffuse + reflectedLight.indirectDiffuse + totalEmissiveRadiance;\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n}\\\\n\\\\\\\",points_vert:\\\\\\\"\\\\nuniform float size;\\\\nuniform float scale;\\\\n\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n\\\\nvoid main() {\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n\\\\tgl_PointSize = size;\\\\n\\\\n\\\\t#ifdef USE_SIZEATTENUATION\\\\n\\\\n\\\\t\\\\tbool isPerspective = isPerspectiveMatrix( projectionMatrix );\\\\n\\\\n\\\\t\\\\tif ( isPerspective ) gl_PointSize *= ( scale / - mvPosition.z );\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n}\\\\n\\\\\\\",points_frag:\\\\\\\"\\\\nuniform vec3 diffuse;\\\\nuniform float opacity;\\\\n\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n\\\\nvoid main() {\\\\n\\\\n\\\\t#include \\\\n\\\\n\\\\tvec3 outgoingLight = vec3( 0.0 );\\\\n\\\\tvec4 diffuseColor = vec4( diffuse, opacity );\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n\\\\toutgoingLight = diffuseColor.rgb;\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n}\\\\n\\\\\\\",shadow_vert:\\\\\\\"\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n\\\\nvoid main() {\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n}\\\\n\\\\\\\",shadow_frag:\\\\\\\"\\\\nuniform vec3 color;\\\\nuniform float opacity;\\\\n\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n\\\\nvoid main() {\\\\n\\\\n\\\\tgl_FragColor = vec4( color, opacity * ( 1.0 - getShadowMask() ) );\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n}\\\\n\\\\\\\",sprite_vert:\\\\\\\"\\\\nuniform float rotation;\\\\nuniform vec2 center;\\\\n\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n\\\\nvoid main() {\\\\n\\\\n\\\\t#include \\\\n\\\\n\\\\tvec4 mvPosition = modelViewMatrix * vec4( 0.0, 0.0, 0.0, 1.0 );\\\\n\\\\n\\\\tvec2 scale;\\\\n\\\\tscale.x = length( vec3( modelMatrix[ 0 ].x, modelMatrix[ 0 ].y, modelMatrix[ 0 ].z ) );\\\\n\\\\tscale.y = length( vec3( modelMatrix[ 1 ].x, modelMatrix[ 1 ].y, modelMatrix[ 1 ].z ) );\\\\n\\\\n\\\\t#ifndef USE_SIZEATTENUATION\\\\n\\\\n\\\\t\\\\tbool isPerspective = isPerspectiveMatrix( projectionMatrix );\\\\n\\\\n\\\\t\\\\tif ( isPerspective ) scale *= - mvPosition.z;\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\tvec2 alignedPosition = ( position.xy - ( center - vec2( 0.5 ) ) ) * scale;\\\\n\\\\n\\\\tvec2 rotatedPosition;\\\\n\\\\trotatedPosition.x = cos( rotation ) * alignedPosition.x - sin( rotation ) * alignedPosition.y;\\\\n\\\\trotatedPosition.y = sin( rotation ) * alignedPosition.x + cos( rotation ) * alignedPosition.y;\\\\n\\\\n\\\\tmvPosition.xy += rotatedPosition;\\\\n\\\\n\\\\tgl_Position = projectionMatrix * mvPosition;\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n}\\\\n\\\\\\\",sprite_frag:\\\\\\\"\\\\nuniform vec3 diffuse;\\\\nuniform float opacity;\\\\n\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n\\\\nvoid main() {\\\\n\\\\n\\\\t#include \\\\n\\\\n\\\\tvec3 outgoingLight = vec3( 0.0 );\\\\n\\\\tvec4 diffuseColor = vec4( diffuse, opacity );\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n\\\\toutgoingLight = diffuseColor.rgb;\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n}\\\\n\\\\\\\"};var G=n(11);const V={common:{diffuse:{value:new D.a(16777215)},opacity:{value:1},map:{value:null},uvTransform:{value:new G.a},uv2Transform:{value:new G.a},alphaMap:{value:null},alphaTest:{value:0}},specularmap:{specularMap:{value:null}},envmap:{envMap:{value:null},flipEnvMap:{value:-1},reflectivity:{value:1},ior:{value:1.5},refractionRatio:{value:.98},maxMipLevel:{value:0}},aomap:{aoMap:{value:null},aoMapIntensity:{value:1}},lightmap:{lightMap:{value:null},lightMapIntensity:{value:1}},emissivemap:{emissiveMap:{value:null}},bumpmap:{bumpMap:{value:null},bumpScale:{value:1}},normalmap:{normalMap:{value:null},normalScale:{value:new d.a(1,1)}},displacementmap:{displacementMap:{value:null},displacementScale:{value:1},displacementBias:{value:0}},roughnessmap:{roughnessMap:{value:null}},metalnessmap:{metalnessMap:{value:null}},gradientmap:{gradientMap:{value:null}},fog:{fogDensity:{value:25e-5},fogNear:{value:1},fogFar:{value:2e3},fogColor:{value:new D.a(16777215)}},lights:{ambientLightColor:{value:[]},lightProbe:{value:[]},directionalLights:{value:[],properties:{direction:{},color:{}}},directionalLightShadows:{value:[],properties:{shadowBias:{},shadowNormalBias:{},shadowRadius:{},shadowMapSize:{}}},directionalShadowMap:{value:[]},directionalShadowMatrix:{value:[]},spotLights:{value:[],properties:{color:{},position:{},direction:{},distance:{},coneCos:{},penumbraCos:{},decay:{}}},spotLightShadows:{value:[],properties:{shadowBias:{},shadowNormalBias:{},shadowRadius:{},shadowMapSize:{}}},spotShadowMap:{value:[]},spotShadowMatrix:{value:[]},pointLights:{value:[],properties:{color:{},position:{},decay:{},distance:{}}},pointLightShadows:{value:[],properties:{shadowBias:{},shadowNormalBias:{},shadowRadius:{},shadowMapSize:{},shadowCameraNear:{},shadowCameraFar:{}}},pointShadowMap:{value:[]},pointShadowMatrix:{value:[]},hemisphereLights:{value:[],properties:{direction:{},skyColor:{},groundColor:{}}},rectAreaLights:{value:[],properties:{color:{},position:{},width:{},height:{}}},ltc_1:{value:null},ltc_2:{value:null}},points:{diffuse:{value:new D.a(16777215)},opacity:{value:1},size:{value:1},scale:{value:1},map:{value:null},alphaMap:{value:null},alphaTest:{value:0},uvTransform:{value:new G.a}},sprite:{diffuse:{value:new D.a(16777215)},opacity:{value:1},center:{value:new d.a(.5,.5)},rotation:{value:0},map:{value:null},alphaMap:{value:null},alphaTest:{value:0},uvTransform:{value:new G.a}}},H={basic:{uniforms:R([V.common,V.specularmap,V.envmap,V.aomap,V.lightmap,V.fog]),vertexShader:U.meshbasic_vert,fragmentShader:U.meshbasic_frag},lambert:{uniforms:R([V.common,V.specularmap,V.envmap,V.aomap,V.lightmap,V.emissivemap,V.fog,V.lights,{emissive:{value:new D.a(0)}}]),vertexShader:U.meshlambert_vert,fragmentShader:U.meshlambert_frag},phong:{uniforms:R([V.common,V.specularmap,V.envmap,V.aomap,V.lightmap,V.emissivemap,V.bumpmap,V.normalmap,V.displacementmap,V.fog,V.lights,{emissive:{value:new D.a(0)},specular:{value:new D.a(1118481)},shininess:{value:30}}]),vertexShader:U.meshphong_vert,fragmentShader:U.meshphong_frag},standard:{uniforms:R([V.common,V.envmap,V.aomap,V.lightmap,V.emissivemap,V.bumpmap,V.normalmap,V.displacementmap,V.roughnessmap,V.metalnessmap,V.fog,V.lights,{emissive:{value:new D.a(0)},roughness:{value:1},metalness:{value:0},envMapIntensity:{value:1}}]),vertexShader:U.meshphysical_vert,fragmentShader:U.meshphysical_frag},toon:{uniforms:R([V.common,V.aomap,V.lightmap,V.emissivemap,V.bumpmap,V.normalmap,V.displacementmap,V.gradientmap,V.fog,V.lights,{emissive:{value:new D.a(0)}}]),vertexShader:U.meshtoon_vert,fragmentShader:U.meshtoon_frag},matcap:{uniforms:R([V.common,V.bumpmap,V.normalmap,V.displacementmap,V.fog,{matcap:{value:null}}]),vertexShader:U.meshmatcap_vert,fragmentShader:U.meshmatcap_frag},points:{uniforms:R([V.points,V.fog]),vertexShader:U.points_vert,fragmentShader:U.points_frag},dashed:{uniforms:R([V.common,V.fog,{scale:{value:1},dashSize:{value:1},totalSize:{value:2}}]),vertexShader:U.linedashed_vert,fragmentShader:U.linedashed_frag},depth:{uniforms:R([V.common,V.displacementmap]),vertexShader:U.depth_vert,fragmentShader:U.depth_frag},normal:{uniforms:R([V.common,V.bumpmap,V.normalmap,V.displacementmap,{opacity:{value:1}}]),vertexShader:U.meshnormal_vert,fragmentShader:U.meshnormal_frag},sprite:{uniforms:R([V.sprite,V.fog]),vertexShader:U.sprite_vert,fragmentShader:U.sprite_frag},background:{uniforms:{uvTransform:{value:new G.a},t2D:{value:null}},vertexShader:U.background_vert,fragmentShader:U.background_frag},cube:{uniforms:R([V.envmap,{opacity:{value:1}}]),vertexShader:U.cube_vert,fragmentShader:U.cube_frag},equirect:{uniforms:{tEquirect:{value:null}},vertexShader:U.equirect_vert,fragmentShader:U.equirect_frag},distanceRGBA:{uniforms:R([V.common,V.displacementmap,{referencePosition:{value:new p.a},nearDistance:{value:1},farDistance:{value:1e3}}]),vertexShader:U.distanceRGBA_vert,fragmentShader:U.distanceRGBA_frag},shadow:{uniforms:R([V.lights,V.fog,{color:{value:new D.a(0)},opacity:{value:1}}]),vertexShader:U.shadow_vert,fragmentShader:U.shadow_frag}};function j(t,e,n,i,s){const r=new D.a(0);let o,a,l=0,c=null,h=0,u=null;function d(t,e){n.buffers.color.setClear(t.r,t.g,t.b,e,s)}return{getClearColor:function(){return r},setClearColor:function(t,e=1){r.set(t),l=e,d(r,l)},getClearAlpha:function(){return l},setClearAlpha:function(t){l=t,d(r,l)},render:function(n,s){let p=!1,_=!0===s.isScene?s.background:null;_&&_.isTexture&&(_=e.get(_));const m=t.xr,f=m.getSession&&m.getSession();f&&\\\\\\\"additive\\\\\\\"===f.environmentBlendMode&&(_=null),null===_?d(r,l):_&&_.isColor&&(d(_,1),p=!0),(t.autoClear||p)&&t.clear(t.autoClearColor,t.autoClearDepth,t.autoClearStencil),_&&(_.isCubeTexture||_.mapping===w.q)?(void 0===a&&(a=new B.a(new N(1,1,1),new F({name:\\\\\\\"BackgroundCubeMaterial\\\\\\\",uniforms:P(H.cube.uniforms),vertexShader:H.cube.vertexShader,fragmentShader:H.cube.fragmentShader,side:w.i,depthTest:!1,depthWrite:!1,fog:!1})),a.geometry.deleteAttribute(\\\\\\\"normal\\\\\\\"),a.geometry.deleteAttribute(\\\\\\\"uv\\\\\\\"),a.onBeforeRender=function(t,e,n){this.matrixWorld.copyPosition(n.matrixWorld)},Object.defineProperty(a.material,\\\\\\\"envMap\\\\\\\",{get:function(){return this.uniforms.envMap.value}}),i.update(a)),a.material.uniforms.envMap.value=_,a.material.uniforms.flipEnvMap.value=_.isCubeTexture&&!1===_.isRenderTargetTexture?-1:1,c===_&&h===_.version&&u===t.toneMapping||(a.material.needsUpdate=!0,c=_,h=_.version,u=t.toneMapping),n.unshift(a,a.geometry,a.material,0,0,null)):_&&_.isTexture&&(void 0===o&&(o=new B.a(new L(2,2),new F({name:\\\\\\\"BackgroundMaterial\\\\\\\",uniforms:P(H.background.uniforms),vertexShader:H.background.vertexShader,fragmentShader:H.background.fragmentShader,side:w.H,depthTest:!1,depthWrite:!1,fog:!1})),o.geometry.deleteAttribute(\\\\\\\"normal\\\\\\\"),Object.defineProperty(o.material,\\\\\\\"map\\\\\\\",{get:function(){return this.uniforms.t2D.value}}),i.update(o)),o.material.uniforms.t2D.value=_,!0===_.matrixAutoUpdate&&_.updateMatrix(),o.material.uniforms.uvTransform.value.copy(_.matrix),c===_&&h===_.version&&u===t.toneMapping||(o.material.needsUpdate=!0,c=_,h=_.version,u=t.toneMapping),n.unshift(o,o.geometry,o.material,0,0,null))}}}function W(t,e,n,i){const s=t.getParameter(t.MAX_VERTEX_ATTRIBS),r=i.isWebGL2?null:e.get(\\\\\\\"OES_vertex_array_object\\\\\\\"),o=i.isWebGL2||null!==r,a={},l=d(null);let c=l;function h(e){return i.isWebGL2?t.bindVertexArray(e):r.bindVertexArrayOES(e)}function u(e){return i.isWebGL2?t.deleteVertexArray(e):r.deleteVertexArrayOES(e)}function d(t){const e=[],n=[],i=[];for(let t=0;t=0){let r=l[e];if(void 0===r&&(\\\\\\\"instanceMatrix\\\\\\\"===e&&s.instanceMatrix&&(r=s.instanceMatrix),\\\\\\\"instanceColor\\\\\\\"===e&&s.instanceColor&&(r=s.instanceColor)),void 0!==r){const e=r.normalized,o=r.itemSize,l=n.get(r);if(void 0===l)continue;const c=l.buffer,h=l.type,u=l.bytesPerElement;if(r.isInterleavedBufferAttribute){const n=r.data,l=n.stride,d=r.offset;if(n&&n.isInstancedInterleavedBuffer){for(let t=0;t0&&t.getShaderPrecisionFormat(t.FRAGMENT_SHADER,t.HIGH_FLOAT).precision>0)return\\\\\\\"highp\\\\\\\";e=\\\\\\\"mediump\\\\\\\"}return\\\\\\\"mediump\\\\\\\"===e&&t.getShaderPrecisionFormat(t.VERTEX_SHADER,t.MEDIUM_FLOAT).precision>0&&t.getShaderPrecisionFormat(t.FRAGMENT_SHADER,t.MEDIUM_FLOAT).precision>0?\\\\\\\"mediump\\\\\\\":\\\\\\\"lowp\\\\\\\"}const r=\\\\\\\"undefined\\\\\\\"!=typeof WebGL2RenderingContext&&t instanceof WebGL2RenderingContext||\\\\\\\"undefined\\\\\\\"!=typeof WebGL2ComputeRenderingContext&&t instanceof WebGL2ComputeRenderingContext;let o=void 0!==n.precision?n.precision:\\\\\\\"highp\\\\\\\";const a=s(o);a!==o&&(console.warn(\\\\\\\"THREE.WebGLRenderer:\\\\\\\",o,\\\\\\\"not supported, using\\\\\\\",a,\\\\\\\"instead.\\\\\\\"),o=a);const l=r||e.has(\\\\\\\"WEBGL_draw_buffers\\\\\\\"),c=!0===n.logarithmicDepthBuffer,h=t.getParameter(t.MAX_TEXTURE_IMAGE_UNITS),u=t.getParameter(t.MAX_VERTEX_TEXTURE_IMAGE_UNITS),d=t.getParameter(t.MAX_TEXTURE_SIZE),p=t.getParameter(t.MAX_CUBE_MAP_TEXTURE_SIZE),_=t.getParameter(t.MAX_VERTEX_ATTRIBS),m=t.getParameter(t.MAX_VERTEX_UNIFORM_VECTORS),f=t.getParameter(t.MAX_VARYING_VECTORS),g=t.getParameter(t.MAX_FRAGMENT_UNIFORM_VECTORS),v=u>0,y=r||e.has(\\\\\\\"OES_texture_float\\\\\\\");return{isWebGL2:r,drawBuffers:l,getMaxAnisotropy:function(){if(void 0!==i)return i;if(!0===e.has(\\\\\\\"EXT_texture_filter_anisotropic\\\\\\\")){const n=e.get(\\\\\\\"EXT_texture_filter_anisotropic\\\\\\\");i=t.getParameter(n.MAX_TEXTURE_MAX_ANISOTROPY_EXT)}else i=0;return i},getMaxPrecision:s,precision:o,logarithmicDepthBuffer:c,maxTextures:h,maxVertexTextures:u,maxTextureSize:d,maxCubemapSize:p,maxAttributes:_,maxVertexUniforms:m,maxVaryings:f,maxFragmentUniforms:g,vertexTextures:v,floatFragmentTextures:y,floatVertexTextures:v&&y,maxSamples:r?t.getParameter(t.MAX_SAMPLES):0}}H.physical={uniforms:R([H.standard.uniforms,{clearcoat:{value:0},clearcoatMap:{value:null},clearcoatRoughness:{value:0},clearcoatRoughnessMap:{value:null},clearcoatNormalScale:{value:new d.a(1,1)},clearcoatNormalMap:{value:null},sheen:{value:0},sheenTint:{value:new D.a(0)},sheenRoughness:{value:0},transmission:{value:0},transmissionMap:{value:null},transmissionSamplerSize:{value:new d.a},transmissionSamplerMap:{value:null},thickness:{value:0},thicknessMap:{value:null},attenuationDistance:{value:0},attenuationTint:{value:new D.a(0)},specularIntensity:{value:0},specularIntensityMap:{value:null},specularTint:{value:new D.a(1,1,1)},specularTintMap:{value:null}}]),vertexShader:U.meshphysical_vert,fragmentShader:U.meshphysical_frag};var Y=n(34);function $(t){const e=this;let n=null,i=0,s=!1,r=!1;const o=new Y.a,a=new G.a,l={value:null,needsUpdate:!1};function c(){l.value!==n&&(l.value=n,l.needsUpdate=i>0),e.numPlanes=i,e.numIntersection=0}function h(t,n,i,s){const r=null!==t?t.length:0;let c=null;if(0!==r){if(c=l.value,!0!==s||null===c){const e=i+4*r,s=n.matrixWorldInverse;a.getNormalMatrix(s),(null===c||c.length\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t#include \\\\n\\\\n\\\\t\\\\t\\\\t\\\\t}\\\\n\\\\t\\\\t\\\\t\\\\\\\",fragmentShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tuniform sampler2D tEquirect;\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tvarying vec3 vWorldDirection;\\\\n\\\\n\\\\t\\\\t\\\\t\\\\t#include \\\\n\\\\n\\\\t\\\\t\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tvec3 direction = normalize( vWorldDirection );\\\\n\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tvec2 sampleUV = equirectUv( direction );\\\\n\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tgl_FragColor = texture2D( tEquirect, sampleUV );\\\\n\\\\n\\\\t\\\\t\\\\t\\\\t}\\\\n\\\\t\\\\t\\\\t\\\\\\\"},i=new N(5,5,5),s=new F({name:\\\\\\\"CubemapFromEquirect\\\\\\\",uniforms:P(n.uniforms),vertexShader:n.vertexShader,fragmentShader:n.fragmentShader,side:w.i,blending:w.ub});s.uniforms.tEquirect.value=e;const r=new B.a(i,s),o=e.minFilter;e.minFilter===w.Y&&(e.minFilter=w.V);return new nt(1,10,this).update(t,r),e.minFilter=o,r.geometry.dispose(),r.material.dispose(),this}clear(t,e,n,i){const s=t.getRenderTarget();for(let s=0;s<6;s++)t.setRenderTarget(this,s),t.clear(e,n,i);t.setRenderTarget(s)}}function rt(t){let e=new WeakMap;function n(t,e){return e===w.D?t.mapping=w.o:e===w.E&&(t.mapping=w.p),t}function i(t){const n=t.target;n.removeEventListener(\\\\\\\"dispose\\\\\\\",i);const s=e.get(n);void 0!==s&&(e.delete(n),s.dispose())}return{get:function(s){if(s&&s.isTexture&&!1===s.isRenderTargetTexture){const r=s.mapping;if(r===w.D||r===w.E){if(e.has(s)){return n(e.get(s).texture,s.mapping)}{const r=s.image;if(r&&r.height>0){const o=t.getRenderTarget(),a=new st(r.height/2);return a.fromEquirectangularTexture(t,s),e.set(s,a),t.setRenderTarget(o),s.addEventListener(\\\\\\\"dispose\\\\\\\",i),n(a.texture,s.mapping)}return null}}}return s},dispose:function(){e=new WeakMap}}}st.prototype.isWebGLCubeRenderTarget=!0;var ot=n(37);class at extends F{constructor(t){super(t),this.type=\\\\\\\"RawShaderMaterial\\\\\\\"}}at.prototype.isRawShaderMaterial=!0;var lt=n(29);const ct=Math.pow(2,8),ht=[.125,.215,.35,.446,.526,.582],ut=5+ht.length,dt=20,pt={[w.U]:0,[w.ld]:1,[w.gc]:2,[w.lc]:3,[w.kc]:4,[w.fc]:5,[w.J]:6},_t=new ot.a,{_lodPlanes:mt,_sizeLods:ft,_sigmas:gt}=Mt(),vt=new D.a;let yt=null;const xt=(1+Math.sqrt(5))/2,bt=1/xt,wt=[new p.a(1,1,1),new p.a(-1,1,1),new p.a(1,1,-1),new p.a(-1,1,-1),new p.a(0,xt,bt),new p.a(0,xt,-bt),new p.a(bt,0,xt),new p.a(-bt,0,xt),new p.a(xt,bt,0),new p.a(-xt,bt,0)];class Tt{constructor(t){this._renderer=t,this._pingPongRenderTarget=null,this._blurMaterial=function(t){const e=new Float32Array(t),n=new p.a(0,1,0);return new at({name:\\\\\\\"SphericalGaussianBlur\\\\\\\",defines:{n:t},uniforms:{envMap:{value:null},samples:{value:1},weights:{value:e},latitudinal:{value:!1},dTheta:{value:0},mipInt:{value:0},poleAxis:{value:n},inputEncoding:{value:pt[w.U]},outputEncoding:{value:pt[w.U]}},vertexShader:Lt(),fragmentShader:`\\\\n\\\\n\\\\t\\\\t\\\\tprecision mediump float;\\\\n\\\\t\\\\t\\\\tprecision mediump int;\\\\n\\\\n\\\\t\\\\t\\\\tvarying vec3 vOutputDirection;\\\\n\\\\n\\\\t\\\\t\\\\tuniform sampler2D envMap;\\\\n\\\\t\\\\t\\\\tuniform int samples;\\\\n\\\\t\\\\t\\\\tuniform float weights[ n ];\\\\n\\\\t\\\\t\\\\tuniform bool latitudinal;\\\\n\\\\t\\\\t\\\\tuniform float dTheta;\\\\n\\\\t\\\\t\\\\tuniform float mipInt;\\\\n\\\\t\\\\t\\\\tuniform vec3 poleAxis;\\\\n\\\\n\\\\t\\\\t\\\\t${Ot()}\\\\n\\\\n\\\\t\\\\t\\\\t#define ENVMAP_TYPE_CUBE_UV\\\\n\\\\t\\\\t\\\\t#include \\\\n\\\\n\\\\t\\\\t\\\\tvec3 getSample( float theta, vec3 axis ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tfloat cosTheta = cos( theta );\\\\n\\\\t\\\\t\\\\t\\\\t// Rodrigues' axis-angle rotation\\\\n\\\\t\\\\t\\\\t\\\\tvec3 sampleDirection = vOutputDirection * cosTheta\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t+ cross( axis, vOutputDirection ) * sin( theta )\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t+ axis * dot( axis, vOutputDirection ) * ( 1.0 - cosTheta );\\\\n\\\\n\\\\t\\\\t\\\\t\\\\treturn bilinearCubeUV( envMap, sampleDirection, mipInt );\\\\n\\\\n\\\\t\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tvec3 axis = latitudinal ? poleAxis : cross( poleAxis, vOutputDirection );\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tif ( all( equal( axis, vec3( 0.0 ) ) ) ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\t\\\\taxis = vec3( vOutputDirection.z, 0.0, - vOutputDirection.x );\\\\n\\\\n\\\\t\\\\t\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\t\\\\t\\\\taxis = normalize( axis );\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tgl_FragColor = vec4( 0.0, 0.0, 0.0, 1.0 );\\\\n\\\\t\\\\t\\\\t\\\\tgl_FragColor.rgb += weights[ 0 ] * getSample( 0.0, axis );\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tfor ( int i = 1; i < n; i++ ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tif ( i >= samples ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tbreak;\\\\n\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tfloat theta = dTheta * float( i );\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tgl_FragColor.rgb += weights[ i ] * getSample( -1.0 * theta, axis );\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tgl_FragColor.rgb += weights[ i ] * getSample( theta, axis );\\\\n\\\\n\\\\t\\\\t\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tgl_FragColor = linearToOutputTexel( gl_FragColor );\\\\n\\\\n\\\\t\\\\t\\\\t}\\\\n\\\\t\\\\t`,blending:w.ub,depthTest:!1,depthWrite:!1})}(dt),this._equirectShader=null,this._cubemapShader=null,this._compileMaterial(this._blurMaterial)}fromScene(t,e=0,n=.1,i=100){yt=this._renderer.getRenderTarget();const s=this._allocateTargets();return this._sceneToCubeUV(t,n,i,s),e>0&&this._blur(s,0,0,e),this._applyPMREM(s),this._cleanup(s),s}fromEquirectangular(t){return this._fromTexture(t)}fromCubemap(t){return this._fromTexture(t)}compileCubemapShader(){null===this._cubemapShader&&(this._cubemapShader=Nt(),this._compileMaterial(this._cubemapShader))}compileEquirectangularShader(){null===this._equirectShader&&(this._equirectShader=Ct(),this._compileMaterial(this._equirectShader))}dispose(){this._blurMaterial.dispose(),null!==this._cubemapShader&&this._cubemapShader.dispose(),null!==this._equirectShader&&this._equirectShader.dispose();for(let t=0;t2?ct:0,ct,ct),a.setRenderTarget(i),p&&a.render(d,s),a.render(t,s)}d.geometry.dispose(),d.material.dispose(),a.toneMapping=h,a.outputEncoding=c,a.autoClear=l,t.background=_}_setEncoding(t,e){!0===this._renderer.capabilities.isWebGL2&&e.format===w.Ib&&e.type===w.Zc&&e.encoding===w.ld?t.value=pt[w.U]:t.value=pt[e.encoding]}_textureToCubeUV(t,e){const n=this._renderer;t.isCubeTexture?null==this._cubemapShader&&(this._cubemapShader=Nt()):null==this._equirectShader&&(this._equirectShader=Ct());const i=t.isCubeTexture?this._cubemapShader:this._equirectShader,s=new B.a(mt[0],i),r=i.uniforms;r.envMap.value=t,t.isCubeTexture||r.texelSize.value.set(1/t.image.width,1/t.image.height),this._setEncoding(r.inputEncoding,t),this._setEncoding(r.outputEncoding,e.texture),St(e,0,0,3*ct,2*ct),n.setRenderTarget(e),n.render(s,_t)}_applyPMREM(t){const e=this._renderer,n=e.autoClear;e.autoClear=!1;for(let e=1;edt&&console.warn(`sigmaRadians, ${s}, is too large and will clip, as it requested ${_} samples when the maximum is set to 20`);const m=[];let f=0;for(let t=0;t4?i-8+4:0),3*g,2*g),a.setRenderTarget(e),a.render(c,_t)}}function At(t){return void 0!==t&&t.type===w.Zc&&(t.encoding===w.U||t.encoding===w.ld||t.encoding===w.J)}function Mt(){const t=[],e=[],n=[];let i=8;for(let s=0;s4?o=ht[s-8+4-1]:0==s&&(o=0),n.push(o);const a=1/(r-1),l=-a/2,c=1+a/2,h=[l,l,c,l,c,c,l,l,c,c,l,c],u=6,d=6,p=3,_=2,m=1,f=new Float32Array(p*d*u),g=new Float32Array(_*d*u),v=new Float32Array(m*d*u);for(let t=0;t2?0:-1,i=[e,n,0,e+2/3,n,0,e+2/3,n+1,0,e,n,0,e+2/3,n+1,0,e,n+1,0];f.set(i,p*d*t),g.set(h,_*d*t);const s=[t,t,t,t,t,t];v.set(s,m*d*t)}const y=new S.a;y.setAttribute(\\\\\\\"position\\\\\\\",new C.a(f,p)),y.setAttribute(\\\\\\\"uv\\\\\\\",new C.a(g,_)),y.setAttribute(\\\\\\\"faceIndex\\\\\\\",new C.a(v,m)),t.push(y),i>4&&i--}return{_lodPlanes:t,_sizeLods:e,_sigmas:n}}function Et(t){const e=new Q(3*ct,3*ct,t);return e.texture.mapping=w.q,e.texture.name=\\\\\\\"PMREM.cubeUv\\\\\\\",e.scissorTest=!0,e}function St(t,e,n,i,s){t.viewport.set(e,n,i,s),t.scissor.set(e,n,i,s)}function Ct(){const t=new d.a(1,1);return new at({name:\\\\\\\"EquirectangularToCubeUV\\\\\\\",uniforms:{envMap:{value:null},texelSize:{value:t},inputEncoding:{value:pt[w.U]},outputEncoding:{value:pt[w.U]}},vertexShader:Lt(),fragmentShader:`\\\\n\\\\n\\\\t\\\\t\\\\tprecision mediump float;\\\\n\\\\t\\\\t\\\\tprecision mediump int;\\\\n\\\\n\\\\t\\\\t\\\\tvarying vec3 vOutputDirection;\\\\n\\\\n\\\\t\\\\t\\\\tuniform sampler2D envMap;\\\\n\\\\t\\\\t\\\\tuniform vec2 texelSize;\\\\n\\\\n\\\\t\\\\t\\\\t${Ot()}\\\\n\\\\n\\\\t\\\\t\\\\t#include \\\\n\\\\n\\\\t\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tgl_FragColor = vec4( 0.0, 0.0, 0.0, 1.0 );\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tvec3 outputDirection = normalize( vOutputDirection );\\\\n\\\\t\\\\t\\\\t\\\\tvec2 uv = equirectUv( outputDirection );\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tvec2 f = fract( uv / texelSize - 0.5 );\\\\n\\\\t\\\\t\\\\t\\\\tuv -= f * texelSize;\\\\n\\\\t\\\\t\\\\t\\\\tvec3 tl = envMapTexelToLinear( texture2D ( envMap, uv ) ).rgb;\\\\n\\\\t\\\\t\\\\t\\\\tuv.x += texelSize.x;\\\\n\\\\t\\\\t\\\\t\\\\tvec3 tr = envMapTexelToLinear( texture2D ( envMap, uv ) ).rgb;\\\\n\\\\t\\\\t\\\\t\\\\tuv.y += texelSize.y;\\\\n\\\\t\\\\t\\\\t\\\\tvec3 br = envMapTexelToLinear( texture2D ( envMap, uv ) ).rgb;\\\\n\\\\t\\\\t\\\\t\\\\tuv.x -= texelSize.x;\\\\n\\\\t\\\\t\\\\t\\\\tvec3 bl = envMapTexelToLinear( texture2D ( envMap, uv ) ).rgb;\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tvec3 tm = mix( tl, tr, f.x );\\\\n\\\\t\\\\t\\\\t\\\\tvec3 bm = mix( bl, br, f.x );\\\\n\\\\t\\\\t\\\\t\\\\tgl_FragColor.rgb = mix( tm, bm, f.y );\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tgl_FragColor = linearToOutputTexel( gl_FragColor );\\\\n\\\\n\\\\t\\\\t\\\\t}\\\\n\\\\t\\\\t`,blending:w.ub,depthTest:!1,depthWrite:!1})}function Nt(){return new at({name:\\\\\\\"CubemapToCubeUV\\\\\\\",uniforms:{envMap:{value:null},inputEncoding:{value:pt[w.U]},outputEncoding:{value:pt[w.U]}},vertexShader:Lt(),fragmentShader:`\\\\n\\\\n\\\\t\\\\t\\\\tprecision mediump float;\\\\n\\\\t\\\\t\\\\tprecision mediump int;\\\\n\\\\n\\\\t\\\\t\\\\tvarying vec3 vOutputDirection;\\\\n\\\\n\\\\t\\\\t\\\\tuniform samplerCube envMap;\\\\n\\\\n\\\\t\\\\t\\\\t${Ot()}\\\\n\\\\n\\\\t\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tgl_FragColor = vec4( 0.0, 0.0, 0.0, 1.0 );\\\\n\\\\t\\\\t\\\\t\\\\tgl_FragColor.rgb = envMapTexelToLinear( textureCube( envMap, vec3( - vOutputDirection.x, vOutputDirection.yz ) ) ).rgb;\\\\n\\\\t\\\\t\\\\t\\\\tgl_FragColor = linearToOutputTexel( gl_FragColor );\\\\n\\\\n\\\\t\\\\t\\\\t}\\\\n\\\\t\\\\t`,blending:w.ub,depthTest:!1,depthWrite:!1})}function Lt(){return\\\\\\\"\\\\n\\\\n\\\\t\\\\tprecision mediump float;\\\\n\\\\t\\\\tprecision mediump int;\\\\n\\\\n\\\\t\\\\tattribute vec3 position;\\\\n\\\\t\\\\tattribute vec2 uv;\\\\n\\\\t\\\\tattribute float faceIndex;\\\\n\\\\n\\\\t\\\\tvarying vec3 vOutputDirection;\\\\n\\\\n\\\\t\\\\t// RH coordinate system; PMREM face-indexing convention\\\\n\\\\t\\\\tvec3 getDirection( vec2 uv, float face ) {\\\\n\\\\n\\\\t\\\\t\\\\tuv = 2.0 * uv - 1.0;\\\\n\\\\n\\\\t\\\\t\\\\tvec3 direction = vec3( uv, 1.0 );\\\\n\\\\n\\\\t\\\\t\\\\tif ( face == 0.0 ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tdirection = direction.zyx; // ( 1, v, u ) pos x\\\\n\\\\n\\\\t\\\\t\\\\t} else if ( face == 1.0 ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tdirection = direction.xzy;\\\\n\\\\t\\\\t\\\\t\\\\tdirection.xz *= -1.0; // ( -u, 1, -v ) pos y\\\\n\\\\n\\\\t\\\\t\\\\t} else if ( face == 2.0 ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tdirection.x *= -1.0; // ( -u, v, 1 ) pos z\\\\n\\\\n\\\\t\\\\t\\\\t} else if ( face == 3.0 ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tdirection = direction.zyx;\\\\n\\\\t\\\\t\\\\t\\\\tdirection.xz *= -1.0; // ( -1, v, -u ) neg x\\\\n\\\\n\\\\t\\\\t\\\\t} else if ( face == 4.0 ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tdirection = direction.xzy;\\\\n\\\\t\\\\t\\\\t\\\\tdirection.xy *= -1.0; // ( -u, -1, v ) neg y\\\\n\\\\n\\\\t\\\\t\\\\t} else if ( face == 5.0 ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tdirection.z *= -1.0; // ( u, v, -1 ) neg z\\\\n\\\\n\\\\t\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\t\\\\treturn direction;\\\\n\\\\n\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\tvOutputDirection = getDirection( uv, faceIndex );\\\\n\\\\t\\\\t\\\\tgl_Position = vec4( position, 1.0 );\\\\n\\\\n\\\\t\\\\t}\\\\n\\\\t\\\\\\\"}function Ot(){return\\\\\\\"\\\\n\\\\n\\\\t\\\\tuniform int inputEncoding;\\\\n\\\\t\\\\tuniform int outputEncoding;\\\\n\\\\n\\\\t\\\\t#include \\\\n\\\\n\\\\t\\\\tvec4 inputTexelToLinear( vec4 value ) {\\\\n\\\\n\\\\t\\\\t\\\\tif ( inputEncoding == 0 ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\treturn value;\\\\n\\\\n\\\\t\\\\t\\\\t} else if ( inputEncoding == 1 ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\treturn sRGBToLinear( value );\\\\n\\\\n\\\\t\\\\t\\\\t} else if ( inputEncoding == 2 ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\treturn RGBEToLinear( value );\\\\n\\\\n\\\\t\\\\t\\\\t} else if ( inputEncoding == 3 ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\treturn RGBMToLinear( value, 7.0 );\\\\n\\\\n\\\\t\\\\t\\\\t} else if ( inputEncoding == 4 ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\treturn RGBMToLinear( value, 16.0 );\\\\n\\\\n\\\\t\\\\t\\\\t} else if ( inputEncoding == 5 ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\treturn RGBDToLinear( value, 256.0 );\\\\n\\\\n\\\\t\\\\t\\\\t} else {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\treturn GammaToLinear( value, 2.2 );\\\\n\\\\n\\\\t\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\tvec4 linearToOutputTexel( vec4 value ) {\\\\n\\\\n\\\\t\\\\t\\\\tif ( outputEncoding == 0 ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\treturn value;\\\\n\\\\n\\\\t\\\\t\\\\t} else if ( outputEncoding == 1 ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\treturn LinearTosRGB( value );\\\\n\\\\n\\\\t\\\\t\\\\t} else if ( outputEncoding == 2 ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\treturn LinearToRGBE( value );\\\\n\\\\n\\\\t\\\\t\\\\t} else if ( outputEncoding == 3 ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\treturn LinearToRGBM( value, 7.0 );\\\\n\\\\n\\\\t\\\\t\\\\t} else if ( outputEncoding == 4 ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\treturn LinearToRGBM( value, 16.0 );\\\\n\\\\n\\\\t\\\\t\\\\t} else if ( outputEncoding == 5 ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\treturn LinearToRGBD( value, 256.0 );\\\\n\\\\n\\\\t\\\\t\\\\t} else {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\treturn LinearToGamma( value, 2.2 );\\\\n\\\\n\\\\t\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\tvec4 envMapTexelToLinear( vec4 color ) {\\\\n\\\\n\\\\t\\\\t\\\\treturn inputTexelToLinear( color );\\\\n\\\\n\\\\t\\\\t}\\\\n\\\\t\\\\\\\"}function Pt(t){let e=new WeakMap,n=null;function i(t){const n=t.target;n.removeEventListener(\\\\\\\"dispose\\\\\\\",i);const s=e.get(n);void 0!==s&&(e.delete(n),s.dispose())}return{get:function(s){if(s&&s.isTexture&&!1===s.isRenderTargetTexture){const r=s.mapping,o=r===w.D||r===w.E,a=r===w.o||r===w.p;if(o||a){if(e.has(s))return e.get(s).texture;{const r=s.image;if(o&&r&&r.height>0||a&&r&&function(t){let e=0;const n=6;for(let i=0;i65535?C.i:C.h)(n,1);a.version=o;const l=r.get(t);l&&e.remove(l),r.set(t,a)}return{get:function(t,e){return!0===s[e.id]||(e.addEventListener(\\\\\\\"dispose\\\\\\\",o),s[e.id]=!0,n.memory.geometries++),e},update:function(n){const i=n.attributes;for(const n in i)e.update(i[n],t.ARRAY_BUFFER);const s=n.morphAttributes;for(const n in s){const i=s[n];for(let n=0,s=i.length;ne.maxTextureSize&&(u=Math.ceil(h/e.maxTextureSize),h=e.maxTextureSize);const p=new Float32Array(h*u*4*i),_=new zt(p,h,u,i);_.format=w.Ib,_.type=w.G;const m=4*l;for(let e=0;e0)return t;const s=e*n;let r=Jt[s];if(void 0===r&&(r=new Float32Array(s),Jt[s]=r),0!==e){i.toArray(r,0);for(let i=1,s=0;i!==e;++i)s+=n,t[i].toArray(r,s)}return r}function ne(t,e){if(t.length!==e.length)return!1;for(let n=0,i=t.length;n/gm;function on(t){return t.replace(rn,an)}function an(t,e){const n=U[e];if(void 0===n)throw new Error(\\\\\\\"Can not resolve #include <\\\\\\\"+e+\\\\\\\">\\\\\\\");return on(n)}const ln=/#pragma unroll_loop[\\\\s]+?for \\\\( int i \\\\= (\\\\d+)\\\\; i < (\\\\d+)\\\\; i \\\\+\\\\+ \\\\) \\\\{([\\\\s\\\\S]+?)(?=\\\\})\\\\}/g,cn=/#pragma unroll_loop_start\\\\s+for\\\\s*\\\\(\\\\s*int\\\\s+i\\\\s*=\\\\s*(\\\\d+)\\\\s*;\\\\s*i\\\\s*<\\\\s*(\\\\d+)\\\\s*;\\\\s*i\\\\s*\\\\+\\\\+\\\\s*\\\\)\\\\s*{([\\\\s\\\\S]+?)}\\\\s+#pragma unroll_loop_end/g;function hn(t){return t.replace(cn,dn).replace(ln,un)}function un(t,e,n,i){return console.warn(\\\\\\\"WebGLProgram: #pragma unroll_loop shader syntax is deprecated. Please use #pragma unroll_loop_start syntax instead.\\\\\\\"),dn(t,e,n,i)}function dn(t,e,n,i){let s=\\\\\\\"\\\\\\\";for(let t=parseInt(e);t0?t.gammaFactor:1,p=n.isWebGL2?\\\\\\\"\\\\\\\":function(t){return[t.extensionDerivatives||t.envMapCubeUV||t.bumpMap||t.tangentSpaceNormalMap||t.clearcoatNormalMap||t.flatShading||\\\\\\\"physical\\\\\\\"===t.shaderID?\\\\\\\"#extension GL_OES_standard_derivatives : enable\\\\\\\":\\\\\\\"\\\\\\\",(t.extensionFragDepth||t.logarithmicDepthBuffer)&&t.rendererExtensionFragDepth?\\\\\\\"#extension GL_EXT_frag_depth : enable\\\\\\\":\\\\\\\"\\\\\\\",t.extensionDrawBuffers&&t.rendererExtensionDrawBuffers?\\\\\\\"#extension GL_EXT_draw_buffers : require\\\\\\\":\\\\\\\"\\\\\\\",(t.extensionShaderTextureLOD||t.envMap||t.transmission)&&t.rendererExtensionShaderTextureLod?\\\\\\\"#extension GL_EXT_shader_texture_lod : enable\\\\\\\":\\\\\\\"\\\\\\\"].filter(en).join(\\\\\\\"\\\\n\\\\\\\")}(n),_=function(t){const e=[];for(const n in t){const i=t[n];!1!==i&&e.push(\\\\\\\"#define \\\\\\\"+n+\\\\\\\" \\\\\\\"+i)}return e.join(\\\\\\\"\\\\n\\\\\\\")}(r),m=s.createProgram();let f,g,v=n.glslVersion?\\\\\\\"#version \\\\\\\"+n.glslVersion+\\\\\\\"\\\\n\\\\\\\":\\\\\\\"\\\\\\\";n.isRawShaderMaterial?(f=[_].filter(en).join(\\\\\\\"\\\\n\\\\\\\"),f.length>0&&(f+=\\\\\\\"\\\\n\\\\\\\"),g=[p,_].filter(en).join(\\\\\\\"\\\\n\\\\\\\"),g.length>0&&(g+=\\\\\\\"\\\\n\\\\\\\")):(f=[pn(n),\\\\\\\"#define SHADER_NAME \\\\\\\"+n.shaderName,_,n.instancing?\\\\\\\"#define USE_INSTANCING\\\\\\\":\\\\\\\"\\\\\\\",n.instancingColor?\\\\\\\"#define USE_INSTANCING_COLOR\\\\\\\":\\\\\\\"\\\\\\\",n.supportsVertexTextures?\\\\\\\"#define VERTEX_TEXTURES\\\\\\\":\\\\\\\"\\\\\\\",\\\\\\\"#define GAMMA_FACTOR \\\\\\\"+d,\\\\\\\"#define MAX_BONES \\\\\\\"+n.maxBones,n.useFog&&n.fog?\\\\\\\"#define USE_FOG\\\\\\\":\\\\\\\"\\\\\\\",n.useFog&&n.fogExp2?\\\\\\\"#define FOG_EXP2\\\\\\\":\\\\\\\"\\\\\\\",n.map?\\\\\\\"#define USE_MAP\\\\\\\":\\\\\\\"\\\\\\\",n.envMap?\\\\\\\"#define USE_ENVMAP\\\\\\\":\\\\\\\"\\\\\\\",n.envMap?\\\\\\\"#define \\\\\\\"+h:\\\\\\\"\\\\\\\",n.lightMap?\\\\\\\"#define USE_LIGHTMAP\\\\\\\":\\\\\\\"\\\\\\\",n.aoMap?\\\\\\\"#define USE_AOMAP\\\\\\\":\\\\\\\"\\\\\\\",n.emissiveMap?\\\\\\\"#define USE_EMISSIVEMAP\\\\\\\":\\\\\\\"\\\\\\\",n.bumpMap?\\\\\\\"#define USE_BUMPMAP\\\\\\\":\\\\\\\"\\\\\\\",n.normalMap?\\\\\\\"#define USE_NORMALMAP\\\\\\\":\\\\\\\"\\\\\\\",n.normalMap&&n.objectSpaceNormalMap?\\\\\\\"#define OBJECTSPACE_NORMALMAP\\\\\\\":\\\\\\\"\\\\\\\",n.normalMap&&n.tangentSpaceNormalMap?\\\\\\\"#define TANGENTSPACE_NORMALMAP\\\\\\\":\\\\\\\"\\\\\\\",n.clearcoatMap?\\\\\\\"#define USE_CLEARCOATMAP\\\\\\\":\\\\\\\"\\\\\\\",n.clearcoatRoughnessMap?\\\\\\\"#define USE_CLEARCOAT_ROUGHNESSMAP\\\\\\\":\\\\\\\"\\\\\\\",n.clearcoatNormalMap?\\\\\\\"#define USE_CLEARCOAT_NORMALMAP\\\\\\\":\\\\\\\"\\\\\\\",n.displacementMap&&n.supportsVertexTextures?\\\\\\\"#define USE_DISPLACEMENTMAP\\\\\\\":\\\\\\\"\\\\\\\",n.specularMap?\\\\\\\"#define USE_SPECULARMAP\\\\\\\":\\\\\\\"\\\\\\\",n.specularIntensityMap?\\\\\\\"#define USE_SPECULARINTENSITYMAP\\\\\\\":\\\\\\\"\\\\\\\",n.specularTintMap?\\\\\\\"#define USE_SPECULARTINTMAP\\\\\\\":\\\\\\\"\\\\\\\",n.roughnessMap?\\\\\\\"#define USE_ROUGHNESSMAP\\\\\\\":\\\\\\\"\\\\\\\",n.metalnessMap?\\\\\\\"#define USE_METALNESSMAP\\\\\\\":\\\\\\\"\\\\\\\",n.alphaMap?\\\\\\\"#define USE_ALPHAMAP\\\\\\\":\\\\\\\"\\\\\\\",n.transmission?\\\\\\\"#define USE_TRANSMISSION\\\\\\\":\\\\\\\"\\\\\\\",n.transmissionMap?\\\\\\\"#define USE_TRANSMISSIONMAP\\\\\\\":\\\\\\\"\\\\\\\",n.thicknessMap?\\\\\\\"#define USE_THICKNESSMAP\\\\\\\":\\\\\\\"\\\\\\\",n.vertexTangents?\\\\\\\"#define USE_TANGENT\\\\\\\":\\\\\\\"\\\\\\\",n.vertexColors?\\\\\\\"#define USE_COLOR\\\\\\\":\\\\\\\"\\\\\\\",n.vertexAlphas?\\\\\\\"#define USE_COLOR_ALPHA\\\\\\\":\\\\\\\"\\\\\\\",n.vertexUvs?\\\\\\\"#define USE_UV\\\\\\\":\\\\\\\"\\\\\\\",n.uvsVertexOnly?\\\\\\\"#define UVS_VERTEX_ONLY\\\\\\\":\\\\\\\"\\\\\\\",n.flatShading?\\\\\\\"#define FLAT_SHADED\\\\\\\":\\\\\\\"\\\\\\\",n.skinning?\\\\\\\"#define USE_SKINNING\\\\\\\":\\\\\\\"\\\\\\\",n.useVertexTexture?\\\\\\\"#define BONE_TEXTURE\\\\\\\":\\\\\\\"\\\\\\\",n.morphTargets?\\\\\\\"#define USE_MORPHTARGETS\\\\\\\":\\\\\\\"\\\\\\\",n.morphNormals&&!1===n.flatShading?\\\\\\\"#define USE_MORPHNORMALS\\\\\\\":\\\\\\\"\\\\\\\",n.morphTargets&&n.isWebGL2?\\\\\\\"#define MORPHTARGETS_TEXTURE\\\\\\\":\\\\\\\"\\\\\\\",n.morphTargets&&n.isWebGL2?\\\\\\\"#define MORPHTARGETS_COUNT \\\\\\\"+n.morphTargetsCount:\\\\\\\"\\\\\\\",n.doubleSided?\\\\\\\"#define DOUBLE_SIDED\\\\\\\":\\\\\\\"\\\\\\\",n.flipSided?\\\\\\\"#define FLIP_SIDED\\\\\\\":\\\\\\\"\\\\\\\",n.shadowMapEnabled?\\\\\\\"#define USE_SHADOWMAP\\\\\\\":\\\\\\\"\\\\\\\",n.shadowMapEnabled?\\\\\\\"#define \\\\\\\"+l:\\\\\\\"\\\\\\\",n.sizeAttenuation?\\\\\\\"#define USE_SIZEATTENUATION\\\\\\\":\\\\\\\"\\\\\\\",n.logarithmicDepthBuffer?\\\\\\\"#define USE_LOGDEPTHBUF\\\\\\\":\\\\\\\"\\\\\\\",n.logarithmicDepthBuffer&&n.rendererExtensionFragDepth?\\\\\\\"#define USE_LOGDEPTHBUF_EXT\\\\\\\":\\\\\\\"\\\\\\\",\\\\\\\"uniform mat4 modelMatrix;\\\\\\\",\\\\\\\"uniform mat4 modelViewMatrix;\\\\\\\",\\\\\\\"uniform mat4 projectionMatrix;\\\\\\\",\\\\\\\"uniform mat4 viewMatrix;\\\\\\\",\\\\\\\"uniform mat3 normalMatrix;\\\\\\\",\\\\\\\"uniform vec3 cameraPosition;\\\\\\\",\\\\\\\"uniform bool isOrthographic;\\\\\\\",\\\\\\\"#ifdef USE_INSTANCING\\\\\\\",\\\\\\\"\\\\tattribute mat4 instanceMatrix;\\\\\\\",\\\\\\\"#endif\\\\\\\",\\\\\\\"#ifdef USE_INSTANCING_COLOR\\\\\\\",\\\\\\\"\\\\tattribute vec3 instanceColor;\\\\\\\",\\\\\\\"#endif\\\\\\\",\\\\\\\"attribute vec3 position;\\\\\\\",\\\\\\\"attribute vec3 normal;\\\\\\\",\\\\\\\"attribute vec2 uv;\\\\\\\",\\\\\\\"#ifdef USE_TANGENT\\\\\\\",\\\\\\\"\\\\tattribute vec4 tangent;\\\\\\\",\\\\\\\"#endif\\\\\\\",\\\\\\\"#if defined( USE_COLOR_ALPHA )\\\\\\\",\\\\\\\"\\\\tattribute vec4 color;\\\\\\\",\\\\\\\"#elif defined( USE_COLOR )\\\\\\\",\\\\\\\"\\\\tattribute vec3 color;\\\\\\\",\\\\\\\"#endif\\\\\\\",\\\\\\\"#if ( defined( USE_MORPHTARGETS ) && ! defined( MORPHTARGETS_TEXTURE ) )\\\\\\\",\\\\\\\"\\\\tattribute vec3 morphTarget0;\\\\\\\",\\\\\\\"\\\\tattribute vec3 morphTarget1;\\\\\\\",\\\\\\\"\\\\tattribute vec3 morphTarget2;\\\\\\\",\\\\\\\"\\\\tattribute vec3 morphTarget3;\\\\\\\",\\\\\\\"\\\\t#ifdef USE_MORPHNORMALS\\\\\\\",\\\\\\\"\\\\t\\\\tattribute vec3 morphNormal0;\\\\\\\",\\\\\\\"\\\\t\\\\tattribute vec3 morphNormal1;\\\\\\\",\\\\\\\"\\\\t\\\\tattribute vec3 morphNormal2;\\\\\\\",\\\\\\\"\\\\t\\\\tattribute vec3 morphNormal3;\\\\\\\",\\\\\\\"\\\\t#else\\\\\\\",\\\\\\\"\\\\t\\\\tattribute vec3 morphTarget4;\\\\\\\",\\\\\\\"\\\\t\\\\tattribute vec3 morphTarget5;\\\\\\\",\\\\\\\"\\\\t\\\\tattribute vec3 morphTarget6;\\\\\\\",\\\\\\\"\\\\t\\\\tattribute vec3 morphTarget7;\\\\\\\",\\\\\\\"\\\\t#endif\\\\\\\",\\\\\\\"#endif\\\\\\\",\\\\\\\"#ifdef USE_SKINNING\\\\\\\",\\\\\\\"\\\\tattribute vec4 skinIndex;\\\\\\\",\\\\\\\"\\\\tattribute vec4 skinWeight;\\\\\\\",\\\\\\\"#endif\\\\\\\",\\\\\\\"\\\\n\\\\\\\"].filter(en).join(\\\\\\\"\\\\n\\\\\\\"),g=[p,pn(n),\\\\\\\"#define SHADER_NAME \\\\\\\"+n.shaderName,_,\\\\\\\"#define GAMMA_FACTOR \\\\\\\"+d,n.useFog&&n.fog?\\\\\\\"#define USE_FOG\\\\\\\":\\\\\\\"\\\\\\\",n.useFog&&n.fogExp2?\\\\\\\"#define FOG_EXP2\\\\\\\":\\\\\\\"\\\\\\\",n.map?\\\\\\\"#define USE_MAP\\\\\\\":\\\\\\\"\\\\\\\",n.matcap?\\\\\\\"#define USE_MATCAP\\\\\\\":\\\\\\\"\\\\\\\",n.envMap?\\\\\\\"#define USE_ENVMAP\\\\\\\":\\\\\\\"\\\\\\\",n.envMap?\\\\\\\"#define \\\\\\\"+c:\\\\\\\"\\\\\\\",n.envMap?\\\\\\\"#define \\\\\\\"+h:\\\\\\\"\\\\\\\",n.envMap?\\\\\\\"#define \\\\\\\"+u:\\\\\\\"\\\\\\\",n.lightMap?\\\\\\\"#define USE_LIGHTMAP\\\\\\\":\\\\\\\"\\\\\\\",n.aoMap?\\\\\\\"#define USE_AOMAP\\\\\\\":\\\\\\\"\\\\\\\",n.emissiveMap?\\\\\\\"#define USE_EMISSIVEMAP\\\\\\\":\\\\\\\"\\\\\\\",n.bumpMap?\\\\\\\"#define USE_BUMPMAP\\\\\\\":\\\\\\\"\\\\\\\",n.normalMap?\\\\\\\"#define USE_NORMALMAP\\\\\\\":\\\\\\\"\\\\\\\",n.normalMap&&n.objectSpaceNormalMap?\\\\\\\"#define OBJECTSPACE_NORMALMAP\\\\\\\":\\\\\\\"\\\\\\\",n.normalMap&&n.tangentSpaceNormalMap?\\\\\\\"#define TANGENTSPACE_NORMALMAP\\\\\\\":\\\\\\\"\\\\\\\",n.clearcoat?\\\\\\\"#define USE_CLEARCOAT\\\\\\\":\\\\\\\"\\\\\\\",n.clearcoatMap?\\\\\\\"#define USE_CLEARCOATMAP\\\\\\\":\\\\\\\"\\\\\\\",n.clearcoatRoughnessMap?\\\\\\\"#define USE_CLEARCOAT_ROUGHNESSMAP\\\\\\\":\\\\\\\"\\\\\\\",n.clearcoatNormalMap?\\\\\\\"#define USE_CLEARCOAT_NORMALMAP\\\\\\\":\\\\\\\"\\\\\\\",n.specularMap?\\\\\\\"#define USE_SPECULARMAP\\\\\\\":\\\\\\\"\\\\\\\",n.specularIntensityMap?\\\\\\\"#define USE_SPECULARINTENSITYMAP\\\\\\\":\\\\\\\"\\\\\\\",n.specularTintMap?\\\\\\\"#define USE_SPECULARTINTMAP\\\\\\\":\\\\\\\"\\\\\\\",n.roughnessMap?\\\\\\\"#define USE_ROUGHNESSMAP\\\\\\\":\\\\\\\"\\\\\\\",n.metalnessMap?\\\\\\\"#define USE_METALNESSMAP\\\\\\\":\\\\\\\"\\\\\\\",n.alphaMap?\\\\\\\"#define USE_ALPHAMAP\\\\\\\":\\\\\\\"\\\\\\\",n.alphaTest?\\\\\\\"#define USE_ALPHATEST\\\\\\\":\\\\\\\"\\\\\\\",n.sheen?\\\\\\\"#define USE_SHEEN\\\\\\\":\\\\\\\"\\\\\\\",n.transmission?\\\\\\\"#define USE_TRANSMISSION\\\\\\\":\\\\\\\"\\\\\\\",n.transmissionMap?\\\\\\\"#define USE_TRANSMISSIONMAP\\\\\\\":\\\\\\\"\\\\\\\",n.thicknessMap?\\\\\\\"#define USE_THICKNESSMAP\\\\\\\":\\\\\\\"\\\\\\\",n.vertexTangents?\\\\\\\"#define USE_TANGENT\\\\\\\":\\\\\\\"\\\\\\\",n.vertexColors||n.instancingColor?\\\\\\\"#define USE_COLOR\\\\\\\":\\\\\\\"\\\\\\\",n.vertexAlphas?\\\\\\\"#define USE_COLOR_ALPHA\\\\\\\":\\\\\\\"\\\\\\\",n.vertexUvs?\\\\\\\"#define USE_UV\\\\\\\":\\\\\\\"\\\\\\\",n.uvsVertexOnly?\\\\\\\"#define UVS_VERTEX_ONLY\\\\\\\":\\\\\\\"\\\\\\\",n.gradientMap?\\\\\\\"#define USE_GRADIENTMAP\\\\\\\":\\\\\\\"\\\\\\\",n.flatShading?\\\\\\\"#define FLAT_SHADED\\\\\\\":\\\\\\\"\\\\\\\",n.doubleSided?\\\\\\\"#define DOUBLE_SIDED\\\\\\\":\\\\\\\"\\\\\\\",n.flipSided?\\\\\\\"#define FLIP_SIDED\\\\\\\":\\\\\\\"\\\\\\\",n.shadowMapEnabled?\\\\\\\"#define USE_SHADOWMAP\\\\\\\":\\\\\\\"\\\\\\\",n.shadowMapEnabled?\\\\\\\"#define \\\\\\\"+l:\\\\\\\"\\\\\\\",n.premultipliedAlpha?\\\\\\\"#define PREMULTIPLIED_ALPHA\\\\\\\":\\\\\\\"\\\\\\\",n.physicallyCorrectLights?\\\\\\\"#define PHYSICALLY_CORRECT_LIGHTS\\\\\\\":\\\\\\\"\\\\\\\",n.logarithmicDepthBuffer?\\\\\\\"#define USE_LOGDEPTHBUF\\\\\\\":\\\\\\\"\\\\\\\",n.logarithmicDepthBuffer&&n.rendererExtensionFragDepth?\\\\\\\"#define USE_LOGDEPTHBUF_EXT\\\\\\\":\\\\\\\"\\\\\\\",(n.extensionShaderTextureLOD||n.envMap)&&n.rendererExtensionShaderTextureLod?\\\\\\\"#define TEXTURE_LOD_EXT\\\\\\\":\\\\\\\"\\\\\\\",\\\\\\\"uniform mat4 viewMatrix;\\\\\\\",\\\\\\\"uniform vec3 cameraPosition;\\\\\\\",\\\\\\\"uniform bool isOrthographic;\\\\\\\",n.toneMapping!==w.vb?\\\\\\\"#define TONE_MAPPING\\\\\\\":\\\\\\\"\\\\\\\",n.toneMapping!==w.vb?U.tonemapping_pars_fragment:\\\\\\\"\\\\\\\",n.toneMapping!==w.vb?tn(\\\\\\\"toneMapping\\\\\\\",n.toneMapping):\\\\\\\"\\\\\\\",n.dithering?\\\\\\\"#define DITHERING\\\\\\\":\\\\\\\"\\\\\\\",n.format===w.ic?\\\\\\\"#define OPAQUE\\\\\\\":\\\\\\\"\\\\\\\",U.encodings_pars_fragment,n.map?Qe(\\\\\\\"mapTexelToLinear\\\\\\\",n.mapEncoding):\\\\\\\"\\\\\\\",n.matcap?Qe(\\\\\\\"matcapTexelToLinear\\\\\\\",n.matcapEncoding):\\\\\\\"\\\\\\\",n.envMap?Qe(\\\\\\\"envMapTexelToLinear\\\\\\\",n.envMapEncoding):\\\\\\\"\\\\\\\",n.emissiveMap?Qe(\\\\\\\"emissiveMapTexelToLinear\\\\\\\",n.emissiveMapEncoding):\\\\\\\"\\\\\\\",n.specularTintMap?Qe(\\\\\\\"specularTintMapTexelToLinear\\\\\\\",n.specularTintMapEncoding):\\\\\\\"\\\\\\\",n.lightMap?Qe(\\\\\\\"lightMapTexelToLinear\\\\\\\",n.lightMapEncoding):\\\\\\\"\\\\\\\",Ke(\\\\\\\"linearToOutputTexel\\\\\\\",n.outputEncoding),n.depthPacking?\\\\\\\"#define DEPTH_PACKING \\\\\\\"+n.depthPacking:\\\\\\\"\\\\\\\",\\\\\\\"\\\\n\\\\\\\"].filter(en).join(\\\\\\\"\\\\n\\\\\\\")),o=on(o),o=nn(o,n),o=sn(o,n),a=on(a),a=nn(a,n),a=sn(a,n),o=hn(o),a=hn(a),n.isWebGL2&&!0!==n.isRawShaderMaterial&&(v=\\\\\\\"#version 300 es\\\\n\\\\\\\",f=[\\\\\\\"precision mediump sampler2DArray;\\\\\\\",\\\\\\\"#define attribute in\\\\\\\",\\\\\\\"#define varying out\\\\\\\",\\\\\\\"#define texture2D texture\\\\\\\"].join(\\\\\\\"\\\\n\\\\\\\")+\\\\\\\"\\\\n\\\\\\\"+f,g=[\\\\\\\"#define varying in\\\\\\\",n.glslVersion===w.I?\\\\\\\"\\\\\\\":\\\\\\\"out highp vec4 pc_fragColor;\\\\\\\",n.glslVersion===w.I?\\\\\\\"\\\\\\\":\\\\\\\"#define gl_FragColor pc_fragColor\\\\\\\",\\\\\\\"#define gl_FragDepthEXT gl_FragDepth\\\\\\\",\\\\\\\"#define texture2D texture\\\\\\\",\\\\\\\"#define textureCube texture\\\\\\\",\\\\\\\"#define texture2DProj textureProj\\\\\\\",\\\\\\\"#define texture2DLodEXT textureLod\\\\\\\",\\\\\\\"#define texture2DProjLodEXT textureProjLod\\\\\\\",\\\\\\\"#define textureCubeLodEXT textureLod\\\\\\\",\\\\\\\"#define texture2DGradEXT textureGrad\\\\\\\",\\\\\\\"#define texture2DProjGradEXT textureProjGrad\\\\\\\",\\\\\\\"#define textureCubeGradEXT textureGrad\\\\\\\"].join(\\\\\\\"\\\\n\\\\\\\")+\\\\\\\"\\\\n\\\\\\\"+g);const y=v+f+o,x=v+g+a,b=Ye(s,s.VERTEX_SHADER,y),T=Ye(s,s.FRAGMENT_SHADER,x);if(s.attachShader(m,b),s.attachShader(m,T),void 0!==n.index0AttributeName?s.bindAttribLocation(m,0,n.index0AttributeName):!0===n.morphTargets&&s.bindAttribLocation(m,0,\\\\\\\"position\\\\\\\"),s.linkProgram(m),t.debug.checkShaderErrors){const t=s.getProgramInfoLog(m).trim(),e=s.getShaderInfoLog(b).trim(),n=s.getShaderInfoLog(T).trim();let i=!0,r=!0;if(!1===s.getProgramParameter(m,s.LINK_STATUS)){i=!1;const e=Ze(s,b,\\\\\\\"vertex\\\\\\\"),n=Ze(s,T,\\\\\\\"fragment\\\\\\\");console.error(\\\\\\\"THREE.WebGLProgram: Shader Error \\\\\\\"+s.getError()+\\\\\\\" - VALIDATE_STATUS \\\\\\\"+s.getProgramParameter(m,s.VALIDATE_STATUS)+\\\\\\\"\\\\n\\\\nProgram Info Log: \\\\\\\"+t+\\\\\\\"\\\\n\\\\\\\"+e+\\\\\\\"\\\\n\\\\\\\"+n)}else\\\\\\\"\\\\\\\"!==t?console.warn(\\\\\\\"THREE.WebGLProgram: Program Info Log:\\\\\\\",t):\\\\\\\"\\\\\\\"!==e&&\\\\\\\"\\\\\\\"!==n||(r=!1);r&&(this.diagnostics={runnable:i,programLog:t,vertexShader:{log:e,prefix:f},fragmentShader:{log:n,prefix:g}})}let A,M;return s.deleteShader(b),s.deleteShader(T),this.getUniforms=function(){return void 0===A&&(A=new Xe(s,m)),A},this.getAttributes=function(){return void 0===M&&(M=function(t,e){const n={},i=t.getProgramParameter(e,t.ACTIVE_ATTRIBUTES);for(let s=0;s0,N=r.clearcoat>0;return{isWebGL2:l,shaderID:T,shaderName:r.type,vertexShader:M,fragmentShader:E,defines:r.defines,isRawShaderMaterial:!0===r.isRawShaderMaterial,glslVersion:r.glslVersion,precision:p,instancing:!0===v.isInstancedMesh,instancingColor:!0===v.isInstancedMesh&&null!==v.instanceColor,supportsVertexTextures:d,outputEncoding:null!==S?f(S.texture):t.outputEncoding,map:!!r.map,mapEncoding:f(r.map),matcap:!!r.matcap,matcapEncoding:f(r.matcap),envMap:!!b,envMapMode:b&&b.mapping,envMapEncoding:f(b),envMapCubeUV:!!b&&(b.mapping===w.q||b.mapping===w.r),lightMap:!!r.lightMap,lightMapEncoding:f(r.lightMap),aoMap:!!r.aoMap,emissiveMap:!!r.emissiveMap,emissiveMapEncoding:f(r.emissiveMap),bumpMap:!!r.bumpMap,normalMap:!!r.normalMap,objectSpaceNormalMap:r.normalMapType===w.zb,tangentSpaceNormalMap:r.normalMapType===w.Uc,clearcoat:N,clearcoatMap:N&&!!r.clearcoatMap,clearcoatRoughnessMap:N&&!!r.clearcoatRoughnessMap,clearcoatNormalMap:N&&!!r.clearcoatNormalMap,displacementMap:!!r.displacementMap,roughnessMap:!!r.roughnessMap,metalnessMap:!!r.metalnessMap,specularMap:!!r.specularMap,specularIntensityMap:!!r.specularIntensityMap,specularTintMap:!!r.specularTintMap,specularTintMapEncoding:f(r.specularTintMap),alphaMap:!!r.alphaMap,alphaTest:C,gradientMap:!!r.gradientMap,sheen:r.sheen>0,transmission:r.transmission>0,transmissionMap:!!r.transmissionMap,thicknessMap:!!r.thicknessMap,combine:r.combine,vertexTangents:!!r.normalMap&&!!v.geometry&&!!v.geometry.attributes.tangent,vertexColors:r.vertexColors,vertexAlphas:!0===r.vertexColors&&!!v.geometry&&!!v.geometry.attributes.color&&4===v.geometry.attributes.color.itemSize,vertexUvs:!!(r.map||r.bumpMap||r.normalMap||r.specularMap||r.alphaMap||r.emissiveMap||r.roughnessMap||r.metalnessMap||r.clearcoatMap||r.clearcoatRoughnessMap||r.clearcoatNormalMap||r.displacementMap||r.transmissionMap||r.thicknessMap||r.specularIntensityMap||r.specularTintMap),uvsVertexOnly:!(r.map||r.bumpMap||r.normalMap||r.specularMap||r.alphaMap||r.emissiveMap||r.roughnessMap||r.metalnessMap||r.clearcoatNormalMap||r.transmission>0||r.transmissionMap||r.thicknessMap||r.specularIntensityMap||r.specularTintMap||!r.displacementMap),fog:!!y,useFog:r.fog,fogExp2:y&&y.isFogExp2,flatShading:!!r.flatShading,sizeAttenuation:r.sizeAttenuation,logarithmicDepthBuffer:c,skinning:!0===v.isSkinnedMesh&&A>0,maxBones:A,useVertexTexture:h,morphTargets:!!v.geometry&&!!v.geometry.morphAttributes.position,morphNormals:!!v.geometry&&!!v.geometry.morphAttributes.normal,morphTargetsCount:v.geometry&&v.geometry.morphAttributes.position?v.geometry.morphAttributes.position.length:0,numDirLights:a.directional.length,numPointLights:a.point.length,numSpotLights:a.spot.length,numRectAreaLights:a.rectArea.length,numHemiLights:a.hemi.length,numDirLightShadows:a.directionalShadowMap.length,numPointLightShadows:a.pointShadowMap.length,numSpotLightShadows:a.spotShadowMap.length,numClippingPlanes:o.numPlanes,numClipIntersection:o.numIntersection,format:r.format,dithering:r.dithering,shadowMapEnabled:t.shadowMap.enabled&&m.length>0,shadowMapType:t.shadowMap.type,toneMapping:r.toneMapped?t.toneMapping:w.vb,physicallyCorrectLights:t.physicallyCorrectLights,premultipliedAlpha:r.premultipliedAlpha,doubleSided:r.side===w.z,flipSided:r.side===w.i,depthPacking:void 0!==r.depthPacking&&r.depthPacking,index0AttributeName:r.index0AttributeName,extensionDerivatives:r.extensions&&r.extensions.derivatives,extensionFragDepth:r.extensions&&r.extensions.fragDepth,extensionDrawBuffers:r.extensions&&r.extensions.drawBuffers,extensionShaderTextureLOD:r.extensions&&r.extensions.shaderTextureLOD,rendererExtensionFragDepth:l||i.has(\\\\\\\"EXT_frag_depth\\\\\\\"),rendererExtensionDrawBuffers:l||i.has(\\\\\\\"WEBGL_draw_buffers\\\\\\\"),rendererExtensionShaderTextureLod:l||i.has(\\\\\\\"EXT_shader_texture_lod\\\\\\\"),customProgramCacheKey:r.customProgramCacheKey()}},getProgramCacheKey:function(e){const n=[];if(e.shaderID?n.push(e.shaderID):(n.push(e.fragmentShader),n.push(e.vertexShader)),void 0!==e.defines)for(const t in e.defines)n.push(t),n.push(e.defines[t]);if(!1===e.isRawShaderMaterial){for(let t=0;t0?s.push(h):!0===n.transparent?r.push(h):i.push(h)},unshift:function(t,e,n,o,l,c){const h=a(t,e,n,o,l,c);n.transmission>0?s.unshift(h):!0===n.transparent?r.unshift(h):i.unshift(h)},finish:function(){for(let t=n,i=e.length;t1&&i.sort(t||gn),s.length>1&&s.sort(e||vn),r.length>1&&r.sort(e||vn)}}}function xn(t){let e=new WeakMap;return{get:function(n,i){let s;return!1===e.has(n)?(s=new yn(t),e.set(n,[s])):i>=e.get(n).length?(s=new yn(t),e.get(n).push(s)):s=e.get(n)[i],s},dispose:function(){e=new WeakMap}}}function bn(){const t={};return{get:function(e){if(void 0!==t[e.id])return t[e.id];let n;switch(e.type){case\\\\\\\"DirectionalLight\\\\\\\":n={direction:new p.a,color:new D.a};break;case\\\\\\\"SpotLight\\\\\\\":n={position:new p.a,direction:new p.a,color:new D.a,distance:0,coneCos:0,penumbraCos:0,decay:0};break;case\\\\\\\"PointLight\\\\\\\":n={position:new p.a,color:new D.a,distance:0,decay:0};break;case\\\\\\\"HemisphereLight\\\\\\\":n={direction:new p.a,skyColor:new D.a,groundColor:new D.a};break;case\\\\\\\"RectAreaLight\\\\\\\":n={color:new D.a,position:new p.a,halfWidth:new p.a,halfHeight:new p.a}}return t[e.id]=n,n}}}let wn=0;function Tn(t,e){return(e.castShadow?1:0)-(t.castShadow?1:0)}function An(t,e){const n=new bn,i=function(){const t={};return{get:function(e){if(void 0!==t[e.id])return t[e.id];let n;switch(e.type){case\\\\\\\"DirectionalLight\\\\\\\":case\\\\\\\"SpotLight\\\\\\\":n={shadowBias:0,shadowNormalBias:0,shadowRadius:1,shadowMapSize:new d.a};break;case\\\\\\\"PointLight\\\\\\\":n={shadowBias:0,shadowNormalBias:0,shadowRadius:1,shadowMapSize:new d.a,shadowCameraNear:1,shadowCameraFar:1e3}}return t[e.id]=n,n}}}(),s={version:0,hash:{directionalLength:-1,pointLength:-1,spotLength:-1,rectAreaLength:-1,hemiLength:-1,numDirectionalShadows:-1,numPointShadows:-1,numSpotShadows:-1},ambient:[0,0,0],probe:[],directional:[],directionalShadow:[],directionalShadowMap:[],directionalShadowMatrix:[],spot:[],spotShadow:[],spotShadowMap:[],spotShadowMatrix:[],rectArea:[],rectAreaLTC1:null,rectAreaLTC2:null,point:[],pointShadow:[],pointShadowMap:[],pointShadowMatrix:[],hemi:[]};for(let t=0;t<9;t++)s.probe.push(new p.a);const r=new p.a,o=new A.a,a=new A.a;return{setup:function(r,o){let a=0,l=0,c=0;for(let t=0;t<9;t++)s.probe[t].set(0,0,0);let h=0,u=0,d=0,p=0,_=0,m=0,f=0,g=0;r.sort(Tn);const v=!0!==o?Math.PI:1;for(let t=0,e=r.length;t0&&(e.isWebGL2||!0===t.has(\\\\\\\"OES_texture_float_linear\\\\\\\")?(s.rectAreaLTC1=V.LTC_FLOAT_1,s.rectAreaLTC2=V.LTC_FLOAT_2):!0===t.has(\\\\\\\"OES_texture_half_float_linear\\\\\\\")?(s.rectAreaLTC1=V.LTC_HALF_1,s.rectAreaLTC2=V.LTC_HALF_2):console.error(\\\\\\\"THREE.WebGLRenderer: Unable to use RectAreaLight. Missing WebGL extensions.\\\\\\\")),s.ambient[0]=a,s.ambient[1]=l,s.ambient[2]=c;const y=s.hash;y.directionalLength===h&&y.pointLength===u&&y.spotLength===d&&y.rectAreaLength===p&&y.hemiLength===_&&y.numDirectionalShadows===m&&y.numPointShadows===f&&y.numSpotShadows===g||(s.directional.length=h,s.spot.length=d,s.rectArea.length=p,s.point.length=u,s.hemi.length=_,s.directionalShadow.length=m,s.directionalShadowMap.length=m,s.pointShadow.length=f,s.pointShadowMap.length=f,s.spotShadow.length=g,s.spotShadowMap.length=g,s.directionalShadowMatrix.length=m,s.pointShadowMatrix.length=f,s.spotShadowMatrix.length=g,y.directionalLength=h,y.pointLength=u,y.spotLength=d,y.rectAreaLength=p,y.hemiLength=_,y.numDirectionalShadows=m,y.numPointShadows=f,y.numSpotShadows=g,s.version=wn++)},setupView:function(t,e){let n=0,i=0,l=0,c=0,h=0;const u=e.matrixWorldInverse;for(let e=0,d=t.length;e=n.get(i).length?(r=new Mn(t,e),n.get(i).push(r)):r=n.get(i)[s],r},dispose:function(){n=new WeakMap}}}class Sn extends O.a{constructor(t){super(),this.type=\\\\\\\"MeshDepthMaterial\\\\\\\",this.depthPacking=w.j,this.map=null,this.alphaMap=null,this.displacementMap=null,this.displacementScale=1,this.displacementBias=0,this.wireframe=!1,this.wireframeLinewidth=1,this.fog=!1,this.setValues(t)}copy(t){return super.copy(t),this.depthPacking=t.depthPacking,this.map=t.map,this.alphaMap=t.alphaMap,this.displacementMap=t.displacementMap,this.displacementScale=t.displacementScale,this.displacementBias=t.displacementBias,this.wireframe=t.wireframe,this.wireframeLinewidth=t.wireframeLinewidth,this}}Sn.prototype.isMeshDepthMaterial=!0;class Cn extends O.a{constructor(t){super(),this.type=\\\\\\\"MeshDistanceMaterial\\\\\\\",this.referencePosition=new p.a,this.nearDistance=1,this.farDistance=1e3,this.map=null,this.alphaMap=null,this.displacementMap=null,this.displacementScale=1,this.displacementBias=0,this.fog=!1,this.setValues(t)}copy(t){return super.copy(t),this.referencePosition.copy(t.referencePosition),this.nearDistance=t.nearDistance,this.farDistance=t.farDistance,this.map=t.map,this.alphaMap=t.alphaMap,this.displacementMap=t.displacementMap,this.displacementScale=t.displacementScale,this.displacementBias=t.displacementBias,this}}Cn.prototype.isMeshDistanceMaterial=!0;function Nn(t,e,n){let i=new T.a;const s=new d.a,r=new d.a,o=new _.a,a=new Sn({depthPacking:w.Hb}),l=new Cn,c={},h=n.maxTextureSize,u={0:w.i,1:w.H,2:w.z},p=new F({uniforms:{shadow_pass:{value:null},resolution:{value:new d.a},radius:{value:4},samples:{value:8}},vertexShader:\\\\\\\"\\\\nvoid main() {\\\\n\\\\n\\\\tgl_Position = vec4( position, 1.0 );\\\\n\\\\n}\\\\n\\\\\\\",fragmentShader:\\\\\\\"\\\\nuniform sampler2D shadow_pass;\\\\nuniform vec2 resolution;\\\\nuniform float radius;\\\\nuniform float samples;\\\\n\\\\n#include \\\\n\\\\nvoid main() {\\\\n\\\\n\\\\tfloat mean = 0.0;\\\\n\\\\tfloat squared_mean = 0.0;\\\\n\\\\n\\\\t// This seems totally useless but it's a crazy work around for a Adreno compiler bug\\\\n\\\\t// float depth = unpackRGBAToDepth( texture2D( shadow_pass, ( gl_FragCoord.xy ) / resolution ) );\\\\n\\\\n\\\\tfloat uvStride = samples <= 1.0 ? 0.0 : 2.0 / ( samples - 1.0 );\\\\n\\\\tfloat uvStart = samples <= 1.0 ? 0.0 : - 1.0;\\\\n\\\\tfor ( float i = 0.0; i < samples; i ++ ) {\\\\n\\\\n\\\\t\\\\tfloat uvOffset = uvStart + i * uvStride;\\\\n\\\\n\\\\t\\\\t#ifdef HORIZONTAL_PASS\\\\n\\\\n\\\\t\\\\t\\\\tvec2 distribution = unpackRGBATo2Half( texture2D( shadow_pass, ( gl_FragCoord.xy + vec2( uvOffset, 0.0 ) * radius ) / resolution ) );\\\\n\\\\t\\\\t\\\\tmean += distribution.x;\\\\n\\\\t\\\\t\\\\tsquared_mean += distribution.y * distribution.y + distribution.x * distribution.x;\\\\n\\\\n\\\\t\\\\t#else\\\\n\\\\n\\\\t\\\\t\\\\tfloat depth = unpackRGBAToDepth( texture2D( shadow_pass, ( gl_FragCoord.xy + vec2( 0.0, uvOffset ) * radius ) / resolution ) );\\\\n\\\\t\\\\t\\\\tmean += depth;\\\\n\\\\t\\\\t\\\\tsquared_mean += depth * depth;\\\\n\\\\n\\\\t\\\\t#endif\\\\n\\\\n\\\\t}\\\\n\\\\n\\\\tmean = mean / samples;\\\\n\\\\tsquared_mean = squared_mean / samples;\\\\n\\\\n\\\\tfloat std_dev = sqrt( squared_mean - mean * mean );\\\\n\\\\n\\\\tgl_FragColor = pack2HalfToRGBA( vec2( mean, std_dev ) );\\\\n\\\\n}\\\\n\\\\\\\"}),m=p.clone();m.defines.HORIZONTAL_PASS=1;const f=new S.a;f.setAttribute(\\\\\\\"position\\\\\\\",new C.a(new Float32Array([-1,-1,.5,3,-1,.5,-1,3,.5]),3));const g=new B.a(f,p),v=this;function y(n,i){const s=e.update(g);p.uniforms.shadow_pass.value=n.map.texture,p.uniforms.resolution.value=n.mapSize,p.uniforms.radius.value=n.radius,p.uniforms.samples.value=n.blurSamples,t.setRenderTarget(n.mapPass),t.clear(),t.renderBufferDirect(i,null,s,p,g,null),m.uniforms.shadow_pass.value=n.mapPass.texture,m.uniforms.resolution.value=n.mapSize,m.uniforms.radius.value=n.radius,m.uniforms.samples.value=n.blurSamples,t.setRenderTarget(n.map),t.clear(),t.renderBufferDirect(i,null,s,m,g,null)}function x(e,n,i,s,r,o,h){let d=null;const p=!0===s.isPointLight?e.customDistanceMaterial:e.customDepthMaterial;if(d=void 0!==p?p:!0===s.isPointLight?l:a,t.localClippingEnabled&&!0===i.clipShadows&&0!==i.clippingPlanes.length||i.displacementMap&&0!==i.displacementScale||i.alphaMap&&i.alphaTest>0){const t=d.uuid,e=i.uuid;let n=c[t];void 0===n&&(n={},c[t]=n);let s=n[e];void 0===s&&(s=d.clone(),n[e]=s),d=s}return d.visible=i.visible,d.wireframe=i.wireframe,h===w.gd?d.side=null!==i.shadowSide?i.shadowSide:i.side:d.side=null!==i.shadowSide?i.shadowSide:u[i.side],d.alphaMap=i.alphaMap,d.alphaTest=i.alphaTest,d.clipShadows=i.clipShadows,d.clippingPlanes=i.clippingPlanes,d.clipIntersection=i.clipIntersection,d.displacementMap=i.displacementMap,d.displacementScale=i.displacementScale,d.displacementBias=i.displacementBias,d.wireframeLinewidth=i.wireframeLinewidth,d.linewidth=i.linewidth,!0===s.isPointLight&&!0===d.isMeshDistanceMaterial&&(d.referencePosition.setFromMatrixPosition(s.matrixWorld),d.nearDistance=r,d.farDistance=o),d}function b(n,s,r,o,a){if(!1===n.visible)return;if(n.layers.test(s.layers)&&(n.isMesh||n.isLine||n.isPoints)&&(n.castShadow||n.receiveShadow&&a===w.gd)&&(!n.frustumCulled||i.intersectsObject(n))){n.modelViewMatrix.multiplyMatrices(r.matrixWorldInverse,n.matrixWorld);const i=e.update(n),s=n.material;if(Array.isArray(s)){const e=i.groups;for(let l=0,c=e.length;lh||s.y>h)&&(s.x>h&&(r.x=Math.floor(h/p.x),s.x=r.x*p.x,u.mapSize.x=r.x),s.y>h&&(r.y=Math.floor(h/p.y),s.y=r.y*p.y,u.mapSize.y=r.y)),null===u.map&&!u.isPointLightShadow&&this.type===w.gd){const t={minFilter:w.V,magFilter:w.V,format:w.Ib};u.map=new Q(s.x,s.y,t),u.map.texture.name=c.name+\\\\\\\".shadowMap\\\\\\\",u.mapPass=new Q(s.x,s.y,t),u.camera.updateProjectionMatrix()}if(null===u.map){const t={minFilter:w.ob,magFilter:w.ob,format:w.Ib};u.map=new Q(s.x,s.y,t),u.map.texture.name=c.name+\\\\\\\".shadowMap\\\\\\\",u.camera.updateProjectionMatrix()}t.setRenderTarget(u.map),t.clear();const _=u.getViewportCount();for(let t=0;t<_;t++){const e=u.getViewport(t);o.set(r.x*e.x,r.y*e.y,r.x*e.z,r.y*e.w),d.viewport(o),u.updateMatrices(c,t),i=u.getFrustum(),b(n,a,u.camera,c,this.type)}u.isPointLightShadow||this.type!==w.gd||y(u,a),u.needsUpdate=!1}v.needsUpdate=!1,t.setRenderTarget(l,c,u)}}function Ln(t,e,n){const i=n.isWebGL2;const s=new function(){let e=!1;const n=new _.a;let i=null;const s=new _.a(0,0,0,0);return{setMask:function(n){i===n||e||(t.colorMask(n,n,n,n),i=n)},setLocked:function(t){e=t},setClear:function(e,i,r,o,a){!0===a&&(e*=o,i*=o,r*=o),n.set(e,i,r,o),!1===s.equals(n)&&(t.clearColor(e,i,r,o),s.copy(n))},reset:function(){e=!1,i=null,s.set(-1,0,0,0)}}},r=new function(){let e=!1,n=null,i=null,s=null;return{setTest:function(e){e?k(t.DEPTH_TEST):U(t.DEPTH_TEST)},setMask:function(i){n===i||e||(t.depthMask(i),n=i)},setFunc:function(e){if(i!==e){if(e)switch(e){case w.tb:t.depthFunc(t.NEVER);break;case w.g:t.depthFunc(t.ALWAYS);break;case w.S:t.depthFunc(t.LESS);break;case w.T:t.depthFunc(t.LEQUAL);break;case w.C:t.depthFunc(t.EQUAL);break;case w.L:t.depthFunc(t.GEQUAL);break;case w.K:t.depthFunc(t.GREATER);break;case w.yb:t.depthFunc(t.NOTEQUAL);break;default:t.depthFunc(t.LEQUAL)}else t.depthFunc(t.LEQUAL);i=e}},setLocked:function(t){e=t},setClear:function(e){s!==e&&(t.clearDepth(e),s=e)},reset:function(){e=!1,n=null,i=null,s=null}}},o=new function(){let e=!1,n=null,i=null,s=null,r=null,o=null,a=null,l=null,c=null;return{setTest:function(n){e||(n?k(t.STENCIL_TEST):U(t.STENCIL_TEST))},setMask:function(i){n===i||e||(t.stencilMask(i),n=i)},setFunc:function(e,n,o){i===e&&s===n&&r===o||(t.stencilFunc(e,n,o),i=e,s=n,r=o)},setOp:function(e,n,i){o===e&&a===n&&l===i||(t.stencilOp(e,n,i),o=e,a=n,l=i)},setLocked:function(t){e=t},setClear:function(e){c!==e&&(t.clearStencil(e),c=e)},reset:function(){e=!1,n=null,i=null,s=null,r=null,o=null,a=null,l=null,c=null}}};let a={},l=null,c={},h=null,u=!1,d=null,p=null,m=null,f=null,g=null,v=null,y=null,x=!1,b=null,T=null,A=null,M=null,E=null;const S=t.getParameter(t.MAX_COMBINED_TEXTURE_IMAGE_UNITS);let C=!1,N=0;const L=t.getParameter(t.VERSION);-1!==L.indexOf(\\\\\\\"WebGL\\\\\\\")?(N=parseFloat(/^WebGL (\\\\d)/.exec(L)[1]),C=N>=1):-1!==L.indexOf(\\\\\\\"OpenGL ES\\\\\\\")&&(N=parseFloat(/^OpenGL ES (\\\\d)/.exec(L)[1]),C=N>=2);let O=null,P={};const R=t.getParameter(t.SCISSOR_BOX),I=t.getParameter(t.VIEWPORT),F=(new _.a).fromArray(R),D=(new _.a).fromArray(I);function B(e,n,i){const s=new Uint8Array(4),r=t.createTexture();t.bindTexture(e,r),t.texParameteri(e,t.TEXTURE_MIN_FILTER,t.NEAREST),t.texParameteri(e,t.TEXTURE_MAG_FILTER,t.NEAREST);for(let e=0;ei||t.height>i)&&(s=i/Math.max(t.width,t.height)),s<1||!0===e){if(\\\\\\\"undefined\\\\\\\"!=typeof HTMLImageElement&&t instanceof HTMLImageElement||\\\\\\\"undefined\\\\\\\"!=typeof HTMLCanvasElement&&t instanceof HTMLCanvasElement||\\\\\\\"undefined\\\\\\\"!=typeof ImageBitmap&&t instanceof ImageBitmap){const i=e?On.g:Math.floor,r=i(s*t.width),o=i(s*t.height);void 0===p&&(p=m(r,o));const a=n?m(r,o):p;a.width=r,a.height=o;return a.getContext(\\\\\\\"2d\\\\\\\").drawImage(t,0,0,r,o),console.warn(\\\\\\\"THREE.WebGLRenderer: Texture has been resized from (\\\\\\\"+t.width+\\\\\\\"x\\\\\\\"+t.height+\\\\\\\") to 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extends J.a{constructor(t,e){super();const n=this,i=t.state;let s=null,r=1,o=null,a=\\\\\\\"local-floor\\\\\\\",l=null,c=null,h=null,u=null,d=null,m=!1,f=null,g=null,v=null,y=null,x=null,b=null;const w=[],T=new Map,A=new tt.a;A.layers.enable(1),A.viewport=new _.a;const E=new tt.a;E.layers.enable(2),E.viewport=new _.a;const S=[A,E],C=new In;C.layers.enable(1),C.layers.enable(2);let N=null,L=null;function O(t){const e=T.get(t.inputSource);e&&e.dispatchEvent({type:t.type,data:t.inputSource})}function P(){T.forEach((function(t,e){t.disconnect(e)})),T.clear(),N=null,L=null,i.bindXRFramebuffer(null),t.setRenderTarget(t.getRenderTarget()),h&&e.deleteFramebuffer(h),f&&e.deleteFramebuffer(f),g&&e.deleteRenderbuffer(g),v&&e.deleteRenderbuffer(v),h=null,f=null,g=null,v=null,d=null,u=null,c=null,s=null,z.stop(),n.isPresenting=!1,n.dispatchEvent({type:\\\\\\\"sessionend\\\\\\\"})}function R(t){const e=s.inputSources;for(let t=0;t0&&(e.alphaTest.value=n.alphaTest);const i=t.get(n).envMap;if(i){e.envMap.value=i,e.flipEnvMap.value=i.isCubeTexture&&!1===i.isRenderTargetTexture?-1:1,e.reflectivity.value=n.reflectivity,e.ior.value=n.ior,e.refractionRatio.value=n.refractionRatio;const s=t.get(i).__maxMipLevel;void 0!==s&&(e.maxMipLevel.value=s)}let s,r;n.lightMap&&(e.lightMap.value=n.lightMap,e.lightMapIntensity.value=n.lightMapIntensity),n.aoMap&&(e.aoMap.value=n.aoMap,e.aoMapIntensity.value=n.aoMapIntensity),n.map?s=n.map:n.specularMap?s=n.specularMap:n.displacementMap?s=n.displacementMap:n.normalMap?s=n.normalMap:n.bumpMap?s=n.bumpMap:n.roughnessMap?s=n.roughnessMap:n.metalnessMap?s=n.metalnessMap:n.alphaMap?s=n.alphaMap:n.emissiveMap?s=n.emissiveMap:n.clearcoatMap?s=n.clearcoatMap:n.clearcoatNormalMap?s=n.clearcoatNormalMap:n.clearcoatRoughnessMap?s=n.clearcoatRoughnessMap:n.specularIntensityMap?s=n.specularIntensityMap:n.specularTintMap?s=n.specularTintMap:n.transmissionMap?s=n.transmissionMap:n.thicknessMap&&(s=n.thicknessMap),void 0!==s&&(s.isWebGLRenderTarget&&(s=s.texture),!0===s.matrixAutoUpdate&&s.updateMatrix(),e.uvTransform.value.copy(s.matrix)),n.aoMap?r=n.aoMap:n.lightMap&&(r=n.lightMap),void 0!==r&&(r.isWebGLRenderTarget&&(r=r.texture),!0===r.matrixAutoUpdate&&r.updateMatrix(),e.uv2Transform.value.copy(r.matrix))}function n(e,n){e.roughness.value=n.roughness,e.metalness.value=n.metalness,n.roughnessMap&&(e.roughnessMap.value=n.roughnessMap),n.metalnessMap&&(e.metalnessMap.value=n.metalnessMap),n.emissiveMap&&(e.emissiveMap.value=n.emissiveMap),n.bumpMap&&(e.bumpMap.value=n.bumpMap,e.bumpScale.value=n.bumpScale,n.side===w.i&&(e.bumpScale.value*=-1)),n.normalMap&&(e.normalMap.value=n.normalMap,e.normalScale.value.copy(n.normalScale),n.side===w.i&&e.normalScale.value.negate()),n.displacementMap&&(e.displacementMap.value=n.displacementMap,e.displacementScale.value=n.displacementScale,e.displacementBias.value=n.displacementBias);t.get(n).envMap&&(e.envMapIntensity.value=n.envMapIntensity)}return{refreshFogUniforms:function(t,e){t.fogColor.value.copy(e.color),e.isFog?(t.fogNear.value=e.near,t.fogFar.value=e.far):e.isFogExp2&&(t.fogDensity.value=e.density)},refreshMaterialUniforms:function(t,i,s,r,o){i.isMeshBasicMaterial?e(t,i):i.isMeshLambertMaterial?(e(t,i),function(t,e){e.emissiveMap&&(t.emissiveMap.value=e.emissiveMap)}(t,i)):i.isMeshToonMaterial?(e(t,i),function(t,e){e.gradientMap&&(t.gradientMap.value=e.gradientMap);e.emissiveMap&&(t.emissiveMap.value=e.emissiveMap);e.bumpMap&&(t.bumpMap.value=e.bumpMap,t.bumpScale.value=e.bumpScale,e.side===w.i&&(t.bumpScale.value*=-1));e.normalMap&&(t.normalMap.value=e.normalMap,t.normalScale.value.copy(e.normalScale),e.side===w.i&&t.normalScale.value.negate());e.displacementMap&&(t.displacementMap.value=e.displacementMap,t.displacementScale.value=e.displacementScale,t.displacementBias.value=e.displacementBias)}(t,i)):i.isMeshPhongMaterial?(e(t,i),function(t,e){t.specular.value.copy(e.specular),t.shininess.value=Math.max(e.shininess,1e-4),e.emissiveMap&&(t.emissiveMap.value=e.emissiveMap);e.bumpMap&&(t.bumpMap.value=e.bumpMap,t.bumpScale.value=e.bumpScale,e.side===w.i&&(t.bumpScale.value*=-1));e.normalMap&&(t.normalMap.value=e.normalMap,t.normalScale.value.copy(e.normalScale),e.side===w.i&&t.normalScale.value.negate());e.displacementMap&&(t.displacementMap.value=e.displacementMap,t.displacementScale.value=e.displacementScale,t.displacementBias.value=e.displacementBias)}(t,i)):i.isMeshStandardMaterial?(e(t,i),i.isMeshPhysicalMaterial?function(t,e,i){n(t,e),t.ior.value=e.ior,e.sheen>0&&(t.sheenTint.value.copy(e.sheenTint).multiplyScalar(e.sheen),t.sheenRoughness.value=e.sheenRoughness);e.clearcoat>0&&(t.clearcoat.value=e.clearcoat,t.clearcoatRoughness.value=e.clearcoatRoughness,e.clearcoatMap&&(t.clearcoatMap.value=e.clearcoatMap),e.clearcoatRoughnessMap&&(t.clearcoatRoughnessMap.value=e.clearcoatRoughnessMap),e.clearcoatNormalMap&&(t.clearcoatNormalScale.value.copy(e.clearcoatNormalScale),t.clearcoatNormalMap.value=e.clearcoatNormalMap,e.side===w.i&&t.clearcoatNormalScale.value.negate()));e.transmission>0&&(t.transmission.value=e.transmission,t.transmissionSamplerMap.value=i.texture,t.transmissionSamplerSize.value.set(i.width,i.height),e.transmissionMap&&(t.transmissionMap.value=e.transmissionMap),t.thickness.value=e.thickness,e.thicknessMap&&(t.thicknessMap.value=e.thicknessMap),t.attenuationDistance.value=e.attenuationDistance,t.attenuationTint.value.copy(e.attenuationTint));t.specularIntensity.value=e.specularIntensity,t.specularTint.value.copy(e.specularTint),e.specularIntensityMap&&(t.specularIntensityMap.value=e.specularIntensityMap);e.specularTintMap&&(t.specularTintMap.value=e.specularTintMap)}(t,i,o):n(t,i)):i.isMeshMatcapMaterial?(e(t,i),function(t,e){e.matcap&&(t.matcap.value=e.matcap);e.bumpMap&&(t.bumpMap.value=e.bumpMap,t.bumpScale.value=e.bumpScale,e.side===w.i&&(t.bumpScale.value*=-1));e.normalMap&&(t.normalMap.value=e.normalMap,t.normalScale.value.copy(e.normalScale),e.side===w.i&&t.normalScale.value.negate());e.displacementMap&&(t.displacementMap.value=e.displacementMap,t.displacementScale.value=e.displacementScale,t.displacementBias.value=e.displacementBias)}(t,i)):i.isMeshDepthMaterial?(e(t,i),function(t,e){e.displacementMap&&(t.displacementMap.value=e.displacementMap,t.displacementScale.value=e.displacementScale,t.displacementBias.value=e.displacementBias)}(t,i)):i.isMeshDistanceMaterial?(e(t,i),function(t,e){e.displacementMap&&(t.displacementMap.value=e.displacementMap,t.displacementScale.value=e.displacementScale,t.displacementBias.value=e.displacementBias);t.referencePosition.value.copy(e.referencePosition),t.nearDistance.value=e.nearDistance,t.farDistance.value=e.farDistance}(t,i)):i.isMeshNormalMaterial?(e(t,i),function(t,e){e.bumpMap&&(t.bumpMap.value=e.bumpMap,t.bumpScale.value=e.bumpScale,e.side===w.i&&(t.bumpScale.value*=-1));e.normalMap&&(t.normalMap.value=e.normalMap,t.normalScale.value.copy(e.normalScale),e.side===w.i&&t.normalScale.value.negate());e.displacementMap&&(t.displacementMap.value=e.displacementMap,t.displacementScale.value=e.displacementScale,t.displacementBias.value=e.displacementBias)}(t,i)):i.isLineBasicMaterial?(function(t,e){t.diffuse.value.copy(e.color),t.opacity.value=e.opacity}(t,i),i.isLineDashedMaterial&&function(t,e){t.dashSize.value=e.dashSize,t.totalSize.value=e.dashSize+e.gapSize,t.scale.value=e.scale}(t,i)):i.isPointsMaterial?function(t,e,n,i){t.diffuse.value.copy(e.color),t.opacity.value=e.opacity,t.size.value=e.size*n,t.scale.value=.5*i,e.map&&(t.map.value=e.map);e.alphaMap&&(t.alphaMap.value=e.alphaMap);e.alphaTest>0&&(t.alphaTest.value=e.alphaTest);let s;e.map?s=e.map:e.alphaMap&&(s=e.alphaMap);void 0!==s&&(!0===s.matrixAutoUpdate&&s.updateMatrix(),t.uvTransform.value.copy(s.matrix))}(t,i,s,r):i.isSpriteMaterial?function(t,e){t.diffuse.value.copy(e.color),t.opacity.value=e.opacity,t.rotation.value=e.rotation,e.map&&(t.map.value=e.map);e.alphaMap&&(t.alphaMap.value=e.alphaMap);e.alphaTest>0&&(t.alphaTest.value=e.alphaTest);let n;e.map?n=e.map:e.alphaMap&&(n=e.alphaMap);void 0!==n&&(!0===n.matrixAutoUpdate&&n.updateMatrix(),t.uvTransform.value.copy(n.matrix))}(t,i):i.isShadowMaterial?(t.color.value.copy(i.color),t.opacity.value=i.opacity):i.isShaderMaterial&&(i.uniformsNeedUpdate=!1)}}}function Un(t={}){const e=void 0!==t.canvas?t.canvas:function(){const t=Object(It.b)(\\\\\\\"canvas\\\\\\\");return t.style.display=\\\\\\\"block\\\\\\\",t}(),n=void 0!==t.context?t.context:null,i=void 0!==t.alpha&&t.alpha,s=void 0===t.depth||t.depth,r=void 0===t.stencil||t.stencil,o=void 0!==t.antialias&&t.antialias,a=void 0===t.premultipliedAlpha||t.premultipliedAlpha,l=void 0!==t.preserveDrawingBuffer&&t.preserveDrawingBuffer,c=void 0!==t.powerPreference?t.powerPreference:\\\\\\\"default\\\\\\\",h=void 0!==t.failIfMajorPerformanceCaveat&&t.failIfMajorPerformanceCaveat;let u=null,d=null;const m=[],f=[];this.domElement=e,this.debug={checkShaderErrors:!0},this.autoClear=!0,this.autoClearColor=!0,this.autoClearDepth=!0,this.autoClearStencil=!0,this.sortObjects=!0,this.clippingPlanes=[],this.localClippingEnabled=!1,this.gammaFactor=2,this.outputEncoding=w.U,this.physicallyCorrectLights=!1,this.toneMapping=w.vb,this.toneMappingExposure=1;const g=this;let v=!1,y=0,x=0,b=null,S=-1,C=null;const N=new _.a,L=new _.a;let O=null,P=e.width,R=e.height,I=1,F=null,D=null;const B=new _.a(0,0,P,R),z=new _.a(0,0,P,R);let k=!1;const U=[],G=new T.a;let V=!1,H=!1,Y=null;const J=new A.a,Z=new p.a,K={background:null,fog:null,environment:null,overrideMaterial:null,isScene:!0};function tt(){return null===b?I:1}let et,nt,it,st,ot,at,lt,ct,ht,ut,dt,pt,_t,mt,ft,gt,vt,yt,xt,bt,wt,Tt,At,Mt=n;function Et(t,n){for(let i=0;i0&&function(t,e,n){if(null===Y){const t=!0===o&&!0===nt.isWebGL2;Y=new(t?Ht:Q)(1024,1024,{generateMipmaps:!0,type:null!==Tt.convert(w.M)?w.M:w.Zc,minFilter:w.Y,magFilter:w.ob,wrapS:w.n,wrapT:w.n})}const i=g.getRenderTarget();g.setRenderTarget(Y),g.clear();const s=g.toneMapping;g.toneMapping=w.vb,Xt(t,e,n),g.toneMapping=s,at.updateMultisampleRenderTarget(Y),at.updateRenderTargetMipmap(Y),g.setRenderTarget(i)}(s,e,n),i&&it.viewport(N.copy(i)),s.length>0&&Xt(s,e,n),r.length>0&&Xt(r,e,n),a.length>0&&Xt(a,e,n)}function Xt(t,e,n){const i=!0===e.isScene?e.overrideMaterial:null;for(let s=0,r=t.length;s0?f[f.length-1]:null,m.pop(),u=m.length>0?m[m.length-1]:null},this.getActiveCubeFace=function(){return y},this.getActiveMipmapLevel=function(){return x},this.getRenderTarget=function(){return b},this.setRenderTarget=function(t,e=0,n=0){b=t,y=e,x=n,t&&void 0===ot.get(t).__webglFramebuffer&&at.setupRenderTarget(t);let i=null,s=!1,r=!1;if(t){const n=t.texture;(n.isDataTexture3D||n.isDataTexture2DArray)&&(r=!0);const o=ot.get(t).__webglFramebuffer;t.isWebGLCubeRenderTarget?(i=o[e],s=!0):i=t.isWebGLMultisampleRenderTarget?ot.get(t).__webglMultisampledFramebuffer:o,N.copy(t.viewport),L.copy(t.scissor),O=t.scissorTest}else N.copy(B).multiplyScalar(I).floor(),L.copy(z).multiplyScalar(I).floor(),O=k;if(it.bindFramebuffer(Mt.FRAMEBUFFER,i)&&nt.drawBuffers){let e=!1;if(t)if(t.isWebGLMultipleRenderTargets){const n=t.texture;if(U.length!==n.length||U[0]!==Mt.COLOR_ATTACHMENT0){for(let t=0,e=n.length;t=0&&e<=t.width-i&&n>=0&&n<=t.height-s&&Mt.readPixels(e,n,i,s,Tt.convert(a),Tt.convert(l),r):console.error(\\\\\\\"THREE.WebGLRenderer.readRenderTargetPixels: readPixels from renderTarget failed. Framebuffer not complete.\\\\\\\")}finally{const t=null!==b?ot.get(b).__webglFramebuffer:null;it.bindFramebuffer(Mt.FRAMEBUFFER,t)}}},this.copyFramebufferToTexture=function(t,e,n=0){const i=Math.pow(2,-n),s=Math.floor(e.image.width*i),r=Math.floor(e.image.height*i);let o=Tt.convert(e.format);nt.isWebGL2&&(o===Mt.RGB&&(o=Mt.RGB8),o===Mt.RGBA&&(o=Mt.RGBA8)),at.setTexture2D(e,0),Mt.copyTexImage2D(Mt.TEXTURE_2D,n,o,t.x,t.y,s,r,0),it.unbindTexture()},this.copyTextureToTexture=function(t,e,n,i=0){const s=e.image.width,r=e.image.height,o=Tt.convert(n.format),a=Tt.convert(n.type);at.setTexture2D(n,0),Mt.pixelStorei(Mt.UNPACK_FLIP_Y_WEBGL,n.flipY),Mt.pixelStorei(Mt.UNPACK_PREMULTIPLY_ALPHA_WEBGL,n.premultiplyAlpha),Mt.pixelStorei(Mt.UNPACK_ALIGNMENT,n.unpackAlignment),e.isDataTexture?Mt.texSubImage2D(Mt.TEXTURE_2D,i,t.x,t.y,s,r,o,a,e.image.data):e.isCompressedTexture?Mt.compressedTexSubImage2D(Mt.TEXTURE_2D,i,t.x,t.y,e.mipmaps[0].width,e.mipmaps[0].height,o,e.mipmaps[0].data):Mt.texSubImage2D(Mt.TEXTURE_2D,i,t.x,t.y,o,a,e.image),0===i&&n.generateMipmaps&&Mt.generateMipmap(Mt.TEXTURE_2D),it.unbindTexture()},this.copyTextureToTexture3D=function(t,e,n,i,s=0){if(g.isWebGL1Renderer)return void console.warn(\\\\\\\"THREE.WebGLRenderer.copyTextureToTexture3D: can only be used with WebGL2.\\\\\\\");const r=t.max.x-t.min.x+1,o=t.max.y-t.min.y+1,a=t.max.z-t.min.z+1,l=Tt.convert(i.format),c=Tt.convert(i.type);let h;if(i.isDataTexture3D)at.setTexture3D(i,0),h=Mt.TEXTURE_3D;else{if(!i.isDataTexture2DArray)return void console.warn(\\\\\\\"THREE.WebGLRenderer.copyTextureToTexture3D: only supports THREE.DataTexture3D and THREE.DataTexture2DArray.\\\\\\\");at.setTexture2DArray(i,0),h=Mt.TEXTURE_2D_ARRAY}Mt.pixelStorei(Mt.UNPACK_FLIP_Y_WEBGL,i.flipY),Mt.pixelStorei(Mt.UNPACK_PREMULTIPLY_ALPHA_WEBGL,i.premultiplyAlpha),Mt.pixelStorei(Mt.UNPACK_ALIGNMENT,i.unpackAlignment);const u=Mt.getParameter(Mt.UNPACK_ROW_LENGTH),d=Mt.getParameter(Mt.UNPACK_IMAGE_HEIGHT),p=Mt.getParameter(Mt.UNPACK_SKIP_PIXELS),_=Mt.getParameter(Mt.UNPACK_SKIP_ROWS),m=Mt.getParameter(Mt.UNPACK_SKIP_IMAGES),f=n.isCompressedTexture?n.mipmaps[0]:n.image;Mt.pixelStorei(Mt.UNPACK_ROW_LENGTH,f.width),Mt.pixelStorei(Mt.UNPACK_IMAGE_HEIGHT,f.height),Mt.pixelStorei(Mt.UNPACK_SKIP_PIXELS,t.min.x),Mt.pixelStorei(Mt.UNPACK_SKIP_ROWS,t.min.y),Mt.pixelStorei(Mt.UNPACK_SKIP_IMAGES,t.min.z),n.isDataTexture||n.isDataTexture3D?Mt.texSubImage3D(h,s,e.x,e.y,e.z,r,o,a,l,c,f.data):n.isCompressedTexture?(console.warn(\\\\\\\"THREE.WebGLRenderer.copyTextureToTexture3D: untested support for compressed srcTexture.\\\\\\\"),Mt.compressedTexSubImage3D(h,s,e.x,e.y,e.z,r,o,a,l,f.data)):Mt.texSubImage3D(h,s,e.x,e.y,e.z,r,o,a,l,c,f),Mt.pixelStorei(Mt.UNPACK_ROW_LENGTH,u),Mt.pixelStorei(Mt.UNPACK_IMAGE_HEIGHT,d),Mt.pixelStorei(Mt.UNPACK_SKIP_PIXELS,p),Mt.pixelStorei(Mt.UNPACK_SKIP_ROWS,_),Mt.pixelStorei(Mt.UNPACK_SKIP_IMAGES,m),0===s&&i.generateMipmaps&&Mt.generateMipmap(h),it.unbindTexture()},this.initTexture=function(t){at.setTexture2D(t,0),it.unbindTexture()},this.resetState=function(){y=0,x=0,b=null,it.reset(),At.reset()},\\\\\\\"undefined\\\\\\\"!=typeof __THREE_DEVTOOLS__&&__THREE_DEVTOOLS__.dispatchEvent(new CustomEvent(\\\\\\\"observe\\\\\\\",{detail:this}))}const Gn={};var Vn,Hn,jn;!function(t){t.WEBGL=\\\\\\\"webgl\\\\\\\",t.WEBGL2=\\\\\\\"webgl2\\\\\\\",t.EXPERIMENTAL_WEBGL=\\\\\\\"experimental-webgl\\\\\\\",t.EXPERIMENTAL_WEBGL2=\\\\\\\"experimental-webgl2\\\\\\\"}(Vn||(Vn={}));class Wn{constructor(){this._next_renderer_id=0,this._renderers={},this._printDebug=!1,this._require_webgl2=!1,this._resolves=[]}setPrintDebug(t=!0){this._printDebug=t}printDebug(){return this._printDebug}printDebugMessage(t){this._printDebug&&console.warn(\\\\\\\"[Poly debug]\\\\\\\",t)}setRequireWebGL2(){this._require_webgl2||(this._require_webgl2=!0)}webgl2Available(){return void 0===this._webgl2_available&&(this._webgl2_available=this._set_webgl2_available()),this._webgl2_available}_set_webgl2_available(){const t=document.createElement(\\\\\\\"canvas\\\\\\\");return null!=(window.WebGL2RenderingContext&&t.getContext(Vn.WEBGL2))}createWebGLRenderer(t){const e=new Un(t);return this.printDebugMessage([\\\\\\\"create renderer:\\\\\\\",t]),e}createRenderingContext(t){let e=null;return this._require_webgl2&&(e=this._getRenderingContextWebgl(t,!0),e||console.warn(\\\\\\\"failed to create webgl2 context\\\\\\\")),e||(e=this._getRenderingContextWebgl(t,!1)),e}_getRenderingContextWebgl(t,e){let n;n=this.webgl2Available()||e?Vn.WEBGL2:Vn.WEBGL;let i=t.getContext(n,Gn);return i?this.printDebugMessage(`create gl context: ${n}.`):(n=e?Vn.EXPERIMENTAL_WEBGL2:Vn.EXPERIMENTAL_WEBGL,this.printDebugMessage(`create gl context: ${n}.`),i=t.getContext(n,Gn)),i}registerRenderer(t){if(t._polygon_id)throw new Error(\\\\\\\"render already registered\\\\\\\");t._polygon_id=this._next_renderer_id+=1,this._renderers[t._polygon_id]=t,1==Object.keys(this._renderers).length&&this.flush_callbacks_with_renderer(t)}deregisterRenderer(t){delete this._renderers[t._polygon_id],t.dispose()}firstRenderer(){const t=Object.keys(this._renderers)[0];return t?this._renderers[t]:null}renderers(){return Object.values(this._renderers)}flush_callbacks_with_renderer(t){let e;for(;e=this._resolves.pop();)e(t)}async waitForRenderer(){const t=this.firstRenderer();return t||new Promise(((t,e)=>{this._resolves.push(t)}))}renderTarget(t,e,n){return this.webgl2Available()?new Ht(t,e,n):new Q(t,e,n)}}class qn{constructor(){this._root=\\\\\\\"/three/js/libs\\\\\\\",this._BASISPath=\\\\\\\"/basis\\\\\\\",this._DRACOPath=\\\\\\\"/draco\\\\\\\",this._DRACOGLTFPath=\\\\\\\"/draco/gltf\\\\\\\"}root(){return this._root}setRoot(t){this._root=t}BASISPath(){return this._BASISPath}DRACOPath(){return this._DRACOPath}DRACOGLTFPath(){return this._DRACOGLTFPath}}class Xn{constructor(t){this.poly=t,this._node_register=new Map,this._node_register_categories=new Map,this._node_register_options=new Map}register(t,e,n){const i=t.context(),s=t.type().toLowerCase();let r=this._node_register.get(i);r||(r=new Map,this._node_register.set(i,r));if(r.get(s))console.error(`node ${i}/${s} already registered`);else{if(r.set(s,t),e){let t=this._node_register_categories.get(i);t||(t=new Map,this._node_register_categories.set(i,t)),t.set(s,e)}if(n){let t=this._node_register_options.get(i);t||(t=new Map,this._node_register_options.set(i,t)),t.set(s,n)}this.poly.pluginsRegister.registerNode(t)}}deregister(t,e){var n,i,s;null===(n=this._node_register.get(t))||void 0===n||n.delete(e),null===(i=this._node_register_categories.get(t))||void 0===i||i.delete(e),null===(s=this._node_register_options.get(t))||void 0===s||s.delete(e)}isRegistered(t,e){const n=this._node_register.get(t);return!!n&&null!=n.get(e)}registeredNodesForContextAndParentType(t,e){var n;if(this._node_register.get(t)){const i=[];return null===(n=this._node_register.get(t))||void 0===n||n.forEach(((t,e)=>{i.push(t)})),i.filter((n=>{var i;const s=n.type().toLowerCase(),r=null===(i=this._node_register_options.get(t))||void 0===i?void 0:i.get(s);if(r){const n=r.only,i=r.except,s=`${t}/${e}`;return n?n.includes(s):!i||!i.includes(s)}return!0}))}return[]}registeredNodes(t,e){const n={},i=this.registeredNodesForContextAndParentType(t,e);for(let t of i){n[t.type().toLowerCase()]=t}return n}registeredCategory(t,e){var n;return null===(n=this._node_register_categories.get(t))||void 0===n?void 0:n.get(e.toLowerCase())}map(){return this._node_register}}class Yn{constructor(t){this.poly=t,this._operation_register=new Map}register(t){const e=t.context();let n=this._operation_register.get(e);n||(n=new Map,this._operation_register.set(e,n));const i=t.type().toLowerCase();if(n.get(i)){const t=`operation ${e}/${i} already registered`;console.error(t)}else n.set(i,t),this.poly.pluginsRegister.registerOperation(t)}registeredOperationsForContextAndParentType(t,e){var n;if(this._operation_register.get(t)){const e=[];return null===(n=this._operation_register.get(t))||void 0===n||n.forEach(((t,n)=>{e.push(t)})),e}return[]}registeredOperation(t,e){const n=this._operation_register.get(t);if(n)return n.get(e.toLowerCase())}}class $n extends class{constructor(){this._methods_names=[],this._methods_by_name=new Map}register(t,e){this._methods_names.push(e),this._methods_by_name.set(e,t)}getMethod(t){return this._methods_by_name.get(t)}availableMethods(){return this._methods_names}}{getMethod(t){return super.getMethod(t)}}!function(t){t.BasisTextureLoader=\\\\\\\"BasisTextureLoader\\\\\\\",t.DRACOLoader=\\\\\\\"DRACOLoader\\\\\\\",t.EXRLoader=\\\\\\\"EXRLoader\\\\\\\",t.FBXLoader=\\\\\\\"FBXLoader\\\\\\\",t.GLTFLoader=\\\\\\\"GLTFLoader\\\\\\\",t.OBJLoader=\\\\\\\"OBJLoader\\\\\\\",t.PDBLoader=\\\\\\\"PDBLoader\\\\\\\",t.PLYLoader=\\\\\\\"PLYLoader\\\\\\\",t.RGBELoader=\\\\\\\"RGBELoader\\\\\\\",t.SVGLoader=\\\\\\\"SVGLoader\\\\\\\",t.STLLoader=\\\\\\\"STLLoader\\\\\\\",t.TTFLoader=\\\\\\\"TTFLoader\\\\\\\"}(Hn||(Hn={}));class Jn extends class{constructor(){this._module_by_name=new Map}register(t,e){this._module_by_name.set(t,e)}moduleNames(){const t=[];return this._module_by_name.forEach(((e,n)=>{t.push(n)})),t}module(t){return this._module_by_name.get(t)}}{}!function(t){t.GL_MESH_BASIC=\\\\\\\"GL_MESH_BASIC\\\\\\\",t.GL_MESH_LAMBERT=\\\\\\\"GL_MESH_LAMBERT\\\\\\\",t.GL_MESH_STANDARD=\\\\\\\"GL_MESH_STANDARD\\\\\\\",t.GL_MESH_PHONG=\\\\\\\"GL_MESH_PHONG\\\\\\\",t.GL_MESH_PHYSICAL=\\\\\\\"GL_MESH_PHYSICAL\\\\\\\",t.GL_PARTICLES=\\\\\\\"GL_PARTICLES\\\\\\\",t.GL_POINTS=\\\\\\\"GL_POINTS\\\\\\\",t.GL_LINE=\\\\\\\"GL_LINE\\\\\\\",t.GL_TEXTURE=\\\\\\\"GL_TEXTURE\\\\\\\",t.GL_VOLUME=\\\\\\\"GL_VOLUME\\\\\\\"}(jn||(jn={}));class Zn extends class{constructor(){this._controller_assembler_by_name=new Map}register(t,e,n){this._controller_assembler_by_name.set(t,{controller:e,assembler:n})}unregister(t){this._controller_assembler_by_name.delete(t)}}{assembler(t,e){const n=this._controller_assembler_by_name.get(e);if(n){return new(0,n.controller)(t,n.assembler)}return n}unregister(t){const e=this._controller_assembler_by_name.get(t);return super.unregister(t),e}}class Qn{constructor(t){this.poly=t,this._plugins_by_name=new Map,this._plugin_name_by_node_context_by_type=new Map,this._plugin_name_by_operation_context_by_type=new Map}register(t){this._current_plugin=t,this._plugins_by_name.set(t.name(),t),t.init(this.poly),this._current_plugin=void 0}pluginByName(t){return this._plugins_by_name.get(t)}registerNode(t){if(!this._current_plugin)return;const e=t.context(),n=t.type();let i=this._plugin_name_by_node_context_by_type.get(e);i||(i=new Map,this._plugin_name_by_node_context_by_type.set(e,i)),i.set(n,this._current_plugin.name())}registerOperation(t){if(!this._current_plugin)return;const e=t.context(),n=t.type();let i=this._plugin_name_by_operation_context_by_type.get(e);i||(i=new Map,this._plugin_name_by_operation_context_by_type.set(e,i)),i.set(n,this._current_plugin.name())}toJson(){const t={plugins:{},nodes:{},operations:{}};return this._plugins_by_name.forEach(((e,n)=>{t.plugins[n]=e.toJSON()})),this._plugin_name_by_node_context_by_type.forEach(((e,n)=>{t.nodes[n]={},e.forEach(((e,i)=>{t.nodes[n][i]=e}))})),this._plugin_name_by_operation_context_by_type.forEach(((e,n)=>{t.operations[n]={},e.forEach(((e,i)=>{t.operations[n][i]=e}))})),t}}class Kn{constructor(t){this._camera_types=[]}register(t){const e=t.type();this._camera_types.includes(e)||this._camera_types.push(e)}registeredTypes(){return this._camera_types}}var ti=n(86);class ei{constructor(){this._blobUrlsByStoredUrl=new Map,this._blobsByStoredUrl=new Map,this._blobDataByNodeId=new Map,this._globalBlobsByStoredUrl=new Map}registerBlobUrl(t){console.log(\\\\\\\"registerBlobUrl\\\\\\\",t,li.playerMode()),li.playerMode()&&this._blobUrlsByStoredUrl.set(t.storedUrl,t.blobUrl)}blobUrl(t){return this._blobUrlsByStoredUrl.get(t)}clear(){this._blobUrlsByStoredUrl.clear(),this._blobsByStoredUrl.clear(),this._blobDataByNodeId.clear()}_clearBlobForNode(t){const e=this._blobDataByNodeId.get(t.graphNodeId());e&&(this._blobsByStoredUrl.delete(e.storedUrl),this._blobUrlsByStoredUrl.delete(e.storedUrl)),this._blobDataByNodeId.delete(t.graphNodeId())}_assignBlobToNode(t,e){this._clearBlobForNode(t),this._blobDataByNodeId.set(t.graphNodeId(),{storedUrl:e.storedUrl,fullUrl:e.fullUrl})}async fetchBlobGlobal(t){if(li.playerMode())return{};try{if(this._blobUrlsByStoredUrl.get(t.storedUrl))return{};const e=li.assetUrls.remapedUrl(t.fullUrl),n=await fetch(e||t.fullUrl);if(n.ok){const e=await n.blob();return this._blobsByStoredUrl.set(t.storedUrl,e),this._blobUrlsByStoredUrl.set(t.storedUrl,this.createBlobUrl(e)),this._globalBlobsByStoredUrl.set(t.storedUrl,e),{blobData:{storedUrl:t.storedUrl,fullUrl:t.fullUrl}}}return{error:`failed to fetch ${t.fullUrl}`}}catch(e){return{error:`failed to fetch ${t.fullUrl}`}}}async fetchBlobForNode(t){if(li.playerMode())return{};try{if(this._blobUrlsByStoredUrl.get(t.storedUrl))return{};const e=li.assetUrls.remapedUrl(t.fullUrl),n=await fetch(e||t.fullUrl);if(n.ok){const e=await n.blob();return this._blobsByStoredUrl.set(t.storedUrl,e),this._blobUrlsByStoredUrl.set(t.storedUrl,this.createBlobUrl(e)),this._scene=t.node.scene(),this._assignBlobToNode(t.node,{storedUrl:t.storedUrl,fullUrl:t.fullUrl}),{blobData:{storedUrl:t.storedUrl,fullUrl:t.fullUrl}}}return{error:`failed to fetch ${t.fullUrl}`}}catch(e){return{error:`failed to fetch ${t.fullUrl}`}}}forEachBlob(t){this._blobDataByNodeId.forEach(((e,n)=>{if(this._scene){if(this._scene.graph.nodeFromId(n)){const{storedUrl:n}=e,i=this._blobsByStoredUrl.get(n);i&&t(i,n)}}}));let e=[];const n=new Map;this._globalBlobsByStoredUrl.forEach(((t,i)=>{e.push(i),n.set(i,t)})),e=e.sort(),e.forEach((e=>{const n=this._globalBlobsByStoredUrl.get(e);n&&t(n,e)}))}createBlobUrl(t){return Object(ti.a)(t)}}class ni{setMap(t){this._map=t}remapedUrl(t){if(!this._map)return;const e=t.split(\\\\\\\"?\\\\\\\"),n=e[0],i=e[1],s=this._map[n];return s?i?`${s}?${i}`:s:void 0}}var ii=n(91),si=n(83);class ri{markAsLoaded(t,e){this._sceneJsonImporterContructor=e,t()}load(t){if(!this._sceneJsonImporterContructor)return;const e=[];t.forEach(((t,n)=>{e.push(n)}));for(let n of e){const e=t.get(n);e&&(this._loadElement(n,e,this._sceneJsonImporterContructor),t.delete(n))}}async _loadElement(t,e,n){const{sceneData:i,assetsManifest:s,unzippedData:r}=e,o=Object.keys(s);for(let t of o){const e=r[`assets/${s[t]}`];if(!e)return void console.error(t,e);const n=new Blob([e]),i={storedUrl:t,blobUrl:li.blobs.createBlobUrl(n)};li.blobs.registerBlobUrl(i)}li.setPlayerMode(!0),li.libs.setRoot(null);const a=`${Math.random()}`.replace(\\\\\\\".\\\\\\\",\\\\\\\"_\\\\\\\"),l={Poly:`___POLY_polyConfig_configurePolygonjs_${a}`,scriptElementId:`___POLY_polyConfig_scriptElement_${a}`,loadSceneArgs:`___POLY_polyConfig_loadSceneArgs_${a}`};window[l.Poly]=li;const c={method:this._loadScene.bind(this),element:t,sceneData:i,sceneJsonImporterContructor:n};window[l.loadSceneArgs]=c;this._loadPolyConfig(l,r)||this._loadScene(t,i,n)}_loadPolyConfig(t,e){const n=e[si.a.POLY_CONFIG];if(!n)return!1;const i=this._createJsBlob(n,\\\\\\\"polyConfig\\\\\\\");let s=document.getElementById(t.scriptElementId);const r=[];return r.push(`import {configurePolygonjs, configureScene} from '${i}';`),r.push(`configurePolygonjs(window.${t.Poly});`),r.push(`window.${t.loadSceneArgs}.method(window.${t.loadSceneArgs}.element, window.${t.loadSceneArgs}.sceneData, window.${t.loadSceneArgs}.sceneJsonImporterContructor, configureScene);`),r.push(`delete window.${t.loadSceneArgs};`),s||(s=document.createElement(\\\\\\\"script\\\\\\\"),s.setAttribute(\\\\\\\"type\\\\\\\",\\\\\\\"module\\\\\\\"),s.text=r.join(\\\\\\\"\\\\n\\\\\\\"),document.body.append(s)),!0}async _loadScene(t,e,n,i){this._fadeOutPoster(t);const s=new n(e),r=await s.scene();i&&i(r);const o=r.mainCameraNode();if(!o)return void console.warn(\\\\\\\"no master camera found\\\\\\\");const a=o.createViewer(t);r.play(),t.scene=r,t.viewer=a}_fadeOutPoster(t){const e=t.firstElementChild;e&&(e.style.pointerEvents=\\\\\\\"none\\\\\\\",ii.a.fadeOut(e).then((()=>{var t;null===(t=e.parentElement)||void 0===t||t.removeChild(e)})))}_createJsBlob(t,e){const n=new Blob([t]),i=new File([n],`${e}.js`,{type:\\\\\\\"application/javascript\\\\\\\"});return Object(ti.a)(i)}}class oi{setPerformanceManager(t){this._performanceManager=t}performanceManager(){return this._performanceManager||window.performance}}class ai{constructor(){this.renderersController=new Wn,this.nodesRegister=new Xn(this),this.operationsRegister=new Yn(this),this.expressionsRegister=new $n,this.modulesRegister=new Jn,this.assemblersRegister=new Zn,this.pluginsRegister=new Qn(this),this.camerasRegister=new Kn(this),this.blobs=new ei,this.assetUrls=new ni,this.selfContainedScenesLoader=new ri,this.performance=new oi,this.scenesByUuid={},this._player_mode=!0,this._logger=null}static _instance_(){if(window.__POLYGONJS_POLY_INSTANCE__)return window.__POLYGONJS_POLY_INSTANCE__;{const t=new ai;return window.__POLYGONJS_POLY_INSTANCE__=t,window.__POLYGONJS_POLY_INSTANCE__}}setPlayerMode(t){this._player_mode=t}playerMode(){return this._player_mode}registerNode(t,e,n){this.nodesRegister.register(t,e,n)}registerOperation(t){this.operationsRegister.register(t)}registerCamera(t){this.camerasRegister.register(t)}registerPlugin(t){this.pluginsRegister.register(t)}registeredNodes(t,e){return this.nodesRegister.registeredNodes(t,e)}registeredOperation(t,e){return this.operationsRegister.registeredOperation(t,e)}registeredCameraTypes(){return this.camerasRegister.registeredTypes()}inWorkerThread(){return!1}desktopController(){}get libs(){return this._libs_controller=this._libs_controller||new qn}setEnv(t){this._env=t}env(){return this._env}setLogger(t){this._logger=t}get logger(){return this._logger}log(t,...e){var n;null===(n=this.logger)||void 0===n||n.log(t,...e)}warn(t,...e){var n;null===(n=this.logger)||void 0===n||n.warn(t,...e)}error(t,...e){var n;null===(n=this.logger)||void 0===n||n.error(t,...e)}}const li=ai._instance_();class ci{constructor(){this._started=!1,this._start_time=0,this._previous_timestamp=0,this._nodes_cook_data={},this._durations_by_name={},this._durations_count_by_name={}}profile(t,e){const n=li.performance.performanceManager(),i=n.now();e();const s=n.now()-i;console.log(`${t}: ${s}`)}start(){if(!this._started){this.reset(),this._started=!0;const t=li.performance.performanceManager();this._start_time=t.now(),this._nodes_cook_data={},this._previous_timestamp=this._start_time}}stop(){this.reset()}reset(){this._started=!1,this._start_time=null,this._durations_by_name={},this._durations_count_by_name={},this._nodes_cook_data={}}started(){return this._started}record_node_cook_data(t,e){const n=t.graphNodeId();null==this._nodes_cook_data[n]&&(this._nodes_cook_data[n]=new c(t)),this._nodes_cook_data[n].update_cook_data(e)}record(t){this.started()||this.start();const e=performance.now();return null==this._durations_by_name[t]&&(this._durations_by_name[t]=0),this._durations_by_name[t]+=e-this._previous_timestamp,null==this._durations_count_by_name[t]&&(this._durations_count_by_name[t]=0),this._durations_count_by_name[t]+=1,this._previous_timestamp=e}print(){this.print_node_cook_data(),this.print_recordings()}print_node_cook_data(){let t=Object.values(this._nodes_cook_data);t=f.sortBy(t,(t=>t.total_cook_time()));const e=t.map((t=>t.print_object()));console.log(\\\\\\\"--------------- NODES COOK TIME -----------\\\\\\\");const n=[],i=f.sortBy(e,(t=>-t.total_cook_time));for(let t of i)n.push(t);return console.table(n),e}print_recordings(){const t=b.clone(this._durations_by_name),e=b.clone(this._durations_count_by_name),n=[],i={};for(let e of Object.keys(t)){const s=t[e];n.push(s),null==i[s]&&(i[s]=[]),i[s].push(e)}n.sort(((t,e)=>t-e));const s=f.uniq(n);console.log(\\\\\\\"--------------- PERF RECORDINGS -----------\\\\\\\");const r=[];for(let t of s){const n=i[t];for(let i of n){const n=e[i],s={duration:t,name:i,count:n,duration_per_iteration:t/n};r.push(s)}}return console.table(r),r}}class hi{constructor(t){this.scene=t}setListener(t){this._events_listener?console.warn(\\\\\\\"scene already has a listener\\\\\\\"):(this._events_listener=t,this.run_on_add_listener_callbacks())}onAddListener(t){this._events_listener?t():(this._on_add_listener_callbacks=this._on_add_listener_callbacks||[],this._on_add_listener_callbacks.push(t))}run_on_add_listener_callbacks(){if(this._on_add_listener_callbacks){let t;for(;t=this._on_add_listener_callbacks.pop();)t();this._on_add_listener_callbacks=void 0}}get eventsListener(){return this._events_listener}dispatch(t,e,n){var i;null===(i=this._events_listener)||void 0===i||i.process_events(t,e,n)}emitAllowed(){return null!=this._events_listener&&this.scene.loadingController.loaded()&&this.scene.loadingController.autoUpdating()}}class ui{constructor(){this._params_by_id=new Map}register_param(t){this._params_by_id.set(t.graphNodeId(),t)}deregister_param(t){this._params_by_id.delete(t.graphNodeId())}regenerate_referring_expressions(t){t.nameController.graph_node.setSuccessorsDirty(t)}}class di{constructor(t){this.scene=t,this._lifecycle_on_create_allowed=!0}onCreateHookAllowed(){return this.scene.loadingController.loaded()&&this._lifecycle_on_create_allowed}onCreatePrevent(t){this._lifecycle_on_create_allowed=!1,t(),this._lifecycle_on_create_allowed=!0}}class pi{constructor(t){this.dispatcher=t,this._nodes_by_graph_node_id=new Map,this._require_canvas_event_listeners=!1,this._activeEventDatas=[]}registerNode(t){this._nodes_by_graph_node_id.set(t.graphNodeId(),t),this.updateViewerEventListeners()}unregisterNode(t){this._nodes_by_graph_node_id.delete(t.graphNodeId()),this.updateViewerEventListeners()}processEvent(t){0!=this._activeEventDatas.length&&this._nodes_by_graph_node_id.forEach((e=>e.processEvent(t)))}updateViewerEventListeners(){this._update_active_event_types(),this._require_canvas_event_listeners&&this.dispatcher.scene.viewersRegister.traverseViewers((t=>{t.eventsController.updateEvents(this)}))}activeEventDatas(){return this._activeEventDatas}_update_active_event_types(){const t=new Map;this._nodes_by_graph_node_id.forEach((e=>{if(e.parent()){const n=e.activeEventDatas();for(let e of n)t.set(e,!0)}})),this._activeEventDatas=[],t.forEach(((t,e)=>{this._activeEventDatas.push(e)}))}}var _i;!function(t){t.LOADED=\\\\\\\"sceneLoaded\\\\\\\",t.PLAY=\\\\\\\"play\\\\\\\",t.PAUSE=\\\\\\\"pause\\\\\\\",t.TICK=\\\\\\\"tick\\\\\\\"}(_i||(_i={}));const mi=[_i.LOADED,_i.PLAY,_i.PAUSE,_i.TICK];class fi extends pi{type(){return\\\\\\\"scene\\\\\\\"}acceptedEventTypes(){return mi.map((t=>`${t}`))}}class gi{constructor(t){this.scene=t,this._loading_state=!1,this._auto_updating=!0,this._first_object_loaded=!1}get LOADED_EVENT_CONTEXT(){return this._LOADED_EVENT_CONTEXT=this._LOADED_EVENT_CONTEXT||{event:new Event(_i.LOADED)}}markAsLoading(){this._set_loading_state(!0)}async markAsLoaded(){this.scene.missingExpressionReferencesController.resolve_missing_references(),await this._set_loading_state(!1),this.trigger_loaded_event()}trigger_loaded_event(){globalThis.Event&&this.scene.eventsDispatcher.sceneEventsController.processEvent(this.LOADED_EVENT_CONTEXT)}async _set_loading_state(t){this._loading_state=t,await this.set_auto_update(!this._loading_state)}isLoading(){return this._loading_state}loaded(){return!this._loading_state}autoUpdating(){return this._auto_updating}async set_auto_update(t){if(this._auto_updating!==t&&(this._auto_updating=t,this._auto_updating)){const t=this.scene.root();t&&await t.processQueue()}}on_first_object_loaded(){var t;if(!this._first_object_loaded){this._first_object_loaded=!0;const e=document.getElementById(\\\\\\\"scene_loading_container\\\\\\\");e&&(null===(t=e.parentElement)||void 0===t||t.removeChild(e))}}}const vi={EMPTY:\\\\\\\"\\\\\\\",UV:\\\\\\\"/COP/imageUv\\\\\\\",ENV_MAP:\\\\\\\"/COP/envMap\\\\\\\",CUBE_MAP:\\\\\\\"/COP/cubeCamera\\\\\\\"};class yi{constructor(t=\\\\\\\"\\\\\\\"){this._path=t,this._node=null}set_path(t){this._path=t}set_node(t){this._node=t}path(){return this._path}node(){return this._node}resolve(t){this._node=bi.findNode(t,this._path)}clone(){const t=new yi(this._path);return t.set_node(this._node),t}nodeWithContext(t,e){const n=this.node();if(!n)return void(null==e||e.set(`no node found at ${this.path()}`));const i=n.context();return i==t?n:void(null==e||e.set(`expected ${t} node, but got a ${i}`))}}class xi{constructor(t=\\\\\\\"\\\\\\\"){this._path=t,this._param=null}set_path(t){this._path=t}set_param(t){this._param=t}path(){return this._path}param(){return this._param}resolve(t){this._param=bi.findParam(t,this._path)}clone(){const t=new xi(this._path);return t.set_param(this._param),t}paramWithType(t,e){const n=this.param();if(n)return n.type()==t?n:void(null==e||e.set(`expected ${t} node, but got a ${n.type()}`));null==e||e.set(`no param found at ${this.path()}`)}}class bi{static split_parent_child(t){const e=t.split(bi.SEPARATOR).filter((t=>t.length>0)),n=e.pop();return{parent:e.join(bi.SEPARATOR),child:n}}static findNode(t,e,n){if(!t)return null;const i=e.split(bi.SEPARATOR).filter((t=>t.length>0)),s=i[0];let r=null;if(e[0]!==bi.SEPARATOR){switch(s){case bi.PARENT:null==n||n.add_path_element(s),r=t.parent();break;case bi.CURRENT:null==n||n.add_path_element(s),r=t;break;default:r=t.node(s),r&&(null==n||n.add_node(s,r))}if(null!=r&&i.length>1){const t=i.slice(1).join(bi.SEPARATOR);r=this.findNode(r,t,n)}return r}{const i=e.substr(1);r=this.findNode(t.root(),i,n)}return r}static findParam(t,e,n){if(!t)return null;const i=e.split(bi.SEPARATOR);if(1===i.length)return t.params.get(i[0]);{const e=i.slice(0,+(i.length-2)+1||void 0).join(bi.SEPARATOR),s=this.findNode(t,e,n);if(null!=s){const t=i[i.length-1],e=s.params.get(t);return n&&e&&n.add_node(t,e),e}return null}}static relativePath(t,e){const n=this.closestCommonParent(t,e);if(n){const i=this.distanceToParent(t,n);let s=\\\\\\\"\\\\\\\";if(i>0){let t=0;const e=[];for(;t++t.length>0)),o=e.path().split(bi.SEPARATOR).filter((t=>t.length>0)),a=[];let l=0;for(let t of o)r[l]||a.push(t),l++;return`${s}${a.join(bi.SEPARATOR)}`}return e.path()}static closestCommonParent(t,e){const n=this.parents(t).reverse().concat([t]),i=this.parents(e).reverse().concat([e]),s=Math.min(n.length,i.length);let r=null;for(let t=0;t{for(let n of e)this._is_reference_resolvable(n)&&t.push(n)}));for(let e of t)e.resolve_missing_dependencies()}_is_reference_resolvable(t){const e=t.absolute_path();if(e){if(this.scene.node(e))return!0;{const t=bi.split_parent_child(e);if(t.child){const e=this.scene.node(t.parent);if(e){if(e.params.get(t.child))return!0}}}}}check_for_missing_references(t){this._check_for_missing_references_for_node(t);for(let e of t.params.all)this._check_for_missing_references_for_param(e)}_check_for_missing_references_for_node(t){const e=t.graphNodeId();this.references.forEach(((n,i)=>{let s=!1;for(let e of n)e.matches_path(t.path())&&(s=!0,e.resolve_missing_dependencies());s&&this.references.delete(e)}))}_check_for_missing_references_for_param(t){const e=t.graphNodeId();this.references.forEach(((n,i)=>{let s=!1;for(let e of n)e.matches_path(t.path())&&(s=!0,e.resolve_missing_dependencies());s&&this.references.delete(e)}))}}class Ai{constructor(t){this.node=t,this._dirty_count=0,this._dirty=!0}dispose(){this._cached_successors=void 0,this._post_dirty_hooks=void 0,this._post_dirty_hook_names=void 0}isDirty(){return!0===this._dirty}dirtyTimestamp(){return this._dirty_timestamp}dirtyCount(){return this._dirty_count}addPostDirtyHook(t,e){this._post_dirty_hook_names=this._post_dirty_hook_names||[],this._post_dirty_hooks=this._post_dirty_hooks||[],this._post_dirty_hook_names.includes(t)?console.warn(`hook with name ${t} already exists`,this.node):(this._post_dirty_hook_names.push(t),this._post_dirty_hooks.push(e))}removePostDirtyHook(t){if(this._post_dirty_hook_names&&this._post_dirty_hooks){const e=this._post_dirty_hook_names.indexOf(t);e>=0&&(this._post_dirty_hook_names.splice(e,1),this._post_dirty_hooks.splice(e,1))}}hasHook(t){return!!this._post_dirty_hook_names&&this._post_dirty_hook_names.includes(t)}removeDirtyState(){this._dirty=!1}setForbiddenTriggerNodes(t){this._forbidden_trigger_nodes=t.map((t=>t.graphNodeId()))}setDirty(t,e){if(null==e&&(e=!0),t&&this._forbidden_trigger_nodes&&this._forbidden_trigger_nodes.includes(t.graphNodeId()))return;null==t&&(t=this.node),this._dirty=!0;const n=li.performance.performanceManager();this._dirty_timestamp=n.now(),this._dirty_count+=1,this.runPostDirtyHooks(t),!0===e&&this.setSuccessorsDirty(t)}runPostDirtyHooks(t){if(this._post_dirty_hooks){const e=this.node.scene().cooker;if(e.blocked)e.enqueue(this.node,t);else for(let e of this._post_dirty_hooks)e(t)}}setSuccessorsDirty(t){this._cached_successors=this._cached_successors||this.node.graphAllSuccessors();for(let e of this._cached_successors)e.dirtyController.setDirty(t,false)}clearSuccessorsCache(){this._cached_successors=void 0}clearSuccessorsCacheWithPredecessors(){this.clearSuccessorsCache();for(let t of this.node.graphAllPredecessors())t.dirtyController.clearSuccessorsCache()}}class Mi{constructor(t,e){this._scene=t,this._name=e,this._dirty_controller=new Ai(this),this._graph_node_id=t.graph.nextId(),t.graph.addNode(this),this._graph=t.graph}dispose(){this._dirty_controller.dispose(),this.graphRemove()}name(){return this._name}setName(t){this._name=t}scene(){return this._scene}graphNodeId(){return this._graph_node_id}get dirtyController(){return this._dirty_controller}setDirty(t){t=t||this,this._dirty_controller.setDirty(t)}setSuccessorsDirty(t){this._dirty_controller.setSuccessorsDirty(t)}removeDirtyState(){this._dirty_controller.removeDirtyState()}isDirty(){return this._dirty_controller.isDirty()}addPostDirtyHook(t,e){this._dirty_controller.addPostDirtyHook(t,e)}graphRemove(){this._graph.removeNode(this)}addGraphInput(t,e=!0){return this._graph.connect(t,this,e)}removeGraphInput(t){this._graph.disconnect(t,this)}graphDisconnectPredecessors(){this._graph.disconnectPredecessors(this)}graphDisconnectSuccessors(){this._graph.disconnectSuccessors(this)}graphPredecessorIds(){return this._graph.predecessorIds(this._graph_node_id)||[]}graphPredecessors(){return this._graph.predecessors(this)}graphSuccessors(){return this._graph.successors(this)}graphAllPredecessors(){return this._graph.allPredecessors(this)}graphAllSuccessors(){return this._graph.allSuccessors(this)}}var Ei;!function(t){t.CREATED=\\\\\\\"node_created\\\\\\\",t.DELETED=\\\\\\\"node_deleted\\\\\\\",t.NAME_UPDATED=\\\\\\\"node_name_update\\\\\\\",t.OVERRIDE_CLONABLE_STATE_UPDATE=\\\\\\\"node_override_clonable_state_update\\\\\\\",t.NAMED_OUTPUTS_UPDATED=\\\\\\\"node_named_outputs_updated\\\\\\\",t.NAMED_INPUTS_UPDATED=\\\\\\\"node_named_inputs_updated\\\\\\\",t.INPUTS_UPDATED=\\\\\\\"node_inputs_updated\\\\\\\",t.PARAMS_UPDATED=\\\\\\\"node_params_updated\\\\\\\",t.UI_DATA_POSITION_UPDATED=\\\\\\\"node_ui_data_position_updated\\\\\\\",t.UI_DATA_COMMENT_UPDATED=\\\\\\\"node_ui_data_comment_updated\\\\\\\",t.ERROR_UPDATED=\\\\\\\"node_error_updated\\\\\\\",t.FLAG_BYPASS_UPDATED=\\\\\\\"bypass_flag_updated\\\\\\\",t.FLAG_DISPLAY_UPDATED=\\\\\\\"display_flag_updated\\\\\\\",t.FLAG_OPTIMIZE_UPDATED=\\\\\\\"optimize_flag_updated\\\\\\\",t.SELECTION_UPDATED=\\\\\\\"selection_updated\\\\\\\"}(Ei||(Ei={}));class Si{constructor(t,e=0,n=0){this.node=t,this._position=new d.a,this._width=50,this._color=new D.a(.75,.75,.75),this._layout_vertical=!0,this._json={x:0,y:0},this._position.x=e,this._position.y=n}setComment(t){this._comment=t,this.node.emit(Ei.UI_DATA_COMMENT_UPDATED)}comment(){return this._comment}setColor(t){this._color=t}color(){return this._color}setLayoutHorizontal(){this._layout_vertical=!1}isLayoutVertical(){return this._layout_vertical}copy(t){this._position.copy(t.position()),this._color.copy(t.color())}position(){return this._position}setPosition(t,e=0){if(m.isNumber(t)){const n=t;this._position.set(n,e)}else this._position.copy(t);this.node.emit(Ei.UI_DATA_POSITION_UPDATED)}translate(t,e=!1){this._position.add(t),e&&(this._position.x=Math.round(this._position.x),this._position.y=Math.round(this._position.y)),this.node.emit(Ei.UI_DATA_POSITION_UPDATED)}toJSON(){return this._json.x=this._position.x,this._json.y=this._position.y,this._json.comment=this._comment,this._json}}class Ci{constructor(t){this.node=t,this._state=!0,this._hooks=null}onUpdate(t){this._hooks=this._hooks||[],this._hooks.push(t)}_on_update(){}set(t){this._state!=t&&(this._state=t,this._on_update(),this.runHooks())}active(){return this._state}toggle(){this.set(!this._state)}runHooks(){if(this._hooks)for(let t of this._hooks)t()}}class Ni extends Ci{constructor(){super(...arguments),this._state=!1}_on_update(){this.node.emit(Ei.FLAG_BYPASS_UPDATED),this.node.setDirty()}}class Li extends Ci{_on_update(){this.node.emit(Ei.FLAG_DISPLAY_UPDATED)}}class Oi extends Ci{constructor(){super(...arguments),this._state=!1}_on_update(){this.node.emit(Ei.FLAG_OPTIMIZE_UPDATED)}}class Pi{constructor(t){this.node=t}hasDisplay(){return!1}hasBypass(){return!1}hasOptimize(){return!1}}function Ri(t){return class extends t{constructor(){super(...arguments),this.display=new Li(this.node)}hasDisplay(){return!0}}}function Ii(t){return class extends t{constructor(){super(...arguments),this.bypass=new Ni(this.node)}hasBypass(){return!0}}}function Fi(t){return class extends t{constructor(){super(...arguments),this.optimize=new Oi(this.node)}hasOptimize(){return!0}}}class Di extends(Ri(Pi)){}class Bi extends(Ii(Pi)){}class zi extends(Ii(Ri(Pi))){}class ki extends(Fi(Ii(Pi))){}class Ui extends(Fi(Ii(Ri(Pi)))){}class Gi{constructor(t){this.node=t}}class Vi extends Gi{active(){return this.paramsTimeDependent()||this.inputsTimeDependent()}paramsTimeDependent(){const t=this.node.params.names;for(let e of t){const t=this.node.params.get(e);if(t&&t.states.timeDependent.active())return!0}return!1}inputsTimeDependent(){const t=this.node.io.inputs.inputs();for(let e of t)if(e&&e.states.timeDependent.active())return!0;return!1}forceTimeDependent(){const t=this.node.graphPredecessors().map((t=>t.graphNodeId())),e=this.node.scene().timeController.graphNode;t.includes(e.graphNodeId())||this.node.addGraphInput(e,!1)}unforceTimeDependent(){const t=this.node.scene().timeController.graphNode;this.node.removeGraphInput(t)}}class Hi extends Gi{set(t){this._message!=t&&(t&&li.warn(`[${this.node.path()}] error: '${t}'`),this._message=t,this.onUpdate())}message(){return this._message}clear(){this.set(void 0)}active(){return null!=this._message}onUpdate(){null!=this._message&&this.node._setContainer(null,`from error '${this._message}'`),this.node.emit(Ei.ERROR_UPDATED)}}class ji{constructor(t){this.node=t,this.timeDependent=new Vi(this.node),this.error=new Hi(this.node)}}class Wi{constructor(t){this.node=t,this._graph_node=new Mi(t.scene(),\\\\\\\"node_name_controller\\\\\\\")}dispose(){this._graph_node.dispose(),this._on_set_name_hooks=void 0,this._on_set_fullPath_hooks=void 0}get graph_node(){return this._graph_node}static base_name(t){let e=t.type();const n=e[e.length-1];return m.isNaN(parseInt(n))||(e+=\\\\\\\"_\\\\\\\"),`${e}1`}request_name_to_parent(t){const e=this.node.parent();e&&e.childrenAllowed()&&e.childrenController?e.childrenController.set_child_name(this.node,t):console.warn(\\\\\\\"request_name_to_parent failed, no parent found\\\\\\\")}setName(t){t!=this.node.name()&&this.request_name_to_parent(t)}update_name_from_parent(t){var e;if(this.node._set_core_name(t),this.post_setName(),this.run_post_set_fullPath_hooks(),this.node.childrenAllowed()){const t=null===(e=this.node.childrenController)||void 0===e?void 0:e.children();if(t)for(let e of t)e.nameController.run_post_set_fullPath_hooks()}this.node.lifecycle.creation_completed&&(this.node.scene().missingExpressionReferencesController.check_for_missing_references(this.node),this.node.scene().expressionsController.regenerate_referring_expressions(this.node)),this.node.scene().referencesController.notify_name_updated(this.node),this.node.emit(Ei.NAME_UPDATED)}add_post_set_name_hook(t){this._on_set_name_hooks=this._on_set_name_hooks||[],this._on_set_name_hooks.push(t)}add_post_set_fullPath_hook(t){this._on_set_fullPath_hooks=this._on_set_fullPath_hooks||[],this._on_set_fullPath_hooks.push(t)}post_setName(){if(this._on_set_name_hooks)for(let t of this._on_set_name_hooks)t()}run_post_set_fullPath_hooks(){if(this._on_set_fullPath_hooks)for(let t of this._on_set_fullPath_hooks)t()}}class qi{constructor(t){this.node=t,this._parent=null}parent(){return this._parent}setParent(t){t!=this.node.parentController.parent()&&(this._parent=t,this._parent&&this.node.nameController.request_name_to_parent(Wi.base_name(this.node)))}is_selected(){var t,e,n;return(null===(n=null===(e=null===(t=this.parent())||void 0===t?void 0:t.childrenController)||void 0===e?void 0:e.selection)||void 0===n?void 0:n.contains(this.node))||!1}path(t){const e=bi.SEPARATOR;if(null!=this._parent){if(this._parent==t)return this.node.name();{const n=this._parent.path(t);return n===e?n+this.node.name():n+e+this.node.name()}}return e}onSetParent(){if(this._on_set_parent_hooks)for(let t of this._on_set_parent_hooks)t()}findNode(t){if(null==t)return null;if(t==bi.CURRENT||t==bi.CURRENT_WITH_SLASH)return this.node;if(t==bi.PARENT||t==bi.PARENT_WITH_SLASH)return this.node.parent();const e=bi.SEPARATOR;if(t===e)return this.node.scene().root();if(t[0]===e)return t=t.substring(1,t.length),this.node.scene().root().node(t);if(t.split){const n=t.split(e);if(1===n.length){const t=n[0];return this.node.childrenController?this.node.childrenController.child_by_name(t):null}return bi.findNode(this.node,t)}return console.error(\\\\\\\"unexpected path given:\\\\\\\",t),null}}const Xi=/[, ]/,Yi=/\\\\d+$/,$i=/^0+/,Ji=/,| /,Zi=/^-?\\\\d+\\\\.?\\\\d*$/;var Qi,Ki,ts,es,ns,is,ss,rs;!function(t){t.TRUE=\\\\\\\"true\\\\\\\",t.FALSE=\\\\\\\"false\\\\\\\"}(Qi||(Qi={}));class os{static isBoolean(t){return t==Qi.TRUE||t==Qi.FALSE}static toBoolean(t){return t==Qi.TRUE}static isNumber(t){return Zi.test(t)}static tailDigits(t){const e=t.match(Yi);return e?parseInt(e[0]):0}static increment(t){const e=t.match(Yi);if(e){let n=e[0],i=\\\\\\\"\\\\\\\";const s=n.match($i);s&&(i=s[0]);const r=parseInt(n);0==r&&i.length>0&&\\\\\\\"0\\\\\\\"==i[i.length-1]&&(i=i.slice(0,-1));return`${t.substring(0,t.length-e[0].length)}${i}${r+1}`}return`${t}1`}static pluralize(t){return\\\\\\\"s\\\\\\\"!==t[t.length-1]?`${t}s`:t}static camelCase(t){const e=t.replace(/_/g,\\\\\\\" \\\\\\\").split(\\\\\\\" \\\\\\\");let n=\\\\\\\"\\\\\\\";for(let t=0;t0&&(i=this.upperFirst(i)),n+=i}return n}static upperFirst(t){return t[0].toUpperCase()+t.substr(1)}static titleize(t){return t.split(/\\\\s|_/g).map((t=>this.upperFirst(t))).join(\\\\\\\" \\\\\\\")}static precision(t,e=2){e=Math.max(e,0);const n=`${t}`.split(\\\\\\\".\\\\\\\");if(e<=0)return n[0];let i=n[1];if(void 0!==i)return i.length>e&&(i=i.substring(0,e)),i=i.padEnd(e,\\\\\\\"0\\\\\\\"),`${n[0]}.${i}`;{const n=`${t}.`,i=n.length+e;return n.padEnd(i,\\\\\\\"0\\\\\\\")}}static ensureFloat(t){const e=`${t}`;return e.indexOf(\\\\\\\".\\\\\\\")>=0?e:`${e}.0`}static ensureInteger(t){const e=`${t}`;return e.indexOf(\\\\\\\".\\\\\\\")>=0?e.split(\\\\\\\".\\\\\\\")[0]:e}static matchMask(t,e){if(\\\\\\\"*\\\\\\\"===e)return!0;if(t==e)return!0;const n=e.split(\\\\\\\" \\\\\\\");if(n.length>1){for(let e of n){if(this.matchMask(t,e))return!0}return!1}e=`^${e=e.split(\\\\\\\"*\\\\\\\").join(\\\\\\\".*\\\\\\\")}$`;return new RegExp(e).test(t)}static matchesOneMask(t,e){let n=!1;for(let i of e)os.matchMask(t,i)&&(n=!0);return n}static attribNames(t){const e=t.split(Xi),n=new Set;for(let t of e)t=t.trim(),t.length>0&&n.add(t);const i=new Array(n.size);let s=0;return n.forEach((t=>{i[s]=t,s++})),i}static indices(t){const e=t.split(Ji);if(e.length>1){const t=e.flatMap((t=>this.indices(t)));return f.uniq(t).sort(((t,e)=>t-e))}{const t=e[0];if(t){const e=\\\\\\\"-\\\\\\\";if(t.indexOf(e)>0){const n=t.split(e);return f.range(parseInt(n[0]),parseInt(n[1])+1)}{const e=parseInt(t);return m.isNumber(e)?[e]:[]}}return[]}}static escapeLineBreaks(t){return t.replace(/(\\\\r\\\\n|\\\\n|\\\\r)/gm,\\\\\\\"\\\\\\\\n\\\\\\\")}static sanitizeName(t){return t=(t=t.replace(/[^A-Za-z0-9]/g,\\\\\\\"_\\\\\\\")).replace(/^[0-9]/,\\\\\\\"_\\\\\\\")}}class as{constructor(t){this._node=t,this._node_ids=[],this._json=[]}node(){return this._node}nodes(){return this._node.scene().graph.nodesFromIds(this._node_ids)}contains(t){return this._node_ids.includes(t.graphNodeId())}equals(t){const e=t.map((t=>t.graphNodeId())).sort();return f.isEqual(e,this._node_ids)}clear(){this._node_ids=[],this.send_update_event()}set(t){this._node_ids=[],this.add(t)}add(t){const e=t.map((t=>t.graphNodeId()));this._node_ids=f.union(this._node_ids,e),this.send_update_event()}remove(t){const e=t.map((t=>t.graphNodeId()));this._node_ids=f.difference(this._node_ids,e),this.send_update_event()}send_update_event(){this._node.emit(Ei.SELECTION_UPDATED)}toJSON(){return this._json=this._json||[],this._json=this._node_ids.map((t=>t)),this._json}}!function(t){t.ALWAYS=\\\\\\\"always\\\\\\\",t.NEVER=\\\\\\\"never\\\\\\\",t.FROM_NODE=\\\\\\\"from_node\\\\\\\"}(Ki||(Ki={}));class ls{static unreachable(t){throw new Error(\\\\\\\"Didn't expect to get here\\\\\\\")}}class cs{constructor(t){this.inputs_controller=t,this._clone_required_states=[],this._overridden=!1}init_inputs_cloned_state(t){m.isArray(t)?this._cloned_states=t:this._cloned_state=t,this._update_clone_required_state()}override_cloned_state_allowed(){if(this._cloned_states)for(let t of this._cloned_states)if(t==Ki.FROM_NODE)return!0;return!!this._cloned_state&&this._cloned_state==Ki.FROM_NODE}clone_required_state(t){return this._clone_required_states[t]}clone_required_states(){return this._clone_required_states}_get_clone_required_state(t){const e=this._cloned_states;if(e){const n=e[t];if(null!=n)return this.clone_required_from_state(n)}return!this._cloned_state||this.clone_required_from_state(this._cloned_state)}clone_required_from_state(t){switch(t){case Ki.ALWAYS:return!0;case Ki.NEVER:return!1;case Ki.FROM_NODE:return!this._overridden}return ls.unreachable(t)}override_cloned_state(t){this._overridden=t,this._update_clone_required_state()}overriden(){return this._overridden}_update_clone_required_state(){if(this._cloned_states){const t=[];for(let e=0;e=1:e;if(m.isString(e)){if(n){if(n instanceof yi)return n.set_path(e);if(n instanceof xi)return n.set_path(e)}return e}m.isArray(e)&&this.params[t].fromArray(e)}setInput(t,e){this._inputs=this._inputs||[],this._inputs[t]=e}inputs_count(){return this._inputs?this._inputs.length:0}inputsController(){return this._inputs_controller=this._inputs_controller||new hs(this)}_init_cloned_states(){const t=this.operation.constructor.INPUT_CLONED_STATE;this.inputsController().init_inputs_cloned_state(t)}input_clone_required(t){return!this._inputs_controller||this._inputs_controller.clone_required(t)}override_input_clone_state(t){this.inputsController().override_cloned_state(t)}cook(t){return this.operation.cook(t,this.params)}}{constructor(t,e,n){super(t,e,n),this.operation=t,this.name=e,this.init_params=n,this._inputs=[],this._current_input_index=0,this._dirty=!0}add_input(t){super.setInput(this._current_input_index,t),this.increment_input_index()}increment_input_index(){this._current_input_index++}current_input_index(){return this._current_input_index}setDirty(){if(!this._dirty){this._compute_result=void 0;for(let t=0;t{n[i]=t[e]}));for(let i=0;i=0&&(this._children_by_type[n].splice(t,1),0==this._children_by_type[n].length&&delete this._children_by_type[n])}this.remove_from_children_and_grandchildren_by_context(t)}add_to_children_and_grandchildren_by_context(t){var e;const n=t.graphNodeId(),i=t.context();this._children_and_grandchildren_by_context[i]=this._children_and_grandchildren_by_context[i]||[],this._children_and_grandchildren_by_context[i].includes(n)||this._children_and_grandchildren_by_context[i].push(n);const s=this.node.parent();s&&s.childrenAllowed()&&(null===(e=s.childrenController)||void 0===e||e.add_to_children_and_grandchildren_by_context(t))}remove_from_children_and_grandchildren_by_context(t){var e;const n=t.graphNodeId(),i=t.context();if(this._children_and_grandchildren_by_context[i]){const t=this._children_and_grandchildren_by_context[i].indexOf(n);t>=0&&(this._children_and_grandchildren_by_context[i].splice(t,1),0==this._children_and_grandchildren_by_context[i].length&&delete this._children_and_grandchildren_by_context[i])}const s=this.node.parent();s&&s.childrenAllowed()&&(null===(e=s.childrenController)||void 0===e||e.remove_from_children_and_grandchildren_by_context(t))}nodesByType(t){const e=this._children_by_type[t]||[],n=this.node.scene().graph,i=[];for(let t of e){const e=n.nodeFromId(t);e&&i.push(e)}return i}child_by_name(t){return this._children[t]}has_children_and_grandchildren_with_context(t){return null!=this._children_and_grandchildren_by_context[t]}children(){return Object.values(this._children)}children_names(){return Object.keys(this._children).sort()}traverse_children(t){var e;for(let n of this.children())t(n),null===(e=n.childrenController)||void 0===e||e.traverse_children(t)}}class ps{constructor(t){this.node=t,this._creation_completed=!1}dispose(){this._on_child_add_hooks=void 0,this._on_child_remove_hooks=void 0,this._on_create_hooks=void 0,this._on_add_hooks=void 0,this._on_delete_hooks=void 0}set_creation_completed(){this._creation_completed||(this._creation_completed=!0)}get creation_completed(){return this.node.scene().loadingController.loaded()&&this._creation_completed}add_on_child_add_hook(t){this._on_child_add_hooks=this._on_child_add_hooks||[],this._on_child_add_hooks.push(t)}run_on_child_add_hooks(t){this.execute_hooks_with_child_node(this._on_child_add_hooks,t)}add_on_child_remove_hook(t){this._on_child_remove_hooks=this._on_child_remove_hooks||[],this._on_child_remove_hooks.push(t)}run_on_child_remove_hooks(t){this.execute_hooks_with_child_node(this._on_child_remove_hooks,t)}add_on_create_hook(t){this._on_create_hooks=this._on_create_hooks||[],this._on_create_hooks.push(t)}run_on_create_hooks(){this.execute_hooks(this._on_create_hooks)}add_on_add_hook(t){this._on_add_hooks=this._on_add_hooks||[],this._on_add_hooks.push(t)}run_on_add_hooks(){this.execute_hooks(this._on_add_hooks)}add_delete_hook(t){this._on_delete_hooks=this._on_delete_hooks||[],this._on_delete_hooks.push(t)}run_on_delete_hooks(){this.execute_hooks(this._on_delete_hooks)}execute_hooks(t){if(t){let e;for(e of t)e()}}execute_hooks_with_child_node(t,e){if(t){let n;for(n of t)n(e)}}}!function(t){t.ANIM=\\\\\\\"anim\\\\\\\",t.COP=\\\\\\\"cop\\\\\\\",t.EVENT=\\\\\\\"event\\\\\\\",t.GL=\\\\\\\"gl\\\\\\\",t.JS=\\\\\\\"js\\\\\\\",t.MANAGER=\\\\\\\"manager\\\\\\\",t.MAT=\\\\\\\"mat\\\\\\\",t.OBJ=\\\\\\\"obj\\\\\\\",t.POST=\\\\\\\"post\\\\\\\",t.ROP=\\\\\\\"rop\\\\\\\",t.SOP=\\\\\\\"sop\\\\\\\"}(ts||(ts={})),function(t){t.ANIM=\\\\\\\"animationsNetwork\\\\\\\",t.COP=\\\\\\\"copNetwork\\\\\\\",t.EVENT=\\\\\\\"eventsNetwork\\\\\\\",t.MAT=\\\\\\\"materialsNetwork\\\\\\\",t.POST=\\\\\\\"postProcessNetwork\\\\\\\",t.ROP=\\\\\\\"renderersNetwork\\\\\\\"}(es||(es={})),function(t){t.INPUT=\\\\\\\"subnetInput\\\\\\\",t.OUTPUT=\\\\\\\"subnetOutput\\\\\\\"}(ns||(ns={})),function(t){t.PERSPECTIVE=\\\\\\\"perspectiveCamera\\\\\\\",t.ORTHOGRAPHIC=\\\\\\\"orthographicCamera\\\\\\\"}(is||(is={})),function(t){t.ATTRIBUTE=\\\\\\\"attribute\\\\\\\"}(ss||(ss={})),function(t){t.DEVICE_ORIENTATION=\\\\\\\"cameraDeviceOrientationControls\\\\\\\",t.MAP=\\\\\\\"cameraMapControls\\\\\\\",t.ORBIT=\\\\\\\"cameraOrbitControls\\\\\\\",t.FIRST_PERSON=\\\\\\\"firstPersonControls\\\\\\\",t.PLAYER=\\\\\\\"playerControls\\\\\\\",t.MOBILE_JOYSTICK=\\\\\\\"mobileJoystickControls\\\\\\\"}(rs||(rs={}));const _s=[rs.DEVICE_ORIENTATION,rs.MAP,rs.ORBIT,rs.FIRST_PERSON,rs.MOBILE_JOYSTICK];class ms{constructor(t){this._node=t}set_node(t){this._node=t}node(){return this._node}set_content(t){this._content=t,this._post_set_content()}has_content(){return null!=this._content}content(){return this._content}_post_set_content(){}coreContent(){return this._content}coreContentCloned(){return this._content}infos(){return[]}}var fs=n(69);class gs extends K.a{constructor(){super(),this.type=\\\\\\\"Scene\\\\\\\",this.background=null,this.environment=null,this.fog=null,this.overrideMaterial=null,this.autoUpdate=!0,\\\\\\\"undefined\\\\\\\"!=typeof __THREE_DEVTOOLS__&&__THREE_DEVTOOLS__.dispatchEvent(new CustomEvent(\\\\\\\"observe\\\\\\\",{detail:this}))}copy(t,e){return super.copy(t,e),null!==t.background&&(this.background=t.background.clone()),null!==t.environment&&(this.environment=t.environment.clone()),null!==t.fog&&(this.fog=t.fog.clone()),null!==t.overrideMaterial&&(this.overrideMaterial=t.overrideMaterial.clone()),this.autoUpdate=t.autoUpdate,this.matrixAutoUpdate=t.matrixAutoUpdate,this}toJSON(t){const e=super.toJSON(t);return null!==this.fog&&(e.object.fog=this.fog.toJSON()),e}}gs.prototype.isScene=!0;var vs=n(49),ys=n(52),xs=n(42),bs=n(55),ws=n(62),Ts=n(24),As=n(35);const Ms=new p.a,Es=new p.a;class Ss extends K.a{constructor(){super(),this._currentLevel=0,this.type=\\\\\\\"LOD\\\\\\\",Object.defineProperties(this,{levels:{enumerable:!0,value:[]},isLOD:{value:!0}}),this.autoUpdate=!0}copy(t){super.copy(t,!1);const e=t.levels;for(let t=0,n=e.length;t0){let n,i;for(n=1,i=e.length;n0){Ms.setFromMatrixPosition(this.matrixWorld);const n=t.ray.origin.distanceTo(Ms);this.getObjectForDistance(n).raycast(t,e)}}update(t){const e=this.levels;if(e.length>1){Ms.setFromMatrixPosition(t.matrixWorld),Es.setFromMatrixPosition(this.matrixWorld);const n=Ms.distanceTo(Es)/t.zoom;let i,s;for(e[0].object.visible=!0,i=1,s=e.length;i=e[i].distance;i++)e[i-1].object.visible=!1,e[i].object.visible=!0;for(this._currentLevel=i-1;in?n:t}static fit01(t,e,n){return this.fit(t,0,1,e,n)}static fit(t,e,n,i,s){return(t-e)/(n-e)*(s-i)+i}static blend(t,e,n){return(1-n)*t+n*e}static degrees_to_radians(t){return t*sr}static radians_to_degrees(t){return t/sr}static deg2rad(t){return this.degrees_to_radians(t)}static rad2deg(t){return this.radians_to_degrees(t)}static rand(t){return m.isNumber(t)?this.randFloat(t):this.randVec2(t)}static round(t,e){const n=t/e;return(t<0?Math.ceil(n):Math.floor(n))*e}static highest_even(t){return 2*Math.ceil(.5*t)}static randFloat(t,e=136574){return this._vec.x=t,this._vec.y=e,this.randVec2(this._vec)}static randVec2(t){const e=(12.9898*t.x+78.233*t.y)%Math.PI;return this.fract(43758.5453*Math.sin(e))}static geodesic_distance(t,e){var n=this.deg2rad(t.lat),i=this.deg2rad(e.lat),s=this.deg2rad(e.lat-t.lat),r=this.deg2rad(e.lng-t.lng),o=Math.sin(s/2)*Math.sin(s/2)+Math.cos(n)*Math.cos(i)*Math.sin(r/2)*Math.sin(r/2);return 6371e3*(2*Math.atan2(Math.sqrt(o),Math.sqrt(1-o)))}static expand_triangle(t,e){t.getMidpoint(this._triangle_mid),this._triangle_mid_to_corner.copy(t.a).sub(this._triangle_mid),this._triangle_mid_to_corner.normalize().multiplyScalar(e),t.a.add(this._triangle_mid_to_corner),this._triangle_mid_to_corner.copy(t.b).sub(this._triangle_mid),this._triangle_mid_to_corner.normalize().multiplyScalar(e),t.b.add(this._triangle_mid_to_corner),this._triangle_mid_to_corner.copy(t.c).sub(this._triangle_mid),this._triangle_mid_to_corner.normalize().multiplyScalar(e),t.c.add(this._triangle_mid_to_corner)}static nearestPower2(t){return Math.pow(2,Math.ceil(Math.log(t)/Math.log(2)))}}rr.Easing=ir,rr.fract=t=>t-Math.floor(t),rr._vec={x:0,y:136574},rr._triangle_mid=new p.a,rr._triangle_mid_to_corner=new p.a;class or{constructor(t,e){this._core_geometry=t,this._index=e,this._geometry=this._core_geometry.geometry()}index(){return this._index}points(){return this._points=this._points||this._get_points()}applyMatrix4(t){for(let e of this.points())e.applyMatrix4(t)}_get_points(){var t;const e=(null===(t=this._geometry.index)||void 0===t?void 0:t.array)||[],n=3*this._index;return[new Qs(this._core_geometry,e[n+0]),new Qs(this._core_geometry,e[n+1]),new Qs(this._core_geometry,e[n+2])]}positions(){return this._positions=this._positions||this._get_positions()}_get_positions(){const t=this.points();return[t[0].position(),t[1].position(),t[2].position()]}triangle(){return this._triangle=this._triangle||this._get_triangle()}_get_triangle(){const t=this.positions();return new Ks.a(t[0],t[1],t[2])}deltas(){return this._deltas=this._deltas||this._get_deltas()}_get_deltas(){const t=this.positions();return[t[1].clone().sub(t[0]),t[2].clone().sub(t[0])]}area(){return this.triangle().getArea()}center(t){const e=this.positions();return t.x=(e[0].x+e[1].x+e[2].x)/3,t.y=(e[0].y+e[1].y+e[2].y)/3,t.z=(e[0].z+e[1].z+e[2].z)/3,t}random_position(t){let e=[rr.randFloat(t),rr.randFloat(6541*t)];return e[0]+e[1]>1&&(e[0]=1-e[0],e[1]=1-e[1]),this.positions()[0].clone().add(this.deltas()[0].clone().multiplyScalar(e[0])).add(this.deltas()[1].clone().multiplyScalar(e[1]))}attrib_value_at_position(t,e){const n=new p.a;this.triangle().getBarycoord(e,n);const i=n.toArray(),s=this._geometry.attributes[t].itemSize,r=this.points().map((e=>e.attribValue(t)));let o,a,l=0;switch(s){case 1:a=0;for(let t of r)a+=t*i[l],l++;o=a;break;default:for(let t of r){const e=t.multiplyScalar(i[l]);a?a.add(e):a=e,l++}o=a}return o}static interpolated_value(t,e,n,i){const s=[e.a,e.b,e.c],r=t.getAttribute(\\\\\\\"position\\\\\\\").array,o=s.map((t=>new p.a(r[3*t+0],r[3*t+1],r[3*t+2]))),a=i.itemSize,l=i.array;let c=[];switch(a){case 1:c=s.map((t=>l[t]));break;case 2:c=s.map((t=>new d.a(l[2*t+0],l[2*t+1])));break;case 3:c=s.map((t=>new p.a(l[3*t+0],l[3*t+1],l[3*t+2])))}const h=s.map(((t,e)=>n.distanceTo(o[e]))),u=f.sum([h[0]*h[1],h[0]*h[2],h[1]*h[2]]),_=[h[1]*h[2]/u,h[0]*h[2]/u,h[0]*h[1]/u];let m;switch(a){case 1:m=f.sum(s.map(((t,e)=>_[e]*c[e])));break;default:var g=s.map(((t,e)=>c[e].multiplyScalar(_[e])));m=null;for(let t of g)m?m.add(t):m=t}return m}}class ar{from_points(t){t=this._filter_points(t);const e=new S.a,n=new _r(e),i=t[0];if(null!=i){const s=i.geometry(),r=i.core_geometry(),o={};for(let e=0;et.indexedAttribValue(i)))),r={};s.forEach(((t,e)=>r[t]=e)),n.userDataAttribs()[i]=s;const o=[];for(let e of t){const t=r[e.indexedAttribValue(i)];o.push(t)}e.setAttribute(i,new C.c(o,1))}else{const n=l[i].itemSize,s=new Array(t.length*n);switch(n){case 1:for(let e=0;enew _r(t))),n=e[0].indexedAttributeNames(),i={};for(let t of n){const n={},s=[];for(let i of e){const e=i.points();for(let i of e){s.push(i);const e=i.indexedAttribValue(t);null!=n[e]?n[e]:n[e]=Object.keys(n).length}}const r=Object.keys(n);for(let e of s){const i=n[e.indexedAttribValue(t)];e.setAttribIndex(t,i)}i[t]=r}const s=hr(t),r=new _r(s);return Object.keys(i).forEach((t=>{const e=i[t];r.setIndexedAttributeValues(t,e)})),s&&delete s.userData.mergedUserData,s}}class _r{constructor(t){this._geometry=t}geometry(){return this._geometry}uuid(){return this._geometry.uuid}boundingBox(){return this._bounding_box=this._bounding_box||this._create_bounding_box()}_create_bounding_box(){if(this._geometry.computeBoundingBox(),this._geometry.boundingBox)return this._geometry.boundingBox}markAsInstance(){this._geometry.userData.isInstance=!0}static markedAsInstance(t){return!0===t.userData.isInstance}markedAsInstance(){return _r.markedAsInstance(this._geometry)}positionAttribName(){let t=\\\\\\\"position\\\\\\\";return this.markedAsInstance()&&(t=\\\\\\\"instancePosition\\\\\\\"),t}computeVertexNormals(){this._geometry.computeVertexNormals()}userDataAttribs(){const t=\\\\\\\"indexed_attrib_values\\\\\\\";return this._geometry.userData[t]=this._geometry.userData[t]||{}}indexedAttributeNames(){return Object.keys(this.userDataAttribs()||{})}userDataAttrib(t){return t=qs.remapName(t),this.userDataAttribs()[t]}isAttribIndexed(t){return t=qs.remapName(t),null!=this.userDataAttrib(t)}hasAttrib(t){return\\\\\\\"ptnum\\\\\\\"===t||(t=qs.remapName(t),null!=this._geometry.attributes[t])}attribType(t){return this.isAttribIndexed(t)?Bs.STRING:Bs.NUMERIC}static attribNames(t){return Object.keys(t.attributes)}attribNames(){return _r.attribNames(this._geometry)}static attribNamesMatchingMask(t,e){const n=os.attribNames(e),i=[];for(let e of this.attribNames(t))for(let t of n)os.matchMask(e,t)&&i.push(e);return f.uniq(i)}attribSizes(){const t={};for(let e of this.attribNames())t[e]=this._geometry.attributes[e].itemSize;return t}attribSize(t){let e;return t=qs.remapName(t),null!=(e=this._geometry.attributes[t])?e.itemSize:\\\\\\\"ptnum\\\\\\\"===t?1:0}setIndexedAttributeValues(t,e){this.userDataAttribs()[t]=e}setIndexedAttribute(t,e,n){this.setIndexedAttributeValues(t,e),this._geometry.setAttribute(t,new C.f(n,1))}addNumericAttrib(t,e=1,n=0){const i=[];let s=!1;if(m.isNumber(n)){for(let t=0;t1)if(m.isArray(n)){for(let t=0;tnew or(this,t)))}}var mr;_r._mesh_builder=new class extends ar{_filter_points(t){var e;const n=t[0];if(n){const i=null===(e=n.geometry().getIndex())||void 0===e?void 0:e.array;if(i){const e={};for(let n of t)e[n.index()]=n;const n=[],s=i.length;let r,o,a;for(let t=0;t{};class gr{static node(t,e){return t.node(e.name)}static clone(t){const e=t.clone(),n=t.uniforms;return n&&(e.uniforms=I.clone(n)),e}static add_user_data_render_hook(t,e){t.userData.POLY_render_hook=e}static apply_render_hook(t,e){if(e.userData){const n=e.userData.POLY_render_hook;if(n)return void(t.onBeforeRender=(e,i,s,r,o,a)=>{n(e,i,s,r,o,a,t)})}t.onBeforeRender=fr}static applyCustomMaterials(t,e){const n=e;if(n.customMaterials)for(let e of Object.keys(n.customMaterials)){const i=e,s=n.customMaterials[i];s&&(t[i]=s,s.needsUpdate=!0)}}static assign_custom_uniforms(t,e,n){const i=t;if(i.customMaterials)for(let t of Object.keys(i.customMaterials)){const s=t,r=i.customMaterials[s];r&&(r.uniforms[e].value=n)}}static init_custom_material_uniforms(t,e,n){const i=t;if(i.customMaterials)for(let t of Object.keys(i.customMaterials)){const s=t,r=i.customMaterials[s];r&&(r.uniforms[e]=r.uniforms[e]||n)}}}const vr=\\\\\\\"name\\\\\\\";class yr extends Xs{constructor(t,e){super(e),this._object=t,null==this._object.userData.attributes&&(this._object.userData.attributes={})}object(){return this._object}geometry(){return this._object.geometry}coreGeometry(){const t=this.geometry();return t?new _r(t):null}points(){var t;return(null===(t=this.coreGeometry())||void 0===t?void 0:t.points())||[]}pointsFromGroup(t){if(t){const e=os.indices(t);if(e){const t=this.points();return e.map((e=>t[e]))}return[]}return this.points()}static isInGroup(t,e){const n=t.trim();if(0==n.length)return!0;const i=n.split(\\\\\\\"=\\\\\\\"),s=i[0];if(\\\\\\\"@\\\\\\\"==s[0]){const t=s.substr(1);return i[1]==this.attribValue(e,t)}return!1}computeVertexNormals(){var t;null===(t=this.coreGeometry())||void 0===t||t.computeVertexNormals()}static _convert_array_to_vector(t){switch(t.length){case 1:return t[0];case 2:return new d.a(t[0],t[1]);case 3:return new p.a(t[0],t[1],t[2]);case 4:return new _.a(t[0],t[1],t[2],t[3])}}static addAttribute(t,e,n){if(m.isArray(n)){if(!this._convert_array_to_vector(n)){const t=\\\\\\\"attribute_value invalid\\\\\\\";throw console.error(t,n),new Error(t)}}const i=n,s=t.userData;s.attributes=s.attributes||{},s.attributes[e]=i}addAttribute(t,e){yr.addAttribute(this._object,t,e)}addNumericAttrib(t,e){this.addAttribute(t,e)}setAttribValue(t,e){this.addAttribute(t,e)}addNumericVertexAttrib(t,e,n){var i;null==n&&(n=qs.default_value(e)),null===(i=this.coreGeometry())||void 0===i||i.addNumericAttrib(t,e,n)}attributeNames(){return Object.keys(this._object.userData.attributes)}attribNames(){return this.attributeNames()}hasAttrib(t){return this.attributeNames().includes(t)}renameAttrib(t,e){const n=this.attribValue(t);null!=n?(this.addAttribute(e,n),this.deleteAttribute(t)):console.warn(`attribute ${t} not found`)}deleteAttribute(t){delete this._object.userData.attributes[t]}static attribValue(t,e,n=0,i){if(\\\\\\\"ptnum\\\\\\\"===e)return n;if(t.userData&&t.userData.attributes){const n=t.userData.attributes[e];if(null==n){if(e==vr)return t.name}else if(m.isArray(n)&&i)return i.fromArray(n),i;return n}return e==vr?t.name:void 0}static stringAttribValue(t,e,n=0){const i=this.attribValue(t,e,n);if(null!=i)return m.isString(i)?i:`${i}`}attribValue(t,e){return yr.attribValue(this._object,t,this._index,e)}stringAttribValue(t){return yr.stringAttribValue(this._object,t,this._index)}name(){return this.attribValue(vr)}humanType(){return Hs.CONSTRUCTOR_NAMES_BY_CONSTRUCTOR_NAME[this._object.constructor.name]}attribTypes(){const t={};for(let e of this.attribNames()){const n=this.attribType(e);null!=n&&(t[e]=n)}return t}attribType(t){const e=this.attribValue(t);return m.isString(e)?Bs.STRING:Bs.NUMERIC}attribSizes(){const t={};for(let e of this.attribNames()){const n=this.attribSize(e);null!=n&&(t[e]=n)}return t}attribSize(t){const e=this.attribValue(t);return null==e?null:qs.attribSizeFromValue(e)}clone(){return yr.clone(this._object)}static clone(t){const e=t.clone();var n=new Map,i=new Map;return yr.parallelTraverse(t,e,(function(t,e){n.set(e,t),i.set(t,e)})),e.traverse((function(e){const s=n.get(e),r=e;if(r.geometry){const t=s.geometry;r.geometry=_r.clone(t);const e=r.geometry;e.userData&&(e.userData=b.cloneDeep(t.userData))}if(r.material){r.material=s.material,gr.applyCustomMaterials(e,r.material);const t=r.material;null==t.color&&(t.color=new D.a(1,1,1))}t.userData&&(e.userData=b.cloneDeep(s.userData));const o=s;o.animations&&(e.animations=o.animations.map((t=>t.clone())));const a=e;if(a.isSkinnedMesh){var l=a,c=s,h=c.skeleton.bones;l.skeleton=c.skeleton.clone(),l.bindMatrix.copy(c.bindMatrix);const t=h.map((function(t){return i.get(t)}));l.skeleton.bones=t,l.bind(l.skeleton,l.bindMatrix)}})),e}static parallelTraverse(t,e,n){n(t,e);for(var i=0;i{const i=e.attributes[n];t[n]=i.itemSize})),t}objectAttributeSizesByName(){let t={};const e=this.firstCoreObject();if(e){const n=e.attribNames();for(let i of n){const n=e.attribSize(i);null!=n&&(t[i]=n)}}return t}pointAttributeTypesByName(){let t={};const e=this.firstGeometry();if(e){const n=new _r(e);Object.keys(e.attributes).forEach((e=>{t[e]=n.attribType(e)}))}return t}objectAttributeTypesByName(){let t={};const e=this.firstCoreObject();if(e)for(let n of e.attribNames())t[n]=e.attribType(n);return t}objectAttributeNames(){let t=[];const e=this.firstObject();return e&&(t=Object.keys(e.userData.attributes||{})),t}pointsCount(){return this._content?this._content.pointsCount():0}totalPointsCount(){return this._content?this._content.totalPointsCount():0}objectsData(){return this._content?this._content.objectsData():[]}boundingBox(){return this._content.boundingBox()}center(){return this._content.center()}size(){return this._content.size()}}};class br{constructor(t){this.node=t,this._callbacks=[],this._callbacks_tmp=[];const e=xr[t.context()];this._container=new e(this.node)}container(){return this._container}async compute(){var t,e;if(null===(e=null===(t=this.node.flags)||void 0===t?void 0:t.bypass)||void 0===e?void 0:e.active()){const t=await this.requestInputContainer(0)||this._container;return this.node.cookController.endCook(),t}return this.node.isDirty()?new Promise(((t,e)=>{this._callbacks.push(t),this.node.cookController.cookMain()})):this._container}async requestInputContainer(t){const e=this.node.io.inputs.input(t);return e?await e.compute():(this.node.states.error.set(`input ${t} required`),this.notifyRequesters(),null)}notifyRequesters(t){let e;for(this._callbacks_tmp=this._callbacks.slice(),this._callbacks.splice(0,this._callbacks.length),t||(t=this.node.containerController.container());e=this._callbacks_tmp.pop();)e(t);this.node.scene().cookController.removeNode(this.node)}}const wr=li.performance.performanceManager();class Tr{constructor(t){this.cookController=t,this._inputs_start=0,this._params_start=0,this._cook_start=0,this._cooksCount=0,this._data={inputsTime:0,paramsTime:0,cookTime:0}}cooksCount(){return this._cooksCount}data2(){return this._data}active(){return this.cookController.performanceRecordStarted()}recordInputsStart(){this.active()&&(this._inputs_start=wr.now())}recordInputsEnd(){this.active()&&(this._data.inputsTime=wr.now()-this._inputs_start)}recordParamsStart(){this.active()&&(this._params_start=wr.now())}recordParamsEnd(){this.active()&&(this._data.paramsTime=wr.now()-this._params_start)}recordCookStart(){this.active()&&(this._cook_start=wr.now())}recordCookEnd(){this.active()&&(this._data.cookTime=wr.now()-this._cook_start,this._cooksCount+=1)}}class Ar{constructor(t){this.node=t,this._cooking=!1,this._performanceController=new Tr(this),this._inputs_evaluation_required=!0,this._core_performance=this.node.scene().performance}performanceRecordStarted(){return this._core_performance.started()}disallowInputsEvaluation(){this._inputs_evaluation_required=!1}isCooking(){return!0===this._cooking}_start_cook_if_no_errors(t){if(this.node.states.error.active())this.endCook();else try{this._performanceController.recordCookStart(),this.node.cook(t)}catch(t){this.node.states.error.set(`node internal error: '${t}'.`),li.warn(t),this.endCook()}}async cookMain(){if(this.isCooking())return;let t;this._initCookingState(),this.node.states.error.clear(),this.node.scene().cookController.addNode(this.node),t=this._inputs_evaluation_required?await this._evaluateInputs():[],this.node.params.paramsEvalRequired()&&await this._evaluateParams(),this._start_cook_if_no_errors(t)}async cookMainWithoutInputs(){this.node.scene().cookController.addNode(this.node),this.isCooking()?li.warn(\\\\\\\"cook_main_without_inputs already cooking\\\\\\\",this.node.path()):(this._initCookingState(),this.node.states.error.clear(),this.node.params.paramsEvalRequired()&&await this._evaluateParams(),this._start_cook_if_no_errors([]))}endCook(t){this._finalizeCookPerformance();const e=this.node.dirtyController.dirtyTimestamp();null==e||e===this._cooking_dirty_timestamp?(this.node.removeDirtyState(),this._terminateCookProcess()):(li.log(\\\\\\\"COOK AGAIN\\\\\\\",e,this._cooking_dirty_timestamp,this.node.path()),this._cooking=!1,this.cookMain())}_initCookingState(){this._cooking=!0,this._cooking_dirty_timestamp=this.node.dirtyController.dirtyTimestamp()}_terminateCookProcess(){this.isCooking()&&(this._cooking=!1,this.node.containerController.notifyRequesters(),this._run_on_cook_complete_hooks())}async _evaluateInputs(){this._performanceController.recordInputsStart();let t=[];const e=this.node.io.inputs;this._inputs_evaluation_required&&(t=e.is_any_input_dirty()?await e.eval_required_inputs():await e.containers_without_evaluation());const n=e.inputs(),i=[];let s;for(let r=0;rt.graphNodeId()))}maxInputsCount(){return this.node.io.inputs.maxInputsCount()}inputIds(){return this.node.io.inputs.inputs().map((t=>null!=t?t.graphNodeId():void 0))}inputConnectionOutputIndices(){var t;return null===(t=this.node.io.connections.inputConnections())||void 0===t?void 0:t.map((t=>null!=t?t.output_index:void 0))}namedInputConnectionPoints(){return this.node.io.inputs.namedInputConnectionPoints().map((t=>t.toJSON()))}namedOutputConnectionPoints(){return this.node.io.outputs.namedOutputConnectionPoints().map((t=>t.toJSON()))}to_json_params_from_names(t,e=!1){return t.map((t=>this.node.params.get(t).graphNodeId()))}to_json_params(t=!1){return this.to_json_params_from_names(this.node.params.names,t)}}var Er,Sr;!function(t){t.BOOLEAN=\\\\\\\"boolean\\\\\\\",t.BUTTON=\\\\\\\"button\\\\\\\",t.COLOR=\\\\\\\"color\\\\\\\",t.FLOAT=\\\\\\\"float\\\\\\\",t.FOLDER=\\\\\\\"folder\\\\\\\",t.INTEGER=\\\\\\\"integer\\\\\\\",t.OPERATOR_PATH=\\\\\\\"operator_path\\\\\\\",t.PARAM_PATH=\\\\\\\"param_path\\\\\\\",t.NODE_PATH=\\\\\\\"node_path\\\\\\\",t.RAMP=\\\\\\\"ramp\\\\\\\",t.STRING=\\\\\\\"string\\\\\\\",t.VECTOR2=\\\\\\\"vector2\\\\\\\",t.VECTOR3=\\\\\\\"vector3\\\\\\\",t.VECTOR4=\\\\\\\"vector4\\\\\\\"}(Er||(Er={})),function(t){t.VISIBLE_UPDATED=\\\\\\\"param_visible_updated\\\\\\\",t.RAW_INPUT_UPDATED=\\\\\\\"raw_input_updated\\\\\\\",t.VALUE_UPDATED=\\\\\\\"param_value_updated\\\\\\\",t.EXPRESSION_UPDATED=\\\\\\\"param_expression_update\\\\\\\",t.ERROR_UPDATED=\\\\\\\"param_error_updated\\\\\\\",t.DELETED=\\\\\\\"param_deleted\\\\\\\"}(Sr||(Sr={}));const Cr=\\\\\\\"dependentOnFoundNode\\\\\\\",Nr=\\\\\\\"visibleIf\\\\\\\";var Lr,Or;!function(t){t.TYPESCRIPT=\\\\\\\"typescript\\\\\\\"}(Lr||(Lr={})),function(t){t.AUDIO=\\\\\\\"audio\\\\\\\",t.TEXTURE_IMAGE=\\\\\\\"texture_image\\\\\\\",t.TEXTURE_VIDEO=\\\\\\\"texture_video\\\\\\\",t.GEOMETRY=\\\\\\\"geometry\\\\\\\",t.FONT=\\\\\\\"font\\\\\\\",t.SVG=\\\\\\\"svg\\\\\\\",t.JSON=\\\\\\\"json\\\\\\\"}(Or||(Or={}));class Pr{constructor(t){this._param=t,this._programatic_visible_state=!0,this._callbackAllowed=!1,this._updateVisibilityAndRemoveDirtyBound=this.updateVisibilityAndRemoveDirty.bind(this),this._ui_data_dependency_set=!1}dispose(){var t;try{this._options.callback=void 0,this._options.callbackString=void 0}catch(t){}null===(t=this._visibility_graph_node)||void 0===t||t.dispose()}set(t){this._default_options=t,this._options=b.cloneDeep(this._default_options),this.post_set_options()}copy(t){this._default_options=b.cloneDeep(t.default()),this._options=b.cloneDeep(t.current()),this.post_set_options()}setOption(t,e){if(this._options[t]=e,this._param.components)for(let n of this._param.components)n.options.setOption(t,e)}post_set_options(){this._handleComputeOnDirty()}param(){return this._param}node(){return this._param.node}default(){return this._default_options}current(){return this._options}hasOptionsOverridden(){return!b.isEqual(this._options,this._default_options)}overriddenOptions(){const t={},e=Object.keys(this._options);for(let n of e)if(!b.isEqual(this._options[n],this._default_options[n])){const e=b.cloneDeep(this._options[n]);Object.assign(t,{[n]:e})}return t}overriddenOptionNames(){return Object.keys(this.overriddenOptions())}computeOnDirty(){return this._options.computeOnDirty||!1}_handleComputeOnDirty(){this.computeOnDirty()&&(this._computeOnDirty_callback_added||(this.param().addPostDirtyHook(\\\\\\\"computeOnDirty\\\\\\\",this._computeParam.bind(this)),this._computeOnDirty_callback_added=!0))}async _computeParam(){await this.param().compute()}hasCallback(){return null!=this._options.callback||null!=this._options.callbackString}allowCallback(){this._callbackAllowed=!0}executeCallback(){if(!this._callbackAllowed)return;if(!this.node())return;const t=this.getCallback();if(!t)return;if(!this.node().scene().loadingController.loaded())return;const e=this.param().parent_param;e?e.options.executeCallback():t(this.node(),this.param())}getCallback(){if(this.hasCallback())return this._options.callback=this._options.callback||this.createCallbackFromString()}createCallbackFromString(){const t=this._options.callbackString;if(t){const e=new Function(\\\\\\\"node\\\\\\\",\\\\\\\"scene\\\\\\\",\\\\\\\"window\\\\\\\",\\\\\\\"location\\\\\\\",t);return()=>{e(this.node(),this.node().scene(),null,null)}}}colorConversion(){return this._options.conversion}makesNodeDirtyWhenDirty(){let t;if(null!=this.param().parent_param)return!1;let e=!0;return null!=(t=this._options.cook)&&(e=t),e}fileBrowseOption(){return this._options.fileBrowse}fileBrowseAllowed(){return null!=this.fileBrowseOption()}fileBrowseType(){const t=this.fileBrowseOption();return t?t.type:null}separatorBefore(){return this._options.separatorBefore}separatorAfter(){return this._options.separatorAfter}isExpressionForEntities(){const t=this._options.expression;return t&&t.forEntities||!1}level(){return this._options.level||0}hasMenu(){return null!=this.menuOptions()||null!=this.menuStringOptions()}menuOptions(){return this._options.menu}menuStringOptions(){return this._options.menuString}menuEntries(){const t=this.menuOptions()||this.menuStringOptions();return t?t.entries:[]}isMultiline(){return!0===this._options.multiline}language(){return this._options.language}isCode(){return null!=this.language()}nodeSelectionOptions(){return this._options.nodeSelection}nodeSelectionContext(){const t=this.nodeSelectionOptions();if(t)return t.context}nodeSelectionTypes(){const t=this.nodeSelectionOptions();if(t)return t.types}dependentOnFoundNode(){return!(Cr in this._options)||this._options.dependentOnFoundNode}isSelectingParam(){return null!=this.paramSelectionOptions()}paramSelectionOptions(){return this._options.paramSelection}paramSelectionType(){const t=this.paramSelectionOptions();if(t){const e=t;if(!m.isBoolean(e))return e}}range(){return this._options.range||[0,1]}step(){return this._options.step}rangeLocked(){return this._options.rangeLocked||[!1,!1]}ensureInRange(t){const e=this.range();return t>=e[0]&&t<=e[1]?t:tObject.keys(t))).flat()):Object.keys(t);const n=this.param().node;return f.compact(e.map((t=>{const e=n.params.get(t);if(e)return e;console.error(`param ${t} not found as visibility condition for ${this.param().name()} in node ${this.param().node.type()}`)})))}setUiDataDependency(){if(this._ui_data_dependency_set)return;this._ui_data_dependency_set=!0;const t=this.visibilityPredecessors();if(t.length>0){this._visibility_graph_node=new Mi(this.param().scene(),\\\\\\\"param_visibility\\\\\\\");for(let e of t)this._visibility_graph_node.addGraphInput(e);this._visibility_graph_node.addPostDirtyHook(\\\\\\\"_update_visibility_and_remove_dirty\\\\\\\",this._updateVisibilityAndRemoveDirtyBound)}}updateVisibilityAndRemoveDirty(){this.updateVisibility(),this.param().removeDirtyState()}async updateVisibility(){const t=this._options.visibleIf;if(t){const e=this.visibilityPredecessors(),n=e.map((t=>{if(t.isDirty())return t.compute()}));if(this._programatic_visible_state=!1,await Promise.all(n),m.isArray(t))for(let n of t){e.filter((t=>t.value==n[t.name()])).length==e.length&&(this._programatic_visible_state=!0)}else{const n=e.filter((e=>e.value==t[e.name()]));this._programatic_visible_state=n.length==e.length}this.param().emit(Sr.VISIBLE_UPDATED)}}}class Rr{constructor(t){this.param=t,this._blocked_emit=!1,this._blocked_parent_emit=!1,this._count_by_event_name={}}emitAllowed(){return!0!==this._blocked_emit&&(!this.param.scene().loadingController.isLoading()&&this.param.scene().dispatchController.emitAllowed())}blockEmit(){if(this._blocked_emit=!0,this.param.isMultiple()&&this.param.components)for(let t of this.param.components)t.emitController.blockEmit();return!0}unblockEmit(){if(this._blocked_emit=!1,this.param.isMultiple()&&this.param.components)for(let t of this.param.components)t.emitController.unblockEmit();return!0}blockParentEmit(){return this._blocked_parent_emit=!0,!0}unblockParentEmit(){return this._blocked_parent_emit=!1,!0}incrementCount(t){this._count_by_event_name[t]=this._count_by_event_name[t]||0,this._count_by_event_name[t]+=1}eventsCount(t){return this._count_by_event_name[t]||0}emit(t){this.emitAllowed()&&(this.param.emit(t),null!=this.param.parent_param&&!0!==this._blocked_parent_emit&&this.param.parent_param.emit(t))}}class Ir{constructor(t){this.param=t}toJSON(){const t={name:this.param.name(),type:this.param.type(),raw_input:this.rawInput(),value:this.value(),value_pre_conversion:this.value_pre_conversion(),expression:this.expression(),graph_node_id:this.param.graphNodeId(),error_message:this.error_message(),is_visible:this.is_visible(),components:void 0};return this.param.isMultiple()&&this.param.components&&(t.components=this.param.components.map((t=>t.graphNodeId()))),t}rawInput(){return this.param.rawInputSerialized()}value(){return this.param.valueSerialized()}value_pre_conversion(){return this.param.valuePreConversionSerialized()}expression(){var t;return this.param.hasExpression()?null===(t=this.param.expressionController)||void 0===t?void 0:t.expression():void 0}error_message(){return this.param.states.error.message()}is_visible(){return this.param.options.isVisible()}}class Fr{constructor(t){this.param=t}active(){const t=this.param.scene().timeController.graphNode.graphNodeId();return this.param.graphPredecessorIds().includes(t)}}class Dr{constructor(t){this.param=t}set(t){this._message!=t&&(this._message=t,this._message&&li.warn(this.param.path(),this._message),this.param.emitController.emit(Sr.ERROR_UPDATED))}message(){return this._message}clear(){this.set(void 0)}active(){return null!=this._message}}class Br{constructor(t){this.param=t,this.timeDependent=new Fr(this.param),this.error=new Dr(this.param)}}class zr extends Mi{constructor(t,e,n){var i;super(t.scene(),\\\\\\\"MethodDependency\\\\\\\"),this.param=t,this.path_argument=e,this.decomposed_path=n,this._update_from_name_change_bound=this._update_from_name_change.bind(this),null===(i=t.expressionController)||void 0===i||i.registerMethodDependency(this),this.addPostDirtyHook(\\\\\\\"_update_from_name_change\\\\\\\",this._update_from_name_change_bound)}_update_from_name_change(t){if(t&&this.decomposed_path){const e=t;this.decomposed_path.update_from_name_change(e);const n=this.decomposed_path.to_path(),i=this.jsep_node;i&&(i.value=`${i.value}`.replace(`${this.path_argument}`,n),i.raw=i.raw.replace(`${this.path_argument}`,n)),this.param.expressionController&&this.param.expressionController.update_from_method_dependency_name_change()}}reset(){this.graphDisconnectPredecessors()}listen_for_name_changes(){if(this.jsep_node&&this.decomposed_path)for(let t of this.decomposed_path.named_nodes())if(t){const e=t;e.nameController&&this.addGraphInput(e.nameController.graph_node)}}set_jsep_node(t){this.jsep_node=t}set_resolved_graph_node(t){this.resolved_graph_node=t}set_unresolved_path(t){this.unresolved_path=t}static create(t,e,n,i){const s=m.isNumber(e),r=new zr(t,e,i);if(n)r.set_resolved_graph_node(n);else if(!s){const t=e;r.set_unresolved_path(t)}return r}}const kr=[];class Ur extends Mi{constructor(t,e){super(t,\\\\\\\"BaseParam\\\\\\\"),this._options=new Pr(this),this._emit_controller=new Rr(this),this._is_computing=!1,this._node=e,this.initialize_param()}get options(){return this._options=this._options||new Pr(this)}get emitController(){return this._emit_controller=this._emit_controller||new Rr(this)}get expressionController(){return this._expression_controller}get serializer(){return this._serializer=this._serializer||new Ir(this)}get states(){return this._states=this._states||new Br(this)}dispose(){var t,e;const n=this.graphPredecessors();for(let t of n)t instanceof zr&&t.dispose();null===(t=this._expression_controller)||void 0===t||t.dispose(),super.dispose(),null===(e=this._options)||void 0===e||e.dispose()}initialize_param(){}static type(){return Er.FLOAT}type(){return this.constructor.type()}isNumeric(){return!1}setName(t){super.setName(t)}get value(){return this._value}copy_value(t){t.type()==this.type()?this._copy_value(t):console.warn(`cannot copy value from ${t.type()} to ${this.type()}`)}_copy_value(t){throw\\\\\\\"abstract method param._copy_value\\\\\\\"}valuePreConversionSerialized(){}convert(t){return null}static are_raw_input_equal(t,e){return!1}is_raw_input_equal(t){return this.constructor.are_raw_input_equal(this._raw_input,t)}static are_values_equal(t,e){return!1}is_value_equal(t){return this.constructor.are_values_equal(this.value,t)}_clone_raw_input(t){return t}set(t){this._raw_input=this._clone_raw_input(this._prefilter_invalid_raw_input(t)),this.emitController.emit(Sr.RAW_INPUT_UPDATED),this.processRawInput()}_prefilter_invalid_raw_input(t){return t}defaultValue(){return this._default_value}isDefault(){return this._raw_input==this._default_value}rawInput(){return this._raw_input}processRawInput(){}async compute(){if(this.scene().loadingController.isLoading()&&console.warn(`param attempt to compute ${this.path()}`),this.isDirty()){if(this._is_computing)return new Promise(((t,e)=>{this._compute_resolves=this._compute_resolves||[],this._compute_resolves.push(t)}));if(this._is_computing=!0,await this.processComputation(),this._is_computing=!1,this._compute_resolves){let t;for(;t=this._compute_resolves.pop();)t()}}}async processComputation(){}setInitValue(t){this._default_value=this._clone_raw_input(this._prefilter_invalid_raw_input(t))}_setupNodeDependencies(t){var e,n;if(t?(this.options.allowCallback(),this.parent_param||(this.options.makesNodeDirtyWhenDirty()?null===(n=t.params.params_node)||void 0===n||n.addGraphInput(this,!1):this.dirtyController.addPostDirtyHook(\\\\\\\"run callback\\\\\\\",(async()=>{await this.compute(),this.options.executeCallback()})))):this._node&&(null===(e=this._node.params.params_node)||void 0===e||e.removeGraphInput(this)),this.components)for(let e of this.components)e._setupNodeDependencies(t)}get node(){return this._node}parent(){return this.node}set_parent_param(t){t.addGraphInput(this,!1),this._parent_param=t}get parent_param(){return this._parent_param}has_parent_param(){return null!=this._parent_param}path(){var t;return(null===(t=this.node)||void 0===t?void 0:t.path())+\\\\\\\"/\\\\\\\"+this.name()}pathRelativeTo(t){const e=bi.relativePath(t,this.node);return e.length>0?`${e}${bi.SEPARATOR}${this.name()}`:this.name()}emit(t){this.emitController.emitAllowed()&&(this.emitController.incrementCount(t),this.scene().dispatchController.dispatch(this,t))}get components(){return this._components}componentNames(){return kr}isMultiple(){return this.componentNames().length>0}initComponents(){}hasExpression(){return null!=this.expressionController&&this.expressionController.active()}toJSON(){return this.serializer.toJSON()}}var Gr=n(94),Vr=n.n(Gr);Vr.a.addUnaryOp(\\\\\\\"@\\\\\\\");Vr.a.addBinaryOp(\\\\\\\"**\\\\\\\",10);class Hr{constructor(){}parse_expression(t){try{this.reset(),this.node=Vr()(t)}catch(e){const n=`could not parse the expression '${t}' (error: ${e})`;this.error_message=n}}parse_expression_for_string_param(t){try{this.reset();const e=Hr.string_value_elements(t),n=[];for(let t=0;t{e.push(Wr.assign_attribute_line(t))})),e.join(\\\\\\\";\\\\n\\\\\\\")}return\\\\\\\"\\\\\\\"}assign_arrays_lines(){var t;if(this._attribute_names){const e=[];return null===(t=this._attribute_names)||void 0===t||t.forEach((t=>{e.push(Wr.assign_item_size_line(t)),e.push(Wr.assign_array_line(t))})),e.join(\\\\\\\";\\\\n\\\\\\\")}return\\\\\\\"\\\\\\\"}attribute_presence_check_line(){var t;if(this._attribute_names){const e=[];if(null===(t=this._attribute_names)||void 0===t||t.forEach((t=>{const n=Wr.var_attribute(t);e.push(n)})),e.length>0)return e.join(\\\\\\\" && \\\\\\\")}return\\\\\\\"true\\\\\\\"}add(t){this._attribute_names=this._attribute_names||new Set,this._attribute_names.add(t)}static assign_attribute_line(t){return`const ${this.var_attribute(t)} = entities[0].geometry().attributes['${t}']`}static assign_item_size_line(t){const e=this.var_attribute(t);return`const ${this.var_attribute_size(t)} = ${e}.itemSize`}static assign_array_line(t){const e=this.var_attribute(t);return`const ${this.var_array(t)} = ${e}.array`}static var_attribute(t){return`attrib_${t}`}static var_attribute_size(t){return`attrib_size_${t}`}static var_array(t){return`array_${t}`}var_attribute_size(t){return Wr.var_attribute_size(t)}var_array(t){return Wr.var_array(t)}}const qr={math_random:\\\\\\\"random\\\\\\\"},Xr=Object.keys(ir),Yr={};[\\\\\\\"abs\\\\\\\",\\\\\\\"acos\\\\\\\",\\\\\\\"acosh\\\\\\\",\\\\\\\"asin\\\\\\\",\\\\\\\"asinh\\\\\\\",\\\\\\\"atan\\\\\\\",\\\\\\\"atan2\\\\\\\",\\\\\\\"atanh\\\\\\\",\\\\\\\"ceil\\\\\\\",\\\\\\\"cos\\\\\\\",\\\\\\\"cosh\\\\\\\",\\\\\\\"exp\\\\\\\",\\\\\\\"expm1\\\\\\\",\\\\\\\"floor\\\\\\\",\\\\\\\"log\\\\\\\",\\\\\\\"log1p\\\\\\\",\\\\\\\"log2\\\\\\\",\\\\\\\"log10\\\\\\\",\\\\\\\"max\\\\\\\",\\\\\\\"min\\\\\\\",\\\\\\\"pow\\\\\\\",\\\\\\\"round\\\\\\\",\\\\\\\"sign\\\\\\\",\\\\\\\"sin\\\\\\\",\\\\\\\"sinh\\\\\\\",\\\\\\\"sqrt\\\\\\\",\\\\\\\"tan\\\\\\\",\\\\\\\"tanh\\\\\\\"].forEach((t=>{Yr[t]=`Math.${t}`})),[\\\\\\\"cbrt\\\\\\\",\\\\\\\"hypot\\\\\\\",\\\\\\\"log10\\\\\\\",\\\\\\\"trunc\\\\\\\"].forEach((t=>{Yr[t]=`Math.${t}`})),Object.keys(qr).forEach((t=>{const e=qr[t];Yr[t]=`Math.${e}`})),[\\\\\\\"fit\\\\\\\",\\\\\\\"fit01\\\\\\\",\\\\\\\"fract\\\\\\\",\\\\\\\"deg2rad\\\\\\\",\\\\\\\"rad2deg\\\\\\\",\\\\\\\"rand\\\\\\\",\\\\\\\"clamp\\\\\\\"].forEach((t=>{Yr[t]=`Core.Math.${t}`})),Xr.forEach((t=>{Yr[t]=`Core.Math.Easing.${t}`})),[\\\\\\\"precision\\\\\\\"].forEach((t=>{Yr[t]=`Core.String.${t}`}));const $r={if:class{static if(t){return`(${t[0]}) ? (${t[1]}) : (${t[2]})`}}.if},Jr={};[\\\\\\\"E\\\\\\\",\\\\\\\"LN2\\\\\\\",\\\\\\\"LN10\\\\\\\",\\\\\\\"LOG10E\\\\\\\",\\\\\\\"LOG2E\\\\\\\",\\\\\\\"PI\\\\\\\",\\\\\\\"SQRT1_2\\\\\\\",\\\\\\\"SQRT2\\\\\\\"].forEach((t=>{Jr[t]=`Math.${t}`}));const Zr={x:0,y:1,z:2,w:3,r:0,g:1,b:2};class Qr extends jr{constructor(t){super(t),this.param=t,this._attribute_requirements_controller=new Wr,this.methods=[],this.method_index=-1,this.method_dependencies=[],this.immutable_dependencies=[]}parse_tree(t){if(this.reset(),null==t.error_message){try{if(this._attribute_requirements_controller.reset(),t.node){const e=this.traverse_node(t.node);e&&!this.is_errored()&&(this.function_main_string=e)}else console.warn(\\\\\\\"no parsed_tree.node\\\\\\\")}catch(t){console.warn(`error in expression for param ${this.param.path()}`),console.warn(t)}if(this.function_main_string)try{this.function=new Function(\\\\\\\"Core\\\\\\\",\\\\\\\"param\\\\\\\",\\\\\\\"methods\\\\\\\",\\\\\\\"_set_error_from_error\\\\\\\",`\\\\n\\\\t\\\\t\\\\t\\\\t\\\\ttry {\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t${this.function_body()}\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t} catch(e) {\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t_set_error_from_error(e)\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\treturn null;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t}`)}catch(t){console.warn(t),this.set_error(\\\\\\\"cannot generate function\\\\\\\")}else this.set_error(\\\\\\\"cannot generate function body\\\\\\\")}else this.set_error(\\\\\\\"cannot parse expression\\\\\\\")}reset(){super.reset(),this.function_main_string=void 0,this.methods=[],this.method_index=-1,this.function=void 0,this.method_dependencies=[],this.immutable_dependencies=[]}function_body(){return this.param.options.isExpressionForEntities()?`\\\\n\\\\t\\\\t\\\\tconst entities = param.expressionController.entities();\\\\n\\\\t\\\\t\\\\tif(entities){\\\\n\\\\t\\\\t\\\\t\\\\treturn new Promise( async (resolve, reject)=>{\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tlet entity;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tconst entity_callback = param.expressionController.entity_callback();\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t${this._attribute_requirements_controller.assign_attributes_lines()}\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tif( ${this._attribute_requirements_controller.attribute_presence_check_line()} ){\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t${this._attribute_requirements_controller.assign_arrays_lines()}\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tfor(let index=0; index < entities.length; index++){\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tentity = entities[index];\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tresult = ${this.function_main_string};\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tentity_callback(entity, result);\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t}\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tresolve()\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t} else {\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tconst error = new Error('attribute not found')\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t_set_error_from_error(error)\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\treject(error)\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t}\\\\n\\\\t\\\\t\\\\t\\\\t})\\\\n\\\\t\\\\t\\\\t}\\\\n\\\\t\\\\t\\\\treturn []`:`\\\\n\\\\t\\\\t\\\\treturn new Promise( async (resolve, reject)=>{\\\\n\\\\t\\\\t\\\\t\\\\ttry {\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tconst value = ${this.function_main_string}\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tresolve(value)\\\\n\\\\t\\\\t\\\\t\\\\t} catch(e) {\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t_set_error_from_error(e)\\\\n\\\\t\\\\t\\\\t\\\\t\\\\treject()\\\\n\\\\t\\\\t\\\\t\\\\t}\\\\n\\\\t\\\\t\\\\t})\\\\n\\\\t\\\\t\\\\t`}eval_allowed(){return null!=this.function}eval_function(){if(this.function){this.clear_error();const t={Math:rr,String:os};return this.function(t,this.param,this.methods,this._set_error_from_error_bound)}}traverse_CallExpression(t){const e=t.arguments.map((t=>this.traverse_node(t))),n=t.callee.name;if(n){const i=$r[n];if(i)return i(e);const s=`${e.join(\\\\\\\", \\\\\\\")}`,r=Yr[n];if(r)return`${r}(${s})`;const o=li.expressionsRegister;if(o.getMethod(n)){const i=t.arguments[0],r=`return ${e[0]}`;let o,a=[];try{o=new Function(r),a=o()}catch{}return this._create_method_and_dependencies(n,a,i),`(await methods[${this.method_index}].processArguments([${s}]))`}{const t=`method not found (${n}), available methods are: ${o.availableMethods().join(\\\\\\\", \\\\\\\")}`;li.warn(t)}}this.set_error(`unknown method: ${n}`)}traverse_BinaryExpression(t){return`(${this.traverse_node(t.left)} ${t.operator} ${this.traverse_node(t.right)})`}traverse_LogicalExpression(t){return`(${this.traverse_node(t.left)} ${t.operator} ${this.traverse_node(t.right)})`}traverse_MemberExpression(t){return`${this.traverse_node(t.object)}.${this.traverse_node(t.property)}`}traverse_UnaryExpression(t){if(\\\\\\\"@\\\\\\\"===t.operator){let e,n,i=t.argument;switch(i.type){case\\\\\\\"Identifier\\\\\\\":e=i.name;break;case\\\\\\\"MemberExpression\\\\\\\":{const t=i,s=t.object,r=t.property;e=s.name,n=r.name;break}}if(e){if(e=qs.remapName(e),\\\\\\\"ptnum\\\\\\\"==e)return\\\\\\\"((entity != null) ? entity.index() : 0)\\\\\\\";{const t=this._attribute_requirements_controller.var_attribute_size(e),i=this._attribute_requirements_controller.var_array(e);if(this._attribute_requirements_controller.add(e),n){return`${i}[entity.index()*${t}+${Zr[n]}]`}return`${i}[entity.index()*${t}]`}}return console.warn(\\\\\\\"attribute not found\\\\\\\"),\\\\\\\"\\\\\\\"}return`${t.operator}${this.traverse_node(t.argument)}`}traverse_Literal(t){return`${t.raw}`}traverse_Identifier(t){if(\\\\\\\"$\\\\\\\"!=t.name[0])return t.name;{const e=t.name.substr(1),n=Jr[e];if(n)return n;const i=`traverse_Identifier_${e}`;if(this[i])return this[i]();this.set_error(`identifier unknown: ${t.name}`)}}traverse_Identifier_F(){return this.immutable_dependencies.push(this.param.scene().timeController.graphNode),\\\\\\\"param.scene().timeController.frame()\\\\\\\"}traverse_Identifier_T(){return this.immutable_dependencies.push(this.param.scene().timeController.graphNode),\\\\\\\"param.scene().timeController.time()\\\\\\\"}traverse_Identifier_OS(){return`'${this.param.node.name()}'`}traverse_Identifier_CH(){return`'${this.param.name()}'`}traverse_Identifier_CEX(){return this._method_centroid(\\\\\\\"x\\\\\\\")}traverse_Identifier_CEY(){return this._method_centroid(\\\\\\\"y\\\\\\\")}traverse_Identifier_CEZ(){return this._method_centroid(\\\\\\\"z\\\\\\\")}_method_centroid(t){const e=[0,`'${t}'`].join(\\\\\\\", \\\\\\\");return this._create_method_and_dependencies(\\\\\\\"centroid\\\\\\\",0),`(await methods[${this.method_index}].processArguments([${e}]))`}_create_method_and_dependencies(t,e,n){const i=li.expressionsRegister,s=i.getMethod(t);if(!s){const e=`method not found (${t}), available methods are: ${i.availableMethods().join(\\\\\\\", \\\\\\\")}`;return this.set_error(e),void li.warn(e)}const r=new s(this.param);if(this.method_index+=1,this.methods[this.method_index]=r,r.require_dependency()){const t=r.findDependency(e);t?(n&&t.set_jsep_node(n),this.method_dependencies.push(t)):n&&m.isString(e)&&this.param.scene().missingExpressionReferencesController.register(this.param,n,e)}}}class Kr extends jr{constructor(t){super(t),this.param=t}parse_tree(t){if(null==t.error_message&&t.node)try{return this.traverse_node(t.node)}catch(t){this.set_error(\\\\\\\"could not traverse tree\\\\\\\")}else this.set_error(\\\\\\\"cannot parse tree\\\\\\\")}traverse_CallExpression(t){const e=`${t.arguments.map((t=>this.traverse_node(t))).join(\\\\\\\", \\\\\\\")}`;return`${t.callee.name}(${e})`}traverse_UnaryExpression(t){return`${t.operator}${this.traverse_node(t.argument)}`}traverse_Identifier(t){return`${t.name}`}}class to{constructor(t){this.param=t,this.cyclic_graph_detected=!1,this.method_dependencies=[]}set_error(t){this.error_message=this.error_message||t}reset(){this.param.graphDisconnectPredecessors(),this.method_dependencies.forEach((t=>{t.reset()})),this.method_dependencies=[]}update(t){this.cyclic_graph_detected=!1,this.connect_immutable_dependencies(t),this.method_dependencies=t.method_dependencies,this.handle_method_dependencies(),this.listen_for_name_changes()}connect_immutable_dependencies(t){t.immutable_dependencies.forEach((t=>{if(0==this.cyclic_graph_detected&&0==this.param.addGraphInput(t))return this.cyclic_graph_detected=!0,this.set_error(\\\\\\\"cannot create expression, infinite graph detected\\\\\\\"),void this.reset()}))}handle_method_dependencies(){this.method_dependencies.forEach((t=>{0==this.cyclic_graph_detected&&this.handle_method_dependency(t)}))}handle_method_dependency(t){const e=t.resolved_graph_node;if(e&&!this.param.addGraphInput(e))return this.cyclic_graph_detected=!0,this.set_error(\\\\\\\"cannot create expression, infinite graph detected\\\\\\\"),void this.reset()}listen_for_name_changes(){this.method_dependencies.forEach((t=>{t.listen_for_name_changes()}))}}class eo{constructor(t){this.param=t,this.parse_completed=!1,this.parse_started=!1,this.parsed_tree=new Hr,this.function_generator=new Qr(this.param),this.dependencies_controller=new to(this.param)}parse_expression(t){if(this.parse_started)throw new Error(`parse in progress for param ${this.param.path()}`);this.parse_started=!0,this.parse_completed=!1,this.parsed_tree=this.parsed_tree||new Hr,this.reset(),this.param.type()==Er.STRING?this.parsed_tree.parse_expression_for_string_param(t):this.parsed_tree.parse_expression(t),this.function_generator.parse_tree(this.parsed_tree),null==this.function_generator.error_message()&&(this.dependencies_controller.update(this.function_generator),this.dependencies_controller.error_message?this.param.states.error.set(this.dependencies_controller.error_message):(this.parse_completed=!0,this.parse_started=!1))}async compute_function(){if(!this.compute_allowed())return new Promise(((t,e)=>{t(null)}));try{return await this.function_generator.eval_function()}catch(t){return}}reset(){this.parse_completed=!1,this.parse_started=!1,this.dependencies_controller.reset(),this.function_generator.reset()}is_errored(){return this.function_generator.is_errored()}error_message(){return this.function_generator.error_message()}compute_allowed(){return this.function_generator.eval_allowed()}update_from_method_dependency_name_change(){this.expression_string_generator=this.expression_string_generator||new Kr(this.param);const t=this.expression_string_generator.parse_tree(this.parsed_tree);t?this.param.set(t):console.warn(\\\\\\\"failed to regenerate expression\\\\\\\")}}class no{constructor(t){this.param=t}dispose(){this._resetMethodDependencies()}_resetMethodDependencies(){var t,e;null===(t=this._method_dependencies_by_graph_node_id)||void 0===t||t.forEach((t=>{t.dispose()})),null===(e=this._method_dependencies_by_graph_node_id)||void 0===e||e.clear()}registerMethodDependency(t){this._method_dependencies_by_graph_node_id=this._method_dependencies_by_graph_node_id||new Map,this._method_dependencies_by_graph_node_id.set(t.graphNodeId(),t)}active(){return null!=this._expression}expression(){return this._expression}is_errored(){return!!this._manager&&this._manager.is_errored()}error_message(){return this._manager?this._manager.error_message():null}requires_entities(){return this.param.options.isExpressionForEntities()}set_expression(t,e=!0){var n;this.param.scene().missingExpressionReferencesController.deregister_param(this.param),this.param.scene().expressionsController.deregister_param(this.param),this._expression!=t&&(this._resetMethodDependencies(),this._expression=t,this._expression?(this._manager=this._manager||new eo(this.param),this._manager.parse_expression(this._expression)):null===(n=this._manager)||void 0===n||n.reset(),e&&this.param.setDirty())}update_from_method_dependency_name_change(){this._manager&&this.active()&&this._manager.update_from_method_dependency_name_change()}async compute_expression(){if(this._manager&&this.active()){return await this._manager.compute_function()}}async compute_expression_for_entities(t,e){var n,i;this.set_entities(t,e),await this.compute_expression(),(null===(n=this._manager)||void 0===n?void 0:n.error_message())&&this.param.node.states.error.set(`expression evalution error: ${null===(i=this._manager)||void 0===i?void 0:i.error_message()}`),this.reset_entities()}compute_expression_for_points(t,e){return this.compute_expression_for_entities(t,e)}compute_expression_for_objects(t,e){return this.compute_expression_for_entities(t,e)}entities(){return this._entities}entity_callback(){return this._entity_callback}set_entities(t,e){this._entities=t,this._entity_callback=e}reset_entities(){this._entities=void 0,this._entity_callback=void 0}}class io extends Ur{isNumeric(){return!0}isDefault(){return this._raw_input==this._default_value}_prefilter_invalid_raw_input(t){return m.isArray(t)?t[0]:t}processRawInput(){this.states.error.clear();const t=this.convert(this._raw_input);null!=t?(this._expression_controller&&(this._expression_controller.set_expression(void 0,!1),this.emitController.emit(Sr.EXPRESSION_UPDATED)),t!=this._value&&(this._update_value(t),this.setSuccessorsDirty(this))):m.isString(this._raw_input)?(this._expression_controller=this._expression_controller||new no(this),this._raw_input!=this._expression_controller.expression()&&(this._expression_controller.set_expression(this._raw_input),this.emitController.emit(Sr.EXPRESSION_UPDATED))):this.states.error.set(`param input is invalid (${this.path()})`)}async processComputation(){var t;if((null===(t=this.expressionController)||void 0===t?void 0:t.active())&&!this.expressionController.requires_entities()){const t=await this.expressionController.compute_expression();if(this.expressionController.is_errored())this.states.error.set(`expression error: \\\\\\\"${this.expressionController.expression()}\\\\\\\" (${this.expressionController.error_message()})`);else{const e=this.convert(t);null!=e?(this.states.error.active()&&this.states.error.clear(),this._update_value(e)):this.states.error.set(`expression returns an invalid type (${t}) (${this.expressionController.expression()})`)}}}_update_value(t){this._value=t,this.parent_param&&this.parent_param.set_value_from_components(),this.options.executeCallback(),this.emitController.emit(Sr.VALUE_UPDATED),this.removeDirtyState()}}class so extends io{static type(){return Er.FLOAT}defaultValueSerialized(){return this._default_value}rawInputSerialized(){return this._raw_input}valueSerialized(){return this.value}_copy_value(t){this.set(t.valueSerialized())}_prefilter_invalid_raw_input(t){return m.isArray(t)?t[0]:m.isString(t)&&os.isNumber(t)?parseFloat(t):t}static are_raw_input_equal(t,e){return t==e}static are_values_equal(t,e){return t==e}static convert(t){if(m.isNumber(t))return t;if(m.isBoolean(t))return t?1:0;if(os.isNumber(t)){const e=parseFloat(t);if(m.isNumber(e))return e}return null}convert(t){const e=so.convert(t);return e?this.options.ensureInRange(e):e}}class ro extends Ur{constructor(){super(...arguments),this._components_contructor=so}get components(){return this._components}isNumeric(){return!0}isDefault(){for(let t of this.components)if(!t.isDefault())return!1;return!0}rawInput(){return this._components.map((t=>t.rawInput()))}rawInputSerialized(){return this._components.map((t=>t.rawInputSerialized()))}_copy_value(t){for(let e=0;ee))}}processRawInput(){const t=this.scene().cooker;t.block();const e=this.components;for(let t of e)t.emitController.blockParentEmit();const n=this._raw_input;let i=0;if(m.isArray(n))for(let t=0;t linear\\\\\\\",t.LINEAR_TO_GAMMA=\\\\\\\"linear -> gamma\\\\\\\",t.SRGB_TO_LINEAR=\\\\\\\"sRGB -> linear\\\\\\\",t.LINEAR_TO_SRGB=\\\\\\\"linear -> sRGB\\\\\\\"}(oo||(oo={}));oo.NONE,oo.GAMMA_TO_LINEAR,oo.LINEAR_TO_GAMMA,oo.SRGB_TO_LINEAR,oo.LINEAR_TO_SRGB;class ao{static set_hsv(t,e,n,i){t=Object(On.f)(t,1),e=Object(On.d)(e,0,1),n=Object(On.d)(n,0,1),i.setHSL(t,e*n/((t=(2-e)*n)<1?t:2-t),.5*t)}}const lo=[\\\\\\\"r\\\\\\\",\\\\\\\"g\\\\\\\",\\\\\\\"b\\\\\\\"];class co extends io{static type(){return Er.INTEGER}defaultValueSerialized(){return this._default_value}rawInputSerialized(){return this._raw_input}valueSerialized(){return this.value}_copy_value(t){this.set(t.valueSerialized())}_prefilter_invalid_raw_input(t){return m.isArray(t)?t[0]:m.isString(t)&&os.isNumber(t)?parseInt(t):t}static are_raw_input_equal(t,e){return t==e}static are_values_equal(t,e){return t==e}static convert(t){if(m.isNumber(t))return Math.round(t);if(m.isBoolean(t))return t?1:0;if(os.isNumber(t)){const e=parseInt(t);if(m.isNumber(e))return e}return null}convert(t){const e=co.convert(t);return e?this.options.ensureInRange(e):e}}class ho{constructor(){this._index=-1,this._path_elements=[],this._named_nodes=[],this._graph_node_ids=[],this._node_element_by_graph_node_id=new Map}reset(){this._index=-1,this._path_elements=[],this._named_nodes=[],this._graph_node_ids=[],this._node_element_by_graph_node_id.clear()}add_node(t,e){this._index+=1,t==e.name()&&(this._named_nodes[this._index]=e),this._graph_node_ids[this._index]=e.graphNodeId(),this._node_element_by_graph_node_id.set(e.graphNodeId(),t)}add_path_element(t){this._index+=1,this._path_elements[this._index]=t}named_graph_nodes(){return this._named_nodes}named_nodes(){const t=[];for(let e of this._named_nodes)if(e){const n=e;n.nameController&&t.push(n)}return t}update_from_name_change(t){this._named_nodes.map((t=>null==t?void 0:t.graphNodeId())).includes(t.graphNodeId())&&this._node_element_by_graph_node_id.set(t.graphNodeId(),t.name())}to_path(){const t=new Array(this._index);for(let e=0;e<=this._index;e++){const n=this._named_nodes[e];if(n){const i=this._node_element_by_graph_node_id.get(n.graphNodeId());i&&(t[e]=i)}else{const n=this._path_elements[e];n&&(t[e]=n)}}let e=t.join(bi.SEPARATOR);const n=e[0];return n&&(bi.NON_LETTER_PREFIXES.includes(n)||(e=`${bi.SEPARATOR}${e}`)),e}}class uo extends Ur{constructor(){super(...arguments),this.decomposed_path=new ho}}var po;!function(t){t.NODE=\\\\\\\"NODE\\\\\\\",t.PARAM=\\\\\\\"PARAM\\\\\\\"}(po||(po={}));class _o extends uo{constructor(){super(...arguments),this._found_node=null,this._found_node_with_expected_type=null,this._found_param=null,this._found_param_with_expected_type=null}static type(){return Er.OPERATOR_PATH}defaultValueSerialized(){return this._default_value}rawInputSerialized(){return`${this._raw_input}`}valueSerialized(){return`${this.value}`}_copy_value(t){this.set(t.valueSerialized())}static are_raw_input_equal(t,e){return t==e}static are_values_equal(t,e){return t==e}isDefault(){return this._value==this._default_value}setNode(t){this.set(t.path())}processRawInput(){this._value!=this._raw_input&&(this._value=this._raw_input,this.setDirty(),this.emitController.emit(Sr.VALUE_UPDATED))}async processComputation(){this.find_target()}find_target(){if(!this.node)return;const t=this._value;let e=null,n=null;const i=null!=t&&\\\\\\\"\\\\\\\"!==t,s=this.options.paramSelectionOptions()?po.PARAM:po.NODE;this.scene().referencesController.reset_reference_from_param(this),this.decomposed_path.reset(),i&&(s==po.PARAM?n=bi.findParam(this.node,t,this.decomposed_path):e=bi.findNode(this.node,t,this.decomposed_path));const r=s==po.PARAM?this._found_param:this._found_node,o=s==po.PARAM?n:e;if(this.scene().referencesController.set_named_nodes_from_param(this),e&&this.scene().referencesController.set_reference_from_param(this,e),(null==r?void 0:r.graphNodeId())!==(null==o?void 0:o.graphNodeId())){const t=this.options.dependentOnFoundNode();this._found_node&&t&&this.removeGraphInput(this._found_node),s==po.PARAM?(this._found_param=n,this._found_node=null):(this._found_node=e,this._found_param=null),e&&this._assign_found_node(e),n&&this._assign_found_param(n),this.options.executeCallback()}this.removeDirtyState()}_assign_found_node(t){const e=this.options.dependentOnFoundNode();this._is_node_expected_context(t)?this._is_node_expected_type(t)?(this._found_node_with_expected_type=t,e&&this.addGraphInput(t)):this.states.error.set(`node type is ${t.type()} but the params expects one of ${(this._expected_node_types()||[]).join(\\\\\\\", \\\\\\\")}`):this.states.error.set(`node context is ${t.context()} but the params expects a ${this._expected_context()}`)}_assign_found_param(t){this._is_param_expected_type(t)?this._found_param_with_expected_type=t:this.states.error.set(`param type is ${t.type()} but the params expects a ${this._expected_param_type()}`)}found_node(){return this._found_node}found_param(){return this._found_param}found_node_with_context(t){return this._found_node_with_expected_type}found_node_with_context_and_type(t,e){const n=this.found_node_with_context(t);if(n)if(m.isArray(e)){for(let t of e)if(n.type()==t)return n;this.states.error.set(`expected node type to be ${e.join(\\\\\\\", \\\\\\\")}, but was instead ${n.type()}`)}else{const t=e;if(n.type()==t)return n;this.states.error.set(`expected node type to be ${t}, but was instead ${n.type()}`)}}found_param_with_type(t){if(this._found_param_with_expected_type)return this._found_param_with_expected_type}found_node_with_expected_type(){return this._found_node_with_expected_type}_expected_context(){return this.options.nodeSelectionContext()}_is_node_expected_context(t){var e,n;const i=this._expected_context();if(null==i)return!0;return i==(null===(n=null===(e=t.parent())||void 0===e?void 0:e.childrenController)||void 0===n?void 0:n.context)}_expected_node_types(){return this.options.nodeSelectionTypes()}_expected_param_type(){return this.options.paramSelectionType()}_is_node_expected_type(t){const e=this._expected_node_types();return null==e||(null==e?void 0:e.includes(t.type()))}_is_param_expected_type(t){const e=this._expected_node_types();return null==e||e.includes(t.type())}notify_path_rebuild_required(t){this.decomposed_path.update_from_name_change(t);const e=this.decomposed_path.to_path();this.set(e)}notify_target_param_owner_params_updated(t){this.setDirty()}}var mo,fo=n(33),go=n(70);class vo{constructor(t=0,e=0){this._position=t,this._value=e}toJSON(){return{position:this._position,value:this._value}}get position(){return this._position}get value(){return this._value}copy(t){this._position=t.position,this._value=t.value}clone(){const t=new vo;return t.copy(this),t}is_equal(t){return this._position==t.position&&this._value==t.value}is_equal_json(t){return this._position==t.position&&this._value==t.value}from_json(t){this._position=t.position,this._value=t.value}static are_equal_json(t,e){return t.position==e.position&&t.value==e.value}static from_json(t){return new vo(t.position,t.value)}}!function(t){t.LINEAR=\\\\\\\"linear\\\\\\\"}(mo||(mo={}));class yo{constructor(t=mo.LINEAR,e=[]){this._interpolation=t,this._points=e,this._uuid=Object(On.h)()}get uuid(){return this._uuid}get interpolation(){return this._interpolation}get points(){return this._points}static from_json(t){const e=[];for(let n of t.points)e.push(vo.from_json(n));return new yo(t.interpolation,e)}toJSON(){return{interpolation:this._interpolation,points:this._points.map((t=>t.toJSON()))}}clone(){const t=new yo;return t.copy(this),t}copy(t){this._interpolation=t.interpolation;let e=0;for(let n of t.points){const t=this._points[e];t?t.copy(n):this._points.push(n.clone()),e+=1}}is_equal(t){if(this._interpolation!=t.interpolation)return!1;const e=t.points;if(this._points.length!=e.length)return!1;let n=0;for(let t of this._points){const i=e[n];if(!t.is_equal(i))return!1;n+=1}return!0}is_equal_json(t){if(this._interpolation!=t.interpolation)return!1;if(this._points.length!=t.points.length)return!1;let e=0;for(let n of this._points){const i=t.points[e];if(!n.is_equal_json(i))return!1;e+=1}return!0}static are_json_equal(t,e){if(t.interpolation!=e.interpolation)return!1;if(t.points.length!=e.points.length)return!1;let n=0;for(let i of t.points){const t=e.points[n];if(!vo.are_equal_json(i,t))return!1;n+=1}return!0}from_json(t){this._interpolation=t.interpolation;let e=0;for(let n of t.points){const t=this._points[e];t?t.from_json(n):this._points.push(vo.from_json(n)),e+=1}}}const xo=1024;class bo extends Ur{constructor(){super(...arguments),this._texture_data=new Uint8Array(3072),this._ramp_texture=new fo.a(this._texture_data,xo,1,w.ic)}static type(){return Er.RAMP}defaultValueSerialized(){return this._default_value instanceof yo?this._default_value.toJSON():this._default_value}_clone_raw_input(t){return t instanceof yo?t.clone():yo.from_json(t).toJSON()}rawInputSerialized(){return this._raw_input instanceof yo?this._raw_input.toJSON():yo.from_json(this._raw_input).toJSON()}valueSerialized(){return this.value.toJSON()}_copy_value(t){this.set(t.valueSerialized())}static are_raw_input_equal(t,e){return t instanceof yo?e instanceof yo?t.is_equal(e):t.is_equal_json(e):e instanceof yo?e.is_equal_json(t):yo.are_json_equal(t,e)}static are_values_equal(t,e){return t.is_equal(e)}isDefault(){return this._default_value instanceof yo?this.value.is_equal(this._default_value):this.value.is_equal_json(this._default_value)}processRawInput(){this._raw_input instanceof yo?this._value?this._value.copy(this._raw_input):this._value=this._raw_input:this._value?this._value.from_json(this._raw_input):this._value=yo.from_json(this._raw_input),this._reset_ramp_interpolant(),this._update_rampTexture(),this.options.executeCallback(),this.emitController.emit(Sr.VALUE_UPDATED),this.setSuccessorsDirty(this)}hasExpression(){return!1}_reset_ramp_interpolant(){this._ramp_interpolant=void 0}rampTexture(){return this._ramp_texture}_update_rampTexture(){this._update_ramp_texture_data(),this.rampTexture().needsUpdate=!0}_update_ramp_texture_data(){let t=0,e=0,n=0;for(var i=0;i<1024;i++)t=3*i,e=i/xo,n=this.value_at_position(e),this._texture_data[t]=255*n}static create_interpolant(t,e){const n=new Float32Array(1);return new go.a(t,e,1,n)}interpolant(){return this._ramp_interpolant=this._ramp_interpolant||this._create_interpolant()}_create_interpolant(){const t=this.value.points,e=f.sortBy(t,(t=>t.position)),n=new Float32Array(e.length),i=new Float32Array(e.length);let s=0;for(let t of e)n[s]=t.position,i[s]=t.value,s++;return bo.create_interpolant(n,i)}value_at_position(t){return this.interpolant().evaluate(t)[0]}}bo.DEFAULT_VALUE=new yo(mo.LINEAR,[new vo(0,0),new vo(1,1)]),bo.DEFAULT_VALUE_JSON=bo.DEFAULT_VALUE.toJSON();class wo extends Ur{static type(){return Er.STRING}defaultValueSerialized(){return this._default_value}_clone_raw_input(t){return`${t}`}rawInputSerialized(){return`${this._raw_input}`}valueSerialized(){return`${this.value}`}_copy_value(t){this.set(t.value)}static are_raw_input_equal(t,e){return t==e}static are_values_equal(t,e){return t==e}isDefault(){return this._raw_input==this._default_value}convert(t){return m.isString(t)?t:`${t}`}rawInput(){return this._raw_input}processRawInput(){this.states.error.clear(),this._value_elements(this._raw_input).length>=3?(this._expression_controller=this._expression_controller||new no(this),this._raw_input!=this._expression_controller.expression()&&(this._expression_controller.set_expression(this._raw_input),this.setDirty(),this.emitController.emit(Sr.EXPRESSION_UPDATED))):this._raw_input!=this._value&&(this._value=this._raw_input,this.removeDirtyState(),this.setSuccessorsDirty(this),this.emitController.emit(Sr.VALUE_UPDATED),this.options.executeCallback(),this._expression_controller&&(this._expression_controller.set_expression(void 0,!1),this.emitController.emit(Sr.EXPRESSION_UPDATED)))}async processComputation(){var t;if((null===(t=this.expressionController)||void 0===t?void 0:t.active())&&!this.expressionController.requires_entities()){const t=await this.expressionController.compute_expression();if(this.expressionController.is_errored())this.states.error.set(`expression error: ${this.expressionController.error_message()}`);else{const e=this.convert(t);null!=e?(this._value=e,this.emitController.emit(Sr.VALUE_UPDATED),this.options.executeCallback()):this.states.error.set(`expression returns an invalid type (${t})`),this.removeDirtyState()}}}_value_elements(t){return Hr.string_value_elements(t)}}const To=[\\\\\\\"x\\\\\\\",\\\\\\\"y\\\\\\\"];const Ao=[\\\\\\\"x\\\\\\\",\\\\\\\"y\\\\\\\",\\\\\\\"z\\\\\\\"];const Mo=[\\\\\\\"x\\\\\\\",\\\\\\\"y\\\\\\\",\\\\\\\"z\\\\\\\",\\\\\\\"w\\\\\\\"];const Eo={[Er.BOOLEAN]:class extends io{static type(){return Er.BOOLEAN}defaultValueSerialized(){return m.isString(this._default_value)?this._default_value:this.convert(this._default_value)||!1}rawInputSerialized(){return this._raw_input}valueSerialized(){return this.value}_copy_value(t){this.set(t.value)}static are_raw_input_equal(t,e){return t==e}static are_values_equal(t,e){return t==e}convert(t){if(m.isBoolean(t))return t;if(m.isNumber(t))return t>=1;if(m.isString(t)){if(os.isBoolean(t))return os.toBoolean(t);if(os.isNumber(t)){return parseFloat(t)>=1}}return null}},[Er.BUTTON]:class extends Ur{static type(){return Er.BUTTON}defaultValueSerialized(){return this._default_value}rawInputSerialized(){return this._raw_input}valueSerialized(){return this.value}_copy_value(t){}static are_raw_input_equal(t,e){return!0}static are_values_equal(t,e){return!0}async pressButton(){(this.node.isDirty()||this.node.cookController.isCooking())&&await this.node.compute(),this.options.executeCallback()}},[Er.COLOR]:class extends ro{constructor(){super(...arguments),this._value=new D.a,this._value_pre_conversion=new D.a,this._value_serialized_dirty=!1,this._value_serialized=[0,0,0],this._value_pre_conversion_serialized=[0,0,0],this._copied_value=[0,0,0]}static type(){return Er.COLOR}componentNames(){return lo}defaultValueSerialized(){return m.isArray(this._default_value)?this._default_value:this._default_value.toArray()}valueSerialized(){return this._update_value_serialized_if_required(),this._value_serialized}valuePreConversionSerialized(){return this._update_value_serialized_if_required(),this._value_pre_conversion_serialized}_copy_value(t){t.value.toArray(this._copied_value),this.set(this._copied_value)}_clone_raw_input(t){if(t instanceof D.a)return t.clone();{const e=[t[0],t[1],t[2]];return null==e[0]&&(e[0]=e[0]||0),null==e[1]&&(e[1]=e[1]||e[0]),null==e[2]&&(e[2]=e[2]||e[1]),e}}static are_raw_input_equal(t,e){return t instanceof D.a?e instanceof D.a?t.equals(e):t.r==e[0]&&t.g==e[1]&&t.b==e[2]:e instanceof D.a?t[0]==e.r&&t[1]==e.g&&t[2]==e.b:t[0]==e[0]&&t[1]==e[1]&&t[2]==e[2]}static are_values_equal(t,e){return t.equals(e)}initComponents(){super.initComponents(),this.r=this.components[0],this.g=this.components[1],this.b=this.components[2],this._value_serialized_dirty=!0}_update_value_serialized_if_required(){this._value_serialized_dirty&&(this._value_serialized[0]=this._value.r,this._value_serialized[1]=this._value.g,this._value_serialized[2]=this._value.b,this._value_pre_conversion_serialized[0]=this._value_pre_conversion.r,this._value_pre_conversion_serialized[1]=this._value_pre_conversion.g,this._value_pre_conversion_serialized[2]=this._value_pre_conversion.b)}valuePreConversion(){return this._value_pre_conversion}set_value_from_components(){this._value_pre_conversion.r=this.r.value,this._value_pre_conversion.g=this.g.value,this._value_pre_conversion.b=this.b.value,this._value.copy(this._value_pre_conversion);const t=this.options.colorConversion();if(null!=t&&t!=oo.NONE){switch(t){case oo.GAMMA_TO_LINEAR:return void this._value.convertGammaToLinear();case oo.LINEAR_TO_GAMMA:return void this._value.convertLinearToGamma();case oo.SRGB_TO_LINEAR:return void this._value.convertSRGBToLinear();case oo.LINEAR_TO_SRGB:return void this._value.convertLinearToSRGB()}ls.unreachable(t)}this._value_serialized_dirty=!0}},[Er.FLOAT]:so,[Er.FOLDER]:class extends Ur{static type(){return Er.FOLDER}defaultValueSerialized(){return this._default_value}rawInputSerialized(){return this._raw_input}valueSerialized(){return this.value}_copy_value(t){}static are_raw_input_equal(t,e){return!0}static are_values_equal(t,e){return!0}},[Er.INTEGER]:co,[Er.OPERATOR_PATH]:_o,[Er.PARAM_PATH]:class extends uo{static type(){return Er.PARAM_PATH}initialize_param(){this._value=new xi}defaultValueSerialized(){return this._default_value}rawInputSerialized(){return`${this._raw_input}`}valueSerialized(){return`${this.value}`}_copy_value(t){this.set(t.valueSerialized())}static are_raw_input_equal(t,e){return t==e}static are_values_equal(t,e){return t==e}isDefault(){return this._raw_input==this._default_value}setParam(t){this.set(t.path())}processRawInput(){this._value.path()!