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e[0]=n[0]*i,e[1]=n[1]*i,e[2]=n[2]*i,e[3]=0,e[4]=n[4]*r,e[5]=n[5]*r,e[6]=n[6]*r,e[7]=0,e[8]=n[8]*o,e[9]=n[9]*o,e[10]=n[10]*o,e[11]=0,e[12]=0,e[13]=0,e[14]=0,e[15]=1,this}makeRotationFromEuler(t){t&&t.isEuler||console.error(\\\\\\\"THREE.Matrix4: .makeRotationFromEuler() now expects a Euler rotation rather than a Vector3 and order.\\\\\\\");const e=this.elements,n=t.x,i=t.y,r=t.z,s=Math.cos(n),o=Math.sin(n),a=Math.cos(i),l=Math.sin(i),c=Math.cos(r),u=Math.sin(r);if(\\\\\\\"XYZ\\\\\\\"===t.order){const t=s*c,n=s*u,i=o*c,r=o*u;e[0]=a*c,e[4]=-a*u,e[8]=l,e[1]=n+i*l,e[5]=t-r*l,e[9]=-o*a,e[2]=r-t*l,e[6]=i+n*l,e[10]=s*a}else if(\\\\\\\"YXZ\\\\\\\"===t.order){const t=a*c,n=a*u,i=l*c,r=l*u;e[0]=t+r*o,e[4]=i*o-n,e[8]=s*l,e[1]=s*u,e[5]=s*c,e[9]=-o,e[2]=n*o-i,e[6]=r+t*o,e[10]=s*a}else if(\\\\\\\"ZXY\\\\\\\"===t.order){const t=a*c,n=a*u,i=l*c,r=l*u;e[0]=t-r*o,e[4]=-s*u,e[8]=i+n*o,e[1]=n+i*o,e[5]=s*c,e[9]=r-t*o,e[2]=-s*l,e[6]=o,e[10]=s*a}else if(\\\\\\\"ZYX\\\\\\\"===t.order){const t=s*c,n=s*u,i=o*c,r=o*u;e[0]=a*c,e[4]=i*l-n,e[8]=t*l+r,e[1]=a*u,e[5]=r*l+t,e[9]=n*l-i,e[2]=-l,e[6]=o*a,e[10]=s*a}else if(\\\\\\\"YZX\\\\\\\"===t.order){const t=s*a,n=s*l,i=o*a,r=o*l;e[0]=a*c,e[4]=r-t*u,e[8]=i*u+n,e[1]=u,e[5]=s*c,e[9]=-o*c,e[2]=-l*c,e[6]=n*u+i,e[10]=t-r*u}else if(\\\\\\\"XZY\\\\\\\"===t.order){const t=s*a,n=s*l,i=o*a,r=o*l;e[0]=a*c,e[4]=-u,e[8]=l*c,e[1]=t*u+r,e[5]=s*c,e[9]=n*u-i,e[2]=i*u-n,e[6]=o*c,e[10]=r*u+t}return e[3]=0,e[7]=0,e[11]=0,e[12]=0,e[13]=0,e[14]=0,e[15]=1,this}makeRotationFromQuaternion(t){return this.compose(a,t,l)}lookAt(t,e,n){const i=this.elements;return h.subVectors(t,e),0===h.lengthSq()&&(h.z=1),h.normalize(),c.crossVectors(n,h),0===c.lengthSq()&&(1===Math.abs(n.z)?h.x+=1e-4:h.z+=1e-4,h.normalize(),c.crossVectors(n,h)),c.normalize(),u.crossVectors(h,c),i[0]=c.x,i[4]=u.x,i[8]=h.x,i[1]=c.y,i[5]=u.y,i[9]=h.y,i[2]=c.z,i[6]=u.z,i[10]=h.z,this}multiply(t,e){return void 0!==e?(console.warn(\\\\\\\"THREE.Matrix4: .multiply() now only accepts one argument. Use .multiplyMatrices( a, b ) instead.\\\\\\\"),this.multiplyMatrices(t,e)):this.multiplyMatrices(this,t)}premultiply(t){return this.multiplyMatrices(t,this)}multiplyMatrices(t,e){const n=t.elements,i=e.elements,r=this.elements,s=n[0],o=n[4],a=n[8],l=n[12],c=n[1],u=n[5],h=n[9],d=n[13],p=n[2],_=n[6],m=n[10],f=n[14],g=n[3],v=n[7],y=n[11],x=n[15],b=i[0],w=i[4],T=i[8],A=i[12],E=i[1],M=i[5],S=i[9],C=i[13],N=i[2],L=i[6],O=i[10],R=i[14],P=i[3],I=i[7],F=i[11],D=i[15];return r[0]=s*b+o*E+a*N+l*P,r[4]=s*w+o*M+a*L+l*I,r[8]=s*T+o*S+a*O+l*F,r[12]=s*A+o*C+a*R+l*D,r[1]=c*b+u*E+h*N+d*P,r[5]=c*w+u*M+h*L+d*I,r[9]=c*T+u*S+h*O+d*F,r[13]=c*A+u*C+h*R+d*D,r[2]=p*b+_*E+m*N+f*P,r[6]=p*w+_*M+m*L+f*I,r[10]=p*T+_*S+m*O+f*F,r[14]=p*A+_*C+m*R+f*D,r[3]=g*b+v*E+y*N+x*P,r[7]=g*w+v*M+y*L+x*I,r[11]=g*T+v*S+y*O+x*F,r[15]=g*A+v*C+y*R+x*D,this}multiplyScalar(t){const e=this.elements;return e[0]*=t,e[4]*=t,e[8]*=t,e[12]*=t,e[1]*=t,e[5]*=t,e[9]*=t,e[13]*=t,e[2]*=t,e[6]*=t,e[10]*=t,e[14]*=t,e[3]*=t,e[7]*=t,e[11]*=t,e[15]*=t,this}determinant(){const t=this.elements,e=t[0],n=t[4],i=t[8],r=t[12],s=t[1],o=t[5],a=t[9],l=t[13],c=t[2],u=t[6],h=t[10],d=t[14];return t[3]*(+r*a*u-i*l*u-r*o*h+n*l*h+i*o*d-n*a*d)+t[7]*(+e*a*d-e*l*h+r*s*h-i*s*d+i*l*c-r*a*c)+t[11]*(+e*l*u-e*o*d-r*s*u+n*s*d+r*o*c-n*l*c)+t[15]*(-i*o*c-e*a*u+e*o*h+i*s*u-n*s*h+n*a*c)}transpose(){const t=this.elements;let e;return e=t[1],t[1]=t[4],t[4]=e,e=t[2],t[2]=t[8],t[8]=e,e=t[6],t[6]=t[9],t[9]=e,e=t[3],t[3]=t[12],t[12]=e,e=t[7],t[7]=t[13],t[13]=e,e=t[11],t[11]=t[14],t[14]=e,this}setPosition(t,e,n){const i=this.elements;return t.isVector3?(i[12]=t.x,i[13]=t.y,i[14]=t.z):(i[12]=t,i[13]=e,i[14]=n),this}invert(){const t=this.elements,e=t[0],n=t[1],i=t[2],r=t[3],s=t[4],o=t[5],a=t[6],l=t[7],c=t[8],u=t[9],h=t[10],d=t[11],p=t[12],_=t[13],m=t[14],f=t[15],g=u*m*l-_*h*l+_*a*d-o*m*d-u*a*f+o*h*f,v=p*h*l-c*m*l-p*a*d+s*m*d+c*a*f-s*h*f,y=c*_*l-p*u*l+p*o*d-s*_*d-c*o*f+s*u*f,x=p*u*a-c*_*a-p*o*h+s*_*h+c*o*m-s*u*m,b=e*g+n*v+i*y+r*x;if(0===b)return this.set(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0);const w=1/b;return t[0]=g*w,t[1]=(_*h*r-u*m*r-_*i*d+n*m*d+u*i*f-n*h*f)*w,t[2]=(o*m*r-_*a*r+_*i*l-n*m*l-o*i*f+n*a*f)*w,t[3]=(u*a*r-o*h*r-u*i*l+n*h*l+o*i*d-n*a*d)*w,t[4]=v*w,t[5]=(c*m*r-p*h*r+p*i*d-e*m*d-c*i*f+e*h*f)*w,t[6]=(p*a*r-s*m*r-p*i*l+e*m*l+s*i*f-e*a*f)*w,t[7]=(s*h*r-c*a*r+c*i*l-e*h*l-s*i*d+e*a*d)*w,t[8]=y*w,t[9]=(p*u*r-c*_*r-p*n*d+e*_*d+c*n*f-e*u*f)*w,t[10]=(s*_*r-p*o*r+p*n*l-e*_*l-s*n*f+e*o*f)*w,t[11]=(c*o*r-s*u*r-c*n*l+e*u*l+s*n*d-e*o*d)*w,t[12]=x*w,t[13]=(c*_*i-p*u*i+p*n*h-e*_*h-c*n*m+e*u*m)*w,t[14]=(p*o*i-s*_*i-p*n*a+e*_*a+s*n*m-e*o*m)*w,t[15]=(s*u*i-c*o*i+c*n*a-e*u*a-s*n*h+e*o*h)*w,this}scale(t){const e=this.elements,n=t.x,i=t.y,r=t.z;return e[0]*=n,e[4]*=i,e[8]*=r,e[1]*=n,e[5]*=i,e[9]*=r,e[2]*=n,e[6]*=i,e[10]*=r,e[3]*=n,e[7]*=i,e[11]*=r,this}getMaxScaleOnAxis(){const t=this.elements,e=t[0]*t[0]+t[1]*t[1]+t[2]*t[2],n=t[4]*t[4]+t[5]*t[5]+t[6]*t[6],i=t[8]*t[8]+t[9]*t[9]+t[10]*t[10];return Math.sqrt(Math.max(e,n,i))}makeTranslation(t,e,n){return this.set(1,0,0,t,0,1,0,e,0,0,1,n,0,0,0,1),this}makeRotationX(t){const e=Math.cos(t),n=Math.sin(t);return this.set(1,0,0,0,0,e,-n,0,0,n,e,0,0,0,0,1),this}makeRotationY(t){const e=Math.cos(t),n=Math.sin(t);return this.set(e,0,n,0,0,1,0,0,-n,0,e,0,0,0,0,1),this}makeRotationZ(t){const e=Math.cos(t),n=Math.sin(t);return this.set(e,-n,0,0,n,e,0,0,0,0,1,0,0,0,0,1),this}makeRotationAxis(t,e){const n=Math.cos(e),i=Math.sin(e),r=1-n,s=t.x,o=t.y,a=t.z,l=r*s,c=r*o;return this.set(l*s+n,l*o-i*a,l*a+i*o,0,l*o+i*a,c*o+n,c*a-i*s,0,l*a-i*o,c*a+i*s,r*a*a+n,0,0,0,0,1),this}makeScale(t,e,n){return this.set(t,0,0,0,0,e,0,0,0,0,n,0,0,0,0,1),this}makeShear(t,e,n,i,r,s){return this.set(1,n,r,0,t,1,s,0,e,i,1,0,0,0,0,1),this}compose(t,e,n){const i=this.elements,r=e._x,s=e._y,o=e._z,a=e._w,l=r+r,c=s+s,u=o+o,h=r*l,d=r*c,p=r*u,_=s*c,m=s*u,f=o*u,g=a*l,v=a*c,y=a*u,x=n.x,b=n.y,w=n.z;return i[0]=(1-(_+f))*x,i[1]=(d+y)*x,i[2]=(p-v)*x,i[3]=0,i[4]=(d-y)*b,i[5]=(1-(h+f))*b,i[6]=(m+g)*b,i[7]=0,i[8]=(p+v)*w,i[9]=(m-g)*w,i[10]=(1-(h+_))*w,i[11]=0,i[12]=t.x,i[13]=t.y,i[14]=t.z,i[15]=1,this}decompose(t,e,n){const i=this.elements;let r=s.set(i[0],i[1],i[2]).length();const a=s.set(i[4],i[5],i[6]).length(),l=s.set(i[8],i[9],i[10]).length();this.determinant()<0&&(r=-r),t.x=i[12],t.y=i[13],t.z=i[14],o.copy(this);const c=1/r,u=1/a,h=1/l;return o.elements[0]*=c,o.elements[1]*=c,o.elements[2]*=c,o.elements[4]*=u,o.elements[5]*=u,o.elements[6]*=u,o.elements[8]*=h,o.elements[9]*=h,o.elements[10]*=h,e.setFromRotationMatrix(o),n.x=r,n.y=a,n.z=l,this}makePerspective(t,e,n,i,r,s){void 0===s&&console.warn(\\\\\\\"THREE.Matrix4: .makePerspective() has been redefined and has a new signature. Please check the docs.\\\\\\\");const o=this.elements,a=2*r/(e-t),l=2*r/(n-i),c=(e+t)/(e-t),u=(n+i)/(n-i),h=-(s+r)/(s-r),d=-2*s*r/(s-r);return o[0]=a,o[4]=0,o[8]=c,o[12]=0,o[1]=0,o[5]=l,o[9]=u,o[13]=0,o[2]=0,o[6]=0,o[10]=h,o[14]=d,o[3]=0,o[7]=0,o[11]=-1,o[15]=0,this}makeOrthographic(t,e,n,i,r,s){const o=this.elements,a=1/(e-t),l=1/(n-i),c=1/(s-r),u=(e+t)*a,h=(n+i)*l,d=(s+r)*c;return o[0]=2*a,o[4]=0,o[8]=0,o[12]=-u,o[1]=0,o[5]=2*l,o[9]=0,o[13]=-h,o[2]=0,o[6]=0,o[10]=-2*c,o[14]=-d,o[3]=0,o[7]=0,o[11]=0,o[15]=1,this}equals(t){const e=this.elements,n=t.elements;for(let t=0;t<16;t++)if(e[t]!==n[t])return!1;return!0}fromArray(t,e=0){for(let n=0;n<16;n++)this.elements[n]=t[n+e];return this}toArray(t=[],e=0){const n=this.elements;return t[e]=n[0],t[e+1]=n[1],t[e+2]=n[2],t[e+3]=n[3],t[e+4]=n[4],t[e+5]=n[5],t[e+6]=n[6],t[e+7]=n[7],t[e+8]=n[8],t[e+9]=n[9],t[e+10]=n[10],t[e+11]=n[11],t[e+12]=n[12],t[e+13]=n[13],t[e+14]=n[14],t[e+15]=n[15],t}}r.prototype.isMatrix4=!0;const s=new i.a,o=new r,a=new i.a(0,0,0),l=new i.a(1,1,1),c=new i.a,u=new i.a,h=new i.a},function(t,e,n){\\\\\\\"use strict\\\\\\\";n.d(e,\\\\\\\"a\\\\\\\",(function(){return u}));var i=n(3);const r={aliceblue:15792383,antiquewhite:16444375,aqua:65535,aquamarine:8388564,azure:15794175,beige:16119260,bisque:16770244,black:0,blanchedalmond:16772045,blue:255,blueviolet:9055202,brown:10824234,burlywood:14596231,cadetblue:6266528,chartreuse:8388352,chocolate:13789470,coral:16744272,cornflowerblue:6591981,cornsilk:16775388,crimson:14423100,cyan:65535,darkblue:139,darkcyan:35723,darkgoldenrod:12092939,darkgray:11119017,darkgreen:25600,darkgrey:11119017,darkkhaki:12433259,darkmagenta:9109643,darkolivegreen:5597999,darkorange:16747520,darkorchid:10040012,darkred:9109504,darksalmon:15308410,darkseagreen:9419919,darkslateblue:4734347,darkslategray:3100495,darkslategrey:3100495,darkturquoise:52945,darkviolet:9699539,deeppink:16716947,deepskyblue:49151,dimgray:6908265,dimgrey:6908265,dodgerblue:2003199,firebrick:11674146,floralwhite:16775920,forestgreen:2263842,fuchsia:16711935,gainsboro:14474460,ghostwhite:16316671,gold:16766720,goldenrod:14329120,gray:8421504,green:32768,greenyellow:11403055,grey:8421504,honeydew:15794160,hotpink:16738740,indianred:13458524,indigo:4915330,ivory:16777200,khaki:15787660,lavender:15132410,lavenderblush:16773365,lawngreen:8190976,lemonchiffon:16775885,lightblue:11393254,lightcoral:15761536,lightcyan:14745599,lightgoldenrodyellow:16448210,lightgray:13882323,lightgreen:9498256,lightgrey:13882323,lightpink:16758465,lightsalmon:16752762,lightseagreen:2142890,lightskyblue:8900346,lightslategray:7833753,lightslategrey:7833753,lightsteelblue:11584734,lightyellow:16777184,lime:65280,limegreen:3329330,linen:16445670,magenta:16711935,maroon:8388608,mediumaquamarine:6737322,mediumblue:205,mediumorchid:12211667,mediumpurple:9662683,mediumseagreen:3978097,mediumslateblue:8087790,mediumspringgreen:64154,mediumturquoise:4772300,mediumvioletred:13047173,midnightblue:1644912,mintcream:16121850,mistyrose:16770273,moccasin:16770229,navajowhite:16768685,navy:128,oldlace:16643558,olive:8421376,olivedrab:7048739,orange:16753920,orangered:16729344,orchid:14315734,palegoldenrod:15657130,palegreen:10025880,paleturquoise:11529966,palevioletred:14381203,papayawhip:16773077,peachpuff:16767673,peru:13468991,pink:16761035,plum:14524637,powderblue:11591910,purple:8388736,rebeccapurple:6697881,red:16711680,rosybrown:12357519,royalblue:4286945,saddlebrown:9127187,salmon:16416882,sandybrown:16032864,seagreen:3050327,seashell:16774638,sienna:10506797,silver:12632256,skyblue:8900331,slateblue:6970061,slategray:7372944,slategrey:7372944,snow:16775930,springgreen:65407,steelblue:4620980,tan:13808780,teal:32896,thistle:14204888,tomato:16737095,turquoise:4251856,violet:15631086,wheat:16113331,white:16777215,whitesmoke:16119285,yellow:16776960,yellowgreen:10145074},s={h:0,s:0,l:0},o={h:0,s:0,l:0};function a(t,e,n){return n<0&&(n+=1),n>1&&(n-=1),n<1/6?t+6*(e-t)*n:n<.5?e:n<2/3?t+6*(e-t)*(2/3-n):t}function l(t){return t<.04045?.0773993808*t:Math.pow(.9478672986*t+.0521327014,2.4)}function c(t){return t<.0031308?12.92*t:1.055*Math.pow(t,.41666)-.055}class u{constructor(t,e,n){return void 0===e&&void 0===n?this.set(t):this.setRGB(t,e,n)}set(t){return t&&t.isColor?this.copy(t):\\\\\\\"number\\\\\\\"==typeof t?this.setHex(t):\\\\\\\"string\\\\\\\"==typeof t&&this.setStyle(t),this}setScalar(t){return this.r=t,this.g=t,this.b=t,this}setHex(t){return t=Math.floor(t),this.r=(t>>16&255)/255,this.g=(t>>8&255)/255,this.b=(255&t)/255,this}setRGB(t,e,n){return this.r=t,this.g=e,this.b=n,this}setHSL(t,e,n){if(t=i.f(t,1),e=i.d(e,0,1),n=i.d(n,0,1),0===e)this.r=this.g=this.b=n;else{const i=n<=.5?n*(1+e):n+e-n*e,r=2*n-i;this.r=a(r,i,t+1/3),this.g=a(r,i,t),this.b=a(r,i,t-1/3)}return this}setStyle(t){function e(e){void 0!==e&&parseFloat(e)<1&&console.warn(\\\\\\\"THREE.Color: Alpha component of \\\\\\\"+t+\\\\\\\" will be ignored.\\\\\\\")}let n;if(n=/^((?:rgb|hsl)a?)\\\\(([^\\\\)]*)\\\\)/.exec(t)){let t;const i=n[1],r=n[2];switch(i){case\\\\\\\"rgb\\\\\\\":case\\\\\\\"rgba\\\\\\\":if(t=/^\\\\s*(\\\\d+)\\\\s*,\\\\s*(\\\\d+)\\\\s*,\\\\s*(\\\\d+)\\\\s*(?:,\\\\s*(\\\\d*\\\\.?\\\\d+)\\\\s*)?$/.exec(r))return this.r=Math.min(255,parseInt(t[1],10))/255,this.g=Math.min(255,parseInt(t[2],10))/255,this.b=Math.min(255,parseInt(t[3],10))/255,e(t[4]),this;if(t=/^\\\\s*(\\\\d+)\\\\%\\\\s*,\\\\s*(\\\\d+)\\\\%\\\\s*,\\\\s*(\\\\d+)\\\\%\\\\s*(?:,\\\\s*(\\\\d*\\\\.?\\\\d+)\\\\s*)?$/.exec(r))return this.r=Math.min(100,parseInt(t[1],10))/100,this.g=Math.min(100,parseInt(t[2],10))/100,this.b=Math.min(100,parseInt(t[3],10))/100,e(t[4]),this;break;case\\\\\\\"hsl\\\\\\\":case\\\\\\\"hsla\\\\\\\":if(t=/^\\\\s*(\\\\d*\\\\.?\\\\d+)\\\\s*,\\\\s*(\\\\d+)\\\\%\\\\s*,\\\\s*(\\\\d+)\\\\%\\\\s*(?:,\\\\s*(\\\\d*\\\\.?\\\\d+)\\\\s*)?$/.exec(r)){const n=parseFloat(t[1])/360,i=parseInt(t[2],10)/100,r=parseInt(t[3],10)/100;return e(t[4]),this.setHSL(n,i,r)}}}else if(n=/^\\\\#([A-Fa-f\\\\d]+)$/.exec(t)){const t=n[1],e=t.length;if(3===e)return this.r=parseInt(t.charAt(0)+t.charAt(0),16)/255,this.g=parseInt(t.charAt(1)+t.charAt(1),16)/255,this.b=parseInt(t.charAt(2)+t.charAt(2),16)/255,this;if(6===e)return this.r=parseInt(t.charAt(0)+t.charAt(1),16)/255,this.g=parseInt(t.charAt(2)+t.charAt(3),16)/255,this.b=parseInt(t.charAt(4)+t.charAt(5),16)/255,this}return t&&t.length>0?this.setColorName(t):this}setColorName(t){const e=r[t.toLowerCase()];return void 0!==e?this.setHex(e):console.warn(\\\\\\\"THREE.Color: Unknown color \\\\\\\"+t),this}clone(){return new this.constructor(this.r,this.g,this.b)}copy(t){return this.r=t.r,this.g=t.g,this.b=t.b,this}copyGammaToLinear(t,e=2){return this.r=Math.pow(t.r,e),this.g=Math.pow(t.g,e),this.b=Math.pow(t.b,e),this}copyLinearToGamma(t,e=2){const n=e>0?1/e:1;return this.r=Math.pow(t.r,n),this.g=Math.pow(t.g,n),this.b=Math.pow(t.b,n),this}convertGammaToLinear(t){return this.copyGammaToLinear(this,t),this}convertLinearToGamma(t){return this.copyLinearToGamma(this,t),this}copySRGBToLinear(t){return this.r=l(t.r),this.g=l(t.g),this.b=l(t.b),this}copyLinearToSRGB(t){return this.r=c(t.r),this.g=c(t.g),this.b=c(t.b),this}convertSRGBToLinear(){return this.copySRGBToLinear(this),this}convertLinearToSRGB(){return this.copyLinearToSRGB(this),this}getHex(){return 255*this.r<<16^255*this.g<<8^255*this.b<<0}getHexString(){return(\\\\\\\"000000\\\\\\\"+this.getHex().toString(16)).slice(-6)}getHSL(t){const e=this.r,n=this.g,i=this.b,r=Math.max(e,n,i),s=Math.min(e,n,i);let o,a;const l=(s+r)/2;if(s===r)o=0,a=0;else{const t=r-s;switch(a=l<=.5?t/(r+s):t/(2-r-s),r){case e:o=(n-i)/t+(n<i?6:0);break;case n:o=(i-e)/t+2;break;case i:o=(e-n)/t+4}o/=6}return t.h=o,t.s=a,t.l=l,t}getStyle(){return\\\\\\\"rgb(\\\\\\\"+(255*this.r|0)+\\\\\\\",\\\\\\\"+(255*this.g|0)+\\\\\\\",\\\\\\\"+(255*this.b|0)+\\\\\\\")\\\\\\\"}offsetHSL(t,e,n){return this.getHSL(s),s.h+=t,s.s+=e,s.l+=n,this.setHSL(s.h,s.s,s.l),this}add(t){return this.r+=t.r,this.g+=t.g,this.b+=t.b,this}addColors(t,e){return this.r=t.r+e.r,this.g=t.g+e.g,this.b=t.b+e.b,this}addScalar(t){return this.r+=t,this.g+=t,this.b+=t,this}sub(t){return this.r=Math.max(0,this.r-t.r),this.g=Math.max(0,this.g-t.g),this.b=Math.max(0,this.b-t.b),this}multiply(t){return this.r*=t.r,this.g*=t.g,this.b*=t.b,this}multiplyScalar(t){return this.r*=t,this.g*=t,this.b*=t,this}lerp(t,e){return this.r+=(t.r-this.r)*e,this.g+=(t.g-this.g)*e,this.b+=(t.b-this.b)*e,this}lerpColors(t,e,n){return this.r=t.r+(e.r-t.r)*n,this.g=t.g+(e.g-t.g)*n,this.b=t.b+(e.b-t.b)*n,this}lerpHSL(t,e){this.getHSL(s),t.getHSL(o);const n=i.j(s.h,o.h,e),r=i.j(s.s,o.s,e),a=i.j(s.l,o.l,e);return this.setHSL(n,r,a),this}equals(t){return t.r===this.r&&t.g===this.g&&t.b===this.b}fromArray(t,e=0){return this.r=t[e],this.g=t[e+1],this.b=t[e+2],this}toArray(t=[],e=0){return t[e]=this.r,t[e+1]=this.g,t[e+2]=this.b,t}fromBufferAttribute(t,e){return this.r=t.getX(e),this.g=t.getY(e),this.b=t.getZ(e),!0===t.normalized&&(this.r/=255,this.g/=255,this.b/=255),this}toJSON(){return this.getHex()}}u.NAMES=r,u.prototype.isColor=!0,u.prototype.r=1,u.prototype.g=1,u.prototype.b=1},function(t,e,n){\\\\\\\"use strict\\\\\\\";n.d(e,\\\\\\\"a\\\\\\\",(function(){return b}));var i=n(0),r=n(2),s=n(16),o=n(15),a=n(4),l=n(18),c=n(10),u=n(5),h=n(11),d=n(3),p=n(20);let _=0;const m=new u.a,f=new c.a,g=new i.a,v=new s.a,y=new s.a,x=new i.a;class b extends o.a{constructor(){super(),Object.defineProperty(this,\\\\\\\"id\\\\\\\",{value:_++}),this.uuid=d.h(),this.name=\\\\\\\"\\\\\\\",this.type=\\\\\\\"BufferGeometry\\\\\\\",this.index=null,this.attributes={},this.morphAttributes={},this.morphTargetsRelative=!1,this.groups=[],this.boundingBox=null,this.boundingSphere=null,this.drawRange={start:0,count:1/0},this.userData={}}getIndex(){return this.index}setIndex(t){return Array.isArray(t)?this.index=new(Object(p.a)(t)>65535?a.i:a.h)(t,1):this.index=t,this}getAttribute(t){return this.attributes[t]}setAttribute(t,e){return this.attributes[t]=e,this}deleteAttribute(t){return delete this.attributes[t],this}hasAttribute(t){return void 0!==this.attributes[t]}addGroup(t,e,n=0){this.groups.push({start:t,count:e,materialIndex:n})}clearGroups(){this.groups=[]}setDrawRange(t,e){this.drawRange.start=t,this.drawRange.count=e}applyMatrix4(t){const e=this.attributes.position;void 0!==e&&(e.applyMatrix4(t),e.needsUpdate=!0);const n=this.attributes.normal;if(void 0!==n){const e=(new h.a).getNormalMatrix(t);n.applyNormalMatrix(e),n.needsUpdate=!0}const i=this.attributes.tangent;return void 0!==i&&(i.transformDirection(t),i.needsUpdate=!0),null!==this.boundingBox&&this.computeBoundingBox(),null!==this.boundingSphere&&this.computeBoundingSphere(),this}applyQuaternion(t){return m.makeRotationFromQuaternion(t),this.applyMatrix4(m),this}rotateX(t){return m.makeRotationX(t),this.applyMatrix4(m),this}rotateY(t){return m.makeRotationY(t),this.applyMatrix4(m),this}rotateZ(t){return m.makeRotationZ(t),this.applyMatrix4(m),this}translate(t,e,n){return m.makeTranslation(t,e,n),this.applyMatrix4(m),this}scale(t,e,n){return m.makeScale(t,e,n),this.applyMatrix4(m),this}lookAt(t){return f.lookAt(t),f.updateMatrix(),this.applyMatrix4(f.matrix),this}center(){return this.computeBoundingBox(),this.boundingBox.getCenter(g).negate(),this.translate(g.x,g.y,g.z),this}setFromPoints(t){const e=[];for(let n=0,i=t.length;n<i;n++){const i=t[n];e.push(i.x,i.y,i.z||0)}return this.setAttribute(\\\\\\\"position\\\\\\\",new a.c(e,3)),this}computeBoundingBox(){null===this.boundingBox&&(this.boundingBox=new s.a);const t=this.attributes.position,e=this.morphAttributes.position;if(t&&t.isGLBufferAttribute)return console.error('THREE.BufferGeometry.computeBoundingBox(): GLBufferAttribute requires a manual bounding box. Alternatively set \\\\\\\"mesh.frustumCulled\\\\\\\" to \\\\\\\"false\\\\\\\".',this),void this.boundingBox.set(new i.a(-1/0,-1/0,-1/0),new i.a(1/0,1/0,1/0));if(void 0!==t){if(this.boundingBox.setFromBufferAttribute(t),e)for(let t=0,n=e.length;t<n;t++){const n=e[t];v.setFromBufferAttribute(n),this.morphTargetsRelative?(x.addVectors(this.boundingBox.min,v.min),this.boundingBox.expandByPoint(x),x.addVectors(this.boundingBox.max,v.max),this.boundingBox.expandByPoint(x)):(this.boundingBox.expandByPoint(v.min),this.boundingBox.expandByPoint(v.max))}}else this.boundingBox.makeEmpty();(isNaN(this.boundingBox.min.x)||isNaN(this.boundingBox.min.y)||isNaN(this.boundingBox.min.z))&&console.error('THREE.BufferGeometry.computeBoundingBox(): Computed min/max have NaN values. The \\\\\\\"position\\\\\\\" attribute is likely to have NaN values.',this)}computeBoundingSphere(){null===this.boundingSphere&&(this.boundingSphere=new l.a);const t=this.attributes.position,e=this.morphAttributes.position;if(t&&t.isGLBufferAttribute)return console.error('THREE.BufferGeometry.computeBoundingSphere(): GLBufferAttribute requires a manual bounding sphere. Alternatively set \\\\\\\"mesh.frustumCulled\\\\\\\" to \\\\\\\"false\\\\\\\".',this),void this.boundingSphere.set(new i.a,1/0);if(t){const n=this.boundingSphere.center;if(v.setFromBufferAttribute(t),e)for(let t=0,n=e.length;t<n;t++){const n=e[t];y.setFromBufferAttribute(n),this.morphTargetsRelative?(x.addVectors(v.min,y.min),v.expandByPoint(x),x.addVectors(v.max,y.max),v.expandByPoint(x)):(v.expandByPoint(y.min),v.expandByPoint(y.max))}v.getCenter(n);let i=0;for(let e=0,r=t.count;e<r;e++)x.fromBufferAttribute(t,e),i=Math.max(i,n.distanceToSquared(x));if(e)for(let r=0,s=e.length;r<s;r++){const s=e[r],o=this.morphTargetsRelative;for(let e=0,r=s.count;e<r;e++)x.fromBufferAttribute(s,e),o&&(g.fromBufferAttribute(t,e),x.add(g)),i=Math.max(i,n.distanceToSquared(x))}this.boundingSphere.radius=Math.sqrt(i),isNaN(this.boundingSphere.radius)&&console.error('THREE.BufferGeometry.computeBoundingSphere(): Computed radius is NaN. The \\\\\\\"position\\\\\\\" attribute is likely to have NaN values.',this)}}computeTangents(){const t=this.index,e=this.attributes;if(null===t||void 0===e.position||void 0===e.normal||void 0===e.uv)return void console.error(\\\\\\\"THREE.BufferGeometry: .computeTangents() failed. Missing required attributes (index, position, normal or uv)\\\\\\\");const n=t.array,s=e.position.array,o=e.normal.array,l=e.uv.array,c=s.length/3;void 0===e.tangent&&this.setAttribute(\\\\\\\"tangent\\\\\\\",new a.a(new Float32Array(4*c),4));const u=e.tangent.array,h=[],d=[];for(let t=0;t<c;t++)h[t]=new i.a,d[t]=new i.a;const p=new i.a,_=new i.a,m=new i.a,f=new r.a,g=new r.a,v=new r.a,y=new i.a,x=new i.a;function b(t,e,n){p.fromArray(s,3*t),_.fromArray(s,3*e),m.fromArray(s,3*n),f.fromArray(l,2*t),g.fromArray(l,2*e),v.fromArray(l,2*n),_.sub(p),m.sub(p),g.sub(f),v.sub(f);const i=1/(g.x*v.y-v.x*g.y);isFinite(i)&&(y.copy(_).multiplyScalar(v.y).addScaledVector(m,-g.y).multiplyScalar(i),x.copy(m).multiplyScalar(g.x).addScaledVector(_,-v.x).multiplyScalar(i),h[t].add(y),h[e].add(y),h[n].add(y),d[t].add(x),d[e].add(x),d[n].add(x))}let w=this.groups;0===w.length&&(w=[{start:0,count:n.length}]);for(let t=0,e=w.length;t<e;++t){const e=w[t],i=e.start;for(let t=i,r=i+e.count;t<r;t+=3)b(n[t+0],n[t+1],n[t+2])}const T=new i.a,A=new i.a,E=new i.a,M=new i.a;function S(t){E.fromArray(o,3*t),M.copy(E);const e=h[t];T.copy(e),T.sub(E.multiplyScalar(E.dot(e))).normalize(),A.crossVectors(M,e);const n=A.dot(d[t])<0?-1:1;u[4*t]=T.x,u[4*t+1]=T.y,u[4*t+2]=T.z,u[4*t+3]=n}for(let t=0,e=w.length;t<e;++t){const e=w[t],i=e.start;for(let t=i,r=i+e.count;t<r;t+=3)S(n[t+0]),S(n[t+1]),S(n[t+2])}}computeVertexNormals(){const t=this.index,e=this.getAttribute(\\\\\\\"position\\\\\\\");if(void 0!==e){let n=this.getAttribute(\\\\\\\"normal\\\\\\\");if(void 0===n)n=new a.a(new Float32Array(3*e.count),3),this.setAttribute(\\\\\\\"normal\\\\\\\",n);else for(let t=0,e=n.count;t<e;t++)n.setXYZ(t,0,0,0);const r=new i.a,s=new i.a,o=new i.a,l=new i.a,c=new i.a,u=new i.a,h=new i.a,d=new i.a;if(t)for(let i=0,a=t.count;i<a;i+=3){const a=t.getX(i+0),p=t.getX(i+1),_=t.getX(i+2);r.fromBufferAttribute(e,a),s.fromBufferAttribute(e,p),o.fromBufferAttribute(e,_),h.subVectors(o,s),d.subVectors(r,s),h.cross(d),l.fromBufferAttribute(n,a),c.fromBufferAttribute(n,p),u.fromBufferAttribute(n,_),l.add(h),c.add(h),u.add(h),n.setXYZ(a,l.x,l.y,l.z),n.setXYZ(p,c.x,c.y,c.z),n.setXYZ(_,u.x,u.y,u.z)}else for(let t=0,i=e.count;t<i;t+=3)r.fromBufferAttribute(e,t+0),s.fromBufferAttribute(e,t+1),o.fromBufferAttribute(e,t+2),h.subVectors(o,s),d.subVectors(r,s),h.cross(d),n.setXYZ(t+0,h.x,h.y,h.z),n.setXYZ(t+1,h.x,h.y,h.z),n.setXYZ(t+2,h.x,h.y,h.z);this.normalizeNormals(),n.needsUpdate=!0}}merge(t,e){if(!t||!t.isBufferGeometry)return void console.error(\\\\\\\"THREE.BufferGeometry.merge(): geometry not an instance of THREE.BufferGeometry.\\\\\\\",t);void 0===e&&(e=0,console.warn(\\\\\\\"THREE.BufferGeometry.merge(): Overwriting original geometry, starting at offset=0. Use BufferGeometryUtils.mergeBufferGeometries() for lossless merge.\\\\\\\"));const n=this.attributes;for(const i in n){if(void 0===t.attributes[i])continue;const r=n[i].array,s=t.attributes[i],o=s.array,a=s.itemSize*e,l=Math.min(o.length,r.length-a);for(let t=0,e=a;t<l;t++,e++)r[e]=o[t]}return this}normalizeNormals(){const t=this.attributes.normal;for(let e=0,n=t.count;e<n;e++)x.fromBufferAttribute(t,e),x.normalize(),t.setXYZ(e,x.x,x.y,x.z)}toNonIndexed(){function t(t,e){const n=t.array,i=t.itemSize,r=t.normalized,s=new n.constructor(e.length*i);let o=0,l=0;for(let r=0,a=e.length;r<a;r++){o=t.isInterleavedBufferAttribute?e[r]*t.data.stride+t.offset:e[r]*i;for(let t=0;t<i;t++)s[l++]=n[o++]}return new a.a(s,i,r)}if(null===this.index)return console.warn(\\\\\\\"THREE.BufferGeometry.toNonIndexed(): BufferGeometry is already non-indexed.\\\\\\\"),this;const e=new b,n=this.index.array,i=this.attributes;for(const r in i){const s=t(i[r],n);e.setAttribute(r,s)}const r=this.morphAttributes;for(const i in r){const s=[],o=r[i];for(let e=0,i=o.length;e<i;e++){const i=t(o[e],n);s.push(i)}e.morphAttributes[i]=s}e.morphTargetsRelative=this.morphTargetsRelative;const s=this.groups;for(let t=0,n=s.length;t<n;t++){const n=s[t];e.addGroup(n.start,n.count,n.materialIndex)}return e}toJSON(){const t={metadata:{version:4.5,type:\\\\\\\"BufferGeometry\\\\\\\",generator:\\\\\\\"BufferGeometry.toJSON\\\\\\\"}};if(t.uuid=this.uuid,t.type=this.type,\\\\\\\"\\\\\\\"!==this.name&&(t.name=this.name),Object.keys(this.userData).length>0&&(t.userData=this.userData),void 0!==this.parameters){const e=this.parameters;for(const n in e)void 0!==e[n]&&(t[n]=e[n]);return t}t.data={attributes:{}};const e=this.index;null!==e&&(t.data.index={type:e.array.constructor.name,array:Array.prototype.slice.call(e.array)});const n=this.attributes;for(const e in n){const i=n[e];t.data.attributes[e]=i.toJSON(t.data)}const i={};let r=!1;for(const e in this.morphAttributes){const n=this.morphAttributes[e],s=[];for(let e=0,i=n.length;e<i;e++){const i=n[e];s.push(i.toJSON(t.data))}s.length>0&&(i[e]=s,r=!0)}r&&(t.data.morphAttributes=i,t.data.morphTargetsRelative=this.morphTargetsRelative);const s=this.groups;s.length>0&&(t.data.groups=JSON.parse(JSON.stringify(s)));const o=this.boundingSphere;return null!==o&&(t.data.boundingSphere={center:o.center.toArray(),radius:o.radius}),t}clone(){return(new this.constructor).copy(this)}copy(t){this.index=null,this.attributes={},this.morphAttributes={},this.groups=[],this.boundingBox=null,this.boundingSphere=null;const e={};this.name=t.name;const n=t.index;null!==n&&this.setIndex(n.clone(e));const i=t.attributes;for(const t in i){const n=i[t];this.setAttribute(t,n.clone(e))}const r=t.morphAttributes;for(const t in r){const n=[],i=r[t];for(let t=0,r=i.length;t<r;t++)n.push(i[t].clone(e));this.morphAttributes[t]=n}this.morphTargetsRelative=t.morphTargetsRelative;const s=t.groups;for(let t=0,e=s.length;t<e;t++){const e=s[t];this.addGroup(e.start,e.count,e.materialIndex)}const o=t.boundingBox;null!==o&&(this.boundingBox=o.clone());const a=t.boundingSphere;return null!==a&&(this.boundingSphere=a.clone()),this.drawRange.start=t.drawRange.start,this.drawRange.count=t.drawRange.count,this.userData=t.userData,void 0!==t.parameters&&(this.parameters=Object.assign({},t.parameters)),this}dispose(){this.dispatchEvent({type:\\\\\\\"dispose\\\\\\\"})}}b.prototype.isBufferGeometry=!0},function(t,e,n){\\\\\\\"use strict\\\\\\\";n.d(e,\\\\\\\"a\\\\\\\",(function(){return r}));var i=n(3);class r{constructor(t=0,e=0,n=0,i=1){this._x=t,this._y=e,this._z=n,this._w=i}static slerp(t,e,n,i){return console.warn(\\\\\\\"THREE.Quaternion: Static .slerp() has been deprecated. Use qm.slerpQuaternions( qa, qb, t ) instead.\\\\\\\"),n.slerpQuaternions(t,e,i)}static slerpFlat(t,e,n,i,r,s,o){let a=n[i+0],l=n[i+1],c=n[i+2],u=n[i+3];const h=r[s+0],d=r[s+1],p=r[s+2],_=r[s+3];if(0===o)return t[e+0]=a,t[e+1]=l,t[e+2]=c,void(t[e+3]=u);if(1===o)return t[e+0]=h,t[e+1]=d,t[e+2]=p,void(t[e+3]=_);if(u!==_||a!==h||l!==d||c!==p){let t=1-o;const e=a*h+l*d+c*p+u*_,n=e>=0?1:-1,i=1-e*e;if(i>Number.EPSILON){const r=Math.sqrt(i),s=Math.atan2(r,e*n);t=Math.sin(t*s)/r,o=Math.sin(o*s)/r}const r=o*n;if(a=a*t+h*r,l=l*t+d*r,c=c*t+p*r,u=u*t+_*r,t===1-o){const t=1/Math.sqrt(a*a+l*l+c*c+u*u);a*=t,l*=t,c*=t,u*=t}}t[e]=a,t[e+1]=l,t[e+2]=c,t[e+3]=u}static multiplyQuaternionsFlat(t,e,n,i,r,s){const o=n[i],a=n[i+1],l=n[i+2],c=n[i+3],u=r[s],h=r[s+1],d=r[s+2],p=r[s+3];return t[e]=o*p+c*u+a*d-l*h,t[e+1]=a*p+c*h+l*u-o*d,t[e+2]=l*p+c*d+o*h-a*u,t[e+3]=c*p-o*u-a*h-l*d,t}get x(){return this._x}set x(t){this._x=t,this._onChangeCallback()}get y(){return this._y}set y(t){this._y=t,this._onChangeCallback()}get z(){return this._z}set z(t){this._z=t,this._onChangeCallback()}get w(){return this._w}set w(t){this._w=t,this._onChangeCallback()}set(t,e,n,i){return this._x=t,this._y=e,this._z=n,this._w=i,this._onChangeCallback(),this}clone(){return new this.constructor(this._x,this._y,this._z,this._w)}copy(t){return this._x=t.x,this._y=t.y,this._z=t.z,this._w=t.w,this._onChangeCallback(),this}setFromEuler(t,e){if(!t||!t.isEuler)throw new Error(\\\\\\\"THREE.Quaternion: .setFromEuler() now expects an Euler rotation rather than a Vector3 and order.\\\\\\\");const n=t._x,i=t._y,r=t._z,s=t._order,o=Math.cos,a=Math.sin,l=o(n/2),c=o(i/2),u=o(r/2),h=a(n/2),d=a(i/2),p=a(r/2);switch(s){case\\\\\\\"XYZ\\\\\\\":this._x=h*c*u+l*d*p,this._y=l*d*u-h*c*p,this._z=l*c*p+h*d*u,this._w=l*c*u-h*d*p;break;case\\\\\\\"YXZ\\\\\\\":this._x=h*c*u+l*d*p,this._y=l*d*u-h*c*p,this._z=l*c*p-h*d*u,this._w=l*c*u+h*d*p;break;case\\\\\\\"ZXY\\\\\\\":this._x=h*c*u-l*d*p,this._y=l*d*u+h*c*p,this._z=l*c*p+h*d*u,this._w=l*c*u-h*d*p;break;case\\\\\\\"ZYX\\\\\\\":this._x=h*c*u-l*d*p,this._y=l*d*u+h*c*p,this._z=l*c*p-h*d*u,this._w=l*c*u+h*d*p;break;case\\\\\\\"YZX\\\\\\\":this._x=h*c*u+l*d*p,this._y=l*d*u+h*c*p,this._z=l*c*p-h*d*u,this._w=l*c*u-h*d*p;break;case\\\\\\\"XZY\\\\\\\":this._x=h*c*u-l*d*p,this._y=l*d*u-h*c*p,this._z=l*c*p+h*d*u,this._w=l*c*u+h*d*p;break;default:console.warn(\\\\\\\"THREE.Quaternion: .setFromEuler() encountered an unknown order: \\\\\\\"+s)}return!1!==e&&this._onChangeCallback(),this}setFromAxisAngle(t,e){const n=e/2,i=Math.sin(n);return this._x=t.x*i,this._y=t.y*i,this._z=t.z*i,this._w=Math.cos(n),this._onChangeCallback(),this}setFromRotationMatrix(t){const e=t.elements,n=e[0],i=e[4],r=e[8],s=e[1],o=e[5],a=e[9],l=e[2],c=e[6],u=e[10],h=n+o+u;if(h>0){const t=.5/Math.sqrt(h+1);this._w=.25/t,this._x=(c-a)*t,this._y=(r-l)*t,this._z=(s-i)*t}else if(n>o&&n>u){const t=2*Math.sqrt(1+n-o-u);this._w=(c-a)/t,this._x=.25*t,this._y=(i+s)/t,this._z=(r+l)/t}else if(o>u){const t=2*Math.sqrt(1+o-n-u);this._w=(r-l)/t,this._x=(i+s)/t,this._y=.25*t,this._z=(a+c)/t}else{const t=2*Math.sqrt(1+u-n-o);this._w=(s-i)/t,this._x=(r+l)/t,this._y=(a+c)/t,this._z=.25*t}return this._onChangeCallback(),this}setFromUnitVectors(t,e){let n=t.dot(e)+1;return n<Number.EPSILON?(n=0,Math.abs(t.x)>Math.abs(t.z)?(this._x=-t.y,this._y=t.x,this._z=0,this._w=n):(this._x=0,this._y=-t.z,this._z=t.y,this._w=n)):(this._x=t.y*e.z-t.z*e.y,this._y=t.z*e.x-t.x*e.z,this._z=t.x*e.y-t.y*e.x,this._w=n),this.normalize()}angleTo(t){return 2*Math.acos(Math.abs(i.d(this.dot(t),-1,1)))}rotateTowards(t,e){const n=this.angleTo(t);if(0===n)return this;const i=Math.min(1,e/n);return this.slerp(t,i),this}identity(){return this.set(0,0,0,1)}invert(){return this.conjugate()}conjugate(){return this._x*=-1,this._y*=-1,this._z*=-1,this._onChangeCallback(),this}dot(t){return this._x*t._x+this._y*t._y+this._z*t._z+this._w*t._w}lengthSq(){return this._x*this._x+this._y*this._y+this._z*this._z+this._w*this._w}length(){return Math.sqrt(this._x*this._x+this._y*this._y+this._z*this._z+this._w*this._w)}normalize(){let t=this.length();return 0===t?(this._x=0,this._y=0,this._z=0,this._w=1):(t=1/t,this._x=this._x*t,this._y=this._y*t,this._z=this._z*t,this._w=this._w*t),this._onChangeCallback(),this}multiply(t,e){return void 0!==e?(console.warn(\\\\\\\"THREE.Quaternion: .multiply() now only accepts one argument. Use .multiplyQuaternions( a, b ) instead.\\\\\\\"),this.multiplyQuaternions(t,e)):this.multiplyQuaternions(this,t)}premultiply(t){return this.multiplyQuaternions(t,this)}multiplyQuaternions(t,e){const n=t._x,i=t._y,r=t._z,s=t._w,o=e._x,a=e._y,l=e._z,c=e._w;return this._x=n*c+s*o+i*l-r*a,this._y=i*c+s*a+r*o-n*l,this._z=r*c+s*l+n*a-i*o,this._w=s*c-n*o-i*a-r*l,this._onChangeCallback(),this}slerp(t,e){if(0===e)return this;if(1===e)return this.copy(t);const n=this._x,i=this._y,r=this._z,s=this._w;let o=s*t._w+n*t._x+i*t._y+r*t._z;if(o<0?(this._w=-t._w,this._x=-t._x,this._y=-t._y,this._z=-t._z,o=-o):this.copy(t),o>=1)return this._w=s,this._x=n,this._y=i,this._z=r,this;const a=1-o*o;if(a<=Number.EPSILON){const t=1-e;return this._w=t*s+e*this._w,this._x=t*n+e*this._x,this._y=t*i+e*this._y,this._z=t*r+e*this._z,this.normalize(),this._onChangeCallback(),this}const l=Math.sqrt(a),c=Math.atan2(l,o),u=Math.sin((1-e)*c)/l,h=Math.sin(e*c)/l;return this._w=s*u+this._w*h,this._x=n*u+this._x*h,this._y=i*u+this._y*h,this._z=r*u+this._z*h,this._onChangeCallback(),this}slerpQuaternions(t,e,n){this.copy(t).slerp(e,n)}random(){const t=Math.random(),e=Math.sqrt(1-t),n=Math.sqrt(t),i=2*Math.PI*Math.random(),r=2*Math.PI*Math.random();return this.set(e*Math.cos(i),n*Math.sin(r),n*Math.cos(r),e*Math.sin(i))}equals(t){return t._x===this._x&&t._y===this._y&&t._z===this._z&&t._w===this._w}fromArray(t,e=0){return this._x=t[e],this._y=t[e+1],this._z=t[e+2],this._w=t[e+3],this._onChangeCallback(),this}toArray(t=[],e=0){return t[e]=this._x,t[e+1]=this._y,t[e+2]=this._z,t[e+3]=this._w,t}fromBufferAttribute(t,e){return this._x=t.getX(e),this._y=t.getY(e),this._z=t.getZ(e),this._w=t.getW(e),this}_onChange(t){return this._onChangeCallback=t,this}_onChangeCallback(){}}r.prototype.isQuaternion=!0},function(t,e,n){\\\\\\\"use strict\\\\\\\";n.d(e,\\\\\\\"a\\\\\\\",(function(){return i}));class i{constructor(t=0,e=0,n=0,i=1){this.x=t,this.y=e,this.z=n,this.w=i}get width(){return this.z}set width(t){this.z=t}get height(){return this.w}set height(t){this.w=t}set(t,e,n,i){return this.x=t,this.y=e,this.z=n,this.w=i,this}setScalar(t){return this.x=t,this.y=t,this.z=t,this.w=t,this}setX(t){return this.x=t,this}setY(t){return this.y=t,this}setZ(t){return this.z=t,this}setW(t){return this.w=t,this}setComponent(t,e){switch(t){case 0:this.x=e;break;case 1:this.y=e;break;case 2:this.z=e;break;case 3:this.w=e;break;default:throw new Error(\\\\\\\"index is out of range: \\\\\\\"+t)}return this}getComponent(t){switch(t){case 0:return this.x;case 1:return this.y;case 2:return this.z;case 3:return this.w;default:throw new Error(\\\\\\\"index is out of range: \\\\\\\"+t)}}clone(){return new this.constructor(this.x,this.y,this.z,this.w)}copy(t){return this.x=t.x,this.y=t.y,this.z=t.z,this.w=void 0!==t.w?t.w:1,this}add(t,e){return void 0!==e?(console.warn(\\\\\\\"THREE.Vector4: .add() now only accepts one argument. Use .addVectors( a, b ) instead.\\\\\\\"),this.addVectors(t,e)):(this.x+=t.x,this.y+=t.y,this.z+=t.z,this.w+=t.w,this)}addScalar(t){return this.x+=t,this.y+=t,this.z+=t,this.w+=t,this}addVectors(t,e){return this.x=t.x+e.x,this.y=t.y+e.y,this.z=t.z+e.z,this.w=t.w+e.w,this}addScaledVector(t,e){return this.x+=t.x*e,this.y+=t.y*e,this.z+=t.z*e,this.w+=t.w*e,this}sub(t,e){return void 0!==e?(console.warn(\\\\\\\"THREE.Vector4: .sub() now only accepts one argument. Use .subVectors( a, b ) instead.\\\\\\\"),this.subVectors(t,e)):(this.x-=t.x,this.y-=t.y,this.z-=t.z,this.w-=t.w,this)}subScalar(t){return this.x-=t,this.y-=t,this.z-=t,this.w-=t,this}subVectors(t,e){return this.x=t.x-e.x,this.y=t.y-e.y,this.z=t.z-e.z,this.w=t.w-e.w,this}multiply(t){return this.x*=t.x,this.y*=t.y,this.z*=t.z,this.w*=t.w,this}multiplyScalar(t){return this.x*=t,this.y*=t,this.z*=t,this.w*=t,this}applyMatrix4(t){const e=this.x,n=this.y,i=this.z,r=this.w,s=t.elements;return this.x=s[0]*e+s[4]*n+s[8]*i+s[12]*r,this.y=s[1]*e+s[5]*n+s[9]*i+s[13]*r,this.z=s[2]*e+s[6]*n+s[10]*i+s[14]*r,this.w=s[3]*e+s[7]*n+s[11]*i+s[15]*r,this}divideScalar(t){return this.multiplyScalar(1/t)}setAxisAngleFromQuaternion(t){this.w=2*Math.acos(t.w);const e=Math.sqrt(1-t.w*t.w);return e<1e-4?(this.x=1,this.y=0,this.z=0):(this.x=t.x/e,this.y=t.y/e,this.z=t.z/e),this}setAxisAngleFromRotationMatrix(t){let e,n,i,r;const s=.01,o=.1,a=t.elements,l=a[0],c=a[4],u=a[8],h=a[1],d=a[5],p=a[9],_=a[2],m=a[6],f=a[10];if(Math.abs(c-h)<s&&Math.abs(u-_)<s&&Math.abs(p-m)<s){if(Math.abs(c+h)<o&&Math.abs(u+_)<o&&Math.abs(p+m)<o&&Math.abs(l+d+f-3)<o)return this.set(1,0,0,0),this;e=Math.PI;const t=(l+1)/2,a=(d+1)/2,g=(f+1)/2,v=(c+h)/4,y=(u+_)/4,x=(p+m)/4;return t>a&&t>g?t<s?(n=0,i=.707106781,r=.707106781):(n=Math.sqrt(t),i=v/n,r=y/n):a>g?a<s?(n=.707106781,i=0,r=.707106781):(i=Math.sqrt(a),n=v/i,r=x/i):g<s?(n=.707106781,i=.707106781,r=0):(r=Math.sqrt(g),n=y/r,i=x/r),this.set(n,i,r,e),this}let g=Math.sqrt((m-p)*(m-p)+(u-_)*(u-_)+(h-c)*(h-c));return Math.abs(g)<.001&&(g=1),this.x=(m-p)/g,this.y=(u-_)/g,this.z=(h-c)/g,this.w=Math.acos((l+d+f-1)/2),this}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.w=Math.min(this.w,t.w),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.w=Math.max(this.w,t.w),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.w=Math.max(t.w,Math.min(e.w,this.w)),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.w=Math.max(t,Math.min(e,this.w)),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.w=Math.floor(this.w),this}ceil(){return this.x=Math.ceil(this.x),this.y=Math.ceil(this.y),this.z=Math.ceil(this.z),this.w=Math.ceil(this.w),this}round(){return this.x=Math.round(this.x),this.y=Math.round(this.y),this.z=Math.round(this.z),this.w=Math.round(this.w),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.w=this.w<0?Math.ceil(this.w):Math.floor(this.w),this}negate(){return this.x=-this.x,this.y=-this.y,this.z=-this.z,this.w=-this.w,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)}manhattanLength(){return Math.abs(this.x)+Math.abs(this.y)+Math.abs(this.z)+Math.abs(this.w)}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.w+=(t.w-this.w)*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.w=t.w+(e.w-t.w)*n,this}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}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,n){return void 0!==n&&console.warn(\\\\\\\"THREE.Vector4: offset has been removed from .fromBufferAttribute().\\\\\\\"),this.x=t.getX(e),this.y=t.getY(e),this.z=t.getZ(e),this.w=t.getW(e),this}random(){return this.x=Math.random(),this.y=Math.random(),this.z=Math.random(),this.w=Math.random(),this}*[Symbol.iterator](){yield this.x,yield this.y,yield this.z,yield this.w}}i.prototype.isVector4=!0},function(t,e,n){\\\\\\\"use strict\\\\\\\";n.d(e,\\\\\\\"a\\\\\\\",(function(){return A}));var i=n(8),r=n(0),s=n(5),o=n(15),a=n(28),l=n(36),c=n(11),u=n(3);let h=0;const d=new r.a,p=new i.a,_=new s.a,m=new r.a,f=new r.a,g=new r.a,v=new i.a,y=new r.a(1,0,0),x=new r.a(0,1,0),b=new r.a(0,0,1),w={type:\\\\\\\"added\\\\\\\"},T={type:\\\\\\\"removed\\\\\\\"};class A extends o.a{constructor(){super(),Object.defineProperty(this,\\\\\\\"id\\\\\\\",{value:h++}),this.uuid=u.h(),this.name=\\\\\\\"\\\\\\\",this.type=\\\\\\\"Object3D\\\\\\\",this.parent=null,this.children=[],this.up=A.DefaultUp.clone();const t=new r.a,e=new a.a,n=new i.a,o=new r.a(1,1,1);e._onChange((function(){n.setFromEuler(e,!1)})),n._onChange((function(){e.setFromQuaternion(n,void 0,!1)})),Object.defineProperties(this,{position:{configurable:!0,enumerable:!0,value:t},rotation:{configurable:!0,enumerable:!0,value:e},quaternion:{configurable:!0,enumerable:!0,value:n},scale:{configurable:!0,enumerable:!0,value:o},modelViewMatrix:{value:new s.a},normalMatrix:{value:new c.a}}),this.matrix=new s.a,this.matrixWorld=new s.a,this.matrixAutoUpdate=A.DefaultMatrixAutoUpdate,this.matrixWorldNeedsUpdate=!1,this.layers=new l.a,this.visible=!0,this.castShadow=!1,this.receiveShadow=!1,this.frustumCulled=!0,this.renderOrder=0,this.animations=[],this.userData={}}onBeforeRender(){}onAfterRender(){}applyMatrix4(t){this.matrixAutoUpdate&&this.updateMatrix(),this.matrix.premultiply(t),this.matrix.decompose(this.position,this.quaternion,this.scale)}applyQuaternion(t){return this.quaternion.premultiply(t),this}setRotationFromAxisAngle(t,e){this.quaternion.setFromAxisAngle(t,e)}setRotationFromEuler(t){this.quaternion.setFromEuler(t,!0)}setRotationFromMatrix(t){this.quaternion.setFromRotationMatrix(t)}setRotationFromQuaternion(t){this.quaternion.copy(t)}rotateOnAxis(t,e){return p.setFromAxisAngle(t,e),this.quaternion.multiply(p),this}rotateOnWorldAxis(t,e){return p.setFromAxisAngle(t,e),this.quaternion.premultiply(p),this}rotateX(t){return this.rotateOnAxis(y,t)}rotateY(t){return this.rotateOnAxis(x,t)}rotateZ(t){return this.rotateOnAxis(b,t)}translateOnAxis(t,e){return d.copy(t).applyQuaternion(this.quaternion),this.position.add(d.multiplyScalar(e)),this}translateX(t){return this.translateOnAxis(y,t)}translateY(t){return this.translateOnAxis(x,t)}translateZ(t){return this.translateOnAxis(b,t)}localToWorld(t){return t.applyMatrix4(this.matrixWorld)}worldToLocal(t){return t.applyMatrix4(_.copy(this.matrixWorld).invert())}lookAt(t,e,n){t.isVector3?m.copy(t):m.set(t,e,n);const i=this.parent;this.updateWorldMatrix(!0,!1),f.setFromMatrixPosition(this.matrixWorld),this.isCamera||this.isLight?_.lookAt(f,m,this.up):_.lookAt(m,f,this.up),this.quaternion.setFromRotationMatrix(_),i&&(_.extractRotation(i.matrixWorld),p.setFromRotationMatrix(_),this.quaternion.premultiply(p.invert()))}add(t){if(arguments.length>1){for(let t=0;t<arguments.length;t++)this.add(arguments[t]);return this}return t===this?(console.error(\\\\\\\"THREE.Object3D.add: object can't be added as a child of itself.\\\\\\\",t),this):(t&&t.isObject3D?(null!==t.parent&&t.parent.remove(t),t.parent=this,this.children.push(t),t.dispatchEvent(w)):console.error(\\\\\\\"THREE.Object3D.add: object not an instance of THREE.Object3D.\\\\\\\",t),this)}remove(t){if(arguments.length>1){for(let t=0;t<arguments.length;t++)this.remove(arguments[t]);return this}const e=this.children.indexOf(t);return-1!==e&&(t.parent=null,this.children.splice(e,1),t.dispatchEvent(T)),this}removeFromParent(){const t=this.parent;return null!==t&&t.remove(this),this}clear(){for(let t=0;t<this.children.length;t++){const e=this.children[t];e.parent=null,e.dispatchEvent(T)}return this.children.length=0,this}attach(t){return this.updateWorldMatrix(!0,!1),_.copy(this.matrixWorld).invert(),null!==t.parent&&(t.parent.updateWorldMatrix(!0,!1),_.multiply(t.parent.matrixWorld)),t.applyMatrix4(_),this.add(t),t.updateWorldMatrix(!1,!0),this}getObjectById(t){return this.getObjectByProperty(\\\\\\\"id\\\\\\\",t)}getObjectByName(t){return this.getObjectByProperty(\\\\\\\"name\\\\\\\",t)}getObjectByProperty(t,e){if(this[t]===e)return this;for(let n=0,i=this.children.length;n<i;n++){const i=this.children[n].getObjectByProperty(t,e);if(void 0!==i)return i}}getWorldPosition(t){return this.updateWorldMatrix(!0,!1),t.setFromMatrixPosition(this.matrixWorld)}getWorldQuaternion(t){return this.updateWorldMatrix(!0,!1),this.matrixWorld.decompose(f,t,g),t}getWorldScale(t){return this.updateWorldMatrix(!0,!1),this.matrixWorld.decompose(f,v,t),t}getWorldDirection(t){this.updateWorldMatrix(!0,!1);const e=this.matrixWorld.elements;return t.set(e[8],e[9],e[10]).normalize()}raycast(){}traverse(t){t(this);const e=this.children;for(let n=0,i=e.length;n<i;n++)e[n].traverse(t)}traverseVisible(t){if(!1===this.visible)return;t(this);const e=this.children;for(let n=0,i=e.length;n<i;n++)e[n].traverseVisible(t)}traverseAncestors(t){const e=this.parent;null!==e&&(t(e),e.traverseAncestors(t))}updateMatrix(){this.matrix.compose(this.position,this.quaternion,this.scale),this.matrixWorldNeedsUpdate=!0}updateMatrixWorld(t){this.matrixAutoUpdate&&this.updateMatrix(),(this.matrixWorldNeedsUpdate||t)&&(null===this.parent?this.matrixWorld.copy(this.matrix):this.matrixWorld.multiplyMatrices(this.parent.matrixWorld,this.matrix),this.matrixWorldNeedsUpdate=!1,t=!0);const e=this.children;for(let n=0,i=e.length;n<i;n++)e[n].updateMatrixWorld(t)}updateWorldMatrix(t,e){const n=this.parent;if(!0===t&&null!==n&&n.updateWorldMatrix(!0,!1),this.matrixAutoUpdate&&this.updateMatrix(),null===this.parent?this.matrixWorld.copy(this.matrix):this.matrixWorld.multiplyMatrices(this.parent.matrixWorld,this.matrix),!0===e){const t=this.children;for(let e=0,n=t.length;e<n;e++)t[e].updateWorldMatrix(!1,!0)}}toJSON(t){const e=void 0===t||\\\\\\\"string\\\\\\\"==typeof t,n={};e&&(t={geometries:{},materials:{},textures:{},images:{},shapes:{},skeletons:{},animations:{}},n.metadata={version:4.5,type:\\\\\\\"Object\\\\\\\",generator:\\\\\\\"Object3D.toJSON\\\\\\\"});const i={};function r(e,n){return void 0===e[n.uuid]&&(e[n.uuid]=n.toJSON(t)),n.uuid}if(i.uuid=this.uuid,i.type=this.type,\\\\\\\"\\\\\\\"!==this.name&&(i.name=this.name),!0===this.castShadow&&(i.castShadow=!0),!0===this.receiveShadow&&(i.receiveShadow=!0),!1===this.visible&&(i.visible=!1),!1===this.frustumCulled&&(i.frustumCulled=!1),0!==this.renderOrder&&(i.renderOrder=this.renderOrder),\\\\\\\"{}\\\\\\\"!==JSON.stringify(this.userData)&&(i.userData=this.userData),i.layers=this.layers.mask,i.matrix=this.matrix.toArray(),!1===this.matrixAutoUpdate&&(i.matrixAutoUpdate=!1),this.isInstancedMesh&&(i.type=\\\\\\\"InstancedMesh\\\\\\\",i.count=this.count,i.instanceMatrix=this.instanceMatrix.toJSON(),null!==this.instanceColor&&(i.instanceColor=this.instanceColor.toJSON())),this.isScene)this.background&&(this.background.isColor?i.background=this.background.toJSON():this.background.isTexture&&(i.background=this.background.toJSON(t).uuid)),this.environment&&this.environment.isTexture&&(i.environment=this.environment.toJSON(t).uuid);else if(this.isMesh||this.isLine||this.isPoints){i.geometry=r(t.geometries,this.geometry);const e=this.geometry.parameters;if(void 0!==e&&void 0!==e.shapes){const n=e.shapes;if(Array.isArray(n))for(let e=0,i=n.length;e<i;e++){const i=n[e];r(t.shapes,i)}else r(t.shapes,n)}}if(this.isSkinnedMesh&&(i.bindMode=this.bindMode,i.bindMatrix=this.bindMatrix.toArray(),void 0!==this.skeleton&&(r(t.skeletons,this.skeleton),i.skeleton=this.skeleton.uuid)),void 0!==this.material)if(Array.isArray(this.material)){const e=[];for(let n=0,i=this.material.length;n<i;n++)e.push(r(t.materials,this.material[n]));i.material=e}else i.material=r(t.materials,this.material);if(this.children.length>0){i.children=[];for(let e=0;e<this.children.length;e++)i.children.push(this.children[e].toJSON(t).object)}if(this.animations.length>0){i.animations=[];for(let e=0;e<this.animations.length;e++){const n=this.animations[e];i.animations.push(r(t.animations,n))}}if(e){const e=s(t.geometries),i=s(t.materials),r=s(t.textures),o=s(t.images),a=s(t.shapes),l=s(t.skeletons),c=s(t.animations);e.length>0&&(n.geometries=e),i.length>0&&(n.materials=i),r.length>0&&(n.textures=r),o.length>0&&(n.images=o),a.length>0&&(n.shapes=a),l.length>0&&(n.skeletons=l),c.length>0&&(n.animations=c)}return n.object=i,n;function s(t){const e=[];for(const n in t){const i=t[n];delete i.metadata,e.push(i)}return e}}clone(t){return(new this.constructor).copy(this,t)}copy(t,e=!0){if(this.name=t.name,this.up.copy(t.up),this.position.copy(t.position),this.rotation.order=t.rotation.order,this.quaternion.copy(t.quaternion),this.scale.copy(t.scale),this.matrix.copy(t.matrix),this.matrixWorld.copy(t.matrixWorld),this.matrixAutoUpdate=t.matrixAutoUpdate,this.matrixWorldNeedsUpdate=t.matrixWorldNeedsUpdate,this.layers.mask=t.layers.mask,this.visible=t.visible,this.castShadow=t.castShadow,this.receiveShadow=t.receiveShadow,this.frustumCulled=t.frustumCulled,this.renderOrder=t.renderOrder,this.userData=JSON.parse(JSON.stringify(t.userData)),!0===e)for(let e=0;e<t.children.length;e++){const n=t.children[e];this.add(n.clone())}return this}}A.DefaultUp=new r.a(0,1,0),A.DefaultMatrixAutoUpdate=!0,A.prototype.isObject3D=!0},function(t,e,n){\\\\\\\"use strict\\\\\\\";n.d(e,\\\\\\\"a\\\\\\\",(function(){return i}));class i{constructor(){this.elements=[1,0,0,0,1,0,0,0,1],arguments.length>0&&console.error(\\\\\\\"THREE.Matrix3: the constructor no longer reads arguments. use .set() instead.\\\\\\\")}set(t,e,n,i,r,s,o,a,l){const c=this.elements;return c[0]=t,c[1]=i,c[2]=o,c[3]=e,c[4]=r,c[5]=a,c[6]=n,c[7]=s,c[8]=l,this}identity(){return this.set(1,0,0,0,1,0,0,0,1),this}copy(t){const e=this.elements,n=t.elements;return e[0]=n[0],e[1]=n[1],e[2]=n[2],e[3]=n[3],e[4]=n[4],e[5]=n[5],e[6]=n[6],e[7]=n[7],e[8]=n[8],this}extractBasis(t,e,n){return t.setFromMatrix3Column(this,0),e.setFromMatrix3Column(this,1),n.setFromMatrix3Column(this,2),this}setFromMatrix4(t){const e=t.elements;return this.set(e[0],e[4],e[8],e[1],e[5],e[9],e[2],e[6],e[10]),this}multiply(t){return this.multiplyMatrices(this,t)}premultiply(t){return this.multiplyMatrices(t,this)}multiplyMatrices(t,e){const n=t.elements,i=e.elements,r=this.elements,s=n[0],o=n[3],a=n[6],l=n[1],c=n[4],u=n[7],h=n[2],d=n[5],p=n[8],_=i[0],m=i[3],f=i[6],g=i[1],v=i[4],y=i[7],x=i[2],b=i[5],w=i[8];return r[0]=s*_+o*g+a*x,r[3]=s*m+o*v+a*b,r[6]=s*f+o*y+a*w,r[1]=l*_+c*g+u*x,r[4]=l*m+c*v+u*b,r[7]=l*f+c*y+u*w,r[2]=h*_+d*g+p*x,r[5]=h*m+d*v+p*b,r[8]=h*f+d*y+p*w,this}multiplyScalar(t){const e=this.elements;return e[0]*=t,e[3]*=t,e[6]*=t,e[1]*=t,e[4]*=t,e[7]*=t,e[2]*=t,e[5]*=t,e[8]*=t,this}determinant(){const t=this.elements,e=t[0],n=t[1],i=t[2],r=t[3],s=t[4],o=t[5],a=t[6],l=t[7],c=t[8];return e*s*c-e*o*l-n*r*c+n*o*a+i*r*l-i*s*a}invert(){const t=this.elements,e=t[0],n=t[1],i=t[2],r=t[3],s=t[4],o=t[5],a=t[6],l=t[7],c=t[8],u=c*s-o*l,h=o*a-c*r,d=l*r-s*a,p=e*u+n*h+i*d;if(0===p)return this.set(0,0,0,0,0,0,0,0,0);const _=1/p;return t[0]=u*_,t[1]=(i*l-c*n)*_,t[2]=(o*n-i*s)*_,t[3]=h*_,t[4]=(c*e-i*a)*_,t[5]=(i*r-o*e)*_,t[6]=d*_,t[7]=(n*a-l*e)*_,t[8]=(s*e-n*r)*_,this}transpose(){let t;const e=this.elements;return t=e[1],e[1]=e[3],e[3]=t,t=e[2],e[2]=e[6],e[6]=t,t=e[5],e[5]=e[7],e[7]=t,this}getNormalMatrix(t){return this.setFromMatrix4(t).invert().transpose()}transposeIntoArray(t){const e=this.elements;return t[0]=e[0],t[1]=e[3],t[2]=e[6],t[3]=e[1],t[4]=e[4],t[5]=e[7],t[6]=e[2],t[7]=e[5],t[8]=e[8],this}setUvTransform(t,e,n,i,r,s,o){const a=Math.cos(r),l=Math.sin(r);return this.set(n*a,n*l,-n*(a*s+l*o)+s+t,-i*l,i*a,-i*(-l*s+a*o)+o+e,0,0,1),this}scale(t,e){const n=this.elements;return n[0]*=t,n[3]*=t,n[6]*=t,n[1]*=e,n[4]*=e,n[7]*=e,this}rotate(t){const e=Math.cos(t),n=Math.sin(t),i=this.elements,r=i[0],s=i[3],o=i[6],a=i[1],l=i[4],c=i[7];return i[0]=e*r+n*a,i[3]=e*s+n*l,i[6]=e*o+n*c,i[1]=-n*r+e*a,i[4]=-n*s+e*l,i[7]=-n*o+e*c,this}translate(t,e){const n=this.elements;return n[0]+=t*n[2],n[3]+=t*n[5],n[6]+=t*n[8],n[1]+=e*n[2],n[4]+=e*n[5],n[7]+=e*n[8],this}equals(t){const e=this.elements,n=t.elements;for(let t=0;t<9;t++)if(e[t]!==n[t])return!1;return!0}fromArray(t,e=0){for(let n=0;n<9;n++)this.elements[n]=t[n+e];return this}toArray(t=[],e=0){const n=this.elements;return t[e]=n[0],t[e+1]=n[1],t[e+2]=n[2],t[e+3]=n[3],t[e+4]=n[4],t[e+5]=n[5],t[e+6]=n[6],t[e+7]=n[7],t[e+8]=n[8],t}clone(){return(new this.constructor).fromArray(this.elements)}}i.prototype.isMatrix3=!0},function(t,e,n){\\\\\\\"use strict\\\\\\\";n.d(e,\\\\\\\"a\\\\\\\",(function(){return a}));var i=n(15),r=n(1),s=n(3);let o=0;class a extends i.a{constructor(){super(),Object.defineProperty(this,\\\\\\\"id\\\\\\\",{value:o++}),this.uuid=s.h(),this.name=\\\\\\\"\\\\\\\",this.type=\\\\\\\"Material\\\\\\\",this.fog=!0,this.blending=r.xb,this.side=r.H,this.vertexColors=!1,this.opacity=1,this.format=r.Ib,this.transparent=!1,this.blendSrc=r.Nc,this.blendDst=r.Db,this.blendEquation=r.b,this.blendSrcAlpha=null,this.blendDstAlpha=null,this.blendEquationAlpha=null,this.depthFunc=r.T,this.depthTest=!0,this.depthWrite=!0,this.stencilWriteMask=255,this.stencilFunc=r.h,this.stencilRef=0,this.stencilFuncMask=255,this.stencilFail=r.R,this.stencilZFail=r.R,this.stencilZPass=r.R,this.stencilWrite=!1,this.clippingPlanes=null,this.clipIntersection=!1,this.clipShadows=!1,this.shadowSide=null,this.colorWrite=!0,this.precision=null,this.polygonOffset=!1,this.polygonOffsetFactor=0,this.polygonOffsetUnits=0,this.dithering=!1,this.alphaToCoverage=!1,this.premultipliedAlpha=!1,this.visible=!0,this.toneMapped=!0,this.userData={},this.version=0,this._alphaTest=0}get alphaTest(){return this._alphaTest}set alphaTest(t){this._alphaTest>0!=t>0&&this.version++,this._alphaTest=t}onBuild(){}onBeforeRender(){}onBeforeCompile(){}customProgramCacheKey(){return this.onBeforeCompile.toString()}setValues(t){if(void 0!==t)for(const e in t){const n=t[e];if(void 0===n){console.warn(\\\\\\\"THREE.Material: '\\\\\\\"+e+\\\\\\\"' parameter is undefined.\\\\\\\");continue}if(\\\\\\\"shading\\\\\\\"===e){console.warn(\\\\\\\"THREE.\\\\\\\"+this.type+\\\\\\\": .shading has been removed. Use the boolean .flatShading instead.\\\\\\\"),this.flatShading=n===r.F;continue}const i=this[e];void 0!==i?i&&i.isColor?i.set(n):i&&i.isVector3&&n&&n.isVector3?i.copy(n):this[e]=n:console.warn(\\\\\\\"THREE.\\\\\\\"+this.type+\\\\\\\": '\\\\\\\"+e+\\\\\\\"' is not a property of this material.\\\\\\\")}}toJSON(t){const e=void 0===t||\\\\\\\"string\\\\\\\"==typeof t;e&&(t={textures:{},images:{}});const n={metadata:{version:4.5,type:\\\\\\\"Material\\\\\\\",generator:\\\\\\\"Material.toJSON\\\\\\\"}};function i(t){const e=[];for(const n in t){const i=t[n];delete i.metadata,e.push(i)}return e}if(n.uuid=this.uuid,n.type=this.type,\\\\\\\"\\\\\\\"!==this.name&&(n.name=this.name),this.color&&this.color.isColor&&(n.color=this.color.getHex()),void 0!==this.roughness&&(n.roughness=this.roughness),void 0!==this.metalness&&(n.metalness=this.metalness),void 0!==this.sheen&&(n.sheen=this.sheen),this.sheenTint&&this.sheenTint.isColor&&(n.sheenTint=this.sheenTint.getHex()),void 0!==this.sheenRoughness&&(n.sheenRoughness=this.sheenRoughness),this.emissive&&this.emissive.isColor&&(n.emissive=this.emissive.getHex()),this.emissiveIntensity&&1!==this.emissiveIntensity&&(n.emissiveIntensity=this.emissiveIntensity),this.specular&&this.specular.isColor&&(n.specular=this.specular.getHex()),void 0!==this.specularIntensity&&(n.specularIntensity=this.specularIntensity),this.specularTint&&this.specularTint.isColor&&(n.specularTint=this.specularTint.getHex()),void 0!==this.shininess&&(n.shininess=this.shininess),void 0!==this.clearcoat&&(n.clearcoat=this.clearcoat),void 0!==this.clearcoatRoughness&&(n.clearcoatRoughness=this.clearcoatRoughness),this.clearcoatMap&&this.clearcoatMap.isTexture&&(n.clearcoatMap=this.clearcoatMap.toJSON(t).uuid),this.clearcoatRoughnessMap&&this.clearcoatRoughnessMap.isTexture&&(n.clearcoatRoughnessMap=this.clearcoatRoughnessMap.toJSON(t).uuid),this.clearcoatNormalMap&&this.clearcoatNormalMap.isTexture&&(n.clearcoatNormalMap=this.clearcoatNormalMap.toJSON(t).uuid,n.clearcoatNormalScale=this.clearcoatNormalScale.toArray()),this.map&&this.map.isTexture&&(n.map=this.map.toJSON(t).uuid),this.matcap&&this.matcap.isTexture&&(n.matcap=this.matcap.toJSON(t).uuid),this.alphaMap&&this.alphaMap.isTexture&&(n.alphaMap=this.alphaMap.toJSON(t).uuid),this.lightMap&&this.lightMap.isTexture&&(n.lightMap=this.lightMap.toJSON(t).uuid,n.lightMapIntensity=this.lightMapIntensity),this.aoMap&&this.aoMap.isTexture&&(n.aoMap=this.aoMap.toJSON(t).uuid,n.aoMapIntensity=this.aoMapIntensity),this.bumpMap&&this.bumpMap.isTexture&&(n.bumpMap=this.bumpMap.toJSON(t).uuid,n.bumpScale=this.bumpScale),this.normalMap&&this.normalMap.isTexture&&(n.normalMap=this.normalMap.toJSON(t).uuid,n.normalMapType=this.normalMapType,n.normalScale=this.normalScale.toArray()),this.displacementMap&&this.displacementMap.isTexture&&(n.displacementMap=this.displacementMap.toJSON(t).uuid,n.displacementScale=this.displacementScale,n.displacementBias=this.displacementBias),this.roughnessMap&&this.roughnessMap.isTexture&&(n.roughnessMap=this.roughnessMap.toJSON(t).uuid),this.metalnessMap&&this.metalnessMap.isTexture&&(n.metalnessMap=this.metalnessMap.toJSON(t).uuid),this.emissiveMap&&this.emissiveMap.isTexture&&(n.emissiveMap=this.emissiveMap.toJSON(t).uuid),this.specularMap&&this.specularMap.isTexture&&(n.specularMap=this.specularMap.toJSON(t).uuid),this.specularIntensityMap&&this.specularIntensityMap.isTexture&&(n.specularIntensityMap=this.specularIntensityMap.toJSON(t).uuid),this.specularTintMap&&this.specularTintMap.isTexture&&(n.specularTintMap=this.specularTintMap.toJSON(t).uuid),this.envMap&&this.envMap.isTexture&&(n.envMap=this.envMap.toJSON(t).uuid,void 0!==this.combine&&(n.combine=this.combine)),void 0!==this.envMapIntensity&&(n.envMapIntensity=this.envMapIntensity),void 0!==this.reflectivity&&(n.reflectivity=this.reflectivity),void 0!==this.refractionRatio&&(n.refractionRatio=this.refractionRatio),this.gradientMap&&this.gradientMap.isTexture&&(n.gradientMap=this.gradientMap.toJSON(t).uuid),void 0!==this.transmission&&(n.transmission=this.transmission),this.transmissionMap&&this.transmissionMap.isTexture&&(n.transmissionMap=this.transmissionMap.toJSON(t).uuid),void 0!==this.thickness&&(n.thickness=this.thickness),this.thicknessMap&&this.thicknessMap.isTexture&&(n.thicknessMap=this.thicknessMap.toJSON(t).uuid),void 0!==this.attenuationDistance&&(n.attenuationDistance=this.attenuationDistance),void 0!==this.attenuationTint&&(n.attenuationTint=this.attenuationTint.getHex()),void 0!==this.size&&(n.size=this.size),null!==this.shadowSide&&(n.shadowSide=this.shadowSide),void 0!==this.sizeAttenuation&&(n.sizeAttenuation=this.sizeAttenuation),this.blending!==r.xb&&(n.blending=this.blending),this.side!==r.H&&(n.side=this.side),this.vertexColors&&(n.vertexColors=!0),this.opacity<1&&(n.opacity=this.opacity),this.format!==r.Ib&&(n.format=this.format),!0===this.transparent&&(n.transparent=this.transparent),n.depthFunc=this.depthFunc,n.depthTest=this.depthTest,n.depthWrite=this.depthWrite,n.colorWrite=this.colorWrite,n.stencilWrite=this.stencilWrite,n.stencilWriteMask=this.stencilWriteMask,n.stencilFunc=this.stencilFunc,n.stencilRef=this.stencilRef,n.stencilFuncMask=this.stencilFuncMask,n.stencilFail=this.stencilFail,n.stencilZFail=this.stencilZFail,n.stencilZPass=this.stencilZPass,this.rotation&&0!==this.rotation&&(n.rotation=this.rotation),!0===this.polygonOffset&&(n.polygonOffset=!0),0!==this.polygonOffsetFactor&&(n.polygonOffsetFactor=this.polygonOffsetFactor),0!==this.polygonOffsetUnits&&(n.polygonOffsetUnits=this.polygonOffsetUnits),this.linewidth&&1!==this.linewidth&&(n.linewidth=this.linewidth),void 0!==this.dashSize&&(n.dashSize=this.dashSize),void 0!==this.gapSize&&(n.gapSize=this.gapSize),void 0!==this.scale&&(n.scale=this.scale),!0===this.dithering&&(n.dithering=!0),this.alphaTest>0&&(n.alphaTest=this.alphaTest),!0===this.alphaToCoverage&&(n.alphaToCoverage=this.alphaToCoverage),!0===this.premultipliedAlpha&&(n.premultipliedAlpha=this.premultipliedAlpha),!0===this.wireframe&&(n.wireframe=this.wireframe),this.wireframeLinewidth>1&&(n.wireframeLinewidth=this.wireframeLinewidth),\\\\\\\"round\\\\\\\"!==this.wireframeLinecap&&(n.wireframeLinecap=this.wireframeLinecap),\\\\\\\"round\\\\\\\"!==this.wireframeLinejoin&&(n.wireframeLinejoin=this.wireframeLinejoin),!0===this.flatShading&&(n.flatShading=this.flatShading),!1===this.visible&&(n.visible=!1),!1===this.toneMapped&&(n.toneMapped=!1),\\\\\\\"{}\\\\\\\"!==JSON.stringify(this.userData)&&(n.userData=this.userData),e){const e=i(t.textures),r=i(t.images);e.length>0&&(n.textures=e),r.length>0&&(n.images=r)}return n}clone(){return(new this.constructor).copy(this)}copy(t){this.name=t.name,this.fog=t.fog,this.blending=t.blending,this.side=t.side,this.vertexColors=t.vertexColors,this.opacity=t.opacity,this.format=t.format,this.transparent=t.transparent,this.blendSrc=t.blendSrc,this.blendDst=t.blendDst,this.blendEquation=t.blendEquation,this.blendSrcAlpha=t.blendSrcAlpha,this.blendDstAlpha=t.blendDstAlpha,this.blendEquationAlpha=t.blendEquationAlpha,this.depthFunc=t.depthFunc,this.depthTest=t.depthTest,this.depthWrite=t.depthWrite,this.stencilWriteMask=t.stencilWriteMask,this.stencilFunc=t.stencilFunc,this.stencilRef=t.stencilRef,this.stencilFuncMask=t.stencilFuncMask,this.stencilFail=t.stencilFail,this.stencilZFail=t.stencilZFail,this.stencilZPass=t.stencilZPass,this.stencilWrite=t.stencilWrite;const e=t.clippingPlanes;let n=null;if(null!==e){const t=e.length;n=new Array(t);for(let i=0;i!==t;++i)n[i]=e[i].clone()}return this.clippingPlanes=n,this.clipIntersection=t.clipIntersection,this.clipShadows=t.clipShadows,this.shadowSide=t.shadowSide,this.colorWrite=t.colorWrite,this.precision=t.precision,this.polygonOffset=t.polygonOffset,this.polygonOffsetFactor=t.polygonOffsetFactor,this.polygonOffsetUnits=t.polygonOffsetUnits,this.dithering=t.dithering,this.alphaTest=t.alphaTest,this.alphaToCoverage=t.alphaToCoverage,this.premultipliedAlpha=t.premultipliedAlpha,this.visible=t.visible,this.toneMapped=t.toneMapped,this.userData=JSON.parse(JSON.stringify(t.userData)),this}dispose(){this.dispatchEvent({type:\\\\\\\"dispose\\\\\\\"})}set needsUpdate(t){!0===t&&this.version++}}a.prototype.isMaterial=!0},function(t,e,n){\\\\\\\"use strict\\\\\\\";n.d(e,\\\\\\\"a\\\\\\\",(function(){return r}));var i=n(29);class r{constructor(t){this.manager=void 0!==t?t:i.a,this.crossOrigin=\\\\\\\"anonymous\\\\\\\",this.withCredentials=!1,this.path=\\\\\\\"\\\\\\\",this.resourcePath=\\\\\\\"\\\\\\\",this.requestHeader={}}load(){}loadAsync(t,e){const n=this;return new Promise((function(i,r){n.load(t,i,e,r)}))}parse(){}setCrossOrigin(t){return this.crossOrigin=t,this}setWithCredentials(t){return this.withCredentials=t,this}setPath(t){return this.path=t,this}setResourcePath(t){return this.resourcePath=t,this}setRequestHeader(t){return this.requestHeader=t,this}}},function(t,e,n){\\\\\\\"use strict\\\\\\\";n.d(e,\\\\\\\"a\\\\\\\",(function(){return L}));var i=n(0),r=n(2),s=n(18),o=n(39),a=n(5),l=n(10),c=n(40),u=n(1),h=n(27),d=n(7);const p=new a.a,_=new o.a,m=new s.a,f=new i.a,g=new i.a,v=new i.a,y=new i.a,x=new i.a,b=new i.a,w=new i.a,T=new i.a,A=new i.a,E=new r.a,M=new r.a,S=new r.a,C=new i.a,N=new i.a;class L extends l.a{constructor(t=new d.a,e=new h.a){super(),this.type=\\\\\\\"Mesh\\\\\\\",this.geometry=t,this.material=e,this.updateMorphTargets()}copy(t){return super.copy(t),void 0!==t.morphTargetInfluences&&(this.morphTargetInfluences=t.morphTargetInfluences.slice()),void 0!==t.morphTargetDictionary&&(this.morphTargetDictionary=Object.assign({},t.morphTargetDictionary)),this.material=t.material,this.geometry=t.geometry,this}updateMorphTargets(){const t=this.geometry;if(t.isBufferGeometry){const e=t.morphAttributes,n=Object.keys(e);if(n.length>0){const t=e[n[0]];if(void 0!==t){this.morphTargetInfluences=[],this.morphTargetDictionary={};for(let e=0,n=t.length;e<n;e++){const n=t[e].name||String(e);this.morphTargetInfluences.push(0),this.morphTargetDictionary[n]=e}}}}else{const e=t.morphTargets;void 0!==e&&e.length>0&&console.error(\\\\\\\"THREE.Mesh.updateMorphTargets() no longer supports THREE.Geometry. Use THREE.BufferGeometry instead.\\\\\\\")}}raycast(t,e){const n=this.geometry,i=this.material,r=this.matrixWorld;if(void 0===i)return;if(null===n.boundingSphere&&n.computeBoundingSphere(),m.copy(n.boundingSphere),m.applyMatrix4(r),!1===t.ray.intersectsSphere(m))return;if(p.copy(r).invert(),_.copy(t.ray).applyMatrix4(p),null!==n.boundingBox&&!1===_.intersectsBox(n.boundingBox))return;let s;if(n.isBufferGeometry){const r=n.index,o=n.attributes.position,a=n.morphAttributes.position,l=n.morphTargetsRelative,c=n.attributes.uv,u=n.attributes.uv2,h=n.groups,d=n.drawRange;if(null!==r)if(Array.isArray(i))for(let n=0,p=h.length;n<p;n++){const p=h[n],m=i[p.materialIndex];for(let n=Math.max(p.start,d.start),i=Math.min(r.count,Math.min(p.start+p.count,d.start+d.count));n<i;n+=3){const i=r.getX(n),h=r.getX(n+1),d=r.getX(n+2);s=O(this,m,t,_,o,a,l,c,u,i,h,d),s&&(s.faceIndex=Math.floor(n/3),s.face.materialIndex=p.materialIndex,e.push(s))}}else{for(let n=Math.max(0,d.start),h=Math.min(r.count,d.start+d.count);n<h;n+=3){const h=r.getX(n),d=r.getX(n+1),p=r.getX(n+2);s=O(this,i,t,_,o,a,l,c,u,h,d,p),s&&(s.faceIndex=Math.floor(n/3),e.push(s))}}else if(void 0!==o)if(Array.isArray(i))for(let n=0,r=h.length;n<r;n++){const r=h[n],p=i[r.materialIndex];for(let n=Math.max(r.start,d.start),i=Math.min(o.count,Math.min(r.start+r.count,d.start+d.count));n<i;n+=3){s=O(this,p,t,_,o,a,l,c,u,n,n+1,n+2),s&&(s.faceIndex=Math.floor(n/3),s.face.materialIndex=r.materialIndex,e.push(s))}}else{for(let n=Math.max(0,d.start),r=Math.min(o.count,d.start+d.count);n<r;n+=3){s=O(this,i,t,_,o,a,l,c,u,n,n+1,n+2),s&&(s.faceIndex=Math.floor(n/3),e.push(s))}}}else n.isGeometry&&console.error(\\\\\\\"THREE.Mesh.raycast() no longer supports THREE.Geometry. Use THREE.BufferGeometry instead.\\\\\\\")}}function O(t,e,n,s,o,a,l,h,d,p,_,m){f.fromBufferAttribute(o,p),g.fromBufferAttribute(o,_),v.fromBufferAttribute(o,m);const L=t.morphTargetInfluences;if(a&&L){w.set(0,0,0),T.set(0,0,0),A.set(0,0,0);for(let t=0,e=a.length;t<e;t++){const e=L[t],n=a[t];0!==e&&(y.fromBufferAttribute(n,p),x.fromBufferAttribute(n,_),b.fromBufferAttribute(n,m),l?(w.addScaledVector(y,e),T.addScaledVector(x,e),A.addScaledVector(b,e)):(w.addScaledVector(y.sub(f),e),T.addScaledVector(x.sub(g),e),A.addScaledVector(b.sub(v),e)))}f.add(w),g.add(T),v.add(A)}t.isSkinnedMesh&&(t.boneTransform(p,f),t.boneTransform(_,g),t.boneTransform(m,v));const O=function(t,e,n,i,r,s,o,a){let l;if(l=e.side===u.i?i.intersectTriangle(o,s,r,!0,a):i.intersectTriangle(r,s,o,e.side!==u.z,a),null===l)return null;N.copy(a),N.applyMatrix4(t.matrixWorld);const c=n.ray.origin.distanceTo(N);return c<n.near||c>n.far?null:{distance:c,point:N.clone(),object:t}}(t,e,n,s,f,g,v,C);if(O){h&&(E.fromBufferAttribute(h,p),M.fromBufferAttribute(h,_),S.fromBufferAttribute(h,m),O.uv=c.a.getUV(C,f,g,v,E,M,S,new r.a)),d&&(E.fromBufferAttribute(d,p),M.fromBufferAttribute(d,_),S.fromBufferAttribute(d,m),O.uv2=c.a.getUV(C,f,g,v,E,M,S,new r.a));const t={a:p,b:_,c:m,normal:new i.a,materialIndex:0};c.a.getNormal(f,g,v,t.normal),O.face=t}return O}L.prototype.isMesh=!0},function(t,e,n){\\\\\\\"use strict\\\\\\\";n.d(e,\\\\\\\"a\\\\\\\",(function(){return i}));class i{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 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0===c&&(c=Object(l.b)(\\\\\\\"canvas\\\\\\\")),c.width=t.width,c.height=t.height;const n=c.getContext(\\\\\\\"2d\\\\\\\");t instanceof ImageData?n.putImageData(t,0,0):n.drawImage(t,0,0,t.width,t.height),e=c}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\\\\\\\")}}.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.\\\\\\\"),{})}h.DEFAULT_IMAGE=void 0,h.DEFAULT_MAPPING=r.Yc,h.prototype.isTexture=!0},function(t,e,n){\\\\\\\"use strict\\\\\\\";n.d(e,\\\\\\\"a\\\\\\\",(function(){return s}));var i=n(12),r=n(6);class s extends i.a{constructor(t){super(),this.type=\\\\\\\"LineBasicMaterial\\\\\\\",this.color=new r.a(16777215),this.linewidth=1,this.linecap=\\\\\\\"round\\\\\\\",this.linejoin=\\\\\\\"round\\\\\\\",this.setValues(t)}copy(t){return super.copy(t),this.color.copy(t.color),this.linewidth=t.linewidth,this.linecap=t.linecap,this.linejoin=t.linejoin,this}}s.prototype.isLineBasicMaterial=!0},function(t,e,n){\\\\\\\"use strict\\\\\\\";n.d(e,\\\\\\\"a\\\\\\\",(function(){return a}));var i=n(3),r=n(2),s=n(0),o=n(5);class a{constructor(){this.type=\\\\\\\"Curve\\\\\\\",this.arcLengthDivisions=200}getPoint(){return console.warn(\\\\\\\"THREE.Curve: .getPoint() not implemented.\\\\\\\"),null}getPointAt(t,e){const n=this.getUtoTmapping(t);return this.getPoint(n,e)}getPoints(t=5){const e=[];for(let n=0;n<=t;n++)e.push(this.getPoint(n/t));return e}getSpacedPoints(t=5){const e=[];for(let n=0;n<=t;n++)e.push(this.getPointAt(n/t));return e}getLength(){const t=this.getLengths();return t[t.length-1]}getLengths(t=this.arcLengthDivisions){if(this.cacheArcLengths&&this.cacheArcLengths.length===t+1&&!this.needsUpdate)return this.cacheArcLengths;this.needsUpdate=!1;const e=[];let n,i=this.getPoint(0),r=0;e.push(0);for(let s=1;s<=t;s++)n=this.getPoint(s/t),r+=n.distanceTo(i),e.push(r),i=n;return this.cacheArcLengths=e,e}updateArcLengths(){this.needsUpdate=!0,this.getLengths()}getUtoTmapping(t,e){const n=this.getLengths();let i=0;const r=n.length;let s;s=e||t*n[r-1];let o,a=0,l=r-1;for(;a<=l;)if(i=Math.floor(a+(l-a)/2),o=n[i]-s,o<0)a=i+1;else{if(!(o>0)){l=i;break}l=i-1}if(i=l,n[i]===s)return i/(r-1);const c=n[i];return(i+(s-c)/(n[i+1]-c))/(r-1)}getTangent(t,e){const n=1e-4;let i=t-n,o=t+n;i<0&&(i=0),o>1&&(o=1);const a=this.getPoint(i),l=this.getPoint(o),c=e||(a.isVector2?new r.a:new s.a);return c.copy(l).sub(a).normalize(),c}getTangentAt(t,e){const n=this.getUtoTmapping(t);return this.getTangent(n,e)}computeFrenetFrames(t,e){const n=new s.a,r=[],a=[],l=[],c=new s.a,u=new o.a;for(let e=0;e<=t;e++){const n=e/t;r[e]=this.getTangentAt(n,new s.a)}a[0]=new s.a,l[0]=new s.a;let h=Number.MAX_VALUE;const d=Math.abs(r[0].x),p=Math.abs(r[0].y),_=Math.abs(r[0].z);d<=h&&(h=d,n.set(1,0,0)),p<=h&&(h=p,n.set(0,1,0)),_<=h&&n.set(0,0,1),c.crossVectors(r[0],n).normalize(),a[0].crossVectors(r[0],c),l[0].crossVectors(r[0],a[0]);for(let e=1;e<=t;e++){if(a[e]=a[e-1].clone(),l[e]=l[e-1].clone(),c.crossVectors(r[e-1],r[e]),c.length()>Number.EPSILON){c.normalize();const t=Math.acos(i.d(r[e-1].dot(r[e]),-1,1));a[e].applyMatrix4(u.makeRotationAxis(c,t))}l[e].crossVectors(r[e],a[e])}if(!0===e){let e=Math.acos(i.d(a[0].dot(a[t]),-1,1));e/=t,r[0].dot(c.crossVectors(a[0],a[t]))>0&&(e=-e);for(let n=1;n<=t;n++)a[n].applyMatrix4(u.makeRotationAxis(r[n],e*n)),l[n].crossVectors(r[n],a[n])}return{tangents:r,normals:a,binormals:l}}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}}},function(t,e,n){\\\\\\\"use strict\\\\\\\";n.d(e,\\\\\\\"a\\\\\\\",(function(){return c}));var i=n(1),r=n(70),s=n(71),o=n(38);class a extends o.a{constructor(t,e,n,i){super(t,e,n,i)}interpolate_(t){return this.copySampleValue_(t-1)}}var l=n(19);class c{constructor(t,e,n,i){if(void 0===t)throw new Error(\\\\\\\"THREE.KeyframeTrack: track name is undefined\\\\\\\");if(void 0===e||0===e.length)throw new Error(\\\\\\\"THREE.KeyframeTrack: no keyframes in track named \\\\\\\"+t);this.name=t,this.times=l.a.convertArray(e,this.TimeBufferType),this.values=l.a.convertArray(n,this.ValueBufferType),this.setInterpolation(i||this.DefaultInterpolation)}static toJSON(t){const e=t.constructor;let n;if(e.toJSON!==this.toJSON)n=e.toJSON(t);else{n={name:t.name,times:l.a.convertArray(t.times,Array),values:l.a.convertArray(t.values,Array)};const e=t.getInterpolation();e!==t.DefaultInterpolation&&(n.interpolation=e)}return n.type=t.ValueTypeName,n}InterpolantFactoryMethodDiscrete(t){return new a(this.times,this.values,this.getValueSize(),t)}InterpolantFactoryMethodLinear(t){return new s.a(this.times,this.values,this.getValueSize(),t)}InterpolantFactoryMethodSmooth(t){return new r.a(this.times,this.values,this.getValueSize(),t)}setInterpolation(t){let e;switch(t){case i.O:e=this.InterpolantFactoryMethodDiscrete;break;case i.P:e=this.InterpolantFactoryMethodLinear;break;case i.Q:e=this.InterpolantFactoryMethodSmooth}if(void 0===e){const e=\\\\\\\"unsupported interpolation for \\\\\\\"+this.ValueTypeName+\\\\\\\" keyframe track named \\\\\\\"+this.name;if(void 0===this.createInterpolant){if(t===this.DefaultInterpolation)throw new Error(e);this.setInterpolation(this.DefaultInterpolation)}return console.warn(\\\\\\\"THREE.KeyframeTrack:\\\\\\\",e),this}return this.createInterpolant=e,this}getInterpolation(){switch(this.createInterpolant){case this.InterpolantFactoryMethodDiscrete:return i.O;case this.InterpolantFactoryMethodLinear:return i.P;case this.InterpolantFactoryMethodSmooth:return i.Q}}getValueSize(){return this.values.length/this.times.length}shift(t){if(0!==t){const e=this.times;for(let n=0,i=e.length;n!==i;++n)e[n]+=t}return this}scale(t){if(1!==t){const e=this.times;for(let n=0,i=e.length;n!==i;++n)e[n]*=t}return this}trim(t,e){const n=this.times,i=n.length;let r=0,s=i-1;for(;r!==i&&n[r]<t;)++r;for(;-1!==s&&n[s]>e;)--s;if(++s,0!==r||s!==i){r>=s&&(s=Math.max(s,1),r=s-1);const t=this.getValueSize();this.times=l.a.arraySlice(n,r,s),this.values=l.a.arraySlice(this.values,r*t,s*t)}return this}validate(){let t=!0;const e=this.getValueSize();e-Math.floor(e)!=0&&(console.error(\\\\\\\"THREE.KeyframeTrack: Invalid value size in track.\\\\\\\",this),t=!1);const n=this.times,i=this.values,r=n.length;0===r&&(console.error(\\\\\\\"THREE.KeyframeTrack: Track is empty.\\\\\\\",this),t=!1);let s=null;for(let e=0;e!==r;e++){const i=n[e];if(\\\\\\\"number\\\\\\\"==typeof i&&isNaN(i)){console.error(\\\\\\\"THREE.KeyframeTrack: Time is not a valid number.\\\\\\\",this,e,i),t=!1;break}if(null!==s&&s>i){console.error(\\\\\\\"THREE.KeyframeTrack: Out of order keys.\\\\\\\",this,e,i,s),t=!1;break}s=i}if(void 0!==i&&l.a.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=l.a.arraySlice(this.times),e=l.a.arraySlice(this.values),n=this.getValueSize(),r=this.getInterpolation()===i.Q,s=t.length-1;let o=1;for(let i=1;i<s;++i){let s=!1;const a=t[i];if(a!==t[i+1]&&(1!==i||a!==t[0]))if(r)s=!0;else{const t=i*n,r=t-n,o=t+n;for(let i=0;i!==n;++i){const n=e[t+i];if(n!==e[r+i]||n!==e[o+i]){s=!0;break}}}if(s){if(i!==o){t[o]=t[i];const r=i*n,s=o*n;for(let t=0;t!==n;++t)e[s+t]=e[r+t]}++o}}if(s>0){t[o]=t[s];for(let t=s*n,i=o*n,r=0;r!==n;++r)e[i+r]=e[t+r];++o}return o!==t.length?(this.times=l.a.arraySlice(t,0,o),this.values=l.a.arraySlice(e,0,o*n)):(this.times=t,this.values=e),this}clone(){const t=l.a.arraySlice(this.times,0),e=l.a.arraySlice(this.values,0),n=new(0,this.constructor)(this.name,t,e);return n.createInterpolant=this.createInterpolant,n}}c.prototype.TimeBufferType=Float32Array,c.prototype.ValueBufferType=Float32Array,c.prototype.DefaultInterpolation=i.P},function(t,e,n){\\\\\\\"use strict\\\\\\\";n.d(e,\\\\\\\"a\\\\\\\",(function(){return o}));var i=n(12),r=n(1),s=n(6);class o extends i.a{constructor(t){super(),this.type=\\\\\\\"MeshBasicMaterial\\\\\\\",this.color=new s.a(16777215),this.map=null,this.lightMap=null,this.lightMapIntensity=1,this.aoMap=null,this.aoMapIntensity=1,this.specularMap=null,this.alphaMap=null,this.envMap=null,this.combine=r.nb,this.reflectivity=1,this.refractionRatio=.98,this.wireframe=!1,this.wireframeLinewidth=1,this.wireframeLinecap=\\\\\\\"round\\\\\\\",this.wireframeLinejoin=\\\\\\\"round\\\\\\\",this.setValues(t)}copy(t){return super.copy(t),this.color.copy(t.color),this.map=t.map,this.lightMap=t.lightMap,this.lightMapIntensity=t.lightMapIntensity,this.aoMap=t.aoMap,this.aoMapIntensity=t.aoMapIntensity,this.specularMap=t.specularMap,this.alphaMap=t.alphaMap,this.envMap=t.envMap,this.combine=t.combine,this.reflectivity=t.reflectivity,this.refractionRatio=t.refractionRatio,this.wireframe=t.wireframe,this.wireframeLinewidth=t.wireframeLinewidth,this.wireframeLinecap=t.wireframeLinecap,this.wireframeLinejoin=t.wireframeLinejoin,this}}o.prototype.isMeshBasicMaterial=!0},function(t,e,n){\\\\\\\"use strict\\\\\\\";n.d(e,\\\\\\\"a\\\\\\\",(function(){return c}));var i=n(8),r=n(0),s=n(5),o=n(3);const a=new s.a,l=new i.a;class c{constructor(t=0,e=0,n=0,i=c.DefaultOrder){this._x=t,this._y=e,this._z=n,this._order=i}get x(){return this._x}set x(t){this._x=t,this._onChangeCallback()}get y(){return this._y}set y(t){this._y=t,this._onChangeCallback()}get z(){return this._z}set z(t){this._z=t,this._onChangeCallback()}get order(){return this._order}set order(t){this._order=t,this._onChangeCallback()}set(t,e,n,i=this._order){return this._x=t,this._y=e,this._z=n,this._order=i,this._onChangeCallback(),this}clone(){return new this.constructor(this._x,this._y,this._z,this._order)}copy(t){return this._x=t._x,this._y=t._y,this._z=t._z,this._order=t._order,this._onChangeCallback(),this}setFromRotationMatrix(t,e=this._order,n=!0){const i=t.elements,r=i[0],s=i[4],a=i[8],l=i[1],c=i[5],u=i[9],h=i[2],d=i[6],p=i[10];switch(e){case\\\\\\\"XYZ\\\\\\\":this._y=Math.asin(Object(o.d)(a,-1,1)),Math.abs(a)<.9999999?(this._x=Math.atan2(-u,p),this._z=Math.atan2(-s,r)):(this._x=Math.atan2(d,c),this._z=0);break;case\\\\\\\"YXZ\\\\\\\":this._x=Math.asin(-Object(o.d)(u,-1,1)),Math.abs(u)<.9999999?(this._y=Math.atan2(a,p),this._z=Math.atan2(l,c)):(this._y=Math.atan2(-h,r),this._z=0);break;case\\\\\\\"ZXY\\\\\\\":this._x=Math.asin(Object(o.d)(d,-1,1)),Math.abs(d)<.9999999?(this._y=Math.atan2(-h,p),this._z=Math.atan2(-s,c)):(this._y=0,this._z=Math.atan2(l,r));break;case\\\\\\\"ZYX\\\\\\\":this._y=Math.asin(-Object(o.d)(h,-1,1)),Math.abs(h)<.9999999?(this._x=Math.atan2(d,p),this._z=Math.atan2(l,r)):(this._x=0,this._z=Math.atan2(-s,c));break;case\\\\\\\"YZX\\\\\\\":this._z=Math.asin(Object(o.d)(l,-1,1)),Math.abs(l)<.9999999?(this._x=Math.atan2(-u,c),this._y=Math.atan2(-h,r)):(this._x=0,this._y=Math.atan2(a,p));break;case\\\\\\\"XZY\\\\\\\":this._z=Math.asin(-Object(o.d)(s,-1,1)),Math.abs(s)<.9999999?(this._x=Math.atan2(d,c),this._y=Math.atan2(a,r)):(this._x=Math.atan2(-u,p),this._y=0);break;default:console.warn(\\\\\\\"THREE.Euler: .setFromRotationMatrix() encountered an unknown order: \\\\\\\"+e)}return this._order=e,!0===n&&this._onChangeCallback(),this}setFromQuaternion(t,e,n){return a.makeRotationFromQuaternion(t),this.setFromRotationMatrix(a,e,n)}setFromVector3(t,e=this._order){return this.set(t.x,t.y,t.z,e)}reorder(t){return l.setFromEuler(this),this.setFromQuaternion(l,t)}equals(t){return t._x===this._x&&t._y===this._y&&t._z===this._z&&t._order===this._order}fromArray(t){return this._x=t[0],this._y=t[1],this._z=t[2],void 0!==t[3]&&(this._order=t[3]),this._onChangeCallback(),this}toArray(t=[],e=0){return t[e]=this._x,t[e+1]=this._y,t[e+2]=this._z,t[e+3]=this._order,t}toVector3(t){return t?t.set(this._x,this._y,this._z):new r.a(this._x,this._y,this._z)}_onChange(t){return this._onChangeCallback=t,this}_onChangeCallback(){}}c.prototype.isEuler=!0,c.DefaultOrder=\\\\\\\"XYZ\\\\\\\",c.RotationOrders=[\\\\\\\"XYZ\\\\\\\",\\\\\\\"YZX\\\\\\\",\\\\\\\"ZXY\\\\\\\",\\\\\\\"XZY\\\\\\\",\\\\\\\"YXZ\\\\\\\",\\\\\\\"ZYX\\\\\\\"]},function(t,e,n){\\\\\\\"use strict\\\\\\\";n.d(e,\\\\\\\"a\\\\\\\",(function(){return r})),n.d(e,\\\\\\\"b\\\\\\\",(function(){return i}));class i{constructor(t,e,n){const i=this;let r,s=!1,o=0,a=0;const l=[];this.onStart=void 0,this.onLoad=t,this.onProgress=e,this.onError=n,this.itemStart=function(t){a++,!1===s&&void 0!==i.onStart&&i.onStart(t,o,a),s=!0},this.itemEnd=function(t){o++,void 0!==i.onProgress&&i.onProgress(t,o,a),o===a&&(s=!1,void 0!==i.onLoad&&i.onLoad())},this.itemError=function(t){void 0!==i.onError&&i.onError(t)},this.resolveURL=function(t){return r?r(t):t},this.setURLModifier=function(t){return r=t,this},this.addHandler=function(t,e){return l.push(t,e),this},this.removeHandler=function(t){const e=l.indexOf(t);return-1!==e&&l.splice(e,2),this},this.getHandler=function(t){for(let e=0,n=l.length;e<n;e+=2){const n=l[e],i=l[e+1];if(n.global&&(n.lastIndex=0),n.test(t))return i}return null}}}const r=new i},function(t,e,n){\\\\\\\"use strict\\\\\\\";n.d(e,\\\\\\\"a\\\\\\\",(function(){return s}));var i=n(43),r=n(3);class s extends i.a{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*r.b*Math.atan(e),this.updateProjectionMatrix()}getFocalLength(){const t=Math.tan(.5*r.a*this.fov);return.5*this.getFilmHeight()/t}getEffectiveFOV(){return 2*r.b*Math.atan(Math.tan(.5*r.a*this.fov)/this.zoom)}getFilmWidth(){return this.filmGauge*Math.min(this.aspect,1)}getFilmHeight(){return this.filmGauge/Math.max(this.aspect,1)}setViewOffset(t,e,n,i,r,s){this.aspect=t/e,null===this.view&&(this.view={enabled:!0,fullWidth:1,fullHeight:1,offsetX:0,offsetY:0,width:1,height:1}),this.view.enabled=!0,this.view.fullWidth=t,this.view.fullHeight=e,this.view.offsetX=n,this.view.offsetY=i,this.view.width=r,this.view.height=s,this.updateProjectionMatrix()}clearViewOffset(){null!==this.view&&(this.view.enabled=!1),this.updateProjectionMatrix()}updateProjectionMatrix(){const t=this.near;let e=t*Math.tan(.5*r.a*this.fov)/this.zoom,n=2*e,i=this.aspect*n,s=-.5*i;const o=this.view;if(null!==this.view&&this.view.enabled){const t=o.fullWidth,r=o.fullHeight;s+=o.offsetX*i/t,e-=o.offsetY*n/r,i*=o.width/t,n*=o.height/r}const a=this.filmOffset;0!==a&&(s+=t*a/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}}s.prototype.isPerspectiveCamera=!0},function(t,e,n){\\\\\\\"use strict\\\\\\\";function i(t,e,n,i,r){const s=.5*(i-e),o=.5*(r-n),a=t*t;return(2*n-2*i+s+o)*(t*a)+(-3*n+3*i-2*s-o)*a+s*t+n}function r(t,e,n,i){return function(t,e){const n=1-t;return n*n*e}(t,e)+function(t,e){return 2*(1-t)*t*e}(t,n)+function(t,e){return t*t*e}(t,i)}function s(t,e,n,i,r){return function(t,e){const n=1-t;return n*n*n*e}(t,e)+function(t,e){const n=1-t;return 3*n*n*t*e}(t,n)+function(t,e){return 3*(1-t)*t*t*e}(t,i)+function(t,e){return t*t*t*e}(t,r)}n.d(e,\\\\\\\"a\\\\\\\",(function(){return i})),n.d(e,\\\\\\\"c\\\\\\\",(function(){return r})),n.d(e,\\\\\\\"b\\\\\\\",(function(){return s}))},function(t,e,n){\\\\\\\"use strict\\\\\\\";n.d(e,\\\\\\\"a\\\\\\\",(function(){return s}));var i=n(10),r=n(6);class s extends i.a{constructor(t,e=1){super(),this.type=\\\\\\\"Light\\\\\\\",this.color=new r.a(t),this.intensity=e}dispose(){}copy(t){return super.copy(t),this.color.copy(t.color),this.intensity=t.intensity,this}toJSON(t){const e=super.toJSON(t);return e.object.color=this.color.getHex(),e.object.intensity=this.intensity,void 0!==this.groundColor&&(e.object.groundColor=this.groundColor.getHex()),void 0!==this.distance&&(e.object.distance=this.distance),void 0!==this.angle&&(e.object.angle=this.angle),void 0!==this.decay&&(e.object.decay=this.decay),void 0!==this.penumbra&&(e.object.penumbra=this.penumbra),void 0!==this.shadow&&(e.object.shadow=this.shadow.toJSON()),e}}s.prototype.isLight=!0},function(t,e,n){\\\\\\\"use strict\\\\\\\";n.d(e,\\\\\\\"a\\\\\\\",(function(){return s}));var i=n(23),r=n(1);class s extends i.a{constructor(t=null,e=1,n=1,i,s,o,a,l,c=r.ob,u=r.ob,h,d){super(null,o,a,l,c,u,i,s,h,d),this.image={data:t,width:e,height:n},this.magFilter=c,this.minFilter=u,this.generateMipmaps=!1,this.flipY=!1,this.unpackAlignment=1,this.needsUpdate=!0}}s.prototype.isDataTexture=!0},function(t,e,n){\\\\\\\"use strict\\\\\\\";n.d(e,\\\\\\\"a\\\\\\\",(function(){return l}));var i=n(11),r=n(0);const s=new r.a,o=new r.a,a=new i.a;class l{constructor(t=new r.a(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=s.subVectors(n,e).cross(o.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(s),i=this.normal.dot(n);if(0===i)return 0===this.distanceToPoint(t.start)?e.copy(t.start):null;const r=-(t.start.dot(this.normal)+this.constant)/i;return r<0||r>1?null:e.copy(n).multiplyScalar(r).add(t.start)}intersectsLine(t){const e=this.distanceToPoint(t.start),n=this.distanceToPoint(t.end);return e<0&&n>0||n<0&&e>0}intersectsBox(t){return t.intersectsPlane(this)}intersectsSphere(t){return t.intersectsPlane(this)}coplanarPoint(t){return t.copy(this.normal).multiplyScalar(-this.constant)}applyMatrix4(t,e){const n=e||a.getNormalMatrix(t),i=this.coplanarPoint(s).applyMatrix4(t),r=this.normal.applyMatrix3(n).normalize();return this.constant=-i.dot(r),this}translate(t){return this.constant-=t.dot(this.normal),this}equals(t){return t.normal.equals(this.normal)&&t.constant===this.constant}clone(){return(new this.constructor).copy(this)}}l.prototype.isPlane=!0},function(t,e,n){\\\\\\\"use strict\\\\\\\";n.d(e,\\\\\\\"a\\\\\\\",(function(){return l}));var i=n(41),r=n(0),s=n(4);const o=new r.a,a=new r.a;class l extends i.a{constructor(t,e){super(t,e),this.type=\\\\\\\"LineSegments\\\\\\\"}computeLineDistances(){const t=this.geometry;if(t.isBufferGeometry)if(null===t.index){const e=t.attributes.position,n=[];for(let t=0,i=e.count;t<i;t+=2)o.fromBufferAttribute(e,t),a.fromBufferAttribute(e,t+1),n[t]=0===t?0:n[t-1],n[t+1]=n[t]+o.distanceTo(a);t.setAttribute(\\\\\\\"lineDistance\\\\\\\",new s.c(n,1))}else console.warn(\\\\\\\"THREE.LineSegments.computeLineDistances(): Computation only possible with non-indexed BufferGeometry.\\\\\\\");else t.isGeometry&&console.error(\\\\\\\"THREE.LineSegments.computeLineDistances() no longer supports THREE.Geometry. Use THREE.BufferGeometry instead.\\\\\\\");return this}}l.prototype.isLineSegments=!0},function(t,e,n){\\\\\\\"use strict\\\\\\\";n.d(e,\\\\\\\"a\\\\\\\",(function(){return i}));class i{constructor(){this.mask=1}set(t){this.mask=1<<t|0}enable(t){this.mask|=1<<t|0}enableAll(){this.mask=-1}toggle(t){this.mask^=1<<t|0}disable(t){this.mask&=~(1<<t|0)}disableAll(){this.mask=0}test(t){return 0!=(this.mask&t.mask)}}},function(t,e,n){\\\\\\\"use strict\\\\\\\";n.d(e,\\\\\\\"a\\\\\\\",(function(){return r}));var i=n(43);class r extends i.a{constructor(t=-1,e=1,n=1,i=-1,r=.1,s=2e3){super(),this.type=\\\\\\\"OrthographicCamera\\\\\\\",this.zoom=1,this.view=null,this.left=t,this.right=e,this.top=n,this.bottom=i,this.near=r,this.far=s,this.updateProjectionMatrix()}copy(t,e){return super.copy(t,e),this.left=t.left,this.right=t.right,this.top=t.top,this.bottom=t.bottom,this.near=t.near,this.far=t.far,this.zoom=t.zoom,this.view=null===t.view?null:Object.assign({},t.view),this}setViewOffset(t,e,n,i,r,s){null===this.view&&(this.view={enabled:!0,fullWidth:1,fullHeight:1,offsetX:0,offsetY:0,width:1,height:1}),this.view.enabled=!0,this.view.fullWidth=t,this.view.fullHeight=e,this.view.offsetX=n,this.view.offsetY=i,this.view.width=r,this.view.height=s,this.updateProjectionMatrix()}clearViewOffset(){null!==this.view&&(this.view.enabled=!1),this.updateProjectionMatrix()}updateProjectionMatrix(){const t=(this.right-this.left)/(2*this.zoom),e=(this.top-this.bottom)/(2*this.zoom),n=(this.right+this.left)/2,i=(this.top+this.bottom)/2;let r=n-t,s=n+t,o=i+e,a=i-e;if(null!==this.view&&this.view.enabled){const t=(this.right-this.left)/this.view.fullWidth/this.zoom,e=(this.top-this.bottom)/this.view.fullHeight/this.zoom;r+=t*this.view.offsetX,s=r+t*this.view.width,o-=e*this.view.offsetY,a=o-e*this.view.height}this.projectionMatrix.makeOrthographic(r,s,o,a,this.near,this.far),this.projectionMatrixInverse.copy(this.projectionMatrix).invert()}toJSON(t){const e=super.toJSON(t);return e.object.zoom=this.zoom,e.object.left=this.left,e.object.right=this.right,e.object.top=this.top,e.object.bottom=this.bottom,e.object.near=this.near,e.object.far=this.far,null!==this.view&&(e.object.view=Object.assign({},this.view)),e}}r.prototype.isOrthographicCamera=!0},function(t,e,n){\\\\\\\"use strict\\\\\\\";n.d(e,\\\\\\\"a\\\\\\\",(function(){return i}));class i{constructor(t,e,n,i){this.parameterPositions=t,this._cachedIndex=0,this.resultBuffer=void 0!==i?i:new e.constructor(n),this.sampleValues=e,this.valueSize=n,this.settings=null,this.DefaultSettings_={}}evaluate(t){const e=this.parameterPositions;let n=this._cachedIndex,i=e[n],r=e[n-1];t:{e:{let s;n:{i:if(!(t<i)){for(let s=n+2;;){if(void 0===i){if(t<r)break i;return n=e.length,this._cachedIndex=n,this.afterEnd_(n-1,t,r)}if(n===s)break;if(r=i,i=e[++n],t<i)break e}s=e.length;break n}if(t>=r)break t;{const o=e[1];t<o&&(n=2,r=o);for(let s=n-2;;){if(void 0===r)return this._cachedIndex=0,this.beforeStart_(0,t,i);if(n===s)break;if(i=r,r=e[--n-1],t>=r)break e}s=n,n=0}}for(;n<s;){const i=n+s>>>1;t<e[i]?s=i:n=i+1}if(i=e[n],r=e[n-1],void 0===r)return this._cachedIndex=0,this.beforeStart_(0,t,i);if(void 0===i)return n=e.length,this._cachedIndex=n,this.afterEnd_(n-1,r,t)}this._cachedIndex=n,this.intervalChanged_(n,r,i)}return this.interpolate_(n,r,t,i)}getSettings_(){return this.settings||this.DefaultSettings_}copySampleValue_(t){const e=this.resultBuffer,n=this.sampleValues,i=this.valueSize,r=t*i;for(let t=0;t!==i;++t)e[t]=n[r+t];return e}interpolate_(){throw new Error(\\\\\\\"call to abstract method\\\\\\\")}intervalChanged_(){}}i.prototype.beforeStart_=i.prototype.copySampleValue_,i.prototype.afterEnd_=i.prototype.copySampleValue_},function(t,e,n){\\\\\\\"use strict\\\\\\\";n.d(e,\\\\\\\"a\\\\\\\",(function(){return h}));var i=n(0);const r=new i.a,s=new i.a,o=new i.a,a=new i.a,l=new i.a,c=new i.a,u=new i.a;class h{constructor(t=new i.a,e=new i.a(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,r)),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=r.subVectors(t,this.origin).dot(this.direction);return e<0?this.origin.distanceToSquared(t):(r.copy(this.direction).multiplyScalar(e).add(this.origin),r.distanceToSquared(t))}distanceSqToSegment(t,e,n,i){s.copy(t).add(e).multiplyScalar(.5),o.copy(e).sub(t).normalize(),a.copy(this.origin).sub(s);const r=.5*t.distanceTo(e),l=-this.direction.dot(o),c=a.dot(this.direction),u=-a.dot(o),h=a.lengthSq(),d=Math.abs(1-l*l);let p,_,m,f;if(d>0)if(p=l*u-c,_=l*c-u,f=r*d,p>=0)if(_>=-f)if(_<=f){const t=1/d;p*=t,_*=t,m=p*(p+l*_+2*c)+_*(l*p+_+2*u)+h}else _=r,p=Math.max(0,-(l*_+c)),m=-p*p+_*(_+2*u)+h;else _=-r,p=Math.max(0,-(l*_+c)),m=-p*p+_*(_+2*u)+h;else _<=-f?(p=Math.max(0,-(-l*r+c)),_=p>0?-r:Math.min(Math.max(-r,-u),r),m=-p*p+_*(_+2*u)+h):_<=f?(p=0,_=Math.min(Math.max(-r,-u),r),m=_*(_+2*u)+h):(p=Math.max(0,-(l*r+c)),_=p>0?r:Math.min(Math.max(-r,-u),r),m=-p*p+_*(_+2*u)+h);else _=l>0?-r:r,p=Math.max(0,-(l*_+c)),m=-p*p+_*(_+2*u)+h;return n&&n.copy(this.direction).multiplyScalar(p).add(this.origin),i&&i.copy(o).multiplyScalar(_).add(s),m}intersectSphere(t,e){r.subVectors(t.center,this.origin);const n=r.dot(this.direction),i=r.dot(r)-n*n,s=t.radius*t.radius;if(i>s)return null;const o=Math.sqrt(s-i),a=n-o,l=n+o;return a<0&&l<0?null:a<0?this.at(l,e):this.at(a,e)}intersectsSphere(t){return this.distanceSqToPoint(t.center)<=t.radius*t.radius}distanceToPlane(t){const e=t.normal.dot(this.direction);if(0===e)return 0===t.distanceToPoint(this.origin)?0:null;const n=-(this.origin.dot(t.normal)+t.constant)/e;return n>=0?n:null}intersectPlane(t,e){const n=this.distanceToPlane(t);return null===n?null:this.at(n,e)}intersectsPlane(t){const e=t.distanceToPoint(this.origin);if(0===e)return!0;return t.normal.dot(this.direction)*e<0}intersectBox(t,e){let n,i,r,s,o,a;const l=1/this.direction.x,c=1/this.direction.y,u=1/this.direction.z,h=this.origin;return l>=0?(n=(t.min.x-h.x)*l,i=(t.max.x-h.x)*l):(n=(t.max.x-h.x)*l,i=(t.min.x-h.x)*l),c>=0?(r=(t.min.y-h.y)*c,s=(t.max.y-h.y)*c):(r=(t.max.y-h.y)*c,s=(t.min.y-h.y)*c),n>s||r>i?null:((r>n||n!=n)&&(n=r),(s<i||i!=i)&&(i=s),u>=0?(o=(t.min.z-h.z)*u,a=(t.max.z-h.z)*u):(o=(t.max.z-h.z)*u,a=(t.min.z-h.z)*u),n>a||o>i?null:((o>n||n!=n)&&(n=o),(a<i||i!=i)&&(i=a),i<0?null:this.at(n>=0?n:i,e)))}intersectsBox(t){return null!==this.intersectBox(t,r)}intersectTriangle(t,e,n,i,r){l.subVectors(e,t),c.subVectors(n,t),u.crossVectors(l,c);let s,o=this.direction.dot(u);if(o>0){if(i)return null;s=1}else{if(!(o<0))return null;s=-1,o=-o}a.subVectors(this.origin,t);const h=s*this.direction.dot(c.crossVectors(a,c));if(h<0)return null;const d=s*this.direction.dot(l.cross(a));if(d<0)return null;if(h+d>o)return null;const p=-s*a.dot(u);return p<0?null:this.at(p/o,r)}applyMatrix4(t){return this.origin.applyMatrix4(t),this.direction.transformDirection(t),this}equals(t){return t.origin.equals(this.origin)&&t.direction.equals(this.direction)}clone(){return(new this.constructor).copy(this)}}},function(t,e,n){\\\\\\\"use strict\\\\\\\";n.d(e,\\\\\\\"a\\\\\\\",(function(){return _}));var i=n(0);const r=new i.a,s=new i.a,o=new i.a,a=new i.a,l=new i.a,c=new i.a,u=new i.a,h=new i.a,d=new i.a,p=new i.a;class _{constructor(t=new i.a,e=new i.a,n=new i.a){this.a=t,this.b=e,this.c=n}static getNormal(t,e,n,i){i.subVectors(n,e),r.subVectors(t,e),i.cross(r);const s=i.lengthSq();return s>0?i.multiplyScalar(1/Math.sqrt(s)):i.set(0,0,0)}static getBarycoord(t,e,n,i,a){r.subVectors(i,e),s.subVectors(n,e),o.subVectors(t,e);const l=r.dot(r),c=r.dot(s),u=r.dot(o),h=s.dot(s),d=s.dot(o),p=l*h-c*c;if(0===p)return a.set(-2,-1,-1);const _=1/p,m=(h*u-c*d)*_,f=(l*d-c*u)*_;return a.set(1-m-f,f,m)}static containsPoint(t,e,n,i){return this.getBarycoord(t,e,n,i,a),a.x>=0&&a.y>=0&&a.x+a.y<=1}static getUV(t,e,n,i,r,s,o,l){return this.getBarycoord(t,e,n,i,a),l.set(0,0),l.addScaledVector(r,a.x),l.addScaledVector(s,a.y),l.addScaledVector(o,a.z),l}static isFrontFacing(t,e,n,i){return r.subVectors(n,e),s.subVectors(t,e),r.cross(s).dot(i)<0}set(t,e,n){return this.a.copy(t),this.b.copy(e),this.c.copy(n),this}setFromPointsAndIndices(t,e,n,i){return this.a.copy(t[e]),this.b.copy(t[n]),this.c.copy(t[i]),this}setFromAttributeAndIndices(t,e,n,i){return this.a.fromBufferAttribute(t,e),this.b.fromBufferAttribute(t,n),this.c.fromBufferAttribute(t,i),this}clone(){return(new this.constructor).copy(this)}copy(t){return this.a.copy(t.a),this.b.copy(t.b),this.c.copy(t.c),this}getArea(){return r.subVectors(this.c,this.b),s.subVectors(this.a,this.b),.5*r.cross(s).length()}getMidpoint(t){return t.addVectors(this.a,this.b).add(this.c).multiplyScalar(1/3)}getNormal(t){return _.getNormal(this.a,this.b,this.c,t)}getPlane(t){return t.setFromCoplanarPoints(this.a,this.b,this.c)}getBarycoord(t,e){return _.getBarycoord(t,this.a,this.b,this.c,e)}getUV(t,e,n,i,r){return _.getUV(t,this.a,this.b,this.c,e,n,i,r)}containsPoint(t){return _.containsPoint(t,this.a,this.b,this.c)}isFrontFacing(t){return _.isFrontFacing(this.a,this.b,this.c,t)}intersectsBox(t){return t.intersectsTriangle(this)}closestPointToPoint(t,e){const n=this.a,i=this.b,r=this.c;let s,o;l.subVectors(i,n),c.subVectors(r,n),h.subVectors(t,n);const a=l.dot(h),_=c.dot(h);if(a<=0&&_<=0)return e.copy(n);d.subVectors(t,i);const m=l.dot(d),f=c.dot(d);if(m>=0&&f<=m)return e.copy(i);const g=a*f-m*_;if(g<=0&&a>=0&&m<=0)return s=a/(a-m),e.copy(n).addScaledVector(l,s);p.subVectors(t,r);const v=l.dot(p),y=c.dot(p);if(y>=0&&v<=y)return e.copy(r);const x=v*_-a*y;if(x<=0&&_>=0&&y<=0)return o=_/(_-y),e.copy(n).addScaledVector(c,o);const b=m*y-v*f;if(b<=0&&f-m>=0&&v-y>=0)return u.subVectors(r,i),o=(f-m)/(f-m+(v-y)),e.copy(i).addScaledVector(u,o);const w=1/(b+x+g);return s=x*w,o=g*w,e.copy(n).addScaledVector(l,s).addScaledVector(c,o)}equals(t){return t.a.equals(this.a)&&t.b.equals(this.b)&&t.c.equals(this.c)}}},function(t,e,n){\\\\\\\"use strict\\\\\\\";n.d(e,\\\\\\\"a\\\\\\\",(function(){return f}));var i=n(18),r=n(39),s=n(5),o=n(10),a=n(0),l=n(24),c=n(7),u=n(4);const h=new a.a,d=new a.a,p=new s.a,_=new r.a,m=new i.a;class f extends o.a{constructor(t=new c.a,e=new l.a){super(),this.type=\\\\\\\"Line\\\\\\\",this.geometry=t,this.material=e,this.updateMorphTargets()}copy(t){return super.copy(t),this.material=t.material,this.geometry=t.geometry,this}computeLineDistances(){const t=this.geometry;if(t.isBufferGeometry)if(null===t.index){const e=t.attributes.position,n=[0];for(let t=1,i=e.count;t<i;t++)h.fromBufferAttribute(e,t-1),d.fromBufferAttribute(e,t),n[t]=n[t-1],n[t]+=h.distanceTo(d);t.setAttribute(\\\\\\\"lineDistance\\\\\\\",new u.c(n,1))}else console.warn(\\\\\\\"THREE.Line.computeLineDistances(): Computation only possible with non-indexed BufferGeometry.\\\\\\\");else t.isGeometry&&console.error(\\\\\\\"THREE.Line.computeLineDistances() no longer supports THREE.Geometry. Use THREE.BufferGeometry instead.\\\\\\\");return this}raycast(t,e){const n=this.geometry,i=this.matrixWorld,r=t.params.Line.threshold,s=n.drawRange;if(null===n.boundingSphere&&n.computeBoundingSphere(),m.copy(n.boundingSphere),m.applyMatrix4(i),m.radius+=r,!1===t.ray.intersectsSphere(m))return;p.copy(i).invert(),_.copy(t.ray).applyMatrix4(p);const o=r/((this.scale.x+this.scale.y+this.scale.z)/3),l=o*o,c=new a.a,u=new a.a,h=new a.a,d=new a.a,f=this.isLineSegments?2:1;if(n.isBufferGeometry){const i=n.index,r=n.attributes.position;if(null!==i){for(let n=Math.max(0,s.start),o=Math.min(i.count,s.start+s.count)-1;n<o;n+=f){const s=i.getX(n),o=i.getX(n+1);c.fromBufferAttribute(r,s),u.fromBufferAttribute(r,o);if(_.distanceSqToSegment(c,u,d,h)>l)continue;d.applyMatrix4(this.matrixWorld);const a=t.ray.origin.distanceTo(d);a<t.near||a>t.far||e.push({distance:a,point:h.clone().applyMatrix4(this.matrixWorld),index:n,face:null,faceIndex:null,object:this})}}else{for(let n=Math.max(0,s.start),i=Math.min(r.count,s.start+s.count)-1;n<i;n+=f){c.fromBufferAttribute(r,n),u.fromBufferAttribute(r,n+1);if(_.distanceSqToSegment(c,u,d,h)>l)continue;d.applyMatrix4(this.matrixWorld);const i=t.ray.origin.distanceTo(d);i<t.near||i>t.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;e<n;e++){const n=t[e].name||String(e);this.morphTargetInfluences.push(0),this.morphTargetDictionary[n]=e}}}}else{const e=t.morphTargets;void 0!==e&&e.length>0&&console.error(\\\\\\\"THREE.Line.updateMorphTargets() does not support THREE.Geometry. Use THREE.BufferGeometry instead.\\\\\\\")}}}f.prototype.isLine=!0},function(t,e,n){\\\\\\\"use strict\\\\\\\";n.d(e,\\\\\\\"a\\\\\\\",(function(){return s}));var i=n(12),r=n(6);class s extends i.a{constructor(t){super(),this.type=\\\\\\\"PointsMaterial\\\\\\\",this.color=new r.a(16777215),this.map=null,this.alphaMap=null,this.size=1,this.sizeAttenuation=!0,this.setValues(t)}copy(t){return super.copy(t),this.color.copy(t.color),this.map=t.map,this.alphaMap=t.alphaMap,this.size=t.size,this.sizeAttenuation=t.sizeAttenuation,this}}s.prototype.isPointsMaterial=!0},function(t,e,n){\\\\\\\"use strict\\\\\\\";n.d(e,\\\\\\\"a\\\\\\\",(function(){return s}));var i=n(5),r=n(10);class s extends r.a{constructor(){super(),this.type=\\\\\\\"Camera\\\\\\\",this.matrixWorldInverse=new i.a,this.projectionMatrix=new i.a,this.projectionMatrixInverse=new i.a}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)}}s.prototype.isCamera=!0},function(t,e,n){\\\\\\\"use strict\\\\\\\";n.d(e,\\\\\\\"a\\\\\\\",(function(){return i}));class i{static decodeText(t){if(\\\\\\\"undefined\\\\\\\"!=typeof TextDecoder)return(new TextDecoder).decode(t);let e=\\\\\\\"\\\\\\\";for(let n=0,i=t.length;n<i;n++)e+=String.fromCharCode(t[n]);try{return decodeURIComponent(escape(e))}catch(t){return e}}static extractUrlBase(t){const e=t.lastIndexOf(\\\\\\\"/\\\\\\\");return-1===e?\\\\\\\"./\\\\\\\":t.substr(0,e+1)}}},function(t,e,n){\\\\\\\"use strict\\\\\\\";n.d(e,\\\\\\\"a\\\\\\\",(function(){return h}));var i=n(5),r=n(2),s=n(0),o=n(9),a=n(59);const l=new i.a,c=new s.a,u=new s.a;class h{constructor(t){this.camera=t,this.bias=0,this.normalBias=0,this.radius=1,this.blurSamples=8,this.mapSize=new r.a(512,512),this.map=null,this.mapPass=null,this.matrix=new i.a,this.autoUpdate=!0,this.needsUpdate=!1,this._frustum=new a.a,this._frameExtents=new r.a(1,1),this._viewportCount=1,this._viewports=[new o.a(0,0,1,1)]}getViewportCount(){return this._viewportCount}getFrustum(){return this._frustum}updateMatrices(t){const e=this.camera,n=this.matrix;c.setFromMatrixPosition(t.matrixWorld),e.position.copy(c),u.setFromMatrixPosition(t.target.matrixWorld),e.lookAt(u),e.updateMatrixWorld(),l.multiplyMatrices(e.projectionMatrix,e.matrixWorldInverse),this._frustum.setFromProjectionMatrix(l),n.set(.5,0,0,.5,0,.5,0,.5,0,0,.5,.5,0,0,0,1),n.multiply(e.projectionMatrix),n.multiply(e.matrixWorldInverse)}getViewport(t){return this._viewports[t]}getFrameExtents(){return this._frameExtents}dispose(){this.map&&this.map.dispose(),this.mapPass&&this.mapPass.dispose()}copy(t){return this.camera=t.camera.clone(),this.bias=t.bias,this.radius=t.radius,this.mapSize.copy(t.mapSize),this}clone(){return(new this.constructor).copy(this)}toJSON(){const t={};return 0!==this.bias&&(t.bias=this.bias),0!==this.normalBias&&(t.normalBias=this.normalBias),1!==this.radius&&(t.radius=this.radius),512===this.mapSize.x&&512===this.mapSize.y||(t.mapSize=this.mapSize.toArray()),t.camera=this.camera.toJSON(!1).object,delete t.camera.matrix,t}}},function(t,e,n){\\\\\\\"use strict\\\\\\\";n.d(e,\\\\\\\"a\\\\\\\",(function(){return s}));var i=n(47),r=n(3);class s extends i.a{constructor(t){super(t),this.uuid=r.h(),this.type=\\\\\\\"Shape\\\\\\\",this.holes=[]}getPointsHoles(t){const e=[];for(let n=0,i=this.holes.length;n<i;n++)e[n]=this.holes[n].getPoints(t);return e}extractPoints(t){return{shape:this.getPoints(t),holes:this.getPointsHoles(t)}}copy(t){super.copy(t),this.holes=[];for(let e=0,n=t.holes.length;e<n;e++){const n=t.holes[e];this.holes.push(n.clone())}return this}toJSON(){const t=super.toJSON();t.uuid=this.uuid,t.holes=[];for(let e=0,n=this.holes.length;e<n;e++){const n=this.holes[e];t.holes.push(n.toJSON())}return t}fromJSON(t){super.fromJSON(t),this.uuid=t.uuid,this.holes=[];for(let e=0,n=t.holes.length;e<n;e++){const n=t.holes[e];this.holes.push((new i.a).fromJSON(n))}return this}}},function(t,e,n){\\\\\\\"use strict\\\\\\\";n.d(e,\\\\\\\"a\\\\\\\",(function(){return d}));var i=n(2),r=n(25),s=n(74),o=n(79);class a extends r.a{constructor(){super(),this.type=\\\\\\\"CurvePath\\\\\\\",this.curves=[],this.autoClose=!1}add(t){this.curves.push(t)}closePath(){const t=this.curves[0].getPoint(0),e=this.curves[this.curves.length-1].getPoint(1);t.equals(e)||this.curves.push(new s.a(e,t))}getPoint(t,e){const n=t*this.getLength(),i=this.getCurveLengths();let r=0;for(;r<i.length;){if(i[r]>=n){const t=i[r]-n,s=this.curves[r],o=s.getLength(),a=0===o?0:1-t/o;return s.getPointAt(a,e)}r++}return null}getLength(){const t=this.getCurveLengths();return t[t.length-1]}updateArcLengths(){this.needsUpdate=!0,this.cacheLengths=null,this.getCurveLengths()}getCurveLengths(){if(this.cacheLengths&&this.cacheLengths.length===this.curves.length)return this.cacheLengths;const t=[];let e=0;for(let n=0,i=this.curves.length;n<i;n++)e+=this.curves[n].getLength(),t.push(e);return this.cacheLengths=t,t}getSpacedPoints(t=40){const e=[];for(let n=0;n<=t;n++)e.push(this.getPoint(n/t));return this.autoClose&&e.push(e[0]),e}getPoints(t=12){const e=[];let n;for(let i=0,r=this.curves;i<r.length;i++){const s=r[i],o=s&&s.isEllipseCurve?2*t:s&&(s.isLineCurve||s.isLineCurve3)?1:s&&s.isSplineCurve?t*s.points.length:t,a=s.getPoints(o);for(let t=0;t<a.length;t++){const i=a[t];n&&n.equals(i)||(e.push(i),n=i)}}return this.autoClose&&e.length>1&&!e[e.length-1].equals(e[0])&&e.push(e[0]),e}copy(t){super.copy(t),this.curves=[];for(let e=0,n=t.curves.length;e<n;e++){const n=t.curves[e];this.curves.push(n.clone())}return this.autoClose=t.autoClose,this}toJSON(){const t=super.toJSON();t.autoClose=this.autoClose,t.curves=[];for(let e=0,n=this.curves.length;e<n;e++){const n=this.curves[e];t.curves.push(n.toJSON())}return t}fromJSON(t){super.fromJSON(t),this.autoClose=t.autoClose,this.curves=[];for(let e=0,n=t.curves.length;e<n;e++){const n=t.curves[e];this.curves.push((new o[n.type]).fromJSON(n))}return this}}var l=n(57),c=n(77),u=n(75),h=n(76);class d extends a{constructor(t){super(),this.type=\\\\\\\"Path\\\\\\\",this.currentPoint=new i.a,t&&this.setFromPoints(t)}setFromPoints(t){this.moveTo(t[0].x,t[0].y);for(let e=1,n=t.length;e<n;e++)this.lineTo(t[e].x,t[e].y);return this}moveTo(t,e){return this.currentPoint.set(t,e),this}lineTo(t,e){const n=new s.a(this.currentPoint.clone(),new i.a(t,e));return this.curves.push(n),this.currentPoint.set(t,e),this}quadraticCurveTo(t,e,n,r){const s=new h.a(this.currentPoint.clone(),new i.a(t,e),new i.a(n,r));return this.curves.push(s),this.currentPoint.set(n,r),this}bezierCurveTo(t,e,n,r,s,o){const a=new u.a(this.currentPoint.clone(),new i.a(t,e),new i.a(n,r),new i.a(s,o));return this.curves.push(a),this.currentPoint.set(s,o),this}splineThru(t){const e=[this.currentPoint.clone()].concat(t),n=new c.a(e);return this.curves.push(n),this.currentPoint.copy(t[t.length-1]),this}arc(t,e,n,i,r,s){const o=this.currentPoint.x,a=this.currentPoint.y;return this.absarc(t+o,e+a,n,i,r,s),this}absarc(t,e,n,i,r,s){return this.absellipse(t,e,n,n,i,r,s),this}ellipse(t,e,n,i,r,s,o,a){const l=this.currentPoint.x,c=this.currentPoint.y;return this.absellipse(t+l,e+c,n,i,r,s,o,a),this}absellipse(t,e,n,i,r,s,o,a){const c=new l.a(t,e,n,i,r,s,o,a);if(this.curves.length>0){const t=c.getPoint(0);t.equals(this.currentPoint)||this.lineTo(t.x,t.y)}this.curves.push(c);const u=c.getPoint(1);return this.currentPoint.copy(u),this}copy(t){return super.copy(t),this.currentPoint.copy(t.currentPoint),this}toJSON(){const t=super.toJSON();return t.currentPoint=this.currentPoint.toArray(),t}fromJSON(t){return super.fromJSON(t),this.currentPoint.fromArray(t.currentPoint),this}}},function(t,e,n){\\\\\\\"use strict\\\\\\\";n.d(e,\\\\\\\"a\\\\\\\",(function(){return a}));var i=n(6),r=n(47),s=n(46),o=n(53);class a{constructor(){this.type=\\\\\\\"ShapePath\\\\\\\",this.color=new i.a,this.subPaths=[],this.currentPath=null}moveTo(t,e){return this.currentPath=new r.a,this.subPaths.push(this.currentPath),this.currentPath.moveTo(t,e),this}lineTo(t,e){return this.currentPath.lineTo(t,e),this}quadraticCurveTo(t,e,n,i){return this.currentPath.quadraticCurveTo(t,e,n,i),this}bezierCurveTo(t,e,n,i,r,s){return this.currentPath.bezierCurveTo(t,e,n,i,r,s),this}splineThru(t){return this.currentPath.splineThru(t),this}toShapes(t,e){function n(t){const e=[];for(let n=0,i=t.length;n<i;n++){const i=t[n],r=new s.a;r.curves=i.curves,e.push(r)}return e}function i(t,e){const n=e.length;let i=!1;for(let r=n-1,s=0;s<n;r=s++){let n=e[r],o=e[s],a=o.x-n.x,l=o.y-n.y;if(Math.abs(l)>Number.EPSILON){if(l<0&&(n=e[s],a=-a,o=e[r],l=-l),t.y<n.y||t.y>o.y)continue;if(t.y===n.y){if(t.x===n.x)return!0}else{const e=l*(t.x-n.x)-a*(t.y-n.y);if(0===e)return!0;if(e<0)continue;i=!i}}else{if(t.y!==n.y)continue;if(o.x<=t.x&&t.x<=n.x||n.x<=t.x&&t.x<=o.x)return!0}}return i}const r=o.a.isClockWise,a=this.subPaths;if(0===a.length)return[];if(!0===e)return n(a);let l,c,u;const h=[];if(1===a.length)return c=a[0],u=new s.a,u.curves=c.curves,h.push(u),h;let d=!r(a[0].getPoints());d=t?!d:d;const p=[],_=[];let m,f,g=[],v=0;_[v]=void 0,g[v]=[];for(let e=0,n=a.length;e<n;e++)c=a[e],m=c.getPoints(),l=r(m),l=t?!l:l,l?(!d&&_[v]&&v++,_[v]={s:new s.a,p:m},_[v].s.curves=c.curves,d&&v++,g[v]=[]):g[v].push({h:c,p:m[0]});if(!_[0])return n(a);if(_.length>1){let t=!1;const e=[];for(let t=0,e=_.length;t<e;t++)p[t]=[];for(let n=0,r=_.length;n<r;n++){const r=g[n];for(let s=0;s<r.length;s++){const o=r[s];let a=!0;for(let r=0;r<_.length;r++)i(o.p,_[r].p)&&(n!==r&&e.push({froms:n,tos:r,hole:s}),a?(a=!1,p[r].push(o)):t=!0);a&&p[n].push(o)}}e.length>0&&(t||(g=p))}for(let t=0,e=_.length;t<e;t++){u=_[t].s,h.push(u),f=g[t];for(let t=0,e=f.length;t<e;t++)u.holes.push(f[t].h)}return h}}},function(t,e,n){\\\\\\\"use strict\\\\\\\";n.d(e,\\\\\\\"a\\\\\\\",(function(){return _}));var i=n(18),r=n(39),s=n(5),o=n(10),a=n(0),l=n(42),c=n(7);const u=new s.a,h=new r.a,d=new i.a,p=new a.a;class _ extends o.a{constructor(t=new c.a,e=new l.a){super(),this.type=\\\\\\\"Points\\\\\\\",this.geometry=t,this.material=e,this.updateMorphTargets()}copy(t){return super.copy(t),this.material=t.material,this.geometry=t.geometry,this}raycast(t,e){const n=this.geometry,i=this.matrixWorld,r=t.params.Points.threshold,s=n.drawRange;if(null===n.boundingSphere&&n.computeBoundingSphere(),d.copy(n.boundingSphere),d.applyMatrix4(i),d.radius+=r,!1===t.ray.intersectsSphere(d))return;u.copy(i).invert(),h.copy(t.ray).applyMatrix4(u);const o=r/((this.scale.x+this.scale.y+this.scale.z)/3),a=o*o;if(n.isBufferGeometry){const r=n.index,o=n.attributes.position;if(null!==r){for(let n=Math.max(0,s.start),l=Math.min(r.count,s.start+s.count);n<l;n++){const s=r.getX(n);p.fromBufferAttribute(o,s),m(p,s,a,i,t,e,this)}}else{for(let n=Math.max(0,s.start),r=Math.min(o.count,s.start+s.count);n<r;n++)p.fromBufferAttribute(o,n),m(p,n,a,i,t,e,this)}}else console.error(\\\\\\\"THREE.Points.raycast() no longer supports 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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;e<n;e++){const n=t[e].name||String(e);this.morphTargetInfluences.push(0),this.morphTargetDictionary[n]=e}}}}else{const e=t.morphTargets;void 0!==e&&e.length>0&&console.error(\\\\\\\"THREE.Points.updateMorphTargets() does not support THREE.Geometry. 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super.copy(t),this.aX=t.aX,this.aY=t.aY,this.xRadius=t.xRadius,this.yRadius=t.yRadius,this.aStartAngle=t.aStartAngle,this.aEndAngle=t.aEndAngle,this.aClockwise=t.aClockwise,this.aRotation=t.aRotation,this}toJSON(){const t=super.toJSON();return t.aX=this.aX,t.aY=this.aY,t.xRadius=this.xRadius,t.yRadius=this.yRadius,t.aStartAngle=this.aStartAngle,t.aEndAngle=this.aEndAngle,t.aClockwise=this.aClockwise,t.aRotation=this.aRotation,t}fromJSON(t){return super.fromJSON(t),this.aX=t.aX,this.aY=t.aY,this.xRadius=t.xRadius,this.yRadius=t.yRadius,this.aStartAngle=t.aStartAngle,this.aEndAngle=t.aEndAngle,this.aClockwise=t.aClockwise,this.aRotation=t.aRotation,this}}s.prototype.isEllipseCurve=!0},function(t,e,n){\\\\\\\"use strict\\\\\\\";n.d(e,\\\\\\\"a\\\\\\\",(function(){return _}));var i=n(32),r=n(45),s=n(30),o=n(5),a=n(2),l=n(0),c=n(9);const u=new o.a,h=new l.a,d=new l.a;class p extends r.a{constructor(){super(new s.a(90,1,.5,500)),this._frameExtents=new a.a(4,2),this._viewportCount=6,this._viewports=[new c.a(2,1,1,1),new c.a(0,1,1,1),new c.a(3,1,1,1),new c.a(1,1,1,1),new c.a(3,0,1,1),new c.a(1,0,1,1)],this._cubeDirections=[new l.a(1,0,0),new l.a(-1,0,0),new l.a(0,0,1),new l.a(0,0,-1),new l.a(0,1,0),new l.a(0,-1,0)],this._cubeUps=[new l.a(0,1,0),new l.a(0,1,0),new l.a(0,1,0),new l.a(0,1,0),new l.a(0,0,1),new l.a(0,0,-1)]}updateMatrices(t,e=0){const n=this.camera,i=this.matrix,r=t.distance||n.far;r!==n.far&&(n.far=r,n.updateProjectionMatrix()),h.setFromMatrixPosition(t.matrixWorld),n.position.copy(h),d.copy(n.position),d.add(this._cubeDirections[e]),n.up.copy(this._cubeUps[e]),n.lookAt(d),n.updateMatrixWorld(),i.makeTranslation(-h.x,-h.y,-h.z),u.multiplyMatrices(n.projectionMatrix,n.matrixWorldInverse),this._frustum.setFromProjectionMatrix(u)}}p.prototype.isPointLightShadow=!0;class _ extends i.a{constructor(t,e,n=0,i=1){super(t,e),this.type=\\\\\\\"PointLight\\\\\\\",this.distance=n,this.decay=i,this.shadow=new p}get power(){return 4*this.intensity*Math.PI}set power(t){this.intensity=t/(4*Math.PI)}dispose(){this.shadow.dispose()}copy(t){return super.copy(t),this.distance=t.distance,this.decay=t.decay,this.shadow=t.shadow.clone(),this}}_.prototype.isPointLight=!0},function(t,e,n){\\\\\\\"use strict\\\\\\\";n.d(e,\\\\\\\"a\\\\\\\",(function(){return l}));var i=n(0),r=n(18),s=n(34);const o=new r.a,a=new i.a;class l{constructor(t=new s.a,e=new s.a,n=new s.a,i=new s.a,r=new s.a,o=new s.a){this.planes=[t,e,n,i,r,o]}set(t,e,n,i,r,s){const o=this.planes;return o[0].copy(t),o[1].copy(e),o[2].copy(n),o[3].copy(i),o[4].copy(r),o[5].copy(s),this}copy(t){const e=this.planes;for(let n=0;n<6;n++)e[n].copy(t.planes[n]);return this}setFromProjectionMatrix(t){const e=this.planes,n=t.elements,i=n[0],r=n[1],s=n[2],o=n[3],a=n[4],l=n[5],c=n[6],u=n[7],h=n[8],d=n[9],p=n[10],_=n[11],m=n[12],f=n[13],g=n[14],v=n[15];return e[0].setComponents(o-i,u-a,_-h,v-m).normalize(),e[1].setComponents(o+i,u+a,_+h,v+m).normalize(),e[2].setComponents(o+r,u+l,_+d,v+f).normalize(),e[3].setComponents(o-r,u-l,_-d,v-f).normalize(),e[4].setComponents(o-s,u-c,_-p,v-g).normalize(),e[5].setComponents(o+s,u+c,_+p,v+g).normalize(),this}intersectsObject(t){const e=t.geometry;return null===e.boundingSphere&&e.computeBoundingSphere(),o.copy(e.boundingSphere).applyMatrix4(t.matrixWorld),this.intersectsSphere(o)}intersectsSprite(t){return o.center.set(0,0,0),o.radius=.7071067811865476,o.applyMatrix4(t.matrixWorld),this.intersectsSphere(o)}intersectsSphere(t){const e=this.planes,n=t.center,i=-t.radius;for(let t=0;t<6;t++){if(e[t].distanceToPoint(n)<i)return!1}return!0}intersectsBox(t){const e=this.planes;for(let n=0;n<6;n++){const i=e[n];if(a.x=i.normal.x>0?t.max.x:t.min.x,a.y=i.normal.y>0?t.max.y:t.min.y,a.z=i.normal.z>0?t.max.z:t.min.z,i.distanceToPoint(a)<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(t,e,n){\\\\\\\"use strict\\\\\\\";n.d(e,\\\\\\\"a\\\\\\\",(function(){return s}));const i=new Float32Array(1),r=new Int32Array(i.buffer);class s{static toHalfFloat(t){t>65504&&(console.warn(\\\\\\\"THREE.DataUtils.toHalfFloat(): value exceeds 65504.\\\\\\\"),t=65504),i[0]=t;const e=r[0];let n=e>>16&32768,s=e>>12&2047;const o=e>>23&255;return o<103?n:o>142?(n|=31744,n|=(255==o?0:1)&&8388607&e,n):o<113?(s|=2048,n|=(s>>114-o)+(s>>113-o&1),n):(n|=o-112<<10|s>>1,n+=1&s,n)}}},function(t,e,n){\\\\\\\"use strict\\\\\\\";n.d(e,\\\\\\\"a\\\\\\\",(function(){return o}));var i=n(12),r=n(1),s=n(6);class o extends i.a{constructor(t){super(),this.type=\\\\\\\"MeshLambertMaterial\\\\\\\",this.color=new s.a(16777215),this.map=null,this.lightMap=null,this.lightMapIntensity=1,this.aoMap=null,this.aoMapIntensity=1,this.emissive=new s.a(0),this.emissiveIntensity=1,this.emissiveMap=null,this.specularMap=null,this.alphaMap=null,this.envMap=null,this.combine=r.nb,this.reflectivity=1,this.refractionRatio=.98,this.wireframe=!1,this.wireframeLinewidth=1,this.wireframeLinecap=\\\\\\\"round\\\\\\\",this.wireframeLinejoin=\\\\\\\"round\\\\\\\",this.setValues(t)}copy(t){return super.copy(t),this.color.copy(t.color),this.map=t.map,this.lightMap=t.lightMap,this.lightMapIntensity=t.lightMapIntensity,this.aoMap=t.aoMap,this.aoMapIntensity=t.aoMapIntensity,this.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}}o.prototype.isMeshLambertMaterial=!0},function(t,e,n){\\\\\\\"use strict\\\\\\\";n.d(e,\\\\\\\"a\\\\\\\",(function(){return o}));var i=n(17),r=n(13),s=n(20);class o extends r.a{constructor(t){super(t)}load(t,e,n,r){void 0!==this.path&&(t=this.path+t),t=this.manager.resolveURL(t);const o=this,a=i.a.get(t);if(void 0!==a)return o.manager.itemStart(t),setTimeout((function(){e&&e(a),o.manager.itemEnd(t)}),0),a;const l=Object(s.b)(\\\\\\\"img\\\\\\\");function c(){l.removeEventListener(\\\\\\\"load\\\\\\\",c,!1),l.removeEventListener(\\\\\\\"error\\\\\\\",u,!1),i.a.add(t,this),e&&e(this),o.manager.itemEnd(t)}function u(e){l.removeEventListener(\\\\\\\"load\\\\\\\",c,!1),l.removeEventListener(\\\\\\\"error\\\\\\\",u,!1),r&&r(e),o.manager.itemError(t),o.manager.itemEnd(t)}return l.addEventListener(\\\\\\\"load\\\\\\\",c,!1),l.addEventListener(\\\\\\\"error\\\\\\\",u,!1),\\\\\\\"data:\\\\\\\"!==t.substr(0,5)&&void 0!==this.crossOrigin&&(l.crossOrigin=this.crossOrigin),o.manager.itemStart(t),l.src=t,l}}},function(t,e,n){\\\\\\\"use strict\\\\\\\";n.d(e,\\\\\\\"a\\\\\\\",(function(){return p}));var i=n(19),r=n(26),s=n(1);class o extends r.a{}o.prototype.ValueTypeName=\\\\\\\"bool\\\\\\\",o.prototype.ValueBufferType=Array,o.prototype.DefaultInterpolation=s.O,o.prototype.InterpolantFactoryMethodLinear=void 0,o.prototype.InterpolantFactoryMethodSmooth=void 0;class a extends r.a{}a.prototype.ValueTypeName=\\\\\\\"color\\\\\\\";var l=n(50),c=n(54);class u extends r.a{}u.prototype.ValueTypeName=\\\\\\\"string\\\\\\\",u.prototype.ValueBufferType=Array,u.prototype.DefaultInterpolation=s.O,u.prototype.InterpolantFactoryMethodLinear=void 0,u.prototype.InterpolantFactoryMethodSmooth=void 0;var h=n(51),d=n(3);class p{constructor(t,e=-1,n,i=s.wb){this.name=t,this.tracks=n,this.duration=e,this.blendMode=i,this.uuid=d.h(),this.duration<0&&this.resetDuration()}static parse(t){const e=[],n=t.tracks,i=1/(t.fps||1);for(let t=0,r=n.length;t!==r;++t)e.push(_(n[t]).scale(i));const r=new this(t.name,t.duration,e,t.blendMode);return r.uuid=t.uuid,r}static toJSON(t){const e=[],n=t.tracks,i={name:t.name,duration:t.duration,tracks:e,uuid:t.uuid,blendMode:t.blendMode};for(let t=0,i=n.length;t!==i;++t)e.push(r.a.toJSON(n[t]));return i}static CreateFromMorphTargetSequence(t,e,n,r){const s=e.length,o=[];for(let t=0;t<s;t++){let a=[],c=[];a.push((t+s-1)%s,t,(t+1)%s),c.push(0,1,0);const u=i.a.getKeyframeOrder(a);a=i.a.sortedArray(a,1,u),c=i.a.sortedArray(c,1,u),r||0!==a[0]||(a.push(s),c.push(c[0])),o.push(new l.a(\\\\\\\".morphTargetInfluences[\\\\\\\"+e[t].name+\\\\\\\"]\\\\\\\",a,c).scale(1/n))}return new this(t,-1,o)}static findByName(t,e){let n=t;if(!Array.isArray(t)){const e=t;n=e.geometry&&e.geometry.animations||e.animations}for(let t=0;t<n.length;t++)if(n[t].name===e)return n[t];return null}static CreateClipsFromMorphTargetSequences(t,e,n){const i={},r=/^([\\\\w-]*?)([\\\\d]+)$/;for(let e=0,n=t.length;e<n;e++){const n=t[e],s=n.name.match(r);if(s&&s.length>1){const t=s[1];let e=i[t];e||(i[t]=e=[]),e.push(n)}}const s=[];for(const t in i)s.push(this.CreateFromMorphTargetSequence(t,i[t],e,n));return s}static parseAnimation(t,e){if(!t)return console.error(\\\\\\\"THREE.AnimationClip: No animation in JSONLoader data.\\\\\\\"),null;const n=function(t,e,n,r,s){if(0!==n.length){const o=[],a=[];i.a.flattenJSON(n,o,a,r),0!==o.length&&s.push(new t(e,o,a))}},r=[],s=t.name||\\\\\\\"default\\\\\\\",o=t.fps||30,a=t.blendMode;let u=t.length||-1;const d=t.hierarchy||[];for(let t=0;t<d.length;t++){const i=d[t].keys;if(i&&0!==i.length)if(i[0].morphTargets){const t={};let e;for(e=0;e<i.length;e++)if(i[e].morphTargets)for(let n=0;n<i[e].morphTargets.length;n++)t[i[e].morphTargets[n]]=-1;for(const n in t){const t=[],s=[];for(let r=0;r!==i[e].morphTargets.length;++r){const r=i[e];t.push(r.time),s.push(r.morphTarget===n?1:0)}r.push(new l.a(\\\\\\\".morphTargetInfluence[\\\\\\\"+n+\\\\\\\"]\\\\\\\",t,s))}u=t.length*(o||1)}else{const s=\\\\\\\".bones[\\\\\\\"+e[t].name+\\\\\\\"]\\\\\\\";n(h.a,s+\\\\\\\".position\\\\\\\",i,\\\\\\\"pos\\\\\\\",r),n(c.a,s+\\\\\\\".quaternion\\\\\\\",i,\\\\\\\"rot\\\\\\\",r),n(h.a,s+\\\\\\\".scale\\\\\\\",i,\\\\\\\"scl\\\\\\\",r)}}if(0===r.length)return null;return new this(s,u,r,a)}resetDuration(){let t=0;for(let e=0,n=this.tracks.length;e!==n;++e){const n=this.tracks[e];t=Math.max(t,n.times[n.times.length-1])}return this.duration=t,this}trim(){for(let t=0;t<this.tracks.length;t++)this.tracks[t].trim(0,this.duration);return this}validate(){let t=!0;for(let e=0;e<this.tracks.length;e++)t=t&&this.tracks[e].validate();return t}optimize(){for(let t=0;t<this.tracks.length;t++)this.tracks[t].optimize();return this}clone(){const t=[];for(let e=0;e<this.tracks.length;e++)t.push(this.tracks[e].clone());return new this.constructor(this.name,this.duration,t,this.blendMode)}toJSON(){return this.constructor.toJSON(this)}}function _(t){if(void 0===t.type)throw new Error(\\\\\\\"THREE.KeyframeTrack: track type undefined, can not parse\\\\\\\");const e=function(t){switch(t.toLowerCase()){case\\\\\\\"scalar\\\\\\\":case\\\\\\\"double\\\\\\\":case\\\\\\\"float\\\\\\\":case\\\\\\\"number\\\\\\\":case\\\\\\\"integer\\\\\\\":return l.a;case\\\\\\\"vector\\\\\\\":case\\\\\\\"vector2\\\\\\\":case\\\\\\\"vector3\\\\\\\":case\\\\\\\"vector4\\\\\\\":return h.a;case\\\\\\\"color\\\\\\\":return a;case\\\\\\\"quaternion\\\\\\\":return c.a;case\\\\\\\"bool\\\\\\\":case\\\\\\\"boolean\\\\\\\":return o;case\\\\\\\"string\\\\\\\":return u}throw new Error(\\\\\\\"THREE.KeyframeTrack: Unsupported typeName: \\\\\\\"+t)}(t.type);if(void 0===t.times){const e=[],n=[];i.a.flattenJSON(t.keys,e,n,\\\\\\\"value\\\\\\\"),t.times=e,t.values=n}return void 0!==e.parse?e.parse(t):new e(t.name,t.times,t.values,t.interpolation)}},function(t,e,n){\\\\\\\"use strict\\\\\\\";n.d(e,\\\\\\\"a\\\\\\\",(function(){return o}));var i=n(0),r=n(4);const s=new i.a;class o{constructor(t,e,n,i=!1){this.name=\\\\\\\"\\\\\\\",this.data=t,this.itemSize=e,this.offset=n,this.normalized=!0===i}get count(){return this.data.count}get array(){return this.data.array}set needsUpdate(t){this.data.needsUpdate=t}applyMatrix4(t){for(let e=0,n=this.data.count;e<n;e++)s.x=this.getX(e),s.y=this.getY(e),s.z=this.getZ(e),s.applyMatrix4(t),this.setXYZ(e,s.x,s.y,s.z);return this}applyNormalMatrix(t){for(let e=0,n=this.count;e<n;e++)s.x=this.getX(e),s.y=this.getY(e),s.z=this.getZ(e),s.applyNormalMatrix(t),this.setXYZ(e,s.x,s.y,s.z);return this}transformDirection(t){for(let e=0,n=this.count;e<n;e++)s.x=this.getX(e),s.y=this.getY(e),s.z=this.getZ(e),s.transformDirection(t),this.setXYZ(e,s.x,s.y,s.z);return this}setX(t,e){return this.data.array[t*this.data.stride+this.offset]=e,this}setY(t,e){return this.data.array[t*this.data.stride+this.offset+1]=e,this}setZ(t,e){return this.data.array[t*this.data.stride+this.offset+2]=e,this}setW(t,e){return this.data.array[t*this.data.stride+this.offset+3]=e,this}getX(t){return this.data.array[t*this.data.stride+this.offset]}getY(t){return this.data.array[t*this.data.stride+this.offset+1]}getZ(t){return this.data.array[t*this.data.stride+this.offset+2]}getW(t){return this.data.array[t*this.data.stride+this.offset+3]}setXY(t,e,n){return t=t*this.data.stride+this.offset,this.data.array[t+0]=e,this.data.array[t+1]=n,this}setXYZ(t,e,n,i){return t=t*this.data.stride+this.offset,this.data.array[t+0]=e,this.data.array[t+1]=n,this.data.array[t+2]=i,this}setXYZW(t,e,n,i,r){return t=t*this.data.stride+this.offset,this.data.array[t+0]=e,this.data.array[t+1]=n,this.data.array[t+2]=i,this.data.array[t+3]=r,this}clone(t){if(void 0===t){console.log(\\\\\\\"THREE.InterleavedBufferAttribute.clone(): Cloning an interlaved buffer attribute will deinterleave buffer data.\\\\\\\");const t=[];for(let e=0;e<this.count;e++){const n=e*this.data.stride+this.offset;for(let e=0;e<this.itemSize;e++)t.push(this.data.array[n+e])}return new r.a(new this.array.constructor(t),this.itemSize,this.normalized)}return void 0===t.interleavedBuffers&&(t.interleavedBuffers={}),void 0===t.interleavedBuffers[this.data.uuid]&&(t.interleavedBuffers[this.data.uuid]=this.data.clone(t)),new o(t.interleavedBuffers[this.data.uuid],this.itemSize,this.offset,this.normalized)}toJSON(t){if(void 0===t){console.log(\\\\\\\"THREE.InterleavedBufferAttribute.toJSON(): Serializing an interlaved buffer attribute will deinterleave buffer data.\\\\\\\");const t=[];for(let e=0;e<this.count;e++){const n=e*this.data.stride+this.offset;for(let e=0;e<this.itemSize;e++)t.push(this.data.array[n+e])}return{itemSize:this.itemSize,type:this.array.constructor.name,array:t,normalized:this.normalized}}return void 0===t.interleavedBuffers&&(t.interleavedBuffers={}),void 0===t.interleavedBuffers[this.data.uuid]&&(t.interleavedBuffers[this.data.uuid]=this.data.toJSON(t)),{isInterleavedBufferAttribute:!0,itemSize:this.itemSize,data:this.data.uuid,offset:this.offset,normalized:this.normalized}}}o.prototype.isInterleavedBufferAttribute=!0},function(t,e,n){\\\\\\\"use strict\\\\\\\";n.d(e,\\\\\\\"a\\\\\\\",(function(){return a}));var i=n(2),r=n(55),s=n(6),o=n(3);class a extends r.a{constructor(t){super(),this.defines={STANDARD:\\\\\\\"\\\\\\\",PHYSICAL:\\\\\\\"\\\\\\\"},this.type=\\\\\\\"MeshPhysicalMaterial\\\\\\\",this.clearcoatMap=null,this.clearcoatRoughness=0,this.clearcoatRoughnessMap=null,this.clearcoatNormalScale=new i.a(1,1),this.clearcoatNormalMap=null,this.ior=1.5,Object.defineProperty(this,\\\\\\\"reflectivity\\\\\\\",{get:function(){return o.d(2.5*(this.ior-1)/(this.ior+1),0,1)},set:function(t){this.ior=(1+.4*t)/(1-.4*t)}}),this.sheenTint=new s.a(0),this.sheenRoughness=1,this.transmissionMap=null,this.thickness=.01,this.thicknessMap=null,this.attenuationDistance=0,this.attenuationTint=new s.a(1,1,1),this.specularIntensity=1,this.specularIntensityMap=null,this.specularTint=new s.a(1,1,1),this.specularTintMap=null,this._sheen=0,this._clearcoat=0,this._transmission=0,this.setValues(t)}get sheen(){return this._sheen}set sheen(t){this._sheen>0!=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}}a.prototype.isMeshPhysicalMaterial=!0},function(t,e,n){\\\\\\\"use strict\\\\\\\";n.d(e,\\\\\\\"a\\\\\\\",(function(){return p}));const i=\\\\\\\"\\\\\\\\[\\\\\\\\]\\\\\\\\.:\\\\\\\\/\\\\\\\",r=new RegExp(\\\\\\\"[\\\\\\\\[\\\\\\\\]\\\\\\\\.:\\\\\\\\/]\\\\\\\",\\\\\\\"g\\\\\\\"),s=\\\\\\\"[^\\\\\\\\[\\\\\\\\]\\\\\\\\.:\\\\\\\\/]\\\\\\\",o=\\\\\\\"[^\\\\\\\"+i.replace(\\\\\\\"\\\\\\\\.\\\\\\\",\\\\\\\"\\\\\\\")+\\\\\\\"]\\\\\\\",a=/((?:WC+[\\\\/:])*)/.source.replace(\\\\\\\"WC\\\\\\\",s),l=/(WCOD+)?/.source.replace(\\\\\\\"WCOD\\\\\\\",o),c=/(?:\\\\.(WC+)(?:\\\\[(.+)\\\\])?)?/.source.replace(\\\\\\\"WC\\\\\\\",s),u=/\\\\.(WC+)(?:\\\\[(.+)\\\\])?/.source.replace(\\\\\\\"WC\\\\\\\",s),h=new RegExp(\\\\\\\"^\\\\\\\"+a+l+c+u+\\\\\\\"$\\\\\\\"),d=[\\\\\\\"material\\\\\\\",\\\\\\\"materials\\\\\\\",\\\\\\\"bones\\\\\\\"];class p{constructor(t,e,n){this.path=e,this.parsedPath=n||p.parseTrackName(e),this.node=p.findNode(t,this.parsedPath.nodeName)||t,this.rootNode=t,this.getValue=this._getValue_unbound,this.setValue=this._setValue_unbound}static create(t,e,n){return t&&t.isAnimationObjectGroup?new p.Composite(t,e,n):new p(t,e,n)}static sanitizeNodeName(t){return t.replace(/\\\\s/g,\\\\\\\"_\\\\\\\").replace(r,\\\\\\\"\\\\\\\")}static parseTrackName(t){const e=h.exec(t);if(!e)throw new Error(\\\\\\\"PropertyBinding: Cannot parse trackName: \\\\\\\"+t);const n={nodeName:e[2],objectName:e[3],objectIndex:e[4],propertyName:e[5],propertyIndex:e[6]},i=n.nodeName&&n.nodeName.lastIndexOf(\\\\\\\".\\\\\\\");if(void 0!==i&&-1!==i){const t=n.nodeName.substring(i+1);-1!==d.indexOf(t)&&(n.nodeName=n.nodeName.substring(0,i),n.objectName=t)}if(null===n.propertyName||0===n.propertyName.length)throw new Error(\\\\\\\"PropertyBinding: can not parse propertyName from trackName: \\\\\\\"+t);return n}static findNode(t,e){if(!e||\\\\\\\"\\\\\\\"===e||\\\\\\\".\\\\\\\"===e||-1===e||e===t.name||e===t.uuid)return t;if(t.skeleton){const n=t.skeleton.getBoneByName(e);if(void 0!==n)return n}if(t.children){const n=function(t){for(let i=0;i<t.length;i++){const r=t[i];if(r.name===e||r.uuid===e)return r;const s=n(r.children);if(s)return s}return null},i=n(t.children);if(i)return i}return null}_getValue_unavailable(){}_setValue_unavailable(){}_getValue_direct(t,e){t[e]=this.targetObject[this.propertyName]}_getValue_array(t,e){const n=this.resolvedProperty;for(let i=0,r=n.length;i!==r;++i)t[e++]=n[i]}_getValue_arrayElement(t,e){t[e]=this.resolvedProperty[this.propertyIndex]}_getValue_toArray(t,e){this.resolvedProperty.toArray(t,e)}_setValue_direct(t,e){this.targetObject[this.propertyName]=t[e]}_setValue_direct_setNeedsUpdate(t,e){this.targetObject[this.propertyName]=t[e],this.targetObject.needsUpdate=!0}_setValue_direct_setMatrixWorldNeedsUpdate(t,e){this.targetObject[this.propertyName]=t[e],this.targetObject.matrixWorldNeedsUpdate=!0}_setValue_array(t,e){const n=this.resolvedProperty;for(let i=0,r=n.length;i!==r;++i)n[i]=t[e++]}_setValue_array_setNeedsUpdate(t,e){const n=this.resolvedProperty;for(let i=0,r=n.length;i!==r;++i)n[i]=t[e++];this.targetObject.needsUpdate=!0}_setValue_array_setMatrixWorldNeedsUpdate(t,e){const n=this.resolvedProperty;for(let i=0,r=n.length;i!==r;++i)n[i]=t[e++];this.targetObject.matrixWorldNeedsUpdate=!0}_setValue_arrayElement(t,e){this.resolvedProperty[this.propertyIndex]=t[e]}_setValue_arrayElement_setNeedsUpdate(t,e){this.resolvedProperty[this.propertyIndex]=t[e],this.targetObject.needsUpdate=!0}_setValue_arrayElement_setMatrixWorldNeedsUpdate(t,e){this.resolvedProperty[this.propertyIndex]=t[e],this.targetObject.matrixWorldNeedsUpdate=!0}_setValue_fromArray(t,e){this.resolvedProperty.fromArray(t,e)}_setValue_fromArray_setNeedsUpdate(t,e){this.resolvedProperty.fromArray(t,e),this.targetObject.needsUpdate=!0}_setValue_fromArray_setMatrixWorldNeedsUpdate(t,e){this.resolvedProperty.fromArray(t,e),this.targetObject.matrixWorldNeedsUpdate=!0}_getValue_unbound(t,e){this.bind(),this.getValue(t,e)}_setValue_unbound(t,e){this.bind(),this.setValue(t,e)}bind(){let t=this.node;const e=this.parsedPath,n=e.objectName,i=e.propertyName;let r=e.propertyIndex;if(t||(t=p.findNode(this.rootNode,e.nodeName)||this.rootNode,this.node=t),this.getValue=this._getValue_unavailable,this.setValue=this._setValue_unavailable,!t)return void console.error(\\\\\\\"THREE.PropertyBinding: Trying to update node for track: \\\\\\\"+this.path+\\\\\\\" but it wasn't found.\\\\\\\");if(n){let i=e.objectIndex;switch(n){case\\\\\\\"materials\\\\\\\":if(!t.material)return void console.error(\\\\\\\"THREE.PropertyBinding: Can not bind to material as node does not have a material.\\\\\\\",this);if(!t.material.materials)return void console.error(\\\\\\\"THREE.PropertyBinding: Can not bind to material.materials as node.material does not have a materials array.\\\\\\\",this);t=t.material.materials;break;case\\\\\\\"bones\\\\\\\":if(!t.skeleton)return void console.error(\\\\\\\"THREE.PropertyBinding: Can not bind to bones as node does not have a skeleton.\\\\\\\",this);t=t.skeleton.bones;for(let e=0;e<t.length;e++)if(t[e].name===i){i=e;break}break;default:if(void 0===t[n])return void console.error(\\\\\\\"THREE.PropertyBinding: Can not bind to objectName of node undefined.\\\\\\\",this);t=t[n]}if(void 0!==i){if(void 0===t[i])return void console.error(\\\\\\\"THREE.PropertyBinding: Trying to bind to objectIndex of objectName, but is undefined.\\\\\\\",this,t);t=t[i]}}const s=t[i];if(void 0===s){const n=e.nodeName;return void console.error(\\\\\\\"THREE.PropertyBinding: Trying to update property for track: \\\\\\\"+n+\\\\\\\".\\\\\\\"+i+\\\\\\\" but it wasn't found.\\\\\\\",t)}let o=this.Versioning.None;this.targetObject=t,void 0!==t.needsUpdate?o=this.Versioning.NeedsUpdate:void 0!==t.matrixWorldNeedsUpdate&&(o=this.Versioning.MatrixWorldNeedsUpdate);let a=this.BindingType.Direct;if(void 0!==r){if(\\\\\\\"morphTargetInfluences\\\\\\\"===i){if(!t.geometry)return void console.error(\\\\\\\"THREE.PropertyBinding: Can not bind to morphTargetInfluences because node does not have a geometry.\\\\\\\",this);if(!t.geometry.isBufferGeometry)return void console.error(\\\\\\\"THREE.PropertyBinding: Can not bind to morphTargetInfluences on THREE.Geometry. Use THREE.BufferGeometry instead.\\\\\\\",this);if(!t.geometry.morphAttributes)return void console.error(\\\\\\\"THREE.PropertyBinding: Can not bind to morphTargetInfluences because node does not have a geometry.morphAttributes.\\\\\\\",this);void 0!==t.morphTargetDictionary[r]&&(r=t.morphTargetDictionary[r])}a=this.BindingType.ArrayElement,this.resolvedProperty=s,this.propertyIndex=r}else void 0!==s.fromArray&&void 0!==s.toArray?(a=this.BindingType.HasFromToArray,this.resolvedProperty=s):Array.isArray(s)?(a=this.BindingType.EntireArray,this.resolvedProperty=s):this.propertyName=i;this.getValue=this.GetterByBindingType[a],this.setValue=this.SetterByBindingTypeAndVersioning[a][o]}unbind(){this.node=null,this.getValue=this._getValue_unbound,this.setValue=this._setValue_unbound}}p.Composite=class{constructor(t,e,n){const i=n||p.parseTrackName(e);this._targetGroup=t,this._bindings=t.subscribe_(e,i)}getValue(t,e){this.bind();const n=this._targetGroup.nCachedObjects_,i=this._bindings[n];void 0!==i&&i.getValue(t,e)}setValue(t,e){const n=this._bindings;for(let i=this._targetGroup.nCachedObjects_,r=n.length;i!==r;++i)n[i].setValue(t,e)}bind(){const t=this._bindings;for(let e=this._targetGroup.nCachedObjects_,n=t.length;e!==n;++e)t[e].bind()}unbind(){const t=this._bindings;for(let e=this._targetGroup.nCachedObjects_,n=t.length;e!==n;++e)t[e].unbind()}},p.prototype.BindingType={Direct:0,EntireArray:1,ArrayElement:2,HasFromToArray:3},p.prototype.Versioning={None:0,NeedsUpdate:1,MatrixWorldNeedsUpdate:2},p.prototype.GetterByBindingType=[p.prototype._getValue_direct,p.prototype._getValue_array,p.prototype._getValue_arrayElement,p.prototype._getValue_toArray],p.prototype.SetterByBindingTypeAndVersioning=[[p.prototype._setValue_direct,p.prototype._setValue_direct_setNeedsUpdate,p.prototype._setValue_direct_setMatrixWorldNeedsUpdate],[p.prototype._setValue_array,p.prototype._setValue_array_setNeedsUpdate,p.prototype._setValue_array_setMatrixWorldNeedsUpdate],[p.prototype._setValue_arrayElement,p.prototype._setValue_arrayElement_setNeedsUpdate,p.prototype._setValue_arrayElement_setMatrixWorldNeedsUpdate],[p.prototype._setValue_fromArray,p.prototype._setValue_fromArray_setNeedsUpdate,p.prototype._setValue_fromArray_setMatrixWorldNeedsUpdate]]},,function(t,e,n){var i=n(120),r=\\\\\\\"object\\\\\\\"==typeof self&&self&&self.Object===Object&&self,s=i||r||Function(\\\\\\\"return this\\\\\\\")();t.exports=s},function(t,e,n){\\\\\\\"use strict\\\\\\\";n.d(e,\\\\\\\"a\\\\\\\",(function(){return d}));var i=n(14),r=n(5),s=n(0),o=n(9);const a=new s.a,l=new o.a,c=new o.a,u=new s.a,h=new r.a;class d extends i.a{constructor(t,e){super(t,e),this.type=\\\\\\\"SkinnedMesh\\\\\\\",this.bindMode=\\\\\\\"attached\\\\\\\",this.bindMatrix=new r.a,this.bindMatrixInverse=new r.a}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 o.a,e=this.geometry.attributes.skinWeight;for(let n=0,i=e.count;n<i;n++){t.x=e.getX(n),t.y=e.getY(n),t.z=e.getZ(n),t.w=e.getW(n);const i=1/t.manhattanLength();i!==1/0?t.multiplyScalar(i):t.set(1,0,0,0),e.setXYZW(n,t.x,t.y,t.z,t.w)}}updateMatrixWorld(t){super.updateMatrixWorld(t),\\\\\\\"attached\\\\\\\"===this.bindMode?this.bindMatrixInverse.copy(this.matrixWorld).invert():\\\\\\\"detached\\\\\\\"===this.bindMode?this.bindMatrixInverse.copy(this.bindMatrix).invert():console.warn(\\\\\\\"THREE.SkinnedMesh: Unrecognized bindMode: \\\\\\\"+this.bindMode)}boneTransform(t,e){const n=this.skeleton,i=this.geometry;l.fromBufferAttribute(i.attributes.skinIndex,t),c.fromBufferAttribute(i.attributes.skinWeight,t),a.copy(e).applyMatrix4(this.bindMatrix),e.set(0,0,0);for(let t=0;t<4;t++){const i=c.getComponent(t);if(0!==i){const r=l.getComponent(t);h.multiplyMatrices(n.bones[r].matrixWorld,n.boneInverses[r]),e.addScaledVector(u.copy(a).applyMatrix4(h),i)}}return e.applyMatrix4(this.bindMatrixInverse)}}d.prototype.isSkinnedMesh=!0},function(t,e,n){\\\\\\\"use strict\\\\\\\";n.d(e,\\\\\\\"a\\\\\\\",(function(){return s}));var i=n(1),r=n(38);class s extends r.a{constructor(t,e,n,r){super(t,e,n,r),this._weightPrev=-0,this._offsetPrev=-0,this._weightNext=-0,this._offsetNext=-0,this.DefaultSettings_={endingStart:i.id,endingEnd:i.id}}intervalChanged_(t,e,n){const r=this.parameterPositions;let s=t-2,o=t+1,a=r[s],l=r[o];if(void 0===a)switch(this.getSettings_().endingStart){case i.kd:s=t,a=2*e-n;break;case i.hd:s=r.length-2,a=e+r[s]-r[s+1];break;default:s=t,a=n}if(void 0===l)switch(this.getSettings_().endingEnd){case i.kd:o=t,l=2*n-e;break;case i.hd:o=1,l=n+r[1]-r[0];break;default:o=t-1,l=e}const c=.5*(n-e),u=this.valueSize;this._weightPrev=c/(e-a),this._weightNext=c/(l-n),this._offsetPrev=s*u,this._offsetNext=o*u}interpolate_(t,e,n,i){const r=this.resultBuffer,s=this.sampleValues,o=this.valueSize,a=t*o,l=a-o,c=this._offsetPrev,u=this._offsetNext,h=this._weightPrev,d=this._weightNext,p=(n-e)/(i-e),_=p*p,m=_*p,f=-h*m+2*h*_-h*p,g=(1+h)*m+(-1.5-2*h)*_+(-.5+h)*p+1,v=(-1-d)*m+(1.5+d)*_+.5*p,y=d*m-d*_;for(let t=0;t!==o;++t)r[t]=f*s[c+t]+g*s[l+t]+v*s[a+t]+y*s[u+t];return r}}},function(t,e,n){\\\\\\\"use strict\\\\\\\";n.d(e,\\\\\\\"a\\\\\\\",(function(){return r}));var i=n(38);class r extends i.a{constructor(t,e,n,i){super(t,e,n,i)}interpolate_(t,e,n,i){const r=this.resultBuffer,s=this.sampleValues,o=this.valueSize,a=t*o,l=a-o,c=(n-e)/(i-e),u=1-c;for(let t=0;t!==o;++t)r[t]=s[l+t]*u+s[a+t]*c;return r}}},function(t,e,n){\\\\\\\"use strict\\\\\\\";n.d(e,\\\\\\\"a\\\\\\\",(function(){return l}));var i=n(32),r=n(45),s=n(37);class o extends r.a{constructor(){super(new s.a(-5,5,5,-5,.5,500))}}o.prototype.isDirectionalLightShadow=!0;var a=n(10);class l extends i.a{constructor(t,e){super(t,e),this.type=\\\\\\\"DirectionalLight\\\\\\\",this.position.copy(a.a.DefaultUp),this.updateMatrix(),this.target=new a.a,this.shadow=new o}dispose(){this.shadow.dispose()}copy(t){return super.copy(t),this.target=t.target.clone(),this.shadow=t.shadow.clone(),this}}l.prototype.isDirectionalLight=!0},function(t,e,n){\\\\\\\"use strict\\\\\\\";n.d(e,\\\\\\\"a\\\\\\\",(function(){return c}));var i=n(32),r=n(45),s=n(3),o=n(30);class a extends r.a{constructor(){super(new o.a(50,1,.5,500)),this.focus=1}updateMatrices(t){const e=this.camera,n=2*s.b*t.angle*this.focus,i=this.mapSize.width/this.mapSize.height,r=t.distance||e.far;n===e.fov&&i===e.aspect&&r===e.far||(e.fov=n,e.aspect=i,e.far=r,e.updateProjectionMatrix()),super.updateMatrices(t)}copy(t){return super.copy(t),this.focus=t.focus,this}}a.prototype.isSpotLightShadow=!0;var l=n(10);class c extends i.a{constructor(t,e,n=0,i=Math.PI/3,r=0,s=1){super(t,e),this.type=\\\\\\\"SpotLight\\\\\\\",this.position.copy(l.a.DefaultUp),this.updateMatrix(),this.target=new l.a,this.distance=n,this.angle=i,this.penumbra=r,this.decay=s,this.shadow=new a}get power(){return this.intensity*Math.PI}set power(t){this.intensity=t/Math.PI}dispose(){this.shadow.dispose()}copy(t){return super.copy(t),this.distance=t.distance,this.angle=t.angle,this.penumbra=t.penumbra,this.decay=t.decay,this.target=t.target.clone(),this.shadow=t.shadow.clone(),this}}c.prototype.isSpotLight=!0},function(t,e,n){\\\\\\\"use strict\\\\\\\";n.d(e,\\\\\\\"a\\\\\\\",(function(){return s}));var i=n(2),r=n(25);class s extends r.a{constructor(t=new i.a,e=new i.a){super(),this.type=\\\\\\\"LineCurve\\\\\\\",this.v1=t,this.v2=e}getPoint(t,e=new i.a){const n=e;return 1===t?n.copy(this.v2):(n.copy(this.v2).sub(this.v1),n.multiplyScalar(t).add(this.v1)),n}getPointAt(t,e){return this.getPoint(t,e)}getTangent(t,e){const n=e||new i.a;return n.copy(this.v2).sub(this.v1).normalize(),n}copy(t){return super.copy(t),this.v1.copy(t.v1),this.v2.copy(t.v2),this}toJSON(){const t=super.toJSON();return t.v1=this.v1.toArray(),t.v2=this.v2.toArray(),t}fromJSON(t){return super.fromJSON(t),this.v1.fromArray(t.v1),this.v2.fromArray(t.v2),this}}s.prototype.isLineCurve=!0},function(t,e,n){\\\\\\\"use strict\\\\\\\";n.d(e,\\\\\\\"a\\\\\\\",(function(){return o}));var i=n(25),r=n(31),s=n(2);class o extends i.a{constructor(t=new s.a,e=new s.a,n=new s.a,i=new s.a){super(),this.type=\\\\\\\"CubicBezierCurve\\\\\\\",this.v0=t,this.v1=e,this.v2=n,this.v3=i}getPoint(t,e=new s.a){const n=e,i=this.v0,o=this.v1,a=this.v2,l=this.v3;return n.set(Object(r.b)(t,i.x,o.x,a.x,l.x),Object(r.b)(t,i.y,o.y,a.y,l.y)),n}copy(t){return super.copy(t),this.v0.copy(t.v0),this.v1.copy(t.v1),this.v2.copy(t.v2),this.v3.copy(t.v3),this}toJSON(){const t=super.toJSON();return t.v0=this.v0.toArray(),t.v1=this.v1.toArray(),t.v2=this.v2.toArray(),t.v3=this.v3.toArray(),t}fromJSON(t){return super.fromJSON(t),this.v0.fromArray(t.v0),this.v1.fromArray(t.v1),this.v2.fromArray(t.v2),this.v3.fromArray(t.v3),this}}o.prototype.isCubicBezierCurve=!0},function(t,e,n){\\\\\\\"use strict\\\\\\\";n.d(e,\\\\\\\"a\\\\\\\",(function(){return o}));var i=n(25),r=n(31),s=n(2);class o extends i.a{constructor(t=new s.a,e=new s.a,n=new s.a){super(),this.type=\\\\\\\"QuadraticBezierCurve\\\\\\\",this.v0=t,this.v1=e,this.v2=n}getPoint(t,e=new s.a){const n=e,i=this.v0,o=this.v1,a=this.v2;return n.set(Object(r.c)(t,i.x,o.x,a.x),Object(r.c)(t,i.y,o.y,a.y)),n}copy(t){return super.copy(t),this.v0.copy(t.v0),this.v1.copy(t.v1),this.v2.copy(t.v2),this}toJSON(){const t=super.toJSON();return t.v0=this.v0.toArray(),t.v1=this.v1.toArray(),t.v2=this.v2.toArray(),t}fromJSON(t){return super.fromJSON(t),this.v0.fromArray(t.v0),this.v1.fromArray(t.v1),this.v2.fromArray(t.v2),this}}o.prototype.isQuadraticBezierCurve=!0},function(t,e,n){\\\\\\\"use strict\\\\\\\";n.d(e,\\\\\\\"a\\\\\\\",(function(){return o}));var i=n(25),r=n(31),s=n(2);class o extends i.a{constructor(t=[]){super(),this.type=\\\\\\\"SplineCurve\\\\\\\",this.points=t}getPoint(t,e=new s.a){const n=e,i=this.points,o=(i.length-1)*t,a=Math.floor(o),l=o-a,c=i[0===a?a:a-1],u=i[a],h=i[a>i.length-2?i.length-1:a+1],d=i[a>i.length-3?i.length-1:a+2];return n.set(Object(r.a)(l,c.x,u.x,h.x,d.x),Object(r.a)(l,c.y,u.y,h.y,d.y)),n}copy(t){super.copy(t),this.points=[];for(let e=0,n=t.points.length;e<n;e++){const n=t.points[e];this.points.push(n.clone())}return this}toJSON(){const t=super.toJSON();t.points=[];for(let e=0,n=this.points.length;e<n;e++){const n=this.points[e];t.points.push(n.toArray())}return t}fromJSON(t){super.fromJSON(t),this.points=[];for(let e=0,n=t.points.length;e<n;e++){const n=t.points[e];this.points.push((new s.a).fromArray(n))}return this}}o.prototype.isSplineCurve=!0},function(t,e,n){\\\\\\\"use strict\\\\\\\";n.d(e,\\\\\\\"a\\\\\\\",(function(){return s}));var i=n(3),r=n(1);class s{constructor(t,e){this.array=t,this.stride=e,this.count=void 0!==t?t.length/e:0,this.usage=r.Qc,this.updateRange={offset:0,count:-1},this.version=0,this.uuid=i.h()}onUploadCallback(){}set needsUpdate(t){!0===t&&this.version++}setUsage(t){return this.usage=t,this}copy(t){return this.array=new t.array.constructor(t.array),this.count=t.count,this.stride=t.stride,this.usage=t.usage,this}copyAt(t,e,n){t*=this.stride,n*=e.stride;for(let i=0,r=this.stride;i<r;i++)this.array[t+i]=e.array[n+i];return this}set(t,e=0){return this.array.set(t,e),this}clone(t){void 0===t.arrayBuffers&&(t.arrayBuffers={}),void 0===this.array.buffer._uuid&&(this.array.buffer._uuid=i.h()),void 0===t.arrayBuffers[this.array.buffer._uuid]&&(t.arrayBuffers[this.array.buffer._uuid]=this.array.slice(0).buffer);const e=new this.array.constructor(t.arrayBuffers[this.array.buffer._uuid]),n=new this.constructor(e,this.stride);return n.setUsage(this.usage),n}onUpload(t){return this.onUploadCallback=t,this}toJSON(t){return void 0===t.arrayBuffers&&(t.arrayBuffers={}),void 0===this.array.buffer._uuid&&(this.array.buffer._uuid=i.h()),void 0===t.arrayBuffers[this.array.buffer._uuid]&&(t.arrayBuffers[this.array.buffer._uuid]=Array.prototype.slice.call(new Uint32Array(this.array.buffer))),{uuid:this.uuid,buffer:this.array.buffer._uuid,type:this.array.constructor.name,stride:this.stride}}}s.prototype.isInterleavedBuffer=!0},function(t,e,n){\\\\\\\"use strict\\\\\\\";n.r(e),n.d(e,\\\\\\\"ArcCurve\\\\\\\",(function(){return r})),n.d(e,\\\\\\\"CatmullRomCurve3\\\\\\\",(function(){return s.a})),n.d(e,\\\\\\\"CubicBezierCurve\\\\\\\",(function(){return o.a})),n.d(e,\\\\\\\"CubicBezierCurve3\\\\\\\",(function(){return u})),n.d(e,\\\\\\\"EllipseCurve\\\\\\\",(function(){return i.a})),n.d(e,\\\\\\\"LineCurve\\\\\\\",(function(){return h.a})),n.d(e,\\\\\\\"LineCurve3\\\\\\\",(function(){return d})),n.d(e,\\\\\\\"QuadraticBezierCurve\\\\\\\",(function(){return p.a})),n.d(e,\\\\\\\"QuadraticBezierCurve3\\\\\\\",(function(){return _.a})),n.d(e,\\\\\\\"SplineCurve\\\\\\\",(function(){return m.a}));var i=n(57);class r extends i.a{constructor(t,e,n,i,r,s){super(t,e,n,n,i,r,s),this.type=\\\\\\\"ArcCurve\\\\\\\"}}r.prototype.isArcCurve=!0;var s=n(85),o=n(75),a=n(25),l=n(31),c=n(0);class u extends a.a{constructor(t=new c.a,e=new c.a,n=new c.a,i=new c.a){super(),this.type=\\\\\\\"CubicBezierCurve3\\\\\\\",this.v0=t,this.v1=e,this.v2=n,this.v3=i}getPoint(t,e=new c.a){const n=e,i=this.v0,r=this.v1,s=this.v2,o=this.v3;return n.set(Object(l.b)(t,i.x,r.x,s.x,o.x),Object(l.b)(t,i.y,r.y,s.y,o.y),Object(l.b)(t,i.z,r.z,s.z,o.z)),n}copy(t){return 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n=o(r.x,s.x),i=o(r.y,s.y);e.quadraticCurveTo(n,i,_[t+0],_[t+1]),s.x=n,s.y=i,r.x=_[t+0],r.y=_[t+1],0===t&&!0===u&&l.copy(r)}break;case\\\\\\\"A\\\\\\\":_=a(p,[3,4],7);for(let t=0,i=_.length;t<i;t+=7){if(_[t+5]==r.x&&_[t+6]==r.y)continue;const i=r.clone();r.x=_[t+5],r.y=_[t+6],s.x=r.x,s.y=r.y,n(e,_[t],_[t+1],_[t+2],_[t+3],_[t+4],i,r),0===t&&!0===u&&l.copy(r)}break;case\\\\\\\"m\\\\\\\":_=a(p);for(let t=0,n=_.length;t<n;t+=2)r.x+=_[t+0],r.y+=_[t+1],s.x=r.x,s.y=r.y,0===t?e.moveTo(r.x,r.y):e.lineTo(r.x,r.y),0===t&&!0===u&&l.copy(r);break;case\\\\\\\"h\\\\\\\":_=a(p);for(let t=0,n=_.length;t<n;t++)r.x+=_[t],s.x=r.x,s.y=r.y,e.lineTo(r.x,r.y),0===t&&!0===u&&l.copy(r);break;case\\\\\\\"v\\\\\\\":_=a(p);for(let t=0,n=_.length;t<n;t++)r.y+=_[t],s.x=r.x,s.y=r.y,e.lineTo(r.x,r.y),0===t&&!0===u&&l.copy(r);break;case\\\\\\\"l\\\\\\\":_=a(p);for(let t=0,n=_.length;t<n;t+=2)r.x+=_[t+0],r.y+=_[t+1],s.x=r.x,s.y=r.y,e.lineTo(r.x,r.y),0===t&&!0===u&&l.copy(r);break;case\\\\\\\"c\\\\\\\":_=a(p);for(let 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i=r.clone();r.x+=_[t+5],r.y+=_[t+6],s.x=r.x,s.y=r.y,n(e,_[t],_[t+1],_[t+2],_[t+3],_[t+4],i,r),0===t&&!0===u&&l.copy(r)}break;case\\\\\\\"Z\\\\\\\":case\\\\\\\"z\\\\\\\":e.currentPath.autoClose=!0,e.currentPath.curves.length>0&&(r.copy(l),e.currentPath.currentPoint.copy(r),c=!0);break;default:console.warn(i)}u=!1}return e}(e));break;case\\\\\\\"rect\\\\\\\":r=s(e,r),m=function(t){const e=d(t.getAttribute(\\\\\\\"x\\\\\\\")||0),n=d(t.getAttribute(\\\\\\\"y\\\\\\\")||0),i=d(t.getAttribute(\\\\\\\"rx\\\\\\\")||t.getAttribute(\\\\\\\"ry\\\\\\\")||0),r=d(t.getAttribute(\\\\\\\"ry\\\\\\\")||t.getAttribute(\\\\\\\"rx\\\\\\\")||0),s=d(t.getAttribute(\\\\\\\"width\\\\\\\")),o=d(t.getAttribute(\\\\\\\"height\\\\\\\")),a=.448084975506,l=new 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t.getAttribute(\\\\\\\"points\\\\\\\").replace(n,e),i.currentPath.autoClose=!1,i}(e);break;case\\\\\\\"circle\\\\\\\":r=s(e,r),m=function(t){const e=d(t.getAttribute(\\\\\\\"cx\\\\\\\")||0),n=d(t.getAttribute(\\\\\\\"cy\\\\\\\")||0),i=d(t.getAttribute(\\\\\\\"r\\\\\\\")||0),r=new h.a;r.absarc(e,n,i,0,2*Math.PI);const s=new p.a;return s.subPaths.push(r),s}(e);break;case\\\\\\\"ellipse\\\\\\\":r=s(e,r),m=function(t){const e=d(t.getAttribute(\\\\\\\"cx\\\\\\\")||0),n=d(t.getAttribute(\\\\\\\"cy\\\\\\\")||0),i=d(t.getAttribute(\\\\\\\"rx\\\\\\\")||0),r=d(t.getAttribute(\\\\\\\"ry\\\\\\\")||0),s=new h.a;s.absellipse(e,n,i,r,0,2*Math.PI);const o=new p.a;return o.subPaths.push(s),o}(e);break;case\\\\\\\"line\\\\\\\":r=s(e,r),m=function(t){const e=d(t.getAttribute(\\\\\\\"x1\\\\\\\")||0),n=d(t.getAttribute(\\\\\\\"y1\\\\\\\")||0),i=d(t.getAttribute(\\\\\\\"x2\\\\\\\")||0),r=d(t.getAttribute(\\\\\\\"y2\\\\\\\")||0),s=new p.a;return s.moveTo(e,n),s.lineTo(i,r),s.currentPath.autoClose=!1,s}(e);break;case\\\\\\\"defs\\\\\\\":c=!1;break;case\\\\\\\"use\\\\\\\":r=s(e,r);const l=e.href.baseVal.substring(1),u=e.viewportElement.getElementById(l);u?t(u,r):console.warn(\\\\\\\"SVGLoader: 'use node' references non-existent node id: \\\\\\\"+l)}if(m&&(void 0!==r.fill&&\\\\\\\"none\\\\\\\"!==r.fill&&m.color.setStyle(r.fill),function(t,e){function n(t){E.set(t.x,t.y,1).applyMatrix3(e),t.set(E.x,E.y)}const i=function(t){return 0!==t.elements[1]||0!==t.elements[3]}(e),r=t.subPaths;for(let t=0,s=r.length;t<s;t++){const s=r[t].curves;for(let t=0;t<s.length;t++){const r=s[t];r.isLineCurve?(n(r.v1),n(r.v2)):r.isCubicBezierCurve?(n(r.v0),n(r.v1),n(r.v2),n(r.v3)):r.isQuadraticBezierCurve?(n(r.v0),n(r.v1),n(r.v2)):r.isEllipseCurve&&(i&&console.warn(\\\\\\\"SVGLoader: Elliptic arc or ellipse rotation or skewing is not 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r})(t,e.points).forEach((t=>{s.push({identifier:e.identifier,isCW:e.isCW,point:t})}))}})),s.sort(((t,e)=>t.point.x-e.point.x)),s}let v=0,y=e,x=-999999999,b=t.subPaths.map((t=>{const n=t.getPoints();let r=-999999999,o=e,a=-999999999,l=e;for(let t=0;t<n.length;t++){const e=n[t];e.y>r&&(r=e.y),e.y<o&&(o=e.y),e.x>a&&(a=e.x),e.x<l&&(l=e.x)}return x<=a&&(x=a+1),y>=l&&(y=l-1),{points:n,isCW:_.a.isClockWise(n),identifier:v++,boundingBox:new s(new i.a(l,o),new i.a(a,r))}}));b=b.filter((t=>t.points.length>1));const w=b.map((e=>function(t,e,n,r,s){null!=s&&\\\\\\\"\\\\\\\"!==s||(s=\\\\\\\"nonzero\\\\\\\");const o=new i.a;t.boundingBox.getCenter(o);const a=g([new i.a(n,o.y),new i.a(r,o.y)],t.boundingBox,e);a.sort(((t,e)=>t.point.x-e.point.x));const l=[],c=[];a.forEach((e=>{e.identifier===t.identifier?l.push(e):c.push(e)}));const u=l[0].point.x,h=[];let d=0;for(;d<c.length&&c[d].point.x<u;)h.length>0&&h[h.length-1]===c[d].identifier?h.pop():h.push(c[d].identifier),d++;if(h.push(t.identifier),\\\\\\\"evenodd\\\\\\\"===s){const e=h.length%2==0,n=h[h.length-2];return{identifier:t.identifier,isHole:e,for:n}}if(\\\\\\\"nonzero\\\\\\\"===s){let n=!0,i=null,r=null;for(let t=0;t<h.length;t++){const s=h[t];n?(r=e[s].isCW,n=!1,i=s):r!==e[s].isCW&&(r=e[s].isCW,n=!0)}return{identifier:t.identifier,isHole:n,for:i}}console.warn('fill-rule: \\\\\\\"'+s+'\\\\\\\" is currently not implemented.')}(e,b,y,x,t.userData.style.fillRule))),T=[];return b.forEach((t=>{if(!w[t.identifier].isHole){const e=new d.a(t.points);w.filter((e=>e.isHole&&e.for===t.identifier)).forEach((t=>{const n=b[t.identifier];e.holes.push(new h.a(n.points))})),T.push(e)}})),T}static getStrokeStyle(t,e,n,i,r){return{strokeColor:e=void 0!==e?e:\\\\\\\"#000\\\\\\\",strokeWidth:t=void 0!==t?t:1,strokeLineJoin:n=void 0!==n?n:\\\\\\\"miter\\\\\\\",strokeLineCap:i=void 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h=a*y-b;C[t]=h*i,C[e]=o*r,C[n]=T,l.push(C.x,C.y,C.z),C[t]=0,C[e]=0,C[n]=m>0?1:-1,c.push(C.x,C.y,C.z),u.push(a/f),u.push(1-s/g),M+=1}}for(let t=0;t<g;t++)for(let e=0;e<f;e++){const n=h+e+A*t,i=h+e+A*(t+1),r=h+(e+1)+A*(t+1),s=h+(e+1)+A*t;a.push(n,i,s),a.push(i,r,s),S+=6}o.addGroup(d,S,v),d+=S,h+=M}_(\\\\\\\"z\\\\\\\",\\\\\\\"y\\\\\\\",\\\\\\\"x\\\\\\\",-1,-1,n,e,t,s,r,0),_(\\\\\\\"z\\\\\\\",\\\\\\\"y\\\\\\\",\\\\\\\"x\\\\\\\",1,-1,n,e,-t,s,r,1),_(\\\\\\\"x\\\\\\\",\\\\\\\"z\\\\\\\",\\\\\\\"y\\\\\\\",1,1,t,n,e,i,s,2),_(\\\\\\\"x\\\\\\\",\\\\\\\"z\\\\\\\",\\\\\\\"y\\\\\\\",1,-1,t,n,-e,i,s,3),_(\\\\\\\"x\\\\\\\",\\\\\\\"y\\\\\\\",\\\\\\\"z\\\\\\\",1,-1,t,e,n,i,r,4),_(\\\\\\\"x\\\\\\\",\\\\\\\"y\\\\\\\",\\\\\\\"z\\\\\\\",-1,-1,t,e,-n,i,r,5),this.setIndex(a),this.setAttribute(\\\\\\\"position\\\\\\\",new C.c(l,3)),this.setAttribute(\\\\\\\"normal\\\\\\\",new C.c(c,3)),this.setAttribute(\\\\\\\"uv\\\\\\\",new C.c(u,2))}static fromJSON(t){return new N(t.width,t.height,t.depth,t.widthSegments,t.heightSegments,t.depthSegments)}}class L extends S.a{constructor(t=1,e=1,n=1,i=1){super(),this.type=\\\\\\\"PlaneGeometry\\\\\\\",this.parameters={width:t,height:e,widthSegments:n,heightSegments:i};const r=t/2,s=e/2,o=Math.floor(n),a=Math.floor(i),l=o+1,c=a+1,u=t/o,h=e/a,d=[],p=[],_=[],m=[];for(let t=0;t<c;t++){const e=t*h-s;for(let n=0;n<l;n++){const i=n*u-r;p.push(i,-e,0),_.push(0,0,1),m.push(n/o),m.push(1-t/a)}}for(let t=0;t<a;t++)for(let e=0;e<o;e++){const n=e+l*t,i=e+l*(t+1),r=e+1+l*(t+1),s=e+1+l*t;d.push(n,i,s),d.push(i,r,s)}this.setIndex(d),this.setAttribute(\\\\\\\"position\\\\\\\",new C.c(p,3)),this.setAttribute(\\\\\\\"normal\\\\\\\",new C.c(_,3)),this.setAttribute(\\\\\\\"uv\\\\\\\",new C.c(m,2))}static fromJSON(t){return new L(t.width,t.height,t.widthSegments,t.heightSegments)}}var O=n(12);function R(t){const e={};for(const n in t){e[n]={};for(const i in t[n]){const r=t[n][i];r&&(r.isColor||r.isMatrix3||r.isMatrix4||r.isVector2||r.isVector3||r.isVector4||r.isTexture||r.isQuaternion)?e[n][i]=r.clone():Array.isArray(r)?e[n][i]=r.slice():e[n][i]=r}}return e}function P(t){const e={};for(let n=0;n<t.length;n++){const i=R(t[n]);for(const t in i)e[t]=i[t]}return e}const I={clone:R,merge:P};class F extends O.a{constructor(t){super(),this.type=\\\\\\\"ShaderMaterial\\\\\\\",this.defines={},this.uniforms={},this.vertexShader=\\\\\\\"\\\\nvoid main() {\\\\n\\\\tgl_Position = projectionMatrix * modelViewMatrix * vec4( position, 1.0 );\\\\n}\\\\n\\\\\\\",this.fragmentShader=\\\\\\\"\\\\nvoid main() {\\\\n\\\\tgl_FragColor = vec4( 1.0, 0.0, 0.0, 1.0 );\\\\n}\\\\n\\\\\\\",this.linewidth=1,this.wireframe=!1,this.wireframeLinewidth=1,this.fog=!1,this.lights=!1,this.clipping=!1,this.extensions={derivatives:!1,fragDepth:!1,drawBuffers:!1,shaderTextureLOD:!1},this.defaultAttributeValues={color:[1,1,1],uv:[0,0],uv2:[0,0]},this.index0AttributeName=void 0,this.uniformsNeedUpdate=!1,this.glslVersion=null,void 0!==t&&(void 0!==t.attributes&&console.error(\\\\\\\"THREE.ShaderMaterial: attributes should now be defined in THREE.BufferGeometry instead.\\\\\\\"),this.setValues(t))}copy(t){return super.copy(t),this.fragmentShader=t.fragmentShader,this.vertexShader=t.vertexShader,this.uniforms=R(t.uniforms),this.defines=Object.assign({},t.defines),this.wireframe=t.wireframe,this.wireframeLinewidth=t.wireframeLinewidth,this.lights=t.lights,this.clipping=t.clipping,this.extensions=Object.assign({},t.extensions),this.glslVersion=t.glslVersion,this}toJSON(t){const e=super.toJSON(t);e.glslVersion=this.glslVersion,e.uniforms={};for(const n in this.uniforms){const i=this.uniforms[n].value;i&&i.isTexture?e.uniforms[n]={type:\\\\\\\"t\\\\\\\",value:i.toJSON(t).uuid}:i&&i.isColor?e.uniforms[n]={type:\\\\\\\"c\\\\\\\",value:i.getHex()}:i&&i.isVector2?e.uniforms[n]={type:\\\\\\\"v2\\\\\\\",value:i.toArray()}:i&&i.isVector3?e.uniforms[n]={type:\\\\\\\"v3\\\\\\\",value:i.toArray()}:i&&i.isVector4?e.uniforms[n]={type:\\\\\\\"v4\\\\\\\",value:i.toArray()}:i&&i.isMatrix3?e.uniforms[n]={type:\\\\\\\"m3\\\\\\\",value:i.toArray()}:i&&i.isMatrix4?e.uniforms[n]={type:\\\\\\\"m4\\\\\\\",value:i.toArray()}:e.uniforms[n]={value:i}}Object.keys(this.defines).length>0&&(e.defines=this.defines),e.vertexShader=this.vertexShader,e.fragmentShader=this.fragmentShader;const n={};for(const t in this.extensions)!0===this.extensions[t]&&(n[t]=!0);return Object.keys(n).length>0&&(e.extensions=n),e}}F.prototype.isShaderMaterial=!0;var D=n(6),k=n(14),B=\\\\\\\"\\\\n#ifdef USE_SHADOWMAP\\\\n\\\\n\\\\t#if NUM_DIR_LIGHT_SHADOWS > 0\\\\n\\\\n\\\\t\\\\tuniform sampler2D directionalShadowMap[ NUM_DIR_LIGHT_SHADOWS ];\\\\n\\\\t\\\\tvarying vec4 vDirectionalShadowCoord[ NUM_DIR_LIGHT_SHADOWS ];\\\\n\\\\n\\\\t\\\\tstruct DirectionalLightShadow {\\\\n\\\\t\\\\t\\\\tfloat shadowBias;\\\\n\\\\t\\\\t\\\\tfloat shadowNormalBias;\\\\n\\\\t\\\\t\\\\tfloat shadowRadius;\\\\n\\\\t\\\\t\\\\tvec2 shadowMapSize;\\\\n\\\\t\\\\t};\\\\n\\\\n\\\\t\\\\tuniform DirectionalLightShadow directionalLightShadows[ NUM_DIR_LIGHT_SHADOWS ];\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\t#if NUM_SPOT_LIGHT_SHADOWS > 0\\\\n\\\\n\\\\t\\\\tuniform sampler2D spotShadowMap[ NUM_SPOT_LIGHT_SHADOWS ];\\\\n\\\\t\\\\tvarying vec4 vSpotShadowCoord[ NUM_SPOT_LIGHT_SHADOWS ];\\\\n\\\\n\\\\t\\\\tstruct SpotLightShadow {\\\\n\\\\t\\\\t\\\\tfloat shadowBias;\\\\n\\\\t\\\\t\\\\tfloat shadowNormalBias;\\\\n\\\\t\\\\t\\\\tfloat shadowRadius;\\\\n\\\\t\\\\t\\\\tvec2 shadowMapSize;\\\\n\\\\t\\\\t};\\\\n\\\\n\\\\t\\\\tuniform SpotLightShadow spotLightShadows[ NUM_SPOT_LIGHT_SHADOWS ];\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\t#if NUM_POINT_LIGHT_SHADOWS > 0\\\\n\\\\n\\\\t\\\\tuniform sampler2D pointShadowMap[ NUM_POINT_LIGHT_SHADOWS ];\\\\n\\\\t\\\\tvarying vec4 vPointShadowCoord[ NUM_POINT_LIGHT_SHADOWS ];\\\\n\\\\n\\\\t\\\\tstruct PointLightShadow {\\\\n\\\\t\\\\t\\\\tfloat shadowBias;\\\\n\\\\t\\\\t\\\\tfloat shadowNormalBias;\\\\n\\\\t\\\\t\\\\tfloat shadowRadius;\\\\n\\\\t\\\\t\\\\tvec2 shadowMapSize;\\\\n\\\\t\\\\t\\\\tfloat shadowCameraNear;\\\\n\\\\t\\\\t\\\\tfloat shadowCameraFar;\\\\n\\\\t\\\\t};\\\\n\\\\n\\\\t\\\\tuniform PointLightShadow pointLightShadows[ NUM_POINT_LIGHT_SHADOWS ];\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\t/*\\\\n\\\\t#if NUM_RECT_AREA_LIGHTS > 0\\\\n\\\\n\\\\t\\\\t// TODO (abelnation): create uniforms for area light shadows\\\\n\\\\n\\\\t#endif\\\\n\\\\t*/\\\\n\\\\n\\\\tfloat texture2DCompare( sampler2D depths, vec2 uv, float compare ) {\\\\n\\\\n\\\\t\\\\treturn step( compare, unpackRGBAToDepth( texture2D( depths, uv ) ) );\\\\n\\\\n\\\\t}\\\\n\\\\n\\\\tvec2 texture2DDistribution( sampler2D shadow, vec2 uv ) {\\\\n\\\\n\\\\t\\\\treturn unpackRGBATo2Half( texture2D( shadow, uv ) );\\\\n\\\\n\\\\t}\\\\n\\\\n\\\\tfloat VSMShadow (sampler2D shadow, vec2 uv, float compare ){\\\\n\\\\n\\\\t\\\\tfloat occlusion = 1.0;\\\\n\\\\n\\\\t\\\\tvec2 distribution = texture2DDistribution( shadow, uv );\\\\n\\\\n\\\\t\\\\tfloat hard_shadow = step( compare , distribution.x ); // Hard Shadow\\\\n\\\\n\\\\t\\\\tif (hard_shadow != 1.0 ) {\\\\n\\\\n\\\\t\\\\t\\\\tfloat distance = compare - distribution.x ;\\\\n\\\\t\\\\t\\\\tfloat variance = max( 0.00000, distribution.y * distribution.y );\\\\n\\\\t\\\\t\\\\tfloat softness_probability = variance / (variance + distance * distance ); // Chebeyshevs inequality\\\\n\\\\t\\\\t\\\\tsoftness_probability = clamp( ( softness_probability - 0.3 ) / ( 0.95 - 0.3 ), 0.0, 1.0 ); // 0.3 reduces light bleed\\\\n\\\\t\\\\t\\\\tocclusion = clamp( max( hard_shadow, softness_probability ), 0.0, 1.0 );\\\\n\\\\n\\\\t\\\\t}\\\\n\\\\t\\\\treturn occlusion;\\\\n\\\\n\\\\t}\\\\n\\\\n\\\\tfloat getShadow( sampler2D shadowMap, vec2 shadowMapSize, float shadowBias, float shadowRadius, vec4 shadowCoord ) {\\\\n\\\\n\\\\t\\\\tfloat shadow = 1.0;\\\\n\\\\n\\\\t\\\\tshadowCoord.xyz /= shadowCoord.w;\\\\n\\\\t\\\\tshadowCoord.z += shadowBias;\\\\n\\\\n\\\\t\\\\t// if ( something && something ) breaks ATI OpenGL shader compiler\\\\n\\\\t\\\\t// if ( all( something, something ) ) using this instead\\\\n\\\\n\\\\t\\\\tbvec4 inFrustumVec = bvec4 ( shadowCoord.x >= 0.0, shadowCoord.x <= 1.0, shadowCoord.y >= 0.0, shadowCoord.y <= 1.0 );\\\\n\\\\t\\\\tbool inFrustum = all( inFrustumVec );\\\\n\\\\n\\\\t\\\\tbvec2 frustumTestVec = bvec2( inFrustum, shadowCoord.z <= 1.0 );\\\\n\\\\n\\\\t\\\\tbool frustumTest = all( frustumTestVec );\\\\n\\\\n\\\\t\\\\tif ( frustumTest ) {\\\\n\\\\n\\\\t\\\\t#if defined( SHADOWMAP_TYPE_PCF )\\\\n\\\\n\\\\t\\\\t\\\\tvec2 texelSize = vec2( 1.0 ) / shadowMapSize;\\\\n\\\\n\\\\t\\\\t\\\\tfloat dx0 = - texelSize.x * shadowRadius;\\\\n\\\\t\\\\t\\\\tfloat dy0 = - texelSize.y * shadowRadius;\\\\n\\\\t\\\\t\\\\tfloat dx1 = + texelSize.x * shadowRadius;\\\\n\\\\t\\\\t\\\\tfloat dy1 = + texelSize.y * shadowRadius;\\\\n\\\\t\\\\t\\\\tfloat dx2 = dx0 / 2.0;\\\\n\\\\t\\\\t\\\\tfloat dy2 = dy0 / 2.0;\\\\n\\\\t\\\\t\\\\tfloat dx3 = dx1 / 2.0;\\\\n\\\\t\\\\t\\\\tfloat dy3 = dy1 / 2.0;\\\\n\\\\n\\\\t\\\\t\\\\tshadow = (\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx0, dy0 ), shadowCoord.z ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( 0.0, dy0 ), shadowCoord.z ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx1, dy0 ), shadowCoord.z ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx2, dy2 ), shadowCoord.z ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( 0.0, dy2 ), shadowCoord.z ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx3, dy2 ), shadowCoord.z ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx0, 0.0 ), shadowCoord.z ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx2, 0.0 ), shadowCoord.z ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, shadowCoord.xy, shadowCoord.z ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx3, 0.0 ), shadowCoord.z ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx1, 0.0 ), shadowCoord.z ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx2, dy3 ), shadowCoord.z ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( 0.0, dy3 ), shadowCoord.z ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx3, dy3 ), shadowCoord.z ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx0, dy1 ), shadowCoord.z ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( 0.0, dy1 ), shadowCoord.z ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx1, dy1 ), shadowCoord.z )\\\\n\\\\t\\\\t\\\\t) * ( 1.0 / 17.0 );\\\\n\\\\n\\\\t\\\\t#elif defined( SHADOWMAP_TYPE_PCF_SOFT )\\\\n\\\\n\\\\t\\\\t\\\\tvec2 texelSize = vec2( 1.0 ) / shadowMapSize;\\\\n\\\\t\\\\t\\\\tfloat dx = texelSize.x;\\\\n\\\\t\\\\t\\\\tfloat dy = texelSize.y;\\\\n\\\\n\\\\t\\\\t\\\\tvec2 uv = shadowCoord.xy;\\\\n\\\\t\\\\t\\\\tvec2 f = fract( uv * shadowMapSize + 0.5 );\\\\n\\\\t\\\\t\\\\tuv -= f * texelSize;\\\\n\\\\n\\\\t\\\\t\\\\tshadow = (\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, uv, shadowCoord.z ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, uv + vec2( dx, 0.0 ), shadowCoord.z ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, uv + vec2( 0.0, dy ), shadowCoord.z ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, uv + texelSize, shadowCoord.z ) +\\\\n\\\\t\\\\t\\\\t\\\\tmix( texture2DCompare( shadowMap, uv + vec2( -dx, 0.0 ), shadowCoord.z ), \\\\n\\\\t\\\\t\\\\t\\\\t\\\\t texture2DCompare( shadowMap, uv + vec2( 2.0 * dx, 0.0 ), shadowCoord.z ),\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t f.x ) +\\\\n\\\\t\\\\t\\\\t\\\\tmix( texture2DCompare( shadowMap, uv + vec2( -dx, dy ), shadowCoord.z ), \\\\n\\\\t\\\\t\\\\t\\\\t\\\\t texture2DCompare( shadowMap, uv + vec2( 2.0 * dx, dy ), shadowCoord.z ),\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t f.x ) +\\\\n\\\\t\\\\t\\\\t\\\\tmix( texture2DCompare( shadowMap, uv + vec2( 0.0, -dy ), shadowCoord.z ), \\\\n\\\\t\\\\t\\\\t\\\\t\\\\t texture2DCompare( shadowMap, uv + vec2( 0.0, 2.0 * dy ), shadowCoord.z ),\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t f.y ) +\\\\n\\\\t\\\\t\\\\t\\\\tmix( texture2DCompare( shadowMap, uv + vec2( dx, -dy ), shadowCoord.z ), \\\\n\\\\t\\\\t\\\\t\\\\t\\\\t texture2DCompare( shadowMap, uv + vec2( dx, 2.0 * dy ), shadowCoord.z ),\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t f.y ) +\\\\n\\\\t\\\\t\\\\t\\\\tmix( mix( texture2DCompare( shadowMap, uv + vec2( -dx, -dy ), shadowCoord.z ), \\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t  texture2DCompare( shadowMap, uv + vec2( 2.0 * dx, -dy ), shadowCoord.z ),\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t  f.x ),\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t mix( texture2DCompare( shadowMap, uv + vec2( -dx, 2.0 * dy ), shadowCoord.z ), \\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t  texture2DCompare( shadowMap, uv + vec2( 2.0 * dx, 2.0 * dy ), shadowCoord.z ),\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t  f.x ),\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t f.y )\\\\n\\\\t\\\\t\\\\t) * ( 1.0 / 9.0 );\\\\n\\\\n\\\\t\\\\t#elif defined( SHADOWMAP_TYPE_VSM )\\\\n\\\\n\\\\t\\\\t\\\\tshadow = VSMShadow( shadowMap, shadowCoord.xy, shadowCoord.z );\\\\n\\\\n\\\\t\\\\t#else // no percentage-closer filtering:\\\\n\\\\n\\\\t\\\\t\\\\tshadow = texture2DCompare( shadowMap, shadowCoord.xy, shadowCoord.z );\\\\n\\\\n\\\\t\\\\t#endif\\\\n\\\\n\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\treturn shadow;\\\\n\\\\n\\\\t}\\\\n\\\\n\\\\t// cubeToUV() maps a 3D direction vector suitable for cube texture mapping to a 2D\\\\n\\\\t// vector suitable for 2D texture mapping. This code uses the following layout for the\\\\n\\\\t// 2D texture:\\\\n\\\\t//\\\\n\\\\t// xzXZ\\\\n\\\\t//  y Y\\\\n\\\\t//\\\\n\\\\t// Y - Positive y direction\\\\n\\\\t// y - Negative y direction\\\\n\\\\t// X - Positive x direction\\\\n\\\\t// x - Negative x direction\\\\n\\\\t// Z - Positive z direction\\\\n\\\\t// z - Negative z direction\\\\n\\\\t//\\\\n\\\\t// Source and test bed:\\\\n\\\\t// https://gist.github.com/tschw/da10c43c467ce8afd0c4\\\\n\\\\n\\\\tvec2 cubeToUV( vec3 v, float texelSizeY ) {\\\\n\\\\n\\\\t\\\\t// Number of texels to avoid at the edge of each square\\\\n\\\\n\\\\t\\\\tvec3 absV = abs( v );\\\\n\\\\n\\\\t\\\\t// Intersect unit cube\\\\n\\\\n\\\\t\\\\tfloat scaleToCube = 1.0 / max( absV.x, max( absV.y, absV.z ) );\\\\n\\\\t\\\\tabsV *= scaleToCube;\\\\n\\\\n\\\\t\\\\t// Apply scale to avoid seams\\\\n\\\\n\\\\t\\\\t// two texels less per square (one texel will do for NEAREST)\\\\n\\\\t\\\\tv *= scaleToCube * ( 1.0 - 2.0 * texelSizeY );\\\\n\\\\n\\\\t\\\\t// Unwrap\\\\n\\\\n\\\\t\\\\t// space: -1 ... 1 range for each square\\\\n\\\\t\\\\t//\\\\n\\\\t\\\\t// #X##\\\\t\\\\tdim    := ( 4 , 2 )\\\\n\\\\t\\\\t//  # #\\\\t\\\\tcenter := ( 1 , 1 )\\\\n\\\\n\\\\t\\\\tvec2 planar = v.xy;\\\\n\\\\n\\\\t\\\\tfloat almostATexel = 1.5 * texelSizeY;\\\\n\\\\t\\\\tfloat almostOne = 1.0 - almostATexel;\\\\n\\\\n\\\\t\\\\tif ( absV.z >= almostOne ) {\\\\n\\\\n\\\\t\\\\t\\\\tif ( v.z > 0.0 )\\\\n\\\\t\\\\t\\\\t\\\\tplanar.x = 4.0 - v.x;\\\\n\\\\n\\\\t\\\\t} else if ( absV.x >= almostOne ) {\\\\n\\\\n\\\\t\\\\t\\\\tfloat signX = sign( v.x );\\\\n\\\\t\\\\t\\\\tplanar.x = v.z * signX + 2.0 * signX;\\\\n\\\\n\\\\t\\\\t} else if ( absV.y >= almostOne ) {\\\\n\\\\n\\\\t\\\\t\\\\tfloat signY = sign( v.y );\\\\n\\\\t\\\\t\\\\tplanar.x = v.x + 2.0 * signY + 2.0;\\\\n\\\\t\\\\t\\\\tplanar.y = v.z * signY - 2.0;\\\\n\\\\n\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\t// Transform to UV space\\\\n\\\\n\\\\t\\\\t// scale := 0.5 / dim\\\\n\\\\t\\\\t// translate := ( center + 0.5 ) / dim\\\\n\\\\t\\\\treturn vec2( 0.125, 0.25 ) * planar + vec2( 0.375, 0.75 );\\\\n\\\\n\\\\t}\\\\n\\\\n\\\\tfloat getPointShadow( sampler2D shadowMap, vec2 shadowMapSize, float shadowBias, float shadowRadius, vec4 shadowCoord, float shadowCameraNear, float shadowCameraFar ) {\\\\n\\\\n\\\\t\\\\tvec2 texelSize = vec2( 1.0 ) / ( shadowMapSize * vec2( 4.0, 2.0 ) );\\\\n\\\\n\\\\t\\\\t// for point lights, the uniform @vShadowCoord is re-purposed to hold\\\\n\\\\t\\\\t// the vector from the light to the world-space position of the fragment.\\\\n\\\\t\\\\tvec3 lightToPosition = shadowCoord.xyz;\\\\n\\\\n\\\\t\\\\t// dp = normalized distance from light to fragment position\\\\n\\\\t\\\\tfloat dp = ( length( lightToPosition ) - shadowCameraNear ) / ( shadowCameraFar - shadowCameraNear ); // need to clamp?\\\\n\\\\t\\\\tdp += shadowBias;\\\\n\\\\n\\\\t\\\\t// bd3D = base direction 3D\\\\n\\\\t\\\\tvec3 bd3D = normalize( lightToPosition );\\\\n\\\\n\\\\t\\\\t#if defined( SHADOWMAP_TYPE_PCF ) || defined( SHADOWMAP_TYPE_PCF_SOFT ) || defined( SHADOWMAP_TYPE_VSM )\\\\n\\\\n\\\\t\\\\t\\\\tvec2 offset = vec2( - 1, 1 ) * shadowRadius * texelSize.y;\\\\n\\\\n\\\\t\\\\t\\\\treturn (\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, cubeToUV( bd3D + offset.xyy, texelSize.y ), dp ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, cubeToUV( bd3D + offset.yyy, texelSize.y ), dp ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, cubeToUV( bd3D + offset.xyx, texelSize.y ), dp ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, cubeToUV( bd3D + offset.yyx, texelSize.y ), dp ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, cubeToUV( bd3D, texelSize.y ), dp ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, cubeToUV( bd3D + offset.xxy, texelSize.y ), dp ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, cubeToUV( bd3D + offset.yxy, texelSize.y ), dp ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, cubeToUV( bd3D + offset.xxx, texelSize.y ), dp ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, cubeToUV( bd3D + offset.yxx, texelSize.y ), dp )\\\\n\\\\t\\\\t\\\\t) * ( 1.0 / 9.0 );\\\\n\\\\n\\\\t\\\\t#else // no percentage-closer filtering\\\\n\\\\n\\\\t\\\\t\\\\treturn texture2DCompare( shadowMap, cubeToUV( bd3D, texelSize.y ), dp );\\\\n\\\\n\\\\t\\\\t#endif\\\\n\\\\n\\\\t}\\\\n\\\\n#endif\\\\n\\\\\\\";const z={alphamap_fragment:\\\\\\\"\\\\n#ifdef USE_ALPHAMAP\\\\n\\\\n\\\\tdiffuseColor.a *= texture2D( alphaMap, vUv ).g;\\\\n\\\\n#endif\\\\n\\\\\\\",alphamap_pars_fragment:\\\\\\\"\\\\n#ifdef USE_ALPHAMAP\\\\n\\\\n\\\\tuniform sampler2D alphaMap;\\\\n\\\\n#endif\\\\n\\\\\\\",alphatest_fragment:\\\\\\\"\\\\n#ifdef USE_ALPHATEST\\\\n\\\\n\\\\tif ( diffuseColor.a < alphaTest ) discard;\\\\n\\\\n#endif\\\\n\\\\\\\",alphatest_pars_fragment:\\\\\\\"\\\\n#ifdef USE_ALPHATEST\\\\n\\\\tuniform float alphaTest;\\\\n#endif\\\\n\\\\\\\",aomap_fragment:\\\\\\\"\\\\n#ifdef USE_AOMAP\\\\n\\\\n\\\\t// reads channel R, compatible with a combined OcclusionRoughnessMetallic (RGB) texture\\\\n\\\\tfloat ambientOcclusion = ( texture2D( aoMap, vUv2 ).r - 1.0 ) * aoMapIntensity + 1.0;\\\\n\\\\n\\\\treflectedLight.indirectDiffuse *= ambientOcclusion;\\\\n\\\\n\\\\t#if defined( USE_ENVMAP ) && defined( STANDARD )\\\\n\\\\n\\\\t\\\\tfloat dotNV = saturate( dot( geometry.normal, geometry.viewDir ) );\\\\n\\\\n\\\\t\\\\treflectedLight.indirectSpecular *= computeSpecularOcclusion( dotNV, ambientOcclusion, material.roughness );\\\\n\\\\n\\\\t#endif\\\\n\\\\n#endif\\\\n\\\\\\\",aomap_pars_fragment:\\\\\\\"\\\\n#ifdef USE_AOMAP\\\\n\\\\n\\\\tuniform sampler2D aoMap;\\\\n\\\\tuniform float aoMapIntensity;\\\\n\\\\n#endif\\\\n\\\\\\\",begin_vertex:\\\\\\\"\\\\nvec3 transformed = vec3( position );\\\\n\\\\\\\",beginnormal_vertex:\\\\\\\"\\\\nvec3 objectNormal = vec3( normal );\\\\n\\\\n#ifdef USE_TANGENT\\\\n\\\\n\\\\tvec3 objectTangent = vec3( tangent.xyz );\\\\n\\\\n#endif\\\\n\\\\\\\",bsdfs:'\\\\n\\\\nvec3 BRDF_Lambert( const in vec3 diffuseColor ) {\\\\n\\\\n\\\\treturn RECIPROCAL_PI * diffuseColor;\\\\n\\\\n} // validated\\\\n\\\\nvec3 F_Schlick( const in vec3 f0, const in float f90, const in float dotVH ) {\\\\n\\\\n\\\\t// Original approximation by Christophe Schlick \\\\'94\\\\n\\\\t// float fresnel = pow( 1.0 - dotVH, 5.0 );\\\\n\\\\n\\\\t// Optimized variant (presented by Epic at SIGGRAPH \\\\'13)\\\\n\\\\t// https://cdn2.unrealengine.com/Resources/files/2013SiggraphPresentationsNotes-26915738.pdf\\\\n\\\\tfloat fresnel = exp2( ( - 5.55473 * dotVH - 6.98316 ) * dotVH );\\\\n\\\\n\\\\treturn f0 * ( 1.0 - fresnel ) + ( f90 * fresnel );\\\\n\\\\n} // validated\\\\n\\\\n// Moving Frostbite to Physically Based Rendering 3.0 - page 12, listing 2\\\\n// https://seblagarde.files.wordpress.com/2015/07/course_notes_moving_frostbite_to_pbr_v32.pdf\\\\nfloat V_GGX_SmithCorrelated( const in float alpha, const in float dotNL, const in float dotNV ) {\\\\n\\\\n\\\\tfloat a2 = pow2( alpha );\\\\n\\\\n\\\\tfloat gv = dotNL * sqrt( a2 + ( 1.0 - a2 ) * pow2( dotNV ) );\\\\n\\\\tfloat gl = dotNV * sqrt( a2 + ( 1.0 - a2 ) * pow2( dotNL ) );\\\\n\\\\n\\\\treturn 0.5 / max( gv + gl, EPSILON );\\\\n\\\\n}\\\\n\\\\n// Microfacet Models for Refraction through Rough Surfaces - equation (33)\\\\n// http://graphicrants.blogspot.com/2013/08/specular-brdf-reference.html\\\\n// alpha is \\\\\\\"roughness squared\\\\\\\" in Disney’s reparameterization\\\\nfloat D_GGX( const in float alpha, const in float dotNH ) {\\\\n\\\\n\\\\tfloat a2 = pow2( alpha );\\\\n\\\\n\\\\tfloat denom = pow2( dotNH ) * ( a2 - 1.0 ) + 1.0; // avoid alpha = 0 with dotNH = 1\\\\n\\\\n\\\\treturn RECIPROCAL_PI * a2 / pow2( denom );\\\\n\\\\n}\\\\n\\\\n// GGX Distribution, Schlick Fresnel, GGX_SmithCorrelated Visibility\\\\nvec3 BRDF_GGX( const in vec3 lightDir, const in vec3 viewDir, const in vec3 normal, const in vec3 f0, const in float f90, const in float roughness ) {\\\\n\\\\n\\\\tfloat alpha = pow2( roughness ); // UE4\\\\'s roughness\\\\n\\\\n\\\\tvec3 halfDir = normalize( lightDir + viewDir );\\\\n\\\\n\\\\tfloat dotNL = saturate( dot( normal, lightDir ) );\\\\n\\\\tfloat dotNV = saturate( dot( normal, viewDir ) );\\\\n\\\\tfloat dotNH = saturate( dot( normal, halfDir ) );\\\\n\\\\tfloat dotVH = saturate( dot( viewDir, halfDir ) );\\\\n\\\\n\\\\tvec3 F = F_Schlick( f0, f90, dotVH );\\\\n\\\\n\\\\tfloat V = V_GGX_SmithCorrelated( alpha, dotNL, dotNV );\\\\n\\\\n\\\\tfloat D = D_GGX( alpha, dotNH );\\\\n\\\\n\\\\treturn F * ( V * D );\\\\n\\\\n}\\\\n\\\\n// Rect Area Light\\\\n\\\\n// Real-Time Polygonal-Light Shading with Linearly Transformed Cosines\\\\n// by Eric Heitz, Jonathan Dupuy, Stephen Hill and David Neubelt\\\\n// code: https://github.com/selfshadow/ltc_code/\\\\n\\\\nvec2 LTC_Uv( const in vec3 N, const in vec3 V, const in float roughness ) {\\\\n\\\\n\\\\tconst float LUT_SIZE = 64.0;\\\\n\\\\tconst float LUT_SCALE = ( LUT_SIZE - 1.0 ) / LUT_SIZE;\\\\n\\\\tconst float LUT_BIAS = 0.5 / LUT_SIZE;\\\\n\\\\n\\\\tfloat dotNV = saturate( dot( N, V ) );\\\\n\\\\n\\\\t// texture parameterized by sqrt( GGX alpha ) and sqrt( 1 - cos( theta ) )\\\\n\\\\tvec2 uv = vec2( roughness, sqrt( 1.0 - dotNV ) );\\\\n\\\\n\\\\tuv = uv * LUT_SCALE + LUT_BIAS;\\\\n\\\\n\\\\treturn uv;\\\\n\\\\n}\\\\n\\\\nfloat LTC_ClippedSphereFormFactor( const in vec3 f ) {\\\\n\\\\n\\\\t// Real-Time Area Lighting: a Journey from Research to Production (p.102)\\\\n\\\\t// An approximation of the form factor of a horizon-clipped rectangle.\\\\n\\\\n\\\\tfloat l = length( f );\\\\n\\\\n\\\\treturn max( ( l * l + f.z ) / ( l + 1.0 ), 0.0 );\\\\n\\\\n}\\\\n\\\\nvec3 LTC_EdgeVectorFormFactor( const in vec3 v1, const in vec3 v2 ) {\\\\n\\\\n\\\\tfloat x = dot( v1, v2 );\\\\n\\\\n\\\\tfloat y = abs( x );\\\\n\\\\n\\\\t// rational polynomial approximation to theta / sin( theta ) / 2PI\\\\n\\\\tfloat a = 0.8543985 + ( 0.4965155 + 0.0145206 * y ) * y;\\\\n\\\\tfloat b = 3.4175940 + ( 4.1616724 + y ) * y;\\\\n\\\\tfloat v = a / b;\\\\n\\\\n\\\\tfloat theta_sintheta = ( x > 0.0 ) ? v : 0.5 * inversesqrt( max( 1.0 - x * x, 1e-7 ) ) - v;\\\\n\\\\n\\\\treturn cross( v1, v2 ) * theta_sintheta;\\\\n\\\\n}\\\\n\\\\nvec3 LTC_Evaluate( const in vec3 N, const in vec3 V, const in vec3 P, const in mat3 mInv, const in vec3 rectCoords[ 4 ] ) {\\\\n\\\\n\\\\t// bail if point is on back side of plane of light\\\\n\\\\t// assumes ccw winding order of light vertices\\\\n\\\\tvec3 v1 = rectCoords[ 1 ] - rectCoords[ 0 ];\\\\n\\\\tvec3 v2 = rectCoords[ 3 ] - rectCoords[ 0 ];\\\\n\\\\tvec3 lightNormal = cross( v1, v2 );\\\\n\\\\n\\\\tif( dot( lightNormal, P - rectCoords[ 0 ] ) < 0.0 ) return vec3( 0.0 );\\\\n\\\\n\\\\t// construct orthonormal basis around N\\\\n\\\\tvec3 T1, T2;\\\\n\\\\tT1 = normalize( V - N * dot( V, N ) );\\\\n\\\\tT2 = - cross( N, T1 ); // negated from paper; possibly due to a different handedness of world coordinate system\\\\n\\\\n\\\\t// compute transform\\\\n\\\\tmat3 mat = mInv * transposeMat3( mat3( T1, T2, N ) );\\\\n\\\\n\\\\t// transform rect\\\\n\\\\tvec3 coords[ 4 ];\\\\n\\\\tcoords[ 0 ] = mat * ( rectCoords[ 0 ] - P );\\\\n\\\\tcoords[ 1 ] = mat * ( rectCoords[ 1 ] - P );\\\\n\\\\tcoords[ 2 ] = mat * ( rectCoords[ 2 ] - P );\\\\n\\\\tcoords[ 3 ] = mat * ( rectCoords[ 3 ] - P );\\\\n\\\\n\\\\t// project rect onto sphere\\\\n\\\\tcoords[ 0 ] = normalize( coords[ 0 ] );\\\\n\\\\tcoords[ 1 ] = normalize( coords[ 1 ] );\\\\n\\\\tcoords[ 2 ] = normalize( coords[ 2 ] );\\\\n\\\\tcoords[ 3 ] = normalize( coords[ 3 ] );\\\\n\\\\n\\\\t// calculate vector form factor\\\\n\\\\tvec3 vectorFormFactor = vec3( 0.0 );\\\\n\\\\tvectorFormFactor += LTC_EdgeVectorFormFactor( coords[ 0 ], coords[ 1 ] );\\\\n\\\\tvectorFormFactor += LTC_EdgeVectorFormFactor( coords[ 1 ], coords[ 2 ] );\\\\n\\\\tvectorFormFactor += LTC_EdgeVectorFormFactor( coords[ 2 ], coords[ 3 ] );\\\\n\\\\tvectorFormFactor += LTC_EdgeVectorFormFactor( coords[ 3 ], coords[ 0 ] );\\\\n\\\\n\\\\t// adjust for horizon clipping\\\\n\\\\tfloat result = LTC_ClippedSphereFormFactor( vectorFormFactor );\\\\n\\\\n/*\\\\n\\\\t// alternate method of adjusting for horizon clipping (see referece)\\\\n\\\\t// refactoring required\\\\n\\\\tfloat len = length( vectorFormFactor );\\\\n\\\\tfloat z = vectorFormFactor.z / len;\\\\n\\\\n\\\\tconst float LUT_SIZE = 64.0;\\\\n\\\\tconst float LUT_SCALE = ( LUT_SIZE - 1.0 ) / LUT_SIZE;\\\\n\\\\tconst float LUT_BIAS = 0.5 / LUT_SIZE;\\\\n\\\\n\\\\t// tabulated horizon-clipped sphere, apparently...\\\\n\\\\tvec2 uv = vec2( z * 0.5 + 0.5, len );\\\\n\\\\tuv = uv * LUT_SCALE + LUT_BIAS;\\\\n\\\\n\\\\tfloat scale = texture2D( ltc_2, uv ).w;\\\\n\\\\n\\\\tfloat result = len * scale;\\\\n*/\\\\n\\\\n\\\\treturn vec3( result );\\\\n\\\\n}\\\\n\\\\n// End Rect Area Light\\\\n\\\\n\\\\nfloat G_BlinnPhong_Implicit( /* const in float dotNL, const in float dotNV */ ) {\\\\n\\\\n\\\\t// geometry term is (n dot l)(n dot v) / 4(n dot l)(n dot v)\\\\n\\\\treturn 0.25;\\\\n\\\\n}\\\\n\\\\nfloat D_BlinnPhong( const in float shininess, const in float dotNH ) {\\\\n\\\\n\\\\treturn RECIPROCAL_PI * ( shininess * 0.5 + 1.0 ) * pow( dotNH, shininess );\\\\n\\\\n}\\\\n\\\\nvec3 BRDF_BlinnPhong( const in vec3 lightDir, const in vec3 viewDir, const in vec3 normal, const in vec3 specularColor, const in float shininess ) {\\\\n\\\\n\\\\tvec3 halfDir = normalize( lightDir + viewDir );\\\\n\\\\n\\\\tfloat dotNH = saturate( dot( normal, halfDir ) );\\\\n\\\\tfloat dotVH = saturate( dot( viewDir, halfDir ) );\\\\n\\\\n\\\\tvec3 F = F_Schlick( specularColor, 1.0, dotVH );\\\\n\\\\n\\\\tfloat G = G_BlinnPhong_Implicit( /* dotNL, dotNV */ );\\\\n\\\\n\\\\tfloat D = D_BlinnPhong( shininess, dotNH );\\\\n\\\\n\\\\treturn F * ( G * D );\\\\n\\\\n} // validated\\\\n\\\\n#if defined( USE_SHEEN )\\\\n\\\\n// https://github.com/google/filament/blob/master/shaders/src/brdf.fs\\\\nfloat D_Charlie( float roughness, float dotNH ) {\\\\n\\\\n\\\\tfloat alpha = pow2( roughness );\\\\n\\\\n\\\\t// Estevez and Kulla 2017, \\\\\\\"Production Friendly Microfacet Sheen BRDF\\\\\\\"\\\\n\\\\tfloat invAlpha = 1.0 / alpha;\\\\n\\\\tfloat cos2h = dotNH * dotNH;\\\\n\\\\tfloat sin2h = max( 1.0 - cos2h, 0.0078125 ); // 2^(-14/2), so sin2h^2 > 0 in fp16\\\\n\\\\n\\\\treturn ( 2.0 + invAlpha ) * pow( sin2h, invAlpha * 0.5 ) / ( 2.0 * PI );\\\\n\\\\n}\\\\n\\\\n// https://github.com/google/filament/blob/master/shaders/src/brdf.fs\\\\nfloat V_Neubelt( float dotNV, float dotNL ) {\\\\n\\\\n\\\\t// Neubelt and Pettineo 2013, \\\\\\\"Crafting a Next-gen Material Pipeline for The Order: 1886\\\\\\\"\\\\n\\\\treturn saturate( 1.0 / ( 4.0 * ( dotNL + dotNV - dotNL * dotNV ) ) );\\\\n\\\\n}\\\\n\\\\nvec3 BRDF_Sheen( const in vec3 lightDir, const in vec3 viewDir, const in vec3 normal, vec3 sheenTint, const in float sheenRoughness ) {\\\\n\\\\n\\\\tvec3 halfDir = normalize( lightDir + viewDir );\\\\n\\\\n\\\\tfloat dotNL = saturate( dot( normal, lightDir ) );\\\\n\\\\tfloat dotNV = saturate( dot( normal, viewDir ) );\\\\n\\\\tfloat dotNH = saturate( dot( normal, halfDir ) );\\\\n\\\\n\\\\tfloat D = D_Charlie( sheenRoughness, dotNH );\\\\n\\\\tfloat V = V_Neubelt( dotNV, dotNL );\\\\n\\\\n\\\\treturn sheenTint * ( D * V );\\\\n\\\\n}\\\\n\\\\n#endif\\\\n',bumpmap_pars_fragment:\\\\\\\"\\\\n#ifdef USE_BUMPMAP\\\\n\\\\n\\\\tuniform sampler2D bumpMap;\\\\n\\\\tuniform float bumpScale;\\\\n\\\\n\\\\t// Bump Mapping Unparametrized Surfaces on the GPU by Morten S. Mikkelsen\\\\n\\\\t// http://api.unrealengine.com/attachments/Engine/Rendering/LightingAndShadows/BumpMappingWithoutTangentSpace/mm_sfgrad_bump.pdf\\\\n\\\\n\\\\t// Evaluate the derivative of the height w.r.t. screen-space using forward differencing (listing 2)\\\\n\\\\n\\\\tvec2 dHdxy_fwd() {\\\\n\\\\n\\\\t\\\\tvec2 dSTdx = dFdx( vUv );\\\\n\\\\t\\\\tvec2 dSTdy = dFdy( vUv );\\\\n\\\\n\\\\t\\\\tfloat Hll = bumpScale * texture2D( bumpMap, vUv ).x;\\\\n\\\\t\\\\tfloat dBx = bumpScale * texture2D( bumpMap, vUv + dSTdx ).x - Hll;\\\\n\\\\t\\\\tfloat dBy = bumpScale * texture2D( bumpMap, vUv + dSTdy ).x - Hll;\\\\n\\\\n\\\\t\\\\treturn vec2( dBx, dBy );\\\\n\\\\n\\\\t}\\\\n\\\\n\\\\tvec3 perturbNormalArb( vec3 surf_pos, vec3 surf_norm, vec2 dHdxy, float faceDirection ) {\\\\n\\\\n\\\\t\\\\t// Workaround for Adreno 3XX dFd*( vec3 ) bug. See #9988\\\\n\\\\n\\\\t\\\\tvec3 vSigmaX = vec3( dFdx( surf_pos.x ), dFdx( surf_pos.y ), dFdx( surf_pos.z ) );\\\\n\\\\t\\\\tvec3 vSigmaY = vec3( dFdy( surf_pos.x ), dFdy( surf_pos.y ), dFdy( surf_pos.z ) );\\\\n\\\\t\\\\tvec3 vN = surf_norm;\\\\t\\\\t// normalized\\\\n\\\\n\\\\t\\\\tvec3 R1 = cross( vSigmaY, vN );\\\\n\\\\t\\\\tvec3 R2 = cross( vN, vSigmaX );\\\\n\\\\n\\\\t\\\\tfloat fDet = dot( vSigmaX, R1 ) * faceDirection;\\\\n\\\\n\\\\t\\\\tvec3 vGrad = sign( fDet ) * ( dHdxy.x * R1 + dHdxy.y * R2 );\\\\n\\\\t\\\\treturn normalize( abs( fDet ) * surf_norm - vGrad );\\\\n\\\\n\\\\t}\\\\n\\\\n#endif\\\\n\\\\\\\",clipping_planes_fragment:\\\\\\\"\\\\n#if NUM_CLIPPING_PLANES > 0\\\\n\\\\n\\\\tvec4 plane;\\\\n\\\\n\\\\t#pragma unroll_loop_start\\\\n\\\\tfor ( int i = 0; i < UNION_CLIPPING_PLANES; i ++ ) {\\\\n\\\\n\\\\t\\\\tplane = clippingPlanes[ i ];\\\\n\\\\t\\\\tif ( dot( vClipPosition, plane.xyz ) > plane.w ) discard;\\\\n\\\\n\\\\t}\\\\n\\\\t#pragma unroll_loop_end\\\\n\\\\n\\\\t#if UNION_CLIPPING_PLANES < NUM_CLIPPING_PLANES\\\\n\\\\n\\\\t\\\\tbool clipped = true;\\\\n\\\\n\\\\t\\\\t#pragma unroll_loop_start\\\\n\\\\t\\\\tfor ( int i = UNION_CLIPPING_PLANES; i < NUM_CLIPPING_PLANES; i ++ ) {\\\\n\\\\n\\\\t\\\\t\\\\tplane = clippingPlanes[ i ];\\\\n\\\\t\\\\t\\\\tclipped = ( dot( vClipPosition, plane.xyz ) > plane.w ) && clipped;\\\\n\\\\n\\\\t\\\\t}\\\\n\\\\t\\\\t#pragma unroll_loop_end\\\\n\\\\n\\\\t\\\\tif ( clipped ) discard;\\\\n\\\\n\\\\t#endif\\\\n\\\\n#endif\\\\n\\\\\\\",clipping_planes_pars_fragment:\\\\\\\"\\\\n#if NUM_CLIPPING_PLANES > 0\\\\n\\\\n\\\\tvarying vec3 vClipPosition;\\\\n\\\\n\\\\tuniform vec4 clippingPlanes[ NUM_CLIPPING_PLANES ];\\\\n\\\\n#endif\\\\n\\\\\\\",clipping_planes_pars_vertex:\\\\\\\"\\\\n#if NUM_CLIPPING_PLANES > 0\\\\n\\\\n\\\\tvarying vec3 vClipPosition;\\\\n\\\\n#endif\\\\n\\\\\\\",clipping_planes_vertex:\\\\\\\"\\\\n#if NUM_CLIPPING_PLANES > 0\\\\n\\\\n\\\\tvClipPosition = - mvPosition.xyz;\\\\n\\\\n#endif\\\\n\\\\\\\",color_fragment:\\\\\\\"\\\\n#if defined( USE_COLOR_ALPHA )\\\\n\\\\n\\\\tdiffuseColor *= vColor;\\\\n\\\\n#elif defined( USE_COLOR )\\\\n\\\\n\\\\tdiffuseColor.rgb *= vColor;\\\\n\\\\n#endif\\\\n\\\\\\\",color_pars_fragment:\\\\\\\"\\\\n#if defined( USE_COLOR_ALPHA )\\\\n\\\\n\\\\tvarying vec4 vColor;\\\\n\\\\n#elif defined( USE_COLOR )\\\\n\\\\n\\\\tvarying vec3 vColor;\\\\n\\\\n#endif\\\\n\\\\\\\",color_pars_vertex:\\\\\\\"\\\\n#if defined( USE_COLOR_ALPHA )\\\\n\\\\n\\\\tvarying vec4 vColor;\\\\n\\\\n#elif defined( USE_COLOR ) || defined( USE_INSTANCING_COLOR )\\\\n\\\\n\\\\tvarying vec3 vColor;\\\\n\\\\n#endif\\\\n\\\\\\\",color_vertex:\\\\\\\"\\\\n#if defined( USE_COLOR_ALPHA )\\\\n\\\\n\\\\tvColor = vec4( 1.0 );\\\\n\\\\n#elif defined( USE_COLOR ) || defined( USE_INSTANCING_COLOR )\\\\n\\\\n\\\\tvColor = vec3( 1.0 );\\\\n\\\\n#endif\\\\n\\\\n#ifdef USE_COLOR\\\\n\\\\n\\\\tvColor *= color;\\\\n\\\\n#endif\\\\n\\\\n#ifdef USE_INSTANCING_COLOR\\\\n\\\\n\\\\tvColor.xyz *= instanceColor.xyz;\\\\n\\\\n#endif\\\\n\\\\\\\",common:\\\\\\\"\\\\n#define PI 3.141592653589793\\\\n#define PI2 6.283185307179586\\\\n#define PI_HALF 1.5707963267948966\\\\n#define RECIPROCAL_PI 0.3183098861837907\\\\n#define RECIPROCAL_PI2 0.15915494309189535\\\\n#define EPSILON 1e-6\\\\n\\\\n#ifndef saturate\\\\n// <tonemapping_pars_fragment> may have defined saturate() already\\\\n#define saturate( a ) clamp( a, 0.0, 1.0 )\\\\n#endif\\\\n#define whiteComplement( a ) ( 1.0 - saturate( a ) )\\\\n\\\\nfloat pow2( const in float x ) { return x*x; }\\\\nfloat pow3( const in float x ) { return x*x*x; }\\\\nfloat pow4( const in float x ) { float x2 = x*x; return x2*x2; }\\\\nfloat max3( const in vec3 v ) { return max( max( v.x, v.y ), v.z ); }\\\\nfloat average( const in vec3 color ) { return dot( color, vec3( 0.3333 ) ); }\\\\n\\\\n// expects values in the range of [0,1]x[0,1], returns values in the [0,1] range.\\\\n// do not collapse into a single function per: http://byteblacksmith.com/improvements-to-the-canonical-one-liner-glsl-rand-for-opengl-es-2-0/\\\\nhighp float rand( const in vec2 uv ) {\\\\n\\\\n\\\\tconst highp float a = 12.9898, b = 78.233, c = 43758.5453;\\\\n\\\\thighp float dt = dot( uv.xy, vec2( a,b ) ), sn = mod( dt, PI );\\\\n\\\\n\\\\treturn fract( sin( sn ) * c );\\\\n\\\\n}\\\\n\\\\n#ifdef HIGH_PRECISION\\\\n\\\\tfloat precisionSafeLength( vec3 v ) { return length( v ); }\\\\n#else\\\\n\\\\tfloat precisionSafeLength( vec3 v ) {\\\\n\\\\t\\\\tfloat maxComponent = max3( abs( v ) );\\\\n\\\\t\\\\treturn length( v / maxComponent ) * maxComponent;\\\\n\\\\t}\\\\n#endif\\\\n\\\\nstruct IncidentLight {\\\\n\\\\tvec3 color;\\\\n\\\\tvec3 direction;\\\\n\\\\tbool visible;\\\\n};\\\\n\\\\nstruct ReflectedLight {\\\\n\\\\tvec3 directDiffuse;\\\\n\\\\tvec3 directSpecular;\\\\n\\\\tvec3 indirectDiffuse;\\\\n\\\\tvec3 indirectSpecular;\\\\n};\\\\n\\\\nstruct GeometricContext {\\\\n\\\\tvec3 position;\\\\n\\\\tvec3 normal;\\\\n\\\\tvec3 viewDir;\\\\n#ifdef USE_CLEARCOAT\\\\n\\\\tvec3 clearcoatNormal;\\\\n#endif\\\\n};\\\\n\\\\nvec3 transformDirection( in vec3 dir, in mat4 matrix ) {\\\\n\\\\n\\\\treturn normalize( ( matrix * vec4( dir, 0.0 ) ).xyz );\\\\n\\\\n}\\\\n\\\\nvec3 inverseTransformDirection( in vec3 dir, in mat4 matrix ) {\\\\n\\\\n\\\\t// dir can be either a direction vector or a normal vector\\\\n\\\\t// upper-left 3x3 of matrix is assumed to be orthogonal\\\\n\\\\n\\\\treturn normalize( ( vec4( dir, 0.0 ) * matrix ).xyz );\\\\n\\\\n}\\\\n\\\\nmat3 transposeMat3( const in mat3 m ) {\\\\n\\\\n\\\\tmat3 tmp;\\\\n\\\\n\\\\ttmp[ 0 ] = vec3( m[ 0 ].x, m[ 1 ].x, m[ 2 ].x );\\\\n\\\\ttmp[ 1 ] = vec3( m[ 0 ].y, m[ 1 ].y, m[ 2 ].y );\\\\n\\\\ttmp[ 2 ] = vec3( m[ 0 ].z, m[ 1 ].z, m[ 2 ].z );\\\\n\\\\n\\\\treturn tmp;\\\\n\\\\n}\\\\n\\\\n// https://en.wikipedia.org/wiki/Relative_luminance\\\\nfloat linearToRelativeLuminance( const in vec3 color ) {\\\\n\\\\n\\\\tvec3 weights = vec3( 0.2126, 0.7152, 0.0722 );\\\\n\\\\n\\\\treturn dot( weights, color.rgb );\\\\n\\\\n}\\\\n\\\\nbool isPerspectiveMatrix( mat4 m ) {\\\\n\\\\n\\\\treturn m[ 2 ][ 3 ] == - 1.0;\\\\n\\\\n}\\\\n\\\\nvec2 equirectUv( in vec3 dir ) {\\\\n\\\\n\\\\t// dir is assumed to be unit length\\\\n\\\\n\\\\tfloat u = atan( dir.z, dir.x ) * RECIPROCAL_PI2 + 0.5;\\\\n\\\\n\\\\tfloat v = asin( clamp( dir.y, - 1.0, 1.0 ) ) * RECIPROCAL_PI + 0.5;\\\\n\\\\n\\\\treturn vec2( u, v );\\\\n\\\\n}\\\\n\\\\\\\",cube_uv_reflection_fragment:\\\\\\\"\\\\n#ifdef ENVMAP_TYPE_CUBE_UV\\\\n\\\\n\\\\t#define cubeUV_maxMipLevel 8.0\\\\n\\\\t#define cubeUV_minMipLevel 4.0\\\\n\\\\t#define cubeUV_maxTileSize 256.0\\\\n\\\\t#define cubeUV_minTileSize 16.0\\\\n\\\\n\\\\t// These shader functions convert between the UV coordinates of a single face of\\\\n\\\\t// a cubemap, the 0-5 integer index of a cube face, and the direction vector for\\\\n\\\\t// sampling a textureCube (not generally normalized ).\\\\n\\\\n\\\\tfloat getFace( vec3 direction ) {\\\\n\\\\n\\\\t\\\\tvec3 absDirection = abs( direction );\\\\n\\\\n\\\\t\\\\tfloat face = - 1.0;\\\\n\\\\n\\\\t\\\\tif ( absDirection.x > absDirection.z ) {\\\\n\\\\n\\\\t\\\\t\\\\tif ( absDirection.x > absDirection.y )\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tface = direction.x > 0.0 ? 0.0 : 3.0;\\\\n\\\\n\\\\t\\\\t\\\\telse\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tface = direction.y > 0.0 ? 1.0 : 4.0;\\\\n\\\\n\\\\t\\\\t} else {\\\\n\\\\n\\\\t\\\\t\\\\tif ( absDirection.z > absDirection.y )\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tface = direction.z > 0.0 ? 2.0 : 5.0;\\\\n\\\\n\\\\t\\\\t\\\\telse\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tface = direction.y > 0.0 ? 1.0 : 4.0;\\\\n\\\\n\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\treturn face;\\\\n\\\\n\\\\t}\\\\n\\\\n\\\\t// RH coordinate system; PMREM face-indexing convention\\\\n\\\\tvec2 getUV( vec3 direction, float face ) {\\\\n\\\\n\\\\t\\\\tvec2 uv;\\\\n\\\\n\\\\t\\\\tif ( face == 0.0 ) {\\\\n\\\\n\\\\t\\\\t\\\\tuv = vec2( direction.z, direction.y ) / abs( direction.x ); // pos x\\\\n\\\\n\\\\t\\\\t} else if ( face == 1.0 ) {\\\\n\\\\n\\\\t\\\\t\\\\tuv = vec2( - direction.x, - direction.z ) / abs( direction.y ); // pos y\\\\n\\\\n\\\\t\\\\t} else if ( face == 2.0 ) {\\\\n\\\\n\\\\t\\\\t\\\\tuv = vec2( - direction.x, direction.y ) / abs( direction.z ); // pos z\\\\n\\\\n\\\\t\\\\t} else if ( face == 3.0 ) {\\\\n\\\\n\\\\t\\\\t\\\\tuv = vec2( - direction.z, direction.y ) / abs( direction.x ); // neg x\\\\n\\\\n\\\\t\\\\t} else if ( face == 4.0 ) {\\\\n\\\\n\\\\t\\\\t\\\\tuv = vec2( - direction.x, direction.z ) / abs( direction.y ); // neg y\\\\n\\\\n\\\\t\\\\t} else {\\\\n\\\\n\\\\t\\\\t\\\\tuv = vec2( direction.x, direction.y ) / abs( direction.z ); // neg z\\\\n\\\\n\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\treturn 0.5 * ( uv + 1.0 );\\\\n\\\\n\\\\t}\\\\n\\\\n\\\\tvec3 bilinearCubeUV( sampler2D envMap, vec3 direction, float mipInt ) {\\\\n\\\\n\\\\t\\\\tfloat face = getFace( direction );\\\\n\\\\n\\\\t\\\\tfloat filterInt = max( cubeUV_minMipLevel - mipInt, 0.0 );\\\\n\\\\n\\\\t\\\\tmipInt = max( mipInt, cubeUV_minMipLevel );\\\\n\\\\n\\\\t\\\\tfloat faceSize = exp2( mipInt );\\\\n\\\\n\\\\t\\\\tfloat texelSize = 1.0 / ( 3.0 * cubeUV_maxTileSize );\\\\n\\\\n\\\\t\\\\tvec2 uv = getUV( direction, face ) * ( faceSize - 1.0 );\\\\n\\\\n\\\\t\\\\tvec2 f = fract( uv );\\\\n\\\\n\\\\t\\\\tuv += 0.5 - f;\\\\n\\\\n\\\\t\\\\tif ( face > 2.0 ) {\\\\n\\\\n\\\\t\\\\t\\\\tuv.y += faceSize;\\\\n\\\\n\\\\t\\\\t\\\\tface -= 3.0;\\\\n\\\\n\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\tuv.x += face * faceSize;\\\\n\\\\n\\\\t\\\\tif ( mipInt < cubeUV_maxMipLevel ) {\\\\n\\\\n\\\\t\\\\t\\\\tuv.y += 2.0 * cubeUV_maxTileSize;\\\\n\\\\n\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\tuv.y += filterInt * 2.0 * cubeUV_minTileSize;\\\\n\\\\n\\\\t\\\\tuv.x += 3.0 * max( 0.0, cubeUV_maxTileSize - 2.0 * faceSize );\\\\n\\\\n\\\\t\\\\tuv *= texelSize;\\\\n\\\\n\\\\t\\\\tvec3 tl = envMapTexelToLinear( texture2D( envMap, uv ) ).rgb;\\\\n\\\\n\\\\t\\\\tuv.x += texelSize;\\\\n\\\\n\\\\t\\\\tvec3 tr = envMapTexelToLinear( texture2D( envMap, uv ) ).rgb;\\\\n\\\\n\\\\t\\\\tuv.y += texelSize;\\\\n\\\\n\\\\t\\\\tvec3 br = envMapTexelToLinear( texture2D( envMap, uv ) ).rgb;\\\\n\\\\n\\\\t\\\\tuv.x -= texelSize;\\\\n\\\\n\\\\t\\\\tvec3 bl = envMapTexelToLinear( texture2D( envMap, uv ) ).rgb;\\\\n\\\\n\\\\t\\\\tvec3 tm = mix( tl, tr, f.x );\\\\n\\\\n\\\\t\\\\tvec3 bm = mix( bl, br, f.x );\\\\n\\\\n\\\\t\\\\treturn mix( tm, bm, f.y );\\\\n\\\\n\\\\t}\\\\n\\\\n\\\\t// These defines must match with PMREMGenerator\\\\n\\\\n\\\\t#define r0 1.0\\\\n\\\\t#define v0 0.339\\\\n\\\\t#define m0 - 2.0\\\\n\\\\t#define r1 0.8\\\\n\\\\t#define v1 0.276\\\\n\\\\t#define m1 - 1.0\\\\n\\\\t#define r4 0.4\\\\n\\\\t#define v4 0.046\\\\n\\\\t#define m4 2.0\\\\n\\\\t#define r5 0.305\\\\n\\\\t#define v5 0.016\\\\n\\\\t#define m5 3.0\\\\n\\\\t#define r6 0.21\\\\n\\\\t#define v6 0.0038\\\\n\\\\t#define m6 4.0\\\\n\\\\n\\\\tfloat roughnessToMip( float roughness ) {\\\\n\\\\n\\\\t\\\\tfloat mip = 0.0;\\\\n\\\\n\\\\t\\\\tif ( roughness >= r1 ) {\\\\n\\\\n\\\\t\\\\t\\\\tmip = ( r0 - roughness ) * ( m1 - m0 ) / ( r0 - r1 ) + m0;\\\\n\\\\n\\\\t\\\\t} else if ( roughness >= r4 ) {\\\\n\\\\n\\\\t\\\\t\\\\tmip = ( r1 - roughness ) * ( m4 - m1 ) / ( r1 - r4 ) + m1;\\\\n\\\\n\\\\t\\\\t} else if ( roughness >= r5 ) {\\\\n\\\\n\\\\t\\\\t\\\\tmip = ( r4 - roughness ) * ( m5 - m4 ) / ( r4 - r5 ) + m4;\\\\n\\\\n\\\\t\\\\t} else if ( roughness >= r6 ) {\\\\n\\\\n\\\\t\\\\t\\\\tmip = ( r5 - roughness ) * ( m6 - m5 ) / ( r5 - r6 ) + m5;\\\\n\\\\n\\\\t\\\\t} else {\\\\n\\\\n\\\\t\\\\t\\\\tmip = - 2.0 * log2( 1.16 * roughness ); // 1.16 = 1.79^0.25\\\\n\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\treturn mip;\\\\n\\\\n\\\\t}\\\\n\\\\n\\\\tvec4 textureCubeUV( sampler2D envMap, vec3 sampleDir, float roughness ) {\\\\n\\\\n\\\\t\\\\tfloat mip = clamp( roughnessToMip( roughness ), m0, cubeUV_maxMipLevel );\\\\n\\\\n\\\\t\\\\tfloat mipF = fract( mip );\\\\n\\\\n\\\\t\\\\tfloat mipInt = floor( mip );\\\\n\\\\n\\\\t\\\\tvec3 color0 = bilinearCubeUV( envMap, sampleDir, mipInt );\\\\n\\\\n\\\\t\\\\tif ( mipF == 0.0 ) {\\\\n\\\\n\\\\t\\\\t\\\\treturn vec4( color0, 1.0 );\\\\n\\\\n\\\\t\\\\t} else {\\\\n\\\\n\\\\t\\\\t\\\\tvec3 color1 = bilinearCubeUV( envMap, sampleDir, mipInt + 1.0 );\\\\n\\\\n\\\\t\\\\t\\\\treturn vec4( mix( color0, color1, mipF ), 1.0 );\\\\n\\\\n\\\\t\\\\t}\\\\n\\\\n\\\\t}\\\\n\\\\n#endif\\\\n\\\\\\\",defaultnormal_vertex:\\\\\\\"\\\\nvec3 transformedNormal = objectNormal;\\\\n\\\\n#ifdef USE_INSTANCING\\\\n\\\\n\\\\t// this is in lieu of a per-instance normal-matrix\\\\n\\\\t// shear transforms in the instance matrix are not supported\\\\n\\\\n\\\\tmat3 m = mat3( instanceMatrix );\\\\n\\\\n\\\\ttransformedNormal /= vec3( dot( m[ 0 ], m[ 0 ] ), dot( m[ 1 ], m[ 1 ] ), dot( m[ 2 ], m[ 2 ] ) );\\\\n\\\\n\\\\ttransformedNormal = m * transformedNormal;\\\\n\\\\n#endif\\\\n\\\\ntransformedNormal = normalMatrix * transformedNormal;\\\\n\\\\n#ifdef FLIP_SIDED\\\\n\\\\n\\\\ttransformedNormal = - transformedNormal;\\\\n\\\\n#endif\\\\n\\\\n#ifdef USE_TANGENT\\\\n\\\\n\\\\tvec3 transformedTangent = ( modelViewMatrix * vec4( objectTangent, 0.0 ) ).xyz;\\\\n\\\\n\\\\t#ifdef FLIP_SIDED\\\\n\\\\n\\\\t\\\\ttransformedTangent = - transformedTangent;\\\\n\\\\n\\\\t#endif\\\\n\\\\n#endif\\\\n\\\\\\\",displacementmap_pars_vertex:\\\\\\\"\\\\n#ifdef USE_DISPLACEMENTMAP\\\\n\\\\n\\\\tuniform sampler2D displacementMap;\\\\n\\\\tuniform float displacementScale;\\\\n\\\\tuniform float displacementBias;\\\\n\\\\n#endif\\\\n\\\\\\\",displacementmap_vertex:\\\\\\\"\\\\n#ifdef USE_DISPLACEMENTMAP\\\\n\\\\n\\\\ttransformed += normalize( objectNormal ) * ( texture2D( displacementMap, vUv ).x * displacementScale + displacementBias );\\\\n\\\\n#endif\\\\n\\\\\\\",emissivemap_fragment:\\\\\\\"\\\\n#ifdef USE_EMISSIVEMAP\\\\n\\\\n\\\\tvec4 emissiveColor = texture2D( emissiveMap, vUv );\\\\n\\\\n\\\\temissiveColor.rgb = emissiveMapTexelToLinear( emissiveColor ).rgb;\\\\n\\\\n\\\\ttotalEmissiveRadiance *= emissiveColor.rgb;\\\\n\\\\n#endif\\\\n\\\\\\\",emissivemap_pars_fragment:\\\\\\\"\\\\n#ifdef USE_EMISSIVEMAP\\\\n\\\\n\\\\tuniform sampler2D emissiveMap;\\\\n\\\\n#endif\\\\n\\\\\\\",encodings_fragment:\\\\\\\"\\\\ngl_FragColor = linearToOutputTexel( gl_FragColor );\\\\n\\\\\\\",encodings_pars_fragment:\\\\\\\"\\\\n// For a discussion of what this is, please read this: http://lousodrome.net/blog/light/2013/05/26/gamma-correct-and-hdr-rendering-in-a-32-bits-buffer/\\\\n\\\\nvec4 LinearToLinear( in vec4 value ) {\\\\n\\\\treturn value;\\\\n}\\\\n\\\\nvec4 GammaToLinear( in vec4 value, in float gammaFactor ) {\\\\n\\\\treturn vec4( pow( value.rgb, vec3( gammaFactor ) ), value.a );\\\\n}\\\\n\\\\nvec4 LinearToGamma( in vec4 value, in float gammaFactor ) {\\\\n\\\\treturn vec4( pow( value.rgb, vec3( 1.0 / gammaFactor ) ), value.a );\\\\n}\\\\n\\\\nvec4 sRGBToLinear( in vec4 value ) {\\\\n\\\\treturn vec4( mix( pow( value.rgb * 0.9478672986 + vec3( 0.0521327014 ), vec3( 2.4 ) ), value.rgb * 0.0773993808, vec3( lessThanEqual( value.rgb, vec3( 0.04045 ) ) ) ), value.a );\\\\n}\\\\n\\\\nvec4 LinearTosRGB( in vec4 value ) {\\\\n\\\\treturn vec4( mix( pow( value.rgb, vec3( 0.41666 ) ) * 1.055 - vec3( 0.055 ), value.rgb * 12.92, vec3( lessThanEqual( value.rgb, vec3( 0.0031308 ) ) ) ), value.a );\\\\n}\\\\n\\\\nvec4 RGBEToLinear( in vec4 value ) {\\\\n\\\\treturn vec4( value.rgb * exp2( value.a * 255.0 - 128.0 ), 1.0 );\\\\n}\\\\n\\\\nvec4 LinearToRGBE( in vec4 value ) {\\\\n\\\\tfloat maxComponent = max( max( value.r, value.g ), value.b );\\\\n\\\\tfloat fExp = clamp( ceil( log2( maxComponent ) ), -128.0, 127.0 );\\\\n\\\\treturn vec4( value.rgb / exp2( fExp ), ( fExp + 128.0 ) / 255.0 );\\\\n\\\\t// return vec4( value.brg, ( 3.0 + 128.0 ) / 256.0 );\\\\n}\\\\n\\\\n// reference: http://iwasbeingirony.blogspot.ca/2010/06/difference-between-rgbm-and-rgbd.html\\\\nvec4 RGBMToLinear( in vec4 value, in float maxRange ) {\\\\n\\\\treturn vec4( value.rgb * value.a * maxRange, 1.0 );\\\\n}\\\\n\\\\nvec4 LinearToRGBM( in vec4 value, in float maxRange ) {\\\\n\\\\tfloat maxRGB = max( value.r, max( value.g, value.b ) );\\\\n\\\\tfloat M = clamp( maxRGB / maxRange, 0.0, 1.0 );\\\\n\\\\tM = ceil( M * 255.0 ) / 255.0;\\\\n\\\\treturn vec4( value.rgb / ( M * maxRange ), M );\\\\n}\\\\n\\\\n// reference: http://iwasbeingirony.blogspot.ca/2010/06/difference-between-rgbm-and-rgbd.html\\\\nvec4 RGBDToLinear( in vec4 value, in float maxRange ) {\\\\n\\\\treturn vec4( value.rgb * ( ( maxRange / 255.0 ) / value.a ), 1.0 );\\\\n}\\\\n\\\\nvec4 LinearToRGBD( in vec4 value, in float maxRange ) {\\\\n\\\\tfloat maxRGB = max( value.r, max( value.g, value.b ) );\\\\n\\\\tfloat D = max( maxRange / maxRGB, 1.0 );\\\\n\\\\t// NOTE: The implementation with min causes the shader to not compile on\\\\n\\\\t// a common Alcatel A502DL in Chrome 78/Android 8.1. Some research suggests \\\\n\\\\t// that the chipset is Mediatek MT6739 w/ IMG PowerVR GE8100 GPU.\\\\n\\\\t// D = min( floor( D ) / 255.0, 1.0 );\\\\n\\\\tD = clamp( floor( D ) / 255.0, 0.0, 1.0 );\\\\n\\\\treturn vec4( value.rgb * ( D * ( 255.0 / maxRange ) ), D );\\\\n}\\\\n\\\\n// LogLuv reference: http://graphicrants.blogspot.ca/2009/04/rgbm-color-encoding.html\\\\n\\\\n// M matrix, for encoding\\\\nconst mat3 cLogLuvM = mat3( 0.2209, 0.3390, 0.4184, 0.1138, 0.6780, 0.7319, 0.0102, 0.1130, 0.2969 );\\\\nvec4 LinearToLogLuv( in vec4 value ) {\\\\n\\\\tvec3 Xp_Y_XYZp = cLogLuvM * value.rgb;\\\\n\\\\tXp_Y_XYZp = max( Xp_Y_XYZp, vec3( 1e-6, 1e-6, 1e-6 ) );\\\\n\\\\tvec4 vResult;\\\\n\\\\tvResult.xy = Xp_Y_XYZp.xy / Xp_Y_XYZp.z;\\\\n\\\\tfloat Le = 2.0 * log2(Xp_Y_XYZp.y) + 127.0;\\\\n\\\\tvResult.w = fract( Le );\\\\n\\\\tvResult.z = ( Le - ( floor( vResult.w * 255.0 ) ) / 255.0 ) / 255.0;\\\\n\\\\treturn vResult;\\\\n}\\\\n\\\\n// Inverse M matrix, for decoding\\\\nconst mat3 cLogLuvInverseM = mat3( 6.0014, -2.7008, -1.7996, -1.3320, 3.1029, -5.7721, 0.3008, -1.0882, 5.6268 );\\\\nvec4 LogLuvToLinear( in vec4 value ) {\\\\n\\\\tfloat Le = value.z * 255.0 + value.w;\\\\n\\\\tvec3 Xp_Y_XYZp;\\\\n\\\\tXp_Y_XYZp.y = exp2( ( Le - 127.0 ) / 2.0 );\\\\n\\\\tXp_Y_XYZp.z = Xp_Y_XYZp.y / value.y;\\\\n\\\\tXp_Y_XYZp.x = value.x * Xp_Y_XYZp.z;\\\\n\\\\tvec3 vRGB = cLogLuvInverseM * Xp_Y_XYZp.rgb;\\\\n\\\\treturn vec4( max( vRGB, 0.0 ), 1.0 );\\\\n}\\\\n\\\\\\\",envmap_fragment:\\\\\\\"\\\\n#ifdef USE_ENVMAP\\\\n\\\\n\\\\t#ifdef ENV_WORLDPOS\\\\n\\\\n\\\\t\\\\tvec3 cameraToFrag;\\\\n\\\\n\\\\t\\\\tif ( isOrthographic ) {\\\\n\\\\n\\\\t\\\\t\\\\tcameraToFrag = normalize( vec3( - viewMatrix[ 0 ][ 2 ], - viewMatrix[ 1 ][ 2 ], - viewMatrix[ 2 ][ 2 ] ) );\\\\n\\\\n\\\\t\\\\t} else {\\\\n\\\\n\\\\t\\\\t\\\\tcameraToFrag = normalize( vWorldPosition - cameraPosition );\\\\n\\\\n\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\t// Transforming Normal Vectors with the Inverse Transformation\\\\n\\\\t\\\\tvec3 worldNormal = inverseTransformDirection( normal, viewMatrix );\\\\n\\\\n\\\\t\\\\t#ifdef ENVMAP_MODE_REFLECTION\\\\n\\\\n\\\\t\\\\t\\\\tvec3 reflectVec = reflect( cameraToFrag, worldNormal );\\\\n\\\\n\\\\t\\\\t#else\\\\n\\\\n\\\\t\\\\t\\\\tvec3 reflectVec = refract( cameraToFrag, worldNormal, refractionRatio );\\\\n\\\\n\\\\t\\\\t#endif\\\\n\\\\n\\\\t#else\\\\n\\\\n\\\\t\\\\tvec3 reflectVec = vReflect;\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\t#ifdef ENVMAP_TYPE_CUBE\\\\n\\\\n\\\\t\\\\tvec4 envColor = textureCube( envMap, vec3( flipEnvMap * reflectVec.x, reflectVec.yz ) );\\\\n\\\\n\\\\t\\\\tenvColor = envMapTexelToLinear( envColor );\\\\n\\\\n\\\\t#elif defined( ENVMAP_TYPE_CUBE_UV )\\\\n\\\\n\\\\t\\\\tvec4 envColor = textureCubeUV( envMap, reflectVec, 0.0 );\\\\n\\\\n\\\\t#else\\\\n\\\\n\\\\t\\\\tvec4 envColor = vec4( 0.0 );\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\t#ifdef ENVMAP_BLENDING_MULTIPLY\\\\n\\\\n\\\\t\\\\toutgoingLight = mix( outgoingLight, outgoingLight * envColor.xyz, specularStrength * reflectivity );\\\\n\\\\n\\\\t#elif defined( ENVMAP_BLENDING_MIX )\\\\n\\\\n\\\\t\\\\toutgoingLight = mix( outgoingLight, envColor.xyz, specularStrength * reflectivity );\\\\n\\\\n\\\\t#elif defined( ENVMAP_BLENDING_ADD )\\\\n\\\\n\\\\t\\\\toutgoingLight += envColor.xyz * specularStrength * reflectivity;\\\\n\\\\n\\\\t#endif\\\\n\\\\n#endif\\\\n\\\\\\\",envmap_common_pars_fragment:\\\\\\\"\\\\n#ifdef USE_ENVMAP\\\\n\\\\n\\\\tuniform float envMapIntensity;\\\\n\\\\tuniform float flipEnvMap;\\\\n\\\\tuniform int maxMipLevel;\\\\n\\\\n\\\\t#ifdef ENVMAP_TYPE_CUBE\\\\n\\\\t\\\\tuniform samplerCube envMap;\\\\n\\\\t#else\\\\n\\\\t\\\\tuniform sampler2D envMap;\\\\n\\\\t#endif\\\\n\\\\t\\\\n#endif\\\\n\\\\\\\",envmap_pars_fragment:\\\\\\\"\\\\n#ifdef USE_ENVMAP\\\\n\\\\n\\\\tuniform float reflectivity;\\\\n\\\\n\\\\t#if defined( USE_BUMPMAP ) || defined( USE_NORMALMAP ) || defined( PHONG )\\\\n\\\\n\\\\t\\\\t#define ENV_WORLDPOS\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\t#ifdef ENV_WORLDPOS\\\\n\\\\n\\\\t\\\\tvarying vec3 vWorldPosition;\\\\n\\\\t\\\\tuniform float refractionRatio;\\\\n\\\\t#else\\\\n\\\\t\\\\tvarying vec3 vReflect;\\\\n\\\\t#endif\\\\n\\\\n#endif\\\\n\\\\\\\",envmap_pars_vertex:\\\\\\\"\\\\n#ifdef USE_ENVMAP\\\\n\\\\n\\\\t#if defined( USE_BUMPMAP ) || defined( USE_NORMALMAP ) ||defined( PHONG )\\\\n\\\\n\\\\t\\\\t#define ENV_WORLDPOS\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\t#ifdef ENV_WORLDPOS\\\\n\\\\t\\\\t\\\\n\\\\t\\\\tvarying vec3 vWorldPosition;\\\\n\\\\n\\\\t#else\\\\n\\\\n\\\\t\\\\tvarying vec3 vReflect;\\\\n\\\\t\\\\tuniform float refractionRatio;\\\\n\\\\n\\\\t#endif\\\\n\\\\n#endif\\\\n\\\\\\\",envmap_physical_pars_fragment:\\\\\\\"\\\\n#if defined( USE_ENVMAP )\\\\n\\\\n\\\\t#ifdef ENVMAP_MODE_REFRACTION\\\\n\\\\n\\\\t\\\\tuniform float refractionRatio;\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\tvec3 getIBLIrradiance( const in vec3 normal ) {\\\\n\\\\n\\\\t\\\\t#if defined( ENVMAP_TYPE_CUBE_UV )\\\\n\\\\n\\\\t\\\\t\\\\tvec3 worldNormal = inverseTransformDirection( normal, viewMatrix );\\\\n\\\\n\\\\t\\\\t\\\\tvec4 envMapColor = textureCubeUV( envMap, worldNormal, 1.0 );\\\\n\\\\n\\\\t\\\\t\\\\treturn PI * envMapColor.rgb * envMapIntensity;\\\\n\\\\n\\\\t\\\\t#else\\\\n\\\\n\\\\t\\\\t\\\\treturn vec3( 0.0 );\\\\n\\\\n\\\\t\\\\t#endif\\\\n\\\\n\\\\t}\\\\n\\\\n\\\\tvec3 getIBLRadiance( const in vec3 viewDir, const in vec3 normal, const in float roughness ) {\\\\n\\\\n\\\\t\\\\t#if defined( ENVMAP_TYPE_CUBE_UV )\\\\n\\\\n\\\\t\\\\t\\\\tvec3 reflectVec;\\\\n\\\\n\\\\t\\\\t\\\\t#ifdef ENVMAP_MODE_REFLECTION\\\\n\\\\n\\\\t\\\\t\\\\t\\\\treflectVec = reflect( - viewDir, normal );\\\\n\\\\n\\\\t\\\\t\\\\t\\\\t// Mixing the reflection with the normal is more accurate and keeps rough objects from gathering light from behind their tangent plane.\\\\n\\\\t\\\\t\\\\t\\\\treflectVec = normalize( mix( reflectVec, normal, roughness * roughness) );\\\\n\\\\n\\\\t\\\\t\\\\t#else\\\\n\\\\n\\\\t\\\\t\\\\t\\\\treflectVec = refract( - viewDir, normal, refractionRatio );\\\\n\\\\n\\\\t\\\\t\\\\t#endif\\\\n\\\\n\\\\t\\\\t\\\\treflectVec = inverseTransformDirection( reflectVec, viewMatrix );\\\\n\\\\n\\\\t\\\\t\\\\tvec4 envMapColor = textureCubeUV( envMap, reflectVec, roughness );\\\\n\\\\n\\\\t\\\\t\\\\treturn envMapColor.rgb * envMapIntensity;\\\\n\\\\n\\\\t\\\\t#else\\\\n\\\\n\\\\t\\\\t\\\\treturn vec3( 0.0 );\\\\n\\\\n\\\\t\\\\t#endif\\\\n\\\\n\\\\t}\\\\n\\\\n#endif\\\\n\\\\\\\",envmap_vertex:\\\\\\\"\\\\n#ifdef USE_ENVMAP\\\\n\\\\n\\\\t#ifdef ENV_WORLDPOS\\\\n\\\\n\\\\t\\\\tvWorldPosition = worldPosition.xyz;\\\\n\\\\n\\\\t#else\\\\n\\\\n\\\\t\\\\tvec3 cameraToVertex;\\\\n\\\\n\\\\t\\\\tif ( isOrthographic ) {\\\\n\\\\n\\\\t\\\\t\\\\tcameraToVertex = normalize( vec3( - viewMatrix[ 0 ][ 2 ], - viewMatrix[ 1 ][ 2 ], - viewMatrix[ 2 ][ 2 ] ) );\\\\n\\\\n\\\\t\\\\t} else {\\\\n\\\\n\\\\t\\\\t\\\\tcameraToVertex = normalize( worldPosition.xyz - cameraPosition );\\\\n\\\\n\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\tvec3 worldNormal = inverseTransformDirection( transformedNormal, viewMatrix );\\\\n\\\\n\\\\t\\\\t#ifdef ENVMAP_MODE_REFLECTION\\\\n\\\\n\\\\t\\\\t\\\\tvReflect = reflect( cameraToVertex, worldNormal );\\\\n\\\\n\\\\t\\\\t#else\\\\n\\\\n\\\\t\\\\t\\\\tvReflect = refract( cameraToVertex, worldNormal, refractionRatio );\\\\n\\\\n\\\\t\\\\t#endif\\\\n\\\\n\\\\t#endif\\\\n\\\\n#endif\\\\n\\\\\\\",fog_vertex:\\\\\\\"\\\\n#ifdef USE_FOG\\\\n\\\\n\\\\tvFogDepth = - mvPosition.z;\\\\n\\\\n#endif\\\\n\\\\\\\",fog_pars_vertex:\\\\\\\"\\\\n#ifdef USE_FOG\\\\n\\\\n\\\\tvarying float vFogDepth;\\\\n\\\\n#endif\\\\n\\\\\\\",fog_fragment:\\\\\\\"\\\\n#ifdef USE_FOG\\\\n\\\\n\\\\t#ifdef FOG_EXP2\\\\n\\\\n\\\\t\\\\tfloat fogFactor = 1.0 - exp( - fogDensity * fogDensity * vFogDepth * vFogDepth );\\\\n\\\\n\\\\t#else\\\\n\\\\n\\\\t\\\\tfloat fogFactor = smoothstep( fogNear, fogFar, vFogDepth );\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\tgl_FragColor.rgb = mix( gl_FragColor.rgb, fogColor, fogFactor );\\\\n\\\\n#endif\\\\n\\\\\\\",fog_pars_fragment:\\\\\\\"\\\\n#ifdef USE_FOG\\\\n\\\\n\\\\tuniform vec3 fogColor;\\\\n\\\\tvarying float vFogDepth;\\\\n\\\\n\\\\t#ifdef FOG_EXP2\\\\n\\\\n\\\\t\\\\tuniform float fogDensity;\\\\n\\\\n\\\\t#else\\\\n\\\\n\\\\t\\\\tuniform float fogNear;\\\\n\\\\t\\\\tuniform float fogFar;\\\\n\\\\n\\\\t#endif\\\\n\\\\n#endif\\\\n\\\\\\\",gradientmap_pars_fragment:\\\\\\\"\\\\n\\\\n#ifdef USE_GRADIENTMAP\\\\n\\\\n\\\\tuniform sampler2D gradientMap;\\\\n\\\\n#endif\\\\n\\\\nvec3 getGradientIrradiance( vec3 normal, vec3 lightDirection ) {\\\\n\\\\n\\\\t// dotNL will be from -1.0 to 1.0\\\\n\\\\tfloat dotNL = dot( normal, lightDirection );\\\\n\\\\tvec2 coord = vec2( dotNL * 0.5 + 0.5, 0.0 );\\\\n\\\\n\\\\t#ifdef USE_GRADIENTMAP\\\\n\\\\n\\\\t\\\\treturn texture2D( gradientMap, coord ).rgb;\\\\n\\\\n\\\\t#else\\\\n\\\\n\\\\t\\\\treturn ( coord.x < 0.7 ) ? vec3( 0.7 ) : vec3( 1.0 );\\\\n\\\\n\\\\t#endif\\\\n\\\\n}\\\\n\\\\\\\",lightmap_fragment:\\\\\\\"\\\\n#ifdef USE_LIGHTMAP\\\\n\\\\n\\\\tvec4 lightMapTexel = texture2D( lightMap, vUv2 );\\\\n\\\\tvec3 lightMapIrradiance = lightMapTexelToLinear( lightMapTexel ).rgb * lightMapIntensity;\\\\n\\\\n\\\\t#ifndef PHYSICALLY_CORRECT_LIGHTS\\\\n\\\\n\\\\t\\\\tlightMapIrradiance *= PI;\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\treflectedLight.indirectDiffuse += lightMapIrradiance;\\\\n\\\\n#endif\\\\n\\\\\\\",lightmap_pars_fragment:\\\\\\\"\\\\n#ifdef USE_LIGHTMAP\\\\n\\\\n\\\\tuniform sampler2D lightMap;\\\\n\\\\tuniform float lightMapIntensity;\\\\n\\\\n#endif\\\\n\\\\\\\",lights_lambert_vertex:\\\\\\\"\\\\nvec3 diffuse = vec3( 1.0 );\\\\n\\\\nGeometricContext geometry;\\\\ngeometry.position = mvPosition.xyz;\\\\ngeometry.normal = normalize( transformedNormal );\\\\ngeometry.viewDir = ( isOrthographic ) ? vec3( 0, 0, 1 ) : normalize( -mvPosition.xyz );\\\\n\\\\nGeometricContext backGeometry;\\\\nbackGeometry.position = geometry.position;\\\\nbackGeometry.normal = -geometry.normal;\\\\nbackGeometry.viewDir = geometry.viewDir;\\\\n\\\\nvLightFront = vec3( 0.0 );\\\\nvIndirectFront = vec3( 0.0 );\\\\n#ifdef DOUBLE_SIDED\\\\n\\\\tvLightBack = vec3( 0.0 );\\\\n\\\\tvIndirectBack = vec3( 0.0 );\\\\n#endif\\\\n\\\\nIncidentLight directLight;\\\\nfloat dotNL;\\\\nvec3 directLightColor_Diffuse;\\\\n\\\\nvIndirectFront += getAmbientLightIrradiance( ambientLightColor );\\\\n\\\\nvIndirectFront += getLightProbeIrradiance( lightProbe, geometry.normal );\\\\n\\\\n#ifdef DOUBLE_SIDED\\\\n\\\\n\\\\tvIndirectBack += getAmbientLightIrradiance( ambientLightColor );\\\\n\\\\n\\\\tvIndirectBack += getLightProbeIrradiance( lightProbe, backGeometry.normal );\\\\n\\\\n#endif\\\\n\\\\n#if NUM_POINT_LIGHTS > 0\\\\n\\\\n\\\\t#pragma unroll_loop_start\\\\n\\\\tfor ( int i = 0; i < NUM_POINT_LIGHTS; i ++ ) {\\\\n\\\\n\\\\t\\\\tgetPointLightInfo( pointLights[ i ], geometry, directLight );\\\\n\\\\n\\\\t\\\\tdotNL = dot( geometry.normal, directLight.direction );\\\\n\\\\t\\\\tdirectLightColor_Diffuse = directLight.color;\\\\n\\\\n\\\\t\\\\tvLightFront += saturate( dotNL ) * directLightColor_Diffuse;\\\\n\\\\n\\\\t\\\\t#ifdef DOUBLE_SIDED\\\\n\\\\n\\\\t\\\\t\\\\tvLightBack += saturate( - dotNL ) * directLightColor_Diffuse;\\\\n\\\\n\\\\t\\\\t#endif\\\\n\\\\n\\\\t}\\\\n\\\\t#pragma unroll_loop_end\\\\n\\\\n#endif\\\\n\\\\n#if NUM_SPOT_LIGHTS > 0\\\\n\\\\n\\\\t#pragma unroll_loop_start\\\\n\\\\tfor ( int i = 0; i < NUM_SPOT_LIGHTS; i ++ ) {\\\\n\\\\n\\\\t\\\\tgetSpotLightInfo( spotLights[ i ], geometry, directLight );\\\\n\\\\n\\\\t\\\\tdotNL = dot( geometry.normal, directLight.direction );\\\\n\\\\t\\\\tdirectLightColor_Diffuse = directLight.color;\\\\n\\\\n\\\\t\\\\tvLightFront += saturate( dotNL ) * directLightColor_Diffuse;\\\\n\\\\n\\\\t\\\\t#ifdef DOUBLE_SIDED\\\\n\\\\n\\\\t\\\\t\\\\tvLightBack += saturate( - dotNL ) * directLightColor_Diffuse;\\\\n\\\\n\\\\t\\\\t#endif\\\\n\\\\t}\\\\n\\\\t#pragma unroll_loop_end\\\\n\\\\n#endif\\\\n\\\\n#if NUM_DIR_LIGHTS > 0\\\\n\\\\n\\\\t#pragma unroll_loop_start\\\\n\\\\tfor ( int i = 0; i < NUM_DIR_LIGHTS; i ++ ) {\\\\n\\\\n\\\\t\\\\tgetDirectionalLightInfo( directionalLights[ i ], geometry, directLight );\\\\n\\\\n\\\\t\\\\tdotNL = dot( geometry.normal, directLight.direction );\\\\n\\\\t\\\\tdirectLightColor_Diffuse = directLight.color;\\\\n\\\\n\\\\t\\\\tvLightFront += saturate( dotNL ) * directLightColor_Diffuse;\\\\n\\\\n\\\\t\\\\t#ifdef DOUBLE_SIDED\\\\n\\\\n\\\\t\\\\t\\\\tvLightBack += saturate( - dotNL ) * directLightColor_Diffuse;\\\\n\\\\n\\\\t\\\\t#endif\\\\n\\\\n\\\\t}\\\\n\\\\t#pragma unroll_loop_end\\\\n\\\\n#endif\\\\n\\\\n#if NUM_HEMI_LIGHTS > 0\\\\n\\\\n\\\\t#pragma unroll_loop_start\\\\n\\\\tfor ( int i = 0; i < NUM_HEMI_LIGHTS; i ++ ) {\\\\n\\\\n\\\\t\\\\tvIndirectFront += getHemisphereLightIrradiance( hemisphereLights[ i ], geometry.normal );\\\\n\\\\n\\\\t\\\\t#ifdef DOUBLE_SIDED\\\\n\\\\n\\\\t\\\\t\\\\tvIndirectBack += getHemisphereLightIrradiance( hemisphereLights[ i ], backGeometry.normal );\\\\n\\\\n\\\\t\\\\t#endif\\\\n\\\\n\\\\t}\\\\n\\\\t#pragma unroll_loop_end\\\\n\\\\n#endif\\\\n\\\\\\\",lights_pars_begin:\\\\\\\"\\\\nuniform bool receiveShadow;\\\\nuniform vec3 ambientLightColor;\\\\nuniform vec3 lightProbe[ 9 ];\\\\n\\\\n// get the irradiance (radiance convolved with cosine lobe) at the point 'normal' on the unit sphere\\\\n// source: https://graphics.stanford.edu/papers/envmap/envmap.pdf\\\\nvec3 shGetIrradianceAt( in vec3 normal, in vec3 shCoefficients[ 9 ] ) {\\\\n\\\\n\\\\t// normal is assumed to have unit length\\\\n\\\\n\\\\tfloat x = normal.x, y = normal.y, z = normal.z;\\\\n\\\\n\\\\t// band 0\\\\n\\\\tvec3 result = shCoefficients[ 0 ] * 0.886227;\\\\n\\\\n\\\\t// band 1\\\\n\\\\tresult += shCoefficients[ 1 ] * 2.0 * 0.511664 * y;\\\\n\\\\tresult += shCoefficients[ 2 ] * 2.0 * 0.511664 * z;\\\\n\\\\tresult += shCoefficients[ 3 ] * 2.0 * 0.511664 * x;\\\\n\\\\n\\\\t// band 2\\\\n\\\\tresult += shCoefficients[ 4 ] * 2.0 * 0.429043 * x * y;\\\\n\\\\tresult += shCoefficients[ 5 ] * 2.0 * 0.429043 * y * z;\\\\n\\\\tresult += shCoefficients[ 6 ] * ( 0.743125 * z * z - 0.247708 );\\\\n\\\\tresult += shCoefficients[ 7 ] * 2.0 * 0.429043 * x * z;\\\\n\\\\tresult += shCoefficients[ 8 ] * 0.429043 * ( x * x - y * y );\\\\n\\\\n\\\\treturn result;\\\\n\\\\n}\\\\n\\\\nvec3 getLightProbeIrradiance( const in vec3 lightProbe[ 9 ], const in vec3 normal ) {\\\\n\\\\n\\\\tvec3 worldNormal = inverseTransformDirection( normal, viewMatrix );\\\\n\\\\n\\\\tvec3 irradiance = shGetIrradianceAt( worldNormal, lightProbe );\\\\n\\\\n\\\\treturn irradiance;\\\\n\\\\n}\\\\n\\\\nvec3 getAmbientLightIrradiance( const in vec3 ambientLightColor ) {\\\\n\\\\n\\\\tvec3 irradiance = ambientLightColor;\\\\n\\\\n\\\\treturn irradiance;\\\\n\\\\n}\\\\n\\\\nfloat getDistanceAttenuation( const in float lightDistance, const in float cutoffDistance, const in float decayExponent ) {\\\\n\\\\n\\\\t#if defined ( PHYSICALLY_CORRECT_LIGHTS )\\\\n\\\\n\\\\t\\\\t// based upon Frostbite 3 Moving to Physically-based Rendering\\\\n\\\\t\\\\t// page 32, equation 26: E[window1]\\\\n\\\\t\\\\t// https://seblagarde.files.wordpress.com/2015/07/course_notes_moving_frostbite_to_pbr_v32.pdf\\\\n\\\\t\\\\tfloat distanceFalloff = 1.0 / max( pow( lightDistance, decayExponent ), 0.01 );\\\\n\\\\n\\\\t\\\\tif ( cutoffDistance > 0.0 ) {\\\\n\\\\n\\\\t\\\\t\\\\tdistanceFalloff *= pow2( saturate( 1.0 - pow4( lightDistance / cutoffDistance ) ) );\\\\n\\\\n\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\treturn distanceFalloff;\\\\n\\\\n\\\\t#else\\\\n\\\\n\\\\t\\\\tif ( cutoffDistance > 0.0 && decayExponent > 0.0 ) {\\\\n\\\\n\\\\t\\\\t\\\\treturn pow( saturate( - lightDistance / cutoffDistance + 1.0 ), decayExponent );\\\\n\\\\n\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\treturn 1.0;\\\\n\\\\n\\\\t#endif\\\\n\\\\n}\\\\n\\\\nfloat getSpotAttenuation( const in float coneCosine, const in float penumbraCosine, const in float angleCosine ) {\\\\n\\\\n\\\\treturn smoothstep( coneCosine, penumbraCosine, angleCosine );\\\\n\\\\n}\\\\n\\\\n#if NUM_DIR_LIGHTS > 0\\\\n\\\\n\\\\tstruct DirectionalLight {\\\\n\\\\t\\\\tvec3 direction;\\\\n\\\\t\\\\tvec3 color;\\\\n\\\\t};\\\\n\\\\n\\\\tuniform DirectionalLight directionalLights[ NUM_DIR_LIGHTS ];\\\\n\\\\n\\\\tvoid getDirectionalLightInfo( const in DirectionalLight directionalLight, const in GeometricContext geometry, out IncidentLight light ) {\\\\n\\\\n\\\\t\\\\tlight.color = directionalLight.color;\\\\n\\\\t\\\\tlight.direction = directionalLight.direction;\\\\n\\\\t\\\\tlight.visible = true;\\\\n\\\\n\\\\t}\\\\n\\\\n#endif\\\\n\\\\n\\\\n#if NUM_POINT_LIGHTS > 0\\\\n\\\\n\\\\tstruct PointLight {\\\\n\\\\t\\\\tvec3 position;\\\\n\\\\t\\\\tvec3 color;\\\\n\\\\t\\\\tfloat distance;\\\\n\\\\t\\\\tfloat decay;\\\\n\\\\t};\\\\n\\\\n\\\\tuniform PointLight pointLights[ NUM_POINT_LIGHTS ];\\\\n\\\\n\\\\t// light is an out parameter as having it as a return value caused compiler errors on some devices\\\\n\\\\tvoid getPointLightInfo( const in PointLight pointLight, const in GeometricContext geometry, out IncidentLight light ) {\\\\n\\\\n\\\\t\\\\tvec3 lVector = pointLight.position - geometry.position;\\\\n\\\\n\\\\t\\\\tlight.direction = normalize( lVector );\\\\n\\\\n\\\\t\\\\tfloat lightDistance = length( lVector );\\\\n\\\\n\\\\t\\\\tlight.color = pointLight.color;\\\\n\\\\t\\\\tlight.color *= getDistanceAttenuation( lightDistance, pointLight.distance, pointLight.decay );\\\\n\\\\t\\\\tlight.visible = ( light.color != vec3( 0.0 ) );\\\\n\\\\n\\\\t}\\\\n\\\\n#endif\\\\n\\\\n\\\\n#if NUM_SPOT_LIGHTS > 0\\\\n\\\\n\\\\tstruct SpotLight {\\\\n\\\\t\\\\tvec3 position;\\\\n\\\\t\\\\tvec3 direction;\\\\n\\\\t\\\\tvec3 color;\\\\n\\\\t\\\\tfloat distance;\\\\n\\\\t\\\\tfloat decay;\\\\n\\\\t\\\\tfloat coneCos;\\\\n\\\\t\\\\tfloat penumbraCos;\\\\n\\\\t};\\\\n\\\\n\\\\tuniform SpotLight spotLights[ NUM_SPOT_LIGHTS ];\\\\n\\\\n\\\\t// light is an out parameter as having it as a return value caused compiler errors on some devices\\\\n\\\\tvoid getSpotLightInfo( const in SpotLight spotLight, const in GeometricContext geometry, out IncidentLight light ) {\\\\n\\\\n\\\\t\\\\tvec3 lVector = spotLight.position - geometry.position;\\\\n\\\\n\\\\t\\\\tlight.direction = normalize( lVector );\\\\n\\\\n\\\\t\\\\tfloat angleCos = dot( light.direction, spotLight.direction );\\\\n\\\\n\\\\t\\\\tfloat spotAttenuation = getSpotAttenuation( spotLight.coneCos, spotLight.penumbraCos, angleCos );\\\\n\\\\n\\\\t\\\\tif ( spotAttenuation > 0.0 ) {\\\\n\\\\n\\\\t\\\\t\\\\tfloat lightDistance = length( lVector );\\\\n\\\\n\\\\t\\\\t\\\\tlight.color = spotLight.color * spotAttenuation;\\\\n\\\\t\\\\t\\\\tlight.color *= getDistanceAttenuation( lightDistance, spotLight.distance, spotLight.decay );\\\\n\\\\t\\\\t\\\\tlight.visible = ( light.color != vec3( 0.0 ) );\\\\n\\\\n\\\\t\\\\t} else {\\\\n\\\\n\\\\t\\\\t\\\\tlight.color = vec3( 0.0 );\\\\n\\\\t\\\\t\\\\tlight.visible = false;\\\\n\\\\n\\\\t\\\\t}\\\\n\\\\n\\\\t}\\\\n\\\\n#endif\\\\n\\\\n\\\\n#if NUM_RECT_AREA_LIGHTS > 0\\\\n\\\\n\\\\tstruct RectAreaLight {\\\\n\\\\t\\\\tvec3 color;\\\\n\\\\t\\\\tvec3 position;\\\\n\\\\t\\\\tvec3 halfWidth;\\\\n\\\\t\\\\tvec3 halfHeight;\\\\n\\\\t};\\\\n\\\\n\\\\t// Pre-computed values of LinearTransformedCosine approximation of BRDF\\\\n\\\\t// BRDF approximation Texture is 64x64\\\\n\\\\tuniform sampler2D ltc_1; // RGBA Float\\\\n\\\\tuniform sampler2D ltc_2; // RGBA Float\\\\n\\\\n\\\\tuniform RectAreaLight rectAreaLights[ NUM_RECT_AREA_LIGHTS ];\\\\n\\\\n#endif\\\\n\\\\n\\\\n#if NUM_HEMI_LIGHTS > 0\\\\n\\\\n\\\\tstruct HemisphereLight {\\\\n\\\\t\\\\tvec3 direction;\\\\n\\\\t\\\\tvec3 skyColor;\\\\n\\\\t\\\\tvec3 groundColor;\\\\n\\\\t};\\\\n\\\\n\\\\tuniform HemisphereLight hemisphereLights[ NUM_HEMI_LIGHTS ];\\\\n\\\\n\\\\tvec3 getHemisphereLightIrradiance( const in HemisphereLight hemiLight, const in vec3 normal ) {\\\\n\\\\n\\\\t\\\\tfloat dotNL = dot( normal, hemiLight.direction );\\\\n\\\\t\\\\tfloat hemiDiffuseWeight = 0.5 * dotNL + 0.5;\\\\n\\\\n\\\\t\\\\tvec3 irradiance = mix( hemiLight.groundColor, hemiLight.skyColor, hemiDiffuseWeight );\\\\n\\\\n\\\\t\\\\treturn irradiance;\\\\n\\\\n\\\\t}\\\\n\\\\n#endif\\\\n\\\\\\\",lights_toon_fragment:\\\\\\\"\\\\nToonMaterial material;\\\\nmaterial.diffuseColor = diffuseColor.rgb;\\\\n\\\\\\\",lights_toon_pars_fragment:\\\\\\\"\\\\nvarying vec3 vViewPosition;\\\\n\\\\nstruct ToonMaterial {\\\\n\\\\n\\\\tvec3 diffuseColor;\\\\n\\\\n};\\\\n\\\\nvoid RE_Direct_Toon( const in IncidentLight directLight, const in GeometricContext geometry, const in ToonMaterial material, inout ReflectedLight reflectedLight ) {\\\\n\\\\n\\\\tvec3 irradiance = getGradientIrradiance( geometry.normal, directLight.direction ) * directLight.color;\\\\n\\\\n\\\\treflectedLight.directDiffuse += irradiance * BRDF_Lambert( material.diffuseColor );\\\\n\\\\n}\\\\n\\\\nvoid RE_IndirectDiffuse_Toon( const in vec3 irradiance, const in GeometricContext geometry, const in ToonMaterial material, inout ReflectedLight reflectedLight ) {\\\\n\\\\n\\\\treflectedLight.indirectDiffuse += irradiance * BRDF_Lambert( material.diffuseColor );\\\\n\\\\n}\\\\n\\\\n#define RE_Direct\\\\t\\\\t\\\\t\\\\tRE_Direct_Toon\\\\n#define RE_IndirectDiffuse\\\\t\\\\tRE_IndirectDiffuse_Toon\\\\n\\\\n#define Material_LightProbeLOD( material )\\\\t(0)\\\\n\\\\\\\",lights_phong_fragment:\\\\\\\"\\\\nBlinnPhongMaterial material;\\\\nmaterial.diffuseColor = diffuseColor.rgb;\\\\nmaterial.specularColor = specular;\\\\nmaterial.specularShininess = shininess;\\\\nmaterial.specularStrength = specularStrength;\\\\n\\\\\\\",lights_phong_pars_fragment:\\\\\\\"\\\\nvarying vec3 vViewPosition;\\\\n\\\\nstruct BlinnPhongMaterial {\\\\n\\\\n\\\\tvec3 diffuseColor;\\\\n\\\\tvec3 specularColor;\\\\n\\\\tfloat specularShininess;\\\\n\\\\tfloat specularStrength;\\\\n\\\\n};\\\\n\\\\nvoid RE_Direct_BlinnPhong( const in IncidentLight directLight, const in GeometricContext geometry, const in BlinnPhongMaterial material, inout ReflectedLight reflectedLight ) {\\\\n\\\\n\\\\tfloat dotNL = saturate( dot( geometry.normal, directLight.direction ) );\\\\n\\\\tvec3 irradiance = dotNL * directLight.color;\\\\n\\\\n\\\\treflectedLight.directDiffuse += irradiance * BRDF_Lambert( material.diffuseColor );\\\\n\\\\n\\\\treflectedLight.directSpecular += irradiance * BRDF_BlinnPhong( directLight.direction, geometry.viewDir, geometry.normal, material.specularColor, material.specularShininess ) * material.specularStrength;\\\\n\\\\n}\\\\n\\\\nvoid RE_IndirectDiffuse_BlinnPhong( const in vec3 irradiance, const in GeometricContext geometry, const in BlinnPhongMaterial material, inout ReflectedLight reflectedLight ) {\\\\n\\\\n\\\\treflectedLight.indirectDiffuse += irradiance * BRDF_Lambert( material.diffuseColor );\\\\n\\\\n}\\\\n\\\\n#define RE_Direct\\\\t\\\\t\\\\t\\\\tRE_Direct_BlinnPhong\\\\n#define RE_IndirectDiffuse\\\\t\\\\tRE_IndirectDiffuse_BlinnPhong\\\\n\\\\n#define Material_LightProbeLOD( material )\\\\t(0)\\\\n\\\\\\\",lights_physical_fragment:\\\\\\\"\\\\nPhysicalMaterial material;\\\\nmaterial.diffuseColor = diffuseColor.rgb * ( 1.0 - metalnessFactor );\\\\n\\\\nvec3 dxy = max( abs( dFdx( geometryNormal ) ), abs( dFdy( geometryNormal ) ) );\\\\nfloat geometryRoughness = max( max( dxy.x, dxy.y ), dxy.z );\\\\n\\\\nmaterial.roughness = max( roughnessFactor, 0.0525 );// 0.0525 corresponds to the base mip of a 256 cubemap.\\\\nmaterial.roughness += geometryRoughness;\\\\nmaterial.roughness = min( material.roughness, 1.0 );\\\\n\\\\n#ifdef IOR\\\\n\\\\n\\\\t#ifdef SPECULAR\\\\n\\\\n\\\\t\\\\tfloat specularIntensityFactor = specularIntensity;\\\\n\\\\t\\\\tvec3 specularTintFactor = specularTint;\\\\n\\\\n\\\\t\\\\t#ifdef USE_SPECULARINTENSITYMAP\\\\n\\\\n\\\\t\\\\t\\\\tspecularIntensityFactor *= texture2D( specularIntensityMap, vUv ).a;\\\\n\\\\n\\\\t\\\\t#endif\\\\n\\\\n\\\\t\\\\t#ifdef USE_SPECULARTINTMAP\\\\n\\\\n\\\\t\\\\t\\\\tspecularTintFactor *= specularTintMapTexelToLinear( texture2D( specularTintMap, vUv ) ).rgb;\\\\n\\\\n\\\\t\\\\t#endif\\\\n\\\\n\\\\t\\\\tmaterial.specularF90 = mix( specularIntensityFactor, 1.0, metalnessFactor );\\\\n\\\\n\\\\t#else\\\\n\\\\n\\\\t\\\\tfloat specularIntensityFactor = 1.0;\\\\n\\\\t\\\\tvec3 specularTintFactor = vec3( 1.0 );\\\\n\\\\t\\\\tmaterial.specularF90 = 1.0;\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\tmaterial.specularColor = mix( min( pow2( ( ior - 1.0 ) / ( ior + 1.0 ) ) * specularTintFactor, vec3( 1.0 ) ) * specularIntensityFactor, diffuseColor.rgb, metalnessFactor );\\\\n\\\\n#else\\\\n\\\\n\\\\tmaterial.specularColor = mix( vec3( 0.04 ), diffuseColor.rgb, metalnessFactor );\\\\n\\\\tmaterial.specularF90 = 1.0;\\\\n\\\\n#endif\\\\n\\\\n#ifdef USE_CLEARCOAT\\\\n\\\\n\\\\tmaterial.clearcoat = clearcoat;\\\\n\\\\tmaterial.clearcoatRoughness = clearcoatRoughness;\\\\n\\\\tmaterial.clearcoatF0 = vec3( 0.04 );\\\\n\\\\tmaterial.clearcoatF90 = 1.0;\\\\n\\\\n\\\\t#ifdef USE_CLEARCOATMAP\\\\n\\\\n\\\\t\\\\tmaterial.clearcoat *= texture2D( clearcoatMap, vUv ).x;\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\t#ifdef USE_CLEARCOAT_ROUGHNESSMAP\\\\n\\\\n\\\\t\\\\tmaterial.clearcoatRoughness *= texture2D( clearcoatRoughnessMap, vUv ).y;\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\tmaterial.clearcoat = saturate( material.clearcoat ); // Burley clearcoat model\\\\n\\\\tmaterial.clearcoatRoughness = max( material.clearcoatRoughness, 0.0525 );\\\\n\\\\tmaterial.clearcoatRoughness += geometryRoughness;\\\\n\\\\tmaterial.clearcoatRoughness = min( material.clearcoatRoughness, 1.0 );\\\\n\\\\n#endif\\\\n\\\\n#ifdef USE_SHEEN\\\\n\\\\n\\\\tmaterial.sheenTint = sheenTint;\\\\n\\\\tmaterial.sheenRoughness = clamp( sheenRoughness, 0.07, 1.0 );\\\\n\\\\n#endif\\\\n\\\\\\\",lights_physical_pars_fragment:'\\\\nstruct PhysicalMaterial {\\\\n\\\\n\\\\tvec3 diffuseColor;\\\\n\\\\tfloat roughness;\\\\n\\\\tvec3 specularColor;\\\\n\\\\tfloat specularF90;\\\\n\\\\n\\\\t#ifdef USE_CLEARCOAT\\\\n\\\\t\\\\tfloat clearcoat;\\\\n\\\\t\\\\tfloat clearcoatRoughness;\\\\n\\\\t\\\\tvec3 clearcoatF0;\\\\n\\\\t\\\\tfloat clearcoatF90;\\\\n\\\\t#endif\\\\n\\\\n\\\\t#ifdef USE_SHEEN\\\\n\\\\t\\\\tvec3 sheenTint;\\\\n\\\\t\\\\tfloat sheenRoughness;\\\\n\\\\t#endif\\\\n\\\\n};\\\\n\\\\n// temporary\\\\nvec3 clearcoatSpecular = vec3( 0.0 );\\\\n\\\\n// Analytical approximation of the DFG LUT, one half of the\\\\n// split-sum approximation used in indirect specular lighting.\\\\n// via \\\\'environmentBRDF\\\\' from \\\\\\\"Physically Based Shading on Mobile\\\\\\\"\\\\n// https://www.unrealengine.com/blog/physically-based-shading-on-mobile\\\\nvec2 DFGApprox( const in vec3 normal, const in vec3 viewDir, const in float roughness ) {\\\\n\\\\n\\\\tfloat dotNV = saturate( dot( normal, viewDir ) );\\\\n\\\\n\\\\tconst vec4 c0 = vec4( - 1, - 0.0275, - 0.572, 0.022 );\\\\n\\\\n\\\\tconst vec4 c1 = vec4( 1, 0.0425, 1.04, - 0.04 );\\\\n\\\\n\\\\tvec4 r = roughness * c0 + c1;\\\\n\\\\n\\\\tfloat a004 = min( r.x * r.x, exp2( - 9.28 * dotNV ) ) * r.x + r.y;\\\\n\\\\n\\\\tvec2 fab = vec2( - 1.04, 1.04 ) * a004 + r.zw;\\\\n\\\\n\\\\treturn fab;\\\\n\\\\n}\\\\n\\\\nvec3 EnvironmentBRDF( const in vec3 normal, const in vec3 viewDir, const in vec3 specularColor, const in float specularF90, const in float roughness ) {\\\\n\\\\n\\\\tvec2 fab = DFGApprox( normal, viewDir, roughness );\\\\n\\\\n\\\\treturn specularColor * fab.x + specularF90 * fab.y;\\\\n\\\\n}\\\\n\\\\n// Fdez-Agüera\\\\'s \\\\\\\"Multiple-Scattering Microfacet Model for Real-Time Image Based Lighting\\\\\\\"\\\\n// Approximates multiscattering in order to preserve energy.\\\\n// http://www.jcgt.org/published/0008/01/03/\\\\nvoid computeMultiscattering( const in vec3 normal, const in vec3 viewDir, const in vec3 specularColor, const in float specularF90, const in float roughness, inout vec3 singleScatter, inout vec3 multiScatter ) {\\\\n\\\\n\\\\tvec2 fab = DFGApprox( normal, viewDir, roughness );\\\\n\\\\n\\\\tvec3 FssEss = specularColor * fab.x + specularF90 * fab.y;\\\\n\\\\n\\\\tfloat Ess = fab.x + fab.y;\\\\n\\\\tfloat Ems = 1.0 - Ess;\\\\n\\\\n\\\\tvec3 Favg = specularColor + ( 1.0 - specularColor ) * 0.047619; // 1/21\\\\n\\\\tvec3 Fms = FssEss * Favg / ( 1.0 - Ems * Favg );\\\\n\\\\n\\\\tsingleScatter += FssEss;\\\\n\\\\tmultiScatter += Fms * Ems;\\\\n\\\\n}\\\\n\\\\n#if NUM_RECT_AREA_LIGHTS > 0\\\\n\\\\n\\\\tvoid RE_Direct_RectArea_Physical( const in RectAreaLight rectAreaLight, const in GeometricContext geometry, const in PhysicalMaterial material, inout ReflectedLight reflectedLight ) {\\\\n\\\\n\\\\t\\\\tvec3 normal = geometry.normal;\\\\n\\\\t\\\\tvec3 viewDir = geometry.viewDir;\\\\n\\\\t\\\\tvec3 position = geometry.position;\\\\n\\\\t\\\\tvec3 lightPos = rectAreaLight.position;\\\\n\\\\t\\\\tvec3 halfWidth = rectAreaLight.halfWidth;\\\\n\\\\t\\\\tvec3 halfHeight = rectAreaLight.halfHeight;\\\\n\\\\t\\\\tvec3 lightColor = rectAreaLight.color;\\\\n\\\\t\\\\tfloat roughness = material.roughness;\\\\n\\\\n\\\\t\\\\tvec3 rectCoords[ 4 ];\\\\n\\\\t\\\\trectCoords[ 0 ] = lightPos + halfWidth - halfHeight; // counterclockwise; light shines in local neg z direction\\\\n\\\\t\\\\trectCoords[ 1 ] = lightPos - halfWidth - halfHeight;\\\\n\\\\t\\\\trectCoords[ 2 ] = lightPos - halfWidth + halfHeight;\\\\n\\\\t\\\\trectCoords[ 3 ] = lightPos + halfWidth + halfHeight;\\\\n\\\\n\\\\t\\\\tvec2 uv = LTC_Uv( normal, viewDir, roughness );\\\\n\\\\n\\\\t\\\\tvec4 t1 = texture2D( ltc_1, uv );\\\\n\\\\t\\\\tvec4 t2 = texture2D( ltc_2, uv );\\\\n\\\\n\\\\t\\\\tmat3 mInv = mat3(\\\\n\\\\t\\\\t\\\\tvec3( t1.x, 0, t1.y ),\\\\n\\\\t\\\\t\\\\tvec3(    0, 1,    0 ),\\\\n\\\\t\\\\t\\\\tvec3( t1.z, 0, t1.w )\\\\n\\\\t\\\\t);\\\\n\\\\n\\\\t\\\\t// LTC Fresnel Approximation by Stephen Hill\\\\n\\\\t\\\\t// http://blog.selfshadow.com/publications/s2016-advances/s2016_ltc_fresnel.pdf\\\\n\\\\t\\\\tvec3 fresnel = ( material.specularColor * t2.x + ( vec3( 1.0 ) - material.specularColor ) * t2.y );\\\\n\\\\n\\\\t\\\\treflectedLight.directSpecular += lightColor * fresnel * LTC_Evaluate( normal, viewDir, position, mInv, rectCoords );\\\\n\\\\n\\\\t\\\\treflectedLight.directDiffuse += lightColor * material.diffuseColor * LTC_Evaluate( normal, viewDir, position, mat3( 1.0 ), rectCoords );\\\\n\\\\n\\\\t}\\\\n\\\\n#endif\\\\n\\\\nvoid RE_Direct_Physical( const in IncidentLight directLight, const in GeometricContext geometry, const in PhysicalMaterial material, inout ReflectedLight reflectedLight ) {\\\\n\\\\n\\\\tfloat dotNL = saturate( dot( geometry.normal, directLight.direction ) );\\\\n\\\\n\\\\tvec3 irradiance = dotNL * directLight.color;\\\\n\\\\n\\\\t#ifdef USE_CLEARCOAT\\\\n\\\\n\\\\t\\\\tfloat dotNLcc = saturate( dot( geometry.clearcoatNormal, directLight.direction ) );\\\\n\\\\n\\\\t\\\\tvec3 ccIrradiance = dotNLcc * directLight.color;\\\\n\\\\n\\\\t\\\\tclearcoatSpecular += ccIrradiance * BRDF_GGX( directLight.direction, geometry.viewDir, geometry.clearcoatNormal, material.clearcoatF0, material.clearcoatF90, material.clearcoatRoughness );\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\t#ifdef USE_SHEEN\\\\n\\\\n\\\\t\\\\treflectedLight.directSpecular += irradiance * BRDF_Sheen( directLight.direction, geometry.viewDir, geometry.normal, material.sheenTint, material.sheenRoughness );\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\treflectedLight.directSpecular += irradiance * BRDF_GGX( directLight.direction, geometry.viewDir, geometry.normal, material.specularColor, material.specularF90, material.roughness );\\\\n\\\\n\\\\n\\\\treflectedLight.directDiffuse += irradiance * BRDF_Lambert( material.diffuseColor );\\\\n}\\\\n\\\\nvoid RE_IndirectDiffuse_Physical( const in vec3 irradiance, const in GeometricContext geometry, const in PhysicalMaterial material, inout ReflectedLight reflectedLight ) {\\\\n\\\\n\\\\treflectedLight.indirectDiffuse += irradiance * BRDF_Lambert( material.diffuseColor );\\\\n\\\\n}\\\\n\\\\nvoid RE_IndirectSpecular_Physical( const in vec3 radiance, const in vec3 irradiance, const in vec3 clearcoatRadiance, const in GeometricContext geometry, const in PhysicalMaterial material, inout ReflectedLight reflectedLight) {\\\\n\\\\n\\\\t#ifdef USE_CLEARCOAT\\\\n\\\\n\\\\t\\\\tclearcoatSpecular += clearcoatRadiance * EnvironmentBRDF( geometry.clearcoatNormal, geometry.viewDir, material.clearcoatF0, material.clearcoatF90, material.clearcoatRoughness );\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\t// Both indirect specular and indirect diffuse light accumulate here\\\\n\\\\n\\\\tvec3 singleScattering = vec3( 0.0 );\\\\n\\\\tvec3 multiScattering = vec3( 0.0 );\\\\n\\\\tvec3 cosineWeightedIrradiance = irradiance * RECIPROCAL_PI;\\\\n\\\\n\\\\tcomputeMultiscattering( geometry.normal, geometry.viewDir, material.specularColor, material.specularF90, material.roughness, singleScattering, multiScattering );\\\\n\\\\n\\\\tvec3 diffuse = material.diffuseColor * ( 1.0 - ( singleScattering + multiScattering ) );\\\\n\\\\n\\\\treflectedLight.indirectSpecular += radiance * singleScattering;\\\\n\\\\treflectedLight.indirectSpecular += multiScattering * cosineWeightedIrradiance;\\\\n\\\\n\\\\treflectedLight.indirectDiffuse += diffuse * cosineWeightedIrradiance;\\\\n\\\\n}\\\\n\\\\n#define RE_Direct\\\\t\\\\t\\\\t\\\\tRE_Direct_Physical\\\\n#define RE_Direct_RectArea\\\\t\\\\tRE_Direct_RectArea_Physical\\\\n#define RE_IndirectDiffuse\\\\t\\\\tRE_IndirectDiffuse_Physical\\\\n#define RE_IndirectSpecular\\\\t\\\\tRE_IndirectSpecular_Physical\\\\n\\\\n// ref: https://seblagarde.files.wordpress.com/2015/07/course_notes_moving_frostbite_to_pbr_v32.pdf\\\\nfloat computeSpecularOcclusion( const in float dotNV, const in float ambientOcclusion, const in float roughness ) {\\\\n\\\\n\\\\treturn saturate( pow( dotNV + ambientOcclusion, exp2( - 16.0 * roughness - 1.0 ) ) - 1.0 + ambientOcclusion );\\\\n\\\\n}\\\\n',lights_fragment_begin:\\\\\\\"\\\\n/**\\\\n * This is a template that can be used to light a material, it uses pluggable\\\\n * RenderEquations (RE)for specific lighting scenarios.