=this._raw_input&&(this._value.set_path(this._raw_input),this.find_target(),this.setDirty(),this.emitController.emit(Sr.VALUE_UPDATED))}async processComputation(){this.find_target()}find_target(){if(!this.node)return;const t=this._raw_input;let e=null;const n=null!=t&&\\\\\\\"\\\\\\\"!==t;this.scene().referencesController.reset_reference_from_param(this),this.decomposed_path.reset(),n&&(e=bi.findParam(this.node,t,this.decomposed_path));const i=this._value.param(),s=e;if(this.scene().referencesController.set_named_nodes_from_param(this),e&&this.scene().referencesController.set_reference_from_param(this,e),(null==i?void 0:i.graphNodeId())!==(null==s?void 0:s.graphNodeId())){const t=this.options.dependentOnFoundNode(),n=this._value.param();n&&t&&this.removeGraphInput(n),e?this._assign_found_node(e):this._value.set_param(null),this.options.executeCallback()}this.removeDirtyState()}_assign_found_node(t){const e=this.options.dependentOnFoundNode();this._value.set_param(t),e&&this.addGraphInput(t)}notify_path_rebuild_required(t){this.decomposed_path.update_from_name_change(t);const e=this.decomposed_path.to_path();this.set(e)}notify_target_param_owner_params_updated(t){this.setDirty()}},[Er.NODE_PATH]:class extends uo{static type(){return Er.NODE_PATH}initialize_param(){this._value=new yi}defaultValueSerialized(){return this._default_value}rawInputSerialized(){return`${this._raw_input}`}valueSerialized(){return`${this.value}`}_copy_value(t){this.set(t.valueSerialized())}static are_raw_input_equal(t,e){return t==e}static are_values_equal(t,e){return t==e}isDefault(){return this._raw_input==this._default_value}setNode(t){this.set(t.path())}processRawInput(){this._value.path()!=this._raw_input&&(this._value.set_path(this._raw_input),this._findTarget(),this.setDirty(),this.emitController.emit(Sr.VALUE_UPDATED))}async processComputation(){this._findTarget()}_findTarget(){if(!this.node)return;const t=this._raw_input;let e=null;const n=null!=t&&\\\\\\\"\\\\\\\"!==t;this.scene().referencesController.reset_reference_from_param(this),this.decomposed_path.reset(),n&&(e=bi.findNode(this.node,t,this.decomposed_path));const i=this._value.node(),s=e;if(this.scene().referencesController.set_named_nodes_from_param(this),e&&this.scene().referencesController.set_reference_from_param(this,e),(null==i?void 0:i.graphNodeId())!==(null==s?void 0:s.graphNodeId())){const t=this.options.dependentOnFoundNode(),n=this._value.node();n&&t&&this.removeGraphInput(n),e?this._assign_found_node(e):this._value.set_node(null),this.options.executeCallback()}n&&!e&&this.scene().loadingController.loaded()&&n&&this.states.error.set(`no node found at path '${t}'`),this.removeDirtyState()}_assign_found_node(t){const e=this.options.dependentOnFoundNode();this._isNodeExpectedContext(t)?this._is_node_expected_type(t)?(this.states.error.clear(),this._value.set_node(t),e&&this.addGraphInput(t)):this.states.error.set(`node type is ${t.type()} but the params expects one of ${(this._expected_node_types()||[]).join(\\\\\\\", \\\\\\\")}`):this.states.error.set(`node context is ${t.context()} but the params expects a ${this._expectedContext()}`)}_expectedContext(){return this.options.nodeSelectionContext()}_isNodeExpectedContext(t){var e,n;const i=this._expectedContext();if(null==i)return!0;return i==(null===(n=null===(e=t.parent())||void 0===e?void 0:e.childrenController)||void 0===n?void 0:n.context)}_expected_node_types(){return this.options.nodeSelectionTypes()}_is_node_expected_type(t){const e=this._expected_node_types();return null==e||(null==e?void 0:e.includes(t.type()))}notify_path_rebuild_required(t){this.decomposed_path.update_from_name_change(t);const e=this.decomposed_path.to_path();this.set(e)}notify_target_param_owner_params_updated(t){this.setDirty()}},[Er.RAMP]:bo,[Er.STRING]:wo,[Er.VECTOR2]:class extends ro{constructor(){super(...arguments),this._value=new d.a,this._copied_value=[0,0]}static type(){return Er.VECTOR2}componentNames(){return To}defaultValueSerialized(){return m.isArray(this._default_value)?this._default_value:this._default_value.toArray()}valueSerialized(){return this.value.toArray()}_copy_value(t){t.value.toArray(this._copied_value),this.set(this._copied_value)}_clone_raw_input(t){if(t instanceof d.a)return t.clone();{const e=[t[0],t[1]];return null==e[0]&&(e[0]=e[0]||0),null==e[1]&&(e[1]=e[1]||e[0]),e}}static are_raw_input_equal(t,e){return t instanceof d.a?e instanceof d.a?t.equals(e):t.x==e[0]&&t.y==e[1]:e instanceof d.a?t[0]==e.x&&t[1]==e.y:t[0]==e[0]&&t[1]==e[1]}static are_values_equal(t,e){return t.equals(e)}initComponents(){super.initComponents(),this.x=this.components[0],this.y=this.components[1]}set_value_from_components(){this._value.x=this.x.value,this._value.y=this.y.value}},[Er.VECTOR3]:class extends ro{constructor(){super(...arguments),this._value=new p.a,this._copied_value=[0,0,0]}static type(){return Er.VECTOR3}componentNames(){return Ao}defaultValueSerialized(){return m.isArray(this._default_value)?this._default_value:this._default_value.toArray()}valueSerialized(){return this.value.toArray()}_copy_value(t){t.value.toArray(this._copied_value),this.set(this._copied_value)}_clone_raw_input(t){if(t instanceof p.a)return t.clone();{const e=[t[0],t[1],t[2]];return null==e[0]&&(e[0]=e[0]||0),null==e[1]&&(e[1]=e[1]||e[0]),null==e[2]&&(e[2]=e[2]||e[1]),e}}static are_raw_input_equal(t,e){return t instanceof p.a?e instanceof p.a?t.equals(e):t.x==e[0]&&t.y==e[1]&&t.z==e[2]:e instanceof p.a?t[0]==e.x&&t[1]==e.y&&t[2]==e.z:t[0]==e[0]&&t[1]==e[1]&&t[2]==e[2]}static are_values_equal(t,e){return t.equals(e)}initComponents(){super.initComponents(),this.x=this.components[0],this.y=this.components[1],this.z=this.components[2]}set_value_from_components(){this._value.x=this.x.value,this._value.y=this.y.value,this._value.z=this.z.value}},[Er.VECTOR4]:class extends ro{constructor(){super(...arguments),this._value=new _.a,this._copied_value=[0,0,0,0]}static type(){return Er.VECTOR4}componentNames(){return Mo}defaultValueSerialized(){return m.isArray(this._default_value)?this._default_value:this._default_value.toArray()}valueSerialized(){return this.value.toArray()}_copy_value(t){t.value.toArray(this._copied_value),this.set(this._copied_value)}_clone_raw_input(t){if(t instanceof _.a)return t.clone();{const e=[t[0],t[1],t[2],t[3]];return null==e[0]&&(e[0]=e[0]||0),null==e[1]&&(e[1]=e[1]||e[0]),null==e[2]&&(e[2]=e[2]||e[1]),null==e[3]&&(e[3]=e[3]||e[2]),e}}static are_raw_input_equal(t,e){return t instanceof _.a?e instanceof _.a?t.equals(e):t.x==e[0]&&t.y==e[1]&&t.z==e[2]&&t.w==e[3]:e instanceof _.a?t[0]==e.x&&t[1]==e.y&&t[2]==e.z&&t[3]==e.w:t[0]==e[0]&&t[1]==e[1]&&t[2]==e[2]&&t[3]==e[3]}static are_values_equal(t,e){return t.equals(e)}initComponents(){super.initComponents(),this.x=this.components[0],this.y=this.components[1],this.z=this.components[2],this.w=this.components[3]}set_value_from_components(){this._value.x=this.x.value,this._value.y=this.y.value,this._value.z=this.z.value,this._value.w=this.w.value}}};class So{dispose(){this._callback=void 0}params(){return this._params}callback(){return this._callback}init(t,e){if(this._params=t,e)this._callback=e;else{const t=this._params[0];switch(t.type()){case Er.STRING:return this._handle_string_param(t);case Er.OPERATOR_PATH:return this._handle_operator_path_param(t);case Er.NODE_PATH:return this._handle_node_path_param(t);case Er.PARAM_PATH:return this._handle_param_path_param(t);case Er.FLOAT:case Er.INTEGER:return this._handle_number_param(t)}}}_handle_string_param(t){this._callback=()=>t.value}_handle_operator_path_param(t){this._callback=()=>t.value}_handle_node_path_param(t){this._callback=()=>t.value.path()}_handle_param_path_param(t){this._callback=()=>t.value.path()}_handle_number_param(t){this._callback=()=>`${t.value}`}}class Co{constructor(t){this.node=t,this._param_create_mode=!1,this._params_created=!1,this._params_by_name={},this._params_list=[],this._param_names=[],this._non_spare_params=[],this._spare_params=[],this._non_spare_param_names=[],this._spare_param_names=[],this._params_added_since_last_params_eval=!1}get label(){return this._label_controller=this._label_controller||new So}hasLabelController(){return null!=this._label_controller}dispose(){var t;this._params_node&&this._params_node.dispose();for(let t of this.all)t.dispose();this._post_create_params_hook_names=void 0,this._post_create_params_hooks=void 0,this._on_scene_load_hooks=void 0,this._on_scene_load_hook_names=void 0,null===(t=this._label_controller)||void 0===t||t.dispose()}initDependencyNode(){this._params_node||(this._params_node=new Mi(this.node.scene(),\\\\\\\"params\\\\\\\"),this.node.addGraphInput(this._params_node,!1))}init(){this.initDependencyNode(),this._param_create_mode=!0,this._initFromParamsConfig(),this.node.createParams(),this._postCreateParams()}_postCreateParams(){this._updateCaches(),this._initParamAccessors(),this._param_create_mode=!1,this._params_created=!0,this._runPostCreateParamsHooks()}postCreateSpareParams(){this._updateCaches(),this._initParamAccessors(),this.node.scene().referencesController.notify_params_updated(this.node),this.node.emit(Ei.PARAMS_UPDATED)}updateParams(t){let e=!1,n=!1;if(t.namesToDelete)for(let e of t.namesToDelete)this.has(e)&&(this._deleteParam(e),n=!0);if(t.toAdd)for(let n of t.toAdd){const t=this.addParam(n.type,n.name,n.init_value,n.options);t&&(null!=n.raw_input&&t.set(n.raw_input),e=!0)}(n||e)&&this.postCreateSpareParams()}_initFromParamsConfig(){const t=this.node.paramsConfig;let e=!1;if(t)for(let n of Object.keys(t)){const i=t[n];let s;this.node.params_init_value_overrides&&(s=this.node.params_init_value_overrides[n],e=!0),this.addParam(i.type,n,i.init_value,i.options,s)}e&&this.node.setDirty(),this.node.params_init_value_overrides=void 0}_initParamAccessors(){let t=Object.getOwnPropertyNames(this.node.pv);this._removeUnneededAccessors(t),t=Object.getOwnPropertyNames(this.node.pv);for(let e of this.all){const n=e.options.isSpare();(!t.includes(e.name())||n)&&(Object.defineProperty(this.node.pv,e.name(),{get:()=>e.value,configurable:n}),Object.defineProperty(this.node.p,e.name(),{get:()=>e,configurable:n}))}}_removeUnneededAccessors(t){const e=this._param_names,n=[];for(let i of t)e.includes(i)||n.push(i);for(let t of n)Object.defineProperty(this.node.pv,t,{get:()=>{},configurable:!0}),Object.defineProperty(this.node.p,t,{get:()=>{},configurable:!0})}get params_node(){return this._params_node}get all(){return this._params_list}get non_spare(){return this._non_spare_params}get spare(){return this._spare_params}get names(){return this._param_names}get non_spare_names(){return this._non_spare_param_names}get spare_names(){return this._spare_param_names}set_with_type(t,e,n){const i=this.param_with_type(t,n);i?i.set(e):li.warn(`param ${t} not found with type ${n}`)}set_float(t,e){this.set_with_type(t,e,Er.FLOAT)}set_vector3(t,e){this.set_with_type(t,e,Er.VECTOR3)}has_param(t){return null!=this._params_by_name[t]}has(t){return this.has_param(t)}get(t){return this.param(t)}param_with_type(t,e){const n=this.param(t);if(n&&n.type()==e)return n}get_float(t){return this.param_with_type(t,Er.FLOAT)}get_operator_path(t){return this.param_with_type(t,Er.OPERATOR_PATH)}value(t){var e;return null===(e=this.param(t))||void 0===e?void 0:e.value}value_with_type(t,e){var n;return null===(n=this.param_with_type(t,e))||void 0===n?void 0:n.value}boolean(t){return this.value_with_type(t,Er.BOOLEAN)}float(t){return this.value_with_type(t,Er.FLOAT)}integer(t){return this.value_with_type(t,Er.INTEGER)}string(t){return this.value_with_type(t,Er.STRING)}vector2(t){return this.value_with_type(t,Er.VECTOR2)}vector3(t){return this.value_with_type(t,Er.VECTOR3)}color(t){return this.value_with_type(t,Er.COLOR)}param(t){const e=this._params_by_name[t];return null!=e?e:(li.warn(`tried to access param '${t}' in node ${this.node.path()}, but existing params are: ${this.names} on node ${this.node.path()}`),null)}_deleteParam(t){const e=this._params_by_name[t];if(!e)throw new Error(`param '${t}' does not exist on node ${this.node.path()}`);if(this._params_node&&this._params_node.removeGraphInput(this._params_by_name[t]),e._setupNodeDependencies(null),delete this._params_by_name[t],e.isMultiple()&&e.components)for(let t of e.components){const e=t.name();delete this._params_by_name[e]}}addParam(t,e,n,i={},s){const r=i.spare||!1;!1!==this._param_create_mode||r||li.warn(`node ${this.node.path()} (${this.node.type()}) param '${e}' cannot be created outside of create_params`),null==this.node.scene()&&li.warn(`node ${this.node.path()} (${this.node.type()}) has no scene assigned`);const o=Eo[t];if(null!=o){const a=this._params_by_name[e];a&&(r?a.type()!=t&&this._deleteParam(a.name()):li.warn(`a param named ${e} already exists`,this.node));const l=new o(this.node.scene(),this.node);if(l.options.set(i),l.setName(e),l.setInitValue(n),l.initComponents(),null==s)l.set(n);else if(l.options.isExpressionForEntities()&&l.set(n),null!=s.raw_input)l.set(s.raw_input);else if(null!=s.simple_data)l.set(s.simple_data);else if(null!=s.complex_data){const t=s.complex_data.raw_input;t?l.set(t):l.set(n);const e=s.complex_data.overriden_options;if(null!=e){const t=Object.keys(e);for(let n of t)l.options.setOption(n,e[n])}}if(l._setupNodeDependencies(this.node),this._params_by_name[l.name()]=l,l.isMultiple()&&l.components)for(let t of l.components)this._params_by_name[t.name()]=t;return this._params_added_since_last_params_eval=!0,l}}_updateCaches(){this._params_list=Object.values(this._params_by_name),this._param_names=Object.keys(this._params_by_name),this._non_spare_params=Object.values(this._params_by_name).filter((t=>!t.options.isSpare())),this._spare_params=Object.values(this._params_by_name).filter((t=>t.options.isSpare())),this._non_spare_param_names=Object.values(this._params_by_name).filter((t=>!t.options.isSpare())).map((t=>t.name())),this._spare_param_names=Object.values(this._params_by_name).filter((t=>t.options.isSpare())).map((t=>t.name()))}async _evalParam(t){t.isDirty()&&(await t.compute(),t.states.error.active()&&this.node.states.error.set(`param '${t.name()}' error: ${t.states.error.message()}`))}async evalParams(t){const e=[];for(let n of t)n.isDirty()&&e.push(this._evalParam(n));await Promise.all(e),this.node.states.error.active()&&this.node._setContainer(null)}paramsEvalRequired(){return null!=this._params_node&&(this._params_node.isDirty()||this._params_added_since_last_params_eval)}async evalAll(){var t;this.paramsEvalRequired()&&(await this.evalParams(this._params_list),null===(t=this._params_node)||void 0===t||t.removeDirtyState(),this._params_added_since_last_params_eval=!1)}onParamsCreated(t,e){if(this._params_created)e();else{if(this._post_create_params_hook_names&&this._post_create_params_hook_names.includes(t))return void li.error(`hook name ${t} already exists`);this._post_create_params_hook_names=this._post_create_params_hook_names||[],this._post_create_params_hook_names.push(t),this._post_create_params_hooks=this._post_create_params_hooks||[],this._post_create_params_hooks.push(e)}}addOnSceneLoadHook(t,e){this._on_scene_load_hook_names=this._on_scene_load_hook_names||[],this._on_scene_load_hooks=this._on_scene_load_hooks||[],this._on_scene_load_hook_names.includes(t)?li.warn(`hook with name ${t} already exists`,this.node):(this._on_scene_load_hook_names.push(t),this._on_scene_load_hooks.push(e))}_runPostCreateParamsHooks(){if(this._post_create_params_hooks)for(let t of this._post_create_params_hooks)t()}runOnSceneLoadHooks(){if(this._on_scene_load_hooks)for(let t of this._on_scene_load_hooks)t()}}class No{constructor(){}}class Lo{constructor(t,e,n=0,i=0){if(this._node_src=t,this._node_dest=e,this._output_index=n,this._input_index=i,null==this._output_index)throw\\\\\\\"bad output index\\\\\\\";if(null==this._input_index)throw\\\\\\\"bad input index\\\\\\\";this._id=Lo._next_id++,this._node_src.io.connections&&this._node_dest.io.connections&&(this._node_src.io.connections.addOutputConnection(this),this._node_dest.io.connections.addInputConnection(this))}get id(){return this._id}get node_src(){return this._node_src}get node_dest(){return this._node_dest}get output_index(){return this._output_index}get input_index(){return this._input_index}src_connection_point(){const t=this._node_src,e=this._output_index;return t.io.outputs.namedOutputConnectionPoints()[e]}dest_connection_point(){const t=this._node_dest,e=this._input_index;return t.io.inputs.namedInputConnectionPoints()[e]}disconnect(t={}){this._node_src.io.connections&&this._node_dest.io.connections&&(this._node_src.io.connections.removeOutputConnection(this),this._node_dest.io.connections.removeInputConnection(this)),!0===t.setInput&&this._node_dest.io.inputs.setInput(this._input_index,null)}}Lo._next_id=0;class Oo{constructor(t){this.inputs_controller=t,this._clone_required_states=[],this._overridden=!1,this.node=t.node}initInputsClonedState(t){m.isArray(t)?this._cloned_states=t:this._cloned_state=t,this._update_clone_required_state()}overrideClonedStateAllowed(){if(this._cloned_states)for(let t of this._cloned_states)if(t==Ki.FROM_NODE)return!0;return!!this._cloned_state&&this._cloned_state==Ki.FROM_NODE}cloneRequiredState(t){return this._clone_required_states[t]}cloneRequiredStates(){return this._clone_required_states}_get_clone_required_state(t){const e=this._cloned_states;if(e){const n=e[t];if(null!=n)return this.clone_required_from_state(n)}return!this._cloned_state||this.clone_required_from_state(this._cloned_state)}clone_required_from_state(t){switch(t){case Ki.ALWAYS:return!0;case Ki.NEVER:return!1;case Ki.FROM_NODE:return!this._overridden}return ls.unreachable(t)}overrideClonedState(t){this._overridden=t,this._update_clone_required_state(),this.node.emit(Ei.OVERRIDE_CLONABLE_STATE_UPDATE),this.node.setDirty()}overriden(){return this._overridden}_update_clone_required_state(){if(this._cloned_states){const t=[];for(let e=0;e=t.length&&n.disconnect({setInput:!0});this._named_input_connection_points=t,this.setMinCount(0),this.setMaxCount(t.length),this._initGraphNodeInputs(),this.node.emit(Ei.NAMED_INPUTS_UPDATED)}hasNamedInputs(){return this._has_named_inputs}namedInputConnectionPoints(){return this._named_input_connection_points||[]}_initGraphNodeInputs(){for(let t=0;t0)for(let e=0;e0){const n=this.existing_input_indices();if(n.length0){const n=[];let i;for(let t=0;tt.name()));return void console.warn(`node ${e.path()} does not have an output named ${n}. inputs are: ${t.join(\\\\\\\", \\\\\\\")}`)}const r=this._graph_node_inputs[i];if(null==r){const t=`graph_input_node not found at index ${i}`;throw console.warn(t),new Error(t)}if(e&&this.node.parent()!=e.parent())return;const o=this._inputs[i];let a,l=null;this.node.io.connections&&(a=this.node.io.connections.inputConnection(i)),a&&(l=a.output_index),e===o&&s==l||(null!=o&&this._depends_on_inputs&&r.removeGraphInput(o),null!=e?r.addGraphInput(e)?(this._depends_on_inputs||r.removeGraphInput(e),a&&a.disconnect({setInput:!1}),this._inputs[i]=e,new Lo(e,this.node,s,i)):console.warn(`cannot connect ${e.path()} to ${this.node.path()}`):(this._inputs[i]=null,a&&a.disconnect({setInput:!1})),this._run_on_set_input_hooks(),r.setSuccessorsDirty(),this.node.emit(Ei.INPUTS_UPDATED))}remove_input(t){const e=this.inputs();let n;for(let i=0;i=0}has_input(t){return null!=this._inputs[t]}inputs(){return this._inputs}initInputsClonedState(t){this._cloned_states_controller||(this._cloned_states_controller=new Oo(this),this._cloned_states_controller.initInputsClonedState(t))}overrideClonedStateAllowed(){var t;return(null===(t=this._cloned_states_controller)||void 0===t?void 0:t.overrideClonedStateAllowed())||!1}overrideClonedState(t){var e;null===(e=this._cloned_states_controller)||void 0===e||e.overrideClonedState(t)}clonedStateOverriden(){var t;return(null===(t=this._cloned_states_controller)||void 0===t?void 0:t.overriden())||!1}cloneRequired(t){var e;const n=null===(e=this._cloned_states_controller)||void 0===e?void 0:e.cloneRequiredState(t);return null==n||n}cloneRequiredStates(){var t;const e=null===(t=this._cloned_states_controller)||void 0===t?void 0:t.cloneRequiredStates();return null==e||e}add_on_set_input_hook(t,e){this._on_update_hooks=this._on_update_hooks||[],this._on_update_hook_names=this._on_update_hook_names||[],this._on_update_hook_names.includes(t)?console.warn(`hook with name ${t} already exists`,this.node):(this._on_update_hooks.push(e),this._on_update_hook_names.push(t))}_run_on_set_input_hooks(){if(this._on_update_hooks)for(let t of this._on_update_hooks)t()}}class Ro{constructor(t){this.node=t,this._has_outputs=!1,this._has_named_outputs=!1}setHasOneOutput(){this._has_outputs=!0}setHasNoOutput(){this._has_outputs=!1}hasOutputs(){return this._has_outputs}hasNamedOutputs(){return this._has_named_outputs}hasNamedOutput(t){return this.getNamedOutputIndex(t)>=0}namedOutputConnectionPoints(){return this._named_output_connection_points||[]}namedOutputConnection(t){if(this._named_output_connection_points)return this._named_output_connection_points[t]}getNamedOutputIndex(t){var e;if(this._named_output_connection_points)for(let n=0;n=t.length&&e.disconnect({setInput:!0});this._named_output_connection_points=t,e&&this.node.scene()&&this.node.setDirty(this.node),this.node.emit(Ei.NAMED_OUTPUTS_UPDATED)}used_output_names(){var t;const e=this.node.io.connections;if(e){let n=e.outputConnections().map((t=>t?t.output_index:null));n=f.uniq(n);const i=[];n.forEach((t=>{m.isNumber(t)&&i.push(t)}));const s=[];for(let e of i){const n=null===(t=this.namedOutputConnectionPoints()[e])||void 0===t?void 0:t.name();n&&s.push(n)}return s}return[]}}class Io{constructor(t){this._node=t,this._output_connections=new Map}initInputs(){const t=this._node.io.inputs.maxInputsCount();for(this._input_connections=this._input_connections||new Array(t);this._input_connections.length1&&void 0===t[t.length-1];)t.pop();return t}addOutputConnection(t){const e=t.output_index,n=t.id;let i=this._output_connections.get(e);i||(i=new Map,this._output_connections.set(e,i)),i.set(n,t)}removeOutputConnection(t){const e=t.output_index,n=t.id;let i=this._output_connections.get(e);i&&i.delete(n)}outputConnections(){let t=[];return this._output_connections.forEach(((e,n)=>{e.forEach(((e,n)=>{e&&t.push(e)}))})),t}}class Fo{constructor(t){this._node=t}set_in(t){this._in=t}set_out(t){this._out=t}clear(){this._in=void 0,this._out=void 0}in(){return this._in}out(){return this._out}}class Do{constructor(t,e,n){this._name=t,this._type=e,this._init_value=n}get init_value(){return this._init_value}name(){return this._name}type(){return this._type}are_types_matched(t,e){return!0}toJSON(){return this._json=this._json||this._create_json()}_create_json(){return{name:this._name,type:this._type}}}var Bo;!function(t){t.BOOL=\\\\\\\"bool\\\\\\\",t.INT=\\\\\\\"int\\\\\\\",t.FLOAT=\\\\\\\"float\\\\\\\",t.VEC2=\\\\\\\"vec2\\\\\\\",t.VEC3=\\\\\\\"vec3\\\\\\\",t.VEC4=\\\\\\\"vec4\\\\\\\",t.SAMPLER_2D=\\\\\\\"sampler2D\\\\\\\",t.SSS_MODEL=\\\\\\\"SSSModel\\\\\\\"}(Bo||(Bo={}));const zo=[Bo.BOOL,Bo.INT,Bo.FLOAT,Bo.VEC2,Bo.VEC3,Bo.VEC4],ko={[Bo.BOOL]:Er.BOOLEAN,[Bo.INT]:Er.INTEGER,[Bo.FLOAT]:Er.FLOAT,[Bo.VEC2]:Er.VECTOR2,[Bo.VEC3]:Er.VECTOR3,[Bo.VEC4]:Er.VECTOR4,[Bo.SAMPLER_2D]:Er.RAMP,[Bo.SSS_MODEL]:Er.STRING},Uo={[Er.BOOLEAN]:Bo.BOOL,[Er.COLOR]:Bo.VEC3,[Er.INTEGER]:Bo.INT,[Er.FLOAT]:Bo.FLOAT,[Er.FOLDER]:void 0,[Er.VECTOR2]:Bo.VEC2,[Er.VECTOR3]:Bo.VEC3,[Er.VECTOR4]:Bo.VEC4,[Er.BUTTON]:void 0,[Er.OPERATOR_PATH]:void 0,[Er.PARAM_PATH]:void 0,[Er.NODE_PATH]:void 0,[Er.RAMP]:void 0,[Er.STRING]:void 0},Go={[Bo.BOOL]:!1,[Bo.INT]:0,[Bo.FLOAT]:0,[Bo.VEC2]:[0,0],[Bo.VEC3]:[0,0,0],[Bo.VEC4]:[0,0,0,0],[Bo.SAMPLER_2D]:bo.DEFAULT_VALUE_JSON,[Bo.SSS_MODEL]:\\\\\\\"SSSModel()\\\\\\\"},Vo={[Bo.BOOL]:1,[Bo.INT]:1,[Bo.FLOAT]:1,[Bo.VEC2]:2,[Bo.VEC3]:3,[Bo.VEC4]:4,[Bo.SAMPLER_2D]:1,[Bo.SSS_MODEL]:1};class Ho extends Do{constructor(t,e,n){super(t,e),this._name=t,this._type=e,this._init_value=n,this._init_value=this._init_value||Go[this._type]}type(){return this._type}are_types_matched(t,e){return t==e}get param_type(){return ko[this._type]}get init_value(){return this._init_value}toJSON(){return this._json=this._json||this._create_json()}_create_json(){return{name:this._name,type:this._type}}}var jo;!function(t){t.BOOL=\\\\\\\"bool\\\\\\\",t.INT=\\\\\\\"int\\\\\\\",t.FLOAT=\\\\\\\"float\\\\\\\",t.VEC2=\\\\\\\"vec2\\\\\\\",t.VEC3=\\\\\\\"vec3\\\\\\\",t.VEC4=\\\\\\\"vec4\\\\\\\"}(jo||(jo={}));const Wo=[jo.BOOL,jo.INT,jo.FLOAT,jo.VEC2,jo.VEC3,jo.VEC4],qo={[jo.BOOL]:Er.BOOLEAN,[jo.INT]:Er.INTEGER,[jo.FLOAT]:Er.FLOAT,[jo.VEC2]:Er.VECTOR2,[jo.VEC3]:Er.VECTOR3,[jo.VEC4]:Er.VECTOR4},Xo={[Er.BOOLEAN]:jo.BOOL,[Er.COLOR]:jo.VEC3,[Er.INTEGER]:jo.INT,[Er.FLOAT]:jo.FLOAT,[Er.FOLDER]:void 0,[Er.VECTOR2]:jo.VEC2,[Er.VECTOR3]:jo.VEC3,[Er.VECTOR4]:jo.VEC4,[Er.BUTTON]:void 0,[Er.OPERATOR_PATH]:void 0,[Er.PARAM_PATH]:void 0,[Er.NODE_PATH]:void 0,[Er.RAMP]:void 0,[Er.STRING]:void 0},Yo={[jo.BOOL]:!1,[jo.INT]:0,[jo.FLOAT]:0,[jo.VEC2]:[0,0],[jo.VEC3]:[0,0,0],[jo.VEC4]:[0,0,0,0]};jo.BOOL,jo.INT,jo.FLOAT,jo.VEC2,jo.VEC3,jo.VEC4;class $o extends Do{constructor(t,e){super(t,e),this._name=t,this._type=e,this._init_value=Yo[this._type]}type(){return this._type}are_types_matched(t,e){return t==e}get param_type(){return qo[this._type]}get init_value(){return this._init_value}toJSON(){return this._json=this._json||this._create_json()}_create_json(){return{name:this._name,type:this._type}}}var Jo;!function(t){t.BASE=\\\\\\\"base\\\\\\\",t.DRAG=\\\\\\\"drag\\\\\\\",t.KEYBOARD=\\\\\\\"keyboard\\\\\\\",t.MOUSE=\\\\\\\"mouse\\\\\\\",t.POINTER=\\\\\\\"pointer\\\\\\\"}(Jo||(Jo={}));class Zo extends Do{constructor(t,e,n){super(t,e),this._name=t,this._type=e,this._event_listener=n}type(){return this._type}get param_type(){return Er.FLOAT}are_types_matched(t,e){return e==Jo.BASE||t==e}get event_listener(){return this._event_listener}toJSON(){return this._json=this._json||this._create_json()}_create_json(){return{name:this._name,type:this._type}}}const Qo={[ts.ANIM]:void 0,[ts.COP]:void 0,[ts.EVENT]:Jo.BASE,[ts.GL]:Bo.FLOAT,[ts.JS]:jo.FLOAT,[ts.MANAGER]:void 0,[ts.MAT]:void 0,[ts.OBJ]:void 0,[ts.POST]:void 0,[ts.ROP]:void 0,[ts.SOP]:void 0};function Ko(t,e,n){switch(t){case ts.EVENT:return new Zo(e,n);case ts.GL:return new Ho(e,n);case ts.JS:return new $o(e,n);default:return}}class ta{constructor(t,e){this.node=t,this._context=e,this._raw_input_serialized_by_param_name=new Map,this._default_value_serialized_by_param_name=new Map,this._initialized=!1}initializeNode(){this._initialized?console.warn(\\\\\\\"already initialized\\\\\\\",this.node):(this._initialized=!0,this.node.params.onParamsCreated(\\\\\\\"create_inputs_from_params\\\\\\\",this.create_inputs_from_params.bind(this)))}initialized(){return this._initialized}create_inputs_from_params(){const t=function(t){switch(t){case ts.EVENT:return;case ts.GL:return Uo;case ts.JS:return Xo;default:return}}(this._context);if(!t)return;const e=[];for(let n of this.node.params.names){let i=!0;if(this._inputless_param_names&&this._inputless_param_names.length>0&&this._inputless_param_names.includes(n)&&(i=!1),i&&this.node.params.has(n)){const i=this.node.params.get(n);if(i&&!i.parent_param){const n=t[i.type()];if(n){const t=Ko(this._context,i.name(),n);t&&e.push(t)}}}}this.node.io.inputs.setNamedInputConnectionPoints(e)}set_inputless_param_names(t){return this._inputless_param_names=t}createSpareParameters(){if(this.node.scene().loadingController.isLoading())return;const t=this.node.params.spare_names,e={};for(let n of t)if(this.node.params.has(n)){const t=this.node.params.get(n);t&&(this._raw_input_serialized_by_param_name.set(n,t.rawInputSerialized()),this._default_value_serialized_by_param_name.set(n,t.defaultValueSerialized()),e.namesToDelete=e.namesToDelete||[],e.namesToDelete.push(n))}for(let t of this.node.io.inputs.namedInputConnectionPoints())if(t){const n=t.name(),i=t.param_type;let s=t.init_value;const r=this._default_value_serialized_by_param_name.get(n);let o=this.node.paramDefaultValue(n);if(s=null!=o?o:null!=r?r:t.init_value,m.isArray(t.init_value))if(m.isNumber(s)){const e=new Array(t.init_value.length);e.fill(s),s=e}else m.isArray(s)&&s.length==t.init_value.length&&null!=r&&(s=t.init_value);null!=s&&(e.toAdd=e.toAdd||[],e.toAdd.push({name:n,type:i,init_value:b.clone(s),raw_input:b.clone(s),options:{spare:!0}}))}this.node.params.updateParams(e);for(let t of this.node.params.spare)if(!t.parent_param){const e=this._raw_input_serialized_by_param_name.get(t.name());e&&t.set(e)}}}class ea{constructor(t,e){this.node=t,this._context=e,this._create_spare_params_from_inputs=!0,this._functions_overridden=!1,this._input_name_function=t=>`in${t}`,this._output_name_function=t=>0==t?\\\\\\\"val\\\\\\\":`val${t}`,this._expected_input_types_function=()=>{const t=this.first_input_connection_type()||this.default_connection_type();return[t,t]},this._expected_output_types_function=()=>[this._expected_input_types_function()[0]],this._update_signature_if_required_bound=this.update_signature_if_required.bind(this),this._initialized=!1,this._spare_params_controller=new ta(this.node,this._context)}default_connection_type(){return Qo[this._context]}create_connection_point(t,e){return Ko(this._context,t,e)}functions_overridden(){return this._functions_overridden}initialized(){return this._initialized}set_create_spare_params_from_inputs(t){this._create_spare_params_from_inputs=t}set_input_name_function(t){this._initialize_if_required(),this._input_name_function=t}set_output_name_function(t){this._initialize_if_required(),this._output_name_function=t}set_expected_input_types_function(t){this._initialize_if_required(),this._functions_overridden=!0,this._expected_input_types_function=t}set_expected_output_types_function(t){this._initialize_if_required(),this._functions_overridden=!0,this._expected_output_types_function=t}input_name(t){return this._wrapped_input_name_function(t)}output_name(t){return this._wrapped_output_name_function(t)}initializeNode(){this._initialized?console.warn(\\\\\\\"already initialized\\\\\\\",this.node):(this._initialized=!0,this.node.io.inputs.add_on_set_input_hook(\\\\\\\"_update_signature_if_required\\\\\\\",this._update_signature_if_required_bound),this.node.params.addOnSceneLoadHook(\\\\\\\"_update_signature_if_required\\\\\\\",this._update_signature_if_required_bound),this.node.params.onParamsCreated(\\\\\\\"_update_signature_if_required_bound\\\\\\\",this._update_signature_if_required_bound),this.node.addPostDirtyHook(\\\\\\\"_update_signature_if_required\\\\\\\",this._update_signature_if_required_bound),this._spare_params_controller.initialized()||this._spare_params_controller.initializeNode())}_initialize_if_required(){this._initialized||this.initializeNode()}get spare_params(){return this._spare_params_controller}update_signature_if_required(t){this.node.lifecycle.creation_completed&&this._connections_match_inputs()||(this.update_connection_types(),this.node.removeDirtyState(),this.node.scene().loadingController.isLoading()||this.make_successors_update_signatures())}make_successors_update_signatures(){const t=this.node.graphAllSuccessors();if(this.node.childrenAllowed()){const e=this.node.nodesByType(ns.INPUT),n=this.node.nodesByType(ns.OUTPUT);for(let n of e)t.push(n);for(let e of n)t.push(e)}for(let e of t){const t=e;t.io&&t.io.has_connection_points_controller&&t.io.connection_points.initialized()&&t.io.connection_points.update_signature_if_required(this.node)}}update_connection_types(){const t=this._wrapped_expected_input_types_function(),e=this._wrapped_expected_output_types_function(),n=[];for(let e=0;enull==t?void 0:t.type())),e=this.node.io.outputs.namedOutputConnectionPoints().map((t=>null==t?void 0:t.type())),n=this._wrapped_expected_input_types_function(),i=this._wrapped_expected_output_types_function();if(n.length!=t.length)return!1;if(i.length!=e.length)return!1;for(let e=0;et.type))}return this._expected_input_types_function()}_wrapped_expected_output_types_function(){if(this.node.scene().loadingController.isLoading()){const t=this.node.io.saved_connection_points_data.out();if(t)return t.map((t=>t.type))}return this._expected_output_types_function()}_wrapped_input_name_function(t){if(this.node.scene().loadingController.isLoading()){const e=this.node.io.saved_connection_points_data.in();if(e)return e[t].name}return this._input_name_function(t)}_wrapped_output_name_function(t){if(this.node.scene().loadingController.isLoading()){const e=this.node.io.saved_connection_points_data.out();if(e)return e[t].name}return this._output_name_function(t)}first_input_connection_type(){return this.input_connection_type(0)}input_connection_type(t){const e=this.node.io.connections.inputConnections();if(e){const n=e[t];if(n)return n.src_connection_point().type()}}}class na{constructor(t){this.node=t,this._connections=new Io(this.node)}get connections(){return this._connections}get inputs(){return this._inputs=this._inputs||new Po(this.node)}has_inputs(){return null!=this._inputs}get outputs(){return this._outputs=this._outputs||new Ro(this.node)}has_outputs(){return null!=this._outputs}get connection_points(){return this._connection_points=this._connection_points||new ea(this.node,this.node.context())}get has_connection_points_controller(){return null!=this._connection_points}get saved_connection_points_data(){return this._saved_connection_points_data=this._saved_connection_points_data||new Fo(this.node)}clear_saved_connection_points_data(){this._saved_connection_points_data&&(this._saved_connection_points_data.clear(),this._saved_connection_points_data=void 0)}}class ia{constructor(){}}class sa extends Mi{constructor(t,e=\\\\\\\"BaseNode\\\\\\\",n){super(t,e),this.params_init_value_overrides=n,this.containerController=new br(this),this.pv=new No,this.p=new ia,this._initialized=!1}copy_param_values(t){const e=this.params.non_spare;for(let n of e){const e=t.params.get(n.name());e&&n.copy_value(e)}}get parentController(){return this._parent_controller=this._parent_controller||new qi(this)}static displayedInputNames(){return[]}get childrenControllerContext(){return this._children_controller_context}_create_children_controller(){if(this._children_controller_context)return new ds(this,this._children_controller_context)}get childrenController(){return this._children_controller=this._children_controller||this._create_children_controller()}childrenAllowed(){return null!=this._children_controller_context}get uiData(){return this._ui_data=this._ui_data||new Si(this)}get states(){return this._states=this._states||new ji(this)}get lifecycle(){return this._lifecycle=this._lifecycle||new ps(this)}get serializer(){return this._serializer=this._serializer||new Mr(this)}get cookController(){return this._cook_controller=this._cook_controller||new Ar(this)}get io(){return this._io=this._io||new na(this)}get nameController(){return this._name_controller=this._name_controller||new Wi(this)}setName(t){this.nameController.setName(t)}_set_core_name(t){this._name=t}get params(){return this._params_controller=this._params_controller||new Co(this)}initialize_base_and_node(){var t;this._initialized?console.warn(\\\\\\\"node already initialized\\\\\\\"):(this._initialized=!0,null===(t=this.displayNodeController)||void 0===t||t.initializeNode(),this.initializeBaseNode(),this.initializeNode(),this.polyNodeController&&this.polyNodeController.initializeNode())}initializeBaseNode(){}initializeNode(){}static type(){throw\\\\\\\"type to be overriden\\\\\\\"}type(){return this.constructor.type()}static context(){throw console.error(\\\\\\\"node has no node_context\\\\\\\",this),\\\\\\\"context requires override\\\\\\\"}context(){return this.constructor.context()}static require_webgl2(){return!1}require_webgl2(){return this.constructor.require_webgl2()}setParent(t){this.parentController.setParent(t)}parent(){return this.parentController.parent()}root(){return this._scene.root()}path(t){return this.parentController.path(t)}createParams(){}addParam(t,e,n,i){var s;return null===(s=this._params_controller)||void 0===s?void 0:s.addParam(t,e,n,i)}paramDefaultValue(t){return null}cook(t){return null}onCookEnd(t,e){this.cookController.registerOnCookEnd(t,e)}async compute(){var t,e;return this.isDirty()||(null===(e=null===(t=this.flags)||void 0===t?void 0:t.bypass)||void 0===e?void 0:e.active())?await this.containerController.compute():this.containerController.container()}_setContainer(t,e=null){this.containerController.container().set_content(t),null!=t&&(t.name||(t.name=this.path()),t.node||(t.node=this)),this.cookController.endCook(e)}createNode(t,e){var n;return null===(n=this.childrenController)||void 0===n?void 0:n.createNode(t,e)}create_operation_container(t,e,n){var i;return null===(i=this.childrenController)||void 0===i?void 0:i.create_operation_container(t,e,n)}removeNode(t){var e;null===(e=this.childrenController)||void 0===e||e.removeNode(t)}dispose(){var t,e;super.dispose(),this.setParent(null),this.io.inputs.dispose(),this.lifecycle.dispose(),null===(t=this.displayNodeController)||void 0===t||t.dispose(),this.nameController.dispose(),null===(e=this.childrenController)||void 0===e||e.dispose(),this.params.dispose()}children(){var t;return(null===(t=this.childrenController)||void 0===t?void 0:t.children())||[]}node(t){var e;return(null===(e=this.parentController)||void 0===e?void 0:e.findNode(t))||null}nodeSibbling(t){var e;const n=this.parent();if(n){const i=null===(e=n.childrenController)||void 0===e?void 0:e.child_by_name(t);if(i)return i}return null}nodesByType(t){var e;return(null===(e=this.childrenController)||void 0===e?void 0:e.nodesByType(t))||[]}setInput(t,e,n=0){this.io.inputs.setInput(t,e,n)}emit(t,e=null){this.scene().dispatchController.dispatch(this,t,e)}toJSON(t=!1){return this.serializer.toJSON(t)}async requiredModules(){}usedAssembler(){}integrationData(){}}class ra extends sa{static context(){return ts.MANAGER}}class oa{constructor(t,e,n){this.type=t,this.init_value=e,this.options=n}}class aa{static BUTTON(t,e){return new oa(Er.BUTTON,t,e)}static BOOLEAN(t,e){return new oa(Er.BOOLEAN,t,e)}static COLOR(t,e){return t instanceof D.a&&(t=t.toArray()),new oa(Er.COLOR,t,e)}static FLOAT(t,e){return new oa(Er.FLOAT,t,e)}static FOLDER(t=null,e){return new oa(Er.FOLDER,t,e)}static INTEGER(t,e){return new oa(Er.INTEGER,t,e)}static RAMP(t=bo.DEFAULT_VALUE,e){return new oa(Er.RAMP,t,e)}static STRING(t=\\\\\\\"\\\\\\\",e){return new oa(Er.STRING,t,e)}static VECTOR2(t,e){return t instanceof d.a&&(t=t.toArray()),new oa(Er.VECTOR2,t,e)}static VECTOR3(t,e){return t instanceof p.a&&(t=t.toArray()),new oa(Er.VECTOR3,t,e)}static VECTOR4(t,e){return t instanceof _.a&&(t=t.toArray()),new oa(Er.VECTOR4,t,e)}static OPERATOR_PATH(t,e){return new oa(Er.OPERATOR_PATH,t,e)}static NODE_PATH(t,e){return new oa(Er.NODE_PATH,t,e)}static PARAM_PATH(t,e){return new oa(Er.PARAM_PATH,t,e)}}class la{}class ca{constructor(t){this.scene=t}findObjectByMask(t){return this.findObjectByMaskInObject(t,this.scene.threejsScene())}findObjectByMaskInObject(t,e,n=\\\\\\\"\\\\\\\"){for(let i of e.children){const e=this._removeTrailingOrHeadingSlash(i.name),s=`${n=this._removeTrailingOrHeadingSlash(n)}/${e}`;if(os.matchMask(s,t))return i;const r=this.findObjectByMaskInObject(t,i,s);if(r)return r}}objectsByMask(t){return this.objectsByMaskInObject(t,this.scene.threejsScene(),[],\\\\\\\"\\\\\\\")}objectsByMaskInObject(t,e,n=[],i=\\\\\\\"\\\\\\\"){for(let s of e.children){const e=this._removeTrailingOrHeadingSlash(s.name),r=`${i=this._removeTrailingOrHeadingSlash(i)}/${e}`;os.matchMask(r,t)&&n.push(s),this.objectsByMaskInObject(t,s,n,r)}return n}_removeTrailingOrHeadingSlash(t){return\\\\\\\"/\\\\\\\"==t[0]&&(t=t.substr(1)),\\\\\\\"/\\\\\\\"==t[t.length-1]&&(t=t.substr(0,t.length-1)),t}}const ha={computeOnDirty:!1,callback:t=>{da.update(t)}};function ua(t){return class extends t{constructor(){super(...arguments),this.autoUpdate=aa.BOOLEAN(1,ha)}}}ua(la);class da{constructor(t){this.node=t}async update(){const t=this.node.object,e=this.node.pv;e.autoUpdate!=t.autoUpdate&&(t.autoUpdate=e.autoUpdate)}static async update(t){t.sceneAutoUpdateController.update()}}var pa;!function(t){t.NONE=\\\\\\\"none\\\\\\\",t.COLOR=\\\\\\\"color\\\\\\\",t.TEXTURE=\\\\\\\"texture\\\\\\\"}(pa||(pa={}));const _a=[pa.NONE,pa.COLOR,pa.TEXTURE],ma={computeOnDirty:!1,callback:t=>{ga.update(t)}};function fa(t){return class extends t{constructor(){super(...arguments),this.backgroundMode=aa.INTEGER(_a.indexOf(pa.NONE),{menu:{entries:_a.map(((t,e)=>({name:t,value:e})))},...ma}),this.bgColor=aa.COLOR([0,0,0],{visibleIf:{backgroundMode:_a.indexOf(pa.COLOR)},...ma}),this.bgTexture=aa.NODE_PATH(\\\\\\\"\\\\\\\",{visibleIf:{backgroundMode:_a.indexOf(pa.TEXTURE)},nodeSelection:{context:ts.COP},dependentOnFoundNode:!1,...ma})}}}fa(la);class ga{constructor(t){this.node=t}update(){const t=this.node.object,e=this.node.pv;if(e.backgroundMode==_a.indexOf(pa.NONE))t.background=null;else if(e.backgroundMode==_a.indexOf(pa.COLOR))t.background=e.bgColor;else{const n=e.bgTexture.nodeWithContext(ts.COP);n?n.compute().then((e=>{t.background=e.texture()})):this.node.states.error.set(\\\\\\\"bgTexture node not found\\\\\\\")}}static update(t){t.sceneBackgroundController.update()}}const va={computeOnDirty:!1,callback:t=>{xa.update(t)}};function ya(t){return class extends t{constructor(){super(...arguments),this.useEnvironment=aa.BOOLEAN(0,va),this.environment=aa.NODE_PATH(\\\\\\\"\\\\\\\",{visibleIf:{useEnvironment:1},nodeSelection:{context:ts.COP},dependentOnFoundNode:!1,...va})}}}ya(la);class xa{constructor(t){this.node=t}async update(){const t=this.node.object,e=this.node.pv;if(e.useEnvironment){const n=e.environment.nodeWithContext(ts.COP);n?n.compute().then((e=>{t.environment=e.texture()})):this.node.states.error.set(\\\\\\\"bgTexture node not found\\\\\\\")}else t.environment=null}static async update(t){t.sceneEnvController.update()}}class ba{constructor(t,e=1,n=1e3){this.name=\\\\\\\"\\\\\\\",this.color=new D.a(t),this.near=e,this.far=n}clone(){return new ba(this.color,this.near,this.far)}toJSON(){return{type:\\\\\\\"Fog\\\\\\\",color:this.color.getHex(),near:this.near,far:this.far}}}ba.prototype.isFog=!0;class wa{constructor(t,e=25e-5){this.name=\\\\\\\"\\\\\\\",this.color=new D.a(t),this.density=e}clone(){return new wa(this.color,this.density)}toJSON(){return{type:\\\\\\\"FogExp2\\\\\\\",color:this.color.getHex(),density:this.density}}}wa.prototype.isFogExp2=!0;const Ta={computeOnDirty:!1,callback:t=>{Sa.update(t)}};var Aa;!function(t){t.LINEAR=\\\\\\\"linear\\\\\\\",t.EXPONENTIAL=\\\\\\\"exponential\\\\\\\"}(Aa||(Aa={}));const Ma=[Aa.LINEAR,Aa.EXPONENTIAL];function Ea(t){return class extends t{constructor(){super(...arguments),this.useFog=aa.BOOLEAN(0,Ta),this.fogType=aa.INTEGER(Ma.indexOf(Aa.EXPONENTIAL),{visibleIf:{useFog:1},menu:{entries:Ma.map(((t,e)=>({name:t,value:e})))},...Ta}),this.fogColor=aa.COLOR([1,1,1],{visibleIf:{useFog:1},...Ta}),this.fogNear=aa.FLOAT(1,{range:[0,100],rangeLocked:[!0,!1],visibleIf:{useFog:1,fogType:Ma.indexOf(Aa.LINEAR)},...Ta}),this.fogFar=aa.FLOAT(100,{range:[0,100],rangeLocked:[!0,!1],visibleIf:{useFog:1,fogType:Ma.indexOf(Aa.LINEAR)},...Ta}),this.fogDensity=aa.FLOAT(25e-5,{visibleIf:{useFog:1,fogType:Ma.indexOf(Aa.EXPONENTIAL)},...Ta})}}}Ea(la);class Sa{constructor(t){this.node=t}async update(){const t=this.node.object,e=this.node.pv;if(e.useFog)if(e.fogType==Ma.indexOf(Aa.LINEAR)){const n=this.fog2(e);t.fog=n,n.color=e.fogColor,n.near=e.fogNear,n.far=e.fogFar}else{const n=this.fogExp2(e);t.fog=this.fogExp2(e),n.color=e.fogColor,n.density=e.fogDensity}else{t.fog&&(t.fog=null)}}fog2(t){return this._fog=this._fog||new ba(16777215,t.fogNear,t.fogFar)}fogExp2(t){return this._fogExp2=this._fogExp2||new wa(16777215,t.fogDensity)}static async update(t){t.sceneFogController.update()}}const Ca={computeOnDirty:!1,callback:t=>{La.update(t)}};function Na(t){return class extends t{constructor(){super(...arguments),this.useOverrideMaterial=aa.BOOLEAN(0,Ca),this.overrideMaterial=aa.NODE_PATH(\\\\\\\"\\\\\\\",{visibleIf:{useOverrideMaterial:1},nodeSelection:{context:ts.MAT},dependentOnFoundNode:!1,...Ca})}}}Na(la);class La{constructor(t){this.node=t}async update(){const t=this.node.object,e=this.node.pv;if(e.useOverrideMaterial){const n=e.overrideMaterial.nodeWithContext(ts.MAT);n?n.compute().then((e=>{t.overrideMaterial=e.material()})):this.node.states.error.set(\\\\\\\"bgTexture node not found\\\\\\\")}else t.overrideMaterial=null}static async update(t){t.SceneMaterialOverrideController.update()}}class Oa extends(Na(ya(Ea(fa(ua(la)))))){}const Pa=new Oa;class Ra extends ra{constructor(){super(...arguments),this.paramsConfig=Pa,this._object=this._createScene(),this._queued_nodes_by_id=new Map,this.sceneAutoUpdateController=new da(this),this.sceneBackgroundController=new ga(this),this.sceneEnvController=new xa(this),this.sceneFogController=new Sa(this),this.sceneMaterialOverrideController=new La(this),this._children_controller_context=ts.OBJ}static type(){return\\\\\\\"obj\\\\\\\"}initializeNode(){this._object.matrixAutoUpdate=!1,this.lifecycle.add_on_child_add_hook(this._on_child_add.bind(this)),this.lifecycle.add_on_child_remove_hook(this._on_child_remove.bind(this))}_createScene(){const t=new gs;return t.name=\\\\\\\"/\\\\\\\",t.matrixAutoUpdate=!1,t}get object(){return this._object}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}_updateScene(){this.sceneAutoUpdateController.update(),this.sceneBackgroundController.update(),this.sceneEnvController.update(),this.sceneFogController.update(),this.sceneMaterialOverrideController.update()}_addToQueue(t){const e=t.graphNodeId();return this._queued_nodes_by_id.has(e)||this._queued_nodes_by_id.set(e,t),t}async processQueue(){this._updateScene();const t=new Map,e=[];this._queued_nodes_by_id.forEach(((n,i)=>{const s=`_____${n.renderOrder}__${n.path()}`;e.push(s),t.set(s,n)})),this._queued_nodes_by_id.clear();for(let n of e){const e=t.get(n);e&&(t.delete(n),this._addToScene(e))}}_update_object(t){return this.scene().loadingController.autoUpdating()?this._addToScene(t):this._addToQueue(t)}getParentForNode(t){if(t.attachableToHierarchy()){const e=t.io.inputs.input(0);return e?e.children_group:this._object}return null}_addToScene(t){var e;if(t.attachableToHierarchy()){const n=this.getParentForNode(t);n&&(t.usedInScene()?(null===(e=t.childrenDisplayController)||void 0===e||e.request_display_node_container(),t.addObjectToParent(n)):t.removeObjectFromParent())}}_removeFromScene(t){t.removeObjectFromParent()}areChildrenCooking(){const t=this.children();for(let e of t)if(e.cookController.isCooking()||e.isDisplayNodeCooking())return!0;return!1}addToParentTransform(t){this._update_object(t)}removeFromParentTransform(t){this._update_object(t)}_on_child_add(t){t&&this._update_object(t)}_on_child_remove(t){t&&this._removeFromScene(t)}}class Ia{constructor(t){this.scene=t,this._node_context_signatures={},this._instanciated_nodes_by_context_and_type={}}init(){this._root=new Ra(this.scene),this._root.initialize_base_and_node(),this._root.params.init(),this._root._set_core_name(\\\\\\\"RootNode\\\\\\\")}root(){return this._root}_traverseNode(t,e){const n=t.children();if(n&&0!=n.length)for(let t of n)e(t),t.childrenController&&this._traverseNode(t,e)}clear(){var t;const e=this.root().children();for(let n of e)null===(t=this.root().childrenController)||void 0===t||t.removeNode(n)}node(t){return\\\\\\\"/\\\\\\\"===t?this.root():this.root().node(t)}allNodes(){let t=[this.root()],e=[this.root()],n=0;for(;e.length>0&&n<10;){const i=e.map((t=>t.childrenAllowed()?t.children():[])).flat();t=t.concat(i),e=i,n+=1}return t.flat()}nodesFromMask(t){const e=this.allNodes(),n=[];for(let i of e){const e=i.path();os.matchMask(e,t)&&n.push(i)}return n}reset_node_context_signatures(){this._node_context_signatures={}}register_node_context_signature(t){t.childrenAllowed()&&t.childrenController&&(this._node_context_signatures[t.childrenController.node_context_signature()]=!0)}node_context_signatures(){return Object.keys(this._node_context_signatures).sort().map((t=>t.toLowerCase()))}addToInstanciatedNode(t){const e=t.context(),n=t.type();this._instanciated_nodes_by_context_and_type[e]=this._instanciated_nodes_by_context_and_type[e]||{},this._instanciated_nodes_by_context_and_type[e][n]=this._instanciated_nodes_by_context_and_type[e][n]||{},this._instanciated_nodes_by_context_and_type[e][n][t.graphNodeId()]=t}removeFromInstanciatedNode(t){const e=t.context(),n=t.type();delete this._instanciated_nodes_by_context_and_type[e][n][t.graphNodeId()]}nodesByType(t){const e=[];return this._traverseNode(this.scene.root(),(n=>{n.type()==t&&e.push(n)})),e}nodesByContextAndType(t,e){const n=[],i=this._instanciated_nodes_by_context_and_type[t];if(i){const t=i[e];if(t)for(let e of Object.keys(t))n.push(t[e])}return n}}class Fa{constructor(t){this.scene=t}toJSON(t=!1){const e={},n={};for(let i of this.scene.nodesController.allNodes()){const s=new Mr(i);e[i.graphNodeId()]=s.toJSON(t);const r=i.params.all;for(let t of r)n[t.graphNodeId()]=t.toJSON()}return{nodes_by_graph_node_id:e,params_by_graph_node_id:n}}}var Da;!function(t){t.auxclick=\\\\\\\"auxclick\\\\\\\",t.click=\\\\\\\"click\\\\\\\",t.contextmenu=\\\\\\\"contextmenu\\\\\\\",t.dblclick=\\\\\\\"dblclick\\\\\\\",t.mousedown=\\\\\\\"mousedown\\\\\\\",t.mouseenter=\\\\\\\"mouseenter\\\\\\\",t.mouseleave=\\\\\\\"mouseleave\\\\\\\",t.mousemove=\\\\\\\"mousemove\\\\\\\",t.mouseover=\\\\\\\"mouseover\\\\\\\",t.mouseout=\\\\\\\"mouseout\\\\\\\",t.mouseup=\\\\\\\"mouseup\\\\\\\",t.pointerlockchange=\\\\\\\"pointerlockchange\\\\\\\",t.pointerlockerror=\\\\\\\"pointerlockerror\\\\\\\",t.select=\\\\\\\"select\\\\\\\",t.wheel=\\\\\\\"wheel\\\\\\\"}(Da||(Da={}));const Ba=[Da.auxclick,Da.click,Da.contextmenu,Da.dblclick,Da.mousedown,Da.mouseenter,Da.mouseleave,Da.mousemove,Da.mouseover,Da.mouseout,Da.mouseup,Da.pointerlockchange,Da.pointerlockerror,Da.select,Da.wheel];class za extends pi{constructor(){super(...arguments),this._require_canvas_event_listeners=!0}type(){return\\\\\\\"mouse\\\\\\\"}acceptedEventTypes(){return Ba.map((t=>`${t}`))}}class ka extends sa{constructor(){super(...arguments),this._cook_without_inputs_bound=this._cook_without_inputs.bind(this)}static context(){return ts.EVENT}initializeBaseNode(){this.uiData.setLayoutHorizontal(),this.addPostDirtyHook(\\\\\\\"cook_without_inputs_on_dirty\\\\\\\",this._cook_without_inputs_bound),this.io.inputs.set_depends_on_inputs(!1),this.io.connections.initInputs(),this.io.connection_points.spare_params.initializeNode()}_cook_without_inputs(){this.cookController.cookMainWithoutInputs()}cook(){this.cookController.endCook()}processEventViaConnectionPoint(t,e){e.event_listener?e.event_listener(t):this.processEvent(t)}processEvent(t){}async dispatchEventToOutput(t,e){this.run_on_dispatch_hook(t,e);const n=this.io.outputs.getOutputIndex(t);if(n>=0){const t=this.io.connections.outputConnections().filter((t=>t.output_index==n));let i;for(let n of t){i=n.node_dest;const t=i.io.inputs.namedInputConnectionPoints()[n.input_index];i.processEventViaConnectionPoint(e,t)}}else console.warn(`requested output '${t}' does not exist on node '${this.path()}'`)}onDispatch(t,e){this._on_dispatch_hooks_by_output_name=this._on_dispatch_hooks_by_output_name||new Map,h.pushOnArrayAtEntry(this._on_dispatch_hooks_by_output_name,t,e)}run_on_dispatch_hook(t,e){if(this._on_dispatch_hooks_by_output_name){const n=this._on_dispatch_hooks_by_output_name.get(t);if(n)for(let t of n)t(e)}}}var Ua;!function(t){t.CANVAS=\\\\\\\"canvas\\\\\\\",t.DOCUMENT=\\\\\\\"document\\\\\\\"}(Ua||(Ua={}));const Ga=[Ua.CANVAS,Ua.DOCUMENT];class Va{constructor(t){this.viewer=t,this._bound_listener_map_by_event_controller_type=new Map}updateEvents(t){const e=this.canvas();if(!e)return;const n=t.type();let i=this._bound_listener_map_by_event_controller_type.get(n);i||(i=new Map,this._bound_listener_map_by_event_controller_type.set(n,i)),i.forEach(((t,n)=>{this._eventOwner(t.data,e).removeEventListener(n,t.listener)})),i.clear();const s=e=>{this.processEvent(e,t)};for(let n of t.activeEventDatas()){this._eventOwner(n,e).addEventListener(n.type,s),i.set(n.type,{listener:s,data:n})}}_eventOwner(t,e){return\\\\\\\"resize\\\\\\\"==t.type?window:t.emitter==Ua.CANVAS?e:document}cameraNode(){return this.viewer.camerasController.cameraNode()}canvas(){return this.viewer.canvas()}init(){this.canvas&&this.viewer.scene().eventsDispatcher.traverseControllers((t=>{this.updateEvents(t)}))}registeredEventTypes(){const t=[];return this._bound_listener_map_by_event_controller_type.forEach((e=>{e.forEach(((e,n)=>{t.push(n)}))})),t}dispose(){const t=this.canvas();this._bound_listener_map_by_event_controller_type.forEach((e=>{t&&e.forEach(((e,n)=>{this._eventOwner(e.data,t).removeEventListener(n,e.listener)}))}))}processEvent(t,e){if(!this.canvas())return;const n={viewer:this.viewer,event:t,cameraNode:this.cameraNode()};e.processEvent(n)}}const Ha={visibleIf:{active:1},callback:t=>{Wa.PARAM_CALLBACK_updateRegister(t)}};class ja extends ka{constructor(){super(...arguments),this._activeEventDatas=[]}initializeBaseNode(){super.initializeBaseNode();this.lifecycle.add_on_add_hook((()=>{this.scene().eventsDispatcher.registerEventNode(this)})),this.lifecycle.add_delete_hook((()=>{this.scene().eventsDispatcher.unregisterEventNode(this)})),this.params.onParamsCreated(\\\\\\\"update_register\\\\\\\",(()=>{this._updateRegister()}))}processEvent(t){this.pv.active&&t.event&&this.dispatchEventToOutput(t.event.type,t)}static PARAM_CALLBACK_updateRegister(t){t._updateRegister()}_updateRegister(){this._updateActiveEventDatas(),this.scene().eventsDispatcher.updateViewerEventListeners(this)}_updateActiveEventDatas(){if(this._activeEventDatas=[],this.pv.active){const t=this.acceptedEventTypes();for(let e of t){const t=this.params.get(e);t&&t.value&&this._activeEventDatas.push({type:e,emitter:Ga[this.pv.element]})}}}activeEventDatas(){return this._activeEventDatas}}class Wa extends ja{acceptedEventTypes(){return[]}}const qa=new class extends la{constructor(){super(...arguments),this.active=aa.BOOLEAN(!0,{callback:t=>{Xa.PARAM_CALLBACK_updateRegister(t)},separatorAfter:!0}),this.element=aa.INTEGER(Ga.indexOf(Ua.CANVAS),{menu:{entries:Ga.map(((t,e)=>({name:t,value:e})))},separatorAfter:!0}),this.auxclick=aa.BOOLEAN(0,Ha),this.click=aa.BOOLEAN(0,Ha),this.contextmenu=aa.BOOLEAN(0,Ha),this.dblclick=aa.BOOLEAN(0,Ha),this.mousedown=aa.BOOLEAN(1,Ha),this.mouseenter=aa.BOOLEAN(0,Ha),this.mouseleave=aa.BOOLEAN(0,Ha),this.mousemove=aa.BOOLEAN(1,Ha),this.mouseover=aa.BOOLEAN(0,Ha),this.mouseout=aa.BOOLEAN(0,Ha),this.mouseup=aa.BOOLEAN(1,Ha),this.pointerlockchange=aa.BOOLEAN(0,Ha),this.pointerlockerror=aa.BOOLEAN(0,Ha),this.select=aa.BOOLEAN(0,Ha),this.wheel=aa.BOOLEAN(0,Ha),this.ctrlKey=aa.BOOLEAN(0,{...Ha,separatorBefore:!0}),this.altKey=aa.BOOLEAN(0,Ha),this.shiftKey=aa.BOOLEAN(0,Ha),this.metaKey=aa.BOOLEAN(0,Ha)}};class Xa extends ja{constructor(){super(...arguments),this.paramsConfig=qa}static type(){return\\\\\\\"mouse\\\\\\\"}acceptedEventTypes(){return Ba.map((t=>`${t}`))}initializeNode(){this.io.outputs.setNamedOutputConnectionPoints(Ba.map((t=>new Zo(t,Jo.MOUSE)))),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{const t=[this.p.auxclick,this.p.click,this.p.dblclick,this.p.mousedown,this.p.mouseenter,this.p.mouseleave,this.p.mousemove,this.p.mouseout,this.p.mouseout,this.p.mouseup,this.p.pointerlockchange,this.p.pointerlockerror,this.p.select,this.p.wheel];this.params.label.init(t,(()=>t.map((t=>t.value?t.name():void 0)).filter((t=>t)).join(\\\\\\\", \\\\\\\")))}))}))}processEvent(t){if(!this.pv.active)return;if(!t.event)return;const e=t.event;e.ctrlKey==this.pv.ctrlKey&&e.shiftKey==this.pv.shiftKey&&e.altKey==this.pv.altKey&&e.metaKey==this.pv.metaKey&&this.dispatchEventToOutput(t.event.type,t)}}var Ya;!function(t){t.pointerdown=\\\\\\\"pointerdown\\\\\\\",t.pointermove=\\\\\\\"pointermove\\\\\\\",t.pointerup=\\\\\\\"pointerup\\\\\\\"}(Ya||(Ya={}));const $a=[Ya.pointerdown,Ya.pointermove,Ya.pointerup];class Ja extends pi{constructor(){super(...arguments),this._require_canvas_event_listeners=!0}type(){return\\\\\\\"pointer\\\\\\\"}acceptedEventTypes(){return $a.map((t=>`${t}`))}}const Za=new class extends la{constructor(){super(...arguments),this.active=aa.BOOLEAN(!0,{callback:t=>{Qa.PARAM_CALLBACK_updateRegister(t)},separatorAfter:!0}),this.element=aa.INTEGER(Ga.indexOf(Ua.CANVAS),{menu:{entries:Ga.map(((t,e)=>({name:t,value:e})))},separatorAfter:!0}),this.pointerdown=aa.BOOLEAN(1,Ha),this.pointermove=aa.BOOLEAN(0,Ha),this.pointerup=aa.BOOLEAN(0,Ha),this.ctrlKey=aa.BOOLEAN(0,{...Ha,separatorBefore:!0}),this.altKey=aa.BOOLEAN(0,Ha),this.shiftKey=aa.BOOLEAN(0,Ha),this.metaKey=aa.BOOLEAN(0,Ha)}};class Qa extends ja{constructor(){super(...arguments),this.paramsConfig=Za}static type(){return\\\\\\\"pointer\\\\\\\"}acceptedEventTypes(){return $a.map((t=>`${t}`))}initializeNode(){this.io.outputs.setNamedOutputConnectionPoints($a.map((t=>new Zo(t,Jo.POINTER)))),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{const t=[this.p.pointerdown,this.p.pointermove,this.p.pointerup];this.params.label.init(t,(()=>t.map((t=>t.value?t.name():void 0)).filter((t=>t)).join(\\\\\\\", \\\\\\\")))}))}))}processEvent(t){if(!this.pv.active)return;if(!t.event)return;const e=t.event;e.ctrlKey==this.pv.ctrlKey&&e.shiftKey==this.pv.shiftKey&&e.altKey==this.pv.altKey&&e.metaKey==this.pv.metaKey&&this.dispatchEventToOutput(t.event.type,t)}}var Ka,tl;!function(t){t.SET_FRAME=\\\\\\\"setFrame\\\\\\\"}(Ka||(Ka={})),function(t){t.TIME_REACHED=\\\\\\\"timeReached\\\\\\\"}(tl||(tl={}));const el=new class extends la{constructor(){super(...arguments),this.active=aa.BOOLEAN(!0,{callback:(t,e)=>{nl.PARAM_CALLBACK_updateRegister(t)},separatorAfter:!0}),this.element=aa.INTEGER(0,{hidden:!0}),this.sceneLoaded=aa.BOOLEAN(1,Ha),this.play=aa.BOOLEAN(1,Ha),this.pause=aa.BOOLEAN(1,Ha),this.tick=aa.BOOLEAN(1,{separatorAfter:!0,...Ha}),this.treachedTime=aa.BOOLEAN(0,{callback:t=>{nl.PARAM_CALLBACK_update_time_dependency(t)}}),this.reachedTime=aa.INTEGER(10,{visibleIf:{treachedTime:1},range:[0,100],separatorAfter:!0}),this.setFrameValue=aa.INTEGER(1,{range:[0,100]}),this.setFrame=aa.BUTTON(null,{callback:t=>{nl.PARAM_CALLBACK_setFrame(t)}})}};class nl extends ja{constructor(){super(...arguments),this.paramsConfig=el}static type(){return\\\\\\\"scene\\\\\\\"}acceptedEventTypes(){return mi.map((t=>`${t}`))}dispose(){var t;null===(t=this.graph_node)||void 0===t||t.dispose(),super.dispose()}initializeNode(){this.io.inputs.setNamedInputConnectionPoints([new Zo(Ka.SET_FRAME,Jo.BASE,this._onSetFrame.bind(this)),new Zo(_i.PLAY,Jo.BASE,this._play.bind(this)),new Zo(_i.PAUSE,Jo.BASE,this._pause.bind(this))]);const t=mi.map((t=>new Zo(t,Jo.BASE)));t.push(new Zo(tl.TIME_REACHED,Jo.BASE)),this.io.outputs.setNamedOutputConnectionPoints(t),this.params.onParamsCreated(\\\\\\\"update_time_dependency\\\\\\\",(()=>{this.update_time_dependency()}))}_onSetFrame(t){this.scene().setFrame(this.pv.setFrameValue)}_play(t){this.scene().play()}_pause(t){this.scene().pause()}_onFrameUpdate(){this.scene().time()>=this.pv.reachedTime&&this.dispatchEventToOutput(tl.TIME_REACHED,{})}update_time_dependency(){this.pv.treachedTime?(this.graph_node=this.graph_node||new Mi(this.scene(),\\\\\\\"scene_node_time_graph_node\\\\\\\"),this.graph_node.addGraphInput(this.scene().timeController.graphNode),this.graph_node.addPostDirtyHook(\\\\\\\"time_update\\\\\\\",this._onFrameUpdate.bind(this))):this.graph_node&&this.graph_node.graphDisconnectPredecessors()}static PARAM_CALLBACK_setFrame(t){t._onSetFrame({})}static PARAM_CALLBACK_update_time_dependency(t){t.update_time_dependency()}}var il;!function(t){t.keydown=\\\\\\\"keydown\\\\\\\",t.keypress=\\\\\\\"keypress\\\\\\\",t.keyup=\\\\\\\"keyup\\\\\\\"}(il||(il={}));const sl=[il.keydown,il.keypress,il.keyup];class rl extends pi{constructor(){super(...arguments),this._require_canvas_event_listeners=!0}type(){return\\\\\\\"keyboard\\\\\\\"}acceptedEventTypes(){return sl.map((t=>`${t}`))}}const ol=new class extends la{constructor(){super(...arguments),this.active=aa.BOOLEAN(!0,{callback:(t,e)=>{al.PARAM_CALLBACK_updateRegister(t)},separatorAfter:!0}),this.element=aa.INTEGER(Ga.indexOf(Ua.CANVAS),{menu:{entries:Ga.map(((t,e)=>({name:t,value:e})))},separatorAfter:!0}),this.keydown=aa.BOOLEAN(1,Ha),this.keypress=aa.BOOLEAN(0,Ha),this.keyup=aa.BOOLEAN(0,Ha),this.keyCodes=aa.STRING(\\\\\\\"Digit1 KeyE ArrowDown\\\\\\\",Ha),this.ctrlKey=aa.BOOLEAN(0,Ha),this.altKey=aa.BOOLEAN(0,Ha),this.shiftKey=aa.BOOLEAN(0,Ha),this.metaKey=aa.BOOLEAN(0,Ha)}};class al extends ja{constructor(){super(...arguments),this.paramsConfig=ol}static type(){return\\\\\\\"keyboard\\\\\\\"}acceptedEventTypes(){return sl.map((t=>`${t}`))}initializeNode(){this.io.outputs.setNamedOutputConnectionPoints(sl.map((t=>new Zo(t,Jo.KEYBOARD)))),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{const t=[this.p.keydown,this.p.keypress,this.p.keyup];this.params.label.init(t.concat([this.p.keyCodes]),(()=>`${t.map((t=>t.value?t.name():void 0)).filter((t=>t)).join(\\\\\\\", \\\\\\\")} (${this.pv.keyCodes})`))}))}))}processEvent(t){if(!this.pv.active)return;if(!t.event)return;const e=t.event;if(e.ctrlKey!=this.pv.ctrlKey)return;if(e.shiftKey!=this.pv.shiftKey)return;if(e.altKey!=this.pv.altKey)return;if(e.metaKey!=this.pv.metaKey)return;if(this.pv.keyCodes.trim().length>0){if(!this.pv.keyCodes.split(\\\\\\\" \\\\\\\").includes(e.code))return}this.dispatchEventToOutput(t.event.type,t)}}var ll;!function(t){t.resize=\\\\\\\"resize\\\\\\\"}(ll||(ll={}));const cl=[ll.resize];class hl extends pi{constructor(){super(...arguments),this._require_canvas_event_listeners=!0}type(){return\\\\\\\"window\\\\\\\"}acceptedEventTypes(){return cl.map((t=>`${t}`))}}const ul=new class extends la{constructor(){super(...arguments),this.active=aa.BOOLEAN(!0,{callback:t=>{dl.PARAM_CALLBACK_updateRegister(t)},separatorAfter:!0}),this.element=aa.INTEGER(0,{hidden:!0}),this.resize=aa.BOOLEAN(1,Ha)}};class dl extends ja{constructor(){super(...arguments),this.paramsConfig=ul}static type(){return\\\\\\\"window\\\\\\\"}acceptedEventTypes(){return cl.map((t=>`${t}`))}initializeNode(){this.io.outputs.setNamedOutputConnectionPoints(cl.map((t=>new Zo(t,Jo.POINTER)))),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{const t=[this.p.resize];this.params.label.init(t,(()=>t.map((t=>t.value?t.name():void 0)).filter((t=>t)).join(\\\\\\\", \\\\\\\")))}))}))}processEvent(t){this.pv.active&&t.event&&this.dispatchEventToOutput(t.event.type,t)}}var pl;!function(t){t.dragover=\\\\\\\"dragover\\\\\\\"}(pl||(pl={}));const _l=[pl.dragover];class ml extends pi{constructor(){super(...arguments),this._require_canvas_event_listeners=!0}type(){return\\\\\\\"drag\\\\\\\"}acceptedEventTypes(){return _l.map((t=>`${t}`))}}var fl;!function(t){t.touchstart=\\\\\\\"touchstart\\\\\\\",t.touchmove=\\\\\\\"touchmove\\\\\\\",t.touchend=\\\\\\\"touchend\\\\\\\"}(fl||(fl={}));const gl=[fl.touchstart,fl.touchmove,fl.touchend];class vl extends pi{constructor(){super(...arguments),this._require_canvas_event_listeners=!0}type(){return\\\\\\\"touch\\\\\\\"}acceptedEventTypes(){return gl.map((t=>`${t}`))}}const yl=new class extends la{constructor(){super(...arguments),this.active=aa.BOOLEAN(!0,{callback:t=>{xl.PARAM_CALLBACK_updateRegister(t)},separatorAfter:!0}),this.element=aa.INTEGER(Ga.indexOf(Ua.CANVAS),{menu:{entries:Ga.map(((t,e)=>({name:t,value:e})))},separatorAfter:!0}),this.dragover=aa.BOOLEAN(1,Ha),this.ctrlKey=aa.BOOLEAN(0,{...Ha,separatorBefore:!0}),this.altKey=aa.BOOLEAN(0,Ha),this.shiftKey=aa.BOOLEAN(0,Ha),this.metaKey=aa.BOOLEAN(0,Ha)}};class xl extends ja{constructor(){super(...arguments),this.paramsConfig=yl}static type(){return\\\\\\\"drag\\\\\\\"}acceptedEventTypes(){return _l.map((t=>`${t}`))}initializeNode(){this.io.outputs.setNamedOutputConnectionPoints(_l.map((t=>new Zo(t,Jo.DRAG)))),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{const t=[this.p.dragover];this.params.label.init(t,(()=>t.map((t=>t.value?t.name():void 0)).filter((t=>t)).join(\\\\\\\", \\\\\\\")))}))}))}processEvent(t){if(!this.pv.active)return;if(!t.event)return;const e=t.event;e.ctrlKey==this.pv.ctrlKey&&e.shiftKey==this.pv.shiftKey&&e.altKey==this.pv.altKey&&e.metaKey==this.pv.metaKey&&this.dispatchEventToOutput(t.event.type,t)}}const bl=new class extends la{constructor(){super(...arguments),this.active=aa.BOOLEAN(!0,{callback:t=>{wl.PARAM_CALLBACK_updateRegister(t)},separatorAfter:!0}),this.element=aa.INTEGER(Ga.indexOf(Ua.CANVAS),{menu:{entries:Ga.map(((t,e)=>({name:t,value:e})))},separatorAfter:!0}),this.touchstart=aa.BOOLEAN(1,Ha),this.touchmove=aa.BOOLEAN(0,Ha),this.touchend=aa.BOOLEAN(0,Ha)}};class wl extends ja{constructor(){super(...arguments),this.paramsConfig=bl}static type(){return\\\\\\\"touch\\\\\\\"}acceptedEventTypes(){return gl.map((t=>`${t}`))}initializeNode(){this.io.outputs.setNamedOutputConnectionPoints(gl.map((t=>new Zo(t,Jo.DRAG)))),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{const t=[this.p.touchstart,this.p.touchmove,this.p.touchend];this.params.label.init(t,(()=>t.map((t=>t.value?t.name():void 0)).filter((t=>t)).join(\\\\\\\", \\\\\\\")))}))}))}processEvent(t){this.pv.active&&t.event&&this.dispatchEventToOutput(t.event.type,t)}}class Tl{constructor(t){this.scene=t,this._controllers=[]}registerEventNode(t){const e=this._find_or_create_controller_for_node(t);e&&e.registerNode(t)}unregisterEventNode(t){const e=this._find_or_create_controller_for_node(t);e&&e.unregisterNode(t)}updateViewerEventListeners(t){const e=this._find_or_create_controller_for_node(t);e&&e.updateViewerEventListeners()}traverseControllers(t){for(let e of this._controllers)t(e)}_find_or_create_controller_for_node(t){switch(t.type()){case al.type():return this.keyboardEventsController;case Xa.type():return this.mouseEventsController;case xl.type():return this.dragEventsController;case Qa.type():return this.pointerEventsController;case nl.type():return this.sceneEventsController;case wl.type():return this.touchEventsController;case dl.type():return this.windowEventsController}}get keyboardEventsController(){return this._keyboard_events_controller=this._keyboard_events_controller||this._create_controller(rl)}get mouseEventsController(){return this._mouse_events_controller=this._mouse_events_controller||this._create_controller(za)}get dragEventsController(){return this._drag_events_controller=this._drag_events_controller||this._create_controller(ml)}get pointerEventsController(){return this._pointer_events_controller=this._pointer_events_controller||this._create_controller(Ja)}get sceneEventsController(){return this._scene_events_controller=this._scene_events_controller||this._create_controller(fi)}get windowEventsController(){return this._window_events_controller=this._window_events_controller||this._create_controller(hl)}get touchEventsController(){return this._touch_events_controller=this._touch_events_controller||this._create_controller(vl)}_create_controller(t){const e=new t(this);return this._controllers.includes(e)||this._controllers.push(e),e}}class Al{constructor(t){this.scene=t,this._referenced_nodes_by_src_param_id=new Map,this._referencing_params_by_referenced_node_id=new Map,this._referencing_params_by_all_named_node_ids=new Map}set_reference_from_param(t,e){this._referenced_nodes_by_src_param_id.set(t.graphNodeId(),e),h.pushOnArrayAtEntry(this._referencing_params_by_referenced_node_id,e.graphNodeId(),t)}set_named_nodes_from_param(t){const e=t.decomposed_path.named_nodes();for(let n of e)h.pushOnArrayAtEntry(this._referencing_params_by_all_named_node_ids,n.graphNodeId(),t)}reset_reference_from_param(t){const e=this._referenced_nodes_by_src_param_id.get(t.graphNodeId());if(e){h.popFromArrayAtEntry(this._referencing_params_by_referenced_node_id,e.graphNodeId(),t);const n=t.decomposed_path.named_nodes();for(let e of n)h.popFromArrayAtEntry(this._referencing_params_by_all_named_node_ids,e.graphNodeId(),t);this._referenced_nodes_by_src_param_id.delete(t.graphNodeId())}}referencing_params(t){return this._referencing_params_by_referenced_node_id.get(t.graphNodeId())}referencing_nodes(t){const e=this._referencing_params_by_referenced_node_id.get(t.graphNodeId());if(e){const t=new Map;for(let n of e){const e=n.node;t.set(e.graphNodeId(),e)}const n=[];return t.forEach((t=>{n.push(t)})),n}}nodes_referenced_by(t){const e=new Set([Er.OPERATOR_PATH,Er.NODE_PATH]),n=[];for(let i of t.params.all)e.has(i.type())&&n.push(i);const i=new Map,s=[];for(let t of n)this._check_param(t,i,s);for(let t of s)i.set(t.node.graphNodeId(),t.node);const r=[];return i.forEach((t=>{r.push(t)})),r}_check_param(t,e,n){if(t instanceof _o){const i=t.found_node(),s=t.found_param();return i&&e.set(i.graphNodeId(),i),void(s&&n.push(s))}}notify_name_updated(t){const e=this._referencing_params_by_all_named_node_ids.get(t.graphNodeId());if(e)for(let n of e)n.notify_path_rebuild_required(t)}notify_params_updated(t){const e=this._referencing_params_by_all_named_node_ids.get(t.graphNodeId());if(e)for(let n of e)n.options.isSelectingParam()&&n.notify_target_param_owner_params_updated(t)}}var Ml;!function(t){t.MAX_FRAME_UPDATED=\\\\\\\"scene_maxFrameUpdated\\\\\\\",t.REALTIME_STATUS_UPDATED=\\\\\\\"scene_realtime_status_updated\\\\\\\",t.FRAME_UPDATED=\\\\\\\"scene_frame_updated\\\\\\\",t.PLAY_STATE_UPDATED=\\\\\\\"scene_play_state_updated\\\\\\\"}(Ml||(Ml={}));class El{constructor(t){this.scene=t,this._frame=0,this._time=0,this._realtimeState=!0,this._maxFrame=600,this._maxFrameLocked=!1,this._playing=!1,this._delta=0,this._graph_node=new Mi(t,\\\\\\\"time controller\\\\\\\")}get PLAY_EVENT_CONTEXT(){return this._PLAY_EVENT_CONTEXT=this._PLAY_EVENT_CONTEXT||{event:new Event(_i.PLAY)}}get PAUSE_EVENT_CONTEXT(){return this._PAUSE_EVENT_CONTEXT=this._PAUSE_EVENT_CONTEXT||{event:new Event(_i.PAUSE)}}get TICK_EVENT_CONTEXT(){return this._TICK_EVENT_CONTEXT=this._TICK_EVENT_CONTEXT||{event:new Event(_i.TICK)}}get graphNode(){return this._graph_node}frame(){return this._frame}time(){return this._time}maxFrame(){return this._maxFrame}maxFrameLocked(){return this._maxFrameLocked}realtimeState(){return this._realtimeState}setMaxFrame(t){this._maxFrame=Math.floor(t),this.scene.dispatchController.dispatch(this._graph_node,Ml.MAX_FRAME_UPDATED)}setMaxFrameLocked(t){this._maxFrameLocked=t,this.scene.dispatchController.dispatch(this._graph_node,Ml.MAX_FRAME_UPDATED)}setRealtimeState(t){this._realtimeState=t,this.scene.dispatchController.dispatch(this._graph_node,Ml.REALTIME_STATUS_UPDATED)}setTime(t,e=!0){if(t!=this._time){if(this._time=t,this._onBeforeTickCallbacks)for(let t of this._onBeforeTickCallbacks)t(this._delta);if(e){const t=Math.floor(60*this._time),e=this._ensureFrameWithinBounds(t);t!=e?this.setFrame(e,!0):this._frame=t}if(this.scene.dispatchController.dispatch(this._graph_node,Ml.FRAME_UPDATED),this.scene.uniformsController.updateTimeDependentUniformOwners(),this.scene.cooker.block(),this.graphNode.setSuccessorsDirty(),this.scene.cooker.unblock(),this.scene.eventsDispatcher.sceneEventsController.processEvent(this.TICK_EVENT_CONTEXT),this._onAfterTickCallbacks)for(let t of this._onAfterTickCallbacks)t(this._delta)}}setFrame(t,e=!0){t!=this._frame&&(t=this._ensureFrameWithinBounds(t))!=this._frame&&(this._frame=t,e&&this.setTime(this._frame/60,!1))}setFrameToStart(){this.setFrame(El.START_FRAME,!0)}incrementTimeIfPlaying(t){this._playing&&(this.scene.root().areChildrenCooking()||this.incrementTime(t))}incrementTime(t){if(this._realtimeState){this._delta=t;const e=this._time+this._delta;this.setTime(e)}else this.setFrame(this.frame()+1)}_ensureFrameWithinBounds(t){if(this._playing){if(this._maxFrameLocked&&t>this._maxFrame)return El.START_FRAME}else{if(this._maxFrameLocked&&t>this._maxFrame)return this._maxFrame;if(t=0&&this._time_dependent_uniform_owners_ids.splice(e,1)}}updateTimeDependentUniformOwners(){const t=this.scene.time();if(this._time_dependent_uniform_owners_ids)for(let e of this._time_dependent_uniform_owners_ids){this._time_dependent_uniform_owners[e].time.value=t}}addResolutionDependentUniformOwner(t,e){this._resolution_dependent_uniform_owners[t]=e,this._resolution_dependent_uniform_owners_ids||(this._resolution_dependent_uniform_owners_ids=[]),this._resolution_dependent_uniform_owners_ids.includes(t)||this._resolution_dependent_uniform_owners_ids.push(t),this._resolution&&this.updateResolutionDependentUniforms(e)}removeResolutionDependentUniformOwner(t){if(delete this._resolution_dependent_uniform_owners[t],this._resolution_dependent_uniform_owners_ids){const e=this._resolution_dependent_uniform_owners_ids.indexOf(t);e>=0&&this._resolution_dependent_uniform_owners_ids.splice(e,1)}}updateResolutionDependentUniformOwners(t){this._resolution.copy(t);for(let t of this._resolution_dependent_uniform_owners_ids){const e=this._resolution_dependent_uniform_owners[t];this.updateResolutionDependentUniforms(e)}}updateResolutionDependentUniforms(t){t.resolution.value.x=this._resolution.x,t.resolution.value.y=this._resolution.y}}class Cl{constructor(t){this.scene=t,this._viewers_by_id=new Map}registerViewer(t){this._viewers_by_id.set(t.id(),t)}unregisterViewer(t){this._viewers_by_id.delete(t.id())}traverseViewers(t){this._viewers_by_id.forEach(t)}}class Nl{constructor(){this._require_webgl2=!1}require_webgl2(){return this._require_webgl2}set_require_webgl2(){this._require_webgl2||(this._require_webgl2=!0,li.renderersController.setRequireWebGL2())}}class Ll{constructor(t){this._scene=t,this._onWindowResizeBound=this._onWindowResize.bind(this)}graphNode(){return this._coreGraphNode=this._coreGraphNode||this._createGraphNode()}_createGraphNode(){const t=new Mi(this._scene,\\\\\\\"SceneWindowController\\\\\\\");return window.addEventListener(\\\\\\\"resize\\\\\\\",this._onWindowResizeBound),t}_onWindowResize(){this.graphNode().setSuccessorsDirty()}dispose(){window.removeEventListener(\\\\\\\"resize\\\\\\\",this._onWindowResizeBound)}}class Ol{constructor(){this._params_by_id=new Map,this._assets_root=null}register_param(t){this._params_by_id.set(t.graphNodeId(),t)}deregister_param(t){this._params_by_id.delete(t.graphNodeId())}traverse_params(t){this._params_by_id.forEach(((e,n)=>{t(e)}))}root(){return this._assets_root}setRoot(t){\\\\\\\"\\\\\\\"==t&&(t=null),this._assets_root=t}}class Pl{constructor(){this._cameras_controller=new r(this),this._cooker=new o(this),this.cookController=new a,this._graph=new l,this._missing_expression_references_controller=new Ti(this),this._expressions_controller=new ui,this._nodes_controller=new Ia(this),this._objects_controller=new ca(this),this._references_controller=new Al(this),this._time_controller=new El(this),this._read_only=!1,this._graph.setScene(this),this.nodesController.init()}threejsScene(){return this.root().object}setUuid(t){return this._uuid=t}get uuid(){return this._uuid}setName(t){return t=os.sanitizeName(t),this._name=t}name(){return this._name}get camerasController(){return this._cameras_controller}mainCameraNode(){return this.camerasController.mainCameraNode()}get cooker(){return this._cooker}get assets(){return this._assets_controller=this._assets_controller||new Ol}async waitForCooksCompleted(){return this.cookController.waitForCooksCompleted()}get dispatchController(){return this._dispatch_controller=this._dispatch_controller||new hi(this)}get eventsDispatcher(){return this._events_dispatcher=this._events_dispatcher||new Tl(this)}get graph(){return this._graph}get lifecycleController(){return this._lifecycle_controller=this._lifecycle_controller||new di(this)}get loadingController(){return this._loading_controller=this._loading_controller||new gi(this)}get missingExpressionReferencesController(){return this._missing_expression_references_controller}get expressionsController(){return this._expressions_controller}get nodesController(){return this._nodes_controller}createNode(t,e){return this.root().createNode(t,e)}nodesByType(t){return this.nodesController.nodesByType(t)}get objectsController(){return this._objects_controller}findObjectByMask(t){return this._objects_controller.findObjectByMask(t)}objectsByMask(t){return this._objects_controller.objectsByMask(t)}get referencesController(){return this._references_controller}get performance(){return this._performance=this._performance||new ci}get viewersRegister(){return this._viewers_register=this._viewers_register||new Cl(this)}get timeController(){return this._time_controller}setFrame(t){this.timeController.setFrame(t)}setFrameToStart(){this.timeController.setFrameToStart()}frame(){return this.timeController.frame()}time(){return this.timeController.time()}maxFrame(){return this.timeController.maxFrame()}play(){this.timeController.play()}pause(){this.timeController.pause()}get serializer(){return this._serializer=this._serializer||new Fa(this)}toJSON(){return this.serializer.toJSON()}markAsReadOnly(t){this._read_only||(this._read_only_requester=t,this._read_only=!0)}readOnly(){return this._read_only}readOnlyRequester(){return this._read_only_requester}get uniformsController(){return this._uniformsController=this._uniformsController||new Sl(this)}get webgl_controller(){return this._webgl_controller=this._webgl_controller||new Nl}get windowController(){return this._windowController=this._windowController||new Ll(this)}dispose(){var t;null===(t=this._windowController)||void 0===t||t.dispose()}batchUpdates(t){this._cooker.block(),t(),this._cooker.unblock()}node(t){return this.nodesController.node(t)}root(){return this.nodesController.root()}registerOnBeforeTick(t,e){this.timeController.registerOnBeforeTick(t,e)}unRegisterOnBeforeTick(t){this.timeController.unRegisterOnBeforeTick(t)}registeredBeforeTickCallbackNames(){return this.timeController.registeredBeforeTickCallbackNames()}registerOnAfterTick(t,e){this.timeController.registerOnAfterTick(t,e)}unRegisterOnAfterTick(t){this.timeController.unRegisterOnAfterTick(t)}registeredAfterTickCallbackNames(){return this.timeController.registeredAfterTickCallbackNames()}}class Rl{constructor(t){this._param=t}process_data(t){const e=t.raw_input;void 0!==e&&this._param.set(e),this.add_main(t)}add_main(t){}static spare_params_data(t){return this.params_data(!0,t)}static non_spare_params_data_value(t){return this.params_data_value(!1,t)}static params_data(t,e){let n;if(e){n={};const t=Object.keys(e);let i;for(let s of t)i=e[s],i&&(n[s]=e)}return n}static params_data_value(t,e){let n;if(e){n={};const i=Object.keys(e);let s;for(let r of i)if(s=e[r],null!=s){const e=s.options,i=s.overriden_options;if(e||i){const o=s;e&&e.spare==t?null!=o.raw_input&&(n[r]={complex_data:o}):i&&(n[r]={complex_data:o})}else{const t=s;(i||null!=t)&&(n[r]={simple_data:t})}}}return n}}const Il=\\\\\\\"operationsComposer\\\\\\\";class Fl{constructor(t,e,n){this._scene=t,this.states=e,this._node=n}static type(){throw\\\\\\\"type to be overriden\\\\\\\"}type(){return this.constructor.type()}static context(){throw console.error(\\\\\\\"operation has no node_context\\\\\\\",this),\\\\\\\"context requires override\\\\\\\"}context(){return this.constructor.context()}scene(){return this._scene}cook(t,e){}}Fl.DEFAULT_PARAMS={},Fl.INPUT_CLONED_STATE=[];class Dl{constructor(t){this._node=t,this._nodes=[],this._optimized_root_node_names=new Set,this._operation_containers_by_name=new Map,this._node_inputs=[]}nodes(){return this._nodes}process_data(t,e){var n,i,s;if(!e)return;if(!this._node.childrenAllowed()||!this._node.childrenController)return;const{optimized_names:r}=Dl.child_names_by_optimized_state(e);this._nodes=[],this._optimized_root_node_names=new Set;for(let t of r)Dl.is_optimized_root_node(e,t)&&this._optimized_root_node_names.add(t);for(let r of this._optimized_root_node_names){const o=e[r],a=this._node.createNode(Il);if(a){a.setName(r),this._nodes.push(a),(null===(n=o.flags)||void 0===n?void 0:n.display)&&(null===(s=null===(i=a.flags)||void 0===i?void 0:i.display)||void 0===s||s.set(!0));const e=this._create_operation_container(t,a,o,a.name());a.set_output_operation_container(e)}}for(let n of this._nodes){const i=n.output_operation_container();if(i){this._node_inputs=[],this._add_optimized_node_inputs(t,n,e,n.name(),i),n.io.inputs.setCount(this._node_inputs.length);for(let t=0;t0}static is_optimized_root_node(t,e){const n=this.node_outputs(t,e);let i=0;return n.forEach((e=>{const n=t[e];this.is_node_optimized(n)||i++})),this.is_optimized_root_node_generic({outputs_count:n.size,non_optimized_count:i})}static is_optimized_root_node_from_node(t){var e,n,i,s;if(!(null===(n=null===(e=t.flags)||void 0===e?void 0:e.optimize)||void 0===n?void 0:n.active()))return!1;const r=t.io.connections.outputConnections().map((t=>t.node_dest));let o=0;for(let t of r)(null===(s=null===(i=t.flags)||void 0===i?void 0:i.optimize)||void 0===s?void 0:s.active())||o++;return this.is_optimized_root_node_generic({outputs_count:r.length,non_optimized_count:o})}static node_outputs(t,e){const n=Object.keys(t),i=new Set;for(let s of n)if(s!=e){const n=t[s].inputs;if(n)for(let t of n)if(m.isString(t)){t==e&&i.add(s)}}return i}_create_operation_container(t,e,n,i){const s=Rl.non_spare_params_data_value(n.params),r=Dl.operation_type(n),o=this._node.create_operation_container(r,i,s);return o&&(this._operation_containers_by_name.set(i,o),o.path_param_resolve_required()&&(e.add_operation_container_with_path_param_resolve_required(o),t.add_operations_composer_node_with_path_param_resolve_required(e))),o}static operation_type(t){return Dl.is_node_bypassed(t)?\\\\\\\"null\\\\\\\":t.type}static is_node_optimized(t){const e=t.flags;return!(!e||!e.optimize)}static is_node_bypassed(t){const e=t.flags;return!(!e||!e.bypass)}}class Bl{constructor(t){this._node=t}process_data(t,e){var n;if(!e)return;if(!this._node.childrenAllowed()||!this._node.childrenController)return;const{optimized_names:i,non_optimized_names:s}=Dl.child_names_by_optimized_state(e),r=[];for(let n of s){const i=e[n],s=i.type.toLowerCase(),o=Rl.non_spare_params_data_value(i.params);try{const t=this._node.createNode(s,o);t&&(t.setName(n),r.push(t))}catch(e){console.error(`error importing node: cannot create with type ${s}`,e);const i=os.camelCase(s);try{const t=this._node.createNode(i,o);t&&(t.setName(n),r.push(t))}catch(e){const a=`${s}Network`;try{const t=this._node.createNode(a,o);t&&(t.setName(n),r.push(t))}catch(e){const n=`failed to create node with type '${s}', '${i}' or '${a}'`;t.report.addWarning(n),li.warn(n,e)}}}}if(i.length>0){const i=new Dl(this._node);if(i.process_data(t,e),this._node.childrenController.context==ts.SOP){const t=Object.keys(e);let s;for(let i of t){(null===(n=e[i].flags)||void 0===n?void 0:n.display)&&(s=i)}if(s){const t=r.map((t=>t.name())),e=i.nodes();for(let n of e)t.push(n.name());if(!t.includes(s)){const t=`node '${`${this._node.path()}/${s}`}' with display flag has been optimized and does not exist in player mode`;console.error(t)}}}}const o=new Map;for(let n of r){if(e[n.name()]){const i=Wl.dispatch_node(n);o.set(n.name(),i),i.process_data(t,e[n.name()])}else li.warn(`possible import error for node ${n.name()}`)}for(let t of r){const n=o.get(t.name());n&&n.process_inputs_data(e[t.name()])}}}const zl=[\\\\\\\"overriden_options\\\\\\\",\\\\\\\"type\\\\\\\"];class kl{constructor(t){this._node=t}process_data(t,e){if(this.set_connection_points(e.connection_points),this._node.childrenAllowed()&&this.create_nodes(t,e.nodes),this.set_selection(e.selection),this._node.io.inputs.overrideClonedStateAllowed()){const t=e.cloned_state_overriden;t&&this._node.io.inputs.overrideClonedState(t)}this.set_flags(e),this.set_params(e.params),e.persisted_config&&this.set_persisted_config(e.persisted_config),this.from_data_custom(e)}process_inputs_data(t){const e=t.maxInputsCount;if(null!=e){const t=this._node.io.inputs.minCount();this._node.io.inputs.setCount(t,e)}this.setInputs(t.inputs)}process_ui_data(t,e){if(!e)return;if(li.playerMode())return;const n=this._node.uiData,i=e.pos;if(i){const t=(new d.a).fromArray(i);n.setPosition(t)}const s=e.comment;s&&n.setComment(s),this._node.childrenAllowed()&&this.process_nodes_ui_data(t,e.nodes)}create_nodes(t,e){if(!e)return;new Bl(this._node).process_data(t,e)}set_selection(t){if(this._node.childrenAllowed()&&this._node.childrenController&&t&&t.length>0){const e=[];t.forEach((t=>{const n=this._node.node(t);n&&e.push(n)})),this._node.childrenController.selection.set(e)}}set_flags(t){var e,n,i,s,r,o;const a=t.flags;if(a){const t=a.bypass;null!=t&&(null===(n=null===(e=this._node.flags)||void 0===e?void 0:e.bypass)||void 0===n||n.set(t));const l=a.display;null!=l&&(null===(s=null===(i=this._node.flags)||void 0===i?void 0:i.display)||void 0===s||s.set(l));const c=a.optimize;null!=c&&(null===(o=null===(r=this._node.flags)||void 0===r?void 0:r.optimize)||void 0===o||o.set(c))}}set_connection_points(t){t&&(t.in&&this._node.io.saved_connection_points_data.set_in(t.in),t.out&&this._node.io.saved_connection_points_data.set_out(t.out),this._node.io.has_connection_points_controller&&this._node.io.connection_points.update_signature_if_required())}setInputs(t){if(!t)return;let e;for(let n=0;n0,s=n.toAdd&&n.toAdd.length>0;if(i||s){this._node.params.updateParams(n);for(let e of this._node.params.spare){const n=t[e.name()];!e.parent_param&&n&&(this._is_param_data_complex(n)?this._process_param_data_complex(e.name(),n):this._process_param_data_simple(e.name(),n))}}this._node.params.runOnSceneLoadHooks()}_process_param_data_simple(t,e){var n;null===(n=this._node.params.get(t))||void 0===n||n.set(e)}_process_param_data_complex(t,e){const n=this._node.params.get(t);n&&Wl.dispatch_param(n).process_data(e)}_is_param_data_complex(t){if(m.isString(t)||m.isNumber(t)||m.isArray(t)||m.isBoolean(t))return!1;if(m.isObject(t)){const e=Object.keys(t);for(let t of zl)if(e.includes(t))return!0}return!1}set_persisted_config(t){this._node.persisted_config&&this._node.persisted_config.load(t)}from_data_custom(t){}}class Ul extends Rl{add_main(t){}}const Gl=/\\\\\\\\n+/g;class Vl extends Rl{add_main(t){let e=t.raw_input;void 0!==e&&(e=e.replace(Gl,\\\\\\\"\\\\n\\\\\\\"),this._param.set(e))}}class Hl extends Rl{add_main(t){const e=t.raw_input;e&&this._param.set(e)}}class jl extends kl{create_nodes(t,e){const n=this._node.polyNodeController;n&&n.createChildNodesFromDefinition()}}class Wl{static dispatch_node(t){return t.polyNodeController?new jl(t):new kl(t)}static dispatch_param(t){return t instanceof ro?new Ul(t):t instanceof wo?new Vl(t):t instanceof bo?new Hl(t):new Rl(t)}}class ql{constructor(t){this._warnings=[]}warnings(){return this._warnings}reset(){this._warnings=[]}addWarning(t){this._warnings.push(t)}}class Xl{constructor(t){this._data=t,this.report=new ql(this)}static async loadData(t){const e=new Xl(t);return await e.scene()}async scene(){const t=new Pl;t.loadingController.markAsLoading();const e=this._data.properties;if(e){const n=e.maxFrame||600;t.timeController.setMaxFrame(n);const i=e.maxFrameLocked;i&&t.timeController.setMaxFrameLocked(i);const s=e.realtimeState;null!=s&&t.timeController.setRealtimeState(s),t.setFrame(e.frame||El.START_FRAME),e.mainCameraNodePath&&t.camerasController.setMainCameraNodePath(e.mainCameraNodePath)}t.cooker.block(),this._base_operations_composer_nodes_with_resolve_required=void 0;const n=Wl.dispatch_node(t.root());return this._data.root&&n.process_data(this,this._data.root),this._data.ui&&n.process_ui_data(this,this._data.ui),this._resolve_operation_containers_with_path_param_resolve(),await t.loadingController.markAsLoaded(),t.cooker.unblock(),t}add_operations_composer_node_with_path_param_resolve_required(t){this._base_operations_composer_nodes_with_resolve_required||(this._base_operations_composer_nodes_with_resolve_required=[]),this._base_operations_composer_nodes_with_resolve_required.push(t)}_resolve_operation_containers_with_path_param_resolve(){if(this._base_operations_composer_nodes_with_resolve_required)for(let t of this._base_operations_composer_nodes_with_resolve_required)t.resolve_operation_containers_path_params()}}class Yl{static async importSceneData(t){null==t.editorMode&&(t.editorMode=!1);const{manifest:e,urlPrefix:n}=t,i=Object.keys(e.nodes),s=[];for(let t of i){const i=`${n}/root/${t}.json?t=${e.nodes[t]}`;s.push(i)}const r=[`${n}/root.json?t=${e.root}`,`${n}/properties.json?t=${e.properties}`];if(t.editorMode){const t=Date.now();r.push(`${n}/ui.json?t=${t}`)}for(let t of s)r.push(t);let o=0;const a=r.length,l=r.map((async e=>{const n=await fetch(e);return t.onProgress&&(o++,t.onProgress({count:o,total:a})),n})),c=await Promise.all(l),h=[];for(let t of c)h.push(await t.json());const u={root:h[0],properties:h[1]};let d=2;t.editorMode&&(u.ui=h[2],d+=1);const p={},_=Object.keys(e.nodes);for(let t=0;t<_.length;t++){const e=_[t],n=h[t+d];p[e]=n}return this.assemble(u,_,p)}static async assemble(t,e,n){const i={root:t.root,properties:t.properties,ui:t.ui};for(let t=0;t{r.matrixAutoUpdate=!0},c.onComplete=()=>{r.matrixAutoUpdate=t,r.matrixAutoUpdate||r.updateMatrix()}}if(o instanceof ah.a&&this._target_value instanceof ah.a){const t={value:0},e=o,n=(new ah.a).copy(o),i=this._target_value;c.onUpdate=()=>{e.slerpQuaternions(n,i,t.value)},a=t,c.value=1}if(m.isNumber(this._target_value)){if(m.isNumber(o))for(let t of l)c[t]=this.withOp(o,this._target_value,i)}else if(!m.isNumber(o))for(let t of l)c[t]=this.withOp(o[t],this._target_value[t],i);a&&this._startTimeline(e,n,c,a)}}}_sceneGraphProps(t,e){const n=e.split(\\\\\\\".\\\\\\\");if(!(n.length>1)){const n=t[e];let i=null;const s=[];return m.isNumber(n)?(i=t,s.push(e)):(i=n,this._target_value instanceof d.a&&s.push(\\\\\\\"x\\\\\\\",\\\\\\\"y\\\\\\\"),this._target_value instanceof p.a&&s.push(\\\\\\\"x\\\\\\\",\\\\\\\"y\\\\\\\",\\\\\\\"z\\\\\\\"),this._target_value instanceof _.a&&s.push(\\\\\\\"x\\\\\\\",\\\\\\\"y\\\\\\\",\\\\\\\"z\\\\\\\",\\\\\\\"w\\\\\\\"),this._target_value,ah.a),{target_property:n,to_target:i,property_names:s}}{const e=t[n.shift()];if(e){const t=n.join(\\\\\\\".\\\\\\\");return this._sceneGraphProps(e,t)}}}_populateWithNode(t,e,n){this._printDebug([\\\\\\\"_populateWithNode\\\\\\\",t]);const i=t.p[this._property_name];this._printDebug([\\\\\\\"target_param\\\\\\\",i]),i?i&&this._populateVarsForParam(i,e,n):li.warn(`${this._property_name} not found on node ${t.path()}`)}_populateVarsForParam(t,e,n){switch(this._printDebug([\\\\\\\"_populateVarsForParam\\\\\\\",t]),t.type()){case Er.INTEGER:return this._populateVarsForParamInteger(t,e,n);case Er.FLOAT:return this._populateVarsForParamFloat(t,e,n);case Er.VECTOR2:return this._populateVarsForParamVector2(t,e,n);case Er.VECTOR3:return this._populateVarsForParamVector3(t,e,n);case Er.VECTOR4:return this._populateVarsForParamVector4(t,e,n)}this._printDebug(`param type cannot be animated (yet): '${t.type()}' '${t.path()}'`)}_populateVarsForParamInteger(t,e,n){if(!m.isNumber(this._target_value))return void li.warn(\\\\\\\"value is not a numbber\\\\\\\",this._target_value);const i=this._commonVars(e),s={num:t.value};i.onUpdate=()=>{t.set(s.num)};const r=e.operation();i.num=this.withOp(t.value,this._target_value,r),this._startTimeline(e,n,i,s)}_populateVarsForParamFloat(t,e,n){if(!m.isNumber(this._target_value))return void li.warn(\\\\\\\"value is not a numbber\\\\\\\",this._target_value);const i=this._commonVars(e),s={num:t.value};i.onUpdate=()=>{t.set(s.num)};const r=e.operation();i.num=this.withOp(t.value,this._target_value,r),this._startTimeline(e,n,i,s)}_populateVarsForParamVector2(t,e,n){if(!(this._target_value instanceof d.a))return;const i=this._commonVars(e),s=t.value.clone(),r=[0,0];i.onUpdate=()=>{s.toArray(r),t.set(r)};const o=e.operation();i.x=this.withOp(t.value.x,this._target_value.x,o),i.y=this.withOp(t.value.y,this._target_value.y,o),this._startTimeline(e,n,i,s)}_populateVarsForParamVector3(t,e,n){if(!(this._target_value instanceof p.a))return;const i=this._commonVars(e),s=t.value.clone(),r=[0,0,0];i.onUpdate=()=>{s.toArray(r),t.set(r)};const o=e.operation();i.x=this.withOp(t.value.x,this._target_value.x,o),i.y=this.withOp(t.value.y,this._target_value.y,o),i.z=this.withOp(t.value.z,this._target_value.z,o),this._startTimeline(e,n,i,s)}_populateVarsForParamVector4(t,e,n){if(!(this._target_value instanceof _.a))return;const i=this._commonVars(e),s=t.value.clone(),r=[0,0,0,0];i.onUpdate=()=>{s.toArray(r),t.set(r)};const o=e.operation();i.x=this.withOp(t.value.x,this._target_value.x,o),i.y=this.withOp(t.value.y,this._target_value.y,o),i.z=this.withOp(t.value.z,this._target_value.z,o),i.w=this.withOp(t.value.w,this._target_value.w,o),this._startTimeline(e,n,i,s)}withOp(t,e,n){switch(n){case N_.SET:return e;case N_.ADD:return t+e;case N_.SUBSTRACT:return t-e}ls.unreachable(n)}_commonVars(t){const e={duration:t.duration()},n=t.easing()||oh.NONE;n&&(e.ease=n);const i=t.delay();null!=i&&(e.delay=i);const s=t.repeatParams();return s&&(e.repeat=s.count,e.repeatDelay=s.delay,e.yoyo=s.yoyo),e}_startTimeline(t,e,n,i){const s=t.position(),r=s?s.toParameter():void 0;e.to(i,n,r)}}function dh(t){if(void 0===t)throw new ReferenceError(\\\\\\\"this hasn't been initialised - super() hasn't been called\\\\\\\");return t}function ph(t,e){t.prototype=Object.create(e.prototype),t.prototype.constructor=t,t.__proto__=e}var _h,mh,fh,gh,vh,yh,xh,bh,wh,Th,Ah,Mh,Eh,Sh={autoSleep:120,force3D:\\\\\\\"auto\\\\\\\",nullTargetWarn:1,units:{lineHeight:\\\\\\\"\\\\\\\"}},Ch={duration:.5,overwrite:!1,delay:0},Nh=1e8,Lh=1e-8,Oh=2*Math.PI,Ph=Oh/4,Rh=0,Ih=Math.sqrt,Fh=Math.cos,Dh=Math.sin,Bh=function(t){return\\\\\\\"string\\\\\\\"==typeof t},zh=function(t){return\\\\\\\"function\\\\\\\"==typeof 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N_,L_=pp.registerPlugin(C_)||pp;L_.core.Tween;!function(t){t.SET=\\\\\\\"set\\\\\\\",t.ADD=\\\\\\\"add\\\\\\\",t.SUBSTRACT=\\\\\\\"substract\\\\\\\"}(N_||(N_={}));const O_=[N_.SET,N_.ADD,N_.SUBSTRACT];class P_{constructor(){this._timeline_builders=[],this._duration=1,this._operation=N_.SET,this._delay=0,this._debug=!1}setDebug(t){this._debug=t}_printDebug(t){this._debug&&console.log(t)}addTimelineBuilder(t){this._timeline_builders.push(t),t.setParent(this)}timelineBuilders(){return this._timeline_builders}setParent(t){this._parent=t}parent(){return this._parent}setTarget(t){this._target=t;for(let e of this._timeline_builders)e.setTarget(t)}target(){return this._target}setDuration(t){if(t>=0){this._duration=t;for(let e of this._timeline_builders)e.setDuration(t)}}duration(){return this._duration}setEasing(t){this._easing=t;for(let e of this._timeline_builders)e.setEasing(t)}easing(){return this._easing}setOperation(t){this._operation=t;for(let e of this._timeline_builders)e.setOperation(t)}operation(){return this._operation}setRepeatParams(t){this._repeat_params=t;for(let e of this._timeline_builders)e.setRepeatParams(t)}repeatParams(){return this._repeat_params}setDelay(t){this._delay=t;for(let e of this._timeline_builders)e.setDelay(t)}delay(){return this._delay}setPosition(t){this._position=t}position(){return this._position}setUpdateCallback(t){this._update_callback=t}updateCallback(){return this._update_callback}clone(){const t=new P_;if(t.setDuration(this._duration),t.setOperation(this._operation),t.setDelay(this._delay),this._target&&t.setTarget(this._target.clone()),this._easing&&t.setEasing(this._easing),this._delay&&t.setDelay(this._delay),this._update_callback&&t.setUpdateCallback(this._update_callback.clone()),this._repeat_params&&t.setRepeatParams({count:this._repeat_params.count,delay:this._repeat_params.delay,yoyo:this._repeat_params.yoyo}),this._property){const e=this._property.name();e&&t.setPropertyName(e);const n=this._property.targetValue();null!=n&&t.setPropertyValue(n)}this._position&&t.setPosition(this._position.clone());for(let e of this._timeline_builders){const n=e.clone();t.addTimelineBuilder(n)}return t}setPropertyName(t){this.property().setName(t)}property(){return this._property=this._property||new uh}propertyName(){return this.property().name()}setPropertyValue(t){this.property().setTargetValue(t)}populate(t){var e;this._printDebug([\\\\\\\"populate\\\\\\\",this,t]);for(let n of this._timeline_builders){const i=L_.timeline();n.setDebug(this._debug),n.populate(i);const s=(null===(e=n.position())||void 0===e?void 0:e.toParameter())||void 0;t.add(i,s)}this._property&&this._target&&(this._property.setDebug(this._debug),this._property.addToTimeline(this,t,this._target))}}const R_=new class extends la{constructor(){super(...arguments),this.count=aa.INTEGER(1,{range:[1,20],rangeLocked:[!0,!1]})}};class I_ extends ih{constructor(){super(...arguments),this.paramsConfig=R_}static type(){return\\\\\\\"copy\\\\\\\"}initializeNode(){this.io.inputs.setCount(1)}async cook(t){const e=new P_;for(let t=0;t{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.delay])}))}))}cook(t){const e=t[0]||new P_;e.setDelay(this.pv.delay),this.setTimelineBuilder(e)}}const B_=new class extends la{constructor(){super(...arguments),this.duration=aa.FLOAT(1,{range:[0,10],rangeLocked:[!0,!1]})}};class z_ extends ih{constructor(){super(...arguments),this.paramsConfig=B_}static type(){return\\\\\\\"duration\\\\\\\"}initializeNode(){this.io.inputs.setCount(0,1),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.duration])}))}))}cook(t){const e=t[0]||new P_;e.setDuration(this.pv.duration),this.setTimelineBuilder(e)}}const k_=new class extends la{constructor(){super(...arguments),this.name=aa.INTEGER(lh.indexOf(oh.POWER4),{menu:{entries:lh.map(((t,e)=>({name:t,value:e})))}}),this.inOut=aa.INTEGER(hh.indexOf(ch.OUT),{menu:{entries:hh.map(((t,e)=>({name:t,value:e})))}})}};class U_ extends ih{constructor(){super(...arguments),this.paramsConfig=k_}static type(){return\\\\\\\"easing\\\\\\\"}initializeNode(){this.io.inputs.setCount(0,1),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.name,this.p.inOut],(()=>this.easing_full_name()))}))}))}easing_full_name(){const t=lh[this.pv.name];if(t==oh.NONE)return t;return`${t}.${hh[this.pv.inOut]}`}cook(t){const e=t[0]||new P_,n=this.easing_full_name();e.setEasing(n),this.setTimelineBuilder(e)}}var G_;!function(t){t.RELATIVE=\\\\\\\"relative\\\\\\\",t.ABSOLUTE=\\\\\\\"absolute\\\\\\\"}(G_||(G_={}));const V_=[G_.RELATIVE,G_.ABSOLUTE];var H_;!function(t){t.START=\\\\\\\"start\\\\\\\",t.END=\\\\\\\"end\\\\\\\"}(H_||(H_={}));const j_=[H_.START,H_.END];class W_{constructor(){this._mode=G_.RELATIVE,this._relativeTo=H_.END,this._offset=0}clone(){const t=new W_;return t.setMode(this._mode),t.setRelativeTo(this._relativeTo),t.setOffset(this._offset),t}setMode(t){this._mode=t}mode(){return this._mode}setRelativeTo(t){this._relativeTo=t}relativeTo(){return this._relativeTo}setOffset(t){this._offset=t}offset(){return this._offset}toParameter(){switch(this._mode){case G_.RELATIVE:return this._relative_position_param();case G_.ABSOLUTE:return this._absolutePositionParam()}ls.unreachable(this._mode)}_relative_position_param(){switch(this._relativeTo){case H_.END:return this._offsetString();case H_.START:return`<${this._offset}`}ls.unreachable(this._relativeTo)}_absolutePositionParam(){return this._offset}_offsetString(){return this._offset>0?`+=${this._offset}`:`-=${Math.abs(this._offset)}`}}var q_;!function(t){t.ALL_TOGETHER=\\\\\\\"play all together\\\\\\\",t.ONE_AT_A_TIME=\\\\\\\"play one at a time\\\\\\\"}(q_||(q_={}));const X_=[q_.ALL_TOGETHER,q_.ONE_AT_A_TIME];const Y_=new class extends la{constructor(){super(...arguments),this.mode=aa.INTEGER(0,{menu:{entries:X_.map(((t,e)=>({name:t,value:e})))}}),this.offset=aa.FLOAT(0,{range:[-1,1]}),this.overridePositions=aa.BOOLEAN(0),this.inputsCount=aa.INTEGER(4,{range:[1,32],rangeLocked:[!0,!1],callback:t=>{$_.PARAM_CALLBACK_setInputsCount(t)}})}};class $_ extends ih{constructor(){super(...arguments),this.paramsConfig=Y_}static type(){return\\\\\\\"merge\\\\\\\"}initializeNode(){this.io.inputs.setCount(0,4),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.mode],(()=>X_[this.pv.mode]))})),this.params.addOnSceneLoadHook(\\\\\\\"update inputs\\\\\\\",(()=>{this._callbackUpdateInputsCount()}))}))}cook(t){const e=new P_;let n=0;for(let i of t)i&&(n>0&&this._update_timeline_builder(i),e.addTimelineBuilder(i),n++);this.setTimelineBuilder(e)}_update_timeline_builder(t){const e=X_[this.pv.mode];switch(e){case q_.ALL_TOGETHER:return this._set_play_all_together(t);case q_.ONE_AT_A_TIME:return this._set_play_one_at_a_time(t)}ls.unreachable(e)}_set_play_all_together(t){let e=t.position();e&&!this.pv.overridePositions||(e=new W_,e.setMode(G_.RELATIVE),e.setRelativeTo(H_.START),e.setOffset(this.pv.offset),t.setPosition(e))}_set_play_one_at_a_time(t){let e=t.position();e&&!this.pv.overridePositions||(e=new W_,e.setMode(G_.RELATIVE),e.setRelativeTo(H_.END),e.setOffset(this.pv.offset),t.setPosition(e))}_callbackUpdateInputsCount(){this.io.inputs.setCount(1,this.pv.inputsCount),this.emit(Ei.INPUTS_UPDATED)}static PARAM_CALLBACK_setInputsCount(t){t._callbackUpdateInputsCount()}}const J_=new class extends la{constructor(){super(...arguments),this.play=aa.BUTTON(null,{callback:t=>{Z_.PARAM_CALLBACK_play(t)}}),this.pause=aa.BUTTON(null,{callback:t=>{Z_.PARAM_CALLBACK_pause(t)}}),this.debug=aa.BOOLEAN(0)}};class Z_ extends ih{constructor(){super(...arguments),this.paramsConfig=J_}static type(){return\\\\\\\"null\\\\\\\"}initializeNode(){this.io.inputs.setCount(0,1),this.io.inputs.initInputsClonedState(Ki.FROM_NODE)}cook(t){const e=t[0]||new P_;this.setTimelineBuilder(e)}async play(){return new Promise((async t=>{const e=await this.compute();e&&(this._timeline_builder=e.coreContent(),this._timeline_builder&&(this._timeline&&this._timeline.kill(),this._timeline=L_.timeline({onComplete:t}),this.pv.debug&&console.log(`play from '${this.path()}'`),this._timeline_builder.setDebug(this.pv.debug),this._timeline_builder.populate(this._timeline)))}))}async pause(){this._timeline&&this._timeline.pause()}static PARAM_CALLBACK_play(t){t.play()}static PARAM_CALLBACK_pause(t){t.pause()}}const Q_=new class extends la{constructor(){super(...arguments),this.operation=aa.INTEGER(0,{menu:{entries:O_.map(((t,e)=>({value:e,name:t})))}})}};class K_ extends ih{constructor(){super(...arguments),this.paramsConfig=Q_}static type(){return\\\\\\\"operation\\\\\\\"}initializeNode(){this.io.inputs.setCount(0,1),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.operation],(()=>O_[this.pv.operation]))}))}))}cook(t){const e=t[0]||new P_;e.setOperation(O_[this.pv.operation]),this.setTimelineBuilder(e)}}const tm=new class extends la{constructor(){super(...arguments),this.mode=aa.INTEGER(0,{menu:{entries:V_.map(((t,e)=>({name:t,value:e})))}}),this.relativeTo=aa.INTEGER(0,{menu:{entries:j_.map(((t,e)=>({name:t,value:e})))}}),this.offset=aa.FLOAT(0)}};class em extends ih{constructor(){super(...arguments),this.paramsConfig=tm}static type(){return\\\\\\\"position\\\\\\\"}initializeNode(){this.io.inputs.setCount(0,1),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.mode,this.p.relativeTo,this.p.offset],(()=>{switch(V_[this.pv.mode]){case G_.RELATIVE:return this._relative_label();case G_.ABSOLUTE:return this._absolute_label()}}))}))}))}_relative_label(){const t=this.pv.offset>0?\\\\\\\"after\\\\\\\":\\\\\\\"before\\\\\\\",e=j_[this.pv.relativeTo];return`${Math.abs(this.pv.offset)} ${t} ${e}`}_absolute_label(){return\\\\\\\"absolute\\\\\\\"}cook(t){const e=t[0]||new P_,n=new W_;n.setMode(V_[this.pv.mode]),n.setRelativeTo(j_[this.pv.relativeTo]),n.setOffset(this.pv.offset),e.setPosition(n),this.setTimelineBuilder(e)}}const nm=new class extends la{constructor(){super(...arguments),this.name=aa.STRING(\\\\\\\"position\\\\\\\")}};class im extends ih{constructor(){super(...arguments),this.paramsConfig=nm}static type(){return\\\\\\\"propertyName\\\\\\\"}initializeNode(){this.io.inputs.setCount(0,1),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.name])}))}))}cook(t){const e=t[0]||new P_;e.setPropertyName(this.pv.name),this.setTimelineBuilder(e)}}var sm;!function(t){t.CUSTOM=\\\\\\\"custom\\\\\\\",t.FROM_SCENE_GRAPH=\\\\\\\"from scene graph\\\\\\\",t.FROM_NODE=\\\\\\\"from node\\\\\\\"}(sm||(sm={}));const rm=[sm.CUSTOM,sm.FROM_SCENE_GRAPH,sm.FROM_NODE],om=rm.indexOf(sm.CUSTOM),am=rm.indexOf(sm.FROM_SCENE_GRAPH),lm=rm.indexOf(sm.FROM_NODE);const cm=new class extends la{constructor(){super(...arguments),this.mode=aa.INTEGER(om,{menu:{entries:rm.map(((t,e)=>({name:t,value:e})))}}),this.nodePath=aa.NODE_PATH(\\\\\\\"\\\\\\\",{visibleIf:{mode:lm}}),this.objectMask=aa.STRING(\\\\\\\"*geo1\\\\\\\",{visibleIf:{mode:am}}),this.printResolve=aa.BUTTON(null,{visibleIf:{mode:am},callback:t=>{hm.PARAM_CALLBACK_print_resolve(t)}}),this.overridePropertyName=aa.BOOLEAN(0,{visibleIf:[{mode:am},{mode:lm}]}),this.propertyName=aa.STRING(\\\\\\\"\\\\\\\",{visibleIf:[{overridePropertyName:!0,mode:am},{overridePropertyName:!0,mode:lm}]}),this.size=aa.INTEGER(3,{range:[1,4],rangeLocked:[!0,!0],visibleIf:{mode:om}}),this.value1=aa.FLOAT(0,{visibleIf:{mode:om,size:1}}),this.value2=aa.VECTOR2([0,0],{visibleIf:{mode:om,size:2}}),this.value3=aa.VECTOR3([0,0,0],{visibleIf:{mode:om,size:3}}),this.value4=aa.VECTOR4([0,0,0,0],{visibleIf:{mode:om,size:4}})}};class hm extends ih{constructor(){super(...arguments),this.paramsConfig=cm}static type(){return\\\\\\\"propertyValue\\\\\\\"}initializeNode(){this.io.inputs.setCount(0,1)}async cook(t){const e=t[0]||new P_;await this._prepare_timeline_builder(e),this.setTimelineBuilder(e)}setMode(t){this.p.mode.set(rm.indexOf(t))}async _prepare_timeline_builder(t){const e=rm[this.pv.mode];switch(e){case sm.CUSTOM:return this._prepare_timebuilder_custom(t);case sm.FROM_SCENE_GRAPH:return this._prepare_timebuilder_from_scene_graph(t);case sm.FROM_NODE:return await this._prepare_timebuilder_from_node(t)}ls.unreachable(e)}_prepare_timebuilder_custom(t){const e=[this.pv.value1,this.pv.value2.clone(),this.pv.value3.clone(),this.pv.value4.clone()][this.pv.size-1];t.setPropertyValue(e)}_prepare_timebuilder_from_scene_graph(t){const e=this.pv.overridePropertyName?this.pv.propertyName:t.propertyName();if(!e)return;const n=this._foundObjectFromSceneGraph();if(n){const i=n[e];i&&(m.isNumber(i)||m.isVector(i)||i instanceof ah.a)&&t.setPropertyValue(i)}}async _prepare_timebuilder_from_node(t){const e=this.pv.overridePropertyName?this.pv.propertyName:t.propertyName();if(!e)return;const n=this.pv.nodePath.node();if(!n)return;const i=n.params.get(e);if(!i)return;i.isDirty()&&await i.compute();const s=i.value;s&&(m.isNumber(s)||m.isVector(s))&&t.setPropertyValue(s)}static PARAM_CALLBACK_print_resolve(t){t.printResolve()}_foundObjectFromSceneGraph(){return this.scene().findObjectByMask(this.pv.objectMask)}printResolve(){const t=this._foundObjectFromSceneGraph();console.log(t)}}const um=new class extends la{constructor(){super(...arguments),this.unlimited=aa.BOOLEAN(0),this.count=aa.INTEGER(1,{range:[0,10],visibleIf:{unlimited:0}}),this.delay=aa.FLOAT(0),this.yoyo=aa.BOOLEAN(0)}};class dm extends ih{constructor(){super(...arguments),this.paramsConfig=um}static type(){return\\\\\\\"repeat\\\\\\\"}initializeNode(){this.io.inputs.setCount(0,1),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.unlimited,this.p.count,this.p.yoyo],(()=>`${`${this.p.unlimited?\\\\\\\"unlimited\\\\\\\":this.pv.count}`} (yoyo: ${this.pv.yoyo})`))}))}))}_repeat_params(){return{count:this.pv.unlimited?-1:this.pv.count,delay:this.pv.delay,yoyo:this.pv.yoyo}}cook(t){const e=t[0]||new P_;e.setRepeatParams(this._repeat_params()),this.setTimelineBuilder(e)}}const pm=new class extends la{constructor(){super(...arguments),this.input=aa.INTEGER(0,{range:[0,3],rangeLocked:[!0,!0]})}};class _m extends ih{constructor(){super(...arguments),this.paramsConfig=pm}static type(){return\\\\\\\"switch\\\\\\\"}initializeNode(){this.io.inputs.setCount(0,4)}cook(t){const e=t[this.pv.input];e?this.setTimelineBuilder(e):this.states.error.set(`input ${this.pv.input} is not valid`)}}class mm{constructor(t,e){this._scene=t,this._options=e}clone(){return new mm(this._scene,this._options)}objects(){const t=this._options.objectMask;if(t)return this._scene.objectsByMask(t)}node(){if(!this._options.node)return;const t=this._options.node;return t.relativeTo.node(t.path)}}class fm{constructor(){this._update_matrix=!1}clone(){const t=new fm;return t.setUpdateMatrix(this._update_matrix),t}setUpdateMatrix(t){this._update_matrix=t}updateMatrix(){return this._update_matrix}}var gm;!function(t){t.SCENE_GRAPH=\\\\\\\"scene graph\\\\\\\",t.NODE=\\\\\\\"node\\\\\\\"}(gm||(gm={}));const vm=[gm.SCENE_GRAPH,gm.NODE],ym=vm.indexOf(gm.SCENE_GRAPH),xm=vm.indexOf(gm.NODE);const bm=new class extends la{constructor(){super(...arguments),this.type=aa.INTEGER(ym,{menu:{entries:vm.map(((t,e)=>({name:t,value:e})))}}),this.nodePath=aa.OPERATOR_PATH(\\\\\\\"\\\\\\\",{visibleIf:{type:xm}}),this.objectMask=aa.STRING(\\\\\\\"/geo*\\\\\\\",{visibleIf:{type:ym}}),this.updateMatrix=aa.BOOLEAN(0,{visibleIf:{type:ym}}),this.printResolve=aa.BUTTON(null,{callback:(t,e)=>{wm.PARAM_CALLBACK_print_resolve(t)}})}};class wm extends ih{constructor(){super(...arguments),this.paramsConfig=bm}static type(){return\\\\\\\"target\\\\\\\"}initializeNode(){this.io.inputs.setCount(0,1),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.type,this.p.nodePath,this.p.objectMask],(()=>{const t=vm[this.pv.type];switch(t){case gm.NODE:return this.pv.nodePath;case gm.SCENE_GRAPH:return this.pv.objectMask}ls.unreachable(t)}))}))}))}cook(t){const e=t[0]||new P_,n=this._create_target(e);e.setTarget(n),this._set_update_callback(e),this.setTimelineBuilder(e)}setTargetType(t){this.p.type.set(vm.indexOf(t))}_create_target(t){const e=vm[this.pv.type];switch(e){case gm.NODE:return new mm(this.scene(),{node:{path:this.pv.nodePath,relativeTo:this}});case gm.SCENE_GRAPH:return new mm(this.scene(),{objectMask:this.pv.objectMask})}ls.unreachable(e)}_set_update_callback(t){const e=vm[this.pv.type];let n=t.updateCallback();switch(e){case gm.NODE:return;case gm.SCENE_GRAPH:return void(this.pv.updateMatrix&&(n=n||new fm,n.setUpdateMatrix(this.pv.updateMatrix),t.setUpdateCallback(n)))}ls.unreachable(e)}static PARAM_CALLBACK_print_resolve(t){t.print_resolve()}print_resolve(){const t=vm[this.pv.type],e=new P_,n=this._create_target(e);switch(t){case gm.NODE:return console.log(n.node());case gm.SCENE_GRAPH:return console.log(n.objects())}}}class Tm extends sa{static context(){return ts.ANIM}cook(){this.cookController.endCook()}}class Am extends Tm{}class Mm extends Am{constructor(){super(...arguments),this._children_controller_context=ts.ANIM}static type(){return es.ANIM}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class Em extends Am{constructor(){super(...arguments),this._children_controller_context=ts.COP}static type(){return es.COP}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class Sm extends Am{constructor(){super(...arguments),this._children_controller_context=ts.EVENT}static type(){return es.EVENT}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class Cm extends Am{constructor(){super(...arguments),this._children_controller_context=ts.MAT}static type(){return es.MAT}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}const Nm={dependsOnDisplayNode:!0};class Lm{constructor(t,e,n=Nm){this.node=t,this.options=n,this._initialized=!1,this._display_node=void 0,this._graph_node=new Mi(t.scene(),\\\\\\\"DisplayNodeController\\\\\\\"),this._graph_node.node=t,this._on_display_node_remove_callback=e.onDisplayNodeRemove,this._on_display_node_set_callback=e.onDisplayNodeSet,this._on_display_node_update_callback=e.onDisplayNodeUpdate}dispose(){this._graph_node.dispose()}displayNode(){return this._display_node}initializeNode(){this._initialized?console.error(\\\\\\\"display node controller already initialed\\\\\\\",this.node):(this._initialized=!0,this.node.lifecycle.add_on_child_add_hook((t=>{var e,n;this._display_node||null===(n=null===(e=t.flags)||void 0===e?void 0:e.display)||void 0===n||n.set(!0)})),this.node.lifecycle.add_on_child_remove_hook((t=>{var e,n,i;if(t.graphNodeId()==(null===(e=this._display_node)||void 0===e?void 0:e.graphNodeId())){const t=this.node.children(),e=t[t.length-1];e?null===(i=null===(n=e.flags)||void 0===n?void 0:n.display)||void 0===i||i.set(!0):this.setDisplayNode(void 0)}})),this._graph_node.dirtyController.addPostDirtyHook(\\\\\\\"_request_display_node_container\\\\\\\",(()=>{this._on_display_node_update_callback&&this._on_display_node_update_callback()})))}async setDisplayNode(t){if(this._initialized||console.error(\\\\\\\"display node controller not initialized\\\\\\\",this.node),this._display_node!=t){const e=this._display_node;e&&(e.flags.display.set(!1),this.options.dependsOnDisplayNode&&this._graph_node.removeGraphInput(e),this._on_display_node_remove_callback&&this._on_display_node_remove_callback()),this._display_node=t,this._display_node&&(this.options.dependsOnDisplayNode&&this._graph_node.addGraphInput(this._display_node),this._on_display_node_set_callback&&this._on_display_node_set_callback())}}}class Om{constructor(t=!0){this.autoStart=t,this.startTime=0,this.oldTime=0,this.elapsedTime=0,this.running=!1}start(){this.startTime=Pm(),this.oldTime=this.startTime,this.elapsedTime=0,this.running=!0}stop(){this.getElapsedTime(),this.running=!1,this.autoStart=!1}getElapsedTime(){return this.getDelta(),this.elapsedTime}getDelta(){let t=0;if(this.autoStart&&!this.running)return this.start(),0;if(this.running){const e=Pm();t=(e-this.oldTime)/1e3,this.oldTime=e,this.elapsedTime+=t}return t}}function Pm(){return(\\\\\\\"undefined\\\\\\\"==typeof performance?Date:performance).now()}var Rm={uniforms:{tDiffuse:{value:null},opacity:{value:1}},vertexShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\tvarying vec2 vUv;\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\tvUv = uv;\\\\n\\\\t\\\\t\\\\tgl_Position = projectionMatrix * modelViewMatrix * vec4( position, 1.0 );\\\\n\\\\n\\\\t\\\\t}\\\\\\\",fragmentShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\tuniform float opacity;\\\\n\\\\n\\\\t\\\\tuniform sampler2D tDiffuse;\\\\n\\\\n\\\\t\\\\tvarying vec2 vUv;\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\tvec4 texel = texture2D( tDiffuse, vUv );\\\\n\\\\t\\\\t\\\\tgl_FragColor = opacity * texel;\\\\n\\\\n\\\\t\\\\t}\\\\\\\"};class Im{constructor(){this.enabled=!0,this.needsSwap=!0,this.clear=!1,this.renderToScreen=!1}setSize(){}render(){console.error(\\\\\\\"THREE.Pass: .render() must be implemented in derived pass.\\\\\\\")}}const Fm=new ot.a(-1,1,1,-1,0,1),Dm=new S.a;Dm.setAttribute(\\\\\\\"position\\\\\\\",new C.c([-1,3,0,-1,-1,0,3,-1,0],3)),Dm.setAttribute(\\\\\\\"uv\\\\\\\",new C.c([0,2,0,0,2,0],2));class Bm{constructor(t){this._mesh=new B.a(Dm,t)}dispose(){this._mesh.geometry.dispose()}render(t){t.render(this._mesh,Fm)}get material(){return this._mesh.material}set material(t){this._mesh.material=t}}class zm extends Im{constructor(t,e){super(),this.textureID=void 0!==e?e:\\\\\\\"tDiffuse\\\\\\\",t instanceof F?(this.uniforms=t.uniforms,this.material=t):t&&(this.uniforms=I.clone(t.uniforms),this.material=new F({defines:Object.assign({},t.defines),uniforms:this.uniforms,vertexShader:t.vertexShader,fragmentShader:t.fragmentShader})),this.fsQuad=new Bm(this.material)}render(t,e,n){this.uniforms[this.textureID]&&(this.uniforms[this.textureID].value=n.texture),this.fsQuad.material=this.material,this.renderToScreen?(t.setRenderTarget(null),this.fsQuad.render(t)):(t.setRenderTarget(e),this.clear&&t.clear(t.autoClearColor,t.autoClearDepth,t.autoClearStencil),this.fsQuad.render(t))}}class km extends Im{constructor(t,e){super(),this.scene=t,this.camera=e,this.clear=!0,this.needsSwap=!1,this.inverse=!1}render(t,e,n){const i=t.getContext(),s=t.state;let r,o;s.buffers.color.setMask(!1),s.buffers.depth.setMask(!1),s.buffers.color.setLocked(!0),s.buffers.depth.setLocked(!0),this.inverse?(r=0,o=1):(r=1,o=0),s.buffers.stencil.setTest(!0),s.buffers.stencil.setOp(i.REPLACE,i.REPLACE,i.REPLACE),s.buffers.stencil.setFunc(i.ALWAYS,r,4294967295),s.buffers.stencil.setClear(o),s.buffers.stencil.setLocked(!0),t.setRenderTarget(n),this.clear&&t.clear(),t.render(this.scene,this.camera),t.setRenderTarget(e),this.clear&&t.clear(),t.render(this.scene,this.camera),s.buffers.color.setLocked(!1),s.buffers.depth.setLocked(!1),s.buffers.stencil.setLocked(!1),s.buffers.stencil.setFunc(i.EQUAL,1,4294967295),s.buffers.stencil.setOp(i.KEEP,i.KEEP,i.KEEP),s.buffers.stencil.setLocked(!0)}}class Um extends Im{constructor(){super(),this.needsSwap=!1}render(t){t.state.buffers.stencil.setLocked(!1),t.state.buffers.stencil.setTest(!1)}}class Gm{constructor(t,e){if(this.renderer=t,void 0===e){const n={minFilter:w.V,magFilter:w.V,format:w.Ib},i=t.getSize(new d.a);this._pixelRatio=t.getPixelRatio(),this._width=i.width,this._height=i.height,(e=new Q(this._width*this._pixelRatio,this._height*this._pixelRatio,n)).texture.name=\\\\\\\"EffectComposer.rt1\\\\\\\"}else this._pixelRatio=1,this._width=e.width,this._height=e.height;this.renderTarget1=e,this.renderTarget2=e.clone(),this.renderTarget2.texture.name=\\\\\\\"EffectComposer.rt2\\\\\\\",this.writeBuffer=this.renderTarget1,this.readBuffer=this.renderTarget2,this.renderToScreen=!0,this.passes=[],void 0===Rm&&console.error(\\\\\\\"THREE.EffectComposer relies on CopyShader\\\\\\\"),void 0===zm&&console.error(\\\\\\\"THREE.EffectComposer relies on ShaderPass\\\\\\\"),this.copyPass=new zm(Rm),this.clock=new Om}swapBuffers(){const t=this.readBuffer;this.readBuffer=this.writeBuffer,this.writeBuffer=t}addPass(t){this.passes.push(t),t.setSize(this._width*this._pixelRatio,this._height*this._pixelRatio)}insertPass(t,e){this.passes.splice(e,0,t),t.setSize(this._width*this._pixelRatio,this._height*this._pixelRatio)}removePass(t){const e=this.passes.indexOf(t);-1!==e&&this.passes.splice(e,1)}isLastEnabledPass(t){for(let e=t+1;e({name:Object.keys(t)[0],value:Object.values(t)[0]}))),$m=Wm.map((t=>({name:Object.keys(t)[0],value:Object.values(t)[0]})));class Jm extends la{constructor(){super(...arguments),this.prependRenderPass=aa.BOOLEAN(1),this.useRenderTarget=aa.BOOLEAN(1),this.tmagFilter=aa.BOOLEAN(0,{visibleIf:{useRenderTarget:1}}),this.magFilter=aa.INTEGER(qm,{visibleIf:{useRenderTarget:1,tmagFilter:1},menu:{entries:Ym}}),this.tminFilter=aa.BOOLEAN(0,{visibleIf:{useRenderTarget:1}}),this.minFilter=aa.INTEGER(Xm,{visibleIf:{useRenderTarget:1,tminFilter:1},menu:{entries:$m}}),this.stencilBuffer=aa.BOOLEAN(0,{visibleIf:{useRenderTarget:1}}),this.sampling=aa.INTEGER(1,{range:[1,4],rangeLocked:[!0,!1]})}}class Zm{constructor(t){this.node=t,this._renderer_size=new d.a}displayNodeControllerCallbacks(){return{onDisplayNodeRemove:()=>{},onDisplayNodeSet:()=>{this.node.setDirty()},onDisplayNodeUpdate:()=>{this.node.setDirty()}}}createEffectsComposer(t){const e=t.renderer;let n;if(this.node.pv.useRenderTarget){const t=this._create_render_target(e);n=new Gm(e,t)}else n=new Gm(e);return n.setPixelRatio(window.devicePixelRatio*this.node.pv.sampling),this._build_passes(n,t),n}_create_render_target(t){let e;t.autoClear=!1;const n={format:w.ic,stencilBuffer:this.node.pv.stencilBuffer};return this.node.pv.tminFilter&&(n.minFilter=this.node.pv.minFilter),this.node.pv.tmagFilter&&(n.magFilter=this.node.pv.magFilter),t.getDrawingBufferSize(this._renderer_size),e=li.renderersController.renderTarget(this._renderer_size.x,this._renderer_size.y,n),e}_build_passes(t,e){if(this.node.pv.prependRenderPass){const n=new Hm(e.scene,e.camera);t.addPass(n)}const n=this.node.displayNodeController.displayNode();n&&n.setupComposer({composer:t,camera:e.camera,resolution:e.resolution,camera_node:e.camera_node,scene:e.scene,requester:e.requester})}}class Qm extends Tm{constructor(){super(...arguments),this.paramsConfig=new Jm,this.effectsComposerController=new Zm(this),this.displayNodeController=new Lm(this,this.effectsComposerController.displayNodeControllerCallbacks()),this._children_controller_context=ts.POST}static type(){return es.POST}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class Km extends Am{constructor(){super(...arguments),this._children_controller_context=ts.ROP}static type(){return es.ROP}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}var tf=n(44);const ef=\\\\\\\"input texture\\\\\\\",nf=[ef,ef,ef,ef];for(var sf=new Uint16Array(32),rf=0;rf<32;rf++)sf[rf]=28898;const of=new fo.a(sf,32,1,w.gb,w.M);class af extends sa{constructor(t){super(t,\\\\\\\"BaseCopNode\\\\\\\"),this.flags=new ki(this)}static context(){return ts.COP}static displayedInputNames(){return nf}initializeBaseNode(){this.io.outputs.setHasOneOutput()}setTexture(t){t.name=this.path();const e=this.containerController.container().texture();if(e){if(e.uuid!=t.uuid){const n=Object.keys(t);for(let i of n)e[i]=t[i];e.needsUpdate=!0}this._setContainer(e)}else this._setContainer(t)}_clearTexture(){this._setContainer(of)}}class lf extends af{}class cf{constructor(){this._id=cf.__next_id++}id(){return this._id}handle_globals_node(t,e,n){}}cf.__next_id=0;class hf{static any(t){return m.isString(t)?t:m.isBoolean(t)?`${t}`:m.isNumber(t)?`${os.ensureFloat(t)}`:m.isArray(t)?this.numeric_array(t):t instanceof d.a||t instanceof p.a||t instanceof _.a||t instanceof D.a?this.numeric_array(t.toArray()):`ThreeToGl error: unknown value type '${t}'`}static numeric_array(t){const e=new Array(t.length);for(let n=0;n`${os.ensureFloat(t)}`)).join(\\\\\\\", \\\\\\\")})`}static vector3(t){if(m.isString(t))return t;return`vec3(${t.toArray().map((t=>`${os.ensureFloat(t)}`)).join(\\\\\\\", \\\\\\\")})`}static vector2(t){if(m.isString(t))return t;return`vec2(${t.toArray().map((t=>`${os.ensureFloat(t)}`)).join(\\\\\\\", \\\\\\\")})`}static vector3_float(t,e){return m.isNumber(e)&&(e=os.ensureFloat(e)),`vec4(${this.vector3(t)}, ${e})`}static float4(t,e,n,i){return m.isNumber(t)&&(t=os.ensureFloat(t)),m.isNumber(e)&&(e=os.ensureFloat(e)),m.isNumber(n)&&(n=os.ensureFloat(n)),m.isNumber(i)&&(i=os.ensureFloat(i)),`vec4(${t}, ${e}, ${n}, ${i})`}static float3(t,e,n){return m.isNumber(t)&&(t=os.ensureFloat(t)),m.isNumber(e)&&(e=os.ensureFloat(e)),m.isNumber(n)&&(n=os.ensureFloat(n)),`vec3(${t}, ${e}, ${n})`}static float2(t,e){return m.isNumber(t)&&(t=os.ensureFloat(t)),m.isNumber(e)&&(e=os.ensureFloat(e)),`vec2(${t}, ${e})`}static float(t){if(m.isNumber(t))return os.ensureFloat(t);{const e=parseFloat(t);return m.isNaN(e)?t:os.ensureFloat(e)}}static integer(t){if(m.isNumber(t))return os.ensureInteger(t);{const e=parseInt(t);return m.isNaN(e)?t:os.ensureInteger(e)}}static bool(t){return m.isBoolean(t)?`${t}`:t}}const uf=/\\\\/+/g;class df extends sa{static context(){return ts.GL}initializeBaseNode(){this.uiData.setLayoutHorizontal(),this.io.connections.initInputs(),this.io.connection_points.spare_params.initializeNode()}cook(){console.warn(\\\\\\\"gl nodes should never cook\\\\\\\")}_set_mat_to_recompile(){var t,e;null===(e=null===(t=this.material_node)||void 0===t?void 0:t.assemblerController)||void 0===e||e.set_compilation_required_and_dirty(this)}get material_node(){var t;const e=this.parent();if(e)return e.context()==ts.GL?null===(t=e)||void 0===t?void 0:t.material_node:e}glVarName(t){return`v_POLY_${this.path(this.material_node).replace(uf,\\\\\\\"_\\\\\\\")}_${t}`}variableForInputParam(t){return this.variableForInput(t.name())}variableForInput(t){var e;const n=this.io.inputs.get_input_index(t),i=this.io.connections.inputConnection(n);if(i){const e=i.node_src,n=e.io.outputs.namedOutputConnectionPoints()[i.output_index];if(n){const t=n.name();return e.glVarName(t)}throw console.warn(`no output called '${t}' for gl node ${e.path()}`),\\\\\\\"variable_for_input ERROR\\\\\\\"}if(this.params.has(t))return hf.any(null===(e=this.params.get(t))||void 0===e?void 0:e.value);{const t=this.io.inputs.namedInputConnectionPoints()[n];return hf.any(t.init_value)}}setLines(t){}reset_code(){var t;null===(t=this._param_configs_controller)||void 0===t||t.reset()}setParamConfigs(){}param_configs(){var t;return null===(t=this._param_configs_controller)||void 0===t?void 0:t.list()}paramsGenerating(){return!1}paramDefaultValue(t){return null}}const pf=new class extends la{};class _f extends df{constructor(){super(...arguments),this.paramsConfig=pf}}const mf=[Bo.FLOAT,Bo.VEC2,Bo.VEC3,Bo.VEC4];const ff=new class extends la{constructor(){super(...arguments),this.name=aa.STRING(\\\\\\\"\\\\\\\"),this.type=aa.INTEGER(0,{menu:{entries:mf.map(((t,e)=>({name:t,value:e})))}}),this.texportWhenConnected=aa.BOOLEAN(0,{hidden:!0}),this.exportWhenConnected=aa.BOOLEAN(0,{visibleIf:{texportWhenConnected:1}})}};class gf extends df{constructor(){super(...arguments),this.paramsConfig=ff,this._on_create_set_name_if_none_bound=this._on_create_set_name_if_none.bind(this),this._bound_setExportWhenConnectedStatus=this._setExportWhenConnectedStatus.bind(this)}static type(){return ss.ATTRIBUTE}initializeNode(){this.addPostDirtyHook(\\\\\\\"_set_mat_to_recompile\\\\\\\",this._set_mat_to_recompile_if_is_exporting.bind(this)),this.lifecycle.add_on_create_hook(this._on_create_set_name_if_none_bound),this.io.connection_points.initializeNode(),this.io.connection_points.set_expected_input_types_function((()=>{var t,e;return(null===(e=null===(t=this.material_node)||void 0===t?void 0:t.assemblerController)||void 0===e?void 0:e.allow_attribute_exports())?[mf[this.pv.type]]:[]})),this.io.connection_points.set_input_name_function((t=>gf.INPUT_NAME)),this.io.connection_points.set_expected_output_types_function((()=>[mf[this.pv.type]])),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.name,this.p.exportWhenConnected],(()=>this.pv.exportWhenConnected?`${this.pv.name} (EXPORTED)`:this.pv.name))}))})),this.lifecycle.add_on_add_hook(this._bound_setExportWhenConnectedStatus),this.params.addOnSceneLoadHook(\\\\\\\"prepare params\\\\\\\",this._bound_setExportWhenConnectedStatus)}_setExportWhenConnectedStatus(){var t,e;(null===(e=null===(t=this.material_node)||void 0===t?void 0:t.assemblerController)||void 0===e?void 0:e.allow_attribute_exports())&&this.p.texportWhenConnected.set(1)}setAttribSize(t){this.p.type.set(t-1)}get input_name(){return gf.INPUT_NAME}get output_name(){return gf.OUTPUT_NAME}setLines(t){t.assembler().set_node_lines_attribute(this,t)}get attribute_name(){return this.pv.name.trim()}gl_type(){return this.io.outputs.namedOutputConnectionPoints()[0].type()}set_gl_type(t){this.p.type.set(mf.indexOf(t))}connected_input_node(){return this.io.inputs.named_input(gf.INPUT_NAME)}connected_input_connection_point(){return this.io.inputs.named_input_connection_point(gf.INPUT_NAME)}output_connection_point(){return this.io.outputs.namedOutputConnectionPointsByName(this.output_name)}isImporting(){return this.io.outputs.used_output_names().length>0}isExporting(){if(this.pv.exportWhenConnected){return null!=this.io.inputs.named_input(gf.INPUT_NAME)}return!1}_set_mat_to_recompile_if_is_exporting(){this.isExporting()&&this._set_mat_to_recompile()}_on_create_set_name_if_none(){\\\\\\\"\\\\\\\"==this.pv.name&&this.p.name.set(this.name())}}gf.INPUT_NAME=\\\\\\\"in\\\\\\\",gf.OUTPUT_NAME=\\\\\\\"val\\\\\\\";class vf{constructor(t=[]){this._definitions=t,this._errored=!1}get errored(){return this._errored}get error_message(){return this._error_message}uniq(){const t=new Map,e=[];for(let n of this._definitions)if(!this._errored){const i=n.name(),s=t.get(i);s?s.data_type!=n.data_type&&(this._errored=!0,this._error_message=`attempt to create '${n.name()}' with types '${n.data_type}' by node '${n.node.path()}', when there is already an existing with type ${s.data_type} from node '${s.node.path()}'`,console.warn(\\\\\\\"emitting error message:\\\\\\\",this._error_message)):(t.set(i,n),e.push(i))}const n=[];for(let i of e){const e=t.get(i);e&&n.push(e)}return n}}var yf,xf;!function(t){t.ATTRIBUTE=\\\\\\\"attribute\\\\\\\",t.FUNCTION=\\\\\\\"function\\\\\\\",t.UNIFORM=\\\\\\\"uniform\\\\\\\",t.VARYING=\\\\\\\"varying\\\\\\\"}(yf||(yf={}));class bf{constructor(t,e,n,i){this._definition_type=t,this._data_type=e,this._node=n,this._name=i}get definition_type(){return this._definition_type}get data_type(){return this._data_type}get node(){return this._node}name(){return this._name}collection_instance(){return new vf}}class wf extends bf{constructor(t,e,n){super(yf.ATTRIBUTE,e,t,n),this._node=t,this._data_type=e,this._name=n}get line(){return`attribute ${this.data_type} ${this.name()}`}}class Tf extends bf{constructor(t,e){super(yf.FUNCTION,Bo.FLOAT,t,e),this._node=t,this._name=e}get line(){return this.name()}}class Af extends bf{constructor(t,e,n){super(yf.UNIFORM,e,t,n),this._node=t,this._data_type=e,this._name=n}get line(){return`uniform ${this.data_type} ${this.name()}`}}class Mf extends bf{constructor(t,e,n){super(yf.VARYING,e,t,n),this._node=t,this._data_type=e,this._name=n}get line(){return`varying ${this.data_type} ${this.name()}`}}!function(t){t.VERTEX=\\\\\\\"vertex\\\\\\\",t.FRAGMENT=\\\\\\\"fragment\\\\\\\",t.LEAVES_FROM_NODES_SHADER=\\\\\\\"leaves_from_nodes_shader\\\\\\\"}(xf||(xf={}));const Ef={position:\\\\\\\"vec3( position )\\\\\\\"};class Sf extends cf{handle_globals_node(t,e,n){var i,s;const r=t.io.outputs.namedOutputConnectionPointsByName(e);if(!r)return;const o=t.glVarName(e),a=r.type(),l=new Mf(t,a,o);n.addDefinitions(t,[l]);const c=null===(s=null===(i=t.material_node)||void 0===i?void 0:i.assemblerController)||void 0===s?void 0:s.assembler;if(!c)return;const h=c.shader_config(n.current_shader_name);if(!h)return;const u=h.dependencies(),d=[],p=`${o} = modelMatrix * vec4( position, 1.0 )`,_=`${o} = normalize( mat3( modelMatrix[0].xyz, modelMatrix[1].xyz, modelMatrix[2].xyz ) * normal )`;switch(e){case\\\\\\\"worldPosition\\\\\\\":d.push(p);break;case\\\\\\\"worldNormal\\\\\\\":d.push(_);break;default:d.push(`${o} = ${a}(${e})`)}for(let e of u)n.addDefinitions(t,[l],e),n.addBodyLines(t,d,e);0==u.length&&n.addBodyLines(t,d)}static variable_config_default(t){return Ef[t]}variable_config_default(t){return Sf.variable_config_default(t)}read_attribute(t,e,n,i){return Sf.read_attribute(t,e,n,i)}static read_attribute(t,e,n,i){var s,r;Sf.PRE_DEFINED_ATTRIBUTES.indexOf(n)<0&&i.addDefinitions(t,[new wf(t,e,n)],xf.VERTEX);const o=i.current_shader_name;switch(o){case xf.VERTEX:return n;case xf.FRAGMENT:{if(!(t instanceof gf))return;const a=\\\\\\\"varying_\\\\\\\"+t.glVarName(t.output_name),l=new Mf(t,e,a),c=new Map;c.set(xf.FRAGMENT,[]);const u=new Map;u.set(xf.FRAGMENT,[]),h.pushOnArrayAtEntry(c,o,l);const d=`${a} = ${e}(${n})`,p=null===(r=null===(s=t.material_node)||void 0===s?void 0:s.assemblerController)||void 0===r?void 0:r.assembler.shader_config(o);if(p){const e=p.dependencies();for(let t of e)h.pushOnArrayAtEntry(c,t,l),h.pushOnArrayAtEntry(u,t,d);c.forEach(((e,n)=>{i.addDefinitions(t,e,n)})),u.forEach(((e,n)=>{i.addBodyLines(t,e,n)}))}return a}}}handle_attribute_node(t,e,n,i){return Sf.read_attribute(t,e,n,i)}}Sf.PRE_DEFINED_ATTRIBUTES=[\\\\\\\"position\\\\\\\",\\\\\\\"color\\\\\\\",\\\\\\\"normal\\\\\\\",\\\\\\\"uv\\\\\\\",\\\\\\\"uv2\\\\\\\",\\\\\\\"morphTarget0\\\\\\\",\\\\\\\"morphTarget1\\\\\\\",\\\\\\\"morphTarget2\\\\\\\",\\\\\\\"morphTarget3\\\\\\\",\\\\\\\"skinIndex\\\\\\\",\\\\\\\"skinWeight\\\\\\\"],Sf.IF_RULE={uv:\\\\\\\"defined( USE_MAP ) || defined( USE_BUMPMAP ) || defined( USE_NORMALMAP ) || defined( USE_SPECULARMAP ) || defined( USE_ALPHAMAP ) || defined( USE_EMISSIVEMAP ) || defined( USE_ROUGHNESSMAP ) || defined( USE_METALNESSMAP )\\\\\\\"};const Cf=[Bo.FLOAT,Bo.VEC2,Bo.VEC3,Bo.VEC4];const Nf=new class extends la{constructor(){super(...arguments),this.name=aa.STRING(\\\\\\\"\\\\\\\"),this.type=aa.INTEGER(0,{menu:{entries:Cf.map(((t,e)=>({name:t,value:e})))}})}};class Lf extends df{constructor(){super(...arguments),this.paramsConfig=Nf,this._on_create_set_name_if_none_bound=this._on_create_set_name_if_none.bind(this)}static type(){return\\\\\\\"varyingWrite\\\\\\\"}initializeNode(){this.addPostDirtyHook(\\\\\\\"_set_mat_to_recompile\\\\\\\",this._set_mat_to_recompile.bind(this)),this.lifecycle.add_on_create_hook(this._on_create_set_name_if_none_bound),this.io.connection_points.initializeNode(),this.io.connection_points.set_input_name_function((()=>this.input_name)),this.io.connection_points.set_expected_input_types_function((()=>[Cf[this.pv.type]])),this.io.connection_points.set_expected_output_types_function((()=>[])),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.name])}))}))}get input_name(){return Lf.INPUT_NAME}setLines(t){if(t.current_shader_name==xf.VERTEX){const e=this.gl_type();if(!e)return;const n=this.pv.name,i=new Mf(this,e,n),s=`${n} = ${hf.any(this.variableForInput(Lf.INPUT_NAME))}`;t.addDefinitions(this,[i],xf.VERTEX),t.addBodyLines(this,[s],xf.VERTEX)}}get attribute_name(){return this.pv.name.trim()}gl_type(){const t=this.io.inputs.namedInputConnectionPoints()[0];if(t)return t.type()}set_gl_type(t){this.p.type.set(Cf.indexOf(t))}_on_create_set_name_if_none(){\\\\\\\"\\\\\\\"==this.pv.name&&this.p.name.set(this.name())}}Lf.INPUT_NAME=\\\\\\\"vertex\\\\\\\";class Of{static findOutputNodes(t){return t.nodesByType(\\\\\\\"output\\\\\\\")}static findParamGeneratingNodes(t){var e;const n=[];return null===(e=t.childrenController)||void 0===e||e.traverse_children((t=>{const e=t;e.paramsGenerating()&&n.push(e)})),n}static findVaryingNodes(t){return t.nodesByType(Lf.type())}static findAttributeExportNodes(t){return t.nodesByType(gf.type()).filter((t=>t.isExporting()))}}class Pf{static overlay(t,e){return new Promise(((n,i)=>{let s=document.createElement(\\\\\\\"canvas\\\\\\\");s.width=Math.max(t.width,e.width),s.height=Math.max(t.height,e.height);let r=s.getContext(\\\\\\\"2d\\\\\\\");r.drawImage(t,0,0,t.width,t.height),r.drawImage(e,0,0,e.width,e.height);const o=s.toDataURL(\\\\\\\"image/png\\\\\\\"),a=new Image;a.onload=()=>{n(a)},a.src=o}))}static create_white_image(t,e){return new Promise(((n,i)=>{let s=document.createElement(\\\\\\\"canvas\\\\\\\");s.width=t,s.height=e;let r=s.getContext(\\\\\\\"2d\\\\\\\");r.beginPath(),r.rect(0,0,t,e),r.fillStyle=\\\\\\\"white\\\\\\\",r.fill();const o=s.toDataURL(\\\\\\\"image/png\\\\\\\"),a=new Image;a.onload=()=>{n(a)},a.src=o}))}static make_square(t){return new Promise(((e,n)=>{let i=document.createElement(\\\\\\\"canvas\\\\\\\");const s=Math.min(t.width,t.height),r=t.width/t.height;i.width=s,i.height=s;let o=i.getContext(\\\\\\\"2d\\\\\\\");const a=r>1,l=a?(t.width-s)/2:(t.height-s)/2;a?o.drawImage(t,l,0,s,s,0,0,s,s):o.drawImage(t,0,l,s,s,0,0,s,s);const c=i.toDataURL(\\\\\\\"image/png\\\\\\\"),h=new Image;h.onload=()=>{e(h)},h.src=c}))}static async image_to_blob(t){return new Promise((function(e,n){try{let i=new XMLHttpRequest;i.open(\\\\\\\"GET\\\\\\\",t.src),i.responseType=\\\\\\\"blob\\\\\\\",i.onerror=function(){n(\\\\\\\"Network error.\\\\\\\")},i.onload=function(){200===i.status?e(i.response):n(\\\\\\\"Loading error:\\\\\\\"+i.statusText)},i.send()}catch(t){n(t.message)}}))}static data_from_url(t){return new Promise(((e,n)=>{const i=new Image;i.crossOrigin=\\\\\\\"Anonymous\\\\\\\",i.onload=()=>{const t=this.data_from_image(i);e(t)},i.src=t}))}static data_from_image(t){const e=document.createElement(\\\\\\\"canvas\\\\\\\");e.width=t.width,e.height=t.height;const n=e.getContext(\\\\\\\"2d\\\\\\\");return n.drawImage(t,0,0,t.width,t.height),n.getImageData(0,0,t.width,t.height)}}var Rf;!function(t){t.Uint8Array=\\\\\\\"Uint8Array\\\\\\\",t.Uint8ClampedArray=\\\\\\\"Uint8ClampedArray\\\\\\\",t.Float32Array=\\\\\\\"Float32Array\\\\\\\"}(Rf||(Rf={}));class If{constructor(t){this.buffer_type=t}from_render_target(t,e){return this._data_texture&&this._same_dimensions(e.texture)||(this._data_texture=this._create_data_texture(e.texture)),this._copy_to_data_texture(t,e),this._data_texture}from_texture(t){const e=Pf.data_from_image(t.image);this._data_texture&&this._same_dimensions(t)||(this._data_texture=this._create_data_texture(t));const n=e.width*e.height,i=e.data,s=this._data_texture.image.data,r=4*n;for(let t=0;t0:s.vertexColors=t.vertexColors),void 0!==t.uniforms)for(const e in t.uniforms){const i=t.uniforms[e];switch(s.uniforms[e]={},i.type){case\\\\\\\"t\\\\\\\":s.uniforms[e].value=n(i.value);break;case\\\\\\\"c\\\\\\\":s.uniforms[e].value=(new D.a).setHex(i.value);break;case\\\\\\\"v2\\\\\\\":s.uniforms[e].value=(new d.a).fromArray(i.value);break;case\\\\\\\"v3\\\\\\\":s.uniforms[e].value=(new p.a).fromArray(i.value);break;case\\\\\\\"v4\\\\\\\":s.uniforms[e].value=(new _.a).fromArray(i.value);break;case\\\\\\\"m3\\\\\\\":s.uniforms[e].value=(new G.a).fromArray(i.value);break;case\\\\\\\"m4\\\\\\\":s.uniforms[e].value=(new A.a).fromArray(i.value);break;default:s.uniforms[e].value=i.value}}if(void 0!==t.defines&&(s.defines=t.defines),void 0!==t.vertexShader&&(s.vertexShader=t.vertexShader),void 0!==t.fragmentShader&&(s.fragmentShader=t.fragmentShader),void 0!==t.extensions)for(const e in t.extensions)s.extensions[e]=t.extensions[e];if(void 0!==t.shading&&(s.flatShading=1===t.shading),void 0!==t.size&&(s.size=t.size),void 0!==t.sizeAttenuation&&(s.sizeAttenuation=t.sizeAttenuation),void 0!==t.map&&(s.map=n(t.map)),void 0!==t.matcap&&(s.matcap=n(t.matcap)),void 0!==t.alphaMap&&(s.alphaMap=n(t.alphaMap)),void 0!==t.bumpMap&&(s.bumpMap=n(t.bumpMap)),void 0!==t.bumpScale&&(s.bumpScale=t.bumpScale),void 0!==t.normalMap&&(s.normalMap=n(t.normalMap)),void 0!==t.normalMapType&&(s.normalMapType=t.normalMapType),void 0!==t.normalScale){let e=t.normalScale;!1===Array.isArray(e)&&(e=[e,e]),s.normalScale=(new d.a).fromArray(e)}return void 0!==t.displacementMap&&(s.displacementMap=n(t.displacementMap)),void 0!==t.displacementScale&&(s.displacementScale=t.displacementScale),void 0!==t.displacementBias&&(s.displacementBias=t.displacementBias),void 0!==t.roughnessMap&&(s.roughnessMap=n(t.roughnessMap)),void 0!==t.metalnessMap&&(s.metalnessMap=n(t.metalnessMap)),void 0!==t.emissiveMap&&(s.emissiveMap=n(t.emissiveMap)),void 0!==t.emissiveIntensity&&(s.emissiveIntensity=t.emissiveIntensity),void 0!==t.specularMap&&(s.specularMap=n(t.specularMap)),void 0!==t.specularIntensityMap&&(s.specularIntensityMap=n(t.specularIntensityMap)),void 0!==t.specularTintMap&&(s.specularTintMap=n(t.specularTintMap)),void 0!==t.envMap&&(s.envMap=n(t.envMap)),void 0!==t.envMapIntensity&&(s.envMapIntensity=t.envMapIntensity),void 0!==t.reflectivity&&(s.reflectivity=t.reflectivity),void 0!==t.refractionRatio&&(s.refractionRatio=t.refractionRatio),void 0!==t.lightMap&&(s.lightMap=n(t.lightMap)),void 0!==t.lightMapIntensity&&(s.lightMapIntensity=t.lightMapIntensity),void 0!==t.aoMap&&(s.aoMap=n(t.aoMap)),void 0!==t.aoMapIntensity&&(s.aoMapIntensity=t.aoMapIntensity),void 0!==t.gradientMap&&(s.gradientMap=n(t.gradientMap)),void 0!==t.clearcoatMap&&(s.clearcoatMap=n(t.clearcoatMap)),void 0!==t.clearcoatRoughnessMap&&(s.clearcoatRoughnessMap=n(t.clearcoatRoughnessMap)),void 0!==t.clearcoatNormalMap&&(s.clearcoatNormalMap=n(t.clearcoatNormalMap)),void 0!==t.clearcoatNormalScale&&(s.clearcoatNormalScale=(new d.a).fromArray(t.clearcoatNormalScale)),void 0!==t.transmissionMap&&(s.transmissionMap=n(t.transmissionMap)),void 0!==t.thicknessMap&&(s.thicknessMap=n(t.thicknessMap)),s}setTextures(t){return this.textures=t,this}}class Xf{constructor(t){this.node=t,this._found_uniform_texture_by_id=new Map,this._found_uniform_textures_id_by_uniform_name=new Map,this._found_param_texture_by_id=new Map,this._found_param_textures_id_by_uniform_name=new Map}toJSON(){}load(t){}_materialToJson(t,e){let n;this._unassignTextures(t);try{n=t.toJSON({}),n&&(n.shadowSide=t.shadowSide,n.colorWrite=t.colorWrite)}catch(e){console.error(\\\\\\\"failed to save material data\\\\\\\"),console.log(t)}return n&&null!=t.lights&&(n.lights=t.lights),n&&(n.uuid=`${e.node.path()}-${e.suffix}`),this._reassignTextures(t),n}_unassignTextures(t){this._found_uniform_texture_by_id.clear(),this._found_uniform_textures_id_by_uniform_name.clear(),this._found_param_texture_by_id.clear(),this._found_param_textures_id_by_uniform_name.clear();const e=t.uniforms,n=Object.keys(e);for(let t of n){const n=e[t].value;if(n&&n.uuid){const i=n;this._found_uniform_texture_by_id.set(i.uuid,n),this._found_uniform_textures_id_by_uniform_name.set(t,i.uuid),e[t].value=null}}const i=Object.keys(t);for(let e of i){const n=t[e];if(n&&n.uuid){const i=n;this._found_param_texture_by_id.set(i.uuid,i),this._found_param_textures_id_by_uniform_name.set(e,i.uuid),t[e]=null}}}_reassignTextures(t){const e=[],n=[];this._found_uniform_textures_id_by_uniform_name.forEach(((t,n)=>{e.push(n)})),this._found_param_textures_id_by_uniform_name.forEach(((t,e)=>{n.push(e)}));const i=t.uniforms;for(let t of e){const e=this._found_uniform_textures_id_by_uniform_name.get(t);if(e){const n=this._found_uniform_texture_by_id.get(e);n&&(i[t].value=n)}}for(let e of n){const n=this._found_param_textures_id_by_uniform_name.get(e);if(n){const i=this._found_param_texture_by_id.get(n);i&&(t[e]=i)}}}_loadMaterial(t){t.color=void 0;const e=(new qf).parse(t);t.shadowSide&&(e.shadowSide=t.shadowSide),null!=t.lights&&(e.lights=t.lights);const n=e.uniforms.uv2Transform;n&&this.mat4ToMat3(n);const i=e.uniforms.uvTransform;return i&&this.mat4ToMat3(i),e}mat4ToMat3(t){const e=t.value;if(null==e.elements[e.elements.length-1]){const n=new G.a;for(let t=0;t{const n=t.texture();e.value=n}));else{const t=n.containerController.container().texture();e.value=t}else e.value=null}static async set_uniform_value_from_texture_from_node_path_param(t,e){t.isDirty()&&await t.compute();const n=t.value.nodeWithContext(ts.COP);if(n)if(n.isDirty())n.compute().then((t=>{const n=t.texture();e.value=n}));else{const t=n.containerController.container().texture();e.value=t}else e.value=null}set_uniform_value_from_ramp(t,e){e.value=t.rampTexture()}}class Jf extends Xf{constructor(t){super(t),this.node=t}toJSON(){const t=this.node.assemblerController;if(!t)return;const e=[],n=t.assembler.param_configs();for(let t of n)e.push([t.name(),t.uniform_name]);return{fragment_shader:this.node.texture_material.fragmentShader,uniforms:this.node.texture_material.uniforms,param_uniform_pairs:e,uniforms_time_dependent:t.assembler.uniformsTimeDependent(),uniforms_resolution_dependent:t.assembler.uniforms_resolution_dependent()}}load(t){this.node.texture_material.fragmentShader=t.fragment_shader,this.node.texture_material.uniforms=t.uniforms,tg.handle_dependencies(this.node,t.uniforms_time_dependent||!1,t.uniforms);for(let e of t.param_uniform_pairs){const n=this.node.params.get(e[0]),i=t.uniforms[e[1]];n&&i&&n.options.set({callback:()=>{$f.callback(n,i)}})}}}class Zf{static isChrome(){return navigator&&null!=navigator.userAgent&&-1!=navigator.userAgent.indexOf(\\\\\\\"Chrome\\\\\\\")}static isMobile(){return/Android|webOS|iPhone|iPad|iPod|BlackBerry|IEMobile|Opera Mini/i.test(navigator.userAgent)}static isiOS(){return/(iPad|iPhone|iPod)/g.test(navigator.userAgent)}static isAndroid(){return/(Android)/g.test(navigator.userAgent)}static isTouchDevice(){var t=document.createElement(\\\\\\\"div\\\\\\\");return t.setAttribute(\\\\\\\"ongesturestart\\\\\\\",\\\\\\\"return;\\\\\\\"),\\\\\\\"function\\\\\\\"==typeof t.ongesturestart}}const Qf=[256,256];const Kf=new class extends la{constructor(){super(...arguments),this.resolution=aa.VECTOR2(Qf),this.useCameraRenderer=aa.BOOLEAN(0)}};class tg extends af{constructor(){super(...arguments),this.paramsConfig=Kf,this.persisted_config=new Jf(this),this._assembler_controller=this._create_assembler_controller(),this._texture_mesh=new B.a(new L(2,2)),this.texture_material=new F({uniforms:{},vertexShader:\\\\\\\"\\\\nvoid main()\\\\t{\\\\n\\\\tgl_Position = vec4( position, 1.0 );\\\\n}\\\\n\\\\\\\",fragmentShader:\\\\\\\"\\\\\\\"}),this._texture_scene=new gs,this._texture_camera=new tf.a,this._children_controller_context=ts.GL,this._cook_main_without_inputs_when_dirty_bound=this._cook_main_without_inputs_when_dirty.bind(this)}static type(){return\\\\\\\"builder\\\\\\\"}usedAssembler(){return jn.GL_TEXTURE}_create_assembler_controller(){const t=li.assemblersRegister.assembler(this,this.usedAssembler());if(t){const e=new Sf;return t.set_assembler_globals_handler(e),t}}get assemblerController(){return this._assembler_controller}initializeNode(){this._texture_mesh.material=this.texture_material,this._texture_mesh.scale.multiplyScalar(.25),this._texture_scene.add(this._texture_mesh),this._texture_camera.position.z=1,this.addPostDirtyHook(\\\\\\\"_cook_main_without_inputs_when_dirty\\\\\\\",(()=>{setTimeout(this._cook_main_without_inputs_when_dirty_bound,0)}))}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}childrenAllowed(){return this.assemblerController?super.childrenAllowed():(this.scene().markAsReadOnly(this),!1)}async _cook_main_without_inputs_when_dirty(){await this.cookController.cookMainWithoutInputs()}async cook(){this.compileIfRequired(),this.renderOnTarget()}shaders_by_name(){return{fragment:this._fragment_shader}}compileIfRequired(){var t;(null===(t=this.assemblerController)||void 0===t?void 0:t.compileRequired())&&this.compile()}compile(){const t=this.assemblerController;if(!t)return;const e=Of.findOutputNodes(this);if(e.length>1)return void this.states.error.set(\\\\\\\"only one output node allowed\\\\\\\");if(e[0]){const n=e;t.assembler.set_root_nodes(n),t.assembler.update_fragment_shader();const i=t.assembler.fragment_shader(),s=t.assembler.uniforms();i&&s&&(this._fragment_shader=i,this._uniforms=s),tg.handle_dependencies(this,t.assembler.uniformsTimeDependent())}this._fragment_shader&&this._uniforms&&(this.texture_material.fragmentShader=this._fragment_shader,this.texture_material.uniforms=this._uniforms,this.texture_material.needsUpdate=!0,this.texture_material.uniforms.resolution={value:this.pv.resolution}),t.post_compile()}static handle_dependencies(t,e,n){const i=t.scene(),s=`${t.graphNodeId()}`;e?(t.states.timeDependent.forceTimeDependent(),n&&i.uniformsController.addTimeDependentUniformOwner(s,n)):(t.states.timeDependent.unforceTimeDependent(),i.uniformsController.removeTimeDependentUniformOwner(s))}async renderOnTarget(){if(this.createRenderTargetIfRequired(),!this._render_target)return;this._renderer_controller=this._renderer_controller||new Ff(this);const t=await this._renderer_controller.renderer(),e=t.getRenderTarget();if(t.setRenderTarget(this._render_target),t.clear(),t.render(this._texture_scene,this._texture_camera),t.setRenderTarget(e),this._render_target.texture)if(this.pv.useCameraRenderer)this.setTexture(this._render_target.texture);else{this._data_texture_controller=this._data_texture_controller||new If(Rf.Float32Array);const e=this._data_texture_controller.from_render_target(t,this._render_target);this.setTexture(e)}else this.cookController.endCook()}renderTarget(){return this._render_target=this._render_target||this._createRenderTarget(this.pv.resolution.x,this.pv.resolution.y)}createRenderTargetIfRequired(){var t;this._render_target&&this._renderTargetResolutionValid()||(this._render_target=this._createRenderTarget(this.pv.resolution.x,this.pv.resolution.y),null===(t=this._data_texture_controller)||void 0===t||t.reset())}_renderTargetResolutionValid(){if(this._render_target){const t=this._render_target.texture.image;return t.width==this.pv.resolution.x&&t.height==this.pv.resolution.y}return!1}_createRenderTarget(t,e){if(this._render_target){const n=this._render_target.texture.image;if(n.width==t&&n.height==e)return this._render_target}const n=w.n,i=w.n,s=w.V,r=w.ob;var o=new Q(t,e,{wrapS:n,wrapT:i,minFilter:s,magFilter:r,format:w.Ib,type:Zf.isiOS()?w.M:w.G,stencilBuffer:!1,depthBuffer:!1});return li.warn(\\\\\\\"created render target\\\\\\\",this.path(),t,e),o}}const eg=[{LinearEncoding:w.U},{sRGBEncoding:w.ld},{GammaEncoding:w.J},{RGBEEncoding:w.gc},{LogLuvEncoding:w.bb},{RGBM7Encoding:w.lc},{RGBM16Encoding:w.kc},{RGBDEncoding:w.fc},{BasicDepthPacking:w.j},{RGBADepthPacking:w.Hb}],ng=[{ClampToEdgeWrapping:w.n},{RepeatWrapping:w.wc},{MirroredRepeatWrapping:w.kb}],ig=[{UVMapping:w.Yc},{CubeReflectionMapping:w.o},{CubeRefractionMapping:w.p},{EquirectangularReflectionMapping:w.D},{EquirectangularRefractionMapping:w.E},{CubeUVReflectionMapping:w.q},{CubeUVRefractionMapping:w.r}],sg=[{UnsignedByteType:w.Zc},{ByteType:w.l},{ShortType:w.Mc},{UnsignedShortType:w.fd},{IntType:w.N},{UnsignedIntType:w.bd},{FloatType:w.G},{HalfFloatType:w.M},{UnsignedShort4444Type:w.cd},{UnsignedShort5551Type:w.dd},{UnsignedShort565Type:w.ed},{UnsignedInt248Type:w.ad}],rg=[{AlphaFormat:w.f},{RedFormat:w.tc},{RedIntegerFormat:w.uc},{RGFormat:w.rc},{RGIntegerFormat:w.sc},{RGBFormat:w.ic},{RGBIntegerFormat:w.jc},{RGBAFormat:w.Ib},{RGBAIntegerFormat:w.Jb},{LuminanceFormat:w.gb},{LuminanceAlphaFormat:w.fb},{DepthFormat:w.x},{DepthStencilFormat:w.y}];function og(t){return{cook:!1,callback:e=>{wg[t](e)}}}const ag={ENCODING:w.U,FORMAT:w.Ib,MAPPING:w.Yc,MIN_FILTER:w.V,MAG_FILTER:w.V,TYPE:w.Zc,WRAPPING:w.wc},lg=og(\\\\\\\"PARAM_CALLBACK_update_encoding\\\\\\\"),cg=og(\\\\\\\"PARAM_CALLBACK_update_mapping\\\\\\\"),hg=og(\\\\\\\"PARAM_CALLBACK_update_wrap\\\\\\\"),ug=og(\\\\\\\"PARAM_CALLBACK_update_filter\\\\\\\"),dg=og(\\\\\\\"PARAM_CALLBACK_update_anisotropy\\\\\\\"),pg=og(\\\\\\\"PARAM_CALLBACK_update_flipY\\\\\\\"),_g=og(\\\\\\\"PARAM_CALLBACK_update_transform\\\\\\\"),mg=og(\\\\\\\"PARAM_CALLBACK_update_repeat\\\\\\\"),fg=og(\\\\\\\"PARAM_CALLBACK_update_offset\\\\\\\"),gg=og(\\\\\\\"PARAM_CALLBACK_update_rotation\\\\\\\"),vg=og(\\\\\\\"PARAM_CALLBACK_update_center\\\\\\\"),yg=og(\\\\\\\"PARAM_CALLBACK_update_advanced\\\\\\\");function xg(t){return class extends t{constructor(){super(...arguments),this.tencoding=aa.BOOLEAN(0,{...lg}),this.encoding=aa.INTEGER(ag.ENCODING,{visibleIf:{tencoding:1},menu:{entries:eg.map((t=>({name:Object.keys(t)[0],value:Object.values(t)[0]})))},...lg}),this.tmapping=aa.BOOLEAN(0,{...cg}),this.mapping=aa.INTEGER(ag.MAPPING,{visibleIf:{tmapping:1},menu:{entries:ig.map((t=>({name:Object.keys(t)[0],value:Object.values(t)[0]})))},...cg}),this.twrap=aa.BOOLEAN(0,{...hg}),this.wrapS=aa.INTEGER(ag.WRAPPING,{visibleIf:{twrap:1},menu:{entries:ng.map((t=>({name:Object.keys(t)[0],value:Object.values(t)[0]})))},...hg}),this.wrapT=aa.INTEGER(ag.WRAPPING,{visibleIf:{twrap:1},menu:{entries:ng.map((t=>({name:Object.keys(t)[0],value:Object.values(t)[0]})))},separatorAfter:!0,...hg}),this.tminFilter=aa.BOOLEAN(0,{...ug}),this.minFilter=aa.INTEGER(Xm,{visibleIf:{tminFilter:1},menu:{entries:$m},...ug}),this.tmagFilter=aa.BOOLEAN(0,{...ug}),this.magFilter=aa.INTEGER(qm,{visibleIf:{tmagFilter:1},menu:{entries:Ym},...ug}),this.tanisotropy=aa.BOOLEAN(0,{...dg}),this.useRendererMaxAnisotropy=aa.BOOLEAN(0,{visibleIf:{tanisotropy:1},...dg}),this.anisotropy=aa.INTEGER(2,{visibleIf:{tanisotropy:1,useRendererMaxAnisotropy:0},range:[0,32],rangeLocked:[!0,!1],...dg}),this.tflipY=aa.BOOLEAN(0,{...pg}),this.flipY=aa.BOOLEAN(0,{visibleIf:{tflipY:1},...pg}),this.ttransform=aa.BOOLEAN(0,{..._g}),this.offset=aa.VECTOR2([0,0],{visibleIf:{ttransform:1},...fg}),this.repeat=aa.VECTOR2([1,1],{visibleIf:{ttransform:1},...mg}),this.rotation=aa.FLOAT(0,{range:[-1,1],visibleIf:{ttransform:1},...gg}),this.center=aa.VECTOR2([0,0],{visibleIf:{ttransform:1},...vg}),this.tadvanced=aa.BOOLEAN(0,{...yg}),this.tformat=aa.BOOLEAN(0,{visibleIf:{tadvanced:1},...yg}),this.format=aa.INTEGER(ag.FORMAT,{visibleIf:{tadvanced:1,tformat:1},menu:{entries:rg.map((t=>({name:Object.keys(t)[0],value:Object.values(t)[0]})))},...yg}),this.ttype=aa.BOOLEAN(0,{visibleIf:{tadvanced:1},...yg}),this.type=aa.INTEGER(ag.TYPE,{visibleIf:{tadvanced:1,ttype:1},menu:{entries:sg.map((t=>({name:Object.keys(t)[0],value:Object.values(t)[0]})))},...yg})}}}class bg extends(xg(la)){}new bg;class wg{constructor(t){this.node=t}async update(t){const e=this.node.pv;this._updateEncoding(t,e),this._updateAdvanced(t,e),this._updateMapping(t,e),this._updateWrap(t,e),this._updateFilter(t,e),this._updateFlip(t,e),await this._updateAnisotropy(t,e),this._updateTransform(t)}_updateEncoding(t,e){e.tencoding?t.encoding=e.encoding:t.encoding=ag.ENCODING,t.needsUpdate=!0}_updateAdvanced(t,e){e.tadvanced&&(e.tformat?t.format=e.format:t.format=ag.FORMAT,e.ttype?t.type=e.type:t.type=ag.TYPE),t.needsUpdate=!0}_updateMapping(t,e){e.tmapping?t.mapping=e.mapping:t.mapping=ag.MAPPING,t.needsUpdate=!0}_updateWrap(t,e){e.twrap?(t.wrapS=e.wrapS,t.wrapT=e.wrapT):(t.wrapS=ag.WRAPPING,t.wrapT=ag.WRAPPING),t.needsUpdate=!0}_updateFilter(t,e){e.tminFilter?t.minFilter=e.minFilter:t.minFilter=w.V,e.tmagFilter?t.magFilter=e.magFilter:t.magFilter=w.V,t.needsUpdate=!0}_updateFlip(t,e){t.flipY=e.tflipY&&e.flipY,t.needsUpdate=!0}async _updateAnisotropy(t,e){if(e.tanisotropy){if(e.useRendererMaxAnisotropy)t.anisotropy=await this._maxRendererAnisotropy();else{const n=e.anisotropy;t.anisotropy=n<=2?n:Math.min(n,await this._maxRendererAnisotropy())}t.needsUpdate=!0}else t.anisotropy=1}async _maxRendererAnisotropy(){this._renderer_controller=this._renderer_controller||new Ff(this.node);return(await this._renderer_controller.renderer()).capabilities.getMaxAnisotropy()}_updateTransform(t){if(!this.node.pv.ttransform)return t.offset.set(0,0),t.rotation=0,t.repeat.set(1,1),void t.center.set(0,0);this._updateTransformOffset(t,!1),this._updateTransformRepeat(t,!1),this._updateTransformRotation(t,!1),this._updateTransformCenter(t,!1),t.updateMatrix()}async _updateTransformOffset(t,e){t.offset.copy(this.node.pv.offset),e&&t.updateMatrix()}async _updateTransformRepeat(t,e){t.repeat.copy(this.node.pv.repeat),e&&t.updateMatrix()}async _updateTransformRotation(t,e){t.rotation=this.node.pv.rotation,e&&t.updateMatrix()}async _updateTransformCenter(t,e){t.center.copy(this.node.pv.center),e&&t.updateMatrix()}static PARAM_CALLBACK_update_encoding(t){const e=t.containerController.container().texture();e&&t.textureParamsController._updateEncoding(e,t.pv)}static PARAM_CALLBACK_update_mapping(t){const e=t.containerController.container().texture();e&&t.textureParamsController._updateMapping(e,t.pv)}static PARAM_CALLBACK_update_wrap(t){const e=t.containerController.container().texture();e&&t.textureParamsController._updateWrap(e,t.pv)}static PARAM_CALLBACK_update_filter(t){const e=t.containerController.container().texture();e&&t.textureParamsController._updateFilter(e,t.pv)}static PARAM_CALLBACK_update_anisotropy(t){const e=t.containerController.container().texture();e&&t.textureParamsController._updateAnisotropy(e,t.pv)}static PARAM_CALLBACK_update_flipY(t){const e=t.containerController.container().texture();e&&t.textureParamsController._updateFlip(e,t.pv)}static PARAM_CALLBACK_update_transform(t){const e=t.containerController.container().texture();e&&t.textureParamsController._updateTransform(e)}static PARAM_CALLBACK_update_offset(t){const e=t.containerController.container().texture();e&&t.textureParamsController._updateTransformOffset(e,!0)}static PARAM_CALLBACK_update_repeat(t){const e=t.containerController.container().texture();e&&t.textureParamsController._updateTransformRepeat(e,!0)}static PARAM_CALLBACK_update_rotation(t){const e=t.containerController.container().texture();e&&t.textureParamsController._updateTransformRotation(e,!0)}static PARAM_CALLBACK_update_center(t){const e=t.containerController.container().texture();e&&t.textureParamsController._updateTransformCenter(e,!0)}static PARAM_CALLBACK_update_advanced(t){const e=t.containerController.container().texture();e&&t.textureParamsController._updateAdvanced(e,t.pv)}static copyTextureAttributes(t,e){t.encoding=e.encoding,t.mapping=e.mapping,t.wrapS=e.wrapS,t.wrapT=e.wrapT,t.minFilter=e.minFilter,t.magFilter=e.magFilter,t.magFilter=e.magFilter,t.anisotropy=e.anisotropy,t.flipY=e.flipY,t.repeat.copy(e.repeat),t.offset.copy(e.offset),t.center.copy(e.center),t.rotation=e.rotation,t.type=e.type,t.format=e.format,t.needsUpdate=!0}paramLabelsParams(){const t=this.node.p;return[t.tencoding,t.encoding,t.tmapping,t.mapping,t.twrap,t.wrapS,t.wrapT,t.tminFilter,t.minFilter,t.tmagFilter,t.magFilter,t.tflipY,t.flipY]}paramLabels(){const t=[],e=this.node.pv;if(e.tencoding)for(let n of eg){const i=Object.keys(n)[0];n[i]==e.encoding&&t.push(`encoding: ${i}`)}if(e.tmapping)for(let n of ig){const i=Object.keys(n)[0];n[i]==e.mapping&&t.push(`mapping: ${i}`)}if(e.twrap){function n(n){for(let i of ng){const s=Object.keys(i)[0];i[s]==e[n]&&t.push(`${n}: ${s}`)}}n(\\\\\\\"wrapS\\\\\\\"),n(\\\\\\\"wrapT\\\\\\\")}if(e.tminFilter)for(let n of Wm){const i=Object.keys(n)[0];n[i]==e.minFilter&&t.push(`minFilter: ${i}`)}if(e.tmagFilter)for(let n of jm){const i=Object.keys(n)[0];n[i]==e.magFilter&&t.push(`magFilter: ${i}`)}return e.tflipY&&t.push(`flipY: ${e.flipY}`),t}}class Tg extends Z.a{constructor(t,e,n,i,s,r,o,a,l){super(t,e,n,i,s,r,o,a,l),this.needsUpdate=!0}}Tg.prototype.isCanvasTexture=!0;class Ag extends(xg(function(t){return class extends t{constructor(){super(...arguments),this.canvasId=aa.STRING(\\\\\\\"canvas-id\\\\\\\"),this.update=aa.BUTTON(null,{cook:!1,callback:t=>{Eg.PARAM_CALLBACK_update(t)}})}}}(la))){}const Mg=new Ag;class Eg extends af{constructor(){super(...arguments),this.paramsConfig=Mg,this.textureParamsController=new wg(this)}static type(){return\\\\\\\"canvas\\\\\\\"}async cook(){const t=this.pv.canvasId,e=document.getElementById(t);if(!e)return this.states.error.set(`element with id '${t}' not found`),void this.cookController.endCook();if(!(e instanceof HTMLCanvasElement))return this.states.error.set(\\\\\\\"element found is not a canvas\\\\\\\"),void this.cookController.endCook();const n=new Tg(e);await this.textureParamsController.update(n),this.setTexture(n)}static PARAM_CALLBACK_update(t){t.markTextureNeedsUpdate()}markTextureNeedsUpdate(){const t=this.containerController.container().texture();t&&(t.needsUpdate=!0)}}const Sg=new class extends la{constructor(){super(...arguments),this.resolution=aa.VECTOR2([256,256],{callback:t=>{Cg.PARAM_CALLBACK_reset(t)}}),this.color=aa.COLOR([1,1,1])}};class Cg extends af{constructor(){super(...arguments),this.paramsConfig=Sg}static type(){return\\\\\\\"color\\\\\\\"}cook(){const t=this.pv.resolution.x,e=this.pv.resolution.y;this._data_texture=this._data_texture||this._create_data_texture(t,e);const n=e*t,i=this.pv.color.toArray(),s=255*i[0],r=255*i[1],o=255*i[2],a=this._data_texture.image.data;for(let t=0;t({name:t,value:e})))}})}};class Ig extends af{constructor(){super(...arguments),this.paramsConfig=Rg}static type(){return Lg.CUBE_CAMERA}async cook(){const t=this.pv.cubeCamera.nodeWithContext(ts.OBJ,this.states.error);if(!t)return this.states.error.set(`cubeCamera not found at '${this.pv.cubeCamera.path()}'`),this.cookController.endCook();const e=t.renderTarget();if(!e)return this.states.error.set(\\\\\\\"cubeCamera has no render target'\\\\\\\"),this.cookController.endCook();const n=e.texture;Pg[this.pv.mode]==Og.REFLECTION?n.mapping=w.o:n.mapping=w.p,this.setTexture(n)}}var Fg;!function(t){t.REFLECTION=\\\\\\\"reflection\\\\\\\",t.REFRACTION=\\\\\\\"refraction\\\\\\\"}(Fg||(Fg={}));const Dg=[Fg.REFLECTION,Fg.REFRACTION];const Bg=new class extends la{constructor(){super(...arguments),this.useCameraRenderer=aa.BOOLEAN(1),this.mode=aa.INTEGER(0,{menu:{entries:Dg.map(((t,e)=>({name:t,value:e})))}})}};class zg extends af{constructor(){super(...arguments),this.paramsConfig=Bg}static type(){return\\\\\\\"envMap\\\\\\\"}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState(Ki.NEVER)}async cook(t){const e=t[0];this.convert_texture_to_env_map(e)}async convert_texture_to_env_map(t){this._renderer_controller=this._renderer_controller||new Ff(this);const e=await this._renderer_controller.renderer();if(e){const n=new Tt(e).fromEquirectangular(t);if(this.pv.useCameraRenderer)this._set_mapping(n.texture),this.setTexture(n.texture);else{this._data_texture_controller=this._data_texture_controller||new If(Rf.Uint8Array);const t=this._data_texture_controller.from_render_target(e,n);this._set_mapping(t),this.setTexture(t)}}else this.states.error.set(\\\\\\\"no renderer found to convert the texture to an env map\\\\\\\"),this.cookController.endCook()}_set_mapping(t){Dg[this.pv.mode]==Fg.REFLECTION?t.mapping=w.q:t.mapping=w.r}}class kg extends Z.a{constructor(t,e,n,i,s,r,o,a,l){super(t,e,n,i,s,r,o,a,l),this.format=void 0!==o?o:w.ic,this.minFilter=void 0!==r?r:w.V,this.magFilter=void 0!==s?s:w.V,this.generateMipmaps=!1;const c=this;\\\\\\\"requestVideoFrameCallback\\\\\\\"in t&&t.requestVideoFrameCallback((function e(){c.needsUpdate=!0,t.requestVideoFrameCallback(e)}))}clone(){return new this.constructor(this.image).copy(this)}update(){const t=this.image;!1===\\\\\\\"requestVideoFrameCallback\\\\\\\"in t&&t.readyState>=t.HAVE_CURRENT_DATA&&(this.needsUpdate=!0)}}kg.prototype.isVideoTexture=!0;var Ug=n(80);const Gg=\\\\\\\"https://raw.githubusercontent.com/polygonjs/polygonjs-assets/master/\\\\\\\";var Vg=n(28);const Hg=new Vg.b;Hg.setURLModifier((t=>{const e=li.assetUrls.remapedUrl(t);if(e)return e;const n=li.blobs.blobUrl(t);return n||t}));class jg{constructor(t,e,n){this._url=t,this._scene=e,this._node=n,this.loadingManager=Hg}static extension(t){let e=null;try{e=new URL(t).searchParams.get(\\\\\\\"ext\\\\\\\")}catch(t){}if(!e){const n=t.split(\\\\\\\"?\\\\\\\")[0].split(\\\\\\\".\\\\\\\");e=n[n.length-1].toLowerCase()}return e}extension(){return jg.extension(this._url)}async _urlToLoad(){let t=this._url;const e=this._url.split(\\\\\\\"?\\\\\\\")[0];if(\\\\\\\"h\\\\\\\"!=t[0]){const e=this._scene.assets.root();e&&(t=`${e}${t}`)}this._node&&await li.blobs.fetchBlobForNode({storedUrl:e,fullUrl:t,node:this._node});return li.blobs.blobUrl(e)||t}static async _loadMultipleBlobGlobal(t){const e=[];for(let n of t.files){const i=n.storedUrl,s=n.fullUrl,r=t.node;e.push(li.blobs.fetchBlobGlobal({storedUrl:i,fullUrl:s,node:r}))}const n=await Promise.all(e);for(let e of n)e.error&&t.node.states.error.set(t.error)}}jg.loadingManager=Hg;const Wg=[\\\\\\\"mp4\\\\\\\",\\\\\\\"ogv\\\\\\\",\\\\\\\"ogg\\\\\\\"];var qg;!function(t){t.JPG=\\\\\\\"jpg\\\\\\\",t.JPEG=\\\\\\\"jpeg\\\\\\\",t.PNG=\\\\\\\"png\\\\\\\",t.EXR=\\\\\\\"exr\\\\\\\",t.BASIS=\\\\\\\"basis\\\\\\\",t.HDR=\\\\\\\"hdr\\\\\\\"}(qg||(qg={}));const Xg=[qg.JPEG,qg.JPG,qg.PNG,qg.EXR,qg.BASIS,qg.HDR];function Yg(t){const e=t.split(\\\\\\\"?\\\\\\\")[0].split(\\\\\\\".\\\\\\\");return e[e.length-1]}class $g extends jg{constructor(t,e,n,i,s){super(t,i,n),this._param=e,this._node=n,this._scene=i,this._forceVideo=!1,this._forceImage=!1,this._forceVideo=(null==s?void 0:s.forceVideo)||this._forceVideo,this._forceImage=(null==s?void 0:s.forceImage)||this._forceImage}static onTextureLoaded(t){this._onTextureLoadedCallback=t}async load_texture_from_url_or_op(t){let e=null,n=null;if(\\\\\\\"op:\\\\\\\"==this._url.substring(0,3)){const t=this._url.substring(3);if(n=bi.findNode(this._node,t),n)if(n instanceof lf){e=(await n.compute()).texture()}else this._node.states.error.set(\\\\\\\"found node is not a texture node\\\\\\\");else this._node.states.error.set(`no node found in path '${t}'`)}else e=await this._loadUrl(t),e||this._node.states.error.set(`could not load texture ${this._url}`);return n&&this._param.graphPredecessors()[0]!=n&&(this._param.graphDisconnectPredecessors(),this._param.addGraphInput(n)),e}async _loadUrl(t){return new Promise((async(e,n)=>{const i=this.extension(),s=await this._urlToLoad();if(this._forceVideo||Wg.includes(i)){e(await this._loadVideo(s))}else if(this._forceImage||Xg.includes(i))try{e(await this._loadImage(s,t))}catch(t){n()}}))}_loadImage(t,e){return new Promise((async(n,i)=>{const s=this.extension();this.loader_for_ext(s,e).then((async e=>{e?($g.incrementInProgressLoadsCount(),await $g.waitForMaxConcurrentLoadsQueueFreed(),e.load(t,(e=>{$g.decrementInProgressLoadsCount();const i=$g._onTextureLoadedCallback;i&&i(t,e),n(e)}),void 0,(t=>{$g.decrementInProgressLoadsCount(),li.warn(\\\\\\\"error\\\\\\\",t),i()}))):i()}))}))}_loadVideo(t){return new Promise((async(e,n)=>{$g.incrementInProgressLoadsCount(),await $g.waitForMaxConcurrentLoadsQueueFreed();const i=document.createElement(\\\\\\\"video\\\\\\\");i.setAttribute(\\\\\\\"crossOrigin\\\\\\\",\\\\\\\"anonymous\\\\\\\"),i.setAttribute(\\\\\\\"autoplay\\\\\\\",\\\\\\\"true\\\\\\\"),i.setAttribute(\\\\\\\"loop\\\\\\\",\\\\\\\"true\\\\\\\"),i.onloadedmetadata=function(){i.pause();const n=new kg(i);$g.decrementInProgressLoadsCount();const s=$g._onTextureLoadedCallback;s&&s(t,n),e(n)};const s=document.createElement(\\\\\\\"source\\\\\\\"),r=jg.extension(t);let o=$g.VIDEO_SOURCE_TYPE_BY_EXT[r];o=o||$g._default_video_source_type(t),s.setAttribute(\\\\\\\"type\\\\\\\",o),s.setAttribute(\\\\\\\"src\\\\\\\",t),i.appendChild(s);let a=t;a=\\\\\\\"mp4\\\\\\\"==r?$g.replaceExtension(t,\\\\\\\"ogv\\\\\\\"):$g.replaceExtension(t,\\\\\\\"mp4\\\\\\\");const l=document.createElement(\\\\\\\"source\\\\\\\"),c=jg.extension(a);o=$g.VIDEO_SOURCE_TYPE_BY_EXT[c],o=o||$g._default_video_source_type(t),l.setAttribute(\\\\\\\"type\\\\\\\",o),l.setAttribute(\\\\\\\"src\\\\\\\",t),i.appendChild(l)}))}static module_names(t){switch(t){case qg.EXR:return[Hn.EXRLoader];case qg.HDR:return[Hn.RGBELoader];case qg.BASIS:return[Hn.BasisTextureLoader]}}async loader_for_ext(t,e){switch(t.toLowerCase()){case qg.EXR:return await this._exr_loader(e);case qg.HDR:return await this._hdr_loader(e);case qg.BASIS:return await $g._basis_loader(this._node)}return new Ug.a(this.loadingManager)}async _exr_loader(t){const e=await li.modulesRegister.module(Hn.EXRLoader);if(e){const n=new e(this.loadingManager);return t.tdataType&&n.setDataType(t.dataType),n}}async _hdr_loader(t){const e=await li.modulesRegister.module(Hn.RGBELoader);if(e){const n=new e(this.loadingManager);return t.tdataType&&n.setDataType(t.dataType),n}}static async _basis_loader(t){const e=await li.modulesRegister.module(Hn.BasisTextureLoader);if(e){const n=new e(this.loadingManager),i=li.libs.root(),s=li.libs.BASISPath();if(i||s){const e=`${i||\\\\\\\"\\\\\\\"}${s||\\\\\\\"\\\\\\\"}/`;if(t){const n=[\\\\\\\"basis_transcoder.js\\\\\\\",\\\\\\\"basis_transcoder.wasm\\\\\\\"];await this._loadMultipleBlobGlobal({files:n.map((t=>({storedUrl:`${s}/${t}`,fullUrl:`${e}${t}`}))),node:t,error:\\\\\\\"failed to load basis libraries. Make sure to install them to load .basis files\\\\\\\"})}n.setTranscoderPath(e)}else n.setTranscoderPath(void 0);const r=await li.renderersController.waitForRenderer();return r?n.detectSupport(r):li.warn(\\\\\\\"texture loader found no renderer for basis texture loader\\\\\\\"),n}}static _default_video_source_type(t){return`video/${jg.extension(t)}`}static pixel_data(t){const e=t.image,n=document.createElement(\\\\\\\"canvas\\\\\\\");n.width=e.width,n.height=e.height;const i=n.getContext(\\\\\\\"2d\\\\\\\");if(i)return i.drawImage(e,0,0,e.width,e.height),i.getImageData(0,0,e.width,e.height)}static replaceExtension(t,e){const n=t.split(\\\\\\\"?\\\\\\\"),i=n[0].split(\\\\\\\".\\\\\\\");return i.pop(),i.push(e),[i.join(\\\\\\\".\\\\\\\"),n[1]].join(\\\\\\\"?\\\\\\\")}static setMaxConcurrentLoadsCount(t){this._maxConcurrentLoadsCountMethod=t}static _init_max_concurrent_loads_count(){return this._maxConcurrentLoadsCountMethod?this._maxConcurrentLoadsCountMethod():Zf.isChrome()?10:4}static _init_concurrent_loads_delay(){return Zf.isChrome()?0:10}static incrementInProgressLoadsCount(){this.in_progress_loads_count++}static decrementInProgressLoadsCount(){this.in_progress_loads_count--;const t=this._queue.pop();if(t){const e=this.CONCURRENT_LOADS_DELAY;setTimeout((()=>{t()}),e)}}static async waitForMaxConcurrentLoadsQueueFreed(){return this.in_progress_loads_count<=this.MAX_CONCURRENT_LOADS_COUNT?void 0:new Promise((t=>{this._queue.push(t)}))}}$g.PARAM_DEFAULT=`${Gg}/textures/uv.jpg`,$g.PARAM_ENV_DEFAULT=`${Gg}/textures/piz_compressed.exr`,$g.VIDEO_SOURCE_TYPE_BY_EXT={ogg:'video/ogg; codecs=\\\\\\\"theora, vorbis\\\\\\\"',ogv:'video/ogg; codecs=\\\\\\\"theora, vorbis\\\\\\\"',mp4:'video/mp4; codecs=\\\\\\\"avc1.42E01E, mp4a.40.2\\\\\\\"'},$g.MAX_CONCURRENT_LOADS_COUNT=$g._init_max_concurrent_loads_count(),$g.CONCURRENT_LOADS_DELAY=$g._init_concurrent_loads_delay(),$g.in_progress_loads_count=0,$g._queue=[];var Jg=n(114);class Zg extends(xg(function(t){return class extends t{constructor(){super(...arguments),this.url=aa.STRING($g.PARAM_DEFAULT,{fileBrowse:{type:[Or.TEXTURE_IMAGE]}}),this.reload=aa.BUTTON(null,{callback:(t,e)=>{Kg.PARAM_CALLBACK_reload(t)}}),this.play=aa.BOOLEAN(1,{cook:!1,callback:t=>{Kg.PARAM_CALLBACK_gifUpdatePlay(t)}}),this.gifFrame=aa.INTEGER(0,{cook:!1,range:[0,100],rangeLocked:[!0,!1],callback:t=>{Kg.PARAM_CALLBACK_gifUpdateFrameIndex(t)}})}}}(la))){}const Qg=new Zg;class Kg extends af{constructor(){super(...arguments),this.paramsConfig=Qg,this.textureParamsController=new wg(this),this._gifCanvasContext=null,this._tmpCanvasContext=null,this._parsedFrames=[],this._frameDelay=100,this._frameIndex=0}static type(){return\\\\\\\"gif\\\\\\\"}async requiredModules(){this.p.url.isDirty()&&await this.p.url.compute();const t=jg.extension(this.pv.url||\\\\\\\"\\\\\\\");return $g.module_names(t)}static displayedInputNames(){return[\\\\\\\"optional texture to copy attributes from\\\\\\\"]}initializeNode(){this.io.inputs.setCount(0,1),this.io.inputs.initInputsClonedState(Ki.NEVER),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{let t=[this.p.url];t=t.concat(this.textureParamsController.paramLabelsParams()),this.params.label.init(t,(()=>{const t=this.p.url.rawInput();if(t){const e=t.split(\\\\\\\"/\\\\\\\"),n=e[e.length-1],i=this.textureParamsController.paramLabels();return[n].concat(i)}return\\\\\\\"\\\\\\\"}))}))}))}async cook(t){if(\\\\\\\"gif\\\\\\\"!=Yg(this.pv.url).toLowerCase())this.states.error.set(\\\\\\\"url is not an image\\\\\\\");else{$g.incrementInProgressLoadsCount(),await $g.waitForMaxConcurrentLoadsQueueFreed();const t=await fetch(this.pv.url),e=await t.arrayBuffer(),n=await Object(Jg.parseGIF)(e),i=!0;this._parsedFrames=await Object(Jg.decompressFrames)(n,i);const s=this._parsedFrames[0];if(this._frameDelay=s.delay,this._frameIndex=this.pv.gifFrame-1,this._createCanvas(),$g.decrementInProgressLoadsCount(),this._gifCanvasElement){const t=new Tg(this._gifCanvasElement);await this.textureParamsController.update(t),this.setTexture(t)}else this.states.error.set(\\\\\\\"failed to create canvas\\\\\\\")}}_createCanvas(){const t=this._parsedFrames[0];this._gifCanvasElement=document.createElement(\\\\\\\"canvas\\\\\\\"),this._tmpCanvasElement=document.createElement(\\\\\\\"canvas\\\\\\\"),this._gifCanvasElement.width=t.dims.width,this._gifCanvasElement.height=t.dims.height,this._tmpCanvasElement.width=t.dims.width,this._tmpCanvasElement.height=t.dims.height,this._gifCanvasContext=this._gifCanvasElement.getContext(\\\\\\\"2d\\\\\\\"),this._tmpCanvasContext=this._tmpCanvasElement.getContext(\\\\\\\"2d\\\\\\\"),this._drawNextFrame()}_drawOnCanvas(){if(!(this._gifCanvasContext&&this._tmpCanvasElement&&this._tmpCanvasContext))return;let t=this._parsedFrames[this._frameIndex];if(t||(console.warn(`no frame at index ${this._frameIndex}, using last frame`),t=this._parsedFrames[this._parsedFrames.length-1]),t){const e=t.dims;this._frameImageData&&e.width==this._frameImageData.width&&e.height==this._frameImageData.height||(this._tmpCanvasElement.width=e.width,this._tmpCanvasElement.height=e.height,this._frameImageData=this._tmpCanvasContext.createImageData(e.width,e.height)),this._frameImageData.data.set(t.patch),this._tmpCanvasContext.putImageData(this._frameImageData,0,0),this._gifCanvasContext.drawImage(this._tmpCanvasElement,e.left,e.top);const n=this.containerController.container().texture();if(!n)return;n.needsUpdate=!0}}_drawNextFrame(){this._frameIndex++,this._frameIndex>=this._parsedFrames.length&&(this._frameIndex=0),this._drawOnCanvas(),this.pv.play&&setTimeout((()=>{this._drawNextFrame()}),this._frameDelay)}gifUpdateFrameIndex(){this._frameIndex=this.pv.gifFrame,this._drawOnCanvas()}static PARAM_CALLBACK_reload(t){t.paramCallbackReload()}paramCallbackReload(){this.p.url.setDirty()}static PARAM_CALLBACK_gifUpdatePlay(t){t.gifUpdatePlay()}gifUpdatePlay(){this.pv.play&&this._drawNextFrame()}static PARAM_CALLBACK_gifUpdateFrameIndex(t){t.gifUpdateFrameIndex()}}function tv(t){return class extends t{constructor(){super(...arguments),this.checkFileType=aa.BOOLEAN(!0)}}}class ev extends(tv(xg(function(t){return class extends t{constructor(){super(...arguments),this.url=aa.STRING($g.PARAM_DEFAULT,{fileBrowse:{type:[Or.TEXTURE_IMAGE]}}),this.reload=aa.BUTTON(null,{callback:(t,e)=>{iv.PARAM_CALLBACK_reload(t,e)}})}}}(la)))){}const nv=new ev;class iv extends af{constructor(){super(...arguments),this.paramsConfig=nv,this.textureParamsController=new wg(this)}static type(){return\\\\\\\"image\\\\\\\"}async requiredModules(){this.p.url.isDirty()&&await this.p.url.compute();const t=jg.extension(this.pv.url||\\\\\\\"\\\\\\\");return $g.module_names(t)}static displayedInputNames(){return[\\\\\\\"optional texture to copy attributes from\\\\\\\"]}initializeNode(){this.io.inputs.setCount(0,1),this.io.inputs.initInputsClonedState(Ki.NEVER),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{let t=[this.p.url];t=t.concat(this.textureParamsController.paramLabelsParams()),this.params.label.init(t,(()=>{const t=this.p.url.rawInput();if(t){const e=t.split(\\\\\\\"/\\\\\\\"),n=e[e.length-1],i=this.textureParamsController.paramLabels();return[n].concat(i)}return\\\\\\\"\\\\\\\"}))}))}))}async cook(t){if(this.pv.checkFileType&&(e=this.pv.url,!Xg.includes(Yg(e).toLowerCase())))this.states.error.set(\\\\\\\"url is not an image\\\\\\\");else{const e=await this._loadTexture(this.pv.url);if(e){const n=t[0];n&&wg.copyTextureAttributes(e,n),await this.textureParamsController.update(e),this.setTexture(e)}else this._clearTexture()}var e}static PARAM_CALLBACK_reload(t,e){t.paramCallbackReload()}paramCallbackReload(){this.p.url.setDirty()}async _loadTexture(t){let e=null;const n=this.p.url,i=new $g(t,n,this,this.scene(),{forceImage:!this.pv.checkFileType});try{e=await i.load_texture_from_url_or_op({tdataType:this.pv.ttype&&this.pv.tadvanced,dataType:this.pv.type}),e&&(e.matrixAutoUpdate=!1)}catch(t){}return e||this.states.error.set(`could not load texture '${t}'`),e}}var sv=n(32);const rv=.005;class ov{constructor(t,e=1024){this.renderer=t,this.res=e,this.objectTargets=[],this.lights=[],this.scene=new gs,this.buffer1Active=!1,this._params={lightRadius:1,iterations:1,iterationBlend:rv,blur:!1,blurAmount:0},this._objectStateByObject=new WeakMap,this._previousRenderTarget=null,this._lightHierarchyStateByLight=new WeakMap,this._lightMatrixStateByLight=new WeakMap,this._t=new p.a,this._q=new ah.a,this._s=new p.a;const n=Zf.isAndroid()||Zf.isiOS()?w.M:w.G;this.progressiveLightMap1=new Q(this.res,this.res,{type:n}),this.progressiveLightMap2=new Q(this.res,this.res,{type:n}),this.uvMat=this._createUVMat()}textureRenderTarget(){return this.progressiveLightMap2}texture(){return this.textureRenderTarget().texture}setParams(t){this._params.lightRadius=t.lightRadius,this._params.iterations=t.iterations,this._params.iterationBlend=t.iterationBlend,this._params.blur=t.blur,this._params.blurAmount=t.blurAmount}init(t,e){this._setObjects(t),this._setLights(e)}_setObjects(t){this.objectTargets=[];for(let e of t)null==this.blurringPlane&&this._initializeBlurPlane(this.res,this.progressiveLightMap1),this.objectTargets.push(e);this._saveObjectsState()}_setLights(t){this.lights=t;for(let e of t)this._saveLightHierarchyState(e),this.scene.attach(e),this._saveLightMatrixState(e)}_saveLightHierarchyState(t){this._lightHierarchyStateByLight.set(t,{parent:t.parent,matrixAutoUpdate:t.matrixAutoUpdate}),t.matrixAutoUpdate=!0}_saveLightMatrixState(t){t.updateMatrix(),t.matrix.decompose(this._t,this._q,this._s),this._lightMatrixStateByLight.set(t,{matrix:t.matrix.clone(),position:this._t.clone()})}_saveObjectsState(){let t=0;for(let e of this.objectTargets)this._objectStateByObject.set(e,{frustumCulled:e.frustumCulled,material:e.material,parent:e.parent,castShadow:e.castShadow,receiveShadow:e.receiveShadow}),e.material=this.uvMat,e.frustumCulled=!1,e.castShadow=!0,e.receiveShadow=!0,e.renderOrder=1e3+t,this.scene.attach(e),t++;this._previousRenderTarget=this.renderer.getRenderTarget()}_moveLights(){const t=this._params.lightRadius;for(let e of this.lights){const n=this._lightMatrixStateByLight.get(e);if(n){const i=n.position;e.position.x=i.x+t*(Math.random()-.5),e.position.y=i.y+t*(Math.random()-.5),e.position.z=i.z+t*(Math.random()-.5)}}}restoreState(){this._restoreObjectsState(),this._restoreLightsState(),this.renderer.setRenderTarget(this._previousRenderTarget)}_restoreObjectsState(){for(let t of this.objectTargets){const e=this._objectStateByObject.get(t);if(e){t.frustumCulled=e.frustumCulled,t.castShadow=e.castShadow,t.receiveShadow=e.receiveShadow,t.material=e.material;const n=e.parent;n&&n.add(t)}}}_restoreLightsState(){var t;for(let e of this.lights){const n=this._lightHierarchyStateByLight.get(e),i=this._lightMatrixStateByLight.get(e);n&&i&&(e.matrixAutoUpdate=n.matrixAutoUpdate,e.matrix.copy(i.matrix),e.matrix.decompose(e.position,e.quaternion,e.scale),e.updateMatrix(),null===(t=n.parent)||void 0===t||t.attach(e))}}runUpdates(t){if(!this.blurMaterial)return;if(null==this.blurringPlane)return;const e=this._params.iterations;this.blurMaterial.uniforms.pixelOffset.value=this._params.blurAmount/this.res,this.blurringPlane.visible=this._params.blur,this.uvMat.uniforms.iterationBlend.value=this._params.iterationBlend,this._clear(t);for(let n=0;n{i.vertexShader=\\\\\\\"#define USE_UV\\\\n\\\\\\\"+i.vertexShader.slice(0,-2)+\\\\\\\"\\\\tgl_Position = vec4((uv - 0.5) * 2.0, 1.0, 1.0); }\\\\\\\";const s=i.fragmentShader.indexOf(\\\\\\\"void main() {\\\\\\\");i.fragmentShader=\\\\\\\"#define USE_UV\\\\n\\\\\\\"+i.fragmentShader.slice(0,s)+\\\\\\\"\\\\tuniform sampler2D previousShadowMap;\\\\n\\\\tuniform float pixelOffset;\\\\n\\\\\\\"+i.fragmentShader.slice(s-1,-2)+\\\\\\\"\\\\tgl_FragColor.rgb = (\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\ttexture2D(previousShadowMap, vUv + vec2( pixelOffset, 0.0 )).rgb +\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\ttexture2D(previousShadowMap, vUv + vec2( 0.0 , pixelOffset)).rgb +\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\ttexture2D(previousShadowMap, vUv + vec2( 0.0 , -pixelOffset)).rgb +\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\ttexture2D(previousShadowMap, vUv + vec2(-pixelOffset, 0.0 )).rgb +\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\ttexture2D(previousShadowMap, vUv + vec2( pixelOffset, pixelOffset)).rgb +\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\ttexture2D(previousShadowMap, vUv + vec2(-pixelOffset, pixelOffset)).rgb +\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\ttexture2D(previousShadowMap, vUv + vec2( pixelOffset, -pixelOffset)).rgb +\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\ttexture2D(previousShadowMap, vUv + vec2(-pixelOffset, -pixelOffset)).rgb)/8.0;\\\\n\\\\t\\\\t\\\\t\\\\t}\\\\\\\";const r={previousShadowMap:{value:e.texture},pixelOffset:{value:.5/t}};i.uniforms.previousShadowMap=r.previousShadowMap,i.uniforms.pixelOffset=r.pixelOffset,n.uniforms.previousShadowMap=r.previousShadowMap,n.uniforms.pixelOffset=r.pixelOffset,n.userData.shader=i},n}_createUVMat(){const t=new Gf.a;return t.uniforms={previousShadowMap:{value:null},iterationBlend:{value:rv}},t.name=\\\\\\\"uvMat\\\\\\\",t.onBeforeCompile=e=>{e.vertexShader=\\\\\\\"#define USE_LIGHTMAP\\\\n\\\\\\\"+e.vertexShader.slice(0,-2)+\\\\\\\"\\\\tgl_Position = vec4((uv2 - 0.5) * 2.0, 1.0, 1.0); }\\\\\\\";const n=e.fragmentShader.indexOf(\\\\\\\"void main() {\\\\\\\");e.fragmentShader=\\\\\\\"varying vec2 vUv2;\\\\n\\\\\\\"+e.fragmentShader.slice(0,n)+\\\\\\\"\\\\tuniform sampler2D previousShadowMap;\\\\n\\\\tuniform float iterationBlend;\\\\n\\\\\\\"+e.fragmentShader.slice(n-1,-2)+\\\\\\\"\\\\nvec3 texelOld = texture2D(previousShadowMap, vUv2).rgb;\\\\n\\\\t\\\\t\\\\t\\\\tgl_FragColor.rgb = mix(texelOld, gl_FragColor.rgb, iterationBlend);\\\\n\\\\t\\\\t\\\\t\\\\t// gl_FragColor.rgb = vec3(vUv2,1.0);\\\\n\\\\t\\\\t\\\\t}\\\\\\\";const i={previousShadowMap:{value:this.progressiveLightMap1.texture},iterationBlend:{value:rv}};e.uniforms.previousShadowMap=i.previousShadowMap,e.uniforms.iterationBlend=i.iterationBlend,t.uniforms.previousShadowMap=i.previousShadowMap,t.uniforms.iterationBlend=i.iterationBlend,t.userData.shader=e},t}}const av=new class extends la{constructor(){super(...arguments),this.update=aa.BUTTON(null,{callback:t=>{lv.PARAM_CALLBACK_updateManual(t)}}),this.useCameraRenderer=aa.BOOLEAN(1),this.lightMapRes=aa.INTEGER(1024,{range:[1,2048],rangeLocked:[!0,!1]}),this.iterations=aa.INTEGER(512,{range:[1,2048],rangeLocked:[!0,!1]}),this.iterationBlend=aa.FLOAT(rv,{range:[0,1],rangeLocked:[!0,!0]}),this.blur=aa.BOOLEAN(1),this.blurAmount=aa.FLOAT(1,{visibleIf:{blur:1},range:[0,1],rangeLocked:[!0,!1]}),this.lightRadius=aa.FLOAT(1,{range:[0,10]}),this.objectsMask=aa.STRING(\\\\\\\"\\\\\\\"),this.lightsMask=aa.STRING(\\\\\\\"*\\\\\\\"),this.printResolveObjectsList=aa.BUTTON(null,{callback:t=>{lv.PARAM_CALLBACK_printResolveObjectsList(t)}})}};class lv extends af{constructor(){super(...arguments),this.paramsConfig=av,this._includedObjects=[],this._includedLights=[]}static type(){return\\\\\\\"lightMap\\\\\\\"}async cook(){this._updateManual()}async _createLightMapController(){const t=await li.renderersController.firstRenderer();if(!t)return void console.warn(\\\\\\\"no renderer found\\\\\\\");return new ov(t,this.pv.lightMapRes)}static PARAM_CALLBACK_update_updateMode(t){}async _updateManual(){if(this.lightMapController=this.lightMapController||await this._createLightMapController(),!this.lightMapController)return;const t=this.scene().mainCameraNode();if(!t)return;this._updateObjectsAndLightsList(),this.lightMapController.init(this._includedObjects,this._includedLights);const e=t.camera();this.lightMapController.setParams({lightRadius:this.pv.lightRadius,iterations:this.pv.iterations,iterationBlend:this.pv.iterationBlend,blur:this.pv.blur,blurAmount:this.pv.blurAmount}),this.lightMapController.runUpdates(e),this.lightMapController.restoreState();const n=this.lightMapController.textureRenderTarget();if(this.pv.useCameraRenderer)this.setTexture(n.texture);else{this._data_texture_controller=this._data_texture_controller||new If(Rf.Float32Array),this._renderer_controller=this._renderer_controller||new Ff(this);const t=await this._renderer_controller.renderer(),e=this._data_texture_controller.from_render_target(t,n);this.setTexture(e)}}static PARAM_CALLBACK_updateManual(t){t._updateManual()}_updateObjectsAndLightsList(){let t=[],e=[];this._includedLights=[],this._includedObjects=[];const n=new WeakSet;if(\\\\\\\"\\\\\\\"!=this.pv.lightsMask){e=this.scene().objectsByMask(this.pv.lightsMask);for(let t of e)t instanceof sv.a&&(this._includedLights.push(t),n.add(t))}if(\\\\\\\"\\\\\\\"!=this.pv.objectsMask){t=this.scene().objectsByMask(this.pv.objectsMask);for(let e of t)e instanceof sv.a||!n.has(e)&&e instanceof B.a&&this._includedObjects.push(e)}}static PARAM_CALLBACK_printResolveObjectsList(t){t._printResolveObjectsList()}_printResolveObjectsList(){this._updateObjectsAndLightsList(),console.log(\\\\\\\"included objects:\\\\\\\"),console.log(this._includedObjects),console.log(\\\\\\\"included lights:\\\\\\\"),console.log(this._includedLights)}}const cv=new la;class hv extends af{constructor(){super(...arguments),this.paramsConfig=cv}static type(){return\\\\\\\"null\\\\\\\"}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState(Ki.NEVER)}async cook(t){const e=t[0];this.setTexture(e)}}const uv=[is.ORTHOGRAPHIC,is.PERSPECTIVE];class dv extends(xg(function(t){return class extends t{constructor(){super(...arguments),this.camera=aa.NODE_PATH(\\\\\\\"\\\\\\\",{nodeSelection:{context:ts.OBJ,types:uv}}),this.resolution=aa.VECTOR2([1024,1024]),this.useCameraRenderer=aa.BOOLEAN(1),this.render=aa.BUTTON(null,{callback:t=>{_v.PARAM_CALLBACK_render(t)}})}}}(la))){}const pv=new dv;class _v extends af{constructor(){super(...arguments),this.paramsConfig=pv,this.textureParamsController=new wg(this)}static type(){return\\\\\\\"render\\\\\\\"}async cook(){this._texture_scene=this.scene().threejsScene(),this._camera_node=this.pv.camera.nodeWithContext(ts.OBJ),this._camera_node&&uv.includes(this._camera_node.type())?(this._texture_camera=this._camera_node.object,await this._camera_node.compute(),this.renderOnTarget()):this._texture_camera=void 0}async renderOnTarget(){if(await this.createRenderTargetIfRequired(),!(this._render_target&&this._texture_scene&&this._texture_camera))return;this._renderer_controller=this._renderer_controller||new Ff(this);const t=await this._renderer_controller.renderer(),e=t.getRenderTarget();if(t.setRenderTarget(this._render_target),t.clear(),t.render(this._texture_scene,this._texture_camera),t.setRenderTarget(e),this._render_target.texture)if(this.pv.useCameraRenderer)this.setTexture(this._render_target.texture);else{this._data_texture_controller=this._data_texture_controller||new If(Rf.Float32Array);const e=this._data_texture_controller.from_render_target(t,this._render_target);await this.textureParamsController.update(e),this.setTexture(e)}else this.cookController.endCook()}async renderTarget(){return this._render_target=this._render_target||await this._createRenderTarget(this.pv.resolution.x,this.pv.resolution.y)}async createRenderTargetIfRequired(){var t;this._render_target&&this._renderTargetResolutionValid()||(this._render_target=await this._createRenderTarget(this.pv.resolution.x,this.pv.resolution.y),null===(t=this._data_texture_controller)||void 0===t||t.reset())}_renderTargetResolutionValid(){if(this._render_target){const t=this._render_target.texture.image;return t.width==this.pv.resolution.x&&t.height==this.pv.resolution.y}return!1}async _createRenderTarget(t,e){if(this._render_target){const n=this._render_target.texture.image;if(n.width==t&&n.height==e)return this._render_target}const n=w.n,i=w.n,s=w.V,r=w.ob;var o=new Q(t,e,{wrapS:n,wrapT:i,minFilter:s,magFilter:r,format:w.Ib,generateMipmaps:!0,type:Zf.isiOS()?w.M:w.G,stencilBuffer:!1,depthBuffer:!1});return await this.textureParamsController.update(o.texture),li.warn(\\\\\\\"created render target\\\\\\\",this.path(),t,e),o}static PARAM_CALLBACK_render(t){t.renderOnTarget()}}const mv=new class extends la{constructor(){super(...arguments),this.input=aa.INTEGER(0,{range:[0,3],rangeLocked:[!0,!0]})}};class fv extends af{constructor(){super(...arguments),this.paramsConfig=mv}static type(){return\\\\\\\"switch\\\\\\\"}initializeNode(){this.io.inputs.setCount(0,4),this.io.inputs.initInputsClonedState(Ki.NEVER),this.cookController.disallowInputsEvaluation()}async cook(){const t=this.pv.input;if(this.io.inputs.has_input(t)){const e=await this.containerController.requestInputContainer(t);if(e)return void this.setTexture(e.texture())}else this.states.error.set(`no input ${t}`);this.cookController.endCook()}}class gv extends(xg(la)){}const vv=new gv;class yv extends af{constructor(){super(...arguments),this.paramsConfig=vv,this.textureParamsController=new wg(this)}static type(){return\\\\\\\"textureProperties\\\\\\\"}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState([Ki.FROM_NODE])}async cook(t){const e=t[0];this.textureParamsController.update(e),this.setTexture(e)}}class xv extends(tv(xg(function(t){return class extends t{constructor(){super(...arguments),this.url=aa.STRING($g.PARAM_DEFAULT,{fileBrowse:{type:[Or.TEXTURE_VIDEO]}}),this.reload=aa.BUTTON(null,{callback:(t,e)=>{wv.PARAM_CALLBACK_reload(t,e)}}),this.play=aa.BOOLEAN(1,{cook:!1,callback:t=>{wv.PARAM_CALLBACK_video_update_play(t)}}),this.muted=aa.BOOLEAN(1,{cook:!1,callback:t=>{wv.PARAM_CALLBACK_video_update_muted(t)}}),this.loop=aa.BOOLEAN(1,{cook:!1,callback:t=>{wv.PARAM_CALLBACK_video_update_loop(t)}}),this.videoTime=aa.FLOAT(0,{cook:!1}),this.setVideoTime=aa.BUTTON(null,{cook:!1,callback:t=>{wv.PARAM_CALLBACK_video_update_time(t)}})}}}(la)))){}const bv=new xv;class wv extends af{constructor(){super(...arguments),this.paramsConfig=bv,this.textureParamsController=new wg(this)}static type(){return Lg.VIDEO}async requiredModules(){this.p.url.isDirty()&&await this.p.url.compute();const t=jg.extension(this.pv.url||\\\\\\\"\\\\\\\");return $g.module_names(t)}HTMLVideoElement(){return this._video}static displayedInputNames(){return[\\\\\\\"optional texture to copy attributes from\\\\\\\"]}initializeNode(){this.io.inputs.setCount(0,1),this.io.inputs.initInputsClonedState(Ki.NEVER),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.url],(()=>{const t=this.p.url.rawInput();if(t){const e=t.split(\\\\\\\"/\\\\\\\");return e[e.length-1]}return\\\\\\\"\\\\\\\"}))}))}))}async cook(t){if(this.pv.checkFileType&&(e=this.pv.url,!Wg.includes(Yg(e).toLowerCase())))this.states.error.set(\\\\\\\"url is not a video\\\\\\\");else{const e=await this._load_texture(this.pv.url);if(e){this._video=e.image,this._video&&document.body.appendChild(this._video);const n=t[0];n&&wg.copyTextureAttributes(e,n),this.video_update_loop(),this.video_update_muted(),this.video_update_play(),this.video_update_time(),await this.textureParamsController.update(e),this.setTexture(e)}else this.cookController.endCook()}var e}dispose(){var t;super.dispose(),this._video&&(null===(t=this._video.parentElement)||void 0===t||t.removeChild(this._video))}static PARAM_CALLBACK_video_update_time(t){t.video_update_time()}static PARAM_CALLBACK_video_update_play(t){t.video_update_play()}static PARAM_CALLBACK_video_update_muted(t){t.video_update_muted()}static PARAM_CALLBACK_video_update_loop(t){t.video_update_loop()}async video_update_time(){if(this._video){const t=this.p.videoTime;t.isDirty()&&await t.compute(),this._video.currentTime=t.value}}video_update_muted(){this._video&&(this._video.muted=this.pv.muted)}video_update_loop(){this._video&&(this._video.loop=this.pv.loop)}video_update_play(){this._video&&(this.pv.play?this._video.play():this._video.pause())}static PARAM_CALLBACK_reload(t,e){t.paramCallbackReload()}paramCallbackReload(){this.p.url.setDirty()}async _load_texture(t){let e=null;const n=this.p.url;this._texture_loader=this._texture_loader||new $g(t,n,this,this.scene(),{forceVideo:!this.pv.checkFileType});try{e=await this._texture_loader.load_texture_from_url_or_op({tdataType:this.pv.ttype&&this.pv.tadvanced,dataType:this.pv.type}),e&&(e.matrixAutoUpdate=!1)}catch(t){}return e||this.states.error.set(`could not load texture '${t}'`),e}}class Tv extends(xg(function(t){return class extends t{constructor(){super(...arguments),this.res=aa.VECTOR2([1024,1024])}}}(la))){}const Av=new Tv;class Mv extends af{constructor(){super(...arguments),this.paramsConfig=Av,this.textureParamsController=new wg(this)}static type(){return Lg.WEB_CAM}HTMLVideoElement(){return this._video}static displayedInputNames(){return[\\\\\\\"optional texture to copy attributes from\\\\\\\"]}initializeNode(){this.io.inputs.setCount(0,1),this.io.inputs.initInputsClonedState(Ki.NEVER)}_createHTMLVideoElement(){this._video&&document.body.removeChild(this._video);const t=document.createElement(\\\\\\\"video\\\\\\\");return t.style.display=\\\\\\\"none\\\\\\\",t.width=this.pv.res.x,t.height=this.pv.res.y,t.autoplay=!0,t.setAttribute(\\\\\\\"autoplay\\\\\\\",\\\\\\\"true\\\\\\\"),t.setAttribute(\\\\\\\"muted\\\\\\\",\\\\\\\"true\\\\\\\"),t.setAttribute(\\\\\\\"playsinline\\\\\\\",\\\\\\\"true\\\\\\\"),document.body.appendChild(t),t}async cook(t){this._video=this._createHTMLVideoElement();const e=new kg(this._video),n=t[0];if(n&&wg.copyTextureAttributes(e,n),await this.textureParamsController.update(e),navigator&&navigator.mediaDevices&&navigator.mediaDevices.getUserMedia){const t={video:{width:this.pv.res.x,height:this.pv.res.y,facingMode:\\\\\\\"user\\\\\\\"}};navigator.mediaDevices.getUserMedia(t).then((t=>{this._video&&(this._video.srcObject=t,this._video.play(),this.setTexture(e))})).catch((t=>{this.states.error.set(\\\\\\\"Unable to access the camera/webcam\\\\\\\")}))}else this.states.error.set(\\\\\\\"MediaDevices interface not available.\\\\\\\")}}class Ev extends sa{static context(){return ts.COP}cook(){this.cookController.endCook()}}class Sv extends Ev{}class Cv extends Sv{constructor(){super(...arguments),this._children_controller_context=ts.ANIM}static type(){return es.ANIM}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class Nv extends Sv{constructor(){super(...arguments),this._children_controller_context=ts.EVENT}static type(){return es.EVENT}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class Lv extends Sv{constructor(){super(...arguments),this._children_controller_context=ts.COP}static type(){return es.COP}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class Ov extends Sv{constructor(){super(...arguments),this._children_controller_context=ts.MAT}static type(){return es.MAT}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class Pv extends Ev{constructor(){super(...arguments),this.paramsConfig=new Jm,this.effectsComposerController=new Zm(this),this.displayNodeController=new Lm(this,this.effectsComposerController.displayNodeControllerCallbacks()),this._children_controller_context=ts.POST}static type(){return es.POST}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class Rv extends Sv{constructor(){super(...arguments),this._children_controller_context=ts.ROP}static type(){return es.ROP}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}var Iv,Fv;!function(t){t.START=\\\\\\\"start\\\\\\\",t.STOP=\\\\\\\"stop\\\\\\\",t.UPDATE=\\\\\\\"update\\\\\\\"}(Iv||(Iv={})),function(t){t.START=\\\\\\\"start\\\\\\\",t.COMPLETE=\\\\\\\"completed\\\\\\\"}(Fv||(Fv={}));const Dv=new class extends la{constructor(){super(...arguments),this.animation=aa.NODE_PATH(\\\\\\\"\\\\\\\",{nodeSelection:{context:ts.ANIM},dependentOnFoundNode:!1}),this.play=aa.BUTTON(null,{callback:t=>{Bv.PARAM_CALLBACK_play(t)}}),this.pause=aa.BUTTON(null,{callback:t=>{Bv.PARAM_CALLBACK_pause(t)}})}};class Bv extends ka{constructor(){super(...arguments),this.paramsConfig=Dv}static type(){return\\\\\\\"animation\\\\\\\"}initializeNode(){this.io.inputs.setNamedInputConnectionPoints([new Zo(Iv.START,Jo.BASE,this._play.bind(this)),new Zo(Iv.STOP,Jo.BASE,this._pause.bind(this))]),this.io.outputs.setNamedOutputConnectionPoints([new Zo(Fv.START,Jo.BASE),new Zo(Fv.COMPLETE,Jo.BASE)]),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.animation],(()=>this.pv.animation.path()))}))}))}processEvent(t){}static PARAM_CALLBACK_play(t){t._play({})}static PARAM_CALLBACK_pause(t){t._pause()}async _play(t){const e=this.p.animation;e.isDirty()&&await e.compute();const n=e.value.nodeWithContext(ts.ANIM);if(!n)return;const i=await n.compute();i&&(this._timeline_builder=i.coreContent(),this._timeline_builder&&(this._timeline&&this._timeline.kill(),this._timeline=L_.timeline(),this._timeline_builder.populate(this._timeline),this._timeline.vars.onStart=()=>{this.trigger_animation_started(t)},this._timeline.vars.onComplete=()=>{this._timeline&&this._timeline.kill(),this.trigger_animation_completed(t)}))}_pause(){this._timeline&&this._timeline.pause()}trigger_animation_started(t){this.dispatchEventToOutput(Fv.START,t)}trigger_animation_completed(t){this.dispatchEventToOutput(Fv.COMPLETE,t)}}const zv=\\\\\\\"event\\\\\\\";const kv=new class extends la{constructor(){super(...arguments),this.active=aa.BOOLEAN(1),this.inputsCount=aa.INTEGER(5,{range:[1,10],rangeLocked:[!0,!1]})}};class Uv extends ka{constructor(){super(...arguments),this.paramsConfig=kv}static type(){return\\\\\\\"any\\\\\\\"}initializeNode(){this.io.connection_points.set_expected_input_types_function(this._expected_input_types.bind(this)),this.io.connection_points.set_input_name_function(this._input_name.bind(this)),this.io.connection_points.set_output_name_function((()=>zv)),this.io.connection_points.set_expected_output_types_function((()=>[Jo.BASE]))}_expected_input_types(){const t=new Array(this.pv.inputsCount);return t.fill(Jo.BASE),t}_input_name(t){return`trigger${t}`}async processEvent(t){this.p.active.isDirty()&&await this.p.active.compute(),this.pv.active&&this.dispatchEventToOutput(zv,t)}}const Gv=new class extends la{constructor(){super(...arguments),this.blocking=aa.BOOLEAN(1)}};class Vv extends ka{constructor(){super(...arguments),this.paramsConfig=Gv}static type(){return\\\\\\\"block\\\\\\\"}initializeNode(){this.io.inputs.setNamedInputConnectionPoints([new Zo(\\\\\\\"in\\\\\\\",Jo.BASE,this._process_incoming_event.bind(this))]),this.io.outputs.setNamedOutputConnectionPoints([new Zo(Vv.OUTPUT,Jo.BASE)]),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.blocking],(()=>this.pv.blocking?\\\\\\\"blocking (X)\\\\\\\":\\\\\\\"pass-through (--\\\\x3e)\\\\\\\"))}))}))}trigger_output(t){this.dispatchEventToOutput(Vv.OUTPUT,t)}_process_incoming_event(t){this.pv.blocking||this.trigger_output(t)}}var Hv;Vv.OUTPUT=\\\\\\\"output\\\\\\\",function(t){t.OUT=\\\\\\\"out\\\\\\\"}(Hv||(Hv={}));const jv=new class extends la{constructor(){super(...arguments),this.dispatch=aa.BUTTON(null,{callback:t=>{Wv.PARAM_CALLBACK_execute(t)}})}};class Wv extends ka{constructor(){super(...arguments),this.paramsConfig=jv}static type(){return\\\\\\\"button\\\\\\\"}initializeNode(){this.io.outputs.setNamedOutputConnectionPoints([new Zo(Hv.OUT,Jo.BASE)])}processEvent(t){}process_event_execute(t){this.dispatchEventToOutput(Hv.OUT,t)}static PARAM_CALLBACK_execute(t){t.process_event_execute({})}}class qv extends ka{constructor(){super(...arguments),this._controls_by_viewer=new Map}async apply_controls(t,e){var n;null===(n=e.controlsController)||void 0===n||n.dispose_controls();const i=e.canvas();if(!i)return;const s=await this.createControlsInstance(t,i),r=this._controls_by_viewer.get(e);r&&r.dispose(),this._controls_by_viewer.set(e,s);const o=li.performance.performanceManager().now();return s.name=`${this.path()}:${t.name}:${o}:${this.controls_id()}`,await this.params.evalAll(),this.setupControls(s),s}controls_id(){return JSON.stringify(this.params.all.map((t=>t.valueSerialized())))}}var Xv=n(27);const Yv=new p.a(0,0,1),$v=new Xv.a,Jv=new ah.a,Zv=new ah.a(-Math.sqrt(.5),0,0,Math.sqrt(.5)),Qv={type:\\\\\\\"change\\\\\\\"};class Kv extends J.a{constructor(t){super(),!1===window.isSecureContext&&console.error(\\\\\\\"THREE.DeviceOrientationControls: DeviceOrientationEvent is only available in secure contexts (https)\\\\\\\");const e=this,n=new ah.a;this.object=t,this.object.rotation.reorder(\\\\\\\"YXZ\\\\\\\"),this.enabled=!0,this.deviceOrientation={},this.screenOrientation=0,this.alphaOffset=0;const i=function(t){e.deviceOrientation=t},s=function(){e.screenOrientation=window.orientation||0};this.connect=function(){s(),void 0!==window.DeviceOrientationEvent&&\\\\\\\"function\\\\\\\"==typeof window.DeviceOrientationEvent.requestPermission?window.DeviceOrientationEvent.requestPermission().then((function(t){\\\\\\\"granted\\\\\\\"==t&&(window.addEventListener(\\\\\\\"orientationchange\\\\\\\",s),window.addEventListener(\\\\\\\"deviceorientation\\\\\\\",i))})).catch((function(t){console.error(\\\\\\\"THREE.DeviceOrientationControls: Unable to use DeviceOrientation API:\\\\\\\",t)})):(window.addEventListener(\\\\\\\"orientationchange\\\\\\\",s),window.addEventListener(\\\\\\\"deviceorientation\\\\\\\",i)),e.enabled=!0},this.disconnect=function(){window.removeEventListener(\\\\\\\"orientationchange\\\\\\\",s),window.removeEventListener(\\\\\\\"deviceorientation\\\\\\\",i),e.enabled=!1},this.update=function(){if(!1===e.enabled)return;const t=e.deviceOrientation;if(t){const i=t.alpha?On.e(t.alpha)+e.alphaOffset:0,s=t.beta?On.e(t.beta):0,r=t.gamma?On.e(t.gamma):0,o=e.screenOrientation?On.e(e.screenOrientation):0;!function(t,e,n,i,s){$v.set(n,e,-i,\\\\\\\"YXZ\\\\\\\"),t.setFromEuler($v),t.multiply(Zv),t.multiply(Jv.setFromAxisAngle(Yv,-s))}(e.object.quaternion,i,s,r,o),8*(1-n.dot(e.object.quaternion))>1e-6&&(n.copy(e.object.quaternion),e.dispatchEvent(Qv))}},this.dispose=function(){e.disconnect()},this.connect()}}const ty=new class extends la{constructor(){super(...arguments),this.enabled=aa.BOOLEAN(1)}};class ey extends qv{constructor(){super(...arguments),this.paramsConfig=ty,this._controls_by_element_id=new Map}static type(){return rs.DEVICE_ORIENTATION}endEventName(){return\\\\\\\"end\\\\\\\"}async createControlsInstance(t,e){const n=new Kv(t);return this._controls_by_element_id.set(e.id,n),n}setupControls(t){t.enabled=this.pv.enabled}updateRequired(){return!0}disposeControlsForHtmlElementId(t){const e=this._controls_by_element_id.get(t);e&&(e.dispose(),this._controls_by_element_id.delete(t))}}class ny{constructor(t=1,e=0,n=0){return this.radius=t,this.phi=e,this.theta=n,this}set(t,e,n){return this.radius=t,this.phi=e,this.theta=n,this}copy(t){return this.radius=t.radius,this.phi=t.phi,this.theta=t.theta,this}makeSafe(){const t=1e-6;return this.phi=Math.max(t,Math.min(Math.PI-t,this.phi)),this}setFromVector3(t){return this.setFromCartesianCoords(t.x,t.y,t.z)}setFromCartesianCoords(t,e,n){return this.radius=Math.sqrt(t*t+e*e+n*n),0===this.radius?(this.theta=0,this.phi=0):(this.theta=Math.atan2(t,n),this.phi=Math.acos(On.d(e/this.radius,-1,1))),this}clone(){return(new this.constructor).copy(this)}}const iy={type:\\\\\\\"change\\\\\\\"},sy={type:\\\\\\\"start\\\\\\\"},ry={type:\\\\\\\"end\\\\\\\"};class oy extends J.a{constructor(t,e){super(),void 0===e&&console.warn('THREE.OrbitControls: The second parameter \\\\\\\"domElement\\\\\\\" is now mandatory.'),e===document&&console.error('THREE.OrbitControls: \\\\\\\"document\\\\\\\" should not be used as the target \\\\\\\"domElement\\\\\\\". Please use \\\\\\\"renderer.domElement\\\\\\\" instead.'),this.object=t,this.domElement=e,this.domElement.style.touchAction=\\\\\\\"none\\\\\\\",this.enabled=!0,this.target=new p.a,this.minDistance=0,this.maxDistance=1/0,this.minZoom=0,this.maxZoom=1/0,this.minPolarAngle=0,this.maxPolarAngle=Math.PI,this.minAzimuthAngle=-1/0,this.maxAzimuthAngle=1/0,this.enableDamping=!1,this.dampingFactor=.05,this.enableZoom=!0,this.zoomSpeed=1,this.enableRotate=!0,this.rotateSpeed=1,this.enablePan=!0,this.panSpeed=1,this.screenSpacePanning=!0,this.keyPanSpeed=7,this.autoRotate=!1,this.autoRotateSpeed=2,this.enableKeys=!0,this.keyMode=\\\\\\\"pan\\\\\\\",this.keyRotateSpeedVertical=1,this.keyRotateSpeedHorizontal=1,this.keys={LEFT:\\\\\\\"ArrowLeft\\\\\\\",UP:\\\\\\\"ArrowUp\\\\\\\",RIGHT:\\\\\\\"ArrowRight\\\\\\\",BOTTOM:\\\\\\\"ArrowDown\\\\\\\"},this.mouseButtons={LEFT:w.hb.ROTATE,MIDDLE:w.hb.DOLLY,RIGHT:w.hb.PAN},this.touches={ONE:w.Tc.ROTATE,TWO:w.Tc.DOLLY_PAN},this.target0=this.target.clone(),this.position0=this.object.position.clone(),this.zoom0=this.object.zoom,this._domElementKeyEvents=null,this.getPolarAngle=function(){return o.phi},this.getAzimuthalAngle=function(){return o.theta},this.getDistance=function(){return this.object.position.distanceTo(this.target)},this.listenToKeyEvents=function(t){t.addEventListener(\\\\\\\"keydown\\\\\\\",q),this._domElementKeyEvents=t},this.saveState=function(){n.target0.copy(n.target),n.position0.copy(n.object.position),n.zoom0=n.object.zoom},this.reset=function(){n.target.copy(n.target0),n.object.position.copy(n.position0),n.object.zoom=n.zoom0,n.object.updateProjectionMatrix(),n.dispatchEvent(iy),n.update(),s=i.NONE},this.update=function(){const e=new p.a,u=(new ah.a).setFromUnitVectors(t.up,new p.a(0,1,0)),d=u.clone().invert(),_=new p.a,m=new ah.a,f=2*Math.PI;let g=!1;return function(){const t=n.object.position;if(e.copy(t).sub(n.target),e.applyQuaternion(u),o.setFromVector3(e),n.autoRotate&&s===i.NONE&&E(2*Math.PI/60/60*n.autoRotateSpeed),n.enableDamping){const t=a.theta*n.dampingFactor,e=a.phi*n.dampingFactor;tMath.PI&&(p-=f),v<-Math.PI?v+=f:v>Math.PI&&(v-=f),o.theta=p(p+v)/2?Math.max(p,o.theta):Math.min(v,o.theta)),o.phi=Math.max(n.minPolarAngle,Math.min(n.maxPolarAngle,o.phi)),o.makeSafe(),o.radius*=l,o.radius=Math.max(n.minDistance,Math.min(n.maxDistance,o.radius)),!0===n.enableDamping?n.target.addScaledVector(c,n.dampingFactor):n.target.add(c),e.setFromSpherical(o),e.applyQuaternion(d),t.copy(n.target).add(e),n.object.lookAt(n.target),!0===n.enableDamping?(a.theta*=1-n.dampingFactor,a.phi*=1-n.dampingFactor,c.multiplyScalar(1-n.dampingFactor)):(a.set(0,0,0),c.set(0,0,0)),l=1,!!(h||_.distanceToSquared(n.object.position)>r||8*(1-m.dot(n.object.quaternion))>r)&&(n.dispatchEvent(iy),_.copy(n.object.position),m.copy(n.object.quaternion),h=!1,!0)}}(),this.dispose=function(){n.domElement.removeEventListener(\\\\\\\"contextmenu\\\\\\\",X,!1),n.domElement.removeEventListener(\\\\\\\"pointerdown\\\\\\\",G,!1),n.domElement.removeEventListener(\\\\\\\"pointercancel\\\\\\\",j),n.domElement.removeEventListener(\\\\\\\"wheel\\\\\\\",W,!1),n.domElement.ownerDocument.removeEventListener(\\\\\\\"pointermove\\\\\\\",V,!1),n.domElement.ownerDocument.removeEventListener(\\\\\\\"pointerup\\\\\\\",H,!1),null!==n._domElementKeyEvents&&n._domElementKeyEvents.removeEventListener(\\\\\\\"keydown\\\\\\\",q)};const n=this,i={NONE:-1,ROTATE:0,DOLLY:1,PAN:2,TOUCH_ROTATE:3,TOUCH_PAN:4,TOUCH_DOLLY_PAN:5,TOUCH_DOLLY_ROTATE:6};let s=i.NONE;const r=1e-6,o=new ny,a=new ny;let l=1;const c=new p.a;let h=!1;const u=new d.a,_=new d.a,m=new d.a,f=new d.a,g=new d.a,v=new d.a,y=new d.a,x=new d.a,b=new d.a,T=[],A={};function M(){return Math.pow(.95,n.zoomSpeed)}function E(t){a.theta-=t}function S(t){a.phi-=t}const C=function(){const t=new p.a;return function(e,n){t.setFromMatrixColumn(n,0),t.multiplyScalar(-e),c.add(t)}}(),N=function(){const t=new p.a;return function(e,i){!0===n.screenSpacePanning?t.setFromMatrixColumn(i,1):(t.setFromMatrixColumn(i,0),t.crossVectors(n.object.up,t)),t.multiplyScalar(e),c.add(t)}}(),L=function(){const t=new p.a;return function(e,i){const s=n.domElement;if(n.object.isPerspectiveCamera){const r=n.object.position;t.copy(r).sub(n.target);let o=t.length();o*=Math.tan(n.object.fov/2*Math.PI/180),C(2*e*o/s.clientHeight,n.object.matrix),N(2*i*o/s.clientHeight,n.object.matrix)}else n.object.isOrthographicCamera?(C(e*(n.object.right-n.object.left)/n.object.zoom/s.clientWidth,n.object.matrix),N(i*(n.object.top-n.object.bottom)/n.object.zoom/s.clientHeight,n.object.matrix)):(console.warn(\\\\\\\"WARNING: OrbitControls.js encountered an unknown camera type - pan disabled.\\\\\\\"),n.enablePan=!1)}}();function O(t){n.object.isPerspectiveCamera?l/=t:n.object.isOrthographicCamera?(n.object.zoom=Math.max(n.minZoom,Math.min(n.maxZoom,n.object.zoom*t)),n.object.updateProjectionMatrix(),h=!0):(console.warn(\\\\\\\"WARNING: OrbitControls.js encountered an unknown camera type - dolly/zoom disabled.\\\\\\\"),n.enableZoom=!1)}function P(t){n.object.isPerspectiveCamera?l*=t:n.object.isOrthographicCamera?(n.object.zoom=Math.max(n.minZoom,Math.min(n.maxZoom,n.object.zoom/t)),n.object.updateProjectionMatrix(),h=!0):(console.warn(\\\\\\\"WARNING: OrbitControls.js encountered an unknown camera type - dolly/zoom disabled.\\\\\\\"),n.enableZoom=!1)}function R(t){u.set(t.clientX,t.clientY)}function I(t){f.set(t.clientX,t.clientY)}function F(){if(1===T.length)u.set(T[0].pageX,T[0].pageY);else{const t=.5*(T[0].pageX+T[1].pageX),e=.5*(T[0].pageY+T[1].pageY);u.set(t,e)}}function D(){if(1===T.length)f.set(T[0].pageX,T[0].pageY);else{const t=.5*(T[0].pageX+T[1].pageX),e=.5*(T[0].pageY+T[1].pageY);f.set(t,e)}}function B(){const t=T[0].pageX-T[1].pageX,e=T[0].pageY-T[1].pageY,n=Math.sqrt(t*t+e*e);y.set(0,n)}function z(t){if(1==T.length)_.set(t.pageX,t.pageY);else{const e=J(t),n=.5*(t.pageX+e.x),i=.5*(t.pageY+e.y);_.set(n,i)}m.subVectors(_,u).multiplyScalar(n.rotateSpeed);const e=n.domElement;E(2*Math.PI*m.x/e.clientHeight),S(2*Math.PI*m.y/e.clientHeight),u.copy(_)}function k(t){if(1===T.length)g.set(t.pageX,t.pageY);else{const e=J(t),n=.5*(t.pageX+e.x),i=.5*(t.pageY+e.y);g.set(n,i)}v.subVectors(g,f).multiplyScalar(n.panSpeed),L(v.x,v.y),f.copy(g)}function U(t){const e=J(t),i=t.pageX-e.x,s=t.pageY-e.y,r=Math.sqrt(i*i+s*s);x.set(0,r),b.set(0,Math.pow(x.y/y.y,n.zoomSpeed)),O(b.y),y.copy(x)}function G(t){!1!==n.enabled&&(0===T.length&&(n.domElement.setPointerCapture(t.pointerId),n.domElement.ownerDocument.addEventListener(\\\\\\\"pointermove\\\\\\\",V),n.domElement.ownerDocument.addEventListener(\\\\\\\"pointerup\\\\\\\",H)),function(t){T.push(t)}(t),\\\\\\\"touch\\\\\\\"===t.pointerType?function(t){switch($(t),T.length){case 1:switch(n.touches.ONE){case w.Tc.ROTATE:if(!1===n.enableRotate)return;F(),s=i.TOUCH_ROTATE;break;case w.Tc.PAN:if(!1===n.enablePan)return;D(),s=i.TOUCH_PAN;break;default:s=i.NONE}break;case 2:switch(n.touches.TWO){case w.Tc.DOLLY_PAN:if(!1===n.enableZoom&&!1===n.enablePan)return;n.enableZoom&&B(),n.enablePan&&D(),s=i.TOUCH_DOLLY_PAN;break;case w.Tc.DOLLY_ROTATE:if(!1===n.enableZoom&&!1===n.enableRotate)return;n.enableZoom&&B(),n.enableRotate&&F(),s=i.TOUCH_DOLLY_ROTATE;break;default:s=i.NONE}break;default:s=i.NONE}s!==i.NONE&&n.dispatchEvent(sy)}(t):function(t){let e;switch(t.button){case 0:e=n.mouseButtons.LEFT;break;case 1:e=n.mouseButtons.MIDDLE;break;case 2:e=n.mouseButtons.RIGHT;break;default:e=-1}switch(e){case w.hb.DOLLY:if(!1===n.enableZoom)return;!function(t){y.set(t.clientX,t.clientY)}(t),s=i.DOLLY;break;case w.hb.ROTATE:if(t.ctrlKey||t.metaKey||t.shiftKey){if(!1===n.enablePan)return;I(t),s=i.PAN}else{if(!1===n.enableRotate)return;R(t),s=i.ROTATE}break;case w.hb.PAN:if(t.ctrlKey||t.metaKey||t.shiftKey){if(!1===n.enableRotate)return;R(t),s=i.ROTATE}else{if(!1===n.enablePan)return;I(t),s=i.PAN}break;default:s=i.NONE}s!==i.NONE&&n.dispatchEvent(sy)}(t))}function V(t){!1!==n.enabled&&(\\\\\\\"touch\\\\\\\"===t.pointerType?function(t){switch($(t),s){case i.TOUCH_ROTATE:if(!1===n.enableRotate)return;z(t),n.update();break;case i.TOUCH_PAN:if(!1===n.enablePan)return;k(t),n.update();break;case i.TOUCH_DOLLY_PAN:if(!1===n.enableZoom&&!1===n.enablePan)return;!function(t){n.enableZoom&&U(t),n.enablePan&&k(t)}(t),n.update();break;case i.TOUCH_DOLLY_ROTATE:if(!1===n.enableZoom&&!1===n.enableRotate)return;!function(t){n.enableZoom&&U(t),n.enableRotate&&z(t)}(t),n.update();break;default:s=i.NONE}}(t):function(t){if(!1===n.enabled)return;switch(s){case i.ROTATE:if(!1===n.enableRotate)return;!function(t){_.set(t.clientX,t.clientY),m.subVectors(_,u).multiplyScalar(n.rotateSpeed);var e=n.domElement;E(2*Math.PI*m.x/e.clientHeight),S(2*Math.PI*m.y/e.clientHeight),u.copy(_),n.update()}(t);break;case i.DOLLY:if(!1===n.enableZoom)return;!function(t){x.set(t.clientX,t.clientY),b.subVectors(x,y),b.y>0?O(M()):b.y<0&&P(M()),y.copy(x),n.update()}(t);break;case i.PAN:if(!1===n.enablePan)return;!function(t){g.set(t.clientX,t.clientY),v.subVectors(g,f).multiplyScalar(n.panSpeed),L(v.x,v.y),f.copy(g),n.update()}(t)}}(t))}function H(t){!1!==n.enabled&&(t.pointerType,n.dispatchEvent(ry),s=i.NONE,Y(t),0===T.length&&(n.domElement.releasePointerCapture(t.pointerId),n.domElement.ownerDocument.removeEventListener(\\\\\\\"pointermove\\\\\\\",V),n.domElement.ownerDocument.removeEventListener(\\\\\\\"pointerup\\\\\\\",H)))}function j(t){Y(t)}function W(t){!1===n.enabled||!1===n.enableZoom||s!==i.NONE&&s!==i.ROTATE||(t.preventDefault(),n.dispatchEvent(sy),function(t){t.deltaY<0?P(M()):t.deltaY>0&&O(M()),n.update()}(t),n.dispatchEvent(ry))}function q(t){!1!==n.enabled&&!1!==n.enablePan&&function(t){let e=!1;if(\\\\\\\"pan\\\\\\\"==n.keyMode)switch(t.code){case n.keys.UP:L(0,n.keyPanSpeed),e=!0;break;case n.keys.BOTTOM:L(0,-n.keyPanSpeed),e=!0;break;case n.keys.LEFT:L(n.keyPanSpeed,0),e=!0;break;case n.keys.RIGHT:L(-n.keyPanSpeed,0),e=!0}else switch(t.code){case n.keys.UP:S(n.keyRotateSpeedVertical),e=!0;break;case n.keys.BOTTOM:S(-n.keyRotateSpeedVertical),e=!0;break;case n.keys.LEFT:E(n.keyRotateSpeedHorizontal),e=!0;break;case n.keys.RIGHT:E(-n.keyRotateSpeedHorizontal),e=!0}e&&(t.preventDefault(),n.update())}(t)}function X(t){!1!==n.enabled&&t.preventDefault()}function Y(t){delete A[t.pointerId];for(let e=0;e{py.PARAM_CALLBACK_update_target(t)}}),this.enableKeys=aa.BOOLEAN(0),this.keysMode=aa.INTEGER(uy.indexOf(hy.PAN),{visibleIf:{enableKeys:1},menu:{entries:uy.map(((t,e)=>({name:t,value:e})))}}),this.keysPanSpeed=aa.FLOAT(7,{range:[0,10],rangeLocked:[!1,!1],visibleIf:{enableKeys:1,keysMode:uy.indexOf(hy.PAN)}}),this.keysRotateSpeedVertical=aa.FLOAT(1,{range:[0,1],rangeLocked:[!1,!1],visibleIf:{enableKeys:1,keysMode:uy.indexOf(hy.ROTATE)}}),this.keysRotateSpeedHorizontal=aa.FLOAT(1,{range:[0,1],rangeLocked:[!1,!1],visibleIf:{enableKeys:1,keysMode:uy.indexOf(hy.ROTATE)}})}};class py extends qv{constructor(){super(...arguments),this.paramsConfig=dy,this._controls_by_element_id=new Map,this._target_array=[0,0,0]}static type(){return rs.ORBIT}endEventName(){return\\\\\\\"end\\\\\\\"}initializeNode(){this.io.outputs.setNamedOutputConnectionPoints([new Zo(ly,Jo.BASE),new Zo(cy,Jo.BASE),new Zo(\\\\\\\"end\\\\\\\",Jo.BASE)])}async createControlsInstance(t,e){const n=new oy(t,e);return n.addEventListener(\\\\\\\"end\\\\\\\",(()=>{this._on_controls_end(n)})),this._controls_by_element_id.set(e.id,n),this._bind_listeners_to_controls_instance(n),n}_bind_listeners_to_controls_instance(t){t.addEventListener(\\\\\\\"start\\\\\\\",(()=>{this.dispatchEventToOutput(ly,{})})),t.addEventListener(\\\\\\\"change\\\\\\\",(()=>{this.dispatchEventToOutput(cy,{})})),t.addEventListener(\\\\\\\"end\\\\\\\",(()=>{this.dispatchEventToOutput(\\\\\\\"end\\\\\\\",{})}))}setupControls(t){t.enabled=this.pv.enabled,t.enablePan=this.pv.allowPan,t.enableRotate=this.pv.allowRotate,t.enableZoom=this.pv.allowZoom,t.enableDamping=this.pv.tdamping,t.dampingFactor=this.pv.damping,t.rotateSpeed=this.pv.rotateSpeed,t.screenSpacePanning=this.pv.screenSpacePanning,t.minDistance=this.pv.minDistance,t.maxDistance=this.pv.maxDistance,this._set_azimuth_angle(t),t.minPolarAngle=this.pv.polarAngleRange.x,t.maxPolarAngle=this.pv.polarAngleRange.y,t.target.copy(this.pv.target),t.enabled&&t.update(),t.enableKeys=this.pv.enableKeys,t.enableKeys&&(t.keyMode=uy[this.pv.keysMode],t.keyRotateSpeedVertical=this.pv.keysRotateSpeedVertical,t.keyRotateSpeedHorizontal=this.pv.keysRotateSpeedHorizontal,t.keyPanSpeed=this.pv.keysPanSpeed)}_set_azimuth_angle(t){this.pv.limitAzimuthAngle?(t.minAzimuthAngle=this.pv.azimuthAngleRange.x,t.maxAzimuthAngle=this.pv.azimuthAngleRange.y):(t.minAzimuthAngle=1/0,t.maxAzimuthAngle=1/0)}updateRequired(){return this.pv.tdamping}_on_controls_end(t){this.pv.allowPan&&(t.target.toArray(this._target_array),this.p.target.set(this._target_array))}static PARAM_CALLBACK_update_target(t){t._update_target()}_update_target(){const t=this.pv.target;this._controls_by_element_id.forEach(((e,n)=>{const i=e.target;i.equals(t)||(i.copy(t),e.update())}))}disposeControlsForHtmlElementId(t){this._controls_by_element_id.get(t)&&this._controls_by_element_id.delete(t)}}class _y extends py{static type(){return rs.MAP}async create_controls_instance(t,e){const n=new ay(t,e);return this._bind_listeners_to_controls_instance(n),n}}const my=new class extends la{constructor(){super(...arguments),this.delay=aa.INTEGER(1e3,{range:[0,1e3],rangeLocked:[!0,!1]})}};class fy extends ka{constructor(){super(...arguments),this.paramsConfig=my}static type(){return\\\\\\\"delay\\\\\\\"}initializeNode(){this.io.inputs.setNamedInputConnectionPoints([new Zo(\\\\\\\"in\\\\\\\",Jo.BASE,this._process_input.bind(this))]),this.io.outputs.setNamedOutputConnectionPoints([new Zo(\\\\\\\"out\\\\\\\",Jo.BASE)])}_process_input(t){setTimeout((()=>{this.dispatchEventToOutput(\\\\\\\"out\\\\\\\",t)}),this.pv.delay)}}const gy={type:\\\\\\\"change\\\\\\\"},vy={type:\\\\\\\"lock\\\\\\\"},yy={type:\\\\\\\"unlock\\\\\\\"},xy=Math.PI/2,by=new p.a,wy=new ny;class Ty extends J.a{constructor(t,e,n){super(),this.camera=t,this.domElement=e,this.player=n,this.isLocked=!1,this.minPolarAngle=0,this.maxPolarAngle=Math.PI,this.rotateSpeed=1,this.euler=new Xv.a(0,0,0,\\\\\\\"YXZ\\\\\\\"),this.boundMethods={onMouseMove:this.onMouseMove.bind(this),onPointerlockChange:this.onPointerlockChange.bind(this),onPointerlockError:this.onPointerlockError.bind(this)},this._azimuthalAngle=0,this.connect()}onMouseMove(t){if(!1!==this.isLocked){var e=t.movementX||t.mozMovementX||t.webkitMovementX||0,n=t.movementY||t.mozMovementY||t.webkitMovementY||0;this.euler.setFromQuaternion(this.camera.quaternion),this.euler.y-=.002*e*this.rotateSpeed,this.euler.x-=.002*n*this.rotateSpeed,this.euler.x=Math.max(xy-this.maxPolarAngle,Math.min(xy-this.minPolarAngle,this.euler.x)),this.camera.quaternion.setFromEuler(this.euler),this._computeAzimuthalAngle(),this.dispatchEvent(gy)}}_computeAzimuthalAngle(){this.camera.updateMatrixWorld(),by.set(0,0,1),this.camera.localToWorld(by),by.sub(this.camera.position),wy.setFromVector3(by),this._azimuthalAngle=wy.theta}onPointerlockChange(){this.domElement.ownerDocument.pointerLockElement===this.domElement?(this.dispatchEvent(vy),this.isLocked=!0):(this.dispatchEvent(yy),this.isLocked=!1)}onPointerlockError(){console.error(\\\\\\\"THREE.PointerLockControls: Unable to use Pointer Lock API (Note that you need to wait for 2 seconds to lock the pointer after having just unlocked it)\\\\\\\")}connect(){this.domElement.ownerDocument.addEventListener(\\\\\\\"mousemove\\\\\\\",this.boundMethods.onMouseMove),this.domElement.ownerDocument.addEventListener(\\\\\\\"pointerlockchange\\\\\\\",this.boundMethods.onPointerlockChange),this.domElement.ownerDocument.addEventListener(\\\\\\\"pointerlockerror\\\\\\\",this.boundMethods.onPointerlockError)}disconnect(){this.domElement.ownerDocument.removeEventListener(\\\\\\\"mousemove\\\\\\\",this.boundMethods.onMouseMove),this.domElement.ownerDocument.removeEventListener(\\\\\\\"pointerlockchange\\\\\\\",this.boundMethods.onPointerlockChange),this.domElement.ownerDocument.removeEventListener(\\\\\\\"pointerlockerror\\\\\\\",this.boundMethods.onPointerlockError)}dispose(){this.disconnect()}getObject(){return this.camera}lock(){this.domElement.requestPointerLock()}unlock(){this.domElement.ownerDocument.exitPointerLock()}update(t){this.player&&(this.player.setAzimuthalAngle(this._azimuthalAngle),this.player.update(t))}}var Ay=n(16);const My=new p.a,Ey=new p.a;class Sy{constructor(t=new p.a,e=new p.a){this.start=t,this.end=e}set(t,e){return this.start.copy(t),this.end.copy(e),this}copy(t){return this.start.copy(t.start),this.end.copy(t.end),this}getCenter(t){return t.addVectors(this.start,this.end).multiplyScalar(.5)}delta(t){return t.subVectors(this.end,this.start)}distanceSq(){return this.start.distanceToSquared(this.end)}distance(){return this.start.distanceTo(this.end)}at(t,e){return this.delta(e).multiplyScalar(t).add(this.start)}closestPointToPointParameter(t,e){My.subVectors(t,this.start),Ey.subVectors(this.end,this.start);const n=Ey.dot(Ey);let i=Ey.dot(My)/n;return e&&(i=On.d(i,0,1)),i}closestPointToPoint(t,e,n){const i=this.closestPointToPointParameter(t,e);return this.delta(n).multiplyScalar(i).add(this.start)}applyMatrix4(t){return this.start.applyMatrix4(t),this.end.applyMatrix4(t),this}equals(t){return t.start.equals(this.start)&&t.end.equals(this.end)}clone(){return(new this.constructor).copy(this)}}const Cy=new p.a;function Ny(t,e,n,i,s,r){const o=2*Math.PI*s/4,a=Math.max(r-2*s,0),l=Math.PI/4;Cy.copy(e),Cy[i]=0,Cy.normalize();const c=.5*o/(o+a),h=1-Cy.angleTo(t)/l;if(1===Math.sign(Cy[n]))return h*c;return a/(o+a)+c+c*(1-h)}class Ly extends N{constructor(t=1,e=1,n=1,i=2,s=.1){if(i=2*i+1,s=Math.min(t/2,e/2,n/2,s),super(1,1,1,i,i,i),1===i)return;const r=this.toNonIndexed();this.index=null,this.attributes.position=r.attributes.position,this.attributes.normal=r.attributes.normal,this.attributes.uv=r.attributes.uv;const o=new p.a,a=new p.a,l=new p.a(t,e,n).divideScalar(2).subScalar(s),c=this.attributes.position.array,h=this.attributes.normal.array,u=this.attributes.uv.array,d=c.length/6,_=new p.a,m=.5/i;for(let i=0,r=0;ithis.object.position.y<-25,this.object=t.object,this.object.matrixAutoUpdate=!0,this.collider=t.collider,t.meshName&&(this._mesh=new B.a,this._mesh.geometry=Oy({radius:this.capsuleInfo.radius,height:1}),this._mesh.name=t.meshName,this._mesh.receiveShadow=!0,this._mesh.castShadow=!0)}setCollider(t){this.collider=t}setCapsule(t){this.capsuleInfo.radius=t.radius,this.capsuleInfo.segment.end.y=-t.height,this._mesh&&(this._mesh.geometry=Oy(t))}setUsePlayerMesh(t){t?(this._mesh=this._mesh||this._createMesh(),this.object.add(this._mesh)):this._mesh&&this.object.remove(this._mesh)}_createMesh(){const t=new B.a;return t.geometry=Oy({radius:this.capsuleInfo.radius,height:1}),t.name=this._meshName||\\\\\\\"defaultPlayerMeshName\\\\\\\",t.receiveShadow=!0,t.castShadow=!0,t}setMaterial(t){this._mesh&&(this._mesh.material=t)}reset(){this.stop(),this.object.position.copy(this.startPosition),ky.copy(this.startRotation).multiplyScalar(On.a),this.object.rotation.setFromVector3(ky)}stop(){this._pressed.forward=!1,this._pressed.backward=!1,this._pressed.left=!1,this._pressed.right=!1,this._running=!1}setResetRequiredCallback(t){this._resetRequiredCallback=t}setAzimuthalAngle(t){this._azimuthalAngle=t}update(t){const e=Math.min(t,.1);for(let t=0;tt.intersectsBox(Dy),intersectsTriangle:t=>{const e=Iy,n=Fy,s=t.closestPointToSegment(zy,e,n);if(sMath.abs(t*this._velocity.y*.25);const o=Math.max(0,r.length()-1e-5);r.normalize().multiplyScalar(o),this.object.position.add(r),this._onGround?this._velocity.set(0,0,0):(r.normalize(),this._velocity.addScaledVector(r,-r.dot(this._velocity))),this._resetRequiredCallback()&&this.reset()}setForward(t){this._pressed.forward=t}setBackward(t){this._pressed.backward=t}setLeft(t){this._pressed.left=t}setRight(t){this._pressed.right=t}jump(){this._onGround&&this.jumpAllowed&&(this._velocity.y=this.jumpStrength)}setRun(t){t?this._onGround&&this.runAllowed&&(this._running=!0):this._running=!1}}function Gy(t){t.preventDefault()}class Vy{constructor(t){this.player=t,this._bounds={keydown:this._onKeyDown.bind(this),keyup:this._onKeyUp.bind(this)}}_onKeyDown(t){if(!t.ctrlKey)switch(t.code){case\\\\\\\"ArrowUp\\\\\\\":case\\\\\\\"KeyW\\\\\\\":this.player.setForward(!0),Gy(t);break;case\\\\\\\"ArrowDown\\\\\\\":case\\\\\\\"KeyS\\\\\\\":this.player.setBackward(!0),Gy(t);break;case\\\\\\\"ArrowRight\\\\\\\":case\\\\\\\"KeyD\\\\\\\":this.player.setRight(!0),Gy(t);break;case\\\\\\\"ArrowLeft\\\\\\\":case\\\\\\\"KeyA\\\\\\\":this.player.setLeft(!0),Gy(t);break;case\\\\\\\"Space\\\\\\\":this.player.jump(),Gy(t);break;case\\\\\\\"ShiftLeft\\\\\\\":case\\\\\\\"ShiftRight\\\\\\\":this.player.setRun(!0),Gy(t)}}_onKeyUp(t){switch(t.code){case\\\\\\\"ArrowUp\\\\\\\":case\\\\\\\"KeyW\\\\\\\":this.player.setForward(!1);break;case\\\\\\\"ArrowDown\\\\\\\":case\\\\\\\"KeyS\\\\\\\":this.player.setBackward(!1);break;case\\\\\\\"ArrowRight\\\\\\\":case\\\\\\\"KeyD\\\\\\\":this.player.setRight(!1);break;case\\\\\\\"ArrowLeft\\\\\\\":case\\\\\\\"KeyA\\\\\\\":this.player.setLeft(!1);break;case\\\\\\\"ShiftLeft\\\\\\\":case\\\\\\\"ShiftRight\\\\\\\":this.player.setRun(!1),Gy(t)}}addEvents(){document.addEventListener(\\\\\\\"keydown\\\\\\\",this._bounds.keydown),document.addEventListener(\\\\\\\"keyup\\\\\\\",this._bounds.keyup)}removeEvents(){document.removeEventListener(\\\\\\\"keydown\\\\\\\",this._bounds.keydown),document.removeEventListener(\\\\\\\"keyup\\\\\\\",this._bounds.keyup)}}const Hy=\\\\\\\"lock\\\\\\\",jy=\\\\\\\"change\\\\\\\",Wy=\\\\\\\"unlock\\\\\\\";function qy(){return{cook:!1,callback:t=>{Yy.PARAM_CALLBACK_updatePlayerParams(t)}}}const Xy=new class extends la{constructor(){super(...arguments),this.main=aa.FOLDER(),this.colliderObject=aa.NODE_PATH(\\\\\\\"\\\\\\\",{nodeSelection:{context:ts.SOP}}),this.lock=aa.BUTTON(null,{callback:t=>{Yy.PARAM_CALLBACK_lockControls(t)}}),this.unlock=aa.BUTTON(null,{callback:t=>{Yy.PARAM_CALLBACK_unlockControls(t)}}),this.capsuleRadius=aa.FLOAT(.5,{range:[0,1],rangeLocked:[!0,!1],...qy()}),this.capsuleHeight=aa.FLOAT(1,{range:[0,2],rangeLocked:[!0,!1],...qy()}),this.physics=aa.FOLDER(),this.physicsSteps=aa.INTEGER(5,{range:[1,10],rangeLocked:[!0,!1],...qy()}),this.gravity=aa.VECTOR3([0,-30,0],{...qy()}),this.translateSpeed=aa.FLOAT(1,{range:[0,10],rangeLocked:[!0,!1],...qy()}),this.rotateSpeed=aa.FLOAT(1,{range:[0,10],rangeLocked:[!0,!1]}),this.jumpAllowed=aa.BOOLEAN(!0,{...qy()}),this.jumpStrength=aa.FLOAT(10,{range:[0,100],rangeLocked:[!0,!1],...qy()}),this.runAllowed=aa.BOOLEAN(!0,{...qy()}),this.runSpeedMult=aa.FLOAT(2,{range:[0,10],rangeLocked:[!0,!1],...qy()}),this.updateCollider=aa.BUTTON(null,{callback:t=>{Yy.PARAM_CALLBACK_updateCollider(t)}}),this.init=aa.FOLDER(),this.startPosition=aa.VECTOR3([0,2,0],{...qy()}),this.startRotation=aa.VECTOR3([0,0,0],{...qy()}),this.reset=aa.BUTTON(null,{callback:t=>{Yy.PARAM_CALLBACK_resetPlayer(t)}}),this.minPolarAngle=aa.FLOAT(0,{range:[0,Math.PI],rangeLocked:[!0,!0]}),this.maxPolarAngle=aa.FLOAT(\\\\\\\"$PI\\\\\\\",{range:[0,Math.PI],rangeLocked:[!0,!0]})}};class Yy extends qv{constructor(){super(...arguments),this.paramsConfig=Xy,this._controls_by_element_id=new Map}static type(){return rs.FIRST_PERSON}endEventName(){return\\\\\\\"unlock\\\\\\\"}initializeNode(){this.io.inputs.setNamedInputConnectionPoints([new Zo(Hy,Jo.BASE,this.lockControls.bind(this))]),this.io.outputs.setNamedOutputConnectionPoints([new Zo(Hy,Jo.BASE),new Zo(jy,Jo.BASE),new Zo(Wy,Jo.BASE)])}async createControlsInstance(t,e){await this._initPlayer(t);const n=new Ty(t,e,this._player);return this._controls_by_element_id.set(e.id,n),this._bind_listeners_to_controls_instance(n),n}async _initPlayer(t){this._player=this._player||await this._createPlayer(t),this._player&&(this._updatePlayerParams(),this._player.reset())}async _updatePlayerParams(){this._player&&(this._player.startPosition.copy(this.pv.startPosition),this._player.startRotation.copy(this.pv.startRotation),this._player.physicsSteps=this.pv.physicsSteps,this._player.jumpAllowed=this.pv.jumpAllowed,this._player.jumpStrength=this.pv.jumpStrength,this._player.runAllowed=this.pv.runAllowed,this._player.runSpeedMult=this.pv.runSpeedMult,this._player.gravity.copy(this.pv.gravity),this._player.speed=this.pv.translateSpeed,this._player.setCapsule({radius:this.pv.capsuleRadius,height:this.pv.capsuleHeight}))}async _createPlayer(t){const e=t,n=await this._getCollider();if(!n)return void this.states.error.set(\\\\\\\"invalid collider\\\\\\\");return new Uy({object:e,collider:n})}_resetPlayer(){var t;null===(t=this._player)||void 0===t||t.reset()}async _getCollider(){const t=this.pv.colliderObject.nodeWithContext(ts.SOP);if(!t)return void this.states.error.set(\\\\\\\"collider node not found\\\\\\\");const e=(await t.compute()).coreContent();if(!e)return void this.states.error.set(\\\\\\\"invalid collider node\\\\\\\");return e.objects()[0]}async _updateCollider(){var t;const e=await this._getCollider();e?null===(t=this._player)||void 0===t||t.setCollider(e):this.states.error.set(\\\\\\\"invalid collider\\\\\\\")}_bind_listeners_to_controls_instance(t){t.addEventListener(Hy,(()=>{this.dispatchEventToOutput(Hy,{})})),t.addEventListener(jy,(()=>{this.dispatchEventToOutput(jy,{})})),t.addEventListener(Wy,(()=>{this.dispatchEventToOutput(Wy,{})}))}updateRequired(){return!0}setupControls(t){t.minPolarAngle=this.pv.minPolarAngle,t.maxPolarAngle=this.pv.maxPolarAngle,t.rotateSpeed=this.pv.rotateSpeed}disposeControlsForHtmlElementId(t){const e=this._controls_by_element_id.get(t);e&&(e.dispose(),this._controls_by_element_id.delete(t))}unlockControls(){var t,e;const n=this._firstControls();n&&(n.unlock(),null===(t=this._corePlayerKeyEvents)||void 0===t||t.removeEvents(),null===(e=this._player)||void 0===e||e.stop())}lockControls(){const t=this._firstControls();t&&(this._player&&(this._corePlayerKeyEvents=this._corePlayerKeyEvents||new Vy(this._player),this._corePlayerKeyEvents.addEvents()),t.lock())}_firstControls(){let t;return this._controls_by_element_id.forEach(((e,n)=>{t=t||e})),t}static PARAM_CALLBACK_lockControls(t){t.lockControls()}static PARAM_CALLBACK_unlockControls(t){t.unlockControls()}static PARAM_CALLBACK_updateCollider(t){t._updateCollider()}static PARAM_CALLBACK_updatePlayerParams(t){t._updatePlayerParams()}static PARAM_CALLBACK_resetPlayer(t){t._resetPlayer()}}var $y,Jy;!function(t){t.TRIGGER=\\\\\\\"trigger\\\\\\\",t.RESET=\\\\\\\"reset\\\\\\\"}($y||($y={})),function(t){t.OUT=\\\\\\\"out\\\\\\\",t.LAST=\\\\\\\"last\\\\\\\"}(Jy||(Jy={}));const Zy=new class extends la{constructor(){super(...arguments),this.maxCount=aa.INTEGER(5,{range:[0,10],rangeLocked:[!0,!1]}),this.reset=aa.BUTTON(null,{callback:t=>{Qy.PARAM_CALLBACK_reset(t)}})}};class Qy extends ka{constructor(){super(...arguments),this.paramsConfig=Zy,this._process_count=0,this._last_dispatched=!1}static type(){return\\\\\\\"limit\\\\\\\"}initializeNode(){this.io.inputs.setNamedInputConnectionPoints([new Zo($y.TRIGGER,Jo.BASE,this.process_event_trigger.bind(this)),new Zo($y.RESET,Jo.BASE,this.process_event_reset.bind(this))]),this.io.outputs.setNamedOutputConnectionPoints([new Zo(Jy.OUT,Jo.BASE),new Zo(Jy.LAST,Jo.BASE)])}processEvent(t){}process_event_trigger(t){this._process_count(t.preventDefault(),!1);class nx{static disableContextMenu(){document.addEventListener(\\\\\\\"contextmenu\\\\\\\",ex)}static reEstablishContextMenu(){document.removeEventListener(\\\\\\\"contextmenu\\\\\\\",ex)}}const ix=100,sx=301,rx=302,ox=303,ax=304,lx=306,cx=307,hx=1e3,ux=1001,dx=1002,px=1003,_x=1004,mx=1005,fx=1006,gx=1007,vx=1008,yx=1009,xx=1012,bx=1014,wx=1015,Tx=1016,Ax=1020,Mx=1022,Ex=1023,Sx=1026,Cx=1027,Nx=2300,Lx=2301,Ox=2302,Px=2400,Rx=2401,Ix=2402,Fx=2500,Dx=3e3,Bx=3001,zx=3007,kx=3002,Ux=7680,Gx=35044,Vx=35048,Hx=\\\\\\\"300 es\\\\\\\";class jx{addEventListener(t,e){void 0===this._listeners&&(this._listeners={});const n=this._listeners;void 0===n[t]&&(n[t]=[]),-1===n[t].indexOf(e)&&n[t].push(e)}hasEventListener(t,e){if(void 0===this._listeners)return!1;const n=this._listeners;return void 0!==n[t]&&-1!==n[t].indexOf(e)}removeEventListener(t,e){if(void 0===this._listeners)return;const n=this._listeners[t];if(void 0!==n){const t=n.indexOf(e);-1!==t&&n.splice(t,1)}}dispatchEvent(t){if(void 0===this._listeners)return;const e=this._listeners[t.type];if(void 0!==e){t.target=this;const n=e.slice(0);for(let e=0,i=n.length;e>8&255]+Yx[t>>16&255]+Yx[t>>24&255]+\\\\\\\"-\\\\\\\"+Yx[255&e]+Yx[e>>8&255]+\\\\\\\"-\\\\\\\"+Yx[e>>16&15|64]+Yx[e>>24&255]+\\\\\\\"-\\\\\\\"+Yx[63&n|128]+Yx[n>>8&255]+\\\\\\\"-\\\\\\\"+Yx[n>>16&255]+Yx[n>>24&255]+Yx[255&i]+Yx[i>>8&255]+Yx[i>>16&255]+Yx[i>>24&255]).toUpperCase()}function Zx(t,e,n){return Math.max(e,Math.min(n,t))}function Qx(t,e){return(t%e+e)%e}function Kx(t,e,n){return(1-n)*t+n*e}function tb(t){return 0==(t&t-1)&&0!==t}function eb(t){return Math.pow(2,Math.ceil(Math.log(t)/Math.LN2))}function nb(t){return Math.pow(2,Math.floor(Math.log(t)/Math.LN2))}var ib=Object.freeze({__proto__:null,DEG2RAD:qx,RAD2DEG:Xx,generateUUID:Jx,clamp:Zx,euclideanModulo:Qx,mapLinear:function(t,e,n,i,s){return i+(t-e)*(s-i)/(n-e)},inverseLerp:function(t,e,n){return t!==e?(n-t)/(e-t):0},lerp:Kx,damp:function(t,e,n,i){return Kx(t,e,1-Math.exp(-n*i))},pingpong:function(t,e=1){return e-Math.abs(Qx(t,2*e)-e)},smoothstep:function(t,e,n){return t<=e?0:t>=n?1:(t=(t-e)/(n-e))*t*(3-2*t)},smootherstep:function(t,e,n){return t<=e?0:t>=n?1:(t=(t-e)/(n-e))*t*t*(t*(6*t-15)+10)},randInt:function(t,e){return t+Math.floor(Math.random()*(e-t+1))},randFloat:function(t,e){return t+Math.random()*(e-t)},randFloatSpread:function(t){return t*(.5-Math.random())},seededRandom:function(t){return void 0!==t&&(Wx=t%2147483647),Wx=16807*Wx%2147483647,(Wx-1)/2147483646},degToRad:function(t){return t*qx},radToDeg:function(t){return t*Xx},isPowerOfTwo:tb,ceilPowerOfTwo:eb,floorPowerOfTwo:nb,setQuaternionFromProperEuler:function(t,e,n,i,s){const r=Math.cos,o=Math.sin,a=r(n/2),l=o(n/2),c=r((e+i)/2),h=o((e+i)/2),u=r((e-i)/2),d=o((e-i)/2),p=r((i-e)/2),_=o((i-e)/2);switch(s){case\\\\\\\"XYX\\\\\\\":t.set(a*h,l*u,l*d,a*c);break;case\\\\\\\"YZY\\\\\\\":t.set(l*d,a*h,l*u,a*c);break;case\\\\\\\"ZXZ\\\\\\\":t.set(l*u,l*d,a*h,a*c);break;case\\\\\\\"XZX\\\\\\\":t.set(a*h,l*_,l*p,a*c);break;case\\\\\\\"YXY\\\\\\\":t.set(l*p,a*h,l*_,a*c);break;case\\\\\\\"ZYZ\\\\\\\":t.set(l*_,l*p,a*h,a*c);break;default:console.warn(\\\\\\\"THREE.MathUtils: .setQuaternionFromProperEuler() encountered an unknown order: \\\\\\\"+s)}}});class sb{constructor(t=0,e=0){this.x=t,this.y=e}get width(){return this.x}set width(t){this.x=t}get height(){return this.y}set height(t){this.y=t}set(t,e){return this.x=t,this.y=e,this}setScalar(t){return this.x=t,this.y=t,this}setX(t){return this.x=t,this}setY(t){return this.y=t,this}setComponent(t,e){switch(t){case 0:this.x=e;break;case 1:this.y=e;break;default:throw new Error(\\\\\\\"index is out of range: \\\\\\\"+t)}return this}getComponent(t){switch(t){case 0:return this.x;case 1:return this.y;default:throw new Error(\\\\\\\"index is out of range: \\\\\\\"+t)}}clone(){return new this.constructor(this.x,this.y)}copy(t){return this.x=t.x,this.y=t.y,this}add(t,e){return void 0!==e?(console.warn(\\\\\\\"THREE.Vector2: .add() now only accepts one argument. Use .addVectors( a, b ) instead.\\\\\\\"),this.addVectors(t,e)):(this.x+=t.x,this.y+=t.y,this)}addScalar(t){return this.x+=t,this.y+=t,this}addVectors(t,e){return this.x=t.x+e.x,this.y=t.y+e.y,this}addScaledVector(t,e){return this.x+=t.x*e,this.y+=t.y*e,this}sub(t,e){return void 0!==e?(console.warn(\\\\\\\"THREE.Vector2: .sub() now only accepts one argument. Use .subVectors( a, b ) instead.\\\\\\\"),this.subVectors(t,e)):(this.x-=t.x,this.y-=t.y,this)}subScalar(t){return this.x-=t,this.y-=t,this}subVectors(t,e){return this.x=t.x-e.x,this.y=t.y-e.y,this}multiply(t){return this.x*=t.x,this.y*=t.y,this}multiplyScalar(t){return this.x*=t,this.y*=t,this}divide(t){return this.x/=t.x,this.y/=t.y,this}divideScalar(t){return this.multiplyScalar(1/t)}applyMatrix3(t){const e=this.x,n=this.y,i=t.elements;return this.x=i[0]*e+i[3]*n+i[6],this.y=i[1]*e+i[4]*n+i[7],this}min(t){return this.x=Math.min(this.x,t.x),this.y=Math.min(this.y,t.y),this}max(t){return this.x=Math.max(this.x,t.x),this.y=Math.max(this.y,t.y),this}clamp(t,e){return this.x=Math.max(t.x,Math.min(e.x,this.x)),this.y=Math.max(t.y,Math.min(e.y,this.y)),this}clampScalar(t,e){return this.x=Math.max(t,Math.min(e,this.x)),this.y=Math.max(t,Math.min(e,this.y)),this}clampLength(t,e){const n=this.length();return this.divideScalar(n||1).multiplyScalar(Math.max(t,Math.min(e,n)))}floor(){return this.x=Math.floor(this.x),this.y=Math.floor(this.y),this}ceil(){return this.x=Math.ceil(this.x),this.y=Math.ceil(this.y),this}round(){return this.x=Math.round(this.x),this.y=Math.round(this.y),this}roundToZero(){return this.x=this.x<0?Math.ceil(this.x):Math.floor(this.x),this.y=this.y<0?Math.ceil(this.y):Math.floor(this.y),this}negate(){return this.x=-this.x,this.y=-this.y,this}dot(t){return this.x*t.x+this.y*t.y}cross(t){return this.x*t.y-this.y*t.x}lengthSq(){return this.x*this.x+this.y*this.y}length(){return Math.sqrt(this.x*this.x+this.y*this.y)}manhattanLength(){return Math.abs(this.x)+Math.abs(this.y)}normalize(){return this.divideScalar(this.length()||1)}angle(){return Math.atan2(-this.y,-this.x)+Math.PI}distanceTo(t){return Math.sqrt(this.distanceToSquared(t))}distanceToSquared(t){const e=this.x-t.x,n=this.y-t.y;return e*e+n*n}manhattanDistanceTo(t){return Math.abs(this.x-t.x)+Math.abs(this.y-t.y)}setLength(t){return this.normalize().multiplyScalar(t)}lerp(t,e){return this.x+=(t.x-this.x)*e,this.y+=(t.y-this.y)*e,this}lerpVectors(t,e,n){return this.x=t.x+(e.x-t.x)*n,this.y=t.y+(e.y-t.y)*n,this}equals(t){return t.x===this.x&&t.y===this.y}fromArray(t,e=0){return this.x=t[e],this.y=t[e+1],this}toArray(t=[],e=0){return t[e]=this.x,t[e+1]=this.y,t}fromBufferAttribute(t,e,n){return void 0!==n&&console.warn(\\\\\\\"THREE.Vector2: offset has been removed from .fromBufferAttribute().\\\\\\\"),this.x=t.getX(e),this.y=t.getY(e),this}rotateAround(t,e){const n=Math.cos(e),i=Math.sin(e),s=this.x-t.x,r=this.y-t.y;return this.x=s*n-r*i+t.x,this.y=s*i+r*n+t.y,this}random(){return this.x=Math.random(),this.y=Math.random(),this}*[Symbol.iterator](){yield this.x,yield this.y}}sb.prototype.isVector2=!0;class rb{constructor(){this.elements=[1,0,0,0,1,0,0,0,1],arguments.length>0&&console.error(\\\\\\\"THREE.Matrix3: the constructor no longer reads arguments. use .set() instead.\\\\\\\")}set(t,e,n,i,s,r,o,a,l){const c=this.elements;return c[0]=t,c[1]=i,c[2]=o,c[3]=e,c[4]=s,c[5]=a,c[6]=n,c[7]=r,c[8]=l,this}identity(){return this.set(1,0,0,0,1,0,0,0,1),this}copy(t){const e=this.elements,n=t.elements;return e[0]=n[0],e[1]=n[1],e[2]=n[2],e[3]=n[3],e[4]=n[4],e[5]=n[5],e[6]=n[6],e[7]=n[7],e[8]=n[8],this}extractBasis(t,e,n){return t.setFromMatrix3Column(this,0),e.setFromMatrix3Column(this,1),n.setFromMatrix3Column(this,2),this}setFromMatrix4(t){const e=t.elements;return this.set(e[0],e[4],e[8],e[1],e[5],e[9],e[2],e[6],e[10]),this}multiply(t){return this.multiplyMatrices(this,t)}premultiply(t){return this.multiplyMatrices(t,this)}multiplyMatrices(t,e){const n=t.elements,i=e.elements,s=this.elements,r=n[0],o=n[3],a=n[6],l=n[1],c=n[4],h=n[7],u=n[2],d=n[5],p=n[8],_=i[0],m=i[3],f=i[6],g=i[1],v=i[4],y=i[7],x=i[2],b=i[5],w=i[8];return s[0]=r*_+o*g+a*x,s[3]=r*m+o*v+a*b,s[6]=r*f+o*y+a*w,s[1]=l*_+c*g+h*x,s[4]=l*m+c*v+h*b,s[7]=l*f+c*y+h*w,s[2]=u*_+d*g+p*x,s[5]=u*m+d*v+p*b,s[8]=u*f+d*y+p*w,this}multiplyScalar(t){const e=this.elements;return e[0]*=t,e[3]*=t,e[6]*=t,e[1]*=t,e[4]*=t,e[7]*=t,e[2]*=t,e[5]*=t,e[8]*=t,this}determinant(){const t=this.elements,e=t[0],n=t[1],i=t[2],s=t[3],r=t[4],o=t[5],a=t[6],l=t[7],c=t[8];return e*r*c-e*o*l-n*s*c+n*o*a+i*s*l-i*r*a}invert(){const t=this.elements,e=t[0],n=t[1],i=t[2],s=t[3],r=t[4],o=t[5],a=t[6],l=t[7],c=t[8],h=c*r-o*l,u=o*a-c*s,d=l*s-r*a,p=e*h+n*u+i*d;if(0===p)return this.set(0,0,0,0,0,0,0,0,0);const _=1/p;return t[0]=h*_,t[1]=(i*l-c*n)*_,t[2]=(o*n-i*r)*_,t[3]=u*_,t[4]=(c*e-i*a)*_,t[5]=(i*s-o*e)*_,t[6]=d*_,t[7]=(n*a-l*e)*_,t[8]=(r*e-n*s)*_,this}transpose(){let t;const e=this.elements;return t=e[1],e[1]=e[3],e[3]=t,t=e[2],e[2]=e[6],e[6]=t,t=e[5],e[5]=e[7],e[7]=t,this}getNormalMatrix(t){return this.setFromMatrix4(t).invert().transpose()}transposeIntoArray(t){const e=this.elements;return t[0]=e[0],t[1]=e[3],t[2]=e[6],t[3]=e[1],t[4]=e[4],t[5]=e[7],t[6]=e[2],t[7]=e[5],t[8]=e[8],this}setUvTransform(t,e,n,i,s,r,o){const a=Math.cos(s),l=Math.sin(s);return this.set(n*a,n*l,-n*(a*r+l*o)+r+t,-i*l,i*a,-i*(-l*r+a*o)+o+e,0,0,1),this}scale(t,e){const n=this.elements;return n[0]*=t,n[3]*=t,n[6]*=t,n[1]*=e,n[4]*=e,n[7]*=e,this}rotate(t){const e=Math.cos(t),n=Math.sin(t),i=this.elements,s=i[0],r=i[3],o=i[6],a=i[1],l=i[4],c=i[7];return i[0]=e*s+n*a,i[3]=e*r+n*l,i[6]=e*o+n*c,i[1]=-n*s+e*a,i[4]=-n*r+e*l,i[7]=-n*o+e*c,this}translate(t,e){const n=this.elements;return n[0]+=t*n[2],n[3]+=t*n[5],n[6]+=t*n[8],n[1]+=e*n[2],n[4]+=e*n[5],n[7]+=e*n[8],this}equals(t){const e=this.elements,n=t.elements;for(let t=0;t<9;t++)if(e[t]!==n[t])return!1;return!0}fromArray(t,e=0){for(let n=0;n<9;n++)this.elements[n]=t[n+e];return this}toArray(t=[],e=0){const n=this.elements;return t[e]=n[0],t[e+1]=n[1],t[e+2]=n[2],t[e+3]=n[3],t[e+4]=n[4],t[e+5]=n[5],t[e+6]=n[6],t[e+7]=n[7],t[e+8]=n[8],t}clone(){return(new this.constructor).fromArray(this.elements)}}function ob(t){if(0===t.length)return-1/0;let e=t[0];for(let n=1,i=t.length;ne&&(e=t[n]);return e}rb.prototype.isMatrix3=!0;Int8Array,Uint8Array,Uint8ClampedArray,Int16Array,Uint16Array,Int32Array,Uint32Array,Float32Array,Float64Array;function ab(t){return document.createElementNS(\\\\\\\"http://www.w3.org/1999/xhtml\\\\\\\",t)}let lb;class cb{static getDataURL(t){if(/^data:/i.test(t.src))return t.src;if(\\\\\\\"undefined\\\\\\\"==typeof HTMLCanvasElement)return t.src;let e;if(t instanceof HTMLCanvasElement)e=t;else{void 0===lb&&(lb=ab(\\\\\\\"canvas\\\\\\\")),lb.width=t.width,lb.height=t.height;const n=lb.getContext(\\\\\\\"2d\\\\\\\");t instanceof ImageData?n.putImageData(t,0,0):n.drawImage(t,0,0,t.width,t.height),e=lb}return e.width>2048||e.height>2048?(console.warn(\\\\\\\"THREE.ImageUtils.getDataURL: Image converted to jpg for performance reasons\\\\\\\",t),e.toDataURL(\\\\\\\"image/jpeg\\\\\\\",.6)):e.toDataURL(\\\\\\\"image/png\\\\\\\")}}let hb=0;class ub extends jx{constructor(t=ub.DEFAULT_IMAGE,e=ub.DEFAULT_MAPPING,n=1001,i=1001,s=1006,r=1008,o=1023,a=1009,l=1,c=3e3){super(),Object.defineProperty(this,\\\\\\\"id\\\\\\\",{value:hb++}),this.uuid=Jx(),this.name=\\\\\\\"\\\\\\\",this.image=t,this.mipmaps=[],this.mapping=e,this.wrapS=n,this.wrapT=i,this.magFilter=s,this.minFilter=r,this.anisotropy=l,this.format=o,this.internalFormat=null,this.type=a,this.offset=new sb(0,0),this.repeat=new sb(1,1),this.center=new sb(0,0),this.rotation=0,this.matrixAutoUpdate=!0,this.matrix=new rb,this.generateMipmaps=!0,this.premultiplyAlpha=!1,this.flipY=!0,this.unpackAlignment=4,this.encoding=c,this.version=0,this.onUpdate=null,this.isRenderTargetTexture=!1}updateMatrix(){this.matrix.setUvTransform(this.offset.x,this.offset.y,this.repeat.x,this.repeat.y,this.rotation,this.center.x,this.center.y)}clone(){return(new this.constructor).copy(this)}copy(t){return this.name=t.name,this.image=t.image,this.mipmaps=t.mipmaps.slice(0),this.mapping=t.mapping,this.wrapS=t.wrapS,this.wrapT=t.wrapT,this.magFilter=t.magFilter,this.minFilter=t.minFilter,this.anisotropy=t.anisotropy,this.format=t.format,this.internalFormat=t.internalFormat,this.type=t.type,this.offset.copy(t.offset),this.repeat.copy(t.repeat),this.center.copy(t.center),this.rotation=t.rotation,this.matrixAutoUpdate=t.matrixAutoUpdate,this.matrix.copy(t.matrix),this.generateMipmaps=t.generateMipmaps,this.premultiplyAlpha=t.premultiplyAlpha,this.flipY=t.flipY,this.unpackAlignment=t.unpackAlignment,this.encoding=t.encoding,this}toJSON(t){const e=void 0===t||\\\\\\\"string\\\\\\\"==typeof t;if(!e&&void 0!==t.textures[this.uuid])return t.textures[this.uuid];const n={metadata:{version:4.5,type:\\\\\\\"Texture\\\\\\\",generator:\\\\\\\"Texture.toJSON\\\\\\\"},uuid:this.uuid,name:this.name,mapping:this.mapping,repeat:[this.repeat.x,this.repeat.y],offset:[this.offset.x,this.offset.y],center:[this.center.x,this.center.y],rotation:this.rotation,wrap:[this.wrapS,this.wrapT],format:this.format,type:this.type,encoding:this.encoding,minFilter:this.minFilter,magFilter:this.magFilter,anisotropy:this.anisotropy,flipY:this.flipY,premultiplyAlpha:this.premultiplyAlpha,unpackAlignment:this.unpackAlignment};if(void 0!==this.image){const i=this.image;if(void 0===i.uuid&&(i.uuid=Jx()),!e&&void 0===t.images[i.uuid]){let e;if(Array.isArray(i)){e=[];for(let t=0,n=i.length;t1)switch(this.wrapS){case hx:t.x=t.x-Math.floor(t.x);break;case ux:t.x=t.x<0?0:1;break;case dx:1===Math.abs(Math.floor(t.x)%2)?t.x=Math.ceil(t.x)-t.x:t.x=t.x-Math.floor(t.x)}if(t.y<0||t.y>1)switch(this.wrapT){case hx:t.y=t.y-Math.floor(t.y);break;case ux:t.y=t.y<0?0:1;break;case dx:1===Math.abs(Math.floor(t.y)%2)?t.y=Math.ceil(t.y)-t.y:t.y=t.y-Math.floor(t.y)}return this.flipY&&(t.y=1-t.y),t}set needsUpdate(t){!0===t&&this.version++}}function db(t){return\\\\\\\"undefined\\\\\\\"!=typeof HTMLImageElement&&t instanceof HTMLImageElement||\\\\\\\"undefined\\\\\\\"!=typeof HTMLCanvasElement&&t instanceof HTMLCanvasElement||\\\\\\\"undefined\\\\\\\"!=typeof ImageBitmap&&t instanceof ImageBitmap?cb.getDataURL(t):t.data?{data:Array.prototype.slice.call(t.data),width:t.width,height:t.height,type:t.data.constructor.name}:(console.warn(\\\\\\\"THREE.Texture: Unable to serialize Texture.\\\\\\\"),{})}ub.DEFAULT_IMAGE=void 0,ub.DEFAULT_MAPPING=300,ub.prototype.isTexture=!0;class pb{constructor(t=0,e=0,n=0,i=1){this.x=t,this.y=e,this.z=n,this.w=i}get width(){return this.z}set width(t){this.z=t}get height(){return this.w}set height(t){this.w=t}set(t,e,n,i){return this.x=t,this.y=e,this.z=n,this.w=i,this}setScalar(t){return this.x=t,this.y=t,this.z=t,this.w=t,this}setX(t){return this.x=t,this}setY(t){return this.y=t,this}setZ(t){return this.z=t,this}setW(t){return this.w=t,this}setComponent(t,e){switch(t){case 0:this.x=e;break;case 1:this.y=e;break;case 2:this.z=e;break;case 3:this.w=e;break;default:throw new Error(\\\\\\\"index is out of range: \\\\\\\"+t)}return this}getComponent(t){switch(t){case 0:return this.x;case 1:return this.y;case 2:return this.z;case 3:return this.w;default:throw new Error(\\\\\\\"index is out of range: \\\\\\\"+t)}}clone(){return new this.constructor(this.x,this.y,this.z,this.w)}copy(t){return this.x=t.x,this.y=t.y,this.z=t.z,this.w=void 0!==t.w?t.w:1,this}add(t,e){return void 0!==e?(console.warn(\\\\\\\"THREE.Vector4: .add() now only accepts one argument. Use .addVectors( a, b ) instead.\\\\\\\"),this.addVectors(t,e)):(this.x+=t.x,this.y+=t.y,this.z+=t.z,this.w+=t.w,this)}addScalar(t){return this.x+=t,this.y+=t,this.z+=t,this.w+=t,this}addVectors(t,e){return this.x=t.x+e.x,this.y=t.y+e.y,this.z=t.z+e.z,this.w=t.w+e.w,this}addScaledVector(t,e){return this.x+=t.x*e,this.y+=t.y*e,this.z+=t.z*e,this.w+=t.w*e,this}sub(t,e){return void 0!==e?(console.warn(\\\\\\\"THREE.Vector4: .sub() now only accepts one argument. Use .subVectors( a, b ) instead.\\\\\\\"),this.subVectors(t,e)):(this.x-=t.x,this.y-=t.y,this.z-=t.z,this.w-=t.w,this)}subScalar(t){return this.x-=t,this.y-=t,this.z-=t,this.w-=t,this}subVectors(t,e){return this.x=t.x-e.x,this.y=t.y-e.y,this.z=t.z-e.z,this.w=t.w-e.w,this}multiply(t){return this.x*=t.x,this.y*=t.y,this.z*=t.z,this.w*=t.w,this}multiplyScalar(t){return this.x*=t,this.y*=t,this.z*=t,this.w*=t,this}applyMatrix4(t){const e=this.x,n=this.y,i=this.z,s=this.w,r=t.elements;return this.x=r[0]*e+r[4]*n+r[8]*i+r[12]*s,this.y=r[1]*e+r[5]*n+r[9]*i+r[13]*s,this.z=r[2]*e+r[6]*n+r[10]*i+r[14]*s,this.w=r[3]*e+r[7]*n+r[11]*i+r[15]*s,this}divideScalar(t){return this.multiplyScalar(1/t)}setAxisAngleFromQuaternion(t){this.w=2*Math.acos(t.w);const e=Math.sqrt(1-t.w*t.w);return e<1e-4?(this.x=1,this.y=0,this.z=0):(this.x=t.x/e,this.y=t.y/e,this.z=t.z/e),this}setAxisAngleFromRotationMatrix(t){let e,n,i,s;const r=.01,o=.1,a=t.elements,l=a[0],c=a[4],h=a[8],u=a[1],d=a[5],p=a[9],_=a[2],m=a[6],f=a[10];if(Math.abs(c-u)a&&t>g?tg?a=0?1:-1,i=1-e*e;if(i>Number.EPSILON){const s=Math.sqrt(i),r=Math.atan2(s,e*n);t=Math.sin(t*r)/s,o=Math.sin(o*r)/s}const s=o*n;if(a=a*t+u*s,l=l*t+d*s,c=c*t+p*s,h=h*t+_*s,t===1-o){const t=1/Math.sqrt(a*a+l*l+c*c+h*h);a*=t,l*=t,c*=t,h*=t}}t[e]=a,t[e+1]=l,t[e+2]=c,t[e+3]=h}static multiplyQuaternionsFlat(t,e,n,i,s,r){const o=n[i],a=n[i+1],l=n[i+2],c=n[i+3],h=s[r],u=s[r+1],d=s[r+2],p=s[r+3];return t[e]=o*p+c*h+a*d-l*u,t[e+1]=a*p+c*u+l*h-o*d,t[e+2]=l*p+c*d+o*u-a*h,t[e+3]=c*p-o*h-a*u-l*d,t}get x(){return this._x}set x(t){this._x=t,this._onChangeCallback()}get y(){return this._y}set y(t){this._y=t,this._onChangeCallback()}get z(){return this._z}set z(t){this._z=t,this._onChangeCallback()}get w(){return this._w}set w(t){this._w=t,this._onChangeCallback()}set(t,e,n,i){return this._x=t,this._y=e,this._z=n,this._w=i,this._onChangeCallback(),this}clone(){return new this.constructor(this._x,this._y,this._z,this._w)}copy(t){return this._x=t.x,this._y=t.y,this._z=t.z,this._w=t.w,this._onChangeCallback(),this}setFromEuler(t,e){if(!t||!t.isEuler)throw new Error(\\\\\\\"THREE.Quaternion: .setFromEuler() now expects an Euler rotation rather than a Vector3 and order.\\\\\\\");const n=t._x,i=t._y,s=t._z,r=t._order,o=Math.cos,a=Math.sin,l=o(n/2),c=o(i/2),h=o(s/2),u=a(n/2),d=a(i/2),p=a(s/2);switch(r){case\\\\\\\"XYZ\\\\\\\":this._x=u*c*h+l*d*p,this._y=l*d*h-u*c*p,this._z=l*c*p+u*d*h,this._w=l*c*h-u*d*p;break;case\\\\\\\"YXZ\\\\\\\":this._x=u*c*h+l*d*p,this._y=l*d*h-u*c*p,this._z=l*c*p-u*d*h,this._w=l*c*h+u*d*p;break;case\\\\\\\"ZXY\\\\\\\":this._x=u*c*h-l*d*p,this._y=l*d*h+u*c*p,this._z=l*c*p+u*d*h,this._w=l*c*h-u*d*p;break;case\\\\\\\"ZYX\\\\\\\":this._x=u*c*h-l*d*p,this._y=l*d*h+u*c*p,this._z=l*c*p-u*d*h,this._w=l*c*h+u*d*p;break;case\\\\\\\"YZX\\\\\\\":this._x=u*c*h+l*d*p,this._y=l*d*h+u*c*p,this._z=l*c*p-u*d*h,this._w=l*c*h-u*d*p;break;case\\\\\\\"XZY\\\\\\\":this._x=u*c*h-l*d*p,this._y=l*d*h-u*c*p,this._z=l*c*p+u*d*h,this._w=l*c*h+u*d*p;break;default:console.warn(\\\\\\\"THREE.Quaternion: .setFromEuler() encountered an unknown order: \\\\\\\"+r)}return!1!==e&&this._onChangeCallback(),this}setFromAxisAngle(t,e){const n=e/2,i=Math.sin(n);return this._x=t.x*i,this._y=t.y*i,this._z=t.z*i,this._w=Math.cos(n),this._onChangeCallback(),this}setFromRotationMatrix(t){const e=t.elements,n=e[0],i=e[4],s=e[8],r=e[1],o=e[5],a=e[9],l=e[2],c=e[6],h=e[10],u=n+o+h;if(u>0){const t=.5/Math.sqrt(u+1);this._w=.25/t,this._x=(c-a)*t,this._y=(s-l)*t,this._z=(r-i)*t}else if(n>o&&n>h){const t=2*Math.sqrt(1+n-o-h);this._w=(c-a)/t,this._x=.25*t,this._y=(i+r)/t,this._z=(s+l)/t}else if(o>h){const t=2*Math.sqrt(1+o-n-h);this._w=(s-l)/t,this._x=(i+r)/t,this._y=.25*t,this._z=(a+c)/t}else{const t=2*Math.sqrt(1+h-n-o);this._w=(r-i)/t,this._x=(s+l)/t,this._y=(a+c)/t,this._z=.25*t}return this._onChangeCallback(),this}setFromUnitVectors(t,e){let n=t.dot(e)+1;return nMath.abs(t.z)?(this._x=-t.y,this._y=t.x,this._z=0,this._w=n):(this._x=0,this._y=-t.z,this._z=t.y,this._w=n)):(this._x=t.y*e.z-t.z*e.y,this._y=t.z*e.x-t.x*e.z,this._z=t.x*e.y-t.y*e.x,this._w=n),this.normalize()}angleTo(t){return 2*Math.acos(Math.abs(Zx(this.dot(t),-1,1)))}rotateTowards(t,e){const n=this.angleTo(t);if(0===n)return this;const i=Math.min(1,e/n);return this.slerp(t,i),this}identity(){return this.set(0,0,0,1)}invert(){return this.conjugate()}conjugate(){return this._x*=-1,this._y*=-1,this._z*=-1,this._onChangeCallback(),this}dot(t){return this._x*t._x+this._y*t._y+this._z*t._z+this._w*t._w}lengthSq(){return this._x*this._x+this._y*this._y+this._z*this._z+this._w*this._w}length(){return Math.sqrt(this._x*this._x+this._y*this._y+this._z*this._z+this._w*this._w)}normalize(){let t=this.length();return 0===t?(this._x=0,this._y=0,this._z=0,this._w=1):(t=1/t,this._x=this._x*t,this._y=this._y*t,this._z=this._z*t,this._w=this._w*t),this._onChangeCallback(),this}multiply(t,e){return void 0!==e?(console.warn(\\\\\\\"THREE.Quaternion: .multiply() now only accepts one argument. Use .multiplyQuaternions( a, b ) instead.\\\\\\\"),this.multiplyQuaternions(t,e)):this.multiplyQuaternions(this,t)}premultiply(t){return this.multiplyQuaternions(t,this)}multiplyQuaternions(t,e){const n=t._x,i=t._y,s=t._z,r=t._w,o=e._x,a=e._y,l=e._z,c=e._w;return this._x=n*c+r*o+i*l-s*a,this._y=i*c+r*a+s*o-n*l,this._z=s*c+r*l+n*a-i*o,this._w=r*c-n*o-i*a-s*l,this._onChangeCallback(),this}slerp(t,e){if(0===e)return this;if(1===e)return this.copy(t);const n=this._x,i=this._y,s=this._z,r=this._w;let o=r*t._w+n*t._x+i*t._y+s*t._z;if(o<0?(this._w=-t._w,this._x=-t._x,this._y=-t._y,this._z=-t._z,o=-o):this.copy(t),o>=1)return this._w=r,this._x=n,this._y=i,this._z=s,this;const a=1-o*o;if(a<=Number.EPSILON){const t=1-e;return this._w=t*r+e*this._w,this._x=t*n+e*this._x,this._y=t*i+e*this._y,this._z=t*s+e*this._z,this.normalize(),this._onChangeCallback(),this}const l=Math.sqrt(a),c=Math.atan2(l,o),h=Math.sin((1-e)*c)/l,u=Math.sin(e*c)/l;return this._w=r*h+this._w*u,this._x=n*h+this._x*u,this._y=i*h+this._y*u,this._z=s*h+this._z*u,this._onChangeCallback(),this}slerpQuaternions(t,e,n){this.copy(t).slerp(e,n)}random(){const t=Math.random(),e=Math.sqrt(1-t),n=Math.sqrt(t),i=2*Math.PI*Math.random(),s=2*Math.PI*Math.random();return this.set(e*Math.cos(i),n*Math.sin(s),n*Math.cos(s),e*Math.sin(i))}equals(t){return t._x===this._x&&t._y===this._y&&t._z===this._z&&t._w===this._w}fromArray(t,e=0){return this._x=t[e],this._y=t[e+1],this._z=t[e+2],this._w=t[e+3],this._onChangeCallback(),this}toArray(t=[],e=0){return t[e]=this._x,t[e+1]=this._y,t[e+2]=this._z,t[e+3]=this._w,t}fromBufferAttribute(t,e){return this._x=t.getX(e),this._y=t.getY(e),this._z=t.getZ(e),this._w=t.getW(e),this}_onChange(t){return this._onChangeCallback=t,this}_onChangeCallback(){}}fb.prototype.isQuaternion=!0;class gb{constructor(t=0,e=0,n=0){this.x=t,this.y=e,this.z=n}set(t,e,n){return void 0===n&&(n=this.z),this.x=t,this.y=e,this.z=n,this}setScalar(t){return this.x=t,this.y=t,this.z=t,this}setX(t){return this.x=t,this}setY(t){return this.y=t,this}setZ(t){return this.z=t,this}setComponent(t,e){switch(t){case 0:this.x=e;break;case 1:this.y=e;break;case 2:this.z=e;break;default:throw new Error(\\\\\\\"index is out of range: \\\\\\\"+t)}return this}getComponent(t){switch(t){case 0:return this.x;case 1:return this.y;case 2:return this.z;default:throw new Error(\\\\\\\"index is out of range: \\\\\\\"+t)}}clone(){return new this.constructor(this.x,this.y,this.z)}copy(t){return this.x=t.x,this.y=t.y,this.z=t.z,this}add(t,e){return void 0!==e?(console.warn(\\\\\\\"THREE.Vector3: .add() now only accepts one argument. Use .addVectors( a, b ) instead.\\\\\\\"),this.addVectors(t,e)):(this.x+=t.x,this.y+=t.y,this.z+=t.z,this)}addScalar(t){return this.x+=t,this.y+=t,this.z+=t,this}addVectors(t,e){return this.x=t.x+e.x,this.y=t.y+e.y,this.z=t.z+e.z,this}addScaledVector(t,e){return this.x+=t.x*e,this.y+=t.y*e,this.z+=t.z*e,this}sub(t,e){return void 0!==e?(console.warn(\\\\\\\"THREE.Vector3: .sub() now only accepts one argument. Use .subVectors( a, b ) instead.\\\\\\\"),this.subVectors(t,e)):(this.x-=t.x,this.y-=t.y,this.z-=t.z,this)}subScalar(t){return this.x-=t,this.y-=t,this.z-=t,this}subVectors(t,e){return this.x=t.x-e.x,this.y=t.y-e.y,this.z=t.z-e.z,this}multiply(t,e){return void 0!==e?(console.warn(\\\\\\\"THREE.Vector3: .multiply() now only accepts one argument. Use .multiplyVectors( a, b ) instead.\\\\\\\"),this.multiplyVectors(t,e)):(this.x*=t.x,this.y*=t.y,this.z*=t.z,this)}multiplyScalar(t){return this.x*=t,this.y*=t,this.z*=t,this}multiplyVectors(t,e){return this.x=t.x*e.x,this.y=t.y*e.y,this.z=t.z*e.z,this}applyEuler(t){return t&&t.isEuler||console.error(\\\\\\\"THREE.Vector3: .applyEuler() now expects an Euler rotation rather than a Vector3 and order.\\\\\\\"),this.applyQuaternion(yb.setFromEuler(t))}applyAxisAngle(t,e){return this.applyQuaternion(yb.setFromAxisAngle(t,e))}applyMatrix3(t){const e=this.x,n=this.y,i=this.z,s=t.elements;return this.x=s[0]*e+s[3]*n+s[6]*i,this.y=s[1]*e+s[4]*n+s[7]*i,this.z=s[2]*e+s[5]*n+s[8]*i,this}applyNormalMatrix(t){return this.applyMatrix3(t).normalize()}applyMatrix4(t){const e=this.x,n=this.y,i=this.z,s=t.elements,r=1/(s[3]*e+s[7]*n+s[11]*i+s[15]);return this.x=(s[0]*e+s[4]*n+s[8]*i+s[12])*r,this.y=(s[1]*e+s[5]*n+s[9]*i+s[13])*r,this.z=(s[2]*e+s[6]*n+s[10]*i+s[14])*r,this}applyQuaternion(t){const e=this.x,n=this.y,i=this.z,s=t.x,r=t.y,o=t.z,a=t.w,l=a*e+r*i-o*n,c=a*n+o*e-s*i,h=a*i+s*n-r*e,u=-s*e-r*n-o*i;return this.x=l*a+u*-s+c*-o-h*-r,this.y=c*a+u*-r+h*-s-l*-o,this.z=h*a+u*-o+l*-r-c*-s,this}project(t){return this.applyMatrix4(t.matrixWorldInverse).applyMatrix4(t.projectionMatrix)}unproject(t){return this.applyMatrix4(t.projectionMatrixInverse).applyMatrix4(t.matrixWorld)}transformDirection(t){const e=this.x,n=this.y,i=this.z,s=t.elements;return this.x=s[0]*e+s[4]*n+s[8]*i,this.y=s[1]*e+s[5]*n+s[9]*i,this.z=s[2]*e+s[6]*n+s[10]*i,this.normalize()}divide(t){return this.x/=t.x,this.y/=t.y,this.z/=t.z,this}divideScalar(t){return this.multiplyScalar(1/t)}min(t){return this.x=Math.min(this.x,t.x),this.y=Math.min(this.y,t.y),this.z=Math.min(this.z,t.z),this}max(t){return this.x=Math.max(this.x,t.x),this.y=Math.max(this.y,t.y),this.z=Math.max(this.z,t.z),this}clamp(t,e){return this.x=Math.max(t.x,Math.min(e.x,this.x)),this.y=Math.max(t.y,Math.min(e.y,this.y)),this.z=Math.max(t.z,Math.min(e.z,this.z)),this}clampScalar(t,e){return this.x=Math.max(t,Math.min(e,this.x)),this.y=Math.max(t,Math.min(e,this.y)),this.z=Math.max(t,Math.min(e,this.z)),this}clampLength(t,e){const n=this.length();return this.divideScalar(n||1).multiplyScalar(Math.max(t,Math.min(e,n)))}floor(){return this.x=Math.floor(this.x),this.y=Math.floor(this.y),this.z=Math.floor(this.z),this}ceil(){return this.x=Math.ceil(this.x),this.y=Math.ceil(this.y),this.z=Math.ceil(this.z),this}round(){return this.x=Math.round(this.x),this.y=Math.round(this.y),this.z=Math.round(this.z),this}roundToZero(){return this.x=this.x<0?Math.ceil(this.x):Math.floor(this.x),this.y=this.y<0?Math.ceil(this.y):Math.floor(this.y),this.z=this.z<0?Math.ceil(this.z):Math.floor(this.z),this}negate(){return this.x=-this.x,this.y=-this.y,this.z=-this.z,this}dot(t){return this.x*t.x+this.y*t.y+this.z*t.z}lengthSq(){return this.x*this.x+this.y*this.y+this.z*this.z}length(){return Math.sqrt(this.x*this.x+this.y*this.y+this.z*this.z)}manhattanLength(){return Math.abs(this.x)+Math.abs(this.y)+Math.abs(this.z)}normalize(){return this.divideScalar(this.length()||1)}setLength(t){return this.normalize().multiplyScalar(t)}lerp(t,e){return this.x+=(t.x-this.x)*e,this.y+=(t.y-this.y)*e,this.z+=(t.z-this.z)*e,this}lerpVectors(t,e,n){return this.x=t.x+(e.x-t.x)*n,this.y=t.y+(e.y-t.y)*n,this.z=t.z+(e.z-t.z)*n,this}cross(t,e){return void 0!==e?(console.warn(\\\\\\\"THREE.Vector3: .cross() now only accepts one argument. Use .crossVectors( a, b ) instead.\\\\\\\"),this.crossVectors(t,e)):this.crossVectors(this,t)}crossVectors(t,e){const n=t.x,i=t.y,s=t.z,r=e.x,o=e.y,a=e.z;return this.x=i*a-s*o,this.y=s*r-n*a,this.z=n*o-i*r,this}projectOnVector(t){const e=t.lengthSq();if(0===e)return this.set(0,0,0);const n=t.dot(this)/e;return this.copy(t).multiplyScalar(n)}projectOnPlane(t){return vb.copy(this).projectOnVector(t),this.sub(vb)}reflect(t){return this.sub(vb.copy(t).multiplyScalar(2*this.dot(t)))}angleTo(t){const e=Math.sqrt(this.lengthSq()*t.lengthSq());if(0===e)return Math.PI/2;const n=this.dot(t)/e;return Math.acos(Zx(n,-1,1))}distanceTo(t){return Math.sqrt(this.distanceToSquared(t))}distanceToSquared(t){const e=this.x-t.x,n=this.y-t.y,i=this.z-t.z;return e*e+n*n+i*i}manhattanDistanceTo(t){return Math.abs(this.x-t.x)+Math.abs(this.y-t.y)+Math.abs(this.z-t.z)}setFromSpherical(t){return this.setFromSphericalCoords(t.radius,t.phi,t.theta)}setFromSphericalCoords(t,e,n){const i=Math.sin(e)*t;return this.x=i*Math.sin(n),this.y=Math.cos(e)*t,this.z=i*Math.cos(n),this}setFromCylindrical(t){return this.setFromCylindricalCoords(t.radius,t.theta,t.y)}setFromCylindricalCoords(t,e,n){return this.x=t*Math.sin(e),this.y=n,this.z=t*Math.cos(e),this}setFromMatrixPosition(t){const e=t.elements;return this.x=e[12],this.y=e[13],this.z=e[14],this}setFromMatrixScale(t){const e=this.setFromMatrixColumn(t,0).length(),n=this.setFromMatrixColumn(t,1).length(),i=this.setFromMatrixColumn(t,2).length();return this.x=e,this.y=n,this.z=i,this}setFromMatrixColumn(t,e){return this.fromArray(t.elements,4*e)}setFromMatrix3Column(t,e){return this.fromArray(t.elements,3*e)}equals(t){return t.x===this.x&&t.y===this.y&&t.z===this.z}fromArray(t,e=0){return this.x=t[e],this.y=t[e+1],this.z=t[e+2],this}toArray(t=[],e=0){return t[e]=this.x,t[e+1]=this.y,t[e+2]=this.z,t}fromBufferAttribute(t,e,n){return void 0!==n&&console.warn(\\\\\\\"THREE.Vector3: offset has been removed from .fromBufferAttribute().\\\\\\\"),this.x=t.getX(e),this.y=t.getY(e),this.z=t.getZ(e),this}random(){return this.x=Math.random(),this.y=Math.random(),this.z=Math.random(),this}randomDirection(){const t=2*(Math.random()-.5),e=Math.random()*Math.PI*2,n=Math.sqrt(1-t**2);return this.x=n*Math.cos(e),this.y=n*Math.sin(e),this.z=t,this}*[Symbol.iterator](){yield this.x,yield this.y,yield this.z}}gb.prototype.isVector3=!0;const vb=new gb,yb=new fb;class xb{constructor(t=new gb(1/0,1/0,1/0),e=new gb(-1/0,-1/0,-1/0)){this.min=t,this.max=e}set(t,e){return this.min.copy(t),this.max.copy(e),this}setFromArray(t){let e=1/0,n=1/0,i=1/0,s=-1/0,r=-1/0,o=-1/0;for(let a=0,l=t.length;as&&(s=l),c>r&&(r=c),h>o&&(o=h)}return this.min.set(e,n,i),this.max.set(s,r,o),this}setFromBufferAttribute(t){let e=1/0,n=1/0,i=1/0,s=-1/0,r=-1/0,o=-1/0;for(let a=0,l=t.count;as&&(s=l),c>r&&(r=c),h>o&&(o=h)}return this.min.set(e,n,i),this.max.set(s,r,o),this}setFromPoints(t){this.makeEmpty();for(let e=0,n=t.length;ethis.max.x||t.ythis.max.y||t.zthis.max.z)}containsBox(t){return this.min.x<=t.min.x&&t.max.x<=this.max.x&&this.min.y<=t.min.y&&t.max.y<=this.max.y&&this.min.z<=t.min.z&&t.max.z<=this.max.z}getParameter(t,e){return e.set((t.x-this.min.x)/(this.max.x-this.min.x),(t.y-this.min.y)/(this.max.y-this.min.y),(t.z-this.min.z)/(this.max.z-this.min.z))}intersectsBox(t){return!(t.max.xthis.max.x||t.max.ythis.max.y||t.max.zthis.max.z)}intersectsSphere(t){return this.clampPoint(t.center,wb),wb.distanceToSquared(t.center)<=t.radius*t.radius}intersectsPlane(t){let e,n;return t.normal.x>0?(e=t.normal.x*this.min.x,n=t.normal.x*this.max.x):(e=t.normal.x*this.max.x,n=t.normal.x*this.min.x),t.normal.y>0?(e+=t.normal.y*this.min.y,n+=t.normal.y*this.max.y):(e+=t.normal.y*this.max.y,n+=t.normal.y*this.min.y),t.normal.z>0?(e+=t.normal.z*this.min.z,n+=t.normal.z*this.max.z):(e+=t.normal.z*this.max.z,n+=t.normal.z*this.min.z),e<=-t.constant&&n>=-t.constant}intersectsTriangle(t){if(this.isEmpty())return!1;this.getCenter(Lb),Ob.subVectors(this.max,Lb),Ab.subVectors(t.a,Lb),Mb.subVectors(t.b,Lb),Eb.subVectors(t.c,Lb),Sb.subVectors(Mb,Ab),Cb.subVectors(Eb,Mb),Nb.subVectors(Ab,Eb);let e=[0,-Sb.z,Sb.y,0,-Cb.z,Cb.y,0,-Nb.z,Nb.y,Sb.z,0,-Sb.x,Cb.z,0,-Cb.x,Nb.z,0,-Nb.x,-Sb.y,Sb.x,0,-Cb.y,Cb.x,0,-Nb.y,Nb.x,0];return!!Ib(e,Ab,Mb,Eb,Ob)&&(e=[1,0,0,0,1,0,0,0,1],!!Ib(e,Ab,Mb,Eb,Ob)&&(Pb.crossVectors(Sb,Cb),e=[Pb.x,Pb.y,Pb.z],Ib(e,Ab,Mb,Eb,Ob)))}clampPoint(t,e){return e.copy(t).clamp(this.min,this.max)}distanceToPoint(t){return wb.copy(t).clamp(this.min,this.max).sub(t).length()}getBoundingSphere(t){return this.getCenter(t.center),t.radius=.5*this.getSize(wb).length(),t}intersect(t){return this.min.max(t.min),this.max.min(t.max),this.isEmpty()&&this.makeEmpty(),this}union(t){return this.min.min(t.min),this.max.max(t.max),this}applyMatrix4(t){return this.isEmpty()||(bb[0].set(this.min.x,this.min.y,this.min.z).applyMatrix4(t),bb[1].set(this.min.x,this.min.y,this.max.z).applyMatrix4(t),bb[2].set(this.min.x,this.max.y,this.min.z).applyMatrix4(t),bb[3].set(this.min.x,this.max.y,this.max.z).applyMatrix4(t),bb[4].set(this.max.x,this.min.y,this.min.z).applyMatrix4(t),bb[5].set(this.max.x,this.min.y,this.max.z).applyMatrix4(t),bb[6].set(this.max.x,this.max.y,this.min.z).applyMatrix4(t),bb[7].set(this.max.x,this.max.y,this.max.z).applyMatrix4(t),this.setFromPoints(bb)),this}translate(t){return this.min.add(t),this.max.add(t),this}equals(t){return t.min.equals(this.min)&&t.max.equals(this.max)}}xb.prototype.isBox3=!0;const bb=[new gb,new gb,new gb,new gb,new gb,new gb,new gb,new gb],wb=new gb,Tb=new xb,Ab=new gb,Mb=new gb,Eb=new gb,Sb=new gb,Cb=new gb,Nb=new gb,Lb=new gb,Ob=new gb,Pb=new gb,Rb=new gb;function Ib(t,e,n,i,s){for(let r=0,o=t.length-3;r<=o;r+=3){Rb.fromArray(t,r);const o=s.x*Math.abs(Rb.x)+s.y*Math.abs(Rb.y)+s.z*Math.abs(Rb.z),a=e.dot(Rb),l=n.dot(Rb),c=i.dot(Rb);if(Math.max(-Math.max(a,l,c),Math.min(a,l,c))>o)return!1}return!0}const Fb=new xb,Db=new gb,Bb=new gb,zb=new gb;class kb{constructor(t=new gb,e=-1){this.center=t,this.radius=e}set(t,e){return this.center.copy(t),this.radius=e,this}setFromPoints(t,e){const n=this.center;void 0!==e?n.copy(e):Fb.setFromPoints(t).getCenter(n);let i=0;for(let e=0,s=t.length;ethis.radius*this.radius&&(e.sub(this.center).normalize(),e.multiplyScalar(this.radius).add(this.center)),e}getBoundingBox(t){return this.isEmpty()?(t.makeEmpty(),t):(t.set(this.center,this.center),t.expandByScalar(this.radius),t)}applyMatrix4(t){return this.center.applyMatrix4(t),this.radius=this.radius*t.getMaxScaleOnAxis(),this}translate(t){return this.center.add(t),this}expandByPoint(t){zb.subVectors(t,this.center);const e=zb.lengthSq();if(e>this.radius*this.radius){const t=Math.sqrt(e),n=.5*(t-this.radius);this.center.add(zb.multiplyScalar(n/t)),this.radius+=n}return this}union(t){return Bb.subVectors(t.center,this.center).normalize().multiplyScalar(t.radius),this.expandByPoint(Db.copy(t.center).add(Bb)),this.expandByPoint(Db.copy(t.center).sub(Bb)),this}equals(t){return t.center.equals(this.center)&&t.radius===this.radius}clone(){return(new this.constructor).copy(this)}}const Ub=new gb,Gb=new gb,Vb=new gb,Hb=new gb,jb=new gb,Wb=new gb,qb=new gb;class Xb{constructor(t=new gb,e=new gb(0,0,-1)){this.origin=t,this.direction=e}set(t,e){return this.origin.copy(t),this.direction.copy(e),this}copy(t){return this.origin.copy(t.origin),this.direction.copy(t.direction),this}at(t,e){return e.copy(this.direction).multiplyScalar(t).add(this.origin)}lookAt(t){return this.direction.copy(t).sub(this.origin).normalize(),this}recast(t){return this.origin.copy(this.at(t,Ub)),this}closestPointToPoint(t,e){e.subVectors(t,this.origin);const n=e.dot(this.direction);return n<0?e.copy(this.origin):e.copy(this.direction).multiplyScalar(n).add(this.origin)}distanceToPoint(t){return Math.sqrt(this.distanceSqToPoint(t))}distanceSqToPoint(t){const e=Ub.subVectors(t,this.origin).dot(this.direction);return e<0?this.origin.distanceToSquared(t):(Ub.copy(this.direction).multiplyScalar(e).add(this.origin),Ub.distanceToSquared(t))}distanceSqToSegment(t,e,n,i){Gb.copy(t).add(e).multiplyScalar(.5),Vb.copy(e).sub(t).normalize(),Hb.copy(this.origin).sub(Gb);const s=.5*t.distanceTo(e),r=-this.direction.dot(Vb),o=Hb.dot(this.direction),a=-Hb.dot(Vb),l=Hb.lengthSq(),c=Math.abs(1-r*r);let h,u,d,p;if(c>0)if(h=r*a-o,u=r*o-a,p=s*c,h>=0)if(u>=-p)if(u<=p){const t=1/c;h*=t,u*=t,d=h*(h+r*u+2*o)+u*(r*h+u+2*a)+l}else u=s,h=Math.max(0,-(r*u+o)),d=-h*h+u*(u+2*a)+l;else u=-s,h=Math.max(0,-(r*u+o)),d=-h*h+u*(u+2*a)+l;else u<=-p?(h=Math.max(0,-(-r*s+o)),u=h>0?-s:Math.min(Math.max(-s,-a),s),d=-h*h+u*(u+2*a)+l):u<=p?(h=0,u=Math.min(Math.max(-s,-a),s),d=u*(u+2*a)+l):(h=Math.max(0,-(r*s+o)),u=h>0?s:Math.min(Math.max(-s,-a),s),d=-h*h+u*(u+2*a)+l);else u=r>0?-s:s,h=Math.max(0,-(r*u+o)),d=-h*h+u*(u+2*a)+l;return n&&n.copy(this.direction).multiplyScalar(h).add(this.origin),i&&i.copy(Vb).multiplyScalar(u).add(Gb),d}intersectSphere(t,e){Ub.subVectors(t.center,this.origin);const n=Ub.dot(this.direction),i=Ub.dot(Ub)-n*n,s=t.radius*t.radius;if(i>s)return null;const r=Math.sqrt(s-i),o=n-r,a=n+r;return o<0&&a<0?null:o<0?this.at(a,e):this.at(o,e)}intersectsSphere(t){return this.distanceSqToPoint(t.center)<=t.radius*t.radius}distanceToPlane(t){const e=t.normal.dot(this.direction);if(0===e)return 0===t.distanceToPoint(this.origin)?0:null;const n=-(this.origin.dot(t.normal)+t.constant)/e;return n>=0?n:null}intersectPlane(t,e){const n=this.distanceToPlane(t);return null===n?null:this.at(n,e)}intersectsPlane(t){const e=t.distanceToPoint(this.origin);if(0===e)return!0;return t.normal.dot(this.direction)*e<0}intersectBox(t,e){let n,i,s,r,o,a;const l=1/this.direction.x,c=1/this.direction.y,h=1/this.direction.z,u=this.origin;return l>=0?(n=(t.min.x-u.x)*l,i=(t.max.x-u.x)*l):(n=(t.max.x-u.x)*l,i=(t.min.x-u.x)*l),c>=0?(s=(t.min.y-u.y)*c,r=(t.max.y-u.y)*c):(s=(t.max.y-u.y)*c,r=(t.min.y-u.y)*c),n>r||s>i?null:((s>n||n!=n)&&(n=s),(r=0?(o=(t.min.z-u.z)*h,a=(t.max.z-u.z)*h):(o=(t.max.z-u.z)*h,a=(t.min.z-u.z)*h),n>a||o>i?null:((o>n||n!=n)&&(n=o),(a=0?n:i,e)))}intersectsBox(t){return null!==this.intersectBox(t,Ub)}intersectTriangle(t,e,n,i,s){jb.subVectors(e,t),Wb.subVectors(n,t),qb.crossVectors(jb,Wb);let r,o=this.direction.dot(qb);if(o>0){if(i)return null;r=1}else{if(!(o<0))return null;r=-1,o=-o}Hb.subVectors(this.origin,t);const a=r*this.direction.dot(Wb.crossVectors(Hb,Wb));if(a<0)return null;const l=r*this.direction.dot(jb.cross(Hb));if(l<0)return null;if(a+l>o)return null;const c=-r*Hb.dot(qb);return c<0?null:this.at(c/o,s)}applyMatrix4(t){return this.origin.applyMatrix4(t),this.direction.transformDirection(t),this}equals(t){return t.origin.equals(this.origin)&&t.direction.equals(this.direction)}clone(){return(new this.constructor).copy(this)}}class Yb{constructor(){this.elements=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1],arguments.length>0&&console.error(\\\\\\\"THREE.Matrix4: the constructor no longer reads arguments. use .set() instead.\\\\\\\")}set(t,e,n,i,s,r,o,a,l,c,h,u,d,p,_,m){const f=this.elements;return f[0]=t,f[4]=e,f[8]=n,f[12]=i,f[1]=s,f[5]=r,f[9]=o,f[13]=a,f[2]=l,f[6]=c,f[10]=h,f[14]=u,f[3]=d,f[7]=p,f[11]=_,f[15]=m,this}identity(){return this.set(1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1),this}clone(){return(new Yb).fromArray(this.elements)}copy(t){const e=this.elements,n=t.elements;return e[0]=n[0],e[1]=n[1],e[2]=n[2],e[3]=n[3],e[4]=n[4],e[5]=n[5],e[6]=n[6],e[7]=n[7],e[8]=n[8],e[9]=n[9],e[10]=n[10],e[11]=n[11],e[12]=n[12],e[13]=n[13],e[14]=n[14],e[15]=n[15],this}copyPosition(t){const e=this.elements,n=t.elements;return e[12]=n[12],e[13]=n[13],e[14]=n[14],this}setFromMatrix3(t){const e=t.elements;return this.set(e[0],e[3],e[6],0,e[1],e[4],e[7],0,e[2],e[5],e[8],0,0,0,0,1),this}extractBasis(t,e,n){return t.setFromMatrixColumn(this,0),e.setFromMatrixColumn(this,1),n.setFromMatrixColumn(this,2),this}makeBasis(t,e,n){return this.set(t.x,e.x,n.x,0,t.y,e.y,n.y,0,t.z,e.z,n.z,0,0,0,0,1),this}extractRotation(t){const e=this.elements,n=t.elements,i=1/$b.setFromMatrixColumn(t,0).length(),s=1/$b.setFromMatrixColumn(t,1).length(),r=1/$b.setFromMatrixColumn(t,2).length();return e[0]=n[0]*i,e[1]=n[1]*i,e[2]=n[2]*i,e[3]=0,e[4]=n[4]*s,e[5]=n[5]*s,e[6]=n[6]*s,e[7]=0,e[8]=n[8]*r,e[9]=n[9]*r,e[10]=n[10]*r,e[11]=0,e[12]=0,e[13]=0,e[14]=0,e[15]=1,this}makeRotationFromEuler(t){t&&t.isEuler||console.error(\\\\\\\"THREE.Matrix4: .makeRotationFromEuler() now expects a Euler rotation rather than a Vector3 and order.\\\\\\\");const e=this.elements,n=t.x,i=t.y,s=t.z,r=Math.cos(n),o=Math.sin(n),a=Math.cos(i),l=Math.sin(i),c=Math.cos(s),h=Math.sin(s);if(\\\\\\\"XYZ\\\\\\\"===t.order){const t=r*c,n=r*h,i=o*c,s=o*h;e[0]=a*c,e[4]=-a*h,e[8]=l,e[1]=n+i*l,e[5]=t-s*l,e[9]=-o*a,e[2]=s-t*l,e[6]=i+n*l,e[10]=r*a}else if(\\\\\\\"YXZ\\\\\\\"===t.order){const t=a*c,n=a*h,i=l*c,s=l*h;e[0]=t+s*o,e[4]=i*o-n,e[8]=r*l,e[1]=r*h,e[5]=r*c,e[9]=-o,e[2]=n*o-i,e[6]=s+t*o,e[10]=r*a}else if(\\\\\\\"ZXY\\\\\\\"===t.order){const t=a*c,n=a*h,i=l*c,s=l*h;e[0]=t-s*o,e[4]=-r*h,e[8]=i+n*o,e[1]=n+i*o,e[5]=r*c,e[9]=s-t*o,e[2]=-r*l,e[6]=o,e[10]=r*a}else if(\\\\\\\"ZYX\\\\\\\"===t.order){const t=r*c,n=r*h,i=o*c,s=o*h;e[0]=a*c,e[4]=i*l-n,e[8]=t*l+s,e[1]=a*h,e[5]=s*l+t,e[9]=n*l-i,e[2]=-l,e[6]=o*a,e[10]=r*a}else if(\\\\\\\"YZX\\\\\\\"===t.order){const t=r*a,n=r*l,i=o*a,s=o*l;e[0]=a*c,e[4]=s-t*h,e[8]=i*h+n,e[1]=h,e[5]=r*c,e[9]=-o*c,e[2]=-l*c,e[6]=n*h+i,e[10]=t-s*h}else if(\\\\\\\"XZY\\\\\\\"===t.order){const t=r*a,n=r*l,i=o*a,s=o*l;e[0]=a*c,e[4]=-h,e[8]=l*c,e[1]=t*h+s,e[5]=r*c,e[9]=n*h-i,e[2]=i*h-n,e[6]=o*c,e[10]=s*h+t}return e[3]=0,e[7]=0,e[11]=0,e[12]=0,e[13]=0,e[14]=0,e[15]=1,this}makeRotationFromQuaternion(t){return this.compose(Zb,t,Qb)}lookAt(t,e,n){const i=this.elements;return ew.subVectors(t,e),0===ew.lengthSq()&&(ew.z=1),ew.normalize(),Kb.crossVectors(n,ew),0===Kb.lengthSq()&&(1===Math.abs(n.z)?ew.x+=1e-4:ew.z+=1e-4,ew.normalize(),Kb.crossVectors(n,ew)),Kb.normalize(),tw.crossVectors(ew,Kb),i[0]=Kb.x,i[4]=tw.x,i[8]=ew.x,i[1]=Kb.y,i[5]=tw.y,i[9]=ew.y,i[2]=Kb.z,i[6]=tw.z,i[10]=ew.z,this}multiply(t,e){return void 0!==e?(console.warn(\\\\\\\"THREE.Matrix4: .multiply() now only accepts one argument. Use .multiplyMatrices( a, b ) instead.\\\\\\\"),this.multiplyMatrices(t,e)):this.multiplyMatrices(this,t)}premultiply(t){return this.multiplyMatrices(t,this)}multiplyMatrices(t,e){const n=t.elements,i=e.elements,s=this.elements,r=n[0],o=n[4],a=n[8],l=n[12],c=n[1],h=n[5],u=n[9],d=n[13],p=n[2],_=n[6],m=n[10],f=n[14],g=n[3],v=n[7],y=n[11],x=n[15],b=i[0],w=i[4],T=i[8],A=i[12],M=i[1],E=i[5],S=i[9],C=i[13],N=i[2],L=i[6],O=i[10],P=i[14],R=i[3],I=i[7],F=i[11],D=i[15];return s[0]=r*b+o*M+a*N+l*R,s[4]=r*w+o*E+a*L+l*I,s[8]=r*T+o*S+a*O+l*F,s[12]=r*A+o*C+a*P+l*D,s[1]=c*b+h*M+u*N+d*R,s[5]=c*w+h*E+u*L+d*I,s[9]=c*T+h*S+u*O+d*F,s[13]=c*A+h*C+u*P+d*D,s[2]=p*b+_*M+m*N+f*R,s[6]=p*w+_*E+m*L+f*I,s[10]=p*T+_*S+m*O+f*F,s[14]=p*A+_*C+m*P+f*D,s[3]=g*b+v*M+y*N+x*R,s[7]=g*w+v*E+y*L+x*I,s[11]=g*T+v*S+y*O+x*F,s[15]=g*A+v*C+y*P+x*D,this}multiplyScalar(t){const e=this.elements;return e[0]*=t,e[4]*=t,e[8]*=t,e[12]*=t,e[1]*=t,e[5]*=t,e[9]*=t,e[13]*=t,e[2]*=t,e[6]*=t,e[10]*=t,e[14]*=t,e[3]*=t,e[7]*=t,e[11]*=t,e[15]*=t,this}determinant(){const t=this.elements,e=t[0],n=t[4],i=t[8],s=t[12],r=t[1],o=t[5],a=t[9],l=t[13],c=t[2],h=t[6],u=t[10],d=t[14];return t[3]*(+s*a*h-i*l*h-s*o*u+n*l*u+i*o*d-n*a*d)+t[7]*(+e*a*d-e*l*u+s*r*u-i*r*d+i*l*c-s*a*c)+t[11]*(+e*l*h-e*o*d-s*r*h+n*r*d+s*o*c-n*l*c)+t[15]*(-i*o*c-e*a*h+e*o*u+i*r*h-n*r*u+n*a*c)}transpose(){const t=this.elements;let e;return e=t[1],t[1]=t[4],t[4]=e,e=t[2],t[2]=t[8],t[8]=e,e=t[6],t[6]=t[9],t[9]=e,e=t[3],t[3]=t[12],t[12]=e,e=t[7],t[7]=t[13],t[13]=e,e=t[11],t[11]=t[14],t[14]=e,this}setPosition(t,e,n){const i=this.elements;return t.isVector3?(i[12]=t.x,i[13]=t.y,i[14]=t.z):(i[12]=t,i[13]=e,i[14]=n),this}invert(){const t=this.elements,e=t[0],n=t[1],i=t[2],s=t[3],r=t[4],o=t[5],a=t[6],l=t[7],c=t[8],h=t[9],u=t[10],d=t[11],p=t[12],_=t[13],m=t[14],f=t[15],g=h*m*l-_*u*l+_*a*d-o*m*d-h*a*f+o*u*f,v=p*u*l-c*m*l-p*a*d+r*m*d+c*a*f-r*u*f,y=c*_*l-p*h*l+p*o*d-r*_*d-c*o*f+r*h*f,x=p*h*a-c*_*a-p*o*u+r*_*u+c*o*m-r*h*m,b=e*g+n*v+i*y+s*x;if(0===b)return this.set(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0);const w=1/b;return t[0]=g*w,t[1]=(_*u*s-h*m*s-_*i*d+n*m*d+h*i*f-n*u*f)*w,t[2]=(o*m*s-_*a*s+_*i*l-n*m*l-o*i*f+n*a*f)*w,t[3]=(h*a*s-o*u*s-h*i*l+n*u*l+o*i*d-n*a*d)*w,t[4]=v*w,t[5]=(c*m*s-p*u*s+p*i*d-e*m*d-c*i*f+e*u*f)*w,t[6]=(p*a*s-r*m*s-p*i*l+e*m*l+r*i*f-e*a*f)*w,t[7]=(r*u*s-c*a*s+c*i*l-e*u*l-r*i*d+e*a*d)*w,t[8]=y*w,t[9]=(p*h*s-c*_*s-p*n*d+e*_*d+c*n*f-e*h*f)*w,t[10]=(r*_*s-p*o*s+p*n*l-e*_*l-r*n*f+e*o*f)*w,t[11]=(c*o*s-r*h*s-c*n*l+e*h*l+r*n*d-e*o*d)*w,t[12]=x*w,t[13]=(c*_*i-p*h*i+p*n*u-e*_*u-c*n*m+e*h*m)*w,t[14]=(p*o*i-r*_*i-p*n*a+e*_*a+r*n*m-e*o*m)*w,t[15]=(r*h*i-c*o*i+c*n*a-e*h*a-r*n*u+e*o*u)*w,this}scale(t){const e=this.elements,n=t.x,i=t.y,s=t.z;return e[0]*=n,e[4]*=i,e[8]*=s,e[1]*=n,e[5]*=i,e[9]*=s,e[2]*=n,e[6]*=i,e[10]*=s,e[3]*=n,e[7]*=i,e[11]*=s,this}getMaxScaleOnAxis(){const t=this.elements,e=t[0]*t[0]+t[1]*t[1]+t[2]*t[2],n=t[4]*t[4]+t[5]*t[5]+t[6]*t[6],i=t[8]*t[8]+t[9]*t[9]+t[10]*t[10];return Math.sqrt(Math.max(e,n,i))}makeTranslation(t,e,n){return this.set(1,0,0,t,0,1,0,e,0,0,1,n,0,0,0,1),this}makeRotationX(t){const e=Math.cos(t),n=Math.sin(t);return this.set(1,0,0,0,0,e,-n,0,0,n,e,0,0,0,0,1),this}makeRotationY(t){const e=Math.cos(t),n=Math.sin(t);return this.set(e,0,n,0,0,1,0,0,-n,0,e,0,0,0,0,1),this}makeRotationZ(t){const e=Math.cos(t),n=Math.sin(t);return this.set(e,-n,0,0,n,e,0,0,0,0,1,0,0,0,0,1),this}makeRotationAxis(t,e){const n=Math.cos(e),i=Math.sin(e),s=1-n,r=t.x,o=t.y,a=t.z,l=s*r,c=s*o;return this.set(l*r+n,l*o-i*a,l*a+i*o,0,l*o+i*a,c*o+n,c*a-i*r,0,l*a-i*o,c*a+i*r,s*a*a+n,0,0,0,0,1),this}makeScale(t,e,n){return this.set(t,0,0,0,0,e,0,0,0,0,n,0,0,0,0,1),this}makeShear(t,e,n,i,s,r){return this.set(1,n,s,0,t,1,r,0,e,i,1,0,0,0,0,1),this}compose(t,e,n){const i=this.elements,s=e._x,r=e._y,o=e._z,a=e._w,l=s+s,c=r+r,h=o+o,u=s*l,d=s*c,p=s*h,_=r*c,m=r*h,f=o*h,g=a*l,v=a*c,y=a*h,x=n.x,b=n.y,w=n.z;return i[0]=(1-(_+f))*x,i[1]=(d+y)*x,i[2]=(p-v)*x,i[3]=0,i[4]=(d-y)*b,i[5]=(1-(u+f))*b,i[6]=(m+g)*b,i[7]=0,i[8]=(p+v)*w,i[9]=(m-g)*w,i[10]=(1-(u+_))*w,i[11]=0,i[12]=t.x,i[13]=t.y,i[14]=t.z,i[15]=1,this}decompose(t,e,n){const i=this.elements;let s=$b.set(i[0],i[1],i[2]).length();const r=$b.set(i[4],i[5],i[6]).length(),o=$b.set(i[8],i[9],i[10]).length();this.determinant()<0&&(s=-s),t.x=i[12],t.y=i[13],t.z=i[14],Jb.copy(this);const a=1/s,l=1/r,c=1/o;return Jb.elements[0]*=a,Jb.elements[1]*=a,Jb.elements[2]*=a,Jb.elements[4]*=l,Jb.elements[5]*=l,Jb.elements[6]*=l,Jb.elements[8]*=c,Jb.elements[9]*=c,Jb.elements[10]*=c,e.setFromRotationMatrix(Jb),n.x=s,n.y=r,n.z=o,this}makePerspective(t,e,n,i,s,r){void 0===r&&console.warn(\\\\\\\"THREE.Matrix4: .makePerspective() has been redefined and has a new signature. Please check the docs.\\\\\\\");const o=this.elements,a=2*s/(e-t),l=2*s/(n-i),c=(e+t)/(e-t),h=(n+i)/(n-i),u=-(r+s)/(r-s),d=-2*r*s/(r-s);return o[0]=a,o[4]=0,o[8]=c,o[12]=0,o[1]=0,o[5]=l,o[9]=h,o[13]=0,o[2]=0,o[6]=0,o[10]=u,o[14]=d,o[3]=0,o[7]=0,o[11]=-1,o[15]=0,this}makeOrthographic(t,e,n,i,s,r){const o=this.elements,a=1/(e-t),l=1/(n-i),c=1/(r-s),h=(e+t)*a,u=(n+i)*l,d=(r+s)*c;return o[0]=2*a,o[4]=0,o[8]=0,o[12]=-h,o[1]=0,o[5]=2*l,o[9]=0,o[13]=-u,o[2]=0,o[6]=0,o[10]=-2*c,o[14]=-d,o[3]=0,o[7]=0,o[11]=0,o[15]=1,this}equals(t){const e=this.elements,n=t.elements;for(let t=0;t<16;t++)if(e[t]!==n[t])return!1;return!0}fromArray(t,e=0){for(let n=0;n<16;n++)this.elements[n]=t[n+e];return this}toArray(t=[],e=0){const n=this.elements;return t[e]=n[0],t[e+1]=n[1],t[e+2]=n[2],t[e+3]=n[3],t[e+4]=n[4],t[e+5]=n[5],t[e+6]=n[6],t[e+7]=n[7],t[e+8]=n[8],t[e+9]=n[9],t[e+10]=n[10],t[e+11]=n[11],t[e+12]=n[12],t[e+13]=n[13],t[e+14]=n[14],t[e+15]=n[15],t}}Yb.prototype.isMatrix4=!0;const $b=new gb,Jb=new Yb,Zb=new gb(0,0,0),Qb=new gb(1,1,1),Kb=new gb,tw=new gb,ew=new gb,nw=new Yb,iw=new fb;class sw{constructor(t=0,e=0,n=0,i=sw.DefaultOrder){this._x=t,this._y=e,this._z=n,this._order=i}get x(){return this._x}set x(t){this._x=t,this._onChangeCallback()}get y(){return this._y}set y(t){this._y=t,this._onChangeCallback()}get z(){return this._z}set z(t){this._z=t,this._onChangeCallback()}get order(){return this._order}set order(t){this._order=t,this._onChangeCallback()}set(t,e,n,i=this._order){return this._x=t,this._y=e,this._z=n,this._order=i,this._onChangeCallback(),this}clone(){return new this.constructor(this._x,this._y,this._z,this._order)}copy(t){return this._x=t._x,this._y=t._y,this._z=t._z,this._order=t._order,this._onChangeCallback(),this}setFromRotationMatrix(t,e=this._order,n=!0){const i=t.elements,s=i[0],r=i[4],o=i[8],a=i[1],l=i[5],c=i[9],h=i[2],u=i[6],d=i[10];switch(e){case\\\\\\\"XYZ\\\\\\\":this._y=Math.asin(Zx(o,-1,1)),Math.abs(o)<.9999999?(this._x=Math.atan2(-c,d),this._z=Math.atan2(-r,s)):(this._x=Math.atan2(u,l),this._z=0);break;case\\\\\\\"YXZ\\\\\\\":this._x=Math.asin(-Zx(c,-1,1)),Math.abs(c)<.9999999?(this._y=Math.atan2(o,d),this._z=Math.atan2(a,l)):(this._y=Math.atan2(-h,s),this._z=0);break;case\\\\\\\"ZXY\\\\\\\":this._x=Math.asin(Zx(u,-1,1)),Math.abs(u)<.9999999?(this._y=Math.atan2(-h,d),this._z=Math.atan2(-r,l)):(this._y=0,this._z=Math.atan2(a,s));break;case\\\\\\\"ZYX\\\\\\\":this._y=Math.asin(-Zx(h,-1,1)),Math.abs(h)<.9999999?(this._x=Math.atan2(u,d),this._z=Math.atan2(a,s)):(this._x=0,this._z=Math.atan2(-r,l));break;case\\\\\\\"YZX\\\\\\\":this._z=Math.asin(Zx(a,-1,1)),Math.abs(a)<.9999999?(this._x=Math.atan2(-c,l),this._y=Math.atan2(-h,s)):(this._x=0,this._y=Math.atan2(o,d));break;case\\\\\\\"XZY\\\\\\\":this._z=Math.asin(-Zx(r,-1,1)),Math.abs(r)<.9999999?(this._x=Math.atan2(u,l),this._y=Math.atan2(o,s)):(this._x=Math.atan2(-c,d),this._y=0);break;default:console.warn(\\\\\\\"THREE.Euler: .setFromRotationMatrix() encountered an unknown order: \\\\\\\"+e)}return this._order=e,!0===n&&this._onChangeCallback(),this}setFromQuaternion(t,e,n){return nw.makeRotationFromQuaternion(t),this.setFromRotationMatrix(nw,e,n)}setFromVector3(t,e=this._order){return this.set(t.x,t.y,t.z,e)}reorder(t){return iw.setFromEuler(this),this.setFromQuaternion(iw,t)}equals(t){return t._x===this._x&&t._y===this._y&&t._z===this._z&&t._order===this._order}fromArray(t){return this._x=t[0],this._y=t[1],this._z=t[2],void 0!==t[3]&&(this._order=t[3]),this._onChangeCallback(),this}toArray(t=[],e=0){return t[e]=this._x,t[e+1]=this._y,t[e+2]=this._z,t[e+3]=this._order,t}toVector3(t){return t?t.set(this._x,this._y,this._z):new gb(this._x,this._y,this._z)}_onChange(t){return this._onChangeCallback=t,this}_onChangeCallback(){}}sw.prototype.isEuler=!0,sw.DefaultOrder=\\\\\\\"XYZ\\\\\\\",sw.RotationOrders=[\\\\\\\"XYZ\\\\\\\",\\\\\\\"YZX\\\\\\\",\\\\\\\"ZXY\\\\\\\",\\\\\\\"XZY\\\\\\\",\\\\\\\"YXZ\\\\\\\",\\\\\\\"ZYX\\\\\\\"];class rw{constructor(){this.mask=1}set(t){this.mask=1<1){for(let t=0;t1){for(let t=0;t0){i.children=[];for(let e=0;e0){i.animations=[];for(let e=0;e0&&(n.geometries=e),i.length>0&&(n.materials=i),s.length>0&&(n.textures=s),o.length>0&&(n.images=o),a.length>0&&(n.shapes=a),l.length>0&&(n.skeletons=l),c.length>0&&(n.animations=c)}return n.object=i,n;function r(t){const e=[];for(const n in t){const i=t[n];delete i.metadata,e.push(i)}return e}}clone(t){return(new this.constructor).copy(this,t)}copy(t,e=!0){if(this.name=t.name,this.up.copy(t.up),this.position.copy(t.position),this.rotation.order=t.rotation.order,this.quaternion.copy(t.quaternion),this.scale.copy(t.scale),this.matrix.copy(t.matrix),this.matrixWorld.copy(t.matrixWorld),this.matrixAutoUpdate=t.matrixAutoUpdate,this.matrixWorldNeedsUpdate=t.matrixWorldNeedsUpdate,this.layers.mask=t.layers.mask,this.visible=t.visible,this.castShadow=t.castShadow,this.receiveShadow=t.receiveShadow,this.frustumCulled=t.frustumCulled,this.renderOrder=t.renderOrder,this.userData=JSON.parse(JSON.stringify(t.userData)),!0===e)for(let e=0;e0?i.multiplyScalar(1/Math.sqrt(s)):i.set(0,0,0)}static getBarycoord(t,e,n,i,s){xw.subVectors(i,e),bw.subVectors(n,e),ww.subVectors(t,e);const r=xw.dot(xw),o=xw.dot(bw),a=xw.dot(ww),l=bw.dot(bw),c=bw.dot(ww),h=r*l-o*o;if(0===h)return s.set(-2,-1,-1);const u=1/h,d=(l*a-o*c)*u,p=(r*c-o*a)*u;return s.set(1-d-p,p,d)}static containsPoint(t,e,n,i){return this.getBarycoord(t,e,n,i,Tw),Tw.x>=0&&Tw.y>=0&&Tw.x+Tw.y<=1}static getUV(t,e,n,i,s,r,o,a){return this.getBarycoord(t,e,n,i,Tw),a.set(0,0),a.addScaledVector(s,Tw.x),a.addScaledVector(r,Tw.y),a.addScaledVector(o,Tw.z),a}static isFrontFacing(t,e,n,i){return xw.subVectors(n,e),bw.subVectors(t,e),xw.cross(bw).dot(i)<0}set(t,e,n){return this.a.copy(t),this.b.copy(e),this.c.copy(n),this}setFromPointsAndIndices(t,e,n,i){return this.a.copy(t[e]),this.b.copy(t[n]),this.c.copy(t[i]),this}setFromAttributeAndIndices(t,e,n,i){return this.a.fromBufferAttribute(t,e),this.b.fromBufferAttribute(t,n),this.c.fromBufferAttribute(t,i),this}clone(){return(new this.constructor).copy(this)}copy(t){return this.a.copy(t.a),this.b.copy(t.b),this.c.copy(t.c),this}getArea(){return xw.subVectors(this.c,this.b),bw.subVectors(this.a,this.b),.5*xw.cross(bw).length()}getMidpoint(t){return t.addVectors(this.a,this.b).add(this.c).multiplyScalar(1/3)}getNormal(t){return Lw.getNormal(this.a,this.b,this.c,t)}getPlane(t){return t.setFromCoplanarPoints(this.a,this.b,this.c)}getBarycoord(t,e){return Lw.getBarycoord(t,this.a,this.b,this.c,e)}getUV(t,e,n,i,s){return Lw.getUV(t,this.a,this.b,this.c,e,n,i,s)}containsPoint(t){return Lw.containsPoint(t,this.a,this.b,this.c)}isFrontFacing(t){return Lw.isFrontFacing(this.a,this.b,this.c,t)}intersectsBox(t){return t.intersectsTriangle(this)}closestPointToPoint(t,e){const n=this.a,i=this.b,s=this.c;let r,o;Aw.subVectors(i,n),Mw.subVectors(s,n),Sw.subVectors(t,n);const a=Aw.dot(Sw),l=Mw.dot(Sw);if(a<=0&&l<=0)return e.copy(n);Cw.subVectors(t,i);const c=Aw.dot(Cw),h=Mw.dot(Cw);if(c>=0&&h<=c)return e.copy(i);const u=a*h-c*l;if(u<=0&&a>=0&&c<=0)return r=a/(a-c),e.copy(n).addScaledVector(Aw,r);Nw.subVectors(t,s);const d=Aw.dot(Nw),p=Mw.dot(Nw);if(p>=0&&d<=p)return e.copy(s);const _=d*l-a*p;if(_<=0&&l>=0&&p<=0)return o=l/(l-p),e.copy(n).addScaledVector(Mw,o);const m=c*p-d*h;if(m<=0&&h-c>=0&&d-p>=0)return Ew.subVectors(s,i),o=(h-c)/(h-c+(d-p)),e.copy(i).addScaledVector(Ew,o);const f=1/(m+_+u);return r=_*f,o=u*f,e.copy(n).addScaledVector(Aw,r).addScaledVector(Mw,o)}equals(t){return t.a.equals(this.a)&&t.b.equals(this.b)&&t.c.equals(this.c)}}let Ow=0;class Pw extends jx{constructor(){super(),Object.defineProperty(this,\\\\\\\"id\\\\\\\",{value:Ow++}),this.uuid=Jx(),this.name=\\\\\\\"\\\\\\\",this.type=\\\\\\\"Material\\\\\\\",this.fog=!0,this.blending=1,this.side=0,this.vertexColors=!1,this.opacity=1,this.format=Ex,this.transparent=!1,this.blendSrc=204,this.blendDst=205,this.blendEquation=ix,this.blendSrcAlpha=null,this.blendDstAlpha=null,this.blendEquationAlpha=null,this.depthFunc=3,this.depthTest=!0,this.depthWrite=!0,this.stencilWriteMask=255,this.stencilFunc=519,this.stencilRef=0,this.stencilFuncMask=255,this.stencilFail=Ux,this.stencilZFail=Ux,this.stencilZPass=Ux,this.stencilWrite=!1,this.clippingPlanes=null,this.clipIntersection=!1,this.clipShadows=!1,this.shadowSide=null,this.colorWrite=!0,this.precision=null,this.polygonOffset=!1,this.polygonOffsetFactor=0,this.polygonOffsetUnits=0,this.dithering=!1,this.alphaToCoverage=!1,this.premultipliedAlpha=!1,this.visible=!0,this.toneMapped=!0,this.userData={},this.version=0,this._alphaTest=0}get alphaTest(){return this._alphaTest}set alphaTest(t){this._alphaTest>0!=t>0&&this.version++,this._alphaTest=t}onBuild(){}onBeforeRender(){}onBeforeCompile(){}customProgramCacheKey(){return this.onBeforeCompile.toString()}setValues(t){if(void 0!==t)for(const e in t){const n=t[e];if(void 0===n){console.warn(\\\\\\\"THREE.Material: '\\\\\\\"+e+\\\\\\\"' parameter is undefined.\\\\\\\");continue}if(\\\\\\\"shading\\\\\\\"===e){console.warn(\\\\\\\"THREE.\\\\\\\"+this.type+\\\\\\\": .shading has been removed. Use the boolean .flatShading instead.\\\\\\\"),this.flatShading=1===n;continue}const i=this[e];void 0!==i?i&&i.isColor?i.set(n):i&&i.isVector3&&n&&n.isVector3?i.copy(n):this[e]=n:console.warn(\\\\\\\"THREE.\\\\\\\"+this.type+\\\\\\\": '\\\\\\\"+e+\\\\\\\"' is not a property of this material.\\\\\\\")}}toJSON(t){const e=void 0===t||\\\\\\\"string\\\\\\\"==typeof t;e&&(t={textures:{},images:{}});const n={metadata:{version:4.5,type:\\\\\\\"Material\\\\\\\",generator:\\\\\\\"Material.toJSON\\\\\\\"}};function i(t){const e=[];for(const n in t){const i=t[n];delete i.metadata,e.push(i)}return e}if(n.uuid=this.uuid,n.type=this.type,\\\\\\\"\\\\\\\"!==this.name&&(n.name=this.name),this.color&&this.color.isColor&&(n.color=this.color.getHex()),void 0!==this.roughness&&(n.roughness=this.roughness),void 0!==this.metalness&&(n.metalness=this.metalness),void 0!==this.sheen&&(n.sheen=this.sheen),this.sheenTint&&this.sheenTint.isColor&&(n.sheenTint=this.sheenTint.getHex()),void 0!==this.sheenRoughness&&(n.sheenRoughness=this.sheenRoughness),this.emissive&&this.emissive.isColor&&(n.emissive=this.emissive.getHex()),this.emissiveIntensity&&1!==this.emissiveIntensity&&(n.emissiveIntensity=this.emissiveIntensity),this.specular&&this.specular.isColor&&(n.specular=this.specular.getHex()),void 0!==this.specularIntensity&&(n.specularIntensity=this.specularIntensity),this.specularTint&&this.specularTint.isColor&&(n.specularTint=this.specularTint.getHex()),void 0!==this.shininess&&(n.shininess=this.shininess),void 0!==this.clearcoat&&(n.clearcoat=this.clearcoat),void 0!==this.clearcoatRoughness&&(n.clearcoatRoughness=this.clearcoatRoughness),this.clearcoatMap&&this.clearcoatMap.isTexture&&(n.clearcoatMap=this.clearcoatMap.toJSON(t).uuid),this.clearcoatRoughnessMap&&this.clearcoatRoughnessMap.isTexture&&(n.clearcoatRoughnessMap=this.clearcoatRoughnessMap.toJSON(t).uuid),this.clearcoatNormalMap&&this.clearcoatNormalMap.isTexture&&(n.clearcoatNormalMap=this.clearcoatNormalMap.toJSON(t).uuid,n.clearcoatNormalScale=this.clearcoatNormalScale.toArray()),this.map&&this.map.isTexture&&(n.map=this.map.toJSON(t).uuid),this.matcap&&this.matcap.isTexture&&(n.matcap=this.matcap.toJSON(t).uuid),this.alphaMap&&this.alphaMap.isTexture&&(n.alphaMap=this.alphaMap.toJSON(t).uuid),this.lightMap&&this.lightMap.isTexture&&(n.lightMap=this.lightMap.toJSON(t).uuid,n.lightMapIntensity=this.lightMapIntensity),this.aoMap&&this.aoMap.isTexture&&(n.aoMap=this.aoMap.toJSON(t).uuid,n.aoMapIntensity=this.aoMapIntensity),this.bumpMap&&this.bumpMap.isTexture&&(n.bumpMap=this.bumpMap.toJSON(t).uuid,n.bumpScale=this.bumpScale),this.normalMap&&this.normalMap.isTexture&&(n.normalMap=this.normalMap.toJSON(t).uuid,n.normalMapType=this.normalMapType,n.normalScale=this.normalScale.toArray()),this.displacementMap&&this.displacementMap.isTexture&&(n.displacementMap=this.displacementMap.toJSON(t).uuid,n.displacementScale=this.displacementScale,n.displacementBias=this.displacementBias),this.roughnessMap&&this.roughnessMap.isTexture&&(n.roughnessMap=this.roughnessMap.toJSON(t).uuid),this.metalnessMap&&this.metalnessMap.isTexture&&(n.metalnessMap=this.metalnessMap.toJSON(t).uuid),this.emissiveMap&&this.emissiveMap.isTexture&&(n.emissiveMap=this.emissiveMap.toJSON(t).uuid),this.specularMap&&this.specularMap.isTexture&&(n.specularMap=this.specularMap.toJSON(t).uuid),this.specularIntensityMap&&this.specularIntensityMap.isTexture&&(n.specularIntensityMap=this.specularIntensityMap.toJSON(t).uuid),this.specularTintMap&&this.specularTintMap.isTexture&&(n.specularTintMap=this.specularTintMap.toJSON(t).uuid),this.envMap&&this.envMap.isTexture&&(n.envMap=this.envMap.toJSON(t).uuid,void 0!==this.combine&&(n.combine=this.combine)),void 0!==this.envMapIntensity&&(n.envMapIntensity=this.envMapIntensity),void 0!==this.reflectivity&&(n.reflectivity=this.reflectivity),void 0!==this.refractionRatio&&(n.refractionRatio=this.refractionRatio),this.gradientMap&&this.gradientMap.isTexture&&(n.gradientMap=this.gradientMap.toJSON(t).uuid),void 0!==this.transmission&&(n.transmission=this.transmission),this.transmissionMap&&this.transmissionMap.isTexture&&(n.transmissionMap=this.transmissionMap.toJSON(t).uuid),void 0!==this.thickness&&(n.thickness=this.thickness),this.thicknessMap&&this.thicknessMap.isTexture&&(n.thicknessMap=this.thicknessMap.toJSON(t).uuid),void 0!==this.attenuationDistance&&(n.attenuationDistance=this.attenuationDistance),void 0!==this.attenuationTint&&(n.attenuationTint=this.attenuationTint.getHex()),void 0!==this.size&&(n.size=this.size),null!==this.shadowSide&&(n.shadowSide=this.shadowSide),void 0!==this.sizeAttenuation&&(n.sizeAttenuation=this.sizeAttenuation),1!==this.blending&&(n.blending=this.blending),0!==this.side&&(n.side=this.side),this.vertexColors&&(n.vertexColors=!0),this.opacity<1&&(n.opacity=this.opacity),this.format!==Ex&&(n.format=this.format),!0===this.transparent&&(n.transparent=this.transparent),n.depthFunc=this.depthFunc,n.depthTest=this.depthTest,n.depthWrite=this.depthWrite,n.colorWrite=this.colorWrite,n.stencilWrite=this.stencilWrite,n.stencilWriteMask=this.stencilWriteMask,n.stencilFunc=this.stencilFunc,n.stencilRef=this.stencilRef,n.stencilFuncMask=this.stencilFuncMask,n.stencilFail=this.stencilFail,n.stencilZFail=this.stencilZFail,n.stencilZPass=this.stencilZPass,this.rotation&&0!==this.rotation&&(n.rotation=this.rotation),!0===this.polygonOffset&&(n.polygonOffset=!0),0!==this.polygonOffsetFactor&&(n.polygonOffsetFactor=this.polygonOffsetFactor),0!==this.polygonOffsetUnits&&(n.polygonOffsetUnits=this.polygonOffsetUnits),this.linewidth&&1!==this.linewidth&&(n.linewidth=this.linewidth),void 0!==this.dashSize&&(n.dashSize=this.dashSize),void 0!==this.gapSize&&(n.gapSize=this.gapSize),void 0!==this.scale&&(n.scale=this.scale),!0===this.dithering&&(n.dithering=!0),this.alphaTest>0&&(n.alphaTest=this.alphaTest),!0===this.alphaToCoverage&&(n.alphaToCoverage=this.alphaToCoverage),!0===this.premultipliedAlpha&&(n.premultipliedAlpha=this.premultipliedAlpha),!0===this.wireframe&&(n.wireframe=this.wireframe),this.wireframeLinewidth>1&&(n.wireframeLinewidth=this.wireframeLinewidth),\\\\\\\"round\\\\\\\"!==this.wireframeLinecap&&(n.wireframeLinecap=this.wireframeLinecap),\\\\\\\"round\\\\\\\"!==this.wireframeLinejoin&&(n.wireframeLinejoin=this.wireframeLinejoin),!0===this.flatShading&&(n.flatShading=this.flatShading),!1===this.visible&&(n.visible=!1),!1===this.toneMapped&&(n.toneMapped=!1),\\\\\\\"{}\\\\\\\"!==JSON.stringify(this.userData)&&(n.userData=this.userData),e){const e=i(t.textures),s=i(t.images);e.length>0&&(n.textures=e),s.length>0&&(n.images=s)}return n}clone(){return(new this.constructor).copy(this)}copy(t){this.name=t.name,this.fog=t.fog,this.blending=t.blending,this.side=t.side,this.vertexColors=t.vertexColors,this.opacity=t.opacity,this.format=t.format,this.transparent=t.transparent,this.blendSrc=t.blendSrc,this.blendDst=t.blendDst,this.blendEquation=t.blendEquation,this.blendSrcAlpha=t.blendSrcAlpha,this.blendDstAlpha=t.blendDstAlpha,this.blendEquationAlpha=t.blendEquationAlpha,this.depthFunc=t.depthFunc,this.depthTest=t.depthTest,this.depthWrite=t.depthWrite,this.stencilWriteMask=t.stencilWriteMask,this.stencilFunc=t.stencilFunc,this.stencilRef=t.stencilRef,this.stencilFuncMask=t.stencilFuncMask,this.stencilFail=t.stencilFail,this.stencilZFail=t.stencilZFail,this.stencilZPass=t.stencilZPass,this.stencilWrite=t.stencilWrite;const e=t.clippingPlanes;let n=null;if(null!==e){const t=e.length;n=new Array(t);for(let i=0;i!==t;++i)n[i]=e[i].clone()}return this.clippingPlanes=n,this.clipIntersection=t.clipIntersection,this.clipShadows=t.clipShadows,this.shadowSide=t.shadowSide,this.colorWrite=t.colorWrite,this.precision=t.precision,this.polygonOffset=t.polygonOffset,this.polygonOffsetFactor=t.polygonOffsetFactor,this.polygonOffsetUnits=t.polygonOffsetUnits,this.dithering=t.dithering,this.alphaTest=t.alphaTest,this.alphaToCoverage=t.alphaToCoverage,this.premultipliedAlpha=t.premultipliedAlpha,this.visible=t.visible,this.toneMapped=t.toneMapped,this.userData=JSON.parse(JSON.stringify(t.userData)),this}dispose(){this.dispatchEvent({type:\\\\\\\"dispose\\\\\\\"})}set needsUpdate(t){!0===t&&this.version++}}Pw.prototype.isMaterial=!0;const Rw={aliceblue:15792383,antiquewhite:16444375,aqua:65535,aquamarine:8388564,azure:15794175,beige:16119260,bisque:16770244,black:0,blanchedalmond:16772045,blue:255,blueviolet:9055202,brown:10824234,burlywood:14596231,cadetblue:6266528,chartreuse:8388352,chocolate:13789470,coral:16744272,cornflowerblue:6591981,cornsilk:16775388,crimson:14423100,cyan:65535,darkblue:139,darkcyan:35723,darkgoldenrod:12092939,darkgray:11119017,darkgreen:25600,darkgrey:11119017,darkkhaki:12433259,darkmagenta:9109643,darkolivegreen:5597999,darkorange:16747520,darkorchid:10040012,darkred:9109504,darksalmon:15308410,darkseagreen:9419919,darkslateblue:4734347,darkslategray:3100495,darkslategrey:3100495,darkturquoise:52945,darkviolet:9699539,deeppink:16716947,deepskyblue:49151,dimgray:6908265,dimgrey:6908265,dodgerblue:2003199,firebrick:11674146,floralwhite:16775920,forestgreen:2263842,fuchsia:16711935,gainsboro:14474460,ghostwhite:16316671,gold:16766720,goldenrod:14329120,gray:8421504,green:32768,greenyellow:11403055,grey:8421504,honeydew:15794160,hotpink:16738740,indianred:13458524,indigo:4915330,ivory:16777200,khaki:15787660,lavender:15132410,lavenderblush:16773365,lawngreen:8190976,lemonchiffon:16775885,lightblue:11393254,lightcoral:15761536,lightcyan:14745599,lightgoldenrodyellow:16448210,lightgray:13882323,lightgreen:9498256,lightgrey:13882323,lightpink:16758465,lightsalmon:16752762,lightseagreen:2142890,lightskyblue:8900346,lightslategray:7833753,lightslategrey:7833753,lightsteelblue:11584734,lightyellow:16777184,lime:65280,limegreen:3329330,linen:16445670,magenta:16711935,maroon:8388608,mediumaquamarine:6737322,mediumblue:205,mediumorchid:12211667,mediumpurple:9662683,mediumseagreen:3978097,mediumslateblue:8087790,mediumspringgreen:64154,mediumturquoise:4772300,mediumvioletred:13047173,midnightblue:1644912,mintcream:16121850,mistyrose:16770273,moccasin:16770229,navajowhite:16768685,navy:128,oldlace:16643558,olive:8421376,olivedrab:7048739,orange:16753920,orangered:16729344,orchid:14315734,palegoldenrod:15657130,palegreen:10025880,paleturquoise:11529966,palevioletred:14381203,papayawhip:16773077,peachpuff:16767673,peru:13468991,pink:16761035,plum:14524637,powderblue:11591910,purple:8388736,rebeccapurple:6697881,red:16711680,rosybrown:12357519,royalblue:4286945,saddlebrown:9127187,salmon:16416882,sandybrown:16032864,seagreen:3050327,seashell:16774638,sienna:10506797,silver:12632256,skyblue:8900331,slateblue:6970061,slategray:7372944,slategrey:7372944,snow:16775930,springgreen:65407,steelblue:4620980,tan:13808780,teal:32896,thistle:14204888,tomato:16737095,turquoise:4251856,violet:15631086,wheat:16113331,white:16777215,whitesmoke:16119285,yellow:16776960,yellowgreen:10145074},Iw={h:0,s:0,l:0},Fw={h:0,s:0,l:0};function Dw(t,e,n){return n<0&&(n+=1),n>1&&(n-=1),n<1/6?t+6*(e-t)*n:n<.5?e:n<2/3?t+6*(e-t)*(2/3-n):t}function Bw(t){return t<.04045?.0773993808*t:Math.pow(.9478672986*t+.0521327014,2.4)}function zw(t){return t<.0031308?12.92*t:1.055*Math.pow(t,.41666)-.055}class kw{constructor(t,e,n){return void 0===e&&void 0===n?this.set(t):this.setRGB(t,e,n)}set(t){return t&&t.isColor?this.copy(t):\\\\\\\"number\\\\\\\"==typeof t?this.setHex(t):\\\\\\\"string\\\\\\\"==typeof t&&this.setStyle(t),this}setScalar(t){return this.r=t,this.g=t,this.b=t,this}setHex(t){return t=Math.floor(t),this.r=(t>>16&255)/255,this.g=(t>>8&255)/255,this.b=(255&t)/255,this}setRGB(t,e,n){return this.r=t,this.g=e,this.b=n,this}setHSL(t,e,n){if(t=Qx(t,1),e=Zx(e,0,1),n=Zx(n,0,1),0===e)this.r=this.g=this.b=n;else{const i=n<=.5?n*(1+e):n+e-n*e,s=2*n-i;this.r=Dw(s,i,t+1/3),this.g=Dw(s,i,t),this.b=Dw(s,i,t-1/3)}return this}setStyle(t){function e(e){void 0!==e&&parseFloat(e)<1&&console.warn(\\\\\\\"THREE.Color: Alpha component of \\\\\\\"+t+\\\\\\\" will be ignored.\\\\\\\")}let n;if(n=/^((?:rgb|hsl)a?)\\\\(([^\\\\)]*)\\\\)/.exec(t)){let t;const i=n[1],s=n[2];switch(i){case\\\\\\\"rgb\\\\\\\":case\\\\\\\"rgba\\\\\\\":if(t=/^\\\\s*(\\\\d+)\\\\s*,\\\\s*(\\\\d+)\\\\s*,\\\\s*(\\\\d+)\\\\s*(?:,\\\\s*(\\\\d*\\\\.?\\\\d+)\\\\s*)?$/.exec(s))return this.r=Math.min(255,parseInt(t[1],10))/255,this.g=Math.min(255,parseInt(t[2],10))/255,this.b=Math.min(255,parseInt(t[3],10))/255,e(t[4]),this;if(t=/^\\\\s*(\\\\d+)\\\\%\\\\s*,\\\\s*(\\\\d+)\\\\%\\\\s*,\\\\s*(\\\\d+)\\\\%\\\\s*(?:,\\\\s*(\\\\d*\\\\.?\\\\d+)\\\\s*)?$/.exec(s))return this.r=Math.min(100,parseInt(t[1],10))/100,this.g=Math.min(100,parseInt(t[2],10))/100,this.b=Math.min(100,parseInt(t[3],10))/100,e(t[4]),this;break;case\\\\\\\"hsl\\\\\\\":case\\\\\\\"hsla\\\\\\\":if(t=/^\\\\s*(\\\\d*\\\\.?\\\\d+)\\\\s*,\\\\s*(\\\\d+)\\\\%\\\\s*,\\\\s*(\\\\d+)\\\\%\\\\s*(?:,\\\\s*(\\\\d*\\\\.?\\\\d+)\\\\s*)?$/.exec(s)){const n=parseFloat(t[1])/360,i=parseInt(t[2],10)/100,s=parseInt(t[3],10)/100;return e(t[4]),this.setHSL(n,i,s)}}}else if(n=/^\\\\#([A-Fa-f\\\\d]+)$/.exec(t)){const t=n[1],e=t.length;if(3===e)return this.r=parseInt(t.charAt(0)+t.charAt(0),16)/255,this.g=parseInt(t.charAt(1)+t.charAt(1),16)/255,this.b=parseInt(t.charAt(2)+t.charAt(2),16)/255,this;if(6===e)return this.r=parseInt(t.charAt(0)+t.charAt(1),16)/255,this.g=parseInt(t.charAt(2)+t.charAt(3),16)/255,this.b=parseInt(t.charAt(4)+t.charAt(5),16)/255,this}return t&&t.length>0?this.setColorName(t):this}setColorName(t){const e=Rw[t.toLowerCase()];return void 0!==e?this.setHex(e):console.warn(\\\\\\\"THREE.Color: Unknown color \\\\\\\"+t),this}clone(){return new this.constructor(this.r,this.g,this.b)}copy(t){return this.r=t.r,this.g=t.g,this.b=t.b,this}copyGammaToLinear(t,e=2){return this.r=Math.pow(t.r,e),this.g=Math.pow(t.g,e),this.b=Math.pow(t.b,e),this}copyLinearToGamma(t,e=2){const n=e>0?1/e:1;return this.r=Math.pow(t.r,n),this.g=Math.pow(t.g,n),this.b=Math.pow(t.b,n),this}convertGammaToLinear(t){return this.copyGammaToLinear(this,t),this}convertLinearToGamma(t){return this.copyLinearToGamma(this,t),this}copySRGBToLinear(t){return this.r=Bw(t.r),this.g=Bw(t.g),this.b=Bw(t.b),this}copyLinearToSRGB(t){return this.r=zw(t.r),this.g=zw(t.g),this.b=zw(t.b),this}convertSRGBToLinear(){return this.copySRGBToLinear(this),this}convertLinearToSRGB(){return this.copyLinearToSRGB(this),this}getHex(){return 255*this.r<<16^255*this.g<<8^255*this.b<<0}getHexString(){return(\\\\\\\"000000\\\\\\\"+this.getHex().toString(16)).slice(-6)}getHSL(t){const e=this.r,n=this.g,i=this.b,s=Math.max(e,n,i),r=Math.min(e,n,i);let o,a;const l=(r+s)/2;if(r===s)o=0,a=0;else{const t=s-r;switch(a=l<=.5?t/(s+r):t/(2-s-r),s){case e:o=(n-i)/t+(n65535?Ww:jw)(t,1):this.index=t,this}getAttribute(t){return this.attributes[t]}setAttribute(t,e){return this.attributes[t]=e,this}deleteAttribute(t){return delete this.attributes[t],this}hasAttribute(t){return void 0!==this.attributes[t]}addGroup(t,e,n=0){this.groups.push({start:t,count:e,materialIndex:n})}clearGroups(){this.groups=[]}setDrawRange(t,e){this.drawRange.start=t,this.drawRange.count=e}applyMatrix4(t){const e=this.attributes.position;void 0!==e&&(e.applyMatrix4(t),e.needsUpdate=!0);const n=this.attributes.normal;if(void 0!==n){const e=(new rb).getNormalMatrix(t);n.applyNormalMatrix(e),n.needsUpdate=!0}const i=this.attributes.tangent;return void 0!==i&&(i.transformDirection(t),i.needsUpdate=!0),null!==this.boundingBox&&this.computeBoundingBox(),null!==this.boundingSphere&&this.computeBoundingSphere(),this}applyQuaternion(t){return Yw.makeRotationFromQuaternion(t),this.applyMatrix4(Yw),this}rotateX(t){return Yw.makeRotationX(t),this.applyMatrix4(Yw),this}rotateY(t){return Yw.makeRotationY(t),this.applyMatrix4(Yw),this}rotateZ(t){return Yw.makeRotationZ(t),this.applyMatrix4(Yw),this}translate(t,e,n){return Yw.makeTranslation(t,e,n),this.applyMatrix4(Yw),this}scale(t,e,n){return Yw.makeScale(t,e,n),this.applyMatrix4(Yw),this}lookAt(t){return $w.lookAt(t),$w.updateMatrix(),this.applyMatrix4($w.matrix),this}center(){return this.computeBoundingBox(),this.boundingBox.getCenter(Jw).negate(),this.translate(Jw.x,Jw.y,Jw.z),this}setFromPoints(t){const e=[];for(let n=0,i=t.length;n0&&(t.userData=this.userData),void 0!==this.parameters){const e=this.parameters;for(const n in e)void 0!==e[n]&&(t[n]=e[n]);return t}t.data={attributes:{}};const e=this.index;null!==e&&(t.data.index={type:e.array.constructor.name,array:Array.prototype.slice.call(e.array)});const n=this.attributes;for(const e in n){const i=n[e];t.data.attributes[e]=i.toJSON(t.data)}const i={};let s=!1;for(const e in this.morphAttributes){const n=this.morphAttributes[e],r=[];for(let e=0,i=n.length;e0&&(i[e]=r,s=!0)}s&&(t.data.morphAttributes=i,t.data.morphTargetsRelative=this.morphTargetsRelative);const r=this.groups;r.length>0&&(t.data.groups=JSON.parse(JSON.stringify(r)));const o=this.boundingSphere;return null!==o&&(t.data.boundingSphere={center:o.center.toArray(),radius:o.radius}),t}clone(){return(new this.constructor).copy(this)}copy(t){this.index=null,this.attributes={},this.morphAttributes={},this.groups=[],this.boundingBox=null,this.boundingSphere=null;const e={};this.name=t.name;const n=t.index;null!==n&&this.setIndex(n.clone(e));const i=t.attributes;for(const t in i){const n=i[t];this.setAttribute(t,n.clone(e))}const s=t.morphAttributes;for(const t in s){const n=[],i=s[t];for(let t=0,s=i.length;t0){const t=e[n[0]];if(void 0!==t){this.morphTargetInfluences=[],this.morphTargetDictionary={};for(let e=0,n=t.length;e0&&console.error(\\\\\\\"THREE.Mesh.updateMorphTargets() no longer supports THREE.Geometry. Use THREE.BufferGeometry instead.\\\\\\\")}}raycast(t,e){const n=this.geometry,i=this.material,s=this.matrixWorld;if(void 0===i)return;if(null===n.boundingSphere&&n.computeBoundingSphere(),iT.copy(n.boundingSphere),iT.applyMatrix4(s),!1===t.ray.intersectsSphere(iT))return;if(eT.copy(s).invert(),nT.copy(t.ray).applyMatrix4(eT),null!==n.boundingBox&&!1===nT.intersectsBox(n.boundingBox))return;let r;if(n.isBufferGeometry){const s=n.index,o=n.attributes.position,a=n.morphAttributes.position,l=n.morphTargetsRelative,c=n.attributes.uv,h=n.attributes.uv2,u=n.groups,d=n.drawRange;if(null!==s)if(Array.isArray(i))for(let n=0,p=u.length;nn.far?null:{distance:c,point:gT.clone(),object:t}}(t,e,n,i,sT,rT,oT,fT);if(p){a&&(pT.fromBufferAttribute(a,c),_T.fromBufferAttribute(a,h),mT.fromBufferAttribute(a,u),p.uv=Lw.getUV(fT,sT,rT,oT,pT,_T,mT,new sb)),l&&(pT.fromBufferAttribute(l,c),_T.fromBufferAttribute(l,h),mT.fromBufferAttribute(l,u),p.uv2=Lw.getUV(fT,sT,rT,oT,pT,_T,mT,new sb));const t={a:c,b:h,c:u,normal:new gb,materialIndex:0};Lw.getNormal(sT,rT,oT,t.normal),p.face=t}return p}vT.prototype.isMesh=!0;class xT extends tT{constructor(t=1,e=1,n=1,i=1,s=1,r=1){super(),this.type=\\\\\\\"BoxGeometry\\\\\\\",this.parameters={width:t,height:e,depth:n,widthSegments:i,heightSegments:s,depthSegments:r};const o=this;i=Math.floor(i),s=Math.floor(s),r=Math.floor(r);const a=[],l=[],c=[],h=[];let u=0,d=0;function p(t,e,n,i,s,r,p,_,m,f,g){const v=r/m,y=p/f,x=r/2,b=p/2,w=_/2,T=m+1,A=f+1;let M=0,E=0;const S=new gb;for(let r=0;r0?1:-1,c.push(S.x,S.y,S.z),h.push(a/m),h.push(1-r/f),M+=1}}for(let t=0;t0&&(e.defines=this.defines),e.vertexShader=this.vertexShader,e.fragmentShader=this.fragmentShader;const n={};for(const t in this.extensions)!0===this.extensions[t]&&(n[t]=!0);return Object.keys(n).length>0&&(e.extensions=n),e}}AT.prototype.isShaderMaterial=!0;class MT extends yw{constructor(){super(),this.type=\\\\\\\"Camera\\\\\\\",this.matrixWorldInverse=new Yb,this.projectionMatrix=new Yb,this.projectionMatrixInverse=new Yb}copy(t,e){return super.copy(t,e),this.matrixWorldInverse.copy(t.matrixWorldInverse),this.projectionMatrix.copy(t.projectionMatrix),this.projectionMatrixInverse.copy(t.projectionMatrixInverse),this}getWorldDirection(t){this.updateWorldMatrix(!0,!1);const e=this.matrixWorld.elements;return t.set(-e[8],-e[9],-e[10]).normalize()}updateMatrixWorld(t){super.updateMatrixWorld(t),this.matrixWorldInverse.copy(this.matrixWorld).invert()}updateWorldMatrix(t,e){super.updateWorldMatrix(t,e),this.matrixWorldInverse.copy(this.matrixWorld).invert()}clone(){return(new this.constructor).copy(this)}}MT.prototype.isCamera=!0;class ET extends MT{constructor(t=50,e=1,n=.1,i=2e3){super(),this.type=\\\\\\\"PerspectiveCamera\\\\\\\",this.fov=t,this.zoom=1,this.near=n,this.far=i,this.focus=10,this.aspect=e,this.view=null,this.filmGauge=35,this.filmOffset=0,this.updateProjectionMatrix()}copy(t,e){return super.copy(t,e),this.fov=t.fov,this.zoom=t.zoom,this.near=t.near,this.far=t.far,this.focus=t.focus,this.aspect=t.aspect,this.view=null===t.view?null:Object.assign({},t.view),this.filmGauge=t.filmGauge,this.filmOffset=t.filmOffset,this}setFocalLength(t){const e=.5*this.getFilmHeight()/t;this.fov=2*Xx*Math.atan(e),this.updateProjectionMatrix()}getFocalLength(){const t=Math.tan(.5*qx*this.fov);return.5*this.getFilmHeight()/t}getEffectiveFOV(){return 2*Xx*Math.atan(Math.tan(.5*qx*this.fov)/this.zoom)}getFilmWidth(){return this.filmGauge*Math.min(this.aspect,1)}getFilmHeight(){return this.filmGauge/Math.max(this.aspect,1)}setViewOffset(t,e,n,i,s,r){this.aspect=t/e,null===this.view&&(this.view={enabled:!0,fullWidth:1,fullHeight:1,offsetX:0,offsetY:0,width:1,height:1}),this.view.enabled=!0,this.view.fullWidth=t,this.view.fullHeight=e,this.view.offsetX=n,this.view.offsetY=i,this.view.width=s,this.view.height=r,this.updateProjectionMatrix()}clearViewOffset(){null!==this.view&&(this.view.enabled=!1),this.updateProjectionMatrix()}updateProjectionMatrix(){const t=this.near;let e=t*Math.tan(.5*qx*this.fov)/this.zoom,n=2*e,i=this.aspect*n,s=-.5*i;const r=this.view;if(null!==this.view&&this.view.enabled){const t=r.fullWidth,o=r.fullHeight;s+=r.offsetX*i/t,e-=r.offsetY*n/o,i*=r.width/t,n*=r.height/o}const o=this.filmOffset;0!==o&&(s+=t*o/this.getFilmWidth()),this.projectionMatrix.makePerspective(s,s+i,e,e-n,t,this.far),this.projectionMatrixInverse.copy(this.projectionMatrix).invert()}toJSON(t){const e=super.toJSON(t);return e.object.fov=this.fov,e.object.zoom=this.zoom,e.object.near=this.near,e.object.far=this.far,e.object.focus=this.focus,e.object.aspect=this.aspect,null!==this.view&&(e.object.view=Object.assign({},this.view)),e.object.filmGauge=this.filmGauge,e.object.filmOffset=this.filmOffset,e}}ET.prototype.isPerspectiveCamera=!0;const ST=90;class CT extends yw{constructor(t,e,n){if(super(),this.type=\\\\\\\"CubeCamera\\\\\\\",!0!==n.isWebGLCubeRenderTarget)return void console.error(\\\\\\\"THREE.CubeCamera: The constructor now expects an instance of WebGLCubeRenderTarget as third parameter.\\\\\\\");this.renderTarget=n;const i=new ET(ST,1,t,e);i.layers=this.layers,i.up.set(0,-1,0),i.lookAt(new gb(1,0,0)),this.add(i);const s=new ET(ST,1,t,e);s.layers=this.layers,s.up.set(0,-1,0),s.lookAt(new gb(-1,0,0)),this.add(s);const r=new ET(ST,1,t,e);r.layers=this.layers,r.up.set(0,0,1),r.lookAt(new gb(0,1,0)),this.add(r);const o=new ET(ST,1,t,e);o.layers=this.layers,o.up.set(0,0,-1),o.lookAt(new gb(0,-1,0)),this.add(o);const a=new ET(ST,1,t,e);a.layers=this.layers,a.up.set(0,-1,0),a.lookAt(new gb(0,0,1)),this.add(a);const l=new ET(ST,1,t,e);l.layers=this.layers,l.up.set(0,-1,0),l.lookAt(new gb(0,0,-1)),this.add(l)}update(t,e){null===this.parent&&this.updateMatrixWorld();const n=this.renderTarget,[i,s,r,o,a,l]=this.children,c=t.xr.enabled,h=t.getRenderTarget();t.xr.enabled=!1;const u=n.texture.generateMipmaps;n.texture.generateMipmaps=!1,t.setRenderTarget(n,0),t.render(e,i),t.setRenderTarget(n,1),t.render(e,s),t.setRenderTarget(n,2),t.render(e,r),t.setRenderTarget(n,3),t.render(e,o),t.setRenderTarget(n,4),t.render(e,a),n.texture.generateMipmaps=u,t.setRenderTarget(n,5),t.render(e,l),t.setRenderTarget(h),t.xr.enabled=c}}class NT extends ub{constructor(t,e,n,i,s,r,o,a,l,c){super(t=void 0!==t?t:[],e=void 0!==e?e:sx,n,i,s,r,o,a,l,c),this.flipY=!1}get images(){return this.image}set images(t){this.image=t}}NT.prototype.isCubeTexture=!0;class LT extends _b{constructor(t,e,n){Number.isInteger(e)&&(console.warn(\\\\\\\"THREE.WebGLCubeRenderTarget: constructor signature is now WebGLCubeRenderTarget( size, options )\\\\\\\"),e=n),super(t,t,e),e=e||{},this.texture=new NT(void 0,e.mapping,e.wrapS,e.wrapT,e.magFilter,e.minFilter,e.format,e.type,e.anisotropy,e.encoding),this.texture.isRenderTargetTexture=!0,this.texture.generateMipmaps=void 0!==e.generateMipmaps&&e.generateMipmaps,this.texture.minFilter=void 0!==e.minFilter?e.minFilter:fx,this.texture._needsFlipEnvMap=!1}fromEquirectangularTexture(t,e){this.texture.type=e.type,this.texture.format=Ex,this.texture.encoding=e.encoding,this.texture.generateMipmaps=e.generateMipmaps,this.texture.minFilter=e.minFilter,this.texture.magFilter=e.magFilter;const n={uniforms:{tEquirect:{value:null}},vertexShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tvarying vec3 vWorldDirection;\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tvec3 transformDirection( in vec3 dir, in mat4 matrix ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\t\\\\treturn normalize( ( matrix * vec4( dir, 0.0 ) ).xyz );\\\\n\\\\n\\\\t\\\\t\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tvWorldDirection = transformDirection( position, modelMatrix );\\\\n\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t#include \\\\n\\\\t\\\\t\\\\t\\\\t\\\\t#include \\\\n\\\\n\\\\t\\\\t\\\\t\\\\t}\\\\n\\\\t\\\\t\\\\t\\\\\\\",fragmentShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tuniform sampler2D tEquirect;\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tvarying vec3 vWorldDirection;\\\\n\\\\n\\\\t\\\\t\\\\t\\\\t#include \\\\n\\\\n\\\\t\\\\t\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tvec3 direction = normalize( vWorldDirection );\\\\n\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tvec2 sampleUV = equirectUv( direction );\\\\n\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tgl_FragColor = texture2D( tEquirect, sampleUV );\\\\n\\\\n\\\\t\\\\t\\\\t\\\\t}\\\\n\\\\t\\\\t\\\\t\\\\\\\"},i=new xT(5,5,5),s=new AT({name:\\\\\\\"CubemapFromEquirect\\\\\\\",uniforms:bT(n.uniforms),vertexShader:n.vertexShader,fragmentShader:n.fragmentShader,side:1,blending:0});s.uniforms.tEquirect.value=e;const r=new vT(i,s),o=e.minFilter;e.minFilter===vx&&(e.minFilter=fx);return new CT(1,10,this).update(t,r),e.minFilter=o,r.geometry.dispose(),r.material.dispose(),this}clear(t,e,n,i){const s=t.getRenderTarget();for(let s=0;s<6;s++)t.setRenderTarget(this,s),t.clear(e,n,i);t.setRenderTarget(s)}}LT.prototype.isWebGLCubeRenderTarget=!0;const OT=new gb,PT=new gb,RT=new rb;class IT{constructor(t=new gb(1,0,0),e=0){this.normal=t,this.constant=e}set(t,e){return this.normal.copy(t),this.constant=e,this}setComponents(t,e,n,i){return this.normal.set(t,e,n),this.constant=i,this}setFromNormalAndCoplanarPoint(t,e){return this.normal.copy(t),this.constant=-e.dot(this.normal),this}setFromCoplanarPoints(t,e,n){const i=OT.subVectors(n,e).cross(PT.subVectors(t,e)).normalize();return this.setFromNormalAndCoplanarPoint(i,t),this}copy(t){return this.normal.copy(t.normal),this.constant=t.constant,this}normalize(){const t=1/this.normal.length();return this.normal.multiplyScalar(t),this.constant*=t,this}negate(){return this.constant*=-1,this.normal.negate(),this}distanceToPoint(t){return this.normal.dot(t)+this.constant}distanceToSphere(t){return this.distanceToPoint(t.center)-t.radius}projectPoint(t,e){return e.copy(this.normal).multiplyScalar(-this.distanceToPoint(t)).add(t)}intersectLine(t,e){const n=t.delta(OT),i=this.normal.dot(n);if(0===i)return 0===this.distanceToPoint(t.start)?e.copy(t.start):null;const s=-(t.start.dot(this.normal)+this.constant)/i;return s<0||s>1?null:e.copy(n).multiplyScalar(s).add(t.start)}intersectsLine(t){const e=this.distanceToPoint(t.start),n=this.distanceToPoint(t.end);return e<0&&n>0||n<0&&e>0}intersectsBox(t){return t.intersectsPlane(this)}intersectsSphere(t){return t.intersectsPlane(this)}coplanarPoint(t){return t.copy(this.normal).multiplyScalar(-this.constant)}applyMatrix4(t,e){const n=e||RT.getNormalMatrix(t),i=this.coplanarPoint(OT).applyMatrix4(t),s=this.normal.applyMatrix3(n).normalize();return this.constant=-i.dot(s),this}translate(t){return this.constant-=t.dot(this.normal),this}equals(t){return t.normal.equals(this.normal)&&t.constant===this.constant}clone(){return(new this.constructor).copy(this)}}IT.prototype.isPlane=!0;const FT=new kb,DT=new gb;class BT{constructor(t=new IT,e=new IT,n=new IT,i=new IT,s=new IT,r=new IT){this.planes=[t,e,n,i,s,r]}set(t,e,n,i,s,r){const o=this.planes;return o[0].copy(t),o[1].copy(e),o[2].copy(n),o[3].copy(i),o[4].copy(s),o[5].copy(r),this}copy(t){const e=this.planes;for(let n=0;n<6;n++)e[n].copy(t.planes[n]);return this}setFromProjectionMatrix(t){const e=this.planes,n=t.elements,i=n[0],s=n[1],r=n[2],o=n[3],a=n[4],l=n[5],c=n[6],h=n[7],u=n[8],d=n[9],p=n[10],_=n[11],m=n[12],f=n[13],g=n[14],v=n[15];return e[0].setComponents(o-i,h-a,_-u,v-m).normalize(),e[1].setComponents(o+i,h+a,_+u,v+m).normalize(),e[2].setComponents(o+s,h+l,_+d,v+f).normalize(),e[3].setComponents(o-s,h-l,_-d,v-f).normalize(),e[4].setComponents(o-r,h-c,_-p,v-g).normalize(),e[5].setComponents(o+r,h+c,_+p,v+g).normalize(),this}intersectsObject(t){const e=t.geometry;return null===e.boundingSphere&&e.computeBoundingSphere(),FT.copy(e.boundingSphere).applyMatrix4(t.matrixWorld),this.intersectsSphere(FT)}intersectsSprite(t){return FT.center.set(0,0,0),FT.radius=.7071067811865476,FT.applyMatrix4(t.matrixWorld),this.intersectsSphere(FT)}intersectsSphere(t){const e=this.planes,n=t.center,i=-t.radius;for(let t=0;t<6;t++){if(e[t].distanceToPoint(n)0?t.max.x:t.min.x,DT.y=i.normal.y>0?t.max.y:t.min.y,DT.z=i.normal.z>0?t.max.z:t.min.z,i.distanceToPoint(DT)<0)return!1}return!0}containsPoint(t){const e=this.planes;for(let n=0;n<6;n++)if(e[n].distanceToPoint(t)<0)return!1;return!0}clone(){return(new this.constructor).copy(this)}}function zT(){let t=null,e=!1,n=null,i=null;function s(e,r){n(e,r),i=t.requestAnimationFrame(s)}return{start:function(){!0!==e&&null!==n&&(i=t.requestAnimationFrame(s),e=!0)},stop:function(){t.cancelAnimationFrame(i),e=!1},setAnimationLoop:function(t){n=t},setContext:function(e){t=e}}}function kT(t,e){const n=e.isWebGL2,i=new WeakMap;return{get:function(t){return t.isInterleavedBufferAttribute&&(t=t.data),i.get(t)},remove:function(e){e.isInterleavedBufferAttribute&&(e=e.data);const n=i.get(e);n&&(t.deleteBuffer(n.buffer),i.delete(e))},update:function(e,s){if(e.isGLBufferAttribute){const t=i.get(e);return void((!t||t.version 0.0 ) ? v : 0.5 * inversesqrt( max( 1.0 - x * x, 1e-7 ) ) - v;\\\\n\\\\treturn cross( v1, v2 ) * theta_sintheta;\\\\n}\\\\nvec3 LTC_Evaluate( const in vec3 N, const in vec3 V, const in vec3 P, const in mat3 mInv, const in vec3 rectCoords[ 4 ] ) {\\\\n\\\\tvec3 v1 = rectCoords[ 1 ] - rectCoords[ 0 ];\\\\n\\\\tvec3 v2 = rectCoords[ 3 ] - rectCoords[ 0 ];\\\\n\\\\tvec3 lightNormal = cross( v1, v2 );\\\\n\\\\tif( dot( lightNormal, P - rectCoords[ 0 ] ) < 0.0 ) return vec3( 0.0 );\\\\n\\\\tvec3 T1, T2;\\\\n\\\\tT1 = normalize( V - N * dot( V, N ) );\\\\n\\\\tT2 = - cross( N, T1 );\\\\n\\\\tmat3 mat = mInv * transposeMat3( mat3( T1, T2, N ) );\\\\n\\\\tvec3 coords[ 4 ];\\\\n\\\\tcoords[ 0 ] = mat * ( rectCoords[ 0 ] - P );\\\\n\\\\tcoords[ 1 ] = mat * ( rectCoords[ 1 ] - P );\\\\n\\\\tcoords[ 2 ] = mat * ( rectCoords[ 2 ] - P );\\\\n\\\\tcoords[ 3 ] = mat * ( rectCoords[ 3 ] - P );\\\\n\\\\tcoords[ 0 ] = normalize( coords[ 0 ] );\\\\n\\\\tcoords[ 1 ] = normalize( coords[ 1 ] );\\\\n\\\\tcoords[ 2 ] = normalize( coords[ 2 ] );\\\\n\\\\tcoords[ 3 ] = normalize( coords[ 3 ] );\\\\n\\\\tvec3 vectorFormFactor = vec3( 0.0 );\\\\n\\\\tvectorFormFactor += LTC_EdgeVectorFormFactor( coords[ 0 ], coords[ 1 ] );\\\\n\\\\tvectorFormFactor += LTC_EdgeVectorFormFactor( coords[ 1 ], coords[ 2 ] );\\\\n\\\\tvectorFormFactor += LTC_EdgeVectorFormFactor( coords[ 2 ], coords[ 3 ] );\\\\n\\\\tvectorFormFactor += LTC_EdgeVectorFormFactor( coords[ 3 ], coords[ 0 ] );\\\\n\\\\tfloat result = LTC_ClippedSphereFormFactor( vectorFormFactor );\\\\n\\\\treturn vec3( result );\\\\n}\\\\nfloat G_BlinnPhong_Implicit( ) {\\\\n\\\\treturn 0.25;\\\\n}\\\\nfloat D_BlinnPhong( const in float shininess, const in float dotNH ) {\\\\n\\\\treturn RECIPROCAL_PI * ( shininess * 0.5 + 1.0 ) * pow( dotNH, shininess );\\\\n}\\\\nvec3 BRDF_BlinnPhong( const in vec3 lightDir, const in vec3 viewDir, const in vec3 normal, const in vec3 specularColor, const in float shininess ) {\\\\n\\\\tvec3 halfDir = normalize( lightDir + viewDir );\\\\n\\\\tfloat dotNH = saturate( dot( normal, halfDir ) );\\\\n\\\\tfloat dotVH = saturate( dot( viewDir, halfDir ) );\\\\n\\\\tvec3 F = F_Schlick( specularColor, 1.0, dotVH );\\\\n\\\\tfloat G = G_BlinnPhong_Implicit( );\\\\n\\\\tfloat D = D_BlinnPhong( shininess, dotNH );\\\\n\\\\treturn F * ( G * D );\\\\n}\\\\n#if defined( USE_SHEEN )\\\\nfloat D_Charlie( float roughness, float dotNH ) {\\\\n\\\\tfloat alpha = pow2( roughness );\\\\n\\\\tfloat invAlpha = 1.0 / alpha;\\\\n\\\\tfloat cos2h = dotNH * dotNH;\\\\n\\\\tfloat sin2h = max( 1.0 - cos2h, 0.0078125 );\\\\n\\\\treturn ( 2.0 + invAlpha ) * pow( sin2h, invAlpha * 0.5 ) / ( 2.0 * PI );\\\\n}\\\\nfloat V_Neubelt( float dotNV, float dotNL ) {\\\\n\\\\treturn saturate( 1.0 / ( 4.0 * ( dotNL + dotNV - dotNL * dotNV ) ) );\\\\n}\\\\nvec3 BRDF_Sheen( const in vec3 lightDir, const in vec3 viewDir, const in vec3 normal, vec3 sheenTint, const in float sheenRoughness ) {\\\\n\\\\tvec3 halfDir = normalize( lightDir + viewDir );\\\\n\\\\tfloat dotNL = saturate( dot( normal, lightDir ) );\\\\n\\\\tfloat dotNV = saturate( dot( normal, viewDir ) );\\\\n\\\\tfloat dotNH = saturate( dot( normal, halfDir ) );\\\\n\\\\tfloat D = D_Charlie( sheenRoughness, dotNH );\\\\n\\\\tfloat V = V_Neubelt( dotNV, dotNL );\\\\n\\\\treturn sheenTint * ( D * V );\\\\n}\\\\n#endif\\\\\\\",bumpmap_pars_fragment:\\\\\\\"#ifdef USE_BUMPMAP\\\\n\\\\tuniform sampler2D bumpMap;\\\\n\\\\tuniform float bumpScale;\\\\n\\\\tvec2 dHdxy_fwd() {\\\\n\\\\t\\\\tvec2 dSTdx = dFdx( vUv );\\\\n\\\\t\\\\tvec2 dSTdy = dFdy( vUv );\\\\n\\\\t\\\\tfloat Hll = bumpScale * texture2D( bumpMap, vUv ).x;\\\\n\\\\t\\\\tfloat dBx = bumpScale * texture2D( bumpMap, vUv + dSTdx ).x - Hll;\\\\n\\\\t\\\\tfloat dBy = bumpScale * texture2D( bumpMap, vUv + dSTdy ).x - Hll;\\\\n\\\\t\\\\treturn vec2( dBx, dBy );\\\\n\\\\t}\\\\n\\\\tvec3 perturbNormalArb( vec3 surf_pos, vec3 surf_norm, vec2 dHdxy, float faceDirection ) {\\\\n\\\\t\\\\tvec3 vSigmaX = vec3( dFdx( surf_pos.x ), dFdx( surf_pos.y ), dFdx( surf_pos.z ) );\\\\n\\\\t\\\\tvec3 vSigmaY = vec3( dFdy( surf_pos.x ), dFdy( surf_pos.y ), dFdy( surf_pos.z ) );\\\\n\\\\t\\\\tvec3 vN = surf_norm;\\\\n\\\\t\\\\tvec3 R1 = cross( vSigmaY, vN );\\\\n\\\\t\\\\tvec3 R2 = cross( vN, vSigmaX );\\\\n\\\\t\\\\tfloat fDet = dot( vSigmaX, R1 ) * faceDirection;\\\\n\\\\t\\\\tvec3 vGrad = sign( fDet ) * ( dHdxy.x * R1 + dHdxy.y * R2 );\\\\n\\\\t\\\\treturn normalize( abs( fDet ) * surf_norm - vGrad );\\\\n\\\\t}\\\\n#endif\\\\\\\",clipping_planes_fragment:\\\\\\\"#if NUM_CLIPPING_PLANES > 0\\\\n\\\\tvec4 plane;\\\\n\\\\t#pragma unroll_loop_start\\\\n\\\\tfor ( int i = 0; i < UNION_CLIPPING_PLANES; i ++ ) {\\\\n\\\\t\\\\tplane = clippingPlanes[ i ];\\\\n\\\\t\\\\tif ( dot( vClipPosition, plane.xyz ) > plane.w ) discard;\\\\n\\\\t}\\\\n\\\\t#pragma unroll_loop_end\\\\n\\\\t#if UNION_CLIPPING_PLANES < NUM_CLIPPING_PLANES\\\\n\\\\t\\\\tbool clipped = true;\\\\n\\\\t\\\\t#pragma unroll_loop_start\\\\n\\\\t\\\\tfor ( int i = UNION_CLIPPING_PLANES; i < NUM_CLIPPING_PLANES; i ++ ) {\\\\n\\\\t\\\\t\\\\tplane = clippingPlanes[ i ];\\\\n\\\\t\\\\t\\\\tclipped = ( dot( vClipPosition, plane.xyz ) > plane.w ) && clipped;\\\\n\\\\t\\\\t}\\\\n\\\\t\\\\t#pragma unroll_loop_end\\\\n\\\\t\\\\tif ( clipped ) discard;\\\\n\\\\t#endif\\\\n#endif\\\\\\\",clipping_planes_pars_fragment:\\\\\\\"#if NUM_CLIPPING_PLANES > 0\\\\n\\\\tvarying vec3 vClipPosition;\\\\n\\\\tuniform vec4 clippingPlanes[ NUM_CLIPPING_PLANES ];\\\\n#endif\\\\\\\",clipping_planes_pars_vertex:\\\\\\\"#if NUM_CLIPPING_PLANES > 0\\\\n\\\\tvarying vec3 vClipPosition;\\\\n#endif\\\\\\\",clipping_planes_vertex:\\\\\\\"#if NUM_CLIPPING_PLANES > 0\\\\n\\\\tvClipPosition = - mvPosition.xyz;\\\\n#endif\\\\\\\",color_fragment:\\\\\\\"#if defined( USE_COLOR_ALPHA )\\\\n\\\\tdiffuseColor *= vColor;\\\\n#elif defined( USE_COLOR )\\\\n\\\\tdiffuseColor.rgb *= vColor;\\\\n#endif\\\\\\\",color_pars_fragment:\\\\\\\"#if defined( USE_COLOR_ALPHA )\\\\n\\\\tvarying vec4 vColor;\\\\n#elif defined( USE_COLOR )\\\\n\\\\tvarying vec3 vColor;\\\\n#endif\\\\\\\",color_pars_vertex:\\\\\\\"#if defined( USE_COLOR_ALPHA )\\\\n\\\\tvarying vec4 vColor;\\\\n#elif defined( USE_COLOR ) || defined( USE_INSTANCING_COLOR )\\\\n\\\\tvarying vec3 vColor;\\\\n#endif\\\\\\\",color_vertex:\\\\\\\"#if defined( USE_COLOR_ALPHA )\\\\n\\\\tvColor = vec4( 1.0 );\\\\n#elif defined( USE_COLOR ) || defined( USE_INSTANCING_COLOR )\\\\n\\\\tvColor = vec3( 1.0 );\\\\n#endif\\\\n#ifdef USE_COLOR\\\\n\\\\tvColor *= color;\\\\n#endif\\\\n#ifdef USE_INSTANCING_COLOR\\\\n\\\\tvColor.xyz *= instanceColor.xyz;\\\\n#endif\\\\\\\",common:\\\\\\\"#define PI 3.141592653589793\\\\n#define PI2 6.283185307179586\\\\n#define PI_HALF 1.5707963267948966\\\\n#define RECIPROCAL_PI 0.3183098861837907\\\\n#define RECIPROCAL_PI2 0.15915494309189535\\\\n#define EPSILON 1e-6\\\\n#ifndef saturate\\\\n#define saturate( a ) clamp( a, 0.0, 1.0 )\\\\n#endif\\\\n#define whiteComplement( a ) ( 1.0 - saturate( a ) )\\\\nfloat pow2( const in float x ) { return x*x; }\\\\nfloat pow3( const in float x ) { return x*x*x; }\\\\nfloat pow4( const in float x ) { float x2 = x*x; return x2*x2; }\\\\nfloat max3( const in vec3 v ) { return max( max( v.x, v.y ), v.z ); }\\\\nfloat average( const in vec3 color ) { return dot( color, vec3( 0.3333 ) ); }\\\\nhighp float rand( const in vec2 uv ) {\\\\n\\\\tconst highp float a = 12.9898, b = 78.233, c = 43758.5453;\\\\n\\\\thighp float dt = dot( uv.xy, vec2( a,b ) ), sn = mod( dt, PI );\\\\n\\\\treturn fract( sin( sn ) * c );\\\\n}\\\\n#ifdef HIGH_PRECISION\\\\n\\\\tfloat precisionSafeLength( vec3 v ) { return length( v ); }\\\\n#else\\\\n\\\\tfloat precisionSafeLength( vec3 v ) {\\\\n\\\\t\\\\tfloat maxComponent = max3( abs( v ) );\\\\n\\\\t\\\\treturn length( v / maxComponent ) * maxComponent;\\\\n\\\\t}\\\\n#endif\\\\nstruct IncidentLight {\\\\n\\\\tvec3 color;\\\\n\\\\tvec3 direction;\\\\n\\\\tbool visible;\\\\n};\\\\nstruct ReflectedLight {\\\\n\\\\tvec3 directDiffuse;\\\\n\\\\tvec3 directSpecular;\\\\n\\\\tvec3 indirectDiffuse;\\\\n\\\\tvec3 indirectSpecular;\\\\n};\\\\nstruct GeometricContext {\\\\n\\\\tvec3 position;\\\\n\\\\tvec3 normal;\\\\n\\\\tvec3 viewDir;\\\\n#ifdef USE_CLEARCOAT\\\\n\\\\tvec3 clearcoatNormal;\\\\n#endif\\\\n};\\\\nvec3 transformDirection( in vec3 dir, in mat4 matrix ) {\\\\n\\\\treturn normalize( ( matrix * vec4( dir, 0.0 ) ).xyz );\\\\n}\\\\nvec3 inverseTransformDirection( in vec3 dir, in mat4 matrix ) {\\\\n\\\\treturn normalize( ( vec4( dir, 0.0 ) * matrix ).xyz );\\\\n}\\\\nmat3 transposeMat3( const in mat3 m ) {\\\\n\\\\tmat3 tmp;\\\\n\\\\ttmp[ 0 ] = vec3( m[ 0 ].x, m[ 1 ].x, m[ 2 ].x );\\\\n\\\\ttmp[ 1 ] = vec3( m[ 0 ].y, m[ 1 ].y, m[ 2 ].y );\\\\n\\\\ttmp[ 2 ] = vec3( m[ 0 ].z, m[ 1 ].z, m[ 2 ].z );\\\\n\\\\treturn tmp;\\\\n}\\\\nfloat linearToRelativeLuminance( const in vec3 color ) {\\\\n\\\\tvec3 weights = vec3( 0.2126, 0.7152, 0.0722 );\\\\n\\\\treturn dot( weights, color.rgb );\\\\n}\\\\nbool isPerspectiveMatrix( mat4 m ) {\\\\n\\\\treturn m[ 2 ][ 3 ] == - 1.0;\\\\n}\\\\nvec2 equirectUv( in vec3 dir ) {\\\\n\\\\tfloat u = atan( dir.z, dir.x ) * RECIPROCAL_PI2 + 0.5;\\\\n\\\\tfloat v = asin( clamp( dir.y, - 1.0, 1.0 ) ) * RECIPROCAL_PI + 0.5;\\\\n\\\\treturn vec2( u, v );\\\\n}\\\\\\\",cube_uv_reflection_fragment:\\\\\\\"#ifdef ENVMAP_TYPE_CUBE_UV\\\\n\\\\t#define cubeUV_maxMipLevel 8.0\\\\n\\\\t#define cubeUV_minMipLevel 4.0\\\\n\\\\t#define cubeUV_maxTileSize 256.0\\\\n\\\\t#define cubeUV_minTileSize 16.0\\\\n\\\\tfloat getFace( vec3 direction ) {\\\\n\\\\t\\\\tvec3 absDirection = abs( direction );\\\\n\\\\t\\\\tfloat face = - 1.0;\\\\n\\\\t\\\\tif ( absDirection.x > absDirection.z ) {\\\\n\\\\t\\\\t\\\\tif ( absDirection.x > absDirection.y )\\\\n\\\\t\\\\t\\\\t\\\\tface = direction.x > 0.0 ? 0.0 : 3.0;\\\\n\\\\t\\\\t\\\\telse\\\\n\\\\t\\\\t\\\\t\\\\tface = direction.y > 0.0 ? 1.0 : 4.0;\\\\n\\\\t\\\\t} else {\\\\n\\\\t\\\\t\\\\tif ( absDirection.z > absDirection.y )\\\\n\\\\t\\\\t\\\\t\\\\tface = direction.z > 0.0 ? 2.0 : 5.0;\\\\n\\\\t\\\\t\\\\telse\\\\n\\\\t\\\\t\\\\t\\\\tface = direction.y > 0.0 ? 1.0 : 4.0;\\\\n\\\\t\\\\t}\\\\n\\\\t\\\\treturn face;\\\\n\\\\t}\\\\n\\\\tvec2 getUV( vec3 direction, float face ) {\\\\n\\\\t\\\\tvec2 uv;\\\\n\\\\t\\\\tif ( face == 0.0 ) {\\\\n\\\\t\\\\t\\\\tuv = vec2( direction.z, direction.y ) / abs( direction.x );\\\\n\\\\t\\\\t} else if ( face == 1.0 ) {\\\\n\\\\t\\\\t\\\\tuv = vec2( - direction.x, - direction.z ) / abs( direction.y );\\\\n\\\\t\\\\t} else if ( face == 2.0 ) {\\\\n\\\\t\\\\t\\\\tuv = vec2( - direction.x, direction.y ) / abs( direction.z );\\\\n\\\\t\\\\t} else if ( face == 3.0 ) {\\\\n\\\\t\\\\t\\\\tuv = vec2( - direction.z, direction.y ) / abs( direction.x );\\\\n\\\\t\\\\t} else if ( face == 4.0 ) {\\\\n\\\\t\\\\t\\\\tuv = vec2( - direction.x, direction.z ) / abs( direction.y );\\\\n\\\\t\\\\t} else {\\\\n\\\\t\\\\t\\\\tuv = vec2( direction.x, direction.y ) / abs( direction.z );\\\\n\\\\t\\\\t}\\\\n\\\\t\\\\treturn 0.5 * ( uv + 1.0 );\\\\n\\\\t}\\\\n\\\\tvec3 bilinearCubeUV( sampler2D envMap, vec3 direction, float mipInt ) {\\\\n\\\\t\\\\tfloat face = getFace( direction );\\\\n\\\\t\\\\tfloat filterInt = max( cubeUV_minMipLevel - mipInt, 0.0 );\\\\n\\\\t\\\\tmipInt = max( mipInt, cubeUV_minMipLevel );\\\\n\\\\t\\\\tfloat faceSize = exp2( mipInt );\\\\n\\\\t\\\\tfloat texelSize = 1.0 / ( 3.0 * cubeUV_maxTileSize );\\\\n\\\\t\\\\tvec2 uv = getUV( direction, face ) * ( faceSize - 1.0 );\\\\n\\\\t\\\\tvec2 f = fract( uv );\\\\n\\\\t\\\\tuv += 0.5 - f;\\\\n\\\\t\\\\tif ( face > 2.0 ) {\\\\n\\\\t\\\\t\\\\tuv.y += faceSize;\\\\n\\\\t\\\\t\\\\tface -= 3.0;\\\\n\\\\t\\\\t}\\\\n\\\\t\\\\tuv.x += face * faceSize;\\\\n\\\\t\\\\tif ( mipInt < cubeUV_maxMipLevel ) {\\\\n\\\\t\\\\t\\\\tuv.y += 2.0 * cubeUV_maxTileSize;\\\\n\\\\t\\\\t}\\\\n\\\\t\\\\tuv.y += filterInt * 2.0 * cubeUV_minTileSize;\\\\n\\\\t\\\\tuv.x += 3.0 * max( 0.0, cubeUV_maxTileSize - 2.0 * faceSize );\\\\n\\\\t\\\\tuv *= texelSize;\\\\n\\\\t\\\\tvec3 tl = envMapTexelToLinear( texture2D( envMap, uv ) ).rgb;\\\\n\\\\t\\\\tuv.x += texelSize;\\\\n\\\\t\\\\tvec3 tr = envMapTexelToLinear( texture2D( envMap, uv ) ).rgb;\\\\n\\\\t\\\\tuv.y += texelSize;\\\\n\\\\t\\\\tvec3 br = envMapTexelToLinear( texture2D( envMap, uv ) ).rgb;\\\\n\\\\t\\\\tuv.x -= texelSize;\\\\n\\\\t\\\\tvec3 bl = envMapTexelToLinear( texture2D( envMap, uv ) ).rgb;\\\\n\\\\t\\\\tvec3 tm = mix( tl, tr, f.x );\\\\n\\\\t\\\\tvec3 bm = mix( bl, br, f.x );\\\\n\\\\t\\\\treturn mix( tm, bm, f.y );\\\\n\\\\t}\\\\n\\\\t#define r0 1.0\\\\n\\\\t#define v0 0.339\\\\n\\\\t#define m0 - 2.0\\\\n\\\\t#define r1 0.8\\\\n\\\\t#define v1 0.276\\\\n\\\\t#define m1 - 1.0\\\\n\\\\t#define r4 0.4\\\\n\\\\t#define v4 0.046\\\\n\\\\t#define m4 2.0\\\\n\\\\t#define r5 0.305\\\\n\\\\t#define v5 0.016\\\\n\\\\t#define m5 3.0\\\\n\\\\t#define r6 0.21\\\\n\\\\t#define v6 0.0038\\\\n\\\\t#define m6 4.0\\\\n\\\\tfloat roughnessToMip( float roughness ) {\\\\n\\\\t\\\\tfloat mip = 0.0;\\\\n\\\\t\\\\tif ( roughness >= r1 ) {\\\\n\\\\t\\\\t\\\\tmip = ( r0 - roughness ) * ( m1 - m0 ) / ( r0 - r1 ) + m0;\\\\n\\\\t\\\\t} else if ( roughness >= r4 ) {\\\\n\\\\t\\\\t\\\\tmip = ( r1 - roughness ) * ( m4 - m1 ) / ( r1 - r4 ) + m1;\\\\n\\\\t\\\\t} else if ( roughness >= r5 ) {\\\\n\\\\t\\\\t\\\\tmip = ( r4 - roughness ) * ( m5 - m4 ) / ( r4 - r5 ) + m4;\\\\n\\\\t\\\\t} else if ( roughness >= r6 ) {\\\\n\\\\t\\\\t\\\\tmip = ( r5 - roughness ) * ( m6 - m5 ) / ( r5 - r6 ) + m5;\\\\n\\\\t\\\\t} else {\\\\n\\\\t\\\\t\\\\tmip = - 2.0 * log2( 1.16 * roughness );\\\\t\\\\t}\\\\n\\\\t\\\\treturn mip;\\\\n\\\\t}\\\\n\\\\tvec4 textureCubeUV( sampler2D envMap, vec3 sampleDir, float roughness ) {\\\\n\\\\t\\\\tfloat mip = clamp( roughnessToMip( roughness ), m0, cubeUV_maxMipLevel );\\\\n\\\\t\\\\tfloat mipF = fract( mip );\\\\n\\\\t\\\\tfloat mipInt = floor( mip );\\\\n\\\\t\\\\tvec3 color0 = bilinearCubeUV( envMap, sampleDir, mipInt );\\\\n\\\\t\\\\tif ( mipF == 0.0 ) {\\\\n\\\\t\\\\t\\\\treturn vec4( color0, 1.0 );\\\\n\\\\t\\\\t} else {\\\\n\\\\t\\\\t\\\\tvec3 color1 = bilinearCubeUV( envMap, sampleDir, mipInt + 1.0 );\\\\n\\\\t\\\\t\\\\treturn vec4( mix( color0, color1, mipF ), 1.0 );\\\\n\\\\t\\\\t}\\\\n\\\\t}\\\\n#endif\\\\\\\",defaultnormal_vertex:\\\\\\\"vec3 transformedNormal = objectNormal;\\\\n#ifdef USE_INSTANCING\\\\n\\\\tmat3 m = mat3( instanceMatrix );\\\\n\\\\ttransformedNormal /= vec3( dot( m[ 0 ], m[ 0 ] ), dot( m[ 1 ], m[ 1 ] ), dot( m[ 2 ], m[ 2 ] ) );\\\\n\\\\ttransformedNormal = m * transformedNormal;\\\\n#endif\\\\ntransformedNormal = normalMatrix * transformedNormal;\\\\n#ifdef FLIP_SIDED\\\\n\\\\ttransformedNormal = - transformedNormal;\\\\n#endif\\\\n#ifdef USE_TANGENT\\\\n\\\\tvec3 transformedTangent = ( modelViewMatrix * vec4( objectTangent, 0.0 ) ).xyz;\\\\n\\\\t#ifdef FLIP_SIDED\\\\n\\\\t\\\\ttransformedTangent = - transformedTangent;\\\\n\\\\t#endif\\\\n#endif\\\\\\\",displacementmap_pars_vertex:\\\\\\\"#ifdef USE_DISPLACEMENTMAP\\\\n\\\\tuniform sampler2D displacementMap;\\\\n\\\\tuniform float displacementScale;\\\\n\\\\tuniform float displacementBias;\\\\n#endif\\\\\\\",displacementmap_vertex:\\\\\\\"#ifdef USE_DISPLACEMENTMAP\\\\n\\\\ttransformed += normalize( objectNormal ) * ( texture2D( displacementMap, vUv ).x * displacementScale + displacementBias );\\\\n#endif\\\\\\\",emissivemap_fragment:\\\\\\\"#ifdef USE_EMISSIVEMAP\\\\n\\\\tvec4 emissiveColor = texture2D( emissiveMap, vUv );\\\\n\\\\temissiveColor.rgb = emissiveMapTexelToLinear( emissiveColor ).rgb;\\\\n\\\\ttotalEmissiveRadiance *= emissiveColor.rgb;\\\\n#endif\\\\\\\",emissivemap_pars_fragment:\\\\\\\"#ifdef USE_EMISSIVEMAP\\\\n\\\\tuniform sampler2D emissiveMap;\\\\n#endif\\\\\\\",encodings_fragment:\\\\\\\"gl_FragColor = linearToOutputTexel( gl_FragColor );\\\\\\\",encodings_pars_fragment:\\\\\\\"\\\\nvec4 LinearToLinear( in vec4 value ) {\\\\n\\\\treturn value;\\\\n}\\\\nvec4 GammaToLinear( in vec4 value, in float gammaFactor ) {\\\\n\\\\treturn vec4( pow( value.rgb, vec3( gammaFactor ) ), value.a );\\\\n}\\\\nvec4 LinearToGamma( in vec4 value, in float gammaFactor ) {\\\\n\\\\treturn vec4( pow( value.rgb, vec3( 1.0 / gammaFactor ) ), value.a );\\\\n}\\\\nvec4 sRGBToLinear( in vec4 value ) {\\\\n\\\\treturn vec4( mix( pow( value.rgb * 0.9478672986 + vec3( 0.0521327014 ), vec3( 2.4 ) ), value.rgb * 0.0773993808, vec3( lessThanEqual( value.rgb, vec3( 0.04045 ) ) ) ), value.a );\\\\n}\\\\nvec4 LinearTosRGB( in vec4 value ) {\\\\n\\\\treturn vec4( mix( pow( value.rgb, vec3( 0.41666 ) ) * 1.055 - vec3( 0.055 ), value.rgb * 12.92, vec3( lessThanEqual( value.rgb, vec3( 0.0031308 ) ) ) ), value.a );\\\\n}\\\\nvec4 RGBEToLinear( in vec4 value ) {\\\\n\\\\treturn vec4( value.rgb * exp2( value.a * 255.0 - 128.0 ), 1.0 );\\\\n}\\\\nvec4 LinearToRGBE( in vec4 value ) {\\\\n\\\\tfloat maxComponent = max( max( value.r, value.g ), value.b );\\\\n\\\\tfloat fExp = clamp( ceil( log2( maxComponent ) ), -128.0, 127.0 );\\\\n\\\\treturn vec4( value.rgb / exp2( fExp ), ( fExp + 128.0 ) / 255.0 );\\\\n}\\\\nvec4 RGBMToLinear( in vec4 value, in float maxRange ) {\\\\n\\\\treturn vec4( value.rgb * value.a * maxRange, 1.0 );\\\\n}\\\\nvec4 LinearToRGBM( in vec4 value, in float maxRange ) {\\\\n\\\\tfloat maxRGB = max( value.r, max( value.g, value.b ) );\\\\n\\\\tfloat M = clamp( maxRGB / maxRange, 0.0, 1.0 );\\\\n\\\\tM = ceil( M * 255.0 ) / 255.0;\\\\n\\\\treturn vec4( value.rgb / ( M * maxRange ), M );\\\\n}\\\\nvec4 RGBDToLinear( in vec4 value, in float maxRange ) {\\\\n\\\\treturn vec4( value.rgb * ( ( maxRange / 255.0 ) / value.a ), 1.0 );\\\\n}\\\\nvec4 LinearToRGBD( in vec4 value, in float maxRange ) {\\\\n\\\\tfloat maxRGB = max( value.r, max( value.g, value.b ) );\\\\n\\\\tfloat D = max( maxRange / maxRGB, 1.0 );\\\\n\\\\tD = clamp( floor( D ) / 255.0, 0.0, 1.0 );\\\\n\\\\treturn vec4( value.rgb * ( D * ( 255.0 / maxRange ) ), D );\\\\n}\\\\nconst mat3 cLogLuvM = mat3( 0.2209, 0.3390, 0.4184, 0.1138, 0.6780, 0.7319, 0.0102, 0.1130, 0.2969 );\\\\nvec4 LinearToLogLuv( in vec4 value ) {\\\\n\\\\tvec3 Xp_Y_XYZp = cLogLuvM * value.rgb;\\\\n\\\\tXp_Y_XYZp = max( Xp_Y_XYZp, vec3( 1e-6, 1e-6, 1e-6 ) );\\\\n\\\\tvec4 vResult;\\\\n\\\\tvResult.xy = Xp_Y_XYZp.xy / Xp_Y_XYZp.z;\\\\n\\\\tfloat Le = 2.0 * log2(Xp_Y_XYZp.y) + 127.0;\\\\n\\\\tvResult.w = fract( Le );\\\\n\\\\tvResult.z = ( Le - ( floor( vResult.w * 255.0 ) ) / 255.0 ) / 255.0;\\\\n\\\\treturn vResult;\\\\n}\\\\nconst mat3 cLogLuvInverseM = mat3( 6.0014, -2.7008, -1.7996, -1.3320, 3.1029, -5.7721, 0.3008, -1.0882, 5.6268 );\\\\nvec4 LogLuvToLinear( in vec4 value ) {\\\\n\\\\tfloat Le = value.z * 255.0 + value.w;\\\\n\\\\tvec3 Xp_Y_XYZp;\\\\n\\\\tXp_Y_XYZp.y = exp2( ( Le - 127.0 ) / 2.0 );\\\\n\\\\tXp_Y_XYZp.z = Xp_Y_XYZp.y / value.y;\\\\n\\\\tXp_Y_XYZp.x = value.x * Xp_Y_XYZp.z;\\\\n\\\\tvec3 vRGB = cLogLuvInverseM * Xp_Y_XYZp.rgb;\\\\n\\\\treturn vec4( max( vRGB, 0.0 ), 1.0 );\\\\n}\\\\\\\",envmap_fragment:\\\\\\\"#ifdef USE_ENVMAP\\\\n\\\\t#ifdef ENV_WORLDPOS\\\\n\\\\t\\\\tvec3 cameraToFrag;\\\\n\\\\t\\\\tif ( isOrthographic ) {\\\\n\\\\t\\\\t\\\\tcameraToFrag = normalize( vec3( - viewMatrix[ 0 ][ 2 ], - viewMatrix[ 1 ][ 2 ], - viewMatrix[ 2 ][ 2 ] ) );\\\\n\\\\t\\\\t} else {\\\\n\\\\t\\\\t\\\\tcameraToFrag = normalize( vWorldPosition - cameraPosition );\\\\n\\\\t\\\\t}\\\\n\\\\t\\\\tvec3 worldNormal = inverseTransformDirection( normal, viewMatrix );\\\\n\\\\t\\\\t#ifdef ENVMAP_MODE_REFLECTION\\\\n\\\\t\\\\t\\\\tvec3 reflectVec = reflect( cameraToFrag, worldNormal );\\\\n\\\\t\\\\t#else\\\\n\\\\t\\\\t\\\\tvec3 reflectVec = refract( cameraToFrag, worldNormal, refractionRatio );\\\\n\\\\t\\\\t#endif\\\\n\\\\t#else\\\\n\\\\t\\\\tvec3 reflectVec = vReflect;\\\\n\\\\t#endif\\\\n\\\\t#ifdef ENVMAP_TYPE_CUBE\\\\n\\\\t\\\\tvec4 envColor = textureCube( envMap, vec3( flipEnvMap * reflectVec.x, reflectVec.yz ) );\\\\n\\\\t\\\\tenvColor = envMapTexelToLinear( envColor );\\\\n\\\\t#elif defined( ENVMAP_TYPE_CUBE_UV )\\\\n\\\\t\\\\tvec4 envColor = textureCubeUV( envMap, reflectVec, 0.0 );\\\\n\\\\t#else\\\\n\\\\t\\\\tvec4 envColor = vec4( 0.0 );\\\\n\\\\t#endif\\\\n\\\\t#ifdef ENVMAP_BLENDING_MULTIPLY\\\\n\\\\t\\\\toutgoingLight = mix( outgoingLight, outgoingLight * envColor.xyz, specularStrength * reflectivity );\\\\n\\\\t#elif defined( ENVMAP_BLENDING_MIX )\\\\n\\\\t\\\\toutgoingLight = mix( outgoingLight, envColor.xyz, specularStrength * reflectivity );\\\\n\\\\t#elif defined( ENVMAP_BLENDING_ADD )\\\\n\\\\t\\\\toutgoingLight += envColor.xyz * specularStrength * reflectivity;\\\\n\\\\t#endif\\\\n#endif\\\\\\\",envmap_common_pars_fragment:\\\\\\\"#ifdef USE_ENVMAP\\\\n\\\\tuniform float envMapIntensity;\\\\n\\\\tuniform float flipEnvMap;\\\\n\\\\tuniform int maxMipLevel;\\\\n\\\\t#ifdef ENVMAP_TYPE_CUBE\\\\n\\\\t\\\\tuniform samplerCube envMap;\\\\n\\\\t#else\\\\n\\\\t\\\\tuniform sampler2D envMap;\\\\n\\\\t#endif\\\\n\\\\t\\\\n#endif\\\\\\\",envmap_pars_fragment:\\\\\\\"#ifdef USE_ENVMAP\\\\n\\\\tuniform float reflectivity;\\\\n\\\\t#if defined( USE_BUMPMAP ) || defined( USE_NORMALMAP ) || defined( PHONG )\\\\n\\\\t\\\\t#define ENV_WORLDPOS\\\\n\\\\t#endif\\\\n\\\\t#ifdef ENV_WORLDPOS\\\\n\\\\t\\\\tvarying vec3 vWorldPosition;\\\\n\\\\t\\\\tuniform float refractionRatio;\\\\n\\\\t#else\\\\n\\\\t\\\\tvarying vec3 vReflect;\\\\n\\\\t#endif\\\\n#endif\\\\\\\",envmap_pars_vertex:\\\\\\\"#ifdef USE_ENVMAP\\\\n\\\\t#if defined( USE_BUMPMAP ) || defined( USE_NORMALMAP ) ||defined( PHONG )\\\\n\\\\t\\\\t#define ENV_WORLDPOS\\\\n\\\\t#endif\\\\n\\\\t#ifdef ENV_WORLDPOS\\\\n\\\\t\\\\t\\\\n\\\\t\\\\tvarying vec3 vWorldPosition;\\\\n\\\\t#else\\\\n\\\\t\\\\tvarying vec3 vReflect;\\\\n\\\\t\\\\tuniform float refractionRatio;\\\\n\\\\t#endif\\\\n#endif\\\\\\\",envmap_physical_pars_fragment:\\\\\\\"#if defined( USE_ENVMAP )\\\\n\\\\t#ifdef ENVMAP_MODE_REFRACTION\\\\n\\\\t\\\\tuniform float refractionRatio;\\\\n\\\\t#endif\\\\n\\\\tvec3 getIBLIrradiance( const in vec3 normal ) {\\\\n\\\\t\\\\t#if defined( ENVMAP_TYPE_CUBE_UV )\\\\n\\\\t\\\\t\\\\tvec3 worldNormal = inverseTransformDirection( normal, viewMatrix );\\\\n\\\\t\\\\t\\\\tvec4 envMapColor = textureCubeUV( envMap, worldNormal, 1.0 );\\\\n\\\\t\\\\t\\\\treturn PI * envMapColor.rgb * envMapIntensity;\\\\n\\\\t\\\\t#else\\\\n\\\\t\\\\t\\\\treturn vec3( 0.0 );\\\\n\\\\t\\\\t#endif\\\\n\\\\t}\\\\n\\\\tvec3 getIBLRadiance( const in vec3 viewDir, const in vec3 normal, const in float roughness ) {\\\\n\\\\t\\\\t#if defined( ENVMAP_TYPE_CUBE_UV )\\\\n\\\\t\\\\t\\\\tvec3 reflectVec;\\\\n\\\\t\\\\t\\\\t#ifdef ENVMAP_MODE_REFLECTION\\\\n\\\\t\\\\t\\\\t\\\\treflectVec = reflect( - viewDir, normal );\\\\n\\\\t\\\\t\\\\t\\\\treflectVec = normalize( mix( reflectVec, normal, roughness * roughness) );\\\\n\\\\t\\\\t\\\\t#else\\\\n\\\\t\\\\t\\\\t\\\\treflectVec = refract( - viewDir, normal, refractionRatio );\\\\n\\\\t\\\\t\\\\t#endif\\\\n\\\\t\\\\t\\\\treflectVec = inverseTransformDirection( reflectVec, viewMatrix );\\\\n\\\\t\\\\t\\\\tvec4 envMapColor = textureCubeUV( envMap, reflectVec, roughness );\\\\n\\\\t\\\\t\\\\treturn envMapColor.rgb * envMapIntensity;\\\\n\\\\t\\\\t#else\\\\n\\\\t\\\\t\\\\treturn vec3( 0.0 );\\\\n\\\\t\\\\t#endif\\\\n\\\\t}\\\\n#endif\\\\\\\",envmap_vertex:\\\\\\\"#ifdef USE_ENVMAP\\\\n\\\\t#ifdef ENV_WORLDPOS\\\\n\\\\t\\\\tvWorldPosition = worldPosition.xyz;\\\\n\\\\t#else\\\\n\\\\t\\\\tvec3 cameraToVertex;\\\\n\\\\t\\\\tif ( isOrthographic ) {\\\\n\\\\t\\\\t\\\\tcameraToVertex = normalize( vec3( - viewMatrix[ 0 ][ 2 ], - viewMatrix[ 1 ][ 2 ], - viewMatrix[ 2 ][ 2 ] ) );\\\\n\\\\t\\\\t} else {\\\\n\\\\t\\\\t\\\\tcameraToVertex = normalize( worldPosition.xyz - cameraPosition );\\\\n\\\\t\\\\t}\\\\n\\\\t\\\\tvec3 worldNormal = inverseTransformDirection( transformedNormal, viewMatrix );\\\\n\\\\t\\\\t#ifdef ENVMAP_MODE_REFLECTION\\\\n\\\\t\\\\t\\\\tvReflect = reflect( cameraToVertex, worldNormal );\\\\n\\\\t\\\\t#else\\\\n\\\\t\\\\t\\\\tvReflect = refract( cameraToVertex, worldNormal, refractionRatio );\\\\n\\\\t\\\\t#endif\\\\n\\\\t#endif\\\\n#endif\\\\\\\",fog_vertex:\\\\\\\"#ifdef USE_FOG\\\\n\\\\tvFogDepth = - mvPosition.z;\\\\n#endif\\\\\\\",fog_pars_vertex:\\\\\\\"#ifdef USE_FOG\\\\n\\\\tvarying float vFogDepth;\\\\n#endif\\\\\\\",fog_fragment:\\\\\\\"#ifdef USE_FOG\\\\n\\\\t#ifdef FOG_EXP2\\\\n\\\\t\\\\tfloat fogFactor = 1.0 - exp( - fogDensity * fogDensity * vFogDepth * vFogDepth );\\\\n\\\\t#else\\\\n\\\\t\\\\tfloat fogFactor = smoothstep( fogNear, fogFar, vFogDepth );\\\\n\\\\t#endif\\\\n\\\\tgl_FragColor.rgb = mix( gl_FragColor.rgb, fogColor, fogFactor );\\\\n#endif\\\\\\\",fog_pars_fragment:\\\\\\\"#ifdef USE_FOG\\\\n\\\\tuniform vec3 fogColor;\\\\n\\\\tvarying float vFogDepth;\\\\n\\\\t#ifdef FOG_EXP2\\\\n\\\\t\\\\tuniform float fogDensity;\\\\n\\\\t#else\\\\n\\\\t\\\\tuniform float fogNear;\\\\n\\\\t\\\\tuniform float fogFar;\\\\n\\\\t#endif\\\\n#endif\\\\\\\",gradientmap_pars_fragment:\\\\\\\"#ifdef USE_GRADIENTMAP\\\\n\\\\tuniform sampler2D gradientMap;\\\\n#endif\\\\nvec3 getGradientIrradiance( vec3 normal, vec3 lightDirection ) {\\\\n\\\\tfloat dotNL = dot( normal, lightDirection );\\\\n\\\\tvec2 coord = vec2( dotNL * 0.5 + 0.5, 0.0 );\\\\n\\\\t#ifdef USE_GRADIENTMAP\\\\n\\\\t\\\\treturn texture2D( gradientMap, coord ).rgb;\\\\n\\\\t#else\\\\n\\\\t\\\\treturn ( coord.x < 0.7 ) ? vec3( 0.7 ) : vec3( 1.0 );\\\\n\\\\t#endif\\\\n}\\\\\\\",lightmap_fragment:\\\\\\\"#ifdef USE_LIGHTMAP\\\\n\\\\tvec4 lightMapTexel = texture2D( lightMap, vUv2 );\\\\n\\\\tvec3 lightMapIrradiance = lightMapTexelToLinear( lightMapTexel ).rgb * lightMapIntensity;\\\\n\\\\t#ifndef PHYSICALLY_CORRECT_LIGHTS\\\\n\\\\t\\\\tlightMapIrradiance *= PI;\\\\n\\\\t#endif\\\\n\\\\treflectedLight.indirectDiffuse += lightMapIrradiance;\\\\n#endif\\\\\\\",lightmap_pars_fragment:\\\\\\\"#ifdef USE_LIGHTMAP\\\\n\\\\tuniform sampler2D lightMap;\\\\n\\\\tuniform float lightMapIntensity;\\\\n#endif\\\\\\\",lights_lambert_vertex:\\\\\\\"vec3 diffuse = vec3( 1.0 );\\\\nGeometricContext geometry;\\\\ngeometry.position = mvPosition.xyz;\\\\ngeometry.normal = normalize( transformedNormal );\\\\ngeometry.viewDir = ( isOrthographic ) ? vec3( 0, 0, 1 ) : normalize( -mvPosition.xyz );\\\\nGeometricContext backGeometry;\\\\nbackGeometry.position = geometry.position;\\\\nbackGeometry.normal = -geometry.normal;\\\\nbackGeometry.viewDir = geometry.viewDir;\\\\nvLightFront = vec3( 0.0 );\\\\nvIndirectFront = vec3( 0.0 );\\\\n#ifdef DOUBLE_SIDED\\\\n\\\\tvLightBack = vec3( 0.0 );\\\\n\\\\tvIndirectBack = vec3( 0.0 );\\\\n#endif\\\\nIncidentLight directLight;\\\\nfloat dotNL;\\\\nvec3 directLightColor_Diffuse;\\\\nvIndirectFront += getAmbientLightIrradiance( ambientLightColor );\\\\nvIndirectFront += getLightProbeIrradiance( lightProbe, geometry.normal );\\\\n#ifdef DOUBLE_SIDED\\\\n\\\\tvIndirectBack += getAmbientLightIrradiance( ambientLightColor );\\\\n\\\\tvIndirectBack += getLightProbeIrradiance( lightProbe, backGeometry.normal );\\\\n#endif\\\\n#if NUM_POINT_LIGHTS > 0\\\\n\\\\t#pragma unroll_loop_start\\\\n\\\\tfor ( int i = 0; i < NUM_POINT_LIGHTS; i ++ ) {\\\\n\\\\t\\\\tgetPointLightInfo( pointLights[ i ], geometry, directLight );\\\\n\\\\t\\\\tdotNL = dot( geometry.normal, directLight.direction );\\\\n\\\\t\\\\tdirectLightColor_Diffuse = directLight.color;\\\\n\\\\t\\\\tvLightFront += saturate( dotNL ) * directLightColor_Diffuse;\\\\n\\\\t\\\\t#ifdef DOUBLE_SIDED\\\\n\\\\t\\\\t\\\\tvLightBack += saturate( - dotNL ) * directLightColor_Diffuse;\\\\n\\\\t\\\\t#endif\\\\n\\\\t}\\\\n\\\\t#pragma unroll_loop_end\\\\n#endif\\\\n#if NUM_SPOT_LIGHTS > 0\\\\n\\\\t#pragma unroll_loop_start\\\\n\\\\tfor ( int i = 0; i < NUM_SPOT_LIGHTS; i ++ ) {\\\\n\\\\t\\\\tgetSpotLightInfo( spotLights[ i ], geometry, directLight );\\\\n\\\\t\\\\tdotNL = dot( geometry.normal, directLight.direction );\\\\n\\\\t\\\\tdirectLightColor_Diffuse = directLight.color;\\\\n\\\\t\\\\tvLightFront += saturate( dotNL ) * directLightColor_Diffuse;\\\\n\\\\t\\\\t#ifdef DOUBLE_SIDED\\\\n\\\\t\\\\t\\\\tvLightBack += saturate( - dotNL ) * directLightColor_Diffuse;\\\\n\\\\t\\\\t#endif\\\\n\\\\t}\\\\n\\\\t#pragma unroll_loop_end\\\\n#endif\\\\n#if NUM_DIR_LIGHTS > 0\\\\n\\\\t#pragma unroll_loop_start\\\\n\\\\tfor ( int i = 0; i < NUM_DIR_LIGHTS; i ++ ) {\\\\n\\\\t\\\\tgetDirectionalLightInfo( directionalLights[ i ], geometry, directLight );\\\\n\\\\t\\\\tdotNL = dot( geometry.normal, directLight.direction );\\\\n\\\\t\\\\tdirectLightColor_Diffuse = directLight.color;\\\\n\\\\t\\\\tvLightFront += saturate( dotNL ) * directLightColor_Diffuse;\\\\n\\\\t\\\\t#ifdef DOUBLE_SIDED\\\\n\\\\t\\\\t\\\\tvLightBack += saturate( - dotNL ) * directLightColor_Diffuse;\\\\n\\\\t\\\\t#endif\\\\n\\\\t}\\\\n\\\\t#pragma unroll_loop_end\\\\n#endif\\\\n#if NUM_HEMI_LIGHTS > 0\\\\n\\\\t#pragma unroll_loop_start\\\\n\\\\tfor ( int i = 0; i < NUM_HEMI_LIGHTS; i ++ ) {\\\\n\\\\t\\\\tvIndirectFront += getHemisphereLightIrradiance( hemisphereLights[ i ], geometry.normal );\\\\n\\\\t\\\\t#ifdef DOUBLE_SIDED\\\\n\\\\t\\\\t\\\\tvIndirectBack += getHemisphereLightIrradiance( hemisphereLights[ i ], backGeometry.normal );\\\\n\\\\t\\\\t#endif\\\\n\\\\t}\\\\n\\\\t#pragma unroll_loop_end\\\\n#endif\\\\\\\",lights_pars_begin:\\\\\\\"uniform bool receiveShadow;\\\\nuniform vec3 ambientLightColor;\\\\nuniform vec3 lightProbe[ 9 ];\\\\nvec3 shGetIrradianceAt( in vec3 normal, in vec3 shCoefficients[ 9 ] ) {\\\\n\\\\tfloat x = normal.x, y = normal.y, z = normal.z;\\\\n\\\\tvec3 result = shCoefficients[ 0 ] * 0.886227;\\\\n\\\\tresult += shCoefficients[ 1 ] * 2.0 * 0.511664 * y;\\\\n\\\\tresult += shCoefficients[ 2 ] * 2.0 * 0.511664 * z;\\\\n\\\\tresult += shCoefficients[ 3 ] * 2.0 * 0.511664 * x;\\\\n\\\\tresult += shCoefficients[ 4 ] * 2.0 * 0.429043 * x * y;\\\\n\\\\tresult += shCoefficients[ 5 ] * 2.0 * 0.429043 * y * z;\\\\n\\\\tresult += shCoefficients[ 6 ] * ( 0.743125 * z * z - 0.247708 );\\\\n\\\\tresult += shCoefficients[ 7 ] * 2.0 * 0.429043 * x * z;\\\\n\\\\tresult += shCoefficients[ 8 ] * 0.429043 * ( x * x - y * y );\\\\n\\\\treturn result;\\\\n}\\\\nvec3 getLightProbeIrradiance( const in vec3 lightProbe[ 9 ], const in vec3 normal ) {\\\\n\\\\tvec3 worldNormal = inverseTransformDirection( normal, viewMatrix );\\\\n\\\\tvec3 irradiance = shGetIrradianceAt( worldNormal, lightProbe );\\\\n\\\\treturn irradiance;\\\\n}\\\\nvec3 getAmbientLightIrradiance( const in vec3 ambientLightColor ) {\\\\n\\\\tvec3 irradiance = ambientLightColor;\\\\n\\\\treturn irradiance;\\\\n}\\\\nfloat getDistanceAttenuation( const in float lightDistance, const in float cutoffDistance, const in float decayExponent ) {\\\\n\\\\t#if defined ( PHYSICALLY_CORRECT_LIGHTS )\\\\n\\\\t\\\\tfloat distanceFalloff = 1.0 / max( pow( lightDistance, decayExponent ), 0.01 );\\\\n\\\\t\\\\tif ( cutoffDistance > 0.0 ) {\\\\n\\\\t\\\\t\\\\tdistanceFalloff *= pow2( saturate( 1.0 - pow4( lightDistance / cutoffDistance ) ) );\\\\n\\\\t\\\\t}\\\\n\\\\t\\\\treturn distanceFalloff;\\\\n\\\\t#else\\\\n\\\\t\\\\tif ( cutoffDistance > 0.0 && decayExponent > 0.0 ) {\\\\n\\\\t\\\\t\\\\treturn pow( saturate( - lightDistance / cutoffDistance + 1.0 ), decayExponent );\\\\n\\\\t\\\\t}\\\\n\\\\t\\\\treturn 1.0;\\\\n\\\\t#endif\\\\n}\\\\nfloat getSpotAttenuation( const in float coneCosine, const in float penumbraCosine, const in float angleCosine ) {\\\\n\\\\treturn smoothstep( coneCosine, penumbraCosine, angleCosine );\\\\n}\\\\n#if NUM_DIR_LIGHTS > 0\\\\n\\\\tstruct DirectionalLight {\\\\n\\\\t\\\\tvec3 direction;\\\\n\\\\t\\\\tvec3 color;\\\\n\\\\t};\\\\n\\\\tuniform DirectionalLight directionalLights[ NUM_DIR_LIGHTS ];\\\\n\\\\tvoid getDirectionalLightInfo( const in DirectionalLight directionalLight, const in GeometricContext geometry, out IncidentLight light ) {\\\\n\\\\t\\\\tlight.color = directionalLight.color;\\\\n\\\\t\\\\tlight.direction = directionalLight.direction;\\\\n\\\\t\\\\tlight.visible = true;\\\\n\\\\t}\\\\n#endif\\\\n#if NUM_POINT_LIGHTS > 0\\\\n\\\\tstruct PointLight {\\\\n\\\\t\\\\tvec3 position;\\\\n\\\\t\\\\tvec3 color;\\\\n\\\\t\\\\tfloat distance;\\\\n\\\\t\\\\tfloat decay;\\\\n\\\\t};\\\\n\\\\tuniform PointLight pointLights[ NUM_POINT_LIGHTS ];\\\\n\\\\tvoid getPointLightInfo( const in PointLight pointLight, const in GeometricContext geometry, out IncidentLight light ) {\\\\n\\\\t\\\\tvec3 lVector = pointLight.position - geometry.position;\\\\n\\\\t\\\\tlight.direction = normalize( lVector );\\\\n\\\\t\\\\tfloat lightDistance = length( lVector );\\\\n\\\\t\\\\tlight.color = pointLight.color;\\\\n\\\\t\\\\tlight.color *= getDistanceAttenuation( lightDistance, pointLight.distance, pointLight.decay );\\\\n\\\\t\\\\tlight.visible = ( light.color != vec3( 0.0 ) );\\\\n\\\\t}\\\\n#endif\\\\n#if NUM_SPOT_LIGHTS > 0\\\\n\\\\tstruct SpotLight {\\\\n\\\\t\\\\tvec3 position;\\\\n\\\\t\\\\tvec3 direction;\\\\n\\\\t\\\\tvec3 color;\\\\n\\\\t\\\\tfloat distance;\\\\n\\\\t\\\\tfloat decay;\\\\n\\\\t\\\\tfloat coneCos;\\\\n\\\\t\\\\tfloat penumbraCos;\\\\n\\\\t};\\\\n\\\\tuniform SpotLight spotLights[ NUM_SPOT_LIGHTS ];\\\\n\\\\tvoid getSpotLightInfo( const in SpotLight spotLight, const in GeometricContext geometry, out IncidentLight light ) {\\\\n\\\\t\\\\tvec3 lVector = spotLight.position - geometry.position;\\\\n\\\\t\\\\tlight.direction = normalize( lVector );\\\\n\\\\t\\\\tfloat angleCos = dot( light.direction, spotLight.direction );\\\\n\\\\t\\\\tfloat spotAttenuation = getSpotAttenuation( spotLight.coneCos, spotLight.penumbraCos, angleCos );\\\\n\\\\t\\\\tif ( spotAttenuation > 0.0 ) {\\\\n\\\\t\\\\t\\\\tfloat lightDistance = length( lVector );\\\\n\\\\t\\\\t\\\\tlight.color = spotLight.color * spotAttenuation;\\\\n\\\\t\\\\t\\\\tlight.color *= getDistanceAttenuation( lightDistance, spotLight.distance, spotLight.decay );\\\\n\\\\t\\\\t\\\\tlight.visible = ( light.color != vec3( 0.0 ) );\\\\n\\\\t\\\\t} else {\\\\n\\\\t\\\\t\\\\tlight.color = vec3( 0.0 );\\\\n\\\\t\\\\t\\\\tlight.visible = false;\\\\n\\\\t\\\\t}\\\\n\\\\t}\\\\n#endif\\\\n#if NUM_RECT_AREA_LIGHTS > 0\\\\n\\\\tstruct RectAreaLight {\\\\n\\\\t\\\\tvec3 color;\\\\n\\\\t\\\\tvec3 position;\\\\n\\\\t\\\\tvec3 halfWidth;\\\\n\\\\t\\\\tvec3 halfHeight;\\\\n\\\\t};\\\\n\\\\tuniform sampler2D ltc_1;\\\\tuniform sampler2D ltc_2;\\\\n\\\\tuniform RectAreaLight rectAreaLights[ NUM_RECT_AREA_LIGHTS ];\\\\n#endif\\\\n#if NUM_HEMI_LIGHTS > 0\\\\n\\\\tstruct HemisphereLight {\\\\n\\\\t\\\\tvec3 direction;\\\\n\\\\t\\\\tvec3 skyColor;\\\\n\\\\t\\\\tvec3 groundColor;\\\\n\\\\t};\\\\n\\\\tuniform HemisphereLight hemisphereLights[ NUM_HEMI_LIGHTS ];\\\\n\\\\tvec3 getHemisphereLightIrradiance( const in HemisphereLight hemiLight, const in vec3 normal ) {\\\\n\\\\t\\\\tfloat dotNL = dot( normal, hemiLight.direction );\\\\n\\\\t\\\\tfloat hemiDiffuseWeight = 0.5 * dotNL + 0.5;\\\\n\\\\t\\\\tvec3 irradiance = mix( hemiLight.groundColor, hemiLight.skyColor, hemiDiffuseWeight );\\\\n\\\\t\\\\treturn irradiance;\\\\n\\\\t}\\\\n#endif\\\\\\\",lights_toon_fragment:\\\\\\\"ToonMaterial material;\\\\nmaterial.diffuseColor = diffuseColor.rgb;\\\\\\\",lights_toon_pars_fragment:\\\\\\\"varying vec3 vViewPosition;\\\\nstruct ToonMaterial {\\\\n\\\\tvec3 diffuseColor;\\\\n};\\\\nvoid RE_Direct_Toon( const in IncidentLight directLight, const in GeometricContext geometry, const in ToonMaterial material, inout ReflectedLight reflectedLight ) {\\\\n\\\\tvec3 irradiance = getGradientIrradiance( geometry.normal, directLight.direction ) * directLight.color;\\\\n\\\\treflectedLight.directDiffuse += irradiance * BRDF_Lambert( material.diffuseColor );\\\\n}\\\\nvoid RE_IndirectDiffuse_Toon( const in vec3 irradiance, const in GeometricContext geometry, const in ToonMaterial material, inout ReflectedLight reflectedLight ) {\\\\n\\\\treflectedLight.indirectDiffuse += irradiance * BRDF_Lambert( material.diffuseColor );\\\\n}\\\\n#define RE_Direct\\\\t\\\\t\\\\t\\\\tRE_Direct_Toon\\\\n#define RE_IndirectDiffuse\\\\t\\\\tRE_IndirectDiffuse_Toon\\\\n#define Material_LightProbeLOD( material )\\\\t(0)\\\\\\\",lights_phong_fragment:\\\\\\\"BlinnPhongMaterial material;\\\\nmaterial.diffuseColor = diffuseColor.rgb;\\\\nmaterial.specularColor = specular;\\\\nmaterial.specularShininess = shininess;\\\\nmaterial.specularStrength = specularStrength;\\\\\\\",lights_phong_pars_fragment:\\\\\\\"varying vec3 vViewPosition;\\\\nstruct BlinnPhongMaterial {\\\\n\\\\tvec3 diffuseColor;\\\\n\\\\tvec3 specularColor;\\\\n\\\\tfloat specularShininess;\\\\n\\\\tfloat specularStrength;\\\\n};\\\\nvoid RE_Direct_BlinnPhong( const in IncidentLight directLight, const in GeometricContext geometry, const in BlinnPhongMaterial material, inout ReflectedLight reflectedLight ) {\\\\n\\\\tfloat dotNL = saturate( dot( geometry.normal, directLight.direction ) );\\\\n\\\\tvec3 irradiance = dotNL * directLight.color;\\\\n\\\\treflectedLight.directDiffuse += irradiance * BRDF_Lambert( material.diffuseColor );\\\\n\\\\treflectedLight.directSpecular += irradiance * BRDF_BlinnPhong( directLight.direction, geometry.viewDir, geometry.normal, material.specularColor, material.specularShininess ) * material.specularStrength;\\\\n}\\\\nvoid RE_IndirectDiffuse_BlinnPhong( const in vec3 irradiance, const in GeometricContext geometry, const in BlinnPhongMaterial material, inout ReflectedLight reflectedLight ) {\\\\n\\\\treflectedLight.indirectDiffuse += irradiance * BRDF_Lambert( material.diffuseColor );\\\\n}\\\\n#define RE_Direct\\\\t\\\\t\\\\t\\\\tRE_Direct_BlinnPhong\\\\n#define RE_IndirectDiffuse\\\\t\\\\tRE_IndirectDiffuse_BlinnPhong\\\\n#define Material_LightProbeLOD( material )\\\\t(0)\\\\\\\",lights_physical_fragment:\\\\\\\"PhysicalMaterial material;\\\\nmaterial.diffuseColor = diffuseColor.rgb * ( 1.0 - metalnessFactor );\\\\nvec3 dxy = max( abs( dFdx( geometryNormal ) ), abs( dFdy( geometryNormal ) ) );\\\\nfloat geometryRoughness = max( max( dxy.x, dxy.y ), dxy.z );\\\\nmaterial.roughness = max( roughnessFactor, 0.0525 );material.roughness += geometryRoughness;\\\\nmaterial.roughness = min( material.roughness, 1.0 );\\\\n#ifdef IOR\\\\n\\\\t#ifdef SPECULAR\\\\n\\\\t\\\\tfloat specularIntensityFactor = specularIntensity;\\\\n\\\\t\\\\tvec3 specularTintFactor = specularTint;\\\\n\\\\t\\\\t#ifdef USE_SPECULARINTENSITYMAP\\\\n\\\\t\\\\t\\\\tspecularIntensityFactor *= texture2D( specularIntensityMap, vUv ).a;\\\\n\\\\t\\\\t#endif\\\\n\\\\t\\\\t#ifdef USE_SPECULARTINTMAP\\\\n\\\\t\\\\t\\\\tspecularTintFactor *= specularTintMapTexelToLinear( texture2D( specularTintMap, vUv ) ).rgb;\\\\n\\\\t\\\\t#endif\\\\n\\\\t\\\\tmaterial.specularF90 = mix( specularIntensityFactor, 1.0, metalnessFactor );\\\\n\\\\t#else\\\\n\\\\t\\\\tfloat specularIntensityFactor = 1.0;\\\\n\\\\t\\\\tvec3 specularTintFactor = vec3( 1.0 );\\\\n\\\\t\\\\tmaterial.specularF90 = 1.0;\\\\n\\\\t#endif\\\\n\\\\tmaterial.specularColor = mix( min( pow2( ( ior - 1.0 ) / ( ior + 1.0 ) ) * specularTintFactor, vec3( 1.0 ) ) * specularIntensityFactor, diffuseColor.rgb, metalnessFactor );\\\\n#else\\\\n\\\\tmaterial.specularColor = mix( vec3( 0.04 ), diffuseColor.rgb, metalnessFactor );\\\\n\\\\tmaterial.specularF90 = 1.0;\\\\n#endif\\\\n#ifdef USE_CLEARCOAT\\\\n\\\\tmaterial.clearcoat = clearcoat;\\\\n\\\\tmaterial.clearcoatRoughness = clearcoatRoughness;\\\\n\\\\tmaterial.clearcoatF0 = vec3( 0.04 );\\\\n\\\\tmaterial.clearcoatF90 = 1.0;\\\\n\\\\t#ifdef USE_CLEARCOATMAP\\\\n\\\\t\\\\tmaterial.clearcoat *= texture2D( clearcoatMap, vUv ).x;\\\\n\\\\t#endif\\\\n\\\\t#ifdef USE_CLEARCOAT_ROUGHNESSMAP\\\\n\\\\t\\\\tmaterial.clearcoatRoughness *= texture2D( clearcoatRoughnessMap, vUv ).y;\\\\n\\\\t#endif\\\\n\\\\tmaterial.clearcoat = saturate( material.clearcoat );\\\\tmaterial.clearcoatRoughness = max( material.clearcoatRoughness, 0.0525 );\\\\n\\\\tmaterial.clearcoatRoughness += geometryRoughness;\\\\n\\\\tmaterial.clearcoatRoughness = min( material.clearcoatRoughness, 1.0 );\\\\n#endif\\\\n#ifdef USE_SHEEN\\\\n\\\\tmaterial.sheenTint = sheenTint;\\\\n\\\\tmaterial.sheenRoughness = clamp( sheenRoughness, 0.07, 1.0 );\\\\n#endif\\\\\\\",lights_physical_pars_fragment:\\\\\\\"struct PhysicalMaterial {\\\\n\\\\tvec3 diffuseColor;\\\\n\\\\tfloat roughness;\\\\n\\\\tvec3 specularColor;\\\\n\\\\tfloat specularF90;\\\\n\\\\t#ifdef USE_CLEARCOAT\\\\n\\\\t\\\\tfloat clearcoat;\\\\n\\\\t\\\\tfloat clearcoatRoughness;\\\\n\\\\t\\\\tvec3 clearcoatF0;\\\\n\\\\t\\\\tfloat clearcoatF90;\\\\n\\\\t#endif\\\\n\\\\t#ifdef USE_SHEEN\\\\n\\\\t\\\\tvec3 sheenTint;\\\\n\\\\t\\\\tfloat sheenRoughness;\\\\n\\\\t#endif\\\\n};\\\\nvec3 clearcoatSpecular = vec3( 0.0 );\\\\nvec2 DFGApprox( const in vec3 normal, const in vec3 viewDir, const in float roughness ) {\\\\n\\\\tfloat dotNV = saturate( dot( normal, viewDir ) );\\\\n\\\\tconst vec4 c0 = vec4( - 1, - 0.0275, - 0.572, 0.022 );\\\\n\\\\tconst vec4 c1 = vec4( 1, 0.0425, 1.04, - 0.04 );\\\\n\\\\tvec4 r = roughness * c0 + c1;\\\\n\\\\tfloat a004 = min( r.x * r.x, exp2( - 9.28 * dotNV ) ) * r.x + r.y;\\\\n\\\\tvec2 fab = vec2( - 1.04, 1.04 ) * a004 + r.zw;\\\\n\\\\treturn fab;\\\\n}\\\\nvec3 EnvironmentBRDF( const in vec3 normal, const in vec3 viewDir, const in vec3 specularColor, const in float specularF90, const in float roughness ) {\\\\n\\\\tvec2 fab = DFGApprox( normal, viewDir, roughness );\\\\n\\\\treturn specularColor * fab.x + specularF90 * fab.y;\\\\n}\\\\nvoid computeMultiscattering( const in vec3 normal, const in vec3 viewDir, const in vec3 specularColor, const in float specularF90, const in float roughness, inout vec3 singleScatter, inout vec3 multiScatter ) {\\\\n\\\\tvec2 fab = DFGApprox( normal, viewDir, roughness );\\\\n\\\\tvec3 FssEss = specularColor * fab.x + specularF90 * fab.y;\\\\n\\\\tfloat Ess = fab.x + fab.y;\\\\n\\\\tfloat Ems = 1.0 - Ess;\\\\n\\\\tvec3 Favg = specularColor + ( 1.0 - specularColor ) * 0.047619;\\\\tvec3 Fms = FssEss * Favg / ( 1.0 - Ems * Favg );\\\\n\\\\tsingleScatter += FssEss;\\\\n\\\\tmultiScatter += Fms * Ems;\\\\n}\\\\n#if NUM_RECT_AREA_LIGHTS > 0\\\\n\\\\tvoid RE_Direct_RectArea_Physical( const in RectAreaLight rectAreaLight, const in GeometricContext geometry, const in PhysicalMaterial material, inout ReflectedLight reflectedLight ) {\\\\n\\\\t\\\\tvec3 normal = geometry.normal;\\\\n\\\\t\\\\tvec3 viewDir = geometry.viewDir;\\\\n\\\\t\\\\tvec3 position = geometry.position;\\\\n\\\\t\\\\tvec3 lightPos = rectAreaLight.position;\\\\n\\\\t\\\\tvec3 halfWidth = rectAreaLight.halfWidth;\\\\n\\\\t\\\\tvec3 halfHeight = rectAreaLight.halfHeight;\\\\n\\\\t\\\\tvec3 lightColor = rectAreaLight.color;\\\\n\\\\t\\\\tfloat roughness = material.roughness;\\\\n\\\\t\\\\tvec3 rectCoords[ 4 ];\\\\n\\\\t\\\\trectCoords[ 0 ] = lightPos + halfWidth - halfHeight;\\\\t\\\\trectCoords[ 1 ] = lightPos - halfWidth - halfHeight;\\\\n\\\\t\\\\trectCoords[ 2 ] = lightPos - halfWidth + halfHeight;\\\\n\\\\t\\\\trectCoords[ 3 ] = lightPos + halfWidth + halfHeight;\\\\n\\\\t\\\\tvec2 uv = LTC_Uv( normal, viewDir, roughness );\\\\n\\\\t\\\\tvec4 t1 = texture2D( ltc_1, uv );\\\\n\\\\t\\\\tvec4 t2 = texture2D( ltc_2, uv );\\\\n\\\\t\\\\tmat3 mInv = mat3(\\\\n\\\\t\\\\t\\\\tvec3( t1.x, 0, t1.y ),\\\\n\\\\t\\\\t\\\\tvec3( 0, 1, 0 ),\\\\n\\\\t\\\\t\\\\tvec3( t1.z, 0, t1.w )\\\\n\\\\t\\\\t);\\\\n\\\\t\\\\tvec3 fresnel = ( material.specularColor * t2.x + ( vec3( 1.0 ) - material.specularColor ) * t2.y );\\\\n\\\\t\\\\treflectedLight.directSpecular += lightColor * fresnel * LTC_Evaluate( normal, viewDir, position, mInv, rectCoords );\\\\n\\\\t\\\\treflectedLight.directDiffuse += lightColor * material.diffuseColor * LTC_Evaluate( normal, viewDir, position, mat3( 1.0 ), rectCoords );\\\\n\\\\t}\\\\n#endif\\\\nvoid RE_Direct_Physical( const in IncidentLight directLight, const in GeometricContext geometry, const in PhysicalMaterial material, inout ReflectedLight reflectedLight ) {\\\\n\\\\tfloat dotNL = saturate( dot( geometry.normal, directLight.direction ) );\\\\n\\\\tvec3 irradiance = dotNL * directLight.color;\\\\n\\\\t#ifdef USE_CLEARCOAT\\\\n\\\\t\\\\tfloat dotNLcc = saturate( dot( geometry.clearcoatNormal, directLight.direction ) );\\\\n\\\\t\\\\tvec3 ccIrradiance = dotNLcc * directLight.color;\\\\n\\\\t\\\\tclearcoatSpecular += ccIrradiance * BRDF_GGX( directLight.direction, geometry.viewDir, geometry.clearcoatNormal, material.clearcoatF0, material.clearcoatF90, material.clearcoatRoughness );\\\\n\\\\t#endif\\\\n\\\\t#ifdef USE_SHEEN\\\\n\\\\t\\\\treflectedLight.directSpecular += irradiance * BRDF_Sheen( directLight.direction, geometry.viewDir, geometry.normal, material.sheenTint, material.sheenRoughness );\\\\n\\\\t#endif\\\\n\\\\treflectedLight.directSpecular += irradiance * BRDF_GGX( directLight.direction, geometry.viewDir, geometry.normal, material.specularColor, material.specularF90, material.roughness );\\\\n\\\\treflectedLight.directDiffuse += irradiance * BRDF_Lambert( material.diffuseColor );\\\\n}\\\\nvoid RE_IndirectDiffuse_Physical( const in vec3 irradiance, const in GeometricContext geometry, const in PhysicalMaterial material, inout ReflectedLight reflectedLight ) {\\\\n\\\\treflectedLight.indirectDiffuse += irradiance * BRDF_Lambert( material.diffuseColor );\\\\n}\\\\nvoid RE_IndirectSpecular_Physical( const in vec3 radiance, const in vec3 irradiance, const in vec3 clearcoatRadiance, const in GeometricContext geometry, const in PhysicalMaterial material, inout ReflectedLight reflectedLight) {\\\\n\\\\t#ifdef USE_CLEARCOAT\\\\n\\\\t\\\\tclearcoatSpecular += clearcoatRadiance * EnvironmentBRDF( geometry.clearcoatNormal, geometry.viewDir, material.clearcoatF0, material.clearcoatF90, material.clearcoatRoughness );\\\\n\\\\t#endif\\\\n\\\\tvec3 singleScattering = vec3( 0.0 );\\\\n\\\\tvec3 multiScattering = vec3( 0.0 );\\\\n\\\\tvec3 cosineWeightedIrradiance = irradiance * RECIPROCAL_PI;\\\\n\\\\tcomputeMultiscattering( geometry.normal, geometry.viewDir, material.specularColor, material.specularF90, material.roughness, singleScattering, multiScattering );\\\\n\\\\tvec3 diffuse = material.diffuseColor * ( 1.0 - ( singleScattering + multiScattering ) );\\\\n\\\\treflectedLight.indirectSpecular += radiance * singleScattering;\\\\n\\\\treflectedLight.indirectSpecular += multiScattering * cosineWeightedIrradiance;\\\\n\\\\treflectedLight.indirectDiffuse += diffuse * cosineWeightedIrradiance;\\\\n}\\\\n#define RE_Direct\\\\t\\\\t\\\\t\\\\tRE_Direct_Physical\\\\n#define RE_Direct_RectArea\\\\t\\\\tRE_Direct_RectArea_Physical\\\\n#define RE_IndirectDiffuse\\\\t\\\\tRE_IndirectDiffuse_Physical\\\\n#define RE_IndirectSpecular\\\\t\\\\tRE_IndirectSpecular_Physical\\\\nfloat computeSpecularOcclusion( const in float dotNV, const in float ambientOcclusion, const in float roughness ) {\\\\n\\\\treturn saturate( pow( dotNV + ambientOcclusion, exp2( - 16.0 * roughness - 1.0 ) ) - 1.0 + ambientOcclusion );\\\\n}\\\\\\\",lights_fragment_begin:\\\\\\\"\\\\nGeometricContext geometry;\\\\ngeometry.position = - vViewPosition;\\\\ngeometry.normal = normal;\\\\ngeometry.viewDir = ( isOrthographic ) ? vec3( 0, 0, 1 ) : normalize( vViewPosition );\\\\n#ifdef USE_CLEARCOAT\\\\n\\\\tgeometry.clearcoatNormal = clearcoatNormal;\\\\n#endif\\\\nIncidentLight directLight;\\\\n#if ( NUM_POINT_LIGHTS > 0 ) && defined( RE_Direct )\\\\n\\\\tPointLight pointLight;\\\\n\\\\t#if defined( USE_SHADOWMAP ) && NUM_POINT_LIGHT_SHADOWS > 0\\\\n\\\\tPointLightShadow pointLightShadow;\\\\n\\\\t#endif\\\\n\\\\t#pragma unroll_loop_start\\\\n\\\\tfor ( int i = 0; i < NUM_POINT_LIGHTS; i ++ ) {\\\\n\\\\t\\\\tpointLight = pointLights[ i ];\\\\n\\\\t\\\\tgetPointLightInfo( pointLight, geometry, directLight );\\\\n\\\\t\\\\t#if defined( USE_SHADOWMAP ) && ( UNROLLED_LOOP_INDEX < NUM_POINT_LIGHT_SHADOWS )\\\\n\\\\t\\\\tpointLightShadow = pointLightShadows[ i ];\\\\n\\\\t\\\\tdirectLight.color *= all( bvec2( directLight.visible, receiveShadow ) ) ? getPointShadow( pointShadowMap[ i ], pointLightShadow.shadowMapSize, pointLightShadow.shadowBias, pointLightShadow.shadowRadius, vPointShadowCoord[ i ], pointLightShadow.shadowCameraNear, pointLightShadow.shadowCameraFar ) : 1.0;\\\\n\\\\t\\\\t#endif\\\\n\\\\t\\\\tRE_Direct( directLight, geometry, material, reflectedLight );\\\\n\\\\t}\\\\n\\\\t#pragma unroll_loop_end\\\\n#endif\\\\n#if ( NUM_SPOT_LIGHTS > 0 ) && defined( RE_Direct )\\\\n\\\\tSpotLight spotLight;\\\\n\\\\t#if defined( USE_SHADOWMAP ) && NUM_SPOT_LIGHT_SHADOWS > 0\\\\n\\\\tSpotLightShadow spotLightShadow;\\\\n\\\\t#endif\\\\n\\\\t#pragma unroll_loop_start\\\\n\\\\tfor ( int i = 0; i < NUM_SPOT_LIGHTS; i ++ ) {\\\\n\\\\t\\\\tspotLight = spotLights[ i ];\\\\n\\\\t\\\\tgetSpotLightInfo( spotLight, geometry, directLight );\\\\n\\\\t\\\\t#if defined( USE_SHADOWMAP ) && ( UNROLLED_LOOP_INDEX < NUM_SPOT_LIGHT_SHADOWS )\\\\n\\\\t\\\\tspotLightShadow = spotLightShadows[ i ];\\\\n\\\\t\\\\tdirectLight.color *= all( bvec2( directLight.visible, receiveShadow ) ) ? getShadow( spotShadowMap[ i ], spotLightShadow.shadowMapSize, spotLightShadow.shadowBias, spotLightShadow.shadowRadius, vSpotShadowCoord[ i ] ) : 1.0;\\\\n\\\\t\\\\t#endif\\\\n\\\\t\\\\tRE_Direct( directLight, geometry, material, reflectedLight );\\\\n\\\\t}\\\\n\\\\t#pragma unroll_loop_end\\\\n#endif\\\\n#if ( NUM_DIR_LIGHTS > 0 ) && defined( RE_Direct )\\\\n\\\\tDirectionalLight directionalLight;\\\\n\\\\t#if defined( USE_SHADOWMAP ) && NUM_DIR_LIGHT_SHADOWS > 0\\\\n\\\\tDirectionalLightShadow directionalLightShadow;\\\\n\\\\t#endif\\\\n\\\\t#pragma unroll_loop_start\\\\n\\\\tfor ( int i = 0; i < NUM_DIR_LIGHTS; i ++ ) {\\\\n\\\\t\\\\tdirectionalLight = directionalLights[ i ];\\\\n\\\\t\\\\tgetDirectionalLightInfo( directionalLight, geometry, directLight );\\\\n\\\\t\\\\t#if defined( USE_SHADOWMAP ) && ( UNROLLED_LOOP_INDEX < NUM_DIR_LIGHT_SHADOWS )\\\\n\\\\t\\\\tdirectionalLightShadow = directionalLightShadows[ i ];\\\\n\\\\t\\\\tdirectLight.color *= all( bvec2( directLight.visible, receiveShadow ) ) ? getShadow( directionalShadowMap[ i ], directionalLightShadow.shadowMapSize, directionalLightShadow.shadowBias, directionalLightShadow.shadowRadius, vDirectionalShadowCoord[ i ] ) : 1.0;\\\\n\\\\t\\\\t#endif\\\\n\\\\t\\\\tRE_Direct( directLight, geometry, material, reflectedLight );\\\\n\\\\t}\\\\n\\\\t#pragma unroll_loop_end\\\\n#endif\\\\n#if ( NUM_RECT_AREA_LIGHTS > 0 ) && defined( RE_Direct_RectArea )\\\\n\\\\tRectAreaLight rectAreaLight;\\\\n\\\\t#pragma unroll_loop_start\\\\n\\\\tfor ( int i = 0; i < NUM_RECT_AREA_LIGHTS; i ++ ) {\\\\n\\\\t\\\\trectAreaLight = rectAreaLights[ i ];\\\\n\\\\t\\\\tRE_Direct_RectArea( rectAreaLight, geometry, material, reflectedLight );\\\\n\\\\t}\\\\n\\\\t#pragma unroll_loop_end\\\\n#endif\\\\n#if defined( RE_IndirectDiffuse )\\\\n\\\\tvec3 iblIrradiance = vec3( 0.0 );\\\\n\\\\tvec3 irradiance = getAmbientLightIrradiance( ambientLightColor );\\\\n\\\\tirradiance += getLightProbeIrradiance( lightProbe, geometry.normal );\\\\n\\\\t#if ( NUM_HEMI_LIGHTS > 0 )\\\\n\\\\t\\\\t#pragma unroll_loop_start\\\\n\\\\t\\\\tfor ( int i = 0; i < NUM_HEMI_LIGHTS; i ++ ) {\\\\n\\\\t\\\\t\\\\tirradiance += getHemisphereLightIrradiance( hemisphereLights[ i ], geometry.normal );\\\\n\\\\t\\\\t}\\\\n\\\\t\\\\t#pragma unroll_loop_end\\\\n\\\\t#endif\\\\n#endif\\\\n#if defined( RE_IndirectSpecular )\\\\n\\\\tvec3 radiance = vec3( 0.0 );\\\\n\\\\tvec3 clearcoatRadiance = vec3( 0.0 );\\\\n#endif\\\\\\\",lights_fragment_maps:\\\\\\\"#if defined( RE_IndirectDiffuse )\\\\n\\\\t#ifdef USE_LIGHTMAP\\\\n\\\\t\\\\tvec4 lightMapTexel = texture2D( lightMap, vUv2 );\\\\n\\\\t\\\\tvec3 lightMapIrradiance = lightMapTexelToLinear( lightMapTexel ).rgb * lightMapIntensity;\\\\n\\\\t\\\\t#ifndef PHYSICALLY_CORRECT_LIGHTS\\\\n\\\\t\\\\t\\\\tlightMapIrradiance *= PI;\\\\n\\\\t\\\\t#endif\\\\n\\\\t\\\\tirradiance += lightMapIrradiance;\\\\n\\\\t#endif\\\\n\\\\t#if defined( USE_ENVMAP ) && defined( STANDARD ) && defined( ENVMAP_TYPE_CUBE_UV )\\\\n\\\\t\\\\tiblIrradiance += getIBLIrradiance( geometry.normal );\\\\n\\\\t#endif\\\\n#endif\\\\n#if defined( USE_ENVMAP ) && defined( RE_IndirectSpecular )\\\\n\\\\tradiance += getIBLRadiance( geometry.viewDir, geometry.normal, material.roughness );\\\\n\\\\t#ifdef USE_CLEARCOAT\\\\n\\\\t\\\\tclearcoatRadiance += getIBLRadiance( geometry.viewDir, geometry.clearcoatNormal, material.clearcoatRoughness );\\\\n\\\\t#endif\\\\n#endif\\\\\\\",lights_fragment_end:\\\\\\\"#if defined( RE_IndirectDiffuse )\\\\n\\\\tRE_IndirectDiffuse( irradiance, geometry, material, reflectedLight );\\\\n#endif\\\\n#if defined( RE_IndirectSpecular )\\\\n\\\\tRE_IndirectSpecular( radiance, iblIrradiance, clearcoatRadiance, geometry, material, reflectedLight );\\\\n#endif\\\\\\\",logdepthbuf_fragment:\\\\\\\"#if defined( USE_LOGDEPTHBUF ) && defined( USE_LOGDEPTHBUF_EXT )\\\\n\\\\tgl_FragDepthEXT = vIsPerspective == 0.0 ? gl_FragCoord.z : log2( vFragDepth ) * logDepthBufFC * 0.5;\\\\n#endif\\\\\\\",logdepthbuf_pars_fragment:\\\\\\\"#if defined( USE_LOGDEPTHBUF ) && defined( USE_LOGDEPTHBUF_EXT )\\\\n\\\\tuniform float logDepthBufFC;\\\\n\\\\tvarying float vFragDepth;\\\\n\\\\tvarying float vIsPerspective;\\\\n#endif\\\\\\\",logdepthbuf_pars_vertex:\\\\\\\"#ifdef USE_LOGDEPTHBUF\\\\n\\\\t#ifdef USE_LOGDEPTHBUF_EXT\\\\n\\\\t\\\\tvarying float vFragDepth;\\\\n\\\\t\\\\tvarying float vIsPerspective;\\\\n\\\\t#else\\\\n\\\\t\\\\tuniform float logDepthBufFC;\\\\n\\\\t#endif\\\\n#endif\\\\\\\",logdepthbuf_vertex:\\\\\\\"#ifdef USE_LOGDEPTHBUF\\\\n\\\\t#ifdef USE_LOGDEPTHBUF_EXT\\\\n\\\\t\\\\tvFragDepth = 1.0 + gl_Position.w;\\\\n\\\\t\\\\tvIsPerspective = float( isPerspectiveMatrix( projectionMatrix ) );\\\\n\\\\t#else\\\\n\\\\t\\\\tif ( isPerspectiveMatrix( projectionMatrix ) ) {\\\\n\\\\t\\\\t\\\\tgl_Position.z = log2( max( EPSILON, gl_Position.w + 1.0 ) ) * logDepthBufFC - 1.0;\\\\n\\\\t\\\\t\\\\tgl_Position.z *= gl_Position.w;\\\\n\\\\t\\\\t}\\\\n\\\\t#endif\\\\n#endif\\\\\\\",map_fragment:\\\\\\\"#ifdef USE_MAP\\\\n\\\\tvec4 texelColor = texture2D( map, vUv );\\\\n\\\\ttexelColor = mapTexelToLinear( texelColor );\\\\n\\\\tdiffuseColor *= texelColor;\\\\n#endif\\\\\\\",map_pars_fragment:\\\\\\\"#ifdef USE_MAP\\\\n\\\\tuniform sampler2D map;\\\\n#endif\\\\\\\",map_particle_fragment:\\\\\\\"#if defined( USE_MAP ) || defined( USE_ALPHAMAP )\\\\n\\\\tvec2 uv = ( uvTransform * vec3( gl_PointCoord.x, 1.0 - gl_PointCoord.y, 1 ) ).xy;\\\\n#endif\\\\n#ifdef USE_MAP\\\\n\\\\tvec4 mapTexel = texture2D( map, uv );\\\\n\\\\tdiffuseColor *= mapTexelToLinear( mapTexel );\\\\n#endif\\\\n#ifdef USE_ALPHAMAP\\\\n\\\\tdiffuseColor.a *= texture2D( alphaMap, uv ).g;\\\\n#endif\\\\\\\",map_particle_pars_fragment:\\\\\\\"#if defined( USE_MAP ) || defined( USE_ALPHAMAP )\\\\n\\\\tuniform mat3 uvTransform;\\\\n#endif\\\\n#ifdef USE_MAP\\\\n\\\\tuniform sampler2D map;\\\\n#endif\\\\n#ifdef USE_ALPHAMAP\\\\n\\\\tuniform sampler2D alphaMap;\\\\n#endif\\\\\\\",metalnessmap_fragment:\\\\\\\"float metalnessFactor = metalness;\\\\n#ifdef USE_METALNESSMAP\\\\n\\\\tvec4 texelMetalness = texture2D( metalnessMap, vUv );\\\\n\\\\tmetalnessFactor *= texelMetalness.b;\\\\n#endif\\\\\\\",metalnessmap_pars_fragment:\\\\\\\"#ifdef USE_METALNESSMAP\\\\n\\\\tuniform sampler2D metalnessMap;\\\\n#endif\\\\\\\",morphnormal_vertex:\\\\\\\"#ifdef USE_MORPHNORMALS\\\\n\\\\tobjectNormal *= morphTargetBaseInfluence;\\\\n\\\\t#ifdef MORPHTARGETS_TEXTURE\\\\n\\\\t\\\\tfor ( int i = 0; i < MORPHTARGETS_COUNT; i ++ ) {\\\\n\\\\t\\\\t\\\\tif ( morphTargetInfluences[ i ] > 0.0 ) objectNormal += getMorph( gl_VertexID, i, 1, 2 ) * morphTargetInfluences[ i ];\\\\n\\\\t\\\\t}\\\\n\\\\t#else\\\\n\\\\t\\\\tobjectNormal += morphNormal0 * morphTargetInfluences[ 0 ];\\\\n\\\\t\\\\tobjectNormal += morphNormal1 * morphTargetInfluences[ 1 ];\\\\n\\\\t\\\\tobjectNormal += morphNormal2 * morphTargetInfluences[ 2 ];\\\\n\\\\t\\\\tobjectNormal += morphNormal3 * morphTargetInfluences[ 3 ];\\\\n\\\\t#endif\\\\n#endif\\\\\\\",morphtarget_pars_vertex:\\\\\\\"#ifdef USE_MORPHTARGETS\\\\n\\\\tuniform float morphTargetBaseInfluence;\\\\n\\\\t#ifdef MORPHTARGETS_TEXTURE\\\\n\\\\t\\\\tuniform float morphTargetInfluences[ MORPHTARGETS_COUNT ];\\\\n\\\\t\\\\tuniform sampler2DArray morphTargetsTexture;\\\\n\\\\t\\\\tuniform vec2 morphTargetsTextureSize;\\\\n\\\\t\\\\tvec3 getMorph( const in int vertexIndex, const in int morphTargetIndex, const in int offset, const in int stride ) {\\\\n\\\\t\\\\t\\\\tfloat texelIndex = float( vertexIndex * stride + offset );\\\\n\\\\t\\\\t\\\\tfloat y = floor( texelIndex / morphTargetsTextureSize.x );\\\\n\\\\t\\\\t\\\\tfloat x = texelIndex - y * morphTargetsTextureSize.x;\\\\n\\\\t\\\\t\\\\tvec3 morphUV = vec3( ( x + 0.5 ) / morphTargetsTextureSize.x, y / morphTargetsTextureSize.y, morphTargetIndex );\\\\n\\\\t\\\\t\\\\treturn texture( morphTargetsTexture, morphUV ).xyz;\\\\n\\\\t\\\\t}\\\\n\\\\t#else\\\\n\\\\t\\\\t#ifndef USE_MORPHNORMALS\\\\n\\\\t\\\\t\\\\tuniform float morphTargetInfluences[ 8 ];\\\\n\\\\t\\\\t#else\\\\n\\\\t\\\\t\\\\tuniform float morphTargetInfluences[ 4 ];\\\\n\\\\t\\\\t#endif\\\\n\\\\t#endif\\\\n#endif\\\\\\\",morphtarget_vertex:\\\\\\\"#ifdef USE_MORPHTARGETS\\\\n\\\\ttransformed *= morphTargetBaseInfluence;\\\\n\\\\t#ifdef MORPHTARGETS_TEXTURE\\\\n\\\\t\\\\tfor ( int i = 0; i < MORPHTARGETS_COUNT; i ++ ) {\\\\n\\\\t\\\\t\\\\t#ifndef USE_MORPHNORMALS\\\\n\\\\t\\\\t\\\\t\\\\tif ( morphTargetInfluences[ i ] > 0.0 ) transformed += getMorph( gl_VertexID, i, 0, 1 ) * morphTargetInfluences[ i ];\\\\n\\\\t\\\\t\\\\t#else\\\\n\\\\t\\\\t\\\\t\\\\tif ( morphTargetInfluences[ i ] > 0.0 ) transformed += getMorph( gl_VertexID, i, 0, 2 ) * morphTargetInfluences[ i ];\\\\n\\\\t\\\\t\\\\t#endif\\\\n\\\\t\\\\t}\\\\n\\\\t#else\\\\n\\\\t\\\\ttransformed += morphTarget0 * morphTargetInfluences[ 0 ];\\\\n\\\\t\\\\ttransformed += morphTarget1 * morphTargetInfluences[ 1 ];\\\\n\\\\t\\\\ttransformed += morphTarget2 * morphTargetInfluences[ 2 ];\\\\n\\\\t\\\\ttransformed += morphTarget3 * morphTargetInfluences[ 3 ];\\\\n\\\\t\\\\t#ifndef USE_MORPHNORMALS\\\\n\\\\t\\\\t\\\\ttransformed += morphTarget4 * morphTargetInfluences[ 4 ];\\\\n\\\\t\\\\t\\\\ttransformed += morphTarget5 * morphTargetInfluences[ 5 ];\\\\n\\\\t\\\\t\\\\ttransformed += morphTarget6 * morphTargetInfluences[ 6 ];\\\\n\\\\t\\\\t\\\\ttransformed += morphTarget7 * morphTargetInfluences[ 7 ];\\\\n\\\\t\\\\t#endif\\\\n\\\\t#endif\\\\n#endif\\\\\\\",normal_fragment_begin:\\\\\\\"float faceDirection = gl_FrontFacing ? 1.0 : - 1.0;\\\\n#ifdef FLAT_SHADED\\\\n\\\\tvec3 fdx = vec3( dFdx( vViewPosition.x ), dFdx( vViewPosition.y ), dFdx( vViewPosition.z ) );\\\\n\\\\tvec3 fdy = vec3( dFdy( vViewPosition.x ), dFdy( vViewPosition.y ), dFdy( vViewPosition.z ) );\\\\n\\\\tvec3 normal = normalize( cross( fdx, fdy ) );\\\\n#else\\\\n\\\\tvec3 normal = normalize( vNormal );\\\\n\\\\t#ifdef DOUBLE_SIDED\\\\n\\\\t\\\\tnormal = normal * faceDirection;\\\\n\\\\t#endif\\\\n\\\\t#ifdef USE_TANGENT\\\\n\\\\t\\\\tvec3 tangent = normalize( vTangent );\\\\n\\\\t\\\\tvec3 bitangent = normalize( vBitangent );\\\\n\\\\t\\\\t#ifdef DOUBLE_SIDED\\\\n\\\\t\\\\t\\\\ttangent = tangent * faceDirection;\\\\n\\\\t\\\\t\\\\tbitangent = bitangent * faceDirection;\\\\n\\\\t\\\\t#endif\\\\n\\\\t\\\\t#if defined( TANGENTSPACE_NORMALMAP ) || defined( USE_CLEARCOAT_NORMALMAP )\\\\n\\\\t\\\\t\\\\tmat3 vTBN = mat3( tangent, bitangent, normal );\\\\n\\\\t\\\\t#endif\\\\n\\\\t#endif\\\\n#endif\\\\nvec3 geometryNormal = normal;\\\\\\\",normal_fragment_maps:\\\\\\\"#ifdef OBJECTSPACE_NORMALMAP\\\\n\\\\tnormal = texture2D( normalMap, vUv ).xyz * 2.0 - 1.0;\\\\n\\\\t#ifdef FLIP_SIDED\\\\n\\\\t\\\\tnormal = - normal;\\\\n\\\\t#endif\\\\n\\\\t#ifdef DOUBLE_SIDED\\\\n\\\\t\\\\tnormal = normal * faceDirection;\\\\n\\\\t#endif\\\\n\\\\tnormal = normalize( normalMatrix * normal );\\\\n#elif defined( TANGENTSPACE_NORMALMAP )\\\\n\\\\tvec3 mapN = texture2D( normalMap, vUv ).xyz * 2.0 - 1.0;\\\\n\\\\tmapN.xy *= normalScale;\\\\n\\\\t#ifdef USE_TANGENT\\\\n\\\\t\\\\tnormal = normalize( vTBN * mapN );\\\\n\\\\t#else\\\\n\\\\t\\\\tnormal = perturbNormal2Arb( - vViewPosition, normal, mapN, faceDirection );\\\\n\\\\t#endif\\\\n#elif defined( USE_BUMPMAP )\\\\n\\\\tnormal = perturbNormalArb( - vViewPosition, normal, dHdxy_fwd(), faceDirection );\\\\n#endif\\\\\\\",normal_pars_fragment:\\\\\\\"#ifndef FLAT_SHADED\\\\n\\\\tvarying vec3 vNormal;\\\\n\\\\t#ifdef USE_TANGENT\\\\n\\\\t\\\\tvarying vec3 vTangent;\\\\n\\\\t\\\\tvarying vec3 vBitangent;\\\\n\\\\t#endif\\\\n#endif\\\\\\\",normal_pars_vertex:\\\\\\\"#ifndef FLAT_SHADED\\\\n\\\\tvarying vec3 vNormal;\\\\n\\\\t#ifdef USE_TANGENT\\\\n\\\\t\\\\tvarying vec3 vTangent;\\\\n\\\\t\\\\tvarying vec3 vBitangent;\\\\n\\\\t#endif\\\\n#endif\\\\\\\",normal_vertex:\\\\\\\"#ifndef FLAT_SHADED\\\\n\\\\tvNormal = normalize( transformedNormal );\\\\n\\\\t#ifdef USE_TANGENT\\\\n\\\\t\\\\tvTangent = normalize( transformedTangent );\\\\n\\\\t\\\\tvBitangent = normalize( cross( vNormal, vTangent ) * tangent.w );\\\\n\\\\t#endif\\\\n#endif\\\\\\\",normalmap_pars_fragment:\\\\\\\"#ifdef USE_NORMALMAP\\\\n\\\\tuniform sampler2D normalMap;\\\\n\\\\tuniform vec2 normalScale;\\\\n#endif\\\\n#ifdef OBJECTSPACE_NORMALMAP\\\\n\\\\tuniform mat3 normalMatrix;\\\\n#endif\\\\n#if ! defined ( USE_TANGENT ) && ( defined ( TANGENTSPACE_NORMALMAP ) || defined ( USE_CLEARCOAT_NORMALMAP ) )\\\\n\\\\tvec3 perturbNormal2Arb( vec3 eye_pos, vec3 surf_norm, vec3 mapN, float faceDirection ) {\\\\n\\\\t\\\\tvec3 q0 = vec3( dFdx( eye_pos.x ), dFdx( eye_pos.y ), dFdx( eye_pos.z ) );\\\\n\\\\t\\\\tvec3 q1 = vec3( dFdy( eye_pos.x ), dFdy( eye_pos.y ), dFdy( eye_pos.z ) );\\\\n\\\\t\\\\tvec2 st0 = dFdx( vUv.st );\\\\n\\\\t\\\\tvec2 st1 = dFdy( vUv.st );\\\\n\\\\t\\\\tvec3 N = surf_norm;\\\\n\\\\t\\\\tvec3 q1perp = cross( q1, N );\\\\n\\\\t\\\\tvec3 q0perp = cross( N, q0 );\\\\n\\\\t\\\\tvec3 T = q1perp * st0.x + q0perp * st1.x;\\\\n\\\\t\\\\tvec3 B = q1perp * st0.y + q0perp * st1.y;\\\\n\\\\t\\\\tfloat det = max( dot( T, T ), dot( B, B ) );\\\\n\\\\t\\\\tfloat scale = ( det == 0.0 ) ? 