\\\\n *\\\\n * Instructions for use:\\\\n * - Ensure that both RE_Direct, RE_IndirectDiffuse and RE_IndirectSpecular are defined\\\\n * - If you have defined an RE_IndirectSpecular, you need to also provide a Material_LightProbeLOD. <---- ???\\\\n * - Create a material parameter that is to be passed as the third parameter to your lighting functions.\\\\n *\\\\n * TODO:\\\\n * - Add area light support.\\\\n * - Add sphere light support.\\\\n * - Add diffuse light probe (irradiance cubemap) support.\\\\n */\\\\n\\\\nGeometricContext geometry;\\\\n\\\\ngeometry.position = - vViewPosition;\\\\ngeometry.normal = normal;\\\\ngeometry.viewDir = ( isOrthographic ) ? vec3( 0, 0, 1 ) : normalize( vViewPosition );\\\\n\\\\n#ifdef USE_CLEARCOAT\\\\n\\\\n\\\\tgeometry.clearcoatNormal = clearcoatNormal;\\\\n\\\\n#endif\\\\n\\\\nIncidentLight directLight;\\\\n\\\\n#if ( NUM_POINT_LIGHTS > 0 ) && defined( RE_Direct )\\\\n\\\\n\\\\tPointLight pointLight;\\\\n\\\\t#if defined( USE_SHADOWMAP ) && NUM_POINT_LIGHT_SHADOWS > 0\\\\n\\\\tPointLightShadow pointLightShadow;\\\\n\\\\t#endif\\\\n\\\\n\\\\t#pragma unroll_loop_start\\\\n\\\\tfor ( int i = 0; i < NUM_POINT_LIGHTS; i ++ ) {\\\\n\\\\n\\\\t\\\\tpointLight = pointLights[ i ];\\\\n\\\\n\\\\t\\\\tgetPointLightInfo( pointLight, geometry, directLight );\\\\n\\\\n\\\\t\\\\t#if defined( USE_SHADOWMAP ) && ( UNROLLED_LOOP_INDEX < NUM_POINT_LIGHT_SHADOWS )\\\\n\\\\t\\\\tpointLightShadow = pointLightShadows[ i ];\\\\n\\\\t\\\\tdirectLight.color *= all( bvec2( directLight.visible, receiveShadow ) ) ? getPointShadow( pointShadowMap[ i ], pointLightShadow.shadowMapSize, pointLightShadow.shadowBias, pointLightShadow.shadowRadius, vPointShadowCoord[ i ], pointLightShadow.shadowCameraNear, pointLightShadow.shadowCameraFar ) : 1.0;\\\\n\\\\t\\\\t#endif\\\\n\\\\n\\\\t\\\\tRE_Direct( directLight, geometry, material, reflectedLight );\\\\n\\\\n\\\\t}\\\\n\\\\t#pragma unroll_loop_end\\\\n\\\\n#endif\\\\n\\\\n#if ( NUM_SPOT_LIGHTS > 0 ) && defined( RE_Direct )\\\\n\\\\n\\\\tSpotLight spotLight;\\\\n\\\\t#if defined( USE_SHADOWMAP ) && NUM_SPOT_LIGHT_SHADOWS > 0\\\\n\\\\tSpotLightShadow spotLightShadow;\\\\n\\\\t#endif\\\\n\\\\n\\\\t#pragma unroll_loop_start\\\\n\\\\tfor ( int i = 0; i < NUM_SPOT_LIGHTS; i ++ ) {\\\\n\\\\n\\\\t\\\\tspotLight = spotLights[ i ];\\\\n\\\\n\\\\t\\\\tgetSpotLightInfo( spotLight, geometry, directLight );\\\\n\\\\n\\\\t\\\\t#if defined( USE_SHADOWMAP ) && ( UNROLLED_LOOP_INDEX < NUM_SPOT_LIGHT_SHADOWS )\\\\n\\\\t\\\\tspotLightShadow = spotLightShadows[ i ];\\\\n\\\\t\\\\tdirectLight.color *= all( bvec2( directLight.visible, receiveShadow ) ) ? getShadow( spotShadowMap[ i ], spotLightShadow.shadowMapSize, spotLightShadow.shadowBias, spotLightShadow.shadowRadius, vSpotShadowCoord[ i ] ) : 1.0;\\\\n\\\\t\\\\t#endif\\\\n\\\\n\\\\t\\\\tRE_Direct( directLight, geometry, material, reflectedLight );\\\\n\\\\n\\\\t}\\\\n\\\\t#pragma unroll_loop_end\\\\n\\\\n#endif\\\\n\\\\n#if ( NUM_DIR_LIGHTS > 0 ) && defined( RE_Direct )\\\\n\\\\n\\\\tDirectionalLight directionalLight;\\\\n\\\\t#if defined( USE_SHADOWMAP ) && NUM_DIR_LIGHT_SHADOWS > 0\\\\n\\\\tDirectionalLightShadow directionalLightShadow;\\\\n\\\\t#endif\\\\n\\\\n\\\\t#pragma unroll_loop_start\\\\n\\\\tfor ( int i = 0; i < NUM_DIR_LIGHTS; i ++ ) {\\\\n\\\\n\\\\t\\\\tdirectionalLight = directionalLights[ i ];\\\\n\\\\n\\\\t\\\\tgetDirectionalLightInfo( directionalLight, geometry, directLight );\\\\n\\\\n\\\\t\\\\t#if defined( USE_SHADOWMAP ) && ( UNROLLED_LOOP_INDEX < NUM_DIR_LIGHT_SHADOWS )\\\\n\\\\t\\\\tdirectionalLightShadow = directionalLightShadows[ i ];\\\\n\\\\t\\\\tdirectLight.color *= all( bvec2( directLight.visible, receiveShadow ) ) ? getShadow( directionalShadowMap[ i ], directionalLightShadow.shadowMapSize, directionalLightShadow.shadowBias, directionalLightShadow.shadowRadius, vDirectionalShadowCoord[ i ] ) : 1.0;\\\\n\\\\t\\\\t#endif\\\\n\\\\n\\\\t\\\\tRE_Direct( directLight, geometry, material, reflectedLight );\\\\n\\\\n\\\\t}\\\\n\\\\t#pragma unroll_loop_end\\\\n\\\\n#endif\\\\n\\\\n#if ( NUM_RECT_AREA_LIGHTS > 0 ) && defined( RE_Direct_RectArea )\\\\n\\\\n\\\\tRectAreaLight rectAreaLight;\\\\n\\\\n\\\\t#pragma unroll_loop_start\\\\n\\\\tfor ( int i = 0; i < NUM_RECT_AREA_LIGHTS; i ++ ) {\\\\n\\\\n\\\\t\\\\trectAreaLight = rectAreaLights[ i ];\\\\n\\\\t\\\\tRE_Direct_RectArea( rectAreaLight, geometry, material, reflectedLight );\\\\n\\\\n\\\\t}\\\\n\\\\t#pragma unroll_loop_end\\\\n\\\\n#endif\\\\n\\\\n#if defined( RE_IndirectDiffuse )\\\\n\\\\n\\\\tvec3 iblIrradiance = vec3( 0.0 );\\\\n\\\\n\\\\tvec3 irradiance = getAmbientLightIrradiance( ambientLightColor );\\\\n\\\\n\\\\tirradiance += getLightProbeIrradiance( lightProbe, geometry.normal );\\\\n\\\\n\\\\t#if ( NUM_HEMI_LIGHTS > 0 )\\\\n\\\\n\\\\t\\\\t#pragma unroll_loop_start\\\\n\\\\t\\\\tfor ( int i = 0; i < NUM_HEMI_LIGHTS; i ++ ) {\\\\n\\\\n\\\\t\\\\t\\\\tirradiance += getHemisphereLightIrradiance( hemisphereLights[ i ], geometry.normal );\\\\n\\\\n\\\\t\\\\t}\\\\n\\\\t\\\\t#pragma unroll_loop_end\\\\n\\\\n\\\\t#endif\\\\n\\\\n#endif\\\\n\\\\n#if defined( RE_IndirectSpecular )\\\\n\\\\n\\\\tvec3 radiance = vec3( 0.0 );\\\\n\\\\tvec3 clearcoatRadiance = vec3( 0.0 );\\\\n\\\\n#endif\\\\n\\\\\\\",lights_fragment_maps:\\\\\\\"\\\\n#if defined( RE_IndirectDiffuse )\\\\n\\\\n\\\\t#ifdef USE_LIGHTMAP\\\\n\\\\n\\\\t\\\\tvec4 lightMapTexel = texture2D( lightMap, vUv2 );\\\\n\\\\t\\\\tvec3 lightMapIrradiance = lightMapTexelToLinear( lightMapTexel ).rgb * lightMapIntensity;\\\\n\\\\n\\\\t\\\\t#ifndef PHYSICALLY_CORRECT_LIGHTS\\\\n\\\\n\\\\t\\\\t\\\\tlightMapIrradiance *= PI;\\\\n\\\\n\\\\t\\\\t#endif\\\\n\\\\n\\\\t\\\\tirradiance += lightMapIrradiance;\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\t#if defined( USE_ENVMAP ) && defined( STANDARD ) && defined( ENVMAP_TYPE_CUBE_UV )\\\\n\\\\n\\\\t\\\\tiblIrradiance += getIBLIrradiance( geometry.normal );\\\\n\\\\n\\\\t#endif\\\\n\\\\n#endif\\\\n\\\\n#if defined( USE_ENVMAP ) && defined( RE_IndirectSpecular )\\\\n\\\\n\\\\tradiance += getIBLRadiance( geometry.viewDir, geometry.normal, material.roughness );\\\\n\\\\n\\\\t#ifdef USE_CLEARCOAT\\\\n\\\\n\\\\t\\\\tclearcoatRadiance += getIBLRadiance( geometry.viewDir, geometry.clearcoatNormal, material.clearcoatRoughness );\\\\n\\\\n\\\\t#endif\\\\n\\\\n#endif\\\\n\\\\\\\",lights_fragment_end:\\\\\\\"\\\\n#if defined( RE_IndirectDiffuse )\\\\n\\\\n\\\\tRE_IndirectDiffuse( irradiance, geometry, material, reflectedLight );\\\\n\\\\n#endif\\\\n\\\\n#if defined( RE_IndirectSpecular )\\\\n\\\\n\\\\tRE_IndirectSpecular( radiance, iblIrradiance, clearcoatRadiance, geometry, material, reflectedLight );\\\\n\\\\n#endif\\\\n\\\\\\\",logdepthbuf_fragment:\\\\\\\"\\\\n#if defined( USE_LOGDEPTHBUF ) && defined( USE_LOGDEPTHBUF_EXT )\\\\n\\\\n\\\\t// Doing a strict comparison with == 1.0 can cause noise artifacts\\\\n\\\\t// on some platforms. See issue #17623.\\\\n\\\\tgl_FragDepthEXT = vIsPerspective == 0.0 ? gl_FragCoord.z : log2( vFragDepth ) * logDepthBufFC * 0.5;\\\\n\\\\n#endif\\\\n\\\\\\\",logdepthbuf_pars_fragment:\\\\\\\"\\\\n#if defined( USE_LOGDEPTHBUF ) && defined( USE_LOGDEPTHBUF_EXT )\\\\n\\\\n\\\\tuniform float logDepthBufFC;\\\\n\\\\tvarying float vFragDepth;\\\\n\\\\tvarying float vIsPerspective;\\\\n\\\\n#endif\\\\n\\\\\\\",logdepthbuf_pars_vertex:\\\\\\\"\\\\n#ifdef USE_LOGDEPTHBUF\\\\n\\\\n\\\\t#ifdef USE_LOGDEPTHBUF_EXT\\\\n\\\\n\\\\t\\\\tvarying float vFragDepth;\\\\n\\\\t\\\\tvarying float vIsPerspective;\\\\n\\\\n\\\\t#else\\\\n\\\\n\\\\t\\\\tuniform float logDepthBufFC;\\\\n\\\\n\\\\t#endif\\\\n\\\\n#endif\\\\n\\\\\\\",logdepthbuf_vertex:\\\\\\\"\\\\n#ifdef USE_LOGDEPTHBUF\\\\n\\\\n\\\\t#ifdef USE_LOGDEPTHBUF_EXT\\\\n\\\\n\\\\t\\\\tvFragDepth = 1.0 + gl_Position.w;\\\\n\\\\t\\\\tvIsPerspective = float( isPerspectiveMatrix( projectionMatrix ) );\\\\n\\\\n\\\\t#else\\\\n\\\\n\\\\t\\\\tif ( isPerspectiveMatrix( projectionMatrix ) ) {\\\\n\\\\n\\\\t\\\\t\\\\tgl_Position.z = log2( max( EPSILON, gl_Position.w + 1.0 ) ) * logDepthBufFC - 1.0;\\\\n\\\\n\\\\t\\\\t\\\\tgl_Position.z *= gl_Position.w;\\\\n\\\\n\\\\t\\\\t}\\\\n\\\\n\\\\t#endif\\\\n\\\\n#endif\\\\n\\\\\\\",map_fragment:\\\\\\\"\\\\n#ifdef USE_MAP\\\\n\\\\n\\\\tvec4 texelColor = texture2D( map, vUv );\\\\n\\\\n\\\\ttexelColor = mapTexelToLinear( texelColor );\\\\n\\\\tdiffuseColor *= texelColor;\\\\n\\\\n#endif\\\\n\\\\\\\",map_pars_fragment:\\\\\\\"\\\\n#ifdef USE_MAP\\\\n\\\\n\\\\tuniform sampler2D map;\\\\n\\\\n#endif\\\\n\\\\\\\",map_particle_fragment:\\\\\\\"\\\\n#if defined( USE_MAP ) || defined( USE_ALPHAMAP )\\\\n\\\\n\\\\tvec2 uv = ( uvTransform * vec3( gl_PointCoord.x, 1.0 - gl_PointCoord.y, 1 ) ).xy;\\\\n\\\\n#endif\\\\n\\\\n#ifdef USE_MAP\\\\n\\\\n\\\\tvec4 mapTexel = texture2D( map, uv );\\\\n\\\\tdiffuseColor *= mapTexelToLinear( mapTexel );\\\\n\\\\n#endif\\\\n\\\\n#ifdef USE_ALPHAMAP\\\\n\\\\n\\\\tdiffuseColor.a *= texture2D( alphaMap, uv ).g;\\\\n\\\\n#endif\\\\n\\\\\\\",map_particle_pars_fragment:\\\\\\\"\\\\n#if defined( USE_MAP ) || defined( USE_ALPHAMAP )\\\\n\\\\n\\\\tuniform mat3 uvTransform;\\\\n\\\\n#endif\\\\n\\\\n#ifdef USE_MAP\\\\n\\\\n\\\\tuniform sampler2D map;\\\\n\\\\n#endif\\\\n\\\\n#ifdef USE_ALPHAMAP\\\\n\\\\n\\\\tuniform sampler2D alphaMap;\\\\n\\\\n#endif\\\\n\\\\\\\",metalnessmap_fragment:\\\\\\\"\\\\nfloat metalnessFactor = metalness;\\\\n\\\\n#ifdef USE_METALNESSMAP\\\\n\\\\n\\\\tvec4 texelMetalness = texture2D( metalnessMap, vUv );\\\\n\\\\n\\\\t// reads channel B, compatible with a combined OcclusionRoughnessMetallic (RGB) texture\\\\n\\\\tmetalnessFactor *= texelMetalness.b;\\\\n\\\\n#endif\\\\n\\\\\\\",metalnessmap_pars_fragment:\\\\\\\"\\\\n#ifdef USE_METALNESSMAP\\\\n\\\\n\\\\tuniform sampler2D metalnessMap;\\\\n\\\\n#endif\\\\n\\\\\\\",morphnormal_vertex:\\\\\\\"\\\\n#ifdef USE_MORPHNORMALS\\\\n\\\\n\\\\t// morphTargetBaseInfluence is set based on BufferGeometry.morphTargetsRelative value:\\\\n\\\\t// When morphTargetsRelative is false, this is set to 1 - sum(influences); this results in normal = sum((target - base) * influence)\\\\n\\\\t// When morphTargetsRelative is true, this is set to 1; as a result, all morph targets are simply added to the base after weighting\\\\n\\\\tobjectNormal *= morphTargetBaseInfluence;\\\\n\\\\n\\\\t#ifdef MORPHTARGETS_TEXTURE\\\\n\\\\n\\\\t\\\\tfor ( int i = 0; i < MORPHTARGETS_COUNT; i ++ ) {\\\\n\\\\n\\\\t\\\\t\\\\tif ( morphTargetInfluences[ i ] > 0.0 ) objectNormal += getMorph( gl_VertexID, i, 1, 2 ) * morphTargetInfluences[ i ];\\\\n\\\\n\\\\t\\\\t}\\\\n\\\\n\\\\t#else\\\\n\\\\n\\\\t\\\\tobjectNormal += morphNormal0 * morphTargetInfluences[ 0 ];\\\\n\\\\t\\\\tobjectNormal += morphNormal1 * morphTargetInfluences[ 1 ];\\\\n\\\\t\\\\tobjectNormal += morphNormal2 * morphTargetInfluences[ 2 ];\\\\n\\\\t\\\\tobjectNormal += morphNormal3 * morphTargetInfluences[ 3 ];\\\\n\\\\n\\\\t#endif\\\\n\\\\n#endif\\\\n\\\\\\\",morphtarget_pars_vertex:\\\\\\\"\\\\n#ifdef USE_MORPHTARGETS\\\\n\\\\n\\\\tuniform float morphTargetBaseInfluence;\\\\n\\\\n\\\\t#ifdef MORPHTARGETS_TEXTURE\\\\n\\\\n\\\\t\\\\tuniform float morphTargetInfluences[ MORPHTARGETS_COUNT ];\\\\n\\\\t\\\\tuniform sampler2DArray morphTargetsTexture;\\\\n\\\\t\\\\tuniform vec2 morphTargetsTextureSize;\\\\n\\\\n\\\\t\\\\tvec3 getMorph( const in int vertexIndex, const in int morphTargetIndex, const in int offset, const in int stride ) {\\\\n\\\\n\\\\t\\\\t\\\\tfloat texelIndex = float( vertexIndex * stride + offset );\\\\n\\\\t\\\\t\\\\tfloat y = floor( texelIndex / morphTargetsTextureSize.x );\\\\n\\\\t\\\\t\\\\tfloat x = texelIndex - y * morphTargetsTextureSize.x;\\\\n\\\\n\\\\t\\\\t\\\\tvec3 morphUV = vec3( ( x + 0.5 ) / morphTargetsTextureSize.x, y / morphTargetsTextureSize.y, morphTargetIndex );\\\\n\\\\t\\\\t\\\\treturn texture( morphTargetsTexture, morphUV ).xyz;\\\\n\\\\n\\\\t\\\\t}\\\\n\\\\n\\\\t#else\\\\n\\\\n\\\\t\\\\t#ifndef USE_MORPHNORMALS\\\\n\\\\n\\\\t\\\\t\\\\tuniform float morphTargetInfluences[ 8 ];\\\\n\\\\n\\\\t\\\\t#else\\\\n\\\\n\\\\t\\\\t\\\\tuniform float morphTargetInfluences[ 4 ];\\\\n\\\\n\\\\t\\\\t#endif\\\\n\\\\n\\\\t#endif\\\\n\\\\n#endif\\\\n\\\\\\\",morphtarget_vertex:\\\\\\\"\\\\n#ifdef USE_MORPHTARGETS\\\\n\\\\n\\\\t// morphTargetBaseInfluence is set based on BufferGeometry.morphTargetsRelative value:\\\\n\\\\t// When morphTargetsRelative is false, this is set to 1 - sum(influences); this results in position = sum((target - base) * influence)\\\\n\\\\t// When morphTargetsRelative is true, this is set to 1; as a result, all morph targets are simply added to the base after weighting\\\\n\\\\ttransformed *= morphTargetBaseInfluence;\\\\n\\\\n\\\\t#ifdef MORPHTARGETS_TEXTURE\\\\n\\\\n\\\\t\\\\tfor ( int i = 0; i < MORPHTARGETS_COUNT; i ++ ) {\\\\n\\\\n\\\\t\\\\t\\\\t#ifndef USE_MORPHNORMALS\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tif ( morphTargetInfluences[ i ] > 0.0 ) transformed += getMorph( gl_VertexID, i, 0, 1 ) * morphTargetInfluences[ i ];\\\\n\\\\n\\\\t\\\\t\\\\t#else\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tif ( morphTargetInfluences[ i ] > 0.0 ) transformed += getMorph( gl_VertexID, i, 0, 2 ) * morphTargetInfluences[ i ];\\\\n\\\\n\\\\t\\\\t\\\\t#endif\\\\n\\\\n\\\\t\\\\t}\\\\n\\\\n\\\\t#else\\\\n\\\\n\\\\t\\\\ttransformed += morphTarget0 * morphTargetInfluences[ 0 ];\\\\n\\\\t\\\\ttransformed += morphTarget1 * morphTargetInfluences[ 1 ];\\\\n\\\\t\\\\ttransformed += morphTarget2 * morphTargetInfluences[ 2 ];\\\\n\\\\t\\\\ttransformed += morphTarget3 * morphTargetInfluences[ 3 ];\\\\n\\\\n\\\\t\\\\t#ifndef USE_MORPHNORMALS\\\\n\\\\n\\\\t\\\\t\\\\ttransformed += morphTarget4 * morphTargetInfluences[ 4 ];\\\\n\\\\t\\\\t\\\\ttransformed += morphTarget5 * morphTargetInfluences[ 5 ];\\\\n\\\\t\\\\t\\\\ttransformed += morphTarget6 * morphTargetInfluences[ 6 ];\\\\n\\\\t\\\\t\\\\ttransformed += morphTarget7 * morphTargetInfluences[ 7 ];\\\\n\\\\n\\\\t\\\\t#endif\\\\n\\\\n\\\\t#endif\\\\n\\\\n#endif\\\\n\\\\\\\",normal_fragment_begin:\\\\\\\"\\\\nfloat faceDirection = gl_FrontFacing ? 1.0 : - 1.0;\\\\n\\\\n#ifdef FLAT_SHADED\\\\n\\\\n\\\\t// Workaround for Adreno GPUs not able to do dFdx( vViewPosition )\\\\n\\\\n\\\\tvec3 fdx = vec3( dFdx( vViewPosition.x ), dFdx( vViewPosition.y ), dFdx( vViewPosition.z ) );\\\\n\\\\tvec3 fdy = vec3( dFdy( vViewPosition.x ), dFdy( vViewPosition.y ), dFdy( vViewPosition.z ) );\\\\n\\\\tvec3 normal = normalize( cross( fdx, fdy ) );\\\\n\\\\n#else\\\\n\\\\n\\\\tvec3 normal = normalize( vNormal );\\\\n\\\\n\\\\t#ifdef DOUBLE_SIDED\\\\n\\\\n\\\\t\\\\tnormal = normal * faceDirection;\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\t#ifdef USE_TANGENT\\\\n\\\\n\\\\t\\\\tvec3 tangent = normalize( vTangent );\\\\n\\\\t\\\\tvec3 bitangent = normalize( vBitangent );\\\\n\\\\n\\\\t\\\\t#ifdef DOUBLE_SIDED\\\\n\\\\n\\\\t\\\\t\\\\ttangent = tangent * faceDirection;\\\\n\\\\t\\\\t\\\\tbitangent = bitangent * faceDirection;\\\\n\\\\n\\\\t\\\\t#endif\\\\n\\\\n\\\\t\\\\t#if defined( TANGENTSPACE_NORMALMAP ) || defined( USE_CLEARCOAT_NORMALMAP )\\\\n\\\\n\\\\t\\\\t\\\\tmat3 vTBN = mat3( tangent, bitangent, normal );\\\\n\\\\n\\\\t\\\\t#endif\\\\n\\\\n\\\\t#endif\\\\n\\\\n#endif\\\\n\\\\n// non perturbed normal for clearcoat among others\\\\n\\\\nvec3 geometryNormal = normal;\\\\n\\\\n\\\\\\\",normal_fragment_maps:\\\\\\\"\\\\n\\\\n#ifdef OBJECTSPACE_NORMALMAP\\\\n\\\\n\\\\tnormal = texture2D( normalMap, vUv ).xyz * 2.0 - 1.0; // overrides both flatShading and attribute normals\\\\n\\\\n\\\\t#ifdef FLIP_SIDED\\\\n\\\\n\\\\t\\\\tnormal = - normal;\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\t#ifdef DOUBLE_SIDED\\\\n\\\\n\\\\t\\\\tnormal = normal * faceDirection;\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\tnormal = normalize( normalMatrix * normal );\\\\n\\\\n#elif defined( TANGENTSPACE_NORMALMAP )\\\\n\\\\n\\\\tvec3 mapN = texture2D( normalMap, vUv ).xyz * 2.0 - 1.0;\\\\n\\\\tmapN.xy *= normalScale;\\\\n\\\\n\\\\t#ifdef USE_TANGENT\\\\n\\\\n\\\\t\\\\tnormal = normalize( vTBN * mapN );\\\\n\\\\n\\\\t#else\\\\n\\\\n\\\\t\\\\tnormal = perturbNormal2Arb( - vViewPosition, normal, mapN, faceDirection );\\\\n\\\\n\\\\t#endif\\\\n\\\\n#elif defined( USE_BUMPMAP )\\\\n\\\\n\\\\tnormal = perturbNormalArb( - vViewPosition, normal, dHdxy_fwd(), faceDirection );\\\\n\\\\n#endif\\\\n\\\\\\\",normal_pars_fragment:\\\\\\\"\\\\n#ifndef FLAT_SHADED\\\\n\\\\n\\\\tvarying vec3 vNormal;\\\\n\\\\n\\\\t#ifdef USE_TANGENT\\\\n\\\\n\\\\t\\\\tvarying vec3 vTangent;\\\\n\\\\t\\\\tvarying vec3 vBitangent;\\\\n\\\\n\\\\t#endif\\\\n\\\\n#endif\\\\n\\\\\\\",normal_pars_vertex:\\\\\\\"\\\\n#ifndef FLAT_SHADED\\\\n\\\\n\\\\tvarying vec3 vNormal;\\\\n\\\\n\\\\t#ifdef USE_TANGENT\\\\n\\\\n\\\\t\\\\tvarying vec3 vTangent;\\\\n\\\\t\\\\tvarying vec3 vBitangent;\\\\n\\\\n\\\\t#endif\\\\n\\\\n#endif\\\\n\\\\\\\",normal_vertex:\\\\\\\"\\\\n#ifndef FLAT_SHADED // normal is computed with derivatives when FLAT_SHADED\\\\n\\\\n\\\\tvNormal = normalize( transformedNormal );\\\\n\\\\n\\\\t#ifdef USE_TANGENT\\\\n\\\\n\\\\t\\\\tvTangent = normalize( transformedTangent );\\\\n\\\\t\\\\tvBitangent = normalize( cross( vNormal, vTangent ) * tangent.w );\\\\n\\\\n\\\\t#endif\\\\n\\\\n#endif\\\\n\\\\\\\",normalmap_pars_fragment:\\\\\\\"\\\\n#ifdef USE_NORMALMAP\\\\n\\\\n\\\\tuniform sampler2D normalMap;\\\\n\\\\tuniform vec2 normalScale;\\\\n\\\\n#endif\\\\n\\\\n#ifdef OBJECTSPACE_NORMALMAP\\\\n\\\\n\\\\tuniform mat3 normalMatrix;\\\\n\\\\n#endif\\\\n\\\\n#if ! defined ( USE_TANGENT ) && ( defined ( TANGENTSPACE_NORMALMAP ) || defined ( USE_CLEARCOAT_NORMALMAP ) )\\\\n\\\\n\\\\t// Normal Mapping Without Precomputed Tangents\\\\n\\\\t// http://www.thetenthplanet.de/archives/1180\\\\n\\\\n\\\\tvec3 perturbNormal2Arb( vec3 eye_pos, vec3 surf_norm, vec3 mapN, float faceDirection ) {\\\\n\\\\n\\\\t\\\\t// Workaround for Adreno 3XX dFd*( vec3 ) bug. See #9988\\\\n\\\\n\\\\t\\\\tvec3 q0 = vec3( dFdx( eye_pos.x ), dFdx( eye_pos.y ), dFdx( eye_pos.z ) );\\\\n\\\\t\\\\tvec3 q1 = vec3( dFdy( eye_pos.x ), dFdy( eye_pos.y ), dFdy( eye_pos.z ) );\\\\n\\\\t\\\\tvec2 st0 = dFdx( vUv.st );\\\\n\\\\t\\\\tvec2 st1 = dFdy( vUv.st );\\\\n\\\\n\\\\t\\\\tvec3 N = surf_norm; // normalized\\\\n\\\\n\\\\t\\\\tvec3 q1perp = cross( q1, N );\\\\n\\\\t\\\\tvec3 q0perp = cross( N, q0 );\\\\n\\\\n\\\\t\\\\tvec3 T = q1perp * st0.x + q0perp * st1.x;\\\\n\\\\t\\\\tvec3 B = q1perp * st0.y + q0perp * st1.y;\\\\n\\\\n\\\\t\\\\tfloat det = max( dot( T, T ), dot( B, B ) );\\\\n\\\\t\\\\tfloat scale = ( det == 0.0 ) ? 0.0 : faceDirection * inversesqrt( det );\\\\n\\\\n\\\\t\\\\treturn normalize( T * ( mapN.x * scale ) + B * ( mapN.y * scale ) + N * mapN.z );\\\\n\\\\n\\\\t}\\\\n\\\\n#endif\\\\n\\\\\\\",clearcoat_normal_fragment_begin:\\\\\\\"\\\\n#ifdef USE_CLEARCOAT\\\\n\\\\n\\\\tvec3 clearcoatNormal = geometryNormal;\\\\n\\\\n#endif\\\\n\\\\\\\",clearcoat_normal_fragment_maps:\\\\\\\"\\\\n#ifdef USE_CLEARCOAT_NORMALMAP\\\\n\\\\n\\\\tvec3 clearcoatMapN = texture2D( clearcoatNormalMap, vUv ).xyz * 2.0 - 1.0;\\\\n\\\\tclearcoatMapN.xy *= clearcoatNormalScale;\\\\n\\\\n\\\\t#ifdef USE_TANGENT\\\\n\\\\n\\\\t\\\\tclearcoatNormal = normalize( vTBN * clearcoatMapN );\\\\n\\\\n\\\\t#else\\\\n\\\\n\\\\t\\\\tclearcoatNormal = perturbNormal2Arb( - vViewPosition, clearcoatNormal, clearcoatMapN, faceDirection );\\\\n\\\\n\\\\t#endif\\\\n\\\\n#endif\\\\n\\\\\\\",clearcoat_pars_fragment:\\\\\\\"\\\\n\\\\n#ifdef USE_CLEARCOATMAP\\\\n\\\\n\\\\tuniform sampler2D clearcoatMap;\\\\n\\\\n#endif\\\\n\\\\n#ifdef USE_CLEARCOAT_ROUGHNESSMAP\\\\n\\\\n\\\\tuniform sampler2D clearcoatRoughnessMap;\\\\n\\\\n#endif\\\\n\\\\n#ifdef USE_CLEARCOAT_NORMALMAP\\\\n\\\\n\\\\tuniform sampler2D clearcoatNormalMap;\\\\n\\\\tuniform vec2 clearcoatNormalScale;\\\\n\\\\n#endif\\\\n\\\\\\\",output_fragment:\\\\\\\"\\\\n#ifdef OPAQUE\\\\ndiffuseColor.a = 1.0;\\\\n#endif\\\\n\\\\n// https://github.com/mrdoob/three.js/pull/22425\\\\n#ifdef USE_TRANSMISSION\\\\ndiffuseColor.a *= transmissionAlpha + 0.1;\\\\n#endif\\\\n\\\\ngl_FragColor = vec4( outgoingLight, diffuseColor.a );\\\\n\\\\\\\",packing:\\\\\\\"\\\\nvec3 packNormalToRGB( const in vec3 normal ) {\\\\n\\\\treturn normalize( normal ) * 0.5 + 0.5;\\\\n}\\\\n\\\\nvec3 unpackRGBToNormal( const in vec3 rgb ) {\\\\n\\\\treturn 2.0 * rgb.xyz - 1.0;\\\\n}\\\\n\\\\nconst float PackUpscale = 256. / 255.; // fraction -> 0..1 (including 1)\\\\nconst float UnpackDownscale = 255. / 256.; // 0..1 -> fraction (excluding 1)\\\\n\\\\nconst vec3 PackFactors = vec3( 256. * 256. * 256., 256. * 256., 256. );\\\\nconst vec4 UnpackFactors = UnpackDownscale / vec4( PackFactors, 1. );\\\\n\\\\nconst float ShiftRight8 = 1. / 256.;\\\\n\\\\nvec4 packDepthToRGBA( const in float v ) {\\\\n\\\\tvec4 r = vec4( fract( v * PackFactors ), v );\\\\n\\\\tr.yzw -= r.xyz * ShiftRight8; // tidy overflow\\\\n\\\\treturn r * PackUpscale;\\\\n}\\\\n\\\\nfloat unpackRGBAToDepth( const in vec4 v ) {\\\\n\\\\treturn dot( v, UnpackFactors );\\\\n}\\\\n\\\\nvec4 pack2HalfToRGBA( vec2 v ) {\\\\n\\\\tvec4 r = vec4( v.x, fract( v.x * 255.0 ), v.y, fract( v.y * 255.0 ) );\\\\n\\\\treturn vec4( r.x - r.y / 255.0, r.y, r.z - r.w / 255.0, r.w );\\\\n}\\\\n\\\\nvec2 unpackRGBATo2Half( vec4 v ) {\\\\n\\\\treturn vec2( v.x + ( v.y / 255.0 ), v.z + ( v.w / 255.0 ) );\\\\n}\\\\n\\\\n// NOTE: viewZ/eyeZ is < 0 when in front of the camera per OpenGL conventions\\\\n\\\\nfloat viewZToOrthographicDepth( const in float viewZ, const in float near, const in float far ) {\\\\n\\\\treturn ( viewZ + near ) / ( near - far );\\\\n}\\\\nfloat orthographicDepthToViewZ( const in float linearClipZ, const in float near, const in float far ) {\\\\n\\\\treturn linearClipZ * ( near - far ) - near;\\\\n}\\\\n\\\\n// NOTE: https://twitter.com/gonnavis/status/1377183786949959682\\\\n\\\\nfloat viewZToPerspectiveDepth( const in float viewZ, const in float near, const in float far ) {\\\\n\\\\treturn ( ( near + viewZ ) * far ) / ( ( far - near ) * viewZ );\\\\n}\\\\nfloat perspectiveDepthToViewZ( const in float invClipZ, const in float near, const in float far ) {\\\\n\\\\treturn ( near * far ) / ( ( far - near ) * invClipZ - far );\\\\n}\\\\n\\\\\\\",premultiplied_alpha_fragment:\\\\\\\"\\\\n#ifdef PREMULTIPLIED_ALPHA\\\\n\\\\n\\\\t// Get get normal blending with premultipled, use with CustomBlending, OneFactor, OneMinusSrcAlphaFactor, AddEquation.\\\\n\\\\tgl_FragColor.rgb *= gl_FragColor.a;\\\\n\\\\n#endif\\\\n\\\\\\\",project_vertex:\\\\\\\"\\\\nvec4 mvPosition = vec4( transformed, 1.0 );\\\\n\\\\n#ifdef USE_INSTANCING\\\\n\\\\n\\\\tmvPosition = instanceMatrix * mvPosition;\\\\n\\\\n#endif\\\\n\\\\nmvPosition = modelViewMatrix * mvPosition;\\\\n\\\\ngl_Position = projectionMatrix * mvPosition;\\\\n\\\\\\\",dithering_fragment:\\\\\\\"\\\\n#ifdef DITHERING\\\\n\\\\n\\\\tgl_FragColor.rgb = dithering( gl_FragColor.rgb );\\\\n\\\\n#endif\\\\n\\\\\\\",dithering_pars_fragment:\\\\\\\"\\\\n#ifdef DITHERING\\\\n\\\\n\\\\t// based on https://www.shadertoy.com/view/MslGR8\\\\n\\\\tvec3 dithering( vec3 color ) {\\\\n\\\\t\\\\t//Calculate grid position\\\\n\\\\t\\\\tfloat grid_position = rand( gl_FragCoord.xy );\\\\n\\\\n\\\\t\\\\t//Shift the individual colors differently, thus making it even harder to see the dithering pattern\\\\n\\\\t\\\\tvec3 dither_shift_RGB = vec3( 0.25 / 255.0, -0.25 / 255.0, 0.25 / 255.0 );\\\\n\\\\n\\\\t\\\\t//modify shift acording to grid position.\\\\n\\\\t\\\\tdither_shift_RGB = mix( 2.0 * dither_shift_RGB, -2.0 * dither_shift_RGB, grid_position );\\\\n\\\\n\\\\t\\\\t//shift the color by dither_shift\\\\n\\\\t\\\\treturn color + dither_shift_RGB;\\\\n\\\\t}\\\\n\\\\n#endif\\\\n\\\\\\\",roughnessmap_fragment:\\\\\\\"\\\\nfloat roughnessFactor = roughness;\\\\n\\\\n#ifdef USE_ROUGHNESSMAP\\\\n\\\\n\\\\tvec4 texelRoughness = texture2D( roughnessMap, vUv );\\\\n\\\\n\\\\t// reads channel G, compatible with a combined OcclusionRoughnessMetallic (RGB) texture\\\\n\\\\troughnessFactor *= texelRoughness.g;\\\\n\\\\n#endif\\\\n\\\\\\\",roughnessmap_pars_fragment:\\\\\\\"\\\\n#ifdef USE_ROUGHNESSMAP\\\\n\\\\n\\\\tuniform sampler2D roughnessMap;\\\\n\\\\n#endif\\\\n\\\\\\\",shadowmap_pars_fragment:B,shadowmap_pars_vertex:\\\\\\\"\\\\n#ifdef USE_SHADOWMAP\\\\n\\\\n\\\\t#if NUM_DIR_LIGHT_SHADOWS > 0\\\\n\\\\n\\\\t\\\\tuniform mat4 directionalShadowMatrix[ NUM_DIR_LIGHT_SHADOWS ];\\\\n\\\\t\\\\tvarying vec4 vDirectionalShadowCoord[ NUM_DIR_LIGHT_SHADOWS ];\\\\n\\\\n\\\\t\\\\tstruct DirectionalLightShadow {\\\\n\\\\t\\\\t\\\\tfloat shadowBias;\\\\n\\\\t\\\\t\\\\tfloat shadowNormalBias;\\\\n\\\\t\\\\t\\\\tfloat shadowRadius;\\\\n\\\\t\\\\t\\\\tvec2 shadowMapSize;\\\\n\\\\t\\\\t};\\\\n\\\\n\\\\t\\\\tuniform DirectionalLightShadow directionalLightShadows[ NUM_DIR_LIGHT_SHADOWS ];\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\t#if NUM_SPOT_LIGHT_SHADOWS > 0\\\\n\\\\n\\\\t\\\\tuniform mat4 spotShadowMatrix[ NUM_SPOT_LIGHT_SHADOWS ];\\\\n\\\\t\\\\tvarying vec4 vSpotShadowCoord[ NUM_SPOT_LIGHT_SHADOWS ];\\\\n\\\\n\\\\t\\\\tstruct SpotLightShadow {\\\\n\\\\t\\\\t\\\\tfloat shadowBias;\\\\n\\\\t\\\\t\\\\tfloat shadowNormalBias;\\\\n\\\\t\\\\t\\\\tfloat shadowRadius;\\\\n\\\\t\\\\t\\\\tvec2 shadowMapSize;\\\\n\\\\t\\\\t};\\\\n\\\\n\\\\t\\\\tuniform SpotLightShadow spotLightShadows[ NUM_SPOT_LIGHT_SHADOWS ];\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\t#if NUM_POINT_LIGHT_SHADOWS > 0\\\\n\\\\n\\\\t\\\\tuniform mat4 pointShadowMatrix[ NUM_POINT_LIGHT_SHADOWS ];\\\\n\\\\t\\\\tvarying vec4 vPointShadowCoord[ NUM_POINT_LIGHT_SHADOWS ];\\\\n\\\\n\\\\t\\\\tstruct PointLightShadow {\\\\n\\\\t\\\\t\\\\tfloat shadowBias;\\\\n\\\\t\\\\t\\\\tfloat shadowNormalBias;\\\\n\\\\t\\\\t\\\\tfloat shadowRadius;\\\\n\\\\t\\\\t\\\\tvec2 shadowMapSize;\\\\n\\\\t\\\\t\\\\tfloat shadowCameraNear;\\\\n\\\\t\\\\t\\\\tfloat shadowCameraFar;\\\\n\\\\t\\\\t};\\\\n\\\\n\\\\t\\\\tuniform PointLightShadow pointLightShadows[ NUM_POINT_LIGHT_SHADOWS ];\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\t/*\\\\n\\\\t#if NUM_RECT_AREA_LIGHTS > 0\\\\n\\\\n\\\\t\\\\t// TODO (abelnation): uniforms for area light shadows\\\\n\\\\n\\\\t#endif\\\\n\\\\t*/\\\\n\\\\n#endif\\\\n\\\\\\\",shadowmap_vertex:\\\\\\\"\\\\n#ifdef USE_SHADOWMAP\\\\n\\\\n\\\\t#if NUM_DIR_LIGHT_SHADOWS > 0 || NUM_SPOT_LIGHT_SHADOWS > 0 || NUM_POINT_LIGHT_SHADOWS > 0\\\\n\\\\n\\\\t\\\\t// Offsetting the position used for querying occlusion along the world normal can be used to reduce shadow acne.\\\\n\\\\t\\\\tvec3 shadowWorldNormal = inverseTransformDirection( transformedNormal, viewMatrix );\\\\n\\\\t\\\\tvec4 shadowWorldPosition;\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\t#if NUM_DIR_LIGHT_SHADOWS > 0\\\\n\\\\n\\\\t#pragma unroll_loop_start\\\\n\\\\tfor ( int i = 0; i < NUM_DIR_LIGHT_SHADOWS; i ++ ) {\\\\n\\\\n\\\\t\\\\tshadowWorldPosition = worldPosition + vec4( shadowWorldNormal * directionalLightShadows[ i ].shadowNormalBias, 0 );\\\\n\\\\t\\\\tvDirectionalShadowCoord[ i ] = directionalShadowMatrix[ i ] * shadowWorldPosition;\\\\n\\\\n\\\\t}\\\\n\\\\t#pragma unroll_loop_end\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\t#if NUM_SPOT_LIGHT_SHADOWS > 0\\\\n\\\\n\\\\t#pragma unroll_loop_start\\\\n\\\\tfor ( int i = 0; i < NUM_SPOT_LIGHT_SHADOWS; i ++ ) {\\\\n\\\\n\\\\t\\\\tshadowWorldPosition = worldPosition + vec4( shadowWorldNormal * spotLightShadows[ i ].shadowNormalBias, 0 );\\\\n\\\\t\\\\tvSpotShadowCoord[ i ] = spotShadowMatrix[ i ] * shadowWorldPosition;\\\\n\\\\n\\\\t}\\\\n\\\\t#pragma unroll_loop_end\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\t#if NUM_POINT_LIGHT_SHADOWS > 0\\\\n\\\\n\\\\t#pragma unroll_loop_start\\\\n\\\\tfor ( int i = 0; i < NUM_POINT_LIGHT_SHADOWS; i ++ ) {\\\\n\\\\n\\\\t\\\\tshadowWorldPosition = worldPosition + vec4( shadowWorldNormal * pointLightShadows[ i ].shadowNormalBias, 0 );\\\\n\\\\t\\\\tvPointShadowCoord[ i ] = pointShadowMatrix[ i ] * shadowWorldPosition;\\\\n\\\\n\\\\t}\\\\n\\\\t#pragma unroll_loop_end\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\t/*\\\\n\\\\t#if NUM_RECT_AREA_LIGHTS > 0\\\\n\\\\n\\\\t\\\\t// TODO (abelnation): update vAreaShadowCoord with area light info\\\\n\\\\n\\\\t#endif\\\\n\\\\t*/\\\\n\\\\n#endif\\\\n\\\\\\\",shadowmask_pars_fragment:\\\\\\\"\\\\nfloat getShadowMask() {\\\\n\\\\n\\\\tfloat shadow = 1.0;\\\\n\\\\n\\\\t#ifdef USE_SHADOWMAP\\\\n\\\\n\\\\t#if NUM_DIR_LIGHT_SHADOWS > 0\\\\n\\\\n\\\\tDirectionalLightShadow directionalLight;\\\\n\\\\n\\\\t#pragma unroll_loop_start\\\\n\\\\tfor ( int i = 0; i < NUM_DIR_LIGHT_SHADOWS; i ++ ) {\\\\n\\\\n\\\\t\\\\tdirectionalLight = directionalLightShadows[ i ];\\\\n\\\\t\\\\tshadow *= receiveShadow ? getShadow( directionalShadowMap[ i ], directionalLight.shadowMapSize, directionalLight.shadowBias, directionalLight.shadowRadius, vDirectionalShadowCoord[ i ] ) : 1.0;\\\\n\\\\n\\\\t}\\\\n\\\\t#pragma unroll_loop_end\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\t#if NUM_SPOT_LIGHT_SHADOWS > 0\\\\n\\\\n\\\\tSpotLightShadow spotLight;\\\\n\\\\n\\\\t#pragma unroll_loop_start\\\\n\\\\tfor ( int i = 0; i < NUM_SPOT_LIGHT_SHADOWS; i ++ ) {\\\\n\\\\n\\\\t\\\\tspotLight = spotLightShadows[ i ];\\\\n\\\\t\\\\tshadow *= receiveShadow ? getShadow( spotShadowMap[ i ], spotLight.shadowMapSize, spotLight.shadowBias, spotLight.shadowRadius, vSpotShadowCoord[ i ] ) : 1.0;\\\\n\\\\n\\\\t}\\\\n\\\\t#pragma unroll_loop_end\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\t#if NUM_POINT_LIGHT_SHADOWS > 0\\\\n\\\\n\\\\tPointLightShadow pointLight;\\\\n\\\\n\\\\t#pragma unroll_loop_start\\\\n\\\\tfor ( int i = 0; i < NUM_POINT_LIGHT_SHADOWS; i ++ ) {\\\\n\\\\n\\\\t\\\\tpointLight = pointLightShadows[ i ];\\\\n\\\\t\\\\tshadow *= receiveShadow ? getPointShadow( pointShadowMap[ i ], pointLight.shadowMapSize, pointLight.shadowBias, pointLight.shadowRadius, vPointShadowCoord[ i ], pointLight.shadowCameraNear, pointLight.shadowCameraFar ) : 1.0;\\\\n\\\\n\\\\t}\\\\n\\\\t#pragma unroll_loop_end\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\t/*\\\\n\\\\t#if NUM_RECT_AREA_LIGHTS > 0\\\\n\\\\n\\\\t\\\\t// TODO (abelnation): update shadow for Area light\\\\n\\\\n\\\\t#endif\\\\n\\\\t*/\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\treturn shadow;\\\\n\\\\n}\\\\n\\\\\\\",skinbase_vertex:\\\\\\\"\\\\n#ifdef USE_SKINNING\\\\n\\\\n\\\\tmat4 boneMatX = getBoneMatrix( skinIndex.x );\\\\n\\\\tmat4 boneMatY = getBoneMatrix( skinIndex.y );\\\\n\\\\tmat4 boneMatZ = getBoneMatrix( skinIndex.z );\\\\n\\\\tmat4 boneMatW = getBoneMatrix( skinIndex.w );\\\\n\\\\n#endif\\\\n\\\\\\\",skinning_pars_vertex:\\\\\\\"\\\\n#ifdef USE_SKINNING\\\\n\\\\n\\\\tuniform mat4 bindMatrix;\\\\n\\\\tuniform mat4 bindMatrixInverse;\\\\n\\\\n\\\\t#ifdef BONE_TEXTURE\\\\n\\\\n\\\\t\\\\tuniform highp sampler2D boneTexture;\\\\n\\\\t\\\\tuniform int boneTextureSize;\\\\n\\\\n\\\\t\\\\tmat4 getBoneMatrix( const in float i ) {\\\\n\\\\n\\\\t\\\\t\\\\tfloat j = i * 4.0;\\\\n\\\\t\\\\t\\\\tfloat x = mod( j, float( boneTextureSize ) );\\\\n\\\\t\\\\t\\\\tfloat y = floor( j / float( boneTextureSize ) );\\\\n\\\\n\\\\t\\\\t\\\\tfloat dx = 1.0 / float( boneTextureSize );\\\\n\\\\t\\\\t\\\\tfloat dy = 1.0 / float( boneTextureSize );\\\\n\\\\n\\\\t\\\\t\\\\ty = dy * ( y + 0.5 );\\\\n\\\\n\\\\t\\\\t\\\\tvec4 v1 = texture2D( boneTexture, vec2( dx * ( x + 0.5 ), y ) );\\\\n\\\\t\\\\t\\\\tvec4 v2 = texture2D( boneTexture, vec2( dx * ( x + 1.5 ), y ) );\\\\n\\\\t\\\\t\\\\tvec4 v3 = texture2D( boneTexture, vec2( dx * ( x + 2.5 ), y ) );\\\\n\\\\t\\\\t\\\\tvec4 v4 = texture2D( boneTexture, vec2( dx * ( x + 3.5 ), y ) );\\\\n\\\\n\\\\t\\\\t\\\\tmat4 bone = mat4( v1, v2, v3, v4 );\\\\n\\\\n\\\\t\\\\t\\\\treturn bone;\\\\n\\\\n\\\\t\\\\t}\\\\n\\\\n\\\\t#else\\\\n\\\\n\\\\t\\\\tuniform mat4 boneMatrices[ MAX_BONES ];\\\\n\\\\n\\\\t\\\\tmat4 getBoneMatrix( const in float i ) {\\\\n\\\\n\\\\t\\\\t\\\\tmat4 bone = boneMatrices[ int(i) ];\\\\n\\\\t\\\\t\\\\treturn bone;\\\\n\\\\n\\\\t\\\\t}\\\\n\\\\n\\\\t#endif\\\\n\\\\n#endif\\\\n\\\\\\\",skinning_vertex:\\\\\\\"\\\\n#ifdef USE_SKINNING\\\\n\\\\n\\\\tvec4 skinVertex = bindMatrix * vec4( transformed, 1.0 );\\\\n\\\\n\\\\tvec4 skinned = vec4( 0.0 );\\\\n\\\\tskinned += boneMatX * skinVertex * skinWeight.x;\\\\n\\\\tskinned += boneMatY * skinVertex * skinWeight.y;\\\\n\\\\tskinned += boneMatZ * skinVertex * skinWeight.z;\\\\n\\\\tskinned += boneMatW * skinVertex * skinWeight.w;\\\\n\\\\n\\\\ttransformed = ( bindMatrixInverse * skinned ).xyz;\\\\n\\\\n#endif\\\\n\\\\\\\",skinnormal_vertex:\\\\\\\"\\\\n#ifdef USE_SKINNING\\\\n\\\\n\\\\tmat4 skinMatrix = mat4( 0.0 );\\\\n\\\\tskinMatrix += skinWeight.x * boneMatX;\\\\n\\\\tskinMatrix += skinWeight.y * boneMatY;\\\\n\\\\tskinMatrix += skinWeight.z * boneMatZ;\\\\n\\\\tskinMatrix += skinWeight.w * boneMatW;\\\\n\\\\tskinMatrix = bindMatrixInverse * skinMatrix * bindMatrix;\\\\n\\\\n\\\\tobjectNormal = vec4( skinMatrix * vec4( objectNormal, 0.0 ) ).xyz;\\\\n\\\\n\\\\t#ifdef USE_TANGENT\\\\n\\\\n\\\\t\\\\tobjectTangent = vec4( skinMatrix * vec4( objectTangent, 0.0 ) ).xyz;\\\\n\\\\n\\\\t#endif\\\\n\\\\n#endif\\\\n\\\\\\\",specularmap_fragment:\\\\\\\"\\\\nfloat specularStrength;\\\\n\\\\n#ifdef USE_SPECULARMAP\\\\n\\\\n\\\\tvec4 texelSpecular = texture2D( specularMap, vUv );\\\\n\\\\tspecularStrength = texelSpecular.r;\\\\n\\\\n#else\\\\n\\\\n\\\\tspecularStrength = 1.0;\\\\n\\\\n#endif\\\\n\\\\\\\",specularmap_pars_fragment:\\\\\\\"\\\\n#ifdef USE_SPECULARMAP\\\\n\\\\n\\\\tuniform sampler2D specularMap;\\\\n\\\\n#endif\\\\n\\\\\\\",tonemapping_fragment:\\\\\\\"\\\\n#if defined( TONE_MAPPING )\\\\n\\\\n\\\\tgl_FragColor.rgb = toneMapping( gl_FragColor.rgb );\\\\n\\\\n#endif\\\\n\\\\\\\",tonemapping_pars_fragment:\\\\\\\"\\\\n#ifndef saturate\\\\n// <common> may have defined saturate() already\\\\n#define saturate( a ) clamp( a, 0.0, 1.0 )\\\\n#endif\\\\n\\\\nuniform float toneMappingExposure;\\\\n\\\\n// exposure only\\\\nvec3 LinearToneMapping( vec3 color ) {\\\\n\\\\n\\\\treturn toneMappingExposure * color;\\\\n\\\\n}\\\\n\\\\n// source: https://www.cs.utah.edu/~reinhard/cdrom/\\\\nvec3 ReinhardToneMapping( vec3 color ) {\\\\n\\\\n\\\\tcolor *= toneMappingExposure;\\\\n\\\\treturn saturate( color / ( vec3( 1.0 ) + color ) );\\\\n\\\\n}\\\\n\\\\n// source: http://filmicworlds.com/blog/filmic-tonemapping-operators/\\\\nvec3 OptimizedCineonToneMapping( vec3 color ) {\\\\n\\\\n\\\\t// optimized filmic operator by Jim Hejl and Richard Burgess-Dawson\\\\n\\\\tcolor *= toneMappingExposure;\\\\n\\\\tcolor = max( vec3( 0.0 ), color - 0.004 );\\\\n\\\\treturn pow( ( color * ( 6.2 * color + 0.5 ) ) / ( color * ( 6.2 * color + 1.7 ) + 0.06 ), vec3( 2.2 ) );\\\\n\\\\n}\\\\n\\\\n// source: https://github.com/selfshadow/ltc_code/blob/master/webgl/shaders/ltc/ltc_blit.fs\\\\nvec3 RRTAndODTFit( vec3 v ) {\\\\n\\\\n\\\\tvec3 a = v * ( v + 0.0245786 ) - 0.000090537;\\\\n\\\\tvec3 b = v * ( 0.983729 * v + 0.4329510 ) + 0.238081;\\\\n\\\\treturn a / b;\\\\n\\\\n}\\\\n\\\\n// this implementation of ACES is modified to accommodate a brighter viewing environment.\\\\n// the scale factor of 1/0.6 is subjective. see discussion in #19621.\\\\n\\\\nvec3 ACESFilmicToneMapping( vec3 color ) {\\\\n\\\\n\\\\t// sRGB => XYZ => D65_2_D60 => AP1 => RRT_SAT\\\\n\\\\tconst mat3 ACESInputMat = mat3(\\\\n\\\\t\\\\tvec3( 0.59719, 0.07600, 0.02840 ), // transposed from source\\\\n\\\\t\\\\tvec3( 0.35458, 0.90834, 0.13383 ),\\\\n\\\\t\\\\tvec3( 0.04823, 0.01566, 0.83777 )\\\\n\\\\t);\\\\n\\\\n\\\\t// ODT_SAT => XYZ => D60_2_D65 => sRGB\\\\n\\\\tconst mat3 ACESOutputMat = mat3(\\\\n\\\\t\\\\tvec3(  1.60475, -0.10208, -0.00327 ), // transposed from source\\\\n\\\\t\\\\tvec3( -0.53108,  1.10813, -0.07276 ),\\\\n\\\\t\\\\tvec3( -0.07367, -0.00605,  1.07602 )\\\\n\\\\t);\\\\n\\\\n\\\\tcolor *= toneMappingExposure / 0.6;\\\\n\\\\n\\\\tcolor = ACESInputMat * color;\\\\n\\\\n\\\\t// Apply RRT and ODT\\\\n\\\\tcolor = RRTAndODTFit( color );\\\\n\\\\n\\\\tcolor = ACESOutputMat * color;\\\\n\\\\n\\\\t// Clamp to [0, 1]\\\\n\\\\treturn saturate( color );\\\\n\\\\n}\\\\n\\\\nvec3 CustomToneMapping( vec3 color ) { return color; }\\\\n\\\\\\\",transmission_fragment:\\\\\\\"\\\\n#ifdef USE_TRANSMISSION\\\\n\\\\n\\\\tfloat transmissionAlpha = 1.0;\\\\n\\\\tfloat transmissionFactor = transmission;\\\\n\\\\tfloat thicknessFactor = thickness;\\\\n\\\\n\\\\t#ifdef USE_TRANSMISSIONMAP\\\\n\\\\n\\\\t\\\\ttransmissionFactor *= texture2D( transmissionMap, vUv ).r;\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\t#ifdef USE_THICKNESSMAP\\\\n\\\\n\\\\t\\\\tthicknessFactor *= texture2D( thicknessMap, vUv ).g;\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\tvec3 pos = vWorldPosition;\\\\n\\\\tvec3 v = normalize( cameraPosition - pos );\\\\n\\\\tvec3 n = inverseTransformDirection( normal, viewMatrix );\\\\n\\\\n\\\\tvec4 transmission = getIBLVolumeRefraction(\\\\n\\\\t\\\\tn, v, roughnessFactor, material.diffuseColor, material.specularColor, material.specularF90,\\\\n\\\\t\\\\tpos, modelMatrix, viewMatrix, projectionMatrix, ior, thicknessFactor,\\\\n\\\\t\\\\tattenuationTint, attenuationDistance );\\\\n\\\\n\\\\ttotalDiffuse = mix( totalDiffuse, transmission.rgb, transmissionFactor );\\\\n\\\\ttransmissionAlpha = mix( transmissionAlpha, transmission.a, transmissionFactor );\\\\n#endif\\\\n\\\\\\\",transmission_pars_fragment:\\\\\\\"\\\\n#ifdef USE_TRANSMISSION\\\\n\\\\n\\\\t// Transmission code is based on glTF-Sampler-Viewer\\\\n\\\\t// https://github.com/KhronosGroup/glTF-Sample-Viewer\\\\n\\\\n\\\\tuniform float transmission;\\\\n\\\\tuniform float thickness;\\\\n\\\\tuniform float attenuationDistance;\\\\n\\\\tuniform vec3 attenuationTint;\\\\n\\\\n\\\\t#ifdef USE_TRANSMISSIONMAP\\\\n\\\\n\\\\t\\\\tuniform sampler2D transmissionMap;\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\t#ifdef USE_THICKNESSMAP\\\\n\\\\n\\\\t\\\\tuniform sampler2D thicknessMap;\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\tuniform vec2 transmissionSamplerSize;\\\\n\\\\tuniform sampler2D transmissionSamplerMap;\\\\n\\\\n\\\\tuniform mat4 modelMatrix;\\\\n\\\\tuniform mat4 projectionMatrix;\\\\n\\\\n\\\\tvarying vec3 vWorldPosition;\\\\n\\\\n\\\\tvec3 getVolumeTransmissionRay( vec3 n, vec3 v, float thickness, float ior, mat4 modelMatrix ) {\\\\n\\\\n\\\\t\\\\t// Direction of refracted light.\\\\n\\\\t\\\\tvec3 refractionVector = refract( - v, normalize( n ), 1.0 / ior );\\\\n\\\\n\\\\t\\\\t// Compute rotation-independant scaling of the model matrix.\\\\n\\\\t\\\\tvec3 modelScale;\\\\n\\\\t\\\\tmodelScale.x = length( vec3( modelMatrix[ 0 ].xyz ) );\\\\n\\\\t\\\\tmodelScale.y = length( vec3( modelMatrix[ 1 ].xyz ) );\\\\n\\\\t\\\\tmodelScale.z = length( vec3( modelMatrix[ 2 ].xyz ) );\\\\n\\\\n\\\\t\\\\t// The thickness is specified in local space.\\\\n\\\\t\\\\treturn normalize( refractionVector ) * thickness * modelScale;\\\\n\\\\n\\\\t}\\\\n\\\\n\\\\tfloat applyIorToRoughness( float roughness, float ior ) {\\\\n\\\\n\\\\t\\\\t// Scale roughness with IOR so that an IOR of 1.0 results in no microfacet refraction and\\\\n\\\\t\\\\t// an IOR of 1.5 results in the default amount of microfacet refraction.\\\\n\\\\t\\\\treturn roughness * clamp( ior * 2.0 - 2.0, 0.0, 1.0 );\\\\n\\\\n\\\\t}\\\\n\\\\n\\\\tvec4 getTransmissionSample( vec2 fragCoord, float roughness, float ior ) {\\\\n\\\\n\\\\t\\\\tfloat framebufferLod = log2( transmissionSamplerSize.x ) * applyIorToRoughness( roughness, ior );\\\\n\\\\n\\\\t\\\\t#ifdef TEXTURE_LOD_EXT\\\\n\\\\n\\\\t\\\\t\\\\treturn texture2DLodEXT( transmissionSamplerMap, fragCoord.xy, framebufferLod );\\\\n\\\\n\\\\t\\\\t#else\\\\n\\\\n\\\\t\\\\t\\\\treturn texture2D( transmissionSamplerMap, fragCoord.xy, framebufferLod );\\\\n\\\\n\\\\t\\\\t#endif\\\\n\\\\n\\\\t}\\\\n\\\\n\\\\tvec3 applyVolumeAttenuation( vec3 radiance, float transmissionDistance, vec3 attenuationColor, float attenuationDistance ) {\\\\n\\\\n\\\\t\\\\tif ( attenuationDistance == 0.0 ) {\\\\n\\\\n\\\\t\\\\t\\\\t// Attenuation distance is +∞ (which we indicate by zero), i.e. the transmitted color is not attenuated at all.\\\\n\\\\t\\\\t\\\\treturn radiance;\\\\n\\\\n\\\\t\\\\t} else {\\\\n\\\\n\\\\t\\\\t\\\\t// Compute light attenuation using Beer's law.\\\\n\\\\t\\\\t\\\\tvec3 attenuationCoefficient = -log( attenuationColor ) / attenuationDistance;\\\\n\\\\t\\\\t\\\\tvec3 transmittance = exp( - attenuationCoefficient * transmissionDistance ); // Beer's law\\\\n\\\\t\\\\t\\\\treturn transmittance * radiance;\\\\n\\\\n\\\\t\\\\t}\\\\n\\\\n\\\\t}\\\\n\\\\n\\\\tvec4 getIBLVolumeRefraction( vec3 n, vec3 v, float roughness, vec3 diffuseColor, vec3 specularColor, float specularF90,\\\\n\\\\t\\\\tvec3 position, mat4 modelMatrix, mat4 viewMatrix, mat4 projMatrix, float ior, float thickness,\\\\n\\\\t\\\\tvec3 attenuationColor, float attenuationDistance ) {\\\\n\\\\n\\\\t\\\\tvec3 transmissionRay = getVolumeTransmissionRay( n, v, thickness, ior, modelMatrix );\\\\n\\\\t\\\\tvec3 refractedRayExit = position + transmissionRay;\\\\n\\\\n\\\\t\\\\t// Project refracted vector on the framebuffer, while mapping to normalized device coordinates.\\\\n\\\\t\\\\tvec4 ndcPos = projMatrix * viewMatrix * vec4( refractedRayExit, 1.0 );\\\\n\\\\t\\\\tvec2 refractionCoords = ndcPos.xy / ndcPos.w;\\\\n\\\\t\\\\trefractionCoords += 1.0;\\\\n\\\\t\\\\trefractionCoords /= 2.0;\\\\n\\\\n\\\\t\\\\t// Sample framebuffer to get pixel the refracted ray hits.\\\\n\\\\t\\\\tvec4 transmittedLight = getTransmissionSample( refractionCoords, roughness, ior );\\\\n\\\\n\\\\t\\\\tvec3 attenuatedColor = applyVolumeAttenuation( transmittedLight.rgb, length( transmissionRay ), attenuationColor, attenuationDistance );\\\\n\\\\n\\\\t\\\\t// Get the specular component.\\\\n\\\\t\\\\tvec3 F = EnvironmentBRDF( n, v, specularColor, specularF90, roughness );\\\\n\\\\n\\\\t\\\\treturn vec4( ( 1.0 - F ) * attenuatedColor * diffuseColor, transmittedLight.a );\\\\n\\\\n\\\\t}\\\\n#endif\\\\n\\\\\\\",uv_pars_fragment:\\\\\\\"\\\\n#if ( defined( USE_UV ) && ! defined( UVS_VERTEX_ONLY ) )\\\\n\\\\n\\\\tvarying vec2 vUv;\\\\n\\\\n#endif\\\\n\\\\\\\",uv_pars_vertex:\\\\\\\"\\\\n#ifdef USE_UV\\\\n\\\\n\\\\t#ifdef UVS_VERTEX_ONLY\\\\n\\\\n\\\\t\\\\tvec2 vUv;\\\\n\\\\n\\\\t#else\\\\n\\\\n\\\\t\\\\tvarying vec2 vUv;\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\tuniform mat3 uvTransform;\\\\n\\\\n#endif\\\\n\\\\\\\",uv_vertex:\\\\\\\"\\\\n#ifdef USE_UV\\\\n\\\\n\\\\tvUv = ( uvTransform * vec3( uv, 1 ) ).xy;\\\\n\\\\n#endif\\\\n\\\\\\\",uv2_pars_fragment:\\\\\\\"\\\\n#if defined( USE_LIGHTMAP ) || defined( USE_AOMAP )\\\\n\\\\n\\\\tvarying vec2 vUv2;\\\\n\\\\n#endif\\\\n\\\\\\\",uv2_pars_vertex:\\\\\\\"\\\\n#if defined( USE_LIGHTMAP ) || defined( USE_AOMAP )\\\\n\\\\n\\\\tattribute vec2 uv2;\\\\n\\\\tvarying vec2 vUv2;\\\\n\\\\n\\\\tuniform mat3 uv2Transform;\\\\n\\\\n#endif\\\\n\\\\\\\",uv2_vertex:\\\\\\\"\\\\n#if defined( USE_LIGHTMAP ) || defined( USE_AOMAP )\\\\n\\\\n\\\\tvUv2 = ( uv2Transform * vec3( uv2, 1 ) ).xy;\\\\n\\\\n#endif\\\\n\\\\\\\",worldpos_vertex:\\\\\\\"\\\\n#if defined( USE_ENVMAP ) || defined( DISTANCE ) || defined ( USE_SHADOWMAP ) || defined ( USE_TRANSMISSION )\\\\n\\\\n\\\\tvec4 worldPosition = vec4( transformed, 1.0 );\\\\n\\\\n\\\\t#ifdef USE_INSTANCING\\\\n\\\\n\\\\t\\\\tworldPosition = instanceMatrix * worldPosition;\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\tworldPosition = modelMatrix * worldPosition;\\\\n\\\\n#endif\\\\n\\\\\\\",background_vert:\\\\\\\"\\\\nvarying vec2 vUv;\\\\nuniform mat3 uvTransform;\\\\n\\\\nvoid main() {\\\\n\\\\n\\\\tvUv = ( uvTransform * vec3( uv, 1 ) ).xy;\\\\n\\\\n\\\\tgl_Position = vec4( position.xy, 1.0, 1.0 );\\\\n\\\\n}\\\\n\\\\\\\",background_frag:\\\\\\\"\\\\nuniform sampler2D t2D;\\\\n\\\\nvarying vec2 vUv;\\\\n\\\\nvoid main() {\\\\n\\\\n\\\\tvec4 texColor = texture2D( t2D, vUv );\\\\n\\\\n\\\\tgl_FragColor = mapTexelToLinear( texColor );\\\\n\\\\n\\\\t#include <tonemapping_fragment>\\\\n\\\\t#include <encodings_fragment>\\\\n\\\\n}\\\\n\\\\\\\",cube_vert:\\\\\\\"\\\\nvarying vec3 vWorldDirection;\\\\n\\\\n#include <common>\\\\n\\\\nvoid main() {\\\\n\\\\n\\\\tvWorldDirection = transformDirection( position, modelMatrix );\\\\n\\\\n\\\\t#include <begin_vertex>\\\\n\\\\t#include <project_vertex>\\\\n\\\\n\\\\tgl_Position.z = gl_Position.w; // set z to camera.far\\\\n\\\\n}\\\\n\\\\\\\",cube_frag:\\\\\\\"\\\\n#include <envmap_common_pars_fragment>\\\\nuniform float opacity;\\\\n\\\\nvarying vec3 vWorldDirection;\\\\n\\\\n#include <cube_uv_reflection_fragment>\\\\n\\\\nvoid main() {\\\\n\\\\n\\\\tvec3 vReflect = vWorldDirection;\\\\n\\\\t#include <envmap_fragment>\\\\n\\\\n\\\\tgl_FragColor = envColor;\\\\n\\\\tgl_FragColor.a *= opacity;\\\\n\\\\n\\\\t#include <tonemapping_fragment>\\\\n\\\\t#include <encodings_fragment>\\\\n\\\\n}\\\\n\\\\\\\",depth_vert:\\\\\\\"\\\\n#include <common>\\\\n#include <uv_pars_vertex>\\\\n#include <displacementmap_pars_vertex>\\\\n#include <morphtarget_pars_vertex>\\\\n#include <skinning_pars_vertex>\\\\n#include <logdepthbuf_pars_vertex>\\\\n#include <clipping_planes_pars_vertex>\\\\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 <uv_vertex>\\\\n\\\\n\\\\t#include <skinbase_vertex>\\\\n\\\\n\\\\t#ifdef USE_DISPLACEMENTMAP\\\\n\\\\n\\\\t\\\\t#include <beginnormal_vertex>\\\\n\\\\t\\\\t#include <morphnormal_vertex>\\\\n\\\\t\\\\t#include <skinnormal_vertex>\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\t#include <begin_vertex>\\\\n\\\\t#include <morphtarget_vertex>\\\\n\\\\t#include <skinning_vertex>\\\\n\\\\t#include <displacementmap_vertex>\\\\n\\\\t#include <project_vertex>\\\\n\\\\t#include <logdepthbuf_vertex>\\\\n\\\\t#include <clipping_planes_vertex>\\\\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 <common>\\\\n#include <packing>\\\\n#include <uv_pars_fragment>\\\\n#include <map_pars_fragment>\\\\n#include <alphamap_pars_fragment>\\\\n#include <alphatest_pars_fragment>\\\\n#include <logdepthbuf_pars_fragment>\\\\n#include <clipping_planes_pars_fragment>\\\\n\\\\nvarying vec2 vHighPrecisionZW;\\\\n\\\\nvoid main() {\\\\n\\\\n\\\\t#include <clipping_planes_fragment>\\\\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 <map_fragment>\\\\n\\\\t#include <alphamap_fragment>\\\\n\\\\t#include <alphatest_fragment>\\\\n\\\\n\\\\t#include <logdepthbuf_fragment>\\\\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 <common>\\\\n#include <uv_pars_vertex>\\\\n#include <displacementmap_pars_vertex>\\\\n#include <morphtarget_pars_vertex>\\\\n#include <skinning_pars_vertex>\\\\n#include <clipping_planes_pars_vertex>\\\\n\\\\nvoid main() {\\\\n\\\\n\\\\t#include <uv_vertex>\\\\n\\\\n\\\\t#include <skinbase_vertex>\\\\n\\\\n\\\\t#ifdef USE_DISPLACEMENTMAP\\\\n\\\\n\\\\t\\\\t#include <beginnormal_vertex>\\\\n\\\\t\\\\t#include <morphnormal_vertex>\\\\n\\\\t\\\\t#include <skinnormal_vertex>\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\t#include <begin_vertex>\\\\n\\\\t#include <morphtarget_vertex>\\\\n\\\\t#include <skinning_vertex>\\\\n\\\\t#include <displacementmap_vertex>\\\\n\\\\t#include <project_vertex>\\\\n\\\\t#include <worldpos_vertex>\\\\n\\\\t#include <clipping_planes_vertex>\\\\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 <common>\\\\n#include <packing>\\\\n#include <uv_pars_fragment>\\\\n#include <map_pars_fragment>\\\\n#include <alphamap_pars_fragment>\\\\n#include <alphatest_pars_fragment>\\\\n#include <clipping_planes_pars_fragment>\\\\n\\\\nvoid main () {\\\\n\\\\n\\\\t#include <clipping_planes_fragment>\\\\n\\\\n\\\\tvec4 diffuseColor = vec4( 1.0 );\\\\n\\\\n\\\\t#include <map_fragment>\\\\n\\\\t#include <alphamap_fragment>\\\\n\\\\t#include <alphatest_fragment>\\\\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 <common>\\\\n\\\\nvoid main() {\\\\n\\\\n\\\\tvWorldDirection = transformDirection( position, modelMatrix );\\\\n\\\\n\\\\t#include <begin_vertex>\\\\n\\\\t#include <project_vertex>\\\\n\\\\n}\\\\n\\\\\\\",equirect_frag:\\\\\\\"\\\\nuniform sampler2D tEquirect;\\\\n\\\\nvarying vec3 vWorldDirection;\\\\n\\\\n#include <common>\\\\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 <tonemapping_fragment>\\\\n\\\\t#include <encodings_fragment>\\\\n\\\\n}\\\\n\\\\\\\",linedashed_vert:\\\\\\\"\\\\nuniform float scale;\\\\nattribute float lineDistance;\\\\n\\\\nvarying float vLineDistance;\\\\n\\\\n#include <common>\\\\n#include <color_pars_vertex>\\\\n#include <fog_pars_vertex>\\\\n#include <morphtarget_pars_vertex>\\\\n#include <logdepthbuf_pars_vertex>\\\\n#include <clipping_planes_pars_vertex>\\\\n\\\\nvoid main() {\\\\n\\\\n\\\\tvLineDistance = scale * lineDistance;\\\\n\\\\n\\\\t#include <color_vertex>\\\\n\\\\t#include <begin_vertex>\\\\n\\\\t#include <morphtarget_vertex>\\\\n\\\\t#include <project_vertex>\\\\n\\\\t#include <logdepthbuf_vertex>\\\\n\\\\t#include <clipping_planes_vertex>\\\\n\\\\t#include <fog_vertex>\\\\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 <common>\\\\n#include <color_pars_fragment>\\\\n#include <fog_pars_fragment>\\\\n#include <logdepthbuf_pars_fragment>\\\\n#include <clipping_planes_pars_fragment>\\\\n\\\\nvoid main() {\\\\n\\\\n\\\\t#include <clipping_planes_fragment>\\\\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 <logdepthbuf_fragment>\\\\n\\\\t#include <color_fragment>\\\\n\\\\n\\\\toutgoingLight = diffuseColor.rgb; // simple shader\\\\n\\\\n\\\\t#include <output_fragment>\\\\n\\\\t#include <tonemapping_fragment>\\\\n\\\\t#include <encodings_fragment>\\\\n\\\\t#include <fog_fragment>\\\\n\\\\t#include <premultiplied_alpha_fragment>\\\\n\\\\n}\\\\n\\\\\\\",meshbasic_vert:\\\\\\\"\\\\n#include <common>\\\\n#include <uv_pars_vertex>\\\\n#include <uv2_pars_vertex>\\\\n#include <envmap_pars_vertex>\\\\n#include <color_pars_vertex>\\\\n#include <fog_pars_vertex>\\\\n#include <morphtarget_pars_vertex>\\\\n#include <skinning_pars_vertex>\\\\n#include <logdepthbuf_pars_vertex>\\\\n#include <clipping_planes_pars_vertex>\\\\n\\\\nvoid main() {\\\\n\\\\n\\\\t#include <uv_vertex>\\\\n\\\\t#include <uv2_vertex>\\\\n\\\\t#include <color_vertex>\\\\n\\\\n\\\\t#if defined ( USE_ENVMAP ) || defined ( USE_SKINNING )\\\\n\\\\n\\\\t\\\\t#include <beginnormal_vertex>\\\\n\\\\t\\\\t#include <morphnormal_vertex>\\\\n\\\\t\\\\t#include <skinbase_vertex>\\\\n\\\\t\\\\t#include <skinnormal_vertex>\\\\n\\\\t\\\\t#include <defaultnormal_vertex>\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\t#include <begin_vertex>\\\\n\\\\t#include <morphtarget_vertex>\\\\n\\\\t#include <skinning_vertex>\\\\n\\\\t#include <project_vertex>\\\\n\\\\t#include <logdepthbuf_vertex>\\\\n\\\\t#include <clipping_planes_vertex>\\\\n\\\\n\\\\t#include <worldpos_vertex>\\\\n\\\\t#include <envmap_vertex>\\\\n\\\\t#include <fog_vertex>\\\\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 <common>\\\\n#include <dithering_pars_fragment>\\\\n#include <color_pars_fragment>\\\\n#include <uv_pars_fragment>\\\\n#include <uv2_pars_fragment>\\\\n#include <map_pars_fragment>\\\\n#include <alphamap_pars_fragment>\\\\n#include <alphatest_pars_fragment>\\\\n#include <aomap_pars_fragment>\\\\n#include <lightmap_pars_fragment>\\\\n#include <envmap_common_pars_fragment>\\\\n#include <envmap_pars_fragment>\\\\n#include <cube_uv_reflection_fragment>\\\\n#include <fog_pars_fragment>\\\\n#include <specularmap_pars_fragment>\\\\n#include <logdepthbuf_pars_fragment>\\\\n#include <clipping_planes_pars_fragment>\\\\n\\\\nvoid main() {\\\\n\\\\n\\\\t#include <clipping_planes_fragment>\\\\n\\\\n\\\\tvec4 diffuseColor = vec4( diffuse, opacity );\\\\n\\\\n\\\\t#include <logdepthbuf_fragment>\\\\n\\\\t#include <map_fragment>\\\\n\\\\t#include <color_fragment>\\\\n\\\\t#include <alphamap_fragment>\\\\n\\\\t#include <alphatest_fragment>\\\\n\\\\t#include <specularmap_fragment>\\\\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 <aomap_fragment>\\\\n\\\\n\\\\treflectedLight.indirectDiffuse *= diffuseColor.rgb;\\\\n\\\\n\\\\tvec3 outgoingLight = reflectedLight.indirectDiffuse;\\\\n\\\\n\\\\t#include <envmap_fragment>\\\\n\\\\n\\\\t#include <output_fragment>\\\\n\\\\t#include <tonemapping_fragment>\\\\n\\\\t#include <encodings_fragment>\\\\n\\\\t#include <fog_fragment>\\\\n\\\\t#include <premultiplied_alpha_fragment>\\\\n\\\\t#include <dithering_fragment>\\\\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 <common>\\\\n#include <uv_pars_vertex>\\\\n#include <uv2_pars_vertex>\\\\n#include <envmap_pars_vertex>\\\\n#include <bsdfs>\\\\n#include <lights_pars_begin>\\\\n#include <color_pars_vertex>\\\\n#include <fog_pars_vertex>\\\\n#include <morphtarget_pars_vertex>\\\\n#include <skinning_pars_vertex>\\\\n#include <shadowmap_pars_vertex>\\\\n#include <logdepthbuf_pars_vertex>\\\\n#include <clipping_planes_pars_vertex>\\\\n\\\\nvoid main() {\\\\n\\\\n\\\\t#include <uv_vertex>\\\\n\\\\t#include <uv2_vertex>\\\\n\\\\t#include <color_vertex>\\\\n\\\\n\\\\t#include <beginnormal_vertex>\\\\n\\\\t#include <morphnormal_vertex>\\\\n\\\\t#include <skinbase_vertex>\\\\n\\\\t#include <skinnormal_vertex>\\\\n\\\\t#include <defaultnormal_vertex>\\\\n\\\\n\\\\t#include <begin_vertex>\\\\n\\\\t#include <morphtarget_vertex>\\\\n\\\\t#include <skinning_vertex>\\\\n\\\\t#include <project_vertex>\\\\n\\\\t#include <logdepthbuf_vertex>\\\\n\\\\t#include <clipping_planes_vertex>\\\\n\\\\n\\\\t#include <worldpos_vertex>\\\\n\\\\t#include <envmap_vertex>\\\\n\\\\t#include <lights_lambert_vertex>\\\\n\\\\t#include <shadowmap_vertex>\\\\n\\\\t#include <fog_vertex>\\\\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 <common>\\\\n#include <packing>\\\\n#include <dithering_pars_fragment>\\\\n#include <color_pars_fragment>\\\\n#include <uv_pars_fragment>\\\\n#include <uv2_pars_fragment>\\\\n#include <map_pars_fragment>\\\\n#include <alphamap_pars_fragment>\\\\n#include <alphatest_pars_fragment>\\\\n#include <aomap_pars_fragment>\\\\n#include <lightmap_pars_fragment>\\\\n#include <emissivemap_pars_fragment>\\\\n#include <envmap_common_pars_fragment>\\\\n#include <envmap_pars_fragment>\\\\n#include <cube_uv_reflection_fragment>\\\\n#include <bsdfs>\\\\n#include <lights_pars_begin>\\\\n#include <fog_pars_fragment>\\\\n#include <shadowmap_pars_fragment>\\\\n#include <shadowmask_pars_fragment>\\\\n#include <specularmap_pars_fragment>\\\\n#include <logdepthbuf_pars_fragment>\\\\n#include <clipping_planes_pars_fragment>\\\\n\\\\nvoid main() {\\\\n\\\\n\\\\t#include <clipping_planes_fragment>\\\\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 <logdepthbuf_fragment>\\\\n\\\\t#include <map_fragment>\\\\n\\\\t#include <color_fragment>\\\\n\\\\t#include <alphamap_fragment>\\\\n\\\\t#include <alphatest_fragment>\\\\n\\\\t#include <specularmap_fragment>\\\\n\\\\t#include <emissivemap_fragment>\\\\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 <lightmap_fragment>\\\\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 <aomap_fragment>\\\\n\\\\n\\\\tvec3 outgoingLight = reflectedLight.directDiffuse + reflectedLight.indirectDiffuse + totalEmissiveRadiance;\\\\n\\\\n\\\\t#include <envmap_fragment>\\\\n\\\\n\\\\t#include <output_fragment>\\\\n\\\\t#include <tonemapping_fragment>\\\\n\\\\t#include <encodings_fragment>\\\\n\\\\t#include <fog_fragment>\\\\n\\\\t#include <premultiplied_alpha_fragment>\\\\n\\\\t#include <dithering_fragment>\\\\n}\\\\n\\\\\\\",meshmatcap_vert:\\\\\\\"\\\\n#define MATCAP\\\\n\\\\nvarying vec3 vViewPosition;\\\\n\\\\n#include <common>\\\\n#include <uv_pars_vertex>\\\\n#include <color_pars_vertex>\\\\n#include <displacementmap_pars_vertex>\\\\n#include <fog_pars_vertex>\\\\n#include <normal_pars_vertex>\\\\n#include <morphtarget_pars_vertex>\\\\n#include <skinning_pars_vertex>\\\\n\\\\n#include <logdepthbuf_pars_vertex>\\\\n#include <clipping_planes_pars_vertex>\\\\n\\\\nvoid main() {\\\\n\\\\n\\\\t#include <uv_vertex>\\\\n\\\\t#include <color_vertex>\\\\n\\\\t#include <beginnormal_vertex>\\\\n\\\\t#include <morphnormal_vertex>\\\\n\\\\t#include <skinbase_vertex>\\\\n\\\\t#include <skinnormal_vertex>\\\\n\\\\t#include <defaultnormal_vertex>\\\\n\\\\t#include <normal_vertex>\\\\n\\\\n\\\\t#include <begin_vertex>\\\\n\\\\t#include <morphtarget_vertex>\\\\n\\\\t#include <skinning_vertex>\\\\n\\\\t#include <displacementmap_vertex>\\\\n\\\\t#include <project_vertex>\\\\n\\\\n\\\\t#include <logdepthbuf_vertex>\\\\n\\\\t#include <clipping_planes_vertex>\\\\n\\\\t#include <fog_vertex>\\\\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 <common>\\\\n#include <dithering_pars_fragment>\\\\n#include <color_pars_fragment>\\\\n#include <uv_pars_fragment>\\\\n#include <map_pars_fragment>\\\\n#include <alphamap_pars_fragment>\\\\n#include <alphatest_pars_fragment>\\\\n#include <fog_pars_fragment>\\\\n#include <normal_pars_fragment>\\\\n#include <bumpmap_pars_fragment>\\\\n#include <normalmap_pars_fragment>\\\\n#include <logdepthbuf_pars_fragment>\\\\n#include <clipping_planes_pars_fragment>\\\\n\\\\nvoid main() {\\\\n\\\\n\\\\t#include <clipping_planes_fragment>\\\\n\\\\n\\\\tvec4 diffuseColor = vec4( diffuse, opacity );\\\\n\\\\n\\\\t#include <logdepthbuf_fragment>\\\\n\\\\t#include <map_fragment>\\\\n\\\\t#include <color_fragment>\\\\n\\\\t#include <alphamap_fragment>\\\\n\\\\t#include <alphatest_fragment>\\\\n\\\\t#include <normal_fragment_begin>\\\\n\\\\t#include <normal_fragment_maps>\\\\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 <output_fragment>\\\\n\\\\t#include <tonemapping_fragment>\\\\n\\\\t#include <encodings_fragment>\\\\n\\\\t#include <fog_fragment>\\\\n\\\\t#include <premultiplied_alpha_fragment>\\\\n\\\\t#include <dithering_fragment>\\\\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 <common>\\\\n#include <uv_pars_vertex>\\\\n#include <displacementmap_pars_vertex>\\\\n#include <normal_pars_vertex>\\\\n#include <morphtarget_pars_vertex>\\\\n#include <skinning_pars_vertex>\\\\n#include <logdepthbuf_pars_vertex>\\\\n#include <clipping_planes_pars_vertex>\\\\n\\\\nvoid main() {\\\\n\\\\n\\\\t#include <uv_vertex>\\\\n\\\\n\\\\t#include <beginnormal_vertex>\\\\n\\\\t#include <morphnormal_vertex>\\\\n\\\\t#include <skinbase_vertex>\\\\n\\\\t#include <skinnormal_vertex>\\\\n\\\\t#include <defaultnormal_vertex>\\\\n\\\\t#include <normal_vertex>\\\\n\\\\n\\\\t#include <begin_vertex>\\\\n\\\\t#include <morphtarget_vertex>\\\\n\\\\t#include <skinning_vertex>\\\\n\\\\t#include <displacementmap_vertex>\\\\n\\\\t#include <project_vertex>\\\\n\\\\t#include <logdepthbuf_vertex>\\\\n\\\\t#include <clipping_planes_vertex>\\\\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 <packing>\\\\n#include <uv_pars_fragment>\\\\n#include <normal_pars_fragment>\\\\n#include <bumpmap_pars_fragment>\\\\n#include <normalmap_pars_fragment>\\\\n#include <logdepthbuf_pars_fragment>\\\\n#include <clipping_planes_pars_fragment>\\\\n\\\\nvoid main() {\\\\n\\\\n\\\\t#include <clipping_planes_fragment>\\\\n\\\\t#include <logdepthbuf_fragment>\\\\n\\\\t#include <normal_fragment_begin>\\\\n\\\\t#include <normal_fragment_maps>\\\\n\\\\n\\\\tgl_FragColor = vec4( packNormalToRGB( normal ), opacity );\\\\n\\\\n}\\\\n\\\\\\\",meshphong_vert:\\\\\\\"\\\\n#define PHONG\\\\n\\\\nvarying vec3 vViewPosition;\\\\n\\\\n#include <common>\\\\n#include <uv_pars_vertex>\\\\n#include <uv2_pars_vertex>\\\\n#include <displacementmap_pars_vertex>\\\\n#include <envmap_pars_vertex>\\\\n#include <color_pars_vertex>\\\\n#include <fog_pars_vertex>\\\\n#include <normal_pars_vertex>\\\\n#include <morphtarget_pars_vertex>\\\\n#include <skinning_pars_vertex>\\\\n#include <shadowmap_pars_vertex>\\\\n#include <logdepthbuf_pars_vertex>\\\\n#include <clipping_planes_pars_vertex>\\\\n\\\\nvoid main() {\\\\n\\\\n\\\\t#include <uv_vertex>\\\\n\\\\t#include <uv2_vertex>\\\\n\\\\t#include <color_vertex>\\\\n\\\\n\\\\t#include <beginnormal_vertex>\\\\n\\\\t#include <morphnormal_vertex>\\\\n\\\\t#include <skinbase_vertex>\\\\n\\\\t#include <skinnormal_vertex>\\\\n\\\\t#include <defaultnormal_vertex>\\\\n\\\\t#include <normal_vertex>\\\\n\\\\n\\\\t#include <begin_vertex>\\\\n\\\\t#include <morphtarget_vertex>\\\\n\\\\t#include <skinning_vertex>\\\\n\\\\t#include <displacementmap_vertex>\\\\n\\\\t#include <project_vertex>\\\\n\\\\t#include <logdepthbuf_vertex>\\\\n\\\\t#include <clipping_planes_vertex>\\\\n\\\\n\\\\tvViewPosition = - mvPosition.xyz;\\\\n\\\\n\\\\t#include <worldpos_vertex>\\\\n\\\\t#include <envmap_vertex>\\\\n\\\\t#include <shadowmap_vertex>\\\\n\\\\t#include <fog_vertex>\\\\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 <common>\\\\n#include <packing>\\\\n#include <dithering_pars_fragment>\\\\n#include <color_pars_fragment>\\\\n#include <uv_pars_fragment>\\\\n#include <uv2_pars_fragment>\\\\n#include <map_pars_fragment>\\\\n#include <alphamap_pars_fragment>\\\\n#include <alphatest_pars_fragment>\\\\n#include <aomap_pars_fragment>\\\\n#include <lightmap_pars_fragment>\\\\n#include <emissivemap_pars_fragment>\\\\n#include <envmap_common_pars_fragment>\\\\n#include <envmap_pars_fragment>\\\\n#include <cube_uv_reflection_fragment>\\\\n#include <fog_pars_fragment>\\\\n#include <bsdfs>\\\\n#include <lights_pars_begin>\\\\n#include <normal_pars_fragment>\\\\n#include <lights_phong_pars_fragment>\\\\n#include <shadowmap_pars_fragment>\\\\n#include <bumpmap_pars_fragment>\\\\n#include <normalmap_pars_fragment>\\\\n#include <specularmap_pars_fragment>\\\\n#include <logdepthbuf_pars_fragment>\\\\n#include <clipping_planes_pars_fragment>\\\\n\\\\nvoid main() {\\\\n\\\\n\\\\t#include <clipping_planes_fragment>\\\\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 <logdepthbuf_fragment>\\\\n\\\\t#include <map_fragment>\\\\n\\\\t#include <color_fragment>\\\\n\\\\t#include <alphamap_fragment>\\\\n\\\\t#include <alphatest_fragment>\\\\n\\\\t#include <specularmap_fragment>\\\\n\\\\t#include <normal_fragment_begin>\\\\n\\\\t#include <normal_fragment_maps>\\\\n\\\\t#include <emissivemap_fragment>\\\\n\\\\n\\\\t// accumulation\\\\n\\\\t#include <lights_phong_fragment>\\\\n\\\\t#include <lights_fragment_begin>\\\\n\\\\t#include <lights_fragment_maps>\\\\n\\\\t#include <lights_fragment_end>\\\\n\\\\n\\\\t// modulation\\\\n\\\\t#include <aomap_fragment>\\\\n\\\\n\\\\tvec3 outgoingLight = reflectedLight.directDiffuse + reflectedLight.indirectDiffuse + reflectedLight.directSpecular + reflectedLight.indirectSpecular + totalEmissiveRadiance;\\\\n\\\\n\\\\t#include <envmap_fragment>\\\\n\\\\t#include <output_fragment>\\\\n\\\\t#include <tonemapping_fragment>\\\\n\\\\t#include <encodings_fragment>\\\\n\\\\t#include <fog_fragment>\\\\n\\\\t#include <premultiplied_alpha_fragment>\\\\n\\\\t#include <dithering_fragment>\\\\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 <common>\\\\n#include <uv_pars_vertex>\\\\n#include <uv2_pars_vertex>\\\\n#include <displacementmap_pars_vertex>\\\\n#include <color_pars_vertex>\\\\n#include <fog_pars_vertex>\\\\n#include <normal_pars_vertex>\\\\n#include <morphtarget_pars_vertex>\\\\n#include <skinning_pars_vertex>\\\\n#include <shadowmap_pars_vertex>\\\\n#include <logdepthbuf_pars_vertex>\\\\n#include <clipping_planes_pars_vertex>\\\\n\\\\nvoid main() {\\\\n\\\\n\\\\t#include <uv_vertex>\\\\n\\\\t#include <uv2_vertex>\\\\n\\\\t#include <color_vertex>\\\\n\\\\n\\\\t#include <beginnormal_vertex>\\\\n\\\\t#include <morphnormal_vertex>\\\\n\\\\t#include <skinbase_vertex>\\\\n\\\\t#include <skinnormal_vertex>\\\\n\\\\t#include <defaultnormal_vertex>\\\\n\\\\t#include <normal_vertex>\\\\n\\\\n\\\\t#include <begin_vertex>\\\\n\\\\t#include <morphtarget_vertex>\\\\n\\\\t#include <skinning_vertex>\\\\n\\\\t#include <displacementmap_vertex>\\\\n\\\\t#include <project_vertex>\\\\n\\\\t#include <logdepthbuf_vertex>\\\\n\\\\t#include <clipping_planes_vertex>\\\\n\\\\n\\\\tvViewPosition = - mvPosition.xyz;\\\\n\\\\n\\\\t#include <worldpos_vertex>\\\\n\\\\t#include <shadowmap_vertex>\\\\n\\\\t#include <fog_vertex>\\\\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 <common>\\\\n#include <packing>\\\\n#include <dithering_pars_fragment>\\\\n#include <color_pars_fragment>\\\\n#include <uv_pars_fragment>\\\\n#include <uv2_pars_fragment>\\\\n#include <map_pars_fragment>\\\\n#include <alphamap_pars_fragment>\\\\n#include <alphatest_pars_fragment>\\\\n#include <aomap_pars_fragment>\\\\n#include <lightmap_pars_fragment>\\\\n#include <emissivemap_pars_fragment>\\\\n#include <bsdfs>\\\\n#include <cube_uv_reflection_fragment>\\\\n#include <envmap_common_pars_fragment>\\\\n#include <envmap_physical_pars_fragment>\\\\n#include <fog_pars_fragment>\\\\n#include <lights_pars_begin>\\\\n#include <normal_pars_fragment>\\\\n#include <lights_physical_pars_fragment>\\\\n#include <transmission_pars_fragment>\\\\n#include <shadowmap_pars_fragment>\\\\n#include <bumpmap_pars_fragment>\\\\n#include <normalmap_pars_fragment>\\\\n#include <clearcoat_pars_fragment>\\\\n#include <roughnessmap_pars_fragment>\\\\n#include <metalnessmap_pars_fragment>\\\\n#include <logdepthbuf_pars_fragment>\\\\n#include <clipping_planes_pars_fragment>\\\\n\\\\nvoid main() {\\\\n\\\\n\\\\t#include <clipping_planes_fragment>\\\\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 <logdepthbuf_fragment>\\\\n\\\\t#include <map_fragment>\\\\n\\\\t#include <color_fragment>\\\\n\\\\t#include <alphamap_fragment>\\\\n\\\\t#include <alphatest_fragment>\\\\n\\\\t#include <roughnessmap_fragment>\\\\n\\\\t#include <metalnessmap_fragment>\\\\n\\\\t#include <normal_fragment_begin>\\\\n\\\\t#include <normal_fragment_maps>\\\\n\\\\t#include <clearcoat_normal_fragment_begin>\\\\n\\\\t#include <clearcoat_normal_fragment_maps>\\\\n\\\\t#include <emissivemap_fragment>\\\\n\\\\n\\\\t// accumulation\\\\n\\\\t#include <lights_physical_fragment>\\\\n\\\\t#include <lights_fragment_begin>\\\\n\\\\t#include <lights_fragment_maps>\\\\n\\\\t#include <lights_fragment_end>\\\\n\\\\n\\\\t// modulation\\\\n\\\\t#include <aomap_fragment>\\\\n\\\\n\\\\tvec3 totalDiffuse = reflectedLight.directDiffuse + reflectedLight.indirectDiffuse;\\\\n\\\\tvec3 totalSpecular = reflectedLight.directSpecular + reflectedLight.indirectSpecular;\\\\n\\\\n\\\\t#include <transmission_fragment>\\\\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 <output_fragment>\\\\n\\\\t#include <tonemapping_fragment>\\\\n\\\\t#include <encodings_fragment>\\\\n\\\\t#include <fog_fragment>\\\\n\\\\t#include <premultiplied_alpha_fragment>\\\\n\\\\t#include <dithering_fragment>\\\\n\\\\n}\\\\n\\\\\\\",meshtoon_vert:\\\\\\\"\\\\n#define TOON\\\\n\\\\nvarying vec3 vViewPosition;\\\\n\\\\n#include <common>\\\\n#include <uv_pars_vertex>\\\\n#include <uv2_pars_vertex>\\\\n#include <displacementmap_pars_vertex>\\\\n#include <color_pars_vertex>\\\\n#include <fog_pars_vertex>\\\\n#include <normal_pars_vertex>\\\\n#include <morphtarget_pars_vertex>\\\\n#include <skinning_pars_vertex>\\\\n#include <shadowmap_pars_vertex>\\\\n#include <logdepthbuf_pars_vertex>\\\\n#include <clipping_planes_pars_vertex>\\\\n\\\\nvoid main() {\\\\n\\\\n\\\\t#include <uv_vertex>\\\\n\\\\t#include <uv2_vertex>\\\\n\\\\t#include <color_vertex>\\\\n\\\\n\\\\t#include <beginnormal_vertex>\\\\n\\\\t#include <morphnormal_vertex>\\\\n\\\\t#include <skinbase_vertex>\\\\n\\\\t#include <skinnormal_vertex>\\\\n\\\\t#include <defaultnormal_vertex>\\\\n\\\\t#include <normal_vertex>\\\\n\\\\n\\\\t#include <begin_vertex>\\\\n\\\\t#include <morphtarget_vertex>\\\\n\\\\t#include <skinning_vertex>\\\\n\\\\t#include <displacementmap_vertex>\\\\n\\\\t#include <project_vertex>\\\\n\\\\t#include <logdepthbuf_vertex>\\\\n\\\\t#include <clipping_planes_vertex>\\\\n\\\\n\\\\tvViewPosition = - mvPosition.xyz;\\\\n\\\\n\\\\t#include <worldpos_vertex>\\\\n\\\\t#include <shadowmap_vertex>\\\\n\\\\t#include <fog_vertex>\\\\n\\\\n}\\\\n\\\\\\\",meshtoon_frag:\\\\\\\"\\\\n#define TOON\\\\n\\\\nuniform vec3 diffuse;\\\\nuniform vec3 emissive;\\\\nuniform float opacity;\\\\n\\\\n#include <common>\\\\n#include <packing>\\\\n#include <dithering_pars_fragment>\\\\n#include <color_pars_fragment>\\\\n#include <uv_pars_fragment>\\\\n#include <uv2_pars_fragment>\\\\n#include <map_pars_fragment>\\\\n#include <alphamap_pars_fragment>\\\\n#include <alphatest_pars_fragment>\\\\n#include <aomap_pars_fragment>\\\\n#include <lightmap_pars_fragment>\\\\n#include <emissivemap_pars_fragment>\\\\n#include <gradientmap_pars_fragment>\\\\n#include <fog_pars_fragment>\\\\n#include <bsdfs>\\\\n#include <lights_pars_begin>\\\\n#include <normal_pars_fragment>\\\\n#include <lights_toon_pars_fragment>\\\\n#include <shadowmap_pars_fragment>\\\\n#include <bumpmap_pars_fragment>\\\\n#include <normalmap_pars_fragment>\\\\n#include <logdepthbuf_pars_fragment>\\\\n#include <clipping_planes_pars_fragment>\\\\n\\\\nvoid main() {\\\\n\\\\n\\\\t#include <clipping_planes_fragment>\\\\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 <logdepthbuf_fragment>\\\\n\\\\t#include <map_fragment>\\\\n\\\\t#include <color_fragment>\\\\n\\\\t#include <alphamap_fragment>\\\\n\\\\t#include <alphatest_fragment>\\\\n\\\\t#include <normal_fragment_begin>\\\\n\\\\t#include <normal_fragment_maps>\\\\n\\\\t#include <emissivemap_fragment>\\\\n\\\\n\\\\t// accumulation\\\\n\\\\t#include <lights_toon_fragment>\\\\n\\\\t#include <lights_fragment_begin>\\\\n\\\\t#include <lights_fragment_maps>\\\\n\\\\t#include <lights_fragment_end>\\\\n\\\\n\\\\t// modulation\\\\n\\\\t#include <aomap_fragment>\\\\n\\\\n\\\\tvec3 outgoingLight = reflectedLight.directDiffuse + reflectedLight.indirectDiffuse + totalEmissiveRadiance;\\\\n\\\\n\\\\t#include <output_fragment>\\\\n\\\\t#include <tonemapping_fragment>\\\\n\\\\t#include <encodings_fragment>\\\\n\\\\t#include <fog_fragment>\\\\n\\\\t#include <premultiplied_alpha_fragment>\\\\n\\\\t#include <dithering_fragment>\\\\n\\\\n}\\\\n\\\\\\\",points_vert:\\\\\\\"\\\\nuniform float size;\\\\nuniform float scale;\\\\n\\\\n#include <common>\\\\n#include <color_pars_vertex>\\\\n#include <fog_pars_vertex>\\\\n#include <morphtarget_pars_vertex>\\\\n#include <logdepthbuf_pars_vertex>\\\\n#include <clipping_planes_pars_vertex>\\\\n\\\\nvoid main() {\\\\n\\\\n\\\\t#include <color_vertex>\\\\n\\\\t#include <begin_vertex>\\\\n\\\\t#include <morphtarget_vertex>\\\\n\\\\t#include <project_vertex>\\\\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 <logdepthbuf_vertex>\\\\n\\\\t#include <clipping_planes_vertex>\\\\n\\\\t#include <worldpos_vertex>\\\\n\\\\t#include <fog_vertex>\\\\n\\\\n}\\\\n\\\\\\\",points_frag:\\\\\\\"\\\\nuniform vec3 diffuse;\\\\nuniform float opacity;\\\\n\\\\n#include <common>\\\\n#include <color_pars_fragment>\\\\n#include <map_particle_pars_fragment>\\\\n#include <alphatest_pars_fragment>\\\\n#include <fog_pars_fragment>\\\\n#include <logdepthbuf_pars_fragment>\\\\n#include <clipping_planes_pars_fragment>\\\\n\\\\nvoid main() {\\\\n\\\\n\\\\t#include <clipping_planes_fragment>\\\\n\\\\n\\\\tvec3 outgoingLight = vec3( 0.0 );\\\\n\\\\tvec4 diffuseColor = vec4( diffuse, opacity );\\\\n\\\\n\\\\t#include <logdepthbuf_fragment>\\\\n\\\\t#include <map_particle_fragment>\\\\n\\\\t#include <color_fragment>\\\\n\\\\t#include <alphatest_fragment>\\\\n\\\\n\\\\toutgoingLight = diffuseColor.rgb;\\\\n\\\\n\\\\t#include <output_fragment>\\\\n\\\\t#include <tonemapping_fragment>\\\\n\\\\t#include <encodings_fragment>\\\\n\\\\t#include <fog_fragment>\\\\n\\\\t#include <premultiplied_alpha_fragment>\\\\n\\\\n}\\\\n\\\\\\\",shadow_vert:\\\\\\\"\\\\n#include <common>\\\\n#include <fog_pars_vertex>\\\\n#include <morphtarget_pars_vertex>\\\\n#include <skinning_pars_vertex>\\\\n#include <shadowmap_pars_vertex>\\\\n\\\\nvoid main() {\\\\n\\\\n\\\\t#include <beginnormal_vertex>\\\\n\\\\t#include <morphnormal_vertex>\\\\n\\\\t#include <skinbase_vertex>\\\\n\\\\t#include <skinnormal_vertex>\\\\n\\\\t#include <defaultnormal_vertex>\\\\n\\\\n\\\\t#include <begin_vertex>\\\\n\\\\t#include <morphtarget_vertex>\\\\n\\\\t#include <skinning_vertex>\\\\n\\\\t#include <project_vertex>\\\\n\\\\n\\\\t#include <worldpos_vertex>\\\\n\\\\t#include <shadowmap_vertex>\\\\n\\\\t#include <fog_vertex>\\\\n\\\\n}\\\\n\\\\\\\",shadow_frag:\\\\\\\"\\\\nuniform vec3 color;\\\\nuniform float opacity;\\\\n\\\\n#include <common>\\\\n#include <packing>\\\\n#include <fog_pars_fragment>\\\\n#include <bsdfs>\\\\n#include <lights_pars_begin>\\\\n#include <shadowmap_pars_fragment>\\\\n#include <shadowmask_pars_fragment>\\\\n\\\\nvoid main() {\\\\n\\\\n\\\\tgl_FragColor = vec4( color, opacity * ( 1.0 - getShadowMask() ) );\\\\n\\\\n\\\\t#include <tonemapping_fragment>\\\\n\\\\t#include <encodings_fragment>\\\\n\\\\t#include <fog_fragment>\\\\n\\\\n}\\\\n\\\\\\\",sprite_vert:\\\\\\\"\\\\nuniform float rotation;\\\\nuniform vec2 center;\\\\n\\\\n#include <common>\\\\n#include <uv_pars_vertex>\\\\n#include <fog_pars_vertex>\\\\n#include <logdepthbuf_pars_vertex>\\\\n#include <clipping_planes_pars_vertex>\\\\n\\\\nvoid main() {\\\\n\\\\n\\\\t#include <uv_vertex>\\\\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 <logdepthbuf_vertex>\\\\n\\\\t#include <clipping_planes_vertex>\\\\n\\\\t#include <fog_vertex>\\\\n\\\\n}\\\\n\\\\\\\",sprite_frag:\\\\\\\"\\\\nuniform vec3 diffuse;\\\\nuniform float opacity;\\\\n\\\\n#include <common>\\\\n#include <uv_pars_fragment>\\\\n#include <map_pars_fragment>\\\\n#include <alphamap_pars_fragment>\\\\n#include <alphatest_pars_fragment>\\\\n#include <fog_pars_fragment>\\\\n#include <logdepthbuf_pars_fragment>\\\\n#include <clipping_planes_pars_fragment>\\\\n\\\\nvoid main() {\\\\n\\\\n\\\\t#include <clipping_planes_fragment>\\\\n\\\\n\\\\tvec3 outgoingLight = vec3( 0.0 );\\\\n\\\\tvec4 diffuseColor = vec4( diffuse, opacity );\\\\n\\\\n\\\\t#include <logdepthbuf_fragment>\\\\n\\\\t#include <map_fragment>\\\\n\\\\t#include <alphamap_fragment>\\\\n\\\\t#include <alphatest_fragment>\\\\n\\\\n\\\\toutgoingLight = diffuseColor.rgb;\\\\n\\\\n\\\\t#include <output_fragment>\\\\n\\\\t#include <tonemapping_fragment>\\\\n\\\\t#include <encodings_fragment>\\\\n\\\\t#include <fog_fragment>\\\\n\\\\n}\\\\n\\\\\\\"};var U=n(11);const G={common:{diffuse:{value:new D.a(16777215)},opacity:{value:1},map:{value:null},uvTransform:{value:new U.a},uv2Transform:{value:new U.a},alphaMap:{value:null},alphaTest:{value:0}},specularmap:{specularMap:{value:null}},envmap:{envMap:{value:null},flipEnvMap:{value:-1},reflectivity:{value:1},ior:{value:1.5},refractionRatio:{value:.98},maxMipLevel:{value:0}},aomap:{aoMap:{value:null},aoMapIntensity:{value:1}},lightmap:{lightMap:{value:null},lightMapIntensity:{value:1}},emissivemap:{emissiveMap:{value:null}},bumpmap:{bumpMap:{value:null},bumpScale:{value:1}},normalmap:{normalMap:{value:null},normalScale:{value:new d.a(1,1)}},displacementmap:{displacementMap:{value:null},displacementScale:{value:1},displacementBias:{value:0}},roughnessmap:{roughnessMap:{value:null}},metalnessmap:{metalnessMap:{value:null}},gradientmap:{gradientMap:{value:null}},fog:{fogDensity:{value:25e-5},fogNear:{value:1},fogFar:{value:2e3},fogColor:{value:new D.a(16777215)}},lights:{ambientLightColor:{value:[]},lightProbe:{value:[]},directionalLights:{value:[],properties:{direction:{},color:{}}},directionalLightShadows:{value:[],properties:{shadowBias:{},shadowNormalBias:{},shadowRadius:{},shadowMapSize:{}}},directionalShadowMap:{value:[]},directionalShadowMatrix:{value:[]},spotLights:{value:[],properties:{color:{},position:{},direction:{},distance:{},coneCos:{},penumbraCos:{},decay:{}}},spotLightShadows:{value:[],properties:{shadowBias:{},shadowNormalBias:{},shadowRadius:{},shadowMapSize:{}}},spotShadowMap:{value:[]},spotShadowMatrix:{value:[]},pointLights:{value:[],properties:{color:{},position:{},decay:{},distance:{}}},pointLightShadows:{value:[],properties:{shadowBias:{},shadowNormalBias:{},shadowRadius:{},shadowMapSize:{},shadowCameraNear:{},shadowCameraFar:{}}},pointShadowMap:{value:[]},pointShadowMatrix:{value:[]},hemisphereLights:{value:[],properties:{direction:{},skyColor:{},groundColor:{}}},rectAreaLights:{value:[],properties:{color:{},position:{},width:{},height:{}}},ltc_1:{value:null},ltc_2:{value:null}},points:{diffuse:{value:new D.a(16777215)},opacity:{value:1},size:{value:1},scale:{value:1},map:{value:null},alphaMap:{value:null},alphaTest:{value:0},uvTransform:{value:new U.a}},sprite:{diffuse:{value:new D.a(16777215)},opacity:{value:1},center:{value:new d.a(.5,.5)},rotation:{value:0},map:{value:null},alphaMap:{value:null},alphaTest:{value:0},uvTransform:{value:new U.a}}},V={basic:{uniforms:P([G.common,G.specularmap,G.envmap,G.aomap,G.lightmap,G.fog]),vertexShader:z.meshbasic_vert,fragmentShader:z.meshbasic_frag},lambert:{uniforms:P([G.common,G.specularmap,G.envmap,G.aomap,G.lightmap,G.emissivemap,G.fog,G.lights,{emissive:{value:new D.a(0)}}]),vertexShader:z.meshlambert_vert,fragmentShader:z.meshlambert_frag},phong:{uniforms:P([G.common,G.specularmap,G.envmap,G.aomap,G.lightmap,G.emissivemap,G.bumpmap,G.normalmap,G.displacementmap,G.fog,G.lights,{emissive:{value:new D.a(0)},specular:{value:new D.a(1118481)},shininess:{value:30}}]),vertexShader:z.meshphong_vert,fragmentShader:z.meshphong_frag},standard:{uniforms:P([G.common,G.envmap,G.aomap,G.lightmap,G.emissivemap,G.bumpmap,G.normalmap,G.displacementmap,G.roughnessmap,G.metalnessmap,G.fog,G.lights,{emissive:{value:new D.a(0)},roughness:{value:1},metalness:{value:0},envMapIntensity:{value:1}}]),vertexShader:z.meshphysical_vert,fragmentShader:z.meshphysical_frag},toon:{uniforms:P([G.common,G.aomap,G.lightmap,G.emissivemap,G.bumpmap,G.normalmap,G.displacementmap,G.gradientmap,G.fog,G.lights,{emissive:{value:new 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i},getMaxPrecision:r,precision:o,logarithmicDepthBuffer:c,maxTextures:u,maxVertexTextures:h,maxTextureSize:d,maxCubemapSize:p,maxAttributes:_,maxVertexUniforms:m,maxVaryings:f,maxFragmentUniforms:g,vertexTextures:v,floatFragmentTextures:y,floatVertexTextures:v&&y,maxSamples:s?t.getParameter(t.MAX_SAMPLES):0}}V.physical={uniforms:P([V.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:z.meshphysical_vert,fragmentShader:z.meshphysical_frag};var X=n(34);function Y(t){const e=this;let n=null,i=0,r=!1,s=!1;const o=new X.a,a=new U.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 u(t,n,i,r){const s=null!==t?t.length:0;let c=null;if(0!==s){if(c=l.value,!0!==r||null===c){const e=i+4*s,r=n.matrixWorldInverse;a.getNormalMatrix(r),(null===c||c.length<e)&&(c=new Float32Array(e));for(let e=0,n=i;e!==s;++e,n+=4)o.copy(t[e]).applyMatrix4(r,a),o.normal.toArray(c,n),c[n+3]=o.constant}l.value=c,l.needsUpdate=!0}return e.numPlanes=s,e.numIntersection=0,c}this.uniform=l,this.numPlanes=0,this.numIntersection=0,this.init=function(t,e,s){const o=0!==t.length||e||0!==i||r;return r=e,n=u(t,s,0),i=t.length,o},this.beginShadows=function(){s=!0,u(null)},this.endShadows=function(){s=!1,c()},this.setState=function(e,o,a){const h=e.clippingPlanes,d=e.clipIntersection,p=e.clipShadows,_=t.get(e);if(!r||null===h||0===h.length||s&&!p)s?u(null):c();else{const t=s?0:i,e=4*t;let r=_.clippingState||null;l.value=r,r=u(h,o,e,a);for(let t=0;t!==e;++t)r[t]=n[t];_.clippingState=r,this.numIntersection=d?this.numPlanes:0,this.numPlanes+=t}}}var $=n(15),J=n(23);class Z extends $.a{constructor(t,e,n={}){super(),this.width=t,this.height=e,this.depth=1,this.scissor=new _.a(0,0,t,e),this.scissorTest=!1,this.viewport=new _.a(0,0,t,e),this.texture=new J.a(void 0,n.mapping,n.wrapS,n.wrapT,n.magFilter,n.minFilter,n.format,n.type,n.anisotropy,n.encoding),this.texture.isRenderTargetTexture=!0,this.texture.image={width:t,height:e,depth:1},this.texture.generateMipmaps=void 0!==n.generateMipmaps&&n.generateMipmaps,this.texture.internalFormat=void 0!==n.internalFormat?n.internalFormat:null,this.texture.minFilter=void 0!==n.minFilter?n.minFilter:w.V,this.depthBuffer=void 0===n.depthBuffer||n.depthBuffer,this.stencilBuffer=void 0!==n.stencilBuffer&&n.stencilBuffer,this.depthTexture=void 0!==n.depthTexture?n.depthTexture:null}setTexture(t){t.image={width:this.width,height:this.height,depth:this.depth},this.texture=t}setSize(t,e,n=1){this.width===t&&this.height===e&&this.depth===n||(this.width=t,this.height=e,this.depth=n,this.texture.image.width=t,this.texture.image.height=e,this.texture.image.depth=n,this.dispose()),this.viewport.set(0,0,t,e),this.scissor.set(0,0,t,e)}clone(){return(new this.constructor).copy(this)}copy(t){return this.width=t.width,this.height=t.height,this.depth=t.depth,this.viewport.copy(t.viewport),this.texture=t.texture.clone(),this.texture.image={...this.texture.image},this.depthBuffer=t.depthBuffer,this.stencilBuffer=t.stencilBuffer,this.depthTexture=t.depthTexture,this}dispose(){this.dispatchEvent({type:\\\\\\\"dispose\\\\\\\"})}}Z.prototype.isWebGLRenderTarget=!0;var Q=n(10),K=n(30);const tt=90;class et extends Q.a{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 K.a(tt,1,t,e);i.layers=this.layers,i.up.set(0,-1,0),i.lookAt(new p.a(1,0,0)),this.add(i);const r=new K.a(tt,1,t,e);r.layers=this.layers,r.up.set(0,-1,0),r.lookAt(new p.a(-1,0,0)),this.add(r);const s=new K.a(tt,1,t,e);s.layers=this.layers,s.up.set(0,0,1),s.lookAt(new p.a(0,1,0)),this.add(s);const o=new K.a(tt,1,t,e);o.layers=this.layers,o.up.set(0,0,-1),o.lookAt(new p.a(0,-1,0)),this.add(o);const a=new K.a(tt,1,t,e);a.layers=this.layers,a.up.set(0,-1,0),a.lookAt(new p.a(0,0,1)),this.add(a);const l=new K.a(tt,1,t,e);l.layers=this.layers,l.up.set(0,-1,0),l.lookAt(new p.a(0,0,-1)),this.add(l)}update(t,e){null===this.parent&&this.updateMatrixWorld();const n=this.renderTarget,[i,r,s,o,a,l]=this.children,c=t.xr.enabled,u=t.getRenderTarget();t.xr.enabled=!1;const h=n.texture.generateMipmaps;n.texture.generateMipmaps=!1,t.setRenderTarget(n,0),t.render(e,i),t.setRenderTarget(n,1),t.render(e,r),t.setRenderTarget(n,2),t.render(e,s),t.setRenderTarget(n,3),t.render(e,o),t.setRenderTarget(n,4),t.render(e,a),n.texture.generateMipmaps=h,t.setRenderTarget(n,5),t.render(e,l),t.setRenderTarget(u),t.xr.enabled=c}}class nt extends J.a{constructor(t,e,n,i,r,s,o,a,l,c){super(t=void 0!==t?t:[],e=void 0!==e?e:w.o,n,i,r,s,o,a,l,c),this.flipY=!1}get images(){return this.image}set images(t){this.image=t}}nt.prototype.isCubeTexture=!0;class it extends Z{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:w.V,this.texture._needsFlipEnvMap=!1}fromEquirectangularTexture(t,e){this.texture.type=e.type,this.texture.format=w.Ib,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 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et(1,10,this).update(t,s),e.minFilter=o,s.geometry.dispose(),s.material.dispose(),this}clear(t,e,n,i){const r=t.getRenderTarget();for(let r=0;r<6;r++)t.setRenderTarget(this,r),t.clear(e,n,i);t.setRenderTarget(r)}}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 r=e.get(n);void 0!==r&&(e.delete(n),r.dispose())}return{get:function(r){if(r&&r.isTexture&&!1===r.isRenderTargetTexture){const s=r.mapping;if(s===w.D||s===w.E){if(e.has(r)){return n(e.get(r).texture,r.mapping)}{const s=r.image;if(s&&s.height>0){const o=t.getRenderTarget(),a=new it(s.height/2);return a.fromEquirectangularTexture(t,r),e.set(r,a),t.setRenderTarget(o),r.addEventListener(\\\\\\\"dispose\\\\\\\",i),n(a.texture,r.mapping)}return null}}}return r},dispose:function(){e=new WeakMap}}}it.prototype.isWebGLCubeRenderTarget=!0;var st=n(37);class ot extends F{constructor(t){super(t),this.type=\\\\\\\"RawShaderMaterial\\\\\\\"}}ot.prototype.isRawShaderMaterial=!0;var at=n(27);const lt=Math.pow(2,8),ct=[.125,.215,.35,.446,.526,.582],ut=5+ct.length,ht=20,dt={[w.U]:0,[w.ld]:1,[w.gc]:2,[w.lc]:3,[w.kc]:4,[w.fc]:5,[w.J]:6},pt=new st.a,{_lodPlanes:_t,_sizeLods:mt,_sigmas:ft}=At(),gt=new D.a;let vt=null;const yt=(1+Math.sqrt(5))/2,xt=1/yt,bt=[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,yt,xt),new p.a(0,yt,-xt),new p.a(xt,0,yt),new p.a(-xt,0,yt),new p.a(yt,xt,0),new p.a(-yt,xt,0)];class wt{constructor(t){this._renderer=t,this._pingPongRenderTarget=null,this._blurMaterial=function(t){const e=new Float32Array(t),n=new p.a(0,1,0);return new ot({name:\\\\\\\"SphericalGaussianBlur\\\\\\\",defines:{n:t},uniforms:{envMap:{value:null},samples:{value:1},weights:{value:e},latitudinal:{value:!1},dTheta:{value:0},mipInt:{value:0},poleAxis:{value:n},inputEncoding:{value:dt[w.U]},outputEncoding:{value:dt[w.U]}},vertexShader:Nt(),fragmentShader:`\\\\n\\\\n\\\\t\\\\t\\\\tprecision mediump float;\\\\n\\\\t\\\\t\\\\tprecision mediump int;\\\\n\\\\n\\\\t\\\\t\\\\tvarying vec3 vOutputDirection;\\\\n\\\\n\\\\t\\\\t\\\\tuniform sampler2D envMap;\\\\n\\\\t\\\\t\\\\tuniform int samples;\\\\n\\\\t\\\\t\\\\tuniform float weights[ n ];\\\\n\\\\t\\\\t\\\\tuniform bool latitudinal;\\\\n\\\\t\\\\t\\\\tuniform float dTheta;\\\\n\\\\t\\\\t\\\\tuniform float mipInt;\\\\n\\\\t\\\\t\\\\tuniform vec3 poleAxis;\\\\n\\\\n\\\\t\\\\t\\\\t${Lt()}\\\\n\\\\n\\\\t\\\\t\\\\t#define ENVMAP_TYPE_CUBE_UV\\\\n\\\\t\\\\t\\\\t#include <cube_uv_reflection_fragment>\\\\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})}(ht),this._equirectShader=null,this._cubemapShader=null,this._compileMaterial(this._blurMaterial)}fromScene(t,e=0,n=.1,i=100){vt=this._renderer.getRenderTarget();const r=this._allocateTargets();return this._sceneToCubeUV(t,n,i,r),e>0&&this._blur(r,0,0,e),this._applyPMREM(r),this._cleanup(r),r}fromEquirectangular(t){return this._fromTexture(t)}fromCubemap(t){return this._fromTexture(t)}compileCubemapShader(){null===this._cubemapShader&&(this._cubemapShader=Ct(),this._compileMaterial(this._cubemapShader))}compileEquirectangularShader(){null===this._equirectShader&&(this._equirectShader=St(),this._compileMaterial(this._equirectShader))}dispose(){this._blurMaterial.dispose(),null!==this._cubemapShader&&this._cubemapShader.dispose(),null!==this._equirectShader&&this._equirectShader.dispose();for(let t=0;t<_t.length;t++)_t[t].dispose()}_cleanup(t){this._pingPongRenderTarget.dispose(),this._renderer.setRenderTarget(vt),t.scissorTest=!1,Mt(t,0,0,t.width,t.height)}_fromTexture(t){vt=this._renderer.getRenderTarget();const e=this._allocateTargets(t);return this._textureToCubeUV(t,e),this._applyPMREM(e),this._cleanup(e),e}_allocateTargets(t){const e={magFilter:w.ob,minFilter:w.ob,generateMipmaps:!1,type:w.Zc,format:w.hc,encoding:Tt(t)?t.encoding:w.gc,depthBuffer:!1},n=Et(e);return n.depthBuffer=!t,this._pingPongRenderTarget=Et(e),n}_compileMaterial(t){const e=new k.a(_t[0],t);this._renderer.compile(e,pt)}_sceneToCubeUV(t,e,n,i){const r=new K.a(90,1,e,n),s=[1,-1,1,1,1,1],o=[1,1,1,-1,-1,-1],a=this._renderer,l=a.autoClear,c=a.outputEncoding,u=a.toneMapping;a.getClearColor(gt),a.toneMapping=w.vb,a.outputEncoding=w.U,a.autoClear=!1;const h=new at.a({name:\\\\\\\"PMREM.Background\\\\\\\",side:w.i,depthWrite:!1,depthTest:!1}),d=new k.a(new N,h);let p=!1;const _=t.background;_?_.isColor&&(h.color.copy(_),t.background=null,p=!0):(h.color.copy(gt),p=!0);for(let e=0;e<6;e++){const n=e%3;0==n?(r.up.set(0,s[e],0),r.lookAt(o[e],0,0)):1==n?(r.up.set(0,0,s[e]),r.lookAt(0,o[e],0)):(r.up.set(0,s[e],0),r.lookAt(0,0,o[e])),Mt(i,n*lt,e>2?lt:0,lt,lt),a.setRenderTarget(i),p&&a.render(d,r),a.render(t,r)}d.geometry.dispose(),d.material.dispose(),a.toneMapping=u,a.outputEncoding=c,a.autoClear=l,t.background=_}_setEncoding(t,e){!0===this._renderer.capabilities.isWebGL2&&e.format===w.Ib&&e.type===w.Zc&&e.encoding===w.ld?t.value=dt[w.U]:t.value=dt[e.encoding]}_textureToCubeUV(t,e){const n=this._renderer;t.isCubeTexture?null==this._cubemapShader&&(this._cubemapShader=Ct()):null==this._equirectShader&&(this._equirectShader=St());const i=t.isCubeTexture?this._cubemapShader:this._equirectShader,r=new k.a(_t[0],i),s=i.uniforms;s.envMap.value=t,t.isCubeTexture||s.texelSize.value.set(1/t.image.width,1/t.image.height),this._setEncoding(s.inputEncoding,t),this._setEncoding(s.outputEncoding,e.texture),Mt(e,0,0,3*lt,2*lt),n.setRenderTarget(e),n.render(r,pt)}_applyPMREM(t){const e=this._renderer,n=e.autoClear;e.autoClear=!1;for(let e=1;e<ut;e++){const n=Math.sqrt(ft[e]*ft[e]-ft[e-1]*ft[e-1]),i=bt[(e-1)%bt.length];this._blur(t,e-1,e,n,i)}e.autoClear=n}_blur(t,e,n,i,r){const s=this._pingPongRenderTarget;this._halfBlur(t,s,e,n,i,\\\\\\\"latitudinal\\\\\\\",r),this._halfBlur(s,t,n,n,i,\\\\\\\"longitudinal\\\\\\\",r)}_halfBlur(t,e,n,i,r,s,o){const a=this._renderer,l=this._blurMaterial;\\\\\\\"latitudinal\\\\\\\"!==s&&\\\\\\\"longitudinal\\\\\\\"!==s&&console.error(\\\\\\\"blur direction must be either latitudinal or longitudinal!\\\\\\\");const c=new k.a(_t[i],l),u=l.uniforms,h=mt[n]-1,d=isFinite(r)?Math.PI/(2*h):2*Math.PI/39,p=r/d,_=isFinite(r)?1+Math.floor(3*p):ht;_>ht&&console.warn(`sigmaRadians, ${r}, is too large and will clip, as it requested ${_} samples when the maximum is set to 20`);const m=[];let f=0;for(let t=0;t<ht;++t){const e=t/p,n=Math.exp(-e*e/2);m.push(n),0==t?f+=n:t<_&&(f+=2*n)}for(let t=0;t<m.length;t++)m[t]=m[t]/f;u.envMap.value=t.texture,u.samples.value=_,u.weights.value=m,u.latitudinal.value=\\\\\\\"latitudinal\\\\\\\"===s,o&&(u.poleAxis.value=o),u.dTheta.value=d,u.mipInt.value=8-n,this._setEncoding(u.inputEncoding,t.texture),this._setEncoding(u.outputEncoding,t.texture);const g=mt[i];Mt(e,3*Math.max(0,lt-2*g),(0===i?0:2*lt)+2*g*(i>4?i-8+4:0),3*g,2*g),a.setRenderTarget(e),a.render(c,pt)}}function Tt(t){return void 0!==t&&t.type===w.Zc&&(t.encoding===w.U||t.encoding===w.ld||t.encoding===w.J)}function At(){const t=[],e=[],n=[];let i=8;for(let r=0;r<ut;r++){const s=Math.pow(2,i);e.push(s);let o=1/s;r>4?o=ct[r-8+4-1]:0==r&&(o=0),n.push(o);const a=1/(s-1),l=-a/2,c=1+a/2,u=[l,l,c,l,c,c,l,l,c,c,l,c],h=6,d=6,p=3,_=2,m=1,f=new Float32Array(p*d*h),g=new Float32Array(_*d*h),v=new Float32Array(m*d*h);for(let t=0;t<h;t++){const e=t%3*2/3-1,n=t>2?0:-1,i=[e,n,0,e+2/3,n,0,e+2/3,n+1,0,e,n,0,e+2/3,n+1,0,e,n+1,0];f.set(i,p*d*t),g.set(u,_*d*t);const r=[t,t,t,t,t,t];v.set(r,m*d*t)}const y=new S.a;y.setAttribute(\\\\\\\"position\\\\\\\",new C.a(f,p)),y.setAttribute(\\\\\\\"uv\\\\\\\",new C.a(g,_)),y.setAttribute(\\\\\\\"faceIndex\\\\\\\",new C.a(v,m)),t.push(y),i>4&&i--}return{_lodPlanes:t,_sizeLods:e,_sigmas:n}}function Et(t){const e=new Z(3*lt,3*lt,t);return e.texture.mapping=w.q,e.texture.name=\\\\\\\"PMREM.cubeUv\\\\\\\",e.scissorTest=!0,e}function Mt(t,e,n,i,r){t.viewport.set(e,n,i,r),t.scissor.set(e,n,i,r)}function St(){const t=new d.a(1,1);return new ot({name:\\\\\\\"EquirectangularToCubeUV\\\\\\\",uniforms:{envMap:{value:null},texelSize:{value:t},inputEncoding:{value:dt[w.U]},outputEncoding:{value:dt[w.U]}},vertexShader:Nt(),fragmentShader:`\\\\n\\\\n\\\\t\\\\t\\\\tprecision mediump float;\\\\n\\\\t\\\\t\\\\tprecision mediump int;\\\\n\\\\n\\\\t\\\\t\\\\tvarying vec3 vOutputDirection;\\\\n\\\\n\\\\t\\\\t\\\\tuniform sampler2D envMap;\\\\n\\\\t\\\\t\\\\tuniform vec2 texelSize;\\\\n\\\\n\\\\t\\\\t\\\\t${Lt()}\\\\n\\\\n\\\\t\\\\t\\\\t#include <common>\\\\n\\\\n\\\\t\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tgl_FragColor = vec4( 0.0, 0.0, 0.0, 1.0 );\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tvec3 outputDirection = normalize( vOutputDirection );\\\\n\\\\t\\\\t\\\\t\\\\tvec2 uv = equirectUv( outputDirection );\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tvec2 f = fract( uv / texelSize - 0.5 );\\\\n\\\\t\\\\t\\\\t\\\\tuv -= f * texelSize;\\\\n\\\\t\\\\t\\\\t\\\\tvec3 tl = envMapTexelToLinear( texture2D ( envMap, uv ) ).rgb;\\\\n\\\\t\\\\t\\\\t\\\\tuv.x += texelSize.x;\\\\n\\\\t\\\\t\\\\t\\\\tvec3 tr = envMapTexelToLinear( texture2D ( envMap, uv ) ).rgb;\\\\n\\\\t\\\\t\\\\t\\\\tuv.y += texelSize.y;\\\\n\\\\t\\\\t\\\\t\\\\tvec3 br = envMapTexelToLinear( texture2D ( envMap, uv ) ).rgb;\\\\n\\\\t\\\\t\\\\t\\\\tuv.x -= texelSize.x;\\\\n\\\\t\\\\t\\\\t\\\\tvec3 bl = envMapTexelToLinear( texture2D ( envMap, uv ) ).rgb;\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tvec3 tm = mix( tl, tr, f.x );\\\\n\\\\t\\\\t\\\\t\\\\tvec3 bm = mix( bl, br, f.x );\\\\n\\\\t\\\\t\\\\t\\\\tgl_FragColor.rgb = mix( tm, bm, f.y );\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tgl_FragColor = linearToOutputTexel( gl_FragColor );\\\\n\\\\n\\\\t\\\\t\\\\t}\\\\n\\\\t\\\\t`,blending:w.ub,depthTest:!1,depthWrite:!1})}function Ct(){return new ot({name:\\\\\\\"CubemapToCubeUV\\\\\\\",uniforms:{envMap:{value:null},inputEncoding:{value:dt[w.U]},outputEncoding:{value:dt[w.U]}},vertexShader:Nt(),fragmentShader:`\\\\n\\\\n\\\\t\\\\t\\\\tprecision mediump float;\\\\n\\\\t\\\\t\\\\tprecision mediump int;\\\\n\\\\n\\\\t\\\\t\\\\tvarying vec3 vOutputDirection;\\\\n\\\\n\\\\t\\\\t\\\\tuniform samplerCube envMap;\\\\n\\\\n\\\\t\\\\t\\\\t${Lt()}\\\\n\\\\n\\\\t\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tgl_FragColor = vec4( 0.0, 0.0, 0.0, 1.0 );\\\\n\\\\t\\\\t\\\\t\\\\tgl_FragColor.rgb = envMapTexelToLinear( textureCube( envMap, vec3( - vOutputDirection.x, vOutputDirection.yz ) ) ).rgb;\\\\n\\\\t\\\\t\\\\t\\\\tgl_FragColor = linearToOutputTexel( gl_FragColor );\\\\n\\\\n\\\\t\\\\t\\\\t}\\\\n\\\\t\\\\t`,blending:w.ub,depthTest:!1,depthWrite:!1})}function Nt(){return\\\\\\\"\\\\n\\\\n\\\\t\\\\tprecision mediump float;\\\\n\\\\t\\\\tprecision mediump int;\\\\n\\\\n\\\\t\\\\tattribute vec3 position;\\\\n\\\\t\\\\tattribute vec2 uv;\\\\n\\\\t\\\\tattribute float faceIndex;\\\\n\\\\n\\\\t\\\\tvarying vec3 vOutputDirection;\\\\n\\\\n\\\\t\\\\t// RH coordinate system; PMREM face-indexing convention\\\\n\\\\t\\\\tvec3 getDirection( vec2 uv, float face ) {\\\\n\\\\n\\\\t\\\\t\\\\tuv = 2.0 * uv - 1.0;\\\\n\\\\n\\\\t\\\\t\\\\tvec3 direction = vec3( uv, 1.0 );\\\\n\\\\n\\\\t\\\\t\\\\tif ( face == 0.0 ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tdirection = direction.zyx; // ( 1, v, u ) pos x\\\\n\\\\n\\\\t\\\\t\\\\t} else if ( face == 1.0 ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tdirection = direction.xzy;\\\\n\\\\t\\\\t\\\\t\\\\tdirection.xz *= -1.0; // ( -u, 1, -v ) pos y\\\\n\\\\n\\\\t\\\\t\\\\t} else if ( face == 2.0 ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tdirection.x *= -1.0; // ( -u, v, 1 ) pos z\\\\n\\\\n\\\\t\\\\t\\\\t} else if ( face == 3.0 ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tdirection = direction.zyx;\\\\n\\\\t\\\\t\\\\t\\\\tdirection.xz *= -1.0; // ( -1, v, -u ) neg x\\\\n\\\\n\\\\t\\\\t\\\\t} else if ( face == 4.0 ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tdirection = direction.xzy;\\\\n\\\\t\\\\t\\\\t\\\\tdirection.xy *= -1.0; // ( -u, -1, v ) neg y\\\\n\\\\n\\\\t\\\\t\\\\t} else if ( face == 5.0 ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tdirection.z *= -1.0; // ( u, v, -1 ) neg z\\\\n\\\\n\\\\t\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\t\\\\treturn direction;\\\\n\\\\n\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\tvOutputDirection = getDirection( uv, faceIndex );\\\\n\\\\t\\\\t\\\\tgl_Position = vec4( position, 1.0 );\\\\n\\\\n\\\\t\\\\t}\\\\n\\\\t\\\\\\\"}function Lt(){return\\\\\\\"\\\\n\\\\n\\\\t\\\\tuniform int inputEncoding;\\\\n\\\\t\\\\tuniform int outputEncoding;\\\\n\\\\n\\\\t\\\\t#include <encodings_pars_fragment>\\\\n\\\\n\\\\t\\\\tvec4 inputTexelToLinear( vec4 value ) {\\\\n\\\\n\\\\t\\\\t\\\\tif ( inputEncoding == 0 ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\treturn value;\\\\n\\\\n\\\\t\\\\t\\\\t} else if ( inputEncoding == 1 ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\treturn sRGBToLinear( value );\\\\n\\\\n\\\\t\\\\t\\\\t} else if ( inputEncoding == 2 ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\treturn RGBEToLinear( value );\\\\n\\\\n\\\\t\\\\t\\\\t} else if ( inputEncoding == 3 ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\treturn RGBMToLinear( value, 7.0 );\\\\n\\\\n\\\\t\\\\t\\\\t} else if ( inputEncoding == 4 ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\treturn RGBMToLinear( value, 16.0 );\\\\n\\\\n\\\\t\\\\t\\\\t} else if ( inputEncoding == 5 ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\treturn RGBDToLinear( value, 256.0 );\\\\n\\\\n\\\\t\\\\t\\\\t} else {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\treturn GammaToLinear( value, 2.2 );\\\\n\\\\n\\\\t\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\tvec4 linearToOutputTexel( vec4 value ) {\\\\n\\\\n\\\\t\\\\t\\\\tif ( outputEncoding == 0 ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\treturn value;\\\\n\\\\n\\\\t\\\\t\\\\t} else if ( outputEncoding == 1 ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\treturn LinearTosRGB( value );\\\\n\\\\n\\\\t\\\\t\\\\t} else if ( outputEncoding == 2 ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\treturn LinearToRGBE( value );\\\\n\\\\n\\\\t\\\\t\\\\t} else if ( outputEncoding == 3 ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\treturn LinearToRGBM( value, 7.0 );\\\\n\\\\n\\\\t\\\\t\\\\t} else if ( outputEncoding == 4 ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\treturn LinearToRGBM( value, 16.0 );\\\\n\\\\n\\\\t\\\\t\\\\t} else if ( outputEncoding == 5 ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\treturn LinearToRGBD( value, 256.0 );\\\\n\\\\n\\\\t\\\\t\\\\t} else {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\treturn LinearToGamma( value, 2.2 );\\\\n\\\\n\\\\t\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\tvec4 envMapTexelToLinear( vec4 color ) {\\\\n\\\\n\\\\t\\\\t\\\\treturn inputTexelToLinear( color );\\\\n\\\\n\\\\t\\\\t}\\\\n\\\\t\\\\\\\"}function Ot(t){let e=new WeakMap,n=null;function i(t){const n=t.target;n.removeEventListener(\\\\\\\"dispose\\\\\\\",i);const r=e.get(n);void 0!==r&&(e.delete(n),r.dispose())}return{get:function(r){if(r&&r.isTexture&&!1===r.isRenderTargetTexture){const s=r.mapping,o=s===w.D||s===w.E,a=s===w.o||s===w.p;if(o||a){if(e.has(r))return e.get(r).texture;{const s=r.image;if(o&&s&&s.height>0||a&&s&&function(t){let e=0;const n=6;for(let i=0;i<n;i++)void 0!==t[i]&&e++;return e===n}(s)){const s=t.getRenderTarget();null===n&&(n=new wt(t));const a=o?n.fromEquirectangular(r):n.fromCubemap(r);return e.set(r,a),t.setRenderTarget(s),r.addEventListener(\\\\\\\"dispose\\\\\\\",i),a.texture}return null}}}return r},dispose:function(){e=new WeakMap,null!==n&&(n.dispose(),n=null)}}}function Rt(t){const e={};function n(n){if(void 0!==e[n])return e[n];let i;switch(n){case\\\\\\\"WEBGL_depth_texture\\\\\\\":i=t.getExtension(\\\\\\\"WEBGL_depth_texture\\\\\\\")||t.getExtension(\\\\\\\"MOZ_WEBGL_depth_texture\\\\\\\")||t.getExtension(\\\\\\\"WEBKIT_WEBGL_depth_texture\\\\\\\");break;case\\\\\\\"EXT_texture_filter_anisotropic\\\\\\\":i=t.getExtension(\\\\\\\"EXT_texture_filter_anisotropic\\\\\\\")||t.getExtension(\\\\\\\"MOZ_EXT_texture_filter_anisotropic\\\\\\\")||t.getExtension(\\\\\\\"WEBKIT_EXT_texture_filter_anisotropic\\\\\\\");break;case\\\\\\\"WEBGL_compressed_texture_s3tc\\\\\\\":i=t.getExtension(\\\\\\\"WEBGL_compressed_texture_s3tc\\\\\\\")||t.getExtension(\\\\\\\"MOZ_WEBGL_compressed_texture_s3tc\\\\\\\")||t.getExtension(\\\\\\\"WEBKIT_WEBGL_compressed_texture_s3tc\\\\\\\");break;case\\\\\\\"WEBGL_compressed_texture_pvrtc\\\\\\\":i=t.getExtension(\\\\\\\"WEBGL_compressed_texture_pvrtc\\\\\\\")||t.getExtension(\\\\\\\"WEBKIT_WEBGL_compressed_texture_pvrtc\\\\\\\");break;default:i=t.getExtension(n)}return e[n]=i,i}return{has:function(t){return null!==n(t)},init:function(t){t.isWebGL2?n(\\\\\\\"EXT_color_buffer_float\\\\\\\"):(n(\\\\\\\"WEBGL_depth_texture\\\\\\\"),n(\\\\\\\"OES_texture_float\\\\\\\"),n(\\\\\\\"OES_texture_half_float\\\\\\\"),n(\\\\\\\"OES_texture_half_float_linear\\\\\\\"),n(\\\\\\\"OES_standard_derivatives\\\\\\\"),n(\\\\\\\"OES_element_index_uint\\\\\\\"),n(\\\\\\\"OES_vertex_array_object\\\\\\\"),n(\\\\\\\"ANGLE_instanced_arrays\\\\\\\")),n(\\\\\\\"OES_texture_float_linear\\\\\\\"),n(\\\\\\\"EXT_color_buffer_half_float\\\\\\\")},get:function(t){const e=n(t);return null===e&&console.warn(\\\\\\\"THREE.WebGLRenderer: \\\\\\\"+t+\\\\\\\" extension not supported.\\\\\\\"),e}}}var Pt=n(20);function It(t,e,n,i){const r={},s=new WeakMap;function o(t){const a=t.target;null!==a.index&&e.remove(a.index);for(const t in a.attributes)e.remove(a.attributes[t]);a.removeEventListener(\\\\\\\"dispose\\\\\\\",o),delete r[a.id];const l=s.get(a);l&&(e.remove(l),s.delete(a)),i.releaseStatesOfGeometry(a),!0===a.isInstancedBufferGeometry&&delete a._maxInstanceCount,n.memory.geometries--}function a(t){const n=[],i=t.index,r=t.attributes.position;let o=0;if(null!==i){const t=i.array;o=i.version;for(let e=0,i=t.length;e<i;e+=3){const i=t[e+0],r=t[e+1],s=t[e+2];n.push(i,r,r,s,s,i)}}else{const t=r.array;o=r.version;for(let e=0,i=t.length/3-1;e<i;e+=3){const t=e+0,i=e+1,r=e+2;n.push(t,i,i,r,r,t)}}const a=new(Object(Pt.a)(n)>65535?C.i:C.h)(n,1);a.version=o;const l=s.get(t);l&&e.remove(l),s.set(t,a)}return{get:function(t,e){return!0===r[e.id]||(e.addEventListener(\\\\\\\"dispose\\\\\\\",o),r[e.id]=!0,n.memory.geometries++),e},update:function(n){const i=n.attributes;for(const n in i)e.update(i[n],t.ARRAY_BUFFER);const r=n.morphAttributes;for(const n in r){const i=r[n];for(let n=0,r=i.length;n<r;n++)e.update(i[n],t.ARRAY_BUFFER)}},getWireframeAttribute:function(t){const e=s.get(t);if(e){const n=t.index;null!==n&&e.version<n.version&&a(t)}else a(t);return s.get(t)}}}function Ft(t,e,n,i){const r=i.isWebGL2;let s,o,a;this.setMode=function(t){s=t},this.setIndex=function(t){o=t.type,a=t.bytesPerElement},this.render=function(e,i){t.drawElements(s,i,o,e*a),n.update(i,s,1)},this.renderInstances=function(i,l,c){if(0===c)return;let u,h;if(r)u=t,h=\\\\\\\"drawElementsInstanced\\\\\\\";else if(u=e.get(\\\\\\\"ANGLE_instanced_arrays\\\\\\\"),h=\\\\\\\"drawElementsInstancedANGLE\\\\\\\",null===u)return void console.error(\\\\\\\"THREE.WebGLIndexedBufferRenderer: using THREE.InstancedBufferGeometry but hardware does not support extension ANGLE_instanced_arrays.\\\\\\\");u[h](s,l,o,i*a,c),n.update(l,s,c)}}function Dt(t){const e={frame:0,calls:0,triangles:0,points:0,lines:0};return{memory:{geometries:0,textures:0},render:e,programs:null,autoReset:!0,reset:function(){e.frame++,e.calls=0,e.triangles=0,e.points=0,e.lines=0},update:function(n,i,r){switch(e.calls++,i){case t.TRIANGLES:e.triangles+=r*(n/3);break;case t.LINES:e.lines+=r*(n/2);break;case t.LINE_STRIP:e.lines+=r*(n-1);break;case t.LINE_LOOP:e.lines+=r*n;break;case t.POINTS:e.points+=r*n;break;default:console.error(\\\\\\\"THREE.WebGLInfo: Unknown draw mode:\\\\\\\",i)}}}}class kt extends J.a{constructor(t=null,e=1,n=1,i=1){super(null),this.image={data:t,width:e,height:n,depth:i},this.magFilter=w.ob,this.minFilter=w.ob,this.wrapR=w.n,this.generateMipmaps=!1,this.flipY=!1,this.unpackAlignment=1,this.needsUpdate=!0}}function Bt(t,e){return t[0]-e[0]}function zt(t,e){return Math.abs(e[1])-Math.abs(t[1])}function Ut(t,e){let n=1;const i=e.isInterleavedBufferAttribute?e.data.array:e.array;i instanceof Int8Array?n=127:i instanceof Int16Array?n=32767:i instanceof Int32Array?n=2147483647:console.error(\\\\\\\"THREE.WebGLMorphtargets: Unsupported morph attribute data type: \\\\\\\",i),t.divideScalar(n)}function Gt(t,e,n){const i={},r=new Float32Array(8),s=new WeakMap,o=new p.a,a=[];for(let t=0;t<8;t++)a[t]=[t,0];return{update:function(l,c,u,h){const p=l.morphTargetInfluences;if(!0===e.isWebGL2){const i=c.morphAttributes.position.length;let r=s.get(c);if(void 0===r||r.count!==i){void 0!==r&&r.texture.dispose();const t=void 0!==c.morphAttributes.normal,n=c.morphAttributes.position,a=c.morphAttributes.normal||[],l=!0===t?2:1;let u=c.attributes.position.count*l,h=1;u>e.maxTextureSize&&(h=Math.ceil(u/e.maxTextureSize),u=e.maxTextureSize);const p=new Float32Array(u*h*4*i),_=new kt(p,u,h,i);_.format=w.Ib,_.type=w.G;const m=4*l;for(let e=0;e<i;e++){const i=n[e],r=a[e],s=u*h*4*e;for(let e=0;e<i.count;e++){o.fromBufferAttribute(i,e),!0===i.normalized&&Ut(o,i);const n=e*m;p[s+n+0]=o.x,p[s+n+1]=o.y,p[s+n+2]=o.z,p[s+n+3]=0,!0===t&&(o.fromBufferAttribute(r,e),!0===r.normalized&&Ut(o,r),p[s+n+4]=o.x,p[s+n+5]=o.y,p[s+n+6]=o.z,p[s+n+7]=0)}}r={count:i,texture:_,size:new d.a(u,h)},s.set(c,r)}let a=0;for(let t=0;t<p.length;t++)a+=p[t];const l=c.morphTargetsRelative?1:1-a;h.getUniforms().setValue(t,\\\\\\\"morphTargetBaseInfluence\\\\\\\",l),h.getUniforms().setValue(t,\\\\\\\"morphTargetInfluences\\\\\\\",p),h.getUniforms().setValue(t,\\\\\\\"morphTargetsTexture\\\\\\\",r.texture,n),h.getUniforms().setValue(t,\\\\\\\"morphTargetsTextureSize\\\\\\\",r.size)}else{const e=void 0===p?0:p.length;let n=i[c.id];if(void 0===n||n.length!==e){n=[];for(let t=0;t<e;t++)n[t]=[t,0];i[c.id]=n}for(let t=0;t<e;t++){const e=n[t];e[0]=t,e[1]=p[t]}n.sort(zt);for(let t=0;t<8;t++)t<e&&n[t][1]?(a[t][0]=n[t][0],a[t][1]=n[t][1]):(a[t][0]=Number.MAX_SAFE_INTEGER,a[t][1]=0);a.sort(Bt);const s=c.morphAttributes.position,o=c.morphAttributes.normal;let l=0;for(let t=0;t<8;t++){const e=a[t],n=e[0],i=e[1];n!==Number.MAX_SAFE_INTEGER&&i?(s&&c.getAttribute(\\\\\\\"morphTarget\\\\\\\"+t)!==s[n]&&c.setAttribute(\\\\\\\"morphTarget\\\\\\\"+t,s[n]),o&&c.getAttribute(\\\\\\\"morphNormal\\\\\\\"+t)!==o[n]&&c.setAttribute(\\\\\\\"morphNormal\\\\\\\"+t,o[n]),r[t]=i,l+=i):(s&&!0===c.hasAttribute(\\\\\\\"morphTarget\\\\\\\"+t)&&c.deleteAttribute(\\\\\\\"morphTarget\\\\\\\"+t),o&&!0===c.hasAttribute(\\\\\\\"morphNormal\\\\\\\"+t)&&c.deleteAttribute(\\\\\\\"morphNormal\\\\\\\"+t),r[t]=0)}const u=c.morphTargetsRelative?1:1-l;h.getUniforms().setValue(t,\\\\\\\"morphTargetBaseInfluence\\\\\\\",u),h.getUniforms().setValue(t,\\\\\\\"morphTargetInfluences\\\\\\\",r)}}}}kt.prototype.isDataTexture2DArray=!0;class Vt extends Z{constructor(t,e,n){super(t,e,n),this.samples=4}copy(t){return super.copy.call(this,t),this.samples=t.samples,this}}function Ht(t,e,n,i){let r=new WeakMap;function s(t){const e=t.target;e.removeEventListener(\\\\\\\"dispose\\\\\\\",s),n.remove(e.instanceMatrix),null!==e.instanceColor&&n.remove(e.instanceColor)}return{update:function(o){const a=i.render.frame,l=o.geometry,c=e.get(o,l);return r.get(c)!==a&&(e.update(c),r.set(c,a)),o.isInstancedMesh&&(!1===o.hasEventListener(\\\\\\\"dispose\\\\\\\",s)&&o.addEventListener(\\\\\\\"dispose\\\\\\\",s),n.update(o.instanceMatrix,t.ARRAY_BUFFER),null!==o.instanceColor&&n.update(o.instanceColor,t.ARRAY_BUFFER)),c},dispose:function(){r=new WeakMap}}}Vt.prototype.isWebGLMultisampleRenderTarget=!0;class jt extends J.a{constructor(t=null,e=1,n=1,i=1){super(null),this.image={data:t,width:e,height:n,depth:i},this.magFilter=w.ob,this.minFilter=w.ob,this.wrapR=w.n,this.generateMipmaps=!1,this.flipY=!1,this.unpackAlignment=1,this.needsUpdate=!0}}jt.prototype.isDataTexture3D=!0;const Wt=new J.a,qt=new kt,Xt=new jt,Yt=new nt,$t=[],Jt=[],Zt=new Float32Array(16),Qt=new Float32Array(9),Kt=new Float32Array(4);function 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i=this.cache,r=n.allocateTextureUnit();i[0]!==r&&(t.uniform1i(this.addr,r),i[0]=r),n.setTexture3D(e||Xt,r)}function be(t,e,n){const i=this.cache,r=n.allocateTextureUnit();i[0]!==r&&(t.uniform1i(this.addr,r),i[0]=r),n.safeSetTextureCube(e||Yt,r)}function we(t,e,n){const i=this.cache,r=n.allocateTextureUnit();i[0]!==r&&(t.uniform1i(this.addr,r),i[0]=r),n.setTexture2DArray(e||qt,r)}function Te(t,e){t.uniform1fv(this.addr,e)}function Ae(t,e){const n=te(e,this.size,2);t.uniform2fv(this.addr,n)}function Ee(t,e){const n=te(e,this.size,3);t.uniform3fv(this.addr,n)}function Me(t,e){const n=te(e,this.size,4);t.uniform4fv(this.addr,n)}function Se(t,e){const n=te(e,this.size,4);t.uniformMatrix2fv(this.addr,!1,n)}function Ce(t,e){const n=te(e,this.size,9);t.uniformMatrix3fv(this.addr,!1,n)}function Ne(t,e){const n=te(e,this.size,16);t.uniformMatrix4fv(this.addr,!1,n)}function Le(t,e){t.uniform1iv(this.addr,e)}function Oe(t,e){t.uniform2iv(this.addr,e)}function 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f(t){let e;return t&&t.isTexture?e=t.encoding:t&&t.isWebGLRenderTarget?(console.warn(\\\\\\\"THREE.WebGLPrograms.getTextureEncodingFromMap: don't use render targets as textures. Use their .texture property instead.\\\\\\\"),e=t.texture.encoding):e=w.U,l&&t&&t.isTexture&&t.format===w.Ib&&t.type===w.Zc&&t.encoding===w.ld&&(e=w.U),e}return{getParameters:function(s,a,m,g,v){const y=g.fog,x=s.isMeshStandardMaterial?g.environment:null,b=(s.isMeshStandardMaterial?n:e).get(s.envMap||x),T=_[s.type],A=v.isSkinnedMesh?function(t){const e=t.skeleton.bones;if(u)return 1024;{const t=h,n=Math.floor((t-20)/4),i=Math.min(n,e.length);return i<e.length?(console.warn(\\\\\\\"THREE.WebGLRenderer: Skeleton has \\\\\\\"+e.length+\\\\\\\" bones. 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0!==s.depthPacking&&s.depthPacking,index0AttributeName:s.index0AttributeName,extensionDerivatives:s.extensions&&s.extensions.derivatives,extensionFragDepth:s.extensions&&s.extensions.fragDepth,extensionDrawBuffers:s.extensions&&s.extensions.drawBuffers,extensionShaderTextureLOD:s.extensions&&s.extensions.shaderTextureLOD,rendererExtensionFragDepth:l||i.has(\\\\\\\"EXT_frag_depth\\\\\\\"),rendererExtensionDrawBuffers:l||i.has(\\\\\\\"WEBGL_draw_buffers\\\\\\\"),rendererExtensionShaderTextureLod:l||i.has(\\\\\\\"EXT_shader_texture_lod\\\\\\\"),customProgramCacheKey:s.customProgramCacheKey()}},getProgramCacheKey:function(e){const n=[];if(e.shaderID?n.push(e.shaderID):(n.push(e.fragmentShader),n.push(e.vertexShader)),void 0!==e.defines)for(const t in e.defines)n.push(t),n.push(e.defines[t]);if(!1===e.isRawShaderMaterial){for(let t=0;t<m.length;t++)n.push(e[m[t]]);n.push(t.outputEncoding),n.push(t.gammaFactor)}return n.push(e.customProgramCacheKey),n.join()},getUniforms:function(t){const e=_[t.type];let n;if(e){const t=V[e];n=I.clone(t.uniforms)}else n=t.uniforms;return n},acquireProgram:function(e,n){let i;for(let t=0,e=a.length;t<e;t++){const e=a[t];if(e.cacheKey===n){i=e,++i.usedTimes;break}}return void 0===i&&(i=new pn(t,n,e,s),a.push(i)),i},releaseProgram:function(t){if(0==--t.usedTimes){const e=a.indexOf(t);a[e]=a[a.length-1],a.pop(),t.destroy()}},programs:a}}function mn(){let t=new WeakMap;return{get:function(e){let n=t.get(e);return void 0===n&&(n={},t.set(e,n)),n},remove:function(e){t.delete(e)},update:function(e,n,i){t.get(e)[n]=i},dispose:function(){t=new WeakMap}}}function fn(t,e){return t.groupOrder!==e.groupOrder?t.groupOrder-e.groupOrder:t.renderOrder!==e.renderOrder?t.renderOrder-e.renderOrder:t.program!==e.program?t.program.id-e.program.id:t.material.id!==e.material.id?t.material.id-e.material.id:t.z!==e.z?t.z-e.z:t.id-e.id}function gn(t,e){return t.groupOrder!==e.groupOrder?t.groupOrder-e.groupOrder:t.renderOrder!==e.renderOrder?t.renderOrder-e.renderOrder:t.z!==e.z?e.z-t.z:t.id-e.id}function vn(t){const e=[];let n=0;const i=[],r=[],s=[],o={id:-1};function a(i,r,s,a,l,c){let u=e[n];const h=t.get(s);return void 0===u?(u={id:i.id,object:i,geometry:r,material:s,program:h.program||o,groupOrder:a,renderOrder:i.renderOrder,z:l,group:c},e[n]=u):(u.id=i.id,u.object=i,u.geometry=r,u.material=s,u.program=h.program||o,u.groupOrder=a,u.renderOrder=i.renderOrder,u.z=l,u.group=c),n++,u}return{opaque:i,transmissive:r,transparent:s,init:function(){n=0,i.length=0,r.length=0,s.length=0},push:function(t,e,n,o,l,c){const u=a(t,e,n,o,l,c);n.transmission>0?r.push(u):!0===n.transparent?s.push(u):i.push(u)},unshift:function(t,e,n,o,l,c){const u=a(t,e,n,o,l,c);n.transmission>0?r.unshift(u):!0===n.transparent?s.unshift(u):i.unshift(u)},finish:function(){for(let t=n,i=e.length;t<i;t++){const n=e[t];if(null===n.id)break;n.id=null,n.object=null,n.geometry=null,n.material=null,n.program=null,n.group=null}},sort:function(t,e){i.length>1&&i.sort(t||fn),r.length>1&&r.sort(e||gn),s.length>1&&s.sort(e||gn)}}}function yn(t){let e=new WeakMap;return{get:function(n,i){let r;return!1===e.has(n)?(r=new vn(t),e.set(n,[r])):i>=e.get(n).length?(r=new vn(t),e.get(n).push(r)):r=e.get(n)[i],r},dispose:function(){e=new WeakMap}}}function xn(){const t={};return{get:function(e){if(void 0!==t[e.id])return t[e.id];let n;switch(e.type){case\\\\\\\"DirectionalLight\\\\\\\":n={direction:new p.a,color:new D.a};break;case\\\\\\\"SpotLight\\\\\\\":n={position:new p.a,direction:new p.a,color:new D.a,distance:0,coneCos:0,penumbraCos:0,decay:0};break;case\\\\\\\"PointLight\\\\\\\":n={position:new p.a,color:new D.a,distance:0,decay:0};break;case\\\\\\\"HemisphereLight\\\\\\\":n={direction:new p.a,skyColor:new D.a,groundColor:new D.a};break;case\\\\\\\"RectAreaLight\\\\\\\":n={color:new D.a,position:new p.a,halfWidth:new p.a,halfHeight:new p.a}}return t[e.id]=n,n}}}let bn=0;function wn(t,e){return(e.castShadow?1:0)-(t.castShadow?1:0)}function Tn(t,e){const n=new xn,i=function(){const t={};return{get:function(e){if(void 0!==t[e.id])return t[e.id];let n;switch(e.type){case\\\\\\\"DirectionalLight\\\\\\\":case\\\\\\\"SpotLight\\\\\\\":n={shadowBias:0,shadowNormalBias:0,shadowRadius:1,shadowMapSize:new d.a};break;case\\\\\\\"PointLight\\\\\\\":n={shadowBias:0,shadowNormalBias:0,shadowRadius:1,shadowMapSize:new d.a,shadowCameraNear:1,shadowCameraFar:1e3}}return t[e.id]=n,n}}}(),r={version:0,hash:{directionalLength:-1,pointLength:-1,spotLength:-1,rectAreaLength:-1,hemiLength:-1,numDirectionalShadows:-1,numPointShadows:-1,numSpotShadows:-1},ambient:[0,0,0],probe:[],directional:[],directionalShadow:[],directionalShadowMap:[],directionalShadowMatrix:[],spot:[],spotShadow:[],spotShadowMap:[],spotShadowMatrix:[],rectArea:[],rectAreaLTC1:null,rectAreaLTC2:null,point:[],pointShadow:[],pointShadowMap:[],pointShadowMatrix:[],hemi:[]};for(let t=0;t<9;t++)r.probe.push(new p.a);const s=new p.a,o=new A.a,a=new A.a;return{setup:function(s,o){let a=0,l=0,c=0;for(let t=0;t<9;t++)r.probe[t].set(0,0,0);let u=0,h=0,d=0,p=0,_=0,m=0,f=0,g=0;s.sort(wn);const v=!0!==o?Math.PI:1;for(let t=0,e=s.length;t<e;t++){const e=s[t],o=e.color,y=e.intensity,x=e.distance,b=e.shadow&&e.shadow.map?e.shadow.map.texture:null;if(e.isAmbientLight)a+=o.r*y*v,l+=o.g*y*v,c+=o.b*y*v;else if(e.isLightProbe)for(let t=0;t<9;t++)r.probe[t].addScaledVector(e.sh.coefficients[t],y);else if(e.isDirectionalLight){const t=n.get(e);if(t.color.copy(e.color).multiplyScalar(e.intensity*v),e.castShadow){const t=e.shadow,n=i.get(e);n.shadowBias=t.bias,n.shadowNormalBias=t.normalBias,n.shadowRadius=t.radius,n.shadowMapSize=t.mapSize,r.directionalShadow[u]=n,r.directionalShadowMap[u]=b,r.directionalShadowMatrix[u]=e.shadow.matrix,m++}r.directional[u]=t,u++}else if(e.isSpotLight){const t=n.get(e);if(t.position.setFromMatrixPosition(e.matrixWorld),t.color.copy(o).multiplyScalar(y*v),t.distance=x,t.coneCos=Math.cos(e.angle),t.penumbraCos=Math.cos(e.angle*(1-e.penumbra)),t.decay=e.decay,e.castShadow){const t=e.shadow,n=i.get(e);n.shadowBias=t.bias,n.shadowNormalBias=t.normalBias,n.shadowRadius=t.radius,n.shadowMapSize=t.mapSize,r.spotShadow[d]=n,r.spotShadowMap[d]=b,r.spotShadowMatrix[d]=e.shadow.matrix,g++}r.spot[d]=t,d++}else if(e.isRectAreaLight){const t=n.get(e);t.color.copy(o).multiplyScalar(y),t.halfWidth.set(.5*e.width,0,0),t.halfHeight.set(0,.5*e.height,0),r.rectArea[p]=t,p++}else if(e.isPointLight){const t=n.get(e);if(t.color.copy(e.color).multiplyScalar(e.intensity*v),t.distance=e.distance,t.decay=e.decay,e.castShadow){const t=e.shadow,n=i.get(e);n.shadowBias=t.bias,n.shadowNormalBias=t.normalBias,n.shadowRadius=t.radius,n.shadowMapSize=t.mapSize,n.shadowCameraNear=t.camera.near,n.shadowCameraFar=t.camera.far,r.pointShadow[h]=n,r.pointShadowMap[h]=b,r.pointShadowMatrix[h]=e.shadow.matrix,f++}r.point[h]=t,h++}else if(e.isHemisphereLight){const t=n.get(e);t.skyColor.copy(e.color).multiplyScalar(y*v),t.groundColor.copy(e.groundColor).multiplyScalar(y*v),r.hemi[_]=t,_++}}p>0&&(e.isWebGL2||!0===t.has(\\\\\\\"OES_texture_float_linear\\\\\\\")?(r.rectAreaLTC1=G.LTC_FLOAT_1,r.rectAreaLTC2=G.LTC_FLOAT_2):!0===t.has(\\\\\\\"OES_texture_half_float_linear\\\\\\\")?(r.rectAreaLTC1=G.LTC_HALF_1,r.rectAreaLTC2=G.LTC_HALF_2):console.error(\\\\\\\"THREE.WebGLRenderer: Unable to use RectAreaLight. Missing WebGL extensions.\\\\\\\")),r.ambient[0]=a,r.ambient[1]=l,r.ambient[2]=c;const y=r.hash;y.directionalLength===u&&y.pointLength===h&&y.spotLength===d&&y.rectAreaLength===p&&y.hemiLength===_&&y.numDirectionalShadows===m&&y.numPointShadows===f&&y.numSpotShadows===g||(r.directional.length=u,r.spot.length=d,r.rectArea.length=p,r.point.length=h,r.hemi.length=_,r.directionalShadow.length=m,r.directionalShadowMap.length=m,r.pointShadow.length=f,r.pointShadowMap.length=f,r.spotShadow.length=g,r.spotShadowMap.length=g,r.directionalShadowMatrix.length=m,r.pointShadowMatrix.length=f,r.spotShadowMatrix.length=g,y.directionalLength=u,y.pointLength=h,y.spotLength=d,y.rectAreaLength=p,y.hemiLength=_,y.numDirectionalShadows=m,y.numPointShadows=f,y.numSpotShadows=g,r.version=bn++)},setupView:function(t,e){let n=0,i=0,l=0,c=0,u=0;const h=e.matrixWorldInverse;for(let e=0,d=t.length;e<d;e++){const d=t[e];if(d.isDirectionalLight){const t=r.directional[n];t.direction.setFromMatrixPosition(d.matrixWorld),s.setFromMatrixPosition(d.target.matrixWorld),t.direction.sub(s),t.direction.transformDirection(h),n++}else if(d.isSpotLight){const t=r.spot[l];t.position.setFromMatrixPosition(d.matrixWorld),t.position.applyMatrix4(h),t.direction.setFromMatrixPosition(d.matrixWorld),s.setFromMatrixPosition(d.target.matrixWorld),t.direction.sub(s),t.direction.transformDirection(h),l++}else if(d.isRectAreaLight){const t=r.rectArea[c];t.position.setFromMatrixPosition(d.matrixWorld),t.position.applyMatrix4(h),a.identity(),o.copy(d.matrixWorld),o.premultiply(h),a.extractRotation(o),t.halfWidth.set(.5*d.width,0,0),t.halfHeight.set(0,.5*d.height,0),t.halfWidth.applyMatrix4(a),t.halfHeight.applyMatrix4(a),c++}else if(d.isPointLight){const t=r.point[i];t.position.setFromMatrixPosition(d.matrixWorld),t.position.applyMatrix4(h),i++}else if(d.isHemisphereLight){const t=r.hemi[u];t.direction.setFromMatrixPosition(d.matrixWorld),t.direction.transformDirection(h),t.direction.normalize(),u++}}},state:r}}function An(t,e){const n=new Tn(t,e),i=[],r=[];return{init:function(){i.length=0,r.length=0},state:{lightsArray:i,shadowsArray:r,lights:n},setupLights:function(t){n.setup(i,t)},setupLightsView:function(t){n.setupView(i,t)},pushLight:function(t){i.push(t)},pushShadow:function(t){r.push(t)}}}function En(t,e){let n=new WeakMap;return{get:function(i,r=0){let s;return!1===n.has(i)?(s=new An(t,e),n.set(i,[s])):r>=n.get(i).length?(s=new An(t,e),n.get(i).push(s)):s=n.get(i)[r],s},dispose:function(){n=new WeakMap}}}class Mn extends O.a{constructor(t){super(),this.type=\\\\\\\"MeshDepthMaterial\\\\\\\",this.depthPacking=w.j,this.map=null,this.alphaMap=null,this.displacementMap=null,this.displacementScale=1,this.displacementBias=0,this.wireframe=!1,this.wireframeLinewidth=1,this.fog=!1,this.setValues(t)}copy(t){return super.copy(t),this.depthPacking=t.depthPacking,this.map=t.map,this.alphaMap=t.alphaMap,this.displacementMap=t.displacementMap,this.displacementScale=t.displacementScale,this.displacementBias=t.displacementBias,this.wireframe=t.wireframe,this.wireframeLinewidth=t.wireframeLinewidth,this}}Mn.prototype.isMeshDepthMaterial=!0;class Sn extends O.a{constructor(t){super(),this.type=\\\\\\\"MeshDistanceMaterial\\\\\\\",this.referencePosition=new p.a,this.nearDistance=1,this.farDistance=1e3,this.map=null,this.alphaMap=null,this.displacementMap=null,this.displacementScale=1,this.displacementBias=0,this.fog=!1,this.setValues(t)}copy(t){return super.copy(t),this.referencePosition.copy(t.referencePosition),this.nearDistance=t.nearDistance,this.farDistance=t.farDistance,this.map=t.map,this.alphaMap=t.alphaMap,this.displacementMap=t.displacementMap,this.displacementScale=t.displacementScale,this.displacementBias=t.displacementBias,this}}Sn.prototype.isMeshDistanceMaterial=!0;function Cn(t,e,n){let i=new T.a;const r=new d.a,s=new d.a,o=new _.a,a=new Mn({depthPacking:w.Hb}),l=new Sn,c={},u=n.maxTextureSize,h={0:w.i,1:w.H,2:w.z},p=new F({uniforms:{shadow_pass:{value:null},resolution:{value:new d.a},radius:{value:4},samples:{value:8}},vertexShader:\\\\\\\"\\\\nvoid main() {\\\\n\\\\n\\\\tgl_Position = vec4( position, 1.0 );\\\\n\\\\n}\\\\n\\\\\\\",fragmentShader:\\\\\\\"\\\\nuniform sampler2D shadow_pass;\\\\nuniform vec2 resolution;\\\\nuniform float radius;\\\\nuniform float samples;\\\\n\\\\n#include <packing>\\\\n\\\\nvoid main() {\\\\n\\\\n\\\\tfloat mean = 0.0;\\\\n\\\\tfloat squared_mean = 0.0;\\\\n\\\\n\\\\t// This seems totally useless but it's a crazy work around for a Adreno compiler bug\\\\n\\\\t// float depth = unpackRGBAToDepth( texture2D( shadow_pass, ( gl_FragCoord.xy ) / resolution ) );\\\\n\\\\n\\\\tfloat uvStride = samples <= 1.0 ? 0.0 : 2.0 / ( samples - 1.0 );\\\\n\\\\tfloat uvStart = samples <= 1.0 ? 0.0 : - 1.0;\\\\n\\\\tfor ( float i = 0.0; i < samples; i ++ ) {\\\\n\\\\n\\\\t\\\\tfloat uvOffset = uvStart + i * uvStride;\\\\n\\\\n\\\\t\\\\t#ifdef HORIZONTAL_PASS\\\\n\\\\n\\\\t\\\\t\\\\tvec2 distribution = unpackRGBATo2Half( texture2D( shadow_pass, ( gl_FragCoord.xy + vec2( uvOffset, 0.0 ) * radius ) / resolution ) );\\\\n\\\\t\\\\t\\\\tmean += distribution.x;\\\\n\\\\t\\\\t\\\\tsquared_mean += distribution.y * distribution.y + distribution.x * distribution.x;\\\\n\\\\n\\\\t\\\\t#else\\\\n\\\\n\\\\t\\\\t\\\\tfloat depth = unpackRGBAToDepth( texture2D( shadow_pass, ( gl_FragCoord.xy + vec2( 0.0, uvOffset ) * radius ) / resolution ) );\\\\n\\\\t\\\\t\\\\tmean += depth;\\\\n\\\\t\\\\t\\\\tsquared_mean += depth * depth;\\\\n\\\\n\\\\t\\\\t#endif\\\\n\\\\n\\\\t}\\\\n\\\\n\\\\tmean = mean / samples;\\\\n\\\\tsquared_mean = squared_mean / samples;\\\\n\\\\n\\\\tfloat std_dev = sqrt( squared_mean - mean * mean );\\\\n\\\\n\\\\tgl_FragColor = pack2HalfToRGBA( vec2( mean, std_dev ) );\\\\n\\\\n}\\\\n\\\\\\\"}),m=p.clone();m.defines.HORIZONTAL_PASS=1;const f=new S.a;f.setAttribute(\\\\\\\"position\\\\\\\",new C.a(new Float32Array([-1,-1,.5,3,-1,.5,-1,3,.5]),3));const g=new k.a(f,p),v=this;function y(n,i){const r=e.update(g);p.uniforms.shadow_pass.value=n.map.texture,p.uniforms.resolution.value=n.mapSize,p.uniforms.radius.value=n.radius,p.uniforms.samples.value=n.blurSamples,t.setRenderTarget(n.mapPass),t.clear(),t.renderBufferDirect(i,null,r,p,g,null),m.uniforms.shadow_pass.value=n.mapPass.texture,m.uniforms.resolution.value=n.mapSize,m.uniforms.radius.value=n.radius,m.uniforms.samples.value=n.blurSamples,t.setRenderTarget(n.map),t.clear(),t.renderBufferDirect(i,null,r,m,g,null)}function x(e,n,i,r,s,o,u){let d=null;const p=!0===r.isPointLight?e.customDistanceMaterial:e.customDepthMaterial;if(d=void 0!==p?p:!0===r.isPointLight?l:a,t.localClippingEnabled&&!0===i.clipShadows&&0!==i.clippingPlanes.length||i.displacementMap&&0!==i.displacementScale||i.alphaMap&&i.alphaTest>0){const t=d.uuid,e=i.uuid;let n=c[t];void 0===n&&(n={},c[t]=n);let r=n[e];void 0===r&&(r=d.clone(),n[e]=r),d=r}return d.visible=i.visible,d.wireframe=i.wireframe,u===w.gd?d.side=null!==i.shadowSide?i.shadowSide:i.side:d.side=null!==i.shadowSide?i.shadowSide:h[i.side],d.alphaMap=i.alphaMap,d.alphaTest=i.alphaTest,d.clipShadows=i.clipShadows,d.clippingPlanes=i.clippingPlanes,d.clipIntersection=i.clipIntersection,d.displacementMap=i.displacementMap,d.displacementScale=i.displacementScale,d.displacementBias=i.displacementBias,d.wireframeLinewidth=i.wireframeLinewidth,d.linewidth=i.linewidth,!0===r.isPointLight&&!0===d.isMeshDistanceMaterial&&(d.referencePosition.setFromMatrixPosition(r.matrixWorld),d.nearDistance=s,d.farDistance=o),d}function b(n,r,s,o,a){if(!1===n.visible)return;if(n.layers.test(r.layers)&&(n.isMesh||n.isLine||n.isPoints)&&(n.castShadow||n.receiveShadow&&a===w.gd)&&(!n.frustumCulled||i.intersectsObject(n))){n.modelViewMatrix.multiplyMatrices(s.matrixWorldInverse,n.matrixWorld);const i=e.update(n),r=n.material;if(Array.isArray(r)){const e=i.groups;for(let l=0,c=e.length;l<c;l++){const c=e[l],u=r[c.materialIndex];if(u&&u.visible){const e=x(n,0,u,o,s.near,s.far,a);t.renderBufferDirect(s,null,i,e,n,c)}}}else if(r.visible){const e=x(n,0,r,o,s.near,s.far,a);t.renderBufferDirect(s,null,i,e,n,null)}}const l=n.children;for(let t=0,e=l.length;t<e;t++)b(l[t],r,s,o,a)}this.enabled=!1,this.autoUpdate=!0,this.needsUpdate=!1,this.type=w.Fb,this.render=function(e,n,a){if(!1===v.enabled)return;if(!1===v.autoUpdate&&!1===v.needsUpdate)return;if(0===e.length)return;const l=t.getRenderTarget(),c=t.getActiveCubeFace(),h=t.getActiveMipmapLevel(),d=t.state;d.setBlending(w.ub),d.buffers.color.setClear(1,1,1,1),d.buffers.depth.setTest(!0),d.setScissorTest(!1);for(let l=0,c=e.length;l<c;l++){const c=e[l],h=c.shadow;if(void 0===h){console.warn(\\\\\\\"THREE.WebGLShadowMap:\\\\\\\",c,\\\\\\\"has no shadow.\\\\\\\");continue}if(!1===h.autoUpdate&&!1===h.needsUpdate)continue;r.copy(h.mapSize);const p=h.getFrameExtents();if(r.multiply(p),s.copy(h.mapSize),(r.x>u||r.y>u)&&(r.x>u&&(s.x=Math.floor(u/p.x),r.x=s.x*p.x,h.mapSize.x=s.x),r.y>u&&(s.y=Math.floor(u/p.y),r.y=s.y*p.y,h.mapSize.y=s.y)),null===h.map&&!h.isPointLightShadow&&this.type===w.gd){const t={minFilter:w.V,magFilter:w.V,format:w.Ib};h.map=new Z(r.x,r.y,t),h.map.texture.name=c.name+\\\\\\\".shadowMap\\\\\\\",h.mapPass=new Z(r.x,r.y,t),h.camera.updateProjectionMatrix()}if(null===h.map){const t={minFilter:w.ob,magFilter:w.ob,format:w.Ib};h.map=new 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e=!1,n=null,i=null,r=null;return{setTest:function(e){e?z(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){r!==e&&(t.clearDepth(e),r=e)},reset:function(){e=!1,n=null,i=null,r=null}}},o=new function(){let 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o=t.TEXTURE_2D;i.isDataTexture2DArray&&(o=t.TEXTURE_2D_ARRAY),i.isDataTexture3D&&(o=t.TEXTURE_3D),O(e,i),n.activeTexture(t.TEXTURE0+r),n.bindTexture(o,e.__webglTexture),t.pixelStorei(t.UNPACK_FLIP_Y_WEBGL,i.flipY),t.pixelStorei(t.UNPACK_PREMULTIPLY_ALPHA_WEBGL,i.premultiplyAlpha),t.pixelStorei(t.UNPACK_ALIGNMENT,i.unpackAlignment),t.pixelStorei(t.UNPACK_COLORSPACE_CONVERSION_WEBGL,t.NONE);const l=function(t){return!a&&(t.wrapS!==w.n||t.wrapT!==w.n||t.minFilter!==w.ob&&t.minFilter!==w.V)}(i)&&!1===g(i.image),c=f(i.image,l,!1,u),h=g(c)||a,d=s.convert(i.format);let p,_=s.convert(i.type),m=x(i.internalFormat,d,_,i.encoding);L(o,i,h);const b=i.mipmaps;if(i.isDepthTexture)m=t.DEPTH_COMPONENT,a?m=i.type===w.G?t.DEPTH_COMPONENT32F:i.type===w.bd?t.DEPTH_COMPONENT24:i.type===w.ad?t.DEPTH24_STENCIL8:t.DEPTH_COMPONENT16:i.type===w.G&&console.error(\\\\\\\"WebGLRenderer: Floating point depth texture requires WebGL2.\\\\\\\"),i.format===w.x&&m===t.DEPTH_COMPONENT&&i.type!==w.fd&&i.type!==w.bd&&(console.warn(\\\\\\\"THREE.WebGLRenderer: Use UnsignedShortType or UnsignedIntType for DepthFormat DepthTexture.\\\\\\\"),i.type=w.fd,_=s.convert(i.type)),i.format===w.y&&m===t.DEPTH_COMPONENT&&(m=t.DEPTH_STENCIL,i.type!==w.ad&&(console.warn(\\\\\\\"THREE.WebGLRenderer: Use UnsignedInt248Type for DepthStencilFormat DepthTexture.\\\\\\\"),i.type=w.ad,_=s.convert(i.type))),n.texImage2D(t.TEXTURE_2D,0,m,c.width,c.height,0,d,_,null);else if(i.isDataTexture)if(b.length>0&&h){for(let e=0,i=b.length;e<i;e++)p=b[e],n.texImage2D(t.TEXTURE_2D,e,m,p.width,p.height,0,d,_,p.data);i.generateMipmaps=!1,e.__maxMipLevel=b.length-1}else n.texImage2D(t.TEXTURE_2D,0,m,c.width,c.height,0,d,_,c.data),e.__maxMipLevel=0;else if(i.isCompressedTexture){for(let e=0,r=b.length;e<r;e++)p=b[e],i.format!==w.Ib&&i.format!==w.ic?null!==d?n.compressedTexImage2D(t.TEXTURE_2D,e,m,p.width,p.height,0,p.data):console.warn(\\\\\\\"THREE.WebGLRenderer: Attempt to load unsupported compressed texture format in .uploadTexture()\\\\\\\"):n.texImage2D(t.TEXTURE_2D,e,m,p.width,p.height,0,d,_,p.data);e.__maxMipLevel=b.length-1}else if(i.isDataTexture2DArray)n.texImage3D(t.TEXTURE_2D_ARRAY,0,m,c.width,c.height,c.depth,0,d,_,c.data),e.__maxMipLevel=0;else if(i.isDataTexture3D)n.texImage3D(t.TEXTURE_3D,0,m,c.width,c.height,c.depth,0,d,_,c.data),e.__maxMipLevel=0;else if(b.length>0&&h){for(let e=0,i=b.length;e<i;e++)p=b[e],n.texImage2D(t.TEXTURE_2D,e,m,d,_,p);i.generateMipmaps=!1,e.__maxMipLevel=b.length-1}else n.texImage2D(t.TEXTURE_2D,0,m,d,_,c),e.__maxMipLevel=0;v(i,h)&&y(o,i,c.width,c.height),e.__version=i.version,i.onUpdate&&i.onUpdate(i)}function P(e,r,o,a,l){const c=s.convert(o.format),u=s.convert(o.type),h=x(o.internalFormat,c,u,o.encoding);l===t.TEXTURE_3D||l===t.TEXTURE_2D_ARRAY?n.texImage3D(l,0,h,r.width,r.height,r.depth,0,c,u,null):n.texImage2D(l,0,h,r.width,r.height,0,c,u,null),n.bindFramebuffer(t.FRAMEBUFFER,e),t.framebufferTexture2D(t.FRAMEBUFFER,a,l,i.get(o).__webglTexture,0),n.bindFramebuffer(t.FRAMEBUFFER,null)}function I(e,n,i){if(t.bindRenderbuffer(t.RENDERBUFFER,e),n.depthBuffer&&!n.stencilBuffer){let r=t.DEPTH_COMPONENT16;if(i){const e=n.depthTexture;e&&e.isDepthTexture&&(e.type===w.G?r=t.DEPTH_COMPONENT32F:e.type===w.bd&&(r=t.DEPTH_COMPONENT24));const i=D(n);t.renderbufferStorageMultisample(t.RENDERBUFFER,i,r,n.width,n.height)}else t.renderbufferStorage(t.RENDERBUFFER,r,n.width,n.height);t.framebufferRenderbuffer(t.FRAMEBUFFER,t.DEPTH_ATTACHMENT,t.RENDERBUFFER,e)}else if(n.depthBuffer&&n.stencilBuffer){if(i){const e=D(n);t.renderbufferStorageMultisample(t.RENDERBUFFER,e,t.DEPTH24_STENCIL8,n.width,n.height)}else t.renderbufferStorage(t.RENDERBUFFER,t.DEPTH_STENCIL,n.width,n.height);t.framebufferRenderbuffer(t.FRAMEBUFFER,t.DEPTH_STENCIL_ATTACHMENT,t.RENDERBUFFER,e)}else{const e=!0===n.isWebGLMultipleRenderTargets?n.texture[0]:n.texture,r=s.convert(e.format),o=s.convert(e.type),a=x(e.internalFormat,r,o,e.encoding);if(i){const e=D(n);t.renderbufferStorageMultisample(t.RENDERBUFFER,e,a,n.width,n.height)}else t.renderbufferStorage(t.RENDERBUFFER,a,n.width,n.height)}t.bindRenderbuffer(t.RENDERBUFFER,null)}function F(e){const r=i.get(e),s=!0===e.isWebGLCubeRenderTarget;if(e.depthTexture){if(s)throw new Error(\\\\\\\"target.depthTexture not supported in Cube render targets\\\\\\\");!function(e,r){if(r&&r.isWebGLCubeRenderTarget)throw new Error(\\\\\\\"Depth Texture with cube render targets is not supported\\\\\\\");if(n.bindFramebuffer(t.FRAMEBUFFER,e),!r.depthTexture||!r.depthTexture.isDepthTexture)throw new Error(\\\\\\\"renderTarget.depthTexture must be an instance of THREE.DepthTexture\\\\\\\");i.get(r.depthTexture).__webglTexture&&r.depthTexture.image.width===r.width&&r.depthTexture.image.height===r.height||(r.depthTexture.image.width=r.width,r.depthTexture.image.height=r.height,r.depthTexture.needsUpdate=!0),M(r.depthTexture,0);const s=i.get(r.depthTexture).__webglTexture;if(r.depthTexture.format===w.x)t.framebufferTexture2D(t.FRAMEBUFFER,t.DEPTH_ATTACHMENT,t.TEXTURE_2D,s,0);else{if(r.depthTexture.format!==w.y)throw new Error(\\\\\\\"Unknown depthTexture format\\\\\\\");t.framebufferTexture2D(t.FRAMEBUFFER,t.DEPTH_STENCIL_ATTACHMENT,t.TEXTURE_2D,s,0)}}(r.__webglFramebuffer,e)}else if(s){r.__webglDepthbuffer=[];for(let i=0;i<6;i++)n.bindFramebuffer(t.FRAMEBUFFER,r.__webglFramebuffer[i]),r.__webglDepthbuffer[i]=t.createRenderbuffer(),I(r.__webglDepthbuffer[i],e,!1)}else n.bindFramebuffer(t.FRAMEBUFFER,r.__webglFramebuffer),r.__webglDepthbuffer=t.createRenderbuffer(),I(r.__webglDepthbuffer,e,!1);n.bindFramebuffer(t.FRAMEBUFFER,null)}function D(t){return a&&t.isWebGLMultisampleRenderTarget?Math.min(h,t.samples):0}let k=!1,B=!1;this.allocateTextureUnit=function(){const t=E;return t>=l&&console.warn(\\\\\\\"THREE.WebGLTextures: Trying to use \\\\\\\"+t+\\\\\\\" texture units while this GPU supports only \\\\\\\"+l),E+=1,t},this.resetTextureUnits=function(){E=0},this.setTexture2D=M,this.setTexture2DArray=function(e,r){const s=i.get(e);e.version>0&&s.__version!==e.version?R(s,e,r):(n.activeTexture(t.TEXTURE0+r),n.bindTexture(t.TEXTURE_2D_ARRAY,s.__webglTexture))},this.setTexture3D=function(e,r){const s=i.get(e);e.version>0&&s.__version!==e.version?R(s,e,r):(n.activeTexture(t.TEXTURE0+r),n.bindTexture(t.TEXTURE_3D,s.__webglTexture))},this.setTextureCube=S,this.setupRenderTarget=function(e){const l=e.texture,c=i.get(e),u=i.get(l);e.addEventListener(\\\\\\\"dispose\\\\\\\",A),!0!==e.isWebGLMultipleRenderTargets&&(u.__webglTexture=t.createTexture(),u.__version=l.version,o.memory.textures++);const h=!0===e.isWebGLCubeRenderTarget,d=!0===e.isWebGLMultipleRenderTargets,p=!0===e.isWebGLMultisampleRenderTarget,_=l.isDataTexture3D||l.isDataTexture2DArray,m=g(e)||a;if(!a||l.format!==w.ic||l.type!==w.G&&l.type!==w.M||(l.format=w.Ib,console.warn(\\\\\\\"THREE.WebGLRenderer: Rendering to textures with RGB format is not supported. Using RGBA format instead.\\\\\\\")),h){c.__webglFramebuffer=[];for(let e=0;e<6;e++)c.__webglFramebuffer[e]=t.createFramebuffer()}else if(c.__webglFramebuffer=t.createFramebuffer(),d)if(r.drawBuffers){const n=e.texture;for(let e=0,r=n.length;e<r;e++){const r=i.get(n[e]);void 0===r.__webglTexture&&(r.__webglTexture=t.createTexture(),o.memory.textures++)}}else console.warn(\\\\\\\"THREE.WebGLRenderer: WebGLMultipleRenderTargets can only be used with WebGL2 or WEBGL_draw_buffers extension.\\\\\\\");else if(p)if(a){c.__webglMultisampledFramebuffer=t.createFramebuffer(),c.__webglColorRenderbuffer=t.createRenderbuffer(),t.bindRenderbuffer(t.RENDERBUFFER,c.__webglColorRenderbuffer);const i=s.convert(l.format),r=s.convert(l.type),o=x(l.internalFormat,i,r,l.encoding),a=D(e);t.renderbufferStorageMultisample(t.RENDERBUFFER,a,o,e.width,e.height),n.bindFramebuffer(t.FRAMEBUFFER,c.__webglMultisampledFramebuffer),t.framebufferRenderbuffer(t.FRAMEBUFFER,t.COLOR_ATTACHMENT0,t.RENDERBUFFER,c.__webglColorRenderbuffer),t.bindRenderbuffer(t.RENDERBUFFER,null),e.depthBuffer&&(c.__webglDepthRenderbuffer=t.createRenderbuffer(),I(c.__webglDepthRenderbuffer,e,!0)),n.bindFramebuffer(t.FRAMEBUFFER,null)}else console.warn(\\\\\\\"THREE.WebGLRenderer: WebGLMultisampleRenderTarget can only be used with WebGL2.\\\\\\\");if(h){n.bindTexture(t.TEXTURE_CUBE_MAP,u.__webglTexture),L(t.TEXTURE_CUBE_MAP,l,m);for(let n=0;n<6;n++)P(c.__webglFramebuffer[n],e,l,t.COLOR_ATTACHMENT0,t.TEXTURE_CUBE_MAP_POSITIVE_X+n);v(l,m)&&y(t.TEXTURE_CUBE_MAP,l,e.width,e.height),n.unbindTexture()}else if(d){const r=e.texture;for(let s=0,o=r.length;s<o;s++){const o=r[s],a=i.get(o);n.bindTexture(t.TEXTURE_2D,a.__webglTexture),L(t.TEXTURE_2D,o,m),P(c.__webglFramebuffer,e,o,t.COLOR_ATTACHMENT0+s,t.TEXTURE_2D),v(o,m)&&y(t.TEXTURE_2D,o,e.width,e.height)}n.unbindTexture()}else{let i=t.TEXTURE_2D;if(_)if(a){i=l.isDataTexture3D?t.TEXTURE_3D:t.TEXTURE_2D_ARRAY}else console.warn(\\\\\\\"THREE.DataTexture3D and THREE.DataTexture2DArray only supported with WebGL2.\\\\\\\");n.bindTexture(i,u.__webglTexture),L(i,l,m),P(c.__webglFramebuffer,e,l,t.COLOR_ATTACHMENT0,i),v(l,m)&&y(i,l,e.width,e.height,e.depth),n.unbindTexture()}e.depthBuffer&&F(e)},this.updateRenderTargetMipmap=function(e){const r=g(e)||a,s=!0===e.isWebGLMultipleRenderTargets?e.texture:[e.texture];for(let o=0,a=s.length;o<a;o++){const a=s[o];if(v(a,r)){const r=e.isWebGLCubeRenderTarget?t.TEXTURE_CUBE_MAP:t.TEXTURE_2D,s=i.get(a).__webglTexture;n.bindTexture(r,s),y(r,a,e.width,e.height),n.unbindTexture()}}},this.updateMultisampleRenderTarget=function(e){if(e.isWebGLMultisampleRenderTarget)if(a){const r=e.width,s=e.height;let o=t.COLOR_BUFFER_BIT;e.depthBuffer&&(o|=t.DEPTH_BUFFER_BIT),e.stencilBuffer&&(o|=t.STENCIL_BUFFER_BIT);const a=i.get(e);n.bindFramebuffer(t.READ_FRAMEBUFFER,a.__webglMultisampledFramebuffer),n.bindFramebuffer(t.DRAW_FRAMEBUFFER,a.__webglFramebuffer),t.blitFramebuffer(0,0,r,s,0,0,r,s,o,t.NEAREST),n.bindFramebuffer(t.READ_FRAMEBUFFER,null),n.bindFramebuffer(t.DRAW_FRAMEBUFFER,a.__webglMultisampledFramebuffer)}else console.warn(\\\\\\\"THREE.WebGLRenderer: WebGLMultisampleRenderTarget can only be used with WebGL2.\\\\\\\")},this.safeSetTexture2D=function(t,e){t&&t.isWebGLRenderTarget&&(!1===k&&(console.warn(\\\\\\\"THREE.WebGLTextures.safeSetTexture2D: don't use render targets as textures. Use their .texture property instead.\\\\\\\"),k=!0),t=t.texture),M(t,e)},this.safeSetTextureCube=function(t,e){t&&t.isWebGLCubeRenderTarget&&(!1===B&&(console.warn(\\\\\\\"THREE.WebGLTextures.safeSetTextureCube: don't use cube render targets as textures. Use their .texture property instead.\\\\\\\"),B=!0),t=t.texture),S(t,e)}}function Rn(t,e,n){const i=n.isWebGL2;return{convert:function(n){let r;if(n===w.Zc)return t.UNSIGNED_BYTE;if(n===w.cd)return t.UNSIGNED_SHORT_4_4_4_4;if(n===w.dd)return t.UNSIGNED_SHORT_5_5_5_1;if(n===w.ed)return t.UNSIGNED_SHORT_5_6_5;if(n===w.l)return t.BYTE;if(n===w.Mc)return t.SHORT;if(n===w.fd)return t.UNSIGNED_SHORT;if(n===w.N)return t.INT;if(n===w.bd)return t.UNSIGNED_INT;if(n===w.G)return t.FLOAT;if(n===w.M)return i?t.HALF_FLOAT:(r=e.get(\\\\\\\"OES_texture_half_float\\\\\\\"),null!==r?r.HALF_FLOAT_OES:null);if(n===w.f)return t.ALPHA;if(n===w.ic)return t.RGB;if(n===w.Ib)return t.RGBA;if(n===w.gb)return t.LUMINANCE;if(n===w.fb)return t.LUMINANCE_ALPHA;if(n===w.x)return t.DEPTH_COMPONENT;if(n===w.y)return t.DEPTH_STENCIL;if(n===w.tc)return t.RED;if(n===w.uc)return t.RED_INTEGER;if(n===w.rc)return t.RG;if(n===w.sc)return t.RG_INTEGER;if(n===w.jc)return t.RGB_INTEGER;if(n===w.Jb)return t.RGBA_INTEGER;if(n===w.qc||n===w.cc||n===w.dc||n===w.ec){if(r=e.get(\\\\\\\"WEBGL_compressed_texture_s3tc\\\\\\\"),null===r)return null;if(n===w.qc)return r.COMPRESSED_RGB_S3TC_DXT1_EXT;if(n===w.cc)return r.COMPRESSED_RGBA_S3TC_DXT1_EXT;if(n===w.dc)return r.COMPRESSED_RGBA_S3TC_DXT3_EXT;if(n===w.ec)return r.COMPRESSED_RGBA_S3TC_DXT5_EXT}if(n===w.pc||n===w.oc||n===w.bc||n===w.ac){if(r=e.get(\\\\\\\"WEBGL_compressed_texture_pvrtc\\\\\\\"),null===r)return null;if(n===w.pc)return r.COMPRESSED_RGB_PVRTC_4BPPV1_IMG;if(n===w.oc)return r.COMPRESSED_RGB_PVRTC_2BPPV1_IMG;if(n===w.bc)return r.COMPRESSED_RGBA_PVRTC_4BPPV1_IMG;if(n===w.ac)return r.COMPRESSED_RGBA_PVRTC_2BPPV1_IMG}if(n===w.mc)return r=e.get(\\\\\\\"WEBGL_compressed_texture_etc1\\\\\\\"),null!==r?r.COMPRESSED_RGB_ETC1_WEBGL:null;if((n===w.nc||n===w.Zb)&&(r=e.get(\\\\\\\"WEBGL_compressed_texture_etc\\\\\\\"),null!==r)){if(n===w.nc)return r.COMPRESSED_RGB8_ETC2;if(n===w.Zb)return r.COMPRESSED_RGBA8_ETC2_EAC}return n===w.Qb||n===w.Rb||n===w.Sb||n===w.Tb||n===w.Ub||n===w.Vb||n===w.Wb||n===w.Xb||n===w.Lb||n===w.Mb||n===w.Nb||n===w.Kb||n===w.Ob||n===w.Pb||n===w.Ec||n===w.Fc||n===w.Gc||n===w.Hc||n===w.Ic||n===w.Jc||n===w.Kc||n===w.Lc||n===w.zc||n===w.Ac||n===w.Bc||n===w.yc||n===w.Cc||n===w.Dc?(r=e.get(\\\\\\\"WEBGL_compressed_texture_astc\\\\\\\"),null!==r?n:null):n===w.Yb?(r=e.get(\\\\\\\"EXT_texture_compression_bptc\\\\\\\"),null!==r?n:null):n===w.ad?i?t.UNSIGNED_INT_24_8:(r=e.get(\\\\\\\"WEBGL_depth_texture\\\\\\\"),null!==r?r.UNSIGNED_INT_24_8_WEBGL:null):void 0}}}class Pn extends K.a{constructor(t=[]){super(),this.cameras=t}}Pn.prototype.isArrayCamera=!0;var In=n(22);const Fn={type:\\\\\\\"move\\\\\\\"};class Dn{constructor(){this._targetRay=null,this._grip=null,this._hand=null}getHandSpace(){return null===this._hand&&(this._hand=new In.a,this._hand.matrixAutoUpdate=!1,this._hand.visible=!1,this._hand.joints={},this._hand.inputState={pinching:!1}),this._hand}getTargetRaySpace(){return null===this._targetRay&&(this._targetRay=new In.a,this._targetRay.matrixAutoUpdate=!1,this._targetRay.visible=!1,this._targetRay.hasLinearVelocity=!1,this._targetRay.linearVelocity=new p.a,this._targetRay.hasAngularVelocity=!1,this._targetRay.angularVelocity=new p.a),this._targetRay}getGripSpace(){return null===this._grip&&(this._grip=new In.a,this._grip.matrixAutoUpdate=!1,this._grip.visible=!1,this._grip.hasLinearVelocity=!1,this._grip.linearVelocity=new p.a,this._grip.hasAngularVelocity=!1,this._grip.angularVelocity=new p.a),this._grip}dispatchEvent(t){return null!==this._targetRay&&this._targetRay.dispatchEvent(t),null!==this._grip&&this._grip.dispatchEvent(t),null!==this._hand&&this._hand.dispatchEvent(t),this}disconnect(t){return this.dispatchEvent({type:\\\\\\\"disconnected\\\\\\\",data:t}),null!==this._targetRay&&(this._targetRay.visible=!1),null!==this._grip&&(this._grip.visible=!1),null!==this._hand&&(this._hand.visible=!1),this}update(t,e,n){let i=null,r=null,s=null;const o=this._targetRay,a=this._grip,l=this._hand;if(t&&\\\\\\\"visible-blurred\\\\\\\"!==e.session.visibilityState)if(null!==o&&(i=e.getPose(t.targetRaySpace,n),null!==i&&(o.matrix.fromArray(i.transform.matrix),o.matrix.decompose(o.position,o.rotation,o.scale),i.linearVelocity?(o.hasLinearVelocity=!0,o.linearVelocity.copy(i.linearVelocity)):o.hasLinearVelocity=!1,i.angularVelocity?(o.hasAngularVelocity=!0,o.angularVelocity.copy(i.angularVelocity)):o.hasAngularVelocity=!1,this.dispatchEvent(Fn))),l&&t.hand){s=!0;for(const i of t.hand.values()){const t=e.getJointPose(i,n);if(void 0===l.joints[i.jointName]){const t=new In.a;t.matrixAutoUpdate=!1,t.visible=!1,l.joints[i.jointName]=t,l.add(t)}const r=l.joints[i.jointName];null!==t&&(r.matrix.fromArray(t.transform.matrix),r.matrix.decompose(r.position,r.rotation,r.scale),r.jointRadius=t.radius),r.visible=null!==t}const i=l.joints[\\\\\\\"index-finger-tip\\\\\\\"],r=l.joints[\\\\\\\"thumb-tip\\\\\\\"],o=i.position.distanceTo(r.position),a=.02,c=.005;l.inputState.pinching&&o>a+c?(l.inputState.pinching=!1,this.dispatchEvent({type:\\\\\\\"pinchend\\\\\\\",handedness:t.handedness,target:this})):!l.inputState.pinching&&o<=a-c&&(l.inputState.pinching=!0,this.dispatchEvent({type:\\\\\\\"pinchstart\\\\\\\",handedness:t.handedness,target:this}))}else null!==a&&t.gripSpace&&(r=e.getPose(t.gripSpace,n),null!==r&&(a.matrix.fromArray(r.transform.matrix),a.matrix.decompose(a.position,a.rotation,a.scale),r.linearVelocity?(a.hasLinearVelocity=!0,a.linearVelocity.copy(r.linearVelocity)):a.hasLinearVelocity=!1,r.angularVelocity?(a.hasAngularVelocity=!0,a.angularVelocity.copy(r.angularVelocity)):a.hasAngularVelocity=!1));return null!==o&&(o.visible=null!==i),null!==a&&(a.visible=null!==r),null!==l&&(l.visible=null!==s),this}}class kn extends $.a{constructor(t,e){super();const n=this,i=t.state;let r=null,s=1,o=null,a=\\\\\\\"local-floor\\\\\\\",l=null,c=null,u=null,h=null,d=null,m=!1,f=null,g=null,v=null,y=null,x=null,b=null;const w=[],T=new Map,A=new K.a;A.layers.enable(1),A.viewport=new _.a;const M=new K.a;M.layers.enable(2),M.viewport=new _.a;const S=[A,M],C=new Pn;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 R(){T.forEach((function(t,e){t.disconnect(e)})),T.clear(),N=null,L=null,i.bindXRFramebuffer(null),t.setRenderTarget(t.getRenderTarget()),u&&e.deleteFramebuffer(u),f&&e.deleteFramebuffer(f),g&&e.deleteRenderbuffer(g),v&&e.deleteRenderbuffer(v),u=null,f=null,g=null,v=null,d=null,h=null,c=null,r=null,B.stop(),n.isPresenting=!1,n.dispatchEvent({type:\\\\\\\"sessionend\\\\\\\"})}function P(t){const e=r.inputSources;for(let t=0;t<w.length;t++)T.set(e[t],w[t]);for(let e=0;e<t.removed.length;e++){const n=t.removed[e],i=T.get(n);i&&(i.dispatchEvent({type:\\\\\\\"disconnected\\\\\\\",data:n}),T.delete(n))}for(let e=0;e<t.added.length;e++){const n=t.added[e],i=T.get(n);i&&i.dispatchEvent({type:\\\\\\\"connected\\\\\\\",data:n})}}this.cameraAutoUpdate=!0,this.enabled=!1,this.isPresenting=!1,this.getController=function(t){let e=w[t];return void 0===e&&(e=new Dn,w[t]=e),e.getTargetRaySpace()},this.getControllerGrip=function(t){let e=w[t];return void 0===e&&(e=new Dn,w[t]=e),e.getGripSpace()},this.getHand=function(t){let e=w[t];return void 0===e&&(e=new Dn,w[t]=e),e.getHandSpace()},this.setFramebufferScaleFactor=function(t){s=t,!0===n.isPresenting&&console.warn(\\\\\\\"THREE.WebXRManager: Cannot change framebuffer scale while presenting.\\\\\\\")},this.setReferenceSpaceType=function(t){a=t,!0===n.isPresenting&&console.warn(\\\\\\\"THREE.WebXRManager: Cannot change reference space type while presenting.\\\\\\\")},this.getReferenceSpace=function(){return o},this.getBaseLayer=function(){return null!==h?h:d},this.getBinding=function(){return c},this.getFrame=function(){return y},this.getSession=function(){return r},this.setSession=async function(t){if(r=t,null!==r){r.addEventListener(\\\\\\\"select\\\\\\\",O),r.addEventListener(\\\\\\\"selectstart\\\\\\\",O),r.addEventListener(\\\\\\\"selectend\\\\\\\",O),r.addEventListener(\\\\\\\"squeeze\\\\\\\",O),r.addEventListener(\\\\\\\"squeezestart\\\\\\\",O),r.addEventListener(\\\\\\\"squeezeend\\\\\\\",O),r.addEventListener(\\\\\\\"end\\\\\\\",R),r.addEventListener(\\\\\\\"inputsourceschange\\\\\\\",P);const t=e.getContextAttributes();if(!0!==t.xrCompatible&&await e.makeXRCompatible(),void 0===r.renderState.layers){const n={antialias:t.antialias,alpha:t.alpha,depth:t.depth,stencil:t.stencil,framebufferScaleFactor:s};d=new XRWebGLLayer(r,e,n),r.updateRenderState({baseLayer:d})}else if(e instanceof WebGLRenderingContext){const n={antialias:!0,alpha:t.alpha,depth:t.depth,stencil:t.stencil,framebufferScaleFactor:s};d=new XRWebGLLayer(r,e,n),r.updateRenderState({layers:[d]})}else{m=t.antialias;let n=null;t.depth&&(b=e.DEPTH_BUFFER_BIT,t.stencil&&(b|=e.STENCIL_BUFFER_BIT),x=t.stencil?e.DEPTH_STENCIL_ATTACHMENT:e.DEPTH_ATTACHMENT,n=t.stencil?e.DEPTH24_STENCIL8:e.DEPTH_COMPONENT24);const o={colorFormat:t.alpha?e.RGBA8:e.RGB8,depthFormat:n,scaleFactor:s};c=new XRWebGLBinding(r,e),h=c.createProjectionLayer(o),u=e.createFramebuffer(),r.updateRenderState({layers:[h]}),m&&(f=e.createFramebuffer(),g=e.createRenderbuffer(),e.bindRenderbuffer(e.RENDERBUFFER,g),e.renderbufferStorageMultisample(e.RENDERBUFFER,4,e.RGBA8,h.textureWidth,h.textureHeight),i.bindFramebuffer(e.FRAMEBUFFER,f),e.framebufferRenderbuffer(e.FRAMEBUFFER,e.COLOR_ATTACHMENT0,e.RENDERBUFFER,g),e.bindRenderbuffer(e.RENDERBUFFER,null),null!==n&&(v=e.createRenderbuffer(),e.bindRenderbuffer(e.RENDERBUFFER,v),e.renderbufferStorageMultisample(e.RENDERBUFFER,4,n,h.textureWidth,h.textureHeight),e.framebufferRenderbuffer(e.FRAMEBUFFER,x,e.RENDERBUFFER,v),e.bindRenderbuffer(e.RENDERBUFFER,null)),i.bindFramebuffer(e.FRAMEBUFFER,null))}o=await r.requestReferenceSpace(a),B.setContext(r),B.start(),n.isPresenting=!0,n.dispatchEvent({type:\\\\\\\"sessionstart\\\\\\\"})}};const I=new p.a,F=new p.a;function D(t,e){null===e?t.matrixWorld.copy(t.matrix):t.matrixWorld.multiplyMatrices(e.matrixWorld,t.matrix),t.matrixWorldInverse.copy(t.matrixWorld).invert()}this.updateCamera=function(t){if(null===r)return;C.near=M.near=A.near=t.near,C.far=M.far=A.far=t.far,N===C.near&&L===C.far||(r.updateRenderState({depthNear:C.near,depthFar:C.far}),N=C.near,L=C.far);const e=t.parent,n=C.cameras;D(C,e);for(let t=0;t<n.length;t++)D(n[t],e);C.matrixWorld.decompose(C.position,C.quaternion,C.scale),t.position.copy(C.position),t.quaternion.copy(C.quaternion),t.scale.copy(C.scale),t.matrix.copy(C.matrix),t.matrixWorld.copy(C.matrixWorld);const i=t.children;for(let t=0,e=i.length;t<e;t++)i[t].updateMatrixWorld(!0);2===n.length?function(t,e,n){I.setFromMatrixPosition(e.matrixWorld),F.setFromMatrixPosition(n.matrixWorld);const i=I.distanceTo(F),r=e.projectionMatrix.elements,s=n.projectionMatrix.elements,o=r[14]/(r[10]-1),a=r[14]/(r[10]+1),l=(r[9]+1)/r[5],c=(r[9]-1)/r[5],u=(r[8]-1)/r[0],h=(s[8]+1)/s[0],d=o*u,p=o*h,_=i/(-u+h),m=_*-u;e.matrixWorld.decompose(t.position,t.quaternion,t.scale),t.translateX(m),t.translateZ(_),t.matrixWorld.compose(t.position,t.quaternion,t.scale),t.matrixWorldInverse.copy(t.matrixWorld).invert();const f=o+_,g=a+_,v=d-m,y=p+(i-m),x=l*a/g*f,b=c*a/g*f;t.projectionMatrix.makePerspective(v,y,x,b,f,g)}(C,A,M):C.projectionMatrix.copy(A.projectionMatrix)},this.getCamera=function(){return C},this.getFoveation=function(){return null!==h?h.fixedFoveation:null!==d?d.fixedFoveation:void 0},this.setFoveation=function(t){null!==h&&(h.fixedFoveation=t),null!==d&&void 0!==d.fixedFoveation&&(d.fixedFoveation=t)};let k=null;const B=new E;B.setAnimationLoop((function(t,n){if(l=n.getViewerPose(o),y=n,null!==l){const t=l.views;null!==d&&i.bindXRFramebuffer(d.framebuffer);let n=!1;t.length!==C.cameras.length&&(C.cameras.length=0,n=!0);for(let r=0;r<t.length;r++){const s=t[r];let o=null;if(null!==d)o=d.getViewport(s);else{const t=c.getViewSubImage(h,s);i.bindXRFramebuffer(u),void 0!==t.depthStencilTexture&&e.framebufferTexture2D(e.FRAMEBUFFER,x,e.TEXTURE_2D,t.depthStencilTexture,0),e.framebufferTexture2D(e.FRAMEBUFFER,e.COLOR_ATTACHMENT0,e.TEXTURE_2D,t.colorTexture,0),o=t.viewport}const a=S[r];a.matrix.fromArray(s.transform.matrix),a.projectionMatrix.fromArray(s.projectionMatrix),a.viewport.set(o.x,o.y,o.width,o.height),0===r&&C.matrix.copy(a.matrix),!0===n&&C.cameras.push(a)}m&&(i.bindXRFramebuffer(f),null!==b&&e.clear(b))}const s=r.inputSources;for(let t=0;t<w.length;t++){const e=w[t],i=s[t];e.update(i,n,o)}if(k&&k(t,n),m){const t=h.textureWidth,n=h.textureHeight;i.bindFramebuffer(e.READ_FRAMEBUFFER,f),i.bindFramebuffer(e.DRAW_FRAMEBUFFER,u),e.invalidateFramebuffer(e.READ_FRAMEBUFFER,[x]),e.invalidateFramebuffer(e.DRAW_FRAMEBUFFER,[x]),e.blitFramebuffer(0,0,t,n,0,0,t,n,e.COLOR_BUFFER_BIT,e.NEAREST),e.invalidateFramebuffer(e.READ_FRAMEBUFFER,[e.COLOR_ATTACHMENT0]),i.bindFramebuffer(e.READ_FRAMEBUFFER,null),i.bindFramebuffer(e.DRAW_FRAMEBUFFER,null),i.bindFramebuffer(e.FRAMEBUFFER,f)}y=null})),this.setAnimationLoop=function(t){k=t},this.dispose=function(){}}}function Bn(t){function e(e,n){e.opacity.value=n.opacity,n.color&&e.diffuse.value.copy(n.color),n.emissive&&e.emissive.value.copy(n.emissive).multiplyScalar(n.emissiveIntensity),n.map&&(e.map.value=n.map),n.alphaMap&&(e.alphaMap.value=n.alphaMap),n.specularMap&&(e.specularMap.value=n.specularMap),n.alphaTest>0&&(e.alphaTest.value=n.alphaTest);const i=t.get(n).envMap;if(i){e.envMap.value=i,e.flipEnvMap.value=i.isCubeTexture&&!1===i.isRenderTargetTexture?-1:1,e.reflectivity.value=n.reflectivity,e.ior.value=n.ior,e.refractionRatio.value=n.refractionRatio;const r=t.get(i).__maxMipLevel;void 0!==r&&(e.maxMipLevel.value=r)}let r,s;n.lightMap&&(e.lightMap.value=n.lightMap,e.lightMapIntensity.value=n.lightMapIntensity),n.aoMap&&(e.aoMap.value=n.aoMap,e.aoMapIntensity.value=n.aoMapIntensity),n.map?r=n.map:n.specularMap?r=n.specularMap:n.displacementMap?r=n.displacementMap:n.normalMap?r=n.normalMap:n.bumpMap?r=n.bumpMap:n.roughnessMap?r=n.roughnessMap:n.metalnessMap?r=n.metalnessMap:n.alphaMap?r=n.alphaMap:n.emissiveMap?r=n.emissiveMap:n.clearcoatMap?r=n.clearcoatMap:n.clearcoatNormalMap?r=n.clearcoatNormalMap:n.clearcoatRoughnessMap?r=n.clearcoatRoughnessMap:n.specularIntensityMap?r=n.specularIntensityMap:n.specularTintMap?r=n.specularTintMap:n.transmissionMap?r=n.transmissionMap:n.thicknessMap&&(r=n.thicknessMap),void 0!==r&&(r.isWebGLRenderTarget&&(r=r.texture),!0===r.matrixAutoUpdate&&r.updateMatrix(),e.uvTransform.value.copy(r.matrix)),n.aoMap?s=n.aoMap:n.lightMap&&(s=n.lightMap),void 0!==s&&(s.isWebGLRenderTarget&&(s=s.texture),!0===s.matrixAutoUpdate&&s.updateMatrix(),e.uv2Transform.value.copy(s.matrix))}function n(e,n){e.roughness.value=n.roughness,e.metalness.value=n.metalness,n.roughnessMap&&(e.roughnessMap.value=n.roughnessMap),n.metalnessMap&&(e.metalnessMap.value=n.metalnessMap),n.emissiveMap&&(e.emissiveMap.value=n.emissiveMap),n.bumpMap&&(e.bumpMap.value=n.bumpMap,e.bumpScale.value=n.bumpScale,n.side===w.i&&(e.bumpScale.value*=-1)),n.normalMap&&(e.normalMap.value=n.normalMap,e.normalScale.value.copy(n.normalScale),n.side===w.i&&e.normalScale.value.negate()),n.displacementMap&&(e.displacementMap.value=n.displacementMap,e.displacementScale.value=n.displacementScale,e.displacementBias.value=n.displacementBias);t.get(n).envMap&&(e.envMapIntensity.value=n.envMapIntensity)}return{refreshFogUniforms:function(t,e){t.fogColor.value.copy(e.color),e.isFog?(t.fogNear.value=e.near,t.fogFar.value=e.far):e.isFogExp2&&(t.fogDensity.value=e.density)},refreshMaterialUniforms:function(t,i,r,s,o){i.isMeshBasicMaterial?e(t,i):i.isMeshLambertMaterial?(e(t,i),function(t,e){e.emissiveMap&&(t.emissiveMap.value=e.emissiveMap)}(t,i)):i.isMeshToonMaterial?(e(t,i),function(t,e){e.gradientMap&&(t.gradientMap.value=e.gradientMap);e.emissiveMap&&(t.emissiveMap.value=e.emissiveMap);e.bumpMap&&(t.bumpMap.value=e.bumpMap,t.bumpScale.value=e.bumpScale,e.side===w.i&&(t.bumpScale.value*=-1));e.normalMap&&(t.normalMap.value=e.normalMap,t.normalScale.value.copy(e.normalScale),e.side===w.i&&t.normalScale.value.negate());e.displacementMap&&(t.displacementMap.value=e.displacementMap,t.displacementScale.value=e.displacementScale,t.displacementBias.value=e.displacementBias)}(t,i)):i.isMeshPhongMaterial?(e(t,i),function(t,e){t.specular.value.copy(e.specular),t.shininess.value=Math.max(e.shininess,1e-4),e.emissiveMap&&(t.emissiveMap.value=e.emissiveMap);e.bumpMap&&(t.bumpMap.value=e.bumpMap,t.bumpScale.value=e.bumpScale,e.side===w.i&&(t.bumpScale.value*=-1));e.normalMap&&(t.normalMap.value=e.normalMap,t.normalScale.value.copy(e.normalScale),e.side===w.i&&t.normalScale.value.negate());e.displacementMap&&(t.displacementMap.value=e.displacementMap,t.displacementScale.value=e.displacementScale,t.displacementBias.value=e.displacementBias)}(t,i)):i.isMeshStandardMaterial?(e(t,i),i.isMeshPhysicalMaterial?function(t,e,i){n(t,e),t.ior.value=e.ior,e.sheen>0&&(t.sheenTint.value.copy(e.sheenTint).multiplyScalar(e.sheen),t.sheenRoughness.value=e.sheenRoughness);e.clearcoat>0&&(t.clearcoat.value=e.clearcoat,t.clearcoatRoughness.value=e.clearcoatRoughness,e.clearcoatMap&&(t.clearcoatMap.value=e.clearcoatMap),e.clearcoatRoughnessMap&&(t.clearcoatRoughnessMap.value=e.clearcoatRoughnessMap),e.clearcoatNormalMap&&(t.clearcoatNormalScale.value.copy(e.clearcoatNormalScale),t.clearcoatNormalMap.value=e.clearcoatNormalMap,e.side===w.i&&t.clearcoatNormalScale.value.negate()));e.transmission>0&&(t.transmission.value=e.transmission,t.transmissionSamplerMap.value=i.texture,t.transmissionSamplerSize.value.set(i.width,i.height),e.transmissionMap&&(t.transmissionMap.value=e.transmissionMap),t.thickness.value=e.thickness,e.thicknessMap&&(t.thicknessMap.value=e.thicknessMap),t.attenuationDistance.value=e.attenuationDistance,t.attenuationTint.value.copy(e.attenuationTint));t.specularIntensity.value=e.specularIntensity,t.specularTint.value.copy(e.specularTint),e.specularIntensityMap&&(t.specularIntensityMap.value=e.specularIntensityMap);e.specularTintMap&&(t.specularTintMap.value=e.specularTintMap)}(t,i,o):n(t,i)):i.isMeshMatcapMaterial?(e(t,i),function(t,e){e.matcap&&(t.matcap.value=e.matcap);e.bumpMap&&(t.bumpMap.value=e.bumpMap,t.bumpScale.value=e.bumpScale,e.side===w.i&&(t.bumpScale.value*=-1));e.normalMap&&(t.normalMap.value=e.normalMap,t.normalScale.value.copy(e.normalScale),e.side===w.i&&t.normalScale.value.negate());e.displacementMap&&(t.displacementMap.value=e.displacementMap,t.displacementScale.value=e.displacementScale,t.displacementBias.value=e.displacementBias)}(t,i)):i.isMeshDepthMaterial?(e(t,i),function(t,e){e.displacementMap&&(t.displacementMap.value=e.displacementMap,t.displacementScale.value=e.displacementScale,t.displacementBias.value=e.displacementBias)}(t,i)):i.isMeshDistanceMaterial?(e(t,i),function(t,e){e.displacementMap&&(t.displacementMap.value=e.displacementMap,t.displacementScale.value=e.displacementScale,t.displacementBias.value=e.displacementBias);t.referencePosition.value.copy(e.referencePosition),t.nearDistance.value=e.nearDistance,t.farDistance.value=e.farDistance}(t,i)):i.isMeshNormalMaterial?(e(t,i),function(t,e){e.bumpMap&&(t.bumpMap.value=e.bumpMap,t.bumpScale.value=e.bumpScale,e.side===w.i&&(t.bumpScale.value*=-1));e.normalMap&&(t.normalMap.value=e.normalMap,t.normalScale.value.copy(e.normalScale),e.side===w.i&&t.normalScale.value.negate());e.displacementMap&&(t.displacementMap.value=e.displacementMap,t.displacementScale.value=e.displacementScale,t.displacementBias.value=e.displacementBias)}(t,i)):i.isLineBasicMaterial?(function(t,e){t.diffuse.value.copy(e.color),t.opacity.value=e.opacity}(t,i),i.isLineDashedMaterial&&function(t,e){t.dashSize.value=e.dashSize,t.totalSize.value=e.dashSize+e.gapSize,t.scale.value=e.scale}(t,i)):i.isPointsMaterial?function(t,e,n,i){t.diffuse.value.copy(e.color),t.opacity.value=e.opacity,t.size.value=e.size*n,t.scale.value=.5*i,e.map&&(t.map.value=e.map);e.alphaMap&&(t.alphaMap.value=e.alphaMap);e.alphaTest>0&&(t.alphaTest.value=e.alphaTest);let r;e.map?r=e.map:e.alphaMap&&(r=e.alphaMap);void 0!==r&&(!0===r.matrixAutoUpdate&&r.updateMatrix(),t.uvTransform.value.copy(r.matrix))}(t,i,r,s):i.isSpriteMaterial?function(t,e){t.diffuse.value.copy(e.color),t.opacity.value=e.opacity,t.rotation.value=e.rotation,e.map&&(t.map.value=e.map);e.alphaMap&&(t.alphaMap.value=e.alphaMap);e.alphaTest>0&&(t.alphaTest.value=e.alphaTest);let n;e.map?n=e.map:e.alphaMap&&(n=e.alphaMap);void 0!==n&&(!0===n.matrixAutoUpdate&&n.updateMatrix(),t.uvTransform.value.copy(n.matrix))}(t,i):i.isShadowMaterial?(t.color.value.copy(i.color),t.opacity.value=i.opacity):i.isShaderMaterial&&(i.uniformsNeedUpdate=!1)}}}function zn(t={}){const e=void 0!==t.canvas?t.canvas:function(){const t=Object(Pt.b)(\\\\\\\"canvas\\\\\\\");return t.style.display=\\\\\\\"block\\\\\\\",t}(),n=void 0!==t.context?t.context:null,i=void 0!==t.alpha&&t.alpha,r=void 0===t.depth||t.depth,s=void 0===t.stencil||t.stencil,o=void 0!==t.antialias&&t.antialias,a=void 0===t.premultipliedAlpha||t.premultipliedAlpha,l=void 0!==t.preserveDrawingBuffer&&t.preserveDrawingBuffer,c=void 0!==t.powerPreference?t.powerPreference:\\\\\\\"default\\\\\\\",u=void 0!==t.failIfMajorPerformanceCaveat&&t.failIfMajorPerformanceCaveat;let h=null,d=null;const m=[],f=[];this.domElement=e,this.debug={checkShaderErrors:!0},this.autoClear=!0,this.autoClearColor=!0,this.autoClearDepth=!0,this.autoClearStencil=!0,this.sortObjects=!0,this.clippingPlanes=[],this.localClippingEnabled=!1,this.gammaFactor=2,this.outputEncoding=w.U,this.physicallyCorrectLights=!1,this.toneMapping=w.vb,this.toneMappingExposure=1;const g=this;let v=!1,y=0,x=0,b=null,S=-1,C=null;const N=new _.a,L=new _.a;let O=null,R=e.width,P=e.height,I=1,F=null,D=null;const k=new _.a(0,0,R,P),B=new _.a(0,0,R,P);let z=!1;const U=[],G=new T.a;let V=!1,X=!1,$=null;const J=new A.a,Q=new p.a,K={background:null,fog:null,environment:null,overrideMaterial:null,isScene:!0};function tt(){return null===b?I:1}let et,nt,it,st,ot,at,lt,ct,ut,ht,dt,pt,_t,mt,ft,gt,vt,yt,xt,bt,wt,Tt,At,Et=n;function Mt(t,n){for(let i=0;i<t.length;i++){const r=t[i],s=e.getContext(r,n);if(null!==s)return s}return null}try{const t={alpha:i,depth:r,stencil:s,antialias:o,premultipliedAlpha:a,preserveDrawingBuffer:l,powerPreference:c,failIfMajorPerformanceCaveat:u};if(e.addEventListener(\\\\\\\"webglcontextlost\\\\\\\",Nt,!1),e.addEventListener(\\\\\\\"webglcontextrestored\\\\\\\",Lt,!1),null===Et){const e=[\\\\\\\"webgl2\\\\\\\",\\\\\\\"webgl\\\\\\\",\\\\\\\"experimental-webgl\\\\\\\"];if(!0===g.isWebGL1Renderer&&e.shift(),Et=Mt(e,t),null===Et)throw Mt(e)?new Error(\\\\\\\"Error creating WebGL context with your selected attributes.\\\\\\\"):new Error(\\\\\\\"Error creating WebGL context.\\\\\\\")}void 0===Et.getShaderPrecisionFormat&&(Et.getShaderPrecisionFormat=function(){return{rangeMin:1,rangeMax:1,precision:1}})}catch(t){throw console.error(\\\\\\\"THREE.WebGLRenderer: \\\\\\\"+t.message),t}function St(){et=new Rt(Et),nt=new q(Et,et,t),et.init(nt),Tt=new Rn(Et,et,nt),it=new Nn(Et,et,nt),U[0]=Et.BACK,st=new Dt(Et),ot=new mn,at=new On(Et,et,it,ot,nt,Tt,st),lt=new rt(g),ct=new Ot(g),ut=new M(Et,nt),At=new j(Et,et,ut,nt),ht=new It(Et,ut,st,At),dt=new Ht(Et,ht,ut,st),xt=new Gt(Et,nt,at),gt=new Y(ot),pt=new _n(g,lt,ct,et,nt,At,gt),_t=new Bn(ot),mt=new yn(ot),ft=new En(et,nt),yt=new H(g,lt,it,dt,a),vt=new Cn(g,dt,nt),bt=new W(Et,et,st,nt),wt=new Ft(Et,et,st,nt),st.programs=pt.programs,g.capabilities=nt,g.extensions=et,g.properties=ot,g.renderLists=mt,g.shadowMap=vt,g.state=it,g.info=st}St();const Ct=new kn(g,Et);function Nt(t){t.preventDefault(),console.log(\\\\\\\"THREE.WebGLRenderer: Context Lost.\\\\\\\"),v=!0}function Lt(){console.log(\\\\\\\"THREE.WebGLRenderer: Context Restored.\\\\\\\"),v=!1;const t=st.autoReset,e=vt.enabled,n=vt.autoUpdate,i=vt.needsUpdate,r=vt.type;St(),st.autoReset=t,vt.enabled=e,vt.autoUpdate=n,vt.needsUpdate=i,vt.type=r}function kt(t){const e=t.target;e.removeEventListener(\\\\\\\"dispose\\\\\\\",kt),function(t){(function(t){const e=ot.get(t).programs;void 0!==e&&e.forEach((function(t){pt.releaseProgram(t)}))})(t),ot.remove(t)}(e)}this.xr=Ct,this.getContext=function(){return Et},this.getContextAttributes=function(){return Et.getContextAttributes()},this.forceContextLoss=function(){const t=et.get(\\\\\\\"WEBGL_lose_context\\\\\\\");t&&t.loseContext()},this.forceContextRestore=function(){const t=et.get(\\\\\\\"WEBGL_lose_context\\\\\\\");t&&t.restoreContext()},this.getPixelRatio=function(){return I},this.setPixelRatio=function(t){void 0!==t&&(I=t,this.setSize(R,P,!1))},this.getSize=function(t){return t.set(R,P)},this.setSize=function(t,n,i){Ct.isPresenting?console.warn(\\\\\\\"THREE.WebGLRenderer: Can't change size while VR device is 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z},this.setScissorTest=function(t){it.setScissorTest(z=t)},this.setOpaqueSort=function(t){F=t},this.setTransparentSort=function(t){D=t},this.getClearColor=function(t){return t.copy(yt.getClearColor())},this.setClearColor=function(){yt.setClearColor.apply(yt,arguments)},this.getClearAlpha=function(){return yt.getClearAlpha()},this.setClearAlpha=function(){yt.setClearAlpha.apply(yt,arguments)},this.clear=function(t,e,n){let i=0;(void 0===t||t)&&(i|=Et.COLOR_BUFFER_BIT),(void 0===e||e)&&(i|=Et.DEPTH_BUFFER_BIT),(void 0===n||n)&&(i|=Et.STENCIL_BUFFER_BIT),Et.clear(i)},this.clearColor=function(){this.clear(!0,!1,!1)},this.clearDepth=function(){this.clear(!1,!0,!1)},this.clearStencil=function(){this.clear(!1,!1,!0)},this.dispose=function(){e.removeEventListener(\\\\\\\"webglcontextlost\\\\\\\",Nt,!1),e.removeEventListener(\\\\\\\"webglcontextrestored\\\\\\\",Lt,!1),mt.dispose(),ft.dispose(),ot.dispose(),lt.dispose(),ct.dispose(),dt.dispose(),At.dispose(),Ct.dispose(),Ct.removeEventListener(\\\\\\\"sessionstart\\\\\\\",zt),Ct.removeEventListener(\\\\\\\"sessionend\\\\\\\",Ut),$&&($.dispose(),$=null),jt.stop()},this.renderBufferImmediate=function(t,e){At.initAttributes();const n=ot.get(t);t.hasPositions&&!n.position&&(n.position=Et.createBuffer()),t.hasNormals&&!n.normal&&(n.normal=Et.createBuffer()),t.hasUvs&&!n.uv&&(n.uv=Et.createBuffer()),t.hasColors&&!n.color&&(n.color=Et.createBuffer());const i=e.getAttributes();t.hasPositions&&(Et.bindBuffer(Et.ARRAY_BUFFER,n.position),Et.bufferData(Et.ARRAY_BUFFER,t.positionArray,Et.DYNAMIC_DRAW),At.enableAttribute(i.position.location),Et.vertexAttribPointer(i.position.location,3,Et.FLOAT,!1,0,0)),t.hasNormals&&(Et.bindBuffer(Et.ARRAY_BUFFER,n.normal),Et.bufferData(Et.ARRAY_BUFFER,t.normalArray,Et.DYNAMIC_DRAW),At.enableAttribute(i.normal.location),Et.vertexAttribPointer(i.normal.location,3,Et.FLOAT,!1,0,0)),t.hasUvs&&(Et.bindBuffer(Et.ARRAY_BUFFER,n.uv),Et.bufferData(Et.ARRAY_BUFFER,t.uvArray,Et.DYNAMIC_DRAW),At.enableAttribute(i.uv.location),Et.vertexAttribPointer(i.uv.location,2,Et.FLOAT,!1,0,0)),t.hasColors&&(Et.bindBuffer(Et.ARRAY_BUFFER,n.color),Et.bufferData(Et.ARRAY_BUFFER,t.colorArray,Et.DYNAMIC_DRAW),At.enableAttribute(i.color.location),Et.vertexAttribPointer(i.color.location,3,Et.FLOAT,!1,0,0)),At.disableUnusedAttributes(),Et.drawArrays(Et.TRIANGLES,0,t.count),t.count=0},this.renderBufferDirect=function(t,e,n,i,r,s){null===e&&(e=K);const o=r.isMesh&&r.matrixWorld.determinant()<0,a=Zt(t,e,n,i,r);it.setMaterial(i,o);let l=n.index;const c=n.attributes.position;if(null===l){if(void 0===c||0===c.count)return}else if(0===l.count)return;let u,h=1;!0===i.wireframe&&(l=ht.getWireframeAttribute(n),h=2),At.setup(r,i,a,n,l);let d=bt;null!==l&&(u=ut.get(l),d=wt,d.setIndex(u));const p=null!==l?l.count:c.count,_=n.drawRange.start*h,m=n.drawRange.count*h,f=null!==s?s.start*h:0,g=null!==s?s.count*h:1/0,v=Math.max(_,f),y=Math.min(p,_+m,f+g)-1,x=Math.max(0,y-v+1);if(0!==x){if(r.isMesh)!0===i.wireframe?(it.setLineWidth(i.wireframeLinewidth*tt()),d.setMode(Et.LINES)):d.setMode(Et.TRIANGLES);else if(r.isLine){let t=i.linewidth;void 0===t&&(t=1),it.setLineWidth(t*tt()),r.isLineSegments?d.setMode(Et.LINES):r.isLineLoop?d.setMode(Et.LINE_LOOP):d.setMode(Et.LINE_STRIP)}else r.isPoints?d.setMode(Et.POINTS):r.isSprite&&d.setMode(Et.TRIANGLES);if(r.isInstancedMesh)d.renderInstances(v,x,r.count);else if(n.isInstancedBufferGeometry){const t=Math.min(n.instanceCount,n._maxInstanceCount);d.renderInstances(v,x,t)}else d.render(v,x)}},this.compile=function(t,e){d=ft.get(t),d.init(),f.push(d),t.traverseVisible((function(t){t.isLight&&t.layers.test(e.layers)&&(d.pushLight(t),t.castShadow&&d.pushShadow(t))})),d.setupLights(g.physicallyCorrectLights),t.traverse((function(e){const n=e.material;if(n)if(Array.isArray(n))for(let i=0;i<n.length;i++){$t(n[i],t,e)}else $t(n,t,e)})),f.pop(),d=null};let Bt=null;function zt(){jt.stop()}function Ut(){jt.start()}const jt=new E;function Wt(t,e,n,i){if(!1===t.visible)return;if(t.layers.test(e.layers))if(t.isGroup)n=t.renderOrder;else if(t.isLOD)!0===t.autoUpdate&&t.update(e);else if(t.isLight)d.pushLight(t),t.castShadow&&d.pushShadow(t);else if(t.isSprite){if(!t.frustumCulled||G.intersectsSprite(t)){i&&Q.setFromMatrixPosition(t.matrixWorld).applyMatrix4(J);const e=dt.update(t),r=t.material;r.visible&&h.push(t,e,r,n,Q.z,null)}}else if(t.isImmediateRenderObject)i&&Q.setFromMatrixPosition(t.matrixWorld).applyMatrix4(J),h.push(t,null,t.material,n,Q.z,null);else if((t.isMesh||t.isLine||t.isPoints)&&(t.isSkinnedMesh&&t.skeleton.frame!==st.render.frame&&(t.skeleton.update(),t.skeleton.frame=st.render.frame),!t.frustumCulled||G.intersectsObject(t))){i&&Q.setFromMatrixPosition(t.matrixWorld).applyMatrix4(J);const e=dt.update(t),r=t.material;if(Array.isArray(r)){const i=e.groups;for(let s=0,o=i.length;s<o;s++){const o=i[s],a=r[o.materialIndex];a&&a.visible&&h.push(t,e,a,n,Q.z,o)}}else r.visible&&h.push(t,e,r,n,Q.z,null)}const r=t.children;for(let t=0,s=r.length;t<s;t++)Wt(r[t],e,n,i)}function qt(t,e,n,i){const r=t.opaque,s=t.transmissive,a=t.transparent;d.setupLightsView(n),s.length>0&&function(t,e,n){if(null===$){const t=!0===o&&!0===nt.isWebGL2;$=new(t?Vt:Z)(1024,1024,{generateMipmaps:!0,type:null!==Tt.convert(w.M)?w.M:w.Zc,minFilter:w.Y,magFilter:w.ob,wrapS:w.n,wrapT:w.n})}const i=g.getRenderTarget();g.setRenderTarget($),g.clear();const r=g.toneMapping;g.toneMapping=w.vb,Xt(t,e,n),g.toneMapping=r,at.updateMultisampleRenderTarget($),at.updateRenderTargetMipmap($),g.setRenderTarget(i)}(r,e,n),i&&it.viewport(N.copy(i)),r.length>0&&Xt(r,e,n),s.length>0&&Xt(s,e,n),a.length>0&&Xt(a,e,n)}function Xt(t,e,n){const i=!0===e.isScene?e.overrideMaterial:null;for(let r=0,s=t.length;r<s;r++){const s=t[r],o=s.object,a=s.geometry,l=null===i?s.material:i,c=s.group;o.layers.test(n.layers)&&Yt(o,e,n,a,l,c)}}function Yt(t,e,n,i,r,s){if(t.onBeforeRender(g,e,n,i,r,s),t.modelViewMatrix.multiplyMatrices(n.matrixWorldInverse,t.matrixWorld),t.normalMatrix.getNormalMatrix(t.modelViewMatrix),r.onBeforeRender(g,e,n,i,t,s),t.isImmediateRenderObject){const s=Zt(n,e,i,r,t);it.setMaterial(r),At.reset(),function(t,e){t.render((function(t){g.renderBufferImmediate(t,e)}))}(t,s)}else!0===r.transparent&&r.side===w.z?(r.side=w.i,r.needsUpdate=!0,g.renderBufferDirect(n,e,i,r,t,s),r.side=w.H,r.needsUpdate=!0,g.renderBufferDirect(n,e,i,r,t,s),r.side=w.z):g.renderBufferDirect(n,e,i,r,t,s);t.onAfterRender(g,e,n,i,r,s)}function $t(t,e,n){!0!==e.isScene&&(e=K);const i=ot.get(t),r=d.state.lights,s=d.state.shadowsArray,o=r.state.version,a=pt.getParameters(t,r.state,s,e,n),l=pt.getProgramCacheKey(a);let c=i.programs;i.environment=t.isMeshStandardMaterial?e.environment:null,i.fog=e.fog,i.envMap=(t.isMeshStandardMaterial?ct:lt).get(t.envMap||i.environment),void 0===c&&(t.addEventListener(\\\\\\\"dispose\\\\\\\",kt),c=new Map,i.programs=c);let u=c.get(l);if(void 0!==u){if(i.currentProgram===u&&i.lightsStateVersion===o)return Jt(t,a),u}else a.uniforms=pt.getUniforms(t),t.onBuild(a,g),t.onBeforeCompile(a,g),u=pt.acquireProgram(a,l),c.set(l,u),i.uniforms=a.uniforms;const h=i.uniforms;(t.isShaderMaterial||t.isRawShaderMaterial)&&!0!==t.clipping||(h.clippingPlanes=gt.uniform),Jt(t,a),i.needsLights=function(t){return t.isMeshLambertMaterial||t.isMeshToonMaterial||t.isMeshPhongMaterial||t.isMeshStandardMaterial||t.isShadowMaterial||t.isShaderMaterial&&!0===t.lights}(t),i.lightsStateVersion=o,i.needsLights&&(h.ambientLightColor.value=r.state.ambient,h.lightProbe.value=r.state.probe,h.directionalLights.value=r.state.directional,h.directionalLightShadows.value=r.state.directionalShadow,h.spotLights.value=r.state.spot,h.spotLightShadows.value=r.state.spotShadow,h.rectAreaLights.value=r.state.rectArea,h.ltc_1.value=r.state.rectAreaLTC1,h.ltc_2.value=r.state.rectAreaLTC2,h.pointLights.value=r.state.point,h.pointLightShadows.value=r.state.pointShadow,h.hemisphereLights.value=r.state.hemi,h.directionalShadowMap.value=r.state.directionalShadowMap,h.directionalShadowMatrix.value=r.state.directionalShadowMatrix,h.spotShadowMap.value=r.state.spotShadowMap,h.spotShadowMatrix.value=r.state.spotShadowMatrix,h.pointShadowMap.value=r.state.pointShadowMap,h.pointShadowMatrix.value=r.state.pointShadowMatrix);const p=u.getUniforms(),_=qe.seqWithValue(p.seq,h);return i.currentProgram=u,i.uniformsList=_,u}function Jt(t,e){const n=ot.get(t);n.outputEncoding=e.outputEncoding,n.instancing=e.instancing,n.skinning=e.skinning,n.morphTargets=e.morphTargets,n.morphNormals=e.morphNormals,n.morphTargetsCount=e.morphTargetsCount,n.numClippingPlanes=e.numClippingPlanes,n.numIntersection=e.numClipIntersection,n.vertexAlphas=e.vertexAlphas,n.vertexTangents=e.vertexTangents}function Zt(t,e,n,i,r){!0!==e.isScene&&(e=K),at.resetTextureUnits();const s=e.fog,o=i.isMeshStandardMaterial?e.environment:null,a=null===b?g.outputEncoding:b.texture.encoding,l=(i.isMeshStandardMaterial?ct:lt).get(i.envMap||o),c=!0===i.vertexColors&&!!n&&!!n.attributes.color&&4===n.attributes.color.itemSize,u=!!i.normalMap&&!!n&&!!n.attributes.tangent,h=!!n&&!!n.morphAttributes.position,p=!!n&&!!n.morphAttributes.normal,_=n&&n.morphAttributes.position?n.morphAttributes.position.length:0,m=ot.get(i),f=d.state.lights;if(!0===V&&(!0===X||t!==C)){const e=t===C&&i.id===S;gt.setState(i,t,e)}let v=!1;i.version===m.__version?m.needsLights&&m.lightsStateVersion!==f.state.version||m.outputEncoding!==a||r.isInstancedMesh&&!1===m.instancing?v=!0:r.isInstancedMesh||!0!==m.instancing?r.isSkinnedMesh&&!1===m.skinning?v=!0:r.isSkinnedMesh||!0!==m.skinning?m.envMap!==l||i.fog&&m.fog!==s?v=!0:void 0===m.numClippingPlanes||m.numClippingPlanes===gt.numPlanes&&m.numIntersection===gt.numIntersection?(m.vertexAlphas!==c||m.vertexTangents!==u||m.morphTargets!==h||m.morphNormals!==p||!0===nt.isWebGL2&&m.morphTargetsCount!==_)&&(v=!0):v=!0:v=!0:v=!0:(v=!0,m.__version=i.version);let y=m.currentProgram;!0===v&&(y=$t(i,e,r));let x=!1,w=!1,T=!1;const A=y.getUniforms(),E=m.uniforms;if(it.useProgram(y.program)&&(x=!0,w=!0,T=!0),i.id!==S&&(S=i.id,w=!0),x||C!==t){if(A.setValue(Et,\\\\\\\"projectionMatrix\\\\\\\",t.projectionMatrix),nt.logarithmicDepthBuffer&&A.setValue(Et,\\\\\\\"logDepthBufFC\\\\\\\",2/(Math.log(t.far+1)/Math.LN2)),C!==t&&(C=t,w=!0,T=!0),i.isShaderMaterial||i.isMeshPhongMaterial||i.isMeshToonMaterial||i.isMeshStandardMaterial||i.envMap){const e=A.map.cameraPosition;void 0!==e&&e.setValue(Et,Q.setFromMatrixPosition(t.matrixWorld))}(i.isMeshPhongMaterial||i.isMeshToonMaterial||i.isMeshLambertMaterial||i.isMeshBasicMaterial||i.isMeshStandardMaterial||i.isShaderMaterial)&&A.setValue(Et,\\\\\\\"isOrthographic\\\\\\\",!0===t.isOrthographicCamera),(i.isMeshPhongMaterial||i.isMeshToonMaterial||i.isMeshLambertMaterial||i.isMeshBasicMaterial||i.isMeshStandardMaterial||i.isShaderMaterial||i.isShadowMaterial||r.isSkinnedMesh)&&A.setValue(Et,\\\\\\\"viewMatrix\\\\\\\",t.matrixWorldInverse)}if(r.isSkinnedMesh){A.setOptional(Et,r,\\\\\\\"bindMatrix\\\\\\\"),A.setOptional(Et,r,\\\\\\\"bindMatrixInverse\\\\\\\");const t=r.skeleton;t&&(nt.floatVertexTextures?(null===t.boneTexture&&t.computeBoneTexture(),A.setValue(Et,\\\\\\\"boneTexture\\\\\\\",t.boneTexture,at),A.setValue(Et,\\\\\\\"boneTextureSize\\\\\\\",t.boneTextureSize)):A.setOptional(Et,t,\\\\\\\"boneMatrices\\\\\\\"))}var M,N;return!n||void 0===n.morphAttributes.position&&void 0===n.morphAttributes.normal||xt.update(r,n,i,y),(w||m.receiveShadow!==r.receiveShadow)&&(m.receiveShadow=r.receiveShadow,A.setValue(Et,\\\\\\\"receiveShadow\\\\\\\",r.receiveShadow)),w&&(A.setValue(Et,\\\\\\\"toneMappingExposure\\\\\\\",g.toneMappingExposure),m.needsLights&&(N=T,(M=E).ambientLightColor.needsUpdate=N,M.lightProbe.needsUpdate=N,M.directionalLights.needsUpdate=N,M.directionalLightShadows.needsUpdate=N,M.pointLights.needsUpdate=N,M.pointLightShadows.needsUpdate=N,M.spotLights.needsUpdate=N,M.spotLightShadows.needsUpdate=N,M.rectAreaLights.needsUpdate=N,M.hemisphereLights.needsUpdate=N),s&&i.fog&&_t.refreshFogUniforms(E,s),_t.refreshMaterialUniforms(E,i,I,P,$),qe.upload(Et,m.uniformsList,E,at)),i.isShaderMaterial&&!0===i.uniformsNeedUpdate&&(qe.upload(Et,m.uniformsList,E,at),i.uniformsNeedUpdate=!1),i.isSpriteMaterial&&A.setValue(Et,\\\\\\\"center\\\\\\\",r.center),A.setValue(Et,\\\\\\\"modelViewMatrix\\\\\\\",r.modelViewMatrix),A.setValue(Et,\\\\\\\"normalMatrix\\\\\\\",r.normalMatrix),A.setValue(Et,\\\\\\\"modelMatrix\\\\\\\",r.matrixWorld),y}jt.setAnimationLoop((function(t){Bt&&Bt(t)})),\\\\\\\"undefined\\\\\\\"!=typeof window&&jt.setContext(window),this.setAnimationLoop=function(t){Bt=t,Ct.setAnimationLoop(t),null===t?jt.stop():jt.start()},Ct.addEventListener(\\\\\\\"sessionstart\\\\\\\",zt),Ct.addEventListener(\\\\\\\"sessionend\\\\\\\",Ut),this.render=function(t,e){if(void 0!==e&&!0!==e.isCamera)return void console.error(\\\\\\\"THREE.WebGLRenderer.render: camera is not an instance of THREE.Camera.\\\\\\\");if(!0===v)return;!0===t.autoUpdate&&t.updateMatrixWorld(),null===e.parent&&e.updateMatrixWorld(),!0===Ct.enabled&&!0===Ct.isPresenting&&(!0===Ct.cameraAutoUpdate&&Ct.updateCamera(e),e=Ct.getCamera()),!0===t.isScene&&t.onBeforeRender(g,t,e,b),d=ft.get(t,f.length),d.init(),f.push(d),J.multiplyMatrices(e.projectionMatrix,e.matrixWorldInverse),G.setFromProjectionMatrix(J),X=this.localClippingEnabled,V=gt.init(this.clippingPlanes,X,e),h=mt.get(t,m.length),h.init(),m.push(h),Wt(t,e,0,g.sortObjects),h.finish(),!0===g.sortObjects&&h.sort(F,D),!0===V&&gt.beginShadows();const n=d.state.shadowsArray;if(vt.render(n,t,e),!0===V&&gt.endShadows(),!0===this.info.autoReset&&this.info.reset(),yt.render(h,t),d.setupLights(g.physicallyCorrectLights),e.isArrayCamera){const n=e.cameras;for(let e=0,i=n.length;e<i;e++){const i=n[e];qt(h,t,i,i.viewport)}}else qt(h,t,e);null!==b&&(at.updateMultisampleRenderTarget(b),at.updateRenderTargetMipmap(b)),!0===t.isScene&&t.onAfterRender(g,t,e),it.buffers.depth.setTest(!0),it.buffers.depth.setMask(!0),it.buffers.color.setMask(!0),it.setPolygonOffset(!1),At.resetDefaultState(),S=-1,C=null,f.pop(),d=f.length>0?f[f.length-1]:null,m.pop(),h=m.length>0?m[m.length-1]:null},this.getActiveCubeFace=function(){return y},this.getActiveMipmapLevel=function(){return x},this.getRenderTarget=function(){return b},this.setRenderTarget=function(t,e=0,n=0){b=t,y=e,x=n,t&&void 0===ot.get(t).__webglFramebuffer&&at.setupRenderTarget(t);let i=null,r=!1,s=!1;if(t){const n=t.texture;(n.isDataTexture3D||n.isDataTexture2DArray)&&(s=!0);const o=ot.get(t).__webglFramebuffer;t.isWebGLCubeRenderTarget?(i=o[e],r=!0):i=t.isWebGLMultisampleRenderTarget?ot.get(t).__webglMultisampledFramebuffer:o,N.copy(t.viewport),L.copy(t.scissor),O=t.scissorTest}else N.copy(k).multiplyScalar(I).floor(),L.copy(B).multiplyScalar(I).floor(),O=z;if(it.bindFramebuffer(Et.FRAMEBUFFER,i)&&nt.drawBuffers){let e=!1;if(t)if(t.isWebGLMultipleRenderTargets){const n=t.texture;if(U.length!==n.length||U[0]!==Et.COLOR_ATTACHMENT0){for(let t=0,e=n.length;t<e;t++)U[t]=Et.COLOR_ATTACHMENT0+t;U.length=n.length,e=!0}}else 1===U.length&&U[0]===Et.COLOR_ATTACHMENT0||(U[0]=Et.COLOR_ATTACHMENT0,U.length=1,e=!0);else 1===U.length&&U[0]===Et.BACK||(U[0]=Et.BACK,U.length=1,e=!0);e&&(nt.isWebGL2?Et.drawBuffers(U):et.get(\\\\\\\"WEBGL_draw_buffers\\\\\\\").drawBuffersWEBGL(U))}if(it.viewport(N),it.scissor(L),it.setScissorTest(O),r){const i=ot.get(t.texture);Et.framebufferTexture2D(Et.FRAMEBUFFER,Et.COLOR_ATTACHMENT0,Et.TEXTURE_CUBE_MAP_POSITIVE_X+e,i.__webglTexture,n)}else if(s){const i=ot.get(t.texture),r=e||0;Et.framebufferTextureLayer(Et.FRAMEBUFFER,Et.COLOR_ATTACHMENT0,i.__webglTexture,n||0,r)}S=-1},this.readRenderTargetPixels=function(t,e,n,i,r,s,o){if(!t||!t.isWebGLRenderTarget)return void console.error(\\\\\\\"THREE.WebGLRenderer.readRenderTargetPixels: renderTarget is not THREE.WebGLRenderTarget.\\\\\\\");let a=ot.get(t).__webglFramebuffer;if(t.isWebGLCubeRenderTarget&&void 0!==o&&(a=a[o]),a){it.bindFramebuffer(Et.FRAMEBUFFER,a);try{const o=t.texture,a=o.format,l=o.type;if(a!==w.Ib&&Tt.convert(a)!==Et.getParameter(Et.IMPLEMENTATION_COLOR_READ_FORMAT))return void console.error(\\\\\\\"THREE.WebGLRenderer.readRenderTargetPixels: renderTarget is not in RGBA or implementation defined format.\\\\\\\");const c=l===w.M&&(et.has(\\\\\\\"EXT_color_buffer_half_float\\\\\\\")||nt.isWebGL2&&et.has(\\\\\\\"EXT_color_buffer_float\\\\\\\"));if(!(l===w.Zc||Tt.convert(l)===Et.getParameter(Et.IMPLEMENTATION_COLOR_READ_TYPE)||l===w.G&&(nt.isWebGL2||et.has(\\\\\\\"OES_texture_float\\\\\\\")||et.has(\\\\\\\"WEBGL_color_buffer_float\\\\\\\"))||c))return void console.error(\\\\\\\"THREE.WebGLRenderer.readRenderTargetPixels: renderTarget is not in UnsignedByteType or implementation defined type.\\\\\\\");Et.checkFramebufferStatus(Et.FRAMEBUFFER)===Et.FRAMEBUFFER_COMPLETE?e>=0&&e<=t.width-i&&n>=0&&n<=t.height-r&&Et.readPixels(e,n,i,r,Tt.convert(a),Tt.convert(l),s):console.error(\\\\\\\"THREE.WebGLRenderer.readRenderTargetPixels: readPixels from renderTarget failed. Framebuffer not complete.\\\\\\\")}finally{const t=null!==b?ot.get(b).__webglFramebuffer:null;it.bindFramebuffer(Et.FRAMEBUFFER,t)}}},this.copyFramebufferToTexture=function(t,e,n=0){const i=Math.pow(2,-n),r=Math.floor(e.image.width*i),s=Math.floor(e.image.height*i);let o=Tt.convert(e.format);nt.isWebGL2&&(o===Et.RGB&&(o=Et.RGB8),o===Et.RGBA&&(o=Et.RGBA8)),at.setTexture2D(e,0),Et.copyTexImage2D(Et.TEXTURE_2D,n,o,t.x,t.y,r,s,0),it.unbindTexture()},this.copyTextureToTexture=function(t,e,n,i=0){const r=e.image.width,s=e.image.height,o=Tt.convert(n.format),a=Tt.convert(n.type);at.setTexture2D(n,0),Et.pixelStorei(Et.UNPACK_FLIP_Y_WEBGL,n.flipY),Et.pixelStorei(Et.UNPACK_PREMULTIPLY_ALPHA_WEBGL,n.premultiplyAlpha),Et.pixelStorei(Et.UNPACK_ALIGNMENT,n.unpackAlignment),e.isDataTexture?Et.texSubImage2D(Et.TEXTURE_2D,i,t.x,t.y,r,s,o,a,e.image.data):e.isCompressedTexture?Et.compressedTexSubImage2D(Et.TEXTURE_2D,i,t.x,t.y,e.mipmaps[0].width,e.mipmaps[0].height,o,e.mipmaps[0].data):Et.texSubImage2D(Et.TEXTURE_2D,i,t.x,t.y,o,a,e.image),0===i&&n.generateMipmaps&&Et.generateMipmap(Et.TEXTURE_2D),it.unbindTexture()},this.copyTextureToTexture3D=function(t,e,n,i,r=0){if(g.isWebGL1Renderer)return void console.warn(\\\\\\\"THREE.WebGLRenderer.copyTextureToTexture3D: can only be used with WebGL2.\\\\\\\");const s=t.max.x-t.min.x+1,o=t.max.y-t.min.y+1,a=t.max.z-t.min.z+1,l=Tt.convert(i.format),c=Tt.convert(i.type);let u;if(i.isDataTexture3D)at.setTexture3D(i,0),u=Et.TEXTURE_3D;else{if(!i.isDataTexture2DArray)return void console.warn(\\\\\\\"THREE.WebGLRenderer.copyTextureToTexture3D: only supports THREE.DataTexture3D and THREE.DataTexture2DArray.\\\\\\\");at.setTexture2DArray(i,0),u=Et.TEXTURE_2D_ARRAY}Et.pixelStorei(Et.UNPACK_FLIP_Y_WEBGL,i.flipY),Et.pixelStorei(Et.UNPACK_PREMULTIPLY_ALPHA_WEBGL,i.premultiplyAlpha),Et.pixelStorei(Et.UNPACK_ALIGNMENT,i.unpackAlignment);const h=Et.getParameter(Et.UNPACK_ROW_LENGTH),d=Et.getParameter(Et.UNPACK_IMAGE_HEIGHT),p=Et.getParameter(Et.UNPACK_SKIP_PIXELS),_=Et.getParameter(Et.UNPACK_SKIP_ROWS),m=Et.getParameter(Et.UNPACK_SKIP_IMAGES),f=n.isCompressedTexture?n.mipmaps[0]:n.image;Et.pixelStorei(Et.UNPACK_ROW_LENGTH,f.width),Et.pixelStorei(Et.UNPACK_IMAGE_HEIGHT,f.height),Et.pixelStorei(Et.UNPACK_SKIP_PIXELS,t.min.x),Et.pixelStorei(Et.UNPACK_SKIP_ROWS,t.min.y),Et.pixelStorei(Et.UNPACK_SKIP_IMAGES,t.min.z),n.isDataTexture||n.isDataTexture3D?Et.texSubImage3D(u,r,e.x,e.y,e.z,s,o,a,l,c,f.data):n.isCompressedTexture?(console.warn(\\\\\\\"THREE.WebGLRenderer.copyTextureToTexture3D: untested support for compressed srcTexture.\\\\\\\"),Et.compressedTexSubImage3D(u,r,e.x,e.y,e.z,s,o,a,l,f.data)):Et.texSubImage3D(u,r,e.x,e.y,e.z,s,o,a,l,c,f),Et.pixelStorei(Et.UNPACK_ROW_LENGTH,h),Et.pixelStorei(Et.UNPACK_IMAGE_HEIGHT,d),Et.pixelStorei(Et.UNPACK_SKIP_PIXELS,p),Et.pixelStorei(Et.UNPACK_SKIP_ROWS,_),Et.pixelStorei(Et.UNPACK_SKIP_IMAGES,m),0===r&&i.generateMipmaps&&Et.generateMipmap(u),it.unbindTexture()},this.initTexture=function(t){at.setTexture2D(t,0),it.unbindTexture()},this.resetState=function(){y=0,x=0,b=null,it.reset(),At.reset()},\\\\\\\"undefined\\\\\\\"!=typeof __THREE_DEVTOOLS__&&__THREE_DEVTOOLS__.dispatchEvent(new CustomEvent(\\\\\\\"observe\\\\\\\",{detail:this}))}const Un={};var Gn,Vn,Hn;!function(t){t.WEBGL=\\\\\\\"webgl\\\\\\\",t.WEBGL2=\\\\\\\"webgl2\\\\\\\",t.EXPERIMENTAL_WEBGL=\\\\\\\"experimental-webgl\\\\\\\",t.EXPERIMENTAL_WEBGL2=\\\\\\\"experimental-webgl2\\\\\\\"}(Gn||(Gn={}));class jn{constructor(){this._next_renderer_id=0,this._renderers={},this._printDebug=!1,this._require_webgl2=!1,this._resolves=[]}setPrintDebug(t=!0){this._printDebug=t}printDebug(){return this._printDebug}printDebugMessage(t){this._printDebug&&console.warn(\\\\\\\"[Poly debug]\\\\\\\",t)}setRequireWebGL2(){this._require_webgl2||(this._require_webgl2=!0)}webgl2Available(){return void 0===this._webgl2_available&&(this._webgl2_available=this._set_webgl2_available()),this._webgl2_available}_set_webgl2_available(){const t=document.createElement(\\\\\\\"canvas\\\\\\\");return null!=(window.WebGL2RenderingContext&&t.getContext(Gn.WEBGL2))}createWebGLRenderer(t){const e=new zn(t);return this.printDebugMessage([\\\\\\\"create renderer:\\\\\\\",t]),e}createRenderingContext(t){let e=null;return this._require_webgl2&&(e=this._getRenderingContextWebgl(t,!0),e||console.warn(\\\\\\\"failed to create webgl2 context\\\\\\\")),e||(e=this._getRenderingContextWebgl(t,!1)),e}_getRenderingContextWebgl(t,e){let n;n=this.webgl2Available()||e?Gn.WEBGL2:Gn.WEBGL;let i=t.getContext(n,Un);return i?this.printDebugMessage(`create gl context: ${n}.`):(n=e?Gn.EXPERIMENTAL_WEBGL2:Gn.EXPERIMENTAL_WEBGL,this.printDebugMessage(`create gl context: ${n}.`),i=t.getContext(n,Un)),i}registerRenderer(t){if(t._polygon_id)throw new Error(\\\\\\\"render already registered\\\\\\\");t._polygon_id=this._next_renderer_id+=1,this._renderers[t._polygon_id]=t,1==Object.keys(this._renderers).length&&this.flush_callbacks_with_renderer(t)}deregisterRenderer(t){delete this._renderers[t._polygon_id],t.dispose()}firstRenderer(){const t=Object.keys(this._renderers)[0];return t?this._renderers[t]:null}renderers(){return Object.values(this._renderers)}flush_callbacks_with_renderer(t){let e;for(;e=this._resolves.pop();)e(t)}async waitForRenderer(){const t=this.firstRenderer();return t||new Promise(((t,e)=>{this._resolves.push(t)}))}renderTarget(t,e,n){return this.webgl2Available()?new Vt(t,e,n):new Z(t,e,n)}}class Wn{constructor(){this._root=\\\\\\\"/three/js/libs\\\\\\\",this._BASISPath=\\\\\\\"/basis\\\\\\\",this._DRACOPath=\\\\\\\"/draco\\\\\\\",this._DRACOGLTFPath=\\\\\\\"/draco/gltf\\\\\\\"}root(){return this._root}setRoot(t){this._root=t}BASISPath(){return this._BASISPath}DRACOPath(){return this._DRACOPath}DRACOGLTFPath(){return this._DRACOGLTFPath}}class qn{constructor(t){this.poly=t,this._node_register=new Map,this._node_register_categories=new Map,this._node_register_options=new Map}register(t,e,n){const i=t.context(),r=t.type().toLowerCase();let s=this._node_register.get(i);s||(s=new Map,this._node_register.set(i,s));if(s.get(r))console.error(`node ${i}/${r} already registered`);else{if(s.set(r,t),e){let t=this._node_register_categories.get(i);t||(t=new Map,this._node_register_categories.set(i,t)),t.set(r,e)}if(n){let t=this._node_register_options.get(i);t||(t=new Map,this._node_register_options.set(i,t)),t.set(r,n)}this.poly.pluginsRegister.registerNode(t)}}deregister(t,e){var n,i,r;null===(n=this._node_register.get(t))||void 0===n||n.delete(e),null===(i=this._node_register_categories.get(t))||void 0===i||i.delete(e),null===(r=this._node_register_options.get(t))||void 0===r||r.delete(e)}isRegistered(t,e){const n=this._node_register.get(t);return!!n&&null!=n.get(e)}registeredNodesForContextAndParentType(t,e){var n;if(this._node_register.get(t)){const i=[];return null===(n=this._node_register.get(t))||void 0===n||n.forEach(((t,e)=>{i.push(t)})),i.filter((n=>{var i;const r=n.type().toLowerCase(),s=null===(i=this._node_register_options.get(t))||void 0===i?void 0:i.get(r);if(s){const n=s.only,i=s.except,r=`${t}/${e}`;return n?n.includes(r):!i||!i.includes(r)}return!0}))}return[]}registeredNodes(t,e){const n={},i=this.registeredNodesForContextAndParentType(t,e);for(let t of i){n[t.type().toLowerCase()]=t}return n}registeredCategory(t,e){var n;return null===(n=this._node_register_categories.get(t))||void 0===n?void 0:n.get(e.toLowerCase())}map(){return this._node_register}}class Xn{constructor(t){this.poly=t,this._operation_register=new Map}register(t){const e=t.context();let n=this._operation_register.get(e);n||(n=new Map,this._operation_register.set(e,n));const i=t.type().toLowerCase();if(n.get(i)){const t=`operation ${e}/${i} already registered`;console.error(t)}else n.set(i,t),this.poly.pluginsRegister.registerOperation(t)}registeredOperationsForContextAndParentType(t,e){var n;if(this._operation_register.get(t)){const e=[];return null===(n=this._operation_register.get(t))||void 0===n||n.forEach(((t,n)=>{e.push(t)})),e}return[]}registeredOperation(t,e){const n=this._operation_register.get(t);if(n)return n.get(e.toLowerCase())}}class Yn extends class{constructor(){this._methods_names=[],this._methods_by_name=new Map}register(t,e){this._methods_names.push(e),this._methods_by_name.set(e,t)}getMethod(t){return this._methods_by_name.get(t)}availableMethods(){return this._methods_names}}{getMethod(t){return super.getMethod(t)}}!function(t){t.BasisTextureLoader=\\\\\\\"BasisTextureLoader\\\\\\\",t.DRACOLoader=\\\\\\\"DRACOLoader\\\\\\\",t.EXRLoader=\\\\\\\"EXRLoader\\\\\\\",t.FBXLoader=\\\\\\\"FBXLoader\\\\\\\",t.GLTFLoader=\\\\\\\"GLTFLoader\\\\\\\",t.OBJLoader=\\\\\\\"OBJLoader\\\\\\\",t.PDBLoader=\\\\\\\"PDBLoader\\\\\\\",t.PLYLoader=\\\\\\\"PLYLoader\\\\\\\",t.RGBELoader=\\\\\\\"RGBELoader\\\\\\\",t.SVGLoader=\\\\\\\"SVGLoader\\\\\\\",t.STLLoader=\\\\\\\"STLLoader\\\\\\\",t.TTFLoader=\\\\\\\"TTFLoader\\\\\\\"}(Vn||(Vn={}));class $n extends class{constructor(){this._module_by_name=new Map}register(t,e){this._module_by_name.set(t,e)}moduleNames(){const t=[];return this._module_by_name.forEach(((e,n)=>{t.push(n)})),t}module(t){return this._module_by_name.get(t)}}{}!function(t){t.GL_MESH_BASIC=\\\\\\\"GL_MESH_BASIC\\\\\\\",t.GL_MESH_LAMBERT=\\\\\\\"GL_MESH_LAMBERT\\\\\\\",t.GL_MESH_STANDARD=\\\\\\\"GL_MESH_STANDARD\\\\\\\",t.GL_MESH_PHONG=\\\\\\\"GL_MESH_PHONG\\\\\\\",t.GL_MESH_PHYSICAL=\\\\\\\"GL_MESH_PHYSICAL\\\\\\\",t.GL_PARTICLES=\\\\\\\"GL_PARTICLES\\\\\\\",t.GL_POINTS=\\\\\\\"GL_POINTS\\\\\\\",t.GL_LINE=\\\\\\\"GL_LINE\\\\\\\",t.GL_TEXTURE=\\\\\\\"GL_TEXTURE\\\\\\\",t.GL_VOLUME=\\\\\\\"GL_VOLUME\\\\\\\"}(Hn||(Hn={}));class Jn extends class{constructor(){this._controller_assembler_by_name=new Map}register(t,e,n){this._controller_assembler_by_name.set(t,{controller:e,assembler:n})}unregister(t){this._controller_assembler_by_name.delete(t)}}{assembler(t,e){const n=this._controller_assembler_by_name.get(e);if(n){return new(0,n.controller)(t,n.assembler)}return n}unregister(t){const e=this._controller_assembler_by_name.get(t);return super.unregister(t),e}}class Zn{constructor(t){this.poly=t,this._plugins_by_name=new Map,this._plugin_name_by_node_context_by_type=new Map,this._plugin_name_by_operation_context_by_type=new Map}register(t){this._current_plugin=t,this._plugins_by_name.set(t.name(),t),t.init(this.poly),this._current_plugin=void 0}pluginByName(t){return this._plugins_by_name.get(t)}registerNode(t){if(!this._current_plugin)return;const e=t.context(),n=t.type();let i=this._plugin_name_by_node_context_by_type.get(e);i||(i=new Map,this._plugin_name_by_node_context_by_type.set(e,i)),i.set(n,this._current_plugin.name())}registerOperation(t){if(!this._current_plugin)return;const e=t.context(),n=t.type();let i=this._plugin_name_by_operation_context_by_type.get(e);i||(i=new Map,this._plugin_name_by_operation_context_by_type.set(e,i)),i.set(n,this._current_plugin.name())}toJson(){const t={plugins:{},nodes:{},operations:{}};return this._plugins_by_name.forEach(((e,n)=>{t.plugins[n]=e.toJSON()})),this._plugin_name_by_node_context_by_type.forEach(((e,n)=>{t.nodes[n]={},e.forEach(((e,i)=>{t.nodes[n][i]=e}))})),this._plugin_name_by_operation_context_by_type.forEach(((e,n)=>{t.operations[n]={},e.forEach(((e,i)=>{t.operations[n][i]=e}))})),t}}class Qn{constructor(t){this._camera_types=[]}register(t){const e=t.type();this._camera_types.includes(e)||this._camera_types.push(e)}registeredTypes(){return this._camera_types}}var Kn=n(86);class ti{constructor(){this._blobUrlsByStoredUrl=new Map,this._blobsByStoredUrl=new Map,this._blobDataByNodeId=new Map,this._globalBlobsByStoredUrl=new Map}registerBlobUrl(t){console.log(\\\\\\\"registerBlobUrl\\\\\\\",t,ai.playerMode()),ai.playerMode()&&this._blobUrlsByStoredUrl.set(t.storedUrl,t.blobUrl)}blobUrl(t){return this._blobUrlsByStoredUrl.get(t)}clear(){this._blobUrlsByStoredUrl.clear(),this._blobsByStoredUrl.clear(),this._blobDataByNodeId.clear()}_clearBlobForNode(t){const e=this._blobDataByNodeId.get(t.graphNodeId());e&&(this._blobsByStoredUrl.delete(e.storedUrl),this._blobUrlsByStoredUrl.delete(e.storedUrl)),this._blobDataByNodeId.delete(t.graphNodeId())}_assignBlobToNode(t,e){this._clearBlobForNode(t),this._blobDataByNodeId.set(t.graphNodeId(),{storedUrl:e.storedUrl,fullUrl:e.fullUrl})}async fetchBlobGlobal(t){if(ai.playerMode())return{};try{if(this._blobUrlsByStoredUrl.get(t.storedUrl))return{};const e=ai.assetUrls.remapedUrl(t.fullUrl),n=await fetch(e||t.fullUrl);if(n.ok){const e=await n.blob();return this._blobsByStoredUrl.set(t.storedUrl,e),this._blobUrlsByStoredUrl.set(t.storedUrl,this.createBlobUrl(e)),this._globalBlobsByStoredUrl.set(t.storedUrl,e),{blobData:{storedUrl:t.storedUrl,fullUrl:t.fullUrl}}}return{error:`failed to fetch ${t.fullUrl}`}}catch(e){return{error:`failed to fetch ${t.fullUrl}`}}}async fetchBlobForNode(t){if(ai.playerMode())return{};try{if(this._blobUrlsByStoredUrl.get(t.storedUrl))return{};const e=ai.assetUrls.remapedUrl(t.fullUrl),n=await fetch(e||t.fullUrl);if(n.ok){const e=await n.blob();return this._blobsByStoredUrl.set(t.storedUrl,e),this._blobUrlsByStoredUrl.set(t.storedUrl,this.createBlobUrl(e)),this._scene=t.node.scene(),this._assignBlobToNode(t.node,{storedUrl:t.storedUrl,fullUrl:t.fullUrl}),{blobData:{storedUrl:t.storedUrl,fullUrl:t.fullUrl}}}return{error:`failed to fetch ${t.fullUrl}`}}catch(e){return{error:`failed to fetch ${t.fullUrl}`}}}forEachBlob(t){this._blobDataByNodeId.forEach(((e,n)=>{if(this._scene){if(this._scene.graph.nodeFromId(n)){const{storedUrl:n}=e,i=this._blobsByStoredUrl.get(n);i&&t(i,n)}}}));let e=[];const n=new Map;this._globalBlobsByStoredUrl.forEach(((t,i)=>{e.push(i),n.set(i,t)})),e=e.sort(),e.forEach((e=>{const n=this._globalBlobsByStoredUrl.get(e);n&&t(n,e)}))}createBlobUrl(t){return Object(Kn.a)(t)}}class ei{setMap(t){this._map=t}remapedUrl(t){if(!this._map)return;const e=t.split(\\\\\\\"?\\\\\\\"),n=e[0],i=e[1],r=this._map[n];return r?i?`${r}?${i}`:r:void 0}}var ni=n(93),ii=n(83);class ri{markAsLoaded(t,e){this._sceneJsonImporterContructor=e,t()}load(t){if(!this._sceneJsonImporterContructor)return;const e=[];t.forEach(((t,n)=>{e.push(n)}));for(let n of e){const e=t.get(n);e&&(this._loadElement(n,e,this._sceneJsonImporterContructor),t.delete(n))}}async _loadElement(t,e,n){const{sceneData:i,assetsManifest:r,unzippedData:s}=e,o=Object.keys(r);for(let t of o){const e=s[`assets/${r[t]}`];if(!e)return void console.error(t,e);const n=new Blob([e]),i={storedUrl:t,blobUrl:ai.blobs.createBlobUrl(n)};ai.blobs.registerBlobUrl(i)}ai.setPlayerMode(!0),ai.libs.setRoot(null);const a=`${Math.random()}`.replace(\\\\\\\".\\\\\\\",\\\\\\\"_\\\\\\\"),l={Poly:`___POLY_polyConfig_configurePolygonjs_${a}`,scriptElementId:`___POLY_polyConfig_scriptElement_${a}`,loadSceneArgs:`___POLY_polyConfig_loadSceneArgs_${a}`};window[l.Poly]=ai;const c={method:this._loadScene.bind(this),element:t,sceneData:i,sceneJsonImporterContructor:n};window[l.loadSceneArgs]=c;this._loadPolyConfig(l,s)||this._loadScene(t,i,n)}_loadPolyConfig(t,e){const n=e[ii.a.POLY_CONFIG];if(!n)return!1;const i=this._createJsBlob(n,\\\\\\\"polyConfig\\\\\\\");let r=document.getElementById(t.scriptElementId);const s=[];return s.push(`import {configurePolygonjs, configureScene} from '${i}';`),s.push(`configurePolygonjs(window.${t.Poly});`),s.push(`window.${t.loadSceneArgs}.method(window.${t.loadSceneArgs}.element, window.${t.loadSceneArgs}.sceneData, window.${t.loadSceneArgs}.sceneJsonImporterContructor, configureScene);`),s.push(`delete window.${t.loadSceneArgs};`),r||(r=document.createElement(\\\\\\\"script\\\\\\\"),r.setAttribute(\\\\\\\"type\\\\\\\",\\\\\\\"module\\\\\\\"),r.text=s.join(\\\\\\\"\\\\n\\\\\\\"),document.body.append(r)),!0}async _loadScene(t,e,n,i){this._fadeOutPoster(t);const r=new n(e),s=await r.scene();i&&i(s);const o=s.mainCameraNode();if(!o)return void console.warn(\\\\\\\"no master camera found\\\\\\\");const a=o.createViewer(t);s.play(),t.scene=s,t.viewer=a}_fadeOutPoster(t){const e=t.firstElementChild;e&&(e.style.pointerEvents=\\\\\\\"none\\\\\\\",ni.a.fadeOut(e).then((()=>{var t;null===(t=e.parentElement)||void 0===t||t.removeChild(e)})))}_createJsBlob(t,e){const n=new Blob([t]),i=new File([n],`${e}.js`,{type:\\\\\\\"application/javascript\\\\\\\"});return Object(Kn.a)(i)}}class si{setPerformanceManager(t){this._performanceManager=t}performanceManager(){return this._performanceManager||window.performance}}class oi{constructor(){this.renderersController=new jn,this.nodesRegister=new qn(this),this.operationsRegister=new Xn(this),this.expressionsRegister=new Yn,this.modulesRegister=new $n,this.assemblersRegister=new Jn,this.pluginsRegister=new Zn(this),this.camerasRegister=new Qn(this),this.blobs=new ti,this.assetUrls=new ei,this.selfContainedScenesLoader=new ri,this.performance=new si,this.scenesByUuid={},this._player_mode=!0,this._logger=null}static _instance_(){if(window.__POLYGONJS_POLY_INSTANCE__)return window.__POLYGONJS_POLY_INSTANCE__;{const t=new oi;return window.__POLYGONJS_POLY_INSTANCE__=t,window.__POLYGONJS_POLY_INSTANCE__}}setPlayerMode(t){this._player_mode=t}playerMode(){return this._player_mode}registerNode(t,e,n){this.nodesRegister.register(t,e,n)}registerOperation(t){this.operationsRegister.register(t)}registerCamera(t){this.camerasRegister.register(t)}registerPlugin(t){this.pluginsRegister.register(t)}registeredNodes(t,e){return this.nodesRegister.registeredNodes(t,e)}registeredOperation(t,e){return this.operationsRegister.registeredOperation(t,e)}registeredCameraTypes(){return this.camerasRegister.registeredTypes()}inWorkerThread(){return!1}desktopController(){}get libs(){return this._libs_controller=this._libs_controller||new Wn}setEnv(t){this._env=t}env(){return this._env}setLogger(t){this._logger=t}get logger(){return this._logger}log(t,...e){var n;null===(n=this.logger)||void 0===n||n.log(t,...e)}warn(t,...e){var n;null===(n=this.logger)||void 0===n||n.warn(t,...e)}error(t,...e){var n;null===(n=this.logger)||void 0===n||n.error(t,...e)}}const ai=oi._instance_();class li{constructor(){this._started=!1,this._start_time=0,this._previous_timestamp=0,this._nodes_cook_data={},this._durations_by_name={},this._durations_count_by_name={}}profile(t,e){const n=ai.performance.performanceManager(),i=n.now();e();const r=n.now()-i;console.log(`${t}: ${r}`)}start(){if(!this._started){this.reset(),this._started=!0;const t=ai.performance.performanceManager();this._start_time=t.now(),this._nodes_cook_data={},this._previous_timestamp=this._start_time}}stop(){this.reset()}reset(){this._started=!1,this._start_time=null,this._durations_by_name={},this._durations_count_by_name={},this._nodes_cook_data={}}started(){return this._started}record_node_cook_data(t,e){const n=t.graphNodeId();null==this._nodes_cook_data[n]&&(this._nodes_cook_data[n]=new c(t)),this._nodes_cook_data[n].update_cook_data(e)}record(t){this.started()||this.start();const e=performance.now();return null==this._durations_by_name[t]&&(this._durations_by_name[t]=0),this._durations_by_name[t]+=e-this._previous_timestamp,null==this._durations_count_by_name[t]&&(this._durations_count_by_name[t]=0),this._durations_count_by_name[t]+=1,this._previous_timestamp=e}print(){this.print_node_cook_data(),this.print_recordings()}print_node_cook_data(){let t=Object.values(this._nodes_cook_data);t=f.sortBy(t,(t=>t.total_cook_time()));const e=t.map((t=>t.print_object()));console.log(\\\\\\\"--------------- NODES COOK TIME -----------\\\\\\\");const n=[],i=f.sortBy(e,(t=>-t.total_cook_time));for(let t of i)n.push(t);return console.table(n),e}print_recordings(){const t=b.clone(this._durations_by_name),e=b.clone(this._durations_count_by_name),n=[],i={};for(let e of Object.keys(t)){const r=t[e];n.push(r),null==i[r]&&(i[r]=[]),i[r].push(e)}n.sort(((t,e)=>t-e));const r=f.uniq(n);console.log(\\\\\\\"--------------- PERF RECORDINGS -----------\\\\\\\");const s=[];for(let t of r){const n=i[t];for(let i of n){const n=e[i],r={duration:t,name:i,count:n,duration_per_iteration:t/n};s.push(r)}}return console.table(s),s}}class ci{constructor(t){this.scene=t}setListener(t){this._events_listener?console.warn(\\\\\\\"scene already has a listener\\\\\\\"):(this._events_listener=t,this.run_on_add_listener_callbacks())}onAddListener(t){this._events_listener?t():(this._on_add_listener_callbacks=this._on_add_listener_callbacks||[],this._on_add_listener_callbacks.push(t))}run_on_add_listener_callbacks(){if(this._on_add_listener_callbacks){let t;for(;t=this._on_add_listener_callbacks.pop();)t();this._on_add_listener_callbacks=void 0}}get eventsListener(){return this._events_listener}dispatch(t,e,n){var i;null===(i=this._events_listener)||void 0===i||i.process_events(t,e,n)}emitAllowed(){return null!=this._events_listener&&this.scene.loadingController.loaded()&&this.scene.loadingController.autoUpdating()}}class ui{constructor(){this._params_by_id=new Map}register_param(t){this._params_by_id.set(t.graphNodeId(),t)}deregister_param(t){this._params_by_id.delete(t.graphNodeId())}regenerate_referring_expressions(t){t.nameController.graph_node.setSuccessorsDirty(t)}}class hi{constructor(t){this.scene=t,this._lifecycle_on_create_allowed=!0}onCreateHookAllowed(){return this.scene.loadingController.loaded()&&this._lifecycle_on_create_allowed}onCreatePrevent(t){this._lifecycle_on_create_allowed=!1,t(),this._lifecycle_on_create_allowed=!0}}class di{constructor(t){this.dispatcher=t,this._nodes_by_graph_node_id=new Map,this._require_canvas_event_listeners=!1,this._activeEventDatas=[]}registerNode(t){this._nodes_by_graph_node_id.set(t.graphNodeId(),t),this.updateViewerEventListeners()}unregisterNode(t){this._nodes_by_graph_node_id.delete(t.graphNodeId()),this.updateViewerEventListeners()}processEvent(t){0!=this._activeEventDatas.length&&this._nodes_by_graph_node_id.forEach((e=>e.processEvent(t)))}updateViewerEventListeners(){this._update_active_event_types(),this._require_canvas_event_listeners&&this.dispatcher.scene.viewersRegister.traverseViewers((t=>{t.eventsController.updateEvents(this)}))}activeEventDatas(){return this._activeEventDatas}_update_active_event_types(){const t=new Map;this._nodes_by_graph_node_id.forEach((e=>{if(e.parent()){const n=e.activeEventDatas();for(let e of n)t.set(e,!0)}})),this._activeEventDatas=[],t.forEach(((t,e)=>{this._activeEventDatas.push(e)}))}}var pi;!function(t){t.LOADED=\\\\\\\"sceneLoaded\\\\\\\",t.PLAY=\\\\\\\"play\\\\\\\",t.PAUSE=\\\\\\\"pause\\\\\\\",t.TICK=\\\\\\\"tick\\\\\\\"}(pi||(pi={}));const _i=[pi.LOADED,pi.PLAY,pi.PAUSE,pi.TICK];class mi extends di{type(){return\\\\\\\"scene\\\\\\\"}acceptedEventTypes(){return _i.map((t=>`${t}`))}}class fi{constructor(t){this.scene=t,this._loading_state=!1,this._auto_updating=!0,this._first_object_loaded=!1}get LOADED_EVENT_CONTEXT(){return this._LOADED_EVENT_CONTEXT=this._LOADED_EVENT_CONTEXT||{event:new Event(pi.LOADED)}}markAsLoading(){this._set_loading_state(!0)}async markAsLoaded(){this.scene.missingExpressionReferencesController.resolve_missing_references(),await this._set_loading_state(!1),this.trigger_loaded_event()}trigger_loaded_event(){globalThis.Event&&this.scene.eventsDispatcher.sceneEventsController.processEvent(this.LOADED_EVENT_CONTEXT)}async _set_loading_state(t){this._loading_state=t,await this.set_auto_update(!this._loading_state)}isLoading(){return this._loading_state}loaded(){return!this._loading_state}autoUpdating(){return this._auto_updating}async set_auto_update(t){if(this._auto_updating!==t&&(this._auto_updating=t,this._auto_updating)){const t=this.scene.root();t&&await t.processQueue()}}on_first_object_loaded(){var t;if(!this._first_object_loaded){this._first_object_loaded=!0;const e=document.getElementById(\\\\\\\"scene_loading_container\\\\\\\");e&&(null===(t=e.parentElement)||void 0===t||t.removeChild(e))}}}const gi={EMPTY:\\\\\\\"\\\\\\\",UV:\\\\\\\"/COP/imageUv\\\\\\\",ENV_MAP:\\\\\\\"/COP/envMap\\\\\\\",CUBE_MAP:\\\\\\\"/COP/cubeCamera\\\\\\\"};class vi{constructor(t=\\\\\\\"\\\\\\\"){this._path=t,this._node=null}set_path(t){this._path=t}set_node(t){this._node=t}path(){return this._path}node(){return this._node}resolve(t){this._node=xi.findNode(t,this._path)}clone(){const t=new vi(this._path);return t.set_node(this._node),t}nodeWithContext(t,e){const n=this.node();if(!n)return void(null==e||e.set(`no node found at ${this.path()}`));const i=n.context();return i==t?n:void(null==e||e.set(`expected ${t} node, but got a ${i}`))}}class yi{constructor(t=\\\\\\\"\\\\\\\"){this._path=t,this._param=null}set_path(t){this._path=t}set_param(t){this._param=t}path(){return this._path}param(){return this._param}resolve(t){this._param=xi.findParam(t,this._path)}clone(){const t=new yi(this._path);return t.set_param(this._param),t}paramWithType(t,e){const n=this.param();if(n)return n.type()==t?n:void(null==e||e.set(`expected ${t} node, but got a ${n.type()}`));null==e||e.set(`no param found at ${this.path()}`)}}class xi{static split_parent_child(t){const e=t.split(xi.SEPARATOR).filter((t=>t.length>0)),n=e.pop();return{parent:e.join(xi.SEPARATOR),child:n}}static findNode(t,e,n){if(!t)return null;const i=e.split(xi.SEPARATOR).filter((t=>t.length>0)),r=i[0];let s=null;if(e[0]!==xi.SEPARATOR){switch(r){case xi.PARENT:null==n||n.add_path_element(r),s=t.parent();break;case xi.CURRENT:null==n||n.add_path_element(r),s=t;break;default:s=t.node(r),s&&(null==n||n.add_node(r,s))}if(null!=s&&i.length>1){const t=i.slice(1).join(xi.SEPARATOR);s=this.findNode(s,t,n)}return s}{const i=e.substr(1);s=this.findNode(t.root(),i,n)}return s}static findParam(t,e,n){if(!t)return null;const i=e.split(xi.SEPARATOR);if(1===i.length)return t.params.get(i[0]);{const e=i.slice(0,+(i.length-2)+1||void 0).join(xi.SEPARATOR),r=this.findNode(t,e,n);if(null!=r){const t=i[i.length-1],e=r.params.get(t);return n&&e&&n.add_node(t,e),e}return null}}static relativePath(t,e){const n=this.closestCommonParent(t,e);if(n){const i=this.distanceToParent(t,n);let r=\\\\\\\"\\\\\\\";if(i>0){let t=0;const e=[];for(;t++<i;)e.push(xi.PARENT);r=e.join(xi.SEPARATOR)+xi.SEPARATOR}const s=n.path().split(xi.SEPARATOR).filter((t=>t.length>0)),o=e.path().split(xi.SEPARATOR).filter((t=>t.length>0)),a=[];let l=0;for(let t of o)s[l]||a.push(t),l++;return`${r}${a.join(xi.SEPARATOR)}`}return e.path()}static closestCommonParent(t,e){const n=this.parents(t).reverse().concat([t]),i=this.parents(e).reverse().concat([e]),r=Math.min(n.length,i.length);let s=null;for(let t=0;t<r;t++)n[t].graphNodeId()==i[t].graphNodeId()&&(s=n[t]);return s}static parents(t){const e=[];let n=t.parent();for(;n;)e.push(n),n=n.parent();return e}static distanceToParent(t,e){let n=0,i=t;const r=e.graphNodeId();for(;i&&i.graphNodeId()!=r;)n+=1,i=i.parent();return i&&i.graphNodeId()==r?n:-1}static makeAbsolutePath(t,e){if(e[0]==xi.SEPARATOR)return e;const n=e.split(xi.SEPARATOR),i=n.shift();if(!i)return t.path();switch(i){case\\\\\\\"..\\\\\\\":{const e=t.parent();return e?this.makeAbsolutePath(e,n.join(xi.SEPARATOR)):null}case\\\\\\\".\\\\\\\":return this.makeAbsolutePath(t,n.join(xi.SEPARATOR));default:return[t.path(),e].join(xi.SEPARATOR)}}}xi.SEPARATOR=\\\\\\\"/\\\\\\\",xi.DOT=\\\\\\\".\\\\\\\",xi.CURRENT=xi.DOT,xi.PARENT=\\\\\\\"..\\\\\\\",xi.CURRENT_WITH_SLASH=`${xi.CURRENT}/`,xi.PARENT_WITH_SLASH=`${xi.PARENT}/`,xi.NON_LETTER_PREFIXES=[xi.SEPARATOR,xi.DOT];class bi{constructor(t,e){this.param=t,this.path=e}absolute_path(){return xi.makeAbsolutePath(this.param.node,this.path)}matches_path(t){return this.absolute_path()==t}update_from_method_dependency_name_change(){var t;null===(t=this.param.expressionController)||void 0===t||t.update_from_method_dependency_name_change()}resolve_missing_dependencies(){const t=this.param.rawInputSerialized();this.param.set(this.param.defaultValue()),this.param.set(t)}}class wi{constructor(t){this.scene=t,this.references=new Map}register(t,e,n){const i=new bi(t,n);return u.pushOnArrayAtEntry(this.references,t.graphNodeId(),i),i}deregister_param(t){this.references.delete(t.graphNodeId())}resolve_missing_references(){const t=[];this.references.forEach((e=>{for(let n of e)this._is_reference_resolvable(n)&&t.push(n)}));for(let e of t)e.resolve_missing_dependencies()}_is_reference_resolvable(t){const e=t.absolute_path();if(e){if(this.scene.node(e))return!0;{const t=xi.split_parent_child(e);if(t.child){const e=this.scene.node(t.parent);if(e){if(e.params.get(t.child))return!0}}}}}check_for_missing_references(t){this._check_for_missing_references_for_node(t);for(let e of t.params.all)this._check_for_missing_references_for_param(e)}_check_for_missing_references_for_node(t){const e=t.graphNodeId();this.references.forEach(((n,i)=>{let r=!1;for(let e of n)e.matches_path(t.path())&&(r=!0,e.resolve_missing_dependencies());r&&this.references.delete(e)}))}_check_for_missing_references_for_param(t){const e=t.graphNodeId();this.references.forEach(((n,i)=>{let r=!1;for(let e of n)e.matches_path(t.path())&&(r=!0,e.resolve_missing_dependencies());r&&this.references.delete(e)}))}}class Ti{constructor(t){this.node=t,this._dirty_count=0,this._dirty=!0}dispose(){this._cached_successors=void 0,this._post_dirty_hooks=void 0,this._post_dirty_hook_names=void 0}isDirty(){return!0===this._dirty}dirtyTimestamp(){return this._dirty_timestamp}dirtyCount(){return this._dirty_count}addPostDirtyHook(t,e){this._post_dirty_hook_names=this._post_dirty_hook_names||[],this._post_dirty_hooks=this._post_dirty_hooks||[],this._post_dirty_hook_names.includes(t)?console.warn(`hook with name ${t} already exists`,this.node):(this._post_dirty_hook_names.push(t),this._post_dirty_hooks.push(e))}removePostDirtyHook(t){if(this._post_dirty_hook_names&&this._post_dirty_hooks){const e=this._post_dirty_hook_names.indexOf(t);e>=0&&(this._post_dirty_hook_names.splice(e,1),this._post_dirty_hooks.splice(e,1))}}hasHook(t){return!!this._post_dirty_hook_names&&this._post_dirty_hook_names.includes(t)}removeDirtyState(){this._dirty=!1}setForbiddenTriggerNodes(t){this._forbidden_trigger_nodes=t.map((t=>t.graphNodeId()))}setDirty(t,e){if(null==e&&(e=!0),t&&this._forbidden_trigger_nodes&&this._forbidden_trigger_nodes.includes(t.graphNodeId()))return;null==t&&(t=this.node),this._dirty=!0;const n=ai.performance.performanceManager();this._dirty_timestamp=n.now(),this._dirty_count+=1,this.runPostDirtyHooks(t),!0===e&&this.setSuccessorsDirty(t)}runPostDirtyHooks(t){if(this._post_dirty_hooks){const e=this.node.scene().cooker;if(e.blocked)e.enqueue(this.node,t);else for(let e of this._post_dirty_hooks)e(t)}}setSuccessorsDirty(t){this._cached_successors=this._cached_successors||this.node.graphAllSuccessors();for(let e of this._cached_successors)e.dirtyController.setDirty(t,false)}clearSuccessorsCache(){this._cached_successors=void 0}clearSuccessorsCacheWithPredecessors(){this.clearSuccessorsCache();for(let t of this.node.graphAllPredecessors())t.dirtyController.clearSuccessorsCache()}}class Ai{constructor(t,e){this._scene=t,this._name=e,this._dirty_controller=new Ti(this),this._graph_node_id=t.graph.nextId(),t.graph.addNode(this),this._graph=t.graph}dispose(){this._dirty_controller.dispose(),this.graphRemove()}name(){return this._name}setName(t){this._name=t}scene(){return this._scene}graphNodeId(){return this._graph_node_id}get dirtyController(){return this._dirty_controller}setDirty(t){t=t||this,this._dirty_controller.setDirty(t)}setSuccessorsDirty(t){this._dirty_controller.setSuccessorsDirty(t)}removeDirtyState(){this._dirty_controller.removeDirtyState()}isDirty(){return this._dirty_controller.isDirty()}addPostDirtyHook(t,e){this._dirty_controller.addPostDirtyHook(t,e)}graphRemove(){this._graph.removeNode(this)}addGraphInput(t,e=!0){return this._graph.connect(t,this,e)}removeGraphInput(t){this._graph.disconnect(t,this)}graphDisconnectPredecessors(){this._graph.disconnectPredecessors(this)}graphDisconnectSuccessors(){this._graph.disconnectSuccessors(this)}graphPredecessorIds(){return this._graph.predecessorIds(this._graph_node_id)||[]}graphPredecessors(){return this._graph.predecessors(this)}graphSuccessors(){return this._graph.successors(this)}graphAllPredecessors(){return this._graph.allPredecessors(this)}graphAllSuccessors(){return this._graph.allSuccessors(this)}}var Ei;!function(t){t.CREATED=\\\\\\\"node_created\\\\\\\",t.DELETED=\\\\\\\"node_deleted\\\\\\\",t.NAME_UPDATED=\\\\\\\"node_name_update\\\\\\\",t.OVERRIDE_CLONABLE_STATE_UPDATE=\\\\\\\"node_override_clonable_state_update\\\\\\\",t.NAMED_OUTPUTS_UPDATED=\\\\\\\"node_named_outputs_updated\\\\\\\",t.NAMED_INPUTS_UPDATED=\\\\\\\"node_named_inputs_updated\\\\\\\",t.INPUTS_UPDATED=\\\\\\\"node_inputs_updated\\\\\\\",t.PARAMS_UPDATED=\\\\\\\"node_params_updated\\\\\\\",t.UI_DATA_POSITION_UPDATED=\\\\\\\"node_ui_data_position_updated\\\\\\\",t.UI_DATA_COMMENT_UPDATED=\\\\\\\"node_ui_data_comment_updated\\\\\\\",t.ERROR_UPDATED=\\\\\\\"node_error_updated\\\\\\\",t.FLAG_BYPASS_UPDATED=\\\\\\\"bypass_flag_updated\\\\\\\",t.FLAG_DISPLAY_UPDATED=\\\\\\\"display_flag_updated\\\\\\\",t.FLAG_OPTIMIZE_UPDATED=\\\\\\\"optimize_flag_updated\\\\\\\",t.SELECTION_UPDATED=\\\\\\\"selection_updated\\\\\\\"}(Ei||(Ei={}));class Mi{constructor(t,e=0,n=0){this.node=t,this._position=new d.a,this._width=50,this._color=new D.a(.75,.75,.75),this._layout_vertical=!0,this._json={x:0,y:0},this._position.x=e,this._position.y=n}setComment(t){this._comment=t,this.node.emit(Ei.UI_DATA_COMMENT_UPDATED)}comment(){return this._comment}setColor(t){this._color=t}color(){return this._color}setLayoutHorizontal(){this._layout_vertical=!1}isLayoutVertical(){return this._layout_vertical}copy(t){this._position.copy(t.position()),this._color.copy(t.color())}position(){return this._position}setPosition(t,e=0){if(m.isNumber(t)){const n=t;this._position.set(n,e)}else this._position.copy(t);this.node.emit(Ei.UI_DATA_POSITION_UPDATED)}translate(t,e=!1){this._position.add(t),e&&(this._position.x=Math.round(this._position.x),this._position.y=Math.round(this._position.y)),this.node.emit(Ei.UI_DATA_POSITION_UPDATED)}toJSON(){return this._json.x=this._position.x,this._json.y=this._position.y,this._json.comment=this._comment,this._json}}class Si{constructor(t){this.node=t,this._state=!0,this._hooks=null}onUpdate(t){this._hooks=this._hooks||[],this._hooks.push(t)}_on_update(){}set(t){this._state!=t&&(this._state=t,this._on_update(),this.runHooks())}active(){return this._state}toggle(){this.set(!this._state)}runHooks(){if(this._hooks)for(let t of this._hooks)t()}}class Ci extends Si{constructor(){super(...arguments),this._state=!1}_on_update(){this.node.emit(Ei.FLAG_BYPASS_UPDATED),this.node.setDirty()}}class Ni extends Si{_on_update(){this.node.emit(Ei.FLAG_DISPLAY_UPDATED)}}class Li extends Si{constructor(){super(...arguments),this._state=!1}_on_update(){this.node.emit(Ei.FLAG_OPTIMIZE_UPDATED)}}class Oi{constructor(t){this.node=t}hasDisplay(){return!1}hasBypass(){return!1}hasOptimize(){return!1}}function Ri(t){return class extends t{constructor(){super(...arguments),this.display=new Ni(this.node)}hasDisplay(){return!0}}}function Pi(t){return class extends t{constructor(){super(...arguments),this.bypass=new Ci(this.node)}hasBypass(){return!0}}}function Ii(t){return class extends t{constructor(){super(...arguments),this.optimize=new Li(this.node)}hasOptimize(){return!0}}}class Fi extends(Ri(Oi)){}class Di extends(Pi(Oi)){}class ki extends(Pi(Ri(Oi))){}class Bi extends(Ii(Pi(Oi))){}class zi extends(Ii(Pi(Ri(Oi)))){}class Ui{constructor(t){this.node=t}}class Gi extends Ui{active(){return this.paramsTimeDependent()||this.inputsTimeDependent()}paramsTimeDependent(){const t=this.node.params.names;for(let e of t){const t=this.node.params.get(e);if(t&&t.states.timeDependent.active())return!0}return!1}inputsTimeDependent(){const t=this.node.io.inputs.inputs();for(let e of t)if(e&&e.states.timeDependent.active())return!0;return!1}forceTimeDependent(){const t=this.node.graphPredecessors().map((t=>t.graphNodeId())),e=this.node.scene().timeController.graphNode;t.includes(e.graphNodeId())||this.node.addGraphInput(e,!1)}unforceTimeDependent(){const t=this.node.scene().timeController.graphNode;this.node.removeGraphInput(t)}}class Vi extends Ui{set(t){this._message!=t&&(t&&ai.warn(`[${this.node.path()}] error: '${t}'`),this._message=t,this.onUpdate())}message(){return this._message}clear(){this.set(void 0)}active(){return null!=this._message}onUpdate(){null!=this._message&&this.node._setContainer(null,`from error '${this._message}'`),this.node.emit(Ei.ERROR_UPDATED)}}class Hi{constructor(t){this.node=t,this.timeDependent=new Gi(this.node),this.error=new Vi(this.node)}}class ji{constructor(t){this.node=t,this._graph_node=new Ai(t.scene(),\\\\\\\"node_name_controller\\\\\\\")}dispose(){this._graph_node.dispose(),this._on_set_name_hooks=void 0,this._on_set_fullPath_hooks=void 0}get graph_node(){return this._graph_node}static base_name(t){let e=t.type();const n=e[e.length-1];return m.isNaN(parseInt(n))||(e+=\\\\\\\"_\\\\\\\"),`${e}1`}request_name_to_parent(t){const e=this.node.parent();e&&e.childrenAllowed()&&e.childrenController?e.childrenController.set_child_name(this.node,t):console.warn(\\\\\\\"request_name_to_parent failed, no parent found\\\\\\\")}setName(t){t!=this.node.name()&&this.request_name_to_parent(t)}update_name_from_parent(t){var e;if(this.node._set_core_name(t),this.post_setName(),this.run_post_set_fullPath_hooks(),this.node.childrenAllowed()){const t=null===(e=this.node.childrenController)||void 0===e?void 0:e.children();if(t)for(let e of t)e.nameController.run_post_set_fullPath_hooks()}this.node.lifecycle.creation_completed&&(this.node.scene().missingExpressionReferencesController.check_for_missing_references(this.node),this.node.scene().expressionsController.regenerate_referring_expressions(this.node)),this.node.scene().referencesController.notify_name_updated(this.node),this.node.emit(Ei.NAME_UPDATED)}add_post_set_name_hook(t){this._on_set_name_hooks=this._on_set_name_hooks||[],this._on_set_name_hooks.push(t)}add_post_set_fullPath_hook(t){this._on_set_fullPath_hooks=this._on_set_fullPath_hooks||[],this._on_set_fullPath_hooks.push(t)}post_setName(){if(this._on_set_name_hooks)for(let t of this._on_set_name_hooks)t()}run_post_set_fullPath_hooks(){if(this._on_set_fullPath_hooks)for(let t of this._on_set_fullPath_hooks)t()}}class Wi{constructor(t){this.node=t,this._parent=null}parent(){return this._parent}setParent(t){t!=this.node.parentController.parent()&&(this._parent=t,this._parent&&this.node.nameController.request_name_to_parent(ji.base_name(this.node)))}is_selected(){var t,e,n;return(null===(n=null===(e=null===(t=this.parent())||void 0===t?void 0:t.childrenController)||void 0===e?void 0:e.selection)||void 0===n?void 0:n.contains(this.node))||!1}path(t){const e=xi.SEPARATOR;if(null!=this._parent){if(this._parent==t)return this.node.name();{const n=this._parent.path(t);return n===e?n+this.node.name():n+e+this.node.name()}}return e}onSetParent(){if(this._on_set_parent_hooks)for(let t of this._on_set_parent_hooks)t()}findNode(t){if(null==t)return null;if(t==xi.CURRENT||t==xi.CURRENT_WITH_SLASH)return this.node;if(t==xi.PARENT||t==xi.PARENT_WITH_SLASH)return this.node.parent();const e=xi.SEPARATOR;if(t===e)return this.node.scene().root();if(t[0]===e)return t=t.substring(1,t.length),this.node.scene().root().node(t);if(t.split){const n=t.split(e);if(1===n.length){const t=n[0];return this.node.childrenController?this.node.childrenController.child_by_name(t):null}return xi.findNode(this.node,t)}return console.error(\\\\\\\"unexpected path given:\\\\\\\",t),null}}const qi=/[, ]/,Xi=/\\\\d+$/,Yi=/^0+/,$i=/,| /,Ji=/^-?\\\\d+\\\\.?\\\\d*$/;var Zi,Qi,Ki,tr,er,nr,ir,rr;!function(t){t.TRUE=\\\\\\\"true\\\\\\\",t.FALSE=\\\\\\\"false\\\\\\\"}(Zi||(Zi={}));class sr{static isBoolean(t){return t==Zi.TRUE||t==Zi.FALSE}static toBoolean(t){return t==Zi.TRUE}static isNumber(t){return Ji.test(t)}static tailDigits(t){const e=t.match(Xi);return e?parseInt(e[0]):0}static increment(t){const e=t.match(Xi);if(e){let n=e[0],i=\\\\\\\"\\\\\\\";const r=n.match(Yi);r&&(i=r[0]);const s=parseInt(n);0==s&&i.length>0&&\\\\\\\"0\\\\\\\"==i[i.length-1]&&(i=i.slice(0,-1));return`${t.substring(0,t.length-e[0].length)}${i}${s+1}`}return`${t}1`}static pluralize(t){return\\\\\\\"s\\\\\\\"!==t[t.length-1]?`${t}s`:t}static camelCase(t){const e=t.replace(/_/g,\\\\\\\" \\\\\\\").split(\\\\\\\" \\\\\\\");let n=\\\\\\\"\\\\\\\";for(let t=0;t<e.length;t++){let i=e[t].toLowerCase();t>0&&(i=this.upperFirst(i)),n+=i}return n}static upperFirst(t){return t[0].toUpperCase()+t.substr(1)}static titleize(t){return t.split(/\\\\s|_/g).map((t=>this.upperFirst(t))).join(\\\\\\\" \\\\\\\")}static precision(t,e=2){e=Math.max(e,0);const n=`${t}`.split(\\\\\\\".\\\\\\\");if(e<=0)return n[0];let i=n[1];if(void 0!==i)return i.length>e&&(i=i.substring(0,e)),i=i.padEnd(e,\\\\\\\"0\\\\\\\"),`${n[0]}.${i}`;{const n=`${t}.`,i=n.length+e;return n.padEnd(i,\\\\\\\"0\\\\\\\")}}static ensureFloat(t){const e=`${t}`;return e.indexOf(\\\\\\\".\\\\\\\")>=0?e:`${e}.0`}static ensureInteger(t){const e=`${t}`;return e.indexOf(\\\\\\\".\\\\\\\")>=0?e.split(\\\\\\\".\\\\\\\")[0]:e}static matchMask(t,e){if(\\\\\\\"*\\\\\\\"===e)return!0;if(t==e)return!0;const n=e.split(\\\\\\\" \\\\\\\");if(n.length>1){for(let e of n){if(this.matchMask(t,e))return!0}return!1}e=`^${e=e.split(\\\\\\\"*\\\\\\\").join(\\\\\\\".*\\\\\\\")}$`;return new RegExp(e).test(t)}static matchesOneMask(t,e){let n=!1;for(let i of e)sr.matchMask(t,i)&&(n=!0);return n}static attribNames(t){const e=t.split(qi),n=new Set;for(let t of e)t=t.trim(),t.length>0&&n.add(t);const i=new Array(n.size);let r=0;return n.forEach((t=>{i[r]=t,r++})),i}static indices(t){const e=t.split($i);if(e.length>1){const t=e.flatMap((t=>this.indices(t)));return f.uniq(t).sort(((t,e)=>t-e))}{const t=e[0];if(t){const e=\\\\\\\"-\\\\\\\";if(t.indexOf(e)>0){const n=t.split(e);return f.range(parseInt(n[0]),parseInt(n[1])+1)}{const e=parseInt(t);return m.isNumber(e)?[e]:[]}}return[]}}static escapeLineBreaks(t){return t.replace(/(\\\\r\\\\n|\\\\n|\\\\r)/gm,\\\\\\\"\\\\\\\\n\\\\\\\")}static sanitizeName(t){return t=(t=t.replace(/[^A-Za-z0-9]/g,\\\\\\\"_\\\\\\\")).replace(/^[0-9]/,\\\\\\\"_\\\\\\\")}}class or{constructor(t){this._node=t,this._node_ids=[],this._json=[]}node(){return this._node}nodes(){return this._node.scene().graph.nodesFromIds(this._node_ids)}contains(t){return this._node_ids.includes(t.graphNodeId())}equals(t){const e=t.map((t=>t.graphNodeId())).sort();return f.isEqual(e,this._node_ids)}clear(){this._node_ids=[],this.send_update_event()}set(t){this._node_ids=[],this.add(t)}add(t){const e=t.map((t=>t.graphNodeId()));this._node_ids=f.union(this._node_ids,e),this.send_update_event()}remove(t){const e=t.map((t=>t.graphNodeId()));this._node_ids=f.difference(this._node_ids,e),this.send_update_event()}send_update_event(){this._node.emit(Ei.SELECTION_UPDATED)}toJSON(){return this._json=this._json||[],this._json=this._node_ids.map((t=>t)),this._json}}!function(t){t.ALWAYS=\\\\\\\"always\\\\\\\",t.NEVER=\\\\\\\"never\\\\\\\",t.FROM_NODE=\\\\\\\"from_node\\\\\\\"}(Qi||(Qi={}));class ar{static unreachable(t){throw new Error(\\\\\\\"Didn't expect to get here\\\\\\\")}}class lr{constructor(t){this.inputs_controller=t,this._clone_required_states=[],this._overridden=!1}init_inputs_cloned_state(t){m.isArray(t)?this._cloned_states=t:this._cloned_state=t,this._update_clone_required_state()}override_cloned_state_allowed(){if(this._cloned_states)for(let t of this._cloned_states)if(t==Qi.FROM_NODE)return!0;return!!this._cloned_state&&this._cloned_state==Qi.FROM_NODE}clone_required_state(t){return this._clone_required_states[t]}clone_required_states(){return this._clone_required_states}_get_clone_required_state(t){const e=this._cloned_states;if(e){const n=e[t];if(null!=n)return this.clone_required_from_state(n)}return!this._cloned_state||this.clone_required_from_state(this._cloned_state)}clone_required_from_state(t){switch(t){case Qi.ALWAYS:return!0;case Qi.NEVER:return!1;case Qi.FROM_NODE:return!this._overridden}return ar.unreachable(t)}override_cloned_state(t){this._overridden=t,this._update_clone_required_state()}overriden(){return this._overridden}_update_clone_required_state(){if(this._cloned_states){const t=[];for(let e=0;e<this._cloned_states.length;e++)t[e]=this._get_clone_required_state(e);this._clone_required_states=t}else if(this._cloned_state){const t=this.inputs_controller.inputs_count(),e=[];for(let n=0;n<t;n++)e[n]=this._get_clone_required_state(n);this._clone_required_states=e}else;}}class cr{constructor(t){this.operation_container=t}inputs_count(){return this.operation_container.inputs_count()}init_inputs_cloned_state(t){this._cloned_states_controller||(this._cloned_states_controller=new lr(this),this._cloned_states_controller.init_inputs_cloned_state(t))}clone_required(t){var e;const n=null===(e=this._cloned_states_controller)||void 0===e?void 0:e.clone_required_state(t);return null==n||n}override_cloned_state(t){var e;null===(e=this._cloned_states_controller)||void 0===e||e.override_cloned_state(t)}}class ur extends class{constructor(t,e,n){this.operation=t,this.name=e,this.params={},this._apply_default_params(),this._apply_init_params(n),this._init_cloned_states()}path_param_resolve_required(){return null!=this._path_params}resolve_path_params(t){if(this._path_params)for(let e of this._path_params)e.resolve(t)}_apply_default_params(){const t=this.operation.constructor.DEFAULT_PARAMS,e=Object.keys(t);for(let n of e){const e=t[n],i=this._convert_param_data(n,e);null!=i&&(this.params[n]=i)}}_apply_init_params(t){const e=Object.keys(t);for(let n of e){const e=t[n];if(null!=e.simple_data){const t=e.simple_data,i=this._convert_export_param_data(n,t);null!