0.0 : faceDirection * inversesqrt( det );\\\\n\\\\t\\\\treturn normalize( T * ( mapN.x * scale ) + B * ( mapN.y * scale ) + N * mapN.z );\\\\n\\\\t}\\\\n#endif\\\\\\\",clearcoat_normal_fragment_begin:\\\\\\\"#ifdef USE_CLEARCOAT\\\\n\\\\tvec3 clearcoatNormal = geometryNormal;\\\\n#endif\\\\\\\",clearcoat_normal_fragment_maps:\\\\\\\"#ifdef USE_CLEARCOAT_NORMALMAP\\\\n\\\\tvec3 clearcoatMapN = texture2D( clearcoatNormalMap, vUv ).xyz * 2.0 - 1.0;\\\\n\\\\tclearcoatMapN.xy *= clearcoatNormalScale;\\\\n\\\\t#ifdef USE_TANGENT\\\\n\\\\t\\\\tclearcoatNormal = normalize( vTBN * clearcoatMapN );\\\\n\\\\t#else\\\\n\\\\t\\\\tclearcoatNormal = perturbNormal2Arb( - vViewPosition, clearcoatNormal, clearcoatMapN, faceDirection );\\\\n\\\\t#endif\\\\n#endif\\\\\\\",clearcoat_pars_fragment:\\\\\\\"#ifdef USE_CLEARCOATMAP\\\\n\\\\tuniform sampler2D clearcoatMap;\\\\n#endif\\\\n#ifdef USE_CLEARCOAT_ROUGHNESSMAP\\\\n\\\\tuniform sampler2D clearcoatRoughnessMap;\\\\n#endif\\\\n#ifdef USE_CLEARCOAT_NORMALMAP\\\\n\\\\tuniform sampler2D clearcoatNormalMap;\\\\n\\\\tuniform vec2 clearcoatNormalScale;\\\\n#endif\\\\\\\",output_fragment:\\\\\\\"#ifdef OPAQUE\\\\ndiffuseColor.a = 1.0;\\\\n#endif\\\\n#ifdef USE_TRANSMISSION\\\\ndiffuseColor.a *= transmissionAlpha + 0.1;\\\\n#endif\\\\ngl_FragColor = vec4( outgoingLight, diffuseColor.a );\\\\\\\",packing:\\\\\\\"vec3 packNormalToRGB( const in vec3 normal ) {\\\\n\\\\treturn normalize( normal ) * 0.5 + 0.5;\\\\n}\\\\nvec3 unpackRGBToNormal( const in vec3 rgb ) {\\\\n\\\\treturn 2.0 * rgb.xyz - 1.0;\\\\n}\\\\nconst float PackUpscale = 256. / 255.;const float UnpackDownscale = 255. / 256.;\\\\nconst vec3 PackFactors = vec3( 256. * 256. * 256., 256. * 256., 256. );\\\\nconst vec4 UnpackFactors = UnpackDownscale / vec4( PackFactors, 1. );\\\\nconst float ShiftRight8 = 1. / 256.;\\\\nvec4 packDepthToRGBA( const in float v ) {\\\\n\\\\tvec4 r = vec4( fract( v * PackFactors ), v );\\\\n\\\\tr.yzw -= r.xyz * ShiftRight8;\\\\treturn r * PackUpscale;\\\\n}\\\\nfloat unpackRGBAToDepth( const in vec4 v ) {\\\\n\\\\treturn dot( v, UnpackFactors );\\\\n}\\\\nvec4 pack2HalfToRGBA( vec2 v ) {\\\\n\\\\tvec4 r = vec4( v.x, fract( v.x * 255.0 ), v.y, fract( v.y * 255.0 ) );\\\\n\\\\treturn vec4( r.x - r.y / 255.0, r.y, r.z - r.w / 255.0, r.w );\\\\n}\\\\nvec2 unpackRGBATo2Half( vec4 v ) {\\\\n\\\\treturn vec2( v.x + ( v.y / 255.0 ), v.z + ( v.w / 255.0 ) );\\\\n}\\\\nfloat viewZToOrthographicDepth( const in float viewZ, const in float near, const in float far ) {\\\\n\\\\treturn ( viewZ + near ) / ( near - far );\\\\n}\\\\nfloat orthographicDepthToViewZ( const in float linearClipZ, const in float near, const in float far ) {\\\\n\\\\treturn linearClipZ * ( near - far ) - near;\\\\n}\\\\nfloat viewZToPerspectiveDepth( const in float viewZ, const in float near, const in float far ) {\\\\n\\\\treturn ( ( near + viewZ ) * far ) / ( ( far - near ) * viewZ );\\\\n}\\\\nfloat perspectiveDepthToViewZ( const in float invClipZ, const in float near, const in float far ) {\\\\n\\\\treturn ( near * far ) / ( ( far - near ) * invClipZ - far );\\\\n}\\\\\\\",premultiplied_alpha_fragment:\\\\\\\"#ifdef PREMULTIPLIED_ALPHA\\\\n\\\\tgl_FragColor.rgb *= gl_FragColor.a;\\\\n#endif\\\\\\\",project_vertex:\\\\\\\"vec4 mvPosition = vec4( transformed, 1.0 );\\\\n#ifdef USE_INSTANCING\\\\n\\\\tmvPosition = instanceMatrix * mvPosition;\\\\n#endif\\\\nmvPosition = modelViewMatrix * mvPosition;\\\\ngl_Position = projectionMatrix * mvPosition;\\\\\\\",dithering_fragment:\\\\\\\"#ifdef DITHERING\\\\n\\\\tgl_FragColor.rgb = dithering( gl_FragColor.rgb );\\\\n#endif\\\\\\\",dithering_pars_fragment:\\\\\\\"#ifdef DITHERING\\\\n\\\\tvec3 dithering( vec3 color ) {\\\\n\\\\t\\\\tfloat grid_position = rand( gl_FragCoord.xy );\\\\n\\\\t\\\\tvec3 dither_shift_RGB = vec3( 0.25 / 255.0, -0.25 / 255.0, 0.25 / 255.0 );\\\\n\\\\t\\\\tdither_shift_RGB = mix( 2.0 * dither_shift_RGB, -2.0 * dither_shift_RGB, grid_position );\\\\n\\\\t\\\\treturn color + dither_shift_RGB;\\\\n\\\\t}\\\\n#endif\\\\\\\",roughnessmap_fragment:\\\\\\\"float roughnessFactor = roughness;\\\\n#ifdef USE_ROUGHNESSMAP\\\\n\\\\tvec4 texelRoughness = texture2D( roughnessMap, vUv );\\\\n\\\\troughnessFactor *= texelRoughness.g;\\\\n#endif\\\\\\\",roughnessmap_pars_fragment:\\\\\\\"#ifdef USE_ROUGHNESSMAP\\\\n\\\\tuniform sampler2D roughnessMap;\\\\n#endif\\\\\\\",shadowmap_pars_fragment:\\\\\\\"#ifdef USE_SHADOWMAP\\\\n\\\\t#if NUM_DIR_LIGHT_SHADOWS > 0\\\\n\\\\t\\\\tuniform sampler2D directionalShadowMap[ NUM_DIR_LIGHT_SHADOWS ];\\\\n\\\\t\\\\tvarying vec4 vDirectionalShadowCoord[ NUM_DIR_LIGHT_SHADOWS ];\\\\n\\\\t\\\\tstruct DirectionalLightShadow {\\\\n\\\\t\\\\t\\\\tfloat shadowBias;\\\\n\\\\t\\\\t\\\\tfloat shadowNormalBias;\\\\n\\\\t\\\\t\\\\tfloat shadowRadius;\\\\n\\\\t\\\\t\\\\tvec2 shadowMapSize;\\\\n\\\\t\\\\t};\\\\n\\\\t\\\\tuniform DirectionalLightShadow directionalLightShadows[ NUM_DIR_LIGHT_SHADOWS ];\\\\n\\\\t#endif\\\\n\\\\t#if NUM_SPOT_LIGHT_SHADOWS > 0\\\\n\\\\t\\\\tuniform sampler2D spotShadowMap[ NUM_SPOT_LIGHT_SHADOWS ];\\\\n\\\\t\\\\tvarying vec4 vSpotShadowCoord[ NUM_SPOT_LIGHT_SHADOWS ];\\\\n\\\\t\\\\tstruct SpotLightShadow {\\\\n\\\\t\\\\t\\\\tfloat shadowBias;\\\\n\\\\t\\\\t\\\\tfloat shadowNormalBias;\\\\n\\\\t\\\\t\\\\tfloat shadowRadius;\\\\n\\\\t\\\\t\\\\tvec2 shadowMapSize;\\\\n\\\\t\\\\t};\\\\n\\\\t\\\\tuniform SpotLightShadow spotLightShadows[ NUM_SPOT_LIGHT_SHADOWS ];\\\\n\\\\t#endif\\\\n\\\\t#if NUM_POINT_LIGHT_SHADOWS > 0\\\\n\\\\t\\\\tuniform sampler2D pointShadowMap[ NUM_POINT_LIGHT_SHADOWS ];\\\\n\\\\t\\\\tvarying vec4 vPointShadowCoord[ NUM_POINT_LIGHT_SHADOWS ];\\\\n\\\\t\\\\tstruct PointLightShadow {\\\\n\\\\t\\\\t\\\\tfloat shadowBias;\\\\n\\\\t\\\\t\\\\tfloat shadowNormalBias;\\\\n\\\\t\\\\t\\\\tfloat shadowRadius;\\\\n\\\\t\\\\t\\\\tvec2 shadowMapSize;\\\\n\\\\t\\\\t\\\\tfloat shadowCameraNear;\\\\n\\\\t\\\\t\\\\tfloat shadowCameraFar;\\\\n\\\\t\\\\t};\\\\n\\\\t\\\\tuniform PointLightShadow pointLightShadows[ NUM_POINT_LIGHT_SHADOWS ];\\\\n\\\\t#endif\\\\n\\\\tfloat texture2DCompare( sampler2D depths, vec2 uv, float compare ) {\\\\n\\\\t\\\\treturn step( compare, unpackRGBAToDepth( texture2D( depths, uv ) ) );\\\\n\\\\t}\\\\n\\\\tvec2 texture2DDistribution( sampler2D shadow, vec2 uv ) {\\\\n\\\\t\\\\treturn unpackRGBATo2Half( texture2D( shadow, uv ) );\\\\n\\\\t}\\\\n\\\\tfloat VSMShadow (sampler2D shadow, vec2 uv, float compare ){\\\\n\\\\t\\\\tfloat occlusion = 1.0;\\\\n\\\\t\\\\tvec2 distribution = texture2DDistribution( shadow, uv );\\\\n\\\\t\\\\tfloat hard_shadow = step( compare , distribution.x );\\\\n\\\\t\\\\tif (hard_shadow != 1.0 ) {\\\\n\\\\t\\\\t\\\\tfloat distance = compare - distribution.x ;\\\\n\\\\t\\\\t\\\\tfloat variance = max( 0.00000, distribution.y * distribution.y );\\\\n\\\\t\\\\t\\\\tfloat softness_probability = variance / (variance + distance * distance );\\\\t\\\\t\\\\tsoftness_probability = clamp( ( softness_probability - 0.3 ) / ( 0.95 - 0.3 ), 0.0, 1.0 );\\\\t\\\\t\\\\tocclusion = clamp( max( hard_shadow, softness_probability ), 0.0, 1.0 );\\\\n\\\\t\\\\t}\\\\n\\\\t\\\\treturn occlusion;\\\\n\\\\t}\\\\n\\\\tfloat getShadow( sampler2D shadowMap, vec2 shadowMapSize, float shadowBias, float shadowRadius, vec4 shadowCoord ) {\\\\n\\\\t\\\\tfloat shadow = 1.0;\\\\n\\\\t\\\\tshadowCoord.xyz /= shadowCoord.w;\\\\n\\\\t\\\\tshadowCoord.z += shadowBias;\\\\n\\\\t\\\\tbvec4 inFrustumVec = bvec4 ( shadowCoord.x >= 0.0, shadowCoord.x <= 1.0, shadowCoord.y >= 0.0, shadowCoord.y <= 1.0 );\\\\n\\\\t\\\\tbool inFrustum = all( inFrustumVec );\\\\n\\\\t\\\\tbvec2 frustumTestVec = bvec2( inFrustum, shadowCoord.z <= 1.0 );\\\\n\\\\t\\\\tbool frustumTest = all( frustumTestVec );\\\\n\\\\t\\\\tif ( frustumTest ) {\\\\n\\\\t\\\\t#if defined( SHADOWMAP_TYPE_PCF )\\\\n\\\\t\\\\t\\\\tvec2 texelSize = vec2( 1.0 ) / shadowMapSize;\\\\n\\\\t\\\\t\\\\tfloat dx0 = - texelSize.x * shadowRadius;\\\\n\\\\t\\\\t\\\\tfloat dy0 = - texelSize.y * shadowRadius;\\\\n\\\\t\\\\t\\\\tfloat dx1 = + texelSize.x * shadowRadius;\\\\n\\\\t\\\\t\\\\tfloat dy1 = + texelSize.y * shadowRadius;\\\\n\\\\t\\\\t\\\\tfloat dx2 = dx0 / 2.0;\\\\n\\\\t\\\\t\\\\tfloat dy2 = dy0 / 2.0;\\\\n\\\\t\\\\t\\\\tfloat dx3 = dx1 / 2.0;\\\\n\\\\t\\\\t\\\\tfloat dy3 = dy1 / 2.0;\\\\n\\\\t\\\\t\\\\tshadow = (\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx0, dy0 ), shadowCoord.z ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( 0.0, dy0 ), shadowCoord.z ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx1, dy0 ), shadowCoord.z ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx2, dy2 ), shadowCoord.z ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( 0.0, dy2 ), shadowCoord.z ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx3, dy2 ), shadowCoord.z ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx0, 0.0 ), shadowCoord.z ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx2, 0.0 ), shadowCoord.z ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, shadowCoord.xy, shadowCoord.z ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx3, 0.0 ), shadowCoord.z ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx1, 0.0 ), shadowCoord.z ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx2, dy3 ), shadowCoord.z ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( 0.0, dy3 ), shadowCoord.z ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx3, dy3 ), shadowCoord.z ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx0, dy1 ), shadowCoord.z ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( 0.0, dy1 ), shadowCoord.z ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx1, dy1 ), shadowCoord.z )\\\\n\\\\t\\\\t\\\\t) * ( 1.0 / 17.0 );\\\\n\\\\t\\\\t#elif defined( SHADOWMAP_TYPE_PCF_SOFT )\\\\n\\\\t\\\\t\\\\tvec2 texelSize = vec2( 1.0 ) / shadowMapSize;\\\\n\\\\t\\\\t\\\\tfloat dx = texelSize.x;\\\\n\\\\t\\\\t\\\\tfloat dy = texelSize.y;\\\\n\\\\t\\\\t\\\\tvec2 uv = shadowCoord.xy;\\\\n\\\\t\\\\t\\\\tvec2 f = fract( uv * shadowMapSize + 0.5 );\\\\n\\\\t\\\\t\\\\tuv -= f * texelSize;\\\\n\\\\t\\\\t\\\\tshadow = (\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, uv, shadowCoord.z ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, uv + vec2( dx, 0.0 ), shadowCoord.z ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, uv + vec2( 0.0, dy ), shadowCoord.z ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, uv + texelSize, shadowCoord.z ) +\\\\n\\\\t\\\\t\\\\t\\\\tmix( texture2DCompare( shadowMap, uv + vec2( -dx, 0.0 ), shadowCoord.z ), \\\\n\\\\t\\\\t\\\\t\\\\t\\\\t texture2DCompare( shadowMap, uv + vec2( 2.0 * dx, 0.0 ), shadowCoord.z ),\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t f.x ) +\\\\n\\\\t\\\\t\\\\t\\\\tmix( texture2DCompare( shadowMap, uv + vec2( -dx, dy ), shadowCoord.z ), \\\\n\\\\t\\\\t\\\\t\\\\t\\\\t texture2DCompare( shadowMap, uv + vec2( 2.0 * dx, dy ), shadowCoord.z ),\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t f.x ) +\\\\n\\\\t\\\\t\\\\t\\\\tmix( texture2DCompare( shadowMap, uv + vec2( 0.0, -dy ), shadowCoord.z ), \\\\n\\\\t\\\\t\\\\t\\\\t\\\\t texture2DCompare( shadowMap, uv + vec2( 0.0, 2.0 * dy ), shadowCoord.z ),\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t f.y ) +\\\\n\\\\t\\\\t\\\\t\\\\tmix( texture2DCompare( shadowMap, uv + vec2( dx, -dy ), shadowCoord.z ), \\\\n\\\\t\\\\t\\\\t\\\\t\\\\t texture2DCompare( shadowMap, uv + vec2( dx, 2.0 * dy ), shadowCoord.z ),\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t f.y ) +\\\\n\\\\t\\\\t\\\\t\\\\tmix( mix( texture2DCompare( shadowMap, uv + vec2( -dx, -dy ), shadowCoord.z ), \\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t texture2DCompare( shadowMap, uv + vec2( 2.0 * dx, -dy ), shadowCoord.z ),\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t f.x ),\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t mix( texture2DCompare( shadowMap, uv + vec2( -dx, 2.0 * dy ), shadowCoord.z ), \\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t texture2DCompare( shadowMap, uv + vec2( 2.0 * dx, 2.0 * dy ), shadowCoord.z ),\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t f.x ),\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t f.y )\\\\n\\\\t\\\\t\\\\t) * ( 1.0 / 9.0 );\\\\n\\\\t\\\\t#elif defined( SHADOWMAP_TYPE_VSM )\\\\n\\\\t\\\\t\\\\tshadow = VSMShadow( shadowMap, shadowCoord.xy, shadowCoord.z );\\\\n\\\\t\\\\t#else\\\\n\\\\t\\\\t\\\\tshadow = texture2DCompare( shadowMap, shadowCoord.xy, shadowCoord.z );\\\\n\\\\t\\\\t#endif\\\\n\\\\t\\\\t}\\\\n\\\\t\\\\treturn shadow;\\\\n\\\\t}\\\\n\\\\tvec2 cubeToUV( vec3 v, float texelSizeY ) {\\\\n\\\\t\\\\tvec3 absV = abs( v );\\\\n\\\\t\\\\tfloat scaleToCube = 1.0 / max( absV.x, max( absV.y, absV.z ) );\\\\n\\\\t\\\\tabsV *= scaleToCube;\\\\n\\\\t\\\\tv *= scaleToCube * ( 1.0 - 2.0 * texelSizeY );\\\\n\\\\t\\\\tvec2 planar = v.xy;\\\\n\\\\t\\\\tfloat almostATexel = 1.5 * texelSizeY;\\\\n\\\\t\\\\tfloat almostOne = 1.0 - almostATexel;\\\\n\\\\t\\\\tif ( absV.z >= almostOne ) {\\\\n\\\\t\\\\t\\\\tif ( v.z > 0.0 )\\\\n\\\\t\\\\t\\\\t\\\\tplanar.x = 4.0 - v.x;\\\\n\\\\t\\\\t} else if ( absV.x >= almostOne ) {\\\\n\\\\t\\\\t\\\\tfloat signX = sign( v.x );\\\\n\\\\t\\\\t\\\\tplanar.x = v.z * signX + 2.0 * signX;\\\\n\\\\t\\\\t} else if ( absV.y >= almostOne ) {\\\\n\\\\t\\\\t\\\\tfloat signY = sign( v.y );\\\\n\\\\t\\\\t\\\\tplanar.x = v.x + 2.0 * signY + 2.0;\\\\n\\\\t\\\\t\\\\tplanar.y = v.z * signY - 2.0;\\\\n\\\\t\\\\t}\\\\n\\\\t\\\\treturn vec2( 0.125, 0.25 ) * planar + vec2( 0.375, 0.75 );\\\\n\\\\t}\\\\n\\\\tfloat getPointShadow( sampler2D shadowMap, vec2 shadowMapSize, float shadowBias, float shadowRadius, vec4 shadowCoord, float shadowCameraNear, float shadowCameraFar ) {\\\\n\\\\t\\\\tvec2 texelSize = vec2( 1.0 ) / ( shadowMapSize * vec2( 4.0, 2.0 ) );\\\\n\\\\t\\\\tvec3 lightToPosition = shadowCoord.xyz;\\\\n\\\\t\\\\tfloat dp = ( length( lightToPosition ) - shadowCameraNear ) / ( shadowCameraFar - shadowCameraNear );\\\\t\\\\tdp += shadowBias;\\\\n\\\\t\\\\tvec3 bd3D = normalize( lightToPosition );\\\\n\\\\t\\\\t#if defined( SHADOWMAP_TYPE_PCF ) || defined( SHADOWMAP_TYPE_PCF_SOFT ) || defined( SHADOWMAP_TYPE_VSM )\\\\n\\\\t\\\\t\\\\tvec2 offset = vec2( - 1, 1 ) * shadowRadius * texelSize.y;\\\\n\\\\t\\\\t\\\\treturn (\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, cubeToUV( bd3D + offset.xyy, texelSize.y ), dp ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, cubeToUV( bd3D + offset.yyy, texelSize.y ), dp ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, cubeToUV( bd3D + offset.xyx, texelSize.y ), dp ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, cubeToUV( bd3D + offset.yyx, texelSize.y ), dp ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, cubeToUV( bd3D, texelSize.y ), dp ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, cubeToUV( bd3D + offset.xxy, texelSize.y ), dp ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, cubeToUV( bd3D + offset.yxy, texelSize.y ), dp ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, cubeToUV( bd3D + offset.xxx, texelSize.y ), dp ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, cubeToUV( bd3D + offset.yxx, texelSize.y ), dp )\\\\n\\\\t\\\\t\\\\t) * ( 1.0 / 9.0 );\\\\n\\\\t\\\\t#else\\\\n\\\\t\\\\t\\\\treturn texture2DCompare( shadowMap, cubeToUV( bd3D, texelSize.y ), dp );\\\\n\\\\t\\\\t#endif\\\\n\\\\t}\\\\n#endif\\\\\\\",shadowmap_pars_vertex:\\\\\\\"#ifdef USE_SHADOWMAP\\\\n\\\\t#if NUM_DIR_LIGHT_SHADOWS > 0\\\\n\\\\t\\\\tuniform mat4 directionalShadowMatrix[ NUM_DIR_LIGHT_SHADOWS ];\\\\n\\\\t\\\\tvarying vec4 vDirectionalShadowCoord[ NUM_DIR_LIGHT_SHADOWS ];\\\\n\\\\t\\\\tstruct DirectionalLightShadow {\\\\n\\\\t\\\\t\\\\tfloat shadowBias;\\\\n\\\\t\\\\t\\\\tfloat shadowNormalBias;\\\\n\\\\t\\\\t\\\\tfloat shadowRadius;\\\\n\\\\t\\\\t\\\\tvec2 shadowMapSize;\\\\n\\\\t\\\\t};\\\\n\\\\t\\\\tuniform DirectionalLightShadow directionalLightShadows[ NUM_DIR_LIGHT_SHADOWS ];\\\\n\\\\t#endif\\\\n\\\\t#if NUM_SPOT_LIGHT_SHADOWS > 0\\\\n\\\\t\\\\tuniform mat4 spotShadowMatrix[ NUM_SPOT_LIGHT_SHADOWS ];\\\\n\\\\t\\\\tvarying vec4 vSpotShadowCoord[ NUM_SPOT_LIGHT_SHADOWS ];\\\\n\\\\t\\\\tstruct SpotLightShadow {\\\\n\\\\t\\\\t\\\\tfloat shadowBias;\\\\n\\\\t\\\\t\\\\tfloat shadowNormalBias;\\\\n\\\\t\\\\t\\\\tfloat shadowRadius;\\\\n\\\\t\\\\t\\\\tvec2 shadowMapSize;\\\\n\\\\t\\\\t};\\\\n\\\\t\\\\tuniform SpotLightShadow spotLightShadows[ NUM_SPOT_LIGHT_SHADOWS ];\\\\n\\\\t#endif\\\\n\\\\t#if NUM_POINT_LIGHT_SHADOWS > 0\\\\n\\\\t\\\\tuniform mat4 pointShadowMatrix[ NUM_POINT_LIGHT_SHADOWS ];\\\\n\\\\t\\\\tvarying vec4 vPointShadowCoord[ NUM_POINT_LIGHT_SHADOWS ];\\\\n\\\\t\\\\tstruct PointLightShadow {\\\\n\\\\t\\\\t\\\\tfloat shadowBias;\\\\n\\\\t\\\\t\\\\tfloat shadowNormalBias;\\\\n\\\\t\\\\t\\\\tfloat shadowRadius;\\\\n\\\\t\\\\t\\\\tvec2 shadowMapSize;\\\\n\\\\t\\\\t\\\\tfloat shadowCameraNear;\\\\n\\\\t\\\\t\\\\tfloat shadowCameraFar;\\\\n\\\\t\\\\t};\\\\n\\\\t\\\\tuniform PointLightShadow pointLightShadows[ NUM_POINT_LIGHT_SHADOWS ];\\\\n\\\\t#endif\\\\n#endif\\\\\\\",shadowmap_vertex:\\\\\\\"#ifdef USE_SHADOWMAP\\\\n\\\\t#if NUM_DIR_LIGHT_SHADOWS > 0 || NUM_SPOT_LIGHT_SHADOWS > 0 || NUM_POINT_LIGHT_SHADOWS > 0\\\\n\\\\t\\\\tvec3 shadowWorldNormal = inverseTransformDirection( transformedNormal, viewMatrix );\\\\n\\\\t\\\\tvec4 shadowWorldPosition;\\\\n\\\\t#endif\\\\n\\\\t#if NUM_DIR_LIGHT_SHADOWS > 0\\\\n\\\\t#pragma unroll_loop_start\\\\n\\\\tfor ( int i = 0; i < NUM_DIR_LIGHT_SHADOWS; i ++ ) {\\\\n\\\\t\\\\tshadowWorldPosition = worldPosition + vec4( shadowWorldNormal * directionalLightShadows[ i ].shadowNormalBias, 0 );\\\\n\\\\t\\\\tvDirectionalShadowCoord[ i ] = directionalShadowMatrix[ i ] * shadowWorldPosition;\\\\n\\\\t}\\\\n\\\\t#pragma unroll_loop_end\\\\n\\\\t#endif\\\\n\\\\t#if NUM_SPOT_LIGHT_SHADOWS > 0\\\\n\\\\t#pragma unroll_loop_start\\\\n\\\\tfor ( int i = 0; i < NUM_SPOT_LIGHT_SHADOWS; i ++ ) {\\\\n\\\\t\\\\tshadowWorldPosition = worldPosition + vec4( shadowWorldNormal * spotLightShadows[ i ].shadowNormalBias, 0 );\\\\n\\\\t\\\\tvSpotShadowCoord[ i ] = spotShadowMatrix[ i ] * shadowWorldPosition;\\\\n\\\\t}\\\\n\\\\t#pragma unroll_loop_end\\\\n\\\\t#endif\\\\n\\\\t#if NUM_POINT_LIGHT_SHADOWS > 0\\\\n\\\\t#pragma unroll_loop_start\\\\n\\\\tfor ( int i = 0; i < NUM_POINT_LIGHT_SHADOWS; i ++ ) {\\\\n\\\\t\\\\tshadowWorldPosition = worldPosition + vec4( shadowWorldNormal * pointLightShadows[ i ].shadowNormalBias, 0 );\\\\n\\\\t\\\\tvPointShadowCoord[ i ] = pointShadowMatrix[ i ] * shadowWorldPosition;\\\\n\\\\t}\\\\n\\\\t#pragma unroll_loop_end\\\\n\\\\t#endif\\\\n#endif\\\\\\\",shadowmask_pars_fragment:\\\\\\\"float getShadowMask() {\\\\n\\\\tfloat shadow = 1.0;\\\\n\\\\t#ifdef USE_SHADOWMAP\\\\n\\\\t#if NUM_DIR_LIGHT_SHADOWS > 0\\\\n\\\\tDirectionalLightShadow directionalLight;\\\\n\\\\t#pragma unroll_loop_start\\\\n\\\\tfor ( int i = 0; i < NUM_DIR_LIGHT_SHADOWS; i ++ ) {\\\\n\\\\t\\\\tdirectionalLight = directionalLightShadows[ i ];\\\\n\\\\t\\\\tshadow *= receiveShadow ? getShadow( directionalShadowMap[ i ], directionalLight.shadowMapSize, directionalLight.shadowBias, directionalLight.shadowRadius, vDirectionalShadowCoord[ i ] ) : 1.0;\\\\n\\\\t}\\\\n\\\\t#pragma unroll_loop_end\\\\n\\\\t#endif\\\\n\\\\t#if NUM_SPOT_LIGHT_SHADOWS > 0\\\\n\\\\tSpotLightShadow spotLight;\\\\n\\\\t#pragma unroll_loop_start\\\\n\\\\tfor ( int i = 0; i < NUM_SPOT_LIGHT_SHADOWS; i ++ ) {\\\\n\\\\t\\\\tspotLight = spotLightShadows[ i ];\\\\n\\\\t\\\\tshadow *= receiveShadow ? getShadow( spotShadowMap[ i ], spotLight.shadowMapSize, spotLight.shadowBias, spotLight.shadowRadius, vSpotShadowCoord[ i ] ) : 1.0;\\\\n\\\\t}\\\\n\\\\t#pragma unroll_loop_end\\\\n\\\\t#endif\\\\n\\\\t#if NUM_POINT_LIGHT_SHADOWS > 0\\\\n\\\\tPointLightShadow pointLight;\\\\n\\\\t#pragma unroll_loop_start\\\\n\\\\tfor ( int i = 0; i < NUM_POINT_LIGHT_SHADOWS; i ++ ) {\\\\n\\\\t\\\\tpointLight = pointLightShadows[ i ];\\\\n\\\\t\\\\tshadow *= receiveShadow ? getPointShadow( pointShadowMap[ i ], pointLight.shadowMapSize, pointLight.shadowBias, pointLight.shadowRadius, vPointShadowCoord[ i ], pointLight.shadowCameraNear, pointLight.shadowCameraFar ) : 1.0;\\\\n\\\\t}\\\\n\\\\t#pragma unroll_loop_end\\\\n\\\\t#endif\\\\n\\\\t#endif\\\\n\\\\treturn shadow;\\\\n}\\\\\\\",skinbase_vertex:\\\\\\\"#ifdef USE_SKINNING\\\\n\\\\tmat4 boneMatX = getBoneMatrix( skinIndex.x );\\\\n\\\\tmat4 boneMatY = getBoneMatrix( skinIndex.y );\\\\n\\\\tmat4 boneMatZ = getBoneMatrix( skinIndex.z );\\\\n\\\\tmat4 boneMatW = getBoneMatrix( skinIndex.w );\\\\n#endif\\\\\\\",skinning_pars_vertex:\\\\\\\"#ifdef USE_SKINNING\\\\n\\\\tuniform mat4 bindMatrix;\\\\n\\\\tuniform mat4 bindMatrixInverse;\\\\n\\\\t#ifdef BONE_TEXTURE\\\\n\\\\t\\\\tuniform highp sampler2D boneTexture;\\\\n\\\\t\\\\tuniform int boneTextureSize;\\\\n\\\\t\\\\tmat4 getBoneMatrix( const in float i ) {\\\\n\\\\t\\\\t\\\\tfloat j = i * 4.0;\\\\n\\\\t\\\\t\\\\tfloat x = mod( j, float( boneTextureSize ) );\\\\n\\\\t\\\\t\\\\tfloat y = floor( j / float( boneTextureSize ) );\\\\n\\\\t\\\\t\\\\tfloat dx = 1.0 / float( boneTextureSize );\\\\n\\\\t\\\\t\\\\tfloat dy = 1.0 / float( boneTextureSize );\\\\n\\\\t\\\\t\\\\ty = dy * ( y + 0.5 );\\\\n\\\\t\\\\t\\\\tvec4 v1 = texture2D( boneTexture, vec2( dx * ( x + 0.5 ), y ) );\\\\n\\\\t\\\\t\\\\tvec4 v2 = texture2D( boneTexture, vec2( dx * ( x + 1.5 ), y ) );\\\\n\\\\t\\\\t\\\\tvec4 v3 = texture2D( boneTexture, vec2( dx * ( x + 2.5 ), y ) );\\\\n\\\\t\\\\t\\\\tvec4 v4 = texture2D( boneTexture, vec2( dx * ( x + 3.5 ), y ) );\\\\n\\\\t\\\\t\\\\tmat4 bone = mat4( v1, v2, v3, v4 );\\\\n\\\\t\\\\t\\\\treturn bone;\\\\n\\\\t\\\\t}\\\\n\\\\t#else\\\\n\\\\t\\\\tuniform mat4 boneMatrices[ MAX_BONES ];\\\\n\\\\t\\\\tmat4 getBoneMatrix( const in float i ) {\\\\n\\\\t\\\\t\\\\tmat4 bone = boneMatrices[ int(i) ];\\\\n\\\\t\\\\t\\\\treturn bone;\\\\n\\\\t\\\\t}\\\\n\\\\t#endif\\\\n#endif\\\\\\\",skinning_vertex:\\\\\\\"#ifdef USE_SKINNING\\\\n\\\\tvec4 skinVertex = bindMatrix * vec4( transformed, 1.0 );\\\\n\\\\tvec4 skinned = vec4( 0.0 );\\\\n\\\\tskinned += boneMatX * skinVertex * skinWeight.x;\\\\n\\\\tskinned += boneMatY * skinVertex * skinWeight.y;\\\\n\\\\tskinned += boneMatZ * skinVertex * skinWeight.z;\\\\n\\\\tskinned += boneMatW * skinVertex * skinWeight.w;\\\\n\\\\ttransformed = ( bindMatrixInverse * skinned ).xyz;\\\\n#endif\\\\\\\",skinnormal_vertex:\\\\\\\"#ifdef USE_SKINNING\\\\n\\\\tmat4 skinMatrix = mat4( 0.0 );\\\\n\\\\tskinMatrix += skinWeight.x * boneMatX;\\\\n\\\\tskinMatrix += skinWeight.y * boneMatY;\\\\n\\\\tskinMatrix += skinWeight.z * boneMatZ;\\\\n\\\\tskinMatrix += skinWeight.w * boneMatW;\\\\n\\\\tskinMatrix = bindMatrixInverse * skinMatrix * bindMatrix;\\\\n\\\\tobjectNormal = vec4( skinMatrix * vec4( objectNormal, 0.0 ) ).xyz;\\\\n\\\\t#ifdef USE_TANGENT\\\\n\\\\t\\\\tobjectTangent = vec4( skinMatrix * vec4( objectTangent, 0.0 ) ).xyz;\\\\n\\\\t#endif\\\\n#endif\\\\\\\",specularmap_fragment:\\\\\\\"float specularStrength;\\\\n#ifdef USE_SPECULARMAP\\\\n\\\\tvec4 texelSpecular = texture2D( specularMap, vUv );\\\\n\\\\tspecularStrength = texelSpecular.r;\\\\n#else\\\\n\\\\tspecularStrength = 1.0;\\\\n#endif\\\\\\\",specularmap_pars_fragment:\\\\\\\"#ifdef USE_SPECULARMAP\\\\n\\\\tuniform sampler2D specularMap;\\\\n#endif\\\\\\\",tonemapping_fragment:\\\\\\\"#if defined( TONE_MAPPING )\\\\n\\\\tgl_FragColor.rgb = toneMapping( gl_FragColor.rgb );\\\\n#endif\\\\\\\",tonemapping_pars_fragment:\\\\\\\"#ifndef saturate\\\\n#define saturate( a ) clamp( a, 0.0, 1.0 )\\\\n#endif\\\\nuniform float toneMappingExposure;\\\\nvec3 LinearToneMapping( vec3 color ) {\\\\n\\\\treturn toneMappingExposure * color;\\\\n}\\\\nvec3 ReinhardToneMapping( vec3 color ) {\\\\n\\\\tcolor *= toneMappingExposure;\\\\n\\\\treturn saturate( color / ( vec3( 1.0 ) + color ) );\\\\n}\\\\nvec3 OptimizedCineonToneMapping( vec3 color ) {\\\\n\\\\tcolor *= toneMappingExposure;\\\\n\\\\tcolor = max( vec3( 0.0 ), color - 0.004 );\\\\n\\\\treturn pow( ( color * ( 6.2 * color + 0.5 ) ) / ( color * ( 6.2 * color + 1.7 ) + 0.06 ), vec3( 2.2 ) );\\\\n}\\\\nvec3 RRTAndODTFit( vec3 v ) {\\\\n\\\\tvec3 a = v * ( v + 0.0245786 ) - 0.000090537;\\\\n\\\\tvec3 b = v * ( 0.983729 * v + 0.4329510 ) + 0.238081;\\\\n\\\\treturn a / b;\\\\n}\\\\nvec3 ACESFilmicToneMapping( vec3 color ) {\\\\n\\\\tconst mat3 ACESInputMat = mat3(\\\\n\\\\t\\\\tvec3( 0.59719, 0.07600, 0.02840 ),\\\\t\\\\tvec3( 0.35458, 0.90834, 0.13383 ),\\\\n\\\\t\\\\tvec3( 0.04823, 0.01566, 0.83777 )\\\\n\\\\t);\\\\n\\\\tconst mat3 ACESOutputMat = mat3(\\\\n\\\\t\\\\tvec3( 1.60475, -0.10208, -0.00327 ),\\\\t\\\\tvec3( -0.53108, 1.10813, -0.07276 ),\\\\n\\\\t\\\\tvec3( -0.07367, -0.00605, 1.07602 )\\\\n\\\\t);\\\\n\\\\tcolor *= toneMappingExposure / 0.6;\\\\n\\\\tcolor = ACESInputMat * color;\\\\n\\\\tcolor = RRTAndODTFit( color );\\\\n\\\\tcolor = ACESOutputMat * color;\\\\n\\\\treturn saturate( color );\\\\n}\\\\nvec3 CustomToneMapping( vec3 color ) { return color; }\\\\\\\",transmission_fragment:\\\\\\\"#ifdef USE_TRANSMISSION\\\\n\\\\tfloat transmissionAlpha = 1.0;\\\\n\\\\tfloat transmissionFactor = transmission;\\\\n\\\\tfloat thicknessFactor = thickness;\\\\n\\\\t#ifdef USE_TRANSMISSIONMAP\\\\n\\\\t\\\\ttransmissionFactor *= texture2D( transmissionMap, vUv ).r;\\\\n\\\\t#endif\\\\n\\\\t#ifdef USE_THICKNESSMAP\\\\n\\\\t\\\\tthicknessFactor *= texture2D( thicknessMap, vUv ).g;\\\\n\\\\t#endif\\\\n\\\\tvec3 pos = vWorldPosition;\\\\n\\\\tvec3 v = normalize( cameraPosition - pos );\\\\n\\\\tvec3 n = inverseTransformDirection( normal, viewMatrix );\\\\n\\\\tvec4 transmission = getIBLVolumeRefraction(\\\\n\\\\t\\\\tn, v, roughnessFactor, material.diffuseColor, material.specularColor, material.specularF90,\\\\n\\\\t\\\\tpos, modelMatrix, viewMatrix, projectionMatrix, ior, thicknessFactor,\\\\n\\\\t\\\\tattenuationTint, attenuationDistance );\\\\n\\\\ttotalDiffuse = mix( totalDiffuse, transmission.rgb, transmissionFactor );\\\\n\\\\ttransmissionAlpha = mix( transmissionAlpha, transmission.a, transmissionFactor );\\\\n#endif\\\\\\\",transmission_pars_fragment:\\\\\\\"#ifdef USE_TRANSMISSION\\\\n\\\\tuniform float transmission;\\\\n\\\\tuniform float thickness;\\\\n\\\\tuniform float attenuationDistance;\\\\n\\\\tuniform vec3 attenuationTint;\\\\n\\\\t#ifdef USE_TRANSMISSIONMAP\\\\n\\\\t\\\\tuniform sampler2D transmissionMap;\\\\n\\\\t#endif\\\\n\\\\t#ifdef USE_THICKNESSMAP\\\\n\\\\t\\\\tuniform sampler2D thicknessMap;\\\\n\\\\t#endif\\\\n\\\\tuniform vec2 transmissionSamplerSize;\\\\n\\\\tuniform sampler2D transmissionSamplerMap;\\\\n\\\\tuniform mat4 modelMatrix;\\\\n\\\\tuniform mat4 projectionMatrix;\\\\n\\\\tvarying vec3 vWorldPosition;\\\\n\\\\tvec3 getVolumeTransmissionRay( vec3 n, vec3 v, float thickness, float ior, mat4 modelMatrix ) {\\\\n\\\\t\\\\tvec3 refractionVector = refract( - v, normalize( n ), 1.0 / ior );\\\\n\\\\t\\\\tvec3 modelScale;\\\\n\\\\t\\\\tmodelScale.x = length( vec3( modelMatrix[ 0 ].xyz ) );\\\\n\\\\t\\\\tmodelScale.y = length( vec3( modelMatrix[ 1 ].xyz ) );\\\\n\\\\t\\\\tmodelScale.z = length( vec3( modelMatrix[ 2 ].xyz ) );\\\\n\\\\t\\\\treturn normalize( refractionVector ) * thickness * modelScale;\\\\n\\\\t}\\\\n\\\\tfloat applyIorToRoughness( float roughness, float ior ) {\\\\n\\\\t\\\\treturn roughness * clamp( ior * 2.0 - 2.0, 0.0, 1.0 );\\\\n\\\\t}\\\\n\\\\tvec4 getTransmissionSample( vec2 fragCoord, float roughness, float ior ) {\\\\n\\\\t\\\\tfloat framebufferLod = log2( transmissionSamplerSize.x ) * applyIorToRoughness( roughness, ior );\\\\n\\\\t\\\\t#ifdef TEXTURE_LOD_EXT\\\\n\\\\t\\\\t\\\\treturn texture2DLodEXT( transmissionSamplerMap, fragCoord.xy, framebufferLod );\\\\n\\\\t\\\\t#else\\\\n\\\\t\\\\t\\\\treturn texture2D( transmissionSamplerMap, fragCoord.xy, framebufferLod );\\\\n\\\\t\\\\t#endif\\\\n\\\\t}\\\\n\\\\tvec3 applyVolumeAttenuation( vec3 radiance, float transmissionDistance, vec3 attenuationColor, float attenuationDistance ) {\\\\n\\\\t\\\\tif ( attenuationDistance == 0.0 ) {\\\\n\\\\t\\\\t\\\\treturn radiance;\\\\n\\\\t\\\\t} else {\\\\n\\\\t\\\\t\\\\tvec3 attenuationCoefficient = -log( attenuationColor ) / attenuationDistance;\\\\n\\\\t\\\\t\\\\tvec3 transmittance = exp( - attenuationCoefficient * transmissionDistance );\\\\t\\\\t\\\\treturn transmittance * radiance;\\\\n\\\\t\\\\t}\\\\n\\\\t}\\\\n\\\\tvec4 getIBLVolumeRefraction( vec3 n, vec3 v, float roughness, vec3 diffuseColor, vec3 specularColor, float specularF90,\\\\n\\\\t\\\\tvec3 position, mat4 modelMatrix, mat4 viewMatrix, mat4 projMatrix, float ior, float thickness,\\\\n\\\\t\\\\tvec3 attenuationColor, float attenuationDistance ) {\\\\n\\\\t\\\\tvec3 transmissionRay = getVolumeTransmissionRay( n, v, thickness, ior, modelMatrix );\\\\n\\\\t\\\\tvec3 refractedRayExit = position + transmissionRay;\\\\n\\\\t\\\\tvec4 ndcPos = projMatrix * viewMatrix * vec4( refractedRayExit, 1.0 );\\\\n\\\\t\\\\tvec2 refractionCoords = ndcPos.xy / ndcPos.w;\\\\n\\\\t\\\\trefractionCoords += 1.0;\\\\n\\\\t\\\\trefractionCoords /= 2.0;\\\\n\\\\t\\\\tvec4 transmittedLight = getTransmissionSample( refractionCoords, roughness, ior );\\\\n\\\\t\\\\tvec3 attenuatedColor = applyVolumeAttenuation( transmittedLight.rgb, length( transmissionRay ), attenuationColor, attenuationDistance );\\\\n\\\\t\\\\tvec3 F = EnvironmentBRDF( n, v, specularColor, specularF90, roughness );\\\\n\\\\t\\\\treturn vec4( ( 1.0 - F ) * attenuatedColor * diffuseColor, transmittedLight.a );\\\\n\\\\t}\\\\n#endif\\\\\\\",uv_pars_fragment:\\\\\\\"#if ( defined( USE_UV ) && ! defined( UVS_VERTEX_ONLY ) )\\\\n\\\\tvarying vec2 vUv;\\\\n#endif\\\\\\\",uv_pars_vertex:\\\\\\\"#ifdef USE_UV\\\\n\\\\t#ifdef UVS_VERTEX_ONLY\\\\n\\\\t\\\\tvec2 vUv;\\\\n\\\\t#else\\\\n\\\\t\\\\tvarying vec2 vUv;\\\\n\\\\t#endif\\\\n\\\\tuniform mat3 uvTransform;\\\\n#endif\\\\\\\",uv_vertex:\\\\\\\"#ifdef USE_UV\\\\n\\\\tvUv = ( uvTransform * vec3( uv, 1 ) ).xy;\\\\n#endif\\\\\\\",uv2_pars_fragment:\\\\\\\"#if defined( USE_LIGHTMAP ) || defined( USE_AOMAP )\\\\n\\\\tvarying vec2 vUv2;\\\\n#endif\\\\\\\",uv2_pars_vertex:\\\\\\\"#if defined( USE_LIGHTMAP ) || defined( USE_AOMAP )\\\\n\\\\tattribute vec2 uv2;\\\\n\\\\tvarying vec2 vUv2;\\\\n\\\\tuniform mat3 uv2Transform;\\\\n#endif\\\\\\\",uv2_vertex:\\\\\\\"#if defined( USE_LIGHTMAP ) || defined( USE_AOMAP )\\\\n\\\\tvUv2 = ( uv2Transform * vec3( uv2, 1 ) ).xy;\\\\n#endif\\\\\\\",worldpos_vertex:\\\\\\\"#if defined( USE_ENVMAP ) || defined( DISTANCE ) || defined ( USE_SHADOWMAP ) || defined ( USE_TRANSMISSION )\\\\n\\\\tvec4 worldPosition = vec4( transformed, 1.0 );\\\\n\\\\t#ifdef USE_INSTANCING\\\\n\\\\t\\\\tworldPosition = instanceMatrix * worldPosition;\\\\n\\\\t#endif\\\\n\\\\tworldPosition = modelMatrix * worldPosition;\\\\n#endif\\\\\\\",background_vert:\\\\\\\"varying vec2 vUv;\\\\nuniform mat3 uvTransform;\\\\nvoid main() {\\\\n\\\\tvUv = ( uvTransform * vec3( uv, 1 ) ).xy;\\\\n\\\\tgl_Position = vec4( position.xy, 1.0, 1.0 );\\\\n}\\\\\\\",background_frag:\\\\\\\"uniform sampler2D t2D;\\\\nvarying vec2 vUv;\\\\nvoid main() {\\\\n\\\\tvec4 texColor = texture2D( t2D, vUv );\\\\n\\\\tgl_FragColor = mapTexelToLinear( texColor );\\\\n\\\\t#include \\\\n\\\\t#include \\\\n}\\\\\\\",cube_vert:\\\\\\\"varying vec3 vWorldDirection;\\\\n#include \\\\nvoid main() {\\\\n\\\\tvWorldDirection = transformDirection( position, modelMatrix );\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\tgl_Position.z = gl_Position.w;\\\\n}\\\\\\\",cube_frag:\\\\\\\"#include \\\\nuniform float opacity;\\\\nvarying vec3 vWorldDirection;\\\\n#include \\\\nvoid main() {\\\\n\\\\tvec3 vReflect = vWorldDirection;\\\\n\\\\t#include \\\\n\\\\tgl_FragColor = envColor;\\\\n\\\\tgl_FragColor.a *= opacity;\\\\n\\\\t#include \\\\n\\\\t#include \\\\n}\\\\\\\",depth_vert:\\\\\\\"#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\nvarying vec2 vHighPrecisionZW;\\\\nvoid main() {\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#ifdef USE_DISPLACEMENTMAP\\\\n\\\\t\\\\t#include \\\\n\\\\t\\\\t#include \\\\n\\\\t\\\\t#include \\\\n\\\\t#endif\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\tvHighPrecisionZW = gl_Position.zw;\\\\n}\\\\\\\",depth_frag:\\\\\\\"#if DEPTH_PACKING == 3200\\\\n\\\\tuniform float opacity;\\\\n#endif\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\nvarying vec2 vHighPrecisionZW;\\\\nvoid main() {\\\\n\\\\t#include \\\\n\\\\tvec4 diffuseColor = vec4( 1.0 );\\\\n\\\\t#if DEPTH_PACKING == 3200\\\\n\\\\t\\\\tdiffuseColor.a = opacity;\\\\n\\\\t#endif\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\tfloat fragCoordZ = 0.5 * vHighPrecisionZW[0] / vHighPrecisionZW[1] + 0.5;\\\\n\\\\t#if DEPTH_PACKING == 3200\\\\n\\\\t\\\\tgl_FragColor = vec4( vec3( 1.0 - fragCoordZ ), opacity );\\\\n\\\\t#elif DEPTH_PACKING == 3201\\\\n\\\\t\\\\tgl_FragColor = packDepthToRGBA( fragCoordZ );\\\\n\\\\t#endif\\\\n}\\\\\\\",distanceRGBA_vert:\\\\\\\"#define DISTANCE\\\\nvarying vec3 vWorldPosition;\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\nvoid main() {\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#ifdef USE_DISPLACEMENTMAP\\\\n\\\\t\\\\t#include \\\\n\\\\t\\\\t#include \\\\n\\\\t\\\\t#include \\\\n\\\\t#endif\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\tvWorldPosition = worldPosition.xyz;\\\\n}\\\\\\\",distanceRGBA_frag:\\\\\\\"#define DISTANCE\\\\nuniform vec3 referencePosition;\\\\nuniform float nearDistance;\\\\nuniform float farDistance;\\\\nvarying vec3 vWorldPosition;\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\nvoid main () {\\\\n\\\\t#include \\\\n\\\\tvec4 diffuseColor = vec4( 1.0 );\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\tfloat dist = length( vWorldPosition - referencePosition );\\\\n\\\\tdist = ( dist - nearDistance ) / ( farDistance - nearDistance );\\\\n\\\\tdist = saturate( dist );\\\\n\\\\tgl_FragColor = packDepthToRGBA( dist );\\\\n}\\\\\\\",equirect_vert:\\\\\\\"varying vec3 vWorldDirection;\\\\n#include \\\\nvoid main() {\\\\n\\\\tvWorldDirection = transformDirection( position, modelMatrix );\\\\n\\\\t#include \\\\n\\\\t#include \\\\n}\\\\\\\",equirect_frag:\\\\\\\"uniform sampler2D tEquirect;\\\\nvarying vec3 vWorldDirection;\\\\n#include \\\\nvoid main() {\\\\n\\\\tvec3 direction = normalize( vWorldDirection );\\\\n\\\\tvec2 sampleUV = equirectUv( direction );\\\\n\\\\tvec4 texColor = texture2D( tEquirect, sampleUV );\\\\n\\\\tgl_FragColor = mapTexelToLinear( texColor );\\\\n\\\\t#include \\\\n\\\\t#include \\\\n}\\\\\\\",linedashed_vert:\\\\\\\"uniform float scale;\\\\nattribute float lineDistance;\\\\nvarying float vLineDistance;\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\nvoid main() {\\\\n\\\\tvLineDistance = scale * lineDistance;\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n}\\\\\\\",linedashed_frag:\\\\\\\"uniform vec3 diffuse;\\\\nuniform float opacity;\\\\nuniform float dashSize;\\\\nuniform float totalSize;\\\\nvarying float vLineDistance;\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\nvoid main() {\\\\n\\\\t#include \\\\n\\\\tif ( mod( vLineDistance, totalSize ) > dashSize ) {\\\\n\\\\t\\\\tdiscard;\\\\n\\\\t}\\\\n\\\\tvec3 outgoingLight = vec3( 0.0 );\\\\n\\\\tvec4 diffuseColor = vec4( diffuse, opacity );\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\toutgoingLight = diffuseColor.rgb;\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n}\\\\\\\",meshbasic_vert:\\\\\\\"#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\nvoid main() {\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#if defined ( USE_ENVMAP ) || defined ( USE_SKINNING )\\\\n\\\\t\\\\t#include \\\\n\\\\t\\\\t#include \\\\n\\\\t\\\\t#include \\\\n\\\\t\\\\t#include \\\\n\\\\t\\\\t#include \\\\n\\\\t#endif\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n}\\\\\\\",meshbasic_frag:\\\\\\\"uniform vec3 diffuse;\\\\nuniform float opacity;\\\\n#ifndef FLAT_SHADED\\\\n\\\\tvarying vec3 vNormal;\\\\n#endif\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\nvoid main() {\\\\n\\\\t#include \\\\n\\\\tvec4 diffuseColor = vec4( diffuse, opacity );\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\tReflectedLight reflectedLight = ReflectedLight( vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ) );\\\\n\\\\t#ifdef USE_LIGHTMAP\\\\n\\\\t\\\\tvec4 lightMapTexel= texture2D( lightMap, vUv2 );\\\\n\\\\t\\\\treflectedLight.indirectDiffuse += lightMapTexelToLinear( lightMapTexel ).rgb * lightMapIntensity;\\\\n\\\\t#else\\\\n\\\\t\\\\treflectedLight.indirectDiffuse += vec3( 1.0 );\\\\n\\\\t#endif\\\\n\\\\t#include \\\\n\\\\treflectedLight.indirectDiffuse *= diffuseColor.rgb;\\\\n\\\\tvec3 outgoingLight = reflectedLight.indirectDiffuse;\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n}\\\\\\\",meshlambert_vert:\\\\\\\"#define LAMBERT\\\\nvarying vec3 vLightFront;\\\\nvarying vec3 vIndirectFront;\\\\n#ifdef DOUBLE_SIDED\\\\n\\\\tvarying vec3 vLightBack;\\\\n\\\\tvarying vec3 vIndirectBack;\\\\n#endif\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\nvoid main() {\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n}\\\\\\\",meshlambert_frag:\\\\\\\"uniform vec3 diffuse;\\\\nuniform vec3 emissive;\\\\nuniform float opacity;\\\\nvarying vec3 vLightFront;\\\\nvarying vec3 vIndirectFront;\\\\n#ifdef DOUBLE_SIDED\\\\n\\\\tvarying vec3 vLightBack;\\\\n\\\\tvarying vec3 vIndirectBack;\\\\n#endif\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\nvoid main() {\\\\n\\\\t#include \\\\n\\\\tvec4 diffuseColor = vec4( diffuse, opacity );\\\\n\\\\tReflectedLight reflectedLight = ReflectedLight( vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ) );\\\\n\\\\tvec3 totalEmissiveRadiance = emissive;\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#ifdef DOUBLE_SIDED\\\\n\\\\t\\\\treflectedLight.indirectDiffuse += ( gl_FrontFacing ) ? vIndirectFront : vIndirectBack;\\\\n\\\\t#else\\\\n\\\\t\\\\treflectedLight.indirectDiffuse += vIndirectFront;\\\\n\\\\t#endif\\\\n\\\\t#include \\\\n\\\\treflectedLight.indirectDiffuse *= BRDF_Lambert( diffuseColor.rgb );\\\\n\\\\t#ifdef DOUBLE_SIDED\\\\n\\\\t\\\\treflectedLight.directDiffuse = ( gl_FrontFacing ) ? vLightFront : vLightBack;\\\\n\\\\t#else\\\\n\\\\t\\\\treflectedLight.directDiffuse = vLightFront;\\\\n\\\\t#endif\\\\n\\\\treflectedLight.directDiffuse *= BRDF_Lambert( diffuseColor.rgb ) * getShadowMask();\\\\n\\\\t#include \\\\n\\\\tvec3 outgoingLight = reflectedLight.directDiffuse + reflectedLight.indirectDiffuse + totalEmissiveRadiance;\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n}\\\\\\\",meshmatcap_vert:\\\\\\\"#define MATCAP\\\\nvarying vec3 vViewPosition;\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\nvoid main() {\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\tvViewPosition = - mvPosition.xyz;\\\\n}\\\\\\\",meshmatcap_frag:\\\\\\\"#define MATCAP\\\\nuniform vec3 diffuse;\\\\nuniform float opacity;\\\\nuniform sampler2D matcap;\\\\nvarying vec3 vViewPosition;\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\nvoid main() {\\\\n\\\\t#include \\\\n\\\\tvec4 diffuseColor = vec4( diffuse, opacity );\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\tvec3 viewDir = normalize( vViewPosition );\\\\n\\\\tvec3 x = normalize( vec3( viewDir.z, 0.0, - viewDir.x ) );\\\\n\\\\tvec3 y = cross( viewDir, x );\\\\n\\\\tvec2 uv = vec2( dot( x, normal ), dot( y, normal ) ) * 0.495 + 0.5;\\\\n\\\\t#ifdef USE_MATCAP\\\\n\\\\t\\\\tvec4 matcapColor = texture2D( matcap, uv );\\\\n\\\\t\\\\tmatcapColor = matcapTexelToLinear( matcapColor );\\\\n\\\\t#else\\\\n\\\\t\\\\tvec4 matcapColor = vec4( 1.0 );\\\\n\\\\t#endif\\\\n\\\\tvec3 outgoingLight = diffuseColor.rgb * matcapColor.rgb;\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n}\\\\\\\",meshnormal_vert:\\\\\\\"#define NORMAL\\\\n#if defined( FLAT_SHADED ) || defined( USE_BUMPMAP ) || defined( TANGENTSPACE_NORMALMAP )\\\\n\\\\tvarying vec3 vViewPosition;\\\\n#endif\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\nvoid main() {\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n#if defined( FLAT_SHADED ) || defined( USE_BUMPMAP ) || defined( TANGENTSPACE_NORMALMAP )\\\\n\\\\tvViewPosition = - mvPosition.xyz;\\\\n#endif\\\\n}\\\\\\\",meshnormal_frag:\\\\\\\"#define NORMAL\\\\nuniform float opacity;\\\\n#if defined( FLAT_SHADED ) || defined( USE_BUMPMAP ) || defined( TANGENTSPACE_NORMALMAP )\\\\n\\\\tvarying vec3 vViewPosition;\\\\n#endif\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\nvoid main() {\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\tgl_FragColor = vec4( packNormalToRGB( normal ), opacity );\\\\n}\\\\\\\",meshphong_vert:\\\\\\\"#define PHONG\\\\nvarying vec3 vViewPosition;\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\nvoid main() {\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\tvViewPosition = - mvPosition.xyz;\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n}\\\\\\\",meshphong_frag:\\\\\\\"#define PHONG\\\\nuniform vec3 diffuse;\\\\nuniform vec3 emissive;\\\\nuniform vec3 specular;\\\\nuniform float shininess;\\\\nuniform float opacity;\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\nvoid main() {\\\\n\\\\t#include \\\\n\\\\tvec4 diffuseColor = vec4( diffuse, opacity );\\\\n\\\\tReflectedLight reflectedLight = ReflectedLight( vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ) );\\\\n\\\\tvec3 totalEmissiveRadiance = emissive;\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\tvec3 outgoingLight = reflectedLight.directDiffuse + reflectedLight.indirectDiffuse + reflectedLight.directSpecular + reflectedLight.indirectSpecular + totalEmissiveRadiance;\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n}\\\\\\\",meshphysical_vert:\\\\\\\"#define STANDARD\\\\nvarying vec3 vViewPosition;\\\\n#ifdef USE_TRANSMISSION\\\\n\\\\tvarying vec3 vWorldPosition;\\\\n#endif\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\nvoid main() {\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\tvViewPosition = - mvPosition.xyz;\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n#ifdef USE_TRANSMISSION\\\\n\\\\tvWorldPosition = worldPosition.xyz;\\\\n#endif\\\\n}\\\\\\\",meshphysical_frag:\\\\\\\"#define STANDARD\\\\n#ifdef PHYSICAL\\\\n\\\\t#define IOR\\\\n\\\\t#define SPECULAR\\\\n#endif\\\\nuniform vec3 diffuse;\\\\nuniform vec3 emissive;\\\\nuniform float roughness;\\\\nuniform float metalness;\\\\nuniform float opacity;\\\\n#ifdef IOR\\\\n\\\\tuniform float ior;\\\\n#endif\\\\n#ifdef SPECULAR\\\\n\\\\tuniform float specularIntensity;\\\\n\\\\tuniform vec3 specularTint;\\\\n\\\\t#ifdef USE_SPECULARINTENSITYMAP\\\\n\\\\t\\\\tuniform sampler2D specularIntensityMap;\\\\n\\\\t#endif\\\\n\\\\t#ifdef USE_SPECULARTINTMAP\\\\n\\\\t\\\\tuniform sampler2D specularTintMap;\\\\n\\\\t#endif\\\\n#endif\\\\n#ifdef USE_CLEARCOAT\\\\n\\\\tuniform float clearcoat;\\\\n\\\\tuniform float clearcoatRoughness;\\\\n#endif\\\\n#ifdef USE_SHEEN\\\\n\\\\tuniform vec3 sheenTint;\\\\n\\\\tuniform float sheenRoughness;\\\\n#endif\\\\nvarying vec3 vViewPosition;\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\nvoid main() {\\\\n\\\\t#include \\\\n\\\\tvec4 diffuseColor = vec4( diffuse, opacity );\\\\n\\\\tReflectedLight reflectedLight = ReflectedLight( vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ) );\\\\n\\\\tvec3 totalEmissiveRadiance = emissive;\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\tvec3 totalDiffuse = reflectedLight.directDiffuse + reflectedLight.indirectDiffuse;\\\\n\\\\tvec3 totalSpecular = reflectedLight.directSpecular + reflectedLight.indirectSpecular;\\\\n\\\\t#include \\\\n\\\\tvec3 outgoingLight = totalDiffuse + totalSpecular + totalEmissiveRadiance;\\\\n\\\\t#ifdef USE_CLEARCOAT\\\\n\\\\t\\\\tfloat dotNVcc = saturate( dot( geometry.clearcoatNormal, geometry.viewDir ) );\\\\n\\\\t\\\\tvec3 Fcc = F_Schlick( material.clearcoatF0, material.clearcoatF90, dotNVcc );\\\\n\\\\t\\\\toutgoingLight = outgoingLight * ( 1.0 - clearcoat * Fcc ) + clearcoatSpecular * clearcoat;\\\\n\\\\t#endif\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n}\\\\\\\",meshtoon_vert:\\\\\\\"#define TOON\\\\nvarying vec3 vViewPosition;\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\nvoid main() {\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\tvViewPosition = - mvPosition.xyz;\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n}\\\\\\\",meshtoon_frag:\\\\\\\"#define TOON\\\\nuniform vec3 diffuse;\\\\nuniform vec3 emissive;\\\\nuniform float opacity;\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\nvoid main() {\\\\n\\\\t#include \\\\n\\\\tvec4 diffuseColor = vec4( diffuse, opacity );\\\\n\\\\tReflectedLight reflectedLight = ReflectedLight( vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ) );\\\\n\\\\tvec3 totalEmissiveRadiance = emissive;\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\tvec3 outgoingLight = reflectedLight.directDiffuse + reflectedLight.indirectDiffuse + totalEmissiveRadiance;\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n}\\\\\\\",points_vert:\\\\\\\"uniform float size;\\\\nuniform float scale;\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\nvoid main() {\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\tgl_PointSize = size;\\\\n\\\\t#ifdef USE_SIZEATTENUATION\\\\n\\\\t\\\\tbool isPerspective = isPerspectiveMatrix( projectionMatrix );\\\\n\\\\t\\\\tif ( isPerspective ) gl_PointSize *= ( scale / - mvPosition.z );\\\\n\\\\t#endif\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n}\\\\\\\",points_frag:\\\\\\\"uniform vec3 diffuse;\\\\nuniform float opacity;\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\nvoid main() {\\\\n\\\\t#include \\\\n\\\\tvec3 outgoingLight = vec3( 0.0 );\\\\n\\\\tvec4 diffuseColor = vec4( diffuse, opacity );\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\toutgoingLight = diffuseColor.rgb;\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n}\\\\\\\",shadow_vert:\\\\\\\"#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\nvoid main() {\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n}\\\\\\\",shadow_frag:\\\\\\\"uniform vec3 color;\\\\nuniform float opacity;\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\nvoid main() {\\\\n\\\\tgl_FragColor = vec4( color, opacity * ( 1.0 - getShadowMask() ) );\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n}\\\\\\\",sprite_vert:\\\\\\\"uniform float rotation;\\\\nuniform vec2 center;\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\nvoid main() {\\\\n\\\\t#include \\\\n\\\\tvec4 mvPosition = modelViewMatrix * vec4( 0.0, 0.0, 0.0, 1.0 );\\\\n\\\\tvec2 scale;\\\\n\\\\tscale.x = length( vec3( modelMatrix[ 0 ].x, modelMatrix[ 0 ].y, modelMatrix[ 0 ].z ) );\\\\n\\\\tscale.y = length( vec3( modelMatrix[ 1 ].x, modelMatrix[ 1 ].y, modelMatrix[ 1 ].z ) );\\\\n\\\\t#ifndef USE_SIZEATTENUATION\\\\n\\\\t\\\\tbool isPerspective = isPerspectiveMatrix( projectionMatrix );\\\\n\\\\t\\\\tif ( isPerspective ) scale *= - mvPosition.z;\\\\n\\\\t#endif\\\\n\\\\tvec2 alignedPosition = ( position.xy - ( center - vec2( 0.5 ) ) ) * scale;\\\\n\\\\tvec2 rotatedPosition;\\\\n\\\\trotatedPosition.x = cos( rotation ) * alignedPosition.x - sin( rotation ) * alignedPosition.y;\\\\n\\\\trotatedPosition.y = sin( rotation ) * alignedPosition.x + cos( rotation ) * alignedPosition.y;\\\\n\\\\tmvPosition.xy += rotatedPosition;\\\\n\\\\tgl_Position = projectionMatrix * mvPosition;\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n}\\\\\\\",sprite_frag:\\\\\\\"uniform vec3 diffuse;\\\\nuniform float opacity;\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\nvoid main() {\\\\n\\\\t#include \\\\n\\\\tvec3 outgoingLight = vec3( 0.0 );\\\\n\\\\tvec4 diffuseColor = vec4( diffuse, opacity );\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\toutgoingLight = diffuseColor.rgb;\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n}\\\\\\\"},VT={common:{diffuse:{value:new kw(16777215)},opacity:{value:1},map:{value:null},uvTransform:{value:new rb},uv2Transform:{value:new rb},alphaMap:{value:null},alphaTest:{value:0}},specularmap:{specularMap:{value:null}},envmap:{envMap:{value:null},flipEnvMap:{value:-1},reflectivity:{value:1},ior:{value:1.5},refractionRatio:{value:.98},maxMipLevel:{value:0}},aomap:{aoMap:{value:null},aoMapIntensity:{value:1}},lightmap:{lightMap:{value:null},lightMapIntensity:{value:1}},emissivemap:{emissiveMap:{value:null}},bumpmap:{bumpMap:{value:null},bumpScale:{value:1}},normalmap:{normalMap:{value:null},normalScale:{value:new sb(1,1)}},displacementmap:{displacementMap:{value:null},displacementScale:{value:1},displacementBias:{value:0}},roughnessmap:{roughnessMap:{value:null}},metalnessmap:{metalnessMap:{value:null}},gradientmap:{gradientMap:{value:null}},fog:{fogDensity:{value:25e-5},fogNear:{value:1},fogFar:{value:2e3},fogColor:{value:new kw(16777215)}},lights:{ambientLightColor:{value:[]},lightProbe:{value:[]},directionalLights:{value:[],properties:{direction:{},color:{}}},directionalLightShadows:{value:[],properties:{shadowBias:{},shadowNormalBias:{},shadowRadius:{},shadowMapSize:{}}},directionalShadowMap:{value:[]},directionalShadowMatrix:{value:[]},spotLights:{value:[],properties:{color:{},position:{},direction:{},distance:{},coneCos:{},penumbraCos:{},decay:{}}},spotLightShadows:{value:[],properties:{shadowBias:{},shadowNormalBias:{},shadowRadius:{},shadowMapSize:{}}},spotShadowMap:{value:[]},spotShadowMatrix:{value:[]},pointLights:{value:[],properties:{color:{},position:{},decay:{},distance:{}}},pointLightShadows:{value:[],properties:{shadowBias:{},shadowNormalBias:{},shadowRadius:{},shadowMapSize:{},shadowCameraNear:{},shadowCameraFar:{}}},pointShadowMap:{value:[]},pointShadowMatrix:{value:[]},hemisphereLights:{value:[],properties:{direction:{},skyColor:{},groundColor:{}}},rectAreaLights:{value:[],properties:{color:{},position:{},width:{},height:{}}},ltc_1:{value:null},ltc_2:{value:null}},points:{diffuse:{value:new kw(16777215)},opacity:{value:1},size:{value:1},scale:{value:1},map:{value:null},alphaMap:{value:null},alphaTest:{value:0},uvTransform:{value:new rb}},sprite:{diffuse:{value:new kw(16777215)},opacity:{value:1},center:{value:new sb(.5,.5)},rotation:{value:0},map:{value:null},alphaMap:{value:null},alphaTest:{value:0},uvTransform:{value:new rb}}},HT={basic:{uniforms:wT([VT.common,VT.specularmap,VT.envmap,VT.aomap,VT.lightmap,VT.fog]),vertexShader:GT.meshbasic_vert,fragmentShader:GT.meshbasic_frag},lambert:{uniforms:wT([VT.common,VT.specularmap,VT.envmap,VT.aomap,VT.lightmap,VT.emissivemap,VT.fog,VT.lights,{emissive:{value:new kw(0)}}]),vertexShader:GT.meshlambert_vert,fragmentShader:GT.meshlambert_frag},phong:{uniforms:wT([VT.common,VT.specularmap,VT.envmap,VT.aomap,VT.lightmap,VT.emissivemap,VT.bumpmap,VT.normalmap,VT.displacementmap,VT.fog,VT.lights,{emissive:{value:new kw(0)},specular:{value:new kw(1118481)},shininess:{value:30}}]),vertexShader:GT.meshphong_vert,fragmentShader:GT.meshphong_frag},standard:{uniforms:wT([VT.common,VT.envmap,VT.aomap,VT.lightmap,VT.emissivemap,VT.bumpmap,VT.normalmap,VT.displacementmap,VT.roughnessmap,VT.metalnessmap,VT.fog,VT.lights,{emissive:{value:new kw(0)},roughness:{value:1},metalness:{value:0},envMapIntensity:{value:1}}]),vertexShader:GT.meshphysical_vert,fragmentShader:GT.meshphysical_frag},toon:{uniforms:wT([VT.common,VT.aomap,VT.lightmap,VT.emissivemap,VT.bumpmap,VT.normalmap,VT.displacementmap,VT.gradientmap,VT.fog,VT.lights,{emissive:{value:new kw(0)}}]),vertexShader:GT.meshtoon_vert,fragmentShader:GT.meshtoon_frag},matcap:{uniforms:wT([VT.common,VT.bumpmap,VT.normalmap,VT.displacementmap,VT.fog,{matcap:{value:null}}]),vertexShader:GT.meshmatcap_vert,fragmentShader:GT.meshmatcap_frag},points:{uniforms:wT([VT.points,VT.fog]),vertexShader:GT.points_vert,fragmentShader:GT.points_frag},dashed:{uniforms:wT([VT.common,VT.fog,{scale:{value:1},dashSize:{value:1},totalSize:{value:2}}]),vertexShader:GT.linedashed_vert,fragmentShader:GT.linedashed_frag},depth:{uniforms:wT([VT.common,VT.displacementmap]),vertexShader:GT.depth_vert,fragmentShader:GT.depth_frag},normal:{uniforms:wT([VT.common,VT.bumpmap,VT.normalmap,VT.displacementmap,{opacity:{value:1}}]),vertexShader:GT.meshnormal_vert,fragmentShader:GT.meshnormal_frag},sprite:{uniforms:wT([VT.sprite,VT.fog]),vertexShader:GT.sprite_vert,fragmentShader:GT.sprite_frag},background:{uniforms:{uvTransform:{value:new rb},t2D:{value:null}},vertexShader:GT.background_vert,fragmentShader:GT.background_frag},cube:{uniforms:wT([VT.envmap,{opacity:{value:1}}]),vertexShader:GT.cube_vert,fragmentShader:GT.cube_frag},equirect:{uniforms:{tEquirect:{value:null}},vertexShader:GT.equirect_vert,fragmentShader:GT.equirect_frag},distanceRGBA:{uniforms:wT([VT.common,VT.displacementmap,{referencePosition:{value:new gb},nearDistance:{value:1},farDistance:{value:1e3}}]),vertexShader:GT.distanceRGBA_vert,fragmentShader:GT.distanceRGBA_frag},shadow:{uniforms:wT([VT.lights,VT.fog,{color:{value:new kw(0)},opacity:{value:1}}]),vertexShader:GT.shadow_vert,fragmentShader:GT.shadow_frag}};function jT(t,e,n,i,s){const r=new kw(0);let o,a,l=0,c=null,h=0,u=null;function d(t,e){n.buffers.color.setClear(t.r,t.g,t.b,e,s)}return{getClearColor:function(){return r},setClearColor:function(t,e=1){r.set(t),l=e,d(r,l)},getClearAlpha:function(){return l},setClearAlpha:function(t){l=t,d(r,l)},render:function(n,s){let p=!1,_=!0===s.isScene?s.background:null;_&&_.isTexture&&(_=e.get(_));const m=t.xr,f=m.getSession&&m.getSession();f&&\\\\\\\"additive\\\\\\\"===f.environmentBlendMode&&(_=null),null===_?d(r,l):_&&_.isColor&&(d(_,1),p=!0),(t.autoClear||p)&&t.clear(t.autoClearColor,t.autoClearDepth,t.autoClearStencil),_&&(_.isCubeTexture||_.mapping===lx)?(void 0===a&&(a=new vT(new xT(1,1,1),new AT({name:\\\\\\\"BackgroundCubeMaterial\\\\\\\",uniforms:bT(HT.cube.uniforms),vertexShader:HT.cube.vertexShader,fragmentShader:HT.cube.fragmentShader,side:1,depthTest:!1,depthWrite:!1,fog:!1})),a.geometry.deleteAttribute(\\\\\\\"normal\\\\\\\"),a.geometry.deleteAttribute(\\\\\\\"uv\\\\\\\"),a.onBeforeRender=function(t,e,n){this.matrixWorld.copyPosition(n.matrixWorld)},Object.defineProperty(a.material,\\\\\\\"envMap\\\\\\\",{get:function(){return this.uniforms.envMap.value}}),i.update(a)),a.material.uniforms.envMap.value=_,a.material.uniforms.flipEnvMap.value=_.isCubeTexture&&!1===_.isRenderTargetTexture?-1:1,c===_&&h===_.version&&u===t.toneMapping||(a.material.needsUpdate=!0,c=_,h=_.version,u=t.toneMapping),n.unshift(a,a.geometry,a.material,0,0,null)):_&&_.isTexture&&(void 0===o&&(o=new vT(new UT(2,2),new AT({name:\\\\\\\"BackgroundMaterial\\\\\\\",uniforms:bT(HT.background.uniforms),vertexShader:HT.background.vertexShader,fragmentShader:HT.background.fragmentShader,side:0,depthTest:!1,depthWrite:!1,fog:!1})),o.geometry.deleteAttribute(\\\\\\\"normal\\\\\\\"),Object.defineProperty(o.material,\\\\\\\"map\\\\\\\",{get:function(){return this.uniforms.t2D.value}}),i.update(o)),o.material.uniforms.t2D.value=_,!0===_.matrixAutoUpdate&&_.updateMatrix(),o.material.uniforms.uvTransform.value.copy(_.matrix),c===_&&h===_.version&&u===t.toneMapping||(o.material.needsUpdate=!0,c=_,h=_.version,u=t.toneMapping),n.unshift(o,o.geometry,o.material,0,0,null))}}}function WT(t,e,n,i){const s=t.getParameter(34921),r=i.isWebGL2?null:e.get(\\\\\\\"OES_vertex_array_object\\\\\\\"),o=i.isWebGL2||null!==r,a={},l=d(null);let c=l;function h(e){return i.isWebGL2?t.bindVertexArray(e):r.bindVertexArrayOES(e)}function u(e){return i.isWebGL2?t.deleteVertexArray(e):r.deleteVertexArrayOES(e)}function d(t){const e=[],n=[],i=[];for(let t=0;t=0){let r=l[e];if(void 0===r&&(\\\\\\\"instanceMatrix\\\\\\\"===e&&s.instanceMatrix&&(r=s.instanceMatrix),\\\\\\\"instanceColor\\\\\\\"===e&&s.instanceColor&&(r=s.instanceColor)),void 0!==r){const e=r.normalized,o=r.itemSize,l=n.get(r);if(void 0===l)continue;const c=l.buffer,h=l.type,u=l.bytesPerElement;if(r.isInterleavedBufferAttribute){const n=r.data,l=n.stride,d=r.offset;if(n&&n.isInstancedInterleavedBuffer){for(let t=0;t0&&t.getShaderPrecisionFormat(35632,36338).precision>0)return\\\\\\\"highp\\\\\\\";e=\\\\\\\"mediump\\\\\\\"}return\\\\\\\"mediump\\\\\\\"===e&&t.getShaderPrecisionFormat(35633,36337).precision>0&&t.getShaderPrecisionFormat(35632,36337).precision>0?\\\\\\\"mediump\\\\\\\":\\\\\\\"lowp\\\\\\\"}const r=\\\\\\\"undefined\\\\\\\"!=typeof WebGL2RenderingContext&&t instanceof WebGL2RenderingContext||\\\\\\\"undefined\\\\\\\"!=typeof WebGL2ComputeRenderingContext&&t instanceof WebGL2ComputeRenderingContext;let o=void 0!==n.precision?n.precision:\\\\\\\"highp\\\\\\\";const a=s(o);a!==o&&(console.warn(\\\\\\\"THREE.WebGLRenderer:\\\\\\\",o,\\\\\\\"not supported, using\\\\\\\",a,\\\\\\\"instead.\\\\\\\"),o=a);const l=r||e.has(\\\\\\\"WEBGL_draw_buffers\\\\\\\"),c=!0===n.logarithmicDepthBuffer,h=t.getParameter(34930),u=t.getParameter(35660),d=t.getParameter(3379),p=t.getParameter(34076),_=t.getParameter(34921),m=t.getParameter(36347),f=t.getParameter(36348),g=t.getParameter(36349),v=u>0,y=r||e.has(\\\\\\\"OES_texture_float\\\\\\\");return{isWebGL2:r,drawBuffers:l,getMaxAnisotropy:function(){if(void 0!==i)return i;if(!0===e.has(\\\\\\\"EXT_texture_filter_anisotropic\\\\\\\")){const n=e.get(\\\\\\\"EXT_texture_filter_anisotropic\\\\\\\");i=t.getParameter(n.MAX_TEXTURE_MAX_ANISOTROPY_EXT)}else i=0;return i},getMaxPrecision:s,precision:o,logarithmicDepthBuffer:c,maxTextures:h,maxVertexTextures:u,maxTextureSize:d,maxCubemapSize:p,maxAttributes:_,maxVertexUniforms:m,maxVaryings:f,maxFragmentUniforms:g,vertexTextures:v,floatFragmentTextures:y,floatVertexTextures:v&&y,maxSamples:r?t.getParameter(36183):0}}function YT(t){const e=this;let n=null,i=0,s=!1,r=!1;const o=new IT,a=new rb,l={value:null,needsUpdate:!1};function c(){l.value!==n&&(l.value=n,l.needsUpdate=i>0),e.numPlanes=i,e.numIntersection=0}function h(t,n,i,s){const r=null!==t?t.length:0;let c=null;if(0!==r){if(c=l.value,!0!==s||null===c){const e=i+4*r,s=n.matrixWorldInverse;a.getNormalMatrix(s),(null===c||c.length0){const o=t.getRenderTarget(),a=new LT(r.height/2);return a.fromEquirectangularTexture(t,s),e.set(s,a),t.setRenderTarget(o),s.addEventListener(\\\\\\\"dispose\\\\\\\",i),n(a.texture,s.mapping)}return null}}}return s},dispose:function(){e=new WeakMap}}}HT.physical={uniforms:wT([HT.standard.uniforms,{clearcoat:{value:0},clearcoatMap:{value:null},clearcoatRoughness:{value:0},clearcoatRoughnessMap:{value:null},clearcoatNormalScale:{value:new sb(1,1)},clearcoatNormalMap:{value:null},sheen:{value:0},sheenTint:{value:new kw(0)},sheenRoughness:{value:0},transmission:{value:0},transmissionMap:{value:null},transmissionSamplerSize:{value:new sb},transmissionSamplerMap:{value:null},thickness:{value:0},thicknessMap:{value:null},attenuationDistance:{value:0},attenuationTint:{value:new kw(0)},specularIntensity:{value:0},specularIntensityMap:{value:null},specularTint:{value:new kw(1,1,1)},specularTintMap:{value:null}}]),vertexShader:GT.meshphysical_vert,fragmentShader:GT.meshphysical_frag};class JT extends MT{constructor(t=-1,e=1,n=1,i=-1,s=.1,r=2e3){super(),this.type=\\\\\\\"OrthographicCamera\\\\\\\",this.zoom=1,this.view=null,this.left=t,this.right=e,this.top=n,this.bottom=i,this.near=s,this.far=r,this.updateProjectionMatrix()}copy(t,e){return super.copy(t,e),this.left=t.left,this.right=t.right,this.top=t.top,this.bottom=t.bottom,this.near=t.near,this.far=t.far,this.zoom=t.zoom,this.view=null===t.view?null:Object.assign({},t.view),this}setViewOffset(t,e,n,i,s,r){null===this.view&&(this.view={enabled:!0,fullWidth:1,fullHeight:1,offsetX:0,offsetY:0,width:1,height:1}),this.view.enabled=!0,this.view.fullWidth=t,this.view.fullHeight=e,this.view.offsetX=n,this.view.offsetY=i,this.view.width=s,this.view.height=r,this.updateProjectionMatrix()}clearViewOffset(){null!==this.view&&(this.view.enabled=!1),this.updateProjectionMatrix()}updateProjectionMatrix(){const t=(this.right-this.left)/(2*this.zoom),e=(this.top-this.bottom)/(2*this.zoom),n=(this.right+this.left)/2,i=(this.top+this.bottom)/2;let s=n-t,r=n+t,o=i+e,a=i-e;if(null!==this.view&&this.view.enabled){const t=(this.right-this.left)/this.view.fullWidth/this.zoom,e=(this.top-this.bottom)/this.view.fullHeight/this.zoom;s+=t*this.view.offsetX,r=s+t*this.view.width,o-=e*this.view.offsetY,a=o-e*this.view.height}this.projectionMatrix.makeOrthographic(s,r,o,a,this.near,this.far),this.projectionMatrixInverse.copy(this.projectionMatrix).invert()}toJSON(t){const e=super.toJSON(t);return e.object.zoom=this.zoom,e.object.left=this.left,e.object.right=this.right,e.object.top=this.top,e.object.bottom=this.bottom,e.object.near=this.near,e.object.far=this.far,null!==this.view&&(e.object.view=Object.assign({},this.view)),e}}JT.prototype.isOrthographicCamera=!0;class ZT extends AT{constructor(t){super(t),this.type=\\\\\\\"RawShaderMaterial\\\\\\\"}}ZT.prototype.isRawShaderMaterial=!0;const QT=Math.pow(2,8),KT=[.125,.215,.35,.446,.526,.582],tA=5+KT.length,eA=20,nA={[Dx]:0,[Bx]:1,[kx]:2,3004:3,3005:4,3006:5,[zx]:6},iA=new JT,{_lodPlanes:sA,_sizeLods:rA,_sigmas:oA}=_A(),aA=new kw;let lA=null;const cA=(1+Math.sqrt(5))/2,hA=1/cA,uA=[new gb(1,1,1),new gb(-1,1,1),new gb(1,1,-1),new gb(-1,1,-1),new gb(0,cA,hA),new gb(0,cA,-hA),new gb(hA,0,cA),new gb(-hA,0,cA),new gb(cA,hA,0),new gb(-cA,hA,0)];class dA{constructor(t){this._renderer=t,this._pingPongRenderTarget=null,this._blurMaterial=function(t){const e=new Float32Array(t),n=new gb(0,1,0);return new ZT({name:\\\\\\\"SphericalGaussianBlur\\\\\\\",defines:{n:t},uniforms:{envMap:{value:null},samples:{value:1},weights:{value:e},latitudinal:{value:!1},dTheta:{value:0},mipInt:{value:0},poleAxis:{value:n},inputEncoding:{value:nA[3e3]},outputEncoding:{value:nA[3e3]}},vertexShader:yA(),fragmentShader:`\\\\n\\\\n\\\\t\\\\t\\\\tprecision mediump float;\\\\n\\\\t\\\\t\\\\tprecision mediump int;\\\\n\\\\n\\\\t\\\\t\\\\tvarying vec3 vOutputDirection;\\\\n\\\\n\\\\t\\\\t\\\\tuniform sampler2D envMap;\\\\n\\\\t\\\\t\\\\tuniform int samples;\\\\n\\\\t\\\\t\\\\tuniform float weights[ n ];\\\\n\\\\t\\\\t\\\\tuniform bool latitudinal;\\\\n\\\\t\\\\t\\\\tuniform float dTheta;\\\\n\\\\t\\\\t\\\\tuniform float mipInt;\\\\n\\\\t\\\\t\\\\tuniform vec3 poleAxis;\\\\n\\\\n\\\\t\\\\t\\\\t${xA()}\\\\n\\\\n\\\\t\\\\t\\\\t#define ENVMAP_TYPE_CUBE_UV\\\\n\\\\t\\\\t\\\\t#include \\\\n\\\\n\\\\t\\\\t\\\\tvec3 getSample( float theta, vec3 axis ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tfloat cosTheta = cos( theta );\\\\n\\\\t\\\\t\\\\t\\\\t// Rodrigues' axis-angle rotation\\\\n\\\\t\\\\t\\\\t\\\\tvec3 sampleDirection = vOutputDirection * cosTheta\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t+ cross( axis, vOutputDirection ) * sin( theta )\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t+ axis * dot( axis, vOutputDirection ) * ( 1.0 - cosTheta );\\\\n\\\\n\\\\t\\\\t\\\\t\\\\treturn bilinearCubeUV( envMap, sampleDirection, mipInt );\\\\n\\\\n\\\\t\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tvec3 axis = latitudinal ? poleAxis : cross( poleAxis, vOutputDirection );\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tif ( all( equal( axis, vec3( 0.0 ) ) ) ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\t\\\\taxis = vec3( vOutputDirection.z, 0.0, - vOutputDirection.x );\\\\n\\\\n\\\\t\\\\t\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\t\\\\t\\\\taxis = normalize( axis );\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tgl_FragColor = vec4( 0.0, 0.0, 0.0, 1.0 );\\\\n\\\\t\\\\t\\\\t\\\\tgl_FragColor.rgb += weights[ 0 ] * getSample( 0.0, axis );\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tfor ( int i = 1; i < n; i++ ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tif ( i >= samples ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tbreak;\\\\n\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tfloat theta = dTheta * float( i );\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tgl_FragColor.rgb += weights[ i ] * getSample( -1.0 * theta, axis );\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tgl_FragColor.rgb += weights[ i ] * getSample( theta, axis );\\\\n\\\\n\\\\t\\\\t\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tgl_FragColor = linearToOutputTexel( gl_FragColor );\\\\n\\\\n\\\\t\\\\t\\\\t}\\\\n\\\\t\\\\t`,blending:0,depthTest:!1,depthWrite:!1})}(eA),this._equirectShader=null,this._cubemapShader=null,this._compileMaterial(this._blurMaterial)}fromScene(t,e=0,n=.1,i=100){lA=this._renderer.getRenderTarget();const s=this._allocateTargets();return this._sceneToCubeUV(t,n,i,s),e>0&&this._blur(s,0,0,e),this._applyPMREM(s),this._cleanup(s),s}fromEquirectangular(t){return this._fromTexture(t)}fromCubemap(t){return this._fromTexture(t)}compileCubemapShader(){null===this._cubemapShader&&(this._cubemapShader=vA(),this._compileMaterial(this._cubemapShader))}compileEquirectangularShader(){null===this._equirectShader&&(this._equirectShader=gA(),this._compileMaterial(this._equirectShader))}dispose(){this._blurMaterial.dispose(),null!==this._cubemapShader&&this._cubemapShader.dispose(),null!==this._equirectShader&&this._equirectShader.dispose();for(let t=0;t2?QT:0,QT,QT),a.setRenderTarget(i),p&&a.render(d,s),a.render(t,s)}d.geometry.dispose(),d.material.dispose(),a.toneMapping=h,a.outputEncoding=c,a.autoClear=l,t.background=_}_setEncoding(t,e){!0===this._renderer.capabilities.isWebGL2&&e.format===Ex&&e.type===yx&&e.encoding===Bx?t.value=nA[3e3]:t.value=nA[e.encoding]}_textureToCubeUV(t,e){const n=this._renderer;t.isCubeTexture?null==this._cubemapShader&&(this._cubemapShader=vA()):null==this._equirectShader&&(this._equirectShader=gA());const i=t.isCubeTexture?this._cubemapShader:this._equirectShader,s=new vT(sA[0],i),r=i.uniforms;r.envMap.value=t,t.isCubeTexture||r.texelSize.value.set(1/t.image.width,1/t.image.height),this._setEncoding(r.inputEncoding,t),this._setEncoding(r.outputEncoding,e.texture),fA(e,0,0,3*QT,2*QT),n.setRenderTarget(e),n.render(s,iA)}_applyPMREM(t){const e=this._renderer,n=e.autoClear;e.autoClear=!1;for(let e=1;eeA&&console.warn(`sigmaRadians, ${s}, is too large and will clip, as it requested ${_} samples when the maximum is set to 20`);const m=[];let f=0;for(let t=0;t4?i-8+4:0),3*g,2*g),a.setRenderTarget(e),a.render(c,iA)}}function pA(t){return void 0!==t&&t.type===yx&&(t.encoding===Dx||t.encoding===Bx||t.encoding===zx)}function _A(){const t=[],e=[],n=[];let i=8;for(let s=0;s4?o=KT[s-8+4-1]:0==s&&(o=0),n.push(o);const a=1/(r-1),l=-a/2,c=1+a/2,h=[l,l,c,l,c,c,l,l,c,c,l,c],u=6,d=6,p=3,_=2,m=1,f=new Float32Array(p*d*u),g=new Float32Array(_*d*u),v=new Float32Array(m*d*u);for(let t=0;t2?0:-1,i=[e,n,0,e+2/3,n,0,e+2/3,n+1,0,e,n,0,e+2/3,n+1,0,e,n+1,0];f.set(i,p*d*t),g.set(h,_*d*t);const s=[t,t,t,t,t,t];v.set(s,m*d*t)}const y=new tT;y.setAttribute(\\\\\\\"position\\\\\\\",new Hw(f,p)),y.setAttribute(\\\\\\\"uv\\\\\\\",new Hw(g,_)),y.setAttribute(\\\\\\\"faceIndex\\\\\\\",new Hw(v,m)),t.push(y),i>4&&i--}return{_lodPlanes:t,_sizeLods:e,_sigmas:n}}function mA(t){const e=new _b(3*QT,3*QT,t);return e.texture.mapping=lx,e.texture.name=\\\\\\\"PMREM.cubeUv\\\\\\\",e.scissorTest=!0,e}function fA(t,e,n,i,s){t.viewport.set(e,n,i,s),t.scissor.set(e,n,i,s)}function gA(){const t=new sb(1,1);return new ZT({name:\\\\\\\"EquirectangularToCubeUV\\\\\\\",uniforms:{envMap:{value:null},texelSize:{value:t},inputEncoding:{value:nA[3e3]},outputEncoding:{value:nA[3e3]}},vertexShader:yA(),fragmentShader:`\\\\n\\\\n\\\\t\\\\t\\\\tprecision mediump float;\\\\n\\\\t\\\\t\\\\tprecision mediump int;\\\\n\\\\n\\\\t\\\\t\\\\tvarying vec3 vOutputDirection;\\\\n\\\\n\\\\t\\\\t\\\\tuniform sampler2D envMap;\\\\n\\\\t\\\\t\\\\tuniform vec2 texelSize;\\\\n\\\\n\\\\t\\\\t\\\\t${xA()}\\\\n\\\\n\\\\t\\\\t\\\\t#include \\\\n\\\\n\\\\t\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tgl_FragColor = vec4( 0.0, 0.0, 0.0, 1.0 );\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tvec3 outputDirection = normalize( vOutputDirection );\\\\n\\\\t\\\\t\\\\t\\\\tvec2 uv = equirectUv( outputDirection );\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tvec2 f = fract( uv / texelSize - 0.5 );\\\\n\\\\t\\\\t\\\\t\\\\tuv -= f * texelSize;\\\\n\\\\t\\\\t\\\\t\\\\tvec3 tl = envMapTexelToLinear( texture2D ( envMap, uv ) ).rgb;\\\\n\\\\t\\\\t\\\\t\\\\tuv.x += texelSize.x;\\\\n\\\\t\\\\t\\\\t\\\\tvec3 tr = envMapTexelToLinear( texture2D ( envMap, uv ) ).rgb;\\\\n\\\\t\\\\t\\\\t\\\\tuv.y += texelSize.y;\\\\n\\\\t\\\\t\\\\t\\\\tvec3 br = envMapTexelToLinear( texture2D ( envMap, uv ) ).rgb;\\\\n\\\\t\\\\t\\\\t\\\\tuv.x -= texelSize.x;\\\\n\\\\t\\\\t\\\\t\\\\tvec3 bl = envMapTexelToLinear( texture2D ( envMap, uv ) ).rgb;\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tvec3 tm = mix( tl, tr, f.x );\\\\n\\\\t\\\\t\\\\t\\\\tvec3 bm = mix( bl, br, f.x );\\\\n\\\\t\\\\t\\\\t\\\\tgl_FragColor.rgb = mix( tm, bm, f.y );\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tgl_FragColor = linearToOutputTexel( gl_FragColor );\\\\n\\\\n\\\\t\\\\t\\\\t}\\\\n\\\\t\\\\t`,blending:0,depthTest:!1,depthWrite:!1})}function vA(){return new ZT({name:\\\\\\\"CubemapToCubeUV\\\\\\\",uniforms:{envMap:{value:null},inputEncoding:{value:nA[3e3]},outputEncoding:{value:nA[3e3]}},vertexShader:yA(),fragmentShader:`\\\\n\\\\n\\\\t\\\\t\\\\tprecision mediump float;\\\\n\\\\t\\\\t\\\\tprecision mediump int;\\\\n\\\\n\\\\t\\\\t\\\\tvarying vec3 vOutputDirection;\\\\n\\\\n\\\\t\\\\t\\\\tuniform samplerCube envMap;\\\\n\\\\n\\\\t\\\\t\\\\t${xA()}\\\\n\\\\n\\\\t\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tgl_FragColor = vec4( 0.0, 0.0, 0.0, 1.0 );\\\\n\\\\t\\\\t\\\\t\\\\tgl_FragColor.rgb = envMapTexelToLinear( textureCube( envMap, vec3( - vOutputDirection.x, vOutputDirection.yz ) ) ).rgb;\\\\n\\\\t\\\\t\\\\t\\\\tgl_FragColor = linearToOutputTexel( gl_FragColor );\\\\n\\\\n\\\\t\\\\t\\\\t}\\\\n\\\\t\\\\t`,blending:0,depthTest:!1,depthWrite:!1})}function yA(){return\\\\\\\"\\\\n\\\\n\\\\t\\\\tprecision mediump float;\\\\n\\\\t\\\\tprecision mediump int;\\\\n\\\\n\\\\t\\\\tattribute vec3 position;\\\\n\\\\t\\\\tattribute vec2 uv;\\\\n\\\\t\\\\tattribute float faceIndex;\\\\n\\\\n\\\\t\\\\tvarying vec3 vOutputDirection;\\\\n\\\\n\\\\t\\\\t// RH coordinate system; PMREM face-indexing convention\\\\n\\\\t\\\\tvec3 getDirection( vec2 uv, float face ) {\\\\n\\\\n\\\\t\\\\t\\\\tuv = 2.0 * uv - 1.0;\\\\n\\\\n\\\\t\\\\t\\\\tvec3 direction = vec3( uv, 1.0 );\\\\n\\\\n\\\\t\\\\t\\\\tif ( face == 0.0 ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tdirection = direction.zyx; // ( 1, v, u ) pos x\\\\n\\\\n\\\\t\\\\t\\\\t} else if ( face == 1.0 ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tdirection = direction.xzy;\\\\n\\\\t\\\\t\\\\t\\\\tdirection.xz *= -1.0; // ( -u, 1, -v ) pos y\\\\n\\\\n\\\\t\\\\t\\\\t} else if ( face == 2.0 ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tdirection.x *= -1.0; // ( -u, v, 1 ) pos z\\\\n\\\\n\\\\t\\\\t\\\\t} else if ( face == 3.0 ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tdirection = direction.zyx;\\\\n\\\\t\\\\t\\\\t\\\\tdirection.xz *= -1.0; // ( -1, v, -u ) neg x\\\\n\\\\n\\\\t\\\\t\\\\t} else if ( face == 4.0 ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tdirection = direction.xzy;\\\\n\\\\t\\\\t\\\\t\\\\tdirection.xy *= -1.0; // ( -u, -1, v ) neg y\\\\n\\\\n\\\\t\\\\t\\\\t} else if ( face == 5.0 ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tdirection.z *= -1.0; // ( u, v, -1 ) neg z\\\\n\\\\n\\\\t\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\t\\\\treturn direction;\\\\n\\\\n\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\tvOutputDirection = getDirection( uv, faceIndex );\\\\n\\\\t\\\\t\\\\tgl_Position = vec4( position, 1.0 );\\\\n\\\\n\\\\t\\\\t}\\\\n\\\\t\\\\\\\"}function xA(){return\\\\\\\"\\\\n\\\\n\\\\t\\\\tuniform int inputEncoding;\\\\n\\\\t\\\\tuniform int outputEncoding;\\\\n\\\\n\\\\t\\\\t#include \\\\n\\\\n\\\\t\\\\tvec4 inputTexelToLinear( vec4 value ) {\\\\n\\\\n\\\\t\\\\t\\\\tif ( inputEncoding == 0 ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\treturn value;\\\\n\\\\n\\\\t\\\\t\\\\t} else if ( inputEncoding == 1 ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\treturn sRGBToLinear( value );\\\\n\\\\n\\\\t\\\\t\\\\t} else if ( inputEncoding == 2 ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\treturn RGBEToLinear( value );\\\\n\\\\n\\\\t\\\\t\\\\t} else if ( inputEncoding == 3 ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\treturn RGBMToLinear( value, 7.0 );\\\\n\\\\n\\\\t\\\\t\\\\t} else if ( inputEncoding == 4 ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\treturn RGBMToLinear( value, 16.0 );\\\\n\\\\n\\\\t\\\\t\\\\t} else if ( inputEncoding == 5 ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\treturn RGBDToLinear( value, 256.0 );\\\\n\\\\n\\\\t\\\\t\\\\t} else {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\treturn GammaToLinear( value, 2.2 );\\\\n\\\\n\\\\t\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\tvec4 linearToOutputTexel( vec4 value ) {\\\\n\\\\n\\\\t\\\\t\\\\tif ( outputEncoding == 0 ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\treturn value;\\\\n\\\\n\\\\t\\\\t\\\\t} else if ( outputEncoding == 1 ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\treturn LinearTosRGB( value );\\\\n\\\\n\\\\t\\\\t\\\\t} else if ( outputEncoding == 2 ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\treturn LinearToRGBE( value );\\\\n\\\\n\\\\t\\\\t\\\\t} else if ( outputEncoding == 3 ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\treturn LinearToRGBM( value, 7.0 );\\\\n\\\\n\\\\t\\\\t\\\\t} else if ( outputEncoding == 4 ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\treturn LinearToRGBM( value, 16.0 );\\\\n\\\\n\\\\t\\\\t\\\\t} else if ( outputEncoding == 5 ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\treturn LinearToRGBD( value, 256.0 );\\\\n\\\\n\\\\t\\\\t\\\\t} else {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\treturn LinearToGamma( value, 2.2 );\\\\n\\\\n\\\\t\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\tvec4 envMapTexelToLinear( vec4 color ) {\\\\n\\\\n\\\\t\\\\t\\\\treturn inputTexelToLinear( color );\\\\n\\\\n\\\\t\\\\t}\\\\n\\\\t\\\\\\\"}function bA(t){let e=new WeakMap,n=null;function i(t){const n=t.target;n.removeEventListener(\\\\\\\"dispose\\\\\\\",i);const s=e.get(n);void 0!==s&&(e.delete(n),s.dispose())}return{get:function(s){if(s&&s.isTexture&&!1===s.isRenderTargetTexture){const 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XM(t,e){const n=GT[e];if(void 0===n)throw new Error(\\\\\\\"Can not resolve #include <\\\\\\\"+e+\\\\\\\">\\\\\\\");return qM(n)}const YM=/#pragma unroll_loop[\\\\s]+?for \\\\( int i \\\\= (\\\\d+)\\\\; i < (\\\\d+)\\\\; i \\\\+\\\\+ \\\\) \\\\{([\\\\s\\\\S]+?)(?=\\\\})\\\\}/g,$M=/#pragma unroll_loop_start\\\\s+for\\\\s*\\\\(\\\\s*int\\\\s+i\\\\s*=\\\\s*(\\\\d+)\\\\s*;\\\\s*i\\\\s*<\\\\s*(\\\\d+)\\\\s*;\\\\s*i\\\\s*\\\\+\\\\+\\\\s*\\\\)\\\\s*{([\\\\s\\\\S]+?)}\\\\s+#pragma unroll_loop_end/g;function JM(t){return t.replace($M,QM).replace(YM,ZM)}function ZM(t,e,n,i){return console.warn(\\\\\\\"WebGLProgram: #pragma unroll_loop shader syntax is deprecated. 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i=t[n];!1!==i&&e.push(\\\\\\\"#define \\\\\\\"+n+\\\\\\\" \\\\\\\"+i)}return e.join(\\\\\\\"\\\\n\\\\\\\")}(r),m=s.createProgram();let f,g,v=n.glslVersion?\\\\\\\"#version \\\\\\\"+n.glslVersion+\\\\\\\"\\\\n\\\\\\\":\\\\\\\"\\\\\\\";n.isRawShaderMaterial?(f=[_].filter(VM).join(\\\\\\\"\\\\n\\\\\\\"),f.length>0&&(f+=\\\\\\\"\\\\n\\\\\\\"),g=[p,_].filter(VM).join(\\\\\\\"\\\\n\\\\\\\"),g.length>0&&(g+=\\\\\\\"\\\\n\\\\\\\")):(f=[KM(n),\\\\\\\"#define SHADER_NAME \\\\\\\"+n.shaderName,_,n.instancing?\\\\\\\"#define USE_INSTANCING\\\\\\\":\\\\\\\"\\\\\\\",n.instancingColor?\\\\\\\"#define USE_INSTANCING_COLOR\\\\\\\":\\\\\\\"\\\\\\\",n.supportsVertexTextures?\\\\\\\"#define VERTEX_TEXTURES\\\\\\\":\\\\\\\"\\\\\\\",\\\\\\\"#define GAMMA_FACTOR \\\\\\\"+d,\\\\\\\"#define MAX_BONES \\\\\\\"+n.maxBones,n.useFog&&n.fog?\\\\\\\"#define USE_FOG\\\\\\\":\\\\\\\"\\\\\\\",n.useFog&&n.fogExp2?\\\\\\\"#define FOG_EXP2\\\\\\\":\\\\\\\"\\\\\\\",n.map?\\\\\\\"#define 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y=v+g+a,x=FM(s,35633,v+f+o),b=FM(s,35632,y);if(s.attachShader(m,x),s.attachShader(m,b),void 0!==n.index0AttributeName?s.bindAttribLocation(m,0,n.index0AttributeName):!0===n.morphTargets&&s.bindAttribLocation(m,0,\\\\\\\"position\\\\\\\"),s.linkProgram(m),t.debug.checkShaderErrors){const t=s.getProgramInfoLog(m).trim(),e=s.getShaderInfoLog(x).trim(),n=s.getShaderInfoLog(b).trim();let i=!0,r=!0;if(!1===s.getProgramParameter(m,35714)){i=!1;const e=zM(s,x,\\\\\\\"vertex\\\\\\\"),n=zM(s,b,\\\\\\\"fragment\\\\\\\");console.error(\\\\\\\"THREE.WebGLProgram: Shader Error \\\\\\\"+s.getError()+\\\\\\\" - VALIDATE_STATUS \\\\\\\"+s.getProgramParameter(m,35715)+\\\\\\\"\\\\n\\\\nProgram Info Log: \\\\\\\"+t+\\\\\\\"\\\\n\\\\\\\"+e+\\\\\\\"\\\\n\\\\\\\"+n)}else\\\\\\\"\\\\\\\"!==t?console.warn(\\\\\\\"THREE.WebGLProgram: Program Info 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s=0;s0,C=r.clearcoat>0;return{isWebGL2:l,shaderID:w,shaderName:r.type,vertexShader:A,fragmentShader:M,defines:r.defines,isRawShaderMaterial:!0===r.isRawShaderMaterial,glslVersion:r.glslVersion,precision:p,instancing:!0===v.isInstancedMesh,instancingColor:!0===v.isInstancedMesh&&null!==v.instanceColor,supportsVertexTextures:d,outputEncoding:null!==E?f(E.texture):t.outputEncoding,map:!!r.map,mapEncoding:f(r.map),matcap:!!r.matcap,matcapEncoding:f(r.matcap),envMap:!!b,envMapMode:b&&b.mapping,envMapEncoding:f(b),envMapCubeUV:!!b&&(b.mapping===lx||b.mapping===cx),lightMap:!!r.lightMap,lightMapEncoding:f(r.lightMap),aoMap:!!r.aoMap,emissiveMap:!!r.emissiveMap,emissiveMapEncoding:f(r.emissiveMap),bumpMap:!!r.bumpMap,normalMap:!!r.normalMap,objectSpaceNormalMap:1===r.normalMapType,tangentSpaceNormalMap:0===r.normalMapType,clearcoat:C,clearcoatMap:C&&!!r.clearcoatMap,clearcoatRoughnessMap:C&&!!r.clearcoatRoughnessMap,clearcoatNormalMap:C&&!!r.clearcoatNormalMap,displacementMap:!!r.displacementMap,roughnessMap:!!r.roughnessMap,metalnessMap:!!r.metalnessMap,specularMap:!!r.specularMap,specularIntensityMap:!!r.specularIntensityMap,specularTintMap:!!r.specularTintMap,specularTintMapEncoding:f(r.specularTintMap),alphaMap:!!r.alphaMap,alphaTest:S,gradientMap:!!r.gradientMap,sheen:r.sheen>0,transmission:r.transmission>0,transmissionMap:!!r.transmissionMap,thicknessMap:!!r.thicknessMap,combine:r.combine,vertexTangents:!!r.normalMap&&!!v.geometry&&!!v.geometry.attributes.tangent,vertexColors:r.vertexColors,vertexAlphas:!0===r.vertexColors&&!!v.geometry&&!!v.geometry.attributes.color&&4===v.geometry.attributes.color.itemSize,vertexUvs:!!(r.map||r.bumpMap||r.normalMap||r.specularMap||r.alphaMap||r.emissiveMap||r.roughnessMap||r.metalnessMap||r.clearcoatMap||r.clearcoatRoughnessMap||r.clearcoatNormalMap||r.displacementMap||r.transmissionMap||r.thicknessMap||r.specularIntensityMap||r.specularTintMap),uvsVertexOnly:!(r.map||r.bumpMap||r.normalMap||r.specularMap||r.alphaMap||r.emissiveMap||r.roughnessMap||r.metalnessMap||r.clearcoatNormalMap||r.transmission>0||r.transmissionMap||r.thicknessMap||r.specularIntensityMap||r.specularTintMap||!r.displacementMap),fog:!!y,useFog:r.fog,fogExp2:y&&y.isFogExp2,flatShading:!!r.flatShading,sizeAttenuation:r.sizeAttenuation,logarithmicDepthBuffer:c,skinning:!0===v.isSkinnedMesh&&T>0,maxBones:T,useVertexTexture:h,morphTargets:!!v.geometry&&!!v.geometry.morphAttributes.position,morphNormals:!!v.geometry&&!!v.geometry.morphAttributes.normal,morphTargetsCount:v.geometry&&v.geometry.morphAttributes.position?v.geometry.morphAttributes.position.length:0,numDirLights:a.directional.length,numPointLights:a.point.length,numSpotLights:a.spot.length,numRectAreaLights:a.rectArea.length,numHemiLights:a.hemi.length,numDirLightShadows:a.directionalShadowMap.length,numPointLightShadows:a.pointShadowMap.length,numSpotLightShadows:a.spotShadowMap.length,numClippingPlanes:o.numPlanes,numClipIntersection:o.numIntersection,format:r.format,dithering:r.dithering,shadowMapEnabled:t.shadowMap.enabled&&m.length>0,shadowMapType:t.shadowMap.type,toneMapping:r.toneMapped?t.toneMapping:0,physicallyCorrectLights:t.physicallyCorrectLights,premultipliedAlpha:r.premultipliedAlpha,doubleSided:2===r.side,flipSided:1===r.side,depthPacking:void 0!==r.depthPacking&&r.depthPacking,index0AttributeName:r.index0AttributeName,extensionDerivatives:r.extensions&&r.extensions.derivatives,extensionFragDepth:r.extensions&&r.extensions.fragDepth,extensionDrawBuffers:r.extensions&&r.extensions.drawBuffers,extensionShaderTextureLOD:r.extensions&&r.extensions.shaderTextureLOD,rendererExtensionFragDepth:l||i.has(\\\\\\\"EXT_frag_depth\\\\\\\"),rendererExtensionDrawBuffers:l||i.has(\\\\\\\"WEBGL_draw_buffers\\\\\\\"),rendererExtensionShaderTextureLod:l||i.has(\\\\\\\"EXT_shader_texture_lod\\\\\\\"),customProgramCacheKey:r.customProgramCacheKey()}},getProgramCacheKey:function(e){const n=[];if(e.shaderID?n.push(e.shaderID):(n.push(e.fragmentShader),n.push(e.vertexShader)),void 0!==e.defines)for(const t in e.defines)n.push(t),n.push(e.defines[t]);if(!1===e.isRawShaderMaterial){for(let t=0;t0?s.push(h):!0===n.transparent?r.push(h):i.push(h)},unshift:function(t,e,n,o,l,c){const h=a(t,e,n,o,l,c);n.transmission>0?s.unshift(h):!0===n.transparent?r.unshift(h):i.unshift(h)},finish:function(){for(let t=n,i=e.length;t1&&i.sort(t||iE),s.length>1&&s.sort(e||sE),r.length>1&&r.sort(e||sE)}}}function oE(t){let e=new WeakMap;return{get:function(n,i){let s;return!1===e.has(n)?(s=new rE(t),e.set(n,[s])):i>=e.get(n).length?(s=new rE(t),e.get(n).push(s)):s=e.get(n)[i],s},dispose:function(){e=new WeakMap}}}function aE(){const t={};return{get:function(e){if(void 0!==t[e.id])return t[e.id];let n;switch(e.type){case\\\\\\\"DirectionalLight\\\\\\\":n={direction:new gb,color:new kw};break;case\\\\\\\"SpotLight\\\\\\\":n={position:new gb,direction:new gb,color:new kw,distance:0,coneCos:0,penumbraCos:0,decay:0};break;case\\\\\\\"PointLight\\\\\\\":n={position:new gb,color:new kw,distance:0,decay:0};break;case\\\\\\\"HemisphereLight\\\\\\\":n={direction:new gb,skyColor:new kw,groundColor:new kw};break;case\\\\\\\"RectAreaLight\\\\\\\":n={color:new kw,position:new gb,halfWidth:new gb,halfHeight:new gb}}return t[e.id]=n,n}}}let lE=0;function cE(t,e){return(e.castShadow?1:0)-(t.castShadow?1:0)}function hE(t,e){const n=new aE,i=function(){const t={};return{get:function(e){if(void 0!==t[e.id])return t[e.id];let n;switch(e.type){case\\\\\\\"DirectionalLight\\\\\\\":case\\\\\\\"SpotLight\\\\\\\":n={shadowBias:0,shadowNormalBias:0,shadowRadius:1,shadowMapSize:new sb};break;case\\\\\\\"PointLight\\\\\\\":n={shadowBias:0,shadowNormalBias:0,shadowRadius:1,shadowMapSize:new sb,shadowCameraNear:1,shadowCameraFar:1e3}}return t[e.id]=n,n}}}(),s={version:0,hash:{directionalLength:-1,pointLength:-1,spotLength:-1,rectAreaLength:-1,hemiLength:-1,numDirectionalShadows:-1,numPointShadows:-1,numSpotShadows:-1},ambient:[0,0,0],probe:[],directional:[],directionalShadow:[],directionalShadowMap:[],directionalShadowMatrix:[],spot:[],spotShadow:[],spotShadowMap:[],spotShadowMatrix:[],rectArea:[],rectAreaLTC1:null,rectAreaLTC2:null,point:[],pointShadow:[],pointShadowMap:[],pointShadowMatrix:[],hemi:[]};for(let t=0;t<9;t++)s.probe.push(new gb);const r=new gb,o=new Yb,a=new Yb;return{setup:function(r,o){let a=0,l=0,c=0;for(let t=0;t<9;t++)s.probe[t].set(0,0,0);let h=0,u=0,d=0,p=0,_=0,m=0,f=0,g=0;r.sort(cE);const v=!0!==o?Math.PI:1;for(let t=0,e=r.length;t0&&(e.isWebGL2||!0===t.has(\\\\\\\"OES_texture_float_linear\\\\\\\")?(s.rectAreaLTC1=VT.LTC_FLOAT_1,s.rectAreaLTC2=VT.LTC_FLOAT_2):!0===t.has(\\\\\\\"OES_texture_half_float_linear\\\\\\\")?(s.rectAreaLTC1=VT.LTC_HALF_1,s.rectAreaLTC2=VT.LTC_HALF_2):console.error(\\\\\\\"THREE.WebGLRenderer: Unable to use RectAreaLight. Missing WebGL extensions.\\\\\\\")),s.ambient[0]=a,s.ambient[1]=l,s.ambient[2]=c;const y=s.hash;y.directionalLength===h&&y.pointLength===u&&y.spotLength===d&&y.rectAreaLength===p&&y.hemiLength===_&&y.numDirectionalShadows===m&&y.numPointShadows===f&&y.numSpotShadows===g||(s.directional.length=h,s.spot.length=d,s.rectArea.length=p,s.point.length=u,s.hemi.length=_,s.directionalShadow.length=m,s.directionalShadowMap.length=m,s.pointShadow.length=f,s.pointShadowMap.length=f,s.spotShadow.length=g,s.spotShadowMap.length=g,s.directionalShadowMatrix.length=m,s.pointShadowMatrix.length=f,s.spotShadowMatrix.length=g,y.directionalLength=h,y.pointLength=u,y.spotLength=d,y.rectAreaLength=p,y.hemiLength=_,y.numDirectionalShadows=m,y.numPointShadows=f,y.numSpotShadows=g,s.version=lE++)},setupView:function(t,e){let n=0,i=0,l=0,c=0,h=0;const u=e.matrixWorldInverse;for(let e=0,d=t.length;e=n.get(i).length?(r=new uE(t,e),n.get(i).push(r)):r=n.get(i)[s],r},dispose:function(){n=new WeakMap}}}class pE extends Pw{constructor(t){super(),this.type=\\\\\\\"MeshDepthMaterial\\\\\\\",this.depthPacking=3200,this.map=null,this.alphaMap=null,this.displacementMap=null,this.displacementScale=1,this.displacementBias=0,this.wireframe=!1,this.wireframeLinewidth=1,this.fog=!1,this.setValues(t)}copy(t){return super.copy(t),this.depthPacking=t.depthPacking,this.map=t.map,this.alphaMap=t.alphaMap,this.displacementMap=t.displacementMap,this.displacementScale=t.displacementScale,this.displacementBias=t.displacementBias,this.wireframe=t.wireframe,this.wireframeLinewidth=t.wireframeLinewidth,this}}pE.prototype.isMeshDepthMaterial=!0;class _E extends Pw{constructor(t){super(),this.type=\\\\\\\"MeshDistanceMaterial\\\\\\\",this.referencePosition=new gb,this.nearDistance=1,this.farDistance=1e3,this.map=null,this.alphaMap=null,this.displacementMap=null,this.displacementScale=1,this.displacementBias=0,this.fog=!1,this.setValues(t)}copy(t){return super.copy(t),this.referencePosition.copy(t.referencePosition),this.nearDistance=t.nearDistance,this.farDistance=t.farDistance,this.map=t.map,this.alphaMap=t.alphaMap,this.displacementMap=t.displacementMap,this.displacementScale=t.displacementScale,this.displacementBias=t.displacementBias,this}}_E.prototype.isMeshDistanceMaterial=!0;function mE(t,e,n){let i=new BT;const s=new sb,r=new sb,o=new pb,a=new pE({depthPacking:3201}),l=new _E,c={},h=n.maxTextureSize,u={0:1,1:0,2:2},d=new AT({uniforms:{shadow_pass:{value:null},resolution:{value:new sb},radius:{value:4},samples:{value:8}},vertexShader:\\\\\\\"void main() {\\\\n\\\\tgl_Position = vec4( position, 1.0 );\\\\n}\\\\\\\",fragmentShader:\\\\\\\"uniform sampler2D shadow_pass;\\\\nuniform vec2 resolution;\\\\nuniform float radius;\\\\nuniform float samples;\\\\n#include \\\\nvoid main() {\\\\n\\\\tfloat mean = 0.0;\\\\n\\\\tfloat squared_mean = 0.0;\\\\n\\\\tfloat uvStride = samples <= 1.0 ? 0.0 : 2.0 / ( samples - 1.0 );\\\\n\\\\tfloat uvStart = samples <= 1.0 ? 0.0 : - 1.0;\\\\n\\\\tfor ( float i = 0.0; i < samples; i ++ ) {\\\\n\\\\t\\\\tfloat uvOffset = uvStart + i * uvStride;\\\\n\\\\t\\\\t#ifdef HORIZONTAL_PASS\\\\n\\\\t\\\\t\\\\tvec2 distribution = unpackRGBATo2Half( texture2D( shadow_pass, ( gl_FragCoord.xy + vec2( uvOffset, 0.0 ) * radius ) / resolution ) );\\\\n\\\\t\\\\t\\\\tmean += distribution.x;\\\\n\\\\t\\\\t\\\\tsquared_mean += distribution.y * distribution.y + distribution.x * distribution.x;\\\\n\\\\t\\\\t#else\\\\n\\\\t\\\\t\\\\tfloat depth = unpackRGBAToDepth( texture2D( shadow_pass, ( gl_FragCoord.xy + vec2( 0.0, uvOffset ) * radius ) / resolution ) );\\\\n\\\\t\\\\t\\\\tmean += depth;\\\\n\\\\t\\\\t\\\\tsquared_mean += depth * depth;\\\\n\\\\t\\\\t#endif\\\\n\\\\t}\\\\n\\\\tmean = mean / samples;\\\\n\\\\tsquared_mean = squared_mean / samples;\\\\n\\\\tfloat std_dev = sqrt( squared_mean - mean * mean );\\\\n\\\\tgl_FragColor = pack2HalfToRGBA( vec2( mean, std_dev ) );\\\\n}\\\\\\\"}),p=d.clone();p.defines.HORIZONTAL_PASS=1;const _=new tT;_.setAttribute(\\\\\\\"position\\\\\\\",new Hw(new Float32Array([-1,-1,.5,3,-1,.5,-1,3,.5]),3));const m=new vT(_,d),f=this;function g(n,i){const s=e.update(m);d.uniforms.shadow_pass.value=n.map.texture,d.uniforms.resolution.value=n.mapSize,d.uniforms.radius.value=n.radius,d.uniforms.samples.value=n.blurSamples,t.setRenderTarget(n.mapPass),t.clear(),t.renderBufferDirect(i,null,s,d,m,null),p.uniforms.shadow_pass.value=n.mapPass.texture,p.uniforms.resolution.value=n.mapSize,p.uniforms.radius.value=n.radius,p.uniforms.samples.value=n.blurSamples,t.setRenderTarget(n.map),t.clear(),t.renderBufferDirect(i,null,s,p,m,null)}function v(e,n,i,s,r,o,h){let d=null;const p=!0===s.isPointLight?e.customDistanceMaterial:e.customDepthMaterial;if(d=void 0!==p?p:!0===s.isPointLight?l:a,t.localClippingEnabled&&!0===i.clipShadows&&0!==i.clippingPlanes.length||i.displacementMap&&0!==i.displacementScale||i.alphaMap&&i.alphaTest>0){const t=d.uuid,e=i.uuid;let n=c[t];void 0===n&&(n={},c[t]=n);let s=n[e];void 0===s&&(s=d.clone(),n[e]=s),d=s}return d.visible=i.visible,d.wireframe=i.wireframe,d.side=3===h?null!==i.shadowSide?i.shadowSide:i.side:null!==i.shadowSide?i.shadowSide:u[i.side],d.alphaMap=i.alphaMap,d.alphaTest=i.alphaTest,d.clipShadows=i.clipShadows,d.clippingPlanes=i.clippingPlanes,d.clipIntersection=i.clipIntersection,d.displacementMap=i.displacementMap,d.displacementScale=i.displacementScale,d.displacementBias=i.displacementBias,d.wireframeLinewidth=i.wireframeLinewidth,d.linewidth=i.linewidth,!0===s.isPointLight&&!0===d.isMeshDistanceMaterial&&(d.referencePosition.setFromMatrixPosition(s.matrixWorld),d.nearDistance=r,d.farDistance=o),d}function y(n,s,r,o,a){if(!1===n.visible)return;if(n.layers.test(s.layers)&&(n.isMesh||n.isLine||n.isPoints)&&(n.castShadow||n.receiveShadow&&3===a)&&(!n.frustumCulled||i.intersectsObject(n))){n.modelViewMatrix.multiplyMatrices(r.matrixWorldInverse,n.matrixWorld);const i=e.update(n),s=n.material;if(Array.isArray(s)){const e=i.groups;for(let l=0,c=e.length;lh||s.y>h)&&(s.x>h&&(r.x=Math.floor(h/p.x),s.x=r.x*p.x,u.mapSize.x=r.x),s.y>h&&(r.y=Math.floor(h/p.y),s.y=r.y*p.y,u.mapSize.y=r.y)),null===u.map&&!u.isPointLightShadow&&3===this.type){const t={minFilter:fx,magFilter:fx,format:Ex};u.map=new _b(s.x,s.y,t),u.map.texture.name=c.name+\\\\\\\".shadowMap\\\\\\\",u.mapPass=new _b(s.x,s.y,t),u.camera.updateProjectionMatrix()}if(null===u.map){const t={minFilter:px,magFilter:px,format:Ex};u.map=new _b(s.x,s.y,t),u.map.texture.name=c.name+\\\\\\\".shadowMap\\\\\\\",u.camera.updateProjectionMatrix()}t.setRenderTarget(u.map),t.clear();const _=u.getViewportCount();for(let t=0;t<_;t++){const e=u.getViewport(t);o.set(r.x*e.x,r.y*e.y,r.x*e.z,r.y*e.w),d.viewport(o),u.updateMatrices(c,t),i=u.getFrustum(),y(n,a,u.camera,c,this.type)}u.isPointLightShadow||3!==this.type||g(u,a),u.needsUpdate=!1}f.needsUpdate=!1,t.setRenderTarget(l,c,u)}}function fE(t,e,n){const i=n.isWebGL2;const s=new function(){let e=!1;const n=new pb;let i=null;const s=new pb(0,0,0,0);return{setMask:function(n){i===n||e||(t.colorMask(n,n,n,n),i=n)},setLocked:function(t){e=t},setClear:function(e,i,r,o,a){!0===a&&(e*=o,i*=o,r*=o),n.set(e,i,r,o),!1===s.equals(n)&&(t.clearColor(e,i,r,o),s.copy(n))},reset:function(){e=!1,i=null,s.set(-1,0,0,0)}}},r=new function(){let e=!1,n=null,i=null,s=null;return{setTest:function(t){t?B(2929):z(2929)},setMask:function(i){n===i||e||(t.depthMask(i),n=i)},setFunc:function(e){if(i!==e){if(e)switch(e){case 0:t.depthFunc(512);break;case 1:t.depthFunc(519);break;case 2:t.depthFunc(513);break;case 3:t.depthFunc(515);break;case 4:t.depthFunc(514);break;case 5:t.depthFunc(518);break;case 6:t.depthFunc(516);break;case 7:t.depthFunc(517);break;default:t.depthFunc(515)}else t.depthFunc(515);i=e}},setLocked:function(t){e=t},setClear:function(e){s!==e&&(t.clearDepth(e),s=e)},reset:function(){e=!1,n=null,i=null,s=null}}},o=new function(){let e=!1,n=null,i=null,s=null,r=null,o=null,a=null,l=null,c=null;return{setTest:function(t){e||(t?B(2960):z(2960))},setMask:function(i){n===i||e||(t.stencilMask(i),n=i)},setFunc:function(e,n,o){i===e&&s===n&&r===o||(t.stencilFunc(e,n,o),i=e,s=n,r=o)},setOp:function(e,n,i){o===e&&a===n&&l===i||(t.stencilOp(e,n,i),o=e,a=n,l=i)},setLocked:function(t){e=t},setClear:function(e){c!==e&&(t.clearStencil(e),c=e)},reset:function(){e=!1,n=null,i=null,s=null,r=null,o=null,a=null,l=null,c=null}}};let a={},l=null,c={},h=null,u=!1,d=null,p=null,_=null,m=null,f=null,g=null,v=null,y=!1,x=null,b=null,w=null,T=null,A=null;const M=t.getParameter(35661);let E=!1,S=0;const C=t.getParameter(7938);-1!==C.indexOf(\\\\\\\"WebGL\\\\\\\")?(S=parseFloat(/^WebGL (\\\\d)/.exec(C)[1]),E=S>=1):-1!==C.indexOf(\\\\\\\"OpenGL ES\\\\\\\")&&(S=parseFloat(/^OpenGL ES (\\\\d)/.exec(C)[1]),E=S>=2);let N=null,L={};const O=t.getParameter(3088),P=t.getParameter(2978),R=(new pb).fromArray(O),I=(new pb).fromArray(P);function F(e,n,i){const s=new Uint8Array(4),r=t.createTexture();t.bindTexture(e,r),t.texParameteri(e,10241,9728),t.texParameteri(e,10240,9728);for(let e=0;ei||t.height>i)&&(s=i/Math.max(t.width,t.height)),s<1||!0===e){if(\\\\\\\"undefined\\\\\\\"!=typeof HTMLImageElement&&t instanceof HTMLImageElement||\\\\\\\"undefined\\\\\\\"!=typeof HTMLCanvasElement&&t instanceof HTMLCanvasElement||\\\\\\\"undefined\\\\\\\"!=typeof ImageBitmap&&t instanceof ImageBitmap){const i=e?nb:Math.floor,r=i(s*t.width),o=i(s*t.height);void 0===p&&(p=m(r,o));const a=n?m(r,o):p;a.width=r,a.height=o;return a.getContext(\\\\\\\"2d\\\\\\\").drawImage(t,0,0,r,o),console.warn(\\\\\\\"THREE.WebGLRenderer: Texture has been resized from (\\\\\\\"+t.width+\\\\\\\"x\\\\\\\"+t.height+\\\\\\\") to (\\\\\\\"+r+\\\\\\\"x\\\\\\\"+o+\\\\\\\").\\\\\\\"),a}return\\\\\\\"data\\\\\\\"in t&&console.warn(\\\\\\\"THREE.WebGLRenderer: Image in DataTexture is too big (\\\\\\\"+t.width+\\\\\\\"x\\\\\\\"+t.height+\\\\\\\").\\\\\\\"),t}return 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0!==s&&(s.isWebGLRenderTarget&&(s=s.texture),!0===s.matrixAutoUpdate&&s.updateMatrix(),e.uvTransform.value.copy(s.matrix)),n.aoMap?r=n.aoMap:n.lightMap&&(r=n.lightMap),void 0!==r&&(r.isWebGLRenderTarget&&(r=r.texture),!0===r.matrixAutoUpdate&&r.updateMatrix(),e.uv2Transform.value.copy(r.matrix))}function n(e,n){e.roughness.value=n.roughness,e.metalness.value=n.metalness,n.roughnessMap&&(e.roughnessMap.value=n.roughnessMap),n.metalnessMap&&(e.metalnessMap.value=n.metalnessMap),n.emissiveMap&&(e.emissiveMap.value=n.emissiveMap),n.bumpMap&&(e.bumpMap.value=n.bumpMap,e.bumpScale.value=n.bumpScale,1===n.side&&(e.bumpScale.value*=-1)),n.normalMap&&(e.normalMap.value=n.normalMap,e.normalScale.value.copy(n.normalScale),1===n.side&&e.normalScale.value.negate()),n.displacementMap&&(e.displacementMap.value=n.displacementMap,e.displacementScale.value=n.displacementScale,e.displacementBias.value=n.displacementBias);t.get(n).envMap&&(e.envMapIntensity.value=n.envMapIntensity)}return{refreshFogUniforms:function(t,e){t.fogColor.value.copy(e.color),e.isFog?(t.fogNear.value=e.near,t.fogFar.value=e.far):e.isFogExp2&&(t.fogDensity.value=e.density)},refreshMaterialUniforms:function(t,i,s,r,o){i.isMeshBasicMaterial?e(t,i):i.isMeshLambertMaterial?(e(t,i),function(t,e){e.emissiveMap&&(t.emissiveMap.value=e.emissiveMap)}(t,i)):i.isMeshToonMaterial?(e(t,i),function(t,e){e.gradientMap&&(t.gradientMap.value=e.gradientMap);e.emissiveMap&&(t.emissiveMap.value=e.emissiveMap);e.bumpMap&&(t.bumpMap.value=e.bumpMap,t.bumpScale.value=e.bumpScale,1===e.side&&(t.bumpScale.value*=-1));e.normalMap&&(t.normalMap.value=e.normalMap,t.normalScale.value.copy(e.normalScale),1===e.side&&t.normalScale.value.negate());e.displacementMap&&(t.displacementMap.value=e.displacementMap,t.displacementScale.value=e.displacementScale,t.displacementBias.value=e.displacementBias)}(t,i)):i.isMeshPhongMaterial?(e(t,i),function(t,e){t.specular.value.copy(e.specular),t.shininess.value=Math.max(e.shininess,1e-4),e.emissiveMap&&(t.emissiveMap.value=e.emissiveMap);e.bumpMap&&(t.bumpMap.value=e.bumpMap,t.bumpScale.value=e.bumpScale,1===e.side&&(t.bumpScale.value*=-1));e.normalMap&&(t.normalMap.value=e.normalMap,t.normalScale.value.copy(e.normalScale),1===e.side&&t.normalScale.value.negate());e.displacementMap&&(t.displacementMap.value=e.displacementMap,t.displacementScale.value=e.displacementScale,t.displacementBias.value=e.displacementBias)}(t,i)):i.isMeshStandardMaterial?(e(t,i),i.isMeshPhysicalMaterial?function(t,e,i){n(t,e),t.ior.value=e.ior,e.sheen>0&&(t.sheenTint.value.copy(e.sheenTint).multiplyScalar(e.sheen),t.sheenRoughness.value=e.sheenRoughness);e.clearcoat>0&&(t.clearcoat.value=e.clearcoat,t.clearcoatRoughness.value=e.clearcoatRoughness,e.clearcoatMap&&(t.clearcoatMap.value=e.clearcoatMap),e.clearcoatRoughnessMap&&(t.clearcoatRoughnessMap.value=e.clearcoatRoughnessMap),e.clearcoatNormalMap&&(t.clearcoatNormalScale.value.copy(e.clearcoatNormalScale),t.clearcoatNormalMap.value=e.clearcoatNormalMap,1===e.side&&t.clearcoatNormalScale.value.negate()));e.transmission>0&&(t.transmission.value=e.transmission,t.transmissionSamplerMap.value=i.texture,t.transmissionSamplerSize.value.set(i.width,i.height),e.transmissionMap&&(t.transmissionMap.value=e.transmissionMap),t.thickness.value=e.thickness,e.thicknessMap&&(t.thicknessMap.value=e.thicknessMap),t.attenuationDistance.value=e.attenuationDistance,t.attenuationTint.value.copy(e.attenuationTint));t.specularIntensity.value=e.specularIntensity,t.specularTint.value.copy(e.specularTint),e.specularIntensityMap&&(t.specularIntensityMap.value=e.specularIntensityMap);e.specularTintMap&&(t.specularTintMap.value=e.specularTintMap)}(t,i,o):n(t,i)):i.isMeshMatcapMaterial?(e(t,i),function(t,e){e.matcap&&(t.matcap.value=e.matcap);e.bumpMap&&(t.bumpMap.value=e.bumpMap,t.bumpScale.value=e.bumpScale,1===e.side&&(t.bumpScale.value*=-1));e.normalMap&&(t.normalMap.value=e.normalMap,t.normalScale.value.copy(e.normalScale),1===e.side&&t.normalScale.value.negate());e.displacementMap&&(t.displacementMap.value=e.displacementMap,t.displacementScale.value=e.displacementScale,t.displacementBias.value=e.displacementBias)}(t,i)):i.isMeshDepthMaterial?(e(t,i),function(t,e){e.displacementMap&&(t.displacementMap.value=e.displacementMap,t.displacementScale.value=e.displacementScale,t.displacementBias.value=e.displacementBias)}(t,i)):i.isMeshDistanceMaterial?(e(t,i),function(t,e){e.displacementMap&&(t.displacementMap.value=e.displacementMap,t.displacementScale.value=e.displacementScale,t.displacementBias.value=e.displacementBias);t.referencePosition.value.copy(e.referencePosition),t.nearDistance.value=e.nearDistance,t.farDistance.value=e.farDistance}(t,i)):i.isMeshNormalMaterial?(e(t,i),function(t,e){e.bumpMap&&(t.bumpMap.value=e.bumpMap,t.bumpScale.value=e.bumpScale,1===e.side&&(t.bumpScale.value*=-1));e.normalMap&&(t.normalMap.value=e.normalMap,t.normalScale.value.copy(e.normalScale),1===e.side&&t.normalScale.value.negate());e.displacementMap&&(t.displacementMap.value=e.displacementMap,t.displacementScale.value=e.displacementScale,t.displacementBias.value=e.displacementBias)}(t,i)):i.isLineBasicMaterial?(function(t,e){t.diffuse.value.copy(e.color),t.opacity.value=e.opacity}(t,i),i.isLineDashedMaterial&&function(t,e){t.dashSize.value=e.dashSize,t.totalSize.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s;e.map?s=e.map:e.alphaMap&&(s=e.alphaMap);void 0!==s&&(!0===s.matrixAutoUpdate&&s.updateMatrix(),t.uvTransform.value.copy(s.matrix))}(t,i,s,r):i.isSpriteMaterial?function(t,e){t.diffuse.value.copy(e.color),t.opacity.value=e.opacity,t.rotation.value=e.rotation,e.map&&(t.map.value=e.map);e.alphaMap&&(t.alphaMap.value=e.alphaMap);e.alphaTest>0&&(t.alphaTest.value=e.alphaTest);let n;e.map?n=e.map:e.alphaMap&&(n=e.alphaMap);void 0!==n&&(!0===n.matrixAutoUpdate&&n.updateMatrix(),t.uvTransform.value.copy(n.matrix))}(t,i):i.isShadowMaterial?(t.color.value.copy(i.color),t.opacity.value=i.opacity):i.isShaderMaterial&&(i.uniformsNeedUpdate=!1)}}}function ME(t={}){const e=void 0!==t.canvas?t.canvas:function(){const t=ab(\\\\\\\"canvas\\\\\\\");return t.style.display=\\\\\\\"block\\\\\\\",t}(),n=void 0!==t.context?t.context:null,i=void 0!==t.alpha&&t.alpha,s=void 0===t.depth||t.depth,r=void 0===t.stencil||t.stencil,o=void 0!==t.antialias&&t.antialias,a=void 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i=null,s=!1,r=!1;if(t){const n=t.texture;(n.isDataTexture3D||n.isDataTexture2DArray)&&(r=!0);const o=q.get(t).__webglFramebuffer;t.isWebGLCubeRenderTarget?(i=o[e],s=!0):i=t.isWebGLMultisampleRenderTarget?q.get(t).__webglMultisampledFramebuffer:o,w.copy(t.viewport),T.copy(t.scissor),A=t.scissorTest}else w.copy(L).multiplyScalar(S).floor(),T.copy(O).multiplyScalar(S).floor(),A=P;if(j.bindFramebuffer(36160,i)&&H.drawBuffers){let e=!1;if(t)if(t.isWebGLMultipleRenderTargets){const n=t.texture;if(R.length!==n.length||36064!==R[0]){for(let t=0,e=n.length;t=0&&e<=t.width-i&&n>=0&&n<=t.height-s&&ut.readPixels(e,n,i,s,ct.convert(a),ct.convert(l),r):console.error(\\\\\\\"THREE.WebGLRenderer.readRenderTargetPixels: readPixels from renderTarget failed. 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s=e.image.width,r=e.image.height,o=ct.convert(n.format),a=ct.convert(n.type);X.setTexture2D(n,0),ut.pixelStorei(37440,n.flipY),ut.pixelStorei(37441,n.premultiplyAlpha),ut.pixelStorei(3317,n.unpackAlignment),e.isDataTexture?ut.texSubImage2D(3553,i,t.x,t.y,s,r,o,a,e.image.data):e.isCompressedTexture?ut.compressedTexSubImage2D(3553,i,t.x,t.y,e.mipmaps[0].width,e.mipmaps[0].height,o,e.mipmaps[0].data):ut.texSubImage2D(3553,i,t.x,t.y,o,a,e.image),0===i&&n.generateMipmaps&&ut.generateMipmap(3553),j.unbindTexture()},this.copyTextureToTexture3D=function(t,e,n,i,s=0){if(m.isWebGL1Renderer)return void console.warn(\\\\\\\"THREE.WebGLRenderer.copyTextureToTexture3D: can only be used with WebGL2.\\\\\\\");const r=t.max.x-t.min.x+1,o=t.max.y-t.min.y+1,a=t.max.z-t.min.z+1,l=ct.convert(i.format),c=ct.convert(i.type);let h;if(i.isDataTexture3D)X.setTexture3D(i,0),h=32879;else{if(!i.isDataTexture2DArray)return void console.warn(\\\\\\\"THREE.WebGLRenderer.copyTextureToTexture3D: only supports THREE.DataTexture3D and THREE.DataTexture2DArray.\\\\\\\");X.setTexture2DArray(i,0),h=35866}ut.pixelStorei(37440,i.flipY),ut.pixelStorei(37441,i.premultiplyAlpha),ut.pixelStorei(3317,i.unpackAlignment);const u=ut.getParameter(3314),d=ut.getParameter(32878),p=ut.getParameter(3316),_=ut.getParameter(3315),f=ut.getParameter(32877),g=n.isCompressedTexture?n.mipmaps[0]:n.image;ut.pixelStorei(3314,g.width),ut.pixelStorei(32878,g.height),ut.pixelStorei(3316,t.min.x),ut.pixelStorei(3315,t.min.y),ut.pixelStorei(32877,t.min.z),n.isDataTexture||n.isDataTexture3D?ut.texSubImage3D(h,s,e.x,e.y,e.z,r,o,a,l,c,g.data):n.isCompressedTexture?(console.warn(\\\\\\\"THREE.WebGLRenderer.copyTextureToTexture3D: untested support for compressed srcTexture.\\\\\\\"),ut.compressedTexSubImage3D(h,s,e.x,e.y,e.z,r,o,a,l,g.data)):ut.texSubImage3D(h,s,e.x,e.y,e.z,r,o,a,l,c,g),ut.pixelStorei(3314,u),ut.pixelStorei(32878,d),ut.pixelStorei(3316,p),ut.pixelStorei(3315,_),ut.pixelStorei(32877,f),0===s&&i.generateMipmaps&&ut.generateMipmap(h),j.unbindTexture()},this.initTexture=function(t){X.setTexture2D(t,0),j.unbindTexture()},this.resetState=function(){g=0,v=0,y=null,j.reset(),ht.reset()},\\\\\\\"undefined\\\\\\\"!=typeof __THREE_DEVTOOLS__&&__THREE_DEVTOOLS__.dispatchEvent(new CustomEvent(\\\\\\\"observe\\\\\\\",{detail:this}))}(class extends ME{}).prototype.isWebGL1Renderer=!0;class EE{constructor(t,e=25e-5){this.name=\\\\\\\"\\\\\\\",this.color=new kw(t),this.density=e}clone(){return new EE(this.color,this.density)}toJSON(){return{type:\\\\\\\"FogExp2\\\\\\\",color:this.color.getHex(),density:this.density}}}EE.prototype.isFogExp2=!0;class SE{constructor(t,e=1,n=1e3){this.name=\\\\\\\"\\\\\\\",this.color=new kw(t),this.near=e,this.far=n}clone(){return new SE(this.color,this.near,this.far)}toJSON(){return{type:\\\\\\\"Fog\\\\\\\",color:this.color.getHex(),near:this.near,far:this.far}}}SE.prototype.isFog=!0;class CE extends yw{constructor(){super(),this.type=\\\\\\\"Scene\\\\\\\",this.background=null,this.environment=null,this.fog=null,this.overrideMaterial=null,this.autoUpdate=!0,\\\\\\\"undefined\\\\\\\"!=typeof __THREE_DEVTOOLS__&&__THREE_DEVTOOLS__.dispatchEvent(new CustomEvent(\\\\\\\"observe\\\\\\\",{detail:this}))}copy(t,e){return super.copy(t,e),null!==t.background&&(this.background=t.background.clone()),null!==t.environment&&(this.environment=t.environment.clone()),null!==t.fog&&(this.fog=t.fog.clone()),null!==t.overrideMaterial&&(this.overrideMaterial=t.overrideMaterial.clone()),this.autoUpdate=t.autoUpdate,this.matrixAutoUpdate=t.matrixAutoUpdate,this}toJSON(t){const e=super.toJSON(t);return null!==this.fog&&(e.object.fog=this.fog.toJSON()),e}}CE.prototype.isScene=!0;class NE{constructor(t,e){this.array=t,this.stride=e,this.count=void 0!==t?t.length/e:0,this.usage=Gx,this.updateRange={offset:0,count:-1},this.version=0,this.uuid=Jx()}onUploadCallback(){}set needsUpdate(t){!0===t&&this.version++}setUsage(t){return this.usage=t,this}copy(t){return this.array=new t.array.constructor(t.array),this.count=t.count,this.stride=t.stride,this.usage=t.usage,this}copyAt(t,e,n){t*=this.stride,n*=e.stride;for(let i=0,s=this.stride;it.far||e.push({distance:a,point:IE.clone(),uv:Lw.getUV(IE,UE,GE,VE,HE,jE,WE,new sb),face:null,object:this})}copy(t){return super.copy(t),void 0!==t.center&&this.center.copy(t.center),this.material=t.material,this}}function XE(t,e,n,i,s,r){BE.subVectors(t,n).addScalar(.5).multiply(i),void 0!==s?(zE.x=r*BE.x-s*BE.y,zE.y=s*BE.x+r*BE.y):zE.copy(BE),t.copy(e),t.x+=zE.x,t.y+=zE.y,t.applyMatrix4(kE)}qE.prototype.isSprite=!0;const YE=new gb,$E=new pb,JE=new pb,ZE=new gb,QE=new Yb;class KE extends vT{constructor(t,e){super(t,e),this.type=\\\\\\\"SkinnedMesh\\\\\\\",this.bindMode=\\\\\\\"attached\\\\\\\",this.bindMatrix=new Yb,this.bindMatrixInverse=new Yb}copy(t){return super.copy(t),this.bindMode=t.bindMode,this.bindMatrix.copy(t.bindMatrix),this.bindMatrixInverse.copy(t.bindMatrixInverse),this.skeleton=t.skeleton,this}bind(t,e){this.skeleton=t,void 0===e&&(this.updateMatrixWorld(!0),this.skeleton.calculateInverses(),e=this.matrixWorld),this.bindMatrix.copy(e),this.bindMatrixInverse.copy(e).invert()}pose(){this.skeleton.pose()}normalizeSkinWeights(){const t=new pb,e=this.geometry.attributes.skinWeight;for(let n=0,i=e.count;na)continue;u.applyMatrix4(this.matrixWorld);const d=t.ray.origin.distanceTo(u);dt.far||e.push({distance:d,point:h.clone().applyMatrix4(this.matrixWorld),index:n,face:null,faceIndex:null,object:this})}}else{for(let n=Math.max(0,r.start),i=Math.min(s.count,r.start+r.count)-1;na)continue;u.applyMatrix4(this.matrixWorld);const i=t.ray.origin.distanceTo(u);it.far||e.push({distance:i,point:h.clone().applyMatrix4(this.matrixWorld),index:n,face:null,faceIndex:null,object:this})}}}else n.isGeometry&&console.error(\\\\\\\"THREE.Line.raycast() no longer supports THREE.Geometry. Use THREE.BufferGeometry instead.\\\\\\\")}updateMorphTargets(){const t=this.geometry;if(t.isBufferGeometry){const e=t.morphAttributes,n=Object.keys(e);if(n.length>0){const t=e[n[0]];if(void 0!==t){this.morphTargetInfluences=[],this.morphTargetDictionary={};for(let e=0,n=t.length;e0&&console.error(\\\\\\\"THREE.Line.updateMorphTargets() does not support THREE.Geometry. Use THREE.BufferGeometry instead.\\\\\\\")}}}_S.prototype.isLine=!0;const mS=new gb,fS=new gb;class gS extends _S{constructor(t,e){super(t,e),this.type=\\\\\\\"LineSegments\\\\\\\"}computeLineDistances(){const t=this.geometry;if(t.isBufferGeometry)if(null===t.index){const e=t.attributes.position,n=[];for(let t=0,i=e.count;t0){const t=e[n[0]];if(void 0!==t){this.morphTargetInfluences=[],this.morphTargetDictionary={};for(let e=0,n=t.length;e0&&console.error(\\\\\\\"THREE.Points.updateMorphTargets() does not support THREE.Geometry. Use THREE.BufferGeometry instead.\\\\\\\")}}}function MS(t,e,n,i,s,r,o){const a=bS.distanceSqToPoint(t);if(as.far)return;r.push({distance:l,distanceToRay:Math.sqrt(a),point:n,index:e,face:null,object:o})}}AS.prototype.isPoints=!0;(class extends ub{constructor(t,e,n,i,s,r,o,a,l){super(t,e,n,i,s,r,o,a,l),this.format=void 0!==o?o:Mx,this.minFilter=void 0!==r?r:fx,this.magFilter=void 0!==s?s:fx,this.generateMipmaps=!1;const c=this;\\\\\\\"requestVideoFrameCallback\\\\\\\"in t&&t.requestVideoFrameCallback((function e(){c.needsUpdate=!0,t.requestVideoFrameCallback(e)}))}clone(){return new this.constructor(this.image).copy(this)}update(){const t=this.image;!1===\\\\\\\"requestVideoFrameCallback\\\\\\\"in t&&t.readyState>=t.HAVE_CURRENT_DATA&&(this.needsUpdate=!0)}}).prototype.isVideoTexture=!0;class ES extends ub{constructor(t,e,n,i,s,r,o,a,l,c,h,u){super(null,r,o,a,l,c,i,s,h,u),this.image={width:e,height:n},this.mipmaps=t,this.flipY=!1,this.generateMipmaps=!1}}ES.prototype.isCompressedTexture=!0;(class extends ub{constructor(t,e,n,i,s,r,o,a,l){super(t,e,n,i,s,r,o,a,l),this.needsUpdate=!0}}).prototype.isCanvasTexture=!0;(class extends ub{constructor(t,e,n,i,s,r,o,a,l,c){if((c=void 0!==c?c:Sx)!==Sx&&c!==Cx)throw new Error(\\\\\\\"DepthTexture format must be either THREE.DepthFormat or THREE.DepthStencilFormat\\\\\\\");void 0===n&&c===Sx&&(n=xx),void 0===n&&c===Cx&&(n=Ax),super(null,i,s,r,o,a,c,n,l),this.image={width:t,height:e},this.magFilter=void 0!==o?o:px,this.minFilter=void 0!==a?a:px,this.flipY=!1,this.generateMipmaps=!1}}).prototype.isDepthTexture=!0;new gb,new gb,new gb,new Lw;class SS{constructor(){this.type=\\\\\\\"Curve\\\\\\\",this.arcLengthDivisions=200}getPoint(){return console.warn(\\\\\\\"THREE.Curve: .getPoint() not implemented.\\\\\\\"),null}getPointAt(t,e){const n=this.getUtoTmapping(t);return this.getPoint(n,e)}getPoints(t=5){const e=[];for(let n=0;n<=t;n++)e.push(this.getPoint(n/t));return e}getSpacedPoints(t=5){const e=[];for(let n=0;n<=t;n++)e.push(this.getPointAt(n/t));return e}getLength(){const t=this.getLengths();return t[t.length-1]}getLengths(t=this.arcLengthDivisions){if(this.cacheArcLengths&&this.cacheArcLengths.length===t+1&&!this.needsUpdate)return this.cacheArcLengths;this.needsUpdate=!1;const e=[];let n,i=this.getPoint(0),s=0;e.push(0);for(let r=1;r<=t;r++)n=this.getPoint(r/t),s+=n.distanceTo(i),e.push(s),i=n;return this.cacheArcLengths=e,e}updateArcLengths(){this.needsUpdate=!0,this.getLengths()}getUtoTmapping(t,e){const n=this.getLengths();let i=0;const s=n.length;let r;r=e||t*n[s-1];let o,a=0,l=s-1;for(;a<=l;)if(i=Math.floor(a+(l-a)/2),o=n[i]-r,o<0)a=i+1;else{if(!(o>0)){l=i;break}l=i-1}if(i=l,n[i]===r)return i/(s-1);const c=n[i];return(i+(r-c)/(n[i+1]-c))/(s-1)}getTangent(t,e){const n=1e-4;let i=t-n,s=t+n;i<0&&(i=0),s>1&&(s=1);const r=this.getPoint(i),o=this.getPoint(s),a=e||(r.isVector2?new sb:new gb);return a.copy(o).sub(r).normalize(),a}getTangentAt(t,e){const n=this.getUtoTmapping(t);return this.getTangent(n,e)}computeFrenetFrames(t,e){const n=new gb,i=[],s=[],r=[],o=new gb,a=new Yb;for(let e=0;e<=t;e++){const n=e/t;i[e]=this.getTangentAt(n,new gb)}s[0]=new gb,r[0]=new gb;let l=Number.MAX_VALUE;const c=Math.abs(i[0].x),h=Math.abs(i[0].y),u=Math.abs(i[0].z);c<=l&&(l=c,n.set(1,0,0)),h<=l&&(l=h,n.set(0,1,0)),u<=l&&n.set(0,0,1),o.crossVectors(i[0],n).normalize(),s[0].crossVectors(i[0],o),r[0].crossVectors(i[0],s[0]);for(let e=1;e<=t;e++){if(s[e]=s[e-1].clone(),r[e]=r[e-1].clone(),o.crossVectors(i[e-1],i[e]),o.length()>Number.EPSILON){o.normalize();const t=Math.acos(Zx(i[e-1].dot(i[e]),-1,1));s[e].applyMatrix4(a.makeRotationAxis(o,t))}r[e].crossVectors(i[e],s[e])}if(!0===e){let e=Math.acos(Zx(s[0].dot(s[t]),-1,1));e/=t,i[0].dot(o.crossVectors(s[0],s[t]))>0&&(e=-e);for(let n=1;n<=t;n++)s[n].applyMatrix4(a.makeRotationAxis(i[n],e*n)),r[n].crossVectors(i[n],s[n])}return{tangents:i,normals:s,binormals:r}}clone(){return(new this.constructor).copy(this)}copy(t){return this.arcLengthDivisions=t.arcLengthDivisions,this}toJSON(){const t={metadata:{version:4.5,type:\\\\\\\"Curve\\\\\\\",generator:\\\\\\\"Curve.toJSON\\\\\\\"}};return t.arcLengthDivisions=this.arcLengthDivisions,t.type=this.type,t}fromJSON(t){return this.arcLengthDivisions=t.arcLengthDivisions,this}}class CS extends SS{constructor(t=0,e=0,n=1,i=1,s=0,r=2*Math.PI,o=!1,a=0){super(),this.type=\\\\\\\"EllipseCurve\\\\\\\",this.aX=t,this.aY=e,this.xRadius=n,this.yRadius=i,this.aStartAngle=s,this.aEndAngle=r,this.aClockwise=o,this.aRotation=a}getPoint(t,e){const n=e||new sb,i=2*Math.PI;let s=this.aEndAngle-this.aStartAngle;const r=Math.abs(s)i;)s-=i;s0?0:(Math.floor(Math.abs(l)/s)+1)*s:0===c&&l===s-1&&(l=s-2,c=1),this.closed||l>0?o=i[(l-1)%s]:(OS.subVectors(i[0],i[1]).add(i[0]),o=OS);const 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hC(t.prev,t,t.next)<0?hC(t,e,t.next)>=0&&hC(t,t.prev,e)>=0:hC(t,e,t.prev)<0||hC(t,t.next,e)<0}function fC(t,e){const n=new yC(t.i,t.x,t.y),i=new yC(e.i,e.x,e.y),s=t.next,r=e.prev;return t.next=e,e.prev=t,n.next=s,s.prev=n,i.next=n,n.prev=i,r.next=i,i.prev=r,i}function gC(t,e,n,i){const s=new yC(t,e,n);return i?(s.next=i.next,s.prev=i,i.next.prev=s,i.next=s):(s.prev=s,s.next=s),s}function vC(t){t.next.prev=t.prev,t.prev.next=t.next,t.prevZ&&(t.prevZ.nextZ=t.nextZ),t.nextZ&&(t.nextZ.prevZ=t.prevZ)}function yC(t,e,n){this.i=t,this.x=e,this.y=n,this.prev=null,this.next=null,this.z=null,this.prevZ=null,this.nextZ=null,this.steiner=!1}class xC{static area(t){const e=t.length;let n=0;for(let i=e-1,s=0;s2&&t[e-1].equals(t[0])&&t.pop()}function wC(t,e){for(let n=0;nNumber.EPSILON){const u=Math.sqrt(h),d=Math.sqrt(l*l+c*c),p=e.x-a/u,_=e.y+o/u,m=((n.x-c/d-p)*c-(n.y+l/d-_)*l)/(o*c-a*l);i=p+o*m-t.x,s=_+a*m-t.y;const f=i*i+s*s;if(f<=2)return new sb(i,s);r=Math.sqrt(f/2)}else{let t=!1;o>Number.EPSILON?l>Number.EPSILON&&(t=!0):o<-Number.EPSILON?l<-Number.EPSILON&&(t=!0):Math.sign(a)===Math.sign(c)&&(t=!0),t?(i=-a,s=o,r=Math.sqrt(h)):(i=o,s=a,r=Math.sqrt(h/2))}return new sb(i/r,s/r)}const O=[];for(let t=0,e=E.length,n=e-1,i=t+1;t=0;t--){const e=t/p,n=h*Math.cos(e*Math.PI/2),i=u*Math.sin(e*Math.PI/2)+d;for(let t=0,e=E.length;t=0;){const i=n;let s=n-1;s<0&&(s=t.length-1);for(let t=0,n=a+2*p;t0!=t>0&&this.version++,this._sheen=t}get clearcoat(){return this._clearcoat}set clearcoat(t){this._clearcoat>0!=t>0&&this.version++,this._clearcoat=t}get transmission(){return this._transmission}set transmission(t){this._transmission>0!=t>0&&this.version++,this._transmission=t}copy(t){return super.copy(t),this.defines={STANDARD:\\\\\\\"\\\\\\\",PHYSICAL:\\\\\\\"\\\\\\\"},this.clearcoat=t.clearcoat,this.clearcoatMap=t.clearcoatMap,this.clearcoatRoughness=t.clearcoatRoughness,this.clearcoatRoughnessMap=t.clearcoatRoughnessMap,this.clearcoatNormalMap=t.clearcoatNormalMap,this.clearcoatNormalScale.copy(t.clearcoatNormalScale),this.ior=t.ior,this.sheen=t.sheen,this.sheenTint.copy(t.sheenTint),this.sheenRoughness=t.sheenRoughness,this.transmission=t.transmission,this.transmissionMap=t.transmissionMap,this.thickness=t.thickness,this.thicknessMap=t.thicknessMap,this.attenuationDistance=t.attenuationDistance,this.attenuationTint.copy(t.attenuationTint),this.specularIntensity=t.specularIntensity,this.specularIntensityMap=t.specularIntensityMap,this.specularTint.copy(t.specularTint),this.specularTintMap=t.specularTintMap,this}}CC.prototype.isMeshPhysicalMaterial=!0;class NC extends Pw{constructor(t){super(),this.type=\\\\\\\"MeshPhongMaterial\\\\\\\",this.color=new kw(16777215),this.specular=new kw(1118481),this.shininess=30,this.map=null,this.lightMap=null,this.lightMapIntensity=1,this.aoMap=null,this.aoMapIntensity=1,this.emissive=new kw(0),this.emissiveIntensity=1,this.emissiveMap=null,this.bumpMap=null,this.bumpScale=1,this.normalMap=null,this.normalMapType=0,this.normalScale=new sb(1,1),this.displacementMap=null,this.displacementScale=1,this.displacementBias=0,this.specularMap=null,this.alphaMap=null,this.envMap=null,this.combine=0,this.reflectivity=1,this.refractionRatio=.98,this.wireframe=!1,this.wireframeLinewidth=1,this.wireframeLinecap=\\\\\\\"round\\\\\\\",this.wireframeLinejoin=\\\\\\\"round\\\\\\\",this.flatShading=!1,this.setValues(t)}copy(t){return super.copy(t),this.color.copy(t.color),this.specular.copy(t.specular),this.shininess=t.shininess,this.map=t.map,this.lightMap=t.lightMap,this.lightMapIntensity=t.lightMapIntensity,this.aoMap=t.aoMap,this.aoMapIntensity=t.aoMapIntensity,this.emissive.copy(t.emissive),this.emissiveMap=t.emissiveMap,this.emissiveIntensity=t.emissiveIntensity,this.bumpMap=t.bumpMap,this.bumpScale=t.bumpScale,this.normalMap=t.normalMap,this.normalMapType=t.normalMapType,this.normalScale.copy(t.normalScale),this.displacementMap=t.displacementMap,this.displacementScale=t.displacementScale,this.displacementBias=t.displacementBias,this.specularMap=t.specularMap,this.alphaMap=t.alphaMap,this.envMap=t.envMap,this.combine=t.combine,this.reflectivity=t.reflectivity,this.refractionRatio=t.refractionRatio,this.wireframe=t.wireframe,this.wireframeLinewidth=t.wireframeLinewidth,this.wireframeLinecap=t.wireframeLinecap,this.wireframeLinejoin=t.wireframeLinejoin,this.flatShading=t.flatShading,this}}NC.prototype.isMeshPhongMaterial=!0;class LC extends Pw{constructor(t){super(),this.defines={TOON:\\\\\\\"\\\\\\\"},this.type=\\\\\\\"MeshToonMaterial\\\\\\\",this.color=new kw(16777215),this.map=null,this.gradientMap=null,this.lightMap=null,this.lightMapIntensity=1,this.aoMap=null,this.aoMapIntensity=1,this.emissive=new kw(0),this.emissiveIntensity=1,this.emissiveMap=null,this.bumpMap=null,this.bumpScale=1,this.normalMap=null,this.normalMapType=0,this.normalScale=new sb(1,1),this.displacementMap=null,this.displacementScale=1,this.displacementBias=0,this.alphaMap=null,this.wireframe=!1,this.wireframeLinewidth=1,this.wireframeLinecap=\\\\\\\"round\\\\\\\",this.wireframeLinejoin=\\\\\\\"round\\\\\\\",this.setValues(t)}copy(t){return super.copy(t),this.color.copy(t.color),this.map=t.map,this.gradientMap=t.gradientMap,this.lightMap=t.lightMap,this.lightMapIntensity=t.lightMapIntensity,this.aoMap=t.aoMap,this.aoMapIntensity=t.aoMapIntensity,this.emissive.copy(t.emissive),this.emissiveMap=t.emissiveMap,this.emissiveIntensity=t.emissiveIntensity,this.bumpMap=t.bumpMap,this.bumpScale=t.bumpScale,this.normalMap=t.normalMap,this.normalMapType=t.normalMapType,this.normalScale.copy(t.normalScale),this.displacementMap=t.displacementMap,this.displacementScale=t.displacementScale,this.displacementBias=t.displacementBias,this.alphaMap=t.alphaMap,this.wireframe=t.wireframe,this.wireframeLinewidth=t.wireframeLinewidth,this.wireframeLinecap=t.wireframeLinecap,this.wireframeLinejoin=t.wireframeLinejoin,this}}LC.prototype.isMeshToonMaterial=!0;class OC extends Pw{constructor(t){super(),this.type=\\\\\\\"MeshNormalMaterial\\\\\\\",this.bumpMap=null,this.bumpScale=1,this.normalMap=null,this.normalMapType=0,this.normalScale=new sb(1,1),this.displacementMap=null,this.displacementScale=1,this.displacementBias=0,this.wireframe=!1,this.wireframeLinewidth=1,this.fog=!1,this.flatShading=!1,this.setValues(t)}copy(t){return super.copy(t),this.bumpMap=t.bumpMap,this.bumpScale=t.bumpScale,this.normalMap=t.normalMap,this.normalMapType=t.normalMapType,this.normalScale.copy(t.normalScale),this.displacementMap=t.displacementMap,this.displacementScale=t.displacementScale,this.displacementBias=t.displacementBias,this.wireframe=t.wireframe,this.wireframeLinewidth=t.wireframeLinewidth,this.flatShading=t.flatShading,this}}OC.prototype.isMeshNormalMaterial=!0;class PC extends Pw{constructor(t){super(),this.type=\\\\\\\"MeshLambertMaterial\\\\\\\",this.color=new kw(16777215),this.map=null,this.lightMap=null,this.lightMapIntensity=1,this.aoMap=null,this.aoMapIntensity=1,this.emissive=new kw(0),this.emissiveIntensity=1,this.emissiveMap=null,this.specularMap=null,this.alphaMap=null,this.envMap=null,this.combine=0,this.reflectivity=1,this.refractionRatio=.98,this.wireframe=!1,this.wireframeLinewidth=1,this.wireframeLinecap=\\\\\\\"round\\\\\\\",this.wireframeLinejoin=\\\\\\\"round\\\\\\\",this.setValues(t)}copy(t){return super.copy(t),this.color.copy(t.color),this.map=t.map,this.lightMap=t.lightMap,this.lightMapIntensity=t.lightMapIntensity,this.aoMap=t.aoMap,this.aoMapIntensity=t.aoMapIntensity,this.emissive.copy(t.emissive),this.emissiveMap=t.emissiveMap,this.emissiveIntensity=t.emissiveIntensity,this.specularMap=t.specularMap,this.alphaMap=t.alphaMap,this.envMap=t.envMap,this.combine=t.combine,this.reflectivity=t.reflectivity,this.refractionRatio=t.refractionRatio,this.wireframe=t.wireframe,this.wireframeLinewidth=t.wireframeLinewidth,this.wireframeLinecap=t.wireframeLinecap,this.wireframeLinejoin=t.wireframeLinejoin,this}}PC.prototype.isMeshLambertMaterial=!0;class RC extends Pw{constructor(t){super(),this.defines={MATCAP:\\\\\\\"\\\\\\\"},this.type=\\\\\\\"MeshMatcapMaterial\\\\\\\",this.color=new kw(16777215),this.matcap=null,this.map=null,this.bumpMap=null,this.bumpScale=1,this.normalMap=null,this.normalMapType=0,this.normalScale=new sb(1,1),this.displacementMap=null,this.displacementScale=1,this.displacementBias=0,this.alphaMap=null,this.flatShading=!1,this.setValues(t)}copy(t){return super.copy(t),this.defines={MATCAP:\\\\\\\"\\\\\\\"},this.color.copy(t.color),this.matcap=t.matcap,this.map=t.map,this.bumpMap=t.bumpMap,this.bumpScale=t.bumpScale,this.normalMap=t.normalMap,this.normalMapType=t.normalMapType,this.normalScale.copy(t.normalScale),this.displacementMap=t.displacementMap,this.displacementScale=t.displacementScale,this.displacementBias=t.displacementBias,this.alphaMap=t.alphaMap,this.flatShading=t.flatShading,this}}RC.prototype.isMeshMatcapMaterial=!0;class IC extends lS{constructor(t){super(),this.type=\\\\\\\"LineDashedMaterial\\\\\\\",this.scale=1,this.dashSize=3,this.gapSize=1,this.setValues(t)}copy(t){return super.copy(t),this.scale=t.scale,this.dashSize=t.dashSize,this.gapSize=t.gapSize,this}}IC.prototype.isLineDashedMaterial=!0;const FC={arraySlice:function(t,e,n){return FC.isTypedArray(t)?new t.constructor(t.subarray(e,void 0!==n?n:t.length)):t.slice(e,n)},convertArray:function(t,e,n){return!t||!n&&t.constructor===e?t:\\\\\\\"number\\\\\\\"==typeof e.BYTES_PER_ELEMENT?new e(t):Array.prototype.slice.call(t)},isTypedArray:function(t){return ArrayBuffer.isView(t)&&!(t instanceof DataView)},getKeyframeOrder:function(t){const e=t.length,n=new Array(e);for(let t=0;t!==e;++t)n[t]=t;return n.sort((function(e,n){return t[e]-t[n]})),n},sortedArray:function(t,e,n){const i=t.length,s=new t.constructor(i);for(let r=0,o=0;o!==i;++r){const i=n[r]*e;for(let n=0;n!==e;++n)s[o++]=t[i+n]}return s},flattenJSON:function(t,e,n,i){let s=1,r=t[0];for(;void 0!==r&&void 0===r[i];)r=t[s++];if(void 0===r)return;let o=r[i];if(void 0!==o)if(Array.isArray(o))do{o=r[i],void 0!==o&&(e.push(r.time),n.push.apply(n,o)),r=t[s++]}while(void 0!==r);else if(void 0!==o.toArray)do{o=r[i],void 0!==o&&(e.push(r.time),o.toArray(n,n.length)),r=t[s++]}while(void 0!==r);else do{o=r[i],void 0!==o&&(e.push(r.time),n.push(o)),r=t[s++]}while(void 0!==r)},subclip:function(t,e,n,i,s=30){const r=t.clone();r.name=e;const o=[];for(let t=0;t=i)){l.push(e.times[t]);for(let n=0;nr.tracks[t].times[0]&&(a=r.tracks[t].times[0]);for(let t=0;t=i.times[u]){const t=u*l+a,e=t+l-a;d=FC.arraySlice(i.values,t,e)}else{const t=i.createInterpolant(),e=a,n=l-a;t.evaluate(r),d=FC.arraySlice(t.resultBuffer,e,n)}if(\\\\\\\"quaternion\\\\\\\"===s){(new fb).fromArray(d).normalize().conjugate().toArray(d)}const p=o.times.length;for(let t=0;t=s)break t;{const o=e[1];t=s)break e}r=n,n=0}}for(;n>>1;te;)--r;if(++r,0!==s||r!==i){s>=r&&(r=Math.max(r,1),s=r-1);const t=this.getValueSize();this.times=FC.arraySlice(n,s,r),this.values=FC.arraySlice(this.values,s*t,r*t)}return this}validate(){let t=!0;const e=this.getValueSize();e-Math.floor(e)!=0&&(console.error(\\\\\\\"THREE.KeyframeTrack: Invalid value size in track.\\\\\\\",this),t=!1);const n=this.times,i=this.values,s=n.length;0===s&&(console.error(\\\\\\\"THREE.KeyframeTrack: Track is empty.\\\\\\\",this),t=!1);let r=null;for(let e=0;e!==s;e++){const i=n[e];if(\\\\\\\"number\\\\\\\"==typeof i&&isNaN(i)){console.error(\\\\\\\"THREE.KeyframeTrack: Time is not a valid number.\\\\\\\",this,e,i),t=!1;break}if(null!==r&&r>i){console.error(\\\\\\\"THREE.KeyframeTrack: Out of order keys.\\\\\\\",this,e,i,r),t=!1;break}r=i}if(void 0!==i&&FC.isTypedArray(i))for(let e=0,n=i.length;e!==n;++e){const n=i[e];if(isNaN(n)){console.error(\\\\\\\"THREE.KeyframeTrack: Value is not a valid number.\\\\\\\",this,e,n),t=!1;break}}return t}optimize(){const t=FC.arraySlice(this.times),e=FC.arraySlice(this.values),n=this.getValueSize(),i=this.getInterpolation()===Ox,s=t.length-1;let r=1;for(let o=1;o0){t[r]=t[s];for(let t=s*n,i=r*n,o=0;o!==n;++o)e[i+o]=e[t+o];++r}return r!==t.length?(this.times=FC.arraySlice(t,0,r),this.values=FC.arraySlice(e,0,r*n)):(this.times=t,this.values=e),this}clone(){const t=FC.arraySlice(this.times,0),e=FC.arraySlice(this.values,0),n=new(0,this.constructor)(this.name,t,e);return n.createInterpolant=this.createInterpolant,n}}UC.prototype.TimeBufferType=Float32Array,UC.prototype.ValueBufferType=Float32Array,UC.prototype.DefaultInterpolation=Lx;class GC extends UC{}GC.prototype.ValueTypeName=\\\\\\\"bool\\\\\\\",GC.prototype.ValueBufferType=Array,GC.prototype.DefaultInterpolation=Nx,GC.prototype.InterpolantFactoryMethodLinear=void 0,GC.prototype.InterpolantFactoryMethodSmooth=void 0;class VC extends UC{}VC.prototype.ValueTypeName=\\\\\\\"color\\\\\\\";class HC extends UC{}HC.prototype.ValueTypeName=\\\\\\\"number\\\\\\\";class jC extends DC{constructor(t,e,n,i){super(t,e,n,i)}interpolate_(t,e,n,i){const s=this.resultBuffer,r=this.sampleValues,o=this.valueSize,a=(n-e)/(i-e);let l=t*o;for(let t=l+o;l!==t;l+=4)fb.slerpFlat(s,0,r,l-o,r,l,a);return s}}class WC extends UC{InterpolantFactoryMethodLinear(t){return new jC(this.times,this.values,this.getValueSize(),t)}}WC.prototype.ValueTypeName=\\\\\\\"quaternion\\\\\\\",WC.prototype.DefaultInterpolation=Lx,WC.prototype.InterpolantFactoryMethodSmooth=void 0;class qC extends UC{}qC.prototype.ValueTypeName=\\\\\\\"string\\\\\\\",qC.prototype.ValueBufferType=Array,qC.prototype.DefaultInterpolation=Nx,qC.prototype.InterpolantFactoryMethodLinear=void 0,qC.prototype.InterpolantFactoryMethodSmooth=void 0;class XC extends UC{}XC.prototype.ValueTypeName=\\\\\\\"vector\\\\\\\";class YC{constructor(t,e=-1,n,i=2500){this.name=t,this.tracks=n,this.duration=e,this.blendMode=i,this.uuid=Jx(),this.duration<0&&this.resetDuration()}static parse(t){const e=[],n=t.tracks,i=1/(t.fps||1);for(let t=0,s=n.length;t!==s;++t)e.push($C(n[t]).scale(i));const s=new this(t.name,t.duration,e,t.blendMode);return s.uuid=t.uuid,s}static toJSON(t){const e=[],n=t.tracks,i={name:t.name,duration:t.duration,tracks:e,uuid:t.uuid,blendMode:t.blendMode};for(let t=0,i=n.length;t!==i;++t)e.push(UC.toJSON(n[t]));return i}static CreateFromMorphTargetSequence(t,e,n,i){const s=e.length,r=[];for(let t=0;t1){const t=r[1];let e=i[t];e||(i[t]=e=[]),e.push(n)}}const r=[];for(const t in i)r.push(this.CreateFromMorphTargetSequence(t,i[t],e,n));return r}static parseAnimation(t,e){if(!t)return console.error(\\\\\\\"THREE.AnimationClip: No animation in JSONLoader data.\\\\\\\"),null;const n=function(t,e,n,i,s){if(0!==n.length){const r=[],o=[];FC.flattenJSON(n,r,o,i),0!==r.length&&s.push(new t(e,r,o))}},i=[],s=t.name||\\\\\\\"default\\\\\\\",r=t.fps||30,o=t.blendMode;let a=t.length||-1;const l=t.hierarchy||[];for(let t=0;t0){this.source.connect(this.filters[0]);for(let t=1,e=this.filters.length;t0){this.source.disconnect(this.filters[0]);for(let t=1,e=this.filters.length;t0&&this._mixBufferRegionAdditive(n,i,this._addIndex*e,1,e);for(let t=e,s=e+e;t!==s;++t)if(n[t]!==n[t+e]){o.setValue(n,i);break}}saveOriginalState(){const t=this.binding,e=this.buffer,n=this.valueSize,i=n*this._origIndex;t.getValue(e,i);for(let t=n,s=i;t!==s;++t)e[t]=e[i+t%n];this._setIdentity(),this.cumulativeWeight=0,this.cumulativeWeightAdditive=0}restoreOriginalState(){const t=3*this.valueSize;this.binding.setValue(this.buffer,t)}_setAdditiveIdentityNumeric(){const t=this._addIndex*this.valueSize,e=t+this.valueSize;for(let n=t;n=.5)for(let i=0;i!==s;++i)t[e+i]=t[n+i]}_slerp(t,e,n,i){fb.slerpFlat(t,e,t,e,t,n,i)}_slerpAdditive(t,e,n,i,s){const r=this._workIndex*s;fb.multiplyQuaternionsFlat(t,r,t,e,t,n),fb.slerpFlat(t,e,t,e,t,r,i)}_lerp(t,e,n,i,s){const r=1-i;for(let o=0;o!==s;++o){const s=e+o;t[s]=t[s]*r+t[n+o]*i}}_lerpAdditive(t,e,n,i,s){for(let r=0;r!==s;++r){const s=e+r;t[s]=t[s]+t[n+r]*i}}}const ON=\\\\\\\"\\\\\\\\[\\\\\\\\]\\\\\\\\.:\\\\\\\\/\\\\\\\",PN=new RegExp(\\\\\\\"[\\\\\\\\[\\\\\\\\]\\\\\\\\.:\\\\\\\\/]\\\\\\\",\\\\\\\"g\\\\\\\"),RN=\\\\\\\"[^\\\\\\\\[\\\\\\\\]\\\\\\\\.:\\\\\\\\/]\\\\\\\",IN=\\\\\\\"[^\\\\\\\"+ON.replace(\\\\\\\"\\\\\\\\.\\\\\\\",\\\\\\\"\\\\\\\")+\\\\\\\"]\\\\\\\",FN=/((?:WC+[\\\\/:])*)/.source.replace(\\\\\\\"WC\\\\\\\",RN),DN=/(WCOD+)?/.source.replace(\\\\\\\"WCOD\\\\\\\",IN),BN=/(?:\\\\.(WC+)(?:\\\\[(.+)\\\\])?)?/.source.replace(\\\\\\\"WC\\\\\\\",RN),zN=/\\\\.(WC+)(?:\\\\[(.+)\\\\])?/.source.replace(\\\\\\\"WC\\\\\\\",RN),kN=new RegExp(\\\\\\\"^\\\\\\\"+FN+DN+BN+zN+\\\\\\\"$\\\\\\\"),UN=[\\\\\\\"material\\\\\\\",\\\\\\\"materials\\\\\\\",\\\\\\\"bones\\\\\\\"];class GN{constructor(t,e,n){this.path=e,this.parsedPath=n||GN.parseTrackName(e),this.node=GN.findNode(t,this.parsedPath.nodeName)||t,this.rootNode=t,this.getValue=this._getValue_unbound,this.setValue=this._setValue_unbound}static create(t,e,n){return t&&t.isAnimationObjectGroup?new GN.Composite(t,e,n):new GN(t,e,n)}static sanitizeNodeName(t){return t.replace(/\\\\s/g,\\\\\\\"_\\\\\\\").replace(PN,\\\\\\\"\\\\\\\")}static parseTrackName(t){const e=kN.exec(t);if(!e)throw new Error(\\\\\\\"PropertyBinding: Cannot parse trackName: \\\\\\\"+t);const n={nodeName:e[2],objectName:e[3],objectIndex:e[4],propertyName:e[5],propertyIndex:e[6]},i=n.nodeName&&n.nodeName.lastIndexOf(\\\\\\\".\\\\\\\");if(void 0!==i&&-1!==i){const t=n.nodeName.substring(i+1);-1!==UN.indexOf(t)&&(n.nodeName=n.nodeName.substring(0,i),n.objectName=t)}if(null===n.propertyName||0===n.propertyName.length)throw new Error(\\\\\\\"PropertyBinding: can not parse propertyName from trackName: \\\\\\\"+t);return n}static findNode(t,e){if(!e||\\\\\\\"\\\\\\\"===e||\\\\\\\".\\\\\\\"===e||-1===e||e===t.name||e===t.uuid)return t;if(t.skeleton){const n=t.skeleton.getBoneByName(e);if(void 0!==n)return n}if(t.children){const n=function(t){for(let i=0;i0){const t=this._interpolants,e=this._propertyBindings;switch(this.blendMode){case 2501:for(let n=0,i=t.length;n!==i;++n)t[n].evaluate(r),e[n].accumulateAdditive(o);break;case Fx:default:for(let n=0,s=t.length;n!==s;++n)t[n].evaluate(r),e[n].accumulate(i,o)}}}_updateWeight(t){let e=0;if(this.enabled){e=this.weight;const n=this._weightInterpolant;if(null!==n){const i=n.evaluate(t)[0];e*=i,t>n.parameterPositions[1]&&(this.stopFading(),0===i&&(this.enabled=!1))}}return this._effectiveWeight=e,e}_updateTimeScale(t){let e=0;if(!this.paused){e=this.timeScale;const n=this._timeScaleInterpolant;if(null!==n){e*=n.evaluate(t)[0],t>n.parameterPositions[1]&&(this.stopWarping(),0===e?this.paused=!0:this.timeScale=e)}}return this._effectiveTimeScale=e,e}_updateTime(t){const e=this._clip.duration,n=this.loop;let i=this.time+t,s=this._loopCount;const r=2202===n;if(0===t)return-1===s?i:r&&1==(1&s)?e-i:i;if(2200===n){-1===s&&(this._loopCount=0,this._setEndings(!0,!0,!1));t:{if(i>=e)i=e;else{if(!(i<0)){this.time=i;break t}i=0}this.clampWhenFinished?this.paused=!0:this.enabled=!1,this.time=i,this._mixer.dispatchEvent({type:\\\\\\\"finished\\\\\\\",action:this,direction:t<0?-1:1})}}else{if(-1===s&&(t>=0?(s=0,this._setEndings(!0,0===this.repetitions,r)):this._setEndings(0===this.repetitions,!0,r)),i>=e||i<0){const n=Math.floor(i/e);i-=e*n,s+=Math.abs(n);const o=this.repetitions-s;if(o<=0)this.clampWhenFinished?this.paused=!0:this.enabled=!1,i=t>0?e:0,this.time=i,this._mixer.dispatchEvent({type:\\\\\\\"finished\\\\\\\",action:this,direction:t>0?1:-1});else{if(1===o){const e=t<0;this._setEndings(e,!e,r)}else this._setEndings(!1,!1,r);this._loopCount=s,this.time=i,this._mixer.dispatchEvent({type:\\\\\\\"loop\\\\\\\",action:this,loopDelta:n})}}else this.time=i;if(r&&1==(1&s))return e-i}return i}_setEndings(t,e,n){const i=this._interpolantSettings;n?(i.endingStart=Rx,i.endingEnd=Rx):(i.endingStart=t?this.zeroSlopeAtStart?Rx:Px:Ix,i.endingEnd=e?this.zeroSlopeAtEnd?Rx:Px:Ix)}_scheduleFading(t,e,n){const i=this._mixer,s=i.time;let r=this._weightInterpolant;null===r&&(r=i._lendControlInterpolant(),this._weightInterpolant=r);const o=r.parameterPositions,a=r.sampleValues;return o[0]=s,a[0]=e,o[1]=s+t,a[1]=n,this}}(class extends jx{constructor(t){super(),this._root=t,this._initMemoryManager(),this._accuIndex=0,this.time=0,this.timeScale=1}_bindAction(t,e){const n=t._localRoot||this._root,i=t._clip.tracks,s=i.length,r=t._propertyBindings,o=t._interpolants,a=n.uuid,l=this._bindingsByRootAndName;let c=l[a];void 0===c&&(c={},l[a]=c);for(let t=0;t!==s;++t){const s=i[t],l=s.name;let h=c[l];if(void 0!==h)r[t]=h;else{if(h=r[t],void 0!==h){null===h._cacheIndex&&(++h.referenceCount,this._addInactiveBinding(h,a,l));continue}const i=e&&e._propertyBindings[t].binding.parsedPath;h=new LN(GN.create(n,l,i),s.ValueTypeName,s.getValueSize()),++h.referenceCount,this._addInactiveBinding(h,a,l),r[t]=h}o[t].resultBuffer=h.buffer}}_activateAction(t){if(!this._isActiveAction(t)){if(null===t._cacheIndex){const e=(t._localRoot||this._root).uuid,n=t._clip.uuid,i=this._actionsByClip[n];this._bindAction(t,i&&i.knownActions[0]),this._addInactiveAction(t,n,e)}const e=t._propertyBindings;for(let t=0,n=e.length;t!==n;++t){const n=e[t];0==n.useCount++&&(this._lendBinding(n),n.saveOriginalState())}this._lendAction(t)}}_deactivateAction(t){if(this._isActiveAction(t)){const e=t._propertyBindings;for(let t=0,n=e.length;t!==n;++t){const n=e[t];0==--n.useCount&&(n.restoreOriginalState(),this._takeBackBinding(n))}this._takeBackAction(t)}}_initMemoryManager(){this._actions=[],this._nActiveActions=0,this._actionsByClip={},this._bindings=[],this._nActiveBindings=0,this._bindingsByRootAndName={},this._controlInterpolants=[],this._nActiveControlInterpolants=0;const t=this;this.stats={actions:{get total(){return t._actions.length},get inUse(){return t._nActiveActions}},bindings:{get total(){return t._bindings.length},get inUse(){return t._nActiveBindings}},controlInterpolants:{get total(){return t._controlInterpolants.length},get inUse(){return t._nActiveControlInterpolants}}}}_isActiveAction(t){const e=t._cacheIndex;return null!==e&&e=0;--e)t[e].stop();return this}update(t){t*=this.timeScale;const e=this._actions,n=this._nActiveActions,i=this.time+=t,s=Math.sign(t),r=this._accuIndex^=1;for(let o=0;o!==n;++o){e[o]._update(i,t,s,r)}const o=this._bindings,a=this._nActiveBindings;for(let t=0;t!==a;++t)o[t].apply(r);return this}setTime(t){this.time=0;for(let t=0;tthis.max.x||t.ythis.max.y)}containsBox(t){return this.min.x<=t.min.x&&t.max.x<=this.max.x&&this.min.y<=t.min.y&&t.max.y<=this.max.y}getParameter(t,e){return e.set((t.x-this.min.x)/(this.max.x-this.min.x),(t.y-this.min.y)/(this.max.y-this.min.y))}intersectsBox(t){return!(t.max.xthis.max.x||t.max.ythis.max.y)}clampPoint(t,e){return e.copy(t).clamp(this.min,this.max)}distanceToPoint(t){return jN.copy(t).clamp(this.min,this.max).sub(t).length()}intersect(t){return this.min.max(t.min),this.max.min(t.max),this}union(t){return this.min.min(t.min),this.max.max(t.max),this}translate(t){return this.min.add(t),this.max.add(t),this}equals(t){return t.min.equals(this.min)&&t.max.equals(this.max)}}WN.prototype.isBox2=!0;const qN=new gb,XN=new gb;class YN{constructor(t=new gb,e=new gb){this.start=t,this.end=e}set(t,e){return this.start.copy(t),this.end.copy(e),this}copy(t){return this.start.copy(t.start),this.end.copy(t.end),this}getCenter(t){return t.addVectors(this.start,this.end).multiplyScalar(.5)}delta(t){return t.subVectors(this.end,this.start)}distanceSq(){return this.start.distanceToSquared(this.end)}distance(){return this.start.distanceTo(this.end)}at(t,e){return this.delta(e).multiplyScalar(t).add(this.start)}closestPointToPointParameter(t,e){qN.subVectors(t,this.start),XN.subVectors(this.end,this.start);const n=XN.dot(XN);let i=XN.dot(qN)/n;return e&&(i=Zx(i,0,1)),i}closestPointToPoint(t,e,n){const i=this.closestPointToPointParameter(t,e);return this.delta(n).multiplyScalar(i).add(this.start)}applyMatrix4(t){return this.start.applyMatrix4(t),this.end.applyMatrix4(t),this}equals(t){return t.start.equals(this.start)&&t.end.equals(this.end)}clone(){return(new this.constructor).copy(this)}}(class extends yw{constructor(t){super(),this.material=t,this.render=function(){},this.hasPositions=!1,this.hasNormals=!1,this.hasColors=!1,this.hasUvs=!1,this.positionArray=null,this.normalArray=null,this.colorArray=null,this.uvArray=null,this.count=0}}).prototype.isImmediateRenderObject=!0;const $N=new gb,JN=new Yb,ZN=new Yb;function QN(t){const e=[];t&&t.isBone&&e.push(t);for(let n=0;n8&&t<16&&e[n].split(t+1),0!==i&&this.subTrees.push(e[n])}return this}build(){return this.calcBox(),this.split(0),this}getRayTriangles(t,e){for(let n=0;n0)for(let t=0;t0&&i>0||n<-t.radius&&i<-t.radius)return!1;const s=Math.abs(n/(Math.abs(n)+Math.abs(i))),r=sL.copy(t.start).lerp(t.end,s);if(e.containsPoint(r))return{normal:oL.normal.clone(),point:r.clone(),depth:Math.abs(Math.min(n,i))};const o=t.radius*t.radius,a=aL.set(t.start,t.end),l=[[e.a,e.b],[e.b,e.c],[e.c,e.a]];for(let e=0;e0)for(let t=0;t0)for(let t=0;ta&&(i=o.clone().add(t.origin),s=a,n=e[r])}}return s<1e100&&{distance:s,triangle:n,position:i}}fromGraphNode(t){return t.updateWorldMatrix(!0,!0),t.traverse((t=>{if(!0===t.isMesh){let e,n=!1;null!==t.geometry.index?(n=!0,e=t.geometry.toNonIndexed()):e=t.geometry;const i=e.getAttribute(\\\\\\\"position\\\\\\\");for(let e=0;e{if(!e){const n=t;n.geometry&&(e=n)}})),e?this._playerCollisionController=new dL(e):console.error(\\\\\\\"no geo found in\\\\\\\",t)}else this._playerCollisionController=void 0}setCollisionCapsule(t){var e;null===(e=this._playerCollisionController)||void 0===e||e.setCapsule(t)}setJumpParams(t){}setGravity(t){}setPlayerMass(t){}}const _L={rotateSpeed:1,rotationRange:{min:.25*-Math.PI,max:.25*Math.PI}},mL={type:\\\\\\\"change\\\\\\\"},fL=new p.a,gL=new ny;class vL extends pL{constructor(t,e,n){super(),this._camera=t,this.domElement=e,this.player=n,this.translationData={direction:new p.a},this.rotationData={direction:{x:0,y:0}},this._boundMethods={onRotateStart:this._onRotateStart.bind(this),onRotateMove:this._onRotateMove.bind(this),onRotateEnd:this._onRotateEnd.bind(this),onTranslateStart:this._onTranslateStart.bind(this),onTranslateMove:this._onTranslateMove.bind(this),onTranslateEnd:this._onTranslateEnd.bind(this)},this._startCameraRotation=new Xv.a,this._rotationSpeed=_L.rotateSpeed,this._rotationRange={min:_L.rotationRange.min,max:_L.rotationRange.max},this._azimuthalAngle=0,this._translateDomElement=this._createTranslateDomElement(),this._translateDomElementRect=this._translateDomElement.getBoundingClientRect(),this.vLeft=new p.a,this.vRight=new p.a,this.vTop=new p.a,this.vBottom=new p.a,this.angleY=0,this.angleX=0,this._rotationStartPosition=new d.a,this._rotationMovePosition=new d.a,this._rotationDelta=new d.a,this._startCameraPosition=new p.a,this._translationStartPosition=new d.a,this._translationMovePosition=new d.a,this._translationDelta=new d.a,this._camera.rotation.order=\\\\\\\"ZYX\\\\\\\",this._addEvents()}dispose(){this._removeEvents()}_createTranslateDomElement(){const t=document.createElement(\\\\\\\"div\\\\\\\"),e=this.domElement.getBoundingClientRect(),n=Math.min(e.width,e.height),i=Math.round(.4*n),s=Math.round(.1*n);return t.style.width=`${i}px`,t.style.height=t.style.width,t.style.border=\\\\\\\"1px solid black\\\\\\\",t.style.borderRadius=`${i}px`,t.style.position=\\\\\\\"absolute\\\\\\\",t.style.bottom=`${s}px`,t.style.left=`${s}px`,t}_addEvents(){var t;nx.disableContextMenu(),this.domElement.addEventListener(\\\\\\\"touchstart\\\\\\\",this._boundMethods.onRotateStart),this.domElement.addEventListener(\\\\\\\"touchmove\\\\\\\",this._boundMethods.onRotateMove),this.domElement.addEventListener(\\\\\\\"touchend\\\\\\\",this._boundMethods.onRotateEnd),this._translateDomElement.addEventListener(\\\\\\\"touchstart\\\\\\\",this._boundMethods.onTranslateStart),this._translateDomElement.addEventListener(\\\\\\\"touchmove\\\\\\\",this._boundMethods.onTranslateMove),this._translateDomElement.addEventListener(\\\\\\\"touchend\\\\\\\",this._boundMethods.onTranslateEnd),null===(t=this.domElement.parentElement)||void 0===t||t.append(this._translateDomElement)}_removeEvents(){var t;nx.reEstablishContextMenu(),this.domElement.removeEventListener(\\\\\\\"touchstart\\\\\\\",this._boundMethods.onRotateStart),this.domElement.removeEventListener(\\\\\\\"touchmove\\\\\\\",this._boundMethods.onRotateMove),this.domElement.removeEventListener(\\\\\\\"touchend\\\\\\\",this._boundMethods.onRotateEnd),this._translateDomElement.removeEventListener(\\\\\\\"touchstart\\\\\\\",this._boundMethods.onTranslateStart),this._translateDomElement.removeEventListener(\\\\\\\"touchmove\\\\\\\",this._boundMethods.onTranslateMove),this._translateDomElement.removeEventListener(\\\\\\\"touchend\\\\\\\",this._boundMethods.onTranslateEnd),null===(t=this._translateDomElement.parentElement)||void 0===t||t.removeChild(this._translateDomElement)}setRotationSpeed(t){this._rotationSpeed=t}setRotationRange(t){this._rotationRange.min=t.min,this._rotationRange.max=t.max}_onRotateStart(t){this._startCameraRotation.copy(this._camera.rotation);const e=this._getTouch(t,this.domElement);e&&(this._rotationStartPosition.set(e.clientX,e.clientY),this.vLeft.set(-1,0,.5),this.vRight.set(1,0,.5),[this.vLeft,this.vRight].forEach((t=>{t.unproject(this._camera),this._camera.worldToLocal(t)})),this.angleY=this.vLeft.angleTo(this.vRight),this.vTop.set(0,1,.5),this.vBottom.set(0,-1,.5),[this.vTop,this.vBottom].forEach((t=>{t.unproject(this._camera),this._camera.worldToLocal(t)})),this.angleX=this.vTop.angleTo(this.vBottom))}_onRotateMove(t){const e=this._getTouch(t,this.domElement);e&&(this._rotationMovePosition.set(e.clientX,e.clientY),this._rotationDelta.copy(this._rotationMovePosition).sub(this._rotationStartPosition),this.rotationData.direction.x=this._rotationDelta.x/this.domElement.clientWidth,this.rotationData.direction.y=this._rotationDelta.y/this.domElement.clientHeight,this._rotateCamera(this.rotationData))}_onRotateEnd(){this.rotationData.direction.x=0,this.rotationData.direction.y=0}_rotateCamera(t){let e=this.angleY*t.direction.x*this._rotationSpeed;this._camera.rotation.y=this._startCameraRotation.y+-e;let n=this.angleX*t.direction.y*this._rotationSpeed;this._camera.rotation.x=rr.clamp(this._startCameraRotation.x+-n,this._rotationRange.min,this._rotationRange.max),this._computeAzimuthalAngle(),this.dispatchEvent(mL)}_computeAzimuthalAngle(){this._camera.updateMatrixWorld(),fL.set(0,0,1),this._camera.localToWorld(fL),fL.sub(this._camera.position),gL.setFromVector3(fL),this._azimuthalAngle=gL.theta}_onTranslateStart(t){this._startCameraPosition.copy(this._camera.position);if(!this._getTouch(t,this._translateDomElement))return;this._translateDomElementRect=this._translateDomElement.getBoundingClientRect();const e=this._translateDomElementRect.left+.5*this._translateDomElementRect.width,n=this._translateDomElementRect.top+.5*this._translateDomElementRect.height;this._translationStartPosition.set(e,n)}_onTranslateMove(t){const e=this._getTouch(t,this._translateDomElement);e&&(this._translationMovePosition.set(e.clientX,e.clientY),this._translationDelta.copy(this._translationMovePosition).sub(this._translationStartPosition),this.translationData.direction.x=this._translationDelta.x/this._translateDomElementRect.width*.5,this.translationData.direction.z=-this._translationDelta.y/this._translateDomElementRect.height*.5,this._updatePlayerTranslate(),this.dispatchEvent(mL))}_onTranslateEnd(){this.translationData.direction.x=0,this.translationData.direction.z=0,this._updatePlayerTranslate()}_updatePlayerTranslate(){if(!this.player)return;const t=this.translationData.direction;this.player.setForward(!1),this.player.setBackward(!1),this.player.setLeft(!1),this.player.setRight(!1);const e=Math.abs(t.x),n=Math.abs(t.z),i=n-e;function s(e){t.z>0&&e.setForward(!0),t.z<0&&e.setBackward(!0)}function r(e){t.x>0&&e.setRight(!0),t.x<0&&e.setLeft(!0)}i>0?(s(this.player),i<.5*n&&r(this.player)):(r(this.player),i<.5*e&&s(this.player))}update(t){this.player&&(this.player.setAzimuthalAngle(this._azimuthalAngle),this.player.update(t))}_getTouch(t,e){for(let n=0;n{AL.PARAM_CALLBACK_updatePlayerParams(t)}}}const TL=new class extends la{constructor(){super(...arguments),this.main=aa.FOLDER(),this.colliderObject=aa.NODE_PATH(\\\\\\\"\\\\\\\",{nodeSelection:{context:ts.SOP}}),this.capsuleRadius=aa.FLOAT(.5,{range:[0,1],rangeLocked:[!0,!1],...wL()}),this.capsuleHeight=aa.FLOAT(1,{range:[0,2],rangeLocked:[!0,!1],...wL()}),this.physics=aa.FOLDER(),this.physicsSteps=aa.INTEGER(5,{range:[1,10],rangeLocked:[!0,!1],...wL()}),this.gravity=aa.VECTOR3([0,-30,0],{...wL()}),this.translateSpeed=aa.FLOAT(1),this.rotateSpeed=aa.FLOAT(_L.rotateSpeed),this.updateCollider=aa.BUTTON(null,{callback:t=>{AL.PARAM_CALLBACK_updateCollider(t)}}),this.init=aa.FOLDER(),this.startPosition=aa.VECTOR3([0,2,0],{...wL()}),this.startRotation=aa.VECTOR3([0,0,0],{...wL()}),this.reset=aa.BUTTON(null,{callback:t=>{AL.PARAM_CALLBACK_resetPlayer(t)}}),this.minPolarAngle=aa.FLOAT(\\\\\\\"-$PI*0.5\\\\\\\",{range:[-Math.PI,Math.PI],rangeLocked:[!0,!0]}),this.maxPolarAngle=aa.FLOAT(\\\\\\\"$PI*0.5\\\\\\\",{range:[-Math.PI,Math.PI],rangeLocked:[!0,!0]})}};class AL extends qv{constructor(){super(...arguments),this.paramsConfig=TL,this._controls_by_element_id=new Map}static type(){return rs.MOBILE_JOYSTICK}endEventName(){return\\\\\\\"end\\\\\\\"}initializeNode(){this.io.outputs.setNamedOutputConnectionPoints([new Zo(yL,Jo.BASE),new Zo(xL,Jo.BASE),new Zo(bL,Jo.BASE)])}async createControlsInstance(t,e){await this._initPlayer(t);const n=new vL(t,e,this._player);return this._controls_by_element_id.set(e.id,n),this._bind_listeners_to_controls_instance(n),n}async _initPlayer(t){this._player=this._player||await this._createPlayer(t),this._player&&(this._updatePlayerParams(),this._player.reset())}async _updatePlayerParams(){this._player&&(this._player.startPosition.copy(this.pv.startPosition),this._player.physicsSteps=this.pv.physicsSteps,this._player.jumpAllowed=!1,this._player.runAllowed=!1,this._player.gravity.copy(this.pv.gravity),this._player.speed=this.pv.translateSpeed,this._player.setCapsule({radius:this.pv.capsuleRadius,height:this.pv.capsuleHeight}))}async _createPlayer(t){const e=t,n=await this._getCollider();if(!n)return void this.states.error.set(\\\\\\\"invalid collider\\\\\\\");return new Uy({object:e,collider:n})}_resetPlayer(){var t;null===(t=this._player)||void 0===t||t.reset()}async _getCollider(){const t=this.pv.colliderObject.nodeWithContext(ts.SOP);if(!t)return void this.states.error.set(\\\\\\\"collider node not found\\\\\\\");const e=(await t.compute()).coreContent();if(!e)return void this.states.error.set(\\\\\\\"invalid collider node\\\\\\\");return e.objects()[0]}async _updateCollider(){var t;const e=await this._getCollider();e?null===(t=this._player)||void 0===t||t.setCollider(e):this.states.error.set(\\\\\\\"invalid collider\\\\\\\")}_bind_listeners_to_controls_instance(t){t.addEventListener(yL,(()=>{this.dispatchEventToOutput(yL,{})})),t.addEventListener(xL,(()=>{this.dispatchEventToOutput(xL,{})})),t.addEventListener(bL,(()=>{this.dispatchEventToOutput(bL,{})}))}updateRequired(){return!0}setupControls(t){t.setRotationSpeed(this.pv.rotateSpeed),t.setRotationRange({min:this.pv.minPolarAngle,max:this.pv.maxPolarAngle})}disposeControlsForHtmlElementId(t){this._controls_by_element_id.get(t)&&this._controls_by_element_id.delete(t)}static PARAM_CALLBACK_updateCollider(t){t._updateCollider()}static PARAM_CALLBACK_updatePlayerParams(t){t._updatePlayerParams()}static PARAM_CALLBACK_resetPlayer(t){t._resetPlayer()}}var ML;!function(t){t.ALL_TOGETHER=\\\\\\\"all together\\\\\\\",t.BATCH=\\\\\\\"batch\\\\\\\"}(ML||(ML={}));const EL=[ML.ALL_TOGETHER,ML.BATCH];const SL=new class extends la{constructor(){super(...arguments),this.mask=aa.STRING(\\\\\\\"/geo*\\\\\\\",{callback:t=>{CL.PARAM_CALLBACK_update_resolved_nodes(t)}}),this.force=aa.BOOLEAN(0),this.cookMode=aa.INTEGER(EL.indexOf(ML.ALL_TOGETHER),{menu:{entries:EL.map(((t,e)=>({name:t,value:e})))}}),this.batchSize=aa.INTEGER(1,{visibleIf:{cookMode:EL.indexOf(ML.BATCH)},separatorAfter:!0}),this.updateResolve=aa.BUTTON(null,{callback:(t,e)=>{CL.PARAM_CALLBACK_update_resolve(t)}}),this.printResolve=aa.BUTTON(null,{callback:(t,e)=>{CL.PARAM_CALLBACK_print_resolve(t)}})}};class CL extends ka{constructor(){super(...arguments),this.paramsConfig=SL,this._resolved_nodes=[],this._dispatched_first_node_cooked=!1,this._dispatched_all_nodes_cooked=!1,this._cook_state_by_node_id=new Map}static type(){return\\\\\\\"nodeCook\\\\\\\"}initializeNode(){this.io.inputs.setNamedInputConnectionPoints([new Zo(CL.INPUT_TRIGGER,Jo.BASE,this.process_event_trigger.bind(this))]),this.io.outputs.setNamedOutputConnectionPoints([new Zo(CL.OUTPUT_FIRST_NODE,Jo.BASE),new Zo(CL.OUTPUT_EACH_NODE,Jo.BASE),new Zo(CL.OUTPUT_ALL_NODES,Jo.BASE)])}trigger(){this.process_event_trigger({})}cook(){this._update_resolved_nodes(),this.cookController.endCook()}dispose(){super.dispose(),this._reset()}process_event_trigger(t){this._cook_nodes_with_mode()}_cook_nodes_with_mode(){this._update_resolved_nodes();const t=EL[this.pv.cookMode];switch(t){case ML.ALL_TOGETHER:return this._cook_nodes_all_together();case ML.BATCH:return this._cook_nodes_batch()}ls.unreachable(t)}_cook_nodes_all_together(){this._cook_nodes(this._resolved_nodes)}async _cook_nodes_batch(){const t=this.pv.batchSize,e=Math.ceil(this._resolved_nodes.length/t);for(let n=0;n{this._on_node_cook_complete(t)})),this._cook_state_by_node_id.set(t.graphNodeId(),!1)}_callbackNameForNode(t){return`owner-${this.graphNodeId()}-target-${t.graphNodeId()}`}_reset(){this._dispatched_first_node_cooked=!1,this._cook_state_by_node_id.clear();for(let t of this._resolved_nodes)t.cookController.deregisterOnCookEnd(this._callbackNameForNode(t));this._resolved_nodes=[]}_all_nodes_have_cooked(){for(let t of this._resolved_nodes){if(!this._cook_state_by_node_id.get(t.graphNodeId()))return!1}return!0}_on_node_cook_complete(t){const e={value:{node:t}};this._dispatched_first_node_cooked||(this._dispatched_first_node_cooked=!0,this.dispatchEventToOutput(CL.OUTPUT_FIRST_NODE,e)),this._cook_state_by_node_id.get(t.graphNodeId())||this.dispatchEventToOutput(CL.OUTPUT_EACH_NODE,e),this._cook_state_by_node_id.set(t.graphNodeId(),!0),this._dispatched_all_nodes_cooked||this._all_nodes_have_cooked()&&(this._dispatched_all_nodes_cooked=!0,this.dispatchEventToOutput(CL.OUTPUT_ALL_NODES,{}))}static PARAM_CALLBACK_update_resolve(t){t._update_resolved_nodes()}static PARAM_CALLBACK_print_resolve(t){t.print_resolve()}print_resolve(){console.log(this._resolved_nodes)}}var NL,LL;CL.INPUT_TRIGGER=\\\\\\\"trigger\\\\\\\",CL.OUTPUT_FIRST_NODE=\\\\\\\"first\\\\\\\",CL.OUTPUT_EACH_NODE=\\\\\\\"each\\\\\\\",CL.OUTPUT_ALL_NODES=\\\\\\\"all\\\\\\\",function(t){t.TRIGGER=\\\\\\\"trigger\\\\\\\"}(NL||(NL={})),function(t){t.OUT=\\\\\\\"out\\\\\\\"}(LL||(LL={}));const OL=new class extends la{};class PL extends ka{constructor(){super(...arguments),this.paramsConfig=OL}static type(){return\\\\\\\"null\\\\\\\"}initializeNode(){this.io.inputs.setNamedInputConnectionPoints([new Zo(NL.TRIGGER,Jo.BASE,this.process_event_trigger.bind(this))]),this.io.outputs.setNamedOutputConnectionPoints([new Zo(LL.OUT,Jo.BASE)])}processEvent(t){}process_event_trigger(t){this.dispatchEventToOutput(LL.OUT,t)}}const RL=\\\\\\\"init\\\\\\\",IL=\\\\\\\"dispose\\\\\\\",FL=\\\\\\\"reset\\\\\\\";function DL(){return{callback:t=>{GL.PARAM_CALLBACK_updatePlayerParams(t)}}}const BL=new p.a,zL=new p.a,kL=new ny;const UL=new class extends la{constructor(){super(...arguments),this.main=aa.FOLDER(),this.playerObject=aa.NODE_PATH(\\\\\\\"\\\\\\\",{nodeSelection:{context:ts.OBJ}}),this.colliderObject=aa.NODE_PATH(\\\\\\\"\\\\\\\",{nodeSelection:{context:ts.SOP}}),this.camera=aa.NODE_PATH(\\\\\\\"\\\\\\\",{nodeSelection:{types:[is.PERSPECTIVE,is.ORTHOGRAPHIC],context:ts.OBJ}}),this.initPlayer=aa.BUTTON(null,{callback:t=>{GL.PARAM_CALLBACK_initPlayer(t)}}),this.capsuleRadius=aa.FLOAT(.5,{range:[0,1],rangeLocked:[!0,!1],...DL()}),this.capsuleHeight=aa.FLOAT(1,{range:[0,2],rangeLocked:[!0,!1],...DL()}),this.physics=aa.FOLDER(),this.physicsSteps=aa.INTEGER(5,{range:[1,10],rangeLocked:[!0,!1],...DL()}),this.gravity=aa.VECTOR3([0,-30,0],{...DL()}),this.speed=aa.FLOAT(1,{range:[0,10],rangeLocked:[!0,!1],...DL()}),this.jumpAllowed=aa.BOOLEAN(!0,{...DL()}),this.jumpStrength=aa.FLOAT(10,{range:[0,100],rangeLocked:[!0,!1],...DL()}),this.runAllowed=aa.BOOLEAN(!0,{...DL()}),this.runSpeedMult=aa.FLOAT(2,{range:[0,10],rangeLocked:[!0,!1],...DL()}),this.updateCollider=aa.BUTTON(null,{callback:t=>{GL.PARAM_CALLBACK_updateCollider(t)}}),this.mesh=aa.FOLDER(),this.useMesh=aa.BOOLEAN(!0,{callback:t=>{GL.PARAM_CALLBACK_updatePlayerMesh(t)}}),this.material=aa.NODE_PATH(\\\\\\\"\\\\\\\",{nodeSelection:{context:ts.MAT},callback:t=>{GL.PARAM_CALLBACK_updatePlayerMaterial(t)}}),this.init=aa.FOLDER(),this.startPosition=aa.VECTOR3([0,5,0],{...DL()}),this.reset=aa.BUTTON(null,{callback:t=>{GL.PARAM_CALLBACK_resetPlayer(t)}})}};class GL extends ka{constructor(){super(...arguments),this.paramsConfig=UL}static type(){return rs.PLAYER}initializeNode(){this.io.inputs.setNamedInputConnectionPoints([new Zo(RL,Jo.BASE,this._initPlayer.bind(this)),new Zo(IL,Jo.BASE,this._disposePlayer.bind(this)),new Zo(FL,Jo.BASE,this._resetPlayer.bind(this))]),this.io.outputs.setNamedOutputConnectionPoints([new Zo(RL,Jo.BASE),new Zo(IL,Jo.BASE),new Zo(FL,Jo.BASE)])}async _initPlayer(){if(this._player=this._player||await this._createPlayer(),!this._player)return void this.states.error.set(\\\\\\\"could not create player\\\\\\\");this._updatePlayerMesh(),this._updatePlayerMaterial(),this._updatePlayerParams(),this._corePlayerKeyEvents=new Vy(this._player),this._corePlayerKeyEvents.addEvents(),this._player.reset();const t=this._player;this.scene().registerOnBeforeTick(this._callbackName(),(e=>{t.setAzimuthalAngle(this._getAzimuthalAngle()),t.update(e)})),this.dispatchEventToOutput(RL,{})}_callbackName(){return`event/PlayerControls-${this.graphNodeId()}`}_disposePlayer(){var t;this._player&&(null===(t=this._corePlayerKeyEvents)||void 0===t||t.removeEvents(),this.scene().unRegisterOnBeforeTick(this._callbackName())),this.dispatchEventToOutput(IL,{})}_resetPlayer(){this._player&&this._player.reset(),this.dispatchEventToOutput(FL,{})}async _updatePlayerParams(){this._player&&(this._player.startPosition.copy(this.pv.startPosition),this._player.physicsSteps=this.pv.physicsSteps,this._player.jumpAllowed=this.pv.jumpAllowed,this._player.jumpStrength=this.pv.jumpStrength,this._player.runAllowed=this.pv.runAllowed,this._player.runSpeedMult=this.pv.runSpeedMult,this._player.gravity.copy(this.pv.gravity),this._player.speed=this.pv.speed,this._player.setCapsule({radius:this.pv.capsuleRadius,height:this.pv.capsuleHeight}))}_updatePlayerMesh(){this._player&&this._player.setUsePlayerMesh(this.pv.useMesh)}async _updatePlayerMaterial(){if(!this._player)return;const t=this.pv.material.nodeWithContext(ts.MAT);if(!t)return void this.states.error.set(\\\\\\\"material node not found\\\\\\\");const e=(await t.compute()).material();this._player.setMaterial(e)}async _createPlayer(){const t=this.pv.playerObject.nodeWithContext(ts.OBJ);if(!t)return void this.states.error.set(\\\\\\\"player node not found\\\\\\\");const e=this.pv.camera.nodeWithContext(ts.OBJ);if(!e)return void this.states.error.set(\\\\\\\"invalid camera node\\\\\\\");this._cameraObject=e.object;const n=t.object,i=await this._getCollider();if(!i)return void this.states.error.set(\\\\\\\"invalid collider\\\\\\\");return new Uy({object:n,collider:i,meshName:this.path()})}async _getCollider(){const t=this.pv.colliderObject.nodeWithContext(ts.SOP);if(!t)return void this.states.error.set(\\\\\\\"collider node not found\\\\\\\");const e=(await t.compute()).coreContent();if(!e)return void this.states.error.set(\\\\\\\"invalid collider node\\\\\\\");return e.objects()[0]}async _updateCollider(){var t;const e=await this._getCollider();e?null===(t=this._player)||void 0===t||t.setCollider(e):this.states.error.set(\\\\\\\"invalid collider\\\\\\\")}_getAzimuthalAngle(){if(!this._cameraObject||!this._player)return 0;const t=this._cameraObject.position,e=this._player.object.position;return BL.copy(t),zL.copy(e),BL.sub(zL),kL.setFromVector3(BL),kL.theta}static PARAM_CALLBACK_initPlayer(t){t._initPlayer()}static PARAM_CALLBACK_updatePlayerParams(t){t._updatePlayerParams()}static PARAM_CALLBACK_updatePlayerMaterial(t){t._updatePlayerMaterial()}static PARAM_CALLBACK_updatePlayerMesh(t){t._updatePlayerMesh()}static PARAM_CALLBACK_updateCollider(t){t._updateCollider()}static PARAM_CALLBACK_resetPlayer(t){t._resetPlayer()}}var VL,HL=n(39),jL=n(36);class WL{constructor(t,e,n=0,i=1/0){this.ray=new HL.a(t,e),this.near=n,this.far=i,this.camera=null,this.layers=new jL.a,this.params={Mesh:{},Line:{threshold:1},LOD:{},Points:{threshold:1},Sprite:{}}}set(t,e){this.ray.set(t,e)}setFromCamera(t,e){e&&e.isPerspectiveCamera?(this.ray.origin.setFromMatrixPosition(e.matrixWorld),this.ray.direction.set(t.x,t.y,.5).unproject(e).sub(this.ray.origin).normalize(),this.camera=e):e&&e.isOrthographicCamera?(this.ray.origin.set(t.x,t.y,(e.near+e.far)/(e.near-e.far)).unproject(e),this.ray.direction.set(0,0,-1).transformDirection(e.matrixWorld),this.camera=e):console.error(\\\\\\\"THREE.Raycaster: Unsupported camera type: \\\\\\\"+e.type)}intersectObject(t,e=!0,n=[]){return XL(t,this,n,e),n.sort(qL),n}intersectObjects(t,e=!0,n=[]){for(let i=0,s=t.length;i{this._mouse.x=t.offsetX/n.offsetWidth*2-1,this._mouse.y=-t.offsetY/n.offsetHeight*2+1,this._mouse.toArray(this._mouse_array),this._node.p.mouse.set(this._mouse_array)})(this._offset),this._raycaster.setFromCamera(this._mouse,i.object)}processEvent(t){this._prepareRaycaster(t);const e=JL[this._node.pv.intersectWith];switch(e){case $L.GEOMETRY:return this._intersect_with_geometry(t);case $L.PLANE:return this._intersect_with_plane(t)}ls.unreachable(e)}_intersect_with_plane(t){this._plane.normal.copy(this._node.pv.planeDirection),this._plane.constant=this._node.pv.planeOffset,this._raycaster.ray.intersectPlane(this._plane,this._plane_intersect_target),this._set_position_param(this._plane_intersect_target),this._node.trigger_hit(t)}_intersect_with_geometry(t){if(this._resolved_targets||this.update_target(),this._resolved_targets){this._intersections.length=0;const e=this._raycaster.intersectObjects(this._resolved_targets,this._node.pv.traverseChildren,this._intersections)[0];e?(this._set_position_param(e.point),this._node.pv.geoAttribute&&this._resolve_geometry_attribute(e),t.value={intersect:e},this._node.trigger_hit(t)):this._node.trigger_miss(t)}}_resolve_geometry_attribute(t){const e=zs[this._node.pv.geoAttributeType],n=QL.resolve_geometry_attribute(t,this._node.pv.geoAttributeName,e);if(null!=n){switch(e){case Bs.NUMERIC:return void this._node.p.geoAttributeValue1.set(n);case Bs.STRING:return void(m.isString(n)&&this._node.p.geoAttributeValues.set(n))}ls.unreachable(e)}}static resolve_geometry_attribute(t,e,n){switch(Ls(t.object.constructor)){case Cs.MESH:return this.resolve_geometry_attribute_for_mesh(t,e,n);case Cs.POINTS:return this.resolve_geometry_attribute_for_point(t,e,n)}}static resolve_geometry_attribute_for_mesh(t,e,n){const i=t.object.geometry;if(i){const s=i.getAttribute(e);if(s){switch(n){case Bs.NUMERIC:{const e=i.getAttribute(\\\\\\\"position\\\\\\\");return t.face?(this._vA.fromBufferAttribute(e,t.face.a),this._vB.fromBufferAttribute(e,t.face.b),this._vC.fromBufferAttribute(e,t.face.c),this._uvA.fromBufferAttribute(s,t.face.a),this._uvB.fromBufferAttribute(s,t.face.b),this._uvC.fromBufferAttribute(s,t.face.c),t.uv=Ks.a.getUV(t.point,this._vA,this._vB,this._vC,this._uvA,this._uvB,this._uvC,this._hitUV),this._hitUV.x):void 0}case Bs.STRING:{const t=new _r(i).points()[0];return t?t.stringAttribValue(e):void 0}}ls.unreachable(n)}}}static resolve_geometry_attribute_for_point(t,e,n){const i=t.object.geometry;if(i&&null!=t.index){switch(n){case Bs.NUMERIC:{const n=i.getAttribute(e);return n?n.array[t.index]:void 0}case Bs.STRING:{const n=new _r(i).points()[t.index];return n?n.stringAttribValue(e):void 0}}ls.unreachable(n)}}_set_position_param(t){if(t.toArray(this._hit_position_array),this._node.pv.tpositionTarget){if(li.playerMode())this._found_position_target_param=this._found_position_target_param||this._node.pv.positionTarget.paramWithType(Er.VECTOR3);else{const t=this._node.pv.positionTarget;this._found_position_target_param=t.paramWithType(Er.VECTOR3)}this._found_position_target_param&&this._found_position_target_param.set(this._hit_position_array)}else this._node.p.position.set(this._hit_position_array);this.velocity_controller.process(t)}_prepareRaycaster(t){const e=this._raycaster.params.Points;e&&(e.threshold=this._node.pv.pointsThreshold);let n=t.cameraNode;if(this._node.pv.overrideCamera)if(this._node.pv.overrideRay)this._raycaster.ray.origin.copy(this._node.pv.rayOrigin),this._raycaster.ray.direction.copy(this._node.pv.rayDirection);else{const t=this._node.p.camera.found_node_with_context(ts.OBJ);t&&(n=t)}n&&!this._node.pv.overrideRay&&n.prepareRaycaster(this._mouse,this._raycaster)}update_target(){const t=lO[this._node.pv.targetType];switch(t){case aO.NODE:return this._update_target_from_node();case aO.SCENE_GRAPH:return this._update_target_from_scene_graph()}ls.unreachable(t)}_update_target_from_node(){const t=this._node.p.targetNode.value.nodeWithContext(ts.OBJ);if(t){const e=this._node.pv.traverseChildren?t.object:t.childrenDisplayController.sopGroup();this._resolved_targets=e?[e]:void 0}else this._node.states.error.set(\\\\\\\"node is not an object\\\\\\\")}_update_target_from_scene_graph(){const t=this._node.scene().objectsByMask(this._node.pv.objectMask);t.length>0?this._resolved_targets=t:this._resolved_targets=void 0}async update_position_target(){this._node.p.positionTarget.isDirty()&&await this._node.p.positionTarget.compute()}static PARAM_CALLBACK_update_target(t){t.cpuController.update_target()}static PARAM_CALLBACK_print_resolve(t){t.cpuController.print_resolve()}print_resolve(){this.update_target(),console.log(this._resolved_targets)}}QL._vA=new p.a,QL._vB=new p.a,QL._vC=new p.a,QL._uvA=new d.a,QL._uvB=new d.a,QL._uvC=new d.a,QL._hitUV=new d.a;class KL{constructor(t){this._node=t,this._resolved_material=null,this._restore_context={scene:{overrideMaterial:null},renderer:{toneMapping:-1,outputEncoding:-1}},this._mouse=new d.a,this._mouse_array=[0,0],this._read=new Float32Array(4),this._param_read=[0,0,0,0]}updateMouse(t){var e;const n=null===(e=t.viewer)||void 0===e?void 0:e.canvas();n&&t.event&&(t.event instanceof MouseEvent||t.event instanceof DragEvent||t.event instanceof PointerEvent?(this._mouse.x=t.event.offsetX/n.offsetWidth,this._mouse.y=1-t.event.offsetY/n.offsetHeight,this._mouse.toArray(this._mouse_array),this._node.p.mouse.set(this._mouse_array)):console.warn(\\\\\\\"event type not implemented\\\\\\\"))}processEvent(t){var e;const n=null===(e=t.viewer)||void 0===e?void 0:e.canvas();if(!n||!t.cameraNode)return;const i=t.cameraNode,s=i.renderController;if(s){if(this._render_target=this._render_target||new Q(n.offsetWidth,n.offsetHeight,{minFilter:w.V,magFilter:w.ob,format:w.Ib,type:w.G}),!this._resolved_material)return this.update_material(),void console.warn(\\\\\\\"no material found\\\\\\\");const e=i,r=s.resolved_scene||i.scene().threejsScene(),o=s.renderer(n);this._modify_scene_and_renderer(r,o),o.setRenderTarget(this._render_target),o.clear(),o.render(r,e.object),o.setRenderTarget(null),this._restore_scene_and_renderer(r,o),o.readRenderTargetPixels(this._render_target,Math.round(this._mouse.x*n.offsetWidth),Math.round(this._mouse.y*n.offsetHeight),1,1,this._read),this._param_read[0]=this._read[0],this._param_read[1]=this._read[1],this._param_read[2]=this._read[2],this._param_read[3]=this._read[3],this._node.p.pixelValue.set(this._param_read),this._node.pv.pixelValue.x>this._node.pv.hitThreshold?this._node.trigger_hit(t):this._node.trigger_miss(t)}}_modify_scene_and_renderer(t,e){this._restore_context.scene.overrideMaterial=t.overrideMaterial,this._restore_context.renderer.outputEncoding=e.outputEncoding,this._restore_context.renderer.toneMapping=e.toneMapping,t.overrideMaterial=this._resolved_material,e.toneMapping=w.vb,e.outputEncoding=w.U}_restore_scene_and_renderer(t,e){t.overrideMaterial=this._restore_context.scene.overrideMaterial,e.outputEncoding=this._restore_context.renderer.outputEncoding,e.toneMapping=this._restore_context.renderer.toneMapping}update_material(){const t=this._node.p.material.found_node();t?t.context()==ts.MAT?this._resolved_material=t.material:this._node.states.error.set(\\\\\\\"target is not an obj\\\\\\\"):this._node.states.error.set(\\\\\\\"no target found\\\\\\\")}static PARAM_CALLBACK_update_material(t){t.gpuController.update_material()}}const tO=1e3/60;var eO;!function(t){t.CPU=\\\\\\\"cpu\\\\\\\",t.GPU=\\\\\\\"gpu\\\\\\\"}(eO||(eO={}));const nO=[eO.CPU,eO.GPU];function iO(t={}){return t.mode=nO.indexOf(eO.CPU),{visibleIf:t}}function sO(t={}){return t.mode=nO.indexOf(eO.CPU),t.intersectWith=JL.indexOf($L.GEOMETRY),{visibleIf:t}}function rO(t={}){return t.mode=nO.indexOf(eO.CPU),t.intersectWith=JL.indexOf($L.PLANE),{visibleIf:t}}function oO(t={}){return t.mode=nO.indexOf(eO.GPU),{visibleIf:t}}var aO;!function(t){t.SCENE_GRAPH=\\\\\\\"scene graph\\\\\\\",t.NODE=\\\\\\\"node\\\\\\\"}(aO||(aO={}));const lO=[aO.SCENE_GRAPH,aO.NODE];const cO=new class extends la{constructor(){super(...arguments),this.mode=aa.INTEGER(nO.indexOf(eO.CPU),{menu:{entries:nO.map(((t,e)=>({name:t,value:e})))}}),this.mouse=aa.VECTOR2([0,0],{cook:!1}),this.overrideCamera=aa.BOOLEAN(0),this.overrideRay=aa.BOOLEAN(0,{visibleIf:{mode:nO.indexOf(eO.CPU),overrideCamera:1}}),this.camera=aa.OPERATOR_PATH(\\\\\\\"/perspective_camera1\\\\\\\",{nodeSelection:{context:ts.OBJ},dependentOnFoundNode:!1,visibleIf:{overrideCamera:1,overrideRay:0}}),this.rayOrigin=aa.VECTOR3([0,0,0],{visibleIf:{overrideCamera:1,overrideRay:1}}),this.rayDirection=aa.VECTOR3([0,0,1],{visibleIf:{overrideCamera:1,overrideRay:1}}),this.material=aa.OPERATOR_PATH(\\\\\\\"/MAT/mesh_basic_builder1\\\\\\\",{nodeSelection:{context:ts.MAT},dependentOnFoundNode:!1,callback:(t,e)=>{KL.PARAM_CALLBACK_update_material(t)},...oO()}),this.pixelValue=aa.VECTOR4([0,0,0,0],{cook:!1,...oO()}),this.hitThreshold=aa.FLOAT(.5,{cook:!1,...oO()}),this.intersectWith=aa.INTEGER(JL.indexOf($L.GEOMETRY),{menu:{entries:JL.map(((t,e)=>({name:t,value:e})))},...iO()}),this.pointsThreshold=aa.FLOAT(1,{range:[0,100],rangeLocked:[!0,!1],...iO()}),this.planeDirection=aa.VECTOR3([0,1,0],{...rO()}),this.planeOffset=aa.FLOAT(0,{...rO()}),this.targetType=aa.INTEGER(0,{menu:{entries:lO.map(((t,e)=>({name:t,value:e})))},...sO()}),this.targetNode=aa.NODE_PATH(\\\\\\\"\\\\\\\",{nodeSelection:{context:ts.OBJ},dependentOnFoundNode:!1,callback:(t,e)=>{QL.PARAM_CALLBACK_update_target(t)},...sO({targetType:lO.indexOf(aO.NODE)})}),this.objectMask=aa.STRING(\\\\\\\"*geo1*\\\\\\\",{callback:(t,e)=>{QL.PARAM_CALLBACK_update_target(t)},...sO({targetType:lO.indexOf(aO.SCENE_GRAPH)})}),this.printFoundObjectsFromMask=aa.BUTTON(null,{callback:(t,e)=>{QL.PARAM_CALLBACK_print_resolve(t)},...sO({targetType:lO.indexOf(aO.SCENE_GRAPH)})}),this.traverseChildren=aa.BOOLEAN(!0,{callback:(t,e)=>{QL.PARAM_CALLBACK_update_target(t)},...sO(),separatorAfter:!0}),this.tpositionTarget=aa.BOOLEAN(0,{cook:!1,...iO()}),this.position=aa.VECTOR3([0,0,0],{cook:!1,...iO({tpositionTarget:0})}),this.positionTarget=aa.PARAM_PATH(\\\\\\\"\\\\\\\",{cook:!1,...iO({tpositionTarget:1}),paramSelection:Er.VECTOR3,computeOnDirty:!0}),this.tvelocity=aa.BOOLEAN(0,{cook:!1}),this.tvelocityTarget=aa.BOOLEAN(0,{cook:!1,...iO({tvelocity:1})}),this.velocity=aa.VECTOR3([0,0,0],{cook:!1,...iO({tvelocity:1,tvelocityTarget:0})}),this.velocityTarget=aa.PARAM_PATH(\\\\\\\"\\\\\\\",{cook:!1,...iO({tvelocity:1,tvelocityTarget:1}),paramSelection:Er.VECTOR3,computeOnDirty:!0}),this.geoAttribute=aa.BOOLEAN(0,sO()),this.geoAttributeName=aa.STRING(\\\\\\\"id\\\\\\\",{cook:!1,...sO({geoAttribute:1})}),this.geoAttributeType=aa.INTEGER(zs.indexOf(Bs.NUMERIC),{menu:{entries:ks},...sO({geoAttribute:1})}),this.geoAttributeValue1=aa.FLOAT(0,{cook:!1,...sO({geoAttribute:1,geoAttributeType:zs.indexOf(Bs.NUMERIC)})}),this.geoAttributeValues=aa.STRING(\\\\\\\"\\\\\\\",{...sO({geoAttribute:1,geoAttributeType:zs.indexOf(Bs.STRING)})})}};class hO extends ka{constructor(){super(...arguments),this.paramsConfig=cO,this.cpuController=new QL(this),this.gpuController=new KL(this),this._last_event_processed_at=-1}static type(){return\\\\\\\"raycast\\\\\\\"}initializeNode(){this.io.inputs.setNamedInputConnectionPoints([new Zo(hO.INPUT_TRIGGER,Jo.BASE,this._process_trigger_event_throttled.bind(this)),new Zo(hO.INPUT_MOUSE,Jo.MOUSE,this._process_mouse_event.bind(this)),new Zo(hO.INPUT_UPDATE_OBJECTS,Jo.BASE,this._process_trigger_update_objects.bind(this)),new Zo(hO.INPUT_TRIGGER_VEL_RESET,Jo.BASE,this._process_trigger_vel_reset.bind(this))]),this.io.outputs.setNamedOutputConnectionPoints([new Zo(hO.OUTPUT_HIT,Jo.BASE),new Zo(hO.OUTPUT_MISS,Jo.BASE)])}trigger_hit(t){this.dispatchEventToOutput(hO.OUTPUT_HIT,t)}trigger_miss(t){this.dispatchEventToOutput(hO.OUTPUT_MISS,t)}_process_mouse_event(t){this.pv.mode==nO.indexOf(eO.CPU)?this.cpuController.updateMouse(t):this.gpuController.updateMouse(t)}_process_trigger_event_throttled(t){const e=this._last_event_processed_at,n=li.performance.performanceManager().now();this._last_event_processed_at=n;const i=n-e;i{this._process_trigger_event(t)}),tO-i):this._process_trigger_event(t)}_process_trigger_event(t){this.pv.mode==nO.indexOf(eO.CPU)?this.cpuController.processEvent(t):this.gpuController.processEvent(t)}_process_trigger_update_objects(t){this.pv.mode==nO.indexOf(eO.CPU)&&this.cpuController.update_target()}_process_trigger_vel_reset(t){this.pv.mode==nO.indexOf(eO.CPU)&&this.cpuController.velocity_controller.reset()}}var uO;hO.INPUT_TRIGGER=\\\\\\\"trigger\\\\\\\",hO.INPUT_MOUSE=\\\\\\\"mouse\\\\\\\",hO.INPUT_UPDATE_OBJECTS=\\\\\\\"updateObjects\\\\\\\",hO.INPUT_TRIGGER_VEL_RESET=\\\\\\\"triggerVelReset\\\\\\\",hO.OUTPUT_HIT=\\\\\\\"hit\\\\\\\",hO.OUTPUT_MISS=\\\\\\\"miss\\\\\\\",function(t){t.SET=\\\\\\\"set\\\\\\\",t.TOGGLE=\\\\\\\"toggle\\\\\\\"}(uO||(uO={}));const dO=[uO.SET,uO.TOGGLE];const pO=new class extends la{constructor(){super(...arguments),this.mask=aa.STRING(\\\\\\\"/geo*\\\\\\\",{separatorAfter:!0}),this.tdisplay=aa.BOOLEAN(0),this.displayMode=aa.INTEGER(dO.indexOf(uO.SET),{visibleIf:{tdisplay:1},menu:{entries:dO.map(((t,e)=>({name:t,value:e})))}}),this.display=aa.BOOLEAN(0,{visibleIf:{tdisplay:1,displayMode:dO.indexOf(uO.SET)},separatorAfter:!0}),this.tbypass=aa.BOOLEAN(0),this.bypassMode=aa.INTEGER(dO.indexOf(uO.SET),{visibleIf:{tbypass:1},menu:{entries:dO.map(((t,e)=>({name:t,value:e})))}}),this.bypass=aa.BOOLEAN(0,{visibleIf:{tbypass:1,displayMode:dO.indexOf(uO.SET)}}),this.execute=aa.BUTTON(null,{callback:t=>{_O.PARAM_CALLBACK_execute(t)}})}};class _O extends ka{constructor(){super(...arguments),this.paramsConfig=pO}static type(){return\\\\\\\"setFlag\\\\\\\"}initializeNode(){this.io.inputs.setNamedInputConnectionPoints([new Zo(\\\\\\\"trigger\\\\\\\",Jo.BASE)])}async processEvent(t){let e=this.pv.mask;if(t.value){const n=t.value.node;if(n){const t=n.parent();t&&(e=`${t.path()}/${e}`)}}const n=this.scene().nodesController.nodesFromMask(e);for(let t of n)this._update_node_flags(t)}_update_node_flags(t){this._update_node_display_flag(t),this._update_node_bypass_flag(t)}_update_node_display_flag(t){var e;if(!this.pv.tdisplay)return;if(!(null===(e=t.flags)||void 0===e?void 0:e.hasDisplay()))return;const n=t.flags.display;if(!n)return;const i=dO[this.pv.displayMode];switch(i){case uO.SET:return void n.set(this.pv.display);case uO.TOGGLE:return void n.set(!n.active())}ls.unreachable(i)}_update_node_bypass_flag(t){var e;if(!this.pv.tbypass)return;if(!(null===(e=t.flags)||void 0===e?void 0:e.hasBypass()))return;const n=t.flags.bypass;if(!n)return;const i=dO[this.pv.bypassMode];switch(i){case uO.SET:return void n.set(this.pv.bypass);case uO.TOGGLE:return void n.set(!n.active())}ls.unreachable(i)}static PARAM_CALLBACK_execute(t){t.processEvent({})}}var mO;!function(t){t.BOOLEAN=\\\\\\\"boolean\\\\\\\",t.BUTTON=\\\\\\\"button\\\\\\\",t.NUMBER=\\\\\\\"number\\\\\\\",t.VECTOR2=\\\\\\\"vector2\\\\\\\",t.VECTOR3=\\\\\\\"vector3\\\\\\\",t.VECTOR4=\\\\\\\"vector4\\\\\\\",t.STRING=\\\\\\\"string\\\\\\\"}(mO||(mO={}));const fO=[mO.BOOLEAN,mO.BUTTON,mO.NUMBER,mO.VECTOR2,mO.VECTOR3,mO.VECTOR4,mO.STRING],gO=fO.indexOf(mO.BOOLEAN),vO=fO.indexOf(mO.NUMBER),yO=fO.indexOf(mO.VECTOR2),xO=fO.indexOf(mO.VECTOR3),bO=fO.indexOf(mO.VECTOR4),wO=fO.indexOf(mO.STRING),TO=\\\\\\\"output\\\\\\\";const AO=new class extends la{constructor(){super(...arguments),this.param=aa.PARAM_PATH(\\\\\\\"\\\\\\\",{paramSelection:!0,computeOnDirty:!0}),this.type=aa.INTEGER(vO,{menu:{entries:fO.map(((t,e)=>({name:t,value:e})))}}),this.toggle=aa.BOOLEAN(0,{visibleIf:{type:gO}}),this.boolean=aa.BOOLEAN(0,{visibleIf:{type:gO,toggle:0}}),this.number=aa.FLOAT(0,{visibleIf:{type:vO}}),this.vector2=aa.VECTOR2([0,0],{visibleIf:{type:yO}}),this.vector3=aa.VECTOR3([0,0,0],{visibleIf:{type:xO}}),this.vector4=aa.VECTOR4([0,0,0,0],{visibleIf:{type:bO}}),this.increment=aa.BOOLEAN(0,{visibleIf:[{type:vO},{type:yO},{type:xO},{type:bO}]}),this.string=aa.STRING(\\\\\\\"\\\\\\\",{visibleIf:{type:wO}}),this.execute=aa.BUTTON(null,{callback:t=>{MO.PARAM_CALLBACK_execute(t)}})}};class MO extends ka{constructor(){super(...arguments),this.paramsConfig=AO,this._tmp_vector2=new d.a,this._tmp_vector3=new p.a,this._tmp_vector4=new _.a,this._tmp_array2=[0,0],this._tmp_array3=[0,0,0],this._tmp_array4=[0,0,0,0]}static type(){return\\\\\\\"setParam\\\\\\\"}initializeNode(){this.io.inputs.setNamedInputConnectionPoints([new Zo(\\\\\\\"trigger\\\\\\\",Jo.BASE)]),this.io.outputs.setNamedOutputConnectionPoints([new Zo(TO,Jo.BASE)]),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.param])}))}))}async processEvent(t){this.p.param.isDirty()&&await this.p.param.compute();const e=this.p.param.value.param();if(e){const t=await this._new_param_value(e);null!=t&&e.set(t)}else this.states.error.set(\\\\\\\"target param not found\\\\\\\");this.dispatchEventToOutput(TO,t)}async _new_param_value(t){const e=fO[this.pv.type];switch(e){case mO.BOOLEAN:return await this._compute_params_if_dirty([this.p.toggle]),this.pv.toggle?t.value?0:1:this.pv.boolean?1:0;case mO.BUTTON:return t.options.executeCallback();case mO.NUMBER:return await this._compute_params_if_dirty([this.p.increment,this.p.number]),this.pv.increment?t.type()==Er.FLOAT?t.value+this.pv.number:t.value:this.pv.number;case mO.VECTOR2:return await this._compute_params_if_dirty([this.p.increment,this.p.vector2]),this.pv.increment?t.type()==Er.VECTOR2?(this._tmp_vector2.copy(t.value),this._tmp_vector2.add(this.pv.vector2),this._tmp_vector2.toArray(this._tmp_array2)):t.value.toArray(this._tmp_array2):this.pv.vector2.toArray(this._tmp_array2),this._tmp_array2;case mO.VECTOR3:return await this._compute_params_if_dirty([this.p.increment,this.p.vector3]),this.pv.increment?t.type()==Er.VECTOR3?(this._tmp_vector3.copy(t.value),this._tmp_vector3.add(this.pv.vector3),this._tmp_vector3.toArray(this._tmp_array3)):t.value.toArray(this._tmp_array3):this.pv.vector3.toArray(this._tmp_array3),this._tmp_array3;case mO.VECTOR4:return await this._compute_params_if_dirty([this.p.increment,this.p.vector4]),this.pv.increment?t.type()==Er.VECTOR4?(this._tmp_vector4.copy(t.value),this._tmp_vector4.add(this.pv.vector4),this._tmp_vector4.toArray(this._tmp_array4)):t.value.toArray(this._tmp_array4):this.pv.vector4.toArray(this._tmp_array4),this._tmp_array4;case mO.STRING:return await this._compute_params_if_dirty([this.p.string]),this.pv.string}ls.unreachable(e)}static PARAM_CALLBACK_execute(t){t.processEvent({})}async _compute_params_if_dirty(t){const e=[];for(let n of t)n.isDirty()&&e.push(n);const n=[];for(let t of e)n.push(t.compute());return await Promise.all(n)}}const EO=new class extends la{constructor(){super(...arguments),this.outputsCount=aa.INTEGER(5,{range:[1,10],rangeLocked:[!0,!1]})}};class SO extends ka{constructor(){super(...arguments),this.paramsConfig=EO}static type(){return\\\\\\\"sequence\\\\\\\"}initializeNode(){this.io.connection_points.set_input_name_function((()=>\\\\\\\"trigger\\\\\\\")),this.io.connection_points.set_expected_input_types_function((()=>[Jo.BASE])),this.io.connection_points.set_expected_output_types_function(this._expected_output_types.bind(this)),this.io.connection_points.set_output_name_function(this._output_name.bind(this))}_expected_output_types(){const t=new Array(this.pv.outputsCount);return t.fill(Jo.BASE),t}_output_name(t){return`out${t}`}processEvent(t){const e=this.pv.outputsCount;for(let n=0;n{this._timer_active&&(this.pv.count<=0||this._current_count{var e;null===(e=t.controlsController)||void 0===e||e.create_controls()}))}_process_trigger_disposeControls(t){this.scene().viewersRegister.traverseViewers((t=>{var e;null===(e=t.controlsController)||void 0===e||e.dispose_controls()}))}}class RO extends sa{static context(){return ts.EVENT}cook(){this.cookController.endCook()}}class IO extends RO{}class FO extends IO{constructor(){super(...arguments),this._children_controller_context=ts.ANIM}static type(){return es.ANIM}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class DO extends IO{constructor(){super(...arguments),this._children_controller_context=ts.COP}static type(){return es.COP}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class BO extends IO{constructor(){super(...arguments),this._children_controller_context=ts.EVENT}static type(){return es.EVENT}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class zO extends IO{constructor(){super(...arguments),this._children_controller_context=ts.MAT}static type(){return es.MAT}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class kO extends RO{constructor(){super(...arguments),this.paramsConfig=new Jm,this.effectsComposerController=new Zm(this),this.displayNodeController=new Lm(this,this.effectsComposerController.displayNodeControllerCallbacks()),this._children_controller_context=ts.POST}static type(){return es.POST}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class UO extends IO{constructor(){super(...arguments),this._children_controller_context=ts.ROP}static type(){return es.ROP}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}const GO=\\\\\\\"int\\\\\\\";const VO=new class extends la{constructor(){super(...arguments),this.float=aa.FLOAT(0)}};class HO extends df{constructor(){super(...arguments),this.paramsConfig=VO}static type(){return\\\\\\\"floatToInt\\\\\\\"}initializeNode(){this.io.outputs.setNamedOutputConnectionPoints([new Ho(GO,Bo.INT)])}setLines(t){const e=this.variableForInputParam(this.p.float),n=`int ${this.glVarName(GO)} = int(${hf.float(e)})`;t.addBodyLines(this,[n])}}const jO=\\\\\\\"float\\\\\\\";const WO=new class extends la{constructor(){super(...arguments),this.int=aa.INTEGER(0)}};class qO extends df{constructor(){super(...arguments),this.paramsConfig=WO}static type(){return\\\\\\\"intToFloat\\\\\\\"}initializeNode(){this.io.outputs.setNamedOutputConnectionPoints([new Ho(jO,Bo.FLOAT)])}setLines(t){const e=this.variableForInputParam(this.p.int),n=`float ${this.glVarName(jO)} = float(${hf.integer(e)})`;t.addBodyLines(this,[n])}}const XO=\\\\\\\"bool\\\\\\\";const YO=new class extends la{constructor(){super(...arguments),this.int=aa.INTEGER(0)}};class $O extends df{constructor(){super(...arguments),this.paramsConfig=YO}static type(){return\\\\\\\"intToBool\\\\\\\"}initializeNode(){this.io.outputs.setNamedOutputConnectionPoints([new Ho(XO,Bo.BOOL)])}setLines(t){const e=this.variableForInputParam(this.p.int),n=`bool ${this.glVarName(XO)} = bool(${hf.integer(e)})`;t.addBodyLines(this,[n])}}const JO=new class extends la{constructor(){super(...arguments),this.bool=aa.BOOLEAN(0)}};class ZO extends df{constructor(){super(...arguments),this.paramsConfig=JO}static type(){return\\\\\\\"boolToInt\\\\\\\"}initializeNode(){this.io.outputs.setNamedOutputConnectionPoints([new Ho(GO,Bo.INT)])}setLines(t){const e=this.variableForInputParam(this.p.bool),n=`int ${this.glVarName(GO)} = int(${hf.bool(e)})`;t.addBodyLines(this,[n])}}const QO=new class extends la{constructor(){super(...arguments),this.x=aa.FLOAT(0),this.y=aa.FLOAT(0)}};class KO extends df{constructor(){super(...arguments),this.paramsConfig=QO}static type(){return\\\\\\\"floatToVec2\\\\\\\"}initializeNode(){this.io.outputs.setNamedOutputConnectionPoints([new Ho(KO.OUTPUT_NAME,Bo.VEC2)])}setLines(t){const e=this.variableForInputParam(this.p.x),n=this.variableForInputParam(this.p.y),i=`vec2 ${this.glVarName(KO.OUTPUT_NAME)} = ${hf.float2(e,n)}`;t.addBodyLines(this,[i])}}KO.OUTPUT_NAME=\\\\\\\"vec2\\\\\\\";const tP=new class extends la{constructor(){super(...arguments),this.x=aa.FLOAT(0),this.y=aa.FLOAT(0),this.z=aa.FLOAT(0)}};class eP extends df{constructor(){super(...arguments),this.paramsConfig=tP}static type(){return\\\\\\\"floatToVec3\\\\\\\"}initializeNode(){this.io.outputs.setNamedOutputConnectionPoints([new Ho(eP.OUTPUT_NAME,Bo.VEC3)])}setLines(t){const e=this.variableForInputParam(this.p.x),n=this.variableForInputParam(this.p.y),i=this.variableForInputParam(this.p.z),s=`vec3 ${this.glVarName(eP.OUTPUT_NAME)} = ${hf.float3(e,n,i)}`;t.addBodyLines(this,[s])}}eP.OUTPUT_NAME=\\\\\\\"vec3\\\\\\\";const nP=new class extends la{constructor(){super(...arguments),this.x=aa.FLOAT(0),this.y=aa.FLOAT(0),this.z=aa.FLOAT(0),this.w=aa.FLOAT(0)}};class iP extends df{constructor(){super(...arguments),this.paramsConfig=nP}static type(){return\\\\\\\"floatToVec4\\\\\\\"}initializeNode(){this.io.outputs.setNamedOutputConnectionPoints([new Ho(iP.OUTPUT_NAME,Bo.VEC4)])}setLines(t){const e=this.variableForInputParam(this.p.x),n=this.variableForInputParam(this.p.y),i=this.variableForInputParam(this.p.z),s=this.variableForInputParam(this.p.w),r=`vec4 ${this.glVarName(iP.OUTPUT_NAME)} = ${hf.float4(e,n,i,s)}`;t.addBodyLines(this,[r])}}iP.OUTPUT_NAME=\\\\\\\"vec4\\\\\\\";const sP=new class extends la{};class rP extends df{constructor(){super(...arguments),this.paramsConfig=sP}}function oP(t,e){const n=e.components,i=e.param_type;return class extends rP{static type(){return t}initializeNode(){this.io.outputs.setNamedOutputConnectionPoints(n.map((t=>new Ho(t,Bo.FLOAT))))}createParams(){this.addParam(i,\\\\\\\"vec\\\\\\\",n.map((t=>0)))}setLines(t){const e=[],n=this.variableForInput(\\\\\\\"vec\\\\\\\");this.io.outputs.used_output_names().forEach((t=>{const i=this.glVarName(t);e.push(`float ${i} = ${n}.${t}`)})),t.addBodyLines(this,e)}}}const aP=[\\\\\\\"x\\\\\\\",\\\\\\\"y\\\\\\\"],lP=[\\\\\\\"x\\\\\\\",\\\\\\\"y\\\\\\\",\\\\\\\"z\\\\\\\"],cP=[\\\\\\\"x\\\\\\\",\\\\\\\"y\\\\\\\",\\\\\\\"z\\\\\\\",\\\\\\\"w\\\\\\\"];class hP extends(oP(\\\\\\\"vec2ToFloat\\\\\\\",{components:[\\\\\\\"x\\\\\\\",\\\\\\\"y\\\\\\\"],param_type:Er.VECTOR2})){}class uP extends(oP(\\\\\\\"vec3ToFloat\\\\\\\",{components:[\\\\\\\"x\\\\\\\",\\\\\\\"y\\\\\\\",\\\\\\\"z\\\\\\\"],param_type:Er.VECTOR3})){}class dP extends(oP(\\\\\\\"vec4ToFloat\\\\\\\",{components:cP,param_type:Er.VECTOR4})){}class pP extends rP{static type(){return\\\\\\\"vec4ToVec3\\\\\\\"}initializeNode(){this.io.outputs.setNamedOutputConnectionPoints([new Ho(pP.OUTPUT_NAME_VEC3,Bo.VEC3),new Ho(pP.OUTPUT_NAME_W,Bo.FLOAT)])}createParams(){this.addParam(Er.VECTOR4,pP.INPUT_NAME_VEC4,cP.map((t=>0)))}setLines(t){const e=[],n=pP.INPUT_NAME_VEC4,i=pP.OUTPUT_NAME_VEC3,s=pP.OUTPUT_NAME_W,r=this.variableForInput(n),o=this.io.outputs.used_output_names();if(o.indexOf(i)>=0){const t=this.glVarName(i);e.push(`vec3 ${t} = ${r}.xyz`)}if(o.indexOf(s)>=0){const t=this.glVarName(s);e.push(`float ${t} = ${r}.w`)}t.addBodyLines(this,e)}}pP.INPUT_NAME_VEC4=\\\\\\\"vec4\\\\\\\",pP.OUTPUT_NAME_VEC3=\\\\\\\"vec3\\\\\\\",pP.OUTPUT_NAME_W=\\\\\\\"w\\\\\\\";class _P extends rP{static type(){return\\\\\\\"vec3ToVec2\\\\\\\"}initializeNode(){this.io.outputs.setNamedOutputConnectionPoints([new Ho(_P.OUTPUT_NAME_VEC2,Bo.VEC2),new Ho(_P.OUTPUT_NAME_Z,Bo.FLOAT)])}createParams(){this.addParam(Er.VECTOR3,_P.INPUT_NAME_VEC3,lP.map((t=>0)))}setLines(t){const e=[],n=_P.INPUT_NAME_VEC3,i=_P.OUTPUT_NAME_VEC2,s=_P.OUTPUT_NAME_Z,r=this.variableForInput(n),o=this.io.outputs.used_output_names();if(o.indexOf(i)>=0){const t=this.glVarName(i);e.push(`vec2 ${t} = ${r}.xy`)}if(o.indexOf(s)>=0){const t=this.glVarName(s);e.push(`float ${t} = ${r}.z`)}t.addBodyLines(this,e)}}_P.INPUT_NAME_VEC3=\\\\\\\"vec3\\\\\\\",_P.OUTPUT_NAME_VEC2=\\\\\\\"vec2\\\\\\\",_P.OUTPUT_NAME_Z=\\\\\\\"z\\\\\\\";class mP extends rP{static type(){return\\\\\\\"vec2ToVec3\\\\\\\"}initializeNode(){this.io.outputs.setNamedOutputConnectionPoints([new Ho(mP.OUTPUT_NAME_VEC3,Bo.VEC3)])}createParams(){this.addParam(Er.VECTOR2,mP.INPUT_NAME_VEC2,aP.map((t=>0))),this.addParam(Er.FLOAT,mP.INPUT_NAME_Z,0)}setLines(t){const e=[],n=mP.INPUT_NAME_VEC2,i=mP.INPUT_NAME_Z,s=mP.OUTPUT_NAME_VEC3,r=this.variableForInput(n),o=this.variableForInput(i),a=this.glVarName(s);e.push(`vec3 ${a} = vec3(${r}.xy, ${o})`),t.addBodyLines(this,e)}}mP.INPUT_NAME_VEC2=\\\\\\\"vec3\\\\\\\",mP.INPUT_NAME_Z=\\\\\\\"z\\\\\\\",mP.OUTPUT_NAME_VEC3=\\\\\\\"vec3\\\\\\\";class fP extends rP{static type(){return\\\\\\\"vec3ToVec4\\\\\\\"}initializeNode(){this.io.outputs.setNamedOutputConnectionPoints([new Ho(fP.OUTPUT_NAME_VEC4,Bo.VEC4)])}createParams(){this.addParam(Er.VECTOR3,fP.INPUT_NAME_VEC3,lP.map((t=>0))),this.addParam(Er.FLOAT,fP.INPUT_NAME_W,0)}setLines(t){const e=[],n=fP.INPUT_NAME_VEC3,i=fP.INPUT_NAME_W,s=fP.OUTPUT_NAME_VEC4,r=this.variableForInput(n),o=this.variableForInput(i),a=this.glVarName(s);e.push(`vec4 ${a} = vec4(${r}.xyz, ${o})`),t.addBodyLines(this,e)}}fP.INPUT_NAME_VEC3=\\\\\\\"vec3\\\\\\\",fP.INPUT_NAME_W=\\\\\\\"w\\\\\\\",fP.OUTPUT_NAME_VEC4=\\\\\\\"vec4\\\\\\\";const gP=new class extends la{};class vP extends df{constructor(){super(...arguments),this.paramsConfig=gP}gl_method_name(){return\\\\\\\"\\\\\\\"}gl_function_definitions(){return[]}initializeNode(){super.initializeNode(),this.io.connection_points.set_expected_input_types_function(this._expected_input_types.bind(this)),this.io.connection_points.set_expected_output_types_function(this._expected_output_types.bind(this)),this.io.connection_points.set_input_name_function(this._gl_input_name.bind(this))}_expected_input_types(){const t=this.io.connection_points.first_input_connection_type()||Bo.FLOAT;if(this.io.connections.firstInputConnection()){const e=this.io.connections.inputConnections();if(e){let n=Math.max(f.compact(e).length+1,2);return f.range(n).map((e=>t))}return[]}return f.range(2).map((e=>t))}_expected_output_types(){return[this._expected_input_types()[0]]}_gl_input_name(t){return\\\\\\\"in\\\\\\\"}setLines(t){const e=this.io.outputs.namedOutputConnectionPoints()[0].type(),n=this.io.inputs.namedInputConnectionPoints().map(((t,e)=>{const n=t.name();return hf.any(this.variableForInput(n))})).join(\\\\\\\", \\\\\\\"),i=`${e} ${this.glVarName(this.io.connection_points.output_name(0))} = ${this.gl_method_name()}(${n})`;t.addBodyLines(this,[i]),t.addDefinitions(this,this.gl_function_definitions())}}class yP extends vP{_gl_input_name(t){return\\\\\\\"in\\\\\\\"}_expected_input_types(){return[this.io.connection_points.first_input_connection_type()||Bo.FLOAT]}}class xP extends vP{_expected_input_types(){const t=this.io.connection_points.first_input_connection_type()||Bo.FLOAT;return[t,t]}}class bP extends vP{_expected_input_types(){const t=this.io.connection_points.first_input_connection_type()||Bo.FLOAT;return[t,t,t]}}class wP extends vP{_expected_input_types(){const t=this.io.connection_points.first_input_connection_type()||Bo.FLOAT;return[t,t,t,t]}}class TP extends vP{_expected_input_types(){const t=this.io.connection_points.first_input_connection_type()||Bo.FLOAT;return[t,t,t,t,t]}}function AP(t,e={}){const n=e.method||t,i=e.out||\\\\\\\"val\\\\\\\",s=e.in||\\\\\\\"in\\\\\\\";return class extends yP{static type(){return t}initializeNode(){super.initializeNode(),this.io.connection_points.set_input_name_function(this._gl_input_name.bind(this)),this.io.connection_points.set_output_name_function(this._gl_output_name.bind(this))}_gl_input_name(t){return s}_gl_output_name(t){return i}gl_method_name(){return n}}}class MP extends(AP(\\\\\\\"abs\\\\\\\")){}class EP extends(AP(\\\\\\\"acos\\\\\\\",{out:\\\\\\\"radians\\\\\\\"})){}class SP extends(AP(\\\\\\\"asin\\\\\\\",{out:\\\\\\\"radians\\\\\\\"})){}class CP extends(AP(\\\\\\\"atan\\\\\\\",{out:\\\\\\\"radians\\\\\\\"})){}class NP extends(AP(\\\\\\\"ceil\\\\\\\")){}class LP extends(AP(\\\\\\\"cos\\\\\\\",{in:\\\\\\\"radians\\\\\\\"})){}class OP extends(AP(\\\\\\\"degrees\\\\\\\",{in:\\\\\\\"radians\\\\\\\",out:\\\\\\\"degrees\\\\\\\"})){}class PP extends(AP(\\\\\\\"exp\\\\\\\")){}class RP extends(AP(\\\\\\\"exp2\\\\\\\")){}class IP extends(AP(\\\\\\\"floor\\\\\\\")){}class FP extends(AP(\\\\\\\"fract\\\\\\\")){}class DP extends(AP(\\\\\\\"inverseSqrt\\\\\\\",{method:\\\\\\\"inversesqrt\\\\\\\"})){}class BP extends(AP(\\\\\\\"log\\\\\\\")){}class zP extends(AP(\\\\\\\"log2\\\\\\\")){}class kP extends(AP(\\\\\\\"normalize\\\\\\\",{out:\\\\\\\"normalized\\\\\\\"})){}class UP extends(AP(\\\\\\\"radians\\\\\\\",{in:\\\\\\\"degrees\\\\\\\",out:\\\\\\\"radians\\\\\\\"})){}class GP extends(AP(\\\\\\\"sign\\\\\\\")){}class VP extends(AP(\\\\\\\"sin\\\\\\\",{in:\\\\\\\"radians\\\\\\\"})){}class HP extends(AP(\\\\\\\"sqrt\\\\\\\")){}class jP extends(AP(\\\\\\\"tan\\\\\\\")){}function WP(t,e={}){const n=e.method||t,i=e.out||\\\\\\\"val\\\\\\\",s=e.in||[\\\\\\\"in0\\\\\\\",\\\\\\\"in1\\\\\\\"],r=e.default_in_type,o=e.allowed_in_types,a=e.out_type,l=e.functions||[];return class extends xP{static type(){return t}initializeNode(){super.initializeNode(),this.io.connection_points.set_input_name_function(this._gl_input_name.bind(this)),this.io.connection_points.set_output_name_function(this._gl_output_name.bind(this)),this.io.connection_points.set_expected_input_types_function(this._expected_input_types.bind(this)),a&&this.io.connection_points.set_expected_output_types_function((()=>[a]))}_gl_input_name(t){return s[t]}_gl_output_name(t){return i}gl_method_name(){return n}gl_function_definitions(){return l?l.map((t=>new Tf(this,t))):[]}_expected_input_types(){let t=this.io.connection_points.first_input_connection_type();if(t&&o&&!o.includes(t)){const e=this.io.inputs.namedInputConnectionPoints()[0];t=e?e.type():r}const e=t||r||Bo.FLOAT;return[e,e]}}}class qP extends(WP(\\\\\\\"distance\\\\\\\",{in:[\\\\\\\"p0\\\\\\\",\\\\\\\"p1\\\\\\\"],default_in_type:Bo.VEC3,allowed_in_types:[Bo.VEC2,Bo.VEC3,Bo.VEC4],out_type:Bo.FLOAT})){}class XP extends(WP(\\\\\\\"dot\\\\\\\",{in:[\\\\\\\"vec0\\\\\\\",\\\\\\\"vec1\\\\\\\"],default_in_type:Bo.VEC3,allowed_in_types:[Bo.VEC2,Bo.VEC3,Bo.VEC4],out_type:Bo.FLOAT})){}class YP extends(WP(\\\\\\\"max\\\\\\\")){}class $P extends(WP(\\\\\\\"min\\\\\\\")){}class JP extends(WP(\\\\\\\"mod\\\\\\\")){paramDefaultValue(t){return{in1:1}[t]}_expected_input_types(){const t=Bo.FLOAT;return[t,t]}}class ZP extends(WP(\\\\\\\"pow\\\\\\\",{in:[\\\\\\\"x\\\\\\\",\\\\\\\"y\\\\\\\"]})){}class QP extends(WP(\\\\\\\"reflect\\\\\\\",{in:[\\\\\\\"I\\\\\\\",\\\\\\\"N\\\\\\\"],default_in_type:Bo.VEC3})){}class KP extends(WP(\\\\\\\"step\\\\\\\",{in:[\\\\\\\"edge\\\\\\\",\\\\\\\"x\\\\\\\"]})){}function tR(t,e={}){const n=e.method||t,i=e.out||\\\\\\\"val\\\\\\\",s=e.in||[\\\\\\\"in0\\\\\\\",\\\\\\\"in1\\\\\\\",\\\\\\\"in2\\\\\\\"],r=e.default||{},o=e.out_type||Bo.FLOAT,a=e.functions||[];return class extends bP{static type(){return t}initializeNode(){super.initializeNode(),this.io.connection_points.set_input_name_function(this._gl_input_name.bind(this)),this.io.connection_points.set_output_name_function(this._gl_output_name.bind(this)),this.io.connection_points.set_expected_output_types_function(this._expected_output_types.bind(this))}_gl_input_name(t){return s[t]}_gl_output_name(t){return i}gl_method_name(){return n}_expected_output_types(){return[o]}paramDefaultValue(t){return r[t]}gl_function_definitions(){return a.map((t=>new Tf(this,t)))}}}class eR extends(tR(\\\\\\\"clamp\\\\\\\",{in:[\\\\\\\"value\\\\\\\",\\\\\\\"min\\\\\\\",\\\\\\\"max\\\\\\\"],default:{max:1}})){_expected_output_types(){return[this._expected_input_types()[0]]}}class nR extends(tR(\\\\\\\"faceForward\\\\\\\",{in:[\\\\\\\"N\\\\\\\",\\\\\\\"I\\\\\\\",\\\\\\\"Nref\\\\\\\"]})){}class iR extends(tR(\\\\\\\"smoothstep\\\\\\\",{in:[\\\\\\\"edge0\\\\\\\",\\\\\\\"edge1\\\\\\\",\\\\\\\"x\\\\\\\"],default:{edge1:1}})){_expected_output_types(){return[this._expected_input_types()[0]]}}function sR(t,e){const n=e.in_prefix||t,i=e.out||\\\\\\\"val\\\\\\\",s=e.operation,r=e.allowed_in_types;return class extends xP{static type(){return t}initializeNode(){super.initializeNode(),this.io.connection_points.set_input_name_function(this._gl_input_name.bind(this)),this.io.connection_points.set_output_name_function(this._gl_output_name.bind(this)),this.io.connection_points.set_expected_input_types_function(this._expected_input_types.bind(this)),this.io.connection_points.set_expected_output_types_function(this._expected_output_types.bind(this))}setLines(t){const e=this.io.outputs.namedOutputConnectionPoints()[0].type(),n=this.io.inputs.namedInputConnectionPoints().map(((t,e)=>{const n=t.name(),i=this.variableForInput(n);if(i)return hf.any(i)})).join(` ${this.gl_operation()} `),i=`${e} ${this.glVarName(this.io.connection_points.output_name(0))} = ${this.gl_method_name()}(${n})`;t.addBodyLines(this,[i])}_gl_input_name(t){return`${n}${t}`}_gl_output_name(t){return i}gl_operation(){return s}_expected_input_types(){let t=this.io.connection_points.first_input_connection_type();if(t&&r&&!r.includes(t)){const e=this.io.inputs.namedInputConnectionPoints()[0];e&&(t=e.type())}const e=t||Bo.FLOAT,n=this.io.connections.existingInputConnections(),i=n?Math.max(n.length+1,2):2,s=[];for(let t=0;t{const n=t.name();return hf.any(this.variableForInput(n))})).join(` ${this.boolean_operation()} `),n=`bool ${this.glVarName(this.io.connection_points.output_name(0))} = ${e}`;t.addBodyLines(this,[n])}}function hR(t,e){return class extends cR{static type(){return t}initializeNode(){super.initializeNode(),this.io.connection_points.set_input_name_function(this._gl_input_name.bind(this)),this.io.connection_points.set_output_name_function(this._gl_output_name.bind(this))}boolean_operation(){return e.op}_gl_output_name(e){return t}_gl_input_name(e=0){return`${t}${e}`}}}class uR extends(hR(\\\\\\\"and\\\\\\\",{op:\\\\\\\"&&\\\\\\\"})){}class dR extends(hR(\\\\\\\"or\\\\\\\",{op:\\\\\\\"||\\\\\\\"})){}var pR;!function(t){t.TIME=\\\\\\\"time\\\\\\\",t.DELTA_TIME=\\\\\\\"delta_time\\\\\\\"}(pR||(pR={}));var _R,mR;!function(t){t.POSITION=\\\\\\\"position\\\\\\\",t.VELOCITY=\\\\\\\"velocity\\\\\\\",t.MASS=\\\\\\\"mass\\\\\\\",t.FORCE=\\\\\\\"force\\\\\\\"}(_R||(_R={})),function(t){t.POSITION=\\\\\\\"position\\\\\\\",t.VELOCITY=\\\\\\\"velocity\\\\\\\"}(mR||(mR={}));const fR=[_R.POSITION,_R.VELOCITY,_R.MASS,_R.FORCE],gR=[mR.POSITION,mR.VELOCITY],vR={[_R.POSITION]:[0,0,0],[_R.VELOCITY]:[0,0,0],[_R.MASS]:1,[_R.FORCE]:[0,-9.8,0]};const yR=new class extends la{};class xR extends df{constructor(){super(...arguments),this.paramsConfig=yR}static type(){return\\\\\\\"acceleration\\\\\\\"}initializeNode(){super.initializeNode(),this.io.outputs.setNamedOutputConnectionPoints([new Ho(mR.POSITION,Bo.VEC3),new Ho(mR.VELOCITY,Bo.VEC3)]),this.io.connection_points.set_expected_input_types_function(this._expected_input_types.bind(this)),this.io.connection_points.set_expected_output_types_function(this._expected_output_types.bind(this)),this.io.connection_points.set_input_name_function(this._gl_input_name.bind(this)),this.io.connection_points.set_output_name_function(this._gl_output_name.bind(this))}_expected_input_types(){const t=this.io.connection_points.first_input_connection_type()||Bo.VEC3;return[t,t,Bo.FLOAT,t]}_expected_output_types(){const t=this._expected_input_types()[0];return[t,t]}_gl_input_name(t){return fR[t]}_gl_output_name(t){return gR[t]}paramDefaultValue(t){return vR[t]}setLines(t){const e=this.io.outputs.namedOutputConnectionPoints()[0].type(),n=new Af(this,Bo.FLOAT,pR.DELTA_TIME),i=new Tf(this,\\\\\\\"float compute_velocity_from_acceleration(float vel, float force, float mass, float time_delta){\\\\n\\\\tfloat impulse = (force * mass) * time_delta;\\\\n\\\\treturn vel + impulse;\\\\n}\\\\nvec2 compute_velocity_from_acceleration(vec2 vel, vec2 force, float mass, float time_delta){\\\\n\\\\tvec2 impulse = (force * mass) * time_delta;\\\\n\\\\treturn vel + impulse;\\\\n}\\\\nvec3 compute_velocity_from_acceleration(vec3 vel, vec3 force, float mass, float time_delta){\\\\n\\\\tvec3 impulse = (force * mass) * time_delta;\\\\n\\\\treturn vel + impulse;\\\\n}\\\\nvec4 compute_velocity_from_acceleration(vec4 vel, vec4 force, float mass, float time_delta){\\\\n\\\\tvec4 impulse = (force * mass) * time_delta;\\\\n\\\\treturn vel + impulse;\\\\n}\\\\nfloat compute_position_from_velocity(float position, float velocity, float time_delta){\\\\n\\\\treturn position + (velocity * time_delta);\\\\n}\\\\nvec2 compute_position_from_velocity(vec2 position, vec2 velocity, float time_delta){\\\\n\\\\treturn position + (velocity * time_delta);\\\\n}\\\\nvec3 compute_position_from_velocity(vec3 position, vec3 velocity, float time_delta){\\\\n\\\\treturn position + (velocity * time_delta);\\\\n}\\\\nvec4 compute_position_from_velocity(vec4 position, vec4 velocity, float time_delta){\\\\n\\\\treturn position + (velocity * time_delta);\\\\n}\\\\\\\");t.addDefinitions(this,[n,i]);const s=hf.any(this.variableForInput(_R.POSITION)),r=hf.any(this.variableForInput(_R.VELOCITY)),o=hf.float(this.variableForInput(_R.MASS)),a=hf.any(this.variableForInput(_R.FORCE)),l=this.glVarName(mR.POSITION),c=this.glVarName(mR.VELOCITY),h=`${e} ${c} = compute_velocity_from_acceleration(${[r,a,o,pR.DELTA_TIME].join(\\\\\\\", \\\\\\\")})`,u=`${e} ${l} = compute_position_from_velocity(${[s,c,pR.DELTA_TIME].join(\\\\\\\", \\\\\\\")})`;t.addBodyLines(this,[h,u])}}var bR,wR=\\\\\\\"\\\\n\\\\n// https://github.com/mattatz/ShibuyaCrowd/blob/master/source/shaders/common/quaternion.glsl\\\\nvec4 quatMult(vec4 q1, vec4 q2)\\\\n{\\\\n\\\\treturn vec4(\\\\n\\\\tq1.w * q2.x + q1.x * q2.w + q1.z * q2.y - q1.y * q2.z,\\\\n\\\\tq1.w * q2.y + q1.y * q2.w + q1.x * q2.z - q1.z * q2.x,\\\\n\\\\tq1.w * q2.z + q1.z * q2.w + q1.y * q2.x - q1.x * q2.y,\\\\n\\\\tq1.w * q2.w - q1.x * q2.x - q1.y * q2.y - q1.z * q2.z\\\\n\\\\t);\\\\n}\\\\n// http://glmatrix.net/docs/quat.js.html#line97\\\\n// let ax = a[0], ay = a[1], az = a[2], aw = a[3];\\\\n\\\\n// let bx = b[0], by = b[1], bz = b[2], bw = b[3];\\\\n\\\\n// out[0] = ax * bw + aw * bx + ay * bz - az * by;\\\\n\\\\n// out[1] = ay * bw + aw * by + az * bx - ax * bz;\\\\n\\\\n// out[2] = az * bw + aw * bz + ax * by - ay * bx;\\\\n\\\\n// out[3] = aw * bw - ax * bx - ay * by - az * bz;\\\\n\\\\n// return out\\\\n\\\\n\\\\n\\\\n// http://www.neilmendoza.com/glsl-rotation-about-an-arbitrary-axis/\\\\nmat4 rotationMatrix(vec3 axis, float angle)\\\\n{\\\\n\\\\taxis = normalize(axis);\\\\n\\\\tfloat s = sin(angle);\\\\n\\\\tfloat c = cos(angle);\\\\n\\\\tfloat oc = 1.0 - c;\\\\n\\\\n \\\\treturn mat4(oc * axis.x * axis.x + c, oc * axis.x * axis.y - axis.z * s, oc * axis.z * axis.x + axis.y * s, 0.0, oc * axis.x * axis.y + axis.z * s, oc * axis.y * axis.y + c, oc * axis.y * axis.z - axis.x * s, 0.0, oc * axis.z * axis.x - axis.y * s, oc * axis.y * axis.z + axis.x * s, oc * axis.z * axis.z + c, 0.0, 0.0, 0.0, 0.0, 1.0);\\\\n}\\\\n\\\\n// https://www.geeks3d.com/20141201/how-to-rotate-a-vertex-by-a-quaternion-in-glsl/\\\\nvec4 quatFromAxisAngle(vec3 axis, float angle)\\\\n{\\\\n\\\\tvec4 qr;\\\\n\\\\tfloat half_angle = (angle * 0.5); // * 3.14159 / 180.0;\\\\n\\\\tfloat sin_half_angle = sin(half_angle);\\\\n\\\\tqr.x = axis.x * sin_half_angle;\\\\n\\\\tqr.y = axis.y * sin_half_angle;\\\\n\\\\tqr.z = axis.z * sin_half_angle;\\\\n\\\\tqr.w = cos(half_angle);\\\\n\\\\treturn qr;\\\\n}\\\\nvec3 rotateWithAxisAngle(vec3 position, vec3 axis, float angle)\\\\n{\\\\n\\\\tvec4 q = quatFromAxisAngle(axis, angle);\\\\n\\\\tvec3 v = position.xyz;\\\\n\\\\treturn v + 2.0 * cross(q.xyz, cross(q.xyz, v) + q.w * v);\\\\n}\\\\n// vec3 applyQuaternionToVector( vec4 q, vec3 v ){\\\\n// \\\\treturn v + 2.0 * cross( q.xyz, cross( q.xyz, v ) + q.w * v );\\\\n// }\\\\nvec3 rotateWithQuat( vec3 v, vec4 q )\\\\n{\\\\n\\\\t// vec4 qv = multQuat( quat, vec4(vec, 0.0) );\\\\n\\\\t// return multQuat( qv, vec4(-quat.x, -quat.y, -quat.z, quat.w) ).xyz;\\\\n\\\\treturn v + 2.0 * cross( q.xyz, cross( q.xyz, v ) + q.w * v );\\\\n}\\\\n// https://github.com/glslify/glsl-look-at/blob/gh-pages/index.glsl\\\\n// mat3 rotation_matrix(vec3 origin, vec3 target, float roll) {\\\\n// \\\\tvec3 rr = vec3(sin(roll), cos(roll), 0.0);\\\\n// \\\\tvec3 ww = normalize(target - origin);\\\\n// \\\\tvec3 uu = normalize(cross(ww, rr));\\\\n// \\\\tvec3 vv = normalize(cross(uu, ww));\\\\n\\\\n// \\\\treturn mat3(uu, vv, ww);\\\\n// }\\\\n// mat3 rotation_matrix(vec3 target, float roll) {\\\\n// \\\\tvec3 rr = vec3(sin(roll), cos(roll), 0.0);\\\\n// \\\\tvec3 ww = normalize(target);\\\\n// \\\\tvec3 uu = normalize(cross(ww, rr));\\\\n// \\\\tvec3 vv = normalize(cross(uu, ww));\\\\n\\\\n// \\\\treturn mat3(uu, vv, ww);\\\\n// }\\\\n\\\\nfloat vectorAngle(vec3 start, vec3 dest){\\\\n\\\\tstart = normalize(start);\\\\n\\\\tdest = normalize(dest);\\\\n\\\\n\\\\tfloat cosTheta = dot(start, dest);\\\\n\\\\tvec3 c1 = cross(start, dest);\\\\n\\\\t// We use the dot product of the cross with the Y axis.