=i&&(this.params[n]=i)}}}_convert_param_data(t,e){if(m.isNumber(e)||m.isBoolean(e)||m.isString(e))return e;if(e instanceof vi){const t=e.clone();return this._path_params||(this._path_params=[]),this._path_params.push(t),t}return e instanceof D.a||e instanceof d.a||e instanceof p.a||e instanceof _.a?e.clone():void 0}_convert_export_param_data(t,e){const n=this.params[t];if(m.isBoolean(e))return e;if(m.isNumber(e))return m.isBoolean(n)?e>=1:e;if(m.isString(e)){if(n){if(n instanceof vi)return n.set_path(e);if(n instanceof yi)return n.set_path(e)}return e}m.isArray(e)&&this.params[t].fromArray(e)}setInput(t,e){this._inputs=this._inputs||[],this._inputs[t]=e}inputs_count(){return this._inputs?this._inputs.length:0}inputsController(){return this._inputs_controller=this._inputs_controller||new cr(this)}_init_cloned_states(){const t=this.operation.constructor.INPUT_CLONED_STATE;this.inputsController().init_inputs_cloned_state(t)}input_clone_required(t){return!this._inputs_controller||this._inputs_controller.clone_required(t)}override_input_clone_state(t){this.inputsController().override_cloned_state(t)}cook(t){return this.operation.cook(t,this.params)}}{constructor(t,e,n){super(t,e,n),this.operation=t,this.name=e,this.init_params=n,this._inputs=[],this._current_input_index=0,this._dirty=!0}add_input(t){super.setInput(this._current_input_index,t),this.increment_input_index()}increment_input_index(){this._current_input_index++}current_input_index(){return this._current_input_index}setDirty(){if(!this._dirty){this._compute_result=void 0;for(let t=0;t<this._inputs.length;t++){this._inputs[t].setDirty()}}}async compute(t,e){if(this._compute_result)return this._compute_result;const n=[],i=e.get(this);i&&i.forEach(((e,i)=>{n[i]=t[e]}));for(let i=0;i<this._inputs.length;i++){const r=this._inputs[i];let s=await r.compute(t,e);s&&(this.input_clone_required(i)&&(s=s.clone()),n[i]=s)}const r=this.operation.cook(n,this.params);return this._compute_result=r?r instanceof Promise?await r:r:void 0,this._dirty=!1,this._compute_result}}class hr{constructor(t,e){this.node=t,this._context=e,this._children={},this._children_by_type={},this._children_and_grandchildren_by_context={}}get selection(){return this._selection=this._selection||new or(this.node)}dispose(){const t=this.children();for(let e of t)this.node.removeNode(e);this._selection=void 0}get context(){return this._context}set_output_node_find_method(t){this._output_node_find_method=t}output_node(){if(this._output_node_find_method)return this._output_node_find_method()}set_child_name(t,e){let n;if(e=sr.sanitizeName(e),null!=(n=this._children[e])){if(t.name()===e&&n.graphNodeId()===t.graphNodeId())return;return e=sr.increment(e),this.set_child_name(t,e)}{const n=t.name();this._children[n]&&delete this._children[n],this._children[e]=t,t.nameController.update_name_from_parent(e),this._add_to_nodesByType(t),this.node.scene().nodesController.addToInstanciatedNode(t)}}node_context_signature(){return`${this.node.context()}/${this.node.type()}`}available_children_classes(){return ai.registeredNodes(this._context,this.node.type())}is_valid_child_type(t){return null!=this.available_children_classes()[t]}createNode(t,e,n=\\\\\\\"\\\\\\\"){if(\\\\\\\"string\\\\\\\"==typeof t){const i=this._find_node_class(t);return this._create_and_init_node(i,e,n)}return this._create_and_init_node(t,e,n)}_create_and_init_node(t,e,n=\\\\\\\"\\\\\\\"){const i=new t(this.node.scene(),`child_node_${n}`,e);return i.initialize_base_and_node(),this.add_node(i),i.lifecycle.set_creation_completed(),i}_find_node_class(t){const e=this.available_children_classes()[t.toLowerCase()];if(null==e){const e=`child node type '${t}' not found for node '${this.node.path()}'. Available types are: ${Object.keys(this.available_children_classes()).join(\\\\\\\", \\\\\\\")}, ${this._context}, ${this.node.type()}`;throw console.error(e),e}return e}create_operation_container(t,e,n){const i=ai.registeredOperation(this._context,t);if(null==i){const e=`no operation found with context ${this._context}/${t}`;throw console.error(e),e}{const t=new i(this.node.scene());return new ur(t,e,n||{})}}add_node(t){if(t.setParent(this.node),t.params.init(),t.parentController.onSetParent(),t.nameController.run_post_set_fullPath_hooks(),t.childrenAllowed()&&t.childrenController)for(let e of t.childrenController.children())e.nameController.run_post_set_fullPath_hooks();return this.node.emit(Ei.CREATED,{child_node_json:t.toJSON()}),this.node.scene().lifecycleController.onCreateHookAllowed()&&t.lifecycle.run_on_create_hooks(),t.lifecycle.run_on_add_hooks(),this.set_child_name(t,ji.base_name(t)),this.node.lifecycle.run_on_child_add_hooks(t),t.require_webgl2()&&this.node.scene().webgl_controller.set_require_webgl2(),this.node.scene().missingExpressionReferencesController.check_for_missing_references(t),t}removeNode(t){if(t.parent()!=this.node)return console.warn(`node ${t.name()} not under parent ${this.node.path()}`);{this.selection.contains(t)&&this.selection.remove([t]);const e=t.io.connections.firstInputConnection(),n=t.io.connections.inputConnections(),i=t.io.connections.outputConnections();if(n)for(let t of n)t&&t.disconnect({setInput:!0});if(i)for(let t of i)if(t&&(t.disconnect({setInput:!0}),e)){const n=e.node_src,i=t.output_index,r=t.node_dest,s=t.input_index;r.io.inputs.setInput(s,n,i)}t.setParent(null),delete this._children[t.name()],this._remove_from_nodesByType(t),this.node.scene().nodesController.removeFromInstanciatedNode(t),t.setSuccessorsDirty(this.node),t.graphDisconnectSuccessors(),this.node.lifecycle.run_on_child_remove_hooks(t),t.lifecycle.run_on_delete_hooks(),t.dispose(),t.emit(Ei.DELETED,{parent_id:this.node.graphNodeId()})}}_add_to_nodesByType(t){const e=t.graphNodeId(),n=t.type();this._children_by_type[n]=this._children_by_type[n]||[],this._children_by_type[n].includes(e)||this._children_by_type[n].push(e),this.add_to_children_and_grandchildren_by_context(t)}_remove_from_nodesByType(t){const e=t.graphNodeId(),n=t.type();if(this._children_by_type[n]){const t=this._children_by_type[n].indexOf(e);t>=0&&(this._children_by_type[n].splice(t,1),0==this._children_by_type[n].length&&delete this._children_by_type[n])}this.remove_from_children_and_grandchildren_by_context(t)}add_to_children_and_grandchildren_by_context(t){var e;const n=t.graphNodeId(),i=t.context();this._children_and_grandchildren_by_context[i]=this._children_and_grandchildren_by_context[i]||[],this._children_and_grandchildren_by_context[i].includes(n)||this._children_and_grandchildren_by_context[i].push(n);const r=this.node.parent();r&&r.childrenAllowed()&&(null===(e=r.childrenController)||void 0===e||e.add_to_children_and_grandchildren_by_context(t))}remove_from_children_and_grandchildren_by_context(t){var e;const n=t.graphNodeId(),i=t.context();if(this._children_and_grandchildren_by_context[i]){const t=this._children_and_grandchildren_by_context[i].indexOf(n);t>=0&&(this._children_and_grandchildren_by_context[i].splice(t,1),0==this._children_and_grandchildren_by_context[i].length&&delete this._children_and_grandchildren_by_context[i])}const r=this.node.parent();r&&r.childrenAllowed()&&(null===(e=r.childrenController)||void 0===e||e.remove_from_children_and_grandchildren_by_context(t))}nodesByType(t){const e=this._children_by_type[t]||[],n=this.node.scene().graph,i=[];for(let t of e){const e=n.nodeFromId(t);e&&i.push(e)}return i}child_by_name(t){return this._children[t]}has_children_and_grandchildren_with_context(t){return null!=this._children_and_grandchildren_by_context[t]}children(){return Object.values(this._children)}children_names(){return Object.keys(this._children).sort()}traverse_children(t){var e;for(let n of this.children())t(n),null===(e=n.childrenController)||void 0===e||e.traverse_children(t)}}class dr{constructor(t){this.node=t,this._creation_completed=!1}dispose(){this._on_child_add_hooks=void 0,this._on_child_remove_hooks=void 0,this._on_create_hooks=void 0,this._on_add_hooks=void 0,this._on_delete_hooks=void 0}set_creation_completed(){this._creation_completed||(this._creation_completed=!0)}get creation_completed(){return this.node.scene().loadingController.loaded()&&this._creation_completed}add_on_child_add_hook(t){this._on_child_add_hooks=this._on_child_add_hooks||[],this._on_child_add_hooks.push(t)}run_on_child_add_hooks(t){this.execute_hooks_with_child_node(this._on_child_add_hooks,t)}add_on_child_remove_hook(t){this._on_child_remove_hooks=this._on_child_remove_hooks||[],this._on_child_remove_hooks.push(t)}run_on_child_remove_hooks(t){this.execute_hooks_with_child_node(this._on_child_remove_hooks,t)}add_on_create_hook(t){this._on_create_hooks=this._on_create_hooks||[],this._on_create_hooks.push(t)}run_on_create_hooks(){this.execute_hooks(this._on_create_hooks)}add_on_add_hook(t){this._on_add_hooks=this._on_add_hooks||[],this._on_add_hooks.push(t)}run_on_add_hooks(){this.execute_hooks(this._on_add_hooks)}add_delete_hook(t){this._on_delete_hooks=this._on_delete_hooks||[],this._on_delete_hooks.push(t)}run_on_delete_hooks(){this.execute_hooks(this._on_delete_hooks)}execute_hooks(t){if(t){let e;for(e of t)e()}}execute_hooks_with_child_node(t,e){if(t){let n;for(n of t)n(e)}}}!function(t){t.ANIM=\\\\\\\"anim\\\\\\\",t.COP=\\\\\\\"cop\\\\\\\",t.EVENT=\\\\\\\"event\\\\\\\",t.GL=\\\\\\\"gl\\\\\\\",t.JS=\\\\\\\"js\\\\\\\",t.MANAGER=\\\\\\\"manager\\\\\\\",t.MAT=\\\\\\\"mat\\\\\\\",t.OBJ=\\\\\\\"obj\\\\\\\",t.POST=\\\\\\\"post\\\\\\\",t.ROP=\\\\\\\"rop\\\\\\\",t.SOP=\\\\\\\"sop\\\\\\\"}(Ki||(Ki={})),function(t){t.ANIM=\\\\\\\"animationsNetwork\\\\\\\",t.COP=\\\\\\\"copNetwork\\\\\\\",t.EVENT=\\\\\\\"eventsNetwork\\\\\\\",t.MAT=\\\\\\\"materialsNetwork\\\\\\\",t.POST=\\\\\\\"postProcessNetwork\\\\\\\",t.ROP=\\\\\\\"renderersNetwork\\\\\\\"}(tr||(tr={})),function(t){t.INPUT=\\\\\\\"subnetInput\\\\\\\",t.OUTPUT=\\\\\\\"subnetOutput\\\\\\\"}(er||(er={})),function(t){t.PERSPECTIVE=\\\\\\\"perspectiveCamera\\\\\\\",t.ORTHOGRAPHIC=\\\\\\\"orthographicCamera\\\\\\\"}(nr||(nr={})),function(t){t.ATTRIBUTE=\\\\\\\"attribute\\\\\\\"}(ir||(ir={})),function(t){t.DEVICE_ORIENTATION=\\\\\\\"cameraDeviceOrientationControls\\\\\\\",t.MAP=\\\\\\\"cameraMapControls\\\\\\\",t.ORBIT=\\\\\\\"cameraOrbitControls\\\\\\\",t.FIRST_PERSON=\\\\\\\"firstPersonControls\\\\\\\",t.MOBILE_JOYSTICK=\\\\\\\"mobileJoystickControls\\\\\\\"}(rr||(rr={}));const pr=[rr.DEVICE_ORIENTATION,rr.MAP,rr.ORBIT,rr.FIRST_PERSON,rr.MOBILE_JOYSTICK];class _r{constructor(t){this._node=t}set_node(t){this._node=t}node(){return this._node}set_content(t){this._content=t,this._post_set_content()}has_content(){return null!=this._content}content(){return this._content}_post_set_content(){}coreContent(){return this._content}coreContentCloned(){return this._content}infos(){return[]}}var mr=n(69);class fr extends Q.a{constructor(){super(),this.type=\\\\\\\"Scene\\\\\\\",this.background=null,this.environment=null,this.fog=null,this.overrideMaterial=null,this.autoUpdate=!0,\\\\\\\"undefined\\\\\\\"!=typeof __THREE_DEVTOOLS__&&__THREE_DEVTOOLS__.dispatchEvent(new CustomEvent(\\\\\\\"observe\\\\\\\",{detail:this}))}copy(t,e){return super.copy(t,e),null!==t.background&&(this.background=t.background.clone()),null!==t.environment&&(this.environment=t.environment.clone()),null!==t.fog&&(this.fog=t.fog.clone()),null!==t.overrideMaterial&&(this.overrideMaterial=t.overrideMaterial.clone()),this.autoUpdate=t.autoUpdate,this.matrixAutoUpdate=t.matrixAutoUpdate,this}toJSON(t){const e=super.toJSON(t);return null!==this.fog&&(e.object.fog=this.fog.toJSON()),e}}fr.prototype.isScene=!0;var gr=n(49),vr=n(52),yr=n(42),xr=n(55),br=n(61),wr=n(24),Tr=n(35);const Ar=new p.a,Er=new p.a;class Mr extends Q.a{constructor(){super(),this._currentLevel=0,this.type=\\\\\\\"LOD\\\\\\\",Object.defineProperties(this,{levels:{enumerable:!0,value:[]},isLOD:{value:!0}}),this.autoUpdate=!0}copy(t){super.copy(t,!1);const e=t.levels;for(let t=0,n=e.length;t<n;t++){const n=e[t];this.addLevel(n.object.clone(),n.distance)}return this.autoUpdate=t.autoUpdate,this}addLevel(t,e=0){e=Math.abs(e);const n=this.levels;let i;for(i=0;i<n.length&&!(e<n[i].distance);i++);return n.splice(i,0,{distance:e,object:t}),this.add(t),this}getCurrentLevel(){return this._currentLevel}getObjectForDistance(t){const e=this.levels;if(e.length>0){let n,i;for(n=1,i=e.length;n<i&&!(t<e[n].distance);n++);return e[n-1].object}return null}raycast(t,e){if(this.levels.length>0){Ar.setFromMatrixPosition(this.matrixWorld);const n=t.ray.origin.distanceTo(Ar);this.getObjectForDistance(n).raycast(t,e)}}update(t){const e=this.levels;if(e.length>1){Ar.setFromMatrixPosition(t.matrixWorld),Er.setFromMatrixPosition(this.matrixWorld);const n=Ar.distanceTo(Er)/t.zoom;let i,r;for(e[0].object.visible=!0,i=1,r=e.length;i<r&&n>=e[i].distance;i++)e[i-1].object.visible=!1,e[i].object.visible=!0;for(this._currentLevel=i-1;i<r;i++)e[i].object.visible=!1}}toJSON(t){const e=super.toJSON(t);!1===this.autoUpdate&&(e.object.autoUpdate=!1),e.object.levels=[];const n=this.levels;for(let t=0,i=n.length;t<i;t++){const i=n[t];e.object.levels.push({object:i.object.uuid,distance:i.distance})}return e}}var Sr;!function(t){t.OBJECT3D=\\\\\\\"Object3D\\\\\\\",t.MESH=\\\\\\\"Mesh\\\\\\\",t.POINTS=\\\\\\\"Points\\\\\\\",t.LINE_SEGMENTS=\\\\\\\"LineSegments\\\\\\\",t.LOD=\\\\\\\"LOD\\\\\\\"}(Sr||(Sr={}));const Cr={[Sr.MESH]:k.a,[Sr.POINTS]:gr.a,[Sr.LINE_SEGMENTS]:Tr.a,[Sr.OBJECT3D]:Q.a,[Sr.LOD]:Mr};function Nr(t){switch(t){case Q.a:return Sr.OBJECT3D;case k.a:return Sr.MESH;case gr.a:return Sr.POINTS;case Tr.a:return Sr.LINE_SEGMENTS;case Mr:return Sr.LOD;default:return ai.warn(\\\\\\\"object type not supported\\\\\\\",t),Sr.MESH}}const Lr=[Sr.MESH,Sr.POINTS,Sr.LINE_SEGMENTS],Or=[{name:\\\\\\\"Mesh\\\\\\\",value:Lr.indexOf(Sr.MESH)},{name:\\\\\\\"Points\\\\\\\",value:Lr.indexOf(Sr.POINTS)},{name:\\\\\\\"LineSegments\\\\\\\",value:Lr.indexOf(Sr.LINE_SEGMENTS)}],Rr={MeshStandard:new xr.a({color:16777215,side:w.H,metalness:.5,roughness:.9}),[Sr.MESH]:new br.a({color:new D.a(1,1,1),side:w.H,vertexColors:!1,transparent:!0,depthTest:!0}),[Sr.POINTS]:new yr.a({color:16777215,size:.1,depthTest:!0}),[Sr.LINE_SEGMENTS]:new wr.a({color:16777215,linewidth:1})};var Pr;!function(t){t[t.VERTEX=0]=\\\\\\\"VERTEX\\\\\\\",t[t.OBJECT=1]=\\\\\\\"OBJECT\\\\\\\"}(Pr||(Pr={}));const Ir=[Pr.VERTEX,Pr.OBJECT],Fr=[{name:\\\\\\\"vertex\\\\\\\",value:Pr.VERTEX},{name:\\\\\\\"object\\\\\\\",value:Pr.OBJECT}];var Dr;!function(t){t[t.NUMERIC=0]=\\\\\\\"NUMERIC\\\\\\\",t[t.STRING=1]=\\\\\\\"STRING\\\\\\\"}(Dr||(Dr={}));const kr=[Dr.NUMERIC,Dr.STRING],Br=[{name:\\\\\\\"numeric\\\\\\\",value:Dr.NUMERIC},{name:\\\\\\\"string\\\\\\\",value:Dr.STRING}];var zr;!function(t){t[t.FLOAT=1]=\\\\\\\"FLOAT\\\\\\\",t[t.VECTOR2=2]=\\\\\\\"VECTOR2\\\\\\\",t[t.VECTOR3=3]=\\\\\\\"VECTOR3\\\\\\\",t[t.VECTOR4=4]=\\\\\\\"VECTOR4\\\\\\\"}(zr||(zr={}));const Ur=[zr.FLOAT,zr.VECTOR2,zr.VECTOR3,zr.VECTOR4],Gr=[zr.FLOAT,zr.VECTOR4],Vr={ATTRIB_CLASS:{VERTEX:Pr.VERTEX,OBJECT:Pr.OBJECT},OBJECT_TYPES:Lr,CONSTRUCTOR_NAMES_BY_CONSTRUCTOR_NAME:{[fr.name]:\\\\\\\"Scene\\\\\\\",[In.a.name]:\\\\\\\"Group\\\\\\\",[Q.a.name]:\\\\\\\"Object3D\\\\\\\",[k.a.name]:\\\\\\\"Mesh\\\\\\\",[gr.a.name]:\\\\\\\"Points\\\\\\\",[Tr.a.name]:\\\\\\\"LineSegments\\\\\\\",[vr.a.name]:\\\\\\\"Bone\\\\\\\",[mr.a.name]:\\\\\\\"SkinnedMesh\\\\\\\"},CONSTRUCTORS_BY_NAME:{[Sr.MESH]:k.a,[Sr.POINTS]:gr.a,[Sr.LINE_SEGMENTS]:Tr.a},MATERIALS:Rr};var Hr;!function(t){t.POSITION=\\\\\\\"position\\\\\\\",t.NORMAL=\\\\\\\"normal\\\\\\\",t.TANGENT=\\\\\\\"tangent\\\\\\\"}(Hr||(Hr={}));const jr={P:\\\\\\\"position\\\\\\\",N:\\\\\\\"normal\\\\\\\",Cd:\\\\\\\"color\\\\\\\"};class Wr{static remapName(t){return jr[t]||t}static arrayToIndexedArrays(t){const e={};let n=0;const i=[],r=[];let s=0;for(;s<t.length;){const o=t[s],a=e[o];null!=a?i.push(a):(r.push(o),i.push(n),e[o]=n,n+=1),s++}return{indices:i,values:r}}static default_value(t){switch(t){case 1:return 0;case 2:return new d.a(0,0);case 3:return new p.a(0,0,0);default:throw`size ${t} not yet implemented`}}static copy(t,e,n=!0){const i=null==t?void 0:t.array,r=null==e?void 0:e.array;if(i&&r){const t=Math.min(i.length,r.length);for(let e=0;e<t;e++)r[e]=i[e];n&&(e.needsUpdate=!0)}}static attribSizeFromValue(t){if(m.isString(t)||m.isNumber(t))return zr.FLOAT;if(m.isArray(t))return t.length;switch(t.constructor){case d.a:return zr.VECTOR2;case p.a:return zr.VECTOR3;case _.a:return zr.VECTOR4}return 0}}class qr{constructor(t){this._index=t}index(){return this._index}}const Xr=\\\\\\\"position\\\\\\\",Yr=\\\\\\\"normal\\\\\\\";var $r;!function(t){t.x=\\\\\\\"x\\\\\\\",t.y=\\\\\\\"y\\\\\\\",t.z=\\\\\\\"z\\\\\\\",t.w=\\\\\\\"w\\\\\\\",t.r=\\\\\\\"r\\\\\\\",t.g=\\\\\\\"g\\\\\\\",t.b=\\\\\\\"b\\\\\\\"}($r||($r={}));const Jr={x:0,y:1,z:2,w:3,r:0,g:1,b:2};class Zr extends qr{constructor(t,e){super(e),this._core_geometry=t,this._geometry=this._core_geometry.geometry()}applyMatrix4(t){this.position().applyMatrix4(t)}core_geometry(){return this._core_geometry}geometry(){return this._geometry=this._geometry||this._core_geometry.geometry()}attribSize(t){return t=Wr.remapName(t),this._geometry.getAttribute(t).itemSize}hasAttrib(t){const e=Wr.remapName(t);return this._core_geometry.hasAttrib(e)}attribValue(t,e){if(\\\\\\\"ptnum\\\\\\\"===t)return this.index();{let n=null,i=null;\\\\\\\".\\\\\\\"===t[t.length-2]&&(n=t[t.length-1],i=Jr[n],t=t.substring(0,t.length-2));const r=Wr.remapName(t),s=this._geometry.getAttribute(r);if(!s){const e=`attrib ${t} not found. availables are: ${Object.keys(this._geometry.attributes||{}).join(\\\\\\\",\\\\\\\")}`;throw console.warn(e),e}{const{array:t}=s;if(this._core_geometry.isAttribIndexed(r))return this.indexedAttribValue(r);{const n=s.itemSize,r=this._index*n;if(null==i)switch(n){case 1:return t[r];case 2:return(e=e||new d.a).fromArray(t,r),e;case 3:return(e=e||new p.a).fromArray(t,r),e;case 4:return(e=e||new _.a).fromArray(t,r),e;default:throw`size not valid (${n})`}else switch(n){case 1:return t[r];default:return t[r+i]}}}}}indexedAttribValue(t){const e=this.attribValueIndex(t);return this._core_geometry.userDataAttrib(t)[e]}stringAttribValue(t){return this.indexedAttribValue(t)}attribValueIndex(t){return this._core_geometry.isAttribIndexed(t)?this._geometry.getAttribute(t).array[this._index]:-1}isAttribIndexed(t){return this._core_geometry.isAttribIndexed(t)}position(){return this._position||(this._position=this.getPosition(new p.a))}getPosition(t){const{array:e}=this._geometry.getAttribute(Xr);return t.fromArray(e,3*this._index)}setPosition(t){this.setAttribValueVector3(Xr,t)}normal(){return this._normal=this._normal||this.getNormal(new p.a)}getNormal(t){const{array:e}=this._geometry.getAttribute(Yr);return t.fromArray(e,3*this._index)}setNormal(t){return this.setAttribValueVector3(Yr,t)}setAttribValue(t,e){if(null==e)return;if(null==t)throw\\\\\\\"Point.set_attrib_value requires a name\\\\\\\";const n=this._geometry.getAttribute(t),i=n.array,r=n.itemSize;if(m.isArray(e))for(let t=0;t<r;t++)i[this._index*r+t]=e[t];else switch(r){case 1:i[this._index]=e;break;case 2:const t=e;i[2*this._index+0]=t.x,i[2*this._index+1]=t.y;break;case 3:if(null!=e.r){const t=e;i[3*this._index+0]=t.r,i[3*this._index+1]=t.g,i[3*this._index+2]=t.b}else{const t=e;i[3*this._index+0]=t.x,i[3*this._index+1]=t.y,i[3*this._index+2]=t.z}break;case 4:const n=e;i[4*this._index+0]=n.x,i[4*this._index+1]=n.y,i[4*this._index+2]=n.z,i[4*this._index+3]=n.w;break;default:throw console.warn(`Point.set_attrib_value does not yet allow attrib size ${r}`),`attrib size ${r} not implemented`}}setAttribValueVector3(t,e){if(null==e)return;if(null==t)throw\\\\\\\"Point.set_attrib_value requires a name\\\\\\\";const n=this._geometry.getAttribute(t).array,i=3*this._index;n[i]=e.x,n[i+1]=e.y,n[i+2]=e.z}setAttribIndex(t,e){return this._geometry.getAttribute(t).array[this._index]=e}}var Qr=n(40);const Kr=function(t){return function(e){return Math.pow(e,t)}},ts=function(t){return function(e){return 1-Math.abs(Math.pow(e-1,t))}},es=function(t){return function(e){return e<.5?Kr(t)(2*e)/2:ts(t)(2*e-1)/2+.5}},ns={linear:es(1),ease_i:function(t,e){return Kr(e)(t)},ease_o:function(t,e){return ts(e)(t)},ease_io:function(t,e){return es(e)(t)},ease_i2:Kr(2),ease_o2:ts(2),ease_io2:es(2),ease_i3:es(3),ease_o3:es(3),ease_io3:es(3),ease_i4:es(4),ease_o4:es(4),ease_io4:es(4),ease_i_sin:function(t){return 1+Math.sin(Math.PI/2*t-Math.PI/2)},ease_o_sin:function(t){return Math.sin(Math.PI/2*t)},ease_io_sin:function(t){return(1+Math.sin(Math.PI*t-Math.PI/2))/2},ease_i_elastic:function(t){return(.04-.04/t)*Math.sin(25*t)+1},ease_o_elastic:function(t){return.04*t/--t*Math.sin(25*t)},ease_io_elastic:function(t){return(t-=.5)<0?(.02+.01/t)*Math.sin(50*t):(.02-.01/t)*Math.sin(50*t)+1}},is=Math.PI/180;class rs{static clamp(t,e,n){return t<e?e:t>n?n:t}static fit01(t,e,n){return this.fit(t,0,1,e,n)}static fit(t,e,n,i,r){return(t-e)/(n-e)*(r-i)+i}static blend(t,e,n){return(1-n)*t+n*e}static degrees_to_radians(t){return t*is}static radians_to_degrees(t){return t/is}static deg2rad(t){return this.degrees_to_radians(t)}static rad2deg(t){return this.radians_to_degrees(t)}static rand(t){return m.isNumber(t)?this.randFloat(t):this.randVec2(t)}static round(t,e){const n=t/e;return(t<0?Math.ceil(n):Math.floor(n))*e}static highest_even(t){return 2*Math.ceil(.5*t)}static randFloat(t,e=136574){return this._vec.x=t,this._vec.y=e,this.randVec2(this._vec)}static randVec2(t){const e=(12.9898*t.x+78.233*t.y)%Math.PI;return this.fract(43758.5453*Math.sin(e))}static geodesic_distance(t,e){var n=this.deg2rad(t.lat),i=this.deg2rad(e.lat),r=this.deg2rad(e.lat-t.lat),s=this.deg2rad(e.lng-t.lng),o=Math.sin(r/2)*Math.sin(r/2)+Math.cos(n)*Math.cos(i)*Math.sin(s/2)*Math.sin(s/2);return 6371e3*(2*Math.atan2(Math.sqrt(o),Math.sqrt(1-o)))}static expand_triangle(t,e){t.getMidpoint(this._triangle_mid),this._triangle_mid_to_corner.copy(t.a).sub(this._triangle_mid),this._triangle_mid_to_corner.normalize().multiplyScalar(e),t.a.add(this._triangle_mid_to_corner),this._triangle_mid_to_corner.copy(t.b).sub(this._triangle_mid),this._triangle_mid_to_corner.normalize().multiplyScalar(e),t.b.add(this._triangle_mid_to_corner),this._triangle_mid_to_corner.copy(t.c).sub(this._triangle_mid),this._triangle_mid_to_corner.normalize().multiplyScalar(e),t.c.add(this._triangle_mid_to_corner)}static nearestPower2(t){return Math.pow(2,Math.ceil(Math.log(t)/Math.log(2)))}}rs.Easing=ns,rs.fract=t=>t-Math.floor(t),rs._vec={x:0,y:136574},rs._triangle_mid=new p.a,rs._triangle_mid_to_corner=new p.a;class ss{constructor(t,e){this._core_geometry=t,this._index=e,this._geometry=this._core_geometry.geometry()}index(){return this._index}points(){return this._points=this._points||this._get_points()}applyMatrix4(t){for(let e of this.points())e.applyMatrix4(t)}_get_points(){var t;const e=(null===(t=this._geometry.index)||void 0===t?void 0:t.array)||[],n=3*this._index;return[new Zr(this._core_geometry,e[n+0]),new Zr(this._core_geometry,e[n+1]),new Zr(this._core_geometry,e[n+2])]}positions(){return this._positions=this._positions||this._get_positions()}_get_positions(){const t=this.points();return[t[0].position(),t[1].position(),t[2].position()]}triangle(){return this._triangle=this._triangle||this._get_triangle()}_get_triangle(){const t=this.positions();return new Qr.a(t[0],t[1],t[2])}deltas(){return this._deltas=this._deltas||this._get_deltas()}_get_deltas(){const t=this.positions();return[t[1].clone().sub(t[0]),t[2].clone().sub(t[0])]}area(){return this.triangle().getArea()}center(t){const e=this.positions();return t.x=(e[0].x+e[1].x+e[2].x)/3,t.y=(e[0].y+e[1].y+e[2].y)/3,t.z=(e[0].z+e[1].z+e[2].z)/3,t}random_position(t){let e=[rs.randFloat(t),rs.randFloat(6541*t)];return e[0]+e[1]>1&&(e[0]=1-e[0],e[1]=1-e[1]),this.positions()[0].clone().add(this.deltas()[0].clone().multiplyScalar(e[0])).add(this.deltas()[1].clone().multiplyScalar(e[1]))}attrib_value_at_position(t,e){const n=new p.a;this.triangle().getBarycoord(e,n);const i=n.toArray(),r=this._geometry.attributes[t].itemSize,s=this.points().map((e=>e.attribValue(t)));let o,a,l=0;switch(r){case 1:a=0;for(let t of s)a+=t*i[l],l++;o=a;break;default:for(let t of s){const e=t.multiplyScalar(i[l]);a?a.add(e):a=e,l++}o=a}return o}static interpolated_value(t,e,n,i){const r=[e.a,e.b,e.c],s=t.getAttribute(\\\\\\\"position\\\\\\\").array,o=r.map((t=>new p.a(s[3*t+0],s[3*t+1],s[3*t+2]))),a=i.itemSize,l=i.array;let c=[];switch(a){case 1:c=r.map((t=>l[t]));break;case 2:c=r.map((t=>new d.a(l[2*t+0],l[2*t+1])));break;case 3:c=r.map((t=>new p.a(l[3*t+0],l[3*t+1],l[3*t+2])))}const u=r.map(((t,e)=>n.distanceTo(o[e]))),h=f.sum([u[0]*u[1],u[0]*u[2],u[1]*u[2]]),_=[u[1]*u[2]/h,u[0]*u[2]/h,u[0]*u[1]/h];let m;switch(a){case 1:m=f.sum(r.map(((t,e)=>_[e]*c[e])));break;default:var g=r.map(((t,e)=>c[e].multiplyScalar(_[e])));m=null;for(let t of g)m?m.add(t):m=t}return m}}class os{from_points(t){t=this._filter_points(t);const e=new S.a,n=new ps(e),i=t[0];if(null!=i){const r=i.geometry(),s=i.core_geometry(),o={};for(let e=0;e<t.length;e++)o[t[e].index()]=e;const a=this._indices_from_points(o,r);a&&e.setIndex(a);const{attributes:l}=r;for(let i of Object.keys(l)){if(null!=s.userDataAttribs()[i]){const r=f.uniq(t.map((t=>t.indexedAttribValue(i)))),s={};r.forEach(((t,e)=>s[t]=e)),n.userDataAttribs()[i]=r;const o=[];for(let e of t){const t=s[e.indexedAttribValue(i)];o.push(t)}e.setAttribute(i,new C.c(o,1))}else{const n=l[i].itemSize,r=new Array(t.length*n);switch(n){case 1:for(let e=0;e<t.length;e++)r[e]=t[e].attribValue(i);break;default:let e;for(let s=0;s<t.length;s++)e=t[s].attribValue(i),e.toArray(r,s*n)}e.setAttribute(i,new C.c(r,n))}}}return e}}var as=n(78),ls=n(64);function cs(t,e=!1){const n=null!==t[0].index,i=new Set(Object.keys(t[0].attributes)),r=new Set(Object.keys(t[0].morphAttributes)),s={},o={},a=t[0].morphTargetsRelative,l=new S.a;let c=0;for(let u=0;u<t.length;++u){const h=t[u];let d=0;if(n!==(null!==h.index))return console.error(\\\\\\\"THREE.BufferGeometryUtils: .mergeBufferGeometries() failed with geometry at index \\\\\\\"+u+\\\\\\\". All geometries must have compatible attributes; make sure index attribute exists among all geometries, or in none of them.\\\\\\\"),null;for(const t in h.attributes){if(!i.has(t))return console.error(\\\\\\\"THREE.BufferGeometryUtils: .mergeBufferGeometries() failed with geometry at index \\\\\\\"+u+'. All geometries must have compatible attributes; make sure \\\\\\\"'+t+'\\\\\\\" attribute exists among all geometries, or in none of them.'),null;void 0===s[t]&&(s[t]=[]),s[t].push(h.attributes[t]),d++}if(d!==i.size)return console.error(\\\\\\\"THREE.BufferGeometryUtils: .mergeBufferGeometries() failed with geometry at index \\\\\\\"+u+\\\\\\\". Make sure all geometries have the same number of attributes.\\\\\\\"),null;if(a!==h.morphTargetsRelative)return console.error(\\\\\\\"THREE.BufferGeometryUtils: .mergeBufferGeometries() failed with geometry at index \\\\\\\"+u+\\\\\\\". .morphTargetsRelative must be consistent throughout all geometries.\\\\\\\"),null;for(const t in h.morphAttributes){if(!r.has(t))return console.error(\\\\\\\"THREE.BufferGeometryUtils: .mergeBufferGeometries() failed with geometry at index \\\\\\\"+u+\\\\\\\".  .morphAttributes must be consistent throughout all geometries.\\\\\\\"),null;void 0===o[t]&&(o[t]=[]),o[t].push(h.morphAttributes[t])}if(l.userData.mergedUserData=l.userData.mergedUserData||[],l.userData.mergedUserData.push(h.userData),e){let t;if(n)t=h.index.count;else{if(void 0===h.attributes.position)return console.error(\\\\\\\"THREE.BufferGeometryUtils: .mergeBufferGeometries() failed with geometry at index \\\\\\\"+u+\\\\\\\". The geometry must have either an index or a position attribute\\\\\\\"),null;t=h.attributes.position.count}l.addGroup(c,t,u),c+=t}}if(n){let e=0;const n=[];for(let i=0;i<t.length;++i){const r=t[i].index;for(let t=0;t<r.count;++t)n.push(r.getX(t)+e);e+=t[i].attributes.position.count}l.setIndex(n)}for(const t in s){const e=us(s[t]);if(!e)return console.error(\\\\\\\"THREE.BufferGeometryUtils: .mergeBufferGeometries() failed while trying to merge the \\\\\\\"+t+\\\\\\\" attribute.\\\\\\\"),null;l.setAttribute(t,e)}for(const t in o){const e=o[t][0].length;if(0===e)break;l.morphAttributes=l.morphAttributes||{},l.morphAttributes[t]=[];for(let n=0;n<e;++n){const e=[];for(let i=0;i<o[t].length;++i)e.push(o[t][i][n]);const i=us(e);if(!i)return console.error(\\\\\\\"THREE.BufferGeometryUtils: .mergeBufferGeometries() failed while trying to merge the \\\\\\\"+t+\\\\\\\" morphAttribute.\\\\\\\"),null;l.morphAttributes[t].push(i)}}return l}function us(t){let e,n,i,r=0;for(let s=0;s<t.length;++s){const o=t[s];if(o.isInterleavedBufferAttribute)return console.error(\\\\\\\"THREE.BufferGeometryUtils: .mergeBufferAttributes() failed. InterleavedBufferAttributes are not supported.\\\\\\\"),null;if(void 0===e&&(e=o.array.constructor),e!==o.array.constructor)return console.error(\\\\\\\"THREE.BufferGeometryUtils: .mergeBufferAttributes() failed. BufferAttribute.array must be of consistent array types across matching attributes.\\\\\\\"),null;if(void 0===n&&(n=o.itemSize),n!==o.itemSize)return console.error(\\\\\\\"THREE.BufferGeometryUtils: .mergeBufferAttributes() failed. BufferAttribute.itemSize must be consistent across matching attributes.\\\\\\\"),null;if(void 0===i&&(i=o.normalized),i!==o.normalized)return console.error(\\\\\\\"THREE.BufferGeometryUtils: .mergeBufferAttributes() failed. BufferAttribute.normalized must be consistent across matching attributes.\\\\\\\"),null;r+=o.array.length}const s=new e(r);let o=0;for(let e=0;e<t.length;++e)s.set(t[e].array,o),o+=t[e].array.length;return new C.a(s,n,i)}class hs{static createIndexIfNone(t){if(!t.index){const e=t.getAttribute(\\\\\\\"position\\\\\\\");if(e){const n=e.array;t.setIndex(f.range(n.length/3))}}}}class ds{static merge(t){if(0===t.length)return;for(let e of t)hs.createIndexIfNone(e);const e=t.map((t=>new ps(t))),n=e[0].indexedAttributeNames(),i={};for(let t of n){const n={},r=[];for(let i of e){const e=i.points();for(let i of e){r.push(i);const e=i.indexedAttribValue(t);null!=n[e]?n[e]:n[e]=Object.keys(n).length}}const s=Object.keys(n);for(let e of r){const i=n[e.indexedAttribValue(t)];e.setAttribIndex(t,i)}i[t]=s}const r=cs(t),s=new ps(r);return Object.keys(i).forEach((t=>{const e=i[t];s.setIndexedAttributeValues(t,e)})),r&&delete r.userData.mergedUserData,r}}class ps{constructor(t){this._geometry=t}geometry(){return this._geometry}uuid(){return this._geometry.uuid}boundingBox(){return this._bounding_box=this._bounding_box||this._create_bounding_box()}_create_bounding_box(){if(this._geometry.computeBoundingBox(),this._geometry.boundingBox)return this._geometry.boundingBox}markAsInstance(){this._geometry.userData.isInstance=!0}static markedAsInstance(t){return!0===t.userData.isInstance}markedAsInstance(){return ps.markedAsInstance(this._geometry)}positionAttribName(){let t=\\\\\\\"position\\\\\\\";return this.markedAsInstance()&&(t=\\\\\\\"instancePosition\\\\\\\"),t}computeVertexNormals(){this._geometry.computeVertexNormals()}userDataAttribs(){const t=\\\\\\\"indexed_attrib_values\\\\\\\";return this._geometry.userData[t]=this._geometry.userData[t]||{}}indexedAttributeNames(){return Object.keys(this.userDataAttribs()||{})}userDataAttrib(t){return t=Wr.remapName(t),this.userDataAttribs()[t]}isAttribIndexed(t){return t=Wr.remapName(t),null!=this.userDataAttrib(t)}hasAttrib(t){return\\\\\\\"ptnum\\\\\\\"===t||(t=Wr.remapName(t),null!=this._geometry.attributes[t])}attribType(t){return this.isAttribIndexed(t)?Dr.STRING:Dr.NUMERIC}static attribNames(t){return Object.keys(t.attributes)}attribNames(){return ps.attribNames(this._geometry)}static attribNamesMatchingMask(t,e){const n=sr.attribNames(e),i=[];for(let e of this.attribNames(t))for(let t of n)sr.matchMask(e,t)&&i.push(e);return f.uniq(i)}attribSizes(){const t={};for(let e of this.attribNames())t[e]=this._geometry.attributes[e].itemSize;return t}attribSize(t){let e;return t=Wr.remapName(t),null!=(e=this._geometry.attributes[t])?e.itemSize:\\\\\\\"ptnum\\\\\\\"===t?1:0}setIndexedAttributeValues(t,e){this.userDataAttribs()[t]=e}setIndexedAttribute(t,e,n){this.setIndexedAttributeValues(t,e),this._geometry.setAttribute(t,new C.f(n,1))}addNumericAttrib(t,e=1,n=0){const i=[];let r=!1;if(m.isNumber(n)){for(let t=0;t<this.pointsCount();t++)for(let t=0;t<e;t++)i.push(n);r=!0}else if(e>1)if(m.isArray(n)){for(let t=0;t<this.pointsCount();t++)for(let t=0;t<e;t++)i.push(n[t]);r=!0}else{const t=n;if(2==e&&null!=t.x&&null!=t.y){for(let e=0;e<this.pointsCount();e++)i.push(t.x),i.push(t.y);r=!0}const s=n;if(3==e&&null!=s.x&&null!=s.y&&null!=s.z){for(let t=0;t<this.pointsCount();t++)i.push(s.x),i.push(s.y),i.push(s.z);r=!0}const o=n;if(3==e&&null!=o.r&&null!=o.g&&null!=o.b){for(let t=0;t<this.pointsCount();t++)i.push(o.r),i.push(o.g),i.push(o.b);r=!0}const a=n;if(4==e&&null!=a.x&&null!=a.y&&null!=a.z&&null!=a.w){for(let t=0;t<this.pointsCount();t++)i.push(a.x),i.push(a.y),i.push(a.z),i.push(a.w);r=!0}}if(!r)throw console.warn(n),`CoreGeometry.add_numeric_attrib error: no other default value allowed for now in add_numeric_attrib (default given: ${n})`;this._geometry.setAttribute(t.trim(),new C.c(i,e))}initPositionAttribute(t,e){const n=[];null==e&&(e=new p.a);for(let i=0;i<t;i++)n.push(e.x),n.push(e.y),n.push(e.z);return this._geometry.setAttribute(\\\\\\\"position\\\\\\\",new C.c(n,3))}addAttribute(t,e){switch(e.type()){case Dr.STRING:return console.log(\\\\\\\"TODO: to implement\\\\\\\");case Dr.NUMERIC:return this.addNumericAttrib(t,e.size())}}renameAttrib(t,e){this.isAttribIndexed(t)&&(this.userDataAttribs()[e]=b.clone(this.userDataAttribs()[t]),delete this.userDataAttribs()[t]);const n=this._geometry.getAttribute(t);return this._geometry.setAttribute(e.trim(),new C.c(n.array,n.itemSize)),this._geometry.deleteAttribute(t)}deleteAttribute(t){return this.isAttribIndexed(t)&&delete this.userDataAttribs()[t],this._geometry.deleteAttribute(t)}clone(){return ps.clone(this._geometry)}static clone(t){let e;const n=t.clone();return null!=(e=t.userData)&&(n.userData=b.cloneDeep(e)),n}pointsCount(){return ps.pointsCount(this._geometry)}static pointsCount(t){let e,n=0;let i=\\\\\\\"position\\\\\\\";if(new this(t).markedAsInstance()&&(i=\\\\\\\"instancePosition\\\\\\\"),null!=(e=t.getAttribute(i))){let t;null!=(t=e.array)&&(n=t.length/3)}return n}points(){return this.pointsFromGeometry()}pointsFromGeometry(){const t=[],e=this._geometry.getAttribute(this.positionAttribName());if(null!=e){const n=e.array.length/3;for(let e=0;e<n;e++){const n=new Zr(this,e);t.push(n)}}return t}static geometryFromPoints(t,e){switch(e){case Sr.MESH:return this._mesh_builder.from_points(t);case Sr.POINTS:return this._points_builder.from_points(t);case Sr.LINE_SEGMENTS:return this._lines_segment_builder.from_points(t);case Sr.OBJECT3D:case Sr.LOD:return null}ar.unreachable(e)}static mergeGeometries(t){return ds.merge(t)}static merge_geometries(t){return ds.merge(t)}segments(){var t;const e=(null===(t=this.geometry().index)||void 0===t?void 0:t.array)||[];return f.chunk(e,2)}faces(){return this.facesFromGeometry()}facesFromGeometry(){var t;const e=((null===(t=this.geometry().index)||void 0===t?void 0:t.array)||[]).length/3;return f.range(e).map((t=>new ss(this,t)))}}var _s;ps._mesh_builder=new class extends os{_filter_points(t){var e;const n=t[0];if(n){const i=null===(e=n.geometry().getIndex())||void 0===e?void 0:e.array;if(i){const e={};for(let n of t)e[n.index()]=n;const n=[],r=i.length;let s,o,a;for(let t=0;t<r;t+=3)s=e[i[t+0]],o=e[i[t+1]],a=e[i[t+2]],s&&o&&a&&(n.push(s),n.push(o),n.push(a));return n}}return[]}_indices_from_points(t,e){const n=e.index;if(null!=n){const e=n.array,i=[];let r,s,o,a,l,c;for(let n=0;n<e.length;n+=3)r=e[n+0],s=e[n+1],o=e[n+2],a=t[r],l=t[s],c=t[o],null!=a&&null!=l&&null!=c&&(i.push(a),i.push(l),i.push(c));return i}}},ps._points_builder=new class extends os{_filter_points(t){return t}_indices_from_points(t,e){const n=e.index;if(null!=n){const e=n.array,i=[];let r,s;for(let n=0;n<e.length;n++)r=e[n],s=t[r],null!=s&&i.push(s);return i}}},ps._lines_segment_builder=new class extends os{_filter_points(t){var e;const n=t[0];if(n){const i=null===(e=n.geometry().getIndex())||void 0===e?void 0:e.array;if(i){const e={};for(let n of t)e[n.index()]=n;const n=[],r=i.length;let s,o;for(let t=0;t<r;t+=2)s=e[i[t+0]],o=e[i[t+1]],s&&o&&(n.push(s),n.push(o));return n}}return[]}_indices_from_points(t,e){const n=e.index;if(null!=n){const e=n.array,i=[];let r,s,o,a;for(let n=0;n<e.length;n+=2)r=e[n],s=e[n+1],o=t[r],a=t[s],null!=o&&null!=a&&(i.push(o),i.push(a));return i}}},function(t){t.customDistanceMaterial=\\\\\\\"customDistanceMaterial\\\\\\\",t.customDepthMaterial=\\\\\\\"customDepthMaterial\\\\\\\",t.customDepthDOFMaterial=\\\\\\\"customDepthDOFMaterial\\\\\\\"}(_s||(_s={}));const ms=(t,e,n,i,r,s)=>{};class fs{static node(t,e){return t.node(e.name)}static clone(t){const e=t.clone(),n=t.uniforms;return n&&(e.uniforms=I.clone(n)),e}static add_user_data_render_hook(t,e){t.userData.POLY_render_hook=e}static apply_render_hook(t,e){if(e.userData){const n=e.userData.POLY_render_hook;if(n)return void(t.onBeforeRender=(e,i,r,s,o,a)=>{n(e,i,r,s,o,a,t)})}t.onBeforeRender=ms}static applyCustomMaterials(t,e){const n=e;if(n.customMaterials)for(let e of Object.keys(n.customMaterials)){const i=e,r=n.customMaterials[i];r&&(t[i]=r,r.needsUpdate=!0)}}static assign_custom_uniforms(t,e,n){const i=t;if(i.customMaterials)for(let t of Object.keys(i.customMaterials)){const r=t,s=i.customMaterials[r];s&&(s.uniforms[e].value=n)}}static init_custom_material_uniforms(t,e,n){const i=t;if(i.customMaterials)for(let t of Object.keys(i.customMaterials)){const r=t,s=i.customMaterials[r];s&&(s.uniforms[e]=s.uniforms[e]||n)}}}const gs=\\\\\\\"name\\\\\\\";class vs extends qr{constructor(t,e){super(e),this._object=t,null==this._object.userData.attributes&&(this._object.userData.attributes={})}object(){return this._object}geometry(){return this._object.geometry}coreGeometry(){const t=this.geometry();return t?new ps(t):null}points(){var t;return(null===(t=this.coreGeometry())||void 0===t?void 0:t.points())||[]}pointsFromGroup(t){if(t){const e=sr.indices(t);if(e){const t=this.points();return e.map((e=>t[e]))}return[]}return this.points()}static isInGroup(t,e){const n=t.trim();if(0==n.length)return!0;const i=n.split(\\\\\\\"=\\\\\\\"),r=i[0];if(\\\\\\\"@\\\\\\\"==r[0]){const t=r.substr(1);return i[1]==this.attribValue(e,t)}return!1}computeVertexNormals(){var t;null===(t=this.coreGeometry())||void 0===t||t.computeVertexNormals()}static _convert_array_to_vector(t){switch(t.length){case 1:return t[0];case 2:return new d.a(t[0],t[1]);case 3:return new p.a(t[0],t[1],t[2]);case 4:return new _.a(t[0],t[1],t[2],t[3])}}static addAttribute(t,e,n){if(m.isArray(n)){if(!this._convert_array_to_vector(n)){const t=\\\\\\\"attribute_value invalid\\\\\\\";throw console.error(t,n),new Error(t)}}const i=n,r=t.userData;r.attributes=r.attributes||{},r.attributes[e]=i}addAttribute(t,e){vs.addAttribute(this._object,t,e)}addNumericAttrib(t,e){this.addAttribute(t,e)}setAttribValue(t,e){this.addAttribute(t,e)}addNumericVertexAttrib(t,e,n){var i;null==n&&(n=Wr.default_value(e)),null===(i=this.coreGeometry())||void 0===i||i.addNumericAttrib(t,e,n)}attributeNames(){return Object.keys(this._object.userData.attributes)}attribNames(){return this.attributeNames()}hasAttrib(t){return this.attributeNames().includes(t)}renameAttrib(t,e){const n=this.attribValue(t);null!=n?(this.addAttribute(e,n),this.deleteAttribute(t)):console.warn(`attribute ${t} not found`)}deleteAttribute(t){delete this._object.userData.attributes[t]}static attribValue(t,e,n=0,i){if(\\\\\\\"ptnum\\\\\\\"===e)return n;if(t.userData&&t.userData.attributes){const n=t.userData.attributes[e];if(null==n){if(e==gs)return t.name}else if(m.isArray(n)&&i)return i.fromArray(n),i;return n}return e==gs?t.name:void 0}static stringAttribValue(t,e,n=0){const i=this.attribValue(t,e,n);if(null!=i)return m.isString(i)?i:`${i}`}attribValue(t,e){return vs.attribValue(this._object,t,this._index,e)}stringAttribValue(t){return vs.stringAttribValue(this._object,t,this._index)}name(){return this.attribValue(gs)}humanType(){return Vr.CONSTRUCTOR_NAMES_BY_CONSTRUCTOR_NAME[this._object.constructor.name]}attribTypes(){const t={};for(let e of this.attribNames()){const n=this.attribType(e);null!=n&&(t[e]=n)}return t}attribType(t){const e=this.attribValue(t);return m.isString(e)?Dr.STRING:Dr.NUMERIC}attribSizes(){const t={};for(let e of this.attribNames()){const n=this.attribSize(e);null!=n&&(t[e]=n)}return t}attribSize(t){const e=this.attribValue(t);return null==e?null:Wr.attribSizeFromValue(e)}clone(){return vs.clone(this._object)}static clone(t){const e=t.clone();var n=new Map,i=new Map;return vs.parallelTraverse(t,e,(function(t,e){n.set(e,t),i.set(t,e)})),e.traverse((function(e){const r=n.get(e),s=e;if(s.geometry){const t=r.geometry;s.geometry=ps.clone(t);const e=s.geometry;e.userData&&(e.userData=b.cloneDeep(t.userData))}if(s.material){s.material=r.material,fs.applyCustomMaterials(e,s.material);const t=s.material;null==t.color&&(t.color=new D.a(1,1,1))}t.userData&&(e.userData=b.cloneDeep(r.userData));const o=r;o.animations&&(e.animations=o.animations.map((t=>t.clone())));const a=e;if(a.isSkinnedMesh){var l=a,c=r,u=c.skeleton.bones;l.skeleton=c.skeleton.clone(),l.bindMatrix.copy(c.bindMatrix);const t=u.map((function(t){return i.get(t)}));l.skeleton.bones=t,l.bind(l.skeleton,l.bindMatrix)}})),e}static parallelTraverse(t,e,n){n(t,e);for(var i=0;i<t.children.length;i++)this.parallelTraverse(t.children[i],e.children[i],n)}}const ys={[Ki.ANIM]:class extends _r{set_content(t){super.set_content(t)}setTimelineBuilder(t){return this.set_content(t)}timeline_builder(){return this.content()}coreContentCloned(){if(this._content)return this._content.clone()}},[Ki.COP]:class extends _r{set_content(t){super.set_content(t)}texture(){return this._content}coreContent(){return this._content}coreContentCloned(){var t;const e=null===(t=this._content)||void 0===t?void 0:t.clone();return e&&(e.needsUpdate=!0),e}object(){return this.texture()}infos(){if(null!=this._content)return[this._content]}resolution(){if(this._content){const t=this._content.image;if(t){if(t instanceof HTMLImageElement||t instanceof Image||t instanceof ImageData||t instanceof HTMLCanvasElement)return[t.width,t.height];if(t.data&&null!=t.width&&null!=t.height)return[t.width,t.height];const e=t;return[e.videoWidth,e.videoHeight]}}return[-1,-1]}},[Ki.EVENT]:class extends _r{set_content(t){super.set_content(t)}},[Ki.GL]:class extends _r{object(){return this._content}},[Ki.JS]:class extends _r{object(){return this._content}},[Ki.MANAGER]:class extends _r{set_content(t){super.set_content(t)}},[Ki.MAT]:class extends _r{set_content(t){super.set_content(t)}set_material(t){null!=this._content&&this._content.dispose(),this.set_content(t)}has_material(){return this.has_content()}material(){return this.content()}},[Ki.OBJ]:class extends _r{set_content(t){super.set_content(t)}set_object(t){return this.set_content(t)}has_object(){return this.has_content()}object(){return this.content()}},[Ki.POST]:class extends _r{set_content(t){super.set_content(t)}render_pass(){return this._content}object(t={}){return this.render_pass()}},[Ki.ROP]:class extends _r{set_content(t){super.set_content(t)}renderer(){return this._content}},[Ki.SOP]:class extends _r{coreContentCloned(){if(this._content)return this._content.clone()}set_content(t){super.set_content(t)}firstObject(){if(this._content)return this._content.objects()[0]}firstCoreObject(){const t=this.firstObject();if(t)return new vs(t,0)}firstGeometry(){const t=this.firstObject();return t?t.geometry:null}objectsCount(){return this._content?this._content.objects().length:0}objectsVisibleCount(){return this._content,0}objectsCountByType(){const t={},e=this._content;if(this._content&&e)for(let n of e.coreObjects()){const e=n.humanType();null==t[e]&&(t[e]=0),t[e]+=1}return t}objectsNamesByType(){const t={},e=this._content;if(this._content&&e)for(let n of e.coreObjects()){const e=n.humanType();t[e]=t[e]||[],t[e].push(n.name())}return t}pointAttributeNames(){let t=[];const e=this.firstGeometry();return e&&(t=Object.keys(e.attributes)),t}pointAttributeSizesByName(){let t={};const e=this.firstGeometry();return e&&Object.keys(e.attributes).forEach((n=>{const i=e.attributes[n];t[n]=i.itemSize})),t}objectAttributeSizesByName(){let t={};const e=this.firstCoreObject();if(e){const n=e.attribNames();for(let i of n){const n=e.attribSize(i);null!=n&&(t[i]=n)}}return t}pointAttributeTypesByName(){let t={};const e=this.firstGeometry();if(e){const n=new ps(e);Object.keys(e.attributes).forEach((e=>{t[e]=n.attribType(e)}))}return t}objectAttributeTypesByName(){let t={};const e=this.firstCoreObject();if(e)for(let n of e.attribNames())t[n]=e.attribType(n);return t}objectAttributeNames(){let t=[];const e=this.firstObject();return e&&(t=Object.keys(e.userData.attributes||{})),t}pointsCount(){return this._content?this._content.pointsCount():0}totalPointsCount(){return this._content?this._content.totalPointsCount():0}objectsData(){return this._content?this._content.objectsData():[]}boundingBox(){return this._content.boundingBox()}center(){return this._content.center()}size(){return this._content.size()}}};class xs{constructor(t){this.node=t,this._callbacks=[],this._callbacks_tmp=[];const e=ys[t.context()];this._container=new e(this.node)}container(){return this._container}async compute(){var t,e;if(null===(e=null===(t=this.node.flags)||void 0===t?void 0:t.bypass)||void 0===e?void 0:e.active()){const t=await this.requestInputContainer(0)||this._container;return this.node.cookController.endCook(),t}return this.node.isDirty()?new Promise(((t,e)=>{this._callbacks.push(t),this.node.cookController.cookMain()})):this._container}async requestInputContainer(t){const e=this.node.io.inputs.input(t);return e?await e.compute():(this.node.states.error.set(`input ${t} required`),this.notifyRequesters(),null)}notifyRequesters(t){let e;for(this._callbacks_tmp=this._callbacks.slice(),this._callbacks.splice(0,this._callbacks.length),t||(t=this.node.containerController.container());e=this._callbacks_tmp.pop();)e(t);this.node.scene().cookController.removeNode(this.node)}}const bs=ai.performance.performanceManager();class ws{constructor(t){this.cookController=t,this._inputs_start=0,this._params_start=0,this._cook_start=0,this._cooksCount=0,this._data={inputsTime:0,paramsTime:0,cookTime:0}}cooksCount(){return this._cooksCount}data2(){return this._data}active(){return this.cookController.performanceRecordStarted()}recordInputsStart(){this.active()&&(this._inputs_start=bs.now())}recordInputsEnd(){this.active()&&(this._data.inputsTime=bs.now()-this._inputs_start)}recordParamsStart(){this.active()&&(this._params_start=bs.now())}recordParamsEnd(){this.active()&&(this._data.paramsTime=bs.now()-this._params_start)}recordCookStart(){this.active()&&(this._cook_start=bs.now())}recordCookEnd(){this.active()&&(this._data.cookTime=bs.now()-this._cook_start,this._cooksCount+=1)}}class Ts{constructor(t){this.node=t,this._cooking=!1,this._performanceController=new ws(this),this._inputs_evaluation_required=!0,this._core_performance=this.node.scene().performance}performanceRecordStarted(){return this._core_performance.started()}disallowInputsEvaluation(){this._inputs_evaluation_required=!1}isCooking(){return!0===this._cooking}_start_cook_if_no_errors(t){if(this.node.states.error.active())this.endCook();else try{this._performanceController.recordCookStart(),this.node.cook(t)}catch(t){this.node.states.error.set(`node internal error: '${t}'.`),ai.warn(t),this.endCook()}}async cookMain(){if(this.isCooking())return;let t;this._initCookingState(),this.node.states.error.clear(),this.node.scene().cookController.addNode(this.node),t=this._inputs_evaluation_required?await this._evaluateInputs():[],this.node.params.paramsEvalRequired()&&await this._evaluateParams(),this._start_cook_if_no_errors(t)}async cookMainWithoutInputs(){this.node.scene().cookController.addNode(this.node),this.isCooking()?ai.warn(\\\\\\\"cook_main_without_inputs already cooking\\\\\\\",this.node.path()):(this._initCookingState(),this.node.states.error.clear(),this.node.params.paramsEvalRequired()&&await this._evaluateParams(),this._start_cook_if_no_errors([]))}endCook(t){this._finalizeCookPerformance();const e=this.node.dirtyController.dirtyTimestamp();null==e||e===this._cooking_dirty_timestamp?(this.node.removeDirtyState(),this._terminateCookProcess()):(ai.log(\\\\\\\"COOK AGAIN\\\\\\\",e,this._cooking_dirty_timestamp,this.node.path()),this._cooking=!1,this.cookMain())}_initCookingState(){this._cooking=!0,this._cooking_dirty_timestamp=this.node.dirtyController.dirtyTimestamp()}_terminateCookProcess(){this.isCooking()&&(this._cooking=!1,this.node.containerController.notifyRequesters(),this._run_on_cook_complete_hooks())}async _evaluateInputs(){this._performanceController.recordInputsStart();let t=[];const e=this.node.io.inputs;this._inputs_evaluation_required&&(t=e.is_any_input_dirty()?await e.eval_required_inputs():await e.containers_without_evaluation());const n=e.inputs(),i=[];let r;for(let s=0;s<n.length;s++)r=t[s],r&&(e.cloneRequired(s)?i[s]=r.coreContentCloned():i[s]=r.coreContent());return this._performanceController.recordInputsEnd(),i}async _evaluateParams(){this._performanceController.recordParamsStart(),await this.node.params.evalAll(),this._performanceController.recordParamsEnd()}cooksCount(){return this._performanceController.cooksCount()}cookTime(){return this._performanceController.data2().cookTime}_finalizeCookPerformance(){this._core_performance.started()&&(this._performanceController.recordCookEnd(),this._core_performance.record_node_cook_data(this.node,this._performanceController.data2()))}registerOnCookEnd(t,e){this._on_cook_complete_hook_names=this._on_cook_complete_hook_names||[],this._on_cook_complete_hooks=this._on_cook_complete_hooks||[],this._on_cook_complete_hook_names.push(t),this._on_cook_complete_hooks.push(e)}deregisterOnCookEnd(t){var e;if(!this._on_cook_complete_hook_names||!this._on_cook_complete_hooks)return;const n=null===(e=this._on_cook_complete_hook_names)||void 0===e?void 0:e.indexOf(t);this._on_cook_complete_hook_names.splice(n,1),this._on_cook_complete_hooks.splice(n,1)}_run_on_cook_complete_hooks(){if(this._on_cook_complete_hooks)for(let t of this._on_cook_complete_hooks)t()}onCookEndCallbackNames(){return this._on_cook_complete_hook_names}}class As{constructor(t){this.node=t}toJSON(t=!1){var e,n,i,r,s,o;const a={name:this.node.name(),type:this.node.type(),graph_node_id:this.node.graphNodeId(),is_dirty:this.node.isDirty(),ui_data_json:this.node.uiData.toJSON(),error_message:this.node.states.error.message(),children:this.childrenIds(),maxInputsCount:this.maxInputsCount(),inputs:this.inputIds(),input_connection_output_indices:this.inputConnectionOutputIndices(),named_input_connection_points:this.namedInputConnectionPoints(),named_output_connection_points:this.namedOutputConnectionPoints(),param_ids:this.to_json_params(t),override_cloned_state_allowed:this.node.io.inputs.overrideClonedStateAllowed(),inputs_clone_required_states:this.node.io.inputs.cloneRequiredStates(),flags:{display:null===(n=null===(e=this.node.flags)||void 0===e?void 0:e.display)||void 0===n?void 0:n.active(),bypass:null===(r=null===(i=this.node.flags)||void 0===i?void 0:i.bypass)||void 0===r?void 0:r.active(),optimize:null===(o=null===(s=this.node.flags)||void 0===s?void 0:s.optimize)||void 0===o?void 0:o.active()},selection:void 0};return this.node.childrenAllowed()&&this.node.childrenController&&(a.selection=this.node.childrenController.selection.toJSON()),a}childrenIds(){return this.node.children().map((t=>t.graphNodeId()))}maxInputsCount(){return this.node.io.inputs.maxInputsCount()}inputIds(){return this.node.io.inputs.inputs().map((t=>null!=t?t.graphNodeId():void 0))}inputConnectionOutputIndices(){var t;return null===(t=this.node.io.connections.inputConnections())||void 0===t?void 0:t.map((t=>null!=t?t.output_index:void 0))}namedInputConnectionPoints(){return this.node.io.inputs.namedInputConnectionPoints().map((t=>t.toJSON()))}namedOutputConnectionPoints(){return this.node.io.outputs.namedOutputConnectionPoints().map((t=>t.toJSON()))}to_json_params_from_names(t,e=!1){return t.map((t=>this.node.params.get(t).graphNodeId()))}to_json_params(t=!1){return this.to_json_params_from_names(this.node.params.names,t)}}var Es,Ms;!function(t){t.BOOLEAN=\\\\\\\"boolean\\\\\\\",t.BUTTON=\\\\\\\"button\\\\\\\",t.COLOR=\\\\\\\"color\\\\\\\",t.FLOAT=\\\\\\\"float\\\\\\\",t.FOLDER=\\\\\\\"folder\\\\\\\",t.INTEGER=\\\\\\\"integer\\\\\\\",t.OPERATOR_PATH=\\\\\\\"operator_path\\\\\\\",t.PARAM_PATH=\\\\\\\"param_path\\\\\\\",t.NODE_PATH=\\\\\\\"node_path\\\\\\\",t.RAMP=\\\\\\\"ramp\\\\\\\",t.STRING=\\\\\\\"string\\\\\\\",t.VECTOR2=\\\\\\\"vector2\\\\\\\",t.VECTOR3=\\\\\\\"vector3\\\\\\\",t.VECTOR4=\\\\\\\"vector4\\\\\\\"}(Es||(Es={})),function(t){t.VISIBLE_UPDATED=\\\\\\\"param_visible_updated\\\\\\\",t.RAW_INPUT_UPDATED=\\\\\\\"raw_input_updated\\\\\\\",t.VALUE_UPDATED=\\\\\\\"param_value_updated\\\\\\\",t.EXPRESSION_UPDATED=\\\\\\\"param_expression_update\\\\\\\",t.ERROR_UPDATED=\\\\\\\"param_error_updated\\\\\\\",t.DELETED=\\\\\\\"param_deleted\\\\\\\"}(Ms||(Ms={}));const Ss=\\\\\\\"dependentOnFoundNode\\\\\\\",Cs=\\\\\\\"visibleIf\\\\\\\";var Ns,Ls;!function(t){t.TYPESCRIPT=\\\\\\\"typescript\\\\\\\"}(Ns||(Ns={})),function(t){t.AUDIO=\\\\\\\"audio\\\\\\\",t.TEXTURE_IMAGE=\\\\\\\"texture_image\\\\\\\",t.TEXTURE_VIDEO=\\\\\\\"texture_video\\\\\\\",t.GEOMETRY=\\\\\\\"geometry\\\\\\\",t.FONT=\\\\\\\"font\\\\\\\",t.SVG=\\\\\\\"svg\\\\\\\",t.JSON=\\\\\\\"json\\\\\\\"}(Ls||(Ls={}));class Os{constructor(t){this._param=t,this._programatic_visible_state=!0,this._callbackAllowed=!1,this._updateVisibilityAndRemoveDirtyBound=this.updateVisibilityAndRemoveDirty.bind(this),this._ui_data_dependency_set=!1}dispose(){var t;try{this._options.callback=void 0,this._options.callbackString=void 0}catch(t){}null===(t=this._visibility_graph_node)||void 0===t||t.dispose()}set(t){this._default_options=t,this._options=b.cloneDeep(this._default_options),this.post_set_options()}copy(t){this._default_options=b.cloneDeep(t.default()),this._options=b.cloneDeep(t.current()),this.post_set_options()}setOption(t,e){if(this._options[t]=e,this._param.components)for(let n of this._param.components)n.options.setOption(t,e)}post_set_options(){this._handleComputeOnDirty()}param(){return this._param}node(){return this._param.node}default(){return this._default_options}current(){return this._options}hasOptionsOverridden(){return!b.isEqual(this._options,this._default_options)}overriddenOptions(){const t={},e=Object.keys(this._options);for(let n of e)if(!b.isEqual(this._options[n],this._default_options[n])){const e=b.cloneDeep(this._options[n]);Object.assign(t,{[n]:e})}return t}overriddenOptionNames(){return Object.keys(this.overriddenOptions())}computeOnDirty(){return this._options.computeOnDirty||!1}_handleComputeOnDirty(){this.computeOnDirty()&&(this._computeOnDirty_callback_added||(this.param().addPostDirtyHook(\\\\\\\"computeOnDirty\\\\\\\",this._computeParam.bind(this)),this._computeOnDirty_callback_added=!0))}async _computeParam(){await this.param().compute()}hasCallback(){return null!=this._options.callback||null!=this._options.callbackString}allowCallback(){this._callbackAllowed=!0}executeCallback(){if(!this._callbackAllowed)return;if(!this.node())return;const t=this.getCallback();if(!t)return;if(!this.node().scene().loadingController.loaded())return;const e=this.param().parent_param;e?e.options.executeCallback():t(this.node(),this.param())}getCallback(){if(this.hasCallback())return this._options.callback=this._options.callback||this.createCallbackFromString()}createCallbackFromString(){const t=this._options.callbackString;if(t){const e=new Function(\\\\\\\"node\\\\\\\",\\\\\\\"scene\\\\\\\",\\\\\\\"window\\\\\\\",\\\\\\\"location\\\\\\\",t);return()=>{e(this.node(),this.node().scene(),null,null)}}}colorConversion(){return this._options.conversion}makesNodeDirtyWhenDirty(){let t;if(null!=this.param().parent_param)return!1;let e=!0;return null!=(t=this._options.cook)&&(e=t),e}fileBrowseOption(){return this._options.fileBrowse}fileBrowseAllowed(){return null!=this.fileBrowseOption()}fileBrowseType(){const t=this.fileBrowseOption();return t?t.type:null}separatorBefore(){return this._options.separatorBefore}separatorAfter(){return this._options.separatorAfter}isExpressionForEntities(){const t=this._options.expression;return t&&t.forEntities||!1}level(){return this._options.level||0}hasMenu(){return null!=this.menuOptions()||null!=this.menuStringOptions()}menuOptions(){return this._options.menu}menuStringOptions(){return this._options.menuString}menuEntries(){const t=this.menuOptions()||this.menuStringOptions();return t?t.entries:[]}isMultiline(){return!0===this._options.multiline}language(){return this._options.language}isCode(){return null!=this.language()}nodeSelectionOptions(){return this._options.nodeSelection}nodeSelectionContext(){const t=this.nodeSelectionOptions();if(t)return t.context}nodeSelectionTypes(){const t=this.nodeSelectionOptions();if(t)return t.types}dependentOnFoundNode(){return!(Ss in this._options)||this._options.dependentOnFoundNode}isSelectingParam(){return null!=this.paramSelectionOptions()}paramSelectionOptions(){return this._options.paramSelection}paramSelectionType(){const t=this.paramSelectionOptions();if(t){const e=t;if(!m.isBoolean(e))return e}}range(){return this._options.range||[0,1]}step(){return this._options.step}rangeLocked(){return this._options.rangeLocked||[!1,!1]}ensureInRange(t){const e=this.range();return t>=e[0]&&t<=e[1]?t:t<e[0]?!0===this.rangeLocked()[0]?e[0]:t:!0===this.rangeLocked()[1]?e[1]:t}isSpare(){return this._options.spare||!1}textureOptions(){return this._options.texture}textureAsEnv(){const t=this.textureOptions();return null!=t&&!0===t.env}isHidden(){return!0===this._options.hidden||!1===this._programatic_visible_state}isVisible(){return!this.isHidden()}setVisibleState(t){this._options.hidden=!t,this.param().emit(Ms.VISIBLE_UPDATED)}label(){return this._options.label}isLabelHidden(){const t=this.param().type();return t===Es.BUTTON||t===Es.BOOLEAN&&this.isFieldHidden()}isFieldHidden(){return!1===this._options.field}uiDataDependsOnOtherParams(){return Cs in this._options}visibilityPredecessors(){const t=this._options.visibleIf;if(!t)return[];let e=[];e=m.isArray(t)?f.uniq(t.map((t=>Object.keys(t))).flat()):Object.keys(t);const n=this.param().node;return f.compact(e.map((t=>{const e=n.params.get(t);if(e)return e;console.error(`param ${t} not found as visibility condition for ${this.param().name()} in node ${this.param().node.type()}`)})))}setUiDataDependency(){if(this._ui_data_dependency_set)return;this._ui_data_dependency_set=!0;const t=this.visibilityPredecessors();if(t.length>0){this._visibility_graph_node=new Ai(this.param().scene(),\\\\\\\"param_visibility\\\\\\\");for(let e of t)this._visibility_graph_node.addGraphInput(e);this._visibility_graph_node.addPostDirtyHook(\\\\\\\"_update_visibility_and_remove_dirty\\\\\\\",this._updateVisibilityAndRemoveDirtyBound)}}updateVisibilityAndRemoveDirty(){this.updateVisibility(),this.param().removeDirtyState()}async updateVisibility(){const t=this._options.visibleIf;if(t){const e=this.visibilityPredecessors(),n=e.map((t=>{if(t.isDirty())return t.compute()}));if(this._programatic_visible_state=!1,await Promise.all(n),m.isArray(t))for(let n of t){e.filter((t=>t.value==n[t.name()])).length==e.length&&(this._programatic_visible_state=!0)}else{const n=e.filter((e=>e.value==t[e.name()]));this._programatic_visible_state=n.length==e.length}this.param().emit(Ms.VISIBLE_UPDATED)}}}class Rs{constructor(t){this.param=t,this._blocked_emit=!1,this._blocked_parent_emit=!1,this._count_by_event_name={}}emitAllowed(){return!0!==this._blocked_emit&&(!this.param.scene().loadingController.isLoading()&&this.param.scene().dispatchController.emitAllowed())}blockEmit(){if(this._blocked_emit=!0,this.param.isMultiple()&&this.param.components)for(let t of this.param.components)t.emitController.blockEmit();return!0}unblockEmit(){if(this._blocked_emit=!1,this.param.isMultiple()&&this.param.components)for(let t of this.param.components)t.emitController.unblockEmit();return!0}blockParentEmit(){return this._blocked_parent_emit=!0,!0}unblockParentEmit(){return this._blocked_parent_emit=!1,!0}incrementCount(t){this._count_by_event_name[t]=this._count_by_event_name[t]||0,this._count_by_event_name[t]+=1}eventsCount(t){return this._count_by_event_name[t]||0}emit(t){this.emitAllowed()&&(this.param.emit(t),null!=this.param.parent_param&&!0!==this._blocked_parent_emit&&this.param.parent_param.emit(t))}}class Ps{constructor(t){this.param=t}toJSON(){const t={name:this.param.name(),type:this.param.type(),raw_input:this.rawInput(),value:this.value(),value_pre_conversion:this.value_pre_conversion(),expression:this.expression(),graph_node_id:this.param.graphNodeId(),error_message:this.error_message(),is_visible:this.is_visible(),components:void 0};return this.param.isMultiple()&&this.param.components&&(t.components=this.param.components.map((t=>t.graphNodeId()))),t}rawInput(){return this.param.rawInputSerialized()}value(){return this.param.valueSerialized()}value_pre_conversion(){return this.param.valuePreConversionSerialized()}expression(){var t;return this.param.hasExpression()?null===(t=this.param.expressionController)||void 0===t?void 0:t.expression():void 0}error_message(){return this.param.states.error.message()}is_visible(){return this.param.options.isVisible()}}class Is{constructor(t){this.param=t}active(){const t=this.param.scene().timeController.graphNode.graphNodeId();return this.param.graphPredecessorIds().includes(t)}}class Fs{constructor(t){this.param=t}set(t){this._message!=t&&(this._message=t,this._message&&ai.warn(this.param.path(),this._message),this.param.emitController.emit(Ms.ERROR_UPDATED))}message(){return this._message}clear(){this.set(void 0)}active(){return null!=this._message}}class Ds{constructor(t){this.param=t,this.timeDependent=new Is(this.param),this.error=new Fs(this.param)}}class ks extends Ai{constructor(t,e,n){var i;super(t.scene(),\\\\\\\"MethodDependency\\\\\\\"),this.param=t,this.path_argument=e,this.decomposed_path=n,this._update_from_name_change_bound=this._update_from_name_change.bind(this),null===(i=t.expressionController)||void 0===i||i.registerMethodDependency(this),this.addPostDirtyHook(\\\\\\\"_update_from_name_change\\\\\\\",this._update_from_name_change_bound)}_update_from_name_change(t){if(t&&this.decomposed_path){const e=t;this.decomposed_path.update_from_name_change(e);const n=this.decomposed_path.to_path(),i=this.jsep_node;i&&(i.value=`${i.value}`.replace(`${this.path_argument}`,n),i.raw=i.raw.replace(`${this.path_argument}`,n)),this.param.expressionController&&this.param.expressionController.update_from_method_dependency_name_change()}}reset(){this.graphDisconnectPredecessors()}listen_for_name_changes(){if(this.jsep_node&&this.decomposed_path)for(let t of this.decomposed_path.named_nodes())if(t){const e=t;e.nameController&&this.addGraphInput(e.nameController.graph_node)}}set_jsep_node(t){this.jsep_node=t}set_resolved_graph_node(t){this.resolved_graph_node=t}set_unresolved_path(t){this.unresolved_path=t}static create(t,e,n,i){const r=m.isNumber(e),s=new ks(t,e,i);if(n)s.set_resolved_graph_node(n);else if(!r){const t=e;s.set_unresolved_path(t)}return s}}const Bs=[];class zs extends Ai{constructor(t,e){super(t,\\\\\\\"BaseParam\\\\\\\"),this._options=new Os(this),this._emit_controller=new Rs(this),this._is_computing=!1,this._node=e,this.initialize_param()}get options(){return this._options=this._options||new Os(this)}get emitController(){return this._emit_controller=this._emit_controller||new Rs(this)}get expressionController(){return this._expression_controller}get serializer(){return this._serializer=this._serializer||new Ps(this)}get states(){return this._states=this._states||new Ds(this)}dispose(){var t,e;const n=this.graphPredecessors();for(let t of n)t instanceof ks&&t.dispose();null===(t=this._expression_controller)||void 0===t||t.dispose(),super.dispose(),null===(e=this._options)||void 0===e||e.dispose()}initialize_param(){}static type(){return Es.FLOAT}type(){return this.constructor.type()}isNumeric(){return!1}setName(t){super.setName(t)}get value(){return this._value}copy_value(t){t.type()==this.type()?this._copy_value(t):console.warn(`cannot copy value from ${t.type()} to ${this.type()}`)}_copy_value(t){throw\\\\\\\"abstract method param._copy_value\\\\\\\"}valuePreConversionSerialized(){}convert(t){return null}static are_raw_input_equal(t,e){return!1}is_raw_input_equal(t){return this.constructor.are_raw_input_equal(this._raw_input,t)}static are_values_equal(t,e){return!1}is_value_equal(t){return this.constructor.are_values_equal(this.value,t)}_clone_raw_input(t){return t}set(t){this._raw_input=this._clone_raw_input(this._prefilter_invalid_raw_input(t)),this.emitController.emit(Ms.RAW_INPUT_UPDATED),this.processRawInput()}_prefilter_invalid_raw_input(t){return t}defaultValue(){return this._default_value}isDefault(){return this._raw_input==this._default_value}rawInput(){return this._raw_input}processRawInput(){}async compute(){if(this.scene().loadingController.isLoading()&&console.warn(`param attempt to compute ${this.path()}`),this.isDirty()){if(this._is_computing)return new Promise(((t,e)=>{this._compute_resolves=this._compute_resolves||[],this._compute_resolves.push(t)}));if(this._is_computing=!0,await this.processComputation(),this._is_computing=!1,this._compute_resolves){let t;for(;t=this._compute_resolves.pop();)t()}}}async processComputation(){}setInitValue(t){this._default_value=this._clone_raw_input(this._prefilter_invalid_raw_input(t))}_setupNodeDependencies(t){var e,n;if(t?(this.options.allowCallback(),this.parent_param||(this.options.makesNodeDirtyWhenDirty()?null===(n=t.params.params_node)||void 0===n||n.addGraphInput(this,!1):this.dirtyController.addPostDirtyHook(\\\\\\\"run callback\\\\\\\",(async()=>{await this.compute(),this.options.executeCallback()})))):this._node&&(null===(e=this._node.params.params_node)||void 0===e||e.removeGraphInput(this)),this.components)for(let e of this.components)e._setupNodeDependencies(t)}get node(){return this._node}parent(){return this.node}set_parent_param(t){t.addGraphInput(this,!1),this._parent_param=t}get parent_param(){return this._parent_param}has_parent_param(){return null!=this._parent_param}path(){var t;return(null===(t=this.node)||void 0===t?void 0:t.path())+\\\\\\\"/\\\\\\\"+this.name()}pathRelativeTo(t){const e=xi.relativePath(t,this.node);return e.length>0?`${e}${xi.SEPARATOR}${this.name()}`:this.name()}emit(t){this.emitController.emitAllowed()&&(this.emitController.incrementCount(t),this.scene().dispatchController.dispatch(this,t))}get components(){return this._components}componentNames(){return Bs}isMultiple(){return this.componentNames().length>0}initComponents(){}hasExpression(){return null!=this.expressionController&&this.expressionController.active()}toJSON(){return this.serializer.toJSON()}}var Us=n(96),Gs=n.n(Us);Gs.a.addUnaryOp(\\\\\\\"@\\\\\\\");Gs.a.addBinaryOp(\\\\\\\"**\\\\\\\",10);class Vs{constructor(){}parse_expression(t){try{this.reset(),this.node=Gs()(t)}catch(e){const n=`could not parse the expression '${t}' (error: ${e})`;this.error_message=n}}parse_expression_for_string_param(t){try{this.reset();const e=Vs.string_value_elements(t),n=[];for(let t=0;t<e.length;t++){const i=e[t];let r;if(t%2==1)r=Gs()(i);else{const t=i.replace(/\\\\'/g,\\\\\\\"\\\\\\\\'\\\\\\\");r={type:\\\\\\\"Literal\\\\\\\",value:`'${t}'`,raw:`'${t}'`}}n.push(r)}this.node={type:\\\\\\\"CallExpression\\\\\\\",arguments:n,callee:{type:\\\\\\\"Identifier\\\\\\\",name:\\\\\\\"strConcat\\\\\\\"}}}catch(e){const n=`could not parse the expression '${t}' (error: ${e})`;this.error_message=n}}static string_value_elements(t){return null!=t&&m.isString(t)?t.split(\\\\\\\"`\\\\\\\"):[]}reset(){this.node=void 0,this.error_message=void 0}}class Hs{constructor(t){this.param=t,this._set_error_from_error_bound=this._set_error_from_error.bind(this)}clear_error(){this._error_message=void 0}set_error(t){this._error_message=this._error_message||t}_set_error_from_error(t){m.isString(t)?this._error_message=t:this._error_message=t.message}is_errored(){return null!=this._error_message}error_message(){return this._error_message}reset(){this._error_message=void 0}traverse_node(t){const e=`traverse_${t.type}`;if(this[e])return this[e](t);this.set_error(`expression unknown node type: ${t.type}`)}traverse_BinaryExpression(t){return`${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)}.${this.traverse_node(t.property)}`}traverse_ConditionalExpression(t){return`(${this.traverse_node(t.test)}) ? (${this.traverse_node(t.consequent)}) : (${this.traverse_node(t.alternate)})`}traverse_Compound(t){const e=t.body;let n=[];for(let t=0;t<e.length;t++){const i=e[t];\\\\\\\"Identifier\\\\\\\"==i.type?\\\\\\\"$\\\\\\\"==i.name[0]?n.push(\\\\\\\"`${\\\\\\\"+this.traverse_node(i)+\\\\\\\"}`\\\\\\\"):n.push(`'${i.name}'`):n.push(\\\\\\\"`${\\\\\\\"+this.traverse_node(i)+\\\\\\\"}`\\\\\\\")}return n.join(\\\\\\\" + \\\\\\\")}traverse_Literal(t){return`${t.raw}`}}class js{constructor(){}reset(){this._attribute_names&&this._attribute_names.clear()}assign_attributes_lines(){var t;if(this._attribute_names){const e=[];return null===(t=this._attribute_names)||void 0===t||t.forEach((t=>{e.push(js.assign_attribute_line(t))})),e.join(\\\\\\\";\\\\n\\\\\\\")}return\\\\\\\"\\\\\\\"}assign_arrays_lines(){var t;if(this._attribute_names){const e=[];return null===(t=this._attribute_names)||void 0===t||t.forEach((t=>{e.push(js.assign_item_size_line(t)),e.push(js.assign_array_line(t))})),e.join(\\\\\\\";\\\\n\\\\\\\")}return\\\\\\\"\\\\\\\"}attribute_presence_check_line(){var t;if(this._attribute_names){const e=[];if(null===(t=this._attribute_names)||void 0===t||t.forEach((t=>{const n=js.var_attribute(t);e.push(n)})),e.length>0)return e.join(\\\\\\\" && \\\\\\\")}return\\\\\\\"true\\\\\\\"}add(t){this._attribute_names=this._attribute_names||new Set,this._attribute_names.add(t)}static assign_attribute_line(t){return`const ${this.var_attribute(t)} = entities[0].geometry().attributes['${t}']`}static assign_item_size_line(t){const e=this.var_attribute(t);return`const ${this.var_attribute_size(t)} = ${e}.itemSize`}static assign_array_line(t){const e=this.var_attribute(t);return`const ${this.var_array(t)} = ${e}.array`}static var_attribute(t){return`attrib_${t}`}static var_attribute_size(t){return`attrib_size_${t}`}static var_array(t){return`array_${t}`}var_attribute_size(t){return js.var_attribute_size(t)}var_array(t){return js.var_array(t)}}const Ws={math_random:\\\\\\\"random\\\\\\\"},qs=Object.keys(ns),Xs={};[\\\\\\\"abs\\\\\\\",\\\\\\\"acos\\\\\\\",\\\\\\\"acosh\\\\\\\",\\\\\\\"asin\\\\\\\",\\\\\\\"asinh\\\\\\\",\\\\\\\"atan\\\\\\\",\\\\\\\"atan2\\\\\\\",\\\\\\\"atanh\\\\\\\",\\\\\\\"ceil\\\\\\\",\\\\\\\"cos\\\\\\\",\\\\\\\"cosh\\\\\\\",\\\\\\\"exp\\\\\\\",\\\\\\\"expm1\\\\\\\",\\\\\\\"floor\\\\\\\",\\\\\\\"log\\\\\\\",\\\\\\\"log1p\\\\\\\",\\\\\\\"log2\\\\\\\",\\\\\\\"log10\\\\\\\",\\\\\\\"max\\\\\\\",\\\\\\\"min\\\\\\\",\\\\\\\"pow\\\\\\\",\\\\\\\"round\\\\\\\",\\\\\\\"sign\\\\\\\",\\\\\\\"sin\\\\\\\",\\\\\\\"sinh\\\\\\\",\\\\\\\"sqrt\\\\\\\",\\\\\\\"tan\\\\\\\",\\\\\\\"tanh\\\\\\\"].forEach((t=>{Xs[t]=`Math.${t}`})),[\\\\\\\"cbrt\\\\\\\",\\\\\\\"hypot\\\\\\\",\\\\\\\"log10\\\\\\\",\\\\\\\"trunc\\\\\\\"].forEach((t=>{Xs[t]=`Math.${t}`})),Object.keys(Ws).forEach((t=>{const e=Ws[t];Xs[t]=`Math.${e}`})),[\\\\\\\"fit\\\\\\\",\\\\\\\"fit01\\\\\\\",\\\\\\\"fract\\\\\\\",\\\\\\\"deg2rad\\\\\\\",\\\\\\\"rad2deg\\\\\\\",\\\\\\\"rand\\\\\\\",\\\\\\\"clamp\\\\\\\"].forEach((t=>{Xs[t]=`Core.Math.${t}`})),qs.forEach((t=>{Xs[t]=`Core.Math.Easing.${t}`})),[\\\\\\\"precision\\\\\\\"].forEach((t=>{Xs[t]=`Core.String.${t}`}));const Ys={if:class{static if(t){return`(${t[0]}) ? (${t[1]}) : (${t[2]})`}}.if},$s={};[\\\\\\\"E\\\\\\\",\\\\\\\"LN2\\\\\\\",\\\\\\\"LN10\\\\\\\",\\\\\\\"LOG10E\\\\\\\",\\\\\\\"LOG2E\\\\\\\",\\\\\\\"PI\\\\\\\",\\\\\\\"SQRT1_2\\\\\\\",\\\\\\\"SQRT2\\\\\\\"].forEach((t=>{$s[t]=`Math.${t}`}));const Js={x:0,y:1,z:2,w:3,r:0,g:1,b:2};class Zs extends Hs{constructor(t){super(t),this.param=t,this._attribute_requirements_controller=new js,this.methods=[],this.method_index=-1,this.method_dependencies=[],this.immutable_dependencies=[]}parse_tree(t){if(this.reset(),null==t.error_message){try{if(this._attribute_requirements_controller.reset(),t.node){const e=this.traverse_node(t.node);e&&!this.is_errored()&&(this.function_main_string=e)}else console.warn(\\\\\\\"no parsed_tree.node\\\\\\\")}catch(t){console.warn(`error in expression for param ${this.param.path()}`),console.warn(t)}if(this.function_main_string)try{this.function=new Function(\\\\\\\"Core\\\\\\\",\\\\\\\"param\\\\\\\",\\\\\\\"methods\\\\\\\",\\\\\\\"_set_error_from_error\\\\\\\",`\\\\n\\\\t\\\\t\\\\t\\\\t\\\\ttry {\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t${this.function_body()}\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t} catch(e) {\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t_set_error_from_error(e)\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\treturn null;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t}`)}catch(t){console.warn(t),this.set_error(\\\\\\\"cannot generate function\\\\\\\")}else this.set_error(\\\\\\\"cannot generate function body\\\\\\\")}else this.set_error(\\\\\\\"cannot parse expression\\\\\\\")}reset(){super.reset(),this.function_main_string=void 0,this.methods=[],this.method_index=-1,this.function=void 0,this.method_dependencies=[],this.immutable_dependencies=[]}function_body(){return this.param.options.isExpressionForEntities()?`\\\\n\\\\t\\\\t\\\\tconst entities = param.expressionController.entities();\\\\n\\\\t\\\\t\\\\tif(entities){\\\\n\\\\t\\\\t\\\\t\\\\treturn new Promise( async (resolve, reject)=>{\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tlet entity;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tconst entity_callback = param.expressionController.entity_callback();\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t${this._attribute_requirements_controller.assign_attributes_lines()}\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tif( ${this._attribute_requirements_controller.attribute_presence_check_line()} ){\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t${this._attribute_requirements_controller.assign_arrays_lines()}\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tfor(let index=0; index < entities.length; index++){\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tentity = entities[index];\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tresult = ${this.function_main_string};\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tentity_callback(entity, result);\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t}\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tresolve()\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t} else {\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tconst error = new Error('attribute not found')\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t_set_error_from_error(error)\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\treject(error)\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t}\\\\n\\\\t\\\\t\\\\t\\\\t})\\\\n\\\\t\\\\t\\\\t}\\\\n\\\\t\\\\t\\\\treturn []`:`\\\\n\\\\t\\\\t\\\\treturn new Promise( async (resolve, reject)=>{\\\\n\\\\t\\\\t\\\\t\\\\ttry {\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tconst value = ${this.function_main_string}\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tresolve(value)\\\\n\\\\t\\\\t\\\\t\\\\t} catch(e) {\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t_set_error_from_error(e)\\\\n\\\\t\\\\t\\\\t\\\\t\\\\treject()\\\\n\\\\t\\\\t\\\\t\\\\t}\\\\n\\\\t\\\\t\\\\t})\\\\n\\\\t\\\\t\\\\t`}eval_allowed(){return null!=this.function}eval_function(){if(this.function){this.clear_error();const t={Math:rs,String:sr};return this.function(t,this.param,this.methods,this._set_error_from_error_bound)}}traverse_CallExpression(t){const e=t.arguments.map((t=>this.traverse_node(t))),n=t.callee.name;if(n){const i=Ys[n];if(i)return i(e);const r=`${e.join(\\\\\\\", \\\\\\\")}`,s=Xs[n];if(s)return`${s}(${r})`;const o=ai.expressionsRegister;if(o.getMethod(n)){const i=t.arguments[0],s=`return ${e[0]}`;let o,a=[];try{o=new Function(s),a=o()}catch{}return this._create_method_and_dependencies(n,a,i),`(await methods[${this.method_index}].processArguments([${r}]))`}{const t=`method not found (${n}), available methods are: ${o.availableMethods().join(\\\\\\\", \\\\\\\")}`;ai.warn(t)}}this.set_error(`unknown method: ${n}`)}traverse_BinaryExpression(t){return`(${this.traverse_node(t.left)} ${t.operator} ${this.traverse_node(t.right)})`}traverse_LogicalExpression(t){return`(${this.traverse_node(t.left)} ${t.operator} ${this.traverse_node(t.right)})`}traverse_MemberExpression(t){return`${this.traverse_node(t.object)}.${this.traverse_node(t.property)}`}traverse_UnaryExpression(t){if(\\\\\\\"@\\\\\\\"===t.operator){let e,n,i=t.argument;switch(i.type){case\\\\\\\"Identifier\\\\\\\":e=i.name;break;case\\\\\\\"MemberExpression\\\\\\\":{const t=i,r=t.object,s=t.property;e=r.name,n=s.name;break}}if(e){if(e=Wr.remapName(e),\\\\\\\"ptnum\\\\\\\"==e)return\\\\\\\"((entity != null) ? entity.index() : 0)\\\\\\\";{const t=this._attribute_requirements_controller.var_attribute_size(e),i=this._attribute_requirements_controller.var_array(e);if(this._attribute_requirements_controller.add(e),n){return`${i}[entity.index()*${t}+${Js[n]}]`}return`${i}[entity.index()*${t}]`}}return console.warn(\\\\\\\"attribute not found\\\\\\\"),\\\\\\\"\\\\\\\"}return`${t.operator}${this.traverse_node(t.argument)}`}traverse_Literal(t){return`${t.raw}`}traverse_Identifier(t){if(\\\\\\\"$\\\\\\\"!=t.name[0])return t.name;{const e=t.name.substr(1),n=$s[e];if(n)return n;const i=`traverse_Identifier_${e}`;if(this[i])return this[i]();this.set_error(`identifier unknown: ${t.name}`)}}traverse_Identifier_F(){return this.immutable_dependencies.push(this.param.scene().timeController.graphNode),\\\\\\\"param.scene().timeController.frame()\\\\\\\"}traverse_Identifier_T(){return this.immutable_dependencies.push(this.param.scene().timeController.graphNode),\\\\\\\"param.scene().timeController.time()\\\\\\\"}traverse_Identifier_OS(){return`'${this.param.node.name()}'`}traverse_Identifier_CH(){return`'${this.param.name()}'`}traverse_Identifier_CEX(){return this._method_centroid(\\\\\\\"x\\\\\\\")}traverse_Identifier_CEY(){return this._method_centroid(\\\\\\\"y\\\\\\\")}traverse_Identifier_CEZ(){return this._method_centroid(\\\\\\\"z\\\\\\\")}_method_centroid(t){const e=[0,`'${t}'`].join(\\\\\\\", \\\\\\\");return this._create_method_and_dependencies(\\\\\\\"centroid\\\\\\\",0),`(await methods[${this.method_index}].processArguments([${e}]))`}_create_method_and_dependencies(t,e,n){const i=ai.expressionsRegister,r=i.getMethod(t);if(!r){const e=`method not found (${t}), available methods are: ${i.availableMethods().join(\\\\\\\", \\\\\\\")}`;return this.set_error(e),void ai.warn(e)}const s=new r(this.param);if(this.method_index+=1,this.methods[this.method_index]=s,s.require_dependency()){const t=s.findDependency(e);t?(n&&t.set_jsep_node(n),this.method_dependencies.push(t)):n&&m.isString(e)&&this.param.scene().missingExpressionReferencesController.register(this.param,n,e)}}}class Qs extends Hs{constructor(t){super(t),this.param=t}parse_tree(t){if(null==t.error_message&&t.node)try{return this.traverse_node(t.node)}catch(t){this.set_error(\\\\\\\"could not traverse tree\\\\\\\")}else this.set_error(\\\\\\\"cannot parse tree\\\\\\\")}traverse_CallExpression(t){const e=`${t.arguments.map((t=>this.traverse_node(t))).join(\\\\\\\", \\\\\\\")}`;return`${t.callee.name}(${e})`}traverse_UnaryExpression(t){return`${t.operator}${this.traverse_node(t.argument)}`}traverse_Identifier(t){return`${t.name}`}}class Ks{constructor(t){this.param=t,this.cyclic_graph_detected=!1,this.method_dependencies=[]}set_error(t){this.error_message=this.error_message||t}reset(){this.param.graphDisconnectPredecessors(),this.method_dependencies.forEach((t=>{t.reset()})),this.method_dependencies=[]}update(t){this.cyclic_graph_detected=!1,this.connect_immutable_dependencies(t),this.method_dependencies=t.method_dependencies,this.handle_method_dependencies(),this.listen_for_name_changes()}connect_immutable_dependencies(t){t.immutable_dependencies.forEach((t=>{if(0==this.cyclic_graph_detected&&0==this.param.addGraphInput(t))return this.cyclic_graph_detected=!0,this.set_error(\\\\\\\"cannot create expression, infinite graph detected\\\\\\\"),void this.reset()}))}handle_method_dependencies(){this.method_dependencies.forEach((t=>{0==this.cyclic_graph_detected&&this.handle_method_dependency(t)}))}handle_method_dependency(t){const e=t.resolved_graph_node;if(e&&!this.param.addGraphInput(e))return this.cyclic_graph_detected=!0,this.set_error(\\\\\\\"cannot create expression, infinite graph detected\\\\\\\"),void this.reset()}listen_for_name_changes(){this.method_dependencies.forEach((t=>{t.listen_for_name_changes()}))}}class to{constructor(t){this.param=t,this.parse_completed=!1,this.parse_started=!1,this.parsed_tree=new Vs,this.function_generator=new Zs(this.param),this.dependencies_controller=new Ks(this.param)}parse_expression(t){if(this.parse_started)throw new Error(`parse in progress for param ${this.param.path()}`);this.parse_started=!0,this.parse_completed=!1,this.parsed_tree=this.parsed_tree||new Vs,this.reset(),this.param.type()==Es.STRING?this.parsed_tree.parse_expression_for_string_param(t):this.parsed_tree.parse_expression(t),this.function_generator.parse_tree(this.parsed_tree),null==this.function_generator.error_message()&&(this.dependencies_controller.update(this.function_generator),this.dependencies_controller.error_message?this.param.states.error.set(this.dependencies_controller.error_message):(this.parse_completed=!0,this.parse_started=!1))}async compute_function(){if(!this.compute_allowed())return new Promise(((t,e)=>{t(null)}));try{return await this.function_generator.eval_function()}catch(t){return}}reset(){this.parse_completed=!1,this.parse_started=!1,this.dependencies_controller.reset(),this.function_generator.reset()}is_errored(){return this.function_generator.is_errored()}error_message(){return this.function_generator.error_message()}compute_allowed(){return this.function_generator.eval_allowed()}update_from_method_dependency_name_change(){this.expression_string_generator=this.expression_string_generator||new Qs(this.param);const t=this.expression_string_generator.parse_tree(this.parsed_tree);t?this.param.set(t):console.warn(\\\\\\\"failed to regenerate expression\\\\\\\")}}class eo{constructor(t){this.param=t}dispose(){this._resetMethodDependencies()}_resetMethodDependencies(){var t,e;null===(t=this._method_dependencies_by_graph_node_id)||void 0===t||t.forEach((t=>{t.dispose()})),null===(e=this._method_dependencies_by_graph_node_id)||void 0===e||e.clear()}registerMethodDependency(t){this._method_dependencies_by_graph_node_id=this._method_dependencies_by_graph_node_id||new Map,this._method_dependencies_by_graph_node_id.set(t.graphNodeId(),t)}active(){return null!=this._expression}expression(){return this._expression}is_errored(){return!!this._manager&&this._manager.is_errored()}error_message(){return this._manager?this._manager.error_message():null}requires_entities(){return this.param.options.isExpressionForEntities()}set_expression(t,e=!0){var n;this.param.scene().missingExpressionReferencesController.deregister_param(this.param),this.param.scene().expressionsController.deregister_param(this.param),this._expression!=t&&(this._resetMethodDependencies(),this._expression=t,this._expression?(this._manager=this._manager||new to(this.param),this._manager.parse_expression(this._expression)):null===(n=this._manager)||void 0===n||n.reset(),e&&this.param.setDirty())}update_from_method_dependency_name_change(){this._manager&&this.active()&&this._manager.update_from_method_dependency_name_change()}async compute_expression(){if(this._manager&&this.active()){return await this._manager.compute_function()}}async compute_expression_for_entities(t,e){var n,i;this.set_entities(t,e),await this.compute_expression(),(null===(n=this._manager)||void 0===n?void 0:n.error_message())&&this.param.node.states.error.set(`expression evalution error: ${null===(i=this._manager)||void 0===i?void 0:i.error_message()}`),this.reset_entities()}compute_expression_for_points(t,e){return this.compute_expression_for_entities(t,e)}compute_expression_for_objects(t,e){return this.compute_expression_for_entities(t,e)}entities(){return this._entities}entity_callback(){return this._entity_callback}set_entities(t,e){this._entities=t,this._entity_callback=e}reset_entities(){this._entities=void 0,this._entity_callback=void 0}}class no extends zs{isNumeric(){return!0}isDefault(){return this._raw_input==this._default_value}_prefilter_invalid_raw_input(t){return m.isArray(t)?t[0]:t}processRawInput(){this.states.error.clear();const t=this.convert(this._raw_input);null!=t?(this._expression_controller&&(this._expression_controller.set_expression(void 0,!1),this.emitController.emit(Ms.EXPRESSION_UPDATED)),t!=this._value&&(this._update_value(t),this.setSuccessorsDirty(this))):m.isString(this._raw_input)?(this._expression_controller=this._expression_controller||new eo(this),this._raw_input!=this._expression_controller.expression()&&(this._expression_controller.set_expression(this._raw_input),this.emitController.emit(Ms.EXPRESSION_UPDATED))):this.states.error.set(`param input is invalid (${this.path()})`)}async processComputation(){var t;if((null===(t=this.expressionController)||void 0===t?void 0:t.active())&&!this.expressionController.requires_entities()){const t=await this.expressionController.compute_expression();if(this.expressionController.is_errored())this.states.error.set(`expression error: \\\\\\\"${this.expressionController.expression()}\\\\\\\" (${this.expressionController.error_message()})`);else{const e=this.convert(t);null!=e?(this.states.error.active()&&this.states.error.clear(),this._update_value(e)):this.states.error.set(`expression returns an invalid type (${t}) (${this.expressionController.expression()})`)}}}_update_value(t){this._value=t,this.parent_param&&this.parent_param.set_value_from_components(),this.options.executeCallback(),this.emitController.emit(Ms.VALUE_UPDATED),this.removeDirtyState()}}class io extends no{static type(){return Es.FLOAT}defaultValueSerialized(){return this._default_value}rawInputSerialized(){return this._raw_input}valueSerialized(){return this.value}_copy_value(t){this.set(t.valueSerialized())}_prefilter_invalid_raw_input(t){return m.isArray(t)?t[0]:m.isString(t)&&sr.isNumber(t)?parseFloat(t):t}static are_raw_input_equal(t,e){return t==e}static are_values_equal(t,e){return t==e}static convert(t){if(m.isNumber(t))return t;if(m.isBoolean(t))return t?1:0;if(sr.isNumber(t)){const e=parseFloat(t);if(m.isNumber(e))return e}return null}convert(t){const e=io.convert(t);return e?this.options.ensureInRange(e):e}}class ro extends zs{constructor(){super(...arguments),this._components_contructor=io}get components(){return this._components}isNumeric(){return!0}isDefault(){for(let t of this.components)if(!t.isDefault())return!1;return!0}rawInput(){return this._components.map((t=>t.rawInput()))}rawInputSerialized(){return this._components.map((t=>t.rawInputSerialized()))}_copy_value(t){for(let e=0;e<this.components.length;e++){const n=this.components[e],i=t.components[e];n.copy_value(i)}}initComponents(){if(null!=this._components)return;let t=0;this._components=new Array(this.componentNames().length);for(let e of this.componentNames()){const n=new this._components_contructor(this.scene(),this._node);let i;i=m.isArray(this._default_value)?this._default_value[t]:this._default_value[e],n.options.copy(this.options),n.setInitValue(i),n.setName(`${this.name()}${e}`),n.set_parent_param(this),this._components[t]=n,t++}}async processComputation(){await this.compute_components(),this.set_value_from_components()}set_value_from_components(){}hasExpression(){var t;for(let e of this.components)if(null===(t=e.expressionController)||void 0===t?void 0:t.active())return!0;return!1}async compute_components(){const t=this.components,e=[];for(let n of t)n.isDirty()&&e.push(n.compute());await Promise.all(e),this.removeDirtyState()}_prefilter_invalid_raw_input(t){if(m.isArray(t))return t;{const e=t;return this.componentNames().map((()=>e))}}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<e.length;t++){let r=n[t];null==r&&(r=i),e[t].set(r),i=r}else for(let t=0;t<e.length;t++){let r=n[this.componentNames()[t]];null==r&&(r=i),e[t].set(r),i=r}t.unblock();for(let t=0;t<e.length;t++)e[t].emitController.unblockParentEmit();this.emitController.emit(Ms.VALUE_UPDATED)}}var so;!function(t){t.NONE=\\\\\\\"no conversion\\\\\\\",t.GAMMA_TO_LINEAR=\\\\\\\"gamma -> linear\\\\\\\",t.LINEAR_TO_GAMMA=\\\\\\\"linear -> gamma\\\\\\\",t.SRGB_TO_LINEAR=\\\\\\\"sRGB -> linear\\\\\\\",t.LINEAR_TO_SRGB=\\\\\\\"linear -> sRGB\\\\\\\"}(so||(so={}));so.NONE,so.GAMMA_TO_LINEAR,so.LINEAR_TO_GAMMA,so.SRGB_TO_LINEAR,so.LINEAR_TO_SRGB;class oo{static set_hsv(t,e,n,i){t=Object(Ln.f)(t,1),e=Object(Ln.d)(e,0,1),n=Object(Ln.d)(n,0,1),i.setHSL(t,e*n/((t=(2-e)*n)<1?t:2-t),.5*t)}}const ao=[\\\\\\\"r\\\\\\\",\\\\\\\"g\\\\\\\",\\\\\\\"b\\\\\\\"];class lo extends no{static type(){return Es.INTEGER}defaultValueSerialized(){return this._default_value}rawInputSerialized(){return this._raw_input}valueSerialized(){return this.value}_copy_value(t){this.set(t.valueSerialized())}_prefilter_invalid_raw_input(t){return m.isArray(t)?t[0]:m.isString(t)&&sr.isNumber(t)?parseInt(t):t}static are_raw_input_equal(t,e){return t==e}static are_values_equal(t,e){return t==e}static convert(t){if(m.isNumber(t))return Math.round(t);if(m.isBoolean(t))return t?1:0;if(sr.isNumber(t)){const e=parseInt(t);if(m.isNumber(e))return e}return null}convert(t){const e=lo.convert(t);return e?this.options.ensureInRange(e):e}}class co{constructor(){this._index=-1,this._path_elements=[],this._named_nodes=[],this._graph_node_ids=[],this._node_element_by_graph_node_id=new Map}reset(){this._index=-1,this._path_elements=[],this._named_nodes=[],this._graph_node_ids=[],this._node_element_by_graph_node_id.clear()}add_node(t,e){this._index+=1,t==e.name()&&(this._named_nodes[this._index]=e),this._graph_node_ids[this._index]=e.graphNodeId(),this._node_element_by_graph_node_id.set(e.graphNodeId(),t)}add_path_element(t){this._index+=1,this._path_elements[this._index]=t}named_graph_nodes(){return this._named_nodes}named_nodes(){const t=[];for(let e of this._named_nodes)if(e){const n=e;n.nameController&&t.push(n)}return t}update_from_name_change(t){this._named_nodes.map((t=>null==t?void 0:t.graphNodeId())).includes(t.graphNodeId())&&this._node_element_by_graph_node_id.set(t.graphNodeId(),t.name())}to_path(){const t=new Array(this._index);for(let e=0;e<=this._index;e++){const n=this._named_nodes[e];if(n){const i=this._node_element_by_graph_node_id.get(n.graphNodeId());i&&(t[e]=i)}else{const n=this._path_elements[e];n&&(t[e]=n)}}let e=t.join(xi.SEPARATOR);const n=e[0];return n&&(xi.NON_LETTER_PREFIXES.includes(n)||(e=`${xi.SEPARATOR}${e}`)),e}}class uo extends zs{constructor(){super(...arguments),this.decomposed_path=new co}}var ho;!function(t){t.NODE=\\\\\\\"NODE\\\\\\\",t.PARAM=\\\\\\\"PARAM\\\\\\\"}(ho||(ho={}));class po extends uo{constructor(){super(...arguments),this._found_node=null,this._found_node_with_expected_type=null,this._found_param=null,this._found_param_with_expected_type=null}static type(){return Es.OPERATOR_PATH}defaultValueSerialized(){return this._default_value}rawInputSerialized(){return`${this._raw_input}`}valueSerialized(){return`${this.value}`}_copy_value(t){this.set(t.valueSerialized())}static are_raw_input_equal(t,e){return t==e}static are_values_equal(t,e){return t==e}isDefault(){return this._value==this._default_value}setNode(t){this.set(t.path())}processRawInput(){this._value!=this._raw_input&&(this._value=this._raw_input,this.setDirty(),this.emitController.emit(Ms.VALUE_UPDATED))}async processComputation(){this.find_target()}find_target(){if(!this.node)return;const t=this._value;let e=null,n=null;const i=null!=t&&\\\\\\\"\\\\\\\"!==t,r=this.options.paramSelectionOptions()?ho.PARAM:ho.NODE;this.scene().referencesController.reset_reference_from_param(this),this.decomposed_path.reset(),i&&(r==ho.PARAM?n=xi.findParam(this.node,t,this.decomposed_path):e=xi.findNode(this.node,t,this.decomposed_path));const s=r==ho.PARAM?this._found_param:this._found_node,o=r==ho.PARAM?n:e;if(this.scene().referencesController.set_named_nodes_from_param(this),e&&this.scene().referencesController.set_reference_from_param(this,e),(null==s?void 0:s.graphNodeId())!==(null==o?void 0:o.graphNodeId())){const t=this.options.dependentOnFoundNode();this._found_node&&t&&this.removeGraphInput(this._found_node),r==ho.PARAM?(this._found_param=n,this._found_node=null):(this._found_node=e,this._found_param=null),e&&this._assign_found_node(e),n&&this._assign_found_param(n),this.options.executeCallback()}this.removeDirtyState()}_assign_found_node(t){const e=this.options.dependentOnFoundNode();this._is_node_expected_context(t)?this._is_node_expected_type(t)?(this._found_node_with_expected_type=t,e&&this.addGraphInput(t)):this.states.error.set(`node type is ${t.type()} but the params expects one of ${(this._expected_node_types()||[]).join(\\\\\\\", \\\\\\\")}`):this.states.error.set(`node context is ${t.context()} but the params expects a ${this._expected_context()}`)}_assign_found_param(t){this._is_param_expected_type(t)?this._found_param_with_expected_type=t:this.states.error.set(`param type is ${t.type()} but the params expects a ${this._expected_param_type()}`)}found_node(){return this._found_node}found_param(){return this._found_param}found_node_with_context(t){return this._found_node_with_expected_type}found_node_with_context_and_type(t,e){const n=this.found_node_with_context(t);if(n)if(m.isArray(e)){for(let t of e)if(n.type()==t)return n;this.states.error.set(`expected node type to be ${e.join(\\\\\\\", \\\\\\\")}, but was instead ${n.type()}`)}else{const t=e;if(n.type()==t)return n;this.states.error.set(`expected node type to be ${t}, but was instead ${n.type()}`)}}found_param_with_type(t){if(this._found_param_with_expected_type)return this._found_param_with_expected_type}found_node_with_expected_type(){return this._found_node_with_expected_type}_expected_context(){return this.options.nodeSelectionContext()}_is_node_expected_context(t){var e,n;const i=this._expected_context();if(null==i)return!0;return i==(null===(n=null===(e=t.parent())||void 0===e?void 0:e.childrenController)||void 0===n?void 0:n.context)}_expected_node_types(){return this.options.nodeSelectionTypes()}_expected_param_type(){return this.options.paramSelectionType()}_is_node_expected_type(t){const e=this._expected_node_types();return null==e||(null==e?void 0:e.includes(t.type()))}_is_param_expected_type(t){const e=this._expected_node_types();return null==e||e.includes(t.type())}notify_path_rebuild_required(t){this.decomposed_path.update_from_name_change(t);const e=this.decomposed_path.to_path();this.set(e)}notify_target_param_owner_params_updated(t){this.setDirty()}}var _o,mo=n(33),fo=n(70);class go{constructor(t=0,e=0){this._position=t,this._value=e}toJSON(){return{position:this._position,value:this._value}}get position(){return this._position}get value(){return this._value}copy(t){this._position=t.position,this._value=t.value}clone(){const t=new go;return t.copy(this),t}is_equal(t){return this._position==t.position&&this._value==t.value}is_equal_json(t){return this._position==t.position&&this._value==t.value}from_json(t){this._position=t.position,this._value=t.value}static are_equal_json(t,e){return t.position==e.position&&t.value==e.value}static from_json(t){return new go(t.position,t.value)}}!function(t){t.LINEAR=\\\\\\\"linear\\\\\\\"}(_o||(_o={}));class vo{constructor(t=_o.LINEAR,e=[]){this._interpolation=t,this._points=e,this._uuid=Object(Ln.h)()}get uuid(){return this._uuid}get interpolation(){return this._interpolation}get points(){return this._points}static from_json(t){const e=[];for(let n of t.points)e.push(go.from_json(n));return new vo(t.interpolation,e)}toJSON(){return{interpolation:this._interpolation,points:this._points.map((t=>t.toJSON()))}}clone(){const t=new vo;return t.copy(this),t}copy(t){this._interpolation=t.interpolation;let e=0;for(let n of t.points){const t=this._points[e];t?t.copy(n):this._points.push(n.clone()),e+=1}}is_equal(t){if(this._interpolation!=t.interpolation)return!1;const e=t.points;if(this._points.length!=e.length)return!1;let n=0;for(let t of this._points){const i=e[n];if(!t.is_equal(i))return!1;n+=1}return!0}is_equal_json(t){if(this._interpolation!=t.interpolation)return!1;if(this._points.length!=t.points.length)return!1;let e=0;for(let n of this._points){const i=t.points[e];if(!n.is_equal_json(i))return!1;e+=1}return!0}static are_json_equal(t,e){if(t.interpolation!=e.interpolation)return!1;if(t.points.length!=e.points.length)return!1;let n=0;for(let i of t.points){const t=e.points[n];if(!go.are_equal_json(i,t))return!1;n+=1}return!0}from_json(t){this._interpolation=t.interpolation;let e=0;for(let n of t.points){const t=this._points[e];t?t.from_json(n):this._points.push(go.from_json(n)),e+=1}}}const yo=1024;class xo extends zs{constructor(){super(...arguments),this._texture_data=new Uint8Array(3072),this._ramp_texture=new mo.a(this._texture_data,yo,1,w.ic)}static type(){return Es.RAMP}defaultValueSerialized(){return this._default_value instanceof vo?this._default_value.toJSON():this._default_value}_clone_raw_input(t){return t instanceof vo?t.clone():vo.from_json(t).toJSON()}rawInputSerialized(){return this._raw_input instanceof vo?this._raw_input.toJSON():vo.from_json(this._raw_input).toJSON()}valueSerialized(){return this.value.toJSON()}_copy_value(t){this.set(t.valueSerialized())}static are_raw_input_equal(t,e){return t instanceof vo?e instanceof vo?t.is_equal(e):t.is_equal_json(e):e instanceof vo?e.is_equal_json(t):vo.are_json_equal(t,e)}static are_values_equal(t,e){return t.is_equal(e)}isDefault(){return this._default_value instanceof vo?this.value.is_equal(this._default_value):this.value.is_equal_json(this._default_value)}processRawInput(){this._raw_input instanceof vo?this._value?this._value.copy(this._raw_input):this._value=this._raw_input:this._value?this._value.from_json(this._raw_input):this._value=vo.from_json(this._raw_input),this._reset_ramp_interpolant(),this._update_rampTexture(),this.options.executeCallback(),this.emitController.emit(Ms.VALUE_UPDATED),this.setSuccessorsDirty(this)}hasExpression(){return!1}_reset_ramp_interpolant(){this._ramp_interpolant=void 0}rampTexture(){return this._ramp_texture}_update_rampTexture(){this._update_ramp_texture_data(),this.rampTexture().needsUpdate=!0}_update_ramp_texture_data(){let t=0,e=0,n=0;for(var i=0;i<1024;i++)t=3*i,e=i/yo,n=this.value_at_position(e),this._texture_data[t]=255*n}static create_interpolant(t,e){const n=new Float32Array(1);return new fo.a(t,e,1,n)}interpolant(){return this._ramp_interpolant=this._ramp_interpolant||this._create_interpolant()}_create_interpolant(){const t=this.value.points,e=f.sortBy(t,(t=>t.position)),n=new Float32Array(e.length),i=new Float32Array(e.length);let r=0;for(let t of e)n[r]=t.position,i[r]=t.value,r++;return xo.create_interpolant(n,i)}value_at_position(t){return this.interpolant().evaluate(t)[0]}}xo.DEFAULT_VALUE=new vo(_o.LINEAR,[new go(0,0),new go(1,1)]),xo.DEFAULT_VALUE_JSON=xo.DEFAULT_VALUE.toJSON();class bo extends zs{static type(){return Es.STRING}defaultValueSerialized(){return this._default_value}_clone_raw_input(t){return`${t}`}rawInputSerialized(){return`${this._raw_input}`}valueSerialized(){return`${this.value}`}_copy_value(t){this.set(t.value)}static are_raw_input_equal(t,e){return t==e}static are_values_equal(t,e){return t==e}isDefault(){return this._raw_input==this._default_value}convert(t){return m.isString(t)?t:`${t}`}rawInput(){return this._raw_input}processRawInput(){this.states.error.clear(),this._value_elements(this._raw_input).length>=3?(this._expression_controller=this._expression_controller||new eo(this),this._raw_input!=this._expression_controller.expression()&&(this._expression_controller.set_expression(this._raw_input),this.setDirty(),this.emitController.emit(Ms.EXPRESSION_UPDATED))):this._raw_input!=this._value&&(this._value=this._raw_input,this.removeDirtyState(),this.setSuccessorsDirty(this),this.emitController.emit(Ms.VALUE_UPDATED),this.options.executeCallback(),this._expression_controller&&(this._expression_controller.set_expression(void 0,!1),this.emitController.emit(Ms.EXPRESSION_UPDATED)))}async processComputation(){var t;if((null===(t=this.expressionController)||void 0===t?void 0:t.active())&&!this.expressionController.requires_entities()){const t=await this.expressionController.compute_expression();if(this.expressionController.is_errored())this.states.error.set(`expression error: ${this.expressionController.error_message()}`);else{const e=this.convert(t);null!=e?(this._value=e,this.emitController.emit(Ms.VALUE_UPDATED),this.options.executeCallback()):this.states.error.set(`expression returns an invalid type (${t})`),this.removeDirtyState()}}}_value_elements(t){return Vs.string_value_elements(t)}}const wo=[\\\\\\\"x\\\\\\\",\\\\\\\"y\\\\\\\"];const To=[\\\\\\\"x\\\\\\\",\\\\\\\"y\\\\\\\",\\\\\\\"z\\\\\\\"];const Ao=[\\\\\\\"x\\\\\\\",\\\\\\\"y\\\\\\\",\\\\\\\"z\\\\\\\",\\\\\\\"w\\\\\\\"];const Eo={[Es.BOOLEAN]:class extends no{static type(){return Es.BOOLEAN}defaultValueSerialized(){return m.isString(this._default_value)?this._default_value:this.convert(this._default_value)||!1}rawInputSerialized(){return this._raw_input}valueSerialized(){return this.value}_copy_value(t){this.set(t.value)}static are_raw_input_equal(t,e){return t==e}static are_values_equal(t,e){return t==e}convert(t){if(m.isBoolean(t))return t;if(m.isNumber(t))return t>=1;if(m.isString(t)){if(sr.isBoolean(t))return sr.toBoolean(t);if(sr.isNumber(t)){return parseFloat(t)>=1}}return null}},[Es.BUTTON]:class extends zs{static type(){return Es.BUTTON}defaultValueSerialized(){return this._default_value}rawInputSerialized(){return this._raw_input}valueSerialized(){return this.value}_copy_value(t){}static are_raw_input_equal(t,e){return!0}static are_values_equal(t,e){return!0}async pressButton(){(this.node.isDirty()||this.node.cookController.isCooking())&&await this.node.compute(),this.options.executeCallback()}},[Es.COLOR]:class extends ro{constructor(){super(...arguments),this._value=new D.a,this._value_pre_conversion=new D.a,this._value_serialized_dirty=!1,this._value_serialized=[0,0,0],this._value_pre_conversion_serialized=[0,0,0],this._copied_value=[0,0,0]}static type(){return Es.COLOR}componentNames(){return ao}defaultValueSerialized(){return m.isArray(this._default_value)?this._default_value:this._default_value.toArray()}valueSerialized(){return this._update_value_serialized_if_required(),this._value_serialized}valuePreConversionSerialized(){return this._update_value_serialized_if_required(),this._value_pre_conversion_serialized}_copy_value(t){t.value.toArray(this._copied_value),this.set(this._copied_value)}_clone_raw_input(t){if(t instanceof D.a)return t.clone();{const e=[t[0],t[1],t[2]];return null==e[0]&&(e[0]=e[0]||0),null==e[1]&&(e[1]=e[1]||e[0]),null==e[2]&&(e[2]=e[2]||e[1]),e}}static are_raw_input_equal(t,e){return t instanceof D.a?e instanceof D.a?t.equals(e):t.r==e[0]&&t.g==e[1]&&t.b==e[2]:e instanceof D.a?t[0]==e.r&&t[1]==e.g&&t[2]==e.b:t[0]==e[0]&&t[1]==e[1]&&t[2]==e[2]}static are_values_equal(t,e){return t.equals(e)}initComponents(){super.initComponents(),this.r=this.components[0],this.g=this.components[1],this.b=this.components[2],this._value_serialized_dirty=!0}_update_value_serialized_if_required(){this._value_serialized_dirty&&(this._value_serialized[0]=this._value.r,this._value_serialized[1]=this._value.g,this._value_serialized[2]=this._value.b,this._value_pre_conversion_serialized[0]=this._value_pre_conversion.r,this._value_pre_conversion_serialized[1]=this._value_pre_conversion.g,this._value_pre_conversion_serialized[2]=this._value_pre_conversion.b)}valuePreConversion(){return this._value_pre_conversion}set_value_from_components(){this._value_pre_conversion.r=this.r.value,this._value_pre_conversion.g=this.g.value,this._value_pre_conversion.b=this.b.value,this._value.copy(this._value_pre_conversion);const t=this.options.colorConversion();if(null!=t&&t!=so.NONE){switch(t){case so.GAMMA_TO_LINEAR:return void this._value.convertGammaToLinear();case so.LINEAR_TO_GAMMA:return void this._value.convertLinearToGamma();case so.SRGB_TO_LINEAR:return void this._value.convertSRGBToLinear();case so.LINEAR_TO_SRGB:return void this._value.convertLinearToSRGB()}ar.unreachable(t)}this._value_serialized_dirty=!0}},[Es.FLOAT]:io,[Es.FOLDER]:class extends zs{static type(){return Es.FOLDER}defaultValueSerialized(){return this._default_value}rawInputSerialized(){return this._raw_input}valueSerialized(){return this.value}_copy_value(t){}static are_raw_input_equal(t,e){return!0}static are_values_equal(t,e){return!0}},[Es.INTEGER]:lo,[Es.OPERATOR_PATH]:po,[Es.PARAM_PATH]:class extends uo{static type(){return Es.PARAM_PATH}initialize_param(){this._value=new yi}defaultValueSerialized(){return this._default_value}rawInputSerialized(){return`${this._raw_input}`}valueSerialized(){return`${this.value}`}_copy_value(t){this.set(t.valueSerialized())}static are_raw_input_equal(t,e){return t==e}static are_values_equal(t,e){return t==e}isDefault(){return this._raw_input==this._default_value}setParam(t){this.set(t.path())}processRawInput(){this._value.path()!=this._raw_input&&(this._value.set_path(this._raw_input),this.find_target(),this.setDirty(),this.emitController.emit(Ms.VALUE_UPDATED))}async processComputation(){this.find_target()}find_target(){if(!this.node)return;const t=this._raw_input;let e=null;const n=null!=t&&\\\\\\\"\\\\\\\"!==t;this.scene().referencesController.reset_reference_from_param(this),this.decomposed_path.reset(),n&&(e=xi.findParam(this.node,t,this.decomposed_path));const i=this._value.param(),r=e;if(this.scene().referencesController.set_named_nodes_from_param(this),e&&this.scene().referencesController.set_reference_from_param(this,e),(null==i?void 0:i.graphNodeId())!==(null==r?void 0:r.graphNodeId())){const t=this.options.dependentOnFoundNode(),n=this._value.param();n&&t&&this.removeGraphInput(n),e?this._assign_found_node(e):this._value.set_param(null),this.options.executeCallback()}this.removeDirtyState()}_assign_found_node(t){const e=this.options.dependentOnFoundNode();this._value.set_param(t),e&&this.addGraphInput(t)}notify_path_rebuild_required(t){this.decomposed_path.update_from_name_change(t);const e=this.decomposed_path.to_path();this.set(e)}notify_target_param_owner_params_updated(t){this.setDirty()}},[Es.NODE_PATH]:class extends uo{static type(){return Es.NODE_PATH}initialize_param(){this._value=new vi}defaultValueSerialized(){return this._default_value}rawInputSerialized(){return`${this._raw_input}`}valueSerialized(){return`${this.value}`}_copy_value(t){this.set(t.valueSerialized())}static are_raw_input_equal(t,e){return t==e}static are_values_equal(t,e){return t==e}isDefault(){return this._raw_input==this._default_value}setNode(t){this.set(t.path())}processRawInput(){this._value.path()!=this._raw_input&&(this._value.set_path(this._raw_input),this._findTarget(),this.setDirty(),this.emitController.emit(Ms.VALUE_UPDATED))}async processComputation(){this._findTarget()}_findTarget(){if(!this.node)return;const t=this._raw_input;let e=null;const n=null!=t&&\\\\\\\"\\\\\\\"!==t;this.scene().referencesController.reset_reference_from_param(this),this.decomposed_path.reset(),n&&(e=xi.findNode(this.node,t,this.decomposed_path));const i=this._value.node(),r=e;if(this.scene().referencesController.set_named_nodes_from_param(this),e&&this.scene().referencesController.set_reference_from_param(this,e),(null==i?void 0:i.graphNodeId())!==(null==r?void 0:r.graphNodeId())){const t=this.options.dependentOnFoundNode(),n=this._value.node();n&&t&&this.removeGraphInput(n),e?this._assign_found_node(e):this._value.set_node(null),this.options.executeCallback()}n&&!e&&this.scene().loadingController.loaded()&&n&&this.states.error.set(`no node found at path '${t}'`),this.removeDirtyState()}_assign_found_node(t){const e=this.options.dependentOnFoundNode();this._isNodeExpectedContext(t)?this._is_node_expected_type(t)?(this.states.error.clear(),this._value.set_node(t),e&&this.addGraphInput(t)):this.states.error.set(`node type is ${t.type()} but the params expects one of ${(this._expected_node_types()||[]).join(\\\\\\\", \\\\\\\")}`):this.states.error.set(`node context is ${t.context()} but the params expects a ${this._expectedContext()}`)}_expectedContext(){return this.options.nodeSelectionContext()}_isNodeExpectedContext(t){var e,n;const i=this._expectedContext();if(null==i)return!0;return i==(null===(n=null===(e=t.parent())||void 0===e?void 0:e.childrenController)||void 0===n?void 0:n.context)}_expected_node_types(){return this.options.nodeSelectionTypes()}_is_node_expected_type(t){const e=this._expected_node_types();return null==e||(null==e?void 0:e.includes(t.type()))}notify_path_rebuild_required(t){this.decomposed_path.update_from_name_change(t);const e=this.decomposed_path.to_path();this.set(e)}notify_target_param_owner_params_updated(t){this.setDirty()}},[Es.RAMP]:xo,[Es.STRING]:bo,[Es.VECTOR2]:class extends ro{constructor(){super(...arguments),this._value=new d.a,this._copied_value=[0,0]}static type(){return Es.VECTOR2}componentNames(){return wo}defaultValueSerialized(){return m.isArray(this._default_value)?this._default_value:this._default_value.toArray()}valueSerialized(){return this.value.toArray()}_copy_value(t){t.value.toArray(this._copied_value),this.set(this._copied_value)}_clone_raw_input(t){if(t instanceof d.a)return t.clone();{const e=[t[0],t[1]];return null==e[0]&&(e[0]=e[0]||0),null==e[1]&&(e[1]=e[1]||e[0]),e}}static are_raw_input_equal(t,e){return t instanceof d.a?e instanceof d.a?t.equals(e):t.x==e[0]&&t.y==e[1]:e instanceof d.a?t[0]==e.x&&t[1]==e.y:t[0]==e[0]&&t[1]==e[1]}static are_values_equal(t,e){return t.equals(e)}initComponents(){super.initComponents(),this.x=this.components[0],this.y=this.components[1]}set_value_from_components(){this._value.x=this.x.value,this._value.y=this.y.value}},[Es.VECTOR3]:class extends ro{constructor(){super(...arguments),this._value=new p.a,this._copied_value=[0,0,0]}static type(){return Es.VECTOR3}componentNames(){return To}defaultValueSerialized(){return m.isArray(this._default_value)?this._default_value:this._default_value.toArray()}valueSerialized(){return this.value.toArray()}_copy_value(t){t.value.toArray(this._copied_value),this.set(this._copied_value)}_clone_raw_input(t){if(t instanceof p.a)return t.clone();{const e=[t[0],t[1],t[2]];return null==e[0]&&(e[0]=e[0]||0),null==e[1]&&(e[1]=e[1]||e[0]),null==e[2]&&(e[2]=e[2]||e[1]),e}}static are_raw_input_equal(t,e){return t instanceof p.a?e instanceof p.a?t.equals(e):t.x==e[0]&&t.y==e[1]&&t.z==e[2]:e instanceof p.a?t[0]==e.x&&t[1]==e.y&&t[2]==e.z:t[0]==e[0]&&t[1]==e[1]&&t[2]==e[2]}static are_values_equal(t,e){return t.equals(e)}initComponents(){super.initComponents(),this.x=this.components[0],this.y=this.components[1],this.z=this.components[2]}set_value_from_components(){this._value.x=this.x.value,this._value.y=this.y.value,this._value.z=this.z.value}},[Es.VECTOR4]:class extends ro{constructor(){super(...arguments),this._value=new _.a,this._copied_value=[0,0,0,0]}static type(){return Es.VECTOR4}componentNames(){return Ao}defaultValueSerialized(){return m.isArray(this._default_value)?this._default_value:this._default_value.toArray()}valueSerialized(){return this.value.toArray()}_copy_value(t){t.value.toArray(this._copied_value),this.set(this._copied_value)}_clone_raw_input(t){if(t instanceof _.a)return t.clone();{const e=[t[0],t[1],t[2],t[3]];return null==e[0]&&(e[0]=e[0]||0),null==e[1]&&(e[1]=e[1]||e[0]),null==e[2]&&(e[2]=e[2]||e[1]),null==e[3]&&(e[3]=e[3]||e[2]),e}}static are_raw_input_equal(t,e){return t instanceof _.a?e instanceof _.a?t.equals(e):t.x==e[0]&&t.y==e[1]&&t.z==e[2]&&t.w==e[3]:e instanceof _.a?t[0]==e.x&&t[1]==e.y&&t[2]==e.z&&t[3]==e.w:t[0]==e[0]&&t[1]==e[1]&&t[2]==e[2]&&t[3]==e[3]}static are_values_equal(t,e){return t.equals(e)}initComponents(){super.initComponents(),this.x=this.components[0],this.y=this.components[1],this.z=this.components[2],this.w=this.components[3]}set_value_from_components(){this._value.x=this.x.value,this._value.y=this.y.value,this._value.z=this.z.value,this._value.w=this.w.value}}};class Mo{dispose(){this._callback=void 0}params(){return this._params}callback(){return this._callback}init(t,e){if(this._params=t,e)this._callback=e;else{const t=this._params[0];switch(t.type()){case Es.STRING:return this._handle_string_param(t);case Es.OPERATOR_PATH:return this._handle_operator_path_param(t);case Es.NODE_PATH:return this._handle_node_path_param(t);case Es.PARAM_PATH:return this._handle_param_path_param(t);case Es.FLOAT:case Es.INTEGER:return this._handle_number_param(t)}}}_handle_string_param(t){this._callback=()=>t.value}_handle_operator_path_param(t){this._callback=()=>t.value}_handle_node_path_param(t){this._callback=()=>t.value.path()}_handle_param_path_param(t){this._callback=()=>t.value.path()}_handle_number_param(t){this._callback=()=>`${t.value}`}}class So{constructor(t){this.node=t,this._param_create_mode=!1,this._params_created=!1,this._params_by_name={},this._params_list=[],this._param_names=[],this._non_spare_params=[],this._spare_params=[],this._non_spare_param_names=[],this._spare_param_names=[],this._params_added_since_last_params_eval=!1}get label(){return this._label_controller=this._label_controller||new Mo}hasLabelController(){return null!=this._label_controller}dispose(){var t;this._params_node&&this._params_node.dispose();for(let t of this.all)t.dispose();this._post_create_params_hook_names=void 0,this._post_create_params_hooks=void 0,this._on_scene_load_hooks=void 0,this._on_scene_load_hook_names=void 0,null===(t=this._label_controller)||void 0===t||t.dispose()}initDependencyNode(){this._params_node||(this._params_node=new Ai(this.node.scene(),\\\\\\\"params\\\\\\\"),this.node.addGraphInput(this._params_node,!1))}init(){this.initDependencyNode(),this._param_create_mode=!0,this._initFromParamsConfig(),this.node.createParams(),this._postCreateParams()}_postCreateParams(){this._updateCaches(),this._initParamAccessors(),this._param_create_mode=!1,this._params_created=!0,this._runPostCreateParamsHooks()}postCreateSpareParams(){this._updateCaches(),this._initParamAccessors(),this.node.scene().referencesController.notify_params_updated(this.node),this.node.emit(Ei.PARAMS_UPDATED)}updateParams(t){let e=!1,n=!1;if(t.namesToDelete)for(let e of t.namesToDelete)this.has(e)&&(this._deleteParam(e),n=!0);if(t.toAdd)for(let n of t.toAdd){const t=this.addParam(n.type,n.name,n.init_value,n.options);t&&(null!=n.raw_input&&t.set(n.raw_input),e=!0)}(n||e)&&this.postCreateSpareParams()}_initFromParamsConfig(){const t=this.node.paramsConfig;let e=!1;if(t)for(let n of Object.keys(t)){const i=t[n];let r;this.node.params_init_value_overrides&&(r=this.node.params_init_value_overrides[n],e=!0),this.addParam(i.type,n,i.init_value,i.options,r)}e&&this.node.setDirty(),this.node.params_init_value_overrides=void 0}_initParamAccessors(){let t=Object.getOwnPropertyNames(this.node.pv);this._removeUnneededAccessors(t),t=Object.getOwnPropertyNames(this.node.pv);for(let e of this.all){const n=e.options.isSpare();(!t.includes(e.name())||n)&&(Object.defineProperty(this.node.pv,e.name(),{get:()=>e.value,configurable:n}),Object.defineProperty(this.node.p,e.name(),{get:()=>e,configurable:n}))}}_removeUnneededAccessors(t){const e=this._param_names,n=[];for(let i of t)e.includes(i)||n.push(i);for(let t of n)Object.defineProperty(this.node.pv,t,{get:()=>{},configurable:!0}),Object.defineProperty(this.node.p,t,{get:()=>{},configurable:!0})}get params_node(){return this._params_node}get all(){return this._params_list}get non_spare(){return this._non_spare_params}get spare(){return this._spare_params}get names(){return this._param_names}get non_spare_names(){return this._non_spare_param_names}get spare_names(){return this._spare_param_names}set_with_type(t,e,n){const i=this.param_with_type(t,n);i?i.set(e):ai.warn(`param ${t} not found with type ${n}`)}set_float(t,e){this.set_with_type(t,e,Es.FLOAT)}set_vector3(t,e){this.set_with_type(t,e,Es.VECTOR3)}has_param(t){return null!=this._params_by_name[t]}has(t){return this.has_param(t)}get(t){return this.param(t)}param_with_type(t,e){const n=this.param(t);if(n&&n.type()==e)return n}get_float(t){return this.param_with_type(t,Es.FLOAT)}get_operator_path(t){return this.param_with_type(t,Es.OPERATOR_PATH)}value(t){var e;return null===(e=this.param(t))||void 0===e?void 0:e.value}value_with_type(t,e){var n;return null===(n=this.param_with_type(t,e))||void 0===n?void 0:n.value}boolean(t){return this.value_with_type(t,Es.BOOLEAN)}float(t){return this.value_with_type(t,Es.FLOAT)}integer(t){return this.value_with_type(t,Es.INTEGER)}string(t){return this.value_with_type(t,Es.STRING)}vector2(t){return this.value_with_type(t,Es.VECTOR2)}vector3(t){return this.value_with_type(t,Es.VECTOR3)}color(t){return this.value_with_type(t,Es.COLOR)}param(t){const e=this._params_by_name[t];return null!=e?e:(ai.warn(`tried to access param '${t}' in node ${this.node.path()}, but existing params are: ${this.names} on node ${this.node.path()}`),null)}_deleteParam(t){const e=this._params_by_name[t];if(!e)throw new Error(`param '${t}' does not exist on node ${this.node.path()}`);if(this._params_node&&this._params_node.removeGraphInput(this._params_by_name[t]),e._setupNodeDependencies(null),delete this._params_by_name[t],e.isMultiple()&&e.components)for(let t of e.components){const e=t.name();delete this._params_by_name[e]}}addParam(t,e,n,i={},r){const s=i.spare||!1;!1!==this._param_create_mode||s||ai.warn(`node ${this.node.path()} (${this.node.type()}) param '${e}' cannot be created outside of create_params`),null==this.node.scene()&&ai.warn(`node ${this.node.path()} (${this.node.type()}) has no scene assigned`);const o=Eo[t];if(null!=o){const a=this._params_by_name[e];a&&(s?a.type()!=t&&this._deleteParam(a.name()):ai.warn(`a param named ${e} already exists`,this.node));const l=new o(this.node.scene(),this.node);if(l.options.set(i),l.setName(e),l.setInitValue(n),l.initComponents(),null==r)l.set(n);else if(l.options.isExpressionForEntities()&&l.set(n),null!=r.raw_input)l.set(r.raw_input);else if(null!=r.simple_data)l.set(r.simple_data);else if(null!=r.complex_data){const t=r.complex_data.raw_input;t?l.set(t):l.set(n);const e=r.complex_data.overriden_options;if(null!=e){const t=Object.keys(e);for(let n of t)l.options.setOption(n,e[n])}}if(l._setupNodeDependencies(this.node),this._params_by_name[l.name()]=l,l.isMultiple()&&l.components)for(let t of l.components)this._params_by_name[t.name()]=t;return this._params_added_since_last_params_eval=!0,l}}_updateCaches(){this._params_list=Object.values(this._params_by_name),this._param_names=Object.keys(this._params_by_name),this._non_spare_params=Object.values(this._params_by_name).filter((t=>!t.options.isSpare())),this._spare_params=Object.values(this._params_by_name).filter((t=>t.options.isSpare())),this._non_spare_param_names=Object.values(this._params_by_name).filter((t=>!t.options.isSpare())).map((t=>t.name())),this._spare_param_names=Object.values(this._params_by_name).filter((t=>t.options.isSpare())).map((t=>t.name()))}async _evalParam(t){t.isDirty()&&(await t.compute(),t.states.error.active()&&this.node.states.error.set(`param '${t.name()}' error: ${t.states.error.message()}`))}async evalParams(t){const e=[];for(let n of t)n.isDirty()&&e.push(this._evalParam(n));await Promise.all(e),this.node.states.error.active()&&this.node._setContainer(null)}paramsEvalRequired(){return null!=this._params_node&&(this._params_node.isDirty()||this._params_added_since_last_params_eval)}async evalAll(){var t;this.paramsEvalRequired()&&(await this.evalParams(this._params_list),null===(t=this._params_node)||void 0===t||t.removeDirtyState(),this._params_added_since_last_params_eval=!1)}onParamsCreated(t,e){if(this._params_created)e();else{if(this._post_create_params_hook_names&&this._post_create_params_hook_names.includes(t))return void ai.error(`hook name ${t} already exists`);this._post_create_params_hook_names=this._post_create_params_hook_names||[],this._post_create_params_hook_names.push(t),this._post_create_params_hooks=this._post_create_params_hooks||[],this._post_create_params_hooks.push(e)}}addOnSceneLoadHook(t,e){this._on_scene_load_hook_names=this._on_scene_load_hook_names||[],this._on_scene_load_hooks=this._on_scene_load_hooks||[],this._on_scene_load_hook_names.includes(t)?ai.warn(`hook with name ${t} already exists`,this.node):(this._on_scene_load_hook_names.push(t),this._on_scene_load_hooks.push(e))}_runPostCreateParamsHooks(){if(this._post_create_params_hooks)for(let t of this._post_create_params_hooks)t()}runOnSceneLoadHooks(){if(this._on_scene_load_hooks)for(let t of this._on_scene_load_hooks)t()}}class Co{constructor(){}}class No{constructor(t,e,n=0,i=0){if(this._node_src=t,this._node_dest=e,this._output_index=n,this._input_index=i,null==this._output_index)throw\\\\\\\"bad output index\\\\\\\";if(null==this._input_index)throw\\\\\\\"bad input index\\\\\\\";this._id=No._next_id++,this._node_src.io.connections&&this._node_dest.io.connections&&(this._node_src.io.connections.addOutputConnection(this),this._node_dest.io.connections.addInputConnection(this))}get id(){return this._id}get node_src(){return this._node_src}get node_dest(){return this._node_dest}get output_index(){return this._output_index}get input_index(){return this._input_index}src_connection_point(){const t=this._node_src,e=this._output_index;return t.io.outputs.namedOutputConnectionPoints()[e]}dest_connection_point(){const t=this._node_dest,e=this._input_index;return t.io.inputs.namedInputConnectionPoints()[e]}disconnect(t={}){this._node_src.io.connections&&this._node_dest.io.connections&&(this._node_src.io.connections.removeOutputConnection(this),this._node_dest.io.connections.removeInputConnection(this)),!0===t.setInput&&this._node_dest.io.inputs.setInput(this._input_index,null)}}No._next_id=0;class Lo{constructor(t){this.inputs_controller=t,this._clone_required_states=[],this._overridden=!1,this.node=t.node}initInputsClonedState(t){m.isArray(t)?this._cloned_states=t:this._cloned_state=t,this._update_clone_required_state()}overrideClonedStateAllowed(){if(this._cloned_states)for(let t of this._cloned_states)if(t==Qi.FROM_NODE)return!0;return!!this._cloned_state&&this._cloned_state==Qi.FROM_NODE}cloneRequiredState(t){return this._clone_required_states[t]}cloneRequiredStates(){return this._clone_required_states}_get_clone_required_state(t){const e=this._cloned_states;if(e){const n=e[t];if(null!=n)return this.clone_required_from_state(n)}return!this._cloned_state||this.clone_required_from_state(this._cloned_state)}clone_required_from_state(t){switch(t){case Qi.ALWAYS:return!0;case Qi.NEVER:return!1;case Qi.FROM_NODE:return!this._overridden}return ar.unreachable(t)}overrideClonedState(t){this._overridden=t,this._update_clone_required_state(),this.node.emit(Ei.OVERRIDE_CLONABLE_STATE_UPDATE),this.node.setDirty()}overriden(){return this._overridden}_update_clone_required_state(){if(this._cloned_states){const t=[];for(let e=0;e<this._cloned_states.length;e++)t[e]=this._get_clone_required_state(e);this._clone_required_states=t}else if(this._cloned_state){const t=this.inputs_controller.maxInputsCount(),e=[];for(let n=0;n<t;n++)e[n]=this._get_clone_required_state(n);this._clone_required_states=e}else;}}class Oo{constructor(t){this.node=t,this._graph_node_inputs=[],this._inputs=[],this._has_named_inputs=!1,this._min_inputs_count=0,this._max_inputs_count=0,this._maxInputsCountOnInput=0,this._depends_on_inputs=!0}dispose(){this._graph_node&&this._graph_node.dispose();for(let t of this._graph_node_inputs)t&&t.dispose();this._on_update_hooks=void 0,this._on_update_hook_names=void 0}set_depends_on_inputs(t){this._depends_on_inputs=t}set_min_inputs_count(t){this._min_inputs_count=t}set_max_inputs_count(t){0==this._max_inputs_count&&(this._maxInputsCountOnInput=t),this._max_inputs_count=t,this.init_graph_node_inputs()}namedInputConnectionPointsByName(t){if(this._named_input_connection_points)for(let e of this._named_input_connection_points)if(e&&e.name()==t)return e}setNamedInputConnectionPoints(t){this._has_named_inputs=!0;const e=this.node.io.connections.inputConnections();if(e)for(let n of e)n&&n.input_index>=t.length&&n.disconnect({setInput:!0});this._named_input_connection_points=t,this.set_min_inputs_count(0),this.set_max_inputs_count(t.length),this.init_graph_node_inputs(),this.node.emit(Ei.NAMED_INPUTS_UPDATED)}hasNamedInputs(){return this._has_named_inputs}namedInputConnectionPoints(){return this._named_input_connection_points||[]}init_graph_node_inputs(){for(let t=0;t<this._max_inputs_count;t++)this._graph_node_inputs[t]=this._graph_node_inputs[t]||this._create_graph_node_input(t)}_create_graph_node_input(t){const e=new Ai(this.node.scene(),`input_${t}`);return this._graph_node||(this._graph_node=new Ai(this.node.scene(),\\\\\\\"inputs\\\\\\\"),this.node.addGraphInput(this._graph_node,!1)),this._graph_node.addGraphInput(e,!1),e}maxInputsCount(){return this._max_inputs_count||0}maxInputsCountOverriden(){return this._max_inputs_count!=this._maxInputsCountOnInput}input_graph_node(t){return this._graph_node_inputs[t]}setCount(t,e){null==e&&(e=t),this.set_min_inputs_count(t),this.set_max_inputs_count(e),this.init_connections_controller_inputs()}init_connections_controller_inputs(){this.node.io.connections.initInputs()}is_any_input_dirty(){var t;return(null===(t=this._graph_node)||void 0===t?void 0:t.isDirty())||!1}async containers_without_evaluation(){const t=[];for(let e=0;e<this._inputs.length;e++){const n=this._inputs[e];let i;n&&(i=await n.compute()),t.push(i)}return t}existing_input_indices(){const t=[];if(this._max_inputs_count>0)for(let e=0;e<this._inputs.length;e++)this._inputs[e]&&t.push(e);return t}async eval_required_inputs(){var t;let e=[];if(this._max_inputs_count>0){const n=this.existing_input_indices();if(n.length<this._min_inputs_count)this.node.states.error.set(\\\\\\\"inputs are missing\\\\\\\");else if(n.length>0){const n=[];let i;for(let t=0;t<this._inputs.length;t++)i=this._inputs[t],i&&n.push(this.eval_required_input(t));e=await Promise.all(n),null===(t=this._graph_node)||void 0===t||t.removeDirtyState()}}return e}async eval_required_input(t){let e;const n=this.input(t);if(n&&(e=await n.compute(),this._graph_node_inputs[t].removeDirtyState()),e&&e.coreContent());else{const e=this.input(t);if(e){const n=e.states.error.message();n&&this.node.states.error.set(`input ${t} is invalid (error: ${n})`)}}return e}get_named_input_index(t){var e;if(this._named_input_connection_points)for(let n=0;n<this._named_input_connection_points.length;n++)if((null===(e=this._named_input_connection_points[n])||void 0===e?void 0:e.name())==t)return n;return-1}get_input_index(t){if(m.isString(t)){if(this.hasNamedInputs())return this.get_named_input_index(t);throw new Error(`node ${this.node.path()} has no named inputs`)}return t}setInput(t,e,n=0){const i=this.get_input_index(t)||0;if(i<0){const e=`invalid input (${t}) for node ${this.node.path()}`;throw console.warn(e),new Error(e)}let r=0;if(e&&e.io.outputs.hasNamedOutputs()&&(r=e.io.outputs.getOutputIndex(n),null==r||r<0)){const t=e.io.outputs.namedOutputConnectionPoints().map((t=>t.name()));return void console.warn(`node ${e.path()} does not have an output named ${n}. inputs are: ${t.join(\\\\\\\", \\\\\\\")}`)}const s=this._graph_node_inputs[i];if(null==s){const t=`graph_input_node not found at index ${i}`;throw console.warn(t),new Error(t)}if(e&&this.node.parent()!=e.parent())return;const o=this._inputs[i];let a,l=null;this.node.io.connections&&(a=this.node.io.connections.inputConnection(i)),a&&(l=a.output_index),e===o&&r==l||(null!=o&&this._depends_on_inputs&&s.removeGraphInput(o),null!=e?s.addGraphInput(e)?(this._depends_on_inputs||s.removeGraphInput(e),a&&a.disconnect({setInput:!1}),this._inputs[i]=e,new No(e,this.node,r,i)):console.warn(`cannot connect ${e.path()} to ${this.node.path()}`):(this._inputs[i]=null,a&&a.disconnect({setInput:!1})),this._run_on_set_input_hooks(),s.setSuccessorsDirty(),this.node.emit(Ei.INPUTS_UPDATED))}remove_input(t){const e=this.inputs();let n;for(let i=0;i<e.length;i++)n=e[i],null!=n&&null!=t&&n.graphNodeId()===t.graphNodeId()&&this.setInput(i,null)}input(t){return this._inputs[t]}named_input(t){if(this.hasNamedInputs()){const e=this.get_input_index(t);return this._inputs[e]}return null}named_input_connection_point(t){if(this.hasNamedInputs()&&this._named_input_connection_points){const e=this.get_input_index(t);return this._named_input_connection_points[e]}}has_named_input(t){return this.get_named_input_index(t)>=0}has_input(t){return null!=this._inputs[t]}inputs(){return this._inputs}initInputsClonedState(t){this._cloned_states_controller||(this._cloned_states_controller=new Lo(this),this._cloned_states_controller.initInputsClonedState(t))}overrideClonedStateAllowed(){var t;return(null===(t=this._cloned_states_controller)||void 0===t?void 0:t.overrideClonedStateAllowed())||!1}overrideClonedState(t){var e;null===(e=this._cloned_states_controller)||void 0===e||e.overrideClonedState(t)}clonedStateOverriden(){var t;return(null===(t=this._cloned_states_controller)||void 0===t?void 0:t.overriden())||!1}cloneRequired(t){var e;const n=null===(e=this._cloned_states_controller)||void 0===e?void 0:e.cloneRequiredState(t);return null==n||n}cloneRequiredStates(){var t;const e=null===(t=this._cloned_states_controller)||void 0===t?void 0:t.cloneRequiredStates();return null==e||e}add_on_set_input_hook(t,e){this._on_update_hooks=this._on_update_hooks||[],this._on_update_hook_names=this._on_update_hook_names||[],this._on_update_hook_names.includes(t)?console.warn(`hook with name ${t} already exists`,this.node):(this._on_update_hooks.push(e),this._on_update_hook_names.push(t))}_run_on_set_input_hooks(){if(this._on_update_hooks)for(let t of this._on_update_hooks)t()}}class Ro{constructor(t){this.node=t,this._has_outputs=!1,this._has_named_outputs=!1}setHasOneOutput(){this._has_outputs=!0}setHasNoOutput(){this._has_outputs=!1}hasOutputs(){return this._has_outputs}hasNamedOutputs(){return this._has_named_outputs}hasNamedOutput(t){return this.getNamedOutputIndex(t)>=0}namedOutputConnectionPoints(){return this._named_output_connection_points||[]}namedOutputConnection(t){if(this._named_output_connection_points)return this._named_output_connection_points[t]}getNamedOutputIndex(t){var e;if(this._named_output_connection_points)for(let n=0;n<this._named_output_connection_points.length;n++)if((null===(e=this._named_output_connection_points[n])||void 0===e?void 0:e.name())==t)return n;return-1}getOutputIndex(t){return null!=t?m.isString(t)?this.hasNamedOutputs()?this.getNamedOutputIndex(t):(console.warn(`node ${this.node.path()} has no named outputs`),-1):t:-1}namedOutputConnectionPointsByName(t){if(this._named_output_connection_points)for(let e of this._named_output_connection_points)if((null==e?void 0:e.name())==t)return e}setNamedOutputConnectionPoints(t,e=!0){this._has_named_outputs=!0;const n=this.node.io.connections.outputConnections();if(n)for(let e of n)e&&e.output_index>=t.length&&e.disconnect({setInput:!0});this._named_output_connection_points=t,e&&this.node.scene()&&this.node.setDirty(this.node),this.node.emit(Ei.NAMED_OUTPUTS_UPDATED)}used_output_names(){var t;const e=this.node.io.connections;if(e){let n=e.outputConnections().map((t=>t?t.output_index:null));n=f.uniq(n);const i=[];n.forEach((t=>{m.isNumber(t)&&i.push(t)}));const r=[];for(let e of i){const n=null===(t=this.namedOutputConnectionPoints()[e])||void 0===t?void 0:t.name();n&&r.push(n)}return r}return[]}}class Po{constructor(t){this._node=t,this._output_connections=new Map}initInputs(){const t=this._node.io.inputs.maxInputsCount();for(this._input_connections=this._input_connections||new Array(t);this._input_connections.length<t;)this._input_connections.push(void 0)}addInputConnection(t){this._input_connections?this._input_connections[t.input_index]=t:console.warn(\\\\\\\"input connections array not initialized\\\\\\\")}removeInputConnection(t){if(this._input_connections)if(t.input_index<this._input_connections.length){this._input_connections[t.input_index]=void 0;let e=!0;for(let n=t.input_index;n<this._input_connections.length;n++)this._input_connections[n]&&(e=!1);e&&(this._input_connections=this._input_connections.slice(0,t.input_index))}else console.warn(`attempt to remove an input connection at index ${t.input_index}`);else console.warn(\\\\\\\"input connections array not initialized\\\\\\\")}inputConnection(t){if(this._input_connections)return this._input_connections[t]}firstInputConnection(){return this._input_connections?f.compact(this._input_connections)[0]:null}inputConnections(){return this._input_connections}existingInputConnections(){const t=this._input_connections;if(t)for(;t.length>1&&void 0===t[t.length-1];)t.pop();return t}addOutputConnection(t){const e=t.output_index,n=t.id;let i=this._output_connections.get(e);i||(i=new Map,this._output_connections.set(e,i)),i.set(n,t)}removeOutputConnection(t){const e=t.output_index,n=t.id;let i=this._output_connections.get(e);i&&i.delete(n)}outputConnections(){let t=[];return this._output_connections.forEach(((e,n)=>{e.forEach(((e,n)=>{e&&t.push(e)}))})),t}}class Io{constructor(t){this._node=t}set_in(t){this._in=t}set_out(t){this._out=t}clear(){this._in=void 0,this._out=void 0}in(){return this._in}out(){return this._out}}class Fo{constructor(t,e,n){this._name=t,this._type=e,this._init_value=n}get init_value(){return this._init_value}name(){return this._name}type(){return this._type}are_types_matched(t,e){return!0}toJSON(){return this._json=this._json||this._create_json()}_create_json(){return{name:this._name,type:this._type}}}var Do;!function(t){t.BOOL=\\\\\\\"bool\\\\\\\",t.INT=\\\\\\\"int\\\\\\\",t.FLOAT=\\\\\\\"float\\\\\\\",t.VEC2=\\\\\\\"vec2\\\\\\\",t.VEC3=\\\\\\\"vec3\\\\\\\",t.VEC4=\\\\\\\"vec4\\\\\\\",t.SAMPLER_2D=\\\\\\\"sampler2D\\\\\\\",t.SSS_MODEL=\\\\\\\"SSSModel\\\\\\\"}(Do||(Do={}));const ko=[Do.BOOL,Do.INT,Do.FLOAT,Do.VEC2,Do.VEC3,Do.VEC4],Bo={[Do.BOOL]:Es.BOOLEAN,[Do.INT]:Es.INTEGER,[Do.FLOAT]:Es.FLOAT,[Do.VEC2]:Es.VECTOR2,[Do.VEC3]:Es.VECTOR3,[Do.VEC4]:Es.VECTOR4,[Do.SAMPLER_2D]:Es.RAMP,[Do.SSS_MODEL]:Es.STRING},zo={[Es.BOOLEAN]:Do.BOOL,[Es.COLOR]:Do.VEC3,[Es.INTEGER]:Do.INT,[Es.FLOAT]:Do.FLOAT,[Es.FOLDER]:void 0,[Es.VECTOR2]:Do.VEC2,[Es.VECTOR3]:Do.VEC3,[Es.VECTOR4]:Do.VEC4,[Es.BUTTON]:void 0,[Es.OPERATOR_PATH]:void 0,[Es.PARAM_PATH]:void 0,[Es.NODE_PATH]:void 0,[Es.RAMP]:void 0,[Es.STRING]:void 0},Uo={[Do.BOOL]:!1,[Do.INT]:0,[Do.FLOAT]:0,[Do.VEC2]:[0,0],[Do.VEC3]:[0,0,0],[Do.VEC4]:[0,0,0,0],[Do.SAMPLER_2D]:xo.DEFAULT_VALUE_JSON,[Do.SSS_MODEL]:\\\\\\\"SSSModel()\\\\\\\"},Go={[Do.BOOL]:1,[Do.INT]:1,[Do.FLOAT]:1,[Do.VEC2]:2,[Do.VEC3]:3,[Do.VEC4]:4,[Do.SAMPLER_2D]:1,[Do.SSS_MODEL]:1};class Vo extends Fo{constructor(t,e,n){super(t,e),this._name=t,this._type=e,this._init_value=n,this._init_value=this._init_value||Uo[this._type]}type(){return this._type}are_types_matched(t,e){return t==e}get param_type(){return Bo[this._type]}get init_value(){return this._init_value}toJSON(){return this._json=this._json||this._create_json()}_create_json(){return{name:this._name,type:this._type}}}var Ho;!function(t){t.BOOL=\\\\\\\"bool\\\\\\\",t.INT=\\\\\\\"int\\\\\\\",t.FLOAT=\\\\\\\"float\\\\\\\",t.VEC2=\\\\\\\"vec2\\\\\\\",t.VEC3=\\\\\\\"vec3\\\\\\\",t.VEC4=\\\\\\\"vec4\\\\\\\"}(Ho||(Ho={}));const jo=[Ho.BOOL,Ho.INT,Ho.FLOAT,Ho.VEC2,Ho.VEC3,Ho.VEC4],Wo={[Ho.BOOL]:Es.BOOLEAN,[Ho.INT]:Es.INTEGER,[Ho.FLOAT]:Es.FLOAT,[Ho.VEC2]:Es.VECTOR2,[Ho.VEC3]:Es.VECTOR3,[Ho.VEC4]:Es.VECTOR4},qo={[Es.BOOLEAN]:Ho.BOOL,[Es.COLOR]:Ho.VEC3,[Es.INTEGER]:Ho.INT,[Es.FLOAT]:Ho.FLOAT,[Es.FOLDER]:void 0,[Es.VECTOR2]:Ho.VEC2,[Es.VECTOR3]:Ho.VEC3,[Es.VECTOR4]:Ho.VEC4,[Es.BUTTON]:void 0,[Es.OPERATOR_PATH]:void 0,[Es.PARAM_PATH]:void 0,[Es.NODE_PATH]:void 0,[Es.RAMP]:void 0,[Es.STRING]:void 0},Xo={[Ho.BOOL]:!1,[Ho.INT]:0,[Ho.FLOAT]:0,[Ho.VEC2]:[0,0],[Ho.VEC3]:[0,0,0],[Ho.VEC4]:[0,0,0,0]};Ho.BOOL,Ho.INT,Ho.FLOAT,Ho.VEC2,Ho.VEC3,Ho.VEC4;class Yo extends Fo{constructor(t,e){super(t,e),this._name=t,this._type=e,this._init_value=Xo[this._type]}type(){return this._type}are_types_matched(t,e){return t==e}get param_type(){return Wo[this._type]}get init_value(){return this._init_value}toJSON(){return this._json=this._json||this._create_json()}_create_json(){return{name:this._name,type:this._type}}}var $o;!function(t){t.BASE=\\\\\\\"base\\\\\\\",t.DRAG=\\\\\\\"drag\\\\\\\",t.KEYBOARD=\\\\\\\"keyboard\\\\\\\",t.MOUSE=\\\\\\\"mouse\\\\\\\",t.POINTER=\\\\\\\"pointer\\\\\\\"}($o||($o={}));class Jo extends Fo{constructor(t,e,n){super(t,e),this._name=t,this._type=e,this._event_listener=n}type(){return this._type}get param_type(){return Es.FLOAT}are_types_matched(t,e){return e==$o.BASE||t==e}get event_listener(){return this._event_listener}toJSON(){return this._json=this._json||this._create_json()}_create_json(){return{name:this._name,type:this._type}}}const Zo={[Ki.ANIM]:void 0,[Ki.COP]:void 0,[Ki.EVENT]:$o.BASE,[Ki.GL]:Do.FLOAT,[Ki.JS]:Ho.FLOAT,[Ki.MANAGER]:void 0,[Ki.MAT]:void 0,[Ki.OBJ]:void 0,[Ki.POST]:void 0,[Ki.ROP]:void 0,[Ki.SOP]:void 0};function Qo(t,e,n){switch(t){case Ki.EVENT:return new Jo(e,n);case Ki.GL:return new Vo(e,n);case Ki.JS:return new Yo(e,n);default:return}}class Ko{constructor(t,e){this.node=t,this._context=e,this._raw_input_serialized_by_param_name=new Map,this._default_value_serialized_by_param_name=new Map,this._initialized=!1}initializeNode(){this._initialized?console.warn(\\\\\\\"already initialized\\\\\\\",this.node):(this._initialized=!0,this.node.params.onParamsCreated(\\\\\\\"create_inputs_from_params\\\\\\\",this.create_inputs_from_params.bind(this)))}initialized(){return this._initialized}create_inputs_from_params(){const t=function(t){switch(t){case Ki.EVENT:return;case Ki.GL:return zo;case Ki.JS:return qo;default:return}}(this._context);if(!t)return;const e=[];for(let n of this.node.params.names){let i=!0;if(this._inputless_param_names&&this._inputless_param_names.length>0&&this._inputless_param_names.includes(n)&&(i=!1),i&&this.node.params.has(n)){const i=this.node.params.get(n);if(i&&!i.parent_param){const n=t[i.type()];if(n){const t=Qo(this._context,i.name(),n);t&&e.push(t)}}}}this.node.io.inputs.setNamedInputConnectionPoints(e)}set_inputless_param_names(t){return this._inputless_param_names=t}createSpareParameters(){if(this.node.scene().loadingController.isLoading())return;const t=this.node.params.spare_names,e={};for(let n of t)if(this.node.params.has(n)){const t=this.node.params.get(n);t&&(this._raw_input_serialized_by_param_name.set(n,t.rawInputSerialized()),this._default_value_serialized_by_param_name.set(n,t.defaultValueSerialized()),e.namesToDelete=e.namesToDelete||[],e.namesToDelete.push(n))}for(let t of this.node.io.inputs.namedInputConnectionPoints())if(t){const n=t.name(),i=t.param_type;let r=t.init_value;const s=this._default_value_serialized_by_param_name.get(n);let o=this.node.paramDefaultValue(n);if(r=null!=o?o:null!=s?s:t.init_value,m.isArray(t.init_value))if(m.isNumber(r)){const e=new Array(t.init_value.length);e.fill(r),r=e}else m.isArray(r)&&r.length==t.init_value.length&&null!=s&&(r=t.init_value);null!=r&&(e.toAdd=e.toAdd||[],e.toAdd.push({name:n,type:i,init_value:b.clone(r),raw_input:b.clone(r),options:{spare:!0}}))}this.node.params.updateParams(e);for(let t of this.node.params.spare)if(!t.parent_param){const e=this._raw_input_serialized_by_param_name.get(t.name());e&&t.set(e)}}}class ta{constructor(t,e){this.node=t,this._context=e,this._create_spare_params_from_inputs=!0,this._functions_overridden=!1,this._input_name_function=t=>`in${t}`,this._output_name_function=t=>0==t?\\\\\\\"val\\\\\\\":`val${t}`,this._expected_input_types_function=()=>{const t=this.first_input_connection_type()||this.default_connection_type();return[t,t]},this._expected_output_types_function=()=>[this._expected_input_types_function()[0]],this._update_signature_if_required_bound=this.update_signature_if_required.bind(this),this._initialized=!1,this._spare_params_controller=new Ko(this.node,this._context)}default_connection_type(){return Zo[this._context]}create_connection_point(t,e){return Qo(this._context,t,e)}functions_overridden(){return this._functions_overridden}initialized(){return this._initialized}set_create_spare_params_from_inputs(t){this._create_spare_params_from_inputs=t}set_input_name_function(t){this._initialize_if_required(),this._input_name_function=t}set_output_name_function(t){this._initialize_if_required(),this._output_name_function=t}set_expected_input_types_function(t){this._initialize_if_required(),this._functions_overridden=!0,this._expected_input_types_function=t}set_expected_output_types_function(t){this._initialize_if_required(),this._functions_overridden=!0,this._expected_output_types_function=t}input_name(t){return this._wrapped_input_name_function(t)}output_name(t){return this._wrapped_output_name_function(t)}initializeNode(){this._initialized?console.warn(\\\\\\\"already initialized\\\\\\\",this.node):(this._initialized=!0,this.node.io.inputs.add_on_set_input_hook(\\\\\\\"_update_signature_if_required\\\\\\\",this._update_signature_if_required_bound),this.node.params.addOnSceneLoadHook(\\\\\\\"_update_signature_if_required\\\\\\\",this._update_signature_if_required_bound),this.node.params.onParamsCreated(\\\\\\\"_update_signature_if_required_bound\\\\\\\",this._update_signature_if_required_bound),this.node.addPostDirtyHook(\\\\\\\"_update_signature_if_required\\\\\\\",this._update_signature_if_required_bound),this._spare_params_controller.initialized()||this._spare_params_controller.initializeNode())}_initialize_if_required(){this._initialized||this.initializeNode()}get spare_params(){return this._spare_params_controller}update_signature_if_required(t){this.node.lifecycle.creation_completed&&this._connections_match_inputs()||(this.update_connection_types(),this.node.removeDirtyState(),this.node.scene().loadingController.isLoading()||this.make_successors_update_signatures())}make_successors_update_signatures(){const t=this.node.graphAllSuccessors();if(this.node.childrenAllowed()){const e=this.node.nodesByType(er.INPUT),n=this.node.nodesByType(er.OUTPUT);for(let n of e)t.push(n);for(let e of n)t.push(e)}for(let e of t){const t=e;t.io&&t.io.has_connection_points_controller&&t.io.connection_points.initialized()&&t.io.connection_points.update_signature_if_required(this.node)}}update_connection_types(){const t=this._wrapped_expected_input_types_function(),e=this._wrapped_expected_output_types_function(),n=[];for(let e=0;e<t.length;e++){const i=t[e],r=this.create_connection_point(this._wrapped_input_name_function(e),i);n.push(r)}const i=[];for(let t=0;t<e.length;t++){const n=e[t],r=this.create_connection_point(this._wrapped_output_name_function(t),n);i.push(r)}this.node.io.inputs.setNamedInputConnectionPoints(n),this.node.io.outputs.setNamedOutputConnectionPoints(i,!1),this._create_spare_params_from_inputs&&this._spare_params_controller.createSpareParameters()}_connections_match_inputs(){const t=this.node.io.inputs.namedInputConnectionPoints().map((t=>null==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;e<t.length;e++)if(t[e]!=n[e])return!1;for(let t=0;t<e.length;t++)if(e[t]!=i[t])return!1;return!0}_wrapped_expected_input_types_function(){if(this.node.scene().loadingController.isLoading()){const t=this.node.io.saved_connection_points_data.in();if(t)return t.map((t=>t.type))}return this._expected_input_types_function()}_wrapped_expected_output_types_function(){if(this.node.scene().loadingController.isLoading()){const t=this.node.io.saved_connection_points_data.out();if(t)return t.map((t=>t.type))}return this._expected_output_types_function()}_wrapped_input_name_function(t){if(this.node.scene().loadingController.isLoading()){const e=this.node.io.saved_connection_points_data.in();if(e)return e[t].name}return this._input_name_function(t)}_wrapped_output_name_function(t){if(this.node.scene().loadingController.isLoading()){const e=this.node.io.saved_connection_points_data.out();if(e)return e[t].name}return this._output_name_function(t)}first_input_connection_type(){return this.input_connection_type(0)}input_connection_type(t){const e=this.node.io.connections.inputConnections();if(e){const n=e[t];if(n)return n.src_connection_point().type()}}}class ea{constructor(t){this.node=t,this._connections=new Po(this.node)}get connections(){return this._connections}get inputs(){return this._inputs=this._inputs||new Oo(this.node)}has_inputs(){return null!=this._inputs}get outputs(){return this._outputs=this._outputs||new Ro(this.node)}has_outputs(){return null!=this._outputs}get connection_points(){return this._connection_points=this._connection_points||new ta(this.node,this.node.context())}get has_connection_points_controller(){return null!=this._connection_points}get saved_connection_points_data(){return this._saved_connection_points_data=this._saved_connection_points_data||new Io(this.node)}clear_saved_connection_points_data(){this._saved_connection_points_data&&(this._saved_connection_points_data.clear(),this._saved_connection_points_data=void 0)}}class na{constructor(){}}class ia extends Ai{constructor(t,e=\\\\\\\"BaseNode\\\\\\\",n){super(t,e),this.params_init_value_overrides=n,this.containerController=new xs(this),this.pv=new Co,this.p=new na,this._initialized=!1}copy_param_values(t){const e=this.params.non_spare;for(let n of e){const e=t.params.get(n.name());e&&n.copy_value(e)}}get parentController(){return this._parent_controller=this._parent_controller||new Wi(this)}static displayedInputNames(){return[]}get childrenControllerContext(){return this._children_controller_context}_create_children_controller(){if(this._children_controller_context)return new hr(this,this._children_controller_context)}get childrenController(){return this._children_controller=this._children_controller||this._create_children_controller()}childrenAllowed(){return null!=this._children_controller_context}get uiData(){return this._ui_data=this._ui_data||new Mi(this)}get states(){return this._states=this._states||new Hi(this)}get lifecycle(){return this._lifecycle=this._lifecycle||new dr(this)}get serializer(){return this._serializer=this._serializer||new As(this)}get cookController(){return this._cook_controller=this._cook_controller||new Ts(this)}get io(){return this._io=this._io||new ea(this)}get nameController(){return this._name_controller=this._name_controller||new ji(this)}setName(t){this.nameController.setName(t)}_set_core_name(t){this._name=t}get params(){return this._params_controller=this._params_controller||new So(this)}initialize_base_and_node(){var t;this._initialized?console.warn(\\\\\\\"node already initialized\\\\\\\"):(this._initialized=!0,null===(t=this.displayNodeController)||void 0===t||t.initializeNode(),this.initializeBaseNode(),this.initializeNode(),this.polyNodeController&&this.polyNodeController.initializeNode())}initializeBaseNode(){}initializeNode(){}static type(){throw\\\\\\\"type to be overriden\\\\\\\"}type(){return this.constructor.type()}static context(){throw console.error(\\\\\\\"node has no node_context\\\\\\\",this),\\\\\\\"context requires override\\\\\\\"}context(){return this.constructor.context()}static require_webgl2(){return!1}require_webgl2(){return this.constructor.require_webgl2()}setParent(t){this.parentController.setParent(t)}parent(){return this.parentController.parent()}root(){return this._scene.root()}path(t){return this.parentController.path(t)}createParams(){}addParam(t,e,n,i){var r;return null===(r=this._params_controller)||void 0===r?void 0:r.addParam(t,e,n,i)}paramDefaultValue(t){return null}cook(t){return null}onCookEnd(t,e){this.cookController.registerOnCookEnd(t,e)}async compute(){var t,e;return this.isDirty()||(null===(e=null===(t=this.flags)||void 0===t?void 0:t.bypass)||void 0===e?void 0:e.active())?await this.containerController.compute():this.containerController.container()}_setContainer(t,e=null){this.containerController.container().set_content(t),null!=t&&(t.name||(t.name=this.path()),t.node||(t.node=this)),this.cookController.endCook(e)}createNode(t,e){var n;return null===(n=this.childrenController)||void 0===n?void 0:n.createNode(t,e)}create_operation_container(t,e,n){var i;return null===(i=this.childrenController)||void 0===i?void 0:i.create_operation_container(t,e,n)}removeNode(t){var e;null===(e=this.childrenController)||void 0===e||e.removeNode(t)}dispose(){var t,e;super.dispose(),this.setParent(null),this.io.inputs.dispose(),this.lifecycle.dispose(),null===(t=this.displayNodeController)||void 0===t||t.dispose(),this.nameController.dispose(),null===(e=this.childrenController)||void 0===e||e.dispose(),this.params.dispose()}children(){var t;return(null===(t=this.childrenController)||void 0===t?void 0:t.children())||[]}node(t){var e;return(null===(e=this.parentController)||void 0===e?void 0:e.findNode(t))||null}nodeSibbling(t){var e;const n=this.parent();if(n){const i=null===(e=n.childrenController)||void 0===e?void 0:e.child_by_name(t);if(i)return i}return null}nodesByType(t){var e;return(null===(e=this.childrenController)||void 0===e?void 0:e.nodesByType(t))||[]}setInput(t,e,n=0){this.io.inputs.setInput(t,e,n)}emit(t,e=null){this.scene().dispatchController.dispatch(this,t,e)}toJSON(t=!1){return this.serializer.toJSON(t)}async requiredModules(){}usedAssembler(){}integrationData(){}}class ra extends ia{static context(){return Ki.MANAGER}}class sa{constructor(t,e,n){this.type=t,this.init_value=e,this.options=n}}class oa{static BUTTON(t,e){return new sa(Es.BUTTON,t,e)}static BOOLEAN(t,e){return new sa(Es.BOOLEAN,t,e)}static COLOR(t,e){return t instanceof D.a&&(t=t.toArray()),new sa(Es.COLOR,t,e)}static FLOAT(t,e){return new sa(Es.FLOAT,t,e)}static FOLDER(t=null,e){return new sa(Es.FOLDER,t,e)}static INTEGER(t,e){return new sa(Es.INTEGER,t,e)}static RAMP(t=xo.DEFAULT_VALUE,e){return new sa(Es.RAMP,t,e)}static STRING(t=\\\\\\\"\\\\\\\",e){return new sa(Es.STRING,t,e)}static VECTOR2(t,e){return t instanceof d.a&&(t=t.toArray()),new sa(Es.VECTOR2,t,e)}static VECTOR3(t,e){return t instanceof p.a&&(t=t.toArray()),new sa(Es.VECTOR3,t,e)}static VECTOR4(t,e){return t instanceof _.a&&(t=t.toArray()),new sa(Es.VECTOR4,t,e)}static OPERATOR_PATH(t,e){return new sa(Es.OPERATOR_PATH,t,e)}static NODE_PATH(t,e){return new sa(Es.NODE_PATH,t,e)}static PARAM_PATH(t,e){return new sa(Es.PARAM_PATH,t,e)}}class aa{}class la{constructor(t){this.scene=t}findObjectByMask(t){return this.findObjectByMaskInObject(t,this.scene.threejsScene())}findObjectByMaskInObject(t,e,n=\\\\\\\"\\\\\\\"){for(let i of e.children){const e=this._removeTrailingOrHeadingSlash(i.name),r=`${n=this._removeTrailingOrHeadingSlash(n)}/${e}`;if(sr.matchMask(r,t))return i;const s=this.findObjectByMaskInObject(t,i,r);if(s)return s}}objectsByMask(t){return this.objectsByMaskInObject(t,this.scene.threejsScene(),[],\\\\\\\"\\\\\\\")}objectsByMaskInObject(t,e,n=[],i=\\\\\\\"\\\\\\\"){for(let r of e.children){const e=this._removeTrailingOrHeadingSlash(r.name),s=`${i=this._removeTrailingOrHeadingSlash(i)}/${e}`;sr.matchMask(s,t)&&n.push(r),this.objectsByMaskInObject(t,r,n,s)}return n}_removeTrailingOrHeadingSlash(t){return\\\\\\\"/\\\\\\\"==t[0]&&(t=t.substr(1)),\\\\\\\"/\\\\\\\"==t[t.length-1]&&(t=t.substr(0,t.length-1)),t}}const ca={computeOnDirty:!1,callback:t=>{ha.update(t)}};function ua(t){return class extends t{constructor(){super(...arguments),this.autoUpdate=oa.BOOLEAN(1,ca)}}}ua(aa);class ha{constructor(t){this.node=t}async update(){const t=this.node.object,e=this.node.pv;e.autoUpdate!=t.autoUpdate&&(t.autoUpdate=e.autoUpdate)}static async update(t){t.sceneAutoUpdateController.update()}}var da;!function(t){t.NONE=\\\\\\\"none\\\\\\\",t.COLOR=\\\\\\\"color\\\\\\\",t.TEXTURE=\\\\\\\"texture\\\\\\\"}(da||(da={}));const pa=[da.NONE,da.COLOR,da.TEXTURE],_a={computeOnDirty:!1,callback:t=>{fa.update(t)}};function ma(t){return class extends t{constructor(){super(...arguments),this.backgroundMode=oa.INTEGER(pa.indexOf(da.NONE),{menu:{entries:pa.map(((t,e)=>({name:t,value:e})))},..._a}),this.bgColor=oa.COLOR([0,0,0],{visibleIf:{backgroundMode:pa.indexOf(da.COLOR)},..._a}),this.bgTexture=oa.NODE_PATH(\\\\\\\"\\\\\\\",{visibleIf:{backgroundMode:pa.indexOf(da.TEXTURE)},nodeSelection:{context:Ki.COP},dependentOnFoundNode:!1,..._a})}}}ma(aa);class fa{constructor(t){this.node=t}update(){const t=this.node.object,e=this.node.pv;if(e.backgroundMode==pa.indexOf(da.NONE))t.background=null;else if(e.backgroundMode==pa.indexOf(da.COLOR))t.background=e.bgColor;else{const n=e.bgTexture.nodeWithContext(Ki.COP);n?n.compute().then((e=>{t.background=e.texture()})):this.node.states.error.set(\\\\\\\"bgTexture node not found\\\\\\\")}}static update(t){t.sceneBackgroundController.update()}}const ga={computeOnDirty:!1,callback:t=>{ya.update(t)}};function va(t){return class extends t{constructor(){super(...arguments),this.useEnvironment=oa.BOOLEAN(0,ga),this.environment=oa.NODE_PATH(\\\\\\\"\\\\\\\",{visibleIf:{useEnvironment:1},nodeSelection:{context:Ki.COP},dependentOnFoundNode:!1,...ga})}}}va(aa);class ya{constructor(t){this.node=t}async update(){const t=this.node.object,e=this.node.pv;if(e.useEnvironment){const n=e.environment.nodeWithContext(Ki.COP);n?n.compute().then((e=>{t.environment=e.texture()})):this.node.states.error.set(\\\\\\\"bgTexture node not found\\\\\\\")}else t.environment=null}static async update(t){t.sceneEnvController.update()}}class xa{constructor(t,e=1,n=1e3){this.name=\\\\\\\"\\\\\\\",this.color=new D.a(t),this.near=e,this.far=n}clone(){return new xa(this.color,this.near,this.far)}toJSON(){return{type:\\\\\\\"Fog\\\\\\\",color:this.color.getHex(),near:this.near,far:this.far}}}xa.prototype.isFog=!0;class ba{constructor(t,e=25e-5){this.name=\\\\\\\"\\\\\\\",this.color=new D.a(t),this.density=e}clone(){return new ba(this.color,this.density)}toJSON(){return{type:\\\\\\\"FogExp2\\\\\\\",color:this.color.getHex(),density:this.density}}}ba.prototype.isFogExp2=!0;const wa={computeOnDirty:!1,callback:t=>{Ma.update(t)}};var Ta;!function(t){t.LINEAR=\\\\\\\"linear\\\\\\\",t.EXPONENTIAL=\\\\\\\"exponential\\\\\\\"}(Ta||(Ta={}));const Aa=[Ta.LINEAR,Ta.EXPONENTIAL];function Ea(t){return class extends t{constructor(){super(...arguments),this.useFog=oa.BOOLEAN(0,wa),this.fogType=oa.INTEGER(Aa.indexOf(Ta.EXPONENTIAL),{visibleIf:{useFog:1},menu:{entries:Aa.map(((t,e)=>({name:t,value:e})))},...wa}),this.fogColor=oa.COLOR([1,1,1],{visibleIf:{useFog:1},...wa}),this.fogNear=oa.FLOAT(1,{range:[0,100],rangeLocked:[!0,!1],visibleIf:{useFog:1,fogType:Aa.indexOf(Ta.LINEAR)},...wa}),this.fogFar=oa.FLOAT(100,{range:[0,100],rangeLocked:[!0,!1],visibleIf:{useFog:1,fogType:Aa.indexOf(Ta.LINEAR)},...wa}),this.fogDensity=oa.FLOAT(25e-5,{visibleIf:{useFog:1,fogType:Aa.indexOf(Ta.EXPONENTIAL)},...wa})}}}Ea(aa);class Ma{constructor(t){this.node=t}async update(){const t=this.node.object,e=this.node.pv;if(e.useFog)if(e.fogType==Aa.indexOf(Ta.LINEAR)){const n=this.fog2(e);t.fog=n,n.color=e.fogColor,n.near=e.fogNear,n.far=e.fogFar}else{const n=this.fogExp2(e);t.fog=this.fogExp2(e),n.color=e.fogColor,n.density=e.fogDensity}else{t.fog&&(t.fog=null)}}fog2(t){return this._fog=this._fog||new xa(16777215,t.fogNear,t.fogFar)}fogExp2(t){return this._fogExp2=this._fogExp2||new ba(16777215,t.fogDensity)}static async update(t){t.sceneFogController.update()}}const Sa={computeOnDirty:!1,callback:t=>{Na.update(t)}};function Ca(t){return class extends t{constructor(){super(...arguments),this.useOverrideMaterial=oa.BOOLEAN(0,Sa),this.overrideMaterial=oa.NODE_PATH(\\\\\\\"\\\\\\\",{visibleIf:{useOverrideMaterial:1},nodeSelection:{context:Ki.MAT},dependentOnFoundNode:!1,...Sa})}}}Ca(aa);class Na{constructor(t){this.node=t}async update(){const t=this.node.object,e=this.node.pv;if(e.useOverrideMaterial){const n=e.overrideMaterial.nodeWithContext(Ki.MAT);n?n.compute().then((e=>{t.overrideMaterial=e.material()})):this.node.states.error.set(\\\\\\\"bgTexture node not found\\\\\\\")}else t.overrideMaterial=null}static async update(t){t.SceneMaterialOverrideController.update()}}class La extends(Ca(va(Ea(ma(ua(aa)))))){}const Oa=new La;class Ra extends ra{constructor(){super(...arguments),this.paramsConfig=Oa,this._object=this._createScene(),this._queued_nodes_by_id=new Map,this.sceneAutoUpdateController=new ha(this),this.sceneBackgroundController=new fa(this),this.sceneEnvController=new ya(this),this.sceneFogController=new Ma(this),this.sceneMaterialOverrideController=new Na(this),this._children_controller_context=Ki.OBJ}static type(){return\\\\\\\"obj\\\\\\\"}initializeNode(){this._object.matrixAutoUpdate=!1,this.lifecycle.add_on_child_add_hook(this._on_child_add.bind(this)),this.lifecycle.add_on_child_remove_hook(this._on_child_remove.bind(this))}_createScene(){const t=new fr;return t.name=\\\\\\\"/\\\\\\\",t.matrixAutoUpdate=!1,t}get object(){return this._object}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}_updateScene(){this.sceneAutoUpdateController.update(),this.sceneBackgroundController.update(),this.sceneEnvController.update(),this.sceneFogController.update(),this.sceneMaterialOverrideController.update()}_addToQueue(t){const e=t.graphNodeId();return this._queued_nodes_by_id.has(e)||this._queued_nodes_by_id.set(e,t),t}async processQueue(){this._updateScene();const t=new Map,e=[];this._queued_nodes_by_id.forEach(((n,i)=>{const r=`_____${n.renderOrder}__${n.path()}`;e.push(r),t.set(r,n)})),this._queued_nodes_by_id.clear();for(let n of e){const e=t.get(n);e&&(t.delete(n),this._addToScene(e))}}_update_object(t){return this.scene().loadingController.autoUpdating()?this._addToScene(t):this._addToQueue(t)}getParentForNode(t){if(t.attachableToHierarchy()){const e=t.io.inputs.input(0);return e?e.children_group:this._object}return null}_addToScene(t){var e;if(t.attachableToHierarchy()){const n=this.getParentForNode(t);n&&(t.usedInScene()?(null===(e=t.childrenDisplayController)||void 0===e||e.request_display_node_container(),t.addObjectToParent(n)):t.removeObjectFromParent())}}_removeFromScene(t){t.removeObjectFromParent()}areChildrenCooking(){const t=this.children();for(let e of t)if(e.cookController.isCooking()||e.isDisplayNodeCooking())return!0;return!1}addToParentTransform(t){this._update_object(t)}removeFromParentTransform(t){this._update_object(t)}_on_child_add(t){t&&this._update_object(t)}_on_child_remove(t){t&&this._removeFromScene(t)}}class Pa{constructor(t){this.scene=t,this._node_context_signatures={},this._instanciated_nodes_by_context_and_type={}}init(){this._root=new Ra(this.scene),this._root.initialize_base_and_node(),this._root.params.init(),this._root._set_core_name(\\\\\\\"RootNode\\\\\\\")}root(){return this._root}_traverseNode(t,e){const n=t.children();if(n&&0!=n.length)for(let t of n)e(t),t.childrenController&&this._traverseNode(t,e)}clear(){var t;const e=this.root().children();for(let n of e)null===(t=this.root().childrenController)||void 0===t||t.removeNode(n)}node(t){return\\\\\\\"/\\\\\\\"===t?this.root():this.root().node(t)}allNodes(){let t=[this.root()],e=[this.root()],n=0;for(;e.length>0&&n<10;){const i=e.map((t=>t.childrenAllowed()?t.children():[])).flat();t=t.concat(i),e=i,n+=1}return t.flat()}nodesFromMask(t){const e=this.allNodes(),n=[];for(let i of e){const e=i.path();sr.matchMask(e,t)&&n.push(i)}return n}reset_node_context_signatures(){this._node_context_signatures={}}register_node_context_signature(t){t.childrenAllowed()&&t.childrenController&&(this._node_context_signatures[t.childrenController.node_context_signature()]=!0)}node_context_signatures(){return Object.keys(this._node_context_signatures).sort().map((t=>t.toLowerCase()))}addToInstanciatedNode(t){const e=t.context(),n=t.type();this._instanciated_nodes_by_context_and_type[e]=this._instanciated_nodes_by_context_and_type[e]||{},this._instanciated_nodes_by_context_and_type[e][n]=this._instanciated_nodes_by_context_and_type[e][n]||{},this._instanciated_nodes_by_context_and_type[e][n][t.graphNodeId()]=t}removeFromInstanciatedNode(t){const e=t.context(),n=t.type();delete this._instanciated_nodes_by_context_and_type[e][n][t.graphNodeId()]}nodesByType(t){const e=[];return this._traverseNode(this.scene.root(),(n=>{n.type()==t&&e.push(n)})),e}nodesByContextAndType(t,e){const n=[],i=this._instanciated_nodes_by_context_and_type[t];if(i){const t=i[e];if(t)for(let e of Object.keys(t))n.push(t[e])}return n}}class Ia{constructor(t){this.scene=t}toJSON(t=!1){const e={},n={};for(let i of this.scene.nodesController.allNodes()){const r=new As(i);e[i.graphNodeId()]=r.toJSON(t);const s=i.params.all;for(let t of s)n[t.graphNodeId()]=t.toJSON()}return{nodes_by_graph_node_id:e,params_by_graph_node_id:n}}}var Fa;!function(t){t.auxclick=\\\\\\\"auxclick\\\\\\\",t.click=\\\\\\\"click\\\\\\\",t.contextmenu=\\\\\\\"contextmenu\\\\\\\",t.dblclick=\\\\\\\"dblclick\\\\\\\",t.mousedown=\\\\\\\"mousedown\\\\\\\",t.mouseenter=\\\\\\\"mouseenter\\\\\\\",t.mouseleave=\\\\\\\"mouseleave\\\\\\\",t.mousemove=\\\\\\\"mousemove\\\\\\\",t.mouseover=\\\\\\\"mouseover\\\\\\\",t.mouseout=\\\\\\\"mouseout\\\\\\\",t.mouseup=\\\\\\\"mouseup\\\\\\\",t.pointerlockchange=\\\\\\\"pointerlockchange\\\\\\\",t.pointerlockerror=\\\\\\\"pointerlockerror\\\\\\\",t.select=\\\\\\\"select\\\\\\\",t.wheel=\\\\\\\"wheel\\\\\\\"}(Fa||(Fa={}));const Da=[Fa.auxclick,Fa.click,Fa.contextmenu,Fa.dblclick,Fa.mousedown,Fa.mouseenter,Fa.mouseleave,Fa.mousemove,Fa.mouseover,Fa.mouseout,Fa.mouseup,Fa.pointerlockchange,Fa.pointerlockerror,Fa.select,Fa.wheel];class ka extends di{constructor(){super(...arguments),this._require_canvas_event_listeners=!0}type(){return\\\\\\\"mouse\\\\\\\"}acceptedEventTypes(){return Da.map((t=>`${t}`))}}class Ba extends ia{constructor(){super(...arguments),this._cook_without_inputs_bound=this._cook_without_inputs.bind(this)}static context(){return Ki.EVENT}initializeBaseNode(){this.uiData.setLayoutHorizontal(),this.addPostDirtyHook(\\\\\\\"cook_without_inputs_on_dirty\\\\\\\",this._cook_without_inputs_bound),this.io.inputs.set_depends_on_inputs(!1),this.io.connections.initInputs(),this.io.connection_points.spare_params.initializeNode()}_cook_without_inputs(){this.cookController.cookMainWithoutInputs()}cook(){this.cookController.endCook()}processEventViaConnectionPoint(t,e){e.event_listener?e.event_listener(t):this.processEvent(t)}processEvent(t){}async dispatchEventToOutput(t,e){this.run_on_dispatch_hook(t,e);const n=this.io.outputs.getOutputIndex(t);if(n>=0){const t=this.io.connections.outputConnections().filter((t=>t.output_index==n));let i;for(let n of t){i=n.node_dest;const t=i.io.inputs.namedInputConnectionPoints()[n.input_index];i.processEventViaConnectionPoint(e,t)}}else console.warn(`requested output '${t}' does not exist on node '${this.path()}'`)}onDispatch(t,e){this._on_dispatch_hooks_by_output_name=this._on_dispatch_hooks_by_output_name||new Map,u.pushOnArrayAtEntry(this._on_dispatch_hooks_by_output_name,t,e)}run_on_dispatch_hook(t,e){if(this._on_dispatch_hooks_by_output_name){const n=this._on_dispatch_hooks_by_output_name.get(t);if(n)for(let t of n)t(e)}}}var za;!function(t){t.CANVAS=\\\\\\\"canvas\\\\\\\",t.DOCUMENT=\\\\\\\"document\\\\\\\"}(za||(za={}));const Ua=[za.CANVAS,za.DOCUMENT];class Ga{constructor(t){this.viewer=t,this._bound_listener_map_by_event_controller_type=new Map}updateEvents(t){const e=this.canvas();if(!e)return;const n=t.type();let i=this._bound_listener_map_by_event_controller_type.get(n);i||(i=new Map,this._bound_listener_map_by_event_controller_type.set(n,i)),i.forEach(((t,n)=>{this._eventOwner(t.data,e).removeEventListener(n,t.listener)})),i.clear();const r=e=>{this.processEvent(e,t)};for(let n of t.activeEventDatas()){this._eventOwner(n,e).addEventListener(n.type,r),i.set(n.type,{listener:r,data:n})}}_eventOwner(t,e){return\\\\\\\"resize\\\\\\\"==t.type?window:t.emitter==za.CANVAS?e:document}cameraNode(){return this.viewer.camerasController.cameraNode()}canvas(){return this.viewer.canvas()}init(){this.canvas&&this.viewer.scene().eventsDispatcher.traverseControllers((t=>{this.updateEvents(t)}))}registeredEventTypes(){const t=[];return this._bound_listener_map_by_event_controller_type.forEach((e=>{e.forEach(((e,n)=>{t.push(n)}))})),t}dispose(){const t=this.canvas();this._bound_listener_map_by_event_controller_type.forEach((e=>{t&&e.forEach(((e,n)=>{this._eventOwner(e.data,t).removeEventListener(n,e.listener)}))}))}processEvent(t,e){if(!this.canvas())return;const n={viewer:this.viewer,event:t,cameraNode:this.cameraNode()};e.processEvent(n)}}const Va={visibleIf:{active:1},callback:t=>{ja.PARAM_CALLBACK_updateRegister(t)}};class Ha extends Ba{constructor(){super(...arguments),this._activeEventDatas=[]}initializeBaseNode(){super.initializeBaseNode();this.lifecycle.add_on_add_hook((()=>{this.scene().eventsDispatcher.registerEventNode(this)})),this.lifecycle.add_delete_hook((()=>{this.scene().eventsDispatcher.unregisterEventNode(this)})),this.params.onParamsCreated(\\\\\\\"update_register\\\\\\\",(()=>{this._updateRegister()}))}processEvent(t){this.pv.active&&t.event&&this.dispatchEventToOutput(t.event.type,t)}static PARAM_CALLBACK_updateRegister(t){t._updateRegister()}_updateRegister(){this._updateActiveEventDatas(),this.scene().eventsDispatcher.updateViewerEventListeners(this)}_updateActiveEventDatas(){if(this._activeEventDatas=[],this.pv.active){const t=this.acceptedEventTypes();for(let e of t){const t=this.params.get(e);t&&t.value&&this._activeEventDatas.push({type:e,emitter:Ua[this.pv.element]})}}}activeEventDatas(){return this._activeEventDatas}}class ja extends Ha{acceptedEventTypes(){return[]}}const Wa=new class extends aa{constructor(){super(...arguments),this.active=oa.BOOLEAN(!0,{callback:t=>{qa.PARAM_CALLBACK_updateRegister(t)},separatorAfter:!0}),this.element=oa.INTEGER(Ua.indexOf(za.CANVAS),{menu:{entries:Ua.map(((t,e)=>({name:t,value:e})))},separatorAfter:!0}),this.auxclick=oa.BOOLEAN(0,Va),this.click=oa.BOOLEAN(0,Va),this.contextmenu=oa.BOOLEAN(0,Va),this.dblclick=oa.BOOLEAN(0,Va),this.mousedown=oa.BOOLEAN(1,Va),this.mouseenter=oa.BOOLEAN(0,Va),this.mouseleave=oa.BOOLEAN(0,Va),this.mousemove=oa.BOOLEAN(1,Va),this.mouseover=oa.BOOLEAN(0,Va),this.mouseout=oa.BOOLEAN(0,Va),this.mouseup=oa.BOOLEAN(1,Va),this.pointerlockchange=oa.BOOLEAN(0,Va),this.pointerlockerror=oa.BOOLEAN(0,Va),this.select=oa.BOOLEAN(0,Va),this.wheel=oa.BOOLEAN(0,Va),this.ctrlKey=oa.BOOLEAN(0,{...Va,separatorBefore:!0}),this.altKey=oa.BOOLEAN(0,Va),this.shiftKey=oa.BOOLEAN(0,Va),this.metaKey=oa.BOOLEAN(0,Va)}};class qa extends Ha{constructor(){super(...arguments),this.paramsConfig=Wa}static type(){return\\\\\\\"mouse\\\\\\\"}acceptedEventTypes(){return Da.map((t=>`${t}`))}initializeNode(){this.io.outputs.setNamedOutputConnectionPoints(Da.map((t=>new Jo(t,$o.MOUSE)))),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{const t=[this.p.auxclick,this.p.click,this.p.dblclick,this.p.mousedown,this.p.mouseenter,this.p.mouseleave,this.p.mousemove,this.p.mouseout,this.p.mouseout,this.p.mouseup,this.p.pointerlockchange,this.p.pointerlockerror,this.p.select,this.p.wheel];this.params.label.init(t,(()=>t.map((t=>t.value?t.name():void 0)).filter((t=>t)).join(\\\\\\\", \\\\\\\")))}))}))}processEvent(t){if(!this.pv.active)return;if(!t.event)return;const e=t.event;e.ctrlKey==this.pv.ctrlKey&&e.shiftKey==this.pv.shiftKey&&e.altKey==this.pv.altKey&&e.metaKey==this.pv.metaKey&&this.dispatchEventToOutput(t.event.type,t)}}var Xa;!function(t){t.pointerdown=\\\\\\\"pointerdown\\\\\\\",t.pointermove=\\\\\\\"pointermove\\\\\\\",t.pointerup=\\\\\\\"pointerup\\\\\\\"}(Xa||(Xa={}));const Ya=[Xa.pointerdown,Xa.pointermove,Xa.pointerup];class $a extends di{constructor(){super(...arguments),this._require_canvas_event_listeners=!0}type(){return\\\\\\\"pointer\\\\\\\"}acceptedEventTypes(){return Ya.map((t=>`${t}`))}}const Ja=new class extends aa{constructor(){super(...arguments),this.active=oa.BOOLEAN(!0,{callback:t=>{Za.PARAM_CALLBACK_updateRegister(t)},separatorAfter:!0}),this.element=oa.INTEGER(Ua.indexOf(za.CANVAS),{menu:{entries:Ua.map(((t,e)=>({name:t,value:e})))},separatorAfter:!0}),this.pointerdown=oa.BOOLEAN(1,Va),this.pointermove=oa.BOOLEAN(0,Va),this.pointerup=oa.BOOLEAN(0,Va),this.ctrlKey=oa.BOOLEAN(0,{...Va,separatorBefore:!0}),this.altKey=oa.BOOLEAN(0,Va),this.shiftKey=oa.BOOLEAN(0,Va),this.metaKey=oa.BOOLEAN(0,Va)}};class Za extends Ha{constructor(){super(...arguments),this.paramsConfig=Ja}static type(){return\\\\\\\"pointer\\\\\\\"}acceptedEventTypes(){return Ya.map((t=>`${t}`))}initializeNode(){this.io.outputs.setNamedOutputConnectionPoints(Ya.map((t=>new Jo(t,$o.POINTER)))),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{const t=[this.p.pointerdown,this.p.pointermove,this.p.pointerup];this.params.label.init(t,(()=>t.map((t=>t.value?t.name():void 0)).filter((t=>t)).join(\\\\\\\", \\\\\\\")))}))}))}processEvent(t){if(!this.pv.active)return;if(!t.event)return;const e=t.event;e.ctrlKey==this.pv.ctrlKey&&e.shiftKey==this.pv.shiftKey&&e.altKey==this.pv.altKey&&e.metaKey==this.pv.metaKey&&this.dispatchEventToOutput(t.event.type,t)}}var Qa,Ka;!function(t){t.SET_FRAME=\\\\\\\"setFrame\\\\\\\"}(Qa||(Qa={})),function(t){t.TIME_REACHED=\\\\\\\"timeReached\\\\\\\"}(Ka||(Ka={}));const tl=new class extends aa{constructor(){super(...arguments),this.active=oa.BOOLEAN(!0,{callback:(t,e)=>{el.PARAM_CALLBACK_updateRegister(t)},separatorAfter:!0}),this.element=oa.INTEGER(0,{hidden:!0}),this.sceneLoaded=oa.BOOLEAN(1,Va),this.play=oa.BOOLEAN(1,Va),this.pause=oa.BOOLEAN(1,Va),this.tick=oa.BOOLEAN(1,{separatorAfter:!0,...Va}),this.treachedTime=oa.BOOLEAN(0,{callback:t=>{el.PARAM_CALLBACK_update_time_dependency(t)}}),this.reachedTime=oa.INTEGER(10,{visibleIf:{treachedTime:1},range:[0,100],separatorAfter:!0}),this.setFrameValue=oa.INTEGER(1,{range:[0,100]}),this.setFrame=oa.BUTTON(null,{callback:t=>{el.PARAM_CALLBACK_setFrame(t)}})}};class el extends Ha{constructor(){super(...arguments),this.paramsConfig=tl}static type(){return\\\\\\\"scene\\\\\\\"}acceptedEventTypes(){return _i.map((t=>`${t}`))}dispose(){var t;null===(t=this.graph_node)||void 0===t||t.dispose(),super.dispose()}initializeNode(){this.io.inputs.setNamedInputConnectionPoints([new Jo(Qa.SET_FRAME,$o.BASE,this.onSetFrame.bind(this))]);const t=_i.map((t=>new Jo(t,$o.BASE)));t.push(new Jo(Ka.TIME_REACHED,$o.BASE)),this.io.outputs.setNamedOutputConnectionPoints(t),this.params.onParamsCreated(\\\\\\\"update_time_dependency\\\\\\\",(()=>{this.update_time_dependency()}))}onSetFrame(t){this.scene().setFrame(this.pv.setFrameValue)}on_frame_update(){this.scene().time()>=this.pv.reachedTime&&this.dispatchEventToOutput(Ka.TIME_REACHED,{})}update_time_dependency(){this.pv.treachedTime?(this.graph_node=this.graph_node||new Ai(this.scene(),\\\\\\\"scene_node_time_graph_node\\\\\\\"),this.graph_node.addGraphInput(this.scene().timeController.graphNode),this.graph_node.addPostDirtyHook(\\\\\\\"time_update\\\\\\\",this.on_frame_update.bind(this))):this.graph_node&&this.graph_node.graphDisconnectPredecessors()}static PARAM_CALLBACK_setFrame(t){t.onSetFrame({})}static PARAM_CALLBACK_update_time_dependency(t){t.update_time_dependency()}}var nl;!function(t){t.keydown=\\\\\\\"keydown\\\\\\\",t.keypress=\\\\\\\"keypress\\\\\\\",t.keyup=\\\\\\\"keyup\\\\\\\"}(nl||(nl={}));const il=[nl.keydown,nl.keypress,nl.keyup];class rl extends di{constructor(){super(...arguments),this._require_canvas_event_listeners=!0}type(){return\\\\\\\"keyboard\\\\\\\"}acceptedEventTypes(){return il.map((t=>`${t}`))}}const sl=new class extends aa{constructor(){super(...arguments),this.active=oa.BOOLEAN(!0,{callback:(t,e)=>{ol.PARAM_CALLBACK_updateRegister(t)},separatorAfter:!0}),this.element=oa.INTEGER(Ua.indexOf(za.CANVAS),{menu:{entries:Ua.map(((t,e)=>({name:t,value:e})))},separatorAfter:!0}),this.keydown=oa.BOOLEAN(1,Va),this.keypress=oa.BOOLEAN(0,Va),this.keyup=oa.BOOLEAN(0,Va),this.keyCodes=oa.STRING(\\\\\\\"Digit1 KeyE ArrowDown\\\\\\\",Va),this.ctrlKey=oa.BOOLEAN(0,Va),this.altKey=oa.BOOLEAN(0,Va),this.shiftKey=oa.BOOLEAN(0,Va),this.metaKey=oa.BOOLEAN(0,Va)}};class ol extends Ha{constructor(){super(...arguments),this.paramsConfig=sl}static type(){return\\\\\\\"keyboard\\\\\\\"}acceptedEventTypes(){return il.map((t=>`${t}`))}initializeNode(){this.io.outputs.setNamedOutputConnectionPoints(il.map((t=>new Jo(t,$o.KEYBOARD)))),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{const t=[this.p.keydown,this.p.keypress,this.p.keyup];this.params.label.init(t.concat([this.p.keyCodes]),(()=>`${t.map((t=>t.value?t.name():void 0)).filter((t=>t)).join(\\\\\\\", \\\\\\\")} (${this.pv.keyCodes})`))}))}))}processEvent(t){if(!this.pv.active)return;if(!t.event)return;const e=t.event;if(e.ctrlKey!=this.pv.ctrlKey)return;if(e.shiftKey!=this.pv.shiftKey)return;if(e.altKey!=this.pv.altKey)return;if(e.metaKey!=this.pv.metaKey)return;if(this.pv.keyCodes.trim().length>0){if(!this.pv.keyCodes.split(\\\\\\\" \\\\\\\").includes(e.code))return}this.dispatchEventToOutput(t.event.type,t)}}var al;!function(t){t.resize=\\\\\\\"resize\\\\\\\"}(al||(al={}));const ll=[al.resize];class cl extends di{constructor(){super(...arguments),this._require_canvas_event_listeners=!0}type(){return\\\\\\\"window\\\\\\\"}acceptedEventTypes(){return ll.map((t=>`${t}`))}}const ul=new class extends aa{constructor(){super(...arguments),this.active=oa.BOOLEAN(!0,{callback:t=>{hl.PARAM_CALLBACK_updateRegister(t)},separatorAfter:!0}),this.element=oa.INTEGER(0,{hidden:!0}),this.resize=oa.BOOLEAN(1,Va)}};class hl extends Ha{constructor(){super(...arguments),this.paramsConfig=ul}static type(){return\\\\\\\"window\\\\\\\"}acceptedEventTypes(){return ll.map((t=>`${t}`))}initializeNode(){this.io.outputs.setNamedOutputConnectionPoints(ll.map((t=>new Jo(t,$o.POINTER)))),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{const t=[this.p.resize];this.params.label.init(t,(()=>t.map((t=>t.value?t.name():void 0)).filter((t=>t)).join(\\\\\\\", \\\\\\\")))}))}))}processEvent(t){this.pv.active&&t.event&&this.dispatchEventToOutput(t.event.type,t)}}var dl;!function(t){t.dragover=\\\\\\\"dragover\\\\\\\"}(dl||(dl={}));const pl=[dl.dragover];class _l extends di{constructor(){super(...arguments),this._require_canvas_event_listeners=!0}type(){return\\\\\\\"drag\\\\\\\"}acceptedEventTypes(){return pl.map((t=>`${t}`))}}var ml;!function(t){t.touchstart=\\\\\\\"touchstart\\\\\\\",t.touchmove=\\\\\\\"touchmove\\\\\\\",t.touchend=\\\\\\\"touchend\\\\\\\"}(ml||(ml={}));const fl=[ml.touchstart,ml.touchmove,ml.touchend];class gl extends di{constructor(){super(...arguments),this._require_canvas_event_listeners=!0}type(){return\\\\\\\"touch\\\\\\\"}acceptedEventTypes(){return fl.map((t=>`${t}`))}}const vl=new class extends aa{constructor(){super(...arguments),this.active=oa.BOOLEAN(!0,{callback:t=>{yl.PARAM_CALLBACK_updateRegister(t)},separatorAfter:!0}),this.element=oa.INTEGER(Ua.indexOf(za.CANVAS),{menu:{entries:Ua.map(((t,e)=>({name:t,value:e})))},separatorAfter:!0}),this.dragover=oa.BOOLEAN(1,Va),this.ctrlKey=oa.BOOLEAN(0,{...Va,separatorBefore:!0}),this.altKey=oa.BOOLEAN(0,Va),this.shiftKey=oa.BOOLEAN(0,Va),this.metaKey=oa.BOOLEAN(0,Va)}};class yl extends Ha{constructor(){super(...arguments),this.paramsConfig=vl}static type(){return\\\\\\\"drag\\\\\\\"}acceptedEventTypes(){return pl.map((t=>`${t}`))}initializeNode(){this.io.outputs.setNamedOutputConnectionPoints(pl.map((t=>new Jo(t,$o.DRAG)))),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{const t=[this.p.dragover];this.params.label.init(t,(()=>t.map((t=>t.value?t.name():void 0)).filter((t=>t)).join(\\\\\\\", \\\\\\\")))}))}))}processEvent(t){if(!this.pv.active)return;if(!t.event)return;const e=t.event;e.ctrlKey==this.pv.ctrlKey&&e.shiftKey==this.pv.shiftKey&&e.altKey==this.pv.altKey&&e.metaKey==this.pv.metaKey&&this.dispatchEventToOutput(t.event.type,t)}}const xl=new class extends aa{constructor(){super(...arguments),this.active=oa.BOOLEAN(!0,{callback:t=>{bl.PARAM_CALLBACK_updateRegister(t)},separatorAfter:!0}),this.element=oa.INTEGER(Ua.indexOf(za.CANVAS),{menu:{entries:Ua.map(((t,e)=>({name:t,value:e})))},separatorAfter:!0}),this.touchstart=oa.BOOLEAN(1,Va),this.touchmove=oa.BOOLEAN(0,Va),this.touchend=oa.BOOLEAN(0,Va)}};class bl extends Ha{constructor(){super(...arguments),this.paramsConfig=xl}static type(){return\\\\\\\"touch\\\\\\\"}acceptedEventTypes(){return fl.map((t=>`${t}`))}initializeNode(){this.io.outputs.setNamedOutputConnectionPoints(fl.map((t=>new Jo(t,$o.DRAG)))),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{const t=[this.p.touchstart,this.p.touchmove,this.p.touchend];this.params.label.init(t,(()=>t.map((t=>t.value?t.name():void 0)).filter((t=>t)).join(\\\\\\\", \\\\\\\")))}))}))}processEvent(t){this.pv.active&&t.event&&this.dispatchEventToOutput(t.event.type,t)}}class wl{constructor(t){this.scene=t,this._controllers=[]}registerEventNode(t){const e=this._find_or_create_controller_for_node(t);e&&e.registerNode(t)}unregisterEventNode(t){const e=this._find_or_create_controller_for_node(t);e&&e.unregisterNode(t)}updateViewerEventListeners(t){const e=this._find_or_create_controller_for_node(t);e&&e.updateViewerEventListeners()}traverseControllers(t){for(let e of this._controllers)t(e)}_find_or_create_controller_for_node(t){switch(t.type()){case ol.type():return this.keyboardEventsController;case qa.type():return this.mouseEventsController;case yl.type():return this.dragEventsController;case Za.type():return this.pointerEventsController;case el.type():return this.sceneEventsController;case bl.type():return this.touchEventsController;case hl.type():return this.windowEventsController}}get keyboardEventsController(){return this._keyboard_events_controller=this._keyboard_events_controller||this._create_controller(rl)}get mouseEventsController(){return this._mouse_events_controller=this._mouse_events_controller||this._create_controller(ka)}get dragEventsController(){return this._drag_events_controller=this._drag_events_controller||this._create_controller(_l)}get pointerEventsController(){return this._pointer_events_controller=this._pointer_events_controller||this._create_controller($a)}get sceneEventsController(){return this._scene_events_controller=this._scene_events_controller||this._create_controller(mi)}get windowEventsController(){return this._window_events_controller=this._window_events_controller||this._create_controller(cl)}get touchEventsController(){return this._touch_events_controller=this._touch_events_controller||this._create_controller(gl)}_create_controller(t){const e=new t(this);return this._controllers.includes(e)||this._controllers.push(e),e}}class Tl{constructor(t){this.scene=t,this._referenced_nodes_by_src_param_id=new Map,this._referencing_params_by_referenced_node_id=new Map,this._referencing_params_by_all_named_node_ids=new Map}set_reference_from_param(t,e){this._referenced_nodes_by_src_param_id.set(t.graphNodeId(),e),u.pushOnArrayAtEntry(this._referencing_params_by_referenced_node_id,e.graphNodeId(),t)}set_named_nodes_from_param(t){const e=t.decomposed_path.named_nodes();for(let n of e)u.pushOnArrayAtEntry(this._referencing_params_by_all_named_node_ids,n.graphNodeId(),t)}reset_reference_from_param(t){const e=this._referenced_nodes_by_src_param_id.get(t.graphNodeId());if(e){u.popFromArrayAtEntry(this._referencing_params_by_referenced_node_id,e.graphNodeId(),t);const n=t.decomposed_path.named_nodes();for(let e of n)u.popFromArrayAtEntry(this._referencing_params_by_all_named_node_ids,e.graphNodeId(),t);this._referenced_nodes_by_src_param_id.delete(t.graphNodeId())}}referencing_params(t){return this._referencing_params_by_referenced_node_id.get(t.graphNodeId())}referencing_nodes(t){const e=this._referencing_params_by_referenced_node_id.get(t.graphNodeId());if(e){const t=new Map;for(let n of e){const e=n.node;t.set(e.graphNodeId(),e)}const n=[];return t.forEach((t=>{n.push(t)})),n}}nodes_referenced_by(t){const e=new Set([Es.OPERATOR_PATH,Es.NODE_PATH]),n=[];for(let i of t.params.all)e.has(i.type())&&n.push(i);const i=new Map,r=[];for(let t of n)this._check_param(t,i,r);for(let t of r)i.set(t.node.graphNodeId(),t.node);const s=[];return i.forEach((t=>{s.push(t)})),s}_check_param(t,e,n){if(t instanceof po){const i=t.found_node(),r=t.found_param();return i&&e.set(i.graphNodeId(),i),void(r&&n.push(r))}}notify_name_updated(t){const e=this._referencing_params_by_all_named_node_ids.get(t.graphNodeId());if(e)for(let n of e)n.notify_path_rebuild_required(t)}notify_params_updated(t){const e=this._referencing_params_by_all_named_node_ids.get(t.graphNodeId());if(e)for(let n of e)n.options.isSelectingParam()&&n.notify_target_param_owner_params_updated(t)}}var Al;!function(t){t.MAX_FRAME_UPDATED=\\\\\\\"scene_maxFrameUpdated\\\\\\\",t.REALTIME_STATUS_UPDATED=\\\\\\\"scene_realtime_status_updated\\\\\\\",t.FRAME_UPDATED=\\\\\\\"scene_frame_updated\\\\\\\",t.PLAY_STATE_UPDATED=\\\\\\\"scene_play_state_updated\\\\\\\"}(Al||(Al={}));const El=ai.performance.performanceManager();class Ml{constructor(t){this.scene=t,this._frame=0,this._time=0,this._prev_performance_now=0,this._realtimeState=!0,this._maxFrame=600,this._maxFrameLocked=!1,this._playing=!1,this._graph_node=new Ai(t,\\\\\\\"time controller\\\\\\\")}get PLAY_EVENT_CONTEXT(){return this._PLAY_EVENT_CONTEXT=this._PLAY_EVENT_CONTEXT||{event:new Event(pi.PLAY)}}get PAUSE_EVENT_CONTEXT(){return this._PAUSE_EVENT_CONTEXT=this._PAUSE_EVENT_CONTEXT||{event:new Event(pi.PAUSE)}}get TICK_EVENT_CONTEXT(){return this._TICK_EVENT_CONTEXT=this._TICK_EVENT_CONTEXT||{event:new Event(pi.TICK)}}get graphNode(){return this._graph_node}frame(){return this._frame}time(){return this._time}maxFrame(){return this._maxFrame}maxFrameLocked(){return this._maxFrameLocked}realtimeState(){return this._realtimeState}setMaxFrame(t){this._maxFrame=Math.floor(t),this.scene.dispatchController.dispatch(this._graph_node,Al.MAX_FRAME_UPDATED)}setMaxFrameLocked(t){this._maxFrameLocked=t,this.scene.dispatchController.dispatch(this._graph_node,Al.MAX_FRAME_UPDATED)}setRealtimeState(t){this._realtimeState=t,this.scene.dispatchController.dispatch(this._graph_node,Al.REALTIME_STATUS_UPDATED)}setTime(t,e=!0){if(t!=this._time){if(this._time=t,this._onBeforeTickCallbacks)for(let t of this._onBeforeTickCallbacks)t();if(e){const t=Math.floor(60*this._time),e=this._ensureFrameWithinBounds(t);t!=e?this.setFrame(e,!0):this._frame=t}if(this.scene.dispatchController.dispatch(this._graph_node,Al.FRAME_UPDATED),this.scene.uniformsController.updateTimeDependentUniformOwners(),this.scene.cooker.block(),this.graphNode.setSuccessorsDirty(),this.scene.cooker.unblock(),this.scene.eventsDispatcher.sceneEventsController.processEvent(this.TICK_EVENT_CONTEXT),this._onAfterTickCallbacks)for(let t of this._onAfterTickCallbacks)t()}}setFrame(t,e=!0){t!=this._frame&&(t=this._ensureFrameWithinBounds(t))!=this._frame&&(this._frame=t,e&&this.setTime(this._frame/60,!1))}setFrameToStart(){this.setFrame(Ml.START_FRAME,!0)}incrementTimeIfPlaying(){this._playing&&(this.scene.root().areChildrenCooking()||this.incrementTime())}incrementTime(){if(this._realtimeState){const t=El.now(),e=(t-this._prev_performance_now)/1e3,n=this._time+e;this._prev_performance_now=t,this.setTime(n)}else this.setFrame(this.frame()+1)}_ensureFrameWithinBounds(t){if(this._playing){if(this._maxFrameLocked&&t>this._maxFrame)return Ml.START_FRAME}else{if(this._maxFrameLocked&&t>this._maxFrame)return this._maxFrame;if(t<Ml.START_FRAME)return Ml.START_FRAME}return t}playing(){return!0===this._playing}pause(){1==this._playing&&(this._playing=!1,this.scene.dispatchController.dispatch(this._graph_node,Al.PLAY_STATE_UPDATED),this.scene.eventsDispatcher.sceneEventsController.processEvent(this.PAUSE_EVENT_CONTEXT))}play(){!0!==this._playing&&(this._playing=!0,this._prev_performance_now=El.now(),this.scene.dispatchController.dispatch(this._graph_node,Al.PLAY_STATE_UPDATED),this.scene.eventsDispatcher.sceneEventsController.processEvent(this.PLAY_EVENT_CONTEXT))}togglePlayPause(){this.playing()?this.pause():this.play()}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}_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)}}Ml.START_FRAME=0;class Sl{constructor(t){this.scene=t,this._time_dependent_uniform_owners={},this._time_dependent_uniform_owners_ids=null,this._resolution=new d.a(1,1),this._resolution_dependent_uniform_owners={},this._resolution_dependent_uniform_owners_ids=[]}addTimeDependentUniformOwner(t,e){this._time_dependent_uniform_owners[t]=e,this._time_dependent_uniform_owners_ids||(this._time_dependent_uniform_owners_ids=[]),this._time_dependent_uniform_owners_ids.includes(t)||this._time_dependent_uniform_owners_ids.push(t)}removeTimeDependentUniformOwner(t){if(delete this._time_dependent_uniform_owners[t],this._time_dependent_uniform_owners_ids){const e=this._time_dependent_uniform_owners_ids.indexOf(t);e>=0&&this._time_dependent_uniform_owners_ids.splice(e,1)}}updateTimeDependentUniformOwners(){const t=this.scene.time();if(this._time_dependent_uniform_owners_ids)for(let e of this._time_dependent_uniform_owners_ids){this._time_dependent_uniform_owners[e].time.value=t}}addResolutionDependentUniformOwner(t,e){this._resolution_dependent_uniform_owners[t]=e,this._resolution_dependent_uniform_owners_ids||(this._resolution_dependent_uniform_owners_ids=[]),this._resolution_dependent_uniform_owners_ids.includes(t)||this._resolution_dependent_uniform_owners_ids.push(t),this._resolution&&this.updateResolutionDependentUniforms(e)}removeResolutionDependentUniformOwner(t){if(delete this._resolution_dependent_uniform_owners[t],this._resolution_dependent_uniform_owners_ids){const e=this._resolution_dependent_uniform_owners_ids.indexOf(t);e>=0&&this._resolution_dependent_uniform_owners_ids.splice(e,1)}}updateResolutionDependentUniformOwners(t){this._resolution.copy(t);for(let t of this._resolution_dependent_uniform_owners_ids){const e=this._resolution_dependent_uniform_owners[t];this.updateResolutionDependentUniforms(e)}}updateResolutionDependentUniforms(t){t.resolution.value.x=this._resolution.x,t.resolution.value.y=this._resolution.y}}class Cl{constructor(t){this.scene=t,this._viewers_by_id=new Map}registerViewer(t){this._viewers_by_id.set(t.id(),t)}unregisterViewer(t){this._viewers_by_id.delete(t.id())}traverseViewers(t){this._viewers_by_id.forEach(t)}}class Nl{constructor(){this._require_webgl2=!1}require_webgl2(){return this._require_webgl2}set_require_webgl2(){this._require_webgl2||(this._require_webgl2=!0,ai.renderersController.setRequireWebGL2())}}class Ll{constructor(t){this._scene=t,this._onWindowResizeBound=this._onWindowResize.bind(this)}graphNode(){return this._coreGraphNode=this._coreGraphNode||this._createGraphNode()}_createGraphNode(){const t=new Ai(this._scene,\\\\\\\"SceneWindowController\\\\\\\");return window.addEventListener(\\\\\\\"resize\\\\\\\",this._onWindowResizeBound),t}_onWindowResize(){this.graphNode().setSuccessorsDirty()}dispose(){window.removeEventListener(\\\\\\\"resize\\\\\\\",this._onWindowResizeBound)}}class Ol{constructor(){this._params_by_id=new Map,this._assets_root=null}register_param(t){this._params_by_id.set(t.graphNodeId(),t)}deregister_param(t){this._params_by_id.delete(t.graphNodeId())}traverse_params(t){this._params_by_id.forEach(((e,n)=>{t(e)}))}root(){return this._assets_root}setRoot(t){\\\\\\\"\\\\\\\"==t&&(t=null),this._assets_root=t}}class Rl{constructor(){this._cameras_controller=new s(this),this._cooker=new o(this),this.cookController=new a,this._graph=new l,this._missing_expression_references_controller=new wi(this),this._expressions_controller=new ui,this._nodes_controller=new Pa(this),this._objects_controller=new la(this),this._references_controller=new Tl(this),this._time_controller=new Ml(this),this._read_only=!1,this._graph.setScene(this),this.nodesController.init()}threejsScene(){return this.root().object}setUuid(t){return this._uuid=t}get uuid(){return this._uuid}setName(t){return t=sr.sanitizeName(t),this._name=t}name(){return this._name}get camerasController(){return this._cameras_controller}mainCameraNode(){return this.camerasController.mainCameraNode()}get cooker(){return this._cooker}get assets(){return this._assets_controller=this._assets_controller||new Ol}async waitForCooksCompleted(){return this.cookController.waitForCooksCompleted()}get dispatchController(){return this._dispatch_controller=this._dispatch_controller||new ci(this)}get eventsDispatcher(){return this._events_dispatcher=this._events_dispatcher||new wl(this)}get graph(){return this._graph}get lifecycleController(){return this._lifecycle_controller=this._lifecycle_controller||new hi(this)}get loadingController(){return this._loading_controller=this._loading_controller||new fi(this)}get missingExpressionReferencesController(){return this._missing_expression_references_controller}get expressionsController(){return this._expressions_controller}get nodesController(){return this._nodes_controller}createNode(t,e){return this.root().createNode(t,e)}nodesByType(t){return this.nodesController.nodesByType(t)}get objectsController(){return this._objects_controller}findObjectByMask(t){return this._objects_controller.findObjectByMask(t)}objectsByMask(t){return this._objects_controller.objectsByMask(t)}get referencesController(){return this._references_controller}get performance(){return this._performance=this._performance||new li}get viewersRegister(){return this._viewers_register=this._viewers_register||new Cl(this)}get timeController(){return this._time_controller}setFrame(t){this.timeController.setFrame(t)}setFrameToStart(){this.timeController.setFrameToStart()}frame(){return this.timeController.frame()}time(){return this.timeController.time()}maxFrame(){return this.timeController.maxFrame()}play(){this.timeController.play()}pause(){this.timeController.pause()}get serializer(){return this._serializer=this._serializer||new Ia(this)}toJSON(){return this.serializer.toJSON()}markAsReadOnly(t){this._read_only||(this._read_only_requester=t,this._read_only=!0)}readOnly(){return this._read_only}readOnlyRequester(){return this._read_only_requester}get uniformsController(){return this._uniformsController=this._uniformsController||new Sl(this)}get webgl_controller(){return this._webgl_controller=this._webgl_controller||new Nl}get windowController(){return this._windowController=this._windowController||new Ll(this)}dispose(){var t;null===(t=this._windowController)||void 0===t||t.dispose()}batchUpdates(t){this._cooker.block(),t(),this._cooker.unblock()}node(t){return this.nodesController.node(t)}root(){return this.nodesController.root()}registerOnBeforeTick(t,e){this.timeController.registerOnBeforeTick(t,e)}unRegisterOnBeforeTick(t){this.timeController.unRegisterOnBeforeTick(t)}registeredBeforeTickCallbackNames(){return this.timeController.registeredBeforeTickCallbackNames()}registerOnAfterTick(t,e){this.timeController.registerOnAfterTick(t,e)}unRegisterOnAfterTick(t){this.timeController.unRegisterOnAfterTick(t)}registeredAfterTickCallbackNames(){return this.timeController.registeredAfterTickCallbackNames()}}class Pl{constructor(t){this._param=t}process_data(t){const e=t.raw_input;void 0!==e&&this._param.set(e),this.add_main(t)}add_main(t){}static spare_params_data(t){return this.params_data(!0,t)}static non_spare_params_data_value(t){return this.params_data_value(!1,t)}static params_data(t,e){let n;if(e){n={};const t=Object.keys(e);let i;for(let r of t)i=e[r],i&&(n[r]=e)}return n}static params_data_value(t,e){let n;if(e){n={};const i=Object.keys(e);let r;for(let s of i)if(r=e[s],null!=r){const e=r.options,i=r.overriden_options;if(e||i){const o=r;e&&e.spare==t?null!=o.raw_input&&(n[s]={complex_data:o}):i&&(n[s]={complex_data:o})}else{const t=r;(i||null!=t)&&(n[s]={simple_data:t})}}}return n}}const Il=\\\\\\\"operationsComposer\\\\\\\";class Fl{constructor(t,e,n){this._scene=t,this.states=e,this._node=n}static type(){throw\\\\\\\"type to be overriden\\\\\\\"}type(){return this.constructor.type()}static context(){throw console.error(\\\\\\\"operation has no node_context\\\\\\\",this),\\\\\\\"context requires override\\\\\\\"}context(){return this.constructor.context()}scene(){return this._scene}cook(t,e){}}Fl.DEFAULT_PARAMS={},Fl.INPUT_CLONED_STATE=[];class Dl{constructor(t){this._node=t,this._nodes=[],this._optimized_root_node_names=new Set,this._operation_containers_by_name=new Map,this._node_inputs=[]}nodes(){return this._nodes}process_data(t,e){var n,i,r;if(!e)return;if(!this._node.childrenAllowed()||!this._node.childrenController)return;const{optimized_names:s}=Dl.child_names_by_optimized_state(e);this._nodes=[],this._optimized_root_node_names=new Set;for(let t of s)Dl.is_optimized_root_node(e,t)&&this._optimized_root_node_names.add(t);for(let s of this._optimized_root_node_names){const o=e[s],a=this._node.createNode(Il);if(a){a.setName(s),this._nodes.push(a),(null===(n=o.flags)||void 0===n?void 0:n.display)&&(null===(r=null===(i=a.flags)||void 0===i?void 0:i.display)||void 0===r||r.set(!0));const e=this._create_operation_container(t,a,o,a.name());a.set_output_operation_container(e)}}for(let n of this._nodes){const i=n.output_operation_container();if(i){this._node_inputs=[],this._add_optimized_node_inputs(t,n,e,n.name(),i),n.io.inputs.setCount(this._node_inputs.length);for(let t=0;t<this._node_inputs.length;t++)n.setInput(t,this._node_inputs[t])}}}_add_optimized_node_inputs(t,e,n,i,r){var s;const o=n[i],a=o.inputs;if(a){for(let i of a)if(m.isString(i)){const o=n[i];if(o)if(Dl.is_node_optimized(o)&&!this._optimized_root_node_names.has(i)){let s=this._operation_containers_by_name.get(i);s||(s=this._create_operation_container(t,e,o,i),s&&this._add_optimized_node_inputs(t,e,n,i,s)),r.add_input(s)}else{const t=null===(s=e.parent())||void 0===s?void 0:s.node(i);if(t){this._node_inputs.push(t);const n=this._node_inputs.length-1;e.add_input_config(r,{operation_input_index:r.current_input_index(),node_input_index:n}),r.increment_input_index()}}}1==o.cloned_state_overriden&&r.override_input_clone_state(o.cloned_state_overriden)}}static child_names_by_optimized_state(t){const e=Object.keys(t),n=[],i=[];for(let r of e){const e=t[r];ai.playerMode()&&this.is_node_optimized(e)?n.push(r):i.push(r)}return{optimized_names:n,non_optimized_names:i}}static is_optimized_root_node_generic(t){return 0==t.outputs_count||t.non_optimized_count>0}static is_optimized_root_node(t,e){const n=this.node_outputs(t,e);let i=0;return n.forEach((e=>{const n=t[e];this.is_node_optimized(n)||i++})),this.is_optimized_root_node_generic({outputs_count:n.size,non_optimized_count:i})}static is_optimized_root_node_from_node(t){var e,n,i,r;if(!(null===(n=null===(e=t.flags)||void 0===e?void 0:e.optimize)||void 0===n?void 0:n.active()))return!1;const s=t.io.connections.outputConnections().map((t=>t.node_dest));let o=0;for(let t of s)(null===(r=null===(i=t.flags)||void 0===i?void 0:i.optimize)||void 0===r?void 0:r.active())||o++;return this.is_optimized_root_node_generic({outputs_count:s.length,non_optimized_count:o})}static node_outputs(t,e){const n=Object.keys(t),i=new Set;for(let r of n)if(r!=e){const n=t[r].inputs;if(n)for(let t of n)if(m.isString(t)){t==e&&i.add(r)}}return i}_create_operation_container(t,e,n,i){const r=Pl.non_spare_params_data_value(n.params),s=Dl.operation_type(n),o=this._node.create_operation_container(s,i,r);return o&&(this._operation_containers_by_name.set(i,o),o.path_param_resolve_required()&&(e.add_operation_container_with_path_param_resolve_required(o),t.add_operations_composer_node_with_path_param_resolve_required(e))),o}static operation_type(t){return Dl.is_node_bypassed(t)?\\\\\\\"null\\\\\\\":t.type}static is_node_optimized(t){const e=t.flags;return!(!e||!e.optimize)}static is_node_bypassed(t){const e=t.flags;return!(!e||!e.bypass)}}class kl{constructor(t){this._node=t}process_data(t,e){var n;if(!e)return;if(!this._node.childrenAllowed()||!this._node.childrenController)return;const{optimized_names:i,non_optimized_names:r}=Dl.child_names_by_optimized_state(e),s=[];for(let n of r){const i=e[n],r=i.type.toLowerCase(),o=Pl.non_spare_params_data_value(i.params);try{const t=this._node.createNode(r,o);t&&(t.setName(n),s.push(t))}catch(e){console.error(`error importing node: cannot create with type ${r}`,e);const i=sr.camelCase(r);try{const t=this._node.createNode(i,o);t&&(t.setName(n),s.push(t))}catch(e){const a=`${r}Network`;try{const t=this._node.createNode(a,o);t&&(t.setName(n),s.push(t))}catch(e){const n=`failed to create node with type '${r}', '${i}' or '${a}'`;t.report.addWarning(n),ai.warn(n,e)}}}}if(i.length>0){const i=new Dl(this._node);if(i.process_data(t,e),this._node.childrenController.context==Ki.SOP){const t=Object.keys(e);let r;for(let i of t){(null===(n=e[i].flags)||void 0===n?void 0:n.display)&&(r=i)}if(r){const t=s.map((t=>t.name())),e=i.nodes();for(let n of e)t.push(n.name());if(!t.includes(r)){const t=`node '${`${this._node.path()}/${r}`}' with display flag has been optimized and does not exist in player mode`;console.error(t)}}}}const o=new Map;for(let n of s){if(e[n.name()]){const i=Wl.dispatch_node(n);o.set(n.name(),i),i.process_data(t,e[n.name()])}else ai.warn(`possible import error for node ${n.name()}`)}for(let t of s){const n=o.get(t.name());n&&n.process_inputs_data(e[t.name()])}}}const Bl=[\\\\\\\"overriden_options\\\\\\\",\\\\\\\"type\\\\\\\"];class zl{constructor(t){this._node=t}process_data(t,e){if(this.set_connection_points(e.connection_points),this._node.childrenAllowed()&&this.create_nodes(t,e.nodes),this.set_selection(e.selection),this._node.io.inputs.overrideClonedStateAllowed()){const t=e.cloned_state_overriden;t&&this._node.io.inputs.overrideClonedState(t)}this.set_flags(e),this.set_params(e.params),e.persisted_config&&this.set_persisted_config(e.persisted_config),this.from_data_custom(e)}process_inputs_data(t){const e=t.maxInputsCount;null!=e&&this._node.io.inputs.setCount(1,e),this.setInputs(t.inputs)}process_ui_data(t,e){if(!e)return;if(ai.playerMode())return;const n=this._node.uiData,i=e.pos;if(i){const t=(new d.a).fromArray(i);n.setPosition(t)}const r=e.comment;r&&n.setComment(r),this._node.childrenAllowed()&&this.process_nodes_ui_data(t,e.nodes)}create_nodes(t,e){if(!e)return;new kl(this._node).process_data(t,e)}set_selection(t){if(this._node.childrenAllowed()&&this._node.childrenController&&t&&t.length>0){const e=[];t.forEach((t=>{const n=this._node.node(t);n&&e.push(n)})),this._node.childrenController.selection.set(e)}}set_flags(t){var e,n,i,r,s,o;const a=t.flags;if(a){const t=a.bypass;null!=t&&(null===(n=null===(e=this._node.flags)||void 0===e?void 0:e.bypass)||void 0===n||n.set(t));const l=a.display;null!=l&&(null===(r=null===(i=this._node.flags)||void 0===i?void 0:i.display)||void 0===r||r.set(l));const c=a.optimize;null!=c&&(null===(o=null===(s=this._node.flags)||void 0===s?void 0:s.optimize)||void 0===o||o.set(c))}}set_connection_points(t){t&&(t.in&&this._node.io.saved_connection_points_data.set_in(t.in),t.out&&this._node.io.saved_connection_points_data.set_out(t.out),this._node.io.has_connection_points_controller&&this._node.io.connection_points.update_signature_if_required())}setInputs(t){if(!t)return;let e;for(let n=0;n<t.length;n++)if(e=t[n],e&&this._node.parent())if(m.isString(e)){const t=e,i=this._node.nodeSibbling(t);this._node.setInput(n,i)}else{const t=this._node.nodeSibbling(e.node),n=e.index;this._node.setInput(n,t,e.output)}}process_nodes_ui_data(t,e){if(!e)return;if(ai.playerMode())return;const n=Object.keys(e);for(let i of n){const n=this._node.node(i);if(n){const r=e[i];Wl.dispatch_node(n).process_ui_data(t,r)}}}set_params(t){if(!t)return;const e=Object.keys(t),n={};for(let i of e){const e=t[i],r=e.options;0;const s=e.type;let o,a=!1;this._node.params.has_param(i)&&(o=this._node.params.get(i),(o&&o.type()==s||null==s)&&(a=!0)),a?this._is_param_data_complex(e)?this._process_param_data_complex(i,e):this._process_param_data_simple(i,e):(n.namesToDelete=n.namesToDelete||[],n.namesToDelete.push(i),n.toAdd=n.toAdd||[],n.toAdd.push({name:i,type:s,init_value:e.default_value,raw_input:e.raw_input,options:r}))}const i=n.namesToDelete&&n.namesToDelete.length>0,r=n.toAdd&&n.toAdd.length>0;if(i||r){this._node.params.updateParams(n);for(let e of this._node.params.spare){const n=t[e.name()];!e.parent_param&&n&&(this._is_param_data_complex(n)?this._process_param_data_complex(e.name(),n):this._process_param_data_simple(e.name(),n))}}this._node.params.runOnSceneLoadHooks()}_process_param_data_simple(t,e){var n;null===(n=this._node.params.get(t))||void 0===n||n.set(e)}_process_param_data_complex(t,e){const n=this._node.params.get(t);n&&Wl.dispatch_param(n).process_data(e)}_is_param_data_complex(t){if(m.isString(t)||m.isNumber(t)||m.isArray(t)||m.isBoolean(t))return!1;if(m.isObject(t)){const e=Object.keys(t);for(let t of Bl)if(e.includes(t))return!0}return!1}set_persisted_config(t){this._node.persisted_config&&this._node.persisted_config.load(t)}from_data_custom(t){}}class Ul extends Pl{add_main(t){}}const Gl=/\\\\\\\\n+/g;class Vl extends Pl{add_main(t){let e=t.raw_input;void 0!==e&&(e=e.replace(Gl,\\\\\\\"\\\\n\\\\\\\"),this._param.set(e))}}class Hl extends Pl{add_main(t){const e=t.raw_input;e&&this._param.set(e)}}class jl extends zl{create_nodes(t,e){const n=this._node.polyNodeController;n&&n.createChildNodesFromDefinition()}}class Wl{static dispatch_node(t){return t.polyNodeController?new jl(t):new zl(t)}static dispatch_param(t){return t instanceof ro?new Ul(t):t instanceof bo?new Vl(t):t instanceof xo?new Hl(t):new Pl(t)}}class ql{constructor(t){this._warnings=[]}warnings(){return this._warnings}reset(){this._warnings=[]}addWarning(t){this._warnings.push(t)}}class Xl{constructor(t){this._data=t,this.report=new ql(this)}static async loadData(t){const e=new Xl(t);return await e.scene()}async scene(){const t=new Rl;t.loadingController.markAsLoading();const e=this._data.properties;if(e){const n=e.maxFrame||600;t.timeController.setMaxFrame(n);const i=e.maxFrameLocked;i&&t.timeController.setMaxFrameLocked(i);const r=e.realtimeState;null!=r&&t.timeController.setRealtimeState(r),t.setFrame(e.frame||Ml.START_FRAME),e.mainCameraNodePath&&t.camerasController.setMainCameraNodePath(e.mainCameraNodePath)}t.cooker.block(),this._base_operations_composer_nodes_with_resolve_required=void 0;const n=Wl.dispatch_node(t.root());return this._data.root&&n.process_data(this,this._data.root),this._data.ui&&n.process_ui_data(this,this._data.ui),this._resolve_operation_containers_with_path_param_resolve(),await t.loadingController.markAsLoaded(),t.cooker.unblock(),t}add_operations_composer_node_with_path_param_resolve_required(t){this._base_operations_composer_nodes_with_resolve_required||(this._base_operations_composer_nodes_with_resolve_required=[]),this._base_operations_composer_nodes_with_resolve_required.push(t)}_resolve_operation_containers_with_path_param_resolve(){if(this._base_operations_composer_nodes_with_resolve_required)for(let t of this._base_operations_composer_nodes_with_resolve_required)t.resolve_operation_containers_path_params()}}class Yl{static async importSceneData(t){null==t.editorMode&&(t.editorMode=!1);const{manifest:e,urlPrefix:n}=t,i=Object.keys(e.nodes),r=[];for(let t of i){const i=`${n}/root/${t}.json?t=${e.nodes[t]}`;r.push(i)}const s=[`${n}/root.json?t=${e.root}`,`${n}/properties.json?t=${e.properties}`];if(t.editorMode){const t=Date.now();s.push(`${n}/ui.json?t=${t}`)}for(let t of r)s.push(t);let o=0;const a=s.length,l=s.map((async e=>{const n=await fetch(e);return t.onProgress&&(o++,t.onProgress({count:o,total:a})),n})),c=await Promise.all(l),u=[];for(let t of c)u.push(await t.json());const h={root:u[0],properties:u[1]};let d=2;t.editorMode&&(h.ui=u[2],d+=1);const p={},_=Object.keys(e.nodes);for(let t=0;t<_.length;t++){const e=_[t],n=u[t+d];p[e]=n}return this.assemble(h,_,p)}static async assemble(t,e,n){const i={root:t.root,properties:t.properties,ui:t.ui};for(let t=0;t<e.length;t++){const r=e[t],s=n[r];this.insert_child_data(i.root,r,s)}return i}static insert_child_data(t,e,n){const i=e.split(\\\\\\\"/\\\\\\\");if(1==i.length)t.nodes||(t.nodes={}),t.nodes[e]=n;else{const e=i.shift(),r=i.join(\\\\\\\"/\\\\\\\"),s=t.nodes[e];this.insert_child_data(s,r,n)}}}async function $l(t){const e=t.scenesSrcRoot||\\\\\\\"/src/polygonjs/scenes\\\\\\\",n=t.scenesSrcRoot||\\\\\\\"/public/polygonjs/scenes\\\\\\\",i=t.sceneName;const r=await async function(){const t=await fetch(`${e}/${i}/manifest.json`);return await t.json()}(),s=await async function(t){return await Yl.importSceneData({manifest:t,urlPrefix:`${n}/${i}`})}(r);return await async function(e){const n=new Xl(e),i=await n.scene(),r=i.mainCameraNode();if(!r)return void console.warn(\\\\\\\"no master camera found\\\\\\\");const s=m.isString(t.domElement)?document.getElementById(t.domElement):t.domElement;if(!s)return void console.warn(\\\\\\\"no element to mount the viewer onto\\\\\\\");const o=r.createViewer(s);return{scene:i,cameraNode:r,viewer:o}}(s)}const Jl=\\\\\\\"networks\\\\\\\",Zl=\\\\\\\"misc\\\\\\\",Ql=\\\\\\\"modifiers\\\\\\\",Kl=Jl,tc=\\\\\\\"prop\\\\\\\",ec=\\\\\\\"timing\\\\\\\",nc=\\\\\\\"advanced\\\\\\\",ic=\\\\\\\"inputs\\\\\\\",rc=\\\\\\\"misc\\\\\\\",sc=Jl,oc=\\\\\\\"cameras\\\\\\\",ac=\\\\\\\"inputs\\\\\\\",lc=\\\\\\\"misc\\\\\\\",cc=\\\\\\\"scene\\\\\\\",uc=Jl,hc=\\\\\\\"color\\\\\\\",dc=\\\\\\\"conversion\\\\\\\",pc=\\\\\\\"geometry\\\\\\\",_c=\\\\\\\"globals\\\\\\\",mc=\\\\\\\"lighting\\\\\\\",fc=\\\\\\\"logic\\\\\\\",gc=\\\\\\\"math\\\\\\\",vc=\\\\\\\"physics\\\\\\\",yc=\\\\\\\"quat\\\\\\\",xc=\\\\\\\"trigo\\\\\\\",bc=\\\\\\\"util\\\\\\\",wc=\\\\\\\"globals\\\\\\\",Tc=\\\\\\\"advanced\\\\\\\",Ac=\\\\\\\"lines\\\\\\\",Ec=\\\\\\\"meshes\\\\\\\",Mc=Jl,Sc=\\\\\\\"points\\\\\\\",Cc=\\\\\\\"volumes\\\\\\\",Nc=\\\\\\\"advanced\\\\\\\",Lc=\\\\\\\"audio\\\\\\\",Oc=\\\\\\\"cameras\\\\\\\",Rc=\\\\\\\"geometries\\\\\\\",Pc=\\\\\\\"lights\\\\\\\",Ic=Jl,Fc=\\\\\\\"transform\\\\\\\",Dc=\\\\\\\"css\\\\\\\",kc=Jl,Bc=\\\\\\\"webgl\\\\\\\",zc=\\\\\\\"advanced\\\\\\\",Uc=\\\\\\\"animation\\\\\\\",Gc=\\\\\\\"attributes\\\\\\\",Vc=\\\\\\\"dynamics\\\\\\\",Hc=\\\\\\\"inputs\\\\\\\",jc=\\\\\\\"lights\\\\\\\",Wc=\\\\\\\"misc\\\\\\\",qc=\\\\\\\"modifiers\\\\\\\",Xc=Jl,Yc=\\\\\\\"primitives\\\\\\\",$c=\\\\\\\"render\\\\\\\",Jc=\\\\\\\"blur\\\\\\\",Zc=\\\\\\\"color\\\\\\\",Qc=\\\\\\\"effect\\\\\\\",Kc=\\\\\\\"misc\\\\\\\",tu=Jl,eu=\\\\\\\"input animation clip\\\\\\\",nu=[eu,eu,eu,eu];class iu extends ia{constructor(){super(...arguments),this.flags=new Di(this)}static context(){return Ki.ANIM}static displayedInputNames(){return nu}initializeBaseNode(){this.io.outputs.setHasOneOutput()}setTimelineBuilder(t){this._setContainer(t)}}class ru extends Ai{constructor(t){super(t,\\\\\\\"CopyStamp\\\\\\\"),this._global_index=0}set_global_index(t){this._global_index=t,this.setDirty(),this.removeDirtyState()}value(t){return this._global_index}}class su extends ru{}var ou,au=n(8);!function(t){t.NONE=\\\\\\\"none\\\\\\\",t.POWER1=\\\\\\\"power1\\\\\\\",t.POWER2=\\\\\\\"power2\\\\\\\",t.POWER3=\\\\\\\"power3\\\\\\\",t.POWER4=\\\\\\\"power4\\\\\\\",t.BACK=\\\\\\\"back\\\\\\\",t.ELASTIC=\\\\\\\"elastic\\\\\\\",t.BOUNCE=\\\\\\\"bounce\\\\\\\",t.SLOW=\\\\\\\"slow\\\\\\\",t.STEPS=\\\\\\\"steps\\\\\\\",t.CIRC=\\\\\\\"circ\\\\\\\",t.EXPO=\\\\\\\"expo\\\\\\\",t.SINE=\\\\\\\"sine\\\\\\\"}(ou||(ou={}));const lu=[ou.NONE,ou.POWER1,ou.POWER2,ou.POWER3,ou.POWER4,ou.BACK,ou.ELASTIC,ou.BOUNCE,ou.SLOW,ou.STEPS,ou.CIRC,ou.EXPO,ou.SINE];var cu;!function(t){t.IN=\\\\\\\"in\\\\\\\",t.OUT=\\\\\\\"out\\\\\\\",t.IN_OUT=\\\\\\\"inOut\\\\\\\"}(cu||(cu={}));const uu=[cu.IN,cu.OUT,cu.IN_OUT];class hu{constructor(){this._debug=!1}setName(t){this._property_name=t}setTargetValue(t){this._target_value=t}name(){return this._property_name}targetValue(){return this._target_value}setDebug(t){this._debug=t}_printDebug(t){this._debug&&console.log(t)}clone(){const t=new hu;if(this._property_name&&t.setName(this._property_name),null!=this._target_value){const e=m.isNumber(this._target_value)?this._target_value:this._target_value.clone();t.setTargetValue(e)}return t}addToTimeline(t,e,n){const i=n.objects();i&&this._populateWithObjects(i,t,e);const r=n.node();r&&this._populateWithNode(r,t,e)}_populateWithObjects(t,e,n){if(this._printDebug([\\\\\\\"_populateWithObjects\\\\\\\",t]),!this._property_name)return void 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bp&&e!==Vp&&(t._gsap.x||n_(t,\\\\\\\"x\\\\\\\"))?n&&yp===n?\\\\\\\"scale\\\\\\\"===e?Bp:kp:(yp=n||{})&&(\\\\\\\"scale\\\\\\\"===e?zp:Up):t.style&&!Uu(t.style[e])?Fp:~e.indexOf(\\\\\\\"-\\\\\\\")?Dp:tp(t,e)},core:{_removeProperty:Qp,_getMatrix:h_}};pp.utils.checkPrefix=qp,S_=vh((E_=\\\\\\\"x,y,z,scale,scaleX,scaleY,xPercent,yPercent\\\\\\\")+\\\\\\\",\\\\\\\"+(M_=\\\\\\\"rotation,rotationX,rotationY,skewX,skewY\\\\\\\")+\\\\\\\",transform,transformOrigin,svgOrigin,force3D,smoothOrigin,transformPerspective\\\\\\\",(function(t){bp[t]=1})),vh(M_,(function(t){Su.units[t]=\\\\\\\"deg\\\\\\\",l_[t]=1})),Cp[S_[13]]=E_+\\\\\\\",\\\\\\\"+M_,vh(\\\\\\\"0:translateX,1:translateY,2:translateZ,8:rotate,8:rotationZ,8:rotateZ,9:rotateX,10:rotateY\\\\\\\",(function(t){var e=t.split(\\\\\\\":\\\\\\\");Cp[e[1]]=S_[e[0]]})),vh(\\\\\\\"x,y,z,top,right,bottom,left,width,height,fontSize,padding,margin,perspective\\\\\\\",(function(t){Su.units[t]=\\\\\\\"px\\\\\\\"})),pp.registerPlugin(C_);var 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 R_{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 R_;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 hu}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 r=(null===(e=n.position())||void 0===e?void 0:e.toParameter())||void 0;t.add(i,r)}this._property&&this._target&&(this._property.setDebug(this._debug),this._property.addToTimeline(this,t,this._target))}}const P_=new class extends aa{constructor(){super(...arguments),this.count=oa.INTEGER(1,{range:[1,20],rangeLocked:[!0,!1]})}};class I_ extends iu{constructor(){super(...arguments),this.paramsConfig=P_}static type(){return\\\\\\\"copy\\\\\\\"}initializeNode(){this.io.inputs.setCount(1)}async cook(t){const e=new R_;for(let t=0;t<this.pv.count;t++){this.stampNode().set_global_index(t);const n=await this.containerController.requestInputContainer(0);if(n){const t=n.coreContentCloned();t&&e.addTimelineBuilder(t)}}this.setTimelineBuilder(e)}stamp_value(t){return this.stampNode().value(t)}stampNode(){return this._stamp_node=this._stamp_node||this.create_stamp_node()}create_stamp_node(){const t=new su(this.scene());return this.dirtyController.setForbiddenTriggerNodes([t]),t}}const F_=new class extends aa{constructor(){super(...arguments),this.delay=oa.FLOAT(1)}};class D_ extends iu{constructor(){super(...arguments),this.paramsConfig=F_}static type(){return\\\\\\\"delay\\\\\\\"}initializeNode(){this.io.inputs.setCount(0,1),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.delay])}))}))}cook(t){const e=t[0]||new R_;e.setDelay(this.pv.delay),this.setTimelineBuilder(e)}}const k_=new class extends aa{constructor(){super(...arguments),this.duration=oa.FLOAT(1,{range:[0,10],rangeLocked:[!0,!1]})}};class B_ extends iu{constructor(){super(...arguments),this.paramsConfig=k_}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 R_;e.setDuration(this.pv.duration),this.setTimelineBuilder(e)}}const z_=new class extends aa{constructor(){super(...arguments),this.name=oa.INTEGER(lu.indexOf(ou.POWER4),{menu:{entries:lu.map(((t,e)=>({name:t,value:e})))}}),this.inOut=oa.INTEGER(uu.indexOf(cu.OUT),{menu:{entries:uu.map(((t,e)=>({name:t,value:e})))}})}};class U_ extends iu{constructor(){super(...arguments),this.paramsConfig=z_}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=lu[this.pv.name];if(t==ou.NONE)return t;return`${t}.${uu[this.pv.inOut]}`}cook(t){const e=t[0]||new R_,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()}ar.unreachable(this._mode)}_relative_position_param(){switch(this._relativeTo){case H_.END:return this._offsetString();case H_.START:return`<${this._offset}`}ar.unreachable(this._relativeTo)}_absolutePositionParam(){return this._offset}_offsetString(){return this._offset>0?`+=${this._offset}`:`-=${Math.abs(this._offset)}`}}var q_;!function(t){t.ALL_TOGETHER=\\\\\\\"play all together\\\\\\\",t.ONE_AT_A_TIME=\\\\\\\"play one at a time\\\\\\\"}(q_||(q_={}));const X_=[q_.ALL_TOGETHER,q_.ONE_AT_A_TIME];const Y_=new class extends aa{constructor(){super(...arguments),this.mode=oa.INTEGER(0,{menu:{entries:X_.map(((t,e)=>({name:t,value:e})))}}),this.offset=oa.FLOAT(0,{range:[-1,1]}),this.overridePositions=oa.BOOLEAN(0),this.inputsCount=oa.INTEGER(4,{range:[1,32],rangeLocked:[!0,!1],callback:t=>{$_.PARAM_CALLBACK_setInputsCount(t)}})}};class $_ extends iu{constructor(){super(...arguments),this.paramsConfig=Y_}static type(){return\\\\\\\"merge\\\\\\\"}initializeNode(){this.io.inputs.setCount(0,4),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.mode],(()=>X_[this.pv.mode]))})),this.params.addOnSceneLoadHook(\\\\\\\"update inputs\\\\\\\",(()=>{this._callbackUpdateInputsCount()}))}))}cook(t){const e=new R_;let n=0;for(let i of t)i&&(n>0&&this._update_timeline_builder(i),e.addTimelineBuilder(i),n++);this.setTimelineBuilder(e)}_update_timeline_builder(t){const e=X_[this.pv.mode];switch(e){case q_.ALL_TOGETHER:return this._set_play_all_together(t);case q_.ONE_AT_A_TIME:return this._set_play_one_at_a_time(t)}ar.unreachable(e)}_set_play_all_together(t){let e=t.position();e&&!this.pv.overridePositions||(e=new W_,e.setMode(G_.RELATIVE),e.setRelativeTo(H_.START),e.setOffset(this.pv.offset),t.setPosition(e))}_set_play_one_at_a_time(t){let e=t.position();e&&!this.pv.overridePositions||(e=new W_,e.setMode(G_.RELATIVE),e.setRelativeTo(H_.END),e.setOffset(this.pv.offset),t.setPosition(e))}_callbackUpdateInputsCount(){this.io.inputs.setCount(1,this.pv.inputsCount),this.emit(Ei.INPUTS_UPDATED)}static PARAM_CALLBACK_setInputsCount(t){t._callbackUpdateInputsCount()}}const J_=new class extends aa{constructor(){super(...arguments),this.play=oa.BUTTON(null,{callback:t=>{Z_.PARAM_CALLBACK_play(t)}}),this.pause=oa.BUTTON(null,{callback:t=>{Z_.PARAM_CALLBACK_pause(t)}}),this.debug=oa.BOOLEAN(0)}};class Z_ extends iu{constructor(){super(...arguments),this.paramsConfig=J_}static type(){return\\\\\\\"null\\\\\\\"}initializeNode(){this.io.inputs.setCount(0,1),this.io.inputs.initInputsClonedState(Qi.FROM_NODE)}cook(t){const e=t[0]||new R_;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 aa{constructor(){super(...arguments),this.operation=oa.INTEGER(0,{menu:{entries:O_.map(((t,e)=>({value:e,name:t})))}})}};class K_ extends iu{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 R_;e.setOperation(O_[this.pv.operation]),this.setTimelineBuilder(e)}}const tm=new class extends aa{constructor(){super(...arguments),this.mode=oa.INTEGER(0,{menu:{entries:V_.map(((t,e)=>({name:t,value:e})))}}),this.relativeTo=oa.INTEGER(0,{menu:{entries:j_.map(((t,e)=>({name:t,value:e})))}}),this.offset=oa.FLOAT(0)}};class em extends iu{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 R_,n=new W_;n.setMode(V_[this.pv.mode]),n.setRelativeTo(j_[this.pv.relativeTo]),n.setOffset(this.pv.offset),e.setPosition(n),this.setTimelineBuilder(e)}}const nm=new class extends aa{constructor(){super(...arguments),this.name=oa.STRING(\\\\\\\"position\\\\\\\")}};class im extends iu{constructor(){super(...arguments),this.paramsConfig=nm}static type(){return\\\\\\\"propertyName\\\\\\\"}initializeNode(){this.io.inputs.setCount(0,1),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.name])}))}))}cook(t){const e=t[0]||new R_;e.setPropertyName(this.pv.name),this.setTimelineBuilder(e)}}var rm;!function(t){t.CUSTOM=\\\\\\\"custom\\\\\\\",t.FROM_SCENE_GRAPH=\\\\\\\"from scene graph\\\\\\\",t.FROM_NODE=\\\\\\\"from node\\\\\\\"}(rm||(rm={}));const sm=[rm.CUSTOM,rm.FROM_SCENE_GRAPH,rm.FROM_NODE],om=sm.indexOf(rm.CUSTOM),am=sm.indexOf(rm.FROM_SCENE_GRAPH),lm=sm.indexOf(rm.FROM_NODE);const cm=new class extends aa{constructor(){super(...arguments),this.mode=oa.INTEGER(om,{menu:{entries:sm.map(((t,e)=>({name:t,value:e})))}}),this.nodePath=oa.NODE_PATH(\\\\\\\"\\\\\\\",{visibleIf:{mode:lm}}),this.objectMask=oa.STRING(\\\\\\\"*geo1\\\\\\\",{visibleIf:{mode:am}}),this.printResolve=oa.BUTTON(null,{visibleIf:{mode:am},callback:t=>{um.PARAM_CALLBACK_print_resolve(t)}}),this.overridePropertyName=oa.BOOLEAN(0,{visibleIf:[{mode:am},{mode:lm}]}),this.propertyName=oa.STRING(\\\\\\\"\\\\\\\",{visibleIf:[{overridePropertyName:!0,mode:am},{overridePropertyName:!0,mode:lm}]}),this.size=oa.INTEGER(3,{range:[1,4],rangeLocked:[!0,!0],visibleIf:{mode:om}}),this.value1=oa.FLOAT(0,{visibleIf:{mode:om,size:1}}),this.value2=oa.VECTOR2([0,0],{visibleIf:{mode:om,size:2}}),this.value3=oa.VECTOR3([0,0,0],{visibleIf:{mode:om,size:3}}),this.value4=oa.VECTOR4([0,0,0,0],{visibleIf:{mode:om,size:4}})}};class um extends iu{constructor(){super(...arguments),this.paramsConfig=cm}static type(){return\\\\\\\"propertyValue\\\\\\\"}initializeNode(){this.io.inputs.setCount(0,1)}async cook(t){const e=t[0]||new R_;await this._prepare_timeline_builder(e),this.setTimelineBuilder(e)}setMode(t){this.p.mode.set(sm.indexOf(t))}async _prepare_timeline_builder(t){const e=sm[this.pv.mode];switch(e){case rm.CUSTOM:return this._prepare_timebuilder_custom(t);case rm.FROM_SCENE_GRAPH:return this._prepare_timebuilder_from_scene_graph(t);case rm.FROM_NODE:return await this._prepare_timebuilder_from_node(t)}ar.unreachable(e)}_prepare_timebuilder_custom(t){const e=[this.pv.value1,this.pv.value2.clone(),this.pv.value3.clone(),this.pv.value4.clone()][this.pv.size-1];t.setPropertyValue(e)}_prepare_timebuilder_from_scene_graph(t){const e=this.pv.overridePropertyName?this.pv.propertyName:t.propertyName();if(!e)return;const n=this._foundObjectFromSceneGraph();if(n){const i=n[e];i&&(m.isNumber(i)||m.isVector(i)||i instanceof au.a)&&t.setPropertyValue(i)}}async _prepare_timebuilder_from_node(t){const e=this.pv.overridePropertyName?this.pv.propertyName:t.propertyName();if(!e)return;const n=this.pv.nodePath.node();if(!n)return;const i=n.params.get(e);if(!i)return;i.isDirty()&&await i.compute();const r=i.value;r&&(m.isNumber(r)||m.isVector(r))&&t.setPropertyValue(r)}static PARAM_CALLBACK_print_resolve(t){t.printResolve()}_foundObjectFromSceneGraph(){return this.scene().findObjectByMask(this.pv.objectMask)}printResolve(){const t=this._foundObjectFromSceneGraph();console.log(t)}}const hm=new class extends aa{constructor(){super(...arguments),this.unlimited=oa.BOOLEAN(0),this.count=oa.INTEGER(1,{range:[0,10],visibleIf:{unlimited:0}}),this.delay=oa.FLOAT(0),this.yoyo=oa.BOOLEAN(0)}};class dm extends iu{constructor(){super(...arguments),this.paramsConfig=hm}static type(){return\\\\\\\"repeat\\\\\\\"}initializeNode(){this.io.inputs.setCount(0,1),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.unlimited,this.p.count,this.p.yoyo],(()=>`${`${this.p.unlimited?\\\\\\\"unlimited\\\\\\\":this.pv.count}`} (yoyo: ${this.pv.yoyo})`))}))}))}_repeat_params(){return{count:this.pv.unlimited?-1:this.pv.count,delay:this.pv.delay,yoyo:this.pv.yoyo}}cook(t){const e=t[0]||new R_;e.setRepeatParams(this._repeat_params()),this.setTimelineBuilder(e)}}const pm=new class extends aa{constructor(){super(...arguments),this.input=oa.INTEGER(0,{range:[0,3],rangeLocked:[!0,!0]})}};class _m extends iu{constructor(){super(...arguments),this.paramsConfig=pm}static type(){return\\\\\\\"switch\\\\\\\"}initializeNode(){this.io.inputs.setCount(0,4)}cook(t){const e=t[this.pv.input];e?this.setTimelineBuilder(e):this.states.error.set(`input ${this.pv.input} is not valid`)}}class mm{constructor(t,e){this._scene=t,this._options=e}clone(){return new mm(this._scene,this._options)}objects(){const t=this._options.objectMask;if(t)return this._scene.objectsByMask(t)}node(){if(!this._options.node)return;const t=this._options.node;return t.relativeTo.node(t.path)}}class fm{constructor(){this._update_matrix=!1}clone(){const t=new fm;return t.setUpdateMatrix(this._update_matrix),t}setUpdateMatrix(t){this._update_matrix=t}updateMatrix(){return this._update_matrix}}var gm;!function(t){t.SCENE_GRAPH=\\\\\\\"scene graph\\\\\\\",t.NODE=\\\\\\\"node\\\\\\\"}(gm||(gm={}));const vm=[gm.SCENE_GRAPH,gm.NODE],ym=vm.indexOf(gm.SCENE_GRAPH),xm=vm.indexOf(gm.NODE);const bm=new class extends aa{constructor(){super(...arguments),this.type=oa.INTEGER(ym,{menu:{entries:vm.map(((t,e)=>({name:t,value:e})))}}),this.nodePath=oa.OPERATOR_PATH(\\\\\\\"\\\\\\\",{visibleIf:{type:xm}}),this.objectMask=oa.STRING(\\\\\\\"/geo*\\\\\\\",{visibleIf:{type:ym}}),this.updateMatrix=oa.BOOLEAN(0,{visibleIf:{type:ym}}),this.printResolve=oa.BUTTON(null,{callback:(t,e)=>{wm.PARAM_CALLBACK_print_resolve(t)}})}};class wm extends iu{constructor(){super(...arguments),this.paramsConfig=bm}static type(){return\\\\\\\"target\\\\\\\"}initializeNode(){this.io.inputs.setCount(0,1),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.type,this.p.nodePath,this.p.objectMask],(()=>{const t=vm[this.pv.type];switch(t){case gm.NODE:return this.pv.nodePath;case gm.SCENE_GRAPH:return this.pv.objectMask}ar.unreachable(t)}))}))}))}cook(t){const e=t[0]||new R_,n=this._create_target(e);e.setTarget(n),this._set_update_callback(e),this.setTimelineBuilder(e)}setTargetType(t){this.p.type.set(vm.indexOf(t))}_create_target(t){const e=vm[this.pv.type];switch(e){case gm.NODE:return new mm(this.scene(),{node:{path:this.pv.nodePath,relativeTo:this}});case gm.SCENE_GRAPH:return new mm(this.scene(),{objectMask:this.pv.objectMask})}ar.unreachable(e)}_set_update_callback(t){const e=vm[this.pv.type];let n=t.updateCallback();switch(e){case gm.NODE:return;case gm.SCENE_GRAPH:return void(this.pv.updateMatrix&&(n=n||new fm,n.setUpdateMatrix(this.pv.updateMatrix),t.setUpdateCallback(n)))}ar.unreachable(e)}static PARAM_CALLBACK_print_resolve(t){t.print_resolve()}print_resolve(){const t=vm[this.pv.type],e=new R_,n=this._create_target(e);switch(t){case gm.NODE:return console.log(n.node());case gm.SCENE_GRAPH:return console.log(n.objects())}}}class Tm extends ia{static context(){return Ki.ANIM}cook(){this.cookController.endCook()}}class Am extends Tm{}class Em extends Am{constructor(){super(...arguments),this._children_controller_context=Ki.ANIM}static type(){return tr.ANIM}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class Mm extends Am{constructor(){super(...arguments),this._children_controller_context=Ki.COP}static type(){return tr.COP}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class Sm extends Am{constructor(){super(...arguments),this._children_controller_context=Ki.EVENT}static type(){return tr.EVENT}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class Cm extends Am{constructor(){super(...arguments),this._children_controller_context=Ki.MAT}static type(){return tr.MAT}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}const Nm={dependsOnDisplayNode:!0};class Lm{constructor(t,e,n=Nm){this.node=t,this.options=n,this._initialized=!1,this._display_node=void 0,this._graph_node=new Ai(t.scene(),\\\\\\\"DisplayNodeController\\\\\\\"),this._graph_node.node=t,this._on_display_node_remove_callback=e.onDisplayNodeRemove,this._on_display_node_set_callback=e.onDisplayNodeSet,this._on_display_node_update_callback=e.onDisplayNodeUpdate}dispose(){this._graph_node.dispose()}displayNode(){return this._display_node}initializeNode(){this._initialized?console.error(\\\\\\\"display node controller already initialed\\\\\\\",this.node):(this._initialized=!0,this.node.lifecycle.add_on_child_add_hook((t=>{var e,n;this._display_node||null===(n=null===(e=t.flags)||void 0===e?void 0:e.display)||void 0===n||n.set(!0)})),this.node.lifecycle.add_on_child_remove_hook((t=>{var e,n,i;if(t.graphNodeId()==(null===(e=this._display_node)||void 0===e?void 0:e.graphNodeId())){const t=this.node.children(),e=t[t.length-1];e?null===(i=null===(n=e.flags)||void 0===n?void 0:n.display)||void 0===i||i.set(!0):this.setDisplayNode(void 0)}})),this._graph_node.dirtyController.addPostDirtyHook(\\\\\\\"_request_display_node_container\\\\\\\",(()=>{this._on_display_node_update_callback&&this._on_display_node_update_callback()})))}async setDisplayNode(t){if(this._initialized||console.error(\\\\\\\"display node controller not initialized\\\\\\\",this.node),this._display_node!=t){const e=this._display_node;e&&(e.flags.display.set(!1),this.options.dependsOnDisplayNode&&this._graph_node.removeGraphInput(e),this._on_display_node_remove_callback&&this._on_display_node_remove_callback()),this._display_node=t,this._display_node&&(this.options.dependsOnDisplayNode&&this._graph_node.addGraphInput(this._display_node),this._on_display_node_set_callback&&this._on_display_node_set_callback())}}}class Om{constructor(t=!0){this.autoStart=t,this.startTime=0,this.oldTime=0,this.elapsedTime=0,this.running=!1}start(){this.startTime=Rm(),this.oldTime=this.startTime,this.elapsedTime=0,this.running=!0}stop(){this.getElapsedTime(),this.running=!1,this.autoStart=!1}getElapsedTime(){return this.getDelta(),this.elapsedTime}getDelta(){let t=0;if(this.autoStart&&!this.running)return this.start(),0;if(this.running){const e=Rm();t=(e-this.oldTime)/1e3,this.oldTime=e,this.elapsedTime+=t}return t}}function Rm(){return(\\\\\\\"undefined\\\\\\\"==typeof performance?Date:performance).now()}var Pm={uniforms:{tDiffuse:{value:null},opacity:{value:1}},vertexShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\tvarying vec2 vUv;\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\tvUv = uv;\\\\n\\\\t\\\\t\\\\tgl_Position = projectionMatrix * modelViewMatrix * vec4( position, 1.0 );\\\\n\\\\n\\\\t\\\\t}\\\\\\\",fragmentShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\tuniform float opacity;\\\\n\\\\n\\\\t\\\\tuniform sampler2D tDiffuse;\\\\n\\\\n\\\\t\\\\tvarying vec2 vUv;\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\tvec4 texel = texture2D( tDiffuse, vUv );\\\\n\\\\t\\\\t\\\\tgl_FragColor = opacity * texel;\\\\n\\\\n\\\\t\\\\t}\\\\\\\"};class Im{constructor(){this.enabled=!0,this.needsSwap=!0,this.clear=!1,this.renderToScreen=!1}setSize(){}render(){console.error(\\\\\\\"THREE.Pass: .render() must be implemented in derived pass.\\\\\\\")}}const Fm=new st.a(-1,1,1,-1,0,1),Dm=new S.a;Dm.setAttribute(\\\\\\\"position\\\\\\\",new C.c([-1,3,0,-1,-1,0,3,-1,0],3)),Dm.setAttribute(\\\\\\\"uv\\\\\\\",new C.c([0,2,0,0,2,0],2));class km{constructor(t){this._mesh=new k.a(Dm,t)}dispose(){this._mesh.geometry.dispose()}render(t){t.render(this._mesh,Fm)}get material(){return this._mesh.material}set material(t){this._mesh.material=t}}class Bm extends Im{constructor(t,e){super(),this.textureID=void 0!==e?e:\\\\\\\"tDiffuse\\\\\\\",t instanceof F?(this.uniforms=t.uniforms,this.material=t):t&&(this.uniforms=I.clone(t.uniforms),this.material=new F({defines:Object.assign({},t.defines),uniforms:this.uniforms,vertexShader:t.vertexShader,fragmentShader:t.fragmentShader})),this.fsQuad=new km(this.material)}render(t,e,n){this.uniforms[this.textureID]&&(this.uniforms[this.textureID].value=n.texture),this.fsQuad.material=this.material,this.renderToScreen?(t.setRenderTarget(null),this.fsQuad.render(t)):(t.setRenderTarget(e),this.clear&&t.clear(t.autoClearColor,t.autoClearDepth,t.autoClearStencil),this.fsQuad.render(t))}}class zm extends Im{constructor(t,e){super(),this.scene=t,this.camera=e,this.clear=!0,this.needsSwap=!1,this.inverse=!1}render(t,e,n){const i=t.getContext(),r=t.state;let s,o;r.buffers.color.setMask(!1),r.buffers.depth.setMask(!1),r.buffers.color.setLocked(!0),r.buffers.depth.setLocked(!0),this.inverse?(s=0,o=1):(s=1,o=0),r.buffers.stencil.setTest(!0),r.buffers.stencil.setOp(i.REPLACE,i.REPLACE,i.REPLACE),r.buffers.stencil.setFunc(i.ALWAYS,s,4294967295),r.buffers.stencil.setClear(o),r.buffers.stencil.setLocked(!0),t.setRenderTarget(n),this.clear&&t.clear(),t.render(this.scene,this.camera),t.setRenderTarget(e),this.clear&&t.clear(),t.render(this.scene,this.camera),r.buffers.color.setLocked(!1),r.buffers.depth.setLocked(!1),r.buffers.stencil.setLocked(!1),r.buffers.stencil.setFunc(i.EQUAL,1,4294967295),r.buffers.stencil.setOp(i.KEEP,i.KEEP,i.KEEP),r.buffers.stencil.setLocked(!0)}}class Um extends Im{constructor(){super(),this.needsSwap=!1}render(t){t.state.buffers.stencil.setLocked(!1),t.state.buffers.stencil.setTest(!1)}}class Gm{constructor(t,e){if(this.renderer=t,void 0===e){const n={minFilter:w.V,magFilter:w.V,format:w.Ib},i=t.getSize(new d.a);this._pixelRatio=t.getPixelRatio(),this._width=i.width,this._height=i.height,(e=new Z(this._width*this._pixelRatio,this._height*this._pixelRatio,n)).texture.name=\\\\\\\"EffectComposer.rt1\\\\\\\"}else this._pixelRatio=1,this._width=e.width,this._height=e.height;this.renderTarget1=e,this.renderTarget2=e.clone(),this.renderTarget2.texture.name=\\\\\\\"EffectComposer.rt2\\\\\\\",this.writeBuffer=this.renderTarget1,this.readBuffer=this.renderTarget2,this.renderToScreen=!0,this.passes=[],void 0===Pm&&console.error(\\\\\\\"THREE.EffectComposer relies on CopyShader\\\\\\\"),void 0===Bm&&console.error(\\\\\\\"THREE.EffectComposer relies on ShaderPass\\\\\\\"),this.copyPass=new Bm(Pm),this.clock=new Om}swapBuffers(){const t=this.readBuffer;this.readBuffer=this.writeBuffer,this.writeBuffer=t}addPass(t){this.passes.push(t),t.setSize(this._width*this._pixelRatio,this._height*this._pixelRatio)}insertPass(t,e){this.passes.splice(e,0,t),t.setSize(this._width*this._pixelRatio,this._height*this._pixelRatio)}removePass(t){const e=this.passes.indexOf(t);-1!==e&&this.passes.splice(e,1)}isLastEnabledPass(t){for(let e=t+1;e<this.passes.length;e++)if(this.passes[e].enabled)return!1;return!0}render(t){void 0===t&&(t=this.clock.getDelta());const e=this.renderer.getRenderTarget();let n=!1;for(let e=0,i=this.passes.length;e<i;e++){const i=this.passes[e];if(!1!==i.enabled){if(i.renderToScreen=this.renderToScreen&&this.isLastEnabledPass(e),i.render(this.renderer,this.writeBuffer,this.readBuffer,t,n),i.needsSwap){if(n){const e=this.renderer.getContext(),n=this.renderer.state.buffers.stencil;n.setFunc(e.NOTEQUAL,1,4294967295),this.copyPass.render(this.renderer,this.writeBuffer,this.readBuffer,t),n.setFunc(e.EQUAL,1,4294967295)}this.swapBuffers()}void 0!==zm&&(i instanceof zm?n=!0:i instanceof Um&&(n=!1))}}this.renderer.setRenderTarget(e)}reset(t){if(void 0===t){const e=this.renderer.getSize(new d.a);this._pixelRatio=this.renderer.getPixelRatio(),this._width=e.width,this._height=e.height,(t=this.renderTarget1.clone()).setSize(this._width*this._pixelRatio,this._height*this._pixelRatio)}this.renderTarget1.dispose(),this.renderTarget2.dispose(),this.renderTarget1=t,this.renderTarget2=t.clone(),this.writeBuffer=this.renderTarget1,this.readBuffer=this.renderTarget2}setSize(t,e){this._width=t,this._height=e;const n=this._width*this._pixelRatio,i=this._height*this._pixelRatio;this.renderTarget1.setSize(n,i),this.renderTarget2.setSize(n,i);for(let t=0;t<this.passes.length;t++)this.passes[t].setSize(n,i)}setPixelRatio(t){this._pixelRatio=t,this.setSize(this._width,this._height)}}new st.a(-1,1,1,-1,0,1);const Vm=new S.a;Vm.setAttribute(\\\\\\\"position\\\\\\\",new C.c([-1,3,0,-1,-1,0,3,-1,0],3)),Vm.setAttribute(\\\\\\\"uv\\\\\\\",new C.c([0,2,0,0,2,0],2));class Hm extends Im{constructor(t,e,n,i,r){super(),this.scene=t,this.camera=e,this.overrideMaterial=n,this.clearColor=i,this.clearAlpha=void 0!==r?r:0,this.clear=!0,this.clearDepth=!1,this.needsSwap=!1,this._oldClearColor=new D.a}render(t,e,n){const i=t.autoClear;let r,s;t.autoClear=!1,void 0!==this.overrideMaterial&&(s=this.scene.overrideMaterial,this.scene.overrideMaterial=this.overrideMaterial),this.clearColor&&(t.getClearColor(this._oldClearColor),r=t.getClearAlpha(),t.setClearColor(this.clearColor,this.clearAlpha)),this.clearDepth&&t.clearDepth(),t.setRenderTarget(this.renderToScreen?null:n),this.clear&&t.clear(t.autoClearColor,t.autoClearDepth,t.autoClearStencil),t.render(this.scene,this.camera),this.clearColor&&t.setClearColor(this._oldClearColor,r),void 0!==this.overrideMaterial&&(this.scene.overrideMaterial=s),t.autoClear=i}}const jm=[{LinearFilter:w.V},{NearestFilter:w.ob}],Wm=[{NearestFilter:w.ob},{NearestMipMapNearestFilter:w.qb},{NearestMipMapLinearFilter:w.pb},{LinearFilter:w.V},{LinearMipMapNearestFilter:w.X},{LinearMipMapLinearFilter:w.W}],qm=Object.values(jm[0])[0],Xm=Object.values(Wm[5])[0],Ym=jm.map((t=>({name:Object.keys(t)[0],value:Object.values(t)[0]}))),$m=Wm.map((t=>({name:Object.keys(t)[0],value:Object.values(t)[0]})));class Jm extends aa{constructor(){super(...arguments),this.prependRenderPass=oa.BOOLEAN(1),this.useRenderTarget=oa.BOOLEAN(1),this.tmagFilter=oa.BOOLEAN(0,{visibleIf:{useRenderTarget:1}}),this.magFilter=oa.INTEGER(qm,{visibleIf:{useRenderTarget:1,tmagFilter:1},menu:{entries:Ym}}),this.tminFilter=oa.BOOLEAN(0,{visibleIf:{useRenderTarget:1}}),this.minFilter=oa.INTEGER(Xm,{visibleIf:{useRenderTarget:1,tminFilter:1},menu:{entries:$m}}),this.stencilBuffer=oa.BOOLEAN(0,{visibleIf:{useRenderTarget:1}}),this.sampling=oa.INTEGER(1,{range:[1,4],rangeLocked:[!0,!1]})}}class Zm{constructor(t){this.node=t,this._renderer_size=new d.a}displayNodeControllerCallbacks(){return{onDisplayNodeRemove:()=>{},onDisplayNodeSet:()=>{this.node.setDirty()},onDisplayNodeUpdate:()=>{this.node.setDirty()}}}createEffectsComposer(t){const e=t.renderer;let n;if(this.node.pv.useRenderTarget){const t=this._create_render_target(e);n=new Gm(e,t)}else n=new Gm(e);return n.setPixelRatio(window.devicePixelRatio*this.node.pv.sampling),this._build_passes(n,t),n}_create_render_target(t){let e;t.autoClear=!1;const n={format:w.ic,stencilBuffer:this.node.pv.stencilBuffer};return this.node.pv.tminFilter&&(n.minFilter=this.node.pv.minFilter),this.node.pv.tmagFilter&&(n.magFilter=this.node.pv.magFilter),t.getDrawingBufferSize(this._renderer_size),e=ai.renderersController.renderTarget(this._renderer_size.x,this._renderer_size.y,n),e}_build_passes(t,e){if(this.node.pv.prependRenderPass){const n=new Hm(e.scene,e.camera);t.addPass(n)}const n=this.node.displayNodeController.displayNode();n&&n.setupComposer({composer:t,camera:e.camera,resolution:e.resolution,camera_node:e.camera_node,scene:e.scene,requester:e.requester})}}class Qm extends Tm{constructor(){super(...arguments),this.paramsConfig=new Jm,this.effectsComposerController=new Zm(this),this.displayNodeController=new Lm(this,this.effectsComposerController.displayNodeControllerCallbacks()),this._children_controller_context=Ki.POST}static type(){return tr.POST}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class Km extends Am{constructor(){super(...arguments),this._children_controller_context=Ki.ROP}static type(){return tr.ROP}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}var tf=n(43);const ef=\\\\\\\"input texture\\\\\\\",nf=[ef,ef,ef,ef];for(var rf=new Uint16Array(32),sf=0;sf<32;sf++)rf[sf]=28898;const of=new mo.a(rf,32,1,w.gb,w.M);class af extends ia{constructor(t){super(t,\\\\\\\"BaseCopNode\\\\\\\"),this.flags=new Bi(this)}static context(){return Ki.COP}static displayedInputNames(){return nf}initializeBaseNode(){this.io.outputs.setHasOneOutput()}setTexture(t){t.name=this.path();const e=this.containerController.container().texture();if(e){if(e.uuid!=t.uuid){const n=Object.keys(t);for(let i of n)e[i]=t[i];e.needsUpdate=!0}this._setContainer(e)}else this._setContainer(t)}_clearTexture(){this._setContainer(of)}}class lf extends af{}class cf{constructor(){this._id=cf.__next_id++}id(){return this._id}handle_globals_node(t,e,n){}}cf.__next_id=0;class uf{static any(t){return m.isString(t)?t:m.isBoolean(t)?`${t}`:m.isNumber(t)?`${sr.ensureFloat(t)}`:m.isArray(t)?this.numeric_array(t):t instanceof d.a||t instanceof p.a||t instanceof _.a||t instanceof D.a?this.numeric_array(t.toArray()):`ThreeToGl error: unknown value type '${t}'`}static numeric_array(t){const e=new Array(t.length);for(let n=0;n<t.length;n++)e[n]=`${sr.ensureFloat(t[n])}`;return`${`vec${t.length}`}(${e.join(\\\\\\\", \\\\\\\")})`}static vector4(t){if(m.isString(t))return t;return`vec4(${t.toArray().map((t=>`${sr.ensureFloat(t)}`)).join(\\\\\\\", \\\\\\\")})`}static vector3(t){if(m.isString(t))return t;return`vec3(${t.toArray().map((t=>`${sr.ensureFloat(t)}`)).join(\\\\\\\", \\\\\\\")})`}static vector2(t){if(m.isString(t))return t;return`vec2(${t.toArray().map((t=>`${sr.ensureFloat(t)}`)).join(\\\\\\\", \\\\\\\")})`}static vector3_float(t,e){return m.isNumber(e)&&(e=sr.ensureFloat(e)),`vec4(${this.vector3(t)}, ${e})`}static float4(t,e,n,i){return m.isNumber(t)&&(t=sr.ensureFloat(t)),m.isNumber(e)&&(e=sr.ensureFloat(e)),m.isNumber(n)&&(n=sr.ensureFloat(n)),m.isNumber(i)&&(i=sr.ensureFloat(i)),`vec4(${t}, ${e}, ${n}, ${i})`}static float3(t,e,n){return m.isNumber(t)&&(t=sr.ensureFloat(t)),m.isNumber(e)&&(e=sr.ensureFloat(e)),m.isNumber(n)&&(n=sr.ensureFloat(n)),`vec3(${t}, ${e}, ${n})`}static float2(t,e){return m.isNumber(t)&&(t=sr.ensureFloat(t)),m.isNumber(e)&&(e=sr.ensureFloat(e)),`vec2(${t}, ${e})`}static float(t){if(m.isNumber(t))return sr.ensureFloat(t);{const e=parseFloat(t);return m.isNaN(e)?t:sr.ensureFloat(e)}}static integer(t){if(m.isNumber(t))return sr.ensureInteger(t);{const e=parseInt(t);return m.isNaN(e)?t:sr.ensureInteger(e)}}static bool(t){return m.isBoolean(t)?`${t}`:t}}const hf=/\\\\/+/g;class df extends ia{static context(){return Ki.GL}initializeBaseNode(){this.uiData.setLayoutHorizontal(),this.io.connections.initInputs(),this.io.connection_points.spare_params.initializeNode()}cook(){console.warn(\\\\\\\"gl nodes should never cook\\\\\\\")}_set_mat_to_recompile(){var t,e;null===(e=null===(t=this.material_node)||void 0===t?void 0:t.assemblerController)||void 0===e||e.set_compilation_required_and_dirty(this)}get material_node(){var t;const e=this.parent();if(e)return e.context()==Ki.GL?null===(t=e)||void 0===t?void 0:t.material_node:e}glVarName(t){return`v_POLY_${this.path(this.material_node).replace(hf,\\\\\\\"_\\\\\\\")}_${t}`}variableForInputParam(t){return this.variableForInput(t.name())}variableForInput(t){var e;const n=this.io.inputs.get_input_index(t),i=this.io.connections.inputConnection(n);if(i){const e=i.node_src,n=e.io.outputs.namedOutputConnectionPoints()[i.output_index];if(n){const t=n.name();return e.glVarName(t)}throw console.warn(`no output called '${t}' for gl node ${e.path()}`),\\\\\\\"variable_for_input ERROR\\\\\\\"}if(this.params.has(t))return uf.any(null===(e=this.params.get(t))||void 0===e?void 0:e.value);{const t=this.io.inputs.namedInputConnectionPoints()[n];return uf.any(t.init_value)}}setLines(t){}reset_code(){var t;null===(t=this._param_configs_controller)||void 0===t||t.reset()}setParamConfigs(){}param_configs(){var t;return null===(t=this._param_configs_controller)||void 0===t?void 0:t.list()}paramsGenerating(){return!1}paramDefaultValue(t){return null}}const pf=new class extends aa{};class _f extends df{constructor(){super(...arguments),this.paramsConfig=pf}}const mf=[Do.FLOAT,Do.VEC2,Do.VEC3,Do.VEC4];const ff=new class extends aa{constructor(){super(...arguments),this.name=oa.STRING(\\\\\\\"\\\\\\\"),this.type=oa.INTEGER(0,{menu:{entries:mf.map(((t,e)=>({name:t,value:e})))}}),this.texportWhenConnected=oa.BOOLEAN(0,{hidden:!0}),this.exportWhenConnected=oa.BOOLEAN(0,{visibleIf:{texportWhenConnected:1}})}};class gf extends df{constructor(){super(...arguments),this.paramsConfig=ff,this._on_create_set_name_if_none_bound=this._on_create_set_name_if_none.bind(this),this._bound_setExportWhenConnectedStatus=this._setExportWhenConnectedStatus.bind(this)}static type(){return ir.ATTRIBUTE}initializeNode(){this.addPostDirtyHook(\\\\\\\"_set_mat_to_recompile\\\\\\\",this._set_mat_to_recompile_if_is_exporting.bind(this)),this.lifecycle.add_on_create_hook(this._on_create_set_name_if_none_bound),this.io.connection_points.initializeNode(),this.io.connection_points.set_expected_input_types_function((()=>{var t,e;return(null===(e=null===(t=this.material_node)||void 0===t?void 0:t.assemblerController)||void 0===e?void 0:e.allow_attribute_exports())?[mf[this.pv.type]]:[]})),this.io.connection_points.set_input_name_function((t=>gf.INPUT_NAME)),this.io.connection_points.set_expected_output_types_function((()=>[mf[this.pv.type]])),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.name,this.p.exportWhenConnected],(()=>this.pv.exportWhenConnected?`${this.pv.name} (EXPORTED)`:this.pv.name))}))})),this.lifecycle.add_on_add_hook(this._bound_setExportWhenConnectedStatus),this.params.addOnSceneLoadHook(\\\\\\\"prepare params\\\\\\\",this._bound_setExportWhenConnectedStatus)}_setExportWhenConnectedStatus(){var t,e;(null===(e=null===(t=this.material_node)||void 0===t?void 0:t.assemblerController)||void 0===e?void 0:e.allow_attribute_exports())&&this.p.texportWhenConnected.set(1)}setAttribSize(t){this.p.type.set(t-1)}get input_name(){return gf.INPUT_NAME}get output_name(){return gf.OUTPUT_NAME}setLines(t){t.assembler().set_node_lines_attribute(this,t)}get attribute_name(){return this.pv.name.trim()}gl_type(){return this.io.outputs.namedOutputConnectionPoints()[0].type()}set_gl_type(t){this.p.type.set(mf.indexOf(t))}connected_input_node(){return this.io.inputs.named_input(gf.INPUT_NAME)}connected_input_connection_point(){return this.io.inputs.named_input_connection_point(gf.INPUT_NAME)}output_connection_point(){return this.io.outputs.namedOutputConnectionPointsByName(this.output_name)}isImporting(){return this.io.outputs.used_output_names().length>0}isExporting(){if(this.pv.exportWhenConnected){return null!=this.io.inputs.named_input(gf.INPUT_NAME)}return!1}_set_mat_to_recompile_if_is_exporting(){this.isExporting()&&this._set_mat_to_recompile()}_on_create_set_name_if_none(){\\\\\\\"\\\\\\\"==this.pv.name&&this.p.name.set(this.name())}}gf.INPUT_NAME=\\\\\\\"in\\\\\\\",gf.OUTPUT_NAME=\\\\\\\"val\\\\\\\";class vf{constructor(t=[]){this._definitions=t,this._errored=!1}get errored(){return this._errored}get error_message(){return this._error_message}uniq(){const t=new Map,e=[];for(let n of this._definitions)if(!this._errored){const i=n.name(),r=t.get(i);r?r.data_type!=n.data_type&&(this._errored=!0,this._error_message=`attempt to create '${n.name()}' with types '${n.data_type}' by node '${n.node.path()}', when there is already an existing with type ${r.data_type} from node '${r.node.path()}'`,console.warn(\\\\\\\"emitting error message:\\\\\\\",this._error_message)):(t.set(i,n),e.push(i))}const n=[];for(let i of e){const e=t.get(i);e&&n.push(e)}return n}}var yf,xf;!function(t){t.ATTRIBUTE=\\\\\\\"attribute\\\\\\\",t.FUNCTION=\\\\\\\"function\\\\\\\",t.UNIFORM=\\\\\\\"uniform\\\\\\\",t.VARYING=\\\\\\\"varying\\\\\\\"}(yf||(yf={}));class bf{constructor(t,e,n,i){this._definition_type=t,this._data_type=e,this._node=n,this._name=i}get definition_type(){return this._definition_type}get data_type(){return this._data_type}get node(){return this._node}name(){return this._name}collection_instance(){return new vf}}class wf extends bf{constructor(t,e,n){super(yf.ATTRIBUTE,e,t,n),this._node=t,this._data_type=e,this._name=n}get line(){return`attribute ${this.data_type} ${this.name()}`}}class Tf extends bf{constructor(t,e){super(yf.FUNCTION,Do.FLOAT,t,e),this._node=t,this._name=e}get line(){return this.name()}}class Af extends bf{constructor(t,e,n){super(yf.UNIFORM,e,t,n),this._node=t,this._data_type=e,this._name=n}get line(){return`uniform ${this.data_type} ${this.name()}`}}class Ef extends bf{constructor(t,e,n){super(yf.VARYING,e,t,n),this._node=t,this._data_type=e,this._name=n}get line(){return`varying ${this.data_type} ${this.name()}`}}!function(t){t.VERTEX=\\\\\\\"vertex\\\\\\\",t.FRAGMENT=\\\\\\\"fragment\\\\\\\",t.LEAVES_FROM_NODES_SHADER=\\\\\\\"leaves_from_nodes_shader\\\\\\\"}(xf||(xf={}));const Mf={position:\\\\\\\"vec3( position )\\\\\\\"};class Sf extends cf{handle_globals_node(t,e,n){var i,r;const s=t.io.outputs.namedOutputConnectionPointsByName(e);if(!s)return;const o=t.glVarName(e),a=s.type(),l=new Ef(t,a,o);n.addDefinitions(t,[l]);const c=null===(r=null===(i=t.material_node)||void 0===i?void 0:i.assemblerController)||void 0===r?void 0:r.assembler;if(!c)return;const u=c.shader_config(n.current_shader_name);if(!u)return;const h=u.dependencies(),d=[],p=`${o} = modelMatrix * vec4( position, 1.0 )`,_=`${o} = normalize( mat3( modelMatrix[0].xyz, modelMatrix[1].xyz, modelMatrix[2].xyz ) * normal )`;switch(e){case\\\\\\\"worldPosition\\\\\\\":d.push(p);break;case\\\\\\\"worldNormal\\\\\\\":d.push(_);break;default:d.push(`${o} = ${a}(${e})`)}for(let e of h)n.addDefinitions(t,[l],e),n.addBodyLines(t,d,e);0==h.length&&n.addBodyLines(t,d)}static variable_config_default(t){return Mf[t]}variable_config_default(t){return Sf.variable_config_default(t)}read_attribute(t,e,n,i){return Sf.read_attribute(t,e,n,i)}static read_attribute(t,e,n,i){var r,s;Sf.PRE_DEFINED_ATTRIBUTES.indexOf(n)<0&&i.addDefinitions(t,[new wf(t,e,n)],xf.VERTEX);const o=i.current_shader_name;switch(o){case xf.VERTEX:return n;case xf.FRAGMENT:{if(!(t instanceof gf))return;const a=\\\\\\\"varying_\\\\\\\"+t.glVarName(t.output_name),l=new Ef(t,e,a),c=new Map;c.set(xf.FRAGMENT,[]);const h=new Map;h.set(xf.FRAGMENT,[]),u.pushOnArrayAtEntry(c,o,l);const d=`${a} = ${e}(${n})`,p=null===(s=null===(r=t.material_node)||void 0===r?void 0:r.assemblerController)||void 0===s?void 0:s.assembler.shader_config(o);if(p){const e=p.dependencies();for(let t of e)u.pushOnArrayAtEntry(c,t,l),u.pushOnArrayAtEntry(h,t,d);c.forEach(((e,n)=>{i.addDefinitions(t,e,n)})),h.forEach(((e,n)=>{i.addBodyLines(t,e,n)}))}return a}}}handle_attribute_node(t,e,n,i){return Sf.read_attribute(t,e,n,i)}}Sf.PRE_DEFINED_ATTRIBUTES=[\\\\\\\"position\\\\\\\",\\\\\\\"color\\\\\\\",\\\\\\\"normal\\\\\\\",\\\\\\\"uv\\\\\\\",\\\\\\\"uv2\\\\\\\",\\\\\\\"morphTarget0\\\\\\\",\\\\\\\"morphTarget1\\\\\\\",\\\\\\\"morphTarget2\\\\\\\",\\\\\\\"morphTarget3\\\\\\\",\\\\\\\"skinIndex\\\\\\\",\\\\\\\"skinWeight\\\\\\\"],Sf.IF_RULE={uv:\\\\\\\"defined( USE_MAP ) || defined( USE_BUMPMAP ) || defined( USE_NORMALMAP ) || defined( USE_SPECULARMAP ) || defined( USE_ALPHAMAP ) || defined( USE_EMISSIVEMAP ) || defined( USE_ROUGHNESSMAP ) || defined( USE_METALNESSMAP )\\\\\\\"};const Cf=[Do.FLOAT,Do.VEC2,Do.VEC3,Do.VEC4];const Nf=new class extends aa{constructor(){super(...arguments),this.name=oa.STRING(\\\\\\\"\\\\\\\"),this.type=oa.INTEGER(0,{menu:{entries:Cf.map(((t,e)=>({name:t,value:e})))}})}};class Lf extends df{constructor(){super(...arguments),this.paramsConfig=Nf,this._on_create_set_name_if_none_bound=this._on_create_set_name_if_none.bind(this)}static type(){return\\\\\\\"varyingWrite\\\\\\\"}initializeNode(){this.addPostDirtyHook(\\\\\\\"_set_mat_to_recompile\\\\\\\",this._set_mat_to_recompile.bind(this)),this.lifecycle.add_on_create_hook(this._on_create_set_name_if_none_bound),this.io.connection_points.initializeNode(),this.io.connection_points.set_input_name_function((()=>this.input_name)),this.io.connection_points.set_expected_input_types_function((()=>[Cf[this.pv.type]])),this.io.connection_points.set_expected_output_types_function((()=>[])),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.name])}))}))}get input_name(){return Lf.INPUT_NAME}setLines(t){if(t.current_shader_name==xf.VERTEX){const e=this.gl_type();if(!e)return;const n=this.pv.name,i=new Ef(this,e,n),r=`${n} = ${uf.any(this.variableForInput(Lf.INPUT_NAME))}`;t.addDefinitions(this,[i],xf.VERTEX),t.addBodyLines(this,[r],xf.VERTEX)}}get attribute_name(){return this.pv.name.trim()}gl_type(){const t=this.io.inputs.namedInputConnectionPoints()[0];if(t)return t.type()}set_gl_type(t){this.p.type.set(Cf.indexOf(t))}_on_create_set_name_if_none(){\\\\\\\"\\\\\\\"==this.pv.name&&this.p.name.set(this.name())}}Lf.INPUT_NAME=\\\\\\\"vertex\\\\\\\";class Of{static findOutputNodes(t){return t.nodesByType(\\\\\\\"output\\\\\\\")}static findParamGeneratingNodes(t){var e;const n=[];return null===(e=t.childrenController)||void 0===e||e.traverse_children((t=>{const e=t;e.paramsGenerating()&&n.push(e)})),n}static findVaryingNodes(t){return t.nodesByType(Lf.type())}static findAttributeExportNodes(t){return t.nodesByType(gf.type()).filter((t=>t.isExporting()))}}class Rf{static overlay(t,e){return new Promise(((n,i)=>{let r=document.createElement(\\\\\\\"canvas\\\\\\\");r.width=Math.max(t.width,e.width),r.height=Math.max(t.height,e.height);let s=r.getContext(\\\\\\\"2d\\\\\\\");s.drawImage(t,0,0,t.width,t.height),s.drawImage(e,0,0,e.width,e.height);const o=r.toDataURL(\\\\\\\"image/png\\\\\\\"),a=new Image;a.onload=()=>{n(a)},a.src=o}))}static create_white_image(t,e){return new Promise(((n,i)=>{let r=document.createElement(\\\\\\\"canvas\\\\\\\");r.width=t,r.height=e;let s=r.getContext(\\\\\\\"2d\\\\\\\");s.beginPath(),s.rect(0,0,t,e),s.fillStyle=\\\\\\\"white\\\\\\\",s.fill();const o=r.toDataURL(\\\\\\\"image/png\\\\\\\"),a=new Image;a.onload=()=>{n(a)},a.src=o}))}static make_square(t){return new Promise(((e,n)=>{let i=document.createElement(\\\\\\\"canvas\\\\\\\");const r=Math.min(t.width,t.height),s=t.width/t.height;i.width=r,i.height=r;let o=i.getContext(\\\\\\\"2d\\\\\\\");const a=s>1,l=a?(t.width-r)/2:(t.height-r)/2;a?o.drawImage(t,l,0,r,r,0,0,r,r):o.drawImage(t,0,l,r,r,0,0,r,r);const c=i.toDataURL(\\\\\\\"image/png\\\\\\\"),u=new Image;u.onload=()=>{e(u)},u.src=c}))}static async image_to_blob(t){return new Promise((function(e,n){try{let i=new XMLHttpRequest;i.open(\\\\\\\"GET\\\\\\\",t.src),i.responseType=\\\\\\\"blob\\\\\\\",i.onerror=function(){n(\\\\\\\"Network error.\\\\\\\")},i.onload=function(){200===i.status?e(i.response):n(\\\\\\\"Loading error:\\\\\\\"+i.statusText)},i.send()}catch(t){n(t.message)}}))}static data_from_url(t){return new Promise(((e,n)=>{const i=new Image;i.crossOrigin=\\\\\\\"Anonymous\\\\\\\",i.onload=()=>{const t=this.data_from_image(i);e(t)},i.src=t}))}static data_from_image(t){const e=document.createElement(\\\\\\\"canvas\\\\\\\");e.width=t.width,e.height=t.height;const n=e.getContext(\\\\\\\"2d\\\\\\\");return n.drawImage(t,0,0,t.width,t.height),n.getImageData(0,0,t.width,t.height)}}var Pf;!function(t){t.Uint8Array=\\\\\\\"Uint8Array\\\\\\\",t.Uint8ClampedArray=\\\\\\\"Uint8ClampedArray\\\\\\\",t.Float32Array=\\\\\\\"Float32Array\\\\\\\"}(Pf||(Pf={}));class If{constructor(t){this.buffer_type=t}from_render_target(t,e){return this._data_texture&&this._same_dimensions(e.texture)||(this._data_texture=this._create_data_texture(e.texture)),this._copy_to_data_texture(t,e),this._data_texture}from_texture(t){const e=Rf.data_from_image(t.image);this._data_texture&&this._same_dimensions(t)||(this._data_texture=this._create_data_texture(t));const n=e.width*e.height,i=e.data,r=this._data_texture.image.data,s=4*n;for(let t=0;t<s;t++)r[t]=i[t];return this._data_texture}get data_texture(){return this._data_texture}reset(){this._data_texture=void 0}_copy_to_data_texture(t,e){const n=e.texture.image;this._data_texture=this._data_texture||this._create_data_texture(e.texture),t.readRenderTargetPixels(e,0,0,n.width,n.height,this._data_texture.image.data),this._data_texture.needsUpdate=!0}_create_data_texture(t){const e=t.image,n=this._create_pixel_buffer(e.width,e.height);return new mo.a(n,e.width,e.height,t.format,t.type,t.mapping,t.wrapS,t.wrapT,t.magFilter,t.minFilter,t.anisotropy,t.encoding)}_create_pixel_buffer(t,e){const n=t*e*4;switch(this.buffer_type){case Pf.Uint8Array:return new Uint8Array(n);case Pf.Uint8ClampedArray:return new Uint8ClampedArray(n);case Pf.Float32Array:return new Float32Array(n)}ar.unreachable(this.buffer_type)}_same_dimensions(t){if(this._data_texture){const e=this._data_texture.image.width==t.image.width,n=this._data_texture.image.height==t.image.height;return e&&n}return!0}}new class extends aa{};class Ff{constructor(t){this.node=t}async renderer(){return await this.cameraRenderer()}reset(){var t;null===(t=this._renderer)||void 0===t||t.dispose(),this._renderer=void 0}async cameraRenderer(){let t=ai.renderersController.firstRenderer();return t||await ai.renderersController.waitForRenderer()}save_state(){this.make_linear()}make_linear(){}restore_state(){}}var Df=n(21),kf=n(13);class Bf extends O.a{constructor(t){super(),this.type=\\\\\\\"ShadowMaterial\\\\\\\",this.color=new D.a(0),this.transparent=!0,this.setValues(t)}copy(t){return super.copy(t),this.color.copy(t.color),this}}Bf.prototype.isShadowMaterial=!0;class zf extends O.a{constructor(t){super(),this.type=\\\\\\\"SpriteMaterial\\\\\\\",this.color=new D.a(16777215),this.map=null,this.alphaMap=null,this.rotation=0,this.sizeAttenuation=!0,this.transparent=!0,this.setValues(t)}copy(t){return super.copy(t),this.color.copy(t.color),this.map=t.map,this.alphaMap=t.alphaMap,this.rotation=t.rotation,this.sizeAttenuation=t.sizeAttenuation,this}}zf.prototype.isSpriteMaterial=!0;var Uf=n(65),Gf=n(56);class Vf extends O.a{constructor(t){super(),this.defines={TOON:\\\\\\\"\\\\\\\"},this.type=\\\\\\\"MeshToonMaterial\\\\\\\",this.color=new D.a(16777215),this.map=null,this.gradientMap=null,this.lightMap=null,this.lightMapIntensity=1,this.aoMap=null,this.aoMapIntensity=1,this.emissive=new D.a(0),this.emissiveIntensity=1,this.emissiveMap=null,this.bumpMap=null,this.bumpScale=1,this.normalMap=null,this.normalMapType=w.Uc,this.normalScale=new d.a(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}}Vf.prototype.isMeshToonMaterial=!0;class Hf extends O.a{constructor(t){super(),this.type=\\\\\\\"MeshNormalMaterial\\\\\\\",this.bumpMap=null,this.bumpScale=1,this.normalMap=null,this.normalMapType=w.Uc,this.normalScale=new d.a(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}}Hf.prototype.isMeshNormalMaterial=!0;class jf extends O.a{constructor(t){super(),this.defines={MATCAP:\\\\\\\"\\\\\\\"},this.type=\\\\\\\"MeshMatcapMaterial\\\\\\\",this.color=new D.a(16777215),this.matcap=null,this.map=null,this.bumpMap=null,this.bumpScale=1,this.normalMap=null,this.normalMapType=w.Uc,this.normalScale=new d.a(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}}jf.prototype.isMeshMatcapMaterial=!0;class Wf extends wr.a{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}}Wf.prototype.isLineDashedMaterial=!0;class qf extends kf.a{constructor(t){super(t),this.textures={}}load(t,e,n,i){const r=this,s=new Df.a(r.manager);s.setPath(r.path),s.setRequestHeader(r.requestHeader),s.setWithCredentials(r.withCredentials),s.load(t,(function(n){try{e(r.parse(JSON.parse(n)))}catch(e){i?i(e):console.error(e),r.manager.itemError(t)}}),n,i)}parse(t){const e=this.textures;function n(t){return void 0===e[t]&&console.warn(\\\\\\\"THREE.MaterialLoader: Undefined texture\\\\\\\",t),e[t]}const r=new i[t.type];if(void 0!==t.uuid&&(r.uuid=t.uuid),void 0!==t.name&&(r.name=t.name),void 0!==t.color&&void 0!==r.color&&r.color.setHex(t.color),void 0!==t.roughness&&(r.roughness=t.roughness),void 0!==t.metalness&&(r.metalness=t.metalness),void 0!==t.sheen&&(r.sheen=t.sheen),void 0!==t.sheenTint&&(r.sheenTint=(new D.a).setHex(t.sheenTint)),void 0!==t.sheenRoughness&&(r.sheenRoughness=t.sheenRoughness),void 0!==t.emissive&&void 0!==r.emissive&&r.emissive.setHex(t.emissive),void 0!==t.specular&&void 0!==r.specular&&r.specular.setHex(t.specular),void 0!==t.specularIntensity&&(r.specularIntensity=t.specularIntensity),void 0!==t.specularTint&&void 0!==r.specularTint&&r.specularTint.setHex(t.specularTint),void 0!==t.shininess&&(r.shininess=t.shininess),void 0!==t.clearcoat&&(r.clearcoat=t.clearcoat),void 0!==t.clearcoatRoughness&&(r.clearcoatRoughness=t.clearcoatRoughness),void 0!==t.transmission&&(r.transmission=t.transmission),void 0!==t.thickness&&(r.thickness=t.thickness),void 0!==t.attenuationDistance&&(r.attenuationDistance=t.attenuationDistance),void 0!==t.attenuationTint&&void 0!==r.attenuationTint&&r.attenuationTint.setHex(t.attenuationTint),void 0!==t.fog&&(r.fog=t.fog),void 0!==t.flatShading&&(r.flatShading=t.flatShading),void 0!==t.blending&&(r.blending=t.blending),void 0!==t.combine&&(r.combine=t.combine),void 0!==t.side&&(r.side=t.side),void 0!==t.shadowSide&&(r.shadowSide=t.shadowSide),void 0!==t.opacity&&(r.opacity=t.opacity),void 0!==t.format&&(r.format=t.format),void 0!==t.transparent&&(r.transparent=t.transparent),void 0!==t.alphaTest&&(r.alphaTest=t.alphaTest),void 0!==t.depthTest&&(r.depthTest=t.depthTest),void 0!==t.depthWrite&&(r.depthWrite=t.depthWrite),void 0!==t.colorWrite&&(r.colorWrite=t.colorWrite),void 0!==t.stencilWrite&&(r.stencilWrite=t.stencilWrite),void 0!==t.stencilWriteMask&&(r.stencilWriteMask=t.stencilWriteMask),void 0!==t.stencilFunc&&(r.stencilFunc=t.stencilFunc),void 0!==t.stencilRef&&(r.stencilRef=t.stencilRef),void 0!==t.stencilFuncMask&&(r.stencilFuncMask=t.stencilFuncMask),void 0!==t.stencilFail&&(r.stencilFail=t.stencilFail),void 0!==t.stencilZFail&&(r.stencilZFail=t.stencilZFail),void 0!==t.stencilZPass&&(r.stencilZPass=t.stencilZPass),void 0!==t.wireframe&&(r.wireframe=t.wireframe),void 0!==t.wireframeLinewidth&&(r.wireframeLinewidth=t.wireframeLinewidth),void 0!==t.wireframeLinecap&&(r.wireframeLinecap=t.wireframeLinecap),void 0!==t.wireframeLinejoin&&(r.wireframeLinejoin=t.wireframeLinejoin),void 0!==t.rotation&&(r.rotation=t.rotation),1!==t.linewidth&&(r.linewidth=t.linewidth),void 0!==t.dashSize&&(r.dashSize=t.dashSize),void 0!==t.gapSize&&(r.gapSize=t.gapSize),void 0!==t.scale&&(r.scale=t.scale),void 0!==t.polygonOffset&&(r.polygonOffset=t.polygonOffset),void 0!==t.polygonOffsetFactor&&(r.polygonOffsetFactor=t.polygonOffsetFactor),void 0!==t.polygonOffsetUnits&&(r.polygonOffsetUnits=t.polygonOffsetUnits),void 0!==t.dithering&&(r.dithering=t.dithering),void 0!==t.alphaToCoverage&&(r.alphaToCoverage=t.alphaToCoverage),void 0!==t.premultipliedAlpha&&(r.premultipliedAlpha=t.premultipliedAlpha),void 0!==t.visible&&(r.visible=t.visible),void 0!==t.toneMapped&&(r.toneMapped=t.toneMapped),void 0!==t.userData&&(r.userData=t.userData),void 0!==t.vertexColors&&(\\\\\\\"number\\\\\\\"==typeof t.vertexColors?r.vertexColors=t.vertexColors>0:r.vertexColors=t.vertexColors),void 0!==t.uniforms)for(const e in t.uniforms){const i=t.uniforms[e];switch(r.uniforms[e]={},i.type){case\\\\\\\"t\\\\\\\":r.uniforms[e].value=n(i.value);break;case\\\\\\\"c\\\\\\\":r.uniforms[e].value=(new D.a).setHex(i.value);break;case\\\\\\\"v2\\\\\\\":r.uniforms[e].value=(new d.a).fromArray(i.value);break;case\\\\\\\"v3\\\\\\\":r.uniforms[e].value=(new p.a).fromArray(i.value);break;case\\\\\\\"v4\\\\\\\":r.uniforms[e].value=(new _.a).fromArray(i.value);break;case\\\\\\\"m3\\\\\\\":r.uniforms[e].value=(new U.a).fromArray(i.value);break;case\\\\\\\"m4\\\\\\\":r.uniforms[e].value=(new A.a).fromArray(i.value);break;default:r.uniforms[e].value=i.value}}if(void 0!==t.defines&&(r.defines=t.defines),void 0!==t.vertexShader&&(r.vertexShader=t.vertexShader),void 0!==t.fragmentShader&&(r.fragmentShader=t.fragmentShader),void 0!==t.extensions)for(const e in t.extensions)r.extensions[e]=t.extensions[e];if(void 0!==t.shading&&(r.flatShading=1===t.shading),void 0!==t.size&&(r.size=t.size),void 0!==t.sizeAttenuation&&(r.sizeAttenuation=t.sizeAttenuation),void 0!==t.map&&(r.map=n(t.map)),void 0!==t.matcap&&(r.matcap=n(t.matcap)),void 0!==t.alphaMap&&(r.alphaMap=n(t.alphaMap)),void 0!==t.bumpMap&&(r.bumpMap=n(t.bumpMap)),void 0!==t.bumpScale&&(r.bumpScale=t.bumpScale),void 0!==t.normalMap&&(r.normalMap=n(t.normalMap)),void 0!==t.normalMapType&&(r.normalMapType=t.normalMapType),void 0!==t.normalScale){let e=t.normalScale;!1===Array.isArray(e)&&(e=[e,e]),r.normalScale=(new d.a).fromArray(e)}return void 0!==t.displacementMap&&(r.displacementMap=n(t.displacementMap)),void 0!==t.displacementScale&&(r.displacementScale=t.displacementScale),void 0!==t.displacementBias&&(r.displacementBias=t.displacementBias),void 0!==t.roughnessMap&&(r.roughnessMap=n(t.roughnessMap)),void 0!==t.metalnessMap&&(r.metalnessMap=n(t.metalnessMap)),void 0!==t.emissiveMap&&(r.emissiveMap=n(t.emissiveMap)),void 0!==t.emissiveIntensity&&(r.emissiveIntensity=t.emissiveIntensity),void 0!==t.specularMap&&(r.specularMap=n(t.specularMap)),void 0!==t.specularIntensityMap&&(r.specularIntensityMap=n(t.specularIntensityMap)),void 0!==t.specularTintMap&&(r.specularTintMap=n(t.specularTintMap)),void 0!==t.envMap&&(r.envMap=n(t.envMap)),void 0!==t.envMapIntensity&&(r.envMapIntensity=t.envMapIntensity),void 0!==t.reflectivity&&(r.reflectivity=t.reflectivity),void 0!==t.refractionRatio&&(r.refractionRatio=t.refractionRatio),void 0!==t.lightMap&&(r.lightMap=n(t.lightMap)),void 0!==t.lightMapIntensity&&(r.lightMapIntensity=t.lightMapIntensity),void 0!==t.aoMap&&(r.aoMap=n(t.aoMap)),void 0!==t.aoMapIntensity&&(r.aoMapIntensity=t.aoMapIntensity),void 0!==t.gradientMap&&(r.gradientMap=n(t.gradientMap)),void 0!==t.clearcoatMap&&(r.clearcoatMap=n(t.clearcoatMap)),void 0!==t.clearcoatRoughnessMap&&(r.clearcoatRoughnessMap=n(t.clearcoatRoughnessMap)),void 0!==t.clearcoatNormalMap&&(r.clearcoatNormalMap=n(t.clearcoatNormalMap)),void 0!==t.clearcoatNormalScale&&(r.clearcoatNormalScale=(new d.a).fromArray(t.clearcoatNormalScale)),void 0!==t.transmissionMap&&(r.transmissionMap=n(t.transmissionMap)),void 0!==t.thicknessMap&&(r.thicknessMap=n(t.thicknessMap)),r}setTextures(t){return this.textures=t,this}}class Xf{constructor(t){this.node=t,this._found_uniform_texture_by_id=new Map,this._found_uniform_textures_id_by_uniform_name=new Map,this._found_param_texture_by_id=new Map,this._found_param_textures_id_by_uniform_name=new Map}toJSON(){}load(t){}_materialToJson(t,e){let n;this._unassignTextures(t);try{n=t.toJSON({}),n&&(n.shadowSide=t.shadowSide,n.colorWrite=t.colorWrite)}catch(e){console.error(\\\\\\\"failed to save material data\\\\\\\"),console.log(t)}return n&&null!=t.lights&&(n.lights=t.lights),n&&(n.uuid=`${e.node.path()}-${e.suffix}`),this._reassignTextures(t),n}_unassignTextures(t){this._found_uniform_texture_by_id.clear(),this._found_uniform_textures_id_by_uniform_name.clear(),this._found_param_texture_by_id.clear(),this._found_param_textures_id_by_uniform_name.clear();const e=t.uniforms,n=Object.keys(e);for(let t of n){const n=e[t].value;if(n&&n.uuid){const i=n;this._found_uniform_texture_by_id.set(i.uuid,n),this._found_uniform_textures_id_by_uniform_name.set(t,i.uuid),e[t].value=null}}const i=Object.keys(t);for(let e of i){const n=t[e];if(n&&n.uuid){const i=n;this._found_param_texture_by_id.set(i.uuid,i),this._found_param_textures_id_by_uniform_name.set(e,i.uuid),t[e]=null}}}_reassignTextures(t){const e=[],n=[];this._found_uniform_textures_id_by_uniform_name.forEach(((t,n)=>{e.push(n)})),this._found_param_textures_id_by_uniform_name.forEach(((t,e)=>{n.push(e)}));const i=t.uniforms;for(let t of e){const e=this._found_uniform_textures_id_by_uniform_name.get(t);if(e){const n=this._found_uniform_texture_by_id.get(e);n&&(i[t].value=n)}}for(let e of n){const n=this._found_param_textures_id_by_uniform_name.get(e);if(n){const i=this._found_param_texture_by_id.get(n);i&&(t[e]=i)}}}_loadMaterial(t){t.color=void 0;const e=(new qf).parse(t);t.shadowSide&&(e.shadowSide=t.shadowSide),null!=t.lights&&(e.lights=t.lights);const n=e.uniforms.uv2Transform;n&&this.mat4ToMat3(n);const i=e.uniforms.uvTransform;return i&&this.mat4ToMat3(i),e}mat4ToMat3(t){const e=t.value;if(null==e.elements[e.elements.length-1]){const n=new U.a;for(let t=0;t<n.elements.length;t++)n.elements[t]=e.elements[t];t.value=n}}}class Yf{constructor(t,e,n){this._type=t,this._name=e,this._default_value=n}static from_param(t){return new Yf(t.type(),t.name(),t.defaultValue())}type(){return this._type}name(){return this._name}get default_value(){return this._default_value}get param_options(){const t=this._callback.bind(this);switch(this._type){case Es.OPERATOR_PATH:return{callback:t,nodeSelection:{context:Ki.COP}};default:return{callback:t}}}_callback(t,e){}}class $f extends Yf{constructor(t,e,n,i){super(t,e,n),this._uniform_name=i}get uniform_name(){return this._uniform_name}get uniform(){return this._uniform=this._uniform||this._create_uniform()}_create_uniform(){return $f.uniform_by_type(this._type)}execute_callback(t,e){this._callback(t,e)}_callback(t,e){$f.callback(e,this.uniform)}static callback(t,e){switch(t.type()){case Es.RAMP:return void(e.value=t.rampTexture());case Es.OPERATOR_PATH:return void $f.set_uniform_value_from_texture(t,e);case Es.NODE_PATH:return void $f.set_uniform_value_from_texture_from_node_path_param(t,e);default:e.value=t.value}}static uniform_by_type(t){switch(t){case Es.BOOLEAN:case Es.BUTTON:return{value:0};case Es.COLOR:return{value:new D.a(0,0,0)};case Es.FLOAT:case Es.FOLDER:case Es.INTEGER:case Es.OPERATOR_PATH:case Es.NODE_PATH:case Es.PARAM_PATH:return{value:0};case Es.RAMP:case Es.STRING:return{value:null};case Es.VECTOR2:return{value:new d.a(0,0)};case Es.VECTOR3:return{value:new p.a(0,0,0)};case Es.VECTOR4:return{value:new _.a(0,0,0,0)}}ar.unreachable(t)}static set_uniform_value_from_texture(t,e){const n=t.found_node();if(n)if(n.isDirty())n.compute().then((t=>{const n=t.texture();e.value=n}));else{const t=n.containerController.container().texture();e.value=t}else e.value=null}static async set_uniform_value_from_texture_from_node_path_param(t,e){t.isDirty()&&await t.compute();const n=t.value.nodeWithContext(Ki.COP);if(n)if(n.isDirty())n.compute().then((t=>{const n=t.texture();e.value=n}));else{const t=n.containerController.container().texture();e.value=t}else e.value=null}set_uniform_value_from_ramp(t,e){e.value=t.rampTexture()}}class Jf extends Xf{constructor(t){super(t),this.node=t}toJSON(){const t=this.node.assemblerController;if(!t)return;const e=[],n=t.assembler.param_configs();for(let t of n)e.push([t.name(),t.uniform_name]);return{fragment_shader:this.node.texture_material.fragmentShader,uniforms:this.node.texture_material.uniforms,param_uniform_pairs:e,uniforms_time_dependent:t.assembler.uniformsTimeDependent(),uniforms_resolution_dependent:t.assembler.uniforms_resolution_dependent()}}load(t){this.node.texture_material.fragmentShader=t.fragment_shader,this.node.texture_material.uniforms=t.uniforms,tg.handle_dependencies(this.node,t.uniforms_time_dependent||!1,t.uniforms);for(let e of t.param_uniform_pairs){const n=this.node.params.get(e[0]),i=t.uniforms[e[1]];n&&i&&n.options.set({callback:()=>{$f.callback(n,i)}})}}}class Zf{static isChrome(){return navigator&&null!=navigator.userAgent&&-1!=navigator.userAgent.indexOf(\\\\\\\"Chrome\\\\\\\")}static isMobile(){return/Android|webOS|iPhone|iPad|iPod|BlackBerry|IEMobile|Opera Mini/i.test(navigator.userAgent)}static isiOS(){return/(iPad|iPhone|iPod)/g.test(navigator.userAgent)}static isAndroid(){return/(Android)/g.test(navigator.userAgent)}static isTouchDevice(){var t=document.createElement(\\\\\\\"div\\\\\\\");return t.setAttribute(\\\\\\\"ongesturestart\\\\\\\",\\\\\\\"return;\\\\\\\"),\\\\\\\"function\\\\\\\"==typeof t.ongesturestart}}const Qf=[256,256];const Kf=new class extends aa{constructor(){super(...arguments),this.resolution=oa.VECTOR2(Qf),this.useCameraRenderer=oa.BOOLEAN(0)}};class tg extends af{constructor(){super(...arguments),this.paramsConfig=Kf,this.persisted_config=new Jf(this),this._assembler_controller=this._create_assembler_controller(),this._texture_mesh=new k.a(new L(2,2)),this.texture_material=new F({uniforms:{},vertexShader:\\\\\\\"\\\\nvoid main()\\\\t{\\\\n\\\\tgl_Position = vec4( position, 1.0 );\\\\n}\\\\n\\\\\\\",fragmentShader:\\\\\\\"\\\\\\\"}),this._texture_scene=new fr,this._texture_camera=new tf.a,this._children_controller_context=Ki.GL,this._cook_main_without_inputs_when_dirty_bound=this._cook_main_without_inputs_when_dirty.bind(this)}static type(){return\\\\\\\"builder\\\\\\\"}usedAssembler(){return Hn.GL_TEXTURE}_create_assembler_controller(){const t=ai.assemblersRegister.assembler(this,this.usedAssembler());if(t){const e=new Sf;return t.set_assembler_globals_handler(e),t}}get assemblerController(){return this._assembler_controller}initializeNode(){this._texture_mesh.material=this.texture_material,this._texture_mesh.scale.multiplyScalar(.25),this._texture_scene.add(this._texture_mesh),this._texture_camera.position.z=1,this.addPostDirtyHook(\\\\\\\"_cook_main_without_inputs_when_dirty\\\\\\\",(()=>{setTimeout(this._cook_main_without_inputs_when_dirty_bound,0)}))}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}childrenAllowed(){return this.assemblerController?super.childrenAllowed():(this.scene().markAsReadOnly(this),!1)}async _cook_main_without_inputs_when_dirty(){await this.cookController.cookMainWithoutInputs()}async cook(){this.compileIfRequired(),this.renderOnTarget()}shaders_by_name(){return{fragment:this._fragment_shader}}compileIfRequired(){var t;(null===(t=this.assemblerController)||void 0===t?void 0:t.compileRequired())&&this.compile()}compile(){const t=this.assemblerController;if(!t)return;const e=Of.findOutputNodes(this);if(e.length>1)return void this.states.error.set(\\\\\\\"only one output node allowed\\\\\\\");if(e[0]){const n=e;t.assembler.set_root_nodes(n),t.assembler.update_fragment_shader();const i=t.assembler.fragment_shader(),r=t.assembler.uniforms();i&&r&&(this._fragment_shader=i,this._uniforms=r),tg.handle_dependencies(this,t.assembler.uniformsTimeDependent())}this._fragment_shader&&this._uniforms&&(this.texture_material.fragmentShader=this._fragment_shader,this.texture_material.uniforms=this._uniforms,this.texture_material.needsUpdate=!0,this.texture_material.uniforms.resolution={value:this.pv.resolution}),t.post_compile()}static handle_dependencies(t,e,n){const i=t.scene(),r=`${t.graphNodeId()}`;e?(t.states.timeDependent.forceTimeDependent(),n&&i.uniformsController.addTimeDependentUniformOwner(r,n)):(t.states.timeDependent.unforceTimeDependent(),i.uniformsController.removeTimeDependentUniformOwner(r))}async renderOnTarget(){if(this.createRenderTargetIfRequired(),!this._render_target)return;this._renderer_controller=this._renderer_controller||new Ff(this);const t=await this._renderer_controller.renderer(),e=t.getRenderTarget();if(t.setRenderTarget(this._render_target),t.clear(),t.render(this._texture_scene,this._texture_camera),t.setRenderTarget(e),this._render_target.texture)if(this.pv.useCameraRenderer)this.setTexture(this._render_target.texture);else{this._data_texture_controller=this._data_texture_controller||new If(Pf.Float32Array);const e=this._data_texture_controller.from_render_target(t,this._render_target);this.setTexture(e)}else this.cookController.endCook()}renderTarget(){return this._render_target=this._render_target||this._createRenderTarget(this.pv.resolution.x,this.pv.resolution.y)}createRenderTargetIfRequired(){var t;this._render_target&&this._renderTargetResolutionValid()||(this._render_target=this._createRenderTarget(this.pv.resolution.x,this.pv.resolution.y),null===(t=this._data_texture_controller)||void 0===t||t.reset())}_renderTargetResolutionValid(){if(this._render_target){const t=this._render_target.texture.image;return t.width==this.pv.resolution.x&&t.height==this.pv.resolution.y}return!1}_createRenderTarget(t,e){if(this._render_target){const n=this._render_target.texture.image;if(n.width==t&&n.height==e)return this._render_target}const n=w.n,i=w.n,r=w.V,s=w.ob;var o=new Z(t,e,{wrapS:n,wrapT:i,minFilter:r,magFilter:s,format:w.Ib,type:Zf.isiOS()?w.M:w.G,stencilBuffer:!1,depthBuffer:!1});return ai.warn(\\\\\\\"created render target\\\\\\\",this.path(),t,e),o}}const eg=[{LinearEncoding:w.U},{sRGBEncoding:w.ld},{GammaEncoding:w.J},{RGBEEncoding:w.gc},{LogLuvEncoding:w.bb},{RGBM7Encoding:w.lc},{RGBM16Encoding:w.kc},{RGBDEncoding:w.fc},{BasicDepthPacking:w.j},{RGBADepthPacking:w.Hb}],ng=[{ClampToEdgeWrapping:w.n},{RepeatWrapping:w.wc},{MirroredRepeatWrapping:w.kb}],ig=[{UVMapping:w.Yc},{CubeReflectionMapping:w.o},{CubeRefractionMapping:w.p},{EquirectangularReflectionMapping:w.D},{EquirectangularRefractionMapping:w.E},{CubeUVReflectionMapping:w.q},{CubeUVRefractionMapping:w.r}],rg=[{UnsignedByteType:w.Zc},{ByteType:w.l},{ShortType:w.Mc},{UnsignedShortType:w.fd},{IntType:w.N},{UnsignedIntType:w.bd},{FloatType:w.G},{HalfFloatType:w.M},{UnsignedShort4444Type:w.cd},{UnsignedShort5551Type:w.dd},{UnsignedShort565Type:w.ed},{UnsignedInt248Type:w.ad}],sg=[{AlphaFormat:w.f},{RedFormat:w.tc},{RedIntegerFormat:w.uc},{RGFormat:w.rc},{RGIntegerFormat:w.sc},{RGBFormat:w.ic},{RGBIntegerFormat:w.jc},{RGBAFormat:w.Ib},{RGBAIntegerFormat:w.Jb},{LuminanceFormat:w.gb},{LuminanceAlphaFormat:w.fb},{DepthFormat:w.x},{DepthStencilFormat:w.y}];function og(t){return{cook:!1,callback:e=>{wg[t](e)}}}const ag={ENCODING:w.U,FORMAT:w.Ib,MAPPING:w.Yc,MIN_FILTER:w.V,MAG_FILTER:w.V,TYPE:w.Zc,WRAPPING:w.wc},lg=og(\\\\\\\"PARAM_CALLBACK_update_encoding\\\\\\\"),cg=og(\\\\\\\"PARAM_CALLBACK_update_mapping\\\\\\\"),ug=og(\\\\\\\"PARAM_CALLBACK_update_wrap\\\\\\\"),hg=og(\\\\\\\"PARAM_CALLBACK_update_filter\\\\\\\"),dg=og(\\\\\\\"PARAM_CALLBACK_update_anisotropy\\\\\\\"),pg=og(\\\\\\\"PARAM_CALLBACK_update_flipY\\\\\\\"),_g=og(\\\\\\\"PARAM_CALLBACK_update_transform\\\\\\\"),mg=og(\\\\\\\"PARAM_CALLBACK_update_repeat\\\\\\\"),fg=og(\\\\\\\"PARAM_CALLBACK_update_offset\\\\\\\"),gg=og(\\\\\\\"PARAM_CALLBACK_update_rotation\\\\\\\"),vg=og(\\\\\\\"PARAM_CALLBACK_update_center\\\\\\\"),yg=og(\\\\\\\"PARAM_CALLBACK_update_advanced\\\\\\\");function xg(t){return class extends t{constructor(){super(...arguments),this.tencoding=oa.BOOLEAN(0,{...lg}),this.encoding=oa.INTEGER(ag.ENCODING,{visibleIf:{tencoding:1},menu:{entries:eg.map((t=>({name:Object.keys(t)[0],value:Object.values(t)[0]})))},...lg}),this.tmapping=oa.BOOLEAN(0,{...cg}),this.mapping=oa.INTEGER(ag.MAPPING,{visibleIf:{tmapping:1},menu:{entries:ig.map((t=>({name:Object.keys(t)[0],value:Object.values(t)[0]})))},...cg}),this.twrap=oa.BOOLEAN(0,{...ug}),this.wrapS=oa.INTEGER(ag.WRAPPING,{visibleIf:{twrap:1},menu:{entries:ng.map((t=>({name:Object.keys(t)[0],value:Object.values(t)[0]})))},...ug}),this.wrapT=oa.INTEGER(ag.WRAPPING,{visibleIf:{twrap:1},menu:{entries:ng.map((t=>({name:Object.keys(t)[0],value:Object.values(t)[0]})))},separatorAfter:!0,...ug}),this.tminFilter=oa.BOOLEAN(0,{...hg}),this.minFilter=oa.INTEGER(Xm,{visibleIf:{tminFilter:1},menu:{entries:$m},...hg}),this.tmagFilter=oa.BOOLEAN(0,{...hg}),this.magFilter=oa.INTEGER(qm,{visibleIf:{tmagFilter:1},menu:{entries:Ym},...hg}),this.tanisotropy=oa.BOOLEAN(0,{...dg}),this.useRendererMaxAnisotropy=oa.BOOLEAN(0,{visibleIf:{tanisotropy:1},...dg}),this.anisotropy=oa.INTEGER(2,{visibleIf:{tanisotropy:1,useRendererMaxAnisotropy:0},range:[0,32],rangeLocked:[!0,!1],...dg}),this.tflipY=oa.BOOLEAN(0,{...pg}),this.flipY=oa.BOOLEAN(0,{visibleIf:{tflipY:1},...pg}),this.ttransform=oa.BOOLEAN(0,{..._g}),this.offset=oa.VECTOR2([0,0],{visibleIf:{ttransform:1},...fg}),this.repeat=oa.VECTOR2([1,1],{visibleIf:{ttransform:1},...mg}),this.rotation=oa.FLOAT(0,{range:[-1,1],visibleIf:{ttransform:1},...gg}),this.center=oa.VECTOR2([0,0],{visibleIf:{ttransform:1},...vg}),this.tadvanced=oa.BOOLEAN(0,{...yg}),this.tformat=oa.BOOLEAN(0,{visibleIf:{tadvanced:1},...yg}),this.format=oa.INTEGER(ag.FORMAT,{visibleIf:{tadvanced:1,tformat:1},menu:{entries:sg.map((t=>({name:Object.keys(t)[0],value:Object.values(t)[0]})))},...yg}),this.ttype=oa.BOOLEAN(0,{visibleIf:{tadvanced:1},...yg}),this.type=oa.INTEGER(ag.TYPE,{visibleIf:{tadvanced:1,ttype:1},menu:{entries:rg.map((t=>({name:Object.keys(t)[0],value:Object.values(t)[0]})))},...yg})}}}class bg extends(xg(aa)){}new bg;class wg{constructor(t){this.node=t}async update(t){const e=this.node.pv;this._updateEncoding(t,e),this._updateAdvanced(t,e),this._updateMapping(t,e),this._updateWrap(t,e),this._updateFilter(t,e),this._updateFlip(t,e),await this._updateAnisotropy(t,e),this._updateTransform(t)}_updateEncoding(t,e){e.tencoding?t.encoding=e.encoding:t.encoding=ag.ENCODING,t.needsUpdate=!0}_updateAdvanced(t,e){e.tadvanced&&(e.tformat?t.format=e.format:t.format=ag.FORMAT,e.ttype?t.type=e.type:t.type=ag.TYPE),t.needsUpdate=!0}_updateMapping(t,e){e.tmapping?t.mapping=e.mapping:t.mapping=ag.MAPPING,t.needsUpdate=!0}_updateWrap(t,e){e.twrap?(t.wrapS=e.wrapS,t.wrapT=e.wrapT):(t.wrapS=ag.WRAPPING,t.wrapT=ag.WRAPPING),t.needsUpdate=!0}_updateFilter(t,e){e.tminFilter?t.minFilter=e.minFilter:t.minFilter=w.V,e.tmagFilter?t.magFilter=e.magFilter:t.magFilter=w.V,t.needsUpdate=!0}_updateFlip(t,e){t.flipY=e.tflipY&&e.flipY,t.needsUpdate=!0}async _updateAnisotropy(t,e){if(e.tanisotropy){if(e.useRendererMaxAnisotropy)t.anisotropy=await this._maxRendererAnisotropy();else{const n=e.anisotropy;t.anisotropy=n<=2?n:Math.min(n,await this._maxRendererAnisotropy())}t.needsUpdate=!0}else t.anisotropy=1}async _maxRendererAnisotropy(){this._renderer_controller=this._renderer_controller||new Ff(this.node);return(await this._renderer_controller.renderer()).capabilities.getMaxAnisotropy()}_updateTransform(t){if(!this.node.pv.ttransform)return t.offset.set(0,0),t.rotation=0,t.repeat.set(1,1),void t.center.set(0,0);this._updateTransformOffset(t,!1),this._updateTransformRepeat(t,!1),this._updateTransformRotation(t,!1),this._updateTransformCenter(t,!1),t.updateMatrix()}async _updateTransformOffset(t,e){t.offset.copy(this.node.pv.offset),e&&t.updateMatrix()}async _updateTransformRepeat(t,e){t.repeat.copy(this.node.pv.repeat),e&&t.updateMatrix()}async _updateTransformRotation(t,e){t.rotation=this.node.pv.rotation,e&&t.updateMatrix()}async _updateTransformCenter(t,e){t.center.copy(this.node.pv.center),e&&t.updateMatrix()}static PARAM_CALLBACK_update_encoding(t){const e=t.containerController.container().texture();e&&t.textureParamsController._updateEncoding(e,t.pv)}static PARAM_CALLBACK_update_mapping(t){const e=t.containerController.container().texture();e&&t.textureParamsController._updateMapping(e,t.pv)}static PARAM_CALLBACK_update_wrap(t){const e=t.containerController.container().texture();e&&t.textureParamsController._updateWrap(e,t.pv)}static PARAM_CALLBACK_update_filter(t){const e=t.containerController.container().texture();e&&t.textureParamsController._updateFilter(e,t.pv)}static PARAM_CALLBACK_update_anisotropy(t){const e=t.containerController.container().texture();e&&t.textureParamsController._updateAnisotropy(e,t.pv)}static PARAM_CALLBACK_update_flipY(t){const e=t.containerController.container().texture();e&&t.textureParamsController._updateFlip(e,t.pv)}static PARAM_CALLBACK_update_transform(t){const e=t.containerController.container().texture();e&&t.textureParamsController._updateTransform(e)}static PARAM_CALLBACK_update_offset(t){const e=t.containerController.container().texture();e&&t.textureParamsController._updateTransformOffset(e,!0)}static PARAM_CALLBACK_update_repeat(t){const e=t.containerController.container().texture();e&&t.textureParamsController._updateTransformRepeat(e,!0)}static PARAM_CALLBACK_update_rotation(t){const e=t.containerController.container().texture();e&&t.textureParamsController._updateTransformRotation(e,!0)}static PARAM_CALLBACK_update_center(t){const e=t.containerController.container().texture();e&&t.textureParamsController._updateTransformCenter(e,!0)}static PARAM_CALLBACK_update_advanced(t){const e=t.containerController.container().texture();e&&t.textureParamsController._updateAdvanced(e,t.pv)}static copyTextureAttributes(t,e){t.encoding=e.encoding,t.mapping=e.mapping,t.wrapS=e.wrapS,t.wrapT=e.wrapT,t.minFilter=e.minFilter,t.magFilter=e.magFilter,t.magFilter=e.magFilter,t.anisotropy=e.anisotropy,t.flipY=e.flipY,t.repeat.copy(e.repeat),t.offset.copy(e.offset),t.center.copy(e.center),t.rotation=e.rotation,t.type=e.type,t.format=e.format,t.needsUpdate=!0}paramLabelsParams(){const t=this.node.p;return[t.tencoding,t.encoding,t.tmapping,t.mapping,t.twrap,t.wrapS,t.wrapT,t.tminFilter,t.minFilter,t.tmagFilter,t.magFilter,t.tflipY,t.flipY]}paramLabels(){const t=[],e=this.node.pv;if(e.tencoding)for(let n of eg){const i=Object.keys(n)[0];n[i]==e.encoding&&t.push(`encoding: ${i}`)}if(e.tmapping)for(let n of ig){const i=Object.keys(n)[0];n[i]==e.mapping&&t.push(`mapping: ${i}`)}if(e.twrap){function n(n){for(let i of ng){const r=Object.keys(i)[0];i[r]==e[n]&&t.push(`${n}: ${r}`)}}n(\\\\\\\"wrapS\\\\\\\"),n(\\\\\\\"wrapT\\\\\\\")}if(e.tminFilter)for(let n of Wm){const i=Object.keys(n)[0];n[i]==e.minFilter&&t.push(`minFilter: ${i}`)}if(e.tmagFilter)for(let n of jm){const i=Object.keys(n)[0];n[i]==e.magFilter&&t.push(`magFilter: ${i}`)}return e.tflipY&&t.push(`flipY: ${e.flipY}`),t}}class Tg extends J.a{constructor(t,e,n,i,r,s,o,a,l){super(t,e,n,i,r,s,o,a,l),this.needsUpdate=!0}}Tg.prototype.isCanvasTexture=!0;class Ag extends(xg(function(t){return class extends t{constructor(){super(...arguments),this.canvasId=oa.STRING(\\\\\\\"canvas-id\\\\\\\"),this.update=oa.BUTTON(null,{cook:!1,callback:t=>{Mg.PARAM_CALLBACK_update(t)}})}}}(aa))){}const Eg=new Ag;class Mg extends af{constructor(){super(...arguments),this.paramsConfig=Eg,this.textureParamsController=new wg(this)}static type(){return\\\\\\\"canvas\\\\\\\"}async cook(){const t=this.pv.canvasId,e=document.getElementById(t);if(!e)return this.states.error.set(`element with id '${t}' not found`),void this.cookController.endCook();if(!(e instanceof HTMLCanvasElement))return this.states.error.set(\\\\\\\"element found is not a canvas\\\\\\\"),void this.cookController.endCook();const n=new Tg(e);await this.textureParamsController.update(n),this.setTexture(n)}static PARAM_CALLBACK_update(t){t.markTextureNeedsUpdate()}markTextureNeedsUpdate(){const t=this.containerController.container().texture();t&&(t.needsUpdate=!0)}}const Sg=new class extends aa{constructor(){super(...arguments),this.resolution=oa.VECTOR2([256,256],{callback:t=>{Cg.PARAM_CALLBACK_reset(t)}}),this.color=oa.COLOR([1,1,1])}};class Cg extends af{constructor(){super(...arguments),this.paramsConfig=Sg}static type(){return\\\\\\\"color\\\\\\\"}cook(){const t=this.pv.resolution.x,e=this.pv.resolution.y;this._data_texture=this._data_texture||this._create_data_texture(t,e);const n=e*t,i=this.pv.color.toArray(),r=255*i[0],s=255*i[1],o=255*i[2],a=this._data_texture.image.data;for(let t=0;t<n;t++)a[4*t+0]=r,a[4*t+1]=s,a[4*t+2]=o,a[4*t+3]=255;this._data_texture.needsUpdate=!0,this.setTexture(this._data_texture)}_create_data_texture(t,e){const n=this._create_pixel_buffer(t,e);return new mo.a(n,t,e)}_create_pixel_buffer(t,e){return new Uint8Array(t*e*4)}static PARAM_CALLBACK_reset(t){t._reset()}_reset(){this._data_texture=void 0}}var Ng,Lg,Og;!function(t){t.GEO=\\\\\\\"geo\\\\\\\",t.CUBE_CAMERA=\\\\\\\"cubeCamera\\\\\\\",t.AUDIO_LISTENER=\\\\\\\"audioListener\\\\\\\",t.POSITIONAL_AUDIO=\\\\\\\"positionalAudio\\\\\\\"}(Ng||(Ng={})),function(t){t.CUBE_CAMERA=\\\\\\\"cubeCamera\\\\\\\",t.VIDEO=\\\\\\\"video\\\\\\\",t.WEB_CAM=\\\\\\\"webCam\\\\\\\"}(Lg||(Lg={})),function(t){t.REFLECTION=\\\\\\\"reflection\\\\\\\",t.REFRACTION=\\\\\\\"refraction\\\\\\\"}(Og||(Og={}));const Rg=[Og.REFLECTION,Og.REFRACTION];const Pg=new class extends aa{constructor(){super(...arguments),this.cubeCamera=oa.NODE_PATH(\\\\\\\"\\\\\\\",{nodeSelection:{context:Ki.OBJ,types:[Ng.CUBE_CAMERA]}}),this.mode=oa.INTEGER(0,{menu:{entries:Rg.map(((t,e)=>({name:t,value:e})))}})}};class Ig extends af{constructor(){super(...arguments),this.paramsConfig=Pg}static type(){return Lg.CUBE_CAMERA}async cook(){const t=this.pv.cubeCamera.nodeWithContext(Ki.OBJ,this.states.error);if(!t)return this.states.error.set(`cubeCamera not found at '${this.pv.cubeCamera.path()}'`),this.cookController.endCook();const e=t.renderTarget();if(!e)return this.states.error.set(\\\\\\\"cubeCamera has no render target'\\\\\\\"),this.cookController.endCook();const n=e.texture;Rg[this.pv.mode]==Og.REFLECTION?n.mapping=w.o:n.mapping=w.p,this.setTexture(n)}}var Fg;!function(t){t.REFLECTION=\\\\\\\"reflection\\\\\\\",t.REFRACTION=\\\\\\\"refraction\\\\\\\"}(Fg||(Fg={}));const Dg=[Fg.REFLECTION,Fg.REFRACTION];const kg=new class extends aa{constructor(){super(...arguments),this.useCameraRenderer=oa.BOOLEAN(1),this.mode=oa.INTEGER(0,{menu:{entries:Dg.map(((t,e)=>({name:t,value:e})))}})}};class Bg extends af{constructor(){super(...arguments),this.paramsConfig=kg}static type(){return\\\\\\\"envMap\\\\\\\"}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState(Qi.NEVER)}async cook(t){const e=t[0];this.convert_texture_to_env_map(e)}async convert_texture_to_env_map(t){this._renderer_controller=this._renderer_controller||new Ff(this);const e=await this._renderer_controller.renderer();if(e){const n=new wt(e).fromEquirectangular(t);if(this.pv.useCameraRenderer)this._set_mapping(n.texture),this.setTexture(n.texture);else{this._data_texture_controller=this._data_texture_controller||new If(Pf.Uint8Array);const t=this._data_texture_controller.from_render_target(e,n);this._set_mapping(t),this.setTexture(t)}}else this.states.error.set(\\\\\\\"no renderer found to convert the texture to an env map\\\\\\\"),this.cookController.endCook()}_set_mapping(t){Dg[this.pv.mode]==Fg.REFLECTION?t.mapping=w.q:t.mapping=w.r}}class zg extends J.a{constructor(t,e,n,i,r,s,o,a,l){super(t,e,n,i,r,s,o,a,l),this.format=void 0!==o?o:w.ic,this.minFilter=void 0!==s?s:w.V,this.magFilter=void 0!==r?r:w.V,this.generateMipmaps=!1;const c=this;\\\\\\\"requestVideoFrameCallback\\\\\\\"in t&&t.requestVideoFrameCallback((function e(){c.needsUpdate=!0,t.requestVideoFrameCallback(e)}))}clone(){return new this.constructor(this.image).copy(this)}update(){const t=this.image;!1===\\\\\\\"requestVideoFrameCallback\\\\\\\"in t&&t.readyState>=t.HAVE_CURRENT_DATA&&(this.needsUpdate=!0)}}zg.prototype.isVideoTexture=!0;var Ug=n(80);const Gg=\\\\\\\"https://raw.githubusercontent.com/polygonjs/polygonjs-assets/master/\\\\\\\";var Vg=n(29);const Hg=new Vg.b;Hg.setURLModifier((t=>{const e=ai.assetUrls.remapedUrl(t);if(e)return e;const n=ai.blobs.blobUrl(t);return n||t}));class jg{constructor(t,e,n){this._url=t,this._scene=e,this._node=n,this.loadingManager=Hg}static extension(t){let e=null;try{e=new URL(t).searchParams.get(\\\\\\\"ext\\\\\\\")}catch(t){}if(!e){const n=t.split(\\\\\\\"?\\\\\\\")[0].split(\\\\\\\".\\\\\\\");e=n[n.length-1].toLowerCase()}return e}extension(){return jg.extension(this._url)}async _urlToLoad(){let t=this._url;const e=this._url.split(\\\\\\\"?\\\\\\\")[0];if(\\\\\\\"h\\\\\\\"!=t[0]){const e=this._scene.assets.root();e&&(t=`${e}${t}`)}this._node&&await ai.blobs.fetchBlobForNode({storedUrl:e,fullUrl:t,node:this._node});return ai.blobs.blobUrl(e)||t}static async _loadMultipleBlobGlobal(t){const e=[];for(let n of t.files){const i=n.storedUrl,r=n.fullUrl,s=t.node;e.push(ai.blobs.fetchBlobGlobal({storedUrl:i,fullUrl:r,node:s}))}const n=await Promise.all(e);for(let e of n)e.error&&t.node.states.error.set(t.error)}}var Wg;jg.loadingManager=Hg,function(t){t.JPG=\\\\\\\"jpg\\\\\\\",t.JPEG=\\\\\\\"jpeg\\\\\\\",t.PNG=\\\\\\\"png\\\\\\\",t.EXR=\\\\\\\"exr\\\\\\\",t.BASIS=\\\\\\\"basis\\\\\\\",t.HDR=\\\\\\\"hdr\\\\\\\"}(Wg||(Wg={}));Wg.JPEG,Wg.JPG,Wg.PNG,Wg.EXR,Wg.BASIS,Wg.HDR;class qg extends jg{constructor(t,e,n,i){super(t,i,n),this._param=e,this._node=n,this._scene=i}static onTextureLoaded(t){this._onTextureLoadedCallback=t}async load_texture_from_url_or_op(t){let e=null,n=null;if(\\\\\\\"op:\\\\\\\"==this._url.substring(0,3)){const t=this._url.substring(3);if(n=xi.findNode(this._node,t),n)if(n instanceof lf){e=(await n.compute()).texture()}else this._node.states.error.set(\\\\\\\"found node is not a texture node\\\\\\\");else this._node.states.error.set(`no node found in path '${t}'`)}else e=await this._loadUrl(t),e||this._node.states.error.set(`could not load texture ${this._url}`);return n&&this._param.graphPredecessors()[0]!=n&&(this._param.graphDisconnectPredecessors(),this._param.addGraphInput(n)),e}async _loadUrl(t){return new Promise((async(e,n)=>{const i=this.extension(),r=await this._urlToLoad();if(qg.VIDEO_EXTENSIONS.includes(i)){const t=await this._load_as_video(r);e(t)}else this.loader_for_ext(i,t).then((async t=>{t?(qg.increment_in_progress_loads_count(),await qg.wait_for_max_concurrent_loads_queue_freed(),t.load(r,(t=>{qg.decrement_in_progress_loads_count();const n=qg._onTextureLoadedCallback;n&&n(r,t),e(t)}),void 0,(t=>{qg.decrement_in_progress_loads_count(),ai.warn(\\\\\\\"error\\\\\\\",t),n()}))):n()}))}))}static module_names(t){switch(t){case Wg.EXR:return[Vn.EXRLoader];case Wg.HDR:return[Vn.RGBELoader];case Wg.BASIS:return[Vn.BasisTextureLoader]}}async loader_for_ext(t,e){switch(t.toLowerCase()){case Wg.EXR:return await this._exr_loader(e);case Wg.HDR:return await this._hdr_loader(e);case Wg.BASIS:return await qg._basis_loader(this._node)}return new Ug.a(this.loadingManager)}async _exr_loader(t){const e=await ai.modulesRegister.module(Vn.EXRLoader);if(e){const n=new e(this.loadingManager);return t.tdataType&&n.setDataType(t.dataType),n}}async _hdr_loader(t){const e=await ai.modulesRegister.module(Vn.RGBELoader);if(e){const n=new e(this.loadingManager);return t.tdataType&&n.setDataType(t.dataType),n}}static async _basis_loader(t){const e=await ai.modulesRegister.module(Vn.BasisTextureLoader);if(e){const n=new e(this.loadingManager),i=ai.libs.root(),r=ai.libs.BASISPath();if(i||r){const e=`${i||\\\\\\\"\\\\\\\"}${r||\\\\\\\"\\\\\\\"}/`;if(t){const n=[\\\\\\\"basis_transcoder.js\\\\\\\",\\\\\\\"basis_transcoder.wasm\\\\\\\"];await this._loadMultipleBlobGlobal({files:n.map((t=>({storedUrl:`${r}/${t}`,fullUrl:`${e}${t}`}))),node:t,error:\\\\\\\"failed to load basis libraries. Make sure to install them to load .basis files\\\\\\\"})}n.setTranscoderPath(e)}else n.setTranscoderPath(void 0);const s=await ai.renderersController.waitForRenderer();return s?n.detectSupport(s):ai.warn(\\\\\\\"texture loader found no renderer for basis texture loader\\\\\\\"),n}}_load_as_video(t){return new Promise(((e,n)=>{const i=document.createElement(\\\\\\\"video\\\\\\\");i.setAttribute(\\\\\\\"crossOrigin\\\\\\\",\\\\\\\"anonymous\\\\\\\"),i.setAttribute(\\\\\\\"autoplay\\\\\\\",\\\\\\\"true\\\\\\\"),i.setAttribute(\\\\\\\"loop\\\\\\\",\\\\\\\"true\\\\\\\"),i.onloadedmetadata=function(){i.pause();const t=new zg(i);e(t)};const r=document.createElement(\\\\\\\"source\\\\\\\"),s=jg.extension(t);let o=qg.VIDEO_SOURCE_TYPE_BY_EXT[s];o=o||qg._default_video_source_type(t),r.setAttribute(\\\\\\\"type\\\\\\\",o),r.setAttribute(\\\\\\\"src\\\\\\\",t),i.appendChild(r);let a=t;a=\\\\\\\"mp4\\\\\\\"==s?qg.replaceExtension(t,\\\\\\\"ogv\\\\\\\"):qg.replaceExtension(t,\\\\\\\"mp4\\\\\\\");const l=document.createElement(\\\\\\\"source\\\\\\\"),c=jg.extension(a);o=qg.VIDEO_SOURCE_TYPE_BY_EXT[c],o=o||qg._default_video_source_type(t),l.setAttribute(\\\\\\\"type\\\\\\\",o),l.setAttribute(\\\\\\\"src\\\\\\\",t),i.appendChild(l)}))}static _default_video_source_type(t){return`video/${jg.extension(t)}`}static pixel_data(t){const e=t.image,n=document.createElement(\\\\\\\"canvas\\\\\\\");n.width=e.width,n.height=e.height;const i=n.getContext(\\\\\\\"2d\\\\\\\");if(i)return i.drawImage(e,0,0,e.width,e.height),i.getImageData(0,0,e.width,e.height)}static replaceExtension(t,e){const n=t.split(\\\\\\\"?\\\\\\\"),i=n[0].split(\\\\\\\".\\\\\\\");return i.pop(),i.push(e),[i.join(\\\\\\\".\\\\\\\"),n[1]].join(\\\\\\\"?\\\\\\\")}static setMaxConcurrentLoadsCount(t){this._maxConcurrentLoadsCountMethod=t}static _init_max_concurrent_loads_count(){return this._maxConcurrentLoadsCountMethod?this._maxConcurrentLoadsCountMethod():Zf.isChrome()?10:4}static _init_concurrent_loads_delay(){return Zf.isChrome()?0:10}static increment_in_progress_loads_count(){this.in_progress_loads_count++}static decrement_in_progress_loads_count(){this.in_progress_loads_count--;const t=this._queue.pop();if(t){const e=this.CONCURRENT_LOADS_DELAY;setTimeout((()=>{t()}),e)}}static async wait_for_max_concurrent_loads_queue_freed(){return this.in_progress_loads_count<=this.MAX_CONCURRENT_LOADS_COUNT?void 0:new Promise((t=>{this._queue.push(t)}))}}qg.PARAM_DEFAULT=`${Gg}/textures/uv.jpg`,qg.PARAM_ENV_DEFAULT=`${Gg}/textures/piz_compressed.exr`,qg.VIDEO_EXTENSIONS=[\\\\\\\"mp4\\\\\\\",\\\\\\\"webm\\\\\\\",\\\\\\\"ogv\\\\\\\"],qg.VIDEO_SOURCE_TYPE_BY_EXT={ogg:'video/ogg; codecs=\\\\\\\"theora, vorbis\\\\\\\"',ogv:'video/ogg; codecs=\\\\\\\"theora, vorbis\\\\\\\"',mp4:'video/mp4; codecs=\\\\\\\"avc1.42E01E, mp4a.40.2\\\\\\\"'},qg.MAX_CONCURRENT_LOADS_COUNT=qg._init_max_concurrent_loads_count(),qg.CONCURRENT_LOADS_DELAY=qg._init_concurrent_loads_delay(),qg.in_progress_loads_count=0,qg._queue=[];var Xg=n(115);class Yg extends(xg(function(t){return class extends t{constructor(){super(...arguments),this.url=oa.STRING(qg.PARAM_DEFAULT,{fileBrowse:{type:[Ls.TEXTURE_IMAGE]}}),this.reload=oa.BUTTON(null,{callback:(t,e)=>{Jg.PARAM_CALLBACK_reload(t)}}),this.play=oa.BOOLEAN(1,{cook:!1,callback:t=>{Jg.PARAM_CALLBACK_gifUpdatePlay(t)}}),this.gifFrame=oa.INTEGER(0,{cook:!1,range:[0,100],rangeLocked:[!0,!1],callback:t=>{Jg.PARAM_CALLBACK_gifUpdateFrameIndex(t)}})}}}(aa))){}const $g=new Yg;class Jg extends af{constructor(){super(...arguments),this.paramsConfig=$g,this.textureParamsController=new wg(this),this._gifCanvasContext=null,this._tmpCanvasContext=null,this._parsedFrames=[],this._frameDelay=100,this._frameIndex=0}static type(){return\\\\\\\"gif\\\\\\\"}async requiredModules(){this.p.url.isDirty()&&await this.p.url.compute();const t=jg.extension(this.pv.url||\\\\\\\"\\\\\\\");return qg.module_names(t)}static displayedInputNames(){return[\\\\\\\"optional texture to copy attributes from\\\\\\\"]}initializeNode(){this.io.inputs.setCount(0,1),this.io.inputs.initInputsClonedState(Qi.NEVER),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{let t=[this.p.url];t=t.concat(this.textureParamsController.paramLabelsParams()),this.params.label.init(t,(()=>{const t=this.p.url.rawInput();if(t){const e=t.split(\\\\\\\"/\\\\\\\"),n=e[e.length-1],i=this.textureParamsController.paramLabels();return[n].concat(i)}return\\\\\\\"\\\\\\\"}))}))}))}async cook(t){const e=await fetch(this.pv.url),n=await e.arrayBuffer(),i=await Object(Xg.parseGIF)(n);this._parsedFrames=await Object(Xg.decompressFrames)(i,!0);const r=this._parsedFrames[0];if(this._frameDelay=r.delay,this._frameIndex=this.pv.gifFrame-1,this._createCanvas(),this._gifCanvasElement){const t=new Tg(this._gifCanvasElement);await this.textureParamsController.update(t),this.setTexture(t)}else this.states.error.set(\\\\\\\"failed to create canvas\\\\\\\")}_createCanvas(){const t=this._parsedFrames[0];this._gifCanvasElement=document.createElement(\\\\\\\"canvas\\\\\\\"),this._tmpCanvasElement=document.createElement(\\\\\\\"canvas\\\\\\\"),this._gifCanvasElement.width=t.dims.width,this._gifCanvasElement.height=t.dims.height,this._tmpCanvasElement.width=t.dims.width,this._tmpCanvasElement.height=t.dims.height,this._gifCanvasContext=this._gifCanvasElement.getContext(\\\\\\\"2d\\\\\\\"),this._tmpCanvasContext=this._tmpCanvasElement.getContext(\\\\\\\"2d\\\\\\\"),this._drawNextFrame()}_drawOnCanvas(){if(!(this._gifCanvasContext&&this._tmpCanvasElement&&this._tmpCanvasContext))return;let t=this._parsedFrames[this._frameIndex];if(t||(console.warn(`no frame at index ${this._frameIndex}, using last frame`),t=this._parsedFrames[this._parsedFrames.length-1]),t){const e=t.dims;this._frameImageData&&e.width==this._frameImageData.width&&e.height==this._frameImageData.height||(this._tmpCanvasElement.width=e.width,this._tmpCanvasElement.height=e.height,this._frameImageData=this._tmpCanvasContext.createImageData(e.width,e.height)),this._frameImageData.data.set(t.patch),this._tmpCanvasContext.putImageData(this._frameImageData,0,0),this._gifCanvasContext.drawImage(this._tmpCanvasElement,e.left,e.top);const n=this.containerController.container().texture();if(!n)return;n.needsUpdate=!0}}_drawNextFrame(){this._frameIndex++,this._frameIndex>=this._parsedFrames.length&&(this._frameIndex=0),this._drawOnCanvas(),this.pv.play&&setTimeout((()=>{this._drawNextFrame()}),this._frameDelay)}gifUpdateFrameIndex(){this._frameIndex=this.pv.gifFrame,this._drawOnCanvas()}static PARAM_CALLBACK_reload(t){t.paramCallbackReload()}paramCallbackReload(){this.p.url.setDirty()}static PARAM_CALLBACK_gifUpdatePlay(t){t.gifUpdatePlay()}gifUpdatePlay(){this.pv.play&&this._drawNextFrame()}static PARAM_CALLBACK_gifUpdateFrameIndex(t){t.gifUpdateFrameIndex()}}const Zg=[\\\\\\\"mp4\\\\\\\",\\\\\\\"ogv\\\\\\\"];class Qg{static isStaticImageUrl(t){const e=t.split(\\\\\\\"?\\\\\\\")[0].split(\\\\\\\".\\\\\\\"),n=e[e.length-1];return!Zg.includes(n)}}class Kg extends(xg(function(t){return class extends t{constructor(){super(...arguments),this.url=oa.STRING(qg.PARAM_DEFAULT,{fileBrowse:{type:[Ls.TEXTURE_IMAGE]}}),this.reload=oa.BUTTON(null,{callback:(t,e)=>{ev.PARAM_CALLBACK_reload(t,e)}})}}}(aa))){}const tv=new Kg;class ev extends af{constructor(){super(...arguments),this.paramsConfig=tv,this.textureParamsController=new wg(this)}static type(){return\\\\\\\"image\\\\\\\"}async requiredModules(){this.p.url.isDirty()&&await this.p.url.compute();const t=jg.extension(this.pv.url||\\\\\\\"\\\\\\\");return qg.module_names(t)}static displayedInputNames(){return[\\\\\\\"optional texture to copy attributes from\\\\\\\"]}initializeNode(){this.io.inputs.setCount(0,1),this.io.inputs.initInputsClonedState(Qi.NEVER),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{let t=[this.p.url];t=t.concat(this.textureParamsController.paramLabelsParams()),this.params.label.init(t,(()=>{const t=this.p.url.rawInput();if(t){const e=t.split(\\\\\\\"/\\\\\\\"),n=e[e.length-1],i=this.textureParamsController.paramLabels();return[n].concat(i)}return\\\\\\\"\\\\\\\"}))}))}))}async cook(t){if(Qg.isStaticImageUrl(this.pv.url)){const e=await this._loadTexture(this.pv.url);if(e){const n=t[0];n&&wg.copyTextureAttributes(e,n),await this.textureParamsController.update(e),this.setTexture(e)}else this._clearTexture()}else this.states.error.set(\\\\\\\"input is not an image\\\\\\\")}static PARAM_CALLBACK_reload(t,e){t.paramCallbackReload()}paramCallbackReload(){this.p.url.setDirty()}async _loadTexture(t){let e=null;const n=this.p.url,i=new qg(t,n,this,this.scene());try{e=await i.load_texture_from_url_or_op({tdataType:this.pv.ttype&&this.pv.tadvanced,dataType:this.pv.type}),e&&(e.matrixAutoUpdate=!1)}catch(t){}return e||this.states.error.set(`could not load texture '${t}'`),e}}var nv=n(32);const iv=.005;class rv{constructor(t,e=1024){this.renderer=t,this.res=e,this.objectTargets=[],this.lights=[],this.scene=new fr,this.buffer1Active=!1,this._params={lightRadius:1,iterations:1,iterationBlend:iv,blur:!1,blurAmount:0},this._objectStateByObject=new WeakMap,this._previousRenderTarget=null,this._lightHierarchyStateByLight=new WeakMap,this._lightMatrixStateByLight=new WeakMap,this._t=new p.a,this._q=new au.a,this._s=new p.a;const n=Zf.isAndroid()||Zf.isiOS()?w.M:w.G;this.progressiveLightMap1=new Z(this.res,this.res,{type:n}),this.progressiveLightMap2=new Z(this.res,this.res,{type:n}),this.uvMat=this._createUVMat()}textureRenderTarget(){return this.progressiveLightMap2}texture(){return this.textureRenderTarget().texture}setParams(t){this._params.lightRadius=t.lightRadius,this._params.iterations=t.iterations,this._params.iterationBlend=t.iterationBlend,this._params.blur=t.blur,this._params.blurAmount=t.blurAmount}init(t,e){this._setObjects(t),this._setLights(e)}_setObjects(t){this.objectTargets=[];for(let e of t)null==this.blurringPlane&&this._initializeBlurPlane(this.res,this.progressiveLightMap1),this.objectTargets.push(e);this._saveObjectsState()}_setLights(t){this.lights=t;for(let e of t)this._saveLightHierarchyState(e),this.scene.attach(e),this._saveLightMatrixState(e)}_saveLightHierarchyState(t){this._lightHierarchyStateByLight.set(t,{parent:t.parent,matrixAutoUpdate:t.matrixAutoUpdate}),t.matrixAutoUpdate=!0}_saveLightMatrixState(t){t.updateMatrix(),t.matrix.decompose(this._t,this._q,this._s),this._lightMatrixStateByLight.set(t,{matrix:t.matrix.clone(),position:this._t.clone()})}_saveObjectsState(){let t=0;for(let e of this.objectTargets)this._objectStateByObject.set(e,{frustumCulled:e.frustumCulled,material:e.material,parent:e.parent,castShadow:e.castShadow,receiveShadow:e.receiveShadow}),e.material=this.uvMat,e.frustumCulled=!1,e.castShadow=!0,e.receiveShadow=!0,e.renderOrder=1e3+t,this.scene.attach(e),t++;this._previousRenderTarget=this.renderer.getRenderTarget()}_moveLights(){const t=this._params.lightRadius;for(let e of this.lights){const n=this._lightMatrixStateByLight.get(e);if(n){const i=n.position;e.position.x=i.x+t*(Math.random()-.5),e.position.y=i.y+t*(Math.random()-.5),e.position.z=i.z+t*(Math.random()-.5)}}}restoreState(){this._restoreObjectsState(),this._restoreLightsState(),this.renderer.setRenderTarget(this._previousRenderTarget)}_restoreObjectsState(){for(let t of this.objectTargets){const e=this._objectStateByObject.get(t);if(e){t.frustumCulled=e.frustumCulled,t.castShadow=e.castShadow,t.receiveShadow=e.receiveShadow,t.material=e.material;const n=e.parent;n&&n.add(t)}}}_restoreLightsState(){var t;for(let e of this.lights){const n=this._lightHierarchyStateByLight.get(e),i=this._lightMatrixStateByLight.get(e);n&&i&&(e.matrixAutoUpdate=n.matrixAutoUpdate,e.matrix.copy(i.matrix),e.matrix.decompose(e.position,e.quaternion,e.scale),e.updateMatrix(),null===(t=n.parent)||void 0===t||t.attach(e))}}runUpdates(t){if(!this.blurMaterial)return;if(null==this.blurringPlane)return;const e=this._params.iterations;this.blurMaterial.uniforms.pixelOffset.value=this._params.blurAmount/this.res,this.blurringPlane.visible=this._params.blur,this.uvMat.uniforms.iterationBlend.value=this._params.iterationBlend,this._clear(t);for(let n=0;n<e;n++)this._moveLights(),this._update(t)}_clear(t){this.scene.visible=!1,this._update(t),this._update(t),this.scene.visible=!0}_update(t){if(!this.blurMaterial)return;const e=this.buffer1Active?this.progressiveLightMap1:this.progressiveLightMap2,n=this.buffer1Active?this.progressiveLightMap2:this.progressiveLightMap1;this.renderer.setRenderTarget(e),this.uvMat.uniforms.previousShadowMap.value=n.texture,this.blurMaterial.uniforms.previousShadowMap.value=n.texture,this.buffer1Active=!this.buffer1Active,this.renderer.render(this.scene,t)}_initializeBlurPlane(t,e){this.blurMaterial=this._createBlurPlaneMaterial(t,e),this.blurringPlane=new k.a(new L(1,1),this.blurMaterial),this.blurringPlane.name=\\\\\\\"Blurring Plane\\\\\\\",this.blurringPlane.frustumCulled=!1,this.blurringPlane.renderOrder=0,this.blurMaterial.depthWrite=!1,this.scene.add(this.blurringPlane)}_createBlurPlaneMaterial(t,e){const n=new at.a;return n.uniforms={previousShadowMap:{value:null},pixelOffset:{value:1/t}},n.onBeforeCompile=i=>{i.vertexShader=\\\\\\\"#define USE_UV\\\\n\\\\\\\"+i.vertexShader.slice(0,-2)+\\\\\\\"\\\\tgl_Position = vec4((uv - 0.5) * 2.0, 1.0, 1.0); }\\\\\\\";const r=i.fragmentShader.indexOf(\\\\\\\"void main() {\\\\\\\");i.fragmentShader=\\\\\\\"#define USE_UV\\\\n\\\\\\\"+i.fragmentShader.slice(0,r)+\\\\\\\"\\\\tuniform sampler2D previousShadowMap;\\\\n\\\\tuniform float pixelOffset;\\\\n\\\\\\\"+i.fragmentShader.slice(r-1,-2)+\\\\\\\"\\\\tgl_FragColor.rgb = (\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\ttexture2D(previousShadowMap, vUv + vec2( pixelOffset,  0.0        )).rgb +\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\ttexture2D(previousShadowMap, vUv + vec2( 0.0        ,  pixelOffset)).rgb +\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\ttexture2D(previousShadowMap, vUv + vec2( 0.0        , -pixelOffset)).rgb +\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\ttexture2D(previousShadowMap, vUv + vec2(-pixelOffset,  0.0        )).rgb +\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\ttexture2D(previousShadowMap, vUv + vec2( pixelOffset,  pixelOffset)).rgb +\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\ttexture2D(previousShadowMap, vUv + vec2(-pixelOffset,  pixelOffset)).rgb +\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\ttexture2D(previousShadowMap, vUv + vec2( pixelOffset, -pixelOffset)).rgb +\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\ttexture2D(previousShadowMap, vUv + vec2(-pixelOffset, -pixelOffset)).rgb)/8.0;\\\\n\\\\t\\\\t\\\\t\\\\t}\\\\\\\";const s={previousShadowMap:{value:e.texture},pixelOffset:{value:.5/t}};i.uniforms.previousShadowMap=s.previousShadowMap,i.uniforms.pixelOffset=s.pixelOffset,n.uniforms.previousShadowMap=s.previousShadowMap,n.uniforms.pixelOffset=s.pixelOffset,n.userData.shader=i},n}_createUVMat(){const t=new Gf.a;return t.uniforms={previousShadowMap:{value:null},iterationBlend:{value:iv}},t.name=\\\\\\\"uvMat\\\\\\\",t.onBeforeCompile=e=>{e.vertexShader=\\\\\\\"#define USE_LIGHTMAP\\\\n\\\\\\\"+e.vertexShader.slice(0,-2)+\\\\\\\"\\\\tgl_Position = vec4((uv2 - 0.5) * 2.0, 1.0, 1.0); }\\\\\\\";const n=e.fragmentShader.indexOf(\\\\\\\"void main() {\\\\\\\");e.fragmentShader=\\\\\\\"varying vec2 vUv2;\\\\n\\\\\\\"+e.fragmentShader.slice(0,n)+\\\\\\\"\\\\tuniform sampler2D previousShadowMap;\\\\n\\\\tuniform float iterationBlend;\\\\n\\\\\\\"+e.fragmentShader.slice(n-1,-2)+\\\\\\\"\\\\nvec3 texelOld = texture2D(previousShadowMap, vUv2).rgb;\\\\n\\\\t\\\\t\\\\t\\\\tgl_FragColor.rgb = mix(texelOld, gl_FragColor.rgb, iterationBlend);\\\\n\\\\t\\\\t\\\\t\\\\t// gl_FragColor.rgb = vec3(vUv2,1.0);\\\\n\\\\t\\\\t\\\\t}\\\\\\\";const i={previousShadowMap:{value:this.progressiveLightMap1.texture},iterationBlend:{value:iv}};e.uniforms.previousShadowMap=i.previousShadowMap,e.uniforms.iterationBlend=i.iterationBlend,t.uniforms.previousShadowMap=i.previousShadowMap,t.uniforms.iterationBlend=i.iterationBlend,t.userData.shader=e},t}}const sv=new class extends aa{constructor(){super(...arguments),this.update=oa.BUTTON(null,{callback:t=>{ov.PARAM_CALLBACK_updateManual(t)}}),this.useCameraRenderer=oa.BOOLEAN(1),this.lightMapRes=oa.INTEGER(1024,{range:[1,2048],rangeLocked:[!0,!1]}),this.iterations=oa.INTEGER(512,{range:[1,2048],rangeLocked:[!0,!1]}),this.iterationBlend=oa.FLOAT(iv,{range:[0,1],rangeLocked:[!0,!0]}),this.blur=oa.BOOLEAN(1),this.blurAmount=oa.FLOAT(1,{visibleIf:{blur:1},range:[0,1],rangeLocked:[!0,!1]}),this.lightRadius=oa.FLOAT(1,{range:[0,10]}),this.objectsMask=oa.STRING(\\\\\\\"\\\\\\\"),this.lightsMask=oa.STRING(\\\\\\\"*\\\\\\\"),this.printResolveObjectsList=oa.BUTTON(null,{callback:t=>{ov.PARAM_CALLBACK_printResolveObjectsList(t)}})}};class ov extends af{constructor(){super(...arguments),this.paramsConfig=sv,this._includedObjects=[],this._includedLights=[]}static type(){return\\\\\\\"lightMap\\\\\\\"}async cook(){this._updateManual()}async _createLightMapController(){const t=await ai.renderersController.firstRenderer();if(!t)return void console.warn(\\\\\\\"no renderer found\\\\\\\");return new rv(t,this.pv.lightMapRes)}static PARAM_CALLBACK_update_updateMode(t){}async _updateManual(){if(this.lightMapController=this.lightMapController||await this._createLightMapController(),!this.lightMapController)return;const t=this.scene().mainCameraNode();if(!t)return;this._updateObjectsAndLightsList(),this.lightMapController.init(this._includedObjects,this._includedLights);const e=t.camera();this.lightMapController.setParams({lightRadius:this.pv.lightRadius,iterations:this.pv.iterations,iterationBlend:this.pv.iterationBlend,blur:this.pv.blur,blurAmount:this.pv.blurAmount}),this.lightMapController.runUpdates(e),this.lightMapController.restoreState();const n=this.lightMapController.textureRenderTarget();if(this.pv.useCameraRenderer)this.setTexture(n.texture);else{this._data_texture_controller=this._data_texture_controller||new If(Pf.Float32Array),this._renderer_controller=this._renderer_controller||new Ff(this);const t=await this._renderer_controller.renderer(),e=this._data_texture_controller.from_render_target(t,n);this.setTexture(e)}}static PARAM_CALLBACK_updateManual(t){t._updateManual()}_updateObjectsAndLightsList(){let t=[],e=[];this._includedLights=[],this._includedObjects=[];const n=new WeakSet;if(\\\\\\\"\\\\\\\"!=this.pv.lightsMask){e=this.scene().objectsByMask(this.pv.lightsMask);for(let t of e)t instanceof nv.a&&(this._includedLights.push(t),n.add(t))}if(\\\\\\\"\\\\\\\"!=this.pv.objectsMask){t=this.scene().objectsByMask(this.pv.objectsMask);for(let e of t)e instanceof nv.a||!n.has(e)&&e instanceof k.a&&this._includedObjects.push(e)}}static PARAM_CALLBACK_printResolveObjectsList(t){t._printResolveObjectsList()}_printResolveObjectsList(){this._updateObjectsAndLightsList(),console.log(\\\\\\\"included objects:\\\\\\\"),console.log(this._includedObjects),console.log(\\\\\\\"included lights:\\\\\\\"),console.log(this._includedLights)}}const av=new aa;class lv extends af{constructor(){super(...arguments),this.paramsConfig=av}static type(){return\\\\\\\"null\\\\\\\"}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState(Qi.NEVER)}async cook(t){const e=t[0];this.setTexture(e)}}const cv=[nr.ORTHOGRAPHIC,nr.PERSPECTIVE];class uv extends(xg(function(t){return class extends t{constructor(){super(...arguments),this.camera=oa.NODE_PATH(\\\\\\\"\\\\\\\",{nodeSelection:{context:Ki.OBJ,types:cv}}),this.resolution=oa.VECTOR2([1024,1024]),this.useCameraRenderer=oa.BOOLEAN(1),this.render=oa.BUTTON(null,{callback:t=>{dv.PARAM_CALLBACK_render(t)}})}}}(aa))){}const hv=new uv;class dv extends af{constructor(){super(...arguments),this.paramsConfig=hv,this.textureParamsController=new wg(this)}static type(){return\\\\\\\"render\\\\\\\"}async cook(){this._texture_scene=this.scene().threejsScene(),this._camera_node=this.pv.camera.nodeWithContext(Ki.OBJ),this._camera_node&&cv.includes(this._camera_node.type())?(this._texture_camera=this._camera_node.object,await this._camera_node.compute(),this.renderOnTarget()):this._texture_camera=void 0}async renderOnTarget(){if(await this.createRenderTargetIfRequired(),!(this._render_target&&this._texture_scene&&this._texture_camera))return;this._renderer_controller=this._renderer_controller||new Ff(this);const t=await this._renderer_controller.renderer(),e=t.getRenderTarget();if(t.setRenderTarget(this._render_target),t.clear(),t.render(this._texture_scene,this._texture_camera),t.setRenderTarget(e),this._render_target.texture)if(this.pv.useCameraRenderer)this.setTexture(this._render_target.texture);else{this._data_texture_controller=this._data_texture_controller||new If(Pf.Float32Array);const e=this._data_texture_controller.from_render_target(t,this._render_target);await this.textureParamsController.update(e),this.setTexture(e)}else this.cookController.endCook()}async renderTarget(){return this._render_target=this._render_target||await this._createRenderTarget(this.pv.resolution.x,this.pv.resolution.y)}async createRenderTargetIfRequired(){var t;this._render_target&&this._renderTargetResolutionValid()||(this._render_target=await this._createRenderTarget(this.pv.resolution.x,this.pv.resolution.y),null===(t=this._data_texture_controller)||void 0===t||t.reset())}_renderTargetResolutionValid(){if(this._render_target){const t=this._render_target.texture.image;return t.width==this.pv.resolution.x&&t.height==this.pv.resolution.y}return!1}async _createRenderTarget(t,e){if(this._render_target){const n=this._render_target.texture.image;if(n.width==t&&n.height==e)return this._render_target}const n=w.n,i=w.n,r=w.V,s=w.ob;var o=new Z(t,e,{wrapS:n,wrapT:i,minFilter:r,magFilter:s,format:w.Ib,generateMipmaps:!0,type:Zf.isiOS()?w.M:w.G,stencilBuffer:!1,depthBuffer:!1});return await this.textureParamsController.update(o.texture),ai.warn(\\\\\\\"created render target\\\\\\\",this.path(),t,e),o}static PARAM_CALLBACK_render(t){t.renderOnTarget()}}const pv=new class extends aa{constructor(){super(...arguments),this.input=oa.INTEGER(0,{range:[0,3],rangeLocked:[!0,!0]})}};class _v extends af{constructor(){super(...arguments),this.paramsConfig=pv}static type(){return\\\\\\\"switch\\\\\\\"}initializeNode(){this.io.inputs.setCount(0,4),this.io.inputs.initInputsClonedState(Qi.NEVER),this.cookController.disallowInputsEvaluation()}async cook(){const t=this.pv.input;if(this.io.inputs.has_input(t)){const e=await this.containerController.requestInputContainer(t);if(e)return void this.setTexture(e.texture())}else this.states.error.set(`no input ${t}`);this.cookController.endCook()}}class mv extends(xg(aa)){}const fv=new mv;class gv extends af{constructor(){super(...arguments),this.paramsConfig=fv,this.textureParamsController=new wg(this)}static type(){return\\\\\\\"textureProperties\\\\\\\"}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState([Qi.FROM_NODE])}async cook(t){const e=t[0];this.textureParamsController.update(e),this.setTexture(e)}}class vv extends(xg(function(t){return class extends t{constructor(){super(...arguments),this.url=oa.STRING(qg.PARAM_DEFAULT,{fileBrowse:{type:[Ls.TEXTURE_VIDEO]}}),this.reload=oa.BUTTON(null,{callback:(t,e)=>{xv.PARAM_CALLBACK_reload(t,e)}}),this.play=oa.BOOLEAN(1,{cook:!1,callback:t=>{xv.PARAM_CALLBACK_video_update_play(t)}}),this.muted=oa.BOOLEAN(1,{cook:!1,callback:t=>{xv.PARAM_CALLBACK_video_update_muted(t)}}),this.loop=oa.BOOLEAN(1,{cook:!1,callback:t=>{xv.PARAM_CALLBACK_video_update_loop(t)}}),this.videoTime=oa.FLOAT(0,{cook:!1}),this.setVideoTime=oa.BUTTON(null,{cook:!1,callback:t=>{xv.PARAM_CALLBACK_video_update_time(t)}})}}}(aa))){}const yv=new vv;class xv extends af{constructor(){super(...arguments),this.paramsConfig=yv,this.textureParamsController=new wg(this)}static type(){return Lg.VIDEO}async requiredModules(){this.p.url.isDirty()&&await this.p.url.compute();const t=jg.extension(this.pv.url||\\\\\\\"\\\\\\\");return qg.module_names(t)}HTMLVideoElement(){return this._video}static displayedInputNames(){return[\\\\\\\"optional texture to copy attributes from\\\\\\\"]}initializeNode(){this.io.inputs.setCount(0,1),this.io.inputs.initInputsClonedState(Qi.NEVER),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.url],(()=>{const t=this.p.url.rawInput();if(t){const e=t.split(\\\\\\\"/\\\\\\\");return e[e.length-1]}return\\\\\\\"\\\\\\\"}))}))}))}async cook(t){if(Qg.isStaticImageUrl(this.pv.url))this.states.error.set(\\\\\\\"input is not a video\\\\\\\");else{const e=await this._load_texture(this.pv.url);if(e){this._video=e.image,this._video&&document.body.appendChild(this._video);const n=t[0];n&&wg.copyTextureAttributes(e,n),this.video_update_loop(),this.video_update_muted(),this.video_update_play(),this.video_update_time(),await this.textureParamsController.update(e),this.setTexture(e)}else this.cookController.endCook()}}static PARAM_CALLBACK_video_update_time(t){t.video_update_time()}static PARAM_CALLBACK_video_update_play(t){t.video_update_play()}static PARAM_CALLBACK_video_update_muted(t){t.video_update_muted()}static PARAM_CALLBACK_video_update_loop(t){t.video_update_loop()}async video_update_time(){if(this._video){const t=this.p.videoTime;t.isDirty()&&await t.compute(),this._video.currentTime=t.value}}video_update_muted(){this._video&&(this._video.muted=this.pv.muted)}video_update_loop(){this._video&&(this._video.loop=this.pv.loop)}video_update_play(){this._video&&(this.pv.play?this._video.play():this._video.pause())}static PARAM_CALLBACK_reload(t,e){t.paramCallbackReload()}paramCallbackReload(){this.p.url.setDirty()}async _load_texture(t){let e=null;const n=this.p.url;this._texture_loader=this._texture_loader||new qg(t,n,this,this.scene());try{e=await this._texture_loader.load_texture_from_url_or_op({tdataType:this.pv.ttype&&this.pv.tadvanced,dataType:this.pv.type}),e&&(e.matrixAutoUpdate=!1)}catch(t){}return e||this.states.error.set(`could not load texture '${t}'`),e}}class bv extends(xg(function(t){return class extends t{constructor(){super(...arguments),this.res=oa.VECTOR2([1024,1024])}}}(aa))){}const wv=new bv;class Tv extends af{constructor(){super(...arguments),this.paramsConfig=wv,this.textureParamsController=new wg(this)}static type(){return Lg.WEB_CAM}HTMLVideoElement(){return this._video}static displayedInputNames(){return[\\\\\\\"optional texture to copy attributes from\\\\\\\"]}initializeNode(){this.io.inputs.setCount(0,1),this.io.inputs.initInputsClonedState(Qi.NEVER)}_createHTMLVideoElement(){this._video&&document.body.removeChild(this._video);const t=document.createElement(\\\\\\\"video\\\\\\\");return t.style.display=\\\\\\\"none\\\\\\\",t.width=this.pv.res.x,t.height=this.pv.res.y,t.autoplay=!0,t.setAttribute(\\\\\\\"autoplay\\\\\\\",\\\\\\\"true\\\\\\\"),t.setAttribute(\\\\\\\"muted\\\\\\\",\\\\\\\"true\\\\\\\"),t.setAttribute(\\\\\\\"playsinline\\\\\\\",\\\\\\\"true\\\\\\\"),document.body.appendChild(t),t}async cook(t){this._video=this._createHTMLVideoElement();const e=new zg(this._video),n=t[0];if(n&&wg.copyTextureAttributes(e,n),await this.textureParamsController.update(e),navigator&&navigator.mediaDevices&&navigator.mediaDevices.getUserMedia){const t={video:{width:this.pv.res.x,height:this.pv.res.y,facingMode:\\\\\\\"user\\\\\\\"}};navigator.mediaDevices.getUserMedia(t).then((t=>{this._video&&(this._video.srcObject=t,this._video.play(),this.setTexture(e))})).catch((t=>{this.states.error.set(\\\\\\\"Unable to access the camera/webcam\\\\\\\")}))}else this.states.error.set(\\\\\\\"MediaDevices interface not available.\\\\\\\")}}class Av extends ia{static context(){return Ki.COP}cook(){this.cookController.endCook()}}class Ev extends Av{}class Mv extends Ev{constructor(){super(...arguments),this._children_controller_context=Ki.ANIM}static type(){return tr.ANIM}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class Sv extends Ev{constructor(){super(...arguments),this._children_controller_context=Ki.EVENT}static type(){return tr.EVENT}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class Cv extends Ev{constructor(){super(...arguments),this._children_controller_context=Ki.COP}static type(){return tr.COP}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class Nv extends Ev{constructor(){super(...arguments),this._children_controller_context=Ki.MAT}static type(){return tr.MAT}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class Lv extends Av{constructor(){super(...arguments),this.paramsConfig=new Jm,this.effectsComposerController=new Zm(this),this.displayNodeController=new Lm(this,this.effectsComposerController.displayNodeControllerCallbacks()),this._children_controller_context=Ki.POST}static type(){return tr.POST}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class Ov extends Ev{constructor(){super(...arguments),this._children_controller_context=Ki.ROP}static type(){return tr.ROP}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}var Rv,Pv;!function(t){t.START=\\\\\\\"start\\\\\\\",t.STOP=\\\\\\\"stop\\\\\\\",t.UPDATE=\\\\\\\"update\\\\\\\"}(Rv||(Rv={})),function(t){t.START=\\\\\\\"start\\\\\\\",t.COMPLETE=\\\\\\\"completed\\\\\\\"}(Pv||(Pv={}));const Iv=new class extends aa{constructor(){super(...arguments),this.animation=oa.NODE_PATH(\\\\\\\"\\\\\\\",{nodeSelection:{context:Ki.ANIM},dependentOnFoundNode:!1}),this.play=oa.BUTTON(null,{callback:t=>{Fv.PARAM_CALLBACK_play(t)}}),this.pause=oa.BUTTON(null,{callback:t=>{Fv.PARAM_CALLBACK_pause(t)}})}};class Fv extends Ba{constructor(){super(...arguments),this.paramsConfig=Iv}static type(){return\\\\\\\"animation\\\\\\\"}initializeNode(){this.io.inputs.setNamedInputConnectionPoints([new Jo(Rv.START,$o.BASE,this._play.bind(this)),new Jo(Rv.STOP,$o.BASE,this._pause.bind(this))]),this.io.outputs.setNamedOutputConnectionPoints([new Jo(Pv.START,$o.BASE),new Jo(Pv.COMPLETE,$o.BASE)]),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.animation],(()=>this.pv.animation.path()))}))}))}processEvent(t){}static PARAM_CALLBACK_play(t){t._play({})}static PARAM_CALLBACK_pause(t){t._pause()}async _play(t){const e=this.p.animation;e.isDirty()&&await e.compute();const n=e.value.nodeWithContext(Ki.ANIM);if(!n)return;const i=await n.compute();i&&(this._timeline_builder=i.coreContent(),this._timeline_builder&&(this._timeline&&this._timeline.kill(),this._timeline=L_.timeline(),this._timeline_builder.populate(this._timeline),this._timeline.vars.onStart=()=>{this.trigger_animation_started(t)},this._timeline.vars.onComplete=()=>{this._timeline&&this._timeline.kill(),this.trigger_animation_completed(t)}))}_pause(){this._timeline&&this._timeline.pause()}trigger_animation_started(t){this.dispatchEventToOutput(Pv.START,t)}trigger_animation_completed(t){this.dispatchEventToOutput(Pv.COMPLETE,t)}}const Dv=\\\\\\\"event\\\\\\\";const kv=new class extends aa{constructor(){super(...arguments),this.active=oa.BOOLEAN(1),this.inputsCount=oa.INTEGER(5,{range:[1,10],rangeLocked:[!0,!1]})}};class Bv extends Ba{constructor(){super(...arguments),this.paramsConfig=kv}static type(){return\\\\\\\"any\\\\\\\"}initializeNode(){this.io.connection_points.set_expected_input_types_function(this._expected_input_types.bind(this)),this.io.connection_points.set_input_name_function(this._input_name.bind(this)),this.io.connection_points.set_output_name_function((()=>Dv)),this.io.connection_points.set_expected_output_types_function((()=>[$o.BASE]))}_expected_input_types(){const t=new Array(this.pv.inputsCount);return t.fill($o.BASE),t}_input_name(t){return`trigger${t}`}async processEvent(t){this.p.active.isDirty()&&await this.p.active.compute(),this.pv.active&&this.dispatchEventToOutput(Dv,t)}}const zv=new class extends aa{constructor(){super(...arguments),this.blocking=oa.BOOLEAN(1)}};class Uv extends Ba{constructor(){super(...arguments),this.paramsConfig=zv}static type(){return\\\\\\\"block\\\\\\\"}initializeNode(){this.io.inputs.setNamedInputConnectionPoints([new Jo(\\\\\\\"in\\\\\\\",$o.BASE,this._process_incoming_event.bind(this))]),this.io.outputs.setNamedOutputConnectionPoints([new Jo(Uv.OUTPUT,$o.BASE)]),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.blocking],(()=>this.pv.blocking?\\\\\\\"blocking (X)\\\\\\\":\\\\\\\"pass-through (--\\\\x3e)\\\\\\\"))}))}))}trigger_output(t){this.dispatchEventToOutput(Uv.OUTPUT,t)}_process_incoming_event(t){this.pv.blocking||this.trigger_output(t)}}var Gv;Uv.OUTPUT=\\\\\\\"output\\\\\\\",function(t){t.OUT=\\\\\\\"out\\\\\\\"}(Gv||(Gv={}));const Vv=new class extends aa{constructor(){super(...arguments),this.dispatch=oa.BUTTON(null,{callback:t=>{Hv.PARAM_CALLBACK_execute(t)}})}};class Hv extends Ba{constructor(){super(...arguments),this.paramsConfig=Vv}static type(){return\\\\\\\"button\\\\\\\"}initializeNode(){this.io.outputs.setNamedOutputConnectionPoints([new Jo(Gv.OUT,$o.BASE)])}processEvent(t){}process_event_execute(t){this.dispatchEventToOutput(Gv.OUT,t)}static PARAM_CALLBACK_execute(t){t.process_event_execute({})}}class jv extends Ba{constructor(){super(...arguments),this._controls_by_viewer=new Map}async apply_controls(t,e){var n;null===(n=e.controlsController)||void 0===n||n.dispose_controls();const i=e.canvas();if(!i)return;const r=await this.create_controls_instance(t,i),s=this._controls_by_viewer.get(e);s&&s.dispose(),this._controls_by_viewer.set(e,r);const o=ai.performance.performanceManager().now();return r.name=`${this.path()}:${t.name}:${o}:${this.controls_id()}`,await this.params.evalAll(),this.setup_controls(r),r}controls_id(){return JSON.stringify(this.params.all.map((t=>t.valueSerialized())))}}var Wv=n(28);const qv=new p.a(0,0,1),Xv=new Wv.a,Yv=new au.a,$v=new au.a(-Math.sqrt(.5),0,0,Math.sqrt(.5)),Jv={type:\\\\\\\"change\\\\\\\"};class Zv extends $.a{constructor(t){super(),!1===window.isSecureContext&&console.error(\\\\\\\"THREE.DeviceOrientationControls: DeviceOrientationEvent is only available in secure contexts (https)\\\\\\\");const e=this,n=new au.a;this.object=t,this.object.rotation.reorder(\\\\\\\"YXZ\\\\\\\"),this.enabled=!0,this.deviceOrientation={},this.screenOrientation=0,this.alphaOffset=0;const i=function(t){e.deviceOrientation=t},r=function(){e.screenOrientation=window.orientation||0};this.connect=function(){r(),void 0!==window.DeviceOrientationEvent&&\\\\\\\"function\\\\\\\"==typeof window.DeviceOrientationEvent.requestPermission?window.DeviceOrientationEvent.requestPermission().then((function(t){\\\\\\\"granted\\\\\\\"==t&&(window.addEventListener(\\\\\\\"orientationchange\\\\\\\",r),window.addEventListener(\\\\\\\"deviceorientation\\\\\\\",i))})).catch((function(t){console.error(\\\\\\\"THREE.DeviceOrientationControls: Unable to use DeviceOrientation API:\\\\\\\",t)})):(window.addEventListener(\\\\\\\"orientationchange\\\\\\\",r),window.addEventListener(\\\\\\\"deviceorientation\\\\\\\",i)),e.enabled=!0},this.disconnect=function(){window.removeEventListener(\\\\\\\"orientationchange\\\\\\\",r),window.removeEventListener(\\\\\\\"deviceorientation\\\\\\\",i),e.enabled=!1},this.update=function(){if(!1===e.enabled)return;const t=e.deviceOrientation;if(t){const i=t.alpha?Ln.e(t.alpha)+e.alphaOffset:0,r=t.beta?Ln.e(t.beta):0,s=t.gamma?Ln.e(t.gamma):0,o=e.screenOrientation?Ln.e(e.screenOrientation):0;!function(t,e,n,i,r){Xv.set(n,e,-i,\\\\\\\"YXZ\\\\\\\"),t.setFromEuler(Xv),t.multiply($v),t.multiply(Yv.setFromAxisAngle(qv,-r))}(e.object.quaternion,i,r,s,o),8*(1-n.dot(e.object.quaternion))>1e-6&&(n.copy(e.object.quaternion),e.dispatchEvent(Jv))}},this.dispose=function(){e.disconnect()},this.connect()}}const Qv=new class extends aa{constructor(){super(...arguments),this.enabled=oa.BOOLEAN(1)}};class Kv extends jv{constructor(){super(...arguments),this.paramsConfig=Qv,this._controls_by_element_id=new Map}static type(){return rr.DEVICE_ORIENTATION}endEventName(){return\\\\\\\"end\\\\\\\"}async create_controls_instance(t,e){const n=new Zv(t);return this._controls_by_element_id.set(e.id,n),n}setup_controls(t){t.enabled=this.pv.enabled}update_required(){return!0}dispose_controls_for_html_element_id(t){const e=this._controls_by_element_id.get(t);e&&(e.dispose(),this._controls_by_element_id.delete(t))}}class ty{constructor(t=1,e=0,n=0){return this.radius=t,this.phi=e,this.theta=n,this}set(t,e,n){return this.radius=t,this.phi=e,this.theta=n,this}copy(t){return this.radius=t.radius,this.phi=t.phi,this.theta=t.theta,this}makeSafe(){const t=1e-6;return this.phi=Math.max(t,Math.min(Math.PI-t,this.phi)),this}setFromVector3(t){return this.setFromCartesianCoords(t.x,t.y,t.z)}setFromCartesianCoords(t,e,n){return this.radius=Math.sqrt(t*t+e*e+n*n),0===this.radius?(this.theta=0,this.phi=0):(this.theta=Math.atan2(t,n),this.phi=Math.acos(Ln.d(e/this.radius,-1,1))),this}clone(){return(new this.constructor).copy(this)}}const ey={type:\\\\\\\"change\\\\\\\"},ny={type:\\\\\\\"start\\\\\\\"},iy={type:\\\\\\\"end\\\\\\\"};class ry extends $.a{constructor(t,e){super(),void 0===e&&console.warn('THREE.OrbitControls: The second parameter \\\\\\\"domElement\\\\\\\" is now mandatory.'),e===document&&console.error('THREE.OrbitControls: \\\\\\\"document\\\\\\\" should not be used as the target \\\\\\\"domElement\\\\\\\". Please use \\\\\\\"renderer.domElement\\\\\\\" instead.'),this.object=t,this.domElement=e,this.domElement.style.touchAction=\\\\\\\"none\\\\\\\",this.enabled=!0,this.target=new p.a,this.minDistance=0,this.maxDistance=1/0,this.minZoom=0,this.maxZoom=1/0,this.minPolarAngle=0,this.maxPolarAngle=Math.PI,this.minAzimuthAngle=-1/0,this.maxAzimuthAngle=1/0,this.enableDamping=!1,this.dampingFactor=.05,this.enableZoom=!0,this.zoomSpeed=1,this.enableRotate=!0,this.rotateSpeed=1,this.enablePan=!0,this.panSpeed=1,this.screenSpacePanning=!0,this.keyPanSpeed=7,this.autoRotate=!1,this.autoRotateSpeed=2,this.enableKeys=!0,this.keyMode=\\\\\\\"pan\\\\\\\",this.keyRotateSpeedVertical=1,this.keyRotateSpeedHorizontal=1,this.keys={LEFT:\\\\\\\"ArrowLeft\\\\\\\",UP:\\\\\\\"ArrowUp\\\\\\\",RIGHT:\\\\\\\"ArrowRight\\\\\\\",BOTTOM:\\\\\\\"ArrowDown\\\\\\\"},this.mouseButtons={LEFT:w.hb.ROTATE,MIDDLE:w.hb.DOLLY,RIGHT:w.hb.PAN},this.touches={ONE:w.Tc.ROTATE,TWO:w.Tc.DOLLY_PAN},this.target0=this.target.clone(),this.position0=this.object.position.clone(),this.zoom0=this.object.zoom,this._domElementKeyEvents=null,this.getPolarAngle=function(){return o.phi},this.getAzimuthalAngle=function(){return o.theta},this.getDistance=function(){return this.object.position.distanceTo(this.target)},this.listenToKeyEvents=function(t){t.addEventListener(\\\\\\\"keydown\\\\\\\",q),this._domElementKeyEvents=t},this.saveState=function(){n.target0.copy(n.target),n.position0.copy(n.object.position),n.zoom0=n.object.zoom},this.reset=function(){n.target.copy(n.target0),n.object.position.copy(n.position0),n.object.zoom=n.zoom0,n.object.updateProjectionMatrix(),n.dispatchEvent(ey),n.update(),r=i.NONE},this.update=function(){const e=new p.a,h=(new au.a).setFromUnitVectors(t.up,new p.a(0,1,0)),d=h.clone().invert(),_=new p.a,m=new au.a,f=2*Math.PI;let g=!1;return function(){const t=n.object.position;if(e.copy(t).sub(n.target),e.applyQuaternion(h),o.setFromVector3(e),n.autoRotate&&r===i.NONE&&M(2*Math.PI/60/60*n.autoRotateSpeed),n.enableDamping){const t=a.theta*n.dampingFactor,e=a.phi*n.dampingFactor;t<s&&e<s?g||(n.dispatchEvent(iy),g=!0):g=!1,o.theta+=t,o.phi+=e}else o.theta+=a.theta,o.phi+=a.phi;let p=n.minAzimuthAngle,v=n.maxAzimuthAngle;return isFinite(p)&&isFinite(v)&&(p<-Math.PI?p+=f:p>Math.PI&&(p-=f),v<-Math.PI?v+=f:v>Math.PI&&(v-=f),o.theta=p<v?Math.max(p,Math.min(v,o.theta)):o.theta>(p+v)/2?Math.max(p,o.theta):Math.min(v,o.theta)),o.phi=Math.max(n.minPolarAngle,Math.min(n.maxPolarAngle,o.phi)),o.makeSafe(),o.radius*=l,o.radius=Math.max(n.minDistance,Math.min(n.maxDistance,o.radius)),!0===n.enableDamping?n.target.addScaledVector(c,n.dampingFactor):n.target.add(c),e.setFromSpherical(o),e.applyQuaternion(d),t.copy(n.target).add(e),n.object.lookAt(n.target),!0===n.enableDamping?(a.theta*=1-n.dampingFactor,a.phi*=1-n.dampingFactor,c.multiplyScalar(1-n.dampingFactor)):(a.set(0,0,0),c.set(0,0,0)),l=1,!!(u||_.distanceToSquared(n.object.position)>s||8*(1-m.dot(n.object.quaternion))>s)&&(n.dispatchEvent(ey),_.copy(n.object.position),m.copy(n.object.quaternion),u=!1,!0)}}(),this.dispose=function(){n.domElement.removeEventListener(\\\\\\\"contextmenu\\\\\\\",X,!1),n.domElement.removeEventListener(\\\\\\\"pointerdown\\\\\\\",G,!1),n.domElement.removeEventListener(\\\\\\\"pointercancel\\\\\\\",j),n.domElement.removeEventListener(\\\\\\\"wheel\\\\\\\",W,!1),n.domElement.ownerDocument.removeEventListener(\\\\\\\"pointermove\\\\\\\",V,!1),n.domElement.ownerDocument.removeEventListener(\\\\\\\"pointerup\\\\\\\",H,!1),null!==n._domElementKeyEvents&&n._domElementKeyEvents.removeEventListener(\\\\\\\"keydown\\\\\\\",q)};const n=this,i={NONE:-1,ROTATE:0,DOLLY:1,PAN:2,TOUCH_ROTATE:3,TOUCH_PAN:4,TOUCH_DOLLY_PAN:5,TOUCH_DOLLY_ROTATE:6};let r=i.NONE;const s=1e-6,o=new ty,a=new ty;let l=1;const c=new p.a;let u=!1;const h=new d.a,_=new d.a,m=new d.a,f=new d.a,g=new d.a,v=new d.a,y=new d.a,x=new d.a,b=new d.a,T=[],A={};function E(){return Math.pow(.95,n.zoomSpeed)}function M(t){a.theta-=t}function S(t){a.phi-=t}const C=function(){const t=new p.a;return function(e,n){t.setFromMatrixColumn(n,0),t.multiplyScalar(-e),c.add(t)}}(),N=function(){const t=new p.a;return function(e,i){!0===n.screenSpacePanning?t.setFromMatrixColumn(i,1):(t.setFromMatrixColumn(i,0),t.crossVectors(n.object.up,t)),t.multiplyScalar(e),c.add(t)}}(),L=function(){const t=new p.a;return function(e,i){const r=n.domElement;if(n.object.isPerspectiveCamera){const s=n.object.position;t.copy(s).sub(n.target);let o=t.length();o*=Math.tan(n.object.fov/2*Math.PI/180),C(2*e*o/r.clientHeight,n.object.matrix),N(2*i*o/r.clientHeight,n.object.matrix)}else n.object.isOrthographicCamera?(C(e*(n.object.right-n.object.left)/n.object.zoom/r.clientWidth,n.object.matrix),N(i*(n.object.top-n.object.bottom)/n.object.zoom/r.clientHeight,n.object.matrix)):(console.warn(\\\\\\\"WARNING: OrbitControls.js encountered an unknown camera type - pan disabled.\\\\\\\"),n.enablePan=!1)}}();function O(t){n.object.isPerspectiveCamera?l/=t:n.object.isOrthographicCamera?(n.object.zoom=Math.max(n.minZoom,Math.min(n.maxZoom,n.object.zoom*t)),n.object.updateProjectionMatrix(),u=!0):(console.warn(\\\\\\\"WARNING: OrbitControls.js encountered an unknown camera type - dolly/zoom disabled.\\\\\\\"),n.enableZoom=!1)}function R(t){n.object.isPerspectiveCamera?l*=t:n.object.isOrthographicCamera?(n.object.zoom=Math.max(n.minZoom,Math.min(n.maxZoom,n.object.zoom/t)),n.object.updateProjectionMatrix(),u=!0):(console.warn(\\\\\\\"WARNING: OrbitControls.js encountered an unknown camera type - dolly/zoom disabled.\\\\\\\"),n.enableZoom=!1)}function P(t){h.set(t.clientX,t.clientY)}function I(t){f.set(t.clientX,t.clientY)}function F(){if(1===T.length)h.set(T[0].pageX,T[0].pageY);else{const t=.5*(T[0].pageX+T[1].pageX),e=.5*(T[0].pageY+T[1].pageY);h.set(t,e)}}function D(){if(1===T.length)f.set(T[0].pageX,T[0].pageY);else{const t=.5*(T[0].pageX+T[1].pageX),e=.5*(T[0].pageY+T[1].pageY);f.set(t,e)}}function k(){const t=T[0].pageX-T[1].pageX,e=T[0].pageY-T[1].pageY,n=Math.sqrt(t*t+e*e);y.set(0,n)}function B(t){if(1==T.length)_.set(t.pageX,t.pageY);else{const e=J(t),n=.5*(t.pageX+e.x),i=.5*(t.pageY+e.y);_.set(n,i)}m.subVectors(_,h).multiplyScalar(n.rotateSpeed);const e=n.domElement;M(2*Math.PI*m.x/e.clientHeight),S(2*Math.PI*m.y/e.clientHeight),h.copy(_)}function z(t){if(1===T.length)g.set(t.pageX,t.pageY);else{const e=J(t),n=.5*(t.pageX+e.x),i=.5*(t.pageY+e.y);g.set(n,i)}v.subVectors(g,f).multiplyScalar(n.panSpeed),L(v.x,v.y),f.copy(g)}function U(t){const e=J(t),i=t.pageX-e.x,r=t.pageY-e.y,s=Math.sqrt(i*i+r*r);x.set(0,s),b.set(0,Math.pow(x.y/y.y,n.zoomSpeed)),O(b.y),y.copy(x)}function G(t){!1!==n.enabled&&(0===T.length&&(n.domElement.setPointerCapture(t.pointerId),n.domElement.ownerDocument.addEventListener(\\\\\\\"pointermove\\\\\\\",V),n.domElement.ownerDocument.addEventListener(\\\\\\\"pointerup\\\\\\\",H)),function(t){T.push(t)}(t),\\\\\\\"touch\\\\\\\"===t.pointerType?function(t){switch($(t),T.length){case 1:switch(n.touches.ONE){case w.Tc.ROTATE:if(!1===n.enableRotate)return;F(),r=i.TOUCH_ROTATE;break;case w.Tc.PAN:if(!1===n.enablePan)return;D(),r=i.TOUCH_PAN;break;default:r=i.NONE}break;case 2:switch(n.touches.TWO){case w.Tc.DOLLY_PAN:if(!1===n.enableZoom&&!1===n.enablePan)return;n.enableZoom&&k(),n.enablePan&&D(),r=i.TOUCH_DOLLY_PAN;break;case w.Tc.DOLLY_ROTATE:if(!1===n.enableZoom&&!1===n.enableRotate)return;n.enableZoom&&k(),n.enableRotate&&F(),r=i.TOUCH_DOLLY_ROTATE;break;default:r=i.NONE}break;default:r=i.NONE}r!==i.NONE&&n.dispatchEvent(ny)}(t):function(t){let e;switch(t.button){case 0:e=n.mouseButtons.LEFT;break;case 1:e=n.mouseButtons.MIDDLE;break;case 2:e=n.mouseButtons.RIGHT;break;default:e=-1}switch(e){case w.hb.DOLLY:if(!1===n.enableZoom)return;!function(t){y.set(t.clientX,t.clientY)}(t),r=i.DOLLY;break;case w.hb.ROTATE:if(t.ctrlKey||t.metaKey||t.shiftKey){if(!1===n.enablePan)return;I(t),r=i.PAN}else{if(!1===n.enableRotate)return;P(t),r=i.ROTATE}break;case w.hb.PAN:if(t.ctrlKey||t.metaKey||t.shiftKey){if(!1===n.enableRotate)return;P(t),r=i.ROTATE}else{if(!1===n.enablePan)return;I(t),r=i.PAN}break;default:r=i.NONE}r!==i.NONE&&n.dispatchEvent(ny)}(t))}function V(t){!1!==n.enabled&&(\\\\\\\"touch\\\\\\\"===t.pointerType?function(t){switch($(t),r){case i.TOUCH_ROTATE:if(!1===n.enableRotate)return;B(t),n.update();break;case i.TOUCH_PAN:if(!1===n.enablePan)return;z(t),n.update();break;case i.TOUCH_DOLLY_PAN:if(!1===n.enableZoom&&!1===n.enablePan)return;!function(t){n.enableZoom&&U(t),n.enablePan&&z(t)}(t),n.update();break;case i.TOUCH_DOLLY_ROTATE:if(!1===n.enableZoom&&!1===n.enableRotate)return;!function(t){n.enableZoom&&U(t),n.enableRotate&&B(t)}(t),n.update();break;default:r=i.NONE}}(t):function(t){if(!1===n.enabled)return;switch(r){case i.ROTATE:if(!1===n.enableRotate)return;!function(t){_.set(t.clientX,t.clientY),m.subVectors(_,h).multiplyScalar(n.rotateSpeed);var e=n.domElement;M(2*Math.PI*m.x/e.clientHeight),S(2*Math.PI*m.y/e.clientHeight),h.copy(_),n.update()}(t);break;case i.DOLLY:if(!1===n.enableZoom)return;!function(t){x.set(t.clientX,t.clientY),b.subVectors(x,y),b.y>0?O(E()):b.y<0&&R(E()),y.copy(x),n.update()}(t);break;case i.PAN:if(!1===n.enablePan)return;!function(t){g.set(t.clientX,t.clientY),v.subVectors(g,f).multiplyScalar(n.panSpeed),L(v.x,v.y),f.copy(g),n.update()}(t)}}(t))}function H(t){!1!==n.enabled&&(t.pointerType,n.dispatchEvent(iy),r=i.NONE,Y(t),0===T.length&&(n.domElement.releasePointerCapture(t.pointerId),n.domElement.ownerDocument.removeEventListener(\\\\\\\"pointermove\\\\\\\",V),n.domElement.ownerDocument.removeEventListener(\\\\\\\"pointerup\\\\\\\",H)))}function j(t){Y(t)}function W(t){!1===n.enabled||!1===n.enableZoom||r!==i.NONE&&r!==i.ROTATE||(t.preventDefault(),n.dispatchEvent(ny),function(t){t.deltaY<0?R(E()):t.deltaY>0&&O(E()),n.update()}(t),n.dispatchEvent(iy))}function q(t){!1!==n.enabled&&!1!==n.enablePan&&function(t){let e=!1;if(\\\\\\\"pan\\\\\\\"==n.keyMode)switch(t.code){case n.keys.UP:L(0,n.keyPanSpeed),e=!0;break;case n.keys.BOTTOM:L(0,-n.keyPanSpeed),e=!0;break;case n.keys.LEFT:L(n.keyPanSpeed,0),e=!0;break;case n.keys.RIGHT:L(-n.keyPanSpeed,0),e=!0}else switch(t.code){case n.keys.UP:S(n.keyRotateSpeedVertical),e=!0;break;case n.keys.BOTTOM:S(-n.keyRotateSpeedVertical),e=!0;break;case n.keys.LEFT:M(n.keyRotateSpeedHorizontal),e=!0;break;case n.keys.RIGHT:M(-n.keyRotateSpeedHorizontal),e=!0}e&&(t.preventDefault(),n.update())}(t)}function X(t){!1!==n.enabled&&t.preventDefault()}function Y(t){delete A[t.pointerId];for(let e=0;e<T.length;e++)if(T[e].pointerId==t.pointerId)return void T.splice(e,1)}function $(t){let e=A[t.pointerId];void 0===e&&(e=new d.a,A[t.pointerId]=e),e.set(t.pageX,t.pageY)}function J(t){const e=t.pointerId===T[0].pointerId?T[1]:T[0];return A[e.pointerId]}n.domElement.addEventListener(\\\\\\\"contextmenu\\\\\\\",X),n.domElement.addEventListener(\\\\\\\"pointerdown\\\\\\\",G),n.domElement.addEventListener(\\\\\\\"pointercancel\\\\\\\",j),n.domElement.addEventListener(\\\\\\\"wheel\\\\\\\",W,{passive:!1}),this.update()}}class sy extends ry{constructor(t,e){super(t,e),this.screenSpacePanning=!1,this.mouseButtons.LEFT=w.hb.PAN,this.mouseButtons.RIGHT=w.hb.ROTATE,this.touches.ONE=w.Tc.PAN,this.touches.TWO=w.Tc.DOLLY_ROTATE}}const oy=\\\\\\\"start\\\\\\\",ay=\\\\\\\"change\\\\\\\";var ly;!function(t){t.PAN=\\\\\\\"pan\\\\\\\",t.ROTATE=\\\\\\\"rotate\\\\\\\"}(ly||(ly={}));const cy=[ly.PAN,ly.ROTATE];const uy=new class extends aa{constructor(){super(...arguments),this.enabled=oa.BOOLEAN(1),this.allowPan=oa.BOOLEAN(1),this.allowRotate=oa.BOOLEAN(1),this.allowZoom=oa.BOOLEAN(1),this.tdamping=oa.BOOLEAN(1),this.damping=oa.FLOAT(.1,{visibleIf:{tdamping:!0}}),this.screenSpacePanning=oa.BOOLEAN(1),this.rotateSpeed=oa.FLOAT(.5),this.minDistance=oa.FLOAT(1,{range:[0,100],rangeLocked:[!0,!1]}),this.maxDistance=oa.FLOAT(50,{range:[0,100],rangeLocked:[!0,!1]}),this.limitAzimuthAngle=oa.BOOLEAN(0),this.azimuthAngleRange=oa.VECTOR2([\\\\\\\"-2*$PI\\\\\\\",\\\\\\\"2*$PI\\\\\\\"],{visibleIf:{limitAzimuthAngle:1}}),this.polarAngleRange=oa.VECTOR2([0,\\\\\\\"$PI\\\\\\\"]),this.target=oa.VECTOR3([0,0,0],{cook:!1,computeOnDirty:!0,callback:t=>{hy.PARAM_CALLBACK_update_target(t)}}),this.enableKeys=oa.BOOLEAN(0),this.keysMode=oa.INTEGER(cy.indexOf(ly.PAN),{visibleIf:{enableKeys:1},menu:{entries:cy.map(((t,e)=>({name:t,value:e})))}}),this.keysPanSpeed=oa.FLOAT(7,{range:[0,10],rangeLocked:[!1,!1],visibleIf:{enableKeys:1,keysMode:cy.indexOf(ly.PAN)}}),this.keysRotateSpeedVertical=oa.FLOAT(1,{range:[0,1],rangeLocked:[!1,!1],visibleIf:{enableKeys:1,keysMode:cy.indexOf(ly.ROTATE)}}),this.keysRotateSpeedHorizontal=oa.FLOAT(1,{range:[0,1],rangeLocked:[!1,!1],visibleIf:{enableKeys:1,keysMode:cy.indexOf(ly.ROTATE)}})}};class hy extends jv{constructor(){super(...arguments),this.paramsConfig=uy,this._controls_by_element_id=new Map,this._target_array=[0,0,0]}static type(){return rr.ORBIT}endEventName(){return\\\\\\\"end\\\\\\\"}initializeNode(){this.io.outputs.setNamedOutputConnectionPoints([new Jo(oy,$o.BASE),new Jo(ay,$o.BASE),new Jo(\\\\\\\"end\\\\\\\",$o.BASE)])}async create_controls_instance(t,e){const n=new ry(t,e);return n.addEventListener(\\\\\\\"end\\\\\\\",(()=>{this._on_controls_end(n)})),this._controls_by_element_id.set(e.id,n),this._bind_listeners_to_controls_instance(n),n}_bind_listeners_to_controls_instance(t){t.addEventListener(\\\\\\\"start\\\\\\\",(()=>{this.dispatchEventToOutput(oy,{})})),t.addEventListener(\\\\\\\"change\\\\\\\",(()=>{this.dispatchEventToOutput(ay,{})})),t.addEventListener(\\\\\\\"end\\\\\\\",(()=>{this.dispatchEventToOutput(\\\\\\\"end\\\\\\\",{})}))}setup_controls(t){t.enabled=this.pv.enabled,t.enablePan=this.pv.allowPan,t.enableRotate=this.pv.allowRotate,t.enableZoom=this.pv.allowZoom,t.enableDamping=this.pv.tdamping,t.dampingFactor=this.pv.damping,t.rotateSpeed=this.pv.rotateSpeed,t.screenSpacePanning=this.pv.screenSpacePanning,t.minDistance=this.pv.minDistance,t.maxDistance=this.pv.maxDistance,this._set_azimuth_angle(t),t.minPolarAngle=this.pv.polarAngleRange.x,t.maxPolarAngle=this.pv.polarAngleRange.y,t.target.copy(this.pv.target),t.enabled&&t.update(),t.enableKeys=this.pv.enableKeys,t.enableKeys&&(t.keyMode=cy[this.pv.keysMode],t.keyRotateSpeedVertical=this.pv.keysRotateSpeedVertical,t.keyRotateSpeedHorizontal=this.pv.keysRotateSpeedHorizontal,t.keyPanSpeed=this.pv.keysPanSpeed)}_set_azimuth_angle(t){this.pv.limitAzimuthAngle?(t.minAzimuthAngle=this.pv.azimuthAngleRange.x,t.maxAzimuthAngle=this.pv.azimuthAngleRange.y):(t.minAzimuthAngle=1/0,t.maxAzimuthAngle=1/0)}update_required(){return this.pv.tdamping}_on_controls_end(t){this.pv.allowPan&&(t.target.toArray(this._target_array),this.p.target.set(this._target_array))}static PARAM_CALLBACK_update_target(t){t._update_target()}_update_target(){const t=this.pv.target;this._controls_by_element_id.forEach(((e,n)=>{const i=e.target;i.equals(t)||(i.copy(t),e.update())}))}dispose_controls_for_html_element_id(t){this._controls_by_element_id.get(t)&&this._controls_by_element_id.delete(t)}}class dy extends hy{static type(){return rr.MAP}async create_controls_instance(t,e){const n=new sy(t,e);return this._bind_listeners_to_controls_instance(n),n}}const py=new class extends aa{constructor(){super(...arguments),this.delay=oa.INTEGER(1e3,{range:[0,1e3],rangeLocked:[!0,!1]})}};class _y extends Ba{constructor(){super(...arguments),this.paramsConfig=py}static type(){return\\\\\\\"delay\\\\\\\"}initializeNode(){this.io.inputs.setNamedInputConnectionPoints([new Jo(\\\\\\\"in\\\\\\\",$o.BASE,this._process_input.bind(this))]),this.io.outputs.setNamedOutputConnectionPoints([new Jo(\\\\\\\"out\\\\\\\",$o.BASE)])}_process_input(t){setTimeout((()=>{this.dispatchEventToOutput(\\\\\\\"out\\\\\\\",t)}),this.pv.delay)}}const my=100,fy=301,gy=302,vy=303,yy=304,xy=306,by=307,wy=1e3,Ty=1001,Ay=1002,Ey=1003,My=1004,Sy=1005,Cy=1006,Ny=1007,Ly=1008,Oy=1009,Ry=1012,Py=1014,Iy=1015,Fy=1016,Dy=1020,ky=1022,By=1023,zy=1026,Uy=1027,Gy=2300,Vy=2301,Hy=2302,jy=2400,Wy=2401,qy=2402,Xy=2500,Yy=3e3,$y=3001,Jy=3007,Zy=3002,Qy=7680,Ky=35044,tx=35048,ex=\\\\\\\"300 es\\\\\\\";class nx{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<i;e++)n[e].call(this,t);t.target=null}}}let ix=1234567;const rx=Math.PI/180,sx=180/Math.PI,ox=[];for(let t=0;t<256;t++)ox[t]=(t<16?\\\\\\\"0\\\\\\\":\\\\\\\"\\\\\\\")+t.toString(16);const ax=\\\\\\\"undefined\\\\\\\"!=typeof crypto&&\\\\\\\"randomUUID\\\\\\\"in crypto;function lx(){if(ax)return crypto.randomUUID().toUpperCase();const t=4294967295*Math.random()|0,e=4294967295*Math.random()|0,n=4294967295*Math.random()|0,i=4294967295*Math.random()|0;return(ox[255&t]+ox[t>>8&255]+ox[t>>16&255]+ox[t>>24&255]+\\\\\\\"-\\\\\\\"+ox[255&e]+ox[e>>8&255]+\\\\\\\"-\\\\\\\"+ox[e>>16&15|64]+ox[e>>24&255]+\\\\\\\"-\\\\\\\"+ox[63&n|128]+ox[n>>8&255]+\\\\\\\"-\\\\\\\"+ox[n>>16&255]+ox[n>>24&255]+ox[255&i]+ox[i>>8&255]+ox[i>>16&255]+ox[i>>24&255]).toUpperCase()}function cx(t,e,n){return Math.max(e,Math.min(n,t))}function ux(t,e){return(t%e+e)%e}function hx(t,e,n){return(1-n)*t+n*e}function dx(t){return 0==(t&t-1)&&0!==t}function px(t){return Math.pow(2,Math.ceil(Math.log(t)/Math.LN2))}function _x(t){return Math.pow(2,Math.floor(Math.log(t)/Math.LN2))}var mx=Object.freeze({__proto__:null,DEG2RAD:rx,RAD2DEG:sx,generateUUID:lx,clamp:cx,euclideanModulo:ux,mapLinear:function(t,e,n,i,r){return i+(t-e)*(r-i)/(n-e)},inverseLerp:function(t,e,n){return t!==e?(n-t)/(e-t):0},lerp:hx,damp:function(t,e,n,i){return hx(t,e,1-Math.exp(-n*i))},pingpong:function(t,e=1){return e-Math.abs(ux(t,2*e)-e)},smoothstep:function(t,e,n){return t<=e?0:t>=n?1:(t=(t-e)/(n-e))*t*(3-2*t)},smootherstep:function(t,e,n){return t<=e?0:t>=n?1:(t=(t-e)/(n-e))*t*t*(t*(6*t-15)+10)},randInt:function(t,e){return t+Math.floor(Math.random()*(e-t+1))},randFloat:function(t,e){return t+Math.random()*(e-t)},randFloatSpread:function(t){return t*(.5-Math.random())},seededRandom:function(t){return void 0!==t&&(ix=t%2147483647),ix=16807*ix%2147483647,(ix-1)/2147483646},degToRad:function(t){return t*rx},radToDeg:function(t){return t*sx},isPowerOfTwo:dx,ceilPowerOfTwo:px,floorPowerOfTwo:_x,setQuaternionFromProperEuler:function(t,e,n,i,r){const s=Math.cos,o=Math.sin,a=s(n/2),l=o(n/2),c=s((e+i)/2),u=o((e+i)/2),h=s((e-i)/2),d=o((e-i)/2),p=s((i-e)/2),_=o((i-e)/2);switch(r){case\\\\\\\"XYX\\\\\\\":t.set(a*u,l*h,l*d,a*c);break;case\\\\\\\"YZY\\\\\\\":t.set(l*d,a*u,l*h,a*c);break;case\\\\\\\"ZXZ\\\\\\\":t.set(l*h,l*d,a*u,a*c);break;case\\\\\\\"XZX\\\\\\\":t.set(a*u,l*_,l*p,a*c);break;case\\\\\\\"YXY\\\\\\\":t.set(l*p,a*u,l*_,a*c);break;case\\\\\\\"ZYZ\\\\\\\":t.set(l*_,l*p,a*u,a*c);break;default:console.warn(\\\\\\\"THREE.MathUtils: .setQuaternionFromProperEuler() encountered an unknown order: \\\\\\\"+r)}}});class fx{constructor(t=0,e=0){this.x=t,this.y=e}get width(){return this.x}set width(t){this.x=t}get height(){return this.y}set height(t){this.y=t}set(t,e){return this.x=t,this.y=e,this}setScalar(t){return this.x=t,this.y=t,this}setX(t){return this.x=t,this}setY(t){return this.y=t,this}setComponent(t,e){switch(t){case 0:this.x=e;break;case 1:this.y=e;break;default:throw new Error(\\\\\\\"index is out of range: \\\\\\\"+t)}return this}getComponent(t){switch(t){case 0:return this.x;case 1:return this.y;default:throw new Error(\\\\\\\"index is out of range: \\\\\\\"+t)}}clone(){return new this.constructor(this.x,this.y)}copy(t){return this.x=t.x,this.y=t.y,this}add(t,e){return void 0!==e?(console.warn(\\\\\\\"THREE.Vector2: .add() now only accepts one argument. 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),r=this.x-t.x,s=this.y-t.y;return this.x=r*n-s*i+t.x,this.y=r*i+s*n+t.y,this}random(){return this.x=Math.random(),this.y=Math.random(),this}*[Symbol.iterator](){yield this.x,yield this.y}}fx.prototype.isVector2=!0;class gx{constructor(){this.elements=[1,0,0,0,1,0,0,0,1],arguments.length>0&&console.error(\\\\\\\"THREE.Matrix3: the constructor no longer reads arguments. use .set() instead.\\\\\\\")}set(t,e,n,i,r,s,o,a,l){const c=this.elements;return c[0]=t,c[1]=i,c[2]=o,c[3]=e,c[4]=r,c[5]=a,c[6]=n,c[7]=s,c[8]=l,this}identity(){return this.set(1,0,0,0,1,0,0,0,1),this}copy(t){const e=this.elements,n=t.elements;return e[0]=n[0],e[1]=n[1],e[2]=n[2],e[3]=n[3],e[4]=n[4],e[5]=n[5],e[6]=n[6],e[7]=n[7],e[8]=n[8],this}extractBasis(t,e,n){return t.setFromMatrix3Column(this,0),e.setFromMatrix3Column(this,1),n.setFromMatrix3Column(this,2),this}setFromMatrix4(t){const e=t.elements;return this.set(e[0],e[4],e[8],e[1],e[5],e[9],e[2],e[6],e[10]),this}multiply(t){return this.multiplyMatrices(this,t)}premultiply(t){return this.multiplyMatrices(t,this)}multiplyMatrices(t,e){const n=t.elements,i=e.elements,r=this.elements,s=n[0],o=n[3],a=n[6],l=n[1],c=n[4],u=n[7],h=n[2],d=n[5],p=n[8],_=i[0],m=i[3],f=i[6],g=i[1],v=i[4],y=i[7],x=i[2],b=i[5],w=i[8];return r[0]=s*_+o*g+a*x,r[3]=s*m+o*v+a*b,r[6]=s*f+o*y+a*w,r[1]=l*_+c*g+u*x,r[4]=l*m+c*v+u*b,r[7]=l*f+c*y+u*w,r[2]=h*_+d*g+p*x,r[5]=h*m+d*v+p*b,r[8]=h*f+d*y+p*w,this}multiplyScalar(t){const e=this.elements;return e[0]*=t,e[3]*=t,e[6]*=t,e[1]*=t,e[4]*=t,e[7]*=t,e[2]*=t,e[5]*=t,e[8]*=t,this}determinant(){const t=this.elements,e=t[0],n=t[1],i=t[2],r=t[3],s=t[4],o=t[5],a=t[6],l=t[7],c=t[8];return e*s*c-e*o*l-n*r*c+n*o*a+i*r*l-i*s*a}invert(){const t=this.elements,e=t[0],n=t[1],i=t[2],r=t[3],s=t[4],o=t[5],a=t[6],l=t[7],c=t[8],u=c*s-o*l,h=o*a-c*r,d=l*r-s*a,p=e*u+n*h+i*d;if(0===p)return this.set(0,0,0,0,0,0,0,0,0);const _=1/p;return t[0]=u*_,t[1]=(i*l-c*n)*_,t[2]=(o*n-i*s)*_,t[3]=h*_,t[4]=(c*e-i*a)*_,t[5]=(i*r-o*e)*_,t[6]=d*_,t[7]=(n*a-l*e)*_,t[8]=(s*e-n*r)*_,this}transpose(){let t;const e=this.elements;return t=e[1],e[1]=e[3],e[3]=t,t=e[2],e[2]=e[6],e[6]=t,t=e[5],e[5]=e[7],e[7]=t,this}getNormalMatrix(t){return this.setFromMatrix4(t).invert().transpose()}transposeIntoArray(t){const e=this.elements;return t[0]=e[0],t[1]=e[3],t[2]=e[6],t[3]=e[1],t[4]=e[4],t[5]=e[7],t[6]=e[2],t[7]=e[5],t[8]=e[8],this}setUvTransform(t,e,n,i,r,s,o){const a=Math.cos(r),l=Math.sin(r);return this.set(n*a,n*l,-n*(a*s+l*o)+s+t,-i*l,i*a,-i*(-l*s+a*o)+o+e,0,0,1),this}scale(t,e){const n=this.elements;return n[0]*=t,n[3]*=t,n[6]*=t,n[1]*=e,n[4]*=e,n[7]*=e,this}rotate(t){const e=Math.cos(t),n=Math.sin(t),i=this.elements,r=i[0],s=i[3],o=i[6],a=i[1],l=i[4],c=i[7];return i[0]=e*r+n*a,i[3]=e*s+n*l,i[6]=e*o+n*c,i[1]=-n*r+e*a,i[4]=-n*s+e*l,i[7]=-n*o+e*c,this}translate(t,e){const n=this.elements;return n[0]+=t*n[2],n[3]+=t*n[5],n[6]+=t*n[8],n[1]+=e*n[2],n[4]+=e*n[5],n[7]+=e*n[8],this}equals(t){const e=this.elements,n=t.elements;for(let t=0;t<9;t++)if(e[t]!==n[t])return!1;return!0}fromArray(t,e=0){for(let n=0;n<9;n++)this.elements[n]=t[n+e];return this}toArray(t=[],e=0){const n=this.elements;return t[e]=n[0],t[e+1]=n[1],t[e+2]=n[2],t[e+3]=n[3],t[e+4]=n[4],t[e+5]=n[5],t[e+6]=n[6],t[e+7]=n[7],t[e+8]=n[8],t}clone(){return(new this.constructor).fromArray(this.elements)}}function vx(t){if(0===t.length)return-1/0;let e=t[0];for(let n=1,i=t.length;n<i;++n)t[n]>e&&(e=t[n]);return e}gx.prototype.isMatrix3=!0;Int8Array,Uint8Array,Uint8ClampedArray,Int16Array,Uint16Array,Int32Array,Uint32Array,Float32Array,Float64Array;function yx(t){return document.createElementNS(\\\\\\\"http://www.w3.org/1999/xhtml\\\\\\\",t)}let xx;class bx{static getDataURL(t){if(/^data:/i.test(t.src))return t.src;if(\\\\\\\"undefined\\\\\\\"==typeof HTMLCanvasElement)return t.src;let e;if(t instanceof HTMLCanvasElement)e=t;else{void 0===xx&&(xx=yx(\\\\\\\"canvas\\\\\\\")),xx.width=t.width,xx.height=t.height;const n=xx.getContext(\\\\\\\"2d\\\\\\\");t instanceof ImageData?n.putImageData(t,0,0):n.drawImage(t,0,0,t.width,t.height),e=xx}return e.width>2048||e.height>2048?(console.warn(\\\\\\\"THREE.ImageUtils.getDataURL: Image converted to jpg for performance reasons\\\\\\\",t),e.toDataURL(\\\\\\\"image/jpeg\\\\\\\",.6)):e.toDataURL(\\\\\\\"image/png\\\\\\\")}}let wx=0;class Tx extends nx{constructor(t=Tx.DEFAULT_IMAGE,e=Tx.DEFAULT_MAPPING,n=1001,i=1001,r=1006,s=1008,o=1023,a=1009,l=1,c=3e3){super(),Object.defineProperty(this,\\\\\\\"id\\\\\\\",{value:wx++}),this.uuid=lx(),this.name=\\\\\\\"\\\\\\\",this.image=t,this.mipmaps=[],this.mapping=e,this.wrapS=n,this.wrapT=i,this.magFilter=r,this.minFilter=s,this.anisotropy=l,this.format=o,this.internalFormat=null,this.type=a,this.offset=new fx(0,0),this.repeat=new fx(1,1),this.center=new fx(0,0),this.rotation=0,this.matrixAutoUpdate=!0,this.matrix=new gx,this.generateMipmaps=!0,this.premultiplyAlpha=!1,this.flipY=!0,this.unpackAlignment=4,this.encoding=c,this.version=0,this.onUpdate=null,this.isRenderTargetTexture=!1}updateMatrix(){this.matrix.setUvTransform(this.offset.x,this.offset.y,this.repeat.x,this.repeat.y,this.rotation,this.center.x,this.center.y)}clone(){return(new this.constructor).copy(this)}copy(t){return this.name=t.name,this.image=t.image,this.mipmaps=t.mipmaps.slice(0),this.mapping=t.mapping,this.wrapS=t.wrapS,this.wrapT=t.wrapT,this.magFilter=t.magFilter,this.minFilter=t.minFilter,this.anisotropy=t.anisotropy,this.format=t.format,this.internalFormat=t.internalFormat,this.type=t.type,this.offset.copy(t.offset),this.repeat.copy(t.repeat),this.center.copy(t.center),this.rotation=t.rotation,this.matrixAutoUpdate=t.matrixAutoUpdate,this.matrix.copy(t.matrix),this.generateMipmaps=t.generateMipmaps,this.premultiplyAlpha=t.premultiplyAlpha,this.flipY=t.flipY,this.unpackAlignment=t.unpackAlignment,this.encoding=t.encoding,this}toJSON(t){const e=void 0===t||\\\\\\\"string\\\\\\\"==typeof t;if(!e&&void 0!==t.textures[this.uuid])return t.textures[this.uuid];const n={metadata:{version:4.5,type:\\\\\\\"Texture\\\\\\\",generator:\\\\\\\"Texture.toJSON\\\\\\\"},uuid:this.uuid,name:this.name,mapping:this.mapping,repeat:[this.repeat.x,this.repeat.y],offset:[this.offset.x,this.offset.y],center:[this.center.x,this.center.y],rotation:this.rotation,wrap:[this.wrapS,this.wrapT],format:this.format,type:this.type,encoding:this.encoding,minFilter:this.minFilter,magFilter:this.magFilter,anisotropy:this.anisotropy,flipY:this.flipY,premultiplyAlpha:this.premultiplyAlpha,unpackAlignment:this.unpackAlignment};if(void 0!==this.image){const i=this.image;if(void 0===i.uuid&&(i.uuid=lx()),!e&&void 0===t.images[i.uuid]){let e;if(Array.isArray(i)){e=[];for(let t=0,n=i.length;t<n;t++)i[t].isDataTexture?e.push(Ax(i[t].image)):e.push(Ax(i[t]))}else e=Ax(i);t.images[i.uuid]={uuid:i.uuid,url:e}}n.image=i.uuid}return e||(t.textures[this.uuid]=n),n}dispose(){this.dispatchEvent({type:\\\\\\\"dispose\\\\\\\"})}transformUv(t){if(300!==this.mapping)return t;if(t.applyMatrix3(this.matrix),t.x<0||t.x>1)switch(this.wrapS){case wy:t.x=t.x-Math.floor(t.x);break;case Ty:t.x=t.x<0?0:1;break;case Ay:1===Math.abs(Math.floor(t.x)%2)?t.x=Math.ceil(t.x)-t.x:t.x=t.x-Math.floor(t.x)}if(t.y<0||t.y>1)switch(this.wrapT){case wy:t.y=t.y-Math.floor(t.y);break;case Ty:t.y=t.y<0?0:1;break;case Ay:1===Math.abs(Math.floor(t.y)%2)?t.y=Math.ceil(t.y)-t.y:t.y=t.y-Math.floor(t.y)}return this.flipY&&(t.y=1-t.y),t}set needsUpdate(t){!0===t&&this.version++}}function Ax(t){return\\\\\\\"undefined\\\\\\\"!=typeof HTMLImageElement&&t instanceof HTMLImageElement||\\\\\\\"undefined\\\\\\\"!=typeof HTMLCanvasElement&&t instanceof HTMLCanvasElement||\\\\\\\"undefined\\\\\\\"!=typeof ImageBitmap&&t instanceof ImageBitmap?bx.getDataURL(t):t.data?{data:Array.prototype.slice.call(t.data),width:t.width,height:t.height,type:t.data.constructor.name}:(console.warn(\\\\\\\"THREE.Texture: Unable to serialize Texture.\\\\\\\"),{})}Tx.DEFAULT_IMAGE=void 0,Tx.DEFAULT_MAPPING=300,Tx.prototype.isTexture=!0;class Ex{constructor(t=0,e=0,n=0,i=1){this.x=t,this.y=e,this.z=n,this.w=i}get width(){return this.z}set width(t){this.z=t}get height(){return this.w}set height(t){this.w=t}set(t,e,n,i){return this.x=t,this.y=e,this.z=n,this.w=i,this}setScalar(t){return this.x=t,this.y=t,this.z=t,this.w=t,this}setX(t){return this.x=t,this}setY(t){return this.y=t,this}setZ(t){return this.z=t,this}setW(t){return this.w=t,this}setComponent(t,e){switch(t){case 0:this.x=e;break;case 1:this.y=e;break;case 2:this.z=e;break;case 3:this.w=e;break;default:throw new Error(\\\\\\\"index is out of range: \\\\\\\"+t)}return this}getComponent(t){switch(t){case 0:return this.x;case 1:return this.y;case 2:return this.z;case 3:return this.w;default:throw new Error(\\\\\\\"index is out of range: \\\\\\\"+t)}}clone(){return new this.constructor(this.x,this.y,this.z,this.w)}copy(t){return this.x=t.x,this.y=t.y,this.z=t.z,this.w=void 0!==t.w?t.w:1,this}add(t,e){return void 0!==e?(console.warn(\\\\\\\"THREE.Vector4: .add() now only accepts one argument. Use .addVectors( a, b ) instead.\\\\\\\"),this.addVectors(t,e)):(this.x+=t.x,this.y+=t.y,this.z+=t.z,this.w+=t.w,this)}addScalar(t){return this.x+=t,this.y+=t,this.z+=t,this.w+=t,this}addVectors(t,e){return this.x=t.x+e.x,this.y=t.y+e.y,this.z=t.z+e.z,this.w=t.w+e.w,this}addScaledVector(t,e){return this.x+=t.x*e,this.y+=t.y*e,this.z+=t.z*e,this.w+=t.w*e,this}sub(t,e){return void 0!==e?(console.warn(\\\\\\\"THREE.Vector4: .sub() now only accepts one argument. Use .subVectors( a, b ) instead.\\\\\\\"),this.subVectors(t,e)):(this.x-=t.x,this.y-=t.y,this.z-=t.z,this.w-=t.w,this)}subScalar(t){return this.x-=t,this.y-=t,this.z-=t,this.w-=t,this}subVectors(t,e){return this.x=t.x-e.x,this.y=t.y-e.y,this.z=t.z-e.z,this.w=t.w-e.w,this}multiply(t){return this.x*=t.x,this.y*=t.y,this.z*=t.z,this.w*=t.w,this}multiplyScalar(t){return this.x*=t,this.y*=t,this.z*=t,this.w*=t,this}applyMatrix4(t){const e=this.x,n=this.y,i=this.z,r=this.w,s=t.elements;return this.x=s[0]*e+s[4]*n+s[8]*i+s[12]*r,this.y=s[1]*e+s[5]*n+s[9]*i+s[13]*r,this.z=s[2]*e+s[6]*n+s[10]*i+s[14]*r,this.w=s[3]*e+s[7]*n+s[11]*i+s[15]*r,this}divideScalar(t){return this.multiplyScalar(1/t)}setAxisAngleFromQuaternion(t){this.w=2*Math.acos(t.w);const e=Math.sqrt(1-t.w*t.w);return e<1e-4?(this.x=1,this.y=0,this.z=0):(this.x=t.x/e,this.y=t.y/e,this.z=t.z/e),this}setAxisAngleFromRotationMatrix(t){let e,n,i,r;const s=.01,o=.1,a=t.elements,l=a[0],c=a[4],u=a[8],h=a[1],d=a[5],p=a[9],_=a[2],m=a[6],f=a[10];if(Math.abs(c-h)<s&&Math.abs(u-_)<s&&Math.abs(p-m)<s){if(Math.abs(c+h)<o&&Math.abs(u+_)<o&&Math.abs(p+m)<o&&Math.abs(l+d+f-3)<o)return this.set(1,0,0,0),this;e=Math.PI;const t=(l+1)/2,a=(d+1)/2,g=(f+1)/2,v=(c+h)/4,y=(u+_)/4,x=(p+m)/4;return t>a&&t>g?t<s?(n=0,i=.707106781,r=.707106781):(n=Math.sqrt(t),i=v/n,r=y/n):a>g?a<s?(n=.707106781,i=0,r=.707106781):(i=Math.sqrt(a),n=v/i,r=x/i):g<s?(n=.707106781,i=.707106781,r=0):(r=Math.sqrt(g),n=y/r,i=x/r),this.set(n,i,r,e),this}let g=Math.sqrt((m-p)*(m-p)+(u-_)*(u-_)+(h-c)*(h-c));return Math.abs(g)<.001&&(g=1),this.x=(m-p)/g,this.y=(u-_)/g,this.z=(h-c)/g,this.w=Math.acos((l+d+f-1)/2),this}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.w=Math.min(this.w,t.w),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.w=Math.max(this.w,t.w),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.w=Math.max(t.w,Math.min(e.w,this.w)),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.w=Math.max(t,Math.min(e,this.w)),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.w=Math.floor(this.w),this}ceil(){return this.x=Math.ceil(this.x),this.y=Math.ceil(this.y),this.z=Math.ceil(this.z),this.w=Math.ceil(this.w),this}round(){return this.x=Math.round(this.x),this.y=Math.round(this.y),this.z=Math.round(this.z),this.w=Math.round(this.w),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.w=this.w<0?Math.ceil(this.w):Math.floor(this.w),this}negate(){return this.x=-this.x,this.y=-this.y,this.z=-this.z,this.w=-this.w,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)}manhattanLength(){return Math.abs(this.x)+Math.abs(this.y)+Math.abs(this.z)+Math.abs(this.w)}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.w+=(t.w-this.w)*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.w=t.w+(e.w-t.w)*n,this}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}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,n){return void 0!==n&&console.warn(\\\\\\\"THREE.Vector4: offset has been removed from .fromBufferAttribute().\\\\\\\"),this.x=t.getX(e),this.y=t.getY(e),this.z=t.getZ(e),this.w=t.getW(e),this}random(){return this.x=Math.random(),this.y=Math.random(),this.z=Math.random(),this.w=Math.random(),this}*[Symbol.iterator](){yield this.x,yield this.y,yield this.z,yield this.w}}Ex.prototype.isVector4=!0;class Mx extends nx{constructor(t,e,n={}){super(),this.width=t,this.height=e,this.depth=1,this.scissor=new Ex(0,0,t,e),this.scissorTest=!1,this.viewport=new Ex(0,0,t,e),this.texture=new Tx(void 0,n.mapping,n.wrapS,n.wrapT,n.magFilter,n.minFilter,n.format,n.type,n.anisotropy,n.encoding),this.texture.isRenderTargetTexture=!0,this.texture.image={width:t,height:e,depth:1},this.texture.generateMipmaps=void 0!==n.generateMipmaps&&n.generateMipmaps,this.texture.internalFormat=void 0!==n.internalFormat?n.internalFormat:null,this.texture.minFilter=void 0!==n.minFilter?n.minFilter:Cy,this.depthBuffer=void 0===n.depthBuffer||n.depthBuffer,this.stencilBuffer=void 0!==n.stencilBuffer&&n.stencilBuffer,this.depthTexture=void 0!==n.depthTexture?n.depthTexture:null}setTexture(t){t.image={width:this.width,height:this.height,depth:this.depth},this.texture=t}setSize(t,e,n=1){this.width===t&&this.height===e&&this.depth===n||(this.width=t,this.height=e,this.depth=n,this.texture.image.width=t,this.texture.image.height=e,this.texture.image.depth=n,this.dispose()),this.viewport.set(0,0,t,e),this.scissor.set(0,0,t,e)}clone(){return(new this.constructor).copy(this)}copy(t){return this.width=t.width,this.height=t.height,this.depth=t.depth,this.viewport.copy(t.viewport),this.texture=t.texture.clone(),this.texture.image={...this.texture.image},this.depthBuffer=t.depthBuffer,this.stencilBuffer=t.stencilBuffer,this.depthTexture=t.depthTexture,this}dispose(){this.dispatchEvent({type:\\\\\\\"dispose\\\\\\\"})}}Mx.prototype.isWebGLRenderTarget=!0;(class extends Mx{constructor(t,e,n){super(t,e);const i=this.texture;this.texture=[];for(let t=0;t<n;t++)this.texture[t]=i.clone()}setSize(t,e,n=1){if(this.width!==t||this.height!==e||this.depth!==n){this.width=t,this.height=e,this.depth=n;for(let i=0,r=this.texture.length;i<r;i++)this.texture[i].image.width=t,this.texture[i].image.height=e,this.texture[i].image.depth=n;this.dispose()}return this.viewport.set(0,0,t,e),this.scissor.set(0,0,t,e),this}copy(t){this.dispose(),this.width=t.width,this.height=t.height,this.depth=t.depth,this.viewport.set(0,0,this.width,this.height),this.scissor.set(0,0,this.width,this.height),this.depthBuffer=t.depthBuffer,this.stencilBuffer=t.stencilBuffer,this.depthTexture=t.depthTexture,this.texture.length=0;for(let e=0,n=t.texture.length;e<n;e++)this.texture[e]=t.texture[e].clone();return this}}).prototype.isWebGLMultipleRenderTargets=!0;class Sx extends Mx{constructor(t,e,n){super(t,e,n),this.samples=4}copy(t){return super.copy.call(this,t),this.samples=t.samples,this}}Sx.prototype.isWebGLMultisampleRenderTarget=!0;class Cx{constructor(t=0,e=0,n=0,i=1){this._x=t,this._y=e,this._z=n,this._w=i}static slerp(t,e,n,i){return console.warn(\\\\\\\"THREE.Quaternion: Static .slerp() has been deprecated. Use qm.slerpQuaternions( qa, qb, t ) instead.\\\\\\\"),n.slerpQuaternions(t,e,i)}static slerpFlat(t,e,n,i,r,s,o){let a=n[i+0],l=n[i+1],c=n[i+2],u=n[i+3];const h=r[s+0],d=r[s+1],p=r[s+2],_=r[s+3];if(0===o)return t[e+0]=a,t[e+1]=l,t[e+2]=c,void(t[e+3]=u);if(1===o)return t[e+0]=h,t[e+1]=d,t[e+2]=p,void(t[e+3]=_);if(u!==_||a!==h||l!==d||c!==p){let t=1-o;const e=a*h+l*d+c*p+u*_,n=e>=0?1:-1,i=1-e*e;if(i>Number.EPSILON){const r=Math.sqrt(i),s=Math.atan2(r,e*n);t=Math.sin(t*s)/r,o=Math.sin(o*s)/r}const r=o*n;if(a=a*t+h*r,l=l*t+d*r,c=c*t+p*r,u=u*t+_*r,t===1-o){const t=1/Math.sqrt(a*a+l*l+c*c+u*u);a*=t,l*=t,c*=t,u*=t}}t[e]=a,t[e+1]=l,t[e+2]=c,t[e+3]=u}static multiplyQuaternionsFlat(t,e,n,i,r,s){const o=n[i],a=n[i+1],l=n[i+2],c=n[i+3],u=r[s],h=r[s+1],d=r[s+2],p=r[s+3];return t[e]=o*p+c*u+a*d-l*h,t[e+1]=a*p+c*h+l*u-o*d,t[e+2]=l*p+c*d+o*h-a*u,t[e+3]=c*p-o*u-a*h-l*d,t}get x(){return this._x}set x(t){this._x=t,this._onChangeCallback()}get y(){return this._y}set y(t){this._y=t,this._onChangeCallback()}get z(){return this._z}set z(t){this._z=t,this._onChangeCallback()}get w(){return this._w}set w(t){this._w=t,this._onChangeCallback()}set(t,e,n,i){return this._x=t,this._y=e,this._z=n,this._w=i,this._onChangeCallback(),this}clone(){return new this.constructor(this._x,this._y,this._z,this._w)}copy(t){return this._x=t.x,this._y=t.y,this._z=t.z,this._w=t.w,this._onChangeCallback(),this}setFromEuler(t,e){if(!t||!t.isEuler)throw new Error(\\\\\\\"THREE.Quaternion: .setFromEuler() now expects an Euler rotation rather than a Vector3 and order.\\\\\\\");const n=t._x,i=t._y,r=t._z,s=t._order,o=Math.cos,a=Math.sin,l=o(n/2),c=o(i/2),u=o(r/2),h=a(n/2),d=a(i/2),p=a(r/2);switch(s){case\\\\\\\"XYZ\\\\\\\":this._x=h*c*u+l*d*p,this._y=l*d*u-h*c*p,this._z=l*c*p+h*d*u,this._w=l*c*u-h*d*p;break;case\\\\\\\"YXZ\\\\\\\":this._x=h*c*u+l*d*p,this._y=l*d*u-h*c*p,this._z=l*c*p-h*d*u,this._w=l*c*u+h*d*p;break;case\\\\\\\"ZXY\\\\\\\":this._x=h*c*u-l*d*p,this._y=l*d*u+h*c*p,this._z=l*c*p+h*d*u,this._w=l*c*u-h*d*p;break;case\\\\\\\"ZYX\\\\\\\":this._x=h*c*u-l*d*p,this._y=l*d*u+h*c*p,this._z=l*c*p-h*d*u,this._w=l*c*u+h*d*p;break;case\\\\\\\"YZX\\\\\\\":this._x=h*c*u+l*d*p,this._y=l*d*u+h*c*p,this._z=l*c*p-h*d*u,this._w=l*c*u-h*d*p;break;case\\\\\\\"XZY\\\\\\\":this._x=h*c*u-l*d*p,this._y=l*d*u-h*c*p,this._z=l*c*p+h*d*u,this._w=l*c*u+h*d*p;break;default:console.warn(\\\\\\\"THREE.Quaternion: .setFromEuler() encountered an unknown order: \\\\\\\"+s)}return!1!==e&&this._onChangeCallback(),this}setFromAxisAngle(t,e){const n=e/2,i=Math.sin(n);return this._x=t.x*i,this._y=t.y*i,this._z=t.z*i,this._w=Math.cos(n),this._onChangeCallback(),this}setFromRotationMatrix(t){const e=t.elements,n=e[0],i=e[4],r=e[8],s=e[1],o=e[5],a=e[9],l=e[2],c=e[6],u=e[10],h=n+o+u;if(h>0){const t=.5/Math.sqrt(h+1);this._w=.25/t,this._x=(c-a)*t,this._y=(r-l)*t,this._z=(s-i)*t}else if(n>o&&n>u){const t=2*Math.sqrt(1+n-o-u);this._w=(c-a)/t,this._x=.25*t,this._y=(i+s)/t,this._z=(r+l)/t}else if(o>u){const t=2*Math.sqrt(1+o-n-u);this._w=(r-l)/t,this._x=(i+s)/t,this._y=.25*t,this._z=(a+c)/t}else{const t=2*Math.sqrt(1+u-n-o);this._w=(s-i)/t,this._x=(r+l)/t,this._y=(a+c)/t,this._z=.25*t}return this._onChangeCallback(),this}setFromUnitVectors(t,e){let n=t.dot(e)+1;return n<Number.EPSILON?(n=0,Math.abs(t.x)>Math.abs(t.z)?(this._x=-t.y,this._y=t.x,this._z=0,this._w=n):(this._x=0,this._y=-t.z,this._z=t.y,this._w=n)):(this._x=t.y*e.z-t.z*e.y,this._y=t.z*e.x-t.x*e.z,this._z=t.x*e.y-t.y*e.x,this._w=n),this.normalize()}angleTo(t){return 2*Math.acos(Math.abs(cx(this.dot(t),-1,1)))}rotateTowards(t,e){const n=this.angleTo(t);if(0===n)return this;const i=Math.min(1,e/n);return this.slerp(t,i),this}identity(){return this.set(0,0,0,1)}invert(){return this.conjugate()}conjugate(){return this._x*=-1,this._y*=-1,this._z*=-1,this._onChangeCallback(),this}dot(t){return this._x*t._x+this._y*t._y+this._z*t._z+this._w*t._w}lengthSq(){return this._x*this._x+this._y*this._y+this._z*this._z+this._w*this._w}length(){return Math.sqrt(this._x*this._x+this._y*this._y+this._z*this._z+this._w*this._w)}normalize(){let t=this.length();return 0===t?(this._x=0,this._y=0,this._z=0,this._w=1):(t=1/t,this._x=this._x*t,this._y=this._y*t,this._z=this._z*t,this._w=this._w*t),this._onChangeCallback(),this}multiply(t,e){return void 0!==e?(console.warn(\\\\\\\"THREE.Quaternion: .multiply() now only accepts one argument. Use .multiplyQuaternions( a, b ) instead.\\\\\\\"),this.multiplyQuaternions(t,e)):this.multiplyQuaternions(this,t)}premultiply(t){return this.multiplyQuaternions(t,this)}multiplyQuaternions(t,e){const n=t._x,i=t._y,r=t._z,s=t._w,o=e._x,a=e._y,l=e._z,c=e._w;return this._x=n*c+s*o+i*l-r*a,this._y=i*c+s*a+r*o-n*l,this._z=r*c+s*l+n*a-i*o,this._w=s*c-n*o-i*a-r*l,this._onChangeCallback(),this}slerp(t,e){if(0===e)return this;if(1===e)return this.copy(t);const n=this._x,i=this._y,r=this._z,s=this._w;let o=s*t._w+n*t._x+i*t._y+r*t._z;if(o<0?(this._w=-t._w,this._x=-t._x,this._y=-t._y,this._z=-t._z,o=-o):this.copy(t),o>=1)return this._w=s,this._x=n,this._y=i,this._z=r,this;const a=1-o*o;if(a<=Number.EPSILON){const t=1-e;return this._w=t*s+e*this._w,this._x=t*n+e*this._x,this._y=t*i+e*this._y,this._z=t*r+e*this._z,this.normalize(),this._onChangeCallback(),this}const l=Math.sqrt(a),c=Math.atan2(l,o),u=Math.sin((1-e)*c)/l,h=Math.sin(e*c)/l;return this._w=s*u+this._w*h,this._x=n*u+this._x*h,this._y=i*u+this._y*h,this._z=r*u+this._z*h,this._onChangeCallback(),this}slerpQuaternions(t,e,n){this.copy(t).slerp(e,n)}random(){const t=Math.random(),e=Math.sqrt(1-t),n=Math.sqrt(t),i=2*Math.PI*Math.random(),r=2*Math.PI*Math.random();return this.set(e*Math.cos(i),n*Math.sin(r),n*Math.cos(r),e*Math.sin(i))}equals(t){return t._x===this._x&&t._y===this._y&&t._z===this._z&&t._w===this._w}fromArray(t,e=0){return this._x=t[e],this._y=t[e+1],this._z=t[e+2],this._w=t[e+3],this._onChangeCallback(),this}toArray(t=[],e=0){return t[e]=this._x,t[e+1]=this._y,t[e+2]=this._z,t[e+3]=this._w,t}fromBufferAttribute(t,e){return this._x=t.getX(e),this._y=t.getY(e),this._z=t.getZ(e),this._w=t.getW(e),this}_onChange(t){return this._onChangeCallback=t,this}_onChangeCallback(){}}Cx.prototype.isQuaternion=!0;class Nx{constructor(t=0,e=0,n=0){this.x=t,this.y=e,this.z=n}set(t,e,n){return void 0===n&&(n=this.z),this.x=t,this.y=e,this.z=n,this}setScalar(t){return this.x=t,this.y=t,this.z=t,this}setX(t){return this.x=t,this}setY(t){return this.y=t,this}setZ(t){return this.z=t,this}setComponent(t,e){switch(t){case 0:this.x=e;break;case 1:this.y=e;break;case 2:this.z=e;break;default:throw new Error(\\\\\\\"index is out of range: \\\\\\\"+t)}return this}getComponent(t){switch(t){case 0:return this.x;case 1:return this.y;case 2:return this.z;default:throw new Error(\\\\\\\"index is out of range: \\\\\\\"+t)}}clone(){return new this.constructor(this.x,this.y,this.z)}copy(t){return this.x=t.x,this.y=t.y,this.z=t.z,this}add(t,e){return void 0!==e?(console.warn(\\\\\\\"THREE.Vector3: .add() now only accepts one argument. Use .addVectors( a, b ) instead.\\\\\\\"),this.addVectors(t,e)):(this.x+=t.x,this.y+=t.y,this.z+=t.z,this)}addScalar(t){return this.x+=t,this.y+=t,this.z+=t,this}addVectors(t,e){return this.x=t.x+e.x,this.y=t.y+e.y,this.z=t.z+e.z,this}addScaledVector(t,e){return this.x+=t.x*e,this.y+=t.y*e,this.z+=t.z*e,this}sub(t,e){return void 0!==e?(console.warn(\\\\\\\"THREE.Vector3: .sub() now only accepts one argument. Use .subVectors( a, b ) instead.\\\\\\\"),this.subVectors(t,e)):(this.x-=t.x,this.y-=t.y,this.z-=t.z,this)}subScalar(t){return this.x-=t,this.y-=t,this.z-=t,this}subVectors(t,e){return this.x=t.x-e.x,this.y=t.y-e.y,this.z=t.z-e.z,this}multiply(t,e){return void 0!==e?(console.warn(\\\\\\\"THREE.Vector3: .multiply() now only accepts one argument. Use .multiplyVectors( a, b ) instead.\\\\\\\"),this.multiplyVectors(t,e)):(this.x*=t.x,this.y*=t.y,this.z*=t.z,this)}multiplyScalar(t){return this.x*=t,this.y*=t,this.z*=t,this}multiplyVectors(t,e){return this.x=t.x*e.x,this.y=t.y*e.y,this.z=t.z*e.z,this}applyEuler(t){return t&&t.isEuler||console.error(\\\\\\\"THREE.Vector3: .applyEuler() now expects an Euler rotation rather than a Vector3 and order.\\\\\\\"),this.applyQuaternion(Ox.setFromEuler(t))}applyAxisAngle(t,e){return this.applyQuaternion(Ox.setFromAxisAngle(t,e))}applyMatrix3(t){const e=this.x,n=this.y,i=this.z,r=t.elements;return this.x=r[0]*e+r[3]*n+r[6]*i,this.y=r[1]*e+r[4]*n+r[7]*i,this.z=r[2]*e+r[5]*n+r[8]*i,this}applyNormalMatrix(t){return this.applyMatrix3(t).normalize()}applyMatrix4(t){const e=this.x,n=this.y,i=this.z,r=t.elements,s=1/(r[3]*e+r[7]*n+r[11]*i+r[15]);return this.x=(r[0]*e+r[4]*n+r[8]*i+r[12])*s,this.y=(r[1]*e+r[5]*n+r[9]*i+r[13])*s,this.z=(r[2]*e+r[6]*n+r[10]*i+r[14])*s,this}applyQuaternion(t){const e=this.x,n=this.y,i=this.z,r=t.x,s=t.y,o=t.z,a=t.w,l=a*e+s*i-o*n,c=a*n+o*e-r*i,u=a*i+r*n-s*e,h=-r*e-s*n-o*i;return this.x=l*a+h*-r+c*-o-u*-s,this.y=c*a+h*-s+u*-r-l*-o,this.z=u*a+h*-o+l*-s-c*-r,this}project(t){return this.applyMatrix4(t.matrixWorldInverse).applyMatrix4(t.projectionMatrix)}unproject(t){return this.applyMatrix4(t.projectionMatrixInverse).applyMatrix4(t.matrixWorld)}transformDirection(t){const e=this.x,n=this.y,i=this.z,r=t.elements;return this.x=r[0]*e+r[4]*n+r[8]*i,this.y=r[1]*e+r[5]*n+r[9]*i,this.z=r[2]*e+r[6]*n+r[10]*i,this.normalize()}divide(t){return this.x/=t.x,this.y/=t.y,this.z/=t.z,this}divideScalar(t){return this.multiplyScalar(1/t)}min(t){return this.x=Math.min(this.x,t.x),this.y=Math.min(this.y,t.y),this.z=Math.min(this.z,t.z),this}max(t){return this.x=Math.max(this.x,t.x),this.y=Math.max(this.y,t.y),this.z=Math.max(this.z,t.z),this}clamp(t,e){return this.x=Math.max(t.x,Math.min(e.x,this.x)),this.y=Math.max(t.y,Math.min(e.y,this.y)),this.z=Math.max(t.z,Math.min(e.z,this.z)),this}clampScalar(t,e){return this.x=Math.max(t,Math.min(e,this.x)),this.y=Math.max(t,Math.min(e,this.y)),this.z=Math.max(t,Math.min(e,this.z)),this}clampLength(t,e){const n=this.length();return this.divideScalar(n||1).multiplyScalar(Math.max(t,Math.min(e,n)))}floor(){return this.x=Math.floor(this.x),this.y=Math.floor(this.y),this.z=Math.floor(this.z),this}ceil(){return this.x=Math.ceil(this.x),this.y=Math.ceil(this.y),this.z=Math.ceil(this.z),this}round(){return this.x=Math.round(this.x),this.y=Math.round(this.y),this.z=Math.round(this.z),this}roundToZero(){return this.x=this.x<0?Math.ceil(this.x):Math.floor(this.x),this.y=this.y<0?Math.ceil(this.y):Math.floor(this.y),this.z=this.z<0?Math.ceil(this.z):Math.floor(this.z),this}negate(){return this.x=-this.x,this.y=-this.y,this.z=-this.z,this}dot(t){return this.x*t.x+this.y*t.y+this.z*t.z}lengthSq(){return this.x*this.x+this.y*this.y+this.z*this.z}length(){return Math.sqrt(this.x*this.x+this.y*this.y+this.z*this.z)}manhattanLength(){return Math.abs(this.x)+Math.abs(this.y)+Math.abs(this.z)}normalize(){return this.divideScalar(this.length()||1)}setLength(t){return this.normalize().multiplyScalar(t)}lerp(t,e){return this.x+=(t.x-this.x)*e,this.y+=(t.y-this.y)*e,this.z+=(t.z-this.z)*e,this}lerpVectors(t,e,n){return this.x=t.x+(e.x-t.x)*n,this.y=t.y+(e.y-t.y)*n,this.z=t.z+(e.z-t.z)*n,this}cross(t,e){return void 0!==e?(console.warn(\\\\\\\"THREE.Vector3: .cross() now only accepts one argument. Use .crossVectors( a, b ) instead.\\\\\\\"),this.crossVectors(t,e)):this.crossVectors(this,t)}crossVectors(t,e){const n=t.x,i=t.y,r=t.z,s=e.x,o=e.y,a=e.z;return this.x=i*a-r*o,this.y=r*s-n*a,this.z=n*o-i*s,this}projectOnVector(t){const e=t.lengthSq();if(0===e)return this.set(0,0,0);const n=t.dot(this)/e;return this.copy(t).multiplyScalar(n)}projectOnPlane(t){return Lx.copy(this).projectOnVector(t),this.sub(Lx)}reflect(t){return this.sub(Lx.copy(t).multiplyScalar(2*this.dot(t)))}angleTo(t){const e=Math.sqrt(this.lengthSq()*t.lengthSq());if(0===e)return Math.PI/2;const n=this.dot(t)/e;return Math.acos(cx(n,-1,1))}distanceTo(t){return Math.sqrt(this.distanceToSquared(t))}distanceToSquared(t){const e=this.x-t.x,n=this.y-t.y,i=this.z-t.z;return e*e+n*n+i*i}manhattanDistanceTo(t){return Math.abs(this.x-t.x)+Math.abs(this.y-t.y)+Math.abs(this.z-t.z)}setFromSpherical(t){return this.setFromSphericalCoords(t.radius,t.phi,t.theta)}setFromSphericalCoords(t,e,n){const i=Math.sin(e)*t;return this.x=i*Math.sin(n),this.y=Math.cos(e)*t,this.z=i*Math.cos(n),this}setFromCylindrical(t){return this.setFromCylindricalCoords(t.radius,t.theta,t.y)}setFromCylindricalCoords(t,e,n){return this.x=t*Math.sin(e),this.y=n,this.z=t*Math.cos(e),this}setFromMatrixPosition(t){const e=t.elements;return this.x=e[12],this.y=e[13],this.z=e[14],this}setFromMatrixScale(t){const e=this.setFromMatrixColumn(t,0).length(),n=this.setFromMatrixColumn(t,1).length(),i=this.setFromMatrixColumn(t,2).length();return this.x=e,this.y=n,this.z=i,this}setFromMatrixColumn(t,e){return this.fromArray(t.elements,4*e)}setFromMatrix3Column(t,e){return this.fromArray(t.elements,3*e)}equals(t){return t.x===this.x&&t.y===this.y&&t.z===this.z}fromArray(t,e=0){return this.x=t[e],this.y=t[e+1],this.z=t[e+2],this}toArray(t=[],e=0){return t[e]=this.x,t[e+1]=this.y,t[e+2]=this.z,t}fromBufferAttribute(t,e,n){return void 0!==n&&console.warn(\\\\\\\"THREE.Vector3: offset has been removed from .fromBufferAttribute().\\\\\\\"),this.x=t.getX(e),this.y=t.getY(e),this.z=t.getZ(e),this}random(){return this.x=Math.random(),this.y=Math.random(),this.z=Math.random(),this}randomDirection(){const t=2*(Math.random()-.5),e=Math.random()*Math.PI*2,n=Math.sqrt(1-t**2);return this.x=n*Math.cos(e),this.y=n*Math.sin(e),this.z=t,this}*[Symbol.iterator](){yield this.x,yield this.y,yield this.z}}Nx.prototype.isVector3=!0;const Lx=new Nx,Ox=new Cx;class Rx{constructor(t=new Nx(1/0,1/0,1/0),e=new Nx(-1/0,-1/0,-1/0)){this.min=t,this.max=e}set(t,e){return this.min.copy(t),this.max.copy(e),this}setFromArray(t){let e=1/0,n=1/0,i=1/0,r=-1/0,s=-1/0,o=-1/0;for(let a=0,l=t.length;a<l;a+=3){const l=t[a],c=t[a+1],u=t[a+2];l<e&&(e=l),c<n&&(n=c),u<i&&(i=u),l>r&&(r=l),c>s&&(s=c),u>o&&(o=u)}return this.min.set(e,n,i),this.max.set(r,s,o),this}setFromBufferAttribute(t){let e=1/0,n=1/0,i=1/0,r=-1/0,s=-1/0,o=-1/0;for(let a=0,l=t.count;a<l;a++){const l=t.getX(a),c=t.getY(a),u=t.getZ(a);l<e&&(e=l),c<n&&(n=c),u<i&&(i=u),l>r&&(r=l),c>s&&(s=c),u>o&&(o=u)}return this.min.set(e,n,i),this.max.set(r,s,o),this}setFromPoints(t){this.makeEmpty();for(let e=0,n=t.length;e<n;e++)this.expandByPoint(t[e]);return this}setFromCenterAndSize(t,e){const n=Ix.copy(e).multiplyScalar(.5);return this.min.copy(t).sub(n),this.max.copy(t).add(n),this}setFromObject(t){return this.makeEmpty(),this.expandByObject(t)}clone(){return(new this.constructor).copy(this)}copy(t){return this.min.copy(t.min),this.max.copy(t.max),this}makeEmpty(){return this.min.x=this.min.y=this.min.z=1/0,this.max.x=this.max.y=this.max.z=-1/0,this}isEmpty(){return this.max.x<this.min.x||this.max.y<this.min.y||this.max.z<this.min.z}getCenter(t){return this.isEmpty()?t.set(0,0,0):t.addVectors(this.min,this.max).multiplyScalar(.5)}getSize(t){return this.isEmpty()?t.set(0,0,0):t.subVectors(this.max,this.min)}expandByPoint(t){return this.min.min(t),this.max.max(t),this}expandByVector(t){return this.min.sub(t),this.max.add(t),this}expandByScalar(t){return this.min.addScalar(-t),this.max.addScalar(t),this}expandByObject(t){t.updateWorldMatrix(!1,!1);const e=t.geometry;void 0!==e&&(null===e.boundingBox&&e.computeBoundingBox(),Fx.copy(e.boundingBox),Fx.applyMatrix4(t.matrixWorld),this.union(Fx));const n=t.children;for(let t=0,e=n.length;t<e;t++)this.expandByObject(n[t]);return this}containsPoint(t){return!(t.x<this.min.x||t.x>this.max.x||t.y<this.min.y||t.y>this.max.y||t.z<this.min.z||t.z>this.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.x<this.min.x||t.min.x>this.max.x||t.max.y<this.min.y||t.min.y>this.max.y||t.max.z<this.min.z||t.min.z>this.max.z)}intersectsSphere(t){return this.clampPoint(t.center,Ix),Ix.distanceToSquared(t.center)<=t.radius*t.radius}intersectsPlane(t){let e,n;return t.normal.x>0?(e=t.normal.x*this.min.x,n=t.normal.x*this.max.x):(e=t.normal.x*this.max.x,n=t.normal.x*this.min.x),t.normal.y>0?(e+=t.normal.y*this.min.y,n+=t.normal.y*this.max.y):(e+=t.normal.y*this.max.y,n+=t.normal.y*this.min.y),t.normal.z>0?(e+=t.normal.z*this.min.z,n+=t.normal.z*this.max.z):(e+=t.normal.z*this.max.z,n+=t.normal.z*this.min.z),e<=-t.constant&&n>=-t.constant}intersectsTriangle(t){if(this.isEmpty())return!1;this.getCenter(Vx),Hx.subVectors(this.max,Vx),Dx.subVectors(t.a,Vx),kx.subVectors(t.b,Vx),Bx.subVectors(t.c,Vx),zx.subVectors(kx,Dx),Ux.subVectors(Bx,kx),Gx.subVectors(Dx,Bx);let e=[0,-zx.z,zx.y,0,-Ux.z,Ux.y,0,-Gx.z,Gx.y,zx.z,0,-zx.x,Ux.z,0,-Ux.x,Gx.z,0,-Gx.x,-zx.y,zx.x,0,-Ux.y,Ux.x,0,-Gx.y,Gx.x,0];return!!qx(e,Dx,kx,Bx,Hx)&&(e=[1,0,0,0,1,0,0,0,1],!!qx(e,Dx,kx,Bx,Hx)&&(jx.crossVectors(zx,Ux),e=[jx.x,jx.y,jx.z],qx(e,Dx,kx,Bx,Hx)))}clampPoint(t,e){return e.copy(t).clamp(this.min,this.max)}distanceToPoint(t){return Ix.copy(t).clamp(this.min,this.max).sub(t).length()}getBoundingSphere(t){return this.getCenter(t.center),t.radius=.5*this.getSize(Ix).length(),t}intersect(t){return this.min.max(t.min),this.max.min(t.max),this.isEmpty()&&this.makeEmpty(),this}union(t){return this.min.min(t.min),this.max.max(t.max),this}applyMatrix4(t){return this.isEmpty()||(Px[0].set(this.min.x,this.min.y,this.min.z).applyMatrix4(t),Px[1].set(this.min.x,this.min.y,this.max.z).applyMatrix4(t),Px[2].set(this.min.x,this.max.y,this.min.z).applyMatrix4(t),Px[3].set(this.min.x,this.max.y,this.max.z).applyMatrix4(t),Px[4].set(this.max.x,this.min.y,this.min.z).applyMatrix4(t),Px[5].set(this.max.x,this.min.y,this.max.z).applyMatrix4(t),Px[6].set(this.max.x,this.max.y,this.min.z).applyMatrix4(t),Px[7].set(this.max.x,this.max.y,this.max.z).applyMatrix4(t),this.setFromPoints(Px)),this}translate(t){return this.min.add(t),this.max.add(t),this}equals(t){return t.min.equals(this.min)&&t.max.equals(this.max)}}Rx.prototype.isBox3=!0;const Px=[new Nx,new Nx,new Nx,new Nx,new Nx,new Nx,new Nx,new Nx],Ix=new Nx,Fx=new Rx,Dx=new Nx,kx=new Nx,Bx=new Nx,zx=new Nx,Ux=new Nx,Gx=new Nx,Vx=new Nx,Hx=new Nx,jx=new Nx,Wx=new Nx;function qx(t,e,n,i,r){for(let s=0,o=t.length-3;s<=o;s+=3){Wx.fromArray(t,s);const o=r.x*Math.abs(Wx.x)+r.y*Math.abs(Wx.y)+r.z*Math.abs(Wx.z),a=e.dot(Wx),l=n.dot(Wx),c=i.dot(Wx);if(Math.max(-Math.max(a,l,c),Math.min(a,l,c))>o)return!1}return!0}const Xx=new Rx,Yx=new Nx,$x=new Nx,Jx=new Nx;class Zx{constructor(t=new Nx,e=-1){this.center=t,this.radius=e}set(t,e){return this.center.copy(t),this.radius=e,this}setFromPoints(t,e){const n=this.center;void 0!==e?n.copy(e):Xx.setFromPoints(t).getCenter(n);let i=0;for(let e=0,r=t.length;e<r;e++)i=Math.max(i,n.distanceToSquared(t[e]));return this.radius=Math.sqrt(i),this}copy(t){return this.center.copy(t.center),this.radius=t.radius,this}isEmpty(){return this.radius<0}makeEmpty(){return this.center.set(0,0,0),this.radius=-1,this}containsPoint(t){return t.distanceToSquared(this.center)<=this.radius*this.radius}distanceToPoint(t){return t.distanceTo(this.center)-this.radius}intersectsSphere(t){const e=this.radius+t.radius;return t.center.distanceToSquared(this.center)<=e*e}intersectsBox(t){return t.intersectsSphere(this)}intersectsPlane(t){return Math.abs(t.distanceToPoint(this.center))<=this.radius}clampPoint(t,e){const n=this.center.distanceToSquared(t);return e.copy(t),n>this.radius*this.radius&&(e.sub(this.center).normalize(),e.multiplyScalar(this.radius).add(this.center)),e}getBoundingBox(t){return this.isEmpty()?(t.makeEmpty(),t):(t.set(this.center,this.center),t.expandByScalar(this.radius),t)}applyMatrix4(t){return this.center.applyMatrix4(t),this.radius=this.radius*t.getMaxScaleOnAxis(),this}translate(t){return this.center.add(t),this}expandByPoint(t){Jx.subVectors(t,this.center);const e=Jx.lengthSq();if(e>this.radius*this.radius){const t=Math.sqrt(e),n=.5*(t-this.radius);this.center.add(Jx.multiplyScalar(n/t)),this.radius+=n}return this}union(t){return $x.subVectors(t.center,this.center).normalize().multiplyScalar(t.radius),this.expandByPoint(Yx.copy(t.center).add($x)),this.expandByPoint(Yx.copy(t.center).sub($x)),this}equals(t){return t.center.equals(this.center)&&t.radius===this.radius}clone(){return(new this.constructor).copy(this)}}const Qx=new Nx,Kx=new Nx,tb=new Nx,eb=new Nx,nb=new Nx,ib=new Nx,rb=new Nx;class sb{constructor(t=new Nx,e=new Nx(0,0,-1)){this.origin=t,this.direction=e}set(t,e){return this.origin.copy(t),this.direction.copy(e),this}copy(t){return this.origin.copy(t.origin),this.direction.copy(t.direction),this}at(t,e){return e.copy(this.direction).multiplyScalar(t).add(this.origin)}lookAt(t){return this.direction.copy(t).sub(this.origin).normalize(),this}recast(t){return this.origin.copy(this.at(t,Qx)),this}closestPointToPoint(t,e){e.subVectors(t,this.origin);const n=e.dot(this.direction);return n<0?e.copy(this.origin):e.copy(this.direction).multiplyScalar(n).add(this.origin)}distanceToPoint(t){return Math.sqrt(this.distanceSqToPoint(t))}distanceSqToPoint(t){const e=Qx.subVectors(t,this.origin).dot(this.direction);return e<0?this.origin.distanceToSquared(t):(Qx.copy(this.direction).multiplyScalar(e).add(this.origin),Qx.distanceToSquared(t))}distanceSqToSegment(t,e,n,i){Kx.copy(t).add(e).multiplyScalar(.5),tb.copy(e).sub(t).normalize(),eb.copy(this.origin).sub(Kx);const r=.5*t.distanceTo(e),s=-this.direction.dot(tb),o=eb.dot(this.direction),a=-eb.dot(tb),l=eb.lengthSq(),c=Math.abs(1-s*s);let u,h,d,p;if(c>0)if(u=s*a-o,h=s*o-a,p=r*c,u>=0)if(h>=-p)if(h<=p){const t=1/c;u*=t,h*=t,d=u*(u+s*h+2*o)+h*(s*u+h+2*a)+l}else h=r,u=Math.max(0,-(s*h+o)),d=-u*u+h*(h+2*a)+l;else h=-r,u=Math.max(0,-(s*h+o)),d=-u*u+h*(h+2*a)+l;else h<=-p?(u=Math.max(0,-(-s*r+o)),h=u>0?-r:Math.min(Math.max(-r,-a),r),d=-u*u+h*(h+2*a)+l):h<=p?(u=0,h=Math.min(Math.max(-r,-a),r),d=h*(h+2*a)+l):(u=Math.max(0,-(s*r+o)),h=u>0?r:Math.min(Math.max(-r,-a),r),d=-u*u+h*(h+2*a)+l);else h=s>0?-r:r,u=Math.max(0,-(s*h+o)),d=-u*u+h*(h+2*a)+l;return n&&n.copy(this.direction).multiplyScalar(u).add(this.origin),i&&i.copy(tb).multiplyScalar(h).add(Kx),d}intersectSphere(t,e){Qx.subVectors(t.center,this.origin);const n=Qx.dot(this.direction),i=Qx.dot(Qx)-n*n,r=t.radius*t.radius;if(i>r)return null;const s=Math.sqrt(r-i),o=n-s,a=n+s;return o<0&&a<0?null:o<0?this.at(a,e):this.at(o,e)}intersectsSphere(t){return this.distanceSqToPoint(t.center)<=t.radius*t.radius}distanceToPlane(t){const e=t.normal.dot(this.direction);if(0===e)return 0===t.distanceToPoint(this.origin)?0:null;const n=-(this.origin.dot(t.normal)+t.constant)/e;return n>=0?n:null}intersectPlane(t,e){const n=this.distanceToPlane(t);return null===n?null:this.at(n,e)}intersectsPlane(t){const e=t.distanceToPoint(this.origin);if(0===e)return!0;return t.normal.dot(this.direction)*e<0}intersectBox(t,e){let n,i,r,s,o,a;const l=1/this.direction.x,c=1/this.direction.y,u=1/this.direction.z,h=this.origin;return l>=0?(n=(t.min.x-h.x)*l,i=(t.max.x-h.x)*l):(n=(t.max.x-h.x)*l,i=(t.min.x-h.x)*l),c>=0?(r=(t.min.y-h.y)*c,s=(t.max.y-h.y)*c):(r=(t.max.y-h.y)*c,s=(t.min.y-h.y)*c),n>s||r>i?null:((r>n||n!=n)&&(n=r),(s<i||i!=i)&&(i=s),u>=0?(o=(t.min.z-h.z)*u,a=(t.max.z-h.z)*u):(o=(t.max.z-h.z)*u,a=(t.min.z-h.z)*u),n>a||o>i?null:((o>n||n!=n)&&(n=o),(a<i||i!=i)&&(i=a),i<0?null:this.at(n>=0?n:i,e)))}intersectsBox(t){return null!==this.intersectBox(t,Qx)}intersectTriangle(t,e,n,i,r){nb.subVectors(e,t),ib.subVectors(n,t),rb.crossVectors(nb,ib);let s,o=this.direction.dot(rb);if(o>0){if(i)return null;s=1}else{if(!(o<0))return null;s=-1,o=-o}eb.subVectors(this.origin,t);const a=s*this.direction.dot(ib.crossVectors(eb,ib));if(a<0)return null;const l=s*this.direction.dot(nb.cross(eb));if(l<0)return null;if(a+l>o)return null;const c=-s*eb.dot(rb);return c<0?null:this.at(c/o,r)}applyMatrix4(t){return this.origin.applyMatrix4(t),this.direction.transformDirection(t),this}equals(t){return t.origin.equals(this.origin)&&t.direction.equals(this.direction)}clone(){return(new this.constructor).copy(this)}}class ob{constructor(){this.elements=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1],arguments.length>0&&console.error(\\\\\\\"THREE.Matrix4: the constructor no longer reads arguments. use .set() instead.\\\\\\\")}set(t,e,n,i,r,s,o,a,l,c,u,h,d,p,_,m){const f=this.elements;return f[0]=t,f[4]=e,f[8]=n,f[12]=i,f[1]=r,f[5]=s,f[9]=o,f[13]=a,f[2]=l,f[6]=c,f[10]=u,f[14]=h,f[3]=d,f[7]=p,f[11]=_,f[15]=m,this}identity(){return this.set(1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1),this}clone(){return(new ob).fromArray(this.elements)}copy(t){const e=this.elements,n=t.elements;return e[0]=n[0],e[1]=n[1],e[2]=n[2],e[3]=n[3],e[4]=n[4],e[5]=n[5],e[6]=n[6],e[7]=n[7],e[8]=n[8],e[9]=n[9],e[10]=n[10],e[11]=n[11],e[12]=n[12],e[13]=n[13],e[14]=n[14],e[15]=n[15],this}copyPosition(t){const e=this.elements,n=t.elements;return e[12]=n[12],e[13]=n[13],e[14]=n[14],this}setFromMatrix3(t){const e=t.elements;return this.set(e[0],e[3],e[6],0,e[1],e[4],e[7],0,e[2],e[5],e[8],0,0,0,0,1),this}extractBasis(t,e,n){return t.setFromMatrixColumn(this,0),e.setFromMatrixColumn(this,1),n.setFromMatrixColumn(this,2),this}makeBasis(t,e,n){return this.set(t.x,e.x,n.x,0,t.y,e.y,n.y,0,t.z,e.z,n.z,0,0,0,0,1),this}extractRotation(t){const e=this.elements,n=t.elements,i=1/ab.setFromMatrixColumn(t,0).length(),r=1/ab.setFromMatrixColumn(t,1).length(),s=1/ab.setFromMatrixColumn(t,2).length();return e[0]=n[0]*i,e[1]=n[1]*i,e[2]=n[2]*i,e[3]=0,e[4]=n[4]*r,e[5]=n[5]*r,e[6]=n[6]*r,e[7]=0,e[8]=n[8]*s,e[9]=n[9]*s,e[10]=n[10]*s,e[11]=0,e[12]=0,e[13]=0,e[14]=0,e[15]=1,this}makeRotationFromEuler(t){t&&t.isEuler||console.error(\\\\\\\"THREE.Matrix4: .makeRotationFromEuler() now expects a Euler rotation rather than a Vector3 and order.\\\\\\\");const e=this.elements,n=t.x,i=t.y,r=t.z,s=Math.cos(n),o=Math.sin(n),a=Math.cos(i),l=Math.sin(i),c=Math.cos(r),u=Math.sin(r);if(\\\\\\\"XYZ\\\\\\\"===t.order){const t=s*c,n=s*u,i=o*c,r=o*u;e[0]=a*c,e[4]=-a*u,e[8]=l,e[1]=n+i*l,e[5]=t-r*l,e[9]=-o*a,e[2]=r-t*l,e[6]=i+n*l,e[10]=s*a}else if(\\\\\\\"YXZ\\\\\\\"===t.order){const t=a*c,n=a*u,i=l*c,r=l*u;e[0]=t+r*o,e[4]=i*o-n,e[8]=s*l,e[1]=s*u,e[5]=s*c,e[9]=-o,e[2]=n*o-i,e[6]=r+t*o,e[10]=s*a}else if(\\\\\\\"ZXY\\\\\\\"===t.order){const t=a*c,n=a*u,i=l*c,r=l*u;e[0]=t-r*o,e[4]=-s*u,e[8]=i+n*o,e[1]=n+i*o,e[5]=s*c,e[9]=r-t*o,e[2]=-s*l,e[6]=o,e[10]=s*a}else if(\\\\\\\"ZYX\\\\\\\"===t.order){const t=s*c,n=s*u,i=o*c,r=o*u;e[0]=a*c,e[4]=i*l-n,e[8]=t*l+r,e[1]=a*u,e[5]=r*l+t,e[9]=n*l-i,e[2]=-l,e[6]=o*a,e[10]=s*a}else if(\\\\\\\"YZX\\\\\\\"===t.order){const t=s*a,n=s*l,i=o*a,r=o*l;e[0]=a*c,e[4]=r-t*u,e[8]=i*u+n,e[1]=u,e[5]=s*c,e[9]=-o*c,e[2]=-l*c,e[6]=n*u+i,e[10]=t-r*u}else if(\\\\\\\"XZY\\\\\\\"===t.order){const t=s*a,n=s*l,i=o*a,r=o*l;e[0]=a*c,e[4]=-u,e[8]=l*c,e[1]=t*u+r,e[5]=s*c,e[9]=n*u-i,e[2]=i*u-n,e[6]=o*c,e[10]=r*u+t}return e[3]=0,e[7]=0,e[11]=0,e[12]=0,e[13]=0,e[14]=0,e[15]=1,this}makeRotationFromQuaternion(t){return this.compose(cb,t,ub)}lookAt(t,e,n){const i=this.elements;return pb.subVectors(t,e),0===pb.lengthSq()&&(pb.z=1),pb.normalize(),hb.crossVectors(n,pb),0===hb.lengthSq()&&(1===Math.abs(n.z)?pb.x+=1e-4:pb.z+=1e-4,pb.normalize(),hb.crossVectors(n,pb)),hb.normalize(),db.crossVectors(pb,hb),i[0]=hb.x,i[4]=db.x,i[8]=pb.x,i[1]=hb.y,i[5]=db.y,i[9]=pb.y,i[2]=hb.z,i[6]=db.z,i[10]=pb.z,this}multiply(t,e){return void 0!==e?(console.warn(\\\\\\\"THREE.Matrix4: .multiply() now only accepts one argument. Use .multiplyMatrices( a, b ) instead.\\\\\\\"),this.multiplyMatrices(t,e)):this.multiplyMatrices(this,t)}premultiply(t){return this.multiplyMatrices(t,this)}multiplyMatrices(t,e){const n=t.elements,i=e.elements,r=this.elements,s=n[0],o=n[4],a=n[8],l=n[12],c=n[1],u=n[5],h=n[9],d=n[13],p=n[2],_=n[6],m=n[10],f=n[14],g=n[3],v=n[7],y=n[11],x=n[15],b=i[0],w=i[4],T=i[8],A=i[12],E=i[1],M=i[5],S=i[9],C=i[13],N=i[2],L=i[6],O=i[10],R=i[14],P=i[3],I=i[7],F=i[11],D=i[15];return r[0]=s*b+o*E+a*N+l*P,r[4]=s*w+o*M+a*L+l*I,r[8]=s*T+o*S+a*O+l*F,r[12]=s*A+o*C+a*R+l*D,r[1]=c*b+u*E+h*N+d*P,r[5]=c*w+u*M+h*L+d*I,r[9]=c*T+u*S+h*O+d*F,r[13]=c*A+u*C+h*R+d*D,r[2]=p*b+_*E+m*N+f*P,r[6]=p*w+_*M+m*L+f*I,r[10]=p*T+_*S+m*O+f*F,r[14]=p*A+_*C+m*R+f*D,r[3]=g*b+v*E+y*N+x*P,r[7]=g*w+v*M+y*L+x*I,r[11]=g*T+v*S+y*O+x*F,r[15]=g*A+v*C+y*R+x*D,this}multiplyScalar(t){const e=this.elements;return e[0]*=t,e[4]*=t,e[8]*=t,e[12]*=t,e[1]*=t,e[5]*=t,e[9]*=t,e[13]*=t,e[2]*=t,e[6]*=t,e[10]*=t,e[14]*=t,e[3]*=t,e[7]*=t,e[11]*=t,e[15]*=t,this}determinant(){const t=this.elements,e=t[0],n=t[4],i=t[8],r=t[12],s=t[1],o=t[5],a=t[9],l=t[13],c=t[2],u=t[6],h=t[10],d=t[14];return t[3]*(+r*a*u-i*l*u-r*o*h+n*l*h+i*o*d-n*a*d)+t[7]*(+e*a*d-e*l*h+r*s*h-i*s*d+i*l*c-r*a*c)+t[11]*(+e*l*u-e*o*d-r*s*u+n*s*d+r*o*c-n*l*c)+t[15]*(-i*o*c-e*a*u+e*o*h+i*s*u-n*s*h+n*a*c)}transpose(){const t=this.elements;let e;return e=t[1],t[1]=t[4],t[4]=e,e=t[2],t[2]=t[8],t[8]=e,e=t[6],t[6]=t[9],t[9]=e,e=t[3],t[3]=t[12],t[12]=e,e=t[7],t[7]=t[13],t[13]=e,e=t[11],t[11]=t[14],t[14]=e,this}setPosition(t,e,n){const i=this.elements;return t.isVector3?(i[12]=t.x,i[13]=t.y,i[14]=t.z):(i[12]=t,i[13]=e,i[14]=n),this}invert(){const t=this.elements,e=t[0],n=t[1],i=t[2],r=t[3],s=t[4],o=t[5],a=t[6],l=t[7],c=t[8],u=t[9],h=t[10],d=t[11],p=t[12],_=t[13],m=t[14],f=t[15],g=u*m*l-_*h*l+_*a*d-o*m*d-u*a*f+o*h*f,v=p*h*l-c*m*l-p*a*d+s*m*d+c*a*f-s*h*f,y=c*_*l-p*u*l+p*o*d-s*_*d-c*o*f+s*u*f,x=p*u*a-c*_*a-p*o*h+s*_*h+c*o*m-s*u*m,b=e*g+n*v+i*y+r*x;if(0===b)return this.set(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0);const w=1/b;return t[0]=g*w,t[1]=(_*h*r-u*m*r-_*i*d+n*m*d+u*i*f-n*h*f)*w,t[2]=(o*m*r-_*a*r+_*i*l-n*m*l-o*i*f+n*a*f)*w,t[3]=(u*a*r-o*h*r-u*i*l+n*h*l+o*i*d-n*a*d)*w,t[4]=v*w,t[5]=(c*m*r-p*h*r+p*i*d-e*m*d-c*i*f+e*h*f)*w,t[6]=(p*a*r-s*m*r-p*i*l+e*m*l+s*i*f-e*a*f)*w,t[7]=(s*h*r-c*a*r+c*i*l-e*h*l-s*i*d+e*a*d)*w,t[8]=y*w,t[9]=(p*u*r-c*_*r-p*n*d+e*_*d+c*n*f-e*u*f)*w,t[10]=(s*_*r-p*o*r+p*n*l-e*_*l-s*n*f+e*o*f)*w,t[11]=(c*o*r-s*u*r-c*n*l+e*u*l+s*n*d-e*o*d)*w,t[12]=x*w,t[13]=(c*_*i-p*u*i+p*n*h-e*_*h-c*n*m+e*u*m)*w,t[14]=(p*o*i-s*_*i-p*n*a+e*_*a+s*n*m-e*o*m)*w,t[15]=(s*u*i-c*o*i+c*n*a-e*u*a-s*n*h+e*o*h)*w,this}scale(t){const e=this.elements,n=t.x,i=t.y,r=t.z;return e[0]*=n,e[4]*=i,e[8]*=r,e[1]*=n,e[5]*=i,e[9]*=r,e[2]*=n,e[6]*=i,e[10]*=r,e[3]*=n,e[7]*=i,e[11]*=r,this}getMaxScaleOnAxis(){const t=this.elements,e=t[0]*t[0]+t[1]*t[1]+t[2]*t[2],n=t[4]*t[4]+t[5]*t[5]+t[6]*t[6],i=t[8]*t[8]+t[9]*t[9]+t[10]*t[10];return Math.sqrt(Math.max(e,n,i))}makeTranslation(t,e,n){return this.set(1,0,0,t,0,1,0,e,0,0,1,n,0,0,0,1),this}makeRotationX(t){const e=Math.cos(t),n=Math.sin(t);return this.set(1,0,0,0,0,e,-n,0,0,n,e,0,0,0,0,1),this}makeRotationY(t){const e=Math.cos(t),n=Math.sin(t);return this.set(e,0,n,0,0,1,0,0,-n,0,e,0,0,0,0,1),this}makeRotationZ(t){const e=Math.cos(t),n=Math.sin(t);return this.set(e,-n,0,0,n,e,0,0,0,0,1,0,0,0,0,1),this}makeRotationAxis(t,e){const n=Math.cos(e),i=Math.sin(e),r=1-n,s=t.x,o=t.y,a=t.z,l=r*s,c=r*o;return this.set(l*s+n,l*o-i*a,l*a+i*o,0,l*o+i*a,c*o+n,c*a-i*s,0,l*a-i*o,c*a+i*s,r*a*a+n,0,0,0,0,1),this}makeScale(t,e,n){return this.set(t,0,0,0,0,e,0,0,0,0,n,0,0,0,0,1),this}makeShear(t,e,n,i,r,s){return this.set(1,n,r,0,t,1,s,0,e,i,1,0,0,0,0,1),this}compose(t,e,n){const i=this.elements,r=e._x,s=e._y,o=e._z,a=e._w,l=r+r,c=s+s,u=o+o,h=r*l,d=r*c,p=r*u,_=s*c,m=s*u,f=o*u,g=a*l,v=a*c,y=a*u,x=n.x,b=n.y,w=n.z;return i[0]=(1-(_+f))*x,i[1]=(d+y)*x,i[2]=(p-v)*x,i[3]=0,i[4]=(d-y)*b,i[5]=(1-(h+f))*b,i[6]=(m+g)*b,i[7]=0,i[8]=(p+v)*w,i[9]=(m-g)*w,i[10]=(1-(h+_))*w,i[11]=0,i[12]=t.x,i[13]=t.y,i[14]=t.z,i[15]=1,this}decompose(t,e,n){const i=this.elements;let r=ab.set(i[0],i[1],i[2]).length();const s=ab.set(i[4],i[5],i[6]).length(),o=ab.set(i[8],i[9],i[10]).length();this.determinant()<0&&(r=-r),t.x=i[12],t.y=i[13],t.z=i[14],lb.copy(this);const a=1/r,l=1/s,c=1/o;return lb.elements[0]*=a,lb.elements[1]*=a,lb.elements[2]*=a,lb.elements[4]*=l,lb.elements[5]*=l,lb.elements[6]*=l,lb.elements[8]*=c,lb.elements[9]*=c,lb.elements[10]*=c,e.setFromRotationMatrix(lb),n.x=r,n.y=s,n.z=o,this}makePerspective(t,e,n,i,r,s){void 0===s&&console.warn(\\\\\\\"THREE.Matrix4: .makePerspective() has been redefined and has a new signature. Please check the docs.\\\\\\\");const o=this.elements,a=2*r/(e-t),l=2*r/(n-i),c=(e+t)/(e-t),u=(n+i)/(n-i),h=-(s+r)/(s-r),d=-2*s*r/(s-r);return o[0]=a,o[4]=0,o[8]=c,o[12]=0,o[1]=0,o[5]=l,o[9]=u,o[13]=0,o[2]=0,o[6]=0,o[10]=h,o[14]=d,o[3]=0,o[7]=0,o[11]=-1,o[15]=0,this}makeOrthographic(t,e,n,i,r,s){const o=this.elements,a=1/(e-t),l=1/(n-i),c=1/(s-r),u=(e+t)*a,h=(n+i)*l,d=(s+r)*c;return o[0]=2*a,o[4]=0,o[8]=0,o[12]=-u,o[1]=0,o[5]=2*l,o[9]=0,o[13]=-h,o[2]=0,o[6]=0,o[10]=-2*c,o[14]=-d,o[3]=0,o[7]=0,o[11]=0,o[15]=1,this}equals(t){const e=this.elements,n=t.elements;for(let t=0;t<16;t++)if(e[t]!==n[t])return!1;return!0}fromArray(t,e=0){for(let n=0;n<16;n++)this.elements[n]=t[n+e];return this}toArray(t=[],e=0){const n=this.elements;return t[e]=n[0],t[e+1]=n[1],t[e+2]=n[2],t[e+3]=n[3],t[e+4]=n[4],t[e+5]=n[5],t[e+6]=n[6],t[e+7]=n[7],t[e+8]=n[8],t[e+9]=n[9],t[e+10]=n[10],t[e+11]=n[11],t[e+12]=n[12],t[e+13]=n[13],t[e+14]=n[14],t[e+15]=n[15],t}}ob.prototype.isMatrix4=!0;const ab=new Nx,lb=new ob,cb=new Nx(0,0,0),ub=new Nx(1,1,1),hb=new Nx,db=new Nx,pb=new Nx,_b=new ob,mb=new Cx;class fb{constructor(t=0,e=0,n=0,i=fb.DefaultOrder){this._x=t,this._y=e,this._z=n,this._order=i}get x(){return this._x}set x(t){this._x=t,this._onChangeCallback()}get y(){return this._y}set y(t){this._y=t,this._onChangeCallback()}get z(){return this._z}set z(t){this._z=t,this._onChangeCallback()}get order(){return this._order}set order(t){this._order=t,this._onChangeCallback()}set(t,e,n,i=this._order){return this._x=t,this._y=e,this._z=n,this._order=i,this._onChangeCallback(),this}clone(){return new this.constructor(this._x,this._y,this._z,this._order)}copy(t){return this._x=t._x,this._y=t._y,this._z=t._z,this._order=t._order,this._onChangeCallback(),this}setFromRotationMatrix(t,e=this._order,n=!0){const i=t.elements,r=i[0],s=i[4],o=i[8],a=i[1],l=i[5],c=i[9],u=i[2],h=i[6],d=i[10];switch(e){case\\\\\\\"XYZ\\\\\\\":this._y=Math.asin(cx(o,-1,1)),Math.abs(o)<.9999999?(this._x=Math.atan2(-c,d),this._z=Math.atan2(-s,r)):(this._x=Math.atan2(h,l),this._z=0);break;case\\\\\\\"YXZ\\\\\\\":this._x=Math.asin(-cx(c,-1,1)),Math.abs(c)<.9999999?(this._y=Math.atan2(o,d),this._z=Math.atan2(a,l)):(this._y=Math.atan2(-u,r),this._z=0);break;case\\\\\\\"ZXY\\\\\\\":this._x=Math.asin(cx(h,-1,1)),Math.abs(h)<.9999999?(this._y=Math.atan2(-u,d),this._z=Math.atan2(-s,l)):(this._y=0,this._z=Math.atan2(a,r));break;case\\\\\\\"ZYX\\\\\\\":this._y=Math.asin(-cx(u,-1,1)),Math.abs(u)<.9999999?(this._x=Math.atan2(h,d),this._z=Math.atan2(a,r)):(this._x=0,this._z=Math.atan2(-s,l));break;case\\\\\\\"YZX\\\\\\\":this._z=Math.asin(cx(a,-1,1)),Math.abs(a)<.9999999?(this._x=Math.atan2(-c,l),this._y=Math.atan2(-u,r)):(this._x=0,this._y=Math.atan2(o,d));break;case\\\\\\\"XZY\\\\\\\":this._z=Math.asin(-cx(s,-1,1)),Math.abs(s)<.9999999?(this._x=Math.atan2(h,l),this._y=Math.atan2(o,r)):(this._x=Math.atan2(-c,d),this._y=0);break;default:console.warn(\\\\\\\"THREE.Euler: .setFromRotationMatrix() encountered an unknown order: \\\\\\\"+e)}return this._order=e,!0===n&&this._onChangeCallback(),this}setFromQuaternion(t,e,n){return _b.makeRotationFromQuaternion(t),this.setFromRotationMatrix(_b,e,n)}setFromVector3(t,e=this._order){return this.set(t.x,t.y,t.z,e)}reorder(t){return mb.setFromEuler(this),this.setFromQuaternion(mb,t)}equals(t){return t._x===this._x&&t._y===this._y&&t._z===this._z&&t._order===this._order}fromArray(t){return this._x=t[0],this._y=t[1],this._z=t[2],void 0!==t[3]&&(this._order=t[3]),this._onChangeCallback(),this}toArray(t=[],e=0){return t[e]=this._x,t[e+1]=this._y,t[e+2]=this._z,t[e+3]=this._order,t}toVector3(t){return t?t.set(this._x,this._y,this._z):new Nx(this._x,this._y,this._z)}_onChange(t){return this._onChangeCallback=t,this}_onChangeCallback(){}}fb.prototype.isEuler=!0,fb.DefaultOrder=\\\\\\\"XYZ\\\\\\\",fb.RotationOrders=[\\\\\\\"XYZ\\\\\\\",\\\\\\\"YZX\\\\\\\",\\\\\\\"ZXY\\\\\\\",\\\\\\\"XZY\\\\\\\",\\\\\\\"YXZ\\\\\\\",\\\\\\\"ZYX\\\\\\\"];class gb{constructor(){this.mask=1}set(t){this.mask=1<<t|0}enable(t){this.mask|=1<<t|0}enableAll(){this.mask=-1}toggle(t){this.mask^=1<<t|0}disable(t){this.mask&=~(1<<t|0)}disableAll(){this.mask=0}test(t){return 0!=(this.mask&t.mask)}}let vb=0;const yb=new Nx,xb=new Cx,bb=new ob,wb=new Nx,Tb=new Nx,Ab=new Nx,Eb=new Cx,Mb=new Nx(1,0,0),Sb=new Nx(0,1,0),Cb=new Nx(0,0,1),Nb={type:\\\\\\\"added\\\\\\\"},Lb={type:\\\\\\\"removed\\\\\\\"};class Ob extends nx{constructor(){super(),Object.defineProperty(this,\\\\\\\"id\\\\\\\",{value:vb++}),this.uuid=lx(),this.name=\\\\\\\"\\\\\\\",this.type=\\\\\\\"Object3D\\\\\\\",this.parent=null,this.children=[],this.up=Ob.DefaultUp.clone();const t=new Nx,e=new fb,n=new Cx,i=new Nx(1,1,1);e._onChange((function(){n.setFromEuler(e,!1)})),n._onChange((function(){e.setFromQuaternion(n,void 0,!1)})),Object.defineProperties(this,{position:{configurable:!0,enumerable:!0,value:t},rotation:{configurable:!0,enumerable:!0,value:e},quaternion:{configurable:!0,enumerable:!0,value:n},scale:{configurable:!0,enumerable:!0,value:i},modelViewMatrix:{value:new ob},normalMatrix:{value:new gx}}),this.matrix=new ob,this.matrixWorld=new ob,this.matrixAutoUpdate=Ob.DefaultMatrixAutoUpdate,this.matrixWorldNeedsUpdate=!1,this.layers=new gb,this.visible=!0,this.castShadow=!1,this.receiveShadow=!1,this.frustumCulled=!0,this.renderOrder=0,this.animations=[],this.userData={}}onBeforeRender(){}onAfterRender(){}applyMatrix4(t){this.matrixAutoUpdate&&this.updateMatrix(),this.matrix.premultiply(t),this.matrix.decompose(this.position,this.quaternion,this.scale)}applyQuaternion(t){return this.quaternion.premultiply(t),this}setRotationFromAxisAngle(t,e){this.quaternion.setFromAxisAngle(t,e)}setRotationFromEuler(t){this.quaternion.setFromEuler(t,!0)}setRotationFromMatrix(t){this.quaternion.setFromRotationMatrix(t)}setRotationFromQuaternion(t){this.quaternion.copy(t)}rotateOnAxis(t,e){return xb.setFromAxisAngle(t,e),this.quaternion.multiply(xb),this}rotateOnWorldAxis(t,e){return xb.setFromAxisAngle(t,e),this.quaternion.premultiply(xb),this}rotateX(t){return this.rotateOnAxis(Mb,t)}rotateY(t){return this.rotateOnAxis(Sb,t)}rotateZ(t){return this.rotateOnAxis(Cb,t)}translateOnAxis(t,e){return yb.copy(t).applyQuaternion(this.quaternion),this.position.add(yb.multiplyScalar(e)),this}translateX(t){return this.translateOnAxis(Mb,t)}translateY(t){return this.translateOnAxis(Sb,t)}translateZ(t){return this.translateOnAxis(Cb,t)}localToWorld(t){return t.applyMatrix4(this.matrixWorld)}worldToLocal(t){return t.applyMatrix4(bb.copy(this.matrixWorld).invert())}lookAt(t,e,n){t.isVector3?wb.copy(t):wb.set(t,e,n);const i=this.parent;this.updateWorldMatrix(!0,!1),Tb.setFromMatrixPosition(this.matrixWorld),this.isCamera||this.isLight?bb.lookAt(Tb,wb,this.up):bb.lookAt(wb,Tb,this.up),this.quaternion.setFromRotationMatrix(bb),i&&(bb.extractRotation(i.matrixWorld),xb.setFromRotationMatrix(bb),this.quaternion.premultiply(xb.invert()))}add(t){if(arguments.length>1){for(let t=0;t<arguments.length;t++)this.add(arguments[t]);return this}return t===this?(console.error(\\\\\\\"THREE.Object3D.add: object can't be added as a child of itself.\\\\\\\",t),this):(t&&t.isObject3D?(null!==t.parent&&t.parent.remove(t),t.parent=this,this.children.push(t),t.dispatchEvent(Nb)):console.error(\\\\\\\"THREE.Object3D.add: object not an instance of THREE.Object3D.\\\\\\\",t),this)}remove(t){if(arguments.length>1){for(let t=0;t<arguments.length;t++)this.remove(arguments[t]);return this}const e=this.children.indexOf(t);return-1!==e&&(t.parent=null,this.children.splice(e,1),t.dispatchEvent(Lb)),this}removeFromParent(){const t=this.parent;return null!==t&&t.remove(this),this}clear(){for(let t=0;t<this.children.length;t++){const e=this.children[t];e.parent=null,e.dispatchEvent(Lb)}return this.children.length=0,this}attach(t){return this.updateWorldMatrix(!0,!1),bb.copy(this.matrixWorld).invert(),null!==t.parent&&(t.parent.updateWorldMatrix(!0,!1),bb.multiply(t.parent.matrixWorld)),t.applyMatrix4(bb),this.add(t),t.updateWorldMatrix(!1,!0),this}getObjectById(t){return this.getObjectByProperty(\\\\\\\"id\\\\\\\",t)}getObjectByName(t){return this.getObjectByProperty(\\\\\\\"name\\\\\\\",t)}getObjectByProperty(t,e){if(this[t]===e)return this;for(let n=0,i=this.children.length;n<i;n++){const i=this.children[n].getObjectByProperty(t,e);if(void 0!==i)return i}}getWorldPosition(t){return this.updateWorldMatrix(!0,!1),t.setFromMatrixPosition(this.matrixWorld)}getWorldQuaternion(t){return this.updateWorldMatrix(!0,!1),this.matrixWorld.decompose(Tb,t,Ab),t}getWorldScale(t){return this.updateWorldMatrix(!0,!1),this.matrixWorld.decompose(Tb,Eb,t),t}getWorldDirection(t){this.updateWorldMatrix(!0,!1);const e=this.matrixWorld.elements;return t.set(e[8],e[9],e[10]).normalize()}raycast(){}traverse(t){t(this);const e=this.children;for(let n=0,i=e.length;n<i;n++)e[n].traverse(t)}traverseVisible(t){if(!1===this.visible)return;t(this);const e=this.children;for(let n=0,i=e.length;n<i;n++)e[n].traverseVisible(t)}traverseAncestors(t){const e=this.parent;null!==e&&(t(e),e.traverseAncestors(t))}updateMatrix(){this.matrix.compose(this.position,this.quaternion,this.scale),this.matrixWorldNeedsUpdate=!0}updateMatrixWorld(t){this.matrixAutoUpdate&&this.updateMatrix(),(this.matrixWorldNeedsUpdate||t)&&(null===this.parent?this.matrixWorld.copy(this.matrix):this.matrixWorld.multiplyMatrices(this.parent.matrixWorld,this.matrix),this.matrixWorldNeedsUpdate=!1,t=!0);const e=this.children;for(let n=0,i=e.length;n<i;n++)e[n].updateMatrixWorld(t)}updateWorldMatrix(t,e){const n=this.parent;if(!0===t&&null!==n&&n.updateWorldMatrix(!0,!1),this.matrixAutoUpdate&&this.updateMatrix(),null===this.parent?this.matrixWorld.copy(this.matrix):this.matrixWorld.multiplyMatrices(this.parent.matrixWorld,this.matrix),!0===e){const t=this.children;for(let e=0,n=t.length;e<n;e++)t[e].updateWorldMatrix(!1,!0)}}toJSON(t){const e=void 0===t||\\\\\\\"string\\\\\\\"==typeof t,n={};e&&(t={geometries:{},materials:{},textures:{},images:{},shapes:{},skeletons:{},animations:{}},n.metadata={version:4.5,type:\\\\\\\"Object\\\\\\\",generator:\\\\\\\"Object3D.toJSON\\\\\\\"});const i={};function r(e,n){return void 0===e[n.uuid]&&(e[n.uuid]=n.toJSON(t)),n.uuid}if(i.uuid=this.uuid,i.type=this.type,\\\\\\\"\\\\\\\"!==this.name&&(i.name=this.name),!0===this.castShadow&&(i.castShadow=!0),!0===this.receiveShadow&&(i.receiveShadow=!0),!1===this.visible&&(i.visible=!1),!1===this.frustumCulled&&(i.frustumCulled=!1),0!==this.renderOrder&&(i.renderOrder=this.renderOrder),\\\\\\\"{}\\\\\\\"!==JSON.stringify(this.userData)&&(i.userData=this.userData),i.layers=this.layers.mask,i.matrix=this.matrix.toArray(),!1===this.matrixAutoUpdate&&(i.matrixAutoUpdate=!1),this.isInstancedMesh&&(i.type=\\\\\\\"InstancedMesh\\\\\\\",i.count=this.count,i.instanceMatrix=this.instanceMatrix.toJSON(),null!==this.instanceColor&&(i.instanceColor=this.instanceColor.toJSON())),this.isScene)this.background&&(this.background.isColor?i.background=this.background.toJSON():this.background.isTexture&&(i.background=this.background.toJSON(t).uuid)),this.environment&&this.environment.isTexture&&(i.environment=this.environment.toJSON(t).uuid);else if(this.isMesh||this.isLine||this.isPoints){i.geometry=r(t.geometries,this.geometry);const e=this.geometry.parameters;if(void 0!==e&&void 0!==e.shapes){const n=e.shapes;if(Array.isArray(n))for(let e=0,i=n.length;e<i;e++){const i=n[e];r(t.shapes,i)}else r(t.shapes,n)}}if(this.isSkinnedMesh&&(i.bindMode=this.bindMode,i.bindMatrix=this.bindMatrix.toArray(),void 0!==this.skeleton&&(r(t.skeletons,this.skeleton),i.skeleton=this.skeleton.uuid)),void 0!==this.material)if(Array.isArray(this.material)){const e=[];for(let n=0,i=this.material.length;n<i;n++)e.push(r(t.materials,this.material[n]));i.material=e}else i.material=r(t.materials,this.material);if(this.children.length>0){i.children=[];for(let e=0;e<this.children.length;e++)i.children.push(this.children[e].toJSON(t).object)}if(this.animations.length>0){i.animations=[];for(let e=0;e<this.animations.length;e++){const n=this.animations[e];i.animations.push(r(t.animations,n))}}if(e){const e=s(t.geometries),i=s(t.materials),r=s(t.textures),o=s(t.images),a=s(t.shapes),l=s(t.skeletons),c=s(t.animations);e.length>0&&(n.geometries=e),i.length>0&&(n.materials=i),r.length>0&&(n.textures=r),o.length>0&&(n.images=o),a.length>0&&(n.shapes=a),l.length>0&&(n.skeletons=l),c.length>0&&(n.animations=c)}return n.object=i,n;function s(t){const e=[];for(const n in t){const i=t[n];delete i.metadata,e.push(i)}return e}}clone(t){return(new this.constructor).copy(this,t)}copy(t,e=!0){if(this.name=t.name,this.up.copy(t.up),this.position.copy(t.position),this.rotation.order=t.rotation.order,this.quaternion.copy(t.quaternion),this.scale.copy(t.scale),this.matrix.copy(t.matrix),this.matrixWorld.copy(t.matrixWorld),this.matrixAutoUpdate=t.matrixAutoUpdate,this.matrixWorldNeedsUpdate=t.matrixWorldNeedsUpdate,this.layers.mask=t.layers.mask,this.visible=t.visible,this.castShadow=t.castShadow,this.receiveShadow=t.receiveShadow,this.frustumCulled=t.frustumCulled,this.renderOrder=t.renderOrder,this.userData=JSON.parse(JSON.stringify(t.userData)),!0===e)for(let e=0;e<t.children.length;e++){const n=t.children[e];this.add(n.clone())}return this}}Ob.DefaultUp=new Nx(0,1,0),Ob.DefaultMatrixAutoUpdate=!0,Ob.prototype.isObject3D=!0;const Rb=new Nx,Pb=new Nx,Ib=new Nx,Fb=new Nx,Db=new Nx,kb=new Nx,Bb=new Nx,zb=new Nx,Ub=new Nx,Gb=new Nx;class Vb{constructor(t=new Nx,e=new Nx,n=new Nx){this.a=t,this.b=e,this.c=n}static getNormal(t,e,n,i){i.subVectors(n,e),Rb.subVectors(t,e),i.cross(Rb);const r=i.lengthSq();return r>0?i.multiplyScalar(1/Math.sqrt(r)):i.set(0,0,0)}static getBarycoord(t,e,n,i,r){Rb.subVectors(i,e),Pb.subVectors(n,e),Ib.subVectors(t,e);const s=Rb.dot(Rb),o=Rb.dot(Pb),a=Rb.dot(Ib),l=Pb.dot(Pb),c=Pb.dot(Ib),u=s*l-o*o;if(0===u)return r.set(-2,-1,-1);const h=1/u,d=(l*a-o*c)*h,p=(s*c-o*a)*h;return r.set(1-d-p,p,d)}static containsPoint(t,e,n,i){return this.getBarycoord(t,e,n,i,Fb),Fb.x>=0&&Fb.y>=0&&Fb.x+Fb.y<=1}static getUV(t,e,n,i,r,s,o,a){return this.getBarycoord(t,e,n,i,Fb),a.set(0,0),a.addScaledVector(r,Fb.x),a.addScaledVector(s,Fb.y),a.addScaledVector(o,Fb.z),a}static isFrontFacing(t,e,n,i){return Rb.subVectors(n,e),Pb.subVectors(t,e),Rb.cross(Pb).dot(i)<0}set(t,e,n){return this.a.copy(t),this.b.copy(e),this.c.copy(n),this}setFromPointsAndIndices(t,e,n,i){return this.a.copy(t[e]),this.b.copy(t[n]),this.c.copy(t[i]),this}setFromAttributeAndIndices(t,e,n,i){return this.a.fromBufferAttribute(t,e),this.b.fromBufferAttribute(t,n),this.c.fromBufferAttribute(t,i),this}clone(){return(new this.constructor).copy(this)}copy(t){return this.a.copy(t.a),this.b.copy(t.b),this.c.copy(t.c),this}getArea(){return Rb.subVectors(this.c,this.b),Pb.subVectors(this.a,this.b),.5*Rb.cross(Pb).length()}getMidpoint(t){return t.addVectors(this.a,this.b).add(this.c).multiplyScalar(1/3)}getNormal(t){return Vb.getNormal(this.a,this.b,this.c,t)}getPlane(t){return t.setFromCoplanarPoints(this.a,this.b,this.c)}getBarycoord(t,e){return Vb.getBarycoord(t,this.a,this.b,this.c,e)}getUV(t,e,n,i,r){return Vb.getUV(t,this.a,this.b,this.c,e,n,i,r)}containsPoint(t){return Vb.containsPoint(t,this.a,this.b,this.c)}isFrontFacing(t){return Vb.isFrontFacing(this.a,this.b,this.c,t)}intersectsBox(t){return t.intersectsTriangle(this)}closestPointToPoint(t,e){const n=this.a,i=this.b,r=this.c;let s,o;Db.subVectors(i,n),kb.subVectors(r,n),zb.subVectors(t,n);const a=Db.dot(zb),l=kb.dot(zb);if(a<=0&&l<=0)return e.copy(n);Ub.subVectors(t,i);const c=Db.dot(Ub),u=kb.dot(Ub);if(c>=0&&u<=c)return e.copy(i);const h=a*u-c*l;if(h<=0&&a>=0&&c<=0)return s=a/(a-c),e.copy(n).addScaledVector(Db,s);Gb.subVectors(t,r);const d=Db.dot(Gb),p=kb.dot(Gb);if(p>=0&&d<=p)return e.copy(r);const _=d*l-a*p;if(_<=0&&l>=0&&p<=0)return o=l/(l-p),e.copy(n).addScaledVector(kb,o);const m=c*p-d*u;if(m<=0&&u-c>=0&&d-p>=0)return Bb.subVectors(r,i),o=(u-c)/(u-c+(d-p)),e.copy(i).addScaledVector(Bb,o);const f=1/(m+_+h);return s=_*f,o=h*f,e.copy(n).addScaledVector(Db,s).addScaledVector(kb,o)}equals(t){return t.a.equals(this.a)&&t.b.equals(this.b)&&t.c.equals(this.c)}}let Hb=0;class jb extends nx{constructor(){super(),Object.defineProperty(this,\\\\\\\"id\\\\\\\",{value:Hb++}),this.uuid=lx(),this.name=\\\\\\\"\\\\\\\",this.type=\\\\\\\"Material\\\\\\\",this.fog=!0,this.blending=1,this.side=0,this.vertexColors=!1,this.opacity=1,this.format=By,this.transparent=!1,this.blendSrc=204,this.blendDst=205,this.blendEquation=my,this.blendSrcAlpha=null,this.blendDstAlpha=null,this.blendEquationAlpha=null,this.depthFunc=3,this.depthTest=!0,this.depthWrite=!0,this.stencilWriteMask=255,this.stencilFunc=519,this.stencilRef=0,this.stencilFuncMask=255,this.stencilFail=Qy,this.stencilZFail=Qy,this.stencilZPass=Qy,this.stencilWrite=!1,this.clippingPlanes=null,this.clipIntersection=!1,this.clipShadows=!1,this.shadowSide=null,this.colorWrite=!0,this.precision=null,this.polygonOffset=!1,this.polygonOffsetFactor=0,this.polygonOffsetUnits=0,this.dithering=!1,this.alphaToCoverage=!1,this.premultipliedAlpha=!1,this.visible=!0,this.toneMapped=!0,this.userData={},this.version=0,this._alphaTest=0}get alphaTest(){return this._alphaTest}set alphaTest(t){this._alphaTest>0!=t>0&&this.version++,this._alphaTest=t}onBuild(){}onBeforeRender(){}onBeforeCompile(){}customProgramCacheKey(){return this.onBeforeCompile.toString()}setValues(t){if(void 0!==t)for(const e in t){const n=t[e];if(void 0===n){console.warn(\\\\\\\"THREE.Material: '\\\\\\\"+e+\\\\\\\"' parameter is undefined.\\\\\\\");continue}if(\\\\\\\"shading\\\\\\\"===e){console.warn(\\\\\\\"THREE.\\\\\\\"+this.type+\\\\\\\": .shading has been removed. Use the boolean .flatShading instead.\\\\\\\"),this.flatShading=1===n;continue}const i=this[e];void 0!==i?i&&i.isColor?i.set(n):i&&i.isVector3&&n&&n.isVector3?i.copy(n):this[e]=n:console.warn(\\\\\\\"THREE.\\\\\\\"+this.type+\\\\\\\": '\\\\\\\"+e+\\\\\\\"' is not a property of this material.\\\\\\\")}}toJSON(t){const e=void 0===t||\\\\\\\"string\\\\\\\"==typeof t;e&&(t={textures:{},images:{}});const n={metadata:{version:4.5,type:\\\\\\\"Material\\\\\\\",generator:\\\\\\\"Material.toJSON\\\\\\\"}};function i(t){const e=[];for(const n in t){const i=t[n];delete i.metadata,e.push(i)}return e}if(n.uuid=this.uuid,n.type=this.type,\\\\\\\"\\\\\\\"!==this.name&&(n.name=this.name),this.color&&this.color.isColor&&(n.color=this.color.getHex()),void 0!==this.roughness&&(n.roughness=this.roughness),void 0!==this.metalness&&(n.metalness=this.metalness),void 0!==this.sheen&&(n.sheen=this.sheen),this.sheenTint&&this.sheenTint.isColor&&(n.sheenTint=this.sheenTint.getHex()),void 0!==this.sheenRoughness&&(n.sheenRoughness=this.sheenRoughness),this.emissive&&this.emissive.isColor&&(n.emissive=this.emissive.getHex()),this.emissiveIntensity&&1!==this.emissiveIntensity&&(n.emissiveIntensity=this.emissiveIntensity),this.specular&&this.specular.isColor&&(n.specular=this.specular.getHex()),void 0!==this.specularIntensity&&(n.specularIntensity=this.specularIntensity),this.specularTint&&this.specularTint.isColor&&(n.specularTint=this.specularTint.getHex()),void 0!==this.shininess&&(n.shininess=this.shininess),void 0!==this.clearcoat&&(n.clearcoat=this.clearcoat),void 0!==this.clearcoatRoughness&&(n.clearcoatRoughness=this.clearcoatRoughness),this.clearcoatMap&&this.clearcoatMap.isTexture&&(n.clearcoatMap=this.clearcoatMap.toJSON(t).uuid),this.clearcoatRoughnessMap&&this.clearcoatRoughnessMap.isTexture&&(n.clearcoatRoughnessMap=this.clearcoatRoughnessMap.toJSON(t).uuid),this.clearcoatNormalMap&&this.clearcoatNormalMap.isTexture&&(n.clearcoatNormalMap=this.clearcoatNormalMap.toJSON(t).uuid,n.clearcoatNormalScale=this.clearcoatNormalScale.toArray()),this.map&&this.map.isTexture&&(n.map=this.map.toJSON(t).uuid),this.matcap&&this.matcap.isTexture&&(n.matcap=this.matcap.toJSON(t).uuid),this.alphaMap&&this.alphaMap.isTexture&&(n.alphaMap=this.alphaMap.toJSON(t).uuid),this.lightMap&&this.lightMap.isTexture&&(n.lightMap=this.lightMap.toJSON(t).uuid,n.lightMapIntensity=this.lightMapIntensity),this.aoMap&&this.aoMap.isTexture&&(n.aoMap=this.aoMap.toJSON(t).uuid,n.aoMapIntensity=this.aoMapIntensity),this.bumpMap&&this.bumpMap.isTexture&&(n.bumpMap=this.bumpMap.toJSON(t).uuid,n.bumpScale=this.bumpScale),this.normalMap&&this.normalMap.isTexture&&(n.normalMap=this.normalMap.toJSON(t).uuid,n.normalMapType=this.normalMapType,n.normalScale=this.normalScale.toArray()),this.displacementMap&&this.displacementMap.isTexture&&(n.displacementMap=this.displacementMap.toJSON(t).uuid,n.displacementScale=this.displacementScale,n.displacementBias=this.displacementBias),this.roughnessMap&&this.roughnessMap.isTexture&&(n.roughnessMap=this.roughnessMap.toJSON(t).uuid),this.metalnessMap&&this.metalnessMap.isTexture&&(n.metalnessMap=this.metalnessMap.toJSON(t).uuid),this.emissiveMap&&this.emissiveMap.isTexture&&(n.emissiveMap=this.emissiveMap.toJSON(t).uuid),this.specularMap&&this.specularMap.isTexture&&(n.specularMap=this.specularMap.toJSON(t).uuid),this.specularIntensityMap&&this.specularIntensityMap.isTexture&&(n.specularIntensityMap=this.specularIntensityMap.toJSON(t).uuid),this.specularTintMap&&this.specularTintMap.isTexture&&(n.specularTintMap=this.specularTintMap.toJSON(t).uuid),this.envMap&&this.envMap.isTexture&&(n.envMap=this.envMap.toJSON(t).uuid,void 0!==this.combine&&(n.combine=this.combine)),void 0!==this.envMapIntensity&&(n.envMapIntensity=this.envMapIntensity),void 0!==this.reflectivity&&(n.reflectivity=this.reflectivity),void 0!==this.refractionRatio&&(n.refractionRatio=this.refractionRatio),this.gradientMap&&this.gradientMap.isTexture&&(n.gradientMap=this.gradientMap.toJSON(t).uuid),void 0!==this.transmission&&(n.transmission=this.transmission),this.transmissionMap&&this.transmissionMap.isTexture&&(n.transmissionMap=this.transmissionMap.toJSON(t).uuid),void 0!==this.thickness&&(n.thickness=this.thickness),this.thicknessMap&&this.thicknessMap.isTexture&&(n.thicknessMap=this.thicknessMap.toJSON(t).uuid),void 0!==this.attenuationDistance&&(n.attenuationDistance=this.attenuationDistance),void 0!==this.attenuationTint&&(n.attenuationTint=this.attenuationTint.getHex()),void 0!==this.size&&(n.size=this.size),null!==this.shadowSide&&(n.shadowSide=this.shadowSide),void 0!==this.sizeAttenuation&&(n.sizeAttenuation=this.sizeAttenuation),1!==this.blending&&(n.blending=this.blending),0!==this.side&&(n.side=this.side),this.vertexColors&&(n.vertexColors=!0),this.opacity<1&&(n.opacity=this.opacity),this.format!==By&&(n.format=this.format),!0===this.transparent&&(n.transparent=this.transparent),n.depthFunc=this.depthFunc,n.depthTest=this.depthTest,n.depthWrite=this.depthWrite,n.colorWrite=this.colorWrite,n.stencilWrite=this.stencilWrite,n.stencilWriteMask=this.stencilWriteMask,n.stencilFunc=this.stencilFunc,n.stencilRef=this.stencilRef,n.stencilFuncMask=this.stencilFuncMask,n.stencilFail=this.stencilFail,n.stencilZFail=this.stencilZFail,n.stencilZPass=this.stencilZPass,this.rotation&&0!==this.rotation&&(n.rotation=this.rotation),!0===this.polygonOffset&&(n.polygonOffset=!0),0!==this.polygonOffsetFactor&&(n.polygonOffsetFactor=this.polygonOffsetFactor),0!==this.polygonOffsetUnits&&(n.polygonOffsetUnits=this.polygonOffsetUnits),this.linewidth&&1!==this.linewidth&&(n.linewidth=this.linewidth),void 0!==this.dashSize&&(n.dashSize=this.dashSize),void 0!==this.gapSize&&(n.gapSize=this.gapSize),void 0!==this.scale&&(n.scale=this.scale),!0===this.dithering&&(n.dithering=!0),this.alphaTest>0&&(n.alphaTest=this.alphaTest),!0===this.alphaToCoverage&&(n.alphaToCoverage=this.alphaToCoverage),!0===this.premultipliedAlpha&&(n.premultipliedAlpha=this.premultipliedAlpha),!0===this.wireframe&&(n.wireframe=this.wireframe),this.wireframeLinewidth>1&&(n.wireframeLinewidth=this.wireframeLinewidth),\\\\\\\"round\\\\\\\"!==this.wireframeLinecap&&(n.wireframeLinecap=this.wireframeLinecap),\\\\\\\"round\\\\\\\"!==this.wireframeLinejoin&&(n.wireframeLinejoin=this.wireframeLinejoin),!0===this.flatShading&&(n.flatShading=this.flatShading),!1===this.visible&&(n.visible=!1),!1===this.toneMapped&&(n.toneMapped=!1),\\\\\\\"{}\\\\\\\"!==JSON.stringify(this.userData)&&(n.userData=this.userData),e){const e=i(t.textures),r=i(t.images);e.length>0&&(n.textures=e),r.length>0&&(n.images=r)}return n}clone(){return(new this.constructor).copy(this)}copy(t){this.name=t.name,this.fog=t.fog,this.blending=t.blending,this.side=t.side,this.vertexColors=t.vertexColors,this.opacity=t.opacity,this.format=t.format,this.transparent=t.transparent,this.blendSrc=t.blendSrc,this.blendDst=t.blendDst,this.blendEquation=t.blendEquation,this.blendSrcAlpha=t.blendSrcAlpha,this.blendDstAlpha=t.blendDstAlpha,this.blendEquationAlpha=t.blendEquationAlpha,this.depthFunc=t.depthFunc,this.depthTest=t.depthTest,this.depthWrite=t.depthWrite,this.stencilWriteMask=t.stencilWriteMask,this.stencilFunc=t.stencilFunc,this.stencilRef=t.stencilRef,this.stencilFuncMask=t.stencilFuncMask,this.stencilFail=t.stencilFail,this.stencilZFail=t.stencilZFail,this.stencilZPass=t.stencilZPass,this.stencilWrite=t.stencilWrite;const e=t.clippingPlanes;let n=null;if(null!==e){const t=e.length;n=new Array(t);for(let i=0;i!==t;++i)n[i]=e[i].clone()}return this.clippingPlanes=n,this.clipIntersection=t.clipIntersection,this.clipShadows=t.clipShadows,this.shadowSide=t.shadowSide,this.colorWrite=t.colorWrite,this.precision=t.precision,this.polygonOffset=t.polygonOffset,this.polygonOffsetFactor=t.polygonOffsetFactor,this.polygonOffsetUnits=t.polygonOffsetUnits,this.dithering=t.dithering,this.alphaTest=t.alphaTest,this.alphaToCoverage=t.alphaToCoverage,this.premultipliedAlpha=t.premultipliedAlpha,this.visible=t.visible,this.toneMapped=t.toneMapped,this.userData=JSON.parse(JSON.stringify(t.userData)),this}dispose(){this.dispatchEvent({type:\\\\\\\"dispose\\\\\\\"})}set needsUpdate(t){!0===t&&this.version++}}jb.prototype.isMaterial=!0;const Wb={aliceblue:15792383,antiquewhite:16444375,aqua:65535,aquamarine:8388564,azure:15794175,beige:16119260,bisque:16770244,black:0,blanchedalmond:16772045,blue:255,blueviolet:9055202,brown:10824234,burlywood:14596231,cadetblue:6266528,chartreuse:8388352,chocolate:13789470,coral:16744272,cornflowerblue:6591981,cornsilk:16775388,crimson:14423100,cyan:65535,darkblue:139,darkcyan:35723,darkgoldenrod:12092939,darkgray:11119017,darkgreen:25600,darkgrey:11119017,darkkhaki:12433259,darkmagenta:9109643,darkolivegreen:5597999,darkorange:16747520,darkorchid:10040012,darkred:9109504,darksalmon:15308410,darkseagreen:9419919,darkslateblue:4734347,darkslategray:3100495,darkslategrey:3100495,darkturquoise:52945,darkviolet:9699539,deeppink:16716947,deepskyblue:49151,dimgray:6908265,dimgrey:6908265,dodgerblue:2003199,firebrick:11674146,floralwhite:16775920,forestgreen:2263842,fuchsia:16711935,gainsboro:14474460,ghostwhite:16316671,gold:16766720,goldenrod:14329120,gray:8421504,green:32768,greenyellow:11403055,grey:8421504,honeydew:15794160,hotpink:16738740,indianred:13458524,indigo:4915330,ivory:16777200,khaki:15787660,lavender:15132410,lavenderblush:16773365,lawngreen:8190976,lemonchiffon:16775885,lightblue:11393254,lightcoral:15761536,lightcyan:14745599,lightgoldenrodyellow:16448210,lightgray:13882323,lightgreen:9498256,lightgrey:13882323,lightpink:16758465,lightsalmon:16752762,lightseagreen:2142890,lightskyblue:8900346,lightslategray:7833753,lightslategrey:7833753,lightsteelblue:11584734,lightyellow:16777184,lime:65280,limegreen:3329330,linen:16445670,magenta:16711935,maroon:8388608,mediumaquamarine:6737322,mediumblue:205,mediumorchid:12211667,mediumpurple:9662683,mediumseagreen:3978097,mediumslateblue:8087790,mediumspringgreen:64154,mediumturquoise:4772300,mediumvioletred:13047173,midnightblue:1644912,mintcream:16121850,mistyrose:16770273,moccasin:16770229,navajowhite:16768685,navy:128,oldlace:16643558,olive:8421376,olivedrab:7048739,orange:16753920,orangered:16729344,orchid:14315734,palegoldenrod:15657130,palegreen:10025880,paleturquoise:11529966,palevioletred:14381203,papayawhip:16773077,peachpuff:16767673,peru:13468991,pink:16761035,plum:14524637,powderblue:11591910,purple:8388736,rebeccapurple:6697881,red:16711680,rosybrown:12357519,royalblue:4286945,saddlebrown:9127187,salmon:16416882,sandybrown:16032864,seagreen:3050327,seashell:16774638,sienna:10506797,silver:12632256,skyblue:8900331,slateblue:6970061,slategray:7372944,slategrey:7372944,snow:16775930,springgreen:65407,steelblue:4620980,tan:13808780,teal:32896,thistle:14204888,tomato:16737095,turquoise:4251856,violet:15631086,wheat:16113331,white:16777215,whitesmoke:16119285,yellow:16776960,yellowgreen:10145074},qb={h:0,s:0,l:0},Xb={h:0,s:0,l:0};function Yb(t,e,n){return n<0&&(n+=1),n>1&&(n-=1),n<1/6?t+6*(e-t)*n:n<.5?e:n<2/3?t+6*(e-t)*(2/3-n):t}function $b(t){return t<.04045?.0773993808*t:Math.pow(.9478672986*t+.0521327014,2.4)}function Jb(t){return t<.0031308?12.92*t:1.055*Math.pow(t,.41666)-.055}class Zb{constructor(t,e,n){return void 0===e&&void 0===n?this.set(t):this.setRGB(t,e,n)}set(t){return t&&t.isColor?this.copy(t):\\\\\\\"number\\\\\\\"==typeof t?this.setHex(t):\\\\\\\"string\\\\\\\"==typeof t&&this.setStyle(t),this}setScalar(t){return this.r=t,this.g=t,this.b=t,this}setHex(t){return t=Math.floor(t),this.r=(t>>16&255)/255,this.g=(t>>8&255)/255,this.b=(255&t)/255,this}setRGB(t,e,n){return this.r=t,this.g=e,this.b=n,this}setHSL(t,e,n){if(t=ux(t,1),e=cx(e,0,1),n=cx(n,0,1),0===e)this.r=this.g=this.b=n;else{const i=n<=.5?n*(1+e):n+e-n*e,r=2*n-i;this.r=Yb(r,i,t+1/3),this.g=Yb(r,i,t),this.b=Yb(r,i,t-1/3)}return this}setStyle(t){function e(e){void 0!==e&&parseFloat(e)<1&&console.warn(\\\\\\\"THREE.Color: Alpha component of \\\\\\\"+t+\\\\\\\" will be ignored.\\\\\\\")}let n;if(n=/^((?:rgb|hsl)a?)\\\\(([^\\\\)]*)\\\\)/.exec(t)){let t;const i=n[1],r=n[2];switch(i){case\\\\\\\"rgb\\\\\\\":case\\\\\\\"rgba\\\\\\\":if(t=/^\\\\s*(\\\\d+)\\\\s*,\\\\s*(\\\\d+)\\\\s*,\\\\s*(\\\\d+)\\\\s*(?:,\\\\s*(\\\\d*\\\\.?\\\\d+)\\\\s*)?$/.exec(r))return this.r=Math.min(255,parseInt(t[1],10))/255,this.g=Math.min(255,parseInt(t[2],10))/255,this.b=Math.min(255,parseInt(t[3],10))/255,e(t[4]),this;if(t=/^\\\\s*(\\\\d+)\\\\%\\\\s*,\\\\s*(\\\\d+)\\\\%\\\\s*,\\\\s*(\\\\d+)\\\\%\\\\s*(?:,\\\\s*(\\\\d*\\\\.?\\\\d+)\\\\s*)?$/.exec(r))return this.r=Math.min(100,parseInt(t[1],10))/100,this.g=Math.min(100,parseInt(t[2],10))/100,this.b=Math.min(100,parseInt(t[3],10))/100,e(t[4]),this;break;case\\\\\\\"hsl\\\\\\\":case\\\\\\\"hsla\\\\\\\":if(t=/^\\\\s*(\\\\d*\\\\.?\\\\d+)\\\\s*,\\\\s*(\\\\d+)\\\\%\\\\s*,\\\\s*(\\\\d+)\\\\%\\\\s*(?:,\\\\s*(\\\\d*\\\\.?\\\\d+)\\\\s*)?$/.exec(r)){const n=parseFloat(t[1])/360,i=parseInt(t[2],10)/100,r=parseInt(t[3],10)/100;return e(t[4]),this.setHSL(n,i,r)}}}else if(n=/^\\\\#([A-Fa-f\\\\d]+)$/.exec(t)){const t=n[1],e=t.length;if(3===e)return this.r=parseInt(t.charAt(0)+t.charAt(0),16)/255,this.g=parseInt(t.charAt(1)+t.charAt(1),16)/255,this.b=parseInt(t.charAt(2)+t.charAt(2),16)/255,this;if(6===e)return this.r=parseInt(t.charAt(0)+t.charAt(1),16)/255,this.g=parseInt(t.charAt(2)+t.charAt(3),16)/255,this.b=parseInt(t.charAt(4)+t.charAt(5),16)/255,this}return t&&t.length>0?this.setColorName(t):this}setColorName(t){const e=Wb[t.toLowerCase()];return void 0!==e?this.setHex(e):console.warn(\\\\\\\"THREE.Color: Unknown color \\\\\\\"+t),this}clone(){return new this.constructor(this.r,this.g,this.b)}copy(t){return this.r=t.r,this.g=t.g,this.b=t.b,this}copyGammaToLinear(t,e=2){return this.r=Math.pow(t.r,e),this.g=Math.pow(t.g,e),this.b=Math.pow(t.b,e),this}copyLinearToGamma(t,e=2){const n=e>0?1/e:1;return this.r=Math.pow(t.r,n),this.g=Math.pow(t.g,n),this.b=Math.pow(t.b,n),this}convertGammaToLinear(t){return this.copyGammaToLinear(this,t),this}convertLinearToGamma(t){return this.copyLinearToGamma(this,t),this}copySRGBToLinear(t){return this.r=$b(t.r),this.g=$b(t.g),this.b=$b(t.b),this}copyLinearToSRGB(t){return this.r=Jb(t.r),this.g=Jb(t.g),this.b=Jb(t.b),this}convertSRGBToLinear(){return this.copySRGBToLinear(this),this}convertLinearToSRGB(){return this.copyLinearToSRGB(this),this}getHex(){return 255*this.r<<16^255*this.g<<8^255*this.b<<0}getHexString(){return(\\\\\\\"000000\\\\\\\"+this.getHex().toString(16)).slice(-6)}getHSL(t){const e=this.r,n=this.g,i=this.b,r=Math.max(e,n,i),s=Math.min(e,n,i);let o,a;const l=(s+r)/2;if(s===r)o=0,a=0;else{const t=r-s;switch(a=l<=.5?t/(r+s):t/(2-r-s),r){case e:o=(n-i)/t+(n<i?6:0);break;case n:o=(i-e)/t+2;break;case i:o=(e-n)/t+4}o/=6}return t.h=o,t.s=a,t.l=l,t}getStyle(){return\\\\\\\"rgb(\\\\\\\"+(255*this.r|0)+\\\\\\\",\\\\\\\"+(255*this.g|0)+\\\\\\\",\\\\\\\"+(255*this.b|0)+\\\\\\\")\\\\\\\"}offsetHSL(t,e,n){return this.getHSL(qb),qb.h+=t,qb.s+=e,qb.l+=n,this.setHSL(qb.h,qb.s,qb.l),this}add(t){return this.r+=t.r,this.g+=t.g,this.b+=t.b,this}addColors(t,e){return this.r=t.r+e.r,this.g=t.g+e.g,this.b=t.b+e.b,this}addScalar(t){return this.r+=t,this.g+=t,this.b+=t,this}sub(t){return this.r=Math.max(0,this.r-t.r),this.g=Math.max(0,this.g-t.g),this.b=Math.max(0,this.b-t.b),this}multiply(t){return this.r*=t.r,this.g*=t.g,this.b*=t.b,this}multiplyScalar(t){return this.r*=t,this.g*=t,this.b*=t,this}lerp(t,e){return this.r+=(t.r-this.r)*e,this.g+=(t.g-this.g)*e,this.b+=(t.b-this.b)*e,this}lerpColors(t,e,n){return this.r=t.r+(e.r-t.r)*n,this.g=t.g+(e.g-t.g)*n,this.b=t.b+(e.b-t.b)*n,this}lerpHSL(t,e){this.getHSL(qb),t.getHSL(Xb);const n=hx(qb.h,Xb.h,e),i=hx(qb.s,Xb.s,e),r=hx(qb.l,Xb.l,e);return this.setHSL(n,i,r),this}equals(t){return t.r===this.r&&t.g===this.g&&t.b===this.b}fromArray(t,e=0){return this.r=t[e],this.g=t[e+1],this.b=t[e+2],this}toArray(t=[],e=0){return t[e]=this.r,t[e+1]=this.g,t[e+2]=this.b,t}fromBufferAttribute(t,e){return this.r=t.getX(e),this.g=t.getY(e),this.b=t.getZ(e),!0===t.normalized&&(this.r/=255,this.g/=255,this.b/=255),this}toJSON(){return this.getHex()}}Zb.NAMES=Wb,Zb.prototype.isColor=!0,Zb.prototype.r=1,Zb.prototype.g=1,Zb.prototype.b=1;class Qb extends jb{constructor(t){super(),this.type=\\\\\\\"MeshBasicMaterial\\\\\\\",this.color=new Zb(16777215),this.map=null,this.lightMap=null,this.lightMapIntensity=1,this.aoMap=null,this.aoMapIntensity=1,this.specularMap=null,this.alphaMap=null,this.envMap=null,this.combine=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.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}}Qb.prototype.isMeshBasicMaterial=!0;const Kb=new Nx,tw=new fx;class ew{constructor(t,e,n){if(Array.isArray(t))throw new TypeError(\\\\\\\"THREE.BufferAttribute: array should be a Typed Array.\\\\\\\");this.name=\\\\\\\"\\\\\\\",this.array=t,this.itemSize=e,this.count=void 0!==t?t.length/e:0,this.normalized=!0===n,this.usage=Ky,this.updateRange={offset:0,count:-1},this.version=0}onUploadCallback(){}set needsUpdate(t){!0===t&&this.version++}setUsage(t){return this.usage=t,this}copy(t){return this.name=t.name,this.array=new t.array.constructor(t.array),this.itemSize=t.itemSize,this.count=t.count,this.normalized=t.normalized,this.usage=t.usage,this}copyAt(t,e,n){t*=this.itemSize,n*=e.itemSize;for(let i=0,r=this.itemSize;i<r;i++)this.array[t+i]=e.array[n+i];return this}copyArray(t){return this.array.set(t),this}copyColorsArray(t){const e=this.array;let n=0;for(let i=0,r=t.length;i<r;i++){let r=t[i];void 0===r&&(console.warn(\\\\\\\"THREE.BufferAttribute.copyColorsArray(): color is undefined\\\\\\\",i),r=new Zb),e[n++]=r.r,e[n++]=r.g,e[n++]=r.b}return this}copyVector2sArray(t){const e=this.array;let n=0;for(let i=0,r=t.length;i<r;i++){let r=t[i];void 0===r&&(console.warn(\\\\\\\"THREE.BufferAttribute.copyVector2sArray(): vector is undefined\\\\\\\",i),r=new fx),e[n++]=r.x,e[n++]=r.y}return this}copyVector3sArray(t){const e=this.array;let n=0;for(let i=0,r=t.length;i<r;i++){let r=t[i];void 0===r&&(console.warn(\\\\\\\"THREE.BufferAttribute.copyVector3sArray(): vector is undefined\\\\\\\",i),r=new Nx),e[n++]=r.x,e[n++]=r.y,e[n++]=r.z}return this}copyVector4sArray(t){const e=this.array;let n=0;for(let i=0,r=t.length;i<r;i++){let r=t[i];void 0===r&&(console.warn(\\\\\\\"THREE.BufferAttribute.copyVector4sArray(): vector is undefined\\\\\\\",i),r=new Ex),e[n++]=r.x,e[n++]=r.y,e[n++]=r.z,e[n++]=r.w}return this}applyMatrix3(t){if(2===this.itemSize)for(let e=0,n=this.count;e<n;e++)tw.fromBufferAttribute(this,e),tw.applyMatrix3(t),this.setXY(e,tw.x,tw.y);else if(3===this.itemSize)for(let e=0,n=this.count;e<n;e++)Kb.fromBufferAttribute(this,e),Kb.applyMatrix3(t),this.setXYZ(e,Kb.x,Kb.y,Kb.z);return this}applyMatrix4(t){for(let e=0,n=this.count;e<n;e++)Kb.x=this.getX(e),Kb.y=this.getY(e),Kb.z=this.getZ(e),Kb.applyMatrix4(t),this.setXYZ(e,Kb.x,Kb.y,Kb.z);return this}applyNormalMatrix(t){for(let e=0,n=this.count;e<n;e++)Kb.x=this.getX(e),Kb.y=this.getY(e),Kb.z=this.getZ(e),Kb.applyNormalMatrix(t),this.setXYZ(e,Kb.x,Kb.y,Kb.z);return this}transformDirection(t){for(let e=0,n=this.count;e<n;e++)Kb.x=this.getX(e),Kb.y=this.getY(e),Kb.z=this.getZ(e),Kb.transformDirection(t),this.setXYZ(e,Kb.x,Kb.y,Kb.z);return this}set(t,e=0){return this.array.set(t,e),this}getX(t){return this.array[t*this.itemSize]}setX(t,e){return this.array[t*this.itemSize]=e,this}getY(t){return this.array[t*this.itemSize+1]}setY(t,e){return this.array[t*this.itemSize+1]=e,this}getZ(t){return this.array[t*this.itemSize+2]}setZ(t,e){return this.array[t*this.itemSize+2]=e,this}getW(t){return this.array[t*this.itemSize+3]}setW(t,e){return this.array[t*this.itemSize+3]=e,this}setXY(t,e,n){return t*=this.itemSize,this.array[t+0]=e,this.array[t+1]=n,this}setXYZ(t,e,n,i){return t*=this.itemSize,this.array[t+0]=e,this.array[t+1]=n,this.array[t+2]=i,this}setXYZW(t,e,n,i,r){return t*=this.itemSize,this.array[t+0]=e,this.array[t+1]=n,this.array[t+2]=i,this.array[t+3]=r,this}onUpload(t){return this.onUploadCallback=t,this}clone(){return new this.constructor(this.array,this.itemSize).copy(this)}toJSON(){const t={itemSize:this.itemSize,type:this.array.constructor.name,array:Array.prototype.slice.call(this.array),normalized:this.normalized};return\\\\\\\"\\\\\\\"!==this.name&&(t.name=this.name),this.usage!==Ky&&(t.usage=this.usage),0===this.updateRange.offset&&-1===this.updateRange.count||(t.updateRange=this.updateRange),t}}ew.prototype.isBufferAttribute=!0;class nw extends ew{constructor(t,e,n){super(new Uint16Array(t),e,n)}}class iw extends ew{constructor(t,e,n){super(new Uint32Array(t),e,n)}}(class extends ew{constructor(t,e,n){super(new Uint16Array(t),e,n)}}).prototype.isFloat16BufferAttribute=!0;class rw extends ew{constructor(t,e,n){super(new Float32Array(t),e,n)}}let sw=0;const ow=new ob,aw=new Ob,lw=new Nx,cw=new Rx,uw=new Rx,hw=new Nx;class dw extends nx{constructor(){super(),Object.defineProperty(this,\\\\\\\"id\\\\\\\",{value:sw++}),this.uuid=lx(),this.name=\\\\\\\"\\\\\\\",this.type=\\\\\\\"BufferGeometry\\\\\\\",this.index=null,this.attributes={},this.morphAttributes={},this.morphTargetsRelative=!1,this.groups=[],this.boundingBox=null,this.boundingSphere=null,this.drawRange={start:0,count:1/0},this.userData={}}getIndex(){return this.index}setIndex(t){return Array.isArray(t)?this.index=new(vx(t)>65535?iw:nw)(t,1):this.index=t,this}getAttribute(t){return this.attributes[t]}setAttribute(t,e){return this.attributes[t]=e,this}deleteAttribute(t){return delete this.attributes[t],this}hasAttribute(t){return void 0!==this.attributes[t]}addGroup(t,e,n=0){this.groups.push({start:t,count:e,materialIndex:n})}clearGroups(){this.groups=[]}setDrawRange(t,e){this.drawRange.start=t,this.drawRange.count=e}applyMatrix4(t){const e=this.attributes.position;void 0!==e&&(e.applyMatrix4(t),e.needsUpdate=!0);const n=this.attributes.normal;if(void 0!==n){const e=(new gx).getNormalMatrix(t);n.applyNormalMatrix(e),n.needsUpdate=!0}const i=this.attributes.tangent;return void 0!==i&&(i.transformDirection(t),i.needsUpdate=!0),null!==this.boundingBox&&this.computeBoundingBox(),null!==this.boundingSphere&&this.computeBoundingSphere(),this}applyQuaternion(t){return ow.makeRotationFromQuaternion(t),this.applyMatrix4(ow),this}rotateX(t){return ow.makeRotationX(t),this.applyMatrix4(ow),this}rotateY(t){return ow.makeRotationY(t),this.applyMatrix4(ow),this}rotateZ(t){return ow.makeRotationZ(t),this.applyMatrix4(ow),this}translate(t,e,n){return ow.makeTranslation(t,e,n),this.applyMatrix4(ow),this}scale(t,e,n){return ow.makeScale(t,e,n),this.applyMatrix4(ow),this}lookAt(t){return aw.lookAt(t),aw.updateMatrix(),this.applyMatrix4(aw.matrix),this}center(){return this.computeBoundingBox(),this.boundingBox.getCenter(lw).negate(),this.translate(lw.x,lw.y,lw.z),this}setFromPoints(t){const e=[];for(let n=0,i=t.length;n<i;n++){const i=t[n];e.push(i.x,i.y,i.z||0)}return this.setAttribute(\\\\\\\"position\\\\\\\",new rw(e,3)),this}computeBoundingBox(){null===this.boundingBox&&(this.boundingBox=new Rx);const t=this.attributes.position,e=this.morphAttributes.position;if(t&&t.isGLBufferAttribute)return console.error('THREE.BufferGeometry.computeBoundingBox(): GLBufferAttribute requires a manual bounding box. Alternatively set \\\\\\\"mesh.frustumCulled\\\\\\\" to \\\\\\\"false\\\\\\\".',this),void this.boundingBox.set(new Nx(-1/0,-1/0,-1/0),new Nx(1/0,1/0,1/0));if(void 0!==t){if(this.boundingBox.setFromBufferAttribute(t),e)for(let t=0,n=e.length;t<n;t++){const n=e[t];cw.setFromBufferAttribute(n),this.morphTargetsRelative?(hw.addVectors(this.boundingBox.min,cw.min),this.boundingBox.expandByPoint(hw),hw.addVectors(this.boundingBox.max,cw.max),this.boundingBox.expandByPoint(hw)):(this.boundingBox.expandByPoint(cw.min),this.boundingBox.expandByPoint(cw.max))}}else this.boundingBox.makeEmpty();(isNaN(this.boundingBox.min.x)||isNaN(this.boundingBox.min.y)||isNaN(this.boundingBox.min.z))&&console.error('THREE.BufferGeometry.computeBoundingBox(): Computed min/max have NaN values. The \\\\\\\"position\\\\\\\" attribute is likely to have NaN values.',this)}computeBoundingSphere(){null===this.boundingSphere&&(this.boundingSphere=new Zx);const t=this.attributes.position,e=this.morphAttributes.position;if(t&&t.isGLBufferAttribute)return console.error('THREE.BufferGeometry.computeBoundingSphere(): GLBufferAttribute requires a manual bounding sphere. Alternatively set \\\\\\\"mesh.frustumCulled\\\\\\\" to \\\\\\\"false\\\\\\\".',this),void this.boundingSphere.set(new Nx,1/0);if(t){const n=this.boundingSphere.center;if(cw.setFromBufferAttribute(t),e)for(let t=0,n=e.length;t<n;t++){const n=e[t];uw.setFromBufferAttribute(n),this.morphTargetsRelative?(hw.addVectors(cw.min,uw.min),cw.expandByPoint(hw),hw.addVectors(cw.max,uw.max),cw.expandByPoint(hw)):(cw.expandByPoint(uw.min),cw.expandByPoint(uw.max))}cw.getCenter(n);let i=0;for(let e=0,r=t.count;e<r;e++)hw.fromBufferAttribute(t,e),i=Math.max(i,n.distanceToSquared(hw));if(e)for(let r=0,s=e.length;r<s;r++){const s=e[r],o=this.morphTargetsRelative;for(let e=0,r=s.count;e<r;e++)hw.fromBufferAttribute(s,e),o&&(lw.fromBufferAttribute(t,e),hw.add(lw)),i=Math.max(i,n.distanceToSquared(hw))}this.boundingSphere.radius=Math.sqrt(i),isNaN(this.boundingSphere.radius)&&console.error('THREE.BufferGeometry.computeBoundingSphere(): Computed radius is NaN. The \\\\\\\"position\\\\\\\" attribute is likely to have NaN values.',this)}}computeTangents(){const t=this.index,e=this.attributes;if(null===t||void 0===e.position||void 0===e.normal||void 0===e.uv)return void console.error(\\\\\\\"THREE.BufferGeometry: .computeTangents() failed. Missing required attributes (index, position, normal or uv)\\\\\\\");const n=t.array,i=e.position.array,r=e.normal.array,s=e.uv.array,o=i.length/3;void 0===e.tangent&&this.setAttribute(\\\\\\\"tangent\\\\\\\",new ew(new Float32Array(4*o),4));const a=e.tangent.array,l=[],c=[];for(let t=0;t<o;t++)l[t]=new Nx,c[t]=new Nx;const u=new Nx,h=new Nx,d=new Nx,p=new fx,_=new fx,m=new fx,f=new Nx,g=new Nx;function v(t,e,n){u.fromArray(i,3*t),h.fromArray(i,3*e),d.fromArray(i,3*n),p.fromArray(s,2*t),_.fromArray(s,2*e),m.fromArray(s,2*n),h.sub(u),d.sub(u),_.sub(p),m.sub(p);const r=1/(_.x*m.y-m.x*_.y);isFinite(r)&&(f.copy(h).multiplyScalar(m.y).addScaledVector(d,-_.y).multiplyScalar(r),g.copy(d).multiplyScalar(_.x).addScaledVector(h,-m.x).multiplyScalar(r),l[t].add(f),l[e].add(f),l[n].add(f),c[t].add(g),c[e].add(g),c[n].add(g))}let y=this.groups;0===y.length&&(y=[{start:0,count:n.length}]);for(let t=0,e=y.length;t<e;++t){const e=y[t],i=e.start;for(let t=i,r=i+e.count;t<r;t+=3)v(n[t+0],n[t+1],n[t+2])}const x=new Nx,b=new Nx,w=new Nx,T=new Nx;function A(t){w.fromArray(r,3*t),T.copy(w);const e=l[t];x.copy(e),x.sub(w.multiplyScalar(w.dot(e))).normalize(),b.crossVectors(T,e);const n=b.dot(c[t])<0?-1:1;a[4*t]=x.x,a[4*t+1]=x.y,a[4*t+2]=x.z,a[4*t+3]=n}for(let t=0,e=y.length;t<e;++t){const e=y[t],i=e.start;for(let t=i,r=i+e.count;t<r;t+=3)A(n[t+0]),A(n[t+1]),A(n[t+2])}}computeVertexNormals(){const t=this.index,e=this.getAttribute(\\\\\\\"position\\\\\\\");if(void 0!==e){let n=this.getAttribute(\\\\\\\"normal\\\\\\\");if(void 0===n)n=new ew(new Float32Array(3*e.count),3),this.setAttribute(\\\\\\\"normal\\\\\\\",n);else for(let t=0,e=n.count;t<e;t++)n.setXYZ(t,0,0,0);const i=new Nx,r=new Nx,s=new Nx,o=new Nx,a=new Nx,l=new Nx,c=new Nx,u=new Nx;if(t)for(let h=0,d=t.count;h<d;h+=3){const d=t.getX(h+0),p=t.getX(h+1),_=t.getX(h+2);i.fromBufferAttribute(e,d),r.fromBufferAttribute(e,p),s.fromBufferAttribute(e,_),c.subVectors(s,r),u.subVectors(i,r),c.cross(u),o.fromBufferAttribute(n,d),a.fromBufferAttribute(n,p),l.fromBufferAttribute(n,_),o.add(c),a.add(c),l.add(c),n.setXYZ(d,o.x,o.y,o.z),n.setXYZ(p,a.x,a.y,a.z),n.setXYZ(_,l.x,l.y,l.z)}else for(let t=0,o=e.count;t<o;t+=3)i.fromBufferAttribute(e,t+0),r.fromBufferAttribute(e,t+1),s.fromBufferAttribute(e,t+2),c.subVectors(s,r),u.subVectors(i,r),c.cross(u),n.setXYZ(t+0,c.x,c.y,c.z),n.setXYZ(t+1,c.x,c.y,c.z),n.setXYZ(t+2,c.x,c.y,c.z);this.normalizeNormals(),n.needsUpdate=!0}}merge(t,e){if(!t||!t.isBufferGeometry)return void console.error(\\\\\\\"THREE.BufferGeometry.merge(): geometry not an instance of THREE.BufferGeometry.\\\\\\\",t);void 0===e&&(e=0,console.warn(\\\\\\\"THREE.BufferGeometry.merge(): Overwriting original geometry, starting at offset=0. Use BufferGeometryUtils.mergeBufferGeometries() for lossless merge.\\\\\\\"));const n=this.attributes;for(const i in n){if(void 0===t.attributes[i])continue;const r=n[i].array,s=t.attributes[i],o=s.array,a=s.itemSize*e,l=Math.min(o.length,r.length-a);for(let t=0,e=a;t<l;t++,e++)r[e]=o[t]}return this}normalizeNormals(){const t=this.attributes.normal;for(let e=0,n=t.count;e<n;e++)hw.fromBufferAttribute(t,e),hw.normalize(),t.setXYZ(e,hw.x,hw.y,hw.z)}toNonIndexed(){function t(t,e){const n=t.array,i=t.itemSize,r=t.normalized,s=new n.constructor(e.length*i);let o=0,a=0;for(let r=0,l=e.length;r<l;r++){o=t.isInterleavedBufferAttribute?e[r]*t.data.stride+t.offset:e[r]*i;for(let t=0;t<i;t++)s[a++]=n[o++]}return new ew(s,i,r)}if(null===this.index)return console.warn(\\\\\\\"THREE.BufferGeometry.toNonIndexed(): BufferGeometry is already non-indexed.\\\\\\\"),this;const e=new dw,n=this.index.array,i=this.attributes;for(const r in i){const s=t(i[r],n);e.setAttribute(r,s)}const r=this.morphAttributes;for(const i in r){const s=[],o=r[i];for(let e=0,i=o.length;e<i;e++){const i=t(o[e],n);s.push(i)}e.morphAttributes[i]=s}e.morphTargetsRelative=this.morphTargetsRelative;const s=this.groups;for(let t=0,n=s.length;t<n;t++){const n=s[t];e.addGroup(n.start,n.count,n.materialIndex)}return e}toJSON(){const t={metadata:{version:4.5,type:\\\\\\\"BufferGeometry\\\\\\\",generator:\\\\\\\"BufferGeometry.toJSON\\\\\\\"}};if(t.uuid=this.uuid,t.type=this.type,\\\\\\\"\\\\\\\"!==this.name&&(t.name=this.name),Object.keys(this.userData).length>0&&(t.userData=this.userData),void 0!==this.parameters){const e=this.parameters;for(const n in e)void 0!==e[n]&&(t[n]=e[n]);return t}t.data={attributes:{}};const e=this.index;null!==e&&(t.data.index={type:e.array.constructor.name,array:Array.prototype.slice.call(e.array)});const n=this.attributes;for(const e in n){const i=n[e];t.data.attributes[e]=i.toJSON(t.data)}const i={};let r=!1;for(const e in this.morphAttributes){const n=this.morphAttributes[e],s=[];for(let e=0,i=n.length;e<i;e++){const i=n[e];s.push(i.toJSON(t.data))}s.length>0&&(i[e]=s,r=!0)}r&&(t.data.morphAttributes=i,t.data.morphTargetsRelative=this.morphTargetsRelative);const s=this.groups;s.length>0&&(t.data.groups=JSON.parse(JSON.stringify(s)));const o=this.boundingSphere;return null!==o&&(t.data.boundingSphere={center:o.center.toArray(),radius:o.radius}),t}clone(){return(new this.constructor).copy(this)}copy(t){this.index=null,this.attributes={},this.morphAttributes={},this.groups=[],this.boundingBox=null,this.boundingSphere=null;const e={};this.name=t.name;const n=t.index;null!==n&&this.setIndex(n.clone(e));const i=t.attributes;for(const t in i){const n=i[t];this.setAttribute(t,n.clone(e))}const r=t.morphAttributes;for(const t in r){const n=[],i=r[t];for(let t=0,r=i.length;t<r;t++)n.push(i[t].clone(e));this.morphAttributes[t]=n}this.morphTargetsRelative=t.morphTargetsRelative;const s=t.groups;for(let t=0,e=s.length;t<e;t++){const e=s[t];this.addGroup(e.start,e.count,e.materialIndex)}const o=t.boundingBox;null!==o&&(this.boundingBox=o.clone());const a=t.boundingSphere;return null!==a&&(this.boundingSphere=a.clone()),this.drawRange.start=t.drawRange.start,this.drawRange.count=t.drawRange.count,this.userData=t.userData,void 0!==t.parameters&&(this.parameters=Object.assign({},t.parameters)),this}dispose(){this.dispatchEvent({type:\\\\\\\"dispose\\\\\\\"})}}dw.prototype.isBufferGeometry=!0;const pw=new ob,_w=new sb,mw=new Zx,fw=new Nx,gw=new Nx,vw=new Nx,yw=new Nx,xw=new Nx,bw=new Nx,ww=new Nx,Tw=new Nx,Aw=new Nx,Ew=new fx,Mw=new fx,Sw=new fx,Cw=new Nx,Nw=new Nx;class Lw extends Ob{constructor(t=new dw,e=new Qb){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;e<n;e++){const n=t[e].name||String(e);this.morphTargetInfluences.push(0),this.morphTargetDictionary[n]=e}}}}else{const e=t.morphTargets;void 0!==e&&e.length>0&&console.error(\\\\\\\"THREE.Mesh.updateMorphTargets() no longer supports THREE.Geometry. Use THREE.BufferGeometry instead.\\\\\\\")}}raycast(t,e){const n=this.geometry,i=this.material,r=this.matrixWorld;if(void 0===i)return;if(null===n.boundingSphere&&n.computeBoundingSphere(),mw.copy(n.boundingSphere),mw.applyMatrix4(r),!1===t.ray.intersectsSphere(mw))return;if(pw.copy(r).invert(),_w.copy(t.ray).applyMatrix4(pw),null!==n.boundingBox&&!1===_w.intersectsBox(n.boundingBox))return;let s;if(n.isBufferGeometry){const r=n.index,o=n.attributes.position,a=n.morphAttributes.position,l=n.morphTargetsRelative,c=n.attributes.uv,u=n.attributes.uv2,h=n.groups,d=n.drawRange;if(null!==r)if(Array.isArray(i))for(let n=0,p=h.length;n<p;n++){const p=h[n],_=i[p.materialIndex];for(let n=Math.max(p.start,d.start),i=Math.min(r.count,Math.min(p.start+p.count,d.start+d.count));n<i;n+=3){const i=r.getX(n),h=r.getX(n+1),d=r.getX(n+2);s=Ow(this,_,t,_w,o,a,l,c,u,i,h,d),s&&(s.faceIndex=Math.floor(n/3),s.face.materialIndex=p.materialIndex,e.push(s))}}else{for(let n=Math.max(0,d.start),h=Math.min(r.count,d.start+d.count);n<h;n+=3){const h=r.getX(n),d=r.getX(n+1),p=r.getX(n+2);s=Ow(this,i,t,_w,o,a,l,c,u,h,d,p),s&&(s.faceIndex=Math.floor(n/3),e.push(s))}}else if(void 0!==o)if(Array.isArray(i))for(let n=0,r=h.length;n<r;n++){const r=h[n],p=i[r.materialIndex];for(let n=Math.max(r.start,d.start),i=Math.min(o.count,Math.min(r.start+r.count,d.start+d.count));n<i;n+=3){s=Ow(this,p,t,_w,o,a,l,c,u,n,n+1,n+2),s&&(s.faceIndex=Math.floor(n/3),s.face.materialIndex=r.materialIndex,e.push(s))}}else{for(let n=Math.max(0,d.start),r=Math.min(o.count,d.start+d.count);n<r;n+=3){s=Ow(this,i,t,_w,o,a,l,c,u,n,n+1,n+2),s&&(s.faceIndex=Math.floor(n/3),e.push(s))}}}else n.isGeometry&&console.error(\\\\\\\"THREE.Mesh.raycast() no longer supports THREE.Geometry. Use THREE.BufferGeometry instead.\\\\\\\")}}function Ow(t,e,n,i,r,s,o,a,l,c,u,h){fw.fromBufferAttribute(r,c),gw.fromBufferAttribute(r,u),vw.fromBufferAttribute(r,h);const d=t.morphTargetInfluences;if(s&&d){ww.set(0,0,0),Tw.set(0,0,0),Aw.set(0,0,0);for(let t=0,e=s.length;t<e;t++){const e=d[t],n=s[t];0!==e&&(yw.fromBufferAttribute(n,c),xw.fromBufferAttribute(n,u),bw.fromBufferAttribute(n,h),o?(ww.addScaledVector(yw,e),Tw.addScaledVector(xw,e),Aw.addScaledVector(bw,e)):(ww.addScaledVector(yw.sub(fw),e),Tw.addScaledVector(xw.sub(gw),e),Aw.addScaledVector(bw.sub(vw),e)))}fw.add(ww),gw.add(Tw),vw.add(Aw)}t.isSkinnedMesh&&(t.boneTransform(c,fw),t.boneTransform(u,gw),t.boneTransform(h,vw));const p=function(t,e,n,i,r,s,o,a){let l;if(l=1===e.side?i.intersectTriangle(o,s,r,!0,a):i.intersectTriangle(r,s,o,2!==e.side,a),null===l)return null;Nw.copy(a),Nw.applyMatrix4(t.matrixWorld);const c=n.ray.origin.distanceTo(Nw);return c<n.near||c>n.far?null:{distance:c,point:Nw.clone(),object:t}}(t,e,n,i,fw,gw,vw,Cw);if(p){a&&(Ew.fromBufferAttribute(a,c),Mw.fromBufferAttribute(a,u),Sw.fromBufferAttribute(a,h),p.uv=Vb.getUV(Cw,fw,gw,vw,Ew,Mw,Sw,new fx)),l&&(Ew.fromBufferAttribute(l,c),Mw.fromBufferAttribute(l,u),Sw.fromBufferAttribute(l,h),p.uv2=Vb.getUV(Cw,fw,gw,vw,Ew,Mw,Sw,new fx));const t={a:c,b:u,c:h,normal:new Nx,materialIndex:0};Vb.getNormal(fw,gw,vw,t.normal),p.face=t}return p}Lw.prototype.isMesh=!0;class Rw extends dw{constructor(t=1,e=1,n=1,i=1,r=1,s=1){super(),this.type=\\\\\\\"BoxGeometry\\\\\\\",this.parameters={width:t,height:e,depth:n,widthSegments:i,heightSegments:r,depthSegments:s};const o=this;i=Math.floor(i),r=Math.floor(r),s=Math.floor(s);const a=[],l=[],c=[],u=[];let h=0,d=0;function p(t,e,n,i,r,s,p,_,m,f,g){const v=s/m,y=p/f,x=s/2,b=p/2,w=_/2,T=m+1,A=f+1;let E=0,M=0;const S=new Nx;for(let s=0;s<A;s++){const o=s*y-b;for(let a=0;a<T;a++){const h=a*v-x;S[t]=h*i,S[e]=o*r,S[n]=w,l.push(S.x,S.y,S.z),S[t]=0,S[e]=0,S[n]=_>0?1:-1,c.push(S.x,S.y,S.z),u.push(a/m),u.push(1-s/f),E+=1}}for(let t=0;t<f;t++)for(let e=0;e<m;e++){const n=h+e+T*t,i=h+e+T*(t+1),r=h+(e+1)+T*(t+1),s=h+(e+1)+T*t;a.push(n,i,s),a.push(i,r,s),M+=6}o.addGroup(d,M,g),d+=M,h+=E}p(\\\\\\\"z\\\\\\\",\\\\\\\"y\\\\\\\",\\\\\\\"x\\\\\\\",-1,-1,n,e,t,s,r,0),p(\\\\\\\"z\\\\\\\",\\\\\\\"y\\\\\\\",\\\\\\\"x\\\\\\\",1,-1,n,e,-t,s,r,1),p(\\\\\\\"x\\\\\\\",\\\\\\\"z\\\\\\\",\\\\\\\"y\\\\\\\",1,1,t,n,e,i,s,2),p(\\\\\\\"x\\\\\\\",\\\\\\\"z\\\\\\\",\\\\\\\"y\\\\\\\",1,-1,t,n,-e,i,s,3),p(\\\\\\\"x\\\\\\\",\\\\\\\"y\\\\\\\",\\\\\\\"z\\\\\\\",1,-1,t,e,n,i,r,4),p(\\\\\\\"x\\\\\\\",\\\\\\\"y\\\\\\\",\\\\\\\"z\\\\\\\",-1,-1,t,e,-n,i,r,5),this.setIndex(a),this.setAttribute(\\\\\\\"position\\\\\\\",new rw(l,3)),this.setAttribute(\\\\\\\"normal\\\\\\\",new rw(c,3)),this.setAttribute(\\\\\\\"uv\\\\\\\",new rw(u,2))}static fromJSON(t){return new Rw(t.width,t.height,t.depth,t.widthSegments,t.heightSegments,t.depthSegments)}}function Pw(t){const e={};for(const n in t){e[n]={};for(const i in t[n]){const r=t[n][i];r&&(r.isColor||r.isMatrix3||r.isMatrix4||r.isVector2||r.isVector3||r.isVector4||r.isTexture||r.isQuaternion)?e[n][i]=r.clone():Array.isArray(r)?e[n][i]=r.slice():e[n][i]=r}}return e}function Iw(t){const e={};for(let n=0;n<t.length;n++){const i=Pw(t[n]);for(const t in i)e[t]=i[t]}return e}const Fw={clone:Pw,merge:Iw};class Dw extends jb{constructor(t){super(),this.type=\\\\\\\"ShaderMaterial\\\\\\\",this.defines={},this.uniforms={},this.vertexShader=\\\\\\\"void main() {\\\\n\\\\tgl_Position = projectionMatrix * modelViewMatrix * vec4( position, 1.0 );\\\\n}\\\\\\\",this.fragmentShader=\\\\\\\"void main() {\\\\n\\\\tgl_FragColor = vec4( 1.0, 0.0, 0.0, 1.0 );\\\\n}\\\\\\\",this.linewidth=1,this.wireframe=!1,this.wireframeLinewidth=1,this.fog=!1,this.lights=!1,this.clipping=!1,this.extensions={derivatives:!1,fragDepth:!1,drawBuffers:!1,shaderTextureLOD:!1},this.defaultAttributeValues={color:[1,1,1],uv:[0,0],uv2:[0,0]},this.index0AttributeName=void 0,this.uniformsNeedUpdate=!1,this.glslVersion=null,void 0!==t&&(void 0!==t.attributes&&console.error(\\\\\\\"THREE.ShaderMaterial: attributes should now be defined in THREE.BufferGeometry instead.\\\\\\\"),this.setValues(t))}copy(t){return super.copy(t),this.fragmentShader=t.fragmentShader,this.vertexShader=t.vertexShader,this.uniforms=Pw(t.uniforms),this.defines=Object.assign({},t.defines),this.wireframe=t.wireframe,this.wireframeLinewidth=t.wireframeLinewidth,this.lights=t.lights,this.clipping=t.clipping,this.extensions=Object.assign({},t.extensions),this.glslVersion=t.glslVersion,this}toJSON(t){const e=super.toJSON(t);e.glslVersion=this.glslVersion,e.uniforms={};for(const n in this.uniforms){const i=this.uniforms[n].value;i&&i.isTexture?e.uniforms[n]={type:\\\\\\\"t\\\\\\\",value:i.toJSON(t).uuid}:i&&i.isColor?e.uniforms[n]={type:\\\\\\\"c\\\\\\\",value:i.getHex()}:i&&i.isVector2?e.uniforms[n]={type:\\\\\\\"v2\\\\\\\",value:i.toArray()}:i&&i.isVector3?e.uniforms[n]={type:\\\\\\\"v3\\\\\\\",value:i.toArray()}:i&&i.isVector4?e.uniforms[n]={type:\\\\\\\"v4\\\\\\\",value:i.toArray()}:i&&i.isMatrix3?e.uniforms[n]={type:\\\\\\\"m3\\\\\\\",value:i.toArray()}:i&&i.isMatrix4?e.uniforms[n]={type:\\\\\\\"m4\\\\\\\",value:i.toArray()}:e.uniforms[n]={value:i}}Object.keys(this.defines).length>0&&(e.defines=this.defines),e.vertexShader=this.vertexShader,e.fragmentShader=this.fragmentShader;const n={};for(const t in this.extensions)!0===this.extensions[t]&&(n[t]=!0);return Object.keys(n).length>0&&(e.extensions=n),e}}Dw.prototype.isShaderMaterial=!0;class kw extends Ob{constructor(){super(),this.type=\\\\\\\"Camera\\\\\\\",this.matrixWorldInverse=new ob,this.projectionMatrix=new ob,this.projectionMatrixInverse=new ob}copy(t,e){return super.copy(t,e),this.matrixWorldInverse.copy(t.matrixWorldInverse),this.projectionMatrix.copy(t.projectionMatrix),this.projectionMatrixInverse.copy(t.projectionMatrixInverse),this}getWorldDirection(t){this.updateWorldMatrix(!0,!1);const e=this.matrixWorld.elements;return t.set(-e[8],-e[9],-e[10]).normalize()}updateMatrixWorld(t){super.updateMatrixWorld(t),this.matrixWorldInverse.copy(this.matrixWorld).invert()}updateWorldMatrix(t,e){super.updateWorldMatrix(t,e),this.matrixWorldInverse.copy(this.matrixWorld).invert()}clone(){return(new this.constructor).copy(this)}}kw.prototype.isCamera=!0;class Bw extends kw{constructor(t=50,e=1,n=.1,i=2e3){super(),this.type=\\\\\\\"PerspectiveCamera\\\\\\\",this.fov=t,this.zoom=1,this.near=n,this.far=i,this.focus=10,this.aspect=e,this.view=null,this.filmGauge=35,this.filmOffset=0,this.updateProjectionMatrix()}copy(t,e){return super.copy(t,e),this.fov=t.fov,this.zoom=t.zoom,this.near=t.near,this.far=t.far,this.focus=t.focus,this.aspect=t.aspect,this.view=null===t.view?null:Object.assign({},t.view),this.filmGauge=t.filmGauge,this.filmOffset=t.filmOffset,this}setFocalLength(t){const e=.5*this.getFilmHeight()/t;this.fov=2*sx*Math.atan(e),this.updateProjectionMatrix()}getFocalLength(){const t=Math.tan(.5*rx*this.fov);return.5*this.getFilmHeight()/t}getEffectiveFOV(){return 2*sx*Math.atan(Math.tan(.5*rx*this.fov)/this.zoom)}getFilmWidth(){return this.filmGauge*Math.min(this.aspect,1)}getFilmHeight(){return this.filmGauge/Math.max(this.aspect,1)}setViewOffset(t,e,n,i,r,s){this.aspect=t/e,null===this.view&&(this.view={enabled:!0,fullWidth:1,fullHeight:1,offsetX:0,offsetY:0,width:1,height:1}),this.view.enabled=!0,this.view.fullWidth=t,this.view.fullHeight=e,this.view.offsetX=n,this.view.offsetY=i,this.view.width=r,this.view.height=s,this.updateProjectionMatrix()}clearViewOffset(){null!==this.view&&(this.view.enabled=!1),this.updateProjectionMatrix()}updateProjectionMatrix(){const t=this.near;let e=t*Math.tan(.5*rx*this.fov)/this.zoom,n=2*e,i=this.aspect*n,r=-.5*i;const s=this.view;if(null!