\\\\n\\\\t// This is a little arbitrary, but can still give a good sense of direction\\\\n\\\\tvec3 y_axis = vec3(0.0, 1.0, 0.0);\\\\n\\\\tfloat d1 = dot(c1, y_axis);\\\\n\\\\tfloat angle = acos(cosTheta) * sign(d1);\\\\n\\\\treturn angle;\\\\n}\\\\n\\\\n// http://www.opengl-tutorial.org/intermediate-tutorials/tutorial-17-quaternions/#i-need-an-equivalent-of-glulookat-how-do-i-orient-an-object-towards-a-point-\\\\nvec4 vectorAlign(vec3 start, vec3 dest){\\\\n\\\\tstart = normalize(start);\\\\n\\\\tdest = normalize(dest);\\\\n\\\\n\\\\tfloat cosTheta = dot(start, dest);\\\\n\\\\tvec3 axis;\\\\n\\\\n\\\\t// if (cosTheta < -1 + 0.001f){\\\\n\\\\t// \\\\t// special case when vectors in opposite directions:\\\\n\\\\t// \\\\t// there is no ideal rotation axis\\\\n\\\\t// \\\\t// So guess one; any will do as long as it's perpendicular to start\\\\n\\\\t// \\\\taxis = cross(vec3(0.0f, 0.0f, 1.0f), start);\\\\n\\\\t// \\\\tif (length2(axis) < 0.01 ) // bad luck, they were parallel, try again!\\\\n\\\\t// \\\\t\\\\taxis = cross(vec3(1.0f, 0.0f, 0.0f), start);\\\\n\\\\n\\\\t// \\\\taxis = normalize(axis);\\\\n\\\\t// \\\\treturn gtx::quaternion::angleAxis(glm::radians(180.0f), axis);\\\\n\\\\t// }\\\\n\\\\tif(cosTheta > (1.0 - 0.0001) || cosTheta < (-1.0 + 0.0001) ){\\\\n\\\\t\\\\taxis = normalize(cross(start, vec3(0.0, 1.0, 0.0)));\\\\n\\\\t\\\\tif (length(axis) < 0.001 ){ // bad luck, they were parallel, try again!\\\\n\\\\t\\\\t\\\\taxis = normalize(cross(start, vec3(1.0, 0.0, 0.0)));\\\\n\\\\t\\\\t}\\\\n\\\\t} else {\\\\n\\\\t\\\\taxis = normalize(cross(start, dest));\\\\n\\\\t}\\\\n\\\\n\\\\tfloat angle = acos(cosTheta);\\\\n\\\\n\\\\treturn quatFromAxisAngle(axis, angle);\\\\n}\\\\nvec4 vectorAlignWithUp(vec3 start, vec3 dest, vec3 up){\\\\n\\\\tvec4 rot1 = vectorAlign(start, dest);\\\\n\\\\tup = normalize(up);\\\\n\\\\n\\\\t// Recompute desiredUp so that it's perpendicular to the direction\\\\n\\\\t// You can skip that part if you really want to force desiredUp\\\\n\\\\t// vec3 right = normalize(cross(dest, up));\\\\n\\\\t// up = normalize(cross(right, dest));\\\\n\\\\n\\\\t// Because of the 1rst rotation, the up is probably completely screwed up.\\\\n\\\\t// Find the rotation between the up of the rotated object, and the desired up\\\\n\\\\tvec3 newUp = rotateWithQuat(vec3(0.0, 1.0, 0.0), rot1);//rot1 * vec3(0.0, 1.0, 0.0);\\\\n\\\\tvec4 rot2 = vectorAlign(up, newUp);\\\\n\\\\n\\\\t// return rot1;\\\\n\\\\treturn rot2;\\\\n\\\\t// return multQuat(rot1, rot2);\\\\n\\\\t// return rot2 * rot1;\\\\n\\\\n}\\\\n\\\\n// https://www.euclideanspace.com/maths/geometry/rotations/conversions/quaternionToAngle/index.htm\\\\nfloat quatToAngle(vec4 q){\\\\n\\\\treturn 2.0 * acos(q.w);\\\\n}\\\\nvec3 quatToAxis(vec4 q){\\\\n\\\\treturn vec3(\\\\n\\\\t\\\\tq.x / sqrt(1.0-q.w*q.w),\\\\n\\\\t\\\\tq.y / sqrt(1.0-q.w*q.w),\\\\n\\\\t\\\\tq.z / sqrt(1.0-q.w*q.w)\\\\n\\\\t);\\\\n}\\\\n\\\\nvec4 align(vec3 dir, vec3 up){\\\\n\\\\tvec3 start_dir = vec3(0.0, 0.0, 1.0);\\\\n\\\\tvec3 start_up = vec3(0.0, 1.0, 0.0);\\\\n\\\\tvec4 rot1 = vectorAlign(start_dir, dir);\\\\n\\\\tup = normalize(up);\\\\n\\\\n\\\\t// Recompute desiredUp so that it's perpendicular to the direction\\\\n\\\\t// You can skip that part if you really want to force desiredUp\\\\n\\\\tvec3 right = normalize(cross(dir, up));\\\\n\\\\tif(length(right)<0.001){\\\\n\\\\t\\\\tright = vec3(1.0, 0.0, 0.0);\\\\n\\\\t}\\\\n\\\\tup = normalize(cross(right, dir));\\\\n\\\\n\\\\t// Because of the 1rst rotation, the up is probably completely screwed up.\\\\n\\\\t// Find the rotation between the up of the rotated object, and the desired up\\\\n\\\\tvec3 newUp = rotateWithQuat(start_up, rot1);//rot1 * vec3(0.0, 1.0, 0.0);\\\\n\\\\tvec4 rot2 = vectorAlign(normalize(newUp), up);\\\\n\\\\n\\\\t// return rot1;\\\\n\\\\treturn quatMult(rot1, rot2);\\\\n\\\\t// return rot2 * rot1;\\\\n\\\\n}\\\\\\\";!function(t){t.DIR=\\\\\\\"dir\\\\\\\",t.UP=\\\\\\\"up\\\\\\\"}(bR||(bR={}));const TR=[bR.DIR,bR.UP],AR={[bR.DIR]:[0,0,1],[bR.UP]:[0,1,0]};class MR extends xP{static type(){return\\\\\\\"align\\\\\\\"}initializeNode(){super.initializeNode(),this.io.connection_points.set_input_name_function((t=>TR[t])),this.io.connection_points.set_expected_input_types_function((()=>[Bo.VEC3,Bo.VEC3])),this.io.connection_points.set_expected_output_types_function((()=>[Bo.VEC4]))}paramDefaultValue(t){return AR[t]}gl_method_name(){return\\\\\\\"align\\\\\\\"}gl_function_definitions(){return[new Tf(this,wR)]}}var ER;!function(t){t.LINEAR=\\\\\\\"Linear\\\\\\\",t.GAMMA=\\\\\\\"Gamma\\\\\\\",t.SRGB=\\\\\\\"sRGB\\\\\\\",t.RGBE=\\\\\\\"RGBE\\\\\\\",t.RGBM=\\\\\\\"RGBM\\\\\\\",t.RGBD=\\\\\\\"RGBD\\\\\\\",t.LogLuv=\\\\\\\"LogLuv\\\\\\\"}(ER||(ER={}));const SR=[ER.LINEAR,ER.GAMMA,ER.SRGB,ER.RGBE,ER.RGBM,ER.RGBD,ER.LogLuv];const CR=new class extends la{constructor(){super(...arguments),this.color=aa.VECTOR4([1,1,1,1]),this.from=aa.INTEGER(SR.indexOf(ER.LINEAR),{menu:{entries:SR.map(((t,e)=>({name:t,value:e})))}}),this.to=aa.INTEGER(SR.indexOf(ER.GAMMA),{menu:{entries:SR.map(((t,e)=>({name:t,value:e})))}}),this.gammaFactor=aa.FLOAT(2.2)}};class NR extends df{constructor(){super(...arguments),this.paramsConfig=CR}static type(){return\\\\\\\"colorCorrect\\\\\\\"}initializeNode(){this.io.connection_points.spare_params.set_inputless_param_names([\\\\\\\"to\\\\\\\",\\\\\\\"from\\\\\\\"]),this.io.outputs.setNamedOutputConnectionPoints([new Ho(NR.OUTPUT_NAME,Bo.VEC4)])}setLines(t){const e=SR[this.pv.from],n=SR[this.pv.to],i=this.glVarName(NR.OUTPUT_NAME),s=hf.any(this.variableForInput(NR.INPUT_NAME)),r=[];if(e!=n){const t=`${e}To${n}`,o=[];if(o.push(s),e==ER.GAMMA||n==ER.GAMMA){const t=hf.any(this.variableForInputParam(this.p.gammaFactor));o.push(t)}r.push(`vec4 ${i} = ${t}(${o.join(\\\\\\\", \\\\\\\")})`)}else r.push(`vec4 ${i} = ${s}`);t.addBodyLines(this,r)}}var LR,OR;NR.INPUT_NAME=\\\\\\\"color\\\\\\\",NR.INPUT_GAMMA_FACTOR=\\\\\\\"gammaFactor\\\\\\\",NR.OUTPUT_NAME=\\\\\\\"out\\\\\\\",function(t){t.EQUAL=\\\\\\\"Equal\\\\\\\",t.LESS_THAN=\\\\\\\"Less Than\\\\\\\",t.GREATER_THAN=\\\\\\\"Greater Than\\\\\\\",t.LESS_THAN_OR_EQUAL=\\\\\\\"Less Than Or Equal\\\\\\\",t.GREATER_THAN_OR_EQUAL=\\\\\\\"Greater Than Or Equal\\\\\\\",t.NOT_EQUAL=\\\\\\\"Not Equal\\\\\\\"}(LR||(LR={})),function(t){t.EQUAL=\\\\\\\"==\\\\\\\",t.LESS_THAN=\\\\\\\"<\\\\\\\",t.GREATER_THAN=\\\\\\\">\\\\\\\",t.LESS_THAN_OR_EQUAL=\\\\\\\"<=\\\\\\\",t.GREATER_THAN_OR_EQUAL=\\\\\\\">=\\\\\\\",t.NOT_EQUAL=\\\\\\\"!=\\\\\\\"}(OR||(OR={}));const PR=[LR.EQUAL,LR.LESS_THAN,LR.GREATER_THAN,LR.LESS_THAN_OR_EQUAL,LR.GREATER_THAN_OR_EQUAL,LR.NOT_EQUAL],RR=[OR.EQUAL,OR.LESS_THAN,OR.GREATER_THAN,OR.LESS_THAN_OR_EQUAL,OR.GREATER_THAN_OR_EQUAL,OR.NOT_EQUAL],IR=[\\\\\\\"x\\\\\\\",\\\\\\\"y\\\\\\\",\\\\\\\"z\\\\\\\",\\\\\\\"w\\\\\\\"];const FR=new class extends la{constructor(){super(...arguments),this.test=aa.INTEGER(0,{menu:{entries:PR.map(((t,e)=>({name:`${RR[e].padEnd(2,\\\\\\\" \\\\\\\")} (${t})`,value:e})))}})}};class DR extends df{constructor(){super(...arguments),this.paramsConfig=FR}static type(){return\\\\\\\"compare\\\\\\\"}initializeNode(){super.initializeNode(),this.io.connection_points.spare_params.set_inputless_param_names([\\\\\\\"test\\\\\\\"]),this.io.connection_points.initializeNode(),this.io.connection_points.set_input_name_function(this._gl_input_name.bind(this)),this.io.connection_points.set_output_name_function((t=>\\\\\\\"val\\\\\\\")),this.io.connection_points.set_expected_input_types_function(this._expected_input_type.bind(this)),this.io.connection_points.set_expected_output_types_function((()=>[Bo.BOOL]))}set_test_name(t){this.p.test.set(PR.indexOf(t))}_gl_input_name(t){return[\\\\\\\"value0\\\\\\\",\\\\\\\"value1\\\\\\\"][t]}_expected_input_type(){const t=this.io.connection_points.first_input_connection_type()||Bo.FLOAT;return[t,t]}setLines(t){const e=[],n=this.glVarName(\\\\\\\"val\\\\\\\"),i=RR[this.pv.test],s=hf.any(this.variableForInput(this._gl_input_name(0))),r=hf.any(this.variableForInput(this._gl_input_name(1))),o=this.io.inputs.namedInputConnectionPoints()[0];let a=1;if(o&&(a=Vo[o.type()]||1),a>1){let t=[];for(let n=0;n({name:t,value:e})))}}),this.bool=aa.BOOLEAN(0,zR(Bo.BOOL)),this.int=aa.INTEGER(0,zR(Bo.INT)),this.float=aa.FLOAT(0,zR(Bo.FLOAT)),this.vec2=aa.VECTOR2([0,0],zR(Bo.VEC2)),this.vec3=aa.VECTOR3([0,0,0],zR(Bo.VEC3)),this.vec4=aa.VECTOR4([0,0,0,0],zR(Bo.VEC4))}};class UR extends df{constructor(){super(...arguments),this.paramsConfig=kR,this._allow_inputs_created_from_params=!1}static type(){return\\\\\\\"constant\\\\\\\"}initializeNode(){this.io.connection_points.set_output_name_function((t=>UR.OUTPUT_NAME)),this.io.connection_points.set_expected_input_types_function((()=>[])),this.io.connection_points.set_expected_output_types_function((()=>[this._current_connection_type]))}setLines(t){const e=this._current_param;if(e){const n=this._current_connection_type;let i=hf.any(e.value);e.name()==this.p.int.name()&&m.isNumber(e.value)&&(i=hf.integer(e.value));const s=`${n} ${this._current_var_name} = ${i}`;t.addBodyLines(this,[s])}else console.warn(`no param found for constant node for type '${this.pv.type}'`)}get _current_connection_type(){null==this.pv.type&&console.warn(\\\\\\\"constant gl node type if not valid\\\\\\\");const t=zo[this.pv.type];return null==t&&console.warn(\\\\\\\"constant gl node type if not valid\\\\\\\"),t}get _current_param(){this._params_by_type=this._params_by_type||new Map([[Bo.BOOL,this.p.bool],[Bo.INT,this.p.int],[Bo.FLOAT,this.p.float],[Bo.VEC2,this.p.vec2],[Bo.VEC3,this.p.vec3],[Bo.VEC4,this.p.vec4]]);const t=zo[this.pv.type];return this._params_by_type.get(t)}get _current_var_name(){return this.glVarName(UR.OUTPUT_NAME)}set_gl_type(t){this.p.type.set(zo.indexOf(t))}}UR.OUTPUT_NAME=\\\\\\\"val\\\\\\\";const GR=\\\\\\\"cross\\\\\\\";const VR=new class extends la{constructor(){super(...arguments),this.x=aa.VECTOR3([0,0,1]),this.y=aa.VECTOR3([0,1,0])}};class HR extends df{constructor(){super(...arguments),this.paramsConfig=VR}static type(){return\\\\\\\"cross\\\\\\\"}initializeNode(){super.initializeNode(),this.io.outputs.setNamedOutputConnectionPoints([new Ho(GR,Bo.VEC3)])}setLines(t){const e=hf.float(this.variableForInputParam(this.p.x)),n=hf.float(this.variableForInputParam(this.p.y)),i=`vec3 ${this.glVarName(GR)} = cross(${e}, ${n})`;t.addBodyLines(this,[i])}}class jR extends(tR(\\\\\\\"cycle\\\\\\\",{in:[\\\\\\\"in\\\\\\\",\\\\\\\"min\\\\\\\",\\\\\\\"max\\\\\\\"],default:{max:1},functions:[\\\\\\\"float cycle(float val, float val_min, float val_max){\\\\n\\\\tif(val >= val_min && val < val_max){\\\\n\\\\t\\\\treturn val;\\\\n\\\\t} else {\\\\n\\\\t\\\\tfloat range = val_max - val_min;\\\\n\\\\t\\\\tif(val >= val_max){\\\\n\\\\t\\\\t\\\\tfloat delta = (val - val_max);\\\\n\\\\t\\\\t\\\\treturn val_min + mod(delta, range);\\\\n\\\\t\\\\t} else {\\\\n\\\\t\\\\t\\\\tfloat delta = (val_min - val);\\\\n\\\\t\\\\t\\\\treturn val_max - mod(delta, range);\\\\n\\\\t\\\\t}\\\\n\\\\t}\\\\n}\\\\\\\"]})){}var WR=\\\\\\\"float disk_feather(float dist, float radius, float feather){\\\\n\\\\tif(feather <= 0.0){\\\\n\\\\t\\\\tif(dist < radius){return 1.0;}else{return 0.0;}\\\\n\\\\t} else {\\\\n\\\\t\\\\tfloat half_feather = feather * 0.5;\\\\n\\\\t\\\\tif(dist < (radius - half_feather)){\\\\n\\\\t\\\\t\\\\treturn 1.0;\\\\n\\\\t\\\\t} else {\\\\n\\\\t\\\\t\\\\tif(dist > (radius + half_feather)){\\\\n\\\\t\\\\t\\\\t\\\\treturn 0.0;\\\\n\\\\t\\\\t\\\\t} else {\\\\n\\\\t\\\\t\\\\t\\\\tfloat feather_start = (radius - half_feather);\\\\n\\\\t\\\\t\\\\t\\\\tfloat blend = 1.0 - (dist - feather_start) / feather;\\\\n\\\\t\\\\t\\\\t\\\\treturn blend;\\\\n\\\\t\\\\t\\\\t}\\\\n\\\\t\\\\t}\\\\n\\\\t}\\\\n}\\\\n\\\\nfloat disk2d(vec2 pos, vec2 center, float radius, float feather){\\\\n\\\\tfloat dist = distance(pos, center);\\\\n\\\\treturn disk_feather(dist, radius, feather);\\\\n}\\\\n\\\\n// function could be called sphere, but is an overload of disk, and is the same\\\\nfloat disk3d(vec3 pos, vec3 center, float radius, float feather){\\\\n\\\\tfloat dist = distance(pos, center);\\\\n\\\\treturn disk_feather(dist, radius, feather);\\\\n}\\\\\\\";const qR=new class extends la{constructor(){super(...arguments),this.position=aa.VECTOR2([0,0]),this.center=aa.VECTOR2([0,0]),this.radius=aa.FLOAT(1),this.feather=aa.FLOAT(.1)}};class XR extends df{constructor(){super(...arguments),this.paramsConfig=qR}static type(){return\\\\\\\"disk\\\\\\\"}initializeNode(){super.initializeNode(),this.io.outputs.setNamedOutputConnectionPoints([new Ho(\\\\\\\"float\\\\\\\",Bo.FLOAT)])}setLines(t){const e=hf.vector2(this.variableForInputParam(this.p.position)),n=hf.vector2(this.variableForInputParam(this.p.center)),i=hf.float(this.variableForInputParam(this.p.radius)),s=hf.float(this.variableForInputParam(this.p.feather)),r=`float ${this.glVarName(\\\\\\\"float\\\\\\\")} = disk2d(${e}, ${n}, ${i}, ${s})`;t.addBodyLines(this,[r]),t.addDefinitions(this,[new Tf(this,WR)])}}var YR=\\\\\\\"\\\\nfloat bounceOut(float t) {\\\\n const float a = 4.0 / 11.0;\\\\n const float b = 8.0 / 11.0;\\\\n const float c = 9.0 / 10.0;\\\\n\\\\n const float ca = 4356.0 / 361.0;\\\\n const float cb = 35442.0 / 1805.0;\\\\n const float cc = 16061.0 / 1805.0;\\\\n\\\\n float t2 = t * t;\\\\n\\\\n return t < a\\\\n ? 7.5625 * t2\\\\n : t < b\\\\n ? 9.075 * t2 - 9.9 * t + 3.4\\\\n : t < c\\\\n ? ca * t2 - cb * t + cc\\\\n : 10.8 * t * t - 20.52 * t + 10.72;\\\\n}\\\\n\\\\n\\\\\\\";const $R=[\\\\\\\"back-in-out\\\\\\\",\\\\\\\"back-in\\\\\\\",\\\\\\\"back-out\\\\\\\",\\\\\\\"bounce-in-out\\\\\\\",\\\\\\\"bounce-in\\\\\\\",\\\\\\\"bounce-out\\\\\\\",\\\\\\\"circular-in-out\\\\\\\",\\\\\\\"circular-in\\\\\\\",\\\\\\\"circular-out\\\\\\\",\\\\\\\"cubic-in-out\\\\\\\",\\\\\\\"cubic-in\\\\\\\",\\\\\\\"cubic-out\\\\\\\",\\\\\\\"elastic-in-out\\\\\\\",\\\\\\\"elastic-in\\\\\\\",\\\\\\\"elastic-out\\\\\\\",\\\\\\\"exponential-in-out\\\\\\\",\\\\\\\"exponential-in\\\\\\\",\\\\\\\"exponential-out\\\\\\\",\\\\\\\"linear\\\\\\\",\\\\\\\"quadratic-in-out\\\\\\\",\\\\\\\"quadratic-in\\\\\\\",\\\\\\\"quadratic-out\\\\\\\",\\\\\\\"sine-in-out\\\\\\\",\\\\\\\"sine-in\\\\\\\",\\\\\\\"sine-out\\\\\\\"],JR={\\\\\\\"circular-in-out\\\\\\\":\\\\\\\"float circularInOut(float t) {\\\\n return t < 0.5\\\\n ? 0.5 * (1.0 - sqrt(1.0 - 4.0 * t * t))\\\\n : 0.5 * (sqrt((3.0 - 2.0 * t) * (2.0 * t - 1.0)) + 1.0);\\\\n}\\\\n\\\\n\\\\\\\",\\\\\\\"exponential-in-out\\\\\\\":\\\\\\\"float exponentialInOut(float t) {\\\\n return t == 0.0 || t == 1.0\\\\n ? t\\\\n : t < 0.5\\\\n ? +0.5 * pow(2.0, (20.0 * t) - 10.0)\\\\n : -0.5 * pow(2.0, 10.0 - (t * 20.0)) + 1.0;\\\\n}\\\\n\\\\n\\\\\\\",\\\\\\\"circular-in\\\\\\\":\\\\\\\"float circularIn(float t) {\\\\n return 1.0 - sqrt(1.0 - t * t);\\\\n}\\\\n\\\\n\\\\\\\",\\\\\\\"elastic-out\\\\\\\":\\\\\\\"#ifndef HALF_PI\\\\n#define HALF_PI 1.5707963267948966\\\\n#endif\\\\n\\\\nfloat elasticOut(float t) {\\\\n return sin(-13.0 * (t + 1.0) * HALF_PI) * pow(2.0, -10.0 * t) + 1.0;\\\\n}\\\\n\\\\n\\\\\\\",\\\\\\\"cubic-in\\\\\\\":\\\\\\\"float cubicIn(float t) {\\\\n return t * t * t;\\\\n}\\\\n\\\\n\\\\\\\",\\\\\\\"exponential-out\\\\\\\":\\\\\\\"float exponentialOut(float t) {\\\\n return t == 1.0 ? t : 1.0 - pow(2.0, -10.0 * t);\\\\n}\\\\n\\\\n\\\\\\\",\\\\\\\"quintic-out\\\\\\\":\\\\\\\"float quinticOut(float t) {\\\\n return 1.0 - (pow(t - 1.0, 5.0));\\\\n}\\\\n\\\\n\\\\\\\",\\\\\\\"elastic-in-out\\\\\\\":\\\\\\\"#ifndef HALF_PI\\\\n#define HALF_PI 1.5707963267948966\\\\n#endif\\\\n\\\\nfloat elasticInOut(float t) {\\\\n return t < 0.5\\\\n ? 0.5 * sin(+13.0 * HALF_PI * 2.0 * t) * pow(2.0, 10.0 * (2.0 * t - 1.0))\\\\n : 0.5 * sin(-13.0 * HALF_PI * ((2.0 * t - 1.0) + 1.0)) * pow(2.0, -10.0 * (2.0 * t - 1.0)) + 1.0;\\\\n}\\\\n\\\\n\\\\\\\",linear:\\\\\\\"float linear(float t) {\\\\n return t;\\\\n}\\\\n\\\\n\\\\\\\",\\\\\\\"circular-out\\\\\\\":\\\\\\\"float circularOut(float t) {\\\\n return sqrt((2.0 - t) * t);\\\\n}\\\\n\\\\n\\\\\\\",\\\\\\\"back-in-out\\\\\\\":\\\\\\\"\\\\nfloat backInOut(float t) {\\\\n float f = t < 0.5\\\\n ? 2.0 * t\\\\n : 1.0 - (2.0 * t - 1.0);\\\\n\\\\n float g = pow(f, 3.0) - f * sin(f * PI);\\\\n\\\\n return t < 0.5\\\\n ? 0.5 * g\\\\n : 0.5 * (1.0 - g) + 0.5;\\\\n}\\\\n\\\\n\\\\\\\",\\\\\\\"back-in\\\\\\\":\\\\\\\"\\\\nfloat backIn(float t) {\\\\n return pow(t, 3.0) - t * sin(t * PI);\\\\n}\\\\n\\\\n\\\\\\\",\\\\\\\"sine-in\\\\\\\":\\\\\\\"#ifndef HALF_PI\\\\n#define HALF_PI 1.5707963267948966\\\\n#endif\\\\n\\\\nfloat sineIn(float t) {\\\\n return sin((t - 1.0) * HALF_PI) + 1.0;\\\\n}\\\\n\\\\n\\\\\\\",\\\\\\\"back-out\\\\\\\":\\\\\\\"\\\\nfloat backOut(float t) {\\\\n float f = 1.0 - t;\\\\n return 1.0 - (pow(f, 3.0) - f * sin(f * PI));\\\\n}\\\\n\\\\n\\\\\\\",\\\\\\\"quartic-in-out\\\\\\\":\\\\\\\"float quarticInOut(float t) {\\\\n return t < 0.5\\\\n ? +8.0 * pow(t, 4.0)\\\\n : -8.0 * pow(t - 1.0, 4.0) + 1.0;\\\\n}\\\\n\\\\n\\\\\\\",\\\\\\\"quadratic-in\\\\\\\":\\\\\\\"float quadraticIn(float t) {\\\\n return t * t;\\\\n}\\\\n\\\\n\\\\\\\",\\\\\\\"cubic-in-out\\\\\\\":\\\\\\\"float cubicInOut(float t) {\\\\n return t < 0.5\\\\n ? 4.0 * t * t * t\\\\n : 0.5 * pow(2.0 * t - 2.0, 3.0) + 1.0;\\\\n}\\\\n\\\\n\\\\\\\",\\\\\\\"elastic-in\\\\\\\":\\\\\\\"#ifndef HALF_PI\\\\n#define HALF_PI 1.5707963267948966\\\\n#endif\\\\n\\\\nfloat elasticIn(float t) {\\\\n return sin(13.0 * t * HALF_PI) * pow(2.0, 10.0 * (t - 1.0));\\\\n}\\\\n\\\\n\\\\\\\",\\\\\\\"bounce-out\\\\\\\":YR,\\\\\\\"quadratic-in-out\\\\\\\":\\\\\\\"float quadraticInOut(float t) {\\\\n float p = 2.0 * t * t;\\\\n return t < 0.5 ? p : -p + (4.0 * t) - 1.0;\\\\n}\\\\n\\\\n\\\\\\\",\\\\\\\"exponential-in\\\\\\\":\\\\\\\"float exponentialIn(float t) {\\\\n return t == 0.0 ? t : pow(2.0, 10.0 * (t - 1.0));\\\\n}\\\\n\\\\n\\\\\\\",\\\\\\\"quintic-in-out\\\\\\\":\\\\\\\"float quinticInOut(float t) {\\\\n return t < 0.5\\\\n ? +16.0 * pow(t, 5.0)\\\\n : -0.5 * pow(2.0 * t - 2.0, 5.0) + 1.0;\\\\n}\\\\n\\\\n\\\\\\\",\\\\\\\"sine-in-out\\\\\\\":\\\\\\\"\\\\nfloat sineInOut(float t) {\\\\n return -0.5 * (cos(PI * t) - 1.0);\\\\n}\\\\n\\\\n\\\\\\\",\\\\\\\"cubic-out\\\\\\\":\\\\\\\"float cubicOut(float t) {\\\\n float f = t - 1.0;\\\\n return f * f * f + 1.0;\\\\n}\\\\n\\\\n\\\\\\\",\\\\\\\"quadratic-out\\\\\\\":\\\\\\\"float quadraticOut(float t) {\\\\n return -t * (t - 2.0);\\\\n}\\\\n\\\\n\\\\\\\",\\\\\\\"bounce-in-out\\\\\\\":\\\\\\\"\\\\nfloat bounceInOut(float t) {\\\\n return t < 0.5\\\\n ? 0.5 * (1.0 - bounceOut(1.0 - t * 2.0))\\\\n : 0.5 * bounceOut(t * 2.0 - 1.0) + 0.5;\\\\n}\\\\n\\\\n\\\\n\\\\n\\\\\\\",\\\\\\\"quintic-in\\\\\\\":\\\\\\\"float quinticIn(float t) {\\\\n return pow(t, 5.0);\\\\n}\\\\n\\\\n\\\\\\\",\\\\\\\"quartic-in\\\\\\\":\\\\\\\"float quarticIn(float t) {\\\\n return pow(t, 4.0);\\\\n}\\\\n\\\\n\\\\\\\",\\\\\\\"quartic-out\\\\\\\":\\\\\\\"float quarticOut(float t) {\\\\n return pow(t - 1.0, 3.0) * (1.0 - t) + 1.0;\\\\n}\\\\n\\\\n\\\\\\\",\\\\\\\"bounce-in\\\\\\\":\\\\\\\"\\\\nfloat bounceIn(float t) {\\\\n return 1.0 - bounceOut(1.0 - t);\\\\n}\\\\n\\\\n\\\\\\\",\\\\\\\"sine-out\\\\\\\":\\\\\\\"#ifndef HALF_PI\\\\n#define HALF_PI 1.5707963267948966\\\\n#endif\\\\n\\\\nfloat sineOut(float t) {\\\\n return sin(t * HALF_PI);\\\\n}\\\\n\\\\n\\\\\\\"},ZR={\\\\\\\"bounce-in\\\\\\\":[YR],\\\\\\\"bounce-in-out\\\\\\\":[YR]},QR={\\\\\\\"circular-in-out\\\\\\\":\\\\\\\"circularInOut\\\\\\\",\\\\\\\"exponential-in-out\\\\\\\":\\\\\\\"exponentialInOut\\\\\\\",\\\\\\\"circular-in\\\\\\\":\\\\\\\"circularIn\\\\\\\",\\\\\\\"elastic-out\\\\\\\":\\\\\\\"elasticOut\\\\\\\",\\\\\\\"cubic-in\\\\\\\":\\\\\\\"cubicIn\\\\\\\",\\\\\\\"exponential-out\\\\\\\":\\\\\\\"exponentialOut\\\\\\\",\\\\\\\"quintic-out\\\\\\\":\\\\\\\"quinticOut\\\\\\\",\\\\\\\"elastic-in-out\\\\\\\":\\\\\\\"elasticInOut\\\\\\\",linear:\\\\\\\"linear\\\\\\\",\\\\\\\"circular-out\\\\\\\":\\\\\\\"circularOut\\\\\\\",\\\\\\\"back-in-out\\\\\\\":\\\\\\\"backInOut\\\\\\\",\\\\\\\"back-in\\\\\\\":\\\\\\\"backIn\\\\\\\",\\\\\\\"sine-in\\\\\\\":\\\\\\\"sineIn\\\\\\\",\\\\\\\"back-out\\\\\\\":\\\\\\\"backOut\\\\\\\",\\\\\\\"quartic-in-out\\\\\\\":\\\\\\\"quarticInOut\\\\\\\",\\\\\\\"quadratic-in\\\\\\\":\\\\\\\"quadraticIn\\\\\\\",\\\\\\\"cubic-in-out\\\\\\\":\\\\\\\"cubicInOut\\\\\\\",\\\\\\\"elastic-in\\\\\\\":\\\\\\\"elasticIn\\\\\\\",\\\\\\\"bounce-out\\\\\\\":\\\\\\\"bounceOut\\\\\\\",\\\\\\\"quadratic-in-out\\\\\\\":\\\\\\\"quadraticInOut\\\\\\\",\\\\\\\"exponential-in\\\\\\\":\\\\\\\"exponentialIn\\\\\\\",\\\\\\\"quintic-in-out\\\\\\\":\\\\\\\"quinticInOut\\\\\\\",\\\\\\\"sine-in-out\\\\\\\":\\\\\\\"sineInOut\\\\\\\",\\\\\\\"cubic-out\\\\\\\":\\\\\\\"cubicOut\\\\\\\",\\\\\\\"quadratic-out\\\\\\\":\\\\\\\"quadraticOut\\\\\\\",\\\\\\\"bounce-in-out\\\\\\\":\\\\\\\"bounceInOut\\\\\\\",\\\\\\\"quintic-in\\\\\\\":\\\\\\\"quinticIn\\\\\\\",\\\\\\\"quartic-in\\\\\\\":\\\\\\\"quarticIn\\\\\\\",\\\\\\\"quartic-out\\\\\\\":\\\\\\\"quarticOut\\\\\\\",\\\\\\\"bounce-in\\\\\\\":\\\\\\\"bounceIn\\\\\\\",\\\\\\\"sine-out\\\\\\\":\\\\\\\"sineOut\\\\\\\"},KR=$R.indexOf(\\\\\\\"sine-in-out\\\\\\\");const tI=new class extends la{constructor(){super(...arguments),this.type=aa.INTEGER(KR,{menu:{entries:$R.map(((t,e)=>({name:t,value:e})))}}),this.input=aa.FLOAT(0)}};class eI extends df{constructor(){super(...arguments),this.paramsConfig=tI}static type(){return\\\\\\\"easing\\\\\\\"}initializeNode(){super.initializeNode(),this.io.connection_points.spare_params.set_inputless_param_names([\\\\\\\"type\\\\\\\"]),this.io.outputs.setNamedOutputConnectionPoints([new Ho(\\\\\\\"out\\\\\\\",Bo.FLOAT)])}setLines(t){const e=$R[this.pv.type],n=QR[e];let i=[new Tf(this,JR[e])];const s=(ZR[e]||[]).map((t=>new Tf(this,t)));s&&(i=s.concat(i));const r=hf.float(this.variableForInputParam(this.p.input)),o=`float ${this.glVarName(\\\\\\\"out\\\\\\\")} = ${n}(${r})`;t.addDefinitions(this,i),t.addBodyLines(this,[o])}}var nI=\\\\\\\"//\\\\n//\\\\n// FIT\\\\n//\\\\n//\\\\nfloat fit(float val, float srcMin, float srcMax, float destMin, float destMax){\\\\n\\\\tfloat src_range = srcMax - srcMin;\\\\n\\\\tfloat dest_range = destMax - destMin;\\\\n\\\\n\\\\tfloat r = (val - srcMin) / src_range;\\\\n\\\\treturn (r * dest_range) + destMin;\\\\n}\\\\nvec2 fit(vec2 val, vec2 srcMin, vec2 srcMax, vec2 destMin, vec2 destMax){\\\\n\\\\treturn vec2(\\\\n\\\\t\\\\tfit(val.x, srcMin.x, srcMax.x, destMin.x, destMax.x),\\\\n\\\\t\\\\tfit(val.y, srcMin.y, srcMax.y, destMin.y, destMax.y)\\\\n\\\\t);\\\\n}\\\\nvec3 fit(vec3 val, vec3 srcMin, vec3 srcMax, vec3 destMin, vec3 destMax){\\\\n\\\\treturn vec3(\\\\n\\\\t\\\\tfit(val.x, srcMin.x, srcMax.x, destMin.x, destMax.x),\\\\n\\\\t\\\\tfit(val.y, srcMin.y, srcMax.y, destMin.y, destMax.y),\\\\n\\\\t\\\\tfit(val.z, srcMin.z, srcMax.z, destMin.z, destMax.z)\\\\n\\\\t);\\\\n}\\\\nvec4 fit(vec4 val, vec4 srcMin, vec4 srcMax, vec4 destMin, vec4 destMax){\\\\n\\\\treturn vec4(\\\\n\\\\t\\\\tfit(val.x, srcMin.x, srcMax.x, destMin.x, destMax.x),\\\\n\\\\t\\\\tfit(val.y, srcMin.y, srcMax.y, destMin.y, destMax.y),\\\\n\\\\t\\\\tfit(val.z, srcMin.z, srcMax.z, destMin.z, destMax.z),\\\\n\\\\t\\\\tfit(val.w, srcMin.w, srcMax.w, destMin.w, destMax.w)\\\\n\\\\t);\\\\n}\\\\n\\\\n//\\\\n//\\\\n// FIT TO 01\\\\n// fits the range [srcMin, srcMax] to [0, 1]\\\\n//\\\\nfloat fitTo01(float val, float srcMin, float srcMax){\\\\n\\\\tfloat size = srcMax - srcMin;\\\\n\\\\treturn (val - srcMin) / size;\\\\n}\\\\nvec2 fitTo01(vec2 val, vec2 srcMin, vec2 srcMax){\\\\n\\\\treturn vec2(\\\\n\\\\t\\\\tfitTo01(val.x, srcMin.x, srcMax.x),\\\\n\\\\t\\\\tfitTo01(val.y, srcMin.y, srcMax.y)\\\\n\\\\t);\\\\n}\\\\nvec3 fitTo01(vec3 val, vec3 srcMin, vec3 srcMax){\\\\n\\\\treturn vec3(\\\\n\\\\t\\\\tfitTo01(val.x, srcMin.x, srcMax.x),\\\\n\\\\t\\\\tfitTo01(val.y, srcMin.y, srcMax.y),\\\\n\\\\t\\\\tfitTo01(val.z, srcMin.z, srcMax.z)\\\\n\\\\t);\\\\n}\\\\nvec4 fitTo01(vec4 val, vec4 srcMin, vec4 srcMax){\\\\n\\\\treturn vec4(\\\\n\\\\t\\\\tfitTo01(val.x, srcMin.x, srcMax.x),\\\\n\\\\t\\\\tfitTo01(val.y, srcMin.y, srcMax.y),\\\\n\\\\t\\\\tfitTo01(val.z, srcMin.z, srcMax.z),\\\\n\\\\t\\\\tfitTo01(val.w, srcMin.w, srcMax.w)\\\\n\\\\t);\\\\n}\\\\n\\\\n//\\\\n//\\\\n// FIT FROM 01\\\\n// fits the range [0, 1] to [destMin, destMax]\\\\n//\\\\nfloat fitFrom01(float val, float destMin, float destMax){\\\\n\\\\treturn fit(val, 0.0, 1.0, destMin, destMax);\\\\n}\\\\nvec2 fitFrom01(vec2 val, vec2 srcMin, vec2 srcMax){\\\\n\\\\treturn vec2(\\\\n\\\\t\\\\tfitFrom01(val.x, srcMin.x, srcMax.x),\\\\n\\\\t\\\\tfitFrom01(val.y, srcMin.y, srcMax.y)\\\\n\\\\t);\\\\n}\\\\nvec3 fitFrom01(vec3 val, vec3 srcMin, vec3 srcMax){\\\\n\\\\treturn vec3(\\\\n\\\\t\\\\tfitFrom01(val.x, srcMin.x, srcMax.x),\\\\n\\\\t\\\\tfitFrom01(val.y, srcMin.y, srcMax.y),\\\\n\\\\t\\\\tfitFrom01(val.z, srcMin.z, srcMax.z)\\\\n\\\\t);\\\\n}\\\\nvec4 fitFrom01(vec4 val, vec4 srcMin, vec4 srcMax){\\\\n\\\\treturn vec4(\\\\n\\\\t\\\\tfitFrom01(val.x, srcMin.x, srcMax.x),\\\\n\\\\t\\\\tfitFrom01(val.y, srcMin.y, srcMax.y),\\\\n\\\\t\\\\tfitFrom01(val.z, srcMin.z, srcMax.z),\\\\n\\\\t\\\\tfitFrom01(val.w, srcMin.w, srcMax.w)\\\\n\\\\t);\\\\n}\\\\n\\\\n//\\\\n//\\\\n// FIT FROM 01 TO VARIANCE\\\\n// fits the range [0, 1] to [center - variance, center + variance]\\\\n//\\\\nfloat fitFrom01ToVariance(float val, float center, float variance){\\\\n\\\\treturn fitFrom01(val, center - variance, center + variance);\\\\n}\\\\nvec2 fitFrom01ToVariance(vec2 val, vec2 center, vec2 variance){\\\\n\\\\treturn vec2(\\\\n\\\\t\\\\tfitFrom01ToVariance(val.x, center.x, variance.x),\\\\n\\\\t\\\\tfitFrom01ToVariance(val.y, center.y, variance.y)\\\\n\\\\t);\\\\n}\\\\nvec3 fitFrom01ToVariance(vec3 val, vec3 center, vec3 variance){\\\\n\\\\treturn vec3(\\\\n\\\\t\\\\tfitFrom01ToVariance(val.x, center.x, variance.x),\\\\n\\\\t\\\\tfitFrom01ToVariance(val.y, center.y, variance.y),\\\\n\\\\t\\\\tfitFrom01ToVariance(val.z, center.z, variance.z)\\\\n\\\\t);\\\\n}\\\\nvec4 fitFrom01ToVariance(vec4 val, vec4 center, vec4 variance){\\\\n\\\\treturn vec4(\\\\n\\\\t\\\\tfitFrom01ToVariance(val.x, center.x, variance.x),\\\\n\\\\t\\\\tfitFrom01ToVariance(val.y, center.y, variance.y),\\\\n\\\\t\\\\tfitFrom01ToVariance(val.z, center.z, variance.z),\\\\n\\\\t\\\\tfitFrom01ToVariance(val.w, center.w, variance.w)\\\\n\\\\t);\\\\n}\\\\\\\";const iI={srcMin:0,srcMax:1,destMin:0,destMax:1};class sI extends TP{static type(){return\\\\\\\"fit\\\\\\\"}_gl_input_name(t){return[\\\\\\\"val\\\\\\\",\\\\\\\"srcMin\\\\\\\",\\\\\\\"srcMax\\\\\\\",\\\\\\\"destMin\\\\\\\",\\\\\\\"destMax\\\\\\\"][t]}paramDefaultValue(t){return iI[t]}gl_method_name(){return\\\\\\\"fit\\\\\\\"}gl_function_definitions(){return[new Tf(this,nI)]}}const rI={srcMin:0,srcMax:1};class oI extends bP{static type(){return\\\\\\\"fitTo01\\\\\\\"}_gl_input_name(t){return[\\\\\\\"val\\\\\\\",\\\\\\\"srcMin\\\\\\\",\\\\\\\"srcMax\\\\\\\"][t]}paramDefaultValue(t){return rI[t]}gl_method_name(){return\\\\\\\"fitTo01\\\\\\\"}gl_function_definitions(){return[new Tf(this,nI)]}}const aI={destMin:0,destMax:1};class lI extends bP{static type(){return\\\\\\\"fitFrom01\\\\\\\"}_gl_input_name(t){return[\\\\\\\"val\\\\\\\",\\\\\\\"destMin\\\\\\\",\\\\\\\"destMax\\\\\\\"][t]}paramDefaultValue(t){return aI[t]}gl_method_name(){return\\\\\\\"fitFrom01\\\\\\\"}gl_function_definitions(){return[new Tf(this,nI)]}}const cI={center:.5,variance:.5};class hI extends bP{static type(){return\\\\\\\"fitFrom01ToVariance\\\\\\\"}_gl_input_name(t){return[\\\\\\\"val\\\\\\\",\\\\\\\"center\\\\\\\",\\\\\\\"variance\\\\\\\"][t]}paramDefaultValue(t){return cI[t]}gl_method_name(){return\\\\\\\"fitFrom01ToVariance\\\\\\\"}gl_function_definitions(){return[new Tf(this,nI)]}}const uI=\\\\\\\"color\\\\\\\";const dI=new class extends la{constructor(){super(...arguments),this.mvPosition=aa.VECTOR4([0,0,0,0]),this.baseColor=aa.COLOR([0,0,0]),this.fogColor=aa.COLOR([1,1,1]),this.near=aa.FLOAT(0),this.far=aa.FLOAT(0)}};class pI extends df{constructor(){super(...arguments),this.paramsConfig=dI}static type(){return\\\\\\\"fog\\\\\\\"}initializeNode(){super.initializeNode(),this.io.outputs.setNamedOutputConnectionPoints([new Ho(uI,Bo.VEC3)])}setLines(t){if(t.current_shader_name==xf.FRAGMENT){const e=this.glVarName(this.name()),n=new Mf(this,Bo.VEC4,e),i=`${e} = modelViewMatrix * vec4(position, 1.0)`;t.addDefinitions(this,[n],xf.VERTEX),t.addBodyLines(this,[i],xf.VERTEX);const s=new Tf(this,\\\\\\\"vec3 compute_fog(vec4 mvPosition, vec3 base_color, vec3 fog_color, float near, float far) {\\\\n\\\\tfloat blend = (-mvPosition.z - near) / (far - near);\\\\n\\\\tblend = clamp(blend, 0.0, 1.0);\\\\n\\\\treturn blend * fog_color + (1.0 - blend) * base_color;\\\\n}\\\\\\\"),r=hf.vector4(this.variableForInputParam(this.p.mvPosition)),o=hf.vector3(this.variableForInputParam(this.p.baseColor)),a=hf.vector3(this.variableForInputParam(this.p.fogColor)),l=hf.vector3(this.variableForInputParam(this.p.near)),c=hf.vector3(this.variableForInputParam(this.p.far)),h=`vec3 ${this.glVarName(uI)} = compute_fog(${[r,o,a,l,c].join(\\\\\\\", \\\\\\\")})`;t.addDefinitions(this,[n,s]),t.addBodyLines(this,[h])}}}const _I=new class extends la{};class mI extends df{constructor(){super(...arguments),this.paramsConfig=_I}static type(){return ns.OUTPUT}initializeNode(){this.io.connection_points.set_input_name_function(this._expected_input_name.bind(this)),this.io.connection_points.set_expected_output_types_function((()=>[])),this.io.connection_points.set_expected_input_types_function(this._expected_input_types.bind(this)),this.io.connection_points.set_create_spare_params_from_inputs(!1),this.addPostDirtyHook(\\\\\\\"setParentDirty\\\\\\\",(()=>{var t;null===(t=this.parent())||void 0===t||t.setDirty(this)}))}parent(){return super.parent()}_expected_input_name(t){const e=this.parent();return(null==e?void 0:e.child_expected_output_connection_point_name(t))||`in${t}`}_expected_input_types(){const t=this.parent();return(null==t?void 0:t.child_expected_output_connection_point_types())||[]}setLines(t){const e=this.parent();if(!e)return;const n=[],i=this.io.connections.inputConnections();if(i)for(let t of i)if(t){const i=t.dest_connection_point(),s=hf.any(this.variableForInput(i.name())),r=`\\\\t${e.glVarName(i.name())} = ${s}`;n.push(r)}t.addBodyLines(this,n),e.set_lines_block_end(t,this)}}class fI extends df{constructor(){super(...arguments),this._children_controller_context=ts.GL}initializeNode(){var t;null===(t=this.childrenController)||void 0===t||t.set_output_node_find_method((()=>this.nodesByType(mI.type())[0])),this.io.connection_points.set_input_name_function(this._expected_input_name.bind(this)),this.io.connection_points.set_output_name_function(this._expected_output_name.bind(this)),this.io.connection_points.set_expected_input_types_function(this._expected_input_types.bind(this)),this.io.connection_points.set_expected_output_types_function(this._expected_output_types.bind(this))}_expected_inputs_count(){const t=this.io.connections.inputConnections();return t?t.length+1:1}_expected_input_types(){const t=[],e=Bo.FLOAT,n=this.io.connections.inputConnections(),i=this._expected_inputs_count();for(let s=0;s=0){const e=t.dest_connection_point(),i=hf.any(this.variableForInput(e.name())),s=`${e.type()} ${this.glVarName(e.name())} = ${i}`;n.push(s)}const r=this.pv.start,o=this.pv.max,a=this.pv.step,l=hf.float(r),c=hf.float(o),h=hf.float(a),u=this.glVarName(\\\\\\\"i\\\\\\\"),d=`for(float ${u} = ${l}; ${u} < ${c}; ${u}+= ${h}){`;n.push(d);const p=`\\\\tfloat ${e.glVarName(yI.START_INDEX)} = ${u}`;if(n.push(p),s)for(let t of s)if(t&&t.input_index>=0){const i=t.dest_connection_point(),s=this.glVarName(i.name()),r=`\\\\t${i.type()} ${e.glVarName(i.name())} = ${s}`;n.push(r)}t.addBodyLines(e,n)}setLines(t){}}const TI=new class extends la{};class AI extends df{constructor(){super(...arguments),this.paramsConfig=TI}static type(){return\\\\\\\"globals\\\\\\\"}initializeNode(){super.initializeNode(),this.lifecycle.add_on_add_hook((()=>{var t,e;null===(e=null===(t=this.material_node)||void 0===t?void 0:t.assemblerController)||void 0===e||e.add_globals_outputs(this)}))}setLines(t){t.assembler().set_node_lines_globals(this,t)}}const MI=new class extends la{constructor(){super(...arguments),this.hsluv=aa.VECTOR3([1,1,1])}};class EI extends df{constructor(){super(...arguments),this.paramsConfig=MI}static type(){return\\\\\\\"hsluvToRgb\\\\\\\"}initializeNode(){super.initializeNode(),this.io.outputs.setNamedOutputConnectionPoints([new Ho(\\\\\\\"rgb\\\\\\\",Bo.VEC3)])}setLines(t){const e=[],n=[];e.push(new Tf(this,\\\\\\\"// from https://github.com/williammalo/hsluv-glsl\\\\n/*\\\\nHSLUV-GLSL v4.2\\\\nHSLUV is a human-friendly alternative to HSL. ( http://www.hsluv.org )\\\\nGLSL port by William Malo ( https://github.com/williammalo )\\\\nPut this code in your fragment shader.\\\\n*/\\\\n\\\\nvec3 hsluv_intersectLineLine(vec3 line1x, vec3 line1y, vec3 line2x, vec3 line2y) {\\\\n\\\\treturn (line1y - line2y) / (line2x - line1x);\\\\n}\\\\n\\\\nvec3 hsluv_distanceFromPole(vec3 pointx,vec3 pointy) {\\\\n\\\\treturn sqrt(pointx*pointx + pointy*pointy);\\\\n}\\\\n\\\\nvec3 hsluv_lengthOfRayUntilIntersect(float theta, vec3 x, vec3 y) {\\\\n\\\\tvec3 len = y / (sin(theta) - x * cos(theta));\\\\n\\\\tif (len.r < 0.0) {len.r=1000.0;}\\\\n\\\\tif (len.g < 0.0) {len.g=1000.0;}\\\\n\\\\tif (len.b < 0.0) {len.b=1000.0;}\\\\n\\\\treturn len;\\\\n}\\\\n\\\\nfloat hsluv_maxSafeChromaForL(float L){\\\\n\\\\tmat3 m2 = mat3(\\\\n\\\\t\\\\t 3.2409699419045214 ,-0.96924363628087983 , 0.055630079696993609,\\\\n\\\\t\\\\t-1.5373831775700935 , 1.8759675015077207 ,-0.20397695888897657 ,\\\\n\\\\t\\\\t-0.49861076029300328 , 0.041555057407175613, 1.0569715142428786 \\\\n\\\\t);\\\\n\\\\tfloat sub0 = L + 16.0;\\\\n\\\\tfloat sub1 = sub0 * sub0 * sub0 * .000000641;\\\\n\\\\tfloat sub2 = sub1 > 0.0088564516790356308 ? sub1 : L / 903.2962962962963;\\\\n\\\\n\\\\tvec3 top1 = (284517.0 * m2[0] - 94839.0 * m2[2]) * sub2;\\\\n\\\\tvec3 bottom = (632260.0 * m2[2] - 126452.0 * m2[1]) * sub2;\\\\n\\\\tvec3 top2 = (838422.0 * m2[2] + 769860.0 * m2[1] + 731718.0 * m2[0]) * L * sub2;\\\\n\\\\n\\\\tvec3 bounds0x = top1 / bottom;\\\\n\\\\tvec3 bounds0y = top2 / bottom;\\\\n\\\\n\\\\tvec3 bounds1x = top1 / (bottom+126452.0);\\\\n\\\\tvec3 bounds1y = (top2-769860.0*L) / (bottom+126452.0);\\\\n\\\\n\\\\tvec3 xs0 = hsluv_intersectLineLine(bounds0x, bounds0y, -1.0/bounds0x, vec3(0.0) );\\\\n\\\\tvec3 xs1 = hsluv_intersectLineLine(bounds1x, bounds1y, -1.0/bounds1x, vec3(0.0) );\\\\n\\\\n\\\\tvec3 lengths0 = hsluv_distanceFromPole( xs0, bounds0y + xs0 * bounds0x );\\\\n\\\\tvec3 lengths1 = hsluv_distanceFromPole( xs1, bounds1y + xs1 * bounds1x );\\\\n\\\\n\\\\treturn min(lengths0.r,\\\\n\\\\t\\\\t\\\\tmin(lengths1.r,\\\\n\\\\t\\\\t\\\\tmin(lengths0.g,\\\\n\\\\t\\\\t\\\\tmin(lengths1.g,\\\\n\\\\t\\\\t\\\\tmin(lengths0.b,\\\\n\\\\t\\\\t\\\\t\\\\tlengths1.b)))));\\\\n}\\\\n\\\\nfloat hsluv_maxChromaForLH(float L, float H) {\\\\n\\\\n\\\\tfloat hrad = radians(H);\\\\n\\\\n\\\\tmat3 m2 = mat3(\\\\n\\\\t\\\\t 3.2409699419045214 ,-0.96924363628087983 , 0.055630079696993609,\\\\n\\\\t\\\\t-1.5373831775700935 , 1.8759675015077207 ,-0.20397695888897657 ,\\\\n\\\\t\\\\t-0.49861076029300328 , 0.041555057407175613, 1.0569715142428786 \\\\n\\\\t);\\\\n\\\\tfloat sub1 = pow(L + 16.0, 3.0) / 1560896.0;\\\\n\\\\tfloat sub2 = sub1 > 0.0088564516790356308 ? sub1 : L / 903.2962962962963;\\\\n\\\\n\\\\tvec3 top1 = (284517.0 * m2[0] - 94839.0 * m2[2]) * sub2;\\\\n\\\\tvec3 bottom = (632260.0 * m2[2] - 126452.0 * m2[1]) * sub2;\\\\n\\\\tvec3 top2 = (838422.0 * m2[2] + 769860.0 * m2[1] + 731718.0 * m2[0]) * L * sub2;\\\\n\\\\n\\\\tvec3 bound0x = top1 / bottom;\\\\n\\\\tvec3 bound0y = top2 / bottom;\\\\n\\\\n\\\\tvec3 bound1x = top1 / (bottom+126452.0);\\\\n\\\\tvec3 bound1y = (top2-769860.0*L) / (bottom+126452.0);\\\\n\\\\n\\\\tvec3 lengths0 = hsluv_lengthOfRayUntilIntersect(hrad, bound0x, bound0y );\\\\n\\\\tvec3 lengths1 = hsluv_lengthOfRayUntilIntersect(hrad, bound1x, bound1y );\\\\n\\\\n\\\\treturn min(lengths0.r,\\\\n\\\\t\\\\t\\\\tmin(lengths1.r,\\\\n\\\\t\\\\t\\\\tmin(lengths0.g,\\\\n\\\\t\\\\t\\\\tmin(lengths1.g,\\\\n\\\\t\\\\t\\\\tmin(lengths0.b,\\\\n\\\\t\\\\t\\\\t\\\\tlengths1.b)))));\\\\n}\\\\n\\\\nfloat hsluv_fromLinear(float c) {\\\\n\\\\treturn c <= 0.0031308 ? 12.92 * c : 1.055 * pow(c, 1.0 / 2.4) - 0.055;\\\\n}\\\\nvec3 hsluv_fromLinear(vec3 c) {\\\\n\\\\treturn vec3( hsluv_fromLinear(c.r), hsluv_fromLinear(c.g), hsluv_fromLinear(c.b) );\\\\n}\\\\n\\\\nfloat hsluv_toLinear(float c) {\\\\n\\\\treturn c > 0.04045 ? pow((c + 0.055) / (1.0 + 0.055), 2.4) : c / 12.92;\\\\n}\\\\n\\\\nvec3 hsluv_toLinear(vec3 c) {\\\\n\\\\treturn vec3( hsluv_toLinear(c.r), hsluv_toLinear(c.g), hsluv_toLinear(c.b) );\\\\n}\\\\n\\\\nfloat hsluv_yToL(float Y){\\\\n\\\\treturn Y <= 0.0088564516790356308 ? Y * 903.2962962962963 : 116.0 * pow(Y, 1.0 / 3.0) - 16.0;\\\\n}\\\\n\\\\nfloat hsluv_lToY(float L) {\\\\n\\\\treturn L <= 8.0 ? L / 903.2962962962963 : pow((L + 16.0) / 116.0, 3.0);\\\\n}\\\\n\\\\nvec3 xyzToRgb(vec3 tuple) {\\\\n\\\\tconst mat3 m = mat3( \\\\n\\\\t\\\\t3.2409699419045214 ,-1.5373831775700935 ,-0.49861076029300328 ,\\\\n\\\\t\\\\t-0.96924363628087983 , 1.8759675015077207 , 0.041555057407175613,\\\\n\\\\t\\\\t0.055630079696993609,-0.20397695888897657, 1.0569715142428786 );\\\\n\\\\t\\\\n\\\\treturn hsluv_fromLinear(tuple*m);\\\\n}\\\\n\\\\nvec3 rgbToXyz(vec3 tuple) {\\\\n\\\\tconst mat3 m = mat3(\\\\n\\\\t\\\\t0.41239079926595948 , 0.35758433938387796, 0.18048078840183429 ,\\\\n\\\\t\\\\t0.21263900587151036 , 0.71516867876775593, 0.072192315360733715,\\\\n\\\\t\\\\t0.019330818715591851, 0.11919477979462599, 0.95053215224966058 \\\\n\\\\t);\\\\n\\\\treturn hsluv_toLinear(tuple) * m;\\\\n}\\\\n\\\\nvec3 xyzToLuv(vec3 tuple){\\\\n\\\\tfloat X = tuple.x;\\\\n\\\\tfloat Y = tuple.y;\\\\n\\\\tfloat Z = tuple.z;\\\\n\\\\n\\\\tfloat L = hsluv_yToL(Y);\\\\n\\\\t\\\\n\\\\tfloat div = 1./dot(tuple,vec3(1,15,3)); \\\\n\\\\n\\\\treturn vec3(\\\\n\\\\t\\\\t1.,\\\\n\\\\t\\\\t(52. * (X*div) - 2.57179),\\\\n\\\\t\\\\t(117.* (Y*div) - 6.08816)\\\\n\\\\t) * L;\\\\n}\\\\n\\\\n\\\\nvec3 luvToXyz(vec3 tuple) {\\\\n\\\\tfloat L = tuple.x;\\\\n\\\\n\\\\tfloat U = tuple.y / (13.0 * L) + 0.19783000664283681;\\\\n\\\\tfloat V = tuple.z / (13.0 * L) + 0.468319994938791;\\\\n\\\\n\\\\tfloat Y = hsluv_lToY(L);\\\\n\\\\tfloat X = 2.25 * U * Y / V;\\\\n\\\\tfloat Z = (3./V - 5.)*Y - (X/3.);\\\\n\\\\n\\\\treturn vec3(X, Y, Z);\\\\n}\\\\n\\\\nvec3 luvToLch(vec3 tuple) {\\\\n\\\\tfloat L = tuple.x;\\\\n\\\\tfloat U = tuple.y;\\\\n\\\\tfloat V = tuple.z;\\\\n\\\\n\\\\tfloat C = length(tuple.yz);\\\\n\\\\tfloat H = degrees(atan(V,U));\\\\n\\\\tif (H < 0.0) {\\\\n\\\\t\\\\tH = 360.0 + H;\\\\n\\\\t}\\\\n\\\\t\\\\n\\\\treturn vec3(L, C, H);\\\\n}\\\\n\\\\nvec3 lchToLuv(vec3 tuple) {\\\\n\\\\tfloat hrad = radians(tuple.b);\\\\n\\\\treturn vec3(\\\\n\\\\t\\\\ttuple.r,\\\\n\\\\t\\\\tcos(hrad) * tuple.g,\\\\n\\\\t\\\\tsin(hrad) * tuple.g\\\\n\\\\t);\\\\n}\\\\n\\\\nvec3 hsluvToLch(vec3 tuple) {\\\\n\\\\ttuple.g *= hsluv_maxChromaForLH(tuple.b, tuple.r) * .01;\\\\n\\\\treturn tuple.bgr;\\\\n}\\\\n\\\\nvec3 lchToHsluv(vec3 tuple) {\\\\n\\\\ttuple.g /= hsluv_maxChromaForLH(tuple.r, tuple.b) * .01;\\\\n\\\\treturn tuple.bgr;\\\\n}\\\\n\\\\nvec3 hpluvToLch(vec3 tuple) {\\\\n\\\\ttuple.g *= hsluv_maxSafeChromaForL(tuple.b) * .01;\\\\n\\\\treturn tuple.bgr;\\\\n}\\\\n\\\\nvec3 lchToHpluv(vec3 tuple) {\\\\n\\\\ttuple.g /= hsluv_maxSafeChromaForL(tuple.r) * .01;\\\\n\\\\treturn tuple.bgr;\\\\n}\\\\n\\\\nvec3 lchToRgb(vec3 tuple) {\\\\n\\\\treturn xyzToRgb(luvToXyz(lchToLuv(tuple)));\\\\n}\\\\n\\\\nvec3 rgbToLch(vec3 tuple) {\\\\n\\\\treturn luvToLch(xyzToLuv(rgbToXyz(tuple)));\\\\n}\\\\n\\\\nvec3 hsluvToRgb(vec3 tuple) {\\\\n\\\\treturn lchToRgb(hsluvToLch(tuple));\\\\n}\\\\n\\\\nvec3 rgbToHsluv(vec3 tuple) {\\\\n\\\\treturn lchToHsluv(rgbToLch(tuple));\\\\n}\\\\n\\\\nvec3 hpluvToRgb(vec3 tuple) {\\\\n\\\\treturn lchToRgb(hpluvToLch(tuple));\\\\n}\\\\n\\\\nvec3 rgbToHpluv(vec3 tuple) {\\\\n\\\\treturn lchToHpluv(rgbToLch(tuple));\\\\n}\\\\n\\\\nvec3 luvToRgb(vec3 tuple){\\\\n\\\\treturn xyzToRgb(luvToXyz(tuple));\\\\n}\\\\n\\\\n// allow vec4's\\\\nvec4 xyzToRgb(vec4 c) {return vec4( xyzToRgb( vec3(c.x,c.y,c.z) ), c.a);}\\\\nvec4 rgbToXyz(vec4 c) {return vec4( rgbToXyz( vec3(c.x,c.y,c.z) ), c.a);}\\\\nvec4 xyzToLuv(vec4 c) {return vec4( xyzToLuv( vec3(c.x,c.y,c.z) ), c.a);}\\\\nvec4 luvToXyz(vec4 c) {return vec4( luvToXyz( vec3(c.x,c.y,c.z) ), c.a);}\\\\nvec4 luvToLch(vec4 c) {return vec4( luvToLch( vec3(c.x,c.y,c.z) ), c.a);}\\\\nvec4 lchToLuv(vec4 c) {return vec4( lchToLuv( vec3(c.x,c.y,c.z) ), c.a);}\\\\nvec4 hsluvToLch(vec4 c) {return vec4( hsluvToLch( vec3(c.x,c.y,c.z) ), c.a);}\\\\nvec4 lchToHsluv(vec4 c) {return vec4( lchToHsluv( vec3(c.x,c.y,c.z) ), c.a);}\\\\nvec4 hpluvToLch(vec4 c) {return vec4( hpluvToLch( vec3(c.x,c.y,c.z) ), c.a);}\\\\nvec4 lchToHpluv(vec4 c) {return vec4( lchToHpluv( vec3(c.x,c.y,c.z) ), c.a);}\\\\nvec4 lchToRgb(vec4 c) {return vec4( lchToRgb( vec3(c.x,c.y,c.z) ), c.a);}\\\\nvec4 rgbToLch(vec4 c) {return vec4( rgbToLch( vec3(c.x,c.y,c.z) ), c.a);}\\\\nvec4 hsluvToRgb(vec4 c) {return vec4( hsluvToRgb( vec3(c.x,c.y,c.z) ), c.a);}\\\\nvec4 rgbToHsluv(vec4 c) {return vec4( rgbToHsluv( vec3(c.x,c.y,c.z) ), c.a);}\\\\nvec4 hpluvToRgb(vec4 c) {return vec4( hpluvToRgb( vec3(c.x,c.y,c.z) ), c.a);}\\\\nvec4 rgbToHpluv(vec4 c) {return vec4( rgbToHpluv( vec3(c.x,c.y,c.z) ), c.a);}\\\\nvec4 luvToRgb(vec4 c) {return vec4( luvToRgb( vec3(c.x,c.y,c.z) ), c.a);}\\\\n// allow 3 floats\\\\nvec3 xyzToRgb(float x, float y, float z) {return xyzToRgb( vec3(x,y,z) );}\\\\nvec3 rgbToXyz(float x, float y, float z) {return rgbToXyz( vec3(x,y,z) );}\\\\nvec3 xyzToLuv(float x, float y, float z) {return xyzToLuv( vec3(x,y,z) );}\\\\nvec3 luvToXyz(float x, float y, float z) {return luvToXyz( vec3(x,y,z) );}\\\\nvec3 luvToLch(float x, float y, float z) {return luvToLch( vec3(x,y,z) );}\\\\nvec3 lchToLuv(float x, float y, float z) {return lchToLuv( vec3(x,y,z) );}\\\\nvec3 hsluvToLch(float x, float y, float z) {return hsluvToLch( vec3(x,y,z) );}\\\\nvec3 lchToHsluv(float x, float y, float z) {return lchToHsluv( vec3(x,y,z) );}\\\\nvec3 hpluvToLch(float x, float y, float z) {return hpluvToLch( vec3(x,y,z) );}\\\\nvec3 lchToHpluv(float x, float y, float z) {return lchToHpluv( vec3(x,y,z) );}\\\\nvec3 lchToRgb(float x, float y, float z) {return lchToRgb( vec3(x,y,z) );}\\\\nvec3 rgbToLch(float x, float y, float z) {return rgbToLch( vec3(x,y,z) );}\\\\nvec3 hsluvToRgb(float x, float y, float z) {return hsluvToRgb( vec3(x,y,z) );}\\\\nvec3 rgbToHsluv(float x, float y, float z) {return rgbToHsluv( vec3(x,y,z) );}\\\\nvec3 hpluvToRgb(float x, float y, float z) {return hpluvToRgb( vec3(x,y,z) );}\\\\nvec3 rgbToHpluv(float x, float y, float z) {return rgbToHpluv( vec3(x,y,z) );}\\\\nvec3 luvToRgb(float x, float y, float z) {return luvToRgb( vec3(x,y,z) );}\\\\n// allow 4 floats\\\\nvec4 xyzToRgb(float x, float y, float z, float a) {return xyzToRgb( vec4(x,y,z,a) );}\\\\nvec4 rgbToXyz(float x, float y, float z, float a) {return rgbToXyz( vec4(x,y,z,a) );}\\\\nvec4 xyzToLuv(float x, float y, float z, float a) {return xyzToLuv( vec4(x,y,z,a) );}\\\\nvec4 luvToXyz(float x, float y, float z, float a) {return luvToXyz( vec4(x,y,z,a) );}\\\\nvec4 luvToLch(float x, float y, float z, float a) {return luvToLch( vec4(x,y,z,a) );}\\\\nvec4 lchToLuv(float x, float y, float z, float a) {return lchToLuv( vec4(x,y,z,a) );}\\\\nvec4 hsluvToLch(float x, float y, float z, float a) {return hsluvToLch( vec4(x,y,z,a) );}\\\\nvec4 lchToHsluv(float x, float y, float z, float a) {return lchToHsluv( vec4(x,y,z,a) );}\\\\nvec4 hpluvToLch(float x, float y, float z, float a) {return hpluvToLch( vec4(x,y,z,a) );}\\\\nvec4 lchToHpluv(float x, float y, float z, float a) {return lchToHpluv( vec4(x,y,z,a) );}\\\\nvec4 lchToRgb(float x, float y, float z, float a) {return lchToRgb( vec4(x,y,z,a) );}\\\\nvec4 rgbToLch(float x, float y, float z, float a) {return rgbToLch( vec4(x,y,z,a) );}\\\\nvec4 hsluvToRgb(float x, float y, float z, float a) {return hsluvToRgb( vec4(x,y,z,a) );}\\\\nvec4 rgbToHslul(float x, float y, float z, float a) {return rgbToHsluv( vec4(x,y,z,a) );}\\\\nvec4 hpluvToRgb(float x, float y, float z, float a) {return hpluvToRgb( vec4(x,y,z,a) );}\\\\nvec4 rgbToHpluv(float x, float y, float z, float a) {return rgbToHpluv( vec4(x,y,z,a) );}\\\\nvec4 luvToRgb(float x, float y, float z, float a) {return luvToRgb( vec4(x,y,z,a) );}\\\\n\\\\n/*\\\\nEND HSLUV-GLSL\\\\n*/\\\\n\\\\n\\\\n// from https://gist.github.com/mattatz/44f081cac87e2f7c8980\\\\n// converted to glsl by gui@polygonjs.com\\\\n// and made function names consistent with the ones above\\\\n/*\\\\n * Conversion between RGB and LAB colorspace.\\\\n * Import from flowabs glsl program : https://code.google.com/p/flowabs/source/browse/glsl/?r=f36cbdcf7790a28d90f09e2cf89ec9a64911f138\\\\n */\\\\n\\\\n\\\\n\\\\nvec3 xyzToLab( vec3 c ) {\\\\n\\\\tvec3 n = c / vec3(95.047, 100, 108.883);\\\\n\\\\tvec3 v;\\\\n\\\\tv.x = ( n.x > 0.008856 ) ? pow( n.x, 1.0 / 3.0 ) : ( 7.787 * n.x ) + ( 16.0 / 116.0 );\\\\n\\\\tv.y = ( n.y > 0.008856 ) ? pow( n.y, 1.0 / 3.0 ) : ( 7.787 * n.y ) + ( 16.0 / 116.0 );\\\\n\\\\tv.z = ( n.z > 0.008856 ) ? pow( n.z, 1.0 / 3.0 ) : ( 7.787 * n.z ) + ( 16.0 / 116.0 );\\\\n\\\\treturn vec3(( 116.0 * v.y ) - 16.0, 500.0 * ( v.x - v.y ), 200.0 * ( v.y - v.z ));\\\\n}\\\\n\\\\nvec3 rgbToLab( vec3 c ) {\\\\n\\\\tvec3 lab = xyzToLab( rgbToXyz( c ) );\\\\n\\\\treturn vec3( lab.x / 100.0, 0.5 + 0.5 * ( lab.y / 127.0 ), 0.5 + 0.5 * ( lab.z / 127.0 ));\\\\n}\\\\n\\\\nvec3 labToXyz( vec3 c ) {\\\\n\\\\tfloat fy = ( c.x + 16.0 ) / 116.0;\\\\n\\\\tfloat fx = c.y / 500.0 + fy;\\\\n\\\\tfloat fz = fy - c.z / 200.0;\\\\n\\\\treturn vec3(\\\\n\\\\t\\\\t 95.047 * (( fx > 0.206897 ) ? fx * fx * fx : ( fx - 16.0 / 116.0 ) / 7.787),\\\\n\\\\t\\\\t100.000 * (( fy > 0.206897 ) ? fy * fy * fy : ( fy - 16.0 / 116.0 ) / 7.787),\\\\n\\\\t\\\\t108.883 * (( fz > 0.206897 ) ? fz * fz * fz : ( fz - 16.0 / 116.0 ) / 7.787)\\\\n\\\\t);\\\\n}\\\\n\\\\n\\\\n\\\\nvec3 labToRgb( vec3 c ) {\\\\n\\\\treturn xyzToRgb( labToXyz( vec3(100.0 * c.x, 2.0 * 127.0 * (c.y - 0.5), 2.0 * 127.0 * (c.z - 0.5)) ) );\\\\n}\\\\\\\"));const i=hf.vector3(this.variableForInputParam(this.p.hsluv)),s=this.glVarName(\\\\\\\"rgb\\\\\\\");n.push(`vec3 ${s} = hsluvToRgb(${i}.x * 360.0, ${i}.y * 100.0, ${i}.z * 100.0)`),t.addDefinitions(this,e),t.addBodyLines(this,n)}}const SI=new class extends la{constructor(){super(...arguments),this.hsv=aa.VECTOR3([1,1,1])}};class CI extends df{constructor(){super(...arguments),this.paramsConfig=SI}static type(){return\\\\\\\"hsvToRgb\\\\\\\"}initializeNode(){super.initializeNode(),this.io.outputs.setNamedOutputConnectionPoints([new Ho(\\\\\\\"rgb\\\\\\\",Bo.VEC3)])}setLines(t){const e=[],n=[];e.push(new Tf(this,\\\\\\\"// https://github.com/hughsk/glsl-hsv2rgb\\\\n// https://stackoverflow.com/questions/15095909/from-rgb-to-hsv-in-opengl-glsl\\\\nvec3 hsv2rgb(vec3 c) {\\\\n\\\\tvec4 K = vec4(1.0, 2.0 / 3.0, 1.0 / 3.0, 3.0);\\\\n\\\\tvec3 p = abs(fract(c.xxx + K.xyz) * 6.0 - K.www);\\\\n\\\\treturn c.z * mix(K.xxx, clamp(p - K.xxx, 0.0, 1.0), c.y);\\\\n}\\\\\\\"));const i=hf.vector3(this.variableForInputParam(this.p.hsv)),s=this.glVarName(\\\\\\\"rgb\\\\\\\");n.push(`vec3 ${s} = hsv2rgb(${i})`),t.addDefinitions(this,e),t.addBodyLines(this,n)}}const NI=\\\\\\\"condition\\\\\\\";const LI=new class extends la{};class OI extends vI{constructor(){super(...arguments),this.paramsConfig=LI}static type(){return\\\\\\\"ifThen\\\\\\\"}_expected_inputs_count(){const t=this.io.connections.inputConnections();return t?Math.max(t.length+1,2):2}_expected_input_types(){const t=[Bo.BOOL],e=Bo.FLOAT,n=this.io.connections.inputConnections(),i=this._expected_inputs_count();for(let s=1;s[\\\\\\\"value0\\\\\\\",\\\\\\\"value1\\\\\\\",\\\\\\\"blend\\\\\\\"][t])),this.io.connection_points.set_output_name_function(this._gl_output_name.bind(this)),this.io.connection_points.set_expected_input_types_function(this._expected_input_types.bind(this)),this.io.connection_points.set_expected_output_types_function(this._expected_output_types.bind(this))}_gl_output_name(){return\\\\\\\"mix\\\\\\\"}_expected_input_types(){const t=this.io.connection_points.first_input_connection_type()||Bo.FLOAT;return[t,t,Bo.FLOAT]}_expected_output_types(){return[this._expected_input_types()[0]]}}const YI=\\\\\\\"mvMult\\\\\\\";const $I=new class extends la{constructor(){super(...arguments),this.vector=aa.VECTOR3([0,0,0])}};class JI extends df{constructor(){super(...arguments),this.paramsConfig=$I}static type(){return\\\\\\\"modelViewMatrixMult\\\\\\\"}initializeNode(){super.initializeNode(),this.io.outputs.setNamedOutputConnectionPoints([new Ho(YI,Bo.VEC4)])}setLines(t){if(t.current_shader_name==xf.VERTEX){const e=hf.vector3(this.variableForInputParam(this.p.vector)),n=`vec4 ${this.glVarName(YI)} = modelViewMatrix * vec4(${e}, 1.0)`;t.addBodyLines(this,[n],xf.VERTEX)}}}const ZI={mult:1};var QI;!function(t){t.VALUE=\\\\\\\"value\\\\\\\",t.PRE_ADD=\\\\\\\"preAdd\\\\\\\",t.MULT=\\\\\\\"mult\\\\\\\",t.POST_ADD=\\\\\\\"postAdd\\\\\\\"}(QI||(QI={}));class KI extends wP{static type(){return\\\\\\\"multAdd\\\\\\\"}_gl_input_name(t){return[QI.VALUE,QI.PRE_ADD,QI.MULT,QI.POST_ADD][t]}paramDefaultValue(t){return ZI[t]}setLines(t){const e=hf.any(this.variableForInput(QI.VALUE)),n=hf.any(this.variableForInput(QI.PRE_ADD)),i=hf.any(this.variableForInput(QI.MULT)),s=hf.any(this.variableForInput(QI.POST_ADD)),r=this._expected_output_types()[0],o=this.io.outputs.namedOutputConnectionPoints()[0].name(),a=`${r} ${this.glVarName(o)} = (${i}*(${e} + ${n})) + ${s}`;t.addBodyLines(this,[a])}}class tF extends yP{static type(){return\\\\\\\"negate\\\\\\\"}initializeNode(){super.initializeNode(),this.io.connection_points.set_input_name_function((t=>[\\\\\\\"in\\\\\\\"][t]))}_gl_input_name(t){return[\\\\\\\"in\\\\\\\"][t]}setLines(t){const e=hf.any(this.variableForInput(this._gl_input_name(0))),n=`${this.io.inputs.namedInputConnectionPoints()[0].type()} ${this.glVarName(this.io.connection_points.output_name(0))} = -1.0 * ${e}`;t.addBodyLines(this,[n])}}var eF;!function(t){t.CLASSIC_PERLIN_2D=\\\\\\\"Classic Perlin 2D\\\\\\\",t.CLASSIC_PERLIN_3D=\\\\\\\"Classic Perlin 3D\\\\\\\",t.CLASSIC_PERLIN_4D=\\\\\\\"Classic Perlin 4D\\\\\\\",t.NOISE_2D=\\\\\\\"noise2D\\\\\\\",t.NOISE_3D=\\\\\\\"noise3D\\\\\\\",t.NOISE_4D=\\\\\\\"noise4D\\\\\\\"}(eF||(eF={}));const nF=[eF.CLASSIC_PERLIN_2D,eF.CLASSIC_PERLIN_3D,eF.CLASSIC_PERLIN_4D,eF.NOISE_2D,eF.NOISE_3D,eF.NOISE_4D],iF={[eF.CLASSIC_PERLIN_2D]:'//\\\\n// GLSL textureless classic 2D noise \\\\\\\"cnoise\\\\\\\",\\\\n// with an RSL-style periodic variant \\\\\\\"pnoise\\\\\\\".\\\\n// Author: Stefan Gustavson (stefan.gustavson@liu.se)\\\\n// Version: 2011-08-22\\\\n//\\\\n// Many thanks to Ian McEwan of Ashima Arts for the\\\\n// ideas for permutation and gradient selection.\\\\n//\\\\n// Copyright (c) 2011 Stefan Gustavson. All rights reserved.\\\\n// Distributed under the MIT license. See LICENSE file.\\\\n// https://github.com/stegu/webgl-noise\\\\n//\\\\n\\\\n\\\\n// Classic Perlin noise\\\\nfloat cnoise(vec2 P)\\\\n{\\\\n vec4 Pi = floor(P.xyxy) + vec4(0.0, 0.0, 1.0, 1.0);\\\\n vec4 Pf = fract(P.xyxy) - vec4(0.0, 0.0, 1.0, 1.0);\\\\n Pi = mod289(Pi); // To avoid truncation effects in permutation\\\\n vec4 ix = Pi.xzxz;\\\\n vec4 iy = Pi.yyww;\\\\n vec4 fx = Pf.xzxz;\\\\n vec4 fy = Pf.yyww;\\\\n\\\\n vec4 i = permute(permute(ix) + iy);\\\\n\\\\n vec4 gx = fract(i * (1.0 / 41.0)) * 2.0 - 1.0 ;\\\\n vec4 gy = abs(gx) - 0.5 ;\\\\n vec4 tx = floor(gx + 0.5);\\\\n gx = gx - tx;\\\\n\\\\n vec2 g00 = vec2(gx.x,gy.x);\\\\n vec2 g10 = vec2(gx.y,gy.y);\\\\n vec2 g01 = vec2(gx.z,gy.z);\\\\n vec2 g11 = vec2(gx.w,gy.w);\\\\n\\\\n vec4 norm = taylorInvSqrt(vec4(dot(g00, g00), dot(g01, g01), dot(g10, g10), dot(g11, g11)));\\\\n g00 *= norm.x; \\\\n g01 *= norm.y; \\\\n g10 *= norm.z; \\\\n g11 *= norm.w; \\\\n\\\\n float n00 = dot(g00, vec2(fx.x, fy.x));\\\\n float n10 = dot(g10, vec2(fx.y, fy.y));\\\\n float n01 = dot(g01, vec2(fx.z, fy.z));\\\\n float n11 = dot(g11, vec2(fx.w, fy.w));\\\\n\\\\n vec2 fade_xy = fade(Pf.xy);\\\\n vec2 n_x = mix(vec2(n00, n01), vec2(n10, n11), fade_xy.x);\\\\n float n_xy = mix(n_x.x, n_x.y, fade_xy.y);\\\\n return 2.3 * n_xy;\\\\n}\\\\n\\\\n// Classic Perlin noise, periodic variant\\\\nfloat pnoise(vec2 P, vec2 rep)\\\\n{\\\\n vec4 Pi = floor(P.xyxy) + vec4(0.0, 0.0, 1.0, 1.0);\\\\n vec4 Pf = fract(P.xyxy) - vec4(0.0, 0.0, 1.0, 1.0);\\\\n Pi = mod(Pi, rep.xyxy); // To create noise with explicit period\\\\n Pi = mod289(Pi); // To avoid truncation effects in permutation\\\\n vec4 ix = Pi.xzxz;\\\\n vec4 iy = Pi.yyww;\\\\n vec4 fx = Pf.xzxz;\\\\n vec4 fy = Pf.yyww;\\\\n\\\\n vec4 i = permute(permute(ix) + iy);\\\\n\\\\n vec4 gx = fract(i * (1.0 / 41.0)) * 2.0 - 1.0 ;\\\\n vec4 gy = abs(gx) - 0.5 ;\\\\n vec4 tx = floor(gx + 0.5);\\\\n gx = gx - tx;\\\\n\\\\n vec2 g00 = vec2(gx.x,gy.x);\\\\n vec2 g10 = vec2(gx.y,gy.y);\\\\n vec2 g01 = vec2(gx.z,gy.z);\\\\n vec2 g11 = vec2(gx.w,gy.w);\\\\n\\\\n vec4 norm = taylorInvSqrt(vec4(dot(g00, g00), dot(g01, g01), dot(g10, g10), dot(g11, g11)));\\\\n g00 *= norm.x; \\\\n g01 *= norm.y; \\\\n g10 *= norm.z; \\\\n g11 *= norm.w; \\\\n\\\\n float n00 = dot(g00, vec2(fx.x, fy.x));\\\\n float n10 = dot(g10, vec2(fx.y, fy.y));\\\\n float n01 = dot(g01, vec2(fx.z, fy.z));\\\\n float n11 = dot(g11, vec2(fx.w, fy.w));\\\\n\\\\n vec2 fade_xy = fade(Pf.xy);\\\\n vec2 n_x = mix(vec2(n00, n01), vec2(n10, n11), fade_xy.x);\\\\n float n_xy = mix(n_x.x, n_x.y, fade_xy.y);\\\\n return 2.3 * n_xy;\\\\n}\\\\n',[eF.CLASSIC_PERLIN_3D]:'//\\\\n// GLSL textureless classic 3D noise \\\\\\\"cnoise\\\\\\\",\\\\n// with an RSL-style periodic variant \\\\\\\"pnoise\\\\\\\".\\\\n// Author: Stefan Gustavson (stefan.gustavson@liu.se)\\\\n// Version: 2011-10-11\\\\n//\\\\n// Many thanks to Ian McEwan of Ashima Arts for the\\\\n// ideas for permutation and gradient selection.\\\\n//\\\\n// Copyright (c) 2011 Stefan Gustavson. All rights reserved.\\\\n// Distributed under the MIT license. See LICENSE file.\\\\n// https://github.com/stegu/webgl-noise\\\\n//\\\\n\\\\n// Classic Perlin noise\\\\nfloat cnoise(vec3 P)\\\\n{\\\\n vec3 Pi0 = floor(P); // Integer part for indexing\\\\n vec3 Pi1 = Pi0 + vec3(1.0); // Integer part + 1\\\\n Pi0 = mod289(Pi0);\\\\n Pi1 = mod289(Pi1);\\\\n vec3 Pf0 = fract(P); // Fractional part for interpolation\\\\n vec3 Pf1 = Pf0 - vec3(1.0); // Fractional part - 1.0\\\\n vec4 ix = vec4(Pi0.x, Pi1.x, Pi0.x, Pi1.x);\\\\n vec4 iy = vec4(Pi0.yy, Pi1.yy);\\\\n vec4 iz0 = Pi0.zzzz;\\\\n vec4 iz1 = Pi1.zzzz;\\\\n\\\\n vec4 ixy = permute(permute(ix) + iy);\\\\n vec4 ixy0 = permute(ixy + iz0);\\\\n vec4 ixy1 = permute(ixy + iz1);\\\\n\\\\n vec4 gx0 = ixy0 * (1.0 / 7.0);\\\\n vec4 gy0 = fract(floor(gx0) * (1.0 / 7.0)) - 0.5;\\\\n gx0 = fract(gx0);\\\\n vec4 gz0 = vec4(0.5) - abs(gx0) - abs(gy0);\\\\n vec4 sz0 = step(gz0, vec4(0.0));\\\\n gx0 -= sz0 * (step(0.0, gx0) - 0.5);\\\\n gy0 -= sz0 * (step(0.0, gy0) - 0.5);\\\\n\\\\n vec4 gx1 = ixy1 * (1.0 / 7.0);\\\\n vec4 gy1 = fract(floor(gx1) * (1.0 / 7.0)) - 0.5;\\\\n gx1 = fract(gx1);\\\\n vec4 gz1 = vec4(0.5) - abs(gx1) - abs(gy1);\\\\n vec4 sz1 = step(gz1, vec4(0.0));\\\\n gx1 -= sz1 * (step(0.0, gx1) - 0.5);\\\\n gy1 -= sz1 * (step(0.0, gy1) - 0.5);\\\\n\\\\n vec3 g000 = vec3(gx0.x,gy0.x,gz0.x);\\\\n vec3 g100 = vec3(gx0.y,gy0.y,gz0.y);\\\\n vec3 g010 = vec3(gx0.z,gy0.z,gz0.z);\\\\n vec3 g110 = vec3(gx0.w,gy0.w,gz0.w);\\\\n vec3 g001 = vec3(gx1.x,gy1.x,gz1.x);\\\\n vec3 g101 = vec3(gx1.y,gy1.y,gz1.y);\\\\n vec3 g011 = vec3(gx1.z,gy1.z,gz1.z);\\\\n vec3 g111 = vec3(gx1.w,gy1.w,gz1.w);\\\\n\\\\n vec4 norm0 = taylorInvSqrt(vec4(dot(g000, g000), dot(g010, g010), dot(g100, g100), dot(g110, g110)));\\\\n g000 *= norm0.x;\\\\n g010 *= norm0.y;\\\\n g100 *= norm0.z;\\\\n g110 *= norm0.w;\\\\n vec4 norm1 = taylorInvSqrt(vec4(dot(g001, g001), dot(g011, g011), dot(g101, g101), dot(g111, g111)));\\\\n g001 *= norm1.x;\\\\n g011 *= norm1.y;\\\\n g101 *= norm1.z;\\\\n g111 *= norm1.w;\\\\n\\\\n float n000 = dot(g000, Pf0);\\\\n float n100 = dot(g100, vec3(Pf1.x, Pf0.yz));\\\\n float n010 = dot(g010, vec3(Pf0.x, Pf1.y, Pf0.z));\\\\n float n110 = dot(g110, vec3(Pf1.xy, Pf0.z));\\\\n float n001 = dot(g001, vec3(Pf0.xy, Pf1.z));\\\\n float n101 = dot(g101, vec3(Pf1.x, Pf0.y, Pf1.z));\\\\n float n011 = dot(g011, vec3(Pf0.x, Pf1.yz));\\\\n float n111 = dot(g111, Pf1);\\\\n\\\\n vec3 fade_xyz = fade(Pf0);\\\\n vec4 n_z = mix(vec4(n000, n100, n010, n110), vec4(n001, n101, n011, n111), fade_xyz.z);\\\\n vec2 n_yz = mix(n_z.xy, n_z.zw, fade_xyz.y);\\\\n float n_xyz = mix(n_yz.x, n_yz.y, fade_xyz.x); \\\\n return 2.2 * n_xyz;\\\\n}\\\\n\\\\n// Classic Perlin noise, periodic variant\\\\nfloat pnoise(vec3 P, vec3 rep)\\\\n{\\\\n vec3 Pi0 = mod(floor(P), rep); // Integer part, modulo period\\\\n vec3 Pi1 = mod(Pi0 + vec3(1.0), rep); // Integer part + 1, mod period\\\\n Pi0 = mod289(Pi0);\\\\n Pi1 = mod289(Pi1);\\\\n vec3 Pf0 = fract(P); // Fractional part for interpolation\\\\n vec3 Pf1 = Pf0 - vec3(1.0); // Fractional part - 1.0\\\\n vec4 ix = vec4(Pi0.x, Pi1.x, Pi0.x, Pi1.x);\\\\n vec4 iy = vec4(Pi0.yy, Pi1.yy);\\\\n vec4 iz0 = Pi0.zzzz;\\\\n vec4 iz1 = Pi1.zzzz;\\\\n\\\\n vec4 ixy = permute(permute(ix) + iy);\\\\n vec4 ixy0 = permute(ixy + iz0);\\\\n vec4 ixy1 = permute(ixy + iz1);\\\\n\\\\n vec4 gx0 = ixy0 * (1.0 / 7.0);\\\\n vec4 gy0 = fract(floor(gx0) * (1.0 / 7.0)) - 0.5;\\\\n gx0 = fract(gx0);\\\\n vec4 gz0 = vec4(0.5) - abs(gx0) - abs(gy0);\\\\n vec4 sz0 = step(gz0, vec4(0.0));\\\\n gx0 -= sz0 * (step(0.0, gx0) - 0.5);\\\\n gy0 -= sz0 * (step(0.0, gy0) - 0.5);\\\\n\\\\n vec4 gx1 = ixy1 * (1.0 / 7.0);\\\\n vec4 gy1 = fract(floor(gx1) * (1.0 / 7.0)) - 0.5;\\\\n gx1 = fract(gx1);\\\\n vec4 gz1 = vec4(0.5) - abs(gx1) - abs(gy1);\\\\n vec4 sz1 = step(gz1, vec4(0.0));\\\\n gx1 -= sz1 * (step(0.0, gx1) - 0.5);\\\\n gy1 -= sz1 * (step(0.0, gy1) - 0.5);\\\\n\\\\n vec3 g000 = vec3(gx0.x,gy0.x,gz0.x);\\\\n vec3 g100 = vec3(gx0.y,gy0.y,gz0.y);\\\\n vec3 g010 = vec3(gx0.z,gy0.z,gz0.z);\\\\n vec3 g110 = vec3(gx0.w,gy0.w,gz0.w);\\\\n vec3 g001 = vec3(gx1.x,gy1.x,gz1.x);\\\\n vec3 g101 = vec3(gx1.y,gy1.y,gz1.y);\\\\n vec3 g011 = vec3(gx1.z,gy1.z,gz1.z);\\\\n vec3 g111 = vec3(gx1.w,gy1.w,gz1.w);\\\\n\\\\n vec4 norm0 = taylorInvSqrt(vec4(dot(g000, g000), dot(g010, g010), dot(g100, g100), dot(g110, g110)));\\\\n g000 *= norm0.x;\\\\n g010 *= norm0.y;\\\\n g100 *= norm0.z;\\\\n g110 *= norm0.w;\\\\n vec4 norm1 = taylorInvSqrt(vec4(dot(g001, g001), dot(g011, g011), dot(g101, g101), dot(g111, g111)));\\\\n g001 *= norm1.x;\\\\n g011 *= norm1.y;\\\\n g101 *= norm1.z;\\\\n g111 *= norm1.w;\\\\n\\\\n float n000 = dot(g000, Pf0);\\\\n float n100 = dot(g100, vec3(Pf1.x, Pf0.yz));\\\\n float n010 = dot(g010, vec3(Pf0.x, Pf1.y, Pf0.z));\\\\n float n110 = dot(g110, vec3(Pf1.xy, Pf0.z));\\\\n float n001 = dot(g001, vec3(Pf0.xy, Pf1.z));\\\\n float n101 = dot(g101, vec3(Pf1.x, Pf0.y, Pf1.z));\\\\n float n011 = dot(g011, vec3(Pf0.x, Pf1.yz));\\\\n float n111 = dot(g111, Pf1);\\\\n\\\\n vec3 fade_xyz = fade(Pf0);\\\\n vec4 n_z = mix(vec4(n000, n100, n010, n110), vec4(n001, n101, n011, n111), fade_xyz.z);\\\\n vec2 n_yz = mix(n_z.xy, n_z.zw, fade_xyz.y);\\\\n float n_xyz = mix(n_yz.x, n_yz.y, fade_xyz.x); \\\\n return 2.2 * n_xyz;\\\\n}\\\\n',[eF.CLASSIC_PERLIN_4D]:'//\\\\n// GLSL textureless classic 4D noise \\\\\\\"cnoise\\\\\\\",\\\\n// with an RSL-style periodic variant \\\\\\\"pnoise\\\\\\\".\\\\n// Author: Stefan Gustavson (stefan.gustavson@liu.se)\\\\n// Version: 2011-08-22\\\\n//\\\\n// Many thanks to Ian McEwan of Ashima Arts for the\\\\n// ideas for permutation and gradient selection.\\\\n//\\\\n// Copyright (c) 2011 Stefan Gustavson. All rights reserved.\\\\n// Distributed under the MIT license. See LICENSE file.\\\\n// https://github.com/stegu/webgl-noise\\\\n//\\\\n\\\\n\\\\n\\\\n// Classic Perlin noise\\\\nfloat cnoise(vec4 P)\\\\n{\\\\n vec4 Pi0 = floor(P); // Integer part for indexing\\\\n vec4 Pi1 = Pi0 + 1.0; // Integer part + 1\\\\n Pi0 = mod289(Pi0);\\\\n Pi1 = mod289(Pi1);\\\\n vec4 Pf0 = fract(P); // Fractional part for interpolation\\\\n vec4 Pf1 = Pf0 - 1.0; // Fractional part - 1.0\\\\n vec4 ix = vec4(Pi0.x, Pi1.x, Pi0.x, Pi1.x);\\\\n vec4 iy = vec4(Pi0.yy, Pi1.yy);\\\\n vec4 iz0 = vec4(Pi0.zzzz);\\\\n vec4 iz1 = vec4(Pi1.zzzz);\\\\n vec4 iw0 = vec4(Pi0.wwww);\\\\n vec4 iw1 = vec4(Pi1.wwww);\\\\n\\\\n vec4 ixy = permute(permute(ix) + iy);\\\\n vec4 ixy0 = permute(ixy + iz0);\\\\n vec4 ixy1 = permute(ixy + iz1);\\\\n vec4 ixy00 = permute(ixy0 + iw0);\\\\n vec4 ixy01 = permute(ixy0 + iw1);\\\\n vec4 ixy10 = permute(ixy1 + iw0);\\\\n vec4 ixy11 = permute(ixy1 + iw1);\\\\n\\\\n vec4 gx00 = ixy00 * (1.0 / 7.0);\\\\n vec4 gy00 = floor(gx00) * (1.0 / 7.0);\\\\n vec4 gz00 = floor(gy00) * (1.0 / 6.0);\\\\n gx00 = fract(gx00) - 0.5;\\\\n gy00 = fract(gy00) - 0.5;\\\\n gz00 = fract(gz00) - 0.5;\\\\n vec4 gw00 = vec4(0.75) - abs(gx00) - abs(gy00) - abs(gz00);\\\\n vec4 sw00 = step(gw00, vec4(0.0));\\\\n gx00 -= sw00 * (step(0.0, gx00) - 0.5);\\\\n gy00 -= sw00 * (step(0.0, gy00) - 0.5);\\\\n\\\\n vec4 gx01 = ixy01 * (1.0 / 7.0);\\\\n vec4 gy01 = floor(gx01) * (1.0 / 7.0);\\\\n vec4 gz01 = floor(gy01) * (1.0 / 6.0);\\\\n gx01 = fract(gx01) - 0.5;\\\\n gy01 = fract(gy01) - 0.5;\\\\n gz01 = fract(gz01) - 0.5;\\\\n vec4 gw01 = vec4(0.75) - abs(gx01) - abs(gy01) - abs(gz01);\\\\n vec4 sw01 = step(gw01, vec4(0.0));\\\\n gx01 -= sw01 * (step(0.0, gx01) - 0.5);\\\\n gy01 -= sw01 * (step(0.0, gy01) - 0.5);\\\\n\\\\n vec4 gx10 = ixy10 * (1.0 / 7.0);\\\\n vec4 gy10 = floor(gx10) * (1.0 / 7.0);\\\\n vec4 gz10 = floor(gy10) * (1.0 / 6.0);\\\\n gx10 = fract(gx10) - 0.5;\\\\n gy10 = fract(gy10) - 0.5;\\\\n gz10 = fract(gz10) - 0.5;\\\\n vec4 gw10 = vec4(0.75) - abs(gx10) - abs(gy10) - abs(gz10);\\\\n vec4 sw10 = step(gw10, vec4(0.0));\\\\n gx10 -= sw10 * (step(0.0, gx10) - 0.5);\\\\n gy10 -= sw10 * (step(0.0, gy10) - 0.5);\\\\n\\\\n vec4 gx11 = ixy11 * (1.0 / 7.0);\\\\n vec4 gy11 = floor(gx11) * (1.0 / 7.0);\\\\n vec4 gz11 = floor(gy11) * (1.0 / 6.0);\\\\n gx11 = fract(gx11) - 0.5;\\\\n gy11 = fract(gy11) - 0.5;\\\\n gz11 = fract(gz11) - 0.5;\\\\n vec4 gw11 = vec4(0.75) - abs(gx11) - abs(gy11) - abs(gz11);\\\\n vec4 sw11 = step(gw11, vec4(0.0));\\\\n gx11 -= sw11 * (step(0.0, gx11) - 0.5);\\\\n gy11 -= sw11 * (step(0.0, gy11) - 0.5);\\\\n\\\\n vec4 g0000 = vec4(gx00.x,gy00.x,gz00.x,gw00.x);\\\\n vec4 g1000 = vec4(gx00.y,gy00.y,gz00.y,gw00.y);\\\\n vec4 g0100 = vec4(gx00.z,gy00.z,gz00.z,gw00.z);\\\\n vec4 g1100 = vec4(gx00.w,gy00.w,gz00.w,gw00.w);\\\\n vec4 g0010 = vec4(gx10.x,gy10.x,gz10.x,gw10.x);\\\\n vec4 g1010 = vec4(gx10.y,gy10.y,gz10.y,gw10.y);\\\\n vec4 g0110 = vec4(gx10.z,gy10.z,gz10.z,gw10.z);\\\\n vec4 g1110 = vec4(gx10.w,gy10.w,gz10.w,gw10.w);\\\\n vec4 g0001 = vec4(gx01.x,gy01.x,gz01.x,gw01.x);\\\\n vec4 g1001 = vec4(gx01.y,gy01.y,gz01.y,gw01.y);\\\\n vec4 g0101 = vec4(gx01.z,gy01.z,gz01.z,gw01.z);\\\\n vec4 g1101 = vec4(gx01.w,gy01.w,gz01.w,gw01.w);\\\\n vec4 g0011 = vec4(gx11.x,gy11.x,gz11.x,gw11.x);\\\\n vec4 g1011 = vec4(gx11.y,gy11.y,gz11.y,gw11.y);\\\\n vec4 g0111 = vec4(gx11.z,gy11.z,gz11.z,gw11.z);\\\\n vec4 g1111 = vec4(gx11.w,gy11.w,gz11.w,gw11.w);\\\\n\\\\n vec4 norm00 = taylorInvSqrt(vec4(dot(g0000, g0000), dot(g0100, g0100), dot(g1000, g1000), dot(g1100, g1100)));\\\\n g0000 *= norm00.x;\\\\n g0100 *= norm00.y;\\\\n g1000 *= norm00.z;\\\\n g1100 *= norm00.w;\\\\n\\\\n vec4 norm01 = taylorInvSqrt(vec4(dot(g0001, g0001), dot(g0101, g0101), dot(g1001, g1001), dot(g1101, g1101)));\\\\n g0001 *= norm01.x;\\\\n g0101 *= norm01.y;\\\\n g1001 *= norm01.z;\\\\n g1101 *= norm01.w;\\\\n\\\\n vec4 norm10 = taylorInvSqrt(vec4(dot(g0010, g0010), dot(g0110, g0110), dot(g1010, g1010), dot(g1110, g1110)));\\\\n g0010 *= norm10.x;\\\\n g0110 *= norm10.y;\\\\n g1010 *= norm10.z;\\\\n g1110 *= norm10.w;\\\\n\\\\n vec4 norm11 = taylorInvSqrt(vec4(dot(g0011, g0011), dot(g0111, g0111), dot(g1011, g1011), dot(g1111, g1111)));\\\\n g0011 *= norm11.x;\\\\n g0111 *= norm11.y;\\\\n g1011 *= norm11.z;\\\\n g1111 *= norm11.w;\\\\n\\\\n float n0000 = dot(g0000, Pf0);\\\\n float n1000 = dot(g1000, vec4(Pf1.x, Pf0.yzw));\\\\n float n0100 = dot(g0100, vec4(Pf0.x, Pf1.y, Pf0.zw));\\\\n float n1100 = dot(g1100, vec4(Pf1.xy, Pf0.zw));\\\\n float n0010 = dot(g0010, vec4(Pf0.xy, Pf1.z, Pf0.w));\\\\n float n1010 = dot(g1010, vec4(Pf1.x, Pf0.y, Pf1.z, Pf0.w));\\\\n float n0110 = dot(g0110, vec4(Pf0.x, Pf1.yz, Pf0.w));\\\\n float n1110 = dot(g1110, vec4(Pf1.xyz, Pf0.w));\\\\n float n0001 = dot(g0001, vec4(Pf0.xyz, Pf1.w));\\\\n float n1001 = dot(g1001, vec4(Pf1.x, Pf0.yz, Pf1.w));\\\\n float n0101 = dot(g0101, vec4(Pf0.x, Pf1.y, Pf0.z, Pf1.w));\\\\n float n1101 = dot(g1101, vec4(Pf1.xy, Pf0.z, Pf1.w));\\\\n float n0011 = dot(g0011, vec4(Pf0.xy, Pf1.zw));\\\\n float n1011 = dot(g1011, vec4(Pf1.x, Pf0.y, Pf1.zw));\\\\n float n0111 = dot(g0111, vec4(Pf0.x, Pf1.yzw));\\\\n float n1111 = dot(g1111, Pf1);\\\\n\\\\n vec4 fade_xyzw = fade(Pf0);\\\\n vec4 n_0w = mix(vec4(n0000, n1000, n0100, n1100), vec4(n0001, n1001, n0101, n1101), fade_xyzw.w);\\\\n vec4 n_1w = mix(vec4(n0010, n1010, n0110, n1110), vec4(n0011, n1011, n0111, n1111), fade_xyzw.w);\\\\n vec4 n_zw = mix(n_0w, n_1w, fade_xyzw.z);\\\\n vec2 n_yzw = mix(n_zw.xy, n_zw.zw, fade_xyzw.y);\\\\n float n_xyzw = mix(n_yzw.x, n_yzw.y, fade_xyzw.x);\\\\n return 2.2 * n_xyzw;\\\\n}\\\\n\\\\n// Classic Perlin noise, periodic version\\\\nfloat pnoise(vec4 P, vec4 rep)\\\\n{\\\\n vec4 Pi0 = mod(floor(P), rep); // Integer part modulo rep\\\\n vec4 Pi1 = mod(Pi0 + 1.0, rep); // Integer part + 1 mod rep\\\\n Pi0 = mod289(Pi0);\\\\n Pi1 = mod289(Pi1);\\\\n vec4 Pf0 = fract(P); // Fractional part for interpolation\\\\n vec4 Pf1 = Pf0 - 1.0; // Fractional part - 1.0\\\\n vec4 ix = vec4(Pi0.x, Pi1.x, Pi0.x, Pi1.x);\\\\n vec4 iy = vec4(Pi0.yy, Pi1.yy);\\\\n vec4 iz0 = vec4(Pi0.zzzz);\\\\n vec4 iz1 = vec4(Pi1.zzzz);\\\\n vec4 iw0 = vec4(Pi0.wwww);\\\\n vec4 iw1 = vec4(Pi1.wwww);\\\\n\\\\n vec4 ixy = permute(permute(ix) + iy);\\\\n vec4 ixy0 = permute(ixy + iz0);\\\\n vec4 ixy1 = permute(ixy + iz1);\\\\n vec4 ixy00 = permute(ixy0 + iw0);\\\\n vec4 ixy01 = permute(ixy0 + iw1);\\\\n vec4 ixy10 = permute(ixy1 + iw0);\\\\n vec4 ixy11 = permute(ixy1 + iw1);\\\\n\\\\n vec4 gx00 = ixy00 * (1.0 / 7.0);\\\\n vec4 gy00 = floor(gx00) * (1.0 / 7.0);\\\\n vec4 gz00 = floor(gy00) * (1.0 / 6.0);\\\\n gx00 = fract(gx00) - 0.5;\\\\n gy00 = fract(gy00) - 0.5;\\\\n gz00 = fract(gz00) - 0.5;\\\\n vec4 gw00 = vec4(0.75) - abs(gx00) - abs(gy00) - abs(gz00);\\\\n vec4 sw00 = step(gw00, vec4(0.0));\\\\n gx00 -= sw00 * (step(0.0, gx00) - 0.5);\\\\n gy00 -= sw00 * (step(0.0, gy00) - 0.5);\\\\n\\\\n vec4 gx01 = ixy01 * (1.0 / 7.0);\\\\n vec4 gy01 = floor(gx01) * (1.0 / 7.0);\\\\n vec4 gz01 = floor(gy01) * (1.0 / 6.0);\\\\n gx01 = fract(gx01) - 0.5;\\\\n gy01 = fract(gy01) - 0.5;\\\\n gz01 = fract(gz01) - 0.5;\\\\n vec4 gw01 = vec4(0.75) - abs(gx01) - abs(gy01) - abs(gz01);\\\\n vec4 sw01 = step(gw01, vec4(0.0));\\\\n gx01 -= sw01 * (step(0.0, gx01) - 0.5);\\\\n gy01 -= sw01 * (step(0.0, gy01) - 0.5);\\\\n\\\\n vec4 gx10 = ixy10 * (1.0 / 7.0);\\\\n vec4 gy10 = floor(gx10) * (1.0 / 7.0);\\\\n vec4 gz10 = floor(gy10) * (1.0 / 6.0);\\\\n gx10 = fract(gx10) - 0.5;\\\\n gy10 = fract(gy10) - 0.5;\\\\n gz10 = fract(gz10) - 0.5;\\\\n vec4 gw10 = vec4(0.75) - abs(gx10) - abs(gy10) - abs(gz10);\\\\n vec4 sw10 = step(gw10, vec4(0.0));\\\\n gx10 -= sw10 * (step(0.0, gx10) - 0.5);\\\\n gy10 -= sw10 * (step(0.0, gy10) - 0.5);\\\\n\\\\n vec4 gx11 = ixy11 * (1.0 / 7.0);\\\\n vec4 gy11 = floor(gx11) * (1.0 / 7.0);\\\\n vec4 gz11 = floor(gy11) * (1.0 / 6.0);\\\\n gx11 = fract(gx11) - 0.5;\\\\n gy11 = fract(gy11) - 0.5;\\\\n gz11 = fract(gz11) - 0.5;\\\\n vec4 gw11 = vec4(0.75) - abs(gx11) - abs(gy11) - abs(gz11);\\\\n vec4 sw11 = step(gw11, vec4(0.0));\\\\n gx11 -= sw11 * (step(0.0, gx11) - 0.5);\\\\n gy11 -= sw11 * (step(0.0, gy11) - 0.5);\\\\n\\\\n vec4 g0000 = vec4(gx00.x,gy00.x,gz00.x,gw00.x);\\\\n vec4 g1000 = vec4(gx00.y,gy00.y,gz00.y,gw00.y);\\\\n vec4 g0100 = vec4(gx00.z,gy00.z,gz00.z,gw00.z);\\\\n vec4 g1100 = vec4(gx00.w,gy00.w,gz00.w,gw00.w);\\\\n vec4 g0010 = vec4(gx10.x,gy10.x,gz10.x,gw10.x);\\\\n vec4 g1010 = vec4(gx10.y,gy10.y,gz10.y,gw10.y);\\\\n vec4 g0110 = vec4(gx10.z,gy10.z,gz10.z,gw10.z);\\\\n vec4 g1110 = vec4(gx10.w,gy10.w,gz10.w,gw10.w);\\\\n vec4 g0001 = vec4(gx01.x,gy01.x,gz01.x,gw01.x);\\\\n vec4 g1001 = vec4(gx01.y,gy01.y,gz01.y,gw01.y);\\\\n vec4 g0101 = vec4(gx01.z,gy01.z,gz01.z,gw01.z);\\\\n vec4 g1101 = vec4(gx01.w,gy01.w,gz01.w,gw01.w);\\\\n vec4 g0011 = vec4(gx11.x,gy11.x,gz11.x,gw11.x);\\\\n vec4 g1011 = vec4(gx11.y,gy11.y,gz11.y,gw11.y);\\\\n vec4 g0111 = vec4(gx11.z,gy11.z,gz11.z,gw11.z);\\\\n vec4 g1111 = vec4(gx11.w,gy11.w,gz11.w,gw11.w);\\\\n\\\\n vec4 norm00 = taylorInvSqrt(vec4(dot(g0000, g0000), dot(g0100, g0100), dot(g1000, g1000), dot(g1100, g1100)));\\\\n g0000 *= norm00.x;\\\\n g0100 *= norm00.y;\\\\n g1000 *= norm00.z;\\\\n g1100 *= norm00.w;\\\\n\\\\n vec4 norm01 = taylorInvSqrt(vec4(dot(g0001, g0001), dot(g0101, g0101), dot(g1001, g1001), dot(g1101, g1101)));\\\\n g0001 *= norm01.x;\\\\n g0101 *= norm01.y;\\\\n g1001 *= norm01.z;\\\\n g1101 *= norm01.w;\\\\n\\\\n vec4 norm10 = taylorInvSqrt(vec4(dot(g0010, g0010), dot(g0110, g0110), dot(g1010, g1010), dot(g1110, g1110)));\\\\n g0010 *= norm10.x;\\\\n g0110 *= norm10.y;\\\\n g1010 *= norm10.z;\\\\n g1110 *= norm10.w;\\\\n\\\\n vec4 norm11 = taylorInvSqrt(vec4(dot(g0011, g0011), dot(g0111, g0111), dot(g1011, g1011), dot(g1111, g1111)));\\\\n g0011 *= norm11.x;\\\\n g0111 *= norm11.y;\\\\n g1011 *= norm11.z;\\\\n g1111 *= norm11.w;\\\\n\\\\n float n0000 = dot(g0000, Pf0);\\\\n float n1000 = dot(g1000, vec4(Pf1.x, Pf0.yzw));\\\\n float n0100 = dot(g0100, vec4(Pf0.x, Pf1.y, Pf0.zw));\\\\n float n1100 = dot(g1100, vec4(Pf1.xy, Pf0.zw));\\\\n float n0010 = dot(g0010, vec4(Pf0.xy, Pf1.z, Pf0.w));\\\\n float n1010 = dot(g1010, vec4(Pf1.x, Pf0.y, Pf1.z, Pf0.w));\\\\n float n0110 = dot(g0110, vec4(Pf0.x, Pf1.yz, Pf0.w));\\\\n float n1110 = dot(g1110, vec4(Pf1.xyz, Pf0.w));\\\\n float n0001 = dot(g0001, vec4(Pf0.xyz, Pf1.w));\\\\n float n1001 = dot(g1001, vec4(Pf1.x, Pf0.yz, Pf1.w));\\\\n float n0101 = dot(g0101, vec4(Pf0.x, Pf1.y, Pf0.z, Pf1.w));\\\\n float n1101 = dot(g1101, vec4(Pf1.xy, Pf0.z, Pf1.w));\\\\n float n0011 = dot(g0011, vec4(Pf0.xy, Pf1.zw));\\\\n float n1011 = dot(g1011, vec4(Pf1.x, Pf0.y, Pf1.zw));\\\\n float n0111 = dot(g0111, vec4(Pf0.x, Pf1.yzw));\\\\n float n1111 = dot(g1111, Pf1);\\\\n\\\\n vec4 fade_xyzw = fade(Pf0);\\\\n vec4 n_0w = mix(vec4(n0000, n1000, n0100, n1100), vec4(n0001, n1001, n0101, n1101), fade_xyzw.w);\\\\n vec4 n_1w = mix(vec4(n0010, n1010, n0110, n1110), vec4(n0011, n1011, n0111, n1111), fade_xyzw.w);\\\\n vec4 n_zw = mix(n_0w, n_1w, fade_xyzw.z);\\\\n vec2 n_yzw = mix(n_zw.xy, n_zw.zw, fade_xyzw.y);\\\\n float n_xyzw = mix(n_yzw.x, n_yzw.y, fade_xyzw.x);\\\\n return 2.2 * n_xyzw;\\\\n}\\\\n',[eF.NOISE_2D]:\\\\\\\"//\\\\n// Description : Array and textureless GLSL 2D simplex noise function.\\\\n// Author : Ian McEwan, Ashima Arts.\\\\n// Maintainer : stegu\\\\n// Lastmod : 20110822 (ijm)\\\\n// License : Copyright (C) 2011 Ashima Arts. All rights reserved.\\\\n// Distributed under the MIT License. See LICENSE file.\\\\n// https://github.com/ashima/webgl-noise\\\\n// https://github.com/stegu/webgl-noise\\\\n// \\\\n\\\\n\\\\nfloat snoise(vec2 v)\\\\n {\\\\n const vec4 C = vec4(0.211324865405187, // (3.0-sqrt(3.0))/6.0\\\\n 0.366025403784439, // 0.5*(sqrt(3.0)-1.0)\\\\n -0.577350269189626, // -1.0 + 2.0 * C.x\\\\n 0.024390243902439); // 1.0 / 41.0\\\\n// First corner\\\\n vec2 i = floor(v + dot(v, C.yy) );\\\\n vec2 x0 = v - i + dot(i, C.xx);\\\\n\\\\n// Other corners\\\\n vec2 i1;\\\\n //i1.x = step( x0.y, x0.x ); // x0.x > x0.y ? 1.0 : 0.0\\\\n //i1.y = 1.0 - i1.x;\\\\n i1 = (x0.x > x0.y) ? vec2(1.0, 0.0) : vec2(0.0, 1.0);\\\\n // x0 = x0 - 0.0 + 0.0 * C.xx ;\\\\n // x1 = x0 - i1 + 1.0 * C.xx ;\\\\n // x2 = x0 - 1.0 + 2.0 * C.xx ;\\\\n vec4 x12 = x0.xyxy + C.xxzz;\\\\n x12.xy -= i1;\\\\n\\\\n// Permutations\\\\n i = mod289(i); // Avoid truncation effects in permutation\\\\n vec3 p = permute( permute( i.y + vec3(0.0, i1.y, 1.0 ))\\\\n\\\\t\\\\t+ i.x + vec3(0.0, i1.x, 1.0 ));\\\\n\\\\n vec3 m = max(0.5 - vec3(dot(x0,x0), dot(x12.xy,x12.xy), dot(x12.zw,x12.zw)), 0.0);\\\\n m = m*m ;\\\\n m = m*m ;\\\\n\\\\n// Gradients: 41 points uniformly over a line, mapped onto a diamond.\\\\n// The ring size 17*17 = 289 is close to a multiple of 41 (41*7 = 287)\\\\n\\\\n vec3 x = 2.0 * fract(p * C.www) - 1.0;\\\\n vec3 h = abs(x) - 0.5;\\\\n vec3 ox = floor(x + 0.5);\\\\n vec3 a0 = x - ox;\\\\n\\\\n// Normalise gradients implicitly by scaling m\\\\n// Approximation of: m *= inversesqrt( a0*a0 + h*h );\\\\n m *= 1.79284291400159 - 0.85373472095314 * ( a0*a0 + h*h );\\\\n\\\\n// Compute final noise value at P\\\\n vec3 g;\\\\n g.x = a0.x * x0.x + h.x * x0.y;\\\\n g.yz = a0.yz * x12.xz + h.yz * x12.yw;\\\\n return 130.0 * dot(m, g);\\\\n}\\\\n\\\\\\\",[eF.NOISE_3D]:\\\\\\\"//\\\\n// Description : Array and textureless GLSL 2D/3D/4D simplex \\\\n// noise functions.\\\\n// Author : Ian McEwan, Ashima Arts.\\\\n// Maintainer : stegu\\\\n// Lastmod : 20110822 (ijm)\\\\n// License : Copyright (C) 2011 Ashima Arts. All rights reserved.\\\\n// Distributed under the MIT License. See LICENSE file.\\\\n// https://github.com/ashima/webgl-noise\\\\n// https://github.com/stegu/webgl-noise\\\\n// \\\\n\\\\n\\\\n\\\\nfloat snoise(vec3 v)\\\\n { \\\\n const vec2 C = vec2(1.0/6.0, 1.0/3.0) ;\\\\n const vec4 D = vec4(0.0, 0.5, 1.0, 2.0);\\\\n\\\\n// First corner\\\\n vec3 i = floor(v + dot(v, C.yyy) );\\\\n vec3 x0 = v - i + dot(i, C.xxx) ;\\\\n\\\\n// Other corners\\\\n vec3 g = step(x0.yzx, x0.xyz);\\\\n vec3 l = 1.0 - g;\\\\n vec3 i1 = min( g.xyz, l.zxy );\\\\n vec3 i2 = max( g.xyz, l.zxy );\\\\n\\\\n // x0 = x0 - 0.0 + 0.0 * C.xxx;\\\\n // x1 = x0 - i1 + 1.0 * C.xxx;\\\\n // x2 = x0 - i2 + 2.0 * C.xxx;\\\\n // x3 = x0 - 1.0 + 3.0 * C.xxx;\\\\n vec3 x1 = x0 - i1 + C.xxx;\\\\n vec3 x2 = x0 - i2 + C.yyy; // 2.0*C.x = 1/3 = C.y\\\\n vec3 x3 = x0 - D.yyy; // -1.0+3.0*C.x = -0.5 = -D.y\\\\n\\\\n// Permutations\\\\n i = mod289(i); \\\\n vec4 p = permute( permute( permute( \\\\n i.z + vec4(0.0, i1.z, i2.z, 1.0 ))\\\\n + i.y + vec4(0.0, i1.y, i2.y, 1.0 )) \\\\n + i.x + vec4(0.0, i1.x, i2.x, 1.0 ));\\\\n\\\\n// Gradients: 7x7 points over a square, mapped onto an octahedron.\\\\n// The ring size 17*17 = 289 is close to a multiple of 49 (49*6 = 294)\\\\n float n_ = 0.142857142857; // 1.0/7.0\\\\n vec3 ns = n_ * D.wyz - D.xzx;\\\\n\\\\n vec4 j = p - 49.0 * floor(p * ns.z * ns.z); // mod(p,7*7)\\\\n\\\\n vec4 x_ = floor(j * ns.z);\\\\n vec4 y_ = floor(j - 7.0 * x_ ); // mod(j,N)\\\\n\\\\n vec4 x = x_ *ns.x + ns.yyyy;\\\\n vec4 y = y_ *ns.x + ns.yyyy;\\\\n vec4 h = 1.0 - abs(x) - abs(y);\\\\n\\\\n vec4 b0 = vec4( x.xy, y.xy );\\\\n vec4 b1 = vec4( x.zw, y.zw );\\\\n\\\\n //vec4 s0 = vec4(lessThan(b0,0.0))*2.0 - 1.0;\\\\n //vec4 s1 = vec4(lessThan(b1,0.0))*2.0 - 1.0;\\\\n vec4 s0 = floor(b0)*2.0 + 1.0;\\\\n vec4 s1 = floor(b1)*2.0 + 1.0;\\\\n vec4 sh = -step(h, vec4(0.0));\\\\n\\\\n vec4 a0 = b0.xzyw + s0.xzyw*sh.xxyy ;\\\\n vec4 a1 = b1.xzyw + s1.xzyw*sh.zzww ;\\\\n\\\\n vec3 p0 = vec3(a0.xy,h.x);\\\\n vec3 p1 = vec3(a0.zw,h.y);\\\\n vec3 p2 = vec3(a1.xy,h.z);\\\\n vec3 p3 = vec3(a1.zw,h.w);\\\\n\\\\n//Normalise gradients\\\\n vec4 norm = taylorInvSqrt(vec4(dot(p0,p0), dot(p1,p1), dot(p2, p2), dot(p3,p3)));\\\\n p0 *= norm.x;\\\\n p1 *= norm.y;\\\\n p2 *= norm.z;\\\\n p3 *= norm.w;\\\\n\\\\n// Mix final noise value\\\\n vec4 m = max(0.6 - vec4(dot(x0,x0), dot(x1,x1), dot(x2,x2), dot(x3,x3)), 0.0);\\\\n m = m * m;\\\\n return 42.0 * dot( m*m, vec4( dot(p0,x0), dot(p1,x1), \\\\n dot(p2,x2), dot(p3,x3) ) );\\\\n }\\\\n\\\\\\\",[eF.NOISE_4D]:\\\\\\\"//\\\\n// Description : Array and textureless GLSL 2D/3D/4D simplex \\\\n// noise functions.\\\\n// Author : Ian McEwan, Ashima Arts.\\\\n// Maintainer : stegu\\\\n// Lastmod : 20110822 (ijm)\\\\n// License : Copyright (C) 2011 Ashima Arts. All rights reserved.\\\\n// Distributed under the MIT License. See LICENSE file.\\\\n// https://github.com/ashima/webgl-noise\\\\n// https://github.com/stegu/webgl-noise\\\\n// \\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\nvec4 grad4(float j, vec4 ip)\\\\n {\\\\n const vec4 ones = vec4(1.0, 1.0, 1.0, -1.0);\\\\n vec4 p,s;\\\\n\\\\n p.xyz = floor( fract (vec3(j) * ip.xyz) * 7.0) * ip.z - 1.0;\\\\n p.w = 1.5 - dot(abs(p.xyz), ones.xyz);\\\\n s = vec4(lessThan(p, vec4(0.0)));\\\\n p.xyz = p.xyz + (s.xyz*2.0 - 1.0) * s.www; \\\\n\\\\n return p;\\\\n }\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\n// (sqrt(5) - 1)/4 = F4, used once below\\\\n#define F4 0.309016994374947451\\\\n\\\\nfloat snoise(vec4 v)\\\\n {\\\\n const vec4 C = vec4( 0.138196601125011, // (5 - sqrt(5))/20 G4\\\\n 0.276393202250021, // 2 * G4\\\\n 0.414589803375032, // 3 * G4\\\\n -0.447213595499958); // -1 + 4 * G4\\\\n\\\\n// First corner\\\\n vec4 i = floor(v + dot(v, vec4(F4)) );\\\\n vec4 x0 = v - i + dot(i, C.xxxx);\\\\n\\\\n// Other corners\\\\n\\\\n// Rank sorting originally contributed by Bill Licea-Kane, AMD (formerly ATI)\\\\n vec4 i0;\\\\n vec3 isX = step( x0.yzw, x0.xxx );\\\\n vec3 isYZ = step( x0.zww, x0.yyz );\\\\n// i0.x = dot( isX, vec3( 1.0 ) );\\\\n i0.x = isX.x + isX.y + isX.z;\\\\n i0.yzw = 1.0 - isX;\\\\n// i0.y += dot( isYZ.xy, vec2( 1.0 ) );\\\\n i0.y += isYZ.x + isYZ.y;\\\\n i0.zw += 1.0 - isYZ.xy;\\\\n i0.z += isYZ.z;\\\\n i0.w += 1.0 - isYZ.z;\\\\n\\\\n // i0 now contains the unique values 0,1,2,3 in each channel\\\\n vec4 i3 = clamp( i0, 0.0, 1.0 );\\\\n vec4 i2 = clamp( i0-1.0, 0.0, 1.0 );\\\\n vec4 i1 = clamp( i0-2.0, 0.0, 1.0 );\\\\n\\\\n // x0 = x0 - 0.0 + 0.0 * C.xxxx\\\\n // x1 = x0 - i1 + 1.0 * C.xxxx\\\\n // x2 = x0 - i2 + 2.0 * C.xxxx\\\\n // x3 = x0 - i3 + 3.0 * C.xxxx\\\\n // x4 = x0 - 1.0 + 4.0 * C.xxxx\\\\n vec4 x1 = x0 - i1 + C.xxxx;\\\\n vec4 x2 = x0 - i2 + C.yyyy;\\\\n vec4 x3 = x0 - i3 + C.zzzz;\\\\n vec4 x4 = x0 + C.wwww;\\\\n\\\\n// Permutations\\\\n i = mod289(i); \\\\n float j0 = permute( permute( permute( permute(i.w) + i.z) + i.y) + i.x);\\\\n vec4 j1 = permute( permute( permute( permute (\\\\n i.w + vec4(i1.w, i2.w, i3.w, 1.0 ))\\\\n + i.z + vec4(i1.z, i2.z, i3.z, 1.0 ))\\\\n + i.y + vec4(i1.y, i2.y, i3.y, 1.0 ))\\\\n + i.x + vec4(i1.x, i2.x, i3.x, 1.0 ));\\\\n\\\\n// Gradients: 7x7x6 points over a cube, mapped onto a 4-cross polytope\\\\n// 7*7*6 = 294, which is close to the ring size 17*17 = 289.\\\\n vec4 ip = vec4(1.0/294.0, 1.0/49.0, 1.0/7.0, 0.0) ;\\\\n\\\\n vec4 p0 = grad4(j0, ip);\\\\n vec4 p1 = grad4(j1.x, ip);\\\\n vec4 p2 = grad4(j1.y, ip);\\\\n vec4 p3 = grad4(j1.z, ip);\\\\n vec4 p4 = grad4(j1.w, ip);\\\\n\\\\n// Normalise gradients\\\\n vec4 norm = taylorInvSqrt(vec4(dot(p0,p0), dot(p1,p1), dot(p2, p2), dot(p3,p3)));\\\\n p0 *= norm.x;\\\\n p1 *= norm.y;\\\\n p2 *= norm.z;\\\\n p3 *= norm.w;\\\\n p4 *= taylorInvSqrt(dot(p4,p4));\\\\n\\\\n// Mix contributions from the five corners\\\\n vec3 m0 = max(0.6 - vec3(dot(x0,x0), dot(x1,x1), dot(x2,x2)), 0.0);\\\\n vec2 m1 = max(0.6 - vec2(dot(x3,x3), dot(x4,x4) ), 0.0);\\\\n m0 = m0 * m0;\\\\n m1 = m1 * m1;\\\\n return 49.0 * ( dot(m0*m0, vec3( dot( p0, x0 ), dot( p1, x1 ), dot( p2, x2 )))\\\\n + dot(m1*m1, vec2( dot( p3, x3 ), dot( p4, x4 ) ) ) ) ;\\\\n\\\\n }\\\\n\\\\\\\"},sF={[eF.CLASSIC_PERLIN_2D]:Bo.VEC2,[eF.CLASSIC_PERLIN_3D]:Bo.VEC3,[eF.CLASSIC_PERLIN_4D]:Bo.VEC4,[eF.NOISE_2D]:Bo.VEC2,[eF.NOISE_3D]:Bo.VEC3,[eF.NOISE_4D]:Bo.VEC4},rF={[eF.CLASSIC_PERLIN_2D]:Bo.FLOAT,[eF.CLASSIC_PERLIN_3D]:Bo.FLOAT,[eF.CLASSIC_PERLIN_4D]:Bo.FLOAT,[eF.NOISE_2D]:Bo.FLOAT,[eF.NOISE_3D]:Bo.FLOAT,[eF.NOISE_4D]:Bo.FLOAT},oF={[eF.CLASSIC_PERLIN_2D]:\\\\\\\"cnoise\\\\\\\",[eF.CLASSIC_PERLIN_3D]:\\\\\\\"cnoise\\\\\\\",[eF.CLASSIC_PERLIN_4D]:\\\\\\\"cnoise\\\\\\\",[eF.NOISE_2D]:\\\\\\\"snoise\\\\\\\",[eF.NOISE_3D]:\\\\\\\"snoise\\\\\\\",[eF.NOISE_4D]:\\\\\\\"snoise\\\\\\\"};var aF;!function(t){t[t.NoChange=0]=\\\\\\\"NoChange\\\\\\\",t[t.Float=1]=\\\\\\\"Float\\\\\\\",t[t.Vec2=2]=\\\\\\\"Vec2\\\\\\\",t[t.Vec3=3]=\\\\\\\"Vec3\\\\\\\",t[t.Vec4=4]=\\\\\\\"Vec4\\\\\\\"}(aF||(aF={}));const lF=[aF.NoChange,aF.Float,aF.Vec2,aF.Vec3,aF.Vec4],cF={[aF.NoChange]:\\\\\\\"Same as noise\\\\\\\",[aF.Float]:\\\\\\\"Float\\\\\\\",[aF.Vec2]:\\\\\\\"Vec2\\\\\\\",[aF.Vec3]:\\\\\\\"Vec3\\\\\\\",[aF.Vec4]:\\\\\\\"Vec4\\\\\\\"},hF={[aF.NoChange]:Bo.FLOAT,[aF.Float]:Bo.FLOAT,[aF.Vec2]:Bo.VEC2,[aF.Vec3]:Bo.VEC3,[aF.Vec4]:Bo.VEC4},uF=[\\\\\\\"x\\\\\\\",\\\\\\\"y\\\\\\\",\\\\\\\"z\\\\\\\",\\\\\\\"w\\\\\\\"],dF=\\\\\\\"noise\\\\\\\",pF=nF.indexOf(eF.NOISE_3D),_F=aF.NoChange,mF={amp:1,freq:1};var fF;!function(t){t.AMP=\\\\\\\"amp\\\\\\\",t.POSITION=\\\\\\\"position\\\\\\\",t.FREQ=\\\\\\\"freq\\\\\\\",t.OFFSET=\\\\\\\"offset\\\\\\\"}(fF||(fF={}));const gF=new class extends la{constructor(){super(...arguments),this.type=aa.INTEGER(pF,{menu:{entries:nF.map(((t,e)=>({name:`${t} (output: ${rF[t]})`,value:e})))}}),this.outputType=aa.INTEGER(_F,{menu:{entries:lF.map((t=>{const e=lF[t];return{name:cF[e],value:e}}))}}),this.octaves=aa.INTEGER(3,{range:[1,10],rangeLocked:[!0,!1]}),this.ampAttenuation=aa.FLOAT(.5,{range:[0,1]}),this.freqIncrease=aa.FLOAT(2,{range:[0,10],separatorAfter:!0})}};class vF extends df{constructor(){super(...arguments),this.paramsConfig=gF}static type(){return\\\\\\\"noise\\\\\\\"}initializeNode(){super.initializeNode(),this.io.connection_points.initializeNode(),this.io.connection_points.spare_params.set_inputless_param_names([\\\\\\\"octaves\\\\\\\",\\\\\\\"ampAttenuation\\\\\\\",\\\\\\\"freqIncrease\\\\\\\"]),this.io.outputs.setNamedOutputConnectionPoints([new Ho(dF,Bo.FLOAT)]),this.io.connection_points.set_expected_input_types_function(this._expected_input_types.bind(this)),this.io.connection_points.set_expected_output_types_function(this._expected_output_types.bind(this)),this.io.connection_points.set_input_name_function(this._gl_input_name.bind(this)),this.io.connection_points.set_output_name_function((()=>dF))}_gl_input_name(t){return[fF.AMP,fF.POSITION,fF.FREQ,fF.OFFSET][t]}paramDefaultValue(t){return mF[t]}_expected_input_types(){const t=nF[this.pv.type],e=this._expected_output_types()[0],n=sF[t];return[e,n,n,n]}_expected_output_types(){const t=nF[this.pv.type],e=lF[this.pv.outputType];return e==aF.NoChange?[sF[t]]:[hF[e]]}setLines(t){const e=[],n=[],i=nF[this.pv.type],s=iF[i],r=rF[i];e.push(new Tf(this,\\\\\\\"// Modulo 289 without a division (only multiplications)\\\\nfloat mod289(float x) {\\\\n return x - floor(x * (1.0 / 289.0)) * 289.0;\\\\n}\\\\nvec2 mod289(vec2 x) {\\\\n return x - floor(x * (1.0 / 289.0)) * 289.0;\\\\n}\\\\nvec3 mod289(vec3 x) {\\\\n return x - floor(x * (1.0 / 289.0)) * 289.0;\\\\n}\\\\nvec4 mod289(vec4 x) {\\\\n return x - floor(x * (1.0 / 289.0)) * 289.0;\\\\n}\\\\n// Modulo 7 without a division\\\\nvec3 mod7(vec3 x) {\\\\n return x - floor(x * (1.0 / 7.0)) * 7.0;\\\\n}\\\\n\\\\n// Permutation polynomial: (34x^2 + x) mod 289\\\\nfloat permute(float x) {\\\\n return mod289(((x*34.0)+1.0)*x);\\\\n}\\\\nvec3 permute(vec3 x) {\\\\n return mod289((34.0 * x + 1.0) * x);\\\\n}\\\\nvec4 permute(vec4 x) {\\\\n return mod289(((x*34.0)+1.0)*x);\\\\n}\\\\n\\\\nfloat taylorInvSqrt(float r)\\\\n{\\\\n return 1.79284291400159 - 0.85373472095314 * r;\\\\n}\\\\nvec4 taylorInvSqrt(vec4 r)\\\\n{\\\\n return 1.79284291400159 - 0.85373472095314 * r;\\\\n}\\\\n\\\\nvec2 fade(vec2 t) {\\\\n return t*t*t*(t*(t*6.0-15.0)+10.0);\\\\n}\\\\nvec3 fade(vec3 t) {\\\\n return t*t*t*(t*(t*6.0-15.0)+10.0);\\\\n}\\\\nvec4 fade(vec4 t) {\\\\n return t*t*t*(t*(t*6.0-15.0)+10.0);\\\\n}\\\\\\\")),e.push(new Tf(this,s)),e.push(new Tf(this,this.fbm_function()));const o=this._expected_output_types()[0];if(o==r){const t=this.single_noise_line();n.push(t)}else{const t=Vo[o],e=[],s=this.glVarName(\\\\\\\"noise\\\\\\\");for(let r=0;rhf.float(1e3*r))).join(\\\\\\\", \\\\\\\")})`,c=this.single_noise_line(t,t,l);n.push(c)}const r=`vec${t} ${s} = vec${t}(${e.join(\\\\\\\", \\\\\\\")})`;n.push(r)}t.addDefinitions(this,e),t.addBodyLines(this,n)}fbm_method_name(){const t=nF[this.pv.type];return`fbm_${oF[t]}_${this.name()}`}fbm_function(){const t=nF[this.pv.type],e=oF[t],n=sF[t];return`\\\\nfloat ${this.fbm_method_name()} (in ${n} st) {\\\\n\\\\tfloat value = 0.0;\\\\n\\\\tfloat amplitude = 1.0;\\\\n\\\\tfor (int i = 0; i < ${hf.integer(this.pv.octaves)}; i++) {\\\\n\\\\t\\\\tvalue += amplitude * ${e}(st);\\\\n\\\\t\\\\tst *= ${hf.float(this.pv.freqIncrease)};\\\\n\\\\t\\\\tamplitude *= ${hf.float(this.pv.ampAttenuation)};\\\\n\\\\t}\\\\n\\\\treturn value;\\\\n}\\\\n`}single_noise_line(t,e,n){const i=this.fbm_method_name(),s=hf.any(this.variableForInput(fF.AMP)),r=hf.any(this.variableForInput(fF.POSITION)),o=hf.any(this.variableForInput(fF.FREQ));let a=hf.any(this.variableForInput(fF.OFFSET));n&&(a=`(${a}+${n})`);const l=[`(${r}*${o})+${a}`].join(\\\\\\\", \\\\\\\"),c=this.glVarName(dF),h=`${s}*${i}(${l})`;if(e)return`float ${c}${t} = (${h}).${e}`;return`${this.io.outputs.namedOutputConnectionPoints()[0].type()} ${c} = ${h}`}}class yF extends yP{static type(){return\\\\\\\"null\\\\\\\"}setLines(t){const e=hf.any(this.variableForInput(this._gl_input_name(0))),n=this.io.outputs.namedOutputConnectionPoints()[0],i=`${n.type()} ${this.glVarName(n.name())} = ${e}`;t.addBodyLines(this,[i])}}const xF=new class extends la{};class bF extends df{constructor(){super(...arguments),this.paramsConfig=xF}static type(){return\\\\\\\"output\\\\\\\"}initializeNode(){super.initializeNode(),this.addPostDirtyHook(\\\\\\\"_set_mat_to_recompile\\\\\\\",this._set_mat_to_recompile.bind(this)),this.lifecycle.add_on_add_hook((()=>{var t,e;null===(e=null===(t=this.material_node)||void 0===t?void 0:t.assemblerController)||void 0===e||e.add_output_inputs(this)}))}setLines(t){t.assembler().set_node_lines_output(this,t)}}class wF{constructor(){this._param_configs=[]}reset(){this._param_configs=[]}push(t){this._param_configs.push(t)}list(){return this._param_configs}}const TF=new class extends la{constructor(){super(...arguments),this.name=aa.STRING(\\\\\\\"\\\\\\\"),this.type=aa.INTEGER(zo.indexOf(Bo.FLOAT),{menu:{entries:zo.map(((t,e)=>({name:t,value:e})))}}),this.asColor=aa.BOOLEAN(0,{visibleIf:{type:zo.indexOf(Bo.VEC3)}})}};class AF extends df{constructor(){super(...arguments),this.paramsConfig=TF,this._allow_inputs_created_from_params=!1,this._on_create_set_name_if_none_bound=this._on_create_set_name_if_none.bind(this)}static type(){return\\\\\\\"param\\\\\\\"}initializeNode(){this.addPostDirtyHook(\\\\\\\"_set_mat_to_recompile\\\\\\\",this._set_mat_to_recompile.bind(this)),this.lifecycle.add_on_create_hook(this._on_create_set_name_if_none_bound),this.io.connection_points.initializeNode(),this.io.connection_points.set_expected_input_types_function((()=>[])),this.io.connection_points.set_expected_output_types_function((()=>[zo[this.pv.type]])),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.name])}))}))}setLines(t){const e=[],n=zo[this.pv.type],i=this.uniform_name();e.push(new Af(this,n,i)),t.addDefinitions(this,e)}paramsGenerating(){return!0}setParamConfigs(){const t=zo[this.pv.type],e=Go[t];let n=ko[t];if(this._param_configs_controller=this._param_configs_controller||new wF,this._param_configs_controller.reset(),n==Er.VECTOR3&&this.p.asColor.value&&m.isArray(e)&&3==e.length){const t=new $f(Er.COLOR,this.pv.name,e,this.uniform_name());this._param_configs_controller.push(t)}else{const t=new $f(n,this.pv.name,e,this.uniform_name());this._param_configs_controller.push(t)}}uniform_name(){const t=this.io.outputs.namedOutputConnectionPoints()[0];return this.glVarName(t.name())}set_gl_type(t){const e=zo.indexOf(t);this.p.type.set(e)}_on_create_set_name_if_none(){\\\\\\\"\\\\\\\"==this.pv.name&&this.p.name.set(this.name())}}class MF extends vP{static type(){return\\\\\\\"refract\\\\\\\"}initializeNode(){super.initializeNode(),this.io.connection_points.set_input_name_function((t=>[\\\\\\\"I\\\\\\\",\\\\\\\"N\\\\\\\",\\\\\\\"eta\\\\\\\"][t])),this.io.connection_points.set_output_name_function((t=>\\\\\\\"refract\\\\\\\")),this.io.connection_points.set_expected_input_types_function(this._expected_input_types.bind(this)),this.io.connection_points.set_expected_output_types_function(this._expected_output_types.bind(this))}gl_method_name(){return\\\\\\\"refract\\\\\\\"}_expected_input_types(){const t=this.io.connection_points.first_input_connection_type()||Bo.VEC3;return[t,t,Bo.FLOAT]}_expected_output_types(){return[this._expected_input_types()[0]]}}const EF=\\\\\\\"SSSModel\\\\\\\";const SF=new class extends la{constructor(){super(...arguments),this.color=aa.COLOR([1,1,1]),this.thickness=aa.FLOAT(.1),this.power=aa.FLOAT(2),this.scale=aa.FLOAT(16),this.distortion=aa.FLOAT(.1),this.ambient=aa.FLOAT(.4),this.attenuation=aa.FLOAT(.8)}};class CF extends df{constructor(){super(...arguments),this.paramsConfig=SF}static type(){return\\\\\\\"SSSModel\\\\\\\"}initializeNode(){this.io.outputs.setNamedOutputConnectionPoints([new Ho(EF,Bo.SSS_MODEL)])}setLines(t){const e=[],n=this.glVarName(EF);e.push(`SSSModel ${n}`),e.push(`${n}.isActive = true;`),e.push(this._paramLineFloat(n,this.p.color)),e.push(this._paramLineFloat(n,this.p.thickness)),e.push(this._paramLineFloat(n,this.p.power)),e.push(this._paramLineFloat(n,this.p.scale)),e.push(this._paramLineFloat(n,this.p.distortion)),e.push(this._paramLineFloat(n,this.p.ambient)),e.push(this._paramLineFloat(n,this.p.attenuation)),t.addBodyLines(this,e)}_paramLineFloat(t,e){return`${t}.${e.name()} = ${hf.vector3(this.variableForInputParam(e))};`}}class NF extends yP{static type(){return\\\\\\\"quatMult\\\\\\\"}initializeNode(){super.initializeNode(),this.io.connection_points.set_input_name_function((t=>[\\\\\\\"quat0\\\\\\\",\\\\\\\"quat1\\\\\\\"][t])),this.io.connection_points.set_expected_input_types_function((()=>[Bo.VEC4,Bo.VEC4])),this.io.connection_points.set_expected_output_types_function((()=>[Bo.VEC4]))}gl_method_name(){return\\\\\\\"quatMult\\\\\\\"}gl_function_definitions(){return[new Tf(this,wR)]}}var LF;!function(t){t.AXIS=\\\\\\\"axis\\\\\\\",t.ANGLE=\\\\\\\"angle\\\\\\\"}(LF||(LF={}));const OF=[LF.AXIS,LF.ANGLE],PF={[LF.AXIS]:[0,0,1],[LF.ANGLE]:0};class RF extends xP{static type(){return\\\\\\\"quatFromAxisAngle\\\\\\\"}initializeNode(){super.initializeNode(),this.io.connection_points.set_input_name_function((t=>OF[t])),this.io.connection_points.set_expected_input_types_function((()=>[Bo.VEC3,Bo.FLOAT])),this.io.connection_points.set_expected_output_types_function((()=>[Bo.VEC4]))}paramDefaultValue(t){return PF[t]}gl_method_name(){return\\\\\\\"quatFromAxisAngle\\\\\\\"}gl_function_definitions(){return[new Tf(this,wR)]}}class IF extends yP{static type(){return\\\\\\\"quatToAngle\\\\\\\"}initializeNode(){super.initializeNode(),this.io.connection_points.set_input_name_function((t=>[\\\\\\\"quat\\\\\\\"][t])),this.io.connection_points.set_expected_input_types_function((()=>[Bo.VEC4])),this.io.connection_points.set_expected_output_types_function((()=>[Bo.FLOAT]))}gl_method_name(){return\\\\\\\"quatToAngle\\\\\\\"}gl_function_definitions(){return[new Tf(this,wR)]}}class FF extends yP{static type(){return\\\\\\\"quatToAxis\\\\\\\"}initializeNode(){super.initializeNode(),this.io.connection_points.set_input_name_function((t=>[\\\\\\\"quat\\\\\\\"][t])),this.io.connection_points.set_expected_input_types_function((()=>[Bo.VEC4])),this.io.connection_points.set_expected_output_types_function((()=>[Bo.VEC3]))}gl_method_name(){return\\\\\\\"quatToAxis\\\\\\\"}gl_function_definitions(){return[new Tf(this,wR)]}}const DF=\\\\\\\"val\\\\\\\";const BF=new class extends la{constructor(){super(...arguments),this.name=aa.STRING(\\\\\\\"ramp\\\\\\\"),this.input=aa.FLOAT(0)}};class zF extends df{constructor(){super(...arguments),this.paramsConfig=BF}static type(){return\\\\\\\"ramp\\\\\\\"}initializeNode(){super.initializeNode(),this.addPostDirtyHook(\\\\\\\"_set_mat_to_recompile\\\\\\\",this._set_mat_to_recompile.bind(this)),this.io.outputs.setNamedOutputConnectionPoints([new Ho(DF,Bo.FLOAT)]),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.name])}))}))}setLines(t){const e=Bo.FLOAT,n=this._uniform_name(),i=this.glVarName(DF),s=new Af(this,Bo.SAMPLER_2D,n);t.addDefinitions(this,[s]);const r=this.variableForInputParam(this.p.input),o=`${e} ${i} = texture2D(${this._uniform_name()}, vec2(${r}, 0.0)).x`;t.addBodyLines(this,[o])}paramsGenerating(){return!0}setParamConfigs(){this._param_configs_controller=this._param_configs_controller||new wF,this._param_configs_controller.reset();const t=new $f(Er.RAMP,this.pv.name,bo.DEFAULT_VALUE,this._uniform_name());this._param_configs_controller.push(t)}_uniform_name(){return\\\\\\\"ramp_texture_\\\\\\\"+this.glVarName(DF)}}const kF=\\\\\\\"rand\\\\\\\";const UF=new class extends la{constructor(){super(...arguments),this.seed=aa.VECTOR2([1,1])}};class GF extends df{constructor(){super(...arguments),this.paramsConfig=UF}static type(){return\\\\\\\"random\\\\\\\"}initializeNode(){super.initializeNode(),this.io.outputs.setNamedOutputConnectionPoints([new Ho(kF,Bo.FLOAT)])}setLines(t){const e=this.io.inputs.namedInputConnectionPoints()[0].name(),n=hf.vector2(this.variableForInput(e)),i=`float ${this.glVarName(kF)} = rand(${n})`;t.addBodyLines(this,[i])}}const VF=new class extends la{constructor(){super(...arguments),this.rgb=aa.VECTOR3([1,1,1])}};class HF extends df{constructor(){super(...arguments),this.paramsConfig=VF}static type(){return\\\\\\\"rgbToHsv\\\\\\\"}initializeNode(){this.io.outputs.setNamedOutputConnectionPoints([new Ho(\\\\\\\"hsv\\\\\\\",Bo.VEC3)])}setLines(t){const e=[],n=[];e.push(new Tf(this,\\\\\\\"// https://stackoverflow.com/questions/15095909/from-rgb-to-hsv-in-opengl-glsl\\\\nvec3 rgb2hsv(vec3 c)\\\\n{\\\\n\\\\tvec4 K = vec4(0.0, -1.0 / 3.0, 2.0 / 3.0, -1.0);\\\\n\\\\tvec4 p = mix(vec4(c.bg, K.wz), vec4(c.gb, K.xy), step(c.b, c.g));\\\\n\\\\tvec4 q = mix(vec4(p.xyw, c.r), vec4(c.r, p.yzx), step(p.x, c.r));\\\\n\\\\n\\\\tfloat d = q.x - min(q.w, q.y);\\\\n\\\\tfloat e = 1.0e-10;\\\\n\\\\treturn vec3(abs(q.z + (q.w - q.y) / (6.0 * d + e)), d / (q.x + e), q.x);\\\\n}\\\\\\\"));const i=hf.vector3(this.variableForInputParam(this.p.rgb)),s=this.glVarName(\\\\\\\"hsv\\\\\\\");n.push(`vec3 ${s} = rgb2hsv(${i})`),t.addDefinitions(this,e),t.addBodyLines(this,n)}}var jF;!function(t){t[t.AXIS=0]=\\\\\\\"AXIS\\\\\\\",t[t.QUAT=1]=\\\\\\\"QUAT\\\\\\\"}(jF||(jF={}));const WF=[jF.AXIS,jF.QUAT],qF={[jF.AXIS]:\\\\\\\"from axis + angle\\\\\\\",[jF.QUAT]:\\\\\\\"from quaternion\\\\\\\"},XF={[jF.AXIS]:[\\\\\\\"vector\\\\\\\",\\\\\\\"axis\\\\\\\",\\\\\\\"angle\\\\\\\"],[jF.QUAT]:[\\\\\\\"vector\\\\\\\",\\\\\\\"quat\\\\\\\"]},YF={[jF.AXIS]:\\\\\\\"rotateWithAxisAngle\\\\\\\",[jF.QUAT]:\\\\\\\"rotateWithQuat\\\\\\\"},$F={[jF.AXIS]:[Bo.VEC3,Bo.VEC3,Bo.FLOAT],[jF.QUAT]:[Bo.VEC3,Bo.VEC4]},JF={vector:[0,0,1],axis:[0,1,0]};const ZF=new class extends la{constructor(){super(...arguments),this.signature=aa.INTEGER(jF.AXIS,{menu:{entries:WF.map(((t,e)=>({name:qF[t],value:e})))}})}};class QF extends df{constructor(){super(...arguments),this.paramsConfig=ZF}static type(){return\\\\\\\"rotate\\\\\\\"}initializeNode(){super.initializeNode(),this.io.connection_points.set_expected_input_types_function(this._expected_input_types.bind(this)),this.io.connection_points.set_expected_output_types_function(this._expected_output_types.bind(this)),this.io.connection_points.set_input_name_function(this._gl_input_name.bind(this))}set_signature(t){const e=WF.indexOf(t);this.p.signature.set(e)}_gl_input_name(t){const e=WF[this.pv.signature];return XF[e][t]}paramDefaultValue(t){return JF[t]}gl_method_name(){const t=WF[this.pv.signature];return YF[t]}_expected_input_types(){const t=WF[this.pv.signature];return $F[t]}_expected_output_types(){return[Bo.VEC3]}gl_function_definitions(){return[new Tf(this,wR)]}setLines(t){const e=this.io.outputs.namedOutputConnectionPoints()[0].type(),n=this.io.inputs.namedInputConnectionPoints().map(((t,e)=>{const n=t.name();return hf.any(this.variableForInput(n))})).join(\\\\\\\", \\\\\\\"),i=`${e} ${this.glVarName(this.io.connection_points.output_name(0))} = ${this.gl_method_name()}(${n})`;t.addBodyLines(this,[i]),t.addDefinitions(this,this.gl_function_definitions())}}const KF=[\\\\\\\"x\\\\\\\",\\\\\\\"y\\\\\\\",\\\\\\\"z\\\\\\\",\\\\\\\"w\\\\\\\"];class tD extends yP{static type(){return\\\\\\\"round\\\\\\\"}setLines(t){const e=this.io.inputs.namedInputConnectionPoints()[0],n=hf.vector2(this.variableForInput(e.name())),i=this.io.outputs.namedOutputConnectionPoints()[0],s=this.glVarName(i.name()),r=[];if(1==Vo[i.type()])r.push(`${i.type()} ${s} = ${this._simple_line(n)}`);else{const t=KF.map((t=>this._simple_line(`${n}.${t}`)));r.push(`${i.type()} ${s} = ${i.type()}(${t.join(\\\\\\\",\\\\\\\")})`)}t.addBodyLines(this,r)}_simple_line(t){return`sign(${t})*floor(abs(${t})+0.5)`}}const eD=new class extends la{constructor(){super(...arguments),this.position=aa.VECTOR3([0,0,0]),this.center=aa.VECTOR3([0,0,0]),this.radius=aa.FLOAT(1),this.feather=aa.FLOAT(.1)}};class nD extends df{constructor(){super(...arguments),this.paramsConfig=eD}static type(){return\\\\\\\"sphere\\\\\\\"}initializeNode(){super.initializeNode(),this.io.outputs.setNamedOutputConnectionPoints([new Ho(\\\\\\\"float\\\\\\\",Bo.FLOAT)])}setLines(t){const e=hf.vector2(this.variableForInputParam(this.p.position)),n=hf.vector2(this.variableForInputParam(this.p.center)),i=hf.float(this.variableForInputParam(this.p.radius)),s=hf.float(this.variableForInputParam(this.p.feather)),r=`float ${this.glVarName(\\\\\\\"float\\\\\\\")} = disk3d(${e}, ${n}, ${i}, ${s})`;t.addBodyLines(this,[r]),t.addDefinitions(this,[new Tf(this,WR)])}}const iD=new class extends la{};class sD extends df{constructor(){super(...arguments),this.paramsConfig=iD}static type(){return ns.INPUT}initializeNode(){this.io.connection_points.set_output_name_function(this._expected_output_names.bind(this)),this.io.connection_points.set_expected_input_types_function((()=>[])),this.io.connection_points.set_expected_output_types_function(this._expected_output_types.bind(this))}parent(){return super.parent()}_expected_output_names(t){const e=this.parent();return(null==e?void 0:e.child_expected_input_connection_point_name(t))||`out${t}`}_expected_output_types(){const t=this.parent();return(null==t?void 0:t.child_expected_input_connection_point_types())||[]}setLines(t){const e=this.parent();e&&e.set_lines_block_start(t,this)}}const rD=new class extends la{};class oD extends df{constructor(){super(...arguments),this.paramsConfig=rD}static type(){return\\\\\\\"switch\\\\\\\"}initializeNode(){this.io.connection_points.set_input_name_function(this._gl_input_name.bind(this)),this.io.connection_points.set_expected_input_types_function(this._expected_input_types.bind(this)),this.io.connection_points.set_expected_output_types_function(this._expected_output_types.bind(this))}_gl_input_name(t){return 0==t?oD.INPUT_INDEX:\\\\\\\"in\\\\\\\"+(t-1)}_expected_input_types(){const t=this.io.connection_points.input_connection_type(1)||Bo.FLOAT,e=this.io.connections.inputConnections(),n=e?rr.clamp(e.length,2,16):2,i=[Bo.INT];for(let e=0;e{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.paramName])}))}))}setLines(t){const e=hf.vector2(this.variableForInputParam(this.p.uv)),n=this.glVarName(lD.OUTPUT_NAME),i=this._uniform_name(),s=new Af(this,Bo.SAMPLER_2D,i),r=`vec4 ${n} = texture2D(${i}, ${e})`;t.addDefinitions(this,[s]),t.addBodyLines(this,[r])}paramsGenerating(){return!0}setParamConfigs(){this._param_configs_controller=this._param_configs_controller||new wF,this._param_configs_controller.reset();const t=new $f(Er.OPERATOR_PATH,this.pv.paramName,this.pv.defaultValue,this._uniform_name());this._param_configs_controller.push(t)}_uniform_name(){return this.glVarName(this.pv.paramName)}}var cD;lD.OUTPUT_NAME=\\\\\\\"rgba\\\\\\\",function(t){t.POSITION=\\\\\\\"position\\\\\\\",t.DIR_VEC=\\\\\\\"direction vector\\\\\\\"}(cD||(cD={}));const hD=[cD.POSITION,cD.DIR_VEC];const uD=new class extends la{constructor(){super(...arguments),this.vec=aa.VECTOR3([0,0,0]),this.interpretation=aa.INTEGER(0,{menu:{entries:hD.map(((t,e)=>({name:t,value:e})))}})}};class dD extends df{constructor(){super(...arguments),this.paramsConfig=uD}static type(){return\\\\\\\"toWorldSpace\\\\\\\"}initializeNode(){this.io.connection_points.spare_params.set_inputless_param_names([\\\\\\\"interpretation\\\\\\\"]),this.io.outputs.setNamedOutputConnectionPoints([new Ho(\\\\\\\"out\\\\\\\",Bo.VEC3)])}setLines(t){const e=[],n=hf.vector3(this.variableForInputParam(this.p.vec)),i=this.glVarName(\\\\\\\"out\\\\\\\");switch(hD[this.pv.interpretation]){case cD.POSITION:e.push(`vec3 ${i} = (modelMatrix * vec4( ${n}, 1.0 )).xyz`);break;case cD.DIR_VEC:e.push(`vec3 ${i} = normalize( mat3( modelMatrix[0].xyz, modelMatrix[1].xyz, modelMatrix[2].xyz ) * ${n} )`)}t.addBodyLines(this,e)}}var pD;!function(t){t.CONDITION=\\\\\\\"condition\\\\\\\",t.IF_TRUE=\\\\\\\"ifTrue\\\\\\\",t.IF_FALSE=\\\\\\\"ifFalse\\\\\\\"}(pD||(pD={}));const _D=[pD.CONDITION,pD.IF_TRUE,pD.IF_FALSE];class mD extends _f{static type(){return\\\\\\\"twoWaySwitch\\\\\\\"}initializeNode(){super.initializeNode(),this.io.connection_points.initializeNode(),this.io.connection_points.set_expected_input_types_function(this._expected_input_types.bind(this)),this.io.connection_points.set_expected_output_types_function(this._expected_output_types.bind(this)),this.io.connection_points.set_input_name_function(this._gl_input_name.bind(this)),this.io.connection_points.set_output_name_function(this._gl_output_name.bind(this))}_gl_input_name(t){return _D[t]}_gl_output_name(){return\\\\\\\"val\\\\\\\"}_expected_input_types(){const t=this.io.connections.inputConnection(1)||this.io.connections.inputConnection(2),e=t?t.src_connection_point().type():Bo.FLOAT;return[Bo.BOOL,e,e]}_expected_output_types(){return[this._expected_input_types()[1]]}setLines(t){const e=[],n=this.glVarName(\\\\\\\"val\\\\\\\"),i=hf.bool(this.variableForInput(pD.CONDITION)),s=hf.any(this.variableForInput(pD.IF_TRUE)),r=hf.any(this.variableForInput(pD.IF_FALSE)),o=this._expected_output_types()[0];e.push(`${o} ${n}`),e.push(`if(${i}){`),e.push(`${n} = ${s}`),e.push(\\\\\\\"} else {\\\\\\\"),e.push(`${n} = ${r}`),e.push(\\\\\\\"}\\\\\\\"),t.addBodyLines(this,e)}}const fD=[Bo.FLOAT,Bo.VEC2,Bo.VEC3,Bo.VEC4];const gD=new class extends la{constructor(){super(...arguments),this.name=aa.STRING(\\\\\\\"\\\\\\\"),this.type=aa.INTEGER(0,{menu:{entries:fD.map(((t,e)=>({name:t,value:e})))}})}};class vD extends df{constructor(){super(...arguments),this.paramsConfig=gD,this._on_create_set_name_if_none_bound=this._on_create_set_name_if_none.bind(this)}static type(){return\\\\\\\"varyingRead\\\\\\\"}initializeNode(){this.addPostDirtyHook(\\\\\\\"_set_mat_to_recompile\\\\\\\",this._set_mat_to_recompile.bind(this)),this.lifecycle.add_on_create_hook(this._on_create_set_name_if_none_bound),this.io.connection_points.initializeNode(),this.io.connection_points.set_output_name_function((()=>this.output_name)),this.io.connection_points.set_expected_input_types_function((()=>[])),this.io.connection_points.set_expected_output_types_function((()=>[fD[this.pv.type]])),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.name])}))}))}get output_name(){return vD.OUTPUT_NAME}setLines(t){if(t.current_shader_name==xf.FRAGMENT){const e=this.pv.name,n=new Mf(this,this.gl_type(),e),i=this.glVarName(vD.OUTPUT_NAME),s=`${this.gl_type()} ${i} = ${e}`;t.addDefinitions(this,[n]),t.addBodyLines(this,[s])}}get attribute_name(){return this.pv.name.trim()}gl_type(){return this.io.outputs.namedOutputConnectionPoints()[0].type()}set_gl_type(t){this.p.type.set(fD.indexOf(t))}_on_create_set_name_if_none(){\\\\\\\"\\\\\\\"==this.pv.name&&this.p.name.set(this.name())}}vD.OUTPUT_NAME=\\\\\\\"fragment\\\\\\\";const yD={start:[0,0,1],end:[1,0,0],up:[0,1,0]};class xD extends(tR(\\\\\\\"vectorAlign\\\\\\\",{in:[\\\\\\\"start\\\\\\\",\\\\\\\"end\\\\\\\",\\\\\\\"up\\\\\\\"],method:\\\\\\\"vectorAlignWithUp\\\\\\\",functions:[wR]})){_expected_input_types(){const t=Bo.VEC3;return[t,t,t]}_expected_output_types(){return[Bo.VEC4]}paramDefaultValue(t){return yD[t]}}const bD={start:[0,0,1],end:[1,0,0]};class wD extends(WP(\\\\\\\"vectorAngle\\\\\\\",{in:[\\\\\\\"start\\\\\\\",\\\\\\\"end\\\\\\\"],method:\\\\\\\"vectorAngle\\\\\\\",functions:[wR]})){_expected_input_types(){const t=Bo.VEC3;return[t,t]}_expected_output_types(){return[Bo.FLOAT]}paramDefaultValue(t){return bD[t]}}const TD={only:[`${OI.context()}/${OI.type()}`,`${vI.context()}/${vI.type()}`,`${wI.context()}/${wI.type()}`]};class AD extends sa{static context(){return ts.JS}initializeBaseNode(){this.uiData.setLayoutHorizontal(),this.io.connection_points.initializeNode()}cook(){console.warn(\\\\\\\"js nodes should never cook\\\\\\\")}_set_function_node_to_recompile(){var t;null===(t=this.function_node)||void 0===t||t.assembler_controller.set_compilation_required_and_dirty(this)}get function_node(){var t;const e=this.parent();if(e)return e.type()==this.type()?null===(t=e)||void 0===t?void 0:t.function_node:e}js_var_name(t){return`v_POLY_${this.name()}_${t}`}variableForInput(t){const e=this.io.inputs.get_input_index(t),n=this.io.connections.inputConnection(e);if(n){const e=n.node_src,i=e.io.outputs.namedOutputConnectionPoints()[n.output_index];if(i){const t=i.name();return e.js_var_name(t)}throw console.warn(`no output called '${t}' for gl node ${e.path()}`),\\\\\\\"variable_for_input ERROR\\\\\\\"}return\\\\\\\"to debug...\\\\\\\"}setLines(t){}reset_code(){var t;null===(t=this._param_configs_controller)||void 0===t||t.reset()}setParamConfigs(){}param_configs(){var t;return null===(t=this._param_configs_controller)||void 0===t?void 0:t.list()}js_input_default_value(t){return null}}new class extends la{};const MD=[jo.FLOAT,jo.VEC2,jo.VEC3,jo.VEC4];const ED=new class extends la{constructor(){super(...arguments),this.name=aa.STRING(\\\\\\\"\\\\\\\"),this.type=aa.INTEGER(0,{menu:{entries:MD.map(((t,e)=>({name:t,value:e})))}})}};class SD extends AD{constructor(){super(...arguments),this.paramsConfig=ED,this._on_create_set_name_if_none_bound=this._on_create_set_name_if_none.bind(this)}static type(){return\\\\\\\"attribute\\\\\\\"}initializeNode(){this.lifecycle.add_on_create_hook(this._on_create_set_name_if_none_bound),this.io.connection_points.initializeNode(),this.io.connection_points.set_expected_input_types_function((()=>[])),this.io.connection_points.set_expected_output_types_function((()=>[MD[this.pv.type]]))}get input_name(){return SD.INPUT_NAME}get output_name(){return SD.OUTPUT_NAME}setLines(t){var e;null===(e=this.function_node)||void 0===e||e.assembler_controller.assembler.set_node_lines_attribute(this,t)}get attribute_name(){return this.pv.name.trim()}gl_type(){return this.io.outputs.namedOutputConnectionPoints()[0].type()}set_gl_type(t){this.p.type.set(MD.indexOf(t))}connected_input_node(){return this.io.inputs.named_input(SD.INPUT_NAME)}connected_input_connection_point(){return this.io.inputs.named_input_connection_point(SD.INPUT_NAME)}output_connection_point(){return this.io.outputs.namedOutputConnectionPointsByName(this.input_name)}get is_importing(){return this.io.outputs.used_output_names().length>0}_on_create_set_name_if_none(){\\\\\\\"\\\\\\\"==this.pv.name&&this.p.name.set(this.name())}}SD.INPUT_NAME=\\\\\\\"export\\\\\\\",SD.OUTPUT_NAME=\\\\\\\"val\\\\\\\";const CD=new class extends la{};class ND extends AD{constructor(){super(...arguments),this.paramsConfig=CD}static type(){return\\\\\\\"globals\\\\\\\"}createParams(){var t;null===(t=this.function_node)||void 0===t||t.assembler_controller.add_globals_outputs(this)}setLines(t){var e,n;null===(n=null===(e=this.function_node)||void 0===e?void 0:e.assembler_controller)||void 0===n||n.assembler.set_node_lines_globals(this,t)}}const LD=new class extends la{};class OD extends AD{constructor(){super(...arguments),this.paramsConfig=LD}static type(){return\\\\\\\"output\\\\\\\"}initializeNode(){super.initializeNode(),this.addPostDirtyHook(\\\\\\\"_set_mat_to_recompile\\\\\\\",this._set_function_node_to_recompile.bind(this))}createParams(){var t;null===(t=this.function_node)||void 0===t||t.assembler_controller.add_output_inputs(this)}setLines(t){var e;null===(e=this.function_node)||void 0===e||e.assembler_controller.assembler.set_node_lines_output(this,t)}}class PD{constructor(t=[]){this._definitions=t,this._errored=!1}get errored(){return this._errored}get error_message(){return this._error_message}uniq(){const t=new Map,e=[];for(let n of this._definitions)if(!this._errored){const i=n.name(),s=t.get(i);s?s.data_type!=n.data_type&&(this._errored=!0,this._error_message=`attempt to create '${n.name()}' with types '${n.data_type}' by node '${n.node.path()}', when there is already an existing with type ${s.data_type} from node '${s.node.path()}'`,console.warn(\\\\\\\"emitting error message:\\\\\\\",this._error_message)):(t.set(i,n),e.push(i))}const n=[];for(let i of e){const e=t.get(i);e&&n.push(e)}return n}}var RD;!function(t){t.ATTRIBUTE=\\\\\\\"attribute\\\\\\\",t.FUNCTION=\\\\\\\"function\\\\\\\",t.UNIFORM=\\\\\\\"uniform\\\\\\\"}(RD||(RD={}));class ID{constructor(t,e,n,i){this._definition_type=t,this._data_type=e,this._node=n,this._name=i}get definition_type(){return this._definition_type}get data_type(){return this._data_type}get node(){return this._node}name(){return this._name}collection_instance(){return new PD}}class FD extends ID{constructor(t,e,n){super(RD.UNIFORM,e,t,n),this._node=t,this._data_type=e,this._name=n}get line(){return`uniform ${this.data_type} ${this.name()}`}}class DD extends Yf{constructor(t,e,n,i){super(t,e,n),this._uniform_name=i}get uniform_name(){return this._uniform_name}static uniform_by_type(t){switch(t){case Er.BOOLEAN:case Er.BUTTON:return{value:0};case Er.COLOR:return{value:new D.a(0,0,0)};case Er.FLOAT:case Er.FOLDER:case Er.INTEGER:case Er.OPERATOR_PATH:case Er.NODE_PATH:case Er.PARAM_PATH:return{value:0};case Er.RAMP:case Er.STRING:return{value:null};case Er.VECTOR2:return{value:new d.a(0,0)};case Er.VECTOR3:return{value:new p.a(0,0,0)};case Er.VECTOR4:return{value:new _.a(0,0,0,0)}}ls.unreachable(t)}}const BD=new class extends la{constructor(){super(...arguments),this.name=aa.STRING(\\\\\\\"\\\\\\\"),this.type=aa.INTEGER(Wo.indexOf(jo.FLOAT),{menu:{entries:Wo.map(((t,e)=>({name:t,value:e})))}}),this.asColor=aa.BOOLEAN(0,{visibleIf:{type:Wo.indexOf(jo.VEC3)}})}};class zD extends AD{constructor(){super(...arguments),this.paramsConfig=BD,this._allow_inputs_created_from_params=!1,this._on_create_set_name_if_none_bound=this._on_create_set_name_if_none.bind(this)}static type(){return\\\\\\\"param\\\\\\\"}initializeNode(){this.addPostDirtyHook(\\\\\\\"_set_mat_to_recompile\\\\\\\",this._set_function_node_to_recompile.bind(this)),this.lifecycle.add_on_create_hook(this._on_create_set_name_if_none_bound),this.io.connection_points.initializeNode(),this.io.connection_points.set_expected_input_types_function((()=>[])),this.io.connection_points.set_expected_output_types_function((()=>[Wo[this.pv.type]]))}setLines(t){const e=[],n=Wo[this.pv.type],i=this.uniform_name();e.push(new FD(this,n,i)),t.addDefinitions(this,e)}setParamConfigs(){const t=Wo[this.pv.type],e=Yo[t];let n=qo[t];if(this._param_configs_controller=this._param_configs_controller||new wF,this._param_configs_controller.reset(),n==Er.VECTOR3&&this.p.asColor.value&&m.isArray(e)&&3==e.length){const t=new DD(Er.COLOR,this.pv.name,e,this.uniform_name());this._param_configs_controller.push(t)}else{const t=new DD(n,this.pv.name,e,this.uniform_name());this._param_configs_controller.push(t)}}uniform_name(){const t=this.io.outputs.namedOutputConnectionPoints()[0];return this.js_var_name(t.name())}set_gl_type(t){const e=Wo.indexOf(t);this.p.type.set(e)}_on_create_set_name_if_none(){\\\\\\\"\\\\\\\"==this.pv.name&&this.p.name.set(this.name())}}class kD extends sa{constructor(){super(...arguments),this._cook_main_without_inputs_when_dirty_bound=this._cook_main_without_inputs_when_dirty.bind(this)}static context(){return ts.MAT}initializeBaseNode(){super.initializeBaseNode(),this.nameController.add_post_set_fullPath_hook(this.set_material_name.bind(this)),this.addPostDirtyHook(\\\\\\\"_cook_main_without_inputs_when_dirty\\\\\\\",(()=>{setTimeout(this._cook_main_without_inputs_when_dirty_bound,0)}))}async _cook_main_without_inputs_when_dirty(){await this.cookController.cookMainWithoutInputs()}set_material_name(){this._material&&(this._material.name=this.path())}get material(){return this._material=this._material||this.createMaterial()}setMaterial(t){this._setContainer(t)}}class UD{constructor(t){this.node=t}add_params(){}update(){}get material(){return this.node.material}}const GD={NoBlending:w.ub,NormalBlending:w.xb,AdditiveBlending:w.e,SubtractiveBlending:w.Sc,MultiplyBlending:w.mb},VD=Object.keys(GD);function HD(t){return class extends t{constructor(){super(...arguments),this.doubleSided=aa.BOOLEAN(0),this.front=aa.BOOLEAN(1,{visibleIf:{doubleSided:!1}}),this.overrideShadowSide=aa.BOOLEAN(0),this.shadowDoubleSided=aa.BOOLEAN(0,{visibleIf:{overrideShadowSide:!0}}),this.shadowFront=aa.BOOLEAN(1,{visibleIf:{overrideShadowSide:!0,shadowDoubleSided:!1}}),this.colorWrite=aa.BOOLEAN(1,{separatorBefore:!0,cook:!1,callback:(t,e)=>{jD.update(t)}}),this.depthWrite=aa.BOOLEAN(1,{cook:!1,callback:(t,e)=>{jD.update(t)}}),this.depthTest=aa.BOOLEAN(1,{cook:!1,callback:(t,e)=>{jD.update(t)}}),this.premultipliedAlpha=aa.BOOLEAN(!1,{separatorAfter:!0}),this.blending=aa.INTEGER(w.xb,{menu:{entries:VD.map((t=>({name:t,value:GD[t]})))}}),this.dithering=aa.BOOLEAN(0),this.polygonOffset=aa.BOOLEAN(!1,{separatorBefore:!0}),this.polygonOffsetFactor=aa.INTEGER(0,{range:[0,1e3],visibleIf:{polygonOffset:1}}),this.polygonOffsetUnits=aa.INTEGER(0,{range:[0,1e3],visibleIf:{polygonOffset:1}})}}}HD(la);class jD extends UD{constructor(t){super(t),this.node=t}initializeNode(){}async update(){const t=this.node.material,e=this.node.pv;this._updateSides(t,e),t.colorWrite=e.colorWrite,t.depthWrite=e.depthWrite,t.depthTest=e.depthTest,t.blending=e.blending,t.premultipliedAlpha=e.premultipliedAlpha,t.dithering=e.dithering,t.polygonOffset=e.polygonOffset,t.polygonOffset&&(t.polygonOffsetFactor=e.polygonOffsetFactor,t.polygonOffsetUnits=e.polygonOffsetUnits,t.needsUpdate=!0)}_updateSides(t,e){const n=e.front?w.H:w.i,i=e.doubleSided?w.z:n;if(i!=t.side&&(t.side=i,t.needsUpdate=!0),e.overrideShadowSide){const t=e.shadowFront?w.H:w.i,n=e.shadowDoubleSided?w.z:t,i=this.node.material;n!=i.shadowSide&&(i.shadowSide=n,i.needsUpdate=!0)}else t.shadowSide=null;const s=t.customMaterials;if(s){const t=Object.keys(s);for(let n of t){const t=s[n];t&&this._updateSides(t,e)}}}static async update(t){t.controllers.advancedCommon.update()}}class WD extends(HD(la)){constructor(){super(...arguments),this.color=aa.COLOR([1,1,1]),this.lineWidth=aa.FLOAT(1,{range:[1,10],rangeLocked:[!0,!1]})}}const qD=new WD;class XD extends kD{constructor(){super(...arguments),this.paramsConfig=qD,this.controllers={advancedCommon:new jD(this)},this.controllerNames=Object.keys(this.controllers)}static type(){return\\\\\\\"lineBasic\\\\\\\"}createMaterial(){return new Ts.a({color:16777215,linewidth:1})}initializeNode(){this.params.onParamsCreated(\\\\\\\"init controllers\\\\\\\",(()=>{for(let t of this.controllerNames)this.controllers[t].initializeNode()}))}async cook(){for(let t of this.controllerNames)this.controllers[t].update();this.material.color.copy(this.pv.color),this.material.linewidth=this.pv.lineWidth,this.setMaterial(this.material)}}function YD(t){return class extends t{constructor(){super(...arguments),this.transparent=aa.BOOLEAN(0),this.opacity=aa.FLOAT(1),this.alphaTest=aa.FLOAT(0)}}}YD(la);class $D extends UD{constructor(t){super(t),this.node=t}static update(t){const e=t.material,n=t.pv;this._updateTransparency(e,n)}static _updateTransparency(t,e){t.transparent=e.transparent,this._updateCommon(t,e)}static _updateCommon(t,e){t.uniforms.opacity&&(t.uniforms.opacity.value=e.opacity),t.opacity=e.opacity,t.alphaTest=e.alphaTest;const n=t.customMaterials;if(n){const t=Object.keys(n);for(let i of t){const t=n[i];t&&this._updateCommon(t,e)}}}}class JD extends Xf{constructor(t){super(t),this.node=t}toJSON(){const t=this.node.assemblerController;if(!t)return;const e={},n=this.node.material.customMaterials;if(n){const t=Object.keys(n);for(let i of t){const t=n[i];if(t){const n=this._materialToJson(t,{node:this.node,suffix:i});n&&(e[i]=n)}}}const i=[],s=t.assembler.param_configs();for(let t of s)i.push([t.name(),t.uniform_name]);const r=this._materialToJson(this.node.material,{node:this.node,suffix:\\\\\\\"main\\\\\\\"});r||console.warn(\\\\\\\"failed to save material from node\\\\\\\",this.node.path());return{material:r||{},uniforms_time_dependent:t.assembler.uniformsTimeDependent(),uniforms_resolution_dependent:t.assembler.uniforms_resolution_dependent(),param_uniform_pairs:i,customMaterials:e}}load(t){if(this._material=this._loadMaterial(t.material),this._material){if(this._material.customMaterials=this._material.customMaterials||{},t.customMaterials){const e=Object.keys(t.customMaterials);for(let n of e){const e=t.customMaterials[n],i=this._loadMaterial(e);i&&(this._material.customMaterials[n]=i)}}if(t.uniforms_time_dependent&&this.node.scene().uniformsController.addTimeDependentUniformOwner(this._material.uuid,this._material.uniforms),t.uniforms_resolution_dependent&&this.node.scene().uniformsController.addResolutionDependentUniformOwner(this._material.uuid,this._material.uniforms),t.param_uniform_pairs)for(let e of t.param_uniform_pairs){const t=e[0],n=e[1],i=this.node.params.get(t),s=this._material.uniforms[n],r=Object.keys(this._material.customMaterials);let o;for(let t of r){const e=this._material.customMaterials[t],i=null==e?void 0:e.uniforms[n];i&&(o=o||[],o.push(i))}i&&(s||o)&&i.options.setOption(\\\\\\\"callback\\\\\\\",(()=>{if(s&&$f.callback(i,s),o)for(let t of o)$f.callback(i,t)}))}}}material(){if(li.playerMode())return this._material}}function ZD(t){return class extends t{constructor(){super(...arguments),this.setBuilderNode=aa.BOOLEAN(0,{callback:t=>{QD.PARAM_CALLBACK_setCompileRequired(t)}}),this.builderNode=aa.NODE_PATH(\\\\\\\"\\\\\\\",{visibleIf:{setBuilderNode:!0},callback:t=>{QD.PARAM_CALLBACK_setCompileRequired(t)}})}}}ZD(la);class QD extends kD{constructor(){super(...arguments),this._children_controller_context=ts.GL,this.persisted_config=new JD(this)}createMaterial(){var t;let e;return this.persisted_config&&(e=this.persisted_config.material()),e||(e=null===(t=this.assemblerController)||void 0===t?void 0:t.assembler.createMaterial()),e}get assemblerController(){return this._assembler_controller=this._assembler_controller||this._create_assembler_controller()}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}childrenAllowed(){return this.assemblerController?super.childrenAllowed():(this.scene().markAsReadOnly(this),!1)}compileIfRequired(){var t;(null===(t=this.assemblerController)||void 0===t?void 0:t.compileRequired())&&this._compile()}_compile(){const t=this.assemblerController;this.material&&t&&(t.assembler.setGlParentNode(this),this._setAssemblerGlParentNode(t),t.assembler.compileMaterial(this.material),t.post_compile())}_setAssemblerGlParentNode(t){if(!this.pv.setBuilderNode)return;const e=this.pv.builderNode.nodeWithContext(ts.MAT);if(!e)return;const n=e;n.assemblerController?n.type()==this.type()?t.assembler.setGlParentNode(n):this.states.error.set(`resolved node '${e.path()}' does not have the same type '${e.type()}' as current node '${this.type()}'`):this.states.error.set(`resolved node '${e.path()}' is not a builder node`)}static PARAM_CALLBACK_setCompileRequired(t){t.PARAM_CALLBACK_setCompileRequired()}PARAM_CALLBACK_setCompileRequired(){var t;null===(t=this.assemblerController)||void 0===t||t.setCompilationRequired(!0)}}function KD(t){return class extends t{constructor(){super(...arguments),this.useFog=aa.BOOLEAN(0)}}}KD(la);class tB extends UD{constructor(t){super(t),this.node=t}static update(t){const e=t.material,n=t.pv;e.fog=n.useFog}}function eB(t){return class extends t{constructor(){super(...arguments),this.default=aa.FOLDER(null)}}}function nB(t){return class extends t{constructor(){super(...arguments),this.advanced=aa.FOLDER(null)}}}class iB extends(KD(HD(ZD(nB(YD(eB(la))))))){constructor(){super(...arguments),this.linewidth=aa.FLOAT(1,{range:[0,10],rangeLocked:[!0,!1]})}}const sB=new iB;class rB extends QD{constructor(){super(...arguments),this.paramsConfig=sB,this.controllers={advancedCommon:new jD(this)},this.controllerNames=Object.keys(this.controllers)}static type(){return\\\\\\\"lineBasicBuilder\\\\\\\"}usedAssembler(){return jn.GL_LINE}_create_assembler_controller(){return li.assemblersRegister.assembler(this,this.usedAssembler())}initializeNode(){this.params.onParamsCreated(\\\\\\\"init controllers\\\\\\\",(()=>{for(let t of this.controllerNames)this.controllers[t].initializeNode()}))}async cook(){for(let t of this.controllerNames)this.controllers[t].update();$D.update(this),tB.update(this),this.compileIfRequired(),this.material.linewidth=this.pv.linewidth,this.setMaterial(this.material)}}function oB(t){return class extends t{constructor(){super(...arguments),this.color=aa.COLOR([1,1,1],{conversion:oo.SRGB_TO_LINEAR}),this.useVertexColors=aa.BOOLEAN(0,{separatorAfter:!0}),this.transparent=aa.BOOLEAN(0),this.opacity=aa.FLOAT(1),this.alphaTest=aa.FLOAT(0)}}}O.a;oB(la);class aB extends UD{constructor(t){super(t),this.node=t}static update(t){const e=t.material,n=t.pv;e.color.copy(n.color);const i=n.useVertexColors;i!=e.vertexColors&&(e.vertexColors=i,e.needsUpdate=!0),e.opacity=n.opacity,e.transparent=n.transparent,e.alphaTest=n.alphaTest}}function lB(t){return class extends t{constructor(){super(...arguments),this.useFog=aa.BOOLEAN(0)}}}lB(la);class cB extends UD{constructor(t){super(t),this.node=t}static update(t){const e=t.material,n=t.pv;e.fog=n.useFog}}function hB(t){return{cook:!1,callback:(e,n)=>{t.update(e)}}}function uB(t,e,n){return{visibleIf:{[e]:1},nodeSelection:{context:ts.COP,types:null==n?void 0:n.types},cook:!1,callback:(e,n)=>{t.update(e)}}}class dB extends UD{constructor(t,e){super(t),this.node=t,this._update_options=e}add_hooks(t,e){t.addPostDirtyHook(\\\\\\\"TextureController\\\\\\\",(()=>{this.update()})),e.addPostDirtyHook(\\\\\\\"TextureController\\\\\\\",(()=>{this.update()}))}static update(t){}async _update(t,e,n,i){if(this._update_options.uniforms){const s=t,r=e;await this._update_texture_on_uniforms(s,r,n,i)}if(this._update_options.directParams){const s=t,r=e;await this._update_texture_on_material(s,r,n,i)}}async _update_texture_on_uniforms(t,e,n,i){this._update_required_attribute(t,t.uniforms,e,n,i,this._apply_texture_on_uniforms.bind(this),this._remove_texture_from_uniforms.bind(this))}_apply_texture_on_uniforms(t,e,n,i){const s=null!=e[n]&&null!=e[n].value;let r=!1;if(s){e[n].value.uuid!=i.uuid&&(r=!0)}if(!s||r){e[n]&&(e[n].value=i),this._apply_texture_on_material(t,t,n,i),t.needsUpdate=!0;const s=t.customMaterials;if(s){const t=Object.keys(s);for(let e of t){const t=s[e];t&&this._apply_texture_on_uniforms(t,t.uniforms,n,i)}}}}_remove_texture_from_uniforms(t,e,n){if(e[n]){if(e[n].value){e[n].value=null,this._remove_texture_from_material(t,t,n),t.needsUpdate=!0;const i=t.customMaterials;if(i){const t=Object.keys(i);for(let e of t){const t=i[e];t&&this._remove_texture_from_uniforms(t,t.uniforms,n)}}}}else li.warn(`'${n}' uniform not found. existing uniforms are:`,Object.keys(e).sort())}async _update_texture_on_material(t,e,n,i){this._update_required_attribute(t,t,e,n,i,this._apply_texture_on_material.bind(this),this._remove_texture_from_material.bind(this))}_apply_texture_on_material(t,e,n,i){const s=null!=e[n];let r=!1;if(s){e[n].uuid!=i.uuid&&(r=!0)}s&&!r||(e[n]=i,t.needsUpdate=!0)}_remove_texture_from_material(t,e,n){e[n]&&(e[n]=null,t.needsUpdate=!0)}async _update_required_attribute(t,e,n,i,s,r,o){i.isDirty()&&await i.compute();if(i.value){s.isDirty()&&await s.compute();const i=s.value.nodeWithContext(ts.COP);if(i){const s=(await i.compute()).texture();if(s)return void r(t,e,n,s)}}o(t,e,n)}}function pB(t){return class extends t{constructor(){super(...arguments),this.useMap=aa.BOOLEAN(0,hB(_B)),this.map=aa.NODE_PATH(vi.EMPTY,uB(_B,\\\\\\\"useMap\\\\\\\"))}}}O.a;pB(la);class _B extends dB{constructor(t,e){super(t,e),this.node=t}initializeNode(){this.add_hooks(this.node.p.useMap,this.node.p.map)}async update(){this._update(this.node.material,\\\\\\\"map\\\\\\\",this.node.p.useMap,this.node.p.map)}static async update(t){t.controllers.map.update()}}function mB(t){return class extends t{constructor(){super(...arguments),this.useAlphaMap=aa.BOOLEAN(0,{separatorBefore:!0,...hB(fB)}),this.alphaMap=aa.NODE_PATH(vi.EMPTY,uB(fB,\\\\\\\"useAlphaMap\\\\\\\"))}}}O.a;mB(la);class fB extends dB{constructor(t,e){super(t,e),this.node=t}initializeNode(){this.add_hooks(this.node.p.useAlphaMap,this.node.p.alphaMap)}async update(){this._update(this.node.material,\\\\\\\"alphaMap\\\\\\\",this.node.p.useAlphaMap,this.node.p.alphaMap)}static async update(t){t.controllers.alphaMap.update()}}function gB(t){return class extends t{constructor(){super(...arguments),this.useAOMap=aa.BOOLEAN(0,{separatorBefore:!0,...hB(vB)}),this.aoMap=aa.NODE_PATH(vi.EMPTY,uB(vB,\\\\\\\"useAOMap\\\\\\\")),this.aoMapIntensity=aa.FLOAT(1,{range:[0,1],rangeLocked:[!1,!1],visibleIf:{useAOMap:1}})}}}O.a;gB(la);class vB extends dB{constructor(t,e){super(t,e),this.node=t}initializeNode(){this.add_hooks(this.node.p.useAOMap,this.node.p.aoMap)}async update(){if(this._update(this.node.material,\\\\\\\"aoMap\\\\\\\",this.node.p.useAOMap,this.node.p.aoMap),this._update_options.uniforms){this.node.material.uniforms.aoMapIntensity.value=this.node.pv.aoMapIntensity}if(this._update_options.directParams){this.node.material.aoMapIntensity=this.node.pv.aoMapIntensity}}static async update(t){t.controllers.aoMap.update()}}var yB;!function(t){t.MULT=\\\\\\\"mult\\\\\\\",t.ADD=\\\\\\\"add\\\\\\\",t.MIX=\\\\\\\"mix\\\\\\\"}(yB||(yB={}));const xB=[yB.MULT,yB.ADD,yB.MIX],bB={[yB.MULT]:w.nb,[yB.ADD]:w.c,[yB.MIX]:w.lb};function wB(t){return class extends t{constructor(){super(...arguments),this.useEnvMap=aa.BOOLEAN(0,hB(TB)),this.envMap=aa.NODE_PATH(vi.EMPTY,uB(TB,\\\\\\\"useEnvMap\\\\\\\",{types:[Lg.CUBE_CAMERA]})),this.combine=aa.INTEGER(0,{visibleIf:{useEnvMap:1},menu:{entries:xB.map(((t,e)=>({name:t,value:e})))}}),this.reflectivity=aa.FLOAT(1,{visibleIf:{useEnvMap:1}}),this.refractionRatio=aa.FLOAT(.98,{range:[-1,1],rangeLocked:[!1,!1],visibleIf:{useEnvMap:1}})}}}wB(la);class TB extends dB{constructor(t,e){super(t,e),this.node=t}initializeNode(){this.add_hooks(this.node.p.useEnvMap,this.node.p.envMap)}async update(){this._update(this.node.material,\\\\\\\"envMap\\\\\\\",this.node.p.useEnvMap,this.node.p.envMap);const t=bB[xB[this.node.pv.combine]];if(this._update_options.uniforms){const t=this.node.material;t.uniforms.reflectivity.value=this.node.pv.reflectivity,t.uniforms.refractionRatio.value=this.node.pv.refractionRatio}if(this._update_options.directParams){const e=this.node.material;e.combine=t,e.reflectivity=this.node.pv.reflectivity,e.refractionRatio=this.node.pv.refractionRatio}}static async update(t){t.controllers.envMap.update()}}function AB(t){return class extends t{constructor(){super(...arguments),this.useLightMap=aa.BOOLEAN(0,{separatorBefore:!0,...hB(MB)}),this.lightMap=aa.NODE_PATH(vi.EMPTY,uB(MB,\\\\\\\"useLightMap\\\\\\\")),this.lightMapIntensity=aa.FLOAT(1,{visibleIf:{useLightMap:1}})}}}O.a;AB(la);class MB extends dB{constructor(t,e){super(t,e),this.node=t}initializeNode(){this.add_hooks(this.node.p.useLightMap,this.node.p.lightMap)}async update(){if(this._update(this.node.material,\\\\\\\"lightMap\\\\\\\",this.node.p.useLightMap,this.node.p.lightMap),this._update_options.uniforms){this.node.material.uniforms.lightMapIntensity.value=this.node.pv.lightMapIntensity}if(this._update_options.directParams){this.node.material.lightMapIntensity=this.node.pv.lightMapIntensity}}static async update(t){t.controllers.lightMap.update()}}var EB;!function(t){t.ROUND=\\\\\\\"round\\\\\\\",t.BUTT=\\\\\\\"butt\\\\\\\",t.SQUARE=\\\\\\\"square\\\\\\\"}(EB||(EB={}));const SB=[EB.ROUND,EB.BUTT,EB.SQUARE];var CB;!function(t){t.ROUND=\\\\\\\"round\\\\\\\",t.BEVEL=\\\\\\\"bevel\\\\\\\",t.MITER=\\\\\\\"miter\\\\\\\"}(CB||(CB={}));const NB=[CB.ROUND,CB.BEVEL,CB.MITER];function LB(t){return class extends t{constructor(){super(...arguments),this.wireframe=aa.BOOLEAN(0,{separatorBefore:!0}),this.wireframeLinecap=aa.INTEGER(0,{menu:{entries:SB.map(((t,e)=>({name:t,value:e})))},visibleIf:{wireframe:1}}),this.wireframeLinejoin=aa.INTEGER(0,{menu:{entries:NB.map(((t,e)=>({name:t,value:e})))},visibleIf:{wireframe:1}})}}}O.a;LB(la);class OB extends UD{constructor(t){super(t),this.node=t}static update(t){const e=t.material,n=t.pv;e.wireframe=n.wireframe,e.wireframeLinecap=SB[n.wireframeLinecap],e.wireframeLinejoin=NB[n.wireframeLinejoin],e.needsUpdate=!0}}function PB(t){return class extends t{constructor(){super(...arguments),this.textures=aa.FOLDER(null)}}}const RB={directParams:!0};class IB extends(lB(LB(HD(nB(AB(wB(gB(mB(pB(PB(oB(eB(la))))))))))))){}const FB=new IB;class DB extends kD{constructor(){super(...arguments),this.paramsConfig=FB,this.controllers={advancedCommon:new jD(this),alphaMap:new fB(this,RB),aoMap:new vB(this,RB),envMap:new TB(this,RB),lightMap:new MB(this,RB),map:new _B(this,RB)},this.controllerNames=Object.keys(this.controllers)}static type(){return\\\\\\\"meshBasic\\\\\\\"}createMaterial(){return new lt.a({vertexColors:!1,side:w.H,color:16777215,opacity:1})}initializeNode(){this.params.onParamsCreated(\\\\\\\"init controllers\\\\\\\",(()=>{for(let t of this.controllerNames)this.controllers[t].initializeNode()}))}async cook(){for(let t of this.controllerNames)this.controllers[t].update();aB.update(this),cB.update(this),OB.update(this),this.setMaterial(this.material)}}function BB(t){return class extends t{constructor(){super(...arguments),this.wireframe=aa.BOOLEAN(0)}}}BB(la);class zB extends UD{constructor(t){super(t),this.node=t}static update(t){const e=t.material,n=t.pv;e.wireframe=n.wireframe,e.needsUpdate=!0}}const kB={uniforms:!0};class UB extends(KD(BB(HD(ZD(nB(wB(gB(mB(pB(PB(YD(eB(la))))))))))))){}const GB=new UB;class VB extends QD{constructor(){super(...arguments),this.paramsConfig=GB,this.controllers={advancedCommon:new jD(this),alphaMap:new fB(this,kB),aoMap:new vB(this,kB),envMap:new TB(this,kB),map:new _B(this,kB)},this.controllerNames=Object.keys(this.controllers)}static type(){return\\\\\\\"meshBasicBuilder\\\\\\\"}usedAssembler(){return jn.GL_MESH_BASIC}_create_assembler_controller(){return li.assemblersRegister.assembler(this,this.usedAssembler())}initializeNode(){this.params.onParamsCreated(\\\\\\\"init controllers\\\\\\\",(()=>{for(let t of this.controllerNames)this.controllers[t].initializeNode()}))}async cook(){for(let t of this.controllerNames)this.controllers[t].update();$D.update(this),tB.update(this),zB.update(this),this.compileIfRequired(),this.setMaterial(this.material)}}function HB(t){return class extends t{constructor(){super(...arguments),this.emissive=aa.COLOR([0,0,0],{separatorBefore:!0}),this.useEmissiveMap=aa.BOOLEAN(0,hB(jB)),this.emissiveMap=aa.NODE_PATH(vi.EMPTY,uB(jB,\\\\\\\"useEmissiveMap\\\\\\\")),this.emissiveIntensity=aa.FLOAT(1)}}}O.a;HB(la);class jB extends dB{constructor(t,e){super(t,e),this.node=t}initializeNode(){this.add_hooks(this.node.p.useEmissiveMap,this.node.p.emissiveMap)}async update(){if(this._update(this.node.material,\\\\\\\"emissiveMap\\\\\\\",this.node.p.useEmissiveMap,this.node.p.emissiveMap),this._update_options.uniforms){this.node.material.uniforms.emissive.value.copy(this.node.pv.emissive)}if(this._update_options.directParams){const t=this.node.material;t.emissive.copy(this.node.pv.emissive),t.emissiveIntensity=this.node.pv.emissiveIntensity}}static async update(t){t.controllers.emissiveMap.update()}}const WB={directParams:!0};class qB extends(lB(LB(HD(nB(AB(wB(HB(gB(mB(pB(PB(oB(eB(la)))))))))))))){}const XB=new qB;class YB extends kD{constructor(){super(...arguments),this.paramsConfig=XB,this.controllers={advancedCommon:new jD(this),alphaMap:new fB(this,WB),aoMap:new vB(this,WB),emissiveMap:new jB(this,WB),envMap:new TB(this,WB),lightMap:new MB(this,WB),map:new _B(this,WB)},this.controllerNames=Object.keys(this.controllers)}static type(){return\\\\\\\"meshLambert\\\\\\\"}createMaterial(){return new ws.a({vertexColors:!1,side:w.H,color:16777215,opacity:1})}initializeNode(){this.params.onParamsCreated(\\\\\\\"init controllers\\\\\\\",(()=>{for(let t of this.controllerNames)this.controllers[t].initializeNode()}))}async cook(){for(let t of this.controllerNames)this.controllers[t].update();aB.update(this),cB.update(this),OB.update(this),this.setMaterial(this.material)}}function $B(t){return class extends t{constructor(){super(...arguments),this.shadowPCSS=aa.BOOLEAN(0,{callback:t=>{JB.PARAM_CALLBACK_setRecompileRequired(t)},separatorBefore:!0}),this.shadowPCSSSamplesCount=aa.INTEGER(16,{visibleIf:{shadowPCSS:1},range:[0,128],rangeLocked:[!0,!1]}),this.shadowPCSSFilterSize=aa.FLOAT(1,{visibleIf:{shadowPCSS:1},range:[0,10],rangeLocked:[!0,!1]})}}}$B(la);class JB extends UD{constructor(t){super(t),this.node=t}initializeNode(){}static filterFragmentShader(t,e){const n=`\\\\n#define NUM_SAMPLES ${hf.integer(t.pv.shadowPCSSSamplesCount)}\\\\n#define PCSS_FILTER_SIZE ${hf.float(t.pv.shadowPCSSFilterSize)}\\\\n#define LIGHT_WORLD_SIZE 0.005\\\\n// #define LIGHT_FRUSTUM_WIDTH 1.0\\\\n// #define PCSS_FILTER_SIZE 1.0\\\\n#define LIGHT_SIZE_UV (PCSS_FILTER_SIZE * LIGHT_WORLD_SIZE)\\\\n#define NEAR_PLANE 9.5\\\\n\\\\n// #define NUM_SAMPLES 32\\\\n#define NUM_RINGS 11\\\\n#define BLOCKER_SEARCH_NUM_SAMPLES NUM_SAMPLES\\\\n#define PCF_NUM_SAMPLES NUM_SAMPLES\\\\n\\\\nvec2 poissonDisk[NUM_SAMPLES];\\\\n\\\\nvoid initPoissonSamples( const in vec2 randomSeed ) {\\\\n\\\\tfloat ANGLE_STEP = PI2 * float( NUM_RINGS ) / float( NUM_SAMPLES );\\\\n\\\\tfloat INV_NUM_SAMPLES = 1.0 / float( NUM_SAMPLES );\\\\n\\\\n\\\\t// jsfiddle that shows sample pattern: https://jsfiddle.net/a16ff1p7/\\\\n\\\\tfloat angle = rand( randomSeed ) * PI2;\\\\n\\\\tfloat radius = INV_NUM_SAMPLES;\\\\n\\\\tfloat radiusStep = radius;\\\\n\\\\n\\\\tfor( int i = 0; i < NUM_SAMPLES; i ++ ) {\\\\n\\\\t\\\\tpoissonDisk[i] = vec2( cos( angle ), sin( angle ) ) * pow( radius, 0.75 );\\\\n\\\\t\\\\tradius += radiusStep;\\\\n\\\\t\\\\tangle += ANGLE_STEP;\\\\n\\\\t}\\\\n}\\\\n\\\\nfloat penumbraSize( const in float zReceiver, const in float zBlocker ) { // Parallel plane estimation\\\\n\\\\treturn (zReceiver - zBlocker) / zBlocker;\\\\n}\\\\n\\\\nfloat findBlocker( sampler2D shadowMap, const in vec2 uv, const in float zReceiver ) {\\\\n\\\\t// This uses similar triangles to compute what\\\\n\\\\t// area of the shadow map we should search\\\\n\\\\tfloat searchRadius = LIGHT_SIZE_UV * ( zReceiver - NEAR_PLANE ) / zReceiver;\\\\n\\\\tfloat blockerDepthSum = 0.0;\\\\n\\\\tint numBlockers = 0;\\\\n\\\\n\\\\tfor( int i = 0; i < BLOCKER_SEARCH_NUM_SAMPLES; i++ ) {\\\\n\\\\t\\\\tfloat shadowMapDepth = unpackRGBAToDepth(texture2D(shadowMap, uv + poissonDisk[i] * searchRadius));\\\\n\\\\t\\\\tif ( shadowMapDepth < zReceiver ) {\\\\n\\\\t\\\\t\\\\tblockerDepthSum += shadowMapDepth;\\\\n\\\\t\\\\t\\\\tnumBlockers ++;\\\\n\\\\t\\\\t}\\\\n\\\\t}\\\\n\\\\n\\\\tif( numBlockers == 0 ) return -1.0;\\\\n\\\\n\\\\treturn blockerDepthSum / float( numBlockers );\\\\n}\\\\n\\\\nfloat PCF_Filter(sampler2D shadowMap, vec2 uv, float zReceiver, float filterRadius ) {\\\\n\\\\tfloat sum = 0.0;\\\\n\\\\tfor( int i = 0; i < PCF_NUM_SAMPLES; i ++ ) {\\\\n\\\\t\\\\tfloat depth = unpackRGBAToDepth( texture2D( shadowMap, uv + poissonDisk[ i ] * filterRadius ) );\\\\n\\\\t\\\\tif( zReceiver <= depth ) sum += 1.0;\\\\n\\\\t}\\\\n\\\\tfor( int i = 0; i < PCF_NUM_SAMPLES; i ++ ) {\\\\n\\\\t\\\\tfloat depth = unpackRGBAToDepth( texture2D( shadowMap, uv + -poissonDisk[ i ].yx * filterRadius ) );\\\\n\\\\t\\\\tif( zReceiver <= depth ) sum += 1.0;\\\\n\\\\t}\\\\n\\\\treturn sum / ( 2.0 * float( PCF_NUM_SAMPLES ) );\\\\n}\\\\n\\\\nfloat PCSS ( sampler2D shadowMap, vec4 coords ) {\\\\n\\\\tvec2 uv = coords.xy;\\\\n\\\\tfloat zReceiver = coords.z; // Assumed to be eye-space z in this code\\\\n\\\\n\\\\tinitPoissonSamples( uv );\\\\n\\\\t// STEP 1: blocker search\\\\n\\\\tfloat avgBlockerDepth = findBlocker( shadowMap, uv, zReceiver );\\\\n\\\\n\\\\t//There are no occluders so early out (this saves filtering)\\\\n\\\\tif( avgBlockerDepth == -1.0 ) return 1.0;\\\\n\\\\n\\\\t// STEP 2: penumbra size\\\\n\\\\tfloat penumbraRatio = penumbraSize( zReceiver, avgBlockerDepth );\\\\n\\\\tfloat filterRadius = penumbraRatio * LIGHT_SIZE_UV * NEAR_PLANE / zReceiver;\\\\n\\\\n\\\\t// STEP 3: filtering\\\\n\\\\t//return avgBlockerDepth;\\\\n\\\\treturn PCF_Filter( shadowMap, uv, zReceiver, filterRadius );\\\\n}\\\\n`;let i=z;return i=i.replace(\\\\\\\"#ifdef USE_SHADOWMAP\\\\\\\",`#ifdef USE_SHADOWMAP\\\\n${n}\\\\n\\\\t\\\\t\\\\t\\\\t`),i=i.replace(\\\\\\\"#if defined( SHADOWMAP_TYPE_PCF )\\\\\\\",\\\\\\\"\\\\n\\\\t\\\\t\\\\t\\\\treturn PCSS( shadowMap, shadowCoord );\\\\n\\\\t\\\\t\\\\t\\\\t#if defined( SHADOWMAP_TYPE_PCF )\\\\\\\"),e=e.replace(\\\\\\\"#include \\\\\\\",i)}async update(){const t=this.node;if(!t.assemblerController)return;const e=\\\\\\\"PCSS\\\\\\\";this.node.pv.shadowPCSS?t.assemblerController.addFilterFragmentShaderCallback(e,(t=>JB.filterFragmentShader(this.node,t))):t.assemblerController.removeFilterFragmentShaderCallback(e)}static async update(t){t.controllers.PCSS.update()}static PARAM_CALLBACK_setRecompileRequired(t){t.controllers.PCSS.update()}}const ZB={uniforms:!0};class QB extends($B(KD(BB(HD(ZD(nB(AB(wB(HB(gB(mB(pB(PB(YD(eB(la)))))))))))))))){}const KB=new QB;class tz extends QD{constructor(){super(...arguments),this.paramsConfig=KB,this.controllers={advancedCommon:new jD(this),alphaMap:new fB(this,ZB),aoMap:new vB(this,ZB),emissiveMap:new jB(this,ZB),envMap:new TB(this,ZB),lightMap:new MB(this,ZB),map:new _B(this,ZB),PCSS:new JB(this)},this.controllerNames=Object.keys(this.controllers)}static type(){return\\\\\\\"meshLambertBuilder\\\\\\\"}usedAssembler(){return jn.GL_MESH_LAMBERT}_create_assembler_controller(){return li.assemblersRegister.assembler(this,this.usedAssembler())}initializeNode(){this.params.onParamsCreated(\\\\\\\"init controllers\\\\\\\",(()=>{for(let t of this.controllerNames)this.controllers[t].initializeNode()}))}async cook(){for(let t of this.controllerNames)this.controllers[t].update();$D.update(this),tB.update(this),zB.update(this),this.compileIfRequired(),this.setMaterial(this.material)}}function ez(t){return class extends t{constructor(){super(...arguments),this.useBumpMap=aa.BOOLEAN(0,{separatorBefore:!0,...hB(nz)}),this.bumpMap=aa.NODE_PATH(\\\\\\\"\\\\\\\",uB(nz,\\\\\\\"useBumpMap\\\\\\\")),this.bumpScale=aa.FLOAT(1,{range:[0,1],rangeLocked:[!1,!1],...uB(nz,\\\\\\\"useBumpMap\\\\\\\")}),this.bumpBias=aa.FLOAT(0,{range:[0,1],rangeLocked:[!1,!1],...uB(nz,\\\\\\\"useBumpMap\\\\\\\")})}}}O.a;ez(la);class nz extends dB{constructor(t,e){super(t,e),this.node=t}initializeNode(){this.add_hooks(this.node.p.useBumpMap,this.node.p.bumpMap)}async update(){if(this._update(this.node.material,\\\\\\\"bumpMap\\\\\\\",this.node.p.useBumpMap,this.node.p.bumpMap),this._update_options.uniforms){this.node.material.uniforms.bumpScale.value=this.node.pv.bumpScale}if(this._update_options.directParams){this.node.material.bumpScale=this.node.pv.bumpScale}}static async update(t){t.controllers.bumpMap.update()}}var iz;!function(t){t.TANGENT=\\\\\\\"tangent\\\\\\\",t.OBJECT=\\\\\\\"object\\\\\\\"}(iz||(iz={}));const sz=[iz.TANGENT,iz.OBJECT],rz={[iz.TANGENT]:w.Uc,[iz.OBJECT]:w.zb};function oz(t){return class extends t{constructor(){super(...arguments),this.useNormalMap=aa.BOOLEAN(0,{separatorBefore:!0,...hB(az)}),this.normalMap=aa.NODE_PATH(vi.EMPTY,uB(az,\\\\\\\"useNormalMap\\\\\\\")),this.normalMapType=aa.INTEGER(0,{visibleIf:{useNormalMap:1},menu:{entries:sz.map(((t,e)=>({name:t,value:e})))}}),this.normalScale=aa.VECTOR2([1,1],{visibleIf:{useNormalMap:1}})}}}O.a;oz(la);class az extends dB{constructor(t,e){super(t,e),this.node=t}initializeNode(){this.add_hooks(this.node.p.useNormalMap,this.node.p.normalMap)}async update(){this._update(this.node.material,\\\\\\\"normalMap\\\\\\\",this.node.p.useNormalMap,this.node.p.normalMap);const t=rz[sz[this.node.pv.normalMapType]];if(this._update_options.uniforms){this.node.material.uniforms.normalScale.value.copy(this.node.pv.normalScale)}const e=this.node.material;e.normalMapType=t,this._update_options.directParams&&e.normalScale.copy(this.node.pv.normalScale)}static async update(t){t.controllers.normalMap.update()}}function lz(t){return class extends t{constructor(){super(...arguments),this.useDisplacementMap=aa.BOOLEAN(0,{separatorBefore:!0,...hB(cz)}),this.displacementMap=aa.NODE_PATH(\\\\\\\"\\\\\\\",uB(cz,\\\\\\\"useDisplacementMap\\\\\\\")),this.displacementScale=aa.FLOAT(1,{range:[0,1],rangeLocked:[!1,!1],...uB(cz,\\\\\\\"useDisplacementMap\\\\\\\")}),this.displacementBias=aa.FLOAT(0,{range:[0,1],rangeLocked:[!1,!1],...uB(cz,\\\\\\\"useDisplacementMap\\\\\\\")})}}}O.a;lz(la);class cz extends dB{constructor(t,e){super(t,e),this.node=t}initializeNode(){this.add_hooks(this.node.p.useDisplacementMap,this.node.p.displacementMap)}async update(){if(this._update(this.node.material,\\\\\\\"displacementMap\\\\\\\",this.node.p.useDisplacementMap,this.node.p.displacementMap),this._update_options.uniforms){const t=this.node.material;t.uniforms.displacementScale.value=this.node.pv.displacementScale,t.uniforms.displacementBias.value=this.node.pv.displacementBias}if(this._update_options.directParams){const t=this.node.material;t.displacementScale=this.node.pv.displacementScale,t.displacementBias=this.node.pv.displacementBias}}static async update(t){t.controllers.displacementMap.update()}}function hz(t){return class extends t{constructor(){super(...arguments),this.useMatcapMap=aa.BOOLEAN(0,hB(uz)),this.matcapMap=aa.NODE_PATH(vi.EMPTY,uB(uz,\\\\\\\"useMatcapMap\\\\\\\"))}}}O.a;hz(la);class uz extends dB{constructor(t,e){super(t,e),this.node=t}initializeNode(){this.add_hooks(this.node.p.useMatcapMap,this.node.p.matcapMap)}async update(){this._update(this.node.material,\\\\\\\"matcap\\\\\\\",this.node.p.useMatcapMap,this.node.p.matcapMap)}static async update(t){t.controllers.matcap.update()}}const dz={directParams:!0};class pz extends(lB(HD(nB(oz(lz(ez(mB(pB(hz(PB(oB(eB(la))))))))))))){}const _z=new pz;class mz extends kD{constructor(){super(...arguments),this.paramsConfig=_z,this.controllers={advancedCommon:new jD(this),alphaMap:new fB(this,dz),bumpMap:new nz(this,dz),displacementMap:new cz(this,dz),map:new _B(this,dz),matcap:new uz(this,dz),normalMap:new az(this,dz)},this.controllerNames=Object.keys(this.controllers)}static type(){return\\\\\\\"meshMatcap\\\\\\\"}createMaterial(){return new jf({vertexColors:!1,side:w.H,color:16777215,opacity:1})}initializeNode(){this.params.onParamsCreated(\\\\\\\"init controllers\\\\\\\",(()=>{for(let t of this.controllerNames)this.controllers[t].initializeNode()}))}async cook(){for(let t of this.controllerNames)this.controllers[t].update();aB.update(this),cB.update(this),this.setMaterial(this.material)}}const fz={directParams:!0};class gz extends(lB(HD(oz(lz(ez(PB(eB(la)))))))){}const vz=new gz;class yz extends kD{constructor(){super(...arguments),this.paramsConfig=vz,this.controllers={advancedCommon:new jD(this),bumpMap:new nz(this,fz),displacementMap:new cz(this,fz),normalMap:new az(this,fz)},this.controllerNames=Object.keys(this.controllers)}static type(){return\\\\\\\"meshNormal\\\\\\\"}createMaterial(){return new Hf({vertexColors:!1,side:w.H,opacity:1})}initializeNode(){this.params.onParamsCreated(\\\\\\\"init controllers\\\\\\\",(()=>{for(let t of this.controllerNames)this.controllers[t].initializeNode()}))}async cook(){for(let t of this.controllerNames)this.controllers[t].update();cB.update(this),this.setMaterial(this.material)}}function xz(t){return class extends t{constructor(){super(...arguments),this.useSpecularMap=aa.BOOLEAN(0,hB(bz)),this.specularMap=aa.NODE_PATH(vi.EMPTY,uB(bz,\\\\\\\"useSpecularMap\\\\\\\"))}}}O.a;xz(la);class bz extends dB{constructor(t,e){super(t,e),this.node=t}initializeNode(){this.add_hooks(this.node.p.useSpecularMap,this.node.p.specularMap)}async update(){this._update(this.node.material,\\\\\\\"specularMap\\\\\\\",this.node.p.useSpecularMap,this.node.p.specularMap)}static async update(t){t.controllers.specularMap.update()}}const wz={directParams:!0};class Tz extends(lB(LB(HD(nB(xz(oz(AB(wB(HB(lz(ez(gB(mB(pB(PB(oB(eB(la)))))))))))))))))){constructor(){super(...arguments),this.flatShading=aa.BOOLEAN(0)}}const Az=new Tz;class Mz extends kD{constructor(){super(...arguments),this.paramsConfig=Az,this.controllers={advancedCommon:new jD(this),alphaMap:new fB(this,wz),aoMap:new vB(this,wz),bumpMap:new nz(this,wz),displacementMap:new cz(this,wz),emissiveMap:new jB(this,wz),envMap:new TB(this,wz),lightMap:new MB(this,wz),map:new _B(this,wz),normalMap:new az(this,wz),specularMap:new bz(this,wz)},this.controllerNames=Object.keys(this.controllers)}static type(){return\\\\\\\"meshPhong\\\\\\\"}createMaterial(){return new Gf.a({vertexColors:!1,side:w.H,color:16777215,opacity:1})}initializeNode(){this.params.onParamsCreated(\\\\\\\"init controllers\\\\\\\",(()=>{for(let t of this.controllerNames)this.controllers[t].initializeNode()}))}async cook(){for(let t of this.controllerNames)this.controllers[t].update();aB.update(this),cB.update(this),OB.update(this),this.material.flatShading!=this.pv.flatShading&&(this.material.flatShading=this.pv.flatShading,this.material.needsUpdate=!0),this.setMaterial(this.material)}}const Ez={uniforms:!0};class Sz extends($B(KD(BB(HD(ZD(nB(xz(oz(AB(wB(HB(lz(ez(gB(mB(pB(PB(YD(eB(la)))))))))))))))))))){}const Cz=new Sz;class Nz extends QD{constructor(){super(...arguments),this.paramsConfig=Cz,this.controllers={advancedCommon:new jD(this),alphaMap:new fB(this,Ez),aoMap:new vB(this,Ez),bumpMap:new nz(this,Ez),displacementMap:new cz(this,Ez),emissiveMap:new jB(this,Ez),envMap:new TB(this,Ez),lightMap:new MB(this,Ez),map:new _B(this,Ez),normalMap:new az(this,Ez),specularMap:new bz(this,Ez),PCSS:new JB(this)},this.controllerNames=Object.keys(this.controllers)}static type(){return\\\\\\\"meshPhongBuilder\\\\\\\"}usedAssembler(){return jn.GL_MESH_PHONG}_create_assembler_controller(){return li.assemblersRegister.assembler(this,this.usedAssembler())}initializeNode(){this.params.onParamsCreated(\\\\\\\"init controllers\\\\\\\",(()=>{for(let t of this.controllerNames)this.controllers[t].initializeNode()}))}async cook(){for(let t of this.controllerNames)this.controllers[t].update();$D.update(this),tB.update(this),zB.update(this),this.compileIfRequired(),this.setMaterial(this.material)}}function Lz(t){return class extends t{constructor(){super(...arguments),this.useEnvMap=aa.BOOLEAN(0,{separatorBefore:!0,...hB(Oz)}),this.envMap=aa.NODE_PATH(vi.EMPTY,uB(Oz,\\\\\\\"useEnvMap\\\\\\\")),this.envMapIntensity=aa.FLOAT(1,{visibleIf:{useEnvMap:1}}),this.refractionRatio=aa.FLOAT(.98,{range:[-1,1],rangeLocked:[!1,!1],visibleIf:{useEnvMap:1}})}}}Lz(la);class Oz extends dB{constructor(t,e){super(t,e),this.node=t}initializeNode(){this.add_hooks(this.node.p.useEnvMap,this.node.p.envMap)}async update(){if(this._update(this.node.material,\\\\\\\"envMap\\\\\\\",this.node.p.useEnvMap,this.node.p.envMap),this._update_options.uniforms){const t=this.node.material;t.uniforms.envMapIntensity.value=this.node.pv.envMapIntensity,t.uniforms.refractionRatio.value=this.node.pv.refractionRatio}if(this._update_options.directParams){const t=this.node.material;t.envMapIntensity=this.node.pv.envMapIntensity,t.refractionRatio=this.node.pv.refractionRatio}}static async update(t){t.controllers.envMap.update()}}function Pz(t){return class extends t{constructor(){super(...arguments),this.useMetalnessMap=aa.BOOLEAN(0,{separatorBefore:!0,...hB(Rz)}),this.metalnessMap=aa.NODE_PATH(vi.EMPTY,uB(Rz,\\\\\\\"useMetalnessMap\\\\\\\")),this.metalness=aa.FLOAT(1),this.useRoughnessMap=aa.BOOLEAN(0,{separatorBefore:!0,...hB(Rz)}),this.roughnessMap=aa.NODE_PATH(vi.EMPTY,uB(Rz,\\\\\\\"useRoughnessMap\\\\\\\")),this.roughness=aa.FLOAT(.5)}}}O.a;Pz(la);class Rz extends dB{constructor(t,e){super(t,e),this.node=t}initializeNode(){this.add_hooks(this.node.p.useMetalnessMap,this.node.p.metalnessMap)}async update(){if(this._update(this.node.material,\\\\\\\"metalnessMap\\\\\\\",this.node.p.useMetalnessMap,this.node.p.metalnessMap),this._update_options.uniforms){this.node.material.uniforms.metalness.value=this.node.pv.metalness}if(this._update_options.directParams){this.node.material.metalness=this.node.pv.metalness}if(this._update(this.node.material,\\\\\\\"roughnessMap\\\\\\\",this.node.p.useRoughnessMap,this.node.p.roughnessMap),this._update_options.uniforms){this.node.material.uniforms.roughness.value=this.node.pv.roughness}if(this._update_options.directParams){this.node.material.roughness=this.node.pv.roughness}}static async update(t){t.controllers.metalnessRoughnessMap.update()}}function Iz(t){return class extends t{constructor(){super(...arguments),this.clearcoat=aa.FLOAT(0,{separatorBefore:!0}),this.useClearCoatMap=aa.BOOLEAN(0,hB(Dz)),this.clearcoatMap=aa.NODE_PATH(vi.EMPTY,uB(Dz,\\\\\\\"useClearCoatMap\\\\\\\")),this.useClearCoatNormalMap=aa.BOOLEAN(0,hB(Dz)),this.clearcoatNormalMap=aa.NODE_PATH(vi.EMPTY,uB(Dz,\\\\\\\"useClearCoatNormalMap\\\\\\\")),this.clearcoatNormalScale=aa.VECTOR2([1,1],{visibleIf:{useClearCoatNormalMap:1}}),this.clearcoatRoughness=aa.FLOAT(0),this.useClearCoatRoughnessMap=aa.BOOLEAN(0,hB(Dz)),this.clearcoatRoughnessMap=aa.NODE_PATH(vi.EMPTY,uB(Dz,\\\\\\\"useClearCoatRoughnessMap\\\\\\\")),this.useSheen=aa.BOOLEAN(0),this.sheen=aa.FLOAT(0,{range:[0,1],rangeLocked:[!0,!1],visibleIf:{useSheen:1}}),this.sheenRoughness=aa.FLOAT(1,{range:[0,1],rangeLocked:[!0,!1],visibleIf:{useSheen:1}}),this.sheenColor=aa.COLOR([1,1,1],{visibleIf:{useSheen:1}}),this.transmission=aa.FLOAT(0,{range:[0,1]}),this.useTransmissionMap=aa.BOOLEAN(0),this.transmissionMap=aa.NODE_PATH(vi.EMPTY,{visibleIf:{useTransmissionMap:1}}),this.ior=aa.FLOAT(1.5,{range:[1,2.3333],rangeLocked:[!0,!0]}),this.thickness=aa.FLOAT(.01,{range:[0,10],rangeLocked:[!0,!1]}),this.useThicknessMap=aa.BOOLEAN(0),this.thicknessMap=aa.NODE_PATH(vi.EMPTY,{visibleIf:{useThicknessMap:1}}),this.attenuationDistance=aa.FLOAT(0,{range:[0,10],rangeLocked:[!0,!1]}),this.attenuationColor=aa.COLOR([1,1,1])}}}Iz(la);const Fz=new Uf.a;class Dz extends dB{constructor(t,e){super(t,e),this.node=t,this._sheenColorClone=new D.a}initializeNode(){this.add_hooks(this.node.p.useClearCoatMap,this.node.p.clearcoatMap),this.add_hooks(this.node.p.useClearCoatNormalMap,this.node.p.clearcoatNormalMap),this.add_hooks(this.node.p.useClearCoatRoughnessMap,this.node.p.clearcoatRoughnessMap),this.add_hooks(this.node.p.useTransmissionMap,this.node.p.transmissionMap),this.add_hooks(this.node.p.useThicknessMap,this.node.p.thicknessMap)}async update(){this._update(this.node.material,\\\\\\\"clearcoatMap\\\\\\\",this.node.p.useClearCoatMap,this.node.p.clearcoatMap),this._update(this.node.material,\\\\\\\"clearcoatNormalMap\\\\\\\",this.node.p.useClearCoatNormalMap,this.node.p.clearcoatNormalMap),this._update(this.node.material,\\\\\\\"clearcoatRoughnessMap\\\\\\\",this.node.p.useClearCoatRoughnessMap,this.node.p.clearcoatRoughnessMap),this._update(this.node.material,\\\\\\\"transmissionMap\\\\\\\",this.node.p.useTransmissionMap,this.node.p.transmissionMap),this._update(this.node.material,\\\\\\\"thicknessMap\\\\\\\",this.node.p.useThicknessMap,this.node.p.thicknessMap);const t=this.node.pv;Fz.ior=t.ior;const e=Fz.reflectivity;if(this._update_options.uniforms){const n=this.node.material;n.uniforms.clearcoat.value=t.clearcoat,n.uniforms.clearcoatNormalScale.value.copy(t.clearcoatNormalScale),n.uniforms.clearcoatRoughness.value=t.clearcoatRoughness,n.uniforms.reflectivity.value=e,n.uniforms.transmission.value=t.transmission,n.uniforms.thickness.value=t.thickness,n.uniforms.attenuationDistance.value=t.attenuationDistance,n.uniforms.attenuationTint.value=t.attenuationColor,t.useSheen?(this._sheenColorClone.copy(t.sheenColor),n.uniforms.sheen.value=t.sheen,n.uniforms.sheenRoughness.value=t.sheenRoughness,n.uniforms.sheenTint.value=this._sheenColorClone):n.uniforms.sheen.value=0,n.uniforms.ior.value=t.ior,n.specularTint=n.uniforms.specularTint.value,n.ior=n.uniforms.ior.value}if(this._update_options.directParams){const n=this.node.material;n.clearcoat=t.clearcoat,null!=n.clearcoatNormalScale&&n.clearcoatNormalScale.copy(t.clearcoatNormalScale),n.clearcoatRoughness=t.clearcoatRoughness,n.reflectivity=e,t.useSheen?(this._sheenColorClone.copy(t.sheenColor),n.sheen=t.sheen,n.sheenRoughness=t.sheenRoughness,n.sheenTint=this._sheenColorClone):n.sheen=0,n.transmission=t.transmission,n.thickness=t.thickness,n.attenuationDistance=t.attenuationDistance,n.attenuationTint=t.attenuationColor}}static async update(t){t.controllers.physical.update()}}const Bz={directParams:!0};class zz extends(lB(LB(HD(nB(Iz(Pz(oz(AB(Lz(HB(lz(ez(gB(mB(pB(PB(oB(eB(la))))))))))))))))))){}const kz=new zz;class Uz extends kD{constructor(){super(...arguments),this.paramsConfig=kz,this.controllers={advancedCommon:new jD(this),alphaMap:new fB(this,Bz),aoMap:new vB(this,Bz),bumpMap:new nz(this,Bz),displacementMap:new cz(this,Bz),emissiveMap:new jB(this,Bz),envMap:new Oz(this,Bz),lightMap:new MB(this,Bz),map:new _B(this,Bz),metalnessRoughnessMap:new Rz(this,Bz),normalMap:new az(this,Bz),physical:new Dz(this,Bz)},this.controllerNames=Object.keys(this.controllers)}static type(){return\\\\\\\"meshPhysical\\\\\\\"}createMaterial(){return new Uf.a({vertexColors:!1,side:w.H,color:16777215,opacity:1,metalness:1,roughness:0})}initializeNode(){this.params.onParamsCreated(\\\\\\\"init controllers\\\\\\\",(()=>{for(let t of this.controllerNames)this.controllers[t].initializeNode()}))}async cook(){for(let t of this.controllerNames)this.controllers[t].update();aB.update(this),cB.update(this),OB.update(this),this.setMaterial(this.material)}}const Gz={uniforms:!0};class Vz extends(function(t){return class extends($B(KD(BB(HD(ZD(t)))))){}}(nB(Iz(Pz(oz(AB(Lz(HB(lz(ez(gB(mB(pB(PB(YD(eB(la))))))))))))))))){}const Hz=new Vz;class jz extends QD{constructor(){super(...arguments),this.paramsConfig=Hz,this.controllers={advancedCommon:new jD(this),alphaMap:new fB(this,Gz),aoMap:new vB(this,Gz),bumpMap:new nz(this,Gz),displacementMap:new cz(this,Gz),emissiveMap:new jB(this,Gz),envMap:new Oz(this,{uniforms:!0,directParams:!0}),lightMap:new MB(this,Gz),map:new _B(this,Gz),metalnessRoughnessMap:new Rz(this,{uniforms:!0,directParams:!0}),normalMap:new az(this,Gz),physical:new Dz(this,{uniforms:!0,directParams:!0}),PCSS:new JB(this)},this.controllerNames=Object.keys(this.controllers)}static type(){return\\\\\\\"meshPhysicalBuilder\\\\\\\"}usedAssembler(){return jn.GL_MESH_PHYSICAL}_create_assembler_controller(){return li.assemblersRegister.assembler(this,this.usedAssembler())}initializeNode(){this.params.onParamsCreated(\\\\\\\"init controllers\\\\\\\",(()=>{for(let t of this.controllerNames)this.controllers[t].initializeNode()}))}createMaterial(){const t=super.createMaterial();return t.isMeshStandardMaterial=!0,t.isMeshPhysicalMaterial=!0,t}async cook(){for(let t of this.controllerNames)this.controllers[t].update();$D.update(this),tB.update(this),zB.update(this),this.compileIfRequired(),this.setMaterial(this.material)}}const Wz={directParams:!0};class qz extends(lB(LB(HD(nB(Pz(oz(AB(Lz(HB(lz(ez(gB(mB(pB(PB(oB(eB(la)))))))))))))))))){}const Xz=new qz;class Yz extends kD{constructor(){super(...arguments),this.paramsConfig=Xz,this.controllers={advancedCommon:new jD(this),alphaMap:new fB(this,Wz),aoMap:new vB(this,Wz),bumpMap:new nz(this,Wz),displacementMap:new cz(this,Wz),emissiveMap:new jB(this,Wz),envMap:new Oz(this,Wz),lightMap:new MB(this,Wz),map:new _B(this,Wz),metalnessRoughnessMap:new Rz(this,Wz),normalMap:new az(this,Wz)},this.controllerNames=Object.keys(this.controllers)}static type(){return\\\\\\\"meshStandard\\\\\\\"}createMaterial(){return new bs.a({vertexColors:!1,side:w.H,color:16777215,opacity:1,metalness:1,roughness:0})}initializeNode(){this.params.onParamsCreated(\\\\\\\"init controllers\\\\\\\",(()=>{for(let t of this.controllerNames)this.controllers[t].initializeNode()}))}async cook(){for(let t of this.controllerNames)this.controllers[t].update();aB.update(this),cB.update(this),OB.update(this),this.setMaterial(this.material)}}const $z={uniforms:!0};class Jz extends($B(KD(BB(HD(ZD(nB(Pz(oz(AB(Lz(HB(lz(ez(gB(mB(pB(PB(YD(eB(la)))))))))))))))))))){}const Zz=new Jz;class Qz extends QD{constructor(){super(...arguments),this.paramsConfig=Zz,this.controllers={advancedCommon:new jD(this),alphaMap:new fB(this,$z),aoMap:new vB(this,$z),bumpMap:new nz(this,$z),displacementMap:new cz(this,$z),emissiveMap:new jB(this,$z),envMap:new Oz(this,$z),lightMap:new MB(this,$z),map:new _B(this,$z),metalnessRoughnessMap:new Rz(this,$z),normalMap:new az(this,$z),PCSS:new JB(this)},this.controllerNames=Object.keys(this.controllers)}static type(){return\\\\\\\"meshStandardBuilder\\\\\\\"}usedAssembler(){return jn.GL_MESH_STANDARD}_create_assembler_controller(){return li.assemblersRegister.assembler(this,this.usedAssembler())}initializeNode(){this.params.onParamsCreated(\\\\\\\"init controllers\\\\\\\",(()=>{for(let t of this.controllerNames)this.controllers[t].initializeNode()}))}async cook(){for(let t of this.controllerNames)this.controllers[t].update();$D.update(this),tB.update(this),zB.update(this),this.compileIfRequired(),this.setMaterial(this.material)}}const Kz=U.meshphong_frag.slice(0,U.meshphong_frag.indexOf(\\\\\\\"void main() {\\\\\\\")),tk=U.meshphong_frag.slice(U.meshphong_frag.indexOf(\\\\\\\"void main() {\\\\\\\")),ek={uniforms:I.merge([H.phong.uniforms,{thicknessMap:{value:null},thicknessColor:{value:new D.a(16777215)},thicknessDistortion:{value:.1},thicknessAmbient:{value:0},thicknessAttenuation:{value:.1},thicknessPower:{value:2},thicknessScale:{value:10}}]),vertexShader:[\\\\\\\"#define USE_UV\\\\\\\",U.meshphong_vert].join(\\\\\\\"\\\\n\\\\\\\"),fragmentShader:[\\\\\\\"#define USE_UV\\\\\\\",\\\\\\\"#define SUBSURFACE\\\\\\\",Kz,\\\\\\\"uniform sampler2D thicknessMap;\\\\\\\",\\\\\\\"uniform float thicknessPower;\\\\\\\",\\\\\\\"uniform float thicknessScale;\\\\\\\",\\\\\\\"uniform float thicknessDistortion;\\\\\\\",\\\\\\\"uniform float thicknessAmbient;\\\\\\\",\\\\\\\"uniform float thicknessAttenuation;\\\\\\\",\\\\\\\"uniform vec3 thicknessColor;\\\\\\\",\\\\\\\"void RE_Direct_Scattering(const in IncidentLight directLight, const in vec2 uv, const in GeometricContext geometry, inout ReflectedLight reflectedLight) {\\\\\\\",\\\\\\\"\\\\tvec3 thickness = thicknessColor * texture2D(thicknessMap, uv).r;\\\\\\\",\\\\\\\"\\\\tvec3 scatteringHalf = normalize(directLight.direction + (geometry.normal * thicknessDistortion));\\\\\\\",\\\\\\\"\\\\tfloat scatteringDot = pow(saturate(dot(geometry.viewDir, -scatteringHalf)), thicknessPower) * thicknessScale;\\\\\\\",\\\\\\\"\\\\tvec3 scatteringIllu = (scatteringDot + thicknessAmbient) * thickness;\\\\\\\",\\\\\\\"\\\\treflectedLight.directDiffuse += scatteringIllu * thicknessAttenuation * directLight.color;\\\\\\\",\\\\\\\"}\\\\\\\",tk.replace(\\\\\\\"#include \\\\\\\",(nk=U.lights_fragment_begin,ik=\\\\\\\"RE_Direct( directLight, geometry, material, reflectedLight );\\\\\\\",sk=[\\\\\\\"RE_Direct( directLight, geometry, material, reflectedLight );\\\\\\\",\\\\\\\"#if defined( SUBSURFACE ) && defined( USE_UV )\\\\\\\",\\\\\\\" RE_Direct_Scattering(directLight, vUv, geometry, reflectedLight);\\\\\\\",\\\\\\\"#endif\\\\\\\"].join(\\\\\\\"\\\\n\\\\\\\"),nk.split(ik).join(sk)))].join(\\\\\\\"\\\\n\\\\\\\")};var nk,ik,sk;function rk(t){return{cook:!1,callback:(e,n)=>{hk.PARAM_CALLBACK_update_uniformColor(e,n,t)}}}function ok(t){return{cook:!1,callback:(e,n)=>{hk.PARAM_CALLBACK_update_uniformN(e,n,t)}}}const ak={uniforms:!0};class lk extends(lB(BB(HD(nB(mB(pB(PB(function(t){return class extends t{constructor(){var t;super(...arguments),this.diffuse=aa.COLOR([1,1,1],{...rk(\\\\\\\"diffuse\\\\\\\")}),this.shininess=aa.FLOAT(1,{range:[0,1e3]}),this.thicknessMap=aa.NODE_PATH(vi.EMPTY,{nodeSelection:{context:ts.COP},...(t=\\\\\\\"thicknessMap\\\\\\\",{cook:!1,callback:(e,n)=>{hk.PARAM_CALLBACK_update_uniformTexture(e,n,t)}})}),this.thicknessColor=aa.COLOR([.5,.3,0],{...rk(\\\\\\\"thicknessColor\\\\\\\")}),this.thicknessDistortion=aa.FLOAT(.1,{...ok(\\\\\\\"thicknessDistortion\\\\\\\")}),this.thicknessAmbient=aa.FLOAT(.4,{...ok(\\\\\\\"thicknessAmbient\\\\\\\")}),this.thicknessAttenuation=aa.FLOAT(.8,{...ok(\\\\\\\"thicknessAttenuation\\\\\\\")}),this.thicknessPower=aa.FLOAT(2,{range:[0,10],...ok(\\\\\\\"thicknessPower\\\\\\\")}),this.thicknessScale=aa.FLOAT(16,{range:[0,100],...ok(\\\\\\\"thicknessScale\\\\\\\")})}}}(eB(la)))))))))){}const ck=new lk;class hk extends kD{constructor(){super(...arguments),this.paramsConfig=ck,this.controllers={advancedCommon:new jD(this),alphaMap:new fB(this,ak),map:new _B(this,ak)},this.controllerNames=Object.keys(this.controllers)}static type(){return\\\\\\\"meshSubsurfaceScattering\\\\\\\"}createMaterial(){const t=I.clone(ek.uniforms),e=new F({uniforms:t,vertexShader:ek.vertexShader,fragmentShader:ek.fragmentShader,lights:!0});return e.extensions.derivatives=!0,e}initializeNode(){this.params.onParamsCreated(\\\\\\\"init controllers\\\\\\\",(()=>{for(let t of this.controllerNames)this.controllers[t].initializeNode()}))}async cook(){for(let t of this.controllerNames)this.controllers[t].update();cB.update(this),zB.update(this),this.update_map(this.p.thicknessMap,\\\\\\\"thicknessMap\\\\\\\"),this.material.uniforms.diffuse.value.copy(this.pv.diffuse),this.material.uniforms.shininess.value=this.pv.shininess,this.material.uniforms.thicknessColor.value.copy(this.pv.thicknessColor),this.material.uniforms.thicknessDistortion.value=this.pv.thicknessDistortion,this.material.uniforms.thicknessAmbient.value=this.pv.thicknessAmbient,this.material.uniforms.thicknessAttenuation.value=this.pv.thicknessAttenuation,this.material.uniforms.thicknessPower.value=this.pv.thicknessPower,this.material.uniforms.thicknessScale.value=this.pv.thicknessScale,this.setMaterial(this.material)}static PARAM_CALLBACK_update_uniformN(t,e,n){t.material.uniforms[n].value=e.value}static PARAM_CALLBACK_update_uniformColor(t,e,n){e.parent_param&&t.material.uniforms[n].value.copy(e.parent_param.value)}static PARAM_CALLBACK_update_uniformTexture(t,e,n){t.update_map(e,n)}async update_map(t,e){const n=t.value.nodeWithContext(ts.COP);n||(this.material.uniforms[e].value=null);const i=n,s=await i.compute();this.material.uniforms[e].value=s.texture()}}function uk(t){return class extends t{constructor(){super(...arguments),this.useGradientMap=aa.BOOLEAN(0,hB(dk)),this.gradientMap=aa.NODE_PATH(vi.EMPTY,uB(dk,\\\\\\\"useGradientMap\\\\\\\"))}}}O.a;uk(la);class dk extends dB{constructor(t,e){super(t,e),this.node=t}initializeNode(){this.add_hooks(this.node.p.useGradientMap,this.node.p.gradientMap)}async update(){this._update(this.node.material,\\\\\\\"gradientMap\\\\\\\",this.node.p.useGradientMap,this.node.p.gradientMap)}static async update(t){t.controllers.gradientMap.update()}}const pk={directParams:!0};class _k extends(lB(LB(HD(nB(oz(AB(uk(HB(lz(ez(gB(mB(pB(PB(oB(eB(la))))))))))))))))){}const mk=new _k;class fk extends kD{constructor(){super(...arguments),this.paramsConfig=mk,this.controllers={advancedCommon:new jD(this),alphaMap:new fB(this,pk),aoMap:new vB(this,pk),bumpMap:new nz(this,pk),displacementMap:new cz(this,pk),emissiveMap:new jB(this,pk),gradientMap:new dk(this,pk),lightMap:new MB(this,pk),map:new _B(this,pk),normalMap:new az(this,pk)},this.controllerNames=Object.keys(this.controllers)}static type(){return\\\\\\\"meshToon\\\\\\\"}createMaterial(){return new Vf({vertexColors:!1,side:w.H,color:16777215,opacity:1})}initializeNode(){this.params.onParamsCreated(\\\\\\\"init controllers\\\\\\\",(()=>{for(let t of this.controllerNames)this.controllers[t].initializeNode()}))}async cook(){for(let t of this.controllerNames)this.controllers[t].update();aB.update(this),cB.update(this),OB.update(this),this.setMaterial(this.material)}}const gk={directParams:!0};class vk extends(KD(HD(nB(mB(pB(PB(oB(function(t){return class extends t{constructor(){super(...arguments),this.size=aa.FLOAT(1),this.sizeAttenuation=aa.BOOLEAN(1)}}}(eB(la)))))))))){}const yk=new vk;class xk extends kD{constructor(){super(...arguments),this.paramsConfig=yk,this.controllers={advancedCommon:new jD(this),alphaMap:new fB(this,gk),map:new _B(this,gk)},this.controllerNames=Object.keys(this.controllers)}static type(){return\\\\\\\"points\\\\\\\"}createMaterial(){return new xs.a({vertexColors:!1,side:w.H,color:16777215,opacity:1})}initializeNode(){this.params.onParamsCreated(\\\\\\\"init controllers\\\\\\\",(()=>{for(let t of this.controllerNames)this.controllers[t].initializeNode()}))}async cook(){for(let t of this.controllerNames)this.controllers[t].update();aB.update(this),tB.update(this),this.material.size=this.pv.size,this.material.sizeAttenuation=this.pv.sizeAttenuation,this.setMaterial(this.material)}}class bk extends(KD(HD(ZD(nB(YD(eB(la))))))){}const wk=new bk;class Tk extends QD{constructor(){super(...arguments),this.paramsConfig=wk,this.controllers={advancedCommon:new jD(this)},this.controllerNames=Object.keys(this.controllers)}static type(){return\\\\\\\"pointsBuilder\\\\\\\"}usedAssembler(){return jn.GL_POINTS}_create_assembler_controller(){return li.assemblersRegister.assembler(this,this.usedAssembler())}initializeNode(){this.params.onParamsCreated(\\\\\\\"init controllers\\\\\\\",(()=>{for(let t of this.controllerNames)this.controllers[t].initializeNode()}))}async cook(){for(let t of this.controllerNames)this.controllers[t].update();$D.update(this),tB.update(this),this.compileIfRequired(),this.setMaterial(this.material)}}class Ak extends(HD(oB(la))){}const Mk=new Ak;class Ek extends kD{constructor(){super(...arguments),this.paramsConfig=Mk,this.controllers={advancedCommon:new jD(this)},this.controllerNames=Object.keys(this.controllers)}static type(){return\\\\\\\"shadow\\\\\\\"}createMaterial(){return new zf({vertexColors:!1,side:w.H,color:16777215,opacity:1})}initializeNode(){this.params.onParamsCreated(\\\\\\\"init controllers\\\\\\\",(()=>{for(let t of this.controllerNames)this.controllers[t].initializeNode()}))}async cook(){for(let t of this.controllerNames)this.controllers[t].update();aB.update(this),this.setMaterial(this.material)}}class Sk extends B.a{constructor(){const t=Sk.SkyShader,e=new F({name:\\\\\\\"SkyShader\\\\\\\",fragmentShader:t.fragmentShader,vertexShader:t.vertexShader,uniforms:I.clone(t.uniforms),side:w.i,depthWrite:!1});super(new N(1,1,1),e)}}Sk.prototype.isSky=!0,Sk.SkyShader={uniforms:{turbidity:{value:2},rayleigh:{value:1},mieCoefficient:{value:.005},mieDirectionalG:{value:.8},sunPosition:{value:new p.a},up:{value:new p.a(0,1,0)}},vertexShader:\\\\\\\"\\\\n\\\\t\\\\tuniform vec3 sunPosition;\\\\n\\\\t\\\\tuniform float rayleigh;\\\\n\\\\t\\\\tuniform float turbidity;\\\\n\\\\t\\\\tuniform float mieCoefficient;\\\\n\\\\t\\\\tuniform vec3 up;\\\\n\\\\n\\\\t\\\\tvarying vec3 vWorldPosition;\\\\n\\\\t\\\\tvarying vec3 vSunDirection;\\\\n\\\\t\\\\tvarying float vSunfade;\\\\n\\\\t\\\\tvarying vec3 vBetaR;\\\\n\\\\t\\\\tvarying vec3 vBetaM;\\\\n\\\\t\\\\tvarying float vSunE;\\\\n\\\\n\\\\t\\\\t// constants for atmospheric scattering\\\\n\\\\t\\\\tconst float e = 2.71828182845904523536028747135266249775724709369995957;\\\\n\\\\t\\\\tconst float pi = 3.141592653589793238462643383279502884197169;\\\\n\\\\n\\\\t\\\\t// wavelength of used primaries, according to preetham\\\\n\\\\t\\\\tconst vec3 lambda = vec3( 680E-9, 550E-9, 450E-9 );\\\\n\\\\t\\\\t// this pre-calcuation replaces older TotalRayleigh(vec3 lambda) function:\\\\n\\\\t\\\\t// (8.0 * pow(pi, 3.0) * pow(pow(n, 2.0) - 1.0, 2.0) * (6.0 + 3.0 * pn)) / (3.0 * N * pow(lambda, vec3(4.0)) * (6.0 - 7.0 * pn))\\\\n\\\\t\\\\tconst vec3 totalRayleigh = vec3( 5.804542996261093E-6, 1.3562911419845635E-5, 3.0265902468824876E-5 );\\\\n\\\\n\\\\t\\\\t// mie stuff\\\\n\\\\t\\\\t// K coefficient for the primaries\\\\n\\\\t\\\\tconst float v = 4.0;\\\\n\\\\t\\\\tconst vec3 K = vec3( 0.686, 0.678, 0.666 );\\\\n\\\\t\\\\t// MieConst = pi * pow( ( 2.0 * pi ) / lambda, vec3( v - 2.0 ) ) * K\\\\n\\\\t\\\\tconst vec3 MieConst = vec3( 1.8399918514433978E14, 2.7798023919660528E14, 4.0790479543861094E14 );\\\\n\\\\n\\\\t\\\\t// earth shadow hack\\\\n\\\\t\\\\t// cutoffAngle = pi / 1.95;\\\\n\\\\t\\\\tconst float cutoffAngle = 1.6110731556870734;\\\\n\\\\t\\\\tconst float steepness = 1.5;\\\\n\\\\t\\\\tconst float EE = 1000.0;\\\\n\\\\n\\\\t\\\\tfloat sunIntensity( float zenithAngleCos ) {\\\\n\\\\t\\\\t\\\\tzenithAngleCos = clamp( zenithAngleCos, -1.0, 1.0 );\\\\n\\\\t\\\\t\\\\treturn EE * max( 0.0, 1.0 - pow( e, -( ( cutoffAngle - acos( zenithAngleCos ) ) / steepness ) ) );\\\\n\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\tvec3 totalMie( float T ) {\\\\n\\\\t\\\\t\\\\tfloat c = ( 0.2 * T ) * 10E-18;\\\\n\\\\t\\\\t\\\\treturn 0.434 * c * MieConst;\\\\n\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\tvec4 worldPosition = modelMatrix * vec4( position, 1.0 );\\\\n\\\\t\\\\t\\\\tvWorldPosition = worldPosition.xyz;\\\\n\\\\n\\\\t\\\\t\\\\tgl_Position = projectionMatrix * modelViewMatrix * vec4( position, 1.0 );\\\\n\\\\t\\\\t\\\\tgl_Position.z = gl_Position.w; // set z to camera.far\\\\n\\\\n\\\\t\\\\t\\\\tvSunDirection = normalize( sunPosition );\\\\n\\\\n\\\\t\\\\t\\\\tvSunE = sunIntensity( dot( vSunDirection, up ) );\\\\n\\\\n\\\\t\\\\t\\\\tvSunfade = 1.0 - clamp( 1.0 - exp( ( sunPosition.y / 450000.0 ) ), 0.0, 1.0 );\\\\n\\\\n\\\\t\\\\t\\\\tfloat rayleighCoefficient = rayleigh - ( 1.0 * ( 1.0 - vSunfade ) );\\\\n\\\\n\\\\t\\\\t\\\\t// extinction (absorbtion + out scattering)\\\\n\\\\t\\\\t\\\\t// rayleigh coefficients\\\\n\\\\t\\\\t\\\\tvBetaR = totalRayleigh * rayleighCoefficient;\\\\n\\\\n\\\\t\\\\t\\\\t// mie coefficients\\\\n\\\\t\\\\t\\\\tvBetaM = totalMie( turbidity ) * mieCoefficient;\\\\n\\\\n\\\\t\\\\t}\\\\\\\",fragmentShader:\\\\\\\"\\\\n\\\\t\\\\tvarying vec3 vWorldPosition;\\\\n\\\\t\\\\tvarying vec3 vSunDirection;\\\\n\\\\t\\\\tvarying float vSunfade;\\\\n\\\\t\\\\tvarying vec3 vBetaR;\\\\n\\\\t\\\\tvarying vec3 vBetaM;\\\\n\\\\t\\\\tvarying float vSunE;\\\\n\\\\n\\\\t\\\\tuniform float mieDirectionalG;\\\\n\\\\t\\\\tuniform vec3 up;\\\\n\\\\n\\\\t\\\\tconst vec3 cameraPos = vec3( 0.0, 0.0, 0.0 );\\\\n\\\\n\\\\t\\\\t// constants for atmospheric scattering\\\\n\\\\t\\\\tconst float pi = 3.141592653589793238462643383279502884197169;\\\\n\\\\n\\\\t\\\\tconst float n = 1.0003; // refractive index of air\\\\n\\\\t\\\\tconst float N = 2.545E25; // number of molecules per unit volume for air at 288.15K and 1013mb (sea level -45 celsius)\\\\n\\\\n\\\\t\\\\t// optical length at zenith for molecules\\\\n\\\\t\\\\tconst float rayleighZenithLength = 8.4E3;\\\\n\\\\t\\\\tconst float mieZenithLength = 1.25E3;\\\\n\\\\t\\\\t// 66 arc seconds -> degrees, and the cosine of that\\\\n\\\\t\\\\tconst float sunAngularDiameterCos = 0.999956676946448443553574619906976478926848692873900859324;\\\\n\\\\n\\\\t\\\\t// 3.0 / ( 16.0 * pi )\\\\n\\\\t\\\\tconst float THREE_OVER_SIXTEENPI = 0.05968310365946075;\\\\n\\\\t\\\\t// 1.0 / ( 4.0 * pi )\\\\n\\\\t\\\\tconst float ONE_OVER_FOURPI = 0.07957747154594767;\\\\n\\\\n\\\\t\\\\tfloat rayleighPhase( float cosTheta ) {\\\\n\\\\t\\\\t\\\\treturn THREE_OVER_SIXTEENPI * ( 1.0 + pow( cosTheta, 2.0 ) );\\\\n\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\tfloat hgPhase( float cosTheta, float g ) {\\\\n\\\\t\\\\t\\\\tfloat g2 = pow( g, 2.0 );\\\\n\\\\t\\\\t\\\\tfloat inverse = 1.0 / pow( 1.0 - 2.0 * g * cosTheta + g2, 1.5 );\\\\n\\\\t\\\\t\\\\treturn ONE_OVER_FOURPI * ( ( 1.0 - g2 ) * inverse );\\\\n\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\tvec3 direction = normalize( vWorldPosition - cameraPos );\\\\n\\\\n\\\\t\\\\t\\\\t// optical length\\\\n\\\\t\\\\t\\\\t// cutoff angle at 90 to avoid singularity in next formula.\\\\n\\\\t\\\\t\\\\tfloat zenithAngle = acos( max( 0.0, dot( up, direction ) ) );\\\\n\\\\t\\\\t\\\\tfloat inverse = 1.0 / ( cos( zenithAngle ) + 0.15 * pow( 93.885 - ( ( zenithAngle * 180.0 ) / pi ), -1.253 ) );\\\\n\\\\t\\\\t\\\\tfloat sR = rayleighZenithLength * inverse;\\\\n\\\\t\\\\t\\\\tfloat sM = mieZenithLength * inverse;\\\\n\\\\n\\\\t\\\\t\\\\t// combined extinction factor\\\\n\\\\t\\\\t\\\\tvec3 Fex = exp( -( vBetaR * sR + vBetaM * sM ) );\\\\n\\\\n\\\\t\\\\t\\\\t// in scattering\\\\n\\\\t\\\\t\\\\tfloat cosTheta = dot( direction, vSunDirection );\\\\n\\\\n\\\\t\\\\t\\\\tfloat rPhase = rayleighPhase( cosTheta * 0.5 + 0.5 );\\\\n\\\\t\\\\t\\\\tvec3 betaRTheta = vBetaR * rPhase;\\\\n\\\\n\\\\t\\\\t\\\\tfloat mPhase = hgPhase( cosTheta, mieDirectionalG );\\\\n\\\\t\\\\t\\\\tvec3 betaMTheta = vBetaM * mPhase;\\\\n\\\\n\\\\t\\\\t\\\\tvec3 Lin = pow( vSunE * ( ( betaRTheta + betaMTheta ) / ( vBetaR + vBetaM ) ) * ( 1.0 - Fex ), vec3( 1.5 ) );\\\\n\\\\t\\\\t\\\\tLin *= mix( vec3( 1.0 ), pow( vSunE * ( ( betaRTheta + betaMTheta ) / ( vBetaR + vBetaM ) ) * Fex, vec3( 1.0 / 2.0 ) ), clamp( pow( 1.0 - dot( up, vSunDirection ), 5.0 ), 0.0, 1.0 ) );\\\\n\\\\n\\\\t\\\\t\\\\t// nightsky\\\\n\\\\t\\\\t\\\\tfloat theta = acos( direction.y ); // elevation --\\\\x3e y-axis, [-pi/2, pi/2]\\\\n\\\\t\\\\t\\\\tfloat phi = atan( direction.z, direction.x ); // azimuth --\\\\x3e x-axis [-pi/2, pi/2]\\\\n\\\\t\\\\t\\\\tvec2 uv = vec2( phi, theta ) / vec2( 2.0 * pi, pi ) + vec2( 0.5, 0.0 );\\\\n\\\\t\\\\t\\\\tvec3 L0 = vec3( 0.1 ) * Fex;\\\\n\\\\n\\\\t\\\\t\\\\t// composition + solar disc\\\\n\\\\t\\\\t\\\\tfloat sundisk = smoothstep( sunAngularDiameterCos, sunAngularDiameterCos + 0.00002, cosTheta );\\\\n\\\\t\\\\t\\\\tL0 += ( vSunE * 19000.0 * Fex ) * sundisk;\\\\n\\\\n\\\\t\\\\t\\\\tvec3 texColor = ( Lin + L0 ) * 0.04 + vec3( 0.0, 0.0003, 0.00075 );\\\\n\\\\n\\\\t\\\\t\\\\tvec3 retColor = pow( texColor, vec3( 1.0 / ( 1.2 + ( 1.2 * vSunfade ) ) ) );\\\\n\\\\n\\\\t\\\\t\\\\tgl_FragColor = vec4( retColor, 1.0 );\\\\n\\\\n\\\\t\\\\t\\\\t#include \\\\n\\\\t\\\\t\\\\t#include \\\\n\\\\n\\\\t\\\\t}\\\\\\\"};const Ck=new class extends la{constructor(){super(...arguments),this.turbidity=aa.FLOAT(2,{range:[0,20]}),this.rayleigh=aa.FLOAT(1,{range:[0,4]}),this.mieCoefficient=aa.FLOAT(.005),this.mieDirectional=aa.FLOAT(.8),this.inclination=aa.FLOAT(.5),this.azimuth=aa.FLOAT(.25),this.up=aa.VECTOR3([0,1,0])}};class Nk extends kD{constructor(){super(...arguments),this.paramsConfig=Ck}static type(){return\\\\\\\"sky\\\\\\\"}createMaterial(){const t=(new Sk).material;return t.depthWrite=!0,t}async cook(){const t=this.material.uniforms;t.turbidity.value=this.pv.turbidity,t.rayleigh.value=this.pv.rayleigh,t.mieCoefficient.value=this.pv.mieCoefficient,t.mieDirectionalG.value=this.pv.mieDirectional,t.up.value.copy(this.pv.up);const e=Math.PI*(this.pv.inclination-.5),n=2*Math.PI*(this.pv.azimuth-.5);t.sunPosition.value.x=Math.cos(n),t.sunPosition.value.y=Math.sin(n)*Math.sin(e),t.sunPosition.value.z=Math.sin(n)*Math.cos(e),this.setMaterial(this.material)}}var Lk=\\\\\\\"precision highp float;\\\\nprecision highp int;\\\\n\\\\nvarying vec3 vPw;\\\\n\\\\n#include \\\\n\\\\nvoid main()\\\\t{\\\\n\\\\n\\\\t// start builder body code\\\\n\\\\n\\\\tvPw = position;\\\\n\\\\tgl_Position = projectionMatrix * modelViewMatrix * vec4( position, 1.0 );\\\\n\\\\n}\\\\\\\",Ok=\\\\\\\"precision highp float;\\\\nprecision highp int;\\\\n\\\\n#include \\\\n\\\\n#define DIR_LIGHTS_COUNT 1\\\\n#define MAX_STEPS_COUNT 4096\\\\n\\\\nuniform vec3 u_Color;\\\\nuniform float u_VolumeDensity;\\\\nuniform float u_ShadowDensity;\\\\nuniform float u_StepSize;\\\\nuniform vec3 u_BoundingBoxMin;\\\\nuniform vec3 u_BoundingBoxMax;\\\\n//const int u_PointsCount = 3;\\\\n//uniform vec3 u_Points[3];\\\\nuniform sampler2D u_Map;\\\\n\\\\n//const int u_DirectionalLightsCount = 1;\\\\nuniform vec3 u_DirectionalLightDirection; //[DIR_LIGHTS_COUNT];\\\\n\\\\nvarying vec3 vPw;\\\\n// varying vec3 vN;\\\\n// varying vec2 vUV;\\\\n//varying vec3 vPCameraSpace;\\\\n// varying vec4 vCd;\\\\n\\\\nvec3 normalize_in_bbox(vec3 point){\\\\n\\\\n\\\\tvec3 min = u_BoundingBoxMin;\\\\n\\\\tvec3 max = u_BoundingBoxMax;\\\\n\\\\n\\\\treturn vec3(\\\\n\\\\t\\\\t(point.x - min.x) / (max.x - min.x),\\\\n\\\\t\\\\t(point.y - min.y) / (max.y - min.y),\\\\n\\\\t\\\\t(point.z - min.z) / (max.z - min.z)\\\\n\\\\t);\\\\n}\\\\n\\\\nbool is_inside_bbox(vec3 Pw){\\\\n\\\\n\\\\tvec3 min = u_BoundingBoxMin;\\\\n\\\\tvec3 max = u_BoundingBoxMax;\\\\n\\\\n\\\\treturn (\\\\n\\\\t\\\\tPw.x > min.x &&\\\\n\\\\t\\\\tPw.y > min.y &&\\\\n\\\\t\\\\tPw.z > min.z &&\\\\n\\\\n\\\\t\\\\tPw.x < max.x &&\\\\n\\\\t\\\\tPw.y < max.y &&\\\\n\\\\t\\\\tPw.z < max.z\\\\n\\\\t\\\\t);\\\\n}\\\\n\\\\nfloat density_to_opacity(float density, float step_size){\\\\n\\\\tfloat curent_density = density;\\\\n\\\\tcurent_density = max(0.0, curent_density);\\\\n\\\\n\\\\tfloat opacity = (1.0-exp(-curent_density * step_size));\\\\n\\\\treturn max(opacity,0.0);\\\\n}\\\\n\\\\nfloat density_function(vec3 position_for_step){\\\\n\\\\tfloat density = 1.0;\\\\n\\\\t// start builder body code\\\\n\\\\n\\\\treturn density;\\\\n}\\\\n\\\\nvec4 raymarch_light(vec3 ray_dir, vec3 start_pos){\\\\n\\\\n\\\\tfloat step_size = u_StepSize;\\\\n\\\\tvec3 step_vector = ray_dir * step_size;\\\\n\\\\n\\\\tvec3 current_pos = start_pos + step_vector*rand(start_pos.x*ray_dir.xy);\\\\n\\\\tfloat opacity = 0.0;\\\\n\\\\tfor(int i=0; i= 0.99){ break; }\\\\n\\\\n\\\\t\\\\tif( is_inside_bbox(current_pos) ){\\\\n\\\\n\\\\t\\\\t\\\\tfloat density = density_function(current_pos) * u_ShadowDensity;\\\\n\\\\t\\\\t\\\\topacity += density_to_opacity(density, step_size);\\\\n\\\\t\\\\t\\\\tcurrent_pos += step_vector;\\\\n\\\\n\\\\t\\\\t}else{\\\\n\\\\t\\\\t\\\\tbreak;\\\\n\\\\t\\\\t}\\\\n\\\\t}\\\\n\\\\n\\\\tvec3 light_color = vec3(1.0, 1.0, 1.0) * u_Color;\\\\n\\\\tlight_color *= (1.0-opacity);\\\\n\\\\treturn vec4(light_color, 1.0-opacity);\\\\n}\\\\n\\\\nvec4 raymarch_bbox(vec3 start_pos, vec3 ray_dir){\\\\n\\\\n\\\\tfloat step_size = u_StepSize;\\\\n\\\\tvec3 step_vector = ray_dir * step_size;\\\\n\\\\n\\\\tvec3 current_pos = start_pos - step_vector*rand(ray_dir.xz);\\\\n\\\\tfloat opacity = 0.0;\\\\n\\\\tvec3 color = vec3(0.0, 0.0, 0.0);\\\\n\\\\tfloat steps_count = 0.0;\\\\n\\\\tbool was_inside_bbox = false;\\\\n\\\\tfor(int i=0; i= 0.99){ break; }\\\\n\\\\n\\\\t\\\\tif( i==0 || is_inside_bbox(current_pos) ){\\\\n\\\\t\\\\t\\\\twas_inside_bbox = true;\\\\n\\\\n\\\\t\\\\t\\\\tfloat density = density_function(current_pos) * u_VolumeDensity;\\\\n\\\\t\\\\t\\\\topacity += density_to_opacity(density, step_size);\\\\n\\\\n\\\\t\\\\t\\\\tvec4 light_color = vec4(0.0,0.0,0.0,1.0); //vec4(1.0,1.0,1.0,1.0);\\\\n\\\\t\\\\t\\\\t// vec3 directional_light_direction;\\\\n\\\\t\\\\t\\\\t// for ( int l = 0; l < DIR_LIGHTS_COUNT; l++ ) {\\\\n\\\\t\\\\t\\\\t// directional_light_direction = u_DirectionalLightsDirection[ l ];\\\\n\\\\t\\\\t\\\\tlight_color += raymarch_light(-u_DirectionalLightDirection, current_pos);\\\\n\\\\t\\\\t\\\\t// }\\\\n\\\\t\\\\t\\\\tfloat blend = 1.0-opacity;\\\\n\\\\t\\\\t\\\\tcolor = mix( color.xyz, light_color.xyz, vec3(blend, blend, blend) );\\\\n\\\\t\\\\t\\\\tsteps_count += 1.0;\\\\n\\\\n\\\\t\\\\t}else{\\\\n\\\\t\\\\t\\\\tif (was_inside_bbox) { break; }\\\\n\\\\t\\\\t}\\\\n\\\\t\\\\tcurrent_pos += step_vector;\\\\n\\\\t}\\\\n\\\\n\\\\treturn vec4(color, opacity);\\\\n\\\\t// steps_count = steps_count / 5.0;\\\\n\\\\t// return vec4(vec3(steps_count, steps_count, steps_count), 1.0);\\\\n}\\\\n\\\\nvoid main()\\\\t{\\\\n\\\\n\\\\tvec3 eye = normalize(vPw - cameraPosition);\\\\n\\\\t// we can start from the bbox, as we are front facing\\\\n\\\\tvec3 start_pos = vPw;\\\\n\\\\n\\\\tvec4 color = raymarch_bbox(start_pos, eye);\\\\n\\\\tgl_FragColor = color;\\\\n\\\\n}\\\\\\\";const Pk={u_Color:{value:new D.a(1,1,1)},u_VolumeDensity:{value:5},u_ShadowDensity:{value:2},u_StepSize:{value:.01},u_BoundingBoxMin:{value:new p.a(-1,-1,-1)},u_BoundingBoxMax:{value:new p.a(1,1,1)},u_DirectionalLightDirection:{value:new p.a(-1,-1,-1)}};function Rk(t){return class extends t{constructor(){super(...arguments),this.color=aa.COLOR([1,1,1]),this.stepSize=aa.FLOAT(.01),this.density=aa.FLOAT(1),this.shadowDensity=aa.FLOAT(1),this.lightDir=aa.VECTOR3([-1,-1,-1])}}}Rk(la);class Ik{constructor(t){this.node=t}static render_hook(t,e,n,i,s,r,o){if(o){this._object_bbox.setFromObject(o);const t=s;t.uniforms.u_BoundingBoxMin.value.copy(this._object_bbox.min),t.uniforms.u_BoundingBoxMax.value.copy(this._object_bbox.max)}}update_uniforms_from_params(){const t=this.node.material.uniforms;t.u_Color.value.copy(this.node.pv.color),t.u_StepSize.value=this.node.pv.stepSize,t.u_VolumeDensity.value=this.node.pv.density,t.u_ShadowDensity.value=this.node.pv.shadowDensity;const e=t.u_DirectionalLightDirection.value,n=this.node.pv.lightDir;e&&(e.x=n.x,e.y=n.y,e.z=n.z)}}Ik._object_bbox=new Ay.a;class Fk extends(Rk(la)){}const Dk=new Fk;class Bk extends kD{constructor(){super(...arguments),this.paramsConfig=Dk,this._volume_controller=new Ik(this)}static type(){return\\\\\\\"volume\\\\\\\"}createMaterial(){const t=new F({vertexShader:Lk,fragmentShader:Ok,side:w.H,transparent:!0,depthTest:!0,uniforms:I.clone(Pk)});return gr.add_user_data_render_hook(t,Ik.render_hook.bind(Ik)),t}initializeNode(){}async cook(){this._volume_controller.update_uniforms_from_params(),this.setMaterial(this.material)}}class zk extends(ZD(Rk(la))){}const kk=new zk;class Uk extends QD{constructor(){super(...arguments),this.paramsConfig=kk,this._volume_controller=new Ik(this)}static type(){return\\\\\\\"volumeBuilder\\\\\\\"}usedAssembler(){return jn.GL_VOLUME}_create_assembler_controller(){return li.assemblersRegister.assembler(this,this.usedAssembler())}initializeNode(){}async cook(){this._volume_controller.update_uniforms_from_params(),this.compileIfRequired(),this.setMaterial(this.material)}}class Gk extends sa{static context(){return ts.MAT}cook(){this.cookController.endCook()}}class Vk extends Gk{}class Hk extends Vk{constructor(){super(...arguments),this._children_controller_context=ts.ANIM}static type(){return es.ANIM}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class jk extends Vk{constructor(){super(...arguments),this._children_controller_context=ts.COP}static type(){return es.COP}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class Wk extends Vk{constructor(){super(...arguments),this._children_controller_context=ts.EVENT}static type(){return es.EVENT}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class qk extends Vk{constructor(){super(...arguments),this._children_controller_context=ts.MAT}static type(){return es.MAT}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class Xk extends Gk{constructor(){super(...arguments),this.paramsConfig=new Jm,this.effectsComposerController=new Zm(this),this.displayNodeController=new Lm(this,this.effectsComposerController.displayNodeControllerCallbacks()),this._children_controller_context=ts.POST}static type(){return es.POST}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class Yk extends Vk{constructor(){super(...arguments),this._children_controller_context=ts.ROP}static type(){return es.ROP}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}var $k=n(87);const Jk=\\\\\\\"parent object\\\\\\\",Zk=[Jk,Jk,Jk,Jk];var Qk;!function(t){t[t.MANAGER=0]=\\\\\\\"MANAGER\\\\\\\",t[t.CAMERA=2]=\\\\\\\"CAMERA\\\\\\\",t[t.LIGHT=3]=\\\\\\\"LIGHT\\\\\\\"}(Qk||(Qk={}));class Kk extends sa{constructor(){super(...arguments),this.renderOrder=Qk.MANAGER,this._children_group=this._create_children_group(),this._attachableToHierarchy=!0,this._used_in_scene=!0}static context(){return ts.OBJ}static displayedInputNames(){return Zk}_create_children_group(){const t=new Fn.a;return t.matrixAutoUpdate=!1,t}attachableToHierarchy(){return this._attachableToHierarchy}usedInScene(){return this._used_in_scene}addObjectToParent(t){this.attachableToHierarchy()&&t.add(this.object)}removeObjectFromParent(){if(this.attachableToHierarchy()){const t=this.object.parent;t&&t.remove(this.object)}}initializeBaseNode(){this._object=this._create_object_with_attributes(),this.nameController.add_post_set_fullPath_hook(this.set_object_name.bind(this)),this.set_object_name()}get children_group(){return this._children_group}get object(){return this._object}_create_object_with_attributes(){const t=this.createObject();return t.node=this,t.add(this._children_group),t}set_object_name(){this._object&&(this._object.name=this.path(),this._children_group.name=`${this.path()}:parented_outputs`)}createObject(){const t=new K.a;return t.matrixAutoUpdate=!1,t}isDisplayNodeCooking(){if(this.displayNodeController){const t=this.displayNodeController.displayNode();if(t)return t.cookController.isCooking()}return!1}isDisplayed(){var t,e;return(null===(e=null===(t=this.flags)||void 0===t?void 0:t.display)||void 0===e?void 0:e.active())||!1}}class tU extends Kk{constructor(){super(...arguments),this.flags=new Di(this),this.renderOrder=Qk.LIGHT,this._color_with_intensity=new D.a(0),this._used_in_scene=!0,this._cook_main_without_inputs_when_dirty_bound=this._cook_main_without_inputs_when_dirty.bind(this)}get light(){return this._light}initializeBaseNode(){super.initializeBaseNode(),this._light=this.createLight(),this.object.add(this._light),this.flags.display.onUpdate((()=>{this._updateLightAttachment()})),this.dirtyController.addPostDirtyHook(\\\\\\\"_cook_main_without_inputs_when_dirty\\\\\\\",this._cook_main_without_inputs_when_dirty_bound)}async _cook_main_without_inputs_when_dirty(){await this.cookController.cookMainWithoutInputs()}set_object_name(){super.set_object_name(),this._light&&(this._light.name=`${this.path()}:light`)}_updateLightAttachment(){this.flags.display.active()?