==this.view&&this.view.enabled){const t=s.fullWidth,o=s.fullHeight;r+=s.offsetX*i/t,e-=s.offsetY*n/o,i*=s.width/t,n*=s.height/o}const o=this.filmOffset;0!==o&&(r+=t*o/this.getFilmWidth()),this.projectionMatrix.makePerspective(r,r+i,e,e-n,t,this.far),this.projectionMatrixInverse.copy(this.projectionMatrix).invert()}toJSON(t){const e=super.toJSON(t);return e.object.fov=this.fov,e.object.zoom=this.zoom,e.object.near=this.near,e.object.far=this.far,e.object.focus=this.focus,e.object.aspect=this.aspect,null!==this.view&&(e.object.view=Object.assign({},this.view)),e.object.filmGauge=this.filmGauge,e.object.filmOffset=this.filmOffset,e}}Bw.prototype.isPerspectiveCamera=!0;const zw=90;class Uw extends Ob{constructor(t,e,n){if(super(),this.type=\\\\\\\"CubeCamera\\\\\\\",!0!==n.isWebGLCubeRenderTarget)return void console.error(\\\\\\\"THREE.CubeCamera: The constructor now expects an instance of WebGLCubeRenderTarget as third parameter.\\\\\\\");this.renderTarget=n;const i=new Bw(zw,1,t,e);i.layers=this.layers,i.up.set(0,-1,0),i.lookAt(new Nx(1,0,0)),this.add(i);const r=new Bw(zw,1,t,e);r.layers=this.layers,r.up.set(0,-1,0),r.lookAt(new Nx(-1,0,0)),this.add(r);const s=new Bw(zw,1,t,e);s.layers=this.layers,s.up.set(0,0,1),s.lookAt(new Nx(0,1,0)),this.add(s);const o=new Bw(zw,1,t,e);o.layers=this.layers,o.up.set(0,0,-1),o.lookAt(new Nx(0,-1,0)),this.add(o);const a=new Bw(zw,1,t,e);a.layers=this.layers,a.up.set(0,-1,0),a.lookAt(new Nx(0,0,1)),this.add(a);const l=new Bw(zw,1,t,e);l.layers=this.layers,l.up.set(0,-1,0),l.lookAt(new Nx(0,0,-1)),this.add(l)}update(t,e){null===this.parent&&this.updateMatrixWorld();const n=this.renderTarget,[i,r,s,o,a,l]=this.children,c=t.xr.enabled,u=t.getRenderTarget();t.xr.enabled=!1;const h=n.texture.generateMipmaps;n.texture.generateMipmaps=!1,t.setRenderTarget(n,0),t.render(e,i),t.setRenderTarget(n,1),t.render(e,r),t.setRenderTarget(n,2),t.render(e,s),t.setRenderTarget(n,3),t.render(e,o),t.setRenderTarget(n,4),t.render(e,a),n.texture.generateMipmaps=h,t.setRenderTarget(n,5),t.render(e,l),t.setRenderTarget(u),t.xr.enabled=c}}class Gw extends Tx{constructor(t,e,n,i,r,s,o,a,l,c){super(t=void 0!==t?t:[],e=void 0!==e?e:fy,n,i,r,s,o,a,l,c),this.flipY=!1}get images(){return this.image}set images(t){this.image=t}}Gw.prototype.isCubeTexture=!0;class Vw extends Mx{constructor(t,e,n){Number.isInteger(e)&&(console.warn(\\\\\\\"THREE.WebGLCubeRenderTarget: constructor signature is now WebGLCubeRenderTarget( size, options )\\\\\\\"),e=n),super(t,t,e),e=e||{},this.texture=new Gw(void 0,e.mapping,e.wrapS,e.wrapT,e.magFilter,e.minFilter,e.format,e.type,e.anisotropy,e.encoding),this.texture.isRenderTargetTexture=!0,this.texture.generateMipmaps=void 0!==e.generateMipmaps&&e.generateMipmaps,this.texture.minFilter=void 0!==e.minFilter?e.minFilter:Cy,this.texture._needsFlipEnvMap=!1}fromEquirectangularTexture(t,e){this.texture.type=e.type,this.texture.format=By,this.texture.encoding=e.encoding,this.texture.generateMipmaps=e.generateMipmaps,this.texture.minFilter=e.minFilter,this.texture.magFilter=e.magFilter;const n={uniforms:{tEquirect:{value:null}},vertexShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tvarying vec3 vWorldDirection;\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tvec3 transformDirection( in vec3 dir, in mat4 matrix ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\t\\\\treturn normalize( ( matrix * vec4( dir, 0.0 ) ).xyz );\\\\n\\\\n\\\\t\\\\t\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tvWorldDirection = transformDirection( position, modelMatrix );\\\\n\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t#include <begin_vertex>\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t#include <project_vertex>\\\\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 <common>\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tvec3 direction = normalize( vWorldDirection );\\\\n\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tvec2 sampleUV = equirectUv( direction );\\\\n\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tgl_FragColor = texture2D( tEquirect, sampleUV );\\\\n\\\\n\\\\t\\\\t\\\\t\\\\t}\\\\n\\\\t\\\\t\\\\t\\\\\\\"},i=new Rw(5,5,5),r=new Dw({name:\\\\\\\"CubemapFromEquirect\\\\\\\",uniforms:Pw(n.uniforms),vertexShader:n.vertexShader,fragmentShader:n.fragmentShader,side:1,blending:0});r.uniforms.tEquirect.value=e;const s=new Lw(i,r),o=e.minFilter;e.minFilter===Ly&&(e.minFilter=Cy);return new Uw(1,10,this).update(t,s),e.minFilter=o,s.geometry.dispose(),s.material.dispose(),this}clear(t,e,n,i){const r=t.getRenderTarget();for(let r=0;r<6;r++)t.setRenderTarget(this,r),t.clear(e,n,i);t.setRenderTarget(r)}}Vw.prototype.isWebGLCubeRenderTarget=!0;const Hw=new Nx,jw=new Nx,Ww=new gx;class qw{constructor(t=new Nx(1,0,0),e=0){this.normal=t,this.constant=e}set(t,e){return this.normal.copy(t),this.constant=e,this}setComponents(t,e,n,i){return this.normal.set(t,e,n),this.constant=i,this}setFromNormalAndCoplanarPoint(t,e){return this.normal.copy(t),this.constant=-e.dot(this.normal),this}setFromCoplanarPoints(t,e,n){const i=Hw.subVectors(n,e).cross(jw.subVectors(t,e)).normalize();return this.setFromNormalAndCoplanarPoint(i,t),this}copy(t){return this.normal.copy(t.normal),this.constant=t.constant,this}normalize(){const t=1/this.normal.length();return this.normal.multiplyScalar(t),this.constant*=t,this}negate(){return this.constant*=-1,this.normal.negate(),this}distanceToPoint(t){return this.normal.dot(t)+this.constant}distanceToSphere(t){return this.distanceToPoint(t.center)-t.radius}projectPoint(t,e){return e.copy(this.normal).multiplyScalar(-this.distanceToPoint(t)).add(t)}intersectLine(t,e){const n=t.delta(Hw),i=this.normal.dot(n);if(0===i)return 0===this.distanceToPoint(t.start)?e.copy(t.start):null;const r=-(t.start.dot(this.normal)+this.constant)/i;return r<0||r>1?null:e.copy(n).multiplyScalar(r).add(t.start)}intersectsLine(t){const e=this.distanceToPoint(t.start),n=this.distanceToPoint(t.end);return e<0&&n>0||n<0&&e>0}intersectsBox(t){return t.intersectsPlane(this)}intersectsSphere(t){return t.intersectsPlane(this)}coplanarPoint(t){return t.copy(this.normal).multiplyScalar(-this.constant)}applyMatrix4(t,e){const n=e||Ww.getNormalMatrix(t),i=this.coplanarPoint(Hw).applyMatrix4(t),r=this.normal.applyMatrix3(n).normalize();return this.constant=-i.dot(r),this}translate(t){return this.constant-=t.dot(this.normal),this}equals(t){return t.normal.equals(this.normal)&&t.constant===this.constant}clone(){return(new this.constructor).copy(this)}}qw.prototype.isPlane=!0;const Xw=new Zx,Yw=new Nx;class $w{constructor(t=new qw,e=new qw,n=new qw,i=new qw,r=new qw,s=new qw){this.planes=[t,e,n,i,r,s]}set(t,e,n,i,r,s){const o=this.planes;return o[0].copy(t),o[1].copy(e),o[2].copy(n),o[3].copy(i),o[4].copy(r),o[5].copy(s),this}copy(t){const e=this.planes;for(let n=0;n<6;n++)e[n].copy(t.planes[n]);return this}setFromProjectionMatrix(t){const e=this.planes,n=t.elements,i=n[0],r=n[1],s=n[2],o=n[3],a=n[4],l=n[5],c=n[6],u=n[7],h=n[8],d=n[9],p=n[10],_=n[11],m=n[12],f=n[13],g=n[14],v=n[15];return e[0].setComponents(o-i,u-a,_-h,v-m).normalize(),e[1].setComponents(o+i,u+a,_+h,v+m).normalize(),e[2].setComponents(o+r,u+l,_+d,v+f).normalize(),e[3].setComponents(o-r,u-l,_-d,v-f).normalize(),e[4].setComponents(o-s,u-c,_-p,v-g).normalize(),e[5].setComponents(o+s,u+c,_+p,v+g).normalize(),this}intersectsObject(t){const e=t.geometry;return null===e.boundingSphere&&e.computeBoundingSphere(),Xw.copy(e.boundingSphere).applyMatrix4(t.matrixWorld),this.intersectsSphere(Xw)}intersectsSprite(t){return Xw.center.set(0,0,0),Xw.radius=.7071067811865476,Xw.applyMatrix4(t.matrixWorld),this.intersectsSphere(Xw)}intersectsSphere(t){const e=this.planes,n=t.center,i=-t.radius;for(let t=0;t<6;t++){if(e[t].distanceToPoint(n)<i)return!1}return!0}intersectsBox(t){const e=this.planes;for(let n=0;n<6;n++){const i=e[n];if(Yw.x=i.normal.x>0?t.max.x:t.min.x,Yw.y=i.normal.y>0?t.max.y:t.min.y,Yw.z=i.normal.z>0?t.max.z:t.min.z,i.distanceToPoint(Yw)<0)return!1}return!0}containsPoint(t){const e=this.planes;for(let n=0;n<6;n++)if(e[n].distanceToPoint(t)<0)return!1;return!0}clone(){return(new this.constructor).copy(this)}}function Jw(){let t=null,e=!1,n=null,i=null;function r(e,s){n(e,s),i=t.requestAnimationFrame(r)}return{start:function(){!0!==e&&null!==n&&(i=t.requestAnimationFrame(r),e=!0)},stop:function(){t.cancelAnimationFrame(i),e=!1},setAnimationLoop:function(t){n=t},setContext:function(e){t=e}}}function Zw(t,e){const n=e.isWebGL2,i=new WeakMap;return{get:function(t){return t.isInterleavedBufferAttribute&&(t=t.data),i.get(t)},remove:function(e){e.isInterleavedBufferAttribute&&(e=e.data);const n=i.get(e);n&&(t.deleteBuffer(n.buffer),i.delete(e))},update:function(e,r){if(e.isGLBufferAttribute){const t=i.get(e);return void((!t||t.version<e.version)&&i.set(e,{buffer:e.buffer,type:e.type,bytesPerElement:e.elementSize,version:e.version}))}e.isInterleavedBufferAttribute&&(e=e.data);const s=i.get(e);void 0===s?i.set(e,function(e,i){const r=e.array,s=e.usage,o=t.createBuffer();t.bindBuffer(i,o),t.bufferData(i,r,s),e.onUploadCallback();let a=5126;return r instanceof Float32Array?a=5126:r instanceof Float64Array?console.warn(\\\\\\\"THREE.WebGLAttributes: Unsupported data buffer format: Float64Array.\\\\\\\"):r instanceof Uint16Array?e.isFloat16BufferAttribute?n?a=5131:console.warn(\\\\\\\"THREE.WebGLAttributes: Usage of Float16BufferAttribute requires WebGL2.\\\\\\\"):a=5123:r instanceof Int16Array?a=5122:r instanceof Uint32Array?a=5125:r instanceof Int32Array?a=5124:r instanceof Int8Array?a=5120:(r instanceof Uint8Array||r instanceof Uint8ClampedArray)&&(a=5121),{buffer:o,type:a,bytesPerElement:r.BYTES_PER_ELEMENT,version:e.version}}(e,r)):s.version<e.version&&(!function(e,i,r){const s=i.array,o=i.updateRange;t.bindBuffer(r,e),-1===o.count?t.bufferSubData(r,0,s):(n?t.bufferSubData(r,o.offset*s.BYTES_PER_ELEMENT,s,o.offset,o.count):t.bufferSubData(r,o.offset*s.BYTES_PER_ELEMENT,s.subarray(o.offset,o.offset+o.count)),o.count=-1)}(s.buffer,e,r),s.version=e.version)}}}class Qw extends dw{constructor(t=1,e=1,n=1,i=1){super(),this.type=\\\\\\\"PlaneGeometry\\\\\\\",this.parameters={width:t,height:e,widthSegments:n,heightSegments:i};const r=t/2,s=e/2,o=Math.floor(n),a=Math.floor(i),l=o+1,c=a+1,u=t/o,h=e/a,d=[],p=[],_=[],m=[];for(let t=0;t<c;t++){const e=t*h-s;for(let n=0;n<l;n++){const i=n*u-r;p.push(i,-e,0),_.push(0,0,1),m.push(n/o),m.push(1-t/a)}}for(let t=0;t<a;t++)for(let e=0;e<o;e++){const n=e+l*t,i=e+l*(t+1),r=e+1+l*(t+1),s=e+1+l*t;d.push(n,i,s),d.push(i,r,s)}this.setIndex(d),this.setAttribute(\\\\\\\"position\\\\\\\",new rw(p,3)),this.setAttribute(\\\\\\\"normal\\\\\\\",new rw(_,3)),this.setAttribute(\\\\\\\"uv\\\\\\\",new rw(m,2))}static fromJSON(t){return new Qw(t.width,t.height,t.widthSegments,t.heightSegments)}}const Kw={alphamap_fragment:\\\\\\\"#ifdef USE_ALPHAMAP\\\\n\\\\tdiffuseColor.a *= texture2D( alphaMap, vUv ).g;\\\\n#endif\\\\\\\",alphamap_pars_fragment:\\\\\\\"#ifdef USE_ALPHAMAP\\\\n\\\\tuniform sampler2D alphaMap;\\\\n#endif\\\\\\\",alphatest_fragment:\\\\\\\"#ifdef USE_ALPHATEST\\\\n\\\\tif ( diffuseColor.a < alphaTest ) discard;\\\\n#endif\\\\\\\",alphatest_pars_fragment:\\\\\\\"#ifdef USE_ALPHATEST\\\\n\\\\tuniform float alphaTest;\\\\n#endif\\\\\\\",aomap_fragment:\\\\\\\"#ifdef USE_AOMAP\\\\n\\\\tfloat ambientOcclusion = ( texture2D( aoMap, vUv2 ).r - 1.0 ) * aoMapIntensity + 1.0;\\\\n\\\\treflectedLight.indirectDiffuse *= ambientOcclusion;\\\\n\\\\t#if defined( USE_ENVMAP ) && defined( STANDARD )\\\\n\\\\t\\\\tfloat dotNV = saturate( dot( geometry.normal, geometry.viewDir ) );\\\\n\\\\t\\\\treflectedLight.indirectSpecular *= computeSpecularOcclusion( dotNV, ambientOcclusion, material.roughness );\\\\n\\\\t#endif\\\\n#endif\\\\\\\",aomap_pars_fragment:\\\\\\\"#ifdef USE_AOMAP\\\\n\\\\tuniform sampler2D aoMap;\\\\n\\\\tuniform float aoMapIntensity;\\\\n#endif\\\\\\\",begin_vertex:\\\\\\\"vec3 transformed = vec3( position );\\\\\\\",beginnormal_vertex:\\\\\\\"vec3 objectNormal = vec3( normal );\\\\n#ifdef USE_TANGENT\\\\n\\\\tvec3 objectTangent = vec3( tangent.xyz );\\\\n#endif\\\\\\\",bsdfs:\\\\\\\"vec3 BRDF_Lambert( const in vec3 diffuseColor ) {\\\\n\\\\treturn RECIPROCAL_PI * diffuseColor;\\\\n}\\\\nvec3 F_Schlick( const in vec3 f0, const in float f90, const in float dotVH ) {\\\\n\\\\tfloat fresnel = exp2( ( - 5.55473 * dotVH - 6.98316 ) * dotVH );\\\\n\\\\treturn f0 * ( 1.0 - fresnel ) + ( f90 * fresnel );\\\\n}\\\\nfloat V_GGX_SmithCorrelated( const in float alpha, const in float dotNL, const in float dotNV ) {\\\\n\\\\tfloat a2 = pow2( alpha );\\\\n\\\\tfloat gv = dotNL * sqrt( a2 + ( 1.0 - a2 ) * pow2( dotNV ) );\\\\n\\\\tfloat gl = dotNV * sqrt( a2 + ( 1.0 - a2 ) * pow2( dotNL ) );\\\\n\\\\treturn 0.5 / max( gv + gl, EPSILON );\\\\n}\\\\nfloat D_GGX( const in float alpha, const in float dotNH ) {\\\\n\\\\tfloat a2 = pow2( alpha );\\\\n\\\\tfloat denom = pow2( dotNH ) * ( a2 - 1.0 ) + 1.0;\\\\n\\\\treturn RECIPROCAL_PI * a2 / pow2( denom );\\\\n}\\\\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\\\\tfloat alpha = pow2( roughness );\\\\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 dotVH = saturate( dot( viewDir, halfDir ) );\\\\n\\\\tvec3 F = F_Schlick( f0, f90, dotVH );\\\\n\\\\tfloat V = V_GGX_SmithCorrelated( alpha, dotNL, dotNV );\\\\n\\\\tfloat D = D_GGX( alpha, dotNH );\\\\n\\\\treturn F * ( V * D );\\\\n}\\\\nvec2 LTC_Uv( const in vec3 N, const in vec3 V, const in float roughness ) {\\\\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\\\\tfloat dotNV = saturate( dot( N, V ) );\\\\n\\\\tvec2 uv = vec2( roughness, sqrt( 1.0 - dotNV ) );\\\\n\\\\tuv = uv * LUT_SCALE + LUT_BIAS;\\\\n\\\\treturn uv;\\\\n}\\\\nfloat LTC_ClippedSphereFormFactor( const in vec3 f ) {\\\\n\\\\tfloat l = length( f );\\\\n\\\\treturn max( ( l * l + f.z ) / ( l + 1.0 ), 0.0 );\\\\n}\\\\nvec3 LTC_EdgeVectorFormFactor( const in vec3 v1, const in vec3 v2 ) {\\\\n\\\\tfloat x = dot( v1, v2 );\\\\n\\\\tfloat y = abs( x );\\\\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\\\\tfloat theta_sintheta = ( x > 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 <tonemapping_fragment>\\\\n\\\\t#include <encodings_fragment>\\\\n}\\\\\\\",cube_vert:\\\\\\\"varying vec3 vWorldDirection;\\\\n#include <common>\\\\nvoid main() {\\\\n\\\\tvWorldDirection = transformDirection( position, modelMatrix );\\\\n\\\\t#include <begin_vertex>\\\\n\\\\t#include <project_vertex>\\\\n\\\\tgl_Position.z = gl_Position.w;\\\\n}\\\\\\\",cube_frag:\\\\\\\"#include <envmap_common_pars_fragment>\\\\nuniform float opacity;\\\\nvarying vec3 vWorldDirection;\\\\n#include <cube_uv_reflection_fragment>\\\\nvoid main() {\\\\n\\\\tvec3 vReflect = vWorldDirection;\\\\n\\\\t#include <envmap_fragment>\\\\n\\\\tgl_FragColor = envColor;\\\\n\\\\tgl_FragColor.a *= opacity;\\\\n\\\\t#include <tonemapping_fragment>\\\\n\\\\t#include <encodings_fragment>\\\\n}\\\\\\\",depth_vert:\\\\\\\"#include <common>\\\\n#include <uv_pars_vertex>\\\\n#include <displacementmap_pars_vertex>\\\\n#include <morphtarget_pars_vertex>\\\\n#include <skinning_pars_vertex>\\\\n#include <logdepthbuf_pars_vertex>\\\\n#include <clipping_planes_pars_vertex>\\\\nvarying vec2 vHighPrecisionZW;\\\\nvoid main() {\\\\n\\\\t#include <uv_vertex>\\\\n\\\\t#include <skinbase_vertex>\\\\n\\\\t#ifdef USE_DISPLACEMENTMAP\\\\n\\\\t\\\\t#include <beginnormal_vertex>\\\\n\\\\t\\\\t#include <morphnormal_vertex>\\\\n\\\\t\\\\t#include <skinnormal_vertex>\\\\n\\\\t#endif\\\\n\\\\t#include <begin_vertex>\\\\n\\\\t#include <morphtarget_vertex>\\\\n\\\\t#include <skinning_vertex>\\\\n\\\\t#include <displacementmap_vertex>\\\\n\\\\t#include <project_vertex>\\\\n\\\\t#include <logdepthbuf_vertex>\\\\n\\\\t#include <clipping_planes_vertex>\\\\n\\\\tvHighPrecisionZW = gl_Position.zw;\\\\n}\\\\\\\",depth_frag:\\\\\\\"#if DEPTH_PACKING == 3200\\\\n\\\\tuniform float opacity;\\\\n#endif\\\\n#include <common>\\\\n#include <packing>\\\\n#include <uv_pars_fragment>\\\\n#include <map_pars_fragment>\\\\n#include <alphamap_pars_fragment>\\\\n#include <alphatest_pars_fragment>\\\\n#include <logdepthbuf_pars_fragment>\\\\n#include <clipping_planes_pars_fragment>\\\\nvarying vec2 vHighPrecisionZW;\\\\nvoid main() {\\\\n\\\\t#include <clipping_planes_fragment>\\\\n\\\\tvec4 diffuseColor = vec4( 1.0 );\\\\n\\\\t#if DEPTH_PACKING == 3200\\\\n\\\\t\\\\tdiffuseColor.a = opacity;\\\\n\\\\t#endif\\\\n\\\\t#include <map_fragment>\\\\n\\\\t#include <alphamap_fragment>\\\\n\\\\t#include <alphatest_fragment>\\\\n\\\\t#include <logdepthbuf_fragment>\\\\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 <common>\\\\n#include <uv_pars_vertex>\\\\n#include <displacementmap_pars_vertex>\\\\n#include <morphtarget_pars_vertex>\\\\n#include <skinning_pars_vertex>\\\\n#include <clipping_planes_pars_vertex>\\\\nvoid main() {\\\\n\\\\t#include <uv_vertex>\\\\n\\\\t#include <skinbase_vertex>\\\\n\\\\t#ifdef USE_DISPLACEMENTMAP\\\\n\\\\t\\\\t#include <beginnormal_vertex>\\\\n\\\\t\\\\t#include <morphnormal_vertex>\\\\n\\\\t\\\\t#include <skinnormal_vertex>\\\\n\\\\t#endif\\\\n\\\\t#include <begin_vertex>\\\\n\\\\t#include <morphtarget_vertex>\\\\n\\\\t#include <skinning_vertex>\\\\n\\\\t#include <displacementmap_vertex>\\\\n\\\\t#include <project_vertex>\\\\n\\\\t#include <worldpos_vertex>\\\\n\\\\t#include <clipping_planes_vertex>\\\\n\\\\tvWorldPosition = worldPosition.xyz;\\\\n}\\\\\\\",distanceRGBA_frag:\\\\\\\"#define DISTANCE\\\\nuniform vec3 referencePosition;\\\\nuniform float nearDistance;\\\\nuniform float farDistance;\\\\nvarying vec3 vWorldPosition;\\\\n#include <common>\\\\n#include <packing>\\\\n#include <uv_pars_fragment>\\\\n#include <map_pars_fragment>\\\\n#include <alphamap_pars_fragment>\\\\n#include <alphatest_pars_fragment>\\\\n#include <clipping_planes_pars_fragment>\\\\nvoid main () {\\\\n\\\\t#include <clipping_planes_fragment>\\\\n\\\\tvec4 diffuseColor = vec4( 1.0 );\\\\n\\\\t#include <map_fragment>\\\\n\\\\t#include <alphamap_fragment>\\\\n\\\\t#include <alphatest_fragment>\\\\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 <common>\\\\nvoid main() {\\\\n\\\\tvWorldDirection = transformDirection( position, modelMatrix );\\\\n\\\\t#include <begin_vertex>\\\\n\\\\t#include <project_vertex>\\\\n}\\\\\\\",equirect_frag:\\\\\\\"uniform sampler2D tEquirect;\\\\nvarying vec3 vWorldDirection;\\\\n#include <common>\\\\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 <tonemapping_fragment>\\\\n\\\\t#include <encodings_fragment>\\\\n}\\\\\\\",linedashed_vert:\\\\\\\"uniform float scale;\\\\nattribute float lineDistance;\\\\nvarying float vLineDistance;\\\\n#include <common>\\\\n#include <color_pars_vertex>\\\\n#include <fog_pars_vertex>\\\\n#include <morphtarget_pars_vertex>\\\\n#include <logdepthbuf_pars_vertex>\\\\n#include <clipping_planes_pars_vertex>\\\\nvoid main() {\\\\n\\\\tvLineDistance = scale * lineDistance;\\\\n\\\\t#include <color_vertex>\\\\n\\\\t#include <begin_vertex>\\\\n\\\\t#include <morphtarget_vertex>\\\\n\\\\t#include <project_vertex>\\\\n\\\\t#include <logdepthbuf_vertex>\\\\n\\\\t#include <clipping_planes_vertex>\\\\n\\\\t#include <fog_vertex>\\\\n}\\\\\\\",linedashed_frag:\\\\\\\"uniform vec3 diffuse;\\\\nuniform float opacity;\\\\nuniform float dashSize;\\\\nuniform float totalSize;\\\\nvarying float vLineDistance;\\\\n#include <common>\\\\n#include <color_pars_fragment>\\\\n#include <fog_pars_fragment>\\\\n#include <logdepthbuf_pars_fragment>\\\\n#include <clipping_planes_pars_fragment>\\\\nvoid main() {\\\\n\\\\t#include <clipping_planes_fragment>\\\\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 <logdepthbuf_fragment>\\\\n\\\\t#include <color_fragment>\\\\n\\\\toutgoingLight = diffuseColor.rgb;\\\\n\\\\t#include <output_fragment>\\\\n\\\\t#include <tonemapping_fragment>\\\\n\\\\t#include <encodings_fragment>\\\\n\\\\t#include <fog_fragment>\\\\n\\\\t#include <premultiplied_alpha_fragment>\\\\n}\\\\\\\",meshbasic_vert:\\\\\\\"#include <common>\\\\n#include <uv_pars_vertex>\\\\n#include <uv2_pars_vertex>\\\\n#include <envmap_pars_vertex>\\\\n#include <color_pars_vertex>\\\\n#include <fog_pars_vertex>\\\\n#include <morphtarget_pars_vertex>\\\\n#include <skinning_pars_vertex>\\\\n#include <logdepthbuf_pars_vertex>\\\\n#include <clipping_planes_pars_vertex>\\\\nvoid main() {\\\\n\\\\t#include <uv_vertex>\\\\n\\\\t#include <uv2_vertex>\\\\n\\\\t#include <color_vertex>\\\\n\\\\t#if defined ( USE_ENVMAP ) || defined ( USE_SKINNING )\\\\n\\\\t\\\\t#include <beginnormal_vertex>\\\\n\\\\t\\\\t#include <morphnormal_vertex>\\\\n\\\\t\\\\t#include <skinbase_vertex>\\\\n\\\\t\\\\t#include <skinnormal_vertex>\\\\n\\\\t\\\\t#include <defaultnormal_vertex>\\\\n\\\\t#endif\\\\n\\\\t#include <begin_vertex>\\\\n\\\\t#include <morphtarget_vertex>\\\\n\\\\t#include <skinning_vertex>\\\\n\\\\t#include <project_vertex>\\\\n\\\\t#include <logdepthbuf_vertex>\\\\n\\\\t#include <clipping_planes_vertex>\\\\n\\\\t#include <worldpos_vertex>\\\\n\\\\t#include <envmap_vertex>\\\\n\\\\t#include <fog_vertex>\\\\n}\\\\\\\",meshbasic_frag:\\\\\\\"uniform vec3 diffuse;\\\\nuniform float opacity;\\\\n#ifndef FLAT_SHADED\\\\n\\\\tvarying vec3 vNormal;\\\\n#endif\\\\n#include <common>\\\\n#include <dithering_pars_fragment>\\\\n#include <color_pars_fragment>\\\\n#include <uv_pars_fragment>\\\\n#include <uv2_pars_fragment>\\\\n#include <map_pars_fragment>\\\\n#include <alphamap_pars_fragment>\\\\n#include <alphatest_pars_fragment>\\\\n#include <aomap_pars_fragment>\\\\n#include <lightmap_pars_fragment>\\\\n#include <envmap_common_pars_fragment>\\\\n#include <envmap_pars_fragment>\\\\n#include <cube_uv_reflection_fragment>\\\\n#include <fog_pars_fragment>\\\\n#include <specularmap_pars_fragment>\\\\n#include <logdepthbuf_pars_fragment>\\\\n#include <clipping_planes_pars_fragment>\\\\nvoid main() {\\\\n\\\\t#include <clipping_planes_fragment>\\\\n\\\\tvec4 diffuseColor = vec4( diffuse, opacity );\\\\n\\\\t#include <logdepthbuf_fragment>\\\\n\\\\t#include <map_fragment>\\\\n\\\\t#include <color_fragment>\\\\n\\\\t#include <alphamap_fragment>\\\\n\\\\t#include <alphatest_fragment>\\\\n\\\\t#include <specularmap_fragment>\\\\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 <aomap_fragment>\\\\n\\\\treflectedLight.indirectDiffuse *= diffuseColor.rgb;\\\\n\\\\tvec3 outgoingLight = reflectedLight.indirectDiffuse;\\\\n\\\\t#include <envmap_fragment>\\\\n\\\\t#include <output_fragment>\\\\n\\\\t#include <tonemapping_fragment>\\\\n\\\\t#include <encodings_fragment>\\\\n\\\\t#include <fog_fragment>\\\\n\\\\t#include <premultiplied_alpha_fragment>\\\\n\\\\t#include <dithering_fragment>\\\\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 <common>\\\\n#include <uv_pars_vertex>\\\\n#include <uv2_pars_vertex>\\\\n#include <envmap_pars_vertex>\\\\n#include <bsdfs>\\\\n#include <lights_pars_begin>\\\\n#include <color_pars_vertex>\\\\n#include <fog_pars_vertex>\\\\n#include <morphtarget_pars_vertex>\\\\n#include <skinning_pars_vertex>\\\\n#include <shadowmap_pars_vertex>\\\\n#include <logdepthbuf_pars_vertex>\\\\n#include <clipping_planes_pars_vertex>\\\\nvoid main() {\\\\n\\\\t#include <uv_vertex>\\\\n\\\\t#include <uv2_vertex>\\\\n\\\\t#include <color_vertex>\\\\n\\\\t#include <beginnormal_vertex>\\\\n\\\\t#include <morphnormal_vertex>\\\\n\\\\t#include <skinbase_vertex>\\\\n\\\\t#include <skinnormal_vertex>\\\\n\\\\t#include <defaultnormal_vertex>\\\\n\\\\t#include <begin_vertex>\\\\n\\\\t#include <morphtarget_vertex>\\\\n\\\\t#include <skinning_vertex>\\\\n\\\\t#include <project_vertex>\\\\n\\\\t#include <logdepthbuf_vertex>\\\\n\\\\t#include <clipping_planes_vertex>\\\\n\\\\t#include <worldpos_vertex>\\\\n\\\\t#include <envmap_vertex>\\\\n\\\\t#include <lights_lambert_vertex>\\\\n\\\\t#include <shadowmap_vertex>\\\\n\\\\t#include <fog_vertex>\\\\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 <common>\\\\n#include <packing>\\\\n#include <dithering_pars_fragment>\\\\n#include <color_pars_fragment>\\\\n#include <uv_pars_fragment>\\\\n#include <uv2_pars_fragment>\\\\n#include <map_pars_fragment>\\\\n#include <alphamap_pars_fragment>\\\\n#include <alphatest_pars_fragment>\\\\n#include <aomap_pars_fragment>\\\\n#include <lightmap_pars_fragment>\\\\n#include <emissivemap_pars_fragment>\\\\n#include <envmap_common_pars_fragment>\\\\n#include <envmap_pars_fragment>\\\\n#include <cube_uv_reflection_fragment>\\\\n#include <bsdfs>\\\\n#include <lights_pars_begin>\\\\n#include <fog_pars_fragment>\\\\n#include <shadowmap_pars_fragment>\\\\n#include <shadowmask_pars_fragment>\\\\n#include <specularmap_pars_fragment>\\\\n#include <logdepthbuf_pars_fragment>\\\\n#include <clipping_planes_pars_fragment>\\\\nvoid main() {\\\\n\\\\t#include <clipping_planes_fragment>\\\\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 <logdepthbuf_fragment>\\\\n\\\\t#include <map_fragment>\\\\n\\\\t#include <color_fragment>\\\\n\\\\t#include <alphamap_fragment>\\\\n\\\\t#include <alphatest_fragment>\\\\n\\\\t#include <specularmap_fragment>\\\\n\\\\t#include <emissivemap_fragment>\\\\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 <lightmap_fragment>\\\\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 <aomap_fragment>\\\\n\\\\tvec3 outgoingLight = reflectedLight.directDiffuse + reflectedLight.indirectDiffuse + totalEmissiveRadiance;\\\\n\\\\t#include <envmap_fragment>\\\\n\\\\t#include <output_fragment>\\\\n\\\\t#include <tonemapping_fragment>\\\\n\\\\t#include <encodings_fragment>\\\\n\\\\t#include <fog_fragment>\\\\n\\\\t#include <premultiplied_alpha_fragment>\\\\n\\\\t#include <dithering_fragment>\\\\n}\\\\\\\",meshmatcap_vert:\\\\\\\"#define MATCAP\\\\nvarying vec3 vViewPosition;\\\\n#include <common>\\\\n#include <uv_pars_vertex>\\\\n#include <color_pars_vertex>\\\\n#include <displacementmap_pars_vertex>\\\\n#include <fog_pars_vertex>\\\\n#include <normal_pars_vertex>\\\\n#include <morphtarget_pars_vertex>\\\\n#include <skinning_pars_vertex>\\\\n#include <logdepthbuf_pars_vertex>\\\\n#include <clipping_planes_pars_vertex>\\\\nvoid main() {\\\\n\\\\t#include <uv_vertex>\\\\n\\\\t#include <color_vertex>\\\\n\\\\t#include <beginnormal_vertex>\\\\n\\\\t#include <morphnormal_vertex>\\\\n\\\\t#include <skinbase_vertex>\\\\n\\\\t#include <skinnormal_vertex>\\\\n\\\\t#include <defaultnormal_vertex>\\\\n\\\\t#include <normal_vertex>\\\\n\\\\t#include <begin_vertex>\\\\n\\\\t#include <morphtarget_vertex>\\\\n\\\\t#include <skinning_vertex>\\\\n\\\\t#include <displacementmap_vertex>\\\\n\\\\t#include <project_vertex>\\\\n\\\\t#include <logdepthbuf_vertex>\\\\n\\\\t#include <clipping_planes_vertex>\\\\n\\\\t#include <fog_vertex>\\\\n\\\\tvViewPosition = - mvPosition.xyz;\\\\n}\\\\\\\",meshmatcap_frag:\\\\\\\"#define MATCAP\\\\nuniform vec3 diffuse;\\\\nuniform float opacity;\\\\nuniform sampler2D matcap;\\\\nvarying vec3 vViewPosition;\\\\n#include <common>\\\\n#include <dithering_pars_fragment>\\\\n#include <color_pars_fragment>\\\\n#include <uv_pars_fragment>\\\\n#include <map_pars_fragment>\\\\n#include <alphamap_pars_fragment>\\\\n#include <alphatest_pars_fragment>\\\\n#include <fog_pars_fragment>\\\\n#include <normal_pars_fragment>\\\\n#include <bumpmap_pars_fragment>\\\\n#include <normalmap_pars_fragment>\\\\n#include <logdepthbuf_pars_fragment>\\\\n#include <clipping_planes_pars_fragment>\\\\nvoid main() {\\\\n\\\\t#include <clipping_planes_fragment>\\\\n\\\\tvec4 diffuseColor = vec4( diffuse, opacity );\\\\n\\\\t#include <logdepthbuf_fragment>\\\\n\\\\t#include <map_fragment>\\\\n\\\\t#include <color_fragment>\\\\n\\\\t#include <alphamap_fragment>\\\\n\\\\t#include <alphatest_fragment>\\\\n\\\\t#include <normal_fragment_begin>\\\\n\\\\t#include <normal_fragment_maps>\\\\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 <output_fragment>\\\\n\\\\t#include <tonemapping_fragment>\\\\n\\\\t#include <encodings_fragment>\\\\n\\\\t#include <fog_fragment>\\\\n\\\\t#include <premultiplied_alpha_fragment>\\\\n\\\\t#include <dithering_fragment>\\\\n}\\\\\\\",meshnormal_vert:\\\\\\\"#define NORMAL\\\\n#if defined( FLAT_SHADED ) || defined( USE_BUMPMAP ) || defined( TANGENTSPACE_NORMALMAP )\\\\n\\\\tvarying vec3 vViewPosition;\\\\n#endif\\\\n#include <common>\\\\n#include <uv_pars_vertex>\\\\n#include <displacementmap_pars_vertex>\\\\n#include <normal_pars_vertex>\\\\n#include <morphtarget_pars_vertex>\\\\n#include <skinning_pars_vertex>\\\\n#include <logdepthbuf_pars_vertex>\\\\n#include <clipping_planes_pars_vertex>\\\\nvoid main() {\\\\n\\\\t#include <uv_vertex>\\\\n\\\\t#include <beginnormal_vertex>\\\\n\\\\t#include <morphnormal_vertex>\\\\n\\\\t#include <skinbase_vertex>\\\\n\\\\t#include <skinnormal_vertex>\\\\n\\\\t#include <defaultnormal_vertex>\\\\n\\\\t#include <normal_vertex>\\\\n\\\\t#include <begin_vertex>\\\\n\\\\t#include <morphtarget_vertex>\\\\n\\\\t#include <skinning_vertex>\\\\n\\\\t#include <displacementmap_vertex>\\\\n\\\\t#include <project_vertex>\\\\n\\\\t#include <logdepthbuf_vertex>\\\\n\\\\t#include <clipping_planes_vertex>\\\\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 <packing>\\\\n#include <uv_pars_fragment>\\\\n#include <normal_pars_fragment>\\\\n#include <bumpmap_pars_fragment>\\\\n#include <normalmap_pars_fragment>\\\\n#include <logdepthbuf_pars_fragment>\\\\n#include <clipping_planes_pars_fragment>\\\\nvoid main() {\\\\n\\\\t#include <clipping_planes_fragment>\\\\n\\\\t#include <logdepthbuf_fragment>\\\\n\\\\t#include <normal_fragment_begin>\\\\n\\\\t#include <normal_fragment_maps>\\\\n\\\\tgl_FragColor = vec4( packNormalToRGB( normal ), opacity );\\\\n}\\\\\\\",meshphong_vert:\\\\\\\"#define PHONG\\\\nvarying vec3 vViewPosition;\\\\n#include <common>\\\\n#include <uv_pars_vertex>\\\\n#include <uv2_pars_vertex>\\\\n#include <displacementmap_pars_vertex>\\\\n#include <envmap_pars_vertex>\\\\n#include <color_pars_vertex>\\\\n#include <fog_pars_vertex>\\\\n#include <normal_pars_vertex>\\\\n#include <morphtarget_pars_vertex>\\\\n#include <skinning_pars_vertex>\\\\n#include <shadowmap_pars_vertex>\\\\n#include <logdepthbuf_pars_vertex>\\\\n#include <clipping_planes_pars_vertex>\\\\nvoid main() {\\\\n\\\\t#include <uv_vertex>\\\\n\\\\t#include <uv2_vertex>\\\\n\\\\t#include <color_vertex>\\\\n\\\\t#include <beginnormal_vertex>\\\\n\\\\t#include <morphnormal_vertex>\\\\n\\\\t#include <skinbase_vertex>\\\\n\\\\t#include <skinnormal_vertex>\\\\n\\\\t#include <defaultnormal_vertex>\\\\n\\\\t#include <normal_vertex>\\\\n\\\\t#include <begin_vertex>\\\\n\\\\t#include <morphtarget_vertex>\\\\n\\\\t#include <skinning_vertex>\\\\n\\\\t#include <displacementmap_vertex>\\\\n\\\\t#include <project_vertex>\\\\n\\\\t#include <logdepthbuf_vertex>\\\\n\\\\t#include <clipping_planes_vertex>\\\\n\\\\tvViewPosition = - mvPosition.xyz;\\\\n\\\\t#include <worldpos_vertex>\\\\n\\\\t#include <envmap_vertex>\\\\n\\\\t#include <shadowmap_vertex>\\\\n\\\\t#include <fog_vertex>\\\\n}\\\\\\\",meshphong_frag:\\\\\\\"#define PHONG\\\\nuniform vec3 diffuse;\\\\nuniform vec3 emissive;\\\\nuniform vec3 specular;\\\\nuniform float shininess;\\\\nuniform float opacity;\\\\n#include <common>\\\\n#include <packing>\\\\n#include <dithering_pars_fragment>\\\\n#include <color_pars_fragment>\\\\n#include <uv_pars_fragment>\\\\n#include <uv2_pars_fragment>\\\\n#include <map_pars_fragment>\\\\n#include <alphamap_pars_fragment>\\\\n#include <alphatest_pars_fragment>\\\\n#include <aomap_pars_fragment>\\\\n#include <lightmap_pars_fragment>\\\\n#include <emissivemap_pars_fragment>\\\\n#include <envmap_common_pars_fragment>\\\\n#include <envmap_pars_fragment>\\\\n#include <cube_uv_reflection_fragment>\\\\n#include <fog_pars_fragment>\\\\n#include <bsdfs>\\\\n#include <lights_pars_begin>\\\\n#include <normal_pars_fragment>\\\\n#include <lights_phong_pars_fragment>\\\\n#include <shadowmap_pars_fragment>\\\\n#include <bumpmap_pars_fragment>\\\\n#include <normalmap_pars_fragment>\\\\n#include <specularmap_pars_fragment>\\\\n#include <logdepthbuf_pars_fragment>\\\\n#include <clipping_planes_pars_fragment>\\\\nvoid main() {\\\\n\\\\t#include <clipping_planes_fragment>\\\\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 <logdepthbuf_fragment>\\\\n\\\\t#include <map_fragment>\\\\n\\\\t#include <color_fragment>\\\\n\\\\t#include <alphamap_fragment>\\\\n\\\\t#include <alphatest_fragment>\\\\n\\\\t#include <specularmap_fragment>\\\\n\\\\t#include <normal_fragment_begin>\\\\n\\\\t#include <normal_fragment_maps>\\\\n\\\\t#include <emissivemap_fragment>\\\\n\\\\t#include <lights_phong_fragment>\\\\n\\\\t#include <lights_fragment_begin>\\\\n\\\\t#include <lights_fragment_maps>\\\\n\\\\t#include <lights_fragment_end>\\\\n\\\\t#include <aomap_fragment>\\\\n\\\\tvec3 outgoingLight = reflectedLight.directDiffuse + reflectedLight.indirectDiffuse + reflectedLight.directSpecular + reflectedLight.indirectSpecular + totalEmissiveRadiance;\\\\n\\\\t#include <envmap_fragment>\\\\n\\\\t#include <output_fragment>\\\\n\\\\t#include <tonemapping_fragment>\\\\n\\\\t#include <encodings_fragment>\\\\n\\\\t#include <fog_fragment>\\\\n\\\\t#include <premultiplied_alpha_fragment>\\\\n\\\\t#include <dithering_fragment>\\\\n}\\\\\\\",meshphysical_vert:\\\\\\\"#define STANDARD\\\\nvarying vec3 vViewPosition;\\\\n#ifdef USE_TRANSMISSION\\\\n\\\\tvarying vec3 vWorldPosition;\\\\n#endif\\\\n#include <common>\\\\n#include <uv_pars_vertex>\\\\n#include <uv2_pars_vertex>\\\\n#include <displacementmap_pars_vertex>\\\\n#include <color_pars_vertex>\\\\n#include <fog_pars_vertex>\\\\n#include <normal_pars_vertex>\\\\n#include <morphtarget_pars_vertex>\\\\n#include <skinning_pars_vertex>\\\\n#include <shadowmap_pars_vertex>\\\\n#include <logdepthbuf_pars_vertex>\\\\n#include <clipping_planes_pars_vertex>\\\\nvoid main() {\\\\n\\\\t#include <uv_vertex>\\\\n\\\\t#include <uv2_vertex>\\\\n\\\\t#include <color_vertex>\\\\n\\\\t#include <beginnormal_vertex>\\\\n\\\\t#include <morphnormal_vertex>\\\\n\\\\t#include <skinbase_vertex>\\\\n\\\\t#include <skinnormal_vertex>\\\\n\\\\t#include <defaultnormal_vertex>\\\\n\\\\t#include <normal_vertex>\\\\n\\\\t#include <begin_vertex>\\\\n\\\\t#include <morphtarget_vertex>\\\\n\\\\t#include <skinning_vertex>\\\\n\\\\t#include <displacementmap_vertex>\\\\n\\\\t#include <project_vertex>\\\\n\\\\t#include <logdepthbuf_vertex>\\\\n\\\\t#include <clipping_planes_vertex>\\\\n\\\\tvViewPosition = - mvPosition.xyz;\\\\n\\\\t#include <worldpos_vertex>\\\\n\\\\t#include <shadowmap_vertex>\\\\n\\\\t#include <fog_vertex>\\\\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 <common>\\\\n#include <packing>\\\\n#include <dithering_pars_fragment>\\\\n#include <color_pars_fragment>\\\\n#include <uv_pars_fragment>\\\\n#include <uv2_pars_fragment>\\\\n#include <map_pars_fragment>\\\\n#include <alphamap_pars_fragment>\\\\n#include <alphatest_pars_fragment>\\\\n#include <aomap_pars_fragment>\\\\n#include <lightmap_pars_fragment>\\\\n#include <emissivemap_pars_fragment>\\\\n#include <bsdfs>\\\\n#include <cube_uv_reflection_fragment>\\\\n#include <envmap_common_pars_fragment>\\\\n#include <envmap_physical_pars_fragment>\\\\n#include <fog_pars_fragment>\\\\n#include <lights_pars_begin>\\\\n#include <normal_pars_fragment>\\\\n#include <lights_physical_pars_fragment>\\\\n#include <transmission_pars_fragment>\\\\n#include <shadowmap_pars_fragment>\\\\n#include <bumpmap_pars_fragment>\\\\n#include <normalmap_pars_fragment>\\\\n#include <clearcoat_pars_fragment>\\\\n#include <roughnessmap_pars_fragment>\\\\n#include <metalnessmap_pars_fragment>\\\\n#include <logdepthbuf_pars_fragment>\\\\n#include <clipping_planes_pars_fragment>\\\\nvoid main() {\\\\n\\\\t#include <clipping_planes_fragment>\\\\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 <logdepthbuf_fragment>\\\\n\\\\t#include <map_fragment>\\\\n\\\\t#include <color_fragment>\\\\n\\\\t#include <alphamap_fragment>\\\\n\\\\t#include <alphatest_fragment>\\\\n\\\\t#include <roughnessmap_fragment>\\\\n\\\\t#include <metalnessmap_fragment>\\\\n\\\\t#include <normal_fragment_begin>\\\\n\\\\t#include <normal_fragment_maps>\\\\n\\\\t#include <clearcoat_normal_fragment_begin>\\\\n\\\\t#include <clearcoat_normal_fragment_maps>\\\\n\\\\t#include <emissivemap_fragment>\\\\n\\\\t#include <lights_physical_fragment>\\\\n\\\\t#include <lights_fragment_begin>\\\\n\\\\t#include <lights_fragment_maps>\\\\n\\\\t#include <lights_fragment_end>\\\\n\\\\t#include <aomap_fragment>\\\\n\\\\tvec3 totalDiffuse = reflectedLight.directDiffuse + reflectedLight.indirectDiffuse;\\\\n\\\\tvec3 totalSpecular = reflectedLight.directSpecular + reflectedLight.indirectSpecular;\\\\n\\\\t#include <transmission_fragment>\\\\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 <output_fragment>\\\\n\\\\t#include <tonemapping_fragment>\\\\n\\\\t#include <encodings_fragment>\\\\n\\\\t#include <fog_fragment>\\\\n\\\\t#include <premultiplied_alpha_fragment>\\\\n\\\\t#include <dithering_fragment>\\\\n}\\\\\\\",meshtoon_vert:\\\\\\\"#define TOON\\\\nvarying vec3 vViewPosition;\\\\n#include <common>\\\\n#include <uv_pars_vertex>\\\\n#include <uv2_pars_vertex>\\\\n#include <displacementmap_pars_vertex>\\\\n#include <color_pars_vertex>\\\\n#include <fog_pars_vertex>\\\\n#include <normal_pars_vertex>\\\\n#include <morphtarget_pars_vertex>\\\\n#include <skinning_pars_vertex>\\\\n#include <shadowmap_pars_vertex>\\\\n#include <logdepthbuf_pars_vertex>\\\\n#include <clipping_planes_pars_vertex>\\\\nvoid main() {\\\\n\\\\t#include <uv_vertex>\\\\n\\\\t#include <uv2_vertex>\\\\n\\\\t#include <color_vertex>\\\\n\\\\t#include <beginnormal_vertex>\\\\n\\\\t#include <morphnormal_vertex>\\\\n\\\\t#include <skinbase_vertex>\\\\n\\\\t#include <skinnormal_vertex>\\\\n\\\\t#include <defaultnormal_vertex>\\\\n\\\\t#include <normal_vertex>\\\\n\\\\t#include <begin_vertex>\\\\n\\\\t#include <morphtarget_vertex>\\\\n\\\\t#include <skinning_vertex>\\\\n\\\\t#include <displacementmap_vertex>\\\\n\\\\t#include <project_vertex>\\\\n\\\\t#include <logdepthbuf_vertex>\\\\n\\\\t#include <clipping_planes_vertex>\\\\n\\\\tvViewPosition = - mvPosition.xyz;\\\\n\\\\t#include <worldpos_vertex>\\\\n\\\\t#include <shadowmap_vertex>\\\\n\\\\t#include <fog_vertex>\\\\n}\\\\\\\",meshtoon_frag:\\\\\\\"#define TOON\\\\nuniform vec3 diffuse;\\\\nuniform vec3 emissive;\\\\nuniform float opacity;\\\\n#include <common>\\\\n#include <packing>\\\\n#include <dithering_pars_fragment>\\\\n#include <color_pars_fragment>\\\\n#include <uv_pars_fragment>\\\\n#include <uv2_pars_fragment>\\\\n#include <map_pars_fragment>\\\\n#include <alphamap_pars_fragment>\\\\n#include <alphatest_pars_fragment>\\\\n#include <aomap_pars_fragment>\\\\n#include <lightmap_pars_fragment>\\\\n#include <emissivemap_pars_fragment>\\\\n#include <gradientmap_pars_fragment>\\\\n#include <fog_pars_fragment>\\\\n#include <bsdfs>\\\\n#include <lights_pars_begin>\\\\n#include <normal_pars_fragment>\\\\n#include <lights_toon_pars_fragment>\\\\n#include <shadowmap_pars_fragment>\\\\n#include <bumpmap_pars_fragment>\\\\n#include <normalmap_pars_fragment>\\\\n#include <logdepthbuf_pars_fragment>\\\\n#include <clipping_planes_pars_fragment>\\\\nvoid main() {\\\\n\\\\t#include <clipping_planes_fragment>\\\\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 <logdepthbuf_fragment>\\\\n\\\\t#include <map_fragment>\\\\n\\\\t#include <color_fragment>\\\\n\\\\t#include <alphamap_fragment>\\\\n\\\\t#include <alphatest_fragment>\\\\n\\\\t#include <normal_fragment_begin>\\\\n\\\\t#include <normal_fragment_maps>\\\\n\\\\t#include <emissivemap_fragment>\\\\n\\\\t#include <lights_toon_fragment>\\\\n\\\\t#include <lights_fragment_begin>\\\\n\\\\t#include <lights_fragment_maps>\\\\n\\\\t#include <lights_fragment_end>\\\\n\\\\t#include <aomap_fragment>\\\\n\\\\tvec3 outgoingLight = reflectedLight.directDiffuse + reflectedLight.indirectDiffuse + totalEmissiveRadiance;\\\\n\\\\t#include <output_fragment>\\\\n\\\\t#include <tonemapping_fragment>\\\\n\\\\t#include <encodings_fragment>\\\\n\\\\t#include <fog_fragment>\\\\n\\\\t#include <premultiplied_alpha_fragment>\\\\n\\\\t#include <dithering_fragment>\\\\n}\\\\\\\",points_vert:\\\\\\\"uniform float size;\\\\nuniform float scale;\\\\n#include <common>\\\\n#include <color_pars_vertex>\\\\n#include <fog_pars_vertex>\\\\n#include <morphtarget_pars_vertex>\\\\n#include <logdepthbuf_pars_vertex>\\\\n#include <clipping_planes_pars_vertex>\\\\nvoid main() {\\\\n\\\\t#include <color_vertex>\\\\n\\\\t#include <begin_vertex>\\\\n\\\\t#include <morphtarget_vertex>\\\\n\\\\t#include <project_vertex>\\\\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 <logdepthbuf_vertex>\\\\n\\\\t#include <clipping_planes_vertex>\\\\n\\\\t#include <worldpos_vertex>\\\\n\\\\t#include <fog_vertex>\\\\n}\\\\\\\",points_frag:\\\\\\\"uniform vec3 diffuse;\\\\nuniform float opacity;\\\\n#include <common>\\\\n#include <color_pars_fragment>\\\\n#include <map_particle_pars_fragment>\\\\n#include <alphatest_pars_fragment>\\\\n#include <fog_pars_fragment>\\\\n#include <logdepthbuf_pars_fragment>\\\\n#include <clipping_planes_pars_fragment>\\\\nvoid main() {\\\\n\\\\t#include <clipping_planes_fragment>\\\\n\\\\tvec3 outgoingLight = vec3( 0.0 );\\\\n\\\\tvec4 diffuseColor = vec4( diffuse, opacity );\\\\n\\\\t#include <logdepthbuf_fragment>\\\\n\\\\t#include <map_particle_fragment>\\\\n\\\\t#include <color_fragment>\\\\n\\\\t#include <alphatest_fragment>\\\\n\\\\toutgoingLight = diffuseColor.rgb;\\\\n\\\\t#include <output_fragment>\\\\n\\\\t#include <tonemapping_fragment>\\\\n\\\\t#include <encodings_fragment>\\\\n\\\\t#include <fog_fragment>\\\\n\\\\t#include <premultiplied_alpha_fragment>\\\\n}\\\\\\\",shadow_vert:\\\\\\\"#include <common>\\\\n#include <fog_pars_vertex>\\\\n#include <morphtarget_pars_vertex>\\\\n#include <skinning_pars_vertex>\\\\n#include <shadowmap_pars_vertex>\\\\nvoid main() {\\\\n\\\\t#include <beginnormal_vertex>\\\\n\\\\t#include <morphnormal_vertex>\\\\n\\\\t#include <skinbase_vertex>\\\\n\\\\t#include <skinnormal_vertex>\\\\n\\\\t#include <defaultnormal_vertex>\\\\n\\\\t#include <begin_vertex>\\\\n\\\\t#include <morphtarget_vertex>\\\\n\\\\t#include <skinning_vertex>\\\\n\\\\t#include <project_vertex>\\\\n\\\\t#include <worldpos_vertex>\\\\n\\\\t#include <shadowmap_vertex>\\\\n\\\\t#include <fog_vertex>\\\\n}\\\\\\\",shadow_frag:\\\\\\\"uniform vec3 color;\\\\nuniform float opacity;\\\\n#include <common>\\\\n#include <packing>\\\\n#include <fog_pars_fragment>\\\\n#include <bsdfs>\\\\n#include <lights_pars_begin>\\\\n#include <shadowmap_pars_fragment>\\\\n#include <shadowmask_pars_fragment>\\\\nvoid main() {\\\\n\\\\tgl_FragColor = vec4( color, opacity * ( 1.0 - getShadowMask() ) );\\\\n\\\\t#include <tonemapping_fragment>\\\\n\\\\t#include <encodings_fragment>\\\\n\\\\t#include <fog_fragment>\\\\n}\\\\\\\",sprite_vert:\\\\\\\"uniform float rotation;\\\\nuniform vec2 center;\\\\n#include <common>\\\\n#include <uv_pars_vertex>\\\\n#include <fog_pars_vertex>\\\\n#include <logdepthbuf_pars_vertex>\\\\n#include <clipping_planes_pars_vertex>\\\\nvoid main() {\\\\n\\\\t#include <uv_vertex>\\\\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 <logdepthbuf_vertex>\\\\n\\\\t#include <clipping_planes_vertex>\\\\n\\\\t#include <fog_vertex>\\\\n}\\\\\\\",sprite_frag:\\\\\\\"uniform vec3 diffuse;\\\\nuniform float opacity;\\\\n#include <common>\\\\n#include <uv_pars_fragment>\\\\n#include <map_pars_fragment>\\\\n#include <alphamap_pars_fragment>\\\\n#include <alphatest_pars_fragment>\\\\n#include <fog_pars_fragment>\\\\n#include <logdepthbuf_pars_fragment>\\\\n#include <clipping_planes_pars_fragment>\\\\nvoid main() {\\\\n\\\\t#include <clipping_planes_fragment>\\\\n\\\\tvec3 outgoingLight = vec3( 0.0 );\\\\n\\\\tvec4 diffuseColor = vec4( diffuse, opacity );\\\\n\\\\t#include <logdepthbuf_fragment>\\\\n\\\\t#include <map_fragment>\\\\n\\\\t#include <alphamap_fragment>\\\\n\\\\t#include <alphatest_fragment>\\\\n\\\\toutgoingLight = diffuseColor.rgb;\\\\n\\\\t#include <output_fragment>\\\\n\\\\t#include <tonemapping_fragment>\\\\n\\\\t#include <encodings_fragment>\\\\n\\\\t#include <fog_fragment>\\\\n}\\\\\\\"},tT={common:{diffuse:{value:new Zb(16777215)},opacity:{value:1},map:{value:null},uvTransform:{value:new gx},uv2Transform:{value:new gx},alphaMap:{value:null},alphaTest:{value:0}},specularmap:{specularMap:{value:null}},envmap:{envMap:{value:null},flipEnvMap:{value:-1},reflectivity:{value:1},ior:{value:1.5},refractionRatio:{value:.98},maxMipLevel:{value:0}},aomap:{aoMap:{value:null},aoMapIntensity:{value:1}},lightmap:{lightMap:{value:null},lightMapIntensity:{value:1}},emissivemap:{emissiveMap:{value:null}},bumpmap:{bumpMap:{value:null},bumpScale:{value:1}},normalmap:{normalMap:{value:null},normalScale:{value:new fx(1,1)}},displacementmap:{displacementMap:{value:null},displacementScale:{value:1},displacementBias:{value:0}},roughnessmap:{roughnessMap:{value:null}},metalnessmap:{metalnessMap:{value:null}},gradientmap:{gradientMap:{value:null}},fog:{fogDensity:{value:25e-5},fogNear:{value:1},fogFar:{value:2e3},fogColor:{value:new Zb(16777215)}},lights:{ambientLightColor:{value:[]},lightProbe:{value:[]},directionalLights:{value:[],properties:{direction:{},color:{}}},directionalLightShadows:{value:[],properties:{shadowBias:{},shadowNormalBias:{},shadowRadius:{},shadowMapSize:{}}},directionalShadowMap:{value:[]},directionalShadowMatrix:{value:[]},spotLights:{value:[],properties:{color:{},position:{},direction:{},distance:{},coneCos:{},penumbraCos:{},decay:{}}},spotLightShadows:{value:[],properties:{shadowBias:{},shadowNormalBias:{},shadowRadius:{},shadowMapSize:{}}},spotShadowMap:{value:[]},spotShadowMatrix:{value:[]},pointLights:{value:[],properties:{color:{},position:{},decay:{},distance:{}}},pointLightShadows:{value:[],properties:{shadowBias:{},shadowNormalBias:{},shadowRadius:{},shadowMapSize:{},shadowCameraNear:{},shadowCameraFar:{}}},pointShadowMap:{value:[]},pointShadowMatrix:{value:[]},hemisphereLights:{value:[],properties:{direction:{},skyColor:{},groundColor:{}}},rectAreaLights:{value:[],properties:{color:{},position:{},width:{},height:{}}},ltc_1:{value:null},ltc_2:{value:null}},points:{diffuse:{value:new Zb(16777215)},opacity:{value:1},size:{value:1},scale:{value:1},map:{value:null},alphaMap:{value:null},alphaTest:{value:0},uvTransform:{value:new gx}},sprite:{diffuse:{value:new Zb(16777215)},opacity:{value:1},center:{value:new fx(.5,.5)},rotation:{value:0},map:{value:null},alphaMap:{value:null},alphaTest:{value:0},uvTransform:{value:new gx}}},eT={basic:{uniforms:Iw([tT.common,tT.specularmap,tT.envmap,tT.aomap,tT.lightmap,tT.fog]),vertexShader:Kw.meshbasic_vert,fragmentShader:Kw.meshbasic_frag},lambert:{uniforms:Iw([tT.common,tT.specularmap,tT.envmap,tT.aomap,tT.lightmap,tT.emissivemap,tT.fog,tT.lights,{emissive:{value:new Zb(0)}}]),vertexShader:Kw.meshlambert_vert,fragmentShader:Kw.meshlambert_frag},phong:{uniforms:Iw([tT.common,tT.specularmap,tT.envmap,tT.aomap,tT.lightmap,tT.emissivemap,tT.bumpmap,tT.normalmap,tT.displacementmap,tT.fog,tT.lights,{emissive:{value:new Zb(0)},specular:{value:new Zb(1118481)},shininess:{value:30}}]),vertexShader:Kw.meshphong_vert,fragmentShader:Kw.meshphong_frag},standard:{uniforms:Iw([tT.common,tT.envmap,tT.aomap,tT.lightmap,tT.emissivemap,tT.bumpmap,tT.normalmap,tT.displacementmap,tT.roughnessmap,tT.metalnessmap,tT.fog,tT.lights,{emissive:{value:new Zb(0)},roughness:{value:1},metalness:{value:0},envMapIntensity:{value:1}}]),vertexShader:Kw.meshphysical_vert,fragmentShader:Kw.meshphysical_frag},toon:{uniforms:Iw([tT.common,tT.aomap,tT.lightmap,tT.emissivemap,tT.bumpmap,tT.normalmap,tT.displacementmap,tT.gradientmap,tT.fog,tT.lights,{emissive:{value:new Zb(0)}}]),vertexShader:Kw.meshtoon_vert,fragmentShader:Kw.meshtoon_frag},matcap:{uniforms:Iw([tT.common,tT.bumpmap,tT.normalmap,tT.displacementmap,tT.fog,{matcap:{value:null}}]),vertexShader:Kw.meshmatcap_vert,fragmentShader:Kw.meshmatcap_frag},points:{uniforms:Iw([tT.points,tT.fog]),vertexShader:Kw.points_vert,fragmentShader:Kw.points_frag},dashed:{uniforms:Iw([tT.common,tT.fog,{scale:{value:1},dashSize:{value:1},totalSize:{value:2}}]),vertexShader:Kw.linedashed_vert,fragmentShader:Kw.linedashed_frag},depth:{uniforms:Iw([tT.common,tT.displacementmap]),vertexShader:Kw.depth_vert,fragmentShader:Kw.depth_frag},normal:{uniforms:Iw([tT.common,tT.bumpmap,tT.normalmap,tT.displacementmap,{opacity:{value:1}}]),vertexShader:Kw.meshnormal_vert,fragmentShader:Kw.meshnormal_frag},sprite:{uniforms:Iw([tT.sprite,tT.fog]),vertexShader:Kw.sprite_vert,fragmentShader:Kw.sprite_frag},background:{uniforms:{uvTransform:{value:new gx},t2D:{value:null}},vertexShader:Kw.background_vert,fragmentShader:Kw.background_frag},cube:{uniforms:Iw([tT.envmap,{opacity:{value:1}}]),vertexShader:Kw.cube_vert,fragmentShader:Kw.cube_frag},equirect:{uniforms:{tEquirect:{value:null}},vertexShader:Kw.equirect_vert,fragmentShader:Kw.equirect_frag},distanceRGBA:{uniforms:Iw([tT.common,tT.displacementmap,{referencePosition:{value:new Nx},nearDistance:{value:1},farDistance:{value:1e3}}]),vertexShader:Kw.distanceRGBA_vert,fragmentShader:Kw.distanceRGBA_frag},shadow:{uniforms:Iw([tT.lights,tT.fog,{color:{value:new Zb(0)},opacity:{value:1}}]),vertexShader:Kw.shadow_vert,fragmentShader:Kw.shadow_frag}};function nT(t,e,n,i,r){const s=new Zb(0);let o,a,l=0,c=null,u=0,h=null;function d(t,e){n.buffers.color.setClear(t.r,t.g,t.b,e,r)}return{getClearColor:function(){return s},setClearColor:function(t,e=1){s.set(t),l=e,d(s,l)},getClearAlpha:function(){return l},setClearAlpha:function(t){l=t,d(s,l)},render:function(n,r){let p=!1,_=!0===r.isScene?r.background:null;_&&_.isTexture&&(_=e.get(_));const m=t.xr,f=m.getSession&&m.getSession();f&&\\\\\\\"additive\\\\\\\"===f.environmentBlendMode&&(_=null),null===_?d(s,l):_&&_.isColor&&(d(_,1),p=!0),(t.autoClear||p)&&t.clear(t.autoClearColor,t.autoClearDepth,t.autoClearStencil),_&&(_.isCubeTexture||_.mapping===xy)?(void 0===a&&(a=new Lw(new Rw(1,1,1),new Dw({name:\\\\\\\"BackgroundCubeMaterial\\\\\\\",uniforms:Pw(eT.cube.uniforms),vertexShader:eT.cube.vertexShader,fragmentShader:eT.cube.fragmentShader,side:1,depthTest:!1,depthWrite:!1,fog:!1})),a.geometry.deleteAttribute(\\\\\\\"normal\\\\\\\"),a.geometry.deleteAttribute(\\\\\\\"uv\\\\\\\"),a.onBeforeRender=function(t,e,n){this.matrixWorld.copyPosition(n.matrixWorld)},Object.defineProperty(a.material,\\\\\\\"envMap\\\\\\\",{get:function(){return this.uniforms.envMap.value}}),i.update(a)),a.material.uniforms.envMap.value=_,a.material.uniforms.flipEnvMap.value=_.isCubeTexture&&!1===_.isRenderTargetTexture?-1:1,c===_&&u===_.version&&h===t.toneMapping||(a.material.needsUpdate=!0,c=_,u=_.version,h=t.toneMapping),n.unshift(a,a.geometry,a.material,0,0,null)):_&&_.isTexture&&(void 0===o&&(o=new Lw(new Qw(2,2),new Dw({name:\\\\\\\"BackgroundMaterial\\\\\\\",uniforms:Pw(eT.background.uniforms),vertexShader:eT.background.vertexShader,fragmentShader:eT.background.fragmentShader,side:0,depthTest:!1,depthWrite:!1,fog:!1})),o.geometry.deleteAttribute(\\\\\\\"normal\\\\\\\"),Object.defineProperty(o.material,\\\\\\\"map\\\\\\\",{get:function(){return this.uniforms.t2D.value}}),i.update(o)),o.material.uniforms.t2D.value=_,!0===_.matrixAutoUpdate&&_.updateMatrix(),o.material.uniforms.uvTransform.value.copy(_.matrix),c===_&&u===_.version&&h===t.toneMapping||(o.material.needsUpdate=!0,c=_,u=_.version,h=t.toneMapping),n.unshift(o,o.geometry,o.material,0,0,null))}}}function iT(t,e,n,i){const r=t.getParameter(34921),s=i.isWebGL2?null:e.get(\\\\\\\"OES_vertex_array_object\\\\\\\"),o=i.isWebGL2||null!==s,a={},l=d(null);let c=l;function u(e){return i.isWebGL2?t.bindVertexArray(e):s.bindVertexArrayOES(e)}function h(e){return i.isWebGL2?t.deleteVertexArray(e):s.deleteVertexArrayOES(e)}function d(t){const e=[],n=[],i=[];for(let t=0;t<r;t++)e[t]=0,n[t]=0,i[t]=0;return{geometry:null,program:null,wireframe:!1,newAttributes:e,enabledAttributes:n,attributeDivisors:i,object:t,attributes:{},index:null}}function p(){const t=c.newAttributes;for(let e=0,n=t.length;e<n;e++)t[e]=0}function _(t){m(t,0)}function m(n,r){const s=c.newAttributes,o=c.enabledAttributes,a=c.attributeDivisors;if(s[n]=1,0===o[n]&&(t.enableVertexAttribArray(n),o[n]=1),a[n]!==r){(i.isWebGL2?t:e.get(\\\\\\\"ANGLE_instanced_arrays\\\\\\\"))[i.isWebGL2?\\\\\\\"vertexAttribDivisor\\\\\\\":\\\\\\\"vertexAttribDivisorANGLE\\\\\\\"](n,r),a[n]=r}}function f(){const e=c.newAttributes,n=c.enabledAttributes;for(let i=0,r=n.length;i<r;i++)n[i]!==e[i]&&(t.disableVertexAttribArray(i),n[i]=0)}function g(e,n,r,s,o,a){!0!==i.isWebGL2||5124!==r&&5125!==r?t.vertexAttribPointer(e,n,r,s,o,a):t.vertexAttribIPointer(e,n,r,o,a)}function v(){y(),c!==l&&(c=l,u(c.object))}function y(){l.geometry=null,l.program=null,l.wireframe=!1}return{setup:function(r,l,h,v,y){let x=!1;if(o){const e=function(e,n,r){const o=!0===r.wireframe;let l=a[e.id];void 0===l&&(l={},a[e.id]=l);let c=l[n.id];void 0===c&&(c={},l[n.id]=c);let u=c[o];void 0===u&&(u=d(i.isWebGL2?t.createVertexArray():s.createVertexArrayOES()),c[o]=u);return u}(v,h,l);c!==e&&(c=e,u(c.object)),x=function(t,e){const n=c.attributes,i=t.attributes;let r=0;for(const t in i){const e=n[t],s=i[t];if(void 0===e)return!0;if(e.attribute!==s)return!0;if(e.data!==s.data)return!0;r++}return c.attributesNum!==r||c.index!==e}(v,y),x&&function(t,e){const n={},i=t.attributes;let r=0;for(const t in i){const e=i[t],s={};s.attribute=e,e.data&&(s.data=e.data),n[t]=s,r++}c.attributes=n,c.attributesNum=r,c.index=e}(v,y)}else{const t=!0===l.wireframe;c.geometry===v.id&&c.program===h.id&&c.wireframe===t||(c.geometry=v.id,c.program=h.id,c.wireframe=t,x=!0)}!0===r.isInstancedMesh&&(x=!0),null!==y&&n.update(y,34963),x&&(!function(r,s,o,a){if(!1===i.isWebGL2&&(r.isInstancedMesh||a.isInstancedBufferGeometry)&&null===e.get(\\\\\\\"ANGLE_instanced_arrays\\\\\\\"))return;p();const l=a.attributes,c=o.getAttributes(),u=s.defaultAttributeValues;for(const e in c){const i=c[e];if(i.location>=0){let s=l[e];if(void 0===s&&(\\\\\\\"instanceMatrix\\\\\\\"===e&&r.instanceMatrix&&(s=r.instanceMatrix),\\\\\\\"instanceColor\\\\\\\"===e&&r.instanceColor&&(s=r.instanceColor)),void 0!==s){const e=s.normalized,o=s.itemSize,l=n.get(s);if(void 0===l)continue;const c=l.buffer,u=l.type,h=l.bytesPerElement;if(s.isInterleavedBufferAttribute){const n=s.data,l=n.stride,d=s.offset;if(n&&n.isInstancedInterleavedBuffer){for(let t=0;t<i.locationSize;t++)m(i.location+t,n.meshPerAttribute);!0!==r.isInstancedMesh&&void 0===a._maxInstanceCount&&(a._maxInstanceCount=n.meshPerAttribute*n.count)}else for(let t=0;t<i.locationSize;t++)_(i.location+t);t.bindBuffer(34962,c);for(let t=0;t<i.locationSize;t++)g(i.location+t,o/i.locationSize,u,e,l*h,(d+o/i.locationSize*t)*h)}else{if(s.isInstancedBufferAttribute){for(let t=0;t<i.locationSize;t++)m(i.location+t,s.meshPerAttribute);!0!==r.isInstancedMesh&&void 0===a._maxInstanceCount&&(a._maxInstanceCount=s.meshPerAttribute*s.count)}else for(let t=0;t<i.locationSize;t++)_(i.location+t);t.bindBuffer(34962,c);for(let t=0;t<i.locationSize;t++)g(i.location+t,o/i.locationSize,u,e,o*h,o/i.locationSize*t*h)}}else if(void 0!==u){const n=u[e];if(void 0!==n)switch(n.length){case 2:t.vertexAttrib2fv(i.location,n);break;case 3:t.vertexAttrib3fv(i.location,n);break;case 4:t.vertexAttrib4fv(i.location,n);break;default:t.vertexAttrib1fv(i.location,n)}}}}f()}(r,l,h,v),null!==y&&t.bindBuffer(34963,n.get(y).buffer))},reset:v,resetDefaultState:y,dispose:function(){v();for(const t in a){const e=a[t];for(const t in e){const n=e[t];for(const t in n)h(n[t].object),delete n[t];delete e[t]}delete a[t]}},releaseStatesOfGeometry:function(t){if(void 0===a[t.id])return;const e=a[t.id];for(const t in e){const n=e[t];for(const t in n)h(n[t].object),delete n[t];delete e[t]}delete a[t.id]},releaseStatesOfProgram:function(t){for(const e in a){const n=a[e];if(void 0===n[t.id])continue;const i=n[t.id];for(const t in i)h(i[t].object),delete i[t];delete n[t.id]}},initAttributes:p,enableAttribute:_,disableUnusedAttributes:f}}function rT(t,e,n,i){const r=i.isWebGL2;let s;this.setMode=function(t){s=t},this.render=function(e,i){t.drawArrays(s,e,i),n.update(i,s,1)},this.renderInstances=function(i,o,a){if(0===a)return;let l,c;if(r)l=t,c=\\\\\\\"drawArraysInstanced\\\\\\\";else if(l=e.get(\\\\\\\"ANGLE_instanced_arrays\\\\\\\"),c=\\\\\\\"drawArraysInstancedANGLE\\\\\\\",null===l)return void console.error(\\\\\\\"THREE.WebGLBufferRenderer: using THREE.InstancedBufferGeometry but hardware does not support extension ANGLE_instanced_arrays.\\\\\\\");l[c](s,i,o,a),n.update(o,s,a)}}function sT(t,e,n){let i;function r(e){if(\\\\\\\"highp\\\\\\\"===e){if(t.getShaderPrecisionFormat(35633,36338).precision>0&&t.getShaderPrecisionFormat(35632,36338).precision>0)return\\\\\\\"highp\\\\\\\";e=\\\\\\\"mediump\\\\\\\"}return\\\\\\\"mediump\\\\\\\"===e&&t.getShaderPrecisionFormat(35633,36337).precision>0&&t.getShaderPrecisionFormat(35632,36337).precision>0?\\\\\\\"mediump\\\\\\\":\\\\\\\"lowp\\\\\\\"}const s=\\\\\\\"undefined\\\\\\\"!=typeof WebGL2RenderingContext&&t instanceof WebGL2RenderingContext||\\\\\\\"undefined\\\\\\\"!=typeof WebGL2ComputeRenderingContext&&t instanceof WebGL2ComputeRenderingContext;let o=void 0!==n.precision?n.precision:\\\\\\\"highp\\\\\\\";const a=r(o);a!==o&&(console.warn(\\\\\\\"THREE.WebGLRenderer:\\\\\\\",o,\\\\\\\"not supported, using\\\\\\\",a,\\\\\\\"instead.\\\\\\\"),o=a);const l=s||e.has(\\\\\\\"WEBGL_draw_buffers\\\\\\\"),c=!0===n.logarithmicDepthBuffer,u=t.getParameter(34930),h=t.getParameter(35660),d=t.getParameter(3379),p=t.getParameter(34076),_=t.getParameter(34921),m=t.getParameter(36347),f=t.getParameter(36348),g=t.getParameter(36349),v=h>0,y=s||e.has(\\\\\\\"OES_texture_float\\\\\\\");return{isWebGL2:s,drawBuffers:l,getMaxAnisotropy:function(){if(void 0!==i)return i;if(!0===e.has(\\\\\\\"EXT_texture_filter_anisotropic\\\\\\\")){const n=e.get(\\\\\\\"EXT_texture_filter_anisotropic\\\\\\\");i=t.getParameter(n.MAX_TEXTURE_MAX_ANISOTROPY_EXT)}else i=0;return i},getMaxPrecision:r,precision:o,logarithmicDepthBuffer:c,maxTextures:u,maxVertexTextures:h,maxTextureSize:d,maxCubemapSize:p,maxAttributes:_,maxVertexUniforms:m,maxVaryings:f,maxFragmentUniforms:g,vertexTextures:v,floatFragmentTextures:y,floatVertexTextures:v&&y,maxSamples:s?t.getParameter(36183):0}}function oT(t){const e=this;let n=null,i=0,r=!1,s=!1;const o=new qw,a=new gx,l={value:null,needsUpdate:!1};function c(){l.value!==n&&(l.value=n,l.needsUpdate=i>0),e.numPlanes=i,e.numIntersection=0}function u(t,n,i,r){const s=null!==t?t.length:0;let c=null;if(0!==s){if(c=l.value,!0!==r||null===c){const e=i+4*s,r=n.matrixWorldInverse;a.getNormalMatrix(r),(null===c||c.length<e)&&(c=new Float32Array(e));for(let e=0,n=i;e!==s;++e,n+=4)o.copy(t[e]).applyMatrix4(r,a),o.normal.toArray(c,n),c[n+3]=o.constant}l.value=c,l.needsUpdate=!0}return e.numPlanes=s,e.numIntersection=0,c}this.uniform=l,this.numPlanes=0,this.numIntersection=0,this.init=function(t,e,s){const o=0!==t.length||e||0!==i||r;return r=e,n=u(t,s,0),i=t.length,o},this.beginShadows=function(){s=!0,u(null)},this.endShadows=function(){s=!1,c()},this.setState=function(e,o,a){const h=e.clippingPlanes,d=e.clipIntersection,p=e.clipShadows,_=t.get(e);if(!r||null===h||0===h.length||s&&!p)s?u(null):c();else{const t=s?0:i,e=4*t;let r=_.clippingState||null;l.value=r,r=u(h,o,e,a);for(let 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fx(1,1)},clearcoatNormalMap:{value:null},sheen:{value:0},sheenTint:{value:new Zb(0)},sheenRoughness:{value:0},transmission:{value:0},transmissionMap:{value:null},transmissionSamplerSize:{value:new fx},transmissionSamplerMap:{value:null},thickness:{value:0},thicknessMap:{value:null},attenuationDistance:{value:0},attenuationTint:{value:new Zb(0)},specularIntensity:{value:0},specularIntensityMap:{value:null},specularTint:{value:new Zb(1,1,1)},specularTintMap:{value:null}}]),vertexShader:Kw.meshphysical_vert,fragmentShader:Kw.meshphysical_frag};class lT extends kw{constructor(t=-1,e=1,n=1,i=-1,r=.1,s=2e3){super(),this.type=\\\\\\\"OrthographicCamera\\\\\\\",this.zoom=1,this.view=null,this.left=t,this.right=e,this.top=n,this.bottom=i,this.near=r,this.far=s,this.updateProjectionMatrix()}copy(t,e){return super.copy(t,e),this.left=t.left,this.right=t.right,this.top=t.top,this.bottom=t.bottom,this.near=t.near,this.far=t.far,this.zoom=t.zoom,this.view=null===t.view?null:Object.assign({},t.view),this}setViewOffset(t,e,n,i,r,s){null===this.view&&(this.view={enabled:!0,fullWidth:1,fullHeight:1,offsetX:0,offsetY:0,width:1,height:1}),this.view.enabled=!0,this.view.fullWidth=t,this.view.fullHeight=e,this.view.offsetX=n,this.view.offsetY=i,this.view.width=r,this.view.height=s,this.updateProjectionMatrix()}clearViewOffset(){null!==this.view&&(this.view.enabled=!1),this.updateProjectionMatrix()}updateProjectionMatrix(){const t=(this.right-this.left)/(2*this.zoom),e=(this.top-this.bottom)/(2*this.zoom),n=(this.right+this.left)/2,i=(this.top+this.bottom)/2;let r=n-t,s=n+t,o=i+e,a=i-e;if(null!==this.view&&this.view.enabled){const t=(this.right-this.left)/this.view.fullWidth/this.zoom,e=(this.top-this.bottom)/this.view.fullHeight/this.zoom;r+=t*this.view.offsetX,s=r+t*this.view.width,o-=e*this.view.offsetY,a=o-e*this.view.height}this.projectionMatrix.makeOrthographic(r,s,o,a,this.near,this.far),this.projectionMatrixInverse.copy(this.projectionMatrix).invert()}toJSON(t){const e=super.toJSON(t);return e.object.zoom=this.zoom,e.object.left=this.left,e.object.right=this.right,e.object.top=this.top,e.object.bottom=this.bottom,e.object.near=this.near,e.object.far=this.far,null!==this.view&&(e.object.view=Object.assign({},this.view)),e}}lT.prototype.isOrthographicCamera=!0;class cT extends Dw{constructor(t){super(t),this.type=\\\\\\\"RawShaderMaterial\\\\\\\"}}cT.prototype.isRawShaderMaterial=!0;const uT=Math.pow(2,8),hT=[.125,.215,.35,.446,.526,.582],dT=5+hT.length,pT=20,_T={[Yy]:0,[$y]:1,[Zy]:2,3004:3,3005:4,3006:5,[Jy]:6},mT=new lT,{_lodPlanes:fT,_sizeLods:gT,_sigmas:vT}=MT(),yT=new Zb;let xT=null;const bT=(1+Math.sqrt(5))/2,wT=1/bT,TT=[new Nx(1,1,1),new Nx(-1,1,1),new Nx(1,1,-1),new Nx(-1,1,-1),new Nx(0,bT,wT),new Nx(0,bT,-wT),new Nx(wT,0,bT),new Nx(-wT,0,bT),new Nx(bT,wT,0),new Nx(-bT,wT,0)];class AT{constructor(t){this._renderer=t,this._pingPongRenderTarget=null,this._blurMaterial=function(t){const e=new Float32Array(t),n=new Nx(0,1,0);return new cT({name:\\\\\\\"SphericalGaussianBlur\\\\\\\",defines:{n:t},uniforms:{envMap:{value:null},samples:{value:1},weights:{value:e},latitudinal:{value:!1},dTheta:{value:0},mipInt:{value:0},poleAxis:{value:n},inputEncoding:{value:_T[3e3]},outputEncoding:{value:_T[3e3]}},vertexShader:OT(),fragmentShader:`\\\\n\\\\n\\\\t\\\\t\\\\tprecision mediump float;\\\\n\\\\t\\\\t\\\\tprecision mediump int;\\\\n\\\\n\\\\t\\\\t\\\\tvarying vec3 vOutputDirection;\\\\n\\\\n\\\\t\\\\t\\\\tuniform sampler2D envMap;\\\\n\\\\t\\\\t\\\\tuniform int samples;\\\\n\\\\t\\\\t\\\\tuniform float weights[ n ];\\\\n\\\\t\\\\t\\\\tuniform bool latitudinal;\\\\n\\\\t\\\\t\\\\tuniform float dTheta;\\\\n\\\\t\\\\t\\\\tuniform float mipInt;\\\\n\\\\t\\\\t\\\\tuniform vec3 poleAxis;\\\\n\\\\n\\\\t\\\\t\\\\t${RT()}\\\\n\\\\n\\\\t\\\\t\\\\t#define ENVMAP_TYPE_CUBE_UV\\\\n\\\\t\\\\t\\\\t#include <cube_uv_reflection_fragment>\\\\n\\\\n\\\\t\\\\t\\\\tvec3 getSample( float theta, vec3 axis ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tfloat cosTheta = cos( theta );\\\\n\\\\t\\\\t\\\\t\\\\t// Rodrigues' axis-angle rotation\\\\n\\\\t\\\\t\\\\t\\\\tvec3 sampleDirection = vOutputDirection * cosTheta\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t+ cross( axis, vOutputDirection ) * sin( theta )\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t+ axis * dot( axis, vOutputDirection ) * ( 1.0 - cosTheta );\\\\n\\\\n\\\\t\\\\t\\\\t\\\\treturn bilinearCubeUV( envMap, sampleDirection, mipInt );\\\\n\\\\n\\\\t\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tvec3 axis = latitudinal ? poleAxis : cross( poleAxis, vOutputDirection );\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tif ( all( equal( axis, vec3( 0.0 ) ) ) ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\t\\\\taxis = vec3( vOutputDirection.z, 0.0, - vOutputDirection.x );\\\\n\\\\n\\\\t\\\\t\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\t\\\\t\\\\taxis = normalize( axis );\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tgl_FragColor = vec4( 0.0, 0.0, 0.0, 1.0 );\\\\n\\\\t\\\\t\\\\t\\\\tgl_FragColor.rgb += weights[ 0 ] * getSample( 0.0, axis );\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tfor ( int i = 1; i < n; i++ ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tif ( i >= samples ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tbreak;\\\\n\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tfloat theta = dTheta * float( i );\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tgl_FragColor.rgb += weights[ i ] * getSample( -1.0 * theta, axis );\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tgl_FragColor.rgb += weights[ i ] * getSample( theta, axis );\\\\n\\\\n\\\\t\\\\t\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tgl_FragColor = linearToOutputTexel( gl_FragColor );\\\\n\\\\n\\\\t\\\\t\\\\t}\\\\n\\\\t\\\\t`,blending:0,depthTest:!1,depthWrite:!1})}(pT),this._equirectShader=null,this._cubemapShader=null,this._compileMaterial(this._blurMaterial)}fromScene(t,e=0,n=.1,i=100){xT=this._renderer.getRenderTarget();const r=this._allocateTargets();return this._sceneToCubeUV(t,n,i,r),e>0&&this._blur(r,0,0,e),this._applyPMREM(r),this._cleanup(r),r}fromEquirectangular(t){return this._fromTexture(t)}fromCubemap(t){return this._fromTexture(t)}compileCubemapShader(){null===this._cubemapShader&&(this._cubemapShader=LT(),this._compileMaterial(this._cubemapShader))}compileEquirectangularShader(){null===this._equirectShader&&(this._equirectShader=NT(),this._compileMaterial(this._equirectShader))}dispose(){this._blurMaterial.dispose(),null!==this._cubemapShader&&this._cubemapShader.dispose(),null!==this._equirectShader&&this._equirectShader.dispose();for(let t=0;t<fT.length;t++)fT[t].dispose()}_cleanup(t){this._pingPongRenderTarget.dispose(),this._renderer.setRenderTarget(xT),t.scissorTest=!1,CT(t,0,0,t.width,t.height)}_fromTexture(t){xT=this._renderer.getRenderTarget();const e=this._allocateTargets(t);return this._textureToCubeUV(t,e),this._applyPMREM(e),this._cleanup(e),e}_allocateTargets(t){const e={magFilter:Ey,minFilter:Ey,generateMipmaps:!1,type:Oy,format:1023,encoding:ET(t)?t.encoding:Zy,depthBuffer:!1},n=ST(e);return n.depthBuffer=!t,this._pingPongRenderTarget=ST(e),n}_compileMaterial(t){const e=new Lw(fT[0],t);this._renderer.compile(e,mT)}_sceneToCubeUV(t,e,n,i){const r=new Bw(90,1,e,n),s=[1,-1,1,1,1,1],o=[1,1,1,-1,-1,-1],a=this._renderer,l=a.autoClear,c=a.outputEncoding,u=a.toneMapping;a.getClearColor(yT),a.toneMapping=0,a.outputEncoding=Yy,a.autoClear=!1;const h=new Qb({name:\\\\\\\"PMREM.Background\\\\\\\",side:1,depthWrite:!1,depthTest:!1}),d=new Lw(new Rw,h);let p=!1;const _=t.background;_?_.isColor&&(h.color.copy(_),t.background=null,p=!0):(h.color.copy(yT),p=!0);for(let e=0;e<6;e++){const n=e%3;0==n?(r.up.set(0,s[e],0),r.lookAt(o[e],0,0)):1==n?(r.up.set(0,0,s[e]),r.lookAt(0,o[e],0)):(r.up.set(0,s[e],0),r.lookAt(0,0,o[e])),CT(i,n*uT,e>2?uT:0,uT,uT),a.setRenderTarget(i),p&&a.render(d,r),a.render(t,r)}d.geometry.dispose(),d.material.dispose(),a.toneMapping=u,a.outputEncoding=c,a.autoClear=l,t.background=_}_setEncoding(t,e){!0===this._renderer.capabilities.isWebGL2&&e.format===By&&e.type===Oy&&e.encoding===$y?t.value=_T[3e3]:t.value=_T[e.encoding]}_textureToCubeUV(t,e){const n=this._renderer;t.isCubeTexture?null==this._cubemapShader&&(this._cubemapShader=LT()):null==this._equirectShader&&(this._equirectShader=NT());const i=t.isCubeTexture?this._cubemapShader:this._equirectShader,r=new Lw(fT[0],i),s=i.uniforms;s.envMap.value=t,t.isCubeTexture||s.texelSize.value.set(1/t.image.width,1/t.image.height),this._setEncoding(s.inputEncoding,t),this._setEncoding(s.outputEncoding,e.texture),CT(e,0,0,3*uT,2*uT),n.setRenderTarget(e),n.render(r,mT)}_applyPMREM(t){const e=this._renderer,n=e.autoClear;e.autoClear=!1;for(let e=1;e<dT;e++){const n=Math.sqrt(vT[e]*vT[e]-vT[e-1]*vT[e-1]),i=TT[(e-1)%TT.length];this._blur(t,e-1,e,n,i)}e.autoClear=n}_blur(t,e,n,i,r){const s=this._pingPongRenderTarget;this._halfBlur(t,s,e,n,i,\\\\\\\"latitudinal\\\\\\\",r),this._halfBlur(s,t,n,n,i,\\\\\\\"longitudinal\\\\\\\",r)}_halfBlur(t,e,n,i,r,s,o){const a=this._renderer,l=this._blurMaterial;\\\\\\\"latitudinal\\\\\\\"!==s&&\\\\\\\"longitudinal\\\\\\\"!==s&&console.error(\\\\\\\"blur direction must be either latitudinal or longitudinal!\\\\\\\");const c=new Lw(fT[i],l),u=l.uniforms,h=gT[n]-1,d=isFinite(r)?Math.PI/(2*h):2*Math.PI/39,p=r/d,_=isFinite(r)?1+Math.floor(3*p):pT;_>pT&&console.warn(`sigmaRadians, ${r}, is too large and will clip, as it requested ${_} samples when the maximum is set to 20`);const m=[];let f=0;for(let t=0;t<pT;++t){const e=t/p,n=Math.exp(-e*e/2);m.push(n),0==t?f+=n:t<_&&(f+=2*n)}for(let t=0;t<m.length;t++)m[t]=m[t]/f;u.envMap.value=t.texture,u.samples.value=_,u.weights.value=m,u.latitudinal.value=\\\\\\\"latitudinal\\\\\\\"===s,o&&(u.poleAxis.value=o),u.dTheta.value=d,u.mipInt.value=8-n,this._setEncoding(u.inputEncoding,t.texture),this._setEncoding(u.outputEncoding,t.texture);const g=gT[i];CT(e,3*Math.max(0,uT-2*g),(0===i?0:2*uT)+2*g*(i>4?i-8+4:0),3*g,2*g),a.setRenderTarget(e),a.render(c,mT)}}function ET(t){return void 0!==t&&t.type===Oy&&(t.encoding===Yy||t.encoding===$y||t.encoding===Jy)}function MT(){const t=[],e=[],n=[];let i=8;for(let r=0;r<dT;r++){const s=Math.pow(2,i);e.push(s);let o=1/s;r>4?o=hT[r-8+4-1]:0==r&&(o=0),n.push(o);const a=1/(s-1),l=-a/2,c=1+a/2,u=[l,l,c,l,c,c,l,l,c,c,l,c],h=6,d=6,p=3,_=2,m=1,f=new Float32Array(p*d*h),g=new Float32Array(_*d*h),v=new Float32Array(m*d*h);for(let t=0;t<h;t++){const e=t%3*2/3-1,n=t>2?0:-1,i=[e,n,0,e+2/3,n,0,e+2/3,n+1,0,e,n,0,e+2/3,n+1,0,e,n+1,0];f.set(i,p*d*t),g.set(u,_*d*t);const r=[t,t,t,t,t,t];v.set(r,m*d*t)}const y=new dw;y.setAttribute(\\\\\\\"position\\\\\\\",new ew(f,p)),y.setAttribute(\\\\\\\"uv\\\\\\\",new ew(g,_)),y.setAttribute(\\\\\\\"faceIndex\\\\\\\",new ew(v,m)),t.push(y),i>4&&i--}return{_lodPlanes:t,_sizeLods:e,_sigmas:n}}function ST(t){const e=new Mx(3*uT,3*uT,t);return e.texture.mapping=xy,e.texture.name=\\\\\\\"PMREM.cubeUv\\\\\\\",e.scissorTest=!0,e}function CT(t,e,n,i,r){t.viewport.set(e,n,i,r),t.scissor.set(e,n,i,r)}function NT(){const t=new fx(1,1);return new cT({name:\\\\\\\"EquirectangularToCubeUV\\\\\\\",uniforms:{envMap:{value:null},texelSize:{value:t},inputEncoding:{value:_T[3e3]},outputEncoding:{value:_T[3e3]}},vertexShader:OT(),fragmentShader:`\\\\n\\\\n\\\\t\\\\t\\\\tprecision mediump float;\\\\n\\\\t\\\\t\\\\tprecision mediump int;\\\\n\\\\n\\\\t\\\\t\\\\tvarying vec3 vOutputDirection;\\\\n\\\\n\\\\t\\\\t\\\\tuniform sampler2D envMap;\\\\n\\\\t\\\\t\\\\tuniform vec2 texelSize;\\\\n\\\\n\\\\t\\\\t\\\\t${RT()}\\\\n\\\\n\\\\t\\\\t\\\\t#include <common>\\\\n\\\\n\\\\t\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tgl_FragColor = vec4( 0.0, 0.0, 0.0, 1.0 );\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tvec3 outputDirection = normalize( vOutputDirection );\\\\n\\\\t\\\\t\\\\t\\\\tvec2 uv = equirectUv( outputDirection );\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tvec2 f = fract( uv / texelSize - 0.5 );\\\\n\\\\t\\\\t\\\\t\\\\tuv -= f * texelSize;\\\\n\\\\t\\\\t\\\\t\\\\tvec3 tl = envMapTexelToLinear( texture2D ( envMap, uv ) ).rgb;\\\\n\\\\t\\\\t\\\\t\\\\tuv.x += texelSize.x;\\\\n\\\\t\\\\t\\\\t\\\\tvec3 tr = envMapTexelToLinear( texture2D ( envMap, uv ) ).rgb;\\\\n\\\\t\\\\t\\\\t\\\\tuv.y += texelSize.y;\\\\n\\\\t\\\\t\\\\t\\\\tvec3 br = envMapTexelToLinear( texture2D ( envMap, uv ) ).rgb;\\\\n\\\\t\\\\t\\\\t\\\\tuv.x -= texelSize.x;\\\\n\\\\t\\\\t\\\\t\\\\tvec3 bl = envMapTexelToLinear( texture2D ( envMap, uv ) ).rgb;\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tvec3 tm = mix( tl, tr, f.x );\\\\n\\\\t\\\\t\\\\t\\\\tvec3 bm = mix( bl, br, f.x );\\\\n\\\\t\\\\t\\\\t\\\\tgl_FragColor.rgb = mix( tm, bm, f.y );\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tgl_FragColor = linearToOutputTexel( gl_FragColor );\\\\n\\\\n\\\\t\\\\t\\\\t}\\\\n\\\\t\\\\t`,blending:0,depthTest:!1,depthWrite:!1})}function LT(){return new cT({name:\\\\\\\"CubemapToCubeUV\\\\\\\",uniforms:{envMap:{value:null},inputEncoding:{value:_T[3e3]},outputEncoding:{value:_T[3e3]}},vertexShader:OT(),fragmentShader:`\\\\n\\\\n\\\\t\\\\t\\\\tprecision mediump float;\\\\n\\\\t\\\\t\\\\tprecision mediump int;\\\\n\\\\n\\\\t\\\\t\\\\tvarying vec3 vOutputDirection;\\\\n\\\\n\\\\t\\\\t\\\\tuniform samplerCube envMap;\\\\n\\\\n\\\\t\\\\t\\\\t${RT()}\\\\n\\\\n\\\\t\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tgl_FragColor = vec4( 0.0, 0.0, 0.0, 1.0 );\\\\n\\\\t\\\\t\\\\t\\\\tgl_FragColor.rgb = envMapTexelToLinear( textureCube( envMap, vec3( - vOutputDirection.x, vOutputDirection.yz ) ) ).rgb;\\\\n\\\\t\\\\t\\\\t\\\\tgl_FragColor = linearToOutputTexel( gl_FragColor );\\\\n\\\\n\\\\t\\\\t\\\\t}\\\\n\\\\t\\\\t`,blending:0,depthTest:!1,depthWrite:!1})}function OT(){return\\\\\\\"\\\\n\\\\n\\\\t\\\\tprecision mediump float;\\\\n\\\\t\\\\tprecision mediump int;\\\\n\\\\n\\\\t\\\\tattribute vec3 position;\\\\n\\\\t\\\\tattribute vec2 uv;\\\\n\\\\t\\\\tattribute float faceIndex;\\\\n\\\\n\\\\t\\\\tvarying vec3 vOutputDirection;\\\\n\\\\n\\\\t\\\\t// RH coordinate system; PMREM face-indexing convention\\\\n\\\\t\\\\tvec3 getDirection( vec2 uv, float face ) {\\\\n\\\\n\\\\t\\\\t\\\\tuv = 2.0 * uv - 1.0;\\\\n\\\\n\\\\t\\\\t\\\\tvec3 direction = vec3( uv, 1.0 );\\\\n\\\\n\\\\t\\\\t\\\\tif ( face == 0.0 ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tdirection = direction.zyx; // ( 1, v, u ) pos x\\\\n\\\\n\\\\t\\\\t\\\\t} else if ( face == 1.0 ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tdirection = direction.xzy;\\\\n\\\\t\\\\t\\\\t\\\\tdirection.xz *= -1.0; // ( -u, 1, -v ) pos y\\\\n\\\\n\\\\t\\\\t\\\\t} else if ( face == 2.0 ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tdirection.x *= -1.0; // ( -u, v, 1 ) pos z\\\\n\\\\n\\\\t\\\\t\\\\t} else if ( face == 3.0 ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tdirection = direction.zyx;\\\\n\\\\t\\\\t\\\\t\\\\tdirection.xz *= -1.0; // ( -1, v, -u ) neg x\\\\n\\\\n\\\\t\\\\t\\\\t} else if ( face == 4.0 ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tdirection = direction.xzy;\\\\n\\\\t\\\\t\\\\t\\\\tdirection.xy *= -1.0; // ( -u, -1, v ) neg y\\\\n\\\\n\\\\t\\\\t\\\\t} else if ( face == 5.0 ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tdirection.z *= -1.0; // ( u, v, -1 ) neg z\\\\n\\\\n\\\\t\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\t\\\\treturn direction;\\\\n\\\\n\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\tvOutputDirection = getDirection( uv, faceIndex );\\\\n\\\\t\\\\t\\\\tgl_Position = vec4( position, 1.0 );\\\\n\\\\n\\\\t\\\\t}\\\\n\\\\t\\\\\\\"}function RT(){return\\\\\\\"\\\\n\\\\n\\\\t\\\\tuniform int inputEncoding;\\\\n\\\\t\\\\tuniform int outputEncoding;\\\\n\\\\n\\\\t\\\\t#include <encodings_pars_fragment>\\\\n\\\\n\\\\t\\\\tvec4 inputTexelToLinear( vec4 value ) {\\\\n\\\\n\\\\t\\\\t\\\\tif ( inputEncoding == 0 ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\treturn value;\\\\n\\\\n\\\\t\\\\t\\\\t} else if ( inputEncoding == 1 ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\treturn sRGBToLinear( value );\\\\n\\\\n\\\\t\\\\t\\\\t} else if ( inputEncoding == 2 ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\treturn RGBEToLinear( value );\\\\n\\\\n\\\\t\\\\t\\\\t} else if ( inputEncoding == 3 ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\treturn RGBMToLinear( value, 7.0 );\\\\n\\\\n\\\\t\\\\t\\\\t} else if ( inputEncoding == 4 ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\treturn RGBMToLinear( value, 16.0 );\\\\n\\\\n\\\\t\\\\t\\\\t} else if ( inputEncoding == 5 ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\treturn RGBDToLinear( value, 256.0 );\\\\n\\\\n\\\\t\\\\t\\\\t} else {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\treturn GammaToLinear( value, 2.2 );\\\\n\\\\n\\\\t\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\tvec4 linearToOutputTexel( vec4 value ) {\\\\n\\\\n\\\\t\\\\t\\\\tif ( outputEncoding == 0 ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\treturn value;\\\\n\\\\n\\\\t\\\\t\\\\t} else if ( outputEncoding == 1 ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\treturn LinearTosRGB( value );\\\\n\\\\n\\\\t\\\\t\\\\t} else if ( outputEncoding == 2 ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\treturn LinearToRGBE( value );\\\\n\\\\n\\\\t\\\\t\\\\t} else if ( outputEncoding == 3 ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\treturn LinearToRGBM( value, 7.0 );\\\\n\\\\n\\\\t\\\\t\\\\t} else if ( outputEncoding == 4 ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\treturn LinearToRGBM( value, 16.0 );\\\\n\\\\n\\\\t\\\\t\\\\t} else if ( outputEncoding == 5 ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\treturn LinearToRGBD( value, 256.0 );\\\\n\\\\n\\\\t\\\\t\\\\t} else {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\treturn LinearToGamma( value, 2.2 );\\\\n\\\\n\\\\t\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\tvec4 envMapTexelToLinear( vec4 color ) {\\\\n\\\\n\\\\t\\\\t\\\\treturn inputTexelToLinear( color );\\\\n\\\\n\\\\t\\\\t}\\\\n\\\\t\\\\\\\"}function PT(t){let e=new WeakMap,n=null;function i(t){const n=t.target;n.removeEventListener(\\\\\\\"dispose\\\\\\\",i);const r=e.get(n);void 0!==r&&(e.delete(n),r.dispose())}return{get:function(r){if(r&&r.isTexture&&!1===r.isRenderTargetTexture){const s=r.mapping,o=s===vy||s===yy,a=s===fy||s===gy;if(o||a){if(e.has(r))return e.get(r).texture;{const s=r.image;if(o&&s&&s.height>0||a&&s&&function(t){let e=0;const n=6;for(let i=0;i<n;i++)void 0!==t[i]&&e++;return e===n}(s)){const s=t.getRenderTarget();null===n&&(n=new AT(t));const a=o?n.fromEquirectangular(r):n.fromCubemap(r);return e.set(r,a),t.setRenderTarget(s),r.addEventListener(\\\\\\\"dispose\\\\\\\",i),a.texture}return null}}}return r},dispose:function(){e=new WeakMap,null!==n&&(n.dispose(),n=null)}}}function IT(t){const e={};function n(n){if(void 0!==e[n])return e[n];let i;switch(n){case\\\\\\\"WEBGL_depth_texture\\\\\\\":i=t.getExtension(\\\\\\\"WEBGL_depth_texture\\\\\\\")||t.getExtension(\\\\\\\"MOZ_WEBGL_depth_texture\\\\\\\")||t.getExtension(\\\\\\\"WEBKIT_WEBGL_depth_texture\\\\\\\");break;case\\\\\\\"EXT_texture_filter_anisotropic\\\\\\\":i=t.getExtension(\\\\\\\"EXT_texture_filter_anisotropic\\\\\\\")||t.getExtension(\\\\\\\"MOZ_EXT_texture_filter_anisotropic\\\\\\\")||t.getExtension(\\\\\\\"WEBKIT_EXT_texture_filter_anisotropic\\\\\\\");break;case\\\\\\\"WEBGL_compressed_texture_s3tc\\\\\\\":i=t.getExtension(\\\\\\\"WEBGL_compressed_texture_s3tc\\\\\\\")||t.getExtension(\\\\\\\"MOZ_WEBGL_compressed_texture_s3tc\\\\\\\")||t.getExtension(\\\\\\\"WEBKIT_WEBGL_compressed_texture_s3tc\\\\\\\");break;case\\\\\\\"WEBGL_compressed_texture_pvrtc\\\\\\\":i=t.getExtension(\\\\\\\"WEBGL_compressed_texture_pvrtc\\\\\\\")||t.getExtension(\\\\\\\"WEBKIT_WEBGL_compressed_texture_pvrtc\\\\\\\");break;default:i=t.getExtension(n)}return e[n]=i,i}return{has:function(t){return null!==n(t)},init:function(t){t.isWebGL2?n(\\\\\\\"EXT_color_buffer_float\\\\\\\"):(n(\\\\\\\"WEBGL_depth_texture\\\\\\\"),n(\\\\\\\"OES_texture_float\\\\\\\"),n(\\\\\\\"OES_texture_half_float\\\\\\\"),n(\\\\\\\"OES_texture_half_float_linear\\\\\\\"),n(\\\\\\\"OES_standard_derivatives\\\\\\\"),n(\\\\\\\"OES_element_index_uint\\\\\\\"),n(\\\\\\\"OES_vertex_array_object\\\\\\\"),n(\\\\\\\"ANGLE_instanced_arrays\\\\\\\")),n(\\\\\\\"OES_texture_float_linear\\\\\\\"),n(\\\\\\\"EXT_color_buffer_half_float\\\\\\\")},get:function(t){const e=n(t);return null===e&&console.warn(\\\\\\\"THREE.WebGLRenderer: \\\\\\\"+t+\\\\\\\" extension not supported.\\\\\\\"),e}}}function FT(t,e,n,i){const r={},s=new WeakMap;function o(t){const a=t.target;null!==a.index&&e.remove(a.index);for(const t in a.attributes)e.remove(a.attributes[t]);a.removeEventListener(\\\\\\\"dispose\\\\\\\",o),delete r[a.id];const l=s.get(a);l&&(e.remove(l),s.delete(a)),i.releaseStatesOfGeometry(a),!0===a.isInstancedBufferGeometry&&delete a._maxInstanceCount,n.memory.geometries--}function a(t){const n=[],i=t.index,r=t.attributes.position;let o=0;if(null!==i){const t=i.array;o=i.version;for(let e=0,i=t.length;e<i;e+=3){const i=t[e+0],r=t[e+1],s=t[e+2];n.push(i,r,r,s,s,i)}}else{const t=r.array;o=r.version;for(let e=0,i=t.length/3-1;e<i;e+=3){const t=e+0,i=e+1,r=e+2;n.push(t,i,i,r,r,t)}}const a=new(vx(n)>65535?iw:nw)(n,1);a.version=o;const l=s.get(t);l&&e.remove(l),s.set(t,a)}return{get:function(t,e){return!0===r[e.id]||(e.addEventListener(\\\\\\\"dispose\\\\\\\",o),r[e.id]=!0,n.memory.geometries++),e},update:function(t){const n=t.attributes;for(const t in n)e.update(n[t],34962);const i=t.morphAttributes;for(const t in i){const n=i[t];for(let t=0,i=n.length;t<i;t++)e.update(n[t],34962)}},getWireframeAttribute:function(t){const e=s.get(t);if(e){const n=t.index;null!==n&&e.version<n.version&&a(t)}else a(t);return s.get(t)}}}function DT(t,e,n,i){const r=i.isWebGL2;let s,o,a;this.setMode=function(t){s=t},this.setIndex=function(t){o=t.type,a=t.bytesPerElement},this.render=function(e,i){t.drawElements(s,i,o,e*a),n.update(i,s,1)},this.renderInstances=function(i,l,c){if(0===c)return;let u,h;if(r)u=t,h=\\\\\\\"drawElementsInstanced\\\\\\\";else if(u=e.get(\\\\\\\"ANGLE_instanced_arrays\\\\\\\"),h=\\\\\\\"drawElementsInstancedANGLE\\\\\\\",null===u)return void console.error(\\\\\\\"THREE.WebGLIndexedBufferRenderer: using THREE.InstancedBufferGeometry but hardware does not support extension ANGLE_instanced_arrays.\\\\\\\");u[h](s,l,o,i*a,c),n.update(l,s,c)}}function kT(t){const e={frame:0,calls:0,triangles:0,points:0,lines:0};return{memory:{geometries:0,textures:0},render:e,programs:null,autoReset:!0,reset:function(){e.frame++,e.calls=0,e.triangles=0,e.points=0,e.lines=0},update:function(t,n,i){switch(e.calls++,n){case 4:e.triangles+=i*(t/3);break;case 1:e.lines+=i*(t/2);break;case 3:e.lines+=i*(t-1);break;case 2:e.lines+=i*t;break;case 0:e.points+=i*t;break;default:console.error(\\\\\\\"THREE.WebGLInfo: Unknown draw mode:\\\\\\\",n)}}}}class BT extends Tx{constructor(t=null,e=1,n=1,i=1){super(null),this.image={data:t,width:e,height:n,depth:i},this.magFilter=Ey,this.minFilter=Ey,this.wrapR=Ty,this.generateMipmaps=!1,this.flipY=!1,this.unpackAlignment=1,this.needsUpdate=!0}}function zT(t,e){return t[0]-e[0]}function UT(t,e){return Math.abs(e[1])-Math.abs(t[1])}function GT(t,e){let n=1;const i=e.isInterleavedBufferAttribute?e.data.array:e.array;i instanceof Int8Array?n=127:i instanceof Int16Array?n=32767:i instanceof Int32Array?n=2147483647:console.error(\\\\\\\"THREE.WebGLMorphtargets: Unsupported morph attribute data type: \\\\\\\",i),t.divideScalar(n)}function VT(t,e,n){const i={},r=new Float32Array(8),s=new WeakMap,o=new Nx,a=[];for(let t=0;t<8;t++)a[t]=[t,0];return{update:function(l,c,u,h){const d=l.morphTargetInfluences;if(!0===e.isWebGL2){const i=c.morphAttributes.position.length;let r=s.get(c);if(void 0===r||r.count!==i){void 0!==r&&r.texture.dispose();const t=void 0!==c.morphAttributes.normal,n=c.morphAttributes.position,a=c.morphAttributes.normal||[],l=!0===t?2:1;let u=c.attributes.position.count*l,h=1;u>e.maxTextureSize&&(h=Math.ceil(u/e.maxTextureSize),u=e.maxTextureSize);const d=new Float32Array(u*h*4*i),p=new BT(d,u,h,i);p.format=By,p.type=Iy;const _=4*l;for(let e=0;e<i;e++){const i=n[e],r=a[e],s=u*h*4*e;for(let e=0;e<i.count;e++){o.fromBufferAttribute(i,e),!0===i.normalized&&GT(o,i);const n=e*_;d[s+n+0]=o.x,d[s+n+1]=o.y,d[s+n+2]=o.z,d[s+n+3]=0,!0===t&&(o.fromBufferAttribute(r,e),!0===r.normalized&&GT(o,r),d[s+n+4]=o.x,d[s+n+5]=o.y,d[s+n+6]=o.z,d[s+n+7]=0)}}r={count:i,texture:p,size:new fx(u,h)},s.set(c,r)}let a=0;for(let t=0;t<d.length;t++)a+=d[t];const l=c.morphTargetsRelative?1:1-a;h.getUniforms().setValue(t,\\\\\\\"morphTargetBaseInfluence\\\\\\\",l),h.getUniforms().setValue(t,\\\\\\\"morphTargetInfluences\\\\\\\",d),h.getUniforms().setValue(t,\\\\\\\"morphTargetsTexture\\\\\\\",r.texture,n),h.getUniforms().setValue(t,\\\\\\\"morphTargetsTextureSize\\\\\\\",r.size)}else{const e=void 0===d?0:d.length;let n=i[c.id];if(void 0===n||n.length!==e){n=[];for(let t=0;t<e;t++)n[t]=[t,0];i[c.id]=n}for(let t=0;t<e;t++){const e=n[t];e[0]=t,e[1]=d[t]}n.sort(UT);for(let t=0;t<8;t++)t<e&&n[t][1]?(a[t][0]=n[t][0],a[t][1]=n[t][1]):(a[t][0]=Number.MAX_SAFE_INTEGER,a[t][1]=0);a.sort(zT);const s=c.morphAttributes.position,o=c.morphAttributes.normal;let l=0;for(let t=0;t<8;t++){const e=a[t],n=e[0],i=e[1];n!==Number.MAX_SAFE_INTEGER&&i?(s&&c.getAttribute(\\\\\\\"morphTarget\\\\\\\"+t)!==s[n]&&c.setAttribute(\\\\\\\"morphTarget\\\\\\\"+t,s[n]),o&&c.getAttribute(\\\\\\\"morphNormal\\\\\\\"+t)!==o[n]&&c.setAttribute(\\\\\\\"morphNormal\\\\\\\"+t,o[n]),r[t]=i,l+=i):(s&&!0===c.hasAttribute(\\\\\\\"morphTarget\\\\\\\"+t)&&c.deleteAttribute(\\\\\\\"morphTarget\\\\\\\"+t),o&&!0===c.hasAttribute(\\\\\\\"morphNormal\\\\\\\"+t)&&c.deleteAttribute(\\\\\\\"morphNormal\\\\\\\"+t),r[t]=0)}const u=c.morphTargetsRelative?1:1-l;h.getUniforms().setValue(t,\\\\\\\"morphTargetBaseInfluence\\\\\\\",u),h.getUniforms().setValue(t,\\\\\\\"morphTargetInfluences\\\\\\\",r)}}}}function HT(t,e,n,i){let r=new WeakMap;function s(t){const e=t.target;e.removeEventListener(\\\\\\\"dispose\\\\\\\",s),n.remove(e.instanceMatrix),null!==e.instanceColor&&n.remove(e.instanceColor)}return{update:function(t){const o=i.render.frame,a=t.geometry,l=e.get(t,a);return r.get(l)!==o&&(e.update(l),r.set(l,o)),t.isInstancedMesh&&(!1===t.hasEventListener(\\\\\\\"dispose\\\\\\\",s)&&t.addEventListener(\\\\\\\"dispose\\\\\\\",s),n.update(t.instanceMatrix,34962),null!==t.instanceColor&&n.update(t.instanceColor,34962)),l},dispose:function(){r=new WeakMap}}}BT.prototype.isDataTexture2DArray=!0;class jT extends Tx{constructor(t=null,e=1,n=1,i=1){super(null),this.image={data:t,width:e,height:n,depth:i},this.magFilter=Ey,this.minFilter=Ey,this.wrapR=Ty,this.generateMipmaps=!1,this.flipY=!1,this.unpackAlignment=1,this.needsUpdate=!0}}jT.prototype.isDataTexture3D=!0;const WT=new Tx,qT=new BT,XT=new jT,YT=new Gw,$T=[],JT=[],ZT=new Float32Array(16),QT=new Float32Array(9),KT=new Float32Array(4);function tA(t,e,n){const i=t[0];if(i<=0||i>0)return t;const r=e*n;let s=$T[r];if(void 0===s&&(s=new Float32Array(r),$T[r]=s),0!==e){i.toArray(s,0);for(let i=1,r=0;i!==e;++i)r+=n,t[i].toArray(s,r)}return s}function eA(t,e){if(t.length!==e.length)return!1;for(let 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0!==e.x)n[0]===e.x&&n[1]===e.y&&n[2]===e.z&&n[3]===e.w||(t.uniform4f(this.addr,e.x,e.y,e.z,e.w),n[0]=e.x,n[1]=e.y,n[2]=e.z,n[3]=e.w);else{if(eA(n,e))return;t.uniform4fv(this.addr,e),nA(n,e)}}function lA(t,e){const n=this.cache,i=e.elements;if(void 0===i){if(eA(n,e))return;t.uniformMatrix2fv(this.addr,!1,e),nA(n,e)}else{if(eA(n,i))return;KT.set(i),t.uniformMatrix2fv(this.addr,!1,KT),nA(n,i)}}function cA(t,e){const n=this.cache,i=e.elements;if(void 0===i){if(eA(n,e))return;t.uniformMatrix3fv(this.addr,!1,e),nA(n,e)}else{if(eA(n,i))return;QT.set(i),t.uniformMatrix3fv(this.addr,!1,QT),nA(n,i)}}function uA(t,e){const n=this.cache,i=e.elements;if(void 0===i){if(eA(n,e))return;t.uniformMatrix4fv(this.addr,!1,e),nA(n,e)}else{if(eA(n,i))return;ZT.set(i),t.uniformMatrix4fv(this.addr,!1,ZT),nA(n,i)}}function hA(t,e){const n=this.cache;n[0]!==e&&(t.uniform1i(this.addr,e),n[0]=e)}function dA(t,e){const n=this.cache;eA(n,e)||(t.uniform2iv(this.addr,e),nA(n,e))}function pA(t,e){const n=this.cache;eA(n,e)||(t.uniform3iv(this.addr,e),nA(n,e))}function _A(t,e){const n=this.cache;eA(n,e)||(t.uniform4iv(this.addr,e),nA(n,e))}function mA(t,e){const n=this.cache;n[0]!==e&&(t.uniform1ui(this.addr,e),n[0]=e)}function fA(t,e){const n=this.cache;eA(n,e)||(t.uniform2uiv(this.addr,e),nA(n,e))}function gA(t,e){const n=this.cache;eA(n,e)||(t.uniform3uiv(this.addr,e),nA(n,e))}function vA(t,e){const n=this.cache;eA(n,e)||(t.uniform4uiv(this.addr,e),nA(n,e))}function yA(t,e,n){const i=this.cache,r=n.allocateTextureUnit();i[0]!==r&&(t.uniform1i(this.addr,r),i[0]=r),n.safeSetTexture2D(e||WT,r)}function xA(t,e,n){const i=this.cache,r=n.allocateTextureUnit();i[0]!==r&&(t.uniform1i(this.addr,r),i[0]=r),n.setTexture3D(e||XT,r)}function bA(t,e,n){const i=this.cache,r=n.allocateTextureUnit();i[0]!==r&&(t.uniform1i(this.addr,r),i[0]=r),n.safeSetTextureCube(e||YT,r)}function wA(t,e,n){const i=this.cache,r=n.allocateTextureUnit();i[0]!==r&&(t.uniform1i(this.addr,r),i[0]=r),n.setTexture2DArray(e||qT,r)}function TA(t,e){t.uniform1fv(this.addr,e)}function AA(t,e){const n=tA(e,this.size,2);t.uniform2fv(this.addr,n)}function EA(t,e){const n=tA(e,this.size,3);t.uniform3fv(this.addr,n)}function MA(t,e){const n=tA(e,this.size,4);t.uniform4fv(this.addr,n)}function SA(t,e){const n=tA(e,this.size,4);t.uniformMatrix2fv(this.addr,!1,n)}function CA(t,e){const n=tA(e,this.size,9);t.uniformMatrix3fv(this.addr,!1,n)}function NA(t,e){const n=tA(e,this.size,16);t.uniformMatrix4fv(this.addr,!1,n)}function LA(t,e){t.uniform1iv(this.addr,e)}function OA(t,e){t.uniform2iv(this.addr,e)}function RA(t,e){t.uniform3iv(this.addr,e)}function PA(t,e){t.uniform4iv(this.addr,e)}function IA(t,e){t.uniform1uiv(this.addr,e)}function FA(t,e){t.uniform2uiv(this.addr,e)}function DA(t,e){t.uniform3uiv(this.addr,e)}function kA(t,e){t.uniform4uiv(this.addr,e)}function BA(t,e,n){const i=e.length,r=iA(n,i);t.uniform1iv(this.addr,r);for(let t=0;t!==i;++t)n.safeSetTexture2D(e[t]||WT,r[t])}function zA(t,e,n){const i=e.length,r=iA(n,i);t.uniform1iv(this.addr,r);for(let t=0;t!==i;++t)n.safeSetTextureCube(e[t]||YT,r[t])}function UA(t,e,n){this.id=t,this.addr=n,this.cache=[],this.setValue=function(t){switch(t){case 5126:return rA;case 35664:return sA;case 35665:return oA;case 35666:return aA;case 35674:return lA;case 35675:return cA;case 35676:return uA;case 5124:case 35670:return hA;case 35667:case 35671:return dA;case 35668:case 35672:return pA;case 35669:case 35673:return _A;case 5125:return mA;case 36294:return fA;case 36295:return gA;case 36296:return vA;case 35678:case 36198:case 36298:case 36306:case 35682:return yA;case 35679:case 36299:case 36307:return xA;case 35680:case 36300:case 36308:case 36293:return bA;case 36289:case 36303:case 36311:case 36292:return wA}}(e.type)}function GA(t,e,n){this.id=t,this.addr=n,this.cache=[],this.size=e.size,this.setValue=function(t){switch(t){case 5126:return TA;case 35664:return AA;case 35665:return EA;case 35666:return MA;case 35674:return SA;case 35675:return CA;case 35676:return NA;case 5124:case 35670:return LA;case 35667:case 35671:return OA;case 35668:case 35672:return RA;case 35669:case 35673:return PA;case 5125:return IA;case 36294:return FA;case 36295:return DA;case 36296:return kA;case 35678:case 36198:case 36298:case 36306:case 35682:return BA;case 35680:case 36300:case 36308:case 36293:return zA}}(e.type)}function VA(t){this.id=t,this.seq=[],this.map={}}GA.prototype.updateCache=function(t){const e=this.cache;t instanceof Float32Array&&e.length!==t.length&&(this.cache=new Float32Array(t.length)),nA(e,t)},VA.prototype.setValue=function(t,e,n){const i=this.seq;for(let r=0,s=i.length;r!==s;++r){const s=i[r];s.setValue(t,e[s.id],n)}};const HA=/(\\\\w+)(\\\\])?(\\\\[|\\\\.)?/g;function 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i&&\\\\\\\"\\\\\\\"===r?\\\\\\\"\\\\\\\":n.toUpperCase()+\\\\\\\"\\\\n\\\\n\\\\\\\"+r+\\\\\\\"\\\\n\\\\n\\\\\\\"+function(t){const e=t.split(\\\\\\\"\\\\n\\\\\\\");for(let t=0;t<e.length;t++)e[t]=t+1+\\\\\\\": \\\\\\\"+e[t];return e.join(\\\\\\\"\\\\n\\\\\\\")}(t.getShaderSource(e))}function ZA(t,e){const n=$A(e);return\\\\\\\"vec4 \\\\\\\"+t+\\\\\\\"( vec4 value ) { return \\\\\\\"+n[0]+\\\\\\\"ToLinear\\\\\\\"+n[1]+\\\\\\\"; }\\\\\\\"}function QA(t,e){const n=$A(e);return\\\\\\\"vec4 \\\\\\\"+t+\\\\\\\"( vec4 value ) { return LinearTo\\\\\\\"+n[0]+n[1]+\\\\\\\"; }\\\\\\\"}function KA(t,e){let n;switch(e){case 1:n=\\\\\\\"Linear\\\\\\\";break;case 2:n=\\\\\\\"Reinhard\\\\\\\";break;case 3:n=\\\\\\\"OptimizedCineon\\\\\\\";break;case 4:n=\\\\\\\"ACESFilmic\\\\\\\";break;case 5:n=\\\\\\\"Custom\\\\\\\";break;default:console.warn(\\\\\\\"THREE.WebGLProgram: Unsupported toneMapping:\\\\\\\",e),n=\\\\\\\"Linear\\\\\\\"}return\\\\\\\"vec3 \\\\\\\"+t+\\\\\\\"( vec3 color ) { return 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1===t.shadowMapType?e=\\\\\\\"SHADOWMAP_TYPE_PCF\\\\\\\":2===t.shadowMapType?e=\\\\\\\"SHADOWMAP_TYPE_PCF_SOFT\\\\\\\":3===t.shadowMapType&&(e=\\\\\\\"SHADOWMAP_TYPE_VSM\\\\\\\"),e}(n),c=function(t){let e=\\\\\\\"ENVMAP_TYPE_CUBE\\\\\\\";if(t.envMap)switch(t.envMapMode){case fy:case gy:e=\\\\\\\"ENVMAP_TYPE_CUBE\\\\\\\";break;case xy:case by:e=\\\\\\\"ENVMAP_TYPE_CUBE_UV\\\\\\\"}return e}(n),u=function(t){let e=\\\\\\\"ENVMAP_MODE_REFLECTION\\\\\\\";if(t.envMap)switch(t.envMapMode){case gy:case by:e=\\\\\\\"ENVMAP_MODE_REFRACTION\\\\\\\"}return e}(n),h=function(t){let e=\\\\\\\"ENVMAP_BLENDING_NONE\\\\\\\";if(t.envMap)switch(t.combine){case 0:e=\\\\\\\"ENVMAP_BLENDING_MULTIPLY\\\\\\\";break;case 1:e=\\\\\\\"ENVMAP_BLENDING_MIX\\\\\\\";break;case 2:e=\\\\\\\"ENVMAP_BLENDING_ADD\\\\\\\"}return 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\\\\\\\"+t+\\\\\\\"\\\\n\\\\\\\"+e+\\\\\\\"\\\\n\\\\\\\"+n)}else\\\\\\\"\\\\\\\"!==t?console.warn(\\\\\\\"THREE.WebGLProgram: Program Info Log:\\\\\\\",t):\\\\\\\"\\\\\\\"!==e&&\\\\\\\"\\\\\\\"!==n||(s=!1);s&&(this.diagnostics={runnable:i,programLog:t,vertexShader:{log:e,prefix:f},fragmentShader:{log:n,prefix:g}})}let w,T;return r.deleteShader(x),r.deleteShader(b),this.getUniforms=function(){return void 0===w&&(w=new qA(r,m)),w},this.getAttributes=function(){return void 0===T&&(T=function(t,e){const n={},i=t.getProgramParameter(e,35721);for(let r=0;r<i;r++){const i=t.getActiveAttrib(e,r),s=i.name;let o=1;35674===i.type&&(o=2),35675===i.type&&(o=3),35676===i.type&&(o=4),n[s]={type:i.type,location:t.getAttribLocation(e,s),locationSize:o}}return n}(r,m)),T},this.destroy=function(){i.releaseStatesOfProgram(this),r.deleteProgram(m),this.program=void 0},this.name=n.shaderName,this.id=YA++,this.cacheKey=e,this.usedTimes=1,this.program=m,this.vertexShader=x,this.fragmentShader=b,this}function pE(t,e,n,i,r,s,o){const a=[],l=r.isWebGL2,c=r.logarithmicDepthBuffer,u=r.floatVertexTextures,h=r.maxVertexUniforms,d=r.vertexTextures;let p=r.precision;const _={MeshDepthMaterial:\\\\\\\"depth\\\\\\\",MeshDistanceMaterial:\\\\\\\"distanceRGBA\\\\\\\",MeshNormalMaterial:\\\\\\\"normal\\\\\\\",MeshBasicMaterial:\\\\\\\"basic\\\\\\\",MeshLambertMaterial:\\\\\\\"lambert\\\\\\\",MeshPhongMaterial:\\\\\\\"phong\\\\\\\",MeshToonMaterial:\\\\\\\"toon\\\\\\\",MeshStandardMaterial:\\\\\\\"physical\\\\\\\",MeshPhysicalMaterial:\\\\\\\"physical\\\\\\\",MeshMatcapMaterial:\\\\\\\"matcap\\\\\\\",LineBasicMaterial:\\\\\\\"basic\\\\\\\",LineDashedMaterial:\\\\\\\"dashed\\\\\\\",PointsMaterial:\\\\\\\"points\\\\\\\",ShadowMaterial:\\\\\\\"shadow\\\\\\\",SpriteMaterial:\\\\\\\"sprite\\\\\\\"},m=[\\\\\\\"precision\\\\\\\",\\\\\\\"isWebGL2\\\\\\\",\\\\\\\"supportsVertexTextures\\\\\\\",\\\\\\\"outputEncoding\\\\\\\",\\\\\\\"instancing\\\\\\\",\\\\\\\"instancingColor\\\\\\\",\\\\\\\"map\\\\\\\",\\\\\\\"mapEncoding\\\\\\\",\\\\\\\"matcap\\\\\\\",\\\\\\\"matcapEncoding\\\\\\\",\\\\\\\"envMap\\\\\\\",\\\\\\\"envMapMode\\\\\\\",\\\\\\\"envMapEncoding\\\\\\\",\\\\\\\"envMapCubeUV\\\\\\\",\\\\\\\"lightMap\\\\\\\",\\\\\\\"lightMapEncoding\\\\\\\",\\\\\\\"aoMap\\\\\\\",\\\\\\\"emissiveMap\\\\\\\",\\\\\\\"emissiveMapEncoding\\\\\\\",\\\\\\\"bumpMap\\\\\\\",\\\\\\\"normalMap\\\\\\\",\\\\\\\"objectSpaceNormalMap\\\\\\\",\\\\\\\"tangentSpaceNormalMap\\\\\\\",\\\\\\\"clearcoat\\\\\\\",\\\\\\\"clearcoatMap\\\\\\\",\\\\\\\"clearcoatRoughnessMap\\\\\\\",\\\\\\\"clearcoatNormalMap\\\\\\\",\\\\\\\"displacementMap\\\\\\\",\\\\\\\"specularMap\\\\\\\",\\\\\\\"specularIntensityMap\\\\\\\",\\\\\\\"specularTintMap\\\\\\\",\\\\\\\"specularTintMapEncoding\\\\\\\",\\\\\\\"roughnessMap\\\\\\\",\\\\\\\"metalnessMap\\\\\\\",\\\\\\\"gradientMap\\\\\\\",\\\\\\\"alphaMap\\\\\\\",\\\\\\\"alphaTest\\\\\\\",\\\\\\\"combine\\\\\\\",\\\\\\\"vertexColors\\\\\\\",\\\\\\\"vertexAlphas\\\\\\\",\\\\\\\"vertexTangents\\\\\\\",\\\\\\\"vertexUvs\\\\\\\",\\\\\\\"uvsVertexOnly\\\\\\\",\\\\\\\"fog\\\\\\\",\\\\\\\"useFog\\\\\\\",\\\\\\\"fogExp2\\\\\\\",\\\\\\\"flatShading\\\\\\\",\\\\\\\"sizeAttenuation\\\\\\\",\\\\\\\"logarithmicDepthBuffer\\\\\\\",\\\\\\\"skinning\\\\\\\",\\\\\\\"maxBones\\\\\\\",\\\\\\\"useVertexTexture\\\\\\\",\\\\\\\"morphTargets\\\\\\\",\\\\\\\"morphNormals\\\\\\\",\\\\\\\"morphTargetsCount\\\\\\\",\\\\\\\"premultipliedAlpha\\\\\\\",\\\\\\\"numDirLights\\\\\\\",\\\\\\\"numPointLights\\\\\\\",\\\\\\\"numSpotLights\\\\\\\",\\\\\\\"numHemiLights\\\\\\\",\\\\\\\"numRectAreaLights\\\\\\\",\\\\\\\"numDirLightShadows\\\\\\\",\\\\\\\"numPointLightShadows\\\\\\\",\\\\\\\"numSpotLightShadows\\\\\\\",\\\\\\\"shadowMapEnabled\\\\\\\",\\\\\\\"shadowMapType\\\\\\\",\\\\\\\"toneMapping\\\\\\\",\\\\\\\"physicallyCorrectLights\\\\\\\",\\\\\\\"doubleSided\\\\\\\",\\\\\\\"flipSided\\\\\\\",\\\\\\\"numClippingPlanes\\\\\\\",\\\\\\\"numClipIntersection\\\\\\\",\\\\\\\"depthPacking\\\\\\\",\\\\\\\"dithering\\\\\\\",\\\\\\\"format\\\\\\\",\\\\\\\"sheen\\\\\\\",\\\\\\\"transmission\\\\\\\",\\\\\\\"transmissionMap\\\\\\\",\\\\\\\"thicknessMap\\\\\\\"];function f(t){let e;return t&&t.isTexture?e=t.encoding:t&&t.isWebGLRenderTarget?(console.warn(\\\\\\\"THREE.WebGLPrograms.getTextureEncodingFromMap: don't use render targets as textures. Use their .texture property instead.\\\\\\\"),e=t.texture.encoding):e=Yy,l&&t&&t.isTexture&&t.format===By&&t.type===Oy&&t.encoding===$y&&(e=Yy),e}return{getParameters:function(s,a,m,g,v){const y=g.fog,x=s.isMeshStandardMaterial?g.environment:null,b=(s.isMeshStandardMaterial?n:e).get(s.envMap||x),w=_[s.type],T=v.isSkinnedMesh?function(t){const e=t.skeleton.bones;if(u)return 1024;{const t=h,n=Math.floor((t-20)/4),i=Math.min(n,e.length);return i<e.length?(console.warn(\\\\\\\"THREE.WebGLRenderer: Skeleton has \\\\\\\"+e.length+\\\\\\\" bones. This GPU supports \\\\\\\"+i+\\\\\\\".\\\\\\\"),0):i}}(v):0;let A,E;if(null!==s.precision&&(p=r.getMaxPrecision(s.precision),p!==s.precision&&console.warn(\\\\\\\"THREE.WebGLProgram.getParameters:\\\\\\\",s.precision,\\\\\\\"not supported, using\\\\\\\",p,\\\\\\\"instead.\\\\\\\")),w){const t=eT[w];A=t.vertexShader,E=t.fragmentShader}else A=s.vertexShader,E=s.fragmentShader;const M=t.getRenderTarget(),S=s.alphaTest>0,C=s.clearcoat>0;return{isWebGL2:l,shaderID:w,shaderName:s.type,vertexShader:A,fragmentShader:E,defines:s.defines,isRawShaderMaterial:!0===s.isRawShaderMaterial,glslVersion:s.glslVersion,precision:p,instancing:!0===v.isInstancedMesh,instancingColor:!0===v.isInstancedMesh&&null!==v.instanceColor,supportsVertexTextures:d,outputEncoding:null!==M?f(M.texture):t.outputEncoding,map:!!s.map,mapEncoding:f(s.map),matcap:!!s.matcap,matcapEncoding:f(s.matcap),envMap:!!b,envMapMode:b&&b.mapping,envMapEncoding:f(b),envMapCubeUV:!!b&&(b.mapping===xy||b.mapping===by),lightMap:!!s.lightMap,lightMapEncoding:f(s.lightMap),aoMap:!!s.aoMap,emissiveMap:!!s.emissiveMap,emissiveMapEncoding:f(s.emissiveMap),bumpMap:!!s.bumpMap,normalMap:!!s.normalMap,objectSpaceNormalMap:1===s.normalMapType,tangentSpaceNormalMap:0===s.normalMapType,clearcoat:C,clearcoatMap:C&&!!s.clearcoatMap,clearcoatRoughnessMap:C&&!!s.clearcoatRoughnessMap,clearcoatNormalMap:C&&!!s.clearcoatNormalMap,displacementMap:!!s.displacementMap,roughnessMap:!!s.roughnessMap,metalnessMap:!!s.metalnessMap,specularMap:!!s.specularMap,specularIntensityMap:!!s.specularIntensityMap,specularTintMap:!!s.specularTintMap,specularTintMapEncoding:f(s.specularTintMap),alphaMap:!!s.alphaMap,alphaTest:S,gradientMap:!!s.gradientMap,sheen:s.sheen>0,transmission:s.transmission>0,transmissionMap:!!s.transmissionMap,thicknessMap:!!s.thicknessMap,combine:s.combine,vertexTangents:!!s.normalMap&&!!v.geometry&&!!v.geometry.attributes.tangent,vertexColors:s.vertexColors,vertexAlphas:!0===s.vertexColors&&!!v.geometry&&!!v.geometry.attributes.color&&4===v.geometry.attributes.color.itemSize,vertexUvs:!!(s.map||s.bumpMap||s.normalMap||s.specularMap||s.alphaMap||s.emissiveMap||s.roughnessMap||s.metalnessMap||s.clearcoatMap||s.clearcoatRoughnessMap||s.clearcoatNormalMap||s.displacementMap||s.transmissionMap||s.thicknessMap||s.specularIntensityMap||s.specularTintMap),uvsVertexOnly:!(s.map||s.bumpMap||s.normalMap||s.specularMap||s.alphaMap||s.emissiveMap||s.roughnessMap||s.metalnessMap||s.clearcoatNormalMap||s.transmission>0||s.transmissionMap||s.thicknessMap||s.specularIntensityMap||s.specularTintMap||!s.displacementMap),fog:!!y,useFog:s.fog,fogExp2:y&&y.isFogExp2,flatShading:!!s.flatShading,sizeAttenuation:s.sizeAttenuation,logarithmicDepthBuffer:c,skinning:!0===v.isSkinnedMesh&&T>0,maxBones:T,useVertexTexture:u,morphTargets:!!v.geometry&&!!v.geometry.morphAttributes.position,morphNormals:!!v.geometry&&!!v.geometry.morphAttributes.normal,morphTargetsCount:v.geometry&&v.geometry.morphAttributes.position?v.geometry.morphAttributes.position.length:0,numDirLights:a.directional.length,numPointLights:a.point.length,numSpotLights:a.spot.length,numRectAreaLights:a.rectArea.length,numHemiLights:a.hemi.length,numDirLightShadows:a.directionalShadowMap.length,numPointLightShadows:a.pointShadowMap.length,numSpotLightShadows:a.spotShadowMap.length,numClippingPlanes:o.numPlanes,numClipIntersection:o.numIntersection,format:s.format,dithering:s.dithering,shadowMapEnabled:t.shadowMap.enabled&&m.length>0,shadowMapType:t.shadowMap.type,toneMapping:s.toneMapped?t.toneMapping:0,physicallyCorrectLights:t.physicallyCorrectLights,premultipliedAlpha:s.premultipliedAlpha,doubleSided:2===s.side,flipSided:1===s.side,depthPacking:void 0!==s.depthPacking&&s.depthPacking,index0AttributeName:s.index0AttributeName,extensionDerivatives:s.extensions&&s.extensions.derivatives,extensionFragDepth:s.extensions&&s.extensions.fragDepth,extensionDrawBuffers:s.extensions&&s.extensions.drawBuffers,extensionShaderTextureLOD:s.extensions&&s.extensions.shaderTextureLOD,rendererExtensionFragDepth:l||i.has(\\\\\\\"EXT_frag_depth\\\\\\\"),rendererExtensionDrawBuffers:l||i.has(\\\\\\\"WEBGL_draw_buffers\\\\\\\"),rendererExtensionShaderTextureLod:l||i.has(\\\\\\\"EXT_shader_texture_lod\\\\\\\"),customProgramCacheKey:s.customProgramCacheKey()}},getProgramCacheKey:function(e){const n=[];if(e.shaderID?n.push(e.shaderID):(n.push(e.fragmentShader),n.push(e.vertexShader)),void 0!==e.defines)for(const t in e.defines)n.push(t),n.push(e.defines[t]);if(!1===e.isRawShaderMaterial){for(let t=0;t<m.length;t++)n.push(e[m[t]]);n.push(t.outputEncoding),n.push(t.gammaFactor)}return n.push(e.customProgramCacheKey),n.join()},getUniforms:function(t){const e=_[t.type];let n;if(e){const t=eT[e];n=Fw.clone(t.uniforms)}else n=t.uniforms;return n},acquireProgram:function(e,n){let i;for(let t=0,e=a.length;t<e;t++){const e=a[t];if(e.cacheKey===n){i=e,++i.usedTimes;break}}return void 0===i&&(i=new dE(t,n,e,s),a.push(i)),i},releaseProgram:function(t){if(0==--t.usedTimes){const e=a.indexOf(t);a[e]=a[a.length-1],a.pop(),t.destroy()}},programs:a}}function _E(){let t=new WeakMap;return{get:function(e){let n=t.get(e);return void 0===n&&(n={},t.set(e,n)),n},remove:function(e){t.delete(e)},update:function(e,n,i){t.get(e)[n]=i},dispose:function(){t=new WeakMap}}}function mE(t,e){return t.groupOrder!==e.groupOrder?t.groupOrder-e.groupOrder:t.renderOrder!==e.renderOrder?t.renderOrder-e.renderOrder:t.program!==e.program?t.program.id-e.program.id:t.material.id!==e.material.id?t.material.id-e.material.id:t.z!==e.z?t.z-e.z:t.id-e.id}function fE(t,e){return t.groupOrder!==e.groupOrder?t.groupOrder-e.groupOrder:t.renderOrder!==e.renderOrder?t.renderOrder-e.renderOrder:t.z!==e.z?e.z-t.z:t.id-e.id}function gE(t){const e=[];let n=0;const i=[],r=[],s=[],o={id:-1};function a(i,r,s,a,l,c){let u=e[n];const h=t.get(s);return void 0===u?(u={id:i.id,object:i,geometry:r,material:s,program:h.program||o,groupOrder:a,renderOrder:i.renderOrder,z:l,group:c},e[n]=u):(u.id=i.id,u.object=i,u.geometry=r,u.material=s,u.program=h.program||o,u.groupOrder=a,u.renderOrder=i.renderOrder,u.z=l,u.group=c),n++,u}return{opaque:i,transmissive:r,transparent:s,init:function(){n=0,i.length=0,r.length=0,s.length=0},push:function(t,e,n,o,l,c){const u=a(t,e,n,o,l,c);n.transmission>0?r.push(u):!0===n.transparent?s.push(u):i.push(u)},unshift:function(t,e,n,o,l,c){const u=a(t,e,n,o,l,c);n.transmission>0?r.unshift(u):!0===n.transparent?s.unshift(u):i.unshift(u)},finish:function(){for(let t=n,i=e.length;t<i;t++){const n=e[t];if(null===n.id)break;n.id=null,n.object=null,n.geometry=null,n.material=null,n.program=null,n.group=null}},sort:function(t,e){i.length>1&&i.sort(t||mE),r.length>1&&r.sort(e||fE),s.length>1&&s.sort(e||fE)}}}function vE(t){let e=new WeakMap;return{get:function(n,i){let r;return!1===e.has(n)?(r=new gE(t),e.set(n,[r])):i>=e.get(n).length?(r=new gE(t),e.get(n).push(r)):r=e.get(n)[i],r},dispose:function(){e=new WeakMap}}}function yE(){const t={};return{get:function(e){if(void 0!==t[e.id])return t[e.id];let n;switch(e.type){case\\\\\\\"DirectionalLight\\\\\\\":n={direction:new Nx,color:new Zb};break;case\\\\\\\"SpotLight\\\\\\\":n={position:new Nx,direction:new Nx,color:new Zb,distance:0,coneCos:0,penumbraCos:0,decay:0};break;case\\\\\\\"PointLight\\\\\\\":n={position:new Nx,color:new Zb,distance:0,decay:0};break;case\\\\\\\"HemisphereLight\\\\\\\":n={direction:new Nx,skyColor:new Zb,groundColor:new Zb};break;case\\\\\\\"RectAreaLight\\\\\\\":n={color:new Zb,position:new Nx,halfWidth:new Nx,halfHeight:new Nx}}return t[e.id]=n,n}}}let xE=0;function bE(t,e){return(e.castShadow?1:0)-(t.castShadow?1:0)}function wE(t,e){const n=new yE,i=function(){const t={};return{get:function(e){if(void 0!==t[e.id])return t[e.id];let n;switch(e.type){case\\\\\\\"DirectionalLight\\\\\\\":case\\\\\\\"SpotLight\\\\\\\":n={shadowBias:0,shadowNormalBias:0,shadowRadius:1,shadowMapSize:new fx};break;case\\\\\\\"PointLight\\\\\\\":n={shadowBias:0,shadowNormalBias:0,shadowRadius:1,shadowMapSize:new fx,shadowCameraNear:1,shadowCameraFar:1e3}}return t[e.id]=n,n}}}(),r={version:0,hash:{directionalLength:-1,pointLength:-1,spotLength:-1,rectAreaLength:-1,hemiLength:-1,numDirectionalShadows:-1,numPointShadows:-1,numSpotShadows:-1},ambient:[0,0,0],probe:[],directional:[],directionalShadow:[],directionalShadowMap:[],directionalShadowMatrix:[],spot:[],spotShadow:[],spotShadowMap:[],spotShadowMatrix:[],rectArea:[],rectAreaLTC1:null,rectAreaLTC2:null,point:[],pointShadow:[],pointShadowMap:[],pointShadowMatrix:[],hemi:[]};for(let t=0;t<9;t++)r.probe.push(new Nx);const s=new Nx,o=new ob,a=new ob;return{setup:function(s,o){let a=0,l=0,c=0;for(let t=0;t<9;t++)r.probe[t].set(0,0,0);let u=0,h=0,d=0,p=0,_=0,m=0,f=0,g=0;s.sort(bE);const v=!0!==o?Math.PI:1;for(let t=0,e=s.length;t<e;t++){const e=s[t],o=e.color,y=e.intensity,x=e.distance,b=e.shadow&&e.shadow.map?e.shadow.map.texture:null;if(e.isAmbientLight)a+=o.r*y*v,l+=o.g*y*v,c+=o.b*y*v;else if(e.isLightProbe)for(let t=0;t<9;t++)r.probe[t].addScaledVector(e.sh.coefficients[t],y);else if(e.isDirectionalLight){const t=n.get(e);if(t.color.copy(e.color).multiplyScalar(e.intensity*v),e.castShadow){const t=e.shadow,n=i.get(e);n.shadowBias=t.bias,n.shadowNormalBias=t.normalBias,n.shadowRadius=t.radius,n.shadowMapSize=t.mapSize,r.directionalShadow[u]=n,r.directionalShadowMap[u]=b,r.directionalShadowMatrix[u]=e.shadow.matrix,m++}r.directional[u]=t,u++}else if(e.isSpotLight){const t=n.get(e);if(t.position.setFromMatrixPosition(e.matrixWorld),t.color.copy(o).multiplyScalar(y*v),t.distance=x,t.coneCos=Math.cos(e.angle),t.penumbraCos=Math.cos(e.angle*(1-e.penumbra)),t.decay=e.decay,e.castShadow){const t=e.shadow,n=i.get(e);n.shadowBias=t.bias,n.shadowNormalBias=t.normalBias,n.shadowRadius=t.radius,n.shadowMapSize=t.mapSize,r.spotShadow[d]=n,r.spotShadowMap[d]=b,r.spotShadowMatrix[d]=e.shadow.matrix,g++}r.spot[d]=t,d++}else if(e.isRectAreaLight){const t=n.get(e);t.color.copy(o).multiplyScalar(y),t.halfWidth.set(.5*e.width,0,0),t.halfHeight.set(0,.5*e.height,0),r.rectArea[p]=t,p++}else if(e.isPointLight){const t=n.get(e);if(t.color.copy(e.color).multiplyScalar(e.intensity*v),t.distance=e.distance,t.decay=e.decay,e.castShadow){const t=e.shadow,n=i.get(e);n.shadowBias=t.bias,n.shadowNormalBias=t.normalBias,n.shadowRadius=t.radius,n.shadowMapSize=t.mapSize,n.shadowCameraNear=t.camera.near,n.shadowCameraFar=t.camera.far,r.pointShadow[h]=n,r.pointShadowMap[h]=b,r.pointShadowMatrix[h]=e.shadow.matrix,f++}r.point[h]=t,h++}else if(e.isHemisphereLight){const t=n.get(e);t.skyColor.copy(e.color).multiplyScalar(y*v),t.groundColor.copy(e.groundColor).multiplyScalar(y*v),r.hemi[_]=t,_++}}p>0&&(e.isWebGL2||!0===t.has(\\\\\\\"OES_texture_float_linear\\\\\\\")?(r.rectAreaLTC1=tT.LTC_FLOAT_1,r.rectAreaLTC2=tT.LTC_FLOAT_2):!0===t.has(\\\\\\\"OES_texture_half_float_linear\\\\\\\")?(r.rectAreaLTC1=tT.LTC_HALF_1,r.rectAreaLTC2=tT.LTC_HALF_2):console.error(\\\\\\\"THREE.WebGLRenderer: Unable to use RectAreaLight. Missing WebGL extensions.\\\\\\\")),r.ambient[0]=a,r.ambient[1]=l,r.ambient[2]=c;const y=r.hash;y.directionalLength===u&&y.pointLength===h&&y.spotLength===d&&y.rectAreaLength===p&&y.hemiLength===_&&y.numDirectionalShadows===m&&y.numPointShadows===f&&y.numSpotShadows===g||(r.directional.length=u,r.spot.length=d,r.rectArea.length=p,r.point.length=h,r.hemi.length=_,r.directionalShadow.length=m,r.directionalShadowMap.length=m,r.pointShadow.length=f,r.pointShadowMap.length=f,r.spotShadow.length=g,r.spotShadowMap.length=g,r.directionalShadowMatrix.length=m,r.pointShadowMatrix.length=f,r.spotShadowMatrix.length=g,y.directionalLength=u,y.pointLength=h,y.spotLength=d,y.rectAreaLength=p,y.hemiLength=_,y.numDirectionalShadows=m,y.numPointShadows=f,y.numSpotShadows=g,r.version=xE++)},setupView:function(t,e){let n=0,i=0,l=0,c=0,u=0;const h=e.matrixWorldInverse;for(let e=0,d=t.length;e<d;e++){const d=t[e];if(d.isDirectionalLight){const t=r.directional[n];t.direction.setFromMatrixPosition(d.matrixWorld),s.setFromMatrixPosition(d.target.matrixWorld),t.direction.sub(s),t.direction.transformDirection(h),n++}else if(d.isSpotLight){const t=r.spot[l];t.position.setFromMatrixPosition(d.matrixWorld),t.position.applyMatrix4(h),t.direction.setFromMatrixPosition(d.matrixWorld),s.setFromMatrixPosition(d.target.matrixWorld),t.direction.sub(s),t.direction.transformDirection(h),l++}else if(d.isRectAreaLight){const t=r.rectArea[c];t.position.setFromMatrixPosition(d.matrixWorld),t.position.applyMatrix4(h),a.identity(),o.copy(d.matrixWorld),o.premultiply(h),a.extractRotation(o),t.halfWidth.set(.5*d.width,0,0),t.halfHeight.set(0,.5*d.height,0),t.halfWidth.applyMatrix4(a),t.halfHeight.applyMatrix4(a),c++}else if(d.isPointLight){const t=r.point[i];t.position.setFromMatrixPosition(d.matrixWorld),t.position.applyMatrix4(h),i++}else if(d.isHemisphereLight){const t=r.hemi[u];t.direction.setFromMatrixPosition(d.matrixWorld),t.direction.transformDirection(h),t.direction.normalize(),u++}}},state:r}}function TE(t,e){const n=new wE(t,e),i=[],r=[];return{init:function(){i.length=0,r.length=0},state:{lightsArray:i,shadowsArray:r,lights:n},setupLights:function(t){n.setup(i,t)},setupLightsView:function(t){n.setupView(i,t)},pushLight:function(t){i.push(t)},pushShadow:function(t){r.push(t)}}}function AE(t,e){let n=new WeakMap;return{get:function(i,r=0){let s;return!1===n.has(i)?(s=new TE(t,e),n.set(i,[s])):r>=n.get(i).length?(s=new TE(t,e),n.get(i).push(s)):s=n.get(i)[r],s},dispose:function(){n=new WeakMap}}}class EE extends jb{constructor(t){super(),this.type=\\\\\\\"MeshDepthMaterial\\\\\\\",this.depthPacking=3200,this.map=null,this.alphaMap=null,this.displacementMap=null,this.displacementScale=1,this.displacementBias=0,this.wireframe=!1,this.wireframeLinewidth=1,this.fog=!1,this.setValues(t)}copy(t){return super.copy(t),this.depthPacking=t.depthPacking,this.map=t.map,this.alphaMap=t.alphaMap,this.displacementMap=t.displacementMap,this.displacementScale=t.displacementScale,this.displacementBias=t.displacementBias,this.wireframe=t.wireframe,this.wireframeLinewidth=t.wireframeLinewidth,this}}EE.prototype.isMeshDepthMaterial=!0;class ME extends jb{constructor(t){super(),this.type=\\\\\\\"MeshDistanceMaterial\\\\\\\",this.referencePosition=new Nx,this.nearDistance=1,this.farDistance=1e3,this.map=null,this.alphaMap=null,this.displacementMap=null,this.displacementScale=1,this.displacementBias=0,this.fog=!1,this.setValues(t)}copy(t){return super.copy(t),this.referencePosition.copy(t.referencePosition),this.nearDistance=t.nearDistance,this.farDistance=t.farDistance,this.map=t.map,this.alphaMap=t.alphaMap,this.displacementMap=t.displacementMap,this.displacementScale=t.displacementScale,this.displacementBias=t.displacementBias,this}}ME.prototype.isMeshDistanceMaterial=!0;function SE(t,e,n){let i=new $w;const r=new fx,s=new fx,o=new Ex,a=new EE({depthPacking:3201}),l=new ME,c={},u=n.maxTextureSize,h={0:1,1:0,2:2},d=new Dw({uniforms:{shadow_pass:{value:null},resolution:{value:new fx},radius:{value:4},samples:{value:8}},vertexShader:\\\\\\\"void main() {\\\\n\\\\tgl_Position = vec4( position, 1.0 );\\\\n}\\\\\\\",fragmentShader:\\\\\\\"uniform sampler2D shadow_pass;\\\\nuniform vec2 resolution;\\\\nuniform float radius;\\\\nuniform float samples;\\\\n#include <packing>\\\\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 ) 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N(n,s,o){if(o?(t.texParameteri(n,10242,S[s.wrapS]),t.texParameteri(n,10243,S[s.wrapT]),32879!==n&&35866!==n||t.texParameteri(n,32882,S[s.wrapR]),t.texParameteri(n,10240,C[s.magFilter]),t.texParameteri(n,10241,C[s.minFilter])):(t.texParameteri(n,10242,33071),t.texParameteri(n,10243,33071),32879!==n&&35866!==n||t.texParameteri(n,32882,33071),s.wrapS===Ty&&s.wrapT===Ty||console.warn(\\\\\\\"THREE.WebGLRenderer: Texture is not power of two. Texture.wrapS and Texture.wrapT should be set to THREE.ClampToEdgeWrapping.\\\\\\\"),t.texParameteri(n,10240,b(s.magFilter)),t.texParameteri(n,10241,b(s.minFilter)),s.minFilter!==Ey&&s.minFilter!==Cy&&console.warn(\\\\\\\"THREE.WebGLRenderer: Texture is not power of two. Texture.minFilter should be set to THREE.NearestFilter or THREE.LinearFilter.\\\\\\\")),!0===e.has(\\\\\\\"EXT_texture_filter_anisotropic\\\\\\\")){const o=e.get(\\\\\\\"EXT_texture_filter_anisotropic\\\\\\\");if(s.type===Iy&&!1===e.has(\\\\\\\"OES_texture_float_linear\\\\\\\"))return;if(!1===a&&s.type===Fy&&!1===e.has(\\\\\\\"OES_texture_half_float_linear\\\\\\\"))return;(s.anisotropy>1||i.get(s).__currentAnisotropy)&&(t.texParameterf(n,o.TEXTURE_MAX_ANISOTROPY_EXT,Math.min(s.anisotropy,r.getMaxAnisotropy())),i.get(s).__currentAnisotropy=s.anisotropy)}}function L(e,n){void 0===e.__webglInit&&(e.__webglInit=!0,n.addEventListener(\\\\\\\"dispose\\\\\\\",w),e.__webglTexture=t.createTexture(),o.memory.textures++)}function O(e,i,r){let o=3553;i.isDataTexture2DArray&&(o=35866),i.isDataTexture3D&&(o=32879),L(e,i),n.activeTexture(33984+r),n.bindTexture(o,e.__webglTexture),t.pixelStorei(37440,i.flipY),t.pixelStorei(37441,i.premultiplyAlpha),t.pixelStorei(3317,i.unpackAlignment),t.pixelStorei(37443,0);const l=function(t){return!a&&(t.wrapS!==Ty||t.wrapT!==Ty||t.minFilter!==Ey&&t.minFilter!==Cy)}(i)&&!1===g(i.image),c=f(i.image,l,!1,u),h=g(c)||a,d=s.convert(i.format);let p,_=s.convert(i.type),m=x(i.internalFormat,d,_,i.encoding);N(o,i,h);const b=i.mipmaps;if(i.isDepthTexture)m=6402,a?m=i.type===Iy?36012:i.type===Py?33190:i.type===Dy?35056:33189:i.type===Iy&&console.error(\\\\\\\"WebGLRenderer: Floating point depth texture requires WebGL2.\\\\\\\"),i.format===zy&&6402===m&&i.type!==Ry&&i.type!==Py&&(console.warn(\\\\\\\"THREE.WebGLRenderer: Use UnsignedShortType or UnsignedIntType for DepthFormat DepthTexture.\\\\\\\"),i.type=Ry,_=s.convert(i.type)),i.format===Uy&&6402===m&&(m=34041,i.type!==Dy&&(console.warn(\\\\\\\"THREE.WebGLRenderer: Use UnsignedInt248Type for DepthStencilFormat DepthTexture.\\\\\\\"),i.type=Dy,_=s.convert(i.type))),n.texImage2D(3553,0,m,c.width,c.height,0,d,_,null);else if(i.isDataTexture)if(b.length>0&&h){for(let t=0,e=b.length;t<e;t++)p=b[t],n.texImage2D(3553,t,m,p.width,p.height,0,d,_,p.data);i.generateMipmaps=!1,e.__maxMipLevel=b.length-1}else n.texImage2D(3553,0,m,c.width,c.height,0,d,_,c.data),e.__maxMipLevel=0;else if(i.isCompressedTexture){for(let t=0,e=b.length;t<e;t++)p=b[t],i.format!==By&&i.format!==ky?null!==d?n.compressedTexImage2D(3553,t,m,p.width,p.height,0,p.data):console.warn(\\\\\\\"THREE.WebGLRenderer: Attempt to load unsupported compressed texture format in .uploadTexture()\\\\\\\"):n.texImage2D(3553,t,m,p.width,p.height,0,d,_,p.data);e.__maxMipLevel=b.length-1}else if(i.isDataTexture2DArray)n.texImage3D(35866,0,m,c.width,c.height,c.depth,0,d,_,c.data),e.__maxMipLevel=0;else if(i.isDataTexture3D)n.texImage3D(32879,0,m,c.width,c.height,c.depth,0,d,_,c.data),e.__maxMipLevel=0;else if(b.length>0&&h){for(let t=0,e=b.length;t<e;t++)p=b[t],n.texImage2D(3553,t,m,d,_,p);i.generateMipmaps=!1,e.__maxMipLevel=b.length-1}else n.texImage2D(3553,0,m,d,_,c),e.__maxMipLevel=0;v(i,h)&&y(o,i,c.width,c.height),e.__version=i.version,i.onUpdate&&i.onUpdate(i)}function R(e,r,o,a,l){const c=s.convert(o.format),u=s.convert(o.type),h=x(o.internalFormat,c,u,o.encoding);32879===l||35866===l?n.texImage3D(l,0,h,r.width,r.height,r.depth,0,c,u,null):n.texImage2D(l,0,h,r.width,r.height,0,c,u,null),n.bindFramebuffer(36160,e),t.framebufferTexture2D(36160,a,l,i.get(o).__webglTexture,0),n.bindFramebuffer(36160,null)}function P(e,n,i){if(t.bindRenderbuffer(36161,e),n.depthBuffer&&!n.stencilBuffer){let r=33189;if(i){const e=n.depthTexture;e&&e.isDepthTexture&&(e.type===Iy?r=36012:e.type===Py&&(r=33190));const i=F(n);t.renderbufferStorageMultisample(36161,i,r,n.width,n.height)}else t.renderbufferStorage(36161,r,n.width,n.height);t.framebufferRenderbuffer(36160,36096,36161,e)}else if(n.depthBuffer&&n.stencilBuffer){if(i){const e=F(n);t.renderbufferStorageMultisample(36161,e,35056,n.width,n.height)}else t.renderbufferStorage(36161,34041,n.width,n.height);t.framebufferRenderbuffer(36160,33306,36161,e)}else{const e=!0===n.isWebGLMultipleRenderTargets?n.texture[0]:n.texture,r=s.convert(e.format),o=s.convert(e.type),a=x(e.internalFormat,r,o,e.encoding);if(i){const e=F(n);t.renderbufferStorageMultisample(36161,e,a,n.width,n.height)}else t.renderbufferStorage(36161,a,n.width,n.height)}t.bindRenderbuffer(36161,null)}function I(e){const r=i.get(e),s=!0===e.isWebGLCubeRenderTarget;if(e.depthTexture){if(s)throw new Error(\\\\\\\"target.depthTexture not supported in Cube render targets\\\\\\\");!function(e,r){if(r&&r.isWebGLCubeRenderTarget)throw new Error(\\\\\\\"Depth Texture with cube render targets is not supported\\\\\\\");if(n.bindFramebuffer(36160,e),!r.depthTexture||!r.depthTexture.isDepthTexture)throw new Error(\\\\\\\"renderTarget.depthTexture must be an instance of THREE.DepthTexture\\\\\\\");i.get(r.depthTexture).__webglTexture&&r.depthTexture.image.width===r.width&&r.depthTexture.image.height===r.height||(r.depthTexture.image.width=r.width,r.depthTexture.image.height=r.height,r.depthTexture.needsUpdate=!0),E(r.depthTexture,0);const s=i.get(r.depthTexture).__webglTexture;if(r.depthTexture.format===zy)t.framebufferTexture2D(36160,36096,3553,s,0);else{if(r.depthTexture.format!==Uy)throw new Error(\\\\\\\"Unknown depthTexture format\\\\\\\");t.framebufferTexture2D(36160,33306,3553,s,0)}}(r.__webglFramebuffer,e)}else if(s){r.__webglDepthbuffer=[];for(let i=0;i<6;i++)n.bindFramebuffer(36160,r.__webglFramebuffer[i]),r.__webglDepthbuffer[i]=t.createRenderbuffer(),P(r.__webglDepthbuffer[i],e,!1)}else n.bindFramebuffer(36160,r.__webglFramebuffer),r.__webglDepthbuffer=t.createRenderbuffer(),P(r.__webglDepthbuffer,e,!1);n.bindFramebuffer(36160,null)}function F(t){return a&&t.isWebGLMultisampleRenderTarget?Math.min(h,t.samples):0}let D=!1,k=!1;this.allocateTextureUnit=function(){const t=A;return t>=l&&console.warn(\\\\\\\"THREE.WebGLTextures: Trying to use \\\\\\\"+t+\\\\\\\" texture units while this GPU supports only \\\\\\\"+l),A+=1,t},this.resetTextureUnits=function(){A=0},this.setTexture2D=E,this.setTexture2DArray=function(t,e){const r=i.get(t);t.version>0&&r.__version!==t.version?O(r,t,e):(n.activeTexture(33984+e),n.bindTexture(35866,r.__webglTexture))},this.setTexture3D=function(t,e){const r=i.get(t);t.version>0&&r.__version!==t.version?O(r,t,e):(n.activeTexture(33984+e),n.bindTexture(32879,r.__webglTexture))},this.setTextureCube=M,this.setupRenderTarget=function(e){const l=e.texture,c=i.get(e),u=i.get(l);e.addEventListener(\\\\\\\"dispose\\\\\\\",T),!0!==e.isWebGLMultipleRenderTargets&&(u.__webglTexture=t.createTexture(),u.__version=l.version,o.memory.textures++);const h=!0===e.isWebGLCubeRenderTarget,d=!0===e.isWebGLMultipleRenderTargets,p=!0===e.isWebGLMultisampleRenderTarget,_=l.isDataTexture3D||l.isDataTexture2DArray,m=g(e)||a;if(!a||l.format!==ky||l.type!==Iy&&l.type!==Fy||(l.format=By,console.warn(\\\\\\\"THREE.WebGLRenderer: Rendering to textures with RGB format is not supported. Using RGBA format instead.\\\\\\\")),h){c.__webglFramebuffer=[];for(let e=0;e<6;e++)c.__webglFramebuffer[e]=t.createFramebuffer()}else if(c.__webglFramebuffer=t.createFramebuffer(),d)if(r.drawBuffers){const n=e.texture;for(let e=0,r=n.length;e<r;e++){const r=i.get(n[e]);void 0===r.__webglTexture&&(r.__webglTexture=t.createTexture(),o.memory.textures++)}}else console.warn(\\\\\\\"THREE.WebGLRenderer: WebGLMultipleRenderTargets can only be used with WebGL2 or WEBGL_draw_buffers extension.\\\\\\\");else if(p)if(a){c.__webglMultisampledFramebuffer=t.createFramebuffer(),c.__webglColorRenderbuffer=t.createRenderbuffer(),t.bindRenderbuffer(36161,c.__webglColorRenderbuffer);const i=s.convert(l.format),r=s.convert(l.type),o=x(l.internalFormat,i,r,l.encoding),a=F(e);t.renderbufferStorageMultisample(36161,a,o,e.width,e.height),n.bindFramebuffer(36160,c.__webglMultisampledFramebuffer),t.framebufferRenderbuffer(36160,36064,36161,c.__webglColorRenderbuffer),t.bindRenderbuffer(36161,null),e.depthBuffer&&(c.__webglDepthRenderbuffer=t.createRenderbuffer(),P(c.__webglDepthRenderbuffer,e,!0)),n.bindFramebuffer(36160,null)}else console.warn(\\\\\\\"THREE.WebGLRenderer: WebGLMultisampleRenderTarget can only be used with WebGL2.\\\\\\\");if(h){n.bindTexture(34067,u.__webglTexture),N(34067,l,m);for(let t=0;t<6;t++)R(c.__webglFramebuffer[t],e,l,36064,34069+t);v(l,m)&&y(34067,l,e.width,e.height),n.unbindTexture()}else if(d){const t=e.texture;for(let r=0,s=t.length;r<s;r++){const s=t[r],o=i.get(s);n.bindTexture(3553,o.__webglTexture),N(3553,s,m),R(c.__webglFramebuffer,e,s,36064+r,3553),v(s,m)&&y(3553,s,e.width,e.height)}n.unbindTexture()}else{let t=3553;if(_)if(a){t=l.isDataTexture3D?32879:35866}else console.warn(\\\\\\\"THREE.DataTexture3D and THREE.DataTexture2DArray only supported with WebGL2.\\\\\\\");n.bindTexture(t,u.__webglTexture),N(t,l,m),R(c.__webglFramebuffer,e,l,36064,t),v(l,m)&&y(t,l,e.width,e.height,e.depth),n.unbindTexture()}e.depthBuffer&&I(e)},this.updateRenderTargetMipmap=function(t){const e=g(t)||a,r=!0===t.isWebGLMultipleRenderTargets?t.texture:[t.texture];for(let s=0,o=r.length;s<o;s++){const o=r[s];if(v(o,e)){const e=t.isWebGLCubeRenderTarget?34067:3553,r=i.get(o).__webglTexture;n.bindTexture(e,r),y(e,o,t.width,t.height),n.unbindTexture()}}},this.updateMultisampleRenderTarget=function(e){if(e.isWebGLMultisampleRenderTarget)if(a){const r=e.width,s=e.height;let o=16384;e.depthBuffer&&(o|=256),e.stencilBuffer&&(o|=1024);const a=i.get(e);n.bindFramebuffer(36008,a.__webglMultisampledFramebuffer),n.bindFramebuffer(36009,a.__webglFramebuffer),t.blitFramebuffer(0,0,r,s,0,0,r,s,o,9728),n.bindFramebuffer(36008,null),n.bindFramebuffer(36009,a.__webglMultisampledFramebuffer)}else console.warn(\\\\\\\"THREE.WebGLRenderer: WebGLMultisampleRenderTarget can only be used with WebGL2.\\\\\\\")},this.safeSetTexture2D=function(t,e){t&&t.isWebGLRenderTarget&&(!1===D&&(console.warn(\\\\\\\"THREE.WebGLTextures.safeSetTexture2D: don't use render targets as textures. Use their .texture property instead.\\\\\\\"),D=!0),t=t.texture),E(t,e)},this.safeSetTextureCube=function(t,e){t&&t.isWebGLCubeRenderTarget&&(!1===k&&(console.warn(\\\\\\\"THREE.WebGLTextures.safeSetTextureCube: don't use cube render targets as textures. Use their .texture property instead.\\\\\\\"),k=!0),t=t.texture),M(t,e)}}function LE(t,e,n){const i=n.isWebGL2;return{convert:function(t){let n;if(t===Oy)return 5121;if(1017===t)return 32819;if(1018===t)return 32820;if(1019===t)return 33635;if(1010===t)return 5120;if(1011===t)return 5122;if(t===Ry)return 5123;if(1013===t)return 5124;if(t===Py)return 5125;if(t===Iy)return 5126;if(t===Fy)return i?5131:(n=e.get(\\\\\\\"OES_texture_half_float\\\\\\\"),null!==n?n.HALF_FLOAT_OES:null);if(1021===t)return 6406;if(t===ky)return 6407;if(t===By)return 6408;if(1024===t)return 6409;if(1025===t)return 6410;if(t===zy)return 6402;if(t===Uy)return 34041;if(1028===t)return 6403;if(1029===t)return 36244;if(1030===t)return 33319;if(1031===t)return 33320;if(1032===t)return 36248;if(1033===t)return 36249;if(33776===t||33777===t||33778===t||33779===t){if(n=e.get(\\\\\\\"WEBGL_compressed_texture_s3tc\\\\\\\"),null===n)return null;if(33776===t)return n.COMPRESSED_RGB_S3TC_DXT1_EXT;if(33777===t)return n.COMPRESSED_RGBA_S3TC_DXT1_EXT;if(33778===t)return n.COMPRESSED_RGBA_S3TC_DXT3_EXT;if(33779===t)return n.COMPRESSED_RGBA_S3TC_DXT5_EXT}if(35840===t||35841===t||35842===t||35843===t){if(n=e.get(\\\\\\\"WEBGL_compressed_texture_pvrtc\\\\\\\"),null===n)return null;if(35840===t)return n.COMPRESSED_RGB_PVRTC_4BPPV1_IMG;if(35841===t)return n.COMPRESSED_RGB_PVRTC_2BPPV1_IMG;if(35842===t)return n.COMPRESSED_RGBA_PVRTC_4BPPV1_IMG;if(35843===t)return n.COMPRESSED_RGBA_PVRTC_2BPPV1_IMG}if(36196===t)return n=e.get(\\\\\\\"WEBGL_compressed_texture_etc1\\\\\\\"),null!==n?n.COMPRESSED_RGB_ETC1_WEBGL:null;if((37492===t||37496===t)&&(n=e.get(\\\\\\\"WEBGL_compressed_texture_etc\\\\\\\"),null!==n)){if(37492===t)return n.COMPRESSED_RGB8_ETC2;if(37496===t)return n.COMPRESSED_RGBA8_ETC2_EAC}return 37808===t||37809===t||37810===t||37811===t||37812===t||37813===t||37814===t||37815===t||37816===t||37817===t||37818===t||37819===t||37820===t||37821===t||37840===t||37841===t||37842===t||37843===t||37844===t||37845===t||37846===t||37847===t||37848===t||37849===t||37850===t||37851===t||37852===t||37853===t?(n=e.get(\\\\\\\"WEBGL_compressed_texture_astc\\\\\\\"),null!==n?t:null):36492===t?(n=e.get(\\\\\\\"EXT_texture_compression_bptc\\\\\\\"),null!==n?t:null):t===Dy?i?34042:(n=e.get(\\\\\\\"WEBGL_depth_texture\\\\\\\"),null!==n?n.UNSIGNED_INT_24_8_WEBGL:null):void 0}}}class OE extends Bw{constructor(t=[]){super(),this.cameras=t}}OE.prototype.isArrayCamera=!0;class RE extends Ob{constructor(){super(),this.type=\\\\\\\"Group\\\\\\\"}}RE.prototype.isGroup=!0;const PE={type:\\\\\\\"move\\\\\\\"};class IE{constructor(){this._targetRay=null,this._grip=null,this._hand=null}getHandSpace(){return null===this._hand&&(this._hand=new RE,this._hand.matrixAutoUpdate=!1,this._hand.visible=!1,this._hand.joints={},this._hand.inputState={pinching:!1}),this._hand}getTargetRaySpace(){return null===this._targetRay&&(this._targetRay=new RE,this._targetRay.matrixAutoUpdate=!1,this._targetRay.visible=!1,this._targetRay.hasLinearVelocity=!1,this._targetRay.linearVelocity=new Nx,this._targetRay.hasAngularVelocity=!1,this._targetRay.angularVelocity=new Nx),this._targetRay}getGripSpace(){return null===this._grip&&(this._grip=new RE,this._grip.matrixAutoUpdate=!1,this._grip.visible=!1,this._grip.hasLinearVelocity=!1,this._grip.linearVelocity=new Nx,this._grip.hasAngularVelocity=!1,this._grip.angularVelocity=new Nx),this._grip}dispatchEvent(t){return null!==this._targetRay&&this._targetRay.dispatchEvent(t),null!==this._grip&&this._grip.dispatchEvent(t),null!==this._hand&&this._hand.dispatchEvent(t),this}disconnect(t){return this.dispatchEvent({type:\\\\\\\"disconnected\\\\\\\",data:t}),null!==this._targetRay&&(this._targetRay.visible=!1),null!==this._grip&&(this._grip.visible=!1),null!==this._hand&&(this._hand.visible=!1),this}update(t,e,n){let i=null,r=null,s=null;const o=this._targetRay,a=this._grip,l=this._hand;if(t&&\\\\\\\"visible-blurred\\\\\\\"!==e.session.visibilityState)if(null!==o&&(i=e.getPose(t.targetRaySpace,n),null!==i&&(o.matrix.fromArray(i.transform.matrix),o.matrix.decompose(o.position,o.rotation,o.scale),i.linearVelocity?(o.hasLinearVelocity=!0,o.linearVelocity.copy(i.linearVelocity)):o.hasLinearVelocity=!1,i.angularVelocity?(o.hasAngularVelocity=!0,o.angularVelocity.copy(i.angularVelocity)):o.hasAngularVelocity=!1,this.dispatchEvent(PE))),l&&t.hand){s=!0;for(const i of t.hand.values()){const t=e.getJointPose(i,n);if(void 0===l.joints[i.jointName]){const t=new RE;t.matrixAutoUpdate=!1,t.visible=!1,l.joints[i.jointName]=t,l.add(t)}const r=l.joints[i.jointName];null!==t&&(r.matrix.fromArray(t.transform.matrix),r.matrix.decompose(r.position,r.rotation,r.scale),r.jointRadius=t.radius),r.visible=null!==t}const i=l.joints[\\\\\\\"index-finger-tip\\\\\\\"],r=l.joints[\\\\\\\"thumb-tip\\\\\\\"],o=i.position.distanceTo(r.position),a=.02,c=.005;l.inputState.pinching&&o>a+c?(l.inputState.pinching=!1,this.dispatchEvent({type:\\\\\\\"pinchend\\\\\\\",handedness:t.handedness,target:this})):!l.inputState.pinching&&o<=a-c&&(l.inputState.pinching=!0,this.dispatchEvent({type:\\\\\\\"pinchstart\\\\\\\",handedness:t.handedness,target:this}))}else null!==a&&t.gripSpace&&(r=e.getPose(t.gripSpace,n),null!==r&&(a.matrix.fromArray(r.transform.matrix),a.matrix.decompose(a.position,a.rotation,a.scale),r.linearVelocity?(a.hasLinearVelocity=!0,a.linearVelocity.copy(r.linearVelocity)):a.hasLinearVelocity=!1,r.angularVelocity?(a.hasAngularVelocity=!0,a.angularVelocity.copy(r.angularVelocity)):a.hasAngularVelocity=!1));return null!==o&&(o.visible=null!==i),null!==a&&(a.visible=null!==r),null!==l&&(l.visible=null!==s),this}}class FE extends nx{constructor(t,e){super();const n=this,i=t.state;let r=null,s=1,o=null,a=\\\\\\\"local-floor\\\\\\\",l=null,c=null,u=null,h=null,d=null,p=!1,_=null,m=null,f=null,g=null,v=null,y=null;const x=[],b=new Map,w=new Bw;w.layers.enable(1),w.viewport=new Ex;const T=new Bw;T.layers.enable(2),T.viewport=new Ex;const A=[w,T],E=new OE;E.layers.enable(1),E.layers.enable(2);let M=null,S=null;function C(t){const e=b.get(t.inputSource);e&&e.dispatchEvent({type:t.type,data:t.inputSource})}function N(){b.forEach((function(t,e){t.disconnect(e)})),b.clear(),M=null,S=null,i.bindXRFramebuffer(null),t.setRenderTarget(t.getRenderTarget()),u&&e.deleteFramebuffer(u),_&&e.deleteFramebuffer(_),m&&e.deleteRenderbuffer(m),f&&e.deleteRenderbuffer(f),u=null,_=null,m=null,f=null,d=null,h=null,c=null,r=null,F.stop(),n.isPresenting=!1,n.dispatchEvent({type:\\\\\\\"sessionend\\\\\\\"})}function L(t){const e=r.inputSources;for(let t=0;t<x.length;t++)b.set(e[t],x[t]);for(let e=0;e<t.removed.length;e++){const n=t.removed[e],i=b.get(n);i&&(i.dispatchEvent({type:\\\\\\\"disconnected\\\\\\\",data:n}),b.delete(n))}for(let e=0;e<t.added.length;e++){const n=t.added[e],i=b.get(n);i&&i.dispatchEvent({type:\\\\\\\"connected\\\\\\\",data:n})}}this.cameraAutoUpdate=!0,this.enabled=!1,this.isPresenting=!1,this.getController=function(t){let e=x[t];return void 0===e&&(e=new IE,x[t]=e),e.getTargetRaySpace()},this.getControllerGrip=function(t){let e=x[t];return void 0===e&&(e=new IE,x[t]=e),e.getGripSpace()},this.getHand=function(t){let e=x[t];return void 0===e&&(e=new IE,x[t]=e),e.getHandSpace()},this.setFramebufferScaleFactor=function(t){s=t,!0===n.isPresenting&&console.warn(\\\\\\\"THREE.WebXRManager: Cannot change framebuffer scale while presenting.\\\\\\\")},this.setReferenceSpaceType=function(t){a=t,!0===n.isPresenting&&console.warn(\\\\\\\"THREE.WebXRManager: Cannot change reference space type while presenting.\\\\\\\")},this.getReferenceSpace=function(){return o},this.getBaseLayer=function(){return null!==h?h:d},this.getBinding=function(){return c},this.getFrame=function(){return g},this.getSession=function(){return r},this.setSession=async function(t){if(r=t,null!==r){r.addEventListener(\\\\\\\"select\\\\\\\",C),r.addEventListener(\\\\\\\"selectstart\\\\\\\",C),r.addEventListener(\\\\\\\"selectend\\\\\\\",C),r.addEventListener(\\\\\\\"squeeze\\\\\\\",C),r.addEventListener(\\\\\\\"squeezestart\\\\\\\",C),r.addEventListener(\\\\\\\"squeezeend\\\\\\\",C),r.addEventListener(\\\\\\\"end\\\\\\\",N),r.addEventListener(\\\\\\\"inputsourceschange\\\\\\\",L);const t=e.getContextAttributes();if(!0!==t.xrCompatible&&await e.makeXRCompatible(),void 0===r.renderState.layers){const n={antialias:t.antialias,alpha:t.alpha,depth:t.depth,stencil:t.stencil,framebufferScaleFactor:s};d=new XRWebGLLayer(r,e,n),r.updateRenderState({baseLayer:d})}else if(e instanceof WebGLRenderingContext){const n={antialias:!0,alpha:t.alpha,depth:t.depth,stencil:t.stencil,framebufferScaleFactor:s};d=new XRWebGLLayer(r,e,n),r.updateRenderState({layers:[d]})}else{p=t.antialias;let n=null;t.depth&&(y=256,t.stencil&&(y|=1024),v=t.stencil?33306:36096,n=t.stencil?35056:33190);const o={colorFormat:t.alpha?32856:32849,depthFormat:n,scaleFactor:s};c=new XRWebGLBinding(r,e),h=c.createProjectionLayer(o),u=e.createFramebuffer(),r.updateRenderState({layers:[h]}),p&&(_=e.createFramebuffer(),m=e.createRenderbuffer(),e.bindRenderbuffer(36161,m),e.renderbufferStorageMultisample(36161,4,32856,h.textureWidth,h.textureHeight),i.bindFramebuffer(36160,_),e.framebufferRenderbuffer(36160,36064,36161,m),e.bindRenderbuffer(36161,null),null!==n&&(f=e.createRenderbuffer(),e.bindRenderbuffer(36161,f),e.renderbufferStorageMultisample(36161,4,n,h.textureWidth,h.textureHeight),e.framebufferRenderbuffer(36160,v,36161,f),e.bindRenderbuffer(36161,null)),i.bindFramebuffer(36160,null))}o=await r.requestReferenceSpace(a),F.setContext(r),F.start(),n.isPresenting=!0,n.dispatchEvent({type:\\\\\\\"sessionstart\\\\\\\"})}};const O=new Nx,R=new Nx;function P(t,e){null===e?t.matrixWorld.copy(t.matrix):t.matrixWorld.multiplyMatrices(e.matrixWorld,t.matrix),t.matrixWorldInverse.copy(t.matrixWorld).invert()}this.updateCamera=function(t){if(null===r)return;E.near=T.near=w.near=t.near,E.far=T.far=w.far=t.far,M===E.near&&S===E.far||(r.updateRenderState({depthNear:E.near,depthFar:E.far}),M=E.near,S=E.far);const e=t.parent,n=E.cameras;P(E,e);for(let t=0;t<n.length;t++)P(n[t],e);E.matrixWorld.decompose(E.position,E.quaternion,E.scale),t.position.copy(E.position),t.quaternion.copy(E.quaternion),t.scale.copy(E.scale),t.matrix.copy(E.matrix),t.matrixWorld.copy(E.matrixWorld);const i=t.children;for(let t=0,e=i.length;t<e;t++)i[t].updateMatrixWorld(!0);2===n.length?function(t,e,n){O.setFromMatrixPosition(e.matrixWorld),R.setFromMatrixPosition(n.matrixWorld);const i=O.distanceTo(R),r=e.projectionMatrix.elements,s=n.projectionMatrix.elements,o=r[14]/(r[10]-1),a=r[14]/(r[10]+1),l=(r[9]+1)/r[5],c=(r[9]-1)/r[5],u=(r[8]-1)/r[0],h=(s[8]+1)/s[0],d=o*u,p=o*h,_=i/(-u+h),m=_*-u;e.matrixWorld.decompose(t.position,t.quaternion,t.scale),t.translateX(m),t.translateZ(_),t.matrixWorld.compose(t.position,t.quaternion,t.scale),t.matrixWorldInverse.copy(t.matrixWorld).invert();const f=o+_,g=a+_,v=d-m,y=p+(i-m),x=l*a/g*f,b=c*a/g*f;t.projectionMatrix.makePerspective(v,y,x,b,f,g)}(E,w,T):E.projectionMatrix.copy(w.projectionMatrix)},this.getCamera=function(){return E},this.getFoveation=function(){return null!==h?h.fixedFoveation:null!==d?d.fixedFoveation:void 0},this.setFoveation=function(t){null!==h&&(h.fixedFoveation=t),null!==d&&void 0!==d.fixedFoveation&&(d.fixedFoveation=t)};let I=null;const F=new Jw;F.setAnimationLoop((function(t,n){if(l=n.getViewerPose(o),g=n,null!==l){const t=l.views;null!==d&&i.bindXRFramebuffer(d.framebuffer);let n=!1;t.length!==E.cameras.length&&(E.cameras.length=0,n=!0);for(let r=0;r<t.length;r++){const s=t[r];let o=null;if(null!==d)o=d.getViewport(s);else{const t=c.getViewSubImage(h,s);i.bindXRFramebuffer(u),void 0!==t.depthStencilTexture&&e.framebufferTexture2D(36160,v,3553,t.depthStencilTexture,0),e.framebufferTexture2D(36160,36064,3553,t.colorTexture,0),o=t.viewport}const a=A[r];a.matrix.fromArray(s.transform.matrix),a.projectionMatrix.fromArray(s.projectionMatrix),a.viewport.set(o.x,o.y,o.width,o.height),0===r&&E.matrix.copy(a.matrix),!0===n&&E.cameras.push(a)}p&&(i.bindXRFramebuffer(_),null!==y&&e.clear(y))}const s=r.inputSources;for(let t=0;t<x.length;t++){const e=x[t],i=s[t];e.update(i,n,o)}if(I&&I(t,n),p){const t=h.textureWidth,n=h.textureHeight;i.bindFramebuffer(36008,_),i.bindFramebuffer(36009,u),e.invalidateFramebuffer(36008,[v]),e.invalidateFramebuffer(36009,[v]),e.blitFramebuffer(0,0,t,n,0,0,t,n,16384,9728),e.invalidateFramebuffer(36008,[36064]),i.bindFramebuffer(36008,null),i.bindFramebuffer(36009,null),i.bindFramebuffer(36160,_)}g=null})),this.setAnimationLoop=function(t){I=t},this.dispose=function(){}}}function DE(t){function e(e,n){e.opacity.value=n.opacity,n.color&&e.diffuse.value.copy(n.color),n.emissive&&e.emissive.value.copy(n.emissive).multiplyScalar(n.emissiveIntensity),n.map&&(e.map.value=n.map),n.alphaMap&&(e.alphaMap.value=n.alphaMap),n.specularMap&&(e.specularMap.value=n.specularMap),n.alphaTest>0&&(e.alphaTest.value=n.alphaTest);const i=t.get(n).envMap;if(i){e.envMap.value=i,e.flipEnvMap.value=i.isCubeTexture&&!1===i.isRenderTargetTexture?-1:1,e.reflectivity.value=n.reflectivity,e.ior.value=n.ior,e.refractionRatio.value=n.refractionRatio;const r=t.get(i).__maxMipLevel;void 0!==r&&(e.maxMipLevel.value=r)}let r,s;n.lightMap&&(e.lightMap.value=n.lightMap,e.lightMapIntensity.value=n.lightMapIntensity),n.aoMap&&(e.aoMap.value=n.aoMap,e.aoMapIntensity.value=n.aoMapIntensity),n.map?r=n.map:n.specularMap?r=n.specularMap:n.displacementMap?r=n.displacementMap:n.normalMap?r=n.normalMap:n.bumpMap?r=n.bumpMap:n.roughnessMap?r=n.roughnessMap:n.metalnessMap?r=n.metalnessMap:n.alphaMap?r=n.alphaMap:n.emissiveMap?r=n.emissiveMap:n.clearcoatMap?r=n.clearcoatMap:n.clearcoatNormalMap?r=n.clearcoatNormalMap:n.clearcoatRoughnessMap?r=n.clearcoatRoughnessMap:n.specularIntensityMap?r=n.specularIntensityMap:n.specularTintMap?r=n.specularTintMap:n.transmissionMap?r=n.transmissionMap:n.thicknessMap&&(r=n.thicknessMap),void 0!==r&&(r.isWebGLRenderTarget&&(r=r.texture),!0===r.matrixAutoUpdate&&r.updateMatrix(),e.uvTransform.value.copy(r.matrix)),n.aoMap?s=n.aoMap:n.lightMap&&(s=n.lightMap),void 0!==s&&(s.isWebGLRenderTarget&&(s=s.texture),!0===s.matrixAutoUpdate&&s.updateMatrix(),e.uv2Transform.value.copy(s.matrix))}function n(e,n){e.roughness.value=n.roughness,e.metalness.value=n.metalness,n.roughnessMap&&(e.roughnessMap.value=n.roughnessMap),n.metalnessMap&&(e.metalnessMap.value=n.metalnessMap),n.emissiveMap&&(e.emissiveMap.value=n.emissiveMap),n.bumpMap&&(e.bumpMap.value=n.bumpMap,e.bumpScale.value=n.bumpScale,1===n.side&&(e.bumpScale.value*=-1)),n.normalMap&&(e.normalMap.value=n.normalMap,e.normalScale.value.copy(n.normalScale),1===n.side&&e.normalScale.value.negate()),n.displacementMap&&(e.displacementMap.value=n.displacementMap,e.displacementScale.value=n.displacementScale,e.displacementBias.value=n.displacementBias);t.get(n).envMap&&(e.envMapIntensity.value=n.envMapIntensity)}return{refreshFogUniforms:function(t,e){t.fogColor.value.copy(e.color),e.isFog?(t.fogNear.value=e.near,t.fogFar.value=e.far):e.isFogExp2&&(t.fogDensity.value=e.density)},refreshMaterialUniforms:function(t,i,r,s,o){i.isMeshBasicMaterial?e(t,i):i.isMeshLambertMaterial?(e(t,i),function(t,e){e.emissiveMap&&(t.emissiveMap.value=e.emissiveMap)}(t,i)):i.isMeshToonMaterial?(e(t,i),function(t,e){e.gradientMap&&(t.gradientMap.value=e.gradientMap);e.emissiveMap&&(t.emissiveMap.value=e.emissiveMap);e.bumpMap&&(t.bumpMap.value=e.bumpMap,t.bumpScale.value=e.bumpScale,1===e.side&&(t.bumpScale.value*=-1));e.normalMap&&(t.normalMap.value=e.normalMap,t.normalScale.value.copy(e.normalScale),1===e.side&&t.normalScale.value.negate());e.displacementMap&&(t.displacementMap.value=e.displacementMap,t.displacementScale.value=e.displacementScale,t.displacementBias.value=e.displacementBias)}(t,i)):i.isMeshPhongMaterial?(e(t,i),function(t,e){t.specular.value.copy(e.specular),t.shininess.value=Math.max(e.shininess,1e-4),e.emissiveMap&&(t.emissiveMap.value=e.emissiveMap);e.bumpMap&&(t.bumpMap.value=e.bumpMap,t.bumpScale.value=e.bumpScale,1===e.side&&(t.bumpScale.value*=-1));e.normalMap&&(t.normalMap.value=e.normalMap,t.normalScale.value.copy(e.normalScale),1===e.side&&t.normalScale.value.negate());e.displacementMap&&(t.displacementMap.value=e.displacementMap,t.displacementScale.value=e.displacementScale,t.displacementBias.value=e.displacementBias)}(t,i)):i.isMeshStandardMaterial?(e(t,i),i.isMeshPhysicalMaterial?function(t,e,i){n(t,e),t.ior.value=e.ior,e.sheen>0&&(t.sheenTint.value.copy(e.sheenTint).multiplyScalar(e.sheen),t.sheenRoughness.value=e.sheenRoughness);e.clearcoat>0&&(t.clearcoat.value=e.clearcoat,t.clearcoatRoughness.value=e.clearcoatRoughness,e.clearcoatMap&&(t.clearcoatMap.value=e.clearcoatMap),e.clearcoatRoughnessMap&&(t.clearcoatRoughnessMap.value=e.clearcoatRoughnessMap),e.clearcoatNormalMap&&(t.clearcoatNormalScale.value.copy(e.clearcoatNormalScale),t.clearcoatNormalMap.value=e.clearcoatNormalMap,1===e.side&&t.clearcoatNormalScale.value.negate()));e.transmission>0&&(t.transmission.value=e.transmission,t.transmissionSamplerMap.value=i.texture,t.transmissionSamplerSize.value.set(i.width,i.height),e.transmissionMap&&(t.transmissionMap.value=e.transmissionMap),t.thickness.value=e.thickness,e.thicknessMap&&(t.thicknessMap.value=e.thicknessMap),t.attenuationDistance.value=e.attenuationDistance,t.attenuationTint.value.copy(e.attenuationTint));t.specularIntensity.value=e.specularIntensity,t.specularTint.value.copy(e.specularTint),e.specularIntensityMap&&(t.specularIntensityMap.value=e.specularIntensityMap);e.specularTintMap&&(t.specularTintMap.value=e.specularTintMap)}(t,i,o):n(t,i)):i.isMeshMatcapMaterial?(e(t,i),function(t,e){e.matcap&&(t.matcap.value=e.matcap);e.bumpMap&&(t.bumpMap.value=e.bumpMap,t.bumpScale.value=e.bumpScale,1===e.side&&(t.bumpScale.value*=-1));e.normalMap&&(t.normalMap.value=e.normalMap,t.normalScale.value.copy(e.normalScale),1===e.side&&t.normalScale.value.negate());e.displacementMap&&(t.displacementMap.value=e.displacementMap,t.displacementScale.value=e.displacementScale,t.displacementBias.value=e.displacementBias)}(t,i)):i.isMeshDepthMaterial?(e(t,i),function(t,e){e.displacementMap&&(t.displacementMap.value=e.displacementMap,t.displacementScale.value=e.displacementScale,t.displacementBias.value=e.displacementBias)}(t,i)):i.isMeshDistanceMaterial?(e(t,i),function(t,e){e.displacementMap&&(t.displacementMap.value=e.displacementMap,t.displacementScale.value=e.displacementScale,t.displacementBias.value=e.displacementBias);t.referencePosition.value.copy(e.referencePosition),t.nearDistance.value=e.nearDistance,t.farDistance.value=e.farDistance}(t,i)):i.isMeshNormalMaterial?(e(t,i),function(t,e){e.bumpMap&&(t.bumpMap.value=e.bumpMap,t.bumpScale.value=e.bumpScale,1===e.side&&(t.bumpScale.value*=-1));e.normalMap&&(t.normalMap.value=e.normalMap,t.normalScale.value.copy(e.normalScale),1===e.side&&t.normalScale.value.negate());e.displacementMap&&(t.displacementMap.value=e.displacementMap,t.displacementScale.value=e.displacementScale,t.displacementBias.value=e.displacementBias)}(t,i)):i.isLineBasicMaterial?(function(t,e){t.diffuse.value.copy(e.color),t.opacity.value=e.opacity}(t,i),i.isLineDashedMaterial&&function(t,e){t.dashSize.value=e.dashSize,t.totalSize.value=e.dashSize+e.gapSize,t.scale.value=e.scale}(t,i)):i.isPointsMaterial?function(t,e,n,i){t.diffuse.value.copy(e.color),t.opacity.value=e.opacity,t.size.value=e.size*n,t.scale.value=.5*i,e.map&&(t.map.value=e.map);e.alphaMap&&(t.alphaMap.value=e.alphaMap);e.alphaTest>0&&(t.alphaTest.value=e.alphaTest);let r;e.map?r=e.map:e.alphaMap&&(r=e.alphaMap);void 0!==r&&(!0===r.matrixAutoUpdate&&r.updateMatrix(),t.uvTransform.value.copy(r.matrix))}(t,i,r,s):i.isSpriteMaterial?function(t,e){t.diffuse.value.copy(e.color),t.opacity.value=e.opacity,t.rotation.value=e.rotation,e.map&&(t.map.value=e.map);e.alphaMap&&(t.alphaMap.value=e.alphaMap);e.alphaTest>0&&(t.alphaTest.value=e.alphaTest);let n;e.map?n=e.map:e.alphaMap&&(n=e.alphaMap);void 0!==n&&(!0===n.matrixAutoUpdate&&n.updateMatrix(),t.uvTransform.value.copy(n.matrix))}(t,i):i.isShadowMaterial?(t.color.value.copy(i.color),t.opacity.value=i.opacity):i.isShaderMaterial&&(i.uniformsNeedUpdate=!1)}}}function kE(t={}){const e=void 0!==t.canvas?t.canvas:function(){const t=yx(\\\\\\\"canvas\\\\\\\");return t.style.display=\\\\\\\"block\\\\\\\",t}(),n=void 0!==t.context?t.context:null,i=void 0!==t.alpha&&t.alpha,r=void 0===t.depth||t.depth,s=void 0===t.stencil||t.stencil,o=void 0!==t.antialias&&t.antialias,a=void 0===t.premultipliedAlpha||t.premultipliedAlpha,l=void 0!==t.preserveDrawingBuffer&&t.preserveDrawingBuffer,c=void 0!==t.powerPreference?t.powerPreference:\\\\\\\"default\\\\\\\",u=void 0!==t.failIfMajorPerformanceCaveat&&t.failIfMajorPerformanceCaveat;let h=null,d=null;const p=[],_=[];this.domElement=e,this.debug={checkShaderErrors:!0},this.autoClear=!0,this.autoClearColor=!0,this.autoClearDepth=!0,this.autoClearStencil=!0,this.sortObjects=!0,this.clippingPlanes=[],this.localClippingEnabled=!1,this.gammaFactor=2,this.outputEncoding=Yy,this.physicallyCorrectLights=!1,this.toneMapping=0,this.toneMappingExposure=1;const m=this;let f=!1,g=0,v=0,y=null,x=-1,b=null;const w=new Ex,T=new Ex;let A=null,E=e.width,M=e.height,S=1,C=null,N=null;const L=new Ex(0,0,E,M),O=new Ex(0,0,E,M);let R=!1;const P=[],I=new $w;let F=!1,D=!1,k=null;const B=new ob,z=new Nx,U={background:null,fog:null,environment:null,overrideMaterial:null,isScene:!0};function G(){return null===y?S:1}let 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h=i.uniforms;(t.isShaderMaterial||t.isRawShaderMaterial)&&!0!==t.clipping||(h.clippingPlanes=it.uniform),St(t,a),i.needsLights=function(t){return t.isMeshLambertMaterial||t.isMeshToonMaterial||t.isMeshPhongMaterial||t.isMeshStandardMaterial||t.isShadowMaterial||t.isShaderMaterial&&!0===t.lights}(t),i.lightsStateVersion=o,i.needsLights&&(h.ambientLightColor.value=r.state.ambient,h.lightProbe.value=r.state.probe,h.directionalLights.value=r.state.directional,h.directionalLightShadows.value=r.state.directionalShadow,h.spotLights.value=r.state.spot,h.spotLightShadows.value=r.state.spotShadow,h.rectAreaLights.value=r.state.rectArea,h.ltc_1.value=r.state.rectAreaLTC1,h.ltc_2.value=r.state.rectAreaLTC2,h.pointLights.value=r.state.point,h.pointLightShadows.value=r.state.pointShadow,h.hemisphereLights.value=r.state.hemi,h.directionalShadowMap.value=r.state.directionalShadowMap,h.directionalShadowMatrix.value=r.state.directionalShadowMatrix,h.spotShadowMap.value=r.state.spotShadowMap,h.spotShadowMatrix.value=r.state.spotShadowMatrix,h.pointShadowMap.value=r.state.pointShadowMap,h.pointShadowMatrix.value=r.state.pointShadowMatrix);const 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C=w.getUniforms(),N=f.uniforms;if(j.useProgram(w.program)&&(T=!0,A=!0,E=!0),i.id!==x&&(x=i.id,A=!0),T||b!==t){if(C.setValue(ht,\\\\\\\"projectionMatrix\\\\\\\",t.projectionMatrix),H.logarithmicDepthBuffer&&C.setValue(ht,\\\\\\\"logDepthBufFC\\\\\\\",2/(Math.log(t.far+1)/Math.LN2)),b!==t&&(b=t,A=!0,E=!0),i.isShaderMaterial||i.isMeshPhongMaterial||i.isMeshToonMaterial||i.isMeshStandardMaterial||i.envMap){const e=C.map.cameraPosition;void 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n=d.state.shadowsArray;if(rt.render(n,t,e),!0===F&&it.endShadows(),!0===this.info.autoReset&&this.info.reset(),st.render(h,t),d.setupLights(m.physicallyCorrectLights),e.isArrayCamera){const n=e.cameras;for(let e=0,i=n.length;e<i;e++){const i=n[e];Tt(h,t,i,i.viewport)}}else Tt(h,t,e);null!==y&&(X.updateMultisampleRenderTarget(y),X.updateRenderTargetMipmap(y)),!0===t.isScene&&t.onAfterRender(m,t,e),j.buffers.depth.setTest(!0),j.buffers.depth.setMask(!0),j.buffers.color.setMask(!0),j.setPolygonOffset(!1),ut.resetDefaultState(),x=-1,b=null,_.pop(),d=_.length>0?_[_.length-1]:null,p.pop(),h=p.length>0?p[p.length-1]:null},this.getActiveCubeFace=function(){return g},this.getActiveMipmapLevel=function(){return v},this.getRenderTarget=function(){return y},this.setRenderTarget=function(t,e=0,n=0){y=t,g=e,v=n,t&&void 0===q.get(t).__webglFramebuffer&&X.setupRenderTarget(t);let i=null,r=!1,s=!1;if(t){const n=t.texture;(n.isDataTexture3D||n.isDataTexture2DArray)&&(s=!0);const o=q.get(t).__webglFramebuffer;t.isWebGLCubeRenderTarget?(i=o[e],r=!0):i=t.isWebGLMultisampleRenderTarget?q.get(t).__webglMultisampledFramebuffer:o,w.copy(t.viewport),T.copy(t.scissor),A=t.scissorTest}else w.copy(L).multiplyScalar(S).floor(),T.copy(O).multiplyScalar(S).floor(),A=R;if(j.bindFramebuffer(36160,i)&&H.drawBuffers){let e=!1;if(t)if(t.isWebGLMultipleRenderTargets){const n=t.texture;if(P.length!==n.length||36064!==P[0]){for(let t=0,e=n.length;t<e;t++)P[t]=36064+t;P.length=n.length,e=!0}}else 1===P.length&&36064===P[0]||(P[0]=36064,P.length=1,e=!0);else 1===P.length&&1029===P[0]||(P[0]=1029,P.length=1,e=!0);e&&(H.isWebGL2?ht.drawBuffers(P):V.get(\\\\\\\"WEBGL_draw_buffers\\\\\\\").drawBuffersWEBGL(P))}if(j.viewport(w),j.scissor(T),j.setScissorTest(A),r){const i=q.get(t.texture);ht.framebufferTexture2D(36160,36064,34069+e,i.__webglTexture,n)}else if(s){const i=q.get(t.texture),r=e||0;ht.framebufferTextureLayer(36160,36064,i.__webglTexture,n||0,r)}x=-1},this.readRenderTargetPixels=function(t,e,n,i,r,s,o){if(!t||!t.isWebGLRenderTarget)return void console.error(\\\\\\\"THREE.WebGLRenderer.readRenderTargetPixels: renderTarget is not THREE.WebGLRenderTarget.\\\\\\\");let a=q.get(t).__webglFramebuffer;if(t.isWebGLCubeRenderTarget&&void 0!==o&&(a=a[o]),a){j.bindFramebuffer(36160,a);try{const o=t.texture,a=o.format,l=o.type;if(a!==By&&ct.convert(a)!==ht.getParameter(35739))return void console.error(\\\\\\\"THREE.WebGLRenderer.readRenderTargetPixels: renderTarget is not in RGBA or implementation defined format.\\\\\\\");const c=l===Fy&&(V.has(\\\\\\\"EXT_color_buffer_half_float\\\\\\\")||H.isWebGL2&&V.has(\\\\\\\"EXT_color_buffer_float\\\\\\\"));if(!(l===Oy||ct.convert(l)===ht.getParameter(35738)||l===Iy&&(H.isWebGL2||V.has(\\\\\\\"OES_texture_float\\\\\\\")||V.has(\\\\\\\"WEBGL_color_buffer_float\\\\\\\"))||c))return void console.error(\\\\\\\"THREE.WebGLRenderer.readRenderTargetPixels: renderTarget is not in UnsignedByteType or implementation defined type.\\\\\\\");36053===ht.checkFramebufferStatus(36160)?e>=0&&e<=t.width-i&&n>=0&&n<=t.height-r&&ht.readPixels(e,n,i,r,ct.convert(a),ct.convert(l),s):console.error(\\\\\\\"THREE.WebGLRenderer.readRenderTargetPixels: readPixels from renderTarget failed. Framebuffer not complete.\\\\\\\")}finally{const t=null!==y?q.get(y).__webglFramebuffer:null;j.bindFramebuffer(36160,t)}}},this.copyFramebufferToTexture=function(t,e,n=0){const i=Math.pow(2,-n),r=Math.floor(e.image.width*i),s=Math.floor(e.image.height*i);let o=ct.convert(e.format);H.isWebGL2&&(6407===o&&(o=32849),6408===o&&(o=32856)),X.setTexture2D(e,0),ht.copyTexImage2D(3553,n,o,t.x,t.y,r,s,0),j.unbindTexture()},this.copyTextureToTexture=function(t,e,n,i=0){const r=e.image.width,s=e.image.height,o=ct.convert(n.format),a=ct.convert(n.type);X.setTexture2D(n,0),ht.pixelStorei(37440,n.flipY),ht.pixelStorei(37441,n.premultiplyAlpha),ht.pixelStorei(3317,n.unpackAlignment),e.isDataTexture?ht.texSubImage2D(3553,i,t.x,t.y,r,s,o,a,e.image.data):e.isCompressedTexture?ht.compressedTexSubImage2D(3553,i,t.x,t.y,e.mipmaps[0].width,e.mipmaps[0].height,o,e.mipmaps[0].data):ht.texSubImage2D(3553,i,t.x,t.y,o,a,e.image),0===i&&n.generateMipmaps&&ht.generateMipmap(3553),j.unbindTexture()},this.copyTextureToTexture3D=function(t,e,n,i,r=0){if(m.isWebGL1Renderer)return void console.warn(\\\\\\\"THREE.WebGLRenderer.copyTextureToTexture3D: can only be used with WebGL2.\\\\\\\");const s=t.max.x-t.min.x+1,o=t.max.y-t.min.y+1,a=t.max.z-t.min.z+1,l=ct.convert(i.format),c=ct.convert(i.type);let u;if(i.isDataTexture3D)X.setTexture3D(i,0),u=32879;else{if(!i.isDataTexture2DArray)return void console.warn(\\\\\\\"THREE.WebGLRenderer.copyTextureToTexture3D: only supports THREE.DataTexture3D and THREE.DataTexture2DArray.\\\\\\\");X.setTexture2DArray(i,0),u=35866}ht.pixelStorei(37440,i.flipY),ht.pixelStorei(37441,i.premultiplyAlpha),ht.pixelStorei(3317,i.unpackAlignment);const h=ht.getParameter(3314),d=ht.getParameter(32878),p=ht.getParameter(3316),_=ht.getParameter(3315),f=ht.getParameter(32877),g=n.isCompressedTexture?n.mipmaps[0]:n.image;ht.pixelStorei(3314,g.width),ht.pixelStorei(32878,g.height),ht.pixelStorei(3316,t.min.x),ht.pixelStorei(3315,t.min.y),ht.pixelStorei(32877,t.min.z),n.isDataTexture||n.isDataTexture3D?ht.texSubImage3D(u,r,e.x,e.y,e.z,s,o,a,l,c,g.data):n.isCompressedTexture?(console.warn(\\\\\\\"THREE.WebGLRenderer.copyTextureToTexture3D: untested support for compressed srcTexture.\\\\\\\"),ht.compressedTexSubImage3D(u,r,e.x,e.y,e.z,s,o,a,l,g.data)):ht.texSubImage3D(u,r,e.x,e.y,e.z,s,o,a,l,c,g),ht.pixelStorei(3314,h),ht.pixelStorei(32878,d),ht.pixelStorei(3316,p),ht.pixelStorei(3315,_),ht.pixelStorei(32877,f),0===r&&i.generateMipmaps&&ht.generateMipmap(u),j.unbindTexture()},this.initTexture=function(t){X.setTexture2D(t,0),j.unbindTexture()},this.resetState=function(){g=0,v=0,y=null,j.reset(),ut.reset()},\\\\\\\"undefined\\\\\\\"!=typeof __THREE_DEVTOOLS__&&__THREE_DEVTOOLS__.dispatchEvent(new CustomEvent(\\\\\\\"observe\\\\\\\",{detail:this}))}(class extends kE{}).prototype.isWebGL1Renderer=!0;class BE{constructor(t,e=25e-5){this.name=\\\\\\\"\\\\\\\",this.color=new Zb(t),this.density=e}clone(){return new BE(this.color,this.density)}toJSON(){return{type:\\\\\\\"FogExp2\\\\\\\",color:this.color.getHex(),density:this.density}}}BE.prototype.isFogExp2=!0;class zE{constructor(t,e=1,n=1e3){this.name=\\\\\\\"\\\\\\\",this.color=new Zb(t),this.near=e,this.far=n}clone(){return new zE(this.color,this.near,this.far)}toJSON(){return{type:\\\\\\\"Fog\\\\\\\",color:this.color.getHex(),near:this.near,far:this.far}}}zE.prototype.isFog=!0;class UE extends Ob{constructor(){super(),this.type=\\\\\\\"Scene\\\\\\\",this.background=null,this.environment=null,this.fog=null,this.overrideMaterial=null,this.autoUpdate=!0,\\\\\\\"undefined\\\\\\\"!=typeof __THREE_DEVTOOLS__&&__THREE_DEVTOOLS__.dispatchEvent(new CustomEvent(\\\\\\\"observe\\\\\\\",{detail:this}))}copy(t,e){return super.copy(t,e),null!==t.background&&(this.background=t.background.clone()),null!==t.environment&&(this.environment=t.environment.clone()),null!==t.fog&&(this.fog=t.fog.clone()),null!==t.overrideMaterial&&(this.overrideMaterial=t.overrideMaterial.clone()),this.autoUpdate=t.autoUpdate,this.matrixAutoUpdate=t.matrixAutoUpdate,this}toJSON(t){const e=super.toJSON(t);return null!==this.fog&&(e.object.fog=this.fog.toJSON()),e}}UE.prototype.isScene=!0;class GE{constructor(t,e){this.array=t,this.stride=e,this.count=void 0!==t?t.length/e:0,this.usage=Ky,this.updateRange={offset:0,count:-1},this.version=0,this.uuid=lx()}onUploadCallback(){}set needsUpdate(t){!0===t&&this.version++}setUsage(t){return this.usage=t,this}copy(t){return this.array=new t.array.constructor(t.array),this.count=t.count,this.stride=t.stride,this.usage=t.usage,this}copyAt(t,e,n){t*=this.stride,n*=e.stride;for(let i=0,r=this.stride;i<r;i++)this.array[t+i]=e.array[n+i];return this}set(t,e=0){return this.array.set(t,e),this}clone(t){void 0===t.arrayBuffers&&(t.arrayBuffers={}),void 0===this.array.buffer._uuid&&(this.array.buffer._uuid=lx()),void 0===t.arrayBuffers[this.array.buffer._uuid]&&(t.arrayBuffers[this.array.buffer._uuid]=this.array.slice(0).buffer);const e=new this.array.constructor(t.arrayBuffers[this.array.buffer._uuid]),n=new this.constructor(e,this.stride);return n.setUsage(this.usage),n}onUpload(t){return this.onUploadCallback=t,this}toJSON(t){return void 0===t.arrayBuffers&&(t.arrayBuffers={}),void 0===this.array.buffer._uuid&&(this.array.buffer._uuid=lx()),void 0===t.arrayBuffers[this.array.buffer._uuid]&&(t.arrayBuffers[this.array.buffer._uuid]=Array.prototype.slice.call(new Uint32Array(this.array.buffer))),{uuid:this.uuid,buffer:this.array.buffer._uuid,type:this.array.constructor.name,stride:this.stride}}}GE.prototype.isInterleavedBuffer=!0;const VE=new Nx;class HE{constructor(t,e,n,i=!1){this.name=\\\\\\\"\\\\\\\",this.data=t,this.itemSize=e,this.offset=n,this.normalized=!0===i}get count(){return this.data.count}get array(){return this.data.array}set needsUpdate(t){this.data.needsUpdate=t}applyMatrix4(t){for(let e=0,n=this.data.count;e<n;e++)VE.x=this.getX(e),VE.y=this.getY(e),VE.z=this.getZ(e),VE.applyMatrix4(t),this.setXYZ(e,VE.x,VE.y,VE.z);return this}applyNormalMatrix(t){for(let e=0,n=this.count;e<n;e++)VE.x=this.getX(e),VE.y=this.getY(e),VE.z=this.getZ(e),VE.applyNormalMatrix(t),this.setXYZ(e,VE.x,VE.y,VE.z);return this}transformDirection(t){for(let e=0,n=this.count;e<n;e++)VE.x=this.getX(e),VE.y=this.getY(e),VE.z=this.getZ(e),VE.transformDirection(t),this.setXYZ(e,VE.x,VE.y,VE.z);return this}setX(t,e){return this.data.array[t*this.data.stride+this.offset]=e,this}setY(t,e){return this.data.array[t*this.data.stride+this.offset+1]=e,this}setZ(t,e){return this.data.array[t*this.data.stride+this.offset+2]=e,this}setW(t,e){return this.data.array[t*this.data.stride+this.offset+3]=e,this}getX(t){return this.data.array[t*this.data.stride+this.offset]}getY(t){return this.data.array[t*this.data.stride+this.offset+1]}getZ(t){return this.data.array[t*this.data.stride+this.offset+2]}getW(t){return this.data.array[t*this.data.stride+this.offset+3]}setXY(t,e,n){return t=t*this.data.stride+this.offset,this.data.array[t+0]=e,this.data.array[t+1]=n,this}setXYZ(t,e,n,i){return t=t*this.data.stride+this.offset,this.data.array[t+0]=e,this.data.array[t+1]=n,this.data.array[t+2]=i,this}setXYZW(t,e,n,i,r){return t=t*this.data.stride+this.offset,this.data.array[t+0]=e,this.data.array[t+1]=n,this.data.array[t+2]=i,this.data.array[t+3]=r,this}clone(t){if(void 0===t){console.log(\\\\\\\"THREE.InterleavedBufferAttribute.clone(): Cloning an interlaved buffer attribute will deinterleave buffer data.\\\\\\\");const t=[];for(let e=0;e<this.count;e++){const n=e*this.data.stride+this.offset;for(let e=0;e<this.itemSize;e++)t.push(this.data.array[n+e])}return new ew(new this.array.constructor(t),this.itemSize,this.normalized)}return void 0===t.interleavedBuffers&&(t.interleavedBuffers={}),void 0===t.interleavedBuffers[this.data.uuid]&&(t.interleavedBuffers[this.data.uuid]=this.data.clone(t)),new HE(t.interleavedBuffers[this.data.uuid],this.itemSize,this.offset,this.normalized)}toJSON(t){if(void 0===t){console.log(\\\\\\\"THREE.InterleavedBufferAttribute.toJSON(): Serializing an interlaved buffer attribute will deinterleave buffer data.\\\\\\\");const t=[];for(let e=0;e<this.count;e++){const n=e*this.data.stride+this.offset;for(let e=0;e<this.itemSize;e++)t.push(this.data.array[n+e])}return{itemSize:this.itemSize,type:this.array.constructor.name,array:t,normalized:this.normalized}}return void 0===t.interleavedBuffers&&(t.interleavedBuffers={}),void 0===t.interleavedBuffers[this.data.uuid]&&(t.interleavedBuffers[this.data.uuid]=this.data.toJSON(t)),{isInterleavedBufferAttribute:!0,itemSize:this.itemSize,data:this.data.uuid,offset:this.offset,normalized:this.normalized}}}HE.prototype.isInterleavedBufferAttribute=!0;class jE extends jb{constructor(t){super(),this.type=\\\\\\\"SpriteMaterial\\\\\\\",this.color=new Zb(16777215),this.map=null,this.alphaMap=null,this.rotation=0,this.sizeAttenuation=!0,this.transparent=!0,this.setValues(t)}copy(t){return super.copy(t),this.color.copy(t.color),this.map=t.map,this.alphaMap=t.alphaMap,this.rotation=t.rotation,this.sizeAttenuation=t.sizeAttenuation,this}}let WE;jE.prototype.isSpriteMaterial=!0;const qE=new Nx,XE=new Nx,YE=new Nx,$E=new fx,JE=new fx,ZE=new ob,QE=new Nx,KE=new Nx,tM=new Nx,eM=new fx,nM=new fx,iM=new fx;class rM extends Ob{constructor(t){if(super(),this.type=\\\\\\\"Sprite\\\\\\\",void 0===WE){WE=new dw;const t=new Float32Array([-.5,-.5,0,0,0,.5,-.5,0,1,0,.5,.5,0,1,1,-.5,.5,0,0,1]),e=new GE(t,5);WE.setIndex([0,1,2,0,2,3]),WE.setAttribute(\\\\\\\"position\\\\\\\",new HE(e,3,0,!1)),WE.setAttribute(\\\\\\\"uv\\\\\\\",new HE(e,2,3,!1))}this.geometry=WE,this.material=void 0!==t?t:new jE,this.center=new fx(.5,.5)}raycast(t,e){null===t.camera&&console.error('THREE.Sprite: \\\\\\\"Raycaster.camera\\\\\\\" needs to be set in order to raycast against sprites.'),XE.setFromMatrixScale(this.matrixWorld),ZE.copy(t.camera.matrixWorld),this.modelViewMatrix.multiplyMatrices(t.camera.matrixWorldInverse,this.matrixWorld),YE.setFromMatrixPosition(this.modelViewMatrix),t.camera.isPerspectiveCamera&&!1===this.material.sizeAttenuation&&XE.multiplyScalar(-YE.z);const n=this.material.rotation;let i,r;0!==n&&(r=Math.cos(n),i=Math.sin(n));const s=this.center;sM(QE.set(-.5,-.5,0),YE,s,XE,i,r),sM(KE.set(.5,-.5,0),YE,s,XE,i,r),sM(tM.set(.5,.5,0),YE,s,XE,i,r),eM.set(0,0),nM.set(1,0),iM.set(1,1);let o=t.ray.intersectTriangle(QE,KE,tM,!1,qE);if(null===o&&(sM(KE.set(-.5,.5,0),YE,s,XE,i,r),nM.set(0,1),o=t.ray.intersectTriangle(QE,tM,KE,!1,qE),null===o))return;const a=t.ray.origin.distanceTo(qE);a<t.near||a>t.far||e.push({distance:a,point:qE.clone(),uv:Vb.getUV(qE,QE,KE,tM,eM,nM,iM,new fx),face:null,object:this})}copy(t){return super.copy(t),void 0!==t.center&&this.center.copy(t.center),this.material=t.material,this}}function sM(t,e,n,i,r,s){$E.subVectors(t,n).addScalar(.5).multiply(i),void 0!==r?(JE.x=s*$E.x-r*$E.y,JE.y=r*$E.x+s*$E.y):JE.copy($E),t.copy(e),t.x+=JE.x,t.y+=JE.y,t.applyMatrix4(ZE)}rM.prototype.isSprite=!0;const oM=new Nx,aM=new Ex,lM=new Ex,cM=new Nx,uM=new ob;class hM extends Lw{constructor(t,e){super(t,e),this.type=\\\\\\\"SkinnedMesh\\\\\\\",this.bindMode=\\\\\\\"attached\\\\\\\",this.bindMatrix=new ob,this.bindMatrixInverse=new ob}copy(t){return super.copy(t),this.bindMode=t.bindMode,this.bindMatrix.copy(t.bindMatrix),this.bindMatrixInverse.copy(t.bindMatrixInverse),this.skeleton=t.skeleton,this}bind(t,e){this.skeleton=t,void 0===e&&(this.updateMatrixWorld(!0),this.skeleton.calculateInverses(),e=this.matrixWorld),this.bindMatrix.copy(e),this.bindMatrixInverse.copy(e).invert()}pose(){this.skeleton.pose()}normalizeSkinWeights(){const t=new Ex,e=this.geometry.attributes.skinWeight;for(let n=0,i=e.count;n<i;n++){t.x=e.getX(n),t.y=e.getY(n),t.z=e.getZ(n),t.w=e.getW(n);const i=1/t.manhattanLength();i!==1/0?t.multiplyScalar(i):t.set(1,0,0,0),e.setXYZW(n,t.x,t.y,t.z,t.w)}}updateMatrixWorld(t){super.updateMatrixWorld(t),\\\\\\\"attached\\\\\\\"===this.bindMode?this.bindMatrixInverse.copy(this.matrixWorld).invert():\\\\\\\"detached\\\\\\\"===this.bindMode?this.bindMatrixInverse.copy(this.bindMatrix).invert():console.warn(\\\\\\\"THREE.SkinnedMesh: Unrecognized bindMode: \\\\\\\"+this.bindMode)}boneTransform(t,e){const n=this.skeleton,i=this.geometry;aM.fromBufferAttribute(i.attributes.skinIndex,t),lM.fromBufferAttribute(i.attributes.skinWeight,t),oM.copy(e).applyMatrix4(this.bindMatrix),e.set(0,0,0);for(let t=0;t<4;t++){const i=lM.getComponent(t);if(0!==i){const r=aM.getComponent(t);uM.multiplyMatrices(n.bones[r].matrixWorld,n.boneInverses[r]),e.addScaledVector(cM.copy(oM).applyMatrix4(uM),i)}}return e.applyMatrix4(this.bindMatrixInverse)}}hM.prototype.isSkinnedMesh=!0;class dM extends Ob{constructor(){super(),this.type=\\\\\\\"Bone\\\\\\\"}}dM.prototype.isBone=!0;class pM extends Tx{constructor(t=null,e=1,n=1,i,r,s,o,a,l=1003,c=1003,u,h){super(null,s,o,a,l,c,i,r,u,h),this.image={data:t,width:e,height:n},this.magFilter=l,this.minFilter=c,this.generateMipmaps=!1,this.flipY=!1,this.unpackAlignment=1,this.needsUpdate=!0}}pM.prototype.isDataTexture=!0;class _M extends ew{constructor(t,e,n,i=1){\\\\\\\"number\\\\\\\"==typeof n&&(i=n,n=!1,console.error(\\\\\\\"THREE.InstancedBufferAttribute: The constructor now expects normalized as the third argument.\\\\\\\")),super(t,e,n),this.meshPerAttribute=i}copy(t){return super.copy(t),this.meshPerAttribute=t.meshPerAttribute,this}toJSON(){const t=super.toJSON();return t.meshPerAttribute=this.meshPerAttribute,t.isInstancedBufferAttribute=!0,t}}_M.prototype.isInstancedBufferAttribute=!0;const mM=new ob,fM=new ob,gM=[],vM=new Lw;class yM extends Lw{constructor(t,e,n){super(t,e),this.instanceMatrix=new _M(new Float32Array(16*n),16),this.instanceColor=null,this.count=n,this.frustumCulled=!1}copy(t){return super.copy(t),this.instanceMatrix.copy(t.instanceMatrix),null!==t.instanceColor&&(this.instanceColor=t.instanceColor.clone()),this.count=t.count,this}getColorAt(t,e){e.fromArray(this.instanceColor.array,3*t)}getMatrixAt(t,e){e.fromArray(this.instanceMatrix.array,16*t)}raycast(t,e){const n=this.matrixWorld,i=this.count;if(vM.geometry=this.geometry,vM.material=this.material,void 0!==vM.material)for(let r=0;r<i;r++){this.getMatrixAt(r,mM),fM.multiplyMatrices(n,mM),vM.matrixWorld=fM,vM.raycast(t,gM);for(let t=0,n=gM.length;t<n;t++){const n=gM[t];n.instanceId=r,n.object=this,e.push(n)}gM.length=0}}setColorAt(t,e){null===this.instanceColor&&(this.instanceColor=new _M(new Float32Array(3*this.instanceMatrix.count),3)),e.toArray(this.instanceColor.array,3*t)}setMatrixAt(t,e){e.toArray(this.instanceMatrix.array,16*t)}updateMorphTargets(){}dispose(){this.dispatchEvent({type:\\\\\\\"dispose\\\\\\\"})}}yM.prototype.isInstancedMesh=!0;class xM extends jb{constructor(t){super(),this.type=\\\\\\\"LineBasicMaterial\\\\\\\",this.color=new Zb(16777215),this.linewidth=1,this.linecap=\\\\\\\"round\\\\\\\",this.linejoin=\\\\\\\"round\\\\\\\",this.setValues(t)}copy(t){return super.copy(t),this.color.copy(t.color),this.linewidth=t.linewidth,this.linecap=t.linecap,this.linejoin=t.linejoin,this}}xM.prototype.isLineBasicMaterial=!0;const bM=new Nx,wM=new Nx,TM=new ob,AM=new sb,EM=new Zx;class MM extends Ob{constructor(t=new dw,e=new xM){super(),this.type=\\\\\\\"Line\\\\\\\",this.geometry=t,this.material=e,this.updateMorphTargets()}copy(t){return super.copy(t),this.material=t.material,this.geometry=t.geometry,this}computeLineDistances(){const t=this.geometry;if(t.isBufferGeometry)if(null===t.index){const e=t.attributes.position,n=[0];for(let t=1,i=e.count;t<i;t++)bM.fromBufferAttribute(e,t-1),wM.fromBufferAttribute(e,t),n[t]=n[t-1],n[t]+=bM.distanceTo(wM);t.setAttribute(\\\\\\\"lineDistance\\\\\\\",new rw(n,1))}else console.warn(\\\\\\\"THREE.Line.computeLineDistances(): Computation only possible with non-indexed BufferGeometry.\\\\\\\");else t.isGeometry&&console.error(\\\\\\\"THREE.Line.computeLineDistances() no longer supports THREE.Geometry. Use THREE.BufferGeometry instead.\\\\\\\");return this}raycast(t,e){const n=this.geometry,i=this.matrixWorld,r=t.params.Line.threshold,s=n.drawRange;if(null===n.boundingSphere&&n.computeBoundingSphere(),EM.copy(n.boundingSphere),EM.applyMatrix4(i),EM.radius+=r,!1===t.ray.intersectsSphere(EM))return;TM.copy(i).invert(),AM.copy(t.ray).applyMatrix4(TM);const o=r/((this.scale.x+this.scale.y+this.scale.z)/3),a=o*o,l=new Nx,c=new Nx,u=new Nx,h=new Nx,d=this.isLineSegments?2:1;if(n.isBufferGeometry){const i=n.index,r=n.attributes.position;if(null!==i){for(let n=Math.max(0,s.start),o=Math.min(i.count,s.start+s.count)-1;n<o;n+=d){const s=i.getX(n),o=i.getX(n+1);l.fromBufferAttribute(r,s),c.fromBufferAttribute(r,o);if(AM.distanceSqToSegment(l,c,h,u)>a)continue;h.applyMatrix4(this.matrixWorld);const d=t.ray.origin.distanceTo(h);d<t.near||d>t.far||e.push({distance:d,point:u.clone().applyMatrix4(this.matrixWorld),index:n,face:null,faceIndex:null,object:this})}}else{for(let n=Math.max(0,s.start),i=Math.min(r.count,s.start+s.count)-1;n<i;n+=d){l.fromBufferAttribute(r,n),c.fromBufferAttribute(r,n+1);if(AM.distanceSqToSegment(l,c,h,u)>a)continue;h.applyMatrix4(this.matrixWorld);const i=t.ray.origin.distanceTo(h);i<t.near||i>t.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;e<n;e++){const n=t[e].name||String(e);this.morphTargetInfluences.push(0),this.morphTargetDictionary[n]=e}}}}else{const e=t.morphTargets;void 0!==e&&e.length>0&&console.error(\\\\\\\"THREE.Line.updateMorphTargets() does not support THREE.Geometry. Use THREE.BufferGeometry instead.\\\\\\\")}}}MM.prototype.isLine=!0;const SM=new Nx,CM=new Nx;class NM extends MM{constructor(t,e){super(t,e),this.type=\\\\\\\"LineSegments\\\\\\\"}computeLineDistances(){const t=this.geometry;if(t.isBufferGeometry)if(null===t.index){const e=t.attributes.position,n=[];for(let t=0,i=e.count;t<i;t+=2)SM.fromBufferAttribute(e,t),CM.fromBufferAttribute(e,t+1),n[t]=0===t?0:n[t-1],n[t+1]=n[t]+SM.distanceTo(CM);t.setAttribute(\\\\\\\"lineDistance\\\\\\\",new rw(n,1))}else console.warn(\\\\\\\"THREE.LineSegments.computeLineDistances(): Computation only possible with non-indexed BufferGeometry.\\\\\\\");else t.isGeometry&&console.error(\\\\\\\"THREE.LineSegments.computeLineDistances() no longer supports THREE.Geometry. Use THREE.BufferGeometry instead.\\\\\\\");return this}}NM.prototype.isLineSegments=!0;class LM extends MM{constructor(t,e){super(t,e),this.type=\\\\\\\"LineLoop\\\\\\\"}}LM.prototype.isLineLoop=!0;class OM extends jb{constructor(t){super(),this.type=\\\\\\\"PointsMaterial\\\\\\\",this.color=new Zb(16777215),this.map=null,this.alphaMap=null,this.size=1,this.sizeAttenuation=!0,this.setValues(t)}copy(t){return super.copy(t),this.color.copy(t.color),this.map=t.map,this.alphaMap=t.alphaMap,this.size=t.size,this.sizeAttenuation=t.sizeAttenuation,this}}OM.prototype.isPointsMaterial=!0;const RM=new ob,PM=new sb,IM=new Zx,FM=new Nx;class DM extends Ob{constructor(t=new dw,e=new OM){super(),this.type=\\\\\\\"Points\\\\\\\",this.geometry=t,this.material=e,this.updateMorphTargets()}copy(t){return super.copy(t),this.material=t.material,this.geometry=t.geometry,this}raycast(t,e){const n=this.geometry,i=this.matrixWorld,r=t.params.Points.threshold,s=n.drawRange;if(null===n.boundingSphere&&n.computeBoundingSphere(),IM.copy(n.boundingSphere),IM.applyMatrix4(i),IM.radius+=r,!1===t.ray.intersectsSphere(IM))return;RM.copy(i).invert(),PM.copy(t.ray).applyMatrix4(RM);const o=r/((this.scale.x+this.scale.y+this.scale.z)/3),a=o*o;if(n.isBufferGeometry){const r=n.index,o=n.attributes.position;if(null!==r){for(let n=Math.max(0,s.start),l=Math.min(r.count,s.start+s.count);n<l;n++){const s=r.getX(n);FM.fromBufferAttribute(o,s),kM(FM,s,a,i,t,e,this)}}else{for(let n=Math.max(0,s.start),r=Math.min(o.count,s.start+s.count);n<r;n++)FM.fromBufferAttribute(o,n),kM(FM,n,a,i,t,e,this)}}else console.error(\\\\\\\"THREE.Points.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;e<n;e++){const n=t[e].name||String(e);this.morphTargetInfluences.push(0),this.morphTargetDictionary[n]=e}}}}else{const e=t.morphTargets;void 0!==e&&e.length>0&&console.error(\\\\\\\"THREE.Points.updateMorphTargets() does not support THREE.Geometry. Use THREE.BufferGeometry instead.\\\\\\\")}}}function kM(t,e,n,i,r,s,o){const a=PM.distanceSqToPoint(t);if(a<n){const n=new Nx;PM.closestPointToPoint(t,n),n.applyMatrix4(i);const l=r.ray.origin.distanceTo(n);if(l<r.near||l>r.far)return;s.push({distance:l,distanceToRay:Math.sqrt(a),point:n,index:e,face:null,object:o})}}DM.prototype.isPoints=!0;(class extends Tx{constructor(t,e,n,i,r,s,o,a,l){super(t,e,n,i,r,s,o,a,l),this.format=void 0!==o?o:ky,this.minFilter=void 0!==s?s:Cy,this.magFilter=void 0!==r?r:Cy,this.generateMipmaps=!1;const c=this;\\\\\\\"requestVideoFrameCallback\\\\\\\"in t&&t.requestVideoFrameCallback((function e(){c.needsUpdate=!0,t.requestVideoFrameCallback(e)}))}clone(){return new this.constructor(this.image).copy(this)}update(){const t=this.image;!1===\\\\\\\"requestVideoFrameCallback\\\\\\\"in t&&t.readyState>=t.HAVE_CURRENT_DATA&&(this.needsUpdate=!0)}}).prototype.isVideoTexture=!0;class BM extends Tx{constructor(t,e,n,i,r,s,o,a,l,c,u,h){super(null,s,o,a,l,c,i,r,u,h),this.image={width:e,height:n},this.mipmaps=t,this.flipY=!1,this.generateMipmaps=!1}}BM.prototype.isCompressedTexture=!0;(class extends Tx{constructor(t,e,n,i,r,s,o,a,l){super(t,e,n,i,r,s,o,a,l),this.needsUpdate=!0}}).prototype.isCanvasTexture=!0;(class extends Tx{constructor(t,e,n,i,r,s,o,a,l,c){if((c=void 0!==c?c:zy)!==zy&&c!==Uy)throw new Error(\\\\\\\"DepthTexture format must be either THREE.DepthFormat or THREE.DepthStencilFormat\\\\\\\");void 0===n&&c===zy&&(n=Ry),void 0===n&&c===Uy&&(n=Dy),super(null,i,r,s,o,a,c,n,l),this.image={width:t,height:e},this.magFilter=void 0!==o?o:Ey,this.minFilter=void 0!==a?a:Ey,this.flipY=!1,this.generateMipmaps=!1}}).prototype.isDepthTexture=!0;new Nx,new Nx,new Nx,new Vb;class zM{constructor(){this.type=\\\\\\\"Curve\\\\\\\",this.arcLengthDivisions=200}getPoint(){return console.warn(\\\\\\\"THREE.Curve: .getPoint() not implemented.\\\\\\\"),null}getPointAt(t,e){const n=this.getUtoTmapping(t);return this.getPoint(n,e)}getPoints(t=5){const e=[];for(let n=0;n<=t;n++)e.push(this.getPoint(n/t));return e}getSpacedPoints(t=5){const e=[];for(let n=0;n<=t;n++)e.push(this.getPointAt(n/t));return e}getLength(){const t=this.getLengths();return t[t.length-1]}getLengths(t=this.arcLengthDivisions){if(this.cacheArcLengths&&this.cacheArcLengths.length===t+1&&!this.needsUpdate)return this.cacheArcLengths;this.needsUpdate=!1;const e=[];let n,i=this.getPoint(0),r=0;e.push(0);for(let s=1;s<=t;s++)n=this.getPoint(s/t),r+=n.distanceTo(i),e.push(r),i=n;return this.cacheArcLengths=e,e}updateArcLengths(){this.needsUpdate=!0,this.getLengths()}getUtoTmapping(t,e){const n=this.getLengths();let i=0;const r=n.length;let s;s=e||t*n[r-1];let o,a=0,l=r-1;for(;a<=l;)if(i=Math.floor(a+(l-a)/2),o=n[i]-s,o<0)a=i+1;else{if(!(o>0)){l=i;break}l=i-1}if(i=l,n[i]===s)return i/(r-1);const c=n[i];return(i+(s-c)/(n[i+1]-c))/(r-1)}getTangent(t,e){const n=1e-4;let i=t-n,r=t+n;i<0&&(i=0),r>1&&(r=1);const s=this.getPoint(i),o=this.getPoint(r),a=e||(s.isVector2?new fx:new Nx);return a.copy(o).sub(s).normalize(),a}getTangentAt(t,e){const n=this.getUtoTmapping(t);return this.getTangent(n,e)}computeFrenetFrames(t,e){const n=new Nx,i=[],r=[],s=[],o=new Nx,a=new ob;for(let e=0;e<=t;e++){const n=e/t;i[e]=this.getTangentAt(n,new Nx)}r[0]=new Nx,s[0]=new Nx;let l=Number.MAX_VALUE;const c=Math.abs(i[0].x),u=Math.abs(i[0].y),h=Math.abs(i[0].z);c<=l&&(l=c,n.set(1,0,0)),u<=l&&(l=u,n.set(0,1,0)),h<=l&&n.set(0,0,1),o.crossVectors(i[0],n).normalize(),r[0].crossVectors(i[0],o),s[0].crossVectors(i[0],r[0]);for(let e=1;e<=t;e++){if(r[e]=r[e-1].clone(),s[e]=s[e-1].clone(),o.crossVectors(i[e-1],i[e]),o.length()>Number.EPSILON){o.normalize();const t=Math.acos(cx(i[e-1].dot(i[e]),-1,1));r[e].applyMatrix4(a.makeRotationAxis(o,t))}s[e].crossVectors(i[e],r[e])}if(!0===e){let e=Math.acos(cx(r[0].dot(r[t]),-1,1));e/=t,i[0].dot(o.crossVectors(r[0],r[t]))>0&&(e=-e);for(let n=1;n<=t;n++)r[n].applyMatrix4(a.makeRotationAxis(i[n],e*n)),s[n].crossVectors(i[n],r[n])}return{tangents:i,normals:r,binormals:s}}clone(){return(new this.constructor).copy(this)}copy(t){return this.arcLengthDivisions=t.arcLengthDivisions,this}toJSON(){const t={metadata:{version:4.5,type:\\\\\\\"Curve\\\\\\\",generator:\\\\\\\"Curve.toJSON\\\\\\\"}};return t.arcLengthDivisions=this.arcLengthDivisions,t.type=this.type,t}fromJSON(t){return this.arcLengthDivisions=t.arcLengthDivisions,this}}class UM extends zM{constructor(t=0,e=0,n=1,i=1,r=0,s=2*Math.PI,o=!1,a=0){super(),this.type=\\\\\\\"EllipseCurve\\\\\\\",this.aX=t,this.aY=e,this.xRadius=n,this.yRadius=i,this.aStartAngle=r,this.aEndAngle=s,this.aClockwise=o,this.aRotation=a}getPoint(t,e){const n=e||new fx,i=2*Math.PI;let r=this.aEndAngle-this.aStartAngle;const s=Math.abs(r)<Number.EPSILON;for(;r<0;)r+=i;for(;r>i;)r-=i;r<Number.EPSILON&&(r=s?0:i),!0!==this.aClockwise||s||(r===i?r=-i:r-=i);const o=this.aStartAngle+t*r;let a=this.aX+this.xRadius*Math.cos(o),l=this.aY+this.yRadius*Math.sin(o);if(0!==this.aRotation){const t=Math.cos(this.aRotation),e=Math.sin(this.aRotation),n=a-this.aX,i=l-this.aY;a=n*t-i*e+this.aX,l=n*e+i*t+this.aY}return n.set(a,l)}copy(t){return super.copy(t),this.aX=t.aX,this.aY=t.aY,this.xRadius=t.xRadius,this.yRadius=t.yRadius,this.aStartAngle=t.aStartAngle,this.aEndAngle=t.aEndAngle,this.aClockwise=t.aClockwise,this.aRotation=t.aRotation,this}toJSON(){const t=super.toJSON();return t.aX=this.aX,t.aY=this.aY,t.xRadius=this.xRadius,t.yRadius=this.yRadius,t.aStartAngle=this.aStartAngle,t.aEndAngle=this.aEndAngle,t.aClockwise=this.aClockwise,t.aRotation=this.aRotation,t}fromJSON(t){return super.fromJSON(t),this.aX=t.aX,this.aY=t.aY,this.xRadius=t.xRadius,this.yRadius=t.yRadius,this.aStartAngle=t.aStartAngle,this.aEndAngle=t.aEndAngle,this.aClockwise=t.aClockwise,this.aRotation=t.aRotation,this}}UM.prototype.isEllipseCurve=!0;class GM extends UM{constructor(t,e,n,i,r,s){super(t,e,n,n,i,r,s),this.type=\\\\\\\"ArcCurve\\\\\\\"}}function VM(){let t=0,e=0,n=0,i=0;function r(r,s,o,a){t=r,e=o,n=-3*r+3*s-2*o-a,i=2*r-2*s+o+a}return{initCatmullRom:function(t,e,n,i,s){r(e,n,s*(n-t),s*(i-e))},initNonuniformCatmullRom:function(t,e,n,i,s,o,a){let l=(e-t)/s-(n-t)/(s+o)+(n-e)/o,c=(n-e)/o-(i-e)/(o+a)+(i-n)/a;l*=o,c*=o,r(e,n,l,c)},calc:function(r){const s=r*r;return t+e*r+n*s+i*(s*r)}}}GM.prototype.isArcCurve=!0;const HM=new Nx,jM=new VM,WM=new VM,qM=new VM;class XM extends zM{constructor(t=[],e=!1,n=\\\\\\\"centripetal\\\\\\\",i=.5){super(),this.type=\\\\\\\"CatmullRomCurve3\\\\\\\",this.points=t,this.closed=e,this.curveType=n,this.tension=i}getPoint(t,e=new Nx){const n=e,i=this.points,r=i.length,s=(r-(this.closed?0:1))*t;let o,a,l=Math.floor(s),c=s-l;this.closed?l+=l>0?0:(Math.floor(Math.abs(l)/r)+1)*r:0===c&&l===r-1&&(l=r-2,c=1),this.closed||l>0?o=i[(l-1)%r]:(HM.subVectors(i[0],i[1]).add(i[0]),o=HM);const u=i[l%r],h=i[(l+1)%r];if(this.closed||l+2<r?a=i[(l+2)%r]:(HM.subVectors(i[r-1],i[r-2]).add(i[r-1]),a=HM),\\\\\\\"centripetal\\\\\\\"===this.curveType||\\\\\\\"chordal\\\\\\\"===this.curveType){const t=\\\\\\\"chordal\\\\\\\"===this.curveType?.5:.25;let e=Math.pow(o.distanceToSquared(u),t),n=Math.pow(u.distanceToSquared(h),t),i=Math.pow(h.distanceToSquared(a),t);n<1e-4&&(n=1),e<1e-4&&(e=n),i<1e-4&&(i=n),jM.initNonuniformCatmullRom(o.x,u.x,h.x,a.x,e,n,i),WM.initNonuniformCatmullRom(o.y,u.y,h.y,a.y,e,n,i),qM.initNonuniformCatmullRom(o.z,u.z,h.z,a.z,e,n,i)}else\\\\\\\"catmullrom\\\\\\\"===this.curveType&&(jM.initCatmullRom(o.x,u.x,h.x,a.x,this.tension),WM.initCatmullRom(o.y,u.y,h.y,a.y,this.tension),qM.initCatmullRom(o.z,u.z,h.z,a.z,this.tension));return n.set(jM.calc(c),WM.calc(c),qM.calc(c)),n}copy(t){super.copy(t),this.points=[];for(let e=0,n=t.points.length;e<n;e++){const n=t.points[e];this.points.push(n.clone())}return this.closed=t.closed,this.curveType=t.curveType,this.tension=t.tension,this}toJSON(){const t=super.toJSON();t.points=[];for(let e=0,n=this.points.length;e<n;e++){const n=this.points[e];t.points.push(n.toArray())}return t.closed=this.closed,t.curveType=this.curveType,t.tension=this.tension,t}fromJSON(t){super.fromJSON(t),this.points=[];for(let e=0,n=t.points.length;e<n;e++){const n=t.points[e];this.points.push((new Nx).fromArray(n))}return this.closed=t.closed,this.curveType=t.curveType,this.tension=t.tension,this}}function YM(t,e,n,i,r){const s=.5*(i-e),o=.5*(r-n),a=t*t;return(2*n-2*i+s+o)*(t*a)+(-3*n+3*i-2*s-o)*a+s*t+n}function $M(t,e,n,i){return function(t,e){const n=1-t;return n*n*e}(t,e)+function(t,e){return 2*(1-t)*t*e}(t,n)+function(t,e){return t*t*e}(t,i)}function JM(t,e,n,i,r){return function(t,e){const n=1-t;return n*n*n*e}(t,e)+function(t,e){const n=1-t;return 3*n*n*t*e}(t,n)+function(t,e){return 3*(1-t)*t*t*e}(t,i)+function(t,e){return t*t*t*e}(t,r)}XM.prototype.isCatmullRomCurve3=!0;class ZM extends zM{constructor(t=new fx,e=new fx,n=new fx,i=new fx){super(),this.type=\\\\\\\"CubicBezierCurve\\\\\\\",this.v0=t,this.v1=e,this.v2=n,this.v3=i}getPoint(t,e=new fx){const n=e,i=this.v0,r=this.v1,s=this.v2,o=this.v3;return n.set(JM(t,i.x,r.x,s.x,o.x),JM(t,i.y,r.y,s.y,o.y)),n}copy(t){return super.copy(t),this.v0.copy(t.v0),this.v1.copy(t.v1),this.v2.copy(t.v2),this.v3.copy(t.v3),this}toJSON(){const t=super.toJSON();return t.v0=this.v0.toArray(),t.v1=this.v1.toArray(),t.v2=this.v2.toArray(),t.v3=this.v3.toArray(),t}fromJSON(t){return super.fromJSON(t),this.v0.fromArray(t.v0),this.v1.fromArray(t.v1),this.v2.fromArray(t.v2),this.v3.fromArray(t.v3),this}}ZM.prototype.isCubicBezierCurve=!0;class QM extends zM{constructor(t=new Nx,e=new Nx,n=new Nx,i=new Nx){super(),this.type=\\\\\\\"CubicBezierCurve3\\\\\\\",this.v0=t,this.v1=e,this.v2=n,this.v3=i}getPoint(t,e=new Nx){const n=e,i=this.v0,r=this.v1,s=this.v2,o=this.v3;return n.set(JM(t,i.x,r.x,s.x,o.x),JM(t,i.y,r.y,s.y,o.y),JM(t,i.z,r.z,s.z,o.z)),n}copy(t){return super.copy(t),this.v0.copy(t.v0),this.v1.copy(t.v1),this.v2.copy(t.v2),this.v3.copy(t.v3),this}toJSON(){const t=super.toJSON();return t.v0=this.v0.toArray(),t.v1=this.v1.toArray(),t.v2=this.v2.toArray(),t.v3=this.v3.toArray(),t}fromJSON(t){return super.fromJSON(t),this.v0.fromArray(t.v0),this.v1.fromArray(t.v1),this.v2.fromArray(t.v2),this.v3.fromArray(t.v3),this}}QM.prototype.isCubicBezierCurve3=!0;class KM extends zM{constructor(t=new fx,e=new fx){super(),this.type=\\\\\\\"LineCurve\\\\\\\",this.v1=t,this.v2=e}getPoint(t,e=new fx){const n=e;return 1===t?n.copy(this.v2):(n.copy(this.v2).sub(this.v1),n.multiplyScalar(t).add(this.v1)),n}getPointAt(t,e){return this.getPoint(t,e)}getTangent(t,e){const n=e||new fx;return n.copy(this.v2).sub(this.v1).normalize(),n}copy(t){return super.copy(t),this.v1.copy(t.v1),this.v2.copy(t.v2),this}toJSON(){const t=super.toJSON();return t.v1=this.v1.toArray(),t.v2=this.v2.toArray(),t}fromJSON(t){return super.fromJSON(t),this.v1.fromArray(t.v1),this.v2.fromArray(t.v2),this}}KM.prototype.isLineCurve=!0;class tS extends zM{constructor(t=new fx,e=new fx,n=new fx){super(),this.type=\\\\\\\"QuadraticBezierCurve\\\\\\\",this.v0=t,this.v1=e,this.v2=n}getPoint(t,e=new fx){const n=e,i=this.v0,r=this.v1,s=this.v2;return n.set($M(t,i.x,r.x,s.x),$M(t,i.y,r.y,s.y)),n}copy(t){return super.copy(t),this.v0.copy(t.v0),this.v1.copy(t.v1),this.v2.copy(t.v2),this}toJSON(){const t=super.toJSON();return t.v0=this.v0.toArray(),t.v1=this.v1.toArray(),t.v2=this.v2.toArray(),t}fromJSON(t){return super.fromJSON(t),this.v0.fromArray(t.v0),this.v1.fromArray(t.v1),this.v2.fromArray(t.v2),this}}tS.prototype.isQuadraticBezierCurve=!0;class eS extends zM{constructor(t=new Nx,e=new Nx,n=new Nx){super(),this.type=\\\\\\\"QuadraticBezierCurve3\\\\\\\",this.v0=t,this.v1=e,this.v2=n}getPoint(t,e=new Nx){const n=e,i=this.v0,r=this.v1,s=this.v2;return n.set($M(t,i.x,r.x,s.x),$M(t,i.y,r.y,s.y),$M(t,i.z,r.z,s.z)),n}copy(t){return super.copy(t),this.v0.copy(t.v0),this.v1.copy(t.v1),this.v2.copy(t.v2),this}toJSON(){const t=super.toJSON();return t.v0=this.v0.toArray(),t.v1=this.v1.toArray(),t.v2=this.v2.toArray(),t}fromJSON(t){return super.fromJSON(t),this.v0.fromArray(t.v0),this.v1.fromArray(t.v1),this.v2.fromArray(t.v2),this}}eS.prototype.isQuadraticBezierCurve3=!0;class nS extends zM{constructor(t=[]){super(),this.type=\\\\\\\"SplineCurve\\\\\\\",this.points=t}getPoint(t,e=new fx){const n=e,i=this.points,r=(i.length-1)*t,s=Math.floor(r),o=r-s,a=i[0===s?s:s-1],l=i[s],c=i[s>i.length-2?i.length-1:s+1],u=i[s>i.length-3?i.length-1:s+2];return n.set(YM(o,a.x,l.x,c.x,u.x),YM(o,a.y,l.y,c.y,u.y)),n}copy(t){super.copy(t),this.points=[];for(let e=0,n=t.points.length;e<n;e++){const n=t.points[e];this.points.push(n.clone())}return this}toJSON(){const t=super.toJSON();t.points=[];for(let e=0,n=this.points.length;e<n;e++){const n=this.points[e];t.points.push(n.toArray())}return t}fromJSON(t){super.fromJSON(t),this.points=[];for(let e=0,n=t.points.length;e<n;e++){const n=t.points[e];this.points.push((new fx).fromArray(n))}return this}}nS.prototype.isSplineCurve=!0;var iS=Object.freeze({__proto__:null,ArcCurve:GM,CatmullRomCurve3:XM,CubicBezierCurve:ZM,CubicBezierCurve3:QM,EllipseCurve:UM,LineCurve:KM,LineCurve3:class extends zM{constructor(t=new Nx,e=new Nx){super(),this.type=\\\\\\\"LineCurve3\\\\\\\",this.isLineCurve3=!0,this.v1=t,this.v2=e}getPoint(t,e=new Nx){const n=e;return 1===t?n.copy(this.v2):(n.copy(this.v2).sub(this.v1),n.multiplyScalar(t).add(this.v1)),n}getPointAt(t,e){return this.getPoint(t,e)}copy(t){return super.copy(t),this.v1.copy(t.v1),this.v2.copy(t.v2),this}toJSON(){const t=super.toJSON();return t.v1=this.v1.toArray(),t.v2=this.v2.toArray(),t}fromJSON(t){return super.fromJSON(t),this.v1.fromArray(t.v1),this.v2.fromArray(t.v2),this}},QuadraticBezierCurve:tS,QuadraticBezierCurve3:eS,SplineCurve:nS});class rS extends zM{constructor(){super(),this.type=\\\\\\\"CurvePath\\\\\\\",this.curves=[],this.autoClose=!1}add(t){this.curves.push(t)}closePath(){const t=this.curves[0].getPoint(0),e=this.curves[this.curves.length-1].getPoint(1);t.equals(e)||this.curves.push(new KM(e,t))}getPoint(t,e){const n=t*this.getLength(),i=this.getCurveLengths();let r=0;for(;r<i.length;){if(i[r]>=n){const t=i[r]-n,s=this.curves[r],o=s.getLength(),a=0===o?0:1-t/o;return s.getPointAt(a,e)}r++}return null}getLength(){const t=this.getCurveLengths();return t[t.length-1]}updateArcLengths(){this.needsUpdate=!0,this.cacheLengths=null,this.getCurveLengths()}getCurveLengths(){if(this.cacheLengths&&this.cacheLengths.length===this.curves.length)return this.cacheLengths;const t=[];let e=0;for(let n=0,i=this.curves.length;n<i;n++)e+=this.curves[n].getLength(),t.push(e);return this.cacheLengths=t,t}getSpacedPoints(t=40){const e=[];for(let n=0;n<=t;n++)e.push(this.getPoint(n/t));return this.autoClose&&e.push(e[0]),e}getPoints(t=12){const e=[];let n;for(let i=0,r=this.curves;i<r.length;i++){const s=r[i],o=s&&s.isEllipseCurve?2*t:s&&(s.isLineCurve||s.isLineCurve3)?1:s&&s.isSplineCurve?t*s.points.length:t,a=s.getPoints(o);for(let t=0;t<a.length;t++){const i=a[t];n&&n.equals(i)||(e.push(i),n=i)}}return this.autoClose&&e.length>1&&!e[e.length-1].equals(e[0])&&e.push(e[0]),e}copy(t){super.copy(t),this.curves=[];for(let e=0,n=t.curves.length;e<n;e++){const n=t.curves[e];this.curves.push(n.clone())}return this.autoClose=t.autoClose,this}toJSON(){const t=super.toJSON();t.autoClose=this.autoClose,t.curves=[];for(let e=0,n=this.curves.length;e<n;e++){const n=this.curves[e];t.curves.push(n.toJSON())}return t}fromJSON(t){super.fromJSON(t),this.autoClose=t.autoClose,this.curves=[];for(let e=0,n=t.curves.length;e<n;e++){const n=t.curves[e];this.curves.push((new iS[n.type]).fromJSON(n))}return this}}class sS extends rS{constructor(t){super(),this.type=\\\\\\\"Path\\\\\\\",this.currentPoint=new fx,t&&this.setFromPoints(t)}setFromPoints(t){this.moveTo(t[0].x,t[0].y);for(let e=1,n=t.length;e<n;e++)this.lineTo(t[e].x,t[e].y);return this}moveTo(t,e){return this.currentPoint.set(t,e),this}lineTo(t,e){const n=new KM(this.currentPoint.clone(),new fx(t,e));return this.curves.push(n),this.currentPoint.set(t,e),this}quadraticCurveTo(t,e,n,i){const r=new tS(this.currentPoint.clone(),new fx(t,e),new fx(n,i));return this.curves.push(r),this.currentPoint.set(n,i),this}bezierCurveTo(t,e,n,i,r,s){const o=new ZM(this.currentPoint.clone(),new fx(t,e),new fx(n,i),new fx(r,s));return this.curves.push(o),this.currentPoint.set(r,s),this}splineThru(t){const e=[this.currentPoint.clone()].concat(t),n=new nS(e);return this.curves.push(n),this.currentPoint.copy(t[t.length-1]),this}arc(t,e,n,i,r,s){const o=this.currentPoint.x,a=this.currentPoint.y;return this.absarc(t+o,e+a,n,i,r,s),this}absarc(t,e,n,i,r,s){return this.absellipse(t,e,n,n,i,r,s),this}ellipse(t,e,n,i,r,s,o,a){const l=this.currentPoint.x,c=this.currentPoint.y;return this.absellipse(t+l,e+c,n,i,r,s,o,a),this}absellipse(t,e,n,i,r,s,o,a){const l=new UM(t,e,n,i,r,s,o,a);if(this.curves.length>0){const t=l.getPoint(0);t.equals(this.currentPoint)||this.lineTo(t.x,t.y)}this.curves.push(l);const c=l.getPoint(1);return this.currentPoint.copy(c),this}copy(t){return super.copy(t),this.currentPoint.copy(t.currentPoint),this}toJSON(){const t=super.toJSON();return t.currentPoint=this.currentPoint.toArray(),t}fromJSON(t){return super.fromJSON(t),this.currentPoint.fromArray(t.currentPoint),this}}class oS extends sS{constructor(t){super(t),this.uuid=lx(),this.type=\\\\\\\"Shape\\\\\\\",this.holes=[]}getPointsHoles(t){const e=[];for(let n=0,i=this.holes.length;n<i;n++)e[n]=this.holes[n].getPoints(t);return e}extractPoints(t){return{shape:this.getPoints(t),holes:this.getPointsHoles(t)}}copy(t){super.copy(t),this.holes=[];for(let e=0,n=t.holes.length;e<n;e++){const n=t.holes[e];this.holes.push(n.clone())}return this}toJSON(){const t=super.toJSON();t.uuid=this.uuid,t.holes=[];for(let e=0,n=this.holes.length;e<n;e++){const n=this.holes[e];t.holes.push(n.toJSON())}return t}fromJSON(t){super.fromJSON(t),this.uuid=t.uuid,this.holes=[];for(let e=0,n=t.holes.length;e<n;e++){const n=t.holes[e];this.holes.push((new sS).fromJSON(n))}return this}}const aS=function(t,e,n=2){const i=e&&e.length,r=i?e[0]*n:t.length;let s=lS(t,0,r,n,!0);const o=[];if(!s||s.next===s.prev)return o;let a,l,c,u,h,d,p;if(i&&(s=function(t,e,n,i){const r=[];let s,o,a,l,c;for(s=0,o=e.length;s<o;s++)a=e[s]*i,l=s<o-1?e[s+1]*i:t.length,c=lS(t,a,l,i,!1),c===c.next&&(c.steiner=!0),r.push(yS(c));for(r.sort(mS),s=0;s<r.length;s++)fS(r[s],n),n=cS(n,n.next);return n}(t,e,s,n)),t.length>80*n){a=c=t[0],l=u=t[1];for(let 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e,n,i,r,s,o,a,l,c=1;do{for(n=t,t=null,s=null,o=0;n;){for(o++,i=n,a=0,e=0;e<c&&(a++,i=i.nextZ,i);e++);for(l=c;a>0||l>0&&i;)0!==a&&(0===l||!i||n.z<=i.z)?(r=n,n=n.nextZ,a--):(r=i,i=i.nextZ,l--),s?s.nextZ=r:t=r,r.prevZ=s,s=r;n=i}s.nextZ=null,c*=2}while(o>1)}(r)}(t,i,r,s);let a,l,c=t;for(;t.prev!==t.next;)if(a=t.prev,l=t.next,s?dS(t,i,r,s):hS(t))e.push(a.i/n),e.push(t.i/n),e.push(l.i/n),LS(t),t=l.next,c=l.next;else if((t=l)===c){o?1===o?uS(t=pS(cS(t),e,n),e,n,i,r,s,2):2===o&&_S(t,e,n,i,r,s):uS(cS(t),e,n,i,r,s,1);break}}function hS(t){const e=t.prev,n=t,i=t.next;if(wS(e,n,i)>=0)return!1;let r=t.next.next;for(;r!==t.prev;){if(xS(e.x,e.y,n.x,n.y,i.x,i.y,r.x,r.y)&&wS(r.prev,r,r.next)>=0)return!1;r=r.next}return!0}function dS(t,e,n,i){const r=t.prev,s=t,o=t.next;if(wS(r,s,o)>=0)return!1;const a=r.x<s.x?r.x<o.x?r.x:o.x:s.x<o.x?s.x:o.x,l=r.y<s.y?r.y<o.y?r.y:o.y:s.y<o.y?s.y:o.y,c=r.x>s.x?r.x>o.x?r.x:o.x:s.x>o.x?s.x:o.x,u=r.y>s.y?r.y>o.y?r.y:o.y:s.y>o.y?s.y:o.y,h=vS(a,l,e,n,i),d=vS(c,u,e,n,i);let p=t.prevZ,_=t.nextZ;for(;p&&p.z>=h&&_&&_.z<=d;){if(p!==t.prev&&p!==t.next&&xS(r.x,r.y,s.x,s.y,o.x,o.y,p.x,p.y)&&wS(p.prev,p,p.next)>=0)return!1;if(p=p.prevZ,_!==t.prev&&_!==t.next&&xS(r.x,r.y,s.x,s.y,o.x,o.y,_.x,_.y)&&wS(_.prev,_,_.next)>=0)return!1;_=_.nextZ}for(;p&&p.z>=h;){if(p!==t.prev&&p!==t.next&&xS(r.x,r.y,s.x,s.y,o.x,o.y,p.x,p.y)&&wS(p.prev,p,p.next)>=0)return!1;p=p.prevZ}for(;_&&_.z<=d;){if(_!==t.prev&&_!==t.next&&xS(r.x,r.y,s.x,s.y,o.x,o.y,_.x,_.y)&&wS(_.prev,_,_.next)>=0)return!1;_=_.nextZ}return!0}function pS(t,e,n){let i=t;do{const r=i.prev,s=i.next.next;!TS(r,s)&&AS(r,i,i.next,s)&&SS(r,s)&&SS(s,r)&&(e.push(r.i/n),e.push(i.i/n),e.push(s.i/n),LS(i),LS(i.next),i=t=s),i=i.next}while(i!==t);return cS(i)}function _S(t,e,n,i,r,s){let o=t;do{let t=o.next.next;for(;t!==o.prev;){if(o.i!==t.i&&bS(o,t)){let a=CS(o,t);return o=cS(o,o.next),a=cS(a,a.next),uS(o,e,n,i,r,s),void uS(a,e,n,i,r,s)}t=t.next}o=o.next}while(o!==t)}function mS(t,e){return t.x-e.x}function fS(t,e){if(e=function(t,e){let n=e;const i=t.x,r=t.y;let s,o=-1/0;do{if(r<=n.y&&r>=n.next.y&&n.next.y!==n.y){const t=n.x+(r-n.y)*(n.next.x-n.x)/(n.next.y-n.y);if(t<=i&&t>o){if(o=t,t===i){if(r===n.y)return n;if(r===n.next.y)return n.next}s=n.x<n.next.x?n:n.next}}n=n.next}while(n!==e);if(!s)return null;if(i===o)return s;const a=s,l=s.x,c=s.y;let u,h=1/0;n=s;do{i>=n.x&&n.x>=l&&i!==n.x&&xS(r<c?i:o,r,l,c,r<c?o:i,r,n.x,n.y)&&(u=Math.abs(r-n.y)/(i-n.x),SS(n,t)&&(u<h||u===h&&(n.x>s.x||n.x===s.x&&gS(s,n)))&&(s=n,h=u)),n=n.next}while(n!==a);return s}(t,e)){const n=CS(e,t);cS(e,e.next),cS(n,n.next)}}function gS(t,e){return wS(t.prev,t,e.prev)<0&&wS(e.next,t,t.next)<0}function vS(t,e,n,i,r){return(t=1431655765&((t=858993459&((t=252645135&((t=16711935&((t=32767*(t-n)*r)|t<<8))|t<<4))|t<<2))|t<<1))|(e=1431655765&((e=858993459&((e=252645135&((e=16711935&((e=32767*(e-i)*r)|e<<8))|e<<4))|e<<2))|e<<1))<<1}function yS(t){let e=t,n=t;do{(e.x<n.x||e.x===n.x&&e.y<n.y)&&(n=e),e=e.next}while(e!==t);return n}function xS(t,e,n,i,r,s,o,a){return(r-o)*(e-a)-(t-o)*(s-a)>=0&&(t-o)*(i-a)-(n-o)*(e-a)>=0&&(n-o)*(s-a)-(r-o)*(i-a)>=0}function bS(t,e){return t.next.i!==e.i&&t.prev.i!==e.i&&!function(t,e){let n=t;do{if(n.i!==t.i&&n.next.i!==t.i&&n.i!==e.i&&n.next.i!==e.i&&AS(n,n.next,t,e))return!0;n=n.next}while(n!==t);return!1}(t,e)&&(SS(t,e)&&SS(e,t)&&function(t,e){let n=t,i=!1;const r=(t.x+e.x)/2,s=(t.y+e.y)/2;do{n.y>s!=n.next.y>s&&n.next.y!==n.y&&r<(n.next.x-n.x)*(s-n.y)/(n.next.y-n.y)+n.x&&(i=!i),n=n.next}while(n!==t);return i}(t,e)&&(wS(t.prev,t,e.prev)||wS(t,e.prev,e))||TS(t,e)&&wS(t.prev,t,t.next)>0&&wS(e.prev,e,e.next)>0)}function wS(t,e,n){return(e.y-t.y)*(n.x-e.x)-(e.x-t.x)*(n.y-e.y)}function TS(t,e){return t.x===e.x&&t.y===e.y}function AS(t,e,n,i){const r=MS(wS(t,e,n)),s=MS(wS(t,e,i)),o=MS(wS(n,i,t)),a=MS(wS(n,i,e));return r!==s&&o!==a||(!(0!==r||!ES(t,n,e))||(!(0!==s||!ES(t,i,e))||(!(0!==o||!ES(n,t,i))||!(0!==a||!ES(n,e,i)))))}function ES(t,e,n){return e.x<=Math.max(t.x,n.x)&&e.x>=Math.min(t.x,n.x)&&e.y<=Math.max(t.y,n.y)&&e.y>=Math.min(t.y,n.y)}function MS(t){return t>0?1:t<0?-1:0}function SS(t,e){return wS(t.prev,t,t.next)<0?wS(t,e,t.next)>=0&&wS(t,t.prev,e)>=0:wS(t,e,t.prev)<0||wS(t,t.next,e)<0}function CS(t,e){const n=new OS(t.i,t.x,t.y),i=new OS(e.i,e.x,e.y),r=t.next,s=e.prev;return t.next=e,e.prev=t,n.next=r,r.prev=n,i.next=n,n.prev=i,s.next=i,i.prev=s,i}function NS(t,e,n,i){const r=new OS(t,e,n);return i?(r.next=i.next,r.prev=i,i.next.prev=r,i.next=r):(r.prev=r,r.next=r),r}function LS(t){t.next.prev=t.prev,t.prev.next=t.next,t.prevZ&&(t.prevZ.nextZ=t.nextZ),t.nextZ&&(t.nextZ.prevZ=t.prevZ)}function OS(t,e,n){this.i=t,this.x=e,this.y=n,this.prev=null,this.next=null,this.z=null,this.prevZ=null,this.nextZ=null,this.steiner=!1}class RS{static area(t){const e=t.length;let n=0;for(let i=e-1,r=0;r<e;i=r++)n+=t[i].x*t[r].y-t[r].x*t[i].y;return.5*n}static isClockWise(t){return RS.area(t)<0}static triangulateShape(t,e){const n=[],i=[],r=[];PS(t),IS(n,t);let s=t.length;e.forEach(PS);for(let t=0;t<e.length;t++)i.push(s),s+=e[t].length,IS(n,e[t]);const o=aS(n,i);for(let t=0;t<o.length;t+=3)r.push(o.slice(t,t+3));return r}}function PS(t){const e=t.length;e>2&&t[e-1].equals(t[0])&&t.pop()}function IS(t,e){for(let n=0;n<e.length;n++)t.push(e[n].x),t.push(e[n].y)}class FS extends dw{constructor(t=new oS([new fx(.5,.5),new fx(-.5,.5),new fx(-.5,-.5),new fx(.5,-.5)]),e={}){super(),this.type=\\\\\\\"ExtrudeGeometry\\\\\\\",this.parameters={shapes:t,options:e},t=Array.isArray(t)?t:[t];const n=this,i=[],r=[];for(let e=0,n=t.length;e<n;e++){s(t[e])}function s(t){const s=[],o=void 0!==e.curveSegments?e.curveSegments:12,a=void 0!==e.steps?e.steps:1;let l=void 0!==e.depth?e.depth:1,c=void 0===e.bevelEnabled||e.bevelEnabled,u=void 0!==e.bevelThickness?e.bevelThickness:.2,h=void 0!==e.bevelSize?e.bevelSize:u-.1,d=void 0!==e.bevelOffset?e.bevelOffset:0,p=void 0!==e.bevelSegments?e.bevelSegments:3;const _=e.extrudePath,m=void 0!==e.UVGenerator?e.UVGenerator:DS;void 0!==e.amount&&(console.warn(\\\\\\\"THREE.ExtrudeBufferGeometry: amount has been renamed to depth.\\\\\\\"),l=e.amount);let f,g,v,y,x,b=!1;_&&(f=_.getSpacedPoints(a),b=!0,c=!1,g=_.computeFrenetFrames(a,!1),v=new Nx,y=new Nx,x=new Nx),c||(p=0,u=0,h=0,d=0);const w=t.extractPoints(o);let T=w.shape;const A=w.holes;if(!RS.isClockWise(T)){T=T.reverse();for(let t=0,e=A.length;t<e;t++){const e=A[t];RS.isClockWise(e)&&(A[t]=e.reverse())}}const E=RS.triangulateShape(T,A),M=T;for(let t=0,e=A.length;t<e;t++){const e=A[t];T=T.concat(e)}function S(t,e,n){return e||console.error(\\\\\\\"THREE.ExtrudeGeometry: vec does not exist\\\\\\\"),e.clone().multiplyScalar(n).add(t)}const C=T.length,N=E.length;function L(t,e,n){let i,r,s;const o=t.x-e.x,a=t.y-e.y,l=n.x-t.x,c=n.y-t.y,u=o*o+a*a,h=o*c-a*l;if(Math.abs(h)>Number.EPSILON){const h=Math.sqrt(u),d=Math.sqrt(l*l+c*c),p=e.x-a/h,_=e.y+o/h,m=((n.x-c/d-p)*c-(n.y+l/d-_)*l)/(o*c-a*l);i=p+o*m-t.x,r=_+a*m-t.y;const f=i*i+r*r;if(f<=2)return new fx(i,r);s=Math.sqrt(f/2)}else{let t=!1;o>Number.EPSILON?l>Number.EPSILON&&(t=!0):o<-Number.EPSILON?l<-Number.EPSILON&&(t=!0):Math.sign(a)===Math.sign(c)&&(t=!0),t?(i=-a,r=o,s=Math.sqrt(u)):(i=o,r=a,s=Math.sqrt(u/2))}return new fx(i/s,r/s)}const O=[];for(let t=0,e=M.length,n=e-1,i=t+1;t<e;t++,n++,i++)n===e&&(n=0),i===e&&(i=0),O[t]=L(M[t],M[n],M[i]);const R=[];let P,I=O.concat();for(let t=0,e=A.length;t<e;t++){const e=A[t];P=[];for(let t=0,n=e.length,i=n-1,r=t+1;t<n;t++,i++,r++)i===n&&(i=0),r===n&&(r=0),P[t]=L(e[t],e[i],e[r]);R.push(P),I=I.concat(P)}for(let t=0;t<p;t++){const e=t/p,n=u*Math.cos(e*Math.PI/2),i=h*Math.sin(e*Math.PI/2)+d;for(let t=0,e=M.length;t<e;t++){const e=S(M[t],O[t],i);k(e.x,e.y,-n)}for(let t=0,e=A.length;t<e;t++){const e=A[t];P=R[t];for(let t=0,r=e.length;t<r;t++){const r=S(e[t],P[t],i);k(r.x,r.y,-n)}}}const F=h+d;for(let t=0;t<C;t++){const e=c?S(T[t],I[t],F):T[t];b?(y.copy(g.normals[0]).multiplyScalar(e.x),v.copy(g.binormals[0]).multiplyScalar(e.y),x.copy(f[0]).add(y).add(v),k(x.x,x.y,x.z)):k(e.x,e.y,0)}for(let t=1;t<=a;t++)for(let e=0;e<C;e++){const n=c?S(T[e],I[e],F):T[e];b?(y.copy(g.normals[t]).multiplyScalar(n.x),v.copy(g.binormals[t]).multiplyScalar(n.y),x.copy(f[t]).add(y).add(v),k(x.x,x.y,x.z)):k(n.x,n.y,l/a*t)}for(let t=p-1;t>=0;t--){const e=t/p,n=u*Math.cos(e*Math.PI/2),i=h*Math.sin(e*Math.PI/2)+d;for(let t=0,e=M.length;t<e;t++){const e=S(M[t],O[t],i);k(e.x,e.y,l+n)}for(let t=0,e=A.length;t<e;t++){const e=A[t];P=R[t];for(let t=0,r=e.length;t<r;t++){const r=S(e[t],P[t],i);b?k(r.x,r.y+f[a-1].y,f[a-1].x+n):k(r.x,r.y,l+n)}}}function D(t,e){let n=t.length;for(;--n>=0;){const i=n;let r=n-1;r<0&&(r=t.length-1);for(let t=0,n=a+2*p;t<n;t++){const n=C*t,s=C*(t+1);z(e+i+n,e+r+n,e+r+s,e+i+s)}}}function k(t,e,n){s.push(t),s.push(e),s.push(n)}function B(t,e,r){U(t),U(e),U(r);const s=i.length/3,o=m.generateTopUV(n,i,s-3,s-2,s-1);G(o[0]),G(o[1]),G(o[2])}function z(t,e,r,s){U(t),U(e),U(s),U(e),U(r),U(s);const o=i.length/3,a=m.generateSideWallUV(n,i,o-6,o-3,o-2,o-1);G(a[0]),G(a[1]),G(a[3]),G(a[1]),G(a[2]),G(a[3])}function U(t){i.push(s[3*t+0]),i.push(s[3*t+1]),i.push(s[3*t+2])}function G(t){r.push(t.x),r.push(t.y)}!function(){const t=i.length/3;if(c){let t=0,e=C*t;for(let t=0;t<N;t++){const n=E[t];B(n[2]+e,n[1]+e,n[0]+e)}t=a+2*p,e=C*t;for(let t=0;t<N;t++){const n=E[t];B(n[0]+e,n[1]+e,n[2]+e)}}else{for(let t=0;t<N;t++){const e=E[t];B(e[2],e[1],e[0])}for(let t=0;t<N;t++){const e=E[t];B(e[0]+C*a,e[1]+C*a,e[2]+C*a)}}n.addGroup(t,i.length/3-t,0)}(),function(){const t=i.length/3;let e=0;D(M,e),e+=M.length;for(let t=0,n=A.length;t<n;t++){const n=A[t];D(n,e),e+=n.length}n.addGroup(t,i.length/3-t,1)}()}this.setAttribute(\\\\\\\"position\\\\\\\",new rw(i,3)),this.setAttribute(\\\\\\\"uv\\\\\\\",new rw(r,2)),this.computeVertexNormals()}toJSON(){const t=super.toJSON();return function(t,e,n){if(n.shapes=[],Array.isArray(t))for(let e=0,i=t.length;e<i;e++){const i=t[e];n.shapes.push(i.uuid)}else n.shapes.push(t.uuid);void 0!==e.extrudePath&&(n.options.extrudePath=e.extrudePath.toJSON());return n}(this.parameters.shapes,this.parameters.options,t)}static fromJSON(t,e){const n=[];for(let i=0,r=t.shapes.length;i<r;i++){const r=e[t.shapes[i]];n.push(r)}const i=t.options.extrudePath;return void 0!==i&&(t.options.extrudePath=(new iS[i.type]).fromJSON(i)),new FS(n,t.options)}}const DS={generateTopUV:function(t,e,n,i,r){const s=e[3*n],o=e[3*n+1],a=e[3*i],l=e[3*i+1],c=e[3*r],u=e[3*r+1];return[new fx(s,o),new fx(a,l),new fx(c,u)]},generateSideWallUV:function(t,e,n,i,r,s){const o=e[3*n],a=e[3*n+1],l=e[3*n+2],c=e[3*i],u=e[3*i+1],h=e[3*i+2],d=e[3*r],p=e[3*r+1],_=e[3*r+2],m=e[3*s],f=e[3*s+1],g=e[3*s+2];return Math.abs(a-u)<Math.abs(o-c)?[new fx(o,1-l),new fx(c,1-h),new fx(d,1-_),new fx(m,1-g)]:[new fx(a,1-l),new fx(u,1-h),new fx(p,1-_),new fx(f,1-g)]}};class kS extends dw{constructor(t=new oS([new fx(0,.5),new fx(-.5,-.5),new fx(.5,-.5)]),e=12){super(),this.type=\\\\\\\"ShapeGeometry\\\\\\\",this.parameters={shapes:t,curveSegments:e};const n=[],i=[],r=[],s=[];let o=0,a=0;if(!1===Array.isArray(t))l(t);else for(let e=0;e<t.length;e++)l(t[e]),this.addGroup(o,a,e),o+=a,a=0;function l(t){const o=i.length/3,l=t.extractPoints(e);let c=l.shape;const u=l.holes;!1===RS.isClockWise(c)&&(c=c.reverse());for(let t=0,e=u.length;t<e;t++){const e=u[t];!0===RS.isClockWise(e)&&(u[t]=e.reverse())}const h=RS.triangulateShape(c,u);for(let t=0,e=u.length;t<e;t++){const e=u[t];c=c.concat(e)}for(let t=0,e=c.length;t<e;t++){const e=c[t];i.push(e.x,e.y,0),r.push(0,0,1),s.push(e.x,e.y)}for(let t=0,e=h.length;t<e;t++){const e=h[t],i=e[0]+o,r=e[1]+o,s=e[2]+o;n.push(i,r,s),a+=3}}this.setIndex(n),this.setAttribute(\\\\\\\"position\\\\\\\",new rw(i,3)),this.setAttribute(\\\\\\\"normal\\\\\\\",new rw(r,3)),this.setAttribute(\\\\\\\"uv\\\\\\\",new rw(s,2))}toJSON(){const t=super.toJSON();return function(t,e){if(e.shapes=[],Array.isArray(t))for(let n=0,i=t.length;n<i;n++){const i=t[n];e.shapes.push(i.uuid)}else e.shapes.push(t.uuid);return e}(this.parameters.shapes,t)}static fromJSON(t,e){const n=[];for(let i=0,r=t.shapes.length;i<r;i++){const r=e[t.shapes[i]];n.push(r)}return new kS(n,t.curveSegments)}}class BS extends jb{constructor(t){super(),this.type=\\\\\\\"ShadowMaterial\\\\\\\",this.color=new Zb(0),this.transparent=!0,this.setValues(t)}copy(t){return super.copy(t),this.color.copy(t.color),this}}BS.prototype.isShadowMaterial=!0;class zS extends jb{constructor(t){super(),this.defines={STANDARD:\\\\\\\"\\\\\\\"},this.type=\\\\\\\"MeshStandardMaterial\\\\\\\",this.color=new Zb(16777215),this.roughness=1,this.metalness=0,this.map=null,this.lightMap=null,this.lightMapIntensity=1,this.aoMap=null,this.aoMapIntensity=1,this.emissive=new Zb(0),this.emissiveIntensity=1,this.emissiveMap=null,this.bumpMap=null,this.bumpScale=1,this.normalMap=null,this.normalMapType=0,this.normalScale=new fx(1,1),this.displacementMap=null,this.displacementScale=1,this.displacementBias=0,this.roughnessMap=null,this.metalnessMap=null,this.alphaMap=null,this.envMap=null,this.envMapIntensity=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.defines={STANDARD:\\\\\\\"\\\\\\\"},this.color.copy(t.color),this.roughness=t.roughness,this.metalness=t.metalness,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.roughnessMap=t.roughnessMap,this.metalnessMap=t.metalnessMap,this.alphaMap=t.alphaMap,this.envMap=t.envMap,this.envMapIntensity=t.envMapIntensity,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}}zS.prototype.isMeshStandardMaterial=!0;class US extends zS{constructor(t){super(),this.defines={STANDARD:\\\\\\\"\\\\\\\",PHYSICAL:\\\\\\\"\\\\\\\"},this.type=\\\\\\\"MeshPhysicalMaterial\\\\\\\",this.clearcoatMap=null,this.clearcoatRoughness=0,this.clearcoatRoughnessMap=null,this.clearcoatNormalScale=new fx(1,1),this.clearcoatNormalMap=null,this.ior=1.5,Object.defineProperty(this,\\\\\\\"reflectivity\\\\\\\",{get:function(){return cx(2.5*(this.ior-1)/(this.ior+1),0,1)},set:function(t){this.ior=(1+.4*t)/(1-.4*t)}}),this.sheenTint=new Zb(0),this.sheenRoughness=1,this.transmissionMap=null,this.thickness=.01,this.thicknessMap=null,this.attenuationDistance=0,this.attenuationTint=new Zb(1,1,1),this.specularIntensity=1,this.specularIntensityMap=null,this.specularTint=new Zb(1,1,1),this.specularTintMap=null,this._sheen=0,this._clearcoat=0,this._transmission=0,this.setValues(t)}get sheen(){return this._sheen}set sheen(t){this._sheen>0!=t>0&&this.version++,this._sheen=t}get clearcoat(){return this._clearcoat}set clearcoat(t){this._clearcoat>0!=t>0&&this.version++,this._clearcoat=t}get transmission(){return this._transmission}set transmission(t){this._transmission>0!=t>0&&this.version++,this._transmission=t}copy(t){return super.copy(t),this.defines={STANDARD:\\\\\\\"\\\\\\\",PHYSICAL:\\\\\\\"\\\\\\\"},this.clearcoat=t.clearcoat,this.clearcoatMap=t.clearcoatMap,this.clearcoatRoughness=t.clearcoatRoughness,this.clearcoatRoughnessMap=t.clearcoatRoughnessMap,this.clearcoatNormalMap=t.clearcoatNormalMap,this.clearcoatNormalScale.copy(t.clearcoatNormalScale),this.ior=t.ior,this.sheen=t.sheen,this.sheenTint.copy(t.sheenTint),this.sheenRoughness=t.sheenRoughness,this.transmission=t.transmission,this.transmissionMap=t.transmissionMap,this.thickness=t.thickness,this.thicknessMap=t.thicknessMap,this.attenuationDistance=t.attenuationDistance,this.attenuationTint.copy(t.attenuationTint),this.specularIntensity=t.specularIntensity,this.specularIntensityMap=t.specularIntensityMap,this.specularTint.copy(t.specularTint),this.specularTintMap=t.specularTintMap,this}}US.prototype.isMeshPhysicalMaterial=!0;class GS extends jb{constructor(t){super(),this.type=\\\\\\\"MeshPhongMaterial\\\\\\\",this.color=new Zb(16777215),this.specular=new Zb(1118481),this.shininess=30,this.map=null,this.lightMap=null,this.lightMapIntensity=1,this.aoMap=null,this.aoMapIntensity=1,this.emissive=new Zb(0),this.emissiveIntensity=1,this.emissiveMap=null,this.bumpMap=null,this.bumpScale=1,this.normalMap=null,this.normalMapType=0,this.normalScale=new fx(1,1),this.displacementMap=null,this.displacementScale=1,this.displacementBias=0,this.specularMap=null,this.alphaMap=null,this.envMap=null,this.combine=0,this.reflectivity=1,this.refractionRatio=.98,this.wireframe=!1,this.wireframeLinewidth=1,this.wireframeLinecap=\\\\\\\"round\\\\\\\",this.wireframeLinejoin=\\\\\\\"round\\\\\\\",this.flatShading=!1,this.setValues(t)}copy(t){return super.copy(t),this.color.copy(t.color),this.specular.copy(t.specular),this.shininess=t.shininess,this.map=t.map,this.lightMap=t.lightMap,this.lightMapIntensity=t.lightMapIntensity,this.aoMap=t.aoMap,this.aoMapIntensity=t.aoMapIntensity,this.emissive.copy(t.emissive),this.emissiveMap=t.emissiveMap,this.emissiveIntensity=t.emissiveIntensity,this.bumpMap=t.bumpMap,this.bumpScale=t.bumpScale,this.normalMap=t.normalMap,this.normalMapType=t.normalMapType,this.normalScale.copy(t.normalScale),this.displacementMap=t.displacementMap,this.displacementScale=t.displacementScale,this.displacementBias=t.displacementBias,this.specularMap=t.specularMap,this.alphaMap=t.alphaMap,this.envMap=t.envMap,this.combine=t.combine,this.reflectivity=t.reflectivity,this.refractionRatio=t.refractionRatio,this.wireframe=t.wireframe,this.wireframeLinewidth=t.wireframeLinewidth,this.wireframeLinecap=t.wireframeLinecap,this.wireframeLinejoin=t.wireframeLinejoin,this.flatShading=t.flatShading,this}}GS.prototype.isMeshPhongMaterial=!0;class VS extends jb{constructor(t){super(),this.defines={TOON:\\\\\\\"\\\\\\\"},this.type=\\\\\\\"MeshToonMaterial\\\\\\\",this.color=new Zb(16777215),this.map=null,this.gradientMap=null,this.lightMap=null,this.lightMapIntensity=1,this.aoMap=null,this.aoMapIntensity=1,this.emissive=new Zb(0),this.emissiveIntensity=1,this.emissiveMap=null,this.bumpMap=null,this.bumpScale=1,this.normalMap=null,this.normalMapType=0,this.normalScale=new fx(1,1),this.displacementMap=null,this.displacementScale=1,this.displacementBias=0,this.alphaMap=null,this.wireframe=!1,this.wireframeLinewidth=1,this.wireframeLinecap=\\\\\\\"round\\\\\\\",this.wireframeLinejoin=\\\\\\\"round\\\\\\\",this.setValues(t)}copy(t){return super.copy(t),this.color.copy(t.color),this.map=t.map,this.gradientMap=t.gradientMap,this.lightMap=t.lightMap,this.lightMapIntensity=t.lightMapIntensity,this.aoMap=t.aoMap,this.aoMapIntensity=t.aoMapIntensity,this.emissive.copy(t.emissive),this.emissiveMap=t.emissiveMap,this.emissiveIntensity=t.emissiveIntensity,this.bumpMap=t.bumpMap,this.bumpScale=t.bumpScale,this.normalMap=t.normalMap,this.normalMapType=t.normalMapType,this.normalScale.copy(t.normalScale),this.displacementMap=t.displacementMap,this.displacementScale=t.displacementScale,this.displacementBias=t.displacementBias,this.alphaMap=t.alphaMap,this.wireframe=t.wireframe,this.wireframeLinewidth=t.wireframeLinewidth,this.wireframeLinecap=t.wireframeLinecap,this.wireframeLinejoin=t.wireframeLinejoin,this}}VS.prototype.isMeshToonMaterial=!0;class HS extends jb{constructor(t){super(),this.type=\\\\\\\"MeshNormalMaterial\\\\\\\",this.bumpMap=null,this.bumpScale=1,this.normalMap=null,this.normalMapType=0,this.normalScale=new fx(1,1),this.displacementMap=null,this.displacementScale=1,this.displacementBias=0,this.wireframe=!1,this.wireframeLinewidth=1,this.fog=!1,this.flatShading=!1,this.setValues(t)}copy(t){return super.copy(t),this.bumpMap=t.bumpMap,this.bumpScale=t.bumpScale,this.normalMap=t.normalMap,this.normalMapType=t.normalMapType,this.normalScale.copy(t.normalScale),this.displacementMap=t.displacementMap,this.displacementScale=t.displacementScale,this.displacementBias=t.displacementBias,this.wireframe=t.wireframe,this.wireframeLinewidth=t.wireframeLinewidth,this.flatShading=t.flatShading,this}}HS.prototype.isMeshNormalMaterial=!0;class jS extends jb{constructor(t){super(),this.type=\\\\\\\"MeshLambertMaterial\\\\\\\",this.color=new Zb(16777215),this.map=null,this.lightMap=null,this.lightMapIntensity=1,this.aoMap=null,this.aoMapIntensity=1,this.emissive=new Zb(0),this.emissiveIntensity=1,this.emissiveMap=null,this.specularMap=null,this.alphaMap=null,this.envMap=null,this.combine=0,this.reflectivity=1,this.refractionRatio=.98,this.wireframe=!1,this.wireframeLinewidth=1,this.wireframeLinecap=\\\\\\\"round\\\\\\\",this.wireframeLinejoin=\\\\\\\"round\\\\\\\",this.setValues(t)}copy(t){return super.copy(t),this.color.copy(t.color),this.map=t.map,this.lightMap=t.lightMap,this.lightMapIntensity=t.lightMapIntensity,this.aoMap=t.aoMap,this.aoMapIntensity=t.aoMapIntensity,this.emissive.copy(t.emissive),this.emissiveMap=t.emissiveMap,this.emissiveIntensity=t.emissiveIntensity,this.specularMap=t.specularMap,this.alphaMap=t.alphaMap,this.envMap=t.envMap,this.combine=t.combine,this.reflectivity=t.reflectivity,this.refractionRatio=t.refractionRatio,this.wireframe=t.wireframe,this.wireframeLinewidth=t.wireframeLinewidth,this.wireframeLinecap=t.wireframeLinecap,this.wireframeLinejoin=t.wireframeLinejoin,this}}jS.prototype.isMeshLambertMaterial=!0;class WS extends jb{constructor(t){super(),this.defines={MATCAP:\\\\\\\"\\\\\\\"},this.type=\\\\\\\"MeshMatcapMaterial\\\\\\\",this.color=new Zb(16777215),this.matcap=null,this.map=null,this.bumpMap=null,this.bumpScale=1,this.normalMap=null,this.normalMapType=0,this.normalScale=new fx(1,1),this.displacementMap=null,this.displacementScale=1,this.displacementBias=0,this.alphaMap=null,this.flatShading=!1,this.setValues(t)}copy(t){return super.copy(t),this.defines={MATCAP:\\\\\\\"\\\\\\\"},this.color.copy(t.color),this.matcap=t.matcap,this.map=t.map,this.bumpMap=t.bumpMap,this.bumpScale=t.bumpScale,this.normalMap=t.normalMap,this.normalMapType=t.normalMapType,this.normalScale.copy(t.normalScale),this.displacementMap=t.displacementMap,this.displacementScale=t.displacementScale,this.displacementBias=t.displacementBias,this.alphaMap=t.alphaMap,this.flatShading=t.flatShading,this}}WS.prototype.isMeshMatcapMaterial=!0;class qS extends xM{constructor(t){super(),this.type=\\\\\\\"LineDashedMaterial\\\\\\\",this.scale=1,this.dashSize=3,this.gapSize=1,this.setValues(t)}copy(t){return super.copy(t),this.scale=t.scale,this.dashSize=t.dashSize,this.gapSize=t.gapSize,this}}qS.prototype.isLineDashedMaterial=!0;const XS={arraySlice:function(t,e,n){return XS.isTypedArray(t)?new t.constructor(t.subarray(e,void 0!==n?n:t.length)):t.slice(e,n)},convertArray:function(t,e,n){return!t||!n&&t.constructor===e?t:\\\\\\\"number\\\\\\\"==typeof e.BYTES_PER_ELEMENT?new e(t):Array.prototype.slice.call(t)},isTypedArray:function(t){return ArrayBuffer.isView(t)&&!(t instanceof DataView)},getKeyframeOrder:function(t){const e=t.length,n=new Array(e);for(let t=0;t!==e;++t)n[t]=t;return n.sort((function(e,n){return t[e]-t[n]})),n},sortedArray:function(t,e,n){const i=t.length,r=new t.constructor(i);for(let s=0,o=0;o!==i;++s){const i=n[s]*e;for(let n=0;n!==e;++n)r[o++]=t[i+n]}return r},flattenJSON:function(t,e,n,i){let r=1,s=t[0];for(;void 0!==s&&void 0===s[i];)s=t[r++];if(void 0===s)return;let o=s[i];if(void 0!==o)if(Array.isArray(o))do{o=s[i],void 0!==o&&(e.push(s.time),n.push.apply(n,o)),s=t[r++]}while(void 0!==s);else if(void 0!==o.toArray)do{o=s[i],void 0!==o&&(e.push(s.time),o.toArray(n,n.length)),s=t[r++]}while(void 0!==s);else do{o=s[i],void 0!==o&&(e.push(s.time),n.push(o)),s=t[r++]}while(void 0!==s)},subclip:function(t,e,n,i,r=30){const s=t.clone();s.name=e;const o=[];for(let t=0;t<s.tracks.length;++t){const e=s.tracks[t],a=e.getValueSize(),l=[],c=[];for(let t=0;t<e.times.length;++t){const s=e.times[t]*r;if(!(s<n||s>=i)){l.push(e.times[t]);for(let n=0;n<a;++n)c.push(e.values[t*a+n])}}0!==l.length&&(e.times=XS.convertArray(l,e.times.constructor),e.values=XS.convertArray(c,e.values.constructor),o.push(e))}s.tracks=o;let a=1/0;for(let t=0;t<s.tracks.length;++t)a>s.tracks[t].times[0]&&(a=s.tracks[t].times[0]);for(let t=0;t<s.tracks.length;++t)s.tracks[t].shift(-1*a);return s.resetDuration(),s},makeClipAdditive:function(t,e=0,n=t,i=30){i<=0&&(i=30);const r=n.tracks.length,s=e/i;for(let e=0;e<r;++e){const i=n.tracks[e],r=i.ValueTypeName;if(\\\\\\\"bool\\\\\\\"===r||\\\\\\\"string\\\\\\\"===r)continue;const o=t.tracks.find((function(t){return t.name===i.name&&t.ValueTypeName===r}));if(void 0===o)continue;let a=0;const l=i.getValueSize();i.createInterpolant.isInterpolantFactoryMethodGLTFCubicSpline&&(a=l/3);let c=0;const u=o.getValueSize();o.createInterpolant.isInterpolantFactoryMethodGLTFCubicSpline&&(c=u/3);const h=i.times.length-1;let d;if(s<=i.times[0]){const t=a,e=l-a;d=XS.arraySlice(i.values,t,e)}else if(s>=i.times[h]){const t=h*l+a,e=t+l-a;d=XS.arraySlice(i.values,t,e)}else{const t=i.createInterpolant(),e=a,n=l-a;t.evaluate(s),d=XS.arraySlice(t.resultBuffer,e,n)}if(\\\\\\\"quaternion\\\\\\\"===r){(new Cx).fromArray(d).normalize().conjugate().toArray(d)}const p=o.times.length;for(let t=0;t<p;++t){const e=t*u+c;if(\\\\\\\"quaternion\\\\\\\"===r)Cx.multiplyQuaternionsFlat(o.values,e,d,0,o.values,e);else{const t=u-2*c;for(let n=0;n<t;++n)o.values[e+n]-=d[n]}}}return t.blendMode=2501,t}};class YS{constructor(t,e,n,i){this.parameterPositions=t,this._cachedIndex=0,this.resultBuffer=void 0!==i?i:new e.constructor(n),this.sampleValues=e,this.valueSize=n,this.settings=null,this.DefaultSettings_={}}evaluate(t){const e=this.parameterPositions;let n=this._cachedIndex,i=e[n],r=e[n-1];t:{e:{let s;n:{i:if(!(t<i)){for(let s=n+2;;){if(void 0===i){if(t<r)break i;return n=e.length,this._cachedIndex=n,this.afterEnd_(n-1,t,r)}if(n===s)break;if(r=i,i=e[++n],t<i)break e}s=e.length;break n}if(t>=r)break t;{const o=e[1];t<o&&(n=2,r=o);for(let s=n-2;;){if(void 0===r)return this._cachedIndex=0,this.beforeStart_(0,t,i);if(n===s)break;if(i=r,r=e[--n-1],t>=r)break e}s=n,n=0}}for(;n<s;){const i=n+s>>>1;t<e[i]?s=i:n=i+1}if(i=e[n],r=e[n-1],void 0===r)return this._cachedIndex=0,this.beforeStart_(0,t,i);if(void 0===i)return n=e.length,this._cachedIndex=n,this.afterEnd_(n-1,r,t)}this._cachedIndex=n,this.intervalChanged_(n,r,i)}return this.interpolate_(n,r,t,i)}getSettings_(){return this.settings||this.DefaultSettings_}copySampleValue_(t){const e=this.resultBuffer,n=this.sampleValues,i=this.valueSize,r=t*i;for(let t=0;t!==i;++t)e[t]=n[r+t];return e}interpolate_(){throw new Error(\\\\\\\"call to abstract method\\\\\\\")}intervalChanged_(){}}YS.prototype.beforeStart_=YS.prototype.copySampleValue_,YS.prototype.afterEnd_=YS.prototype.copySampleValue_;class $S extends YS{constructor(t,e,n,i){super(t,e,n,i),this._weightPrev=-0,this._offsetPrev=-0,this._weightNext=-0,this._offsetNext=-0,this.DefaultSettings_={endingStart:jy,endingEnd:jy}}intervalChanged_(t,e,n){const i=this.parameterPositions;let r=t-2,s=t+1,o=i[r],a=i[s];if(void 0===o)switch(this.getSettings_().endingStart){case Wy:r=t,o=2*e-n;break;case qy:r=i.length-2,o=e+i[r]-i[r+1];break;default:r=t,o=n}if(void 0===a)switch(this.getSettings_().endingEnd){case Wy:s=t,a=2*n-e;break;case qy:s=1,a=n+i[1]-i[0];break;default:s=t-1,a=e}const l=.5*(n-e),c=this.valueSize;this._weightPrev=l/(e-o),this._weightNext=l/(a-n),this._offsetPrev=r*c,this._offsetNext=s*c}interpolate_(t,e,n,i){const r=this.resultBuffer,s=this.sampleValues,o=this.valueSize,a=t*o,l=a-o,c=this._offsetPrev,u=this._offsetNext,h=this._weightPrev,d=this._weightNext,p=(n-e)/(i-e),_=p*p,m=_*p,f=-h*m+2*h*_-h*p,g=(1+h)*m+(-1.5-2*h)*_+(-.5+h)*p+1,v=(-1-d)*m+(1.5+d)*_+.5*p,y=d*m-d*_;for(let t=0;t!==o;++t)r[t]=f*s[c+t]+g*s[l+t]+v*s[a+t]+y*s[u+t];return r}}class JS extends YS{constructor(t,e,n,i){super(t,e,n,i)}interpolate_(t,e,n,i){const r=this.resultBuffer,s=this.sampleValues,o=this.valueSize,a=t*o,l=a-o,c=(n-e)/(i-e),u=1-c;for(let t=0;t!==o;++t)r[t]=s[l+t]*u+s[a+t]*c;return r}}class ZS extends YS{constructor(t,e,n,i){super(t,e,n,i)}interpolate_(t){return this.copySampleValue_(t-1)}}class QS{constructor(t,e,n,i){if(void 0===t)throw new Error(\\\\\\\"THREE.KeyframeTrack: track name is undefined\\\\\\\");if(void 0===e||0===e.length)throw new Error(\\\\\\\"THREE.KeyframeTrack: no keyframes in track named \\\\\\\"+t);this.name=t,this.times=XS.convertArray(e,this.TimeBufferType),this.values=XS.convertArray(n,this.ValueBufferType),this.setInterpolation(i||this.DefaultInterpolation)}static toJSON(t){const e=t.constructor;let n;if(e.toJSON!==this.toJSON)n=e.toJSON(t);else{n={name:t.name,times:XS.convertArray(t.times,Array),values:XS.convertArray(t.values,Array)};const e=t.getInterpolation();e!==t.DefaultInterpolation&&(n.interpolation=e)}return n.type=t.ValueTypeName,n}InterpolantFactoryMethodDiscrete(t){return new ZS(this.times,this.values,this.getValueSize(),t)}InterpolantFactoryMethodLinear(t){return new JS(this.times,this.values,this.getValueSize(),t)}InterpolantFactoryMethodSmooth(t){return new $S(this.times,this.values,this.getValueSize(),t)}setInterpolation(t){let e;switch(t){case Gy:e=this.InterpolantFactoryMethodDiscrete;break;case Vy:e=this.InterpolantFactoryMethodLinear;break;case Hy:e=this.InterpolantFactoryMethodSmooth}if(void 0===e){const e=\\\\\\\"unsupported interpolation for \\\\\\\"+this.ValueTypeName+\\\\\\\" keyframe track named \\\\\\\"+this.name;if(void 0===this.createInterpolant){if(t===this.DefaultInterpolation)throw new Error(e);this.setInterpolation(this.DefaultInterpolation)}return console.warn(\\\\\\\"THREE.KeyframeTrack:\\\\\\\",e),this}return this.createInterpolant=e,this}getInterpolation(){switch(this.createInterpolant){case this.InterpolantFactoryMethodDiscrete:return Gy;case this.InterpolantFactoryMethodLinear:return Vy;case this.InterpolantFactoryMethodSmooth:return Hy}}getValueSize(){return this.values.length/this.times.length}shift(t){if(0!==t){const e=this.times;for(let n=0,i=e.length;n!==i;++n)e[n]+=t}return this}scale(t){if(1!==t){const e=this.times;for(let n=0,i=e.length;n!==i;++n)e[n]*=t}return this}trim(t,e){const n=this.times,i=n.length;let r=0,s=i-1;for(;r!==i&&n[r]<t;)++r;for(;-1!==s&&n[s]>e;)--s;if(++s,0!==r||s!==i){r>=s&&(s=Math.max(s,1),r=s-1);const t=this.getValueSize();this.times=XS.arraySlice(n,r,s),this.values=XS.arraySlice(this.values,r*t,s*t)}return this}validate(){let t=!0;const e=this.getValueSize();e-Math.floor(e)!=0&&(console.error(\\\\\\\"THREE.KeyframeTrack: Invalid value size in track.\\\\\\\",this),t=!1);const n=this.times,i=this.values,r=n.length;0===r&&(console.error(\\\\\\\"THREE.KeyframeTrack: Track is empty.\\\\\\\",this),t=!1);let s=null;for(let e=0;e!==r;e++){const i=n[e];if(\\\\\\\"number\\\\\\\"==typeof i&&isNaN(i)){console.error(\\\\\\\"THREE.KeyframeTrack: Time is not a valid number.\\\\\\\",this,e,i),t=!1;break}if(null!==s&&s>i){console.error(\\\\\\\"THREE.KeyframeTrack: Out of order keys.\\\\\\\",this,e,i,s),t=!1;break}s=i}if(void 0!==i&&XS.isTypedArray(i))for(let e=0,n=i.length;e!==n;++e){const n=i[e];if(isNaN(n)){console.error(\\\\\\\"THREE.KeyframeTrack: Value is not a valid number.\\\\\\\",this,e,n),t=!1;break}}return t}optimize(){const t=XS.arraySlice(this.times),e=XS.arraySlice(this.values),n=this.getValueSize(),i=this.getInterpolation()===Hy,r=t.length-1;let s=1;for(let o=1;o<r;++o){let r=!1;const a=t[o];if(a!==t[o+1]&&(1!==o||a!==t[0]))if(i)r=!0;else{const t=o*n,i=t-n,s=t+n;for(let o=0;o!==n;++o){const n=e[t+o];if(n!==e[i+o]||n!==e[s+o]){r=!0;break}}}if(r){if(o!==s){t[s]=t[o];const i=o*n,r=s*n;for(let t=0;t!==n;++t)e[r+t]=e[i+t]}++s}}if(r>0){t[s]=t[r];for(let t=r*n,i=s*n,o=0;o!==n;++o)e[i+o]=e[t+o];++s}return s!==t.length?(this.times=XS.arraySlice(t,0,s),this.values=XS.arraySlice(e,0,s*n)):(this.times=t,this.values=e),this}clone(){const t=XS.arraySlice(this.times,0),e=XS.arraySlice(this.values,0),n=new(0,this.constructor)(this.name,t,e);return n.createInterpolant=this.createInterpolant,n}}QS.prototype.TimeBufferType=Float32Array,QS.prototype.ValueBufferType=Float32Array,QS.prototype.DefaultInterpolation=Vy;class KS extends QS{}KS.prototype.ValueTypeName=\\\\\\\"bool\\\\\\\",KS.prototype.ValueBufferType=Array,KS.prototype.DefaultInterpolation=Gy,KS.prototype.InterpolantFactoryMethodLinear=void 0,KS.prototype.InterpolantFactoryMethodSmooth=void 0;class tC extends QS{}tC.prototype.ValueTypeName=\\\\\\\"color\\\\\\\";class eC extends QS{}eC.prototype.ValueTypeName=\\\\\\\"number\\\\\\\";class nC extends YS{constructor(t,e,n,i){super(t,e,n,i)}interpolate_(t,e,n,i){const r=this.resultBuffer,s=this.sampleValues,o=this.valueSize,a=(n-e)/(i-e);let l=t*o;for(let t=l+o;l!==t;l+=4)Cx.slerpFlat(r,0,s,l-o,s,l,a);return r}}class iC extends QS{InterpolantFactoryMethodLinear(t){return new nC(this.times,this.values,this.getValueSize(),t)}}iC.prototype.ValueTypeName=\\\\\\\"quaternion\\\\\\\",iC.prototype.DefaultInterpolation=Vy,iC.prototype.InterpolantFactoryMethodSmooth=void 0;class rC extends QS{}rC.prototype.ValueTypeName=\\\\\\\"string\\\\\\\",rC.prototype.ValueBufferType=Array,rC.prototype.DefaultInterpolation=Gy,rC.prototype.InterpolantFactoryMethodLinear=void 0,rC.prototype.InterpolantFactoryMethodSmooth=void 0;class sC extends QS{}sC.prototype.ValueTypeName=\\\\\\\"vector\\\\\\\";class oC{constructor(t,e=-1,n,i=2500){this.name=t,this.tracks=n,this.duration=e,this.blendMode=i,this.uuid=lx(),this.duration<0&&this.resetDuration()}static parse(t){const e=[],n=t.tracks,i=1/(t.fps||1);for(let t=0,r=n.length;t!==r;++t)e.push(aC(n[t]).scale(i));const r=new this(t.name,t.duration,e,t.blendMode);return r.uuid=t.uuid,r}static toJSON(t){const e=[],n=t.tracks,i={name:t.name,duration:t.duration,tracks:e,uuid:t.uuid,blendMode:t.blendMode};for(let t=0,i=n.length;t!==i;++t)e.push(QS.toJSON(n[t]));return i}static CreateFromMorphTargetSequence(t,e,n,i){const r=e.length,s=[];for(let t=0;t<r;t++){let o=[],a=[];o.push((t+r-1)%r,t,(t+1)%r),a.push(0,1,0);const l=XS.getKeyframeOrder(o);o=XS.sortedArray(o,1,l),a=XS.sortedArray(a,1,l),i||0!==o[0]||(o.push(r),a.push(a[0])),s.push(new eC(\\\\\\\".morphTargetInfluences[\\\\\\\"+e[t].name+\\\\\\\"]\\\\\\\",o,a).scale(1/n))}return new this(t,-1,s)}static findByName(t,e){let n=t;if(!Array.isArray(t)){const e=t;n=e.geometry&&e.geometry.animations||e.animations}for(let t=0;t<n.length;t++)if(n[t].name===e)return n[t];return null}static CreateClipsFromMorphTargetSequences(t,e,n){const i={},r=/^([\\\\w-]*?)([\\\\d]+)$/;for(let e=0,n=t.length;e<n;e++){const n=t[e],s=n.name.match(r);if(s&&s.length>1){const t=s[1];let e=i[t];e||(i[t]=e=[]),e.push(n)}}const s=[];for(const t in i)s.push(this.CreateFromMorphTargetSequence(t,i[t],e,n));return s}static parseAnimation(t,e){if(!t)return console.error(\\\\\\\"THREE.AnimationClip: No animation in JSONLoader data.\\\\\\\"),null;const n=function(t,e,n,i,r){if(0!==n.length){const s=[],o=[];XS.flattenJSON(n,s,o,i),0!==s.length&&r.push(new t(e,s,o))}},i=[],r=t.name||\\\\\\\"default\\\\\\\",s=t.fps||30,o=t.blendMode;let a=t.length||-1;const l=t.hierarchy||[];for(let t=0;t<l.length;t++){const r=l[t].keys;if(r&&0!==r.length)if(r[0].morphTargets){const t={};let e;for(e=0;e<r.length;e++)if(r[e].morphTargets)for(let n=0;n<r[e].morphTargets.length;n++)t[r[e].morphTargets[n]]=-1;for(const n in t){const t=[],s=[];for(let i=0;i!==r[e].morphTargets.length;++i){const i=r[e];t.push(i.time),s.push(i.morphTarget===n?1:0)}i.push(new eC(\\\\\\\".morphTargetInfluence[\\\\\\\"+n+\\\\\\\"]\\\\\\\",t,s))}a=t.length*(s||1)}else{const s=\\\\\\\".bones[\\\\\\\"+e[t].name+\\\\\\\"]\\\\\\\";n(sC,s+\\\\\\\".position\\\\\\\",r,\\\\\\\"pos\\\\\\\",i),n(iC,s+\\\\\\\".quaternion\\\\\\\",r,\\\\\\\"rot\\\\\\\",i),n(sC,s+\\\\\\\".scale\\\\\\\",r,\\\\\\\"scl\\\\\\\",i)}}if(0===i.length)return null;return new this(r,a,i,o)}resetDuration(){let t=0;for(let e=0,n=this.tracks.length;e!==n;++e){const n=this.tracks[e];t=Math.max(t,n.times[n.times.length-1])}return this.duration=t,this}trim(){for(let t=0;t<this.tracks.length;t++)this.tracks[t].trim(0,this.duration);return this}validate(){let t=!0;for(let e=0;e<this.tracks.length;e++)t=t&&this.tracks[e].validate();return t}optimize(){for(let t=0;t<this.tracks.length;t++)this.tracks[t].optimize();return this}clone(){const t=[];for(let e=0;e<this.tracks.length;e++)t.push(this.tracks[e].clone());return new this.constructor(this.name,this.duration,t,this.blendMode)}toJSON(){return this.constructor.toJSON(this)}}function aC(t){if(void 0===t.type)throw new Error(\\\\\\\"THREE.KeyframeTrack: track type undefined, can not parse\\\\\\\");const e=function(t){switch(t.toLowerCase()){case\\\\\\\"scalar\\\\\\\":case\\\\\\\"double\\\\\\\":case\\\\\\\"float\\\\\\\":case\\\\\\\"number\\\\\\\":case\\\\\\\"integer\\\\\\\":return eC;case\\\\\\\"vector\\\\\\\":case\\\\\\\"vector2\\\\\\\":case\\\\\\\"vector3\\\\\\\":case\\\\\\\"vector4\\\\\\\":return sC;case\\\\\\\"color\\\\\\\":return tC;case\\\\\\\"quaternion\\\\\\\":return iC;case\\\\\\\"bool\\\\\\\":case\\\\\\\"boolean\\\\\\\":return KS;case\\\\\\\"string\\\\\\\":return rC}throw new Error(\\\\\\\"THREE.KeyframeTrack: Unsupported typeName: \\\\\\\"+t)}(t.type);if(void 0===t.times){const e=[],n=[];XS.flattenJSON(t.keys,e,n,\\\\\\\"value\\\\\\\"),t.times=e,t.values=n}return void 0!==e.parse?e.parse(t):new e(t.name,t.times,t.values,t.interpolation)}const lC={enabled:!1,files:{},add:function(t,e){!1!==this.enabled&&(this.files[t]=e)},get:function(t){if(!1!==this.enabled)return this.files[t]},remove:function(t){delete this.files[t]},clear:function(){this.files={}}};class cC{constructor(t,e,n){const i=this;let r,s=!1,o=0,a=0;const l=[];this.onStart=void 0,this.onLoad=t,this.onProgress=e,this.onError=n,this.itemStart=function(t){a++,!1===s&&void 0!==i.onStart&&i.onStart(t,o,a),s=!0},this.itemEnd=function(t){o++,void 0!==i.onProgress&&i.onProgress(t,o,a),o===a&&(s=!1,void 0!==i.onLoad&&i.onLoad())},this.itemError=function(t){void 0!==i.onError&&i.onError(t)},this.resolveURL=function(t){return r?r(t):t},this.setURLModifier=function(t){return r=t,this},this.addHandler=function(t,e){return l.push(t,e),this},this.removeHandler=function(t){const e=l.indexOf(t);return-1!==e&&l.splice(e,2),this},this.getHandler=function(t){for(let e=0,n=l.length;e<n;e+=2){const n=l[e],i=l[e+1];if(n.global&&(n.lastIndex=0),n.test(t))return i}return null}}}const uC=new cC;class hC{constructor(t){this.manager=void 0!==t?t:uC,this.crossOrigin=\\\\\\\"anonymous\\\\\\\",this.withCredentials=!1,this.path=\\\\\\\"\\\\\\\",this.resourcePath=\\\\\\\"\\\\\\\",this.requestHeader={}}load(){}loadAsync(t,e){const n=this;return new Promise((function(i,r){n.load(t,i,e,r)}))}parse(){}setCrossOrigin(t){return this.crossOrigin=t,this}setWithCredentials(t){return this.withCredentials=t,this}setPath(t){return this.path=t,this}setResourcePath(t){return this.resourcePath=t,this}setRequestHeader(t){return this.requestHeader=t,this}}const dC={};class pC extends hC{constructor(t){super(t)}load(t,e,n,i){void 0===t&&(t=\\\\\\\"\\\\\\\"),void 0!==this.path&&(t=this.path+t),t=this.manager.resolveURL(t);const r=this,s=lC.get(t);if(void 0!==s)return r.manager.itemStart(t),setTimeout((function(){e&&e(s),r.manager.itemEnd(t)}),0),s;if(void 0!==dC[t])return void dC[t].push({onLoad:e,onProgress:n,onError:i});const o=t.match(/^data:(.*?)(;base64)?,(.*)$/);let a;if(o){const n=o[1],s=!!o[2];let a=o[3];a=decodeURIComponent(a),s&&(a=atob(a));try{let i;const s=(this.responseType||\\\\\\\"\\\\\\\").toLowerCase();switch(s){case\\\\\\\"arraybuffer\\\\\\\":case\\\\\\\"blob\\\\\\\":const t=new Uint8Array(a.length);for(let e=0;e<a.length;e++)t[e]=a.charCodeAt(e);i=\\\\\\\"blob\\\\\\\"===s?new Blob([t.buffer],{type:n}):t.buffer;break;case\\\\\\\"document\\\\\\\":const e=new DOMParser;i=e.parseFromString(a,n);break;case\\\\\\\"json\\\\\\\":i=JSON.parse(a);break;default:i=a}setTimeout((function(){e&&e(i),r.manager.itemEnd(t)}),0)}catch(e){setTimeout((function(){i&&i(e),r.manager.itemError(t),r.manager.itemEnd(t)}),0)}}else{dC[t]=[],dC[t].push({onLoad:e,onProgress:n,onError:i}),a=new XMLHttpRequest,a.open(\\\\\\\"GET\\\\\\\",t,!0),a.addEventListener(\\\\\\\"load\\\\\\\",(function(e){const n=this.response,i=dC[t];if(delete dC[t],200===this.status||0===this.status){0===this.status&&console.warn(\\\\\\\"THREE.FileLoader: HTTP Status 0 received.\\\\\\\"),lC.add(t,n);for(let t=0,e=i.length;t<e;t++){const e=i[t];e.onLoad&&e.onLoad(n)}r.manager.itemEnd(t)}else{for(let t=0,n=i.length;t<n;t++){const n=i[t];n.onError&&n.onError(e)}r.manager.itemError(t),r.manager.itemEnd(t)}}),!1),a.addEventListener(\\\\\\\"progress\\\\\\\",(function(e){const n=dC[t];for(let t=0,i=n.length;t<i;t++){const i=n[t];i.onProgress&&i.onProgress(e)}}),!1),a.addEventListener(\\\\\\\"error\\\\\\\",(function(e){const n=dC[t];delete dC[t];for(let t=0,i=n.length;t<i;t++){const i=n[t];i.onError&&i.onError(e)}r.manager.itemError(t),r.manager.itemEnd(t)}),!1),a.addEventListener(\\\\\\\"abort\\\\\\\",(function(e){const n=dC[t];delete dC[t];for(let t=0,i=n.length;t<i;t++){const i=n[t];i.onError&&i.onError(e)}r.manager.itemError(t),r.manager.itemEnd(t)}),!1),void 0!==this.responseType&&(a.responseType=this.responseType),void 0!==this.withCredentials&&(a.withCredentials=this.withCredentials),a.overrideMimeType&&a.overrideMimeType(void 0!==this.mimeType?this.mimeType:\\\\\\\"text/plain\\\\\\\");for(const t in this.requestHeader)a.setRequestHeader(t,this.requestHeader[t]);a.send(null)}return r.manager.itemStart(t),a}setResponseType(t){return this.responseType=t,this}setMimeType(t){return this.mimeType=t,this}}class _C extends hC{constructor(t){super(t)}load(t,e,n,i){void 0!==this.path&&(t=this.path+t),t=this.manager.resolveURL(t);const r=this,s=lC.get(t);if(void 0!==s)return r.manager.itemStart(t),setTimeout((function(){e&&e(s),r.manager.itemEnd(t)}),0),s;const o=yx(\\\\\\\"img\\\\\\\");function a(){o.removeEventListener(\\\\\\\"load\\\\\\\",a,!1),o.removeEventListener(\\\\\\\"error\\\\\\\",l,!1),lC.add(t,this),e&&e(this),r.manager.itemEnd(t)}function l(e){o.removeEventListener(\\\\\\\"load\\\\\\\",a,!1),o.removeEventListener(\\\\\\\"error\\\\\\\",l,!1),i&&i(e),r.manager.itemError(t),r.manager.itemEnd(t)}return o.addEventListener(\\\\\\\"load\\\\\\\",a,!1),o.addEventListener(\\\\\\\"error\\\\\\\",l,!1),\\\\\\\"data:\\\\\\\"!==t.substr(0,5)&&void 0!==this.crossOrigin&&(o.crossOrigin=this.crossOrigin),r.manager.itemStart(t),o.src=t,o}}class mC extends hC{constructor(t){super(t)}load(t,e,n,i){const r=new Gw,s=new _C(this.manager);s.setCrossOrigin(this.crossOrigin),s.setPath(this.path);let o=0;function a(n){s.load(t[n],(function(t){r.images[n]=t,o++,6===o&&(r.needsUpdate=!0,e&&e(r))}),void 0,i)}for(let e=0;e<t.length;++e)a(e);return r}}class fC extends hC{constructor(t){super(t)}load(t,e,n,i){const r=new Tx,s=new _C(this.manager);return s.setCrossOrigin(this.crossOrigin),s.setPath(this.path),s.load(t,(function(t){r.image=t,r.needsUpdate=!0,void 0!==e&&e(r)}),n,i),r}}class gC extends Ob{constructor(t,e=1){super(),this.type=\\\\\\\"Light\\\\\\\",this.color=new Zb(t),this.intensity=e}dispose(){}copy(t){return super.copy(t),this.color.copy(t.color),this.intensity=t.intensity,this}toJSON(t){const e=super.toJSON(t);return e.object.color=this.color.getHex(),e.object.intensity=this.intensity,void 0!==this.groundColor&&(e.object.groundColor=this.groundColor.getHex()),void 0!==this.distance&&(e.object.distance=this.distance),void 0!==this.angle&&(e.object.angle=this.angle),void 0!==this.decay&&(e.object.decay=this.decay),void 0!==this.penumbra&&(e.object.penumbra=this.penumbra),void 0!==this.shadow&&(e.object.shadow=this.shadow.toJSON()),e}}gC.prototype.isLight=!0;class vC extends gC{constructor(t,e,n){super(t,n),this.type=\\\\\\\"HemisphereLight\\\\\\\",this.position.copy(Ob.DefaultUp),this.updateMatrix(),this.groundColor=new Zb(e)}copy(t){return gC.prototype.copy.call(this,t),this.groundColor.copy(t.groundColor),this}}vC.prototype.isHemisphereLight=!0;const yC=new ob,xC=new Nx,bC=new Nx;class wC{constructor(t){this.camera=t,this.bias=0,this.normalBias=0,this.radius=1,this.blurSamples=8,this.mapSize=new fx(512,512),this.map=null,this.mapPass=null,this.matrix=new ob,this.autoUpdate=!0,this.needsUpdate=!1,this._frustum=new $w,this._frameExtents=new fx(1,1),this._viewportCount=1,this._viewports=[new Ex(0,0,1,1)]}getViewportCount(){return this._viewportCount}getFrustum(){return this._frustum}updateMatrices(t){const e=this.camera,n=this.matrix;xC.setFromMatrixPosition(t.matrixWorld),e.position.copy(xC),bC.setFromMatrixPosition(t.target.matrixWorld),e.lookAt(bC),e.updateMatrixWorld(),yC.multiplyMatrices(e.projectionMatrix,e.matrixWorldInverse),this._frustum.setFromProjectionMatrix(yC),n.set(.5,0,0,.5,0,.5,0,.5,0,0,.5,.5,0,0,0,1),n.multiply(e.projectionMatrix),n.multiply(e.matrixWorldInverse)}getViewport(t){return this._viewports[t]}getFrameExtents(){return this._frameExtents}dispose(){this.map&&this.map.dispose(),this.mapPass&&this.mapPass.dispose()}copy(t){return this.camera=t.camera.clone(),this.bias=t.bias,this.radius=t.radius,this.mapSize.copy(t.mapSize),this}clone(){return(new this.constructor).copy(this)}toJSON(){const t={};return 0!==this.bias&&(t.bias=this.bias),0!==this.normalBias&&(t.normalBias=this.normalBias),1!==this.radius&&(t.radius=this.radius),512===this.mapSize.x&&512===this.mapSize.y||(t.mapSize=this.mapSize.toArray()),t.camera=this.camera.toJSON(!1).object,delete t.camera.matrix,t}}class TC extends wC{constructor(){super(new Bw(50,1,.5,500)),this.focus=1}updateMatrices(t){const e=this.camera,n=2*sx*t.angle*this.focus,i=this.mapSize.width/this.mapSize.height,r=t.distance||e.far;n===e.fov&&i===e.aspect&&r===e.far||(e.fov=n,e.aspect=i,e.far=r,e.updateProjectionMatrix()),super.updateMatrices(t)}copy(t){return super.copy(t),this.focus=t.focus,this}}TC.prototype.isSpotLightShadow=!0;class AC extends gC{constructor(t,e,n=0,i=Math.PI/3,r=0,s=1){super(t,e),this.type=\\\\\\\"SpotLight\\\\\\\",this.position.copy(Ob.DefaultUp),this.updateMatrix(),this.target=new Ob,this.distance=n,this.angle=i,this.penumbra=r,this.decay=s,this.shadow=new TC}get power(){return this.intensity*Math.PI}set power(t){this.intensity=t/Math.PI}dispose(){this.shadow.dispose()}copy(t){return super.copy(t),this.distance=t.distance,this.angle=t.angle,this.penumbra=t.penumbra,this.decay=t.decay,this.target=t.target.clone(),this.shadow=t.shadow.clone(),this}}AC.prototype.isSpotLight=!0;const EC=new ob,MC=new Nx,SC=new Nx;class CC extends wC{constructor(){super(new Bw(90,1,.5,500)),this._frameExtents=new fx(4,2),this._viewportCount=6,this._viewports=[new Ex(2,1,1,1),new Ex(0,1,1,1),new Ex(3,1,1,1),new Ex(1,1,1,1),new Ex(3,0,1,1),new Ex(1,0,1,1)],this._cubeDirections=[new Nx(1,0,0),new Nx(-1,0,0),new Nx(0,0,1),new Nx(0,0,-1),new Nx(0,1,0),new Nx(0,-1,0)],this._cubeUps=[new Nx(0,1,0),new Nx(0,1,0),new Nx(0,1,0),new Nx(0,1,0),new Nx(0,0,1),new Nx(0,0,-1)]}updateMatrices(t,e=0){const n=this.camera,i=this.matrix,r=t.distance||n.far;r!==n.far&&(n.far=r,n.updateProjectionMatrix()),MC.setFromMatrixPosition(t.matrixWorld),n.position.copy(MC),SC.copy(n.position),SC.add(this._cubeDirections[e]),n.up.copy(this._cubeUps[e]),n.lookAt(SC),n.updateMatrixWorld(),i.makeTranslation(-MC.x,-MC.y,-MC.z),EC.multiplyMatrices(n.projectionMatrix,n.matrixWorldInverse),this._frustum.setFromProjectionMatrix(EC)}}CC.prototype.isPointLightShadow=!0;class NC extends gC{constructor(t,e,n=0,i=1){super(t,e),this.type=\\\\\\\"PointLight\\\\\\\",this.distance=n,this.decay=i,this.shadow=new CC}get power(){return 4*this.intensity*Math.PI}set power(t){this.intensity=t/(4*Math.PI)}dispose(){this.shadow.dispose()}copy(t){return super.copy(t),this.distance=t.distance,this.decay=t.decay,this.shadow=t.shadow.clone(),this}}NC.prototype.isPointLight=!0;class LC extends wC{constructor(){super(new lT(-5,5,5,-5,.5,500))}}LC.prototype.isDirectionalLightShadow=!0;class OC extends gC{constructor(t,e){super(t,e),this.type=\\\\\\\"DirectionalLight\\\\\\\",this.position.copy(Ob.DefaultUp),this.updateMatrix(),this.target=new Ob,this.shadow=new LC}dispose(){this.shadow.dispose()}copy(t){return super.copy(t),this.target=t.target.clone(),this.shadow=t.shadow.clone(),this}}OC.prototype.isDirectionalLight=!0;class RC extends gC{constructor(t,e){super(t,e),this.type=\\\\\\\"AmbientLight\\\\\\\"}}RC.prototype.isAmbientLight=!0;class PC extends gC{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}}PC.prototype.isRectAreaLight=!0;class IC{constructor(){this.coefficients=[];for(let t=0;t<9;t++)this.coefficients.push(new Nx)}set(t){for(let e=0;e<9;e++)this.coefficients[e].copy(t[e]);return this}zero(){for(let t=0;t<9;t++)this.coefficients[t].set(0,0,0);return this}getAt(t,e){const n=t.x,i=t.y,r=t.z,s=this.coefficients;return e.copy(s[0]).multiplyScalar(.282095),e.addScaledVector(s[1],.488603*i),e.addScaledVector(s[2],.488603*r),e.addScaledVector(s[3],.488603*n),e.addScaledVector(s[4],n*i*1.092548),e.addScaledVector(s[5],i*r*1.092548),e.addScaledVector(s[6],.315392*(3*r*r-1)),e.addScaledVector(s[7],n*r*1.092548),e.addScaledVector(s[8],.546274*(n*n-i*i)),e}getIrradianceAt(t,e){const n=t.x,i=t.y,r=t.z,s=this.coefficients;return e.copy(s[0]).multiplyScalar(.886227),e.addScaledVector(s[1],1.023328*i),e.addScaledVector(s[2],1.023328*r),e.addScaledVector(s[3],1.023328*n),e.addScaledVector(s[4],.858086*n*i),e.addScaledVector(s[5],.858086*i*r),e.addScaledVector(s[6],.743125*r*r-.247708),e.addScaledVector(s[7],.858086*n*r),e.addScaledVector(s[8],.429043*(n*n-i*i)),e}add(t){for(let e=0;e<9;e++)this.coefficients[e].add(t.coefficients[e]);return this}addScaledSH(t,e){for(let n=0;n<9;n++)this.coefficients[n].addScaledVector(t.coefficients[n],e);return this}scale(t){for(let e=0;e<9;e++)this.coefficients[e].multiplyScalar(t);return this}lerp(t,e){for(let n=0;n<9;n++)this.coefficients[n].lerp(t.coefficients[n],e);return this}equals(t){for(let e=0;e<9;e++)if(!this.coefficients[e].equals(t.coefficients[e]))return!1;return!0}copy(t){return this.set(t.coefficients)}clone(){return(new this.constructor).copy(this)}fromArray(t,e=0){const n=this.coefficients;for(let i=0;i<9;i++)n[i].fromArray(t,e+3*i);return this}toArray(t=[],e=0){const n=this.coefficients;for(let i=0;i<9;i++)n[i].toArray(t,e+3*i);return t}static getBasisAt(t,e){const n=t.x,i=t.y,r=t.z;e[0]=.282095,e[1]=.488603*i,e[2]=.488603*r,e[3]=.488603*n,e[4]=1.092548*n*i,e[5]=1.092548*i*r,e[6]=.315392*(3*r*r-1),e[7]=1.092548*n*r,e[8]=.546274*(n*n-i*i)}}IC.prototype.isSphericalHarmonics3=!0;class FC extends gC{constructor(t=new IC,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}}FC.prototype.isLightProbe=!0;class DC{static decodeText(t){if(\\\\\\\"undefined\\\\\\\"!=typeof TextDecoder)return(new TextDecoder).decode(t);let e=\\\\\\\"\\\\\\\";for(let n=0,i=t.length;n<i;n++)e+=String.fromCharCode(t[n]);try{return decodeURIComponent(escape(e))}catch(t){return e}}static extractUrlBase(t){const e=t.lastIndexOf(\\\\\\\"/\\\\\\\");return-1===e?\\\\\\\"./\\\\\\\":t.substr(0,e+1)}}class kC extends dw{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}}kC.prototype.isInstancedBufferGeometry=!0;let BC;(class extends hC{constructor(t){super(t),\\\\\\\"undefined\\\\\\\"==typeof createImageBitmap&&console.warn(\\\\\\\"THREE.ImageBitmapLoader: createImageBitmap() not supported.\\\\\\\"),\\\\\\\"undefined\\\\\\\"==typeof fetch&&console.warn(\\\\\\\"THREE.ImageBitmapLoader: fetch() not supported.\\\\\\\"),this.options={premultiplyAlpha:\\\\\\\"none\\\\\\\"}}setOptions(t){return this.options=t,this}load(t,e,n,i){void 0===t&&(t=\\\\\\\"\\\\\\\"),void 0!==this.path&&(t=this.path+t),t=this.manager.resolveURL(t);const r=this,s=lC.get(t);if(void 0!==s)return r.manager.itemStart(t),setTimeout((function(){e&&e(s),r.manager.itemEnd(t)}),0),s;const o={};o.credentials=\\\\\\\"anonymous\\\\\\\"===this.crossOrigin?\\\\\\\"same-origin\\\\\\\":\\\\\\\"include\\\\\\\",o.headers=this.requestHeader,fetch(t,o).then((function(t){return t.blob()})).then((function(t){return createImageBitmap(t,Object.assign(r.options,{colorSpaceConversion:\\\\\\\"none\\\\\\\"}))})).then((function(n){lC.add(t,n),e&&e(n),r.manager.itemEnd(t)})).catch((function(e){i&&i(e),r.manager.itemError(t),r.manager.itemEnd(t)})),r.manager.itemStart(t)}}).prototype.isImageBitmapLoader=!0;const zC=function(){return void 0===BC&&(BC=new(window.AudioContext||window.webkitAudioContext)),BC};class UC extends hC{constructor(t){super(t)}load(t,e,n,i){const r=this,s=new pC(this.manager);s.setResponseType(\\\\\\\"arraybuffer\\\\\\\"),s.setPath(this.path),s.setRequestHeader(this.requestHeader),s.setWithCredentials(this.withCredentials),s.load(t,(function(n){try{const t=n.slice(0);zC().decodeAudioData(t,(function(t){e(t)}))}catch(e){i?i(e):console.error(e),r.manager.itemError(t)}}),n,i)}}(class extends FC{constructor(t,e,n=1){super(void 0,n);const i=(new Zb).set(t),r=(new Zb).set(e),s=new Nx(i.r,i.g,i.b),o=new Nx(r.r,r.g,r.b),a=Math.sqrt(Math.PI),l=a*Math.sqrt(.75);this.sh.coefficients[0].copy(s).add(o).multiplyScalar(a),this.sh.coefficients[1].copy(s).sub(o).multiplyScalar(l)}}).prototype.isHemisphereLightProbe=!0;(class extends FC{constructor(t,e=1){super(void 0,e);const n=(new Zb).set(t);this.sh.coefficients[0].set(n.r,n.g,n.b).multiplyScalar(2*Math.sqrt(Math.PI))}}).prototype.isAmbientLightProbe=!0;class GC extends Ob{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;t<e;t++)this.filters[t-1].connect(this.filters[t]);this.filters[this.filters.length-1].connect(this.getOutput())}else this.source.connect(this.getOutput());return this._connected=!0,this}disconnect(){if(this.filters.length>0){this.source.disconnect(this.filters[0]);for(let t=1,e=this.filters.length;t<e;t++)this.filters[t-1].disconnect(this.filters[t]);this.filters[this.filters.length-1].disconnect(this.getOutput())}else this.source.disconnect(this.getOutput());return this._connected=!1,this}getFilters(){return this.filters}setFilters(t){return t||(t=[]),!0===this._connected?(this.disconnect(),this.filters=t.slice(),this.connect()):this.filters=t.slice(),this}setDetune(t){if(this.detune=t,void 0!==this.source.detune)return!0===this.isPlaying&&this.source.detune.setTargetAtTime(this.detune,this.context.currentTime,.01),this}getDetune(){return this.detune}getFilter(){return this.getFilters()[0]}setFilter(t){return this.setFilters(t?[t]:[])}setPlaybackRate(t){if(!1!==this.hasPlaybackControl)return this.playbackRate=t,!0===this.isPlaying&&this.source.playbackRate.setTargetAtTime(this.playbackRate,this.context.currentTime,.01),this;console.warn(\\\\\\\"THREE.Audio: this Audio has no playback control.\\\\\\\")}getPlaybackRate(){return this.playbackRate}onEnded(){this.isPlaying=!1}getLoop(){return!1===this.hasPlaybackControl?(console.warn(\\\\\\\"THREE.Audio: this Audio has no playback control.\\\\\\\"),!1):this.loop}setLoop(t){if(!1!==this.hasPlaybackControl)return this.loop=t,!0===this.isPlaying&&(this.source.loop=this.loop),this;console.warn(\\\\\\\"THREE.Audio: this Audio has no playback control.\\\\\\\")}setLoopStart(t){return this.loopStart=t,this}setLoopEnd(t){return this.loopEnd=t,this}getVolume(){return this.gain.gain.value}setVolume(t){return this.gain.gain.setTargetAtTime(t,this.context.currentTime,.01),this}}class VC{constructor(t,e,n){let i,r,s;switch(this.binding=t,this.valueSize=n,e){case\\\\\\\"quaternion\\\\\\\":i=this._slerp,r=this._slerpAdditive,s=this._setAdditiveIdentityQuaternion,this.buffer=new Float64Array(6*n),this._workIndex=5;break;case\\\\\\\"string\\\\\\\":case\\\\\\\"bool\\\\\\\":i=this._select,r=this._select,s=this._setAdditiveIdentityOther,this.buffer=new Array(5*n);break;default:i=this._lerp,r=this._lerpAdditive,s=this._setAdditiveIdentityNumeric,this.buffer=new Float64Array(5*n)}this._mixBufferRegion=i,this._mixBufferRegionAdditive=r,this._setIdentity=s,this._origIndex=3,this._addIndex=4,this.cumulativeWeight=0,this.cumulativeWeightAdditive=0,this.useCount=0,this.referenceCount=0}accumulate(t,e){const n=this.buffer,i=this.valueSize,r=t*i+i;let s=this.cumulativeWeight;if(0===s){for(let t=0;t!==i;++t)n[r+t]=n[t];s=e}else{s+=e;const t=e/s;this._mixBufferRegion(n,r,0,t,i)}this.cumulativeWeight=s}accumulateAdditive(t){const e=this.buffer,n=this.valueSize,i=n*this._addIndex;0===this.cumulativeWeightAdditive&&this._setIdentity(),this._mixBufferRegionAdditive(e,i,0,t,n),this.cumulativeWeightAdditive+=t}apply(t){const e=this.valueSize,n=this.buffer,i=t*e+e,r=this.cumulativeWeight,s=this.cumulativeWeightAdditive,o=this.binding;if(this.cumulativeWeight=0,this.cumulativeWeightAdditive=0,r<1){const t=e*this._origIndex;this._mixBufferRegion(n,i,t,1-r,e)}s>0&&this._mixBufferRegionAdditive(n,i,this._addIndex*e,1,e);for(let t=e,r=e+e;t!==r;++t)if(n[t]!==n[t+e]){o.setValue(n,i);break}}saveOriginalState(){const t=this.binding,e=this.buffer,n=this.valueSize,i=n*this._origIndex;t.getValue(e,i);for(let t=n,r=i;t!==r;++t)e[t]=e[i+t%n];this._setIdentity(),this.cumulativeWeight=0,this.cumulativeWeightAdditive=0}restoreOriginalState(){const t=3*this.valueSize;this.binding.setValue(this.buffer,t)}_setAdditiveIdentityNumeric(){const t=this._addIndex*this.valueSize,e=t+this.valueSize;for(let n=t;n<e;n++)this.buffer[n]=0}_setAdditiveIdentityQuaternion(){this._setAdditiveIdentityNumeric(),this.buffer[this._addIndex*this.valueSize+3]=1}_setAdditiveIdentityOther(){const t=this._origIndex*this.valueSize,e=this._addIndex*this.valueSize;for(let n=0;n<this.valueSize;n++)this.buffer[e+n]=this.buffer[t+n]}_select(t,e,n,i,r){if(i>=.5)for(let i=0;i!==r;++i)t[e+i]=t[n+i]}_slerp(t,e,n,i){Cx.slerpFlat(t,e,t,e,t,n,i)}_slerpAdditive(t,e,n,i,r){const s=this._workIndex*r;Cx.multiplyQuaternionsFlat(t,s,t,e,t,n),Cx.slerpFlat(t,e,t,e,t,s,i)}_lerp(t,e,n,i,r){const s=1-i;for(let o=0;o!==r;++o){const r=e+o;t[r]=t[r]*s+t[n+o]*i}}_lerpAdditive(t,e,n,i,r){for(let s=0;s!==r;++s){const r=e+s;t[r]=t[r]+t[n+s]*i}}}const HC=\\\\\\\"\\\\\\\\[\\\\\\\\]\\\\\\\\.:\\\\\\\\/\\\\\\\",jC=new RegExp(\\\\\\\"[\\\\\\\\[\\\\\\\\]\\\\\\\\.:\\\\\\\\/]\\\\\\\",\\\\\\\"g\\\\\\\"),WC=\\\\\\\"[^\\\\\\\\[\\\\\\\\]\\\\\\\\.:\\\\\\\\/]\\\\\\\",qC=\\\\\\\"[^\\\\\\\"+HC.replace(\\\\\\\"\\\\\\\\.\\\\\\\",\\\\\\\"\\\\\\\")+\\\\\\\"]\\\\\\\",XC=/((?:WC+[\\\\/:])*)/.source.replace(\\\\\\\"WC\\\\\\\",WC),YC=/(WCOD+)?/.source.replace(\\\\\\\"WCOD\\\\\\\",qC),$C=/(?:\\\\.(WC+)(?:\\\\[(.+)\\\\])?)?/.source.replace(\\\\\\\"WC\\\\\\\",WC),JC=/\\\\.(WC+)(?:\\\\[(.+)\\\\])?/.source.replace(\\\\\\\"WC\\\\\\\",WC),ZC=new RegExp(\\\\\\\"^\\\\\\\"+XC+YC+$C+JC+\\\\\\\"$\\\\\\\"),QC=[\\\\\\\"material\\\\\\\",\\\\\\\"materials\\\\\\\",\\\\\\\"bones\\\\\\\"];class KC{constructor(t,e,n){this.path=e,this.parsedPath=n||KC.parseTrackName(e),this.node=KC.findNode(t,this.parsedPath.nodeName)||t,this.rootNode=t,this.getValue=this._getValue_unbound,this.setValue=this._setValue_unbound}static create(t,e,n){return t&&t.isAnimationObjectGroup?new KC.Composite(t,e,n):new KC(t,e,n)}static sanitizeNodeName(t){return t.replace(/\\\\s/g,\\\\\\\"_\\\\\\\").replace(jC,\\\\\\\"\\\\\\\")}static parseTrackName(t){const e=ZC.exec(t);if(!e)throw new Error(\\\\\\\"PropertyBinding: Cannot parse trackName: \\\\\\\"+t);const n={nodeName:e[2],objectName:e[3],objectIndex:e[4],propertyName:e[5],propertyIndex:e[6]},i=n.nodeName&&n.nodeName.lastIndexOf(\\\\\\\".\\\\\\\");if(void 0!==i&&-1!==i){const t=n.nodeName.substring(i+1);-1!==QC.indexOf(t)&&(n.nodeName=n.nodeName.substring(0,i),n.objectName=t)}if(null===n.propertyName||0===n.propertyName.length)throw new Error(\\\\\\\"PropertyBinding: can not parse propertyName from trackName: \\\\\\\"+t);return n}static findNode(t,e){if(!e||\\\\\\\"\\\\\\\"===e||\\\\\\\".\\\\\\\"===e||-1===e||e===t.name||e===t.uuid)return t;if(t.skeleton){const n=t.skeleton.getBoneByName(e);if(void 0!==n)return n}if(t.children){const n=function(t){for(let i=0;i<t.length;i++){const r=t[i];if(r.name===e||r.uuid===e)return r;const s=n(r.children);if(s)return s}return null},i=n(t.children);if(i)return i}return null}_getValue_unavailable(){}_setValue_unavailable(){}_getValue_direct(t,e){t[e]=this.targetObject[this.propertyName]}_getValue_array(t,e){const n=this.resolvedProperty;for(let i=0,r=n.length;i!==r;++i)t[e++]=n[i]}_getValue_arrayElement(t,e){t[e]=this.resolvedProperty[this.propertyIndex]}_getValue_toArray(t,e){this.resolvedProperty.toArray(t,e)}_setValue_direct(t,e){this.targetObject[this.propertyName]=t[e]}_setValue_direct_setNeedsUpdate(t,e){this.targetObject[this.propertyName]=t[e],this.targetObject.needsUpdate=!0}_setValue_direct_setMatrixWorldNeedsUpdate(t,e){this.targetObject[this.propertyName]=t[e],this.targetObject.matrixWorldNeedsUpdate=!0}_setValue_array(t,e){const n=this.resolvedProperty;for(let i=0,r=n.length;i!==r;++i)n[i]=t[e++]}_setValue_array_setNeedsUpdate(t,e){const n=this.resolvedProperty;for(let i=0,r=n.length;i!==r;++i)n[i]=t[e++];this.targetObject.needsUpdate=!0}_setValue_array_setMatrixWorldNeedsUpdate(t,e){const n=this.resolvedProperty;for(let i=0,r=n.length;i!==r;++i)n[i]=t[e++];this.targetObject.matrixWorldNeedsUpdate=!0}_setValue_arrayElement(t,e){this.resolvedProperty[this.propertyIndex]=t[e]}_setValue_arrayElement_setNeedsUpdate(t,e){this.resolvedProperty[this.propertyIndex]=t[e],this.targetObject.needsUpdate=!0}_setValue_arrayElement_setMatrixWorldNeedsUpdate(t,e){this.resolvedProperty[this.propertyIndex]=t[e],this.targetObject.matrixWorldNeedsUpdate=!0}_setValue_fromArray(t,e){this.resolvedProperty.fromArray(t,e)}_setValue_fromArray_setNeedsUpdate(t,e){this.resolvedProperty.fromArray(t,e),this.targetObject.needsUpdate=!0}_setValue_fromArray_setMatrixWorldNeedsUpdate(t,e){this.resolvedProperty.fromArray(t,e),this.targetObject.matrixWorldNeedsUpdate=!0}_getValue_unbound(t,e){this.bind(),this.getValue(t,e)}_setValue_unbound(t,e){this.bind(),this.setValue(t,e)}bind(){let t=this.node;const e=this.parsedPath,n=e.objectName,i=e.propertyName;let r=e.propertyIndex;if(t||(t=KC.findNode(this.rootNode,e.nodeName)||this.rootNode,this.node=t),this.getValue=this._getValue_unavailable,this.setValue=this._setValue_unavailable,!t)return void console.error(\\\\\\\"THREE.PropertyBinding: Trying to update node for track: \\\\\\\"+this.path+\\\\\\\" but it wasn't found.\\\\\\\");if(n){let i=e.objectIndex;switch(n){case\\\\\\\"materials\\\\\\\":if(!t.material)return void console.error(\\\\\\\"THREE.PropertyBinding: Can not bind to material as node does not have a material.\\\\\\\",this);if(!t.material.materials)return void console.error(\\\\\\\"THREE.PropertyBinding: Can not bind to material.materials as node.material does not have a materials array.\\\\\\\",this);t=t.material.materials;break;case\\\\\\\"bones\\\\\\\":if(!t.skeleton)return void console.error(\\\\\\\"THREE.PropertyBinding: Can not bind to bones as node does not have a skeleton.\\\\\\\",this);t=t.skeleton.bones;for(let e=0;e<t.length;e++)if(t[e].name===i){i=e;break}break;default:if(void 0===t[n])return void console.error(\\\\\\\"THREE.PropertyBinding: Can not bind to objectName of node undefined.\\\\\\\",this);t=t[n]}if(void 0!==i){if(void 0===t[i])return void console.error(\\\\\\\"THREE.PropertyBinding: Trying to bind to objectIndex of objectName, but is undefined.\\\\\\\",this,t);t=t[i]}}const s=t[i];if(void 0===s){const n=e.nodeName;return void console.error(\\\\\\\"THREE.PropertyBinding: Trying to update property for track: \\\\\\\"+n+\\\\\\\".\\\\\\\"+i+\\\\\\\" but it wasn't found.\\\\\\\",t)}let o=this.Versioning.None;this.targetObject=t,void 0!==t.needsUpdate?o=this.Versioning.NeedsUpdate:void 0!==t.matrixWorldNeedsUpdate&&(o=this.Versioning.MatrixWorldNeedsUpdate);let a=this.BindingType.Direct;if(void 0!==r){if(\\\\\\\"morphTargetInfluences\\\\\\\"===i){if(!t.geometry)return void console.error(\\\\\\\"THREE.PropertyBinding: Can not bind to morphTargetInfluences because node does not have a geometry.\\\\\\\",this);if(!t.geometry.isBufferGeometry)return void console.error(\\\\\\\"THREE.PropertyBinding: Can not bind to morphTargetInfluences on THREE.Geometry. Use THREE.BufferGeometry instead.\\\\\\\",this);if(!t.geometry.morphAttributes)return void console.error(\\\\\\\"THREE.PropertyBinding: Can not bind to morphTargetInfluences because node does not have a geometry.morphAttributes.\\\\\\\",this);void 0!==t.morphTargetDictionary[r]&&(r=t.morphTargetDictionary[r])}a=this.BindingType.ArrayElement,this.resolvedProperty=s,this.propertyIndex=r}else void 0!==s.fromArray&&void 0!==s.toArray?(a=this.BindingType.HasFromToArray,this.resolvedProperty=s):Array.isArray(s)?(a=this.BindingType.EntireArray,this.resolvedProperty=s):this.propertyName=i;this.getValue=this.GetterByBindingType[a],this.setValue=this.SetterByBindingTypeAndVersioning[a][o]}unbind(){this.node=null,this.getValue=this._getValue_unbound,this.setValue=this._setValue_unbound}}KC.Composite=class{constructor(t,e,n){const i=n||KC.parseTrackName(e);this._targetGroup=t,this._bindings=t.subscribe_(e,i)}getValue(t,e){this.bind();const n=this._targetGroup.nCachedObjects_,i=this._bindings[n];void 0!==i&&i.getValue(t,e)}setValue(t,e){const n=this._bindings;for(let i=this._targetGroup.nCachedObjects_,r=n.length;i!==r;++i)n[i].setValue(t,e)}bind(){const t=this._bindings;for(let e=this._targetGroup.nCachedObjects_,n=t.length;e!==n;++e)t[e].bind()}unbind(){const t=this._bindings;for(let e=this._targetGroup.nCachedObjects_,n=t.length;e!==n;++e)t[e].unbind()}},KC.prototype.BindingType={Direct:0,EntireArray:1,ArrayElement:2,HasFromToArray:3},KC.prototype.Versioning={None:0,NeedsUpdate:1,MatrixWorldNeedsUpdate:2},KC.prototype.GetterByBindingType=[KC.prototype._getValue_direct,KC.prototype._getValue_array,KC.prototype._getValue_arrayElement,KC.prototype._getValue_toArray],KC.prototype.SetterByBindingTypeAndVersioning=[[KC.prototype._setValue_direct,KC.prototype._setValue_direct_setNeedsUpdate,KC.prototype._setValue_direct_setMatrixWorldNeedsUpdate],[KC.prototype._setValue_array,KC.prototype._setValue_array_setNeedsUpdate,KC.prototype._setValue_array_setMatrixWorldNeedsUpdate],[KC.prototype._setValue_arrayElement,KC.prototype._setValue_arrayElement_setNeedsUpdate,KC.prototype._setValue_arrayElement_setMatrixWorldNeedsUpdate],[KC.prototype._setValue_fromArray,KC.prototype._setValue_fromArray_setNeedsUpdate,KC.prototype._setValue_fromArray_setMatrixWorldNeedsUpdate]];class tN{constructor(t,e,n=null,i=e.blendMode){this._mixer=t,this._clip=e,this._localRoot=n,this.blendMode=i;const r=e.tracks,s=r.length,o=new Array(s),a={endingStart:jy,endingEnd:jy};for(let t=0;t!==s;++t){const e=r[t].createInterpolant(null);o[t]=e,e.settings=a}this._interpolantSettings=a,this._interpolants=o,this._propertyBindings=new Array(s),this._cacheIndex=null,this._byClipCacheIndex=null,this._timeScaleInterpolant=null,this._weightInterpolant=null,this.loop=2201,this._loopCount=-1,this._startTime=null,this.time=0,this.timeScale=1,this._effectiveTimeScale=1,this.weight=1,this._effectiveWeight=1,this.repetitions=1/0,this.paused=!1,this.enabled=!0,this.clampWhenFinished=!1,this.zeroSlopeAtStart=!0,this.zeroSlopeAtEnd=!0}play(){return this._mixer._activateAction(this),this}stop(){return this._mixer._deactivateAction(this),this.reset()}reset(){return this.paused=!1,this.enabled=!0,this.time=0,this._loopCount=-1,this._startTime=null,this.stopFading().stopWarping()}isRunning(){return this.enabled&&!this.paused&&0!==this.timeScale&&null===this._startTime&&this._mixer._isActiveAction(this)}isScheduled(){return this._mixer._isActiveAction(this)}startAt(t){return this._startTime=t,this}setLoop(t,e){return this.loop=t,this.repetitions=e,this}setEffectiveWeight(t){return this.weight=t,this._effectiveWeight=this.enabled?t:0,this.stopFading()}getEffectiveWeight(){return this._effectiveWeight}fadeIn(t){return this._scheduleFading(t,0,1)}fadeOut(t){return this._scheduleFading(t,1,0)}crossFadeFrom(t,e,n){if(t.fadeOut(e),this.fadeIn(e),n){const n=this._clip.duration,i=t._clip.duration,r=i/n,s=n/i;t.warp(1,r,e),this.warp(s,1,e)}return this}crossFadeTo(t,e,n){return t.crossFadeFrom(this,e,n)}stopFading(){const t=this._weightInterpolant;return null!==t&&(this._weightInterpolant=null,this._mixer._takeBackControlInterpolant(t)),this}setEffectiveTimeScale(t){return this.timeScale=t,this._effectiveTimeScale=this.paused?0:t,this.stopWarping()}getEffectiveTimeScale(){return this._effectiveTimeScale}setDuration(t){return this.timeScale=this._clip.duration/t,this.stopWarping()}syncWith(t){return this.time=t.time,this.timeScale=t.timeScale,this.stopWarping()}halt(t){return this.warp(this._effectiveTimeScale,0,t)}warp(t,e,n){const i=this._mixer,r=i.time,s=this.timeScale;let o=this._timeScaleInterpolant;null===o&&(o=i._lendControlInterpolant(),this._timeScaleInterpolant=o);const a=o.parameterPositions,l=o.sampleValues;return a[0]=r,a[1]=r+n,l[0]=t/s,l[1]=e/s,this}stopWarping(){const t=this._timeScaleInterpolant;return null!==t&&(this._timeScaleInterpolant=null,this._mixer._takeBackControlInterpolant(t)),this}getMixer(){return this._mixer}getClip(){return this._clip}getRoot(){return this._localRoot||this._mixer._root}_update(t,e,n,i){if(!this.enabled)return void this._updateWeight(t);const r=this._startTime;if(null!==r){const i=(t-r)*n;if(i<0||0===n)return;this._startTime=null,e=n*i}e*=this._updateTimeScale(t);const s=this._updateTime(e),o=this._updateWeight(t);if(o>0){const t=this._interpolants,e=this._propertyBindings;switch(this.blendMode){case 2501:for(let n=0,i=t.length;n!==i;++n)t[n].evaluate(s),e[n].accumulateAdditive(o);break;case Xy:default:for(let n=0,r=t.length;n!==r;++n)t[n].evaluate(s),e[n].accumulate(i,o)}}}_updateWeight(t){let e=0;if(this.enabled){e=this.weight;const n=this._weightInterpolant;if(null!==n){const i=n.evaluate(t)[0];e*=i,t>n.parameterPositions[1]&&(this.stopFading(),0===i&&(this.enabled=!1))}}return this._effectiveWeight=e,e}_updateTimeScale(t){let e=0;if(!this.paused){e=this.timeScale;const n=this._timeScaleInterpolant;if(null!==n){e*=n.evaluate(t)[0],t>n.parameterPositions[1]&&(this.stopWarping(),0===e?this.paused=!0:this.timeScale=e)}}return this._effectiveTimeScale=e,e}_updateTime(t){const e=this._clip.duration,n=this.loop;let i=this.time+t,r=this._loopCount;const s=2202===n;if(0===t)return-1===r?i:s&&1==(1&r)?e-i:i;if(2200===n){-1===r&&(this._loopCount=0,this._setEndings(!0,!0,!1));t:{if(i>=e)i=e;else{if(!(i<0)){this.time=i;break t}i=0}this.clampWhenFinished?this.paused=!0:this.enabled=!1,this.time=i,this._mixer.dispatchEvent({type:\\\\\\\"finished\\\\\\\",action:this,direction:t<0?-1:1})}}else{if(-1===r&&(t>=0?(r=0,this._setEndings(!0,0===this.repetitions,s)):this._setEndings(0===this.repetitions,!0,s)),i>=e||i<0){const n=Math.floor(i/e);i-=e*n,r+=Math.abs(n);const o=this.repetitions-r;if(o<=0)this.clampWhenFinished?this.paused=!0:this.enabled=!1,i=t>0?e:0,this.time=i,this._mixer.dispatchEvent({type:\\\\\\\"finished\\\\\\\",action:this,direction:t>0?1:-1});else{if(1===o){const e=t<0;this._setEndings(e,!e,s)}else this._setEndings(!1,!1,s);this._loopCount=r,this.time=i,this._mixer.dispatchEvent({type:\\\\\\\"loop\\\\\\\",action:this,loopDelta:n})}}else this.time=i;if(s&&1==(1&r))return e-i}return i}_setEndings(t,e,n){const i=this._interpolantSettings;n?(i.endingStart=Wy,i.endingEnd=Wy):(i.endingStart=t?this.zeroSlopeAtStart?Wy:jy:qy,i.endingEnd=e?this.zeroSlopeAtEnd?Wy:jy:qy)}_scheduleFading(t,e,n){const i=this._mixer,r=i.time;let s=this._weightInterpolant;null===s&&(s=i._lendControlInterpolant(),this._weightInterpolant=s);const o=s.parameterPositions,a=s.sampleValues;return o[0]=r,a[0]=e,o[1]=r+t,a[1]=n,this}}(class extends nx{constructor(t){super(),this._root=t,this._initMemoryManager(),this._accuIndex=0,this.time=0,this.timeScale=1}_bindAction(t,e){const n=t._localRoot||this._root,i=t._clip.tracks,r=i.length,s=t._propertyBindings,o=t._interpolants,a=n.uuid,l=this._bindingsByRootAndName;let c=l[a];void 0===c&&(c={},l[a]=c);for(let t=0;t!==r;++t){const r=i[t],l=r.name;let u=c[l];if(void 0!==u)s[t]=u;else{if(u=s[t],void 0!==u){null===u._cacheIndex&&(++u.referenceCount,this._addInactiveBinding(u,a,l));continue}const i=e&&e._propertyBindings[t].binding.parsedPath;u=new VC(KC.create(n,l,i),r.ValueTypeName,r.getValueSize()),++u.referenceCount,this._addInactiveBinding(u,a,l),s[t]=u}o[t].resultBuffer=u.buffer}}_activateAction(t){if(!this._isActiveAction(t)){if(null===t._cacheIndex){const e=(t._localRoot||this._root).uuid,n=t._clip.uuid,i=this._actionsByClip[n];this._bindAction(t,i&&i.knownActions[0]),this._addInactiveAction(t,n,e)}const e=t._propertyBindings;for(let t=0,n=e.length;t!==n;++t){const n=e[t];0==n.useCount++&&(this._lendBinding(n),n.saveOriginalState())}this._lendAction(t)}}_deactivateAction(t){if(this._isActiveAction(t)){const e=t._propertyBindings;for(let t=0,n=e.length;t!==n;++t){const n=e[t];0==--n.useCount&&(n.restoreOriginalState(),this._takeBackBinding(n))}this._takeBackAction(t)}}_initMemoryManager(){this._actions=[],this._nActiveActions=0,this._actionsByClip={},this._bindings=[],this._nActiveBindings=0,this._bindingsByRootAndName={},this._controlInterpolants=[],this._nActiveControlInterpolants=0;const t=this;this.stats={actions:{get total(){return t._actions.length},get inUse(){return t._nActiveActions}},bindings:{get total(){return t._bindings.length},get inUse(){return t._nActiveBindings}},controlInterpolants:{get total(){return t._controlInterpolants.length},get inUse(){return t._nActiveControlInterpolants}}}}_isActiveAction(t){const e=t._cacheIndex;return null!==e&&e<this._nActiveActions}_addInactiveAction(t,e,n){const i=this._actions,r=this._actionsByClip;let s=r[e];if(void 0===s)s={knownActions:[t],actionByRoot:{}},t._byClipCacheIndex=0,r[e]=s;else{const e=s.knownActions;t._byClipCacheIndex=e.length,e.push(t)}t._cacheIndex=i.length,i.push(t),s.actionByRoot[n]=t}_removeInactiveAction(t){const e=this._actions,n=e[e.length-1],i=t._cacheIndex;n._cacheIndex=i,e[i]=n,e.pop(),t._cacheIndex=null;const r=t._clip.uuid,s=this._actionsByClip,o=s[r],a=o.knownActions,l=a[a.length-1],c=t._byClipCacheIndex;l._byClipCacheIndex=c,a[c]=l,a.pop(),t._byClipCacheIndex=null;delete o.actionByRoot[(t._localRoot||this._root).uuid],0===a.length&&delete s[r],this._removeInactiveBindingsForAction(t)}_removeInactiveBindingsForAction(t){const e=t._propertyBindings;for(let t=0,n=e.length;t!==n;++t){const n=e[t];0==--n.referenceCount&&this._removeInactiveBinding(n)}}_lendAction(t){const e=this._actions,n=t._cacheIndex,i=this._nActiveActions++,r=e[i];t._cacheIndex=i,e[i]=t,r._cacheIndex=n,e[n]=r}_takeBackAction(t){const e=this._actions,n=t._cacheIndex,i=--this._nActiveActions,r=e[i];t._cacheIndex=i,e[i]=t,r._cacheIndex=n,e[n]=r}_addInactiveBinding(t,e,n){const i=this._bindingsByRootAndName,r=this._bindings;let s=i[e];void 0===s&&(s={},i[e]=s),s[n]=t,t._cacheIndex=r.length,r.push(t)}_removeInactiveBinding(t){const e=this._bindings,n=t.binding,i=n.rootNode.uuid,r=n.path,s=this._bindingsByRootAndName,o=s[i],a=e[e.length-1],l=t._cacheIndex;a._cacheIndex=l,e[l]=a,e.pop(),delete o[r],0===Object.keys(o).length&&delete s[i]}_lendBinding(t){const e=this._bindings,n=t._cacheIndex,i=this._nActiveBindings++,r=e[i];t._cacheIndex=i,e[i]=t,r._cacheIndex=n,e[n]=r}_takeBackBinding(t){const e=this._bindings,n=t._cacheIndex,i=--this._nActiveBindings,r=e[i];t._cacheIndex=i,e[i]=t,r._cacheIndex=n,e[n]=r}_lendControlInterpolant(){const t=this._controlInterpolants,e=this._nActiveControlInterpolants++;let n=t[e];return void 0===n&&(n=new JS(new Float32Array(2),new Float32Array(2),1,this._controlInterpolantsResultBuffer),n.__cacheIndex=e,t[e]=n),n}_takeBackControlInterpolant(t){const e=this._controlInterpolants,n=t.__cacheIndex,i=--this._nActiveControlInterpolants,r=e[i];t.__cacheIndex=i,e[i]=t,r.__cacheIndex=n,e[n]=r}clipAction(t,e,n){const i=e||this._root,r=i.uuid;let s=\\\\\\\"string\\\\\\\"==typeof t?oC.findByName(i,t):t;const o=null!==s?s.uuid:t,a=this._actionsByClip[o];let l=null;if(void 0===n&&(n=null!==s?s.blendMode:Xy),void 0!==a){const t=a.actionByRoot[r];if(void 0!==t&&t.blendMode===n)return t;l=a.knownActions[0],null===s&&(s=l._clip)}if(null===s)return null;const c=new tN(this,s,e,n);return this._bindAction(c,l),this._addInactiveAction(c,o,r),c}existingAction(t,e){const n=e||this._root,i=n.uuid,r=\\\\\\\"string\\\\\\\"==typeof t?oC.findByName(n,t):t,s=r?r.uuid:t,o=this._actionsByClip[s];return void 0!==o&&o.actionByRoot[i]||null}stopAllAction(){const t=this._actions;for(let e=this._nActiveActions-1;e>=0;--e)t[e].stop();return this}update(t){t*=this.timeScale;const e=this._actions,n=this._nActiveActions,i=this.time+=t,r=Math.sign(t),s=this._accuIndex^=1;for(let o=0;o!==n;++o){e[o]._update(i,t,r,s)}const o=this._bindings,a=this._nActiveBindings;for(let t=0;t!==a;++t)o[t].apply(s);return this}setTime(t){this.time=0;for(let t=0;t<this._actions.length;t++)this._actions[t].time=0;return this.update(t)}getRoot(){return this._root}uncacheClip(t){const e=this._actions,n=t.uuid,i=this._actionsByClip,r=i[n];if(void 0!==r){const t=r.knownActions;for(let n=0,i=t.length;n!==i;++n){const i=t[n];this._deactivateAction(i);const r=i._cacheIndex,s=e[e.length-1];i._cacheIndex=null,i._byClipCacheIndex=null,s._cacheIndex=r,e[r]=s,e.pop(),this._removeInactiveBindingsForAction(i)}delete i[n]}}uncacheRoot(t){const e=t.uuid,n=this._actionsByClip;for(const t in n){const i=n[t].actionByRoot[e];void 0!==i&&(this._deactivateAction(i),this._removeInactiveAction(i))}const i=this._bindingsByRootAndName[e];if(void 0!==i)for(const t in i){const e=i[t];e.restoreOriginalState(),this._removeInactiveBinding(e)}}uncacheAction(t,e){const n=this.existingAction(t,e);null!==n&&(this._deactivateAction(n),this._removeInactiveAction(n))}}).prototype._controlInterpolantsResultBuffer=new Float32Array(1);class eN{constructor(t){\\\\\\\"string\\\\\\\"==typeof t&&(console.warn(\\\\\\\"THREE.Uniform: Type parameter is no longer needed.\\\\\\\"),t=arguments[1]),this.value=t}clone(){return new eN(void 0===this.value.clone?this.value:this.value.clone())}}(class extends GE{constructor(t,e,n=1){super(t,e),this.meshPerAttribute=n}copy(t){return super.copy(t),this.meshPerAttribute=t.meshPerAttribute,this}clone(t){const e=super.clone(t);return e.meshPerAttribute=this.meshPerAttribute,e}toJSON(t){const e=super.toJSON(t);return e.isInstancedInterleavedBuffer=!0,e.meshPerAttribute=this.meshPerAttribute,e}}).prototype.isInstancedInterleavedBuffer=!0;const nN=new fx;class iN{constructor(t=new fx(1/0,1/0),e=new fx(-1/0,-1/0)){this.min=t,this.max=e}set(t,e){return this.min.copy(t),this.max.copy(e),this}setFromPoints(t){this.makeEmpty();for(let e=0,n=t.length;e<n;e++)this.expandByPoint(t[e]);return this}setFromCenterAndSize(t,e){const n=nN.copy(e).multiplyScalar(.5);return this.min.copy(t).sub(n),this.max.copy(t).add(n),this}clone(){return(new this.constructor).copy(this)}copy(t){return this.min.copy(t.min),this.max.copy(t.max),this}makeEmpty(){return this.min.x=this.min.y=1/0,this.max.x=this.max.y=-1/0,this}isEmpty(){return this.max.x<this.min.x||this.max.y<this.min.y}getCenter(t){return this.isEmpty()?t.set(0,0):t.addVectors(this.min,this.max).multiplyScalar(.5)}getSize(t){return this.isEmpty()?t.set(0,0):t.subVectors(this.max,this.min)}expandByPoint(t){return this.min.min(t),this.max.max(t),this}expandByVector(t){return this.min.sub(t),this.max.add(t),this}expandByScalar(t){return this.min.addScalar(-t),this.max.addScalar(t),this}containsPoint(t){return!(t.x<this.min.x||t.x>this.max.x||t.y<this.min.y||t.y>this.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.x<this.min.x||t.min.x>this.max.x||t.max.y<this.min.y||t.min.y>this.max.y)}clampPoint(t,e){return e.copy(t).clamp(this.min,this.max)}distanceToPoint(t){return nN.copy(t).clamp(this.min,this.max).sub(t).length()}intersect(t){return this.min.max(t.min),this.max.min(t.max),this}union(t){return this.min.min(t.min),this.max.max(t.max),this}translate(t){return this.min.add(t),this.max.add(t),this}equals(t){return t.min.equals(this.min)&&t.max.equals(this.max)}}iN.prototype.isBox2=!0;const rN=new Nx,sN=new Nx;class oN{constructor(t=new Nx,e=new Nx){this.start=t,this.end=e}set(t,e){return this.start.copy(t),this.end.copy(e),this}copy(t){return this.start.copy(t.start),this.end.copy(t.end),this}getCenter(t){return t.addVectors(this.start,this.end).multiplyScalar(.5)}delta(t){return t.subVectors(this.end,this.start)}distanceSq(){return this.start.distanceToSquared(this.end)}distance(){return this.start.distanceTo(this.end)}at(t,e){return this.delta(e).multiplyScalar(t).add(this.start)}closestPointToPointParameter(t,e){rN.subVectors(t,this.start),sN.subVectors(this.end,this.start);const n=sN.dot(sN);let i=sN.dot(rN)/n;return e&&(i=cx(i,0,1)),i}closestPointToPoint(t,e,n){const i=this.closestPointToPointParameter(t,e);return this.delta(n).multiplyScalar(i).add(this.start)}applyMatrix4(t){return this.start.applyMatrix4(t),this.end.applyMatrix4(t),this}equals(t){return t.start.equals(this.start)&&t.end.equals(this.end)}clone(){return(new this.constructor).copy(this)}}(class extends Ob{constructor(t){super(),this.material=t,this.render=function(){},this.hasPositions=!1,this.hasNormals=!1,this.hasColors=!1,this.hasUvs=!1,this.positionArray=null,this.normalArray=null,this.colorArray=null,this.uvArray=null,this.count=0}}).prototype.isImmediateRenderObject=!0;const aN=new Nx,lN=new ob,cN=new ob;function uN(t){const e=[];t&&t.isBone&&e.push(t);for(let n=0;n<t.children.length;n++)e.push.apply(e,uN(t.children[n]));return e}const hN=new Float32Array(1);new Int32Array(hN.buffer);zM.create=function(t,e){return console.log(\\\\\\\"THREE.Curve.create() has been deprecated\\\\\\\"),t.prototype=Object.create(zM.prototype),t.prototype.constructor=t,t.prototype.getPoint=e,t},sS.prototype.fromPoints=function(t){return console.warn(\\\\\\\"THREE.Path: .fromPoints() has been renamed to .setFromPoints().\\\\\\\"),this.setFromPoints(t)},class extends NM{constructor(t=10,e=10,n=4473924,i=8947848){n=new Zb(n),i=new Zb(i);const r=e/2,s=t/e,o=t/2,a=[],l=[];for(let t=0,c=0,u=-o;t<=e;t++,u+=s){a.push(-o,0,u,o,0,u),a.push(u,0,-o,u,0,o);const e=t===r?n:i;e.toArray(l,c),c+=3,e.toArray(l,c),c+=3,e.toArray(l,c),c+=3,e.toArray(l,c),c+=3}const c=new dw;c.setAttribute(\\\\\\\"position\\\\\\\",new rw(a,3)),c.setAttribute(\\\\\\\"color\\\\\\\",new rw(l,3));super(c,new xM({vertexColors:!0,toneMapped:!1})),this.type=\\\\\\\"GridHelper\\\\\\\"}}.prototype.setColors=function(){console.error(\\\\\\\"THREE.GridHelper: setColors() has been deprecated, pass them in the constructor instead.\\\\\\\")},class extends NM{constructor(t){const e=uN(t),n=new dw,i=[],r=[],s=new Zb(0,0,1),o=new Zb(0,1,0);for(let t=0;t<e.length;t++){const n=e[t];n.parent&&n.parent.isBone&&(i.push(0,0,0),i.push(0,0,0),r.push(s.r,s.g,s.b),r.push(o.r,o.g,o.b))}n.setAttribute(\\\\\\\"position\\\\\\\",new rw(i,3)),n.setAttribute(\\\\\\\"color\\\\\\\",new rw(r,3));super(n,new xM({vertexColors:!0,depthTest:!1,depthWrite:!1,toneMapped:!1,transparent:!0})),this.type=\\\\\\\"SkeletonHelper\\\\\\\",this.isSkeletonHelper=!0,this.root=t,this.bones=e,this.matrix=t.matrixWorld,this.matrixAutoUpdate=!1}updateMatrixWorld(t){const e=this.bones,n=this.geometry,i=n.getAttribute(\\\\\\\"position\\\\\\\");cN.copy(this.root.matrixWorld).invert();for(let t=0,n=0;t<e.length;t++){const r=e[t];r.parent&&r.parent.isBone&&(lN.multiplyMatrices(cN,r.matrixWorld),aN.setFromMatrixPosition(lN),i.setXYZ(n,aN.x,aN.y,aN.z),lN.multiplyMatrices(cN,r.parent.matrixWorld),aN.setFromMatrixPosition(lN),i.setXYZ(n+1,aN.x,aN.y,aN.z),n+=2)}n.getAttribute(\\\\\\\"position\\\\\\\").needsUpdate=!0,super.updateMatrixWorld(t)}}.prototype.update=function(){console.error(\\\\\\\"THREE.SkeletonHelper: update() no longer needs to be called.\\\\\\\")},hC.prototype.extractUrlBase=function(t){return console.warn(\\\\\\\"THREE.Loader: .extractUrlBase() has been deprecated. Use THREE.LoaderUtils.extractUrlBase() instead.\\\\\\\"),DC.extractUrlBase(t)},hC.Handlers={add:function(){console.error(\\\\\\\"THREE.Loader: Handlers.add() has been removed. Use LoadingManager.addHandler() instead.\\\\\\\")},get:function(){console.error(\\\\\\\"THREE.Loader: Handlers.get() has been removed. Use LoadingManager.getHandler() instead.\\\\\\\")}},iN.prototype.center=function(t){return console.warn(\\\\\\\"THREE.Box2: .center() has been renamed to .getCenter().\\\\\\\"),this.getCenter(t)},iN.prototype.empty=function(){return console.warn(\\\\\\\"THREE.Box2: .empty() has been renamed to .isEmpty().\\\\\\\"),this.isEmpty()},iN.prototype.isIntersectionBox=function(t){return console.warn(\\\\\\\"THREE.Box2: .isIntersectionBox() has been renamed to .intersectsBox().\\\\\\\"),this.intersectsBox(t)},iN.prototype.size=function(t){return console.warn(\\\\\\\"THREE.Box2: .size() has been renamed to .getSize().\\\\\\\"),this.getSize(t)},Rx.prototype.center=function(t){return console.warn(\\\\\\\"THREE.Box3: .center() has been renamed to .getCenter().\\\\\\\"),this.getCenter(t)},Rx.prototype.empty=function(){return console.warn(\\\\\\\"THREE.Box3: .empty() has been renamed to .isEmpty().\\\\\\\"),this.isEmpty()},Rx.prototype.isIntersectionBox=function(t){return console.warn(\\\\\\\"THREE.Box3: .isIntersectionBox() has been renamed to .intersectsBox().\\\\\\\"),this.intersectsBox(t)},Rx.prototype.isIntersectionSphere=function(t){return console.warn(\\\\\\\"THREE.Box3: .isIntersectionSphere() has been renamed to .intersectsSphere().\\\\\\\"),this.intersectsSphere(t)},Rx.prototype.size=function(t){return console.warn(\\\\\\\"THREE.Box3: .size() has been renamed to .getSize().\\\\\\\"),this.getSize(t)},Zx.prototype.empty=function(){return console.warn(\\\\\\\"THREE.Sphere: .empty() has been renamed to .isEmpty().\\\\\\\"),this.isEmpty()},$w.prototype.setFromMatrix=function(t){return console.warn(\\\\\\\"THREE.Frustum: .setFromMatrix() has been renamed to .setFromProjectionMatrix().\\\\\\\"),this.setFromProjectionMatrix(t)},oN.prototype.center=function(t){return console.warn(\\\\\\\"THREE.Line3: .center() has been renamed to .getCenter().\\\\\\\"),this.getCenter(t)},gx.prototype.flattenToArrayOffset=function(t,e){return console.warn(\\\\\\\"THREE.Matrix3: .flattenToArrayOffset() has been deprecated. Use .toArray() instead.\\\\\\\"),this.toArray(t,e)},gx.prototype.multiplyVector3=function(t){return console.warn(\\\\\\\"THREE.Matrix3: .multiplyVector3() has been removed. Use vector.applyMatrix3( matrix ) instead.\\\\\\\"),t.applyMatrix3(this)},gx.prototype.multiplyVector3Array=function(){console.error(\\\\\\\"THREE.Matrix3: .multiplyVector3Array() has been removed.\\\\\\\")},gx.prototype.applyToBufferAttribute=function(t){return console.warn(\\\\\\\"THREE.Matrix3: .applyToBufferAttribute() has been removed. Use attribute.applyMatrix3( matrix ) instead.\\\\\\\"),t.applyMatrix3(this)},gx.prototype.applyToVector3Array=function(){console.error(\\\\\\\"THREE.Matrix3: .applyToVector3Array() has been removed.\\\\\\\")},gx.prototype.getInverse=function(t){return console.warn(\\\\\\\"THREE.Matrix3: .getInverse() has been removed. Use matrixInv.copy( matrix ).invert(); instead.\\\\\\\"),this.copy(t).invert()},ob.prototype.extractPosition=function(t){return console.warn(\\\\\\\"THREE.Matrix4: .extractPosition() has been renamed to .copyPosition().\\\\\\\"),this.copyPosition(t)},ob.prototype.flattenToArrayOffset=function(t,e){return console.warn(\\\\\\\"THREE.Matrix4: .flattenToArrayOffset() has been deprecated. Use .toArray() instead.\\\\\\\"),this.toArray(t,e)},ob.prototype.getPosition=function(){return console.warn(\\\\\\\"THREE.Matrix4: .getPosition() has been removed. Use Vector3.setFromMatrixPosition( matrix ) instead.\\\\\\\"),(new Nx).setFromMatrixColumn(this,3)},ob.prototype.setRotationFromQuaternion=function(t){return console.warn(\\\\\\\"THREE.Matrix4: .setRotationFromQuaternion() has been renamed to .makeRotationFromQuaternion().\\\\\\\"),this.makeRotationFromQuaternion(t)},ob.prototype.multiplyToArray=function(){console.warn(\\\\\\\"THREE.Matrix4: .multiplyToArray() has been removed.\\\\\\\")},ob.prototype.multiplyVector3=function(t){return console.warn(\\\\\\\"THREE.Matrix4: .multiplyVector3() has been removed. Use vector.applyMatrix4( matrix ) instead.\\\\\\\"),t.applyMatrix4(this)},ob.prototype.multiplyVector4=function(t){return console.warn(\\\\\\\"THREE.Matrix4: .multiplyVector4() has been removed. Use vector.applyMatrix4( matrix ) instead.\\\\\\\"),t.applyMatrix4(this)},ob.prototype.multiplyVector3Array=function(){console.error(\\\\\\\"THREE.Matrix4: .multiplyVector3Array() has been removed.\\\\\\\")},ob.prototype.rotateAxis=function(t){console.warn(\\\\\\\"THREE.Matrix4: .rotateAxis() has been removed. Use Vector3.transformDirection( matrix ) instead.\\\\\\\"),t.transformDirection(this)},ob.prototype.crossVector=function(t){return console.warn(\\\\\\\"THREE.Matrix4: .crossVector() has been removed. Use vector.applyMatrix4( matrix ) instead.\\\\\\\"),t.applyMatrix4(this)},ob.prototype.translate=function(){console.error(\\\\\\\"THREE.Matrix4: .translate() has been removed.\\\\\\\")},ob.prototype.rotateX=function(){console.error(\\\\\\\"THREE.Matrix4: .rotateX() has been removed.\\\\\\\")},ob.prototype.rotateY=function(){console.error(\\\\\\\"THREE.Matrix4: .rotateY() has been removed.\\\\\\\")},ob.prototype.rotateZ=function(){console.error(\\\\\\\"THREE.Matrix4: .rotateZ() has been removed.\\\\\\\")},ob.prototype.rotateByAxis=function(){console.error(\\\\\\\"THREE.Matrix4: .rotateByAxis() has been removed.\\\\\\\")},ob.prototype.applyToBufferAttribute=function(t){return console.warn(\\\\\\\"THREE.Matrix4: .applyToBufferAttribute() has been removed. Use attribute.applyMatrix4( matrix ) instead.\\\\\\\"),t.applyMatrix4(this)},ob.prototype.applyToVector3Array=function(){console.error(\\\\\\\"THREE.Matrix4: .applyToVector3Array() has been removed.\\\\\\\")},ob.prototype.makeFrustum=function(t,e,n,i,r,s){return console.warn(\\\\\\\"THREE.Matrix4: .makeFrustum() has been removed. Use .makePerspective( left, right, top, bottom, near, far ) instead.\\\\\\\"),this.makePerspective(t,e,i,n,r,s)},ob.prototype.getInverse=function(t){return console.warn(\\\\\\\"THREE.Matrix4: .getInverse() has been removed. Use matrixInv.copy( matrix ).invert(); instead.\\\\\\\"),this.copy(t).invert()},qw.prototype.isIntersectionLine=function(t){return console.warn(\\\\\\\"THREE.Plane: .isIntersectionLine() has been renamed to .intersectsLine().\\\\\\\"),this.intersectsLine(t)},Cx.prototype.multiplyVector3=function(t){return console.warn(\\\\\\\"THREE.Quaternion: .multiplyVector3() has been removed. Use is now vector.applyQuaternion( quaternion ) instead.\\\\\\\"),t.applyQuaternion(this)},Cx.prototype.inverse=function(){return console.warn(\\\\\\\"THREE.Quaternion: .inverse() has been renamed to invert().\\\\\\\"),this.invert()},sb.prototype.isIntersectionBox=function(t){return console.warn(\\\\\\\"THREE.Ray: .isIntersectionBox() has been renamed to .intersectsBox().\\\\\\\"),this.intersectsBox(t)},sb.prototype.isIntersectionPlane=function(t){return console.warn(\\\\\\\"THREE.Ray: .isIntersectionPlane() has been renamed to .intersectsPlane().\\\\\\\"),this.intersectsPlane(t)},sb.prototype.isIntersectionSphere=function(t){return console.warn(\\\\\\\"THREE.Ray: .isIntersectionSphere() has been renamed to .intersectsSphere().\\\\\\\"),this.intersectsSphere(t)},Vb.prototype.area=function(){return console.warn(\\\\\\\"THREE.Triangle: .area() has been renamed to .getArea().\\\\\\\"),this.getArea()},Vb.prototype.barycoordFromPoint=function(t,e){return console.warn(\\\\\\\"THREE.Triangle: .barycoordFromPoint() has been renamed to .getBarycoord().\\\\\\\"),this.getBarycoord(t,e)},Vb.prototype.midpoint=function(t){return console.warn(\\\\\\\"THREE.Triangle: .midpoint() has been renamed to .getMidpoint().\\\\\\\"),this.getMidpoint(t)},Vb.prototypenormal=function(t){return console.warn(\\\\\\\"THREE.Triangle: .normal() has been renamed to .getNormal().\\\\\\\"),this.getNormal(t)},Vb.prototype.plane=function(t){return console.warn(\\\\\\\"THREE.Triangle: .plane() has been renamed to .getPlane().\\\\\\\"),this.getPlane(t)},Vb.barycoordFromPoint=function(t,e,n,i,r){return console.warn(\\\\\\\"THREE.Triangle: .barycoordFromPoint() has been renamed to .getBarycoord().\\\\\\\"),Vb.getBarycoord(t,e,n,i,r)},Vb.normal=function(t,e,n,i){return console.warn(\\\\\\\"THREE.Triangle: .normal() has been renamed to .getNormal().\\\\\\\"),Vb.getNormal(t,e,n,i)},oS.prototype.extractAllPoints=function(t){return console.warn(\\\\\\\"THREE.Shape: .extractAllPoints() has been removed. Use .extractPoints() instead.\\\\\\\"),this.extractPoints(t)},oS.prototype.extrude=function(t){return console.warn(\\\\\\\"THREE.Shape: .extrude() has been removed. Use ExtrudeGeometry() instead.\\\\\\\"),new FS(this,t)},oS.prototype.makeGeometry=function(t){return console.warn(\\\\\\\"THREE.Shape: .makeGeometry() has been removed. Use ShapeGeometry() instead.\\\\\\\"),new kS(this,t)},fx.prototype.fromAttribute=function(t,e,n){return console.warn(\\\\\\\"THREE.Vector2: .fromAttribute() has been renamed to .fromBufferAttribute().\\\\\\\"),this.fromBufferAttribute(t,e,n)},fx.prototype.distanceToManhattan=function(t){return console.warn(\\\\\\\"THREE.Vector2: .distanceToManhattan() has been renamed to .manhattanDistanceTo().\\\\\\\"),this.manhattanDistanceTo(t)},fx.prototype.lengthManhattan=function(){return console.warn(\\\\\\\"THREE.Vector2: .lengthManhattan() has been renamed to .manhattanLength().\\\\\\\"),this.manhattanLength()},Nx.prototype.setEulerFromRotationMatrix=function(){console.error(\\\\\\\"THREE.Vector3: .setEulerFromRotationMatrix() has been removed. Use Euler.setFromRotationMatrix() instead.\\\\\\\")},Nx.prototype.setEulerFromQuaternion=function(){console.error(\\\\\\\"THREE.Vector3: .setEulerFromQuaternion() has been removed. Use Euler.setFromQuaternion() instead.\\\\\\\")},Nx.prototype.getPositionFromMatrix=function(t){return console.warn(\\\\\\\"THREE.Vector3: .getPositionFromMatrix() has been renamed to .setFromMatrixPosition().\\\\\\\"),this.setFromMatrixPosition(t)},Nx.prototype.getScaleFromMatrix=function(t){return console.warn(\\\\\\\"THREE.Vector3: .getScaleFromMatrix() has been renamed to .setFromMatrixScale().\\\\\\\"),this.setFromMatrixScale(t)},Nx.prototype.getColumnFromMatrix=function(t,e){return console.warn(\\\\\\\"THREE.Vector3: .getColumnFromMatrix() has been renamed to .setFromMatrixColumn().\\\\\\\"),this.setFromMatrixColumn(e,t)},Nx.prototype.applyProjection=function(t){return console.warn(\\\\\\\"THREE.Vector3: .applyProjection() has been removed. Use .applyMatrix4( m ) instead.\\\\\\\"),this.applyMatrix4(t)},Nx.prototype.fromAttribute=function(t,e,n){return console.warn(\\\\\\\"THREE.Vector3: .fromAttribute() has been renamed to .fromBufferAttribute().\\\\\\\"),this.fromBufferAttribute(t,e,n)},Nx.prototype.distanceToManhattan=function(t){return console.warn(\\\\\\\"THREE.Vector3: .distanceToManhattan() has been renamed to .manhattanDistanceTo().\\\\\\\"),this.manhattanDistanceTo(t)},Nx.prototype.lengthManhattan=function(){return console.warn(\\\\\\\"THREE.Vector3: .lengthManhattan() has been renamed to .manhattanLength().\\\\\\\"),this.manhattanLength()},Ex.prototype.fromAttribute=function(t,e,n){return console.warn(\\\\\\\"THREE.Vector4: .fromAttribute() has been renamed to .fromBufferAttribute().\\\\\\\"),this.fromBufferAttribute(t,e,n)},Ex.prototype.lengthManhattan=function(){return console.warn(\\\\\\\"THREE.Vector4: .lengthManhattan() has been renamed to .manhattanLength().\\\\\\\"),this.manhattanLength()},Ob.prototype.getChildByName=function(t){return console.warn(\\\\\\\"THREE.Object3D: .getChildByName() has been renamed to .getObjectByName().\\\\\\\"),this.getObjectByName(t)},Ob.prototype.renderDepth=function(){console.warn(\\\\\\\"THREE.Object3D: .renderDepth has been removed. Use .renderOrder, instead.\\\\\\\")},Ob.prototype.translate=function(t,e){return console.warn(\\\\\\\"THREE.Object3D: .translate() has been removed. Use .translateOnAxis( axis, distance ) instead.\\\\\\\"),this.translateOnAxis(e,t)},Ob.prototype.getWorldRotation=function(){console.error(\\\\\\\"THREE.Object3D: .getWorldRotation() has been removed. Use THREE.Object3D.getWorldQuaternion( target ) instead.\\\\\\\")},Ob.prototype.applyMatrix=function(t){return console.warn(\\\\\\\"THREE.Object3D: .applyMatrix() has been renamed to .applyMatrix4().\\\\\\\"),this.applyMatrix4(t)},Object.defineProperties(Ob.prototype,{eulerOrder:{get:function(){return console.warn(\\\\\\\"THREE.Object3D: .eulerOrder is now .rotation.order.\\\\\\\"),this.rotation.order},set:function(t){console.warn(\\\\\\\"THREE.Object3D: .eulerOrder is now .rotation.order.\\\\\\\"),this.rotation.order=t}},useQuaternion:{get:function(){console.warn(\\\\\\\"THREE.Object3D: .useQuaternion has been removed. The library now uses quaternions by default.\\\\\\\")},set:function(){console.warn(\\\\\\\"THREE.Object3D: .useQuaternion has been removed. The library now uses quaternions by default.\\\\\\\")}}}),Lw.prototype.setDrawMode=function(){console.error(\\\\\\\"THREE.Mesh: .setDrawMode() has been removed. The renderer now always assumes THREE.TrianglesDrawMode. Transform your geometry via BufferGeometryUtils.toTrianglesDrawMode() if necessary.\\\\\\\")},Object.defineProperties(Lw.prototype,{drawMode:{get:function(){return console.error(\\\\\\\"THREE.Mesh: .drawMode has been removed. The renderer now always assumes THREE.TrianglesDrawMode.\\\\\\\"),0},set:function(){console.error(\\\\\\\"THREE.Mesh: .drawMode has been removed. The renderer now always assumes THREE.TrianglesDrawMode. Transform your geometry via BufferGeometryUtils.toTrianglesDrawMode() if necessary.\\\\\\\")}}}),hM.prototype.initBones=function(){console.error(\\\\\\\"THREE.SkinnedMesh: initBones() has been removed.\\\\\\\")},Bw.prototype.setLens=function(t,e){console.warn(\\\\\\\"THREE.PerspectiveCamera.setLens is deprecated. Use .setFocalLength and .filmGauge for a photographic setup.\\\\\\\"),void 0!==e&&(this.filmGauge=e),this.setFocalLength(t)},Object.defineProperties(gC.prototype,{onlyShadow:{set:function(){console.warn(\\\\\\\"THREE.Light: .onlyShadow has been removed.\\\\\\\")}},shadowCameraFov:{set:function(t){console.warn(\\\\\\\"THREE.Light: .shadowCameraFov is now .shadow.camera.fov.\\\\\\\"),this.shadow.camera.fov=t}},shadowCameraLeft:{set:function(t){console.warn(\\\\\\\"THREE.Light: .shadowCameraLeft is now .shadow.camera.left.\\\\\\\"),this.shadow.camera.left=t}},shadowCameraRight:{set:function(t){console.warn(\\\\\\\"THREE.Light: .shadowCameraRight is now .shadow.camera.right.\\\\\\\"),this.shadow.camera.right=t}},shadowCameraTop:{set:function(t){console.warn(\\\\\\\"THREE.Light: .shadowCameraTop is now .shadow.camera.top.\\\\\\\"),this.shadow.camera.top=t}},shadowCameraBottom:{set:function(t){console.warn(\\\\\\\"THREE.Light: .shadowCameraBottom is now .shadow.camera.bottom.\\\\\\\"),this.shadow.camera.bottom=t}},shadowCameraNear:{set:function(t){console.warn(\\\\\\\"THREE.Light: .shadowCameraNear is now .shadow.camera.near.\\\\\\\"),this.shadow.camera.near=t}},shadowCameraFar:{set:function(t){console.warn(\\\\\\\"THREE.Light: .shadowCameraFar is now .shadow.camera.far.\\\\\\\"),this.shadow.camera.far=t}},shadowCameraVisible:{set:function(){console.warn(\\\\\\\"THREE.Light: .shadowCameraVisible has been removed. Use new THREE.CameraHelper( light.shadow.camera ) instead.\\\\\\\")}},shadowBias:{set:function(t){console.warn(\\\\\\\"THREE.Light: .shadowBias is now .shadow.bias.\\\\\\\"),this.shadow.bias=t}},shadowDarkness:{set:function(){console.warn(\\\\\\\"THREE.Light: .shadowDarkness has been removed.\\\\\\\")}},shadowMapWidth:{set:function(t){console.warn(\\\\\\\"THREE.Light: .shadowMapWidth is now .shadow.mapSize.width.\\\\\\\"),this.shadow.mapSize.width=t}},shadowMapHeight:{set:function(t){console.warn(\\\\\\\"THREE.Light: .shadowMapHeight is now .shadow.mapSize.height.\\\\\\\"),this.shadow.mapSize.height=t}}}),Object.defineProperties(ew.prototype,{length:{get:function(){return console.warn(\\\\\\\"THREE.BufferAttribute: .length has been deprecated. Use .count instead.\\\\\\\"),this.array.length}},dynamic:{get:function(){return console.warn(\\\\\\\"THREE.BufferAttribute: .dynamic has been deprecated. Use .usage instead.\\\\\\\"),this.usage===tx},set:function(){console.warn(\\\\\\\"THREE.BufferAttribute: .dynamic has been deprecated. Use .usage instead.\\\\\\\"),this.setUsage(tx)}}}),ew.prototype.setDynamic=function(t){return console.warn(\\\\\\\"THREE.BufferAttribute: .setDynamic() has been deprecated. Use .setUsage() instead.\\\\\\\"),this.setUsage(!0===t?tx:Ky),this},ew.prototype.copyIndicesArray=function(){console.error(\\\\\\\"THREE.BufferAttribute: .copyIndicesArray() has been removed.\\\\\\\")},ew.prototype.setArray=function(){console.error(\\\\\\\"THREE.BufferAttribute: .setArray has been removed. Use BufferGeometry .setAttribute to replace/resize attribute buffers\\\\\\\")},dw.prototype.addIndex=function(t){console.warn(\\\\\\\"THREE.BufferGeometry: .addIndex() has been renamed to .setIndex().\\\\\\\"),this.setIndex(t)},dw.prototype.addAttribute=function(t,e){return console.warn(\\\\\\\"THREE.BufferGeometry: .addAttribute() has been renamed to .setAttribute().\\\\\\\"),e&&e.isBufferAttribute||e&&e.isInterleavedBufferAttribute?\\\\\\\"index\\\\\\\"===t?(console.warn(\\\\\\\"THREE.BufferGeometry.addAttribute: Use .setIndex() for index attribute.\\\\\\\"),this.setIndex(e),this):this.setAttribute(t,e):(console.warn(\\\\\\\"THREE.BufferGeometry: .addAttribute() now expects ( name, attribute ).\\\\\\\"),this.setAttribute(t,new ew(arguments[1],arguments[2])))},dw.prototype.addDrawCall=function(t,e,n){void 0!==n&&console.warn(\\\\\\\"THREE.BufferGeometry: .addDrawCall() no longer supports indexOffset.\\\\\\\"),console.warn(\\\\\\\"THREE.BufferGeometry: .addDrawCall() is now .addGroup().\\\\\\\"),this.addGroup(t,e)},dw.prototype.clearDrawCalls=function(){console.warn(\\\\\\\"THREE.BufferGeometry: .clearDrawCalls() is now .clearGroups().\\\\\\\"),this.clearGroups()},dw.prototype.computeOffsets=function(){console.warn(\\\\\\\"THREE.BufferGeometry: .computeOffsets() has been removed.\\\\\\\")},dw.prototype.removeAttribute=function(t){return console.warn(\\\\\\\"THREE.BufferGeometry: .removeAttribute() has been renamed to .deleteAttribute().\\\\\\\"),this.deleteAttribute(t)},dw.prototype.applyMatrix=function(t){return console.warn(\\\\\\\"THREE.BufferGeometry: .applyMatrix() has been renamed to .applyMatrix4().\\\\\\\"),this.applyMatrix4(t)},Object.defineProperties(dw.prototype,{drawcalls:{get:function(){return console.error(\\\\\\\"THREE.BufferGeometry: .drawcalls has been renamed to .groups.\\\\\\\"),this.groups}},offsets:{get:function(){return console.warn(\\\\\\\"THREE.BufferGeometry: .offsets has been renamed to .groups.\\\\\\\"),this.groups}}}),GE.prototype.setDynamic=function(t){return console.warn(\\\\\\\"THREE.InterleavedBuffer: .setDynamic() has been deprecated. Use .setUsage() instead.\\\\\\\"),this.setUsage(!0===t?tx:Ky),this},GE.prototype.setArray=function(){console.error(\\\\\\\"THREE.InterleavedBuffer: .setArray has been removed. Use BufferGeometry .setAttribute to replace/resize attribute buffers\\\\\\\")},FS.prototype.getArrays=function(){console.error(\\\\\\\"THREE.ExtrudeGeometry: .getArrays() has been removed.\\\\\\\")},FS.prototype.addShapeList=function(){console.error(\\\\\\\"THREE.ExtrudeGeometry: .addShapeList() has been removed.\\\\\\\")},FS.prototype.addShape=function(){console.error(\\\\\\\"THREE.ExtrudeGeometry: .addShape() has been removed.\\\\\\\")},UE.prototype.dispose=function(){console.error(\\\\\\\"THREE.Scene: .dispose() has been removed.\\\\\\\")},eN.prototype.onUpdate=function(){return console.warn(\\\\\\\"THREE.Uniform: .onUpdate() has been removed. Use object.onBeforeRender() instead.\\\\\\\"),this},Object.defineProperties(jb.prototype,{wrapAround:{get:function(){console.warn(\\\\\\\"THREE.Material: .wrapAround has been removed.\\\\\\\")},set:function(){console.warn(\\\\\\\"THREE.Material: .wrapAround has been removed.\\\\\\\")}},overdraw:{get:function(){console.warn(\\\\\\\"THREE.Material: .overdraw has been removed.\\\\\\\")},set:function(){console.warn(\\\\\\\"THREE.Material: .overdraw has been removed.\\\\\\\")}},wrapRGB:{get:function(){return console.warn(\\\\\\\"THREE.Material: .wrapRGB has been removed.\\\\\\\"),new Zb}},shading:{get:function(){console.error(\\\\\\\"THREE.\\\\\\\"+this.type+\\\\\\\": .shading has been removed. Use the boolean .flatShading instead.\\\\\\\")},set:function(t){console.warn(\\\\\\\"THREE.\\\\\\\"+this.type+\\\\\\\": .shading has been removed. Use the boolean .flatShading instead.\\\\\\\"),this.flatShading=1===t}},stencilMask:{get:function(){return console.warn(\\\\\\\"THREE.\\\\\\\"+this.type+\\\\\\\": .stencilMask has been removed. Use .stencilFuncMask instead.\\\\\\\"),this.stencilFuncMask},set:function(t){console.warn(\\\\\\\"THREE.\\\\\\\"+this.type+\\\\\\\": .stencilMask has been removed. Use .stencilFuncMask instead.\\\\\\\"),this.stencilFuncMask=t}},vertexTangents:{get:function(){console.warn(\\\\\\\"THREE.\\\\\\\"+this.type+\\\\\\\": .vertexTangents has been removed.\\\\\\\")},set:function(){console.warn(\\\\\\\"THREE.\\\\\\\"+this.type+\\\\\\\": .vertexTangents has been removed.\\\\\\\")}}}),Object.defineProperties(Dw.prototype,{derivatives:{get:function(){return console.warn(\\\\\\\"THREE.ShaderMaterial: .derivatives has been moved to .extensions.derivatives.\\\\\\\"),this.extensions.derivatives},set:function(t){console.warn(\\\\\\\"THREE. ShaderMaterial: .derivatives has been moved to .extensions.derivatives.\\\\\\\"),this.extensions.derivatives=t}}}),kE.prototype.clearTarget=function(t,e,n,i){console.warn(\\\\\\\"THREE.WebGLRenderer: .clearTarget() has been deprecated. Use .setRenderTarget() and .clear() instead.\\\\\\\"),this.setRenderTarget(t),this.clear(e,n,i)},kE.prototype.animate=function(t){console.warn(\\\\\\\"THREE.WebGLRenderer: .animate() is now .setAnimationLoop().\\\\\\\"),this.setAnimationLoop(t)},kE.prototype.getCurrentRenderTarget=function(){return console.warn(\\\\\\\"THREE.WebGLRenderer: .getCurrentRenderTarget() is now .getRenderTarget().\\\\\\\"),this.getRenderTarget()},kE.prototype.getMaxAnisotropy=function(){return console.warn(\\\\\\\"THREE.WebGLRenderer: .getMaxAnisotropy() is now .capabilities.getMaxAnisotropy().\\\\\\\"),this.capabilities.getMaxAnisotropy()},kE.prototype.getPrecision=function(){return console.warn(\\\\\\\"THREE.WebGLRenderer: .getPrecision() is now .capabilities.precision.\\\\\\\"),this.capabilities.precision},kE.prototype.resetGLState=function(){return console.warn(\\\\\\\"THREE.WebGLRenderer: .resetGLState() is now .state.reset().\\\\\\\"),this.state.reset()},kE.prototype.supportsFloatTextures=function(){return console.warn(\\\\\\\"THREE.WebGLRenderer: .supportsFloatTextures() is now .extensions.get( 'OES_texture_float' ).\\\\\\\"),this.extensions.get(\\\\\\\"OES_texture_float\\\\\\\")},kE.prototype.supportsHalfFloatTextures=function(){return console.warn(\\\\\\\"THREE.WebGLRenderer: .supportsHalfFloatTextures() is now .extensions.get( 'OES_texture_half_float' ).\\\\\\\"),this.extensions.get(\\\\\\\"OES_texture_half_float\\\\\\\")},kE.prototype.supportsStandardDerivatives=function(){return console.warn(\\\\\\\"THREE.WebGLRenderer: .supportsStandardDerivatives() is now .extensions.get( 'OES_standard_derivatives' ).\\\\\\\"),this.extensions.get(\\\\\\\"OES_standard_derivatives\\\\\\\")},kE.prototype.supportsCompressedTextureS3TC=function(){return console.warn(\\\\\\\"THREE.WebGLRenderer: .supportsCompressedTextureS3TC() is now .extensions.get( 'WEBGL_compressed_texture_s3tc' ).\\\\\\\"),this.extensions.get(\\\\\\\"WEBGL_compressed_texture_s3tc\\\\\\\")},kE.prototype.supportsCompressedTexturePVRTC=function(){return console.warn(\\\\\\\"THREE.WebGLRenderer: .supportsCompressedTexturePVRTC() is now .extensions.get( 'WEBGL_compressed_texture_pvrtc' ).\\\\\\\"),this.extensions.get(\\\\\\\"WEBGL_compressed_texture_pvrtc\\\\\\\")},kE.prototype.supportsBlendMinMax=function(){return console.warn(\\\\\\\"THREE.WebGLRenderer: .supportsBlendMinMax() is now .extensions.get( 'EXT_blend_minmax' ).\\\\\\\"),this.extensions.get(\\\\\\\"EXT_blend_minmax\\\\\\\")},kE.prototype.supportsVertexTextures=function(){return console.warn(\\\\\\\"THREE.WebGLRenderer: .supportsVertexTextures() is now .capabilities.vertexTextures.\\\\\\\"),this.capabilities.vertexTextures},kE.prototype.supportsInstancedArrays=function(){return console.warn(\\\\\\\"THREE.WebGLRenderer: .supportsInstancedArrays() is now .extensions.get( 'ANGLE_instanced_arrays' ).\\\\\\\"),this.extensions.get(\\\\\\\"ANGLE_instanced_arrays\\\\\\\")},kE.prototype.enableScissorTest=function(t){console.warn(\\\\\\\"THREE.WebGLRenderer: .enableScissorTest() is now .setScissorTest().\\\\\\\"),this.setScissorTest(t)},kE.prototype.initMaterial=function(){console.warn(\\\\\\\"THREE.WebGLRenderer: .initMaterial() has been removed.\\\\\\\")},kE.prototype.addPrePlugin=function(){console.warn(\\\\\\\"THREE.WebGLRenderer: .addPrePlugin() has been removed.\\\\\\\")},kE.prototype.addPostPlugin=function(){console.warn(\\\\\\\"THREE.WebGLRenderer: .addPostPlugin() has been removed.\\\\\\\")},kE.prototype.updateShadowMap=function(){console.warn(\\\\\\\"THREE.WebGLRenderer: .updateShadowMap() has been removed.\\\\\\\")},kE.prototype.setFaceCulling=function(){console.warn(\\\\\\\"THREE.WebGLRenderer: .setFaceCulling() has been removed.\\\\\\\")},kE.prototype.allocTextureUnit=function(){console.warn(\\\\\\\"THREE.WebGLRenderer: .allocTextureUnit() has been removed.\\\\\\\")},kE.prototype.setTexture=function(){console.warn(\\\\\\\"THREE.WebGLRenderer: .setTexture() has been removed.\\\\\\\")},kE.prototype.setTexture2D=function(){console.warn(\\\\\\\"THREE.WebGLRenderer: .setTexture2D() has been removed.\\\\\\\")},kE.prototype.setTextureCube=function(){console.warn(\\\\\\\"THREE.WebGLRenderer: .setTextureCube() has been removed.\\\\\\\")},kE.prototype.getActiveMipMapLevel=function(){return console.warn(\\\\\\\"THREE.WebGLRenderer: .getActiveMipMapLevel() is now .getActiveMipmapLevel().\\\\\\\"),this.getActiveMipmapLevel()},Object.defineProperties(kE.prototype,{shadowMapEnabled:{get:function(){return this.shadowMap.enabled},set:function(t){console.warn(\\\\\\\"THREE.WebGLRenderer: .shadowMapEnabled is now .shadowMap.enabled.\\\\\\\"),this.shadowMap.enabled=t}},shadowMapType:{get:function(){return this.shadowMap.type},set:function(t){console.warn(\\\\\\\"THREE.WebGLRenderer: .shadowMapType is now .shadowMap.type.\\\\\\\"),this.shadowMap.type=t}},shadowMapCullFace:{get:function(){console.warn(\\\\\\\"THREE.WebGLRenderer: .shadowMapCullFace has been removed. Set Material.shadowSide instead.\\\\\\\")},set:function(){console.warn(\\\\\\\"THREE.WebGLRenderer: .shadowMapCullFace has been removed. Set Material.shadowSide instead.\\\\\\\")}},context:{get:function(){return console.warn(\\\\\\\"THREE.WebGLRenderer: .context has been removed. Use .getContext() instead.\\\\\\\"),this.getContext()}},vr:{get:function(){return console.warn(\\\\\\\"THREE.WebGLRenderer: .vr has been renamed to .xr\\\\\\\"),this.xr}},gammaInput:{get:function(){return console.warn(\\\\\\\"THREE.WebGLRenderer: .gammaInput has been removed. Set the encoding for textures via Texture.encoding instead.\\\\\\\"),!1},set:function(){console.warn(\\\\\\\"THREE.WebGLRenderer: .gammaInput has been removed. Set the encoding for textures via Texture.encoding instead.\\\\\\\")}},gammaOutput:{get:function(){return console.warn(\\\\\\\"THREE.WebGLRenderer: .gammaOutput has been removed. Set WebGLRenderer.outputEncoding instead.\\\\\\\"),!1},set:function(t){console.warn(\\\\\\\"THREE.WebGLRenderer: .gammaOutput has been removed. Set WebGLRenderer.outputEncoding instead.\\\\\\\"),this.outputEncoding=!0===t?$y:Yy}},toneMappingWhitePoint:{get:function(){return console.warn(\\\\\\\"THREE.WebGLRenderer: .toneMappingWhitePoint has been removed.\\\\\\\"),1},set:function(){console.warn(\\\\\\\"THREE.WebGLRenderer: .toneMappingWhitePoint has been removed.\\\\\\\")}}}),Object.defineProperties(SE.prototype,{cullFace:{get:function(){console.warn(\\\\\\\"THREE.WebGLRenderer: .shadowMap.cullFace has been removed. Set Material.shadowSide instead.\\\\\\\")},set:function(){console.warn(\\\\\\\"THREE.WebGLRenderer: .shadowMap.cullFace has been removed. Set Material.shadowSide instead.\\\\\\\")}},renderReverseSided:{get:function(){console.warn(\\\\\\\"THREE.WebGLRenderer: .shadowMap.renderReverseSided has been removed. Set Material.shadowSide instead.\\\\\\\")},set:function(){console.warn(\\\\\\\"THREE.WebGLRenderer: .shadowMap.renderReverseSided has been removed. Set Material.shadowSide instead.\\\\\\\")}},renderSingleSided:{get:function(){console.warn(\\\\\\\"THREE.WebGLRenderer: .shadowMap.renderSingleSided has been removed. Set Material.shadowSide instead.\\\\\\\")},set:function(){console.warn(\\\\\\\"THREE.WebGLRenderer: .shadowMap.renderSingleSided has been removed. Set Material.shadowSide instead.\\\\\\\")}}}),Object.defineProperties(Mx.prototype,{wrapS:{get:function(){return console.warn(\\\\\\\"THREE.WebGLRenderTarget: .wrapS is now .texture.wrapS.\\\\\\\"),this.texture.wrapS},set:function(t){console.warn(\\\\\\\"THREE.WebGLRenderTarget: .wrapS is now .texture.wrapS.\\\\\\\"),this.texture.wrapS=t}},wrapT:{get:function(){return console.warn(\\\\\\\"THREE.WebGLRenderTarget: .wrapT is now .texture.wrapT.\\\\\\\"),this.texture.wrapT},set:function(t){console.warn(\\\\\\\"THREE.WebGLRenderTarget: .wrapT is now .texture.wrapT.\\\\\\\"),this.texture.wrapT=t}},magFilter:{get:function(){return console.warn(\\\\\\\"THREE.WebGLRenderTarget: .magFilter is now .texture.magFilter.\\\\\\\"),this.texture.magFilter},set:function(t){console.warn(\\\\\\\"THREE.WebGLRenderTarget: .magFilter is now .texture.magFilter.\\\\\\\"),this.texture.magFilter=t}},minFilter:{get:function(){return console.warn(\\\\\\\"THREE.WebGLRenderTarget: .minFilter is now .texture.minFilter.\\\\\\\"),this.texture.minFilter},set:function(t){console.warn(\\\\\\\"THREE.WebGLRenderTarget: .minFilter is now .texture.minFilter.\\\\\\\"),this.texture.minFilter=t}},anisotropy:{get:function(){return console.warn(\\\\\\\"THREE.WebGLRenderTarget: .anisotropy is now .texture.anisotropy.\\\\\\\"),this.texture.anisotropy},set:function(t){console.warn(\\\\\\\"THREE.WebGLRenderTarget: .anisotropy is now .texture.anisotropy.\\\\\\\"),this.texture.anisotropy=t}},offset:{get:function(){return console.warn(\\\\\\\"THREE.WebGLRenderTarget: .offset is now .texture.offset.\\\\\\\"),this.texture.offset},set:function(t){console.warn(\\\\\\\"THREE.WebGLRenderTarget: .offset is now .texture.offset.\\\\\\\"),this.texture.offset=t}},repeat:{get:function(){return console.warn(\\\\\\\"THREE.WebGLRenderTarget: .repeat is now .texture.repeat.\\\\\\\"),this.texture.repeat},set:function(t){console.warn(\\\\\\\"THREE.WebGLRenderTarget: .repeat is now .texture.repeat.\\\\\\\"),this.texture.repeat=t}},format:{get:function(){return console.warn(\\\\\\\"THREE.WebGLRenderTarget: .format is now .texture.format.\\\\\\\"),this.texture.format},set:function(t){console.warn(\\\\\\\"THREE.WebGLRenderTarget: .format is now .texture.format.\\\\\\\"),this.texture.format=t}},type:{get:function(){return console.warn(\\\\\\\"THREE.WebGLRenderTarget: .type is now .texture.type.\\\\\\\"),this.texture.type},set:function(t){console.warn(\\\\\\\"THREE.WebGLRenderTarget: .type is now .texture.type.\\\\\\\"),this.texture.type=t}},generateMipmaps:{get:function(){return console.warn(\\\\\\\"THREE.WebGLRenderTarget: .generateMipmaps is now .texture.generateMipmaps.\\\\\\\"),this.texture.generateMipmaps},set:function(t){console.warn(\\\\\\\"THREE.WebGLRenderTarget: .generateMipmaps is now .texture.generateMipmaps.\\\\\\\"),this.texture.generateMipmaps=t}}}),GC.prototype.load=function(t){console.warn(\\\\\\\"THREE.Audio: .load has been deprecated. Use THREE.AudioLoader instead.\\\\\\\");const e=this;return(new UC).load(t,(function(t){e.setBuffer(t)})),this},Uw.prototype.updateCubeMap=function(t,e){return console.warn(\\\\\\\"THREE.CubeCamera: .updateCubeMap() is now .update().\\\\\\\"),this.update(t,e)},Uw.prototype.clear=function(t,e,n,i){return console.warn(\\\\\\\"THREE.CubeCamera: .clear() is now .renderTarget.clear().\\\\\\\"),this.renderTarget.clear(t,e,n,i)},bx.crossOrigin=void 0,bx.loadTexture=function(t,e,n,i){console.warn(\\\\\\\"THREE.ImageUtils.loadTexture has been deprecated. Use THREE.TextureLoader() instead.\\\\\\\");const r=new fC;r.setCrossOrigin(this.crossOrigin);const s=r.load(t,n,void 0,i);return e&&(s.mapping=e),s},bx.loadTextureCube=function(t,e,n,i){console.warn(\\\\\\\"THREE.ImageUtils.loadTextureCube has been deprecated. Use THREE.CubeTextureLoader() instead.\\\\\\\");const r=new mC;r.setCrossOrigin(this.crossOrigin);const s=r.load(t,n,void 0,i);return e&&(s.mapping=e),s},bx.loadCompressedTexture=function(){console.error(\\\\\\\"THREE.ImageUtils.loadCompressedTexture has been removed. Use THREE.DDSLoader instead.\\\\\\\")},bx.loadCompressedTextureCube=function(){console.error(\\\\\\\"THREE.ImageUtils.loadCompressedTextureCube has been removed. Use THREE.DDSLoader instead.\\\\\\\")};\\\\\\\"undefined\\\\\\\"!=typeof __THREE_DEVTOOLS__&&__THREE_DEVTOOLS__.dispatchEvent(new CustomEvent(\\\\\\\"register\\\\\\\",{detail:{revision:\\\\\\\"133\\\\\\\"}})),\\\\\\\"undefined\\\\\\\"!=typeof window&&(window.__THREE__?console.warn(\\\\\\\"WARNING: Multiple instances of Three.js being imported.\\\\\\\"):window.__THREE__=\\\\\\\"133\\\\\\\");const dN=new Nx,pN=new Nx,_N=new Nx;class mN{constructor(t=new Nx(0,0,0),e=new Nx(0,1,0),n=1){this.start=t,this.end=e,this.radius=n}clone(){return new mN(this.start.clone(),this.end.clone(),this.radius)}set(t,e,n){this.start.copy(t),this.end.copy(e),this.radius=n}copy(t){this.start.copy(t.start),this.end.copy(t.end),this.radius=t.radius}getCenter(t){return t.copy(this.end).add(this.start).multiplyScalar(.5)}translate(t){this.start.add(t),this.end.add(t)}checkAABBAxis(t,e,n,i,r,s,o,a,l){return(r-t<l||r-n<l)&&(t-s<l||n-s<l)&&(o-e<l||o-i<l)&&(e-a<l||i-a<l)}intersectsBox(t){return this.checkAABBAxis(this.start.x,this.start.y,this.end.x,this.end.y,t.min.x,t.max.x,t.min.y,t.max.y,this.radius)&&this.checkAABBAxis(this.start.x,this.start.z,this.end.x,this.end.z,t.min.x,t.max.x,t.min.z,t.max.z,this.radius)&&this.checkAABBAxis(this.start.y,this.start.z,this.end.y,this.end.z,t.min.y,t.max.y,t.min.z,t.max.z,this.radius)}lineLineMinimumPoints(t,e){const n=dN.copy(t.end).sub(t.start),i=pN.copy(e.end).sub(e.start),r=_N.copy(e.start).sub(t.start),s=n.dot(i),o=n.dot(n),a=i.dot(i),l=i.dot(r),c=n.dot(r);let u,h;const d=o*a-s*s;if(Math.abs(d)<1e-10){const t=-l/a,e=(s-l)/a;Math.abs(t-.5)<Math.abs(e-.5)?(u=0,h=t):(u=1,h=e)}else u=(l*s+c*a)/d,h=(u*s-l)/a;h=Math.max(0,Math.min(1,h)),u=Math.max(0,Math.min(1,u));return[n.multiplyScalar(u).add(t.start),i.multiplyScalar(h).add(e.start)]}}const fN=new Nx,gN=new Nx,vN=new qw,yN=new oN,xN=new oN,bN=new Zx,wN=new mN;class TN{constructor(t){this.triangles=[],this.box=t,this.subTrees=[]}addTriangle(t){return this.bounds||(this.bounds=new Rx),this.bounds.min.x=Math.min(this.bounds.min.x,t.a.x,t.b.x,t.c.x),this.bounds.min.y=Math.min(this.bounds.min.y,t.a.y,t.b.y,t.c.y),this.bounds.min.z=Math.min(this.bounds.min.z,t.a.z,t.b.z,t.c.z),this.bounds.max.x=Math.max(this.bounds.max.x,t.a.x,t.b.x,t.c.x),this.bounds.max.y=Math.max(this.bounds.max.y,t.a.y,t.b.y,t.c.y),this.bounds.max.z=Math.max(this.bounds.max.z,t.a.z,t.b.z,t.c.z),this.triangles.push(t),this}calcBox(){return this.box=this.bounds.clone(),this.box.min.x-=.01,this.box.min.y-=.01,this.box.min.z-=.01,this}split(t){if(!this.box)return;const e=[],n=gN.copy(this.box.max).sub(this.box.min).multiplyScalar(.5);for(let t=0;t<2;t++)for(let i=0;i<2;i++)for(let r=0;r<2;r++){const s=new Rx,o=fN.set(t,i,r);s.min.copy(this.box.min).add(o.multiply(n)),s.max.copy(s.min).add(n),e.push(new TN(s))}let i;for(;i=this.triangles.pop();)for(let t=0;t<e.length;t++)e[t].box.intersectsTriangle(i)&&e[t].triangles.push(i);for(let n=0;n<e.length;n++){const i=e[n].triangles.length;i>8&&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;n<this.subTrees.length;n++){const i=this.subTrees[n];if(t.intersectsBox(i.box))if(i.triangles.length>0)for(let t=0;t<i.triangles.length;t++)-1===e.indexOf(i.triangles[t])&&e.push(i.triangles[t]);else i.getRayTriangles(t,e)}return e}triangleCapsuleIntersect(t,e){e.getPlane(vN);const n=vN.distanceToPoint(t.start)-t.radius,i=vN.distanceToPoint(t.end)-t.radius;if(n>0&&i>0||n<-t.radius&&i<-t.radius)return!1;const r=Math.abs(n/(Math.abs(n)+Math.abs(i))),s=fN.copy(t.start).lerp(t.end,r);if(e.containsPoint(s))return{normal:vN.normal.clone(),point:s.clone(),depth:Math.abs(Math.min(n,i))};const o=t.radius*t.radius,a=yN.set(t.start,t.end),l=[[e.a,e.b],[e.b,e.c],[e.c,e.a]];for(let e=0;e<l.length;e++){const n=xN.set(l[e][0],l[e][1]),[i,r]=t.lineLineMinimumPoints(a,n);if(i.distanceToSquared(r)<o)return{normal:i.clone().sub(r).normalize(),point:r.clone(),depth:t.radius-i.distanceTo(r)}}return!1}triangleSphereIntersect(t,e){if(e.getPlane(vN),!t.intersectsPlane(vN))return!1;const n=Math.abs(vN.distanceToSphere(t)),i=t.radius*t.radius-n*n,r=vN.projectPoint(t.center,fN);if(e.containsPoint(t.center))return{normal:vN.normal.clone(),point:r.clone(),depth:Math.abs(vN.distanceToSphere(t))};const s=[[e.a,e.b],[e.b,e.c],[e.c,e.a]];for(let e=0;e<s.length;e++){yN.set(s[e][0],s[e][1]),yN.closestPointToPoint(r,!0,gN);const n=gN.distanceToSquared(t.center);if(n<i)return{normal:t.center.clone().sub(gN).normalize(),point:gN.clone(),depth:t.radius-Math.sqrt(n)}}return!1}getSphereTriangles(t,e){for(let n=0;n<this.subTrees.length;n++){const i=this.subTrees[n];if(t.intersectsBox(i.box))if(i.triangles.length>0)for(let t=0;t<i.triangles.length;t++)-1===e.indexOf(i.triangles[t])&&e.push(i.triangles[t]);else i.getSphereTriangles(t,e)}}getCapsuleTriangles(t,e){for(let n=0;n<this.subTrees.length;n++){const i=this.subTrees[n];if(t.intersectsBox(i.box))if(i.triangles.length>0)for(let t=0;t<i.triangles.length;t++)-1===e.indexOf(i.triangles[t])&&e.push(i.triangles[t]);else i.getCapsuleTriangles(t,e)}}sphereIntersect(t){bN.copy(t);const e=[];let n,i=!1;this.getSphereTriangles(t,e);for(let t=0;t<e.length;t++)(n=this.triangleSphereIntersect(bN,e[t]))&&(i=!0,bN.center.add(n.normal.multiplyScalar(n.depth)));if(i){const e=bN.center.clone().sub(t.center),n=e.length();return{normal:e.normalize(),depth:n}}return!1}capsuleIntersect(t){wN.copy(t);const e=[];let n,i=!1;this.getCapsuleTriangles(wN,e);for(let t=0;t<e.length;t++)(n=this.triangleCapsuleIntersect(wN,e[t]))&&(i=!0,wN.translate(n.normal.multiplyScalar(n.depth)));if(i){const e=wN.getCenter(new Nx).sub(t.getCenter(fN)),n=e.length();return{normal:e.normalize(),depth:n}}return!1}rayIntersect(t){if(0===t.direction.length())return;const e=[];let n,i,r=1e100;this.getRayTriangles(t,e);for(let s=0;s<e.length;s++){const o=t.intersectTriangle(e[s].a,e[s].b,e[s].c,!0,fN);if(o){const a=o.sub(t.origin).length();r>a&&(i=o.clone().add(t.origin),r=a,n=e[s])}}return r<1e100&&{distance:r,triangle:n,position:i}}fromGraphNode(t){return t.updateWorldMatrix(!0,!0),t.traverse((t=>{if(!0===t.isMesh){let e,n=!1;null!==t.geometry.index?(n=!0,e=t.geometry.toNonIndexed()):e=t.geometry;const i=e.getAttribute(\\\\\\\"position\\\\\\\");for(let e=0;e<i.count;e+=3){const n=(new Nx).fromBufferAttribute(i,e),r=(new Nx).fromBufferAttribute(i,e+1),s=(new Nx).fromBufferAttribute(i,e+2);n.applyMatrix4(t.matrixWorld),r.applyMatrix4(t.matrixWorld),s.applyMatrix4(t.matrixWorld),this.addTriangle(new Vb(n,r,s))}n&&e.dispose()}})),this.build(),this}}class AN{constructor(t){this._object=t,this._octree=new TN,this._capsule=new mN(new p.a(0,.35,0),new p.a(0,1,0),.6),this._octree.fromGraphNode(this._object)}setCapsule(t){this._capsule.copy(t)}testPosition(t){return this._capsule.start.x=t.x,this._capsule.start.z=t.z,this._capsule.end.x=t.x,this._capsule.end.z=t.z,this._octree.capsuleIntersect(this._capsule)}}class EN extends $.a{setCheckCollisions(t){if(t){let e;t.traverse((t=>{if(!e){const n=t;n.geometry&&(e=n)}})),e?this._playerCollisionController=new AN(e):console.error(\\\\\\\"no geo found in\\\\\\\",t)}else this._playerCollisionController=void 0}setCollisionCapsule(t){var e;null===(e=this._playerCollisionController)||void 0===e||e.setCapsule(t)}}const MN={type:\\\\\\\"change\\\\\\\"},SN={type:\\\\\\\"lock\\\\\\\"},CN={type:\\\\\\\"unlock\\\\\\\"},NN=Math.PI/2;class LN extends EN{constructor(t,e){super(),this.camera=t,this.domElement=e,this.isLocked=!1,this.minPolarAngle=0,this.maxPolarAngle=Math.PI,this.speed=1,this.euler=new Wv.a(0,0,0,\\\\\\\"YXZ\\\\\\\"),this.vec=new p.a,this.boundMethods={onMouseMove:this.onMouseMove.bind(this),onPointerlockChange:this.onPointerlockChange.bind(this),onPointerlockError:this.onPointerlockError.bind(this)},this._cameraTmp=new tf.a,this.velocity=new p.a,this.direction=new p.a,this._moveForward=!1,this._moveBackward=!1,this._moveLeft=!1,this._moveRight=!1,this.prevTime=0,this.connect()}onMouseMove(t){if(!1!==this.isLocked){var e=t.movementX||t.mozMovementX||t.webkitMovementX||0,n=t.movementY||t.mozMovementY||t.webkitMovementY||0;this.euler.setFromQuaternion(this.camera.quaternion),this.euler.y-=.002*e,this.euler.x-=.002*n,this.euler.x=Math.max(NN-this.maxPolarAngle,Math.min(NN-this.minPolarAngle,this.euler.x)),this.camera.quaternion.setFromEuler(this.euler),this.dispatchEvent(MN)}}onPointerlockChange(){this.velocity.set(0,0,0),this.domElement.ownerDocument.pointerLockElement===this.domElement?(this.dispatchEvent(SN),this.isLocked=!0):(this.dispatchEvent(CN),this.isLocked=!1)}onPointerlockError(){console.error(\\\\\\\"THREE.PointerLockControls: Unable to use Pointer Lock API (Note that you need to wait for 2 seconds to lock the pointer after having just unlocked it)\\\\\\\")}connect(){this.domElement.ownerDocument.addEventListener(\\\\\\\"mousemove\\\\\\\",this.boundMethods.onMouseMove),this.domElement.ownerDocument.addEventListener(\\\\\\\"pointerlockchange\\\\\\\",this.boundMethods.onPointerlockChange),this.domElement.ownerDocument.addEventListener(\\\\\\\"pointerlockerror\\\\\\\",this.boundMethods.onPointerlockError)}disconnect(){this.domElement.ownerDocument.removeEventListener(\\\\\\\"mousemove\\\\\\\",this.boundMethods.onMouseMove),this.domElement.ownerDocument.removeEventListener(\\\\\\\"pointerlockchange\\\\\\\",this.boundMethods.onPointerlockChange),this.domElement.ownerDocument.removeEventListener(\\\\\\\"pointerlockerror\\\\\\\",this.boundMethods.onPointerlockError)}dispose(){this.disconnect()}getObject(){return this.camera}moveForward(t,e){this.vec.setFromMatrixColumn(t.matrix,0),this.vec.crossVectors(t.up,this.vec),t.position.addScaledVector(this.vec,e)}moveRight(t,e){this.vec.setFromMatrixColumn(t.matrix,0),t.position.addScaledVector(this.vec,e)}_copyToCameraTmp(){this._cameraTmp.position.copy(this.camera.position),this._cameraTmp.matrix.copy(this.camera.matrix),this._cameraTmp.up.copy(this.camera.up)}lock(){this.domElement.requestPointerLock()}unlock(){this.domElement.ownerDocument.exitPointerLock()}setMoveForward(t){this._moveForward=t}setMoveBackward(t){this._moveBackward=t}setMoveLeft(t){this._moveLeft=t}setMoveRight(t){this._moveRight=t}update(){const t=performance.now();if(!0===this.isLocked){const e=(t-this.prevTime)/1e3;if(this.velocity.x-=10*this.velocity.x*e,this.velocity.z-=10*this.velocity.z*e,this.velocity.y-=9.8*100*e,this.direction.z=Number(this._moveForward)-Number(this._moveBackward),this.direction.x=Number(this._moveRight)-Number(this._moveLeft),this.direction.normalize(),(this._moveForward||this._moveBackward)&&(this.velocity.z-=400*this.direction.z*e*this.speed),(this._moveLeft||this._moveRight)&&(this.velocity.x-=400*this.direction.x*e*this.speed),this._playerCollisionController){this._copyToCameraTmp(),this.moveRight(this._cameraTmp,-this.velocity.x*e),this.moveForward(this._cameraTmp,-this.velocity.z*e);const t=this._playerCollisionController.testPosition(this._cameraTmp.position);t?(this._cameraTmp.position.add(t.normal.multiplyScalar(t.depth)),this.camera.position.copy(this._cameraTmp.position)):this._applyVelocity(e)}this._applyVelocity(e)}this.prevTime=t}_applyVelocity(t){this.moveRight(this.camera,-this.velocity.x*t),this.moveForward(this.camera,-this.velocity.z*t)}}async function ON(t,e){var n;if(e.pv.collideWithGeo){const i=e.pv.collidingGeo.nodeWithContext(Ki.OBJ);if(i){await i.compute();const r=await(null===(n=i.displayNodeController)||void 0===n?void 0:n.displayNode()),s=(await(null==r?void 0:r.compute())).coreContent();if(!s)return void console.error(\\\\\\\"obj node contains invalid sop\\\\\\\");const o=s.objectsWithGeo()[0];t.setCheckCollisions(o),t.setCollisionCapsule(new mN(new p.a(0,e.pv.capsuleHeightRange.x,0),new p.a(e.pv.capsuleHeightRange.y),e.pv.capsuleRadius))}}else t.setCheckCollisions()}const RN=\\\\\\\"lock\\\\\\\",PN=\\\\\\\"change\\\\\\\",IN=\\\\\\\"unlock\\\\\\\";const FN=new class extends aa{constructor(){super(...arguments),this.lock=oa.BUTTON(null,{callback:t=>{DN.PARAM_CALLBACK_lock_controls(t)}}),this.minPolarAngle=oa.FLOAT(0,{range:[0,Math.PI],rangeLocked:[!0,!0]}),this.maxPolarAngle=oa.FLOAT(\\\\\\\"$PI\\\\\\\",{range:[0,Math.PI],rangeLocked:[!0,!0]}),this.speed=oa.FLOAT(1),this.collideWithGeo=oa.BOOLEAN(0),this.collidingGeo=oa.NODE_PATH(\\\\\\\"\\\\\\\",{nodeSelection:{context:Ki.OBJ,types:[Ng.GEO]},visibleIf:{collideWithGeo:!0}}),this.recomputeCollidingGeo=oa.BUTTON(null,{callback:t=>{DN.PARAM_CALLBACK_recomputeCollidingGeo(t)},visibleIf:{collideWithGeo:!0}}),this.capsuleHeightRange=oa.VECTOR2([.3,1],{visibleIf:{collideWithGeo:!0}}),this.capsuleRadius=oa.FLOAT(.3,{visibleIf:{collideWithGeo:!0}})}};class DN extends jv{constructor(){super(...arguments),this.paramsConfig=FN,this._controls_by_element_id=new Map}static type(){return rr.FIRST_PERSON}endEventName(){return\\\\\\\"unlock\\\\\\\"}initializeNode(){this.io.inputs.setNamedInputConnectionPoints([new Jo(RN,$o.BASE,this.lockControls.bind(this))]),this.io.outputs.setNamedOutputConnectionPoints([new Jo(RN,$o.BASE),new Jo(PN,$o.BASE),new Jo(IN,$o.BASE)])}async create_controls_instance(t,e){const n=new LN(t,e);return this._controls_by_element_id.set(e.id,n),this._bind_listeners_to_controls_instance(n),n}_bind_listeners_to_controls_instance(t){t.addEventListener(RN,(()=>{this._createKeysEvents(t),this.dispatchEventToOutput(RN,{})})),t.addEventListener(PN,(()=>{this.dispatchEventToOutput(PN,{})})),t.addEventListener(IN,(()=>{this._removeKeysEvents(),this.dispatchEventToOutput(IN,{})}))}update_required(){return!0}setup_controls(t){t.minPolarAngle=this.pv.minPolarAngle,t.maxPolarAngle=this.pv.maxPolarAngle,t.speed=this.pv.speed,this._setupCollisionGeo(t)}async _setupCollisionGeo(t){ON(t,this)}dispose_controls_for_html_element_id(t){this._controls_by_element_id.get(t)&&this._controls_by_element_id.delete(t)}static PARAM_CALLBACK_recomputeCollidingGeo(t){t._recomputeCollidingGeo()}_recomputeCollidingGeo(){this._controls_by_element_id.forEach(((t,e)=>{this._setupCollisionGeo(t)}))}lockControls(){let t;this._controls_by_element_id.forEach(((e,n)=>{t=t||e})),t&&t.lock()}static PARAM_CALLBACK_lock_controls(t){t.lockControls()}_onKeyDown(t,e){switch(t.code){case\\\\\\\"ArrowUp\\\\\\\":case\\\\\\\"KeyW\\\\\\\":e.setMoveForward(!0);break;case\\\\\\\"ArrowLeft\\\\\\\":case\\\\\\\"KeyA\\\\\\\":e.setMoveLeft(!0);break;case\\\\\\\"ArrowDown\\\\\\\":case\\\\\\\"KeyS\\\\\\\":e.setMoveBackward(!0);break;case\\\\\\\"ArrowRight\\\\\\\":case\\\\\\\"KeyD\\\\\\\":e.setMoveRight(!0)}}_onKeyUp(t,e){switch(t.code){case\\\\\\\"ArrowUp\\\\\\\":case\\\\\\\"KeyW\\\\\\\":e.setMoveForward(!1);break;case\\\\\\\"ArrowLeft\\\\\\\":case\\\\\\\"KeyA\\\\\\\":e.setMoveLeft(!1);break;case\\\\\\\"ArrowDown\\\\\\\":case\\\\\\\"KeyS\\\\\\\":e.setMoveBackward(!1);break;case\\\\\\\"ArrowRight\\\\\\\":case\\\\\\\"KeyD\\\\\\\":e.setMoveRight(!1)}}_createKeysEvents(t){this._onKeyDownBound=e=>{this._onKeyDown(e,t)},this._onKeyUpBound=e=>{this._onKeyUp(e,t)},document.addEventListener(\\\\\\\"keydown\\\\\\\",this._onKeyDownBound),document.addEventListener(\\\\\\\"keyup\\\\\\\",this._onKeyUpBound)}_removeKeysEvents(){this._onKeyDownBound&&this._onKeyUpBound&&(document.removeEventListener(\\\\\\\"keydown\\\\\\\",this._onKeyDownBound),document.removeEventListener(\\\\\\\"keyup\\\\\\\",this._onKeyUpBound))}}var kN,BN;!function(t){t.TRIGGER=\\\\\\\"trigger\\\\\\\",t.RESET=\\\\\\\"reset\\\\\\\"}(kN||(kN={})),function(t){t.OUT=\\\\\\\"out\\\\\\\",t.LAST=\\\\\\\"last\\\\\\\"}(BN||(BN={}));const zN=new class extends aa{constructor(){super(...arguments),this.maxCount=oa.INTEGER(5,{range:[0,10],rangeLocked:[!0,!1]}),this.reset=oa.BUTTON(null,{callback:t=>{UN.PARAM_CALLBACK_reset(t)}})}};class UN extends Ba{constructor(){super(...arguments),this.paramsConfig=zN,this._process_count=0,this._last_dispatched=!1}static type(){return\\\\\\\"limit\\\\\\\"}initializeNode(){this.io.inputs.setNamedInputConnectionPoints([new Jo(kN.TRIGGER,$o.BASE,this.process_event_trigger.bind(this)),new Jo(kN.RESET,$o.BASE,this.process_event_reset.bind(this))]),this.io.outputs.setNamedOutputConnectionPoints([new Jo(BN.OUT,$o.BASE),new Jo(BN.LAST,$o.BASE)])}processEvent(t){}process_event_trigger(t){this._process_count<this.pv.maxCount?(this._process_count+=1,this.dispatchEventToOutput(BN.OUT,t)):this._last_dispatched||(this._last_dispatched=!0,this.dispatchEventToOutput(BN.LAST,t))}process_event_reset(t){this._process_count=0,this._last_dispatched=!1}static PARAM_CALLBACK_reset(t){t.process_event_reset({})}}const GN=new class extends aa{constructor(){super(...arguments),this.alert=oa.BOOLEAN(0),this.console=oa.BOOLEAN(1)}};class VN extends Ba{constructor(){super(...arguments),this.paramsConfig=GN}static type(){return\\\\\\\"message\\\\\\\"}initializeNode(){this.io.inputs.setNamedInputConnectionPoints([new Jo(\\\\\\\"trigger\\\\\\\",$o.BASE,this._process_trigger_event.bind(this))]),this.io.outputs.setNamedOutputConnectionPoints([new Jo(VN.OUTPUT,$o.BASE)])}trigger_output(t){this.dispatchEventToOutput(VN.OUTPUT,t)}_process_trigger_event(t){this.pv.alert&&alert(t),this.pv.console&&console.log(this.path(),Date.now(),t),this.trigger_output(t)}}VN.OUTPUT=\\\\\\\"output\\\\\\\";const HN=t=>(t.preventDefault(),!1);class jN{static disableContextMenu(){document.addEventListener(\\\\\\\"contextmenu\\\\\\\",HN)}static reEstablishContextMenu(){document.removeEventListener(\\\\\\\"contextmenu\\\\\\\",HN)}}const WN={rotationSpeed:1,rotationRange:{min:.25*-Math.PI,max:.25*Math.PI},translationSpeed:.1},qN={type:\\\\\\\"change\\\\\\\"};class XN extends EN{constructor(t,e){super(),this._camera=t,this.domElement=e,this.translationData={direction:{x:0,y:0}},this.rotationData={direction:{x:0,y:0}},this._boundMethods={onRotateStart:this._onRotateStart.bind(this),onRotateMove:this._onRotateMove.bind(this),onRotateEnd:this._onRotateEnd.bind(this),onTranslateStart:this._onTranslateStart.bind(this),onTranslateMove:this._onTranslateMove.bind(this),onTranslateEnd:this._onTranslateEnd.bind(this)},this._startCameraRotation=new Wv.a,this._velocity=new p.a,this._rotationSpeed=WN.rotationSpeed,this._rotationRange={min:WN.rotationRange.min,max:WN.rotationRange.max},this._translationSpeed=WN.translationSpeed,this._translateDomElement=this._createTranslateDomElement(),this._translateDomElementRect=this._translateDomElement.getBoundingClientRect(),this.vLeft=new p.a,this.vRight=new p.a,this.vTop=new p.a,this.vBottom=new p.a,this.angleY=0,this.angleX=0,this._rotationStartPosition=new d.a,this._rotationMovePosition=new d.a,this._rotationDelta=new d.a,this._startCameraPosition=new p.a,this._translationStartPosition=new d.a,this._translationMovePosition=new d.a,this._translationDelta=new d.a,this.prevTime=performance.now(),this._camTmpPost=new p.a,this._camWorldDir=new p.a,this._up=new p.a(0,1,0),this._camSideVector=new p.a,this._camera.rotation.order=\\\\\\\"ZYX\\\\\\\",this._addEvents()}dispose(){this._removeEvents()}_createTranslateDomElement(){const t=document.createElement(\\\\\\\"div\\\\\\\"),e=this.domElement.getBoundingClientRect(),n=Math.min(e.width,e.height),i=Math.round(.4*n),r=Math.round(.1*n);return t.style.width=`${i}px`,t.style.height=t.style.width,t.style.border=\\\\\\\"1px solid black\\\\\\\",t.style.borderRadius=`${i}px`,t.style.position=\\\\\\\"absolute\\\\\\\",t.style.bottom=`${r}px`,t.style.left=`${r}px`,t}_addEvents(){var t;jN.disableContextMenu(),this.domElement.addEventListener(\\\\\\\"touchstart\\\\\\\",this._boundMethods.onRotateStart),this.domElement.addEventListener(\\\\\\\"touchmove\\\\\\\",this._boundMethods.onRotateMove),this.domElement.addEventListener(\\\\\\\"touchend\\\\\\\",this._boundMethods.onRotateEnd),this._translateDomElement.addEventListener(\\\\\\\"touchstart\\\\\\\",this._boundMethods.onTranslateStart),this._translateDomElement.addEventListener(\\\\\\\"touchmove\\\\\\\",this._boundMethods.onTranslateMove),this._translateDomElement.addEventListener(\\\\\\\"touchend\\\\\\\",this._boundMethods.onTranslateEnd),null===(t=this.domElement.parentElement)||void 0===t||t.append(this._translateDomElement)}_removeEvents(){var t;jN.reEstablishContextMenu(),this.domElement.removeEventListener(\\\\\\\"touchstart\\\\\\\",this._boundMethods.onRotateStart),this.domElement.removeEventListener(\\\\\\\"touchmove\\\\\\\",this._boundMethods.onRotateMove),this.domElement.removeEventListener(\\\\\\\"touchend\\\\\\\",this._boundMethods.onRotateEnd),this._translateDomElement.removeEventListener(\\\\\\\"touchstart\\\\\\\",this._boundMethods.onTranslateStart),this._translateDomElement.removeEventListener(\\\\\\\"touchmove\\\\\\\",this._boundMethods.onTranslateMove),this._translateDomElement.removeEventListener(\\\\\\\"touchend\\\\\\\",this._boundMethods.onTranslateEnd),null===(t=this.domElement.parentElement)||void 0===t||t.removeChild(this._translateDomElement)}setRotationSpeed(t){this._rotationSpeed=t}setRotationRange(t){this._rotationRange.min=t.min,this._rotationRange.max=t.max}setTranslationSpeed(t){this._translationSpeed=t}_onRotateStart(t){this._startCameraRotation.copy(this._camera.rotation);const e=this._getTouch(t,this.domElement);e&&(this._rotationStartPosition.set(e.clientX,e.clientY),this.vLeft.set(-1,0,.5),this.vRight.set(1,0,.5),[this.vLeft,this.vRight].forEach((t=>{t.unproject(this._camera),this._camera.worldToLocal(t)})),this.angleY=this.vLeft.angleTo(this.vRight),this.vTop.set(0,1,.5),this.vBottom.set(0,-1,.5),[this.vTop,this.vBottom].forEach((t=>{t.unproject(this._camera),this._camera.worldToLocal(t)})),this.angleX=this.vTop.angleTo(this.vBottom))}_onRotateMove(t){const e=this._getTouch(t,this.domElement);e&&(this._rotationMovePosition.set(e.clientX,e.clientY),this._rotationDelta.copy(this._rotationMovePosition).sub(this._rotationStartPosition),this.rotationData.direction.x=this._rotationDelta.x/this.domElement.clientWidth,this.rotationData.direction.y=this._rotationDelta.y/this.domElement.clientHeight,this._rotateCamera(this.rotationData))}_onRotateEnd(){this.rotationData.direction.x=0,this.rotationData.direction.y=0}_rotateCamera(t){let e=this.angleY*t.direction.x*this._rotationSpeed;this._camera.rotation.y=this._startCameraRotation.y+-e;let n=this.angleX*t.direction.y*this._rotationSpeed;this._camera.rotation.x=rs.clamp(this._startCameraRotation.x+-n,this._rotationRange.min,this._rotationRange.max),this.dispatchEvent(qN)}_onTranslateStart(t){this._startCameraPosition.copy(this._camera.position);const e=this._getTouch(t,this._translateDomElement);e&&(this._translationStartPosition.set(e.clientX,e.clientY),this._translateDomElementRect=this._translateDomElement.getBoundingClientRect())}_onTranslateMove(t){const e=this._getTouch(t,this._translateDomElement);e&&(this._translationMovePosition.set(e.clientX,e.clientY),this._translationDelta.copy(this._translationMovePosition).sub(this._translationStartPosition),this.translationData.direction.x=this._translationSpeed*this._translationDelta.x/this._translateDomElementRect.width,this.translationData.direction.y=this._translationSpeed*-this._translationDelta.y/this._translateDomElementRect.height,this.dispatchEvent(qN))}_onTranslateEnd(){this.translationData.direction.x=0,this.translationData.direction.y=0}update(){const t=performance.now(),e=t-this.prevTime;this.prevTime=t,this._translateCamera(this.translationData,e)}_translateCamera(t,e){this._camera.getWorldDirection(this._camWorldDir),this._camWorldDir.y=0,this._camWorldDir.normalize(),this._camSideVector.crossVectors(this._up,this._camWorldDir),this._camSideVector.normalize(),this._camSideVector.multiplyScalar(-t.direction.x),this._camWorldDir.multiplyScalar(t.direction.y),this._velocity.copy(this._camWorldDir),this._velocity.add(this._camSideVector);const n=this._camera.position.y;if(this._camTmpPost.copy(this._camera.position),this._playerCollisionController){const t=1;this._velocity.addScaledVector(this._velocity,t);const n=this._velocity.clone().multiplyScalar(e);this._camTmpPost.add(n);const i=this._playerCollisionController.testPosition(this._camTmpPost);i&&this._camTmpPost.add(i.normal.multiplyScalar(i.depth)),this._camera.position.copy(this._camTmpPost)}else this._camTmpPost.add(this._camSideVector),this._camTmpPost.add(this._camWorldDir),this._camera.position.copy(this._camTmpPost);this._camera.position.y=n}_getTouch(t,e){for(let n=0;n<t.touches.length;n++){const i=t.touches[n];if(i.target===e)return i}}}const YN=\\\\\\\"start\\\\\\\",$N=\\\\\\\"change\\\\\\\",JN=\\\\\\\"end\\\\\\\";const ZN=new class extends aa{constructor(){super(...arguments),this.minPolarAngle=oa.FLOAT(0,{range:[0,Math.PI],rangeLocked:[!0,!0]}),this.maxPolarAngle=oa.FLOAT(\\\\\\\"$PI\\\\\\\",{range:[0,Math.PI],rangeLocked:[!0,!0]}),this.rotationSpeed=oa.FLOAT(WN.rotationSpeed),this.translationSpeed=oa.FLOAT(WN.translationSpeed),this.collideWithGeo=oa.BOOLEAN(0),this.collidingGeo=oa.NODE_PATH(\\\\\\\"\\\\\\\",{nodeSelection:{context:Ki.OBJ,types:[Ng.GEO]},visibleIf:{collideWithGeo:!0}}),this.recomputeCollidingGeo=oa.BUTTON(null,{callback:t=>{QN.PARAM_CALLBACK_recomputeCollidingGeo(t)},visibleIf:{collideWithGeo:!0}}),this.capsuleHeightRange=oa.VECTOR2([.3,1],{visibleIf:{collideWithGeo:!0}}),this.capsuleRadius=oa.FLOAT(.3,{visibleIf:{collideWithGeo:!0}})}};class QN extends jv{constructor(){super(...arguments),this.paramsConfig=ZN,this._controls_by_element_id=new Map}static type(){return rr.MOBILE_JOYSTICK}endEventName(){return\\\\\\\"end\\\\\\\"}initializeNode(){this.io.outputs.setNamedOutputConnectionPoints([new Jo(YN,$o.BASE),new Jo($N,$o.BASE),new Jo(JN,$o.BASE)])}async create_controls_instance(t,e){const n=new XN(t,e);return this._controls_by_element_id.set(e.id,n),this._bind_listeners_to_controls_instance(n),n}_bind_listeners_to_controls_instance(t){t.addEventListener(YN,(()=>{this.dispatchEventToOutput(YN,{})})),t.addEventListener($N,(()=>{this.dispatchEventToOutput($N,{})})),t.addEventListener(JN,(()=>{this.dispatchEventToOutput(JN,{})}))}update_required(){return!0}setup_controls(t){t.setRotationSpeed(this.pv.rotationSpeed),t.setRotationRange({min:this.pv.minPolarAngle,max:this.pv.maxPolarAngle}),t.setTranslationSpeed(this.pv.translationSpeed),this._setupCollisionGeo(t)}async _setupCollisionGeo(t){ON(t,this)}dispose_controls_for_html_element_id(t){this._controls_by_element_id.get(t)&&this._controls_by_element_id.delete(t)}static PARAM_CALLBACK_recomputeCollidingGeo(t){t._recomputeCollidingGeo()}_recomputeCollidingGeo(){this._controls_by_element_id.forEach(((t,e)=>{this._setupCollisionGeo(t)}))}}var KN;!function(t){t.ALL_TOGETHER=\\\\\\\"all together\\\\\\\",t.BATCH=\\\\\\\"batch\\\\\\\"}(KN||(KN={}));const tL=[KN.ALL_TOGETHER,KN.BATCH];const eL=new class extends aa{constructor(){super(...arguments),this.mask=oa.STRING(\\\\\\\"/geo*\\\\\\\",{callback:t=>{nL.PARAM_CALLBACK_update_resolved_nodes(t)}}),this.force=oa.BOOLEAN(0),this.cookMode=oa.INTEGER(tL.indexOf(KN.ALL_TOGETHER),{menu:{entries:tL.map(((t,e)=>({name:t,value:e})))}}),this.batchSize=oa.INTEGER(1,{visibleIf:{cookMode:tL.indexOf(KN.BATCH)},separatorAfter:!0}),this.updateResolve=oa.BUTTON(null,{callback:(t,e)=>{nL.PARAM_CALLBACK_update_resolve(t)}}),this.printResolve=oa.BUTTON(null,{callback:(t,e)=>{nL.PARAM_CALLBACK_print_resolve(t)}})}};class nL extends Ba{constructor(){super(...arguments),this.paramsConfig=eL,this._resolved_nodes=[],this._dispatched_first_node_cooked=!1,this._dispatched_all_nodes_cooked=!1,this._cook_state_by_node_id=new Map}static type(){return\\\\\\\"nodeCook\\\\\\\"}initializeNode(){this.io.inputs.setNamedInputConnectionPoints([new Jo(nL.INPUT_TRIGGER,$o.BASE,this.process_event_trigger.bind(this))]),this.io.outputs.setNamedOutputConnectionPoints([new Jo(nL.OUTPUT_FIRST_NODE,$o.BASE),new Jo(nL.OUTPUT_EACH_NODE,$o.BASE),new Jo(nL.OUTPUT_ALL_NODES,$o.BASE)])}trigger(){this.process_event_trigger({})}cook(){this._update_resolved_nodes(),this.cookController.endCook()}dispose(){super.dispose(),this._reset()}process_event_trigger(t){this._cook_nodes_with_mode()}_cook_nodes_with_mode(){this._update_resolved_nodes();const t=tL[this.pv.cookMode];switch(t){case KN.ALL_TOGETHER:return this._cook_nodes_all_together();case KN.BATCH:return this._cook_nodes_batch()}ar.unreachable(t)}_cook_nodes_all_together(){this._cook_nodes(this._resolved_nodes)}async _cook_nodes_batch(){const t=this.pv.batchSize,e=Math.ceil(this._resolved_nodes.length/t);for(let n=0;n<e;n++){const e=n*t,i=(n+1)*t,r=this._resolved_nodes.slice(e,i);await this._cook_nodes(r)}}async _cook_nodes(t){const e=[];for(let n of t)e.push(this._cook_node(n));return await Promise.all(e)}_cook_node(t){return this.pv.force&&t.setDirty(this),t.compute()}static PARAM_CALLBACK_update_resolved_nodes(t){t._update_resolved_nodes()}_update_resolved_nodes(){this._reset(),this._resolved_nodes=this.scene().nodesController.nodesFromMask(this.pv.mask||\\\\\\\"\\\\\\\");for(let t of this._resolved_nodes)t.cookController.registerOnCookEnd(this._callbackNameForNode(t),(()=>{this._on_node_cook_complete(t)})),this._cook_state_by_node_id.set(t.graphNodeId(),!1)}_callbackNameForNode(t){return`owner-${this.graphNodeId()}-target-${t.graphNodeId()}`}_reset(){this._dispatched_first_node_cooked=!1,this._cook_state_by_node_id.clear();for(let t of this._resolved_nodes)t.cookController.deregisterOnCookEnd(this._callbackNameForNode(t));this._resolved_nodes=[]}_all_nodes_have_cooked(){for(let t of this._resolved_nodes){if(!this._cook_state_by_node_id.get(t.graphNodeId()))return!1}return!0}_on_node_cook_complete(t){const e={value:{node:t}};this._dispatched_first_node_cooked||(this._dispatched_first_node_cooked=!0,this.dispatchEventToOutput(nL.OUTPUT_FIRST_NODE,e)),this._cook_state_by_node_id.get(t.graphNodeId())||this.dispatchEventToOutput(nL.OUTPUT_EACH_NODE,e),this._cook_state_by_node_id.set(t.graphNodeId(),!0),this._dispatched_all_nodes_cooked||this._all_nodes_have_cooked()&&(this._dispatched_all_nodes_cooked=!0,this.dispatchEventToOutput(nL.OUTPUT_ALL_NODES,{}))}static PARAM_CALLBACK_update_resolve(t){t._update_resolved_nodes()}static PARAM_CALLBACK_print_resolve(t){t.print_resolve()}print_resolve(){console.log(this._resolved_nodes)}}var iL,rL;nL.INPUT_TRIGGER=\\\\\\\"trigger\\\\\\\",nL.OUTPUT_FIRST_NODE=\\\\\\\"first\\\\\\\",nL.OUTPUT_EACH_NODE=\\\\\\\"each\\\\\\\",nL.OUTPUT_ALL_NODES=\\\\\\\"all\\\\\\\",function(t){t.TRIGGER=\\\\\\\"trigger\\\\\\\"}(iL||(iL={})),function(t){t.OUT=\\\\\\\"out\\\\\\\"}(rL||(rL={}));const sL=new class extends aa{};class oL extends Ba{constructor(){super(...arguments),this.paramsConfig=sL}static type(){return\\\\\\\"null\\\\\\\"}initializeNode(){this.io.inputs.setNamedInputConnectionPoints([new Jo(iL.TRIGGER,$o.BASE,this.process_event_trigger.bind(this))]),this.io.outputs.setNamedOutputConnectionPoints([new Jo(rL.OUT,$o.BASE)])}processEvent(t){}process_event_trigger(t){this.dispatchEventToOutput(rL.OUT,t)}}var aL,lL=n(39),cL=n(36);class uL{constructor(t,e,n=0,i=1/0){this.ray=new lL.a(t,e),this.near=n,this.far=i,this.camera=null,this.layers=new cL.a,this.params={Mesh:{},Line:{threshold:1},LOD:{},Points:{threshold:1},Sprite:{}}}set(t,e){this.ray.set(t,e)}setFromCamera(t,e){e&&e.isPerspectiveCamera?(this.ray.origin.setFromMatrixPosition(e.matrixWorld),this.ray.direction.set(t.x,t.y,.5).unproject(e).sub(this.ray.origin).normalize(),this.camera=e):e&&e.isOrthographicCamera?(this.ray.origin.set(t.x,t.y,(e.near+e.far)/(e.near-e.far)).unproject(e),this.ray.direction.set(0,0,-1).transformDirection(e.matrixWorld),this.camera=e):console.error(\\\\\\\"THREE.Raycaster: Unsupported camera type: \\\\\\\"+e.type)}intersectObject(t,e=!0,n=[]){return dL(t,this,n,e),n.sort(hL),n}intersectObjects(t,e=!0,n=[]){for(let i=0,r=t.length;i<r;i++)dL(t[i],this,n,e);return n.sort(hL),n}}function hL(t,e){return t.distance-e.distance}function dL(t,e,n,i){if(t.layers.test(e.layers)&&t.raycast(e,n),!0===i){const i=t.children;for(let t=0,r=i.length;t<r;t++)dL(i[t],e,n,!0)}}!function(t){t.GEOMETRY=\\\\\\\"geometry\\\\\\\",t.PLANE=\\\\\\\"plane\\\\\\\"}(aL||(aL={}));aL.GEOMETRY,aL.PLANE;class pL{constructor(t){this._node=t,this._set_pos_timestamp=-1,this._hit_velocity=new p.a(0,0,0),this._hit_velocity_array=[0,0,0]}process(t){if(!this._node.pv.tvelocity)return;if(!this._prev_position)return this._prev_position=this._prev_position||new p.a,void this._prev_position.copy(t);const e=ai.performance.performanceManager().now(),n=e-this._set_pos_timestamp;if(this._set_pos_timestamp=e,this._hit_velocity.copy(t).sub(this._prev_position).divideScalar(n).multiplyScalar(1e3),this._hit_velocity.toArray(this._hit_velocity_array),this._node.pv.tvelocityTarget){if(ai.playerMode())this._found_velocity_target_param=this._found_velocity_target_param||this._node.pv.velocityTarget.paramWithType(Es.VECTOR3);else{const t=this._node.pv.velocityTarget;this._found_velocity_target_param=t.paramWithType(Es.VECTOR3)}this._found_velocity_target_param&&this._found_velocity_target_param.set(this._hit_velocity_array)}else this._node.p.velocity.set(this._hit_velocity_array);this._prev_position.copy(t)}reset(){this._prev_position=void 0}}var _L;!function(t){t.GEOMETRY=\\\\\\\"geometry\\\\\\\",t.PLANE=\\\\\\\"plane\\\\\\\"}(_L||(_L={}));const mL=[_L.GEOMETRY,_L.PLANE];function fL(t,e,n){var i=e.getBoundingClientRect();n.offsetX=t.pageX-i.left,n.offsetY=t.pageY-i.top}class gL{constructor(t){this._node=t,this._offset={offsetX:0,offsetY:0},this._mouse=new d.a,this._mouse_array=[0,0],this._raycaster=new uL,this._plane=new X.a,this._plane_intersect_target=new p.a,this._intersections=[],this._hit_position_array=[0,0,0],this.velocity_controller=new pL(this._node)}updateMouse(t){var e;const n=null===(e=t.viewer)||void 0===e?void 0:e.canvas(),i=t.cameraNode;if(!n||!i)return;const r=t.event;if((r instanceof MouseEvent||r instanceof DragEvent||r instanceof PointerEvent)&&fL(r,n,this._offset),window.TouchEvent&&r instanceof TouchEvent){fL(r.touches[0],n,this._offset)}(t=>{this._mouse.x=t.offsetX/n.offsetWidth*2-1,this._mouse.y=-t.offsetY/n.offsetHeight*2+1,this._mouse.toArray(this._mouse_array),this._node.p.mouse.set(this._mouse_array)})(this._offset),this._raycaster.setFromCamera(this._mouse,i.object)}processEvent(t){this._prepareRaycaster(t);const e=mL[this._node.pv.intersectWith];switch(e){case _L.GEOMETRY:return this._intersect_with_geometry(t);case _L.PLANE:return this._intersect_with_plane(t)}ar.unreachable(e)}_intersect_with_plane(t){this._plane.normal.copy(this._node.pv.planeDirection),this._plane.constant=this._node.pv.planeOffset,this._raycaster.ray.intersectPlane(this._plane,this._plane_intersect_target),this._set_position_param(this._plane_intersect_target),this._node.trigger_hit(t)}_intersect_with_geometry(t){if(this._resolved_targets||this.update_target(),this._resolved_targets){this._intersections.length=0;const e=this._raycaster.intersectObjects(this._resolved_targets,this._node.pv.traverseChildren,this._intersections)[0];e?(this._set_position_param(e.point),this._node.pv.geoAttribute&&this._resolve_geometry_attribute(e),t.value={intersect:e},this._node.trigger_hit(t)):this._node.trigger_miss(t)}}_resolve_geometry_attribute(t){const e=kr[this._node.pv.geoAttributeType],n=gL.resolve_geometry_attribute(t,this._node.pv.geoAttributeName,e);if(null!=n){switch(e){case Dr.NUMERIC:return void this._node.p.geoAttributeValue1.set(n);case Dr.STRING:return void(m.isString(n)&&this._node.p.geoAttributeValues.set(n))}ar.unreachable(e)}}static resolve_geometry_attribute(t,e,n){switch(Nr(t.object.constructor)){case Sr.MESH:return this.resolve_geometry_attribute_for_mesh(t,e,n);case Sr.POINTS:return this.resolve_geometry_attribute_for_point(t,e,n)}}static resolve_geometry_attribute_for_mesh(t,e,n){const i=t.object.geometry;if(i){const r=i.getAttribute(e);if(r){switch(n){case Dr.NUMERIC:{const e=i.getAttribute(\\\\\\\"position\\\\\\\");return t.face?(this._vA.fromBufferAttribute(e,t.face.a),this._vB.fromBufferAttribute(e,t.face.b),this._vC.fromBufferAttribute(e,t.face.c),this._uvA.fromBufferAttribute(r,t.face.a),this._uvB.fromBufferAttribute(r,t.face.b),this._uvC.fromBufferAttribute(r,t.face.c),t.uv=Qr.a.getUV(t.point,this._vA,this._vB,this._vC,this._uvA,this._uvB,this._uvC,this._hitUV),this._hitUV.x):void 0}case Dr.STRING:{const t=new ps(i).points()[0];return t?t.stringAttribValue(e):void 0}}ar.unreachable(n)}}}static resolve_geometry_attribute_for_point(t,e,n){const i=t.object.geometry;if(i&&null!=t.index){switch(n){case Dr.NUMERIC:{const n=i.getAttribute(e);return n?n.array[t.index]:void 0}case Dr.STRING:{const n=new ps(i).points()[t.index];return n?n.stringAttribValue(e):void 0}}ar.unreachable(n)}}_set_position_param(t){if(t.toArray(this._hit_position_array),this._node.pv.tpositionTarget){if(ai.playerMode())this._found_position_target_param=this._found_position_target_param||this._node.pv.positionTarget.paramWithType(Es.VECTOR3);else{const t=this._node.pv.positionTarget;this._found_position_target_param=t.paramWithType(Es.VECTOR3)}this._found_position_target_param&&this._found_position_target_param.set(this._hit_position_array)}else this._node.p.position.set(this._hit_position_array);this.velocity_controller.process(t)}_prepareRaycaster(t){const e=this._raycaster.params.Points;e&&(e.threshold=this._node.pv.pointsThreshold);let n=t.cameraNode;if(this._node.pv.overrideCamera)if(this._node.pv.overrideRay)this._raycaster.ray.origin.copy(this._node.pv.rayOrigin),this._raycaster.ray.direction.copy(this._node.pv.rayDirection);else{const t=this._node.p.camera.found_node_with_context(Ki.OBJ);t&&(n=t)}n&&!this._node.pv.overrideRay&&n.prepareRaycaster(this._mouse,this._raycaster)}update_target(){const t=SL[this._node.pv.targetType];switch(t){case ML.NODE:return this._update_target_from_node();case ML.SCENE_GRAPH:return this._update_target_from_scene_graph()}ar.unreachable(t)}_update_target_from_node(){const t=this._node.p.targetNode.value.nodeWithContext(Ki.OBJ);if(t){const e=this._node.pv.traverseChildren?t.object:t.childrenDisplayController.sopGroup();this._resolved_targets=e?[e]:void 0}else this._node.states.error.set(\\\\\\\"node is not an object\\\\\\\")}_update_target_from_scene_graph(){const t=this._node.scene().objectsByMask(this._node.pv.objectMask);t.length>0?this._resolved_targets=t:this._resolved_targets=void 0}async update_position_target(){this._node.p.positionTarget.isDirty()&&await this._node.p.positionTarget.compute()}static PARAM_CALLBACK_update_target(t){t.cpuController.update_target()}static PARAM_CALLBACK_print_resolve(t){t.cpuController.print_resolve()}print_resolve(){this.update_target(),console.log(this._resolved_targets)}}gL._vA=new p.a,gL._vB=new p.a,gL._vC=new p.a,gL._uvA=new d.a,gL._uvB=new d.a,gL._uvC=new d.a,gL._hitUV=new d.a;class vL{constructor(t){this._node=t,this._resolved_material=null,this._restore_context={scene:{overrideMaterial:null},renderer:{toneMapping:-1,outputEncoding:-1}},this._mouse=new d.a,this._mouse_array=[0,0],this._read=new Float32Array(4),this._param_read=[0,0,0,0]}updateMouse(t){var e;const n=null===(e=t.viewer)||void 0===e?void 0:e.canvas();n&&t.event&&(t.event instanceof MouseEvent||t.event instanceof DragEvent||t.event instanceof PointerEvent?(this._mouse.x=t.event.offsetX/n.offsetWidth,this._mouse.y=1-t.event.offsetY/n.offsetHeight,this._mouse.toArray(this._mouse_array),this._node.p.mouse.set(this._mouse_array)):console.warn(\\\\\\\"event type not implemented\\\\\\\"))}processEvent(t){var e;const n=null===(e=t.viewer)||void 0===e?void 0:e.canvas();if(!n||!t.cameraNode)return;const i=t.cameraNode,r=i.renderController;if(r){if(this._render_target=this._render_target||new Z(n.offsetWidth,n.offsetHeight,{minFilter:w.V,magFilter:w.ob,format:w.Ib,type:w.G}),!this._resolved_material)return this.update_material(),void console.warn(\\\\\\\"no material found\\\\\\\");const e=i,s=r.resolved_scene||i.scene().threejsScene(),o=r.renderer(n);this._modify_scene_and_renderer(s,o),o.setRenderTarget(this._render_target),o.clear(),o.render(s,e.object),o.setRenderTarget(null),this._restore_scene_and_renderer(s,o),o.readRenderTargetPixels(this._render_target,Math.round(this._mouse.x*n.offsetWidth),Math.round(this._mouse.y*n.offsetHeight),1,1,this._read),this._param_read[0]=this._read[0],this._param_read[1]=this._read[1],this._param_read[2]=this._read[2],this._param_read[3]=this._read[3],this._node.p.pixelValue.set(this._param_read),this._node.pv.pixelValue.x>this._node.pv.hitThreshold?this._node.trigger_hit(t):this._node.trigger_miss(t)}}_modify_scene_and_renderer(t,e){this._restore_context.scene.overrideMaterial=t.overrideMaterial,this._restore_context.renderer.outputEncoding=e.outputEncoding,this._restore_context.renderer.toneMapping=e.toneMapping,t.overrideMaterial=this._resolved_material,e.toneMapping=w.vb,e.outputEncoding=w.U}_restore_scene_and_renderer(t,e){t.overrideMaterial=this._restore_context.scene.overrideMaterial,e.outputEncoding=this._restore_context.renderer.outputEncoding,e.toneMapping=this._restore_context.renderer.toneMapping}update_material(){const t=this._node.p.material.found_node();t?t.context()==Ki.MAT?this._resolved_material=t.material:this._node.states.error.set(\\\\\\\"target is not an obj\\\\\\\"):this._node.states.error.set(\\\\\\\"no target found\\\\\\\")}static PARAM_CALLBACK_update_material(t){t.gpuController.update_material()}}const yL=1e3/60;var xL;!function(t){t.CPU=\\\\\\\"cpu\\\\\\\",t.GPU=\\\\\\\"gpu\\\\\\\"}(xL||(xL={}));const bL=[xL.CPU,xL.GPU];function wL(t={}){return t.mode=bL.indexOf(xL.CPU),{visibleIf:t}}function TL(t={}){return t.mode=bL.indexOf(xL.CPU),t.intersectWith=mL.indexOf(_L.GEOMETRY),{visibleIf:t}}function AL(t={}){return t.mode=bL.indexOf(xL.CPU),t.intersectWith=mL.indexOf(_L.PLANE),{visibleIf:t}}function EL(t={}){return t.mode=bL.indexOf(xL.GPU),{visibleIf:t}}var ML;!function(t){t.SCENE_GRAPH=\\\\\\\"scene graph\\\\\\\",t.NODE=\\\\\\\"node\\\\\\\"}(ML||(ML={}));const SL=[ML.SCENE_GRAPH,ML.NODE];const CL=new class extends aa{constructor(){super(...arguments),this.mode=oa.INTEGER(bL.indexOf(xL.CPU),{menu:{entries:bL.map(((t,e)=>({name:t,value:e})))}}),this.mouse=oa.VECTOR2([0,0],{cook:!1}),this.overrideCamera=oa.BOOLEAN(0),this.overrideRay=oa.BOOLEAN(0,{visibleIf:{mode:bL.indexOf(xL.CPU),overrideCamera:1}}),this.camera=oa.OPERATOR_PATH(\\\\\\\"/perspective_camera1\\\\\\\",{nodeSelection:{context:Ki.OBJ},dependentOnFoundNode:!1,visibleIf:{overrideCamera:1,overrideRay:0}}),this.rayOrigin=oa.VECTOR3([0,0,0],{visibleIf:{overrideCamera:1,overrideRay:1}}),this.rayDirection=oa.VECTOR3([0,0,1],{visibleIf:{overrideCamera:1,overrideRay:1}}),this.material=oa.OPERATOR_PATH(\\\\\\\"/MAT/mesh_basic_builder1\\\\\\\",{nodeSelection:{context:Ki.MAT},dependentOnFoundNode:!1,callback:(t,e)=>{vL.PARAM_CALLBACK_update_material(t)},...EL()}),this.pixelValue=oa.VECTOR4([0,0,0,0],{cook:!1,...EL()}),this.hitThreshold=oa.FLOAT(.5,{cook:!1,...EL()}),this.intersectWith=oa.INTEGER(mL.indexOf(_L.GEOMETRY),{menu:{entries:mL.map(((t,e)=>({name:t,value:e})))},...wL()}),this.pointsThreshold=oa.FLOAT(1,{range:[0,100],rangeLocked:[!0,!1],...wL()}),this.planeDirection=oa.VECTOR3([0,1,0],{...AL()}),this.planeOffset=oa.FLOAT(0,{...AL()}),this.targetType=oa.INTEGER(0,{menu:{entries:SL.map(((t,e)=>({name:t,value:e})))},...TL()}),this.targetNode=oa.NODE_PATH(\\\\\\\"\\\\\\\",{nodeSelection:{context:Ki.OBJ},dependentOnFoundNode:!1,callback:(t,e)=>{gL.PARAM_CALLBACK_update_target(t)},...TL({targetType:SL.indexOf(ML.NODE)})}),this.objectMask=oa.STRING(\\\\\\\"*geo1*\\\\\\\",{callback:(t,e)=>{gL.PARAM_CALLBACK_update_target(t)},...TL({targetType:SL.indexOf(ML.SCENE_GRAPH)})}),this.printFoundObjectsFromMask=oa.BUTTON(null,{callback:(t,e)=>{gL.PARAM_CALLBACK_print_resolve(t)},...TL({targetType:SL.indexOf(ML.SCENE_GRAPH)})}),this.traverseChildren=oa.BOOLEAN(!0,{callback:(t,e)=>{gL.PARAM_CALLBACK_update_target(t)},...TL(),separatorAfter:!0}),this.tpositionTarget=oa.BOOLEAN(0,{cook:!1,...wL()}),this.position=oa.VECTOR3([0,0,0],{cook:!1,...wL({tpositionTarget:0})}),this.positionTarget=oa.PARAM_PATH(\\\\\\\"\\\\\\\",{cook:!1,...wL({tpositionTarget:1}),paramSelection:Es.VECTOR3,computeOnDirty:!0}),this.tvelocity=oa.BOOLEAN(0,{cook:!1}),this.tvelocityTarget=oa.BOOLEAN(0,{cook:!1,...wL({tvelocity:1})}),this.velocity=oa.VECTOR3([0,0,0],{cook:!1,...wL({tvelocity:1,tvelocityTarget:0})}),this.velocityTarget=oa.PARAM_PATH(\\\\\\\"\\\\\\\",{cook:!1,...wL({tvelocity:1,tvelocityTarget:1}),paramSelection:Es.VECTOR3,computeOnDirty:!0}),this.geoAttribute=oa.BOOLEAN(0,TL()),this.geoAttributeName=oa.STRING(\\\\\\\"id\\\\\\\",{cook:!1,...TL({geoAttribute:1})}),this.geoAttributeType=oa.INTEGER(kr.indexOf(Dr.NUMERIC),{menu:{entries:Br},...TL({geoAttribute:1})}),this.geoAttributeValue1=oa.FLOAT(0,{cook:!1,...TL({geoAttribute:1,geoAttributeType:kr.indexOf(Dr.NUMERIC)})}),this.geoAttributeValues=oa.STRING(\\\\\\\"\\\\\\\",{...TL({geoAttribute:1,geoAttributeType:kr.indexOf(Dr.STRING)})})}};class NL extends Ba{constructor(){super(...arguments),this.paramsConfig=CL,this.cpuController=new gL(this),this.gpuController=new vL(this),this._last_event_processed_at=-1}static type(){return\\\\\\\"raycast\\\\\\\"}initializeNode(){this.io.inputs.setNamedInputConnectionPoints([new Jo(NL.INPUT_TRIGGER,$o.BASE,this._process_trigger_event_throttled.bind(this)),new Jo(NL.INPUT_MOUSE,$o.MOUSE,this._process_mouse_event.bind(this)),new Jo(NL.INPUT_UPDATE_OBJECTS,$o.BASE,this._process_trigger_update_objects.bind(this)),new Jo(NL.INPUT_TRIGGER_VEL_RESET,$o.BASE,this._process_trigger_vel_reset.bind(this))]),this.io.outputs.setNamedOutputConnectionPoints([new Jo(NL.OUTPUT_HIT,$o.BASE),new Jo(NL.OUTPUT_MISS,$o.BASE)])}trigger_hit(t){this.dispatchEventToOutput(NL.OUTPUT_HIT,t)}trigger_miss(t){this.dispatchEventToOutput(NL.OUTPUT_MISS,t)}_process_mouse_event(t){this.pv.mode==bL.indexOf(xL.CPU)?this.cpuController.updateMouse(t):this.gpuController.updateMouse(t)}_process_trigger_event_throttled(t){const e=this._last_event_processed_at,n=ai.performance.performanceManager().now();this._last_event_processed_at=n;const i=n-e;i<yL?setTimeout((()=>{this._process_trigger_event(t)}),yL-i):this._process_trigger_event(t)}_process_trigger_event(t){this.pv.mode==bL.indexOf(xL.CPU)?this.cpuController.processEvent(t):this.gpuController.processEvent(t)}_process_trigger_update_objects(t){this.pv.mode==bL.indexOf(xL.CPU)&&this.cpuController.update_target()}_process_trigger_vel_reset(t){this.pv.mode==bL.indexOf(xL.CPU)&&this.cpuController.velocity_controller.reset()}}var LL;NL.INPUT_TRIGGER=\\\\\\\"trigger\\\\\\\",NL.INPUT_MOUSE=\\\\\\\"mouse\\\\\\\",NL.INPUT_UPDATE_OBJECTS=\\\\\\\"updateObjects\\\\\\\",NL.INPUT_TRIGGER_VEL_RESET=\\\\\\\"triggerVelReset\\\\\\\",NL.OUTPUT_HIT=\\\\\\\"hit\\\\\\\",NL.OUTPUT_MISS=\\\\\\\"miss\\\\\\\",function(t){t.SET=\\\\\\\"set\\\\\\\",t.TOGGLE=\\\\\\\"toggle\\\\\\\"}(LL||(LL={}));const OL=[LL.SET,LL.TOGGLE];const RL=new class extends aa{constructor(){super(...arguments),this.mask=oa.STRING(\\\\\\\"/geo*\\\\\\\",{separatorAfter:!0}),this.tdisplay=oa.BOOLEAN(0),this.displayMode=oa.INTEGER(OL.indexOf(LL.SET),{visibleIf:{tdisplay:1},menu:{entries:OL.map(((t,e)=>({name:t,value:e})))}}),this.display=oa.BOOLEAN(0,{visibleIf:{tdisplay:1,displayMode:OL.indexOf(LL.SET)},separatorAfter:!0}),this.tbypass=oa.BOOLEAN(0),this.bypassMode=oa.INTEGER(OL.indexOf(LL.SET),{visibleIf:{tbypass:1},menu:{entries:OL.map(((t,e)=>({name:t,value:e})))}}),this.bypass=oa.BOOLEAN(0,{visibleIf:{tbypass:1,displayMode:OL.indexOf(LL.SET)}}),this.execute=oa.BUTTON(null,{callback:t=>{PL.PARAM_CALLBACK_execute(t)}})}};class PL extends Ba{constructor(){super(...arguments),this.paramsConfig=RL}static type(){return\\\\\\\"setFlag\\\\\\\"}initializeNode(){this.io.inputs.setNamedInputConnectionPoints([new Jo(\\\\\\\"trigger\\\\\\\",$o.BASE)])}async processEvent(t){let e=this.pv.mask;if(t.value){const n=t.value.node;if(n){const t=n.parent();t&&(e=`${t.path()}/${e}`)}}const n=this.scene().nodesController.nodesFromMask(e);for(let t of n)this._update_node_flags(t)}_update_node_flags(t){this._update_node_display_flag(t),this._update_node_bypass_flag(t)}_update_node_display_flag(t){var e;if(!this.pv.tdisplay)return;if(!(null===(e=t.flags)||void 0===e?void 0:e.hasDisplay()))return;const n=t.flags.display;if(!n)return;const i=OL[this.pv.displayMode];switch(i){case LL.SET:return void n.set(this.pv.display);case LL.TOGGLE:return void n.set(!n.active())}ar.unreachable(i)}_update_node_bypass_flag(t){var e;if(!this.pv.tbypass)return;if(!(null===(e=t.flags)||void 0===e?void 0:e.hasBypass()))return;const n=t.flags.bypass;if(!n)return;const i=OL[this.pv.bypassMode];switch(i){case LL.SET:return void n.set(this.pv.bypass);case LL.TOGGLE:return void n.set(!n.active())}ar.unreachable(i)}static PARAM_CALLBACK_execute(t){t.processEvent({})}}var IL;!function(t){t.BOOLEAN=\\\\\\\"boolean\\\\\\\",t.BUTTON=\\\\\\\"button\\\\\\\",t.NUMBER=\\\\\\\"number\\\\\\\",t.VECTOR2=\\\\\\\"vector2\\\\\\\",t.VECTOR3=\\\\\\\"vector3\\\\\\\",t.VECTOR4=\\\\\\\"vector4\\\\\\\",t.STRING=\\\\\\\"string\\\\\\\"}(IL||(IL={}));const FL=[IL.BOOLEAN,IL.BUTTON,IL.NUMBER,IL.VECTOR2,IL.VECTOR3,IL.VECTOR4,IL.STRING],DL=FL.indexOf(IL.BOOLEAN),kL=FL.indexOf(IL.NUMBER),BL=FL.indexOf(IL.VECTOR2),zL=FL.indexOf(IL.VECTOR3),UL=FL.indexOf(IL.VECTOR4),GL=FL.indexOf(IL.STRING),VL=\\\\\\\"output\\\\\\\";const HL=new class extends aa{constructor(){super(...arguments),this.param=oa.PARAM_PATH(\\\\\\\"\\\\\\\",{paramSelection:!0,computeOnDirty:!0}),this.type=oa.INTEGER(kL,{menu:{entries:FL.map(((t,e)=>({name:t,value:e})))}}),this.toggle=oa.BOOLEAN(0,{visibleIf:{type:DL}}),this.boolean=oa.BOOLEAN(0,{visibleIf:{type:DL,toggle:0}}),this.number=oa.FLOAT(0,{visibleIf:{type:kL}}),this.vector2=oa.VECTOR2([0,0],{visibleIf:{type:BL}}),this.vector3=oa.VECTOR3([0,0,0],{visibleIf:{type:zL}}),this.vector4=oa.VECTOR4([0,0,0,0],{visibleIf:{type:UL}}),this.increment=oa.BOOLEAN(0,{visibleIf:[{type:kL},{type:BL},{type:zL},{type:UL}]}),this.string=oa.STRING(\\\\\\\"\\\\\\\",{visibleIf:{type:GL}}),this.execute=oa.BUTTON(null,{callback:t=>{jL.PARAM_CALLBACK_execute(t)}})}};class jL extends Ba{constructor(){super(...arguments),this.paramsConfig=HL,this._tmp_vector2=new d.a,this._tmp_vector3=new p.a,this._tmp_vector4=new _.a,this._tmp_array2=[0,0],this._tmp_array3=[0,0,0],this._tmp_array4=[0,0,0,0]}static type(){return\\\\\\\"setParam\\\\\\\"}initializeNode(){this.io.inputs.setNamedInputConnectionPoints([new Jo(\\\\\\\"trigger\\\\\\\",$o.BASE)]),this.io.outputs.setNamedOutputConnectionPoints([new Jo(VL,$o.BASE)]),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.param])}))}))}async processEvent(t){this.p.param.isDirty()&&await this.p.param.compute();const e=this.p.param.value.param();if(e){const t=await this._new_param_value(e);null!=t&&e.set(t)}else this.states.error.set(\\\\\\\"target param not found\\\\\\\");this.dispatchEventToOutput(VL,t)}async _new_param_value(t){const e=FL[this.pv.type];switch(e){case IL.BOOLEAN:return await this._compute_params_if_dirty([this.p.toggle]),this.pv.toggle?t.value?0:1:this.pv.boolean?1:0;case IL.BUTTON:return t.options.executeCallback();case IL.NUMBER:return await this._compute_params_if_dirty([this.p.increment,this.p.number]),this.pv.increment?t.type()==Es.FLOAT?t.value+this.pv.number:t.value:this.pv.number;case IL.VECTOR2:return await this._compute_params_if_dirty([this.p.increment,this.p.vector2]),this.pv.increment?t.type()==Es.VECTOR2?(this._tmp_vector2.copy(t.value),this._tmp_vector2.add(this.pv.vector2),this._tmp_vector2.toArray(this._tmp_array2)):t.value.toArray(this._tmp_array2):this.pv.vector2.toArray(this._tmp_array2),this._tmp_array2;case IL.VECTOR3:return await this._compute_params_if_dirty([this.p.increment,this.p.vector3]),this.pv.increment?t.type()==Es.VECTOR3?(this._tmp_vector3.copy(t.value),this._tmp_vector3.add(this.pv.vector3),this._tmp_vector3.toArray(this._tmp_array3)):t.value.toArray(this._tmp_array3):this.pv.vector3.toArray(this._tmp_array3),this._tmp_array3;case IL.VECTOR4:return await this._compute_params_if_dirty([this.p.increment,this.p.vector4]),this.pv.increment?t.type()==Es.VECTOR4?(this._tmp_vector4.copy(t.value),this._tmp_vector4.add(this.pv.vector4),this._tmp_vector4.toArray(this._tmp_array4)):t.value.toArray(this._tmp_array4):this.pv.vector4.toArray(this._tmp_array4),this._tmp_array4;case IL.STRING:return await this._compute_params_if_dirty([this.p.string]),this.pv.string}ar.unreachable(e)}static PARAM_CALLBACK_execute(t){t.processEvent({})}async _compute_params_if_dirty(t){const e=[];for(let n of t)n.isDirty()&&e.push(n);const n=[];for(let t of e)n.push(t.compute());return await Promise.all(n)}}const WL=new class extends aa{constructor(){super(...arguments),this.outputsCount=oa.INTEGER(5,{range:[1,10],rangeLocked:[!0,!1]})}};class qL extends Ba{constructor(){super(...arguments),this.paramsConfig=WL}static type(){return\\\\\\\"sequence\\\\\\\"}initializeNode(){this.io.connection_points.set_input_name_function((()=>\\\\\\\"trigger\\\\\\\")),this.io.connection_points.set_expected_input_types_function((()=>[$o.BASE])),this.io.connection_points.set_expected_output_types_function(this._expected_output_types.bind(this)),this.io.connection_points.set_output_name_function(this._output_name.bind(this))}_expected_output_types(){const t=new Array(this.pv.outputsCount);return t.fill($o.BASE),t}_output_name(t){return`out${t}`}processEvent(t){const e=this.pv.outputsCount;for(let n=0;n<e;n++){const e=this.io.outputs.namedOutputConnectionPoints()[n];this.dispatchEventToOutput(e.name(),t)}}}const XL=\\\\\\\"tick\\\\\\\";const YL=new class extends aa{constructor(){super(...arguments),this.period=oa.INTEGER(1e3),this.count=oa.INTEGER(-1)}};class $L extends Ba{constructor(){super(...arguments),this.paramsConfig=YL,this._timer_active=!1,this._current_count=0}static type(){return\\\\\\\"timer\\\\\\\"}initializeNode(){this.io.inputs.setNamedInputConnectionPoints([new Jo(\\\\\\\"start\\\\\\\",$o.BASE,this._start_timer.bind(this)),new Jo(\\\\\\\"stop\\\\\\\",$o.BASE,this._stop_timer.bind(this))]),this.io.outputs.setNamedOutputConnectionPoints([new Jo(XL,$o.BASE)])}_start_timer(t){this._timer_active||(this._timer_active=!0,this._current_count=0),this._run_timer(t)}_stop_timer(){this._timer_active=!1}_run_timer(t){setTimeout((()=>{this._timer_active&&(this.pv.count<=0||this._current_count<this.pv.count?(this.dispatchEventToOutput(XL,t),this._current_count+=1,this._run_timer(t)):this._stop_timer())}),this.pv.period)}}const JL=new class extends aa{constructor(){super(...arguments),this.className=oa.STRING(\\\\\\\"active\\\\\\\")}};class ZL extends Ba{constructor(){super(...arguments),this.paramsConfig=JL}static type(){return\\\\\\\"viewer\\\\\\\"}initializeNode(){this.io.inputs.setNamedInputConnectionPoints([new Jo(\\\\\\\"setCss\\\\\\\",$o.BASE,this._process_trigger_setClass.bind(this)),new Jo(\\\\\\\"unSetCss\\\\\\\",$o.BASE,this._process_trigger_unsetClass.bind(this)),new Jo(\\\\\\\"createControls\\\\\\\",$o.BASE,this._process_trigger_createControls.bind(this)),new Jo(\\\\\\\"disposeControls\\\\\\\",$o.BASE,this._process_trigger_disposeControls.bind(this))])}_process_trigger_setClass(t){var e;const n=null===(e=t.viewer)||void 0===e?void 0:e.canvas();n&&n.classList.add(this.pv.className)}_process_trigger_unsetClass(t){var e;const n=null===(e=t.viewer)||void 0===e?void 0:e.canvas();n&&n.classList.remove(this.pv.className)}_process_trigger_createControls(t){this.scene().viewersRegister.traverseViewers((t=>{var e;null===(e=t.controlsController)||void 0===e||e.create_controls()}))}_process_trigger_disposeControls(t){this.scene().viewersRegister.traverseViewers((t=>{var e;null===(e=t.controlsController)||void 0===e||e.dispose_controls()}))}}class QL extends ia{static context(){return Ki.EVENT}cook(){this.cookController.endCook()}}class KL extends QL{}class tO extends KL{constructor(){super(...arguments),this._children_controller_context=Ki.ANIM}static type(){return tr.ANIM}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class eO extends KL{constructor(){super(...arguments),this._children_controller_context=Ki.COP}static type(){return tr.COP}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class nO extends KL{constructor(){super(...arguments),this._children_controller_context=Ki.EVENT}static type(){return tr.EVENT}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class iO extends KL{constructor(){super(...arguments),this._children_controller_context=Ki.MAT}static type(){return tr.MAT}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class rO extends QL{constructor(){super(...arguments),this.paramsConfig=new Jm,this.effectsComposerController=new Zm(this),this.displayNodeController=new Lm(this,this.effectsComposerController.displayNodeControllerCallbacks()),this._children_controller_context=Ki.POST}static type(){return tr.POST}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class sO extends KL{constructor(){super(...arguments),this._children_controller_context=Ki.ROP}static type(){return tr.ROP}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}const oO=\\\\\\\"int\\\\\\\";const aO=new class extends aa{constructor(){super(...arguments),this.float=oa.FLOAT(0)}};class lO extends df{constructor(){super(...arguments),this.paramsConfig=aO}static type(){return\\\\\\\"floatToInt\\\\\\\"}initializeNode(){this.io.outputs.setNamedOutputConnectionPoints([new Vo(oO,Do.INT)])}setLines(t){const e=this.variableForInputParam(this.p.float),n=`int ${this.glVarName(oO)} = int(${uf.float(e)})`;t.addBodyLines(this,[n])}}const cO=\\\\\\\"float\\\\\\\";const uO=new class extends aa{constructor(){super(...arguments),this.int=oa.INTEGER(0)}};class hO extends df{constructor(){super(...arguments),this.paramsConfig=uO}static type(){return\\\\\\\"intToFloat\\\\\\\"}initializeNode(){this.io.outputs.setNamedOutputConnectionPoints([new Vo(cO,Do.FLOAT)])}setLines(t){const e=this.variableForInputParam(this.p.int),n=`float ${this.glVarName(cO)} = float(${uf.integer(e)})`;t.addBodyLines(this,[n])}}const dO=\\\\\\\"bool\\\\\\\";const pO=new class extends aa{constructor(){super(...arguments),this.int=oa.INTEGER(0)}};class _O extends df{constructor(){super(...arguments),this.paramsConfig=pO}static type(){return\\\\\\\"intToBool\\\\\\\"}initializeNode(){this.io.outputs.setNamedOutputConnectionPoints([new Vo(dO,Do.BOOL)])}setLines(t){const e=this.variableForInputParam(this.p.int),n=`bool ${this.glVarName(dO)} = bool(${uf.integer(e)})`;t.addBodyLines(this,[n])}}const mO=new class extends aa{constructor(){super(...arguments),this.bool=oa.BOOLEAN(0)}};class fO extends df{constructor(){super(...arguments),this.paramsConfig=mO}static type(){return\\\\\\\"boolToInt\\\\\\\"}initializeNode(){this.io.outputs.setNamedOutputConnectionPoints([new Vo(oO,Do.INT)])}setLines(t){const e=this.variableForInputParam(this.p.bool),n=`int ${this.glVarName(oO)} = int(${uf.bool(e)})`;t.addBodyLines(this,[n])}}const gO=new class extends aa{constructor(){super(...arguments),this.x=oa.FLOAT(0),this.y=oa.FLOAT(0)}};class vO extends df{constructor(){super(...arguments),this.paramsConfig=gO}static type(){return\\\\\\\"floatToVec2\\\\\\\"}initializeNode(){this.io.outputs.setNamedOutputConnectionPoints([new Vo(vO.OUTPUT_NAME,Do.VEC2)])}setLines(t){const e=this.variableForInputParam(this.p.x),n=this.variableForInputParam(this.p.y),i=`vec2 ${this.glVarName(vO.OUTPUT_NAME)} = ${uf.float2(e,n)}`;t.addBodyLines(this,[i])}}vO.OUTPUT_NAME=\\\\\\\"vec2\\\\\\\";const yO=new class extends aa{constructor(){super(...arguments),this.x=oa.FLOAT(0),this.y=oa.FLOAT(0),this.z=oa.FLOAT(0)}};class xO extends df{constructor(){super(...arguments),this.paramsConfig=yO}static type(){return\\\\\\\"floatToVec3\\\\\\\"}initializeNode(){this.io.outputs.setNamedOutputConnectionPoints([new Vo(xO.OUTPUT_NAME,Do.VEC3)])}setLines(t){const e=this.variableForInputParam(this.p.x),n=this.variableForInputParam(this.p.y),i=this.variableForInputParam(this.p.z),r=`vec3 ${this.glVarName(xO.OUTPUT_NAME)} = ${uf.float3(e,n,i)}`;t.addBodyLines(this,[r])}}xO.OUTPUT_NAME=\\\\\\\"vec3\\\\\\\";const bO=new class extends aa{constructor(){super(...arguments),this.x=oa.FLOAT(0),this.y=oa.FLOAT(0),this.z=oa.FLOAT(0),this.w=oa.FLOAT(0)}};class wO extends df{constructor(){super(...arguments),this.paramsConfig=bO}static type(){return\\\\\\\"floatToVec4\\\\\\\"}initializeNode(){this.io.outputs.setNamedOutputConnectionPoints([new Vo(wO.OUTPUT_NAME,Do.VEC4)])}setLines(t){const e=this.variableForInputParam(this.p.x),n=this.variableForInputParam(this.p.y),i=this.variableForInputParam(this.p.z),r=this.variableForInputParam(this.p.w),s=`vec4 ${this.glVarName(wO.OUTPUT_NAME)} = ${uf.float4(e,n,i,r)}`;t.addBodyLines(this,[s])}}wO.OUTPUT_NAME=\\\\\\\"vec4\\\\\\\";const TO=new class extends aa{};class AO extends df{constructor(){super(...arguments),this.paramsConfig=TO}}function EO(t,e){const n=e.components,i=e.param_type;return class extends AO{static type(){return t}initializeNode(){this.io.outputs.setNamedOutputConnectionPoints(n.map((t=>new Vo(t,Do.FLOAT))))}createParams(){this.addParam(i,\\\\\\\"vec\\\\\\\",n.map((t=>0)))}setLines(t){const e=[],n=this.variableForInput(\\\\\\\"vec\\\\\\\");this.io.outputs.used_output_names().forEach((t=>{const i=this.glVarName(t);e.push(`float ${i} = ${n}.${t}`)})),t.addBodyLines(this,e)}}}const MO=[\\\\\\\"x\\\\\\\",\\\\\\\"y\\\\\\\"],SO=[\\\\\\\"x\\\\\\\",\\\\\\\"y\\\\\\\",\\\\\\\"z\\\\\\\"],CO=[\\\\\\\"x\\\\\\\",\\\\\\\"y\\\\\\\",\\\\\\\"z\\\\\\\",\\\\\\\"w\\\\\\\"];class NO extends(EO(\\\\\\\"vec2ToFloat\\\\\\\",{components:[\\\\\\\"x\\\\\\\",\\\\\\\"y\\\\\\\"],param_type:Es.VECTOR2})){}class LO extends(EO(\\\\\\\"vec3ToFloat\\\\\\\",{components:[\\\\\\\"x\\\\\\\",\\\\\\\"y\\\\\\\",\\\\\\\"z\\\\\\\"],param_type:Es.VECTOR3})){}class OO extends(EO(\\\\\\\"vec4ToFloat\\\\\\\",{components:CO,param_type:Es.VECTOR4})){}class RO extends AO{static type(){return\\\\\\\"vec4ToVec3\\\\\\\"}initializeNode(){this.io.outputs.setNamedOutputConnectionPoints([new Vo(RO.OUTPUT_NAME_VEC3,Do.VEC3),new Vo(RO.OUTPUT_NAME_W,Do.FLOAT)])}createParams(){this.addParam(Es.VECTOR4,RO.INPUT_NAME_VEC4,CO.map((t=>0)))}setLines(t){const e=[],n=RO.INPUT_NAME_VEC4,i=RO.OUTPUT_NAME_VEC3,r=RO.OUTPUT_NAME_W,s=this.variableForInput(n),o=this.io.outputs.used_output_names();if(o.indexOf(i)>=0){const t=this.glVarName(i);e.push(`vec3 ${t} = ${s}.xyz`)}if(o.indexOf(r)>=0){const t=this.glVarName(r);e.push(`float ${t} = ${s}.w`)}t.addBodyLines(this,e)}}RO.INPUT_NAME_VEC4=\\\\\\\"vec4\\\\\\\",RO.OUTPUT_NAME_VEC3=\\\\\\\"vec3\\\\\\\",RO.OUTPUT_NAME_W=\\\\\\\"w\\\\\\\";class PO extends AO{static type(){return\\\\\\\"vec3ToVec2\\\\\\\"}initializeNode(){this.io.outputs.setNamedOutputConnectionPoints([new Vo(PO.OUTPUT_NAME_VEC2,Do.VEC2),new Vo(PO.OUTPUT_NAME_Z,Do.FLOAT)])}createParams(){this.addParam(Es.VECTOR3,PO.INPUT_NAME_VEC3,SO.map((t=>0)))}setLines(t){const e=[],n=PO.INPUT_NAME_VEC3,i=PO.OUTPUT_NAME_VEC2,r=PO.OUTPUT_NAME_Z,s=this.variableForInput(n),o=this.io.outputs.used_output_names();if(o.indexOf(i)>=0){const t=this.glVarName(i);e.push(`vec2 ${t} = ${s}.xy`)}if(o.indexOf(r)>=0){const t=this.glVarName(r);e.push(`float ${t} = ${s}.z`)}t.addBodyLines(this,e)}}PO.INPUT_NAME_VEC3=\\\\\\\"vec3\\\\\\\",PO.OUTPUT_NAME_VEC2=\\\\\\\"vec2\\\\\\\",PO.OUTPUT_NAME_Z=\\\\\\\"z\\\\\\\";class IO extends AO{static type(){return\\\\\\\"vec2ToVec3\\\\\\\"}initializeNode(){this.io.outputs.setNamedOutputConnectionPoints([new Vo(IO.OUTPUT_NAME_VEC3,Do.VEC3)])}createParams(){this.addParam(Es.VECTOR2,IO.INPUT_NAME_VEC2,MO.map((t=>0))),this.addParam(Es.FLOAT,IO.INPUT_NAME_Z,0)}setLines(t){const e=[],n=IO.INPUT_NAME_VEC2,i=IO.INPUT_NAME_Z,r=IO.OUTPUT_NAME_VEC3,s=this.variableForInput(n),o=this.variableForInput(i),a=this.glVarName(r);e.push(`vec3 ${a} = vec3(${s}.xy, ${o})`),t.addBodyLines(this,e)}}IO.INPUT_NAME_VEC2=\\\\\\\"vec3\\\\\\\",IO.INPUT_NAME_Z=\\\\\\\"z\\\\\\\",IO.OUTPUT_NAME_VEC3=\\\\\\\"vec3\\\\\\\";class FO extends AO{static type(){return\\\\\\\"vec3ToVec4\\\\\\\"}initializeNode(){this.io.outputs.setNamedOutputConnectionPoints([new Vo(FO.OUTPUT_NAME_VEC4,Do.VEC4)])}createParams(){this.addParam(Es.VECTOR3,FO.INPUT_NAME_VEC3,SO.map((t=>0))),this.addParam(Es.FLOAT,FO.INPUT_NAME_W,0)}setLines(t){const e=[],n=FO.INPUT_NAME_VEC3,i=FO.INPUT_NAME_W,r=FO.OUTPUT_NAME_VEC4,s=this.variableForInput(n),o=this.variableForInput(i),a=this.glVarName(r);e.push(`vec4 ${a} = vec4(${s}.xyz, ${o})`),t.addBodyLines(this,e)}}FO.INPUT_NAME_VEC3=\\\\\\\"vec3\\\\\\\",FO.INPUT_NAME_W=\\\\\\\"w\\\\\\\",FO.OUTPUT_NAME_VEC4=\\\\\\\"vec4\\\\\\\";const DO=new class extends aa{};class kO extends df{constructor(){super(...arguments),this.paramsConfig=DO}gl_method_name(){return\\\\\\\"\\\\\\\"}gl_function_definitions(){return[]}initializeNode(){super.initializeNode(),this.io.connection_points.set_expected_input_types_function(this._expected_input_types.bind(this)),this.io.connection_points.set_expected_output_types_function(this._expected_output_types.bind(this)),this.io.connection_points.set_input_name_function(this._gl_input_name.bind(this))}_expected_input_types(){const t=this.io.connection_points.first_input_connection_type()||Do.FLOAT;if(this.io.connections.firstInputConnection()){const e=this.io.connections.inputConnections();if(e){let n=Math.max(f.compact(e).length+1,2);return f.range(n).map((e=>t))}return[]}return f.range(2).map((e=>t))}_expected_output_types(){return[this._expected_input_types()[0]]}_gl_input_name(t){return\\\\\\\"in\\\\\\\"}setLines(t){const e=this.io.outputs.namedOutputConnectionPoints()[0].type(),n=this.io.inputs.namedInputConnectionPoints().map(((t,e)=>{const n=t.name();return uf.any(this.variableForInput(n))})).join(\\\\\\\", \\\\\\\"),i=`${e} ${this.glVarName(this.io.connection_points.output_name(0))} = ${this.gl_method_name()}(${n})`;t.addBodyLines(this,[i]),t.addDefinitions(this,this.gl_function_definitions())}}class BO extends kO{_gl_input_name(t){return\\\\\\\"in\\\\\\\"}_expected_input_types(){return[this.io.connection_points.first_input_connection_type()||Do.FLOAT]}}class zO extends kO{_expected_input_types(){const t=this.io.connection_points.first_input_connection_type()||Do.FLOAT;return[t,t]}}class UO extends kO{_expected_input_types(){const t=this.io.connection_points.first_input_connection_type()||Do.FLOAT;return[t,t,t]}}class GO extends kO{_expected_input_types(){const t=this.io.connection_points.first_input_connection_type()||Do.FLOAT;return[t,t,t,t]}}class VO extends kO{_expected_input_types(){const t=this.io.connection_points.first_input_connection_type()||Do.FLOAT;return[t,t,t,t,t]}}function HO(t,e={}){const n=e.method||t,i=e.out||\\\\\\\"val\\\\\\\",r=e.in||\\\\\\\"in\\\\\\\";return class extends BO{static type(){return t}initializeNode(){super.initializeNode(),this.io.connection_points.set_input_name_function(this._gl_input_name.bind(this)),this.io.connection_points.set_output_name_function(this._gl_output_name.bind(this))}_gl_input_name(t){return r}_gl_output_name(t){return i}gl_method_name(){return n}}}class jO extends(HO(\\\\\\\"abs\\\\\\\")){}class WO extends(HO(\\\\\\\"acos\\\\\\\",{out:\\\\\\\"radians\\\\\\\"})){}class qO extends(HO(\\\\\\\"asin\\\\\\\",{out:\\\\\\\"radians\\\\\\\"})){}class XO extends(HO(\\\\\\\"atan\\\\\\\",{out:\\\\\\\"radians\\\\\\\"})){}class YO extends(HO(\\\\\\\"ceil\\\\\\\")){}class $O extends(HO(\\\\\\\"cos\\\\\\\",{in:\\\\\\\"radians\\\\\\\"})){}class JO extends(HO(\\\\\\\"degrees\\\\\\\",{in:\\\\\\\"radians\\\\\\\",out:\\\\\\\"degrees\\\\\\\"})){}class ZO extends(HO(\\\\\\\"exp\\\\\\\")){}class QO extends(HO(\\\\\\\"exp2\\\\\\\")){}class KO extends(HO(\\\\\\\"floor\\\\\\\")){}class tR extends(HO(\\\\\\\"fract\\\\\\\")){}class eR extends(HO(\\\\\\\"inverseSqrt\\\\\\\",{method:\\\\\\\"inversesqrt\\\\\\\"})){}class nR extends(HO(\\\\\\\"log\\\\\\\")){}class iR extends(HO(\\\\\\\"log2\\\\\\\")){}class rR extends(HO(\\\\\\\"normalize\\\\\\\",{out:\\\\\\\"normalized\\\\\\\"})){}class sR extends(HO(\\\\\\\"radians\\\\\\\",{in:\\\\\\\"degrees\\\\\\\",out:\\\\\\\"radians\\\\\\\"})){}class oR extends(HO(\\\\\\\"sign\\\\\\\")){}class aR extends(HO(\\\\\\\"sin\\\\\\\",{in:\\\\\\\"radians\\\\\\\"})){}class lR extends(HO(\\\\\\\"sqrt\\\\\\\")){}class cR extends(HO(\\\\\\\"tan\\\\\\\")){}function uR(t,e={}){const n=e.method||t,i=e.out||\\\\\\\"val\\\\\\\",r=e.in||[\\\\\\\"in0\\\\\\\",\\\\\\\"in1\\\\\\\"],s=e.default_in_type,o=e.allowed_in_types,a=e.out_type,l=e.functions||[];return class extends zO{static type(){return t}initializeNode(){super.initializeNode(),this.io.connection_points.set_input_name_function(this._gl_input_name.bind(this)),this.io.connection_points.set_output_name_function(this._gl_output_name.bind(this)),this.io.connection_points.set_expected_input_types_function(this._expected_input_types.bind(this)),a&&this.io.connection_points.set_expected_output_types_function((()=>[a]))}_gl_input_name(t){return r[t]}_gl_output_name(t){return i}gl_method_name(){return n}gl_function_definitions(){return l?l.map((t=>new Tf(this,t))):[]}_expected_input_types(){let t=this.io.connection_points.first_input_connection_type();if(t&&o&&!o.includes(t)){const e=this.io.inputs.namedInputConnectionPoints()[0];t=e?e.type():s}const e=t||s||Do.FLOAT;return[e,e]}}}class hR extends(uR(\\\\\\\"distance\\\\\\\",{in:[\\\\\\\"p0\\\\\\\",\\\\\\\"p1\\\\\\\"],default_in_type:Do.VEC3,allowed_in_types:[Do.VEC2,Do.VEC3,Do.VEC4],out_type:Do.FLOAT})){}class dR extends(uR(\\\\\\\"dot\\\\\\\",{in:[\\\\\\\"vec0\\\\\\\",\\\\\\\"vec1\\\\\\\"],default_in_type:Do.VEC3,allowed_in_types:[Do.VEC2,Do.VEC3,Do.VEC4],out_type:Do.FLOAT})){}class pR extends(uR(\\\\\\\"max\\\\\\\")){}class _R extends(uR(\\\\\\\"min\\\\\\\")){}class mR extends(uR(\\\\\\\"mod\\\\\\\")){paramDefaultValue(t){return{in1:1}[t]}_expected_input_types(){const t=Do.FLOAT;return[t,t]}}class fR extends(uR(\\\\\\\"pow\\\\\\\",{in:[\\\\\\\"x\\\\\\\",\\\\\\\"y\\\\\\\"]})){}class gR extends(uR(\\\\\\\"reflect\\\\\\\",{in:[\\\\\\\"I\\\\\\\",\\\\\\\"N\\\\\\\"],default_in_type:Do.VEC3})){}class vR extends(uR(\\\\\\\"step\\\\\\\",{in:[\\\\\\\"edge\\\\\\\",\\\\\\\"x\\\\\\\"]})){}function yR(t,e={}){const n=e.method||t,i=e.out||\\\\\\\"val\\\\\\\",r=e.in||[\\\\\\\"in0\\\\\\\",\\\\\\\"in1\\\\\\\",\\\\\\\"in2\\\\\\\"],s=e.default||{},o=e.out_type||Do.FLOAT,a=e.functions||[];return class extends UO{static type(){return t}initializeNode(){super.initializeNode(),this.io.connection_points.set_input_name_function(this._gl_input_name.bind(this)),this.io.connection_points.set_output_name_function(this._gl_output_name.bind(this)),this.io.connection_points.set_expected_output_types_function(this._expected_output_types.bind(this))}_gl_input_name(t){return r[t]}_gl_output_name(t){return i}gl_method_name(){return n}_expected_output_types(){return[o]}paramDefaultValue(t){return s[t]}gl_function_definitions(){return a.map((t=>new Tf(this,t)))}}}class xR extends(yR(\\\\\\\"clamp\\\\\\\",{in:[\\\\\\\"value\\\\\\\",\\\\\\\"min\\\\\\\",\\\\\\\"max\\\\\\\"],default:{max:1}})){_expected_output_types(){return[this._expected_input_types()[0]]}}class bR extends(yR(\\\\\\\"faceForward\\\\\\\",{in:[\\\\\\\"N\\\\\\\",\\\\\\\"I\\\\\\\",\\\\\\\"Nref\\\\\\\"]})){}class wR extends(yR(\\\\\\\"smoothstep\\\\\\\",{in:[\\\\\\\"edge0\\\\\\\",\\\\\\\"edge1\\\\\\\",\\\\\\\"x\\\\\\\"],default:{edge1:1}})){_expected_output_types(){return[this._expected_input_types()[0]]}}function TR(t,e){const n=e.in_prefix||t,i=e.out||\\\\\\\"val\\\\\\\",r=e.operation,s=e.allowed_in_types;return class extends zO{static type(){return t}initializeNode(){super.initializeNode(),this.io.connection_points.set_input_name_function(this._gl_input_name.bind(this)),this.io.connection_points.set_output_name_function(this._gl_output_name.bind(this)),this.io.connection_points.set_expected_input_types_function(this._expected_input_types.bind(this)),this.io.connection_points.set_expected_output_types_function(this._expected_output_types.bind(this))}setLines(t){const e=this.io.outputs.namedOutputConnectionPoints()[0].type(),n=this.io.inputs.namedInputConnectionPoints().map(((t,e)=>{const n=t.name(),i=this.variableForInput(n);if(i)return uf.any(i)})).join(` ${this.gl_operation()} `),i=`${e} ${this.glVarName(this.io.connection_points.output_name(0))} = ${this.gl_method_name()}(${n})`;t.addBodyLines(this,[i])}_gl_input_name(t){return`${n}${t}`}_gl_output_name(t){return i}gl_operation(){return r}_expected_input_types(){let t=this.io.connection_points.first_input_connection_type();if(t&&s&&!s.includes(t)){const e=this.io.inputs.namedInputConnectionPoints()[0];e&&(t=e.type())}const e=t||Do.FLOAT,n=this.io.connections.existingInputConnections(),i=n?Math.max(n.length+1,2):2,r=[];for(let t=0;t<i;t++)r.push(e);return r}_expected_output_types(){const t=this._expected_input_types();return[t[1]||t[0]||Do.FLOAT]}}}class AR extends(TR(\\\\\\\"add\\\\\\\",{in_prefix:\\\\\\\"add\\\\\\\",out:\\\\\\\"sum\\\\\\\",operation:\\\\\\\"+\\\\\\\"})){}class ER extends(TR(\\\\\\\"divide\\\\\\\",{in_prefix:\\\\\\\"div\\\\\\\",out:\\\\\\\"divide\\\\\\\",operation:\\\\\\\"/\\\\\\\"})){paramDefaultValue(t){return 1}}class MR extends(TR(\\\\\\\"substract\\\\\\\",{in_prefix:\\\\\\\"sub\\\\\\\",out:\\\\\\\"substract\\\\\\\",operation:\\\\\\\"-\\\\\\\"})){}class SR extends(TR(\\\\\\\"mult\\\\\\\",{in_prefix:\\\\\\\"mult\\\\\\\",out:\\\\\\\"product\\\\\\\",operation:\\\\\\\"*\\\\\\\"})){static type(){return\\\\\\\"mult\\\\\\\"}paramDefaultValue(t){return 1}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))}_expected_output_type(){const t=this._expected_input_types();return[t[t.length-1]]}_expected_input_types(){const t=this.io.connections.existingInputConnections();if(t){const e=t[0];if(e){const n=e.node_src.io.outputs.namedOutputConnectionPoints()[e.output_index].type(),i=Math.max(t.length+1,2),r=new Array(i);if(n==Do.FLOAT){const e=t[1];if(e){const t=e.node_src.io.outputs.namedOutputConnectionPoints()[e.output_index].type();return t==Do.FLOAT?r.fill(n):[n,t]}return[n,n]}return r.fill(n)}}return[Do.FLOAT,Do.FLOAT]}}class CR extends zO{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))}_expected_input_types(){return[Do.BOOL,Do.BOOL]}_expected_output_types(){return[Do.BOOL]}setLines(t){const e=this.io.inputs.namedInputConnectionPoints().map(((t,e)=>{const n=t.name();return uf.any(this.variableForInput(n))})).join(` ${this.boolean_operation()} `),n=`bool ${this.glVarName(this.io.connection_points.output_name(0))} = ${e}`;t.addBodyLines(this,[n])}}function NR(t,e){return class extends CR{static type(){return t}initializeNode(){super.initializeNode(),this.io.connection_points.set_input_name_function(this._gl_input_name.bind(this)),this.io.connection_points.set_output_name_function(this._gl_output_name.bind(this))}boolean_operation(){return e.op}_gl_output_name(e){return t}_gl_input_name(e=0){return`${t}${e}`}}}class LR extends(NR(\\\\\\\"and\\\\\\\",{op:\\\\\\\"&&\\\\\\\"})){}class OR extends(NR(\\\\\\\"or\\\\\\\",{op:\\\\\\\"||\\\\\\\"})){}var RR;!function(t){t.TIME=\\\\\\\"time\\\\\\\",t.DELTA_TIME=\\\\\\\"delta_time\\\\\\\"}(RR||(RR={}));var PR,IR;!function(t){t.POSITION=\\\\\\\"position\\\\\\\",t.VELOCITY=\\\\\\\"velocity\\\\\\\",t.MASS=\\\\\\\"mass\\\\\\\",t.FORCE=\\\\\\\"force\\\\\\\"}(PR||(PR={})),function(t){t.POSITION=\\\\\\\"position\\\\\\\",t.VELOCITY=\\\\\\\"velocity\\\\\\\"}(IR||(IR={}));const FR=[PR.POSITION,PR.VELOCITY,PR.MASS,PR.FORCE],DR=[IR.POSITION,IR.VELOCITY],kR={[PR.POSITION]:[0,0,0],[PR.VELOCITY]:[0,0,0],[PR.MASS]:1,[PR.FORCE]:[0,-9.8,0]};const BR=new class extends aa{};class zR extends df{constructor(){super(...arguments),this.paramsConfig=BR}static type(){return\\\\\\\"acceleration\\\\\\\"}initializeNode(){super.initializeNode(),this.io.outputs.setNamedOutputConnectionPoints([new Vo(IR.POSITION,Do.VEC3),new Vo(IR.VELOCITY,Do.VEC3)]),this.io.connection_points.set_expected_input_types_function(this._expected_input_types.bind(this)),this.io.connection_points.set_expected_output_types_function(this._expected_output_types.bind(this)),this.io.connection_points.set_input_name_function(this._gl_input_name.bind(this)),this.io.connection_points.set_output_name_function(this._gl_output_name.bind(this))}_expected_input_types(){const t=this.io.connection_points.first_input_connection_type()||Do.VEC3;return[t,t,Do.FLOAT,t]}_expected_output_types(){const t=this._expected_input_types()[0];return[t,t]}_gl_input_name(t){return FR[t]}_gl_output_name(t){return DR[t]}paramDefaultValue(t){return kR[t]}setLines(t){const e=this.io.outputs.namedOutputConnectionPoints()[0].type(),n=new Af(this,Do.FLOAT,RR.DELTA_TIME),i=new Tf(this,\\\\\\\"float compute_velocity_from_acceleration(float vel, float force, float mass, float time_delta){\\\\n\\\\tfloat impulse = (force * mass) * time_delta;\\\\n\\\\treturn vel + impulse;\\\\n}\\\\nvec2 compute_velocity_from_acceleration(vec2 vel, vec2 force, float mass, float time_delta){\\\\n\\\\tvec2 impulse = (force * mass) * time_delta;\\\\n\\\\treturn vel + impulse;\\\\n}\\\\nvec3 compute_velocity_from_acceleration(vec3 vel, vec3 force, float mass, float time_delta){\\\\n\\\\tvec3 impulse = (force * mass) * time_delta;\\\\n\\\\treturn vel + impulse;\\\\n}\\\\nvec4 compute_velocity_from_acceleration(vec4 vel, vec4 force, float mass, float time_delta){\\\\n\\\\tvec4 impulse = (force * mass) * time_delta;\\\\n\\\\treturn vel + impulse;\\\\n}\\\\nfloat compute_position_from_velocity(float position, float velocity, float time_delta){\\\\n\\\\treturn position + (velocity * time_delta);\\\\n}\\\\nvec2 compute_position_from_velocity(vec2 position, vec2 velocity, float time_delta){\\\\n\\\\treturn position + (velocity * time_delta);\\\\n}\\\\nvec3 compute_position_from_velocity(vec3 position, vec3 velocity, float time_delta){\\\\n\\\\treturn position + (velocity * time_delta);\\\\n}\\\\nvec4 compute_position_from_velocity(vec4 position, vec4 velocity, float time_delta){\\\\n\\\\treturn position + (velocity * time_delta);\\\\n}\\\\\\\");t.addDefinitions(this,[n,i]);const r=uf.any(this.variableForInput(PR.POSITION)),s=uf.any(this.variableForInput(PR.VELOCITY)),o=uf.float(this.variableForInput(PR.MASS)),a=uf.any(this.variableForInput(PR.FORCE)),l=this.glVarName(IR.POSITION),c=this.glVarName(IR.VELOCITY),u=`${e} ${c} = compute_velocity_from_acceleration(${[s,a,o,RR.DELTA_TIME].join(\\\\\\\", \\\\\\\")})`,h=`${e} ${l} = compute_position_from_velocity(${[r,c,RR.DELTA_TIME].join(\\\\\\\", \\\\\\\")})`;t.addBodyLines(this,[u,h])}}var UR,GR=\\\\\\\"\\\\n\\\\n// https://github.com/mattatz/ShibuyaCrowd/blob/master/source/shaders/common/quaternion.glsl\\\\nvec4 quatMult(vec4 q1, vec4 q2)\\\\n{\\\\n\\\\treturn vec4(\\\\n\\\\tq1.w * q2.x + q1.x * q2.w + q1.z * q2.y - q1.y * q2.z,\\\\n\\\\tq1.w * q2.y + q1.y * q2.w + q1.x * q2.z - q1.z * q2.x,\\\\n\\\\tq1.w * q2.z + q1.z * q2.w + q1.y * q2.x - q1.x * q2.y,\\\\n\\\\tq1.w * q2.w - q1.x * q2.x - q1.y * q2.y - q1.z * q2.z\\\\n\\\\t);\\\\n}\\\\n// http://glmatrix.net/docs/quat.js.html#line97\\\\n//   let ax = a[0], ay = a[1], az = a[2], aw = a[3];\\\\n\\\\n//   let bx = b[0], by = b[1], bz = b[2], bw = b[3];\\\\n\\\\n//   out[0] = ax * bw + aw * bx + ay * bz - az * by;\\\\n\\\\n//   out[1] = ay * bw + aw * by + az * bx - ax * bz;\\\\n\\\\n//   out[2] = az * bw + aw * bz + ax * by - ay * bx;\\\\n\\\\n//   out[3] = aw * bw - ax * bx - ay * by - az * bz;\\\\n\\\\n//   return out\\\\n\\\\n\\\\n\\\\n// http://www.neilmendoza.com/glsl-rotation-about-an-arbitrary-axis/\\\\nmat4 rotationMatrix(vec3 axis, float angle)\\\\n{\\\\n\\\\taxis = normalize(axis);\\\\n\\\\tfloat s = sin(angle);\\\\n\\\\tfloat c = cos(angle);\\\\n\\\\tfloat oc = 1.0 - c;\\\\n\\\\n \\\\treturn mat4(oc * axis.x * axis.x + c, oc * axis.x * axis.y - axis.z * s,  oc * axis.z * axis.x + axis.y * s, 0.0, oc * axis.x * axis.y + axis.z * s,  oc * axis.y * axis.y + c, oc * axis.y * axis.z - axis.x * s,  0.0, oc * axis.z * axis.x - axis.y * s,  oc * axis.y * axis.z + axis.x * s,  oc * axis.z * axis.z + c, 0.0, 0.0, 0.0, 0.0, 1.0);\\\\n}\\\\n\\\\n// https://www.geeks3d.com/20141201/how-to-rotate-a-vertex-by-a-quaternion-in-glsl/\\\\nvec4 quatFromAxisAngle(vec3 axis, float angle)\\\\n{\\\\n\\\\tvec4 qr;\\\\n\\\\tfloat half_angle = (angle * 0.5); // * 3.14159 / 180.0;\\\\n\\\\tfloat sin_half_angle = sin(half_angle);\\\\n\\\\tqr.x = axis.x * sin_half_angle;\\\\n\\\\tqr.y = axis.y * sin_half_angle;\\\\n\\\\tqr.z = axis.z * sin_half_angle;\\\\n\\\\tqr.w = cos(half_angle);\\\\n\\\\treturn qr;\\\\n}\\\\nvec3 rotateWithAxisAngle(vec3 position, vec3 axis, float angle)\\\\n{\\\\n\\\\tvec4 q = quatFromAxisAngle(axis, angle);\\\\n\\\\tvec3 v = position.xyz;\\\\n\\\\treturn v + 2.0 * cross(q.xyz, cross(q.xyz, v) + q.w * v);\\\\n}\\\\n// vec3 applyQuaternionToVector( vec4 q, vec3 v ){\\\\n// \\\\treturn v + 2.0 * cross( q.xyz, cross( q.xyz, v ) + q.w * v );\\\\n// }\\\\nvec3 rotateWithQuat( vec3 v, vec4 q )\\\\n{\\\\n\\\\t// vec4 qv = multQuat( quat, vec4(vec, 0.0) );\\\\n\\\\t// return multQuat( qv, vec4(-quat.x, -quat.y, -quat.z, quat.w) ).xyz;\\\\n\\\\treturn v + 2.0 * cross( q.xyz, cross( q.xyz, v ) + q.w * v );\\\\n}\\\\n// https://github.com/glslify/glsl-look-at/blob/gh-pages/index.glsl\\\\n// mat3 rotation_matrix(vec3 origin, vec3 target, float roll) {\\\\n// \\\\tvec3 rr = vec3(sin(roll), cos(roll), 0.0);\\\\n// \\\\tvec3 ww = normalize(target - origin);\\\\n// \\\\tvec3 uu = normalize(cross(ww, rr));\\\\n// \\\\tvec3 vv = normalize(cross(uu, ww));\\\\n\\\\n// \\\\treturn mat3(uu, vv, ww);\\\\n// }\\\\n// mat3 rotation_matrix(vec3 target, float roll) {\\\\n// \\\\tvec3 rr = vec3(sin(roll), cos(roll), 0.0);\\\\n// \\\\tvec3 ww = normalize(target);\\\\n// \\\\tvec3 uu = normalize(cross(ww, rr));\\\\n// \\\\tvec3 vv = normalize(cross(uu, ww));\\\\n\\\\n// \\\\treturn mat3(uu, vv, ww);\\\\n// }\\\\n\\\\nfloat vectorAngle(vec3 start, vec3 dest){\\\\n\\\\tstart = normalize(start);\\\\n\\\\tdest = normalize(dest);\\\\n\\\\n\\\\tfloat cosTheta = dot(start, dest);\\\\n\\\\tvec3 c1 = cross(start, dest);\\\\n\\\\t// We use the dot product of the cross with the Y axis.\\\\n\\\\t// This is a little arbitrary, but can still give a good sense of direction\\\\n\\\\tvec3 y_axis = vec3(0.0, 1.0, 0.0);\\\\n\\\\tfloat d1 = dot(c1, y_axis);\\\\n\\\\tfloat angle = acos(cosTheta) * sign(d1);\\\\n\\\\treturn angle;\\\\n}\\\\n\\\\n// http://www.opengl-tutorial.org/intermediate-tutorials/tutorial-17-quaternions/#i-need-an-equivalent-of-glulookat-how-do-i-orient-an-object-towards-a-point-\\\\nvec4 vectorAlign(vec3 start, vec3 dest){\\\\n\\\\tstart = normalize(start);\\\\n\\\\tdest = normalize(dest);\\\\n\\\\n\\\\tfloat cosTheta = dot(start, dest);\\\\n\\\\tvec3 axis;\\\\n\\\\n\\\\t// if (cosTheta < -1 + 0.001f){\\\\n\\\\t// \\\\t// special case when vectors in opposite directions:\\\\n\\\\t// \\\\t// there is no ideal rotation axis\\\\n\\\\t// \\\\t// So guess one; any will do as long as it's perpendicular to start\\\\n\\\\t// \\\\taxis = cross(vec3(0.0f, 0.0f, 1.0f), start);\\\\n\\\\t// \\\\tif (length2(axis) < 0.01 ) // bad luck, they were parallel, try again!\\\\n\\\\t// \\\\t\\\\taxis = cross(vec3(1.0f, 0.0f, 0.0f), start);\\\\n\\\\n\\\\t// \\\\taxis = normalize(axis);\\\\n\\\\t// \\\\treturn gtx::quaternion::angleAxis(glm::radians(180.0f), axis);\\\\n\\\\t// }\\\\n\\\\tif(cosTheta > (1.0 - 0.0001) || cosTheta < (-1.0 + 0.0001) ){\\\\n\\\\t\\\\taxis = normalize(cross(start, vec3(0.0, 1.0, 0.0)));\\\\n\\\\t\\\\tif (length(axis) < 0.001 ){ // bad luck, they were parallel, try again!\\\\n\\\\t\\\\t\\\\taxis = normalize(cross(start, vec3(1.0, 0.0, 0.0)));\\\\n\\\\t\\\\t}\\\\n\\\\t} else {\\\\n\\\\t\\\\taxis = normalize(cross(start, dest));\\\\n\\\\t}\\\\n\\\\n\\\\tfloat angle = acos(cosTheta);\\\\n\\\\n\\\\treturn quatFromAxisAngle(axis, angle);\\\\n}\\\\nvec4 vectorAlignWithUp(vec3 start, vec3 dest, vec3 up){\\\\n\\\\tvec4 rot1 = vectorAlign(start, dest);\\\\n\\\\tup = normalize(up);\\\\n\\\\n\\\\t// Recompute desiredUp so that it's perpendicular to the direction\\\\n\\\\t// You can skip that part if you really want to force desiredUp\\\\n\\\\t// vec3 right = normalize(cross(dest, up));\\\\n\\\\t// up = normalize(cross(right, dest));\\\\n\\\\n\\\\t// Because of the 1rst rotation, the up is probably completely screwed up.\\\\n\\\\t// Find the rotation between the up of the rotated object, and the desired up\\\\n\\\\tvec3 newUp = rotateWithQuat(vec3(0.0, 1.0, 0.0), rot1);//rot1 * vec3(0.0, 1.0, 0.0);\\\\n\\\\tvec4 rot2 = vectorAlign(up, newUp);\\\\n\\\\n\\\\t// return rot1;\\\\n\\\\treturn rot2;\\\\n\\\\t// return multQuat(rot1, rot2);\\\\n\\\\t// return rot2 * rot1;\\\\n\\\\n}\\\\n\\\\n// https://www.euclideanspace.com/maths/geometry/rotations/conversions/quaternionToAngle/index.htm\\\\nfloat quatToAngle(vec4 q){\\\\n\\\\treturn 2.0 * acos(q.w);\\\\n}\\\\nvec3 quatToAxis(vec4 q){\\\\n\\\\treturn vec3(\\\\n\\\\t\\\\tq.x / sqrt(1.0-q.w*q.w),\\\\n\\\\t\\\\tq.y / sqrt(1.0-q.w*q.w),\\\\n\\\\t\\\\tq.z / sqrt(1.0-q.w*q.w)\\\\n\\\\t);\\\\n}\\\\n\\\\nvec4 align(vec3 dir, vec3 up){\\\\n\\\\tvec3 start_dir = vec3(0.0, 0.0, 1.0);\\\\n\\\\tvec3 start_up = vec3(0.0, 1.0, 0.0);\\\\n\\\\tvec4 rot1 = vectorAlign(start_dir, dir);\\\\n\\\\tup = normalize(up);\\\\n\\\\n\\\\t// Recompute desiredUp so that it's perpendicular to the direction\\\\n\\\\t// You can skip that part if you really want to force desiredUp\\\\n\\\\tvec3 right = normalize(cross(dir, up));\\\\n\\\\tif(length(right)<0.001){\\\\n\\\\t\\\\tright = vec3(1.0, 0.0, 0.0);\\\\n\\\\t}\\\\n\\\\tup = normalize(cross(right, dir));\\\\n\\\\n\\\\t// Because of the 1rst rotation, the up is probably completely screwed up.\\\\n\\\\t// Find the rotation between the up of the rotated object, and the desired up\\\\n\\\\tvec3 newUp = rotateWithQuat(start_up, rot1);//rot1 * vec3(0.0, 1.0, 0.0);\\\\n\\\\tvec4 rot2 = vectorAlign(normalize(newUp), up);\\\\n\\\\n\\\\t// return rot1;\\\\n\\\\treturn quatMult(rot1, rot2);\\\\n\\\\t// return rot2 * rot1;\\\\n\\\\n}\\\\\\\";!function(t){t.DIR=\\\\\\\"dir\\\\\\\",t.UP=\\\\\\\"up\\\\\\\"}(UR||(UR={}));const VR=[UR.DIR,UR.UP],HR={[UR.DIR]:[0,0,1],[UR.UP]:[0,1,0]};class jR extends zO{static type(){return\\\\\\\"align\\\\\\\"}initializeNode(){super.initializeNode(),this.io.connection_points.set_input_name_function((t=>VR[t])),this.io.connection_points.set_expected_input_types_function((()=>[Do.VEC3,Do.VEC3])),this.io.connection_points.set_expected_output_types_function((()=>[Do.VEC4]))}paramDefaultValue(t){return HR[t]}gl_method_name(){return\\\\\\\"align\\\\\\\"}gl_function_definitions(){return[new Tf(this,GR)]}}var WR;!function(t){t.LINEAR=\\\\\\\"Linear\\\\\\\",t.GAMMA=\\\\\\\"Gamma\\\\\\\",t.SRGB=\\\\\\\"sRGB\\\\\\\",t.RGBE=\\\\\\\"RGBE\\\\\\\",t.RGBM=\\\\\\\"RGBM\\\\\\\",t.RGBD=\\\\\\\"RGBD\\\\\\\",t.LogLuv=\\\\\\\"LogLuv\\\\\\\"}(WR||(WR={}));const qR=[WR.LINEAR,WR.GAMMA,WR.SRGB,WR.RGBE,WR.RGBM,WR.RGBD,WR.LogLuv];const XR=new class extends aa{constructor(){super(...arguments),this.color=oa.VECTOR4([1,1,1,1]),this.from=oa.INTEGER(qR.indexOf(WR.LINEAR),{menu:{entries:qR.map(((t,e)=>({name:t,value:e})))}}),this.to=oa.INTEGER(qR.indexOf(WR.GAMMA),{menu:{entries:qR.map(((t,e)=>({name:t,value:e})))}}),this.gammaFactor=oa.FLOAT(2.2)}};class YR extends df{constructor(){super(...arguments),this.paramsConfig=XR}static type(){return\\\\\\\"colorCorrect\\\\\\\"}initializeNode(){this.io.connection_points.spare_params.set_inputless_param_names([\\\\\\\"to\\\\\\\",\\\\\\\"from\\\\\\\"]),this.io.outputs.setNamedOutputConnectionPoints([new Vo(YR.OUTPUT_NAME,Do.VEC4)])}setLines(t){const e=qR[this.pv.from],n=qR[this.pv.to],i=this.glVarName(YR.OUTPUT_NAME),r=uf.any(this.variableForInput(YR.INPUT_NAME)),s=[];if(e!=n){const t=`${e}To${n}`,o=[];if(o.push(r),e==WR.GAMMA||n==WR.GAMMA){const t=uf.any(this.variableForInputParam(this.p.gammaFactor));o.push(t)}s.push(`vec4 ${i} = ${t}(${o.join(\\\\\\\", \\\\\\\")})`)}else s.push(`vec4 ${i} = ${r}`);t.addBodyLines(this,s)}}var $R,JR;YR.INPUT_NAME=\\\\\\\"color\\\\\\\",YR.INPUT_GAMMA_FACTOR=\\\\\\\"gammaFactor\\\\\\\",YR.OUTPUT_NAME=\\\\\\\"out\\\\\\\",function(t){t.EQUAL=\\\\\\\"Equal\\\\\\\",t.LESS_THAN=\\\\\\\"Less Than\\\\\\\",t.GREATER_THAN=\\\\\\\"Greater Than\\\\\\\",t.LESS_THAN_OR_EQUAL=\\\\\\\"Less Than Or Equal\\\\\\\",t.GREATER_THAN_OR_EQUAL=\\\\\\\"Greater Than Or Equal\\\\\\\",t.NOT_EQUAL=\\\\\\\"Not Equal\\\\\\\"}($R||($R={})),function(t){t.EQUAL=\\\\\\\"==\\\\\\\",t.LESS_THAN=\\\\\\\"<\\\\\\\",t.GREATER_THAN=\\\\\\\">\\\\\\\",t.LESS_THAN_OR_EQUAL=\\\\\\\"<=\\\\\\\",t.GREATER_THAN_OR_EQUAL=\\\\\\\">=\\\\\\\",t.NOT_EQUAL=\\\\\\\"!=\\\\\\\"}(JR||(JR={}));const ZR=[$R.EQUAL,$R.LESS_THAN,$R.GREATER_THAN,$R.LESS_THAN_OR_EQUAL,$R.GREATER_THAN_OR_EQUAL,$R.NOT_EQUAL],QR=[JR.EQUAL,JR.LESS_THAN,JR.GREATER_THAN,JR.LESS_THAN_OR_EQUAL,JR.GREATER_THAN_OR_EQUAL,JR.NOT_EQUAL],KR=[\\\\\\\"x\\\\\\\",\\\\\\\"y\\\\\\\",\\\\\\\"z\\\\\\\",\\\\\\\"w\\\\\\\"];const tP=new class extends aa{constructor(){super(...arguments),this.test=oa.INTEGER(0,{menu:{entries:ZR.map(((t,e)=>({name:`${QR[e].padEnd(2,\\\\\\\" \\\\\\\")} (${t})`,value:e})))}})}};class eP extends df{constructor(){super(...arguments),this.paramsConfig=tP}static type(){return\\\\\\\"compare\\\\\\\"}initializeNode(){super.initializeNode(),this.io.connection_points.spare_params.set_inputless_param_names([\\\\\\\"test\\\\\\\"]),this.io.connection_points.initializeNode(),this.io.connection_points.set_input_name_function(this._gl_input_name.bind(this)),this.io.connection_points.set_output_name_function((t=>\\\\\\\"val\\\\\\\")),this.io.connection_points.set_expected_input_types_function(this._expected_input_type.bind(this)),this.io.connection_points.set_expected_output_types_function((()=>[Do.BOOL]))}set_test_name(t){this.p.test.set(ZR.indexOf(t))}_gl_input_name(t){return[\\\\\\\"value0\\\\\\\",\\\\\\\"value1\\\\\\\"][t]}_expected_input_type(){const t=this.io.connection_points.first_input_connection_type()||Do.FLOAT;return[t,t]}setLines(t){const e=[],n=this.glVarName(\\\\\\\"val\\\\\\\"),i=QR[this.pv.test],r=uf.any(this.variableForInput(this._gl_input_name(0))),s=uf.any(this.variableForInput(this._gl_input_name(1))),o=this.io.inputs.namedInputConnectionPoints()[0];let a=1;if(o&&(a=Go[o.type()]||1),a>1){let t=[];for(let n=0;n<a;n++){const o=this.glVarName(`tmp_value_${n}`),a=KR[n];t.push(o),e.push(`bool ${o} = (${r}.${a} ${i} ${s}.${a})`)}e.push(`bool ${n} = (${t.join(\\\\\\\" && \\\\\\\")})`)}else e.push(`bool ${n} = (${r} ${i} ${s})`);t.addBodyLines(this,e)}}class nP extends BO{static type(){return\\\\\\\"complement\\\\\\\"}gl_method_name(){return\\\\\\\"complement\\\\\\\"}gl_function_definitions(){return[new Tf(this,\\\\\\\"float complement(float x){return 1.0-x;}\\\\nvec2 complement(vec2 x){return vec2(1.0-x.x, 1.0-x.y);}\\\\nvec3 complement(vec3 x){return vec3(1.0-x.x, 1.0-x.y, 1.0-x.z);}\\\\nvec4 complement(vec4 x){return vec4(1.0-x.x, 1.0-x.y, 1.0-x.z, 1.0-x.w);}\\\\n\\\\\\\")]}}function iP(t){return{visibleIf:{type:ko.indexOf(t)}}}const rP=new class extends aa{constructor(){super(...arguments),this.type=oa.INTEGER(ko.indexOf(Do.FLOAT),{menu:{entries:ko.map(((t,e)=>({name:t,value:e})))}}),this.bool=oa.BOOLEAN(0,iP(Do.BOOL)),this.int=oa.INTEGER(0,iP(Do.INT)),this.float=oa.FLOAT(0,iP(Do.FLOAT)),this.vec2=oa.VECTOR2([0,0],iP(Do.VEC2)),this.vec3=oa.VECTOR3([0,0,0],iP(Do.VEC3)),this.vec4=oa.VECTOR4([0,0,0,0],iP(Do.VEC4))}};class sP extends df{constructor(){super(...arguments),this.paramsConfig=rP,this._allow_inputs_created_from_params=!1}static type(){return\\\\\\\"constant\\\\\\\"}initializeNode(){this.io.connection_points.set_output_name_function((t=>sP.OUTPUT_NAME)),this.io.connection_points.set_expected_input_types_function((()=>[])),this.io.connection_points.set_expected_output_types_function((()=>[this._current_connection_type]))}setLines(t){const e=this._current_param;if(e){const n=this._current_connection_type;let i=uf.any(e.value);e.name()==this.p.int.name()&&m.isNumber(e.value)&&(i=uf.integer(e.value));const r=`${n} ${this._current_var_name} = ${i}`;t.addBodyLines(this,[r])}else console.warn(`no param found for constant node for type '${this.pv.type}'`)}get _current_connection_type(){null==this.pv.type&&console.warn(\\\\\\\"constant gl node type if not valid\\\\\\\");const t=ko[this.pv.type];return null==t&&console.warn(\\\\\\\"constant gl node type if not valid\\\\\\\"),t}get _current_param(){this._params_by_type=this._params_by_type||new Map([[Do.BOOL,this.p.bool],[Do.INT,this.p.int],[Do.FLOAT,this.p.float],[Do.VEC2,this.p.vec2],[Do.VEC3,this.p.vec3],[Do.VEC4,this.p.vec4]]);const t=ko[this.pv.type];return this._params_by_type.get(t)}get _current_var_name(){return this.glVarName(sP.OUTPUT_NAME)}set_gl_type(t){this.p.type.set(ko.indexOf(t))}}sP.OUTPUT_NAME=\\\\\\\"val\\\\\\\";const oP=\\\\\\\"cross\\\\\\\";const aP=new class extends aa{constructor(){super(...arguments),this.x=oa.VECTOR3([0,0,1]),this.y=oa.VECTOR3([0,1,0])}};class lP extends df{constructor(){super(...arguments),this.paramsConfig=aP}static type(){return\\\\\\\"cross\\\\\\\"}initializeNode(){super.initializeNode(),this.io.outputs.setNamedOutputConnectionPoints([new Vo(oP,Do.VEC3)])}setLines(t){const e=uf.float(this.variableForInputParam(this.p.x)),n=uf.float(this.variableForInputParam(this.p.y)),i=`vec3 ${this.glVarName(oP)} = cross(${e}, ${n})`;t.addBodyLines(this,[i])}}class cP extends(yR(\\\\\\\"cycle\\\\\\\",{in:[\\\\\\\"in\\\\\\\",\\\\\\\"min\\\\\\\",\\\\\\\"max\\\\\\\"],default:{max:1},functions:[\\\\\\\"float cycle(float val, float val_min, float val_max){\\\\n\\\\tif(val >= val_min && val < val_max){\\\\n\\\\t\\\\treturn val;\\\\n\\\\t} else {\\\\n\\\\t\\\\tfloat range = val_max - val_min;\\\\n\\\\t\\\\tif(val >= val_max){\\\\n\\\\t\\\\t\\\\tfloat delta = (val - val_max);\\\\n\\\\t\\\\t\\\\treturn val_min + mod(delta, range);\\\\n\\\\t\\\\t} else {\\\\n\\\\t\\\\t\\\\tfloat delta = (val_min - val);\\\\n\\\\t\\\\t\\\\treturn val_max - mod(delta, range);\\\\n\\\\t\\\\t}\\\\n\\\\t}\\\\n}\\\\\\\"]})){}var uP=\\\\\\\"float disk_feather(float dist, float radius, float feather){\\\\n\\\\tif(feather <= 0.0){\\\\n\\\\t\\\\tif(dist < radius){return 1.0;}else{return 0.0;}\\\\n\\\\t} else {\\\\n\\\\t\\\\tfloat half_feather = feather * 0.5;\\\\n\\\\t\\\\tif(dist < (radius - half_feather)){\\\\n\\\\t\\\\t\\\\treturn 1.0;\\\\n\\\\t\\\\t} else {\\\\n\\\\t\\\\t\\\\tif(dist > (radius + half_feather)){\\\\n\\\\t\\\\t\\\\t\\\\treturn 0.0;\\\\n\\\\t\\\\t\\\\t} else {\\\\n\\\\t\\\\t\\\\t\\\\tfloat feather_start = (radius - half_feather);\\\\n\\\\t\\\\t\\\\t\\\\tfloat blend = 1.0 - (dist - feather_start) / feather;\\\\n\\\\t\\\\t\\\\t\\\\treturn blend;\\\\n\\\\t\\\\t\\\\t}\\\\n\\\\t\\\\t}\\\\n\\\\t}\\\\n}\\\\n\\\\nfloat disk2d(vec2 pos, vec2 center, float radius, float feather){\\\\n\\\\tfloat dist = distance(pos, center);\\\\n\\\\treturn disk_feather(dist, radius, feather);\\\\n}\\\\n\\\\n// function could be called sphere, but is an overload of disk, and is the same\\\\nfloat disk3d(vec3 pos, vec3 center, float radius, float feather){\\\\n\\\\tfloat dist = distance(pos, center);\\\\n\\\\treturn disk_feather(dist, radius, feather);\\\\n}\\\\\\\";const hP=new class extends aa{constructor(){super(...arguments),this.position=oa.VECTOR2([0,0]),this.center=oa.VECTOR2([0,0]),this.radius=oa.FLOAT(1),this.feather=oa.FLOAT(.1)}};class dP extends df{constructor(){super(...arguments),this.paramsConfig=hP}static type(){return\\\\\\\"disk\\\\\\\"}initializeNode(){super.initializeNode(),this.io.outputs.setNamedOutputConnectionPoints([new Vo(\\\\\\\"float\\\\\\\",Do.FLOAT)])}setLines(t){const e=uf.vector2(this.variableForInputParam(this.p.position)),n=uf.vector2(this.variableForInputParam(this.p.center)),i=uf.float(this.variableForInputParam(this.p.radius)),r=uf.float(this.variableForInputParam(this.p.feather)),s=`float ${this.glVarName(\\\\\\\"float\\\\\\\")} = disk2d(${e}, ${n}, ${i}, ${r})`;t.addBodyLines(this,[s]),t.addDefinitions(this,[new Tf(this,uP)])}}var pP=\\\\\\\"\\\\nfloat bounceOut(float t) {\\\\n  const float a = 4.0 / 11.0;\\\\n  const float b = 8.0 / 11.0;\\\\n  const float c = 9.0 / 10.0;\\\\n\\\\n  const float ca = 4356.0 / 361.0;\\\\n  const float cb = 35442.0 / 1805.0;\\\\n  const float cc = 16061.0 / 1805.0;\\\\n\\\\n  float t2 = t * t;\\\\n\\\\n  return t < a\\\\n    ? 7.5625 * t2\\\\n    : t < b\\\\n      ? 9.075 * t2 - 9.9 * t + 3.4\\\\n      : t < c\\\\n        ? ca * t2 - cb * t + cc\\\\n        : 10.8 * t * t - 20.52 * t + 10.72;\\\\n}\\\\n\\\\n\\\\\\\";const _P=[\\\\\\\"back-in-out\\\\\\\",\\\\\\\"back-in\\\\\\\",\\\\\\\"back-out\\\\\\\",\\\\\\\"bounce-in-out\\\\\\\",\\\\\\\"bounce-in\\\\\\\",\\\\\\\"bounce-out\\\\\\\",\\\\\\\"circular-in-out\\\\\\\",\\\\\\\"circular-in\\\\\\\",\\\\\\\"circular-out\\\\\\\",\\\\\\\"cubic-in-out\\\\\\\",\\\\\\\"cubic-in\\\\\\\",\\\\\\\"cubic-out\\\\\\\",\\\\\\\"elastic-in-out\\\\\\\",\\\\\\\"elastic-in\\\\\\\",\\\\\\\"elastic-out\\\\\\\",\\\\\\\"exponential-in-out\\\\\\\",\\\\\\\"exponential-in\\\\\\\",\\\\\\\"exponential-out\\\\\\\",\\\\\\\"linear\\\\\\\",\\\\\\\"quadratic-in-out\\\\\\\",\\\\\\\"quadratic-in\\\\\\\",\\\\\\\"quadratic-out\\\\\\\",\\\\\\\"sine-in-out\\\\\\\",\\\\\\\"sine-in\\\\\\\",\\\\\\\"sine-out\\\\\\\"],mP={\\\\\\\"circular-in-out\\\\\\\":\\\\\\\"float circularInOut(float t) {\\\\n  return t < 0.5\\\\n    ? 0.5 * (1.0 - sqrt(1.0 - 4.0 * t * t))\\\\n    : 0.5 * (sqrt((3.0 - 2.0 * t) * (2.0 * t - 1.0)) + 1.0);\\\\n}\\\\n\\\\n\\\\\\\",\\\\\\\"exponential-in-out\\\\\\\":\\\\\\\"float exponentialInOut(float t) {\\\\n  return t == 0.0 || t == 1.0\\\\n    ? t\\\\n    : t < 0.5\\\\n      ? +0.5 * pow(2.0, (20.0 * t) - 10.0)\\\\n      : -0.5 * pow(2.0, 10.0 - (t * 20.0)) + 1.0;\\\\n}\\\\n\\\\n\\\\\\\",\\\\\\\"circular-in\\\\\\\":\\\\\\\"float circularIn(float t) {\\\\n  return 1.0 - sqrt(1.0 - t * t);\\\\n}\\\\n\\\\n\\\\\\\",\\\\\\\"elastic-out\\\\\\\":\\\\\\\"#ifndef HALF_PI\\\\n#define HALF_PI 1.5707963267948966\\\\n#endif\\\\n\\\\nfloat elasticOut(float t) {\\\\n  return sin(-13.0 * (t + 1.0) * HALF_PI) * pow(2.0, -10.0 * t) + 1.0;\\\\n}\\\\n\\\\n\\\\\\\",\\\\\\\"cubic-in\\\\\\\":\\\\\\\"float cubicIn(float t) {\\\\n  return t * t * t;\\\\n}\\\\n\\\\n\\\\\\\",\\\\\\\"exponential-out\\\\\\\":\\\\\\\"float exponentialOut(float t) {\\\\n  return t == 1.0 ? t : 1.0 - pow(2.0, -10.0 * t);\\\\n}\\\\n\\\\n\\\\\\\",\\\\\\\"quintic-out\\\\\\\":\\\\\\\"float quinticOut(float t) {\\\\n  return 1.0 - (pow(t - 1.0, 5.0));\\\\n}\\\\n\\\\n\\\\\\\",\\\\\\\"elastic-in-out\\\\\\\":\\\\\\\"#ifndef HALF_PI\\\\n#define HALF_PI 1.5707963267948966\\\\n#endif\\\\n\\\\nfloat elasticInOut(float t) {\\\\n  return t < 0.5\\\\n    ? 0.5 * sin(+13.0 * HALF_PI * 2.0 * t) * pow(2.0, 10.0 * (2.0 * t - 1.0))\\\\n    : 0.5 * sin(-13.0 * HALF_PI * ((2.0 * t - 1.0) + 1.0)) * pow(2.0, -10.0 * (2.0 * t - 1.0)) + 1.0;\\\\n}\\\\n\\\\n\\\\\\\",linear:\\\\\\\"float linear(float t) {\\\\n  return t;\\\\n}\\\\n\\\\n\\\\\\\",\\\\\\\"circular-out\\\\\\\":\\\\\\\"float circularOut(float t) {\\\\n  return sqrt((2.0 - t) * t);\\\\n}\\\\n\\\\n\\\\\\\",\\\\\\\"back-in-out\\\\\\\":\\\\\\\"\\\\nfloat backInOut(float t) {\\\\n  float f = t < 0.5\\\\n    ? 2.0 * t\\\\n    : 1.0 - (2.0 * t - 1.0);\\\\n\\\\n  float g = pow(f, 3.0) - f * sin(f * PI);\\\\n\\\\n  return t < 0.5\\\\n    ? 0.5 * g\\\\n    : 0.5 * (1.0 - g) + 0.5;\\\\n}\\\\n\\\\n\\\\\\\",\\\\\\\"back-in\\\\\\\":\\\\\\\"\\\\nfloat backIn(float t) {\\\\n  return pow(t, 3.0) - t * sin(t * PI);\\\\n}\\\\n\\\\n\\\\\\\",\\\\\\\"sine-in\\\\\\\":\\\\\\\"#ifndef HALF_PI\\\\n#define HALF_PI 1.5707963267948966\\\\n#endif\\\\n\\\\nfloat sineIn(float t) {\\\\n  return sin((t - 1.0) * HALF_PI) + 1.0;\\\\n}\\\\n\\\\n\\\\\\\",\\\\\\\"back-out\\\\\\\":\\\\\\\"\\\\nfloat backOut(float t) {\\\\n  float f = 1.0 - t;\\\\n  return 1.0 - (pow(f, 3.0) - f * sin(f * PI));\\\\n}\\\\n\\\\n\\\\\\\",\\\\\\\"quartic-in-out\\\\\\\":\\\\\\\"float quarticInOut(float t) {\\\\n  return t < 0.5\\\\n    ? +8.0 * pow(t, 4.0)\\\\n    : -8.0 * pow(t - 1.0, 4.0) + 1.0;\\\\n}\\\\n\\\\n\\\\\\\",\\\\\\\"quadratic-in\\\\\\\":\\\\\\\"float quadraticIn(float t) {\\\\n  return t * t;\\\\n}\\\\n\\\\n\\\\\\\",\\\\\\\"cubic-in-out\\\\\\\":\\\\\\\"float cubicInOut(float t) {\\\\n  return t < 0.5\\\\n    ? 4.0 * t * t * t\\\\n    : 0.5 * pow(2.0 * t - 2.0, 3.0) + 1.0;\\\\n}\\\\n\\\\n\\\\\\\",\\\\\\\"elastic-in\\\\\\\":\\\\\\\"#ifndef HALF_PI\\\\n#define HALF_PI 1.5707963267948966\\\\n#endif\\\\n\\\\nfloat elasticIn(float t) {\\\\n  return sin(13.0 * t * HALF_PI) * pow(2.0, 10.0 * (t - 1.0));\\\\n}\\\\n\\\\n\\\\\\\",\\\\\\\"bounce-out\\\\\\\":pP,\\\\\\\"quadratic-in-out\\\\\\\":\\\\\\\"float quadraticInOut(float t) {\\\\n  float p = 2.0 * t * t;\\\\n  return t < 0.5 ? p : -p + (4.0 * t) - 1.0;\\\\n}\\\\n\\\\n\\\\\\\",\\\\\\\"exponential-in\\\\\\\":\\\\\\\"float exponentialIn(float t) {\\\\n  return t == 0.0 ? t : pow(2.0, 10.0 * (t - 1.0));\\\\n}\\\\n\\\\n\\\\\\\",\\\\\\\"quintic-in-out\\\\\\\":\\\\\\\"float quinticInOut(float t) {\\\\n  return t < 0.5\\\\n    ? +16.0 * pow(t, 5.0)\\\\n    : -0.5 * pow(2.0 * t - 2.0, 5.0) + 1.0;\\\\n}\\\\n\\\\n\\\\\\\",\\\\\\\"sine-in-out\\\\\\\":\\\\\\\"\\\\nfloat sineInOut(float t) {\\\\n  return -0.5 * (cos(PI * t) - 1.0);\\\\n}\\\\n\\\\n\\\\\\\",\\\\\\\"cubic-out\\\\\\\":\\\\\\\"float cubicOut(float t) {\\\\n  float f = t - 1.0;\\\\n  return f * f * f + 1.0;\\\\n}\\\\n\\\\n\\\\\\\",\\\\\\\"quadratic-out\\\\\\\":\\\\\\\"float quadraticOut(float t) {\\\\n  return -t * (t - 2.0);\\\\n}\\\\n\\\\n\\\\\\\",\\\\\\\"bounce-in-out\\\\\\\":\\\\\\\"\\\\nfloat bounceInOut(float t) {\\\\n  return t < 0.5\\\\n    ? 0.5 * (1.0 - bounceOut(1.0 - t * 2.0))\\\\n    : 0.5 * bounceOut(t * 2.0 - 1.0) + 0.5;\\\\n}\\\\n\\\\n\\\\n\\\\n\\\\\\\",\\\\\\\"quintic-in\\\\\\\":\\\\\\\"float quinticIn(float t) {\\\\n  return pow(t, 5.0);\\\\n}\\\\n\\\\n\\\\\\\",\\\\\\\"quartic-in\\\\\\\":\\\\\\\"float quarticIn(float t) {\\\\n  return pow(t, 4.0);\\\\n}\\\\n\\\\n\\\\\\\",\\\\\\\"quartic-out\\\\\\\":\\\\\\\"float quarticOut(float t) {\\\\n  return pow(t - 1.0, 3.0) * (1.0 - t) + 1.0;\\\\n}\\\\n\\\\n\\\\\\\",\\\\\\\"bounce-in\\\\\\\":\\\\\\\"\\\\nfloat bounceIn(float t) {\\\\n  return 1.0 - bounceOut(1.0 - t);\\\\n}\\\\n\\\\n\\\\\\\",\\\\\\\"sine-out\\\\\\\":\\\\\\\"#ifndef HALF_PI\\\\n#define HALF_PI 1.5707963267948966\\\\n#endif\\\\n\\\\nfloat sineOut(float t) {\\\\n  return sin(t * HALF_PI);\\\\n}\\\\n\\\\n\\\\\\\"},fP={\\\\\\\"bounce-in\\\\\\\":[pP],\\\\\\\"bounce-in-out\\\\\\\":[pP]},gP={\\\\\\\"circular-in-out\\\\\\\":\\\\\\\"circularInOut\\\\\\\",\\\\\\\"exponential-in-out\\\\\\\":\\\\\\\"exponentialInOut\\\\\\\",\\\\\\\"circular-in\\\\\\\":\\\\\\\"circularIn\\\\\\\",\\\\\\\"elastic-out\\\\\\\":\\\\\\\"elasticOut\\\\\\\",\\\\\\\"cubic-in\\\\\\\":\\\\\\\"cubicIn\\\\\\\",\\\\\\\"exponential-out\\\\\\\":\\\\\\\"exponentialOut\\\\\\\",\\\\\\\"quintic-out\\\\\\\":\\\\\\\"quinticOut\\\\\\\",\\\\\\\"elastic-in-out\\\\\\\":\\\\\\\"elasticInOut\\\\\\\",linear:\\\\\\\"linear\\\\\\\",\\\\\\\"circular-out\\\\\\\":\\\\\\\"circularOut\\\\\\\",\\\\\\\"back-in-out\\\\\\\":\\\\\\\"backInOut\\\\\\\",\\\\\\\"back-in\\\\\\\":\\\\\\\"backIn\\\\\\\",\\\\\\\"sine-in\\\\\\\":\\\\\\\"sineIn\\\\\\\",\\\\\\\"back-out\\\\\\\":\\\\\\\"backOut\\\\\\\",\\\\\\\"quartic-in-out\\\\\\\":\\\\\\\"quarticInOut\\\\\\\",\\\\\\\"quadratic-in\\\\\\\":\\\\\\\"quadraticIn\\\\\\\",\\\\\\\"cubic-in-out\\\\\\\":\\\\\\\"cubicInOut\\\\\\\",\\\\\\\"elastic-in\\\\\\\":\\\\\\\"elasticIn\\\\\\\",\\\\\\\"bounce-out\\\\\\\":\\\\\\\"bounceOut\\\\\\\",\\\\\\\"quadratic-in-out\\\\\\\":\\\\\\\"quadraticInOut\\\\\\\",\\\\\\\"exponential-in\\\\\\\":\\\\\\\"exponentialIn\\\\\\\",\\\\\\\"quintic-in-out\\\\\\\":\\\\\\\"quinticInOut\\\\\\\",\\\\\\\"sine-in-out\\\\\\\":\\\\\\\"sineInOut\\\\\\\",\\\\\\\"cubic-out\\\\\\\":\\\\\\\"cubicOut\\\\\\\",\\\\\\\"quadratic-out\\\\\\\":\\\\\\\"quadraticOut\\\\\\\",\\\\\\\"bounce-in-out\\\\\\\":\\\\\\\"bounceInOut\\\\\\\",\\\\\\\"quintic-in\\\\\\\":\\\\\\\"quinticIn\\\\\\\",\\\\\\\"quartic-in\\\\\\\":\\\\\\\"quarticIn\\\\\\\",\\\\\\\"quartic-out\\\\\\\":\\\\\\\"quarticOut\\\\\\\",\\\\\\\"bounce-in\\\\\\\":\\\\\\\"bounceIn\\\\\\\",\\\\\\\"sine-out\\\\\\\":\\\\\\\"sineOut\\\\\\\"},vP=_P.indexOf(\\\\\\\"sine-in-out\\\\\\\");const yP=new class extends aa{constructor(){super(...arguments),this.type=oa.INTEGER(vP,{menu:{entries:_P.map(((t,e)=>({name:t,value:e})))}}),this.input=oa.FLOAT(0)}};class xP extends df{constructor(){super(...arguments),this.paramsConfig=yP}static type(){return\\\\\\\"easing\\\\\\\"}initializeNode(){super.initializeNode(),this.io.connection_points.spare_params.set_inputless_param_names([\\\\\\\"type\\\\\\\"]),this.io.outputs.setNamedOutputConnectionPoints([new Vo(\\\\\\\"out\\\\\\\",Do.FLOAT)])}setLines(t){const e=_P[this.pv.type],n=gP[e];let i=[new Tf(this,mP[e])];const r=(fP[e]||[]).map((t=>new Tf(this,t)));r&&(i=r.concat(i));const s=uf.float(this.variableForInputParam(this.p.input)),o=`float ${this.glVarName(\\\\\\\"out\\\\\\\")} = ${n}(${s})`;t.addDefinitions(this,i),t.addBodyLines(this,[o])}}var bP=\\\\\\\"//\\\\n//\\\\n// FIT\\\\n//\\\\n//\\\\nfloat fit(float val, float srcMin, float srcMax, float destMin, float destMax){\\\\n\\\\tfloat src_range = srcMax - srcMin;\\\\n\\\\tfloat dest_range = destMax - destMin;\\\\n\\\\n\\\\tfloat r = (val - srcMin) / src_range;\\\\n\\\\treturn (r * dest_range) + destMin;\\\\n}\\\\nvec2 fit(vec2 val, vec2 srcMin, vec2 srcMax, vec2 destMin, vec2 destMax){\\\\n\\\\treturn vec2(\\\\n\\\\t\\\\tfit(val.x, srcMin.x, srcMax.x, destMin.x, destMax.x),\\\\n\\\\t\\\\tfit(val.y, srcMin.y, srcMax.y, destMin.y, destMax.y)\\\\n\\\\t);\\\\n}\\\\nvec3 fit(vec3 val, vec3 srcMin, vec3 srcMax, vec3 destMin, vec3 destMax){\\\\n\\\\treturn vec3(\\\\n\\\\t\\\\tfit(val.x, srcMin.x, srcMax.x, destMin.x, destMax.x),\\\\n\\\\t\\\\tfit(val.y, srcMin.y, srcMax.y, destMin.y, destMax.y),\\\\n\\\\t\\\\tfit(val.z, srcMin.z, srcMax.z, destMin.z, destMax.z)\\\\n\\\\t);\\\\n}\\\\nvec4 fit(vec4 val, vec4 srcMin, vec4 srcMax, vec4 destMin, vec4 destMax){\\\\n\\\\treturn vec4(\\\\n\\\\t\\\\tfit(val.x, srcMin.x, srcMax.x, destMin.x, destMax.x),\\\\n\\\\t\\\\tfit(val.y, srcMin.y, srcMax.y, destMin.y, destMax.y),\\\\n\\\\t\\\\tfit(val.z, srcMin.z, srcMax.z, destMin.z, destMax.z),\\\\n\\\\t\\\\tfit(val.w, srcMin.w, srcMax.w, destMin.w, destMax.w)\\\\n\\\\t);\\\\n}\\\\n\\\\n//\\\\n//\\\\n// FIT TO 01\\\\n// fits the range [srcMin, srcMax] to [0, 1]\\\\n//\\\\nfloat fitTo01(float val, float srcMin, float srcMax){\\\\n\\\\tfloat size = srcMax - srcMin;\\\\n\\\\treturn (val - srcMin) / size;\\\\n}\\\\nvec2 fitTo01(vec2 val, vec2 srcMin, vec2 srcMax){\\\\n\\\\treturn vec2(\\\\n\\\\t\\\\tfitTo01(val.x, srcMin.x, srcMax.x),\\\\n\\\\t\\\\tfitTo01(val.y, srcMin.y, srcMax.y)\\\\n\\\\t);\\\\n}\\\\nvec3 fitTo01(vec3 val, vec3 srcMin, vec3 srcMax){\\\\n\\\\treturn vec3(\\\\n\\\\t\\\\tfitTo01(val.x, srcMin.x, srcMax.x),\\\\n\\\\t\\\\tfitTo01(val.y, srcMin.y, srcMax.y),\\\\n\\\\t\\\\tfitTo01(val.z, srcMin.z, srcMax.z)\\\\n\\\\t);\\\\n}\\\\nvec4 fitTo01(vec4 val, vec4 srcMin, vec4 srcMax){\\\\n\\\\treturn vec4(\\\\n\\\\t\\\\tfitTo01(val.x, srcMin.x, srcMax.x),\\\\n\\\\t\\\\tfitTo01(val.y, srcMin.y, srcMax.y),\\\\n\\\\t\\\\tfitTo01(val.z, srcMin.z, srcMax.z),\\\\n\\\\t\\\\tfitTo01(val.w, srcMin.w, srcMax.w)\\\\n\\\\t);\\\\n}\\\\n\\\\n//\\\\n//\\\\n// FIT FROM 01\\\\n// fits the range [0, 1] to [destMin, destMax]\\\\n//\\\\nfloat fitFrom01(float val, float destMin, float destMax){\\\\n\\\\treturn fit(val, 0.0, 1.0, destMin, destMax);\\\\n}\\\\nvec2 fitFrom01(vec2 val, vec2 srcMin, vec2 srcMax){\\\\n\\\\treturn vec2(\\\\n\\\\t\\\\tfitFrom01(val.x, srcMin.x, srcMax.x),\\\\n\\\\t\\\\tfitFrom01(val.y, srcMin.y, srcMax.y)\\\\n\\\\t);\\\\n}\\\\nvec3 fitFrom01(vec3 val, vec3 srcMin, vec3 srcMax){\\\\n\\\\treturn vec3(\\\\n\\\\t\\\\tfitFrom01(val.x, srcMin.x, srcMax.x),\\\\n\\\\t\\\\tfitFrom01(val.y, srcMin.y, srcMax.y),\\\\n\\\\t\\\\tfitFrom01(val.z, srcMin.z, srcMax.z)\\\\n\\\\t);\\\\n}\\\\nvec4 fitFrom01(vec4 val, vec4 srcMin, vec4 srcMax){\\\\n\\\\treturn vec4(\\\\n\\\\t\\\\tfitFrom01(val.x, srcMin.x, srcMax.x),\\\\n\\\\t\\\\tfitFrom01(val.y, srcMin.y, srcMax.y),\\\\n\\\\t\\\\tfitFrom01(val.z, srcMin.z, srcMax.z),\\\\n\\\\t\\\\tfitFrom01(val.w, srcMin.w, srcMax.w)\\\\n\\\\t);\\\\n}\\\\n\\\\n//\\\\n//\\\\n// FIT FROM 01 TO VARIANCE\\\\n// fits the range [0, 1] to [center - variance, center + variance]\\\\n//\\\\nfloat fitFrom01ToVariance(float val, float center, float variance){\\\\n\\\\treturn fitFrom01(val, center - variance, center + variance);\\\\n}\\\\nvec2 fitFrom01ToVariance(vec2 val, vec2 center, vec2 variance){\\\\n\\\\treturn vec2(\\\\n\\\\t\\\\tfitFrom01ToVariance(val.x, center.x, variance.x),\\\\n\\\\t\\\\tfitFrom01ToVariance(val.y, center.y, variance.y)\\\\n\\\\t);\\\\n}\\\\nvec3 fitFrom01ToVariance(vec3 val, vec3 center, vec3 variance){\\\\n\\\\treturn vec3(\\\\n\\\\t\\\\tfitFrom01ToVariance(val.x, center.x, variance.x),\\\\n\\\\t\\\\tfitFrom01ToVariance(val.y, center.y, variance.y),\\\\n\\\\t\\\\tfitFrom01ToVariance(val.z, center.z, variance.z)\\\\n\\\\t);\\\\n}\\\\nvec4 fitFrom01ToVariance(vec4 val, vec4 center, vec4 variance){\\\\n\\\\treturn vec4(\\\\n\\\\t\\\\tfitFrom01ToVariance(val.x, center.x, variance.x),\\\\n\\\\t\\\\tfitFrom01ToVariance(val.y, center.y, variance.y),\\\\n\\\\t\\\\tfitFrom01ToVariance(val.z, center.z, variance.z),\\\\n\\\\t\\\\tfitFrom01ToVariance(val.w, center.w, variance.w)\\\\n\\\\t);\\\\n}\\\\\\\";const wP={srcMin:0,srcMax:1,destMin:0,destMax:1};class TP extends VO{static type(){return\\\\\\\"fit\\\\\\\"}_gl_input_name(t){return[\\\\\\\"val\\\\\\\",\\\\\\\"srcMin\\\\\\\",\\\\\\\"srcMax\\\\\\\",\\\\\\\"destMin\\\\\\\",\\\\\\\"destMax\\\\\\\"][t]}paramDefaultValue(t){return wP[t]}gl_method_name(){return\\\\\\\"fit\\\\\\\"}gl_function_definitions(){return[new Tf(this,bP)]}}const AP={srcMin:0,srcMax:1};class EP extends UO{static type(){return\\\\\\\"fitTo01\\\\\\\"}_gl_input_name(t){return[\\\\\\\"val\\\\\\\",\\\\\\\"srcMin\\\\\\\",\\\\\\\"srcMax\\\\\\\"][t]}paramDefaultValue(t){return AP[t]}gl_method_name(){return\\\\\\\"fitTo01\\\\\\\"}gl_function_definitions(){return[new Tf(this,bP)]}}const MP={destMin:0,destMax:1};class SP extends UO{static type(){return\\\\\\\"fitFrom01\\\\\\\"}_gl_input_name(t){return[\\\\\\\"val\\\\\\\",\\\\\\\"destMin\\\\\\\",\\\\\\\"destMax\\\\\\\"][t]}paramDefaultValue(t){return MP[t]}gl_method_name(){return\\\\\\\"fitFrom01\\\\\\\"}gl_function_definitions(){return[new Tf(this,bP)]}}const CP={center:.5,variance:.5};class NP extends UO{static type(){return\\\\\\\"fitFrom01ToVariance\\\\\\\"}_gl_input_name(t){return[\\\\\\\"val\\\\\\\",\\\\\\\"center\\\\\\\",\\\\\\\"variance\\\\\\\"][t]}paramDefaultValue(t){return CP[t]}gl_method_name(){return\\\\\\\"fitFrom01ToVariance\\\\\\\"}gl_function_definitions(){return[new Tf(this,bP)]}}const LP=\\\\\\\"color\\\\\\\";const OP=new class extends aa{constructor(){super(...arguments),this.mvPosition=oa.VECTOR4([0,0,0,0]),this.baseColor=oa.COLOR([0,0,0]),this.fogColor=oa.COLOR([1,1,1]),this.near=oa.FLOAT(0),this.far=oa.FLOAT(0)}};class RP extends df{constructor(){super(...arguments),this.paramsConfig=OP}static type(){return\\\\\\\"fog\\\\\\\"}initializeNode(){super.initializeNode(),this.io.outputs.setNamedOutputConnectionPoints([new Vo(LP,Do.VEC3)])}setLines(t){if(t.current_shader_name==xf.FRAGMENT){const e=this.glVarName(this.name()),n=new Ef(this,Do.VEC4,e),i=`${e} = modelViewMatrix * vec4(position, 1.0)`;t.addDefinitions(this,[n],xf.VERTEX),t.addBodyLines(this,[i],xf.VERTEX);const r=new Tf(this,\\\\\\\"vec3 compute_fog(vec4 mvPosition, vec3 base_color, vec3 fog_color, float near, float far) {\\\\n\\\\tfloat blend = (-mvPosition.z - near) / (far - near);\\\\n\\\\tblend = clamp(blend, 0.0, 1.0);\\\\n\\\\treturn blend * fog_color + (1.0 - blend) * base_color;\\\\n}\\\\\\\"),s=uf.vector4(this.variableForInputParam(this.p.mvPosition)),o=uf.vector3(this.variableForInputParam(this.p.baseColor)),a=uf.vector3(this.variableForInputParam(this.p.fogColor)),l=uf.vector3(this.variableForInputParam(this.p.near)),c=uf.vector3(this.variableForInputParam(this.p.far)),u=`vec3 ${this.glVarName(LP)} = compute_fog(${[s,o,a,l,c].join(\\\\\\\", \\\\\\\")})`;t.addDefinitions(this,[n,r]),t.addBodyLines(this,[u])}}}const PP=new class extends aa{};class IP extends df{constructor(){super(...arguments),this.paramsConfig=PP}static type(){return er.OUTPUT}initializeNode(){this.io.connection_points.set_input_name_function(this._expected_input_name.bind(this)),this.io.connection_points.set_expected_output_types_function((()=>[])),this.io.connection_points.set_expected_input_types_function(this._expected_input_types.bind(this)),this.io.connection_points.set_create_spare_params_from_inputs(!1),this.addPostDirtyHook(\\\\\\\"setParentDirty\\\\\\\",(()=>{var t;null===(t=this.parent())||void 0===t||t.setDirty(this)}))}parent(){return super.parent()}_expected_input_name(t){const e=this.parent();return(null==e?void 0:e.child_expected_output_connection_point_name(t))||`in${t}`}_expected_input_types(){const t=this.parent();return(null==t?void 0:t.child_expected_output_connection_point_types())||[]}setLines(t){const e=this.parent();if(!e)return;const n=[],i=this.io.connections.inputConnections();if(i)for(let t of i)if(t){const i=t.dest_connection_point(),r=uf.any(this.variableForInput(i.name())),s=`\\\\t${e.glVarName(i.name())} = ${r}`;n.push(s)}t.addBodyLines(this,n),e.set_lines_block_end(t,this)}}class FP extends df{constructor(){super(...arguments),this._children_controller_context=Ki.GL}initializeNode(){var t;null===(t=this.childrenController)||void 0===t||t.set_output_node_find_method((()=>this.nodesByType(IP.type())[0])),this.io.connection_points.set_input_name_function(this._expected_input_name.bind(this)),this.io.connection_points.set_output_name_function(this._expected_output_name.bind(this)),this.io.connection_points.set_expected_input_types_function(this._expected_input_types.bind(this)),this.io.connection_points.set_expected_output_types_function(this._expected_output_types.bind(this))}_expected_inputs_count(){const t=this.io.connections.inputConnections();return t?t.length+1:1}_expected_input_types(){const t=[],e=Do.FLOAT,n=this.io.connections.inputConnections(),i=this._expected_inputs_count();for(let r=0;r<i;r++)if(n){const i=n[r];if(i){const e=i.src_connection_point().type();t.push(e)}else t.push(e)}else t.push(e);return t}_expected_output_types(){const t=[],e=this._expected_input_types();for(let n=0;n<e.length;n++)t.push(e[n]);return t}_expected_input_name(t){const e=this.io.connections.inputConnection(t);if(e){return e.src_connection_point().name()}return`in${t}`}_expected_output_name(t){return this._expected_input_name(t)}child_expected_input_connection_point_types(){return this._expected_input_types()}child_expected_output_connection_point_types(){return this._expected_output_types()}child_expected_input_connection_point_name(t){return this._expected_input_name(t)}child_expected_output_connection_point_name(t){return this._expected_output_name(t)}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}set_lines_block_start(t,e){const n=[],i=this.io.inputs.namedInputConnectionPoints();for(let t=0;t<i.length;t++){const e=i[t],r=`${e.type()} ${this.glVarName(e.name())} = ${uf.any(this.variableForInput(e.name()))}`;n.push(r)}n.push(\\\\\\\"if(true){\\\\\\\");const r=this.io.connections.inputConnections();if(r)for(let t of r)if(t){const i=t.dest_connection_point(),r=uf.any(this.variableForInput(i.name())),s=`\\\\t${i.type()} ${e.glVarName(i.name())} = ${r}`;n.push(s)}t.addBodyLines(e,n)}set_lines_block_end(t,e){t.addBodyLines(e,[\\\\\\\"}\\\\\\\"])}setLines(t){}}const DP=new class extends aa{};class kP extends FP{constructor(){super(...arguments),this.paramsConfig=DP}static type(){return\\\\\\\"subnet\\\\\\\"}}var BP;!function(t){t.START_INDEX=\\\\\\\"i\\\\\\\",t.MAX=\\\\\\\"max\\\\\\\",t.STEP=\\\\\\\"step\\\\\\\"}(BP||(BP={}));const zP={[BP.START_INDEX]:0,[BP.MAX]:10,[BP.STEP]:1};const UP=new class extends aa{constructor(){super(...arguments),this.start=oa.FLOAT(0),this.max=oa.FLOAT(10,{range:[0,100],rangeLocked:[!1,!1]}),this.step=oa.FLOAT(1)}};class GP extends FP{constructor(){super(...arguments),this.paramsConfig=UP}static type(){return\\\\\\\"forLoop\\\\\\\"}paramDefaultValue(t){return zP[t]}_expected_inputs_count(){const t=this.io.connections.inputConnections();return t?t.length+1:1}_expected_input_types(){const t=[],e=Do.FLOAT,n=this.io.connections.inputConnections(),i=this._expected_inputs_count();for(let r=0;r<i;r++)if(n){const i=n[r];if(i){const e=i.src_connection_point().type();t.push(e)}else t.push(e)}else t.push(e);return t}_expected_output_types(){const t=[],e=this._expected_input_types();for(let n=0;n<e.length;n++)t.push(e[n]);return t}_expected_input_name(t){const e=this.io.connections.inputConnection(t);if(e){return e.src_connection_point().name()}return`in${t}`}_expected_output_name(t){return this._expected_input_name(t+0)}child_expected_input_connection_point_types(){return this._expected_input_types()}child_expected_input_connection_point_name(t){return this._expected_input_name(t)}child_expected_output_connection_point_types(){return this._expected_output_types()}child_expected_output_connection_point_name(t){return this._expected_output_name(t)}set_lines_block_start(t,e){const n=[],i=this.io.inputs.namedInputConnectionPoints();for(let t=0;t<i.length;t++){const e=i[t],r=`${e.type()} ${this.glVarName(e.name())} = ${uf.any(this.variableForInput(e.name()))}`;n.push(r)}const r=this.io.connections.inputConnections();if(r)for(let t of r)if(t&&t.input_index>=0){const e=t.dest_connection_point(),i=uf.any(this.variableForInput(e.name())),r=`${e.type()} ${this.glVarName(e.name())} = ${i}`;n.push(r)}const s=this.pv.start,o=this.pv.max,a=this.pv.step,l=uf.float(s),c=uf.float(o),u=uf.float(a),h=this.glVarName(\\\\\\\"i\\\\\\\"),d=`for(float ${h} = ${l}; ${h} < ${c}; ${h}+= ${u}){`;n.push(d);const p=`\\\\tfloat ${e.glVarName(BP.START_INDEX)} = ${h}`;if(n.push(p),r)for(let t of r)if(t&&t.input_index>=0){const i=t.dest_connection_point(),r=this.glVarName(i.name()),s=`\\\\t${i.type()} ${e.glVarName(i.name())} = ${r}`;n.push(s)}t.addBodyLines(e,n)}setLines(t){}}const VP=new class extends aa{};class HP extends df{constructor(){super(...arguments),this.paramsConfig=VP}static type(){return\\\\\\\"globals\\\\\\\"}initializeNode(){super.initializeNode(),this.lifecycle.add_on_add_hook((()=>{var t,e;null===(e=null===(t=this.material_node)||void 0===t?void 0:t.assemblerController)||void 0===e||e.add_globals_outputs(this)}))}setLines(t){t.assembler().set_node_lines_globals(this,t)}}const jP=new class extends aa{constructor(){super(...arguments),this.hsluv=oa.VECTOR3([1,1,1])}};class WP extends df{constructor(){super(...arguments),this.paramsConfig=jP}static type(){return\\\\\\\"hsluvToRgb\\\\\\\"}initializeNode(){super.initializeNode(),this.io.outputs.setNamedOutputConnectionPoints([new Vo(\\\\\\\"rgb\\\\\\\",Do.VEC3)])}setLines(t){const e=[],n=[];e.push(new Tf(this,\\\\\\\"// from https://github.com/williammalo/hsluv-glsl\\\\n/*\\\\nHSLUV-GLSL v4.2\\\\nHSLUV is a human-friendly alternative to HSL. ( http://www.hsluv.org )\\\\nGLSL port by William Malo ( https://github.com/williammalo )\\\\nPut this code in your fragment shader.\\\\n*/\\\\n\\\\nvec3 hsluv_intersectLineLine(vec3 line1x, vec3 line1y, vec3 line2x, vec3 line2y) {\\\\n\\\\treturn (line1y - line2y) / (line2x - line1x);\\\\n}\\\\n\\\\nvec3 hsluv_distanceFromPole(vec3 pointx,vec3 pointy) {\\\\n\\\\treturn sqrt(pointx*pointx + pointy*pointy);\\\\n}\\\\n\\\\nvec3 hsluv_lengthOfRayUntilIntersect(float theta, vec3 x, vec3 y) {\\\\n\\\\tvec3 len = y / (sin(theta) - x * cos(theta));\\\\n\\\\tif (len.r < 0.0) {len.r=1000.0;}\\\\n\\\\tif (len.g < 0.0) {len.g=1000.0;}\\\\n\\\\tif (len.b < 0.0) {len.b=1000.0;}\\\\n\\\\treturn len;\\\\n}\\\\n\\\\nfloat hsluv_maxSafeChromaForL(float L){\\\\n\\\\tmat3 m2 = mat3(\\\\n\\\\t\\\\t 3.2409699419045214  ,-0.96924363628087983 , 0.055630079696993609,\\\\n\\\\t\\\\t-1.5373831775700935  , 1.8759675015077207  ,-0.20397695888897657 ,\\\\n\\\\t\\\\t-0.49861076029300328 , 0.041555057407175613, 1.0569715142428786  \\\\n\\\\t);\\\\n\\\\tfloat sub0 = L + 16.0;\\\\n\\\\tfloat sub1 = sub0 * sub0 * sub0 * .000000641;\\\\n\\\\tfloat sub2 = sub1 > 0.0088564516790356308 ? sub1 : L / 903.2962962962963;\\\\n\\\\n\\\\tvec3 top1   = (284517.0 * m2[0] - 94839.0  * m2[2]) * sub2;\\\\n\\\\tvec3 bottom = (632260.0 * m2[2] - 126452.0 * m2[1]) * sub2;\\\\n\\\\tvec3 top2   = (838422.0 * m2[2] + 769860.0 * m2[1] + 731718.0 * m2[0]) * L * sub2;\\\\n\\\\n\\\\tvec3 bounds0x = top1 / bottom;\\\\n\\\\tvec3 bounds0y = top2 / bottom;\\\\n\\\\n\\\\tvec3 bounds1x =              top1 / (bottom+126452.0);\\\\n\\\\tvec3 bounds1y = (top2-769860.0*L) / (bottom+126452.0);\\\\n\\\\n\\\\tvec3 xs0 = hsluv_intersectLineLine(bounds0x, bounds0y, -1.0/bounds0x, vec3(0.0) );\\\\n\\\\tvec3 xs1 = hsluv_intersectLineLine(bounds1x, bounds1y, -1.0/bounds1x, vec3(0.0) );\\\\n\\\\n\\\\tvec3 lengths0 = hsluv_distanceFromPole( xs0, bounds0y + xs0 * bounds0x );\\\\n\\\\tvec3 lengths1 = hsluv_distanceFromPole( xs1, bounds1y + xs1 * bounds1x );\\\\n\\\\n\\\\treturn  min(lengths0.r,\\\\n\\\\t\\\\t\\\\tmin(lengths1.r,\\\\n\\\\t\\\\t\\\\tmin(lengths0.g,\\\\n\\\\t\\\\t\\\\tmin(lengths1.g,\\\\n\\\\t\\\\t\\\\tmin(lengths0.b,\\\\n\\\\t\\\\t\\\\t\\\\tlengths1.b)))));\\\\n}\\\\n\\\\nfloat hsluv_maxChromaForLH(float L, float H) {\\\\n\\\\n\\\\tfloat hrad = radians(H);\\\\n\\\\n\\\\tmat3 m2 = mat3(\\\\n\\\\t\\\\t 3.2409699419045214  ,-0.96924363628087983 , 0.055630079696993609,\\\\n\\\\t\\\\t-1.5373831775700935  , 1.8759675015077207  ,-0.20397695888897657 ,\\\\n\\\\t\\\\t-0.49861076029300328 , 0.041555057407175613, 1.0569715142428786  \\\\n\\\\t);\\\\n\\\\tfloat sub1 = pow(L + 16.0, 3.0) / 1560896.0;\\\\n\\\\tfloat sub2 = sub1 > 0.0088564516790356308 ? sub1 : L / 903.2962962962963;\\\\n\\\\n\\\\tvec3 top1   = (284517.0 * m2[0] - 94839.0  * m2[2]) * sub2;\\\\n\\\\tvec3 bottom = (632260.0 * m2[2] - 126452.0 * m2[1]) * sub2;\\\\n\\\\tvec3 top2   = (838422.0 * m2[2] + 769860.0 * m2[1] + 731718.0 * m2[0]) * L * sub2;\\\\n\\\\n\\\\tvec3 bound0x = top1 / bottom;\\\\n\\\\tvec3 bound0y = top2 / bottom;\\\\n\\\\n\\\\tvec3 bound1x =              top1 / (bottom+126452.0);\\\\n\\\\tvec3 bound1y = (top2-769860.0*L) / (bottom+126452.0);\\\\n\\\\n\\\\tvec3 lengths0 = hsluv_lengthOfRayUntilIntersect(hrad, bound0x, bound0y );\\\\n\\\\tvec3 lengths1 = hsluv_lengthOfRayUntilIntersect(hrad, bound1x, bound1y );\\\\n\\\\n\\\\treturn  min(lengths0.r,\\\\n\\\\t\\\\t\\\\tmin(lengths1.r,\\\\n\\\\t\\\\t\\\\tmin(lengths0.g,\\\\n\\\\t\\\\t\\\\tmin(lengths1.g,\\\\n\\\\t\\\\t\\\\tmin(lengths0.b,\\\\n\\\\t\\\\t\\\\t\\\\tlengths1.b)))));\\\\n}\\\\n\\\\nfloat hsluv_fromLinear(float c) {\\\\n\\\\treturn c <= 0.0031308 ? 12.92 * c : 1.055 * pow(c, 1.0 / 2.4) - 0.055;\\\\n}\\\\nvec3 hsluv_fromLinear(vec3 c) {\\\\n\\\\treturn vec3( hsluv_fromLinear(c.r), hsluv_fromLinear(c.g), hsluv_fromLinear(c.b) );\\\\n}\\\\n\\\\nfloat hsluv_toLinear(float c) {\\\\n\\\\treturn c > 0.04045 ? pow((c + 0.055) / (1.0 + 0.055), 2.4) : c / 12.92;\\\\n}\\\\n\\\\nvec3 hsluv_toLinear(vec3 c) {\\\\n\\\\treturn vec3( hsluv_toLinear(c.r), hsluv_toLinear(c.g), hsluv_toLinear(c.b) );\\\\n}\\\\n\\\\nfloat hsluv_yToL(float Y){\\\\n\\\\treturn Y <= 0.0088564516790356308 ? Y * 903.2962962962963 : 116.0 * pow(Y, 1.0 / 3.0) - 16.0;\\\\n}\\\\n\\\\nfloat hsluv_lToY(float L) {\\\\n\\\\treturn L <= 8.0 ? L / 903.2962962962963 : pow((L + 16.0) / 116.0, 3.0);\\\\n}\\\\n\\\\nvec3 xyzToRgb(vec3 tuple) {\\\\n\\\\tconst mat3 m = mat3( \\\\n\\\\t\\\\t3.2409699419045214  ,-1.5373831775700935 ,-0.49861076029300328 ,\\\\n\\\\t\\\\t-0.96924363628087983 , 1.8759675015077207 , 0.041555057407175613,\\\\n\\\\t\\\\t0.055630079696993609,-0.20397695888897657, 1.0569715142428786  );\\\\n\\\\t\\\\n\\\\treturn hsluv_fromLinear(tuple*m);\\\\n}\\\\n\\\\nvec3 rgbToXyz(vec3 tuple) {\\\\n\\\\tconst mat3 m = mat3(\\\\n\\\\t\\\\t0.41239079926595948 , 0.35758433938387796, 0.18048078840183429 ,\\\\n\\\\t\\\\t0.21263900587151036 , 0.71516867876775593, 0.072192315360733715,\\\\n\\\\t\\\\t0.019330818715591851, 0.11919477979462599, 0.95053215224966058 \\\\n\\\\t);\\\\n\\\\treturn hsluv_toLinear(tuple) * m;\\\\n}\\\\n\\\\nvec3 xyzToLuv(vec3 tuple){\\\\n\\\\tfloat X = tuple.x;\\\\n\\\\tfloat Y = tuple.y;\\\\n\\\\tfloat Z = tuple.z;\\\\n\\\\n\\\\tfloat L = hsluv_yToL(Y);\\\\n\\\\t\\\\n\\\\tfloat div = 1./dot(tuple,vec3(1,15,3)); \\\\n\\\\n\\\\treturn vec3(\\\\n\\\\t\\\\t1.,\\\\n\\\\t\\\\t(52. * (X*div) - 2.57179),\\\\n\\\\t\\\\t(117.* (Y*div) - 6.08816)\\\\n\\\\t) * L;\\\\n}\\\\n\\\\n\\\\nvec3 luvToXyz(vec3 tuple) {\\\\n\\\\tfloat L = tuple.x;\\\\n\\\\n\\\\tfloat U = tuple.y / (13.0 * L) + 0.19783000664283681;\\\\n\\\\tfloat V = tuple.z / (13.0 * L) + 0.468319994938791;\\\\n\\\\n\\\\tfloat Y = hsluv_lToY(L);\\\\n\\\\tfloat X = 2.25 * U * Y / V;\\\\n\\\\tfloat Z = (3./V - 5.)*Y - (X/3.);\\\\n\\\\n\\\\treturn vec3(X, Y, Z);\\\\n}\\\\n\\\\nvec3 luvToLch(vec3 tuple) {\\\\n\\\\tfloat L = tuple.x;\\\\n\\\\tfloat U = tuple.y;\\\\n\\\\tfloat V = tuple.z;\\\\n\\\\n\\\\tfloat C = length(tuple.yz);\\\\n\\\\tfloat H = degrees(atan(V,U));\\\\n\\\\tif (H < 0.0) {\\\\n\\\\t\\\\tH = 360.0 + H;\\\\n\\\\t}\\\\n\\\\t\\\\n\\\\treturn vec3(L, C, H);\\\\n}\\\\n\\\\nvec3 lchToLuv(vec3 tuple) {\\\\n\\\\tfloat hrad = radians(tuple.b);\\\\n\\\\treturn vec3(\\\\n\\\\t\\\\ttuple.r,\\\\n\\\\t\\\\tcos(hrad) * tuple.g,\\\\n\\\\t\\\\tsin(hrad) * tuple.g\\\\n\\\\t);\\\\n}\\\\n\\\\nvec3 hsluvToLch(vec3 tuple) {\\\\n\\\\ttuple.g *= hsluv_maxChromaForLH(tuple.b, tuple.r) * .01;\\\\n\\\\treturn tuple.bgr;\\\\n}\\\\n\\\\nvec3 lchToHsluv(vec3 tuple) {\\\\n\\\\ttuple.g /= hsluv_maxChromaForLH(tuple.r, tuple.b) * .01;\\\\n\\\\treturn tuple.bgr;\\\\n}\\\\n\\\\nvec3 hpluvToLch(vec3 tuple) {\\\\n\\\\ttuple.g *= hsluv_maxSafeChromaForL(tuple.b) * .01;\\\\n\\\\treturn tuple.bgr;\\\\n}\\\\n\\\\nvec3 lchToHpluv(vec3 tuple) {\\\\n\\\\ttuple.g /= hsluv_maxSafeChromaForL(tuple.r) * .01;\\\\n\\\\treturn tuple.bgr;\\\\n}\\\\n\\\\nvec3 lchToRgb(vec3 tuple) {\\\\n\\\\treturn xyzToRgb(luvToXyz(lchToLuv(tuple)));\\\\n}\\\\n\\\\nvec3 rgbToLch(vec3 tuple) {\\\\n\\\\treturn luvToLch(xyzToLuv(rgbToXyz(tuple)));\\\\n}\\\\n\\\\nvec3 hsluvToRgb(vec3 tuple) {\\\\n\\\\treturn lchToRgb(hsluvToLch(tuple));\\\\n}\\\\n\\\\nvec3 rgbToHsluv(vec3 tuple) {\\\\n\\\\treturn lchToHsluv(rgbToLch(tuple));\\\\n}\\\\n\\\\nvec3 hpluvToRgb(vec3 tuple) {\\\\n\\\\treturn lchToRgb(hpluvToLch(tuple));\\\\n}\\\\n\\\\nvec3 rgbToHpluv(vec3 tuple) {\\\\n\\\\treturn lchToHpluv(rgbToLch(tuple));\\\\n}\\\\n\\\\nvec3 luvToRgb(vec3 tuple){\\\\n\\\\treturn xyzToRgb(luvToXyz(tuple));\\\\n}\\\\n\\\\n// allow vec4's\\\\nvec4   xyzToRgb(vec4 c) {return vec4(   xyzToRgb( vec3(c.x,c.y,c.z) ), c.a);}\\\\nvec4   rgbToXyz(vec4 c) {return vec4(   rgbToXyz( vec3(c.x,c.y,c.z) ), c.a);}\\\\nvec4   xyzToLuv(vec4 c) {return vec4(   xyzToLuv( vec3(c.x,c.y,c.z) ), c.a);}\\\\nvec4   luvToXyz(vec4 c) {return vec4(   luvToXyz( vec3(c.x,c.y,c.z) ), c.a);}\\\\nvec4   luvToLch(vec4 c) {return vec4(   luvToLch( vec3(c.x,c.y,c.z) ), c.a);}\\\\nvec4   lchToLuv(vec4 c) {return vec4(   lchToLuv( vec3(c.x,c.y,c.z) ), c.a);}\\\\nvec4 hsluvToLch(vec4 c) {return vec4( hsluvToLch( vec3(c.x,c.y,c.z) ), c.a);}\\\\nvec4 lchToHsluv(vec4 c) {return vec4( lchToHsluv( vec3(c.x,c.y,c.z) ), c.a);}\\\\nvec4 hpluvToLch(vec4 c) {return vec4( hpluvToLch( vec3(c.x,c.y,c.z) ), c.a);}\\\\nvec4 lchToHpluv(vec4 c) {return vec4( lchToHpluv( vec3(c.x,c.y,c.z) ), c.a);}\\\\nvec4   lchToRgb(vec4 c) {return vec4(   lchToRgb( vec3(c.x,c.y,c.z) ), c.a);}\\\\nvec4   rgbToLch(vec4 c) {return vec4(   rgbToLch( vec3(c.x,c.y,c.z) ), c.a);}\\\\nvec4 hsluvToRgb(vec4 c) {return vec4( hsluvToRgb( vec3(c.x,c.y,c.z) ), c.a);}\\\\nvec4 rgbToHsluv(vec4 c) {return vec4( rgbToHsluv( vec3(c.x,c.y,c.z) ), c.a);}\\\\nvec4 hpluvToRgb(vec4 c) {return vec4( hpluvToRgb( vec3(c.x,c.y,c.z) ), c.a);}\\\\nvec4 rgbToHpluv(vec4 c) {return vec4( rgbToHpluv( vec3(c.x,c.y,c.z) ), c.a);}\\\\nvec4   luvToRgb(vec4 c) {return vec4(   luvToRgb( vec3(c.x,c.y,c.z) ), c.a);}\\\\n// allow 3 floats\\\\nvec3   xyzToRgb(float x, float y, float z) {return   xyzToRgb( vec3(x,y,z) );}\\\\nvec3   rgbToXyz(float x, float y, float z) {return   rgbToXyz( vec3(x,y,z) );}\\\\nvec3   xyzToLuv(float x, float y, float z) {return   xyzToLuv( vec3(x,y,z) );}\\\\nvec3   luvToXyz(float x, float y, float z) {return   luvToXyz( vec3(x,y,z) );}\\\\nvec3   luvToLch(float x, float y, float z) {return   luvToLch( vec3(x,y,z) );}\\\\nvec3   lchToLuv(float x, float y, float z) {return   lchToLuv( vec3(x,y,z) );}\\\\nvec3 hsluvToLch(float x, float y, float z) {return hsluvToLch( vec3(x,y,z) );}\\\\nvec3 lchToHsluv(float x, float y, float z) {return lchToHsluv( vec3(x,y,z) );}\\\\nvec3 hpluvToLch(float x, float y, float z) {return hpluvToLch( vec3(x,y,z) );}\\\\nvec3 lchToHpluv(float x, float y, float z) {return lchToHpluv( vec3(x,y,z) );}\\\\nvec3   lchToRgb(float x, float y, float z) {return   lchToRgb( vec3(x,y,z) );}\\\\nvec3   rgbToLch(float x, float y, float z) {return   rgbToLch( vec3(x,y,z) );}\\\\nvec3 hsluvToRgb(float x, float y, float z) {return hsluvToRgb( vec3(x,y,z) );}\\\\nvec3 rgbToHsluv(float x, float y, float z) {return rgbToHsluv( vec3(x,y,z) );}\\\\nvec3 hpluvToRgb(float x, float y, float z) {return hpluvToRgb( vec3(x,y,z) );}\\\\nvec3 rgbToHpluv(float x, float y, float z) {return rgbToHpluv( vec3(x,y,z) );}\\\\nvec3   luvToRgb(float x, float y, float z) {return   luvToRgb( vec3(x,y,z) );}\\\\n// allow 4 floats\\\\nvec4   xyzToRgb(float x, float y, float z, float a) {return   xyzToRgb( vec4(x,y,z,a) );}\\\\nvec4   rgbToXyz(float x, float y, float z, float a) {return   rgbToXyz( vec4(x,y,z,a) );}\\\\nvec4   xyzToLuv(float x, float y, float z, float a) {return   xyzToLuv( vec4(x,y,z,a) );}\\\\nvec4   luvToXyz(float x, float y, float z, float a) {return   luvToXyz( vec4(x,y,z,a) );}\\\\nvec4   luvToLch(float x, float y, float z, float a) {return   luvToLch( vec4(x,y,z,a) );}\\\\nvec4   lchToLuv(float x, float y, float z, float a) {return   lchToLuv( vec4(x,y,z,a) );}\\\\nvec4 hsluvToLch(float x, float y, float z, float a) {return hsluvToLch( vec4(x,y,z,a) );}\\\\nvec4 lchToHsluv(float x, float y, float z, float a) {return lchToHsluv( vec4(x,y,z,a) );}\\\\nvec4 hpluvToLch(float x, float y, float z, float a) {return hpluvToLch( vec4(x,y,z,a) );}\\\\nvec4 lchToHpluv(float x, float y, float z, float a) {return lchToHpluv( vec4(x,y,z,a) );}\\\\nvec4   lchToRgb(float x, float y, float z, float a) {return   lchToRgb( vec4(x,y,z,a) );}\\\\nvec4   rgbToLch(float x, float y, float z, float a) {return   rgbToLch( vec4(x,y,z,a) );}\\\\nvec4 hsluvToRgb(float x, float y, float z, float a) {return hsluvToRgb( vec4(x,y,z,a) );}\\\\nvec4 rgbToHslul(float x, float y, float z, float a) {return rgbToHsluv( vec4(x,y,z,a) );}\\\\nvec4 hpluvToRgb(float x, float y, float z, float a) {return hpluvToRgb( vec4(x,y,z,a) );}\\\\nvec4 rgbToHpluv(float x, float y, float z, float a) {return rgbToHpluv( vec4(x,y,z,a) );}\\\\nvec4   luvToRgb(float x, float y, float z, float a) {return   luvToRgb( vec4(x,y,z,a) );}\\\\n\\\\n/*\\\\nEND HSLUV-GLSL\\\\n*/\\\\n\\\\n\\\\n// from https://gist.github.com/mattatz/44f081cac87e2f7c8980\\\\n// converted to glsl by gui@polygonjs.com\\\\n// and made function names consistent with the ones above\\\\n/*\\\\n * Conversion between RGB and LAB colorspace.\\\\n * Import from flowabs glsl program : https://code.google.com/p/flowabs/source/browse/glsl/?r=f36cbdcf7790a28d90f09e2cf89ec9a64911f138\\\\n */\\\\n\\\\n\\\\n\\\\nvec3 xyzToLab( vec3 c ) {\\\\n\\\\tvec3 n = c / vec3(95.047, 100, 108.883);\\\\n\\\\tvec3 v;\\\\n\\\\tv.x = ( n.x > 0.008856 ) ? pow( n.x, 1.0 / 3.0 ) : ( 7.787 * n.x ) + ( 16.0 / 116.0 );\\\\n\\\\tv.y = ( n.y > 0.008856 ) ? pow( n.y, 1.0 / 3.0 ) : ( 7.787 * n.y ) + ( 16.0 / 116.0 );\\\\n\\\\tv.z = ( n.z > 0.008856 ) ? pow( n.z, 1.0 / 3.0 ) : ( 7.787 * n.z ) + ( 16.0 / 116.0 );\\\\n\\\\treturn vec3(( 116.0 * v.y ) - 16.0, 500.0 * ( v.x - v.y ), 200.0 * ( v.y - v.z ));\\\\n}\\\\n\\\\nvec3 rgbToLab( vec3 c ) {\\\\n\\\\tvec3 lab = xyzToLab( rgbToXyz( c ) );\\\\n\\\\treturn vec3( lab.x / 100.0, 0.5 + 0.5 * ( lab.y / 127.0 ), 0.5 + 0.5 * ( lab.z / 127.0 ));\\\\n}\\\\n\\\\nvec3 labToXyz( vec3 c ) {\\\\n\\\\tfloat fy = ( c.x + 16.0 ) / 116.0;\\\\n\\\\tfloat fx = c.y / 500.0 + fy;\\\\n\\\\tfloat fz = fy - c.z / 200.0;\\\\n\\\\treturn vec3(\\\\n\\\\t\\\\t 95.047 * (( fx > 0.206897 ) ? fx * fx * fx : ( fx - 16.0 / 116.0 ) / 7.787),\\\\n\\\\t\\\\t100.000 * (( fy > 0.206897 ) ? fy * fy * fy : ( fy - 16.0 / 116.0 ) / 7.787),\\\\n\\\\t\\\\t108.883 * (( fz > 0.206897 ) ? fz * fz * fz : ( fz - 16.0 / 116.0 ) / 7.787)\\\\n\\\\t);\\\\n}\\\\n\\\\n\\\\n\\\\nvec3 labToRgb( vec3 c ) {\\\\n\\\\treturn xyzToRgb( labToXyz( vec3(100.0 * c.x, 2.0 * 127.0 * (c.y - 0.5), 2.0 * 127.0 * (c.z - 0.5)) ) );\\\\n}\\\\\\\"));const i=uf.vector3(this.variableForInputParam(this.p.hsluv)),r=this.glVarName(\\\\\\\"rgb\\\\\\\");n.push(`vec3 ${r} = hsluvToRgb(${i}.x * 360.0, ${i}.y * 100.0, ${i}.z * 100.0)`),t.addDefinitions(this,e),t.addBodyLines(this,n)}}const qP=new class extends aa{constructor(){super(...arguments),this.hsv=oa.VECTOR3([1,1,1])}};class XP extends df{constructor(){super(...arguments),this.paramsConfig=qP}static type(){return\\\\\\\"hsvToRgb\\\\\\\"}initializeNode(){super.initializeNode(),this.io.outputs.setNamedOutputConnectionPoints([new Vo(\\\\\\\"rgb\\\\\\\",Do.VEC3)])}setLines(t){const e=[],n=[];e.push(new Tf(this,\\\\\\\"// https://github.com/hughsk/glsl-hsv2rgb\\\\n// https://stackoverflow.com/questions/15095909/from-rgb-to-hsv-in-opengl-glsl\\\\nvec3 hsv2rgb(vec3 c) {\\\\n\\\\tvec4 K = vec4(1.0, 2.0 / 3.0, 1.0 / 3.0, 3.0);\\\\n\\\\tvec3 p = abs(fract(c.xxx + K.xyz) * 6.0 - K.www);\\\\n\\\\treturn c.z * mix(K.xxx, clamp(p - K.xxx, 0.0, 1.0), c.y);\\\\n}\\\\\\\"));const i=uf.vector3(this.variableForInputParam(this.p.hsv)),r=this.glVarName(\\\\\\\"rgb\\\\\\\");n.push(`vec3 ${r} = hsv2rgb(${i})`),t.addDefinitions(this,e),t.addBodyLines(this,n)}}const YP=\\\\\\\"condition\\\\\\\";const $P=new class extends aa{};class JP extends kP{constructor(){super(...arguments),this.paramsConfig=$P}static type(){return\\\\\\\"ifThen\\\\\\\"}_expected_inputs_count(){const t=this.io.connections.inputConnections();return t?Math.max(t.length+1,2):2}_expected_input_types(){const t=[Do.BOOL],e=Do.FLOAT,n=this.io.connections.inputConnections(),i=this._expected_inputs_count();for(let r=1;r<i;r++)if(n){const i=n[r];if(i){const e=i.src_connection_point().type();t.push(e)}else t.push(e)}else t.push(e);return t}_expected_output_types(){const t=[],e=this._expected_input_types();for(let n=1;n<e.length;n++)t.push(e[n]);return t}_expected_input_name(t){if(0==t)return YP;{const e=this.io.connections.inputConnection(t);if(e){return e.src_connection_point().name()}return`in${t}`}}_expected_output_name(t){return this._expected_input_name(t+1)}child_expected_input_connection_point_types(){return this._expected_output_types()}child_expected_input_connection_point_name(t){return this._expected_output_name(t)}child_expected_output_connection_point_types(){return this._expected_output_types()}child_expected_output_connection_point_name(t){return this._expected_output_name(t)}set_lines_block_start(t,e){const n=[],i=this.io.inputs.namedInputConnectionPoints();for(let t=1;t<i.length;t++){const e=i[t],r=`${e.type()} ${this.glVarName(e.name())} = ${uf.any(this.variableForInput(e.name()))}`;n.push(r)}const r=`if(${uf.any(this.variableForInput(YP))}){`;n.push(r);const s=this.io.connections.inputConnections();if(s)for(let t of s)if(t&&0!=t.input_index){const i=t.dest_connection_point(),r=uf.any(this.variableForInput(i.name())),s=`\\\\t${i.type()} ${e.glVarName(i.name())} = ${r}`;n.push(s)}t.addBodyLines(e,n)}setLines(t){}}const ZP=new class extends aa{constructor(){super(...arguments),this.center=oa.VECTOR3([0,0,0]),this.cameraPos=oa.VECTOR3([0,0,0]),this.uv=oa.VECTOR2([0,0]),this.tilesCount=oa.INTEGER(8,{range:[0,32],rangeLocked:[!0,!1]}),this.offset=oa.FLOAT(0)}};class QP extends df{constructor(){super(...arguments),this.paramsConfig=ZP}static type(){return\\\\\\\"impostorUv\\\\\\\"}initializeNode(){super.initializeNode(),this.io.outputs.setNamedOutputConnectionPoints([new Vo(\\\\\\\"uv\\\\\\\",Do.VEC2)])}setLines(t){const e=[];t.addDefinitions(this,[new Tf(this,GR),new Tf(this,\\\\\\\"// ANGLE_NORMALIZER = 1 / (2*PI)\\\\n# define IMPOSTOR_UV_ANGLE_NORMALIZER 0.15915494309189535\\\\nvec2 impostor_uv(vec3 center, vec3 camera_pos, vec2 imp_uv, float tiles_count, float offset){\\\\n\\\\timp_uv.x /= tiles_count;\\\\n\\\\n\\\\tcamera_pos.y = center.y;\\\\n\\\\tvec3 delta = normalize(center - camera_pos);\\\\n\\\\tvec3 angle_start = vec3(-1.0,0.0,0.0);\\\\n\\\\tfloat angle = vector_angle(delta, angle_start) + offset;\\\\n\\\\tangle *= IMPOSTOR_UV_ANGLE_NORMALIZER;\\\\n\\\\tangle *= tiles_count;\\\\n\\\\tangle = floor(angle);\\\\n\\\\tangle /= tiles_count;\\\\n\\\\timp_uv.x -= angle;\\\\n\\\\n\\\\treturn imp_uv;\\\\n}\\\\n\\\\\\\")]);const n=uf.vector3(this.variableForInputParam(this.p.center)),i=uf.vector3(this.variableForInputParam(this.p.cameraPos)),r=uf.vector2(this.variableForInputParam(this.p.uv)),s=uf.float(this.variableForInputParam(this.p.tilesCount)),o=uf.float(this.variableForInputParam(this.p.offset)),a=this.glVarName(\\\\\\\"uv\\\\\\\"),l=[n,i,r,s,o].join(\\\\\\\", \\\\\\\");e.push(`vec2 ${a} = impostor_uv(${l})`),t.addBodyLines(this,e)}}const KP=\\\\\\\"position\\\\\\\",tI=\\\\\\\"normal\\\\\\\",eI=\\\\\\\"instancePosition\\\\\\\",nI=\\\\\\\"instanceOrientation\\\\\\\",iI=\\\\\\\"instanceScale\\\\\\\";const rI=new class extends aa{constructor(){super(...arguments),this.position=oa.VECTOR3([0,0,0]),this.normal=oa.VECTOR3([0,0,1]),this.instancePosition=oa.VECTOR3([0,0,0]),this.instanceOrientation=oa.VECTOR4([0,0,0,0]),this.instanceScale=oa.VECTOR3([1,1,1])}};class sI extends df{constructor(){super(...arguments),this.paramsConfig=rI}static type(){return\\\\\\\"instanceTransform\\\\\\\"}initializeNode(){super.initializeNode(),this.io.outputs.setNamedOutputConnectionPoints([new Vo(this.gl_output_name_position(),Do.VEC3),new Vo(this.gl_output_name_normal(),Do.VEC3)])}setLines(t){const e=[],n=[];n.push(new Tf(this,GR));const i=this.io.inputs.named_input(this.p.position.name())?uf.float(this.variableForInputParam(this.p.position)):this._default_position(),r=this.io.inputs.named_input(this.p.normal.name())?uf.float(this.variableForInputParam(this.p.normal)):this._default_normal(),s=this.io.inputs.named_input(this.p.instancePosition.name())?uf.float(this.variableForInputParam(this.p.instancePosition)):this._default_instancePosition(t),o=this.io.inputs.named_input(this.p.instanceOrientation.name())?uf.float(this.variableForInputParam(this.p.instanceOrientation)):this._default_input_instanceOrientation(t),a=this.io.inputs.named_input(this.p.instanceScale.name())?uf.float(this.variableForInputParam(this.p.instanceScale)):this._default_input_instanceScale(t),l=this.glVarName(this.gl_output_name_position()),c=this.glVarName(this.gl_output_name_normal());e.push(`vec3 ${l} = vec3(${i})`),e.push(`${l} *= ${a}`),e.push(`${l} = rotateWithQuat( ${l}, ${o} )`),e.push(`${l} += ${s}`),e.push(`vec3 ${c} = vec3(${r})`),e.push(`${c} = rotateWithQuat( ${c}, ${o} )`),t.addBodyLines(this,e),t.addDefinitions(this,n)}gl_output_name_position(){return\\\\\\\"position\\\\\\\"}gl_output_name_normal(){return\\\\\\\"normal\\\\\\\"}_default_position(){return KP}_default_normal(){return tI}_default_instancePosition(t){var e;return null===(e=t.assembler().globals_handler)||void 0===e?void 0:e.read_attribute(this,Do.VEC3,eI,t)}_default_input_instanceOrientation(t){var e;return null===(e=t.assembler().globals_handler)||void 0===e?void 0:e.read_attribute(this,Do.VEC4,nI,t)}_default_input_instanceScale(t){var e;return null===(e=t.assembler().globals_handler)||void 0===e?void 0:e.read_attribute(this,Do.VEC3,iI,t)}}class oI extends BO{static type(){return\\\\\\\"length\\\\\\\"}initializeNode(){super.initializeNode(),this.io.connection_points.set_input_name_function(this._gl_input_name.bind(this)),this.io.connection_points.set_expected_output_types_function(this._expected_output_types.bind(this))}_gl_input_name(t){return[\\\\\\\"x\\\\\\\"][t]}gl_method_name(){return\\\\\\\"length\\\\\\\"}_expected_output_types(){return[Do.FLOAT]}}const aI=new class extends aa{constructor(){super(...arguments),this.color=oa.VECTOR3([1,1,1])}};class lI extends df{constructor(){super(...arguments),this.paramsConfig=aI}static type(){return\\\\\\\"luminance\\\\\\\"}initializeNode(){super.initializeNode(),this.io.outputs.setNamedOutputConnectionPoints([new Vo(\\\\\\\"lum\\\\\\\",Do.FLOAT)])}setLines(t){const e=uf.vector3(this.variableForInputParam(this.p.color)),n=`float ${this.glVarName(\\\\\\\"lum\\\\\\\")} = linearToRelativeLuminance(${e})`;t.addBodyLines(this,[n])}}const cI={max:1};class uI extends zO{static type(){return\\\\\\\"maxLength\\\\\\\"}_expected_input_types(){return[this.io.connection_points.first_input_connection_type()||Do.VEC3,Do.FLOAT]}_gl_input_name(t){return[\\\\\\\"val\\\\\\\",\\\\\\\"max\\\\\\\"][t]}paramDefaultValue(t){return cI[t]}gl_method_name(){return\\\\\\\"maxLength\\\\\\\"}gl_function_definitions(){return[new Tf(this,\\\\\\\"//\\\\n//\\\\n// CLAMP_LENGTH\\\\n//\\\\n//\\\\nfloat maxLength(float val, float max_l){\\\\n\\\\treturn min(val, max_l);\\\\n}\\\\nvec2 maxLength(vec2 val, float max_l){\\\\n\\\\tfloat vec_length = length(val);\\\\n\\\\tif(vec_length == 0.0){\\\\n\\\\t\\\\treturn val;\\\\n\\\\t} else {\\\\n\\\\t\\\\tfloat new_length = min(vec_length, max_l);\\\\n\\\\t\\\\treturn new_length * normalize(val);\\\\n\\\\t}\\\\n}\\\\nvec3 maxLength(vec3 val, float max_l){\\\\n\\\\tfloat vec_length = length(val);\\\\n\\\\tif(vec_length == 0.0){\\\\n\\\\t\\\\treturn val;\\\\n\\\\t} else {\\\\n\\\\t\\\\tfloat new_length = min(vec_length, max_l);\\\\n\\\\t\\\\treturn new_length * normalize(val);\\\\n\\\\t}\\\\n}\\\\nvec4 maxLength(vec4 val, float max_l){\\\\n\\\\tfloat vec_length = length(val);\\\\n\\\\tif(vec_length == 0.0){\\\\n\\\\t\\\\treturn val;\\\\n\\\\t} else {\\\\n\\\\t\\\\tfloat new_length = min(vec_length, max_l);\\\\n\\\\t\\\\treturn new_length * normalize(val);\\\\n\\\\t}\\\\n}\\\\n\\\\\\\")]}}const hI={blend:.5};class dI extends kO{static type(){return\\\\\\\"mix\\\\\\\"}gl_method_name(){return\\\\\\\"mix\\\\\\\"}paramDefaultValue(t){return hI[t]}initializeNode(){super.initializeNode(),this.io.connection_points.set_input_name_function((t=>[\\\\\\\"value0\\\\\\\",\\\\\\\"value1\\\\\\\",\\\\\\\"blend\\\\\\\"][t])),this.io.connection_points.set_output_name_function(this._gl_output_name.bind(this)),this.io.connection_points.set_expected_input_types_function(this._expected_input_types.bind(this)),this.io.connection_points.set_expected_output_types_function(this._expected_output_types.bind(this))}_gl_output_name(){return\\\\\\\"mix\\\\\\\"}_expected_input_types(){const t=this.io.connection_points.first_input_connection_type()||Do.FLOAT;return[t,t,Do.FLOAT]}_expected_output_types(){return[this._expected_input_types()[0]]}}const pI=\\\\\\\"mvMult\\\\\\\";const _I=new class extends aa{constructor(){super(...arguments),this.vector=oa.VECTOR3([0,0,0])}};class mI extends df{constructor(){super(...arguments),this.paramsConfig=_I}static type(){return\\\\\\\"modelViewMatrixMult\\\\\\\"}initializeNode(){super.initializeNode(),this.io.outputs.setNamedOutputConnectionPoints([new Vo(pI,Do.VEC4)])}setLines(t){if(t.current_shader_name==xf.VERTEX){const e=uf.vector3(this.variableForInputParam(this.p.vector)),n=`vec4 ${this.glVarName(pI)} = modelViewMatrix * vec4(${e}, 1.0)`;t.addBodyLines(this,[n],xf.VERTEX)}}}const fI={mult:1};var gI;!function(t){t.VALUE=\\\\\\\"value\\\\\\\",t.PRE_ADD=\\\\\\\"preAdd\\\\\\\",t.MULT=\\\\\\\"mult\\\\\\\",t.POST_ADD=\\\\\\\"postAdd\\\\\\\"}(gI||(gI={}));class vI extends GO{static type(){return\\\\\\\"multAdd\\\\\\\"}_gl_input_name(t){return[gI.VALUE,gI.PRE_ADD,gI.MULT,gI.POST_ADD][t]}paramDefaultValue(t){return fI[t]}setLines(t){const e=uf.any(this.variableForInput(gI.VALUE)),n=uf.any(this.variableForInput(gI.PRE_ADD)),i=uf.any(this.variableForInput(gI.MULT)),r=uf.any(this.variableForInput(gI.POST_ADD)),s=this._expected_output_types()[0],o=this.io.outputs.namedOutputConnectionPoints()[0].name(),a=`${s} ${this.glVarName(o)} = (${i}*(${e} + ${n})) + ${r}`;t.addBodyLines(this,[a])}}class yI extends BO{static type(){return\\\\\\\"negate\\\\\\\"}initializeNode(){super.initializeNode(),this.io.connection_points.set_input_name_function((t=>[\\\\\\\"in\\\\\\\"][t]))}_gl_input_name(t){return[\\\\\\\"in\\\\\\\"][t]}setLines(t){const e=uf.any(this.variableForInput(this._gl_input_name(0))),n=`${this.io.inputs.namedInputConnectionPoints()[0].type()} ${this.glVarName(this.io.connection_points.output_name(0))} = -1.0 * ${e}`;t.addBodyLines(this,[n])}}var xI;!function(t){t.CLASSIC_PERLIN_2D=\\\\\\\"Classic Perlin 2D\\\\\\\",t.CLASSIC_PERLIN_3D=\\\\\\\"Classic Perlin 3D\\\\\\\",t.CLASSIC_PERLIN_4D=\\\\\\\"Classic Perlin 4D\\\\\\\",t.NOISE_2D=\\\\\\\"noise2D\\\\\\\",t.NOISE_3D=\\\\\\\"noise3D\\\\\\\",t.NOISE_4D=\\\\\\\"noise4D\\\\\\\"}(xI||(xI={}));const bI=[xI.CLASSIC_PERLIN_2D,xI.CLASSIC_PERLIN_3D,xI.CLASSIC_PERLIN_4D,xI.NOISE_2D,xI.NOISE_3D,xI.NOISE_4D],wI={[xI.CLASSIC_PERLIN_2D]:'//\\\\n// GLSL textureless classic 2D noise \\\\\\\"cnoise\\\\\\\",\\\\n// with an RSL-style periodic variant \\\\\\\"pnoise\\\\\\\".\\\\n// Author:  Stefan Gustavson (stefan.gustavson@liu.se)\\\\n// Version: 2011-08-22\\\\n//\\\\n// Many thanks to Ian McEwan of Ashima Arts for the\\\\n// ideas for permutation and gradient selection.\\\\n//\\\\n// Copyright (c) 2011 Stefan Gustavson. All rights reserved.\\\\n// Distributed under the MIT license. See LICENSE file.