(this.object.add(this.light),this._cook_main_without_inputs_when_dirty()):this.object.remove(this.light)}cook(){this.updateLightParams(),this.updateShadowParams(),this.cookController.endCook()}updateLightParams(){}updateShadowParams(){}}const eU=new class extends la{constructor(){super(...arguments),this.color=aa.COLOR([1,1,1],{conversion:oo.SRGB_TO_LINEAR}),this.intensity=aa.FLOAT(1)}};class nU extends tU{constructor(){super(...arguments),this.paramsConfig=eU}static type(){return\\\\\\\"ambientLight\\\\\\\"}createLight(){const t=new $k.a;return t.matrixAutoUpdate=!1,t}initializeNode(){this.io.inputs.setCount(0,1)}updateLightParams(){this.light.color=this.pv.color,this.light.intensity=this.pv.intensity}}class iU extends sv.a{constructor(t,e,n=10,i=10){super(t,e),this.type=\\\\\\\"RectAreaLight\\\\\\\",this.width=n,this.height=i}get power(){return this.intensity*this.width*this.height*Math.PI}set power(t){this.intensity=t/(this.width*this.height*Math.PI)}copy(t){return super.copy(t),this.width=t.width,this.height=t.height,this}toJSON(t){const e=super.toJSON(t);return e.object.width=this.width,e.object.height=this.height,e}}iU.prototype.isRectAreaLight=!0;var sU,rU=n(61);class oU{static init(){const 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pU=\\\\\\\"_cook_main_without_inputs_when_dirty\\\\\\\";class _U{constructor(t){this.node=t,this._cook_main_without_inputs_when_dirty_bound=this._cook_main_without_inputs_when_dirty.bind(this),this._core_transform=new uU,this._keep_pos_when_parenting_m_object=new A.a,this._keep_pos_when_parenting_m_new_parent_inv=new A.a}initializeNode(){this.node.dirtyController.hasHook(pU)||this.node.dirtyController.addPostDirtyHook(pU,this._cook_main_without_inputs_when_dirty_bound)}async _cook_main_without_inputs_when_dirty(){await this.node.cookController.cookMainWithoutInputs()}update(){this.update_transform_with_matrix();this.node.object.matrixAutoUpdate=this.node.pv.matrixAutoUpdate}update_transform_with_matrix(t){const e=this.node.object;null==t||t.equals(e.matrix)?this._update_matrix_from_params_with_core_transform():(e.matrix.copy(t),e.dispatchEvent({type:\\\\\\\"change\\\\\\\"}))}_update_matrix_from_params_with_core_transform(){const t=this.node.object;let e=t.matrixAutoUpdate;e&&(t.matrixAutoUpdate=!1);const n=this._core_transform.matrix(this.node.pv.t,this.node.pv.r,this.node.pv.s,this.node.pv.scale,cU[this.node.pv.rotationOrder]);t.matrix.identity(),t.applyMatrix4(n),this._apply_look_at(),t.updateMatrix(),e&&(t.matrixAutoUpdate=!0),t.dispatchEvent({type:\\\\\\\"change\\\\\\\"})}_apply_look_at(){}set_params_from_matrix(t,e={}){uU.set_params_from_matrix(t,this.node,e)}static update_node_transform_params_if_required(t,e){t.transformController.update_node_transform_params_if_required(e)}update_node_transform_params_if_required(t){if(!this.node.pv.keepPosWhenParenting)return;if(!this.node.scene().loadingController.loaded())return;if(t==this.node.object.parent)return;const e=this.node.object;e.updateMatrixWorld(!0),t.updateMatrixWorld(!0),this._keep_pos_when_parenting_m_object.copy(e.matrixWorld),this._keep_pos_when_parenting_m_new_parent_inv.copy(t.matrixWorld),this._keep_pos_when_parenting_m_new_parent_inv.invert(),this._keep_pos_when_parenting_m_object.premultiply(this._keep_pos_when_parenting_m_new_parent_inv),uU.set_params_from_matrix(this._keep_pos_when_parenting_m_object,this.node,{scale:!0})}update_node_transform_params_from_object(t=!1){const e=this.node.object;t&&e.updateMatrix(),uU.set_params_from_matrix(e.matrix,this.node,{scale:!0})}static PARAM_CALLBACK_update_transform_from_object(t){t.transformController.update_node_transform_params_from_object()}}class mU{constructor(t){this.node=t}initializeNode(){this.node.io.inputs.setCount(0,1),this.node.io.inputs.set_depends_on_inputs(!1),this.node.io.outputs.setHasOneOutput(),this.node.io.inputs.add_on_set_input_hook(\\\\\\\"on_input_updated:update_parent\\\\\\\",(()=>{this.on_input_updated()}))}static on_input_updated(t){const e=t.root().getParentForNode(t);t.transformController&&e&&_U.update_node_transform_params_if_required(t,e),null!=t.io.inputs.input(0)?t.root().addToParentTransform(t):t.root().removeFromParentTransform(t)}on_input_updated(){mU.on_input_updated(this.node)}}dU(la);class fU extends tU{constructor(){super(...arguments),this.flags=new Di(this),this.hierarchyController=new mU(this),this.transformController=new _U(this)}initializeBaseNode(){super.initializeBaseNode(),this.hierarchyController.initializeNode(),this.transformController.initializeNode()}cook(){this.transformController.update(),this.updateLightParams(),this.updateShadowParams(),this.cookController.endCook()}}class 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lt.a({side:w.i,fog:!1})))}updateMatrixWorld(){if(this.scale.set(.5*this.light.width,.5*this.light.height,1),void 0!==this.color)this.material.color.set(this.color),this.children[0].material.color.set(this.color);else{this.material.color.copy(this.light.color).multiplyScalar(this.light.intensity);const t=this.material.color,e=Math.max(t.r,t.g,t.b);e>1&&t.multiplyScalar(1/e),this.children[0].material.color.copy(this.material.color)}this.matrixWorld.extractRotation(this.light.matrixWorld).scale(this.scale).copyPosition(this.light.matrixWorld),this.children[0].matrixWorld.copy(this.matrixWorld)}dispose(){this.geometry.dispose(),this.material.dispose(),this.children[0].geometry.dispose(),this.children[0].material.dispose()}}xU=la;var xU;class bU{constructor(t,e){this.node=t,this._name=e,this._object=this.createObject(),this._material=new lt.a({wireframe:!0,fog:!1})}build(){this._object.matrixAutoUpdate=!1,this._object.name=this._name,this.buildHelper()}get object(){return this._object}}class wU extends bU{createObject(){return new yU(this.node.light)}buildHelper(){}update(){this._object.updateMatrixWorld()}}class TU extends(function(t){return class extends t{constructor(){super(...arguments),this.light=aa.FOLDER(),this.color=aa.COLOR([1,1,1],{conversion:oo.SRGB_TO_LINEAR}),this.intensity=aa.FLOAT(1,{range:[0,10]}),this.width=aa.FLOAT(1,{range:[0,10]}),this.height=aa.FLOAT(1,{range:[0,10]}),this.showHelper=aa.BOOLEAN(0)}}}(dU(la))){}const AU=new TU;class MU extends fU{constructor(){super(...arguments),this.paramsConfig=AU,this._helperController=new gU(this,wU,\\\\\\\"RectAreaLightObjNodeHelper\\\\\\\")}static type(){return\\\\\\\"areaLight\\\\\\\"}initializeNode(){this._helperController.initializeNode()}createLight(){const t=new iU(16777215,1,1,1);return t.matrixAutoUpdate=!1,oU.initialized||(oU.init(),oU.initialized=!0),t}updateLightParams(){this.light.color=this.pv.color,this.light.intensity=this.pv.intensity,this.light.width=this.pv.width,this.light.height=this.pv.height,this._helperController.update()}}var EU=n(72);const SU=new p.a,CU=new tf.a;class NU extends As.a{constructor(t){const e=new S.a,n=new Ts.a({color:16777215,vertexColors:!0,toneMapped:!1}),i=[],s=[],r={},o=new D.a(16755200),a=new D.a(16711680),l=new D.a(43775),c=new D.a(16777215),h=new D.a(3355443);function u(t,e,n){d(t,n),d(e,n)}function d(t,e){i.push(0,0,0),s.push(e.r,e.g,e.b),void 0===r[t]&&(r[t]=[]),r[t].push(i.length/3-1)}u(\\\\\\\"n1\\\\\\\",\\\\\\\"n2\\\\\\\",o),u(\\\\\\\"n2\\\\\\\",\\\\\\\"n4\\\\\\\",o),u(\\\\\\\"n4\\\\\\\",\\\\\\\"n3\\\\\\\",o),u(\\\\\\\"n3\\\\\\\",\\\\\\\"n1\\\\\\\",o),u(\\\\\\\"f1\\\\\\\",\\\\\\\"f2\\\\\\\",o),u(\\\\\\\"f2\\\\\\\",\\\\\\\"f4\\\\\\\",o),u(\\\\\\\"f4\\\\\\\",\\\\\\\"f3\\\\\\\",o),u(\\\\\\\"f3\\\\\\\",\\\\\\\"f1\\\\\\\",o),u(\\\\\\\"n1\\\\\\\",\\\\\\\"f1\\\\\\\",o),u(\\\\\\\"n2\\\\\\\",\\\\\\\"f2\\\\\\\",o),u(\\\\\\\"n3\\\\\\\",\\\\\\\"f3\\\\\\\",o),u(\\\\\\\"n4\\\\\\\",\\\\\\\"f4\\\\\\\",o),u(\\\\\\\"p\\\\\\\",\\\\\\\"n1\\\\\\\",a),u(\\\\\\\"p\\\\\\\",\\\\\\\"n2\\\\\\\",a),u(\\\\\\\"p\\\\\\\",\\\\\\\"n3\\\\\\\",a),u(\\\\\\\"p\\\\\\\",\\\\\\\"n4\\\\\\\",a),u(\\\\\\\"u1\\\\\\\",\\\\\\\"u2\\\\\\\",l),u(\\\\\\\"u2\\\\\\\",\\\\\\\"u3\\\\\\\",l),u(\\\\\\\"u3\\\\\\\",\\\\\\\"u1\\\\\\\",l),u(\\\\\\\"c\\\\\\\",\\\\\\\"t\\\\\\\",c),u(\\\\\\\"p\\\\\\\",\\\\\\\"c\\\\\\\",h),u(\\\\\\\"cn1\\\\\\\",\\\\\\\"cn2\\\\\\\",h),u(\\\\\\\"cn3\\\\\\\",\\\\\\\"cn4\\\\\\\",h),u(\\\\\\\"cf1\\\\\\\",\\\\\\\"cf2\\\\\\\",h),u(\\\\\\\"cf3\\\\\\\",\\\\\\\"cf4\\\\\\\",h),e.setAttribute(\\\\\\\"position\\\\\\\",new C.c(i,3)),e.setAttribute(\\\\\\\"color\\\\\\\",new C.c(s,3)),super(e,n),this.type=\\\\\\\"CameraHelper\\\\\\\",this.camera=t,this.camera.updateProjectionMatrix&&this.camera.updateProjectionMatrix(),this.matrixAutoUpdate=!1,this.pointMap=r,this.update()}update(){const t=this.geometry,e=this.pointMap;CU.projectionMatrixInverse.copy(this.camera.projectionMatrixInverse),LU(\\\\\\\"c\\\\\\\",e,t,CU,0,0,-1),LU(\\\\\\\"t\\\\\\\",e,t,CU,0,0,1),LU(\\\\\\\"n1\\\\\\\",e,t,CU,-1,-1,-1),LU(\\\\\\\"n2\\\\\\\",e,t,CU,1,-1,-1),LU(\\\\\\\"n3\\\\\\\",e,t,CU,-1,1,-1),LU(\\\\\\\"n4\\\\\\\",e,t,CU,1,1,-1),LU(\\\\\\\"f1\\\\\\\",e,t,CU,-1,-1,1),LU(\\\\\\\"f2\\\\\\\",e,t,CU,1,-1,1),LU(\\\\\\\"f3\\\\\\\",e,t,CU,-1,1,1),LU(\\\\\\\"f4\\\\\\\",e,t,CU,1,1,1),LU(\\\\\\\"u1\\\\\\\",e,t,CU,.7,1.1,-1),LU(\\\\\\\"u2\\\\\\\",e,t,CU,-.7,1.1,-1),LU(\\\\\\\"u3\\\\\\\",e,t,CU,0,2,-1),LU(\\\\\\\"cf1\\\\\\\",e,t,CU,-1,0,1),LU(\\\\\\\"cf2\\\\\\\",e,t,CU,1,0,1),LU(\\\\\\\"cf3\\\\\\\",e,t,CU,0,-1,1),LU(\\\\\\\"cf4\\\\\\\",e,t,CU,0,1,1),LU(\\\\\\\"cn1\\\\\\\",e,t,CU,-1,0,-1),LU(\\\\\\\"cn2\\\\\\\",e,t,CU,1,0,-1),LU(\\\\\\\"cn3\\\\\\\",e,t,CU,0,-1,-1),LU(\\\\\\\"cn4\\\\\\\",e,t,CU,0,1,-1),t.getAttribute(\\\\\\\"position\\\\\\\").needsUpdate=!0}}function LU(t,e,n,i,s,r,o){SU.set(s,r,o).unproject(i);const a=e[t];if(void 0!==a){const t=n.getAttribute(\\\\\\\"position\\\\\\\");for(let e=0,n=a.length;e.9&&o<.1&&(e<.2&&(r[t+0]+=1),n<.2&&(r[t+2]+=1),i<.2&&(r[t+4]+=1))}}()}(),this.setAttribute(\\\\\\\"position\\\\\\\",new C.c(s,3)),this.setAttribute(\\\\\\\"normal\\\\\\\",new C.c(s.slice(),3)),this.setAttribute(\\\\\\\"uv\\\\\\\",new C.c(r,2)),0===i?this.computeVertexNormals():this.normalizeNormals()}static fromJSON(t){return new zU(t.vertices,t.indices,t.radius,t.details)}}class kU extends zU{constructor(t=1,e=0){super([1,0,0,-1,0,0,0,1,0,0,-1,0,0,0,1,0,0,-1],[0,2,4,0,4,3,0,3,5,0,5,2,1,2,5,1,5,3,1,3,4,1,4,2],t,e),this.type=\\\\\\\"OctahedronGeometry\\\\\\\",this.parameters={radius:t,detail:e}}static fromJSON(t){return new kU(t.radius,t.detail)}}class UU extends bU{constructor(){super(...arguments),this._geometry=new kU(1),this._quat=new ah.a,this._default_position=new p.a(0,1,0),this._color1=new D.a,this._color2=new D.a}createObject(){return new B.a}buildHelper(){this._geometry.rotateZ(.5*Math.PI),this._material.vertexColors=!0;const t=this._geometry.getAttribute(\\\\\\\"position\\\\\\\"),e=new Float32Array(3*t.count);this._geometry.setAttribute(\\\\\\\"color\\\\\\\",new C.a(e,3)),this._object.geometry=this._geometry,this._object.material=this._material,this._object.matrixAutoUpdate=!1}update(){if(!this.node.pv.position)return;this._object.position.copy(this.node.pv.position).multiplyScalar(-1),this._quat.setFromUnitVectors(this._default_position,this.node.pv.position),this._object.setRotationFromQuaternion(this._quat),this._object.scale.setScalar(this.node.pv.helperSize),this._object.updateMatrix();const t=this._geometry.getAttribute(\\\\\\\"color\\\\\\\");this._color1.copy(this.node.light.color),this._color2.copy(this.node.light.groundColor);for(let e=0,n=t.count;e0)&&d.push(e,s,l),(t!==n-1||a0&&y(!0),e>0&&y(!1)),this.setIndex(c),this.setAttribute(\\\\\\\"position\\\\\\\",new C.c(h,3)),this.setAttribute(\\\\\\\"normal\\\\\\\",new C.c(u,3)),this.setAttribute(\\\\\\\"uv\\\\\\\",new C.c(_,2))}static fromJSON(t){return new QU(t.radiusTop,t.radiusBottom,t.height,t.radialSegments,t.heightSegments,t.openEnded,t.thetaStart,t.thetaLength)}}class KU extends QU{constructor(t=1,e=1,n=8,i=1,s=!1,r=0,o=2*Math.PI){super(0,t,e,n,i,s,r,o),this.type=\\\\\\\"ConeGeometry\\\\\\\",this.parameters={radius:t,height:e,radialSegments:n,heightSegments:i,openEnded:s,thetaStart:r,thetaLength:o}}static fromJSON(t){return new KU(t.radius,t.height,t.radialSegments,t.heightSegments,t.openEnded,t.thetaStart,t.thetaLength)}}class tG{constructor(t){this.node=t}update(){const t=this.node.pv;if(t.tvolumetric){const e=this.object(),n=this.node.light;ZU.updateConeObject(e,{sizeMult:t.helperSize,distance:n.distance,angle:n.angle});const i=e.material.uniforms;i.lightColor.value.copy(n.color),i.attenuation.value=t.volAttenuation,i.anglePower.value=t.volAnglePower,this.node.light.add(e)}else this._mesh&&this.node.light.remove(this._mesh)}object(){return this._mesh=this._mesh||this._createMesh()}_createMesh(){const t=new KU(1,1,256,1);t.applyMatrix4((new A.a).makeTranslation(0,-.5,0)),t.applyMatrix4((new A.a).makeRotationX(-Math.PI/2));const e=this._createMaterial(),n=new B.a(t,e);return n.matrixAutoUpdate=!1,n.name=\\\\\\\"Volumetric\\\\\\\",e.uniforms.lightColor.value.set(\\\\\\\"white\\\\\\\"),n}_createMaterial(){return new F({uniforms:{attenuation:{value:5},anglePower:{value:1.2},lightColor:{value:new D.a(\\\\\\\"cyan\\\\\\\")}},vertexShader:\\\\\\\"varying vec3 vNormal;\\\\nvarying vec3 vWorldPosition;\\\\nvarying vec3 vWorldOrigin;\\\\n\\\\nvoid main(){\\\\n\\\\t// compute intensity\\\\n\\\\tvNormal\\\\t\\\\t= normalize( normalMatrix * normal );\\\\n\\\\n\\\\tvec4 worldPosition\\\\t= modelMatrix * vec4( position, 1.0 );\\\\n\\\\tvWorldPosition\\\\t\\\\t= worldPosition.xyz;\\\\n\\\\n\\\\tvec4 worldOrigin\\\\t= modelMatrix * vec4( 0.0, 0.0, 0.0, 1.0 );\\\\n\\\\tvWorldOrigin\\\\t\\\\t= worldOrigin.xyz;\\\\n\\\\n\\\\t// set gl_Position\\\\n\\\\tgl_Position\\\\t= projectionMatrix * modelViewMatrix * vec4( position, 1.0 );\\\\n}\\\\\\\",fragmentShader:\\\\\\\"varying vec3 vNormal;\\\\nvarying vec3 vWorldPosition;\\\\nvarying vec3 vWorldOrigin;\\\\n\\\\nuniform vec3 lightColor;\\\\n\\\\n// uniform vec3 spotPosition;\\\\n\\\\nuniform float attenuation;\\\\nuniform float anglePower;\\\\n\\\\nvoid main(){\\\\n\\\\n\\\\t//////////////////////////////////////////////////////////\\\\n\\\\t// distance attenuation //\\\\n\\\\t//////////////////////////////////////////////////////////\\\\n\\\\tfloat intensity = distance(vWorldPosition, vWorldOrigin) / attenuation;\\\\n\\\\tintensity = 1.0 - clamp(intensity, 0.0, 1.0);\\\\n\\\\n\\\\t//////////////////////////////////////////////////////////\\\\n\\\\t// intensity on angle //\\\\n\\\\t//////////////////////////////////////////////////////////\\\\n\\\\tvec3 normal = vec3(vNormal.x, vNormal.y, abs(vNormal.z));\\\\n\\\\tfloat angleIntensity = pow( dot(normal, vec3(0.0, 0.0, 1.0)), anglePower );\\\\n\\\\tintensity = intensity * angleIntensity;\\\\n\\\\t// 'gl_FragColor = vec4( lightColor, intensity );\\\\n\\\\n\\\\t//////////////////////////////////////////////////////////\\\\n\\\\t// final color //\\\\n\\\\t//////////////////////////////////////////////////////////\\\\n\\\\n\\\\t// set the final color\\\\n\\\\tgl_FragColor = vec4( lightColor, intensity);\\\\n}\\\\\\\",transparent:!0,depthWrite:!1})}}class eG extends(dU(la)){constructor(){super(...arguments),this.light=aa.FOLDER(),this.color=aa.COLOR([1,1,1],{conversion:oo.SRGB_TO_LINEAR}),this.intensity=aa.FLOAT(1),this.angle=aa.FLOAT(45,{range:[0,180]}),this.penumbra=aa.FLOAT(.1),this.decay=aa.FLOAT(.1,{range:[0,1]}),this.distance=aa.FLOAT(100,{range:[0,100]}),this.showHelper=aa.BOOLEAN(0),this.helperSize=aa.FLOAT(1,{visibleIf:{showHelper:1}}),this.shadow=aa.FOLDER(),this.castShadow=aa.BOOLEAN(1),this.shadowAutoUpdate=aa.BOOLEAN(1,{visibleIf:{castShadow:1}}),this.shadowUpdateOnNextRender=aa.BOOLEAN(0,{visibleIf:{castShadow:1,shadowAutoUpdate:0}}),this.shadowRes=aa.VECTOR2([256,256],{visibleIf:{castShadow:1}}),this.shadowBias=aa.FLOAT(.001,{visibleIf:{castShadow:1},range:[-.01,.01],rangeLocked:[!1,!1]}),this.shadowNear=aa.FLOAT(.1,{visibleIf:{castShadow:1},range:[0,100],rangeLocked:[!0,!1]}),this.shadowFar=aa.FLOAT(100,{visibleIf:{castShadow:1},range:[0,100],rangeLocked:[!0,!1]}),this.shadowRadius=aa.FLOAT(0,{visibleIf:{castShadow:1},range:[0,10],rangeLocked:[!0,!1]}),this.volumetric=aa.FOLDER(),this.tvolumetric=aa.BOOLEAN(0),this.volAttenuation=aa.FLOAT(5,{range:[0,10],rangeLocked:[!0,!1]}),this.volAnglePower=aa.FLOAT(10,{range:[0,20],rangeLocked:[!0,!1]})}}const nG=new eG;class iG extends fU{constructor(){super(...arguments),this.paramsConfig=nG,this._helperController=new gU(this,ZU,\\\\\\\"SpotLightHelper\\\\\\\"),this._volumetricController=new tG(this)}static type(){return PU.SPOT}initializeNode(){this._helperController.initializeNode()}createLight(){const t=new JU.a;return t.matrixAutoUpdate=!1,t.castShadow=!0,t.shadow.bias=-.001,t.shadow.mapSize.x=256,t.shadow.mapSize.y=256,t.shadow.camera.near=.1,this._target_target=t.target,this._target_target.name=\\\\\\\"SpotLight Default Target\\\\\\\",this._target_target.matrixAutoUpdate=!1,this.object.add(this._target_target),t}updateLightParams(){this.light.color=this.pv.color,this.light.intensity=this.pv.intensity,this.light.angle=this.pv.angle*(Math.PI/180),this.light.penumbra=this.pv.penumbra,this.light.decay=this.pv.decay,this.light.distance=this.pv.distance,this._helperController.update(),this._volumetricController.update()}updateShadowParams(){this.light.castShadow=this.pv.castShadow,this.light.shadow.autoUpdate=this.pv.shadowAutoUpdate,this.light.shadow.needsUpdate=this.pv.shadowUpdateOnNextRender,this.light.shadow.mapSize.copy(this.pv.shadowRes),this.light.shadow.camera.near=this.pv.shadowNear,this.light.shadow.camera.far=this.pv.shadowFar,this.light.shadow.bias=this.pv.shadowBias,this.light.shadow.radius=this.pv.shadowRadius}}let sG;const rG=function(){return void 0===sG&&(sG=new(window.AudioContext||window.webkitAudioContext)),sG},oG=new p.a,aG=new ah.a,lG=new p.a,cG=new p.a;class hG extends K.a{constructor(){super(),this.type=\\\\\\\"AudioListener\\\\\\\",this.context=rG(),this.gain=this.context.createGain(),this.gain.connect(this.context.destination),this.filter=null,this.timeDelta=0,this._clock=new Om}getInput(){return this.gain}removeFilter(){return null!==this.filter&&(this.gain.disconnect(this.filter),this.filter.disconnect(this.context.destination),this.gain.connect(this.context.destination),this.filter=null),this}getFilter(){return this.filter}setFilter(t){return null!==this.filter?(this.gain.disconnect(this.filter),this.filter.disconnect(this.context.destination)):this.gain.disconnect(this.context.destination),this.filter=t,this.gain.connect(this.filter),this.filter.connect(this.context.destination),this}getMasterVolume(){return this.gain.gain.value}setMasterVolume(t){return this.gain.gain.setTargetAtTime(t,this.context.currentTime,.01),this}updateMatrixWorld(t){super.updateMatrixWorld(t);const e=this.context.listener,n=this.up;if(this.timeDelta=this._clock.getDelta(),this.matrixWorld.decompose(oG,aG,lG),cG.set(0,0,-1).applyQuaternion(aG),e.positionX){const t=this.context.currentTime+this.timeDelta;e.positionX.linearRampToValueAtTime(oG.x,t),e.positionY.linearRampToValueAtTime(oG.y,t),e.positionZ.linearRampToValueAtTime(oG.z,t),e.forwardX.linearRampToValueAtTime(cG.x,t),e.forwardY.linearRampToValueAtTime(cG.y,t),e.forwardZ.linearRampToValueAtTime(cG.z,t),e.upX.linearRampToValueAtTime(n.x,t),e.upY.linearRampToValueAtTime(n.y,t),e.upZ.linearRampToValueAtTime(n.z,t)}else e.setPosition(oG.x,oG.y,oG.z),e.setOrientation(cG.x,cG.y,cG.z,n.x,n.y,n.z)}}class uG extends(dU(la)){}const dG=new uG;class pG extends Kk{constructor(){super(...arguments),this.paramsConfig=dG,this.hierarchyController=new mU(this),this.transformController=new _U(this),this.flags=new Di(this)}static type(){return Ng.AUDIO_LISTENER}createObject(){const t=new hG;return t.matrixAutoUpdate=!1,t}initializeNode(){this.hierarchyController.initializeNode(),this.transformController.initializeNode()}cook(){this.transformController.update(),this.cookController.endCook()}}class _G extends As.a{constructor(t=1){const e=[0,0,0,t,0,0,0,0,0,0,t,0,0,0,0,0,0,t],n=new S.a;n.setAttribute(\\\\\\\"position\\\\\\\",new C.c(e,3)),n.setAttribute(\\\\\\\"color\\\\\\\",new C.c([1,0,0,1,.6,0,0,1,0,.6,1,0,0,0,1,0,.6,1],3));super(n,new Ts.a({vertexColors:!0,toneMapped:!1})),this.type=\\\\\\\"AxesHelper\\\\\\\"}setColors(t,e,n){const i=new D.a,s=this.geometry.attributes.color.array;return i.set(t),i.toArray(s,0),i.toArray(s,3),i.set(e),i.toArray(s,6),i.toArray(s,9),i.set(n),i.toArray(s,12),i.toArray(s,15),this.geometry.attributes.color.needsUpdate=!0,this}dispose(){this.geometry.dispose(),this.material.dispose()}}var mG;!function(t){t.TOGETHER=\\\\\\\"translate + rotate together\\\\\\\",t.SEPARATELY=\\\\\\\"translate + rotate separately\\\\\\\"}(mG||(mG={}));const fG=[mG.TOGETHER,mG.SEPARATELY];const gG=new class extends la{constructor(){super(...arguments),this.object0=aa.OPERATOR_PATH(\\\\\\\"/geo1\\\\\\\",{nodeSelection:{context:ts.OBJ}}),this.object1=aa.OPERATOR_PATH(\\\\\\\"/geo2\\\\\\\",{nodeSelection:{context:ts.OBJ}}),this.mode=aa.INTEGER(fG.indexOf(mG.TOGETHER),{menu:{entries:fG.map(((t,e)=>({name:t,value:e})))}}),this.blend=aa.FLOAT(0,{visibleIf:{mode:fG.indexOf(mG.TOGETHER)},range:[0,1],rangeLocked:[!1,!1]}),this.blendT=aa.FLOAT(0,{visibleIf:{mode:fG.indexOf(mG.SEPARATELY)},range:[0,1],rangeLocked:[!1,!1]}),this.blendR=aa.FLOAT(0,{visibleIf:{mode:fG.indexOf(mG.SEPARATELY)},range:[0,1],rangeLocked:[!1,!1]})}};class vG extends Kk{constructor(){super(...arguments),this.paramsConfig=gG,this.hierarchyController=new mU(this),this.flags=new Di(this),this._helper=new _G(1),this._t0=new p.a,this._q0=new ah.a,this._s0=new p.a,this._t1=new p.a,this._q1=new ah.a,this._s1=new p.a}static type(){return\\\\\\\"blend\\\\\\\"}createObject(){const t=new Fn.a;return t.matrixAutoUpdate=!1,t}initializeNode(){this.hierarchyController.initializeNode(),this.io.inputs.setCount(0),this.addPostDirtyHook(\\\\\\\"blend_on_dirty\\\\\\\",(()=>{this.cookController.cookMainWithoutInputs()})),this._updateHelperHierarchy(),this.flags.display.onUpdate((()=>{this._updateHelperHierarchy()}))}_updateHelperHierarchy(){this.flags.display.active()?this.object.add(this._helper):this.object.remove(this._helper)}cook(){const t=this.p.object0.found_node_with_context(ts.OBJ),e=this.p.object1.found_node_with_context(ts.OBJ);t&&e&&this._blend(t.object,e.object),this.cookController.endCook()}_blend(t,e){const n=fG[this.pv.mode];switch(n){case mG.TOGETHER:return this._blend_together(t,e);case mG.SEPARATELY:return this._blend_separately(t,e)}ls.unreachable(n)}_blend_together(t,e){this._decompose_matrices(t,e),this._object.position.copy(this._t0).lerp(this._t1,this.pv.blend),this._object.quaternion.copy(this._q0).slerp(this._q1,this.pv.blend),this._object.matrixAutoUpdate||this._object.updateMatrix()}_blend_separately(t,e){this._decompose_matrices(t,e),this._object.position.copy(this._t0).lerp(this._t1,this.pv.blendT),this._object.quaternion.copy(this._q0).slerp(this._q1,this.pv.blendR),this._object.matrixAutoUpdate||this._object.updateMatrix()}_decompose_matrices(t,e){t.matrixWorld.decompose(this._t0,this._q0,this._s0),e.matrixWorld.decompose(this._t1,this._q1,this._s1)}}var yG={uniforms:{tDiffuse:{value:null},h:{value:1/512}},vertexShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\tvarying vec2 vUv;\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\tvUv = uv;\\\\n\\\\t\\\\t\\\\tgl_Position = projectionMatrix * modelViewMatrix * vec4( position, 1.0 );\\\\n\\\\n\\\\t\\\\t}\\\\\\\",fragmentShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\tuniform sampler2D tDiffuse;\\\\n\\\\t\\\\tuniform float h;\\\\n\\\\n\\\\t\\\\tvarying vec2 vUv;\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\tvec4 sum = vec4( 0.0 );\\\\n\\\\n\\\\t\\\\t\\\\tsum += texture2D( tDiffuse, vec2( vUv.x - 4.0 * h, vUv.y ) ) * 0.051;\\\\n\\\\t\\\\t\\\\tsum += texture2D( tDiffuse, vec2( vUv.x - 3.0 * h, vUv.y ) ) * 0.0918;\\\\n\\\\t\\\\t\\\\tsum += texture2D( tDiffuse, vec2( vUv.x - 2.0 * h, vUv.y ) ) * 0.12245;\\\\n\\\\t\\\\t\\\\tsum += texture2D( tDiffuse, vec2( vUv.x - 1.0 * h, vUv.y ) ) * 0.1531;\\\\n\\\\t\\\\t\\\\tsum += texture2D( tDiffuse, vec2( vUv.x, vUv.y ) ) * 0.1633;\\\\n\\\\t\\\\t\\\\tsum += texture2D( tDiffuse, vec2( vUv.x + 1.0 * h, vUv.y ) ) * 0.1531;\\\\n\\\\t\\\\t\\\\tsum += texture2D( tDiffuse, vec2( vUv.x + 2.0 * h, vUv.y ) ) * 0.12245;\\\\n\\\\t\\\\t\\\\tsum += texture2D( tDiffuse, vec2( vUv.x + 3.0 * h, vUv.y ) ) * 0.0918;\\\\n\\\\t\\\\t\\\\tsum += texture2D( tDiffuse, vec2( vUv.x + 4.0 * h, vUv.y ) ) * 0.051;\\\\n\\\\n\\\\t\\\\t\\\\tgl_FragColor = sum;\\\\n\\\\n\\\\t\\\\t}\\\\\\\"};const xG={uniforms:{tDiffuse:{value:null},v:{value:1/512}},vertexShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\tvarying vec2 vUv;\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\tvUv = uv;\\\\n\\\\t\\\\t\\\\tgl_Position = projectionMatrix * modelViewMatrix * vec4( position, 1.0 );\\\\n\\\\n\\\\t\\\\t}\\\\\\\",fragmentShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\tuniform sampler2D tDiffuse;\\\\n\\\\t\\\\tuniform float v;\\\\n\\\\n\\\\t\\\\tvarying vec2 vUv;\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\tvec4 sum = vec4( 0.0 );\\\\n\\\\n\\\\t\\\\t\\\\tsum += texture2D( tDiffuse, vec2( vUv.x, vUv.y - 4.0 * v ) ) * 0.051;\\\\n\\\\t\\\\t\\\\tsum += texture2D( tDiffuse, vec2( vUv.x, vUv.y - 3.0 * v ) ) * 0.0918;\\\\n\\\\t\\\\t\\\\tsum += texture2D( tDiffuse, vec2( vUv.x, vUv.y - 2.0 * v ) ) * 0.12245;\\\\n\\\\t\\\\t\\\\tsum += texture2D( tDiffuse, vec2( vUv.x, vUv.y - 1.0 * v ) ) * 0.1531;\\\\n\\\\t\\\\t\\\\tsum += texture2D( tDiffuse, vec2( vUv.x, vUv.y ) ) * 0.1633;\\\\n\\\\t\\\\t\\\\tsum += texture2D( tDiffuse, vec2( vUv.x, vUv.y + 1.0 * v ) ) * 0.1531;\\\\n\\\\t\\\\t\\\\tsum += texture2D( tDiffuse, vec2( vUv.x, vUv.y + 2.0 * v ) ) * 0.12245;\\\\n\\\\t\\\\t\\\\tsum += texture2D( tDiffuse, vec2( vUv.x, vUv.y + 3.0 * v ) ) * 0.0918;\\\\n\\\\t\\\\t\\\\tsum += texture2D( tDiffuse, vec2( vUv.x, vUv.y + 4.0 * v ) ) * 0.051;\\\\n\\\\n\\\\t\\\\t\\\\tgl_FragColor = sum;\\\\n\\\\n\\\\t\\\\t}\\\\\\\"},bG=1/256e3;class wG{constructor(t){this._renderTargetBlur=this._createRenderTarget(t),this._camera=this._createCamera(),this._blurPlane=this._createBlurPlane(),this._horizontalBlurMaterial=new F(yG),this._horizontalBlurMaterial.depthTest=!1,this._verticalBlurMaterial=new F(xG),this._verticalBlurMaterial.depthTest=!1}setSize(t,e){this._renderTargetBlur.setSize(t,e)}_createRenderTarget(t){const e=new Q(t.x,t.y);return e.texture.generateMipmaps=!1,e}_createCamera(){const t=new ot.a(-.5,.5,.5,-.5,0,1);return t.position.z=.5,t}_createBlurPlane(){const t=new L(1,1);return new B.a(t)}applyBlur(t,e,n,i){const s=Math.max(this._renderTargetBlur.width,this._renderTargetBlur.height);this._horizontalBlurMaterial.uniforms.tDiffuse.value=t.texture,this._horizontalBlurMaterial.uniforms.h.value=n*s*bG,this._blurPlane.material=this._horizontalBlurMaterial,e.setRenderTarget(this._renderTargetBlur),e.render(this._blurPlane,this._camera),this._verticalBlurMaterial.uniforms.tDiffuse.value=this._renderTargetBlur.texture,this._verticalBlurMaterial.uniforms.v.value=i*s*bG,this._blurPlane.material=this._verticalBlurMaterial,e.setRenderTarget(t),e.render(this._blurPlane,this._camera)}}var TG;!function(t){t.ON_RENDER=\\\\\\\"On Every Render\\\\\\\",t.MANUAL=\\\\\\\"Manual\\\\\\\"}(TG||(TG={}));const AG=[TG.ON_RENDER,TG.MANUAL];class MG extends(dU(la)){constructor(){super(...arguments),this.shadow=aa.FOLDER(),this.dist=aa.FLOAT(1,{range:[0,10],rangeLocked:[!0,!1]}),this.planeSize=aa.VECTOR2([1,1]),this.shadowRes=aa.VECTOR2([256,256]),this.blur=aa.FLOAT(1,{range:[0,10],rangeLocked:[!0,!1]}),this.tblur2=aa.BOOLEAN(1),this.blur2=aa.FLOAT(1,{range:[0,10],rangeLocked:[!0,!1],visibleIf:{tblur2:1}}),this.darkness=aa.FLOAT(1),this.opacity=aa.FLOAT(1),this.showHelper=aa.BOOLEAN(0),this.updateMode=aa.INTEGER(AG.indexOf(TG.ON_RENDER),{callback:t=>{CG.PARAM_CALLBACK_update_updateMode(t)},menu:{entries:AG.map(((t,e)=>({name:t,value:e})))}}),this.update=aa.BUTTON(null,{callback:t=>{CG.PARAM_CALLBACK_updateManual(t)},visibleIf:{updateMode:AG.indexOf(TG.MANUAL)}}),this.scene=aa.FOLDER(),this.include=aa.STRING(\\\\\\\"\\\\\\\"),this.exclude=aa.STRING(\\\\\\\"\\\\\\\"),this.updateObjectsList=aa.BUTTON(null,{callback:t=>{CG.PARAM_CALLBACK_updateObjectsList(t)}}),this.printResolveObjectsList=aa.BUTTON(null,{callback:t=>{CG.PARAM_CALLBACK_printResolveObjectsList(t)}})}}const EG=new MG,SG=new d.a(256,256);class CG extends Kk{constructor(){super(...arguments),this.paramsConfig=EG,this.hierarchyController=new mU(this),this.flags=new Di(this),this._renderTarget=this._createRenderTarget(SG),this._coreRenderBlur=this._createCoreRenderBlur(SG),this._includedObjects=[],this._includedAncestors=[],this._excludedObjects=[],this.transformController=new _U(this),this._darknessUniform={value:1},this._emptyOnBeforeRender=()=>{},this._emptyRenderHook=()=>{},this._on_object_before_render_bound=this._update.bind(this),this._initialVisibilityState=new WeakMap}static type(){return\\\\\\\"contactShadow\\\\\\\"}_createRenderTarget(t){const e=new Q(t.x,t.y);return e.texture.generateMipmaps=!1,e}_createCoreRenderBlur(t){return new wG(t)}createObject(){const t=new Fn.a;this._shadowGroup=new Fn.a,t.add(this._shadowGroup),this._shadowGroup.name=\\\\\\\"shadowGroup\\\\\\\";const e=new L(1,1).rotateX(-Math.PI/2),n=e.getAttribute(\\\\\\\"uv\\\\\\\").array;for(let t of[1,3,5,7])n[t]=1-n[t];return this._planeMaterial=new lt.a({map:this._renderTarget.texture,opacity:1,transparent:!0,depthWrite:!1}),this._plane=new B.a(e,this._planeMaterial),this._plane.renderOrder=1,this._plane.matrixAutoUpdate=!1,this._shadowGroup.add(this._plane),this._createDepthCamera(this._shadowGroup),this._createMaterials(),t}initializeNode(){this.hierarchyController.initializeNode(),this.transformController.initializeNode(),this._updateShadowGroupVisibility(),this._updateHelperVisibility(),this.flags.display.onUpdate((()=>{this._updateShadowGroupVisibility(),this._updateHelperVisibility()}))}async cook(){this.transformController.update(),this._updateRenderHook(),this._updateHelperVisibility(),this._updateObjectsList(),this._planeMaterial&&(this._planeMaterial.opacity=this.pv.opacity),this._darknessUniform.value=this.pv.darkness,this._plane&&this._shadowCamera&&this._helper&&(this._plane.scale.x=this.pv.planeSize.x,this._plane.scale.z=this.pv.planeSize.y,this._plane.updateMatrix(),this._shadowCamera.left=-this.pv.planeSize.x/2,this._shadowCamera.right=this.pv.planeSize.x/2,this._shadowCamera.bottom=-this.pv.planeSize.y/2,this._shadowCamera.top=this.pv.planeSize.y/2,this._shadowCamera.far=this.pv.dist,this._shadowCamera.updateProjectionMatrix(),this._helper.update()),this._renderTarget.width==this.pv.shadowRes.x&&this._renderTarget.height==this.pv.shadowRes.y||this._planeMaterial&&(this._renderTarget=this._createRenderTarget(this.pv.shadowRes),this._coreRenderBlur=this._createCoreRenderBlur(this.pv.shadowRes),this._planeMaterial.map=this._renderTarget.texture),this.cookController.endCook()}_createDepthCamera(t){this._shadowCamera=new ot.a(-.5,.5,.5,-.5,0,1),this._shadowCamera.rotation.x=Math.PI/2,t.add(this._shadowCamera),this._helper=new NU(this._shadowCamera),this._helper.visible=!1,this._shadowCamera.add(this._helper)}_createMaterials(){this._depthMaterial=new Sn,this._depthMaterial.onBeforeCompile=t=>{t.uniforms.darkness=this._darknessUniform,t.fragmentShader=`\\\\n\\\\t\\\\t\\\\tuniform float darkness;\\\\n\\\\t\\\\t\\\\t${t.fragmentShader.replace(\\\\\\\"gl_FragColor = vec4( vec3( 1.0 - fragCoordZ ), opacity );\\\\\\\",\\\\\\\"gl_FragColor = vec4( vec3( 0.0 ), ( 1.0 - fragCoordZ ) * darkness );\\\\\\\")}\\\\n\\\\t\\\\t`},this._depthMaterial.depthTest=!1,this._depthMaterial.depthWrite=!1}_renderShadow(t,e){if(!this._helper)return;if(!this._depthMaterial)return;if(!this._shadowCamera)return;if(!this._helper)return;if(!this._plane)return;const n=this._plane.onBeforeRender,i=e.background,s=this._helper.visible;e.background=null,this._plane.onBeforeRender=this._emptyOnBeforeRender,this._helper.visible=!1,e.overrideMaterial=this._depthMaterial,this._initVisibility(e),t.setRenderTarget(this._renderTarget),t.render(e,this._shadowCamera),this._coreRenderBlur.applyBlur(this._renderTarget,t,this.pv.blur,this.pv.blur),this.pv.tblur2&&this._coreRenderBlur.applyBlur(this._renderTarget,t,this.pv.blur2,this.pv.blur2),this._restoreVisibility(e),e.overrideMaterial=null,this._helper.visible=s,t.setRenderTarget(null),e.background=i,this._plane.onBeforeRender=n}_updateShadowGroupVisibility(){this._shadowGroup&&(this.flags.display.active()?this._shadowGroup.visible=!0:this._shadowGroup.visible=!1)}_updateHelperVisibility(){this._helper&&(this.flags.display.active()&&this.pv.showHelper?this._helper.visible=!0:this._helper.visible=!1)}_updateRenderHook(){const t=AG[this.pv.updateMode];switch(t){case TG.ON_RENDER:return this._addRenderHook();case TG.MANUAL:return this._removeRenderHook()}ls.unreachable(t)}_addRenderHook(){this._plane&&this._plane.onBeforeRender!=this._on_object_before_render_bound&&(this._plane.onBeforeRender=this._on_object_before_render_bound)}_removeRenderHook(){this._plane&&this._plane.onBeforeRender!=this._emptyRenderHook&&(this._plane.onBeforeRender=this._emptyRenderHook)}_update(t,e,n,i,s,r){t&&e?this._renderShadow(t,e):console.log(\\\\\\\"no renderer or scene\\\\\\\")}_updateManual(){const t=li.renderersController.firstRenderer();if(!t)return void console.log(\\\\\\\"no renderer found\\\\\\\");const e=this.scene().threejsScene();this._renderShadow(t,e)}static PARAM_CALLBACK_update_updateMode(t){t._updateRenderHook()}static PARAM_CALLBACK_updateManual(t){t._updateManual()}static PARAM_CALLBACK_updateObjectsList(t){t._updateObjectsList()}_updateObjectsList(){\\\\\\\"\\\\\\\"!=this.pv.include?this._includedObjects=this.scene().objectsByMask(this.pv.include):this._includedObjects=[];const t=new Map;for(let e of this._includedObjects)e.traverseAncestors((e=>{t.set(e.uuid,e)}));this._includedAncestors=[],t.forEach(((t,e)=>{this._includedAncestors.push(t)})),\\\\\\\"\\\\\\\"!=this.pv.exclude?this._excludedObjects=this.scene().objectsByMask(this.pv.exclude):this._excludedObjects=[]}static PARAM_CALLBACK_printResolveObjectsList(t){t._printResolveObjectsList()}_printResolveObjectsList(){console.log(\\\\\\\"included objects:\\\\\\\"),console.log(this._includedObjects),console.log(\\\\\\\"included parents:\\\\\\\"),console.log(this._includedAncestors),console.log(\\\\\\\"excluded objects:\\\\\\\"),console.log(this._excludedObjects)}_initVisibility(t){this._includedObjects.length>0?t.traverse((t=>{this._initialVisibilityState.set(t,t.visible),t.visible=!1})):(this._storeObjectsVisibility(this._includedObjects),this._storeObjectsVisibility(this._includedAncestors),this._storeObjectsVisibility(this._excludedObjects)),this._setObjectsVisibility(this._includedObjects,!0),this._setObjectsVisibility(this._includedAncestors,!0),this._setObjectsVisibility(this._excludedObjects,!1)}_storeObjectsVisibility(t){for(let e of t)this._initialVisibilityState.set(e,e.visible)}_setObjectsVisibility(t,e){for(let n of t)n.visible=e}_restoreVisibility(t){this._includedObjects.length>0?t.traverse((t=>{const e=this._initialVisibilityState.get(t);e&&(t.visible=e)})):(this._restoreObjectsVisibility(this._includedObjects),this._restoreObjectsVisibility(this._includedAncestors),this._restoreObjectsVisibility(this._excludedObjects))}_restoreObjectsVisibility(t){for(let e of t){const t=this._initialVisibilityState.get(e);t&&(e.visible=t)}}}const NG=\\\\\\\"display\\\\\\\";class LG{constructor(t){this.node=t,this._children_uuids_dict=new Map,this._children_length=0,this._sop_group=this._create_sop_group()}_create_sop_group(){const t=new Fn.a;return t.matrixAutoUpdate=!1,t}sopGroup(){return this._sop_group}set_sop_group_name(){this._sop_group.name=`${this.node.name()}:sop_group`}displayNodeControllerCallbacks(){return{onDisplayNodeRemove:()=>{this.remove_children()},onDisplayNodeSet:()=>{setTimeout((()=>{this.request_display_node_container()}),0)},onDisplayNodeUpdate:()=>{this.request_display_node_container()}}}initializeNode(){var t;this.node.object.add(this.sopGroup()),this.node.nameController.add_post_set_fullPath_hook(this.set_sop_group_name.bind(this)),this._create_sop_group();const e=null===(t=this.node.flags)||void 0===t?void 0:t.display;e&&e.onUpdate((()=>{this._updateSopGroupHierarchy(),e.active()&&this.request_display_node_container()}))}_updateSopGroupHierarchy(){var t;if(null===(t=this.node.flags)||void 0===t?void 0:t.display){const t=this.sopGroup();this.usedInScene()?(t.visible=!0,this.node.object.add(t),t.updateMatrix()):(t.visible=!1,this.node.object.remove(t))}}usedInScene(){var t,e;const n=this.node.params.has(NG),i=this.node.params.boolean(NG),s=this.node.usedInScene(),r=(null===(e=null===(t=this.node.flags)||void 0===t?void 0:t.display)||void 0===e?void 0:e.active())||!1;return s&&r&&(!n||i)}async request_display_node_container(){this.node.scene().loadingController.loaded()&&this.usedInScene()&&await this._set_content_under_sop_group()}remove_children(){if(0==this._sop_group.children.length)return;let t;for(;t=this._sop_group.children[0];)this._sop_group.remove(t);this._children_uuids_dict.clear(),this._children_length=0}async _set_content_under_sop_group(){var t;const e=this.node.displayNodeController.displayNode();if(e&&(null===(t=e.parent())||void 0===t?void 0:t.graphNodeId())==this.node.graphNodeId()){const t=(await e.compute()).coreContent();if(t){const e=t.objects();let n=e.length!=this._children_length;if(!n)for(let t of e)this._children_uuids_dict.get(t.uuid)||(n=!0);if(n){this.remove_children();for(let t of e)this._sop_group.add(t),t.updateMatrix(),this._children_uuids_dict.set(t.uuid,!0);this._children_length=e.length}return}}this.remove_children()}}class OG extends(dU(la)){constructor(){super(...arguments),this.display=aa.BOOLEAN(1),this.renderOrder=aa.INTEGER(0,{range:[0,10],rangeLocked:[!0,!1]})}}const PG=new OG;class RG extends Kk{constructor(){super(...arguments),this.paramsConfig=PG,this.hierarchyController=new mU(this),this.transformController=new _U(this),this.flags=new Di(this),this.childrenDisplayController=new LG(this),this.displayNodeController=new Lm(this,this.childrenDisplayController.displayNodeControllerCallbacks()),this._children_controller_context=ts.SOP,this._onChildAddBound=this._onChildAdd.bind(this)}static type(){return Ng.GEO}createObject(){const t=new Fn.a;return t.matrixAutoUpdate=!1,t}initializeNode(){this.lifecycle.add_on_child_add_hook(this._onChildAddBound),this.hierarchyController.initializeNode(),this.transformController.initializeNode(),this.childrenDisplayController.initializeNode()}isDisplayNodeCooking(){if(this.flags.display.active()){const t=this.displayNodeController.displayNode();return!!t&&t.isDirty()}return!1}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}_onChildAdd(t){var e,n;this.scene().loadingController.loaded()&&1==this.children().length&&(null===(n=null===(e=t.flags)||void 0===e?void 0:e.display)||void 0===n||n.set(!0))}cook(){this.transformController.update(),this.object.visible=this.pv.display,this.object.renderOrder=this.pv.renderOrder,this.cookController.endCook()}}class IG extends(dU(la)){}const FG=new IG;class DG extends Kk{constructor(){super(...arguments),this.paramsConfig=FG,this.hierarchyController=new mU(this),this.transformController=new _U(this),this.flags=new Di(this),this._helper=new _G(1)}static type(){return\\\\\\\"null\\\\\\\"}createObject(){const t=new Fn.a;return t.matrixAutoUpdate=!1,t}initializeNode(){this.hierarchyController.initializeNode(),this.transformController.initializeNode(),this._updateHelperHierarchy(),this._helper.matrixAutoUpdate=!1,this.flags.display.onUpdate((()=>{this._updateHelperHierarchy()}))}_updateHelperHierarchy(){this.flags.display.active()?(this.object.add(this._helper),this._helper.updateMatrix()):this.object.remove(this._helper)}cook(){this.transformController.update(),this.cookController.endCook()}}const BG=new class extends la{constructor(){super(...arguments),this.center=aa.VECTOR3([0,0,0]),this.longitude=aa.FLOAT(0,{range:[0,360]}),this.latitude=aa.FLOAT(0,{range:[-180,180]}),this.depth=aa.FLOAT(1,{range:[0,10]})}},zG=\\\\\\\"_cook_main_without_inputs_when_dirty\\\\\\\",kG=new p.a(0,1,0),UG=new p.a(-1,0,0);class GG extends Kk{constructor(){super(...arguments),this.paramsConfig=BG,this.hierarchyController=new mU(this),this.flags=new Di(this),this._helper=new _G(1),this._cook_main_without_inputs_when_dirty_bound=this._cook_main_without_inputs_when_dirty.bind(this),this._centerMatrix=new A.a,this._longitudeMatrix=new A.a,this._latitudeMatrix=new A.a,this._depthMatrix=new A.a,this._fullMatrix=new A.a,this._decomposed={t:new p.a,q:new ah.a,s:new p.a}}static type(){return\\\\\\\"polarTransform\\\\\\\"}createObject(){const t=new Fn.a;return t.matrixAutoUpdate=!1,t}initializeNode(){this.hierarchyController.initializeNode(),this.dirtyController.hasHook(zG)||this.dirtyController.addPostDirtyHook(zG,this._cook_main_without_inputs_when_dirty_bound),this._updateHelperHierarchy(),this._helper.matrixAutoUpdate=!1,this.flags.display.onUpdate((()=>{this._updateHelperHierarchy()}))}_updateHelperHierarchy(){this.flags.display.active()?(this.object.add(this._helper),this._helper.updateMatrix()):this.object.remove(this._helper)}async _cook_main_without_inputs_when_dirty(){await this.cookController.cookMainWithoutInputs()}cook(){const t=this.object;this._centerMatrix.identity(),this._longitudeMatrix.identity(),this._latitudeMatrix.identity(),this._depthMatrix.identity(),this._centerMatrix.makeTranslation(this.pv.center.x,this.pv.center.y,this.pv.center.z),this._longitudeMatrix.makeRotationAxis(kG,Object(On.e)(this.pv.longitude)),this._latitudeMatrix.makeRotationAxis(UG,Object(On.e)(this.pv.latitude)),this._depthMatrix.makeTranslation(0,0,this.pv.depth),this._fullMatrix.copy(this._centerMatrix).multiply(this._longitudeMatrix).multiply(this._latitudeMatrix).multiply(this._depthMatrix),this._fullMatrix.decompose(this._decomposed.t,this._decomposed.q,this._decomposed.s),t.position.copy(this._decomposed.t),t.quaternion.copy(this._decomposed.q),t.scale.copy(this._decomposed.s),t.updateMatrix(),this.cookController.endCook()}}class VG{constructor(t){this._scene=t,this._data={}}data(t){this._scene.nodesController.reset_node_context_signatures();const e=JG.dispatch_node(this._scene.root()),n=e.data(),i=e.ui_data();return this._data={properties:{frame:this._scene.frame()||El.START_FRAME,maxFrame:this._scene.maxFrame(),maxFrameLocked:this._scene.timeController.maxFrameLocked(),realtimeState:this._scene.timeController.realtimeState(),mainCameraNodePath:this._scene.camerasController.mainCameraNodePath(),versions:t},root:n,ui:i},this._data}static sanitize_string(t){return t=t.replace(/'/g,\\\\\\\"'\\\\\\\"),t=os.escapeLineBreaks(t)}}class HG{constructor(t){this._node=t}data(t={}){var e,n,i,s,r,o,a;this.is_root()||this._node.scene().nodesController.register_node_context_signature(this._node),this._data={type:this._node.type()};const l=this.nodes_data(t);Object.keys(l).length>0&&(this._data.nodes=l);const c=this.params_data();if(Object.keys(c).length>0&&(this._data.params=c),!this.is_root()){this._node.io.inputs.maxInputsCountOverriden()&&(this._data.maxInputsCount=this._node.io.inputs.maxInputsCount());const t=this.inputs_data();t.length>0&&(this._data.inputs=t);const e=this.connection_points_data();e&&(this._data.connection_points=e)}if(this._node.flags){const t={};(this._node.flags.hasBypass()||this._node.flags.hasDisplay()||this._node.flags.hasOptimize())&&(this._node.flags.hasBypass()&&(null===(e=this._node.flags.bypass)||void 0===e?void 0:e.active())&&(t.bypass=this._node.flags.bypass.active()),this._node.flags.hasDisplay()&&(!(null===(n=this._node.flags.display)||void 0===n?void 0:n.active())&&(null===(i=this._node.parent())||void 0===i?void 0:i.displayNodeController)||(t.display=null===(s=this._node.flags.display)||void 0===s?void 0:s.active())),this._node.flags.hasOptimize()&&(null===(r=this._node.flags.optimize)||void 0===r?void 0:r.active())&&(t.optimize=null===(o=this._node.flags.optimize)||void 0===o?void 0:o.active())),Object.keys(t).length>0&&(this._data.flags=t)}if(this._node.childrenAllowed()){const t=null===(a=this._node.childrenController)||void 0===a?void 0:a.selection;if(t&&this._node.children().length>0){const e=[],n={};for(let e of t.nodes())n[e.graphNodeId()]=!0;for(let t of this._node.children())t.graphNodeId()in n&&e.push(t);const i=e.map((t=>t.name()));i.length>0&&(this._data.selection=i)}}if(this._node.io.inputs.overrideClonedStateAllowed()){const t=this._node.io.inputs.clonedStateOverriden();t&&(this._data.cloned_state_overriden=t)}if(this._node.persisted_config){const t=this._node.persisted_config.toJSON();t&&(this._data.persisted_config=t)}return this.add_custom(),this._data}ui_data(t={}){const e=this.ui_data_without_children(),n=this._node.children();return n.length>0&&(e.nodes={},n.forEach((n=>{const i=JG.dispatch_node(n);e.nodes[n.name()]=i.ui_data(t)}))),e}ui_data_without_children(){const t={};if(!this.is_root()){const e=this._node.uiData;t.pos=e.position().toArray();const n=e.comment();n&&(t.comment=VG.sanitize_string(n))}return t}is_root(){return null===this._node.parent()&&this._node.graphNodeId()==this._node.root().graphNodeId()}inputs_data(){const t=[];return this._node.io.inputs.inputs().forEach(((e,n)=>{var i;if(e){const s=this._node.io.connections.inputConnection(n);if(this._node.io.inputs.hasNamedInputs()){const r=s.output_index,o=null===(i=e.io.outputs.namedOutputConnectionPoints()[r])||void 0===i?void 0:i.name();o&&(t[n]={index:n,node:e.name(),output:o})}else t[n]=e.name()}})),t}connection_points_data(){if(this._node.io.has_connection_points_controller&&this._node.io.connection_points.initialized()&&(this._node.io.inputs.hasNamedInputs()||this._node.io.outputs.hasNamedOutputs())){const t={};if(this._node.io.inputs.hasNamedInputs()){t.in=[];for(let e of this._node.io.inputs.namedInputConnectionPoints())e&&t.in.push(e.toJSON())}if(this._node.io.outputs.hasNamedOutputs()){t.out=[];for(let e of this._node.io.outputs.namedOutputConnectionPoints())e&&t.out.push(e.toJSON())}return t}}params_data(){const t={};for(let e of this._node.params.names){const n=this._node.params.get(e);if(n&&!n.parent_param){const e=JG.dispatch_param(n);if(e.required()){const i=e.data();t[n.name()]=i}}}return t}nodes_data(t={}){const e={};for(let n of this._node.children()){const i=JG.dispatch_node(n);e[n.name()]=i.data(t)}return e}add_custom(){}}class jG{constructor(t){this._param=t,this._complex_data={}}required(){const t=this._param.options.isSpare()&&!this._param.parent_param,e=!this._param.isDefault();return t||e||this._param.options.hasOptionsOverridden()}data(){if(this._param.parent_param)throw console.warn(\\\\\\\"no component should be saved\\\\\\\"),\\\\\\\"no component should be saved\\\\\\\";return this._require_data_complex()?this._data_complex():this._data_simple()}_data_simple(){return this._param.rawInputSerialized()}_data_complex(){if(this._complex_data={},this._param.options.isSpare()&&!this._param.parent_param&&(this._complex_data.type=this._param.type(),this._complex_data.default_value=this._param.defaultValueSerialized(),this._complex_data.options=this._param.options.current()),this._param.isDefault()||(this._complex_data.raw_input=this._param.rawInputSerialized()),this._param.options.hasOptionsOverridden()){const t={},e=this._param.options.overriddenOptions();for(let n of Object.keys(e)){const i=e[n];m.isString(i)||m.isNumber(i)?t[n]=i:t[n]=JSON.stringify(i)}this._complex_data.overriden_options=t}return this._complex_data}_require_data_complex(){return!!this._param.options.isSpare()||!!this._param.options.hasOptionsOverridden()}add_main(){}}class WG extends jG{add_main(){if(!this._require_data_complex())return this._param.rawInputSerialized();this._complex_data.raw_input=this._param.rawInputSerialized()}}class qG extends jG{add_main(){let t=this._param.rawInput();if(t=VG.sanitize_string(t),!this._require_data_complex())return t;this._complex_data.raw_input=t}}class XG extends jG{add_main(){let t=this._param.rawInput();if(t=VG.sanitize_string(t),!this._require_data_complex())return t;this._complex_data.raw_input=t}}class YG extends jG{add_main(){if(!this._require_data_complex())return this._param.rawInputSerialized();this._complex_data.raw_input=this._param.rawInputSerialized()}}class $G extends HG{nodes_data(t={}){return t.showPolyNodesData?super.nodes_data(t):{}}ui_data(t={}){return t.showPolyNodesData?super.ui_data(t):this.ui_data_without_children()}}class JG{static dispatch_node(t){return t.polyNodeController?new $G(t):new HG(t)}static dispatch_param(t){return t instanceof io?new WG(t):t instanceof _o?new qG(t):t instanceof wo?new XG(t):t instanceof bo?new YG(t):new jG(t)}}class ZG{constructor(){this._objects=[],this._objects_with_geo=[],this.touch()}timestamp(){return this._timestamp}touch(){const t=li.performance.performanceManager();this._timestamp=t.now(),this.reset()}reset(){this._bounding_box=void 0,this._core_geometries=void 0,this._core_objects=void 0}clone(){const t=new ZG;if(this._objects){const e=[];for(let t of this._objects)e.push(yr.clone(t));t.setObjects(e)}return t}setObjects(t){this._objects=t,this._objects_with_geo=t.filter((t=>null!=t.geometry)),this.touch()}objects(){return this._objects}objectsWithGeo(){return this._objects_with_geo}coreObjects(){return this._core_objects=this._core_objects||this._create_core_objects()}_create_core_objects(){return this._objects?this._objects.map(((t,e)=>new yr(t,e))):[]}objectsData(){return this._objects?this._objects.map((t=>this._objectData(t))):[]}_objectData(t){let e=0;return t.geometry&&(e=_r.pointsCount(t.geometry)),{type:Ls(t.constructor),name:t.name,children_count:t.children.length,points_count:e}}geometries(){const t=[];for(let e of this.coreObjects()){const n=e.object().geometry;n&&t.push(n)}return t}coreGeometries(){return this._core_geometries=this._core_geometries||this._createCoreGeometries()}_createCoreGeometries(){const t=[];for(let e of this.geometries())t.push(new _r(e));return t}static geometryFromObject(t){return t.isMesh||t.isLine||t.isPoints?t.geometry:null}faces(){const t=[];for(let e of this.objectsWithGeo())if(e.geometry){const n=new _r(e.geometry).faces();for(let i of n)i.applyMatrix4(e.matrix),t.push(i)}return t}points(){return this.coreGeometries().map((t=>t.points())).flat()}pointsCount(){return f.sum(this.coreGeometries().map((t=>t.pointsCount())))}totalPointsCount(){if(this._objects){let t=0;for(let e of this._objects)e.traverse((e=>{const n=e.geometry;n&&(t+=_r.pointsCount(n))}));return t}return 0}pointsFromGroup(t){if(t){const e=os.indices(t),n=this.points();return f.compact(e.map((t=>n[t])))}return this.points()}static _fromObjects(t){const e=new ZG;return e.setObjects(t),e}objectsFromGroup(t){return this.coreObjectsFromGroup(t).map((t=>t.object()))}coreObjectsFromGroup(t){if(\\\\\\\"\\\\\\\"!==(t=t.trim())){const e=parseInt(t);return m.isNaN(e)?this.coreObjects().filter((e=>os.matchMask(t,e.name()))):f.compact([this.coreObjects()[e]])}return this.coreObjects()}boundingBox(){return this._bounding_box=this._bounding_box||this._compute_bounding_box()}center(){const t=new p.a;return this.boundingBox().getCenter(t),t}size(){const t=new p.a;return this.boundingBox().getSize(t),t}_compute_bounding_box(){let t;if(this._objects)for(let e of this._objects){const n=e.geometry;n&&(n.computeBoundingBox(),t?t.expandByObject(e):n.boundingBox&&(t=n.boundingBox.clone()))}return t=t||new Ay.a(new p.a(-1,-1,-1),new p.a(1,1,1)),t}computeVertexNormals(){for(let t of this.coreObjects())t.computeVertexNormals()}hasAttrib(t){let e;return null!=(e=this.coreGeometries()[0])&&e.hasAttrib(t)}attribType(t){const e=this.coreGeometries()[0];return null!=e?e.attribType(t):null}objectAttribType(t){const e=this.coreObjects()[0];return null!=e?e.attribType(t):null}renameAttrib(t,e,n){switch(n){case Hs.ATTRIB_CLASS.VERTEX:if(this.hasAttrib(t)&&this._objects)for(let n of this._objects)n.traverse((n=>{const i=ZG.geometryFromObject(n);if(i){new _r(i).renameAttrib(t,e)}}));break;case Hs.ATTRIB_CLASS.OBJECT:if(this.hasAttrib(t)&&this._objects)for(let n of this._objects)n.traverse((n=>{new yr(n,0).renameAttrib(t,e)}))}}attribNames(){let t;return null!=(t=this.coreGeometries()[0])?t.attribNames():[]}objectAttribNames(){let t;return null!=(t=this.coreObjects()[0])?t.attribNames():[]}attribNamesMatchingMask(t){const e=os.attribNames(t),n=[];for(let t of this.attribNames())for(let i of e)if(os.matchMask(t,i))n.push(t);else{t==qs.remapName(i)&&n.push(t)}return f.uniq(n)}attribSizes(){let t;return null!=(t=this.coreGeometries()[0])?t.attribSizes():{}}objectAttribSizes(){let t;return null!=(t=this.coreObjects()[0])?t.attribSizes():{}}attribSize(t){let e;return null!=(e=this.coreGeometries()[0])?e.attribSize(t):0}addNumericVertexAttrib(t,e,n){null==n&&(n=qs.default_value(e));for(let i of this.coreGeometries())i.addNumericAttrib(t,e,n)}static clone(t){const e=new Fn.a;return t.children.forEach((t=>{const n=yr.clone(t);e.add(n)})),e}}class QG extends Fl{static context(){return ts.SOP}cook(t,e){}createCoreGroupFromObjects(t){const e=new ZG;return e.setObjects(t),e}createCoreGroupFromGeometry(t,e=Cs.MESH){const n=QG.createObject(t,e);return this.createCoreGroupFromObjects([n])}createObject(t,e,n){return QG.createObject(t,e,n)}static createObject(t,e,n){this.createIndexIfNone(t);const i=new(0,Ns[e])(t,n=n||Hs.MATERIALS[e].clone());return i.castShadow=!0,i.receiveShadow=!0,i.frustumCulled=!1,i.matrixAutoUpdate=!1,i}createIndexIfNone(t){QG.createIndexIfNone(t)}static createIndexIfNone(t){dr.createIndexIfNone(t)}}var KG;!function(t){t.FROM_SET_CORE_GROUP=\\\\\\\"from set_core_group\\\\\\\",t.FROM_SET_GROUP=\\\\\\\"from set_group\\\\\\\",t.FROM_SET_OBJECTS=\\\\\\\"from set_objects\\\\\\\",t.FROM_SET_OBJECT=\\\\\\\"from set_object\\\\\\\",t.FROM_SET_GEOMETRIES=\\\\\\\"from set_geometries\\\\\\\",t.FROM_SET_GEOMETRY=\\\\\\\"from set_geometry\\\\\\\"}(KG||(KG={}));const tV=\\\\\\\"input geometry\\\\\\\",eV=[tV,tV,tV,tV];class nV extends sa{constructor(){super(...arguments),this.flags=new Ui(this)}static context(){return ts.SOP}static displayedInputNames(){return eV}initializeBaseNode(){this.flags.display.set(!1),this.flags.display.onUpdate((()=>{if(this.flags.display.active()){const t=this.parent();t&&t.displayNodeController&&t.displayNodeController.setDisplayNode(this)}})),this.io.outputs.setHasOneOutput()}setCoreGroup(t){this._setContainer(t,KG.FROM_SET_CORE_GROUP)}setObject(t){this._setContainerObjects([t],KG.FROM_SET_OBJECT)}setObjects(t){this._setContainerObjects(t,KG.FROM_SET_OBJECTS)}setGeometry(t,e=Cs.MESH){const n=this.createObject(t,e);this._setContainerObjects([n],KG.FROM_SET_GEOMETRY)}setGeometries(t,e=Cs.MESH){const n=[];let i;for(let s of t)i=this.createObject(s,e),n.push(i);this._setContainerObjects(n,KG.FROM_SET_GEOMETRIES)}_setContainerObjects(t,e){const n=this.containerController.container().coreContent()||new ZG;n.setObjects(t),n.touch(),this._setContainer(n)}static createObject(t,e,n){return QG.createObject(t,e,n)}createObject(t,e,n){return nV.createObject(t,e,n)}static createIndexIfNone(t){QG.createIndexIfNone(t)}_createIndexIfNone(t){nV.createIndexIfNone(t)}}const iV=new class extends la{};class sV extends nV{constructor(){super(...arguments),this.paramsConfig=iV}static type(){return ns.OUTPUT}initializeNode(){this.io.inputs.setCount(1),this.io.outputs.setHasNoOutput(),this.io.inputs.initInputsClonedState(Ki.NEVER)}cook(t){this.setCoreGroup(t[0])}}class rV extends nV{constructor(){super(...arguments),this.childrenDisplayController=new aV(this),this.displayNodeController=new Lm(this,this.childrenDisplayController.displayNodeControllerCallbacks()),this._children_controller_context=ts.SOP}initializeBaseNode(){super.initializeBaseNode(),this.childrenDisplayController.initializeNode(),this.cookController.disallowInputsEvaluation()}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}async cook(t){const e=this.childrenDisplayController.output_node();if(e){const t=(await e.compute()).coreContent();t?this.setCoreGroup(t):e.states.error.active()?this.states.error.set(e.states.error.message()):this.setObjects([])}else this.states.error.set(\\\\\\\"no output node found inside subnet\\\\\\\")}}const oV={dependsOnDisplayNode:!0};class aV{constructor(t,e=oV){this.node=t,this.options=e,this._output_node_needs_update=!0}dispose(){var t;null===(t=this._graph_node)||void 0===t||t.dispose()}displayNodeControllerCallbacks(){return{onDisplayNodeRemove:()=>{this.node.setDirty()},onDisplayNodeSet:()=>{this.node.setDirty()},onDisplayNodeUpdate:()=>{this.node.setDirty()}}}output_node(){return this._output_node_needs_update&&this._update_output_node(),this._output_node}initializeNode(){var t;const e=null===(t=this.node.flags)||void 0===t?void 0:t.display;e&&e.onUpdate((()=>{e.active()&&this.node.setDirty()})),this.node.lifecycle.add_on_child_add_hook((()=>{this._output_node_needs_update=!0,this.node.setDirty()})),this.node.lifecycle.add_on_child_remove_hook((()=>{this._output_node_needs_update=!0,this.node.setDirty()}))}_update_output_node(){const t=this.node.nodesByType(sV.type())[0];null!=this._output_node&&null!=t&&this._output_node.graphNodeId()==t.graphNodeId()||(this._graph_node&&this._output_node&&this._graph_node.removeGraphInput(this._output_node),this._output_node=t,this._output_node&&this.options.dependsOnDisplayNode&&(this._graph_node=this._graph_node||this._create_graph_node(),this._graph_node.addGraphInput(this._output_node)))}_create_graph_node(){const t=new Mi(this.node.scene(),\\\\\\\"subnetChildrenDisplayController\\\\\\\");return t.addPostDirtyHook(\\\\\\\"subnetChildrenDisplayController\\\\\\\",(()=>{this.node.setDirty()})),t}}function lV(t,e){const n=new class extends la{constructor(){super(...arguments),this.template=aa.OPERATOR_PATH(\\\\\\\"../template\\\\\\\"),this.debug=aa.BUTTON(null,{callback:t=>{i.PARAM_CALLBACK_debug(t)}})}};class i extends rV{constructor(){super(...arguments),this.paramsConfig=n,this.polyNodeController=new uV(this,e)}static type(){return t}static PARAM_CALLBACK_debug(t){t._debug()}_debug(){this.polyNodeController.debug(this.p.template)}}return i}const cV=lV(\\\\\\\"poly\\\\\\\",{nodeContext:ts.SOP,inputs:[0,4]});class hV extends cV{}class uV{constructor(t,e){this.node=t,this._definition=e}initializeNode(){this.init_inputs(),this.node.params.onParamsCreated(\\\\\\\"poly_node_init\\\\\\\",(()=>{this.create_params_from_definition()})),this.node.lifecycle.add_on_create_hook((()=>{this.create_params_from_definition(),this.createChildNodesFromDefinition()}))}init_inputs(){const t=this._definition.inputs;t&&this.node.io.inputs.setCount(t[0],t[1])}create_params_from_definition(){const t=this._definition.params;if(t){for(let e of t)e.options=e.options||{},e.options.spare=!0;this.node.params.updateParams({toAdd:t})}}createChildNodesFromDefinition(){const t=this._definition.nodes;if(!t)return;const e=this.node.scene().loadingController.loaded();e&&this.node.scene().loadingController.markAsLoading();const n=new Xl({}),i=new kl(this.node);i.create_nodes(n,t);const s=this._definition.ui;s&&i.process_nodes_ui_data(n,s),e&&this.node.scene().loadingController.markAsLoaded()}debug(t){const e=t.found_node();if(e){const t=JG.dispatch_node(e),n=t.data({showPolyNodesData:!0}),i=t.ui_data({showPolyNodesData:!0}),s={nodeContext:e.context(),inputs:[0,0],params:[],nodes:n.nodes,ui:i.nodes};console.log(JSON.stringify(s))}}static createNodeClass(t,e,n){switch(e){case ts.SOP:return lV(t,n);case ts.OBJ:return dV(t,n)}}}function dV(t,e){const n=new class extends la{constructor(){super(...arguments),this.display=aa.BOOLEAN(1),this.template=aa.OPERATOR_PATH(\\\\\\\"../template\\\\\\\"),this.debug=aa.BUTTON(null,{callback:t=>{i.PARAM_CALLBACK_debug(t)}})}};class i extends Kk{constructor(){super(...arguments),this.paramsConfig=n,this.hierarchyController=new mU(this),this.flags=new Di(this),this.childrenDisplayController=new LG(this),this.displayNodeController=new Lm(this,this.childrenDisplayController.displayNodeControllerCallbacks()),this._children_controller_context=ts.SOP,this.polyNodeController=new uV(this,e)}static type(){return t}createObject(){const t=new Fn.a;return t.matrixAutoUpdate=!1,t}initializeNode(){this.hierarchyController.initializeNode(),this.childrenDisplayController.initializeNode()}isDisplayNodeCooking(){if(this.flags.display.active()){const t=this.displayNodeController.displayNode();return!!t&&t.isDirty()}return!1}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}cook(){this.object.visible=this.pv.display,this.cookController.endCook()}static PARAM_CALLBACK_debug(t){t._debug()}_debug(){this.polyNodeController.debug(this.p.template)}}return i}const pV=dV(\\\\\\\"poly\\\\\\\",{nodeContext:ts.OBJ});class _V extends pV{}class mV extends K.a{constructor(t){super(),this.type=\\\\\\\"Audio\\\\\\\",this.listener=t,this.context=t.context,this.gain=this.context.createGain(),this.gain.connect(t.getInput()),this.autoplay=!1,this.buffer=null,this.detune=0,this.loop=!1,this.loopStart=0,this.loopEnd=0,this.offset=0,this.duration=void 0,this.playbackRate=1,this.isPlaying=!1,this.hasPlaybackControl=!0,this.source=null,this.sourceType=\\\\\\\"empty\\\\\\\",this._startedAt=0,this._progress=0,this._connected=!1,this.filters=[]}getOutput(){return this.gain}setNodeSource(t){return this.hasPlaybackControl=!1,this.sourceType=\\\\\\\"audioNode\\\\\\\",this.source=t,this.connect(),this}setMediaElementSource(t){return this.hasPlaybackControl=!1,this.sourceType=\\\\\\\"mediaNode\\\\\\\",this.source=this.context.createMediaElementSource(t),this.connect(),this}setMediaStreamSource(t){return this.hasPlaybackControl=!1,this.sourceType=\\\\\\\"mediaStreamNode\\\\\\\",this.source=this.context.createMediaStreamSource(t),this.connect(),this}setBuffer(t){return this.buffer=t,this.sourceType=\\\\\\\"buffer\\\\\\\",this.autoplay&&this.play(),this}play(t=0){if(!0===this.isPlaying)return void console.warn(\\\\\\\"THREE.Audio: Audio is already playing.\\\\\\\");if(!1===this.hasPlaybackControl)return void console.warn(\\\\\\\"THREE.Audio: this Audio has no playback control.\\\\\\\");this._startedAt=this.context.currentTime+t;const e=this.context.createBufferSource();return e.buffer=this.buffer,e.loop=this.loop,e.loopStart=this.loopStart,e.loopEnd=this.loopEnd,e.onended=this.onEnded.bind(this),e.start(this._startedAt,this._progress+this.offset,this.duration),this.isPlaying=!0,this.source=e,this.setDetune(this.detune),this.setPlaybackRate(this.playbackRate),this.connect()}pause(){if(!1!==this.hasPlaybackControl)return!0===this.isPlaying&&(this._progress+=Math.max(this.context.currentTime-this._startedAt,0)*this.playbackRate,!0===this.loop&&(this._progress=this._progress%(this.duration||this.buffer.duration)),this.source.stop(),this.source.onended=null,this.isPlaying=!1),this;console.warn(\\\\\\\"THREE.Audio: this Audio has no playback control.\\\\\\\")}stop(){if(!1!==this.hasPlaybackControl)return this._progress=0,this.source.stop(),this.source.onended=null,this.isPlaying=!1,this;console.warn(\\\\\\\"THREE.Audio: this Audio has no playback control.\\\\\\\")}connect(){if(this.filters.length>0){this.source.connect(this.filters[0]);for(let t=1,e=this.filters.length;t0){this.source.disconnect(this.filters[0]);for(let t=1,e=this.filters.length;t{t.load(e,(function(t){n(t)}))}))}}var MV;!function(t){t.LINEAR=\\\\\\\"linear\\\\\\\",t.INVERSE=\\\\\\\"inverse\\\\\\\",t.EXPONENTIAL=\\\\\\\"exponential\\\\\\\"}(MV||(MV={}));const EV=[MV.LINEAR,MV.INVERSE,MV.EXPONENTIAL];class SV extends(dU(la)){constructor(){super(...arguments),this.audio=aa.FOLDER(),this.listener=aa.NODE_PATH(\\\\\\\"\\\\\\\",{nodeSelection:{context:ts.OBJ,types:[Ng.AUDIO_LISTENER]}}),this.url=aa.STRING(\\\\\\\"\\\\\\\",{fileBrowse:{type:[Or.AUDIO]}}),this.volume=aa.FLOAT(1),this.loop=aa.BOOLEAN(1,{separatorBefore:!0}),this.loopStart=aa.FLOAT(0,{visibleIf:{loop:1}}),this.loopEnd=aa.FLOAT(0,{visibleIf:{loop:1},separatorAfter:!0}),this.refDistance=aa.FLOAT(10,{range:[0,10],rangeLocked:[!0,!1]}),this.rolloffFactor=aa.FLOAT(10,{range:[0,10],rangeLocked:[!0,!1]}),this.maxDistance=aa.FLOAT(100,{range:[.001,100],rangeLocked:[!0,!1]}),this.distanceModel=aa.INTEGER(EV.indexOf(MV.LINEAR),{menu:{entries:EV.map(((t,e)=>({name:t,value:e})))}}),this.coneInnerAngle=aa.FLOAT(180,{range:[0,360],rangeLocked:[!0,!0]}),this.coneOuterAngle=aa.FLOAT(230,{range:[0,360],rangeLocked:[!0,!0]}),this.coneOuterGain=aa.FLOAT(.1,{range:[0,1],rangeLocked:[!0,!0]}),this.autoplay=aa.BOOLEAN(1),this.showHelper=aa.BOOLEAN(0),this.play=aa.BUTTON(null,{callback:t=>{NV.PARAM_CALLBACK_play(t)}}),this.pause=aa.BUTTON(null,{callback:t=>{NV.PARAM_CALLBACK_pause(t)}})}}const CV=new SV;class NV extends Kk{constructor(){super(...arguments),this.paramsConfig=CV,this.hierarchyController=new mU(this),this.transformController=new _U(this),this.flags=new Di(this)}static type(){return Ng.POSITIONAL_AUDIO}createObject(){const t=new Fn.a;return t.matrixAutoUpdate=!1,t}initializeNode(){this.hierarchyController.initializeNode(),this.transformController.initializeNode(),this._updateHelperHierarchy(),this.flags.display.onUpdate((()=>{this._updateHelperHierarchy()}))}_updateHelperHierarchy(){this._helper&&(this.flags.display.active()?this.object.add(this._helper):this.object.remove(this._helper))}cook(){this.transformController.update(),this._updatePositionalAudio(),this.cookController.endCook()}async _updatePositionalAudio(){this.p.listener.isDirty()&&await this.p.listener.compute();const t=this.pv.url;if(this._loadedUrl!=t)try{await this._createPositionalAudio()}catch(t){this.states.error.set(`error when creating audio: ${t}`)}this._positionalAudio&&(this._positionalAudio.setVolume(this.pv.volume),this._positionalAudio.setLoop(this.pv.loop),this._positionalAudio.setLoopStart(this.pv.loopStart),this._positionalAudio.setLoopEnd(this.pv.loopEnd),this._positionalAudio.setRefDistance(this.pv.refDistance),this._positionalAudio.setRolloffFactor(this.pv.rolloffFactor),this._positionalAudio.setMaxDistance(this.pv.maxDistance),this._positionalAudio.setDistanceModel(EV[this.pv.distanceModel]),this._positionalAudio.setDirectionalCone(this.pv.coneInnerAngle,this.pv.coneOuterAngle,this.pv.coneOuterGain),this.pv.showHelper&&(this._helper=this._helper||this._createHelper(this._positionalAudio),this.object.add(this._helper)),this._helper&&(this._helper.visible=this.pv.showHelper,this._helper.update()))}_createHelper(t){const e=new bV(t);return e.matrixAutoUpdate=!1,e}async _createPositionalAudio(){const t=this.pv.listener.nodeWithContext(ts.OBJ);if(!t)return;const e=t.object;this._positionalAudio&&(this._positionalAudio.source&&(this._positionalAudio.stop(),this._positionalAudio.disconnect()),this.object.remove(this._positionalAudio),this._positionalAudio=void 0),this._helper&&(this._helper.dispose(),this._helper=void 0),this._positionalAudio=new xV(e),this._positionalAudio.matrixAutoUpdate=!1;const n=new AV(this.pv.url,this.scene(),this),i=await n.load();this._loadedUrl=this.pv.url,this._positionalAudio.autoplay=this.pv.autoplay,this._positionalAudio.setBuffer(i),this.object.add(this._positionalAudio)}isPlaying(){return!!this._positionalAudio&&this._positionalAudio.isPlaying}static PARAM_CALLBACK_play(t){t.PARAM_CALLBACK_play()}static PARAM_CALLBACK_pause(t){t.PARAM_CALLBACK_pause()}PARAM_CALLBACK_play(){this._positionalAudio&&(this.isPlaying()||this._positionalAudio.play())}PARAM_CALLBACK_pause(){this._positionalAudio&&this.isPlaying()&&this._positionalAudio.pause()}}var LV;!function(t){t.ON_RENDER=\\\\\\\"On Every Render\\\\\\\",t.MANUAL=\\\\\\\"Manual\\\\\\\"}(LV||(LV={}));const OV=[LV.ON_RENDER,LV.MANUAL];const PV=new class extends la{constructor(){super(...arguments),this.object=aa.OPERATOR_PATH(\\\\\\\"\\\\\\\",{nodeSelection:{context:ts.OBJ},dependentOnFoundNode:!1,computeOnDirty:!0,callback:t=>{RV.PARAM_CALLBACK_update_resolved_object(t)}}),this.pointIndex=aa.INTEGER(0,{range:[0,100]}),this.updateMode=aa.INTEGER(OV.indexOf(LV.ON_RENDER),{callback:t=>{RV.PARAM_CALLBACK_update_updateMode(t)},menu:{entries:OV.map(((t,e)=>({name:t,value:e})))}}),this.update=aa.BUTTON(null,{callback:t=>{RV.PARAM_CALLBACK_update(t)},visibleIf:{updateMode:OV.indexOf(LV.MANUAL)}})}};class RV extends Kk{constructor(){super(...arguments),this.paramsConfig=PV,this.hierarchyController=new mU(this),this.flags=new Di(this),this._helper=new _G(1),this._found_point_post=new p.a,this._on_object_before_render_bound=this._update.bind(this)}static type(){return\\\\\\\"rivet\\\\\\\"}createObject(){const t=new B.a;return t.matrixAutoUpdate=!1,t}initializeNode(){this.hierarchyController.initializeNode(),this.addPostDirtyHook(\\\\\\\"rivet_on_dirty\\\\\\\",(()=>{this.cookController.cookMainWithoutInputs()})),this._updateHelperHierarchy(),this.flags.display.onUpdate((()=>{this._updateHelperHierarchy()}))}_updateHelperHierarchy(){this.flags.display.active()?this.object.add(this._helper):this.object.remove(this._helper)}async cook(){await this._update_resolved_object(),this._update_render_hook(),this.cookController.endCook()}_update_render_hook(){const t=OV[this.pv.updateMode];switch(t){case LV.ON_RENDER:return this._add_render_hook();case LV.MANUAL:return this._remove_render_hook()}ls.unreachable(t)}_add_render_hook(){this.object.onBeforeRender=this._on_object_before_render_bound,this.object.frustumCulled=!1}_remove_render_hook(){this.object.onBeforeRender=()=>{}}_update(t,e,n,i,s,r){const o=this._resolved_object();if(o){const t=o.geometry;if(t){const e=t.attributes.position;if(e){const t=e.array;this._found_point_post.fromArray(t,3*this.pv.pointIndex),o.updateWorldMatrix(!0,!1),o.localToWorld(this._found_point_post),this.object.matrix.makeTranslation(this._found_point_post.x,this._found_point_post.y,this._found_point_post.z)}}}}static PARAM_CALLBACK_update_resolved_object(t){t._update_resolved_object()}async _update_resolved_object(){this.p.object.isDirty()&&await this.p.object.compute();const t=this.p.object.found_node();if(t)if(t.context()==ts.OBJ&&t.type()==RG.type()){const e=t;this._resolved_sop_group=e.childrenDisplayController.sopGroup()}else this.states.error.set(\\\\\\\"found node is not a geo node\\\\\\\")}_resolved_object(){if(!this._resolved_sop_group)return;const t=this._resolved_sop_group.children[0];return t||void 0}static PARAM_CALLBACK_update_updateMode(t){t._update_render_hook()}static PARAM_CALLBACK_update(t){t._update()}}class IV extends(Na(Ea(ya(fa(ua(la)))))){}const FV=new IV;class DV extends Kk{constructor(){super(...arguments),this.paramsConfig=FV,this.hierarchyController=new mU(this),this.SceneAutoUpdateController=new da(this),this.sceneBackgroundController=new ga(this),this.SceneEnvController=new xa(this),this.sceneFogController=new Sa(this),this.sceneMaterialOverrideController=new La(this)}static type(){return\\\\\\\"scene\\\\\\\"}createObject(){const t=new gs;return t.matrixAutoUpdate=!1,t}initializeNode(){this.hierarchyController.initializeNode()}cook(){this.SceneAutoUpdateController.update(),this.sceneBackgroundController.update(),this.SceneEnvController.update(),this.sceneFogController.update(),this.sceneMaterialOverrideController.update(),this.cookController.endCook()}}class BV{constructor(t,e,n){this._camera_node_id=t,this._controls_node=e,this._controls=n,this._updateRequired=this._controls_node.updateRequired()}updateRequired(){return this._updateRequired}get camera_node_id(){return this._camera_node_id}get controls(){return this._controls}get controls_node(){return this._controls_node}is_equal(t){return t.camera_node_id==this._camera_node_id&&t.controls_node.graphNodeId()==this._controls_node.graphNodeId()}}const zV=\\\\\\\"controls\\\\\\\";class kV{constructor(t){this.node=t,this._applied_controls_by_element_id=new Map,this._controls_node=null}controls_param(){return this.node.params.has(zV)?this.node.params.get(zV):null}async controls_node(){const t=this.node.p.controls,e=t.rawInput();if(e&&\\\\\\\"\\\\\\\"!=e){t.isDirty()&&await t.compute();const e=t.value.node();if(e){if(_s.includes(e.type()))return e;this.node.states.error.set(\\\\\\\"found node is not of a camera control type\\\\\\\")}else this.node.states.error.set(\\\\\\\"no node has been found\\\\\\\")}return null}async update_controls(){const t=await this.controls_node();t&&this._controls_node!=t&&this._dispose_control_refs(),this._controls_node=t}async apply_controls(t){const e=t.canvas();if(!e)return;const n=await this.controls_node();if(n){this._controlsEndEventName=n.endEventName();const i=n.controls_id();let s=!1,r=this._applied_controls_by_element_id.get(e.id);if(r&&r.get(i)&&(s=!0),!s){r=new Map,this._applied_controls_by_element_id.set(e.id,r),r.set(i,n);const s=await n.apply_controls(this.node.object,t);if(!s)return;const o=new BV(this.node.graphNodeId(),n,s);return this.set_controls_events(s),o}}}_dispose_control_refs(){this._applied_controls_by_element_id.forEach(((t,e)=>{this._dispose_controls_for_element_id(e)})),this._applied_controls_by_element_id.clear(),this._controlsEndEventName=void 0}_dispose_controls_for_element_id(t){const e=this._applied_controls_by_element_id.get(t);e&&e.forEach(((e,n)=>{e.disposeControlsForHtmlElementId(t)})),this._applied_controls_by_element_id.delete(t)}async dispose_controls(t){this._dispose_controls_for_element_id(t.id)}set_controls_events(t){const e=OH[this.node.pv.updateFromControlsMode];switch(e){case LH.ON_END:return this._set_controls_events_to_update_on_end(t);case LH.ALWAYS:return this._set_controls_events_to_update_always(t);case LH.NEVER:return this._reset(t)}ls.unreachable(e)}_reset(t){this.controls_change_listener&&(t.removeEventListener(\\\\\\\"change\\\\\\\",this.controls_change_listener),this.controls_change_listener=void 0),this.controls_end_listener&&this._controlsEndEventName&&(t.removeEventListener(this._controlsEndEventName,this.controls_end_listener),this.controls_end_listener=void 0)}_set_controls_events_to_update_on_end(t){this._reset(t),this._controlsEndEventName&&(this.controls_end_listener=()=>{this.node.update_transform_params_from_object()},t.addEventListener(this._controlsEndEventName,this.controls_end_listener))}_set_controls_events_to_update_always(t){this._reset(t),this.controls_change_listener=()=>{this.node.update_transform_params_from_object()},t.addEventListener(\\\\\\\"change\\\\\\\",this.controls_change_listener)}}function UV(t){return class extends t{constructor(){super(...arguments),this.layer=aa.INTEGER(0,{range:[0,31],rangeLocked:[!0,!0]})}}}class GV{constructor(t){this.node=t}update(){const t=this.node.object;t.layers.set(0),t.layers.enable(this.node.params.integer(\\\\\\\"layer\\\\\\\"))}}const VV={callback:t=>{UH.PARAM_CALLBACK_reset_effects_composer(t)}};function HV(t){return class extends t{constructor(){super(...arguments),this.doPostProcess=aa.BOOLEAN(0),this.postProcessNode=aa.NODE_PATH(\\\\\\\"\\\\\\\",{visibleIf:{doPostProcess:1},nodeSelection:{types:[es.POST]},...VV})}}}class jV{constructor(t){this.node=t,this._composers_by_canvas_id={},this.node.p.postProcessNode?this._add_param_dirty_hook():this.node.params.onParamsCreated(\\\\\\\"post process add param dirty hook\\\\\\\",(()=>{this._add_param_dirty_hook()}))}_add_param_dirty_hook(){this.node.p.postProcessNode.addPostDirtyHook(\\\\\\\"on_post_node_dirty\\\\\\\",(()=>{this.reset()}))}render(t,e){const n=this.composer(t);n&&(e&&n.setSize(e.x,e.y),n.render())}reset(){const t=Object.keys(this._composers_by_canvas_id);for(let e of t)delete this._composers_by_canvas_id[e]}composer(t){return this._composers_by_canvas_id[t.id]=this._composers_by_canvas_id[t.id]||this._create_composer(t)}_create_composer(t){const e=this.node.renderController.renderer(t);if(e){const n=this.node.renderController.resolved_scene||this.node.scene().threejsScene(),i=this.node.object,s=this.node.p.postProcessNode.value.node();if(s){if(s.type()==es.POST){const r=s,o=this.node.renderController.canvas_resolution(t);return r.effectsComposerController.createEffectsComposer({renderer:e,scene:n,camera:i,resolution:o,requester:this.node,camera_node:this.node})}this.node.states.error.set(\\\\\\\"found node is not a post process node\\\\\\\")}else this.node.states.error.set(\\\\\\\"no post node found\\\\\\\")}}}class WV extends sa{constructor(){super(...arguments),this.flags=new Pi(this)}static context(){return ts.ROP}initializeBaseNode(){this.dirtyController.addPostDirtyHook(\\\\\\\"cook_immediately\\\\\\\",(()=>{this.cookController.cookMainWithoutInputs()}))}cook(){this.cookController.endCook()}}var qV,XV,YV,$V;!function(t){t.CSS2D=\\\\\\\"CSS2DRenderer\\\\\\\",t.CSS3D=\\\\\\\"CSS3DRenderer\\\\\\\",t.WEBGL=\\\\\\\"WebGLRenderer\\\\\\\"}(qV||(qV={})),function(t){t.Linear=\\\\\\\"Linear\\\\\\\",t.sRGB=\\\\\\\"sRGB\\\\\\\",t.Gamma=\\\\\\\"Gamma\\\\\\\",t.RGBE=\\\\\\\"RGBE\\\\\\\",t.LogLuv=\\\\\\\"LogLuv\\\\\\\",t.RGBM7=\\\\\\\"RGBM7\\\\\\\",t.RGBM16=\\\\\\\"RGBM16\\\\\\\",t.RGBD=\\\\\\\"RGBD\\\\\\\"}(XV||(XV={})),($V=YV||(YV={}))[$V.Linear=w.U]=\\\\\\\"Linear\\\\\\\",$V[$V.sRGB=w.ld]=\\\\\\\"sRGB\\\\\\\",$V[$V.Gamma=w.J]=\\\\\\\"Gamma\\\\\\\",$V[$V.RGBE=w.gc]=\\\\\\\"RGBE\\\\\\\",$V[$V.LogLuv=w.bb]=\\\\\\\"LogLuv\\\\\\\",$V[$V.RGBM7=w.lc]=\\\\\\\"RGBM7\\\\\\\",$V[$V.RGBM16=w.kc]=\\\\\\\"RGBM16\\\\\\\",$V[$V.RGBD=w.fc]=\\\\\\\"RGBD\\\\\\\";const JV=[XV.Linear,XV.sRGB,XV.Gamma,XV.RGBE,XV.LogLuv,XV.RGBM7,XV.RGBM16,XV.RGBD],ZV=[YV.Linear,YV.sRGB,YV.Gamma,YV.RGBE,YV.LogLuv,YV.RGBM7,YV.RGBM16,YV.RGBD],QV=YV.sRGB;var KV,tH,eH;!function(t){t.No=\\\\\\\"No\\\\\\\",t.Linear=\\\\\\\"Linear\\\\\\\",t.Reinhard=\\\\\\\"Reinhard\\\\\\\",t.Cineon=\\\\\\\"Cineon\\\\\\\",t.ACESFilmic=\\\\\\\"ACESFilmic\\\\\\\"}(KV||(KV={})),(eH=tH||(tH={}))[eH.No=w.vb]=\\\\\\\"No\\\\\\\",eH[eH.Linear=w.ab]=\\\\\\\"Linear\\\\\\\",eH[eH.Reinhard=w.vc]=\\\\\\\"Reinhard\\\\\\\",eH[eH.Cineon=w.m]=\\\\\\\"Cineon\\\\\\\",eH[eH.ACESFilmic=w.a]=\\\\\\\"ACESFilmic\\\\\\\";const nH=[KV.No,KV.Linear,KV.Reinhard,KV.Cineon,KV.ACESFilmic],iH=[tH.No,tH.Linear,tH.Reinhard,tH.Cineon,tH.ACESFilmic],sH=tH.ACESFilmic,rH=nH.map(((t,e)=>({name:t,value:iH[e]})));var oH;!function(t){t.HIGH=\\\\\\\"highp\\\\\\\",t.MEDIUM=\\\\\\\"mediump\\\\\\\",t.LOW=\\\\\\\"lowp\\\\\\\"}(oH||(oH={}));const aH=[oH.HIGH,oH.MEDIUM,oH.LOW];var lH;!function(t){t.HIGH=\\\\\\\"high-performance\\\\\\\",t.LOW=\\\\\\\"low-power\\\\\\\",t.DEFAULT=\\\\\\\"default\\\\\\\"}(lH||(lH={}));const cH=[lH.HIGH,lH.LOW,lH.DEFAULT];var hH,uH,dH;!function(t){t.Basic=\\\\\\\"Basic\\\\\\\",t.PCF=\\\\\\\"PCF\\\\\\\",t.PCFSoft=\\\\\\\"PCFSoft\\\\\\\",t.VSM=\\\\\\\"VSM\\\\\\\"}(hH||(hH={})),(dH=uH||(uH={}))[dH.Basic=w.k]=\\\\\\\"Basic\\\\\\\",dH[dH.PCF=w.Fb]=\\\\\\\"PCF\\\\\\\",dH[dH.PCFSoft=w.Gb]=\\\\\\\"PCFSoft\\\\\\\",dH[dH.VSM=w.gd]=\\\\\\\"VSM\\\\\\\";const pH=[hH.Basic,hH.PCF,hH.PCFSoft,hH.VSM],_H=[uH.Basic,uH.PCF,uH.PCFSoft,uH.VSM],mH=(w.k,w.Fb,w.Gb,w.gd,uH.PCFSoft),fH={alpha:!1,precision:oH.HIGH,premultipliedAlpha:!0,antialias:!1,stencil:!0,preserveDrawingBuffer:!1,powerPreference:lH.DEFAULT,depth:!0,logarithmicDepthBuffer:!1};const gH=new class extends la{constructor(){super(...arguments),this.tprecision=aa.BOOLEAN(0),this.precision=aa.INTEGER(aH.indexOf(oH.HIGH),{visibleIf:{tprecision:1},menu:{entries:aH.map(((t,e)=>({value:e,name:t})))}}),this.tpowerPreference=aa.BOOLEAN(0),this.powerPreference=aa.INTEGER(cH.indexOf(lH.DEFAULT),{visibleIf:{tpowerPreference:1},menu:{entries:cH.map(((t,e)=>({value:e,name:t})))}}),this.alpha=aa.BOOLEAN(1),this.premultipliedAlpha=aa.BOOLEAN(1),this.antialias=aa.BOOLEAN(1),this.stencil=aa.BOOLEAN(1),this.depth=aa.BOOLEAN(1),this.logarithmicDepthBuffer=aa.BOOLEAN(0),this.toneMapping=aa.INTEGER(sH,{menu:{entries:rH}}),this.toneMappingExposure=aa.FLOAT(1,{range:[0,2]}),this.outputEncoding=aa.INTEGER(QV,{menu:{entries:JV.map(((t,e)=>({name:t,value:ZV[e]})))}}),this.physicallyCorrectLights=aa.BOOLEAN(1),this.sortObjects=aa.BOOLEAN(1),this.tpixelRatio=aa.BOOLEAN(0),this.pixelRatio=aa.INTEGER(2,{visibleIf:{tpixelRatio:!0},range:[1,4],rangeLocked:[!0,!1]}),this.tshadowMap=aa.BOOLEAN(1),this.shadowMapAutoUpdate=aa.BOOLEAN(1,{visibleIf:{tshadowMap:1}}),this.shadowMapNeedsUpdate=aa.BOOLEAN(0,{visibleIf:{tshadowMap:1}}),this.shadowMapType=aa.INTEGER(mH,{visibleIf:{tshadowMap:1},menu:{entries:pH.map(((t,e)=>({name:t,value:_H[e]})))}})}};class vH extends WV{constructor(){super(...arguments),this.paramsConfig=gH,this._renderers_by_canvas_id={}}static type(){return qV.WEBGL}createRenderer(t,e){const n={},i=Object.keys(fH);let s;for(s of i)n[s]=fH[s];if(this.pv.tprecision){const t=aH[this.pv.precision];n.precision=t}if(this.pv.tpowerPreference){const t=cH[this.pv.powerPreference];n.powerPreference=t}n.antialias=this.pv.antialias,n.antialias=this.pv.antialias,n.alpha=this.pv.alpha,n.premultipliedAlpha=this.pv.premultipliedAlpha,n.depth=this.pv.depth,n.stencil=this.pv.stencil,n.logarithmicDepthBuffer=this.pv.logarithmicDepthBuffer,n.canvas=t,n.context=e;const r=li.renderersController.createWebGLRenderer(n);return li.renderersController.printDebug()&&(li.renderersController.printDebugMessage(`create renderer from node '${this.path()}'`),li.renderersController.printDebugMessage({params:n})),this._update_renderer(r),this._renderers_by_canvas_id[t.id]=r,r}cook(){const t=Object.keys(this._renderers_by_canvas_id);for(let e of t){const t=this._renderers_by_canvas_id[e];this._update_renderer(t)}this._traverse_scene_and_update_materials(),this.cookController.endCook()}_update_renderer(t){t.physicallyCorrectLights=this.pv.physicallyCorrectLights,t.outputEncoding=this.pv.outputEncoding,t.toneMapping=this.pv.toneMapping,t.toneMappingExposure=this.pv.toneMappingExposure,t.shadowMap.enabled=this.pv.tshadowMap,t.shadowMap.autoUpdate=this.pv.shadowMapAutoUpdate,t.shadowMap.needsUpdate=this.pv.shadowMapNeedsUpdate,t.shadowMap.type=this.pv.shadowMapType,t.sortObjects=this.pv.sortObjects;const e=this.pv.tpixelRatio?this.pv.pixelRatio:xH.defaultPixelRatio();li.renderersController.printDebug()&&(li.renderersController.printDebugMessage(`set renderer pixelRatio from '${this.path()}'`),li.renderersController.printDebugMessage({pixelRatio:e})),t.setPixelRatio(e)}_traverse_scene_and_update_materials(){this.scene().threejsScene().traverse((t=>{const e=t.material;if(e)if(m.isArray(e))for(let t of e)t.needsUpdate=!0;else e.needsUpdate=!0}))}}function yH(t){return class extends t{constructor(){super(...arguments),this.render=aa.FOLDER(),this.setScene=aa.BOOLEAN(0),this.scene=aa.OPERATOR_PATH(\\\\\\\"\\\\\\\",{visibleIf:{setScene:1},nodeSelection:{context:ts.OBJ,types:[DV.type()]}}),this.setRenderer=aa.BOOLEAN(0),this.renderer=aa.OPERATOR_PATH(\\\\\\\"\\\\\\\",{visibleIf:{setRenderer:1},nodeSelection:{context:ts.ROP,types:[vH.type()]}}),this.setCSSRenderer=aa.BOOLEAN(0),this.CSSRenderer=aa.OPERATOR_PATH(\\\\\\\"\\\\\\\",{visibleIf:{setCSSRenderer:1},nodeSelection:{context:ts.ROP,types:[qV.CSS2D,qV.CSS3D]}})}}}class xH{constructor(t){this.node=t,this._renderers_by_canvas_id={},this._resolution_by_canvas_id={},this._super_sampling_size=new d.a}render(t,e,n){if(this.node.pv.doPostProcess?this.node.postProcessController.render(t,e):this.render_with_renderer(t),this._resolved_cssRenderer_rop&&this._resolved_scene&&this.node.pv.setCSSRenderer){const e=this.cssRenderer(t);e&&e.render(this._resolved_scene,this.node.object)}}render_with_renderer(t){const e=this.renderer(t);e&&this._resolved_scene&&e.render(this._resolved_scene,this.node.object)}async update(){this.update_scene(),this.update_renderer(),this.update_cssRenderer()}get resolved_scene(){return this._resolved_scene}update_scene(){if(this.node.pv.setScene){const t=this.node.p.scene;t.isDirty()&&t.find_target();const e=t.found_node_with_context_and_type(ts.OBJ,DV.type());e&&(e.isDirty()&&e.cookController.cookMainWithoutInputs(),this._resolved_scene=e.object)}else this._resolved_scene=this.node.scene().threejsScene()}update_renderer(){if(this.node.pv.setRenderer){const t=this.node.p.renderer;t.isDirty()&&t.find_target(),this._resolved_renderer_rop=t.found_node_with_context_and_type(ts.ROP,qV.WEBGL)}else this._resolved_renderer_rop=void 0}update_cssRenderer(){if(this.node.pv.setCSSRenderer){const t=this.node.p.CSSRenderer;t.isDirty()&&t.find_target(),this._resolved_cssRenderer_rop=t.found_node_with_context_and_type(ts.ROP,[qV.CSS2D,qV.CSS3D])}else this._resolved_cssRenderer_rop,this._resolved_cssRenderer_rop=void 0}renderer(t){return this._renderers_by_canvas_id[t.id]}cssRenderer(t){if(this._resolved_cssRenderer_rop&&this.node.pv.setCSSRenderer)return this._resolved_cssRenderer_rop.renderer(t)}createRenderer(t,e){const n=li.renderersController.createRenderingContext(t);if(!n)return void console.error(\\\\\\\"failed to create webgl context\\\\\\\");let i;return this.node.pv.setRenderer&&(this.update_renderer(),this._resolved_renderer_rop&&(i=this._resolved_renderer_rop.createRenderer(t,n))),i||(i=xH._createDefaultRenderer(t,n)),li.renderersController.registerRenderer(i),this._renderers_by_canvas_id[t.id]=i,this._super_sampling_size.copy(e),this.set_renderer_size(t,this._super_sampling_size),i}static defaultPixelRatio(){return Zf.isMobile()?1:Math.max(2,window.devicePixelRatio)}static _createDefaultRenderer(t,e){const n={canvas:t,antialias:!1,alpha:!1,context:e},i=li.renderersController.createWebGLRenderer(n),s=this.defaultPixelRatio();return i.setPixelRatio(s),i.shadowMap.enabled=!0,i.shadowMap.type=mH,i.physicallyCorrectLights=!0,i.toneMapping=sH,i.toneMappingExposure=1,i.outputEncoding=QV,li.renderersController.printDebug()&&(li.renderersController.printDebugMessage(\\\\\\\"create default renderer\\\\\\\"),li.renderersController.printDebugMessage({params:n,pixelRatio:s})),i}delete_renderer(t){const e=this.renderer(t);e&&li.renderersController.deregisterRenderer(e)}canvas_resolution(t){return this._resolution_by_canvas_id[t.id]}set_renderer_size(t,e){this._resolution_by_canvas_id[t.id]=this._resolution_by_canvas_id[t.id]||new d.a,this._resolution_by_canvas_id[t.id].copy(e);const n=this.renderer(t);if(n){const t=!1;n.setSize(e.x,e.y,t)}if(this._resolved_cssRenderer_rop){const n=this.cssRenderer(t);n&&n.setSize(e.x,e.y)}}}class bH{constructor(t){this.viewer=t,this._active=!1,this._controls=null,this._bound_on_controls_start=this._on_controls_start.bind(this),this._bound_on_controls_end=this._on_controls_end.bind(this),this._update_graph_node()}controls(){return this._controls}async create_controls(){var t;this.dispose_controls();this.viewer.canvas()&&(this._config=await(null===(t=this.viewer.cameraControlsController)||void 0===t?void 0:t.apply_controls(this.viewer)),this._config&&(this._controls=this._config.controls,this._controls&&(this.viewer.active()?(this._controls.addEventListener(\\\\\\\"start\\\\\\\",this._bound_on_controls_start),this._controls.addEventListener(\\\\\\\"end\\\\\\\",this._bound_on_controls_end)):this.dispose_controls())))}update(t){this._config&&this._controls&&this._config.updateRequired()&&this._controls.update(t)}dispose(){var t;null===(t=this._graph_node)||void 0===t||t.graphDisconnectPredecessors(),this.dispose_controls()}dispose_controls(){var t;if(this._controls){const e=this.viewer.canvas();e&&(null===(t=this.viewer)||void 0===t||t.cameraControlsController.dispose_controls(e)),this._bound_on_controls_start&&this._controls.removeEventListener(\\\\\\\"start\\\\\\\",this._bound_on_controls_start),this._bound_on_controls_end&&this._controls.removeEventListener(\\\\\\\"end\\\\\\\",this._bound_on_controls_end),this._controls.dispose(),this._controls=null}}_on_controls_start(){this._active=!0}_on_controls_end(){this._active=!1}_update_graph_node(){const t=this.viewer.cameraNode().p.controls;this._graph_node=this._graph_node||this._create_graph_node(),this._graph_node&&(this._graph_node.graphDisconnectPredecessors(),this._graph_node.addGraphInput(t))}_create_graph_node(){const t=new Mi(this.viewer.cameraNode().scene(),\\\\\\\"viewer-controls\\\\\\\");return t.addPostDirtyHook(\\\\\\\"this.viewer.controls_controller\\\\\\\",(async()=>{await this.create_controls()})),t}}class wH{constructor(t){this._viewer=t,this._size=new d.a(100,100),this._aspect=1}cameraNode(){return this._viewer.cameraNode()}get size(){return this._size}get aspect(){return this._aspect}computeSizeAndAspect(){this._updateSize(),this.cameraNode().scene().uniformsController.updateResolutionDependentUniformOwners(this._size),this._aspect=this._getAspect()}_updateSize(){this._size.x=this._viewer.domElement().offsetWidth,this._size.y=this._viewer.domElement().offsetHeight}_getAspect(){return this._size.x/this._size.y}updateCameraAspect(){this.cameraNode().setupForAspectRatio(this._aspect)}async prepareCurrentCamera(){await this.cameraNode().compute(),await this._updateFromCameraContainer()}async _updateFromCameraContainer(){var t;this.updateCameraAspect(),await(null===(t=this._viewer.controlsController)||void 0===t?void 0:t.create_controls())}}class TH{constructor(t){this.viewer=t}init(){const t=this.viewer.canvas();t&&(t.onwebglcontextlost=this._on_webglcontextlost.bind(this),t.onwebglcontextrestored=this._on_webglcontextrestored.bind(this))}_on_webglcontextlost(){console.warn(\\\\\\\"context lost at frame\\\\\\\",this.viewer.scene().frame()),this.request_animation_frame_id?cancelAnimationFrame(this.request_animation_frame_id):console.warn(\\\\\\\"request_animation_frame_id not initialized\\\\\\\"),console.warn(\\\\\\\"not canceled\\\\\\\",this.request_animation_frame_id)}_on_webglcontextrestored(){console.log(\\\\\\\"context restored\\\\\\\")}}const AH=\\\\\\\"hovered\\\\\\\";class MH{constructor(t,e,n){this._container=t,this._scene=e,this._camera_node=n,this._active=!1,this._id=MH._next_viewer_id++,this._scene.viewersRegister.registerViewer(this)}active(){return this._active}activate(){this._active=!0}deactivate(){this._active=!1}get camerasController(){return this._cameras_controller=this._cameras_controller||new wH(this)}get controlsController(){return this._controls_controller}get eventsController(){return this._events_controller=this._events_controller||new Va(this)}get webglController(){return this._webgl_controller=this._webgl_controller||new TH(this)}domElement(){return this._container}scene(){return this._scene}canvas(){return this._canvas}cameraNode(){return this._camera_node}get cameraControlsController(){}id(){return this._id}dispose(){let t;for(this._scene.viewersRegister.unregisterViewer(this),this.eventsController.dispose();t=this._container.children[0];)this._container.removeChild(t)}resetContainerClass(){this.domElement().classList.remove(AH)}setContainerClassHovered(){this.domElement().classList.add(AH)}registerOnBeforeTick(t,e){this._onBeforeTickCallbackNames=this._onBeforeTickCallbackNames||[],this._onBeforeTickCallbacks=this._onBeforeTickCallbacks||[],this._registerCallback(t,e,this._onBeforeTickCallbackNames,this._onBeforeTickCallbacks)}unRegisterOnBeforeTick(t){this._unregisterCallback(t,this._onBeforeTickCallbackNames,this._onBeforeTickCallbacks)}registeredBeforeTickCallbackNames(){return this._onBeforeTickCallbackNames}registerOnAfterTick(t,e){this._onAfterTickCallbacks=this._onAfterTickCallbacks||[],this._onAfterTickCallbackNames=this._onAfterTickCallbackNames||[],this._registerCallback(t,e,this._onAfterTickCallbackNames,this._onAfterTickCallbacks)}unRegisterOnAfterTick(t){this._unregisterCallback(t,this._onAfterTickCallbackNames,this._onAfterTickCallbacks)}registeredAfterTickCallbackNames(){return this._onAfterTickCallbackNames}registerOnBeforeRender(t,e){this._onBeforeRenderCallbackNames=this._onBeforeRenderCallbackNames||[],this._onBeforeRenderCallbacks=this._onBeforeRenderCallbacks||[],this._registerCallback(t,e,this._onBeforeRenderCallbackNames,this._onBeforeRenderCallbacks)}unRegisterOnBeforeRender(t){this._unregisterCallback(t,this._onBeforeRenderCallbackNames,this._onBeforeRenderCallbacks)}registeredBeforeRenderCallbackNames(){return this._onBeforeRenderCallbackNames}registerOnAfterRender(t,e){this._onAfterRenderCallbackNames=this._onAfterRenderCallbackNames||[],this._onAfterRenderCallbacks=this._onAfterRenderCallbacks||[],this._registerCallback(t,e,this._onAfterRenderCallbackNames,this._onAfterRenderCallbacks)}unRegisterOnAfterRender(t){this._unregisterCallback(t,this._onAfterRenderCallbackNames,this._onAfterRenderCallbacks)}registeredAfterRenderCallbackNames(){return this._onAfterRenderCallbackNames}_registerCallback(t,e,n,i){(null==n?void 0:n.includes(t))?console.warn(`callback ${t} already registered`):(i.push(e),n.push(t))}_unregisterCallback(t,e,n){if(!e||!n)return;const i=e.indexOf(t);e.splice(i,1),n.splice(i,1)}}MH._next_viewer_id=0;class EH extends MH{constructor(t,e,n,i){super(t,e,n),this._scene=e,this._camera_node=n,this._properties=i,this._do_render=!0,this._clock=new Om,this._delta=0,this._animate_method=this.animate.bind(this),this._onResizeBound=this.onResize.bind(this),this._do_render=null==this._properties||this._properties.autoRender,this._canvas=document.createElement(\\\\\\\"canvas\\\\\\\"),this._canvas.id=`canvas_id_${Math.random()}`.replace(\\\\\\\".\\\\\\\",\\\\\\\"_\\\\\\\"),this._canvas.style.display=\\\\\\\"block\\\\\\\",this._canvas.style.outline=\\\\\\\"none\\\\\\\",this._container.appendChild(this._canvas),this._container.classList.add(\\\\\\\"CoreThreejsViewer\\\\\\\"),this._build(),this._setEvents()}get controlsController(){return this._controls_controller=this._controls_controller||new bH(this)}_build(){this._init_display(),this.activate()}dispose(){this._cancel_animate(),this.controlsController.dispose(),this._disposeEvents(),super.dispose()}get cameraControlsController(){return this._camera_node.controls_controller}_setEvents(){this.eventsController.init(),this.webglController.init(),window.addEventListener(\\\\\\\"resize\\\\\\\",this._onResizeBound.bind(this),!1)}_disposeEvents(){window.removeEventListener(\\\\\\\"resize\\\\\\\",this._onResizeBound.bind(this),!1)}onResize(){const t=this.canvas();t&&(this.camerasController.computeSizeAndAspect(),this._camera_node.renderController.set_renderer_size(t,this.camerasController.size),this.camerasController.updateCameraAspect())}_init_display(){if(!this._canvas)return void console.warn(\\\\\\\"no canvas found for viewer\\\\\\\");this.camerasController.computeSizeAndAspect();const t=this.camerasController.size;this._camera_node.renderController.createRenderer(this._canvas,t),this.camerasController.prepareCurrentCamera(),this.animate()}setAutoRender(t=!0){this._do_render=t,this._do_render&&this.animate()}animate(){var t;if(this._do_render){if(this._delta=this._clock.getDelta(),this._request_animation_frame_id=requestAnimationFrame(this._animate_method),this._onBeforeTickCallbacks)for(let t of this._onBeforeTickCallbacks)t(this._delta);if(this._scene.timeController.incrementTimeIfPlaying(this._delta),this._onAfterTickCallbacks)for(let t of this._onAfterTickCallbacks)t(this._delta);this.render(this._delta),null===(t=this._controls_controller)||void 0===t||t.update(this._delta)}}_cancel_animate(){this._do_render=!1,this._request_animation_frame_id&&cancelAnimationFrame(this._request_animation_frame_id),this._canvas&&this._camera_node.renderController.delete_renderer(this._canvas)}render(t){if(this.camerasController.cameraNode()&&this._canvas){if(this._onBeforeRenderCallbacks)for(let e of this._onBeforeRenderCallbacks)e(t);const e=this.camerasController.size,n=this.camerasController.aspect;if(this._camera_node.renderController.render(this._canvas,e,n),this._onAfterRenderCallbacks)for(let e of this._onAfterRenderCallbacks)e(t)}else console.warn(\\\\\\\"no camera to render with\\\\\\\")}renderer(){if(this._canvas)return this._camera_node.renderController.renderer(this._canvas)}}const SH={type:\\\\\\\"change\\\\\\\"},CH=1,NH=100;var LH;!function(t){t.ON_END=\\\\\\\"on move end\\\\\\\",t.ALWAYS=\\\\\\\"always\\\\\\\",t.NEVER=\\\\\\\"never\\\\\\\"}(LH||(LH={}));const OH=[LH.ON_END,LH.ALWAYS,LH.NEVER];function PH(t){return class extends t{constructor(){super(...arguments),this.setMainCamera=aa.BUTTON(null,{callback:(t,e)=>{kH.PARAM_CALLBACK_setMasterCamera(t)}})}}}function RH(t){return class extends t{constructor(){super(...arguments),this.camera=aa.FOLDER(),this.controls=aa.NODE_PATH(\\\\\\\"\\\\\\\",{nodeSelection:{context:ts.EVENT}}),this.updateFromControlsMode=aa.INTEGER(OH.indexOf(LH.ON_END),{menu:{entries:OH.map(((t,e)=>({name:t,value:e})))}}),this.near=aa.FLOAT(CH,{range:[0,100],cook:!1,computeOnDirty:!0,callback:(t,e)=>{UH.PARAM_CALLBACK_update_near_far_from_param(t,e)}}),this.far=aa.FLOAT(NH,{range:[0,100],cook:!1,computeOnDirty:!0,callback:(t,e)=>{UH.PARAM_CALLBACK_update_near_far_from_param(t,e)}}),this.display=aa.BOOLEAN(1),this.showHelper=aa.BOOLEAN(0)}}}var IH;!function(t){t.DEFAULT=\\\\\\\"default\\\\\\\",t.COVER=\\\\\\\"cover\\\\\\\",t.CONTAIN=\\\\\\\"contain\\\\\\\"}(IH||(IH={}));const FH=[IH.DEFAULT,IH.COVER,IH.CONTAIN];function DH(t){return class extends t{constructor(){super(...arguments),this.fovAdjustMode=aa.INTEGER(FH.indexOf(IH.DEFAULT),{menu:{entries:FH.map(((t,e)=>({name:t,value:e})))}}),this.expectedAspectRatio=aa.FLOAT(\\\\\\\"16/9\\\\\\\",{visibleIf:[{fovAdjustMode:FH.indexOf(IH.COVER)},{fovAdjustMode:FH.indexOf(IH.CONTAIN)}],range:[0,2],rangeLocked:[!0,!1]})}}}PH(la);HV(yH(dU(UV(RH(PH(la))))));class BH extends Kk{constructor(){super(...arguments),this.renderOrder=Qk.CAMERA,this._aspect=-1}get object(){return this._object}async cook(){this.updateCamera(),this._object.dispatchEvent(SH),this.cookController.endCook()}on_create(){}on_delete(){}prepareRaycaster(t,e){}camera(){return this._object}updateCamera(){}static PARAM_CALLBACK_setMasterCamera(t){t.set_as_master_camera()}set_as_master_camera(){this.scene().camerasController.setMainCameraNodePath(this.path())}setupForAspectRatio(t){}_updateForAspectRatio(){}update_transform_params_from_object(){uU.set_params_from_object(this._object,this)}static PARAM_CALLBACK_update_from_param(t,e){t.object[e.name()]=t.pv[e.name()]}}class zH extends BH{constructor(){super(...arguments),this.flags=new Di(this),this.hierarchyController=new mU(this),this.transformController=new _U(this),this.childrenDisplayController=new LG(this),this.displayNodeController=new Lm(this,this.childrenDisplayController.displayNodeControllerCallbacks()),this._children_controller_context=ts.SOP}get controls_controller(){return this._controls_controller=this._controls_controller||new kV(this)}get layers_controller(){return this._layers_controller=this._layers_controller||new GV(this)}get renderController(){return this._render_controller=this._render_controller||new xH(this)}get postProcessController(){return this._post_process_controller=this._post_process_controller||new jV(this)}initializeBaseNode(){super.initializeBaseNode(),this.io.outputs.setHasOneOutput(),this.hierarchyController.initializeNode(),this.transformController.initializeNode(),this.childrenDisplayController.initializeNode(),this.initHelperHook()}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}prepareRaycaster(t,e){e.setFromCamera(t,this._object)}async cook(){this.transformController.update(),this.layers_controller.update(),this.updateNearFar(),this.renderController.update(),this.updateCamera(),this._updateHelper(),this.controls_controller.update_controls(),this._object.dispatchEvent(SH),this.cookController.endCook()}static PARAM_CALLBACK_update_near_far_from_param(t,e){t.updateNearFar()}updateNearFar(){this._object.near==this.pv.near&&this._object.far==this.pv.far||(this._object.near=this.pv.near,this._object.far=this.pv.far,this._object.updateProjectionMatrix(),this._updateHelper())}setupForAspectRatio(t){m.isNaN(t)||t&&this._aspect!=t&&(this._aspect=t,this._updateForAspectRatio())}createViewer(t,e){return new EH(t,this.scene(),this,e)}static PARAM_CALLBACK_reset_effects_composer(t){t.postProcessController.reset()}initHelperHook(){this.flags.display.onUpdate((()=>{this._updateHelper()}))}helperVisible(){return this.flags.display.active()&&this.pv.showHelper}_createHelper(){const t=new NU(this.object);return t.update(),t}_updateHelper(){this.helperVisible()?(this._helper||(this._helper=this._createHelper()),this._helper&&(this.object.add(this._helper),this._helper.update())):this._helper&&this.object.remove(this._helper)}}class kH extends BH{}class UH extends zH{PARAM_CALLBACK_update_effects_composer(t){}}const GH=-.5,VH=.5,HH=.5,jH=-.5;class WH extends(HV(yH(UV(PH(DH(function(t){return class extends t{constructor(){super(...arguments),this.size=aa.FLOAT(1)}}}(RH(dU(la,{matrixAutoUpdate:!0}))))))))){}const qH=new WH;class XH extends zH{constructor(){super(...arguments),this.paramsConfig=qH}static type(){return is.ORTHOGRAPHIC}createObject(){return new ot.a(2*GH,2*VH,2*HH,2*jH,CH,NH)}updateCamera(){this._updateForAspectRatio()}_updateForAspectRatio(){this._aspect&&(this._adjustFOVFromMode(),this._object.updateProjectionMatrix())}_adjustFOVFromMode(){const t=FH[this.pv.fovAdjustMode];switch(t){case IH.DEFAULT:return this._adjustFOVFromModeDefault();case IH.COVER:return this._adjustFOVFromModeCover();case IH.CONTAIN:return this._adjustFOVFromModeContain()}ls.unreachable(t)}_adjustFOVFromModeDefault(){this._adjustFOVFromSize(this.pv.size||1)}_adjustFOVFromModeCover(){const t=this.pv.size||1;this._aspect>this.pv.expectedAspectRatio?this._adjustFOVFromSize(this.pv.expectedAspectRatio*t/this._aspect):this._adjustFOVFromSize(t)}_adjustFOVFromModeContain(){const t=this.pv.size||1;this._aspect>this.pv.expectedAspectRatio?this._adjustFOVFromSize(t):this._adjustFOVFromSize(this.pv.expectedAspectRatio*t/this._aspect)}_adjustFOVFromSize(t){const e=t*this._aspect;this._object.left=GH*e*1,this._object.right=VH*e*1,this._object.top=HH*t*1,this._object.bottom=jH*t*1}}const YH=50;class $H extends(HV(yH(UV(PH(DH(function(t){return class extends t{constructor(){super(...arguments),this.fov=aa.FLOAT(YH,{range:[0,100]})}}}(RH(dU(la,{matrixAutoUpdate:!0}))))))))){}const JH=new $H;class ZH extends zH{constructor(){super(...arguments),this.paramsConfig=JH}static type(){return is.PERSPECTIVE}createObject(){return new tt.a(YH,1,CH,NH)}updateCamera(){this._object.fov!=this.pv.fov&&(this._object.fov=this.pv.fov,this._object.updateProjectionMatrix()),this._updateForAspectRatio()}_updateForAspectRatio(){this._aspect&&(this._object.aspect=this._aspect,this._adjustFOVFromMode(),this._object.updateProjectionMatrix())}_adjustFOVFromMode(){const t=FH[this.pv.fovAdjustMode];switch(t){case IH.DEFAULT:return this._adjustFOVFromModeDefault();case IH.COVER:return this._adjustFOVFromModeCover();case IH.CONTAIN:return this._adjustFOVFromModeContain()}ls.unreachable(t)}_adjustFOVFromModeDefault(){this._object.fov=this.pv.fov}_adjustFOVFromModeCover(){if(this._object.aspect>this.pv.expectedAspectRatio){const t=Math.tan(Object(On.e)(this.pv.fov/2))/(this._object.aspect/this.pv.expectedAspectRatio);this._object.fov=2*Object(On.k)(Math.atan(t))}else this._object.fov=this.pv.fov}_adjustFOVFromModeContain(){if(this._object.aspect>this.pv.expectedAspectRatio)this._object.fov=this.pv.fov;else{const t=Math.tan(Object(On.e)(this.pv.fov/2))/(this._object.aspect/this.pv.expectedAspectRatio);this._object.fov=2*Object(On.k)(Math.atan(t))}}}class QH extends(function(t){return class extends t{constructor(){super(...arguments),this.main=aa.FOLDER(),this.resolution=aa.INTEGER(256),this.excludedObjects=aa.STRING(\\\\\\\"*`$OS`\\\\\\\"),this.printResolve=aa.BUTTON(null,{callback:t=>{tj.PARAM_CALLBACK_printResolve(t)}}),this.near=aa.FLOAT(1),this.far=aa.FLOAT(100),this.render=aa.BUTTON(null,{callback:t=>{tj.PARAM_CALLBACK_render(t)}}),this.renderTarget=aa.FOLDER(),this.tencoding=aa.BOOLEAN(0),this.encoding=aa.INTEGER(w.ld,{visibleIf:{tencoding:1},menu:{entries:eg.map((t=>({name:Object.keys(t)[0],value:Object.values(t)[0]})))}}),this.tminFilter=aa.BOOLEAN(0),this.minFilter=aa.INTEGER(Xm,{visibleIf:{tminFilter:1},menu:{entries:$m}}),this.tmagFilter=aa.BOOLEAN(0),this.magFilter=aa.INTEGER(qm,{visibleIf:{tmagFilter:1},menu:{entries:Ym}})}}}(dU(la))){}const KH=new QH;class tj extends Kk{constructor(){super(...arguments),this.paramsConfig=KH,this.hierarchyController=new mU(this),this.transformController=new _U(this),this.flags=new Di(this),this._excludedObjects=[],this._previousVisibleStateByUuid=new Map,this._helper=new _G(1)}static type(){return Ng.CUBE_CAMERA}initializeNode(){this.hierarchyController.initializeNode(),this.transformController.initializeNode(),this._updateHelperHierarchy(),this._helper.matrixAutoUpdate=!1,this.flags.display.onUpdate((()=>{this._updateHelperHierarchy()})),this.io.inputs.setCount(0,1)}createObject(){const t=new Fn.a;return t.matrixAutoUpdate=!0,t}cook(){this.transformController.update(),this._resolveObjects();const t=this._setupCubeCamera();this._cubeCamera&&!t||this._createCubeCamera(),this.cookController.endCook()}_updateHelperHierarchy(){this.flags.display.active()?this.object.add(this._helper):this.object.remove(this._helper)}_setupCubeCamera(){let t=!1;if(this._cubeCamera){const e=this._cubeCamera.children[0],n=e.near,i=e.far,s=this._cubeCamera.renderTarget.width;n==this.pv.near&&i==this.pv.far&&s==this.pv.resolution||(t=!0),t&&this.object.remove(this._cubeCamera)}return t}_createCubeCamera(){const t=new st(this.pv.resolution,{encoding:this.pv.tencoding?this.pv.encoding:w.ld,minFilter:this.pv.tminFilter?this.pv.minFilter:void 0,magFilter:this.pv.tmagFilter?this.pv.magFilter:void 0});this._cubeCamera=new nt(this.pv.near,this.pv.far,t),this._cubeCamera.matrixAutoUpdate=!0,this.object.add(this._cubeCamera)}renderTarget(){if(this._cubeCamera)return this._cubeCamera.renderTarget}render(){const t=li.renderersController.firstRenderer();if(t)if(this._cubeCamera){for(let t of this._excludedObjects)this._previousVisibleStateByUuid.set(t.uuid,t.visible),t.visible=!1;this._cubeCamera.update(t,this.scene().threejsScene());for(let t of this._excludedObjects){const e=this._previousVisibleStateByUuid.get(t.uuid);e&&(t.visible=e)}this._previousVisibleStateByUuid.clear()}else console.warn(`no cubeCamera for ${this.path()}`);else console.warn(`no renderer found for ${this.path()}`)}_resolveObjects(){const t=this.scene().objectsByMask(this.pv.excludedObjects),e=new Map;for(let n of t)e.set(n.uuid,n);this._excludedObjects=[];for(let n of t){const t=n.parent;t&&(e.get(t.uuid)||this._excludedObjects.push(n))}}static PARAM_CALLBACK_printResolve(t){t.param_callback_printResolve()}param_callback_printResolve(){this._resolveObjects(),console.log(this._excludedObjects)}static PARAM_CALLBACK_render(t){t.param_callback_render()}param_callback_render(){this.render()}}class ej extends Kk{constructor(){super(...arguments),this._attachableToHierarchy=!1}createObject(){const t=new Fn.a;return t.matrixAutoUpdate=!1,t}cook(){this.cookController.endCook()}}class nj extends ej{}class ij extends nj{constructor(){super(...arguments),this._children_controller_context=ts.ANIM}static type(){return es.ANIM}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class sj extends ij{constructor(){super(...arguments),this.renderOrder=Qk.MANAGER}}class rj extends nj{constructor(){super(...arguments),this._children_controller_context=ts.COP}static type(){return es.COP}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class oj extends nj{constructor(){super(...arguments),this.renderOrder=Qk.MANAGER,this._children_controller_context=ts.EVENT}static type(){return es.EVENT}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class aj extends nj{constructor(){super(...arguments),this.renderOrder=Qk.MANAGER,this._children_controller_context=ts.MAT}static type(){return es.MAT}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class lj extends ej{constructor(){super(...arguments),this.paramsConfig=new Jm,this.effectsComposerController=new Zm(this),this.displayNodeController=new Lm(this,this.effectsComposerController.displayNodeControllerCallbacks()),this._children_controller_context=ts.POST}static type(){return es.POST}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class cj extends nj{constructor(){super(...arguments),this.renderOrder=Qk.MANAGER,this._children_controller_context=ts.ROP}static type(){return es.ROP}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}const hj=[\\\\\\\"input pass\\\\\\\"];const uj={cook:!1,callback:function(t,e){dj.PARAM_CALLBACK_updatePasses(t)},computeOnDirty:!0};class dj extends sa{constructor(){super(...arguments),this.flags=new zi(this),this._passes_by_requester_id=new Map,this._update_pass_bound=this.updatePass.bind(this)}static context(){return ts.POST}static displayedInputNames(){return hj}initializeNode(){this.flags.display.set(!1),this.flags.display.onUpdate((()=>{if(this.flags.display.active()){const t=this.parent();t&&t.displayNodeController&&t.displayNodeController.setDisplayNode(this)}})),this.io.inputs.setCount(0,1),this.io.outputs.setHasOneOutput()}cook(){this.cookController.endCook()}setupComposer(t){if(this._addPassFromInput(0,t),!this.flags.bypass.active()){let e=this._passes_by_requester_id.get(t.requester.graphNodeId());e||(e=this._createPass(t),e&&this._passes_by_requester_id.set(t.requester.graphNodeId(),e)),e&&t.composer.addPass(e)}}_addPassFromInput(t,e){const n=this.io.inputs.input(t);n&&n.setupComposer(e)}_createPass(t){}static PARAM_CALLBACK_updatePasses(t){t._updatePasses()}_updatePasses(){this._passes_by_requester_id.forEach(this._update_pass_bound)}updatePass(t){}}const pj={uniforms:{tDiffuse:{value:null}},vertexShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\tvarying vec2 vUv;\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\tvUv = uv;\\\\n\\\\n\\\\t\\\\t\\\\tgl_Position = projectionMatrix * modelViewMatrix * vec4( position, 1.0 );\\\\n\\\\n\\\\t\\\\t}\\\\\\\",fragmentShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\t#include \\\\n\\\\n\\\\t\\\\tuniform sampler2D tDiffuse;\\\\n\\\\n\\\\t\\\\tvarying vec2 vUv;\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\tvec4 texel = texture2D( tDiffuse, vUv );\\\\n\\\\n\\\\t\\\\t\\\\tfloat l = linearToRelativeLuminance( texel.rgb );\\\\n\\\\n\\\\t\\\\t\\\\tgl_FragColor = vec4( l, l, l, texel.w );\\\\n\\\\n\\\\t\\\\t}\\\\\\\"};var _j={uniforms:{tDiffuse:{value:null},averageLuminance:{value:1},luminanceMap:{value:null},maxLuminance:{value:16},minLuminance:{value:.01},middleGrey:{value:.6}},vertexShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\tvarying vec2 vUv;\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\tvUv = uv;\\\\n\\\\t\\\\t\\\\tgl_Position = projectionMatrix * modelViewMatrix * vec4( position, 1.0 );\\\\n\\\\n\\\\t\\\\t}\\\\\\\",fragmentShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\t#include \\\\n\\\\n\\\\t\\\\tuniform sampler2D tDiffuse;\\\\n\\\\n\\\\t\\\\tvarying vec2 vUv;\\\\n\\\\n\\\\t\\\\tuniform float middleGrey;\\\\n\\\\t\\\\tuniform float minLuminance;\\\\n\\\\t\\\\tuniform float maxLuminance;\\\\n\\\\t\\\\t#ifdef ADAPTED_LUMINANCE\\\\n\\\\t\\\\t\\\\tuniform sampler2D luminanceMap;\\\\n\\\\t\\\\t#else\\\\n\\\\t\\\\t\\\\tuniform float averageLuminance;\\\\n\\\\t\\\\t#endif\\\\n\\\\n\\\\t\\\\tvec3 ToneMap( vec3 vColor ) {\\\\n\\\\t\\\\t\\\\t#ifdef ADAPTED_LUMINANCE\\\\n\\\\t\\\\t\\\\t\\\\t// Get the calculated average luminance\\\\n\\\\t\\\\t\\\\t\\\\tfloat fLumAvg = texture2D(luminanceMap, vec2(0.5, 0.5)).r;\\\\n\\\\t\\\\t\\\\t#else\\\\n\\\\t\\\\t\\\\t\\\\tfloat fLumAvg = averageLuminance;\\\\n\\\\t\\\\t\\\\t#endif\\\\n\\\\n\\\\t\\\\t\\\\t// Calculate the luminance of the current pixel\\\\n\\\\t\\\\t\\\\tfloat fLumPixel = linearToRelativeLuminance( vColor );\\\\n\\\\n\\\\t\\\\t\\\\t// Apply the modified operator (Eq. 4)\\\\n\\\\t\\\\t\\\\tfloat fLumScaled = (fLumPixel * middleGrey) / max( minLuminance, fLumAvg );\\\\n\\\\n\\\\t\\\\t\\\\tfloat fLumCompressed = (fLumScaled * (1.0 + (fLumScaled / (maxLuminance * maxLuminance)))) / (1.0 + fLumScaled);\\\\n\\\\t\\\\t\\\\treturn fLumCompressed * vColor;\\\\n\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\tvec4 texel = texture2D( tDiffuse, vUv );\\\\n\\\\n\\\\t\\\\t\\\\tgl_FragColor = vec4( ToneMap( texel.xyz ), texel.w );\\\\n\\\\n\\\\t\\\\t}\\\\\\\"};class mj extends Im{constructor(t,e){super(),this.resolution=void 0!==e?e:256,this.needsInit=!0,this.adaptive=void 0===t||!!t,this.luminanceRT=null,this.previousLuminanceRT=null,this.currentLuminanceRT=null,void 0===Rm&&console.error(\\\\\\\"THREE.AdaptiveToneMappingPass relies on CopyShader\\\\\\\");const n=Rm;this.copyUniforms=I.clone(n.uniforms),this.materialCopy=new F({uniforms:this.copyUniforms,vertexShader:n.vertexShader,fragmentShader:n.fragmentShader,blending:w.ub,depthTest:!1}),void 0===pj&&console.error(\\\\\\\"THREE.AdaptiveToneMappingPass relies on LuminosityShader\\\\\\\"),this.materialLuminance=new F({uniforms:I.clone(pj.uniforms),vertexShader:pj.vertexShader,fragmentShader:pj.fragmentShader,blending:w.ub}),this.adaptLuminanceShader={defines:{MIP_LEVEL_1X1:(Math.log(this.resolution)/Math.log(2)).toFixed(1)},uniforms:{lastLum:{value:null},currentLum:{value:null},minLuminance:{value:.01},delta:{value:.016},tau:{value:1}},vertexShader:\\\\\\\"varying vec2 vUv;\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tvUv = uv;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tgl_Position = projectionMatrix * modelViewMatrix * vec4( position, 1.0 );\\\\n\\\\n\\\\t\\\\t\\\\t\\\\t}\\\\\\\",fragmentShader:\\\\\\\"varying vec2 vUv;\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tuniform sampler2D lastLum;\\\\n\\\\t\\\\t\\\\t\\\\tuniform sampler2D currentLum;\\\\n\\\\t\\\\t\\\\t\\\\tuniform float minLuminance;\\\\n\\\\t\\\\t\\\\t\\\\tuniform float delta;\\\\n\\\\t\\\\t\\\\t\\\\tuniform float tau;\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tvec4 lastLum = texture2D( lastLum, vUv, MIP_LEVEL_1X1 );\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tvec4 currentLum = texture2D( currentLum, vUv, MIP_LEVEL_1X1 );\\\\n\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tfloat fLastLum = max( minLuminance, lastLum.r );\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tfloat fCurrentLum = max( minLuminance, currentLum.r );\\\\n\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t//The adaption seems to work better in extreme lighting differences\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t//if the input luminance is squared.\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tfCurrentLum *= fCurrentLum;\\\\n\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t// Adapt the luminance using Pattanaik's technique\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tfloat fAdaptedLum = fLastLum + (fCurrentLum - fLastLum) * (1.0 - exp(-delta * tau));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t// \\\\\\\\\\\"fAdaptedLum = sqrt(fAdaptedLum);\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tgl_FragColor.r = fAdaptedLum;\\\\n\\\\t\\\\t\\\\t\\\\t}\\\\\\\"},this.materialAdaptiveLum=new F({uniforms:I.clone(this.adaptLuminanceShader.uniforms),vertexShader:this.adaptLuminanceShader.vertexShader,fragmentShader:this.adaptLuminanceShader.fragmentShader,defines:Object.assign({},this.adaptLuminanceShader.defines),blending:w.ub}),void 0===_j&&console.error(\\\\\\\"THREE.AdaptiveToneMappingPass relies on ToneMapShader\\\\\\\"),this.materialToneMap=new F({uniforms:I.clone(_j.uniforms),vertexShader:_j.vertexShader,fragmentShader:_j.fragmentShader,blending:w.ub}),this.fsQuad=new Bm(null)}render(t,e,n,i){this.needsInit&&(this.reset(t),this.luminanceRT.texture.type=n.texture.type,this.previousLuminanceRT.texture.type=n.texture.type,this.currentLuminanceRT.texture.type=n.texture.type,this.needsInit=!1),this.adaptive&&(this.fsQuad.material=this.materialLuminance,this.materialLuminance.uniforms.tDiffuse.value=n.texture,t.setRenderTarget(this.currentLuminanceRT),this.fsQuad.render(t),this.fsQuad.material=this.materialAdaptiveLum,this.materialAdaptiveLum.uniforms.delta.value=i,this.materialAdaptiveLum.uniforms.lastLum.value=this.previousLuminanceRT.texture,this.materialAdaptiveLum.uniforms.currentLum.value=this.currentLuminanceRT.texture,t.setRenderTarget(this.luminanceRT),this.fsQuad.render(t),this.fsQuad.material=this.materialCopy,this.copyUniforms.tDiffuse.value=this.luminanceRT.texture,t.setRenderTarget(this.previousLuminanceRT),this.fsQuad.render(t)),this.fsQuad.material=this.materialToneMap,this.materialToneMap.uniforms.tDiffuse.value=n.texture,this.renderToScreen?(t.setRenderTarget(null),this.fsQuad.render(t)):(t.setRenderTarget(e),this.clear&&t.clear(),this.fsQuad.render(t))}reset(){this.luminanceRT&&this.luminanceRT.dispose(),this.currentLuminanceRT&&this.currentLuminanceRT.dispose(),this.previousLuminanceRT&&this.previousLuminanceRT.dispose();const t={minFilter:w.V,magFilter:w.V,format:w.Ib};this.luminanceRT=new Q(this.resolution,this.resolution,t),this.luminanceRT.texture.name=\\\\\\\"AdaptiveToneMappingPass.l\\\\\\\",this.luminanceRT.texture.generateMipmaps=!1,this.previousLuminanceRT=new Q(this.resolution,this.resolution,t),this.previousLuminanceRT.texture.name=\\\\\\\"AdaptiveToneMappingPass.pl\\\\\\\",this.previousLuminanceRT.texture.generateMipmaps=!1,t.minFilter=w.Y,t.generateMipmaps=!0,this.currentLuminanceRT=new Q(this.resolution,this.resolution,t),this.currentLuminanceRT.texture.name=\\\\\\\"AdaptiveToneMappingPass.cl\\\\\\\",this.adaptive&&(this.materialToneMap.defines.ADAPTED_LUMINANCE=\\\\\\\"\\\\\\\",this.materialToneMap.uniforms.luminanceMap.value=this.luminanceRT.texture),this.fsQuad.material=new lt.a({color:7829367}),this.materialLuminance.needsUpdate=!0,this.materialAdaptiveLum.needsUpdate=!0,this.materialToneMap.needsUpdate=!0}setAdaptive(t){t?(this.adaptive=!0,this.materialToneMap.defines.ADAPTED_LUMINANCE=\\\\\\\"\\\\\\\",this.materialToneMap.uniforms.luminanceMap.value=this.luminanceRT.texture):(this.adaptive=!1,delete this.materialToneMap.defines.ADAPTED_LUMINANCE,this.materialToneMap.uniforms.luminanceMap.value=null),this.materialToneMap.needsUpdate=!0}setAdaptionRate(t){t&&(this.materialAdaptiveLum.uniforms.tau.value=Math.abs(t))}setMinLuminance(t){t&&(this.materialToneMap.uniforms.minLuminance.value=t,this.materialAdaptiveLum.uniforms.minLuminance.value=t)}setMaxLuminance(t){t&&(this.materialToneMap.uniforms.maxLuminance.value=t)}setAverageLuminance(t){t&&(this.materialToneMap.uniforms.averageLuminance.value=t)}setMiddleGrey(t){t&&(this.materialToneMap.uniforms.middleGrey.value=t)}dispose(){this.luminanceRT&&this.luminanceRT.dispose(),this.previousLuminanceRT&&this.previousLuminanceRT.dispose(),this.currentLuminanceRT&&this.currentLuminanceRT.dispose(),this.materialLuminance&&this.materialLuminance.dispose(),this.materialAdaptiveLum&&this.materialAdaptiveLum.dispose(),this.materialCopy&&this.materialCopy.dispose(),this.materialToneMap&&this.materialToneMap.dispose()}}const fj=new class extends la{constructor(){super(...arguments),this.adaptive=aa.BOOLEAN(1,{...uj}),this.averageLuminance=aa.FLOAT(.7,{...uj}),this.midGrey=aa.FLOAT(.04,{...uj}),this.maxLuminance=aa.FLOAT(16,{range:[0,20],...uj}),this.adaptiveRange=aa.FLOAT(2,{range:[0,10],...uj})}};class gj extends dj{constructor(){super(...arguments),this.paramsConfig=fj}static type(){return\\\\\\\"adaptiveToneMapping\\\\\\\"}_createPass(t){const e=new mj(this.pv.adaptive,t.resolution.x);return this.updatePass(e),e}updatePass(t){t.setMaxLuminance(this.pv.maxLuminance),t.setMiddleGrey(this.pv.midGrey),t.setAverageLuminance(this.pv.averageLuminance)}}const vj={uniforms:{damp:{value:.96},tOld:{value:null},tNew:{value:null}},vertexShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\tvarying vec2 vUv;\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\tvUv = uv;\\\\n\\\\t\\\\t\\\\tgl_Position = projectionMatrix * modelViewMatrix * vec4( position, 1.0 );\\\\n\\\\n\\\\t\\\\t}\\\\\\\",fragmentShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\tuniform float damp;\\\\n\\\\n\\\\t\\\\tuniform sampler2D tOld;\\\\n\\\\t\\\\tuniform sampler2D tNew;\\\\n\\\\n\\\\t\\\\tvarying vec2 vUv;\\\\n\\\\n\\\\t\\\\tvec4 when_gt( vec4 x, float y ) {\\\\n\\\\n\\\\t\\\\t\\\\treturn max( sign( x - y ), 0.0 );\\\\n\\\\n\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\tvec4 texelOld = texture2D( tOld, vUv );\\\\n\\\\t\\\\t\\\\tvec4 texelNew = texture2D( tNew, vUv );\\\\n\\\\n\\\\t\\\\t\\\\ttexelOld *= damp * when_gt( texelOld, 0.1 );\\\\n\\\\n\\\\t\\\\t\\\\tgl_FragColor = max(texelNew, texelOld);\\\\n\\\\n\\\\t\\\\t}\\\\\\\"};class yj extends Im{constructor(t=.96){super(),void 0===vj&&console.error(\\\\\\\"THREE.AfterimagePass relies on AfterimageShader\\\\\\\"),this.shader=vj,this.uniforms=I.clone(this.shader.uniforms),this.uniforms.damp.value=t,this.textureComp=new Q(window.innerWidth,window.innerHeight,{minFilter:w.V,magFilter:w.ob,format:w.Ib}),this.textureOld=new Q(window.innerWidth,window.innerHeight,{minFilter:w.V,magFilter:w.ob,format:w.Ib}),this.shaderMaterial=new F({uniforms:this.uniforms,vertexShader:this.shader.vertexShader,fragmentShader:this.shader.fragmentShader}),this.compFsQuad=new Bm(this.shaderMaterial);const e=new lt.a;this.copyFsQuad=new Bm(e)}render(t,e,n){this.uniforms.tOld.value=this.textureOld.texture,this.uniforms.tNew.value=n.texture,t.setRenderTarget(this.textureComp),this.compFsQuad.render(t),this.copyFsQuad.material.map=this.textureComp.texture,this.renderToScreen?(t.setRenderTarget(null),this.copyFsQuad.render(t)):(t.setRenderTarget(e),this.clear&&t.clear(),this.copyFsQuad.render(t));const i=this.textureOld;this.textureOld=this.textureComp,this.textureComp=i}setSize(t,e){this.textureComp.setSize(t,e),this.textureOld.setSize(t,e)}}const xj=new class extends la{constructor(){super(...arguments),this.damp=aa.FLOAT(.96,{range:[0,1],rangeLocked:[!0,!0],...uj})}};class bj extends dj{constructor(){super(...arguments),this.paramsConfig=xj}static type(){return\\\\\\\"afterImage\\\\\\\"}_createPass(t){const e=new yj;return this.updatePass(e),e}updatePass(t){t.uniforms.damp.value=this.pv.damp}}const wj={uniforms:{tDiffuse:{value:null},opacity:{value:1}},vertexShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\tvarying vec2 vUv;\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\tvUv = uv;\\\\n\\\\t\\\\t\\\\tgl_Position = projectionMatrix * modelViewMatrix * vec4( position, 1.0 );\\\\n\\\\n\\\\t\\\\t}\\\\\\\",fragmentShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\tuniform float opacity;\\\\n\\\\n\\\\t\\\\tuniform sampler2D tDiffuse;\\\\n\\\\n\\\\t\\\\tvarying vec2 vUv;\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\tvec4 base = texture2D( tDiffuse, vUv );\\\\n\\\\n\\\\t\\\\t\\\\tvec3 lumCoeff = vec3( 0.25, 0.65, 0.1 );\\\\n\\\\t\\\\t\\\\tfloat lum = dot( lumCoeff, base.rgb );\\\\n\\\\t\\\\t\\\\tvec3 blend = vec3( lum );\\\\n\\\\n\\\\t\\\\t\\\\tfloat L = min( 1.0, max( 0.0, 10.0 * ( lum - 0.45 ) ) );\\\\n\\\\n\\\\t\\\\t\\\\tvec3 result1 = 2.0 * base.rgb * blend;\\\\n\\\\t\\\\t\\\\tvec3 result2 = 1.0 - 2.0 * ( 1.0 - blend ) * ( 1.0 - base.rgb );\\\\n\\\\n\\\\t\\\\t\\\\tvec3 newColor = mix( result1, result2, L );\\\\n\\\\n\\\\t\\\\t\\\\tfloat A2 = opacity * base.a;\\\\n\\\\t\\\\t\\\\tvec3 mixRGB = A2 * newColor.rgb;\\\\n\\\\t\\\\t\\\\tmixRGB += ( ( 1.0 - A2 ) * base.rgb );\\\\n\\\\n\\\\t\\\\t\\\\tgl_FragColor = vec4( mixRGB, base.a );\\\\n\\\\n\\\\t\\\\t}\\\\\\\"};const Tj=new class extends la{constructor(){super(...arguments),this.opacity=aa.FLOAT(.95,{range:[-5,5],rangeLocked:[!0,!0],...uj})}};class Aj extends dj{constructor(){super(...arguments),this.paramsConfig=Tj}static type(){return\\\\\\\"bleach\\\\\\\"}_createPass(t){const e=new zm(wj);return this.updatePass(e),e}updatePass(t){t.uniforms.opacity.value=this.pv.opacity}}const Mj={uniforms:{tDiffuse:{value:null},brightness:{value:0},contrast:{value:0}},vertexShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\tvarying vec2 vUv;\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\tvUv = uv;\\\\n\\\\n\\\\t\\\\t\\\\tgl_Position = projectionMatrix * modelViewMatrix * vec4( position, 1.0 );\\\\n\\\\n\\\\t\\\\t}\\\\\\\",fragmentShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\tuniform sampler2D tDiffuse;\\\\n\\\\t\\\\tuniform float brightness;\\\\n\\\\t\\\\tuniform float contrast;\\\\n\\\\n\\\\t\\\\tvarying vec2 vUv;\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\tgl_FragColor = texture2D( tDiffuse, vUv );\\\\n\\\\n\\\\t\\\\t\\\\tgl_FragColor.rgb += brightness;\\\\n\\\\n\\\\t\\\\t\\\\tif (contrast > 0.0) {\\\\n\\\\t\\\\t\\\\t\\\\tgl_FragColor.rgb = (gl_FragColor.rgb - 0.5) / (1.0 - contrast) + 0.5;\\\\n\\\\t\\\\t\\\\t} else {\\\\n\\\\t\\\\t\\\\t\\\\tgl_FragColor.rgb = (gl_FragColor.rgb - 0.5) * (1.0 + contrast) + 0.5;\\\\n\\\\t\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\t}\\\\\\\"};const Ej=new class extends la{constructor(){super(...arguments),this.brightness=aa.FLOAT(0,{range:[-1,1],rangeLocked:[!1,!1],...uj}),this.contrast=aa.FLOAT(0,{range:[-1,1],rangeLocked:[!1,!1],...uj}),this.transparent=aa.BOOLEAN(1,uj)}};class Sj extends dj{constructor(){super(...arguments),this.paramsConfig=Ej}static type(){return\\\\\\\"brightnessContrast\\\\\\\"}_createPass(t){const e=new zm(Mj);return e.fsQuad.material.transparent=!0,this.updatePass(e),e}updatePass(t){t.uniforms.brightness.value=this.pv.brightness,t.uniforms.contrast.value=this.pv.contrast,t.material.transparent=this.pv.transparent}}class Cj extends Im{constructor(t,e){super(),this.needsSwap=!1,this.clearColor=void 0!==t?t:0,this.clearAlpha=void 0!==e?e:0,this._oldClearColor=new D.a}render(t,e,n){let i;this.clearColor&&(t.getClearColor(this._oldClearColor),i=t.getClearAlpha(),t.setClearColor(this.clearColor,this.clearAlpha)),t.setRenderTarget(this.renderToScreen?null:n),t.clear(),this.clearColor&&t.setClearColor(this._oldClearColor,i)}}const Nj=new class extends la{};class Lj extends dj{constructor(){super(...arguments),this.paramsConfig=Nj}static type(){return\\\\\\\"clear\\\\\\\"}_createPass(t){const e=new Cj;return this.updatePass(e),e}updatePass(t){}}const Oj=new class extends la{};class Pj extends dj{constructor(){super(...arguments),this.paramsConfig=Oj}static type(){return\\\\\\\"clearMask\\\\\\\"}_createPass(t){const e=new Um;return this.updatePass(e),e}updatePass(t){}}const Rj={uniforms:{tDiffuse:{value:null},powRGB:{value:new p.a(2,2,2)},mulRGB:{value:new p.a(1,1,1)},addRGB:{value:new p.a(0,0,0)}},vertexShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\tvarying vec2 vUv;\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\tvUv = uv;\\\\n\\\\n\\\\t\\\\t\\\\tgl_Position = projectionMatrix * modelViewMatrix * vec4( position, 1.0 );\\\\n\\\\n\\\\t\\\\t}\\\\\\\",fragmentShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\tuniform sampler2D tDiffuse;\\\\n\\\\t\\\\tuniform vec3 powRGB;\\\\n\\\\t\\\\tuniform vec3 mulRGB;\\\\n\\\\t\\\\tuniform vec3 addRGB;\\\\n\\\\n\\\\t\\\\tvarying vec2 vUv;\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\tgl_FragColor = texture2D( tDiffuse, vUv );\\\\n\\\\t\\\\t\\\\tgl_FragColor.rgb = mulRGB * pow( ( gl_FragColor.rgb + addRGB ), powRGB );\\\\n\\\\n\\\\t\\\\t}\\\\\\\"};const Ij=new class extends la{constructor(){super(...arguments),this.pow=aa.VECTOR3([2,2,2],{...uj}),this.mult=aa.COLOR([1,1,1],{...uj}),this.add=aa.COLOR([0,0,0],{...uj})}};class Fj extends dj{constructor(){super(...arguments),this.paramsConfig=Ij}static type(){return\\\\\\\"colorCorrection\\\\\\\"}_createPass(t){const e=new zm(Rj);return this.updatePass(e),e}updatePass(t){t.uniforms.powRGB.value.copy(this.pv.pow),t.uniforms.mulRGB.value.set(this.pv.mult.r,this.pv.mult.g,this.pv.mult.b),t.uniforms.addRGB.value.set(this.pv.add.r,this.pv.add.g,this.pv.add.b)}}const Dj=new class extends la{constructor(){super(...arguments),this.opacity=aa.FLOAT(1,{range:[0,1],rangeLocked:[!0,!0],...uj}),this.transparent=aa.BOOLEAN(1,uj)}};class Bj extends dj{constructor(){super(...arguments),this.paramsConfig=Dj}static type(){return\\\\\\\"copy\\\\\\\"}_createPass(t){const e=new zm(Rm);return this.updatePass(e),e}updatePass(t){t.uniforms.opacity.value=this.pv.opacity,t.material.transparent=this.pv.transparent}}const zj={uniforms:{textureWidth:{value:1},textureHeight:{value:1},focalDepth:{value:1},focalLength:{value:24},fstop:{value:.9},tColor:{value:null},tDepth:{value:null},maxblur:{value:1},showFocus:{value:0},manualdof:{value:0},vignetting:{value:0},depthblur:{value:0},threshold:{value:.5},gain:{value:2},bias:{value:.5},fringe:{value:.7},znear:{value:.1},zfar:{value:100},noise:{value:1},dithering:{value:1e-4},pentagon:{value:0},shaderFocus:{value:1},focusCoords:{value:new d.a}},vertexShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\tvarying vec2 vUv;\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\tvUv = uv;\\\\n\\\\t\\\\t\\\\tgl_Position = projectionMatrix * modelViewMatrix * vec4( position, 1.0 );\\\\n\\\\n\\\\t\\\\t}\\\\\\\",fragmentShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\t#include \\\\n\\\\n\\\\t\\\\tvarying vec2 vUv;\\\\n\\\\n\\\\t\\\\tuniform sampler2D tColor;\\\\n\\\\t\\\\tuniform sampler2D tDepth;\\\\n\\\\t\\\\tuniform float textureWidth;\\\\n\\\\t\\\\tuniform float textureHeight;\\\\n\\\\n\\\\t\\\\tuniform float focalDepth; //focal distance value in meters, but you may use autofocus option below\\\\n\\\\t\\\\tuniform float focalLength; //focal length in mm\\\\n\\\\t\\\\tuniform float fstop; //f-stop value\\\\n\\\\t\\\\tuniform bool showFocus; //show debug focus point and focal range (red = focal point, green = focal range)\\\\n\\\\n\\\\t\\\\t/*\\\\n\\\\t\\\\tmake sure that these two values are the same for your camera, otherwise distances will be wrong.\\\\n\\\\t\\\\t*/\\\\n\\\\n\\\\t\\\\tuniform float znear; // camera clipping start\\\\n\\\\t\\\\tuniform float zfar; // camera clipping end\\\\n\\\\n\\\\t\\\\t//------------------------------------------\\\\n\\\\t\\\\t//user variables\\\\n\\\\n\\\\t\\\\tconst int samples = SAMPLES; //samples on the first ring\\\\n\\\\t\\\\tconst int rings = RINGS; //ring count\\\\n\\\\n\\\\t\\\\tconst int maxringsamples = rings * samples;\\\\n\\\\n\\\\t\\\\tuniform bool manualdof; // manual dof calculation\\\\n\\\\t\\\\tfloat ndofstart = 1.0; // near dof blur start\\\\n\\\\t\\\\tfloat ndofdist = 2.0; // near dof blur falloff distance\\\\n\\\\t\\\\tfloat fdofstart = 1.0; // far dof blur start\\\\n\\\\t\\\\tfloat fdofdist = 3.0; // far dof blur falloff distance\\\\n\\\\n\\\\t\\\\tfloat CoC = 0.03; //circle of confusion size in mm (35mm film = 0.03mm)\\\\n\\\\n\\\\t\\\\tuniform bool vignetting; // use optical lens vignetting\\\\n\\\\n\\\\t\\\\tfloat vignout = 1.3; // vignetting outer border\\\\n\\\\t\\\\tfloat vignin = 0.0; // vignetting inner border\\\\n\\\\t\\\\tfloat vignfade = 22.0; // f-stops till vignete fades\\\\n\\\\n\\\\t\\\\tuniform bool shaderFocus;\\\\n\\\\t\\\\t// disable if you use external focalDepth value\\\\n\\\\n\\\\t\\\\tuniform vec2 focusCoords;\\\\n\\\\t\\\\t// autofocus point on screen (0.0,0.0 - left lower corner, 1.0,1.0 - upper right)\\\\n\\\\t\\\\t// if center of screen use vec2(0.5, 0.5);\\\\n\\\\n\\\\t\\\\tuniform float maxblur;\\\\n\\\\t\\\\t//clamp value of max blur (0.0 = no blur, 1.0 default)\\\\n\\\\n\\\\t\\\\tuniform float threshold; // highlight threshold;\\\\n\\\\t\\\\tuniform float gain; // highlight gain;\\\\n\\\\n\\\\t\\\\tuniform float bias; // bokeh edge bias\\\\n\\\\t\\\\tuniform float fringe; // bokeh chromatic aberration / fringing\\\\n\\\\n\\\\t\\\\tuniform bool noise; //use noise instead of pattern for sample dithering\\\\n\\\\n\\\\t\\\\tuniform float dithering;\\\\n\\\\n\\\\t\\\\tuniform bool depthblur; // blur the depth buffer\\\\n\\\\t\\\\tfloat dbsize = 1.25; // depth blur size\\\\n\\\\n\\\\t\\\\t/*\\\\n\\\\t\\\\tnext part is experimental\\\\n\\\\t\\\\tnot looking good with small sample and ring count\\\\n\\\\t\\\\tlooks okay starting from samples = 4, rings = 4\\\\n\\\\t\\\\t*/\\\\n\\\\n\\\\t\\\\tuniform bool pentagon; //use pentagon as bokeh shape?\\\\n\\\\t\\\\tfloat feather = 0.4; //pentagon shape feather\\\\n\\\\n\\\\t\\\\t//------------------------------------------\\\\n\\\\n\\\\t\\\\tfloat penta(vec2 coords) {\\\\n\\\\t\\\\t\\\\t//pentagonal shape\\\\n\\\\t\\\\t\\\\tfloat scale = float(rings) - 1.3;\\\\n\\\\t\\\\t\\\\tvec4 HS0 = vec4( 1.0, 0.0, 0.0, 1.0);\\\\n\\\\t\\\\t\\\\tvec4 HS1 = vec4( 0.309016994, 0.951056516, 0.0, 1.0);\\\\n\\\\t\\\\t\\\\tvec4 HS2 = vec4(-0.809016994, 0.587785252, 0.0, 1.0);\\\\n\\\\t\\\\t\\\\tvec4 HS3 = vec4(-0.809016994,-0.587785252, 0.0, 1.0);\\\\n\\\\t\\\\t\\\\tvec4 HS4 = vec4( 0.309016994,-0.951056516, 0.0, 1.0);\\\\n\\\\t\\\\t\\\\tvec4 HS5 = vec4( 0.0 ,0.0 , 1.0, 1.0);\\\\n\\\\n\\\\t\\\\t\\\\tvec4 one = vec4( 1.0 );\\\\n\\\\n\\\\t\\\\t\\\\tvec4 P = vec4((coords),vec2(scale, scale));\\\\n\\\\n\\\\t\\\\t\\\\tvec4 dist = vec4(0.0);\\\\n\\\\t\\\\t\\\\tfloat inorout = -4.0;\\\\n\\\\n\\\\t\\\\t\\\\tdist.x = dot( P, HS0 );\\\\n\\\\t\\\\t\\\\tdist.y = dot( P, HS1 );\\\\n\\\\t\\\\t\\\\tdist.z = dot( P, HS2 );\\\\n\\\\t\\\\t\\\\tdist.w = dot( P, HS3 );\\\\n\\\\n\\\\t\\\\t\\\\tdist = smoothstep( -feather, feather, dist );\\\\n\\\\n\\\\t\\\\t\\\\tinorout += dot( dist, one );\\\\n\\\\n\\\\t\\\\t\\\\tdist.x = dot( P, HS4 );\\\\n\\\\t\\\\t\\\\tdist.y = HS5.w - abs( P.z );\\\\n\\\\n\\\\t\\\\t\\\\tdist = smoothstep( -feather, feather, dist );\\\\n\\\\t\\\\t\\\\tinorout += dist.x;\\\\n\\\\n\\\\t\\\\t\\\\treturn clamp( inorout, 0.0, 1.0 );\\\\n\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\tfloat bdepth(vec2 coords) {\\\\n\\\\t\\\\t\\\\t// Depth buffer blur\\\\n\\\\t\\\\t\\\\tfloat d = 0.0;\\\\n\\\\t\\\\t\\\\tfloat kernel[9];\\\\n\\\\t\\\\t\\\\tvec2 offset[9];\\\\n\\\\n\\\\t\\\\t\\\\tvec2 wh = vec2(1.0/textureWidth,1.0/textureHeight) * dbsize;\\\\n\\\\n\\\\t\\\\t\\\\toffset[0] = vec2(-wh.x,-wh.y);\\\\n\\\\t\\\\t\\\\toffset[1] = vec2( 0.0, -wh.y);\\\\n\\\\t\\\\t\\\\toffset[2] = vec2( wh.x -wh.y);\\\\n\\\\n\\\\t\\\\t\\\\toffset[3] = vec2(-wh.x, 0.0);\\\\n\\\\t\\\\t\\\\toffset[4] = vec2( 0.0, 0.0);\\\\n\\\\t\\\\t\\\\toffset[5] = vec2( wh.x, 0.0);\\\\n\\\\n\\\\t\\\\t\\\\toffset[6] = vec2(-wh.x, wh.y);\\\\n\\\\t\\\\t\\\\toffset[7] = vec2( 0.0, wh.y);\\\\n\\\\t\\\\t\\\\toffset[8] = vec2( wh.x, wh.y);\\\\n\\\\n\\\\t\\\\t\\\\tkernel[0] = 1.0/16.0; kernel[1] = 2.0/16.0; kernel[2] = 1.0/16.0;\\\\n\\\\t\\\\t\\\\tkernel[3] = 2.0/16.0; kernel[4] = 4.0/16.0; kernel[5] = 2.0/16.0;\\\\n\\\\t\\\\t\\\\tkernel[6] = 1.0/16.0; kernel[7] = 2.0/16.0; kernel[8] = 1.0/16.0;\\\\n\\\\n\\\\n\\\\t\\\\t\\\\tfor( int i=0; i<9; i++ ) {\\\\n\\\\t\\\\t\\\\t\\\\tfloat tmp = texture2D(tDepth, coords + offset[i]).r;\\\\n\\\\t\\\\t\\\\t\\\\td += tmp * kernel[i];\\\\n\\\\t\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\t\\\\treturn d;\\\\n\\\\t\\\\t}\\\\n\\\\n\\\\n\\\\t\\\\tvec3 color(vec2 coords,float blur) {\\\\n\\\\t\\\\t\\\\t//processing the sample\\\\n\\\\n\\\\t\\\\t\\\\tvec3 col = vec3(0.0);\\\\n\\\\t\\\\t\\\\tvec2 texel = vec2(1.0/textureWidth,1.0/textureHeight);\\\\n\\\\n\\\\t\\\\t\\\\tcol.r = texture2D(tColor,coords + vec2(0.0,1.0)*texel*fringe*blur).r;\\\\n\\\\t\\\\t\\\\tcol.g = texture2D(tColor,coords + vec2(-0.866,-0.5)*texel*fringe*blur).g;\\\\n\\\\t\\\\t\\\\tcol.b = texture2D(tColor,coords + vec2(0.866,-0.5)*texel*fringe*blur).b;\\\\n\\\\n\\\\t\\\\t\\\\tvec3 lumcoeff = vec3(0.299,0.587,0.114);\\\\n\\\\t\\\\t\\\\tfloat lum = dot(col.rgb, lumcoeff);\\\\n\\\\t\\\\t\\\\tfloat thresh = max((lum-threshold)*gain, 0.0);\\\\n\\\\t\\\\t\\\\treturn col+mix(vec3(0.0),col,thresh*blur);\\\\n\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\tvec3 debugFocus(vec3 col, float blur, float depth) {\\\\n\\\\t\\\\t\\\\tfloat edge = 0.002*depth; //distance based edge smoothing\\\\n\\\\t\\\\t\\\\tfloat m = clamp(smoothstep(0.0,edge,blur),0.0,1.0);\\\\n\\\\t\\\\t\\\\tfloat e = clamp(smoothstep(1.0-edge,1.0,blur),0.0,1.0);\\\\n\\\\n\\\\t\\\\t\\\\tcol = mix(col,vec3(1.0,0.5,0.0),(1.0-m)*0.6);\\\\n\\\\t\\\\t\\\\tcol = mix(col,vec3(0.0,0.5,1.0),((1.0-e)-(1.0-m))*0.2);\\\\n\\\\n\\\\t\\\\t\\\\treturn col;\\\\n\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\tfloat linearize(float depth) {\\\\n\\\\t\\\\t\\\\treturn -zfar * znear / (depth * (zfar - znear) - zfar);\\\\n\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\tfloat vignette() {\\\\n\\\\t\\\\t\\\\tfloat dist = distance(vUv.xy, vec2(0.5,0.5));\\\\n\\\\t\\\\t\\\\tdist = smoothstep(vignout+(fstop/vignfade), vignin+(fstop/vignfade), dist);\\\\n\\\\t\\\\t\\\\treturn clamp(dist,0.0,1.0);\\\\n\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\tfloat gather(float i, float j, int ringsamples, inout vec3 col, float w, float h, float blur) {\\\\n\\\\t\\\\t\\\\tfloat rings2 = float(rings);\\\\n\\\\t\\\\t\\\\tfloat step = PI*2.0 / float(ringsamples);\\\\n\\\\t\\\\t\\\\tfloat pw = cos(j*step)*i;\\\\n\\\\t\\\\t\\\\tfloat ph = sin(j*step)*i;\\\\n\\\\t\\\\t\\\\tfloat p = 1.0;\\\\n\\\\t\\\\t\\\\tif (pentagon) {\\\\n\\\\t\\\\t\\\\t\\\\tp = penta(vec2(pw,ph));\\\\n\\\\t\\\\t\\\\t}\\\\n\\\\t\\\\t\\\\tcol += color(vUv.xy + vec2(pw*w,ph*h), blur) * mix(1.0, i/rings2, bias) * p;\\\\n\\\\t\\\\t\\\\treturn 1.0 * mix(1.0, i /rings2, bias) * p;\\\\n\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\t\\\\t\\\\t//scene depth calculation\\\\n\\\\n\\\\t\\\\t\\\\tfloat depth = linearize(texture2D(tDepth,vUv.xy).x);\\\\n\\\\n\\\\t\\\\t\\\\t// Blur depth?\\\\n\\\\t\\\\t\\\\tif ( depthblur ) {\\\\n\\\\t\\\\t\\\\t\\\\tdepth = linearize(bdepth(vUv.xy));\\\\n\\\\t\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\t\\\\t//focal plane calculation\\\\n\\\\n\\\\t\\\\t\\\\tfloat fDepth = focalDepth;\\\\n\\\\n\\\\t\\\\t\\\\tif (shaderFocus) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tfDepth = linearize(texture2D(tDepth,focusCoords).x);\\\\n\\\\n\\\\t\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\t\\\\t// dof blur factor calculation\\\\n\\\\n\\\\t\\\\t\\\\tfloat blur = 0.0;\\\\n\\\\n\\\\t\\\\t\\\\tif (manualdof) {\\\\n\\\\t\\\\t\\\\t\\\\tfloat a = depth-fDepth; // Focal plane\\\\n\\\\t\\\\t\\\\t\\\\tfloat b = (a-fdofstart)/fdofdist; // Far DoF\\\\n\\\\t\\\\t\\\\t\\\\tfloat c = (-a-ndofstart)/ndofdist; // Near Dof\\\\n\\\\t\\\\t\\\\t\\\\tblur = (a>0.0) ? b : c;\\\\n\\\\t\\\\t\\\\t} else {\\\\n\\\\t\\\\t\\\\t\\\\tfloat f = focalLength; // focal length in mm\\\\n\\\\t\\\\t\\\\t\\\\tfloat d = fDepth*1000.0; // focal plane in mm\\\\n\\\\t\\\\t\\\\t\\\\tfloat o = depth*1000.0; // depth in mm\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tfloat a = (o*f)/(o-f);\\\\n\\\\t\\\\t\\\\t\\\\tfloat b = (d*f)/(d-f);\\\\n\\\\t\\\\t\\\\t\\\\tfloat c = (d-f)/(d*fstop*CoC);\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tblur = abs(a-b)*c;\\\\n\\\\t\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\t\\\\tblur = clamp(blur,0.0,1.0);\\\\n\\\\n\\\\t\\\\t\\\\t// calculation of pattern for dithering\\\\n\\\\n\\\\t\\\\t\\\\tvec2 noise = vec2(rand(vUv.xy), rand( vUv.xy + vec2( 0.4, 0.6 ) ) )*dithering*blur;\\\\n\\\\n\\\\t\\\\t\\\\t// getting blur x and y step factor\\\\n\\\\n\\\\t\\\\t\\\\tfloat w = (1.0/textureWidth)*blur*maxblur+noise.x;\\\\n\\\\t\\\\t\\\\tfloat h = (1.0/textureHeight)*blur*maxblur+noise.y;\\\\n\\\\n\\\\t\\\\t\\\\t// calculation of final color\\\\n\\\\n\\\\t\\\\t\\\\tvec3 col = vec3(0.0);\\\\n\\\\n\\\\t\\\\t\\\\tif(blur < 0.05) {\\\\n\\\\t\\\\t\\\\t\\\\t//some optimization thingy\\\\n\\\\t\\\\t\\\\t\\\\tcol = texture2D(tColor, vUv.xy).rgb;\\\\n\\\\t\\\\t\\\\t} else {\\\\n\\\\t\\\\t\\\\t\\\\tcol = texture2D(tColor, vUv.xy).rgb;\\\\n\\\\t\\\\t\\\\t\\\\tfloat s = 1.0;\\\\n\\\\t\\\\t\\\\t\\\\tint ringsamples;\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tfor (int i = 1; i <= rings; i++) {\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t/*unboxstart*/\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tringsamples = i * samples;\\\\n\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tfor (int j = 0 ; j < maxringsamples ; j++) {\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif (j >= ringsamples) break;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\ts += gather(float(i), float(j), ringsamples, col, w, h, blur);\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t}\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t/*unboxend*/\\\\n\\\\t\\\\t\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tcol /= s; //divide by sample count\\\\n\\\\t\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\t\\\\tif (showFocus) {\\\\n\\\\t\\\\t\\\\t\\\\tcol = debugFocus(col, blur, depth);\\\\n\\\\t\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\t\\\\tif (vignetting) {\\\\n\\\\t\\\\t\\\\t\\\\tcol *= vignette();\\\\n\\\\t\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\t\\\\tgl_FragColor.rgb = col;\\\\n\\\\t\\\\t\\\\tgl_FragColor.a = 1.0;\\\\n\\\\t\\\\t}\\\\\\\"},kj={uniforms:{mNear:{value:1},mFar:{value:1e3}},vertexShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\tvarying float vViewZDepth;\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\t#include \\\\n\\\\t\\\\t\\\\t#include \\\\n\\\\n\\\\t\\\\t\\\\tvViewZDepth = - mvPosition.z;\\\\n\\\\n\\\\t\\\\t}\\\\\\\",fragmentShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\tuniform float mNear;\\\\n\\\\t\\\\tuniform float mFar;\\\\n\\\\n\\\\t\\\\tvarying float vViewZDepth;\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\tfloat color = 1.0 - smoothstep( mNear, mFar, vViewZDepth );\\\\n\\\\t\\\\t\\\\tgl_FragColor = vec4( vec3( color ), 1.0 );\\\\n\\\\n\\\\t\\\\t}\\\\\\\"};class Uj{constructor(t){this._scene=t}scene(){return this._scene}with_overriden_material(t,e,n,i){const s={};let r;this._scene.traverse((i=>{const o=i;if(o.material){const i=o.geometry;if(i){const a=o.customDepthDOFMaterial;if(a){if(r=a,r.uniforms)for(let t of Object.keys(n))r.uniforms[t].value=n[t].value}else r=_r.markedAsInstance(i)?e:t;r&&(s[o.uuid]=o.material,o.material=r)}}})),i(),this._scene.traverse((t=>{const e=t;if(e.material){e.geometry&&(e.material=s[e.uuid])}}));for(let t of Object.keys(s))delete s[t]}}class Gj{constructor(t,e,n,i){this._depth_of_field_node=t,this._scene=e,this._camera=n,this._resolution=i,this._camera_uniforms={mNear:{value:0},mFar:{value:0}},this.enabled=!0,this.needsSwap=!0,this.clear=!0,this.renderToScreen=!0,this._processing_scene=new gs,this.clear_color=new D.a(1,1,1),this._prev_clear_color=new D.a,this._core_scene=new Uj(this._scene);const s=3,r=4;this._processing_camera=new ot.a(this._resolution.x/-2,this._resolution.x/2,this._resolution.y/2,this._resolution.y/-2,-1e4,1e4),this._processing_camera.position.z=100,this._processing_scene.add(this._processing_camera);var o={minFilter:w.V,magFilter:w.V,format:w.ic};this._rtTextureDepth=new Q(this._resolution.x,this._resolution.y,o),this._rtTextureColor=new Q(this._resolution.x,this._resolution.y,o);var a=zj;a||console.error(\\\\\\\"BokehPass relies on BokehShader\\\\\\\"),this.bokeh_uniforms=I.clone(a.uniforms),this.bokeh_uniforms.tColor.value=this._rtTextureColor.texture,this.bokeh_uniforms.tDepth.value=this._rtTextureDepth.texture,this.bokeh_uniforms.textureWidth.value=this._resolution.x,this.bokeh_uniforms.textureHeight.value=this._resolution.y,this.bokeh_material=new F({uniforms:this.bokeh_uniforms,vertexShader:a.vertexShader,fragmentShader:a.fragmentShader,defines:{RINGS:s,SAMPLES:r}}),this._quad=new B.a(new L(this._resolution.x,this._resolution.y),this.bokeh_material),this._quad.position.z=-500,this._processing_scene.add(this._quad);var l=kj;l||console.error(\\\\\\\"BokehPass relies on BokehDepthShader\\\\\\\"),this.materialDepth=new F({uniforms:l.uniforms,vertexShader:l.vertexShader,fragmentShader:l.fragmentShader}),this.materialDepthInstance=new F({uniforms:l.uniforms,vertexShader:\\\\\\\"#include \\\\n\\\\nvec3 rotate_with_quat( vec3 v, vec4 q )\\\\n{\\\\n\\\\treturn v + 2.0 * cross( q.xyz, cross( q.xyz, v ) + q.w * v );\\\\n}\\\\n\\\\n\\\\nattribute vec4 instanceOrientation;\\\\nattribute vec3 instancePosition;\\\\nattribute vec3 instanceScale;\\\\nvarying float vViewZDepth;\\\\n\\\\n\\\\n\\\\nvoid main() {\\\\n\\\\n\\\\tvec3 v_POLYGON_instance_transform1_position = vec3(position);\\\\n\\\\tv_POLYGON_instance_transform1_position *= instanceScale;\\\\n\\\\tv_POLYGON_instance_transform1_position = rotate_with_quat( v_POLYGON_instance_transform1_position, instanceOrientation );\\\\n\\\\tv_POLYGON_instance_transform1_position += instancePosition;\\\\n\\\\t\\\\n\\\\t// replaces #include \\\\n\\\\tvec3 transformed = v_POLYGON_instance_transform1_position;\\\\n\\\\n\\\\n\\\\t#include \\\\n\\\\n\\\\tvViewZDepth = - mvPosition.z;\\\\n}\\\\\\\",fragmentShader:l.fragmentShader}),this.update_camera_uniforms_with_node(this._depth_of_field_node,this._camera)}setSize(t,e){this._rtTextureDepth.setSize(t,e),this._rtTextureColor.setSize(t,e),this.bokeh_uniforms.textureWidth.value=t,this.bokeh_uniforms.textureHeight.value=e}dispose(){this._rtTextureDepth.dispose(),this._rtTextureColor.dispose()}render(t,e,n){t.getClearColor(this._prev_clear_color),t.setClearColor(this.clear_color),t.clear(),t.setRenderTarget(this._rtTextureColor),t.clear(),t.render(this._scene,this._camera),t.setClearColor(0),this._core_scene.with_overriden_material(this.materialDepth,this.materialDepthInstance,this._camera_uniforms,(()=>{t.setRenderTarget(this._rtTextureDepth),t.clear(),t.render(this._scene,this._camera)})),t.setRenderTarget(null),t.clear(),t.render(this._processing_scene,this._processing_camera),t.setClearColor(this._prev_clear_color)}update_camera_uniforms_with_node(t,e){this.bokeh_uniforms.focalLength.value=e.getFocalLength(),this.bokeh_uniforms.znear.value=e.near,this.bokeh_uniforms.zfar.value=e.far;var n=Hj.smoothstep(e.near,e.far,t.pv.focalDepth),i=Hj.linearize(1-n,e.near,e.far);this.bokeh_uniforms.focalDepth.value=i,this._camera_uniforms={mNear:{value:e.near},mFar:{value:e.far}};for(let t of[this.materialDepth,this.materialDepthInstance])t.uniforms.mNear.value=this._camera_uniforms.mNear.value,t.uniforms.mFar.value=this._camera_uniforms.mFar.value}}const Vj=new class extends la{constructor(){super(...arguments),this.focalDepth=aa.FLOAT(10,{range:[0,50],rangeLocked:[!0,!1],step:.001,...uj}),this.fStep=aa.FLOAT(10,{range:[.1,22],rangeLocked:[!0,!0],...uj}),this.maxBlur=aa.FLOAT(2,{range:[0,10],rangeLocked:[!0,!1],...uj}),this.vignetting=aa.BOOLEAN(0,{...uj}),this.depthBlur=aa.BOOLEAN(0,{...uj}),this.threshold=aa.FLOAT(.5,{range:[0,1],rangeLocked:[!0,!0],step:.001,...uj}),this.gain=aa.FLOAT(1,{range:[0,100],rangeLocked:[!0,!0],step:.001,...uj}),this.bias=aa.FLOAT(1,{range:[0,3],rangeLocked:[!0,!0],step:.001,...uj}),this.fringe=aa.FLOAT(.7,{range:[0,5],rangeLocked:[!0,!1],step:.001,...uj}),this.noise=aa.BOOLEAN(0,{...uj}),this.dithering=aa.FLOAT(0,{range:[0,.001],rangeLocked:[!0,!0],step:1e-4,...uj}),this.pentagon=aa.BOOLEAN(0,{...uj}),this.rings=aa.INTEGER(3,{range:[1,8],rangeLocked:[!0,!0],...uj}),this.samples=aa.INTEGER(4,{range:[1,13],rangeLocked:[!0,!0],...uj}),this.clearColor=aa.COLOR([1,1,1],{...uj})}};class Hj extends dj{constructor(){super(...arguments),this.paramsConfig=Vj}static type(){return\\\\\\\"depthOfField\\\\\\\"}static saturate(t){return Math.max(0,Math.min(1,t))}static linearize(t,e,n){return-n*e/(t*(n-e)-n)}static smoothstep(t,e,n){var i=this.saturate((n-t)/(e-t));return i*i*(3-2*i)}_createPass(t){if(t.camera.isPerspectiveCamera){const e=t.camera_node;if(e){const n=new Gj(this,t.scene,e.object,t.resolution);this.updatePass(n);const i=new Mi(this.scene(),\\\\\\\"DOF\\\\\\\");return i.addGraphInput(e.p.near),i.addGraphInput(e.p.far),i.addGraphInput(e.p.fov),i.addGraphInput(this.p.focalDepth),i.addPostDirtyHook(\\\\\\\"post/DOF\\\\\\\",(()=>{this.update_pass_from_camera_node(n,e)})),n}}}update_pass_from_camera_node(t,e){t.update_camera_uniforms_with_node(this,e.object)}updatePass(t){t.bokeh_uniforms.fstop.value=this.pv.fStep,t.bokeh_uniforms.maxblur.value=this.pv.maxBlur,t.bokeh_uniforms.threshold.value=this.pv.threshold,t.bokeh_uniforms.gain.value=this.pv.gain,t.bokeh_uniforms.bias.value=this.pv.bias,t.bokeh_uniforms.fringe.value=this.pv.fringe,t.bokeh_uniforms.dithering.value=this.pv.dithering,t.bokeh_uniforms.noise.value=this.pv.noise?1:0,t.bokeh_uniforms.pentagon.value=this.pv.pentagon?1:0,t.bokeh_uniforms.vignetting.value=this.pv.vignetting?1:0,t.bokeh_uniforms.depthblur.value=this.pv.depthBlur?1:0,t.bokeh_uniforms.shaderFocus.value=0,t.bokeh_uniforms.showFocus.value=0,t.bokeh_uniforms.manualdof.value=0,t.bokeh_uniforms.focusCoords.value.set(.5,.5),t.bokeh_material.defines.RINGS=this.pv.rings,t.bokeh_material.defines.SAMPLES=this.pv.samples,t.bokeh_material.needsUpdate=!0,t.clear_color.copy(this.pv.clearColor)}}const jj={uniforms:{tDiffuse:{value:null},tSize:{value:new d.a(256,256)},center:{value:new d.a(.5,.5)},angle:{value:1.57},scale:{value:1}},vertexShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\tvarying vec2 vUv;\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\tvUv = uv;\\\\n\\\\t\\\\t\\\\tgl_Position = projectionMatrix * modelViewMatrix * vec4( position, 1.0 );\\\\n\\\\n\\\\t\\\\t}\\\\\\\",fragmentShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\tuniform vec2 center;\\\\n\\\\t\\\\tuniform float angle;\\\\n\\\\t\\\\tuniform float scale;\\\\n\\\\t\\\\tuniform vec2 tSize;\\\\n\\\\n\\\\t\\\\tuniform sampler2D tDiffuse;\\\\n\\\\n\\\\t\\\\tvarying vec2 vUv;\\\\n\\\\n\\\\t\\\\tfloat pattern() {\\\\n\\\\n\\\\t\\\\t\\\\tfloat s = sin( angle ), c = cos( angle );\\\\n\\\\n\\\\t\\\\t\\\\tvec2 tex = vUv * tSize - center;\\\\n\\\\t\\\\t\\\\tvec2 point = vec2( c * tex.x - s * tex.y, s * tex.x + c * tex.y ) * scale;\\\\n\\\\n\\\\t\\\\t\\\\treturn ( sin( point.x ) * sin( point.y ) ) * 4.0;\\\\n\\\\n\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\tvec4 color = texture2D( tDiffuse, vUv );\\\\n\\\\n\\\\t\\\\t\\\\tfloat average = ( color.r + color.g + color.b ) / 3.0;\\\\n\\\\n\\\\t\\\\t\\\\tgl_FragColor = vec4( vec3( average * 10.0 - 5.0 + pattern() ), color.a );\\\\n\\\\n\\\\t\\\\t}\\\\\\\"};const Wj=new class extends la{constructor(){super(...arguments),this.center=aa.VECTOR2([.5,.5],{...uj}),this.angle=aa.FLOAT(\\\\\\\"$PI*0.5\\\\\\\",{range:[0,10],rangeLocked:[!1,!1],...uj}),this.scale=aa.FLOAT(1,{range:[0,1],rangeLocked:[!1,!1],...uj})}};class qj extends dj{constructor(){super(...arguments),this.paramsConfig=Wj}static type(){return\\\\\\\"dotScreen\\\\\\\"}_createPass(t){const e=new zm(jj);return this.updatePass(e),e}updatePass(t){t.uniforms.center.value=this.pv.center,t.uniforms.angle.value=this.pv.angle,t.uniforms.scale.value=this.pv.scale}}const Xj={uniforms:{tDiffuse:{value:null},time:{value:0},nIntensity:{value:.5},sIntensity:{value:.05},sCount:{value:4096},grayscale:{value:1}},vertexShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\tvarying vec2 vUv;\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\tvUv = uv;\\\\n\\\\t\\\\t\\\\tgl_Position = projectionMatrix * modelViewMatrix * vec4( position, 1.0 );\\\\n\\\\n\\\\t\\\\t}\\\\\\\",fragmentShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\t#include \\\\n\\\\n\\\\t\\\\t// control parameter\\\\n\\\\t\\\\tuniform float time;\\\\n\\\\n\\\\t\\\\tuniform bool grayscale;\\\\n\\\\n\\\\t\\\\t// noise effect intensity value (0 = no effect, 1 = full effect)\\\\n\\\\t\\\\tuniform float nIntensity;\\\\n\\\\n\\\\t\\\\t// scanlines effect intensity value (0 = no effect, 1 = full effect)\\\\n\\\\t\\\\tuniform float sIntensity;\\\\n\\\\n\\\\t\\\\t// scanlines effect count value (0 = no effect, 4096 = full effect)\\\\n\\\\t\\\\tuniform float sCount;\\\\n\\\\n\\\\t\\\\tuniform sampler2D tDiffuse;\\\\n\\\\n\\\\t\\\\tvarying vec2 vUv;\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t// sample the source\\\\n\\\\t\\\\t\\\\tvec4 cTextureScreen = texture2D( tDiffuse, vUv );\\\\n\\\\n\\\\t\\\\t// make some noise\\\\n\\\\t\\\\t\\\\tfloat dx = rand( vUv + time );\\\\n\\\\n\\\\t\\\\t// add noise\\\\n\\\\t\\\\t\\\\tvec3 cResult = cTextureScreen.rgb + cTextureScreen.rgb * clamp( 0.1 + dx, 0.0, 1.0 );\\\\n\\\\n\\\\t\\\\t// get us a sine and cosine\\\\n\\\\t\\\\t\\\\tvec2 sc = vec2( sin( vUv.y * sCount ), cos( vUv.y * sCount ) );\\\\n\\\\n\\\\t\\\\t// add scanlines\\\\n\\\\t\\\\t\\\\tcResult += cTextureScreen.rgb * vec3( sc.x, sc.y, sc.x ) * sIntensity;\\\\n\\\\n\\\\t\\\\t// interpolate between source and result by intensity\\\\n\\\\t\\\\t\\\\tcResult = cTextureScreen.rgb + clamp( nIntensity, 0.0,1.0 ) * ( cResult - cTextureScreen.rgb );\\\\n\\\\n\\\\t\\\\t// convert to grayscale if desired\\\\n\\\\t\\\\t\\\\tif( grayscale ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tcResult = vec3( cResult.r * 0.3 + cResult.g * 0.59 + cResult.b * 0.11 );\\\\n\\\\n\\\\t\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\t\\\\tgl_FragColor = vec4( cResult, cTextureScreen.a );\\\\n\\\\n\\\\t\\\\t}\\\\\\\"};class Yj extends Im{constructor(t,e,n,i){super(),void 0===Xj&&console.error(\\\\\\\"THREE.FilmPass relies on FilmShader\\\\\\\");const s=Xj;this.uniforms=I.clone(s.uniforms),this.material=new F({uniforms:this.uniforms,vertexShader:s.vertexShader,fragmentShader:s.fragmentShader}),void 0!==i&&(this.uniforms.grayscale.value=i),void 0!==t&&(this.uniforms.nIntensity.value=t),void 0!==e&&(this.uniforms.sIntensity.value=e),void 0!==n&&(this.uniforms.sCount.value=n),this.fsQuad=new Bm(this.material)}render(t,e,n,i){this.uniforms.tDiffuse.value=n.texture,this.uniforms.time.value+=i,this.renderToScreen?(t.setRenderTarget(null),this.fsQuad.render(t)):(t.setRenderTarget(e),this.clear&&t.clear(),this.fsQuad.render(t))}}const $j=new class extends la{constructor(){super(...arguments),this.noiseIntensity=aa.FLOAT(.5,{range:[0,1],rangeLocked:[!1,!1],...uj}),this.scanlinesIntensity=aa.FLOAT(.05,{range:[0,1],rangeLocked:[!0,!1],...uj}),this.scanlinesCount=aa.FLOAT(4096,{range:[0,4096],rangeLocked:[!0,!1],...uj}),this.grayscale=aa.BOOLEAN(1,{...uj})}};class Jj extends dj{constructor(){super(...arguments),this.paramsConfig=$j}static type(){return\\\\\\\"film\\\\\\\"}_createPass(t){const e=new Yj(this.pv.noiseIntensity,this.pv.scanlinesIntensity,this.pv.scanlinesCount,this.pv.grayscale?1:0);return this.updatePass(e),e}updatePass(t){t.uniforms.nIntensity.value=this.pv.noiseIntensity,t.uniforms.sIntensity.value=this.pv.scanlinesIntensity,t.uniforms.sCount.value=this.pv.scanlinesCount,t.uniforms.grayscale.value=this.pv.grayscale?1:0}}const Zj={uniforms:{tDiffuse:{value:null},resolution:{value:new d.a(1/1024,1/512)}},vertexShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\tvarying vec2 vUv;\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\tvUv = uv;\\\\n\\\\t\\\\t\\\\tgl_Position = projectionMatrix * modelViewMatrix * vec4( position, 1.0 );\\\\n\\\\n\\\\t\\\\t}\\\\\\\",fragmentShader:'\\\\n\\\\n\\\\t\\\\tprecision highp float;\\\\n\\\\n\\\\t\\\\tuniform sampler2D tDiffuse;\\\\n\\\\n\\\\t\\\\tuniform vec2 resolution;\\\\n\\\\n\\\\t\\\\tvarying vec2 vUv;\\\\n\\\\n\\\\t\\\\t#define FXAA_PC 1\\\\n\\\\t\\\\t#define FXAA_GLSL_100 1\\\\n\\\\t\\\\t#define FXAA_QUALITY_PRESET 12\\\\n\\\\n\\\\t\\\\t#define FXAA_GREEN_AS_LUMA 1\\\\n\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t#ifndef FXAA_PC_CONSOLE\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\n\\\\t\\\\t\\\\t\\\\t// The console algorithm for PC is included\\\\n\\\\t\\\\t\\\\t\\\\t// for developers targeting really low spec machines.\\\\n\\\\t\\\\t\\\\t\\\\t// Likely better to just run FXAA_PC, and use a really low preset.\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_PC_CONSOLE 0\\\\n\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t#ifndef FXAA_GLSL_120\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_GLSL_120 0\\\\n\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t#ifndef FXAA_GLSL_130\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_GLSL_130 0\\\\n\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t#ifndef FXAA_HLSL_3\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_HLSL_3 0\\\\n\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t#ifndef FXAA_HLSL_4\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_HLSL_4 0\\\\n\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t#ifndef FXAA_HLSL_5\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_HLSL_5 0\\\\n\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*==========================================================================*/\\\\n\\\\t\\\\t#ifndef FXAA_GREEN_AS_LUMA\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\n\\\\t\\\\t\\\\t\\\\t// For those using non-linear color,\\\\n\\\\t\\\\t\\\\t\\\\t// and either not able to get luma in alpha, or not wanting to,\\\\n\\\\t\\\\t\\\\t\\\\t// this enables FXAA to run using green as a proxy for luma.\\\\n\\\\t\\\\t\\\\t\\\\t// So with this enabled, no need to pack luma in alpha.\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\n\\\\t\\\\t\\\\t\\\\t// This will turn off AA on anything which lacks some amount of green.\\\\n\\\\t\\\\t\\\\t\\\\t// Pure red and blue or combination of only R and B, will get no AA.\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\n\\\\t\\\\t\\\\t\\\\t// Might want to lower the settings for both,\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t\\\\tfxaaConsoleEdgeThresholdMin\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t\\\\tfxaaQualityEdgeThresholdMin\\\\n\\\\t\\\\t\\\\t\\\\t// In order to insure AA does not get turned off on colors\\\\n\\\\t\\\\t\\\\t\\\\t// which contain a minor amount of green.\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\n\\\\t\\\\t\\\\t\\\\t// 1 = On.\\\\n\\\\t\\\\t\\\\t\\\\t// 0 = Off.\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_GREEN_AS_LUMA 0\\\\n\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t#ifndef FXAA_EARLY_EXIT\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\n\\\\t\\\\t\\\\t\\\\t// Controls algorithm\\\\'s early exit path.\\\\n\\\\t\\\\t\\\\t\\\\t// On PS3 turning this ON adds 2 cycles to the shader.\\\\n\\\\t\\\\t\\\\t\\\\t// On 360 turning this OFF adds 10ths of a millisecond to the shader.\\\\n\\\\t\\\\t\\\\t\\\\t// Turning this off on console will result in a more blurry image.\\\\n\\\\t\\\\t\\\\t\\\\t// So this defaults to on.\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\n\\\\t\\\\t\\\\t\\\\t// 1 = On.\\\\n\\\\t\\\\t\\\\t\\\\t// 0 = Off.\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_EARLY_EXIT 1\\\\n\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t#ifndef FXAA_DISCARD\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\n\\\\t\\\\t\\\\t\\\\t// Only valid for PC OpenGL currently.\\\\n\\\\t\\\\t\\\\t\\\\t// Probably will not work when FXAA_GREEN_AS_LUMA = 1.\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\n\\\\t\\\\t\\\\t\\\\t// 1 = Use discard on pixels which don\\\\'t need AA.\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t\\\\t For APIs which enable concurrent TEX+ROP from same surface.\\\\n\\\\t\\\\t\\\\t\\\\t// 0 = Return unchanged color on pixels which don\\\\'t need AA.\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_DISCARD 0\\\\n\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t#ifndef FXAA_FAST_PIXEL_OFFSET\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\n\\\\t\\\\t\\\\t\\\\t// Used for GLSL 120 only.\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\n\\\\t\\\\t\\\\t\\\\t// 1 = GL API supports fast pixel offsets\\\\n\\\\t\\\\t\\\\t\\\\t// 0 = do not use fast pixel offsets\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\n\\\\t\\\\t\\\\t\\\\t#ifdef GL_EXT_gpu_shader4\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#define FXAA_FAST_PIXEL_OFFSET 1\\\\n\\\\t\\\\t\\\\t\\\\t#endif\\\\n\\\\t\\\\t\\\\t\\\\t#ifdef GL_NV_gpu_shader5\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#define FXAA_FAST_PIXEL_OFFSET 1\\\\n\\\\t\\\\t\\\\t\\\\t#endif\\\\n\\\\t\\\\t\\\\t\\\\t#ifdef GL_ARB_gpu_shader5\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#define FXAA_FAST_PIXEL_OFFSET 1\\\\n\\\\t\\\\t\\\\t\\\\t#endif\\\\n\\\\t\\\\t\\\\t\\\\t#ifndef FXAA_FAST_PIXEL_OFFSET\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#define FXAA_FAST_PIXEL_OFFSET 0\\\\n\\\\t\\\\t\\\\t\\\\t#endif\\\\n\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t#ifndef FXAA_GATHER4_ALPHA\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\n\\\\t\\\\t\\\\t\\\\t// 1 = API supports gather4 on alpha channel.\\\\n\\\\t\\\\t\\\\t\\\\t// 0 = API does not support gather4 on alpha channel.\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\n\\\\t\\\\t\\\\t\\\\t#if (FXAA_HLSL_5 == 1)\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#define FXAA_GATHER4_ALPHA 1\\\\n\\\\t\\\\t\\\\t\\\\t#endif\\\\n\\\\t\\\\t\\\\t\\\\t#ifdef GL_ARB_gpu_shader5\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#define FXAA_GATHER4_ALPHA 1\\\\n\\\\t\\\\t\\\\t\\\\t#endif\\\\n\\\\t\\\\t\\\\t\\\\t#ifdef GL_NV_gpu_shader5\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#define FXAA_GATHER4_ALPHA 1\\\\n\\\\t\\\\t\\\\t\\\\t#endif\\\\n\\\\t\\\\t\\\\t\\\\t#ifndef FXAA_GATHER4_ALPHA\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#define FXAA_GATHER4_ALPHA 0\\\\n\\\\t\\\\t\\\\t\\\\t#endif\\\\n\\\\t\\\\t#endif\\\\n\\\\n\\\\n\\\\t\\\\t/*============================================================================\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tFXAA QUALITY - TUNING KNOBS\\\\n\\\\t\\\\t------------------------------------------------------------------------------\\\\n\\\\t\\\\tNOTE the other tuning knobs are now in the shader function inputs!\\\\n\\\\t\\\\t============================================================================*/\\\\n\\\\t\\\\t#ifndef FXAA_QUALITY_PRESET\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\n\\\\t\\\\t\\\\t\\\\t// Choose the quality preset.\\\\n\\\\t\\\\t\\\\t\\\\t// This needs to be compiled into the shader as it effects code.\\\\n\\\\t\\\\t\\\\t\\\\t// Best option to include multiple presets is to\\\\n\\\\t\\\\t\\\\t\\\\t// in each shader define the preset, then include this file.\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\n\\\\t\\\\t\\\\t\\\\t// OPTIONS\\\\n\\\\t\\\\t\\\\t\\\\t// -----------------------------------------------------------------------\\\\n\\\\t\\\\t\\\\t\\\\t// 10 to 15 - default medium dither (10=fastest, 15=highest quality)\\\\n\\\\t\\\\t\\\\t\\\\t// 20 to 29 - less dither, more expensive (20=fastest, 29=highest quality)\\\\n\\\\t\\\\t\\\\t\\\\t// 39\\\\t\\\\t\\\\t - no dither, very expensive\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\n\\\\t\\\\t\\\\t\\\\t// NOTES\\\\n\\\\t\\\\t\\\\t\\\\t// -----------------------------------------------------------------------\\\\n\\\\t\\\\t\\\\t\\\\t// 12 = slightly faster then FXAA 3.9 and higher edge quality (default)\\\\n\\\\t\\\\t\\\\t\\\\t// 13 = about same speed as FXAA 3.9 and better than 12\\\\n\\\\t\\\\t\\\\t\\\\t// 23 = closest to FXAA 3.9 visually and performance wise\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t_ = the lowest digit is directly related to performance\\\\n\\\\t\\\\t\\\\t\\\\t// _\\\\t= the highest digit is directly related to style\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_PRESET 12\\\\n\\\\t\\\\t#endif\\\\n\\\\n\\\\n\\\\t\\\\t/*============================================================================\\\\n\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t FXAA QUALITY - PRESETS\\\\n\\\\n\\\\t\\\\t============================================================================*/\\\\n\\\\n\\\\t\\\\t/*============================================================================\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t FXAA QUALITY - MEDIUM DITHER PRESETS\\\\n\\\\t\\\\t============================================================================*/\\\\n\\\\t\\\\t#if (FXAA_QUALITY_PRESET == 10)\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_PS 3\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P0 1.5\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P1 3.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P2 12.0\\\\n\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t#if (FXAA_QUALITY_PRESET == 11)\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_PS 4\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P0 1.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P1 1.5\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P2 3.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P3 12.0\\\\n\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t#if (FXAA_QUALITY_PRESET == 12)\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_PS 5\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P0 1.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P1 1.5\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P2 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P3 4.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P4 12.0\\\\n\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t#if (FXAA_QUALITY_PRESET == 13)\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_PS 6\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P0 1.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P1 1.5\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P2 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P3 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P4 4.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P5 12.0\\\\n\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t#if (FXAA_QUALITY_PRESET == 14)\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_PS 7\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P0 1.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P1 1.5\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P2 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P3 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P4 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P5 4.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P6 12.0\\\\n\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t#if (FXAA_QUALITY_PRESET == 15)\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_PS 8\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P0 1.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P1 1.5\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P2 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P3 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P4 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P5 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P6 4.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P7 12.0\\\\n\\\\t\\\\t#endif\\\\n\\\\n\\\\t\\\\t/*============================================================================\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t FXAA QUALITY - LOW DITHER PRESETS\\\\n\\\\t\\\\t============================================================================*/\\\\n\\\\t\\\\t#if (FXAA_QUALITY_PRESET == 20)\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_PS 3\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P0 1.5\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P1 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P2 8.0\\\\n\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t#if (FXAA_QUALITY_PRESET == 21)\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_PS 4\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P0 1.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P1 1.5\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P2 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P3 8.0\\\\n\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t#if (FXAA_QUALITY_PRESET == 22)\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_PS 5\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P0 1.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P1 1.5\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P2 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P3 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P4 8.0\\\\n\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t#if (FXAA_QUALITY_PRESET == 23)\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_PS 6\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P0 1.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P1 1.5\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P2 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P3 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P4 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P5 8.0\\\\n\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t#if (FXAA_QUALITY_PRESET == 24)\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_PS 7\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P0 1.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P1 1.5\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P2 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P3 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P4 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P5 3.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P6 8.0\\\\n\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t#if (FXAA_QUALITY_PRESET == 25)\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_PS 8\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P0 1.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P1 1.5\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P2 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P3 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P4 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P5 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P6 4.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P7 8.0\\\\n\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t#if (FXAA_QUALITY_PRESET == 26)\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_PS 9\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P0 1.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P1 1.5\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P2 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P3 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P4 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P5 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P6 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P7 4.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P8 8.0\\\\n\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t#if (FXAA_QUALITY_PRESET == 27)\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_PS 10\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P0 1.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P1 1.5\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P2 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P3 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P4 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P5 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P6 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P7 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P8 4.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P9 8.0\\\\n\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t#if (FXAA_QUALITY_PRESET == 28)\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_PS 11\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P0 1.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P1 1.5\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P2 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P3 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P4 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P5 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P6 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P7 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P8 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P9 4.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P10 8.0\\\\n\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t#if (FXAA_QUALITY_PRESET == 29)\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_PS 12\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P0 1.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P1 1.5\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P2 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P3 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P4 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P5 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P6 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P7 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P8 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P9 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P10 4.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P11 8.0\\\\n\\\\t\\\\t#endif\\\\n\\\\n\\\\t\\\\t/*============================================================================\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t FXAA QUALITY - EXTREME QUALITY\\\\n\\\\t\\\\t============================================================================*/\\\\n\\\\t\\\\t#if (FXAA_QUALITY_PRESET == 39)\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_PS 12\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P0 1.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P1 1.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P2 1.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P3 1.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P4 1.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P5 1.5\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P6 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P7 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P8 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P9 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P10 4.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P11 8.0\\\\n\\\\t\\\\t#endif\\\\n\\\\n\\\\n\\\\n\\\\t\\\\t/*============================================================================\\\\n\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tAPI PORTING\\\\n\\\\n\\\\t\\\\t============================================================================*/\\\\n\\\\t\\\\t#if (FXAA_GLSL_100 == 1) || (FXAA_GLSL_120 == 1) || (FXAA_GLSL_130 == 1)\\\\n\\\\t\\\\t\\\\t\\\\t#define FxaaBool bool\\\\n\\\\t\\\\t\\\\t\\\\t#define FxaaDiscard discard\\\\n\\\\t\\\\t\\\\t\\\\t#define FxaaFloat float\\\\n\\\\t\\\\t\\\\t\\\\t#define FxaaFloat2 vec2\\\\n\\\\t\\\\t\\\\t\\\\t#define FxaaFloat3 vec3\\\\n\\\\t\\\\t\\\\t\\\\t#define FxaaFloat4 vec4\\\\n\\\\t\\\\t\\\\t\\\\t#define FxaaHalf float\\\\n\\\\t\\\\t\\\\t\\\\t#define FxaaHalf2 vec2\\\\n\\\\t\\\\t\\\\t\\\\t#define FxaaHalf3 vec3\\\\n\\\\t\\\\t\\\\t\\\\t#define FxaaHalf4 vec4\\\\n\\\\t\\\\t\\\\t\\\\t#define FxaaInt2 ivec2\\\\n\\\\t\\\\t\\\\t\\\\t#define FxaaSat(x) clamp(x, 0.0, 1.0)\\\\n\\\\t\\\\t\\\\t\\\\t#define FxaaTex sampler2D\\\\n\\\\t\\\\t#else\\\\n\\\\t\\\\t\\\\t\\\\t#define FxaaBool bool\\\\n\\\\t\\\\t\\\\t\\\\t#define FxaaDiscard clip(-1)\\\\n\\\\t\\\\t\\\\t\\\\t#define FxaaFloat float\\\\n\\\\t\\\\t\\\\t\\\\t#define FxaaFloat2 float2\\\\n\\\\t\\\\t\\\\t\\\\t#define FxaaFloat3 float3\\\\n\\\\t\\\\t\\\\t\\\\t#define FxaaFloat4 float4\\\\n\\\\t\\\\t\\\\t\\\\t#define FxaaHalf half\\\\n\\\\t\\\\t\\\\t\\\\t#define FxaaHalf2 half2\\\\n\\\\t\\\\t\\\\t\\\\t#define FxaaHalf3 half3\\\\n\\\\t\\\\t\\\\t\\\\t#define FxaaHalf4 half4\\\\n\\\\t\\\\t\\\\t\\\\t#define FxaaSat(x) saturate(x)\\\\n\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t#if (FXAA_GLSL_100 == 1)\\\\n\\\\t\\\\t\\\\t#define FxaaTexTop(t, p) texture2D(t, p, 0.0)\\\\n\\\\t\\\\t\\\\t#define FxaaTexOff(t, p, o, r) texture2D(t, p + (o * r), 0.0)\\\\n\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t#if (FXAA_GLSL_120 == 1)\\\\n\\\\t\\\\t\\\\t\\\\t// Requires,\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t#version 120\\\\n\\\\t\\\\t\\\\t\\\\t// And at least,\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t#extension GL_EXT_gpu_shader4 : enable\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t(or set FXAA_FAST_PIXEL_OFFSET 1 to work like DX9)\\\\n\\\\t\\\\t\\\\t\\\\t#define FxaaTexTop(t, p) texture2DLod(t, p, 0.0)\\\\n\\\\t\\\\t\\\\t\\\\t#if (FXAA_FAST_PIXEL_OFFSET == 1)\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#define FxaaTexOff(t, p, o, r) texture2DLodOffset(t, p, 0.0, o)\\\\n\\\\t\\\\t\\\\t\\\\t#else\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#define FxaaTexOff(t, p, o, r) texture2DLod(t, p + (o * r), 0.0)\\\\n\\\\t\\\\t\\\\t\\\\t#endif\\\\n\\\\t\\\\t\\\\t\\\\t#if (FXAA_GATHER4_ALPHA == 1)\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t// use #extension GL_ARB_gpu_shader5 : enable\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#define FxaaTexAlpha4(t, p) textureGather(t, p, 3)\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#define FxaaTexOffAlpha4(t, p, o) textureGatherOffset(t, p, o, 3)\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#define FxaaTexGreen4(t, p) textureGather(t, p, 1)\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#define FxaaTexOffGreen4(t, p, o) textureGatherOffset(t, p, o, 1)\\\\n\\\\t\\\\t\\\\t\\\\t#endif\\\\n\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t#if (FXAA_GLSL_130 == 1)\\\\n\\\\t\\\\t\\\\t\\\\t// Requires \\\\\\\"#version 130\\\\\\\" or better\\\\n\\\\t\\\\t\\\\t\\\\t#define FxaaTexTop(t, p) textureLod(t, p, 0.0)\\\\n\\\\t\\\\t\\\\t\\\\t#define FxaaTexOff(t, p, o, r) textureLodOffset(t, p, 0.0, o)\\\\n\\\\t\\\\t\\\\t\\\\t#if (FXAA_GATHER4_ALPHA == 1)\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t// use #extension GL_ARB_gpu_shader5 : enable\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#define FxaaTexAlpha4(t, p) textureGather(t, p, 3)\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#define FxaaTexOffAlpha4(t, p, o) textureGatherOffset(t, p, o, 3)\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#define FxaaTexGreen4(t, p) textureGather(t, p, 1)\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#define FxaaTexOffGreen4(t, p, o) textureGatherOffset(t, p, o, 1)\\\\n\\\\t\\\\t\\\\t\\\\t#endif\\\\n\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t#if (FXAA_HLSL_3 == 1)\\\\n\\\\t\\\\t\\\\t\\\\t#define FxaaInt2 float2\\\\n\\\\t\\\\t\\\\t\\\\t#define FxaaTex sampler2D\\\\n\\\\t\\\\t\\\\t\\\\t#define FxaaTexTop(t, p) tex2Dlod(t, float4(p, 0.0, 0.0))\\\\n\\\\t\\\\t\\\\t\\\\t#define FxaaTexOff(t, p, o, r) tex2Dlod(t, float4(p + (o * r), 0, 0))\\\\n\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t#if (FXAA_HLSL_4 == 1)\\\\n\\\\t\\\\t\\\\t\\\\t#define FxaaInt2 int2\\\\n\\\\t\\\\t\\\\t\\\\tstruct FxaaTex { SamplerState smpl; Texture2D tex; };\\\\n\\\\t\\\\t\\\\t\\\\t#define FxaaTexTop(t, p) t.tex.SampleLevel(t.smpl, p, 0.0)\\\\n\\\\t\\\\t\\\\t\\\\t#define FxaaTexOff(t, p, o, r) t.tex.SampleLevel(t.smpl, p, 0.0, o)\\\\n\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t#if (FXAA_HLSL_5 == 1)\\\\n\\\\t\\\\t\\\\t\\\\t#define FxaaInt2 int2\\\\n\\\\t\\\\t\\\\t\\\\tstruct FxaaTex { SamplerState smpl; Texture2D tex; };\\\\n\\\\t\\\\t\\\\t\\\\t#define FxaaTexTop(t, p) t.tex.SampleLevel(t.smpl, p, 0.0)\\\\n\\\\t\\\\t\\\\t\\\\t#define FxaaTexOff(t, p, o, r) t.tex.SampleLevel(t.smpl, p, 0.0, o)\\\\n\\\\t\\\\t\\\\t\\\\t#define FxaaTexAlpha4(t, p) t.tex.GatherAlpha(t.smpl, p)\\\\n\\\\t\\\\t\\\\t\\\\t#define FxaaTexOffAlpha4(t, p, o) t.tex.GatherAlpha(t.smpl, p, o)\\\\n\\\\t\\\\t\\\\t\\\\t#define FxaaTexGreen4(t, p) t.tex.GatherGreen(t.smpl, p)\\\\n\\\\t\\\\t\\\\t\\\\t#define FxaaTexOffGreen4(t, p, o) t.tex.GatherGreen(t.smpl, p, o)\\\\n\\\\t\\\\t#endif\\\\n\\\\n\\\\n\\\\t\\\\t/*============================================================================\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t GREEN AS LUMA OPTION SUPPORT FUNCTION\\\\n\\\\t\\\\t============================================================================*/\\\\n\\\\t\\\\t#if (FXAA_GREEN_AS_LUMA == 0)\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat FxaaLuma(FxaaFloat4 rgba) { return rgba.w; }\\\\n\\\\t\\\\t#else\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat FxaaLuma(FxaaFloat4 rgba) { return rgba.y; }\\\\n\\\\t\\\\t#endif\\\\n\\\\n\\\\n\\\\n\\\\n\\\\t\\\\t/*============================================================================\\\\n\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t FXAA3 QUALITY - PC\\\\n\\\\n\\\\t\\\\t============================================================================*/\\\\n\\\\t\\\\t#if (FXAA_PC == 1)\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\tFxaaFloat4 FxaaPixelShader(\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\n\\\\t\\\\t\\\\t\\\\t// Use noperspective interpolation here (turn off perspective interpolation).\\\\n\\\\t\\\\t\\\\t\\\\t// {xy} = center of pixel\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat2 pos,\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\n\\\\t\\\\t\\\\t\\\\t// Used only for FXAA Console, and not used on the 360 version.\\\\n\\\\t\\\\t\\\\t\\\\t// Use noperspective interpolation here (turn off perspective interpolation).\\\\n\\\\t\\\\t\\\\t\\\\t// {xy_} = upper left of pixel\\\\n\\\\t\\\\t\\\\t\\\\t// {_zw} = lower right of pixel\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat4 fxaaConsolePosPos,\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\n\\\\t\\\\t\\\\t\\\\t// Input color texture.\\\\n\\\\t\\\\t\\\\t\\\\t// {rgb_} = color in linear or perceptual color space\\\\n\\\\t\\\\t\\\\t\\\\t// if (FXAA_GREEN_AS_LUMA == 0)\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t\\\\t {__a} = luma in perceptual color space (not linear)\\\\n\\\\t\\\\t\\\\t\\\\tFxaaTex tex,\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\n\\\\t\\\\t\\\\t\\\\t// Only used on the optimized 360 version of FXAA Console.\\\\n\\\\t\\\\t\\\\t\\\\t// For everything but 360, just use the same input here as for \\\\\\\"tex\\\\\\\".\\\\n\\\\t\\\\t\\\\t\\\\t// For 360, same texture, just alias with a 2nd sampler.\\\\n\\\\t\\\\t\\\\t\\\\t// This sampler needs to have an exponent bias of -1.\\\\n\\\\t\\\\t\\\\t\\\\tFxaaTex fxaaConsole360TexExpBiasNegOne,\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\n\\\\t\\\\t\\\\t\\\\t// Only used on the optimized 360 version of FXAA Console.\\\\n\\\\t\\\\t\\\\t\\\\t// For everything but 360, just use the same input here as for \\\\\\\"tex\\\\\\\".\\\\n\\\\t\\\\t\\\\t\\\\t// For 360, same texture, just alias with a 3nd sampler.\\\\n\\\\t\\\\t\\\\t\\\\t// This sampler needs to have an exponent bias of -2.\\\\n\\\\t\\\\t\\\\t\\\\tFxaaTex fxaaConsole360TexExpBiasNegTwo,\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\n\\\\t\\\\t\\\\t\\\\t// Only used on FXAA Quality.\\\\n\\\\t\\\\t\\\\t\\\\t// This must be from a constant/uniform.\\\\n\\\\t\\\\t\\\\t\\\\t// {x_} = 1.0/screenWidthInPixels\\\\n\\\\t\\\\t\\\\t\\\\t// {_y} = 1.0/screenHeightInPixels\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat2 fxaaQualityRcpFrame,\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\n\\\\t\\\\t\\\\t\\\\t// Only used on FXAA Console.\\\\n\\\\t\\\\t\\\\t\\\\t// This must be from a constant/uniform.\\\\n\\\\t\\\\t\\\\t\\\\t// This effects sub-pixel AA quality and inversely sharpness.\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t Where N ranges between,\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t\\\\t N = 0.50 (default)\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t\\\\t N = 0.33 (sharper)\\\\n\\\\t\\\\t\\\\t\\\\t// {x__} = -N/screenWidthInPixels\\\\n\\\\t\\\\t\\\\t\\\\t// {_y_} = -N/screenHeightInPixels\\\\n\\\\t\\\\t\\\\t\\\\t// {_z_} =\\\\tN/screenWidthInPixels\\\\n\\\\t\\\\t\\\\t\\\\t// {__w} =\\\\tN/screenHeightInPixels\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat4 fxaaConsoleRcpFrameOpt,\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\n\\\\t\\\\t\\\\t\\\\t// Only used on FXAA Console.\\\\n\\\\t\\\\t\\\\t\\\\t// Not used on 360, but used on PS3 and PC.\\\\n\\\\t\\\\t\\\\t\\\\t// This must be from a constant/uniform.\\\\n\\\\t\\\\t\\\\t\\\\t// {x__} = -2.0/screenWidthInPixels\\\\n\\\\t\\\\t\\\\t\\\\t// {_y_} = -2.0/screenHeightInPixels\\\\n\\\\t\\\\t\\\\t\\\\t// {_z_} =\\\\t2.0/screenWidthInPixels\\\\n\\\\t\\\\t\\\\t\\\\t// {__w} =\\\\t2.0/screenHeightInPixels\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat4 fxaaConsoleRcpFrameOpt2,\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\n\\\\t\\\\t\\\\t\\\\t// Only used on FXAA Console.\\\\n\\\\t\\\\t\\\\t\\\\t// Only used on 360 in place of fxaaConsoleRcpFrameOpt2.\\\\n\\\\t\\\\t\\\\t\\\\t// This must be from a constant/uniform.\\\\n\\\\t\\\\t\\\\t\\\\t// {x__} =\\\\t8.0/screenWidthInPixels\\\\n\\\\t\\\\t\\\\t\\\\t// {_y_} =\\\\t8.0/screenHeightInPixels\\\\n\\\\t\\\\t\\\\t\\\\t// {_z_} = -4.0/screenWidthInPixels\\\\n\\\\t\\\\t\\\\t\\\\t// {__w} = -4.0/screenHeightInPixels\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat4 fxaaConsole360RcpFrameOpt2,\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\n\\\\t\\\\t\\\\t\\\\t// Only used on FXAA Quality.\\\\n\\\\t\\\\t\\\\t\\\\t// This used to be the FXAA_QUALITY_SUBPIX define.\\\\n\\\\t\\\\t\\\\t\\\\t// It is here now to allow easier tuning.\\\\n\\\\t\\\\t\\\\t\\\\t// Choose the amount of sub-pixel aliasing removal.\\\\n\\\\t\\\\t\\\\t\\\\t// This can effect sharpness.\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t 1.00 - upper limit (softer)\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t 0.75 - default amount of filtering\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t 0.50 - lower limit (sharper, less sub-pixel aliasing removal)\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t 0.25 - almost off\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t 0.00 - completely off\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat fxaaQualitySubpix,\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\n\\\\t\\\\t\\\\t\\\\t// Only used on FXAA Quality.\\\\n\\\\t\\\\t\\\\t\\\\t// This used to be the FXAA_QUALITY_EDGE_THRESHOLD define.\\\\n\\\\t\\\\t\\\\t\\\\t// It is here now to allow easier tuning.\\\\n\\\\t\\\\t\\\\t\\\\t// The minimum amount of local contrast required to apply algorithm.\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t 0.333 - too little (faster)\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t 0.250 - low quality\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t 0.166 - default\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t 0.125 - high quality\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t 0.063 - overkill (slower)\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat fxaaQualityEdgeThreshold,\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\n\\\\t\\\\t\\\\t\\\\t// Only used on FXAA Quality.\\\\n\\\\t\\\\t\\\\t\\\\t// This used to be the FXAA_QUALITY_EDGE_THRESHOLD_MIN define.\\\\n\\\\t\\\\t\\\\t\\\\t// It is here now to allow easier tuning.\\\\n\\\\t\\\\t\\\\t\\\\t// Trims the algorithm from processing darks.\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t 0.0833 - upper limit (default, the start of visible unfiltered edges)\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t 0.0625 - high quality (faster)\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t 0.0312 - visible limit (slower)\\\\n\\\\t\\\\t\\\\t\\\\t// Special notes when using FXAA_GREEN_AS_LUMA,\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t Likely want to set this to zero.\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t As colors that are mostly not-green\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t will appear very dark in the green channel!\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t Tune by looking at mostly non-green content,\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t then start at zero and increase until aliasing is a problem.\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat fxaaQualityEdgeThresholdMin,\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\n\\\\t\\\\t\\\\t\\\\t// Only used on FXAA Console.\\\\n\\\\t\\\\t\\\\t\\\\t// This used to be the FXAA_CONSOLE_EDGE_SHARPNESS define.\\\\n\\\\t\\\\t\\\\t\\\\t// It is here now to allow easier tuning.\\\\n\\\\t\\\\t\\\\t\\\\t// This does not effect PS3, as this needs to be compiled in.\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t Use FXAA_CONSOLE_PS3_EDGE_SHARPNESS for PS3.\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t Due to the PS3 being ALU bound,\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t there are only three safe values here: 2 and 4 and 8.\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t These options use the shaders ability to a free *|/ by 2|4|8.\\\\n\\\\t\\\\t\\\\t\\\\t// For all other platforms can be a non-power of two.\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t 8.0 is sharper (default!!!)\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t 4.0 is softer\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t 2.0 is really soft (good only for vector graphics inputs)\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat fxaaConsoleEdgeSharpness,\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\n\\\\t\\\\t\\\\t\\\\t// Only used on FXAA Console.\\\\n\\\\t\\\\t\\\\t\\\\t// This used to be the FXAA_CONSOLE_EDGE_THRESHOLD define.\\\\n\\\\t\\\\t\\\\t\\\\t// It is here now to allow easier tuning.\\\\n\\\\t\\\\t\\\\t\\\\t// This does not effect PS3, as this needs to be compiled in.\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t Use FXAA_CONSOLE_PS3_EDGE_THRESHOLD for PS3.\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t Due to the PS3 being ALU bound,\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t there are only two safe values here: 1/4 and 1/8.\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t These options use the shaders ability to a free *|/ by 2|4|8.\\\\n\\\\t\\\\t\\\\t\\\\t// The console setting has a different mapping than the quality setting.\\\\n\\\\t\\\\t\\\\t\\\\t// Other platforms can use other values.\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t 0.125 leaves less aliasing, but is softer (default!!!)\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t 0.25 leaves more aliasing, and is sharper\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat fxaaConsoleEdgeThreshold,\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\n\\\\t\\\\t\\\\t\\\\t// Only used on FXAA Console.\\\\n\\\\t\\\\t\\\\t\\\\t// This used to be the FXAA_CONSOLE_EDGE_THRESHOLD_MIN define.\\\\n\\\\t\\\\t\\\\t\\\\t// It is here now to allow easier tuning.\\\\n\\\\t\\\\t\\\\t\\\\t// Trims the algorithm from processing darks.\\\\n\\\\t\\\\t\\\\t\\\\t// The console setting has a different mapping than the quality setting.\\\\n\\\\t\\\\t\\\\t\\\\t// This only applies when FXAA_EARLY_EXIT is 1.\\\\n\\\\t\\\\t\\\\t\\\\t// This does not apply to PS3,\\\\n\\\\t\\\\t\\\\t\\\\t// PS3 was simplified to avoid more shader instructions.\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t 0.06 - faster but more aliasing in darks\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t 0.05 - default\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t 0.04 - slower and less aliasing in darks\\\\n\\\\t\\\\t\\\\t\\\\t// Special notes when using FXAA_GREEN_AS_LUMA,\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t Likely want to set this to zero.\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t As colors that are mostly not-green\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t will appear very dark in the green channel!\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t Tune by looking at mostly non-green content,\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t then start at zero and increase until aliasing is a problem.\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat fxaaConsoleEdgeThresholdMin,\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\n\\\\t\\\\t\\\\t\\\\t// Extra constants for 360 FXAA Console only.\\\\n\\\\t\\\\t\\\\t\\\\t// Use zeros or anything else for other platforms.