\\\\n// https://github.com/stegu/webgl-noise\\\\n//\\\\n\\\\n\\\\n// Classic Perlin noise\\\\nfloat cnoise(vec2 P)\\\\n{\\\\n  vec4 Pi = floor(P.xyxy) + vec4(0.0, 0.0, 1.0, 1.0);\\\\n  vec4 Pf = fract(P.xyxy) - vec4(0.0, 0.0, 1.0, 1.0);\\\\n  Pi = mod289(Pi); // To avoid truncation effects in permutation\\\\n  vec4 ix = Pi.xzxz;\\\\n  vec4 iy = Pi.yyww;\\\\n  vec4 fx = Pf.xzxz;\\\\n  vec4 fy = Pf.yyww;\\\\n\\\\n  vec4 i = permute(permute(ix) + iy);\\\\n\\\\n  vec4 gx = fract(i * (1.0 / 41.0)) * 2.0 - 1.0 ;\\\\n  vec4 gy = abs(gx) - 0.5 ;\\\\n  vec4 tx = floor(gx + 0.5);\\\\n  gx = gx - tx;\\\\n\\\\n  vec2 g00 = vec2(gx.x,gy.x);\\\\n  vec2 g10 = vec2(gx.y,gy.y);\\\\n  vec2 g01 = vec2(gx.z,gy.z);\\\\n  vec2 g11 = vec2(gx.w,gy.w);\\\\n\\\\n  vec4 norm = taylorInvSqrt(vec4(dot(g00, g00), dot(g01, g01), dot(g10, g10), dot(g11, g11)));\\\\n  g00 *= norm.x;  \\\\n  g01 *= norm.y;  \\\\n  g10 *= norm.z;  \\\\n  g11 *= norm.w;  \\\\n\\\\n  float n00 = dot(g00, vec2(fx.x, fy.x));\\\\n  float n10 = dot(g10, vec2(fx.y, fy.y));\\\\n  float n01 = dot(g01, vec2(fx.z, fy.z));\\\\n  float n11 = dot(g11, vec2(fx.w, fy.w));\\\\n\\\\n  vec2 fade_xy = fade(Pf.xy);\\\\n  vec2 n_x = mix(vec2(n00, n01), vec2(n10, n11), fade_xy.x);\\\\n  float n_xy = mix(n_x.x, n_x.y, fade_xy.y);\\\\n  return 2.3 * n_xy;\\\\n}\\\\n\\\\n// Classic Perlin noise, periodic variant\\\\nfloat pnoise(vec2 P, vec2 rep)\\\\n{\\\\n  vec4 Pi = floor(P.xyxy) + vec4(0.0, 0.0, 1.0, 1.0);\\\\n  vec4 Pf = fract(P.xyxy) - vec4(0.0, 0.0, 1.0, 1.0);\\\\n  Pi = mod(Pi, rep.xyxy); // To create noise with explicit period\\\\n  Pi = mod289(Pi);        // To avoid truncation effects in permutation\\\\n  vec4 ix = Pi.xzxz;\\\\n  vec4 iy = Pi.yyww;\\\\n  vec4 fx = Pf.xzxz;\\\\n  vec4 fy = Pf.yyww;\\\\n\\\\n  vec4 i = permute(permute(ix) + iy);\\\\n\\\\n  vec4 gx = fract(i * (1.0 / 41.0)) * 2.0 - 1.0 ;\\\\n  vec4 gy = abs(gx) - 0.5 ;\\\\n  vec4 tx = floor(gx + 0.5);\\\\n  gx = gx - tx;\\\\n\\\\n  vec2 g00 = vec2(gx.x,gy.x);\\\\n  vec2 g10 = vec2(gx.y,gy.y);\\\\n  vec2 g01 = vec2(gx.z,gy.z);\\\\n  vec2 g11 = vec2(gx.w,gy.w);\\\\n\\\\n  vec4 norm = taylorInvSqrt(vec4(dot(g00, g00), dot(g01, g01), dot(g10, g10), dot(g11, g11)));\\\\n  g00 *= norm.x;  \\\\n  g01 *= norm.y;  \\\\n  g10 *= norm.z;  \\\\n  g11 *= norm.w;  \\\\n\\\\n  float n00 = dot(g00, vec2(fx.x, fy.x));\\\\n  float n10 = dot(g10, vec2(fx.y, fy.y));\\\\n  float n01 = dot(g01, vec2(fx.z, fy.z));\\\\n  float n11 = dot(g11, vec2(fx.w, fy.w));\\\\n\\\\n  vec2 fade_xy = fade(Pf.xy);\\\\n  vec2 n_x = mix(vec2(n00, n01), vec2(n10, n11), fade_xy.x);\\\\n  float n_xy = mix(n_x.x, n_x.y, fade_xy.y);\\\\n  return 2.3 * n_xy;\\\\n}\\\\n',[xI.CLASSIC_PERLIN_3D]:'//\\\\n// GLSL textureless classic 3D noise \\\\\\\"cnoise\\\\\\\",\\\\n// with an RSL-style periodic variant \\\\\\\"pnoise\\\\\\\".\\\\n// Author:  Stefan Gustavson (stefan.gustavson@liu.se)\\\\n// Version: 2011-10-11\\\\n//\\\\n// Many thanks to Ian McEwan of Ashima Arts for the\\\\n// ideas for permutation and gradient selection.\\\\n//\\\\n// Copyright (c) 2011 Stefan Gustavson. All rights reserved.\\\\n// Distributed under the MIT license. See LICENSE file.\\\\n// https://github.com/stegu/webgl-noise\\\\n//\\\\n\\\\n// Classic Perlin noise\\\\nfloat cnoise(vec3 P)\\\\n{\\\\n  vec3 Pi0 = floor(P); // Integer part for indexing\\\\n  vec3 Pi1 = Pi0 + vec3(1.0); // Integer part + 1\\\\n  Pi0 = mod289(Pi0);\\\\n  Pi1 = mod289(Pi1);\\\\n  vec3 Pf0 = fract(P); // Fractional part for interpolation\\\\n  vec3 Pf1 = Pf0 - vec3(1.0); // Fractional part - 1.0\\\\n  vec4 ix = vec4(Pi0.x, Pi1.x, Pi0.x, Pi1.x);\\\\n  vec4 iy = vec4(Pi0.yy, Pi1.yy);\\\\n  vec4 iz0 = Pi0.zzzz;\\\\n  vec4 iz1 = Pi1.zzzz;\\\\n\\\\n  vec4 ixy = permute(permute(ix) + iy);\\\\n  vec4 ixy0 = permute(ixy + iz0);\\\\n  vec4 ixy1 = permute(ixy + iz1);\\\\n\\\\n  vec4 gx0 = ixy0 * (1.0 / 7.0);\\\\n  vec4 gy0 = fract(floor(gx0) * (1.0 / 7.0)) - 0.5;\\\\n  gx0 = fract(gx0);\\\\n  vec4 gz0 = vec4(0.5) - abs(gx0) - abs(gy0);\\\\n  vec4 sz0 = step(gz0, vec4(0.0));\\\\n  gx0 -= sz0 * (step(0.0, gx0) - 0.5);\\\\n  gy0 -= sz0 * (step(0.0, gy0) - 0.5);\\\\n\\\\n  vec4 gx1 = ixy1 * (1.0 / 7.0);\\\\n  vec4 gy1 = fract(floor(gx1) * (1.0 / 7.0)) - 0.5;\\\\n  gx1 = fract(gx1);\\\\n  vec4 gz1 = vec4(0.5) - abs(gx1) - abs(gy1);\\\\n  vec4 sz1 = step(gz1, vec4(0.0));\\\\n  gx1 -= sz1 * (step(0.0, gx1) - 0.5);\\\\n  gy1 -= sz1 * (step(0.0, gy1) - 0.5);\\\\n\\\\n  vec3 g000 = vec3(gx0.x,gy0.x,gz0.x);\\\\n  vec3 g100 = vec3(gx0.y,gy0.y,gz0.y);\\\\n  vec3 g010 = vec3(gx0.z,gy0.z,gz0.z);\\\\n  vec3 g110 = vec3(gx0.w,gy0.w,gz0.w);\\\\n  vec3 g001 = vec3(gx1.x,gy1.x,gz1.x);\\\\n  vec3 g101 = vec3(gx1.y,gy1.y,gz1.y);\\\\n  vec3 g011 = vec3(gx1.z,gy1.z,gz1.z);\\\\n  vec3 g111 = vec3(gx1.w,gy1.w,gz1.w);\\\\n\\\\n  vec4 norm0 = taylorInvSqrt(vec4(dot(g000, g000), dot(g010, g010), dot(g100, g100), dot(g110, g110)));\\\\n  g000 *= norm0.x;\\\\n  g010 *= norm0.y;\\\\n  g100 *= norm0.z;\\\\n  g110 *= norm0.w;\\\\n  vec4 norm1 = taylorInvSqrt(vec4(dot(g001, g001), dot(g011, g011), dot(g101, g101), dot(g111, g111)));\\\\n  g001 *= norm1.x;\\\\n  g011 *= norm1.y;\\\\n  g101 *= norm1.z;\\\\n  g111 *= norm1.w;\\\\n\\\\n  float n000 = dot(g000, Pf0);\\\\n  float n100 = dot(g100, vec3(Pf1.x, Pf0.yz));\\\\n  float n010 = dot(g010, vec3(Pf0.x, Pf1.y, Pf0.z));\\\\n  float n110 = dot(g110, vec3(Pf1.xy, Pf0.z));\\\\n  float n001 = dot(g001, vec3(Pf0.xy, Pf1.z));\\\\n  float n101 = dot(g101, vec3(Pf1.x, Pf0.y, Pf1.z));\\\\n  float n011 = dot(g011, vec3(Pf0.x, Pf1.yz));\\\\n  float n111 = dot(g111, Pf1);\\\\n\\\\n  vec3 fade_xyz = fade(Pf0);\\\\n  vec4 n_z = mix(vec4(n000, n100, n010, n110), vec4(n001, n101, n011, n111), fade_xyz.z);\\\\n  vec2 n_yz = mix(n_z.xy, n_z.zw, fade_xyz.y);\\\\n  float n_xyz = mix(n_yz.x, n_yz.y, fade_xyz.x); \\\\n  return 2.2 * n_xyz;\\\\n}\\\\n\\\\n// Classic Perlin noise, periodic variant\\\\nfloat pnoise(vec3 P, vec3 rep)\\\\n{\\\\n  vec3 Pi0 = mod(floor(P), rep); // Integer part, modulo period\\\\n  vec3 Pi1 = mod(Pi0 + vec3(1.0), rep); // Integer part + 1, mod period\\\\n  Pi0 = mod289(Pi0);\\\\n  Pi1 = mod289(Pi1);\\\\n  vec3 Pf0 = fract(P); // Fractional part for interpolation\\\\n  vec3 Pf1 = Pf0 - vec3(1.0); // Fractional part - 1.0\\\\n  vec4 ix = vec4(Pi0.x, Pi1.x, Pi0.x, Pi1.x);\\\\n  vec4 iy = vec4(Pi0.yy, Pi1.yy);\\\\n  vec4 iz0 = Pi0.zzzz;\\\\n  vec4 iz1 = Pi1.zzzz;\\\\n\\\\n  vec4 ixy = permute(permute(ix) + iy);\\\\n  vec4 ixy0 = permute(ixy + iz0);\\\\n  vec4 ixy1 = permute(ixy + iz1);\\\\n\\\\n  vec4 gx0 = ixy0 * (1.0 / 7.0);\\\\n  vec4 gy0 = fract(floor(gx0) * (1.0 / 7.0)) - 0.5;\\\\n  gx0 = fract(gx0);\\\\n  vec4 gz0 = vec4(0.5) - abs(gx0) - abs(gy0);\\\\n  vec4 sz0 = step(gz0, vec4(0.0));\\\\n  gx0 -= sz0 * (step(0.0, gx0) - 0.5);\\\\n  gy0 -= sz0 * (step(0.0, gy0) - 0.5);\\\\n\\\\n  vec4 gx1 = ixy1 * (1.0 / 7.0);\\\\n  vec4 gy1 = fract(floor(gx1) * (1.0 / 7.0)) - 0.5;\\\\n  gx1 = fract(gx1);\\\\n  vec4 gz1 = vec4(0.5) - abs(gx1) - abs(gy1);\\\\n  vec4 sz1 = step(gz1, vec4(0.0));\\\\n  gx1 -= sz1 * (step(0.0, gx1) - 0.5);\\\\n  gy1 -= sz1 * (step(0.0, gy1) - 0.5);\\\\n\\\\n  vec3 g000 = vec3(gx0.x,gy0.x,gz0.x);\\\\n  vec3 g100 = vec3(gx0.y,gy0.y,gz0.y);\\\\n  vec3 g010 = vec3(gx0.z,gy0.z,gz0.z);\\\\n  vec3 g110 = vec3(gx0.w,gy0.w,gz0.w);\\\\n  vec3 g001 = vec3(gx1.x,gy1.x,gz1.x);\\\\n  vec3 g101 = vec3(gx1.y,gy1.y,gz1.y);\\\\n  vec3 g011 = vec3(gx1.z,gy1.z,gz1.z);\\\\n  vec3 g111 = vec3(gx1.w,gy1.w,gz1.w);\\\\n\\\\n  vec4 norm0 = taylorInvSqrt(vec4(dot(g000, g000), dot(g010, g010), dot(g100, g100), dot(g110, g110)));\\\\n  g000 *= norm0.x;\\\\n  g010 *= norm0.y;\\\\n  g100 *= norm0.z;\\\\n  g110 *= norm0.w;\\\\n  vec4 norm1 = taylorInvSqrt(vec4(dot(g001, g001), dot(g011, g011), dot(g101, g101), dot(g111, g111)));\\\\n  g001 *= norm1.x;\\\\n  g011 *= norm1.y;\\\\n  g101 *= norm1.z;\\\\n  g111 *= norm1.w;\\\\n\\\\n  float n000 = dot(g000, Pf0);\\\\n  float n100 = dot(g100, vec3(Pf1.x, Pf0.yz));\\\\n  float n010 = dot(g010, vec3(Pf0.x, Pf1.y, Pf0.z));\\\\n  float n110 = dot(g110, vec3(Pf1.xy, Pf0.z));\\\\n  float n001 = dot(g001, vec3(Pf0.xy, Pf1.z));\\\\n  float n101 = dot(g101, vec3(Pf1.x, Pf0.y, Pf1.z));\\\\n  float n011 = dot(g011, vec3(Pf0.x, Pf1.yz));\\\\n  float n111 = dot(g111, Pf1);\\\\n\\\\n  vec3 fade_xyz = fade(Pf0);\\\\n  vec4 n_z = mix(vec4(n000, n100, n010, n110), vec4(n001, n101, n011, n111), fade_xyz.z);\\\\n  vec2 n_yz = mix(n_z.xy, n_z.zw, fade_xyz.y);\\\\n  float n_xyz = mix(n_yz.x, n_yz.y, fade_xyz.x); \\\\n  return 2.2 * n_xyz;\\\\n}\\\\n',[xI.CLASSIC_PERLIN_4D]:'//\\\\n// GLSL textureless classic 4D noise \\\\\\\"cnoise\\\\\\\",\\\\n// with an RSL-style periodic variant \\\\\\\"pnoise\\\\\\\".\\\\n// Author:  Stefan Gustavson (stefan.gustavson@liu.se)\\\\n// Version: 2011-08-22\\\\n//\\\\n// Many thanks to Ian McEwan of Ashima Arts for the\\\\n// ideas for permutation and gradient selection.\\\\n//\\\\n// Copyright (c) 2011 Stefan Gustavson. All rights reserved.\\\\n// Distributed under the MIT license. See LICENSE file.\\\\n// https://github.com/stegu/webgl-noise\\\\n//\\\\n\\\\n\\\\n\\\\n// Classic Perlin noise\\\\nfloat cnoise(vec4 P)\\\\n{\\\\n  vec4 Pi0 = floor(P); // Integer part for indexing\\\\n  vec4 Pi1 = Pi0 + 1.0; // Integer part + 1\\\\n  Pi0 = mod289(Pi0);\\\\n  Pi1 = mod289(Pi1);\\\\n  vec4 Pf0 = fract(P); // Fractional part for interpolation\\\\n  vec4 Pf1 = Pf0 - 1.0; // Fractional part - 1.0\\\\n  vec4 ix = vec4(Pi0.x, Pi1.x, Pi0.x, Pi1.x);\\\\n  vec4 iy = vec4(Pi0.yy, Pi1.yy);\\\\n  vec4 iz0 = vec4(Pi0.zzzz);\\\\n  vec4 iz1 = vec4(Pi1.zzzz);\\\\n  vec4 iw0 = vec4(Pi0.wwww);\\\\n  vec4 iw1 = vec4(Pi1.wwww);\\\\n\\\\n  vec4 ixy = permute(permute(ix) + iy);\\\\n  vec4 ixy0 = permute(ixy + iz0);\\\\n  vec4 ixy1 = permute(ixy + iz1);\\\\n  vec4 ixy00 = permute(ixy0 + iw0);\\\\n  vec4 ixy01 = permute(ixy0 + iw1);\\\\n  vec4 ixy10 = permute(ixy1 + iw0);\\\\n  vec4 ixy11 = permute(ixy1 + iw1);\\\\n\\\\n  vec4 gx00 = ixy00 * (1.0 / 7.0);\\\\n  vec4 gy00 = floor(gx00) * (1.0 / 7.0);\\\\n  vec4 gz00 = floor(gy00) * (1.0 / 6.0);\\\\n  gx00 = fract(gx00) - 0.5;\\\\n  gy00 = fract(gy00) - 0.5;\\\\n  gz00 = fract(gz00) - 0.5;\\\\n  vec4 gw00 = vec4(0.75) - abs(gx00) - abs(gy00) - abs(gz00);\\\\n  vec4 sw00 = step(gw00, vec4(0.0));\\\\n  gx00 -= sw00 * (step(0.0, gx00) - 0.5);\\\\n  gy00 -= sw00 * (step(0.0, gy00) - 0.5);\\\\n\\\\n  vec4 gx01 = ixy01 * (1.0 / 7.0);\\\\n  vec4 gy01 = floor(gx01) * (1.0 / 7.0);\\\\n  vec4 gz01 = floor(gy01) * (1.0 / 6.0);\\\\n  gx01 = fract(gx01) - 0.5;\\\\n  gy01 = fract(gy01) - 0.5;\\\\n  gz01 = fract(gz01) - 0.5;\\\\n  vec4 gw01 = vec4(0.75) - abs(gx01) - abs(gy01) - abs(gz01);\\\\n  vec4 sw01 = step(gw01, vec4(0.0));\\\\n  gx01 -= sw01 * (step(0.0, gx01) - 0.5);\\\\n  gy01 -= sw01 * (step(0.0, gy01) - 0.5);\\\\n\\\\n  vec4 gx10 = ixy10 * (1.0 / 7.0);\\\\n  vec4 gy10 = floor(gx10) * (1.0 / 7.0);\\\\n  vec4 gz10 = floor(gy10) * (1.0 / 6.0);\\\\n  gx10 = fract(gx10) - 0.5;\\\\n  gy10 = fract(gy10) - 0.5;\\\\n  gz10 = fract(gz10) - 0.5;\\\\n  vec4 gw10 = vec4(0.75) - abs(gx10) - abs(gy10) - abs(gz10);\\\\n  vec4 sw10 = step(gw10, vec4(0.0));\\\\n  gx10 -= sw10 * (step(0.0, gx10) - 0.5);\\\\n  gy10 -= sw10 * (step(0.0, gy10) - 0.5);\\\\n\\\\n  vec4 gx11 = ixy11 * (1.0 / 7.0);\\\\n  vec4 gy11 = floor(gx11) * (1.0 / 7.0);\\\\n  vec4 gz11 = floor(gy11) * (1.0 / 6.0);\\\\n  gx11 = fract(gx11) - 0.5;\\\\n  gy11 = fract(gy11) - 0.5;\\\\n  gz11 = fract(gz11) - 0.5;\\\\n  vec4 gw11 = vec4(0.75) - abs(gx11) - abs(gy11) - abs(gz11);\\\\n  vec4 sw11 = step(gw11, vec4(0.0));\\\\n  gx11 -= sw11 * (step(0.0, gx11) - 0.5);\\\\n  gy11 -= sw11 * (step(0.0, gy11) - 0.5);\\\\n\\\\n  vec4 g0000 = vec4(gx00.x,gy00.x,gz00.x,gw00.x);\\\\n  vec4 g1000 = vec4(gx00.y,gy00.y,gz00.y,gw00.y);\\\\n  vec4 g0100 = vec4(gx00.z,gy00.z,gz00.z,gw00.z);\\\\n  vec4 g1100 = vec4(gx00.w,gy00.w,gz00.w,gw00.w);\\\\n  vec4 g0010 = vec4(gx10.x,gy10.x,gz10.x,gw10.x);\\\\n  vec4 g1010 = vec4(gx10.y,gy10.y,gz10.y,gw10.y);\\\\n  vec4 g0110 = vec4(gx10.z,gy10.z,gz10.z,gw10.z);\\\\n  vec4 g1110 = vec4(gx10.w,gy10.w,gz10.w,gw10.w);\\\\n  vec4 g0001 = vec4(gx01.x,gy01.x,gz01.x,gw01.x);\\\\n  vec4 g1001 = vec4(gx01.y,gy01.y,gz01.y,gw01.y);\\\\n  vec4 g0101 = vec4(gx01.z,gy01.z,gz01.z,gw01.z);\\\\n  vec4 g1101 = vec4(gx01.w,gy01.w,gz01.w,gw01.w);\\\\n  vec4 g0011 = vec4(gx11.x,gy11.x,gz11.x,gw11.x);\\\\n  vec4 g1011 = vec4(gx11.y,gy11.y,gz11.y,gw11.y);\\\\n  vec4 g0111 = vec4(gx11.z,gy11.z,gz11.z,gw11.z);\\\\n  vec4 g1111 = vec4(gx11.w,gy11.w,gz11.w,gw11.w);\\\\n\\\\n  vec4 norm00 = taylorInvSqrt(vec4(dot(g0000, g0000), dot(g0100, g0100), dot(g1000, g1000), dot(g1100, g1100)));\\\\n  g0000 *= norm00.x;\\\\n  g0100 *= norm00.y;\\\\n  g1000 *= norm00.z;\\\\n  g1100 *= norm00.w;\\\\n\\\\n  vec4 norm01 = taylorInvSqrt(vec4(dot(g0001, g0001), dot(g0101, g0101), dot(g1001, g1001), dot(g1101, g1101)));\\\\n  g0001 *= norm01.x;\\\\n  g0101 *= norm01.y;\\\\n  g1001 *= norm01.z;\\\\n  g1101 *= norm01.w;\\\\n\\\\n  vec4 norm10 = taylorInvSqrt(vec4(dot(g0010, g0010), dot(g0110, g0110), dot(g1010, g1010), dot(g1110, g1110)));\\\\n  g0010 *= norm10.x;\\\\n  g0110 *= norm10.y;\\\\n  g1010 *= norm10.z;\\\\n  g1110 *= norm10.w;\\\\n\\\\n  vec4 norm11 = taylorInvSqrt(vec4(dot(g0011, g0011), dot(g0111, g0111), dot(g1011, g1011), dot(g1111, g1111)));\\\\n  g0011 *= norm11.x;\\\\n  g0111 *= norm11.y;\\\\n  g1011 *= norm11.z;\\\\n  g1111 *= norm11.w;\\\\n\\\\n  float n0000 = dot(g0000, Pf0);\\\\n  float n1000 = dot(g1000, vec4(Pf1.x, Pf0.yzw));\\\\n  float n0100 = dot(g0100, vec4(Pf0.x, Pf1.y, Pf0.zw));\\\\n  float n1100 = dot(g1100, vec4(Pf1.xy, Pf0.zw));\\\\n  float n0010 = dot(g0010, vec4(Pf0.xy, Pf1.z, Pf0.w));\\\\n  float n1010 = dot(g1010, vec4(Pf1.x, Pf0.y, Pf1.z, Pf0.w));\\\\n  float n0110 = dot(g0110, vec4(Pf0.x, Pf1.yz, Pf0.w));\\\\n  float n1110 = dot(g1110, vec4(Pf1.xyz, Pf0.w));\\\\n  float n0001 = dot(g0001, vec4(Pf0.xyz, Pf1.w));\\\\n  float n1001 = dot(g1001, vec4(Pf1.x, Pf0.yz, Pf1.w));\\\\n  float n0101 = dot(g0101, vec4(Pf0.x, Pf1.y, Pf0.z, Pf1.w));\\\\n  float n1101 = dot(g1101, vec4(Pf1.xy, Pf0.z, Pf1.w));\\\\n  float n0011 = dot(g0011, vec4(Pf0.xy, Pf1.zw));\\\\n  float n1011 = dot(g1011, vec4(Pf1.x, Pf0.y, Pf1.zw));\\\\n  float n0111 = dot(g0111, vec4(Pf0.x, Pf1.yzw));\\\\n  float n1111 = dot(g1111, Pf1);\\\\n\\\\n  vec4 fade_xyzw = fade(Pf0);\\\\n  vec4 n_0w = mix(vec4(n0000, n1000, n0100, n1100), vec4(n0001, n1001, n0101, n1101), fade_xyzw.w);\\\\n  vec4 n_1w = mix(vec4(n0010, n1010, n0110, n1110), vec4(n0011, n1011, n0111, n1111), fade_xyzw.w);\\\\n  vec4 n_zw = mix(n_0w, n_1w, fade_xyzw.z);\\\\n  vec2 n_yzw = mix(n_zw.xy, n_zw.zw, fade_xyzw.y);\\\\n  float n_xyzw = mix(n_yzw.x, n_yzw.y, fade_xyzw.x);\\\\n  return 2.2 * n_xyzw;\\\\n}\\\\n\\\\n// Classic Perlin noise, periodic version\\\\nfloat pnoise(vec4 P, vec4 rep)\\\\n{\\\\n  vec4 Pi0 = mod(floor(P), rep); // Integer part modulo rep\\\\n  vec4 Pi1 = mod(Pi0 + 1.0, rep); // Integer part + 1 mod rep\\\\n  Pi0 = mod289(Pi0);\\\\n  Pi1 = mod289(Pi1);\\\\n  vec4 Pf0 = fract(P); // Fractional part for interpolation\\\\n  vec4 Pf1 = Pf0 - 1.0; // Fractional part - 1.0\\\\n  vec4 ix = vec4(Pi0.x, Pi1.x, Pi0.x, Pi1.x);\\\\n  vec4 iy = vec4(Pi0.yy, Pi1.yy);\\\\n  vec4 iz0 = vec4(Pi0.zzzz);\\\\n  vec4 iz1 = vec4(Pi1.zzzz);\\\\n  vec4 iw0 = vec4(Pi0.wwww);\\\\n  vec4 iw1 = vec4(Pi1.wwww);\\\\n\\\\n  vec4 ixy = permute(permute(ix) + iy);\\\\n  vec4 ixy0 = permute(ixy + iz0);\\\\n  vec4 ixy1 = permute(ixy + iz1);\\\\n  vec4 ixy00 = permute(ixy0 + iw0);\\\\n  vec4 ixy01 = permute(ixy0 + iw1);\\\\n  vec4 ixy10 = permute(ixy1 + iw0);\\\\n  vec4 ixy11 = permute(ixy1 + iw1);\\\\n\\\\n  vec4 gx00 = ixy00 * (1.0 / 7.0);\\\\n  vec4 gy00 = floor(gx00) * (1.0 / 7.0);\\\\n  vec4 gz00 = floor(gy00) * (1.0 / 6.0);\\\\n  gx00 = fract(gx00) - 0.5;\\\\n  gy00 = fract(gy00) - 0.5;\\\\n  gz00 = fract(gz00) - 0.5;\\\\n  vec4 gw00 = vec4(0.75) - abs(gx00) - abs(gy00) - abs(gz00);\\\\n  vec4 sw00 = step(gw00, vec4(0.0));\\\\n  gx00 -= sw00 * (step(0.0, gx00) - 0.5);\\\\n  gy00 -= sw00 * (step(0.0, gy00) - 0.5);\\\\n\\\\n  vec4 gx01 = ixy01 * (1.0 / 7.0);\\\\n  vec4 gy01 = floor(gx01) * (1.0 / 7.0);\\\\n  vec4 gz01 = floor(gy01) * (1.0 / 6.0);\\\\n  gx01 = fract(gx01) - 0.5;\\\\n  gy01 = fract(gy01) - 0.5;\\\\n  gz01 = fract(gz01) - 0.5;\\\\n  vec4 gw01 = vec4(0.75) - abs(gx01) - abs(gy01) - abs(gz01);\\\\n  vec4 sw01 = step(gw01, vec4(0.0));\\\\n  gx01 -= sw01 * (step(0.0, gx01) - 0.5);\\\\n  gy01 -= sw01 * (step(0.0, gy01) - 0.5);\\\\n\\\\n  vec4 gx10 = ixy10 * (1.0 / 7.0);\\\\n  vec4 gy10 = floor(gx10) * (1.0 / 7.0);\\\\n  vec4 gz10 = floor(gy10) * (1.0 / 6.0);\\\\n  gx10 = fract(gx10) - 0.5;\\\\n  gy10 = fract(gy10) - 0.5;\\\\n  gz10 = fract(gz10) - 0.5;\\\\n  vec4 gw10 = vec4(0.75) - abs(gx10) - abs(gy10) - abs(gz10);\\\\n  vec4 sw10 = step(gw10, vec4(0.0));\\\\n  gx10 -= sw10 * (step(0.0, gx10) - 0.5);\\\\n  gy10 -= sw10 * (step(0.0, gy10) - 0.5);\\\\n\\\\n  vec4 gx11 = ixy11 * (1.0 / 7.0);\\\\n  vec4 gy11 = floor(gx11) * (1.0 / 7.0);\\\\n  vec4 gz11 = floor(gy11) * (1.0 / 6.0);\\\\n  gx11 = fract(gx11) - 0.5;\\\\n  gy11 = fract(gy11) - 0.5;\\\\n  gz11 = fract(gz11) - 0.5;\\\\n  vec4 gw11 = vec4(0.75) - abs(gx11) - abs(gy11) - abs(gz11);\\\\n  vec4 sw11 = step(gw11, vec4(0.0));\\\\n  gx11 -= sw11 * (step(0.0, gx11) - 0.5);\\\\n  gy11 -= sw11 * (step(0.0, gy11) - 0.5);\\\\n\\\\n  vec4 g0000 = vec4(gx00.x,gy00.x,gz00.x,gw00.x);\\\\n  vec4 g1000 = vec4(gx00.y,gy00.y,gz00.y,gw00.y);\\\\n  vec4 g0100 = vec4(gx00.z,gy00.z,gz00.z,gw00.z);\\\\n  vec4 g1100 = vec4(gx00.w,gy00.w,gz00.w,gw00.w);\\\\n  vec4 g0010 = vec4(gx10.x,gy10.x,gz10.x,gw10.x);\\\\n  vec4 g1010 = vec4(gx10.y,gy10.y,gz10.y,gw10.y);\\\\n  vec4 g0110 = vec4(gx10.z,gy10.z,gz10.z,gw10.z);\\\\n  vec4 g1110 = vec4(gx10.w,gy10.w,gz10.w,gw10.w);\\\\n  vec4 g0001 = vec4(gx01.x,gy01.x,gz01.x,gw01.x);\\\\n  vec4 g1001 = vec4(gx01.y,gy01.y,gz01.y,gw01.y);\\\\n  vec4 g0101 = vec4(gx01.z,gy01.z,gz01.z,gw01.z);\\\\n  vec4 g1101 = vec4(gx01.w,gy01.w,gz01.w,gw01.w);\\\\n  vec4 g0011 = vec4(gx11.x,gy11.x,gz11.x,gw11.x);\\\\n  vec4 g1011 = vec4(gx11.y,gy11.y,gz11.y,gw11.y);\\\\n  vec4 g0111 = vec4(gx11.z,gy11.z,gz11.z,gw11.z);\\\\n  vec4 g1111 = vec4(gx11.w,gy11.w,gz11.w,gw11.w);\\\\n\\\\n  vec4 norm00 = taylorInvSqrt(vec4(dot(g0000, g0000), dot(g0100, g0100), dot(g1000, g1000), dot(g1100, g1100)));\\\\n  g0000 *= norm00.x;\\\\n  g0100 *= norm00.y;\\\\n  g1000 *= norm00.z;\\\\n  g1100 *= norm00.w;\\\\n\\\\n  vec4 norm01 = taylorInvSqrt(vec4(dot(g0001, g0001), dot(g0101, g0101), dot(g1001, g1001), dot(g1101, g1101)));\\\\n  g0001 *= norm01.x;\\\\n  g0101 *= norm01.y;\\\\n  g1001 *= norm01.z;\\\\n  g1101 *= norm01.w;\\\\n\\\\n  vec4 norm10 = taylorInvSqrt(vec4(dot(g0010, g0010), dot(g0110, g0110), dot(g1010, g1010), dot(g1110, g1110)));\\\\n  g0010 *= norm10.x;\\\\n  g0110 *= norm10.y;\\\\n  g1010 *= norm10.z;\\\\n  g1110 *= norm10.w;\\\\n\\\\n  vec4 norm11 = taylorInvSqrt(vec4(dot(g0011, g0011), dot(g0111, g0111), dot(g1011, g1011), dot(g1111, g1111)));\\\\n  g0011 *= norm11.x;\\\\n  g0111 *= norm11.y;\\\\n  g1011 *= norm11.z;\\\\n  g1111 *= norm11.w;\\\\n\\\\n  float n0000 = dot(g0000, Pf0);\\\\n  float n1000 = dot(g1000, vec4(Pf1.x, Pf0.yzw));\\\\n  float n0100 = dot(g0100, vec4(Pf0.x, Pf1.y, Pf0.zw));\\\\n  float n1100 = dot(g1100, vec4(Pf1.xy, Pf0.zw));\\\\n  float n0010 = dot(g0010, vec4(Pf0.xy, Pf1.z, Pf0.w));\\\\n  float n1010 = dot(g1010, vec4(Pf1.x, Pf0.y, Pf1.z, Pf0.w));\\\\n  float n0110 = dot(g0110, vec4(Pf0.x, Pf1.yz, Pf0.w));\\\\n  float n1110 = dot(g1110, vec4(Pf1.xyz, Pf0.w));\\\\n  float n0001 = dot(g0001, vec4(Pf0.xyz, Pf1.w));\\\\n  float n1001 = dot(g1001, vec4(Pf1.x, Pf0.yz, Pf1.w));\\\\n  float n0101 = dot(g0101, vec4(Pf0.x, Pf1.y, Pf0.z, Pf1.w));\\\\n  float n1101 = dot(g1101, vec4(Pf1.xy, Pf0.z, Pf1.w));\\\\n  float n0011 = dot(g0011, vec4(Pf0.xy, Pf1.zw));\\\\n  float n1011 = dot(g1011, vec4(Pf1.x, Pf0.y, Pf1.zw));\\\\n  float n0111 = dot(g0111, vec4(Pf0.x, Pf1.yzw));\\\\n  float n1111 = dot(g1111, Pf1);\\\\n\\\\n  vec4 fade_xyzw = fade(Pf0);\\\\n  vec4 n_0w = mix(vec4(n0000, n1000, n0100, n1100), vec4(n0001, n1001, n0101, n1101), fade_xyzw.w);\\\\n  vec4 n_1w = mix(vec4(n0010, n1010, n0110, n1110), vec4(n0011, n1011, n0111, n1111), fade_xyzw.w);\\\\n  vec4 n_zw = mix(n_0w, n_1w, fade_xyzw.z);\\\\n  vec2 n_yzw = mix(n_zw.xy, n_zw.zw, fade_xyzw.y);\\\\n  float n_xyzw = mix(n_yzw.x, n_yzw.y, fade_xyzw.x);\\\\n  return 2.2 * n_xyzw;\\\\n}\\\\n',[xI.NOISE_2D]:\\\\\\\"//\\\\n// Description : Array and textureless GLSL 2D simplex noise function.\\\\n//      Author : Ian McEwan, Ashima Arts.\\\\n//  Maintainer : stegu\\\\n//     Lastmod : 20110822 (ijm)\\\\n//     License : Copyright (C) 2011 Ashima Arts. All rights reserved.\\\\n//               Distributed under the MIT License. See LICENSE file.\\\\n//               https://github.com/ashima/webgl-noise\\\\n//               https://github.com/stegu/webgl-noise\\\\n// \\\\n\\\\n\\\\nfloat snoise(vec2 v)\\\\n  {\\\\n  const vec4 C = vec4(0.211324865405187,  // (3.0-sqrt(3.0))/6.0\\\\n                      0.366025403784439,  // 0.5*(sqrt(3.0)-1.0)\\\\n                     -0.577350269189626,  // -1.0 + 2.0 * C.x\\\\n                      0.024390243902439); // 1.0 / 41.0\\\\n// First corner\\\\n  vec2 i  = floor(v + dot(v, C.yy) );\\\\n  vec2 x0 = v -   i + dot(i, C.xx);\\\\n\\\\n// Other corners\\\\n  vec2 i1;\\\\n  //i1.x = step( x0.y, x0.x ); // x0.x > x0.y ? 1.0 : 0.0\\\\n  //i1.y = 1.0 - i1.x;\\\\n  i1 = (x0.x > x0.y) ? vec2(1.0, 0.0) : vec2(0.0, 1.0);\\\\n  // x0 = x0 - 0.0 + 0.0 * C.xx ;\\\\n  // x1 = x0 - i1 + 1.0 * C.xx ;\\\\n  // x2 = x0 - 1.0 + 2.0 * C.xx ;\\\\n  vec4 x12 = x0.xyxy + C.xxzz;\\\\n  x12.xy -= i1;\\\\n\\\\n// Permutations\\\\n  i = mod289(i); // Avoid truncation effects in permutation\\\\n  vec3 p = permute( permute( i.y + vec3(0.0, i1.y, 1.0 ))\\\\n\\\\t\\\\t+ i.x + vec3(0.0, i1.x, 1.0 ));\\\\n\\\\n  vec3 m = max(0.5 - vec3(dot(x0,x0), dot(x12.xy,x12.xy), dot(x12.zw,x12.zw)), 0.0);\\\\n  m = m*m ;\\\\n  m = m*m ;\\\\n\\\\n// Gradients: 41 points uniformly over a line, mapped onto a diamond.\\\\n// The ring size 17*17 = 289 is close to a multiple of 41 (41*7 = 287)\\\\n\\\\n  vec3 x = 2.0 * fract(p * C.www) - 1.0;\\\\n  vec3 h = abs(x) - 0.5;\\\\n  vec3 ox = floor(x + 0.5);\\\\n  vec3 a0 = x - ox;\\\\n\\\\n// Normalise gradients implicitly by scaling m\\\\n// Approximation of: m *= inversesqrt( a0*a0 + h*h );\\\\n  m *= 1.79284291400159 - 0.85373472095314 * ( a0*a0 + h*h );\\\\n\\\\n// Compute final noise value at P\\\\n  vec3 g;\\\\n  g.x  = a0.x  * x0.x  + h.x  * x0.y;\\\\n  g.yz = a0.yz * x12.xz + h.yz * x12.yw;\\\\n  return 130.0 * dot(m, g);\\\\n}\\\\n\\\\\\\",[xI.NOISE_3D]:\\\\\\\"//\\\\n// Description : Array and textureless GLSL 2D/3D/4D simplex \\\\n//               noise functions.\\\\n//      Author : Ian McEwan, Ashima Arts.\\\\n//  Maintainer : stegu\\\\n//     Lastmod : 20110822 (ijm)\\\\n//     License : Copyright (C) 2011 Ashima Arts. All rights reserved.\\\\n//               Distributed under the MIT License. See LICENSE file.\\\\n//               https://github.com/ashima/webgl-noise\\\\n//               https://github.com/stegu/webgl-noise\\\\n// \\\\n\\\\n\\\\n\\\\nfloat snoise(vec3 v)\\\\n  { \\\\n  const vec2  C = vec2(1.0/6.0, 1.0/3.0) ;\\\\n  const vec4  D = vec4(0.0, 0.5, 1.0, 2.0);\\\\n\\\\n// First corner\\\\n  vec3 i  = floor(v + dot(v, C.yyy) );\\\\n  vec3 x0 =   v - i + dot(i, C.xxx) ;\\\\n\\\\n// Other corners\\\\n  vec3 g = step(x0.yzx, x0.xyz);\\\\n  vec3 l = 1.0 - g;\\\\n  vec3 i1 = min( g.xyz, l.zxy );\\\\n  vec3 i2 = max( g.xyz, l.zxy );\\\\n\\\\n  //   x0 = x0 - 0.0 + 0.0 * C.xxx;\\\\n  //   x1 = x0 - i1  + 1.0 * C.xxx;\\\\n  //   x2 = x0 - i2  + 2.0 * C.xxx;\\\\n  //   x3 = x0 - 1.0 + 3.0 * C.xxx;\\\\n  vec3 x1 = x0 - i1 + C.xxx;\\\\n  vec3 x2 = x0 - i2 + C.yyy; // 2.0*C.x = 1/3 = C.y\\\\n  vec3 x3 = x0 - D.yyy;      // -1.0+3.0*C.x = -0.5 = -D.y\\\\n\\\\n// Permutations\\\\n  i = mod289(i); \\\\n  vec4 p = permute( permute( permute( \\\\n             i.z + vec4(0.0, i1.z, i2.z, 1.0 ))\\\\n           + i.y + vec4(0.0, i1.y, i2.y, 1.0 )) \\\\n           + i.x + vec4(0.0, i1.x, i2.x, 1.0 ));\\\\n\\\\n// Gradients: 7x7 points over a square, mapped onto an octahedron.\\\\n// The ring size 17*17 = 289 is close to a multiple of 49 (49*6 = 294)\\\\n  float n_ = 0.142857142857; // 1.0/7.0\\\\n  vec3  ns = n_ * D.wyz - D.xzx;\\\\n\\\\n  vec4 j = p - 49.0 * floor(p * ns.z * ns.z);  //  mod(p,7*7)\\\\n\\\\n  vec4 x_ = floor(j * ns.z);\\\\n  vec4 y_ = floor(j - 7.0 * x_ );    // mod(j,N)\\\\n\\\\n  vec4 x = x_ *ns.x + ns.yyyy;\\\\n  vec4 y = y_ *ns.x + ns.yyyy;\\\\n  vec4 h = 1.0 - abs(x) - abs(y);\\\\n\\\\n  vec4 b0 = vec4( x.xy, y.xy );\\\\n  vec4 b1 = vec4( x.zw, y.zw );\\\\n\\\\n  //vec4 s0 = vec4(lessThan(b0,0.0))*2.0 - 1.0;\\\\n  //vec4 s1 = vec4(lessThan(b1,0.0))*2.0 - 1.0;\\\\n  vec4 s0 = floor(b0)*2.0 + 1.0;\\\\n  vec4 s1 = floor(b1)*2.0 + 1.0;\\\\n  vec4 sh = -step(h, vec4(0.0));\\\\n\\\\n  vec4 a0 = b0.xzyw + s0.xzyw*sh.xxyy ;\\\\n  vec4 a1 = b1.xzyw + s1.xzyw*sh.zzww ;\\\\n\\\\n  vec3 p0 = vec3(a0.xy,h.x);\\\\n  vec3 p1 = vec3(a0.zw,h.y);\\\\n  vec3 p2 = vec3(a1.xy,h.z);\\\\n  vec3 p3 = vec3(a1.zw,h.w);\\\\n\\\\n//Normalise gradients\\\\n  vec4 norm = taylorInvSqrt(vec4(dot(p0,p0), dot(p1,p1), dot(p2, p2), dot(p3,p3)));\\\\n  p0 *= norm.x;\\\\n  p1 *= norm.y;\\\\n  p2 *= norm.z;\\\\n  p3 *= norm.w;\\\\n\\\\n// Mix final noise value\\\\n  vec4 m = max(0.6 - vec4(dot(x0,x0), dot(x1,x1), dot(x2,x2), dot(x3,x3)), 0.0);\\\\n  m = m * m;\\\\n  return 42.0 * dot( m*m, vec4( dot(p0,x0), dot(p1,x1), \\\\n                                dot(p2,x2), dot(p3,x3) ) );\\\\n  }\\\\n\\\\\\\",[xI.NOISE_4D]:\\\\\\\"//\\\\n// Description : Array and textureless GLSL 2D/3D/4D simplex \\\\n//               noise functions.\\\\n//      Author : Ian McEwan, Ashima Arts.\\\\n//  Maintainer : stegu\\\\n//     Lastmod : 20110822 (ijm)\\\\n//     License : Copyright (C) 2011 Ashima Arts. All rights reserved.\\\\n//               Distributed under the MIT License. See LICENSE file.\\\\n//               https://github.com/ashima/webgl-noise\\\\n//               https://github.com/stegu/webgl-noise\\\\n// \\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\nvec4 grad4(float j, vec4 ip)\\\\n  {\\\\n  const vec4 ones = vec4(1.0, 1.0, 1.0, -1.0);\\\\n  vec4 p,s;\\\\n\\\\n  p.xyz = floor( fract (vec3(j) * ip.xyz) * 7.0) * ip.z - 1.0;\\\\n  p.w = 1.5 - dot(abs(p.xyz), ones.xyz);\\\\n  s = vec4(lessThan(p, vec4(0.0)));\\\\n  p.xyz = p.xyz + (s.xyz*2.0 - 1.0) * s.www; \\\\n\\\\n  return p;\\\\n  }\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\n// (sqrt(5) - 1)/4 = F4, used once below\\\\n#define F4 0.309016994374947451\\\\n\\\\nfloat snoise(vec4 v)\\\\n  {\\\\n  const vec4  C = vec4( 0.138196601125011,  // (5 - sqrt(5))/20  G4\\\\n                        0.276393202250021,  // 2 * G4\\\\n                        0.414589803375032,  // 3 * G4\\\\n                       -0.447213595499958); // -1 + 4 * G4\\\\n\\\\n// First corner\\\\n  vec4 i  = floor(v + dot(v, vec4(F4)) );\\\\n  vec4 x0 = v -   i + dot(i, C.xxxx);\\\\n\\\\n// Other corners\\\\n\\\\n// Rank sorting originally contributed by Bill Licea-Kane, AMD (formerly ATI)\\\\n  vec4 i0;\\\\n  vec3 isX = step( x0.yzw, x0.xxx );\\\\n  vec3 isYZ = step( x0.zww, x0.yyz );\\\\n//  i0.x = dot( isX, vec3( 1.0 ) );\\\\n  i0.x = isX.x + isX.y + isX.z;\\\\n  i0.yzw = 1.0 - isX;\\\\n//  i0.y += dot( isYZ.xy, vec2( 1.0 ) );\\\\n  i0.y += isYZ.x + isYZ.y;\\\\n  i0.zw += 1.0 - isYZ.xy;\\\\n  i0.z += isYZ.z;\\\\n  i0.w += 1.0 - isYZ.z;\\\\n\\\\n  // i0 now contains the unique values 0,1,2,3 in each channel\\\\n  vec4 i3 = clamp( i0, 0.0, 1.0 );\\\\n  vec4 i2 = clamp( i0-1.0, 0.0, 1.0 );\\\\n  vec4 i1 = clamp( i0-2.0, 0.0, 1.0 );\\\\n\\\\n  //  x0 = x0 - 0.0 + 0.0 * C.xxxx\\\\n  //  x1 = x0 - i1  + 1.0 * C.xxxx\\\\n  //  x2 = x0 - i2  + 2.0 * C.xxxx\\\\n  //  x3 = x0 - i3  + 3.0 * C.xxxx\\\\n  //  x4 = x0 - 1.0 + 4.0 * C.xxxx\\\\n  vec4 x1 = x0 - i1 + C.xxxx;\\\\n  vec4 x2 = x0 - i2 + C.yyyy;\\\\n  vec4 x3 = x0 - i3 + C.zzzz;\\\\n  vec4 x4 = x0 + C.wwww;\\\\n\\\\n// Permutations\\\\n  i = mod289(i); \\\\n  float j0 = permute( permute( permute( permute(i.w) + i.z) + i.y) + i.x);\\\\n  vec4 j1 = permute( permute( permute( permute (\\\\n             i.w + vec4(i1.w, i2.w, i3.w, 1.0 ))\\\\n           + i.z + vec4(i1.z, i2.z, i3.z, 1.0 ))\\\\n           + i.y + vec4(i1.y, i2.y, i3.y, 1.0 ))\\\\n           + i.x + vec4(i1.x, i2.x, i3.x, 1.0 ));\\\\n\\\\n// Gradients: 7x7x6 points over a cube, mapped onto a 4-cross polytope\\\\n// 7*7*6 = 294, which is close to the ring size 17*17 = 289.\\\\n  vec4 ip = vec4(1.0/294.0, 1.0/49.0, 1.0/7.0, 0.0) ;\\\\n\\\\n  vec4 p0 = grad4(j0,   ip);\\\\n  vec4 p1 = grad4(j1.x, ip);\\\\n  vec4 p2 = grad4(j1.y, ip);\\\\n  vec4 p3 = grad4(j1.z, ip);\\\\n  vec4 p4 = grad4(j1.w, ip);\\\\n\\\\n// Normalise gradients\\\\n  vec4 norm = taylorInvSqrt(vec4(dot(p0,p0), dot(p1,p1), dot(p2, p2), dot(p3,p3)));\\\\n  p0 *= norm.x;\\\\n  p1 *= norm.y;\\\\n  p2 *= norm.z;\\\\n  p3 *= norm.w;\\\\n  p4 *= taylorInvSqrt(dot(p4,p4));\\\\n\\\\n// Mix contributions from the five corners\\\\n  vec3 m0 = max(0.6 - vec3(dot(x0,x0), dot(x1,x1), dot(x2,x2)), 0.0);\\\\n  vec2 m1 = max(0.6 - vec2(dot(x3,x3), dot(x4,x4)            ), 0.0);\\\\n  m0 = m0 * m0;\\\\n  m1 = m1 * m1;\\\\n  return 49.0 * ( dot(m0*m0, vec3( dot( p0, x0 ), dot( p1, x1 ), dot( p2, x2 )))\\\\n               + dot(m1*m1, vec2( dot( p3, x3 ), dot( p4, x4 ) ) ) ) ;\\\\n\\\\n  }\\\\n\\\\\\\"},TI={[xI.CLASSIC_PERLIN_2D]:Do.VEC2,[xI.CLASSIC_PERLIN_3D]:Do.VEC3,[xI.CLASSIC_PERLIN_4D]:Do.VEC4,[xI.NOISE_2D]:Do.VEC2,[xI.NOISE_3D]:Do.VEC3,[xI.NOISE_4D]:Do.VEC4},AI={[xI.CLASSIC_PERLIN_2D]:Do.FLOAT,[xI.CLASSIC_PERLIN_3D]:Do.FLOAT,[xI.CLASSIC_PERLIN_4D]:Do.FLOAT,[xI.NOISE_2D]:Do.FLOAT,[xI.NOISE_3D]:Do.FLOAT,[xI.NOISE_4D]:Do.FLOAT},EI={[xI.CLASSIC_PERLIN_2D]:\\\\\\\"cnoise\\\\\\\",[xI.CLASSIC_PERLIN_3D]:\\\\\\\"cnoise\\\\\\\",[xI.CLASSIC_PERLIN_4D]:\\\\\\\"cnoise\\\\\\\",[xI.NOISE_2D]:\\\\\\\"snoise\\\\\\\",[xI.NOISE_3D]:\\\\\\\"snoise\\\\\\\",[xI.NOISE_4D]:\\\\\\\"snoise\\\\\\\"};var MI;!function(t){t[t.NoChange=0]=\\\\\\\"NoChange\\\\\\\",t[t.Float=1]=\\\\\\\"Float\\\\\\\",t[t.Vec2=2]=\\\\\\\"Vec2\\\\\\\",t[t.Vec3=3]=\\\\\\\"Vec3\\\\\\\",t[t.Vec4=4]=\\\\\\\"Vec4\\\\\\\"}(MI||(MI={}));const SI=[MI.NoChange,MI.Float,MI.Vec2,MI.Vec3,MI.Vec4],CI={[MI.NoChange]:\\\\\\\"Same as noise\\\\\\\",[MI.Float]:\\\\\\\"Float\\\\\\\",[MI.Vec2]:\\\\\\\"Vec2\\\\\\\",[MI.Vec3]:\\\\\\\"Vec3\\\\\\\",[MI.Vec4]:\\\\\\\"Vec4\\\\\\\"},NI={[MI.NoChange]:Do.FLOAT,[MI.Float]:Do.FLOAT,[MI.Vec2]:Do.VEC2,[MI.Vec3]:Do.VEC3,[MI.Vec4]:Do.VEC4},LI=[\\\\\\\"x\\\\\\\",\\\\\\\"y\\\\\\\",\\\\\\\"z\\\\\\\",\\\\\\\"w\\\\\\\"],OI=\\\\\\\"noise\\\\\\\",RI=bI.indexOf(xI.NOISE_3D),PI=MI.NoChange,II={amp:1,freq:1};var FI;!function(t){t.AMP=\\\\\\\"amp\\\\\\\",t.POSITION=\\\\\\\"position\\\\\\\",t.FREQ=\\\\\\\"freq\\\\\\\",t.OFFSET=\\\\\\\"offset\\\\\\\"}(FI||(FI={}));const DI=new class extends aa{constructor(){super(...arguments),this.type=oa.INTEGER(RI,{menu:{entries:bI.map(((t,e)=>({name:`${t} (output: ${AI[t]})`,value:e})))}}),this.outputType=oa.INTEGER(PI,{menu:{entries:SI.map((t=>{const e=SI[t];return{name:CI[e],value:e}}))}}),this.octaves=oa.INTEGER(3,{range:[1,10],rangeLocked:[!0,!1]}),this.ampAttenuation=oa.FLOAT(.5,{range:[0,1]}),this.freqIncrease=oa.FLOAT(2,{range:[0,10],separatorAfter:!0})}};class kI extends df{constructor(){super(...arguments),this.paramsConfig=DI}static type(){return\\\\\\\"noise\\\\\\\"}initializeNode(){super.initializeNode(),this.io.connection_points.initializeNode(),this.io.connection_points.spare_params.set_inputless_param_names([\\\\\\\"octaves\\\\\\\",\\\\\\\"ampAttenuation\\\\\\\",\\\\\\\"freqIncrease\\\\\\\"]),this.io.outputs.setNamedOutputConnectionPoints([new Vo(OI,Do.FLOAT)]),this.io.connection_points.set_expected_input_types_function(this._expected_input_types.bind(this)),this.io.connection_points.set_expected_output_types_function(this._expected_output_types.bind(this)),this.io.connection_points.set_input_name_function(this._gl_input_name.bind(this)),this.io.connection_points.set_output_name_function((()=>OI))}_gl_input_name(t){return[FI.AMP,FI.POSITION,FI.FREQ,FI.OFFSET][t]}paramDefaultValue(t){return II[t]}_expected_input_types(){const t=bI[this.pv.type],e=this._expected_output_types()[0],n=TI[t];return[e,n,n,n]}_expected_output_types(){const t=bI[this.pv.type],e=SI[this.pv.outputType];return e==MI.NoChange?[TI[t]]:[NI[e]]}setLines(t){const e=[],n=[],i=bI[this.pv.type],r=wI[i],s=AI[i];e.push(new Tf(this,\\\\\\\"// Modulo 289 without a division (only multiplications)\\\\nfloat mod289(float x) {\\\\n  return x - floor(x * (1.0 / 289.0)) * 289.0;\\\\n}\\\\nvec2 mod289(vec2 x) {\\\\n  return x - floor(x * (1.0 / 289.0)) * 289.0;\\\\n}\\\\nvec3 mod289(vec3 x) {\\\\n  return x - floor(x * (1.0 / 289.0)) * 289.0;\\\\n}\\\\nvec4 mod289(vec4 x) {\\\\n  return x - floor(x * (1.0 / 289.0)) * 289.0;\\\\n}\\\\n// Modulo 7 without a division\\\\nvec3 mod7(vec3 x) {\\\\n  return x - floor(x * (1.0 / 7.0)) * 7.0;\\\\n}\\\\n\\\\n// Permutation polynomial: (34x^2 + x) mod 289\\\\nfloat permute(float x) {\\\\n     return mod289(((x*34.0)+1.0)*x);\\\\n}\\\\nvec3 permute(vec3 x) {\\\\n  return mod289((34.0 * x + 1.0) * x);\\\\n}\\\\nvec4 permute(vec4 x) {\\\\n     return mod289(((x*34.0)+1.0)*x);\\\\n}\\\\n\\\\nfloat taylorInvSqrt(float r)\\\\n{\\\\n  return 1.79284291400159 - 0.85373472095314 * r;\\\\n}\\\\nvec4 taylorInvSqrt(vec4 r)\\\\n{\\\\n  return 1.79284291400159 - 0.85373472095314 * r;\\\\n}\\\\n\\\\nvec2 fade(vec2 t) {\\\\n  return t*t*t*(t*(t*6.0-15.0)+10.0);\\\\n}\\\\nvec3 fade(vec3 t) {\\\\n  return t*t*t*(t*(t*6.0-15.0)+10.0);\\\\n}\\\\nvec4 fade(vec4 t) {\\\\n  return t*t*t*(t*(t*6.0-15.0)+10.0);\\\\n}\\\\\\\")),e.push(new Tf(this,r)),e.push(new Tf(this,this.fbm_function()));const o=this._expected_output_types()[0];if(o==s){const t=this.single_noise_line();n.push(t)}else{const t=Go[o],e=[],r=this.glVarName(\\\\\\\"noise\\\\\\\");for(let s=0;s<t;s++){const t=LI[s];e.push(`${r}${t}`);const o=TI[i],a=Go[o],l=`${o}(${f.range(a).map((t=>uf.float(1e3*s))).join(\\\\\\\", \\\\\\\")})`,c=this.single_noise_line(t,t,l);n.push(c)}const s=`vec${t} ${r} = vec${t}(${e.join(\\\\\\\", \\\\\\\")})`;n.push(s)}t.addDefinitions(this,e),t.addBodyLines(this,n)}fbm_method_name(){const t=bI[this.pv.type];return`fbm_${EI[t]}_${this.name()}`}fbm_function(){const t=bI[this.pv.type],e=EI[t],n=TI[t];return`\\\\nfloat ${this.fbm_method_name()} (in ${n} st) {\\\\n\\\\tfloat value = 0.0;\\\\n\\\\tfloat amplitude = 1.0;\\\\n\\\\tfor (int i = 0; i < ${uf.integer(this.pv.octaves)}; i++) {\\\\n\\\\t\\\\tvalue += amplitude * ${e}(st);\\\\n\\\\t\\\\tst *= ${uf.float(this.pv.freqIncrease)};\\\\n\\\\t\\\\tamplitude *= ${uf.float(this.pv.ampAttenuation)};\\\\n\\\\t}\\\\n\\\\treturn value;\\\\n}\\\\n`}single_noise_line(t,e,n){const i=this.fbm_method_name(),r=uf.any(this.variableForInput(FI.AMP)),s=uf.any(this.variableForInput(FI.POSITION)),o=uf.any(this.variableForInput(FI.FREQ));let a=uf.any(this.variableForInput(FI.OFFSET));n&&(a=`(${a}+${n})`);const l=[`(${s}*${o})+${a}`].join(\\\\\\\", \\\\\\\"),c=this.glVarName(OI),u=`${r}*${i}(${l})`;if(e)return`float ${c}${t} = (${u}).${e}`;return`${this.io.outputs.namedOutputConnectionPoints()[0].type()} ${c} = ${u}`}}class BI extends BO{static type(){return\\\\\\\"null\\\\\\\"}setLines(t){const e=uf.any(this.variableForInput(this._gl_input_name(0))),n=this.io.outputs.namedOutputConnectionPoints()[0],i=`${n.type()} ${this.glVarName(n.name())} = ${e}`;t.addBodyLines(this,[i])}}const zI=new class extends aa{};class UI extends df{constructor(){super(...arguments),this.paramsConfig=zI}static type(){return\\\\\\\"output\\\\\\\"}initializeNode(){super.initializeNode(),this.addPostDirtyHook(\\\\\\\"_set_mat_to_recompile\\\\\\\",this._set_mat_to_recompile.bind(this)),this.lifecycle.add_on_add_hook((()=>{var t,e;null===(e=null===(t=this.material_node)||void 0===t?void 0:t.assemblerController)||void 0===e||e.add_output_inputs(this)}))}setLines(t){t.assembler().set_node_lines_output(this,t)}}class GI{constructor(){this._param_configs=[]}reset(){this._param_configs=[]}push(t){this._param_configs.push(t)}list(){return this._param_configs}}const VI=new class extends aa{constructor(){super(...arguments),this.name=oa.STRING(\\\\\\\"\\\\\\\"),this.type=oa.INTEGER(ko.indexOf(Do.FLOAT),{menu:{entries:ko.map(((t,e)=>({name:t,value:e})))}}),this.asColor=oa.BOOLEAN(0,{visibleIf:{type:ko.indexOf(Do.VEC3)}})}};class HI extends df{constructor(){super(...arguments),this.paramsConfig=VI,this._allow_inputs_created_from_params=!1,this._on_create_set_name_if_none_bound=this._on_create_set_name_if_none.bind(this)}static type(){return\\\\\\\"param\\\\\\\"}initializeNode(){this.addPostDirtyHook(\\\\\\\"_set_mat_to_recompile\\\\\\\",this._set_mat_to_recompile.bind(this)),this.lifecycle.add_on_create_hook(this._on_create_set_name_if_none_bound),this.io.connection_points.initializeNode(),this.io.connection_points.set_expected_input_types_function((()=>[])),this.io.connection_points.set_expected_output_types_function((()=>[ko[this.pv.type]])),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.name])}))}))}setLines(t){const e=[],n=ko[this.pv.type],i=this.uniform_name();e.push(new Af(this,n,i)),t.addDefinitions(this,e)}paramsGenerating(){return!0}setParamConfigs(){const t=ko[this.pv.type],e=Uo[t];let n=Bo[t];if(this._param_configs_controller=this._param_configs_controller||new GI,this._param_configs_controller.reset(),n==Es.VECTOR3&&this.p.asColor.value&&m.isArray(e)&&3==e.length){const t=new $f(Es.COLOR,this.pv.name,e,this.uniform_name());this._param_configs_controller.push(t)}else{const t=new $f(n,this.pv.name,e,this.uniform_name());this._param_configs_controller.push(t)}}uniform_name(){const t=this.io.outputs.namedOutputConnectionPoints()[0];return this.glVarName(t.name())}set_gl_type(t){const e=ko.indexOf(t);this.p.type.set(e)}_on_create_set_name_if_none(){\\\\\\\"\\\\\\\"==this.pv.name&&this.p.name.set(this.name())}}class jI extends kO{static type(){return\\\\\\\"refract\\\\\\\"}initializeNode(){super.initializeNode(),this.io.connection_points.set_input_name_function((t=>[\\\\\\\"I\\\\\\\",\\\\\\\"N\\\\\\\",\\\\\\\"eta\\\\\\\"][t])),this.io.connection_points.set_output_name_function((t=>\\\\\\\"refract\\\\\\\")),this.io.connection_points.set_expected_input_types_function(this._expected_input_types.bind(this)),this.io.connection_points.set_expected_output_types_function(this._expected_output_types.bind(this))}gl_method_name(){return\\\\\\\"refract\\\\\\\"}_expected_input_types(){const t=this.io.connection_points.first_input_connection_type()||Do.VEC3;return[t,t,Do.FLOAT]}_expected_output_types(){return[this._expected_input_types()[0]]}}const WI=\\\\\\\"SSSModel\\\\\\\";const qI=new class extends aa{constructor(){super(...arguments),this.color=oa.COLOR([1,1,1]),this.thickness=oa.FLOAT(.1),this.power=oa.FLOAT(2),this.scale=oa.FLOAT(16),this.distortion=oa.FLOAT(.1),this.ambient=oa.FLOAT(.4),this.attenuation=oa.FLOAT(.8)}};class XI extends df{constructor(){super(...arguments),this.paramsConfig=qI}static type(){return\\\\\\\"SSSModel\\\\\\\"}initializeNode(){this.io.outputs.setNamedOutputConnectionPoints([new Vo(WI,Do.SSS_MODEL)])}setLines(t){const e=[],n=this.glVarName(WI);e.push(`SSSModel ${n}`),e.push(`${n}.isActive = true;`),e.push(this._paramLineFloat(n,this.p.color)),e.push(this._paramLineFloat(n,this.p.thickness)),e.push(this._paramLineFloat(n,this.p.power)),e.push(this._paramLineFloat(n,this.p.scale)),e.push(this._paramLineFloat(n,this.p.distortion)),e.push(this._paramLineFloat(n,this.p.ambient)),e.push(this._paramLineFloat(n,this.p.attenuation)),t.addBodyLines(this,e)}_paramLineFloat(t,e){return`${t}.${e.name()} = ${uf.vector3(this.variableForInputParam(e))};`}}class YI extends BO{static type(){return\\\\\\\"quatMult\\\\\\\"}initializeNode(){super.initializeNode(),this.io.connection_points.set_input_name_function((t=>[\\\\\\\"quat0\\\\\\\",\\\\\\\"quat1\\\\\\\"][t])),this.io.connection_points.set_expected_input_types_function((()=>[Do.VEC4,Do.VEC4])),this.io.connection_points.set_expected_output_types_function((()=>[Do.VEC4]))}gl_method_name(){return\\\\\\\"quatMult\\\\\\\"}gl_function_definitions(){return[new Tf(this,GR)]}}var $I;!function(t){t.AXIS=\\\\\\\"axis\\\\\\\",t.ANGLE=\\\\\\\"angle\\\\\\\"}($I||($I={}));const JI=[$I.AXIS,$I.ANGLE],ZI={[$I.AXIS]:[0,0,1],[$I.ANGLE]:0};class QI extends zO{static type(){return\\\\\\\"quatFromAxisAngle\\\\\\\"}initializeNode(){super.initializeNode(),this.io.connection_points.set_input_name_function((t=>JI[t])),this.io.connection_points.set_expected_input_types_function((()=>[Do.VEC3,Do.FLOAT])),this.io.connection_points.set_expected_output_types_function((()=>[Do.VEC4]))}paramDefaultValue(t){return ZI[t]}gl_method_name(){return\\\\\\\"quatFromAxisAngle\\\\\\\"}gl_function_definitions(){return[new Tf(this,GR)]}}class KI extends BO{static type(){return\\\\\\\"quatToAngle\\\\\\\"}initializeNode(){super.initializeNode(),this.io.connection_points.set_input_name_function((t=>[\\\\\\\"quat\\\\\\\"][t])),this.io.connection_points.set_expected_input_types_function((()=>[Do.VEC4])),this.io.connection_points.set_expected_output_types_function((()=>[Do.FLOAT]))}gl_method_name(){return\\\\\\\"quatToAngle\\\\\\\"}gl_function_definitions(){return[new Tf(this,GR)]}}class tF extends BO{static type(){return\\\\\\\"quatToAxis\\\\\\\"}initializeNode(){super.initializeNode(),this.io.connection_points.set_input_name_function((t=>[\\\\\\\"quat\\\\\\\"][t])),this.io.connection_points.set_expected_input_types_function((()=>[Do.VEC4])),this.io.connection_points.set_expected_output_types_function((()=>[Do.VEC3]))}gl_method_name(){return\\\\\\\"quatToAxis\\\\\\\"}gl_function_definitions(){return[new Tf(this,GR)]}}const eF=\\\\\\\"val\\\\\\\";const nF=new class extends aa{constructor(){super(...arguments),this.name=oa.STRING(\\\\\\\"ramp\\\\\\\"),this.input=oa.FLOAT(0)}};class iF extends df{constructor(){super(...arguments),this.paramsConfig=nF}static type(){return\\\\\\\"ramp\\\\\\\"}initializeNode(){super.initializeNode(),this.addPostDirtyHook(\\\\\\\"_set_mat_to_recompile\\\\\\\",this._set_mat_to_recompile.bind(this)),this.io.outputs.setNamedOutputConnectionPoints([new Vo(eF,Do.FLOAT)]),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.name])}))}))}setLines(t){const e=Do.FLOAT,n=this._uniform_name(),i=this.glVarName(eF),r=new Af(this,Do.SAMPLER_2D,n);t.addDefinitions(this,[r]);const s=this.variableForInputParam(this.p.input),o=`${e} ${i} = texture2D(${this._uniform_name()}, vec2(${s}, 0.0)).x`;t.addBodyLines(this,[o])}paramsGenerating(){return!0}setParamConfigs(){this._param_configs_controller=this._param_configs_controller||new GI,this._param_configs_controller.reset();const t=new $f(Es.RAMP,this.pv.name,xo.DEFAULT_VALUE,this._uniform_name());this._param_configs_controller.push(t)}_uniform_name(){return\\\\\\\"ramp_texture_\\\\\\\"+this.glVarName(eF)}}const rF=\\\\\\\"rand\\\\\\\";const sF=new class extends aa{constructor(){super(...arguments),this.seed=oa.VECTOR2([1,1])}};class oF extends df{constructor(){super(...arguments),this.paramsConfig=sF}static type(){return\\\\\\\"random\\\\\\\"}initializeNode(){super.initializeNode(),this.io.outputs.setNamedOutputConnectionPoints([new Vo(rF,Do.FLOAT)])}setLines(t){const e=this.io.inputs.namedInputConnectionPoints()[0].name(),n=uf.vector2(this.variableForInput(e)),i=`float ${this.glVarName(rF)} = rand(${n})`;t.addBodyLines(this,[i])}}const aF=new class extends aa{constructor(){super(...arguments),this.rgb=oa.VECTOR3([1,1,1])}};class lF extends df{constructor(){super(...arguments),this.paramsConfig=aF}static type(){return\\\\\\\"rgbToHsv\\\\\\\"}initializeNode(){this.io.outputs.setNamedOutputConnectionPoints([new Vo(\\\\\\\"hsv\\\\\\\",Do.VEC3)])}setLines(t){const e=[],n=[];e.push(new Tf(this,\\\\\\\"// https://stackoverflow.com/questions/15095909/from-rgb-to-hsv-in-opengl-glsl\\\\nvec3 rgb2hsv(vec3 c)\\\\n{\\\\n\\\\tvec4 K = vec4(0.0, -1.0 / 3.0, 2.0 / 3.0, -1.0);\\\\n\\\\tvec4 p = mix(vec4(c.bg, K.wz), vec4(c.gb, K.xy), step(c.b, c.g));\\\\n\\\\tvec4 q = mix(vec4(p.xyw, c.r), vec4(c.r, p.yzx), step(p.x, c.r));\\\\n\\\\n\\\\tfloat d = q.x - min(q.w, q.y);\\\\n\\\\tfloat e = 1.0e-10;\\\\n\\\\treturn vec3(abs(q.z + (q.w - q.y) / (6.0 * d + e)), d / (q.x + e), q.x);\\\\n}\\\\\\\"));const i=uf.vector3(this.variableForInputParam(this.p.rgb)),r=this.glVarName(\\\\\\\"hsv\\\\\\\");n.push(`vec3 ${r} = rgb2hsv(${i})`),t.addDefinitions(this,e),t.addBodyLines(this,n)}}var cF;!function(t){t[t.AXIS=0]=\\\\\\\"AXIS\\\\\\\",t[t.QUAT=1]=\\\\\\\"QUAT\\\\\\\"}(cF||(cF={}));const uF=[cF.AXIS,cF.QUAT],hF={[cF.AXIS]:\\\\\\\"from axis + angle\\\\\\\",[cF.QUAT]:\\\\\\\"from quaternion\\\\\\\"},dF={[cF.AXIS]:[\\\\\\\"vector\\\\\\\",\\\\\\\"axis\\\\\\\",\\\\\\\"angle\\\\\\\"],[cF.QUAT]:[\\\\\\\"vector\\\\\\\",\\\\\\\"quat\\\\\\\"]},pF={[cF.AXIS]:\\\\\\\"rotateWithAxisAngle\\\\\\\",[cF.QUAT]:\\\\\\\"rotateWithQuat\\\\\\\"},_F={[cF.AXIS]:[Do.VEC3,Do.VEC3,Do.FLOAT],[cF.QUAT]:[Do.VEC3,Do.VEC4]},mF={vector:[0,0,1],axis:[0,1,0]};const fF=new class extends aa{constructor(){super(...arguments),this.signature=oa.INTEGER(cF.AXIS,{menu:{entries:uF.map(((t,e)=>({name:hF[t],value:e})))}})}};class gF extends df{constructor(){super(...arguments),this.paramsConfig=fF}static type(){return\\\\\\\"rotate\\\\\\\"}initializeNode(){super.initializeNode(),this.io.connection_points.set_expected_input_types_function(this._expected_input_types.bind(this)),this.io.connection_points.set_expected_output_types_function(this._expected_output_types.bind(this)),this.io.connection_points.set_input_name_function(this._gl_input_name.bind(this))}set_signature(t){const e=uF.indexOf(t);this.p.signature.set(e)}_gl_input_name(t){const e=uF[this.pv.signature];return dF[e][t]}paramDefaultValue(t){return mF[t]}gl_method_name(){const t=uF[this.pv.signature];return pF[t]}_expected_input_types(){const t=uF[this.pv.signature];return _F[t]}_expected_output_types(){return[Do.VEC3]}gl_function_definitions(){return[new Tf(this,GR)]}setLines(t){const e=this.io.outputs.namedOutputConnectionPoints()[0].type(),n=this.io.inputs.namedInputConnectionPoints().map(((t,e)=>{const n=t.name();return uf.any(this.variableForInput(n))})).join(\\\\\\\", \\\\\\\"),i=`${e} ${this.glVarName(this.io.connection_points.output_name(0))} = ${this.gl_method_name()}(${n})`;t.addBodyLines(this,[i]),t.addDefinitions(this,this.gl_function_definitions())}}const vF=[\\\\\\\"x\\\\\\\",\\\\\\\"y\\\\\\\",\\\\\\\"z\\\\\\\",\\\\\\\"w\\\\\\\"];class yF extends BO{static type(){return\\\\\\\"round\\\\\\\"}setLines(t){const e=this.io.inputs.namedInputConnectionPoints()[0],n=uf.vector2(this.variableForInput(e.name())),i=this.io.outputs.namedOutputConnectionPoints()[0],r=this.glVarName(i.name()),s=[];if(1==Go[i.type()])s.push(`${i.type()} ${r} = ${this._simple_line(n)}`);else{const t=vF.map((t=>this._simple_line(`${n}.${t}`)));s.push(`${i.type()} ${r} = ${i.type()}(${t.join(\\\\\\\",\\\\\\\")})`)}t.addBodyLines(this,s)}_simple_line(t){return`sign(${t})*floor(abs(${t})+0.5)`}}const xF=new class extends aa{constructor(){super(...arguments),this.position=oa.VECTOR3([0,0,0]),this.center=oa.VECTOR3([0,0,0]),this.radius=oa.FLOAT(1),this.feather=oa.FLOAT(.1)}};class bF extends df{constructor(){super(...arguments),this.paramsConfig=xF}static type(){return\\\\\\\"sphere\\\\\\\"}initializeNode(){super.initializeNode(),this.io.outputs.setNamedOutputConnectionPoints([new Vo(\\\\\\\"float\\\\\\\",Do.FLOAT)])}setLines(t){const e=uf.vector2(this.variableForInputParam(this.p.position)),n=uf.vector2(this.variableForInputParam(this.p.center)),i=uf.float(this.variableForInputParam(this.p.radius)),r=uf.float(this.variableForInputParam(this.p.feather)),s=`float ${this.glVarName(\\\\\\\"float\\\\\\\")} = disk3d(${e}, ${n}, ${i}, ${r})`;t.addBodyLines(this,[s]),t.addDefinitions(this,[new Tf(this,uP)])}}const wF=new class extends aa{};class TF extends df{constructor(){super(...arguments),this.paramsConfig=wF}static type(){return er.INPUT}initializeNode(){this.io.connection_points.set_output_name_function(this._expected_output_names.bind(this)),this.io.connection_points.set_expected_input_types_function((()=>[])),this.io.connection_points.set_expected_output_types_function(this._expected_output_types.bind(this))}parent(){return super.parent()}_expected_output_names(t){const e=this.parent();return(null==e?void 0:e.child_expected_input_connection_point_name(t))||`out${t}`}_expected_output_types(){const t=this.parent();return(null==t?void 0:t.child_expected_input_connection_point_types())||[]}setLines(t){const e=this.parent();e&&e.set_lines_block_start(t,this)}}const AF=new class extends aa{};class EF extends df{constructor(){super(...arguments),this.paramsConfig=AF}static type(){return\\\\\\\"switch\\\\\\\"}initializeNode(){this.io.connection_points.set_input_name_function(this._gl_input_name.bind(this)),this.io.connection_points.set_expected_input_types_function(this._expected_input_types.bind(this)),this.io.connection_points.set_expected_output_types_function(this._expected_output_types.bind(this))}_gl_input_name(t){return 0==t?EF.INPUT_INDEX:\\\\\\\"in\\\\\\\"+(t-1)}_expected_input_types(){const t=this.io.connection_points.input_connection_type(1)||Do.FLOAT,e=this.io.connections.inputConnections(),n=e?rs.clamp(e.length,2,16):2,i=[Do.INT];for(let e=0;e<n;e++)i.push(t);return i}_expected_output_types(){return[this._expected_input_types()[1]||Do.FLOAT]}setLines(t){const e=this.io.outputs.namedOutputConnectionPoints()[0].type(),n=this.glVarName(this.io.connection_points.output_name(0)),i=this.io.connection_points.input_name(0),r=uf.integer(this.variableForInput(i)),s=this.glVarName(\\\\\\\"index\\\\\\\"),o=[`${e} ${n};`,`int ${s} = ${r}`],a=this._expected_input_types().length-1;for(let t=0;t<a;t++){const e=0==t?\\\\\\\"if\\\\\\\":\\\\\\\"else if\\\\\\\",i=`${s} == ${t}`,r=this.io.connection_points.input_name(t+1),a=`${e}(${i}){${`${n} = ${uf.any(this.variableForInput(r))};`}}`;o.push(a)}t.addBodyLines(this,o)}}EF.INPUT_INDEX=\\\\\\\"index\\\\\\\";const MF=new class extends aa{constructor(){super(...arguments),this.paramName=oa.STRING(\\\\\\\"textureMap\\\\\\\"),this.defaultValue=oa.STRING(gi.UV),this.uv=oa.VECTOR2([0,0])}};class SF extends df{constructor(){super(...arguments),this.paramsConfig=MF}static type(){return\\\\\\\"texture\\\\\\\"}initializeNode(){this.addPostDirtyHook(\\\\\\\"_set_mat_to_recompile\\\\\\\",this._set_mat_to_recompile.bind(this)),this.io.outputs.setNamedOutputConnectionPoints([new Vo(SF.OUTPUT_NAME,Do.VEC4)]),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.paramName])}))}))}setLines(t){const e=uf.vector2(this.variableForInputParam(this.p.uv)),n=this.glVarName(SF.OUTPUT_NAME),i=this._uniform_name(),r=new Af(this,Do.SAMPLER_2D,i),s=`vec4 ${n} = texture2D(${i}, ${e})`;t.addDefinitions(this,[r]),t.addBodyLines(this,[s])}paramsGenerating(){return!0}setParamConfigs(){this._param_configs_controller=this._param_configs_controller||new GI,this._param_configs_controller.reset();const t=new $f(Es.OPERATOR_PATH,this.pv.paramName,this.pv.defaultValue,this._uniform_name());this._param_configs_controller.push(t)}_uniform_name(){return this.glVarName(this.pv.paramName)}}var CF;SF.OUTPUT_NAME=\\\\\\\"rgba\\\\\\\",function(t){t.POSITION=\\\\\\\"position\\\\\\\",t.DIR_VEC=\\\\\\\"direction vector\\\\\\\"}(CF||(CF={}));const NF=[CF.POSITION,CF.DIR_VEC];const LF=new class extends aa{constructor(){super(...arguments),this.vec=oa.VECTOR3([0,0,0]),this.interpretation=oa.INTEGER(0,{menu:{entries:NF.map(((t,e)=>({name:t,value:e})))}})}};class OF extends df{constructor(){super(...arguments),this.paramsConfig=LF}static type(){return\\\\\\\"toWorldSpace\\\\\\\"}initializeNode(){this.io.connection_points.spare_params.set_inputless_param_names([\\\\\\\"interpretation\\\\\\\"]),this.io.outputs.setNamedOutputConnectionPoints([new Vo(\\\\\\\"out\\\\\\\",Do.VEC3)])}setLines(t){const e=[],n=uf.vector3(this.variableForInputParam(this.p.vec)),i=this.glVarName(\\\\\\\"out\\\\\\\");switch(NF[this.pv.interpretation]){case CF.POSITION:e.push(`vec3 ${i} = (modelMatrix * vec4( ${n}, 1.0 )).xyz`);break;case CF.DIR_VEC:e.push(`vec3 ${i} = normalize( mat3( modelMatrix[0].xyz, modelMatrix[1].xyz, modelMatrix[2].xyz ) * ${n} )`)}t.addBodyLines(this,e)}}var RF;!function(t){t.CONDITION=\\\\\\\"condition\\\\\\\",t.IF_TRUE=\\\\\\\"ifTrue\\\\\\\",t.IF_FALSE=\\\\\\\"ifFalse\\\\\\\"}(RF||(RF={}));const PF=[RF.CONDITION,RF.IF_TRUE,RF.IF_FALSE];class IF extends _f{static type(){return\\\\\\\"twoWaySwitch\\\\\\\"}initializeNode(){super.initializeNode(),this.io.connection_points.initializeNode(),this.io.connection_points.set_expected_input_types_function(this._expected_input_types.bind(this)),this.io.connection_points.set_expected_output_types_function(this._expected_output_types.bind(this)),this.io.connection_points.set_input_name_function(this._gl_input_name.bind(this)),this.io.connection_points.set_output_name_function(this._gl_output_name.bind(this))}_gl_input_name(t){return PF[t]}_gl_output_name(){return\\\\\\\"val\\\\\\\"}_expected_input_types(){const t=this.io.connections.inputConnection(1)||this.io.connections.inputConnection(2),e=t?t.src_connection_point().type():Do.FLOAT;return[Do.BOOL,e,e]}_expected_output_types(){return[this._expected_input_types()[1]]}setLines(t){const e=[],n=this.glVarName(\\\\\\\"val\\\\\\\"),i=uf.bool(this.variableForInput(RF.CONDITION)),r=uf.any(this.variableForInput(RF.IF_TRUE)),s=uf.any(this.variableForInput(RF.IF_FALSE)),o=this._expected_output_types()[0];e.push(`${o} ${n}`),e.push(`if(${i}){`),e.push(`${n} = ${r}`),e.push(\\\\\\\"} else {\\\\\\\"),e.push(`${n} = ${s}`),e.push(\\\\\\\"}\\\\\\\"),t.addBodyLines(this,e)}}const FF=[Do.FLOAT,Do.VEC2,Do.VEC3,Do.VEC4];const DF=new class extends aa{constructor(){super(...arguments),this.name=oa.STRING(\\\\\\\"\\\\\\\"),this.type=oa.INTEGER(0,{menu:{entries:FF.map(((t,e)=>({name:t,value:e})))}})}};class kF extends df{constructor(){super(...arguments),this.paramsConfig=DF,this._on_create_set_name_if_none_bound=this._on_create_set_name_if_none.bind(this)}static type(){return\\\\\\\"varyingRead\\\\\\\"}initializeNode(){this.addPostDirtyHook(\\\\\\\"_set_mat_to_recompile\\\\\\\",this._set_mat_to_recompile.bind(this)),this.lifecycle.add_on_create_hook(this._on_create_set_name_if_none_bound),this.io.connection_points.initializeNode(),this.io.connection_points.set_output_name_function((()=>this.output_name)),this.io.connection_points.set_expected_input_types_function((()=>[])),this.io.connection_points.set_expected_output_types_function((()=>[FF[this.pv.type]])),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.name])}))}))}get output_name(){return kF.OUTPUT_NAME}setLines(t){if(t.current_shader_name==xf.FRAGMENT){const e=this.pv.name,n=new Ef(this,this.gl_type(),e),i=this.glVarName(kF.OUTPUT_NAME),r=`${this.gl_type()} ${i} = ${e}`;t.addDefinitions(this,[n]),t.addBodyLines(this,[r])}}get attribute_name(){return this.pv.name.trim()}gl_type(){return this.io.outputs.namedOutputConnectionPoints()[0].type()}set_gl_type(t){this.p.type.set(FF.indexOf(t))}_on_create_set_name_if_none(){\\\\\\\"\\\\\\\"==this.pv.name&&this.p.name.set(this.name())}}kF.OUTPUT_NAME=\\\\\\\"fragment\\\\\\\";const BF={start:[0,0,1],end:[1,0,0],up:[0,1,0]};class zF extends(yR(\\\\\\\"vectorAlign\\\\\\\",{in:[\\\\\\\"start\\\\\\\",\\\\\\\"end\\\\\\\",\\\\\\\"up\\\\\\\"],method:\\\\\\\"vectorAlignWithUp\\\\\\\",functions:[GR]})){_expected_input_types(){const t=Do.VEC3;return[t,t,t]}_expected_output_types(){return[Do.VEC4]}paramDefaultValue(t){return BF[t]}}const UF={start:[0,0,1],end:[1,0,0]};class GF extends(uR(\\\\\\\"vectorAngle\\\\\\\",{in:[\\\\\\\"start\\\\\\\",\\\\\\\"end\\\\\\\"],method:\\\\\\\"vectorAngle\\\\\\\",functions:[GR]})){_expected_input_types(){const t=Do.VEC3;return[t,t]}_expected_output_types(){return[Do.FLOAT]}paramDefaultValue(t){return UF[t]}}const VF={only:[`${JP.context()}/${JP.type()}`,`${kP.context()}/${kP.type()}`,`${GP.context()}/${GP.type()}`]};class HF extends ia{static context(){return Ki.JS}initializeBaseNode(){this.uiData.setLayoutHorizontal(),this.io.connection_points.initializeNode()}cook(){console.warn(\\\\\\\"js nodes should never cook\\\\\\\")}_set_function_node_to_recompile(){var t;null===(t=this.function_node)||void 0===t||t.assembler_controller.set_compilation_required_and_dirty(this)}get function_node(){var t;const e=this.parent();if(e)return e.type()==this.type()?null===(t=e)||void 0===t?void 0:t.function_node:e}js_var_name(t){return`v_POLY_${this.name()}_${t}`}variableForInput(t){const e=this.io.inputs.get_input_index(t),n=this.io.connections.inputConnection(e);if(n){const e=n.node_src,i=e.io.outputs.namedOutputConnectionPoints()[n.output_index];if(i){const t=i.name();return e.js_var_name(t)}throw console.warn(`no output called '${t}' for gl node ${e.path()}`),\\\\\\\"variable_for_input ERROR\\\\\\\"}return\\\\\\\"to debug...\\\\\\\"}setLines(t){}reset_code(){var t;null===(t=this._param_configs_controller)||void 0===t||t.reset()}setParamConfigs(){}param_configs(){var t;return null===(t=this._param_configs_controller)||void 0===t?void 0:t.list()}js_input_default_value(t){return null}}new class extends aa{};const jF=[Ho.FLOAT,Ho.VEC2,Ho.VEC3,Ho.VEC4];const WF=new class extends aa{constructor(){super(...arguments),this.name=oa.STRING(\\\\\\\"\\\\\\\"),this.type=oa.INTEGER(0,{menu:{entries:jF.map(((t,e)=>({name:t,value:e})))}})}};class qF extends HF{constructor(){super(...arguments),this.paramsConfig=WF,this._on_create_set_name_if_none_bound=this._on_create_set_name_if_none.bind(this)}static type(){return\\\\\\\"attribute\\\\\\\"}initializeNode(){this.lifecycle.add_on_create_hook(this._on_create_set_name_if_none_bound),this.io.connection_points.initializeNode(),this.io.connection_points.set_expected_input_types_function((()=>[])),this.io.connection_points.set_expected_output_types_function((()=>[jF[this.pv.type]]))}get input_name(){return qF.INPUT_NAME}get output_name(){return qF.OUTPUT_NAME}setLines(t){var e;null===(e=this.function_node)||void 0===e||e.assembler_controller.assembler.set_node_lines_attribute(this,t)}get attribute_name(){return this.pv.name.trim()}gl_type(){return this.io.outputs.namedOutputConnectionPoints()[0].type()}set_gl_type(t){this.p.type.set(jF.indexOf(t))}connected_input_node(){return this.io.inputs.named_input(qF.INPUT_NAME)}connected_input_connection_point(){return this.io.inputs.named_input_connection_point(qF.INPUT_NAME)}output_connection_point(){return this.io.outputs.namedOutputConnectionPointsByName(this.input_name)}get is_importing(){return this.io.outputs.used_output_names().length>0}_on_create_set_name_if_none(){\\\\\\\"\\\\\\\"==this.pv.name&&this.p.name.set(this.name())}}qF.INPUT_NAME=\\\\\\\"export\\\\\\\",qF.OUTPUT_NAME=\\\\\\\"val\\\\\\\";const XF=new class extends aa{};class YF extends HF{constructor(){super(...arguments),this.paramsConfig=XF}static type(){return\\\\\\\"globals\\\\\\\"}createParams(){var t;null===(t=this.function_node)||void 0===t||t.assembler_controller.add_globals_outputs(this)}setLines(t){var e,n;null===(n=null===(e=this.function_node)||void 0===e?void 0:e.assembler_controller)||void 0===n||n.assembler.set_node_lines_globals(this,t)}}const $F=new class extends aa{};class JF extends HF{constructor(){super(...arguments),this.paramsConfig=$F}static type(){return\\\\\\\"output\\\\\\\"}initializeNode(){super.initializeNode(),this.addPostDirtyHook(\\\\\\\"_set_mat_to_recompile\\\\\\\",this._set_function_node_to_recompile.bind(this))}createParams(){var t;null===(t=this.function_node)||void 0===t||t.assembler_controller.add_output_inputs(this)}setLines(t){var e;null===(e=this.function_node)||void 0===e||e.assembler_controller.assembler.set_node_lines_output(this,t)}}class ZF{constructor(t=[]){this._definitions=t,this._errored=!1}get errored(){return this._errored}get error_message(){return this._error_message}uniq(){const t=new Map,e=[];for(let n of this._definitions)if(!this._errored){const i=n.name(),r=t.get(i);r?r.data_type!=n.data_type&&(this._errored=!0,this._error_message=`attempt to create '${n.name()}' with types '${n.data_type}' by node '${n.node.path()}', when there is already an existing with type ${r.data_type} from node '${r.node.path()}'`,console.warn(\\\\\\\"emitting error message:\\\\\\\",this._error_message)):(t.set(i,n),e.push(i))}const n=[];for(let i of e){const e=t.get(i);e&&n.push(e)}return n}}var QF;!function(t){t.ATTRIBUTE=\\\\\\\"attribute\\\\\\\",t.FUNCTION=\\\\\\\"function\\\\\\\",t.UNIFORM=\\\\\\\"uniform\\\\\\\"}(QF||(QF={}));class KF{constructor(t,e,n,i){this._definition_type=t,this._data_type=e,this._node=n,this._name=i}get definition_type(){return this._definition_type}get data_type(){return this._data_type}get node(){return this._node}name(){return this._name}collection_instance(){return new ZF}}class tD extends KF{constructor(t,e,n){super(QF.UNIFORM,e,t,n),this._node=t,this._data_type=e,this._name=n}get line(){return`uniform ${this.data_type} ${this.name()}`}}class eD extends Yf{constructor(t,e,n,i){super(t,e,n),this._uniform_name=i}get uniform_name(){return this._uniform_name}static uniform_by_type(t){switch(t){case Es.BOOLEAN:case Es.BUTTON:return{value:0};case Es.COLOR:return{value:new D.a(0,0,0)};case Es.FLOAT:case Es.FOLDER:case Es.INTEGER:case Es.OPERATOR_PATH:case Es.NODE_PATH:case Es.PARAM_PATH:return{value:0};case Es.RAMP:case Es.STRING:return{value:null};case Es.VECTOR2:return{value:new d.a(0,0)};case Es.VECTOR3:return{value:new p.a(0,0,0)};case Es.VECTOR4:return{value:new _.a(0,0,0,0)}}ar.unreachable(t)}}const nD=new class extends aa{constructor(){super(...arguments),this.name=oa.STRING(\\\\\\\"\\\\\\\"),this.type=oa.INTEGER(jo.indexOf(Ho.FLOAT),{menu:{entries:jo.map(((t,e)=>({name:t,value:e})))}}),this.asColor=oa.BOOLEAN(0,{visibleIf:{type:jo.indexOf(Ho.VEC3)}})}};class iD extends HF{constructor(){super(...arguments),this.paramsConfig=nD,this._allow_inputs_created_from_params=!1,this._on_create_set_name_if_none_bound=this._on_create_set_name_if_none.bind(this)}static type(){return\\\\\\\"param\\\\\\\"}initializeNode(){this.addPostDirtyHook(\\\\\\\"_set_mat_to_recompile\\\\\\\",this._set_function_node_to_recompile.bind(this)),this.lifecycle.add_on_create_hook(this._on_create_set_name_if_none_bound),this.io.connection_points.initializeNode(),this.io.connection_points.set_expected_input_types_function((()=>[])),this.io.connection_points.set_expected_output_types_function((()=>[jo[this.pv.type]]))}setLines(t){const e=[],n=jo[this.pv.type],i=this.uniform_name();e.push(new tD(this,n,i)),t.addDefinitions(this,e)}setParamConfigs(){const t=jo[this.pv.type],e=Xo[t];let n=Wo[t];if(this._param_configs_controller=this._param_configs_controller||new GI,this._param_configs_controller.reset(),n==Es.VECTOR3&&this.p.asColor.value&&m.isArray(e)&&3==e.length){const t=new eD(Es.COLOR,this.pv.name,e,this.uniform_name());this._param_configs_controller.push(t)}else{const t=new eD(n,this.pv.name,e,this.uniform_name());this._param_configs_controller.push(t)}}uniform_name(){const t=this.io.outputs.namedOutputConnectionPoints()[0];return this.js_var_name(t.name())}set_gl_type(t){const e=jo.indexOf(t);this.p.type.set(e)}_on_create_set_name_if_none(){\\\\\\\"\\\\\\\"==this.pv.name&&this.p.name.set(this.name())}}class rD extends ia{constructor(){super(...arguments),this._cook_main_without_inputs_when_dirty_bound=this._cook_main_without_inputs_when_dirty.bind(this)}static context(){return Ki.MAT}initializeBaseNode(){super.initializeBaseNode(),this.nameController.add_post_set_fullPath_hook(this.set_material_name.bind(this)),this.addPostDirtyHook(\\\\\\\"_cook_main_without_inputs_when_dirty\\\\\\\",(()=>{setTimeout(this._cook_main_without_inputs_when_dirty_bound,0)}))}async _cook_main_without_inputs_when_dirty(){await this.cookController.cookMainWithoutInputs()}set_material_name(){this._material&&(this._material.name=this.path())}get material(){return this._material=this._material||this.createMaterial()}setMaterial(t){this._setContainer(t)}}class sD{constructor(t){this.node=t}add_params(){}update(){}get material(){return this.node.material}}const oD={NoBlending:w.ub,NormalBlending:w.xb,AdditiveBlending:w.e,SubtractiveBlending:w.Sc,MultiplyBlending:w.mb},aD=Object.keys(oD);function lD(t){return class extends t{constructor(){super(...arguments),this.doubleSided=oa.BOOLEAN(0),this.front=oa.BOOLEAN(1,{visibleIf:{doubleSided:!1}}),this.overrideShadowSide=oa.BOOLEAN(0),this.shadowDoubleSided=oa.BOOLEAN(0,{visibleIf:{overrideShadowSide:!0}}),this.shadowFront=oa.BOOLEAN(1,{visibleIf:{overrideShadowSide:!0,shadowDoubleSided:!1}}),this.colorWrite=oa.BOOLEAN(1,{separatorBefore:!0,cook:!1,callback:(t,e)=>{cD.update(t)}}),this.depthWrite=oa.BOOLEAN(1,{cook:!1,callback:(t,e)=>{cD.update(t)}}),this.depthTest=oa.BOOLEAN(1,{cook:!1,callback:(t,e)=>{cD.update(t)}}),this.premultipliedAlpha=oa.BOOLEAN(!1,{separatorAfter:!0}),this.blending=oa.INTEGER(w.xb,{menu:{entries:aD.map((t=>({name:t,value:oD[t]})))}}),this.dithering=oa.BOOLEAN(0),this.polygonOffset=oa.BOOLEAN(!1,{separatorBefore:!0}),this.polygonOffsetFactor=oa.INTEGER(0,{range:[0,1e3],visibleIf:{polygonOffset:1}}),this.polygonOffsetUnits=oa.INTEGER(0,{range:[0,1e3],visibleIf:{polygonOffset:1}})}}}lD(aa);class cD extends sD{constructor(t){super(t),this.node=t}initializeNode(){}async update(){const t=this.node.material,e=this.node.pv;this._updateSides(t,e),t.colorWrite=e.colorWrite,t.depthWrite=e.depthWrite,t.depthTest=e.depthTest,t.blending=e.blending,t.premultipliedAlpha=e.premultipliedAlpha,t.dithering=e.dithering,t.polygonOffset=e.polygonOffset,t.polygonOffset&&(t.polygonOffsetFactor=e.polygonOffsetFactor,t.polygonOffsetUnits=e.polygonOffsetUnits,t.needsUpdate=!0)}_updateSides(t,e){const n=e.front?w.H:w.i,i=e.doubleSided?w.z:n;if(i!=t.side&&(t.side=i,t.needsUpdate=!0),e.overrideShadowSide){const t=e.shadowFront?w.H:w.i,n=e.shadowDoubleSided?w.z:t,i=this.node.material;n!=i.shadowSide&&(i.shadowSide=n,i.needsUpdate=!0)}else t.shadowSide=null;const r=t.customMaterials;if(r){const t=Object.keys(r);for(let n of t){const t=r[n];t&&this._updateSides(t,e)}}}static async update(t){t.controllers.advancedCommon.update()}}class uD extends(lD(aa)){constructor(){super(...arguments),this.color=oa.COLOR([1,1,1]),this.lineWidth=oa.FLOAT(1,{range:[1,10],rangeLocked:[!0,!1]})}}const hD=new uD;class dD extends rD{constructor(){super(...arguments),this.paramsConfig=hD,this.controllers={advancedCommon:new cD(this)},this.controllerNames=Object.keys(this.controllers)}static type(){return\\\\\\\"lineBasic\\\\\\\"}createMaterial(){return new wr.a({color:16777215,linewidth:1})}initializeNode(){this.params.onParamsCreated(\\\\\\\"init controllers\\\\\\\",(()=>{for(let t of this.controllerNames)this.controllers[t].initializeNode()}))}async cook(){for(let t of this.controllerNames)this.controllers[t].update();this.material.color.copy(this.pv.color),this.material.linewidth=this.pv.lineWidth,this.setMaterial(this.material)}}function pD(t){return class extends t{constructor(){super(...arguments),this.transparent=oa.BOOLEAN(0),this.opacity=oa.FLOAT(1),this.alphaTest=oa.FLOAT(0)}}}pD(aa);class _D extends sD{constructor(t){super(t),this.node=t}static update(t){const e=t.material,n=t.pv;this._updateTransparency(e,n)}static _updateTransparency(t,e){t.transparent=e.transparent,this._updateCommon(t,e)}static _updateCommon(t,e){t.uniforms.opacity&&(t.uniforms.opacity.value=e.opacity),t.opacity=e.opacity,t.alphaTest=e.alphaTest;const n=t.customMaterials;if(n){const t=Object.keys(n);for(let i of t){const t=n[i];t&&this._updateCommon(t,e)}}}}class mD extends Xf{constructor(t){super(t),this.node=t}toJSON(){const t=this.node.assemblerController;if(!t)return;const e={},n=this.node.material.customMaterials;if(n){const t=Object.keys(n);for(let i of t){const t=n[i];if(t){const n=this._materialToJson(t,{node:this.node,suffix:i});n&&(e[i]=n)}}}const i=[],r=t.assembler.param_configs();for(let t of r)i.push([t.name(),t.uniform_name]);const s=this._materialToJson(this.node.material,{node:this.node,suffix:\\\\\\\"main\\\\\\\"});s||console.warn(\\\\\\\"failed to save material from node\\\\\\\",this.node.path());return{material:s||{},uniforms_time_dependent:t.assembler.uniformsTimeDependent(),uniforms_resolution_dependent:t.assembler.uniforms_resolution_dependent(),param_uniform_pairs:i,customMaterials:e}}load(t){if(this._material=this._loadMaterial(t.material),this._material){if(this._material.customMaterials=this._material.customMaterials||{},t.customMaterials){const e=Object.keys(t.customMaterials);for(let n of e){const e=t.customMaterials[n],i=this._loadMaterial(e);i&&(this._material.customMaterials[n]=i)}}if(t.uniforms_time_dependent&&this.node.scene().uniformsController.addTimeDependentUniformOwner(this._material.uuid,this._material.uniforms),t.uniforms_resolution_dependent&&this.node.scene().uniformsController.addResolutionDependentUniformOwner(this._material.uuid,this._material.uniforms),t.param_uniform_pairs)for(let e of t.param_uniform_pairs){const t=e[0],n=e[1],i=this.node.params.get(t),r=this._material.uniforms[n],s=Object.keys(this._material.customMaterials);let o;for(let t of s){const e=this._material.customMaterials[t],i=null==e?void 0:e.uniforms[n];i&&(o=o||[],o.push(i))}i&&(r||o)&&i.options.setOption(\\\\\\\"callback\\\\\\\",(()=>{if(r&&$f.callback(i,r),o)for(let t of o)$f.callback(i,t)}))}}}material(){if(ai.playerMode())return this._material}}function fD(t){return class extends t{constructor(){super(...arguments),this.setBuilderNode=oa.BOOLEAN(0,{callback:t=>{gD.PARAM_CALLBACK_setCompileRequired(t)}}),this.builderNode=oa.NODE_PATH(\\\\\\\"\\\\\\\",{visibleIf:{setBuilderNode:!0},callback:t=>{gD.PARAM_CALLBACK_setCompileRequired(t)}})}}}fD(aa);class gD extends rD{constructor(){super(...arguments),this._children_controller_context=Ki.GL,this.persisted_config=new mD(this)}createMaterial(){var t;let e;return this.persisted_config&&(e=this.persisted_config.material()),e||(e=null===(t=this.assemblerController)||void 0===t?void 0:t.assembler.createMaterial()),e}get assemblerController(){return this._assembler_controller=this._assembler_controller||this._create_assembler_controller()}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}childrenAllowed(){return this.assemblerController?super.childrenAllowed():(this.scene().markAsReadOnly(this),!1)}compileIfRequired(){var t;(null===(t=this.assemblerController)||void 0===t?void 0:t.compileRequired())&&this._compile()}_compile(){const t=this.assemblerController;this.material&&t&&(t.assembler.setGlParentNode(this),this._setAssemblerGlParentNode(t),t.assembler.compileMaterial(this.material),t.post_compile())}_setAssemblerGlParentNode(t){if(!this.pv.setBuilderNode)return;const e=this.pv.builderNode.nodeWithContext(Ki.MAT);if(!e)return;const n=e;n.assemblerController?n.type()==this.type()?t.assembler.setGlParentNode(n):this.states.error.set(`resolved node '${e.path()}' does not have the same type '${e.type()}' as current node '${this.type()}'`):this.states.error.set(`resolved node '${e.path()}' is not a builder node`)}static PARAM_CALLBACK_setCompileRequired(t){t.PARAM_CALLBACK_setCompileRequired()}PARAM_CALLBACK_setCompileRequired(){var t;null===(t=this.assemblerController)||void 0===t||t.setCompilationRequired(!0)}}function vD(t){return class extends t{constructor(){super(...arguments),this.useFog=oa.BOOLEAN(0)}}}vD(aa);class yD extends sD{constructor(t){super(t),this.node=t}static update(t){const e=t.material,n=t.pv;e.fog=n.useFog}}function xD(t){return class extends t{constructor(){super(...arguments),this.default=oa.FOLDER(null)}}}function bD(t){return class extends t{constructor(){super(...arguments),this.advanced=oa.FOLDER(null)}}}class wD extends(vD(lD(fD(bD(pD(xD(aa))))))){constructor(){super(...arguments),this.linewidth=oa.FLOAT(1,{range:[0,10],rangeLocked:[!0,!1]})}}const TD=new wD;class AD extends gD{constructor(){super(...arguments),this.paramsConfig=TD,this.controllers={advancedCommon:new cD(this)},this.controllerNames=Object.keys(this.controllers)}static type(){return\\\\\\\"lineBasicBuilder\\\\\\\"}usedAssembler(){return Hn.GL_LINE}_create_assembler_controller(){return ai.assemblersRegister.assembler(this,this.usedAssembler())}initializeNode(){this.params.onParamsCreated(\\\\\\\"init controllers\\\\\\\",(()=>{for(let t of this.controllerNames)this.controllers[t].initializeNode()}))}async cook(){for(let t of this.controllerNames)this.controllers[t].update();_D.update(this),yD.update(this),this.compileIfRequired(),this.material.linewidth=this.pv.linewidth,this.setMaterial(this.material)}}function ED(t){return class extends t{constructor(){super(...arguments),this.color=oa.COLOR([1,1,1],{conversion:so.SRGB_TO_LINEAR}),this.useVertexColors=oa.BOOLEAN(0,{separatorAfter:!0}),this.transparent=oa.BOOLEAN(0),this.opacity=oa.FLOAT(1),this.alphaTest=oa.FLOAT(0)}}}O.a;ED(aa);class MD extends sD{constructor(t){super(t),this.node=t}static update(t){const e=t.material,n=t.pv;e.color.copy(n.color);const i=n.useVertexColors;i!=e.vertexColors&&(e.vertexColors=i,e.needsUpdate=!0),e.opacity=n.opacity,e.transparent=n.transparent,e.alphaTest=n.alphaTest}}function SD(t){return class extends t{constructor(){super(...arguments),this.useFog=oa.BOOLEAN(0)}}}SD(aa);class CD extends sD{constructor(t){super(t),this.node=t}static update(t){const e=t.material,n=t.pv;e.fog=n.useFog}}function ND(t){return{cook:!1,callback:(e,n)=>{t.update(e)}}}function LD(t,e,n){return{visibleIf:{[e]:1},nodeSelection:{context:Ki.COP,types:null==n?void 0:n.types},cook:!1,callback:(e,n)=>{t.update(e)}}}class OD extends sD{constructor(t,e){super(t),this.node=t,this._update_options=e}add_hooks(t,e){t.addPostDirtyHook(\\\\\\\"TextureController\\\\\\\",(()=>{this.update()})),e.addPostDirtyHook(\\\\\\\"TextureController\\\\\\\",(()=>{this.update()}))}static update(t){}async _update(t,e,n,i){if(this._update_options.uniforms){const r=t,s=e;await this._update_texture_on_uniforms(r,s,n,i)}if(this._update_options.directParams){const r=t,s=e;await this._update_texture_on_material(r,s,n,i)}}async _update_texture_on_uniforms(t,e,n,i){this._update_required_attribute(t,t.uniforms,e,n,i,this._apply_texture_on_uniforms.bind(this),this._remove_texture_from_uniforms.bind(this))}_apply_texture_on_uniforms(t,e,n,i){const r=null!=e[n]&&null!=e[n].value;let s=!1;if(r){e[n].value.uuid!=i.uuid&&(s=!0)}if(!r||s){e[n]&&(e[n].value=i),this._apply_texture_on_material(t,t,n,i),t.needsUpdate=!0;const r=t.customMaterials;if(r){const t=Object.keys(r);for(let e of t){const t=r[e];t&&this._apply_texture_on_uniforms(t,t.uniforms,n,i)}}}}_remove_texture_from_uniforms(t,e,n){if(e[n]){if(e[n].value){e[n].value=null,this._remove_texture_from_material(t,t,n),t.needsUpdate=!0;const i=t.customMaterials;if(i){const t=Object.keys(i);for(let e of t){const t=i[e];t&&this._remove_texture_from_uniforms(t,t.uniforms,n)}}}}else ai.warn(`'${n}' uniform not found. existing uniforms are:`,Object.keys(e).sort())}async _update_texture_on_material(t,e,n,i){this._update_required_attribute(t,t,e,n,i,this._apply_texture_on_material.bind(this),this._remove_texture_from_material.bind(this))}_apply_texture_on_material(t,e,n,i){const r=null!=e[n];let s=!1;if(r){e[n].uuid!=i.uuid&&(s=!0)}r&&!s||(e[n]=i,t.needsUpdate=!0)}_remove_texture_from_material(t,e,n){e[n]&&(e[n]=null,t.needsUpdate=!0)}async _update_required_attribute(t,e,n,i,r,s,o){i.isDirty()&&await i.compute();if(i.value){r.isDirty()&&await r.compute();const i=r.value.nodeWithContext(Ki.COP);if(i){const r=(await i.compute()).texture();if(r)return void s(t,e,n,r)}}o(t,e,n)}}function RD(t){return class extends t{constructor(){super(...arguments),this.useMap=oa.BOOLEAN(0,ND(PD)),this.map=oa.NODE_PATH(gi.EMPTY,LD(PD,\\\\\\\"useMap\\\\\\\"))}}}O.a;RD(aa);class PD extends OD{constructor(t,e){super(t,e),this.node=t}initializeNode(){this.add_hooks(this.node.p.useMap,this.node.p.map)}async update(){this._update(this.node.material,\\\\\\\"map\\\\\\\",this.node.p.useMap,this.node.p.map)}static async update(t){t.controllers.map.update()}}function ID(t){return class extends t{constructor(){super(...arguments),this.useAlphaMap=oa.BOOLEAN(0,{separatorBefore:!0,...ND(FD)}),this.alphaMap=oa.NODE_PATH(gi.EMPTY,LD(FD,\\\\\\\"useAlphaMap\\\\\\\"))}}}O.a;ID(aa);class FD extends OD{constructor(t,e){super(t,e),this.node=t}initializeNode(){this.add_hooks(this.node.p.useAlphaMap,this.node.p.alphaMap)}async update(){this._update(this.node.material,\\\\\\\"alphaMap\\\\\\\",this.node.p.useAlphaMap,this.node.p.alphaMap)}static async update(t){t.controllers.alphaMap.update()}}function DD(t){return class extends t{constructor(){super(...arguments),this.useAOMap=oa.BOOLEAN(0,{separatorBefore:!0,...ND(kD)}),this.aoMap=oa.NODE_PATH(gi.EMPTY,LD(kD,\\\\\\\"useAOMap\\\\\\\")),this.aoMapIntensity=oa.FLOAT(1,{range:[0,1],rangeLocked:[!1,!1],visibleIf:{useAOMap:1}})}}}O.a;DD(aa);class kD extends OD{constructor(t,e){super(t,e),this.node=t}initializeNode(){this.add_hooks(this.node.p.useAOMap,this.node.p.aoMap)}async update(){if(this._update(this.node.material,\\\\\\\"aoMap\\\\\\\",this.node.p.useAOMap,this.node.p.aoMap),this._update_options.uniforms){this.node.material.uniforms.aoMapIntensity.value=this.node.pv.aoMapIntensity}if(this._update_options.directParams){this.node.material.aoMapIntensity=this.node.pv.aoMapIntensity}}static async update(t){t.controllers.aoMap.update()}}var BD;!function(t){t.MULT=\\\\\\\"mult\\\\\\\",t.ADD=\\\\\\\"add\\\\\\\",t.MIX=\\\\\\\"mix\\\\\\\"}(BD||(BD={}));const zD=[BD.MULT,BD.ADD,BD.MIX],UD={[BD.MULT]:w.nb,[BD.ADD]:w.c,[BD.MIX]:w.lb};function GD(t){return class extends t{constructor(){super(...arguments),this.useEnvMap=oa.BOOLEAN(0,ND(VD)),this.envMap=oa.NODE_PATH(gi.EMPTY,LD(VD,\\\\\\\"useEnvMap\\\\\\\",{types:[Lg.CUBE_CAMERA]})),this.combine=oa.INTEGER(0,{visibleIf:{useEnvMap:1},menu:{entries:zD.map(((t,e)=>({name:t,value:e})))}}),this.reflectivity=oa.FLOAT(1,{visibleIf:{useEnvMap:1}}),this.refractionRatio=oa.FLOAT(.98,{range:[-1,1],rangeLocked:[!1,!1],visibleIf:{useEnvMap:1}})}}}GD(aa);class VD extends OD{constructor(t,e){super(t,e),this.node=t}initializeNode(){this.add_hooks(this.node.p.useEnvMap,this.node.p.envMap)}async update(){this._update(this.node.material,\\\\\\\"envMap\\\\\\\",this.node.p.useEnvMap,this.node.p.envMap);const t=UD[zD[this.node.pv.combine]];if(this._update_options.uniforms){const t=this.node.material;t.uniforms.reflectivity.value=this.node.pv.reflectivity,t.uniforms.refractionRatio.value=this.node.pv.refractionRatio}if(this._update_options.directParams){const e=this.node.material;e.combine=t,e.reflectivity=this.node.pv.reflectivity,e.refractionRatio=this.node.pv.refractionRatio}}static async update(t){t.controllers.envMap.update()}}function HD(t){return class extends t{constructor(){super(...arguments),this.useLightMap=oa.BOOLEAN(0,{separatorBefore:!0,...ND(jD)}),this.lightMap=oa.NODE_PATH(gi.EMPTY,LD(jD,\\\\\\\"useLightMap\\\\\\\")),this.lightMapIntensity=oa.FLOAT(1,{visibleIf:{useLightMap:1}})}}}O.a;HD(aa);class jD extends OD{constructor(t,e){super(t,e),this.node=t}initializeNode(){this.add_hooks(this.node.p.useLightMap,this.node.p.lightMap)}async update(){if(this._update(this.node.material,\\\\\\\"lightMap\\\\\\\",this.node.p.useLightMap,this.node.p.lightMap),this._update_options.uniforms){this.node.material.uniforms.lightMapIntensity.value=this.node.pv.lightMapIntensity}if(this._update_options.directParams){this.node.material.lightMapIntensity=this.node.pv.lightMapIntensity}}static async update(t){t.controllers.lightMap.update()}}var WD;!function(t){t.ROUND=\\\\\\\"round\\\\\\\",t.BUTT=\\\\\\\"butt\\\\\\\",t.SQUARE=\\\\\\\"square\\\\\\\"}(WD||(WD={}));const qD=[WD.ROUND,WD.BUTT,WD.SQUARE];var XD;!function(t){t.ROUND=\\\\\\\"round\\\\\\\",t.BEVEL=\\\\\\\"bevel\\\\\\\",t.MITER=\\\\\\\"miter\\\\\\\"}(XD||(XD={}));const YD=[XD.ROUND,XD.BEVEL,XD.MITER];function $D(t){return class extends t{constructor(){super(...arguments),this.wireframe=oa.BOOLEAN(0,{separatorBefore:!0}),this.wireframeLinecap=oa.INTEGER(0,{menu:{entries:qD.map(((t,e)=>({name:t,value:e})))},visibleIf:{wireframe:1}}),this.wireframeLinejoin=oa.INTEGER(0,{menu:{entries:YD.map(((t,e)=>({name:t,value:e})))},visibleIf:{wireframe:1}})}}}O.a;$D(aa);class JD extends sD{constructor(t){super(t),this.node=t}static update(t){const e=t.material,n=t.pv;e.wireframe=n.wireframe,e.wireframeLinecap=qD[n.wireframeLinecap],e.wireframeLinejoin=YD[n.wireframeLinejoin],e.needsUpdate=!0}}function ZD(t){return class extends t{constructor(){super(...arguments),this.textures=oa.FOLDER(null)}}}const QD={directParams:!0};class KD extends(SD($D(lD(bD(HD(GD(DD(ID(RD(ZD(ED(xD(aa))))))))))))){}const tk=new KD;class ek extends rD{constructor(){super(...arguments),this.paramsConfig=tk,this.controllers={advancedCommon:new cD(this),alphaMap:new FD(this,QD),aoMap:new kD(this,QD),envMap:new VD(this,QD),lightMap:new jD(this,QD),map:new PD(this,QD)},this.controllerNames=Object.keys(this.controllers)}static type(){return\\\\\\\"meshBasic\\\\\\\"}createMaterial(){return new at.a({vertexColors:!1,side:w.H,color:16777215,opacity:1})}initializeNode(){this.params.onParamsCreated(\\\\\\\"init controllers\\\\\\\",(()=>{for(let t of this.controllerNames)this.controllers[t].initializeNode()}))}async cook(){for(let t of this.controllerNames)this.controllers[t].update();MD.update(this),CD.update(this),JD.update(this),this.setMaterial(this.material)}}function nk(t){return class extends t{constructor(){super(...arguments),this.wireframe=oa.BOOLEAN(0)}}}nk(aa);class ik extends sD{constructor(t){super(t),this.node=t}static update(t){const e=t.material,n=t.pv;e.wireframe=n.wireframe,e.needsUpdate=!0}}const rk={uniforms:!0};class sk extends(vD(nk(lD(fD(bD(GD(DD(ID(RD(ZD(pD(xD(aa))))))))))))){}const ok=new sk;class ak extends gD{constructor(){super(...arguments),this.paramsConfig=ok,this.controllers={advancedCommon:new cD(this),alphaMap:new FD(this,rk),aoMap:new kD(this,rk),envMap:new VD(this,rk),map:new PD(this,rk)},this.controllerNames=Object.keys(this.controllers)}static type(){return\\\\\\\"meshBasicBuilder\\\\\\\"}usedAssembler(){return Hn.GL_MESH_BASIC}_create_assembler_controller(){return ai.assemblersRegister.assembler(this,this.usedAssembler())}initializeNode(){this.params.onParamsCreated(\\\\\\\"init controllers\\\\\\\",(()=>{for(let t of this.controllerNames)this.controllers[t].initializeNode()}))}async cook(){for(let t of this.controllerNames)this.controllers[t].update();_D.update(this),yD.update(this),ik.update(this),this.compileIfRequired(),this.setMaterial(this.material)}}function lk(t){return class extends t{constructor(){super(...arguments),this.emissive=oa.COLOR([0,0,0],{separatorBefore:!0}),this.useEmissiveMap=oa.BOOLEAN(0,ND(ck)),this.emissiveMap=oa.NODE_PATH(gi.EMPTY,LD(ck,\\\\\\\"useEmissiveMap\\\\\\\")),this.emissiveIntensity=oa.FLOAT(1)}}}O.a;lk(aa);class ck extends OD{constructor(t,e){super(t,e),this.node=t}initializeNode(){this.add_hooks(this.node.p.useEmissiveMap,this.node.p.emissiveMap)}async update(){if(this._update(this.node.material,\\\\\\\"emissiveMap\\\\\\\",this.node.p.useEmissiveMap,this.node.p.emissiveMap),this._update_options.uniforms){this.node.material.uniforms.emissive.value.copy(this.node.pv.emissive)}if(this._update_options.directParams){const t=this.node.material;t.emissive.copy(this.node.pv.emissive),t.emissiveIntensity=this.node.pv.emissiveIntensity}}static async update(t){t.controllers.emissiveMap.update()}}const uk={directParams:!0};class hk extends(SD($D(lD(bD(HD(GD(lk(DD(ID(RD(ZD(ED(xD(aa)))))))))))))){}const dk=new hk;class pk extends rD{constructor(){super(...arguments),this.paramsConfig=dk,this.controllers={advancedCommon:new cD(this),alphaMap:new FD(this,uk),aoMap:new kD(this,uk),emissiveMap:new ck(this,uk),envMap:new VD(this,uk),lightMap:new jD(this,uk),map:new PD(this,uk)},this.controllerNames=Object.keys(this.controllers)}static type(){return\\\\\\\"meshLambert\\\\\\\"}createMaterial(){return new br.a({vertexColors:!1,side:w.H,color:16777215,opacity:1})}initializeNode(){this.params.onParamsCreated(\\\\\\\"init controllers\\\\\\\",(()=>{for(let t of this.controllerNames)this.controllers[t].initializeNode()}))}async cook(){for(let t of this.controllerNames)this.controllers[t].update();MD.update(this),CD.update(this),JD.update(this),this.setMaterial(this.material)}}function _k(t){return class extends t{constructor(){super(...arguments),this.shadowPCSS=oa.BOOLEAN(0,{callback:t=>{mk.PARAM_CALLBACK_setRecompileRequired(t)},separatorBefore:!0}),this.shadowPCSSSamplesCount=oa.INTEGER(16,{visibleIf:{shadowPCSS:1},range:[0,128],rangeLocked:[!0,!1]}),this.shadowPCSSFilterSize=oa.FLOAT(1,{visibleIf:{shadowPCSS:1},range:[0,10],rangeLocked:[!0,!1]})}}}_k(aa);class mk extends sD{constructor(t){super(t),this.node=t}initializeNode(){}static filterFragmentShader(t,e){const n=`\\\\n#define NUM_SAMPLES ${uf.integer(t.pv.shadowPCSSSamplesCount)}\\\\n#define PCSS_FILTER_SIZE ${uf.float(t.pv.shadowPCSSFilterSize)}\\\\n#define LIGHT_WORLD_SIZE 0.005\\\\n// #define LIGHT_FRUSTUM_WIDTH 1.0\\\\n// #define PCSS_FILTER_SIZE 1.0\\\\n#define LIGHT_SIZE_UV (PCSS_FILTER_SIZE * LIGHT_WORLD_SIZE)\\\\n#define NEAR_PLANE 9.5\\\\n\\\\n// #define NUM_SAMPLES 32\\\\n#define NUM_RINGS 11\\\\n#define BLOCKER_SEARCH_NUM_SAMPLES NUM_SAMPLES\\\\n#define PCF_NUM_SAMPLES NUM_SAMPLES\\\\n\\\\nvec2 poissonDisk[NUM_SAMPLES];\\\\n\\\\nvoid initPoissonSamples( const in vec2 randomSeed ) {\\\\n\\\\tfloat ANGLE_STEP = PI2 * float( NUM_RINGS ) / float( NUM_SAMPLES );\\\\n\\\\tfloat INV_NUM_SAMPLES = 1.0 / float( NUM_SAMPLES );\\\\n\\\\n\\\\t// jsfiddle that shows sample pattern: https://jsfiddle.net/a16ff1p7/\\\\n\\\\tfloat angle = rand( randomSeed ) * PI2;\\\\n\\\\tfloat radius = INV_NUM_SAMPLES;\\\\n\\\\tfloat radiusStep = radius;\\\\n\\\\n\\\\tfor( int i = 0; i < NUM_SAMPLES; i ++ ) {\\\\n\\\\t\\\\tpoissonDisk[i] = vec2( cos( angle ), sin( angle ) ) * pow( radius, 0.75 );\\\\n\\\\t\\\\tradius += radiusStep;\\\\n\\\\t\\\\tangle += ANGLE_STEP;\\\\n\\\\t}\\\\n}\\\\n\\\\nfloat penumbraSize( const in float zReceiver, const in float zBlocker ) { // Parallel plane estimation\\\\n\\\\treturn (zReceiver - zBlocker) / zBlocker;\\\\n}\\\\n\\\\nfloat findBlocker( sampler2D shadowMap, const in vec2 uv, const in float zReceiver ) {\\\\n\\\\t// This uses similar triangles to compute what\\\\n\\\\t// area of the shadow map we should search\\\\n\\\\tfloat searchRadius = LIGHT_SIZE_UV * ( zReceiver - NEAR_PLANE ) / zReceiver;\\\\n\\\\tfloat blockerDepthSum = 0.0;\\\\n\\\\tint numBlockers = 0;\\\\n\\\\n\\\\tfor( int i = 0; i < BLOCKER_SEARCH_NUM_SAMPLES; i++ ) {\\\\n\\\\t\\\\tfloat shadowMapDepth = unpackRGBAToDepth(texture2D(shadowMap, uv + poissonDisk[i] * searchRadius));\\\\n\\\\t\\\\tif ( shadowMapDepth < zReceiver ) {\\\\n\\\\t\\\\t\\\\tblockerDepthSum += shadowMapDepth;\\\\n\\\\t\\\\t\\\\tnumBlockers ++;\\\\n\\\\t\\\\t}\\\\n\\\\t}\\\\n\\\\n\\\\tif( numBlockers == 0 ) return -1.0;\\\\n\\\\n\\\\treturn blockerDepthSum / float( numBlockers );\\\\n}\\\\n\\\\nfloat PCF_Filter(sampler2D shadowMap, vec2 uv, float zReceiver, float filterRadius ) {\\\\n\\\\tfloat sum = 0.0;\\\\n\\\\tfor( int i = 0; i < PCF_NUM_SAMPLES; i ++ ) {\\\\n\\\\t\\\\tfloat depth = unpackRGBAToDepth( texture2D( shadowMap, uv + poissonDisk[ i ] * filterRadius ) );\\\\n\\\\t\\\\tif( zReceiver <= depth ) sum += 1.0;\\\\n\\\\t}\\\\n\\\\tfor( int i = 0; i < PCF_NUM_SAMPLES; i ++ ) {\\\\n\\\\t\\\\tfloat depth = unpackRGBAToDepth( texture2D( shadowMap, uv + -poissonDisk[ i ].yx * filterRadius ) );\\\\n\\\\t\\\\tif( zReceiver <= depth ) sum += 1.0;\\\\n\\\\t}\\\\n\\\\treturn sum / ( 2.0 * float( PCF_NUM_SAMPLES ) );\\\\n}\\\\n\\\\nfloat PCSS ( sampler2D shadowMap, vec4 coords ) {\\\\n\\\\tvec2 uv = coords.xy;\\\\n\\\\tfloat zReceiver = coords.z; // Assumed to be eye-space z in this code\\\\n\\\\n\\\\tinitPoissonSamples( uv );\\\\n\\\\t// STEP 1: blocker search\\\\n\\\\tfloat avgBlockerDepth = findBlocker( shadowMap, uv, zReceiver );\\\\n\\\\n\\\\t//There are no occluders so early out (this saves filtering)\\\\n\\\\tif( avgBlockerDepth == -1.0 ) return 1.0;\\\\n\\\\n\\\\t// STEP 2: penumbra size\\\\n\\\\tfloat penumbraRatio = penumbraSize( zReceiver, avgBlockerDepth );\\\\n\\\\tfloat filterRadius = penumbraRatio * LIGHT_SIZE_UV * NEAR_PLANE / zReceiver;\\\\n\\\\n\\\\t// STEP 3: filtering\\\\n\\\\t//return avgBlockerDepth;\\\\n\\\\treturn PCF_Filter( shadowMap, uv, zReceiver, filterRadius );\\\\n}\\\\n`;let i=B;return i=i.replace(\\\\\\\"#ifdef USE_SHADOWMAP\\\\\\\",`#ifdef USE_SHADOWMAP\\\\n${n}\\\\n\\\\t\\\\t\\\\t\\\\t`),i=i.replace(\\\\\\\"#if defined( SHADOWMAP_TYPE_PCF )\\\\\\\",\\\\\\\"\\\\n\\\\t\\\\t\\\\t\\\\treturn PCSS( shadowMap, shadowCoord );\\\\n\\\\t\\\\t\\\\t\\\\t#if defined( SHADOWMAP_TYPE_PCF )\\\\\\\"),e=e.replace(\\\\\\\"#include <shadowmap_pars_fragment>\\\\\\\",i)}async update(){const t=this.node;if(!t.assemblerController)return;const e=\\\\\\\"PCSS\\\\\\\";this.node.pv.shadowPCSS?t.assemblerController.addFilterFragmentShaderCallback(e,(t=>mk.filterFragmentShader(this.node,t))):t.assemblerController.removeFilterFragmentShaderCallback(e)}static async update(t){t.controllers.PCSS.update()}static PARAM_CALLBACK_setRecompileRequired(t){t.controllers.PCSS.update()}}const fk={uniforms:!0};class gk extends(_k(vD(nk(lD(fD(bD(HD(GD(lk(DD(ID(RD(ZD(pD(xD(aa)))))))))))))))){}const vk=new gk;class yk extends gD{constructor(){super(...arguments),this.paramsConfig=vk,this.controllers={advancedCommon:new cD(this),alphaMap:new FD(this,fk),aoMap:new kD(this,fk),emissiveMap:new ck(this,fk),envMap:new VD(this,fk),lightMap:new jD(this,fk),map:new PD(this,fk),PCSS:new mk(this)},this.controllerNames=Object.keys(this.controllers)}static type(){return\\\\\\\"meshLambertBuilder\\\\\\\"}usedAssembler(){return Hn.GL_MESH_LAMBERT}_create_assembler_controller(){return ai.assemblersRegister.assembler(this,this.usedAssembler())}initializeNode(){this.params.onParamsCreated(\\\\\\\"init controllers\\\\\\\",(()=>{for(let t of this.controllerNames)this.controllers[t].initializeNode()}))}async cook(){for(let t of this.controllerNames)this.controllers[t].update();_D.update(this),yD.update(this),ik.update(this),this.compileIfRequired(),this.setMaterial(this.material)}}function xk(t){return class extends t{constructor(){super(...arguments),this.useBumpMap=oa.BOOLEAN(0,{separatorBefore:!0,...ND(bk)}),this.bumpMap=oa.NODE_PATH(\\\\\\\"\\\\\\\",LD(bk,\\\\\\\"useBumpMap\\\\\\\")),this.bumpScale=oa.FLOAT(1,{range:[0,1],rangeLocked:[!1,!1],...LD(bk,\\\\\\\"useBumpMap\\\\\\\")}),this.bumpBias=oa.FLOAT(0,{range:[0,1],rangeLocked:[!1,!1],...LD(bk,\\\\\\\"useBumpMap\\\\\\\")})}}}O.a;xk(aa);class bk extends OD{constructor(t,e){super(t,e),this.node=t}initializeNode(){this.add_hooks(this.node.p.useBumpMap,this.node.p.bumpMap)}async update(){if(this._update(this.node.material,\\\\\\\"bumpMap\\\\\\\",this.node.p.useBumpMap,this.node.p.bumpMap),this._update_options.uniforms){this.node.material.uniforms.bumpScale.value=this.node.pv.bumpScale}if(this._update_options.directParams){this.node.material.bumpScale=this.node.pv.bumpScale}}static async update(t){t.controllers.bumpMap.update()}}var wk;!function(t){t.TANGENT=\\\\\\\"tangent\\\\\\\",t.OBJECT=\\\\\\\"object\\\\\\\"}(wk||(wk={}));const Tk=[wk.TANGENT,wk.OBJECT],Ak={[wk.TANGENT]:w.Uc,[wk.OBJECT]:w.zb};function Ek(t){return class extends t{constructor(){super(...arguments),this.useNormalMap=oa.BOOLEAN(0,{separatorBefore:!0,...ND(Mk)}),this.normalMap=oa.NODE_PATH(gi.EMPTY,LD(Mk,\\\\\\\"useNormalMap\\\\\\\")),this.normalMapType=oa.INTEGER(0,{visibleIf:{useNormalMap:1},menu:{entries:Tk.map(((t,e)=>({name:t,value:e})))}}),this.normalScale=oa.VECTOR2([1,1],{visibleIf:{useNormalMap:1}})}}}O.a;Ek(aa);class Mk extends OD{constructor(t,e){super(t,e),this.node=t}initializeNode(){this.add_hooks(this.node.p.useNormalMap,this.node.p.normalMap)}async update(){this._update(this.node.material,\\\\\\\"normalMap\\\\\\\",this.node.p.useNormalMap,this.node.p.normalMap);const t=Ak[Tk[this.node.pv.normalMapType]];if(this._update_options.uniforms){this.node.material.uniforms.normalScale.value.copy(this.node.pv.normalScale)}const e=this.node.material;e.normalMapType=t,this._update_options.directParams&&e.normalScale.copy(this.node.pv.normalScale)}static async update(t){t.controllers.normalMap.update()}}function Sk(t){return class extends t{constructor(){super(...arguments),this.useDisplacementMap=oa.BOOLEAN(0,{separatorBefore:!0,...ND(Ck)}),this.displacementMap=oa.NODE_PATH(\\\\\\\"\\\\\\\",LD(Ck,\\\\\\\"useDisplacementMap\\\\\\\")),this.displacementScale=oa.FLOAT(1,{range:[0,1],rangeLocked:[!1,!1],...LD(Ck,\\\\\\\"useDisplacementMap\\\\\\\")}),this.displacementBias=oa.FLOAT(0,{range:[0,1],rangeLocked:[!1,!1],...LD(Ck,\\\\\\\"useDisplacementMap\\\\\\\")})}}}O.a;Sk(aa);class Ck extends OD{constructor(t,e){super(t,e),this.node=t}initializeNode(){this.add_hooks(this.node.p.useDisplacementMap,this.node.p.displacementMap)}async update(){if(this._update(this.node.material,\\\\\\\"displacementMap\\\\\\\",this.node.p.useDisplacementMap,this.node.p.displacementMap),this._update_options.uniforms){const t=this.node.material;t.uniforms.displacementScale.value=this.node.pv.displacementScale,t.uniforms.displacementBias.value=this.node.pv.displacementBias}if(this._update_options.directParams){const t=this.node.material;t.displacementScale=this.node.pv.displacementScale,t.displacementBias=this.node.pv.displacementBias}}static async update(t){t.controllers.displacementMap.update()}}function Nk(t){return class extends t{constructor(){super(...arguments),this.useMatcapMap=oa.BOOLEAN(0,ND(Lk)),this.matcapMap=oa.NODE_PATH(gi.EMPTY,LD(Lk,\\\\\\\"useMatcapMap\\\\\\\"))}}}O.a;Nk(aa);class Lk extends OD{constructor(t,e){super(t,e),this.node=t}initializeNode(){this.add_hooks(this.node.p.useMatcapMap,this.node.p.matcapMap)}async update(){this._update(this.node.material,\\\\\\\"matcap\\\\\\\",this.node.p.useMatcapMap,this.node.p.matcapMap)}static async update(t){t.controllers.matcap.update()}}const Ok={directParams:!0};class Rk extends(SD(lD(bD(Ek(Sk(xk(ID(RD(Nk(ZD(ED(xD(aa))))))))))))){}const Pk=new Rk;class Ik extends rD{constructor(){super(...arguments),this.paramsConfig=Pk,this.controllers={advancedCommon:new cD(this),alphaMap:new FD(this,Ok),bumpMap:new bk(this,Ok),displacementMap:new Ck(this,Ok),map:new PD(this,Ok),matcap:new Lk(this,Ok),normalMap:new Mk(this,Ok)},this.controllerNames=Object.keys(this.controllers)}static type(){return\\\\\\\"meshMatcap\\\\\\\"}createMaterial(){return new jf({vertexColors:!1,side:w.H,color:16777215,opacity:1})}initializeNode(){this.params.onParamsCreated(\\\\\\\"init controllers\\\\\\\",(()=>{for(let t of this.controllerNames)this.controllers[t].initializeNode()}))}async cook(){for(let t of this.controllerNames)this.controllers[t].update();MD.update(this),CD.update(this),this.setMaterial(this.material)}}function Fk(t){return class extends t{constructor(){super(...arguments),this.useSpecularMap=oa.BOOLEAN(0,ND(Dk)),this.specularMap=oa.NODE_PATH(gi.EMPTY,LD(Dk,\\\\\\\"useSpecularMap\\\\\\\"))}}}O.a;Fk(aa);class Dk extends OD{constructor(t,e){super(t,e),this.node=t}initializeNode(){this.add_hooks(this.node.p.useSpecularMap,this.node.p.specularMap)}async update(){this._update(this.node.material,\\\\\\\"specularMap\\\\\\\",this.node.p.useSpecularMap,this.node.p.specularMap)}static async update(t){t.controllers.specularMap.update()}}const kk={directParams:!0};class Bk extends(SD($D(lD(bD(Fk(Ek(HD(GD(lk(Sk(xk(DD(ID(RD(ZD(ED(xD(aa)))))))))))))))))){constructor(){super(...arguments),this.flatShading=oa.BOOLEAN(0)}}const zk=new Bk;class Uk extends rD{constructor(){super(...arguments),this.paramsConfig=zk,this.controllers={advancedCommon:new cD(this),alphaMap:new FD(this,kk),aoMap:new kD(this,kk),bumpMap:new bk(this,kk),displacementMap:new Ck(this,kk),emissiveMap:new ck(this,kk),envMap:new VD(this,kk),lightMap:new jD(this,kk),map:new PD(this,kk),normalMap:new Mk(this,kk),specularMap:new Dk(this,kk)},this.controllerNames=Object.keys(this.controllers)}static type(){return\\\\\\\"meshPhong\\\\\\\"}createMaterial(){return new Gf.a({vertexColors:!1,side:w.H,color:16777215,opacity:1})}initializeNode(){this.params.onParamsCreated(\\\\\\\"init controllers\\\\\\\",(()=>{for(let t of this.controllerNames)this.controllers[t].initializeNode()}))}async cook(){for(let t of this.controllerNames)this.controllers[t].update();MD.update(this),CD.update(this),JD.update(this),this.material.flatShading!=this.pv.flatShading&&(this.material.flatShading=this.pv.flatShading,this.material.needsUpdate=!0),this.setMaterial(this.material)}}const Gk={uniforms:!0};class Vk extends(_k(vD(nk(lD(fD(bD(Fk(Ek(HD(GD(lk(Sk(xk(DD(ID(RD(ZD(pD(xD(aa)))))))))))))))))))){}const Hk=new Vk;class jk extends gD{constructor(){super(...arguments),this.paramsConfig=Hk,this.controllers={advancedCommon:new cD(this),alphaMap:new FD(this,Gk),aoMap:new kD(this,Gk),bumpMap:new bk(this,Gk),displacementMap:new Ck(this,Gk),emissiveMap:new ck(this,Gk),envMap:new VD(this,Gk),lightMap:new jD(this,Gk),map:new PD(this,Gk),normalMap:new Mk(this,Gk),specularMap:new Dk(this,Gk),PCSS:new mk(this)},this.controllerNames=Object.keys(this.controllers)}static type(){return\\\\\\\"meshPhongBuilder\\\\\\\"}usedAssembler(){return Hn.GL_MESH_PHONG}_create_assembler_controller(){return ai.assemblersRegister.assembler(this,this.usedAssembler())}initializeNode(){this.params.onParamsCreated(\\\\\\\"init controllers\\\\\\\",(()=>{for(let t of this.controllerNames)this.controllers[t].initializeNode()}))}async cook(){for(let t of this.controllerNames)this.controllers[t].update();_D.update(this),yD.update(this),ik.update(this),this.compileIfRequired(),this.setMaterial(this.material)}}function Wk(t){return class extends t{constructor(){super(...arguments),this.useEnvMap=oa.BOOLEAN(0,{separatorBefore:!0,...ND(qk)}),this.envMap=oa.NODE_PATH(gi.EMPTY,LD(qk,\\\\\\\"useEnvMap\\\\\\\")),this.envMapIntensity=oa.FLOAT(1,{visibleIf:{useEnvMap:1}}),this.refractionRatio=oa.FLOAT(.98,{range:[-1,1],rangeLocked:[!1,!1],visibleIf:{useEnvMap:1}})}}}Wk(aa);class qk extends OD{constructor(t,e){super(t,e),this.node=t}initializeNode(){this.add_hooks(this.node.p.useEnvMap,this.node.p.envMap)}async update(){if(this._update(this.node.material,\\\\\\\"envMap\\\\\\\",this.node.p.useEnvMap,this.node.p.envMap),this._update_options.uniforms){const t=this.node.material;t.uniforms.envMapIntensity.value=this.node.pv.envMapIntensity,t.uniforms.refractionRatio.value=this.node.pv.refractionRatio}if(this._update_options.directParams){const t=this.node.material;t.envMapIntensity=this.node.pv.envMapIntensity,t.refractionRatio=this.node.pv.refractionRatio}}static async update(t){t.controllers.envMap.update()}}function Xk(t){return class extends t{constructor(){super(...arguments),this.useMetalnessMap=oa.BOOLEAN(0,{separatorBefore:!0,...ND(Yk)}),this.metalnessMap=oa.NODE_PATH(gi.EMPTY,LD(Yk,\\\\\\\"useMetalnessMap\\\\\\\")),this.metalness=oa.FLOAT(1),this.useRoughnessMap=oa.BOOLEAN(0,{separatorBefore:!0,...ND(Yk)}),this.roughnessMap=oa.NODE_PATH(gi.EMPTY,LD(Yk,\\\\\\\"useRoughnessMap\\\\\\\")),this.roughness=oa.FLOAT(.5)}}}O.a;Xk(aa);class Yk extends OD{constructor(t,e){super(t,e),this.node=t}initializeNode(){this.add_hooks(this.node.p.useMetalnessMap,this.node.p.metalnessMap)}async update(){if(this._update(this.node.material,\\\\\\\"metalnessMap\\\\\\\",this.node.p.useMetalnessMap,this.node.p.metalnessMap),this._update_options.uniforms){this.node.material.uniforms.metalness.value=this.node.pv.metalness}if(this._update_options.directParams){this.node.material.metalness=this.node.pv.metalness}if(this._update(this.node.material,\\\\\\\"roughnessMap\\\\\\\",this.node.p.useRoughnessMap,this.node.p.roughnessMap),this._update_options.uniforms){this.node.material.uniforms.roughness.value=this.node.pv.roughness}if(this._update_options.directParams){this.node.material.roughness=this.node.pv.roughness}}static async update(t){t.controllers.metalnessRoughnessMap.update()}}function $k(t){return class extends t{constructor(){super(...arguments),this.clearcoat=oa.FLOAT(0,{separatorBefore:!0}),this.useClearCoatMap=oa.BOOLEAN(0,ND(Jk)),this.clearcoatMap=oa.NODE_PATH(gi.EMPTY,LD(Jk,\\\\\\\"useClearCoatMap\\\\\\\")),this.useClearCoatNormalMap=oa.BOOLEAN(0,ND(Jk)),this.clearcoatNormalMap=oa.NODE_PATH(gi.EMPTY,LD(Jk,\\\\\\\"useClearCoatNormalMap\\\\\\\")),this.clearcoatNormalScale=oa.VECTOR2([1,1],{visibleIf:{useClearCoatNormalMap:1}}),this.clearcoatRoughness=oa.FLOAT(0),this.useClearCoatRoughnessMap=oa.BOOLEAN(0,ND(Jk)),this.clearcoatRoughnessMap=oa.NODE_PATH(gi.EMPTY,LD(Jk,\\\\\\\"useClearCoatRoughnessMap\\\\\\\")),this.useSheen=oa.BOOLEAN(0),this.sheen=oa.FLOAT(0,{range:[0,1],rangeLocked:[!0,!1],visibleIf:{useSheen:1}}),this.sheenRoughness=oa.FLOAT(1,{range:[0,1],rangeLocked:[!0,!1],visibleIf:{useSheen:1}}),this.sheenColor=oa.COLOR([1,1,1],{visibleIf:{useSheen:1}}),this.reflectivity=oa.FLOAT(.5,{range:[0,1],rangeLocked:[!0,!0]}),this.transmission=oa.FLOAT(0,{range:[0,1]}),this.useTransmissionMap=oa.BOOLEAN(0),this.transmissionMap=oa.NODE_PATH(gi.EMPTY,{visibleIf:{useTransmissionMap:1}}),this.thickness=oa.FLOAT(.01,{range:[0,1],rangeLocked:[!0,!1]}),this.useThicknessMap=oa.BOOLEAN(0),this.thicknessMap=oa.NODE_PATH(gi.EMPTY,{visibleIf:{useThicknessMap:1}}),this.attenuationDistance=oa.FLOAT(0),this.attenuationColor=oa.COLOR([1,1,1])}}}$k(aa);class Jk extends OD{constructor(t,e){super(t,e),this.node=t,this._sheenColorClone=new D.a}initializeNode(){this.add_hooks(this.node.p.useClearCoatMap,this.node.p.clearcoatMap),this.add_hooks(this.node.p.useClearCoatNormalMap,this.node.p.clearcoatNormalMap),this.add_hooks(this.node.p.useClearCoatRoughnessMap,this.node.p.clearcoatRoughnessMap),this.add_hooks(this.node.p.useTransmissionMap,this.node.p.transmissionMap),this.add_hooks(this.node.p.useThicknessMap,this.node.p.thicknessMap)}async update(){this._update(this.node.material,\\\\\\\"clearcoatMap\\\\\\\",this.node.p.useClearCoatMap,this.node.p.clearcoatMap),this._update(this.node.material,\\\\\\\"clearcoatNormalMap\\\\\\\",this.node.p.useClearCoatNormalMap,this.node.p.clearcoatNormalMap),this._update(this.node.material,\\\\\\\"clearcoatRoughnessMap\\\\\\\",this.node.p.useClearCoatRoughnessMap,this.node.p.clearcoatRoughnessMap),this._update(this.node.material,\\\\\\\"transmissionMap\\\\\\\",this.node.p.useTransmissionMap,this.node.p.transmissionMap),this._update(this.node.material,\\\\\\\"thicknessMap\\\\\\\",this.node.p.useThicknessMap,this.node.p.thicknessMap);const t=this.node.pv;if(this._update_options.uniforms){const e=this.node.material;e.uniforms.clearcoat.value=t.clearcoat,e.uniforms.clearcoatNormalScale.value.copy(t.clearcoatNormalScale),e.uniforms.clearcoatRoughness.value=t.clearcoatRoughness,e.uniforms.reflectivity.value=t.reflectivity,e.uniforms.transmission.value=t.transmission,e.uniforms.thickness.value=t.thickness,e.uniforms.attenuationDistance.value=t.attenuationDistance,e.uniforms.attenuationTint.value=t.attenuationColor,t.useSheen?(this._sheenColorClone.copy(t.sheenColor),e.uniforms.sheen.value=t.sheen,e.uniforms.sheenRoughness.value=t.sheenRoughness,e.uniforms.sheenTint.value=this._sheenColorClone):e.uniforms.sheen.value=0}if(this._update_options.directParams){const e=this.node.material;e.clearcoat=t.clearcoat,e.clearcoatNormalScale.copy(t.clearcoatNormalScale),e.clearcoatRoughness=t.clearcoatRoughness,e.reflectivity=t.reflectivity,t.useSheen?(this._sheenColorClone.copy(t.sheenColor),e.sheen=t.sheen,e.sheenRoughness=t.sheenRoughness,e.sheenTint=this._sheenColorClone):e.sheen=0,e.transmission=t.transmission,e.thickness=t.thickness,e.attenuationDistance=t.attenuationDistance,e.attenuationTint=t.attenuationColor}}static async update(t){t.controllers.physical.update()}}const Zk={directParams:!0};class Qk extends(SD($D(lD(bD($k(Xk(Ek(HD(Wk(lk(Sk(xk(DD(ID(RD(ZD(ED(xD(aa))))))))))))))))))){}const Kk=new Qk;class tB extends rD{constructor(){super(...arguments),this.paramsConfig=Kk,this.controllers={advancedCommon:new cD(this),alphaMap:new FD(this,Zk),aoMap:new kD(this,Zk),bumpMap:new bk(this,Zk),displacementMap:new Ck(this,Zk),emissiveMap:new ck(this,Zk),envMap:new qk(this,Zk),lightMap:new jD(this,Zk),map:new PD(this,Zk),metalnessRoughnessMap:new Yk(this,Zk),normalMap:new Mk(this,Zk),physical:new Jk(this,Zk)},this.controllerNames=Object.keys(this.controllers)}static type(){return\\\\\\\"meshPhysical\\\\\\\"}createMaterial(){return new Uf.a({vertexColors:!1,side:w.H,color:16777215,opacity:1,metalness:1,roughness:0})}initializeNode(){this.params.onParamsCreated(\\\\\\\"init controllers\\\\\\\",(()=>{for(let t of this.controllerNames)this.controllers[t].initializeNode()}))}async cook(){for(let t of this.controllerNames)this.controllers[t].update();MD.update(this),CD.update(this),JD.update(this),this.setMaterial(this.material)}}const eB={uniforms:!0};class nB extends(function(t){return class extends(_k(vD(nk(lD(fD(t)))))){}}(bD($k(Xk(Ek(HD(Wk(lk(Sk(xk(DD(ID(RD(ZD(pD(xD(aa))))))))))))))))){}const iB=new nB;class rB extends gD{constructor(){super(...arguments),this.paramsConfig=iB,this.controllers={advancedCommon:new cD(this),alphaMap:new FD(this,eB),aoMap:new kD(this,eB),bumpMap:new bk(this,eB),displacementMap:new Ck(this,eB),emissiveMap:new ck(this,eB),envMap:new qk(this,eB),lightMap:new jD(this,eB),map:new PD(this,eB),metalnessRoughnessMap:new Yk(this,eB),normalMap:new Mk(this,eB),physical:new Jk(this,eB),PCSS:new mk(this)},this.controllerNames=Object.keys(this.controllers)}static type(){return\\\\\\\"meshPhysicalBuilder\\\\\\\"}usedAssembler(){return Hn.GL_MESH_PHYSICAL}_create_assembler_controller(){return ai.assemblersRegister.assembler(this,this.usedAssembler())}initializeNode(){this.params.onParamsCreated(\\\\\\\"init controllers\\\\\\\",(()=>{for(let t of this.controllerNames)this.controllers[t].initializeNode()}))}async cook(){for(let t of this.controllerNames)this.controllers[t].update();_D.update(this),yD.update(this),ik.update(this),this.compileIfRequired(),this.setMaterial(this.material)}}const sB={directParams:!0};class oB extends(SD($D(lD(bD(Xk(Ek(HD(Wk(lk(Sk(xk(DD(ID(RD(ZD(ED(xD(aa)))))))))))))))))){}const aB=new oB;class lB extends rD{constructor(){super(...arguments),this.paramsConfig=aB,this.controllers={advancedCommon:new cD(this),alphaMap:new FD(this,sB),aoMap:new kD(this,sB),bumpMap:new bk(this,sB),displacementMap:new Ck(this,sB),emissiveMap:new ck(this,sB),envMap:new qk(this,sB),lightMap:new jD(this,sB),map:new PD(this,sB),metalnessRoughnessMap:new Yk(this,sB),normalMap:new Mk(this,sB)},this.controllerNames=Object.keys(this.controllers)}static type(){return\\\\\\\"meshStandard\\\\\\\"}createMaterial(){return new xr.a({vertexColors:!1,side:w.H,color:16777215,opacity:1,metalness:1,roughness:0})}initializeNode(){this.params.onParamsCreated(\\\\\\\"init controllers\\\\\\\",(()=>{for(let t of this.controllerNames)this.controllers[t].initializeNode()}))}async cook(){for(let t of this.controllerNames)this.controllers[t].update();MD.update(this),CD.update(this),JD.update(this),this.setMaterial(this.material)}}const cB={uniforms:!0};class uB extends(_k(vD(nk(lD(fD(bD(Xk(Ek(HD(Wk(lk(Sk(xk(DD(ID(RD(ZD(pD(xD(aa)))))))))))))))))))){}const hB=new uB;class dB extends gD{constructor(){super(...arguments),this.paramsConfig=hB,this.controllers={advancedCommon:new cD(this),alphaMap:new FD(this,cB),aoMap:new kD(this,cB),bumpMap:new bk(this,cB),displacementMap:new Ck(this,cB),emissiveMap:new ck(this,cB),envMap:new qk(this,cB),lightMap:new jD(this,cB),map:new PD(this,cB),metalnessRoughnessMap:new Yk(this,cB),normalMap:new Mk(this,cB),PCSS:new mk(this)},this.controllerNames=Object.keys(this.controllers)}static type(){return\\\\\\\"meshStandardBuilder\\\\\\\"}usedAssembler(){return Hn.GL_MESH_STANDARD}_create_assembler_controller(){return ai.assemblersRegister.assembler(this,this.usedAssembler())}initializeNode(){this.params.onParamsCreated(\\\\\\\"init controllers\\\\\\\",(()=>{for(let t of this.controllerNames)this.controllers[t].initializeNode()}))}async cook(){for(let t of this.controllerNames)this.controllers[t].update();_D.update(this),yD.update(this),ik.update(this),this.compileIfRequired(),this.setMaterial(this.material)}}const pB=z.meshphong_frag.slice(0,z.meshphong_frag.indexOf(\\\\\\\"void main() {\\\\\\\")),_B=z.meshphong_frag.slice(z.meshphong_frag.indexOf(\\\\\\\"void main() {\\\\\\\")),mB={uniforms:I.merge([V.phong.uniforms,{thicknessMap:{value:null},thicknessColor:{value:new D.a(16777215)},thicknessDistortion:{value:.1},thicknessAmbient:{value:0},thicknessAttenuation:{value:.1},thicknessPower:{value:2},thicknessScale:{value:10}}]),vertexShader:[\\\\\\\"#define USE_UV\\\\\\\",z.meshphong_vert].join(\\\\\\\"\\\\n\\\\\\\"),fragmentShader:[\\\\\\\"#define USE_UV\\\\\\\",\\\\\\\"#define SUBSURFACE\\\\\\\",pB,\\\\\\\"uniform sampler2D thicknessMap;\\\\\\\",\\\\\\\"uniform float thicknessPower;\\\\\\\",\\\\\\\"uniform float thicknessScale;\\\\\\\",\\\\\\\"uniform float thicknessDistortion;\\\\\\\",\\\\\\\"uniform float thicknessAmbient;\\\\\\\",\\\\\\\"uniform float thicknessAttenuation;\\\\\\\",\\\\\\\"uniform vec3 thicknessColor;\\\\\\\",\\\\\\\"void RE_Direct_Scattering(const in IncidentLight directLight, const in vec2 uv, const in GeometricContext geometry, inout ReflectedLight reflectedLight) {\\\\\\\",\\\\\\\"\\\\tvec3 thickness = thicknessColor * texture2D(thicknessMap, uv).r;\\\\\\\",\\\\\\\"\\\\tvec3 scatteringHalf = normalize(directLight.direction + (geometry.normal * thicknessDistortion));\\\\\\\",\\\\\\\"\\\\tfloat scatteringDot = pow(saturate(dot(geometry.viewDir, -scatteringHalf)), thicknessPower) * thicknessScale;\\\\\\\",\\\\\\\"\\\\tvec3 scatteringIllu = (scatteringDot + thicknessAmbient) * thickness;\\\\\\\",\\\\\\\"\\\\treflectedLight.directDiffuse += scatteringIllu * thicknessAttenuation * directLight.color;\\\\\\\",\\\\\\\"}\\\\\\\",_B.replace(\\\\\\\"#include <lights_fragment_begin>\\\\\\\",(fB=z.lights_fragment_begin,gB=\\\\\\\"RE_Direct( directLight, geometry, material, reflectedLight );\\\\\\\",vB=[\\\\\\\"RE_Direct( directLight, geometry, material, reflectedLight );\\\\\\\",\\\\\\\"#if defined( SUBSURFACE ) && defined( USE_UV )\\\\\\\",\\\\\\\" RE_Direct_Scattering(directLight, vUv, geometry, reflectedLight);\\\\\\\",\\\\\\\"#endif\\\\\\\"].join(\\\\\\\"\\\\n\\\\\\\"),fB.split(gB).join(vB)))].join(\\\\\\\"\\\\n\\\\\\\")};var fB,gB,vB;function yB(t){return{cook:!1,callback:(e,n)=>{AB.PARAM_CALLBACK_update_uniformColor(e,n,t)}}}function xB(t){return{cook:!1,callback:(e,n)=>{AB.PARAM_CALLBACK_update_uniformN(e,n,t)}}}const bB={uniforms:!0};class wB extends(SD(nk(lD(bD(ID(RD(ZD(function(t){return class extends t{constructor(){var t;super(...arguments),this.diffuse=oa.COLOR([1,1,1],{...yB(\\\\\\\"diffuse\\\\\\\")}),this.shininess=oa.FLOAT(1,{range:[0,1e3]}),this.thicknessMap=oa.NODE_PATH(gi.EMPTY,{nodeSelection:{context:Ki.COP},...(t=\\\\\\\"thicknessMap\\\\\\\",{cook:!1,callback:(e,n)=>{AB.PARAM_CALLBACK_update_uniformTexture(e,n,t)}})}),this.thicknessColor=oa.COLOR([.5,.3,0],{...yB(\\\\\\\"thicknessColor\\\\\\\")}),this.thicknessDistortion=oa.FLOAT(.1,{...xB(\\\\\\\"thicknessDistortion\\\\\\\")}),this.thicknessAmbient=oa.FLOAT(.4,{...xB(\\\\\\\"thicknessAmbient\\\\\\\")}),this.thicknessAttenuation=oa.FLOAT(.8,{...xB(\\\\\\\"thicknessAttenuation\\\\\\\")}),this.thicknessPower=oa.FLOAT(2,{range:[0,10],...xB(\\\\\\\"thicknessPower\\\\\\\")}),this.thicknessScale=oa.FLOAT(16,{range:[0,100],...xB(\\\\\\\"thicknessScale\\\\\\\")})}}}(xD(aa)))))))))){}const TB=new wB;class AB extends rD{constructor(){super(...arguments),this.paramsConfig=TB,this.controllers={advancedCommon:new cD(this),alphaMap:new FD(this,bB),map:new PD(this,bB)},this.controllerNames=Object.keys(this.controllers)}static type(){return\\\\\\\"meshSubsurfaceScattering\\\\\\\"}createMaterial(){const t=I.clone(mB.uniforms),e=new F({uniforms:t,vertexShader:mB.vertexShader,fragmentShader:mB.fragmentShader,lights:!0});return e.extensions.derivatives=!0,e}initializeNode(){this.params.onParamsCreated(\\\\\\\"init controllers\\\\\\\",(()=>{for(let t of this.controllerNames)this.controllers[t].initializeNode()}))}async cook(){for(let t of this.controllerNames)this.controllers[t].update();CD.update(this),ik.update(this),this.update_map(this.p.thicknessMap,\\\\\\\"thicknessMap\\\\\\\"),this.material.uniforms.diffuse.value.copy(this.pv.diffuse),this.material.uniforms.shininess.value=this.pv.shininess,this.material.uniforms.thicknessColor.value.copy(this.pv.thicknessColor),this.material.uniforms.thicknessDistortion.value=this.pv.thicknessDistortion,this.material.uniforms.thicknessAmbient.value=this.pv.thicknessAmbient,this.material.uniforms.thicknessAttenuation.value=this.pv.thicknessAttenuation,this.material.uniforms.thicknessPower.value=this.pv.thicknessPower,this.material.uniforms.thicknessScale.value=this.pv.thicknessScale,this.setMaterial(this.material)}static PARAM_CALLBACK_update_uniformN(t,e,n){t.material.uniforms[n].value=e.value}static PARAM_CALLBACK_update_uniformColor(t,e,n){e.parent_param&&t.material.uniforms[n].value.copy(e.parent_param.value)}static PARAM_CALLBACK_update_uniformTexture(t,e,n){t.update_map(e,n)}async update_map(t,e){const n=t.value.nodeWithContext(Ki.COP);n||(this.material.uniforms[e].value=null);const i=n,r=await i.compute();this.material.uniforms[e].value=r.texture()}}function EB(t){return class extends t{constructor(){super(...arguments),this.useGradientMap=oa.BOOLEAN(0,ND(MB)),this.gradientMap=oa.NODE_PATH(gi.EMPTY,LD(MB,\\\\\\\"useGradientMap\\\\\\\"))}}}O.a;EB(aa);class MB extends OD{constructor(t,e){super(t,e),this.node=t}initializeNode(){this.add_hooks(this.node.p.useGradientMap,this.node.p.gradientMap)}async update(){this._update(this.node.material,\\\\\\\"gradientMap\\\\\\\",this.node.p.useGradientMap,this.node.p.gradientMap)}static async update(t){t.controllers.gradientMap.update()}}const SB={directParams:!0};class CB extends(SD($D(lD(bD(Ek(HD(EB(lk(Sk(xk(DD(ID(RD(ZD(ED(xD(aa))))))))))))))))){}const NB=new CB;class LB extends rD{constructor(){super(...arguments),this.paramsConfig=NB,this.controllers={advancedCommon:new cD(this),alphaMap:new FD(this,SB),aoMap:new kD(this,SB),bumpMap:new bk(this,SB),displacementMap:new Ck(this,SB),emissiveMap:new ck(this,SB),gradientMap:new MB(this,SB),lightMap:new jD(this,SB),map:new PD(this,SB),normalMap:new Mk(this,SB)},this.controllerNames=Object.keys(this.controllers)}static type(){return\\\\\\\"meshToon\\\\\\\"}createMaterial(){return new Vf({vertexColors:!1,side:w.H,color:16777215,opacity:1})}initializeNode(){this.params.onParamsCreated(\\\\\\\"init controllers\\\\\\\",(()=>{for(let t of this.controllerNames)this.controllers[t].initializeNode()}))}async cook(){for(let t of this.controllerNames)this.controllers[t].update();MD.update(this),CD.update(this),JD.update(this),this.setMaterial(this.material)}}const OB={directParams:!0};class RB extends(vD(lD(bD(ID(RD(ZD(ED(function(t){return class extends t{constructor(){super(...arguments),this.size=oa.FLOAT(1),this.sizeAttenuation=oa.BOOLEAN(1)}}}(xD(aa)))))))))){}const PB=new RB;class IB extends rD{constructor(){super(...arguments),this.paramsConfig=PB,this.controllers={advancedCommon:new cD(this),alphaMap:new FD(this,OB),map:new PD(this,OB)},this.controllerNames=Object.keys(this.controllers)}static type(){return\\\\\\\"points\\\\\\\"}createMaterial(){return new yr.a({vertexColors:!1,side:w.H,color:16777215,opacity:1})}initializeNode(){this.params.onParamsCreated(\\\\\\\"init controllers\\\\\\\",(()=>{for(let t of this.controllerNames)this.controllers[t].initializeNode()}))}async cook(){for(let t of this.controllerNames)this.controllers[t].update();MD.update(this),yD.update(this),this.material.size=this.pv.size,this.material.sizeAttenuation=this.pv.sizeAttenuation,this.setMaterial(this.material)}}class FB extends(vD(lD(fD(bD(pD(xD(aa))))))){}const DB=new FB;class kB extends gD{constructor(){super(...arguments),this.paramsConfig=DB,this.controllers={advancedCommon:new cD(this)},this.controllerNames=Object.keys(this.controllers)}static type(){return\\\\\\\"pointsBuilder\\\\\\\"}usedAssembler(){return Hn.GL_POINTS}_create_assembler_controller(){return ai.assemblersRegister.assembler(this,this.usedAssembler())}initializeNode(){this.params.onParamsCreated(\\\\\\\"init controllers\\\\\\\",(()=>{for(let t of this.controllerNames)this.controllers[t].initializeNode()}))}async cook(){for(let t of this.controllerNames)this.controllers[t].update();_D.update(this),yD.update(this),this.compileIfRequired(),this.setMaterial(this.material)}}class BB extends(lD(ED(aa))){}const zB=new BB;class UB extends rD{constructor(){super(...arguments),this.paramsConfig=zB,this.controllers={advancedCommon:new cD(this)},this.controllerNames=Object.keys(this.controllers)}static type(){return\\\\\\\"shadow\\\\\\\"}createMaterial(){return new Bf({vertexColors:!1,side:w.H,color:16777215,opacity:1})}initializeNode(){this.params.onParamsCreated(\\\\\\\"init controllers\\\\\\\",(()=>{for(let t of this.controllerNames)this.controllers[t].initializeNode()}))}async cook(){for(let t of this.controllerNames)this.controllers[t].update();MD.update(this),this.setMaterial(this.material)}}class GB extends k.a{constructor(){const t=GB.SkyShader,e=new F({name:\\\\\\\"SkyShader\\\\\\\",fragmentShader:t.fragmentShader,vertexShader:t.vertexShader,uniforms:I.clone(t.uniforms),side:w.i,depthWrite:!1});super(new N(1,1,1),e)}}GB.prototype.isSky=!0,GB.SkyShader={uniforms:{turbidity:{value:2},rayleigh:{value:1},mieCoefficient:{value:.005},mieDirectionalG:{value:.8},sunPosition:{value:new p.a},up:{value:new p.a(0,1,0)}},vertexShader:\\\\\\\"\\\\n\\\\t\\\\tuniform vec3 sunPosition;\\\\n\\\\t\\\\tuniform float rayleigh;\\\\n\\\\t\\\\tuniform float turbidity;\\\\n\\\\t\\\\tuniform float mieCoefficient;\\\\n\\\\t\\\\tuniform vec3 up;\\\\n\\\\n\\\\t\\\\tvarying vec3 vWorldPosition;\\\\n\\\\t\\\\tvarying vec3 vSunDirection;\\\\n\\\\t\\\\tvarying float vSunfade;\\\\n\\\\t\\\\tvarying vec3 vBetaR;\\\\n\\\\t\\\\tvarying vec3 vBetaM;\\\\n\\\\t\\\\tvarying float vSunE;\\\\n\\\\n\\\\t\\\\t// constants for atmospheric scattering\\\\n\\\\t\\\\tconst float e = 2.71828182845904523536028747135266249775724709369995957;\\\\n\\\\t\\\\tconst float pi = 3.141592653589793238462643383279502884197169;\\\\n\\\\n\\\\t\\\\t// wavelength of used primaries, according to preetham\\\\n\\\\t\\\\tconst vec3 lambda = vec3( 680E-9, 550E-9, 450E-9 );\\\\n\\\\t\\\\t// this pre-calcuation replaces older TotalRayleigh(vec3 lambda) function:\\\\n\\\\t\\\\t// (8.0 * pow(pi, 3.0) * pow(pow(n, 2.0) - 1.0, 2.0) * (6.0 + 3.0 * pn)) / (3.0 * N * pow(lambda, vec3(4.0)) * (6.0 - 7.0 * pn))\\\\n\\\\t\\\\tconst vec3 totalRayleigh = vec3( 5.804542996261093E-6, 1.3562911419845635E-5, 3.0265902468824876E-5 );\\\\n\\\\n\\\\t\\\\t// mie stuff\\\\n\\\\t\\\\t// K coefficient for the primaries\\\\n\\\\t\\\\tconst float v = 4.0;\\\\n\\\\t\\\\tconst vec3 K = vec3( 0.686, 0.678, 0.666 );\\\\n\\\\t\\\\t// MieConst = pi * pow( ( 2.0 * pi ) / lambda, vec3( v - 2.0 ) ) * K\\\\n\\\\t\\\\tconst vec3 MieConst = vec3( 1.8399918514433978E14, 2.7798023919660528E14, 4.0790479543861094E14 );\\\\n\\\\n\\\\t\\\\t// earth shadow hack\\\\n\\\\t\\\\t// cutoffAngle = pi / 1.95;\\\\n\\\\t\\\\tconst float cutoffAngle = 1.6110731556870734;\\\\n\\\\t\\\\tconst float steepness = 1.5;\\\\n\\\\t\\\\tconst float EE = 1000.0;\\\\n\\\\n\\\\t\\\\tfloat sunIntensity( float zenithAngleCos ) {\\\\n\\\\t\\\\t\\\\tzenithAngleCos = clamp( zenithAngleCos, -1.0, 1.0 );\\\\n\\\\t\\\\t\\\\treturn EE * max( 0.0, 1.0 - pow( e, -( ( cutoffAngle - acos( zenithAngleCos ) ) / steepness ) ) );\\\\n\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\tvec3 totalMie( float T ) {\\\\n\\\\t\\\\t\\\\tfloat c = ( 0.2 * T ) * 10E-18;\\\\n\\\\t\\\\t\\\\treturn 0.434 * c * MieConst;\\\\n\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\tvec4 worldPosition = modelMatrix * vec4( position, 1.0 );\\\\n\\\\t\\\\t\\\\tvWorldPosition = worldPosition.xyz;\\\\n\\\\n\\\\t\\\\t\\\\tgl_Position = projectionMatrix * modelViewMatrix * vec4( position, 1.0 );\\\\n\\\\t\\\\t\\\\tgl_Position.z = gl_Position.w; // set z to camera.far\\\\n\\\\n\\\\t\\\\t\\\\tvSunDirection = normalize( sunPosition );\\\\n\\\\n\\\\t\\\\t\\\\tvSunE = sunIntensity( dot( vSunDirection, up ) );\\\\n\\\\n\\\\t\\\\t\\\\tvSunfade = 1.0 - clamp( 1.0 - exp( ( sunPosition.y / 450000.0 ) ), 0.0, 1.0 );\\\\n\\\\n\\\\t\\\\t\\\\tfloat rayleighCoefficient = rayleigh - ( 1.0 * ( 1.0 - vSunfade ) );\\\\n\\\\n\\\\t\\\\t\\\\t// extinction (absorbtion + out scattering)\\\\n\\\\t\\\\t\\\\t// rayleigh coefficients\\\\n\\\\t\\\\t\\\\tvBetaR = totalRayleigh * rayleighCoefficient;\\\\n\\\\n\\\\t\\\\t\\\\t// mie coefficients\\\\n\\\\t\\\\t\\\\tvBetaM = totalMie( turbidity ) * mieCoefficient;\\\\n\\\\n\\\\t\\\\t}\\\\\\\",fragmentShader:\\\\\\\"\\\\n\\\\t\\\\tvarying vec3 vWorldPosition;\\\\n\\\\t\\\\tvarying vec3 vSunDirection;\\\\n\\\\t\\\\tvarying float vSunfade;\\\\n\\\\t\\\\tvarying vec3 vBetaR;\\\\n\\\\t\\\\tvarying vec3 vBetaM;\\\\n\\\\t\\\\tvarying float vSunE;\\\\n\\\\n\\\\t\\\\tuniform float mieDirectionalG;\\\\n\\\\t\\\\tuniform vec3 up;\\\\n\\\\n\\\\t\\\\tconst vec3 cameraPos = vec3( 0.0, 0.0, 0.0 );\\\\n\\\\n\\\\t\\\\t// constants for atmospheric scattering\\\\n\\\\t\\\\tconst float pi = 3.141592653589793238462643383279502884197169;\\\\n\\\\n\\\\t\\\\tconst float n = 1.0003; // refractive index of air\\\\n\\\\t\\\\tconst float N = 2.545E25; // number of molecules per unit volume for air at 288.15K and 1013mb (sea level -45 celsius)\\\\n\\\\n\\\\t\\\\t// optical length at zenith for molecules\\\\n\\\\t\\\\tconst float rayleighZenithLength = 8.4E3;\\\\n\\\\t\\\\tconst float mieZenithLength = 1.25E3;\\\\n\\\\t\\\\t// 66 arc seconds -> degrees, and the cosine of that\\\\n\\\\t\\\\tconst float sunAngularDiameterCos = 0.999956676946448443553574619906976478926848692873900859324;\\\\n\\\\n\\\\t\\\\t// 3.0 / ( 16.0 * pi )\\\\n\\\\t\\\\tconst float THREE_OVER_SIXTEENPI = 0.05968310365946075;\\\\n\\\\t\\\\t// 1.0 / ( 4.0 * pi )\\\\n\\\\t\\\\tconst float ONE_OVER_FOURPI = 0.07957747154594767;\\\\n\\\\n\\\\t\\\\tfloat rayleighPhase( float cosTheta ) {\\\\n\\\\t\\\\t\\\\treturn THREE_OVER_SIXTEENPI * ( 1.0 + pow( cosTheta, 2.0 ) );\\\\n\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\tfloat hgPhase( float cosTheta, float g ) {\\\\n\\\\t\\\\t\\\\tfloat g2 = pow( g, 2.0 );\\\\n\\\\t\\\\t\\\\tfloat inverse = 1.0 / pow( 1.0 - 2.0 * g * cosTheta + g2, 1.5 );\\\\n\\\\t\\\\t\\\\treturn ONE_OVER_FOURPI * ( ( 1.0 - g2 ) * inverse );\\\\n\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\tvec3 direction = normalize( vWorldPosition - cameraPos );\\\\n\\\\n\\\\t\\\\t\\\\t// optical length\\\\n\\\\t\\\\t\\\\t// cutoff angle at 90 to avoid singularity in next formula.\\\\n\\\\t\\\\t\\\\tfloat zenithAngle = acos( max( 0.0, dot( up, direction ) ) );\\\\n\\\\t\\\\t\\\\tfloat inverse = 1.0 / ( cos( zenithAngle ) + 0.15 * pow( 93.885 - ( ( zenithAngle * 180.0 ) / pi ), -1.253 ) );\\\\n\\\\t\\\\t\\\\tfloat sR = rayleighZenithLength * inverse;\\\\n\\\\t\\\\t\\\\tfloat sM = mieZenithLength * inverse;\\\\n\\\\n\\\\t\\\\t\\\\t// combined extinction factor\\\\n\\\\t\\\\t\\\\tvec3 Fex = exp( -( vBetaR * sR + vBetaM * sM ) );\\\\n\\\\n\\\\t\\\\t\\\\t// in scattering\\\\n\\\\t\\\\t\\\\tfloat cosTheta = dot( direction, vSunDirection );\\\\n\\\\n\\\\t\\\\t\\\\tfloat rPhase = rayleighPhase( cosTheta * 0.5 + 0.5 );\\\\n\\\\t\\\\t\\\\tvec3 betaRTheta = vBetaR * rPhase;\\\\n\\\\n\\\\t\\\\t\\\\tfloat mPhase = hgPhase( cosTheta, mieDirectionalG );\\\\n\\\\t\\\\t\\\\tvec3 betaMTheta = vBetaM * mPhase;\\\\n\\\\n\\\\t\\\\t\\\\tvec3 Lin = pow( vSunE * ( ( betaRTheta + betaMTheta ) / ( vBetaR + vBetaM ) ) * ( 1.0 - Fex ), vec3( 1.5 ) );\\\\n\\\\t\\\\t\\\\tLin *= mix( vec3( 1.0 ), pow( vSunE * ( ( betaRTheta + betaMTheta ) / ( vBetaR + vBetaM ) ) * Fex, vec3( 1.0 / 2.0 ) ), clamp( pow( 1.0 - dot( up, vSunDirection ), 5.0 ), 0.0, 1.0 ) );\\\\n\\\\n\\\\t\\\\t\\\\t// nightsky\\\\n\\\\t\\\\t\\\\tfloat theta = acos( direction.y ); // elevation --\\\\x3e y-axis, [-pi/2, pi/2]\\\\n\\\\t\\\\t\\\\tfloat phi = atan( direction.z, direction.x ); // azimuth --\\\\x3e x-axis [-pi/2, pi/2]\\\\n\\\\t\\\\t\\\\tvec2 uv = vec2( phi, theta ) / vec2( 2.0 * pi, pi ) + vec2( 0.5, 0.0 );\\\\n\\\\t\\\\t\\\\tvec3 L0 = vec3( 0.1 ) * Fex;\\\\n\\\\n\\\\t\\\\t\\\\t// composition + solar disc\\\\n\\\\t\\\\t\\\\tfloat sundisk = smoothstep( sunAngularDiameterCos, sunAngularDiameterCos + 0.00002, cosTheta );\\\\n\\\\t\\\\t\\\\tL0 += ( vSunE * 19000.0 * Fex ) * sundisk;\\\\n\\\\n\\\\t\\\\t\\\\tvec3 texColor = ( Lin + L0 ) * 0.04 + vec3( 0.0, 0.0003, 0.00075 );\\\\n\\\\n\\\\t\\\\t\\\\tvec3 retColor = pow( texColor, vec3( 1.0 / ( 1.2 + ( 1.2 * vSunfade ) ) ) );\\\\n\\\\n\\\\t\\\\t\\\\tgl_FragColor = vec4( retColor, 1.0 );\\\\n\\\\n\\\\t\\\\t\\\\t#include <tonemapping_fragment>\\\\n\\\\t\\\\t\\\\t#include <encodings_fragment>\\\\n\\\\n\\\\t\\\\t}\\\\\\\"};const VB=new class extends aa{constructor(){super(...arguments),this.turbidity=oa.FLOAT(2,{range:[0,20]}),this.rayleigh=oa.FLOAT(1,{range:[0,4]}),this.mieCoefficient=oa.FLOAT(.005),this.mieDirectional=oa.FLOAT(.8),this.inclination=oa.FLOAT(.5),this.azimuth=oa.FLOAT(.25),this.up=oa.VECTOR3([0,1,0])}};class HB extends rD{constructor(){super(...arguments),this.paramsConfig=VB}static type(){return\\\\\\\"sky\\\\\\\"}createMaterial(){const t=(new GB).material;return t.depthWrite=!0,t}async cook(){const t=this.material.uniforms;t.turbidity.value=this.pv.turbidity,t.rayleigh.value=this.pv.rayleigh,t.mieCoefficient.value=this.pv.mieCoefficient,t.mieDirectionalG.value=this.pv.mieDirectional,t.up.value.copy(this.pv.up);const e=Math.PI*(this.pv.inclination-.5),n=2*Math.PI*(this.pv.azimuth-.5);t.sunPosition.value.x=Math.cos(n),t.sunPosition.value.y=Math.sin(n)*Math.sin(e),t.sunPosition.value.z=Math.sin(n)*Math.cos(e),this.setMaterial(this.material)}}var jB=\\\\\\\"precision highp float;\\\\nprecision highp int;\\\\n\\\\nvarying vec3 vPw;\\\\n\\\\n#include <common>\\\\n\\\\nvoid main()\\\\t{\\\\n\\\\n\\\\t// start builder body code\\\\n\\\\n\\\\tvPw = position;\\\\n\\\\tgl_Position = projectionMatrix * modelViewMatrix * vec4( position, 1.0 );\\\\n\\\\n}\\\\\\\",WB=\\\\\\\"precision highp float;\\\\nprecision highp int;\\\\n\\\\n#include <common>\\\\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<MAX_STEPS_COUNT; i++){\\\\n\\\\t\\\\tif(opacity >= 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<MAX_STEPS_COUNT; i++){\\\\n\\\\t\\\\tif(opacity >= 0.99){ break; }\\\\n\\\\n\\\\t\\\\tif( i==0 || is_inside_bbox(current_pos) ){\\\\n\\\\t\\\\t\\\\twas_inside_bbox = true;\\\\n\\\\n\\\\t\\\\t\\\\tfloat density = density_function(current_pos) * u_VolumeDensity;\\\\n\\\\t\\\\t\\\\topacity += density_to_opacity(density, step_size);\\\\n\\\\n\\\\t\\\\t\\\\tvec4 light_color = vec4(0.0,0.0,0.0,1.0); //vec4(1.0,1.0,1.0,1.0);\\\\n\\\\t\\\\t\\\\t// vec3 directional_light_direction;\\\\n\\\\t\\\\t\\\\t// for ( int l = 0; l < DIR_LIGHTS_COUNT; l++ ) {\\\\n\\\\t\\\\t\\\\t// directional_light_direction = u_DirectionalLightsDirection[ l ];\\\\n\\\\t\\\\t\\\\tlight_color += raymarch_light(-u_DirectionalLightDirection, current_pos);\\\\n\\\\t\\\\t\\\\t// }\\\\n\\\\t\\\\t\\\\tfloat blend = 1.0-opacity;\\\\n\\\\t\\\\t\\\\tcolor = mix( color.xyz, light_color.xyz, vec3(blend, blend, blend) );\\\\n\\\\t\\\\t\\\\tsteps_count += 1.0;\\\\n\\\\n\\\\t\\\\t}else{\\\\n\\\\t\\\\t\\\\tif (was_inside_bbox) { break; }\\\\n\\\\t\\\\t}\\\\n\\\\t\\\\tcurrent_pos += step_vector;\\\\n\\\\t}\\\\n\\\\n\\\\treturn vec4(color, opacity);\\\\n\\\\t// steps_count = steps_count / 5.0;\\\\n\\\\t// return vec4(vec3(steps_count, steps_count, steps_count), 1.0);\\\\n}\\\\n\\\\nvoid main()\\\\t{\\\\n\\\\n\\\\tvec3 eye = normalize(vPw - cameraPosition);\\\\n\\\\t// we can start from the bbox, as we are front facing\\\\n\\\\tvec3 start_pos = vPw;\\\\n\\\\n\\\\tvec4 color = raymarch_bbox(start_pos, eye);\\\\n\\\\tgl_FragColor = color;\\\\n\\\\n}\\\\\\\";const qB={u_Color:{value:new D.a(1,1,1)},u_VolumeDensity:{value:5},u_ShadowDensity:{value:2},u_StepSize:{value:.01},u_BoundingBoxMin:{value:new p.a(-1,-1,-1)},u_BoundingBoxMax:{value:new p.a(1,1,1)},u_DirectionalLightDirection:{value:new p.a(-1,-1,-1)}};var XB=n(16);function YB(t){return class extends t{constructor(){super(...arguments),this.color=oa.COLOR([1,1,1]),this.stepSize=oa.FLOAT(.01),this.density=oa.FLOAT(1),this.shadowDensity=oa.FLOAT(1),this.lightDir=oa.VECTOR3([-1,-1,-1])}}}YB(aa);class $B{constructor(t){this.node=t}static render_hook(t,e,n,i,r,s,o){if(o){this._object_bbox.setFromObject(o);const t=r;t.uniforms.u_BoundingBoxMin.value.copy(this._object_bbox.min),t.uniforms.u_BoundingBoxMax.value.copy(this._object_bbox.max)}}update_uniforms_from_params(){const t=this.node.material.uniforms;t.u_Color.value.copy(this.node.pv.color),t.u_StepSize.value=this.node.pv.stepSize,t.u_VolumeDensity.value=this.node.pv.density,t.u_ShadowDensity.value=this.node.pv.shadowDensity;const e=t.u_DirectionalLightDirection.value,n=this.node.pv.lightDir;e&&(e.x=n.x,e.y=n.y,e.z=n.z)}}$B._object_bbox=new XB.a;class JB extends(YB(aa)){}const ZB=new JB;class QB extends rD{constructor(){super(...arguments),this.paramsConfig=ZB,this._volume_controller=new $B(this)}static type(){return\\\\\\\"volume\\\\\\\"}createMaterial(){const t=new F({vertexShader:jB,fragmentShader:WB,side:w.H,transparent:!0,depthTest:!0,uniforms:I.clone(qB)});return fs.add_user_data_render_hook(t,$B.render_hook.bind($B)),t}initializeNode(){}async cook(){this._volume_controller.update_uniforms_from_params(),this.setMaterial(this.material)}}class KB extends(fD(YB(aa))){}const tz=new KB;class ez extends gD{constructor(){super(...arguments),this.paramsConfig=tz,this._volume_controller=new $B(this)}static type(){return\\\\\\\"volumeBuilder\\\\\\\"}usedAssembler(){return Hn.GL_VOLUME}_create_assembler_controller(){return ai.assemblersRegister.assembler(this,this.usedAssembler())}initializeNode(){}async cook(){this._volume_controller.update_uniforms_from_params(),this.compileIfRequired(),this.setMaterial(this.material)}}class nz extends ia{static context(){return Ki.MAT}cook(){this.cookController.endCook()}}class iz extends nz{}class rz extends iz{constructor(){super(...arguments),this._children_controller_context=Ki.ANIM}static type(){return tr.ANIM}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class sz extends iz{constructor(){super(...arguments),this._children_controller_context=Ki.COP}static type(){return tr.COP}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class oz extends iz{constructor(){super(...arguments),this._children_controller_context=Ki.EVENT}static type(){return tr.EVENT}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class az extends iz{constructor(){super(...arguments),this._children_controller_context=Ki.MAT}static type(){return tr.MAT}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class lz extends nz{constructor(){super(...arguments),this.paramsConfig=new Jm,this.effectsComposerController=new Zm(this),this.displayNodeController=new Lm(this,this.effectsComposerController.displayNodeControllerCallbacks()),this._children_controller_context=Ki.POST}static type(){return tr.POST}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class cz extends iz{constructor(){super(...arguments),this._children_controller_context=Ki.ROP}static type(){return tr.ROP}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}var uz=n(87);const hz=\\\\\\\"parent object\\\\\\\",dz=[hz,hz,hz,hz];var pz;!function(t){t[t.MANAGER=0]=\\\\\\\"MANAGER\\\\\\\",t[t.CAMERA=2]=\\\\\\\"CAMERA\\\\\\\",t[t.LIGHT=3]=\\\\\\\"LIGHT\\\\\\\"}(pz||(pz={}));class _z extends ia{constructor(){super(...arguments),this.renderOrder=pz.MANAGER,this._children_group=this._create_children_group(),this._attachableToHierarchy=!0,this._used_in_scene=!0}static context(){return Ki.OBJ}static displayedInputNames(){return dz}_create_children_group(){const t=new In.a;return t.matrixAutoUpdate=!1,t}attachableToHierarchy(){return this._attachableToHierarchy}usedInScene(){return this._used_in_scene}addObjectToParent(t){this.attachableToHierarchy()&&t.add(this.object)}removeObjectFromParent(){if(this.attachableToHierarchy()){const t=this.object.parent;t&&t.remove(this.object)}}initializeBaseNode(){this._object=this._create_object_with_attributes(),this.nameController.add_post_set_fullPath_hook(this.set_object_name.bind(this)),this.set_object_name()}get children_group(){return this._children_group}get object(){return this._object}_create_object_with_attributes(){const t=this.createObject();return t.node=this,t.add(this._children_group),t}set_object_name(){this._object&&(this._object.name=this.path(),this._children_group.name=`${this.path()}:parented_outputs`)}createObject(){const t=new Q.a;return t.matrixAutoUpdate=!1,t}isDisplayNodeCooking(){if(this.displayNodeController){const t=this.displayNodeController.displayNode();if(t)return t.cookController.isCooking()}return!1}isDisplayed(){var t,e;return(null===(e=null===(t=this.flags)||void 0===t?void 0:t.display)||void 0===e?void 0:e.active())||!1}}class mz extends _z{constructor(){super(...arguments),this.flags=new Fi(this),this.renderOrder=pz.LIGHT,this._color_with_intensity=new D.a(0),this._used_in_scene=!0,this._cook_main_without_inputs_when_dirty_bound=this._cook_main_without_inputs_when_dirty.bind(this)}get light(){return this._light}initializeBaseNode(){super.initializeBaseNode(),this._light=this.createLight(),this.object.add(this._light),this.flags.display.onUpdate((()=>{this._updateLightAttachment()})),this.dirtyController.addPostDirtyHook(\\\\\\\"_cook_main_without_inputs_when_dirty\\\\\\\",this._cook_main_without_inputs_when_dirty_bound)}async _cook_main_without_inputs_when_dirty(){await this.cookController.cookMainWithoutInputs()}set_object_name(){super.set_object_name(),this._light&&(this._light.name=`${this.path()}:light`)}_updateLightAttachment(){this.flags.display.active()?(this.object.add(this.light),this._cook_main_without_inputs_when_dirty()):this.object.remove(this.light)}cook(){this.updateLightParams(),this.updateShadowParams(),this.cookController.endCook()}updateLightParams(){}updateShadowParams(){}}const fz=new class extends aa{constructor(){super(...arguments),this.color=oa.COLOR([1,1,1],{conversion:so.SRGB_TO_LINEAR}),this.intensity=oa.FLOAT(1)}};class gz extends mz{constructor(){super(...arguments),this.paramsConfig=fz}static type(){return\\\\\\\"ambientLight\\\\\\\"}createLight(){const t=new uz.a;return t.matrixAutoUpdate=!1,t}initializeNode(){this.io.inputs.setCount(0,1)}updateLightParams(){this.light.color=this.pv.color,this.light.intensity=this.pv.intensity}}class vz extends nv.a{constructor(t,e,n=10,i=10){super(t,e),this.type=\\\\\\\"RectAreaLight\\\\\\\",this.width=n,this.height=i}get power(){return this.intensity*this.width*this.height*Math.PI}set power(t){this.intensity=t/(this.width*this.height*Math.PI)}copy(t){return super.copy(t),this.width=t.width,this.height=t.height,this}toJSON(t){const e=super.toJSON(t);return e.object.width=this.width,e.object.height=this.height,e}}vz.prototype.isRectAreaLight=!0;var yz,xz=n(60);class bz{static init(){const 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Float32Array(t),i=new Float32Array(e);G.LTC_FLOAT_1=new mo.a(n,64,64,w.Ib,w.G,w.Yc,w.n,w.n,w.V,w.ob,1),G.LTC_FLOAT_2=new mo.a(i,64,64,w.Ib,w.G,w.Yc,w.n,w.n,w.V,w.ob,1);const r=new Uint16Array(t.length);t.forEach((function(t,e){r[e]=xz.a.toHalfFloat(t)}));const s=new Uint16Array(e.length);e.forEach((function(t,e){s[e]=xz.a.toHalfFloat(t)})),G.LTC_HALF_1=new mo.a(r,64,64,w.Ib,w.M,w.Yc,w.n,w.n,w.V,w.ob,1),G.LTC_HALF_2=new mo.a(s,64,64,w.Ib,w.M,w.Yc,w.n,w.n,w.V,w.ob,1)}}!function(t){t.OBJECTS=\\\\\\\"objects\\\\\\\",t.GEOMETRIES=\\\\\\\"geometries\\\\\\\"}(yz||(yz={}));const wz=[yz.GEOMETRIES,yz.OBJECTS];var Tz;!function(t){t.XYZ=\\\\\\\"XYZ\\\\\\\",t.XZY=\\\\\\\"XZY\\\\\\\",t.YXZ=\\\\\\\"YXZ\\\\\\\",t.YZX=\\\\\\\"YZX\\\\\\\",t.ZYX=\\\\\\\"ZYX\\\\\\\",t.ZXY=\\\\\\\"ZXY\\\\\\\"}(Tz||(Tz={}));const Az=[Tz.XYZ,Tz.XZY,Tz.YXZ,Tz.YZX,Tz.ZXY,Tz.ZYX],Ez=Tz.XYZ;class Mz{constructor(){this._translation_matrix=new A.a,this._translation_matrix_q=new au.a,this._translation_matrix_s=new 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set_params_from_object(t,e){t.position.toArray(this.set_params_from_object_position_array),t.rotation.toArray(this.set_params_from_object_rotation_array),this.set_params_from_object_rotation_deg.fromArray(this.set_params_from_object_rotation_array),this.set_params_from_object_rotation_deg.multiplyScalar(180/Math.PI),this.set_params_from_object_rotation_deg.toArray(this.set_params_from_object_rotation_array),e.scene().batchUpdates((()=>{e.params.set_vector3(\\\\\\\"t\\\\\\\",this.set_params_from_object_position_array),e.params.set_vector3(\\\\\\\"r\\\\\\\",this.set_params_from_object_rotation_array)}))}translation_matrix(t){return this._translation_matrix.compose(t,this._translation_matrix_q,this._translation_matrix_s),this._translation_matrix}matrix(t,e,n,i,r){return this._matrix_euler.set(Object(Ln.e)(e.x),Object(Ln.e)(e.y),Object(Ln.e)(e.z),r),this._matrix_q.setFromEuler(this._matrix_euler),this._matrix_s.copy(n).multiplyScalar(i),this._matrix.compose(t,this._matrix_q,this._matrix_s),this._matrix}rotate_geometry(t,e,n){this._rotate_geometry_vec_dest.copy(n),this._rotate_geometry_vec_dest.normalize(),this._rotate_geometry_q.setFromUnitVectors(e,this._rotate_geometry_vec_dest),this._rotate_geometry_m.makeRotationFromQuaternion(this._rotate_geometry_q),t.applyMatrix4(this._rotate_geometry_m)}static decompose_matrix(t){t.matrix.decompose(t.position,t.quaternion,t.scale)}}function Sz(t,e){const n=(null==e?void 0:e.matrixAutoUpdate)||!1;return class extends t{constructor(){super(...arguments),this.transform=oa.FOLDER(),this.keepPosWhenParenting=oa.BOOLEAN(0),this.rotationOrder=oa.INTEGER(Az.indexOf(Tz.XYZ),{menu:{entries:Az.map(((t,e)=>({name:t,value:e})))}}),this.t=oa.VECTOR3([0,0,0]),this.r=oa.VECTOR3([0,0,0]),this.s=oa.VECTOR3([1,1,1]),this.scale=oa.FLOAT(1),this.matrixAutoUpdate=oa.BOOLEAN(n?1:0),this.updateTransformFromObject=oa.BUTTON(null,{callback:t=>{Nz.PARAM_CALLBACK_update_transform_from_object(t)}})}}}Mz.set_params_from_matrix_position=new p.a,Mz.set_params_from_matrix_quaternion=new au.a,Mz.set_params_from_matrix_scale=new p.a,Mz.set_params_from_matrix_euler=new Wv.a,Mz.set_params_from_matrix_rotation=new p.a,Mz.set_params_from_matrix_t=[0,0,0],Mz.set_params_from_matrix_r=[0,0,0],Mz.set_params_from_matrix_s=[0,0,0],Mz.set_params_from_object_position_array=[0,0,0],Mz.set_params_from_object_rotation_deg=new p.a,Mz.set_params_from_object_rotation_array=[0,0,0];Sz(aa);const Cz=\\\\\\\"_cook_main_without_inputs_when_dirty\\\\\\\";class Nz{constructor(t){this.node=t,this._cook_main_without_inputs_when_dirty_bound=this._cook_main_without_inputs_when_dirty.bind(this),this._core_transform=new Mz,this._keep_pos_when_parenting_m_object=new A.a,this._keep_pos_when_parenting_m_new_parent_inv=new A.a}initializeNode(){this.node.dirtyController.hasHook(Cz)||this.node.dirtyController.addPostDirtyHook(Cz,this._cook_main_without_inputs_when_dirty_bound)}async _cook_main_without_inputs_when_dirty(){await this.node.cookController.cookMainWithoutInputs()}update(){this.update_transform_with_matrix();this.node.object.matrixAutoUpdate=this.node.pv.matrixAutoUpdate}update_transform_with_matrix(t){const e=this.node.object;null==t||t.equals(e.matrix)?this._update_matrix_from_params_with_core_transform():(e.matrix.copy(t),e.dispatchEvent({type:\\\\\\\"change\\\\\\\"}))}_update_matrix_from_params_with_core_transform(){const t=this.node.object;let e=t.matrixAutoUpdate;e&&(t.matrixAutoUpdate=!1);const n=this._core_transform.matrix(this.node.pv.t,this.node.pv.r,this.node.pv.s,this.node.pv.scale,Az[this.node.pv.rotationOrder]);t.matrix.identity(),t.applyMatrix4(n),this._apply_look_at(),t.updateMatrix(),e&&(t.matrixAutoUpdate=!0),t.dispatchEvent({type:\\\\\\\"change\\\\\\\"})}_apply_look_at(){}set_params_from_matrix(t,e={}){Mz.set_params_from_matrix(t,this.node,e)}static update_node_transform_params_if_required(t,e){t.transformController.update_node_transform_params_if_required(e)}update_node_transform_params_if_required(t){if(!this.node.pv.keepPosWhenParenting)return;if(!this.node.scene().loadingController.loaded())return;if(t==this.node.object.parent)return;const e=this.node.object;e.updateMatrixWorld(!0),t.updateMatrixWorld(!0),this._keep_pos_when_parenting_m_object.copy(e.matrixWorld),this._keep_pos_when_parenting_m_new_parent_inv.copy(t.matrixWorld),this._keep_pos_when_parenting_m_new_parent_inv.invert(),this._keep_pos_when_parenting_m_object.premultiply(this._keep_pos_when_parenting_m_new_parent_inv),Mz.set_params_from_matrix(this._keep_pos_when_parenting_m_object,this.node,{scale:!0})}update_node_transform_params_from_object(t=!1){const e=this.node.object;t&&e.updateMatrix(),Mz.set_params_from_matrix(e.matrix,this.node,{scale:!0})}static PARAM_CALLBACK_update_transform_from_object(t){t.transformController.update_node_transform_params_from_object()}}class Lz{constructor(t){this.node=t}initializeNode(){this.node.io.inputs.setCount(0,1),this.node.io.inputs.set_depends_on_inputs(!1),this.node.io.outputs.setHasOneOutput(),this.node.io.inputs.add_on_set_input_hook(\\\\\\\"on_input_updated:update_parent\\\\\\\",(()=>{this.on_input_updated()}))}static on_input_updated(t){const e=t.root().getParentForNode(t);t.transformController&&e&&Nz.update_node_transform_params_if_required(t,e),null!=t.io.inputs.input(0)?t.root().addToParentTransform(t):t.root().removeFromParentTransform(t)}on_input_updated(){Lz.on_input_updated(this.node)}}Sz(aa);class Oz extends mz{constructor(){super(...arguments),this.flags=new Fi(this),this.hierarchyController=new Lz(this),this.transformController=new Nz(this)}initializeBaseNode(){super.initializeBaseNode(),this.hierarchyController.initializeNode(),this.transformController.initializeNode()}cook(){this.transformController.update(),this.updateLightParams(),this.updateShadowParams(),this.cookController.endCook()}}class 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t.matrixAutoUpdate=!1,bz.initialized||(bz.init(),bz.initialized=!0),t}updateLightParams(){this.light.color=this.pv.color,this.light.intensity=this.pv.intensity,this.light.width=this.pv.width,this.light.height=this.pv.height,this._helperController.update()}}var Gz=n(72);const Vz=new p.a,Hz=new tf.a;class jz extends Tr.a{constructor(t){const e=new S.a,n=new wr.a({color:16777215,vertexColors:!0,toneMapped:!1}),i=[],r=[],s={},o=new D.a(16755200),a=new D.a(16711680),l=new D.a(43775),c=new D.a(16777215),u=new D.a(3355443);function h(t,e,n){d(t,n),d(e,n)}function d(t,e){i.push(0,0,0),r.push(e.r,e.g,e.b),void 0===s[t]&&(s[t]=[]),s[t].push(i.length/3-1)}h(\\\\\\\"n1\\\\\\\",\\\\\\\"n2\\\\\\\",o),h(\\\\\\\"n2\\\\\\\",\\\\\\\"n4\\\\\\\",o),h(\\\\\\\"n4\\\\\\\",\\\\\\\"n3\\\\\\\",o),h(\\\\\\\"n3\\\\\\\",\\\\\\\"n1\\\\\\\",o),h(\\\\\\\"f1\\\\\\\",\\\\\\\"f2\\\\\\\",o),h(\\\\\\\"f2\\\\\\\",\\\\\\\"f4\\\\\\\",o),h(\\\\\\\"f4\\\\\\\",\\\\\\\"f3\\\\\\\",o),h(\\\\\\\"f3\\\\\\\",\\\\\\\"f1\\\\\\\",o),h(\\\\\\\"n1\\\\\\\",\\\\\\\"f1\\\\\\\",o),h(\\\\\\\"n2\\\\\\\",\\\\\\\"f2\\\\\\\",o),h(\\\\\\\"n3\\\\\\\",\\\\\\\"f3\\\\\\\",o),h(\\\\\\\"n4\\\\\\\",\\\\\\\"f4\\\\\\\",o),h(\\\\\\\"p\\\\\\\",\\\\\\\"n1\\\\\\\",a),h(\\\\\\\"p\\\\\\\",\\\\\\\"n2\\\\\\\",a),h(\\\\\\\"p\\\\\\\",\\\\\\\"n3\\\\\\\",a),h(\\\\\\\"p\\\\\\\",\\\\\\\"n4\\\\\\\",a),h(\\\\\\\"u1\\\\\\\",\\\\\\\"u2\\\\\\\",l),h(\\\\\\\"u2\\\\\\\",\\\\\\\"u3\\\\\\\",l),h(\\\\\\\"u3\\\\\\\",\\\\\\\"u1\\\\\\\",l),h(\\\\\\\"c\\\\\\\",\\\\\\\"t\\\\\\\",c),h(\\\\\\\"p\\\\\\\",\\\\\\\"c\\\\\\\",u),h(\\\\\\\"cn1\\\\\\\",\\\\\\\"cn2\\\\\\\",u),h(\\\\\\\"cn3\\\\\\\",\\\\\\\"cn4\\\\\\\",u),h(\\\\\\\"cf1\\\\\\\",\\\\\\\"cf2\\\\\\\",u),h(\\\\\\\"cf3\\\\\\\",\\\\\\\"cf4\\\\\\\",u),e.setAttribute(\\\\\\\"position\\\\\\\",new C.c(i,3)),e.setAttribute(\\\\\\\"color\\\\\\\",new C.c(r,3)),super(e,n),this.type=\\\\\\\"CameraHelper\\\\\\\",this.camera=t,this.camera.updateProjectionMatrix&&this.camera.updateProjectionMatrix(),this.matrixAutoUpdate=!1,this.pointMap=s,this.update()}update(){const t=this.geometry,e=this.pointMap;Hz.projectionMatrixInverse.copy(this.camera.projectionMatrixInverse),Wz(\\\\\\\"c\\\\\\\",e,t,Hz,0,0,-1),Wz(\\\\\\\"t\\\\\\\",e,t,Hz,0,0,1),Wz(\\\\\\\"n1\\\\\\\",e,t,Hz,-1,-1,-1),Wz(\\\\\\\"n2\\\\\\\",e,t,Hz,1,-1,-1),Wz(\\\\\\\"n3\\\\\\\",e,t,Hz,-1,1,-1),Wz(\\\\\\\"n4\\\\\\\",e,t,Hz,1,1,-1),Wz(\\\\\\\"f1\\\\\\\",e,t,Hz,-1,-1,1),Wz(\\\\\\\"f2\\\\\\\",e,t,Hz,1,-1,1),Wz(\\\\\\\"f3\\\\\\\",e,t,Hz,-1,1,1),Wz(\\\\\\\"f4\\\\\\\",e,t,Hz,1,1,1),Wz(\\\\\\\"u1\\\\\\\",e,t,Hz,.7,1.1,-1),Wz(\\\\\\\"u2\\\\\\\",e,t,Hz,-.7,1.1,-1),Wz(\\\\\\\"u3\\\\\\\",e,t,Hz,0,2,-1),Wz(\\\\\\\"cf1\\\\\\\",e,t,Hz,-1,0,1),Wz(\\\\\\\"cf2\\\\\\\",e,t,Hz,1,0,1),Wz(\\\\\\\"cf3\\\\\\\",e,t,Hz,0,-1,1),Wz(\\\\\\\"cf4\\\\\\\",e,t,Hz,0,1,1),Wz(\\\\\\\"cn1\\\\\\\",e,t,Hz,-1,0,-1),Wz(\\\\\\\"cn2\\\\\\\",e,t,Hz,1,0,-1),Wz(\\\\\\\"cn3\\\\\\\",e,t,Hz,0,-1,-1),Wz(\\\\\\\"cn4\\\\\\\",e,t,Hz,0,1,-1),t.getAttribute(\\\\\\\"position\\\\\\\").needsUpdate=!0}}function Wz(t,e,n,i,r,s,o){Vz.set(r,s,o).unproject(i);const a=e[t];if(void 0!==a){const t=n.getAttribute(\\\\\\\"position\\\\\\\");for(let e=0,n=a.length;e<n;e++)t.setXYZ(a[e],Vz.x,Vz.y,Vz.z)}}class qz extends Dz{constructor(){super(...arguments),this._square=new Pz.a,this._line_material=new wr.a({fog:!1})}createObject(){return new k.a}buildHelper(){const t=new S.a;t.setAttribute(\\\\\\\"position\\\\\\\",new C.c([-1,1,0,1,1,0,1,-1,0,-1,-1,0,-1,1,0],3)),this._square.geometry=t,this._square.material=this._line_material,this._square.rotateX(.5*Math.PI),this._square.updateMatrix(),this._square.matrixAutoUpdate=!1,this.object.add(this._square),this._cameraHelper=new jz(this.node.light.shadow.camera),this._cameraHelper.rotateX(.5*-Math.PI),this._cameraHelper.updateMatrix(),this._cameraHelper.matrixAutoUpdate=!1,this.object.add(this._cameraHelper)}update(){this._object.updateMatrix(),this._cameraHelper.update(),this._line_material.color.copy(this.node.light.color)}}var Xz,Yz;!function(t){t.DIRECTIONAL=\\\\\\\"directionalLight\\\\\\\",t.HEMISPHERE=\\\\\\\"hemisphereLight\\\\\\\",t.POINT=\\\\\\\"pointLight\\\\\\\",t.SPOT=\\\\\\\"spotLight\\\\\\\"}(Xz||(Xz={})),function(t){t.DIRECTIONAL=\\\\\\\"DirectionalLight\\\\\\\",t.HEMISPHERE=\\\\\\\"HemisphereLight\\\\\\\",t.POINT=\\\\\\\"PointLight\\\\\\\",t.SPOT=\\\\\\\"SpotLight\\\\\\\"}(Yz||(Yz={}));class $z extends(function(t){return class extends t{constructor(){super(...arguments),this.light=oa.FOLDER(),this.color=oa.COLOR([1,1,1],{conversion:so.SRGB_TO_LINEAR}),this.intensity=oa.FLOAT(1),this.distance=oa.FLOAT(100,{range:[0,100]}),this.showHelper=oa.BOOLEAN(0),this.shadow=oa.FOLDER(),this.castShadow=oa.BOOLEAN(1),this.shadowRes=oa.VECTOR2([1024,1024],{visibleIf:{castShadow:!0}}),this.shadowSize=oa.VECTOR2([2,2],{visibleIf:{castShadow:!0}}),this.shadowBias=oa.FLOAT(.001,{visibleIf:{castShadow:!0}}),this.shadowRadius=oa.FLOAT(0,{visibleIf:{castShadow:1},range:[0,10],rangeLocked:[!0,!1]})}}}(Sz(aa))){}const Jz=new $z;class Zz extends Oz{constructor(){super(...arguments),this.paramsConfig=Jz,this._helperController=new Rz(this,qz,\\\\\\\"DirectionalLightHelper\\\\\\\")}static type(){return Xz.DIRECTIONAL}initializeNode(){this._helperController.initializeNode()}createLight(){const t=new Gz.a;return t.matrixAutoUpdate=!1,t.castShadow=!0,t.shadow.bias=-.001,t.shadow.mapSize.x=1024,t.shadow.mapSize.y=1024,t.shadow.camera.near=.1,this._target_target=t.target,this._target_target.name=\\\\\\\"DirectionalLight Default Target\\\\\\\",this.object.add(this._target_target),t}updateLightParams(){this.light.color=this.pv.color,this.light.intensity=this.pv.intensity,this.light.shadow.camera.far=this.pv.distance}updateShadowParams(){this.light.castShadow=this.pv.castShadow,this.light.shadow.mapSize.copy(this.pv.shadowRes),this.light.shadow.bias=this.pv.shadowBias,this.light.shadow.radius=this.pv.shadowRadius;const t=this.light.shadow.camera,e=this.pv.shadowSize;t.left=.5*-e.x,t.right=.5*e.x,t.top=.5*e.y,t.bottom=.5*-e.y,this.light.shadow.camera.updateProjectionMatrix(),this._helperController.update()}}class Qz extends nv.a{constructor(t,e,n){super(t,n),this.type=\\\\\\\"HemisphereLight\\\\\\\",this.position.copy(Q.a.DefaultUp),this.updateMatrix(),this.groundColor=new D.a(e)}copy(t){return nv.a.prototype.copy.call(this,t),this.groundColor.copy(t.groundColor),this}}Qz.prototype.isHemisphereLight=!0;class Kz extends S.a{constructor(t=[],e=[],n=1,i=0){super(),this.type=\\\\\\\"PolyhedronGeometry\\\\\\\",this.parameters={vertices:t,indices:e,radius:n,detail:i};const r=[],s=[];function o(t,e,n,i){const r=i+1,s=[];for(let i=0;i<=r;i++){s[i]=[];const o=t.clone().lerp(n,i/r),a=e.clone().lerp(n,i/r),l=r-i;for(let t=0;t<=l;t++)s[i][t]=0===t&&i===r?o:o.clone().lerp(a,t/l)}for(let t=0;t<r;t++)for(let e=0;e<2*(r-t)-1;e++){const n=Math.floor(e/2);e%2==0?(a(s[t][n+1]),a(s[t+1][n]),a(s[t][n])):(a(s[t][n+1]),a(s[t+1][n+1]),a(s[t+1][n]))}}function a(t){r.push(t.x,t.y,t.z)}function l(e,n){const i=3*e;n.x=t[i+0],n.y=t[i+1],n.z=t[i+2]}function c(t,e,n,i){i<0&&1===t.x&&(s[e]=t.x-1),0===n.x&&0===n.z&&(s[e]=i/2/Math.PI+.5)}function u(t){return Math.atan2(t.z,-t.x)}!function(t){const n=new p.a,i=new p.a,r=new p.a;for(let s=0;s<e.length;s+=3)l(e[s+0],n),l(e[s+1],i),l(e[s+2],r),o(n,i,r,t)}(i),function(t){const e=new p.a;for(let n=0;n<r.length;n+=3)e.x=r[n+0],e.y=r[n+1],e.z=r[n+2],e.normalize().multiplyScalar(t),r[n+0]=e.x,r[n+1]=e.y,r[n+2]=e.z}(n),function(){const t=new p.a;for(let n=0;n<r.length;n+=3){t.x=r[n+0],t.y=r[n+1],t.z=r[n+2];const i=u(t)/2/Math.PI+.5,o=(e=t,Math.atan2(-e.y,Math.sqrt(e.x*e.x+e.z*e.z))/Math.PI+.5);s.push(i,1-o)}var e;(function(){const t=new p.a,e=new p.a,n=new p.a,i=new p.a,o=new d.a,a=new d.a,l=new d.a;for(let h=0,d=0;h<r.length;h+=9,d+=6){t.set(r[h+0],r[h+1],r[h+2]),e.set(r[h+3],r[h+4],r[h+5]),n.set(r[h+6],r[h+7],r[h+8]),o.set(s[d+0],s[d+1]),a.set(s[d+2],s[d+3]),l.set(s[d+4],s[d+5]),i.copy(t).add(e).add(n).divideScalar(3);const p=u(i);c(o,d+0,t,p),c(a,d+2,e,p),c(l,d+4,n,p)}})(),function(){for(let t=0;t<s.length;t+=6){const e=s[t+0],n=s[t+2],i=s[t+4],r=Math.max(e,n,i),o=Math.min(e,n,i);r>.9&&o<.1&&(e<.2&&(s[t+0]+=1),n<.2&&(s[t+2]+=1),i<.2&&(s[t+4]+=1))}}()}(),this.setAttribute(\\\\\\\"position\\\\\\\",new C.c(r,3)),this.setAttribute(\\\\\\\"normal\\\\\\\",new C.c(r.slice(),3)),this.setAttribute(\\\\\\\"uv\\\\\\\",new C.c(s,2)),0===i?this.computeVertexNormals():this.normalizeNormals()}static fromJSON(t){return new Kz(t.vertices,t.indices,t.radius,t.details)}}class tU extends Kz{constructor(t=1,e=0){super([1,0,0,-1,0,0,0,1,0,0,-1,0,0,0,1,0,0,-1],[0,2,4,0,4,3,0,3,5,0,5,2,1,2,5,1,5,3,1,3,4,1,4,2],t,e),this.type=\\\\\\\"OctahedronGeometry\\\\\\\",this.parameters={radius:t,detail:e}}static fromJSON(t){return new tU(t.radius,t.detail)}}class eU extends Dz{constructor(){super(...arguments),this._geometry=new tU(1),this._quat=new au.a,this._default_position=new p.a(0,1,0),this._color1=new D.a,this._color2=new D.a}createObject(){return new k.a}buildHelper(){this._geometry.rotateZ(.5*Math.PI),this._material.vertexColors=!0;const t=this._geometry.getAttribute(\\\\\\\"position\\\\\\\"),e=new Float32Array(3*t.count);this._geometry.setAttribute(\\\\\\\"color\\\\\\\",new C.a(e,3)),this._object.geometry=this._geometry,this._object.material=this._material,this._object.matrixAutoUpdate=!1}update(){if(!this.node.pv.position)return;this._object.position.copy(this.node.pv.position).multiplyScalar(-1),this._quat.setFromUnitVectors(this._default_position,this.node.pv.position),this._object.setRotationFromQuaternion(this._quat),this._object.scale.setScalar(this.node.pv.helperSize),this._object.updateMatrix();const t=this._geometry.getAttribute(\\\\\\\"color\\\\\\\");this._color1.copy(this.node.light.color),this._color2.copy(this.node.light.groundColor);for(let e=0,n=t.count;e<n;e++){const i=e<n/2?this._color1:this._color2;t.setXYZ(e,i.r,i.g,i.b)}t.needsUpdate=!0}}const nU={skyColor:new D.a(1,1,1),groundColor:new D.a(0,0,0)};const iU=new class extends aa{constructor(){super(...arguments),this.skyColor=oa.COLOR(nU.skyColor,{conversion:so.SRGB_TO_LINEAR}),this.groundColor=oa.COLOR(nU.groundColor,{conversion:so.SRGB_TO_LINEAR}),this.intensity=oa.FLOAT(1),this.position=oa.VECTOR3([0,1,0]),this.showHelper=oa.BOOLEAN(0),this.helperSize=oa.FLOAT(1,{visibleIf:{showHelper:1}})}};class rU extends mz{constructor(){super(...arguments),this.paramsConfig=iU,this._helperController=new Rz(this,eU,\\\\\\\"HemisphereLightHelper\\\\\\\")}static type(){return Xz.HEMISPHERE}createLight(){const t=new Qz;return t.matrixAutoUpdate=!1,t.color.copy(nU.skyColor),t.groundColor.copy(nU.groundColor),t}initializeNode(){this.io.inputs.setCount(0,1),this._helperController.initializeNode()}updateLightParams(){this.light.color=this.pv.skyColor,this.light.groundColor=this.pv.groundColor,this.light.position.copy(this.pv.position),this.light.intensity=this.pv.intensity,this._helperController.update()}}var sU=n(58);class oU extends S.a{constructor(t=1,e=32,n=16,i=0,r=2*Math.PI,s=0,o=Math.PI){super(),this.type=\\\\\\\"SphereGeometry\\\\\\\",this.parameters={radius:t,widthSegments:e,heightSegments:n,phiStart:i,phiLength:r,thetaStart:s,thetaLength:o},e=Math.max(3,Math.floor(e)),n=Math.max(2,Math.floor(n));const a=Math.min(s+o,Math.PI);let l=0;const c=[],u=new p.a,h=new p.a,d=[],_=[],m=[],f=[];for(let d=0;d<=n;d++){const p=[],g=d/n;let v=0;0==d&&0==s?v=.5/e:d==n&&a==Math.PI&&(v=-.5/e);for(let n=0;n<=e;n++){const a=n/e;u.x=-t*Math.cos(i+a*r)*Math.sin(s+g*o),u.y=t*Math.cos(s+g*o),u.z=t*Math.sin(i+a*r)*Math.sin(s+g*o),_.push(u.x,u.y,u.z),h.copy(u).normalize(),m.push(h.x,h.y,h.z),f.push(a+v,1-g),p.push(l++)}c.push(p)}for(let t=0;t<n;t++)for(let i=0;i<e;i++){const e=c[t][i+1],r=c[t][i],o=c[t+1][i],l=c[t+1][i+1];(0!==t||s>0)&&d.push(e,r,l),(t!==n-1||a<Math.PI)&&d.push(r,o,l)}this.setIndex(d),this.setAttribute(\\\\\\\"position\\\\\\\",new C.c(_,3)),this.setAttribute(\\\\\\\"normal\\\\\\\",new C.c(m,3)),this.setAttribute(\\\\\\\"uv\\\\\\\",new C.c(f,2))}static fromJSON(t){return new oU(t.radius,t.widthSegments,t.heightSegments,t.phiStart,t.phiLength,t.thetaStart,t.thetaLength)}}class aU extends Dz{constructor(){super(...arguments),this._matrix_scale=new p.a(1,1,1)}createObject(){return new k.a}buildHelper(){this._object.geometry=new oU(1,4,2),this._object.matrixAutoUpdate=!1,this._object.material=this._material}update(){const t=this.node.pv.helperSize;this._matrix_scale.set(t,t,t),this._object.matrix.identity(),this._object.matrix.scale(this._matrix_scale),this._material.color.copy(this.node.light.color)}}class lU extends(Sz(aa)){constructor(){super(...arguments),this.light=oa.FOLDER(),this.color=oa.COLOR([1,1,1],{conversion:so.SRGB_TO_LINEAR}),this.intensity=oa.FLOAT(1),this.decay=oa.FLOAT(.1),this.distance=oa.FLOAT(100),this.castShadows=oa.BOOLEAN(1),this.shadowRes=oa.VECTOR2([1024,1024],{visibleIf:{castShadows:1}}),this.shadowBias=oa.FLOAT(.001,{visibleIf:{castShadows:1}}),this.shadowNear=oa.FLOAT(1,{visibleIf:{castShadows:1}}),this.shadowFar=oa.FLOAT(100,{visibleIf:{castShadows:1}}),this.showHelper=oa.BOOLEAN(0),this.helperSize=oa.FLOAT(1,{visibleIf:{showHelper:1}})}}const cU=new lU;class uU extends Oz{constructor(){super(...arguments),this.paramsConfig=cU,this._helperController=new Rz(this,aU,\\\\\\\"PointLightHelper\\\\\\\")}static type(){return Xz.POINT}initializeNode(){this._helperController.initializeNode()}createLight(){const t=new sU.a;return t.matrixAutoUpdate=!1,t.castShadow=!0,t.shadow.bias=-.001,t.shadow.mapSize.x=1024,t.shadow.mapSize.y=1024,t.shadow.camera.near=.1,t}updateLightParams(){this.light.color=this.pv.color,this.light.intensity=this.pv.intensity,this.light.decay=this.pv.decay,this.light.distance=this.pv.distance,this._helperController.update()}updateShadowParams(){this.light.castShadow=this.pv.castShadows,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}}var hU=n(73);class dU extends Dz{constructor(){super(...arguments),this._cone=new Tr.a,this._line_material=new wr.a({fog:!1})}createObject(){return new k.a}static buildConeGeometry(){const t=new S.a,e=[0,0,0,0,0,1,0,0,0,1,0,1,0,0,0,-1,0,1,0,0,0,0,1,1,0,0,0,0,-1,1];for(let t=0,n=1,i=32;t<i;t++,n++){const r=t/i*Math.PI*2,s=n/i*Math.PI*2;e.push(Math.cos(r),Math.sin(r),1,Math.cos(s),Math.sin(s),1)}return t.setAttribute(\\\\\\\"position\\\\\\\",new C.c(e,3)),t}static updateConeObject(t,e){const n=(e.distance?e.distance:1e3)*e.sizeMult,i=n*Math.tan(e.angle);this._matrix_scale.set(i,i,n),t.matrix.identity(),t.matrix.makeRotationX(.5*Math.PI),t.matrix.scale(this._matrix_scale)}buildHelper(){this._cone.geometry=dU.buildConeGeometry(),this._cone.material=this._line_material,this._cone.matrixAutoUpdate=!1,this.object.add(this._cone)}update(){dU.updateConeObject(this._cone,{sizeMult:this.node.pv.helperSize,distance:this.node.light.distance,angle:this.node.light.angle}),this._line_material.color.copy(this.node.light.color)}}dU._matrix_scale=new p.a;class pU extends S.a{constructor(t=1,e=1,n=1,i=8,r=1,s=!1,o=0,a=2*Math.PI){super(),this.type=\\\\\\\"CylinderGeometry\\\\\\\",this.parameters={radiusTop:t,radiusBottom:e,height:n,radialSegments:i,heightSegments:r,openEnded:s,thetaStart:o,thetaLength:a};const l=this;i=Math.floor(i),r=Math.floor(r);const c=[],u=[],h=[],_=[];let m=0;const f=[],g=n/2;let v=0;function y(n){const r=m,s=new d.a,f=new p.a;let y=0;const x=!0===n?t:e,b=!0===n?1:-1;for(let t=1;t<=i;t++)u.push(0,g*b,0),h.push(0,b,0),_.push(.5,.5),m++;const w=m;for(let t=0;t<=i;t++){const e=t/i*a+o,n=Math.cos(e),r=Math.sin(e);f.x=x*r,f.y=g*b,f.z=x*n,u.push(f.x,f.y,f.z),h.push(0,b,0),s.x=.5*n+.5,s.y=.5*r*b+.5,_.push(s.x,s.y),m++}for(let t=0;t<i;t++){const e=r+t,i=w+t;!0===n?c.push(i,i+1,e):c.push(i+1,i,e),y+=3}l.addGroup(v,y,!0===n?1:2),v+=y}!function(){const s=new p.a,d=new p.a;let y=0;const x=(e-t)/n;for(let l=0;l<=r;l++){const c=[],p=l/r,v=p*(e-t)+t;for(let t=0;t<=i;t++){const e=t/i,r=e*a+o,l=Math.sin(r),f=Math.cos(r);d.x=v*l,d.y=-p*n+g,d.z=v*f,u.push(d.x,d.y,d.z),s.set(l,x,f).normalize(),h.push(s.x,s.y,s.z),_.push(e,1-p),c.push(m++)}f.push(c)}for(let t=0;t<i;t++)for(let e=0;e<r;e++){const n=f[e][t],i=f[e+1][t],r=f[e+1][t+1],s=f[e][t+1];c.push(n,i,s),c.push(i,r,s),y+=6}l.addGroup(v,y,0),v+=y}(),!1===s&&(t>0&&y(!0),e>0&&y(!1)),this.setIndex(c),this.setAttribute(\\\\\\\"position\\\\\\\",new C.c(u,3)),this.setAttribute(\\\\\\\"normal\\\\\\\",new C.c(h,3)),this.setAttribute(\\\\\\\"uv\\\\\\\",new C.c(_,2))}static fromJSON(t){return new pU(t.radiusTop,t.radiusBottom,t.height,t.radialSegments,t.heightSegments,t.openEnded,t.thetaStart,t.thetaLength)}}class _U extends pU{constructor(t=1,e=1,n=8,i=1,r=!1,s=0,o=2*Math.PI){super(0,t,e,n,i,r,s,o),this.type=\\\\\\\"ConeGeometry\\\\\\\",this.parameters={radius:t,height:e,radialSegments:n,heightSegments:i,openEnded:r,thetaStart:s,thetaLength:o}}static fromJSON(t){return new _U(t.radius,t.height,t.radialSegments,t.heightSegments,t.openEnded,t.thetaStart,t.thetaLength)}}class mU{constructor(t){this.node=t}update(){const t=this.node.pv;if(t.tvolumetric){const e=this.object(),n=this.node.light;dU.updateConeObject(e,{sizeMult:t.helperSize,distance:n.distance,angle:n.angle});const i=e.material.uniforms;i.lightColor.value.copy(n.color),i.attenuation.value=t.volAttenuation,i.anglePower.value=t.volAnglePower,this.node.light.add(e)}else this._mesh&&this.node.light.remove(this._mesh)}object(){return this._mesh=this._mesh||this._createMesh()}_createMesh(){const t=new _U(1,1,256,1);t.applyMatrix4((new A.a).makeTranslation(0,-.5,0)),t.applyMatrix4((new A.a).makeRotationX(-Math.PI/2));const e=this._createMaterial(),n=new k.a(t,e);return n.matrixAutoUpdate=!1,n.name=\\\\\\\"Volumetric\\\\\\\",e.uniforms.lightColor.value.set(\\\\\\\"white\\\\\\\"),n}_createMaterial(){return new F({uniforms:{attenuation:{value:5},anglePower:{value:1.2},lightColor:{value:new D.a(\\\\\\\"cyan\\\\\\\")}},vertexShader:\\\\\\\"varying vec3 vNormal;\\\\nvarying vec3 vWorldPosition;\\\\nvarying vec3 vWorldOrigin;\\\\n\\\\nvoid main(){\\\\n\\\\t// compute intensity\\\\n\\\\tvNormal\\\\t\\\\t= normalize( normalMatrix * normal );\\\\n\\\\n\\\\tvec4 worldPosition\\\\t= modelMatrix * vec4( position, 1.0 );\\\\n\\\\tvWorldPosition\\\\t\\\\t= worldPosition.xyz;\\\\n\\\\n\\\\tvec4 worldOrigin\\\\t= modelMatrix * vec4( 0.0, 0.0, 0.0, 1.0 );\\\\n\\\\tvWorldOrigin\\\\t\\\\t= worldOrigin.xyz;\\\\n\\\\n\\\\t// set gl_Position\\\\n\\\\tgl_Position\\\\t= projectionMatrix * modelViewMatrix * vec4( position, 1.0 );\\\\n}\\\\\\\",fragmentShader:\\\\\\\"varying vec3 vNormal;\\\\nvarying vec3 vWorldPosition;\\\\nvarying vec3 vWorldOrigin;\\\\n\\\\nuniform vec3 lightColor;\\\\n\\\\n// uniform vec3 spotPosition;\\\\n\\\\nuniform float attenuation;\\\\nuniform float anglePower;\\\\n\\\\nvoid main(){\\\\n\\\\n\\\\t//////////////////////////////////////////////////////////\\\\n\\\\t// distance attenuation   //\\\\n\\\\t//////////////////////////////////////////////////////////\\\\n\\\\tfloat intensity = distance(vWorldPosition, vWorldOrigin) / attenuation;\\\\n\\\\tintensity = 1.0 - clamp(intensity, 0.0, 1.0);\\\\n\\\\n\\\\t//////////////////////////////////////////////////////////\\\\n\\\\t// intensity on angle   //\\\\n\\\\t//////////////////////////////////////////////////////////\\\\n\\\\tvec3 normal = vec3(vNormal.x, vNormal.y, abs(vNormal.z));\\\\n\\\\tfloat angleIntensity = pow( dot(normal, vec3(0.0, 0.0, 1.0)), anglePower );\\\\n\\\\tintensity = intensity * angleIntensity;\\\\n\\\\t// 'gl_FragColor = vec4( lightColor, intensity );\\\\n\\\\n\\\\t//////////////////////////////////////////////////////////\\\\n\\\\t// final color   //\\\\n\\\\t//////////////////////////////////////////////////////////\\\\n\\\\n\\\\t// set the final color\\\\n\\\\tgl_FragColor = vec4( lightColor, intensity);\\\\n}\\\\\\\",transparent:!0,depthWrite:!1})}}class fU extends(Sz(aa)){constructor(){super(...arguments),this.light=oa.FOLDER(),this.color=oa.COLOR([1,1,1],{conversion:so.SRGB_TO_LINEAR}),this.intensity=oa.FLOAT(1),this.angle=oa.FLOAT(45,{range:[0,180]}),this.penumbra=oa.FLOAT(.1),this.decay=oa.FLOAT(.1,{range:[0,1]}),this.distance=oa.FLOAT(100,{range:[0,100]}),this.showHelper=oa.BOOLEAN(0),this.helperSize=oa.FLOAT(1,{visibleIf:{showHelper:1}}),this.shadow=oa.FOLDER(),this.castShadow=oa.BOOLEAN(1),this.shadowAutoUpdate=oa.BOOLEAN(1,{visibleIf:{castShadow:1}}),this.shadowUpdateOnNextRender=oa.BOOLEAN(0,{visibleIf:{castShadow:1,shadowAutoUpdate:0}}),this.shadowRes=oa.VECTOR2([256,256],{visibleIf:{castShadow:1}}),this.shadowBias=oa.FLOAT(.001,{visibleIf:{castShadow:1},range:[-.01,.01],rangeLocked:[!1,!1]}),this.shadowNear=oa.FLOAT(.1,{visibleIf:{castShadow:1},range:[0,100],rangeLocked:[!0,!1]}),this.shadowFar=oa.FLOAT(100,{visibleIf:{castShadow:1},range:[0,100],rangeLocked:[!0,!1]}),this.shadowRadius=oa.FLOAT(0,{visibleIf:{castShadow:1},range:[0,10],rangeLocked:[!0,!1]}),this.volumetric=oa.FOLDER(),this.tvolumetric=oa.BOOLEAN(0),this.volAttenuation=oa.FLOAT(5,{range:[0,10],rangeLocked:[!0,!1]}),this.volAnglePower=oa.FLOAT(10,{range:[0,20],rangeLocked:[!0,!1]})}}const gU=new fU;class vU extends Oz{constructor(){super(...arguments),this.paramsConfig=gU,this._helperController=new Rz(this,dU,\\\\\\\"SpotLightHelper\\\\\\\"),this._volumetricController=new mU(this)}static type(){return Xz.SPOT}initializeNode(){this._helperController.initializeNode()}createLight(){const t=new hU.a;return t.matrixAutoUpdate=!1,t.castShadow=!0,t.shadow.bias=-.001,t.shadow.mapSize.x=256,t.shadow.mapSize.y=256,t.shadow.camera.near=.1,this._target_target=t.target,this._target_target.name=\\\\\\\"SpotLight Default Target\\\\\\\",this._target_target.matrixAutoUpdate=!1,this.object.add(this._target_target),t}updateLightParams(){this.light.color=this.pv.color,this.light.intensity=this.pv.intensity,this.light.angle=this.pv.angle*(Math.PI/180),this.light.penumbra=this.pv.penumbra,this.light.decay=this.pv.decay,this.light.distance=this.pv.distance,this._helperController.update(),this._volumetricController.update()}updateShadowParams(){this.light.castShadow=this.pv.castShadow,this.light.shadow.autoUpdate=this.pv.shadowAutoUpdate,this.light.shadow.needsUpdate=this.pv.shadowUpdateOnNextRender,this.light.shadow.mapSize.copy(this.pv.shadowRes),this.light.shadow.camera.near=this.pv.shadowNear,this.light.shadow.camera.far=this.pv.shadowFar,this.light.shadow.bias=this.pv.shadowBias,this.light.shadow.radius=this.pv.shadowRadius}}let yU;const xU=function(){return void 0===yU&&(yU=new(window.AudioContext||window.webkitAudioContext)),yU},bU=new p.a,wU=new au.a,TU=new p.a,AU=new p.a;class EU extends Q.a{constructor(){super(),this.type=\\\\\\\"AudioListener\\\\\\\",this.context=xU(),this.gain=this.context.createGain(),this.gain.connect(this.context.destination),this.filter=null,this.timeDelta=0,this._clock=new Om}getInput(){return this.gain}removeFilter(){return null!==this.filter&&(this.gain.disconnect(this.filter),this.filter.disconnect(this.context.destination),this.gain.connect(this.context.destination),this.filter=null),this}getFilter(){return this.filter}setFilter(t){return null!==this.filter?(this.gain.disconnect(this.filter),this.filter.disconnect(this.context.destination)):this.gain.disconnect(this.context.destination),this.filter=t,this.gain.connect(this.filter),this.filter.connect(this.context.destination),this}getMasterVolume(){return this.gain.gain.value}setMasterVolume(t){return this.gain.gain.setTargetAtTime(t,this.context.currentTime,.01),this}updateMatrixWorld(t){super.updateMatrixWorld(t);const e=this.context.listener,n=this.up;if(this.timeDelta=this._clock.getDelta(),this.matrixWorld.decompose(bU,wU,TU),AU.set(0,0,-1).applyQuaternion(wU),e.positionX){const t=this.context.currentTime+this.timeDelta;e.positionX.linearRampToValueAtTime(bU.x,t),e.positionY.linearRampToValueAtTime(bU.y,t),e.positionZ.linearRampToValueAtTime(bU.z,t),e.forwardX.linearRampToValueAtTime(AU.x,t),e.forwardY.linearRampToValueAtTime(AU.y,t),e.forwardZ.linearRampToValueAtTime(AU.z,t),e.upX.linearRampToValueAtTime(n.x,t),e.upY.linearRampToValueAtTime(n.y,t),e.upZ.linearRampToValueAtTime(n.z,t)}else e.setPosition(bU.x,bU.y,bU.z),e.setOrientation(AU.x,AU.y,AU.z,n.x,n.y,n.z)}}class MU extends(Sz(aa)){}const SU=new MU;class CU extends _z{constructor(){super(...arguments),this.paramsConfig=SU,this.hierarchyController=new Lz(this),this.transformController=new Nz(this),this.flags=new Fi(this)}static type(){return Ng.AUDIO_LISTENER}createObject(){const t=new EU;return t.matrixAutoUpdate=!1,t}initializeNode(){this.hierarchyController.initializeNode(),this.transformController.initializeNode()}cook(){this.transformController.update(),this.cookController.endCook()}}class NU extends Tr.a{constructor(t=1){const e=[0,0,0,t,0,0,0,0,0,0,t,0,0,0,0,0,0,t],n=new S.a;n.setAttribute(\\\\\\\"position\\\\\\\",new C.c(e,3)),n.setAttribute(\\\\\\\"color\\\\\\\",new C.c([1,0,0,1,.6,0,0,1,0,.6,1,0,0,0,1,0,.6,1],3));super(n,new wr.a({vertexColors:!0,toneMapped:!1})),this.type=\\\\\\\"AxesHelper\\\\\\\"}setColors(t,e,n){const i=new D.a,r=this.geometry.attributes.color.array;return i.set(t),i.toArray(r,0),i.toArray(r,3),i.set(e),i.toArray(r,6),i.toArray(r,9),i.set(n),i.toArray(r,12),i.toArray(r,15),this.geometry.attributes.color.needsUpdate=!0,this}dispose(){this.geometry.dispose(),this.material.dispose()}}var LU;!function(t){t.TOGETHER=\\\\\\\"translate + rotate together\\\\\\\",t.SEPARATELY=\\\\\\\"translate + rotate separately\\\\\\\"}(LU||(LU={}));const OU=[LU.TOGETHER,LU.SEPARATELY];const RU=new class extends aa{constructor(){super(...arguments),this.object0=oa.OPERATOR_PATH(\\\\\\\"/geo1\\\\\\\",{nodeSelection:{context:Ki.OBJ}}),this.object1=oa.OPERATOR_PATH(\\\\\\\"/geo2\\\\\\\",{nodeSelection:{context:Ki.OBJ}}),this.mode=oa.INTEGER(OU.indexOf(LU.TOGETHER),{menu:{entries:OU.map(((t,e)=>({name:t,value:e})))}}),this.blend=oa.FLOAT(0,{visibleIf:{mode:OU.indexOf(LU.TOGETHER)},range:[0,1],rangeLocked:[!1,!1]}),this.blendT=oa.FLOAT(0,{visibleIf:{mode:OU.indexOf(LU.SEPARATELY)},range:[0,1],rangeLocked:[!1,!1]}),this.blendR=oa.FLOAT(0,{visibleIf:{mode:OU.indexOf(LU.SEPARATELY)},range:[0,1],rangeLocked:[!1,!1]})}};class PU extends _z{constructor(){super(...arguments),this.paramsConfig=RU,this.hierarchyController=new Lz(this),this.flags=new Fi(this),this._helper=new NU(1),this._t0=new p.a,this._q0=new au.a,this._s0=new p.a,this._t1=new p.a,this._q1=new au.a,this._s1=new p.a}static type(){return\\\\\\\"blend\\\\\\\"}createObject(){const t=new In.a;return t.matrixAutoUpdate=!1,t}initializeNode(){this.hierarchyController.initializeNode(),this.io.inputs.setCount(0),this.addPostDirtyHook(\\\\\\\"blend_on_dirty\\\\\\\",(()=>{this.cookController.cookMainWithoutInputs()})),this._updateHelperHierarchy(),this.flags.display.onUpdate((()=>{this._updateHelperHierarchy()}))}_updateHelperHierarchy(){this.flags.display.active()?this.object.add(this._helper):this.object.remove(this._helper)}cook(){const t=this.p.object0.found_node_with_context(Ki.OBJ),e=this.p.object1.found_node_with_context(Ki.OBJ);t&&e&&this._blend(t.object,e.object),this.cookController.endCook()}_blend(t,e){const n=OU[this.pv.mode];switch(n){case LU.TOGETHER:return this._blend_together(t,e);case LU.SEPARATELY:return this._blend_separately(t,e)}ar.unreachable(n)}_blend_together(t,e){this._decompose_matrices(t,e),this._object.position.copy(this._t0).lerp(this._t1,this.pv.blend),this._object.quaternion.copy(this._q0).slerp(this._q1,this.pv.blend),this._object.matrixAutoUpdate||this._object.updateMatrix()}_blend_separately(t,e){this._decompose_matrices(t,e),this._object.position.copy(this._t0).lerp(this._t1,this.pv.blendT),this._object.quaternion.copy(this._q0).slerp(this._q1,this.pv.blendR),this._object.matrixAutoUpdate||this._object.updateMatrix()}_decompose_matrices(t,e){t.matrixWorld.decompose(this._t0,this._q0,this._s0),e.matrixWorld.decompose(this._t1,this._q1,this._s1)}}var IU={uniforms:{tDiffuse:{value:null},h:{value:1/512}},vertexShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\tvarying vec2 vUv;\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\tvUv = uv;\\\\n\\\\t\\\\t\\\\tgl_Position = projectionMatrix * modelViewMatrix * vec4( position, 1.0 );\\\\n\\\\n\\\\t\\\\t}\\\\\\\",fragmentShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\tuniform sampler2D tDiffuse;\\\\n\\\\t\\\\tuniform float h;\\\\n\\\\n\\\\t\\\\tvarying vec2 vUv;\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\tvec4 sum = vec4( 0.0 );\\\\n\\\\n\\\\t\\\\t\\\\tsum += texture2D( tDiffuse, vec2( vUv.x - 4.0 * h, vUv.y ) ) * 0.051;\\\\n\\\\t\\\\t\\\\tsum += texture2D( tDiffuse, vec2( vUv.x - 3.0 * h, vUv.y ) ) * 0.0918;\\\\n\\\\t\\\\t\\\\tsum += texture2D( tDiffuse, vec2( vUv.x - 2.0 * h, vUv.y ) ) * 0.12245;\\\\n\\\\t\\\\t\\\\tsum += texture2D( tDiffuse, vec2( vUv.x - 1.0 * h, vUv.y ) ) * 0.1531;\\\\n\\\\t\\\\t\\\\tsum += texture2D( tDiffuse, vec2( vUv.x, vUv.y ) ) * 0.1633;\\\\n\\\\t\\\\t\\\\tsum += texture2D( tDiffuse, vec2( vUv.x + 1.0 * h, vUv.y ) ) * 0.1531;\\\\n\\\\t\\\\t\\\\tsum += texture2D( tDiffuse, vec2( vUv.x + 2.0 * h, vUv.y ) ) * 0.12245;\\\\n\\\\t\\\\t\\\\tsum += texture2D( tDiffuse, vec2( vUv.x + 3.0 * h, vUv.y ) ) * 0.0918;\\\\n\\\\t\\\\t\\\\tsum += texture2D( tDiffuse, vec2( vUv.x + 4.0 * h, vUv.y ) ) * 0.051;\\\\n\\\\n\\\\t\\\\t\\\\tgl_FragColor = sum;\\\\n\\\\n\\\\t\\\\t}\\\\\\\"};const FU={uniforms:{tDiffuse:{value:null},v:{value:1/512}},vertexShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\tvarying vec2 vUv;\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\tvUv = uv;\\\\n\\\\t\\\\t\\\\tgl_Position = projectionMatrix * modelViewMatrix * vec4( position, 1.0 );\\\\n\\\\n\\\\t\\\\t}\\\\\\\",fragmentShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\tuniform sampler2D tDiffuse;\\\\n\\\\t\\\\tuniform float v;\\\\n\\\\n\\\\t\\\\tvarying vec2 vUv;\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\tvec4 sum = vec4( 0.0 );\\\\n\\\\n\\\\t\\\\t\\\\tsum += texture2D( tDiffuse, vec2( vUv.x, vUv.y - 4.0 * v ) ) * 0.051;\\\\n\\\\t\\\\t\\\\tsum += texture2D( tDiffuse, vec2( vUv.x, vUv.y - 3.0 * v ) ) * 0.0918;\\\\n\\\\t\\\\t\\\\tsum += texture2D( tDiffuse, vec2( vUv.x, vUv.y - 2.0 * v ) ) * 0.12245;\\\\n\\\\t\\\\t\\\\tsum += texture2D( tDiffuse, vec2( vUv.x, vUv.y - 1.0 * v ) ) * 0.1531;\\\\n\\\\t\\\\t\\\\tsum += texture2D( tDiffuse, vec2( vUv.x, vUv.y ) ) * 0.1633;\\\\n\\\\t\\\\t\\\\tsum += texture2D( tDiffuse, vec2( vUv.x, vUv.y + 1.0 * v ) ) * 0.1531;\\\\n\\\\t\\\\t\\\\tsum += texture2D( tDiffuse, vec2( vUv.x, vUv.y + 2.0 * v ) ) * 0.12245;\\\\n\\\\t\\\\t\\\\tsum += texture2D( tDiffuse, vec2( vUv.x, vUv.y + 3.0 * v ) ) * 0.0918;\\\\n\\\\t\\\\t\\\\tsum += texture2D( tDiffuse, vec2( vUv.x, vUv.y + 4.0 * v ) ) * 0.051;\\\\n\\\\n\\\\t\\\\t\\\\tgl_FragColor = sum;\\\\n\\\\n\\\\t\\\\t}\\\\\\\"},DU=1/256e3;class kU{constructor(t){this._renderTargetBlur=this._createRenderTarget(t),this._camera=this._createCamera(),this._blurPlane=this._createBlurPlane(),this._horizontalBlurMaterial=new F(IU),this._horizontalBlurMaterial.depthTest=!1,this._verticalBlurMaterial=new F(FU),this._verticalBlurMaterial.depthTest=!1}setSize(t,e){this._renderTargetBlur.setSize(t,e)}_createRenderTarget(t){const e=new Z(t.x,t.y);return e.texture.generateMipmaps=!1,e}_createCamera(){const t=new st.a(-.5,.5,.5,-.5,0,1);return t.position.z=.5,t}_createBlurPlane(){const t=new L(1,1);return new k.a(t)}applyBlur(t,e,n,i){const r=Math.max(this._renderTargetBlur.width,this._renderTargetBlur.height);this._horizontalBlurMaterial.uniforms.tDiffuse.value=t.texture,this._horizontalBlurMaterial.uniforms.h.value=n*r*DU,this._blurPlane.material=this._horizontalBlurMaterial,e.setRenderTarget(this._renderTargetBlur),e.render(this._blurPlane,this._camera),this._verticalBlurMaterial.uniforms.tDiffuse.value=this._renderTargetBlur.texture,this._verticalBlurMaterial.uniforms.v.value=i*r*DU,this._blurPlane.material=this._verticalBlurMaterial,e.setRenderTarget(t),e.render(this._blurPlane,this._camera)}}var BU;!function(t){t.ON_RENDER=\\\\\\\"On Every Render\\\\\\\",t.MANUAL=\\\\\\\"Manual\\\\\\\"}(BU||(BU={}));const zU=[BU.ON_RENDER,BU.MANUAL];class UU extends(Sz(aa)){constructor(){super(...arguments),this.shadow=oa.FOLDER(),this.dist=oa.FLOAT(1,{range:[0,10],rangeLocked:[!0,!1]}),this.planeSize=oa.VECTOR2([1,1]),this.shadowRes=oa.VECTOR2([256,256]),this.blur=oa.FLOAT(1,{range:[0,10],rangeLocked:[!0,!1]}),this.tblur2=oa.BOOLEAN(1),this.blur2=oa.FLOAT(1,{range:[0,10],rangeLocked:[!0,!1],visibleIf:{tblur2:1}}),this.darkness=oa.FLOAT(1),this.opacity=oa.FLOAT(1),this.showHelper=oa.BOOLEAN(0),this.updateMode=oa.INTEGER(zU.indexOf(BU.ON_RENDER),{callback:t=>{HU.PARAM_CALLBACK_update_updateMode(t)},menu:{entries:zU.map(((t,e)=>({name:t,value:e})))}}),this.update=oa.BUTTON(null,{callback:t=>{HU.PARAM_CALLBACK_updateManual(t)},visibleIf:{updateMode:zU.indexOf(BU.MANUAL)}}),this.scene=oa.FOLDER(),this.include=oa.STRING(\\\\\\\"\\\\\\\"),this.exclude=oa.STRING(\\\\\\\"\\\\\\\"),this.updateObjectsList=oa.BUTTON(null,{callback:t=>{HU.PARAM_CALLBACK_updateObjectsList(t)}}),this.printResolveObjectsList=oa.BUTTON(null,{callback:t=>{HU.PARAM_CALLBACK_printResolveObjectsList(t)}})}}const GU=new UU,VU=new d.a(256,256);class HU extends _z{constructor(){super(...arguments),this.paramsConfig=GU,this.hierarchyController=new Lz(this),this.flags=new Fi(this),this._renderTarget=this._createRenderTarget(VU),this._coreRenderBlur=this._createCoreRenderBlur(VU),this._includedObjects=[],this._includedAncestors=[],this._excludedObjects=[],this.transformController=new Nz(this),this._darknessUniform={value:1},this._emptyOnBeforeRender=()=>{},this._emptyRenderHook=()=>{},this._on_object_before_render_bound=this._update.bind(this),this._initialVisibilityState=new WeakMap}static type(){return\\\\\\\"contactShadow\\\\\\\"}_createRenderTarget(t){const e=new Z(t.x,t.y);return e.texture.generateMipmaps=!1,e}_createCoreRenderBlur(t){return new kU(t)}createObject(){const t=new In.a;this._shadowGroup=new In.a,t.add(this._shadowGroup),this._shadowGroup.name=\\\\\\\"shadowGroup\\\\\\\";const e=new L(1,1).rotateX(-Math.PI/2),n=e.getAttribute(\\\\\\\"uv\\\\\\\").array;for(let t of[1,3,5,7])n[t]=1-n[t];return this._planeMaterial=new at.a({map:this._renderTarget.texture,opacity:1,transparent:!0,depthWrite:!1}),this._plane=new k.a(e,this._planeMaterial),this._plane.renderOrder=1,this._plane.matrixAutoUpdate=!1,this._shadowGroup.add(this._plane),this._createDepthCamera(this._shadowGroup),this._createMaterials(),t}initializeNode(){this.hierarchyController.initializeNode(),this.transformController.initializeNode(),this._updateShadowGroupVisibility(),this._updateHelperVisibility(),this.flags.display.onUpdate((()=>{this._updateShadowGroupVisibility(),this._updateHelperVisibility()}))}async cook(){this.transformController.update(),this._updateRenderHook(),this._updateHelperVisibility(),this._updateObjectsList(),this._planeMaterial&&(this._planeMaterial.opacity=this.pv.opacity),this._darknessUniform.value=this.pv.darkness,this._plane&&this._shadowCamera&&this._helper&&(this._plane.scale.x=this.pv.planeSize.x,this._plane.scale.z=this.pv.planeSize.y,this._plane.updateMatrix(),this._shadowCamera.left=-this.pv.planeSize.x/2,this._shadowCamera.right=this.pv.planeSize.x/2,this._shadowCamera.bottom=-this.pv.planeSize.y/2,this._shadowCamera.top=this.pv.planeSize.y/2,this._shadowCamera.far=this.pv.dist,this._shadowCamera.updateProjectionMatrix(),this._helper.update()),this._renderTarget.width==this.pv.shadowRes.x&&this._renderTarget.height==this.pv.shadowRes.y||this._planeMaterial&&(this._renderTarget=this._createRenderTarget(this.pv.shadowRes),this._coreRenderBlur=this._createCoreRenderBlur(this.pv.shadowRes),this._planeMaterial.map=this._renderTarget.texture),this.cookController.endCook()}_createDepthCamera(t){this._shadowCamera=new st.a(-.5,.5,.5,-.5,0,1),this._shadowCamera.rotation.x=Math.PI/2,t.add(this._shadowCamera),this._helper=new jz(this._shadowCamera),this._helper.visible=!1,this._shadowCamera.add(this._helper)}_createMaterials(){this._depthMaterial=new Mn,this._depthMaterial.onBeforeCompile=t=>{t.uniforms.darkness=this._darknessUniform,t.fragmentShader=`\\\\n\\\\t\\\\t\\\\tuniform float darkness;\\\\n\\\\t\\\\t\\\\t${t.fragmentShader.replace(\\\\\\\"gl_FragColor = vec4( vec3( 1.0 - fragCoordZ ), opacity );\\\\\\\",\\\\\\\"gl_FragColor = vec4( vec3( 0.0 ), ( 1.0 - fragCoordZ ) * darkness );\\\\\\\")}\\\\n\\\\t\\\\t`},this._depthMaterial.depthTest=!1,this._depthMaterial.depthWrite=!1}_renderShadow(t,e){if(!this._helper)return;if(!this._depthMaterial)return;if(!this._shadowCamera)return;if(!this._helper)return;if(!this._plane)return;const n=this._plane.onBeforeRender,i=e.background,r=this._helper.visible;e.background=null,this._plane.onBeforeRender=this._emptyOnBeforeRender,this._helper.visible=!1,e.overrideMaterial=this._depthMaterial,this._initVisibility(e),t.setRenderTarget(this._renderTarget),t.render(e,this._shadowCamera),this._coreRenderBlur.applyBlur(this._renderTarget,t,this.pv.blur,this.pv.blur),this.pv.tblur2&&this._coreRenderBlur.applyBlur(this._renderTarget,t,this.pv.blur2,this.pv.blur2),this._restoreVisibility(e),e.overrideMaterial=null,this._helper.visible=r,t.setRenderTarget(null),e.background=i,this._plane.onBeforeRender=n}_updateShadowGroupVisibility(){this._shadowGroup&&(this.flags.display.active()?this._shadowGroup.visible=!0:this._shadowGroup.visible=!1)}_updateHelperVisibility(){this._helper&&(this.flags.display.active()&&this.pv.showHelper?this._helper.visible=!0:this._helper.visible=!1)}_updateRenderHook(){const t=zU[this.pv.updateMode];switch(t){case BU.ON_RENDER:return this._addRenderHook();case BU.MANUAL:return this._removeRenderHook()}ar.unreachable(t)}_addRenderHook(){this._plane&&this._plane.onBeforeRender!=this._on_object_before_render_bound&&(this._plane.onBeforeRender=this._on_object_before_render_bound)}_removeRenderHook(){this._plane&&this._plane.onBeforeRender!=this._emptyRenderHook&&(this._plane.onBeforeRender=this._emptyRenderHook)}_update(t,e,n,i,r,s){t&&e?this._renderShadow(t,e):console.log(\\\\\\\"no renderer or scene\\\\\\\")}_updateManual(){const t=ai.renderersController.firstRenderer();if(!t)return void console.log(\\\\\\\"no renderer found\\\\\\\");const e=this.scene().threejsScene();this._renderShadow(t,e)}static PARAM_CALLBACK_update_updateMode(t){t._updateRenderHook()}static PARAM_CALLBACK_updateManual(t){t._updateManual()}static PARAM_CALLBACK_updateObjectsList(t){t._updateObjectsList()}_updateObjectsList(){\\\\\\\"\\\\\\\"!=this.pv.include?this._includedObjects=this.scene().objectsByMask(this.pv.include):this._includedObjects=[];const t=new Map;for(let e of this._includedObjects)e.traverseAncestors((e=>{t.set(e.uuid,e)}));this._includedAncestors=[],t.forEach(((t,e)=>{this._includedAncestors.push(t)})),\\\\\\\"\\\\\\\"!=this.pv.exclude?this._excludedObjects=this.scene().objectsByMask(this.pv.exclude):this._excludedObjects=[]}static PARAM_CALLBACK_printResolveObjectsList(t){t._printResolveObjectsList()}_printResolveObjectsList(){console.log(\\\\\\\"included objects:\\\\\\\"),console.log(this._includedObjects),console.log(\\\\\\\"included parents:\\\\\\\"),console.log(this._includedAncestors),console.log(\\\\\\\"excluded objects:\\\\\\\"),console.log(this._excludedObjects)}_initVisibility(t){this._includedObjects.length>0?t.traverse((t=>{this._initialVisibilityState.set(t,t.visible),t.visible=!1})):(this._storeObjectsVisibility(this._includedObjects),this._storeObjectsVisibility(this._includedAncestors),this._storeObjectsVisibility(this._excludedObjects)),this._setObjectsVisibility(this._includedObjects,!0),this._setObjectsVisibility(this._includedAncestors,!0),this._setObjectsVisibility(this._excludedObjects,!1)}_storeObjectsVisibility(t){for(let e of t)this._initialVisibilityState.set(e,e.visible)}_setObjectsVisibility(t,e){for(let n of t)n.visible=e}_restoreVisibility(t){this._includedObjects.length>0?t.traverse((t=>{const e=this._initialVisibilityState.get(t);e&&(t.visible=e)})):(this._restoreObjectsVisibility(this._includedObjects),this._restoreObjectsVisibility(this._includedAncestors),this._restoreObjectsVisibility(this._excludedObjects))}_restoreObjectsVisibility(t){for(let e of t){const t=this._initialVisibilityState.get(e);t&&(e.visible=t)}}}const jU=\\\\\\\"display\\\\\\\";class WU{constructor(t){this.node=t,this._children_uuids_dict=new Map,this._children_length=0,this._sop_group=this._create_sop_group()}_create_sop_group(){const t=new In.a;return t.matrixAutoUpdate=!1,t}sopGroup(){return this._sop_group}set_sop_group_name(){this._sop_group.name=`${this.node.name()}:sop_group`}displayNodeControllerCallbacks(){return{onDisplayNodeRemove:()=>{this.remove_children()},onDisplayNodeSet:()=>{setTimeout((()=>{this.request_display_node_container()}),0)},onDisplayNodeUpdate:()=>{this.request_display_node_container()}}}initializeNode(){var t;this.node.object.add(this.sopGroup()),this.node.nameController.add_post_set_fullPath_hook(this.set_sop_group_name.bind(this)),this._create_sop_group();const e=null===(t=this.node.flags)||void 0===t?void 0:t.display;e&&e.onUpdate((()=>{this._updateSopGroupHierarchy(),e.active()&&this.request_display_node_container()}))}_updateSopGroupHierarchy(){var t;if(null===(t=this.node.flags)||void 0===t?void 0:t.display){const t=this.sopGroup();this.usedInScene()?(t.visible=!0,this.node.object.add(t),t.updateMatrix()):(t.visible=!1,this.node.object.remove(t))}}usedInScene(){var t,e;const n=this.node.params.has(jU),i=this.node.params.boolean(jU),r=this.node.usedInScene(),s=(null===(e=null===(t=this.node.flags)||void 0===t?void 0:t.display)||void 0===e?void 0:e.active())||!1;return r&&s&&(!n||i)}async request_display_node_container(){this.node.scene().loadingController.loaded()&&this.usedInScene()&&await this._set_content_under_sop_group()}remove_children(){if(0==this._sop_group.children.length)return;let t;for(;t=this._sop_group.children[0];)this._sop_group.remove(t);this._children_uuids_dict.clear(),this._children_length=0}async _set_content_under_sop_group(){var t;const e=this.node.displayNodeController.displayNode();if(e&&(null===(t=e.parent())||void 0===t?void 0:t.graphNodeId())==this.node.graphNodeId()){const t=(await e.compute()).coreContent();if(t){const e=t.objects();let n=e.length!=this._children_length;if(!n)for(let t of e)this._children_uuids_dict.get(t.uuid)||(n=!0);if(n){this.remove_children();for(let t of e)this._sop_group.add(t),t.updateMatrix(),this._children_uuids_dict.set(t.uuid,!0);this._children_length=e.length}return}}this.remove_children()}}class qU extends(Sz(aa)){constructor(){super(...arguments),this.display=oa.BOOLEAN(1),this.renderOrder=oa.INTEGER(0,{range:[0,10],rangeLocked:[!0,!1]})}}const XU=new qU;class YU extends _z{constructor(){super(...arguments),this.paramsConfig=XU,this.hierarchyController=new Lz(this),this.transformController=new Nz(this),this.flags=new Fi(this),this.childrenDisplayController=new WU(this),this.displayNodeController=new Lm(this,this.childrenDisplayController.displayNodeControllerCallbacks()),this._children_controller_context=Ki.SOP,this._onChildAddBound=this._onChildAdd.bind(this)}static type(){return Ng.GEO}createObject(){const t=new In.a;return t.matrixAutoUpdate=!1,t}initializeNode(){this.lifecycle.add_on_child_add_hook(this._onChildAddBound),this.hierarchyController.initializeNode(),this.transformController.initializeNode(),this.childrenDisplayController.initializeNode()}isDisplayNodeCooking(){if(this.flags.display.active()){const t=this.displayNodeController.displayNode();return!!t&&t.isDirty()}return!1}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}_onChildAdd(t){var e,n;this.scene().loadingController.loaded()&&1==this.children().length&&(null===(n=null===(e=t.flags)||void 0===e?void 0:e.display)||void 0===n||n.set(!0))}cook(){this.transformController.update(),this.object.visible=this.pv.display,this.object.renderOrder=this.pv.renderOrder,this.cookController.endCook()}}class $U extends(Sz(aa)){}const JU=new $U;class ZU extends _z{constructor(){super(...arguments),this.paramsConfig=JU,this.hierarchyController=new Lz(this),this.transformController=new Nz(this),this.flags=new Fi(this),this._helper=new NU(1)}static type(){return\\\\\\\"null\\\\\\\"}createObject(){const t=new In.a;return t.matrixAutoUpdate=!1,t}initializeNode(){this.hierarchyController.initializeNode(),this.transformController.initializeNode(),this._updateHelperHierarchy(),this._helper.matrixAutoUpdate=!1,this.flags.display.onUpdate((()=>{this._updateHelperHierarchy()}))}_updateHelperHierarchy(){this.flags.display.active()?(this.object.add(this._helper),this._helper.updateMatrix()):this.object.remove(this._helper)}cook(){this.transformController.update(),this.cookController.endCook()}}const QU=new class extends aa{constructor(){super(...arguments),this.center=oa.VECTOR3([0,0,0]),this.longitude=oa.FLOAT(0,{range:[0,360]}),this.latitude=oa.FLOAT(0,{range:[-180,180]}),this.depth=oa.FLOAT(1,{range:[0,10]})}},KU=\\\\\\\"_cook_main_without_inputs_when_dirty\\\\\\\",tG=new p.a(0,1,0),eG=new p.a(-1,0,0);class nG extends _z{constructor(){super(...arguments),this.paramsConfig=QU,this.hierarchyController=new Lz(this),this.flags=new Fi(this),this._helper=new NU(1),this._cook_main_without_inputs_when_dirty_bound=this._cook_main_without_inputs_when_dirty.bind(this),this._centerMatrix=new A.a,this._longitudeMatrix=new A.a,this._latitudeMatrix=new A.a,this._depthMatrix=new A.a,this._fullMatrix=new A.a,this._decomposed={t:new p.a,q:new au.a,s:new p.a}}static type(){return\\\\\\\"polarTransform\\\\\\\"}createObject(){const t=new In.a;return t.matrixAutoUpdate=!1,t}initializeNode(){this.hierarchyController.initializeNode(),this.dirtyController.hasHook(KU)||this.dirtyController.addPostDirtyHook(KU,this._cook_main_without_inputs_when_dirty_bound),this._updateHelperHierarchy(),this._helper.matrixAutoUpdate=!1,this.flags.display.onUpdate((()=>{this._updateHelperHierarchy()}))}_updateHelperHierarchy(){this.flags.display.active()?(this.object.add(this._helper),this._helper.updateMatrix()):this.object.remove(this._helper)}async _cook_main_without_inputs_when_dirty(){await this.cookController.cookMainWithoutInputs()}cook(){const t=this.object;this._centerMatrix.identity(),this._longitudeMatrix.identity(),this._latitudeMatrix.identity(),this._depthMatrix.identity(),this._centerMatrix.makeTranslation(this.pv.center.x,this.pv.center.y,this.pv.center.z),this._longitudeMatrix.makeRotationAxis(tG,Object(Ln.e)(this.pv.longitude)),this._latitudeMatrix.makeRotationAxis(eG,Object(Ln.e)(this.pv.latitude)),this._depthMatrix.makeTranslation(0,0,this.pv.depth),this._fullMatrix.copy(this._centerMatrix).multiply(this._longitudeMatrix).multiply(this._latitudeMatrix).multiply(this._depthMatrix),this._fullMatrix.decompose(this._decomposed.t,this._decomposed.q,this._decomposed.s),t.position.copy(this._decomposed.t),t.quaternion.copy(this._decomposed.q),t.scale.copy(this._decomposed.s),t.updateMatrix(),this.cookController.endCook()}}class iG{constructor(t){this._scene=t,this._data={}}data(t){this._scene.nodesController.reset_node_context_signatures();const e=hG.dispatch_node(this._scene.root()),n=e.data(),i=e.ui_data();return this._data={properties:{frame:this._scene.frame()||Ml.START_FRAME,maxFrame:this._scene.maxFrame(),maxFrameLocked:this._scene.timeController.maxFrameLocked(),realtimeState:this._scene.timeController.realtimeState(),mainCameraNodePath:this._scene.camerasController.mainCameraNodePath(),versions:t},root:n,ui:i},this._data}static sanitize_string(t){return t=t.replace(/'/g,\\\\\\\"'\\\\\\\"),t=sr.escapeLineBreaks(t)}}class rG{constructor(t){this._node=t}data(t={}){var e,n,i,r,s,o,a;this.is_root()||this._node.scene().nodesController.register_node_context_signature(this._node),this._data={type:this._node.type()};const l=this.nodes_data(t);Object.keys(l).length>0&&(this._data.nodes=l);const c=this.params_data();if(Object.keys(c).length>0&&(this._data.params=c),!this.is_root()){this._node.io.inputs.maxInputsCountOverriden()&&(this._data.maxInputsCount=this._node.io.inputs.maxInputsCount());const t=this.inputs_data();t.length>0&&(this._data.inputs=t);const e=this.connection_points_data();e&&(this._data.connection_points=e)}if(this._node.flags){const t={};(this._node.flags.hasBypass()||this._node.flags.hasDisplay()||this._node.flags.hasOptimize())&&(this._node.flags.hasBypass()&&(null===(e=this._node.flags.bypass)||void 0===e?void 0:e.active())&&(t.bypass=this._node.flags.bypass.active()),this._node.flags.hasDisplay()&&(!(null===(n=this._node.flags.display)||void 0===n?void 0:n.active())&&(null===(i=this._node.parent())||void 0===i?void 0:i.displayNodeController)||(t.display=null===(r=this._node.flags.display)||void 0===r?void 0:r.active())),this._node.flags.hasOptimize()&&(null===(s=this._node.flags.optimize)||void 0===s?void 0:s.active())&&(t.optimize=null===(o=this._node.flags.optimize)||void 0===o?void 0:o.active())),Object.keys(t).length>0&&(this._data.flags=t)}if(this._node.childrenAllowed()){const t=null===(a=this._node.childrenController)||void 0===a?void 0:a.selection;if(t&&this._node.children().length>0){const e=[],n={};for(let e of t.nodes())n[e.graphNodeId()]=!0;for(let t of this._node.children())t.graphNodeId()in n&&e.push(t);const i=e.map((t=>t.name()));i.length>0&&(this._data.selection=i)}}if(this._node.io.inputs.overrideClonedStateAllowed()){const t=this._node.io.inputs.clonedStateOverriden();t&&(this._data.cloned_state_overriden=t)}if(this._node.persisted_config){const t=this._node.persisted_config.toJSON();t&&(this._data.persisted_config=t)}return this.add_custom(),this._data}ui_data(t={}){const e=this.ui_data_without_children(),n=this._node.children();return n.length>0&&(e.nodes={},n.forEach((n=>{const i=hG.dispatch_node(n);e.nodes[n.name()]=i.ui_data(t)}))),e}ui_data_without_children(){const t={};if(!this.is_root()){const e=this._node.uiData;t.pos=e.position().toArray();const n=e.comment();n&&(t.comment=iG.sanitize_string(n))}return t}is_root(){return null===this._node.parent()&&this._node.graphNodeId()==this._node.root().graphNodeId()}inputs_data(){const t=[];return this._node.io.inputs.inputs().forEach(((e,n)=>{var i;if(e){const r=this._node.io.connections.inputConnection(n);if(this._node.io.inputs.hasNamedInputs()){const s=r.output_index,o=null===(i=e.io.outputs.namedOutputConnectionPoints()[s])||void 0===i?void 0:i.name();o&&(t[n]={index:n,node:e.name(),output:o})}else t[n]=e.name()}})),t}connection_points_data(){if(this._node.io.has_connection_points_controller&&this._node.io.connection_points.initialized()&&(this._node.io.inputs.hasNamedInputs()||this._node.io.outputs.hasNamedOutputs())){const t={};if(this._node.io.inputs.hasNamedInputs()){t.in=[];for(let e of this._node.io.inputs.namedInputConnectionPoints())e&&t.in.push(e.toJSON())}if(this._node.io.outputs.hasNamedOutputs()){t.out=[];for(let e of this._node.io.outputs.namedOutputConnectionPoints())e&&t.out.push(e.toJSON())}return t}}params_data(){const t={};for(let e of this._node.params.names){const n=this._node.params.get(e);if(n&&!n.parent_param){const e=hG.dispatch_param(n);if(e.required()){const i=e.data();t[n.name()]=i}}}return t}nodes_data(t={}){const e={};for(let n of this._node.children()){const i=hG.dispatch_node(n);e[n.name()]=i.data(t)}return e}add_custom(){}}class sG{constructor(t){this._param=t,this._complex_data={}}required(){const t=this._param.options.isSpare()&&!this._param.parent_param,e=!this._param.isDefault();return t||e||this._param.options.hasOptionsOverridden()}data(){if(this._param.parent_param)throw console.warn(\\\\\\\"no component should be saved\\\\\\\"),\\\\\\\"no component should be saved\\\\\\\";return this._require_data_complex()?this._data_complex():this._data_simple()}_data_simple(){return this._param.rawInputSerialized()}_data_complex(){if(this._complex_data={},this._param.options.isSpare()&&!this._param.parent_param&&(this._complex_data.type=this._param.type(),this._complex_data.default_value=this._param.defaultValueSerialized(),this._complex_data.options=this._param.options.current()),this._param.isDefault()||(this._complex_data.raw_input=this._param.rawInputSerialized()),this._param.options.hasOptionsOverridden()){const t={},e=this._param.options.overriddenOptions();for(let n of Object.keys(e)){const i=e[n];m.isString(i)||m.isNumber(i)?t[n]=i:t[n]=JSON.stringify(i)}this._complex_data.overriden_options=t}return this._complex_data}_require_data_complex(){return!!this._param.options.isSpare()||!!this._param.options.hasOptionsOverridden()}add_main(){}}class oG extends sG{add_main(){if(!this._require_data_complex())return this._param.rawInputSerialized();this._complex_data.raw_input=this._param.rawInputSerialized()}}class aG extends sG{add_main(){let t=this._param.rawInput();if(t=iG.sanitize_string(t),!this._require_data_complex())return t;this._complex_data.raw_input=t}}class lG extends sG{add_main(){let t=this._param.rawInput();if(t=iG.sanitize_string(t),!this._require_data_complex())return t;this._complex_data.raw_input=t}}class cG extends sG{add_main(){if(!this._require_data_complex())return this._param.rawInputSerialized();this._complex_data.raw_input=this._param.rawInputSerialized()}}class uG extends rG{nodes_data(t={}){return t.showPolyNodesData?super.nodes_data(t):{}}ui_data(t={}){return t.showPolyNodesData?super.ui_data(t):this.ui_data_without_children()}}class hG{static dispatch_node(t){return t.polyNodeController?new uG(t):new rG(t)}static dispatch_param(t){return t instanceof no?new oG(t):t instanceof po?new aG(t):t instanceof bo?new lG(t):t instanceof xo?new cG(t):new sG(t)}}class dG{constructor(){this._objects=[],this._objects_with_geo=[],this.touch()}timestamp(){return this._timestamp}touch(){const t=ai.performance.performanceManager();this._timestamp=t.now(),this.reset()}reset(){this._bounding_box=void 0,this._core_geometries=void 0,this._core_objects=void 0}clone(){const t=new dG;if(this._objects){const e=[];for(let t of this._objects)e.push(vs.clone(t));t.setObjects(e)}return t}setObjects(t){this._objects=t,this._objects_with_geo=t.filter((t=>null!=t.geometry)),this.touch()}objects(){return this._objects}objectsWithGeo(){return this._objects_with_geo}coreObjects(){return this._core_objects=this._core_objects||this._create_core_objects()}_create_core_objects(){return this._objects?this._objects.map(((t,e)=>new vs(t,e))):[]}objectsData(){return this._objects?this._objects.map((t=>this._objectData(t))):[]}_objectData(t){let e=0;return t.geometry&&(e=ps.pointsCount(t.geometry)),{type:Nr(t.constructor),name:t.name,children_count:t.children.length,points_count:e}}geometries(){const t=[];for(let e of this.coreObjects()){const n=e.object().geometry;n&&t.push(n)}return t}coreGeometries(){return this._core_geometries=this._core_geometries||this._createCoreGeometries()}_createCoreGeometries(){const t=[];for(let e of this.geometries())t.push(new ps(e));return t}static geometryFromObject(t){return t.isMesh||t.isLine||t.isPoints?t.geometry:null}faces(){const t=[];for(let e of this.objectsWithGeo())if(e.geometry){const n=new ps(e.geometry).faces();for(let i of n)i.applyMatrix4(e.matrix),t.push(i)}return t}points(){return this.coreGeometries().map((t=>t.points())).flat()}pointsCount(){return f.sum(this.coreGeometries().map((t=>t.pointsCount())))}totalPointsCount(){if(this._objects){let t=0;for(let e of this._objects)e.traverse((e=>{const n=e.geometry;n&&(t+=ps.pointsCount(n))}));return t}return 0}pointsFromGroup(t){if(t){const e=sr.indices(t),n=this.points();return f.compact(e.map((t=>n[t])))}return this.points()}static _fromObjects(t){const e=new dG;return e.setObjects(t),e}objectsFromGroup(t){return this.coreObjectsFromGroup(t).map((t=>t.object()))}coreObjectsFromGroup(t){if(\\\\\\\"\\\\\\\"!==(t=t.trim())){const e=parseInt(t);return m.isNaN(e)?this.coreObjects().filter((e=>sr.matchMask(t,e.name()))):f.compact([this.coreObjects()[e]])}return this.coreObjects()}boundingBox(){return this._bounding_box=this._bounding_box||this._compute_bounding_box()}center(){const t=new p.a;return this.boundingBox().getCenter(t),t}size(){const t=new p.a;return this.boundingBox().getSize(t),t}_compute_bounding_box(){let t;if(this._objects)for(let e of this._objects){const n=e.geometry;n&&(n.computeBoundingBox(),t?t.expandByObject(e):n.boundingBox&&(t=n.boundingBox.clone()))}return t=t||new XB.a(new p.a(-1,-1,-1),new p.a(1,1,1)),t}computeVertexNormals(){for(let t of this.coreObjects())t.computeVertexNormals()}hasAttrib(t){let e;return null!=(e=this.coreGeometries()[0])&&e.hasAttrib(t)}attribType(t){const e=this.coreGeometries()[0];return null!=e?e.attribType(t):null}objectAttribType(t){const e=this.coreObjects()[0];return null!=e?e.attribType(t):null}renameAttrib(t,e,n){switch(n){case Vr.ATTRIB_CLASS.VERTEX:if(this.hasAttrib(t)&&this._objects)for(let n of this._objects)n.traverse((n=>{const i=dG.geometryFromObject(n);if(i){new ps(i).renameAttrib(t,e)}}));break;case Vr.ATTRIB_CLASS.OBJECT:if(this.hasAttrib(t)&&this._objects)for(let n of this._objects)n.traverse((n=>{new vs(n,0).renameAttrib(t,e)}))}}attribNames(){let t;return null!=(t=this.coreGeometries()[0])?t.attribNames():[]}objectAttribNames(){let t;return null!=(t=this.coreObjects()[0])?t.attribNames():[]}attribNamesMatchingMask(t){const e=sr.attribNames(t),n=[];for(let t of this.attribNames())for(let i of e)if(sr.matchMask(t,i))n.push(t);else{t==Wr.remapName(i)&&n.push(t)}return f.uniq(n)}attribSizes(){let t;return null!=(t=this.coreGeometries()[0])?t.attribSizes():{}}objectAttribSizes(){let t;return null!=(t=this.coreObjects()[0])?t.attribSizes():{}}attribSize(t){let e;return null!=(e=this.coreGeometries()[0])?e.attribSize(t):0}addNumericVertexAttrib(t,e,n){null==n&&(n=Wr.default_value(e));for(let i of this.coreGeometries())i.addNumericAttrib(t,e,n)}static clone(t){const e=new In.a;return t.children.forEach((t=>{const n=vs.clone(t);e.add(n)})),e}}class pG extends Fl{static context(){return Ki.SOP}cook(t,e){}createCoreGroupFromObjects(t){const e=new dG;return e.setObjects(t),e}createCoreGroupFromGeometry(t,e=Sr.MESH){const n=pG.createObject(t,e);return this.createCoreGroupFromObjects([n])}createObject(t,e,n){return pG.createObject(t,e,n)}static createObject(t,e,n){this.createIndexIfNone(t);const i=new(0,Cr[e])(t,n=n||Vr.MATERIALS[e].clone());return i.castShadow=!0,i.receiveShadow=!0,i.frustumCulled=!1,i.matrixAutoUpdate=!1,i}createIndexIfNone(t){pG.createIndexIfNone(t)}static createIndexIfNone(t){hs.createIndexIfNone(t)}}var _G;!function(t){t.FROM_SET_CORE_GROUP=\\\\\\\"from set_core_group\\\\\\\",t.FROM_SET_GROUP=\\\\\\\"from set_group\\\\\\\",t.FROM_SET_OBJECTS=\\\\\\\"from set_objects\\\\\\\",t.FROM_SET_OBJECT=\\\\\\\"from set_object\\\\\\\",t.FROM_SET_GEOMETRIES=\\\\\\\"from set_geometries\\\\\\\",t.FROM_SET_GEOMETRY=\\\\\\\"from set_geometry\\\\\\\"}(_G||(_G={}));const mG=\\\\\\\"input geometry\\\\\\\",fG=[mG,mG,mG,mG];class gG extends ia{constructor(){super(...arguments),this.flags=new zi(this)}static context(){return Ki.SOP}static displayedInputNames(){return fG}initializeBaseNode(){this.flags.display.set(!1),this.flags.display.onUpdate((()=>{if(this.flags.display.active()){const t=this.parent();t&&t.displayNodeController&&t.displayNodeController.setDisplayNode(this)}})),this.io.outputs.setHasOneOutput()}setCoreGroup(t){this._setContainer(t,_G.FROM_SET_CORE_GROUP)}setObject(t){this._setContainerObjects([t],_G.FROM_SET_OBJECT)}setObjects(t){this._setContainerObjects(t,_G.FROM_SET_OBJECTS)}setGeometry(t,e=Sr.MESH){const n=this.createObject(t,e);this._setContainerObjects([n],_G.FROM_SET_GEOMETRY)}setGeometries(t,e=Sr.MESH){const n=[];let i;for(let r of t)i=this.createObject(r,e),n.push(i);this._setContainerObjects(n,_G.FROM_SET_GEOMETRIES)}_setContainerObjects(t,e){const n=this.containerController.container().coreContent()||new dG;n.setObjects(t),n.touch(),this._setContainer(n)}static createObject(t,e,n){return pG.createObject(t,e,n)}createObject(t,e,n){return gG.createObject(t,e,n)}static createIndexIfNone(t){pG.createIndexIfNone(t)}_createIndexIfNone(t){gG.createIndexIfNone(t)}}const vG=new class extends aa{};class yG extends gG{constructor(){super(...arguments),this.paramsConfig=vG}static type(){return er.OUTPUT}initializeNode(){this.io.inputs.setCount(1),this.io.outputs.setHasNoOutput(),this.io.inputs.initInputsClonedState(Qi.NEVER)}cook(t){this.setCoreGroup(t[0])}}class xG extends gG{constructor(){super(...arguments),this.childrenDisplayController=new wG(this),this.displayNodeController=new Lm(this,this.childrenDisplayController.displayNodeControllerCallbacks()),this._children_controller_context=Ki.SOP}initializeBaseNode(){super.initializeBaseNode(),this.childrenDisplayController.initializeNode(),this.cookController.disallowInputsEvaluation()}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}async cook(t){const e=this.childrenDisplayController.output_node();if(e){const t=(await e.compute()).coreContent();t?this.setCoreGroup(t):e.states.error.active()?this.states.error.set(e.states.error.message()):this.setObjects([])}else this.states.error.set(\\\\\\\"no output node found inside subnet\\\\\\\")}}const bG={dependsOnDisplayNode:!0};class wG{constructor(t,e=bG){this.node=t,this.options=e,this._output_node_needs_update=!0}dispose(){var t;null===(t=this._graph_node)||void 0===t||t.dispose()}displayNodeControllerCallbacks(){return{onDisplayNodeRemove:()=>{this.node.setDirty()},onDisplayNodeSet:()=>{this.node.setDirty()},onDisplayNodeUpdate:()=>{this.node.setDirty()}}}output_node(){return this._output_node_needs_update&&this._update_output_node(),this._output_node}initializeNode(){var t;const e=null===(t=this.node.flags)||void 0===t?void 0:t.display;e&&e.onUpdate((()=>{e.active()&&this.node.setDirty()})),this.node.lifecycle.add_on_child_add_hook((()=>{this._output_node_needs_update=!0,this.node.setDirty()})),this.node.lifecycle.add_on_child_remove_hook((()=>{this._output_node_needs_update=!0,this.node.setDirty()}))}_update_output_node(){const t=this.node.nodesByType(yG.type())[0];null!=this._output_node&&null!=t&&this._output_node.graphNodeId()==t.graphNodeId()||(this._graph_node&&this._output_node&&this._graph_node.removeGraphInput(this._output_node),this._output_node=t,this._output_node&&this.options.dependsOnDisplayNode&&(this._graph_node=this._graph_node||this._create_graph_node(),this._graph_node.addGraphInput(this._output_node)))}_create_graph_node(){const t=new Ai(this.node.scene(),\\\\\\\"subnetChildrenDisplayController\\\\\\\");return t.addPostDirtyHook(\\\\\\\"subnetChildrenDisplayController\\\\\\\",(()=>{this.node.setDirty()})),t}}function TG(t,e){const n=new class extends aa{constructor(){super(...arguments),this.template=oa.OPERATOR_PATH(\\\\\\\"../template\\\\\\\"),this.debug=oa.BUTTON(null,{callback:t=>{i.PARAM_CALLBACK_debug(t)}})}};class i extends xG{constructor(){super(...arguments),this.paramsConfig=n,this.polyNodeController=new MG(this,e)}static type(){return t}static PARAM_CALLBACK_debug(t){t._debug()}_debug(){this.polyNodeController.debug(this.p.template)}}return i}const AG=TG(\\\\\\\"poly\\\\\\\",{nodeContext:Ki.SOP,inputs:[0,4]});class EG extends AG{}class MG{constructor(t,e){this.node=t,this._definition=e}initializeNode(){this.init_inputs(),this.node.params.onParamsCreated(\\\\\\\"poly_node_init\\\\\\\",(()=>{this.create_params_from_definition()})),this.node.lifecycle.add_on_create_hook((()=>{this.create_params_from_definition(),this.createChildNodesFromDefinition()}))}init_inputs(){const t=this._definition.inputs;t&&this.node.io.inputs.setCount(t[0],t[1])}create_params_from_definition(){const t=this._definition.params;if(t){for(let e of t)e.options=e.options||{},e.options.spare=!0;this.node.params.updateParams({toAdd:t})}}createChildNodesFromDefinition(){const t=this._definition.nodes;if(!t)return;const e=this.node.scene().loadingController.loaded();e&&this.node.scene().loadingController.markAsLoading();const n=new Xl({}),i=new zl(this.node);i.create_nodes(n,t);const r=this._definition.ui;r&&i.process_nodes_ui_data(n,r),e&&this.node.scene().loadingController.markAsLoaded()}debug(t){const e=t.found_node();if(e){const t=hG.dispatch_node(e),n=t.data({showPolyNodesData:!0}),i=t.ui_data({showPolyNodesData:!0}),r={nodeContext:e.context(),inputs:[0,0],params:[],nodes:n.nodes,ui:i.nodes};console.log(JSON.stringify(r))}}static createNodeClass(t,e,n){switch(e){case Ki.SOP:return TG(t,n);case Ki.OBJ:return SG(t,n)}}}function SG(t,e){const n=new class extends aa{constructor(){super(...arguments),this.display=oa.BOOLEAN(1),this.template=oa.OPERATOR_PATH(\\\\\\\"../template\\\\\\\"),this.debug=oa.BUTTON(null,{callback:t=>{i.PARAM_CALLBACK_debug(t)}})}};class i extends _z{constructor(){super(...arguments),this.paramsConfig=n,this.hierarchyController=new Lz(this),this.flags=new Fi(this),this.childrenDisplayController=new WU(this),this.displayNodeController=new Lm(this,this.childrenDisplayController.displayNodeControllerCallbacks()),this._children_controller_context=Ki.SOP,this.polyNodeController=new MG(this,e)}static type(){return t}createObject(){const t=new In.a;return t.matrixAutoUpdate=!1,t}initializeNode(){this.hierarchyController.initializeNode(),this.childrenDisplayController.initializeNode()}isDisplayNodeCooking(){if(this.flags.display.active()){const t=this.displayNodeController.displayNode();return!!t&&t.isDirty()}return!1}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}cook(){this.object.visible=this.pv.display,this.cookController.endCook()}static PARAM_CALLBACK_debug(t){t._debug()}_debug(){this.polyNodeController.debug(this.p.template)}}return i}const CG=SG(\\\\\\\"poly\\\\\\\",{nodeContext:Ki.OBJ});class NG extends CG{}class LG extends Q.a{constructor(t){super(),this.type=\\\\\\\"Audio\\\\\\\",this.listener=t,this.context=t.context,this.gain=this.context.createGain(),this.gain.connect(t.getInput()),this.autoplay=!1,this.buffer=null,this.detune=0,this.loop=!1,this.loopStart=0,this.loopEnd=0,this.offset=0,this.duration=void 0,this.playbackRate=1,this.isPlaying=!1,this.hasPlaybackControl=!0,this.source=null,this.sourceType=\\\\\\\"empty\\\\\\\",this._startedAt=0,this._progress=0,this._connected=!1,this.filters=[]}getOutput(){return this.gain}setNodeSource(t){return this.hasPlaybackControl=!1,this.sourceType=\\\\\\\"audioNode\\\\\\\",this.source=t,this.connect(),this}setMediaElementSource(t){return this.hasPlaybackControl=!1,this.sourceType=\\\\\\\"mediaNode\\\\\\\",this.source=this.context.createMediaElementSource(t),this.connect(),this}setMediaStreamSource(t){return this.hasPlaybackControl=!1,this.sourceType=\\\\\\\"mediaStreamNode\\\\\\\",this.source=this.context.createMediaStreamSource(t),this.connect(),this}setBuffer(t){return this.buffer=t,this.sourceType=\\\\\\\"buffer\\\\\\\",this.autoplay&&this.play(),this}play(t=0){if(!0===this.isPlaying)return void console.warn(\\\\\\\"THREE.Audio: Audio is already playing.\\\\\\\");if(!1===this.hasPlaybackControl)return void console.warn(\\\\\\\"THREE.Audio: this Audio has no playback control.\\\\\\\");this._startedAt=this.context.currentTime+t;const e=this.context.createBufferSource();return e.buffer=this.buffer,e.loop=this.loop,e.loopStart=this.loopStart,e.loopEnd=this.loopEnd,e.onended=this.onEnded.bind(this),e.start(this._startedAt,this._progress+this.offset,this.duration),this.isPlaying=!0,this.source=e,this.setDetune(this.detune),this.setPlaybackRate(this.playbackRate),this.connect()}pause(){if(!1!==this.hasPlaybackControl)return!0===this.isPlaying&&(this._progress+=Math.max(this.context.currentTime-this._startedAt,0)*this.playbackRate,!0===this.loop&&(this._progress=this._progress%(this.duration||this.buffer.duration)),this.source.stop(),this.source.onended=null,this.isPlaying=!1),this;console.warn(\\\\\\\"THREE.Audio: this Audio has no playback control.\\\\\\\")}stop(){if(!1!==this.hasPlaybackControl)return this._progress=0,this.source.stop(),this.source.onended=null,this.isPlaying=!1,this;console.warn(\\\\\\\"THREE.Audio: this Audio has no playback control.\\\\\\\")}connect(){if(this.filters.length>0){this.source.connect(this.filters[0]);for(let t=1,e=this.filters.length;t<e;t++)this.filters[t-1].connect(this.filters[t]);this.filters[this.filters.length-1].connect(this.getOutput())}else this.source.connect(this.getOutput());return this._connected=!0,this}disconnect(){if(this.filters.length>0){this.source.disconnect(this.filters[0]);for(let t=1,e=this.filters.length;t<e;t++)this.filters[t-1].disconnect(this.filters[t]);this.filters[this.filters.length-1].disconnect(this.getOutput())}else this.source.disconnect(this.getOutput());return this._connected=!1,this}getFilters(){return this.filters}setFilters(t){return t||(t=[]),!0===this._connected?(this.disconnect(),this.filters=t.slice(),this.connect()):this.filters=t.slice(),this}setDetune(t){if(this.detune=t,void 0!==this.source.detune)return!0===this.isPlaying&&this.source.detune.setTargetAtTime(this.detune,this.context.currentTime,.01),this}getDetune(){return this.detune}getFilter(){return this.getFilters()[0]}setFilter(t){return this.setFilters(t?[t]:[])}setPlaybackRate(t){if(!1!==this.hasPlaybackControl)return this.playbackRate=t,!0===this.isPlaying&&this.source.playbackRate.setTargetAtTime(this.playbackRate,this.context.currentTime,.01),this;console.warn(\\\\\\\"THREE.Audio: this Audio has no playback control.\\\\\\\")}getPlaybackRate(){return this.playbackRate}onEnded(){this.isPlaying=!1}getLoop(){return!1===this.hasPlaybackControl?(console.warn(\\\\\\\"THREE.Audio: this Audio has no playback control.\\\\\\\"),!1):this.loop}setLoop(t){if(!1!==this.hasPlaybackControl)return this.loop=t,!0===this.isPlaying&&(this.source.loop=this.loop),this;console.warn(\\\\\\\"THREE.Audio: this Audio has no playback control.\\\\\\\")}setLoopStart(t){return this.loopStart=t,this}setLoopEnd(t){return this.loopEnd=t,this}getVolume(){return this.gain.gain.value}setVolume(t){return this.gain.gain.setTargetAtTime(t,this.context.currentTime,.01),this}}const OG=new p.a,RG=new au.a,PG=new p.a,IG=new p.a;class FG extends LG{constructor(t){super(t),this.panner=this.context.createPanner(),this.panner.panningModel=\\\\\\\"HRTF\\\\\\\",this.panner.connect(this.gain)}getOutput(){return this.panner}getRefDistance(){return this.panner.refDistance}setRefDistance(t){return this.panner.refDistance=t,this}getRolloffFactor(){return this.panner.rolloffFactor}setRolloffFactor(t){return this.panner.rolloffFactor=t,this}getDistanceModel(){return this.panner.distanceModel}setDistanceModel(t){return this.panner.distanceModel=t,this}getMaxDistance(){return this.panner.maxDistance}setMaxDistance(t){return this.panner.maxDistance=t,this}setDirectionalCone(t,e,n){return this.panner.coneInnerAngle=t,this.panner.coneOuterAngle=e,this.panner.coneOuterGain=n,this}updateMatrixWorld(t){if(super.updateMatrixWorld(t),!0===this.hasPlaybackControl&&!1===this.isPlaying)return;this.matrixWorld.decompose(OG,RG,PG),IG.set(0,0,1).applyQuaternion(RG);const e=this.panner;if(e.positionX){const t=this.context.currentTime+this.listener.timeDelta;e.positionX.linearRampToValueAtTime(OG.x,t),e.positionY.linearRampToValueAtTime(OG.y,t),e.positionZ.linearRampToValueAtTime(OG.z,t),e.orientationX.linearRampToValueAtTime(IG.x,t),e.orientationY.linearRampToValueAtTime(IG.y,t),e.orientationZ.linearRampToValueAtTime(IG.z,t)}else e.setPosition(OG.x,OG.y,OG.z),e.setOrientation(IG.x,IG.y,IG.z)}}class DG extends Pz.a{constructor(t,e=1,n=16,i=2){const r=new S.a,s=new Float32Array(3*(3*(n+2*i)+3));r.setAttribute(\\\\\\\"position\\\\\\\",new C.a(s,3));const o=new wr.a({color:65280});super(r,[new wr.a({color:16776960}),o]),this.audio=t,this.range=e,this.divisionsInnerAngle=n,this.divisionsOuterAngle=i,this.type=\\\\\\\"PositionalAudioHelper\\\\\\\",this.update()}update(){const t=this.audio,e=this.range,n=this.divisionsInnerAngle,i=this.divisionsOuterAngle,r=Ln.e(t.panner.coneInnerAngle),s=Ln.e(t.panner.coneOuterAngle),o=r/2,a=s/2;let l,c,u=0,h=0;const d=this.geometry,p=d.attributes.position;function _(t,n,i,r){const s=(n-t)/i;for(p.setXYZ(u,0,0,0),h++,l=t;l<n;l+=s)c=u+h,p.setXYZ(c,Math.sin(l)*e,0,Math.cos(l)*e),p.setXYZ(c+1,Math.sin(Math.min(l+s,n))*e,0,Math.cos(Math.min(l+s,n))*e),p.setXYZ(c+2,0,0,0),h+=3;d.addGroup(u,h,r),u+=h,h=0}d.clearGroups(),_(-a,-o,i,0),_(-o,o,n,1),_(o,a,i,0),p.needsUpdate=!0,r===s&&(this.material[0].visible=!1)}dispose(){this.geometry.dispose(),this.material[0].dispose(),this.material[1].dispose()}}class kG extends kf.a{constructor(t){super(t)}load(t,e,n,i){const r=this,s=new Df.a(this.manager);s.setResponseType(\\\\\\\"arraybuffer\\\\\\\"),s.setPath(this.path),s.setRequestHeader(this.requestHeader),s.setWithCredentials(this.withCredentials),s.load(t,(function(n){try{const t=n.slice(0);xU().decodeAudioData(t,(function(t){e(t)}))}catch(e){i?i(e):console.error(e),r.manager.itemError(t)}}),n,i)}}var BG;!function(t){t.MP3=\\\\\\\"mp3\\\\\\\",t.WAV=\\\\\\\"wav\\\\\\\"}(BG||(BG={}));BG.MP3,BG.WAV;class zG extends jg{async load(){const t=new kG(this.loadingManager),e=await this._urlToLoad();return new Promise((n=>{t.load(e,(function(t){n(t)}))}))}}var UG;!function(t){t.LINEAR=\\\\\\\"linear\\\\\\\",t.INVERSE=\\\\\\\"inverse\\\\\\\",t.EXPONENTIAL=\\\\\\\"exponential\\\\\\\"}(UG||(UG={}));const GG=[UG.LINEAR,UG.INVERSE,UG.EXPONENTIAL];class VG extends(Sz(aa)){constructor(){super(...arguments),this.audio=oa.FOLDER(),this.listener=oa.NODE_PATH(\\\\\\\"\\\\\\\",{nodeSelection:{context:Ki.OBJ,types:[Ng.AUDIO_LISTENER]}}),this.url=oa.STRING(\\\\\\\"\\\\\\\",{fileBrowse:{type:[Ls.AUDIO]}}),this.volume=oa.FLOAT(1),this.loop=oa.BOOLEAN(1,{separatorBefore:!0}),this.loopStart=oa.FLOAT(0,{visibleIf:{loop:1}}),this.loopEnd=oa.FLOAT(0,{visibleIf:{loop:1},separatorAfter:!0}),this.refDistance=oa.FLOAT(10,{range:[0,10],rangeLocked:[!0,!1]}),this.rolloffFactor=oa.FLOAT(10,{range:[0,10],rangeLocked:[!0,!1]}),this.maxDistance=oa.FLOAT(100,{range:[.001,100],rangeLocked:[!0,!1]}),this.distanceModel=oa.INTEGER(GG.indexOf(UG.LINEAR),{menu:{entries:GG.map(((t,e)=>({name:t,value:e})))}}),this.coneInnerAngle=oa.FLOAT(180,{range:[0,360],rangeLocked:[!0,!0]}),this.coneOuterAngle=oa.FLOAT(230,{range:[0,360],rangeLocked:[!0,!0]}),this.coneOuterGain=oa.FLOAT(.1,{range:[0,1],rangeLocked:[!0,!0]}),this.autoplay=oa.BOOLEAN(1),this.showHelper=oa.BOOLEAN(0),this.play=oa.BUTTON(null,{callback:t=>{jG.PARAM_CALLBACK_play(t)}}),this.pause=oa.BUTTON(null,{callback:t=>{jG.PARAM_CALLBACK_pause(t)}})}}const HG=new VG;class jG extends _z{constructor(){super(...arguments),this.paramsConfig=HG,this.hierarchyController=new Lz(this),this.transformController=new Nz(this),this.flags=new Fi(this)}static type(){return Ng.POSITIONAL_AUDIO}createObject(){const t=new In.a;return t.matrixAutoUpdate=!1,t}initializeNode(){this.hierarchyController.initializeNode(),this.transformController.initializeNode(),this._updateHelperHierarchy(),this.flags.display.onUpdate((()=>{this._updateHelperHierarchy()}))}_updateHelperHierarchy(){this._helper&&(this.flags.display.active()?this.object.add(this._helper):this.object.remove(this._helper))}cook(){this.transformController.update(),this._updatePositionalAudio(),this.cookController.endCook()}async _updatePositionalAudio(){this.p.listener.isDirty()&&await this.p.listener.compute();const t=this.pv.url;if(this._loadedUrl!=t)try{await this._createPositionalAudio()}catch(t){this.states.error.set(`error when creating audio: ${t}`)}this._positionalAudio&&(this._positionalAudio.setVolume(this.pv.volume),this._positionalAudio.setLoop(this.pv.loop),this._positionalAudio.setLoopStart(this.pv.loopStart),this._positionalAudio.setLoopEnd(this.pv.loopEnd),this._positionalAudio.setRefDistance(this.pv.refDistance),this._positionalAudio.setRolloffFactor(this.pv.rolloffFactor),this._positionalAudio.setMaxDistance(this.pv.maxDistance),this._positionalAudio.setDistanceModel(GG[this.pv.distanceModel]),this._positionalAudio.setDirectionalCone(this.pv.coneInnerAngle,this.pv.coneOuterAngle,this.pv.coneOuterGain),this.pv.showHelper&&(this._helper=this._helper||this._createHelper(this._positionalAudio),this.object.add(this._helper)),this._helper&&(this._helper.visible=this.pv.showHelper,this._helper.update()))}_createHelper(t){const e=new DG(t);return e.matrixAutoUpdate=!1,e}async _createPositionalAudio(){const t=this.pv.listener.nodeWithContext(Ki.OBJ);if(!t)return;const e=t.object;this._positionalAudio&&(this._positionalAudio.source&&(this._positionalAudio.stop(),this._positionalAudio.disconnect()),this.object.remove(this._positionalAudio),this._positionalAudio=void 0),this._helper&&(this._helper.dispose(),this._helper=void 0),this._positionalAudio=new FG(e),this._positionalAudio.matrixAutoUpdate=!1;const n=new zG(this.pv.url,this.scene(),this),i=await n.load();this._loadedUrl=this.pv.url,this._positionalAudio.autoplay=this.pv.autoplay,this._positionalAudio.setBuffer(i),this.object.add(this._positionalAudio)}isPlaying(){return!!this._positionalAudio&&this._positionalAudio.isPlaying}static PARAM_CALLBACK_play(t){t.PARAM_CALLBACK_play()}static PARAM_CALLBACK_pause(t){t.PARAM_CALLBACK_pause()}PARAM_CALLBACK_play(){this._positionalAudio&&(this.isPlaying()||this._positionalAudio.play())}PARAM_CALLBACK_pause(){this._positionalAudio&&this.isPlaying()&&this._positionalAudio.pause()}}var WG;!function(t){t.ON_RENDER=\\\\\\\"On Every Render\\\\\\\",t.MANUAL=\\\\\\\"Manual\\\\\\\"}(WG||(WG={}));const qG=[WG.ON_RENDER,WG.MANUAL];const XG=new class extends aa{constructor(){super(...arguments),this.object=oa.OPERATOR_PATH(\\\\\\\"\\\\\\\",{nodeSelection:{context:Ki.OBJ},dependentOnFoundNode:!1,computeOnDirty:!0,callback:t=>{YG.PARAM_CALLBACK_update_resolved_object(t)}}),this.pointIndex=oa.INTEGER(0,{range:[0,100]}),this.updateMode=oa.INTEGER(qG.indexOf(WG.ON_RENDER),{callback:t=>{YG.PARAM_CALLBACK_update_updateMode(t)},menu:{entries:qG.map(((t,e)=>({name:t,value:e})))}}),this.update=oa.BUTTON(null,{callback:t=>{YG.PARAM_CALLBACK_update(t)},visibleIf:{updateMode:qG.indexOf(WG.MANUAL)}})}};class YG extends _z{constructor(){super(...arguments),this.paramsConfig=XG,this.hierarchyController=new Lz(this),this.flags=new Fi(this),this._helper=new NU(1),this._found_point_post=new p.a,this._on_object_before_render_bound=this._update.bind(this)}static type(){return\\\\\\\"rivet\\\\\\\"}createObject(){const t=new k.a;return t.matrixAutoUpdate=!1,t}initializeNode(){this.hierarchyController.initializeNode(),this.addPostDirtyHook(\\\\\\\"rivet_on_dirty\\\\\\\",(()=>{this.cookController.cookMainWithoutInputs()})),this._updateHelperHierarchy(),this.flags.display.onUpdate((()=>{this._updateHelperHierarchy()}))}_updateHelperHierarchy(){this.flags.display.active()?this.object.add(this._helper):this.object.remove(this._helper)}async cook(){await this._update_resolved_object(),this._update_render_hook(),this.cookController.endCook()}_update_render_hook(){const t=qG[this.pv.updateMode];switch(t){case WG.ON_RENDER:return this._add_render_hook();case WG.MANUAL:return this._remove_render_hook()}ar.unreachable(t)}_add_render_hook(){this.object.onBeforeRender=this._on_object_before_render_bound,this.object.frustumCulled=!1}_remove_render_hook(){this.object.onBeforeRender=()=>{}}_update(t,e,n,i,r,s){const o=this._resolved_object();if(o){const t=o.geometry;if(t){const e=t.attributes.position;if(e){const t=e.array;this._found_point_post.fromArray(t,3*this.pv.pointIndex),o.updateWorldMatrix(!0,!1),o.localToWorld(this._found_point_post),this.object.matrix.makeTranslation(this._found_point_post.x,this._found_point_post.y,this._found_point_post.z)}}}}static PARAM_CALLBACK_update_resolved_object(t){t._update_resolved_object()}async _update_resolved_object(){this.p.object.isDirty()&&await this.p.object.compute();const t=this.p.object.found_node();if(t)if(t.context()==Ki.OBJ&&t.type()==YU.type()){const e=t;this._resolved_sop_group=e.childrenDisplayController.sopGroup()}else this.states.error.set(\\\\\\\"found node is not a geo node\\\\\\\")}_resolved_object(){if(!this._resolved_sop_group)return;const t=this._resolved_sop_group.children[0];return t||void 0}static PARAM_CALLBACK_update_updateMode(t){t._update_render_hook()}static PARAM_CALLBACK_update(t){t._update()}}class $G extends(Ca(Ea(va(ma(ua(aa)))))){}const JG=new $G;class ZG extends _z{constructor(){super(...arguments),this.paramsConfig=JG,this.hierarchyController=new Lz(this),this.SceneAutoUpdateController=new ha(this),this.sceneBackgroundController=new fa(this),this.SceneEnvController=new ya(this),this.sceneFogController=new Ma(this),this.sceneMaterialOverrideController=new Na(this)}static type(){return\\\\\\\"scene\\\\\\\"}createObject(){const t=new fr;return t.matrixAutoUpdate=!1,t}initializeNode(){this.hierarchyController.initializeNode()}cook(){this.SceneAutoUpdateController.update(),this.sceneBackgroundController.update(),this.SceneEnvController.update(),this.sceneFogController.update(),this.sceneMaterialOverrideController.update(),this.cookController.endCook()}}class QG{constructor(t,e,n){this._camera_node_id=t,this._controls_node=e,this._controls=n,this._update_required=this._controls_node.update_required()}update_required(){return this._update_required}get camera_node_id(){return this._camera_node_id}get controls(){return this._controls}get controls_node(){return this._controls_node}is_equal(t){return t.camera_node_id==this._camera_node_id&&t.controls_node.graphNodeId()==this._controls_node.graphNodeId()}}const KG=\\\\\\\"controls\\\\\\\";class tV{constructor(t){this.node=t,this._applied_controls_by_element_id=new Map,this._controls_node=null}controls_param(){return this.node.params.has(KG)?this.node.params.get(KG):null}async controls_node(){const t=this.node.p.controls,e=t.rawInput();if(e&&\\\\\\\"\\\\\\\"!=e){t.isDirty()&&await t.compute();const e=t.value.node();if(e){if(pr.includes(e.type()))return e;this.node.states.error.set(\\\\\\\"found node is not of a camera control type\\\\\\\")}else this.node.states.error.set(\\\\\\\"no node has been found\\\\\\\")}return null}async update_controls(){const t=await this.controls_node();t&&this._controls_node!