\\\\n\\\\t\\\\t\\\\t\\\\t// These must be in physical constant registers and NOT immediates.\\\\n\\\\t\\\\t\\\\t\\\\t// Immediates will result in compiler un-optimizing.\\\\n\\\\t\\\\t\\\\t\\\\t// {xyzw} = float4(1.0, -1.0, 0.25, -0.25)\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat4 fxaaConsole360ConstDir\\\\n\\\\t\\\\t) {\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat2 posM;\\\\n\\\\t\\\\t\\\\t\\\\tposM.x = pos.x;\\\\n\\\\t\\\\t\\\\t\\\\tposM.y = pos.y;\\\\n\\\\t\\\\t\\\\t\\\\t#if (FXAA_GATHER4_ALPHA == 1)\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#if (FXAA_DISCARD == 0)\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tFxaaFloat4 rgbyM = FxaaTexTop(tex, posM);\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#if (FXAA_GREEN_AS_LUMA == 0)\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#define lumaM rgbyM.w\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#else\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#define lumaM rgbyM.y\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#endif\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#endif\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#if (FXAA_GREEN_AS_LUMA == 0)\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tFxaaFloat4 luma4A = FxaaTexAlpha4(tex, posM);\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tFxaaFloat4 luma4B = FxaaTexOffAlpha4(tex, posM, FxaaInt2(-1, -1));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#else\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tFxaaFloat4 luma4A = FxaaTexGreen4(tex, posM);\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tFxaaFloat4 luma4B = FxaaTexOffGreen4(tex, posM, FxaaInt2(-1, -1));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#endif\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#if (FXAA_DISCARD == 1)\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#define lumaM luma4A.w\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#endif\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#define lumaE luma4A.z\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#define lumaS luma4A.x\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#define lumaSE luma4A.y\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#define lumaNW luma4B.w\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#define lumaN luma4B.z\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#define lumaW luma4B.x\\\\n\\\\t\\\\t\\\\t\\\\t#else\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tFxaaFloat4 rgbyM = FxaaTexTop(tex, posM);\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#if (FXAA_GREEN_AS_LUMA == 0)\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#define lumaM rgbyM.w\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#else\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#define lumaM rgbyM.y\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#endif\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#if (FXAA_GLSL_100 == 1)\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tFxaaFloat lumaS = FxaaLuma(FxaaTexOff(tex, posM, FxaaFloat2( 0.0, 1.0), fxaaQualityRcpFrame.xy));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tFxaaFloat lumaE = FxaaLuma(FxaaTexOff(tex, posM, FxaaFloat2( 1.0, 0.0), fxaaQualityRcpFrame.xy));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tFxaaFloat lumaN = FxaaLuma(FxaaTexOff(tex, posM, FxaaFloat2( 0.0,-1.0), fxaaQualityRcpFrame.xy));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tFxaaFloat lumaW = FxaaLuma(FxaaTexOff(tex, posM, FxaaFloat2(-1.0, 0.0), fxaaQualityRcpFrame.xy));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#else\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tFxaaFloat lumaS = FxaaLuma(FxaaTexOff(tex, posM, FxaaInt2( 0, 1), fxaaQualityRcpFrame.xy));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tFxaaFloat lumaE = FxaaLuma(FxaaTexOff(tex, posM, FxaaInt2( 1, 0), fxaaQualityRcpFrame.xy));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tFxaaFloat lumaN = FxaaLuma(FxaaTexOff(tex, posM, FxaaInt2( 0,-1), fxaaQualityRcpFrame.xy));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tFxaaFloat lumaW = FxaaLuma(FxaaTexOff(tex, posM, FxaaInt2(-1, 0), fxaaQualityRcpFrame.xy));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#endif\\\\n\\\\t\\\\t\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat maxSM = max(lumaS, lumaM);\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat minSM = min(lumaS, lumaM);\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat maxESM = max(lumaE, maxSM);\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat minESM = min(lumaE, minSM);\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat maxWN = max(lumaN, lumaW);\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat minWN = min(lumaN, lumaW);\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat rangeMax = max(maxWN, maxESM);\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat rangeMin = min(minWN, minESM);\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat rangeMaxScaled = rangeMax * fxaaQualityEdgeThreshold;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat range = rangeMax - rangeMin;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat rangeMaxClamped = max(fxaaQualityEdgeThresholdMin, rangeMaxScaled);\\\\n\\\\t\\\\t\\\\t\\\\tFxaaBool earlyExit = range < rangeMaxClamped;\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t\\\\t\\\\tif(earlyExit)\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#if (FXAA_DISCARD == 1)\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tFxaaDiscard;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#else\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\treturn rgbyM;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t\\\\t\\\\t#if (FXAA_GATHER4_ALPHA == 0)\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#if (FXAA_GLSL_100 == 1)\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tFxaaFloat lumaNW = FxaaLuma(FxaaTexOff(tex, posM, FxaaFloat2(-1.0,-1.0), fxaaQualityRcpFrame.xy));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tFxaaFloat lumaSE = FxaaLuma(FxaaTexOff(tex, posM, FxaaFloat2( 1.0, 1.0), fxaaQualityRcpFrame.xy));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tFxaaFloat lumaNE = FxaaLuma(FxaaTexOff(tex, posM, FxaaFloat2( 1.0,-1.0), fxaaQualityRcpFrame.xy));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tFxaaFloat lumaSW = FxaaLuma(FxaaTexOff(tex, posM, FxaaFloat2(-1.0, 1.0), fxaaQualityRcpFrame.xy));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#else\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tFxaaFloat lumaNW = FxaaLuma(FxaaTexOff(tex, posM, FxaaInt2(-1,-1), fxaaQualityRcpFrame.xy));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tFxaaFloat lumaSE = FxaaLuma(FxaaTexOff(tex, posM, FxaaInt2( 1, 1), fxaaQualityRcpFrame.xy));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tFxaaFloat lumaNE = FxaaLuma(FxaaTexOff(tex, posM, FxaaInt2( 1,-1), fxaaQualityRcpFrame.xy));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tFxaaFloat lumaSW = FxaaLuma(FxaaTexOff(tex, posM, FxaaInt2(-1, 1), fxaaQualityRcpFrame.xy));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#endif\\\\n\\\\t\\\\t\\\\t\\\\t#else\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tFxaaFloat lumaNE = FxaaLuma(FxaaTexOff(tex, posM, FxaaInt2(1, -1), fxaaQualityRcpFrame.xy));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tFxaaFloat lumaSW = FxaaLuma(FxaaTexOff(tex, posM, FxaaInt2(-1, 1), fxaaQualityRcpFrame.xy));\\\\n\\\\t\\\\t\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat lumaNS = lumaN + lumaS;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat lumaWE = lumaW + lumaE;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat subpixRcpRange = 1.0/range;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat subpixNSWE = lumaNS + lumaWE;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat edgeHorz1 = (-2.0 * lumaM) + lumaNS;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat edgeVert1 = (-2.0 * lumaM) + lumaWE;\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat lumaNESE = lumaNE + lumaSE;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat lumaNWNE = lumaNW + lumaNE;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat edgeHorz2 = (-2.0 * lumaE) + lumaNESE;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat edgeVert2 = (-2.0 * lumaN) + lumaNWNE;\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat lumaNWSW = lumaNW + lumaSW;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat lumaSWSE = lumaSW + lumaSE;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat edgeHorz4 = (abs(edgeHorz1) * 2.0) + abs(edgeHorz2);\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat edgeVert4 = (abs(edgeVert1) * 2.0) + abs(edgeVert2);\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat edgeHorz3 = (-2.0 * lumaW) + lumaNWSW;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat edgeVert3 = (-2.0 * lumaS) + lumaSWSE;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat edgeHorz = abs(edgeHorz3) + edgeHorz4;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat edgeVert = abs(edgeVert3) + edgeVert4;\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat subpixNWSWNESE = lumaNWSW + lumaNESE;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat lengthSign = fxaaQualityRcpFrame.x;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaBool horzSpan = edgeHorz >= edgeVert;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat subpixA = subpixNSWE * 2.0 + subpixNWSWNESE;\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t\\\\t\\\\tif(!horzSpan) lumaN = lumaW;\\\\n\\\\t\\\\t\\\\t\\\\tif(!horzSpan) lumaS = lumaE;\\\\n\\\\t\\\\t\\\\t\\\\tif(horzSpan) lengthSign = fxaaQualityRcpFrame.y;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat subpixB = (subpixA * (1.0/12.0)) - lumaM;\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat gradientN = lumaN - lumaM;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat gradientS = lumaS - lumaM;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat lumaNN = lumaN + lumaM;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat lumaSS = lumaS + lumaM;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaBool pairN = abs(gradientN) >= abs(gradientS);\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat gradient = max(abs(gradientN), abs(gradientS));\\\\n\\\\t\\\\t\\\\t\\\\tif(pairN) lengthSign = -lengthSign;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat subpixC = FxaaSat(abs(subpixB) * subpixRcpRange);\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat2 posB;\\\\n\\\\t\\\\t\\\\t\\\\tposB.x = posM.x;\\\\n\\\\t\\\\t\\\\t\\\\tposB.y = posM.y;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat2 offNP;\\\\n\\\\t\\\\t\\\\t\\\\toffNP.x = (!horzSpan) ? 0.0 : fxaaQualityRcpFrame.x;\\\\n\\\\t\\\\t\\\\t\\\\toffNP.y = ( horzSpan) ? 0.0 : fxaaQualityRcpFrame.y;\\\\n\\\\t\\\\t\\\\t\\\\tif(!horzSpan) posB.x += lengthSign * 0.5;\\\\n\\\\t\\\\t\\\\t\\\\tif( horzSpan) posB.y += lengthSign * 0.5;\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat2 posN;\\\\n\\\\t\\\\t\\\\t\\\\tposN.x = posB.x - offNP.x * FXAA_QUALITY_P0;\\\\n\\\\t\\\\t\\\\t\\\\tposN.y = posB.y - offNP.y * FXAA_QUALITY_P0;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat2 posP;\\\\n\\\\t\\\\t\\\\t\\\\tposP.x = posB.x + offNP.x * FXAA_QUALITY_P0;\\\\n\\\\t\\\\t\\\\t\\\\tposP.y = posB.y + offNP.y * FXAA_QUALITY_P0;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat subpixD = ((-2.0)*subpixC) + 3.0;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat lumaEndN = FxaaLuma(FxaaTexTop(tex, posN));\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat subpixE = subpixC * subpixC;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat lumaEndP = FxaaLuma(FxaaTexTop(tex, posP));\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t\\\\t\\\\tif(!pairN) lumaNN = lumaSS;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat gradientScaled = gradient * 1.0/4.0;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat lumaMM = lumaM - lumaNN * 0.5;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat subpixF = subpixD * subpixE;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaBool lumaMLTZero = lumaMM < 0.0;\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t\\\\t\\\\tlumaEndN -= lumaNN * 0.5;\\\\n\\\\t\\\\t\\\\t\\\\tlumaEndP -= lumaNN * 0.5;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaBool doneN = abs(lumaEndN) >= gradientScaled;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaBool doneP = abs(lumaEndP) >= gradientScaled;\\\\n\\\\t\\\\t\\\\t\\\\tif(!doneN) posN.x -= offNP.x * FXAA_QUALITY_P1;\\\\n\\\\t\\\\t\\\\t\\\\tif(!doneN) posN.y -= offNP.y * FXAA_QUALITY_P1;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaBool doneNP = (!doneN) || (!doneP);\\\\n\\\\t\\\\t\\\\t\\\\tif(!doneP) posP.x += offNP.x * FXAA_QUALITY_P1;\\\\n\\\\t\\\\t\\\\t\\\\tif(!doneP) posP.y += offNP.y * FXAA_QUALITY_P1;\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t\\\\t\\\\tif(doneNP) {\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) lumaEndN = FxaaLuma(FxaaTexTop(tex, posN.xy));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) lumaEndP = FxaaLuma(FxaaTexTop(tex, posP.xy));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) lumaEndN = lumaEndN - lumaNN * 0.5;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) lumaEndP = lumaEndP - lumaNN * 0.5;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tdoneN = abs(lumaEndN) >= gradientScaled;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tdoneP = abs(lumaEndP) >= gradientScaled;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) posN.x -= offNP.x * FXAA_QUALITY_P2;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) posN.y -= offNP.y * FXAA_QUALITY_P2;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tdoneNP = (!doneN) || (!doneP);\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) posP.x += offNP.x * FXAA_QUALITY_P2;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) posP.y += offNP.y * FXAA_QUALITY_P2;\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#if (FXAA_QUALITY_PS > 3)\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(doneNP) {\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) lumaEndN = FxaaLuma(FxaaTexTop(tex, posN.xy));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) lumaEndP = FxaaLuma(FxaaTexTop(tex, posP.xy));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) lumaEndN = lumaEndN - lumaNN * 0.5;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) lumaEndP = lumaEndP - lumaNN * 0.5;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tdoneN = abs(lumaEndN) >= gradientScaled;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tdoneP = abs(lumaEndP) >= gradientScaled;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) posN.x -= offNP.x * FXAA_QUALITY_P3;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) posN.y -= offNP.y * FXAA_QUALITY_P3;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tdoneNP = (!doneN) || (!doneP);\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) posP.x += offNP.x * FXAA_QUALITY_P3;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) posP.y += offNP.y * FXAA_QUALITY_P3;\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#if (FXAA_QUALITY_PS > 4)\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(doneNP) {\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) lumaEndN = FxaaLuma(FxaaTexTop(tex, posN.xy));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) lumaEndP = FxaaLuma(FxaaTexTop(tex, posP.xy));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) lumaEndN = lumaEndN - lumaNN * 0.5;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) lumaEndP = lumaEndP - lumaNN * 0.5;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tdoneN = abs(lumaEndN) >= gradientScaled;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tdoneP = abs(lumaEndP) >= gradientScaled;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) posN.x -= offNP.x * FXAA_QUALITY_P4;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) posN.y -= offNP.y * FXAA_QUALITY_P4;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tdoneNP = (!doneN) || (!doneP);\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) posP.x += offNP.x * FXAA_QUALITY_P4;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) posP.y += offNP.y * FXAA_QUALITY_P4;\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#if (FXAA_QUALITY_PS > 5)\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(doneNP) {\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) lumaEndN = FxaaLuma(FxaaTexTop(tex, posN.xy));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) lumaEndP = FxaaLuma(FxaaTexTop(tex, posP.xy));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) lumaEndN = lumaEndN - lumaNN * 0.5;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) lumaEndP = lumaEndP - lumaNN * 0.5;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tdoneN = abs(lumaEndN) >= gradientScaled;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tdoneP = abs(lumaEndP) >= gradientScaled;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) posN.x -= offNP.x * FXAA_QUALITY_P5;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) posN.y -= offNP.y * FXAA_QUALITY_P5;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tdoneNP = (!doneN) || (!doneP);\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) posP.x += offNP.x * FXAA_QUALITY_P5;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) posP.y += offNP.y * FXAA_QUALITY_P5;\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#if (FXAA_QUALITY_PS > 6)\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(doneNP) {\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) lumaEndN = FxaaLuma(FxaaTexTop(tex, posN.xy));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) lumaEndP = FxaaLuma(FxaaTexTop(tex, posP.xy));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) lumaEndN = lumaEndN - lumaNN * 0.5;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) lumaEndP = lumaEndP - lumaNN * 0.5;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tdoneN = abs(lumaEndN) >= gradientScaled;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tdoneP = abs(lumaEndP) >= gradientScaled;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) posN.x -= offNP.x * FXAA_QUALITY_P6;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) posN.y -= offNP.y * FXAA_QUALITY_P6;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tdoneNP = (!doneN) || (!doneP);\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) posP.x += offNP.x * FXAA_QUALITY_P6;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) posP.y += offNP.y * FXAA_QUALITY_P6;\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#if (FXAA_QUALITY_PS > 7)\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(doneNP) {\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) lumaEndN = FxaaLuma(FxaaTexTop(tex, posN.xy));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) lumaEndP = FxaaLuma(FxaaTexTop(tex, posP.xy));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) lumaEndN = lumaEndN - lumaNN * 0.5;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) lumaEndP = lumaEndP - lumaNN * 0.5;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tdoneN = abs(lumaEndN) >= gradientScaled;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tdoneP = abs(lumaEndP) >= gradientScaled;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) posN.x -= offNP.x * FXAA_QUALITY_P7;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) posN.y -= offNP.y * FXAA_QUALITY_P7;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tdoneNP = (!doneN) || (!doneP);\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) posP.x += offNP.x * FXAA_QUALITY_P7;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) posP.y += offNP.y * FXAA_QUALITY_P7;\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t\\\\t\\\\t#if (FXAA_QUALITY_PS > 8)\\\\n\\\\t\\\\t\\\\t\\\\tif(doneNP) {\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) lumaEndN = FxaaLuma(FxaaTexTop(tex, posN.xy));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) lumaEndP = FxaaLuma(FxaaTexTop(tex, posP.xy));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) lumaEndN = lumaEndN - lumaNN * 0.5;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) lumaEndP = lumaEndP - lumaNN * 0.5;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tdoneN = abs(lumaEndN) >= gradientScaled;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tdoneP = abs(lumaEndP) >= gradientScaled;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) posN.x -= offNP.x * FXAA_QUALITY_P8;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) posN.y -= offNP.y * FXAA_QUALITY_P8;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tdoneNP = (!doneN) || (!doneP);\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) posP.x += offNP.x * FXAA_QUALITY_P8;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) posP.y += offNP.y * FXAA_QUALITY_P8;\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#if (FXAA_QUALITY_PS > 9)\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(doneNP) {\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) lumaEndN = FxaaLuma(FxaaTexTop(tex, posN.xy));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) lumaEndP = FxaaLuma(FxaaTexTop(tex, posP.xy));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) lumaEndN = lumaEndN - lumaNN * 0.5;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) lumaEndP = lumaEndP - lumaNN * 0.5;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tdoneN = abs(lumaEndN) >= gradientScaled;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tdoneP = abs(lumaEndP) >= gradientScaled;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) posN.x -= offNP.x * FXAA_QUALITY_P9;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) posN.y -= offNP.y * FXAA_QUALITY_P9;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tdoneNP = (!doneN) || (!doneP);\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) posP.x += offNP.x * FXAA_QUALITY_P9;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) posP.y += offNP.y * FXAA_QUALITY_P9;\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#if (FXAA_QUALITY_PS > 10)\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(doneNP) {\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) lumaEndN = FxaaLuma(FxaaTexTop(tex, posN.xy));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) lumaEndP = FxaaLuma(FxaaTexTop(tex, posP.xy));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) lumaEndN = lumaEndN - lumaNN * 0.5;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) lumaEndP = lumaEndP - lumaNN * 0.5;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tdoneN = abs(lumaEndN) >= gradientScaled;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tdoneP = abs(lumaEndP) >= gradientScaled;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) posN.x -= offNP.x * FXAA_QUALITY_P10;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) posN.y -= offNP.y * FXAA_QUALITY_P10;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tdoneNP = (!doneN) || (!doneP);\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) posP.x += offNP.x * FXAA_QUALITY_P10;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) posP.y += offNP.y * FXAA_QUALITY_P10;\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#if (FXAA_QUALITY_PS > 11)\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(doneNP) {\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) lumaEndN = FxaaLuma(FxaaTexTop(tex, posN.xy));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) lumaEndP = FxaaLuma(FxaaTexTop(tex, posP.xy));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) lumaEndN = lumaEndN - lumaNN * 0.5;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) lumaEndP = lumaEndP - lumaNN * 0.5;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tdoneN = abs(lumaEndN) >= gradientScaled;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tdoneP = abs(lumaEndP) >= gradientScaled;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) posN.x -= offNP.x * FXAA_QUALITY_P11;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) posN.y -= offNP.y * FXAA_QUALITY_P11;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tdoneNP = (!doneN) || (!doneP);\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) posP.x += offNP.x * FXAA_QUALITY_P11;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) posP.y += offNP.y * FXAA_QUALITY_P11;\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#if (FXAA_QUALITY_PS > 12)\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(doneNP) {\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) lumaEndN = FxaaLuma(FxaaTexTop(tex, posN.xy));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) lumaEndP = FxaaLuma(FxaaTexTop(tex, posP.xy));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) lumaEndN = lumaEndN - lumaNN * 0.5;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) lumaEndP = lumaEndP - lumaNN * 0.5;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tdoneN = abs(lumaEndN) >= gradientScaled;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tdoneP = abs(lumaEndP) >= gradientScaled;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) posN.x -= offNP.x * FXAA_QUALITY_P12;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) posN.y -= offNP.y * FXAA_QUALITY_P12;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tdoneNP = (!doneN) || (!doneP);\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) posP.x += offNP.x * FXAA_QUALITY_P12;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) posP.y += offNP.y * FXAA_QUALITY_P12;\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t}\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t}\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t}\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t}\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t\\\\t\\\\t}\\\\n\\\\t\\\\t\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t}\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t}\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t}\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t}\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t}\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t\\\\t\\\\t}\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat dstN = posM.x - posN.x;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat dstP = posP.x - posM.x;\\\\n\\\\t\\\\t\\\\t\\\\tif(!horzSpan) dstN = posM.y - posN.y;\\\\n\\\\t\\\\t\\\\t\\\\tif(!horzSpan) dstP = posP.y - posM.y;\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t\\\\t\\\\tFxaaBool goodSpanN = (lumaEndN < 0.0) != lumaMLTZero;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat spanLength = (dstP + dstN);\\\\n\\\\t\\\\t\\\\t\\\\tFxaaBool goodSpanP = (lumaEndP < 0.0) != lumaMLTZero;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat spanLengthRcp = 1.0/spanLength;\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t\\\\t\\\\tFxaaBool directionN = dstN < dstP;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat dst = min(dstN, dstP);\\\\n\\\\t\\\\t\\\\t\\\\tFxaaBool goodSpan = directionN ? goodSpanN : goodSpanP;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat subpixG = subpixF * subpixF;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat pixelOffset = (dst * (-spanLengthRcp)) + 0.5;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat subpixH = subpixG * fxaaQualitySubpix;\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat pixelOffsetGood = goodSpan ? pixelOffset : 0.0;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat pixelOffsetSubpix = max(pixelOffsetGood, subpixH);\\\\n\\\\t\\\\t\\\\t\\\\tif(!horzSpan) posM.x += pixelOffsetSubpix * lengthSign;\\\\n\\\\t\\\\t\\\\t\\\\tif( horzSpan) posM.y += pixelOffsetSubpix * lengthSign;\\\\n\\\\t\\\\t\\\\t\\\\t#if (FXAA_DISCARD == 1)\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\treturn FxaaTexTop(tex, posM);\\\\n\\\\t\\\\t\\\\t\\\\t#else\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\treturn FxaaFloat4(FxaaTexTop(tex, posM).xyz, lumaM);\\\\n\\\\t\\\\t\\\\t\\\\t#endif\\\\n\\\\t\\\\t}\\\\n\\\\t\\\\t/*==========================================================================*/\\\\n\\\\t\\\\t#endif\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\t\\\\t\\\\tgl_FragColor = FxaaPixelShader(\\\\n\\\\t\\\\t\\\\t\\\\tvUv,\\\\n\\\\t\\\\t\\\\t\\\\tvec4(0.0),\\\\n\\\\t\\\\t\\\\t\\\\ttDiffuse,\\\\n\\\\t\\\\t\\\\t\\\\ttDiffuse,\\\\n\\\\t\\\\t\\\\t\\\\ttDiffuse,\\\\n\\\\t\\\\t\\\\t\\\\tresolution,\\\\n\\\\t\\\\t\\\\t\\\\tvec4(0.0),\\\\n\\\\t\\\\t\\\\t\\\\tvec4(0.0),\\\\n\\\\t\\\\t\\\\t\\\\tvec4(0.0),\\\\n\\\\t\\\\t\\\\t\\\\t0.75,\\\\n\\\\t\\\\t\\\\t\\\\t0.166,\\\\n\\\\t\\\\t\\\\t\\\\t0.0833,\\\\n\\\\t\\\\t\\\\t\\\\t0.0,\\\\n\\\\t\\\\t\\\\t\\\\t0.0,\\\\n\\\\t\\\\t\\\\t\\\\t0.0,\\\\n\\\\t\\\\t\\\\t\\\\tvec4(0.0)\\\\n\\\\t\\\\t\\\\t);\\\\n\\\\n\\\\t\\\\t\\\\t// TODO avoid querying texture twice for same texel\\\\n\\\\t\\\\t\\\\tgl_FragColor.a = texture2D(tDiffuse, vUv).a;\\\\n\\\\t\\\\t}'};const Qj=new class extends la{constructor(){super(...arguments),this.transparent=aa.BOOLEAN(1,uj)}};class Kj extends dj{constructor(){super(...arguments),this.paramsConfig=Qj}static type(){return\\\\\\\"FXAA\\\\\\\"}_createPass(t){const e=new zm(Zj);return e.uniforms.resolution.value.set(1/t.resolution.x,1/t.resolution.y),e.material.transparent=!0,this.updatePass(e),e}updatePass(t){t.material.transparent=this.pv.transparent}}const tW={uniforms:{tDiffuse:{value:null}},vertexShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\tvarying vec2 vUv;\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\tvUv = uv;\\\\n\\\\t\\\\t\\\\tgl_Position = projectionMatrix * modelViewMatrix * vec4( position, 1.0 );\\\\n\\\\n\\\\t\\\\t}\\\\\\\",fragmentShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\tuniform sampler2D tDiffuse;\\\\n\\\\n\\\\t\\\\tvarying vec2 vUv;\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\tvec4 tex = texture2D( tDiffuse, vUv );\\\\n\\\\n\\\\t\\\\t\\\\tgl_FragColor = LinearTosRGB( tex ); // optional: LinearToGamma( tex, float( GAMMA_FACTOR ) );\\\\n\\\\n\\\\t\\\\t}\\\\\\\"};const eW=new class extends la{};class nW extends dj{constructor(){super(...arguments),this.paramsConfig=eW}static type(){return\\\\\\\"gammaCorrection\\\\\\\"}_createPass(t){const e=new zm(tW);return this.updatePass(e),e}updatePass(t){}}const iW=new class extends la{constructor(){super(...arguments),this.amount=aa.FLOAT(2,{range:[0,10],rangeLocked:[!0,!1],step:.01,...uj}),this.transparent=aa.BOOLEAN(1,uj)}};class sW extends dj{constructor(){super(...arguments),this.paramsConfig=iW}static type(){return\\\\\\\"horizontalBlur\\\\\\\"}_createPass(t){const e=new zm(yG);return e.resolution_x=t.resolution.x,this.updatePass(e),e}updatePass(t){t.uniforms.h.value=this.pv.amount/(t.resolution_x*window.devicePixelRatio),t.material.transparent=this.pv.transparent}}const rW=new class extends la{constructor(){super(...arguments),this.map=aa.OPERATOR_PATH(vi.UV,{nodeSelection:{context:ts.COP},...uj}),this.darkness=aa.FLOAT(0,{range:[0,2],rangeLocked:[!0,!1],...uj}),this.offset=aa.FLOAT(0,{range:[0,2],rangeLocked:[!0,!1],...uj})}};class oW extends dj{constructor(){super(...arguments),this.paramsConfig=rW}static type(){return\\\\\\\"image\\\\\\\"}static _create_shader(){return{uniforms:{tDiffuse:{value:null},map:{value:null},offset:{value:1},darkness:{value:1}},vertexShader:\\\\\\\"varying vec2 vUv;\\\\nvoid main() {\\\\n\\\\tvUv = uv;\\\\n\\\\tgl_Position = projectionMatrix * modelViewMatrix * vec4( position, 1.0 );\\\\n}\\\\\\\",fragmentShader:\\\\\\\"uniform float offset;\\\\nuniform float darkness;\\\\nuniform sampler2D tDiffuse;\\\\nuniform sampler2D map;\\\\nvarying vec2 vUv;\\\\n\\\\nvoid main() {\\\\n\\\\n\\\\tvec4 texel = texture2D( tDiffuse, vUv );\\\\n\\\\tvec4 map_val = texture2D( map, vUv );\\\\n\\\\tvec2 uv = ( vUv - vec2( 0.5 ) ) * vec2( offset );\\\\n\\\\t// gl_FragColor = vec4( mix( texel.rgb, vec3( 1.0 - darkness ), dot( uv, uv ) ), texel.a );\\\\n\\\\tgl_FragColor = vec4( mix( texel.rgb, map_val.rgb, map_val.a ), texel.a );\\\\n\\\\n}\\\\n\\\\\\\"}}_createPass(t){const e=new zm(oW._create_shader());return this.updatePass(e),e}updatePass(t){t.uniforms.darkness.value=this.pv.darkness,t.uniforms.offset.value=this.pv.offset,this._update_map(t)}async _update_map(t){this.p.map.isDirty()&&await this.p.map.compute();const e=this.p.map.found_node();if(e)if(e.context()==ts.COP){const n=e,i=(await n.compute()).coreContent();t.uniforms.map.value=i}else this.states.error.set(\\\\\\\"node is not COP\\\\\\\");else this.states.error.set(\\\\\\\"no map found\\\\\\\")}}const aW={tDiffuse:{value:null},texture1:{value:null},texture2:{value:null},h:{value:1/512}},lW=\\\\\\\"varying vec2 vUv;\\\\n\\\\nvoid main() {\\\\n\\\\n\\\\tvUv = uv;\\\\n\\\\tgl_Position = projectionMatrix * modelViewMatrix * vec4( position, 1.0 );\\\\n\\\\n}\\\\\\\",cW=\\\\\\\"uniform sampler2D texture1;\\\\nuniform sampler2D texture2;\\\\nvarying vec2 vUv;\\\\n\\\\nvoid main() {\\\\n\\\\n\\\\tvec4 t1 = texture2D( texture1, vUv);\\\\n\\\\tvec4 t2 = texture2D( texture2, vUv);\\\\n\\\\n\\\\tvec3 c1 = t1.rgb * t1.a * (1.0-t2.a);\\\\n\\\\tvec3 c2 = t2.rgb * t2.a;\\\\n\\\\tfloat a = t2.a + t1.a;\\\\n\\\\tvec3 c = max(c1,c2);\\\\n\\\\n\\\\tgl_FragColor = vec4(c,a);\\\\n\\\\n}\\\\\\\";class hW extends Im{constructor(t,e){super(),this._composer1=t,this._composer2=e,this.uniforms=I.clone(aW),this.material=new F({uniforms:this.uniforms,vertexShader:lW,fragmentShader:cW,transparent:!0}),this.fsQuad=new Bm(this.material)}render(t,e){this._composer1.render(),this._composer2.render(),this.uniforms.texture1.value=this._composer1.readBuffer.texture,this.uniforms.texture2.value=this._composer2.readBuffer.texture,this.renderToScreen?(t.setRenderTarget(null),this.fsQuad.render(t)):(t.setRenderTarget(e),this.clear&&t.clear(t.autoClearColor,t.autoClearDepth,t.autoClearStencil),this.fsQuad.render(t))}}const uW=new class extends la{};class dW extends dj{constructor(){super(...arguments),this.paramsConfig=uW}static type(){return\\\\\\\"layer\\\\\\\"}initializeNode(){super.initializeNode(),this.io.inputs.setCount(2)}setupComposer(t){const e=t.composer.renderer,n={minFilter:w.V,magFilter:w.V,format:w.Ib,stencilBuffer:!0},i=li.renderersController.renderTarget(e.domElement.offsetWidth,e.domElement.offsetHeight,n),s=li.renderersController.renderTarget(e.domElement.offsetWidth,e.domElement.offsetHeight,n),r=new Gm(e,i),o=new Gm(e,s);r.renderToScreen=!1,o.renderToScreen=!1;const a={...t},l={...t};a.composer=r,l.composer=o,this._addPassFromInput(0,a),this._addPassFromInput(1,l);const c=new hW(r,o);this.updatePass(c),t.composer.addPass(c)}updatePass(t){}}const pW=new class extends la{constructor(){super(...arguments),this.overrideScene=aa.BOOLEAN(0,uj),this.scene=aa.OPERATOR_PATH(\\\\\\\"/scene1\\\\\\\",{visibleIf:{overrideScene:1},nodeSelection:{context:ts.OBJ,types:[DV.type()]},...uj}),this.overrideCamera=aa.BOOLEAN(0,uj),this.camera=aa.OPERATOR_PATH(\\\\\\\"/perspective_camera1\\\\\\\",{visibleIf:{overrideCamera:1},nodeSelection:{context:ts.OBJ},...uj}),this.inverse=aa.BOOLEAN(0,uj)}};class _W extends dj{constructor(){super(...arguments),this.paramsConfig=pW}static type(){return\\\\\\\"mask\\\\\\\"}_createPass(t){const e=new km(t.scene,t.camera);return e.context={scene:t.scene,camera:t.camera},this.updatePass(e),e}updatePass(t){t.inverse=this.pv.inverse,this._update_scene(t),this._updateCamera(t)}async _update_scene(t){if(this.pv.overrideScene){this.p.scene.isDirty()&&await this.p.scene.compute();const e=this.p.scene.found_node_with_expected_type();if(e)return void(t.scene=e.object)}t.scene=t.context.scene}async _updateCamera(t){if(this.pv.overrideCamera){this.p.camera.isDirty()&&await this.p.camera.compute();const e=this.p.camera.found_node_with_expected_type();if(e)return void(t.camera=e.object)}t.camera=t.context.camera}}const mW=new class extends la{};class fW extends dj{constructor(){super(...arguments),this.paramsConfig=mW}static type(){return\\\\\\\"null\\\\\\\"}}class gW extends Im{constructor(t,e,n,i){super(),this.renderScene=e,this.renderCamera=n,this.selectedObjects=void 0!==i?i:[],this.visibleEdgeColor=new D.a(1,1,1),this.hiddenEdgeColor=new D.a(.1,.04,.02),this.edgeGlow=0,this.usePatternTexture=!1,this.edgeThickness=1,this.edgeStrength=3,this.downSampleRatio=2,this.pulsePeriod=0,this._visibilityCache=new Map,this.resolution=void 0!==t?new d.a(t.x,t.y):new d.a(256,256);const s={minFilter:w.V,magFilter:w.V,format:w.Ib},r=Math.round(this.resolution.x/this.downSampleRatio),o=Math.round(this.resolution.y/this.downSampleRatio);this.maskBufferMaterial=new lt.a({color:16777215}),this.maskBufferMaterial.side=w.z,this.renderTargetMaskBuffer=new Q(this.resolution.x,this.resolution.y,s),this.renderTargetMaskBuffer.texture.name=\\\\\\\"OutlinePass.mask\\\\\\\",this.renderTargetMaskBuffer.texture.generateMipmaps=!1,this.depthMaterial=new Sn,this.depthMaterial.side=w.z,this.depthMaterial.depthPacking=w.Hb,this.depthMaterial.blending=w.ub,this.prepareMaskMaterial=this.getPrepareMaskMaterial(),this.prepareMaskMaterial.side=w.z,this.prepareMaskMaterial.fragmentShader=function(t,e){var n=e.isPerspectiveCamera?\\\\\\\"perspective\\\\\\\":\\\\\\\"orthographic\\\\\\\";return t.replace(/DEPTH_TO_VIEW_Z/g,n+\\\\\\\"DepthToViewZ\\\\\\\")}(this.prepareMaskMaterial.fragmentShader,this.renderCamera),this.renderTargetDepthBuffer=new Q(this.resolution.x,this.resolution.y,s),this.renderTargetDepthBuffer.texture.name=\\\\\\\"OutlinePass.depth\\\\\\\",this.renderTargetDepthBuffer.texture.generateMipmaps=!1,this.renderTargetMaskDownSampleBuffer=new Q(r,o,s),this.renderTargetMaskDownSampleBuffer.texture.name=\\\\\\\"OutlinePass.depthDownSample\\\\\\\",this.renderTargetMaskDownSampleBuffer.texture.generateMipmaps=!1,this.renderTargetBlurBuffer1=new Q(r,o,s),this.renderTargetBlurBuffer1.texture.name=\\\\\\\"OutlinePass.blur1\\\\\\\",this.renderTargetBlurBuffer1.texture.generateMipmaps=!1,this.renderTargetBlurBuffer2=new Q(Math.round(r/2),Math.round(o/2),s),this.renderTargetBlurBuffer2.texture.name=\\\\\\\"OutlinePass.blur2\\\\\\\",this.renderTargetBlurBuffer2.texture.generateMipmaps=!1,this.edgeDetectionMaterial=this.getEdgeDetectionMaterial(),this.renderTargetEdgeBuffer1=new Q(r,o,s),this.renderTargetEdgeBuffer1.texture.name=\\\\\\\"OutlinePass.edge1\\\\\\\",this.renderTargetEdgeBuffer1.texture.generateMipmaps=!1,this.renderTargetEdgeBuffer2=new Q(Math.round(r/2),Math.round(o/2),s),this.renderTargetEdgeBuffer2.texture.name=\\\\\\\"OutlinePass.edge2\\\\\\\",this.renderTargetEdgeBuffer2.texture.generateMipmaps=!1;this.separableBlurMaterial1=this.getSeperableBlurMaterial(4),this.separableBlurMaterial1.uniforms.texSize.value.set(r,o),this.separableBlurMaterial1.uniforms.kernelRadius.value=1,this.separableBlurMaterial2=this.getSeperableBlurMaterial(4),this.separableBlurMaterial2.uniforms.texSize.value.set(Math.round(r/2),Math.round(o/2)),this.separableBlurMaterial2.uniforms.kernelRadius.value=4,this.overlayMaterial=this.getOverlayMaterial(),void 0===Rm&&console.error(\\\\\\\"THREE.OutlinePass relies on CopyShader\\\\\\\");const a=Rm;this.copyUniforms=I.clone(a.uniforms),this.copyUniforms.opacity.value=1,this.materialCopy=new F({uniforms:this.copyUniforms,vertexShader:a.vertexShader,fragmentShader:a.fragmentShader,blending:w.ub,depthTest:!1,depthWrite:!1,transparent:!0}),this.enabled=!0,this.needsSwap=!1,this._oldClearColor=new D.a,this.oldClearAlpha=1,this.fsQuad=new Bm(null),this.tempPulseColor1=new D.a,this.tempPulseColor2=new D.a,this.textureMatrix=new A.a}dispose(){this.renderTargetMaskBuffer.dispose(),this.renderTargetDepthBuffer.dispose(),this.renderTargetMaskDownSampleBuffer.dispose(),this.renderTargetBlurBuffer1.dispose(),this.renderTargetBlurBuffer2.dispose(),this.renderTargetEdgeBuffer1.dispose(),this.renderTargetEdgeBuffer2.dispose()}setSize(t,e){this.renderTargetMaskBuffer.setSize(t,e),this.renderTargetDepthBuffer.setSize(t,e);let n=Math.round(t/this.downSampleRatio),i=Math.round(e/this.downSampleRatio);this.renderTargetMaskDownSampleBuffer.setSize(n,i),this.renderTargetBlurBuffer1.setSize(n,i),this.renderTargetEdgeBuffer1.setSize(n,i),this.separableBlurMaterial1.uniforms.texSize.value.set(n,i),n=Math.round(n/2),i=Math.round(i/2),this.renderTargetBlurBuffer2.setSize(n,i),this.renderTargetEdgeBuffer2.setSize(n,i),this.separableBlurMaterial2.uniforms.texSize.value.set(n,i)}changeVisibilityOfSelectedObjects(t){const e=this._visibilityCache;function n(n){n.isMesh&&(!0===t?n.visible=e.get(n):(e.set(n,n.visible),n.visible=t))}for(let t=0;t0){t.getClearColor(this._oldClearColor),this.oldClearAlpha=t.getClearAlpha();const e=t.autoClear;t.autoClear=!1,s&&t.state.buffers.stencil.setTest(!1),t.setClearColor(16777215,1),this.changeVisibilityOfSelectedObjects(!1);const i=this.renderScene.background;if(this.renderScene.background=null,this.renderScene.overrideMaterial=this.depthMaterial,t.setRenderTarget(this.renderTargetDepthBuffer),t.clear(),t.render(this.renderScene,this.renderCamera),this.changeVisibilityOfSelectedObjects(!0),this._visibilityCache.clear(),this.updateTextureMatrix(),this.changeVisibilityOfNonSelectedObjects(!1),this.renderScene.overrideMaterial=this.prepareMaskMaterial,this.prepareMaskMaterial.uniforms.cameraNearFar.value.set(this.renderCamera.near,this.renderCamera.far),this.prepareMaskMaterial.uniforms.depthTexture.value=this.renderTargetDepthBuffer.texture,this.prepareMaskMaterial.uniforms.textureMatrix.value=this.textureMatrix,t.setRenderTarget(this.renderTargetMaskBuffer),t.clear(),t.render(this.renderScene,this.renderCamera),this.renderScene.overrideMaterial=null,this.changeVisibilityOfNonSelectedObjects(!0),this._visibilityCache.clear(),this.renderScene.background=i,this.fsQuad.material=this.materialCopy,this.copyUniforms.tDiffuse.value=this.renderTargetMaskBuffer.texture,t.setRenderTarget(this.renderTargetMaskDownSampleBuffer),t.clear(),this.fsQuad.render(t),this.tempPulseColor1.copy(this.visibleEdgeColor),this.tempPulseColor2.copy(this.hiddenEdgeColor),this.pulsePeriod>0){const t=.625+.75*Math.cos(.01*performance.now()/this.pulsePeriod)/2;this.tempPulseColor1.multiplyScalar(t),this.tempPulseColor2.multiplyScalar(t)}this.fsQuad.material=this.edgeDetectionMaterial,this.edgeDetectionMaterial.uniforms.maskTexture.value=this.renderTargetMaskDownSampleBuffer.texture,this.edgeDetectionMaterial.uniforms.texSize.value.set(this.renderTargetMaskDownSampleBuffer.width,this.renderTargetMaskDownSampleBuffer.height),this.edgeDetectionMaterial.uniforms.visibleEdgeColor.value=this.tempPulseColor1,this.edgeDetectionMaterial.uniforms.hiddenEdgeColor.value=this.tempPulseColor2,t.setRenderTarget(this.renderTargetEdgeBuffer1),t.clear(),this.fsQuad.render(t),this.fsQuad.material=this.separableBlurMaterial1,this.separableBlurMaterial1.uniforms.colorTexture.value=this.renderTargetEdgeBuffer1.texture,this.separableBlurMaterial1.uniforms.direction.value=gW.BlurDirectionX,this.separableBlurMaterial1.uniforms.kernelRadius.value=this.edgeThickness,t.setRenderTarget(this.renderTargetBlurBuffer1),t.clear(),this.fsQuad.render(t),this.separableBlurMaterial1.uniforms.colorTexture.value=this.renderTargetBlurBuffer1.texture,this.separableBlurMaterial1.uniforms.direction.value=gW.BlurDirectionY,t.setRenderTarget(this.renderTargetEdgeBuffer1),t.clear(),this.fsQuad.render(t),this.fsQuad.material=this.separableBlurMaterial2,this.separableBlurMaterial2.uniforms.colorTexture.value=this.renderTargetEdgeBuffer1.texture,this.separableBlurMaterial2.uniforms.direction.value=gW.BlurDirectionX,t.setRenderTarget(this.renderTargetBlurBuffer2),t.clear(),this.fsQuad.render(t),this.separableBlurMaterial2.uniforms.colorTexture.value=this.renderTargetBlurBuffer2.texture,this.separableBlurMaterial2.uniforms.direction.value=gW.BlurDirectionY,t.setRenderTarget(this.renderTargetEdgeBuffer2),t.clear(),this.fsQuad.render(t),this.fsQuad.material=this.overlayMaterial,this.overlayMaterial.uniforms.maskTexture.value=this.renderTargetMaskBuffer.texture,this.overlayMaterial.uniforms.edgeTexture1.value=this.renderTargetEdgeBuffer1.texture,this.overlayMaterial.uniforms.edgeTexture2.value=this.renderTargetEdgeBuffer2.texture,this.overlayMaterial.uniforms.patternTexture.value=this.patternTexture,this.overlayMaterial.uniforms.edgeStrength.value=this.edgeStrength,this.overlayMaterial.uniforms.edgeGlow.value=this.edgeGlow,this.overlayMaterial.uniforms.usePatternTexture.value=this.usePatternTexture,s&&t.state.buffers.stencil.setTest(!0),t.setRenderTarget(n),this.fsQuad.render(t),t.setClearColor(this._oldClearColor,this.oldClearAlpha),t.autoClear=e}this.renderToScreen&&(this.fsQuad.material=this.materialCopy,this.copyUniforms.tDiffuse.value=n.texture,t.setRenderTarget(null),this.fsQuad.render(t))}getPrepareMaskMaterial(){return new F({uniforms:{depthTexture:{value:null},cameraNearFar:{value:new d.a(.5,.5)},textureMatrix:{value:null}},vertexShader:\\\\\\\"#include \\\\n\\\\t\\\\t\\\\t\\\\t#include \\\\n\\\\n\\\\t\\\\t\\\\t\\\\tvarying vec4 projTexCoord;\\\\n\\\\t\\\\t\\\\t\\\\tvarying vec4 vPosition;\\\\n\\\\t\\\\t\\\\t\\\\tuniform mat4 textureMatrix;\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t#include \\\\n\\\\t\\\\t\\\\t\\\\t\\\\t#include \\\\n\\\\t\\\\t\\\\t\\\\t\\\\t#include \\\\n\\\\t\\\\t\\\\t\\\\t\\\\t#include \\\\n\\\\t\\\\t\\\\t\\\\t\\\\t#include \\\\n\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tvPosition = mvPosition;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tvec4 worldPosition = modelMatrix * vec4( transformed, 1.0 );\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tprojTexCoord = textureMatrix * worldPosition;\\\\n\\\\n\\\\t\\\\t\\\\t\\\\t}\\\\\\\",fragmentShader:\\\\\\\"#include \\\\n\\\\t\\\\t\\\\t\\\\tvarying vec4 vPosition;\\\\n\\\\t\\\\t\\\\t\\\\tvarying vec4 projTexCoord;\\\\n\\\\t\\\\t\\\\t\\\\tuniform sampler2D depthTexture;\\\\n\\\\t\\\\t\\\\t\\\\tuniform vec2 cameraNearFar;\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tfloat depth = unpackRGBAToDepth(texture2DProj( depthTexture, projTexCoord ));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tfloat viewZ = - DEPTH_TO_VIEW_Z( depth, cameraNearFar.x, cameraNearFar.y );\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tfloat depthTest = (-vPosition.z > viewZ) ? 1.0 : 0.0;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tgl_FragColor = vec4(0.0, depthTest, 1.0, 1.0);\\\\n\\\\n\\\\t\\\\t\\\\t\\\\t}\\\\\\\"})}getEdgeDetectionMaterial(){return new F({uniforms:{maskTexture:{value:null},texSize:{value:new d.a(.5,.5)},visibleEdgeColor:{value:new p.a(1,1,1)},hiddenEdgeColor:{value:new p.a(1,1,1)}},vertexShader:\\\\\\\"varying vec2 vUv;\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tvoid main() {\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tvUv = uv;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tgl_Position = projectionMatrix * modelViewMatrix * vec4( position, 1.0 );\\\\n\\\\t\\\\t\\\\t\\\\t}\\\\\\\",fragmentShader:\\\\\\\"varying vec2 vUv;\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tuniform sampler2D maskTexture;\\\\n\\\\t\\\\t\\\\t\\\\tuniform vec2 texSize;\\\\n\\\\t\\\\t\\\\t\\\\tuniform vec3 visibleEdgeColor;\\\\n\\\\t\\\\t\\\\t\\\\tuniform vec3 hiddenEdgeColor;\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tvoid main() {\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tvec2 invSize = 1.0 / texSize;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tvec4 uvOffset = vec4(1.0, 0.0, 0.0, 1.0) * vec4(invSize, invSize);\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tvec4 c1 = texture2D( maskTexture, vUv + uvOffset.xy);\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tvec4 c2 = texture2D( maskTexture, vUv - uvOffset.xy);\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tvec4 c3 = texture2D( maskTexture, vUv + uvOffset.yw);\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tvec4 c4 = texture2D( maskTexture, vUv - uvOffset.yw);\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tfloat diff1 = (c1.r - c2.r)*0.5;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tfloat diff2 = (c3.r - c4.r)*0.5;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tfloat d = length( vec2(diff1, diff2) );\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tfloat a1 = min(c1.g, c2.g);\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tfloat a2 = min(c3.g, c4.g);\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tfloat visibilityFactor = min(a1, a2);\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tvec3 edgeColor = 1.0 - visibilityFactor > 0.001 ? visibleEdgeColor : hiddenEdgeColor;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tgl_FragColor = vec4(edgeColor, 1.0) * vec4(d);\\\\n\\\\t\\\\t\\\\t\\\\t}\\\\\\\"})}getSeperableBlurMaterial(t){return new F({defines:{MAX_RADIUS:t},uniforms:{colorTexture:{value:null},texSize:{value:new d.a(.5,.5)},direction:{value:new d.a(.5,.5)},kernelRadius:{value:1}},vertexShader:\\\\\\\"varying vec2 vUv;\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tvoid main() {\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tvUv = uv;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tgl_Position = projectionMatrix * modelViewMatrix * vec4( position, 1.0 );\\\\n\\\\t\\\\t\\\\t\\\\t}\\\\\\\",fragmentShader:\\\\\\\"#include \\\\n\\\\t\\\\t\\\\t\\\\tvarying vec2 vUv;\\\\n\\\\t\\\\t\\\\t\\\\tuniform sampler2D colorTexture;\\\\n\\\\t\\\\t\\\\t\\\\tuniform vec2 texSize;\\\\n\\\\t\\\\t\\\\t\\\\tuniform vec2 direction;\\\\n\\\\t\\\\t\\\\t\\\\tuniform float kernelRadius;\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tfloat gaussianPdf(in float x, in float sigma) {\\\\n\\\\t\\\\t\\\\t\\\\t\\\\treturn 0.39894 * exp( -0.5 * x * x/( sigma * sigma))/sigma;\\\\n\\\\t\\\\t\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tvoid main() {\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tvec2 invSize = 1.0 / texSize;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tfloat weightSum = gaussianPdf(0.0, kernelRadius);\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tvec4 diffuseSum = texture2D( colorTexture, vUv) * weightSum;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tvec2 delta = direction * invSize * kernelRadius/float(MAX_RADIUS);\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tvec2 uvOffset = delta;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tfor( int i = 1; i <= MAX_RADIUS; i ++ ) {\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tfloat w = gaussianPdf(uvOffset.x, kernelRadius);\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tvec4 sample1 = texture2D( colorTexture, vUv + uvOffset);\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tvec4 sample2 = texture2D( colorTexture, vUv - uvOffset);\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tdiffuseSum += ((sample1 + sample2) * w);\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tweightSum += (2.0 * w);\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tuvOffset += delta;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t}\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tgl_FragColor = diffuseSum/weightSum;\\\\n\\\\t\\\\t\\\\t\\\\t}\\\\\\\"})}getOverlayMaterial(){return new F({uniforms:{maskTexture:{value:null},edgeTexture1:{value:null},edgeTexture2:{value:null},patternTexture:{value:null},edgeStrength:{value:1},edgeGlow:{value:1},usePatternTexture:{value:0}},vertexShader:\\\\\\\"varying vec2 vUv;\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tvoid main() {\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tvUv = uv;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tgl_Position = projectionMatrix * modelViewMatrix * vec4( position, 1.0 );\\\\n\\\\t\\\\t\\\\t\\\\t}\\\\\\\",fragmentShader:\\\\\\\"varying vec2 vUv;\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tuniform sampler2D maskTexture;\\\\n\\\\t\\\\t\\\\t\\\\tuniform sampler2D edgeTexture1;\\\\n\\\\t\\\\t\\\\t\\\\tuniform sampler2D edgeTexture2;\\\\n\\\\t\\\\t\\\\t\\\\tuniform sampler2D patternTexture;\\\\n\\\\t\\\\t\\\\t\\\\tuniform float edgeStrength;\\\\n\\\\t\\\\t\\\\t\\\\tuniform float edgeGlow;\\\\n\\\\t\\\\t\\\\t\\\\tuniform bool usePatternTexture;\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tvoid main() {\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tvec4 edgeValue1 = texture2D(edgeTexture1, vUv);\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tvec4 edgeValue2 = texture2D(edgeTexture2, vUv);\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tvec4 maskColor = texture2D(maskTexture, vUv);\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tvec4 patternColor = texture2D(patternTexture, 6.0 * vUv);\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tfloat visibilityFactor = 1.0 - maskColor.g > 0.0 ? 1.0 : 0.5;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tvec4 edgeValue = edgeValue1 + edgeValue2 * edgeGlow;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tvec4 finalColor = edgeStrength * maskColor.r * edgeValue;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tif(usePatternTexture)\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tfinalColor += + visibilityFactor * (1.0 - maskColor.r) * (1.0 - patternColor.r);\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tgl_FragColor = finalColor;\\\\n\\\\t\\\\t\\\\t\\\\t}\\\\\\\",blending:w.e,depthTest:!1,depthWrite:!1,transparent:!0})}}gW.BlurDirectionX=new d.a(1,0),gW.BlurDirectionY=new d.a(0,1);const vW=new class extends la{constructor(){super(...arguments),this.objectsMask=aa.STRING(\\\\\\\"*outlined*\\\\\\\",{...uj}),this.refreshObjects=aa.BUTTON(null,{...uj}),this.printObjects=aa.BUTTON(null,{cook:!1,callback:t=>{yW.PARAM_CALLBACK_printResolve(t)}}),this.edgeStrength=aa.FLOAT(3,{range:[0,10],rangeLocked:[!0,!1],...uj}),this.edgeThickness=aa.FLOAT(1,{range:[0,4],rangeLocked:[!0,!1],...uj}),this.edgeGlow=aa.FLOAT(0,{range:[0,1],rangeLocked:[!0,!1],...uj}),this.pulsePeriod=aa.FLOAT(0,{range:[0,5],rangeLocked:[!0,!1],...uj}),this.visibleEdgeColor=aa.COLOR([1,1,1],{...uj}),this.hiddenEdgeColor=aa.COLOR([.2,.1,.4],{...uj})}};class yW extends dj{constructor(){super(...arguments),this.paramsConfig=vW,this._resolvedObjects=[],this._map=new Map}static type(){return\\\\\\\"outline\\\\\\\"}_createPass(t){const e=new gW(new d.a(t.resolution.x,t.resolution.y),t.scene,t.camera,t.scene.children);return this.updatePass(e),e}updatePass(t){t.edgeStrength=this.pv.edgeStrength,t.edgeThickness=this.pv.edgeThickness,t.edgeGlow=this.pv.edgeGlow,t.pulsePeriod=this.pv.pulsePeriod,t.visibleEdgeColor=this.pv.visibleEdgeColor,t.hiddenEdgeColor=this.pv.hiddenEdgeColor,this._setSelectedObjects(t)}_setSelectedObjects(t){const e=this.scene().objectsByMask(this.pv.objectsMask);this._map.clear();for(let t of e)this._map.set(t.uuid,t);this._resolvedObjects=e.filter((t=>{let e=!1;return t.traverseAncestors((t=>{this._map.has(t.uuid)&&(e=!0)})),!e})),t.selectedObjects=this._resolvedObjects}static PARAM_CALLBACK_printResolve(t){t.printResolve()}printResolve(){console.log(this._resolvedObjects)}}const xW={uniforms:{tDiffuse:{value:null},resolution:{value:null},pixelSize:{value:1}},vertexShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\tvarying highp vec2 vUv;\\\\n\\\\n\\\\t\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tvUv = uv;\\\\n\\\\t\\\\t\\\\t\\\\tgl_Position = projectionMatrix * modelViewMatrix * vec4( position, 1.0 );\\\\n\\\\n\\\\t\\\\t}\\\\\\\",fragmentShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\tuniform sampler2D tDiffuse;\\\\n\\\\t\\\\tuniform float pixelSize;\\\\n\\\\t\\\\tuniform vec2 resolution;\\\\n\\\\n\\\\t\\\\tvarying highp vec2 vUv;\\\\n\\\\n\\\\t\\\\tvoid main(){\\\\n\\\\n\\\\t\\\\t\\\\tvec2 dxy = pixelSize / resolution;\\\\n\\\\t\\\\t\\\\tvec2 coord = dxy * floor( vUv / dxy );\\\\n\\\\t\\\\t\\\\tgl_FragColor = texture2D(tDiffuse, coord);\\\\n\\\\n\\\\t\\\\t}\\\\\\\"};const bW=new class extends la{constructor(){super(...arguments),this.pixelSize=aa.INTEGER(16,{range:[1,50],rangeLocked:[!0,!1],...uj})}};class wW extends dj{constructor(){super(...arguments),this.paramsConfig=bW}static type(){return\\\\\\\"pixel\\\\\\\"}_createPass(t){const e=new zm(xW);return e.uniforms.resolution.value=t.resolution,e.uniforms.resolution.value.multiplyScalar(window.devicePixelRatio),this.updatePass(e),e}updatePass(t){t.uniforms.pixelSize.value=this.pv.pixelSize}}const TW=new class extends la{constructor(){super(...arguments),this.overrideScene=aa.BOOLEAN(0,uj),this.scene=aa.OPERATOR_PATH(\\\\\\\"/scene1\\\\\\\",{visibleIf:{overrideScene:1},nodeSelection:{context:ts.OBJ,types:[DV.type()]},...uj}),this.overrideCamera=aa.BOOLEAN(0,uj),this.camera=aa.OPERATOR_PATH(\\\\\\\"/perspective_camera1\\\\\\\",{visibleIf:{overrideCamera:1},nodeSelection:{context:ts.OBJ},...uj})}};class AW extends dj{constructor(){super(...arguments),this.paramsConfig=TW}static type(){return\\\\\\\"render\\\\\\\"}_createPass(t){const e=new Hm(t.scene,t.camera);return e.context={camera:t.camera,scene:t.scene},this.updatePass(e),e}updatePass(t){this._updateCamera(t),this._update_scene(t)}async _updateCamera(t){if(this.pv.overrideCamera){this.p.camera.isDirty()&&await this.p.camera.compute();const e=this.p.camera.found_node_with_context(ts.OBJ);if(e&&(e.type()==is.PERSPECTIVE||e.type()==is.ORTHOGRAPHIC)){const n=e.object;t.camera=n}}else t.camera=t.context.camera}async _update_scene(t){if(this.pv.overrideScene){this.p.camera.isDirty()&&await this.p.scene.compute();const e=this.p.scene.found_node_with_context(ts.OBJ);if(e&&e.type()==DV.type()){const n=e.object;t.scene=n}}else t.scene=t.context.scene}}const MW={uniforms:{tDiffuse:{value:null},amount:{value:.005},angle:{value:0}},vertexShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\tvarying vec2 vUv;\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\tvUv = uv;\\\\n\\\\t\\\\t\\\\tgl_Position = projectionMatrix * modelViewMatrix * vec4( position, 1.0 );\\\\n\\\\n\\\\t\\\\t}\\\\\\\",fragmentShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\tuniform sampler2D tDiffuse;\\\\n\\\\t\\\\tuniform float amount;\\\\n\\\\t\\\\tuniform float angle;\\\\n\\\\n\\\\t\\\\tvarying vec2 vUv;\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\tvec2 offset = amount * vec2( cos(angle), sin(angle));\\\\n\\\\t\\\\t\\\\tvec4 cr = texture2D(tDiffuse, vUv + offset);\\\\n\\\\t\\\\t\\\\tvec4 cga = texture2D(tDiffuse, vUv);\\\\n\\\\t\\\\t\\\\tvec4 cb = texture2D(tDiffuse, vUv - offset);\\\\n\\\\t\\\\t\\\\tgl_FragColor = vec4(cr.r, cga.g, cb.b, cga.a);\\\\n\\\\n\\\\t\\\\t}\\\\\\\"};const EW=new class extends la{constructor(){super(...arguments),this.amount=aa.FLOAT(.005,{range:[0,1],rangeLocked:[!0,!1],...uj}),this.angle=aa.FLOAT(0,{range:[0,10],rangeLocked:[!0,!1],...uj})}};class SW extends dj{constructor(){super(...arguments),this.paramsConfig=EW}static type(){return\\\\\\\"RGBShift\\\\\\\"}_createPass(t){const e=new zm(MW);return this.updatePass(e),e}updatePass(t){t.uniforms.amount.value=this.pv.amount,t.uniforms.angle.value=this.pv.angle}}const CW={uniforms:{tDiffuse:{value:null},amount:{value:1}},vertexShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\tvarying vec2 vUv;\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\tvUv = uv;\\\\n\\\\t\\\\t\\\\tgl_Position = projectionMatrix * modelViewMatrix * vec4( position, 1.0 );\\\\n\\\\n\\\\t\\\\t}\\\\\\\",fragmentShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\tuniform float amount;\\\\n\\\\n\\\\t\\\\tuniform sampler2D tDiffuse;\\\\n\\\\n\\\\t\\\\tvarying vec2 vUv;\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\tvec4 color = texture2D( tDiffuse, vUv );\\\\n\\\\t\\\\t\\\\tvec3 c = color.rgb;\\\\n\\\\n\\\\t\\\\t\\\\tcolor.r = dot( c, vec3( 1.0 - 0.607 * amount, 0.769 * amount, 0.189 * amount ) );\\\\n\\\\t\\\\t\\\\tcolor.g = dot( c, vec3( 0.349 * amount, 1.0 - 0.314 * amount, 0.168 * amount ) );\\\\n\\\\t\\\\t\\\\tcolor.b = dot( c, vec3( 0.272 * amount, 0.534 * amount, 1.0 - 0.869 * amount ) );\\\\n\\\\n\\\\t\\\\t\\\\tgl_FragColor = vec4( min( vec3( 1.0 ), color.rgb ), color.a );\\\\n\\\\n\\\\t\\\\t}\\\\\\\"};const NW=new class extends la{constructor(){super(...arguments),this.amount=aa.FLOAT(.5,{range:[0,2],rangeLocked:[!1,!1],...uj})}};class LW extends dj{constructor(){super(...arguments),this.paramsConfig=NW}static type(){return\\\\\\\"sepia\\\\\\\"}_createPass(t){const e=new zm(CW);return this.updatePass(e),e}updatePass(t){t.uniforms.amount.value=this.pv.amount}}const OW=new class extends la{};class PW extends dj{constructor(){super(...arguments),this.paramsConfig=OW}static type(){return\\\\\\\"sequence\\\\\\\"}initializeNode(){super.initializeNode(),this.io.inputs.setCount(0,4)}setupComposer(t){this._addPassFromInput(0,t),this._addPassFromInput(1,t),this._addPassFromInput(2,t),this._addPassFromInput(3,t)}}const RW=I.clone(yG.uniforms);RW.delta={value:new d.a};const IW={uniforms:RW,vertexShader:yG.vertexShader,fragmentShader:\\\\\\\"\\\\n#include \\\\n#define ITERATIONS 10.0\\\\nuniform sampler2D tDiffuse;\\\\nuniform vec2 delta;\\\\nvarying vec2 vUv;\\\\nvoid main() {\\\\n\\\\tvec4 color = vec4( 0.0 );\\\\n\\\\tfloat total = 0.0;\\\\n\\\\tfloat offset = rand( vUv );\\\\n\\\\tfor ( float t = -ITERATIONS; t <= ITERATIONS; t ++ ) {\\\\n\\\\t\\\\tfloat percent = ( t + offset - 0.5 ) / ITERATIONS;\\\\n\\\\t\\\\tfloat weight = 1.0 - abs( percent );\\\\n\\\\t\\\\tcolor += texture2D( tDiffuse, vUv + delta * percent ) * weight;\\\\n\\\\t\\\\ttotal += weight;\\\\n\\\\t}\\\\n\\\\tgl_FragColor = color / total;\\\\n}\\\\\\\"};const FW=new class extends la{constructor(){super(...arguments),this.delta=aa.VECTOR2([2,2],{...uj})}};class DW extends dj{constructor(){super(...arguments),this.paramsConfig=FW}static type(){return\\\\\\\"triangleBlur\\\\\\\"}_createPass(t){const e=new zm(IW);return e.resolution=t.resolution.clone(),this.updatePass(e),e}updatePass(t){t.uniforms.delta.value.copy(this.pv.delta).divide(t.resolution).multiplyScalar(window.devicePixelRatio)}}const BW={shaderID:\\\\\\\"luminosityHighPass\\\\\\\",uniforms:{tDiffuse:{value:null},luminosityThreshold:{value:1},smoothWidth:{value:1},defaultColor:{value:new D.a(0)},defaultOpacity:{value:0}},vertexShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\tvarying vec2 vUv;\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\tvUv = uv;\\\\n\\\\n\\\\t\\\\t\\\\tgl_Position = projectionMatrix * modelViewMatrix * vec4( position, 1.0 );\\\\n\\\\n\\\\t\\\\t}\\\\\\\",fragmentShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\tuniform sampler2D tDiffuse;\\\\n\\\\t\\\\tuniform vec3 defaultColor;\\\\n\\\\t\\\\tuniform float defaultOpacity;\\\\n\\\\t\\\\tuniform float luminosityThreshold;\\\\n\\\\t\\\\tuniform float smoothWidth;\\\\n\\\\n\\\\t\\\\tvarying vec2 vUv;\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\tvec4 texel = texture2D( tDiffuse, vUv );\\\\n\\\\n\\\\t\\\\t\\\\tvec3 luma = vec3( 0.299, 0.587, 0.114 );\\\\n\\\\n\\\\t\\\\t\\\\tfloat v = dot( texel.xyz, luma );\\\\n\\\\n\\\\t\\\\t\\\\tvec4 outputColor = vec4( defaultColor.rgb, defaultOpacity );\\\\n\\\\n\\\\t\\\\t\\\\tfloat alpha = smoothstep( luminosityThreshold, luminosityThreshold + smoothWidth, v );\\\\n\\\\n\\\\t\\\\t\\\\tgl_FragColor = mix( outputColor, texel, alpha );\\\\n\\\\n\\\\t\\\\t}\\\\\\\"};class zW extends Im{constructor(t,e,n,i){super(),this.strength=void 0!==e?e:1,this.radius=n,this.threshold=i,this.resolution=void 0!==t?new d.a(t.x,t.y):new d.a(256,256),this.clearColor=new D.a(0,0,0);const s={minFilter:w.V,magFilter:w.V,format:w.Ib};this.renderTargetsHorizontal=[],this.renderTargetsVertical=[],this.nMips=5;let r=Math.round(this.resolution.x/2),o=Math.round(this.resolution.y/2);this.renderTargetBright=new Q(r,o,s),this.renderTargetBright.texture.name=\\\\\\\"UnrealBloomPass.bright\\\\\\\",this.renderTargetBright.texture.generateMipmaps=!1;for(let t=0;t\\\\n\\\\t\\\\t\\\\t\\\\tvarying vec2 vUv;\\\\n\\\\t\\\\t\\\\t\\\\tuniform sampler2D colorTexture;\\\\n\\\\t\\\\t\\\\t\\\\tuniform vec2 texSize;\\\\n\\\\t\\\\t\\\\t\\\\tuniform vec2 direction;\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tfloat gaussianPdf(in float x, in float sigma) {\\\\n\\\\t\\\\t\\\\t\\\\t\\\\treturn 0.39894 * exp( -0.5 * x * x/( sigma * sigma))/sigma;\\\\n\\\\t\\\\t\\\\t\\\\t}\\\\n\\\\t\\\\t\\\\t\\\\tvoid main() {\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tvec2 invSize = 1.0 / texSize;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tfloat fSigma = float(SIGMA);\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tfloat weightSum = gaussianPdf(0.0, fSigma);\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tvec3 diffuseSum = texture2D( colorTexture, vUv).rgb * weightSum;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tfor( int i = 1; i < KERNEL_RADIUS; i ++ ) {\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tfloat x = float(i);\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tfloat w = gaussianPdf(x, fSigma);\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tvec2 uvOffset = direction * invSize * x;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tvec3 sample1 = texture2D( colorTexture, vUv + uvOffset).rgb;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tvec3 sample2 = texture2D( colorTexture, vUv - uvOffset).rgb;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tdiffuseSum += (sample1 + sample2) * w;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tweightSum += 2.0 * w;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t}\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tgl_FragColor = vec4(diffuseSum/weightSum, 1.0);\\\\n\\\\t\\\\t\\\\t\\\\t}\\\\\\\"})}getCompositeMaterial(t){return new F({defines:{NUM_MIPS:t},uniforms:{blurTexture1:{value:null},blurTexture2:{value:null},blurTexture3:{value:null},blurTexture4:{value:null},blurTexture5:{value:null},dirtTexture:{value:null},bloomStrength:{value:1},bloomFactors:{value:null},bloomTintColors:{value:null},bloomRadius:{value:0}},vertexShader:\\\\\\\"varying vec2 vUv;\\\\n\\\\t\\\\t\\\\t\\\\tvoid main() {\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tvUv = uv;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tgl_Position = projectionMatrix * modelViewMatrix * vec4( position, 1.0 );\\\\n\\\\t\\\\t\\\\t\\\\t}\\\\\\\",fragmentShader:\\\\\\\"varying vec2 vUv;\\\\n\\\\t\\\\t\\\\t\\\\tuniform sampler2D blurTexture1;\\\\n\\\\t\\\\t\\\\t\\\\tuniform sampler2D blurTexture2;\\\\n\\\\t\\\\t\\\\t\\\\tuniform sampler2D blurTexture3;\\\\n\\\\t\\\\t\\\\t\\\\tuniform sampler2D blurTexture4;\\\\n\\\\t\\\\t\\\\t\\\\tuniform sampler2D blurTexture5;\\\\n\\\\t\\\\t\\\\t\\\\tuniform sampler2D dirtTexture;\\\\n\\\\t\\\\t\\\\t\\\\tuniform float bloomStrength;\\\\n\\\\t\\\\t\\\\t\\\\tuniform float bloomRadius;\\\\n\\\\t\\\\t\\\\t\\\\tuniform float bloomFactors[NUM_MIPS];\\\\n\\\\t\\\\t\\\\t\\\\tuniform vec3 bloomTintColors[NUM_MIPS];\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tfloat lerpBloomFactor(const in float factor) {\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tfloat mirrorFactor = 1.2 - factor;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\treturn mix(factor, mirrorFactor, bloomRadius);\\\\n\\\\t\\\\t\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tvoid main() {\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tgl_FragColor = bloomStrength * ( lerpBloomFactor(bloomFactors[0]) * vec4(bloomTintColors[0], 1.0) * texture2D(blurTexture1, vUv) +\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tlerpBloomFactor(bloomFactors[1]) * vec4(bloomTintColors[1], 1.0) * texture2D(blurTexture2, vUv) +\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tlerpBloomFactor(bloomFactors[2]) * vec4(bloomTintColors[2], 1.0) * texture2D(blurTexture3, vUv) +\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tlerpBloomFactor(bloomFactors[3]) * vec4(bloomTintColors[3], 1.0) * texture2D(blurTexture4, vUv) +\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tlerpBloomFactor(bloomFactors[4]) * vec4(bloomTintColors[4], 1.0) * texture2D(blurTexture5, vUv) );\\\\n\\\\t\\\\t\\\\t\\\\t}\\\\\\\"})}}zW.BlurDirectionX=new d.a(1,0),zW.BlurDirectionY=new d.a(0,1);const kW=new class extends la{constructor(){super(...arguments),this.strength=aa.FLOAT(1.5,{range:[0,3],rangeLocked:[!0,!1],...uj}),this.radius=aa.FLOAT(1,{...uj}),this.threshold=aa.FLOAT(0,{...uj})}};class UW extends dj{constructor(){super(...arguments),this.paramsConfig=kW}static type(){return\\\\\\\"unrealBloom\\\\\\\"}_createPass(t){return new zW(new d.a(t.resolution.x,t.resolution.y),this.pv.strength,this.pv.radius,this.pv.threshold)}updatePass(t){t.strength=this.pv.strength,t.radius=this.pv.radius,t.threshold=this.pv.threshold}}const GW=new class extends la{constructor(){super(...arguments),this.amount=aa.FLOAT(2,{range:[0,10],rangeLocked:[!0,!1],step:.01,...uj}),this.transparent=aa.BOOLEAN(1,uj)}};class VW extends dj{constructor(){super(...arguments),this.paramsConfig=GW}static type(){return\\\\\\\"verticalBlur\\\\\\\"}_createPass(t){const e=new zm(xG);return e.resolution_y=t.resolution.y,this.updatePass(e),e}updatePass(t){t.uniforms.v.value=this.pv.amount/(t.resolution_y*window.devicePixelRatio),t.material.transparent=this.pv.transparent}}const HW={uniforms:{tDiffuse:{value:null},offset:{value:1},darkness:{value:1}},vertexShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\tvarying vec2 vUv;\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\tvUv = uv;\\\\n\\\\t\\\\t\\\\tgl_Position = projectionMatrix * modelViewMatrix * vec4( position, 1.0 );\\\\n\\\\n\\\\t\\\\t}\\\\\\\",fragmentShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\tuniform float offset;\\\\n\\\\t\\\\tuniform float darkness;\\\\n\\\\n\\\\t\\\\tuniform sampler2D tDiffuse;\\\\n\\\\n\\\\t\\\\tvarying vec2 vUv;\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\t// Eskil's vignette\\\\n\\\\n\\\\t\\\\t\\\\tvec4 texel = texture2D( tDiffuse, vUv );\\\\n\\\\t\\\\t\\\\tvec2 uv = ( vUv - vec2( 0.5 ) ) * vec2( offset );\\\\n\\\\t\\\\t\\\\tgl_FragColor = vec4( mix( texel.rgb, vec3( 1.0 - darkness ), dot( uv, uv ) ), texel.a );\\\\n\\\\n\\\\t\\\\t}\\\\\\\"};const jW=new class extends la{constructor(){super(...arguments),this.offset=aa.FLOAT(1,{range:[0,1],rangeLocked:[!1,!1],...uj}),this.darkness=aa.FLOAT(1,{range:[0,2],rangeLocked:[!0,!1],...uj})}};class WW extends dj{constructor(){super(...arguments),this.paramsConfig=jW}static type(){return\\\\\\\"vignette\\\\\\\"}_createPass(t){const e=new zm(HW);return this.updatePass(e),e}updatePass(t){t.uniforms.offset.value=this.pv.offset,t.uniforms.darkness.value=this.pv.darkness}}class qW extends sa{static context(){return ts.POST}cook(){this.cookController.endCook()}}class XW extends qW{}class YW extends XW{constructor(){super(...arguments),this._children_controller_context=ts.ANIM}static type(){return es.ANIM}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class $W extends XW{constructor(){super(...arguments),this._children_controller_context=ts.COP}static type(){return es.COP}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class JW extends XW{constructor(){super(...arguments),this._children_controller_context=ts.EVENT}static type(){return es.EVENT}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class ZW extends XW{constructor(){super(...arguments),this._children_controller_context=ts.MAT}static type(){return es.MAT}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class QW extends qW{constructor(){super(...arguments),this.paramsConfig=new Jm,this.effectsComposerController=new Zm(this),this.displayNodeController=new Lm(this,this.effectsComposerController.displayNodeControllerCallbacks()),this._children_controller_context=ts.POST}static type(){return es.POST}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class KW extends XW{constructor(){super(...arguments),this._children_controller_context=ts.ROP}static type(){return es.ROP}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class tq extends sa{static context(){return ts.ROP}cook(){this.cookController.endCook()}}class eq extends tq{}class nq extends eq{constructor(){super(...arguments),this._children_controller_context=ts.COP}static type(){return es.COP}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class iq extends K.a{constructor(t){super(),this._element=t,this._element.style.position=\\\\\\\"absolute\\\\\\\",this.addEventListener(\\\\\\\"removed\\\\\\\",this._on_removed.bind(this))}_on_removed(){this.traverse((function(t){t instanceof iq&&t.element instanceof Element&&null!==t.element.parentNode&&t.element.parentNode.removeChild(t.element)}))}get element(){return this._element}copy(t,e){return K.a.prototype.copy.call(this,t,e),this._element=t.element.cloneNode(!0),this.matrixAutoUpdate=t.matrixAutoUpdate,this}}class sq{constructor(){this._width=0,this._height=0,this._widthHalf=0,this._heightHalf=0,this.vector=new p.a,this.viewMatrix=new A.a,this.viewProjectionMatrix=new A.a,this.cache_distanceToCameraSquared=new WeakMap,this.domElement=document.createElement(\\\\\\\"div\\\\\\\"),this._sort_objects=!1,this._use_fog=!1,this._fog_near=1,this._fog_far=100,this.a=new p.a,this.b=new p.a,this.domElement.classList.add(\\\\\\\"polygonjs-CSS2DRenderer\\\\\\\")}getSize(){return{width:this._width,height:this._height}}setSize(t,e){this._width=t,this._height=e,this._widthHalf=this._width/2,this._heightHalf=this._height/2,this.domElement.style.width=t+\\\\\\\"px\\\\\\\",this.domElement.style.height=e+\\\\\\\"px\\\\\\\"}renderObject(t,e,n){if(t instanceof iq){this.vector.setFromMatrixPosition(t.matrixWorld),this.vector.applyMatrix4(this.viewProjectionMatrix);var i=t.element,s=\\\\\\\"translate(-50%,-50%) translate(\\\\\\\"+(this.vector.x*this._widthHalf+this._widthHalf)+\\\\\\\"px,\\\\\\\"+(-this.vector.y*this._heightHalf+this._heightHalf)+\\\\\\\"px)\\\\\\\";if(i.style.webkitTransform=s,i.style.transform=s,i.style.display=t.visible&&this.vector.z>=-1&&this.vector.z<=1?\\\\\\\"\\\\\\\":\\\\\\\"none\\\\\\\",this._sort_objects||this._use_fog){const e=this.getDistanceToSquared(n,t);if(this._use_fog){const t=Math.sqrt(e),n=rr.fit(t,this._fog_near,this._fog_far,0,1),s=rr.clamp(1-n,0,1);i.style.opacity=`${s}`,0==s&&(i.style.display=\\\\\\\"none\\\\\\\")}this.cache_distanceToCameraSquared.set(t,e)}i.parentNode!==this.domElement&&this.domElement.appendChild(i)}for(var r=0,o=t.children.length;r{const n=this.cache_distanceToCameraSquared.get(t),i=this.cache_distanceToCameraSquared.get(e);return null!=n&&null!=i?n-i:0})),n=e.length;for(let t=0,i=e.length;t{this._update_renderer(t)})),this.cookController.endCook()}_update_renderer(t){t.set_sorting(this.pv.sortObjects),t.set_use_fog(this.pv.useFog),t.set_fog_range(this.pv.fogNear,this.pv.fogFar)}_update_css(){this.css_element().innerHTML=this.pv.css}css_element(){return this._css_element=this._css_element||this._find_element()||this._create_element()}_find_element(){return document.getElementById(this._css_element_id())}_create_element(){const t=document.createElement(\\\\\\\"style\\\\\\\");return t.appendChild(document.createTextNode(\\\\\\\"\\\\\\\")),document.head.appendChild(t),t.id=this._css_element_id(),t}_css_element_id(){return`css_2d_renderer-${this.graphNodeId()}`}}class aq extends eq{constructor(){super(...arguments),this._children_controller_context=ts.ANIM}static type(){return es.ANIM}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class lq extends eq{constructor(){super(...arguments),this._children_controller_context=ts.EVENT}static type(){return es.EVENT}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class cq extends eq{constructor(){super(...arguments),this._children_controller_context=ts.MAT}static type(){return es.MAT}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class hq extends tq{constructor(){super(...arguments),this.paramsConfig=new Jm,this.effectsComposerController=new Zm(this),this.displayNodeController=new Lm(this,this.effectsComposerController.displayNodeControllerCallbacks()),this._children_controller_context=ts.POST}static type(){return es.POST}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class uq extends eq{constructor(){super(...arguments),this._children_controller_context=ts.ROP}static type(){return es.ROP}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class dq extends QG{static type(){return\\\\\\\"add\\\\\\\"}cook(t,e){const n=[];return this._create_point(n,e),this._create_polygon(t[0],n,e),this.createCoreGroupFromObjects(n)}_create_point(t,e){if(!e.createPoint)return;const n=new S.a,i=[];for(let t=0;t0&&this._create_polygon_open(t,e,n)}_create_polygon_open(t,e,n){const i=t.points();let s=[];const r=[];let o;for(let t=0;t0&&(r.push(t-1),r.push(t));if(i.length>2&&n.connectToLastPoint){i[0].position().toArray(s,s.length);const t=r[r.length-1];r.push(t),r.push(0)}const a=new S.a;a.setAttribute(\\\\\\\"position\\\\\\\",new C.c(s,3)),a.setIndex(r);const l=this.createObject(a,Cs.LINE_SEGMENTS);e.push(l)}}dq.DEFAULT_PARAMS={createPoint:!0,pointsCount:1,position:new p.a(0,0,0),connectInputPoints:!1,connectToLastPoint:!1};const pq=dq.DEFAULT_PARAMS;const _q=new class extends la{constructor(){super(...arguments),this.createPoint=aa.BOOLEAN(pq.createPoint),this.pointsCount=aa.INTEGER(pq.pointsCount,{range:[1,100],rangeLocked:[!0,!1],visibleIf:{createPoint:!0}}),this.position=aa.VECTOR3(pq.position,{visibleIf:{createPoint:!0}}),this.connectInputPoints=aa.BOOLEAN(pq.connectInputPoints),this.connectToLastPoint=aa.BOOLEAN(pq.connectToLastPoint)}};class mq extends nV{constructor(){super(...arguments),this.paramsConfig=_q}static type(){return\\\\\\\"add\\\\\\\"}static displayedInputNames(){return[\\\\\\\"geometry to create polygons from (optional)\\\\\\\"]}initializeNode(){this.io.inputs.setCount(0,1)}cook(t){this._operation=this._operation||new dq(this.scene(),this.states);const e=this._operation.cook(t,this.pv);this.setCoreGroup(e)}}const fq=new class extends la{};class gq extends nV{constructor(){super(...arguments),this.paramsConfig=fq}static type(){return\\\\\\\"animationCopy\\\\\\\"}static displayedInputNames(){return[\\\\\\\"geometry to copy animation to\\\\\\\",\\\\\\\"geometry to copy animation from\\\\\\\"]}initializeNode(){this.io.inputs.setCount(2),this.io.inputs.initInputsClonedState([Ki.FROM_NODE,Ki.NEVER])}cook(t){const e=t[0],n=t[1].objects()[0],i=e.objects()[0],s=n.animations;s?(i.animations=s.map((t=>t.clone())),this.setCoreGroup(e)):this.states.error.set(\\\\\\\"no animation found\\\\\\\")}}class vq{constructor(t,e,n=null,i=e.blendMode){this._mixer=t,this._clip=e,this._localRoot=n,this.blendMode=i;const s=e.tracks,r=s.length,o=new Array(r),a={endingStart:w.id,endingEnd:w.id};for(let t=0;t!==r;++t){const e=s[t].createInterpolant(null);o[t]=e,e.settings=a}this._interpolantSettings=a,this._interpolants=o,this._propertyBindings=new Array(r),this._cacheIndex=null,this._byClipCacheIndex=null,this._timeScaleInterpolant=null,this._weightInterpolant=null,this.loop=w.eb,this._loopCount=-1,this._startTime=null,this.time=0,this.timeScale=1,this._effectiveTimeScale=1,this.weight=1,this._effectiveWeight=1,this.repetitions=1/0,this.paused=!1,this.enabled=!0,this.clampWhenFinished=!1,this.zeroSlopeAtStart=!0,this.zeroSlopeAtEnd=!0}play(){return this._mixer._activateAction(this),this}stop(){return this._mixer._deactivateAction(this),this.reset()}reset(){return this.paused=!1,this.enabled=!0,this.time=0,this._loopCount=-1,this._startTime=null,this.stopFading().stopWarping()}isRunning(){return this.enabled&&!this.paused&&0!==this.timeScale&&null===this._startTime&&this._mixer._isActiveAction(this)}isScheduled(){return this._mixer._isActiveAction(this)}startAt(t){return this._startTime=t,this}setLoop(t,e){return this.loop=t,this.repetitions=e,this}setEffectiveWeight(t){return this.weight=t,this._effectiveWeight=this.enabled?t:0,this.stopFading()}getEffectiveWeight(){return this._effectiveWeight}fadeIn(t){return this._scheduleFading(t,0,1)}fadeOut(t){return this._scheduleFading(t,1,0)}crossFadeFrom(t,e,n){if(t.fadeOut(e),this.fadeIn(e),n){const n=this._clip.duration,i=t._clip.duration,s=i/n,r=n/i;t.warp(1,s,e),this.warp(r,1,e)}return this}crossFadeTo(t,e,n){return t.crossFadeFrom(this,e,n)}stopFading(){const t=this._weightInterpolant;return null!==t&&(this._weightInterpolant=null,this._mixer._takeBackControlInterpolant(t)),this}setEffectiveTimeScale(t){return this.timeScale=t,this._effectiveTimeScale=this.paused?0:t,this.stopWarping()}getEffectiveTimeScale(){return this._effectiveTimeScale}setDuration(t){return this.timeScale=this._clip.duration/t,this.stopWarping()}syncWith(t){return this.time=t.time,this.timeScale=t.timeScale,this.stopWarping()}halt(t){return this.warp(this._effectiveTimeScale,0,t)}warp(t,e,n){const i=this._mixer,s=i.time,r=this.timeScale;let o=this._timeScaleInterpolant;null===o&&(o=i._lendControlInterpolant(),this._timeScaleInterpolant=o);const a=o.parameterPositions,l=o.sampleValues;return a[0]=s,a[1]=s+n,l[0]=t/r,l[1]=e/r,this}stopWarping(){const t=this._timeScaleInterpolant;return null!==t&&(this._timeScaleInterpolant=null,this._mixer._takeBackControlInterpolant(t)),this}getMixer(){return this._mixer}getClip(){return this._clip}getRoot(){return this._localRoot||this._mixer._root}_update(t,e,n,i){if(!this.enabled)return void this._updateWeight(t);const s=this._startTime;if(null!==s){const i=(t-s)*n;if(i<0||0===n)return;this._startTime=null,e=n*i}e*=this._updateTimeScale(t);const r=this._updateTime(e),o=this._updateWeight(t);if(o>0){const t=this._interpolants,e=this._propertyBindings;switch(this.blendMode){case w.d:for(let n=0,i=t.length;n!==i;++n)t[n].evaluate(r),e[n].accumulateAdditive(o);break;case w.wb:default:for(let n=0,s=t.length;n!==s;++n)t[n].evaluate(r),e[n].accumulate(i,o)}}}_updateWeight(t){let e=0;if(this.enabled){e=this.weight;const n=this._weightInterpolant;if(null!==n){const i=n.evaluate(t)[0];e*=i,t>n.parameterPositions[1]&&(this.stopFading(),0===i&&(this.enabled=!1))}}return this._effectiveWeight=e,e}_updateTimeScale(t){let e=0;if(!this.paused){e=this.timeScale;const n=this._timeScaleInterpolant;if(null!==n){e*=n.evaluate(t)[0],t>n.parameterPositions[1]&&(this.stopWarping(),0===e?this.paused=!0:this.timeScale=e)}}return this._effectiveTimeScale=e,e}_updateTime(t){const e=this._clip.duration,n=this.loop;let i=this.time+t,s=this._loopCount;const r=n===w.db;if(0===t)return-1===s?i:r&&1==(1&s)?e-i:i;if(n===w.cb){-1===s&&(this._loopCount=0,this._setEndings(!0,!0,!1));t:{if(i>=e)i=e;else{if(!(i<0)){this.time=i;break t}i=0}this.clampWhenFinished?this.paused=!0:this.enabled=!1,this.time=i,this._mixer.dispatchEvent({type:\\\\\\\"finished\\\\\\\",action:this,direction:t<0?-1:1})}}else{if(-1===s&&(t>=0?(s=0,this._setEndings(!0,0===this.repetitions,r)):this._setEndings(0===this.repetitions,!0,r)),i>=e||i<0){const n=Math.floor(i/e);i-=e*n,s+=Math.abs(n);const o=this.repetitions-s;if(o<=0)this.clampWhenFinished?this.paused=!0:this.enabled=!1,i=t>0?e:0,this.time=i,this._mixer.dispatchEvent({type:\\\\\\\"finished\\\\\\\",action:this,direction:t>0?1:-1});else{if(1===o){const e=t<0;this._setEndings(e,!e,r)}else this._setEndings(!1,!1,r);this._loopCount=s,this.time=i,this._mixer.dispatchEvent({type:\\\\\\\"loop\\\\\\\",action:this,loopDelta:n})}}else this.time=i;if(r&&1==(1&s))return e-i}return i}_setEndings(t,e,n){const i=this._interpolantSettings;n?(i.endingStart=w.kd,i.endingEnd=w.kd):(i.endingStart=t?this.zeroSlopeAtStart?w.kd:w.id:w.hd,i.endingEnd=e?this.zeroSlopeAtEnd?w.kd:w.id:w.hd)}_scheduleFading(t,e,n){const i=this._mixer,s=i.time;let r=this._weightInterpolant;null===r&&(r=i._lendControlInterpolant(),this._weightInterpolant=r);const o=r.parameterPositions,a=r.sampleValues;return o[0]=s,a[0]=e,o[1]=s+t,a[1]=n,this}}var yq=n(71),xq=n(66);class bq{constructor(t,e,n){let i,s,r;switch(this.binding=t,this.valueSize=n,e){case\\\\\\\"quaternion\\\\\\\":i=this._slerp,s=this._slerpAdditive,r=this._setAdditiveIdentityQuaternion,this.buffer=new Float64Array(6*n),this._workIndex=5;break;case\\\\\\\"string\\\\\\\":case\\\\\\\"bool\\\\\\\":i=this._select,s=this._select,r=this._setAdditiveIdentityOther,this.buffer=new Array(5*n);break;default:i=this._lerp,s=this._lerpAdditive,r=this._setAdditiveIdentityNumeric,this.buffer=new Float64Array(5*n)}this._mixBufferRegion=i,this._mixBufferRegionAdditive=s,this._setIdentity=r,this._origIndex=3,this._addIndex=4,this.cumulativeWeight=0,this.cumulativeWeightAdditive=0,this.useCount=0,this.referenceCount=0}accumulate(t,e){const n=this.buffer,i=this.valueSize,s=t*i+i;let r=this.cumulativeWeight;if(0===r){for(let t=0;t!==i;++t)n[s+t]=n[t];r=e}else{r+=e;const t=e/r;this._mixBufferRegion(n,s,0,t,i)}this.cumulativeWeight=r}accumulateAdditive(t){const e=this.buffer,n=this.valueSize,i=n*this._addIndex;0===this.cumulativeWeightAdditive&&this._setIdentity(),this._mixBufferRegionAdditive(e,i,0,t,n),this.cumulativeWeightAdditive+=t}apply(t){const e=this.valueSize,n=this.buffer,i=t*e+e,s=this.cumulativeWeight,r=this.cumulativeWeightAdditive,o=this.binding;if(this.cumulativeWeight=0,this.cumulativeWeightAdditive=0,s<1){const t=e*this._origIndex;this._mixBufferRegion(n,i,t,1-s,e)}r>0&&this._mixBufferRegionAdditive(n,i,this._addIndex*e,1,e);for(let t=e,s=e+e;t!==s;++t)if(n[t]!==n[t+e]){o.setValue(n,i);break}}saveOriginalState(){const t=this.binding,e=this.buffer,n=this.valueSize,i=n*this._origIndex;t.getValue(e,i);for(let t=n,s=i;t!==s;++t)e[t]=e[i+t%n];this._setIdentity(),this.cumulativeWeight=0,this.cumulativeWeightAdditive=0}restoreOriginalState(){const t=3*this.valueSize;this.binding.setValue(this.buffer,t)}_setAdditiveIdentityNumeric(){const t=this._addIndex*this.valueSize,e=t+this.valueSize;for(let n=t;n=.5)for(let i=0;i!==s;++i)t[e+i]=t[n+i]}_slerp(t,e,n,i){ah.a.slerpFlat(t,e,t,e,t,n,i)}_slerpAdditive(t,e,n,i,s){const r=this._workIndex*s;ah.a.multiplyQuaternionsFlat(t,r,t,e,t,n),ah.a.slerpFlat(t,e,t,e,t,r,i)}_lerp(t,e,n,i,s){const r=1-i;for(let o=0;o!==s;++o){const s=e+o;t[s]=t[s]*r+t[n+o]*i}}_lerpAdditive(t,e,n,i,s){for(let r=0;r!==s;++r){const s=e+r;t[s]=t[s]+t[n+r]*i}}}var wq=n(64);class Tq extends J.a{constructor(t){super(),this._root=t,this._initMemoryManager(),this._accuIndex=0,this.time=0,this.timeScale=1}_bindAction(t,e){const n=t._localRoot||this._root,i=t._clip.tracks,s=i.length,r=t._propertyBindings,o=t._interpolants,a=n.uuid,l=this._bindingsByRootAndName;let c=l[a];void 0===c&&(c={},l[a]=c);for(let t=0;t!==s;++t){const s=i[t],l=s.name;let h=c[l];if(void 0!==h)r[t]=h;else{if(h=r[t],void 0!==h){null===h._cacheIndex&&(++h.referenceCount,this._addInactiveBinding(h,a,l));continue}const i=e&&e._propertyBindings[t].binding.parsedPath;h=new bq(xq.a.create(n,l,i),s.ValueTypeName,s.getValueSize()),++h.referenceCount,this._addInactiveBinding(h,a,l),r[t]=h}o[t].resultBuffer=h.buffer}}_activateAction(t){if(!this._isActiveAction(t)){if(null===t._cacheIndex){const e=(t._localRoot||this._root).uuid,n=t._clip.uuid,i=this._actionsByClip[n];this._bindAction(t,i&&i.knownActions[0]),this._addInactiveAction(t,n,e)}const e=t._propertyBindings;for(let t=0,n=e.length;t!==n;++t){const n=e[t];0==n.useCount++&&(this._lendBinding(n),n.saveOriginalState())}this._lendAction(t)}}_deactivateAction(t){if(this._isActiveAction(t)){const e=t._propertyBindings;for(let t=0,n=e.length;t!==n;++t){const n=e[t];0==--n.useCount&&(n.restoreOriginalState(),this._takeBackBinding(n))}this._takeBackAction(t)}}_initMemoryManager(){this._actions=[],this._nActiveActions=0,this._actionsByClip={},this._bindings=[],this._nActiveBindings=0,this._bindingsByRootAndName={},this._controlInterpolants=[],this._nActiveControlInterpolants=0;const t=this;this.stats={actions:{get total(){return t._actions.length},get inUse(){return t._nActiveActions}},bindings:{get total(){return t._bindings.length},get inUse(){return t._nActiveBindings}},controlInterpolants:{get total(){return t._controlInterpolants.length},get inUse(){return t._nActiveControlInterpolants}}}}_isActiveAction(t){const e=t._cacheIndex;return null!==e&&e=0;--e)t[e].stop();return this}update(t){t*=this.timeScale;const e=this._actions,n=this._nActiveActions,i=this.time+=t,s=Math.sign(t),r=this._accuIndex^=1;for(let o=0;o!==n;++o){e[o]._update(i,t,s,r)}const o=this._bindings,a=this._nActiveBindings;for(let t=0;t!==a;++t)o[t].apply(r);return this}setTime(t){this.time=0;for(let t=0;t{Mq.PARAM_CALLBACK_reset(t,e)}})}};class Mq extends nV{constructor(){super(...arguments),this.paramsConfig=Aq}static type(){return\\\\\\\"animationMixer\\\\\\\"}static displayedInputNames(){return[\\\\\\\"geometry to be animated\\\\\\\"]}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState(Ki.NEVER)}async cook(t){const e=t[0].objects()[0];e&&(await this.create_mixer_if_required(e),this._update_mixer()),this.setObjects([e])}async create_mixer_if_required(t){if(!this._mixer){const e=await this._create_mixer(t);e&&(this._mixer=e)}}async _create_mixer(t){this.p.clip.isDirty()&&await this.p.clip.compute();if(this.p.clip.found_node_with_context(ts.ANIM)){return new Tq(t)}}_update_mixer(){this._set_mixer_time()}_set_mixer_time(){this.pv.time!=this._previous_time&&(this._mixer&&this._mixer.setTime(this.pv.time),this._previous_time=this.pv.time)}static PARAM_CALLBACK_reset(t,e){e.setDirty(),t.reset_animation_mixer()}async reset_animation_mixer(){this._mixer=void 0,this._previous_time=void 0,this.setDirty()}}class Eq extends QG{static type(){return\\\\\\\"attribAddMult\\\\\\\"}cook(t,e){const n=t[0],i=n.attribNamesMatchingMask(e.name);for(let t of i){const i=n.geometries();for(let n of i)this._update_attrib(t,n,e)}return n}_update_attrib(t,e,n){const i=e.getAttribute(t);if(i){const t=i.array,e=n.preAdd,s=n.mult,r=n.postAdd;for(let n=0;n({name:t,value:e})))}})}};class Bq extends nV{constructor(){super(...arguments),this.paramsConfig=Dq}static type(){return\\\\\\\"attribCast\\\\\\\"}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState(Iq.INPUT_CLONED_STATE),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.type],(()=>Oq[this.pv.type]))}))}))}cook(t){this._operation=this._operation||new Iq(this.scene(),this.states);const e=this._operation.cook(t,this.pv);this.setCoreGroup(e)}}class zq extends QG{static type(){return\\\\\\\"attribCopy\\\\\\\"}cook(t,e){const n=t[0],i=t[1]||n,s=i.attribNamesMatchingMask(e.name);for(let t of s)this.copy_vertex_attribute_between_core_groups(n,i,t,e);return n}copy_vertex_attribute_between_core_groups(t,e,n,i){var s;const r=e.objectsWithGeo(),o=t.objectsWithGeo();if(o.length>r.length)null===(s=this.states)||void 0===s||s.error.set(\\\\\\\"second input does not have enough objects to copy attributes from\\\\\\\");else for(let t=0;ta&&(null===(s=this.states)||void 0===s||s.error.set(\\\\\\\"not enough points in second input\\\\\\\"));const c=i.tnewName?i.newName:n;let h=t.getAttribute(c);if(h)this._fill_dest_array(h,o,i),h.needsUpdate=!0;else{const e=o.array.slice(0,l*r);t.setAttribute(c,new C.c(e,r))}}else null===(r=this.states)||void 0===r||r.error.set(`attribute '${n}' does not exist on second input`)}_fill_dest_array(t,e,n){const i=t.array,s=e.array,r=i.length,o=t.itemSize,a=e.itemSize,l=n.srcOffset,c=n.destOffset;if(t.itemSize==e.itemSize){t.copyArray(e.array);for(let t=0;t{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.name,this.p.tnewName,this.p.newName],(()=>this.pv.tnewName?`${this.pv.name} -> ${this.pv.newName}`:this.pv.name))}))}))}cook(t){this._operation=this._operation||new zq(this.scene(),this.states);const e=this._operation.cook(t,this.pv);this.setCoreGroup(e)}}class Vq extends QG{static type(){return\\\\\\\"attribCreate\\\\\\\"}cook(t,e){var n;const i=t[0];return e.name&&\\\\\\\"\\\\\\\"!=e.name.trim()?this._add_attribute(Fs[e.class],i,e):null===(n=this.states)||void 0===n||n.error.set(\\\\\\\"attribute name is not valid\\\\\\\"),i}async _add_attribute(t,e,n){const i=zs[n.type];switch(t){case Is.VERTEX:return void await this.add_point_attribute(i,e,n);case Is.OBJECT:return void await this.add_object_attribute(i,e,n)}ls.unreachable(t)}async add_point_attribute(t,e,n){const i=e.coreObjects();switch(t){case Bs.NUMERIC:for(let t=0;t{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.name])}))}))}cook(t){if(this._is_using_expression())this.pv.name&&\\\\\\\"\\\\\\\"!=this.pv.name.trim()?this._add_attribute(Fs[this.pv.class],t[0]):this.states.error.set(\\\\\\\"attribute name is not valid\\\\\\\");else{this._operation=this._operation||new Vq(this.scene(),this.states);const e=this._operation.cook(t,this.pv);this.setCoreGroup(e)}}async _add_attribute(t,e){const n=zs[this.pv.type];switch(t){case Is.VERTEX:return await this.add_point_attribute(n,e),this.setCoreGroup(e);case Is.OBJECT:return await this.add_object_attribute(n,e),this.setCoreGroup(e)}ls.unreachable(t)}async add_point_attribute(t,e){const n=e.coreObjects();switch(t){case Bs.NUMERIC:for(let t=0;t{s[t.index()*this.pv.size+0]=e}));else{let e=[this.p.value2,this.p.value3,this.p.value4][this.pv.size-2].components;const i=new Array(e.length);let r;const o=[this._x_arrays_by_geometry_uuid,this._y_arrays_by_geometry_uuid,this._z_arrays_by_geometry_uuid,this._w_arrays_by_geometry_uuid];for(let a=0;a{i[a][t.index()]=e}));else{const t=r.value;for(let e of n)s[e.index()*this.pv.size+a]=t}for(let t=0;t{t.setAttribValue(this.pv.name,e)}));else{let e=[this.p.value2,this.p.value3,this.p.value4][this.pv.size-2].components,n={};const i=this._vector_by_attrib_size(this.pv.size);if(i){for(let e of t)n[e.index()]=i;for(let i=0;i{n[t.index()][r]=e}));else for(let e of t){n[e.index()][r]=s.value}}for(let e=0;e{i[t.index()]=e}));const s=qs.arrayToIndexedArrays(i),r=t.coreGeometry();r&&r.setIndexedAttribute(this.pv.name,s.values,s.indices)}async add_string_attribute_to_object(t){const e=this.p.string;e.hasExpression()&&e.expressionController&&await e.expressionController.compute_expression_for_objects(t,((t,e)=>{t.setAttribValue(this.pv.name,e)}))}_init_array_if_required(t,e,n){const i=t.uuid,s=e[i];return s?s.length{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.name])}))}))}cook(t){const e=t[0],n=e.attribNamesMatchingMask(this.pv.name);for(let t of n)switch(this.pv.class){case Is.VERTEX:this.delete_vertex_attribute(e,t);case Is.OBJECT:this.delete_object_attribute(e,t)}this.setCoreGroup(e)}delete_vertex_attribute(t,e){for(let n of t.objects())n.traverse((t=>{const n=t;if(n.geometry){new _r(n.geometry).deleteAttribute(e)}}))}delete_object_attribute(t,e){for(let n of t.objects()){let t=0;n.traverse((n=>{new yr(n,t).deleteAttribute(e),t++}))}}}class $q{set_attrib(t){const e=t.geometry,n=t.targetAttribSize;if(n<1||n>4)return;const i=t.add,s=t.mult,r=this._data_from_texture(t.texture);if(!r)return;const{data:o,resx:a,resy:l}=r,c=o.length/(a*l),h=e.getAttribute(t.uvAttribName).array,u=h.length/2,d=new Array(u*n);let p,_,m,f,g,v,y,x,b;const w=rr.clamp;for(v=0;v{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.attrib])}))}))}async cook(t){this._operation=this._operation||new Jq(this.scene(),this.states);const e=await this._operation.cook(t,this.pv);this.setCoreGroup(e)}}var tX;!function(t){t.MIN_MAX_TO_01=\\\\\\\"min/max to 0/1\\\\\\\",t.VECTOR_TO_LENGTH_1=\\\\\\\"vectors to length 1\\\\\\\"}(tX||(tX={}));const eX=[tX.MIN_MAX_TO_01,tX.VECTOR_TO_LENGTH_1];class nX extends QG{constructor(){super(...arguments),this.min3=new p.a,this.max3=new p.a,this._vec=new p.a}static type(){return\\\\\\\"attribNormalize\\\\\\\"}cook(t,e){const n=t[0],i=t[0].objectsWithGeo(),s=os.attribNames(e.name);for(let t of i){const n=t.geometry;for(let t of s){const i=n.getAttribute(t);if(i){let t=i;e.changeName&&\\\\\\\"\\\\\\\"!=e.newName&&(t=n.getAttribute(e.newName),t&&(t.needsUpdate=!0),t=t||i.clone()),this._normalize_attribute(i,t,e)}}}return n}_normalize_attribute(t,e,n){switch(eX[n.mode]){case tX.MIN_MAX_TO_01:return this._normalize_from_min_max_to_01(t,e);case tX.VECTOR_TO_LENGTH_1:return this._normalize_vectors(t,e)}}_normalize_from_min_max_to_01(t,e){const n=t.itemSize,i=t.array,s=e.array;switch(n){case 1:{const t=Math.min(...i),e=Math.max(...i);for(let n=0;n({name:t,value:e})))}}),this.name=aa.STRING(iX.name),this.changeName=aa.BOOLEAN(iX.changeName),this.newName=aa.STRING(iX.newName,{visibleIf:{changeName:1}})}};class rX extends nV{constructor(){super(...arguments),this.paramsConfig=sX}static type(){return\\\\\\\"attribNormalize\\\\\\\"}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState(nX.INPUT_CLONED_STATE),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.name])}))}))}set_mode(t){this.p.mode.set(eX.indexOf(t))}cook(t){this._operation=this._operation||new nX(this.scene(),this.states);const e=this._operation.cook(t,this.pv);this.setCoreGroup(e)}}var oX;!function(t){t[t.MIN=0]=\\\\\\\"MIN\\\\\\\",t[t.MAX=1]=\\\\\\\"MAX\\\\\\\",t[t.FIRST_FOUND=2]=\\\\\\\"FIRST_FOUND\\\\\\\"}(oX||(oX={}));class aX extends QG{constructor(){super(...arguments),this._values_per_attrib_name={},this._filtered_values_per_attrib_name={}}static type(){return\\\\\\\"attribPromote\\\\\\\"}cook(t,e){this._core_group=t[0],this._values_per_attrib_name={},this._filtered_values_per_attrib_name={};for(let t of this._core_group.coreObjects())this._core_object=t,this.find_values(e),this.filter_values(e),this.set_values(e);return this._core_group}find_values(t){const e=os.attribNames(t.name);for(let n of e)this._find_values_for_attrib_name(n,t)}_find_values_for_attrib_name(t,e){switch(e.classFrom){case Is.VERTEX:return this.find_values_from_points(t,e);case Is.OBJECT:return this.find_values_from_object(t,e)}}find_values_from_points(t,e){if(this._core_object){const e=this._core_object.points(),n=e[0];if(n&&!n.isAttribIndexed(t)){const n=new Array(e.length);let i;for(let s=0;s{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.name,this.p.classFrom,this.p.classTo],(()=>{if(\\\\\\\"\\\\\\\"!=this.pv.name){const t=Ds.filter((t=>t.value==this.pv.classFrom))[0].name,e=Ds.filter((t=>t.value==this.pv.classTo))[0].name;return`${this.pv.name} (${t} -> ${e})`}return\\\\\\\"\\\\\\\"}))}))}))}cook(t){this._operation=this._operation||new aX(this.scene(),this.states);const e=this._operation.cook(t,this.pv);this.setCoreGroup(e)}}const dX=new class extends la{constructor(){super(...arguments),this.name=aa.STRING(),this.ramp=aa.RAMP(),this.changeName=aa.BOOLEAN(0),this.newName=aa.STRING(\\\\\\\"\\\\\\\",{visibleIf:{changeName:1}})}};class pX extends nV{constructor(){super(...arguments),this.paramsConfig=dX}static type(){return\\\\\\\"attribRemap\\\\\\\"}initializeNode(){this.io.inputs.setCount(1)}cook(t){const e=t[0];this._remap_attribute(e),this.setCoreGroup(e)}_remap_attribute(t){const e=t.points();if(0===e.length)return;if(\\\\\\\"\\\\\\\"===this.pv.name)return;const n=e[0].attribSize(this.pv.name),i=e.map((t=>t.attribValue(this.pv.name)));let s=new Array(e.length);this._get_remaped_values(n,i,s);let r=this.pv.name;this.pv.changeName&&(r=this.pv.newName,t.hasAttrib(r)||t.addNumericVertexAttrib(r,n,0));let o=0;for(let t of s){e[o].setAttribValue(r,t),o++}}_get_remaped_values(t,e,n){switch(t){case Us.FLOAT:return this._get_normalized_float(e,n);case Us.VECTOR2:return this._get_normalized_vector2(e,n);case Us.VECTOR3:return this._get_normalized_vector3(e,n);case Us.VECTOR4:return this._get_normalized_vector4(e,n)}ls.unreachable(t)}_get_normalized_float(t,e){const n=t,i=this.p.ramp;for(let t=0;t{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.oldName,this.p.newName],(()=>\\\\\\\"\\\\\\\"!=this.pv.oldName&&\\\\\\\"\\\\\\\"!=this.pv.newName?`${this.pv.oldName} -> ${this.pv.newName}`:\\\\\\\"\\\\\\\"))}))}))}cook(t){const e=t[0];e.renameAttrib(this.pv.oldName,this.pv.newName,this.pv.class),this.setCoreGroup(e)}}var fX=n(18);class gX{constructor(t,e=0){this._bbox=t,this._level=e,this._leaves_by_octant={},this._points_by_octant_id={},this._leaves=[],this._bounding_boxes_by_octant={},this._bounding_boxes_by_octant_prepared=!1,this._center=this._bbox.max.clone().add(this._bbox.min).multiplyScalar(.5)}level(){return this._level}traverse(t){t(this);Object.values(this._leaves_by_octant).forEach((e=>{e.traverse(t)}))}intersects_sphere(t){return!!this._bbox&&this._bbox.intersectsSphere(t)}points_in_sphere(t,e){if(0==this._leaves.length){Object.values(this._points_by_octant_id).flat().filter((e=>t.containsPoint(e.position()))).forEach((t=>{e.push(t)}))}else{this._leaves.filter((e=>e.intersects_sphere(t))).forEach((n=>n.points_in_sphere(t,e)))}}bounding_box(){return this._bbox}set_points(t){this._points_by_octant_id={};for(let e of t)this.add_point(e);const e=Object.keys(this._points_by_octant_id);e.length>1&&e.forEach((t=>{this.create_leaf(t)}))}create_leaf(t){const e=this._leaf_bbox(t),n=new gX(e,this._level+1);this._leaves_by_octant[t]=n,this._leaves.push(n),n.set_points(this._points_by_octant_id[t])}add_point(t){const e=this._octant_id(t.position());null==this._points_by_octant_id[e]&&(this._points_by_octant_id[e]=[]),this._points_by_octant_id[e].push(t)}_octant_id(t){return`${t.x>this._center.x?1:0}${t.y>this._center.y?1:0}${t.z>this._center.z?1:0}`}_leaf_bbox(t){return this._bounding_boxes_by_octant_prepared||(this._prepare_leaves_bboxes(),this._bounding_boxes_by_octant_prepared=!0),this._bounding_boxes_by_octant[t]}_bbox_center(t,e,n){const i=this._bbox.min.clone();return t&&(i.x=this._bbox.max.x),e&&(i.y=this._bbox.max.y),n&&(i.z=this._bbox.max.z),i.clone().add(this._center).multiplyScalar(.5)}_prepare_leaves_bboxes(){const t=[];t.push(this._bbox_center(0,0,0)),t.push(this._bbox_center(0,0,1)),t.push(this._bbox_center(0,1,0)),t.push(this._bbox_center(0,1,1)),t.push(this._bbox_center(1,0,0)),t.push(this._bbox_center(1,0,1)),t.push(this._bbox_center(1,1,0)),t.push(this._bbox_center(1,1,1));const e=this._bbox.max.clone().sub(this._bbox.min).multiplyScalar(.25);for(let n of t){const t=this._octant_id(n),i=new Ay.a(n.clone().sub(e),n.clone().add(e));this._bounding_boxes_by_octant[t]=i}}}class vX{constructor(t){this._root=new gX(t)}set_points(t){this._root.set_points(t)}traverse(t){this._root.traverse(t)}find_points(t,e,n){const i=new fX.a(t,e);let s=[];return this._root.intersects_sphere(i)&&this._root.points_in_sphere(i,s),null==n||s.length>n&&(s=f.sortBy(s,(e=>e.position().distanceTo(t))),s=s.slice(0,n)),s}}class yX{constructor(t={}){this._array_index=0,this._count=0,this._current_count_index=0,this._resolve=null,this._max_time_per_chunk=t.max_time_per_chunk||10,this._check_every_interations=t.check_every_interations||100}async startWithCount(t,e){if(this._count=t,this._current_count_index=0,this._iteratee_method_count=e,this._bound_next_with_count=this.nextWithCount.bind(this),this._resolve)throw\\\\\\\"an iterator cannot be started twice\\\\\\\";return new Promise(((t,e)=>{this._resolve=t,this.nextWithCount()}))}nextWithCount(){const t=li.performance.performanceManager(),e=t.now();if(this._iteratee_method_count&&this._bound_next_with_count)for(;this._current_count_indexthis._max_time_per_chunk){setTimeout(this._bound_next_with_count,1);break}this._current_count_index>=this._count&&this._resolve&&this._resolve()}async startWithArray(t,e){if(this._array=t,this._array_index=0,this._iteratee_method_array=e,this._bound_next_with_array=this.nextWithArray.bind(this),this._resolve)throw\\\\\\\"an iterator cannot be started twice\\\\\\\";return new Promise(((t,e)=>{this._resolve=t,this.nextWithArray()}))}nextWithArray(){const t=li.performance.performanceManager(),e=t.now();if(this._iteratee_method_array&&this._bound_next_with_array&&this._array)for(;this._current_array_element=this._array[this._array_index];)if(this._iteratee_method_array(this._current_array_element,this._array_index),this._array_index++,this._array_index%this._check_every_interations==0&&t.now()-e>this._max_time_per_chunk){setTimeout(this._bound_next_with_array,1);break}void 0===this._current_array_element&&this._resolve&&this._resolve()}}const xX=new class extends la{constructor(){super(...arguments),this.srcGroup=aa.STRING(),this.destGroup=aa.STRING(),this.name=aa.STRING(),this.maxSamplesCount=aa.INTEGER(1,{range:[1,10],rangeLocked:[!0,!1]}),this.distanceThreshold=aa.FLOAT(1),this.blendWidth=aa.FLOAT(0)}};class bX extends nV{constructor(){super(...arguments),this.paramsConfig=xX}static type(){return\\\\\\\"attribTransfer\\\\\\\"}static displayedInputNames(){return[\\\\\\\"geometry to transfer attributes to\\\\\\\",\\\\\\\"geometry to transfer attributes from\\\\\\\"]}initializeNode(){this.io.inputs.setCount(2),this.io.inputs.initInputsClonedState([Ki.FROM_NODE,Ki.NEVER])}async cook(t){this._core_group_dest=t[0];const e=this._core_group_dest.pointsFromGroup(this.pv.destGroup);this._core_group_src=t[1],this._attrib_names=this._core_group_src.attribNamesMatchingMask(this.pv.name),this._error_if_attribute_not_found_on_second_input(),this._build_octree_if_required(this._core_group_src),this._add_attribute_if_required(),await this._transfer_attributes(e),this.setCoreGroup(this._core_group_dest)}_error_if_attribute_not_found_on_second_input(){for(let t of this._attrib_names)this._core_group_src.hasAttrib(t)||this.states.error.set(`attribute '${t}' not found on second input`)}_build_octree_if_required(t){const e=null==this._octree_timestamp||this._octree_timestamp!==t.timestamp();if(this._prev_param_srcGroup!==this.pv.srcGroup||e){this._octree_timestamp=t.timestamp(),this._prev_param_srcGroup=this.pv.srcGroup;const e=this._core_group_src.pointsFromGroup(this.pv.srcGroup);this._octree=new vX(this._core_group_src.boundingBox()),this._octree.set_points(e)}}_add_attribute_if_required(){for(let t of this._attrib_names)if(!this._core_group_dest.hasAttrib(t)){const e=this._core_group_src.attribSize(t);this._core_group_dest.addNumericVertexAttrib(t,e,0)}}async _transfer_attributes(t){const e=new yX;await e.startWithArray(t,this._transfer_attributes_for_point.bind(this))}_transfer_attributes_for_point(t){var e;const n=this.pv.distanceThreshold+this.pv.blendWidth,i=(null===(e=this._octree)||void 0===e?void 0:e.find_points(t.position(),n,this.pv.maxSamplesCount))||[];for(let e of this._attrib_names)this._interpolate_points(t,i,e)}_interpolate_points(t,e,n){let i;i=class{static perform(t,e,n,i,s){switch(e.length){case 0:return t.attribValue(n);case 1:return this._interpolate_with_1_point(t,e[0],n,i,s);default:return this._interpolate_with_multiple_points(t,e,n,i,s)}}static _interpolate_with_1_point(t,e,n,i,s){const r=t.position(),o=e.position(),a=r.distanceTo(o),l=e.attribValue(n);return m.isNumber(l)?this._weighted_value_from_distance(t,l,n,a,i,s):(console.warn(\\\\\\\"value is not a number\\\\\\\",l),0)}static _weight_from_distance(t,e,n){return(t-e)/n}static _weighted_value_from_distance(t,e,n,i,s,r){if(i<=s)return e;{const o=t.attribValue(n);if(m.isNumber(o)){const t=this._weight_from_distance(i,s,r);return t*o+(1-t)*e}return console.warn(\\\\\\\"value is not a number\\\\\\\",o),0}}static _interpolate_with_multiple_points(t,e,n,i,s){const r=e.map((e=>this._interpolate_with_1_point(t,e,n,i,s)));return f.max(r)||0}static weights(t,e){switch(e.length){case 1:return 1;case 2:return this._weights_from_2(t,e);default:return e=e.slice(0,3),this._weights_from_3(t,e)}}static _weights_from_2(t,e){const n=e.map((e=>t.distanceTo(e))),i=f.sum(n);return[n[1]/i,n[0]/i]}static _weights_from_3(t,e){const n=e.map((e=>t.distanceTo(e))),i=f.sum([n[0]*n[1],n[0]*n[2],n[1]*n[2]]);return[n[1]*n[2]/i,n[0]*n[2]/i,n[0]*n[1]/i]}}.perform(t,e,n,this.pv.distanceThreshold,this.pv.blendWidth),null!=i&&t.setAttribValue(n,i)}}const wX=new class extends la{constructor(){super(...arguments),this.stepSize=aa.FLOAT(.1)}};class TX extends nV{constructor(){super(...arguments),this.paramsConfig=wX}static type(){return\\\\\\\"bboxScatter\\\\\\\"}static displayedInputNames(){return[\\\\\\\"geometry to create points from\\\\\\\"]}initializeNode(){this.io.inputs.setCount(1)}cook(t){const e=t[0],n=this.pv.stepSize,i=e.boundingBox(),s=i.min,r=i.max,o=[];for(let t=s.x;t<=r.x;t+=n)for(let e=s.y;e<=r.y;e+=n)for(let i=s.z;i<=r.z;i+=n)o.push(t),o.push(e),o.push(i);const a=new S.a;a.setAttribute(\\\\\\\"position\\\\\\\",new C.a(new Float32Array(o),3)),this.setGeometry(a,Cs.POINTS)}}const AX=new class extends la{constructor(){super(...arguments),this.attribName=aa.STRING(\\\\\\\"position\\\\\\\"),this.blend=aa.FLOAT(.5,{range:[0,1],rangeLocked:[!0,!0]})}};class MX extends nV{constructor(){super(...arguments),this.paramsConfig=AX}static type(){return\\\\\\\"blend\\\\\\\"}static displayedInputNames(){return[\\\\\\\"geometry to blend from\\\\\\\",\\\\\\\"geometry to blend to\\\\\\\"]}initializeNode(){this.io.inputs.setCount(2),this.io.inputs.initInputsClonedState([Ki.FROM_NODE,Ki.NEVER])}cook(t){const e=t[0],n=t[1],i=e.objects(),s=n.objects();let r,o;for(let t=0;tt.flip())),t}}EX.fromPolygons=function(t){let e=new EX;return e.polygons=t,e};class SX{constructor(t=0,e=0,n=0){this.x=t,this.y=e,this.z=n}copy(t){return this.x=t.x,this.y=t.y,this.z=t.z,this}clone(){return new SX(this.x,this.y,this.z)}negate(){return this.x*=-1,this.y*=-1,this.z*=-1,this}add(t){return this.x+=t.x,this.y+=t.y,this.z+=t.z,this}sub(t){return this.x-=t.x,this.y-=t.y,this.z-=t.z,this}times(t){return this.x*=t,this.y*=t,this.z*=t,this}dividedBy(t){return this.x/=t,this.y/=t,this.z/=t,this}lerp(t,e){return this.add(CX.copy(t).sub(this).times(e))}unit(){return this.dividedBy(this.length())}length(){return Math.sqrt(this.x**2+this.y**2+this.z**2)}normalize(){return this.unit()}cross(t){let e=this;const n=e.x,i=e.y,s=e.z,r=t.x,o=t.y,a=t.z;return this.x=i*a-s*o,this.y=s*r-n*a,this.z=n*o-i*r,this}dot(t){return this.x*t.x+this.y*t.y+this.z*t.z}}let CX=new SX,NX=new SX;class LX{constructor(t,e,n,i){this.pos=(new SX).copy(t),this.normal=(new SX).copy(e),this.uv=(new SX).copy(n),this.uv.z=0,i&&(this.color=(new SX).copy(i))}clone(){return new LX(this.pos,this.normal,this.uv,this.color)}flip(){this.normal.negate()}interpolate(t,e){return new LX(this.pos.clone().lerp(t.pos,e),this.normal.clone().lerp(t.normal,e),this.uv.clone().lerp(t.uv,e),this.color&&t.color&&this.color.clone().lerp(t.color,e))}}class OX{constructor(t,e){this.normal=t,this.w=e}clone(){return new OX(this.normal.clone(),this.w)}flip(){this.normal.negate(),this.w=-this.w}splitPolygon(t,e,n,i,s){let r=0,o=[];for(let e=0;eOX.EPSILON?1:0;r|=i,o.push(i)}switch(r){case 0:(this.normal.dot(t.plane.normal)>0?e:n).push(t);break;case 1:i.push(t);break;case 2:s.push(t);break;case 3:let r=[],a=[];for(let e=0;e=3&&i.push(new PX(r,t.shared)),a.length>=3&&s.push(new PX(a,t.shared))}}}OX.EPSILON=1e-5,OX.fromPoints=function(t,e,n){let i=CX.copy(e).sub(t).cross(NX.copy(n).sub(t)).normalize();return new OX(i.clone(),i.dot(t))};class PX{constructor(t,e){this.vertices=t,this.shared=e,this.plane=OX.fromPoints(t[0].pos,t[1].pos,t[2].pos)}clone(){return new PX(this.vertices.map((t=>t.clone())),this.shared)}flip(){this.vertices.reverse().map((t=>t.flip())),this.plane.flip()}}class RX{constructor(t){this.plane=null,this.front=null,this.back=null,this.polygons=[],t&&this.build(t)}clone(){let t=new RX;return t.plane=this.plane&&this.plane.clone(),t.front=this.front&&this.front.clone(),t.back=this.back&&this.back.clone(),t.polygons=this.polygons.map((t=>t.clone())),t}invert(){for(let t=0;tnew PX(t.vertices.map((t=>new LX(t.pos,t.normal,t.uv))),t.shared))))},EX.fromGeometry=function(t,e){let n=[];if(t.isGeometry){let i=t.faces,s=t.vertices,r=[\\\\\\\"a\\\\\\\",\\\\\\\"b\\\\\\\",\\\\\\\"c\\\\\\\"];for(let o=0;o({top:0,array:new Float32Array(t),write:function(t){this.array[this.top++]=t.x,this.array[this.top++]=t.y,this.array[this.top++]=t.z}}),BX=t=>({top:0,array:new Float32Array(t),write:function(t){this.array[this.top++]=t.x,this.array[this.top++]=t.y}});var zX;EX.toMesh=function(t,e,n){let i,s,r=t.polygons;{let t=0;r.forEach((e=>t+=e.vertices.length-2)),i=new S.a;let e,n=DX(3*t*3),o=DX(3*t*3),a=BX(2*t*3),l=[];if(r.forEach((i=>{let s=i.vertices,r=s.length;void 0!==i.shared&&(l[i.shared]||(l[i.shared]=[])),r&&void 0!==s[0].color&&(e||(e=DX(3*t*3)));for(let t=3;t<=r;t++)void 0!==i.shared&&l[i.shared].push(n.top/3,n.top/3+1,n.top/3+2),n.write(s[0].pos),n.write(s[t-2].pos),n.write(s[t-1].pos),o.write(s[0].normal),o.write(s[t-2].normal),o.write(s[t-1].normal),a.write(s[0].uv),a.write(s[t-2].uv),a.write(s[t-1].uv),e&&(e.write(s[0].color)||e.write(s[t-2].color)||e.write(s[t-1].color))})),i.setAttribute(\\\\\\\"position\\\\\\\",new C.a(n.array,3)),i.setAttribute(\\\\\\\"normal\\\\\\\",new C.a(o.array,3)),i.setAttribute(\\\\\\\"uv\\\\\\\",new C.a(a.array,2)),e&&i.setAttribute(\\\\\\\"color\\\\\\\",new C.a(e.array,3)),l.length){let t=[],e=0;for(let n=0;n({name:t,value:e})))}}),this.useBothMaterials=aa.BOOLEAN(GX.useBothMaterials)}};class HX extends nV{constructor(){super(...arguments),this.paramsConfig=VX}static type(){return\\\\\\\"boolean\\\\\\\"}initializeNode(){super.initializeNode(),this.io.inputs.setCount(2),this.io.inputs.initInputsClonedState(UX.INPUT_CLONED_STATE),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.operation],(()=>kX[this.pv.operation]))}))}))}setOperation(t){this.p.operation.set(kX.indexOf(t))}async cook(t){this._operation=this._operation||new UX(this.scene(),this.states,this);const e=await this._operation.cook(t,this.pv);this.setCoreGroup(e)}}class jX extends QG{constructor(){super(...arguments),this._core_transform=new uU}static type(){return\\\\\\\"box\\\\\\\"}cook(t,e){const n=t[0],i=n?this._cook_with_input(n,e):this._cook_without_input(e);return this.createCoreGroupFromGeometry(i)}_cook_without_input(t){const e=t.divisions,n=t.size,i=new N(n,n,n,e,e,e);return i.translate(t.center.x,t.center.y,t.center.z),i.computeVertexNormals(),i}_cook_with_input(t,e){const n=e.divisions,i=t.boundingBox(),s=i.max.clone().sub(i.min),r=i.max.clone().add(i.min).multiplyScalar(.5),o=new N(s.x,s.y,s.z,n,n,n),a=this._core_transform.translation_matrix(r);return o.applyMatrix4(a),o}}jX.DEFAULT_PARAMS={size:1,divisions:1,center:new p.a(0,0,0)},jX.INPUT_CLONED_STATE=Ki.NEVER;const WX=jX.DEFAULT_PARAMS;const qX=new class extends la{constructor(){super(...arguments),this.size=aa.FLOAT(WX.size),this.divisions=aa.INTEGER(WX.divisions,{range:[1,10],rangeLocked:[!0,!1]}),this.center=aa.VECTOR3(WX.center)}};class XX extends nV{constructor(){super(...arguments),this.paramsConfig=qX}static type(){return\\\\\\\"box\\\\\\\"}static displayedInputNames(){return[\\\\\\\"geometry to create bounding box from (optional)\\\\\\\"]}initializeNode(){this.io.inputs.setCount(0,1),this.io.inputs.initInputsClonedState(jX.INPUT_CLONED_STATE)}cook(t){this._operation=this._operation||new jX(this._scene,this.states);const e=this._operation.cook(t,this.pv);this.setCoreGroup(e)}}class YX{constructor(){}}function $X(t,e,n){return n.min.x=e[t],n.min.y=e[t+1],n.min.z=e[t+2],n.max.x=e[t+3],n.max.y=e[t+4],n.max.z=e[t+5],n}function JX(t){let e=-1,n=-1/0;for(let i=0;i<3;i++){const s=t[i+3]-t[i];s>n&&(n=s,e=i)}return e}function ZX(t,e){e.set(t)}function QX(t,e,n){let i,s;for(let r=0;r<3;r++){const o=r+3;i=t[r],s=e[r],n[r]=is?i:s}}function KX(t){const e=t[3]-t[0],n=t[4]-t[1],i=t[5]-t[2];return 2*(e*n+n*i+i*e)}const tY=Math.pow(2,-24);function eY(t,e,n,i,s=null){let r=1/0,o=1/0,a=1/0,l=-1/0,c=-1/0,h=-1/0,u=1/0,d=1/0,p=1/0,_=-1/0,m=-1/0,f=-1/0;const g=null!==s;for(let i=6*e,s=6*(e+n);il&&(l=v),g&&e_&&(_=e);const y=t[i+2],x=t[i+3],b=y-x,w=y+x;bc&&(c=w),g&&ym&&(m=y);const T=t[i+4],A=t[i+5],M=T-A,E=T+A;Mh&&(h=E),g&&Tf&&(f=T)}i[0]=r,i[1]=o,i[2]=a,i[3]=l,i[4]=c,i[5]=h,g&&(s[0]=u,s[1]=d,s[2]=p,s[3]=_,s[4]=m,s[5]=f)}const nY=32,iY=new Array(nY).fill().map((()=>({count:0,bounds:new Float32Array(6),rightCacheBounds:new Float32Array(6),candidate:0}))),sY=new Float32Array(6);function rY(t,e){function n(e,i,d,p=null,_=0){if(!u&&_>=a&&(u=!0,l&&(console.warn(`MeshBVH: Max depth of ${a} reached when generating BVH. Consider increasing maxDepth.`),console.warn(t))),d<=c||_>=a)return e.offset=i,e.count=d,e;const m=function(t,e,n,i,s,r){let o=-1,a=0;if(0===r)o=JX(e),-1!==o&&(a=(e[o]+e[o+3])/2);else if(1===r)o=JX(t),-1!==o&&(a=function(t,e,n,i){let s=0;for(let r=e,o=e+n;r=nY&&(s=31);const r=iY[s];r.count++;const o=r.bounds;for(let t=0;t<3;t++){const i=n[e+2*t],s=n[e+2*t+1],r=i-s,a=i+s;ro[t+3]&&(o[t+3]=a)}}const d=iY[31];ZX(d.bounds,d.rightCacheBounds);for(let t=30;t>=0;t--){const e=iY[t],n=iY[t+1];QX(e.bounds,n.rightCacheBounds,e.rightCacheBounds)}let p=0;for(let e=0;e<31;e++){const n=iY[e],i=n.count,c=n.bounds,h=iY[e+1].rightCacheBounds;0!==i&&(0===p?ZX(c,sY):QX(c,sY,sY)),p+=i;let u=0,d=0;0!==p&&(u=KX(sY)/r);const _=s-p;0!==_&&(d=KX(h)/r);const m=1+1.25*(u*p+d*_);m=a;)o--;if(!(r65535?new Uint32Array(new i(4*n)):new Uint16Array(new i(2*n)),t.setIndex(new Hw(s,1));for(let t=0;td&&(d=s),a>d&&(d=a);const p=(d-l)/2,_=2*t;o[r+_+0]=l+p,o[r+_+1]=p+(Math.abs(l)+p)*tY,le[t+3]&&(e[t+3]=d)}}return o}(t,i),o=t.index.array,a=e.maxDepth,l=e.verbose,c=e.maxLeafTris,h=e.strategy;let u=!1;const d=[],p=function(t){if(!t.groups||!t.groups.length)return[{offset:0,count:t.index.count/3}];const e=[],n=new Set;for(const e of t.groups)n.add(e.start),n.add(e.start+e.count);const i=Array.from(n.values()).sort(((t,e)=>t-e));for(let t=0;ta&&(a=e);const n=t[i+2];nl&&(l=n);const h=t[i+4];hc&&(c=h)}i[0]=s,i[1]=r,i[2]=o,i[3]=a,i[4]=l,i[5]=c}(r,t.offset,t.count,s),n(e,t.offset,t.count,s),d.push(e)}else for(let t of p){const e=new YX;e.boundingData=new Float32Array(6),eY(r,t.offset,t.count,e.boundingData,s),n(e,t.offset,t.count,s),d.push(e)}return d}const oY=65535;class aY{constructor(){this.min=1/0,this.max=-1/0}setFromPointsField(t,e){let n=1/0,i=-1/0;for(let s=0,r=t.length;si?r:i}this.min=n,this.max=i}setFromPoints(t,e){let n=1/0,i=-1/0;for(let s=0,r=e.length;si?o:i}this.min=n,this.max=i}isSeparated(t){return this.min>t.max||t.min>this.max}}aY.prototype.setFromBox=function(){const t=new gb;return function(e,n){const i=n.min,s=n.max;let r=1/0,o=-1/0;for(let n=0;n<=1;n++)for(let a=0;a<=1;a++)for(let l=0;l<=1;l++){t.x=i.x*n+s.x*(1-n),t.y=i.y*a+s.y*(1-a),t.z=i.z*l+s.z*(1-l);const c=e.dot(t);r=Math.min(c,r),o=Math.max(c,o)}this.min=r,this.max=o}}();!function(){const t=new aY}();const lY=function(){const t=new gb,e=new gb,n=new gb;return function(i,s,r){const o=i.start,a=t,l=s.start,c=e;n.subVectors(o,l),t.subVectors(i.end,s.start),e.subVectors(s.end,s.start);const h=n.dot(c),u=c.dot(a),d=c.dot(c),p=n.dot(a),_=a.dot(a)*d-u*u;let m,f;m=0!==_?(h*u-p*d)/_:0,f=(h+m*u)/d,r.x=m,r.y=f}}(),cY=function(){const t=new sb,e=new gb,n=new gb;return function(i,s,r,o){lY(i,s,t);let a=t.x,l=t.y;if(a>=0&&a<=1&&l>=0&&l<=1)return i.at(a,r),void s.at(l,o);if(a>=0&&a<=1)return l<0?s.at(0,o):s.at(1,o),void i.closestPointToPoint(o,!0,r);if(l>=0&&l<=1)return a<0?i.at(0,r):i.at(1,r),void s.closestPointToPoint(r,!0,o);{let t,c;t=a<0?i.start:i.end,c=l<0?s.start:s.end;const h=e,u=n;return i.closestPointToPoint(c,!0,e),s.closestPointToPoint(t,!0,n),h.distanceToSquared(c)<=u.distanceToSquared(t)?(r.copy(h),void o.copy(c)):(r.copy(t),void o.copy(u))}}}(),hY=function(){const t=new gb,e=new gb,n=new IT,i=new YN;return function(s,r){const{radius:o,center:a}=s,{a:l,b:c,c:h}=r;i.start=l,i.end=c;if(i.closestPointToPoint(a,!0,t).distanceTo(a)<=o)return!0;i.start=l,i.end=h;if(i.closestPointToPoint(a,!0,t).distanceTo(a)<=o)return!0;i.start=c,i.end=h;if(i.closestPointToPoint(a,!0,t).distanceTo(a)<=o)return!0;const u=r.getPlane(n);if(Math.abs(u.distanceToPoint(a))<=o){const t=u.projectPoint(a,e);if(r.containsPoint(t))return!0}return!1}}();class uY extends Lw{constructor(...t){super(...t),this.isSeparatingAxisTriangle=!0,this.satAxes=new Array(4).fill().map((()=>new gb)),this.satBounds=new Array(4).fill().map((()=>new aY)),this.points=[this.a,this.b,this.c],this.sphere=new kb,this.plane=new IT,this.needsUpdate=!1}intersectsSphere(t){return hY(t,this)}update(){const t=this.a,e=this.b,n=this.c,i=this.points,s=this.satAxes,r=this.satBounds,o=s[0],a=r[0];this.getNormal(o),a.setFromPoints(o,i);const l=s[1],c=r[1];l.subVectors(t,e),c.setFromPoints(l,i);const h=s[2],u=r[2];h.subVectors(e,n),u.setFromPoints(h,i);const d=s[3],p=r[3];d.subVectors(n,t),p.setFromPoints(d,i),this.sphere.setFromPoints(this.points),this.plane.setFromNormalAndCoplanarPoint(o,t),this.needsUpdate=!1}}uY.prototype.closestPointToSegment=function(){const t=new gb,e=new gb,n=new YN;return function(i,s=null,r=null){const{start:o,end:a}=i,l=this.points;let c,h=1/0;for(let o=0;o<3;o++){const a=(o+1)%3;n.start.copy(l[o]),n.end.copy(l[a]),cY(n,i,t,e),c=t.distanceToSquared(e),c1-1e-10)console.warn(\\\\\\\"SeparatingAxisTriangle.intersectsTriangle: Triangles are coplanar which does not support an output edge. Setting edge to 0, 0, 0.\\\\\\\"),p.start.set(0,0,0),p.end.set(0,0,0);else{const n=this.points;let i=!1;for(let t=0;t<3;t++){const s=n[t],r=n[(t+1)%3];if(c.start.copy(s),c.end.copy(r),e.intersectLine(c,i?h.start:h.end)){if(i)break;i=!0}}const s=d.points;let r=!1;for(let e=0;e<3;e++){const n=s[e],i=s[(e+1)%3];if(c.start.copy(n),c.end.copy(i),t.intersectLine(c,r?u.start:u.end)){if(r)break;r=!0}}if(h.delta(o),u.delta(a),o.dot(a)<0){let t=u.start;u.start=u.end,u.end=t}l.subVectors(h.start,u.start),l.dot(o)>0?p.start.copy(h.start):p.start.copy(u.start),l.subVectors(h.end,u.end),l.dot(o)<0?p.end.copy(h.end):p.end.copy(u.end)}}return!0}}(),uY.prototype.distanceToPoint=function(){const t=new gb;return function(e){return this.closestPointToPoint(e,t),e.distanceTo(t)}}(),uY.prototype.distanceToTriangle=function(){const t=new gb,e=new gb,n=[\\\\\\\"a\\\\\\\",\\\\\\\"b\\\\\\\",\\\\\\\"c\\\\\\\"],i=new YN,s=new YN;return function(r,o=null,a=null){const l=o||a?i:null;if(this.intersectsTriangle(r,l))return(o||a)&&(o&&l.getCenter(o),a&&l.getCenter(a)),0;let c=1/0;for(let e=0;e<3;e++){let i;const s=n[e],l=r[s];this.closestPointToPoint(l,t),i=l.distanceToSquared(t),inew gb)),this.satAxes=new Array(3).fill().map((()=>new gb)),this.satBounds=new Array(3).fill().map((()=>new aY)),this.alignedSatBounds=new Array(3).fill().map((()=>new aY)),this.needsUpdate=!1}set(t,e,n){super.set(t,e),this.matrix=n,this.needsUpdate=!0}copy(t){super.copy(t),this.matrix.copy(t.matrix),this.needsUpdate=!0}}dY.prototype.update=function(){const t=this.matrix,e=this.min,n=this.max,i=this.points;for(let s=0;s<=1;s++)for(let r=0;r<=1;r++)for(let o=0;o<=1;o++){const a=i[1*s|2*r|4*o];a.x=s?n.x:e.x,a.y=r?n.y:e.y,a.z=o?n.z:e.z,a.applyMatrix4(t)}const s=this.satBounds,r=this.satAxes,o=i[0];for(let t=0;t<3;t++){const e=r[t],n=s[t],a=i[1<new YN)),n=new Array(12).fill().map((()=>new YN)),i=new gb,s=new gb;return function(r,o=0,a=null,l=null){if(this.needsUpdate&&this.update(),this.intersectsBox(r))return(a||l)&&(r.getCenter(s),this.closestPointToPoint(s,i),r.closestPointToPoint(i,s),a&&a.copy(i),l&&l.copy(s)),0;const c=o*o,h=r.min,u=r.max,d=this.points;let p=1/0;for(let t=0;t<8;t++){const e=d[t];s.copy(e).clamp(h,u);const n=e.distanceToSquared(s);if(nn.far?null:t)}function wY(t,e,n,i){const s=t.a,r=t.b,o=t.c;let a=e,l=e+1,c=e+2;n&&(a=n.getX(e),l=n.getX(e+1),c=n.getX(e+2)),s.x=i.getX(a),s.y=i.getY(a),s.z=i.getZ(a),r.x=i.getX(l),r.y=i.getY(l),r.z=i.getZ(l),o.x=i.getX(c),o.y=i.getY(c),o.z=i.getZ(c)}function TY(t,e,n,i,s,r,o){const a=n.index,l=n.attributes.position;for(let n=t,c=e+t;n=0;let c,h;l?(c=CY(t),h=NY(t,a)):(c=NY(t,a),h=CY(t));const u=BY(c,r,i,OY)?IY(c,e,n,i):null;if(u){const t=u.point[o];if(l?t<=r[h+s]:t>=r[h+s+3])return u}const d=BY(h,r,i,OY)?IY(h,e,n,i):null;return u&&d?u.distance<=d.distance?u:d:u||d||null}}const FY=function(){let t,e;const n=[],i=new AY((()=>new xb));return function(...r){t=i.getPrimitive(),e=i.getPrimitive(),n.push(t,e);const o=s(...r);i.releasePrimitive(t),i.releasePrimitive(e),n.pop(),n.pop();const a=n.length;return a>0&&(e=n[a-1],t=n[a-2]),o};function s(n,i,r,o,a=null,l=0,c=0){function h(t){let e=2*t,n=GY,i=VY;for(;!MY(e,n);)e=2*(t=CY(t));return EY(t,i)}function u(t){let e=2*t,n=GY,i=VY;for(;!MY(e,n);)e=2*(t=NY(t,i));return EY(t,i)+SY(e,n)}let d=2*n,p=UY,_=GY,m=VY;if(MY(d,_)){const e=EY(n,m),i=SY(d,_);return $X(n,p,t),o(e,i,!1,c,l+n,t)}{const d=CY(n),f=NY(n,m);let g,v,y,x,b=d,w=f;if(a&&(y=t,x=e,$X(b,p,y),$X(w,p,x),g=a(y),v=a(x),vs.intersectsBox(t),intersectsTriangle:t=>{t.a.applyMatrix4(c),t.b.applyMatrix4(c),t.c.applyMatrix4(c),t.needsUpdate=!0;for(let n=3*g,i=3*(v+g);nnew uY));class s${static serialize(t,e={}){if(e.isBufferGeometry)return console.warn(\\\\\\\"MeshBVH.serialize: The arguments for the function have changed. 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See docs for new signature.');const t=new Set;n.forEach((e=>t.add(e))),e&&e.forEach((e=>t.add(e))),c$.call(this,t)}else c$.apply(this,t)},[\\\\\\\"intersectsGeometry\\\\\\\",\\\\\\\"shapecast\\\\\\\",\\\\\\\"intersectsBox\\\\\\\",\\\\\\\"intersectsSphere\\\\\\\"].forEach((t=>{const e=s$.prototype[t];s$.prototype[t]=function(...n){return(null===n[0]||n[0].isMesh)&&(n.shift(),console.warn(`MeshBVH: The function signature for \\\\\\\"${t}\\\\\\\" has changed and no longer takes Mesh. See docs for new signature.`)),e.apply(this,n)}}));const h$=new Xb,u$=new Yb,d$=vT.prototype.raycast;function p$(t,e){if(this.geometry.boundsTree){if(void 0===this.material)return;u$.copy(this.matrixWorld).invert(),h$.copy(t.ray).applyMatrix4(u$);const n=this.geometry.boundsTree;if(!0===t.firstHitOnly){const i=bY(n.raycastFirst(h$,this.material),this,t);i&&e.push(i)}else{const i=n.raycast(h$,this.material);for(let n=0,s=i.length;n{if(t===i||e)return r++,!0;s&&r++}),n);let o=0;const a=new Float32Array(24*r);let l,c;e.traverse(((t,e,n)=>{const r=t===i||e;if(r||s){$X(0,n,_$);const{min:t,max:e}=_$;for(let n=-1;n<=1;n+=2){const i=n<0?t.x:e.x;for(let n=-1;n<=1;n+=2){const s=n<0?t.y:e.y;for(let n=-1;n<=1;n+=2){const r=n<0?t.z:e.z;a[o+0]=i,a[o+1]=s,a[o+2]=r,o+=3}}}return r}}),n),c=this.displayEdges?new Uint8Array([0,4,1,5,2,6,3,7,0,2,1,3,4,6,5,7,0,1,2,3,4,5,6,7]):new Uint8Array([0,1,2,2,1,3,4,6,5,6,7,5,1,4,5,0,4,1,2,3,6,3,7,6,0,2,4,2,6,4,1,5,3,3,5,7]),l=a.length>65535?new Uint32Array(c.length*r):new Uint16Array(c.length*r);const h=c.length;for(let t=0;te;)this._roots.pop();for(let t=0;t=this._roots.length){const e=new m$(this.mesh,this.edgeMaterial,this.depth,t);this.add(e),this._roots.push(e)}const e=this._roots[t];e.depth=this.depth,e.mesh=this.mesh,e.displayParents=this.displayParents,e.displayEdges=this.displayEdges,e.material=this.displayEdges?this.edgeMaterial:this.meshMaterial,e.update()}}updateMatrixWorld(...t){this.position.copy(this.mesh.position),this.rotation.copy(this.mesh.rotation),this.scale.copy(this.mesh.scale),super.updateMatrixWorld(...t)}copy(t){this.depth=t.depth,this.mesh=t.mesh}clone(){return new f$(this.mesh,this.depth)}dispose(){this.edgeMaterial.dispose(),this.meshMaterial.dispose();const t=this.children;for(let e=0,n=t.length;e{const e=t;if(e.isMesh){const t=e.geometry;for(const e in t.attributes)\\\\\\\"position\\\\\\\"!==e&&t.deleteAttribute(e);n.push(e)}}))}const i=this._makeCompact(n);if(i){i.matrixAutoUpdate=!1,i.raycast=p$;const t=new s$(i.geometry,{verbose:!1});return i.geometry.boundsTree=t,this.createCoreGroupFromObjects([i])}return this.createCoreGroupFromObjects([])}_makeCompact(t){var e,n;const i=[];let s;for(let e of t){s=s||e.material;const t=e.geometry;t.applyMatrix4(e.matrix),i.push(t)}try{const t=_r.mergeGeometries(i);if(t){return this.createObject(t,Cs.MESH,s)}null===(e=this.states)||void 0===e||e.error.set(\\\\\\\"merge failed, check that input geometries have the same attributes\\\\\\\")}catch(t){null===(n=this.states)||void 0===n||n.error.set(t.message)}}}g$.DEFAULT_PARAMS={},g$.INPUT_CLONED_STATE=Ki.ALWAYS;const v$=new class extends la{};class y$ extends nV{constructor(){super(...arguments),this.paramsConfig=v$}static type(){return\\\\\\\"BVH\\\\\\\"}static displayedInputNames(){return[\\\\\\\"geometry to create BVH 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super.copy(t),this.instanceMatrix.copy(t.instanceMatrix),null!==t.instanceColor&&(this.instanceColor=t.instanceColor.clone()),this.count=t.count,this}getColorAt(t,e){e.fromArray(this.instanceColor.array,3*t)}getMatrixAt(t,e){e.fromArray(this.instanceMatrix.array,16*t)}raycast(t,e){const n=this.matrixWorld,i=this.count;if(C$.geometry=this.geometry,C$.material=this.material,void 0!==C$.material)for(let s=0;st.far||e.push({distance:a,point:O$.clone(),uv:Ks.a.getUV(O$,B$,z$,k$,U$,G$,V$,new d.a),face:null,object:this})}copy(t){return super.copy(t),void 0!==t.center&&this.center.copy(t.center),this.material=t.material,this}}function j$(t,e,n,i,s,r){I$.subVectors(t,n).addScalar(.5).multiply(i),void 0!==s?(F$.x=r*I$.x-s*I$.y,F$.y=s*I$.x+r*I$.y):F$.copy(I$),t.copy(e),t.x+=F$.x,t.y+=F$.y,t.applyMatrix4(D$)}H$.prototype.isSprite=!0;var W$=n(92),q$=n(81),X$=n(46);class Y${constructor(){this.coefficients=[];for(let t=0;t<9;t++)this.coefficients.push(new p.a)}set(t){for(let 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e=0;e<9;e++)this.coefficients[e].add(t.coefficients[e]);return this}addScaledSH(t,e){for(let n=0;n<9;n++)this.coefficients[n].addScaledVector(t.coefficients[n],e);return this}scale(t){for(let e=0;e<9;e++)this.coefficients[e].multiplyScalar(t);return this}lerp(t,e){for(let n=0;n<9;n++)this.coefficients[n].lerp(t.coefficients[n],e);return this}equals(t){for(let e=0;e<9;e++)if(!this.coefficients[e].equals(t.coefficients[e]))return!1;return!0}copy(t){return this.set(t.coefficients)}clone(){return(new this.constructor).copy(this)}fromArray(t,e=0){const n=this.coefficients;for(let i=0;i<9;i++)n[i].fromArray(t,e+3*i);return this}toArray(t=[],e=0){const n=this.coefficients;for(let i=0;i<9;i++)n[i].toArray(t,e+3*i);return t}static getBasisAt(t,e){const n=t.x,i=t.y,s=t.z;e[0]=.282095,e[1]=.488603*i,e[2]=.488603*s,e[3]=.488603*n,e[4]=1.092548*n*i,e[5]=1.092548*i*s,e[6]=.315392*(3*s*s-1),e[7]=1.092548*n*s,e[8]=.546274*(n*n-i*i)}}Y$.prototype.isSphericalHarmonics3=!0;class $$ extends sv.a{constructor(t=new Y$,e=1){super(void 0,e),this.sh=t}copy(t){return super.copy(t),this.sh.copy(t.sh),this}fromJSON(t){return this.intensity=t.intensity,this.sh.fromArray(t.sh),this}toJSON(t){const e=super.toJSON(t);return e.object.sh=this.sh.toArray(),e}}$$.prototype.isLightProbe=!0;var J$=n(63),Z$=n(43);class Q$ extends S.a{constructor(){super(),this.type=\\\\\\\"InstancedBufferGeometry\\\\\\\",this.instanceCount=1/0}copy(t){return super.copy(t),this.instanceCount=t.instanceCount,this}clone(){return(new this.constructor).copy(this)}toJSON(){const t=super.toJSON(this);return t.instanceCount=this.instanceCount,t.isInstancedBufferGeometry=!0,t}}Q$.prototype.isInstancedBufferGeometry=!0;class K$ extends Bf.a{constructor(t){super(t)}load(t,e,n,i){const s=this,r=new Df.a(s.manager);r.setPath(s.path),r.setRequestHeader(s.requestHeader),r.setWithCredentials(s.withCredentials),r.load(t,(function(n){try{e(s.parse(JSON.parse(n)))}catch(e){i?i(e):console.error(e),s.manager.itemError(t)}}),n,i)}parse(t){const e={},n={};function i(t,i){if(void 0!==e[i])return e[i];const s=t.interleavedBuffers[i],r=function(t,e){if(void 0!==n[e])return n[e];const i=t.arrayBuffers[e],s=new Uint32Array(i).buffer;return n[e]=s,s}(t,s.buffer),o=Object(It.c)(s.type,r),a=new lr.a(o,s.stride);return a.uuid=s.uuid,e[i]=a,a}const s=t.isInstancedBufferGeometry?new Q$:new S.a,r=t.data.index;if(void 0!==r){const t=Object(It.c)(r.type,r.array);s.setIndex(new C.a(t,1))}const o=t.data.attributes;for(const e in o){const n=o[e];let r;if(n.isInterleavedBufferAttribute){const e=i(t.data,n.data);r=new cr.a(e,n.itemSize,n.offset,n.normalized)}else{const t=Object(It.c)(n.type,n.array);r=new(n.isInstancedBufferAttribute?A$:C.a)(t,n.itemSize,n.normalized)}void 0!==n.name&&(r.name=n.name),void 0!==n.usage&&r.setUsage(n.usage),void 0!==n.updateRange&&(r.updateRange.offset=n.updateRange.offset,r.updateRange.count=n.updateRange.count),s.setAttribute(e,r)}const a=t.data.morphAttributes;if(a)for(const e in a){const n=a[e],r=[];for(let e=0,s=n.length;eNumber.EPSILON){const u=Math.sqrt(h),p=Math.sqrt(l*l+c*c),_=e.x-a/u,m=e.y+o/u,f=((n.x-c/p-_)*c-(n.y+l/p-m)*l)/(o*c-a*l);i=_+o*f-t.x,s=m+a*f-t.y;const g=i*i+s*s;if(g<=2)return new d.a(i,s);r=Math.sqrt(g/2)}else{let t=!1;o>Number.EPSILON?l>Number.EPSILON&&(t=!0):o<-Number.EPSILON?l<-Number.EPSILON&&(t=!0):Math.sign(a)===Math.sign(c)&&(t=!0),t?(i=-a,s=o,r=Math.sqrt(h)):(i=o,s=a,r=Math.sqrt(h/2))}return new d.a(i/r,s/r)}const R=[];for(let t=0,e=C.length,n=e-1,i=t+1;t=0;t--){const e=t/m,n=h*Math.cos(e*Math.PI/2),i=u*Math.sin(e*Math.PI/2)+_;for(let t=0,e=C.length;t=0;){const i=n;let s=n-1;s<0&&(s=t.length-1);for(let t=0,n=a+2*m;t0){const n=new Vg.b(e);s=new J$.a(n),s.setCrossOrigin(this.crossOrigin);for(let e=0,n=t.length;e0){i=new J$.a(this.manager),i.setCrossOrigin(this.crossOrigin);for(let e=0,i=t.length;e{SJ.PARAM_CALLBACK_reset(t,e)}})}};class SJ extends nV{constructor(){super(...arguments),this.paramsConfig=EJ}static type(){return\\\\\\\"cache\\\\\\\"}static displayedInputNames(){return[\\\\\\\"geometry to cache\\\\\\\"]}initializeNode(){this.io.inputs.setCount(0,1)}cook(t){const e=\\\\\\\"\\\\\\\"==this.pv.cache||null==this.pv.cache,n=t[0];if(e&&n){const t=[];for(let e of n.objects())t.push(e.toJSON());this.setCoreGroup(n),this.p.cache.set(JSON.stringify(t))}else if(this.pv.cache){const t=new wJ,e=JSON.parse(this.pv.cache),n=[];for(let i of e){const e=t.parse(i);n.push(e)}this.setObjects(n)}else this.setObjects([])}static PARAM_CALLBACK_reset(t,e){t.param_callback_PARAM_CALLBACK_reset()}async param_callback_PARAM_CALLBACK_reset(){this.p.cache.set(\\\\\\\"\\\\\\\"),this.compute()}}const CJ=[is.ORTHOGRAPHIC,is.PERSPECTIVE],NJ={direction:new p.a(0,1,0)},LJ=[new d.a(-1,-1),new d.a(-1,1),new d.a(1,1),new d.a(1,-1)],OJ=new p.a(0,0,1);const PJ=new class extends la{constructor(){super(...arguments),this.camera=aa.NODE_PATH(\\\\\\\"\\\\\\\",{nodeSelection:{context:ts.OBJ,types:CJ}}),this.direction=aa.VECTOR3(NJ.direction),this.offset=aa.FLOAT(0,{range:[-10,10],rangeLocked:[!1,!1]}),this.useSegmentsCount=aa.BOOLEAN(!0),this.stepSize=aa.FLOAT(1,{range:[.001,1],rangeLocked:[!1,!1],visibleIf:{useSegmentsCount:0}}),this.segments=aa.VECTOR2([10,10],{visibleIf:{useSegmentsCount:1}}),this.sizeMult=aa.FLOAT(1,{range:[0,2],rangeLocked:[!0,!1]}),this.updateOnWindowResize=aa.BOOLEAN(1),this.update=aa.BUTTON(null,{callback:t=>{RJ.PARAM_CALLBACK_update(t)}})}};class RJ extends nV{constructor(){super(...arguments),this.paramsConfig=PJ,this._plane=new Y.a,this._raycaster=new WL,this._planeCorners=[new p.a,new p.a,new p.a,new p.a],this._planeCenter=new p.a,this._core_transform=new uU,this.segments_count=new d.a(1,1),this.planeSize=new d.a}static type(){return\\\\\\\"cameraPlane\\\\\\\"}cook(){this._updateWindowControllerDependency();const t=this.pv.camera.nodeWithContext(ts.OBJ);if(!t)return this.states.error.set(\\\\\\\"no camera found\\\\\\\"),void this.cookController.endCook();if(!CJ.includes(t.type()))return this.states.error.set(\\\\\\\"node found is not a camera\\\\\\\"),void this.cookController.endCook();const e=t.object;this._computePlaneParams(e)}_updateWindowControllerDependency(){this.pv.updateOnWindowResize?this.addGraphInput(this.scene().windowController.graphNode()):this.removeGraphInput(this.scene().windowController.graphNode())}_computePlaneParams(t){this._plane.normal.copy(this.pv.direction),this._plane.constant=this.pv.offset;let e=0;this._planeCenter.set(0,0,0);for(let n of LJ){this._raycaster.setFromCamera(n,t);const i=this._planeCorners[e];this._raycaster.ray.intersectPlane(this._plane,i),this._planeCenter.add(i),e++}this._planeCenter.multiplyScalar(.25);const n=this._planeCorners[1].distanceTo(this._planeCorners[2]),i=this._planeCorners[0].distanceTo(this._planeCorners[3]),s=this._planeCorners[0].distanceTo(this._planeCorners[1]),r=this._planeCorners[2].distanceTo(this._planeCorners[3]),o=Math.max(n,i)*this.pv.sizeMult,a=Math.max(s,r)*this.pv.sizeMult;this.planeSize.set(o,a);const l=this._createPlane(this.planeSize);this._core_transform.rotate_geometry(l,OJ,this.pv.direction);const c=this._core_transform.translation_matrix(this._planeCenter);l.applyMatrix4(c),this.setGeometry(l)}_createPlane(t){return t=t.clone(),this.pv.useSegmentsCount?(this.segments_count.x=Math.floor(this.pv.segments.x),this.segments_count.y=Math.floor(this.pv.segments.y)):this.pv.stepSize>0&&(this.segments_count.x=Math.floor(t.x/this.pv.stepSize),this.segments_count.y=Math.floor(t.y/this.pv.stepSize),t.x=this.segments_count.x*this.pv.stepSize,t.y=this.segments_count.y*this.pv.stepSize),new L(t.x,t.y,this.segments_count.x,this.segments_count.y)}static PARAM_CALLBACK_update(t){t._paramCallbackUpdate()}_paramCallbackUpdate(){this.setDirty()}}class IJ extends QG{constructor(){super(...arguments),this._geo_center=new p.a}static type(){return\\\\\\\"center\\\\\\\"}cook(t,e){var n;const i=t[0].objectsWithGeo(),s=new Array(3*i.length);s.fill(0);for(let t=0;t0&&(r.push(t-1),r.push(t));r.push(e-1),r.push(0);const a=new S.a;return a.setAttribute(\\\\\\\"position\\\\\\\",new C.c(s,3)),a.setIndex(r),a}}const zJ=new p.a(0,0,1);class kJ extends QG{constructor(){super(...arguments),this._core_transform=new uU}static type(){return\\\\\\\"circle\\\\\\\"}cook(t,e){return e.open?this._create_circle(e):this._create_disk(e)}_create_circle(t){const e=BJ.create(t.radius,t.segments,t.arcAngle);return this._core_transform.rotate_geometry(e,zJ,t.direction),this.createCoreGroupFromGeometry(e,Cs.LINE_SEGMENTS)}_create_disk(t){const e=new tJ(t.radius,t.segments);return this._core_transform.rotate_geometry(e,zJ,t.direction),this.createCoreGroupFromGeometry(e)}}kJ.DEFAULT_PARAMS={radius:1,segments:12,open:!0,arcAngle:360,direction:new p.a(0,1,0)};const UJ=kJ.DEFAULT_PARAMS;const GJ=new class extends la{constructor(){super(...arguments),this.radius=aa.FLOAT(UJ.radius),this.segments=aa.INTEGER(UJ.segments,{range:[1,50],rangeLocked:[!0,!1]}),this.open=aa.BOOLEAN(UJ.open),this.arcAngle=aa.FLOAT(UJ.arcAngle,{range:[0,360],rangeLocked:[!1,!1],visibleIf:{open:1}}),this.direction=aa.VECTOR3(UJ.direction)}};class VJ extends nV{constructor(){super(...arguments),this.paramsConfig=GJ}static type(){return\\\\\\\"circle\\\\\\\"}initializeNode(){}cook(){this._operation=this._operation||new kJ(this._scene,this.states);const t=this._operation.cook([],this.pv);this.setCoreGroup(t)}}var HJ;!function(t){t.SEGMENTS_COUNT=\\\\\\\"segments count\\\\\\\",t.SEGMENTS_LENGTH=\\\\\\\"segments length\\\\\\\"}(HJ||(HJ={}));const jJ=[HJ.SEGMENTS_COUNT,HJ.SEGMENTS_LENGTH];var WJ;!function(t){t.ABC=\\\\\\\"abc\\\\\\\",t.ACB=\\\\\\\"acb\\\\\\\",t.AB=\\\\\\\"ab\\\\\\\",t.BC=\\\\\\\"bc\\\\\\\",t.AC=\\\\\\\"ac\\\\\\\"}(WJ||(WJ={}));const qJ=[WJ.ABC,WJ.ACB,WJ.AB,WJ.AC,WJ.BC];class XJ{constructor(t){this.params=t,this.a=new p.a,this.b=new p.a,this.c=new p.a,this.an=new p.a,this.bn=new p.a,this.cn=new p.a,this.ac=new p.a,this.ab=new p.a,this.ab_x_ac=new p.a,this.part0=new p.a,this.part1=new p.a,this.divider=1,this.a_center=new p.a,this.center=new p.a,this.normal=new p.a,this.radius=1,this.x=new p.a,this.y=new p.a,this.z=new p.a,this.angle_ab=1,this.angle_ac=1,this.angle_bc=1,this.angle=2*Math.PI,this.x_rotated=new p.a,this._created_geometries={}}created_geometries(){return this._created_geometries}create(t,e,n){this.a.copy(t),this.b.copy(e),this.c.copy(n),this._compute_axis(),this._create_arc(),this._create_center()}_create_arc(){this._compute_angle();const t=this._points_count(),e=new Array(3*t),n=new Array(t),i=this.angle/(t-1);this.x_rotated.copy(this.x).multiplyScalar(this.radius);let s=0;for(s=0;s0&&(n[2*(s-1)]=s-1,n[2*(s-1)+1]=s);this.params.full&&(n.push(s-1),n.push(0));const r=new S.a;if(r.setAttribute(\\\\\\\"position\\\\\\\",new C.a(new Float32Array(e),3)),r.setIndex(n),this.params.addIdAttribute||this.params.addIdnAttribute){const e=new Array(t);for(let t=0;te/(t-1)));this.params.addIdnAttribute&&r.setAttribute(\\\\\\\"idn\\\\\\\",new C.a(new Float32Array(n),1))}this._created_geometries.arc=r}_create_center(){if(!this.params.center)return;const t=new S.a,e=[this.center.x,this.center.y,this.center.z];t.setAttribute(\\\\\\\"position\\\\\\\",new C.a(new Float32Array(e),3)),this._created_geometries.center=t}_compute_axis(){this.ac.copy(this.c).sub(this.a),this.ab.copy(this.b).sub(this.a),this.ab_x_ac.copy(this.ab).cross(this.ac),this.divider=2*this.ab_x_ac.lengthSq(),this.part0.copy(this.ab_x_ac).cross(this.ab).multiplyScalar(this.ac.lengthSq()),this.part1.copy(this.ac).cross(this.ab_x_ac).multiplyScalar(this.ab.lengthSq()),this.a_center.copy(this.part0).add(this.part1).divideScalar(this.divider),this.radius=this.a_center.length(),this.normal.copy(this.ab_x_ac).normalize(),this.center.copy(this.a).add(this.a_center)}_compute_angle(){this.params.arc&&(this.params.full?(this.x.copy(this.a).sub(this.center).normalize(),this.angle=2*Math.PI):(this.an.copy(this.a).sub(this.center).normalize(),this.bn.copy(this.b).sub(this.center).normalize(),this.cn.copy(this.c).sub(this.center).normalize(),this._set_x_from_joinMode(),this.y.copy(this.normal),this.z.copy(this.x).cross(this.y).normalize(),this.angle_ab=this.an.angleTo(this.bn),this.angle_ac=this.an.angleTo(this.cn),this.angle_bc=this.bn.angleTo(this.cn),this._set_angle_from_joinMode()))}_points_count(){const t=this.params.pointsCountMode;switch(t){case HJ.SEGMENTS_COUNT:return this.params.segmentsCount+1;case HJ.SEGMENTS_LENGTH:{let t=Math.PI*this.radius*this.radius;return this.params.full||(t*=Math.abs(this.angle)/(2*Math.PI)),Math.ceil(t/this.params.segmentsLength)}}ls.unreachable(t)}_set_x_from_joinMode(){const t=this.params.joinMode;switch(this.x.copy(this.a).sub(this.center).normalize(),t){case WJ.ABC:case WJ.ACB:case WJ.AB:case WJ.AC:return this.x.copy(this.an);case WJ.BC:return this.x.copy(this.bn)}ls.unreachable(t)}_set_angle_from_joinMode(){const t=this.params.joinMode;switch(t){case WJ.ABC:return void(this.angle=this.angle_ab+this.angle_bc);case WJ.ACB:return this.angle=this.angle_ac+this.angle_bc,void(this.angle*=-1);case WJ.AB:return void(this.angle=this.angle_ab);case WJ.AC:return this.angle=this.angle_ac,void(this.angle*=-1);case WJ.BC:return void(this.angle=this.angle_bc)}ls.unreachable(t)}}const YJ=new class extends la{constructor(){super(...arguments),this.arc=aa.BOOLEAN(1),this.pointsCountMode=aa.INTEGER(jJ.indexOf(HJ.SEGMENTS_COUNT),{visibleIf:{arc:1},menu:{entries:jJ.map(((t,e)=>({value:e,name:t})))}}),this.segmentsLength=aa.FLOAT(.1,{visibleIf:{arc:1,pointsCountMode:jJ.indexOf(HJ.SEGMENTS_LENGTH)},range:[0,1],rangeLocked:[!0,!1]}),this.segmentsCount=aa.INTEGER(100,{visibleIf:{arc:1,pointsCountMode:jJ.indexOf(HJ.SEGMENTS_COUNT)},range:[1,100],rangeLocked:[!0,!1]}),this.full=aa.BOOLEAN(1,{visibleIf:{arc:1}}),this.joinMode=aa.INTEGER(qJ.indexOf(WJ.ABC),{visibleIf:{arc:1,full:0},menu:{entries:qJ.map(((t,e)=>({value:e,name:t})))}}),this.addIdAttribute=aa.BOOLEAN(1),this.addIdnAttribute=aa.BOOLEAN(1),this.center=aa.BOOLEAN(0)}};class $J extends nV{constructor(){super(...arguments),this.paramsConfig=YJ,this.a=new p.a,this.b=new p.a,this.c=new p.a}static type(){return\\\\\\\"circle3Points\\\\\\\"}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState([Ki.NEVER])}cook(t){const e=t[0].points();e.length<3?this.states.error.set(`only ${e.length} points found, when 3 are required`):this._create_circle(e)}_create_circle(t){const e=new XJ({arc:this.pv.arc,center:this.pv.center,pointsCountMode:jJ[this.pv.pointsCountMode],segmentsLength:this.pv.segmentsLength,segmentsCount:this.pv.segmentsCount,full:this.pv.full,joinMode:qJ[this.pv.joinMode],addIdAttribute:this.pv.addIdAttribute,addIdnAttribute:this.pv.addIdnAttribute});t[0].getPosition(this.a),t[1].getPosition(this.b),t[2].getPosition(this.c),e.create(this.a,this.b,this.c);const n=[],i=e.created_geometries();i.arc&&n.push(this.createObject(i.arc,Cs.LINE_SEGMENTS)),i.center&&n.push(this.createObject(i.center,Cs.POINTS)),this.setObjects(n)}}class JJ extends QG{static type(){return\\\\\\\"color\\\\\\\"}cook(t,e){}}JJ.DEFAULT_PARAMS={fromAttribute:!1,attribName:\\\\\\\"\\\\\\\",color:new D.a(1,1,1),asHsv:!1};const ZJ=new D.a(1,1,1),QJ=\\\\\\\"color\\\\\\\",KJ=JJ.DEFAULT_PARAMS;const tZ=new class extends la{constructor(){super(...arguments),this.fromAttribute=aa.BOOLEAN(KJ.fromAttribute),this.attribName=aa.STRING(KJ.attribName,{visibleIf:{fromAttribute:1}}),this.color=aa.COLOR(KJ.color,{visibleIf:{fromAttribute:0},expression:{forEntities:!0}}),this.asHsv=aa.BOOLEAN(KJ.asHsv,{visibleIf:{fromAttribute:0}})}};class eZ extends nV{constructor(){super(...arguments),this.paramsConfig=tZ,this._r_arrays_by_geometry_uuid={},this._g_arrays_by_geometry_uuid={},this._b_arrays_by_geometry_uuid={}}static type(){return\\\\\\\"color\\\\\\\"}static displayedInputNames(){return[\\\\\\\"geometry to update color of\\\\\\\"]}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState(Ki.FROM_NODE)}async cook(t){const e=t[0],n=e.coreObjects();for(let t of n)if(this.pv.fromAttribute)this._set_fromAttribute(t);else{this.p.color.hasExpression()?await this._eval_expressions(t):this._eval_simple_values(t)}if(!this.io.inputs.cloneRequired(0)){const t=e.geometries();for(let e of t)e.getAttribute(QJ).needsUpdate=!0}this.setCoreGroup(e)}_set_fromAttribute(t){const e=t.coreGeometry();if(!e)return;this._create_init_color(e,ZJ);const n=e.points(),i=e.attribSize(this.pv.attribName),s=e.geometry(),r=s.getAttribute(this.pv.attribName).array,o=s.getAttribute(QJ).array;switch(i){case 1:for(let t=0;t{a[t.index()]=e}));else for(let t of n)e[3*t.index()+i]=r;return a}_init_array_if_required(t,e,n){const i=t.uuid,s=e[i];return s?s.length{const e=new A.a;return this._matrix_from_point(t,e),e}))}_matrix_from_point(t,e){const n=t.position();this._is_scale_present?t.attribValue(\\\\\\\"scale\\\\\\\",this._point_scale):this._point_scale.copy(rZ.SCALE);const i=this._is_pscale_present?t.attribValue(\\\\\\\"pscale\\\\\\\"):rZ.PSCALE;this._point_scale.multiplyScalar(i);const s=this._matrices.scale;s.makeScale(this._point_scale.x,this._point_scale.y,this._point_scale.z);const r=this._matrices.translate;if(r.makeTranslation(n.x,n.y,n.z),e.multiply(r),this._do_rotate_matrices){const n=this._matrices.rotate,i=rZ.EYE;t.attribValue(\\\\\\\"normal\\\\\\\",this._point_normal),this._point_normal.multiplyScalar(-1),this._is_up_present?t.attribValue(\\\\\\\"up\\\\\\\",this._point_up):this._point_up.copy(rZ.UP),this._point_up.normalize(),n.lookAt(i,this._point_normal,this._point_up),e.multiply(n)}e.multiply(s)}static create_instance_buffer_geo(t,e,n){const i=e.points(),s=new Q$;s.copy(t),s.instanceCount=1/0;const r=i.length,o=new Float32Array(3*r),a=new Float32Array(3*r),l=new Float32Array(3*r),c=new Float32Array(4*r),h=e.hasAttrib(lZ),u=new p.a(0,0,0),d=new ah.a,_=new p.a(1,1,1),f=new uZ(e).matrices();i.forEach(((t,e)=>{const n=3*e,i=4*e;f[e].decompose(u,d,_),u.toArray(o,n),d.toArray(c,i),_.toArray(l,n);(h?t.attribValue(lZ,this._point_color):oZ).toArray(a,n)}));const g=e.hasAttrib(\\\\\\\"uv\\\\\\\");if(g){const t=new Float32Array(2*r);i.forEach(((e,n)=>{const i=2*n;(g?e.attribValue(\\\\\\\"uv\\\\\\\",this._point_uv):aZ).toArray(t,i)})),s.setAttribute(cZ.UV,new A$(t,2))}s.setAttribute(cZ.POSITION,new A$(o,3)),s.setAttribute(cZ.SCALE,new A$(l,3)),s.setAttribute(cZ.ORIENTATION,new A$(c,4)),s.setAttribute(cZ.COLOR,new A$(a,3));e.attribNamesMatchingMask(n).forEach((t=>{const n=e.attribSize(t),o=new Float32Array(r*n);i.forEach(((e,i)=>{const s=e.attribValue(t);m.isNumber(s)?o[i]=s:s.toArray(o,i*n)})),s.setAttribute(t,new A$(o,n))}));return new _r(s).markAsInstance(),s}}uZ._point_color=new p.a,uZ._point_uv=new d.a;class dZ extends sh{set_point(t){this._point=t,this.setDirty(),this.removeDirtyState()}value(t){return this._point?t?this._point.attribValue(t):this._point.index():this._global_index}}!function(t){t[t.OBJECT=0]=\\\\\\\"OBJECT\\\\\\\",t[t.GEOMETRY=1]=\\\\\\\"GEOMETRY\\\\\\\"}(hZ||(hZ={}));const pZ=[hZ.OBJECT,hZ.GEOMETRY],_Z=[{name:\\\\\\\"object\\\\\\\",value:hZ.OBJECT},{name:\\\\\\\"geometry\\\\\\\",value:hZ.GEOMETRY}];const mZ=new class extends la{constructor(){super(...arguments),this.count=aa.INTEGER(1,{range:[1,20],rangeLocked:[!0,!1]}),this.transformOnly=aa.BOOLEAN(0),this.transformMode=aa.INTEGER(0,{menu:{entries:_Z}}),this.copyAttributes=aa.BOOLEAN(0),this.attributesToCopy=aa.STRING(\\\\\\\"\\\\\\\",{visibleIf:{copyAttributes:!0}}),this.useCopyExpr=aa.BOOLEAN(0)}};class fZ extends nV{constructor(){super(...arguments),this.paramsConfig=mZ,this._attribute_names_to_copy=[],this._objects=[],this._object_position=new p.a}static type(){return\\\\\\\"copy\\\\\\\"}static displayedInputNames(){return[\\\\\\\"geometry to be copied\\\\\\\",\\\\\\\"points to copy to\\\\\\\"]}initializeNode(){this.io.inputs.setCount(1,2),this.io.inputs.initInputsClonedState([Ki.ALWAYS,Ki.NEVER])}async cook(t){const e=t[0];if(!this.io.inputs.has_input(1))return void await this.cook_without_template(e);const n=t[1];n?await this.cook_with_template(e,n):this.states.error.set(\\\\\\\"second input invalid\\\\\\\")}async cook_with_template(t,e){this._objects=[];const n=e.points();let i=new uZ(e).matrices();const s=new p.a,r=new ah.a,o=new p.a;i[0].decompose(s,r,o),this._attribute_names_to_copy=os.attribNames(this.pv.attributesToCopy).filter((t=>e.hasAttrib(t))),await this._copy_moved_objects_on_template_points(t,i,n),this.setObjects(this._objects)}async _copy_moved_objects_on_template_points(t,e,n){for(let i=0;i{const e=yr.clone(t);this._objects.push(e)}))}async cook_without_template(t){this._objects=[],await this._copy_moved_objects_for_each_instance(t),this.setObjects(this._objects)}_copyAttributes_from_template(t,e){this._attribute_names_to_copy.forEach(((n,i)=>{const s=e.attribValue(n);new yr(t,i).addAttribute(n,s)}))}stamp_value(t){return this.stamp_node.value(t)}get stamp_node(){return this._stamp_node=this._stamp_node||this.create_stamp_node()}create_stamp_node(){const t=new dZ(this.scene());return this.dirtyController.setForbiddenTriggerNodes([t]),t}dispose(){super.dispose(),this._stamp_node&&this._stamp_node.dispose()}}const gZ=\\\\\\\"id\\\\\\\",vZ=\\\\\\\"class\\\\\\\",yZ=\\\\\\\"html\\\\\\\";class xZ extends QG{static type(){return\\\\\\\"CSS2DObject\\\\\\\"}cook(t,e){const n=t[0];if(n){const t=this._create_objects_from_input_points(n,e);return this.createCoreGroupFromObjects(t)}{const t=this._create_object_from_scratch(e);return this.createCoreGroupFromObjects([t])}}_create_objects_from_input_points(t,e){const n=t.points(),i=[];for(let t of n){const n=e.useIdAttrib?t.attribValue(gZ):e.className,s=e.useClassAttrib?t.attribValue(vZ):e.className,r=e.useHtmlAttrib?t.attribValue(yZ):e.html,o=xZ.create_css_object({id:n,className:s,html:r}),a=o.element;if(e.copyAttributes){const n=os.attribNames(e.attributesToCopy);for(let e of n){const n=t.attribValue(e);m.isString(n)?a.setAttribute(e,n):m.isNumber(n)&&a.setAttribute(e,`${n}`)}}o.position.copy(t.position()),o.updateMatrix(),i.push(o)}return i}_create_object_from_scratch(t){return xZ.create_css_object({id:t.id,className:t.className,html:t.html})}static create_css_object(t){const e=document.createElement(\\\\\\\"div\\\\\\\");e.id=t.id,e.className=t.className,e.innerHTML=t.html;const n=new iq(e);return n.matrixAutoUpdate=!1,n}}xZ.DEFAULT_PARAMS={useIdAttrib:!1,id:\\\\\\\"my_css_object\\\\\\\",useClassAttrib:!1,className:\\\\\\\"CSS2DObject\\\\\\\",useHtmlAttrib:!1,html:\\\\\\\"
default html
\\\\\\\",copyAttributes:!1,attributesToCopy:\\\\\\\"\\\\\\\"},xZ.INPUT_CLONED_STATE=Ki.FROM_NODE;const bZ=xZ.DEFAULT_PARAMS;const wZ=new class extends la{constructor(){super(...arguments),this.useIdAttrib=aa.BOOLEAN(bZ.useIdAttrib),this.id=aa.STRING(bZ.id,{visibleIf:{useIdAttrib:0}}),this.useClassAttrib=aa.BOOLEAN(bZ.useClassAttrib),this.className=aa.STRING(bZ.className,{visibleIf:{useClassAttrib:0}}),this.useHtmlAttrib=aa.BOOLEAN(bZ.useHtmlAttrib),this.html=aa.STRING(bZ.html,{visibleIf:{useHtmlAttrib:0},multiline:!0}),this.copyAttributes=aa.BOOLEAN(bZ.copyAttributes),this.attributesToCopy=aa.STRING(bZ.attributesToCopy,{visibleIf:{copyAttributes:!0}})}};class TZ extends nV{constructor(){super(...arguments),this.paramsConfig=wZ}static type(){return\\\\\\\"CSS2DObject\\\\\\\"}initializeNode(){this.io.inputs.setCount(0,1)}cook(t){this._operation=this._operation||new xZ(this.scene(),this.states);const e=this._operation.cook(t,this.pv);this.setCoreGroup(e)}}class AZ{constructor(t,e){this._size=t,this._type=e}size(){return this._size}type(){return this._type}static from_value(t){const e=m.isString(t)?Bs.STRING:Bs.NUMERIC;return new this(m.isArray(t)?t.length:1,e)}}class MZ{constructor(t={}){this._attribute_datas_by_name={},this._options={},this._options.dataKeysPrefix=t.dataKeysPrefix,this._options.skipEntries=t.skipEntries,this._options.doConvert=t.doConvert||!1,this._options.convertToNumeric=t.convertToNumeric}dataKeysPrefix(){return this._options.dataKeysPrefix}get_prefixed_json(t,e){if(0==e.length)return t;{const n=e.shift();if(n)return this.get_prefixed_json(t[n],e)}return[]}setJSON(t){return this._json=t}createObject(){const t=new S.a,e=new _r(t);if(null!=this._json){const n=this._json.length;e.initPositionAttribute(n),this._find_attributes();const i=os.attribNames(this._options.convertToNumeric||\\\\\\\"\\\\\\\");for(let n of Object.keys(this._attribute_datas_by_name)){const s=qs.remapName(n);let r=this._attribute_values_for_name(n).flat();const o=this._attribute_datas_by_name[n],a=o.size();if(o.type()===Bs.STRING)if(this._options.doConvert&&os.matchesOneMask(n,i)){const e=r.map((t=>m.isString(t)?parseFloat(t)||0:t));t.setAttribute(s,new C.c(e,a))}else{const t=qs.arrayToIndexedArrays(r);e.setIndexedAttribute(s,t.values,t.indices)}else{const e=r;t.setAttribute(s,new C.c(e,a))}}}return t}_find_attributes(){let t;const e=os.attribNames(this._options.skipEntries||\\\\\\\"\\\\\\\");if(this._json&&null!=(t=this._json[0]))for(let n of Object.keys(t)){const i=t[n];if(this._value_has_subentries(i))for(let t of Object.keys(i)){const s=[n,t].join(\\\\\\\":\\\\\\\"),r=i[n];os.matchesOneMask(s,e)||(this._attribute_datas_by_name[s]=AZ.from_value(r))}else os.matchesOneMask(n,e)||(this._attribute_datas_by_name[n]=AZ.from_value(i))}}_attribute_values_for_name(t){return this._json?this._json.map((e=>{const n=t.split(\\\\\\\":\\\\\\\")[0],i=e[n];if(this._value_has_subentries(i)){return i[t.substring(n.length+1)]||0}return i||0})):[]}_value_has_subentries(t){return m.isObject(t)&&!m.isArray(t)}}const EZ=JSON.stringify([{value:-40},{value:-30},{value:-20},{value:-10},{value:0},{value:10},{value:20},{value:30},{value:40},{value:50},{value:60},{value:70},{value:80}]);const SZ=new class extends la{constructor(){super(...arguments),this.data=aa.STRING(EZ)}};class CZ extends nV{constructor(){super(...arguments),this.paramsConfig=SZ}static type(){return\\\\\\\"data\\\\\\\"}cook(){let t=null;try{t=JSON.parse(this.pv.data)}catch(t){this.states.error.set(\\\\\\\"could not parse json\\\\\\\")}if(t)try{const e=new MZ;e.setJSON(t);const n=e.createObject();this.setGeometry(n,Cs.POINTS)}catch(t){this.states.error.set(\\\\\\\"could not build geometry from json\\\\\\\")}else this.cookController.endCook()}}class NZ extends jg{constructor(t,e,n={},i){super(t,e,i),this._node=i,this._parser=new MZ(n)}async load(t,e,n){const i=await this._urlToLoad();fetch(i).then((async e=>{let n=await e.json();const i=this._parser.dataKeysPrefix();null!=i&&\\\\\\\"\\\\\\\"!=i&&(n=this._parser.get_prefixed_json(n,i.split(\\\\\\\".\\\\\\\"))),this._parser.setJSON(n);const s=this._parser.createObject();t(s)})).catch((t=>{li.error(\\\\\\\"error\\\\\\\",t),n(t)}))}}const LZ=\\\\\\\"position\\\\\\\";class OZ{constructor(t){this.attribute_names=t,this.attribute_names_from_first_line=!1,this.lines=[],this.points_count=0,this.attribute_values_by_name={},this.attribute_data_by_name={},this._loading=!1,this.attribute_names||(this.attribute_names_from_first_line=!0)}async load(t){if(this._loading)return void console.warn(\\\\\\\"is already loading\\\\\\\");this._loading=!0,this.points_count=0,await this.load_data(t),this.infer_types(),this.read_values();return this.create_points()}async load_data(t){const e=await fetch(t),n=await e.text();this.lines=n.split(\\\\\\\"\\\\n\\\\\\\"),this.attribute_names||(this.attribute_names=this.lines[0].split(OZ.SEPARATOR)),this.attribute_names=this.attribute_names.map((t=>qs.remapName(t)));for(let t of this.attribute_names)this.attribute_values_by_name[t]=[]}infer_types(){const t=this.attribute_names_from_first_line?1:0;let e=this.lines[t].split(OZ.SEPARATOR);for(let t=0;tparseFloat(t)))}return t}return t}read_values(){if(!this.attribute_names)return;let t;for(let e=this.attribute_names_from_first_line?1:0;e=this.attribute_names.length){for(let t=0;t({name:t,value:e})))}}),this.url=aa.STRING(IZ,{fileBrowse:{type:[Or.JSON]}}),this.jsonDataKeysPrefix=aa.STRING(\\\\\\\"\\\\\\\",{visibleIf:{dataType:RZ.indexOf(PZ.JSON)}}),this.skipEntries=aa.STRING(\\\\\\\"\\\\\\\",{visibleIf:{dataType:RZ.indexOf(PZ.JSON)}}),this.convert=aa.BOOLEAN(0,{visibleIf:{dataType:RZ.indexOf(PZ.JSON)}}),this.convertToNumeric=aa.STRING(\\\\\\\"\\\\\\\",{visibleIf:{dataType:RZ.indexOf(PZ.JSON),convert:1}}),this.readAttribNamesFromFile=aa.BOOLEAN(1,{visibleIf:{dataType:RZ.indexOf(PZ.CSV)}}),this.attribNames=aa.STRING(\\\\\\\"height scale\\\\\\\",{visibleIf:{dataType:RZ.indexOf(PZ.CSV),readAttribNamesFromFile:0}}),this.reload=aa.BUTTON(null,{callback:(t,e)=>{DZ.PARAM_CALLBACK_reload(t,e)}})}};class DZ extends nV{constructor(){super(...arguments),this.paramsConfig=FZ}static type(){return\\\\\\\"dataUrl\\\\\\\"}initializeNode(){this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.url],(()=>{const t=this.p.url.rawInput();if(t){const e=t.split(\\\\\\\"/\\\\\\\");return e[e.length-1]}return\\\\\\\"\\\\\\\"}))}))}))}async cook(){switch(RZ[this.pv.dataType]){case PZ.JSON:return this._load_json();case PZ.CSV:return this._load_csv()}}_url(){const t=this.scene().assets.root();return t?`${t}${this.pv.url}`:this.pv.url}_load_json(){new NZ(this._url(),this.scene(),{dataKeysPrefix:this.pv.jsonDataKeysPrefix,skipEntries:this.pv.skipEntries,doConvert:this.pv.convert,convertToNumeric:this.pv.convertToNumeric},this).load(this._on_load.bind(this),void 0,this._on_error.bind(this))}_on_load(t){this.setGeometry(t,Cs.POINTS)}_on_error(t){this.states.error.set(`could not load geometry from ${this._url()} (${t})`),this.cookController.endCook()}async _load_csv(){const t=this.pv.readAttribNamesFromFile?void 0:this.pv.attribNames.split(\\\\\\\" \\\\\\\"),e=new OZ(t),n=await e.load(this._url());n?this.setGeometry(n,Cs.POINTS):this.states.error.set(\\\\\\\"could not generate points\\\\\\\")}static PARAM_CALLBACK_reload(t,e){t.param_callback_reload()}param_callback_reload(){this.p.url.setDirty()}}class BZ extends S.a{constructor(t,e,n,i){super();const s=[],r=[],o=[],a=new p.a,l=new A.a;l.makeRotationFromEuler(n),l.setPosition(e);const c=new A.a;function h(e,n,i){n.applyMatrix4(t.matrixWorld),n.applyMatrix4(c),i.transformDirection(t.matrixWorld),e.push(new zZ(n.clone(),i.clone()))}function u(t,e){const n=[],s=.5*Math.abs(i.dot(e));for(let i=0;i0,u=t[i+1].position.dot(e)-s>0,p=t[i+2].position.dot(e)-s>0;switch(c=(h?1:0)+(u?1:0)+(p?1:0),c){case 0:n.push(t[i]),n.push(t[i+1]),n.push(t[i+2]);break;case 1:if(h&&(r=t[i+1],o=t[i+2],a=d(t[i],r,e,s),l=d(t[i],o,e,s)),u){r=t[i],o=t[i+2],a=d(t[i+1],r,e,s),l=d(t[i+1],o,e,s),n.push(a),n.push(o.clone()),n.push(r.clone()),n.push(o.clone()),n.push(a.clone()),n.push(l);break}p&&(r=t[i],o=t[i+1],a=d(t[i+2],r,e,s),l=d(t[i+2],o,e,s)),n.push(r.clone()),n.push(o.clone()),n.push(a),n.push(l),n.push(a.clone()),n.push(o.clone());break;case 2:h||(r=t[i].clone(),o=d(r,t[i+1],e,s),a=d(r,t[i+2],e,s),n.push(r),n.push(o),n.push(a)),u||(r=t[i+1].clone(),o=d(r,t[i+2],e,s),a=d(r,t[i],e,s),n.push(r),n.push(o),n.push(a)),p||(r=t[i+2].clone(),o=d(r,t[i],e,s),a=d(r,t[i+1],e,s),n.push(r),n.push(o),n.push(a))}}return n}function d(t,e,n,i){const s=t.position.dot(n)-i,r=s/(s-(e.position.dot(n)-i));return new zZ(new p.a(t.position.x+r*(e.position.x-t.position.x),t.position.y+r*(e.position.y-t.position.y),t.position.z+r*(e.position.z-t.position.z)),new p.a(t.normal.x+r*(e.normal.x-t.normal.x),t.normal.y+r*(e.normal.y-t.normal.y),t.normal.z+r*(e.normal.z-t.normal.z)))}c.copy(l).invert(),function(){let e=[];const n=new p.a,c=new p.a;if(!0===t.geometry.isGeometry)return void console.error(\\\\\\\"THREE.DecalGeometry no longer supports THREE.Geometry. Use BufferGeometry instead.\\\\\\\");const d=t.geometry,_=d.attributes.position,m=d.attributes.normal;if(null!==d.index){const t=d.index;for(let i=0;i{this.setCoreGroup(e)}),Math.max(this.pv.duration,0))}}class WZ{constructor(t){this.node=t,this.selected_state=new Map,this._entities_count=0,this._selected_entities_count=0}init(t){this.selected_state.clear();for(let e of t)this.selected_state.set(e,!1);this._entities_count=t.length,this._selected_entities_count=0}select(t){const e=this.selected_state.get(t);null!=e&&0==e&&(this.selected_state.set(t,!0),this._selected_entities_count++)}entities_to_keep(){return this._entities_for_state(this.node.pv.invert)}entities_to_delete(){return this._entities_for_state(!this.node.pv.invert)}_entities_for_state(t){const e=!!t,n=t?this._selected_entities_count:this._entities_count-this._selected_entities_count;if(0==n)return[];{const t=new Array(n);let i=0;return this.selected_state.forEach(((n,s)=>{n==e&&(t[i]=s,i++)})),t}}}var qZ;!function(t){t.EQUAL=\\\\\\\"==\\\\\\\",t.LESS_THAN=\\\\\\\"<\\\\\\\",t.EQUAL_OR_LESS_THAN=\\\\\\\"<=\\\\\\\",t.EQUAL_OR_GREATER_THAN=\\\\\\\">=\\\\\\\",t.GREATER_THAN=\\\\\\\">\\\\\\\",t.DIFFERENT=\\\\\\\"!=\\\\\\\"}(qZ||(qZ={}));const XZ=[qZ.EQUAL,qZ.LESS_THAN,qZ.EQUAL_OR_LESS_THAN,qZ.EQUAL_OR_GREATER_THAN,qZ.GREATER_THAN,qZ.DIFFERENT],YZ={[qZ.EQUAL]:(t,e)=>t==e,[qZ.LESS_THAN]:(t,e)=>tt<=e,[qZ.EQUAL_OR_GREATER_THAN]:(t,e)=>t>=e,[qZ.GREATER_THAN]:(t,e)=>t>e,[qZ.DIFFERENT]:(t,e)=>t!=e},$Z=XZ.map(((t,e)=>({name:t,value:e})));class JZ{constructor(t){this.node=t}evalForEntities(t){const e=zs[this.node.pv.attribType];switch(e){case Bs.NUMERIC:return void this._eval_for_numeric(t);case Bs.STRING:return void this._eval_for_string(t)}ls.unreachable(e)}_eval_for_string(t){let e;for(let n of t)e=n.stringAttribValue(this.node.pv.attribName),e==this.node.pv.attrib_string&&this.node.entitySelectionHelper.select(n)}_eval_for_numeric(t){const e=Gs[this.node.pv.attribSize-1];switch(e){case Us.FLOAT:return this._eval_for_points_numeric_float(t);case Us.VECTOR2:return this._eval_for_points_numeric_vector2(t);case Us.VECTOR3:return this._eval_for_points_numeric_vector3(t);case Us.VECTOR4:return this._eval_for_points_numeric_vector4(t)}ls.unreachable(e)}_eval_for_points_numeric_float(t){let e=this.node.pv.attribName;const n=this.node.pv.attribValue1;let i;const s=XZ[this.node.pv.attribComparisonOperator],r=YZ[s];for(let s of t)i=s.attribValue(e),r(i,n)&&this.node.entitySelectionHelper.select(s)}_eval_for_points_numeric_vector2(t){let e=this.node.pv.attribName;const n=this.node.pv.attribValue2;let i=new d.a;for(let s of t){const t=s.attribValue(e,i);n.equals(t)&&this.node.entitySelectionHelper.select(s)}}_eval_for_points_numeric_vector3(t){let e=this.node.pv.attribName;const n=this.node.pv.attribValue3;let i=new p.a;for(let s of t){const t=s.attribValue(e,i);n.equals(t)&&this.node.entitySelectionHelper.select(s)}}_eval_for_points_numeric_vector4(t){let e=this.node.pv.attribName;const n=this.node.pv.attribValue4;let i=new _.a;for(let s of t){const t=s.attribValue(e,i);n.equals(t)&&this.node.entitySelectionHelper.select(s)}}}class ZZ{constructor(t){this.node=t}async evalForEntities(t){const e=this.node.p.expression;this.node.p.expression.hasExpression()&&e.expressionController?await this.eval_expressions_for_points_with_expression(t):this.eval_expressions_without_expression(t)}async eval_expressions_for_points_with_expression(t){const e=this.node.p.expression;e.expressionController&&await e.expressionController.compute_expression_for_entities(t,((t,e)=>{e&&this.node.entitySelectionHelper.select(t)}))}eval_expressions_without_expression(t){if(this.node.pv.expression)for(let e of t)this.node.entitySelectionHelper.select(e)}}class QZ{constructor(t){this.node=t,this._point_position=new p.a}evalForPoints(t){const e=this._createBbox();for(let n of t){e.containsPoint(n.getPosition(this._point_position))&&this.node.entitySelectionHelper.select(n)}}_createBbox(){return new Ay.a(this.node.pv.bboxCenter.clone().sub(this.node.pv.bboxSize.clone().multiplyScalar(.5)),this.node.pv.bboxCenter.clone().add(this.node.pv.bboxSize.clone().multiplyScalar(.5)))}}class KZ{constructor(t){this.node=t}eval_for_objects(t){const e=Os[this.node.pv.objectType];for(let n of t){Ls(n.object().constructor)==e&&this.node.entitySelectionHelper.select(n)}}}class tQ{constructor(){this._sidePropertyByMaterial=new WeakMap,this._bound_setMat=this._setObjectMaterialDoubleSided.bind(this),this._bound_restoreMat=this._restoreObjectMaterialSide.bind(this)}setCoreGroupMaterialDoubleSided(t){const e=t.objects();for(let t of e)t.traverse(this._bound_setMat)}restoreMaterialSideProperty(t){const e=t.objects();for(let t of e)t.traverse(this._bound_restoreMat)}_setObjectMaterialDoubleSided(t){const e=t.material;if(e)if(m.isArray(e))for(let t of e)this._setMaterialDoubleSided(t);else this._setMaterialDoubleSided(e)}_restoreObjectMaterialSide(t){const e=t.material;if(e)if(m.isArray(e))for(let t of e)this._restoreMaterialDoubleSided(t);else this._restoreMaterialDoubleSided(e)}_setMaterialDoubleSided(t){this._sidePropertyByMaterial.set(t,t.side),t.side=w.z}_restoreMaterialDoubleSided(t){t.side=this._sidePropertyByMaterial.get(t)||w.z}}const eQ=new p.a(0,1,0),nQ=new p.a(0,-1,0);class iQ{constructor(t){this.node=t,this._matDoubleSideTmpSetter=new tQ,this._point_position=new p.a,this._raycaster=new WL,this._intersections=[]}evalForPoints(t,e){if(!e)return;const n=null==e?void 0:e.objectsWithGeo()[0];if(!n)return;const i=n;if(!i.isMesh)return;this._matDoubleSideTmpSetter.setCoreGroupMaterialDoubleSided(e);const s=n.geometry;s.computeBoundingBox();const r=s.boundingBox;for(let e of t)e.getPosition(this._point_position),r.containsPoint(this._point_position)?this._isPositionInObject(this._point_position,i,eQ)&&this._isPositionInObject(this._point_position,i,nQ)&&this.node.entitySelectionHelper.select(e):this.node.entitySelectionHelper.select(e);this._matDoubleSideTmpSetter.restoreMaterialSideProperty(e)}_isPositionInObject(t,e,n){var i;this._raycaster.ray.direction.copy(n),this._raycaster.ray.origin.copy(t),this._intersections.length=0;const s=this._raycaster.intersectObject(e,!1,this._intersections);if(!s)return!1;if(0==s.length)return!1;const r=null===(i=s[0].face)||void 0===i?void 0:i.normal;if(!r)return!1;return this._raycaster.ray.direction.dot(r)>=0}}const sQ=new class extends la{constructor(){super(...arguments),this.class=aa.INTEGER(Fs.indexOf(Is.VERTEX),{menu:{entries:Ds}}),this.invert=aa.BOOLEAN(0),this.byObjectType=aa.BOOLEAN(0,{visibleIf:{class:Fs.indexOf(Is.OBJECT)}}),this.objectType=aa.INTEGER(Os.indexOf(Cs.MESH),{menu:{entries:Ps},visibleIf:{class:Fs.indexOf(Is.OBJECT),byObjectType:!0},separatorAfter:!0}),this.byExpression=aa.BOOLEAN(0),this.expression=aa.BOOLEAN(\\\\\\\"@ptnum==0\\\\\\\",{visibleIf:{byExpression:!0},expression:{forEntities:!0},separatorAfter:!0}),this.byAttrib=aa.BOOLEAN(0),this.attribType=aa.INTEGER(zs.indexOf(Bs.NUMERIC),{menu:{entries:ks},visibleIf:{byAttrib:1}}),this.attribName=aa.STRING(\\\\\\\"\\\\\\\",{visibleIf:{byAttrib:1}}),this.attribSize=aa.INTEGER(1,{range:Vs,rangeLocked:[!0,!0],visibleIf:{byAttrib:1,attribType:zs.indexOf(Bs.NUMERIC)}}),this.attribComparisonOperator=aa.INTEGER(XZ.indexOf(qZ.EQUAL),{menu:{entries:$Z},visibleIf:{byAttrib:!0,attribType:zs.indexOf(Bs.NUMERIC),attribSize:Us.FLOAT}}),this.attribValue1=aa.FLOAT(0,{visibleIf:{byAttrib:1,attribType:zs.indexOf(Bs.NUMERIC),attribSize:1}}),this.attribValue2=aa.VECTOR2([0,0],{visibleIf:{byAttrib:1,attribType:zs.indexOf(Bs.NUMERIC),attribSize:2}}),this.attribValue3=aa.VECTOR3([0,0,0],{visibleIf:{byAttrib:1,attribType:zs.indexOf(Bs.NUMERIC),attribSize:3}}),this.attribValue4=aa.VECTOR4([0,0,0,0],{visibleIf:{byAttrib:1,attribType:zs.indexOf(Bs.NUMERIC),attribSize:4}}),this.attribString=aa.STRING(\\\\\\\"\\\\\\\",{visibleIf:{byAttrib:1,attribType:zs.indexOf(Bs.STRING)},separatorAfter:!0}),this.byBbox=aa.BOOLEAN(0,{visibleIf:{class:Fs.indexOf(Is.VERTEX)}}),this.bboxSize=aa.VECTOR3([1,1,1],{visibleIf:{class:Fs.indexOf(Is.VERTEX),byBbox:!0}}),this.bboxCenter=aa.VECTOR3([0,0,0],{visibleIf:{class:Fs.indexOf(Is.VERTEX),byBbox:!0},separatorAfter:!0}),this.byBoundingObject=aa.BOOLEAN(0,{visibleIf:{class:Fs.indexOf(Is.VERTEX)}}),this.keepPoints=aa.BOOLEAN(0,{visibleIf:{class:Fs.indexOf(Is.OBJECT)}})}};class rQ extends nV{constructor(){super(...arguments),this.paramsConfig=sQ,this._marked_for_deletion_per_object_index=new Map,this.entitySelectionHelper=new WZ(this),this.byExpressionHelper=new ZZ(this),this.byAttributeHelper=new JZ(this),this.byObjectTypeHelper=new KZ(this),this.byBboxHelper=new QZ(this),this.byBoundingObjectHelper=new iQ(this)}static type(){return\\\\\\\"delete\\\\\\\"}static displayedInputNames(){return[\\\\\\\"geometry to delete from\\\\\\\",\\\\\\\"points inside this geometry will be deleted (optional)\\\\\\\"]}initializeNode(){this.io.inputs.setCount(1,2),this.io.inputs.initInputsClonedState(Ki.FROM_NODE)}async cook(t){const e=t[0],n=t[1];switch(this.pv.class){case Is.VERTEX:await this._eval_for_points(e,n);break;case Is.OBJECT:await this._eval_for_objects(e)}}set_class(t){this.p.class.set(t)}async _eval_for_objects(t){const e=t.coreObjects();this.entitySelectionHelper.init(e),this._marked_for_deletion_per_object_index=new Map;for(let t of e)this._marked_for_deletion_per_object_index.set(t.index(),!1);this.pv.byExpression&&await this.byExpressionHelper.evalForEntities(e),this.pv.byObjectType&&this.byObjectTypeHelper.eval_for_objects(e),this.pv.byAttrib&&\\\\\\\"\\\\\\\"!=this.pv.attribName&&this.byAttributeHelper.evalForEntities(e);const n=this.entitySelectionHelper.entities_to_keep().map((t=>t.object()));if(this.pv.keepPoints){const t=this.entitySelectionHelper.entities_to_delete();for(let e of t){const t=this._point_object(e);t&&n.push(t)}}this.setObjects(n)}async _eval_for_points(t,e){const n=t.coreObjects();let i,s=[];for(let t=0;t0){const e=_r.geometryFromPoints(a,Ls(t.constructor));e&&(t.geometry=e,s.push(t))}}}this.setObjects(s)}_point_object(t){const e=t.points(),n=_r.geometryFromPoints(e,Cs.POINTS);if(n)return this.createObject(n,Cs.POINTS)}}const oQ=new class extends la{constructor(){super(...arguments),this.start=aa.INTEGER(0,{range:[0,100],rangeLocked:[!0,!1]}),this.useCount=aa.BOOLEAN(0),this.count=aa.INTEGER(0,{range:[0,100],rangeLocked:[!0,!1],visibleIf:{useCount:1}})}};class aQ extends nV{constructor(){super(...arguments),this.paramsConfig=oQ}static type(){return\\\\\\\"drawRange\\\\\\\"}initializeNode(){this.io.inputs.setCount(0,1),this.io.inputs.initInputsClonedState(Ki.FROM_NODE)}cook(t){const e=t[0],n=e.objects();for(let t of n){const e=t.geometry;if(e){const t=e.drawRange;t.start=this.pv.start,this.pv.useCount?t.count=this.pv.count:t.count=1/0}}this.setCoreGroup(e)}}class lQ{constructor(){this.pluginCallbacks=[],this.register((function(t){return new DQ(t)})),this.register((function(t){return new BQ(t)})),this.register((function(t){return new zQ(t)})),this.register((function(t){return new kQ(t)})),this.register((function(t){return new UQ(t)}))}register(t){return-1===this.pluginCallbacks.indexOf(t)&&this.pluginCallbacks.push(t),this}unregister(t){return-1!==this.pluginCallbacks.indexOf(t)&&this.pluginCallbacks.splice(this.pluginCallbacks.indexOf(t),1),this}parse(t,e,n){const i=new FQ,s=[];for(let t=0,e=this.pluginCallbacks.length;t0&&(this.options.trs=!0),this.processInput(t);const i=this;Promise.all(this.pending).then((function(){const t=i.buffers,n=i.json,s=i.options,r=i.extensionsUsed,o=new Blob(t,{type:\\\\\\\"application/octet-stream\\\\\\\"}),a=Object.keys(r);if(a.length>0&&(n.extensionsUsed=a),n.buffers&&n.buffers.length>0&&(n.buffers[0].byteLength=o.size),!0===s.binary){const t=new window.FileReader;t.readAsArrayBuffer(o),t.onloadend=function(){const i=RQ(t.result),s=new DataView(new ArrayBuffer(8));s.setUint32(0,i.byteLength,!0),s.setUint32(4,5130562,!0);const r=RQ(function(t){if(void 0!==window.TextEncoder)return(new TextEncoder).encode(t).buffer;const e=new Uint8Array(new ArrayBuffer(t.length));for(let n=0,i=t.length;n255?32:i}return e.buffer}(JSON.stringify(n)),32),o=new DataView(new ArrayBuffer(8));o.setUint32(0,r.byteLength,!0),o.setUint32(4,1313821514,!0);const a=new ArrayBuffer(12),l=new DataView(a);l.setUint32(0,1179937895,!0),l.setUint32(4,2,!0);const c=12+o.byteLength+r.byteLength+s.byteLength+i.byteLength;l.setUint32(8,c,!0);const h=new Blob([a,o,r,s,i],{type:\\\\\\\"application/octet-stream\\\\\\\"}),u=new window.FileReader;u.readAsArrayBuffer(h),u.onloadend=function(){e(u.result)}}}else if(n.buffers&&n.buffers.length>0){const t=new window.FileReader;t.readAsDataURL(o),t.onloadend=function(){const i=t.result;n.buffers[0].uri=i,e(n)}}else e(n)}))}serializeUserData(t,e){if(0===Object.keys(t.userData).length)return;const n=this.options,i=this.extensionsUsed;try{const s=JSON.parse(JSON.stringify(t.userData));if(n.includeCustomExtensions&&s.gltfExtensions){void 0===e.extensions&&(e.extensions={});for(const t in s.gltfExtensions)e.extensions[t]=s.gltfExtensions[t],i[t]=!0;delete s.gltfExtensions}Object.keys(s).length>0&&(e.extras=s)}catch(e){console.warn(\\\\\\\"THREE.GLTFExporter: userData of '\\\\\\\"+t.name+\\\\\\\"' won't be serialized because of JSON.stringify error - \\\\\\\"+e.message)}}getUID(t){return this.uids.has(t)||this.uids.set(t,this.uid++),this.uids.get(t)}isNormalizedNormalAttribute(t){if(this.cache.attributesNormalized.has(t))return!1;const e=new gb;for(let n=0,i=t.count;n5e-4)return!1;return!0}createNormalizedNormalAttribute(t){const e=this.cache;if(e.attributesNormalized.has(t))return e.attributesNormalized.get(t);const n=t.clone(),i=new gb;for(let t=0,e=n.count;t4?i=t.array[s*t.itemSize+n]:0===n?i=t.getX(s):1===n?i=t.getY(s):2===n?i=t.getZ(s):3===n&&(i=t.getW(s)),e===fQ?l.setFloat32(c,i,!0):e===gQ?l.setUint32(c,i,!0):e===mQ?l.setUint16(c,i,!0):e===_Q&&l.setUint8(c,i),c+=o}const h={buffer:this.processBuffer(l.buffer),byteOffset:this.byteOffset,byteLength:a};void 0!==s&&(h.target=s),s===vQ&&(h.byteStride=t.itemSize*o),this.byteOffset+=a,r.bufferViews.push(h);return{id:r.bufferViews.length-1,byteLength:0}}processBufferViewImage(t){const e=this,n=e.json;return n.bufferViews||(n.bufferViews=[]),new Promise((function(i){const s=new window.FileReader;s.readAsArrayBuffer(t),s.onloadend=function(){const t=RQ(s.result),r={buffer:e.processBuffer(t),byteOffset:e.byteOffset,byteLength:t.byteLength};e.byteOffset+=t.byteLength,i(n.bufferViews.push(r)-1)}}))}processAccessor(t,e,n,i){const s=this.options,r=this.json;let o;if(t.array.constructor===Float32Array)o=fQ;else if(t.array.constructor===Uint32Array)o=gQ;else if(t.array.constructor===Uint16Array)o=mQ;else{if(t.array.constructor!==Uint8Array)throw new Error(\\\\\\\"THREE.GLTFExporter: Unsupported bufferAttribute component type.\\\\\\\");o=_Q}if(void 0===n&&(n=0),void 0===i&&(i=t.count),s.truncateDrawRange&&void 0!==e&&null===e.index){const s=n+i,r=e.drawRange.count===1/0?t.count:e.drawRange.start+e.drawRange.count;n=Math.max(n,e.drawRange.start),(i=Math.min(s,r)-n)<0&&(i=0)}if(0===i)return null;const a=function(t,e,n){const i={min:new Array(t.itemSize).fill(Number.POSITIVE_INFINITY),max:new Array(t.itemSize).fill(Number.NEGATIVE_INFINITY)};for(let s=e;s4?n=t.array[s*t.itemSize+e]:0===e?n=t.getX(s):1===e?n=t.getY(s):2===e?n=t.getZ(s):3===e&&(n=t.getW(s)),i.min[e]=Math.min(i.min[e],n),i.max[e]=Math.max(i.max[e],n)}return i}(t,n,i);let l;void 0!==e&&(l=t===e.index?yQ:vQ);const c=this.processBufferView(t,o,n,i,l),h={bufferView:c.id,byteOffset:c.byteOffset,componentType:o,count:i,max:a.max,min:a.min,type:{1:\\\\\\\"SCALAR\\\\\\\",2:\\\\\\\"VEC2\\\\\\\",3:\\\\\\\"VEC3\\\\\\\",4:\\\\\\\"VEC4\\\\\\\",16:\\\\\\\"MAT4\\\\\\\"}[t.itemSize]};return!0===t.normalized&&(h.normalized=!0),r.accessors||(r.accessors=[]),r.accessors.push(h)-1}processImage(t,e,n){const i=this,s=i.cache,r=i.json,o=i.options,a=i.pending;s.images.has(t)||s.images.set(t,{});const l=s.images.get(t),c=e===Ex?\\\\\\\"image/png\\\\\\\":\\\\\\\"image/jpeg\\\\\\\",h=c+\\\\\\\":flipY/\\\\\\\"+n.toString();if(void 0!==l[h])return l[h];r.images||(r.images=[]);const u={mimeType:c};if(o.embedImages){const s=IQ=IQ||document.createElement(\\\\\\\"canvas\\\\\\\");s.width=Math.min(t.width,o.maxTextureSize),s.height=Math.min(t.height,o.maxTextureSize);const r=s.getContext(\\\\\\\"2d\\\\\\\");if(!0===n&&(r.translate(0,s.height),r.scale(1,-1)),\\\\\\\"undefined\\\\\\\"!=typeof HTMLImageElement&&t instanceof HTMLImageElement||\\\\\\\"undefined\\\\\\\"!=typeof HTMLCanvasElement&&t instanceof HTMLCanvasElement||\\\\\\\"undefined\\\\\\\"!=typeof OffscreenCanvas&&t instanceof OffscreenCanvas||\\\\\\\"undefined\\\\\\\"!=typeof ImageBitmap&&t instanceof ImageBitmap)r.drawImage(t,0,0,s.width,s.height);else{e!==Ex&&e!==Mx&&console.error(\\\\\\\"GLTFExporter: Only RGB and RGBA formats are supported.\\\\\\\"),(t.width>o.maxTextureSize||t.height>o.maxTextureSize)&&console.warn(\\\\\\\"GLTFExporter: Image size is bigger than maxTextureSize\\\\\\\",t);const n=new Uint8ClampedArray(t.height*t.width*4);if(e===Ex)for(let e=0;e1&&(e.multiplyScalar(1/n),console.warn(\\\\\\\"THREE.GLTFExporter: Some emissive components exceed 1; emissive has been limited\\\\\\\")),n>0&&(i.emissiveFactor=e.toArray()),t.emissiveMap){const e={index:this.processTexture(t.emissiveMap)};this.applyTextureTransform(e,t.emissiveMap),i.emissiveTexture=e}}if(t.normalMap){const e={index:this.processTexture(t.normalMap)};t.normalScale&&1!==t.normalScale.x&&(e.scale=t.normalScale.x),this.applyTextureTransform(e,t.normalMap),i.normalTexture=e}if(t.aoMap){const e={index:this.processTexture(t.aoMap),texCoord:1};1!==t.aoMapIntensity&&(e.strength=t.aoMapIntensity),this.applyTextureTransform(e,t.aoMap),i.occlusionTexture=e}t.transparent?i.alphaMode=\\\\\\\"BLEND\\\\\\\":t.alphaTest>0&&(i.alphaMode=\\\\\\\"MASK\\\\\\\",i.alphaCutoff=t.alphaTest),2===t.side&&(i.doubleSided=!0),\\\\\\\"\\\\\\\"!==t.name&&(i.name=t.name),this.serializeUserData(t,i),this._invokeAll((function(e){e.writeMaterial&&e.writeMaterial(t,i)}));const r=n.materials.push(i)-1;return e.materials.set(t,r),r}processMesh(t){const e=this.cache,n=this.json,i=[t.geometry.uuid];if(Array.isArray(t.material))for(let e=0,n=t.material.length;e0){const n=[],i=[],s={};if(void 0!==t.morphTargetDictionary)for(const e in t.morphTargetDictionary)s[t.morphTargetDictionary[e]]=e;for(let o=0;o0&&(a.extras={},a.extras.targetNames=i)}const _=Array.isArray(t.material);if(_&&0===r.groups.length)return null;const m=_?t.material:[t.material],f=_?r.groups:[{materialIndex:0,start:void 0,count:void 0}];for(let t=0,n=f.length;t0&&(n.targets=h),null!==r.index){let i=this.getUID(r.index);void 0===f[t].start&&void 0===f[t].count||(i+=\\\\\\\":\\\\\\\"+f[t].start+\\\\\\\":\\\\\\\"+f[t].count),e.attributes.has(i)?n.indices=e.attributes.get(i):(n.indices=this.processAccessor(r.index,r,f[t].start,f[t].count),e.attributes.set(i,n.indices)),null===n.indices&&delete n.indices}const i=this.processMaterial(m[f[t].materialIndex]);null!==i&&(n.material=i),c.push(n)}a.primitives=c,n.meshes||(n.meshes=[]),this._invokeAll((function(e){e.writeMesh&&e.writeMesh(t,a)}));const g=n.meshes.push(a)-1;return e.meshes.set(s,g),g}processCamera(t){const e=this.json;e.cameras||(e.cameras=[]);const n=t.isOrthographicCamera,i={type:n?\\\\\\\"orthographic\\\\\\\":\\\\\\\"perspective\\\\\\\"};return n?i.orthographic={xmag:2*t.right,ymag:2*t.top,zfar:t.far<=0?.001:t.far,znear:t.near<0?0:t.near}:i.perspective={aspectRatio:t.aspect,yfov:ib.degToRad(t.fov),zfar:t.far<=0?.001:t.far,znear:t.near<0?0:t.near},\\\\\\\"\\\\\\\"!==t.name&&(i.name=t.type),e.cameras.push(i)-1}processAnimation(t,e){const n=this.json,i=this.nodeMap;n.animations||(n.animations=[]);const s=(t=lQ.Utils.mergeMorphTargetTracks(t.clone(),e)).tracks,r=[],o=[];for(let t=0;t0){const e=[];for(let i=0,s=t.children.length;i0&&(s.children=e)}this._invokeAll((function(e){e.writeNode&&e.writeNode(t,s)}));const r=e.nodes.push(s)-1;return i.set(t,r),r}processScene(t){const e=this.json,n=this.options;e.scenes||(e.scenes=[],e.scene=0);const i={};\\\\\\\"\\\\\\\"!==t.name&&(i.name=t.name),e.scenes.push(i);const s=[];for(let e=0,i=t.children.length;e0&&(i.nodes=s),this.serializeUserData(t,i)}processObjects(t){const e=new CE;e.name=\\\\\\\"AuxScene\\\\\\\";for(let n=0;n0&&this.processObjects(n);for(let t=0;t0&&(r.range=t.distance)):t.isSpotLight&&(r.type=\\\\\\\"spot\\\\\\\",t.distance>0&&(r.range=t.distance),r.spot={},r.spot.innerConeAngle=(t.penumbra-1)*t.angle*-1,r.spot.outerConeAngle=t.angle),void 0!==t.decay&&2!==t.decay&&console.warn(\\\\\\\"THREE.GLTFExporter: Light decay may be lost. glTF is physically-based, and expects light.decay=2.\\\\\\\"),!t.target||t.target.parent===t&&0===t.target.position.x&&0===t.target.position.y&&-1===t.target.position.z||console.warn(\\\\\\\"THREE.GLTFExporter: Light direction may be lost. For best results, make light.target a child of the light with position 0,0,-1.\\\\\\\"),s[this.name]||(i.extensions=i.extensions||{},i.extensions[this.name]={lights:[]},s[this.name]=!0);const o=i.extensions[this.name].lights;o.push(r),e.extensions=e.extensions||{},e.extensions[this.name]={light:o.length-1}}}class BQ{constructor(t){this.writer=t,this.name=\\\\\\\"KHR_materials_unlit\\\\\\\"}writeMaterial(t,e){if(!t.isMeshBasicMaterial)return;const n=this.writer.extensionsUsed;e.extensions=e.extensions||{},e.extensions[this.name]={},n[this.name]=!0,e.pbrMetallicRoughness.metallicFactor=0,e.pbrMetallicRoughness.roughnessFactor=.9}}class zQ{constructor(t){this.writer=t,this.name=\\\\\\\"KHR_materials_pbrSpecularGlossiness\\\\\\\"}writeMaterial(t,e){if(!t.isGLTFSpecularGlossinessMaterial)return;const n=this.writer,i=n.extensionsUsed,s={};e.pbrMetallicRoughness.baseColorFactor&&(s.diffuseFactor=e.pbrMetallicRoughness.baseColorFactor);const r=[1,1,1];if(t.specular.toArray(r,0),s.specularFactor=r,s.glossinessFactor=t.glossiness,e.pbrMetallicRoughness.baseColorTexture&&(s.diffuseTexture=e.pbrMetallicRoughness.baseColorTexture),t.specularMap){const e={index:n.processTexture(t.specularMap)};n.applyTextureTransform(e,t.specularMap),s.specularGlossinessTexture=e}e.extensions=e.extensions||{},e.extensions[this.name]=s,i[this.name]=!0}}class kQ{constructor(t){this.writer=t,this.name=\\\\\\\"KHR_materials_transmission\\\\\\\"}writeMaterial(t,e){if(!t.isMeshPhysicalMaterial||0===t.transmission)return;const n=this.writer,i=n.extensionsUsed,s={};if(s.transmissionFactor=t.transmission,t.transmissionMap){const e={index:n.processTexture(t.transmissionMap)};n.applyTextureTransform(e,t.transmissionMap),s.transmissionTexture=e}e.extensions=e.extensions||{},e.extensions[this.name]=s,i[this.name]=!0}}class UQ{constructor(t){this.writer=t,this.name=\\\\\\\"KHR_materials_volume\\\\\\\"}writeMaterial(t,e){if(!t.isMeshPhysicalMaterial||0===t.thickness)return;const n=this.writer,i=n.extensionsUsed,s={};if(s.thicknessFactor=t.thickness,t.thicknessMap){const e={index:n.processTexture(t.thicknessMap)};n.applyTextureTransform(e,t.thicknessMap),s.thicknessTexture=e}s.attenuationDistance=t.attenuationDistance,s.attenuationColor=t.attenuationTint.toArray(),e.extensions=e.extensions||{},e.extensions[this.name]=s,i[this.name]=!0}}function GQ(t,e){const n=document.createElement(\\\\\\\"a\\\\\\\");n.style.display=\\\\\\\"none\\\\\\\",document.body.appendChild(n),n.href=URL.createObjectURL(t),n.download=e,n.click(),setTimeout((()=>{document.body.removeChild(n)}),10)}lQ.Utils={insertKeyframe:function(t,e){const n=.001,i=t.getValueSize(),s=new t.TimeBufferType(t.times.length+1),r=new t.ValueBufferType(t.values.length+i),o=t.createInterpolant(new t.ValueBufferType(i));let a;if(0===t.times.length){s[0]=e;for(let t=0;tt.times[t.times.length-1]){if(Math.abs(t.times[t.times.length-1]-e)e){s.set(t.times.slice(0,l+1),0),s[l+1]=e,s.set(t.times.slice(l+1),l+2),r.set(t.values.slice(0,(l+1)*i),0),r.set(o.evaluate(e),(l+1)*i),r.set(t.values.slice((l+1)*i),(l+2)*i),a=l+1;break}}return t.times=s,t.values=r,a},mergeMorphTargetTracks:function(t,e){const n=[],i={},s=t.tracks;for(let t=0;t{HQ.PARAM_CALLBACK_export(t)}})}};class HQ extends nV{constructor(){super(...arguments),this.paramsConfig=VQ}static type(){return\\\\\\\"exporter\\\\\\\"}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState(Ki.NEVER)}async cook(t){this.setCoreGroup(t[0])}static PARAM_CALLBACK_export(t){t._paramCallbackExport()}async _paramCallbackExport(){const t=(await this.compute()).coreContent();if(!t)return void console.error(\\\\\\\"input invalid\\\\\\\");const e=new WeakMap,n=t.objects();for(let t of n)e.set(t,t.parent);const i=new gs;for(let t of n)i.add(t);(new lQ).parse(i,(t=>{if(t instanceof ArrayBuffer)i=\\\\\\\"scene.glb\\\\\\\",GQ(new Blob([t],{type:\\\\\\\"application/octet-stream\\\\\\\"}),i);else{!function(t,e){GQ(new Blob([t],{type:\\\\\\\"text/plain\\\\\\\"}),e)}(JSON.stringify(t,null,2),\\\\\\\"scene.gltf\\\\\\\")}var i;for(let t of n){const n=e.get(t);n&&n.add(t)}}),{embedImages:!0})}}const jQ=new class extends la{constructor(){super(...arguments),this.makeFacesUnique=aa.BOOLEAN(0),this.addFaceCenterAttribute=aa.BOOLEAN(0,{visibleIf:{makeFacesUnique:1}}),this.addFaceId=aa.BOOLEAN(0,{visibleIf:{makeFacesUnique:1}}),this.transform=aa.BOOLEAN(0,{visibleIf:{makeFacesUnique:1}}),this.scale=aa.FLOAT(1,{visibleIf:{makeFacesUnique:1,transform:1}})}};class WQ extends nV{constructor(){super(...arguments),this.paramsConfig=jQ}static type(){return\\\\\\\"face\\\\\\\"}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState(Ki.FROM_NODE)}cook(t){const e=t[0];this.pv.makeFacesUnique&&(this._makeFacesUnique(e),this.pv.addFaceCenterAttribute&&this._addFaceCenterAttribute(e),this.pv.addFaceId&&this._addFaceId(e),this.pv.transform&&this._transform_faces(e)),this.setCoreGroup(e)}_makeFacesUnique(t){var e;for(let n of t.objects())if(n.isMesh){const t=n.geometry,i=f.chunk((null===(e=t.index)||void 0===e?void 0:e.array)||[],3),s=3*i.length;for(let e of Object.keys(t.attributes)){const n=t.attributes[e],r=n.itemSize,o=new Float32Array(s*r);let a=0;i.forEach((t=>{t.forEach((t=>{for(let e=0;e{const a=t.object(),l=t.coreGeometry();if(a.isMesh&&l){i=l.faces(),l.hasAttrib(e)||l.addNumericAttrib(e,3,-1);for(let t=0;t{const n=t.object(),i=t.coreGeometry();if(n.isMesh&&i){const t=i.faces();i.hasAttrib(e)||i.addNumericAttrib(e,1,-1);for(let n=0;n{const c=t.object(),h=t.coreGeometry();if(c.isMesh&&h){r=h.faces(),h.hasAttrib(e)||h.addNumericAttrib(e,3,-1);for(let t=0;t{t(e)})).catch((t=>{e(t)}))}_load(){return new Promise((async(t,e)=>{const n=await this._urlToLoad(),i=this.extension();if(i==qQ.JSON&&this._options.format==qQ.AUTO)$Q.increment_in_progress_loads_count(),await $Q.wait_for_max_concurrent_loads_queue_freed(),fetch(n).then((async e=>{const n=await e.json();new wJ(this.loadingManager).parse(n,(e=>{$Q.decrement_in_progress_loads_count(),t(this.on_load_success(e.children[0]))}))})).catch((t=>{$Q.decrement_in_progress_loads_count(),e(t)}));else{const s=await this._loaderForFormat();if(s)$Q.increment_in_progress_loads_count(),await $Q.wait_for_max_concurrent_loads_queue_freed(),s.load(n,(e=>{this.on_load_success(e).then((e=>{$Q.decrement_in_progress_loads_count(),t(e)}))}),void 0,(t=>{li.warn(\\\\\\\"error loading\\\\\\\",n,t),$Q.decrement_in_progress_loads_count(),e(t)}));else{e(`format not supported (${i})`)}}}))}async on_load_success(t){const e=this.extension();if(e==qQ.JSON)return[t];const n=t;if(n.isObject3D)switch(e){case YQ.PDB:return this.on_load_succes_pdb(t);case YQ.OBJ:default:return[n]}const i=t;if(i.isBufferGeometry)switch(e){case YQ.DRC:return this.on_load_succes_drc(i);default:return[new B.a(i)]}const s=t;if(null!=s.scene)switch(e){case YQ.GLTF:case YQ.GLB:return this.on_load_succes_gltf(s);default:return[n]}const r=t;if(r.geometryAtoms||r.geometryBonds)switch(e){case YQ.PDB:return this.on_load_succes_pdb(r);default:return[]}return[]}on_load_succes_drc(t){return[new B.a(t,$Q._default_mat_mesh)]}on_load_succes_gltf(t){const e=t.scene;return e.animations=t.animations,[e]}on_load_succes_pdb(t){return[new vs.a(t.geometryAtoms,$Q._default_mat_point),new As.a(t.geometryBonds,$Q._default_mat_line)]}static moduleNamesFromFormat(t,e){switch(t){case qQ.AUTO:return this.moduleNamesFromExt(e);case qQ.DRC:return[Hn.DRACOLoader];case qQ.FBX:return[Hn.FBXLoader];case qQ.JSON:return[];case qQ.GLTF:return[Hn.GLTFLoader];case qQ.GLTF_WITH_DRACO:return[Hn.GLTFLoader,Hn.DRACOLoader];case qQ.OBJ:return[Hn.OBJLoader];case qQ.PDB:return[Hn.PDBLoader];case qQ.PLY:return[Hn.PLYLoader];case qQ.STL:return[Hn.STLLoader]}ls.unreachable(t)}static moduleNamesFromExt(t){switch(t){case YQ.DRC:return[Hn.DRACOLoader];case YQ.FBX:return[Hn.FBXLoader];case YQ.GLTF:return[Hn.GLTFLoader];case YQ.GLB:return[Hn.GLTFLoader,Hn.DRACOLoader];case YQ.OBJ:return[Hn.OBJLoader];case YQ.PDB:return[Hn.PDBLoader];case YQ.PLY:return[Hn.PLYLoader];case YQ.STL:return[Hn.STLLoader]}}async _loaderForFormat(){const t=this._options.format;switch(t){case qQ.AUTO:return this._loaderForExt();case qQ.DRC:return this.loader_for_drc(this._node);case qQ.FBX:return this.loader_for_fbx();case qQ.JSON:return;case qQ.GLTF:return this.loader_for_gltf();case qQ.GLTF_WITH_DRACO:return this.loader_for_glb(this._node);case qQ.OBJ:return this.loader_for_obj();case qQ.PDB:return this.loader_for_pdb();case qQ.PLY:return this.loader_for_ply();case qQ.STL:return this.loader_for_stl()}ls.unreachable(t)}async _loaderForExt(){switch(this.extension().toLowerCase()){case YQ.DRC:return this.loader_for_drc(this._node);case YQ.FBX:return this.loader_for_fbx();case YQ.GLTF:return this.loader_for_gltf();case YQ.GLB:return this.loader_for_glb(this._node);case YQ.OBJ:return this.loader_for_obj();case YQ.PDB:return this.loader_for_pdb();case YQ.PLY:return this.loader_for_ply();case YQ.STL:return this.loader_for_stl()}}loader_for_fbx(){const t=li.modulesRegister.module(Hn.FBXLoader);if(t)return new t(this.loadingManager)}loader_for_gltf(){const t=li.modulesRegister.module(Hn.GLTFLoader);if(t)return new t(this.loadingManager)}static async loader_for_drc(t){const e=li.modulesRegister.module(Hn.DRACOLoader);if(e){const n=new e(this.loadingManager),i=li.libs.root(),s=li.libs.DRACOPath();if(i||s){const e=`${i||\\\\\\\"\\\\\\\"}${s||\\\\\\\"\\\\\\\"}/`;if(t){const n=[\\\\\\\"draco_decoder.js\\\\\\\",\\\\\\\"draco_decoder.wasm\\\\\\\",\\\\\\\"draco_wasm_wrapper.js\\\\\\\"];await this._loadMultipleBlobGlobal({files:n.map((t=>({storedUrl:`${s}/${t}`,fullUrl:`${e}${t}`}))),node:t,error:\\\\\\\"failed to load draco libraries. Make sure to install them to load .glb files\\\\\\\"})}n.setDecoderPath(e)}else n.setDecoderPath(void 0);return n.setDecoderConfig({type:\\\\\\\"js\\\\\\\"}),n}}loader_for_drc(t){return $Q.loader_for_drc(t)}static async loader_for_glb(t){const e=li.modulesRegister.module(Hn.GLTFLoader),n=li.modulesRegister.module(Hn.DRACOLoader);if(e&&n){this.gltf_loader=this.gltf_loader||new e(this.loadingManager),this.draco_loader=this.draco_loader||new n(this.loadingManager);const i=li.libs.root(),s=li.libs.DRACOGLTFPath();if(i||s){const e=`${i||\\\\\\\"\\\\\\\"}${s||\\\\\\\"\\\\\\\"}/`;if(t){const n=[\\\\\\\"draco_decoder.js\\\\\\\",\\\\\\\"draco_decoder.wasm\\\\\\\",\\\\\\\"draco_wasm_wrapper.js\\\\\\\"];await this._loadMultipleBlobGlobal({files:n.map((t=>({storedUrl:`${s}/${t}`,fullUrl:`${e}${t}`}))),node:t,error:\\\\\\\"failed to load draco libraries. Make sure to install them to load .glb files\\\\\\\"})}this.draco_loader.setDecoderPath(e)}else this.draco_loader.setDecoderPath(void 0);return this.gltf_loader.setDRACOLoader(this.draco_loader),this.gltf_loader}}loader_for_glb(t){return $Q.loader_for_glb(t)}loader_for_obj(){const t=li.modulesRegister.module(Hn.OBJLoader);if(t)return new t(this.loadingManager)}loader_for_pdb(){const t=li.modulesRegister.module(Hn.PDBLoader);if(t)return new t(this.loadingManager)}loader_for_ply(){const t=li.modulesRegister.module(Hn.PLYLoader);if(t)return new t(this.loadingManager)}loader_for_stl(){const t=li.modulesRegister.module(Hn.STLLoader);if(t)return new t(this.loadingManager)}static setMaxConcurrentLoadsCount(t){this._maxConcurrentLoadsCountMethod=t}static _init_max_concurrent_loads_count(){return this._maxConcurrentLoadsCountMethod?this._maxConcurrentLoadsCountMethod():Zf.isChrome()?4:1}static _init_concurrent_loads_delay(){return Zf.isChrome()?1:10}static increment_in_progress_loads_count(){this.in_progress_loads_count++}static decrement_in_progress_loads_count(){this.in_progress_loads_count--;const t=this._queue.pop();if(t){const e=this.CONCURRENT_LOADS_DELAY;setTimeout((()=>{t()}),e)}}static async wait_for_max_concurrent_loads_queue_freed(){return this.in_progress_loads_count<=this.MAX_CONCURRENT_LOADS_COUNT?void 0:new Promise((t=>{this._queue.push(t)}))}}$Q._default_mat_mesh=new ws.a,$Q._default_mat_point=new xs.a,$Q._default_mat_line=new Ts.a,$Q.MAX_CONCURRENT_LOADS_COUNT=$Q._init_max_concurrent_loads_count(),$Q.CONCURRENT_LOADS_DELAY=$Q._init_concurrent_loads_delay(),$Q.in_progress_loads_count=0,$Q._queue=[];const JQ=`${Gg}/models/wolf.obj`;class ZQ extends QG{static type(){return\\\\\\\"file\\\\\\\"}cook(t,e){const n=new $Q({url:e.url,format:e.format},this.scene(),this._node);return new Promise((t=>{n.load((e=>{const n=this._on_load(e);t(this.createCoreGroupFromObjects(n))}),(t=>{this._on_error(t,e)}))}))}_on_load(t){t=t.flat();for(let e of t)e.traverse((t=>{this._ensure_geometry_has_index(t),t.matrixAutoUpdate=!1}));return t}_on_error(t,e){var n;null===(n=this.states)||void 0===n||n.error.set(`could not load geometry from ${e.url} (${t})`)}_ensure_geometry_has_index(t){const e=t.geometry;e&&this.createIndexIfNone(e)}}ZQ.DEFAULT_PARAMS={url:JQ,format:qQ.AUTO};const QQ=ZQ.DEFAULT_PARAMS;const KQ=new class extends la{constructor(){super(...arguments),this.url=aa.STRING(QQ.url,{fileBrowse:{type:[Or.GEOMETRY]}}),this.format=aa.STRING(QQ.format,{menuString:{entries:XQ.map((t=>({name:t,value:t})))}}),this.reload=aa.BUTTON(null,{callback:t=>{tK.PARAM_CALLBACK_reload(t)}})}};class tK extends nV{constructor(){super(...arguments),this.paramsConfig=KQ}static type(){return\\\\\\\"file\\\\\\\"}async requiredModules(){for(let t of[this.p.url,this.p.format])t.isDirty()&&await t.compute();const t=jg.extension(this.pv.url||\\\\\\\"\\\\\\\"),e=this.pv.format;return $Q.moduleNamesFromFormat(e,t)}initializeNode(){this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.url],(()=>{const t=this.p.url.rawInput();if(t){const e=t.split(\\\\\\\"/\\\\\\\");return e[e.length-1]}return\\\\\\\"\\\\\\\"}))}))}))}async cook(t){this._operation=this._operation||new ZQ(this.scene(),this.states,this);const e=await this._operation.cook(t,this.pv);this.setCoreGroup(e)}static PARAM_CALLBACK_reload(t){t._paramCallbackReload()}_paramCallbackReload(){this.p.url.setDirty()}}const eK=new class extends la{constructor(){super(...arguments),this.dist=aa.FLOAT(.1,{range:[0,1],rangeLocked:[!0,!1]})}};class nK extends nV{constructor(){super(...arguments),this.paramsConfig=eK}static type(){return\\\\\\\"fuse\\\\\\\"}static displayedInputNames(){return[\\\\\\\"points to fuse together\\\\\\\"]}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState(Ki.FROM_NODE)}cook(t){const e=t[0],n=[];let i;for(let t of e.coreObjects())i=this._fuse_core_object(t),i&&n.push(i);this.setObjects(n)}_fuse_core_object(t){const e=t.object();if(!e)return;const n=t.points(),i=this.pv.dist,s={};for(let t of n){const e=t.position(),n=new p.a(Math.round(e.x/i),Math.round(e.y/i),Math.round(e.z/i)).toArray().join(\\\\\\\"-\\\\\\\");s[n]=s[n]||[],s[n].push(t)}const r=[];if(Object.keys(s).forEach((t=>{r.push(s[t][0])})),e.geometry.dispose(),r.length>0){const t=_r.geometryFromPoints(r,Ls(e.constructor));return t&&(e.geometry=t),e}}}class iK{constructor(t,e,n){this._param_size=t,this._param_hexagon_radius=e,this._param_points_only=n}process(){const t=this._param_hexagon_radius,e=.5*t,n=t,i=Math.cos(Math.PI/6)*this._param_hexagon_radius,s=Math.floor(this._param_size.x/n),r=Math.floor(this._param_size.y/i);let o=[],a=[];for(let t=0;t=1&&(0==r||r==s-1?0==r?a.push([r+1+(t-1)*s,r+(t-1)*s,r+t*s]):a.push([r+t*s,r+(t-1)*s,r-1+t*s]):(a.push([r+t*s,r+(t-1)*s,r-1+t*s]),a.push([r+t*s,r+1+(t-1)*s,r+(t-1)*s])));const l=new S.a;return l.setAttribute(\\\\\\\"position\\\\\\\",new C.a(new Float32Array(o.flat()),3)),this._param_points_only||(l.setIndex(a.flat()),l.computeVertexNormals()),l}}const sK=new p.a(0,1,0);const rK=new class extends la{constructor(){super(...arguments),this.size=aa.VECTOR2([1,1]),this.hexagonRadius=aa.FLOAT(.1,{range:[.001,1],rangeLocked:[!1,!1]}),this.direction=aa.VECTOR3([0,1,0]),this.pointsOnly=aa.BOOLEAN(0)}};class oK extends nV{constructor(){super(...arguments),this.paramsConfig=rK,this._core_transform=new uU}static type(){return\\\\\\\"hexagons\\\\\\\"}initializeNode(){}cook(){if(this.pv.hexagonRadius>0){const t=new iK(this.pv.size,this.pv.hexagonRadius,this.pv.pointsOnly).process();this._core_transform.rotate_geometry(t,sK,this.pv.direction),this.pv.pointsOnly?this.setGeometry(t,Cs.POINTS):this.setGeometry(t)}else this.setObjects([])}}var aK;!function(t){t.ADD_PARENT=\\\\\\\"add_parent\\\\\\\",t.REMOVE_PARENT=\\\\\\\"remove_parent\\\\\\\",t.ADD_CHILD=\\\\\\\"add_child\\\\\\\"}(aK||(aK={}));const lK=[aK.ADD_PARENT,aK.REMOVE_PARENT,aK.ADD_CHILD];class cK extends QG{static type(){return\\\\\\\"hierarchy\\\\\\\"}cook(t,e){const n=t[0],i=lK[e.mode];switch(i){case aK.ADD_PARENT:{const t=this._add_parent_to_core_group(n,e);return this.createCoreGroupFromObjects(t)}case aK.REMOVE_PARENT:{const t=this._remove_parent_from_core_group(n,e);return this.createCoreGroupFromObjects(t)}case aK.ADD_CHILD:{const i=this._add_child_to_core_group(n,t[1],e);return this.createCoreGroupFromObjects(i)}}ls.unreachable(i)}_add_parent_to_core_group(t,e){if(0==e.levels)return t.objects();return[this._add_parent_to_object(t.objects(),e)]}_add_parent_to_object(t,e){let n=new Fn.a;if(n.matrixAutoUpdate=!1,n.add(...t),e.levels>0)for(let t=0;t({name:t,value:e})))}}),this.levels=aa.INTEGER(uK.levels,{range:[0,5],visibleIf:[{mode:lK.indexOf(aK.ADD_PARENT)},{mode:lK.indexOf(aK.REMOVE_PARENT)}]}),this.objectMask=aa.STRING(\\\\\\\"\\\\\\\",{visibleIf:{mode:lK.indexOf(aK.ADD_CHILD)}}),this.debugObjectMask=aa.BOOLEAN(0,{visibleIf:{mode:lK.indexOf(aK.ADD_CHILD)}})}};class pK extends nV{constructor(){super(...arguments),this.paramsConfig=dK}static type(){return\\\\\\\"hierarchy\\\\\\\"}static displayedInputNames(){return[\\\\\\\"geometry to add or remove parents to/from\\\\\\\",\\\\\\\"objects to use as parent or children (optional)\\\\\\\"]}initializeNode(){this.io.inputs.setCount(1,2),this.io.inputs.initInputsClonedState(cK.INPUT_CLONED_STATE),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.mode,this.p.levels,this.p.objectMask],(()=>{const t=lK[this.pv.mode];return hK.includes(t)?`${t} ${this.pv.levels}`:`${t} (with mask: ${this.pv.objectMask})`}))}))}))}cook(t){this._operation=this._operation||new cK(this._scene,this.states);const e=this._operation.cook(t,this.pv);this.setCoreGroup(e)}}const _K=new class extends la{constructor(){super(...arguments),this.texture=aa.OPERATOR_PATH(vi.UV,{nodeSelection:{context:ts.COP}}),this.mult=aa.FLOAT(1)}};class mK extends nV{constructor(){super(...arguments),this.paramsConfig=_K}static type(){return\\\\\\\"heightMap\\\\\\\"}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState(Ki.FROM_NODE)}async cook(t){const e=t[0],n=this.p.texture.found_node();if(n){if(n.context()==ts.COP){const t=n,i=(await t.compute()).texture();for(let t of e.coreObjects())this._set_position_from_data_texture(t,i)}else this.states.error.set(\\\\\\\"found node is not a texture\\\\\\\")}e.computeVertexNormals(),this.setCoreGroup(e)}_set_position_from_data_texture(t,e){var n;const i=this._data_from_texture(e);if(!i)return;const{data:s,resx:r,resy:o}=i,a=s.length/(r*o),l=null===(n=t.coreGeometry())||void 0===n?void 0:n.geometry();if(!l)return;const c=l.getAttribute(\\\\\\\"position\\\\\\\").array,h=l.getAttribute(\\\\\\\"uv\\\\\\\"),u=l.getAttribute(\\\\\\\"normal\\\\\\\");if(null==h)return void this.states.error.set(\\\\\\\"uvs are required\\\\\\\");if(null==u)return void this.states.error.set(\\\\\\\"normals are required\\\\\\\");const d=h.array,p=u.array,_=c.length/3;let m,f,g,v,y,x,b,w=0;for(let t=0;t<_;t++)m=2*t,f=d[m],g=d[m+1],v=Math.floor((r-1)*f),y=Math.floor((o-1)*(1-g)),x=y*r+v,b=s[a*x],w=3*t,c[w+0]+=p[w+0]*b*this.pv.mult,c[w+1]+=p[w+1]*b*this.pv.mult,c[w+2]+=p[w+2]*b*this.pv.mult}_data_from_texture(t){if(t.image)return t.image.data?this._data_from_data_texture(t):this._data_from_default_texture(t)}_data_from_default_texture(t){const e=t.image.width,n=t.image.height;return{data:Pf.data_from_image(t.image).data,resx:e,resy:n}}_data_from_data_texture(t){return{data:t.image.data,resx:t.image.width,resy:t.image.height}}}function fK(t){return Math.atan2(-t.y,Math.sqrt(t.x*t.x+t.z*t.z))}class gK extends S.a{constructor(t,e,n,i,s){super(),this.type=\\\\\\\"PolyhedronBufferGeometry\\\\\\\",this.parameters={vertices:t,indices:e,radius:n,detail:i},n=n||1,i=i||0;const r=[],o=[],a=new Map;function l(t,e,n,i){const s=i+1,r=[];for(let i=0;i<=s;i++){r[i]=[];const o=t.clone().lerp(n,i/s),a=e.clone().lerp(n,i/s),l=s-i;for(let t=0;t<=l;t++)r[i][t]=0===t&&i===s?o:o.clone().lerp(a,t/l)}for(let t=0;t{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.layer])}))}))}cook(t){const e=t[0];for(let t of e.objects())t.layers.set(this.pv.layer);this.setCoreGroup(e)}}const FK=new class extends la{constructor(){super(...arguments),this.length=aa.FLOAT(1,{range:[0,10]}),this.pointsCount=aa.INTEGER(1,{range:[2,100],rangeLocked:[!0,!1]}),this.origin=aa.VECTOR3([0,0,0]),this.direction=aa.VECTOR3([0,1,0])}};class DK extends nV{constructor(){super(...arguments),this.paramsConfig=FK}static type(){return\\\\\\\"line\\\\\\\"}initializeNode(){}cook(){const t=Math.max(2,this.pv.pointsCount),e=new Array(3*t),n=new Array(t),i=this.pv.direction.clone().normalize().multiplyScalar(this.pv.length);for(let s=0;s0&&(n[2*(s-1)]=s-1,n[2*(s-1)+1]=s)}const s=new S.a;s.setAttribute(\\\\\\\"position\\\\\\\",new C.c(e,3)),s.setIndex(n),this.setGeometry(s,Cs.LINE_SEGMENTS)}}const BK=new class extends la{constructor(){super(...arguments),this.distance0=aa.FLOAT(1),this.distance1=aa.FLOAT(2),this.autoUpdate=aa.BOOLEAN(1),this.update=aa.BUTTON(null,{callback:t=>{zK.PARAM_CALLBACK_update(t)}}),this.camera=aa.OPERATOR_PATH(\\\\\\\"/perspective_camera1\\\\\\\",{visibleIf:{autoUpdate:0},dependentOnFoundNode:!1})}};class zK extends nV{constructor(){super(...arguments),this.paramsConfig=BK,this._lod=this._create_LOD()}static type(){return\\\\\\\"lod\\\\\\\"}static displayedInputNames(){return[\\\\\\\"high res\\\\\\\",\\\\\\\"mid res\\\\\\\",\\\\\\\"low res\\\\\\\"]}initializeNode(){this.io.inputs.setCount(1,3),this.io.inputs.initInputsClonedState(Ki.FROM_NODE)}_create_LOD(){const t=new Ss;return t.matrixAutoUpdate=!1,t}cook(t){this._clear_lod(),this._add_level(t[0],0),this._add_level(t[1],this.pv.distance0),this._add_level(t[2],this.pv.distance1),this._lod.autoUpdate=this.pv.autoUpdate,this.setObject(this._lod)}_add_level(t,e){if(t){const n=t.objects();let i;for(let t=0;t{this._apply_material(t,n,e)}));else for(let i of t.objectsFromGroup(e.group))this._apply_material(i,n,e);return t}null===(i=this.states)||void 0===i||i.error.set(`material invalid. (error: '${r.states.error.message()}')`)}else null===(s=this.states)||void 0===s||s.error.set(\\\\\\\"no material node found\\\\\\\")}_swap_textures(t,e){if(e.swapCurrentTex){this._materials_by_uuid.clear();for(let n of t.objectsFromGroup(e.group))if(e.applyToChildren)n.traverse((t=>{const e=n.material;this._materials_by_uuid.set(e.uuid,e)}));else{const t=n.material;this._materials_by_uuid.set(t.uuid,t)}this._materials_by_uuid.forEach(((t,n)=>{this._swap_texture(t,e)}))}}_apply_material(t,e,n){if(n.group&&!yr.isInGroup(n.group,t))return;const i=n.cloneMat?gr.clone(e):e;if(e instanceof F&&i instanceof F)for(let t in e.uniforms)i.uniforms[t]=e.uniforms[t];const s=t;this._old_mat_by_old_new_id.set(i.uuid,s.material),s.material=i,gr.apply_render_hook(t,i),gr.applyCustomMaterials(t,i)}_swap_texture(t,e){if(\\\\\\\"\\\\\\\"==e.texSrc0||\\\\\\\"\\\\\\\"==e.texDest0)return;let n=this._old_mat_by_old_new_id.get(t.uuid);n=n||t;const i=n[e.texSrc0];if(i){t[e.texDest0]=i;const n=t.uniforms;if(n){n[e.texDest0]&&(n[e.texDest0]={value:i})}}}}kK.DEFAULT_PARAMS={group:\\\\\\\"\\\\\\\",assignMat:!0,material:new yi(\\\\\\\"\\\\\\\"),applyToChildren:!0,cloneMat:!1,shareUniforms:!0,swapCurrentTex:!1,texSrc0:\\\\\\\"emissiveMap\\\\\\\",texDest0:\\\\\\\"map\\\\\\\"},kK.INPUT_CLONED_STATE=Ki.FROM_NODE;const UK=kK.DEFAULT_PARAMS;const GK=new class extends la{constructor(){super(...arguments),this.group=aa.STRING(UK.group),this.assignMat=aa.BOOLEAN(UK.assignMat),this.material=aa.NODE_PATH(UK.material.path(),{nodeSelection:{context:ts.MAT},dependentOnFoundNode:!1,visibleIf:{assignMat:1}}),this.applyToChildren=aa.BOOLEAN(UK.applyToChildren,{visibleIf:{assignMat:1}}),this.cloneMat=aa.BOOLEAN(UK.cloneMat,{visibleIf:{assignMat:1}}),this.shareUniforms=aa.BOOLEAN(UK.shareUniforms,{visibleIf:{assignMat:1,cloneMat:1}}),this.swapCurrentTex=aa.BOOLEAN(UK.swapCurrentTex),this.texSrc0=aa.STRING(UK.texSrc0,{visibleIf:{swapCurrentTex:1}}),this.texDest0=aa.STRING(UK.texDest0,{visibleIf:{swapCurrentTex:1}})}};class VK extends nV{constructor(){super(...arguments),this.paramsConfig=GK}static type(){return\\\\\\\"material\\\\\\\"}static displayedInputNames(){return[\\\\\\\"objects to assign material to\\\\\\\"]}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState(kK.INPUT_CLONED_STATE),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.material],(()=>this.p.material.rawInput()))}))}))}async cook(t){this._operation=this._operation||new kK(this._scene,this.states);const e=await this._operation.cook(t,this.pv);this.setCoreGroup(e)}}class HK extends QG{static type(){return\\\\\\\"merge\\\\\\\"}cook(t,e){let n=[];for(let i of t)if(i){const t=i.objects();if(e.compact)for(let e of t)e.traverse((t=>{n.push(t)}));else for(let t of i.objects())n.push(t)}e.compact&&(n=this._makeCompact(n));for(let t of n)t.traverse((t=>{t.matrixAutoUpdate=!1}));return this.createCoreGroupFromObjects(n)}_makeCompact(t){const e=new Map,n=new Map,i=[];for(let s of t)s.traverse((t=>{if(t instanceof Fn.a)return;const s=t;if(s.geometry){const t=Ls(s.constructor);if(i.includes(t)||i.push(t),t){e.get(t)||e.set(t,s.material),h.pushOnArrayAtEntry(n,t,s)}}}));const s=[];return i.forEach((t=>{var i,r;const o=n.get(t);if(o){const n=[];for(let t of o){const e=t.geometry;e.applyMatrix4(t.matrix),n.push(e)}try{const r=_r.mergeGeometries(n);if(r){const n=e.get(t),i=this.createObject(r,t,n);s.push(i)}else null===(i=this.states)||void 0===i||i.error.set(\\\\\\\"merge failed, check that input geometries have the same attributes\\\\\\\")}catch(t){null===(r=this.states)||void 0===r||r.error.set(t.message)}}})),s}}HK.DEFAULT_PARAMS={compact:!1},HK.INPUT_CLONED_STATE=Ki.FROM_NODE;const jK=\\\\\\\"geometry to merge\\\\\\\",WK=HK.DEFAULT_PARAMS;const qK=new class extends la{constructor(){super(...arguments),this.compact=aa.BOOLEAN(WK.compact),this.inputsCount=aa.INTEGER(4,{range:[1,32],rangeLocked:[!0,!1],callback:t=>{XK.PARAM_CALLBACK_setInputsCount(t)}})}};class XK extends nV{constructor(){super(...arguments),this.paramsConfig=qK}static type(){return\\\\\\\"merge\\\\\\\"}static displayedInputNames(){return[jK,jK,jK,jK]}setCompactMode(t){this.p.compact.set(t)}initializeNode(){this.io.inputs.setCount(1,4),this.io.inputs.initInputsClonedState(HK.INPUT_CLONED_STATE),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.compact],(()=>this.pv.compact?\\\\\\\"compact\\\\\\\":\\\\\\\"separate objects\\\\\\\"))})),this.params.addOnSceneLoadHook(\\\\\\\"update inputs\\\\\\\",(()=>{this._callbackUpdateInputsCount()}))}))}cook(t){this._operation=this._operation||new HK(this.scene(),this.states);const e=this._operation.cook(t,this.pv);this.setCoreGroup(e)}_callbackUpdateInputsCount(){this.io.inputs.setCount(1,this.pv.inputsCount),this.emit(Ei.INPUTS_UPDATED)}static PARAM_CALLBACK_setInputsCount(t){t._callbackUpdateInputsCount()}}class YK extends K.a{constructor(t){super(),this.material=t,this.render=function(){},this.hasPositions=!1,this.hasNormals=!1,this.hasColors=!1,this.hasUvs=!1,this.positionArray=null,this.normalArray=null,this.colorArray=null,this.uvArray=null,this.count=0}}YK.prototype.isImmediateRenderObject=!0;class $K extends YK{constructor(t,e,n,i){super(e);const s=this,r=new Float32Array(36),o=new Float32Array(36),a=new Float32Array(36);function l(t,e,n){return t+(e-t)*n}function c(t,e,n,i,c,h,u,d,p,_){const m=(n-u)/(d-u),f=s.normal_cache;r[e+0]=i+m*s.delta,r[e+1]=c,r[e+2]=h,o[e+0]=l(f[t+0],f[t+3],m),o[e+1]=l(f[t+1],f[t+4],m),o[e+2]=l(f[t+2],f[t+5],m),a[e+0]=l(s.palette[3*p+0],s.palette[3*_+0],m),a[e+1]=l(s.palette[3*p+1],s.palette[3*_+1],m),a[e+2]=l(s.palette[3*p+2],s.palette[3*_+2],m)}function h(t,e,n,i,c,h,u,d,p,_){const m=(n-u)/(d-u),f=s.normal_cache;r[e+0]=i,r[e+1]=c+m*s.delta,r[e+2]=h;const g=t+3*s.yd;o[e+0]=l(f[t+0],f[g+0],m),o[e+1]=l(f[t+1],f[g+1],m),o[e+2]=l(f[t+2],f[g+2],m),a[e+0]=l(s.palette[3*p+0],s.palette[3*_+0],m),a[e+1]=l(s.palette[3*p+1],s.palette[3*_+1],m),a[e+2]=l(s.palette[3*p+2],s.palette[3*_+2],m)}function u(t,e,n,i,c,h,u,d,p,_){const m=(n-u)/(d-u),f=s.normal_cache;r[e+0]=i,r[e+1]=c,r[e+2]=h+m*s.delta;const g=t+3*s.zd;o[e+0]=l(f[t+0],f[g+0],m),o[e+1]=l(f[t+1],f[g+1],m),o[e+2]=l(f[t+2],f[g+2],m),a[e+0]=l(s.palette[3*p+0],s.palette[3*_+0],m),a[e+1]=l(s.palette[3*p+1],s.palette[3*_+1],m),a[e+2]=l(s.palette[3*p+2],s.palette[3*_+2],m)}function d(t){const e=3*t;0===s.normal_cache[e]&&(s.normal_cache[e+0]=s.field[t-1]-s.field[t+1],s.normal_cache[e+1]=s.field[t-s.yd]-s.field[t+s.yd],s.normal_cache[e+2]=s.field[t-s.zd]-s.field[t+s.zd])}function p(t,e,n,i,l,p){const m=i+1,f=i+s.yd,g=i+s.zd,v=m+s.yd,y=m+s.zd,x=i+s.yd+s.zd,b=m+s.yd+s.zd;let w=0;const T=s.field[i],A=s.field[m],M=s.field[f],E=s.field[v],S=s.field[g],C=s.field[y],N=s.field[x],L=s.field[b];T=s.maxCount-3&&(s.hasPositions=!0,s.hasNormals=!0,s.enableUvs&&(s.hasUvs=!0),s.enableColors&&(s.hasColors=!0),a(s))}function m(t,e,n){const i=new Float32Array(t.length+n);return i.set(t,0),i.set(e.slice(0,n),t.length),i}this.enableUvs=void 0!==n&&n,this.enableColors=void 0!==i&&i,this.init=function(t){this.resolution=t,this.isolation=80,this.size=t,this.size2=this.size*this.size,this.size3=this.size2*this.size,this.halfsize=this.size/2,this.delta=2/this.size,this.yd=this.size,this.zd=this.size2,this.field=new Float32Array(this.size3),this.normal_cache=new Float32Array(3*this.size3),this.palette=new Float32Array(3*this.size3),this.maxCount=4096,this.count=0,this.hasPositions=!1,this.hasNormals=!1,this.hasColors=!1,this.hasUvs=!1,this.positionArray=new Float32Array(3*this.maxCount),this.normalArray=new Float32Array(3*this.maxCount),this.enableUvs&&(this.uvArray=new Float32Array(2*this.maxCount)),this.enableColors&&(this.colorArray=new Float32Array(3*this.maxCount))},this.begin=function(){this.count=0,this.hasPositions=!1,this.hasNormals=!1,this.hasUvs=!1,this.hasColors=!1},this.end=function(t){if(0!==this.count){for(let t=3*this.count;tthis.size-1&&(_=this.size-1);let m=Math.floor(u-c);m<1&&(m=1);let f=Math.floor(u+c);f>this.size-1&&(f=this.size-1);let g=Math.floor(d-c);g<1&&(g=1);let v,y,x,b,w,T,A,M,E,S,C,N=Math.floor(d+c);for(N>this.size-1&&(N=this.size-1),x=p;x<_;x++)for(w=this.size2*x,M=x/this.size-n,E=M*M,y=m;y0){this.field[b+v]+=C*o;const t=Math.sqrt((v-d)*(v-d)+(y-u)*(y-u)+(x-h)*(x-h))/c,e=1-t*t*t*(t*(6*t-15)+10);this.palette[3*(b+v)+0]+=l.r*e,this.palette[3*(b+v)+1]+=l.g*e,this.palette[3*(b+v)+2]+=l.b*e}},this.addPlaneX=function(t,e){const n=this.size,i=this.yd,s=this.zd,r=this.field;let o,a,l,c,h,u,d,p=n*Math.sqrt(t/e);for(p>n&&(p=n),o=0;o0)for(a=0;an&&(_=n),a=0;a<_;a++)if(u=a/n,c=u*u,h=t/(1e-4+c)-e,h>0)for(d=a*i,o=0;on&&(_=n),l=0;l<_;l++)if(u=l/n,c=u*u,h=t/(1e-4+c)-e,h>0)for(d=s*l,a=0;a=i))for(let e=-1;e<=1;e+=2){const r=e+o;if(!(r<0||r>=i))for(let e=-1;e<=1;e+=2){const o=e+a;if(o<0||o>=i)continue;const u=n[s*o+i*r+l];h++,c+=t*(u-c)/h}}}e[l]=c}},this.reset=function(){for(let t=0;tu?(d=1,p=0):(d=0,p=1);const _=h-d+l,m=u-p+l,f=h-1+2*l,g=u-1+2*l,v=255&o,y=255&a,x=this.perm[v+this.perm[y]]%12,b=this.perm[v+d+this.perm[y+p]]%12,w=this.perm[v+1+this.perm[y+1]]%12;let T=.5-h*h-u*u;T<0?n=0:(T*=T,n=T*T*this.dot(this.grad3[x],h,u));let A=.5-_*_-m*m;A<0?i=0:(A*=A,i=A*A*this.dot(this.grad3[b],_,m));let M=.5-f*f-g*g;return M<0?s=0:(M*=M,s=M*M*this.dot(this.grad3[w],f,g)),70*(n+i+s)}noise3d(t,e,n){let i,s,r,o;const a=(t+e+n)*(1/3),l=Math.floor(t+a),c=Math.floor(e+a),h=Math.floor(n+a),u=1/6,d=(l+c+h)*u,p=t-(l-d),_=e-(c-d),m=n-(h-d);let f,g,v,y,x,b;p>=_?_>=m?(f=1,g=0,v=0,y=1,x=1,b=0):p>=m?(f=1,g=0,v=0,y=1,x=0,b=1):(f=0,g=0,v=1,y=1,x=0,b=1):_b?32:0)+(x>w?16:0)+(b>w?8:0)+(x>T?4:0)+(b>T?2:0)+(w>T?1:0),M=r[A][0]>=3?1:0,E=r[A][1]>=3?1:0,S=r[A][2]>=3?1:0,C=r[A][3]>=3?1:0,N=r[A][0]>=2?1:0,L=r[A][1]>=2?1:0,O=r[A][2]>=2?1:0,P=r[A][3]>=2?1:0,R=r[A][0]>=1?1:0,I=r[A][1]>=1?1:0,F=r[A][2]>=1?1:0,D=r[A][3]>=1?1:0,B=x-M+l,z=b-E+l,k=w-S+l,U=T-C+l,G=x-N+2*l,V=b-L+2*l,H=w-O+2*l,j=T-P+2*l,W=x-R+3*l,q=b-I+3*l,X=w-F+3*l,Y=T-D+3*l,$=x-1+4*l,J=b-1+4*l,Z=w-1+4*l,Q=T-1+4*l,K=255&m,tt=255&f,et=255&g,nt=255&v,it=o[K+o[tt+o[et+o[nt]]]]%32,st=o[K+M+o[tt+E+o[et+S+o[nt+C]]]]%32,rt=o[K+N+o[tt+L+o[et+O+o[nt+P]]]]%32,ot=o[K+R+o[tt+I+o[et+F+o[nt+D]]]]%32,at=o[K+1+o[tt+1+o[et+1+o[nt+1]]]]%32;let lt=.6-x*x-b*b-w*w-T*T;lt<0?c=0:(lt*=lt,c=lt*lt*this.dot4(s[it],x,b,w,T));let ct=.6-B*B-z*z-k*k-U*U;ct<0?h=0:(ct*=ct,h=ct*ct*this.dot4(s[st],B,z,k,U));let ht=.6-G*G-V*V-H*H-j*j;ht<0?u=0:(ht*=ht,u=ht*ht*this.dot4(s[rt],G,V,H,j));let ut=.6-W*W-q*q-X*X-Y*Y;ut<0?d=0:(ut*=ut,d=ut*ut*this.dot4(s[ot],W,q,X,Y));let dt=.6-$*$-J*J-Z*Z-Q*Q;return dt<0?p=0:(dt*=dt,p=dt*dt*this.dot4(s[at],$,J,Z,Q)),27*(c+h+u+d+p)}}var s0;!function(t){t.ADD=\\\\\\\"add\\\\\\\",t.SET=\\\\\\\"set\\\\\\\",t.MULT=\\\\\\\"mult\\\\\\\",t.SUBSTRACT=\\\\\\\"substract\\\\\\\",t.DIVIDE=\\\\\\\"divide\\\\\\\"}(s0||(s0={}));const r0=[s0.ADD,s0.SET,s0.MULT,s0.SUBSTRACT,s0.DIVIDE];const o0=new class extends la{constructor(){super(...arguments),this.amplitude=aa.FLOAT(1),this.tamplitudeAttrib=aa.BOOLEAN(0),this.amplitudeAttrib=aa.STRING(\\\\\\\"amp\\\\\\\",{visibleIf:{tamplitudeAttrib:!0}}),this.freq=aa.VECTOR3([1,1,1]),this.offset=aa.VECTOR3([0,0,0]),this.octaves=aa.INTEGER(3,{range:[1,8],rangeLocked:[!0,!1]}),this.ampAttenuation=aa.FLOAT(.5,{range:[0,1]}),this.freqIncrease=aa.FLOAT(2,{range:[0,10]}),this.seed=aa.INTEGER(0,{range:[0,100],separatorAfter:!0}),this.useNormals=aa.BOOLEAN(0),this.attribName=aa.STRING(\\\\\\\"position\\\\\\\"),this.useRestAttributes=aa.BOOLEAN(0),this.restP=aa.STRING(\\\\\\\"restP\\\\\\\",{visibleIf:{useRestAttributes:!0}}),this.restN=aa.STRING(\\\\\\\"restN\\\\\\\",{visibleIf:{useRestAttributes:!0}}),this.operation=aa.INTEGER(r0.indexOf(s0.ADD),{menu:{entries:r0.map((t=>({name:t,value:r0.indexOf(t)})))}}),this.computeNormals=aa.BOOLEAN(1)}};class a0 extends nV{constructor(){super(...arguments),this.paramsConfig=o0,this._simplex_by_seed=new Map,this._rest_pos=new p.a,this._rest_value2=new d.a,this._noise_value_v=new p.a}static type(){return\\\\\\\"noise\\\\\\\"}static displayedInputNames(){return[\\\\\\\"geometry to add noise to\\\\\\\"]}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState([Ki.FROM_NODE])}setOperation(t){this.p.operation.set(r0.indexOf(t))}async cook(t){const e=t[0],n=e.points(),i=this.pv.attribName;if(!e.hasAttrib(i))return this.states.error.set(`attribute ${i} not found`),void this.cookController.endCook();if(e.attribType(i)!=Bs.NUMERIC)return this.states.error.set(`attribute ${i} is not a numeric attribute`),void this.cookController.endCook();const s=this._get_simplex(),r=this.pv.useNormals&&e.hasAttrib(\\\\\\\"normal\\\\\\\"),o=e.attribSize(this.pv.attribName),a=r0[this.pv.operation],l=this.pv.useRestAttributes,c=this.pv.amplitude,h=this.pv.tamplitudeAttrib;let u,f,g=new p.a;for(let t=0;t{s[3*t.index()+0]=e}));else{let t;for(let e=0;e{s[3*t.index()+1]=e}));else{let t;for(let e=0;e{s[3*t.index()+2]=e}));else{let t;for(let e=0;e{this._update_object(t,e)})):this._update_object(t,e);return n}_update_object(t,e){e.tname&&(t.name=e.name),e.trenderOrder&&(t.renderOrder=e.renderOrder),e.tfrustumCulled&&(t.frustumCulled=e.frustumCulled),e.tmatrixAutoUpdate&&(t.matrixAutoUpdate=e.matrixAutoUpdate),e.tvisible&&(t.visible=e.visible),e.tcastShadow&&(t.castShadow=e.castShadow),e.treceiveShadow&&(t.receiveShadow=e.receiveShadow)}}m0.DEFAULT_PARAMS={applyToChildren:!0,tname:!1,name:\\\\\\\"\\\\\\\",trenderOrder:!1,renderOrder:0,tfrustumCulled:!1,frustumCulled:!0,tmatrixAutoUpdate:!1,matrixAutoUpdate:!1,tvisible:!1,visible:!0,tcastShadow:!1,castShadow:!0,treceiveShadow:!1,receiveShadow:!0},m0.INPUT_CLONED_STATE=Ki.FROM_NODE;const f0=m0.DEFAULT_PARAMS;const g0=new class extends la{constructor(){super(...arguments),this.applyToChildren=aa.BOOLEAN(f0.applyToChildren,{separatorAfter:!0}),this.tname=aa.BOOLEAN(f0.tname),this.name=aa.STRING(f0.name,{visibleIf:{tname:!0},separatorAfter:!0}),this.trenderOrder=aa.BOOLEAN(f0.trenderOrder),this.renderOrder=aa.INTEGER(f0.renderOrder,{visibleIf:{trenderOrder:!0},range:[0,10],rangeLocked:[!1,!1],separatorAfter:!0}),this.tfrustumCulled=aa.BOOLEAN(f0.tfrustumCulled),this.frustumCulled=aa.BOOLEAN(f0.frustumCulled,{visibleIf:{tfrustumCulled:!0},separatorAfter:!0}),this.tmatrixAutoUpdate=aa.BOOLEAN(f0.tmatrixAutoUpdate),this.matrixAutoUpdate=aa.BOOLEAN(f0.matrixAutoUpdate,{visibleIf:{tmatrixAutoUpdate:!0},separatorAfter:!0}),this.tvisible=aa.BOOLEAN(f0.tvisible),this.visible=aa.BOOLEAN(f0.visible,{visibleIf:{tvisible:!0},separatorAfter:!0}),this.tcastShadow=aa.BOOLEAN(f0.tcastShadow),this.castShadow=aa.BOOLEAN(f0.castShadow,{visibleIf:{tcastShadow:!0},separatorAfter:!0}),this.treceiveShadow=aa.BOOLEAN(f0.treceiveShadow),this.receiveShadow=aa.BOOLEAN(f0.receiveShadow,{visibleIf:{treceiveShadow:!0}})}};class v0 extends nV{constructor(){super(...arguments),this.paramsConfig=g0}static type(){return\\\\\\\"objectProperties\\\\\\\"}static displayedInputNames(){return[\\\\\\\"objects to change properties of\\\\\\\"]}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState(m0.INPUT_CLONED_STATE)}async cook(t){this._operation=this._operation||new m0(this.scene(),this.states);const e=this._operation.cook(t,this.pv);this.setCoreGroup(e)}}const y0=new class extends la{};class x0 extends nV{constructor(){super(...arguments),this.paramsConfig=y0,this._input_configs_by_operation_container=new WeakMap}static type(){return Il}initializeNode(){this.io.inputs.initInputsClonedState(Ki.FROM_NODE)}set_output_operation_container(t){this._output_operation_container=t}output_operation_container(){return this._output_operation_container}add_input_config(t,e){let n=this._input_configs_by_operation_container.get(t);n||(n=new Map,this._input_configs_by_operation_container.set(t,n)),n.set(e.operation_input_index,e.node_input_index)}add_operation_container_with_path_param_resolve_required(t){this._operation_containers_requiring_resolve||(this._operation_containers_requiring_resolve=[]),this._operation_containers_requiring_resolve.push(t)}resolve_operation_containers_path_params(){if(this._operation_containers_requiring_resolve)for(let t of this._operation_containers_requiring_resolve)t.resolve_path_params(this)}async cook(t){if(this._output_operation_container){this._output_operation_container.setDirty();const e=await this._output_operation_container.compute(t,this._input_configs_by_operation_container);e&&this.setCoreGroup(e)}}}class b0 extends cf{constructor(t){super(),this._uv_name=t}set_texture_allocations_controller(t){this._texture_allocations_controller=t}handle_globals_node(t,e,n){if(!this._texture_allocations_controller)return;const i=t.io.outputs.namedOutputConnectionPointsByName(e),s=t.glVarName(e);if(this._texture_allocations_controller.variable(e)&&i){const r=i.type(),o=`${r} ${s} = ${this.read_attribute(t,r,e,n)}`;n.addBodyLines(t,[o])}else this.globals_geometry_handler=this.globals_geometry_handler||new Sf,this.globals_geometry_handler.handle_globals_node(t,e,n)}read_attribute(t,e,n,i){if(!this._texture_allocations_controller)return;const s=this._texture_allocations_controller.variable(n);if(!s)return Sf.read_attribute(t,e,n,i);{this.add_particles_sim_uv_attribute(t,i);const e=s.component(),n=s.allocation();if(n){const s=n.textureName(),r=new Af(t,Bo.SAMPLER_2D,s);i.addDefinitions(t,[r]);return`texture2D( ${s}, ${this._uv_name} ).${e}`}}}add_particles_sim_uv_attribute(t,e){const n=new wf(t,Bo.VEC2,b0.UV_ATTRIB),i=new Mf(t,Bo.VEC2,b0.UV_VARYING);e.addDefinitions(t,[n,i],xf.VERTEX),e.addDefinitions(t,[i],xf.FRAGMENT),e.addBodyLines(t,[`${b0.UV_VARYING} = ${b0.UV_ATTRIB}`],xf.VERTEX)}}b0.UV_ATTRIB=\\\\\\\"particles_sim_uv_attrib\\\\\\\",b0.UV_VARYING=\\\\\\\"particles_sim_uv_varying\\\\\\\",b0.PARTICLE_SIM_UV=\\\\\\\"particleUV\\\\\\\";class w0{constructor(t){this.node=t,this._particles_group_objects=[],this._all_shader_names=[],this._all_uniform_names=[],this.globals_handler=new b0(b0.UV_VARYING)}setShadersByName(t){this._shaders_by_name=t,this._all_shader_names=[],this._all_uniform_names=[],this._shaders_by_name.forEach(((t,e)=>{this._all_shader_names.push(e),this._all_uniform_names.push(`texture_${e}`)})),this.reset_render_material()}assign_render_material(){if(this._render_material){for(let t of this._particles_group_objects){const e=t;e.geometry&&(e.material=this._render_material,gr.applyCustomMaterials(e,this._render_material),e.matrixAutoUpdate=!1,e.updateMatrix())}this._render_material.needsUpdate=!0,this.update_render_material_uniforms()}}update_render_material_uniforms(){var t;if(!this._render_material)return;let e,n;for(let i=0;i=e&&(null==this._last_simulated_frame&&(this._last_simulated_frame=e-1),null==this._last_simulated_time&&(this._last_simulated_time=this.node.scene().time()),t>this._last_simulated_frame&&this._compute_simulation(t-this._last_simulated_frame))}_compute_simulation(t=1){if(!this._gpu_compute||null==this._last_simulated_time)return;this.update_simulation_material_uniforms();for(let e=0;et)return void this.node.states.error.set(`max particles is set to (${this.node.pv.texturesSize.x}x${this.node.pv.texturesSize.y}=) ${t}`);this._used_textures_size.copy(this.node.pv.texturesSize)}this._forceTimeDependent(),this._init_particles_uvs(),this.node.renderController.reset_render_material();const n=await li.renderersController.waitForRenderer();if(n?this._renderer=n:this.node.states.error.set(\\\\\\\"no renderer found\\\\\\\"),!this._renderer)return;const i=new A0(this._used_textures_size.x,this._used_textures_size.y,this._renderer);if(i.setDataType(this._data_type()),this._gpu_compute=i,this._gpu_compute){this._last_simulated_frame=void 0,this.variables_by_name.forEach(((t,e)=>{t.renderTargets[0].dispose(),t.renderTargets[1].dispose(),this.variables_by_name.delete(e)})),this._all_variables=[],null===(t=this._shaders_by_name)||void 0===t||t.forEach(((t,e)=>{if(this._gpu_compute){const n=this._gpu_compute.addVariable(this._textureNameForShaderName(e),t,this._created_textures_by_name.get(e));this.variables_by_name.set(e,n),this._all_variables.push(n)}})),null===(e=this.variables_by_name)||void 0===e||e.forEach(((t,e)=>{this._gpu_compute&&this._gpu_compute.setVariableDependencies(t,this._all_variables)})),this._create_texture_render_targets(),this._fill_textures(),this.create_simulation_material_uniforms();var s=this._gpu_compute.init();null!==s&&(console.error(s),this.node.states.error.set(s))}else this.node.states.error.set(\\\\\\\"failed to create the GPUComputationRenderer\\\\\\\")}_forceTimeDependent(){this._graph_node||(this._graph_node=new Mi(this.node.scene(),\\\\\\\"gpu_compute\\\\\\\"),this._graph_node.addGraphInput(this.node.scene().timeController.graphNode),this._graph_node.addPostDirtyHook(\\\\\\\"on_time_change\\\\\\\",this._on_graph_node_dirty.bind(this)))}_on_graph_node_dirty(){this.node.is_on_frame_start()?this.node.setDirty():this.compute_similation_if_required()}materials(){const t=[];return this.variables_by_name.forEach(((e,n)=>{t.push(e.material)})),t}create_simulation_material_uniforms(){const t=this.node.assemblerController,e=null==t?void 0:t.assembler;if(!e&&!this._persisted_texture_allocations_controller)return;const n=[];this.variables_by_name.forEach(((t,e)=>{n.push(t.material)}));const i=this._readonlyAllocations();for(let t of n)t.uniforms[pR.TIME]={value:this.node.scene().time()},t.uniforms[pR.DELTA_TIME]={value:this.node.scene().time()},i&&this._assignReadonlyTextures(t,i);if(e)for(let t of n)for(let n of e.param_configs())t.uniforms[n.uniform_name]=n.uniform;else{const t=this.node.persisted_config.loaded_data();if(t){const e=this.node.persisted_config.uniforms();if(e){const s=t.param_uniform_pairs;for(let t of s){const s=t[0],r=t[1],o=this.node.params.get(s),a=e[r];for(let t of n)t.uniforms[r]=a,i&&this._assignReadonlyTextures(t,i);o&&a&&o.options.setOption(\\\\\\\"callback\\\\\\\",(()=>{for(let t of n)$f.callback(o,t.uniforms[r])}))}}}}}_assignReadonlyTextures(t,e){for(let n of e){const e=n.shaderName(),i=this._created_textures_by_name.get(e);if(i){const n=this._textureNameForShaderName(e);t.uniforms[n]={value:i}}}}update_simulation_material_uniforms(){for(let t of this._all_variables)t.material.uniforms[pR.TIME].value=this.node.scene().time(),t.material.uniforms[pR.DELTA_TIME].value=this._delta_time}_init_particles_uvs(){var t=new Float32Array(2*this._points.length);let e=0;for(var n=0,i=0;i=t.length));s++);const r=b0.UV_ATTRIB;if(this._particles_core_group)for(let e of this._particles_core_group.coreGeometries()){const n=e.geometry(),i=e.markedAsInstance()?A$:C.a;n.setAttribute(r,new i(t,2))}}createdTexturesByName(){return this._created_textures_by_name}_fill_textures(){const t=this._textureAllocationsController();t&&this._created_textures_by_name.forEach(((e,n)=>{const i=t.allocationForShaderName(n);if(!i)return void console.warn(`no allocation found for shader ${n}`);const s=i.variables();if(!s)return void console.warn(\\\\\\\"allocation has no variables\\\\\\\");const r=e.image.data;for(let t of s){const e=t.position();let n=t.name();const i=this._points[0];if(i){if(i.hasAttrib(n)){const t=i.attribSize(n);let s=e;for(let e of this._points){if(1==t){const t=e.attribValue(n);r[s]=t}else e.attribValue(n).toArray(r,s);s+=4}}}}}))}reset_gpu_compute(){this._gpu_compute=void 0,this._simulation_restart_required=!0}set_restart_not_required(){this._simulation_restart_required=!1}reset_gpu_compute_and_set_dirty(){this.reset_gpu_compute(),this.node.setDirty()}reset_particle_groups(){this._particles_core_group=void 0}initialized(){return null!=this._particles_core_group&&null!=this._gpu_compute}_create_texture_render_targets(){this._created_textures_by_name.forEach(((t,e)=>{t.dispose()})),this._created_textures_by_name.clear(),this.variables_by_name.forEach(((t,e)=>{this._gpu_compute&&this._created_textures_by_name.set(e,this._gpu_compute.createTexture())}));const t=this._readonlyAllocations();if(t&&this._gpu_compute)for(let e of t)this._created_textures_by_name.set(e.shaderName(),this._gpu_compute.createTexture())}_textureAllocationsController(){var t;return(null===(t=this.node.assemblerController)||void 0===t?void 0:t.assembler.textureAllocationsController())||this._persisted_texture_allocations_controller}_readonlyAllocations(){var t;return null===(t=this._textureAllocationsController())||void 0===t?void 0:t.readonlyAllocations()}restart_simulation_if_required(){this._simulation_restart_required&&this._restart_simulation()}_restart_simulation(){this._last_simulated_time=void 0,this._create_texture_render_targets();this._get_points()&&(this._fill_textures(),this.variables_by_name.forEach(((t,e)=>{const n=this._created_textures_by_name.get(e);this._gpu_compute&&n&&(this._gpu_compute.renderTexture(n,t.renderTargets[0]),this._gpu_compute.renderTexture(n,t.renderTargets[1]))})))}_get_points(){if(!this._particles_core_group)return;let t=this._particles_core_group.coreGeometries();const e=t[0];if(e){const n=e.markedAsInstance(),i=[];for(let e of t)e.markedAsInstance()==n&&i.push(e);const s=[];for(let t of i)for(let e of t.points())s.push(e);return s}return[]}}class C0{constructor(t,e){if(this._name=t,this._size=e,this._position=-1,this._readonly=!1,!t)throw\\\\\\\"TextureVariable requires a name\\\\\\\"}merge(t){var e;t.readonly()||this.setReadonly(!1),null===(e=t.graphNodeIds())||void 0===e||e.forEach(((t,e)=>{this.addGraphNodeId(e)}))}setReadonly(t){this._readonly=t}readonly(){return this._readonly}setAllocation(t){this._allocation=t}allocation(){return this._allocation}graphNodeIds(){return this._graph_node_ids}addGraphNodeId(t){this._graph_node_ids=this._graph_node_ids||new Map,this._graph_node_ids.set(t,!0)}name(){return this._name}size(){return this._size}setPosition(t){this._position=t}position(){return this._position}component(){return\\\\\\\"xyzw\\\\\\\".split(\\\\\\\"\\\\\\\").splice(this._position,this._size).join(\\\\\\\"\\\\\\\")}static fromJSON(t){return new C0(t.name,t.size)}toJSON(t){const e=[];return this._graph_node_ids&&this._graph_node_ids.forEach(((n,i)=>{const s=t.graph.nodeFromId(i);if(s){const t=s.path();t&&e.push(t)}})),{name:this.name(),size:this.size(),nodes:e}}}class N0{constructor(){this._size=0}addVariable(t){this._variables=this._variables||[],this._variables.push(t),t.setPosition(this._size),t.setAllocation(this),this._size+=t.size()}hasSpaceForVariable(t){return this._size+t.size()<=4}shaderName(){var t;return((null===(t=this.variables())||void 0===t?void 0:t.map((t=>t.name())))||[\\\\\\\"no_variables_allocated\\\\\\\"]).join(\\\\\\\"_SEPARATOR_\\\\\\\")}textureName(){return`texture_${this.shaderName()}`}variables(){return this._variables}variablesForInputNode(t){var e;return null===(e=this._variables)||void 0===e?void 0:e.filter((e=>{var n;return(null===(n=e.graphNodeIds())||void 0===n?void 0:n.has(t.graphNodeId()))||!1}))}inputNamesForNode(t){const e=this.variablesForInputNode(t);if(e)return t.type()==ss.ATTRIBUTE?[gf.INPUT_NAME]:e.map((t=>t.name()))}variable(t){if(this._variables)for(let e of this._variables)if(e.name()==t)return e}static fromJSON(t){const e=new N0;for(let n of t){const t=C0.fromJSON(n);e.addVariable(t)}return e}toJSON(t){return this._variables?this._variables.map((e=>e.toJSON(t))):[]}}const L0=[\\\\\\\"position\\\\\\\",\\\\\\\"normal\\\\\\\",\\\\\\\"color\\\\\\\",\\\\\\\"uv\\\\\\\"];class O0{constructor(){this._writableAllocations=[],this._readonlyAllocations=[]}static _sortNodes(t){const e=t.filter((t=>t.type()==bF.type())),n=t.filter((t=>t.type()!=bF.type())),i=n.map((t=>t.name())).sort(),s=new Map;for(let t of n)s.set(t.name(),t);for(let t of i){const n=s.get(t);n&&e.push(n)}return e}allocateConnectionsFromRootNodes(t,e){const n=[];t=O0._sortNodes(t),e=O0._sortNodes(e);for(let e of t){const t=e.graphNodeId();switch(e.type()){case bF.type():for(let i of e.io.inputs.namedInputConnectionPoints()){if(e.io.inputs.named_input(i.name())){const e=new C0(i.name(),Vo[i.type()]);e.addGraphNodeId(t),n.push(e)}}break;case gf.type():{const i=e,s=i.connected_input_node(),r=i.connected_input_connection_point();if(s&&r){const e=new C0(i.attribute_name,Vo[r.type()]);e.addGraphNodeId(t),n.push(e)}break}}}for(let t of e){const e=t.graphNodeId();switch(t.type()){case AI.type():{const i=t;for(let t of i.io.outputs.used_output_names()){if(L0.includes(t)){const s=i.io.outputs.namedOutputConnectionPointsByName(t);if(s){const i=s.type(),r=new C0(t,Vo[i]);r.addGraphNodeId(e),n.push(r)}}}break}case gf.type():{const i=t,s=i.output_connection_point();if(s){const t=new C0(i.attribute_name,Vo[s.type()]);i.isExporting()||t.setReadonly(!0),t.addGraphNodeId(e),n.push(t)}break}}}this._allocateVariables(n)}_allocateVariables(t){const e=f.sortBy(t,(t=>-t.size())),n=this._ensureVariablesAreUnique(e);for(let t of n)t.readonly()?this._allocateVariable(t,this._readonlyAllocations):this._allocateVariable(t,this._writableAllocations)}_ensureVariablesAreUnique(t){const e=new Map;for(let n of t)h.pushOnArrayAtEntry(e,n.name(),n);const n=[];return e.forEach(((t,e)=>{const i=t[0];n.push(i);for(let e=1;et.shaderName()));return f.uniq(t)}createShaderConfigs(){return[]}allocationForShaderName(t){const e=this._writableAllocations.filter((e=>e.shaderName()==t))[0];return e||this._readonlyAllocations.filter((e=>e.shaderName()==t))[0]}inputNamesForShaderName(t,e){const n=this.allocationForShaderName(e);if(n)return n.inputNamesForNode(t)}variable(t){for(let e of this._writableAllocations){const n=e.variable(t);if(n)return n}for(let e of this._readonlyAllocations){const n=e.variable(t);if(n)return n}}variables(){const t=this._writableAllocations.map((t=>t.variables()||[])).flat(),e=this._writableAllocations.map((t=>t.variables()||[])).flat();return t.concat(e)}hasVariable(t){return this.variables().map((t=>t.name())).includes(t)}static fromJSON(t){const e=new O0;for(let n of t.writable){const t=n[Object.keys(n)[0]],i=N0.fromJSON(t);e._addWritableAllocation(i)}for(let n of t.readonly){const t=n[Object.keys(n)[0]],i=N0.fromJSON(t);e._addReadonlyAllocation(i)}return e}toJSON(t){return{writable:this._writableAllocations.map((e=>({[e.shaderName()]:e.toJSON(t)}))),readonly:this._readonlyAllocations.map((e=>({[e.shaderName()]:e.toJSON(t)})))}}print(t){console.warn(JSON.stringify(this.toJSON(t),[\\\\\\\"\\\\\\\"],2))}}class P0 extends Xf{constructor(t){super(t),this.node=t}toJSON(){const t=this.node.assemblerController;if(!t)return;const e={};this.node.shaders_by_name().forEach(((t,n)=>{e[n]=t}));const n=t.assembler.textureAllocationsController().toJSON(this.node.scene()),i=[],s=new F,r=t.assembler.param_configs();for(let t of r)i.push([t.name(),t.uniform_name]),s.uniforms[t.uniform_name]=t.uniform;const o=this._materialToJson(s,{node:this.node,suffix:\\\\\\\"main\\\\\\\"});return{shaders_by_name:e,texture_allocations:n,param_uniform_pairs:i,uniforms_owner:o||{}}}load(t){li.playerMode()&&(this._loaded_data=t,this.node.init_with_persisted_config())}loaded_data(){return this._loaded_data}shaders_by_name(){if(this._loaded_data){const t=new Map,e=Object.keys(this._loaded_data.shaders_by_name);for(let n of e)t.set(n,this._loaded_data.shaders_by_name[n]);return t}}texture_allocations_controller(){if(this._loaded_data)return O0.fromJSON(this._loaded_data.texture_allocations)}uniforms(){if(this._loaded_data){const t=this._loadMaterial(this._loaded_data.uniforms_owner);return(null==t?void 0:t.uniforms)||{}}}}const R0=new class extends la{constructor(){super(...arguments),this.startFrame=aa.FLOAT(El.START_FRAME,{range:[0,1e3],rangeLocked:[!0,!1]}),this.autoTexturesSize=aa.BOOLEAN(1),this.maxTexturesSize=aa.VECTOR2([1024,1024],{visibleIf:{autoTexturesSize:1}}),this.texturesSize=aa.VECTOR2([64,64],{visibleIf:{autoTexturesSize:0}}),this.dataType=aa.INTEGER(0,{menu:{entries:M0.map(((t,e)=>({value:e,name:t})))}}),this.reset=aa.BUTTON(null,{callback:(t,e)=>{I0.PARAM_CALLBACK_reset(t)}}),this.material=aa.OPERATOR_PATH(\\\\\\\"\\\\\\\",{nodeSelection:{context:ts.MAT},dependentOnFoundNode:!1})}};class I0 extends nV{constructor(){super(...arguments),this.paramsConfig=R0,this._assembler_controller=this._create_assembler_controller(),this.persisted_config=new P0(this),this.globals_handler=new b0(b0.PARTICLE_SIM_UV),this._shaders_by_name=new Map,this.gpuController=new S0(this),this.renderController=new w0(this),this._reset_material_if_dirty_bound=this._reset_material_if_dirty.bind(this),this._children_controller_context=ts.GL}static type(){return\\\\\\\"particlesSystemGpu\\\\\\\"}dispose(){super.dispose(),this.gpuController.dispose()}get assemblerController(){return this._assembler_controller}usedAssembler(){return jn.GL_PARTICLES}_create_assembler_controller(){return li.assemblersRegister.assembler(this,this.usedAssembler())}shaders_by_name(){return this._shaders_by_name}static require_webgl2(){return!0}static PARAM_CALLBACK_reset(t){t.PARAM_CALLBACK_reset()}PARAM_CALLBACK_reset(){this.gpuController.reset_gpu_compute_and_set_dirty()}static displayedInputNames(){return[\\\\\\\"points to emit particles from\\\\\\\"]}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState(Ki.NEVER),this.addPostDirtyHook(\\\\\\\"_reset_material_if_dirty\\\\\\\",this._reset_material_if_dirty_bound)}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}childrenAllowed(){return this.assemblerController?super.childrenAllowed():(this.scene().markAsReadOnly(this),!1)}async _reset_material_if_dirty(){this.p.material.isDirty()&&(this.renderController.reset_render_material(),this.is_on_frame_start()||await this.renderController.init_render_material())}is_on_frame_start(){return this.scene().frame()==this.pv.startFrame}async cook(t){this.gpuController.set_restart_not_required();const e=t[0];this.compileIfRequired(),this.is_on_frame_start()&&this.gpuController.reset_particle_groups(),this.gpuController.initialized()||await this.gpuController.init(e),this.renderController.initialized()||(this.renderController.init_core_group(e),await this.renderController.init_render_material()),this.gpuController.restart_simulation_if_required(),this.gpuController.compute_similation_if_required(),this.is_on_frame_start()?this.setCoreGroup(e):this.cookController.endCook()}async compileIfRequired(){var t;(null===(t=this.assemblerController)||void 0===t?void 0:t.compileRequired())&&await this.run_assembler()}async run_assembler(){const t=this.assemblerController;if(!t)return;const e=this._find_export_nodes();if(e.length>0){const n=e;t.set_assembler_globals_handler(this.globals_handler),t.assembler.set_root_nodes(n),t.assembler.compile(),t.post_compile()}const n=t.assembler.shaders_by_name();this._setShaderNames(n)}_setShaderNames(t){this._shaders_by_name=t,this.gpuController.setShadersByName(this._shaders_by_name),this.renderController.setShadersByName(this._shaders_by_name),this.gpuController.reset_gpu_compute(),this.gpuController.reset_particle_groups()}init_with_persisted_config(){const t=this.persisted_config.shaders_by_name(),e=this.persisted_config.texture_allocations_controller();t&&e&&(this._setShaderNames(t),this.gpuController.set_persisted_texture_allocation_controller(e))}_find_export_nodes(){const t=Of.findAttributeExportNodes(this),e=Of.findOutputNodes(this);if(e.length>1)return this.states.error.set(\\\\\\\"only one output node is allowed\\\\\\\"),[];const n=e[0];return n&&t.push(n),t}}class F0 extends QG{static type(){return\\\\\\\"peak\\\\\\\"}cook(t,e){const n=t[0];let i,s;for(let t of n.objects())t.traverse((t=>{let n;if(null!=(n=t.geometry)){for(s of(i=new _r(n),i.points())){const t=s.normal(),n=s.position().clone().add(t.multiplyScalar(e.amount));s.setPosition(n)}i.geometry().getAttribute(\\\\\\\"position\\\\\\\").needsUpdate=!0}}));return t[0]}}F0.DEFAULT_PARAMS={amount:0};const D0=F0.DEFAULT_PARAMS;const B0=new class extends la{constructor(){super(...arguments),this.amount=aa.FLOAT(D0.amount,{range:[-1,1]})}};class z0 extends nV{constructor(){super(...arguments),this.paramsConfig=B0}static type(){return\\\\\\\"peak\\\\\\\"}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState(Ki.FROM_NODE)}cook(t){this._operation=this._operation||new F0(this.scene(),this.states);const e=this._operation.cook(t,this.pv);this.setCoreGroup(e)}}const k0=new p.a(0,0,1),U0=new p.a(0,0,1),G0=new p.a(0,1,0);class V0 extends QG{constructor(){super(...arguments),this._core_transform=new uU,this._size=new p.a,this._center=new p.a,this._segmentsCount=new d.a(1,1)}static type(){return\\\\\\\"plane\\\\\\\"}cook(t,e){const n=t[0];return n?this._cook_with_input(n,e):this._cook_without_input(e)}_cook_without_input(t){const e=this._create_plane(t.size,t);this._core_transform.rotate_geometry(e,k0,t.direction);const n=this._core_transform.translation_matrix(t.center);return e.applyMatrix4(n),this.createCoreGroupFromGeometry(e)}_cook_with_input(t,e){const n=t.boundingBox();n.getSize(this._size),n.getCenter(this._center);const i=new d.a(this._size.x,this._size.z),s=this._create_plane(i,e);this._core_transform.rotate_geometry(s,U0,G0);const r=this._core_transform.translation_matrix(this._center);return s.applyMatrix4(r),this.createCoreGroupFromGeometry(s)}_create_plane(t,e){return t=t.clone(),e.useSegmentsCount?(this._segmentsCount.x=Math.floor(e.segments.x),this._segmentsCount.y=Math.floor(e.segments.y)):e.stepSize>0&&(this._segmentsCount.x=Math.floor(t.x/e.stepSize),this._segmentsCount.y=Math.floor(t.y/e.stepSize),t.x=this._segmentsCount.x*e.stepSize,t.y=this._segmentsCount.y*e.stepSize),new L(t.x,t.y,this._segmentsCount.x,this._segmentsCount.y)}}V0.DEFAULT_PARAMS={size:new d.a(1,1),useSegmentsCount:!1,stepSize:1,segments:new d.a(1,1),direction:new p.a(0,1,0),center:new p.a(0,0,0)},V0.INPUT_CLONED_STATE=Ki.NEVER;const H0=V0.DEFAULT_PARAMS;const j0=new class extends la{constructor(){super(...arguments),this.size=aa.VECTOR2(H0.size),this.useSegmentsCount=aa.BOOLEAN(H0.useSegmentsCount),this.stepSize=aa.FLOAT(H0.stepSize,{range:[.001,1],rangeLocked:[!1,!1],visibleIf:{useSegmentsCount:0}}),this.segments=aa.VECTOR2(H0.segments,{visibleIf:{useSegmentsCount:1}}),this.direction=aa.VECTOR3(H0.direction),this.center=aa.VECTOR3(H0.center)}};class W0 extends nV{constructor(){super(...arguments),this.paramsConfig=j0}static type(){return\\\\\\\"plane\\\\\\\"}static displayedInputNames(){return[\\\\\\\"geometry to create plane from (optional)\\\\\\\"]}initializeNode(){this.io.inputs.setCount(0,1),this.io.inputs.initInputsClonedState(V0.INPUT_CLONED_STATE)}cook(t){this._operation=this._operation||new V0(this.scene(),this.states);const e=this._operation.cook(t,this.pv);this.setCoreGroup(e)}}class q0 extends QG{static type(){return\\\\\\\"playerCapsule\\\\\\\"}cook(t,e){return this.createCoreGroupFromGeometry(Oy(e))}}q0.DEFAULT_PARAMS={radius:.5,height:1};const X0=q0.DEFAULT_PARAMS;const Y0=new class extends la{constructor(){super(...arguments),this.radius=aa.FLOAT(X0.radius,{range:[0,1],rangeLocked:[!0,!1]}),this.height=aa.FLOAT(X0.height,{range:[0,1],rangeLocked:[!0,!1]})}};class $0 extends nV{constructor(){super(...arguments),this.paramsConfig=Y0}static type(){return\\\\\\\"playerCapsule\\\\\\\"}initializeNode(){this.io.inputs.setCount(0)}cook(t){this._operation=this._operation||new q0(this._scene,this.states);const e=this._operation.cook(t,this.pv);this.setCoreGroup(e)}}const J0=\\\\\\\"position\\\\\\\";const Z0=new class extends la{constructor(){super(...arguments),this.updateX=aa.BOOLEAN(0),this.x=aa.FLOAT(\\\\\\\"@P.x\\\\\\\",{visibleIf:{updateX:1},expression:{forEntities:!0}}),this.updateY=aa.BOOLEAN(0),this.y=aa.FLOAT(\\\\\\\"@P.y\\\\\\\",{visibleIf:{updateY:1},expression:{forEntities:!0}}),this.updateZ=aa.BOOLEAN(0),this.z=aa.FLOAT(\\\\\\\"@P.z\\\\\\\",{visibleIf:{updateZ:1},expression:{forEntities:!0}}),this.updateNormals=aa.BOOLEAN(1)}};class Q0 extends nV{constructor(){super(...arguments),this.paramsConfig=Z0,this._x_arrays_by_geometry_uuid=new Map,this._y_arrays_by_geometry_uuid=new Map,this._z_arrays_by_geometry_uuid=new Map}static type(){return\\\\\\\"point\\\\\\\"}static displayedInputNames(){return[\\\\\\\"points to move\\\\\\\"]}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState(Ki.FROM_NODE)}async cook(t){const e=t[0];await this._eval_expressions_for_core_group(e)}async _eval_expressions_for_core_group(t){const e=t.coreObjects();for(let t=0;t{h[t.index()]=e}));else{let t;for(let e=0;e({name:t,value:e})))}}),this.center=aa.VECTOR3(o1.center.toArray()),this.longitude=aa.FLOAT(o1.longitude,{range:[0,360]}),this.latitude=aa.FLOAT(o1.latitude,{range:[-180,180]}),this.depth=aa.FLOAT(o1.depth,{range:[0,10]})}};class l1 extends nV{constructor(){super(...arguments),this.paramsConfig=a1}static type(){return\\\\\\\"polarTransform\\\\\\\"}static displayedInputNames(){return[\\\\\\\"geometries or objects to transform\\\\\\\"]}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState(r1.INPUT_CLONED_STATE)}cook(t){this._operation=this._operation||new r1(this.scene(),this.states);const e=this._operation.cook(t,this.pv);this.setCoreGroup(e)}}class c1{static accumulated_curve_point_indices(t){let e=[];const n=[];let i,s=null;for(let r=0;r{r[t]=[]})),e.forEach(((e,o)=>{const a=t[e];n.forEach((t=>{const e=a.attribValue(t);let n;n=i[t]>1?e.toArray():[e],n.forEach((e=>{r[t].push(e)}))})),o>0&&(s.push(o-1),s.push(o))}));const o=new S.a;return n.forEach((t=>{const e=i[t],n=r[t];o.setAttribute(t,new C.c(n,e))})),o.setIndex(s),o}static line_segment_to_geometries(t){var e;const n=[],i=new _r(t),s=i.attribNames(),r=i.points(),o=(null===(e=t.getIndex())||void 0===e?void 0:e.array)||[],a=this.accumulated_curve_point_indices(o);if(a.length>0){const e=i.attribSizes();a.forEach(((i,o)=>{t=this.create_line_segment_geometry(r,i,s,e),n.push(t)}))}return n}}class h1{constructor(t,e,n){this.geometry=t,this.geometry1=e,this.geometry0=n}process(){const t=new _r(this.geometry0),e=new _r(this.geometry1),n=t.segments(),i=e.segments();if(0===n.length||0===i.length)return;const s=n.length{const n=l[e];p.push(t[0]),p.push(t[1]),p.push(n[0]+u),p.push(t[1]),p.push(n[1]+u),p.push(n[0]+u)}));f.intersection(r.attribNames(),o.attribNames()).forEach((t=>{const e=r.attribSize(t);let n,i=d.map((e=>e.attribValue(t)));n=1==e?i:i.map((t=>t.toArray())).flat(),this.geometry.setAttribute(t,new C.c(n,e))})),this.geometry.setIndex(p),this.geometry.computeVertexNormals()}}const u1=new p.a(0,0,0),d1=new p.a(1,1,1);const p1=new class extends la{constructor(){super(...arguments),this.radius=aa.FLOAT(1),this.segmentsRadial=aa.INTEGER(8,{range:[3,20],rangeLocked:[!0,!1]}),this.closed=aa.BOOLEAN(0)}};class _1 extends nV{constructor(){super(...arguments),this.paramsConfig=p1,this._core_transform=new uU,this._geometries=[]}static type(){return\\\\\\\"polywire\\\\\\\"}static displayedInputNames(){return[\\\\\\\"lines to create tubes from\\\\\\\"]}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState(Ki.NEVER)}cook(t){const e=t[0];this._geometries=[];for(let t of e.objects())t instanceof As.a&&this._create_tube(t);const n=_r.mergeGeometries(this._geometries);for(let t of this._geometries)t.dispose();if(n){const t=this.createObject(n,Cs.MESH);this.setObject(t)}else this.setObjects([])}_create_tube(t){var e;const n=t.geometry,i=new _r(n).points(),s=null===(e=n.getIndex())||void 0===e?void 0:e.array,r=c1.accumulated_curve_point_indices(s);for(let t of r){const e=t.map((t=>i[t]));this._create_tube_from_points(e)}}_create_tube_from_points(t){if(t.length<=1)return;const e=t.map((t=>t.attribValue(\\\\\\\"position\\\\\\\"))),n=BJ.create(this.pv.radius,this.pv.segmentsRadial),i=[];for(let t of e){const e=t,s=this._core_transform.matrix(e,u1,d1,1,hU),r=n.clone();r.applyMatrix4(s),i.push(r)}for(let t=0;t0){const e=i[t],n=i[t-1],s=this._skin(n,e);this._geometries.push(s)}}_skin(t,e){const n=new S.a;return new h1(n,t,e).process(),n}}const m1=\\\\\\\"dist\\\\\\\";class f1 extends QG{constructor(){super(...arguments),this._matDoubleSideTmpSetter=new tQ,this._raycaster=function(){const t=new WL;return t.firstHitOnly=!0,t}(),this._pointPos=new p.a,this._pointNormal=new p.a}static type(){return\\\\\\\"ray\\\\\\\"}cook(t,e){const n=t[0],i=t[1];return this._ray(n,i,e)}_ray(t,e,n){let i,s;this._matDoubleSideTmpSetter.setCoreGroupMaterialDoubleSided(e),n.addDistAttribute&&(t.hasAttrib(m1)||t.addNumericVertexAttrib(m1,1,-1));const r=t.points();for(let t of r)if(t.getPosition(this._pointPos),i=n.direction,n.useNormals&&(t.getNormal(this._pointNormal),i=this._pointNormal),this._raycaster.set(this._pointPos,i),s=this._raycaster.intersectObjects(e.objects(),!0)[0],s){if(n.transformPoints&&t.setPosition(s.point),n.addDistAttribute){const e=this._pointPos.distanceTo(s.point);console.log(e),t.setAttribValue(m1,e)}n.transferFaceNormals&&s.face&&t.setNormal(s.face.normal)}return this._matDoubleSideTmpSetter.restoreMaterialSideProperty(e),t}}f1.DEFAULT_PARAMS={useNormals:!0,direction:new p.a(0,-1,0),transformPoints:!0,transferFaceNormals:!0,addDistAttribute:!1},f1.INPUT_CLONED_STATE=[Ki.FROM_NODE,Ki.NEVER];const g1=f1.DEFAULT_PARAMS;const v1=new class extends la{constructor(){super(...arguments),this.useNormals=aa.BOOLEAN(g1.useNormals),this.direction=aa.VECTOR3(g1.direction.toArray(),{visibleIf:{useNormals:0}}),this.transformPoints=aa.BOOLEAN(g1.transformPoints),this.transferFaceNormals=aa.BOOLEAN(g1.transferFaceNormals),this.addDistAttribute=aa.BOOLEAN(g1.addDistAttribute)}};class y1 extends nV{constructor(){super(...arguments),this.paramsConfig=v1}static type(){return\\\\\\\"ray\\\\\\\"}static displayedInputNames(){return[\\\\\\\"geometry to move\\\\\\\",\\\\\\\"geometry to ray onto\\\\\\\"]}initializeNode(){this.io.inputs.setCount(2),this.io.inputs.initInputsClonedState(f1.INPUT_CLONED_STATE)}cook(t){this._operation=this._operation||new f1(this._scene,this.states);const e=this._operation.cook(t,this.pv);this.setCoreGroup(e)}}const x1={color:{value:null},tDiffuse:{value:null},textureMatrix:{value:null},opacity:{value:.5}},b1=\\\\\\\"uniform mat4 textureMatrix;\\\\nvarying vec4 vUv;\\\\n\\\\nvoid main() {\\\\n\\\\n\\\\tvUv = textureMatrix * vec4( position, 1.0 );\\\\n\\\\tgl_Position = projectionMatrix * modelViewMatrix * vec4( position, 1.0 );\\\\n\\\\n}\\\\\\\",w1=\\\\\\\"uniform vec3 color;\\\\nuniform sampler2D tDiffuse;\\\\nvarying vec4 vUv;\\\\nuniform float opacity;\\\\n\\\\nfloat blendOverlay( float base, float blend ) {\\\\n\\\\n\\\\treturn( base < 0.5 ? ( 2.0 * base * blend ) : ( 1.0 - 2.0 * ( 1.0 - base ) * ( 1.0 - blend ) ) );\\\\n\\\\n}\\\\n\\\\nvec3 blendOverlay( vec3 base, vec3 blend ) {\\\\n\\\\n\\\\treturn vec3( blendOverlay( base.r, blend.r ), blendOverlay( base.g, blend.g ), blendOverlay( base.b, blend.b ) );\\\\n\\\\n}\\\\n\\\\nvoid main() {\\\\n\\\\n\\\\tvec4 base = texture2DProj( tDiffuse, vUv );\\\\n\\\\tgl_FragColor = vec4( blendOverlay( base.rgb, color ), opacity );\\\\n\\\\n}\\\\\\\",T1={minFilter:w.V,magFilter:w.V,format:w.ic};class A1 extends B.a{constructor(t,e){super(),this.geometry=t,this._options=e,this.type=\\\\\\\"Reflector\\\\\\\",this.reflectorPlane=new Y.a,this.normal=new p.a,this.reflectorWorldPosition=new p.a,this.cameraWorldPosition=new p.a,this.rotationMatrix=new A.a,this.lookAtPosition=new p.a(0,0,-1),this.clipPlane=new _.a,this.view=new p.a,this.target=new p.a,this.q=new _.a,this.textureMatrix=new A.a,this.virtualCamera=new tt.a,this.onBeforeRender=this._onBeforeRender.bind(this),this._onWindowResizeBound=this._onWindowResize.bind(this);const{width:n,height:i}=this._getRendererSize(this._options.renderer);this.renderTarget=new Q(n,i,T1),Object(On.i)(n)&&Object(On.i)(i)||(this.renderTarget.texture.generateMipmaps=!1),this._coreRenderBlur=new wG(new d.a(n,i)),this.material=new F({uniforms:I.clone(x1),fragmentShader:w1,vertexShader:b1}),this.material.uniforms.tDiffuse.value=this.renderTarget.texture,this.material.uniforms.color.value=this._options.color,this.material.uniforms.textureMatrix.value=this.textureMatrix,this.material.uniforms.opacity.value=this._options.opacity,this.material.transparent=this._options.opacity<1,this._addWindowResizeEvent()}_addWindowResizeEvent(){window.addEventListener(\\\\\\\"resize\\\\\\\",this._onWindowResizeBound.bind(this),!1)}_removeWindowResizeEvent(){window.removeEventListener(\\\\\\\"resize\\\\\\\",this._onWindowResizeBound.bind(this),!1)}_onWindowResize(){this.traverseAncestors((t=>{t.parent||t.uuid!=this._options.scene.uuid&&this._removeWindowResizeEvent()}));const{width:t,height:e}=this._getRendererSize(this._options.renderer);this.renderTarget.setSize(t,e),this._coreRenderBlur.setSize(t,e)}_getRendererSize(t){const e=t.domElement;return{width:e.width*this._options.pixelRatio,height:e.height*this._options.pixelRatio}}_onBeforeRender(t,e,n,i,s,r){if(!this._options.active)return;const o=n;if(this.reflectorWorldPosition.setFromMatrixPosition(this.matrixWorld),this.cameraWorldPosition.setFromMatrixPosition(o.matrixWorld),this.rotationMatrix.extractRotation(this.matrixWorld),this.normal.set(0,0,1),this.normal.applyMatrix4(this.rotationMatrix),this.view.subVectors(this.reflectorWorldPosition,this.cameraWorldPosition),!(this.view.dot(this.normal)>0)){this.view.reflect(this.normal).negate(),this.view.add(this.reflectorWorldPosition),this.rotationMatrix.extractRotation(o.matrixWorld),this.lookAtPosition.set(0,0,-1),this.lookAtPosition.applyMatrix4(this.rotationMatrix),this.lookAtPosition.add(this.cameraWorldPosition),this.target.subVectors(this.reflectorWorldPosition,this.lookAtPosition),this.target.reflect(this.normal).negate(),this.target.add(this.reflectorWorldPosition),this.virtualCamera.position.copy(this.view),this.virtualCamera.up.set(0,1,0),this.virtualCamera.up.applyMatrix4(this.rotationMatrix),this.virtualCamera.up.reflect(this.normal),this.virtualCamera.lookAt(this.target),this.virtualCamera.far=o.far,this.virtualCamera.updateMatrixWorld(),this.virtualCamera.projectionMatrix.copy(o.projectionMatrix),this.textureMatrix.set(.5,0,0,.5,0,.5,0,.5,0,0,.5,.5,0,0,0,1),this.textureMatrix.multiply(this.virtualCamera.projectionMatrix),this.textureMatrix.multiply(this.virtualCamera.matrixWorldInverse),this.textureMatrix.multiply(this.matrixWorld),this.reflectorPlane.setFromNormalAndCoplanarPoint(this.normal,this.reflectorWorldPosition),this.reflectorPlane.applyMatrix4(this.virtualCamera.matrixWorldInverse),this.clipPlane.set(this.reflectorPlane.normal.x,this.reflectorPlane.normal.y,this.reflectorPlane.normal.z,this.reflectorPlane.constant);var a=this.virtualCamera.projectionMatrix;this.q.x=(Math.sign(this.clipPlane.x)+a.elements[8])/a.elements[0],this.q.y=(Math.sign(this.clipPlane.y)+a.elements[9])/a.elements[5],this.q.z=-1,this.q.w=(1+a.elements[10])/a.elements[14],this.clipPlane.multiplyScalar(2/this.clipPlane.dot(this.q)),a.elements[2]=this.clipPlane.x,a.elements[6]=this.clipPlane.y,a.elements[10]=this.clipPlane.z+1-this._options.clipBias,a.elements[14]=this.clipPlane.w,this.renderTarget.texture.encoding=t.outputEncoding,this.visible=!1;var l=t.getRenderTarget(),c=t.xr.enabled,h=t.shadowMap.autoUpdate;if(t.xr.enabled=!1,t.shadowMap.autoUpdate=!1,t.setRenderTarget(this.renderTarget),t.state.buffers.depth.setMask(!0),!1===t.autoClear&&t.clear(),t.render(e,this.virtualCamera),this._options.tblur){const e=this._options.blur*this._options.pixelRatio,n=e*this._options.verticalBlurMult;if(this._coreRenderBlur.applyBlur(this.renderTarget,t,e,n),this._options.tblur2){const e=this._options.blur2*this._options.pixelRatio,n=e*this._options.verticalBlur2Mult;this._coreRenderBlur.applyBlur(this.renderTarget,t,e,n)}}t.xr.enabled=c,t.shadowMap.autoUpdate=h,t.setRenderTarget(l);var u=o.viewport;void 0!==u&&t.state.viewport(u),this.visible=!0}}}class M1 extends QG{static type(){return\\\\\\\"reflector\\\\\\\"}async cook(t,e){const n=t[0],i=[],s=await li.renderersController.firstRenderer();if(!s)return this.createCoreGroupFromObjects(i);const r=n.objectsWithGeo();for(let t of r){const n=new A1(t.geometry,{clipBias:e.clipBias,renderer:s,scene:this.scene().threejsScene(),pixelRatio:e.pixelRatio,color:e.color,opacity:e.opacity,active:e.active,tblur:e.tblur,blur:e.blur,verticalBlurMult:e.verticalBlurMult,tblur2:e.tblur2,blur2:e.blur2,verticalBlur2Mult:e.verticalBlur2Mult});n.position.copy(t.position),n.rotation.copy(t.rotation),n.scale.copy(t.scale),n.updateMatrix(),i.push(n)}return this.createCoreGroupFromObjects(i)}}M1.DEFAULT_PARAMS={active:!0,clipBias:.003,color:new D.a(1,1,1),opacity:1,pixelRatio:1,tblur:!1,blur:1,verticalBlurMult:1,tblur2:!1,blur2:1,verticalBlur2Mult:1},M1.INPUT_CLONED_STATE=Ki.NEVER;const E1=M1.DEFAULT_PARAMS;const S1=new class extends la{constructor(){super(...arguments),this.active=aa.BOOLEAN(E1.active),this.clipBias=aa.FLOAT(E1.clipBias),this.color=aa.COLOR(E1.color.toArray()),this.opacity=aa.FLOAT(E1.opacity),this.pixelRatio=aa.INTEGER(E1.pixelRatio,{range:[1,4],rangeLocked:[!0,!1]}),this.tblur=aa.BOOLEAN(E1.tblur),this.blur=aa.FLOAT(E1.blur,{visibleIf:{tblur:1}}),this.verticalBlurMult=aa.FLOAT(E1.verticalBlurMult,{visibleIf:{tblur:1}}),this.tblur2=aa.BOOLEAN(E1.tblur2,{visibleIf:{tblur:1}}),this.blur2=aa.FLOAT(E1.blur2,{visibleIf:{tblur:1,tblur2:1}}),this.verticalBlur2Mult=aa.FLOAT(E1.verticalBlur2Mult,{visibleIf:{tblur:1,tblur2:1}})}};class C1 extends nV{constructor(){super(...arguments),this.paramsConfig=S1}static type(){return\\\\\\\"reflector\\\\\\\"}static displayedInputNames(){return[\\\\\\\"geometry to create a reflector from\\\\\\\"]}initializeNode(){this.io.inputs.setCount(0,1),this.io.inputs.initInputsClonedState(M1.INPUT_CLONED_STATE)}async cook(t){this._operation=this._operation||new M1(this._scene,this.states);const e=await this._operation.cook(t,this.pv);this.setCoreGroup(e)}}var N1=n(85);var L1;!function(t){t.POINTS_COUNT=\\\\\\\"pointsCount\\\\\\\",t.SEGMENT_LENGTH=\\\\\\\"segmentLength\\\\\\\"}(L1||(L1={}));const O1=[L1.POINTS_COUNT,L1.SEGMENT_LENGTH];var P1;!function(t){t.CENTRIPETAL=\\\\\\\"centripetal\\\\\\\",t.CHORDAL=\\\\\\\"chordal\\\\\\\",t.CATMULLROM=\\\\\\\"catmullrom\\\\\\\"}(P1||(P1={}));const R1=[P1.CENTRIPETAL,P1.CHORDAL,P1.CATMULLROM];const I1=new class extends la{constructor(){super(...arguments),this.method=aa.INTEGER(O1.indexOf(L1.POINTS_COUNT),{menu:{entries:O1.map(((t,e)=>({name:t,value:e})))}}),this.curveType=aa.INTEGER(R1.indexOf(P1.CATMULLROM),{range:[0,2],rangeLocked:[!0,!0],menu:{entries:R1.map(((t,e)=>({name:t,value:e})))}}),this.tension=aa.FLOAT(.01,{range:[0,1],rangeLocked:[!0,!0]}),this.pointsCount=aa.INTEGER(100,{visibleIf:{method:O1.indexOf(L1.POINTS_COUNT)},range:[1,1e3],rangeLocked:[!0,!1]}),this.segmentLength=aa.FLOAT(1,{visibleIf:{method:O1.indexOf(L1.SEGMENT_LENGTH)}})}};class F1 extends nV{constructor(){super(...arguments),this.paramsConfig=I1}static type(){return\\\\\\\"resample\\\\\\\"}initializeNode(){this.io.inputs.setCount(1)}cook(t){const e=t[0],n=[];if(this.pv.pointsCount>=2){const t=e.coreObjects();for(let e=0;ei[t])),n=this._create_curve_from_points(e);n&&o.push(n)}const a=hr(o);return this.createObject(a,Cs.LINE_SEGMENTS)}_create_curve_from_points(t){if(t.length<=1)return;const e=t.map((t=>t.attribValue(\\\\\\\"position\\\\\\\"))),n=R1[this.pv.curveType],i=this.pv.tension,s=new N1.a(e,!1,n,i),r=this._get_points_from_curve(s);let o=[];const a=[];for(let t=0;t0&&(a.push(t-1),a.push(t))}const l=new S.a;return l.setAttribute(\\\\\\\"position\\\\\\\",new C.c(o.flat(),3)),l.setIndex(a),l}_get_points_from_curve(t){const e=O1[this.pv.method];switch(e){case L1.POINTS_COUNT:return t.getSpacedPoints(Math.max(2,this.pv.pointsCount));case L1.SEGMENT_LENGTH:var n=t.getLength(),i=0!==this.pv.segmentLength?1+n/this.pv.segmentLength:2;return i=Math.max(2,i),t.getSpacedPoints(i)}ls.unreachable(e)}}class D1 extends QG{static type(){return\\\\\\\"restAttributes\\\\\\\"}cook(t,e){const n=t[0].objectsWithGeo();return e.tposition&&this._create_rest_attribute(n,e.position,e.restP),e.tnormal&&this._create_rest_attribute(n,e.normal,e.restN),this.createCoreGroupFromObjects(n)}_create_rest_attribute(t,e,n){for(let i of t){const t=i.geometry;if(t){const i=t.getAttribute(e);i&&t.setAttribute(n,i.clone())}}}}D1.DEFAULT_PARAMS={tposition:!0,position:\\\\\\\"position\\\\\\\",restP:\\\\\\\"restP\\\\\\\",tnormal:!0,normal:\\\\\\\"normal\\\\\\\",restN:\\\\\\\"restN\\\\\\\"};const B1=D1.DEFAULT_PARAMS;const z1=new class extends la{constructor(){super(...arguments),this.tposition=aa.BOOLEAN(B1.tposition),this.position=aa.STRING(B1.position,{visibleIf:{tposition:!0}}),this.restP=aa.STRING(B1.restP,{visibleIf:{tposition:!0}}),this.tnormal=aa.BOOLEAN(B1.tnormal),this.normal=aa.STRING(B1.normal,{visibleIf:{tnormal:!0}}),this.restN=aa.STRING(B1.restN,{visibleIf:{tnormal:!0}})}};class k1 extends nV{constructor(){super(...arguments),this.paramsConfig=z1}static type(){return\\\\\\\"restAttributes\\\\\\\"}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState([Ki.FROM_NODE])}cook(t){this._operation=this._operation||new D1(this.scene(),this.states);const e=this._operation.cook(t,this.pv);this.setCoreGroup(e)}}class U1 extends QG{constructor(){super(...arguments),this._core_transform=new uU}static type(){return\\\\\\\"roundedBox\\\\\\\"}cook(t,e){const n=t[0],i=n?this._cook_with_input(n,e):this._cook_without_input(e);return this.createCoreGroupFromGeometry(i)}_cook_without_input(t){const e=t.size,n=new Ly(e.x,e.y,e.z,t.divisions,t.bevel);return n.translate(t.center.x,t.center.y,t.center.z),n.computeVertexNormals(),n}_cook_with_input(t,e){const n=e.divisions,i=t.boundingBox(),s=i.max.clone().sub(i.min),r=i.max.clone().add(i.min).multiplyScalar(.5),o=new Ly(s.x,s.y,s.z,n,e.bevel),a=this._core_transform.translation_matrix(r);return o.applyMatrix4(a),o}}U1.DEFAULT_PARAMS={size:new p.a(1,1,1),divisions:2,bevel:.1,center:new p.a(0,0,0)},U1.INPUT_CLONED_STATE=Ki.NEVER;const G1=U1.DEFAULT_PARAMS;const V1=new class extends la{constructor(){super(...arguments),this.size=aa.VECTOR3(G1.size),this.divisions=aa.INTEGER(G1.divisions,{range:[1,10],rangeLocked:[!0,!1]}),this.bevel=aa.FLOAT(G1.bevel,{range:[0,1],rangeLocked:[!0,!1]}),this.center=aa.VECTOR3(G1.center)}};class H1 extends nV{constructor(){super(...arguments),this.paramsConfig=V1}static type(){return\\\\\\\"roundedBox\\\\\\\"}static displayedInputNames(){return[\\\\\\\"geometry to create bounding box from (optional)\\\\\\\"]}initializeNode(){this.io.inputs.setCount(0,1),this.io.inputs.initInputsClonedState(U1.INPUT_CLONED_STATE)}cook(t){this._operation=this._operation||new U1(this._scene,this.states);const e=this._operation.cook(t,this.pv);this.setCoreGroup(e)}}class j1 extends QG{static type(){return\\\\\\\"scatter\\\\\\\"}async cook(t,e){const n=t[0];let i=n.faces();const s=[];let r=0;const o=new Map;for(let t of i){const e=t.area();o.set(t.index(),e)}const a=f.sortBy(i,(t=>o.get(t.index())||-1));let l=0;for(let t of a)r+=o.get(t.index()),s[l]=r,l++;const c=[];let h=[];e.transferAttributes&&(h=n.attribNamesMatchingMask(e.attributesToTransfer));const u=new Map,d=new Map;for(let t of h)u.set(t,[]),d.set(t,n.attribSize(t));const p=new yX,_=2454*e.seed%Number.MAX_SAFE_INTEGER;await p.startWithCount(e.pointsCount,(t=>{const e=rr.randFloat(_+t)*r;for(let t=0;te/(t-1)));e.addIdnAttribute&&g.setAttribute(\\\\\\\"idn\\\\\\\",new C.a(new Float32Array(i),1))}const v=this.createObject(g,Cs.POINTS);return this.createCoreGroupFromObjects([v])}}j1.DEFAULT_PARAMS={pointsCount:100,seed:0,transferAttributes:!0,attributesToTransfer:\\\\\\\"normal\\\\\\\",addIdAttribute:!0,addIdnAttribute:!0},j1.INPUT_CLONED_STATE=Ki.FROM_NODE;const W1=j1.DEFAULT_PARAMS;const q1=new class extends la{constructor(){super(...arguments),this.pointsCount=aa.INTEGER(W1.pointsCount,{range:[0,100],rangeLocked:[!0,!1]}),this.seed=aa.INTEGER(W1.seed,{range:[0,100],rangeLocked:[!1,!1]}),this.transferAttributes=aa.BOOLEAN(W1.transferAttributes),this.attributesToTransfer=aa.STRING(W1.attributesToTransfer,{visibleIf:{transferAttributes:1}}),this.addIdAttribute=aa.BOOLEAN(W1.addIdAttribute),this.addIdnAttribute=aa.BOOLEAN(W1.addIdnAttribute)}};class X1 extends nV{constructor(){super(...arguments),this.paramsConfig=q1}static type(){return\\\\\\\"scatter\\\\\\\"}static displayedInputNames(){return[\\\\\\\"geometry to scatter points onto\\\\\\\"]}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState(Ki.NEVER)}async cook(t){this._operation=this._operation||new j1(this.scene(),this.states);const e=await this._operation.cook(t,this.pv);this.setCoreGroup(e)}}var Y1;!function(t){t.MATRIX=\\\\\\\"matrix\\\\\\\",t.AXIS=\\\\\\\"axis\\\\\\\"}(Y1||(Y1={}));const $1=[Y1.MATRIX,Y1.AXIS];var J1;!function(t){t.BBOX_CENTER=\\\\\\\"bbox center\\\\\\\",t.BBOX_CENTER_OFFSET=\\\\\\\"bbox center offset\\\\\\\",t.CUSTOM=\\\\\\\"custom\\\\\\\"}(J1||(J1={}));const Z1=[J1.BBOX_CENTER,J1.BBOX_CENTER_OFFSET,J1.CUSTOM];class Q1 extends QG{constructor(){super(...arguments),this._m4=new A.a,this._axisNormalized=new p.a,this._center=new p.a,this._pointPos=new p.a,this._axisPlane=new Y.a,this._pointOnPlane=new p.a,this._delta=new p.a,this._deltaNormalized=new p.a,this._offset=new p.a}static type(){return\\\\\\\"shear\\\\\\\"}cook(t,e){const n=t[0].objects();return this._applyShear(n,e),t[0]}_applyShear(t,e){const n=$1[e.mode];switch(n){case Y1.MATRIX:return this._applyMatrixShear(t,e);case Y1.AXIS:return this._applyAxisShear(t,e)}ls.unreachable(n)}_applyMatrixShear(t,e){this._m4.makeShear(e.xy,e.xz,e.yx,e.yz,e.zx,e.zy);for(let e of t){const t=e.geometry;t&&t.applyMatrix4(this._m4)}}_applyAxisShear(t,e){this._axisNormalized.copy(e.axis),this._axisNormalized.normalize();for(let n of t){const t=n.geometry;if(t){this._getAxisModeCenter(t,e),this._axisPlane.setFromNormalAndCoplanarPoint(e.planeAxis,this._center);const n=new _r(t).points();for(let t of n){t.getPosition(this._pointPos),this._axisPlane.projectPoint(this._pointPos,this._pointOnPlane),this._delta.copy(this._pointOnPlane).sub(this._pointPos);const n=this._delta.length();this._deltaNormalized.copy(this._delta).normalize(),this._offset.copy(this._axisNormalized).multiplyScalar(e.axisAmount*n),this._delta.dot(e.planeAxis)>0&&this._offset.multiplyScalar(-1),this._pointPos.add(this._offset),t.setPosition(this._pointPos)}}}}_getAxisModeCenter(t,e){const n=Z1[e.centerMode];switch(n){case J1.BBOX_CENTER:return this._getAxisModeCenterBbox(t,e);case J1.BBOX_CENTER_OFFSET:return this._getAxisModeCenterBboxOffset(t,e);case J1.CUSTOM:return this._getAxisModeCenterCustom(e)}ls.unreachable(n)}_getAxisModeCenterBbox(t,e){t.computeBoundingBox();const n=t.boundingBox;n?n.getCenter(this._center):this._center.set(0,0,0)}_getAxisModeCenterBboxOffset(t,e){this._getAxisModeCenterBbox(t,e),this._center.add(e.centerOffset)}_getAxisModeCenterCustom(t){return this._center.copy(t.center)}}var K1;Q1.DEFAULT_PARAMS={mode:$1.indexOf(Y1.AXIS),xy:0,xz:0,yx:0,yz:0,zx:0,zy:0,centerMode:Z1.indexOf(J1.BBOX_CENTER),centerOffset:new p.a(0,0,0),center:new p.a(0,0,0),planeAxis:new p.a(0,0,1),axis:new p.a(0,1,0),axisAmount:0},Q1.INPUT_CLONED_STATE=Ki.FROM_NODE,function(t){t.SHEAR=\\\\\\\"shear\\\\\\\",t.TRANSFORM=\\\\\\\"transform\\\\\\\",t.UV_LAYOUT=\\\\\\\"uvLayout\\\\\\\",t.UV_TRANSFORM=\\\\\\\"uvTransform\\\\\\\",t.UV_UNWRAP=\\\\\\\"uvUnwrap\\\\\\\"}(K1||(K1={}));const t2=Q1.DEFAULT_PARAMS;const e2=new class extends la{constructor(){super(...arguments),this.mode=aa.INTEGER(t2.mode,{menu:{entries:$1.map(((t,e)=>({name:t,value:e})))}}),this.xy=aa.FLOAT(t2.xy,{visibleIf:{mode:$1.indexOf(Y1.MATRIX)}}),this.xz=aa.FLOAT(t2.xz,{visibleIf:{mode:$1.indexOf(Y1.MATRIX)}}),this.yx=aa.FLOAT(t2.yx,{visibleIf:{mode:$1.indexOf(Y1.MATRIX)}}),this.yz=aa.FLOAT(t2.yz,{visibleIf:{mode:$1.indexOf(Y1.MATRIX)}}),this.zx=aa.FLOAT(t2.zx,{visibleIf:{mode:$1.indexOf(Y1.MATRIX)}}),this.zy=aa.FLOAT(t2.zy,{visibleIf:{mode:$1.indexOf(Y1.MATRIX)}}),this.centerMode=aa.INTEGER(t2.centerMode,{visibleIf:{mode:$1.indexOf(Y1.AXIS)},menu:{entries:Z1.map(((t,e)=>({name:t,value:e})))}}),this.centerOffset=aa.VECTOR3(t2.centerOffset.toArray(),{visibleIf:{mode:$1.indexOf(Y1.AXIS),centerMode:Z1.indexOf(J1.BBOX_CENTER_OFFSET)}}),this.center=aa.VECTOR3(t2.center.toArray(),{visibleIf:{mode:$1.indexOf(Y1.AXIS),centerMode:Z1.indexOf(J1.CUSTOM)}}),this.planeAxis=aa.VECTOR3(t2.planeAxis.toArray(),{visibleIf:{mode:$1.indexOf(Y1.AXIS)}}),this.axis=aa.VECTOR3(t2.axis.toArray(),{visibleIf:{mode:$1.indexOf(Y1.AXIS)}}),this.axisAmount=aa.FLOAT(t2.axisAmount,{range:[-1,1],visibleIf:{mode:$1.indexOf(Y1.AXIS)}})}};class n2 extends nV{constructor(){super(...arguments),this.paramsConfig=e2}static type(){return K1.SHEAR}static displayedInputNames(){return[\\\\\\\"geometries or objects to transform\\\\\\\"]}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState(Q1.INPUT_CLONED_STATE)}setMode(t){this.p.mode.set($1.indexOf(t))}cook(t){this._operation=this._operation||new Q1(this.scene(),this.states);const e=this._operation.cook(t,this.pv);this.setCoreGroup(e)}}const i2=new class extends la{};class s2 extends nV{constructor(){super(...arguments),this.paramsConfig=i2}static type(){return\\\\\\\"skin\\\\\\\"}static displayedInputNames(){return[\\\\\\\"lines to create polygons from\\\\\\\",\\\\\\\"if used, lines from both inputs will be used\\\\\\\"]}initializeNode(){this.io.inputs.setCount(1,2)}cook(t){switch(f.compact(this.io.inputs.inputs()).length){case 1:return this.process_one_input(t);case 2:return this.process_two_inputs(t);default:return this.states.error.set(\\\\\\\"inputs count not valid\\\\\\\")}}process_one_input(t){const e=t[0],n=this._get_line_segments(e),i=[];if(n){const t=n[0];if(t){const e=c1.line_segment_to_geometries(t.geometry);e.forEach(((t,n)=>{if(n>0){const s=e[n-1],r=this._skin(s,t);i.push(r)}}))}}this.setGeometries(i)}process_two_inputs(t){const e=t[0],n=t[1],i=this._get_line_segments(e),s=this._get_line_segments(n),r=f.sortBy([i,s],(t=>-t.length)),o=r[0],a=r[1],l=[];o.forEach(((t,e)=>{const n=a[e];if(null!=t&&null!=n){const e=t.geometry,i=n.geometry,s=this._skin(e,i);l.push(s)}})),this.setGeometries(l)}_get_line_segments(t){return t.objects().filter((t=>t.isLineSegments))}_skin(t,e){const n=new S.a;return new h1(n,t,e).process(),n}}var r2;!function(t){t.X=\\\\\\\"x\\\\\\\",t.Y=\\\\\\\"y\\\\\\\",t.Z=\\\\\\\"z\\\\\\\"}(r2||(r2={}));const o2=[r2.X,r2.Y,r2.Z];class a2 extends QG{constructor(){super(...arguments),this._pointPos=new p.a,this._positions=[],this._indicesByPos=new Map,this._indexDest=new Map,this._debugActive=!1}static type(){return\\\\\\\"sort\\\\\\\"}cook(t,e){const n=t[0],i=n.objectsWithGeo();for(let t of i)this._sortObject(t,e);return n}_debug(t){this._debugActive}_sortObject(t,e){const n=new yr(t,0).points(),i=t.geometry.getIndex();if(!i)return void console.warn(\\\\\\\"geometry cannot be sorted since it has no index\\\\\\\");const s=i.array;this._positions=new Array(n.length),this._indicesByPos.clear(),this._indexDest.clear();const r=o2[e.axis];let o=0,a=0;for(let t of n){switch(t.getPosition(this._pointPos),r){case r2.X:o=this._pointPos.x;break;case r2.Y:o=this._pointPos.y;break;case r2.Z:o=this._pointPos.z}this._positions[a]=o,h.pushOnArrayAtEntry(this._indicesByPos,o,t.index()),a++}let l=this._positions.sort(((t,e)=>t-e));e.invert&&l.reverse();const c=new Array(n.length);a=0;const u=f.uniq(l);for(let t of u){const e=this._indicesByPos.get(t);if(e)for(let t of e)c[a]=t,this._indexDest.set(t,a),a++}const d=new Array(s.length);for(let t=0;t ${e}`),null!=e)for(let n=0;n({name:t,value:e})))}}),this.invert=aa.BOOLEAN(l2.invert)}};class h2 extends nV{constructor(){super(...arguments),this.paramsConfig=c2}static type(){return\\\\\\\"sort\\\\\\\"}static displayedInputNames(){return[\\\\\\\"geometry to sort\\\\\\\"]}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState([Ki.FROM_NODE])}cook(t){this._operation=this._operation||new a2(this._scene,this.states);const e=this._operation.cook(t,this.pv);this.setCoreGroup(e)}}const u2=new class extends la{constructor(){super(...arguments),this.startFrame=aa.INTEGER(El.START_FRAME)}};class d2 extends rV{constructor(){super(...arguments),this.paramsConfig=u2,this._last_simulated_frame=null,this.childrenDisplayController=new aV(this,{dependsOnDisplayNode:!1}),this.displayNodeController=new Lm(this,{onDisplayNodeRemove:()=>{},onDisplayNodeSet:()=>{},onDisplayNodeUpdate:()=>{}},{dependsOnDisplayNode:!1})}static type(){return\\\\\\\"solver\\\\\\\"}initializeNode(){this.io.inputs.setCount(0,4),this.io.inputs.initInputsClonedState(Ki.NEVER),this.addGraphInput(this.scene().timeController.graphNode)}previousFrameCoreGroup(){return this._previousFrameCoreGroup}async cook(t){this.pv.startFrame==this.scene().frame()&&this._reset(),this.computeSolverIfRequired()}_reset(){this._previousFrameCoreGroup=void 0,this._last_simulated_frame=null}computeSolverIfRequired(){const t=this.scene().frame(),e=this.pv.startFrame;t>=e&&(null==this._last_simulated_frame&&(this._last_simulated_frame=e-1),t>this._last_simulated_frame&&this._computeSolverMultipleTimes(t-this._last_simulated_frame))}_computeSolverMultipleTimes(t=1){for(let e=0;e({name:t,value:f2[t]})))}}),this.radius=aa.FLOAT(y2.radius,{visibleIf:{type:f2.default}}),this.resolution=aa.VECTOR2(y2.resolution,{visibleIf:{type:f2.default}}),this.open=aa.BOOLEAN(y2.open,{visibleIf:{type:f2.default}}),this.phiStart=aa.FLOAT(y2.phiStart,{range:[0,2*Math.PI],visibleIf:{type:f2.default,open:!0}}),this.phiLength=aa.FLOAT(\\\\\\\"$PI*2\\\\\\\",{range:[0,2*Math.PI],visibleIf:{type:f2.default,open:!0}}),this.thetaStart=aa.FLOAT(y2.thetaStart,{range:[0,Math.PI],visibleIf:{type:f2.default,open:!0}}),this.thetaLength=aa.FLOAT(\\\\\\\"$PI\\\\\\\",{range:[0,Math.PI],visibleIf:{type:f2.default,open:!0}}),this.detail=aa.INTEGER(y2.detail,{range:[0,5],rangeLocked:[!0,!1],visibleIf:{type:f2.isocahedron}}),this.center=aa.VECTOR3(y2.center)}};class b2 extends nV{constructor(){super(...arguments),this.paramsConfig=x2}static type(){return\\\\\\\"sphere\\\\\\\"}initializeNode(){this.io.inputs.setCount(0,1),this.io.inputs.initInputsClonedState(v2.INPUT_CLONED_STATE)}cook(t){this._operation=this._operation||new v2(this.scene(),this.states);const e=this._operation.cook(t,this.pv);this.setCoreGroup(e)}}const w2=new class extends la{constructor(){super(...arguments),this.attribType=aa.INTEGER(zs.indexOf(Bs.NUMERIC),{menu:{entries:ks}}),this.attribName=aa.STRING(\\\\\\\"\\\\\\\")}};class T2 extends nV{constructor(){super(...arguments),this.paramsConfig=w2,this._new_objects=[]}static type(){return\\\\\\\"split\\\\\\\"}static displayedInputNames(){return[\\\\\\\"geometry to split in multiple objects\\\\\\\"]}initializeNode(){this.io.inputs.setCount(1)}async cook(t){const e=t[0];this._new_objects=[],\\\\\\\"\\\\\\\"!=this.pv.attribName&&this._split_core_group(e),this.setObjects(this._new_objects)}async _split_core_group(t){const e=t.coreObjects();for(let t of e)this._split_core_object(t)}_split_core_object(t){let e=t.coreGeometry(),n=this.pv.attribName,i=new Map;if(e){const s=t.object(),r=e.pointsFromGeometry(),o=r[0];if(o){if(o.attribSize(n)!=Us.FLOAT&&!o.isAttribIndexed(n))return void this.states.error.set(`attrib '${n}' must be a float or a string`);let t;if(o.isAttribIndexed(n))for(let e of r)t=e.indexedAttribValue(n),h.pushOnArrayAtEntry(i,t,e);else for(let e of r)t=e.attribValue(n),h.pushOnArrayAtEntry(i,t,e)}const a=Ls(s.constructor);i.forEach(((t,e)=>{const i=_r.geometryFromPoints(t,a);if(i){const t=this.createObject(i,a);yr.addAttribute(t,n,e),this._new_objects.push(t)}}))}}}const A2=new A.a,M2=new K.a,E2=new p.a;class S2 extends J.a{constructor(){super(),this.uuid=On.h(),this.name=\\\\\\\"\\\\\\\",this.type=\\\\\\\"Geometry\\\\\\\",this.vertices=[],this.colors=[],this.faces=[],this.faceVertexUvs=[[]],this.morphTargets=[],this.morphNormals=[],this.skinWeights=[],this.skinIndices=[],this.lineDistances=[],this.boundingBox=null,this.boundingSphere=null,this.elementsNeedUpdate=!1,this.verticesNeedUpdate=!1,this.uvsNeedUpdate=!1,this.normalsNeedUpdate=!1,this.colorsNeedUpdate=!1,this.lineDistancesNeedUpdate=!1,this.groupsNeedUpdate=!1}applyMatrix4(t){const e=(new G.a).getNormalMatrix(t);for(let e=0,n=this.vertices.length;e0)for(let t=0;t0&&(this.normalsNeedUpdate=!0)}computeFlatVertexNormals(){this.computeFaceNormals();for(let t=0,e=this.faces.length;t0&&(this.normalsNeedUpdate=!0)}computeMorphNormals(){for(let t=0,e=this.faces.length;t=0;t--){const e=r[t];this.faces.splice(e,1);for(let t=0,n=this.faceVertexUvs.length;t0,a=e.vertexNormals.length>0,l=1!==e.color.r||1!==e.color.g||1!==e.color.b,p=e.vertexColors.length>0;let _=0;if(_=c(_,0,0),_=c(_,1,i),_=c(_,2,s),_=c(_,3,r),_=c(_,4,o),_=c(_,5,a),_=c(_,6,l),_=c(_,7,p),n.push(_),n.push(e.a,e.b,e.c),n.push(e.materialIndex),r){const e=this.faceVertexUvs[0][t];n.push(d(e[0]),d(e[1]),d(e[2]))}if(o&&n.push(h(e.normal)),a){const t=e.vertexNormals;n.push(h(t[0]),h(t[1]),h(t[2]))}if(l&&n.push(u(e.color)),p){const t=e.vertexColors;n.push(u(t[0]),u(t[1]),u(t[2]))}}function c(t,e,n){return n?t|1<0&&(t.data.colors=r),a.length>0&&(t.data.uvs=[a]),t.data.faces=n,t}clone(){return(new S2).copy(this)}copy(t){this.vertices=[],this.colors=[],this.faces=[],this.faceVertexUvs=[[]],this.morphTargets=[],this.morphNormals=[],this.skinWeights=[],this.skinIndices=[],this.lineDistances=[],this.boundingBox=null,this.boundingSphere=null,this.name=t.name;const e=t.vertices;for(let t=0,n=e.length;t0){const n=new Float32Array(3*t.normals.length);e.setAttribute(\\\\\\\"normal\\\\\\\",new C.a(n,3).copyVector3sArray(t.normals))}if(t.colors.length>0){const n=new Float32Array(3*t.colors.length);e.setAttribute(\\\\\\\"color\\\\\\\",new C.a(n,3).copyColorsArray(t.colors))}if(t.uvs.length>0){const n=new Float32Array(2*t.uvs.length);e.setAttribute(\\\\\\\"uv\\\\\\\",new C.a(n,2).copyVector2sArray(t.uvs))}if(t.uvs2.length>0){const n=new Float32Array(2*t.uvs2.length);e.setAttribute(\\\\\\\"uv2\\\\\\\",new C.a(n,2).copyVector2sArray(t.uvs2))}e.groups=t.groups;for(const n in t.morphTargets){const i=[],s=t.morphTargets[n];for(let t=0,e=s.length;t0){const n=new C.c(4*t.skinIndices.length,4);e.setAttribute(\\\\\\\"skinIndex\\\\\\\",n.copyVector4sArray(t.skinIndices))}if(t.skinWeights.length>0){const n=new C.c(4*t.skinWeights.length,4);e.setAttribute(\\\\\\\"skinWeight\\\\\\\",n.copyVector4sArray(t.skinWeights))}return null!==t.boundingSphere&&(e.boundingSphere=t.boundingSphere.clone()),null!==t.boundingBox&&(e.boundingBox=t.boundingBox.clone()),e}computeTangents(){console.error(\\\\\\\"THREE.Geometry: .computeTangents() has been removed.\\\\\\\")}computeLineDistances(){console.error(\\\\\\\"THREE.Geometry: .computeLineDistances() has been removed. Use THREE.Line.computeLineDistances() instead.\\\\\\\")}applyMatrix(t){return console.warn(\\\\\\\"THREE.Geometry: .applyMatrix() has been renamed to .applyMatrix4().\\\\\\\"),this.applyMatrix4(t)}dispose(){this.dispatchEvent({type:\\\\\\\"dispose\\\\\\\"})}static createBufferGeometryFromObject(t){let e=new S.a;const n=t.geometry;if(t.isPoints||t.isLine){const t=new C.c(3*n.vertices.length,3),i=new C.c(3*n.colors.length,3);if(e.setAttribute(\\\\\\\"position\\\\\\\",t.copyVector3sArray(n.vertices)),e.setAttribute(\\\\\\\"color\\\\\\\",i.copyColorsArray(n.colors)),n.lineDistances&&n.lineDistances.length===n.vertices.length){const t=new C.c(n.lineDistances.length,1);e.setAttribute(\\\\\\\"lineDistance\\\\\\\",t.copyArray(n.lineDistances))}null!==n.boundingSphere&&(e.boundingSphere=n.boundingSphere.clone()),null!==n.boundingBox&&(e.boundingBox=n.boundingBox.clone())}else t.isMesh&&(e=n.toBufferGeometry());return e}}S2.prototype.isGeometry=!0;class C2{constructor(){this.vertices=[],this.normals=[],this.colors=[],this.uvs=[],this.uvs2=[],this.groups=[],this.morphTargets={},this.skinWeights=[],this.skinIndices=[],this.boundingBox=null,this.boundingSphere=null,this.verticesNeedUpdate=!1,this.normalsNeedUpdate=!1,this.colorsNeedUpdate=!1,this.uvsNeedUpdate=!1,this.groupsNeedUpdate=!1}computeGroups(t){const e=[];let n,i,s;const r=t.faces;for(i=0;i0,r=i[1]&&i[1].length>0,o=t.morphTargets,a=o.length;let l;if(a>0){l=[];for(let t=0;t0){u=[];for(let t=0;t0&&0===e.length&&console.error(\\\\\\\"THREE.DirectGeometry: Faceless geometries are not supported.\\\\\\\");for(let t=0;t0;)this.smooth(t);return t.computeFaceNormals(),t.computeVertexNormals(),e?t.toBufferGeometry():t},function(){var t=[\\\\\\\"a\\\\\\\",\\\\\\\"b\\\\\\\",\\\\\\\"c\\\\\\\"];function e(t,e,n){return n[Math.min(t,e)+\\\\\\\"_\\\\\\\"+Math.max(t,e)]}function n(t,e,n,i,s,r){var o,a=Math.min(t,e),l=Math.max(t,e),c=a+\\\\\\\"_\\\\\\\"+l;c in i?o=i[c]:(o={a:n[a],b:n[l],newEdge:null,faces:[]},i[c]=o);o.faces.push(s),r[t].edges.push(o),r[e].edges.push(o)}function i(t,e,n,i,s){t.push(new N2(e,n,i,void 0,void 0,s))}function s(t,e){return Math.abs(e-t)/2+Math.min(t,e)}function r(t,e,n,i){t.push([e.clone(),n.clone(),i.clone()])}L2.prototype.smooth=function(o){var a,l,c,h,u,_,m,f,g,v,y,x,b,w=new p.a,T=[];a=o.vertices,l=o.faces;var A,M,E,S,C,N,L,O,P,R,I,F,D,B,z=void 0!==(c=o.faceVertexUvs)[0]&&c[0].length>0;if(z)for(var k=0;k3&&(O=3/(8*_)),P=1-_*O,R=O,_<=2&&2==_&&(P=3/4,R=1/8),B=D.clone().multiplyScalar(P),w.set(0,0,0),k=0;k<_;k++)A=(I=F[k]).a!==D?I.a:I.b,w.add(A);w.multiplyScalar(R),B.add(w),b.push(B)}h=b.concat(x);var U,G,V,H,j,W,q,X=b.length;u=[];var Y=new d.a,$=new d.a,J=new d.a;for(m=0,f=l.length;m{z2.PARAM_CALLBACK_reset(t)}})}};class z2 extends nV{constructor(){super(...arguments),this.paramsConfig=B2}static type(){return ns.INPUT}initializeNode(){this.io.inputs.setCount(0),this.lifecycle.add_on_add_hook((()=>{this.set_parent_input_dependency()}))}async cook(){const t=this.pv.input,e=this.parent();if(e){if(e.io.inputs.has_input(t)){const n=await e.containerController.requestInputContainer(t);if(n){const t=n.coreContent();if(t)return void this.setCoreGroup(t)}}else this.states.error.set(`parent has no input ${t}`);this.cookController.endCook()}else this.states.error.set(\\\\\\\"subnet input has no parent\\\\\\\")}static PARAM_CALLBACK_reset(t){t.set_parent_input_dependency()}set_parent_input_dependency(){this._current_parent_input_graph_node&&this.removeGraphInput(this._current_parent_input_graph_node);const t=this.parent();t&&(this._current_parent_input_graph_node=t.io.inputs.inputGraphNode(this.pv.input),this.addGraphInput(this._current_parent_input_graph_node))}}var k2=n(82);class U2 extends jg{constructor(t,e,n){super(t,e,n)}load(t){return new Promise((async(e,n)=>{const i=new k2.a(this.loadingManager),s=await this._urlToLoad();i.load(s,(i=>{try{const n=this._onLoaded(i,t);e(n)}catch(t){n([])}}))}))}parse(t,e){const n=new k2.a(this.loadingManager).parse(t);return this._onLoaded(n,e)}_onLoaded(t,e){const n=t.paths,i=new Fn.a;for(let t=0;t{const i=await n.load(e);for(let t of i.children)this._ensure_geometry_has_index(t);t(this.createCoreGroupFromObjects(i.children))}))}_ensure_geometry_has_index(t){const e=t.geometry;e&&this.createIndexIfNone(e)}}V2.DEFAULT_PARAMS={url:G2,drawFillShapes:!0,fillShapesWireframe:!1,drawStrokes:!0,strokesWireframe:!1};const H2=V2.DEFAULT_PARAMS;const j2=new class extends la{constructor(){super(...arguments),this.url=aa.STRING(H2.url,{fileBrowse:{type:[Or.SVG]}}),this.reload=aa.BUTTON(null,{callback:(t,e)=>{W2.PARAM_CALLBACK_reload(t)}}),this.drawFillShapes=aa.BOOLEAN(H2.drawFillShapes),this.fillShapesWireframe=aa.BOOLEAN(H2.fillShapesWireframe),this.drawStrokes=aa.BOOLEAN(H2.drawStrokes),this.strokesWireframe=aa.BOOLEAN(H2.strokesWireframe)}};class W2 extends nV{constructor(){super(...arguments),this.paramsConfig=j2}static type(){return\\\\\\\"svg\\\\\\\"}async requiredModules(){return[Hn.SVGLoader]}initializeNode(){this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.url],(()=>{const t=this.pv.url;if(t){const e=t.split(\\\\\\\"/\\\\\\\");return e[e.length-1]}return\\\\\\\"\\\\\\\"}))}))}))}async cook(t){this._operation=this._operation||new V2(this.scene(),this.states,this);const e=await this._operation.cook(t,this.pv);this.setCoreGroup(e)}static PARAM_CALLBACK_reload(t){t.param_callback_reload()}param_callback_reload(){this.p.url.setDirty()}}const q2=\\\\\\\"geometry to switch to\\\\\\\";const X2=new class extends la{constructor(){super(...arguments),this.input=aa.INTEGER(0,{range:[0,3],rangeLocked:[!0,!0]})}};class Y2 extends nV{constructor(){super(...arguments),this.paramsConfig=X2}static type(){return\\\\\\\"switch\\\\\\\"}static displayedInputNames(){return[q2,q2,q2,q2]}initializeNode(){this.io.inputs.setCount(0,4),this.io.inputs.initInputsClonedState(Ki.NEVER),this.cookController.disallowInputsEvaluation()}async cook(){const t=this.pv.input;if(this.io.inputs.has_input(t)){const e=await this.containerController.requestInputContainer(t);if(e){const t=e.coreContent();if(t)return void this.setCoreGroup(t)}}else this.states.error.set(`no input ${t}`);this.cookController.endCook()}}class $2 extends gK{constructor(t,e,n){super([1,1,1,-1,-1,1,-1,1,-1,1,-1,-1],[2,1,0,0,3,2,1,3,0,2,3,1],t,e,n),this.type=\\\\\\\"TetrahedronBufferGeometry\\\\\\\",this.parameters={radius:t,detail:e}}}const J2=new class extends la{constructor(){super(...arguments),this.radius=aa.FLOAT(1),this.detail=aa.INTEGER(0,{range:[0,10],rangeLocked:[!0,!1]}),this.pointsOnly=aa.BOOLEAN(0),this.center=aa.VECTOR3([0,0,0])}};class Z2 extends nV{constructor(){super(...arguments),this.paramsConfig=J2}static type(){return\\\\\\\"tetrahedron\\\\\\\"}cook(){const t=this.pv.pointsOnly,e=new $2(this.pv.radius,this.pv.detail,t);if(e.translate(this.pv.center.x,this.pv.center.y,this.pv.center.z),t){const t=this.createObject(e,Cs.POINTS);this.setObject(t)}else e.computeVertexNormals(),this.setGeometry(e)}}class Q2 extends cJ{constructor(t,e={}){const n=e.font;if(!n||!n.isFont)return new S.a;const i=n.generateShapes(t,e.size);e.depth=void 0!==e.height?e.height:50,void 0===e.bevelThickness&&(e.bevelThickness=10),void 0===e.bevelSize&&(e.bevelSize=8),void 0===e.bevelEnabled&&(e.bevelEnabled=!1),super(i,e),this.type=\\\\\\\"TextGeometry\\\\\\\"}}var K2=n(48);class t9 extends Bf.a{constructor(t){super(t)}load(t,e,n,i){const s=this,r=new Df.a(this.manager);r.setPath(this.path),r.setRequestHeader(this.requestHeader),r.setWithCredentials(s.withCredentials),r.load(t,(function(t){let n;try{n=JSON.parse(t)}catch(e){console.warn(\\\\\\\"THREE.FontLoader: typeface.js support is being deprecated. Use typeface.json instead.\\\\\\\"),n=JSON.parse(t.substring(65,t.length-2))}const i=s.parse(n);e&&e(i)}),n,i)}parse(t){return new e9(t)}}class e9{constructor(t){this.type=\\\\\\\"Font\\\\\\\",this.data=t}generateShapes(t,e=100){const n=[],i=function(t,e,n){const i=Array.from(t),s=e/n.resolution,r=(n.boundingBox.yMax-n.boundingBox.yMin+n.underlineThickness)*s,o=[];let a=0,l=0;for(let t=0;t{const i=await this._loadTTFLoader();i&&i.load(t,(t=>{const n=this._font_loader.parse(t);e(n)}),void 0,(()=>{n()}))}))}_loadJSON(t){return new Promise(((e,n)=>{this._font_loader.load(t,(t=>{e(t)}),void 0,(()=>{n()}))}))}async _loadTTFLoader(){const t=await li.modulesRegister.module(Hn.TTFLoader);if(t)return new t(this.loadingManager)}static async loadSVGLoader(){const t=await li.modulesRegister.module(Hn.SVGLoader);if(t)return t}}var s9;!function(t){t.MESH=\\\\\\\"mesh\\\\\\\",t.FLAT=\\\\\\\"flat\\\\\\\",t.LINE=\\\\\\\"line\\\\\\\",t.STROKE=\\\\\\\"stroke\\\\\\\"}(s9||(s9={}));const r9=[s9.MESH,s9.FLAT,s9.LINE,s9.STROKE],o9=\\\\\\\"failed to generate geometry. Try to remove some characters\\\\\\\";const a9=new class extends la{constructor(){super(...arguments),this.font=aa.STRING(\\\\\\\"https://raw.githubusercontent.com/polygonjs/polygonjs-assets/master/fonts/droid_sans_regular.typeface.json\\\\\\\",{fileBrowse:{type:[Or.FONT]}}),this.text=aa.STRING(\\\\\\\"polygonjs\\\\\\\",{multiline:!0}),this.type=aa.INTEGER(0,{menu:{entries:r9.map(((t,e)=>({name:t,value:e})))}}),this.size=aa.FLOAT(1,{range:[0,1],rangeLocked:[!0,!1]}),this.extrude=aa.FLOAT(.1,{visibleIf:{type:r9.indexOf(s9.MESH)}}),this.segments=aa.INTEGER(1,{range:[1,20],rangeLocked:[!0,!1],visibleIf:{type:r9.indexOf(s9.MESH)}}),this.strokeWidth=aa.FLOAT(.02,{visibleIf:{type:r9.indexOf(s9.STROKE)}})}};class l9 extends nV{constructor(){super(...arguments),this.paramsConfig=a9,this._loaded_fonts={}}static type(){return\\\\\\\"text\\\\\\\"}initializeNode(){this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.text],(()=>this.p.text.rawInput()))}))}))}async cook(){try{this._loaded_fonts[this.pv.font]=this._loaded_fonts[this.pv.font]||await this._loadFont()}catch(t){return void this.states.error.set(`count not load font (${this.pv.font})`)}const t=this._loaded_fonts[this.pv.font];if(t)switch(r9[this.pv.type]){case s9.MESH:return this._create_geometry_from_type_mesh(t);case s9.FLAT:return this._create_geometry_from_type_flat(t);case s9.LINE:return this._create_geometry_from_type_line(t);case s9.STROKE:return this._create_geometry_from_type_stroke(t);default:console.warn(\\\\\\\"type is not valid\\\\\\\")}}_create_geometry_from_type_mesh(t){const e=this.displayed_text(),n={font:t,size:this.pv.size,height:this.pv.extrude,curveSegments:this.pv.segments};try{const t=new Q2(e,n);if(!t.index){const e=t.getAttribute(\\\\\\\"position\\\\\\\").array;t.setIndex(f.range(e.length/3))}this.setGeometry(t)}catch(t){this.states.error.set(o9)}}_create_geometry_from_type_flat(t){const e=this._get_shapes(t);if(e){var n=new _J(e);this.setGeometry(n)}}_create_geometry_from_type_line(t){const e=this.shapes_from_font(t);if(e){const t=[],n=[];let i=0;for(let s=0;s0&&e0)for(let e=0;e{const n=t.material;n&&(m.isArray(n)||s||(s=n[e.textureName]))}));if(s)for(let t of n.objects())t.traverse((t=>{const n=t.material;if(n&&!m.isArray(n)){n[e.textureName]=s;const t=n.uniforms;if(t){const n=t[e.textureName];n&&(n.value=s)}n.needsUpdate=!0}}));return n}}c9.DEFAULT_PARAMS={textureName:\\\\\\\"map\\\\\\\"},c9.INPUT_CLONED_STATE=[Ki.FROM_NODE,Ki.NEVER];const h9=c9.DEFAULT_PARAMS;const u9=new class extends la{constructor(){super(...arguments),this.textureName=aa.STRING(h9.textureName)}};class d9 extends nV{constructor(){super(...arguments),this.paramsConfig=u9}static type(){return\\\\\\\"TextureCopy\\\\\\\"}static displayedInputNames(){return[\\\\\\\"objects to copy textures to\\\\\\\",\\\\\\\"objects to copy textures from\\\\\\\"]}initializeNode(){this.io.inputs.setCount(2),this.io.inputs.initInputsClonedState(c9.INPUT_CLONED_STATE)}async cook(t){this._operation=this._operation||new c9(this.scene(),this.states);const e=await this._operation.cook(t,this.pv);this.setCoreGroup(e)}}class p9 extends QG{static type(){return\\\\\\\"textureProperties\\\\\\\"}async cook(t,e){const n=t[0],i=[];for(let t of n.objects())e.applyToChildren?t.traverse((t=>{i.push(t)})):i.push(t);const s=i.map((t=>this._update_object(t,e)));return await Promise.all(s),n}async _update_object(t,e){const n=t.material;n&&await this._update_material(n,e)}async _update_material(t,e){let n=t.map;n&&await this._update_texture(n,e)}async _update_texture(t,e){this._updateEncoding(t,e),this._updateMapping(t,e),this._updateWrap(t,e),await this._updateAnisotropy(t,e),this._updateFilter(t,e)}_updateEncoding(t,e){e.tencoding&&(t.encoding=e.encoding,t.needsUpdate=!0)}_updateMapping(t,e){e.tmapping&&(t.mapping=e.mapping)}_updateWrap(t,e){e.twrap&&(t.wrapS=e.wrapS,t.wrapT=e.wrapT)}async _updateAnisotropy(t,e){if(e.tanisotropy)if(e.useRendererMaxAnisotropy){const e=await li.renderersController.firstRenderer();e&&(t.anisotropy=e.capabilities.getMaxAnisotropy())}else t.anisotropy=e.anisotropy}_updateFilter(t,e){e.tminFilter&&(t.minFilter=e.minFilter),e.tmagFilter&&(t.magFilter=e.magFilter)}}p9.DEFAULT_PARAMS={applyToChildren:!1,tencoding:!1,encoding:w.U,tmapping:!1,mapping:w.Yc,twrap:!1,wrapS:w.wc,wrapT:w.wc,tanisotropy:!1,useRendererMaxAnisotropy:!1,anisotropy:2,tminFilter:!1,minFilter:Xm,tmagFilter:!1,magFilter:qm},p9.INPUT_CLONED_STATE=Ki.FROM_NODE;const _9=p9.DEFAULT_PARAMS;const m9=new class extends la{constructor(){super(...arguments),this.applyToChildren=aa.BOOLEAN(_9.applyToChildren,{separatorAfter:!0}),this.tencoding=aa.BOOLEAN(_9.tencoding),this.encoding=aa.INTEGER(_9.encoding,{visibleIf:{tencoding:1},menu:{entries:eg.map((t=>({name:Object.keys(t)[0],value:Object.values(t)[0]})))}}),this.tmapping=aa.BOOLEAN(_9.tmapping),this.mapping=aa.INTEGER(_9.mapping,{visibleIf:{tmapping:1},menu:{entries:ig.map((t=>({name:Object.keys(t)[0],value:Object.values(t)[0]})))}}),this.twrap=aa.BOOLEAN(_9.twrap),this.wrapS=aa.INTEGER(_9.wrapS,{visibleIf:{twrap:1},menu:{entries:ng.map((t=>({name:Object.keys(t)[0],value:Object.values(t)[0]})))}}),this.wrapT=aa.INTEGER(_9.wrapT,{visibleIf:{twrap:1},menu:{entries:ng.map((t=>({name:Object.keys(t)[0],value:Object.values(t)[0]})))},separatorAfter:!0}),this.tanisotropy=aa.BOOLEAN(_9.tanisotropy),this.useRendererMaxAnisotropy=aa.BOOLEAN(_9.useRendererMaxAnisotropy,{visibleIf:{tanisotropy:1}}),this.anisotropy=aa.INTEGER(_9.anisotropy,{visibleIf:{tanisotropy:1,useRendererMaxAnisotropy:0},range:[0,32],rangeLocked:[!0,!1]}),this.tminFilter=aa.BOOLEAN(0),this.minFilter=aa.INTEGER(_9.minFilter,{visibleIf:{tminFilter:1},menu:{entries:$m}}),this.tmagFilter=aa.BOOLEAN(0),this.magFilter=aa.INTEGER(_9.magFilter,{visibleIf:{tmagFilter:1},menu:{entries:Ym}})}};class f9 extends nV{constructor(){super(...arguments),this.paramsConfig=m9}static type(){return\\\\\\\"textureProperties\\\\\\\"}static displayedInputNames(){return[\\\\\\\"objects with textures to change properties of\\\\\\\"]}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState(p9.INPUT_CLONED_STATE)}async cook(t){this._operation=this._operation||new p9(this.scene(),this.states);const e=await this._operation.cook(t,this.pv);this.setCoreGroup(e)}}const g9=new p.a(0,0,1);class v9 extends QG{constructor(){super(...arguments),this._core_transform=new uU}static type(){return\\\\\\\"torus\\\\\\\"}cook(t,e){const n=e.radius,i=e.radiusTube,s=e.segmentsRadial,r=e.segmentsTube,o=new fJ(n,i,s,r);return o.translate(e.center.x,e.center.y,e.center.z),this._core_transform.rotate_geometry(o,g9,e.direction),this.createCoreGroupFromGeometry(o)}}v9.DEFAULT_PARAMS={radius:1,radiusTube:1,segmentsRadial:20,segmentsTube:12,direction:new p.a(0,1,0),center:new p.a(0,0,0)},v9.INPUT_CLONED_STATE=Ki.FROM_NODE;const y9=v9.DEFAULT_PARAMS;const x9=new class extends la{constructor(){super(...arguments),this.radius=aa.FLOAT(y9.radius,{range:[0,1]}),this.radiusTube=aa.FLOAT(y9.radiusTube,{range:[0,1]}),this.segmentsRadial=aa.INTEGER(y9.segmentsRadial,{range:[1,50],rangeLocked:[!0,!1]}),this.segmentsTube=aa.INTEGER(y9.segmentsTube,{range:[1,50],rangeLocked:[!0,!1]}),this.direction=aa.VECTOR3(y9.direction),this.center=aa.VECTOR3(y9.center)}};class b9 extends nV{constructor(){super(...arguments),this.paramsConfig=x9}static type(){return\\\\\\\"torus\\\\\\\"}cook(t){this._operation=this._operation||new v9(this.scene(),this.states);const e=this._operation.cook(t,this.pv);this.setCoreGroup(e)}}class w9 extends QG{static type(){return\\\\\\\"torusKnot\\\\\\\"}cook(t,e){const n=e.radius,i=e.radiusTube,s=e.segmentsRadial,r=e.segmentsTube,o=e.p,a=e.q,l=new gJ(n,i,s,r,o,a);return l.translate(e.center.x,e.center.y,e.center.z),this.createCoreGroupFromGeometry(l)}}w9.DEFAULT_PARAMS={radius:1,radiusTube:1,segmentsRadial:64,segmentsTube:8,p:2,q:3,center:new p.a(0,0,0)},w9.INPUT_CLONED_STATE=Ki.FROM_NODE;const T9=w9.DEFAULT_PARAMS;const A9=new class extends la{constructor(){super(...arguments),this.radius=aa.FLOAT(T9.radius),this.radiusTube=aa.FLOAT(T9.radiusTube),this.segmentsRadial=aa.INTEGER(T9.segmentsRadial,{range:[1,128]}),this.segmentsTube=aa.INTEGER(T9.segmentsTube,{range:[1,32]}),this.p=aa.INTEGER(T9.p,{range:[1,10]}),this.q=aa.INTEGER(T9.q,{range:[1,10]}),this.center=aa.VECTOR3(T9.center)}};class M9 extends nV{constructor(){super(...arguments),this.paramsConfig=A9}static type(){return\\\\\\\"torusKnot\\\\\\\"}initializeNode(){}cook(t){this._operation=this._operation||new w9(this.scene(),this.states);const e=this._operation.cook(t,this.pv);this.setCoreGroup(e)}}var E9;!function(t){t.SET_PARAMS=\\\\\\\"set params\\\\\\\",t.UPDATE_MATRIX=\\\\\\\"update matrix\\\\\\\"}(E9||(E9={}));const S9=[E9.SET_PARAMS,E9.UPDATE_MATRIX];class C9 extends QG{constructor(){super(...arguments),this._core_transform=new uU,this._point_pos=new p.a,this._object_scale=new p.a,this._r=new p.a,this._object_position=new p.a}static type(){return\\\\\\\"transform\\\\\\\"}cook(t,e){const n=t[0].objects();return this._apply_transform(n,e),t[0]}_apply_transform(t,e){const n=aU[e.applyOn];switch(n){case sU.GEOMETRIES:return this._update_geometries(t,e);case sU.OBJECTS:return this._update_objects(t,e)}ls.unreachable(n)}_update_geometries(t,e){const n=this._matrix(e);if(\\\\\\\"\\\\\\\"===e.group.trim())for(let i of t){const t=i.geometry;t&&(t.translate(-e.pivot.x,-e.pivot.y,-e.pivot.z),t.applyMatrix4(n),t.translate(e.pivot.x,e.pivot.y,e.pivot.z))}else{const i=ZG._fromObjects(t).pointsFromGroup(e.group);for(let t of i){const i=t.getPosition(this._point_pos).sub(e.pivot);i.applyMatrix4(n),t.setPosition(i.add(e.pivot))}}}_update_objects(t,e){const n=S9[e.objectMode];switch(n){case E9.SET_PARAMS:return this._update_objects_params(t,e);case E9.UPDATE_MATRIX:return this._update_objects_matrix(t,e)}ls.unreachable(n)}_update_objects_params(t,e){for(let n of t){n.position.copy(e.t);const t=cU[e.rotationOrder];this._r.copy(e.r).multiplyScalar(On.a),n.rotation.set(this._r.x,this._r.y,this._r.z,t),this._object_scale.copy(e.s).multiplyScalar(e.scale),n.scale.copy(this._object_scale),n.updateMatrix()}}_update_objects_matrix(t,e){const n=this._matrix(e);for(let e of t)this._object_position.copy(e.position),e.position.multiplyScalar(0),e.updateMatrix(),e.applyMatrix4(n),e.position.add(this._object_position),e.updateMatrix()}_matrix(t){return this._core_transform.matrix(t.t,t.r,t.s,t.scale,cU[t.rotationOrder])}}C9.DEFAULT_PARAMS={applyOn:aU.indexOf(sU.GEOMETRIES),objectMode:S9.indexOf(E9.SET_PARAMS),group:\\\\\\\"\\\\\\\",rotationOrder:cU.indexOf(lU.XYZ),t:new p.a(0,0,0),r:new p.a(0,0,0),s:new p.a(1,1,1),scale:1,pivot:new p.a(0,0,0)},C9.INPUT_CLONED_STATE=Ki.FROM_NODE;const N9=C9.DEFAULT_PARAMS;const L9=new class extends la{constructor(){super(...arguments),this.applyOn=aa.INTEGER(N9.applyOn,{menu:{entries:aU.map(((t,e)=>({name:t,value:e})))}}),this.objectMode=aa.INTEGER(N9.objectMode,{visibleIf:{applyOn:aU.indexOf(sU.OBJECTS)},menu:{entries:S9.map(((t,e)=>({name:t,value:e})))}}),this.group=aa.STRING(N9.group,{visibleIf:{applyOn:aU.indexOf(sU.GEOMETRIES)}}),this.rotationOrder=aa.INTEGER(N9.rotationOrder,{menu:{entries:cU.map(((t,e)=>({name:t,value:e})))}}),this.t=aa.VECTOR3(N9.t),this.r=aa.VECTOR3(N9.r),this.s=aa.VECTOR3(N9.s),this.scale=aa.FLOAT(N9.scale,{range:[0,10]}),this.pivot=aa.VECTOR3(N9.pivot,{visibleIf:{applyOn:aU.indexOf(sU.GEOMETRIES)}})}};class O9 extends nV{constructor(){super(...arguments),this.paramsConfig=L9}static type(){return K1.TRANSFORM}static displayedInputNames(){return[\\\\\\\"geometries or objects to transform\\\\\\\"]}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState(C9.INPUT_CLONED_STATE),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.applyOn],(()=>aU[this.pv.applyOn]))}))}))}setApplyOn(t){this.p.applyOn.set(aU.indexOf(t))}setObjectMode(t){this.p.objectMode.set(S9.indexOf(t))}cook(t){this._operation=this._operation||new C9(this.scene(),this.states);const e=this._operation.cook(t,this.pv);this.setCoreGroup(e)}}const P9=new class extends la{constructor(){super(...arguments),this.useSecondInput=aa.BOOLEAN(1),this.reference=aa.OPERATOR_PATH(\\\\\\\"\\\\\\\",{nodeSelection:{context:ts.SOP},visibleIf:{useSecondInput:0}})}};class R9 extends nV{constructor(){super(...arguments),this.paramsConfig=P9}static type(){return\\\\\\\"transformCopy\\\\\\\"}static displayedInputNames(){return[\\\\\\\"objects to transform\\\\\\\",\\\\\\\"objects to copy transform from\\\\\\\"]}initializeNode(){this.io.inputs.setCount(1,2),this.io.inputs.initInputsClonedState([Ki.FROM_NODE,Ki.NEVER])}cook(t){this.pv.useSecondInput&&t[1]?this._copy_from_src_objects(t[0].objects(),t[1].objects()):this._copy_from_found_node(t[0].objects())}_copy_from_src_objects(t,e){let n,i;for(let s=0;s({name:t,value:e})))}};function D9(t){const e=[];for(let n=t+1;n<=6;n++)e.push({count:n});return{visibleIf:e}}const B9=new class extends la{constructor(){super(...arguments),this.applyOn=aa.INTEGER(aU.indexOf(sU.GEOMETRIES),{menu:{entries:aU.map(((t,e)=>({name:t,value:e})))}}),this.count=aa.INTEGER(2,{range:[0,6],rangeLocked:[!0,!0]}),this.rotationOrder0=aa.INTEGER(I9,{separatorBefore:!0,...F9,...D9(0)}),this.r0=aa.VECTOR3([0,0,0],{...D9(0)}),this.rotationOrder1=aa.INTEGER(I9,{separatorBefore:!0,...F9,...D9(1)}),this.r1=aa.VECTOR3([0,0,0],{...D9(1)}),this.rotationOrder2=aa.INTEGER(I9,{separatorBefore:!0,...F9,...D9(2)}),this.r2=aa.VECTOR3([0,0,0],{...D9(2)}),this.rotationOrder3=aa.INTEGER(I9,{separatorBefore:!0,...F9,...D9(3)}),this.r3=aa.VECTOR3([0,0,0],{...D9(3)}),this.rotationOrder4=aa.INTEGER(I9,{separatorBefore:!0,...F9,...D9(4)}),this.r4=aa.VECTOR3([0,0,0],{...D9(4)}),this.rotationOrder5=aa.INTEGER(I9,{separatorBefore:!0,...F9,...D9(5)}),this.r5=aa.VECTOR3([0,0,0],{...D9(5)})}};class z9 extends nV{constructor(){super(...arguments),this.paramsConfig=B9,this._core_transform=new uU,this._t=new p.a(0,0,0),this._s=new p.a(1,1,1),this._scale=1}static type(){return\\\\\\\"transformMulti\\\\\\\"}static displayedInputNames(){return[\\\\\\\"objects to transform\\\\\\\",\\\\\\\"objects to copy initial transform from\\\\\\\"]}initializeNode(){this.io.inputs.setCount(1,2),this.io.inputs.initInputsClonedState([Ki.FROM_NODE,Ki.NEVER]),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.applyOn],(()=>aU[this.pv.applyOn]))}))})),this.params.onParamsCreated(\\\\\\\"cache param pairs\\\\\\\",(()=>{this._rot_and_index_pairs=[[this.p.r0,this.p.rotationOrder0],[this.p.r1,this.p.rotationOrder1],[this.p.r2,this.p.rotationOrder2],[this.p.r3,this.p.rotationOrder3],[this.p.r4,this.p.rotationOrder4],[this.p.r5,this.p.rotationOrder5]]}))}cook(t){const e=t[0].objectsWithGeo(),n=t[1]?t[1].objectsWithGeo()[0]:void 0;this._apply_transforms(e,n),this.setObjects(e)}_apply_transforms(t,e){const n=aU[this.pv.applyOn];switch(n){case sU.GEOMETRIES:return this._apply_matrix_to_geometries(t,e);case sU.OBJECTS:return this._apply_matrix_to_objects(t,e)}ls.unreachable(n)}_apply_matrix_to_geometries(t,e){if(!this._rot_and_index_pairs)return;if(e){const n=e.geometry;if(n){const e=[js.POSITION,js.NORMAL,js.TANGENT];for(let i of e){const e=n.attributes[i];for(let n of t){const t=n.geometry.attributes[i];e&&t&&qs.copy(e,t)}}}}let n;for(let e=0;e({name:t,value:e})))}})}};class V9 extends nV{constructor(){super(...arguments),this.paramsConfig=G9,this._bbox_center=new p.a,this._translate_matrix=new A.a}static type(){return\\\\\\\"transformReset\\\\\\\"}static displayedInputNames(){return[\\\\\\\"objects to reset transform\\\\\\\",\\\\\\\"optional reference for center\\\\\\\"]}initializeNode(){this.io.inputs.setCount(1,2),this.io.inputs.initInputsClonedState(Ki.FROM_NODE)}setMode(t){this.p.mode.set(U9.indexOf(t))}cook(t){const e=U9[this.pv.mode];this._select_mode(e,t)}_select_mode(t,e){switch(t){case k9.RESET_OBJECT:return this._reset_objects(e);case k9.CENTER_GEO:return this._center_geos(e,!1);case k9.PROMOTE_GEO_TO_OBJECT:return this._center_geos(e,!0)}ls.unreachable(t)}_reset_objects(t){const e=t[0],n=e.objects();for(let t of n)t.matrix.identity(),uU.decompose_matrix(t);this.setCoreGroup(e)}_center_geos(t,e){const n=t[0],i=n.objectsWithGeo();let s=i;const r=t[1];r&&(s=r.objectsWithGeo());for(let t=0;te.h-t.h));const i=[{x:0,y:0,w:Math.max(Math.ceil(Math.sqrt(e/.95)),n),h:1/0}];let s=0,r=0;for(const e of t)for(let t=i.length-1;t>=0;t--){const n=i[t];if(!(e.w>n.w||e.h>n.h)){if(e.x=n.x,e.y=n.y,r=Math.max(r,e.y+e.h),s=Math.max(s,e.x+e.w),e.w===n.w&&e.h===n.h){const e=i.pop();t=this.minAllowedArgumentsCount()&&t<=this.maxAllowedArgumentsCount()}processArguments(t){throw\\\\\\\"Expression.Method._Base.process_arguments virtual method call. Please override\\\\\\\"}async getReferencedNodeContainer(t){const e=this.getReferencedNode(t);if(e){let t;if(t=e.isDirty()?await e.compute():e.containerController.container(),t){if(t.coreContent())return t}throw`referenced node invalid: ${e.path()}`}throw`invalid input (${t})`}getReferencedParam(t,e){const n=this.node();return n?bi.findParam(n,t,e):null}findReferencedGraphNode(t,e){if(!m.isNumber(t)){const n=t;return this.getReferencedNode(n,e)}{const e=t,n=this.node();if(n){return n.io.inputs.inputGraphNode(e)}}return null}getReferencedNode(t,e){let n=null;const i=this.node();if(m.isString(t)){if(i){const s=t;n=bi.findNode(i,s,e)}}else if(i){const e=t;n=i.io.inputs.input(e)}return n||null}findDependency(t){return null}createDependencyFromIndexOrPath(t){const e=new ho,n=this.findReferencedGraphNode(t,e);return n?this.createDependency(n,t,e):(li.warn(\\\\\\\"node not found for path\\\\\\\",t),null)}createDependency(t,e,n){return zr.create(this.param,e,t,n)}}class f3 extends m3{constructor(){super(...arguments),this._require_dependency=!0}static requiredArguments(){return[[\\\\\\\"string\\\\\\\",\\\\\\\"arguments list\\\\\\\"],[\\\\\\\"number\\\\\\\",\\\\\\\"index\\\\\\\"]]}processArguments(t){return new Promise(((e,n)=>{if(2==t.length){const n=t[0],i=t[1];e(n.split(\\\\\\\" \\\\\\\")[i])}else e(0)}))}}class g3 extends m3{constructor(){super(...arguments),this._require_dependency=!0}static requiredArguments(){return[[\\\\\\\"string\\\\\\\",\\\\\\\"arguments list\\\\\\\"]]}processArguments(t){return new Promise(((e,n)=>{if(1==t.length){e(t[0].split(\\\\\\\" \\\\\\\").length)}else e(0)}))}}const v3=[\\\\\\\"min\\\\\\\",\\\\\\\"max\\\\\\\",\\\\\\\"size\\\\\\\",\\\\\\\"center\\\\\\\"],y3=[\\\\\\\"x\\\\\\\",\\\\\\\"y\\\\\\\",\\\\\\\"z\\\\\\\"];class x3 extends m3{constructor(){super(...arguments),this._require_dependency=!0}static requiredArguments(){return[[\\\\\\\"string\\\\\\\",\\\\\\\"path to node\\\\\\\"],[\\\\\\\"string\\\\\\\",\\\\\\\"vector name, min, max, size or center\\\\\\\"],[\\\\\\\"string\\\\\\\",\\\\\\\"component_name, x,y or z\\\\\\\"]]}findDependency(t){return this.createDependencyFromIndexOrPath(t)}processArguments(t){let e=0;return new Promise((async(n,i)=>{if(t.length>=1){const s=t[0],r=t[1],o=t[2];let a=null;try{a=await this.getReferencedNodeContainer(s)}catch(t){i(t)}a&&(e=this._get_value_from_container(a,r,o),n(e))}else n(0)}))}_get_value_from_container(t,e,n){const i=t.boundingBox();if(!e)return i;if(v3.indexOf(e)>=0){let t=new p.a;switch(e){case\\\\\\\"size\\\\\\\":i.getSize(t);break;case\\\\\\\"center\\\\\\\":i.getCenter(t);break;default:t=i[e]}return n?y3.indexOf(n)>=0?t[n]:-1:t}return-1}}class b3 extends m3{constructor(){super(...arguments),this._require_dependency=!0}static requiredArguments(){return[[\\\\\\\"string\\\\\\\",\\\\\\\"path to node\\\\\\\"],[\\\\\\\"string\\\\\\\",\\\\\\\"component_name, x,y or z\\\\\\\"]]}findDependency(t){return this.createDependencyFromIndexOrPath(t)}processArguments(t){return new Promise((async(e,n)=>{if(t.length>=1){const i=t[0],s=t[1];let r=null;try{r=await this.getReferencedNodeContainer(i)}catch(t){n(t)}if(r){const t=r.boundingBox(),n=t.min.clone().add(t.max).multiplyScalar(.5);if(s){const t=n[s];e(null!=t?t:0)}else e(n)}}else e(0)}))}}class w3 extends m3{constructor(){super(...arguments),this._require_dependency=!0}static requiredArguments(){return[[\\\\\\\"string\\\\\\\",\\\\\\\"path to param\\\\\\\"]]}findDependency(t){const e=new ho,n=this.getReferencedParam(t,e);return n?this.createDependency(n,t,e):null}async processArguments(t){return new Promise((async(e,n)=>{let i=0;if(1==t.length){const s=t[0],r=this.getReferencedParam(s);if(r){r.isDirty()&&await r.compute();const t=r.value;null!=t&&(i=t,e(i))}else n(0)}}))}}class T3 extends m3{constructor(){super(...arguments),this._require_dependency=!0}static requiredArguments(){return[[\\\\\\\"string\\\\\\\",\\\\\\\"path to copy\\\\\\\"],[\\\\\\\"integer\\\\\\\",\\\\\\\"default value\\\\\\\"]]}static optionalArguments(){return[[\\\\\\\"string\\\\\\\",\\\\\\\"attribute name (optional)\\\\\\\"]]}findDependency(t){const e=this.findReferencedGraphNode(t);if(e&&\\\\\\\"copy\\\\\\\"==e.type()){const n=e.stamp_node;return this.createDependency(n,t)}return null}processArguments(t){return new Promise(((e,n)=>{if(2==t.length||3==t.length){const n=t[0],i=t[1],s=t[2],r=this.node(),o=r?bi.findNode(r,n):null;let a;o&&o.type()==fZ.type()&&(a=o.stamp_value(s)),null==a&&(a=i),e(a)}else e(0)}))}}class A3 extends m3{constructor(){super(...arguments),this._require_dependency=!0,this._resolution=new d.a}static requiredArguments(){return[[\\\\\\\"string\\\\\\\",\\\\\\\"path to node\\\\\\\"],[\\\\\\\"string\\\\\\\",\\\\\\\"component_name: x or y\\\\\\\"]]}findDependency(t){return this.createDependencyFromIndexOrPath(t)}async processArguments(t){if(1==t.length||2==t.length){const e=t[0],n=t[1],i=await this.getReferencedNodeContainer(e);if(i){const t=i.resolution();if(!n)return this._resolution.set(t[0],t[1]),this._resolution;if([0,\\\\\\\"0\\\\\\\",\\\\\\\"x\\\\\\\"].includes(n))return t[0];if([1,\\\\\\\"1\\\\\\\",\\\\\\\"y\\\\\\\"].includes(n))return t[1]}}return-1}}class M3 extends m3{constructor(){super(...arguments),this._require_dependency=!0}static requiredArguments(){return[]}async processArguments(t){return new Promise((async(t,e)=>{t(Zf.isMobile())}))}}class E3 extends m3{constructor(){super(...arguments),this._require_dependency=!0}static requiredArguments(){return[]}async processArguments(t){return new Promise((async(t,e)=>{t(Zf.isTouchDevice())}))}}class S3 extends m3{static requiredArguments(){return[[\\\\\\\"string\\\\\\\",\\\\\\\"javascript expression\\\\\\\"]]}async processArguments(t){let e=0;if(1==t.length){const n=t[0];if(this._function=this._function||this._create_function(n),this._function)try{e=this._function(this.param.scene(),this.param.node,this.param)}catch(t){console.warn(\\\\\\\"expression error\\\\\\\"),console.warn(t)}}return e}_create_function(t){return new Function(\\\\\\\"scene\\\\\\\",\\\\\\\"node\\\\\\\",\\\\\\\"param\\\\\\\",`return ${t}`)}}class C3 extends m3{constructor(){super(...arguments),this._require_dependency=!0}static requiredArguments(){return[[\\\\\\\"string\\\\\\\",\\\\\\\"path to node\\\\\\\"],[\\\\\\\"string\\\\\\\",\\\\\\\"attribute name\\\\\\\"],[\\\\\\\"index\\\\\\\",\\\\\\\"object index\\\\\\\"]]}findDependency(t){return this.createDependencyFromIndexOrPath(t)}processArguments(t){return new Promise((async(e,n)=>{if(3==t.length){const i=t[0],s=t[1],r=t[2];let o=null;try{o=await this.getReferencedNodeContainer(i)}catch(t){n(t)}if(o){e(this._get_value_from_container(o,s,r))}}else console.warn(`${t.length} given when expected 3`),e(0)}))}_get_value_from_container(t,e,n){const i=t.coreContent();if(i){const t=i.coreObjects()[n];return t?t.attribValue(e):0}return null}}class N3 extends m3{constructor(){super(...arguments),this._require_dependency=!0}static requiredArguments(){return[[\\\\\\\"string\\\\\\\",\\\\\\\"path to node\\\\\\\"]]}findDependency(t){return this.createDependencyFromIndexOrPath(t)}processArguments(t){return new Promise((async(e,n)=>{if(1==t.length){const i=t[0];let s;try{s=await this.getReferencedNodeContainer(i)}catch(t){return void n(t)}if(s){e(s.objectsCount())}}else e(0)}))}}class L3 extends m3{constructor(){super(...arguments),this._require_dependency=!0}static requiredArguments(){return[[\\\\\\\"string\\\\\\\",\\\\\\\"path to node\\\\\\\"]]}findDependency(t){const e=this.findReferencedGraphNode(t);if(e){const n=e;if(n.nameController){const e=n.nameController.graph_node;return this.createDependency(e,t)}}return null}processArguments(t){return new Promise(((e,n)=>{if(1==t.length){const n=t[0],i=this.getReferencedNode(n);if(i){const t=i.name();e(os.tailDigits(t))}else e(0)}else e(0)}))}}class O3 extends m3{constructor(){super(...arguments),this._require_dependency=!0}static requiredArguments(){return[[\\\\\\\"string\\\\\\\",\\\\\\\"path to node\\\\\\\"]]}findDependency(t){const e=this.findReferencedGraphNode(t);if(e){const n=e;if(n.nameController){const e=n.nameController.graph_node;return this.createDependency(e,t)}}return null}processArguments(t){return new Promise(((e,n)=>{if(1==t.length){const n=t[0],i=this.getReferencedNode(n);if(i){e(i.name())}else e(0)}else e(0)}))}}class P3 extends m3{static requiredArguments(){return[[\\\\\\\"string\\\\\\\",\\\\\\\"number\\\\\\\"]]}processArguments(t){return new Promise((e=>{const n=t[0]||2;e(`${t[1]||0}`.padStart(n,\\\\\\\"0\\\\\\\"))}))}}class R3 extends m3{constructor(){super(...arguments),this._require_dependency=!0}static requiredArguments(){return[[\\\\\\\"string\\\\\\\",\\\\\\\"path to node\\\\\\\"],[\\\\\\\"string\\\\\\\",\\\\\\\"attribute name\\\\\\\"],[\\\\\\\"index\\\\\\\",\\\\\\\"point index\\\\\\\"]]}findDependency(t){return this.createDependencyFromIndexOrPath(t)}processArguments(t){return new Promise((async(e,n)=>{if(3==t.length){const i=t[0],s=t[1],r=t[2];let o=null;try{o=await this.getReferencedNodeContainer(i)}catch(t){n(t)}if(o){e(this._get_value_from_container(o,s,r))}}else console.warn(`${t.length} given when expected 3`),e(0)}))}_get_value_from_container(t,e,n){const i=t.coreContent();if(i){const t=i.points()[n];return t?t.attribValue(e):0}return null}}class I3 extends m3{constructor(){super(...arguments),this._require_dependency=!0}static requiredArguments(){return[[\\\\\\\"string\\\\\\\",\\\\\\\"path to node\\\\\\\"]]}findDependency(t){return this.createDependencyFromIndexOrPath(t)}processArguments(t){return new Promise((async(e,n)=>{if(1==t.length){const i=t[0];let s;try{s=await this.getReferencedNodeContainer(i)}catch(t){return void n(t)}if(s){e(s.pointsCount())}}else e(0)}))}}class F3 extends m3{static requiredArguments(){return[[\\\\\\\"string\\\\\\\",\\\\\\\"string to count characters of\\\\\\\"]]}async processArguments(t){let e=0;if(1==t.length){e=t[0].length}return e}}class D3 extends m3{static requiredArguments(){return[]}async processArguments(t){let e=\\\\\\\"\\\\\\\";for(let n of t)null==n&&(n=\\\\\\\"\\\\\\\"),e+=`${n}`;return e}}class B3 extends m3{static requiredArguments(){return[[\\\\\\\"string\\\\\\\",\\\\\\\"string to get index from\\\\\\\"],[\\\\\\\"string\\\\\\\",\\\\\\\"char to find index of\\\\\\\"]]}async processArguments(t){let e=-1;if(2==t.length){const n=t[0],i=t[1];e=n.indexOf(i)}return e}}class z3 extends m3{static requiredArguments(){return[[\\\\\\\"string\\\\\\\",\\\\\\\"string to get range from\\\\\\\"],[\\\\\\\"integer\\\\\\\",\\\\\\\"range start\\\\\\\"],[\\\\\\\"integer\\\\\\\",\\\\\\\"range size\\\\\\\"]]}async processArguments(t){let e=\\\\\\\"\\\\\\\";const n=t[0],i=t[1]||0;let s=t[2]||1;return n&&(e=n.substr(i,s)),e}}class k3 extends m3{constructor(){super(...arguments),this._require_dependency=!0,this._windowSize=new d.a}static requiredArguments(){return[[]]}findDependency(t){return this.param.addGraphInput(this.param.scene().windowController.graphNode()),null}processArguments(t){return new Promise((t=>{this._windowSize.set(window.innerWidth,window.innerHeight),t(this._windowSize)}))}}class U3{constructor(t,e){this._controller=t,this._node=e,this._deleted_params_data=new Map,this._created_spare_param_names=[],this._raw_input_serialized_by_param_name=new Map,this._init_value_serialized_by_param_name=new Map}get assembler(){return this._controller.assembler}createSpareParameters(){var t;const e={},n=this.assembler.param_configs(),i=n.map((t=>t.name())),s=b.clone(i);if(0==this._validateNames(s))return;b.clone(this._created_spare_param_names).concat(s).forEach((t=>{const n=this._node.params.get(t);if(n){this._raw_input_serialized_by_param_name.set(n.name(),n.rawInputSerialized()),this._init_value_serialized_by_param_name.set(n.name(),n.defaultValueSerialized());const t=JG.dispatch_param(n);if(t.required()){const e=t.data();this._deleted_params_data.set(n.name(),e)}}e.namesToDelete=e.namesToDelete||[],e.namesToDelete.push(t)}));for(let t of n)if(s.indexOf(t.name())>=0){const n=b.clone(t.param_options),i={spare:!0,computeOnDirty:!0,cook:!1},s=b.merge(n,i);let r=this._init_value_serialized_by_param_name.get(t.name());null==r&&(r=t.default_value);let o=this._raw_input_serialized_by_param_name.get(t.name());null==o&&(o=t.default_value),e.toAdd=e.toAdd||[],e.toAdd.push({name:t.name(),type:t.type(),init_value:r,raw_input:o,options:s})}this._node.params.updateParams(e),this._created_spare_param_names=(null===(t=e.toAdd)||void 0===t?void 0:t.map((t=>t.name)))||[];for(let t of n){const e=this._node.params.get(t.name());e&&(t.execute_callback(this._node,e),e.type()==Er.OPERATOR_PATH&&setTimeout((async()=>{e.isDirty()&&await e.compute(),e.options.executeCallback()}),200))}}_validateNames(t){const e=b.clone(this._node.params.non_spare_names),n=f.intersection(t,e);if(n.length>0){const t=`${this._node.path()} attempts to create spare params called '${n.join(\\\\\\\", \\\\\\\")}' with same name as params`;return this._node.states.error.set(t),!1}return!0}}class G3{constructor(t,e){this.node=t,this._globals_handler=new Sf,this._compile_required=!0,this._assembler=new e(this.node),this._spare_params_controller=new U3(this,this.node)}set_assembler_globals_handler(t){(this._globals_handler?this._globals_handler.id():null)!=(t?t.id():null)&&(this._globals_handler=t,this.set_compilation_required_and_dirty(),this._assembler.reset_configs())}get assembler(){return this._assembler}get globals_handler(){return this._globals_handler}add_output_inputs(t){this._assembler.add_output_inputs(t)}add_globals_outputs(t){this._assembler.add_globals_outputs(t)}allow_attribute_exports(){return this._assembler.allow_attribute_exports()}setCompilationRequired(t=!0){this._compile_required=t}set_compilation_required_and_dirty(t){this.setCompilationRequired(),this.node.setDirty(t)}compileRequired(){return this._compile_required}post_compile(){this.createSpareParameters(),this.setCompilationRequired(!1)}createSpareParameters(){this._spare_params_controller.createSpareParameters()}addFilterFragmentShaderCallback(t,e){this.assembler._addFilterFragmentShaderCallback(t,e),this.setCompilationRequired()}removeFilterFragmentShaderCallback(t){this.assembler._removeFilterFragmentShaderCallback(t),this.setCompilationRequired()}}var V3;!function(t){t.FUNCTION_DECLARATION=\\\\\\\"function_declaration\\\\\\\",t.DEFINE=\\\\\\\"define\\\\\\\",t.BODY=\\\\\\\"body\\\\\\\"}(V3||(V3={}));class H3{constructor(t,e,n){this._name=t,this._input_names=e,this._dependencies=n}name(){return this._name}input_names(){return this._input_names}dependencies(){return this._dependencies}}class j3{constructor(t,e={}){this._name=t,this._options=e}name(){return this._name}default_from_attribute(){return this._options.default_from_attribute||!1}default(){return this._options.default}if_condition(){return this._options.if}prefix(){return this._options.prefix||\\\\\\\"\\\\\\\"}suffix(){return this._options.suffix||\\\\\\\"\\\\\\\"}postLines(){return this._options.postLines}}class W3{constructor(t){this._shader_name=t,this._definitions_by_node_id=new Map,this._body_lines_by_node_id=new Map}get shader_name(){return this._shader_name}addDefinitions(t,e){for(let n of e)h.pushOnArrayAtEntry(this._definitions_by_node_id,t.graphNodeId(),n)}definitions(t){return this._definitions_by_node_id.get(t.graphNodeId())}addBodyLines(t,e){for(let n of e)h.pushOnArrayAtEntry(this._body_lines_by_node_id,t.graphNodeId(),n)}body_lines(t){return this._body_lines_by_node_id.get(t.graphNodeId())}}class q3{constructor(t,e,n){this._shader_names=t,this._current_shader_name=e,this._assembler=n,this._lines_controller_by_shader_name=new Map;for(let t of this._shader_names)this._lines_controller_by_shader_name.set(t,new W3(t))}assembler(){return this._assembler}shaderNames(){return this._shader_names}set_current_shader_name(t){this._current_shader_name=t}get current_shader_name(){return this._current_shader_name}addDefinitions(t,e,n){if(0==e.length)return;n=n||this._current_shader_name;const i=this._lines_controller_by_shader_name.get(n);i&&i.addDefinitions(t,e)}definitions(t,e){const n=this._lines_controller_by_shader_name.get(t);if(n)return n.definitions(e)}addBodyLines(t,e,n){if(0==e.length)return;n=n||this._current_shader_name;const i=this._lines_controller_by_shader_name.get(n);i&&i.addBodyLines(t,e)}body_lines(t,e){const n=this._lines_controller_by_shader_name.get(t);if(n)return n.body_lines(e)}}const X3={[V3.FUNCTION_DECLARATION]:\\\\\\\"\\\\\\\",[V3.DEFINE]:\\\\\\\";\\\\\\\",[V3.BODY]:\\\\\\\";\\\\\\\"},Y3={[V3.FUNCTION_DECLARATION]:\\\\\\\"\\\\\\\",[V3.DEFINE]:\\\\\\\"\\\\\\\",[V3.BODY]:\\\\\\\"\\\\t\\\\\\\"};class $3{static node_comment(t,e){let n=`// ${t.path()}`,i=Y3[e];if(e==V3.BODY){let e=this.node_distance_to_material(t);t.type()==ns.OUTPUT&&(e+=1),i=i.repeat(e)}return e==V3.BODY&&(n=`${i}${n}`),n}static line_wrap(t,e,n){let i=!0;0!=e.indexOf(\\\\\\\"#if\\\\\\\")&&0!=e.indexOf(\\\\\\\"#endif\\\\\\\")||(i=!1);let s=Y3[n];if(n==V3.BODY&&(s=s.repeat(this.node_distance_to_material(t))),e=`${s}${e}`,i){const t=e[e.length-1],i=X3[n];t!=i&&\\\\\\\"{\\\\\\\"!=t&&\\\\\\\"}\\\\\\\"!=t&&(e+=i)}return e}static post_line_separator(t){return t==V3.BODY?\\\\\\\"\\\\t\\\\\\\":\\\\\\\"\\\\\\\"}static node_distance_to_material(t){const e=t.parent();if(!e)return 0;if(e.context()!=t.context())return 1;{let n=1;return t.type()!=ns.INPUT&&t.type()!=ns.OUTPUT||(n=0),n+this.node_distance_to_material(e)}}}class J3{constructor(t,e,n){this._node_traverser=t,this._root_nodes_for_shader_method=e,this._assembler=n,this._param_configs_controller=new wF,this._param_configs_set_allowed=!0,this._lines=new Map}shaderNames(){return this._node_traverser.shaderNames()}buildFromNodes(t,e){this._node_traverser.traverse(t);const n=new Map;for(let t of this.shaderNames()){const e=this._node_traverser.nodes_for_shader_name(t);n.set(t,e)}const i=this._node_traverser.sorted_nodes();for(let t of this.shaderNames()){const e=this._root_nodes_for_shader_method(t);for(let i of e)h.pushOnArrayAtEntry(n,t,i)}const s=new Map;for(let t of i)s.set(t.graphNodeId(),!0);for(let e of t)s.get(e.graphNodeId())||(i.push(e),s.set(e.graphNodeId(),!0));for(let t of i)t.reset_code();for(let t of e)t.reset_code();this._shaders_collection_controller=new q3(this.shaderNames(),this.shaderNames()[0],this._assembler),this.reset();for(let t of this.shaderNames()){let e=n.get(t)||[];if(e=f.uniq(e),this._shaders_collection_controller.set_current_shader_name(t),e)for(let t of e)t.setLines(this._shaders_collection_controller)}if(this._param_configs_set_allowed){for(let t of e)t.setParamConfigs();this.setParamConfigs(e)}this.set_code_lines(i)}shaders_collection_controller(){return this._shaders_collection_controller}disallow_new_param_configs(){this._param_configs_set_allowed=!1}allow_new_param_configs(){this._param_configs_set_allowed=!0}reset(){for(let t of this.shaderNames()){const e=new Map;this._lines.set(t,e)}}param_configs(){return this._param_configs_controller.list()||[]}lines(t,e){var n;return(null===(n=this._lines.get(t))||void 0===n?void 0:n.get(e))||[]}all_lines(){return this._lines}setParamConfigs(t){this._param_configs_controller.reset();for(let e of t){const t=e.param_configs();if(t)for(let e of t)this._param_configs_controller.push(e)}}set_code_lines(t){for(let e of this.shaderNames())this.add_code_lines(t,e)}add_code_lines(t,e){this.addDefinitions(t,e,yf.FUNCTION,V3.FUNCTION_DECLARATION),this.addDefinitions(t,e,yf.UNIFORM,V3.DEFINE),this.addDefinitions(t,e,yf.VARYING,V3.DEFINE),this.addDefinitions(t,e,yf.ATTRIBUTE,V3.DEFINE),this.add_code_line_for_nodes_and_line_type(t,e,V3.BODY)}addDefinitions(t,e,n,i){if(!this._shaders_collection_controller)return;const s=[];for(let i of t){let t=this._shaders_collection_controller.definitions(e,i);if(t){t=t.filter((t=>t.definition_type==n));for(let e of t)s.push(e)}}if(s.length>0){const t=new vf(s),n=t.uniq();if(t.errored)throw`code builder error: ${t.error_message}`;const r=new Map,o=new Map;for(let t of n){const e=t.node.graphNodeId();o.has(e)||o.set(e,!0),h.pushOnArrayAtEntry(r,e,t)}const a=this._lines.get(e);o.forEach(((t,e)=>{const n=r.get(e);if(n){const t=n[0];if(t){const e=$3.node_comment(t.node,i);h.pushOnArrayAtEntry(a,i,e);for(let e of n){const n=$3.line_wrap(t.node,e.line,i);h.pushOnArrayAtEntry(a,i,n)}const s=$3.post_line_separator(i);h.pushOnArrayAtEntry(a,i,s)}}}))}}add_code_line_for_nodes_and_line_type(t,e,n){var i=(t=t.filter((t=>{if(this._shaders_collection_controller){const n=this._shaders_collection_controller.body_lines(e,t);return n&&n.length>0}}))).length;for(let s=0;s0){const r=this._lines.get(e),o=$3.node_comment(t,n);if(h.pushOnArrayAtEntry(r,n,o),f.uniq(s).forEach((e=>{e=$3.line_wrap(t,e,n),h.pushOnArrayAtEntry(r,n,e)})),n!=V3.BODY||!i){const t=$3.post_line_separator(n);h.pushOnArrayAtEntry(r,n,t)}}}}class Z3{constructor(t,e,n){this._parent_node=t,this._shader_names=e,this._input_names_for_shader_name_method=n,this._leaves_graph_id=new Map,this._graph_ids_by_shader_name=new Map,this._outputs_by_graph_id=new Map,this._depth_by_graph_id=new Map,this._graph_id_by_depth=new Map,this._graph=this._parent_node.scene().graph}reset(){this._leaves_graph_id.clear(),this._graph_ids_by_shader_name.clear(),this._outputs_by_graph_id.clear(),this._depth_by_graph_id.clear(),this._graph_id_by_depth.clear(),this._shader_names.forEach((t=>{this._graph_ids_by_shader_name.set(t,new Map)}))}shaderNames(){return this._shader_names}input_names_for_shader_name(t,e){return this._input_names_for_shader_name_method(t,e)}traverse(t){this.reset();for(let t of this.shaderNames())this._leaves_graph_id.set(t,new Map);for(let e of this.shaderNames()){this._shader_name=e;for(let e of t)this.find_leaves_from_root_node(e),this.set_nodes_depth()}this._depth_by_graph_id.forEach(((t,e)=>{null!=t&&h.pushOnArrayAtEntry(this._graph_id_by_depth,t,e)}))}leaves_from_nodes(t){var e;this._shader_name=xf.LEAVES_FROM_NODES_SHADER,this._graph_ids_by_shader_name.set(this._shader_name,new Map),this._leaves_graph_id.set(this._shader_name,new Map);for(let e of t)this.find_leaves(e);const n=[];return null===(e=this._leaves_graph_id.get(this._shader_name))||void 0===e||e.forEach(((t,e)=>{n.push(e)})),this._graph.nodesFromIds(n)}nodes_for_shader_name(t){const e=[];this._graph_id_by_depth.forEach(((t,n)=>{e.push(n)})),e.sort(((t,e)=>t-e));const n=[],i=new Map;return e.forEach((e=>{const s=this._graph_id_by_depth.get(e);s&&s.forEach((e=>{var s;if(null===(s=this._graph_ids_by_shader_name.get(t))||void 0===s?void 0:s.get(e)){const s=this._graph.nodeFromId(e);this.add_nodes_with_children(s,i,n,t)}}))})),n}sorted_nodes(){const t=[];this._graph_id_by_depth.forEach(((e,n)=>{t.push(n)})),t.sort(((t,e)=>t-e));const e=[],n=new Map;return t.forEach((t=>{const i=this._graph_id_by_depth.get(t);if(i)for(let t of i){const i=this._graph.nodeFromId(t);i&&this.add_nodes_with_children(i,n,e)}})),e}add_nodes_with_children(t,e,n,i){if(e.get(t.graphNodeId())||(n.push(t),e.set(t.graphNodeId(),!0)),t.type()==ns.INPUT){const s=t.parent();if(s){const r=this.sorted_nodes_for_shader_name_for_parent(s,i);for(let s of r)s.graphNodeId()!=t.graphNodeId()&&this.add_nodes_with_children(s,e,n,i)}}}sorted_nodes_for_shader_name_for_parent(t,e){const n=[];this._graph_id_by_depth.forEach(((t,e)=>{n.push(e)})),n.sort(((t,e)=>t-e));const i=[];n.forEach((n=>{const s=this._graph_id_by_depth.get(n);s&&s.forEach((n=>{var s;if(!e||(null===(s=this._graph_ids_by_shader_name.get(e))||void 0===s?void 0:s.get(n))){const e=this._graph.nodeFromId(n);e.parent()==t&&i.push(e)}}))}));const s=i[0];return t.context()==s.context()&&i.push(t),i}find_leaves_from_root_node(t){var e;null===(e=this._graph_ids_by_shader_name.get(this._shader_name))||void 0===e||e.set(t.graphNodeId(),!0);const n=this.input_names_for_shader_name(t,this._shader_name);if(n)for(let e of n){const n=t.io.inputs.named_input(e);n&&(h.pushOnArrayAtEntry(this._outputs_by_graph_id,n.graphNodeId(),t.graphNodeId()),this.find_leaves(n))}this._outputs_by_graph_id.forEach(((t,e)=>{this._outputs_by_graph_id.set(e,f.uniq(t))}))}find_leaves(t){var e;null===(e=this._graph_ids_by_shader_name.get(this._shader_name))||void 0===e||e.set(t.graphNodeId(),!0);const n=this._find_inputs_or_children(t),i=f.compact(n),s=f.uniq(i.map((t=>t.graphNodeId()))).map((t=>this._graph.nodeFromId(t)));if(s.length>0)for(let e of s)h.pushOnArrayAtEntry(this._outputs_by_graph_id,e.graphNodeId(),t.graphNodeId()),this.find_leaves(e);else this._leaves_graph_id.get(this._shader_name).set(t.graphNodeId(),!0)}_find_inputs_or_children(t){var e,n;if(t.type()==ns.INPUT)return(null===(e=t.parent())||void 0===e?void 0:e.io.inputs.inputs())||[];if(t.childrenAllowed()){return[null===(n=t.childrenController)||void 0===n?void 0:n.output_node()]}return t.io.inputs.inputs()}set_nodes_depth(){this._leaves_graph_id.forEach(((t,e)=>{t.forEach(((t,e)=>{this.set_node_depth(e)}))}))}set_node_depth(t,e=0){const n=this._depth_by_graph_id.get(t);null!=n?this._depth_by_graph_id.set(t,Math.max(n,e)):this._depth_by_graph_id.set(t,e);const i=this._outputs_by_graph_id.get(t);i&&i.forEach((t=>{this.set_node_depth(t,e+1)}))}}const Q3=new Map([[xf.VERTEX,\\\\\\\"#include \\\\\\\"],[xf.FRAGMENT,\\\\\\\"#include \\\\\\\"]]),K3=new Map([[xf.VERTEX,\\\\\\\"#include \\\\\\\"],[xf.FRAGMENT,\\\\\\\"vec4 diffuseColor = vec4( diffuse, opacity );\\\\\\\"]]),t4=new Map([[xf.VERTEX,[\\\\\\\"#include \\\\\\\",\\\\\\\"#include \\\\\\\"]],[xf.FRAGMENT,[]]]);class e4 extends class{}{constructor(t){super(),this._gl_parent_node=t,this._shaders_by_name=new Map,this._lines=new Map,this._root_nodes=[],this._leaf_nodes=[],this._uniforms_time_dependent=!1,this._uniforms_resolution_dependent=!1}setGlParentNode(t){this._overriden_gl_parent_node=t}currentGlParentNode(){return this._overriden_gl_parent_node||this._gl_parent_node}compile(){}_template_shader_for_shader_name(t){var e,n;switch(t){case xf.VERTEX:return null===(e=this.templateShader())||void 0===e?void 0:e.vertexShader;case xf.FRAGMENT:return null===(n=this.templateShader())||void 0===n?void 0:n.fragmentShader}}get globals_handler(){var t;return null===(t=this.currentGlParentNode().assemblerController)||void 0===t?void 0:t.globals_handler}compileAllowed(){var t;return null!=(null===(t=this.currentGlParentNode().assemblerController)||void 0===t?void 0:t.globals_handler)}shaders_by_name(){return this._shaders_by_name}_build_lines(){for(let t of this.shaderNames()){const e=this._template_shader_for_shader_name(t);e&&this._replace_template(e,t)}}set_root_nodes(t){this._root_nodes=t}templateShader(){}addUniforms(t){for(let e of this.param_configs())t[e.uniform_name]=e.uniform;this.uniformsTimeDependent()&&(t.time={value:this.currentGlParentNode().scene().time()}),this.uniforms_resolution_dependent()&&(t.resolution={value:new d.a(1e3,1e3)})}root_nodes_by_shader_name(t){const e=[];for(let t of this._root_nodes)switch(t.type()){case bF.type():case AF.type():e.push(t);break;case gf.type():case Lf.type():e.push(t)}return e}leaf_nodes_by_shader_name(t){const e=[];for(let t of this._leaf_nodes)switch(t.type()){case AI.type():e.push(t);break;case gf.type():}return e}set_node_lines_globals(t,e){}set_node_lines_output(t,e){}set_node_lines_attribute(t,e){}codeBuilder(){return this._code_builder=this._code_builder||this._create_code_builder()}_resetCodeBuilder(){this._code_builder=void 0}_create_code_builder(){const t=new Z3(this.currentGlParentNode(),this.shaderNames(),((t,e)=>this.input_names_for_shader_name(t,e)));return new J3(t,(t=>this.root_nodes_by_shader_name(t)),this)}build_code_from_nodes(t){const e=Of.findParamGeneratingNodes(this.currentGlParentNode());this.codeBuilder().buildFromNodes(t,e)}allow_new_param_configs(){this.codeBuilder().allow_new_param_configs()}disallow_new_param_configs(){this.codeBuilder().disallow_new_param_configs()}builder_param_configs(){return this.codeBuilder().param_configs()}builder_lines(t,e){return this.codeBuilder().lines(t,e)}all_builder_lines(){return this.codeBuilder().all_lines()}param_configs(){return(this._param_config_owner||this.codeBuilder()).param_configs()}set_param_configs_owner(t){this._param_config_owner=t,this._param_config_owner?this.codeBuilder().disallow_new_param_configs():this.codeBuilder().allow_new_param_configs()}static output_input_connection_points(){return[new Ho(\\\\\\\"position\\\\\\\",Bo.VEC3),new Ho(\\\\\\\"normal\\\\\\\",Bo.VEC3),new Ho(\\\\\\\"color\\\\\\\",Bo.VEC3),new Ho(\\\\\\\"alpha\\\\\\\",Bo.FLOAT),new Ho(\\\\\\\"uv\\\\\\\",Bo.VEC2)]}add_output_inputs(t){t.io.inputs.setNamedInputConnectionPoints(e4.output_input_connection_points())}static create_globals_node_output_connections(){return[new Ho(\\\\\\\"position\\\\\\\",Bo.VEC3),new Ho(\\\\\\\"normal\\\\\\\",Bo.VEC3),new Ho(\\\\\\\"color\\\\\\\",Bo.VEC3),new Ho(\\\\\\\"uv\\\\\\\",Bo.VEC2),new Ho(\\\\\\\"mvPosition\\\\\\\",Bo.VEC4),new Ho(\\\\\\\"worldPosition\\\\\\\",Bo.VEC4),new Ho(\\\\\\\"worldNormal\\\\\\\",Bo.VEC3),new Ho(\\\\\\\"gl_Position\\\\\\\",Bo.VEC4),new Ho(\\\\\\\"gl_FragCoord\\\\\\\",Bo.VEC4),new Ho(\\\\\\\"cameraPosition\\\\\\\",Bo.VEC3),new Ho(\\\\\\\"resolution\\\\\\\",Bo.VEC2),new Ho(\\\\\\\"time\\\\\\\",Bo.FLOAT)]}create_globals_node_output_connections(){return e4.create_globals_node_output_connections()}add_globals_outputs(t){t.io.outputs.setNamedOutputConnectionPoints(this.create_globals_node_output_connections())}allow_attribute_exports(){return!1}reset_configs(){this._reset_shader_configs(),this._reset_variable_configs(),this._resetUniformsTimeDependency(),this._reset_uniforms_resolution_dependency()}shaderConfigs(){return this._shader_configs=this._shader_configs||this.create_shader_configs()}set_shader_configs(t){this._shader_configs=t}shaderNames(){var t;return(null===(t=this.shaderConfigs())||void 0===t?void 0:t.map((t=>t.name())))||[]}_reset_shader_configs(){this._shader_configs=void 0}create_shader_configs(){return[new H3(xf.VERTEX,[\\\\\\\"position\\\\\\\",\\\\\\\"normal\\\\\\\",\\\\\\\"uv\\\\\\\",Lf.INPUT_NAME],[]),new H3(xf.FRAGMENT,[\\\\\\\"color\\\\\\\",\\\\\\\"alpha\\\\\\\"],[xf.VERTEX])]}shader_config(t){var e;return null===(e=this.shaderConfigs())||void 0===e?void 0:e.filter((e=>e.name()==t))[0]}variable_configs(){return this._variable_configs=this._variable_configs||this.create_variable_configs()}set_variable_configs(t){this._variable_configs=t}variable_config(t){return this.variable_configs().filter((e=>e.name()==t))[0]}static create_variable_configs(){return[new j3(\\\\\\\"position\\\\\\\",{default_from_attribute:!0,prefix:\\\\\\\"vec3 transformed = \\\\\\\"}),new j3(\\\\\\\"normal\\\\\\\",{default_from_attribute:!0,prefix:\\\\\\\"vec3 objectNormal = \\\\\\\",postLines:[\\\\\\\"#ifdef USE_TANGENT\\\\\\\",\\\\\\\"\\\\tvec3 objectTangent = vec3( tangent.xyz );\\\\\\\",\\\\\\\"#endif\\\\\\\"]}),new j3(\\\\\\\"color\\\\\\\",{prefix:\\\\\\\"diffuseColor.xyz = \\\\\\\"}),new j3(\\\\\\\"alpha\\\\\\\",{prefix:\\\\\\\"diffuseColor.a = \\\\\\\"}),new j3(\\\\\\\"uv\\\\\\\",{prefix:\\\\\\\"vUv = \\\\\\\",if:Sf.IF_RULE.uv})]}create_variable_configs(){return e4.create_variable_configs()}_reset_variable_configs(){this._variable_configs=void 0,this.variable_configs()}input_names_for_shader_name(t,e){var n;return(null===(n=this.shader_config(e))||void 0===n?void 0:n.input_names())||[]}_resetUniformsTimeDependency(){this._uniforms_time_dependent=!1}setUniformsTimeDependent(){this._uniforms_time_dependent=!0}uniformsTimeDependent(){return this._uniforms_time_dependent}_reset_uniforms_resolution_dependency(){this._uniforms_resolution_dependent=!1}set_uniforms_resolution_dependent(){this._uniforms_resolution_dependent=!0}uniforms_resolution_dependent(){return this._uniforms_resolution_dependent}insert_define_after(t){return Q3.get(t)}insert_body_after(t){return K3.get(t)}lines_to_remove(t){return t4.get(t)}_replace_template(t,e){const n=this.builder_lines(e,V3.FUNCTION_DECLARATION),i=this.builder_lines(e,V3.DEFINE),s=this.builder_lines(e,V3.BODY);let r=t.split(\\\\\\\"\\\\n\\\\\\\");const o=[],a=this.insert_define_after(e),l=this.insert_body_after(e),c=this.lines_to_remove(e);let h=!1,u=!1;for(let t of r){1==h&&(n&&this._insert_lines(o,n),i&&this._insert_lines(o,i),h=!1),1==u&&(s&&this._insert_lines(o,s),u=!1);let e=!1;if(c)for(let n of c)t.indexOf(n)>=0&&(e=!0);e?(o.push(\\\\\\\"// removed:\\\\\\\"),o.push(`//${t}`)):o.push(t),a&&t.indexOf(a)>=0&&(h=!0),l&&t.indexOf(l)>=0&&(u=!0)}this._lines.set(e,o)}_insert_lines(t,e){if(e.length>0){for(let e=0;e<3;e++)t.push(\\\\\\\"\\\\\\\");for(let n of e)t.push(n);for(let e=0;e<3;e++)t.push(\\\\\\\"\\\\\\\")}}_addFilterFragmentShaderCallback(t,e){}_removeFilterFragmentShaderCallback(t){}getCustomMaterials(){return new Map}static expandShader(t){return function t(e){return e.replace(/^[ \\\\t]*#include +<([\\\\w\\\\d./]+)>/gm,(function(e,n){var i=U[n];if(void 0===i)throw new Error(\\\\\\\"Can not resolve #include <\\\\\\\"+n+\\\\\\\">\\\\\\\");return t(i)}))}(t)}}var n4,i4;!function(t){t.DISTANCE=\\\\\\\"customDistanceMaterial\\\\\\\",t.DEPTH=\\\\\\\"customDepthMaterial\\\\\\\",t.DEPTH_DOF=\\\\\\\"customDepthDOFMaterial\\\\\\\"}(n4||(n4={})),function(t){t.TIME=\\\\\\\"time\\\\\\\",t.RESOLUTION=\\\\\\\"resolution\\\\\\\",t.MV_POSITION=\\\\\\\"mvPosition\\\\\\\",t.GL_POSITION=\\\\\\\"gl_Position\\\\\\\",t.GL_FRAGCOORD=\\\\\\\"gl_FragCoord\\\\\\\",t.GL_POINTCOORD=\\\\\\\"gl_PointCoord\\\\\\\"}(i4||(i4={}));const s4=[i4.GL_FRAGCOORD,i4.GL_POINTCOORD];class r4 extends e4{constructor(){super(...arguments),this._assemblers_by_custom_name=new Map,this._filterFragmentShaderCallbacks=new Map}createMaterial(){return new F}custom_assembler_class_by_custom_name(){}_addCustomMaterials(t){const e=this.custom_assembler_class_by_custom_name();e&&e.forEach(((e,n)=>{this._add_custom_material(t,n,e)}))}_add_custom_material(t,e,n){let i=this._assemblers_by_custom_name.get(e);i||(i=new n(this.currentGlParentNode()),this._assemblers_by_custom_name.set(e,i)),t.customMaterials=t.customMaterials||{};const s=i.createMaterial();s.name=e,t.customMaterials[e]=s}compileCustomMaterials(t){const e=this.custom_assembler_class_by_custom_name();e&&e.forEach(((e,n)=>{if(this._code_builder){let i=this._assemblers_by_custom_name.get(n);i||(i=new e(this.currentGlParentNode()),this._assemblers_by_custom_name.set(n,i)),i.set_root_nodes(this._root_nodes),i.set_param_configs_owner(this._code_builder),i.set_shader_configs(this.shaderConfigs()),i.set_variable_configs(this.variable_configs());const s=t.customMaterials[n];s&&(i.setFilterFragmentShaderMethodOwner(this),i.compileMaterial(s),i.setFilterFragmentShaderMethodOwner(void 0))}}))}_resetFilterFragmentShaderCallbacks(){this._filterFragmentShaderCallbacks.clear()}_addFilterFragmentShaderCallback(t,e){this._filterFragmentShaderCallbacks.set(t,e)}_removeFilterFragmentShaderCallback(t){this._filterFragmentShaderCallbacks.delete(t)}setFilterFragmentShaderMethodOwner(t){this._filterFragmentShaderMethodOwner=t}filterFragmentShader(t){return this._filterFragmentShaderCallbacks.forEach(((e,n)=>{t=e(t)})),t}processFilterFragmentShader(t){return this._filterFragmentShaderMethodOwner?this._filterFragmentShaderMethodOwner.filterFragmentShader(t):this.filterFragmentShader(t)}compileMaterial(t){if(!this.compileAllowed())return;const e=Of.findOutputNodes(this.currentGlParentNode());e.length>1&&this.currentGlParentNode().states.error.set(\\\\\\\"only one output node allowed\\\\\\\");const n=Of.findVaryingNodes(this.currentGlParentNode()),i=e.concat(n);this.set_root_nodes(i),this._update_shaders();const s=this._shaders_by_name.get(xf.VERTEX),r=this._shaders_by_name.get(xf.FRAGMENT);s&&r&&(t.vertexShader=s,t.fragmentShader=this.processFilterFragmentShader(r),this.addUniforms(t.uniforms),t.needsUpdate=!0);const o=this.currentGlParentNode().scene();this.uniformsTimeDependent()?o.uniformsController.addTimeDependentUniformOwner(t.uuid,t.uniforms):o.uniformsController.removeTimeDependentUniformOwner(t.uuid),this.uniforms_resolution_dependent()?o.uniformsController.addResolutionDependentUniformOwner(t.uuid,t.uniforms):o.uniformsController.removeResolutionDependentUniformOwner(t.uuid),t.customMaterials&&this.compileCustomMaterials(t)}_update_shaders(){this._shaders_by_name=new Map,this._lines=new Map;for(let t of this.shaderNames()){const e=this._template_shader_for_shader_name(t);e&&this._lines.set(t,e.split(\\\\\\\"\\\\n\\\\\\\"))}this._root_nodes.length>0&&(this.build_code_from_nodes(this._root_nodes),this._build_lines());for(let t of this.shaderNames()){const e=this._lines.get(t);e&&this._shaders_by_name.set(t,e.join(\\\\\\\"\\\\n\\\\\\\"))}}shadow_assembler_class_by_custom_name(){return{}}add_output_body_line(t,e,n){var i;const s=t.io.inputs.named_input(n),r=t.variableForInput(n),o=this.variable_config(n);let a=null;if(s)a=hf.vector3(r);else if(o.default_from_attribute()){const s=t.io.inputs.namedInputConnectionPointsByName(n);if(s){const r=s.type(),o=null===(i=this.globals_handler)||void 0===i?void 0:i.read_attribute(t,r,n,e);o&&(a=o)}}else{const t=o.default();t&&(a=t)}if(a){const n=o.prefix(),i=o.suffix(),s=o.if_condition();s&&e.addBodyLines(t,[`#if ${s}`]),e.addBodyLines(t,[`${n}${a}${i}`]);const r=o.postLines();r&&e.addBodyLines(t,r),s&&e.addBodyLines(t,[\\\\\\\"#endif\\\\\\\"])}}set_node_lines_output(t,e){var n;const i=e.current_shader_name,s=null===(n=this.shader_config(i))||void 0===n?void 0:n.input_names();if(s)for(let n of s)t.io.inputs.has_named_input(n)&&this.add_output_body_line(t,e,n)}set_node_lines_attribute(t,e){var n;const i=t.gl_type(),s=null===(n=this.globals_handler)||void 0===n?void 0:n.read_attribute(t,i,t.attribute_name,e),r=t.glVarName(t.output_name);e.addBodyLines(t,[`${i} ${r} = ${s}`])}handle_globals_output_name(t){var e;switch(t.output_name){case i4.TIME:return void this.handleTime(t);case i4.RESOLUTION:return void this.handle_resolution(t);case i4.MV_POSITION:return void this.handle_mvPosition(t);case i4.GL_POSITION:return void this.handle_gl_Position(t);case i4.GL_FRAGCOORD:return void this.handle_gl_FragCoord(t);case i4.GL_POINTCOORD:return void this.handle_gl_PointCoord(t);default:null===(e=this.globals_handler)||void 0===e||e.handle_globals_node(t.globals_node,t.output_name,t.shaders_collection_controller)}}handleTime(t){const e=new Af(t.globals_node,Bo.FLOAT,t.output_name);t.globals_shader_name&&h.pushOnArrayAtEntry(t.definitions_by_shader_name,t.globals_shader_name,e);const n=`float ${t.var_name} = ${t.output_name}`;for(let i of t.dependencies)h.pushOnArrayAtEntry(t.definitions_by_shader_name,i,e),h.pushOnArrayAtEntry(t.body_lines_by_shader_name,i,n);t.body_lines.push(n),this.setUniformsTimeDependent()}handle_resolution(t){t.body_lines.push(`vec2 ${t.var_name} = resolution`);const e=new Af(t.globals_node,Bo.VEC2,t.output_name);t.globals_shader_name&&h.pushOnArrayAtEntry(t.definitions_by_shader_name,t.globals_shader_name,e);for(let n of t.dependencies)h.pushOnArrayAtEntry(t.definitions_by_shader_name,n,e);this.set_uniforms_resolution_dependent()}handle_mvPosition(t){if(t.shader_name==xf.FRAGMENT){const e=t.globals_node,n=t.shaders_collection_controller,i=new Mf(e,Bo.VEC4,t.var_name),s=`${t.var_name} = modelViewMatrix * vec4(position, 1.0)`;n.addDefinitions(e,[i],xf.VERTEX),n.addBodyLines(e,[s],xf.VERTEX),n.addDefinitions(e,[i])}}handle_gl_Position(t){if(t.shader_name==xf.FRAGMENT){const e=t.globals_node,n=t.shaders_collection_controller,i=new Mf(e,Bo.VEC4,t.var_name),s=`${t.var_name} = projectionMatrix * modelViewMatrix * vec4(position, 1.0)`;n.addDefinitions(e,[i],xf.VERTEX),n.addBodyLines(e,[s],xf.VERTEX),n.addDefinitions(e,[i])}}handle_gl_FragCoord(t){t.shader_name==xf.FRAGMENT&&t.body_lines.push(`vec4 ${t.var_name} = gl_FragCoord`)}handle_gl_PointCoord(t){t.shader_name==xf.FRAGMENT?t.body_lines.push(`vec2 ${t.var_name} = gl_PointCoord`):t.body_lines.push(`vec2 ${t.var_name} = vec2(0.0, 0.0)`)}set_node_lines_globals(t,e){const n=[],i=e.current_shader_name,s=this.shader_config(i);if(!s)return;const r=s.dependencies(),o=new Map,a=new Map,l=this.used_output_names_for_shader(t,i);for(let s of l){const l=t.glVarName(s),c=e.current_shader_name,h={globals_node:t,shaders_collection_controller:e,output_name:s,globals_shader_name:c,definitions_by_shader_name:o,body_lines:n,var_name:l,shader_name:i,dependencies:r,body_lines_by_shader_name:a};this.handle_globals_output_name(h)}o.forEach(((n,i)=>{e.addDefinitions(t,n,i)})),a.forEach(((n,i)=>{e.addBodyLines(t,n,i)})),e.addBodyLines(t,n)}used_output_names_for_shader(t,e){const n=t.io.outputs.used_output_names(),i=[];for(let t of n)e==xf.VERTEX&&s4.includes(t)||i.push(t);return i}}const o4=new Map([[xf.VERTEX,\\\\\\\"#include \\\\\\\"],[xf.FRAGMENT,\\\\\\\"vec4 diffuseColor = vec4( 1.0 );\\\\\\\"]]);const a4=new Map([[xf.VERTEX,\\\\\\\"#include \\\\\\\"],[xf.FRAGMENT,\\\\\\\"vec4 diffuseColor = vec4( 1.0 );\\\\\\\"]]);var l4=\\\\\\\"uniform float mNear;\\\\nuniform float mFar;\\\\n\\\\nvarying float vViewZDepth;\\\\n\\\\nvoid main() {\\\\n\\\\n\\\\tfloat color = 1.0 - smoothstep( mNear, mFar, vViewZDepth );\\\\n\\\\tgl_FragColor = vec4( vec3( color ), 1.0 );\\\\n\\\\n}\\\\n\\\\\\\";const c4=new Map([[xf.VERTEX,\\\\\\\"// INSERT DEFINES\\\\\\\"]]),h4=new Map([[xf.VERTEX,\\\\\\\"// INSERT BODY\\\\\\\"]]);const u4=new Map([]);u4.set(n4.DISTANCE,class extends r4{templateShader(){const t=H.distanceRGBA;return{vertexShader:t.vertexShader,fragmentShader:t.fragmentShader,uniforms:t.uniforms}}insert_body_after(t){return o4.get(t)}createMaterial(){const t=this.templateShader();return new F({defines:{DEPTH_PACKING:[w.Hb,w.j][0]},uniforms:I.clone(t.uniforms),vertexShader:t.vertexShader,fragmentShader:t.fragmentShader})}}),u4.set(n4.DEPTH,class extends r4{templateShader(){const t=H.depth;return{vertexShader:t.vertexShader,fragmentShader:t.fragmentShader,uniforms:t.uniforms}}insert_body_after(t){return a4.get(t)}createMaterial(){const t=this.templateShader();return new F({defines:{DEPTH_PACKING:[w.Hb,w.j][0]},uniforms:I.clone(t.uniforms),vertexShader:t.vertexShader,fragmentShader:t.fragmentShader})}}),u4.set(n4.DEPTH_DOF,class extends r4{templateShader(){return{vertexShader:\\\\\\\"#include \\\\n\\\\nvarying float vViewZDepth;\\\\n\\\\n// INSERT DEFINES\\\\n\\\\n\\\\n\\\\nvoid main() {\\\\n\\\\n\\\\t// INSERT BODY\\\\n\\\\n\\\\n\\\\t#include \\\\n\\\\n\\\\tvViewZDepth = - mvPosition.z;\\\\n}\\\\\\\",fragmentShader:l4,uniforms:{mNear:{value:0},mFar:{value:10}}}}insert_define_after(t){return c4.get(t)}insert_body_after(t){return h4.get(t)}createMaterial(){const t=this.templateShader();return new F({uniforms:I.clone(t.uniforms),vertexShader:t.vertexShader,fragmentShader:t.fragmentShader})}});class d4 extends r4{custom_assembler_class_by_custom_name(){return u4}}class p4 extends d4{templateShader(){const t=H.basic;return{vertexShader:t.vertexShader,fragmentShader:t.fragmentShader,uniforms:t.uniforms}}createMaterial(){const t=this.templateShader(),e=new F({lights:!1,uniforms:I.clone(t.uniforms),vertexShader:t.vertexShader,fragmentShader:t.fragmentShader});return this._addCustomMaterials(e),e}}class _4 extends d4{templateShader(){const t=H.lambert;return{vertexShader:t.vertexShader,fragmentShader:t.fragmentShader,uniforms:t.uniforms}}createMaterial(){const t=this.templateShader(),e=new F({lights:!0,uniforms:I.clone(t.uniforms),vertexShader:t.vertexShader,fragmentShader:t.fragmentShader});return this._addCustomMaterials(e),e}}class m4 extends d4{templateShader(){const t=H.phong;return{vertexShader:t.vertexShader,fragmentShader:t.fragmentShader,uniforms:t.uniforms}}createMaterial(){const t=this.templateShader(),e=new F({lights:!0,uniforms:I.clone(t.uniforms),vertexShader:t.vertexShader,fragmentShader:t.fragmentShader});return this._addCustomMaterials(e),e}}var f4=\\\\\\\"SSSModel(/*isActive*/false,/*color*/vec3(1.0, 1.0, 1.0), /*thickness*/0.1, /*power*/2.0, /*scale*/16.0, /*distortion*/0.1,/*ambient*/0.4,/*attenuation*/0.8 )\\\\\\\";class g4 extends d4{constructor(t){super(t),this._gl_parent_node=t,this._addFilterFragmentShaderCallback(\\\\\\\"MeshStandardBuilderMatNode\\\\\\\",g4.filterFragmentShader)}isPhysical(){return!1}templateShader(){const t=this.isPhysical()?H.physical:H.standard;return{vertexShader:t.vertexShader,fragmentShader:t.fragmentShader,uniforms:t.uniforms}}static filterFragmentShader(t){return t=(t=(t=t.replace(\\\\\\\"#include \\\\\\\",\\\\\\\"float metalnessFactor = metalness * POLY_metalness;\\\\n\\\\n#ifdef USE_METALNESSMAP\\\\n\\\\n\\\\tvec4 texelMetalness = texture2D( metalnessMap, vUv );\\\\n\\\\n\\\\t// reads channel B, compatible with a combined OcclusionRoughnessMetallic (RGB) texture\\\\n\\\\tmetalnessFactor *= texelMetalness.b;\\\\n\\\\n#endif\\\\n\\\\\\\")).replace(\\\\\\\"#include \\\\\\\",\\\\\\\"float roughnessFactor = roughness * POLY_roughness;\\\\n\\\\n#ifdef USE_ROUGHNESSMAP\\\\n\\\\n\\\\tvec4 texelRoughness = texture2D( roughnessMap, vUv );\\\\n\\\\n\\\\t// reads channel G, compatible with a combined OcclusionRoughnessMetallic (RGB) texture\\\\n\\\\troughnessFactor *= texelRoughness.g;\\\\n\\\\n#endif\\\\n\\\\\\\")).replace(\\\\\\\"vec3 totalEmissiveRadiance = emissive;\\\\\\\",\\\\\\\"vec3 totalEmissiveRadiance = emissive * POLY_emissive;\\\\\\\"),g4.USE_SSS&&(t=(t=t.replace(/void main\\\\s?\\\\(\\\\) {/,\\\\\\\"struct SSSModel {\\\\n\\\\tbool isActive;\\\\n\\\\tvec3 color;\\\\n\\\\tfloat thickness;\\\\n\\\\tfloat power;\\\\n\\\\tfloat scale;\\\\n\\\\tfloat distortion;\\\\n\\\\tfloat ambient;\\\\n\\\\tfloat attenuation;\\\\n};\\\\n\\\\nvoid RE_Direct_Scattering(\\\\n\\\\tconst in IncidentLight directLight,\\\\n\\\\tconst in GeometricContext geometry,\\\\n\\\\tconst in SSSModel sssModel,\\\\n\\\\tinout ReflectedLight reflectedLight\\\\n\\\\t){\\\\n\\\\tvec3 scatteringHalf = normalize(directLight.direction + (geometry.normal * sssModel.distortion));\\\\n\\\\tfloat scatteringDot = pow(saturate(dot(geometry.viewDir, -scatteringHalf)), sssModel.power) * sssModel.scale;\\\\n\\\\tvec3 scatteringIllu = (scatteringDot + sssModel.ambient) * (sssModel.color * (1.0-sssModel.thickness));\\\\n\\\\treflectedLight.directDiffuse += scatteringIllu * sssModel.attenuation * directLight.color;\\\\n}\\\\n\\\\nvoid main() {\\\\\\\")).replace(\\\\\\\"#include \\\\\\\",\\\\\\\"#include \\\\nif(POLY_SSSModel.isActive){\\\\n\\\\tRE_Direct_Scattering(directLight, geometry, POLY_SSSModel, reflectedLight);\\\\n}\\\\n\\\\n\\\\\\\")),t}createMaterial(){const t=this.templateShader(),e={lights:!0,extensions:{derivatives:!0},uniforms:I.clone(t.uniforms),vertexShader:t.vertexShader,fragmentShader:t.fragmentShader},n=new F(e);return this.isPhysical()&&(n.defines.PHYSICAL=!0),this._addCustomMaterials(n),n}add_output_inputs(t){const e=e4.output_input_connection_points();e.push(new Ho(\\\\\\\"metalness\\\\\\\",Bo.FLOAT,1)),e.push(new Ho(\\\\\\\"roughness\\\\\\\",Bo.FLOAT,1)),e.push(new Ho(\\\\\\\"emissive\\\\\\\",Bo.VEC3,[1,1,1])),g4.USE_SSS&&e.push(new Ho(\\\\\\\"SSSModel\\\\\\\",Bo.SSS_MODEL,f4)),this.isPhysical()&&(e.push(new Ho(\\\\\\\"transmission\\\\\\\",Bo.FLOAT,1)),e.push(new Ho(\\\\\\\"thickness\\\\\\\",Bo.FLOAT,1))),t.io.inputs.setNamedInputConnectionPoints(e)}create_shader_configs(){const t=[\\\\\\\"color\\\\\\\",\\\\\\\"alpha\\\\\\\",\\\\\\\"metalness\\\\\\\",\\\\\\\"roughness\\\\\\\",\\\\\\\"emissive\\\\\\\",\\\\\\\"SSSModel\\\\\\\"];return this.isPhysical()&&(t.push(\\\\\\\"transmission\\\\\\\"),t.push(\\\\\\\"thickness\\\\\\\")),[new H3(xf.VERTEX,[\\\\\\\"position\\\\\\\",\\\\\\\"normal\\\\\\\",\\\\\\\"uv\\\\\\\"],[]),new H3(xf.FRAGMENT,t,[xf.VERTEX])]}create_variable_configs(){const t=e4.create_variable_configs();return t.push(new j3(\\\\\\\"metalness\\\\\\\",{default:\\\\\\\"1.0\\\\\\\",prefix:\\\\\\\"float POLY_metalness = \\\\\\\"})),t.push(new j3(\\\\\\\"roughness\\\\\\\",{default:\\\\\\\"1.0\\\\\\\",prefix:\\\\\\\"float POLY_roughness = \\\\\\\"})),t.push(new j3(\\\\\\\"emissive\\\\\\\",{default:\\\\\\\"vec3(1.0, 1.0, 1.0)\\\\\\\",prefix:\\\\\\\"vec3 POLY_emissive = \\\\\\\"})),g4.USE_SSS&&t.push(new j3(\\\\\\\"SSSModel\\\\\\\",{default:f4,prefix:\\\\\\\"SSSModel POLY_SSSModel = \\\\\\\"})),this.isPhysical()&&(t.push(new j3(\\\\\\\"transmission\\\\\\\",{default:\\\\\\\"1.0\\\\\\\",prefix:\\\\\\\"float POLY_transmission = \\\\\\\"})),t.push(new j3(\\\\\\\"thickness\\\\\\\",{default:\\\\\\\"1.0\\\\\\\",prefix:\\\\\\\"float POLY_thickness = \\\\\\\"}))),t}}g4.USE_SSS=!0;class v4 extends g4{constructor(t){super(t),this._gl_parent_node=t,this._addFilterFragmentShaderCallback(\\\\\\\"MeshPhysicalBuilderMatNode\\\\\\\",v4.filterFragmentShader)}isPhysical(){return!0}static filterFragmentShader(t){return t=t.replace(\\\\\\\"#include \\\\\\\",function(t){const e=t.split(\\\\\\\"\\\\n\\\\\\\");let n=0;for(let t of e)t.includes(\\\\\\\"float transmissionFactor = transmission;\\\\\\\")&&(t=\\\\\\\"float transmissionFactor = transmission * POLY_transmission;\\\\\\\",e[n]=t),t.includes(\\\\\\\"float thicknessFactor = thickness;\\\\\\\")&&(t=\\\\\\\"float thicknessFactor = thickness * POLY_thickness;\\\\\\\",e[n]=t),n++;return e.join(\\\\\\\"\\\\n\\\\\\\")}(k))}}const y4=new Map([[xf.VERTEX,\\\\\\\"// INSERT DEFINES\\\\\\\"]]),x4=new Map([[xf.VERTEX,\\\\\\\"// INSERT BODY\\\\\\\"]]);const b4=new Map([[xf.VERTEX,\\\\\\\"// INSERT DEFINES\\\\\\\"]]),w4=new Map([[xf.VERTEX,\\\\\\\"// INSERT BODY\\\\\\\"]]);const T4=new Map([[xf.VERTEX,[\\\\\\\"#include \\\\\\\",\\\\\\\"gl_PointSize = size;\\\\\\\"]],[xf.FRAGMENT,[]]]),A4=new Map;A4.set(n4.DISTANCE,class extends r4{templateShader(){const t=H.distanceRGBA,e=I.clone(t.uniforms);return e.size={value:1},e.scale={value:1},{vertexShader:\\\\\\\"uniform float size;\\\\nuniform float scale;\\\\n#define DISTANCE\\\\nvarying vec3 vWorldPosition;\\\\n#include \\\\n#include \\\\nvarying float vViewZDepth;\\\\n\\\\n// INSERT DEFINES\\\\n\\\\n\\\\n\\\\nvoid main() {\\\\n\\\\n\\\\t// INSERT BODY\\\\n\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n\\\\t#ifdef USE_SIZEATTENUATION\\\\n\\\\t\\\\tbool isPerspective = ( projectionMatrix[ 2 ][ 3 ] == - 1.0 );\\\\n\\\\t\\\\tif ( isPerspective ) gl_PointSize *= ( scale / - mvPosition.z );\\\\n\\\\t#endif\\\\n\\\\tvWorldPosition = worldPosition.xyz;\\\\n}\\\\n\\\\n// #define DISTANCE\\\\n// varying vec3 vWorldPosition;\\\\n// #include \\\\n// #include \\\\n// #include \\\\n// #include \\\\n// #include \\\\n// #include \\\\n// void main() {\\\\n// \\\\t#include \\\\n// \\\\t#include \\\\n// \\\\t#ifdef USE_DISPLACEMENTMAP\\\\n// \\\\t\\\\t#include \\\\n// \\\\t\\\\t#include \\\\n// \\\\t\\\\t#include \\\\n// \\\\t#endif\\\\n// \\\\t#include \\\\n// \\\\t#include \\\\n// \\\\t#include \\\\n// \\\\t#include \\\\n// \\\\t#include \\\\n// \\\\t#include \\\\n// \\\\t#include \\\\n// \\\\tvWorldPosition = worldPosition.xyz;\\\\n// }\\\\n\\\\n\\\\n\\\\\\\",fragmentShader:t.fragmentShader,uniforms:e}}insert_define_after(t){return y4.get(t)}insert_body_after(t){return x4.get(t)}createMaterial(){const t=this.templateShader();return new F({defines:{USE_SIZEATTENUATION:1,DEPTH_PACKING:[w.Hb,w.j][0]},uniforms:I.clone(t.uniforms),vertexShader:t.vertexShader,fragmentShader:t.fragmentShader})}}),A4.set(n4.DEPTH_DOF,class extends r4{templateShader(){return{vertexShader:\\\\\\\"uniform float size;\\\\nuniform float scale;\\\\n#include \\\\n\\\\nvarying float vViewZDepth;\\\\n\\\\n// INSERT DEFINES\\\\n\\\\n\\\\n\\\\nvoid main() {\\\\n\\\\n\\\\t// INSERT BODY\\\\n\\\\n\\\\n\\\\t#include \\\\n\\\\n\\\\tvViewZDepth = - mvPosition.z;\\\\n\\\\t#ifdef USE_SIZEATTENUATION\\\\n\\\\t\\\\tbool isPerspective = ( projectionMatrix[ 2 ][ 3 ] == - 1.0 );\\\\n\\\\t\\\\tif ( isPerspective ) gl_PointSize *= ( scale / - mvPosition.z );\\\\n\\\\t#endif\\\\n\\\\n}\\\\n\\\\n\\\\\\\",fragmentShader:l4,uniforms:{size:{value:1},scale:{value:1},mNear:{value:0},mFar:{value:10}}}}insert_define_after(t){return b4.get(t)}insert_body_after(t){return w4.get(t)}createMaterial(){const t=this.templateShader();return new F({depthTest:!0,defines:{USE_SIZEATTENUATION:1},uniforms:I.clone(t.uniforms),vertexShader:t.vertexShader,fragmentShader:t.fragmentShader})}});class M4 extends r4{custom_assembler_class_by_custom_name(){return A4}templateShader(){const t=H.points;return{vertexShader:t.vertexShader,fragmentShader:t.fragmentShader,uniforms:t.uniforms}}createMaterial(){const t=this.templateShader(),e=new F({transparent:!0,fog:!0,defines:{USE_SIZEATTENUATION:1},uniforms:I.clone(t.uniforms),vertexShader:t.vertexShader,fragmentShader:t.fragmentShader});return this._addCustomMaterials(e),e}add_output_inputs(t){const e=e4.output_input_connection_points();e.push(new Ho(\\\\\\\"gl_PointSize\\\\\\\",Bo.FLOAT)),t.io.inputs.setNamedInputConnectionPoints(e)}create_globals_node_output_connections(){return e4.create_globals_node_output_connections().concat([new Ho(i4.GL_POINTCOORD,Bo.VEC2)])}create_shader_configs(){return[new H3(xf.VERTEX,[\\\\\\\"position\\\\\\\",\\\\\\\"normal\\\\\\\",\\\\\\\"uv\\\\\\\",\\\\\\\"gl_PointSize\\\\\\\"],[]),new H3(xf.FRAGMENT,[\\\\\\\"color\\\\\\\",\\\\\\\"alpha\\\\\\\"],[xf.VERTEX])]}create_variable_configs(){return e4.create_variable_configs().concat([new j3(\\\\\\\"gl_PointSize\\\\\\\",{default:\\\\\\\"1.0\\\\\\\",prefix:\\\\\\\"gl_PointSize = \\\\\\\",suffix:\\\\\\\" * size * 10.0\\\\\\\"})])}lines_to_remove(t){return T4.get(t)}}const E4=new Map([[xf.VERTEX,\\\\\\\"// INSERT DEFINES\\\\\\\"]]),S4=new Map([[xf.VERTEX,\\\\\\\"// INSERT BODY\\\\\\\"]]);const C4=new Map([]);C4.set(n4.DEPTH_DOF,class extends r4{templateShader(){return{vertexShader:\\\\\\\"uniform float scale;\\\\nattribute float lineDistance;\\\\nvarying float vLineDistance;\\\\n#include \\\\n\\\\nvarying float vViewZDepth;\\\\n\\\\n// INSERT DEFINES\\\\n\\\\n\\\\n\\\\nvoid main() {\\\\n\\\\n\\\\t// INSERT BODY\\\\n\\\\n\\\\n\\\\tvLineDistance = scale * lineDistance;\\\\n\\\\tgl_Position = projectionMatrix * mvPosition;\\\\n\\\\n\\\\tvViewZDepth = - mvPosition.z;\\\\n\\\\n\\\\n}\\\\n\\\\n\\\\n\\\\n\\\\\\\",fragmentShader:l4,uniforms:{scale:{value:1},mNear:{value:0},mFar:{value:10}}}}insert_define_after(t){return E4.get(t)}insert_body_after(t){return S4.get(t)}createMaterial(){const t=this.templateShader();return new F({depthTest:!0,linewidth:100,uniforms:I.clone(t.uniforms),vertexShader:t.vertexShader,fragmentShader:t.fragmentShader})}});const N4=new Map([[xf.VERTEX,[\\\\\\\"#include \\\\\\\",\\\\\\\"#include \\\\\\\"]],[xf.FRAGMENT,[]]]);class L4 extends r4{templateShader(){const t=H.dashed;return{vertexShader:t.vertexShader,fragmentShader:t.fragmentShader,uniforms:t.uniforms}}createMaterial(){const t=this.templateShader(),e=new F({depthTest:!0,alphaTest:.5,linewidth:1,uniforms:I.clone(t.uniforms),vertexShader:t.vertexShader,fragmentShader:t.fragmentShader});return this._addCustomMaterials(e),e}custom_assembler_class_by_custom_name(){return console.log(\\\\\\\"custom_assembler_class_by_custom_name\\\\\\\",C4),C4}create_shader_configs(){return[new H3(xf.VERTEX,[\\\\\\\"position\\\\\\\",\\\\\\\"uv\\\\\\\"],[]),new H3(xf.FRAGMENT,[\\\\\\\"color\\\\\\\",\\\\\\\"alpha\\\\\\\"],[xf.VERTEX])]}static output_input_connection_points(){return[new Ho(\\\\\\\"position\\\\\\\",Bo.VEC3),new Ho(\\\\\\\"color\\\\\\\",Bo.VEC3),new Ho(\\\\\\\"alpha\\\\\\\",Bo.FLOAT),new Ho(\\\\\\\"uv\\\\\\\",Bo.VEC2)]}add_output_inputs(t){t.io.inputs.setNamedInputConnectionPoints(L4.output_input_connection_points())}static create_globals_node_output_connections(){return[new Ho(\\\\\\\"position\\\\\\\",Bo.VEC3),new Ho(\\\\\\\"color\\\\\\\",Bo.VEC3),new Ho(\\\\\\\"uv\\\\\\\",Bo.VEC2),new Ho(\\\\\\\"gl_FragCoord\\\\\\\",Bo.VEC4),new Ho(\\\\\\\"resolution\\\\\\\",Bo.VEC2),new Ho(\\\\\\\"time\\\\\\\",Bo.FLOAT)]}create_globals_node_output_connections(){return L4.create_globals_node_output_connections()}create_variable_configs(){return[new j3(\\\\\\\"position\\\\\\\",{default:\\\\\\\"vec3( position )\\\\\\\",prefix:\\\\\\\"vec3 transformed = \\\\\\\",suffix:\\\\\\\";vec4 mvPosition = vec4( transformed, 1.0 ); gl_Position = projectionMatrix * modelViewMatrix * mvPosition;\\\\\\\"}),new j3(\\\\\\\"color\\\\\\\",{prefix:\\\\\\\"diffuseColor.xyz = \\\\\\\"}),new j3(\\\\\\\"alpha\\\\\\\",{prefix:\\\\\\\"diffuseColor.w = \\\\\\\"}),new j3(\\\\\\\"uv\\\\\\\",{prefix:\\\\\\\"vUv = \\\\\\\",if:Sf.IF_RULE.uv})]}lines_to_remove(t){return N4.get(t)}}class O4 extends e4{templateShader(){}_template_shader_for_shader_name(t){return\\\\\\\"#include \\\\n\\\\n// INSERT DEFINE\\\\n\\\\nvoid main() {\\\\n\\\\n\\\\tvec2 particleUV = (gl_FragCoord.xy / resolution.xy);\\\\n\\\\n\\\\t// INSERT BODY\\\\n\\\\n}\\\\\\\"}compile(){this.setup_shader_names_and_variables(),this.update_shaders()}root_nodes_by_shader_name(t){var e,n;const i=[];for(let s of this._root_nodes)switch(s.type()){case bF.type():i.push(s);break;case gf.type():{const r=s.attribute_name,o=null===(e=this._texture_allocations_controller)||void 0===e?void 0:e.variable(r);if(o&&o.allocation()){(null===(n=o.allocation())||void 0===n?void 0:n.shaderName())==t&&i.push(s)}break}}return i}leaf_nodes_by_shader_name(t){var e,n;const i=[];for(let s of this._leaf_nodes)switch(s.type()){case AI.type():i.push(s);break;case gf.type():{const r=s.attribute_name,o=null===(e=this._texture_allocations_controller)||void 0===e?void 0:e.variable(r);if(o&&o.allocation()){(null===(n=o.allocation())||void 0===n?void 0:n.shaderName())==t&&i.push(s)}break}}return i}setup_shader_names_and_variables(){var t;const e=new Z3(this.currentGlParentNode(),this.shaderNames(),((t,e)=>this.input_names_for_shader_name(t,e)));this._leaf_nodes=e.leaves_from_nodes(this._root_nodes),this._texture_allocations_controller=new O0,this._texture_allocations_controller.allocateConnectionsFromRootNodes(this._root_nodes,this._leaf_nodes),this.globals_handler&&(null===(t=this.globals_handler)||void 0===t||t.set_texture_allocations_controller(this._texture_allocations_controller)),this._reset_shader_configs()}update_shaders(){this._shaders_by_name.clear(),this._lines.clear();for(let t of this.shaderNames()){const e=this._template_shader_for_shader_name(t);this._lines.set(t,e.split(\\\\\\\"\\\\n\\\\\\\"))}this._root_nodes.length>0&&(this._resetCodeBuilder(),this.build_code_from_nodes(this._root_nodes),this._build_lines());for(let t of this.shaderNames()){const e=this._lines.get(t);e&&this._shaders_by_name.set(t,e.join(\\\\\\\"\\\\n\\\\\\\"))}}add_output_inputs(t){t.io.inputs.setNamedInputConnectionPoints([new Ho(\\\\\\\"position\\\\\\\",Bo.VEC3),new Ho(\\\\\\\"velocity\\\\\\\",Bo.VEC3)])}add_globals_outputs(t){t.io.outputs.setNamedOutputConnectionPoints([new Ho(\\\\\\\"position\\\\\\\",Bo.VEC3),new Ho(\\\\\\\"velocity\\\\\\\",Bo.VEC3),new Ho(\\\\\\\"time\\\\\\\",Bo.FLOAT)])}allow_attribute_exports(){return!0}textureAllocationsController(){return this._texture_allocations_controller=this._texture_allocations_controller||new O0}create_shader_configs(){var t;return(null===(t=this._texture_allocations_controller)||void 0===t?void 0:t.createShaderConfigs())||[]}create_variable_configs(){return[]}shaderNames(){return this.textureAllocationsController().shaderNames()||[]}input_names_for_shader_name(t,e){return this.textureAllocationsController().inputNamesForShaderName(t,e)||[]}insert_define_after(t){return\\\\\\\"// INSERT DEFINE\\\\\\\"}insert_body_after(t){return\\\\\\\"// INSERT BODY\\\\\\\"}lines_to_remove(t){return[\\\\\\\"// INSERT DEFINE\\\\\\\",\\\\\\\"// INSERT BODY\\\\\\\"]}add_export_body_line(t,e,n,i,s){var r;if(n){const n=t.variableForInput(e),o=hf.vector3(n);if(o){const e=this.textureAllocationsController().variable(i),n=s.current_shader_name;if(e&&(null===(r=e.allocation())||void 0===r?void 0:r.shaderName())==n){const i=`gl_FragColor.${e.component()} = ${o}`;s.addBodyLines(t,[i],n)}}}}set_node_lines_output(t,e){const n=e.current_shader_name,i=this.textureAllocationsController().inputNamesForShaderName(t,n);if(i)for(let n of i){const i=t.io.inputs.named_input(n);if(i){const s=n;this.add_export_body_line(t,n,i,s,e)}}}set_node_lines_attribute(t,e){var n,i;if(t.isImporting()){const s=t.gl_type(),r=t.attribute_name,o=null===(n=this.globals_handler)||void 0===n?void 0:n.read_attribute(t,s,r,e),a=t.glVarName(t.output_name),l=`${s} ${a} = ${o}`;e.addBodyLines(t,[l]);const c=this.textureAllocationsController().variable(r),h=e.current_shader_name;if(c&&(null===(i=c.allocation())||void 0===i?void 0:i.shaderName())==h){const n=this.textureAllocationsController().variable(r);if(n){const i=`gl_FragColor.${n.component()} = ${a}`;e.addBodyLines(t,[i])}}}if(t.isExporting()){const n=t.connected_input_node();if(n){const i=t.attribute_name;this.add_export_body_line(t,t.input_name,n,i,e)}}}set_node_lines_globals(t,e){for(let n of t.io.outputs.used_output_names())switch(n){case\\\\\\\"time\\\\\\\":this._handle_globals_time(t,n,e);break;default:this._handle_globals_default(t,n,e)}}_handle_globals_time(t,e,n){const i=new Af(t,Bo.FLOAT,e);n.addDefinitions(t,[i]);const s=`float ${t.glVarName(e)} = ${e}`;n.addBodyLines(t,[s]),this.setUniformsTimeDependent()}_handle_globals_default(t,e,n){var i;const s=t.io.outputs.namedOutputConnectionPointsByName(e);if(s){const r=s.type(),o=null===(i=this.globals_handler)||void 0===i?void 0:i.read_attribute(t,r,e,n);if(o){const i=`${r} ${t.glVarName(e)} = ${o}`;n.addBodyLines(t,[i])}}}}class P4 extends e4{templateShader(){return{fragmentShader:\\\\\\\"#include \\\\n\\\\nuniform vec2 resolution;\\\\n\\\\n// INSERT DEFINE\\\\n\\\\nvoid main() {\\\\n\\\\n\\\\tvec4 diffuseColor = vec4(0.0,0.0,0.0,1.0);\\\\n\\\\n\\\\n\\\\t// INSERT BODY\\\\n\\\\n\\\\tgl_FragColor = vec4( diffuseColor );\\\\n}\\\\\\\",vertexShader:void 0,uniforms:void 0}}fragment_shader(){return this._shaders_by_name.get(xf.FRAGMENT)}uniforms(){return this._uniforms}update_fragment_shader(){this._lines=new Map,this._shaders_by_name=new Map;for(let t of this.shaderNames())if(t==xf.FRAGMENT){const e=this.templateShader().fragmentShader;this._lines.set(t,e.split(\\\\\\\"\\\\n\\\\\\\"))}this._root_nodes.length>0&&(this.build_code_from_nodes(this._root_nodes),this._build_lines()),this._uniforms=this._uniforms||{},this.addUniforms(this._uniforms);for(let t of this.shaderNames()){const e=this._lines.get(t);e&&this._shaders_by_name.set(t,e.join(\\\\\\\"\\\\n\\\\\\\"))}tg.handle_dependencies(this.currentGlParentNode(),this.uniformsTimeDependent(),this._uniforms)}add_output_inputs(t){t.io.inputs.setNamedInputConnectionPoints([new Ho(\\\\\\\"color\\\\\\\",Bo.VEC3),new Ho(\\\\\\\"alpha\\\\\\\",Bo.FLOAT)])}add_globals_outputs(t){t.io.outputs.setNamedOutputConnectionPoints([new Ho(\\\\\\\"gl_FragCoord\\\\\\\",Bo.VEC2),new Ho(\\\\\\\"time\\\\\\\",Bo.FLOAT)])}create_shader_configs(){return[new H3(xf.FRAGMENT,[\\\\\\\"color\\\\\\\",\\\\\\\"alpha\\\\\\\"],[])]}create_variable_configs(){return[new j3(\\\\\\\"color\\\\\\\",{prefix:\\\\\\\"diffuseColor.xyz = \\\\\\\"}),new j3(\\\\\\\"alpha\\\\\\\",{prefix:\\\\\\\"diffuseColor.a = \\\\\\\",default:\\\\\\\"1.0\\\\\\\"})]}insert_define_after(t){return\\\\\\\"// INSERT DEFINE\\\\\\\"}insert_body_after(t){return\\\\\\\"// INSERT BODY\\\\\\\"}lines_to_remove(t){return[\\\\\\\"// INSERT DEFINE\\\\\\\",\\\\\\\"// INSERT BODY\\\\\\\"]}handle_gl_FragCoord(t,e,n){\\\\\\\"fragment\\\\\\\"==e&&t.push(`vec2 ${n} = vec2(gl_FragCoord.x / resolution.x, gl_FragCoord.y / resolution.y)`)}set_node_lines_output(t,e){const n=this.input_names_for_shader_name(t,e.current_shader_name);if(n)for(let i of n){if(t.io.inputs.named_input(i)){const n=t.variableForInput(i);let s;\\\\\\\"color\\\\\\\"==i&&(s=`diffuseColor.xyz = ${hf.any(n)}`),\\\\\\\"alpha\\\\\\\"==i&&(s=`diffuseColor.a = ${hf.any(n)}`),s&&e.addBodyLines(t,[s])}}}set_node_lines_globals(t,e){const n=e.current_shader_name;if(!this.shader_config(n))return;const i=[],s=[];for(let e of t.io.outputs.used_output_names()){const r=t.glVarName(e);switch(e){case\\\\\\\"time\\\\\\\":s.push(new Af(t,Bo.FLOAT,e)),i.push(`float ${r} = ${e}`),this.setUniformsTimeDependent();break;case\\\\\\\"gl_FragCoord\\\\\\\":this.handle_gl_FragCoord(i,n,r)}}e.addDefinitions(t,s,n),e.addBodyLines(t,i)}}const R4=new Map([]);class I4 extends r4{custom_assembler_class_by_custom_name(){return R4}}const F4=new Map([[xf.VERTEX,\\\\\\\"// start builder body code\\\\\\\"],[xf.FRAGMENT,\\\\\\\"// start builder body code\\\\\\\"]]),D4=new Map([[xf.FRAGMENT,[]]]);class B4 extends I4{templateShader(){return{vertexShader:Lk,fragmentShader:Ok,uniforms:I.clone(Pk)}}createMaterial(){const t=this.templateShader(),e=new F({vertexShader:t.vertexShader,fragmentShader:t.fragmentShader,side:w.H,transparent:!0,depthTest:!0,uniforms:I.clone(t.uniforms)});return gr.add_user_data_render_hook(e,Ik.render_hook.bind(Ik)),this._addCustomMaterials(e),e}add_output_inputs(t){t.io.inputs.setNamedInputConnectionPoints([new Ho(\\\\\\\"density\\\\\\\",Bo.FLOAT,1)])}static create_globals_node_output_connections(){return[new Ho(\\\\\\\"position\\\\\\\",Bo.VEC3),new Ho(\\\\\\\"pos_normalized\\\\\\\",Bo.VEC3),new Ho(\\\\\\\"time\\\\\\\",Bo.FLOAT)]}create_globals_node_output_connections(){return B4.create_globals_node_output_connections()}insert_body_after(t){return F4.get(t)}lines_to_remove(t){return D4.get(t)}create_shader_configs(){return[new H3(xf.VERTEX,[],[]),new H3(xf.FRAGMENT,[\\\\\\\"density\\\\\\\"],[xf.VERTEX])]}static create_variable_configs(){return[new j3(\\\\\\\"position\\\\\\\",{}),new j3(\\\\\\\"density\\\\\\\",{prefix:\\\\\\\"density *= \\\\\\\"})]}create_variable_configs(){return B4.create_variable_configs()}set_node_lines_globals(t,e){const n=[],i=e.current_shader_name,s=this.shader_config(i);if(!s)return;const r=s.dependencies(),o=new Map,a=new Map;let l,c;for(let s of t.io.outputs.used_output_names()){const u=t.glVarName(s),d=e.current_shader_name;switch(s){case\\\\\\\"time\\\\\\\":l=new Af(t,Bo.FLOAT,s),d&&h.pushOnArrayAtEntry(o,d,l),c=`float ${u} = ${s}`;for(let t of r)h.pushOnArrayAtEntry(o,t,l),h.pushOnArrayAtEntry(a,t,c);n.push(c),this.setUniformsTimeDependent();break;case\\\\\\\"position\\\\\\\":i==xf.FRAGMENT&&n.push(`vec3 ${u} = position_for_step`);break;case\\\\\\\"pos_normalized\\\\\\\":i==xf.FRAGMENT&&n.push(`vec3 ${u} = (position_for_step - u_BoundingBoxMax) / (u_BoundingBoxMax - u_BoundingBoxMin)`)}}o.forEach(((n,i)=>{e.addDefinitions(t,n,i)})),a.forEach(((n,i)=>{e.addBodyLines(t,n,i)})),e.addBodyLines(t,n)}}class z4{static async run(){this._started||(this._started=!0,class{static async run(t){(class{static run(t){t.registerNode(I_,Ql),t.registerNode(D_,ec),t.registerNode(z_,Ql),t.registerNode(U_,Ql),t.registerNode($_,Ql),t.registerNode(Z_,Zl),t.registerNode(K_,Ql),t.registerNode(em,ec),t.registerNode(im,tc),t.registerNode(hm,tc),t.registerNode(dm,Ql),t.registerNode(_m,Zl),t.registerNode(wm,tc),t.registerNode(Mm,Kl),t.registerNode(Em,Kl),t.registerNode(Sm,Kl),t.registerNode(Cm,Kl),t.registerNode(Qm,Kl),t.registerNode(Km,Kl)}}).run(t),class{static run(t){t.registerNode(tg,nc),t.registerNode(Eg,ic),t.registerNode(Cg,ic),t.registerNode(Ig,ic),t.registerNode(zg,ic),t.registerNode(Kg,ic),t.registerNode(iv,ic),t.registerNode(lv,sc),t.registerNode(hv,sc),t.registerNode(_v,sc),t.registerNode(fv,sc),t.registerNode(yv,nc),t.registerNode(wv,ic),t.registerNode(Mv,nc),t.registerNode(Cv,rc),t.registerNode(Lv,rc),t.registerNode(Nv,rc),t.registerNode(Ov,rc),t.registerNode(Pv,rc),t.registerNode(Rv,rc)}}.run(t),class{static run(t){t.registerNode(Bv,cc),t.registerNode(Uv,lc),t.registerNode(Vv,lc),t.registerNode(Wv,lc),t.registerNode(ey,oc),t.registerNode(_y,oc),t.registerNode(py,oc),t.registerNode(fy,lc),t.registerNode(xl,ac),t.registerNode(Yy,oc),t.registerNode(al,ac),t.registerNode(Qy,lc),t.registerNode(tx,lc),t.registerNode(AL,oc),t.registerNode(Xa,ac),t.registerNode(CL,cc),t.registerNode(PL,lc),t.registerNode(GL,ac),t.registerNode(Qa,ac),t.registerNode(hO,lc),t.registerNode(nl,cc),t.registerNode(_O,cc),t.registerNode(MO,cc),t.registerNode(SO,lc),t.registerNode(LO,lc),t.registerNode(wl,ac),t.registerNode(PO,lc),t.registerNode(dl,ac),t.registerNode(FO,hc),t.registerNode(DO,hc),t.registerNode(BO,hc),t.registerNode(zO,hc),t.registerNode(kO,hc),t.registerNode(UO,hc)}}.run(t),class{static run(t){t.registerNode(MP,gc),t.registerNode(xR,vc),t.registerNode(EP,xc),t.registerNode(rR,gc),t.registerNode(MR,xc),t.registerNode(uR,fc),t.registerNode(SP,xc),t.registerNode(CP,xc),t.registerNode(gf,_c,{except:[`${ts.COP}/builder`]}),t.registerNode(ZO,dc),t.registerNode(NP,gc),t.registerNode(eR,gc),t.registerNode(NR,uc),t.registerNode(DR,fc),t.registerNode(BR,gc),t.registerNode(UR,_c),t.registerNode(LP,xc),t.registerNode(HR,pc),t.registerNode(jR,gc),t.registerNode(OP,dc),t.registerNode(XR,pc),t.registerNode(qP,pc),t.registerNode(oR,gc),t.registerNode(XP,pc),t.registerNode(eI,gc),t.registerNode(PP,gc),t.registerNode(RP,gc),t.registerNode(nR,pc),t.registerNode(sI,gc),t.registerNode(oI,gc),t.registerNode(lI,gc),t.registerNode(hI,gc),t.registerNode(HO,dc),t.registerNode(KO,dc),t.registerNode(eP,dc),t.registerNode(iP,dc),t.registerNode(IP,gc),t.registerNode(pI,uc),t.registerNode(wI,fc),t.registerNode(FP,gc),t.registerNode(AI,_c),t.registerNode(EI,uc),t.registerNode(CI,uc),t.registerNode(OI,fc),t.registerNode(RI,bc),t.registerNode($O,dc),t.registerNode(qO,dc),t.registerNode(DP,gc),t.registerNode(UI,pc),t.registerNode(GI,pc),t.registerNode(HI,uc),t.registerNode(BP,gc),t.registerNode(zP,gc),t.registerNode(YP,gc),t.registerNode(WI,gc),t.registerNode($P,gc),t.registerNode(JP,gc),t.registerNode(JI,gc),t.registerNode(XI,gc),t.registerNode(lR,gc),t.registerNode(KI,gc),t.registerNode(tF,gc),t.registerNode(yF,bc),t.registerNode(vF,pc),t.registerNode(kP,gc),t.registerNode(dR,fc),t.registerNode(bF,_c),t.registerNode(AF,_c),t.registerNode(ZP,gc),t.registerNode(NF,yc),t.registerNode(RF,yc),t.registerNode(IF,yc),t.registerNode(FF,yc),t.registerNode(zF,_c),t.registerNode(GF,_c),t.registerNode(UP,dc),t.registerNode(QP,pc),t.registerNode(MF,pc),t.registerNode(HF,uc),t.registerNode(QF,pc),t.registerNode(tD,gc),t.registerNode(GP,gc),t.registerNode(VP,xc),t.registerNode(iR,gc),t.registerNode(nD,pc),t.registerNode(HP,gc),t.registerNode(CF,mc),t.registerNode(KP,pc),t.registerNode(vI,fc),t.registerNode(sD,fc,TD),t.registerNode(mI,fc,TD),t.registerNode(aR,gc),t.registerNode(oD,fc),t.registerNode(jP,xc),t.registerNode(lD,uc),t.registerNode(dD,_c),t.registerNode(mD,fc),t.registerNode(Lf,_c),t.registerNode(vD,_c),t.registerNode(hP,dc),t.registerNode(mP,dc),t.registerNode(uP,dc),t.registerNode(_P,dc),t.registerNode(fP,dc),t.registerNode(dP,dc),t.registerNode(pP,dc),t.registerNode(xD,pc),t.registerNode(wD,pc)}}.run(t),class{static run(t){t.registerNode(SD,wc),t.registerNode(ND,wc),t.registerNode(OD,wc),t.registerNode(zD,wc)}}.run(t),class{static run(t){t.registerNode(XD,Ac),t.registerNode(rB,Ac),t.registerNode(DB,Mc),t.registerNode(VB,Tc),t.registerNode(YB,Mc),t.registerNode(tz,Tc),t.registerNode(mz,Mc),t.registerNode(yz,Mc),t.registerNode(Mz,Mc),t.registerNode(Nz,Tc),t.registerNode(Uz,Mc),t.registerNode(jz,Tc),t.registerNode(Yz,Mc),t.registerNode(Qz,Tc),t.registerNode(hk,Mc),t.registerNode(fk,Mc),t.registerNode(xk,Sc),t.registerNode(Tk,Tc),t.registerNode(Ek,Tc),t.registerNode(Nk,Mc),t.registerNode(Bk,Cc),t.registerNode(Uk,Cc),t.registerNode(Hk,Ec),t.registerNode(jk,Ec),t.registerNode(Wk,Ec),t.registerNode(qk,Ec),t.registerNode(Xk,Ec),t.registerNode(Yk,Ec)}}.run(t),class{static run(t){t.registerNode(nU,Rc),t.registerNode(MU,Rc),t.registerNode(DU,Rc),t.registerNode(HU,Rc),t.registerNode($U,Rc),t.registerNode(iG,Rc),t.registerNode(pG,Lc),t.registerNode(vG,Fc),t.registerNode(CG,Nc),t.registerNode(RG,Pc),t.registerNode(DG,Fc),t.registerNode(GG,Fc),t.registerNode(_V,Nc),t.registerNode(NV,Lc),t.registerNode(RV,Fc),t.registerNode(DV,Nc),t.registerNode(XH,Oc),t.registerNode(ZH,Oc),t.registerNode(tj,Oc),t.registerNode(sj,Ic),t.registerNode(rj,Ic),t.registerNode(oj,Ic),t.registerNode(aj,Ic),t.registerNode(lj,Ic),t.registerNode(cj,Ic)}}.run(t),class{static run(t){t.registerNode(gj,Qc),t.registerNode(bj,Qc),t.registerNode(Aj,Zc),t.registerNode(Sj,Zc),t.registerNode(Lj,Kc),t.registerNode(Pj,Kc),t.registerNode(Fj,Kc),t.registerNode(Bj,Kc),t.registerNode(qj,Qc),t.registerNode(Hj,Qc),t.registerNode(Jj,Qc),t.registerNode(Kj,Kc),t.registerNode(nW,Zc),t.registerNode(sW,Jc),t.registerNode(oW,Kc),t.registerNode(dW,Kc),t.registerNode(_W,Kc),t.registerNode(fW,Kc),t.registerNode(yW,Qc),t.registerNode(wW,Qc),t.registerNode(AW,Kc),t.registerNode(SW,Qc),t.registerNode(LW,Zc),t.registerNode(PW,Kc),t.registerNode(DW,Jc),t.registerNode(UW,Qc),t.registerNode(VW,Jc),t.registerNode(WW,Qc),t.registerNode(YW,th),t.registerNode($W,th),t.registerNode(JW,th),t.registerNode(ZW,th),t.registerNode(QW,th),t.registerNode(KW,th)}}.run(t),class{static run(t){t.registerNode(oq,Dc),t.registerNode(vH,zc),t.registerNode(aq,Bc),t.registerNode(nq,Bc),t.registerNode(lq,Bc),t.registerNode(cq,Bc),t.registerNode(hq,Bc),t.registerNode(uq,Bc)}}.run(t),class{static run(t){t.registerOperation(dq),t.registerOperation(Eq),t.registerOperation(Iq),t.registerOperation(zq),t.registerOperation(Vq),t.registerOperation(nX),t.registerOperation(Jq),t.registerOperation(aX),t.registerOperation(UX),t.registerOperation(jX),t.registerOperation(g$),t.registerOperation(x$),t.registerOperation(IJ),t.registerOperation(kJ),t.registerOperation(xZ),t.registerOperation(kZ),t.registerOperation(ZQ),t.registerOperation(cK),t.registerOperation(yK),t.registerOperation(TK),t.registerOperation(NK),t.registerOperation(HK),t.registerOperation(KK),t.registerOperation(kK),t.registerOperation(h0),t.registerOperation(m0),t.registerOperation(F0),t.registerOperation(V0),t.registerOperation(q0),t.registerOperation(K0),t.registerOperation(r1),t.registerOperation(f1),t.registerOperation(M1),t.registerOperation(D1),t.registerOperation(U1),t.registerOperation(j1),t.registerOperation(Q1),t.registerOperation(a2),t.registerOperation(v2),t.registerOperation(O2),t.registerOperation(V2),t.registerOperation(c9),t.registerOperation(p9),t.registerOperation(v9),t.registerOperation(w9),t.registerOperation(C9),t.registerOperation(X9),t.registerOperation(t3),t.registerOperation(s3),t.registerNode(mq,Hc),t.registerNode(gq,Uc),t.registerNode(Mq,Uc),t.registerNode(Nq,Gc),t.registerNode(Bq,Gc),t.registerNode(Gq,Gc),t.registerNode(qq,Gc),t.registerNode(Yq,Gc),t.registerNode(Kq,Gc),t.registerNode(rX,Gc),t.registerNode(uX,Gc),t.registerNode(pX,Gc),t.registerNode(mX,Gc),t.registerNode(bX,Gc),t.registerNode(TX,qc),t.registerNode(MX,qc),t.registerNode(HX,qc),t.registerNode(XX,Yc),t.registerNode(y$,kc),t.registerNode(T$,kc),t.registerNode(SJ,Wc),t.registerNode(RJ,Yc),t.registerNode(DJ,Yc),t.registerNode(VJ,Yc),t.registerNode($J,Yc),t.registerNode(eZ,qc),t.registerNode(sZ,Yc),t.registerNode(fZ,qc),t.registerNode(TZ,Yc),t.registerNode(CZ,Hc),t.registerNode(DZ,Hc),t.registerNode(VZ,Wc),t.registerNode(jZ,Wc),t.registerNode(rQ,qc),t.registerNode(aQ,qc),t.registerNode(HQ,kc),t.registerNode(WQ,qc),t.registerNode(tK,Hc),t.registerNode(nK,qc),t.registerNode(oK,Yc),t.registerNode(mK,qc),t.registerNode(pK,Wc),t.registerNode(wK,Yc),t.registerNode(EK,$c),t.registerNode(CK,$c),t.registerNode(PK,qc),t.registerNode(IK,qc),t.registerNode(DK,Yc),t.registerNode(zK,kc),t.registerNode(VK,$c),t.registerNode(XK,Wc),t.registerNode(n0,Yc),t.registerNode(a0,Wc),t.registerNode(c0,qc),t.registerNode(d0,Wc),t.registerNode(_0,Hc),t.registerNode(v0,qc),t.registerNode(x0,kc,{userAllowed:!1}),t.registerNode(I0,Vc),t.registerNode(z0,qc),t.registerNode(W0,Yc),t.registerNode($0,qc),t.registerNode(l1,qc),t.registerNode(Q0,qc),t.registerNode(n1,jc),t.registerNode(hV,kc),t.registerNode(_1,qc),t.registerNode(y1,qc),t.registerNode(C1,$c),t.registerNode(F1,qc),t.registerNode(k1,Gc),t.registerNode(H1,Hc),t.registerNode(X1,qc),t.registerNode(s2,qc),t.registerNode(n2,qc),t.registerNode(d2,kc),t.registerNode(_2,kc),t.registerNode(h2,qc),t.registerNode(b2,Yc),t.registerNode(T2,qc),t.registerNode(I2,qc),t.registerNode(D2,Wc),t.registerNode(z2,Wc),t.registerNode(sV,Wc),t.registerNode(W2,Hc),t.registerNode(Y2,Wc),t.registerNode(Z2,Yc),t.registerNode(l9,Yc),t.registerNode(d9,qc),t.registerNode(f9,qc),t.registerNode(b9,Yc),t.registerNode(M9,Yc),t.registerNode(O9,qc),t.registerNode(R9,qc),t.registerNode(z9,qc),t.registerNode(V9,qc),t.registerNode(W9,Yc),t.registerNode($9,qc),t.registerNode(K9,qc),t.registerNode(i3,qc),t.registerNode(o3,qc),t.registerNode(c3,Xc),t.registerNode(h3,Xc),t.registerNode(u3,Xc),t.registerNode(d3,Xc),t.registerNode(p3,Xc),t.registerNode(_3,Xc)}}.run(t)}}.run(li),class{static run(t){t.registerCamera(XH),t.registerCamera(ZH)}}.run(li),class{static run(t){t.expressionsRegister.register(f3,\\\\\\\"arg\\\\\\\"),t.expressionsRegister.register(g3,\\\\\\\"argc\\\\\\\"),t.expressionsRegister.register(x3,\\\\\\\"bbox\\\\\\\"),t.expressionsRegister.register(b3,\\\\\\\"centroid\\\\\\\"),t.expressionsRegister.register(w3,\\\\\\\"ch\\\\\\\"),t.expressionsRegister.register(T3,\\\\\\\"copy\\\\\\\"),t.expressionsRegister.register(A3,\\\\\\\"copRes\\\\\\\"),t.expressionsRegister.register(M3,\\\\\\\"isDeviceMobile\\\\\\\"),t.expressionsRegister.register(E3,\\\\\\\"isDeviceTouch\\\\\\\"),t.expressionsRegister.register(S3,\\\\\\\"js\\\\\\\"),t.expressionsRegister.register(C3,\\\\\\\"object\\\\\\\"),t.expressionsRegister.register(N3,\\\\\\\"objectsCount\\\\\\\"),t.expressionsRegister.register(L3,\\\\\\\"opdigits\\\\\\\"),t.expressionsRegister.register(O3,\\\\\\\"opname\\\\\\\"),t.expressionsRegister.register(P3,\\\\\\\"padzero\\\\\\\"),t.expressionsRegister.register(R3,\\\\\\\"point\\\\\\\"),t.expressionsRegister.register(I3,\\\\\\\"pointsCount\\\\\\\"),t.expressionsRegister.register(F3,\\\\\\\"strCharsCount\\\\\\\"),t.expressionsRegister.register(D3,\\\\\\\"strConcat\\\\\\\"),t.expressionsRegister.register(B3,\\\\\\\"strIndex\\\\\\\"),t.expressionsRegister.register(z3,\\\\\\\"strSub\\\\\\\"),t.expressionsRegister.register(k3,\\\\\\\"windowSize\\\\\\\")}}.run(li),class{static run(t){t.assemblersRegister.register(jn.GL_MESH_BASIC,G3,p4),t.assemblersRegister.register(jn.GL_MESH_LAMBERT,G3,_4),t.assemblersRegister.register(jn.GL_MESH_PHONG,G3,m4),t.assemblersRegister.register(jn.GL_MESH_STANDARD,G3,g4),t.assemblersRegister.register(jn.GL_MESH_PHYSICAL,G3,v4),t.assemblersRegister.register(jn.GL_PARTICLES,G3,O4),t.assemblersRegister.register(jn.GL_POINTS,G3,M4),t.assemblersRegister.register(jn.GL_LINE,G3,L4),t.assemblersRegister.register(jn.GL_TEXTURE,G3,P4),t.assemblersRegister.register(jn.GL_VOLUME,G3,B4)}}.run(li))}}z4._started=!1,z4.run()}]);void 0===POLY&&console.error(\\\\\\\"esm-webpack-plugin: nothing exported!\\\\\\\");const _POLY$PolyScene=POLY.PolyScene,_POLY$Poly=POLY.Poly,_POLY$SceneJsonImporter=POLY.SceneJsonImporter,_POLY$SceneDataManifestImporter=POLY.SceneDataManifestImporter,_POLY$mountScene=POLY.mountScene;export{_POLY$PolyScene as PolyScene,_POLY$Poly as Poly,_POLY$SceneJsonImporter as SceneJsonImporter,_POLY$SceneDataManifestImporter as SceneDataManifestImporter,_POLY$mountScene as mountScene};\\n//# sourceMappingURL=all.js.map\"","status":200,"headers":{"content-type":"application/javascript","content-length":"2813173"}},"type":2,"external":true,"timestamp":1723912707334},{"data":{"url":"blob:https://ipfs.arkivo.art/a8500b3d-ac2d-4440-8a25-c7dbb1bff87c","host":"","path":"https://ipfs.arkivo.art/a8500b3d-ac2d-4440-8a25-c7dbb1bff87c","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":1723912707366},{"data":{"url":"blob:https://ipfs.arkivo.art/a8500b3d-ac2d-4440-8a25-c7dbb1bff87c","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":1723912713242}],"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":1723912706629},{"text":"[.WebGL-0x247001bcd500]GL Driver Message (OpenGL, Performance, GL_CLOSE_PATH_NV, High): GPU stall due to ReadPixels","level":"warning","timestamp":1723912713271},{"text":"[.WebGL-0x247001bcd500]GL Driver Message (OpenGL, Performance, GL_CLOSE_PATH_NV, High): GPU stall due to ReadPixels","level":"warning","timestamp":1723912713272},{"text":"[.WebGL-0x247001bcd500]GL Driver Message (OpenGL, Performance, GL_CLOSE_PATH_NV, High): GPU stall due to ReadPixels","level":"warning","timestamp":1723912713272},{"text":"[.WebGL-0x247001bcd500]GL Driver Message (OpenGL, Performance, GL_CLOSE_PATH_NV, High): GPU stall due to ReadPixels (this message will no longer repeat)","level":"warning","timestamp":1723912713272}],"screenshotDelay":10000},"timestamp":1723912706054},"created_at":"2024-08-17T16:38:52.225+00:00","updated_at":"2024-08-17T16:38:52.225+00:00"}