=t&&this._dispose_control_refs(),this._controls_node=t}async apply_controls(t){const e=t.canvas();if(!e)return;const n=await this.controls_node();if(n){this._controlsEndEventName=n.endEventName();const i=n.controls_id();let r=!1,s=this._applied_controls_by_element_id.get(e.id);if(s&&s.get(i)&&(r=!0),!r){s=new Map,this._applied_controls_by_element_id.set(e.id,s),s.set(i,n);const r=await n.apply_controls(this.node.object,t);if(!r)return;const o=new QG(this.node.graphNodeId(),n,r);return this.set_controls_events(r),o}}}_dispose_control_refs(){this._applied_controls_by_element_id.forEach(((t,e)=>{this._dispose_controls_for_element_id(e)})),this._applied_controls_by_element_id.clear(),this._controlsEndEventName=void 0}_dispose_controls_for_element_id(t){const e=this._applied_controls_by_element_id.get(t);e&&e.forEach(((e,n)=>{e.dispose_controls_for_html_element_id(t)})),this._applied_controls_by_element_id.delete(t)}async dispose_controls(t){this._dispose_controls_for_element_id(t.id)}set_controls_events(t){const e=qV[this.node.pv.updateFromControlsMode];switch(e){case WV.ON_END:return this._set_controls_events_to_update_on_end(t);case WV.ALWAYS:return this._set_controls_events_to_update_always(t);case WV.NEVER:return this._reset(t)}ar.unreachable(e)}_reset(t){this.controls_change_listener&&(t.removeEventListener(\\\\\\\"change\\\\\\\",this.controls_change_listener),this.controls_change_listener=void 0),this.controls_end_listener&&this._controlsEndEventName&&(t.removeEventListener(this._controlsEndEventName,this.controls_end_listener),this.controls_end_listener=void 0)}_set_controls_events_to_update_on_end(t){this._reset(t),this._controlsEndEventName&&(this.controls_end_listener=()=>{this.node.update_transform_params_from_object()},t.addEventListener(this._controlsEndEventName,this.controls_end_listener))}_set_controls_events_to_update_always(t){this._reset(t),this.controls_change_listener=()=>{this.node.update_transform_params_from_object()},t.addEventListener(\\\\\\\"change\\\\\\\",this.controls_change_listener)}}function eV(t){return class extends t{constructor(){super(...arguments),this.layer=oa.INTEGER(0,{range:[0,31],rangeLocked:[!0,!0]})}}}class nV{constructor(t){this.node=t}update(){const t=this.node.object;t.layers.set(0),t.layers.enable(this.node.params.integer(\\\\\\\"layer\\\\\\\"))}}const iV={callback:t=>{eH.PARAM_CALLBACK_reset_effects_composer(t)}};function rV(t){return class extends t{constructor(){super(...arguments),this.doPostProcess=oa.BOOLEAN(0),this.postProcessNode=oa.NODE_PATH(\\\\\\\"\\\\\\\",{visibleIf:{doPostProcess:1},nodeSelection:{types:[tr.POST]},...iV})}}}class sV{constructor(t){this.node=t,this._composers_by_canvas_id={},this.node.p.postProcessNode?this._add_param_dirty_hook():this.node.params.onParamsCreated(\\\\\\\"post process add param dirty hook\\\\\\\",(()=>{this._add_param_dirty_hook()}))}_add_param_dirty_hook(){this.node.p.postProcessNode.addPostDirtyHook(\\\\\\\"on_post_node_dirty\\\\\\\",(()=>{this.reset()}))}render(t,e){const n=this.composer(t);n&&(e&&n.setSize(e.x,e.y),n.render())}reset(){const t=Object.keys(this._composers_by_canvas_id);for(let e of t)delete this._composers_by_canvas_id[e]}composer(t){return this._composers_by_canvas_id[t.id]=this._composers_by_canvas_id[t.id]||this._create_composer(t)}_create_composer(t){const e=this.node.renderController.renderer(t);if(e){const n=this.node.renderController.resolved_scene||this.node.scene().threejsScene(),i=this.node.object,r=this.node.p.postProcessNode.value.node();if(r){if(r.type()==tr.POST){const s=r,o=this.node.renderController.canvas_resolution(t);return s.effectsComposerController.createEffectsComposer({renderer:e,scene:n,camera:i,resolution:o,requester:this.node,camera_node:this.node})}this.node.states.error.set(\\\\\\\"found node is not a post process node\\\\\\\")}else this.node.states.error.set(\\\\\\\"no post node found\\\\\\\")}}}class oV extends ia{constructor(){super(...arguments),this.flags=new Oi(this)}static context(){return Ki.ROP}initializeBaseNode(){this.dirtyController.addPostDirtyHook(\\\\\\\"cook_immediately\\\\\\\",(()=>{this.cookController.cookMainWithoutInputs()}))}cook(){this.cookController.endCook()}}var aV,lV,cV,uV;!function(t){t.CSS2D=\\\\\\\"CSS2DRenderer\\\\\\\",t.CSS3D=\\\\\\\"CSS3DRenderer\\\\\\\",t.WEBGL=\\\\\\\"WebGLRenderer\\\\\\\"}(aV||(aV={})),function(t){t.Linear=\\\\\\\"Linear\\\\\\\",t.sRGB=\\\\\\\"sRGB\\\\\\\",t.Gamma=\\\\\\\"Gamma\\\\\\\",t.RGBE=\\\\\\\"RGBE\\\\\\\",t.LogLuv=\\\\\\\"LogLuv\\\\\\\",t.RGBM7=\\\\\\\"RGBM7\\\\\\\",t.RGBM16=\\\\\\\"RGBM16\\\\\\\",t.RGBD=\\\\\\\"RGBD\\\\\\\"}(lV||(lV={})),(uV=cV||(cV={}))[uV.Linear=w.U]=\\\\\\\"Linear\\\\\\\",uV[uV.sRGB=w.ld]=\\\\\\\"sRGB\\\\\\\",uV[uV.Gamma=w.J]=\\\\\\\"Gamma\\\\\\\",uV[uV.RGBE=w.gc]=\\\\\\\"RGBE\\\\\\\",uV[uV.LogLuv=w.bb]=\\\\\\\"LogLuv\\\\\\\",uV[uV.RGBM7=w.lc]=\\\\\\\"RGBM7\\\\\\\",uV[uV.RGBM16=w.kc]=\\\\\\\"RGBM16\\\\\\\",uV[uV.RGBD=w.fc]=\\\\\\\"RGBD\\\\\\\";const hV=[lV.Linear,lV.sRGB,lV.Gamma,lV.RGBE,lV.LogLuv,lV.RGBM7,lV.RGBM16,lV.RGBD],dV=[cV.Linear,cV.sRGB,cV.Gamma,cV.RGBE,cV.LogLuv,cV.RGBM7,cV.RGBM16,cV.RGBD],pV=cV.sRGB;var _V,mV,fV;!function(t){t.No=\\\\\\\"No\\\\\\\",t.Linear=\\\\\\\"Linear\\\\\\\",t.Reinhard=\\\\\\\"Reinhard\\\\\\\",t.Cineon=\\\\\\\"Cineon\\\\\\\",t.ACESFilmic=\\\\\\\"ACESFilmic\\\\\\\"}(_V||(_V={})),(fV=mV||(mV={}))[fV.No=w.vb]=\\\\\\\"No\\\\\\\",fV[fV.Linear=w.ab]=\\\\\\\"Linear\\\\\\\",fV[fV.Reinhard=w.vc]=\\\\\\\"Reinhard\\\\\\\",fV[fV.Cineon=w.m]=\\\\\\\"Cineon\\\\\\\",fV[fV.ACESFilmic=w.a]=\\\\\\\"ACESFilmic\\\\\\\";const gV=[_V.No,_V.Linear,_V.Reinhard,_V.Cineon,_V.ACESFilmic],vV=[mV.No,mV.Linear,mV.Reinhard,mV.Cineon,mV.ACESFilmic],yV=mV.ACESFilmic,xV=gV.map(((t,e)=>({name:t,value:vV[e]})));var bV;!function(t){t.HIGH=\\\\\\\"highp\\\\\\\",t.MEDIUM=\\\\\\\"mediump\\\\\\\",t.LOW=\\\\\\\"lowp\\\\\\\"}(bV||(bV={}));const wV=[bV.HIGH,bV.MEDIUM,bV.LOW];var TV;!function(t){t.HIGH=\\\\\\\"high-performance\\\\\\\",t.LOW=\\\\\\\"low-power\\\\\\\",t.DEFAULT=\\\\\\\"default\\\\\\\"}(TV||(TV={}));const AV=[TV.HIGH,TV.LOW,TV.DEFAULT];var EV,MV,SV;!function(t){t.Basic=\\\\\\\"Basic\\\\\\\",t.PCF=\\\\\\\"PCF\\\\\\\",t.PCFSoft=\\\\\\\"PCFSoft\\\\\\\",t.VSM=\\\\\\\"VSM\\\\\\\"}(EV||(EV={})),(SV=MV||(MV={}))[SV.Basic=w.k]=\\\\\\\"Basic\\\\\\\",SV[SV.PCF=w.Fb]=\\\\\\\"PCF\\\\\\\",SV[SV.PCFSoft=w.Gb]=\\\\\\\"PCFSoft\\\\\\\",SV[SV.VSM=w.gd]=\\\\\\\"VSM\\\\\\\";const CV=[EV.Basic,EV.PCF,EV.PCFSoft,EV.VSM],NV=[MV.Basic,MV.PCF,MV.PCFSoft,MV.VSM],LV=(w.k,w.Fb,w.Gb,w.gd,MV.PCFSoft),OV={alpha:!1,precision:bV.HIGH,premultipliedAlpha:!0,antialias:!1,stencil:!0,preserveDrawingBuffer:!1,powerPreference:TV.DEFAULT,depth:!0,logarithmicDepthBuffer:!1};const RV=new class extends aa{constructor(){super(...arguments),this.tprecision=oa.BOOLEAN(0),this.precision=oa.INTEGER(wV.indexOf(bV.HIGH),{visibleIf:{tprecision:1},menu:{entries:wV.map(((t,e)=>({value:e,name:t})))}}),this.tpowerPreference=oa.BOOLEAN(0),this.powerPreference=oa.INTEGER(AV.indexOf(TV.DEFAULT),{visibleIf:{tpowerPreference:1},menu:{entries:AV.map(((t,e)=>({value:e,name:t})))}}),this.alpha=oa.BOOLEAN(1),this.premultipliedAlpha=oa.BOOLEAN(1),this.antialias=oa.BOOLEAN(1),this.stencil=oa.BOOLEAN(1),this.depth=oa.BOOLEAN(1),this.logarithmicDepthBuffer=oa.BOOLEAN(0),this.toneMapping=oa.INTEGER(yV,{menu:{entries:xV}}),this.toneMappingExposure=oa.FLOAT(1,{range:[0,2]}),this.outputEncoding=oa.INTEGER(pV,{menu:{entries:hV.map(((t,e)=>({name:t,value:dV[e]})))}}),this.physicallyCorrectLights=oa.BOOLEAN(1),this.sortObjects=oa.BOOLEAN(1),this.tpixelRatio=oa.BOOLEAN(0),this.pixelRatio=oa.INTEGER(2,{visibleIf:{tpixelRatio:!0},range:[1,4],rangeLocked:[!0,!1]}),this.tshadowMap=oa.BOOLEAN(1),this.shadowMapAutoUpdate=oa.BOOLEAN(1,{visibleIf:{tshadowMap:1}}),this.shadowMapNeedsUpdate=oa.BOOLEAN(0,{visibleIf:{tshadowMap:1}}),this.shadowMapType=oa.INTEGER(LV,{visibleIf:{tshadowMap:1},menu:{entries:CV.map(((t,e)=>({name:t,value:NV[e]})))}})}};class PV extends oV{constructor(){super(...arguments),this.paramsConfig=RV,this._renderers_by_canvas_id={}}static type(){return aV.WEBGL}createRenderer(t,e){const n={},i=Object.keys(OV);let r;for(r of i)n[r]=OV[r];if(this.pv.tprecision){const t=wV[this.pv.precision];n.precision=t}if(this.pv.tpowerPreference){const t=AV[this.pv.powerPreference];n.powerPreference=t}n.antialias=this.pv.antialias,n.antialias=this.pv.antialias,n.alpha=this.pv.alpha,n.premultipliedAlpha=this.pv.premultipliedAlpha,n.depth=this.pv.depth,n.stencil=this.pv.stencil,n.logarithmicDepthBuffer=this.pv.logarithmicDepthBuffer,n.canvas=t,n.context=e;const s=ai.renderersController.createWebGLRenderer(n);return ai.renderersController.printDebug()&&(ai.renderersController.printDebugMessage(`create renderer from node '${this.path()}'`),ai.renderersController.printDebugMessage({params:n})),this._update_renderer(s),this._renderers_by_canvas_id[t.id]=s,s}cook(){const t=Object.keys(this._renderers_by_canvas_id);for(let e of t){const t=this._renderers_by_canvas_id[e];this._update_renderer(t)}this._traverse_scene_and_update_materials(),this.cookController.endCook()}_update_renderer(t){t.physicallyCorrectLights=this.pv.physicallyCorrectLights,t.outputEncoding=this.pv.outputEncoding,t.toneMapping=this.pv.toneMapping,t.toneMappingExposure=this.pv.toneMappingExposure,t.shadowMap.enabled=this.pv.tshadowMap,t.shadowMap.autoUpdate=this.pv.shadowMapAutoUpdate,t.shadowMap.needsUpdate=this.pv.shadowMapNeedsUpdate,t.shadowMap.type=this.pv.shadowMapType,t.sortObjects=this.pv.sortObjects;const e=this.pv.tpixelRatio?this.pv.pixelRatio:FV.defaultPixelRatio();ai.renderersController.printDebug()&&(ai.renderersController.printDebugMessage(`set renderer pixelRatio from '${this.path()}'`),ai.renderersController.printDebugMessage({pixelRatio:e})),t.setPixelRatio(e)}_traverse_scene_and_update_materials(){this.scene().threejsScene().traverse((t=>{const e=t.material;if(e)if(m.isArray(e))for(let t of e)t.needsUpdate=!0;else e.needsUpdate=!0}))}}function IV(t){return class extends t{constructor(){super(...arguments),this.render=oa.FOLDER(),this.setScene=oa.BOOLEAN(0),this.scene=oa.OPERATOR_PATH(\\\\\\\"\\\\\\\",{visibleIf:{setScene:1},nodeSelection:{context:Ki.OBJ,types:[ZG.type()]}}),this.setRenderer=oa.BOOLEAN(0),this.renderer=oa.OPERATOR_PATH(\\\\\\\"\\\\\\\",{visibleIf:{setRenderer:1},nodeSelection:{context:Ki.ROP,types:[PV.type()]}}),this.setCSSRenderer=oa.BOOLEAN(0),this.CSSRenderer=oa.OPERATOR_PATH(\\\\\\\"\\\\\\\",{visibleIf:{setCSSRenderer:1},nodeSelection:{context:Ki.ROP,types:[aV.CSS2D,aV.CSS3D]}})}}}class FV{constructor(t){this.node=t,this._renderers_by_canvas_id={},this._resolution_by_canvas_id={},this._super_sampling_size=new d.a}render(t,e,n){if(this.node.pv.doPostProcess?this.node.postProcessController.render(t,e):this.render_with_renderer(t),this._resolved_cssRenderer_rop&&this._resolved_scene&&this.node.pv.setCSSRenderer){const e=this.cssRenderer(t);e&&e.render(this._resolved_scene,this.node.object)}}render_with_renderer(t){const e=this.renderer(t);e&&this._resolved_scene&&e.render(this._resolved_scene,this.node.object)}async update(){this.update_scene(),this.update_renderer(),this.update_cssRenderer()}get resolved_scene(){return this._resolved_scene}update_scene(){if(this.node.pv.setScene){const t=this.node.p.scene;t.isDirty()&&t.find_target();const e=t.found_node_with_context_and_type(Ki.OBJ,ZG.type());e&&(e.isDirty()&&e.cookController.cookMainWithoutInputs(),this._resolved_scene=e.object)}else this._resolved_scene=this.node.scene().threejsScene()}update_renderer(){if(this.node.pv.setRenderer){const t=this.node.p.renderer;t.isDirty()&&t.find_target(),this._resolved_renderer_rop=t.found_node_with_context_and_type(Ki.ROP,aV.WEBGL)}else this._resolved_renderer_rop=void 0}update_cssRenderer(){if(this.node.pv.setCSSRenderer){const t=this.node.p.CSSRenderer;t.isDirty()&&t.find_target(),this._resolved_cssRenderer_rop=t.found_node_with_context_and_type(Ki.ROP,[aV.CSS2D,aV.CSS3D])}else this._resolved_cssRenderer_rop,this._resolved_cssRenderer_rop=void 0}renderer(t){return this._renderers_by_canvas_id[t.id]}cssRenderer(t){if(this._resolved_cssRenderer_rop&&this.node.pv.setCSSRenderer)return this._resolved_cssRenderer_rop.renderer(t)}createRenderer(t,e){const n=ai.renderersController.createRenderingContext(t);if(!n)return void console.error(\\\\\\\"failed to create webgl context\\\\\\\");let i;return this.node.pv.setRenderer&&(this.update_renderer(),this._resolved_renderer_rop&&(i=this._resolved_renderer_rop.createRenderer(t,n))),i||(i=FV._createDefaultRenderer(t,n)),ai.renderersController.registerRenderer(i),this._renderers_by_canvas_id[t.id]=i,this._super_sampling_size.copy(e),this.set_renderer_size(t,this._super_sampling_size),i}static defaultPixelRatio(){return Zf.isMobile()?1:Math.max(2,window.devicePixelRatio)}static _createDefaultRenderer(t,e){const n={canvas:t,antialias:!1,alpha:!1,context:e},i=ai.renderersController.createWebGLRenderer(n),r=this.defaultPixelRatio();return i.setPixelRatio(r),i.shadowMap.enabled=!0,i.shadowMap.type=LV,i.physicallyCorrectLights=!0,i.toneMapping=yV,i.toneMappingExposure=1,i.outputEncoding=pV,ai.renderersController.printDebug()&&(ai.renderersController.printDebugMessage(\\\\\\\"create default renderer\\\\\\\"),ai.renderersController.printDebugMessage({params:n,pixelRatio:r})),i}delete_renderer(t){const e=this.renderer(t);e&&ai.renderersController.deregisterRenderer(e)}canvas_resolution(t){return this._resolution_by_canvas_id[t.id]}set_renderer_size(t,e){this._resolution_by_canvas_id[t.id]=this._resolution_by_canvas_id[t.id]||new d.a,this._resolution_by_canvas_id[t.id].copy(e);const n=this.renderer(t);if(n){const t=!1;n.setSize(e.x,e.y,t)}if(this._resolved_cssRenderer_rop){const n=this.cssRenderer(t);n&&n.setSize(e.x,e.y)}}}class DV{constructor(t){this.viewer=t,this._active=!1,this._controls=null,this._bound_on_controls_start=this._on_controls_start.bind(this),this._bound_on_controls_end=this._on_controls_end.bind(this),this._update_graph_node()}controls(){return this._controls}async create_controls(){var t;this.dispose_controls();this.viewer.canvas()&&(this._config=await(null===(t=this.viewer.cameraControlsController)||void 0===t?void 0:t.apply_controls(this.viewer)),this._config&&(this._controls=this._config.controls,this._controls&&(this.viewer.active()?(this._controls.addEventListener(\\\\\\\"start\\\\\\\",this._bound_on_controls_start),this._controls.addEventListener(\\\\\\\"end\\\\\\\",this._bound_on_controls_end)):this.dispose_controls())))}update(){this._config&&this._controls&&this._config.update_required()&&this._controls.update()}dispose(){var t;null===(t=this._graph_node)||void 0===t||t.graphDisconnectPredecessors(),this.dispose_controls()}dispose_controls(){var t;if(this._controls){const e=this.viewer.canvas();e&&(null===(t=this.viewer)||void 0===t||t.cameraControlsController.dispose_controls(e)),this._bound_on_controls_start&&this._controls.removeEventListener(\\\\\\\"start\\\\\\\",this._bound_on_controls_start),this._bound_on_controls_end&&this._controls.removeEventListener(\\\\\\\"end\\\\\\\",this._bound_on_controls_end),this._controls.dispose(),this._controls=null}}_on_controls_start(){this._active=!0}_on_controls_end(){this._active=!1}_update_graph_node(){const t=this.viewer.cameraNode().p.controls;this._graph_node=this._graph_node||this._create_graph_node(),this._graph_node&&(this._graph_node.graphDisconnectPredecessors(),this._graph_node.addGraphInput(t))}_create_graph_node(){const t=new Ai(this.viewer.cameraNode().scene(),\\\\\\\"viewer-controls\\\\\\\");return t.addPostDirtyHook(\\\\\\\"this.viewer.controls_controller\\\\\\\",(async()=>{await this.viewer.controlsController.create_controls()})),t}}class kV{constructor(t){this._viewer=t,this._size=new d.a(100,100),this._aspect=1}cameraNode(){return this._viewer.cameraNode()}get size(){return this._size}get aspect(){return this._aspect}computeSizeAndAspect(){this._updateSize(),this.cameraNode().scene().uniformsController.updateResolutionDependentUniformOwners(this._size),this._aspect=this._getAspect()}_updateSize(){this._size.x=this._viewer.domElement().offsetWidth,this._size.y=this._viewer.domElement().offsetHeight}_getAspect(){return this._size.x/this._size.y}updateCameraAspect(){this.cameraNode().setupForAspectRatio(this._aspect)}async prepareCurrentCamera(){await this.cameraNode().compute(),await this._updateFromCameraContainer()}async _updateFromCameraContainer(){var t;this.updateCameraAspect(),await(null===(t=this._viewer.controlsController)||void 0===t?void 0:t.create_controls())}}class BV{constructor(t){this.viewer=t}init(){const t=this.viewer.canvas();t&&(t.onwebglcontextlost=this._on_webglcontextlost.bind(this),t.onwebglcontextrestored=this._on_webglcontextrestored.bind(this))}_on_webglcontextlost(){console.warn(\\\\\\\"context lost at frame\\\\\\\",this.viewer.scene().frame()),this.request_animation_frame_id?cancelAnimationFrame(this.request_animation_frame_id):console.warn(\\\\\\\"request_animation_frame_id not initialized\\\\\\\"),console.warn(\\\\\\\"not canceled\\\\\\\",this.request_animation_frame_id)}_on_webglcontextrestored(){console.log(\\\\\\\"context restored\\\\\\\")}}const zV=\\\\\\\"hovered\\\\\\\";class UV{constructor(t,e,n){this._container=t,this._scene=e,this._camera_node=n,this._active=!1,this._id=UV._next_viewer_id++,this._scene.viewersRegister.registerViewer(this)}active(){return this._active}activate(){this._active=!0}deactivate(){this._active=!1}get camerasController(){return this._cameras_controller=this._cameras_controller||new kV(this)}get controlsController(){return this._controls_controller}get eventsController(){return this._events_controller=this._events_controller||new Ga(this)}get webglController(){return this._webgl_controller=this._webgl_controller||new BV(this)}domElement(){return this._container}scene(){return this._scene}canvas(){return this._canvas}cameraNode(){return this._camera_node}get cameraControlsController(){}id(){return this._id}dispose(){let t;for(this._scene.viewersRegister.unregisterViewer(this),this.eventsController.dispose();t=this._container.children[0];)this._container.removeChild(t)}resetContainerClass(){this.domElement().classList.remove(zV)}setContainerClassHovered(){this.domElement().classList.add(zV)}registerOnBeforeTick(t,e){this._onBeforeTickCallbackNames=this._onBeforeTickCallbackNames||[],this._onBeforeTickCallbacks=this._onBeforeTickCallbacks||[],this._registerCallback(t,e,this._onBeforeTickCallbackNames,this._onBeforeTickCallbacks)}unRegisterOnBeforeTick(t){this._unregisterCallback(t,this._onBeforeTickCallbackNames,this._onBeforeTickCallbacks)}registeredBeforeTickCallbackNames(){return this._onBeforeTickCallbackNames}registerOnAfterTick(t,e){this._onAfterTickCallbacks=this._onAfterTickCallbacks||[],this._onAfterTickCallbackNames=this._onAfterTickCallbackNames||[],this._registerCallback(t,e,this._onAfterTickCallbackNames,this._onAfterTickCallbacks)}unRegisterOnAfterTick(t){this._unregisterCallback(t,this._onAfterTickCallbackNames,this._onAfterTickCallbacks)}registeredAfterTickCallbackNames(){return this._onAfterTickCallbackNames}registerOnBeforeRender(t,e){this._onBeforeRenderCallbackNames=this._onBeforeRenderCallbackNames||[],this._onBeforeRenderCallbacks=this._onBeforeRenderCallbacks||[],this._registerCallback(t,e,this._onBeforeRenderCallbackNames,this._onBeforeRenderCallbacks)}unRegisterOnBeforeRender(t){this._unregisterCallback(t,this._onBeforeRenderCallbackNames,this._onBeforeRenderCallbacks)}registeredBeforeRenderCallbackNames(){return this._onBeforeRenderCallbackNames}registerOnAfterRender(t,e){this._onAfterRenderCallbackNames=this._onAfterRenderCallbackNames||[],this._onAfterRenderCallbacks=this._onAfterRenderCallbacks||[],this._registerCallback(t,e,this._onAfterRenderCallbackNames,this._onAfterRenderCallbacks)}unRegisterOnAfterRender(t){this._unregisterCallback(t,this._onAfterRenderCallbackNames,this._onAfterRenderCallbacks)}registeredAfterRenderCallbackNames(){return this._onAfterRenderCallbackNames}_registerCallback(t,e,n,i){(null==n?void 0:n.includes(t))?console.warn(`callback ${t} already registered`):(i.push(e),n.push(t))}_unregisterCallback(t,e,n){if(!e||!n)return;const i=e.indexOf(t);e.splice(i,1),n.splice(i,1)}}UV._next_viewer_id=0;class GV extends UV{constructor(t,e,n,i){super(t,e,n),this._scene=e,this._camera_node=n,this._properties=i,this._do_render=!0,this._animate_method=this.animate.bind(this),this._onResizeBound=this.onResize.bind(this),this._do_render=null==this._properties||this._properties.autoRender,this._canvas=document.createElement(\\\\\\\"canvas\\\\\\\"),this._canvas.id=`canvas_id_${Math.random()}`.replace(\\\\\\\".\\\\\\\",\\\\\\\"_\\\\\\\"),this._canvas.style.display=\\\\\\\"block\\\\\\\",this._canvas.style.outline=\\\\\\\"none\\\\\\\",this._container.appendChild(this._canvas),this._container.classList.add(\\\\\\\"CoreThreejsViewer\\\\\\\"),this._build(),this._setEvents()}get controlsController(){return this._controls_controller=this._controls_controller||new DV(this)}_build(){this._init_display(),this.activate()}dispose(){this._cancel_animate(),this.controlsController.dispose(),this._disposeEvents(),super.dispose()}get cameraControlsController(){return this._camera_node.controls_controller}_setEvents(){this.eventsController.init(),this.webglController.init(),window.addEventListener(\\\\\\\"resize\\\\\\\",this._onResizeBound.bind(this),!1)}_disposeEvents(){window.removeEventListener(\\\\\\\"resize\\\\\\\",this._onResizeBound.bind(this),!1)}onResize(){const t=this.canvas();t&&(this.camerasController.computeSizeAndAspect(),this._camera_node.renderController.set_renderer_size(t,this.camerasController.size),this.camerasController.updateCameraAspect())}_init_display(){if(!this._canvas)return void console.warn(\\\\\\\"no canvas found for viewer\\\\\\\");this.camerasController.computeSizeAndAspect();const t=this.camerasController.size;this._camera_node.renderController.createRenderer(this._canvas,t),this.camerasController.prepareCurrentCamera(),this.animate()}setAutoRender(t=!0){this._do_render=t,this._do_render&&this.animate()}animate(){var t;if(this._do_render){if(this._request_animation_frame_id=requestAnimationFrame(this._animate_method),this._onBeforeTickCallbacks)for(let t of this._onBeforeTickCallbacks)t();if(this._scene.timeController.incrementTimeIfPlaying(),this._onAfterTickCallbacks)for(let t of this._onAfterTickCallbacks)t();this.render(),null===(t=this._controls_controller)||void 0===t||t.update()}}_cancel_animate(){this._do_render=!1,this._request_animation_frame_id&&cancelAnimationFrame(this._request_animation_frame_id),this._canvas&&this._camera_node.renderController.delete_renderer(this._canvas)}render(){if(this.camerasController.cameraNode()&&this._canvas){if(this._onBeforeRenderCallbacks)for(let t of this._onBeforeRenderCallbacks)t();const t=this.camerasController.size,e=this.camerasController.aspect;if(this._camera_node.renderController.render(this._canvas,t,e),this._onAfterRenderCallbacks)for(let t of this._onAfterRenderCallbacks)t()}else console.warn(\\\\\\\"no camera to render with\\\\\\\")}renderer(){if(this._canvas)return this._camera_node.renderController.renderer(this._canvas)}}const VV={type:\\\\\\\"change\\\\\\\"},HV=1,jV=100;var WV;!function(t){t.ON_END=\\\\\\\"on move end\\\\\\\",t.ALWAYS=\\\\\\\"always\\\\\\\",t.NEVER=\\\\\\\"never\\\\\\\"}(WV||(WV={}));const qV=[WV.ON_END,WV.ALWAYS,WV.NEVER];function XV(t){return class extends t{constructor(){super(...arguments),this.setMainCamera=oa.BUTTON(null,{callback:(t,e)=>{tH.PARAM_CALLBACK_setMasterCamera(t)}})}}}function YV(t){return class extends t{constructor(){super(...arguments),this.camera=oa.FOLDER(),this.controls=oa.NODE_PATH(\\\\\\\"\\\\\\\",{nodeSelection:{context:Ki.EVENT}}),this.updateFromControlsMode=oa.INTEGER(qV.indexOf(WV.ON_END),{menu:{entries:qV.map(((t,e)=>({name:t,value:e})))}}),this.near=oa.FLOAT(HV,{range:[0,100],cook:!1,computeOnDirty:!0,callback:(t,e)=>{eH.PARAM_CALLBACK_update_near_far_from_param(t,e)}}),this.far=oa.FLOAT(jV,{range:[0,100],cook:!1,computeOnDirty:!0,callback:(t,e)=>{eH.PARAM_CALLBACK_update_near_far_from_param(t,e)}}),this.display=oa.BOOLEAN(1),this.showHelper=oa.BOOLEAN(0)}}}var $V;!function(t){t.DEFAULT=\\\\\\\"default\\\\\\\",t.COVER=\\\\\\\"cover\\\\\\\",t.CONTAIN=\\\\\\\"contain\\\\\\\"}($V||($V={}));const JV=[$V.DEFAULT,$V.COVER,$V.CONTAIN];function ZV(t){return class extends t{constructor(){super(...arguments),this.fovAdjustMode=oa.INTEGER(JV.indexOf($V.DEFAULT),{menu:{entries:JV.map(((t,e)=>({name:t,value:e})))}}),this.expectedAspectRatio=oa.FLOAT(\\\\\\\"16/9\\\\\\\",{visibleIf:[{fovAdjustMode:JV.indexOf($V.COVER)},{fovAdjustMode:JV.indexOf($V.CONTAIN)}],range:[0,2],rangeLocked:[!0,!1]})}}}XV(aa);rV(IV(Sz(eV(YV(XV(aa))))));class QV extends _z{constructor(){super(...arguments),this.renderOrder=pz.CAMERA,this._aspect=-1}get object(){return this._object}async cook(){this.updateCamera(),this._object.dispatchEvent(VV),this.cookController.endCook()}on_create(){}on_delete(){}prepareRaycaster(t,e){}camera(){return this._object}updateCamera(){}static PARAM_CALLBACK_setMasterCamera(t){t.set_as_master_camera()}set_as_master_camera(){this.scene().camerasController.setMainCameraNodePath(this.path())}setupForAspectRatio(t){}_updateForAspectRatio(){}update_transform_params_from_object(){Mz.set_params_from_object(this._object,this)}static PARAM_CALLBACK_update_from_param(t,e){t.object[e.name()]=t.pv[e.name()]}}class KV extends QV{constructor(){super(...arguments),this.flags=new Fi(this),this.hierarchyController=new Lz(this),this.transformController=new Nz(this),this.childrenDisplayController=new WU(this),this.displayNodeController=new Lm(this,this.childrenDisplayController.displayNodeControllerCallbacks()),this._children_controller_context=Ki.SOP}get controls_controller(){return this._controls_controller=this._controls_controller||new tV(this)}get layers_controller(){return this._layers_controller=this._layers_controller||new nV(this)}get renderController(){return this._render_controller=this._render_controller||new FV(this)}get postProcessController(){return this._post_process_controller=this._post_process_controller||new sV(this)}initializeBaseNode(){super.initializeBaseNode(),this.io.outputs.setHasOneOutput(),this.hierarchyController.initializeNode(),this.transformController.initializeNode(),this.childrenDisplayController.initializeNode(),this.initHelperHook()}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}prepareRaycaster(t,e){e.setFromCamera(t,this._object)}async cook(){this.transformController.update(),this.layers_controller.update(),this.updateNearFar(),this.renderController.update(),this.updateCamera(),this._updateHelper(),this.controls_controller.update_controls(),this._object.dispatchEvent(VV),this.cookController.endCook()}static PARAM_CALLBACK_update_near_far_from_param(t,e){t.updateNearFar()}updateNearFar(){this._object.near==this.pv.near&&this._object.far==this.pv.far||(this._object.near=this.pv.near,this._object.far=this.pv.far,this._object.updateProjectionMatrix(),this._updateHelper())}setupForAspectRatio(t){m.isNaN(t)||t&&this._aspect!=t&&(this._aspect=t,this._updateForAspectRatio())}createViewer(t,e){return new GV(t,this.scene(),this,e)}static PARAM_CALLBACK_reset_effects_composer(t){t.postProcessController.reset()}initHelperHook(){this.flags.display.onUpdate((()=>{this._updateHelper()}))}helperVisible(){return this.flags.display.active()&&this.pv.showHelper}_createHelper(){const t=new jz(this.object);return t.update(),t}_updateHelper(){this.helperVisible()?(this._helper||(this._helper=this._createHelper()),this._helper&&(this.object.add(this._helper),this._helper.update())):this._helper&&this.object.remove(this._helper)}}class tH extends QV{}class eH extends KV{PARAM_CALLBACK_update_effects_composer(t){}}const nH=-.5,iH=.5,rH=.5,sH=-.5;class oH extends(rV(IV(eV(XV(ZV(function(t){return class extends t{constructor(){super(...arguments),this.size=oa.FLOAT(1)}}}(YV(Sz(aa,{matrixAutoUpdate:!0}))))))))){}const aH=new oH;class lH extends KV{constructor(){super(...arguments),this.paramsConfig=aH}static type(){return nr.ORTHOGRAPHIC}createObject(){return new st.a(2*nH,2*iH,2*rH,2*sH,HV,jV)}updateCamera(){this._updateForAspectRatio()}_updateForAspectRatio(){this._aspect&&(this._adjustFOVFromMode(),this._object.updateProjectionMatrix())}_adjustFOVFromMode(){const t=JV[this.pv.fovAdjustMode];switch(t){case $V.DEFAULT:return this._adjustFOVFromModeDefault();case $V.COVER:return this._adjustFOVFromModeCover();case $V.CONTAIN:return this._adjustFOVFromModeContain()}ar.unreachable(t)}_adjustFOVFromModeDefault(){this._adjustFOVFromSize(this.pv.size||1)}_adjustFOVFromModeCover(){const t=this.pv.size||1;this._aspect>this.pv.expectedAspectRatio?this._adjustFOVFromSize(this.pv.expectedAspectRatio*t/this._aspect):this._adjustFOVFromSize(t)}_adjustFOVFromModeContain(){const t=this.pv.size||1;this._aspect>this.pv.expectedAspectRatio?this._adjustFOVFromSize(t):this._adjustFOVFromSize(this.pv.expectedAspectRatio*t/this._aspect)}_adjustFOVFromSize(t){const e=t*this._aspect;this._object.left=nH*e*1,this._object.right=iH*e*1,this._object.top=rH*t*1,this._object.bottom=sH*t*1}}const cH=50;class uH extends(rV(IV(eV(XV(ZV(function(t){return class extends t{constructor(){super(...arguments),this.fov=oa.FLOAT(cH,{range:[0,100]})}}}(YV(Sz(aa,{matrixAutoUpdate:!0}))))))))){}const hH=new uH;class dH extends KV{constructor(){super(...arguments),this.paramsConfig=hH}static type(){return nr.PERSPECTIVE}createObject(){return new K.a(cH,1,HV,jV)}updateCamera(){this._object.fov!=this.pv.fov&&(this._object.fov=this.pv.fov,this._object.updateProjectionMatrix()),this._updateForAspectRatio()}_updateForAspectRatio(){this._aspect&&(this._object.aspect=this._aspect,this._adjustFOVFromMode(),this._object.updateProjectionMatrix())}_adjustFOVFromMode(){const t=JV[this.pv.fovAdjustMode];switch(t){case $V.DEFAULT:return this._adjustFOVFromModeDefault();case $V.COVER:return this._adjustFOVFromModeCover();case $V.CONTAIN:return this._adjustFOVFromModeContain()}ar.unreachable(t)}_adjustFOVFromModeDefault(){this._object.fov=this.pv.fov}_adjustFOVFromModeCover(){if(this._object.aspect>this.pv.expectedAspectRatio){const t=Math.tan(Object(Ln.e)(this.pv.fov/2))/(this._object.aspect/this.pv.expectedAspectRatio);this._object.fov=2*Object(Ln.k)(Math.atan(t))}else this._object.fov=this.pv.fov}_adjustFOVFromModeContain(){if(this._object.aspect>this.pv.expectedAspectRatio)this._object.fov=this.pv.fov;else{const t=Math.tan(Object(Ln.e)(this.pv.fov/2))/(this._object.aspect/this.pv.expectedAspectRatio);this._object.fov=2*Object(Ln.k)(Math.atan(t))}}}class pH extends(function(t){return class extends t{constructor(){super(...arguments),this.main=oa.FOLDER(),this.resolution=oa.INTEGER(256),this.excludedObjects=oa.STRING(\\\\\\\"*`$OS`\\\\\\\"),this.printResolve=oa.BUTTON(null,{callback:t=>{mH.PARAM_CALLBACK_printResolve(t)}}),this.near=oa.FLOAT(1),this.far=oa.FLOAT(100),this.render=oa.BUTTON(null,{callback:t=>{mH.PARAM_CALLBACK_render(t)}}),this.renderTarget=oa.FOLDER(),this.tencoding=oa.BOOLEAN(0),this.encoding=oa.INTEGER(w.ld,{visibleIf:{tencoding:1},menu:{entries:eg.map((t=>({name:Object.keys(t)[0],value:Object.values(t)[0]})))}}),this.tminFilter=oa.BOOLEAN(0),this.minFilter=oa.INTEGER(Xm,{visibleIf:{tminFilter:1},menu:{entries:$m}}),this.tmagFilter=oa.BOOLEAN(0),this.magFilter=oa.INTEGER(qm,{visibleIf:{tmagFilter:1},menu:{entries:Ym}})}}}(Sz(aa))){}const _H=new pH;class mH extends _z{constructor(){super(...arguments),this.paramsConfig=_H,this.hierarchyController=new Lz(this),this.transformController=new Nz(this),this.flags=new Fi(this),this._excludedObjects=[],this._previousVisibleStateByUuid=new Map,this._helper=new NU(1)}static type(){return Ng.CUBE_CAMERA}initializeNode(){this.hierarchyController.initializeNode(),this.transformController.initializeNode(),this._updateHelperHierarchy(),this._helper.matrixAutoUpdate=!1,this.flags.display.onUpdate((()=>{this._updateHelperHierarchy()})),this.io.inputs.setCount(0,1)}createObject(){const t=new In.a;return t.matrixAutoUpdate=!0,t}cook(){this.transformController.update(),this._resolveObjects();const t=this._setupCubeCamera();this._cubeCamera&&!t||this._createCubeCamera(),this.cookController.endCook()}_updateHelperHierarchy(){this.flags.display.active()?this.object.add(this._helper):this.object.remove(this._helper)}_setupCubeCamera(){let t=!1;if(this._cubeCamera){const e=this._cubeCamera.children[0],n=e.near,i=e.far,r=this._cubeCamera.renderTarget.width;n==this.pv.near&&i==this.pv.far&&r==this.pv.resolution||(t=!0),t&&this.object.remove(this._cubeCamera)}return t}_createCubeCamera(){const t=new it(this.pv.resolution,{encoding:this.pv.tencoding?this.pv.encoding:w.ld,minFilter:this.pv.tminFilter?this.pv.minFilter:void 0,magFilter:this.pv.tmagFilter?this.pv.magFilter:void 0});this._cubeCamera=new et(this.pv.near,this.pv.far,t),this._cubeCamera.matrixAutoUpdate=!0,this.object.add(this._cubeCamera)}renderTarget(){if(this._cubeCamera)return this._cubeCamera.renderTarget}render(){const t=ai.renderersController.firstRenderer();if(t)if(this._cubeCamera){for(let t of this._excludedObjects)this._previousVisibleStateByUuid.set(t.uuid,t.visible),t.visible=!1;this._cubeCamera.update(t,this.scene().threejsScene());for(let t of this._excludedObjects){const e=this._previousVisibleStateByUuid.get(t.uuid);e&&(t.visible=e)}this._previousVisibleStateByUuid.clear()}else console.warn(`no cubeCamera for ${this.path()}`);else console.warn(`no renderer found for ${this.path()}`)}_resolveObjects(){const t=this.scene().objectsByMask(this.pv.excludedObjects),e=new Map;for(let n of t)e.set(n.uuid,n);this._excludedObjects=[];for(let n of t){const t=n.parent;t&&(e.get(t.uuid)||this._excludedObjects.push(n))}}static PARAM_CALLBACK_printResolve(t){t.param_callback_printResolve()}param_callback_printResolve(){this._resolveObjects(),console.log(this._excludedObjects)}static PARAM_CALLBACK_render(t){t.param_callback_render()}param_callback_render(){this.render()}}class fH extends _z{constructor(){super(...arguments),this._attachableToHierarchy=!1}createObject(){const t=new In.a;return t.matrixAutoUpdate=!1,t}cook(){this.cookController.endCook()}}class gH extends fH{}class vH extends gH{constructor(){super(...arguments),this._children_controller_context=Ki.ANIM}static type(){return tr.ANIM}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class yH extends vH{constructor(){super(...arguments),this.renderOrder=pz.MANAGER}}class xH extends gH{constructor(){super(...arguments),this._children_controller_context=Ki.COP}static type(){return tr.COP}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class bH extends gH{constructor(){super(...arguments),this.renderOrder=pz.MANAGER,this._children_controller_context=Ki.EVENT}static type(){return tr.EVENT}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class wH extends gH{constructor(){super(...arguments),this.renderOrder=pz.MANAGER,this._children_controller_context=Ki.MAT}static type(){return tr.MAT}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class TH extends fH{constructor(){super(...arguments),this.paramsConfig=new Jm,this.effectsComposerController=new Zm(this),this.displayNodeController=new Lm(this,this.effectsComposerController.displayNodeControllerCallbacks()),this._children_controller_context=Ki.POST}static type(){return tr.POST}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class AH extends gH{constructor(){super(...arguments),this.renderOrder=pz.MANAGER,this._children_controller_context=Ki.ROP}static type(){return tr.ROP}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}const EH=[\\\\\\\"input pass\\\\\\\"];const MH={cook:!1,callback:function(t,e){SH.PARAM_CALLBACK_updatePasses(t)},computeOnDirty:!0};class SH extends ia{constructor(){super(...arguments),this.flags=new ki(this),this._passes_by_requester_id=new Map,this._update_pass_bound=this.updatePass.bind(this)}static context(){return Ki.POST}static displayedInputNames(){return EH}initializeNode(){this.flags.display.set(!1),this.flags.display.onUpdate((()=>{if(this.flags.display.active()){const t=this.parent();t&&t.displayNodeController&&t.displayNodeController.setDisplayNode(this)}})),this.io.inputs.setCount(0,1),this.io.outputs.setHasOneOutput()}cook(){this.cookController.endCook()}setupComposer(t){if(this._addPassFromInput(0,t),!this.flags.bypass.active()){let e=this._passes_by_requester_id.get(t.requester.graphNodeId());e||(e=this._createPass(t),e&&this._passes_by_requester_id.set(t.requester.graphNodeId(),e)),e&&t.composer.addPass(e)}}_addPassFromInput(t,e){const n=this.io.inputs.input(t);n&&n.setupComposer(e)}_createPass(t){}static PARAM_CALLBACK_updatePasses(t){t._updatePasses()}_updatePasses(){this._passes_by_requester_id.forEach(this._update_pass_bound)}updatePass(t){}}const CH={uniforms:{tDiffuse:{value:null}},vertexShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\tvarying vec2 vUv;\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\tvUv = uv;\\\\n\\\\n\\\\t\\\\t\\\\tgl_Position = projectionMatrix * modelViewMatrix * vec4( position, 1.0 );\\\\n\\\\n\\\\t\\\\t}\\\\\\\",fragmentShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\t#include <common>\\\\n\\\\n\\\\t\\\\tuniform sampler2D tDiffuse;\\\\n\\\\n\\\\t\\\\tvarying vec2 vUv;\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\tvec4 texel = texture2D( tDiffuse, vUv );\\\\n\\\\n\\\\t\\\\t\\\\tfloat l = linearToRelativeLuminance( texel.rgb );\\\\n\\\\n\\\\t\\\\t\\\\tgl_FragColor = vec4( l, l, l, texel.w );\\\\n\\\\n\\\\t\\\\t}\\\\\\\"};var NH={uniforms:{tDiffuse:{value:null},averageLuminance:{value:1},luminanceMap:{value:null},maxLuminance:{value:16},minLuminance:{value:.01},middleGrey:{value:.6}},vertexShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\tvarying vec2 vUv;\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\tvUv = uv;\\\\n\\\\t\\\\t\\\\tgl_Position = projectionMatrix * modelViewMatrix * vec4( position, 1.0 );\\\\n\\\\n\\\\t\\\\t}\\\\\\\",fragmentShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\t#include <common>\\\\n\\\\n\\\\t\\\\tuniform sampler2D tDiffuse;\\\\n\\\\n\\\\t\\\\tvarying vec2 vUv;\\\\n\\\\n\\\\t\\\\tuniform float middleGrey;\\\\n\\\\t\\\\tuniform float minLuminance;\\\\n\\\\t\\\\tuniform float maxLuminance;\\\\n\\\\t\\\\t#ifdef ADAPTED_LUMINANCE\\\\n\\\\t\\\\t\\\\tuniform sampler2D luminanceMap;\\\\n\\\\t\\\\t#else\\\\n\\\\t\\\\t\\\\tuniform float averageLuminance;\\\\n\\\\t\\\\t#endif\\\\n\\\\n\\\\t\\\\tvec3 ToneMap( vec3 vColor ) {\\\\n\\\\t\\\\t\\\\t#ifdef ADAPTED_LUMINANCE\\\\n\\\\t\\\\t\\\\t\\\\t// Get the calculated average luminance\\\\n\\\\t\\\\t\\\\t\\\\tfloat fLumAvg = texture2D(luminanceMap, vec2(0.5, 0.5)).r;\\\\n\\\\t\\\\t\\\\t#else\\\\n\\\\t\\\\t\\\\t\\\\tfloat fLumAvg = averageLuminance;\\\\n\\\\t\\\\t\\\\t#endif\\\\n\\\\n\\\\t\\\\t\\\\t// Calculate the luminance of the current pixel\\\\n\\\\t\\\\t\\\\tfloat fLumPixel = linearToRelativeLuminance( vColor );\\\\n\\\\n\\\\t\\\\t\\\\t// Apply the modified operator (Eq. 4)\\\\n\\\\t\\\\t\\\\tfloat fLumScaled = (fLumPixel * middleGrey) / max( minLuminance, fLumAvg );\\\\n\\\\n\\\\t\\\\t\\\\tfloat fLumCompressed = (fLumScaled * (1.0 + (fLumScaled / (maxLuminance * maxLuminance)))) / (1.0 + fLumScaled);\\\\n\\\\t\\\\t\\\\treturn fLumCompressed * vColor;\\\\n\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\tvec4 texel = texture2D( tDiffuse, vUv );\\\\n\\\\n\\\\t\\\\t\\\\tgl_FragColor = vec4( ToneMap( texel.xyz ), texel.w );\\\\n\\\\n\\\\t\\\\t}\\\\\\\"};class LH extends Im{constructor(t,e){super(),this.resolution=void 0!==e?e:256,this.needsInit=!0,this.adaptive=void 0===t||!!t,this.luminanceRT=null,this.previousLuminanceRT=null,this.currentLuminanceRT=null,void 0===Pm&&console.error(\\\\\\\"THREE.AdaptiveToneMappingPass relies on CopyShader\\\\\\\");const n=Pm;this.copyUniforms=I.clone(n.uniforms),this.materialCopy=new F({uniforms:this.copyUniforms,vertexShader:n.vertexShader,fragmentShader:n.fragmentShader,blending:w.ub,depthTest:!1}),void 0===CH&&console.error(\\\\\\\"THREE.AdaptiveToneMappingPass relies on LuminosityShader\\\\\\\"),this.materialLuminance=new F({uniforms:I.clone(CH.uniforms),vertexShader:CH.vertexShader,fragmentShader:CH.fragmentShader,blending:w.ub}),this.adaptLuminanceShader={defines:{MIP_LEVEL_1X1:(Math.log(this.resolution)/Math.log(2)).toFixed(1)},uniforms:{lastLum:{value:null},currentLum:{value:null},minLuminance:{value:.01},delta:{value:.016},tau:{value:1}},vertexShader:\\\\\\\"varying vec2 vUv;\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tvUv = uv;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tgl_Position = projectionMatrix * modelViewMatrix * vec4( position, 1.0 );\\\\n\\\\n\\\\t\\\\t\\\\t\\\\t}\\\\\\\",fragmentShader:\\\\\\\"varying vec2 vUv;\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tuniform sampler2D lastLum;\\\\n\\\\t\\\\t\\\\t\\\\tuniform sampler2D currentLum;\\\\n\\\\t\\\\t\\\\t\\\\tuniform float minLuminance;\\\\n\\\\t\\\\t\\\\t\\\\tuniform float delta;\\\\n\\\\t\\\\t\\\\t\\\\tuniform float tau;\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tvec4 lastLum = texture2D( lastLum, vUv, MIP_LEVEL_1X1 );\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tvec4 currentLum = texture2D( currentLum, vUv, MIP_LEVEL_1X1 );\\\\n\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tfloat fLastLum = max( minLuminance, lastLum.r );\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tfloat fCurrentLum = max( minLuminance, currentLum.r );\\\\n\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t//The adaption seems to work better in extreme lighting differences\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t//if the input luminance is squared.\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tfCurrentLum *= fCurrentLum;\\\\n\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t// Adapt the luminance using Pattanaik's technique\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tfloat fAdaptedLum = fLastLum + (fCurrentLum - fLastLum) * (1.0 - exp(-delta * tau));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t// \\\\\\\\\\\"fAdaptedLum = sqrt(fAdaptedLum);\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tgl_FragColor.r = fAdaptedLum;\\\\n\\\\t\\\\t\\\\t\\\\t}\\\\\\\"},this.materialAdaptiveLum=new F({uniforms:I.clone(this.adaptLuminanceShader.uniforms),vertexShader:this.adaptLuminanceShader.vertexShader,fragmentShader:this.adaptLuminanceShader.fragmentShader,defines:Object.assign({},this.adaptLuminanceShader.defines),blending:w.ub}),void 0===NH&&console.error(\\\\\\\"THREE.AdaptiveToneMappingPass relies on ToneMapShader\\\\\\\"),this.materialToneMap=new F({uniforms:I.clone(NH.uniforms),vertexShader:NH.vertexShader,fragmentShader:NH.fragmentShader,blending:w.ub}),this.fsQuad=new km(null)}render(t,e,n,i){this.needsInit&&(this.reset(t),this.luminanceRT.texture.type=n.texture.type,this.previousLuminanceRT.texture.type=n.texture.type,this.currentLuminanceRT.texture.type=n.texture.type,this.needsInit=!1),this.adaptive&&(this.fsQuad.material=this.materialLuminance,this.materialLuminance.uniforms.tDiffuse.value=n.texture,t.setRenderTarget(this.currentLuminanceRT),this.fsQuad.render(t),this.fsQuad.material=this.materialAdaptiveLum,this.materialAdaptiveLum.uniforms.delta.value=i,this.materialAdaptiveLum.uniforms.lastLum.value=this.previousLuminanceRT.texture,this.materialAdaptiveLum.uniforms.currentLum.value=this.currentLuminanceRT.texture,t.setRenderTarget(this.luminanceRT),this.fsQuad.render(t),this.fsQuad.material=this.materialCopy,this.copyUniforms.tDiffuse.value=this.luminanceRT.texture,t.setRenderTarget(this.previousLuminanceRT),this.fsQuad.render(t)),this.fsQuad.material=this.materialToneMap,this.materialToneMap.uniforms.tDiffuse.value=n.texture,this.renderToScreen?(t.setRenderTarget(null),this.fsQuad.render(t)):(t.setRenderTarget(e),this.clear&&t.clear(),this.fsQuad.render(t))}reset(){this.luminanceRT&&this.luminanceRT.dispose(),this.currentLuminanceRT&&this.currentLuminanceRT.dispose(),this.previousLuminanceRT&&this.previousLuminanceRT.dispose();const t={minFilter:w.V,magFilter:w.V,format:w.Ib};this.luminanceRT=new Z(this.resolution,this.resolution,t),this.luminanceRT.texture.name=\\\\\\\"AdaptiveToneMappingPass.l\\\\\\\",this.luminanceRT.texture.generateMipmaps=!1,this.previousLuminanceRT=new Z(this.resolution,this.resolution,t),this.previousLuminanceRT.texture.name=\\\\\\\"AdaptiveToneMappingPass.pl\\\\\\\",this.previousLuminanceRT.texture.generateMipmaps=!1,t.minFilter=w.Y,t.generateMipmaps=!0,this.currentLuminanceRT=new Z(this.resolution,this.resolution,t),this.currentLuminanceRT.texture.name=\\\\\\\"AdaptiveToneMappingPass.cl\\\\\\\",this.adaptive&&(this.materialToneMap.defines.ADAPTED_LUMINANCE=\\\\\\\"\\\\\\\",this.materialToneMap.uniforms.luminanceMap.value=this.luminanceRT.texture),this.fsQuad.material=new at.a({color:7829367}),this.materialLuminance.needsUpdate=!0,this.materialAdaptiveLum.needsUpdate=!0,this.materialToneMap.needsUpdate=!0}setAdaptive(t){t?(this.adaptive=!0,this.materialToneMap.defines.ADAPTED_LUMINANCE=\\\\\\\"\\\\\\\",this.materialToneMap.uniforms.luminanceMap.value=this.luminanceRT.texture):(this.adaptive=!1,delete this.materialToneMap.defines.ADAPTED_LUMINANCE,this.materialToneMap.uniforms.luminanceMap.value=null),this.materialToneMap.needsUpdate=!0}setAdaptionRate(t){t&&(this.materialAdaptiveLum.uniforms.tau.value=Math.abs(t))}setMinLuminance(t){t&&(this.materialToneMap.uniforms.minLuminance.value=t,this.materialAdaptiveLum.uniforms.minLuminance.value=t)}setMaxLuminance(t){t&&(this.materialToneMap.uniforms.maxLuminance.value=t)}setAverageLuminance(t){t&&(this.materialToneMap.uniforms.averageLuminance.value=t)}setMiddleGrey(t){t&&(this.materialToneMap.uniforms.middleGrey.value=t)}dispose(){this.luminanceRT&&this.luminanceRT.dispose(),this.previousLuminanceRT&&this.previousLuminanceRT.dispose(),this.currentLuminanceRT&&this.currentLuminanceRT.dispose(),this.materialLuminance&&this.materialLuminance.dispose(),this.materialAdaptiveLum&&this.materialAdaptiveLum.dispose(),this.materialCopy&&this.materialCopy.dispose(),this.materialToneMap&&this.materialToneMap.dispose()}}const OH=new class extends aa{constructor(){super(...arguments),this.adaptive=oa.BOOLEAN(1,{...MH}),this.averageLuminance=oa.FLOAT(.7,{...MH}),this.midGrey=oa.FLOAT(.04,{...MH}),this.maxLuminance=oa.FLOAT(16,{range:[0,20],...MH}),this.adaptiveRange=oa.FLOAT(2,{range:[0,10],...MH})}};class RH extends SH{constructor(){super(...arguments),this.paramsConfig=OH}static type(){return\\\\\\\"adaptiveToneMapping\\\\\\\"}_createPass(t){const e=new LH(this.pv.adaptive,t.resolution.x);return this.updatePass(e),e}updatePass(t){t.setMaxLuminance(this.pv.maxLuminance),t.setMiddleGrey(this.pv.midGrey),t.setAverageLuminance(this.pv.averageLuminance)}}const PH={uniforms:{damp:{value:.96},tOld:{value:null},tNew:{value:null}},vertexShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\tvarying vec2 vUv;\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\tvUv = uv;\\\\n\\\\t\\\\t\\\\tgl_Position = projectionMatrix * modelViewMatrix * vec4( position, 1.0 );\\\\n\\\\n\\\\t\\\\t}\\\\\\\",fragmentShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\tuniform float damp;\\\\n\\\\n\\\\t\\\\tuniform sampler2D tOld;\\\\n\\\\t\\\\tuniform sampler2D tNew;\\\\n\\\\n\\\\t\\\\tvarying vec2 vUv;\\\\n\\\\n\\\\t\\\\tvec4 when_gt( vec4 x, float y ) {\\\\n\\\\n\\\\t\\\\t\\\\treturn max( sign( x - y ), 0.0 );\\\\n\\\\n\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\tvec4 texelOld = texture2D( tOld, vUv );\\\\n\\\\t\\\\t\\\\tvec4 texelNew = texture2D( tNew, vUv );\\\\n\\\\n\\\\t\\\\t\\\\ttexelOld *= damp * when_gt( texelOld, 0.1 );\\\\n\\\\n\\\\t\\\\t\\\\tgl_FragColor = max(texelNew, texelOld);\\\\n\\\\n\\\\t\\\\t}\\\\\\\"};class IH extends Im{constructor(t=.96){super(),void 0===PH&&console.error(\\\\\\\"THREE.AfterimagePass relies on AfterimageShader\\\\\\\"),this.shader=PH,this.uniforms=I.clone(this.shader.uniforms),this.uniforms.damp.value=t,this.textureComp=new Z(window.innerWidth,window.innerHeight,{minFilter:w.V,magFilter:w.ob,format:w.Ib}),this.textureOld=new Z(window.innerWidth,window.innerHeight,{minFilter:w.V,magFilter:w.ob,format:w.Ib}),this.shaderMaterial=new F({uniforms:this.uniforms,vertexShader:this.shader.vertexShader,fragmentShader:this.shader.fragmentShader}),this.compFsQuad=new km(this.shaderMaterial);const e=new at.a;this.copyFsQuad=new km(e)}render(t,e,n){this.uniforms.tOld.value=this.textureOld.texture,this.uniforms.tNew.value=n.texture,t.setRenderTarget(this.textureComp),this.compFsQuad.render(t),this.copyFsQuad.material.map=this.textureComp.texture,this.renderToScreen?(t.setRenderTarget(null),this.copyFsQuad.render(t)):(t.setRenderTarget(e),this.clear&&t.clear(),this.copyFsQuad.render(t));const i=this.textureOld;this.textureOld=this.textureComp,this.textureComp=i}setSize(t,e){this.textureComp.setSize(t,e),this.textureOld.setSize(t,e)}}const FH=new class extends aa{constructor(){super(...arguments),this.damp=oa.FLOAT(.96,{range:[0,1],rangeLocked:[!0,!0],...MH})}};class DH extends SH{constructor(){super(...arguments),this.paramsConfig=FH}static type(){return\\\\\\\"afterImage\\\\\\\"}_createPass(t){const e=new IH;return this.updatePass(e),e}updatePass(t){t.uniforms.damp.value=this.pv.damp}}const kH={uniforms:{tDiffuse:{value:null},opacity:{value:1}},vertexShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\tvarying vec2 vUv;\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\tvUv = uv;\\\\n\\\\t\\\\t\\\\tgl_Position = projectionMatrix * modelViewMatrix * vec4( position, 1.0 );\\\\n\\\\n\\\\t\\\\t}\\\\\\\",fragmentShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\tuniform float opacity;\\\\n\\\\n\\\\t\\\\tuniform sampler2D tDiffuse;\\\\n\\\\n\\\\t\\\\tvarying vec2 vUv;\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\tvec4 base = texture2D( tDiffuse, vUv );\\\\n\\\\n\\\\t\\\\t\\\\tvec3 lumCoeff = vec3( 0.25, 0.65, 0.1 );\\\\n\\\\t\\\\t\\\\tfloat lum = dot( lumCoeff, base.rgb );\\\\n\\\\t\\\\t\\\\tvec3 blend = vec3( lum );\\\\n\\\\n\\\\t\\\\t\\\\tfloat L = min( 1.0, max( 0.0, 10.0 * ( lum - 0.45 ) ) );\\\\n\\\\n\\\\t\\\\t\\\\tvec3 result1 = 2.0 * base.rgb * blend;\\\\n\\\\t\\\\t\\\\tvec3 result2 = 1.0 - 2.0 * ( 1.0 - blend ) * ( 1.0 - base.rgb );\\\\n\\\\n\\\\t\\\\t\\\\tvec3 newColor = mix( result1, result2, L );\\\\n\\\\n\\\\t\\\\t\\\\tfloat A2 = opacity * base.a;\\\\n\\\\t\\\\t\\\\tvec3 mixRGB = A2 * newColor.rgb;\\\\n\\\\t\\\\t\\\\tmixRGB += ( ( 1.0 - A2 ) * base.rgb );\\\\n\\\\n\\\\t\\\\t\\\\tgl_FragColor = vec4( mixRGB, base.a );\\\\n\\\\n\\\\t\\\\t}\\\\\\\"};const BH=new class extends aa{constructor(){super(...arguments),this.opacity=oa.FLOAT(.95,{range:[-5,5],rangeLocked:[!0,!0],...MH})}};class zH extends SH{constructor(){super(...arguments),this.paramsConfig=BH}static type(){return\\\\\\\"bleach\\\\\\\"}_createPass(t){const e=new Bm(kH);return this.updatePass(e),e}updatePass(t){t.uniforms.opacity.value=this.pv.opacity}}const UH={uniforms:{tDiffuse:{value:null},brightness:{value:0},contrast:{value:0}},vertexShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\tvarying vec2 vUv;\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\tvUv = uv;\\\\n\\\\n\\\\t\\\\t\\\\tgl_Position = projectionMatrix * modelViewMatrix * vec4( position, 1.0 );\\\\n\\\\n\\\\t\\\\t}\\\\\\\",fragmentShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\tuniform sampler2D tDiffuse;\\\\n\\\\t\\\\tuniform float brightness;\\\\n\\\\t\\\\tuniform float contrast;\\\\n\\\\n\\\\t\\\\tvarying vec2 vUv;\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\tgl_FragColor = texture2D( tDiffuse, vUv );\\\\n\\\\n\\\\t\\\\t\\\\tgl_FragColor.rgb += brightness;\\\\n\\\\n\\\\t\\\\t\\\\tif (contrast > 0.0) {\\\\n\\\\t\\\\t\\\\t\\\\tgl_FragColor.rgb = (gl_FragColor.rgb - 0.5) / (1.0 - contrast) + 0.5;\\\\n\\\\t\\\\t\\\\t} else {\\\\n\\\\t\\\\t\\\\t\\\\tgl_FragColor.rgb = (gl_FragColor.rgb - 0.5) * (1.0 + contrast) + 0.5;\\\\n\\\\t\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\t}\\\\\\\"};const GH=new class extends aa{constructor(){super(...arguments),this.brightness=oa.FLOAT(0,{range:[-1,1],rangeLocked:[!1,!1],...MH}),this.contrast=oa.FLOAT(0,{range:[-1,1],rangeLocked:[!1,!1],...MH}),this.transparent=oa.BOOLEAN(1,MH)}};class VH extends SH{constructor(){super(...arguments),this.paramsConfig=GH}static type(){return\\\\\\\"brightnessContrast\\\\\\\"}_createPass(t){const e=new Bm(UH);return console.log(\\\\\\\"brightness\\\\\\\",e),e.fsQuad.material.transparent=!0,this.updatePass(e),e}updatePass(t){t.uniforms.brightness.value=this.pv.brightness,t.uniforms.contrast.value=this.pv.contrast,t.material.transparent=this.pv.transparent}}class HH extends Im{constructor(t,e){super(),this.needsSwap=!1,this.clearColor=void 0!==t?t:0,this.clearAlpha=void 0!==e?e:0,this._oldClearColor=new D.a}render(t,e,n){let i;this.clearColor&&(t.getClearColor(this._oldClearColor),i=t.getClearAlpha(),t.setClearColor(this.clearColor,this.clearAlpha)),t.setRenderTarget(this.renderToScreen?null:n),t.clear(),this.clearColor&&t.setClearColor(this._oldClearColor,i)}}const jH=new class extends aa{};class WH extends SH{constructor(){super(...arguments),this.paramsConfig=jH}static type(){return\\\\\\\"clear\\\\\\\"}_createPass(t){const e=new HH;return this.updatePass(e),e}updatePass(t){}}const qH=new class extends aa{};class XH extends SH{constructor(){super(...arguments),this.paramsConfig=qH}static type(){return\\\\\\\"clearMask\\\\\\\"}_createPass(t){const e=new Um;return this.updatePass(e),e}updatePass(t){}}const YH={uniforms:{tDiffuse:{value:null},powRGB:{value:new p.a(2,2,2)},mulRGB:{value:new p.a(1,1,1)},addRGB:{value:new p.a(0,0,0)}},vertexShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\tvarying vec2 vUv;\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\tvUv = uv;\\\\n\\\\n\\\\t\\\\t\\\\tgl_Position = projectionMatrix * modelViewMatrix * vec4( position, 1.0 );\\\\n\\\\n\\\\t\\\\t}\\\\\\\",fragmentShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\tuniform sampler2D tDiffuse;\\\\n\\\\t\\\\tuniform vec3 powRGB;\\\\n\\\\t\\\\tuniform vec3 mulRGB;\\\\n\\\\t\\\\tuniform vec3 addRGB;\\\\n\\\\n\\\\t\\\\tvarying vec2 vUv;\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\tgl_FragColor = texture2D( tDiffuse, vUv );\\\\n\\\\t\\\\t\\\\tgl_FragColor.rgb = mulRGB * pow( ( gl_FragColor.rgb + addRGB ), powRGB );\\\\n\\\\n\\\\t\\\\t}\\\\\\\"};const $H=new class extends aa{constructor(){super(...arguments),this.pow=oa.VECTOR3([2,2,2],{...MH}),this.mult=oa.COLOR([1,1,1],{...MH}),this.add=oa.COLOR([0,0,0],{...MH})}};class JH extends SH{constructor(){super(...arguments),this.paramsConfig=$H}static type(){return\\\\\\\"colorCorrection\\\\\\\"}_createPass(t){const e=new Bm(YH);return this.updatePass(e),e}updatePass(t){t.uniforms.powRGB.value.copy(this.pv.pow),t.uniforms.mulRGB.value.set(this.pv.mult.r,this.pv.mult.g,this.pv.mult.b),t.uniforms.addRGB.value.set(this.pv.add.r,this.pv.add.g,this.pv.add.b)}}const ZH=new class extends aa{constructor(){super(...arguments),this.opacity=oa.FLOAT(1,{range:[0,1],rangeLocked:[!0,!0],...MH}),this.transparent=oa.BOOLEAN(1,MH)}};class QH extends SH{constructor(){super(...arguments),this.paramsConfig=ZH}static type(){return\\\\\\\"copy\\\\\\\"}_createPass(t){const e=new Bm(Pm);return this.updatePass(e),e}updatePass(t){t.uniforms.opacity.value=this.pv.opacity,t.material.transparent=this.pv.transparent}}const KH={uniforms:{textureWidth:{value:1},textureHeight:{value:1},focalDepth:{value:1},focalLength:{value:24},fstop:{value:.9},tColor:{value:null},tDepth:{value:null},maxblur:{value:1},showFocus:{value:0},manualdof:{value:0},vignetting:{value:0},depthblur:{value:0},threshold:{value:.5},gain:{value:2},bias:{value:.5},fringe:{value:.7},znear:{value:.1},zfar:{value:100},noise:{value:1},dithering:{value:1e-4},pentagon:{value:0},shaderFocus:{value:1},focusCoords:{value:new d.a}},vertexShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\tvarying vec2 vUv;\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\tvUv = uv;\\\\n\\\\t\\\\t\\\\tgl_Position = projectionMatrix * modelViewMatrix * vec4( position, 1.0 );\\\\n\\\\n\\\\t\\\\t}\\\\\\\",fragmentShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\t#include <common>\\\\n\\\\n\\\\t\\\\tvarying vec2 vUv;\\\\n\\\\n\\\\t\\\\tuniform sampler2D tColor;\\\\n\\\\t\\\\tuniform sampler2D tDepth;\\\\n\\\\t\\\\tuniform float textureWidth;\\\\n\\\\t\\\\tuniform float textureHeight;\\\\n\\\\n\\\\t\\\\tuniform float focalDepth;  //focal distance value in meters, but you may use autofocus option below\\\\n\\\\t\\\\tuniform float focalLength; //focal length in mm\\\\n\\\\t\\\\tuniform float fstop; //f-stop value\\\\n\\\\t\\\\tuniform bool showFocus; //show debug focus point and focal range (red = focal point, green = focal range)\\\\n\\\\n\\\\t\\\\t/*\\\\n\\\\t\\\\tmake sure that these two values are the same for your camera, otherwise distances will be wrong.\\\\n\\\\t\\\\t*/\\\\n\\\\n\\\\t\\\\tuniform float znear; // camera clipping start\\\\n\\\\t\\\\tuniform float zfar; // camera clipping end\\\\n\\\\n\\\\t\\\\t//------------------------------------------\\\\n\\\\t\\\\t//user variables\\\\n\\\\n\\\\t\\\\tconst int samples = SAMPLES; //samples on the first ring\\\\n\\\\t\\\\tconst int rings = RINGS; //ring count\\\\n\\\\n\\\\t\\\\tconst int maxringsamples = rings * samples;\\\\n\\\\n\\\\t\\\\tuniform bool manualdof; // manual dof calculation\\\\n\\\\t\\\\tfloat ndofstart = 1.0; // near dof blur start\\\\n\\\\t\\\\tfloat ndofdist = 2.0; // near dof blur falloff distance\\\\n\\\\t\\\\tfloat fdofstart = 1.0; // far dof blur start\\\\n\\\\t\\\\tfloat fdofdist = 3.0; // far dof blur falloff distance\\\\n\\\\n\\\\t\\\\tfloat CoC = 0.03; //circle of confusion size in mm (35mm film = 0.03mm)\\\\n\\\\n\\\\t\\\\tuniform bool vignetting; // use optical lens vignetting\\\\n\\\\n\\\\t\\\\tfloat vignout = 1.3; // vignetting outer border\\\\n\\\\t\\\\tfloat vignin = 0.0; // vignetting inner border\\\\n\\\\t\\\\tfloat vignfade = 22.0; // f-stops till vignete fades\\\\n\\\\n\\\\t\\\\tuniform bool shaderFocus;\\\\n\\\\t\\\\t// disable if you use external focalDepth value\\\\n\\\\n\\\\t\\\\tuniform vec2 focusCoords;\\\\n\\\\t\\\\t// autofocus point on screen (0.0,0.0 - left lower corner, 1.0,1.0 - upper right)\\\\n\\\\t\\\\t// if center of screen use vec2(0.5, 0.5);\\\\n\\\\n\\\\t\\\\tuniform float maxblur;\\\\n\\\\t\\\\t//clamp value of max blur (0.0 = no blur, 1.0 default)\\\\n\\\\n\\\\t\\\\tuniform float threshold; // highlight threshold;\\\\n\\\\t\\\\tuniform float gain; // highlight gain;\\\\n\\\\n\\\\t\\\\tuniform float bias; // bokeh edge bias\\\\n\\\\t\\\\tuniform float fringe; // bokeh chromatic aberration / fringing\\\\n\\\\n\\\\t\\\\tuniform bool noise; //use noise instead of pattern for sample dithering\\\\n\\\\n\\\\t\\\\tuniform float dithering;\\\\n\\\\n\\\\t\\\\tuniform bool depthblur; // blur the depth buffer\\\\n\\\\t\\\\tfloat dbsize = 1.25; // depth blur size\\\\n\\\\n\\\\t\\\\t/*\\\\n\\\\t\\\\tnext part is experimental\\\\n\\\\t\\\\tnot looking good with small sample and ring count\\\\n\\\\t\\\\tlooks okay starting from samples = 4, rings = 4\\\\n\\\\t\\\\t*/\\\\n\\\\n\\\\t\\\\tuniform bool pentagon; //use pentagon as bokeh shape?\\\\n\\\\t\\\\tfloat feather = 0.4; //pentagon shape feather\\\\n\\\\n\\\\t\\\\t//------------------------------------------\\\\n\\\\n\\\\t\\\\tfloat penta(vec2 coords) {\\\\n\\\\t\\\\t\\\\t//pentagonal shape\\\\n\\\\t\\\\t\\\\tfloat scale = float(rings) - 1.3;\\\\n\\\\t\\\\t\\\\tvec4  HS0 = vec4( 1.0,         0.0,         0.0,  1.0);\\\\n\\\\t\\\\t\\\\tvec4  HS1 = vec4( 0.309016994, 0.951056516, 0.0,  1.0);\\\\n\\\\t\\\\t\\\\tvec4  HS2 = vec4(-0.809016994, 0.587785252, 0.0,  1.0);\\\\n\\\\t\\\\t\\\\tvec4  HS3 = vec4(-0.809016994,-0.587785252, 0.0,  1.0);\\\\n\\\\t\\\\t\\\\tvec4  HS4 = vec4( 0.309016994,-0.951056516, 0.0,  1.0);\\\\n\\\\t\\\\t\\\\tvec4  HS5 = vec4( 0.0        ,0.0         , 1.0,  1.0);\\\\n\\\\n\\\\t\\\\t\\\\tvec4  one = vec4( 1.0 );\\\\n\\\\n\\\\t\\\\t\\\\tvec4 P = vec4((coords),vec2(scale, scale));\\\\n\\\\n\\\\t\\\\t\\\\tvec4 dist = vec4(0.0);\\\\n\\\\t\\\\t\\\\tfloat inorout = -4.0;\\\\n\\\\n\\\\t\\\\t\\\\tdist.x = dot( P, HS0 );\\\\n\\\\t\\\\t\\\\tdist.y = dot( P, HS1 );\\\\n\\\\t\\\\t\\\\tdist.z = dot( P, HS2 );\\\\n\\\\t\\\\t\\\\tdist.w = dot( P, HS3 );\\\\n\\\\n\\\\t\\\\t\\\\tdist = smoothstep( -feather, feather, dist );\\\\n\\\\n\\\\t\\\\t\\\\tinorout += dot( dist, one );\\\\n\\\\n\\\\t\\\\t\\\\tdist.x = dot( P, HS4 );\\\\n\\\\t\\\\t\\\\tdist.y = HS5.w - abs( P.z );\\\\n\\\\n\\\\t\\\\t\\\\tdist = smoothstep( -feather, feather, dist );\\\\n\\\\t\\\\t\\\\tinorout += dist.x;\\\\n\\\\n\\\\t\\\\t\\\\treturn clamp( inorout, 0.0, 1.0 );\\\\n\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\tfloat bdepth(vec2 coords) {\\\\n\\\\t\\\\t\\\\t// Depth buffer blur\\\\n\\\\t\\\\t\\\\tfloat d = 0.0;\\\\n\\\\t\\\\t\\\\tfloat kernel[9];\\\\n\\\\t\\\\t\\\\tvec2 offset[9];\\\\n\\\\n\\\\t\\\\t\\\\tvec2 wh = vec2(1.0/textureWidth,1.0/textureHeight) * dbsize;\\\\n\\\\n\\\\t\\\\t\\\\toffset[0] = vec2(-wh.x,-wh.y);\\\\n\\\\t\\\\t\\\\toffset[1] = vec2( 0.0, -wh.y);\\\\n\\\\t\\\\t\\\\toffset[2] = vec2( wh.x -wh.y);\\\\n\\\\n\\\\t\\\\t\\\\toffset[3] = vec2(-wh.x,  0.0);\\\\n\\\\t\\\\t\\\\toffset[4] = vec2( 0.0,   0.0);\\\\n\\\\t\\\\t\\\\toffset[5] = vec2( wh.x,  0.0);\\\\n\\\\n\\\\t\\\\t\\\\toffset[6] = vec2(-wh.x, wh.y);\\\\n\\\\t\\\\t\\\\toffset[7] = vec2( 0.0,  wh.y);\\\\n\\\\t\\\\t\\\\toffset[8] = vec2( wh.x, wh.y);\\\\n\\\\n\\\\t\\\\t\\\\tkernel[0] = 1.0/16.0;   kernel[1] = 2.0/16.0;   kernel[2] = 1.0/16.0;\\\\n\\\\t\\\\t\\\\tkernel[3] = 2.0/16.0;   kernel[4] = 4.0/16.0;   kernel[5] = 2.0/16.0;\\\\n\\\\t\\\\t\\\\tkernel[6] = 1.0/16.0;   kernel[7] = 2.0/16.0;   kernel[8] = 1.0/16.0;\\\\n\\\\n\\\\n\\\\t\\\\t\\\\tfor( int i=0; i<9; i++ ) {\\\\n\\\\t\\\\t\\\\t\\\\tfloat tmp = texture2D(tDepth, coords + offset[i]).r;\\\\n\\\\t\\\\t\\\\t\\\\td += tmp * kernel[i];\\\\n\\\\t\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\t\\\\treturn d;\\\\n\\\\t\\\\t}\\\\n\\\\n\\\\n\\\\t\\\\tvec3 color(vec2 coords,float blur) {\\\\n\\\\t\\\\t\\\\t//processing the sample\\\\n\\\\n\\\\t\\\\t\\\\tvec3 col = vec3(0.0);\\\\n\\\\t\\\\t\\\\tvec2 texel = vec2(1.0/textureWidth,1.0/textureHeight);\\\\n\\\\n\\\\t\\\\t\\\\tcol.r = texture2D(tColor,coords + vec2(0.0,1.0)*texel*fringe*blur).r;\\\\n\\\\t\\\\t\\\\tcol.g = texture2D(tColor,coords + vec2(-0.866,-0.5)*texel*fringe*blur).g;\\\\n\\\\t\\\\t\\\\tcol.b = texture2D(tColor,coords + vec2(0.866,-0.5)*texel*fringe*blur).b;\\\\n\\\\n\\\\t\\\\t\\\\tvec3 lumcoeff = vec3(0.299,0.587,0.114);\\\\n\\\\t\\\\t\\\\tfloat lum = dot(col.rgb, lumcoeff);\\\\n\\\\t\\\\t\\\\tfloat thresh = max((lum-threshold)*gain, 0.0);\\\\n\\\\t\\\\t\\\\treturn col+mix(vec3(0.0),col,thresh*blur);\\\\n\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\tvec3 debugFocus(vec3 col, float blur, float depth) {\\\\n\\\\t\\\\t\\\\tfloat edge = 0.002*depth; //distance based edge smoothing\\\\n\\\\t\\\\t\\\\tfloat m = clamp(smoothstep(0.0,edge,blur),0.0,1.0);\\\\n\\\\t\\\\t\\\\tfloat e = clamp(smoothstep(1.0-edge,1.0,blur),0.0,1.0);\\\\n\\\\n\\\\t\\\\t\\\\tcol = mix(col,vec3(1.0,0.5,0.0),(1.0-m)*0.6);\\\\n\\\\t\\\\t\\\\tcol = mix(col,vec3(0.0,0.5,1.0),((1.0-e)-(1.0-m))*0.2);\\\\n\\\\n\\\\t\\\\t\\\\treturn col;\\\\n\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\tfloat linearize(float depth) {\\\\n\\\\t\\\\t\\\\treturn -zfar * znear / (depth * (zfar - znear) - zfar);\\\\n\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\tfloat vignette() {\\\\n\\\\t\\\\t\\\\tfloat dist = distance(vUv.xy, vec2(0.5,0.5));\\\\n\\\\t\\\\t\\\\tdist = smoothstep(vignout+(fstop/vignfade), vignin+(fstop/vignfade), dist);\\\\n\\\\t\\\\t\\\\treturn clamp(dist,0.0,1.0);\\\\n\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\tfloat gather(float i, float j, int ringsamples, inout vec3 col, float w, float h, float blur) {\\\\n\\\\t\\\\t\\\\tfloat rings2 = float(rings);\\\\n\\\\t\\\\t\\\\tfloat step = PI*2.0 / float(ringsamples);\\\\n\\\\t\\\\t\\\\tfloat pw = cos(j*step)*i;\\\\n\\\\t\\\\t\\\\tfloat ph = sin(j*step)*i;\\\\n\\\\t\\\\t\\\\tfloat p = 1.0;\\\\n\\\\t\\\\t\\\\tif (pentagon) {\\\\n\\\\t\\\\t\\\\t\\\\tp = penta(vec2(pw,ph));\\\\n\\\\t\\\\t\\\\t}\\\\n\\\\t\\\\t\\\\tcol += color(vUv.xy + vec2(pw*w,ph*h), blur) * mix(1.0, i/rings2, bias) * p;\\\\n\\\\t\\\\t\\\\treturn 1.0 * mix(1.0, i /rings2, bias) * p;\\\\n\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\t\\\\t\\\\t//scene depth calculation\\\\n\\\\n\\\\t\\\\t\\\\tfloat depth = linearize(texture2D(tDepth,vUv.xy).x);\\\\n\\\\n\\\\t\\\\t\\\\t// Blur depth?\\\\n\\\\t\\\\t\\\\tif ( depthblur ) {\\\\n\\\\t\\\\t\\\\t\\\\tdepth = linearize(bdepth(vUv.xy));\\\\n\\\\t\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\t\\\\t//focal plane calculation\\\\n\\\\n\\\\t\\\\t\\\\tfloat fDepth = focalDepth;\\\\n\\\\n\\\\t\\\\t\\\\tif (shaderFocus) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tfDepth = linearize(texture2D(tDepth,focusCoords).x);\\\\n\\\\n\\\\t\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\t\\\\t// dof blur factor calculation\\\\n\\\\n\\\\t\\\\t\\\\tfloat blur = 0.0;\\\\n\\\\n\\\\t\\\\t\\\\tif (manualdof) {\\\\n\\\\t\\\\t\\\\t\\\\tfloat a = depth-fDepth; // Focal plane\\\\n\\\\t\\\\t\\\\t\\\\tfloat b = (a-fdofstart)/fdofdist; // Far DoF\\\\n\\\\t\\\\t\\\\t\\\\tfloat c = (-a-ndofstart)/ndofdist; // Near Dof\\\\n\\\\t\\\\t\\\\t\\\\tblur = (a>0.0) ? b : c;\\\\n\\\\t\\\\t\\\\t} else {\\\\n\\\\t\\\\t\\\\t\\\\tfloat f = focalLength; // focal length in mm\\\\n\\\\t\\\\t\\\\t\\\\tfloat d = fDepth*1000.0; // focal plane in mm\\\\n\\\\t\\\\t\\\\t\\\\tfloat o = depth*1000.0; // depth in mm\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tfloat a = (o*f)/(o-f);\\\\n\\\\t\\\\t\\\\t\\\\tfloat b = (d*f)/(d-f);\\\\n\\\\t\\\\t\\\\t\\\\tfloat c = (d-f)/(d*fstop*CoC);\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tblur = abs(a-b)*c;\\\\n\\\\t\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\t\\\\tblur = clamp(blur,0.0,1.0);\\\\n\\\\n\\\\t\\\\t\\\\t// calculation of pattern for dithering\\\\n\\\\n\\\\t\\\\t\\\\tvec2 noise = vec2(rand(vUv.xy), rand( vUv.xy + vec2( 0.4, 0.6 ) ) )*dithering*blur;\\\\n\\\\n\\\\t\\\\t\\\\t// getting blur x and y step factor\\\\n\\\\n\\\\t\\\\t\\\\tfloat w = (1.0/textureWidth)*blur*maxblur+noise.x;\\\\n\\\\t\\\\t\\\\tfloat h = (1.0/textureHeight)*blur*maxblur+noise.y;\\\\n\\\\n\\\\t\\\\t\\\\t// calculation of final color\\\\n\\\\n\\\\t\\\\t\\\\tvec3 col = vec3(0.0);\\\\n\\\\n\\\\t\\\\t\\\\tif(blur < 0.05) {\\\\n\\\\t\\\\t\\\\t\\\\t//some optimization thingy\\\\n\\\\t\\\\t\\\\t\\\\tcol = texture2D(tColor, vUv.xy).rgb;\\\\n\\\\t\\\\t\\\\t} else {\\\\n\\\\t\\\\t\\\\t\\\\tcol = texture2D(tColor, vUv.xy).rgb;\\\\n\\\\t\\\\t\\\\t\\\\tfloat s = 1.0;\\\\n\\\\t\\\\t\\\\t\\\\tint ringsamples;\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tfor (int i = 1; i <= rings; i++) {\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t/*unboxstart*/\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tringsamples = i * samples;\\\\n\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tfor (int j = 0 ; j < maxringsamples ; j++) {\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif (j >= ringsamples) break;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\ts += gather(float(i), float(j), ringsamples, col, w, h, blur);\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t}\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t/*unboxend*/\\\\n\\\\t\\\\t\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tcol /= s; //divide by sample count\\\\n\\\\t\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\t\\\\tif (showFocus) {\\\\n\\\\t\\\\t\\\\t\\\\tcol = debugFocus(col, blur, depth);\\\\n\\\\t\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\t\\\\tif (vignetting) {\\\\n\\\\t\\\\t\\\\t\\\\tcol *= vignette();\\\\n\\\\t\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\t\\\\tgl_FragColor.rgb = col;\\\\n\\\\t\\\\t\\\\tgl_FragColor.a = 1.0;\\\\n\\\\t\\\\t}\\\\\\\"},tj={uniforms:{mNear:{value:1},mFar:{value:1e3}},vertexShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\tvarying float vViewZDepth;\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\t#include <begin_vertex>\\\\n\\\\t\\\\t\\\\t#include <project_vertex>\\\\n\\\\n\\\\t\\\\t\\\\tvViewZDepth = - mvPosition.z;\\\\n\\\\n\\\\t\\\\t}\\\\\\\",fragmentShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\tuniform float mNear;\\\\n\\\\t\\\\tuniform float mFar;\\\\n\\\\n\\\\t\\\\tvarying float vViewZDepth;\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\tfloat color = 1.0 - smoothstep( mNear, mFar, vViewZDepth );\\\\n\\\\t\\\\t\\\\tgl_FragColor = vec4( vec3( color ), 1.0 );\\\\n\\\\n\\\\t\\\\t}\\\\\\\"};class ej{constructor(t){this._scene=t}scene(){return this._scene}with_overriden_material(t,e,n,i){const r={};let s;this._scene.traverse((i=>{const o=i;if(o.material){const i=o.geometry;if(i){const a=o.customDepthDOFMaterial;if(a){if(s=a,s.uniforms)for(let t of Object.keys(n))s.uniforms[t].value=n[t].value}else s=ps.markedAsInstance(i)?e:t;s&&(r[o.uuid]=o.material,o.material=s)}}})),i(),this._scene.traverse((t=>{const e=t;if(e.material){e.geometry&&(e.material=r[e.uuid])}}));for(let t of Object.keys(r))delete r[t]}}class nj{constructor(t,e,n,i){this._depth_of_field_node=t,this._scene=e,this._camera=n,this._resolution=i,this._camera_uniforms={mNear:{value:0},mFar:{value:0}},this.enabled=!0,this.needsSwap=!0,this.clear=!0,this.renderToScreen=!0,this._processing_scene=new fr,this.clear_color=new D.a(1,1,1),this._prev_clear_color=new D.a,this._core_scene=new ej(this._scene);const r=3,s=4;this._processing_camera=new st.a(this._resolution.x/-2,this._resolution.x/2,this._resolution.y/2,this._resolution.y/-2,-1e4,1e4),this._processing_camera.position.z=100,this._processing_scene.add(this._processing_camera);var o={minFilter:w.V,magFilter:w.V,format:w.ic};this._rtTextureDepth=new Z(this._resolution.x,this._resolution.y,o),this._rtTextureColor=new Z(this._resolution.x,this._resolution.y,o);var a=KH;a||console.error(\\\\\\\"BokehPass relies on BokehShader\\\\\\\"),this.bokeh_uniforms=I.clone(a.uniforms),this.bokeh_uniforms.tColor.value=this._rtTextureColor.texture,this.bokeh_uniforms.tDepth.value=this._rtTextureDepth.texture,this.bokeh_uniforms.textureWidth.value=this._resolution.x,this.bokeh_uniforms.textureHeight.value=this._resolution.y,this.bokeh_material=new F({uniforms:this.bokeh_uniforms,vertexShader:a.vertexShader,fragmentShader:a.fragmentShader,defines:{RINGS:r,SAMPLES:s}}),this._quad=new k.a(new L(this._resolution.x,this._resolution.y),this.bokeh_material),this._quad.position.z=-500,this._processing_scene.add(this._quad);var l=tj;l||console.error(\\\\\\\"BokehPass relies on BokehDepthShader\\\\\\\"),this.materialDepth=new F({uniforms:l.uniforms,vertexShader:l.vertexShader,fragmentShader:l.fragmentShader}),this.materialDepthInstance=new F({uniforms:l.uniforms,vertexShader:\\\\\\\"#include <common>\\\\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 <begin_vertex>\\\\n\\\\tvec3 transformed = v_POLYGON_instance_transform1_position;\\\\n\\\\n\\\\n\\\\t#include <project_vertex>\\\\n\\\\n\\\\tvViewZDepth = - mvPosition.z;\\\\n}\\\\\\\",fragmentShader:l.fragmentShader}),this.update_camera_uniforms_with_node(this._depth_of_field_node,this._camera)}setSize(t,e){this._rtTextureDepth.setSize(t,e),this._rtTextureColor.setSize(t,e),this.bokeh_uniforms.textureWidth.value=t,this.bokeh_uniforms.textureHeight.value=e}dispose(){this._rtTextureDepth.dispose(),this._rtTextureColor.dispose()}render(t,e,n){t.getClearColor(this._prev_clear_color),t.setClearColor(this.clear_color),t.clear(),t.setRenderTarget(this._rtTextureColor),t.clear(),t.render(this._scene,this._camera),t.setClearColor(0),this._core_scene.with_overriden_material(this.materialDepth,this.materialDepthInstance,this._camera_uniforms,(()=>{t.setRenderTarget(this._rtTextureDepth),t.clear(),t.render(this._scene,this._camera)})),t.setRenderTarget(null),t.clear(),t.render(this._processing_scene,this._processing_camera),t.setClearColor(this._prev_clear_color)}update_camera_uniforms_with_node(t,e){this.bokeh_uniforms.focalLength.value=e.getFocalLength(),this.bokeh_uniforms.znear.value=e.near,this.bokeh_uniforms.zfar.value=e.far;var n=rj.smoothstep(e.near,e.far,t.pv.focalDepth),i=rj.linearize(1-n,e.near,e.far);this.bokeh_uniforms.focalDepth.value=i,this._camera_uniforms={mNear:{value:e.near},mFar:{value:e.far}};for(let t of[this.materialDepth,this.materialDepthInstance])t.uniforms.mNear.value=this._camera_uniforms.mNear.value,t.uniforms.mFar.value=this._camera_uniforms.mFar.value}}const ij=new class extends aa{constructor(){super(...arguments),this.focalDepth=oa.FLOAT(10,{range:[0,50],rangeLocked:[!0,!1],step:.001,...MH}),this.fStep=oa.FLOAT(10,{range:[.1,22],rangeLocked:[!0,!0],...MH}),this.maxBlur=oa.FLOAT(2,{range:[0,10],rangeLocked:[!0,!1],...MH}),this.vignetting=oa.BOOLEAN(0,{...MH}),this.depthBlur=oa.BOOLEAN(0,{...MH}),this.threshold=oa.FLOAT(.5,{range:[0,1],rangeLocked:[!0,!0],step:.001,...MH}),this.gain=oa.FLOAT(1,{range:[0,100],rangeLocked:[!0,!0],step:.001,...MH}),this.bias=oa.FLOAT(1,{range:[0,3],rangeLocked:[!0,!0],step:.001,...MH}),this.fringe=oa.FLOAT(.7,{range:[0,5],rangeLocked:[!0,!1],step:.001,...MH}),this.noise=oa.BOOLEAN(0,{...MH}),this.dithering=oa.FLOAT(0,{range:[0,.001],rangeLocked:[!0,!0],step:1e-4,...MH}),this.pentagon=oa.BOOLEAN(0,{...MH}),this.rings=oa.INTEGER(3,{range:[1,8],rangeLocked:[!0,!0],...MH}),this.samples=oa.INTEGER(4,{range:[1,13],rangeLocked:[!0,!0],...MH}),this.clearColor=oa.COLOR([1,1,1],{...MH})}};class rj extends SH{constructor(){super(...arguments),this.paramsConfig=ij}static type(){return\\\\\\\"depthOfField\\\\\\\"}static saturate(t){return Math.max(0,Math.min(1,t))}static linearize(t,e,n){return-n*e/(t*(n-e)-n)}static smoothstep(t,e,n){var i=this.saturate((n-t)/(e-t));return i*i*(3-2*i)}_createPass(t){if(t.camera.isPerspectiveCamera){const e=t.camera_node;if(e){const n=new nj(this,t.scene,e.object,t.resolution);this.updatePass(n);const i=new Ai(this.scene(),\\\\\\\"DOF\\\\\\\");return i.addGraphInput(e.p.near),i.addGraphInput(e.p.far),i.addGraphInput(e.p.fov),i.addGraphInput(this.p.focalDepth),i.addPostDirtyHook(\\\\\\\"post/DOF\\\\\\\",(()=>{this.update_pass_from_camera_node(n,e)})),n}}}update_pass_from_camera_node(t,e){t.update_camera_uniforms_with_node(this,e.object)}updatePass(t){t.bokeh_uniforms.fstop.value=this.pv.fStep,t.bokeh_uniforms.maxblur.value=this.pv.maxBlur,t.bokeh_uniforms.threshold.value=this.pv.threshold,t.bokeh_uniforms.gain.value=this.pv.gain,t.bokeh_uniforms.bias.value=this.pv.bias,t.bokeh_uniforms.fringe.value=this.pv.fringe,t.bokeh_uniforms.dithering.value=this.pv.dithering,t.bokeh_uniforms.noise.value=this.pv.noise?1:0,t.bokeh_uniforms.pentagon.value=this.pv.pentagon?1:0,t.bokeh_uniforms.vignetting.value=this.pv.vignetting?1:0,t.bokeh_uniforms.depthblur.value=this.pv.depthBlur?1:0,t.bokeh_uniforms.shaderFocus.value=0,t.bokeh_uniforms.showFocus.value=0,t.bokeh_uniforms.manualdof.value=0,t.bokeh_uniforms.focusCoords.value.set(.5,.5),t.bokeh_material.defines.RINGS=this.pv.rings,t.bokeh_material.defines.SAMPLES=this.pv.samples,t.bokeh_material.needsUpdate=!0,t.clear_color.copy(this.pv.clearColor)}}const sj={uniforms:{tDiffuse:{value:null},tSize:{value:new d.a(256,256)},center:{value:new d.a(.5,.5)},angle:{value:1.57},scale:{value:1}},vertexShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\tvarying vec2 vUv;\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\tvUv = uv;\\\\n\\\\t\\\\t\\\\tgl_Position = projectionMatrix * modelViewMatrix * vec4( position, 1.0 );\\\\n\\\\n\\\\t\\\\t}\\\\\\\",fragmentShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\tuniform vec2 center;\\\\n\\\\t\\\\tuniform float angle;\\\\n\\\\t\\\\tuniform float scale;\\\\n\\\\t\\\\tuniform vec2 tSize;\\\\n\\\\n\\\\t\\\\tuniform sampler2D tDiffuse;\\\\n\\\\n\\\\t\\\\tvarying vec2 vUv;\\\\n\\\\n\\\\t\\\\tfloat pattern() {\\\\n\\\\n\\\\t\\\\t\\\\tfloat s = sin( angle ), c = cos( angle );\\\\n\\\\n\\\\t\\\\t\\\\tvec2 tex = vUv * tSize - center;\\\\n\\\\t\\\\t\\\\tvec2 point = vec2( c * tex.x - s * tex.y, s * tex.x + c * tex.y ) * scale;\\\\n\\\\n\\\\t\\\\t\\\\treturn ( sin( point.x ) * sin( point.y ) ) * 4.0;\\\\n\\\\n\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\tvec4 color = texture2D( tDiffuse, vUv );\\\\n\\\\n\\\\t\\\\t\\\\tfloat average = ( color.r + color.g + color.b ) / 3.0;\\\\n\\\\n\\\\t\\\\t\\\\tgl_FragColor = vec4( vec3( average * 10.0 - 5.0 + pattern() ), color.a );\\\\n\\\\n\\\\t\\\\t}\\\\\\\"};const oj=new class extends aa{constructor(){super(...arguments),this.center=oa.VECTOR2([.5,.5],{...MH}),this.angle=oa.FLOAT(\\\\\\\"$PI*0.5\\\\\\\",{range:[0,10],rangeLocked:[!1,!1],...MH}),this.scale=oa.FLOAT(1,{range:[0,1],rangeLocked:[!1,!1],...MH})}};class aj extends SH{constructor(){super(...arguments),this.paramsConfig=oj}static type(){return\\\\\\\"dotScreen\\\\\\\"}_createPass(t){const e=new Bm(sj);return this.updatePass(e),e}updatePass(t){t.uniforms.center.value=this.pv.center,t.uniforms.angle.value=this.pv.angle,t.uniforms.scale.value=this.pv.scale}}const lj={uniforms:{tDiffuse:{value:null},time:{value:0},nIntensity:{value:.5},sIntensity:{value:.05},sCount:{value:4096},grayscale:{value:1}},vertexShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\tvarying vec2 vUv;\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\tvUv = uv;\\\\n\\\\t\\\\t\\\\tgl_Position = projectionMatrix * modelViewMatrix * vec4( position, 1.0 );\\\\n\\\\n\\\\t\\\\t}\\\\\\\",fragmentShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\t#include <common>\\\\n\\\\n\\\\t\\\\t// control parameter\\\\n\\\\t\\\\tuniform float time;\\\\n\\\\n\\\\t\\\\tuniform bool grayscale;\\\\n\\\\n\\\\t\\\\t// noise effect intensity value (0 = no effect, 1 = full effect)\\\\n\\\\t\\\\tuniform float nIntensity;\\\\n\\\\n\\\\t\\\\t// scanlines effect intensity value (0 = no effect, 1 = full effect)\\\\n\\\\t\\\\tuniform float sIntensity;\\\\n\\\\n\\\\t\\\\t// scanlines effect count value (0 = no effect, 4096 = full effect)\\\\n\\\\t\\\\tuniform float sCount;\\\\n\\\\n\\\\t\\\\tuniform sampler2D tDiffuse;\\\\n\\\\n\\\\t\\\\tvarying vec2 vUv;\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t// sample the source\\\\n\\\\t\\\\t\\\\tvec4 cTextureScreen = texture2D( tDiffuse, vUv );\\\\n\\\\n\\\\t\\\\t// make some noise\\\\n\\\\t\\\\t\\\\tfloat dx = rand( vUv + time );\\\\n\\\\n\\\\t\\\\t// add noise\\\\n\\\\t\\\\t\\\\tvec3 cResult = cTextureScreen.rgb + cTextureScreen.rgb * clamp( 0.1 + dx, 0.0, 1.0 );\\\\n\\\\n\\\\t\\\\t// get us a sine and cosine\\\\n\\\\t\\\\t\\\\tvec2 sc = vec2( sin( vUv.y * sCount ), cos( vUv.y * sCount ) );\\\\n\\\\n\\\\t\\\\t// add scanlines\\\\n\\\\t\\\\t\\\\tcResult += cTextureScreen.rgb * vec3( sc.x, sc.y, sc.x ) * sIntensity;\\\\n\\\\n\\\\t\\\\t// interpolate between source and result by intensity\\\\n\\\\t\\\\t\\\\tcResult = cTextureScreen.rgb + clamp( nIntensity, 0.0,1.0 ) * ( cResult - cTextureScreen.rgb );\\\\n\\\\n\\\\t\\\\t// convert to grayscale if desired\\\\n\\\\t\\\\t\\\\tif( grayscale ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tcResult = vec3( cResult.r * 0.3 + cResult.g * 0.59 + cResult.b * 0.11 );\\\\n\\\\n\\\\t\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\t\\\\tgl_FragColor =  vec4( cResult, cTextureScreen.a );\\\\n\\\\n\\\\t\\\\t}\\\\\\\"};class cj extends Im{constructor(t,e,n,i){super(),void 0===lj&&console.error(\\\\\\\"THREE.FilmPass relies on FilmShader\\\\\\\");const r=lj;this.uniforms=I.clone(r.uniforms),this.material=new F({uniforms:this.uniforms,vertexShader:r.vertexShader,fragmentShader:r.fragmentShader}),void 0!==i&&(this.uniforms.grayscale.value=i),void 0!==t&&(this.uniforms.nIntensity.value=t),void 0!==e&&(this.uniforms.sIntensity.value=e),void 0!==n&&(this.uniforms.sCount.value=n),this.fsQuad=new km(this.material)}render(t,e,n,i){this.uniforms.tDiffuse.value=n.texture,this.uniforms.time.value+=i,this.renderToScreen?(t.setRenderTarget(null),this.fsQuad.render(t)):(t.setRenderTarget(e),this.clear&&t.clear(),this.fsQuad.render(t))}}const uj=new class extends aa{constructor(){super(...arguments),this.noiseIntensity=oa.FLOAT(.5,{range:[0,1],rangeLocked:[!1,!1],...MH}),this.scanlinesIntensity=oa.FLOAT(.05,{range:[0,1],rangeLocked:[!0,!1],...MH}),this.scanlinesCount=oa.FLOAT(4096,{range:[0,4096],rangeLocked:[!0,!1],...MH}),this.grayscale=oa.BOOLEAN(1,{...MH})}};class hj extends SH{constructor(){super(...arguments),this.paramsConfig=uj}static type(){return\\\\\\\"film\\\\\\\"}_createPass(t){const e=new cj(this.pv.noiseIntensity,this.pv.scanlinesIntensity,this.pv.scanlinesCount,this.pv.grayscale?1:0);return this.updatePass(e),e}updatePass(t){t.uniforms.nIntensity.value=this.pv.noiseIntensity,t.uniforms.sIntensity.value=this.pv.scanlinesIntensity,t.uniforms.sCount.value=this.pv.scanlinesCount,t.uniforms.grayscale.value=this.pv.grayscale?1:0}}const dj={uniforms:{tDiffuse:{value:null},resolution:{value:new d.a(1/1024,1/512)}},vertexShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\tvarying vec2 vUv;\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\tvUv = uv;\\\\n\\\\t\\\\t\\\\tgl_Position = projectionMatrix * modelViewMatrix * vec4( position, 1.0 );\\\\n\\\\n\\\\t\\\\t}\\\\\\\",fragmentShader:'\\\\n\\\\n\\\\t\\\\tprecision highp float;\\\\n\\\\n\\\\t\\\\tuniform sampler2D tDiffuse;\\\\n\\\\n\\\\t\\\\tuniform vec2 resolution;\\\\n\\\\n\\\\t\\\\tvarying vec2 vUv;\\\\n\\\\n\\\\t\\\\t#define FXAA_PC 1\\\\n\\\\t\\\\t#define FXAA_GLSL_100 1\\\\n\\\\t\\\\t#define FXAA_QUALITY_PRESET 12\\\\n\\\\n\\\\t\\\\t#define FXAA_GREEN_AS_LUMA 1\\\\n\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t#ifndef FXAA_PC_CONSOLE\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\n\\\\t\\\\t\\\\t\\\\t// The console algorithm for PC is included\\\\n\\\\t\\\\t\\\\t\\\\t// for developers targeting really low spec machines.\\\\n\\\\t\\\\t\\\\t\\\\t// Likely better to just run FXAA_PC, and use a really low preset.\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_PC_CONSOLE 0\\\\n\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t#ifndef FXAA_GLSL_120\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_GLSL_120 0\\\\n\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t#ifndef FXAA_GLSL_130\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_GLSL_130 0\\\\n\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t#ifndef FXAA_HLSL_3\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_HLSL_3 0\\\\n\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t#ifndef FXAA_HLSL_4\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_HLSL_4 0\\\\n\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t#ifndef FXAA_HLSL_5\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_HLSL_5 0\\\\n\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*==========================================================================*/\\\\n\\\\t\\\\t#ifndef FXAA_GREEN_AS_LUMA\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\n\\\\t\\\\t\\\\t\\\\t// For those using non-linear color,\\\\n\\\\t\\\\t\\\\t\\\\t// and either not able to get luma in alpha, or not wanting to,\\\\n\\\\t\\\\t\\\\t\\\\t// this enables FXAA to run using green as a proxy for luma.\\\\n\\\\t\\\\t\\\\t\\\\t// So with this enabled, no need to pack luma in alpha.\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\n\\\\t\\\\t\\\\t\\\\t// This will turn off AA on anything which lacks some amount of green.\\\\n\\\\t\\\\t\\\\t\\\\t// Pure red and blue or combination of only R and B, will get no AA.\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\n\\\\t\\\\t\\\\t\\\\t// Might want to lower the settings for both,\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t\\\\tfxaaConsoleEdgeThresholdMin\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t\\\\tfxaaQualityEdgeThresholdMin\\\\n\\\\t\\\\t\\\\t\\\\t// In order to insure AA does not get turned off on colors\\\\n\\\\t\\\\t\\\\t\\\\t// which contain a minor amount of green.\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\n\\\\t\\\\t\\\\t\\\\t// 1 = On.\\\\n\\\\t\\\\t\\\\t\\\\t// 0 = Off.\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_GREEN_AS_LUMA 0\\\\n\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t#ifndef FXAA_EARLY_EXIT\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\n\\\\t\\\\t\\\\t\\\\t// Controls algorithm\\\\'s early exit path.\\\\n\\\\t\\\\t\\\\t\\\\t// On PS3 turning this ON adds 2 cycles to the shader.\\\\n\\\\t\\\\t\\\\t\\\\t// On 360 turning this OFF adds 10ths of a millisecond to the shader.\\\\n\\\\t\\\\t\\\\t\\\\t// Turning this off on console will result in a more blurry image.\\\\n\\\\t\\\\t\\\\t\\\\t// So this defaults to on.\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\n\\\\t\\\\t\\\\t\\\\t// 1 = On.\\\\n\\\\t\\\\t\\\\t\\\\t// 0 = Off.\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_EARLY_EXIT 1\\\\n\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t#ifndef FXAA_DISCARD\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\n\\\\t\\\\t\\\\t\\\\t// Only valid for PC OpenGL currently.\\\\n\\\\t\\\\t\\\\t\\\\t// Probably will not work when FXAA_GREEN_AS_LUMA = 1.\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\n\\\\t\\\\t\\\\t\\\\t// 1 = Use discard on pixels which don\\\\'t need AA.\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t\\\\t For APIs which enable concurrent TEX+ROP from same surface.\\\\n\\\\t\\\\t\\\\t\\\\t// 0 = Return unchanged color on pixels which don\\\\'t need AA.\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_DISCARD 0\\\\n\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t#ifndef FXAA_FAST_PIXEL_OFFSET\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\n\\\\t\\\\t\\\\t\\\\t// Used for GLSL 120 only.\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\n\\\\t\\\\t\\\\t\\\\t// 1 = GL API supports fast pixel offsets\\\\n\\\\t\\\\t\\\\t\\\\t// 0 = do not use fast pixel offsets\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\n\\\\t\\\\t\\\\t\\\\t#ifdef GL_EXT_gpu_shader4\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#define FXAA_FAST_PIXEL_OFFSET 1\\\\n\\\\t\\\\t\\\\t\\\\t#endif\\\\n\\\\t\\\\t\\\\t\\\\t#ifdef GL_NV_gpu_shader5\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#define FXAA_FAST_PIXEL_OFFSET 1\\\\n\\\\t\\\\t\\\\t\\\\t#endif\\\\n\\\\t\\\\t\\\\t\\\\t#ifdef GL_ARB_gpu_shader5\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#define FXAA_FAST_PIXEL_OFFSET 1\\\\n\\\\t\\\\t\\\\t\\\\t#endif\\\\n\\\\t\\\\t\\\\t\\\\t#ifndef FXAA_FAST_PIXEL_OFFSET\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#define FXAA_FAST_PIXEL_OFFSET 0\\\\n\\\\t\\\\t\\\\t\\\\t#endif\\\\n\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t#ifndef FXAA_GATHER4_ALPHA\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\n\\\\t\\\\t\\\\t\\\\t// 1 = API supports gather4 on alpha channel.\\\\n\\\\t\\\\t\\\\t\\\\t// 0 = API does not support gather4 on alpha channel.\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\n\\\\t\\\\t\\\\t\\\\t#if (FXAA_HLSL_5 == 1)\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#define FXAA_GATHER4_ALPHA 1\\\\n\\\\t\\\\t\\\\t\\\\t#endif\\\\n\\\\t\\\\t\\\\t\\\\t#ifdef GL_ARB_gpu_shader5\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#define FXAA_GATHER4_ALPHA 1\\\\n\\\\t\\\\t\\\\t\\\\t#endif\\\\n\\\\t\\\\t\\\\t\\\\t#ifdef GL_NV_gpu_shader5\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#define FXAA_GATHER4_ALPHA 1\\\\n\\\\t\\\\t\\\\t\\\\t#endif\\\\n\\\\t\\\\t\\\\t\\\\t#ifndef FXAA_GATHER4_ALPHA\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#define FXAA_GATHER4_ALPHA 0\\\\n\\\\t\\\\t\\\\t\\\\t#endif\\\\n\\\\t\\\\t#endif\\\\n\\\\n\\\\n\\\\t\\\\t/*============================================================================\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tFXAA QUALITY - TUNING KNOBS\\\\n\\\\t\\\\t------------------------------------------------------------------------------\\\\n\\\\t\\\\tNOTE the other tuning knobs are now in the shader function inputs!\\\\n\\\\t\\\\t============================================================================*/\\\\n\\\\t\\\\t#ifndef FXAA_QUALITY_PRESET\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\n\\\\t\\\\t\\\\t\\\\t// Choose the quality preset.\\\\n\\\\t\\\\t\\\\t\\\\t// This needs to be compiled into the shader as it effects code.\\\\n\\\\t\\\\t\\\\t\\\\t// Best option to include multiple presets is to\\\\n\\\\t\\\\t\\\\t\\\\t// in each shader define the preset, then include this file.\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\n\\\\t\\\\t\\\\t\\\\t// OPTIONS\\\\n\\\\t\\\\t\\\\t\\\\t// -----------------------------------------------------------------------\\\\n\\\\t\\\\t\\\\t\\\\t// 10 to 15 - default medium dither (10=fastest, 15=highest quality)\\\\n\\\\t\\\\t\\\\t\\\\t// 20 to 29 - less dither, more expensive (20=fastest, 29=highest quality)\\\\n\\\\t\\\\t\\\\t\\\\t// 39\\\\t\\\\t\\\\t - no dither, very expensive\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\n\\\\t\\\\t\\\\t\\\\t// NOTES\\\\n\\\\t\\\\t\\\\t\\\\t// -----------------------------------------------------------------------\\\\n\\\\t\\\\t\\\\t\\\\t// 12 = slightly faster then FXAA 3.9 and higher edge quality (default)\\\\n\\\\t\\\\t\\\\t\\\\t// 13 = about same speed as FXAA 3.9 and better than 12\\\\n\\\\t\\\\t\\\\t\\\\t// 23 = closest to FXAA 3.9 visually and performance wise\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t_ = the lowest digit is directly related to performance\\\\n\\\\t\\\\t\\\\t\\\\t// _\\\\t= the highest digit is directly related to style\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_PRESET 12\\\\n\\\\t\\\\t#endif\\\\n\\\\n\\\\n\\\\t\\\\t/*============================================================================\\\\n\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t FXAA QUALITY - PRESETS\\\\n\\\\n\\\\t\\\\t============================================================================*/\\\\n\\\\n\\\\t\\\\t/*============================================================================\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t FXAA QUALITY - MEDIUM DITHER PRESETS\\\\n\\\\t\\\\t============================================================================*/\\\\n\\\\t\\\\t#if (FXAA_QUALITY_PRESET == 10)\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_PS 3\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P0 1.5\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P1 3.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P2 12.0\\\\n\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t#if (FXAA_QUALITY_PRESET == 11)\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_PS 4\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P0 1.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P1 1.5\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P2 3.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P3 12.0\\\\n\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t#if (FXAA_QUALITY_PRESET == 12)\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_PS 5\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P0 1.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P1 1.5\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P2 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P3 4.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P4 12.0\\\\n\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t#if (FXAA_QUALITY_PRESET == 13)\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_PS 6\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P0 1.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P1 1.5\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P2 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P3 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P4 4.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P5 12.0\\\\n\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t#if (FXAA_QUALITY_PRESET == 14)\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_PS 7\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P0 1.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P1 1.5\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P2 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P3 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P4 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P5 4.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P6 12.0\\\\n\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t#if (FXAA_QUALITY_PRESET == 15)\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_PS 8\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P0 1.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P1 1.5\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P2 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P3 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P4 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P5 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P6 4.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P7 12.0\\\\n\\\\t\\\\t#endif\\\\n\\\\n\\\\t\\\\t/*============================================================================\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t FXAA QUALITY - LOW DITHER PRESETS\\\\n\\\\t\\\\t============================================================================*/\\\\n\\\\t\\\\t#if (FXAA_QUALITY_PRESET == 20)\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_PS 3\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P0 1.5\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P1 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P2 8.0\\\\n\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t#if (FXAA_QUALITY_PRESET == 21)\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_PS 4\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P0 1.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P1 1.5\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P2 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P3 8.0\\\\n\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t#if (FXAA_QUALITY_PRESET == 22)\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_PS 5\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P0 1.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P1 1.5\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P2 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P3 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P4 8.0\\\\n\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t#if (FXAA_QUALITY_PRESET == 23)\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_PS 6\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P0 1.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P1 1.5\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P2 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P3 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P4 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P5 8.0\\\\n\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t#if (FXAA_QUALITY_PRESET == 24)\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_PS 7\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P0 1.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P1 1.5\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P2 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P3 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P4 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P5 3.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P6 8.0\\\\n\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t#if (FXAA_QUALITY_PRESET == 25)\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_PS 8\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P0 1.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P1 1.5\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P2 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P3 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P4 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P5 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P6 4.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P7 8.0\\\\n\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t#if (FXAA_QUALITY_PRESET == 26)\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_PS 9\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P0 1.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P1 1.5\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P2 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P3 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P4 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P5 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P6 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P7 4.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P8 8.0\\\\n\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t#if (FXAA_QUALITY_PRESET == 27)\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_PS 10\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P0 1.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P1 1.5\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P2 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P3 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P4 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P5 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P6 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P7 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P8 4.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P9 8.0\\\\n\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t#if (FXAA_QUALITY_PRESET == 28)\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_PS 11\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P0 1.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P1 1.5\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P2 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P3 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P4 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P5 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P6 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P7 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P8 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P9 4.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P10 8.0\\\\n\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t#if (FXAA_QUALITY_PRESET == 29)\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_PS 12\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P0 1.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P1 1.5\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P2 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P3 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P4 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P5 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P6 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P7 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P8 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P9 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P10 4.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P11 8.0\\\\n\\\\t\\\\t#endif\\\\n\\\\n\\\\t\\\\t/*============================================================================\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t FXAA QUALITY - EXTREME QUALITY\\\\n\\\\t\\\\t============================================================================*/\\\\n\\\\t\\\\t#if (FXAA_QUALITY_PRESET == 39)\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_PS 12\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P0 1.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P1 1.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P2 1.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P3 1.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P4 1.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P5 1.5\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P6 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P7 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P8 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P9 2.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P10 4.0\\\\n\\\\t\\\\t\\\\t\\\\t#define FXAA_QUALITY_P11 8.0\\\\n\\\\t\\\\t#endif\\\\n\\\\n\\\\n\\\\n\\\\t\\\\t/*============================================================================\\\\n\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tAPI PORTING\\\\n\\\\n\\\\t\\\\t============================================================================*/\\\\n\\\\t\\\\t#if (FXAA_GLSL_100 == 1) || (FXAA_GLSL_120 == 1) || (FXAA_GLSL_130 == 1)\\\\n\\\\t\\\\t\\\\t\\\\t#define FxaaBool bool\\\\n\\\\t\\\\t\\\\t\\\\t#define FxaaDiscard discard\\\\n\\\\t\\\\t\\\\t\\\\t#define FxaaFloat float\\\\n\\\\t\\\\t\\\\t\\\\t#define FxaaFloat2 vec2\\\\n\\\\t\\\\t\\\\t\\\\t#define FxaaFloat3 vec3\\\\n\\\\t\\\\t\\\\t\\\\t#define FxaaFloat4 vec4\\\\n\\\\t\\\\t\\\\t\\\\t#define FxaaHalf float\\\\n\\\\t\\\\t\\\\t\\\\t#define FxaaHalf2 vec2\\\\n\\\\t\\\\t\\\\t\\\\t#define FxaaHalf3 vec3\\\\n\\\\t\\\\t\\\\t\\\\t#define FxaaHalf4 vec4\\\\n\\\\t\\\\t\\\\t\\\\t#define FxaaInt2 ivec2\\\\n\\\\t\\\\t\\\\t\\\\t#define FxaaSat(x) clamp(x, 0.0, 1.0)\\\\n\\\\t\\\\t\\\\t\\\\t#define FxaaTex sampler2D\\\\n\\\\t\\\\t#else\\\\n\\\\t\\\\t\\\\t\\\\t#define FxaaBool bool\\\\n\\\\t\\\\t\\\\t\\\\t#define FxaaDiscard clip(-1)\\\\n\\\\t\\\\t\\\\t\\\\t#define FxaaFloat float\\\\n\\\\t\\\\t\\\\t\\\\t#define FxaaFloat2 float2\\\\n\\\\t\\\\t\\\\t\\\\t#define FxaaFloat3 float3\\\\n\\\\t\\\\t\\\\t\\\\t#define FxaaFloat4 float4\\\\n\\\\t\\\\t\\\\t\\\\t#define FxaaHalf half\\\\n\\\\t\\\\t\\\\t\\\\t#define FxaaHalf2 half2\\\\n\\\\t\\\\t\\\\t\\\\t#define FxaaHalf3 half3\\\\n\\\\t\\\\t\\\\t\\\\t#define FxaaHalf4 half4\\\\n\\\\t\\\\t\\\\t\\\\t#define FxaaSat(x) saturate(x)\\\\n\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t#if (FXAA_GLSL_100 == 1)\\\\n\\\\t\\\\t\\\\t#define FxaaTexTop(t, p) texture2D(t, p, 0.0)\\\\n\\\\t\\\\t\\\\t#define FxaaTexOff(t, p, o, r) texture2D(t, p + (o * r), 0.0)\\\\n\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t#if (FXAA_GLSL_120 == 1)\\\\n\\\\t\\\\t\\\\t\\\\t// Requires,\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t#version 120\\\\n\\\\t\\\\t\\\\t\\\\t// And at least,\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t#extension GL_EXT_gpu_shader4 : enable\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t(or set FXAA_FAST_PIXEL_OFFSET 1 to work like DX9)\\\\n\\\\t\\\\t\\\\t\\\\t#define FxaaTexTop(t, p) texture2DLod(t, p, 0.0)\\\\n\\\\t\\\\t\\\\t\\\\t#if (FXAA_FAST_PIXEL_OFFSET == 1)\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#define FxaaTexOff(t, p, o, r) texture2DLodOffset(t, p, 0.0, o)\\\\n\\\\t\\\\t\\\\t\\\\t#else\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#define FxaaTexOff(t, p, o, r) texture2DLod(t, p + (o * r), 0.0)\\\\n\\\\t\\\\t\\\\t\\\\t#endif\\\\n\\\\t\\\\t\\\\t\\\\t#if (FXAA_GATHER4_ALPHA == 1)\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t// use #extension GL_ARB_gpu_shader5 : enable\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#define FxaaTexAlpha4(t, p) textureGather(t, p, 3)\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#define FxaaTexOffAlpha4(t, p, o) textureGatherOffset(t, p, o, 3)\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#define FxaaTexGreen4(t, p) textureGather(t, p, 1)\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#define FxaaTexOffGreen4(t, p, o) textureGatherOffset(t, p, o, 1)\\\\n\\\\t\\\\t\\\\t\\\\t#endif\\\\n\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t#if (FXAA_GLSL_130 == 1)\\\\n\\\\t\\\\t\\\\t\\\\t// Requires \\\\\\\"#version 130\\\\\\\" or better\\\\n\\\\t\\\\t\\\\t\\\\t#define FxaaTexTop(t, p) textureLod(t, p, 0.0)\\\\n\\\\t\\\\t\\\\t\\\\t#define FxaaTexOff(t, p, o, r) textureLodOffset(t, p, 0.0, o)\\\\n\\\\t\\\\t\\\\t\\\\t#if (FXAA_GATHER4_ALPHA == 1)\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t// use #extension GL_ARB_gpu_shader5 : enable\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#define FxaaTexAlpha4(t, p) textureGather(t, p, 3)\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#define FxaaTexOffAlpha4(t, p, o) textureGatherOffset(t, p, o, 3)\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#define FxaaTexGreen4(t, p) textureGather(t, p, 1)\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#define FxaaTexOffGreen4(t, p, o) textureGatherOffset(t, p, o, 1)\\\\n\\\\t\\\\t\\\\t\\\\t#endif\\\\n\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t#if (FXAA_HLSL_3 == 1)\\\\n\\\\t\\\\t\\\\t\\\\t#define FxaaInt2 float2\\\\n\\\\t\\\\t\\\\t\\\\t#define FxaaTex sampler2D\\\\n\\\\t\\\\t\\\\t\\\\t#define FxaaTexTop(t, p) tex2Dlod(t, float4(p, 0.0, 0.0))\\\\n\\\\t\\\\t\\\\t\\\\t#define FxaaTexOff(t, p, o, r) tex2Dlod(t, float4(p + (o * r), 0, 0))\\\\n\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t#if (FXAA_HLSL_4 == 1)\\\\n\\\\t\\\\t\\\\t\\\\t#define FxaaInt2 int2\\\\n\\\\t\\\\t\\\\t\\\\tstruct FxaaTex { SamplerState smpl; Texture2D tex; };\\\\n\\\\t\\\\t\\\\t\\\\t#define FxaaTexTop(t, p) t.tex.SampleLevel(t.smpl, p, 0.0)\\\\n\\\\t\\\\t\\\\t\\\\t#define FxaaTexOff(t, p, o, r) t.tex.SampleLevel(t.smpl, p, 0.0, o)\\\\n\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t#if (FXAA_HLSL_5 == 1)\\\\n\\\\t\\\\t\\\\t\\\\t#define FxaaInt2 int2\\\\n\\\\t\\\\t\\\\t\\\\tstruct FxaaTex { SamplerState smpl; Texture2D tex; };\\\\n\\\\t\\\\t\\\\t\\\\t#define FxaaTexTop(t, p) t.tex.SampleLevel(t.smpl, p, 0.0)\\\\n\\\\t\\\\t\\\\t\\\\t#define FxaaTexOff(t, p, o, r) t.tex.SampleLevel(t.smpl, p, 0.0, o)\\\\n\\\\t\\\\t\\\\t\\\\t#define FxaaTexAlpha4(t, p) t.tex.GatherAlpha(t.smpl, p)\\\\n\\\\t\\\\t\\\\t\\\\t#define FxaaTexOffAlpha4(t, p, o) t.tex.GatherAlpha(t.smpl, p, o)\\\\n\\\\t\\\\t\\\\t\\\\t#define FxaaTexGreen4(t, p) t.tex.GatherGreen(t.smpl, p)\\\\n\\\\t\\\\t\\\\t\\\\t#define FxaaTexOffGreen4(t, p, o) t.tex.GatherGreen(t.smpl, p, o)\\\\n\\\\t\\\\t#endif\\\\n\\\\n\\\\n\\\\t\\\\t/*============================================================================\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t GREEN AS LUMA OPTION SUPPORT FUNCTION\\\\n\\\\t\\\\t============================================================================*/\\\\n\\\\t\\\\t#if (FXAA_GREEN_AS_LUMA == 0)\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat FxaaLuma(FxaaFloat4 rgba) { return rgba.w; }\\\\n\\\\t\\\\t#else\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat FxaaLuma(FxaaFloat4 rgba) { return rgba.y; }\\\\n\\\\t\\\\t#endif\\\\n\\\\n\\\\n\\\\n\\\\n\\\\t\\\\t/*============================================================================\\\\n\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t FXAA3 QUALITY - PC\\\\n\\\\n\\\\t\\\\t============================================================================*/\\\\n\\\\t\\\\t#if (FXAA_PC == 1)\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\tFxaaFloat4 FxaaPixelShader(\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\n\\\\t\\\\t\\\\t\\\\t// Use noperspective interpolation here (turn off perspective interpolation).\\\\n\\\\t\\\\t\\\\t\\\\t// {xy} = center of pixel\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat2 pos,\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\n\\\\t\\\\t\\\\t\\\\t// Used only for FXAA Console, and not used on the 360 version.\\\\n\\\\t\\\\t\\\\t\\\\t// Use noperspective interpolation here (turn off perspective interpolation).\\\\n\\\\t\\\\t\\\\t\\\\t// {xy_} = upper left of pixel\\\\n\\\\t\\\\t\\\\t\\\\t// {_zw} = lower right of pixel\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat4 fxaaConsolePosPos,\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\n\\\\t\\\\t\\\\t\\\\t// Input color texture.\\\\n\\\\t\\\\t\\\\t\\\\t// {rgb_} = color in linear or perceptual color space\\\\n\\\\t\\\\t\\\\t\\\\t// if (FXAA_GREEN_AS_LUMA == 0)\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t\\\\t {__a} = luma in perceptual color space (not linear)\\\\n\\\\t\\\\t\\\\t\\\\tFxaaTex tex,\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\n\\\\t\\\\t\\\\t\\\\t// Only used on the optimized 360 version of FXAA Console.\\\\n\\\\t\\\\t\\\\t\\\\t// For everything but 360, just use the same input here as for \\\\\\\"tex\\\\\\\".\\\\n\\\\t\\\\t\\\\t\\\\t// For 360, same texture, just alias with a 2nd sampler.\\\\n\\\\t\\\\t\\\\t\\\\t// This sampler needs to have an exponent bias of -1.\\\\n\\\\t\\\\t\\\\t\\\\tFxaaTex fxaaConsole360TexExpBiasNegOne,\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\n\\\\t\\\\t\\\\t\\\\t// Only used on the optimized 360 version of FXAA Console.\\\\n\\\\t\\\\t\\\\t\\\\t// For everything but 360, just use the same input here as for \\\\\\\"tex\\\\\\\".\\\\n\\\\t\\\\t\\\\t\\\\t// For 360, same texture, just alias with a 3nd sampler.\\\\n\\\\t\\\\t\\\\t\\\\t// This sampler needs to have an exponent bias of -2.\\\\n\\\\t\\\\t\\\\t\\\\tFxaaTex fxaaConsole360TexExpBiasNegTwo,\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\n\\\\t\\\\t\\\\t\\\\t// Only used on FXAA Quality.\\\\n\\\\t\\\\t\\\\t\\\\t// This must be from a constant/uniform.\\\\n\\\\t\\\\t\\\\t\\\\t// {x_} = 1.0/screenWidthInPixels\\\\n\\\\t\\\\t\\\\t\\\\t// {_y} = 1.0/screenHeightInPixels\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat2 fxaaQualityRcpFrame,\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\n\\\\t\\\\t\\\\t\\\\t// Only used on FXAA Console.\\\\n\\\\t\\\\t\\\\t\\\\t// This must be from a constant/uniform.\\\\n\\\\t\\\\t\\\\t\\\\t// This effects sub-pixel AA quality and inversely sharpness.\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t Where N ranges between,\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t\\\\t N = 0.50 (default)\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t\\\\t N = 0.33 (sharper)\\\\n\\\\t\\\\t\\\\t\\\\t// {x__} = -N/screenWidthInPixels\\\\n\\\\t\\\\t\\\\t\\\\t// {_y_} = -N/screenHeightInPixels\\\\n\\\\t\\\\t\\\\t\\\\t// {_z_} =\\\\tN/screenWidthInPixels\\\\n\\\\t\\\\t\\\\t\\\\t// {__w} =\\\\tN/screenHeightInPixels\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat4 fxaaConsoleRcpFrameOpt,\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\n\\\\t\\\\t\\\\t\\\\t// Only used on FXAA Console.\\\\n\\\\t\\\\t\\\\t\\\\t// Not used on 360, but used on PS3 and PC.\\\\n\\\\t\\\\t\\\\t\\\\t// This must be from a constant/uniform.\\\\n\\\\t\\\\t\\\\t\\\\t// {x__} = -2.0/screenWidthInPixels\\\\n\\\\t\\\\t\\\\t\\\\t// {_y_} = -2.0/screenHeightInPixels\\\\n\\\\t\\\\t\\\\t\\\\t// {_z_} =\\\\t2.0/screenWidthInPixels\\\\n\\\\t\\\\t\\\\t\\\\t// {__w} =\\\\t2.0/screenHeightInPixels\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat4 fxaaConsoleRcpFrameOpt2,\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\n\\\\t\\\\t\\\\t\\\\t// Only used on FXAA Console.\\\\n\\\\t\\\\t\\\\t\\\\t// Only used on 360 in place of fxaaConsoleRcpFrameOpt2.\\\\n\\\\t\\\\t\\\\t\\\\t// This must be from a constant/uniform.\\\\n\\\\t\\\\t\\\\t\\\\t// {x__} =\\\\t8.0/screenWidthInPixels\\\\n\\\\t\\\\t\\\\t\\\\t// {_y_} =\\\\t8.0/screenHeightInPixels\\\\n\\\\t\\\\t\\\\t\\\\t// {_z_} = -4.0/screenWidthInPixels\\\\n\\\\t\\\\t\\\\t\\\\t// {__w} = -4.0/screenHeightInPixels\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat4 fxaaConsole360RcpFrameOpt2,\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\n\\\\t\\\\t\\\\t\\\\t// Only used on FXAA Quality.\\\\n\\\\t\\\\t\\\\t\\\\t// This used to be the FXAA_QUALITY_SUBPIX define.\\\\n\\\\t\\\\t\\\\t\\\\t// It is here now to allow easier tuning.\\\\n\\\\t\\\\t\\\\t\\\\t// Choose the amount of sub-pixel aliasing removal.\\\\n\\\\t\\\\t\\\\t\\\\t// This can effect sharpness.\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t 1.00 - upper limit (softer)\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t 0.75 - default amount of filtering\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t 0.50 - lower limit (sharper, less sub-pixel aliasing removal)\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t 0.25 - almost off\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t 0.00 - completely off\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat fxaaQualitySubpix,\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\n\\\\t\\\\t\\\\t\\\\t// Only used on FXAA Quality.\\\\n\\\\t\\\\t\\\\t\\\\t// This used to be the FXAA_QUALITY_EDGE_THRESHOLD define.\\\\n\\\\t\\\\t\\\\t\\\\t// It is here now to allow easier tuning.\\\\n\\\\t\\\\t\\\\t\\\\t// The minimum amount of local contrast required to apply algorithm.\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t 0.333 - too little (faster)\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t 0.250 - low quality\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t 0.166 - default\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t 0.125 - high quality\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t 0.063 - overkill (slower)\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat fxaaQualityEdgeThreshold,\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\n\\\\t\\\\t\\\\t\\\\t// Only used on FXAA Quality.\\\\n\\\\t\\\\t\\\\t\\\\t// This used to be the FXAA_QUALITY_EDGE_THRESHOLD_MIN define.\\\\n\\\\t\\\\t\\\\t\\\\t// It is here now to allow easier tuning.\\\\n\\\\t\\\\t\\\\t\\\\t// Trims the algorithm from processing darks.\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t 0.0833 - upper limit (default, the start of visible unfiltered edges)\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t 0.0625 - high quality (faster)\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t 0.0312 - visible limit (slower)\\\\n\\\\t\\\\t\\\\t\\\\t// Special notes when using FXAA_GREEN_AS_LUMA,\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t Likely want to set this to zero.\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t As colors that are mostly not-green\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t will appear very dark in the green channel!\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t Tune by looking at mostly non-green content,\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t then start at zero and increase until aliasing is a problem.\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat fxaaQualityEdgeThresholdMin,\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\n\\\\t\\\\t\\\\t\\\\t// Only used on FXAA Console.\\\\n\\\\t\\\\t\\\\t\\\\t// This used to be the FXAA_CONSOLE_EDGE_SHARPNESS define.\\\\n\\\\t\\\\t\\\\t\\\\t// It is here now to allow easier tuning.\\\\n\\\\t\\\\t\\\\t\\\\t// This does not effect PS3, as this needs to be compiled in.\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t Use FXAA_CONSOLE_PS3_EDGE_SHARPNESS for PS3.\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t Due to the PS3 being ALU bound,\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t there are only three safe values here: 2 and 4 and 8.\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t These options use the shaders ability to a free *|/ by 2|4|8.\\\\n\\\\t\\\\t\\\\t\\\\t// For all other platforms can be a non-power of two.\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t 8.0 is sharper (default!!!)\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t 4.0 is softer\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t 2.0 is really soft (good only for vector graphics inputs)\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat fxaaConsoleEdgeSharpness,\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\n\\\\t\\\\t\\\\t\\\\t// Only used on FXAA Console.\\\\n\\\\t\\\\t\\\\t\\\\t// This used to be the FXAA_CONSOLE_EDGE_THRESHOLD define.\\\\n\\\\t\\\\t\\\\t\\\\t// It is here now to allow easier tuning.\\\\n\\\\t\\\\t\\\\t\\\\t// This does not effect PS3, as this needs to be compiled in.\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t Use FXAA_CONSOLE_PS3_EDGE_THRESHOLD for PS3.\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t Due to the PS3 being ALU bound,\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t there are only two safe values here: 1/4 and 1/8.\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t These options use the shaders ability to a free *|/ by 2|4|8.\\\\n\\\\t\\\\t\\\\t\\\\t// The console setting has a different mapping than the quality setting.\\\\n\\\\t\\\\t\\\\t\\\\t// Other platforms can use other values.\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t 0.125 leaves less aliasing, but is softer (default!!!)\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t 0.25 leaves more aliasing, and is sharper\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat fxaaConsoleEdgeThreshold,\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\n\\\\t\\\\t\\\\t\\\\t// Only used on FXAA Console.\\\\n\\\\t\\\\t\\\\t\\\\t// This used to be the FXAA_CONSOLE_EDGE_THRESHOLD_MIN define.\\\\n\\\\t\\\\t\\\\t\\\\t// It is here now to allow easier tuning.\\\\n\\\\t\\\\t\\\\t\\\\t// Trims the algorithm from processing darks.\\\\n\\\\t\\\\t\\\\t\\\\t// The console setting has a different mapping than the quality setting.\\\\n\\\\t\\\\t\\\\t\\\\t// This only applies when FXAA_EARLY_EXIT is 1.\\\\n\\\\t\\\\t\\\\t\\\\t// This does not apply to PS3,\\\\n\\\\t\\\\t\\\\t\\\\t// PS3 was simplified to avoid more shader instructions.\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t 0.06 - faster but more aliasing in darks\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t 0.05 - default\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t 0.04 - slower and less aliasing in darks\\\\n\\\\t\\\\t\\\\t\\\\t// Special notes when using FXAA_GREEN_AS_LUMA,\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t Likely want to set this to zero.\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t As colors that are mostly not-green\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t will appear very dark in the green channel!\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t Tune by looking at mostly non-green content,\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\t then start at zero and increase until aliasing is a problem.\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat fxaaConsoleEdgeThresholdMin,\\\\n\\\\t\\\\t\\\\t\\\\t//\\\\n\\\\t\\\\t\\\\t\\\\t// Extra constants for 360 FXAA Console only.\\\\n\\\\t\\\\t\\\\t\\\\t// Use zeros or anything else for other platforms.\\\\n\\\\t\\\\t\\\\t\\\\t// These must be in physical constant registers and NOT immediates.\\\\n\\\\t\\\\t\\\\t\\\\t// Immediates will result in compiler un-optimizing.\\\\n\\\\t\\\\t\\\\t\\\\t// {xyzw} = float4(1.0, -1.0, 0.25, -0.25)\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat4 fxaaConsole360ConstDir\\\\n\\\\t\\\\t) {\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat2 posM;\\\\n\\\\t\\\\t\\\\t\\\\tposM.x = pos.x;\\\\n\\\\t\\\\t\\\\t\\\\tposM.y = pos.y;\\\\n\\\\t\\\\t\\\\t\\\\t#if (FXAA_GATHER4_ALPHA == 1)\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#if (FXAA_DISCARD == 0)\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tFxaaFloat4 rgbyM = FxaaTexTop(tex, posM);\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#if (FXAA_GREEN_AS_LUMA == 0)\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#define lumaM rgbyM.w\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#else\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#define lumaM rgbyM.y\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#endif\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#endif\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#if (FXAA_GREEN_AS_LUMA == 0)\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tFxaaFloat4 luma4A = FxaaTexAlpha4(tex, posM);\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tFxaaFloat4 luma4B = FxaaTexOffAlpha4(tex, posM, FxaaInt2(-1, -1));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#else\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tFxaaFloat4 luma4A = FxaaTexGreen4(tex, posM);\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tFxaaFloat4 luma4B = FxaaTexOffGreen4(tex, posM, FxaaInt2(-1, -1));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#endif\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#if (FXAA_DISCARD == 1)\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#define lumaM luma4A.w\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#endif\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#define lumaE luma4A.z\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#define lumaS luma4A.x\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#define lumaSE luma4A.y\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#define lumaNW luma4B.w\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#define lumaN luma4B.z\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#define lumaW luma4B.x\\\\n\\\\t\\\\t\\\\t\\\\t#else\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tFxaaFloat4 rgbyM = FxaaTexTop(tex, posM);\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#if (FXAA_GREEN_AS_LUMA == 0)\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#define lumaM rgbyM.w\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#else\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#define lumaM rgbyM.y\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#endif\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#if (FXAA_GLSL_100 == 1)\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tFxaaFloat lumaS = FxaaLuma(FxaaTexOff(tex, posM, FxaaFloat2( 0.0, 1.0), fxaaQualityRcpFrame.xy));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tFxaaFloat lumaE = FxaaLuma(FxaaTexOff(tex, posM, FxaaFloat2( 1.0, 0.0), fxaaQualityRcpFrame.xy));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tFxaaFloat lumaN = FxaaLuma(FxaaTexOff(tex, posM, FxaaFloat2( 0.0,-1.0), fxaaQualityRcpFrame.xy));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tFxaaFloat lumaW = FxaaLuma(FxaaTexOff(tex, posM, FxaaFloat2(-1.0, 0.0), fxaaQualityRcpFrame.xy));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#else\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tFxaaFloat lumaS = FxaaLuma(FxaaTexOff(tex, posM, FxaaInt2( 0, 1), fxaaQualityRcpFrame.xy));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tFxaaFloat lumaE = FxaaLuma(FxaaTexOff(tex, posM, FxaaInt2( 1, 0), fxaaQualityRcpFrame.xy));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tFxaaFloat lumaN = FxaaLuma(FxaaTexOff(tex, posM, FxaaInt2( 0,-1), fxaaQualityRcpFrame.xy));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tFxaaFloat lumaW = FxaaLuma(FxaaTexOff(tex, posM, FxaaInt2(-1, 0), fxaaQualityRcpFrame.xy));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#endif\\\\n\\\\t\\\\t\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat maxSM = max(lumaS, lumaM);\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat minSM = min(lumaS, lumaM);\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat maxESM = max(lumaE, maxSM);\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat minESM = min(lumaE, minSM);\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat maxWN = max(lumaN, lumaW);\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat minWN = min(lumaN, lumaW);\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat rangeMax = max(maxWN, maxESM);\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat rangeMin = min(minWN, minESM);\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat rangeMaxScaled = rangeMax * fxaaQualityEdgeThreshold;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat range = rangeMax - rangeMin;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat rangeMaxClamped = max(fxaaQualityEdgeThresholdMin, rangeMaxScaled);\\\\n\\\\t\\\\t\\\\t\\\\tFxaaBool earlyExit = range < rangeMaxClamped;\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t\\\\t\\\\tif(earlyExit)\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#if (FXAA_DISCARD == 1)\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tFxaaDiscard;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#else\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\treturn rgbyM;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t\\\\t\\\\t#if (FXAA_GATHER4_ALPHA == 0)\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#if (FXAA_GLSL_100 == 1)\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tFxaaFloat lumaNW = FxaaLuma(FxaaTexOff(tex, posM, FxaaFloat2(-1.0,-1.0), fxaaQualityRcpFrame.xy));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tFxaaFloat lumaSE = FxaaLuma(FxaaTexOff(tex, posM, FxaaFloat2( 1.0, 1.0), fxaaQualityRcpFrame.xy));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tFxaaFloat lumaNE = FxaaLuma(FxaaTexOff(tex, posM, FxaaFloat2( 1.0,-1.0), fxaaQualityRcpFrame.xy));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tFxaaFloat lumaSW = FxaaLuma(FxaaTexOff(tex, posM, FxaaFloat2(-1.0, 1.0), fxaaQualityRcpFrame.xy));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#else\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tFxaaFloat lumaNW = FxaaLuma(FxaaTexOff(tex, posM, FxaaInt2(-1,-1), fxaaQualityRcpFrame.xy));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tFxaaFloat lumaSE = FxaaLuma(FxaaTexOff(tex, posM, FxaaInt2( 1, 1), fxaaQualityRcpFrame.xy));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tFxaaFloat lumaNE = FxaaLuma(FxaaTexOff(tex, posM, FxaaInt2( 1,-1), fxaaQualityRcpFrame.xy));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tFxaaFloat lumaSW = FxaaLuma(FxaaTexOff(tex, posM, FxaaInt2(-1, 1), fxaaQualityRcpFrame.xy));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#endif\\\\n\\\\t\\\\t\\\\t\\\\t#else\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tFxaaFloat lumaNE = FxaaLuma(FxaaTexOff(tex, posM, FxaaInt2(1, -1), fxaaQualityRcpFrame.xy));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tFxaaFloat lumaSW = FxaaLuma(FxaaTexOff(tex, posM, FxaaInt2(-1, 1), fxaaQualityRcpFrame.xy));\\\\n\\\\t\\\\t\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat lumaNS = lumaN + lumaS;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat lumaWE = lumaW + lumaE;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat subpixRcpRange = 1.0/range;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat subpixNSWE = lumaNS + lumaWE;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat edgeHorz1 = (-2.0 * lumaM) + lumaNS;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat edgeVert1 = (-2.0 * lumaM) + lumaWE;\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat lumaNESE = lumaNE + lumaSE;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat lumaNWNE = lumaNW + lumaNE;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat edgeHorz2 = (-2.0 * lumaE) + lumaNESE;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat edgeVert2 = (-2.0 * lumaN) + lumaNWNE;\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat lumaNWSW = lumaNW + lumaSW;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat lumaSWSE = lumaSW + lumaSE;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat edgeHorz4 = (abs(edgeHorz1) * 2.0) + abs(edgeHorz2);\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat edgeVert4 = (abs(edgeVert1) * 2.0) + abs(edgeVert2);\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat edgeHorz3 = (-2.0 * lumaW) + lumaNWSW;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat edgeVert3 = (-2.0 * lumaS) + lumaSWSE;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat edgeHorz = abs(edgeHorz3) + edgeHorz4;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat edgeVert = abs(edgeVert3) + edgeVert4;\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat subpixNWSWNESE = lumaNWSW + lumaNESE;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat lengthSign = fxaaQualityRcpFrame.x;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaBool horzSpan = edgeHorz >= edgeVert;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat subpixA = subpixNSWE * 2.0 + subpixNWSWNESE;\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t\\\\t\\\\tif(!horzSpan) lumaN = lumaW;\\\\n\\\\t\\\\t\\\\t\\\\tif(!horzSpan) lumaS = lumaE;\\\\n\\\\t\\\\t\\\\t\\\\tif(horzSpan) lengthSign = fxaaQualityRcpFrame.y;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat subpixB = (subpixA * (1.0/12.0)) - lumaM;\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat gradientN = lumaN - lumaM;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat gradientS = lumaS - lumaM;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat lumaNN = lumaN + lumaM;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat lumaSS = lumaS + lumaM;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaBool pairN = abs(gradientN) >= abs(gradientS);\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat gradient = max(abs(gradientN), abs(gradientS));\\\\n\\\\t\\\\t\\\\t\\\\tif(pairN) lengthSign = -lengthSign;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat subpixC = FxaaSat(abs(subpixB) * subpixRcpRange);\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat2 posB;\\\\n\\\\t\\\\t\\\\t\\\\tposB.x = posM.x;\\\\n\\\\t\\\\t\\\\t\\\\tposB.y = posM.y;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat2 offNP;\\\\n\\\\t\\\\t\\\\t\\\\toffNP.x = (!horzSpan) ? 0.0 : fxaaQualityRcpFrame.x;\\\\n\\\\t\\\\t\\\\t\\\\toffNP.y = ( horzSpan) ? 0.0 : fxaaQualityRcpFrame.y;\\\\n\\\\t\\\\t\\\\t\\\\tif(!horzSpan) posB.x += lengthSign * 0.5;\\\\n\\\\t\\\\t\\\\t\\\\tif( horzSpan) posB.y += lengthSign * 0.5;\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat2 posN;\\\\n\\\\t\\\\t\\\\t\\\\tposN.x = posB.x - offNP.x * FXAA_QUALITY_P0;\\\\n\\\\t\\\\t\\\\t\\\\tposN.y = posB.y - offNP.y * FXAA_QUALITY_P0;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat2 posP;\\\\n\\\\t\\\\t\\\\t\\\\tposP.x = posB.x + offNP.x * FXAA_QUALITY_P0;\\\\n\\\\t\\\\t\\\\t\\\\tposP.y = posB.y + offNP.y * FXAA_QUALITY_P0;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat subpixD = ((-2.0)*subpixC) + 3.0;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat lumaEndN = FxaaLuma(FxaaTexTop(tex, posN));\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat subpixE = subpixC * subpixC;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat lumaEndP = FxaaLuma(FxaaTexTop(tex, posP));\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t\\\\t\\\\tif(!pairN) lumaNN = lumaSS;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat gradientScaled = gradient * 1.0/4.0;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat lumaMM = lumaM - lumaNN * 0.5;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat subpixF = subpixD * subpixE;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaBool lumaMLTZero = lumaMM < 0.0;\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t\\\\t\\\\tlumaEndN -= lumaNN * 0.5;\\\\n\\\\t\\\\t\\\\t\\\\tlumaEndP -= lumaNN * 0.5;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaBool doneN = abs(lumaEndN) >= gradientScaled;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaBool doneP = abs(lumaEndP) >= gradientScaled;\\\\n\\\\t\\\\t\\\\t\\\\tif(!doneN) posN.x -= offNP.x * FXAA_QUALITY_P1;\\\\n\\\\t\\\\t\\\\t\\\\tif(!doneN) posN.y -= offNP.y * FXAA_QUALITY_P1;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaBool doneNP = (!doneN) || (!doneP);\\\\n\\\\t\\\\t\\\\t\\\\tif(!doneP) posP.x += offNP.x * FXAA_QUALITY_P1;\\\\n\\\\t\\\\t\\\\t\\\\tif(!doneP) posP.y += offNP.y * FXAA_QUALITY_P1;\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t\\\\t\\\\tif(doneNP) {\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) lumaEndN = FxaaLuma(FxaaTexTop(tex, posN.xy));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) lumaEndP = FxaaLuma(FxaaTexTop(tex, posP.xy));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) lumaEndN = lumaEndN - lumaNN * 0.5;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) lumaEndP = lumaEndP - lumaNN * 0.5;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tdoneN = abs(lumaEndN) >= gradientScaled;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tdoneP = abs(lumaEndP) >= gradientScaled;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) posN.x -= offNP.x * FXAA_QUALITY_P2;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) posN.y -= offNP.y * FXAA_QUALITY_P2;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tdoneNP = (!doneN) || (!doneP);\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) posP.x += offNP.x * FXAA_QUALITY_P2;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) posP.y += offNP.y * FXAA_QUALITY_P2;\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#if (FXAA_QUALITY_PS > 3)\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(doneNP) {\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) lumaEndN = FxaaLuma(FxaaTexTop(tex, posN.xy));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) lumaEndP = FxaaLuma(FxaaTexTop(tex, posP.xy));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) lumaEndN = lumaEndN - lumaNN * 0.5;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) lumaEndP = lumaEndP - lumaNN * 0.5;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tdoneN = abs(lumaEndN) >= gradientScaled;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tdoneP = abs(lumaEndP) >= gradientScaled;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) posN.x -= offNP.x * FXAA_QUALITY_P3;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) posN.y -= offNP.y * FXAA_QUALITY_P3;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tdoneNP = (!doneN) || (!doneP);\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) posP.x += offNP.x * FXAA_QUALITY_P3;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) posP.y += offNP.y * FXAA_QUALITY_P3;\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#if (FXAA_QUALITY_PS > 4)\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(doneNP) {\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) lumaEndN = FxaaLuma(FxaaTexTop(tex, posN.xy));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) lumaEndP = FxaaLuma(FxaaTexTop(tex, posP.xy));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) lumaEndN = lumaEndN - lumaNN * 0.5;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) lumaEndP = lumaEndP - lumaNN * 0.5;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tdoneN = abs(lumaEndN) >= gradientScaled;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tdoneP = abs(lumaEndP) >= gradientScaled;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) posN.x -= offNP.x * FXAA_QUALITY_P4;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) posN.y -= offNP.y * FXAA_QUALITY_P4;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tdoneNP = (!doneN) || (!doneP);\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) posP.x += offNP.x * FXAA_QUALITY_P4;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) posP.y += offNP.y * FXAA_QUALITY_P4;\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#if (FXAA_QUALITY_PS > 5)\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(doneNP) {\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) lumaEndN = FxaaLuma(FxaaTexTop(tex, posN.xy));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) lumaEndP = FxaaLuma(FxaaTexTop(tex, posP.xy));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) lumaEndN = lumaEndN - lumaNN * 0.5;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) lumaEndP = lumaEndP - lumaNN * 0.5;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tdoneN = abs(lumaEndN) >= gradientScaled;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tdoneP = abs(lumaEndP) >= gradientScaled;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) posN.x -= offNP.x * FXAA_QUALITY_P5;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) posN.y -= offNP.y * FXAA_QUALITY_P5;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tdoneNP = (!doneN) || (!doneP);\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) posP.x += offNP.x * FXAA_QUALITY_P5;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) posP.y += offNP.y * FXAA_QUALITY_P5;\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#if (FXAA_QUALITY_PS > 6)\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(doneNP) {\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) lumaEndN = FxaaLuma(FxaaTexTop(tex, posN.xy));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) lumaEndP = FxaaLuma(FxaaTexTop(tex, posP.xy));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) lumaEndN = lumaEndN - lumaNN * 0.5;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) lumaEndP = lumaEndP - lumaNN * 0.5;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tdoneN = abs(lumaEndN) >= gradientScaled;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tdoneP = abs(lumaEndP) >= gradientScaled;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) posN.x -= offNP.x * FXAA_QUALITY_P6;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) posN.y -= offNP.y * FXAA_QUALITY_P6;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tdoneNP = (!doneN) || (!doneP);\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) posP.x += offNP.x * FXAA_QUALITY_P6;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) posP.y += offNP.y * FXAA_QUALITY_P6;\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#if (FXAA_QUALITY_PS > 7)\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(doneNP) {\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) lumaEndN = FxaaLuma(FxaaTexTop(tex, posN.xy));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) lumaEndP = FxaaLuma(FxaaTexTop(tex, posP.xy));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) lumaEndN = lumaEndN - lumaNN * 0.5;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) lumaEndP = lumaEndP - lumaNN * 0.5;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tdoneN = abs(lumaEndN) >= gradientScaled;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tdoneP = abs(lumaEndP) >= gradientScaled;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) posN.x -= offNP.x * FXAA_QUALITY_P7;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) posN.y -= offNP.y * FXAA_QUALITY_P7;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tdoneNP = (!doneN) || (!doneP);\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) posP.x += offNP.x * FXAA_QUALITY_P7;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) posP.y += offNP.y * FXAA_QUALITY_P7;\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t\\\\t\\\\t#if (FXAA_QUALITY_PS > 8)\\\\n\\\\t\\\\t\\\\t\\\\tif(doneNP) {\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) lumaEndN = FxaaLuma(FxaaTexTop(tex, posN.xy));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) lumaEndP = FxaaLuma(FxaaTexTop(tex, posP.xy));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) lumaEndN = lumaEndN - lumaNN * 0.5;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) lumaEndP = lumaEndP - lumaNN * 0.5;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tdoneN = abs(lumaEndN) >= gradientScaled;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tdoneP = abs(lumaEndP) >= gradientScaled;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) posN.x -= offNP.x * FXAA_QUALITY_P8;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) posN.y -= offNP.y * FXAA_QUALITY_P8;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tdoneNP = (!doneN) || (!doneP);\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) posP.x += offNP.x * FXAA_QUALITY_P8;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) posP.y += offNP.y * FXAA_QUALITY_P8;\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#if (FXAA_QUALITY_PS > 9)\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(doneNP) {\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) lumaEndN = FxaaLuma(FxaaTexTop(tex, posN.xy));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) lumaEndP = FxaaLuma(FxaaTexTop(tex, posP.xy));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) lumaEndN = lumaEndN - lumaNN * 0.5;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) lumaEndP = lumaEndP - lumaNN * 0.5;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tdoneN = abs(lumaEndN) >= gradientScaled;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tdoneP = abs(lumaEndP) >= gradientScaled;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) posN.x -= offNP.x * FXAA_QUALITY_P9;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) posN.y -= offNP.y * FXAA_QUALITY_P9;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tdoneNP = (!doneN) || (!doneP);\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) posP.x += offNP.x * FXAA_QUALITY_P9;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) posP.y += offNP.y * FXAA_QUALITY_P9;\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#if (FXAA_QUALITY_PS > 10)\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(doneNP) {\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) lumaEndN = FxaaLuma(FxaaTexTop(tex, posN.xy));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) lumaEndP = FxaaLuma(FxaaTexTop(tex, posP.xy));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) lumaEndN = lumaEndN - lumaNN * 0.5;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) lumaEndP = lumaEndP - lumaNN * 0.5;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tdoneN = abs(lumaEndN) >= gradientScaled;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tdoneP = abs(lumaEndP) >= gradientScaled;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) posN.x -= offNP.x * FXAA_QUALITY_P10;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) posN.y -= offNP.y * FXAA_QUALITY_P10;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tdoneNP = (!doneN) || (!doneP);\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) posP.x += offNP.x * FXAA_QUALITY_P10;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) posP.y += offNP.y * FXAA_QUALITY_P10;\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#if (FXAA_QUALITY_PS > 11)\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(doneNP) {\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) lumaEndN = FxaaLuma(FxaaTexTop(tex, posN.xy));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) lumaEndP = FxaaLuma(FxaaTexTop(tex, posP.xy));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) lumaEndN = lumaEndN - lumaNN * 0.5;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) lumaEndP = lumaEndP - lumaNN * 0.5;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tdoneN = abs(lumaEndN) >= gradientScaled;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tdoneP = abs(lumaEndP) >= gradientScaled;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) posN.x -= offNP.x * FXAA_QUALITY_P11;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) posN.y -= offNP.y * FXAA_QUALITY_P11;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tdoneNP = (!doneN) || (!doneP);\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) posP.x += offNP.x * FXAA_QUALITY_P11;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) posP.y += offNP.y * FXAA_QUALITY_P11;\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#if (FXAA_QUALITY_PS > 12)\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(doneNP) {\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) lumaEndN = FxaaLuma(FxaaTexTop(tex, posN.xy));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) lumaEndP = FxaaLuma(FxaaTexTop(tex, posP.xy));\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) lumaEndN = lumaEndN - lumaNN * 0.5;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) lumaEndP = lumaEndP - lumaNN * 0.5;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tdoneN = abs(lumaEndN) >= gradientScaled;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tdoneP = abs(lumaEndP) >= gradientScaled;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) posN.x -= offNP.x * FXAA_QUALITY_P12;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneN) posN.y -= offNP.y * FXAA_QUALITY_P12;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tdoneNP = (!doneN) || (!doneP);\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) posP.x += offNP.x * FXAA_QUALITY_P12;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tif(!doneP) posP.y += offNP.y * FXAA_QUALITY_P12;\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t}\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t}\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t}\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t}\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t\\\\t\\\\t}\\\\n\\\\t\\\\t\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t}\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t}\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t}\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t}\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t}\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t#endif\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t\\\\t\\\\t}\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat dstN = posM.x - posN.x;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat dstP = posP.x - posM.x;\\\\n\\\\t\\\\t\\\\t\\\\tif(!horzSpan) dstN = posM.y - posN.y;\\\\n\\\\t\\\\t\\\\t\\\\tif(!horzSpan) dstP = posP.y - posM.y;\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t\\\\t\\\\tFxaaBool goodSpanN = (lumaEndN < 0.0) != lumaMLTZero;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat spanLength = (dstP + dstN);\\\\n\\\\t\\\\t\\\\t\\\\tFxaaBool goodSpanP = (lumaEndP < 0.0) != lumaMLTZero;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat spanLengthRcp = 1.0/spanLength;\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t\\\\t\\\\tFxaaBool directionN = dstN < dstP;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat dst = min(dstN, dstP);\\\\n\\\\t\\\\t\\\\t\\\\tFxaaBool goodSpan = directionN ? goodSpanN : goodSpanP;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat subpixG = subpixF * subpixF;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat pixelOffset = (dst * (-spanLengthRcp)) + 0.5;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat subpixH = subpixG * fxaaQualitySubpix;\\\\n\\\\t\\\\t/*--------------------------------------------------------------------------*/\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat pixelOffsetGood = goodSpan ? pixelOffset : 0.0;\\\\n\\\\t\\\\t\\\\t\\\\tFxaaFloat pixelOffsetSubpix = max(pixelOffsetGood, subpixH);\\\\n\\\\t\\\\t\\\\t\\\\tif(!horzSpan) posM.x += pixelOffsetSubpix * lengthSign;\\\\n\\\\t\\\\t\\\\t\\\\tif( horzSpan) posM.y += pixelOffsetSubpix * lengthSign;\\\\n\\\\t\\\\t\\\\t\\\\t#if (FXAA_DISCARD == 1)\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\treturn FxaaTexTop(tex, posM);\\\\n\\\\t\\\\t\\\\t\\\\t#else\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\treturn FxaaFloat4(FxaaTexTop(tex, posM).xyz, lumaM);\\\\n\\\\t\\\\t\\\\t\\\\t#endif\\\\n\\\\t\\\\t}\\\\n\\\\t\\\\t/*==========================================================================*/\\\\n\\\\t\\\\t#endif\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\t\\\\t\\\\tgl_FragColor = FxaaPixelShader(\\\\n\\\\t\\\\t\\\\t\\\\tvUv,\\\\n\\\\t\\\\t\\\\t\\\\tvec4(0.0),\\\\n\\\\t\\\\t\\\\t\\\\ttDiffuse,\\\\n\\\\t\\\\t\\\\t\\\\ttDiffuse,\\\\n\\\\t\\\\t\\\\t\\\\ttDiffuse,\\\\n\\\\t\\\\t\\\\t\\\\tresolution,\\\\n\\\\t\\\\t\\\\t\\\\tvec4(0.0),\\\\n\\\\t\\\\t\\\\t\\\\tvec4(0.0),\\\\n\\\\t\\\\t\\\\t\\\\tvec4(0.0),\\\\n\\\\t\\\\t\\\\t\\\\t0.75,\\\\n\\\\t\\\\t\\\\t\\\\t0.166,\\\\n\\\\t\\\\t\\\\t\\\\t0.0833,\\\\n\\\\t\\\\t\\\\t\\\\t0.0,\\\\n\\\\t\\\\t\\\\t\\\\t0.0,\\\\n\\\\t\\\\t\\\\t\\\\t0.0,\\\\n\\\\t\\\\t\\\\t\\\\tvec4(0.0)\\\\n\\\\t\\\\t\\\\t);\\\\n\\\\n\\\\t\\\\t\\\\t// TODO avoid querying texture twice for same texel\\\\n\\\\t\\\\t\\\\tgl_FragColor.a = texture2D(tDiffuse, vUv).a;\\\\n\\\\t\\\\t}'};const pj=new class extends aa{constructor(){super(...arguments),this.transparent=oa.BOOLEAN(1,MH)}};class _j extends SH{constructor(){super(...arguments),this.paramsConfig=pj}static type(){return\\\\\\\"FXAA\\\\\\\"}_createPass(t){const e=new Bm(dj);return e.uniforms.resolution.value.set(1/t.resolution.x,1/t.resolution.y),e.material.transparent=!0,this.updatePass(e),e}updatePass(t){t.material.transparent=this.pv.transparent}}const mj={uniforms:{tDiffuse:{value:null}},vertexShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\tvarying vec2 vUv;\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\tvUv = uv;\\\\n\\\\t\\\\t\\\\tgl_Position = projectionMatrix * modelViewMatrix * vec4( position, 1.0 );\\\\n\\\\n\\\\t\\\\t}\\\\\\\",fragmentShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\tuniform sampler2D tDiffuse;\\\\n\\\\n\\\\t\\\\tvarying vec2 vUv;\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\tvec4 tex = texture2D( tDiffuse, vUv );\\\\n\\\\n\\\\t\\\\t\\\\tgl_FragColor = LinearTosRGB( tex ); // optional: LinearToGamma( tex, float( GAMMA_FACTOR ) );\\\\n\\\\n\\\\t\\\\t}\\\\\\\"};const fj=new class extends aa{};class gj extends SH{constructor(){super(...arguments),this.paramsConfig=fj}static type(){return\\\\\\\"gammaCorrection\\\\\\\"}_createPass(t){const e=new Bm(mj);return this.updatePass(e),e}updatePass(t){}}const vj=new class extends aa{constructor(){super(...arguments),this.amount=oa.FLOAT(2,{range:[0,10],rangeLocked:[!0,!1],step:.01,...MH}),this.transparent=oa.BOOLEAN(1,MH)}};class yj extends SH{constructor(){super(...arguments),this.paramsConfig=vj}static type(){return\\\\\\\"horizontalBlur\\\\\\\"}_createPass(t){const e=new Bm(IU);return e.resolution_x=t.resolution.x,this.updatePass(e),e}updatePass(t){t.uniforms.h.value=this.pv.amount/(t.resolution_x*window.devicePixelRatio),t.material.transparent=this.pv.transparent}}const xj=new class extends aa{constructor(){super(...arguments),this.map=oa.OPERATOR_PATH(gi.UV,{nodeSelection:{context:Ki.COP},...MH}),this.darkness=oa.FLOAT(0,{range:[0,2],rangeLocked:[!0,!1],...MH}),this.offset=oa.FLOAT(0,{range:[0,2],rangeLocked:[!0,!1],...MH})}};class bj extends SH{constructor(){super(...arguments),this.paramsConfig=xj}static type(){return\\\\\\\"image\\\\\\\"}static _create_shader(){return{uniforms:{tDiffuse:{value:null},map:{value:null},offset:{value:1},darkness:{value:1}},vertexShader:\\\\\\\"varying vec2 vUv;\\\\nvoid main() {\\\\n\\\\tvUv = uv;\\\\n\\\\tgl_Position = projectionMatrix * modelViewMatrix * vec4( position, 1.0 );\\\\n}\\\\\\\",fragmentShader:\\\\\\\"uniform float offset;\\\\nuniform float darkness;\\\\nuniform sampler2D tDiffuse;\\\\nuniform sampler2D map;\\\\nvarying vec2 vUv;\\\\n\\\\nvoid main() {\\\\n\\\\n\\\\tvec4 texel = texture2D( tDiffuse, vUv );\\\\n\\\\tvec4 map_val = texture2D( map, vUv );\\\\n\\\\tvec2 uv = ( vUv - vec2( 0.5 ) ) * vec2( offset );\\\\n\\\\t// gl_FragColor = vec4( mix( texel.rgb, vec3( 1.0 - darkness ), dot( uv, uv ) ), texel.a );\\\\n\\\\tgl_FragColor = vec4( mix( texel.rgb, map_val.rgb, map_val.a ), texel.a );\\\\n\\\\n}\\\\n\\\\\\\"}}_createPass(t){const e=new Bm(bj._create_shader());return this.updatePass(e),e}updatePass(t){t.uniforms.darkness.value=this.pv.darkness,t.uniforms.offset.value=this.pv.offset,this._update_map(t)}async _update_map(t){this.p.map.isDirty()&&await this.p.map.compute();const e=this.p.map.found_node();if(e)if(e.context()==Ki.COP){const n=e,i=(await n.compute()).coreContent();t.uniforms.map.value=i}else this.states.error.set(\\\\\\\"node is not COP\\\\\\\");else this.states.error.set(\\\\\\\"no map found\\\\\\\")}}const wj={tDiffuse:{value:null},texture1:{value:null},texture2:{value:null},h:{value:1/512}},Tj=\\\\\\\"varying vec2 vUv;\\\\n\\\\nvoid main() {\\\\n\\\\n\\\\tvUv = uv;\\\\n\\\\tgl_Position = projectionMatrix * modelViewMatrix * vec4( position, 1.0 );\\\\n\\\\n}\\\\\\\",Aj=\\\\\\\"uniform sampler2D texture1;\\\\nuniform sampler2D texture2;\\\\nvarying vec2 vUv;\\\\n\\\\nvoid main() {\\\\n\\\\n\\\\tvec4 t1 = texture2D( texture1, vUv);\\\\n\\\\tvec4 t2 = texture2D( texture2, vUv);\\\\n\\\\n\\\\tvec3 c1 = t1.rgb * t1.a * (1.0-t2.a);\\\\n\\\\tvec3 c2 = t2.rgb * t2.a;\\\\n\\\\tfloat a = t2.a + t1.a;\\\\n\\\\tvec3 c = max(c1,c2);\\\\n\\\\n\\\\tgl_FragColor = vec4(c,a);\\\\n\\\\n}\\\\\\\";class Ej extends Im{constructor(t,e){super(),this._composer1=t,this._composer2=e,this.uniforms=I.clone(wj),this.material=new F({uniforms:this.uniforms,vertexShader:Tj,fragmentShader:Aj,transparent:!0}),this.fsQuad=new km(this.material)}render(t,e){this._composer1.render(),this._composer2.render(),this.uniforms.texture1.value=this._composer1.readBuffer.texture,this.uniforms.texture2.value=this._composer2.readBuffer.texture,this.renderToScreen?(t.setRenderTarget(null),this.fsQuad.render(t)):(t.setRenderTarget(e),this.clear&&t.clear(t.autoClearColor,t.autoClearDepth,t.autoClearStencil),this.fsQuad.render(t))}}const Mj=new class extends aa{};class Sj extends SH{constructor(){super(...arguments),this.paramsConfig=Mj}static type(){return\\\\\\\"layer\\\\\\\"}initializeNode(){super.initializeNode(),this.io.inputs.setCount(2)}setupComposer(t){const e=t.composer.renderer,n={minFilter:w.V,magFilter:w.V,format:w.Ib,stencilBuffer:!0},i=ai.renderersController.renderTarget(e.domElement.offsetWidth,e.domElement.offsetHeight,n),r=ai.renderersController.renderTarget(e.domElement.offsetWidth,e.domElement.offsetHeight,n),s=new Gm(e,i),o=new Gm(e,r);s.renderToScreen=!1,o.renderToScreen=!1;const a={...t},l={...t};a.composer=s,l.composer=o,this._addPassFromInput(0,a),this._addPassFromInput(1,l);const c=new Ej(s,o);this.updatePass(c),t.composer.addPass(c)}updatePass(t){}}const Cj=new class extends aa{constructor(){super(...arguments),this.overrideScene=oa.BOOLEAN(0,MH),this.scene=oa.OPERATOR_PATH(\\\\\\\"/scene1\\\\\\\",{visibleIf:{overrideScene:1},nodeSelection:{context:Ki.OBJ,types:[ZG.type()]},...MH}),this.overrideCamera=oa.BOOLEAN(0,MH),this.camera=oa.OPERATOR_PATH(\\\\\\\"/perspective_camera1\\\\\\\",{visibleIf:{overrideCamera:1},nodeSelection:{context:Ki.OBJ},...MH}),this.inverse=oa.BOOLEAN(0,MH)}};class Nj extends SH{constructor(){super(...arguments),this.paramsConfig=Cj}static type(){return\\\\\\\"mask\\\\\\\"}_createPass(t){const e=new zm(t.scene,t.camera);return e.context={scene:t.scene,camera:t.camera},this.updatePass(e),e}updatePass(t){t.inverse=this.pv.inverse,this._update_scene(t),this._updateCamera(t)}async _update_scene(t){if(this.pv.overrideScene){this.p.scene.isDirty()&&await this.p.scene.compute();const e=this.p.scene.found_node_with_expected_type();if(e)return void(t.scene=e.object)}t.scene=t.context.scene}async _updateCamera(t){if(this.pv.overrideCamera){this.p.camera.isDirty()&&await this.p.camera.compute();const e=this.p.camera.found_node_with_expected_type();if(e)return void(t.camera=e.object)}t.camera=t.context.camera}}const Lj=new class extends aa{};class Oj extends SH{constructor(){super(...arguments),this.paramsConfig=Lj}static type(){return\\\\\\\"null\\\\\\\"}}class Rj extends Im{constructor(t,e,n,i){super(),this.renderScene=e,this.renderCamera=n,this.selectedObjects=void 0!==i?i:[],this.visibleEdgeColor=new D.a(1,1,1),this.hiddenEdgeColor=new D.a(.1,.04,.02),this.edgeGlow=0,this.usePatternTexture=!1,this.edgeThickness=1,this.edgeStrength=3,this.downSampleRatio=2,this.pulsePeriod=0,this._visibilityCache=new Map,this.resolution=void 0!==t?new d.a(t.x,t.y):new d.a(256,256);const r={minFilter:w.V,magFilter:w.V,format:w.Ib},s=Math.round(this.resolution.x/this.downSampleRatio),o=Math.round(this.resolution.y/this.downSampleRatio);this.maskBufferMaterial=new at.a({color:16777215}),this.maskBufferMaterial.side=w.z,this.renderTargetMaskBuffer=new Z(this.resolution.x,this.resolution.y,r),this.renderTargetMaskBuffer.texture.name=\\\\\\\"OutlinePass.mask\\\\\\\",this.renderTargetMaskBuffer.texture.generateMipmaps=!1,this.depthMaterial=new Mn,this.depthMaterial.side=w.z,this.depthMaterial.depthPacking=w.Hb,this.depthMaterial.blending=w.ub,this.prepareMaskMaterial=this.getPrepareMaskMaterial(),this.prepareMaskMaterial.side=w.z,this.prepareMaskMaterial.fragmentShader=function(t,e){var n=e.isPerspectiveCamera?\\\\\\\"perspective\\\\\\\":\\\\\\\"orthographic\\\\\\\";return t.replace(/DEPTH_TO_VIEW_Z/g,n+\\\\\\\"DepthToViewZ\\\\\\\")}(this.prepareMaskMaterial.fragmentShader,this.renderCamera),this.renderTargetDepthBuffer=new Z(this.resolution.x,this.resolution.y,r),this.renderTargetDepthBuffer.texture.name=\\\\\\\"OutlinePass.depth\\\\\\\",this.renderTargetDepthBuffer.texture.generateMipmaps=!1,this.renderTargetMaskDownSampleBuffer=new Z(s,o,r),this.renderTargetMaskDownSampleBuffer.texture.name=\\\\\\\"OutlinePass.depthDownSample\\\\\\\",this.renderTargetMaskDownSampleBuffer.texture.generateMipmaps=!1,this.renderTargetBlurBuffer1=new Z(s,o,r),this.renderTargetBlurBuffer1.texture.name=\\\\\\\"OutlinePass.blur1\\\\\\\",this.renderTargetBlurBuffer1.texture.generateMipmaps=!1,this.renderTargetBlurBuffer2=new Z(Math.round(s/2),Math.round(o/2),r),this.renderTargetBlurBuffer2.texture.name=\\\\\\\"OutlinePass.blur2\\\\\\\",this.renderTargetBlurBuffer2.texture.generateMipmaps=!1,this.edgeDetectionMaterial=this.getEdgeDetectionMaterial(),this.renderTargetEdgeBuffer1=new Z(s,o,r),this.renderTargetEdgeBuffer1.texture.name=\\\\\\\"OutlinePass.edge1\\\\\\\",this.renderTargetEdgeBuffer1.texture.generateMipmaps=!1,this.renderTargetEdgeBuffer2=new Z(Math.round(s/2),Math.round(o/2),r),this.renderTargetEdgeBuffer2.texture.name=\\\\\\\"OutlinePass.edge2\\\\\\\",this.renderTargetEdgeBuffer2.texture.generateMipmaps=!1;this.separableBlurMaterial1=this.getSeperableBlurMaterial(4),this.separableBlurMaterial1.uniforms.texSize.value.set(s,o),this.separableBlurMaterial1.uniforms.kernelRadius.value=1,this.separableBlurMaterial2=this.getSeperableBlurMaterial(4),this.separableBlurMaterial2.uniforms.texSize.value.set(Math.round(s/2),Math.round(o/2)),this.separableBlurMaterial2.uniforms.kernelRadius.value=4,this.overlayMaterial=this.getOverlayMaterial(),void 0===Pm&&console.error(\\\\\\\"THREE.OutlinePass relies on CopyShader\\\\\\\");const a=Pm;this.copyUniforms=I.clone(a.uniforms),this.copyUniforms.opacity.value=1,this.materialCopy=new F({uniforms:this.copyUniforms,vertexShader:a.vertexShader,fragmentShader:a.fragmentShader,blending:w.ub,depthTest:!1,depthWrite:!1,transparent:!0}),this.enabled=!0,this.needsSwap=!1,this._oldClearColor=new D.a,this.oldClearAlpha=1,this.fsQuad=new km(null),this.tempPulseColor1=new D.a,this.tempPulseColor2=new D.a,this.textureMatrix=new A.a}dispose(){this.renderTargetMaskBuffer.dispose(),this.renderTargetDepthBuffer.dispose(),this.renderTargetMaskDownSampleBuffer.dispose(),this.renderTargetBlurBuffer1.dispose(),this.renderTargetBlurBuffer2.dispose(),this.renderTargetEdgeBuffer1.dispose(),this.renderTargetEdgeBuffer2.dispose()}setSize(t,e){this.renderTargetMaskBuffer.setSize(t,e),this.renderTargetDepthBuffer.setSize(t,e);let n=Math.round(t/this.downSampleRatio),i=Math.round(e/this.downSampleRatio);this.renderTargetMaskDownSampleBuffer.setSize(n,i),this.renderTargetBlurBuffer1.setSize(n,i),this.renderTargetEdgeBuffer1.setSize(n,i),this.separableBlurMaterial1.uniforms.texSize.value.set(n,i),n=Math.round(n/2),i=Math.round(i/2),this.renderTargetBlurBuffer2.setSize(n,i),this.renderTargetEdgeBuffer2.setSize(n,i),this.separableBlurMaterial2.uniforms.texSize.value.set(n,i)}changeVisibilityOfSelectedObjects(t){const e=this._visibilityCache;function n(n){n.isMesh&&(!0===t?n.visible=e.get(n):(e.set(n,n.visible),n.visible=t))}for(let t=0;t<this.selectedObjects.length;t++){this.selectedObjects[t].traverse(n)}}changeVisibilityOfNonSelectedObjects(t){const e=this._visibilityCache,n=[];function i(t){t.isMesh&&n.push(t)}for(let t=0;t<this.selectedObjects.length;t++){this.selectedObjects[t].traverse(i)}this.renderScene.traverse((function(i){if(i.isMesh||i.isSprite){let r=!1;for(let t=0;t<n.length;t++){if(n[t].id===i.id){r=!0;break}}if(!1===r){const n=i.visible;!1!==t&&!0!==e.get(i)||(i.visible=t),e.set(i,n)}}else(i.isPoints||i.isLine)&&(!0===t?i.visible=e.get(i):(e.set(i,i.visible),i.visible=t))}))}updateTextureMatrix(){this.textureMatrix.set(.5,0,0,.5,0,.5,0,.5,0,0,.5,.5,0,0,0,1),this.textureMatrix.multiply(this.renderCamera.projectionMatrix),this.textureMatrix.multiply(this.renderCamera.matrixWorldInverse)}render(t,e,n,i,r){if(this.selectedObjects.length>0){t.getClearColor(this._oldClearColor),this.oldClearAlpha=t.getClearAlpha();const e=t.autoClear;t.autoClear=!1,r&&t.state.buffers.stencil.setTest(!1),t.setClearColor(16777215,1),this.changeVisibilityOfSelectedObjects(!1);const i=this.renderScene.background;if(this.renderScene.background=null,this.renderScene.overrideMaterial=this.depthMaterial,t.setRenderTarget(this.renderTargetDepthBuffer),t.clear(),t.render(this.renderScene,this.renderCamera),this.changeVisibilityOfSelectedObjects(!0),this._visibilityCache.clear(),this.updateTextureMatrix(),this.changeVisibilityOfNonSelectedObjects(!1),this.renderScene.overrideMaterial=this.prepareMaskMaterial,this.prepareMaskMaterial.uniforms.cameraNearFar.value.set(this.renderCamera.near,this.renderCamera.far),this.prepareMaskMaterial.uniforms.depthTexture.value=this.renderTargetDepthBuffer.texture,this.prepareMaskMaterial.uniforms.textureMatrix.value=this.textureMatrix,t.setRenderTarget(this.renderTargetMaskBuffer),t.clear(),t.render(this.renderScene,this.renderCamera),this.renderScene.overrideMaterial=null,this.changeVisibilityOfNonSelectedObjects(!0),this._visibilityCache.clear(),this.renderScene.background=i,this.fsQuad.material=this.materialCopy,this.copyUniforms.tDiffuse.value=this.renderTargetMaskBuffer.texture,t.setRenderTarget(this.renderTargetMaskDownSampleBuffer),t.clear(),this.fsQuad.render(t),this.tempPulseColor1.copy(this.visibleEdgeColor),this.tempPulseColor2.copy(this.hiddenEdgeColor),this.pulsePeriod>0){const t=.625+.75*Math.cos(.01*performance.now()/this.pulsePeriod)/2;this.tempPulseColor1.multiplyScalar(t),this.tempPulseColor2.multiplyScalar(t)}this.fsQuad.material=this.edgeDetectionMaterial,this.edgeDetectionMaterial.uniforms.maskTexture.value=this.renderTargetMaskDownSampleBuffer.texture,this.edgeDetectionMaterial.uniforms.texSize.value.set(this.renderTargetMaskDownSampleBuffer.width,this.renderTargetMaskDownSampleBuffer.height),this.edgeDetectionMaterial.uniforms.visibleEdgeColor.value=this.tempPulseColor1,this.edgeDetectionMaterial.uniforms.hiddenEdgeColor.value=this.tempPulseColor2,t.setRenderTarget(this.renderTargetEdgeBuffer1),t.clear(),this.fsQuad.render(t),this.fsQuad.material=this.separableBlurMaterial1,this.separableBlurMaterial1.uniforms.colorTexture.value=this.renderTargetEdgeBuffer1.texture,this.separableBlurMaterial1.uniforms.direction.value=Rj.BlurDirectionX,this.separableBlurMaterial1.uniforms.kernelRadius.value=this.edgeThickness,t.setRenderTarget(this.renderTargetBlurBuffer1),t.clear(),this.fsQuad.render(t),this.separableBlurMaterial1.uniforms.colorTexture.value=this.renderTargetBlurBuffer1.texture,this.separableBlurMaterial1.uniforms.direction.value=Rj.BlurDirectionY,t.setRenderTarget(this.renderTargetEdgeBuffer1),t.clear(),this.fsQuad.render(t),this.fsQuad.material=this.separableBlurMaterial2,this.separableBlurMaterial2.uniforms.colorTexture.value=this.renderTargetEdgeBuffer1.texture,this.separableBlurMaterial2.uniforms.direction.value=Rj.BlurDirectionX,t.setRenderTarget(this.renderTargetBlurBuffer2),t.clear(),this.fsQuad.render(t),this.separableBlurMaterial2.uniforms.colorTexture.value=this.renderTargetBlurBuffer2.texture,this.separableBlurMaterial2.uniforms.direction.value=Rj.BlurDirectionY,t.setRenderTarget(this.renderTargetEdgeBuffer2),t.clear(),this.fsQuad.render(t),this.fsQuad.material=this.overlayMaterial,this.overlayMaterial.uniforms.maskTexture.value=this.renderTargetMaskBuffer.texture,this.overlayMaterial.uniforms.edgeTexture1.value=this.renderTargetEdgeBuffer1.texture,this.overlayMaterial.uniforms.edgeTexture2.value=this.renderTargetEdgeBuffer2.texture,this.overlayMaterial.uniforms.patternTexture.value=this.patternTexture,this.overlayMaterial.uniforms.edgeStrength.value=this.edgeStrength,this.overlayMaterial.uniforms.edgeGlow.value=this.edgeGlow,this.overlayMaterial.uniforms.usePatternTexture.value=this.usePatternTexture,r&&t.state.buffers.stencil.setTest(!0),t.setRenderTarget(n),this.fsQuad.render(t),t.setClearColor(this._oldClearColor,this.oldClearAlpha),t.autoClear=e}this.renderToScreen&&(this.fsQuad.material=this.materialCopy,this.copyUniforms.tDiffuse.value=n.texture,t.setRenderTarget(null),this.fsQuad.render(t))}getPrepareMaskMaterial(){return new F({uniforms:{depthTexture:{value:null},cameraNearFar:{value:new d.a(.5,.5)},textureMatrix:{value:null}},vertexShader:\\\\\\\"#include <morphtarget_pars_vertex>\\\\n\\\\t\\\\t\\\\t\\\\t#include <skinning_pars_vertex>\\\\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 <skinbase_vertex>\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t#include <begin_vertex>\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t#include <morphtarget_vertex>\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t#include <skinning_vertex>\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t#include <project_vertex>\\\\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 <packing>\\\\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 <common>\\\\n\\\\t\\\\t\\\\t\\\\tvarying vec2 vUv;\\\\n\\\\t\\\\t\\\\t\\\\tuniform sampler2D colorTexture;\\\\n\\\\t\\\\t\\\\t\\\\tuniform vec2 texSize;\\\\n\\\\t\\\\t\\\\t\\\\tuniform vec2 direction;\\\\n\\\\t\\\\t\\\\t\\\\tuniform float kernelRadius;\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tfloat gaussianPdf(in float x, in float sigma) {\\\\n\\\\t\\\\t\\\\t\\\\t\\\\treturn 0.39894 * exp( -0.5 * x * x/( sigma * sigma))/sigma;\\\\n\\\\t\\\\t\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tvoid main() {\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tvec2 invSize = 1.0 / texSize;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tfloat weightSum = gaussianPdf(0.0, kernelRadius);\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tvec4 diffuseSum = texture2D( colorTexture, vUv) * weightSum;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tvec2 delta = direction * invSize * kernelRadius/float(MAX_RADIUS);\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tvec2 uvOffset = delta;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tfor( int i = 1; i <= MAX_RADIUS; i ++ ) {\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tfloat w = gaussianPdf(uvOffset.x, kernelRadius);\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tvec4 sample1 = texture2D( colorTexture, vUv + uvOffset);\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tvec4 sample2 = texture2D( colorTexture, vUv - uvOffset);\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tdiffuseSum += ((sample1 + sample2) * w);\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tweightSum += (2.0 * w);\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tuvOffset += delta;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t}\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tgl_FragColor = diffuseSum/weightSum;\\\\n\\\\t\\\\t\\\\t\\\\t}\\\\\\\"})}getOverlayMaterial(){return new F({uniforms:{maskTexture:{value:null},edgeTexture1:{value:null},edgeTexture2:{value:null},patternTexture:{value:null},edgeStrength:{value:1},edgeGlow:{value:1},usePatternTexture:{value:0}},vertexShader:\\\\\\\"varying vec2 vUv;\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tvoid main() {\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tvUv = uv;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tgl_Position = projectionMatrix * modelViewMatrix * vec4( position, 1.0 );\\\\n\\\\t\\\\t\\\\t\\\\t}\\\\\\\",fragmentShader:\\\\\\\"varying vec2 vUv;\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tuniform sampler2D maskTexture;\\\\n\\\\t\\\\t\\\\t\\\\tuniform sampler2D edgeTexture1;\\\\n\\\\t\\\\t\\\\t\\\\tuniform sampler2D edgeTexture2;\\\\n\\\\t\\\\t\\\\t\\\\tuniform sampler2D patternTexture;\\\\n\\\\t\\\\t\\\\t\\\\tuniform float edgeStrength;\\\\n\\\\t\\\\t\\\\t\\\\tuniform float edgeGlow;\\\\n\\\\t\\\\t\\\\t\\\\tuniform bool usePatternTexture;\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tvoid main() {\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tvec4 edgeValue1 = texture2D(edgeTexture1, vUv);\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tvec4 edgeValue2 = texture2D(edgeTexture2, vUv);\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tvec4 maskColor = texture2D(maskTexture, vUv);\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tvec4 patternColor = texture2D(patternTexture, 6.0 * vUv);\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tfloat visibilityFactor = 1.0 - maskColor.g > 0.0 ? 1.0 : 0.5;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tvec4 edgeValue = edgeValue1 + edgeValue2 * edgeGlow;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tvec4 finalColor = edgeStrength * maskColor.r * edgeValue;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tif(usePatternTexture)\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tfinalColor += + visibilityFactor * (1.0 - maskColor.r) * (1.0 - patternColor.r);\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tgl_FragColor = finalColor;\\\\n\\\\t\\\\t\\\\t\\\\t}\\\\\\\",blending:w.e,depthTest:!1,depthWrite:!1,transparent:!0})}}Rj.BlurDirectionX=new d.a(1,0),Rj.BlurDirectionY=new d.a(0,1);const Pj=new class extends aa{constructor(){super(...arguments),this.objectsMask=oa.STRING(\\\\\\\"*outlined*\\\\\\\",{...MH}),this.refreshObjects=oa.BUTTON(null,{...MH}),this.printObjects=oa.BUTTON(null,{cook:!1,callback:t=>{Ij.PARAM_CALLBACK_printResolve(t)}}),this.edgeStrength=oa.FLOAT(3,{range:[0,10],rangeLocked:[!0,!1],...MH}),this.edgeThickness=oa.FLOAT(1,{range:[0,4],rangeLocked:[!0,!1],...MH}),this.edgeGlow=oa.FLOAT(0,{range:[0,1],rangeLocked:[!0,!1],...MH}),this.pulsePeriod=oa.FLOAT(0,{range:[0,5],rangeLocked:[!0,!1],...MH}),this.visibleEdgeColor=oa.COLOR([1,1,1],{...MH}),this.hiddenEdgeColor=oa.COLOR([.2,.1,.4],{...MH})}};class Ij extends SH{constructor(){super(...arguments),this.paramsConfig=Pj,this._resolvedObjects=[],this._map=new Map}static type(){return\\\\\\\"outline\\\\\\\"}_createPass(t){const e=new Rj(new d.a(t.resolution.x,t.resolution.y),t.scene,t.camera,t.scene.children);return this.updatePass(e),e}updatePass(t){t.edgeStrength=this.pv.edgeStrength,t.edgeThickness=this.pv.edgeThickness,t.edgeGlow=this.pv.edgeGlow,t.pulsePeriod=this.pv.pulsePeriod,t.visibleEdgeColor=this.pv.visibleEdgeColor,t.hiddenEdgeColor=this.pv.hiddenEdgeColor,this._setSelectedObjects(t)}_setSelectedObjects(t){const e=this.scene().objectsByMask(this.pv.objectsMask);this._map.clear();for(let t of e)this._map.set(t.uuid,t);this._resolvedObjects=e.filter((t=>{let e=!1;return t.traverseAncestors((t=>{this._map.has(t.uuid)&&(e=!0)})),!e})),t.selectedObjects=this._resolvedObjects}static PARAM_CALLBACK_printResolve(t){t.printResolve()}printResolve(){console.log(this._resolvedObjects)}}const Fj={uniforms:{tDiffuse:{value:null},resolution:{value:null},pixelSize:{value:1}},vertexShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\tvarying highp vec2 vUv;\\\\n\\\\n\\\\t\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tvUv = uv;\\\\n\\\\t\\\\t\\\\t\\\\tgl_Position = projectionMatrix * modelViewMatrix * vec4( position, 1.0 );\\\\n\\\\n\\\\t\\\\t}\\\\\\\",fragmentShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\tuniform sampler2D tDiffuse;\\\\n\\\\t\\\\tuniform float pixelSize;\\\\n\\\\t\\\\tuniform vec2 resolution;\\\\n\\\\n\\\\t\\\\tvarying highp vec2 vUv;\\\\n\\\\n\\\\t\\\\tvoid main(){\\\\n\\\\n\\\\t\\\\t\\\\tvec2 dxy = pixelSize / resolution;\\\\n\\\\t\\\\t\\\\tvec2 coord = dxy * floor( vUv / dxy );\\\\n\\\\t\\\\t\\\\tgl_FragColor = texture2D(tDiffuse, coord);\\\\n\\\\n\\\\t\\\\t}\\\\\\\"};const Dj=new class extends aa{constructor(){super(...arguments),this.pixelSize=oa.INTEGER(16,{range:[1,50],rangeLocked:[!0,!1],...MH})}};class kj extends SH{constructor(){super(...arguments),this.paramsConfig=Dj}static type(){return\\\\\\\"pixel\\\\\\\"}_createPass(t){const e=new Bm(Fj);return e.uniforms.resolution.value=t.resolution,e.uniforms.resolution.value.multiplyScalar(window.devicePixelRatio),this.updatePass(e),e}updatePass(t){t.uniforms.pixelSize.value=this.pv.pixelSize}}const Bj=new class extends aa{constructor(){super(...arguments),this.overrideScene=oa.BOOLEAN(0,MH),this.scene=oa.OPERATOR_PATH(\\\\\\\"/scene1\\\\\\\",{visibleIf:{overrideScene:1},nodeSelection:{context:Ki.OBJ,types:[ZG.type()]},...MH}),this.overrideCamera=oa.BOOLEAN(0,MH),this.camera=oa.OPERATOR_PATH(\\\\\\\"/perspective_camera1\\\\\\\",{visibleIf:{overrideCamera:1},nodeSelection:{context:Ki.OBJ},...MH})}};class zj extends SH{constructor(){super(...arguments),this.paramsConfig=Bj}static type(){return\\\\\\\"render\\\\\\\"}_createPass(t){const e=new Hm(t.scene,t.camera);return e.context={camera:t.camera,scene:t.scene},this.updatePass(e),e}updatePass(t){this._updateCamera(t),this._update_scene(t)}async _updateCamera(t){if(this.pv.overrideCamera){this.p.camera.isDirty()&&await this.p.camera.compute();const e=this.p.camera.found_node_with_context(Ki.OBJ);if(e&&(e.type()==nr.PERSPECTIVE||e.type()==nr.ORTHOGRAPHIC)){const n=e.object;t.camera=n}}else t.camera=t.context.camera}async _update_scene(t){if(this.pv.overrideScene){this.p.camera.isDirty()&&await this.p.scene.compute();const e=this.p.scene.found_node_with_context(Ki.OBJ);if(e&&e.type()==ZG.type()){const n=e.object;t.scene=n}}else t.scene=t.context.scene}}const Uj={uniforms:{tDiffuse:{value:null},amount:{value:.005},angle:{value:0}},vertexShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\tvarying vec2 vUv;\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\tvUv = uv;\\\\n\\\\t\\\\t\\\\tgl_Position = projectionMatrix * modelViewMatrix * vec4( position, 1.0 );\\\\n\\\\n\\\\t\\\\t}\\\\\\\",fragmentShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\tuniform sampler2D tDiffuse;\\\\n\\\\t\\\\tuniform float amount;\\\\n\\\\t\\\\tuniform float angle;\\\\n\\\\n\\\\t\\\\tvarying vec2 vUv;\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\tvec2 offset = amount * vec2( cos(angle), sin(angle));\\\\n\\\\t\\\\t\\\\tvec4 cr = texture2D(tDiffuse, vUv + offset);\\\\n\\\\t\\\\t\\\\tvec4 cga = texture2D(tDiffuse, vUv);\\\\n\\\\t\\\\t\\\\tvec4 cb = texture2D(tDiffuse, vUv - offset);\\\\n\\\\t\\\\t\\\\tgl_FragColor = vec4(cr.r, cga.g, cb.b, cga.a);\\\\n\\\\n\\\\t\\\\t}\\\\\\\"};const Gj=new class extends aa{constructor(){super(...arguments),this.amount=oa.FLOAT(.005,{range:[0,1],rangeLocked:[!0,!1],...MH}),this.angle=oa.FLOAT(0,{range:[0,10],rangeLocked:[!0,!1],...MH})}};class Vj extends SH{constructor(){super(...arguments),this.paramsConfig=Gj}static type(){return\\\\\\\"RGBShift\\\\\\\"}_createPass(t){const e=new Bm(Uj);return this.updatePass(e),e}updatePass(t){t.uniforms.amount.value=this.pv.amount,t.uniforms.angle.value=this.pv.angle}}const Hj={uniforms:{tDiffuse:{value:null},amount:{value:1}},vertexShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\tvarying vec2 vUv;\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\tvUv = uv;\\\\n\\\\t\\\\t\\\\tgl_Position = projectionMatrix * modelViewMatrix * vec4( position, 1.0 );\\\\n\\\\n\\\\t\\\\t}\\\\\\\",fragmentShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\tuniform float amount;\\\\n\\\\n\\\\t\\\\tuniform sampler2D tDiffuse;\\\\n\\\\n\\\\t\\\\tvarying vec2 vUv;\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\tvec4 color = texture2D( tDiffuse, vUv );\\\\n\\\\t\\\\t\\\\tvec3 c = color.rgb;\\\\n\\\\n\\\\t\\\\t\\\\tcolor.r = dot( c, vec3( 1.0 - 0.607 * amount, 0.769 * amount, 0.189 * amount ) );\\\\n\\\\t\\\\t\\\\tcolor.g = dot( c, vec3( 0.349 * amount, 1.0 - 0.314 * amount, 0.168 * amount ) );\\\\n\\\\t\\\\t\\\\tcolor.b = dot( c, vec3( 0.272 * amount, 0.534 * amount, 1.0 - 0.869 * amount ) );\\\\n\\\\n\\\\t\\\\t\\\\tgl_FragColor = vec4( min( vec3( 1.0 ), color.rgb ), color.a );\\\\n\\\\n\\\\t\\\\t}\\\\\\\"};const jj=new class extends aa{constructor(){super(...arguments),this.amount=oa.FLOAT(.5,{range:[0,2],rangeLocked:[!1,!1],...MH})}};class Wj extends SH{constructor(){super(...arguments),this.paramsConfig=jj}static type(){return\\\\\\\"sepia\\\\\\\"}_createPass(t){const e=new Bm(Hj);return this.updatePass(e),e}updatePass(t){t.uniforms.amount.value=this.pv.amount}}const qj=new class extends aa{};class Xj extends SH{constructor(){super(...arguments),this.paramsConfig=qj}static type(){return\\\\\\\"sequence\\\\\\\"}initializeNode(){super.initializeNode(),this.io.inputs.setCount(0,4)}setupComposer(t){this._addPassFromInput(0,t),this._addPassFromInput(1,t),this._addPassFromInput(2,t),this._addPassFromInput(3,t)}}const Yj=I.clone(IU.uniforms);Yj.delta={value:new d.a};const $j={uniforms:Yj,vertexShader:IU.vertexShader,fragmentShader:\\\\\\\"\\\\n#include <common>\\\\n#define ITERATIONS 10.0\\\\nuniform sampler2D tDiffuse;\\\\nuniform vec2 delta;\\\\nvarying vec2 vUv;\\\\nvoid main() {\\\\n\\\\tvec4 color = vec4( 0.0 );\\\\n\\\\tfloat total = 0.0;\\\\n\\\\tfloat offset = rand( vUv );\\\\n\\\\tfor ( float t = -ITERATIONS; t <= ITERATIONS; t ++ ) {\\\\n\\\\t\\\\tfloat percent = ( t + offset - 0.5 ) / ITERATIONS;\\\\n\\\\t\\\\tfloat weight = 1.0 - abs( percent );\\\\n\\\\t\\\\tcolor += texture2D( tDiffuse, vUv + delta * percent ) * weight;\\\\n\\\\t\\\\ttotal += weight;\\\\n\\\\t}\\\\n\\\\tgl_FragColor = color / total;\\\\n}\\\\\\\"};const Jj=new class extends aa{constructor(){super(...arguments),this.delta=oa.VECTOR2([2,2],{...MH})}};class Zj extends SH{constructor(){super(...arguments),this.paramsConfig=Jj}static type(){return\\\\\\\"triangleBlur\\\\\\\"}_createPass(t){const e=new Bm($j);return e.resolution=t.resolution.clone(),this.updatePass(e),e}updatePass(t){t.uniforms.delta.value.copy(this.pv.delta).divide(t.resolution).multiplyScalar(window.devicePixelRatio)}}const Qj={shaderID:\\\\\\\"luminosityHighPass\\\\\\\",uniforms:{tDiffuse:{value:null},luminosityThreshold:{value:1},smoothWidth:{value:1},defaultColor:{value:new D.a(0)},defaultOpacity:{value:0}},vertexShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\tvarying vec2 vUv;\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\tvUv = uv;\\\\n\\\\n\\\\t\\\\t\\\\tgl_Position = projectionMatrix * modelViewMatrix * vec4( position, 1.0 );\\\\n\\\\n\\\\t\\\\t}\\\\\\\",fragmentShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\tuniform sampler2D tDiffuse;\\\\n\\\\t\\\\tuniform vec3 defaultColor;\\\\n\\\\t\\\\tuniform float defaultOpacity;\\\\n\\\\t\\\\tuniform float luminosityThreshold;\\\\n\\\\t\\\\tuniform float smoothWidth;\\\\n\\\\n\\\\t\\\\tvarying vec2 vUv;\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\tvec4 texel = texture2D( tDiffuse, vUv );\\\\n\\\\n\\\\t\\\\t\\\\tvec3 luma = vec3( 0.299, 0.587, 0.114 );\\\\n\\\\n\\\\t\\\\t\\\\tfloat v = dot( texel.xyz, luma );\\\\n\\\\n\\\\t\\\\t\\\\tvec4 outputColor = vec4( defaultColor.rgb, defaultOpacity );\\\\n\\\\n\\\\t\\\\t\\\\tfloat alpha = smoothstep( luminosityThreshold, luminosityThreshold + smoothWidth, v );\\\\n\\\\n\\\\t\\\\t\\\\tgl_FragColor = mix( outputColor, texel, alpha );\\\\n\\\\n\\\\t\\\\t}\\\\\\\"};class Kj extends Im{constructor(t,e,n,i){super(),this.strength=void 0!==e?e:1,this.radius=n,this.threshold=i,this.resolution=void 0!==t?new d.a(t.x,t.y):new d.a(256,256),this.clearColor=new D.a(0,0,0);const r={minFilter:w.V,magFilter:w.V,format:w.Ib};this.renderTargetsHorizontal=[],this.renderTargetsVertical=[],this.nMips=5;let s=Math.round(this.resolution.x/2),o=Math.round(this.resolution.y/2);this.renderTargetBright=new Z(s,o,r),this.renderTargetBright.texture.name=\\\\\\\"UnrealBloomPass.bright\\\\\\\",this.renderTargetBright.texture.generateMipmaps=!1;for(let t=0;t<this.nMips;t++){const e=new Z(s,o,r);e.texture.name=\\\\\\\"UnrealBloomPass.h\\\\\\\"+t,e.texture.generateMipmaps=!1,this.renderTargetsHorizontal.push(e);const n=new Z(s,o,r);n.texture.name=\\\\\\\"UnrealBloomPass.v\\\\\\\"+t,n.texture.generateMipmaps=!1,this.renderTargetsVertical.push(n),s=Math.round(s/2),o=Math.round(o/2)}void 0===Qj&&console.error(\\\\\\\"THREE.UnrealBloomPass relies on LuminosityHighPassShader\\\\\\\");const a=Qj;this.highPassUniforms=I.clone(a.uniforms),this.highPassUniforms.luminosityThreshold.value=i,this.highPassUniforms.smoothWidth.value=.01,this.materialHighPassFilter=new F({uniforms:this.highPassUniforms,vertexShader:a.vertexShader,fragmentShader:a.fragmentShader,defines:{}}),this.separableBlurMaterials=[];const l=[3,5,7,9,11];s=Math.round(this.resolution.x/2),o=Math.round(this.resolution.y/2);for(let t=0;t<this.nMips;t++)this.separableBlurMaterials.push(this.getSeperableBlurMaterial(l[t])),this.separableBlurMaterials[t].uniforms.texSize.value=new d.a(s,o),s=Math.round(s/2),o=Math.round(o/2);this.compositeMaterial=this.getCompositeMaterial(this.nMips),this.compositeMaterial.uniforms.blurTexture1.value=this.renderTargetsVertical[0].texture,this.compositeMaterial.uniforms.blurTexture2.value=this.renderTargetsVertical[1].texture,this.compositeMaterial.uniforms.blurTexture3.value=this.renderTargetsVertical[2].texture,this.compositeMaterial.uniforms.blurTexture4.value=this.renderTargetsVertical[3].texture,this.compositeMaterial.uniforms.blurTexture5.value=this.renderTargetsVertical[4].texture,this.compositeMaterial.uniforms.bloomStrength.value=e,this.compositeMaterial.uniforms.bloomRadius.value=.1,this.compositeMaterial.needsUpdate=!0;this.compositeMaterial.uniforms.bloomFactors.value=[1,.8,.6,.4,.2],this.bloomTintColors=[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(1,1,1)],this.compositeMaterial.uniforms.bloomTintColors.value=this.bloomTintColors,void 0===Pm&&console.error(\\\\\\\"THREE.UnrealBloomPass relies on CopyShader\\\\\\\");const c=Pm;this.copyUniforms=I.clone(c.uniforms),this.copyUniforms.opacity.value=1,this.materialCopy=new F({uniforms:this.copyUniforms,vertexShader:c.vertexShader,fragmentShader:c.fragmentShader,blending:w.e,depthTest:!1,depthWrite:!1,transparent:!0}),this.enabled=!0,this.needsSwap=!1,this._oldClearColor=new D.a,this.oldClearAlpha=1,this.basic=new at.a,this.fsQuad=new km(null)}dispose(){for(let t=0;t<this.renderTargetsHorizontal.length;t++)this.renderTargetsHorizontal[t].dispose();for(let t=0;t<this.renderTargetsVertical.length;t++)this.renderTargetsVertical[t].dispose();this.renderTargetBright.dispose()}setSize(t,e){let n=Math.round(t/2),i=Math.round(e/2);this.renderTargetBright.setSize(n,i);for(let t=0;t<this.nMips;t++)this.renderTargetsHorizontal[t].setSize(n,i),this.renderTargetsVertical[t].setSize(n,i),this.separableBlurMaterials[t].uniforms.texSize.value=new d.a(n,i),n=Math.round(n/2),i=Math.round(i/2)}render(t,e,n,i,r){t.getClearColor(this._oldClearColor),this.oldClearAlpha=t.getClearAlpha();const s=t.autoClear;t.autoClear=!1,t.setClearColor(this.clearColor,0),r&&t.state.buffers.stencil.setTest(!1),this.renderToScreen&&(this.fsQuad.material=this.basic,this.basic.map=n.texture,t.setRenderTarget(null),t.clear(),this.fsQuad.render(t)),this.highPassUniforms.tDiffuse.value=n.texture,this.highPassUniforms.luminosityThreshold.value=this.threshold,this.fsQuad.material=this.materialHighPassFilter,t.setRenderTarget(this.renderTargetBright),t.clear(),this.fsQuad.render(t);let o=this.renderTargetBright;for(let e=0;e<this.nMips;e++)this.fsQuad.material=this.separableBlurMaterials[e],this.separableBlurMaterials[e].uniforms.colorTexture.value=o.texture,this.separableBlurMaterials[e].uniforms.direction.value=Kj.BlurDirectionX,t.setRenderTarget(this.renderTargetsHorizontal[e]),t.clear(),this.fsQuad.render(t),this.separableBlurMaterials[e].uniforms.colorTexture.value=this.renderTargetsHorizontal[e].texture,this.separableBlurMaterials[e].uniforms.direction.value=Kj.BlurDirectionY,t.setRenderTarget(this.renderTargetsVertical[e]),t.clear(),this.fsQuad.render(t),o=this.renderTargetsVertical[e];this.fsQuad.material=this.compositeMaterial,this.compositeMaterial.uniforms.bloomStrength.value=this.strength,this.compositeMaterial.uniforms.bloomRadius.value=this.radius,this.compositeMaterial.uniforms.bloomTintColors.value=this.bloomTintColors,t.setRenderTarget(this.renderTargetsHorizontal[0]),t.clear(),this.fsQuad.render(t),this.fsQuad.material=this.materialCopy,this.copyUniforms.tDiffuse.value=this.renderTargetsHorizontal[0].texture,r&&t.state.buffers.stencil.setTest(!0),this.renderToScreen?(t.setRenderTarget(null),this.fsQuad.render(t)):(t.setRenderTarget(n),this.fsQuad.render(t)),t.setClearColor(this._oldClearColor,this.oldClearAlpha),t.autoClear=s}getSeperableBlurMaterial(t){return new F({defines:{KERNEL_RADIUS:t,SIGMA:t},uniforms:{colorTexture:{value:null},texSize:{value:new d.a(.5,.5)},direction:{value:new d.a(.5,.5)}},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:\\\\\\\"#include <common>\\\\n\\\\t\\\\t\\\\t\\\\tvarying vec2 vUv;\\\\n\\\\t\\\\t\\\\t\\\\tuniform sampler2D colorTexture;\\\\n\\\\t\\\\t\\\\t\\\\tuniform vec2 texSize;\\\\n\\\\t\\\\t\\\\t\\\\tuniform vec2 direction;\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tfloat gaussianPdf(in float x, in float sigma) {\\\\n\\\\t\\\\t\\\\t\\\\t\\\\treturn 0.39894 * exp( -0.5 * x * x/( sigma * sigma))/sigma;\\\\n\\\\t\\\\t\\\\t\\\\t}\\\\n\\\\t\\\\t\\\\t\\\\tvoid main() {\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tvec2 invSize = 1.0 / texSize;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tfloat fSigma = float(SIGMA);\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tfloat weightSum = gaussianPdf(0.0, fSigma);\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tvec3 diffuseSum = texture2D( colorTexture, vUv).rgb * weightSum;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tfor( int i = 1; i < KERNEL_RADIUS; i ++ ) {\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tfloat x = float(i);\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tfloat w = gaussianPdf(x, fSigma);\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tvec2 uvOffset = direction * invSize * x;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tvec3 sample1 = texture2D( colorTexture, vUv + uvOffset).rgb;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tvec3 sample2 = texture2D( colorTexture, vUv - uvOffset).rgb;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tdiffuseSum += (sample1 + sample2) * w;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tweightSum += 2.0 * w;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t}\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tgl_FragColor = vec4(diffuseSum/weightSum, 1.0);\\\\n\\\\t\\\\t\\\\t\\\\t}\\\\\\\"})}getCompositeMaterial(t){return new F({defines:{NUM_MIPS:t},uniforms:{blurTexture1:{value:null},blurTexture2:{value:null},blurTexture3:{value:null},blurTexture4:{value:null},blurTexture5:{value:null},dirtTexture:{value:null},bloomStrength:{value:1},bloomFactors:{value:null},bloomTintColors:{value:null},bloomRadius:{value:0}},vertexShader:\\\\\\\"varying vec2 vUv;\\\\n\\\\t\\\\t\\\\t\\\\tvoid main() {\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tvUv = uv;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tgl_Position = projectionMatrix * modelViewMatrix * vec4( position, 1.0 );\\\\n\\\\t\\\\t\\\\t\\\\t}\\\\\\\",fragmentShader:\\\\\\\"varying vec2 vUv;\\\\n\\\\t\\\\t\\\\t\\\\tuniform sampler2D blurTexture1;\\\\n\\\\t\\\\t\\\\t\\\\tuniform sampler2D blurTexture2;\\\\n\\\\t\\\\t\\\\t\\\\tuniform sampler2D blurTexture3;\\\\n\\\\t\\\\t\\\\t\\\\tuniform sampler2D blurTexture4;\\\\n\\\\t\\\\t\\\\t\\\\tuniform sampler2D blurTexture5;\\\\n\\\\t\\\\t\\\\t\\\\tuniform sampler2D dirtTexture;\\\\n\\\\t\\\\t\\\\t\\\\tuniform float bloomStrength;\\\\n\\\\t\\\\t\\\\t\\\\tuniform float bloomRadius;\\\\n\\\\t\\\\t\\\\t\\\\tuniform float bloomFactors[NUM_MIPS];\\\\n\\\\t\\\\t\\\\t\\\\tuniform vec3 bloomTintColors[NUM_MIPS];\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tfloat lerpBloomFactor(const in float factor) {\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tfloat mirrorFactor = 1.2 - factor;\\\\n\\\\t\\\\t\\\\t\\\\t\\\\treturn mix(factor, mirrorFactor, bloomRadius);\\\\n\\\\t\\\\t\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tvoid main() {\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tgl_FragColor = bloomStrength * ( lerpBloomFactor(bloomFactors[0]) * vec4(bloomTintColors[0], 1.0) * texture2D(blurTexture1, vUv) +\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tlerpBloomFactor(bloomFactors[1]) * vec4(bloomTintColors[1], 1.0) * texture2D(blurTexture2, vUv) +\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tlerpBloomFactor(bloomFactors[2]) * vec4(bloomTintColors[2], 1.0) * texture2D(blurTexture3, vUv) +\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tlerpBloomFactor(bloomFactors[3]) * vec4(bloomTintColors[3], 1.0) * texture2D(blurTexture4, vUv) +\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tlerpBloomFactor(bloomFactors[4]) * vec4(bloomTintColors[4], 1.0) * texture2D(blurTexture5, vUv) );\\\\n\\\\t\\\\t\\\\t\\\\t}\\\\\\\"})}}Kj.BlurDirectionX=new d.a(1,0),Kj.BlurDirectionY=new d.a(0,1);const tW=new class extends aa{constructor(){super(...arguments),this.strength=oa.FLOAT(1.5,{range:[0,3],rangeLocked:[!0,!1],...MH}),this.radius=oa.FLOAT(1,{...MH}),this.threshold=oa.FLOAT(0,{...MH})}};class eW extends SH{constructor(){super(...arguments),this.paramsConfig=tW}static type(){return\\\\\\\"unrealBloom\\\\\\\"}_createPass(t){return new Kj(new d.a(t.resolution.x,t.resolution.y),this.pv.strength,this.pv.radius,this.pv.threshold)}updatePass(t){t.strength=this.pv.strength,t.radius=this.pv.radius,t.threshold=this.pv.threshold}}const nW=new class extends aa{constructor(){super(...arguments),this.amount=oa.FLOAT(2,{range:[0,10],rangeLocked:[!0,!1],step:.01,...MH}),this.transparent=oa.BOOLEAN(1,MH)}};class iW extends SH{constructor(){super(...arguments),this.paramsConfig=nW}static type(){return\\\\\\\"verticalBlur\\\\\\\"}_createPass(t){const e=new Bm(FU);return e.resolution_y=t.resolution.y,this.updatePass(e),e}updatePass(t){t.uniforms.v.value=this.pv.amount/(t.resolution_y*window.devicePixelRatio),t.material.transparent=this.pv.transparent}}const rW={uniforms:{tDiffuse:{value:null},offset:{value:1},darkness:{value:1}},vertexShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\tvarying vec2 vUv;\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\tvUv = uv;\\\\n\\\\t\\\\t\\\\tgl_Position = projectionMatrix * modelViewMatrix * vec4( position, 1.0 );\\\\n\\\\n\\\\t\\\\t}\\\\\\\",fragmentShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\tuniform float offset;\\\\n\\\\t\\\\tuniform float darkness;\\\\n\\\\n\\\\t\\\\tuniform sampler2D tDiffuse;\\\\n\\\\n\\\\t\\\\tvarying vec2 vUv;\\\\n\\\\n\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\t// Eskil's vignette\\\\n\\\\n\\\\t\\\\t\\\\tvec4 texel = texture2D( tDiffuse, vUv );\\\\n\\\\t\\\\t\\\\tvec2 uv = ( vUv - vec2( 0.5 ) ) * vec2( offset );\\\\n\\\\t\\\\t\\\\tgl_FragColor = vec4( mix( texel.rgb, vec3( 1.0 - darkness ), dot( uv, uv ) ), texel.a );\\\\n\\\\n\\\\t\\\\t}\\\\\\\"};const sW=new class extends aa{constructor(){super(...arguments),this.offset=oa.FLOAT(1,{range:[0,1],rangeLocked:[!1,!1],...MH}),this.darkness=oa.FLOAT(1,{range:[0,2],rangeLocked:[!0,!1],...MH})}};class oW extends SH{constructor(){super(...arguments),this.paramsConfig=sW}static type(){return\\\\\\\"vignette\\\\\\\"}_createPass(t){const e=new Bm(rW);return this.updatePass(e),e}updatePass(t){t.uniforms.offset.value=this.pv.offset,t.uniforms.darkness.value=this.pv.darkness}}class aW extends ia{static context(){return Ki.POST}cook(){this.cookController.endCook()}}class lW extends aW{}class cW extends lW{constructor(){super(...arguments),this._children_controller_context=Ki.ANIM}static type(){return tr.ANIM}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class uW extends lW{constructor(){super(...arguments),this._children_controller_context=Ki.COP}static type(){return tr.COP}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class hW extends lW{constructor(){super(...arguments),this._children_controller_context=Ki.EVENT}static type(){return tr.EVENT}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class dW extends lW{constructor(){super(...arguments),this._children_controller_context=Ki.MAT}static type(){return tr.MAT}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class pW extends aW{constructor(){super(...arguments),this.paramsConfig=new Jm,this.effectsComposerController=new Zm(this),this.displayNodeController=new Lm(this,this.effectsComposerController.displayNodeControllerCallbacks()),this._children_controller_context=Ki.POST}static type(){return tr.POST}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class _W extends lW{constructor(){super(...arguments),this._children_controller_context=Ki.ROP}static type(){return tr.ROP}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class mW extends ia{static context(){return Ki.ROP}cook(){this.cookController.endCook()}}class fW extends mW{}class gW extends fW{constructor(){super(...arguments),this._children_controller_context=Ki.COP}static type(){return tr.COP}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class vW extends Q.a{constructor(t){super(),this._element=t,this._element.style.position=\\\\\\\"absolute\\\\\\\",this.addEventListener(\\\\\\\"removed\\\\\\\",this._on_removed.bind(this))}_on_removed(){this.traverse((function(t){t instanceof vW&&t.element instanceof Element&&null!==t.element.parentNode&&t.element.parentNode.removeChild(t.element)}))}get element(){return this._element}copy(t,e){return Q.a.prototype.copy.call(this,t,e),this._element=t.element.cloneNode(!0),this.matrixAutoUpdate=t.matrixAutoUpdate,this}}class yW{constructor(){this._width=0,this._height=0,this._widthHalf=0,this._heightHalf=0,this.vector=new p.a,this.viewMatrix=new A.a,this.viewProjectionMatrix=new A.a,this.cache_distanceToCameraSquared=new WeakMap,this.domElement=document.createElement(\\\\\\\"div\\\\\\\"),this._sort_objects=!1,this._use_fog=!1,this._fog_near=1,this._fog_far=100,this.a=new p.a,this.b=new p.a,this.domElement.classList.add(\\\\\\\"polygonjs-CSS2DRenderer\\\\\\\")}getSize(){return{width:this._width,height:this._height}}setSize(t,e){this._width=t,this._height=e,this._widthHalf=this._width/2,this._heightHalf=this._height/2,this.domElement.style.width=t+\\\\\\\"px\\\\\\\",this.domElement.style.height=e+\\\\\\\"px\\\\\\\"}renderObject(t,e,n){if(t instanceof vW){this.vector.setFromMatrixPosition(t.matrixWorld),this.vector.applyMatrix4(this.viewProjectionMatrix);var i=t.element,r=\\\\\\\"translate(-50%,-50%) translate(\\\\\\\"+(this.vector.x*this._widthHalf+this._widthHalf)+\\\\\\\"px,\\\\\\\"+(-this.vector.y*this._heightHalf+this._heightHalf)+\\\\\\\"px)\\\\\\\";if(i.style.webkitTransform=r,i.style.transform=r,i.style.display=t.visible&&this.vector.z>=-1&&this.vector.z<=1?\\\\\\\"\\\\\\\":\\\\\\\"none\\\\\\\",this._sort_objects||this._use_fog){const e=this.getDistanceToSquared(n,t);if(this._use_fog){const t=Math.sqrt(e),n=rs.fit(t,this._fog_near,this._fog_far,0,1),r=rs.clamp(1-n,0,1);i.style.opacity=`${r}`,0==r&&(i.style.display=\\\\\\\"none\\\\\\\")}this.cache_distanceToCameraSquared.set(t,e)}i.parentNode!==this.domElement&&this.domElement.appendChild(i)}for(var s=0,o=t.children.length;s<o;s++)this.renderObject(t.children[s],e,n)}getDistanceToSquared(t,e){return this.a.setFromMatrixPosition(t.matrixWorld),this.b.setFromMatrixPosition(e.matrixWorld),this.a.distanceToSquared(this.b)}filterAndFlatten(t){const e=[];return t.traverse((function(t){t instanceof vW&&e.push(t)})),e}render(t,e){!0===t.autoUpdate&&t.updateMatrixWorld(),null===e.parent&&e.updateMatrixWorld(),this.viewMatrix.copy(e.matrixWorldInverse),this.viewProjectionMatrix.multiplyMatrices(e.projectionMatrix,this.viewMatrix),this.renderObject(t,t,e),this._sort_objects&&this.zOrder(t)}set_sorting(t){this._sort_objects=t}zOrder(t){const e=this.filterAndFlatten(t).sort(((t,e)=>{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<i;t++)e[t].element.style.zIndex=\\\\\\\"\\\\\\\"+(n-t)}set_use_fog(t){this._use_fog=t}set_fog_range(t,e){this._fog_near=t,this._fog_far=e}}const xW=new class extends aa{constructor(){super(...arguments),this.css=oa.STRING(\\\\\\\"\\\\\\\",{multiline:!0}),this.sortObjects=oa.BOOLEAN(0),this.useFog=oa.BOOLEAN(0),this.fogNear=oa.FLOAT(1,{range:[0,100],rangeLocked:[!0,!1],visibleIf:{useFog:1}}),this.fogFar=oa.FLOAT(100,{range:[0,100],rangeLocked:[!0,!1],visibleIf:{useFog:1}})}};class bW extends oV{constructor(){super(...arguments),this.paramsConfig=xW,this._renderers_by_canvas_id=new Map}static type(){return aV.CSS2D}createRenderer(t){const e=new yW;this._renderers_by_canvas_id.set(t.id,e);const n=t.parentElement;n&&(n.prepend(e.domElement),n.style.position=\\\\\\\"relative\\\\\\\"),e.domElement.style.position=\\\\\\\"absolute\\\\\\\",e.domElement.style.top=\\\\\\\"0px\\\\\\\",e.domElement.style.left=\\\\\\\"0px\\\\\\\",e.domElement.style.pointerEvents=\\\\\\\"none\\\\\\\";const i=t.getBoundingClientRect();return e.setSize(i.width,i.height),this._update_renderer(e),e}renderer(t){return this._renderers_by_canvas_id.get(t.id)||this.createRenderer(t)}cook(){this._update_css(),this._renderers_by_canvas_id.forEach((t=>{this._update_renderer(t)})),this.cookController.endCook()}_update_renderer(t){t.set_sorting(this.pv.sortObjects),t.set_use_fog(this.pv.useFog),t.set_fog_range(this.pv.fogNear,this.pv.fogFar)}_update_css(){this.css_element().innerHTML=this.pv.css}css_element(){return this._css_element=this._css_element||this._find_element()||this._create_element()}_find_element(){return document.getElementById(this._css_element_id())}_create_element(){const t=document.createElement(\\\\\\\"style\\\\\\\");return t.appendChild(document.createTextNode(\\\\\\\"\\\\\\\")),document.head.appendChild(t),t.id=this._css_element_id(),t}_css_element_id(){return`css_2d_renderer-${this.graphNodeId()}`}}class wW extends fW{constructor(){super(...arguments),this._children_controller_context=Ki.ANIM}static type(){return tr.ANIM}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class TW extends fW{constructor(){super(...arguments),this._children_controller_context=Ki.EVENT}static type(){return tr.EVENT}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class AW extends fW{constructor(){super(...arguments),this._children_controller_context=Ki.MAT}static type(){return tr.MAT}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class EW extends mW{constructor(){super(...arguments),this.paramsConfig=new Jm,this.effectsComposerController=new Zm(this),this.displayNodeController=new Lm(this,this.effectsComposerController.displayNodeControllerCallbacks()),this._children_controller_context=Ki.POST}static type(){return tr.POST}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class MW extends fW{constructor(){super(...arguments),this._children_controller_context=Ki.ROP}static type(){return tr.ROP}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class SW extends pG{static type(){return\\\\\\\"add\\\\\\\"}cook(t,e){const n=[];return this._create_point(n,e),this._create_polygon(t[0],n,e),this.createCoreGroupFromObjects(n)}_create_point(t,e){if(!e.createPoint)return;const n=new S.a,i=[];for(let t=0;t<e.pointsCount;t++)e.position.toArray(i,3*t);n.setAttribute(\\\\\\\"position\\\\\\\",new C.a(new Float32Array(i),3));const r=this.createObject(n,Sr.POINTS);t&&t.push(r)}_create_polygon(t,e,n){if(!n.connectInputPoints)return;t.points().length>0&&this._create_polygon_open(t,e,n)}_create_polygon_open(t,e,n){const i=t.points();let r=[];const s=[];let o;for(let t=0;t<i.length;t++)o=i[t],o.position().toArray(r,3*t),t>0&&(s.push(t-1),s.push(t));if(i.length>2&&n.connectToLastPoint){i[0].position().toArray(r,r.length);const t=s[s.length-1];s.push(t),s.push(0)}const a=new S.a;a.setAttribute(\\\\\\\"position\\\\\\\",new C.c(r,3)),a.setIndex(s);const l=this.createObject(a,Sr.LINE_SEGMENTS);e.push(l)}}SW.DEFAULT_PARAMS={createPoint:!0,pointsCount:1,position:new p.a(0,0,0),connectInputPoints:!1,connectToLastPoint:!1};const CW=SW.DEFAULT_PARAMS;const NW=new class extends aa{constructor(){super(...arguments),this.createPoint=oa.BOOLEAN(CW.createPoint),this.pointsCount=oa.INTEGER(CW.pointsCount,{range:[1,100],rangeLocked:[!0,!1],visibleIf:{createPoint:!0}}),this.position=oa.VECTOR3(CW.position,{visibleIf:{createPoint:!0}}),this.connectInputPoints=oa.BOOLEAN(CW.connectInputPoints),this.connectToLastPoint=oa.BOOLEAN(CW.connectToLastPoint)}};class LW extends gG{constructor(){super(...arguments),this.paramsConfig=NW}static type(){return\\\\\\\"add\\\\\\\"}static displayedInputNames(){return[\\\\\\\"geometry to create polygons from (optional)\\\\\\\"]}initializeNode(){this.io.inputs.setCount(0,1)}cook(t){this._operation=this._operation||new SW(this.scene(),this.states);const e=this._operation.cook(t,this.pv);this.setCoreGroup(e)}}const OW=new class extends aa{};class RW extends gG{constructor(){super(...arguments),this.paramsConfig=OW}static type(){return\\\\\\\"animationCopy\\\\\\\"}static displayedInputNames(){return[\\\\\\\"geometry to copy animation to\\\\\\\",\\\\\\\"geometry to copy animation from\\\\\\\"]}initializeNode(){this.io.inputs.setCount(2),this.io.inputs.initInputsClonedState([Qi.FROM_NODE,Qi.NEVER])}cook(t){const e=t[0],n=t[1].objects()[0],i=e.objects()[0],r=n.animations;r?(i.animations=r.map((t=>t.clone())),this.setCoreGroup(e)):this.states.error.set(\\\\\\\"no animation found\\\\\\\")}}class PW{constructor(t,e,n=null,i=e.blendMode){this._mixer=t,this._clip=e,this._localRoot=n,this.blendMode=i;const r=e.tracks,s=r.length,o=new Array(s),a={endingStart:w.id,endingEnd:w.id};for(let t=0;t!==s;++t){const e=r[t].createInterpolant(null);o[t]=e,e.settings=a}this._interpolantSettings=a,this._interpolants=o,this._propertyBindings=new Array(s),this._cacheIndex=null,this._byClipCacheIndex=null,this._timeScaleInterpolant=null,this._weightInterpolant=null,this.loop=w.eb,this._loopCount=-1,this._startTime=null,this.time=0,this.timeScale=1,this._effectiveTimeScale=1,this.weight=1,this._effectiveWeight=1,this.repetitions=1/0,this.paused=!1,this.enabled=!0,this.clampWhenFinished=!1,this.zeroSlopeAtStart=!0,this.zeroSlopeAtEnd=!0}play(){return this._mixer._activateAction(this),this}stop(){return this._mixer._deactivateAction(this),this.reset()}reset(){return this.paused=!1,this.enabled=!0,this.time=0,this._loopCount=-1,this._startTime=null,this.stopFading().stopWarping()}isRunning(){return this.enabled&&!this.paused&&0!==this.timeScale&&null===this._startTime&&this._mixer._isActiveAction(this)}isScheduled(){return this._mixer._isActiveAction(this)}startAt(t){return this._startTime=t,this}setLoop(t,e){return this.loop=t,this.repetitions=e,this}setEffectiveWeight(t){return this.weight=t,this._effectiveWeight=this.enabled?t:0,this.stopFading()}getEffectiveWeight(){return this._effectiveWeight}fadeIn(t){return this._scheduleFading(t,0,1)}fadeOut(t){return this._scheduleFading(t,1,0)}crossFadeFrom(t,e,n){if(t.fadeOut(e),this.fadeIn(e),n){const n=this._clip.duration,i=t._clip.duration,r=i/n,s=n/i;t.warp(1,r,e),this.warp(s,1,e)}return this}crossFadeTo(t,e,n){return t.crossFadeFrom(this,e,n)}stopFading(){const t=this._weightInterpolant;return null!==t&&(this._weightInterpolant=null,this._mixer._takeBackControlInterpolant(t)),this}setEffectiveTimeScale(t){return this.timeScale=t,this._effectiveTimeScale=this.paused?0:t,this.stopWarping()}getEffectiveTimeScale(){return this._effectiveTimeScale}setDuration(t){return this.timeScale=this._clip.duration/t,this.stopWarping()}syncWith(t){return this.time=t.time,this.timeScale=t.timeScale,this.stopWarping()}halt(t){return this.warp(this._effectiveTimeScale,0,t)}warp(t,e,n){const i=this._mixer,r=i.time,s=this.timeScale;let o=this._timeScaleInterpolant;null===o&&(o=i._lendControlInterpolant(),this._timeScaleInterpolant=o);const a=o.parameterPositions,l=o.sampleValues;return a[0]=r,a[1]=r+n,l[0]=t/s,l[1]=e/s,this}stopWarping(){const t=this._timeScaleInterpolant;return null!==t&&(this._timeScaleInterpolant=null,this._mixer._takeBackControlInterpolant(t)),this}getMixer(){return this._mixer}getClip(){return this._clip}getRoot(){return this._localRoot||this._mixer._root}_update(t,e,n,i){if(!this.enabled)return void this._updateWeight(t);const r=this._startTime;if(null!==r){const i=(t-r)*n;if(i<0||0===n)return;this._startTime=null,e=n*i}e*=this._updateTimeScale(t);const s=this._updateTime(e),o=this._updateWeight(t);if(o>0){const t=this._interpolants,e=this._propertyBindings;switch(this.blendMode){case w.d:for(let n=0,i=t.length;n!==i;++n)t[n].evaluate(s),e[n].accumulateAdditive(o);break;case w.wb:default:for(let n=0,r=t.length;n!==r;++n)t[n].evaluate(s),e[n].accumulate(i,o)}}}_updateWeight(t){let e=0;if(this.enabled){e=this.weight;const n=this._weightInterpolant;if(null!==n){const i=n.evaluate(t)[0];e*=i,t>n.parameterPositions[1]&&(this.stopFading(),0===i&&(this.enabled=!1))}}return this._effectiveWeight=e,e}_updateTimeScale(t){let e=0;if(!this.paused){e=this.timeScale;const n=this._timeScaleInterpolant;if(null!==n){e*=n.evaluate(t)[0],t>n.parameterPositions[1]&&(this.stopWarping(),0===e?this.paused=!0:this.timeScale=e)}}return this._effectiveTimeScale=e,e}_updateTime(t){const e=this._clip.duration,n=this.loop;let i=this.time+t,r=this._loopCount;const s=n===w.db;if(0===t)return-1===r?i:s&&1==(1&r)?e-i:i;if(n===w.cb){-1===r&&(this._loopCount=0,this._setEndings(!0,!0,!1));t:{if(i>=e)i=e;else{if(!(i<0)){this.time=i;break t}i=0}this.clampWhenFinished?this.paused=!0:this.enabled=!1,this.time=i,this._mixer.dispatchEvent({type:\\\\\\\"finished\\\\\\\",action:this,direction:t<0?-1:1})}}else{if(-1===r&&(t>=0?(r=0,this._setEndings(!0,0===this.repetitions,s)):this._setEndings(0===this.repetitions,!0,s)),i>=e||i<0){const n=Math.floor(i/e);i-=e*n,r+=Math.abs(n);const o=this.repetitions-r;if(o<=0)this.clampWhenFinished?this.paused=!0:this.enabled=!1,i=t>0?e:0,this.time=i,this._mixer.dispatchEvent({type:\\\\\\\"finished\\\\\\\",action:this,direction:t>0?1:-1});else{if(1===o){const e=t<0;this._setEndings(e,!e,s)}else this._setEndings(!1,!1,s);this._loopCount=r,this.time=i,this._mixer.dispatchEvent({type:\\\\\\\"loop\\\\\\\",action:this,loopDelta:n})}}else this.time=i;if(s&&1==(1&r))return e-i}return i}_setEndings(t,e,n){const i=this._interpolantSettings;n?(i.endingStart=w.kd,i.endingEnd=w.kd):(i.endingStart=t?this.zeroSlopeAtStart?w.kd:w.id:w.hd,i.endingEnd=e?this.zeroSlopeAtEnd?w.kd:w.id:w.hd)}_scheduleFading(t,e,n){const i=this._mixer,r=i.time;let s=this._weightInterpolant;null===s&&(s=i._lendControlInterpolant(),this._weightInterpolant=s);const o=s.parameterPositions,a=s.sampleValues;return o[0]=r,a[0]=e,o[1]=r+t,a[1]=n,this}}var IW=n(71),FW=n(66);class DW{constructor(t,e,n){let i,r,s;switch(this.binding=t,this.valueSize=n,e){case\\\\\\\"quaternion\\\\\\\":i=this._slerp,r=this._slerpAdditive,s=this._setAdditiveIdentityQuaternion,this.buffer=new Float64Array(6*n),this._workIndex=5;break;case\\\\\\\"string\\\\\\\":case\\\\\\\"bool\\\\\\\":i=this._select,r=this._select,s=this._setAdditiveIdentityOther,this.buffer=new Array(5*n);break;default:i=this._lerp,r=this._lerpAdditive,s=this._setAdditiveIdentityNumeric,this.buffer=new Float64Array(5*n)}this._mixBufferRegion=i,this._mixBufferRegionAdditive=r,this._setIdentity=s,this._origIndex=3,this._addIndex=4,this.cumulativeWeight=0,this.cumulativeWeightAdditive=0,this.useCount=0,this.referenceCount=0}accumulate(t,e){const n=this.buffer,i=this.valueSize,r=t*i+i;let s=this.cumulativeWeight;if(0===s){for(let t=0;t!==i;++t)n[r+t]=n[t];s=e}else{s+=e;const t=e/s;this._mixBufferRegion(n,r,0,t,i)}this.cumulativeWeight=s}accumulateAdditive(t){const e=this.buffer,n=this.valueSize,i=n*this._addIndex;0===this.cumulativeWeightAdditive&&this._setIdentity(),this._mixBufferRegionAdditive(e,i,0,t,n),this.cumulativeWeightAdditive+=t}apply(t){const e=this.valueSize,n=this.buffer,i=t*e+e,r=this.cumulativeWeight,s=this.cumulativeWeightAdditive,o=this.binding;if(this.cumulativeWeight=0,this.cumulativeWeightAdditive=0,r<1){const t=e*this._origIndex;this._mixBufferRegion(n,i,t,1-r,e)}s>0&&this._mixBufferRegionAdditive(n,i,this._addIndex*e,1,e);for(let t=e,r=e+e;t!==r;++t)if(n[t]!==n[t+e]){o.setValue(n,i);break}}saveOriginalState(){const t=this.binding,e=this.buffer,n=this.valueSize,i=n*this._origIndex;t.getValue(e,i);for(let t=n,r=i;t!==r;++t)e[t]=e[i+t%n];this._setIdentity(),this.cumulativeWeight=0,this.cumulativeWeightAdditive=0}restoreOriginalState(){const t=3*this.valueSize;this.binding.setValue(this.buffer,t)}_setAdditiveIdentityNumeric(){const t=this._addIndex*this.valueSize,e=t+this.valueSize;for(let n=t;n<e;n++)this.buffer[n]=0}_setAdditiveIdentityQuaternion(){this._setAdditiveIdentityNumeric(),this.buffer[this._addIndex*this.valueSize+3]=1}_setAdditiveIdentityOther(){const t=this._origIndex*this.valueSize,e=this._addIndex*this.valueSize;for(let n=0;n<this.valueSize;n++)this.buffer[e+n]=this.buffer[t+n]}_select(t,e,n,i,r){if(i>=.5)for(let i=0;i!==r;++i)t[e+i]=t[n+i]}_slerp(t,e,n,i){au.a.slerpFlat(t,e,t,e,t,n,i)}_slerpAdditive(t,e,n,i,r){const s=this._workIndex*r;au.a.multiplyQuaternionsFlat(t,s,t,e,t,n),au.a.slerpFlat(t,e,t,e,t,s,i)}_lerp(t,e,n,i,r){const s=1-i;for(let o=0;o!==r;++o){const r=e+o;t[r]=t[r]*s+t[n+o]*i}}_lerpAdditive(t,e,n,i,r){for(let s=0;s!==r;++s){const r=e+s;t[r]=t[r]+t[n+s]*i}}}var kW=n(63);class BW extends $.a{constructor(t){super(),this._root=t,this._initMemoryManager(),this._accuIndex=0,this.time=0,this.timeScale=1}_bindAction(t,e){const n=t._localRoot||this._root,i=t._clip.tracks,r=i.length,s=t._propertyBindings,o=t._interpolants,a=n.uuid,l=this._bindingsByRootAndName;let c=l[a];void 0===c&&(c={},l[a]=c);for(let t=0;t!==r;++t){const r=i[t],l=r.name;let u=c[l];if(void 0!==u)s[t]=u;else{if(u=s[t],void 0!==u){null===u._cacheIndex&&(++u.referenceCount,this._addInactiveBinding(u,a,l));continue}const i=e&&e._propertyBindings[t].binding.parsedPath;u=new DW(FW.a.create(n,l,i),r.ValueTypeName,r.getValueSize()),++u.referenceCount,this._addInactiveBinding(u,a,l),s[t]=u}o[t].resultBuffer=u.buffer}}_activateAction(t){if(!this._isActiveAction(t)){if(null===t._cacheIndex){const e=(t._localRoot||this._root).uuid,n=t._clip.uuid,i=this._actionsByClip[n];this._bindAction(t,i&&i.knownActions[0]),this._addInactiveAction(t,n,e)}const e=t._propertyBindings;for(let t=0,n=e.length;t!==n;++t){const n=e[t];0==n.useCount++&&(this._lendBinding(n),n.saveOriginalState())}this._lendAction(t)}}_deactivateAction(t){if(this._isActiveAction(t)){const e=t._propertyBindings;for(let t=0,n=e.length;t!==n;++t){const n=e[t];0==--n.useCount&&(n.restoreOriginalState(),this._takeBackBinding(n))}this._takeBackAction(t)}}_initMemoryManager(){this._actions=[],this._nActiveActions=0,this._actionsByClip={},this._bindings=[],this._nActiveBindings=0,this._bindingsByRootAndName={},this._controlInterpolants=[],this._nActiveControlInterpolants=0;const t=this;this.stats={actions:{get total(){return t._actions.length},get inUse(){return t._nActiveActions}},bindings:{get total(){return t._bindings.length},get inUse(){return t._nActiveBindings}},controlInterpolants:{get total(){return t._controlInterpolants.length},get inUse(){return t._nActiveControlInterpolants}}}}_isActiveAction(t){const e=t._cacheIndex;return null!==e&&e<this._nActiveActions}_addInactiveAction(t,e,n){const i=this._actions,r=this._actionsByClip;let s=r[e];if(void 0===s)s={knownActions:[t],actionByRoot:{}},t._byClipCacheIndex=0,r[e]=s;else{const e=s.knownActions;t._byClipCacheIndex=e.length,e.push(t)}t._cacheIndex=i.length,i.push(t),s.actionByRoot[n]=t}_removeInactiveAction(t){const e=this._actions,n=e[e.length-1],i=t._cacheIndex;n._cacheIndex=i,e[i]=n,e.pop(),t._cacheIndex=null;const r=t._clip.uuid,s=this._actionsByClip,o=s[r],a=o.knownActions,l=a[a.length-1],c=t._byClipCacheIndex;l._byClipCacheIndex=c,a[c]=l,a.pop(),t._byClipCacheIndex=null;delete o.actionByRoot[(t._localRoot||this._root).uuid],0===a.length&&delete s[r],this._removeInactiveBindingsForAction(t)}_removeInactiveBindingsForAction(t){const e=t._propertyBindings;for(let t=0,n=e.length;t!==n;++t){const n=e[t];0==--n.referenceCount&&this._removeInactiveBinding(n)}}_lendAction(t){const e=this._actions,n=t._cacheIndex,i=this._nActiveActions++,r=e[i];t._cacheIndex=i,e[i]=t,r._cacheIndex=n,e[n]=r}_takeBackAction(t){const e=this._actions,n=t._cacheIndex,i=--this._nActiveActions,r=e[i];t._cacheIndex=i,e[i]=t,r._cacheIndex=n,e[n]=r}_addInactiveBinding(t,e,n){const i=this._bindingsByRootAndName,r=this._bindings;let s=i[e];void 0===s&&(s={},i[e]=s),s[n]=t,t._cacheIndex=r.length,r.push(t)}_removeInactiveBinding(t){const e=this._bindings,n=t.binding,i=n.rootNode.uuid,r=n.path,s=this._bindingsByRootAndName,o=s[i],a=e[e.length-1],l=t._cacheIndex;a._cacheIndex=l,e[l]=a,e.pop(),delete o[r],0===Object.keys(o).length&&delete s[i]}_lendBinding(t){const e=this._bindings,n=t._cacheIndex,i=this._nActiveBindings++,r=e[i];t._cacheIndex=i,e[i]=t,r._cacheIndex=n,e[n]=r}_takeBackBinding(t){const e=this._bindings,n=t._cacheIndex,i=--this._nActiveBindings,r=e[i];t._cacheIndex=i,e[i]=t,r._cacheIndex=n,e[n]=r}_lendControlInterpolant(){const t=this._controlInterpolants,e=this._nActiveControlInterpolants++;let n=t[e];return void 0===n&&(n=new IW.a(new Float32Array(2),new Float32Array(2),1,this._controlInterpolantsResultBuffer),n.__cacheIndex=e,t[e]=n),n}_takeBackControlInterpolant(t){const e=this._controlInterpolants,n=t.__cacheIndex,i=--this._nActiveControlInterpolants,r=e[i];t.__cacheIndex=i,e[i]=t,r.__cacheIndex=n,e[n]=r}clipAction(t,e,n){const i=e||this._root,r=i.uuid;let s=\\\\\\\"string\\\\\\\"==typeof t?kW.a.findByName(i,t):t;const o=null!==s?s.uuid:t,a=this._actionsByClip[o];let l=null;if(void 0===n&&(n=null!==s?s.blendMode:w.wb),void 0!==a){const t=a.actionByRoot[r];if(void 0!==t&&t.blendMode===n)return t;l=a.knownActions[0],null===s&&(s=l._clip)}if(null===s)return null;const c=new PW(this,s,e,n);return this._bindAction(c,l),this._addInactiveAction(c,o,r),c}existingAction(t,e){const n=e||this._root,i=n.uuid,r=\\\\\\\"string\\\\\\\"==typeof t?kW.a.findByName(n,t):t,s=r?r.uuid:t,o=this._actionsByClip[s];return void 0!==o&&o.actionByRoot[i]||null}stopAllAction(){const t=this._actions;for(let e=this._nActiveActions-1;e>=0;--e)t[e].stop();return this}update(t){t*=this.timeScale;const e=this._actions,n=this._nActiveActions,i=this.time+=t,r=Math.sign(t),s=this._accuIndex^=1;for(let o=0;o!==n;++o){e[o]._update(i,t,r,s)}const o=this._bindings,a=this._nActiveBindings;for(let t=0;t!==a;++t)o[t].apply(s);return this}setTime(t){this.time=0;for(let t=0;t<this._actions.length;t++)this._actions[t].time=0;return this.update(t)}getRoot(){return this._root}uncacheClip(t){const e=this._actions,n=t.uuid,i=this._actionsByClip,r=i[n];if(void 0!==r){const t=r.knownActions;for(let n=0,i=t.length;n!==i;++n){const i=t[n];this._deactivateAction(i);const r=i._cacheIndex,s=e[e.length-1];i._cacheIndex=null,i._byClipCacheIndex=null,s._cacheIndex=r,e[r]=s,e.pop(),this._removeInactiveBindingsForAction(i)}delete i[n]}}uncacheRoot(t){const e=t.uuid,n=this._actionsByClip;for(const t in n){const i=n[t].actionByRoot[e];void 0!==i&&(this._deactivateAction(i),this._removeInactiveAction(i))}const i=this._bindingsByRootAndName[e];if(void 0!==i)for(const t in i){const e=i[t];e.restoreOriginalState(),this._removeInactiveBinding(e)}}uncacheAction(t,e){const n=this.existingAction(t,e);null!==n&&(this._deactivateAction(n),this._removeInactiveAction(n))}}BW.prototype._controlInterpolantsResultBuffer=new Float32Array(1);const zW=new class extends aa{constructor(){super(...arguments),this.time=oa.FLOAT(\\\\\\\"$T\\\\\\\",{range:[0,10]}),this.clip=oa.OPERATOR_PATH(\\\\\\\"/ANIM/OUT\\\\\\\",{nodeSelection:{context:Ki.ANIM},dependentOnFoundNode:!1}),this.reset=oa.BUTTON(null,{callback:(t,e)=>{UW.PARAM_CALLBACK_reset(t,e)}})}};class UW extends gG{constructor(){super(...arguments),this.paramsConfig=zW}static type(){return\\\\\\\"animationMixer\\\\\\\"}static displayedInputNames(){return[\\\\\\\"geometry to be animated\\\\\\\"]}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState(Qi.NEVER)}async cook(t){const e=t[0].objects()[0];e&&(await this.create_mixer_if_required(e),this._update_mixer()),this.setObjects([e])}async create_mixer_if_required(t){if(!this._mixer){const e=await this._create_mixer(t);e&&(this._mixer=e)}}async _create_mixer(t){this.p.clip.isDirty()&&await this.p.clip.compute();if(this.p.clip.found_node_with_context(Ki.ANIM)){return new BW(t)}}_update_mixer(){this._set_mixer_time()}_set_mixer_time(){this.pv.time!=this._previous_time&&(this._mixer&&this._mixer.setTime(this.pv.time),this._previous_time=this.pv.time)}static PARAM_CALLBACK_reset(t,e){e.setDirty(),t.reset_animation_mixer()}async reset_animation_mixer(){this._mixer=void 0,this._previous_time=void 0,this.setDirty()}}class GW extends pG{static type(){return\\\\\\\"attribAddMult\\\\\\\"}cook(t,e){const n=t[0],i=n.attribNamesMatchingMask(e.name);for(let t of i){const i=n.geometries();for(let n of i)this._update_attrib(t,n,e)}return n}_update_attrib(t,e,n){const i=e.getAttribute(t);if(i){const t=i.array,e=n.preAdd,r=n.mult,s=n.postAdd;for(let n=0;n<t.length;n++){const i=t[n];t[n]=(i+e)*r+s}i.needsUpdate=!0}}}GW.DEFAULT_PARAMS={name:\\\\\\\"\\\\\\\",preAdd:0,mult:1,postAdd:0},GW.INPUT_CLONED_STATE=Qi.FROM_NODE;const VW=GW.DEFAULT_PARAMS;const HW=new class extends aa{constructor(){super(...arguments),this.name=oa.STRING(VW.name),this.preAdd=oa.FLOAT(VW.preAdd,{range:[0,1]}),this.mult=oa.FLOAT(VW.mult,{range:[0,1]}),this.postAdd=oa.FLOAT(VW.postAdd,{range:[0,1]})}};class jW extends gG{constructor(){super(...arguments),this.paramsConfig=HW}static type(){return\\\\\\\"attribAddMult\\\\\\\"}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState(GW.INPUT_CLONED_STATE)}cook(t){this._operation=this._operation||new GW(this.scene(),this.states);const e=this._operation.cook(t,this.pv);this.setCoreGroup(e)}}var WW;!function(t){t.Float64BufferAttribute=\\\\\\\"Float64BufferAttribute\\\\\\\",t.Float32BufferAttribute=\\\\\\\"Float32BufferAttribute\\\\\\\",t.Float16BufferAttribute=\\\\\\\"Float16BufferAttribute\\\\\\\",t.Uint32BufferAttribute=\\\\\\\"Uint32BufferAttribute\\\\\\\",t.Int32BufferAttribute=\\\\\\\"Int32BufferAttribute\\\\\\\",t.Uint16BufferAttribute=\\\\\\\"Uint16BufferAttribute\\\\\\\",t.Int16BufferAttribute=\\\\\\\"Int16BufferAttribute\\\\\\\",t.Uint8ClampedBufferAttribute=\\\\\\\"Uint8ClampedBufferAttribute\\\\\\\",t.Uint8BufferAttribute=\\\\\\\"Uint8BufferAttribute\\\\\\\",t.Int8BufferAttribute=\\\\\\\"Int8BufferAttribute\\\\\\\"}(WW||(WW={}));const qW=[WW.Float64BufferAttribute,WW.Float32BufferAttribute,WW.Float16BufferAttribute,WW.Uint32BufferAttribute,WW.Int32BufferAttribute,WW.Uint16BufferAttribute,WW.Int16BufferAttribute,WW.Uint8ClampedBufferAttribute,WW.Uint8BufferAttribute,WW.Int8BufferAttribute],XW={[WW.Float64BufferAttribute]:C.d,[WW.Float32BufferAttribute]:C.c,[WW.Float16BufferAttribute]:C.b,[WW.Uint32BufferAttribute]:C.i,[WW.Int32BufferAttribute]:C.f,[WW.Uint16BufferAttribute]:C.h,[WW.Int16BufferAttribute]:C.e,[WW.Uint8ClampedBufferAttribute]:C.k,[WW.Uint8BufferAttribute]:C.j,[WW.Int8BufferAttribute]:C.g},YW={[WW.Float64BufferAttribute]:Float64Array,[WW.Float32BufferAttribute]:Float32Array,[WW.Float16BufferAttribute]:Uint16Array,[WW.Uint32BufferAttribute]:Uint32Array,[WW.Int32BufferAttribute]:Int32Array,[WW.Uint16BufferAttribute]:Uint16Array,[WW.Int16BufferAttribute]:Int16Array,[WW.Uint8ClampedBufferAttribute]:Uint8Array,[WW.Uint8BufferAttribute]:Uint8Array,[WW.Int8BufferAttribute]:Int8Array};class $W extends pG{static type(){return\\\\\\\"attribCast\\\\\\\"}cook(t,e){const n=t[0],i=n.objectsWithGeo();for(let t of i)this._castGeoAttributes(t.geometry,e);return n}_castGeoAttributes(t,e){const n=qW[e.type],i=XW[n],r=YW[n];if(e.castAttributes){const n=ps.attribNamesMatchingMask(t,e.mask);for(let e of n){const n=t.attributes[e],s=n.array,o=new r(n.count*n.itemSize);for(let t=0;t<s.length;t++)o[t]=s[t];const a=new i(o,1);t.setAttribute(e,a)}}if(e.castIndex){const e=t.getIndex();if(e){const n=e.array,s=new r(e.count*1);for(let t=0;t<n.length;t++)s[t]=n[t];const o=new i(s,1);t.setIndex(o)}}}}$W.DEFAULT_PARAMS={castAttributes:!0,mask:\\\\\\\"*\\\\\\\",castIndex:!1,type:qW.indexOf(WW.Float32BufferAttribute)},$W.INPUT_CLONED_STATE=Qi.FROM_NODE;const JW=$W.DEFAULT_PARAMS;const ZW=new class extends aa{constructor(){super(...arguments),this.castAttributes=oa.BOOLEAN(JW.castAttributes),this.mask=oa.STRING(JW.mask,{visibleIf:{castAttributes:1}}),this.castIndex=oa.BOOLEAN(JW.castIndex),this.type=oa.INTEGER(JW.type,{menu:{entries:qW.map(((t,e)=>({name:t,value:e})))}})}};class QW extends gG{constructor(){super(...arguments),this.paramsConfig=ZW}static type(){return\\\\\\\"attribCast\\\\\\\"}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState($W.INPUT_CLONED_STATE),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.type],(()=>qW[this.pv.type]))}))}))}cook(t){this._operation=this._operation||new $W(this.scene(),this.states);const e=this._operation.cook(t,this.pv);this.setCoreGroup(e)}}class KW extends pG{static type(){return\\\\\\\"attribCopy\\\\\\\"}cook(t,e){const n=t[0],i=t[1]||n,r=i.attribNamesMatchingMask(e.name);for(let t of r)this.copy_vertex_attribute_between_core_groups(n,i,t,e);return n}copy_vertex_attribute_between_core_groups(t,e,n,i){var r;const s=e.objectsWithGeo(),o=t.objectsWithGeo();if(o.length>s.length)null===(r=this.states)||void 0===r||r.error.set(\\\\\\\"second input does not have enough objects to copy attributes from\\\\\\\");else for(let t=0;t<o.length;t++){const e=o[t].geometry,r=s[t].geometry;this.copy_vertex_attribute_between_geometries(e,r,n,i)}}copy_vertex_attribute_between_geometries(t,e,n,i){var r,s;const o=e.getAttribute(n);if(o){const s=o.itemSize,a=e.getAttribute(\\\\\\\"position\\\\\\\").array.length/3,l=t.getAttribute(\\\\\\\"position\\\\\\\").array.length/3;l>a&&(null===(r=this.states)||void 0===r||r.error.set(\\\\\\\"not enough points in second input\\\\\\\"));const c=i.tnewName?i.newName:n;let u=t.getAttribute(c);if(u)this._fill_dest_array(u,o,i),u.needsUpdate=!0;else{const e=o.array.slice(0,l*s);t.setAttribute(c,new C.c(e,s))}}else null===(s=this.states)||void 0===s||s.error.set(`attribute '${n}' does not exist on second input`)}_fill_dest_array(t,e,n){const i=t.array,r=e.array,s=i.length,o=t.itemSize,a=e.itemSize,l=n.srcOffset,c=n.destOffset;if(t.itemSize==e.itemSize){t.copyArray(e.array);for(let t=0;t<s;t++)i[t]=r[t]}else{const t=i.length/o;if(o<a)for(let e=0;e<t;e++)for(let t=0;t<o;t++)i[e*o+t+c]=r[e*a+t+l];else for(let e=0;e<t;e++)for(let t=0;t<a;t++)i[e*o+t+c]=r[e*a+t+l]}}}KW.DEFAULT_PARAMS={name:\\\\\\\"\\\\\\\",tnewName:!1,newName:\\\\\\\"\\\\\\\",srcOffset:0,destOffset:0},KW.INPUT_CLONED_STATE=[Qi.FROM_NODE,Qi.NEVER];const tq=KW.DEFAULT_PARAMS;const eq=new class extends aa{constructor(){super(...arguments),this.name=oa.STRING(tq.name),this.tnewName=oa.BOOLEAN(tq.tnewName),this.newName=oa.STRING(tq.newName,{visibleIf:{tnewName:1}}),this.srcOffset=oa.INTEGER(tq.srcOffset,{range:[0,3],rangeLocked:[!0,!0]}),this.destOffset=oa.INTEGER(tq.destOffset,{range:[0,3],rangeLocked:[!0,!0]})}};class nq extends gG{constructor(){super(...arguments),this.paramsConfig=eq}static type(){return\\\\\\\"attribCopy\\\\\\\"}static displayedInputNames(){return[\\\\\\\"geometry to copy attributes to\\\\\\\",\\\\\\\"geometry to copy attributes from\\\\\\\"]}initializeNode(){this.io.inputs.setCount(1,2),this.io.inputs.initInputsClonedState(KW.INPUT_CLONED_STATE),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.name,this.p.tnewName,this.p.newName],(()=>this.pv.tnewName?`${this.pv.name} -> ${this.pv.newName}`:this.pv.name))}))}))}cook(t){this._operation=this._operation||new KW(this.scene(),this.states);const e=this._operation.cook(t,this.pv);this.setCoreGroup(e)}}class iq extends pG{static type(){return\\\\\\\"attribCreate\\\\\\\"}cook(t,e){var n;const i=t[0];return e.name&&\\\\\\\"\\\\\\\"!=e.name.trim()?this._add_attribute(Ir[e.class],i,e):null===(n=this.states)||void 0===n||n.error.set(\\\\\\\"attribute name is not valid\\\\\\\"),i}async _add_attribute(t,e,n){const i=kr[n.type];switch(t){case Pr.VERTEX:return void await this.add_point_attribute(i,e,n);case Pr.OBJECT:return void await this.add_object_attribute(i,e,n)}ar.unreachable(t)}async add_point_attribute(t,e,n){const i=e.coreObjects();switch(t){case Dr.NUMERIC:for(let t=0;t<i.length;t++)await this.add_numeric_attribute_to_points(i[t],n);return;case Dr.STRING:for(let t=0;t<i.length;t++)await this.add_string_attribute_to_points(i[t],n);return}ar.unreachable(t)}async add_object_attribute(t,e,n){const i=e.coreObjectsFromGroup(n.group);switch(t){case Dr.NUMERIC:return void await this.add_numeric_attribute_to_object(i,n);case Dr.STRING:return void await this.add_string_attribute_to_object(i,n)}ar.unreachable(t)}async add_numeric_attribute_to_points(t,e){if(!t.coreGeometry())return;const n=[e.value1,e.value2,e.value3,e.value4][e.size-1];t.addNumericVertexAttrib(e.name,e.size,n)}async add_numeric_attribute_to_object(t,e){const n=[e.value1,e.value2,e.value3,e.value4][e.size-1];for(let i of t)i.setAttribValue(e.name,n)}async add_string_attribute_to_points(t,e){const n=t.pointsFromGroup(e.group),i=e.string,r=new Array(n.length);for(let t=0;t<n.length;t++)r[t]=i;const s=Wr.arrayToIndexedArrays(r),o=t.coreGeometry();o&&o.setIndexedAttribute(e.name,s.values,s.indices)}async add_string_attribute_to_object(t,e){const n=e.string;for(let i of t)i.setAttribValue(e.name,n)}}iq.DEFAULT_PARAMS={group:\\\\\\\"\\\\\\\",class:Ir.indexOf(Pr.VERTEX),type:kr.indexOf(Dr.NUMERIC),name:\\\\\\\"new_attrib\\\\\\\",size:1,value1:0,value2:new d.a(0,0),value3:new p.a(0,0,0),value4:new _.a(0,0,0,0),string:\\\\\\\"\\\\\\\"},iq.INPUT_CLONED_STATE=Qi.FROM_NODE;const rq=[\\\\\\\"x\\\\\\\",\\\\\\\"y\\\\\\\",\\\\\\\"z\\\\\\\",\\\\\\\"w\\\\\\\"],sq=iq.DEFAULT_PARAMS;const oq=new class extends aa{constructor(){super(...arguments),this.group=oa.STRING(sq.group),this.class=oa.INTEGER(sq.class,{menu:{entries:Fr}}),this.type=oa.INTEGER(sq.type,{menu:{entries:Br}}),this.name=oa.STRING(sq.name),this.size=oa.INTEGER(sq.size,{range:[1,4],rangeLocked:[!0,!0],visibleIf:{type:Dr.NUMERIC}}),this.value1=oa.FLOAT(sq.value1,{visibleIf:{type:Dr.NUMERIC,size:1},expression:{forEntities:!0}}),this.value2=oa.VECTOR2(sq.value2,{visibleIf:{type:Dr.NUMERIC,size:2},expression:{forEntities:!0}}),this.value3=oa.VECTOR3(sq.value3,{visibleIf:{type:Dr.NUMERIC,size:3},expression:{forEntities:!0}}),this.value4=oa.VECTOR4(sq.value4,{visibleIf:{type:Dr.NUMERIC,size:4},expression:{forEntities:!0}}),this.string=oa.STRING(sq.string,{visibleIf:{type:Dr.STRING},expression:{forEntities:!0}})}};class aq extends gG{constructor(){super(...arguments),this.paramsConfig=oq,this._x_arrays_by_geometry_uuid={},this._y_arrays_by_geometry_uuid={},this._z_arrays_by_geometry_uuid={},this._w_arrays_by_geometry_uuid={}}static type(){return\\\\\\\"attribCreate\\\\\\\"}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.name])}))}))}cook(t){if(this._is_using_expression())this.pv.name&&\\\\\\\"\\\\\\\"!=this.pv.name.trim()?this._add_attribute(Ir[this.pv.class],t[0]):this.states.error.set(\\\\\\\"attribute name is not valid\\\\\\\");else{this._operation=this._operation||new iq(this.scene(),this.states);const e=this._operation.cook(t,this.pv);this.setCoreGroup(e)}}async _add_attribute(t,e){const n=kr[this.pv.type];switch(t){case Pr.VERTEX:return await this.add_point_attribute(n,e),this.setCoreGroup(e);case Pr.OBJECT:return await this.add_object_attribute(n,e),this.setCoreGroup(e)}ar.unreachable(t)}async add_point_attribute(t,e){const n=e.coreObjects();switch(t){case Dr.NUMERIC:for(let t=0;t<n.length;t++)await this.add_numeric_attribute_to_points(n[t]);return;case Dr.STRING:for(let t=0;t<n.length;t++)await this.add_string_attribute_to_points(n[t]);return}ar.unreachable(t)}async add_object_attribute(t,e){const n=e.coreObjectsFromGroup(this.pv.group);switch(t){case Dr.NUMERIC:return void await this.add_numeric_attribute_to_object(n);case Dr.STRING:return void await this.add_string_attribute_to_object(n)}ar.unreachable(t)}async add_numeric_attribute_to_points(t){const e=t.coreGeometry();if(!e)return;const n=t.pointsFromGroup(this.pv.group),i=[this.p.value1,this.p.value2,this.p.value3,this.p.value4][this.pv.size-1];if(i.hasExpression()){e.hasAttrib(this.pv.name)||e.addNumericAttrib(this.pv.name,this.pv.size,i.value);const t=e.geometry(),r=t.getAttribute(this.pv.name).array;if(1==this.pv.size)this.p.value1.expressionController&&await this.p.value1.expressionController.compute_expression_for_points(n,((t,e)=>{r[t.index()*this.pv.size+0]=e}));else{let e=[this.p.value2,this.p.value3,this.p.value4][this.pv.size-2].components;const i=new Array(e.length);let s;const o=[this._x_arrays_by_geometry_uuid,this._y_arrays_by_geometry_uuid,this._z_arrays_by_geometry_uuid,this._w_arrays_by_geometry_uuid];for(let a=0;a<e.length;a++)if(s=e[a],s.hasExpression()&&s.expressionController)i[a]=this._init_array_if_required(t,o[a],n.length),await s.expressionController.compute_expression_for_points(n,((t,e)=>{i[a][t.index()]=e}));else{const t=s.value;for(let e of n)r[e.index()*this.pv.size+a]=t}for(let t=0;t<i.length;t++){const e=i[t];if(e)for(let n=0;n<e.length;n++)r[n*this.pv.size+t]=e[n]}}}}async add_numeric_attribute_to_object(t){if([this.p.value1,this.p.value2,this.p.value3,this.p.value4][this.pv.size-1].hasExpression())if(1==this.pv.size)this.p.value1.expressionController&&await this.p.value1.expressionController.compute_expression_for_objects(t,((t,e)=>{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<e.length;i++){const r=e[i],s=rq[i];if(r.hasExpression()&&r.expressionController)await r.expressionController.compute_expression_for_objects(t,((t,e)=>{n[t.index()][s]=e}));else for(let e of t){n[e.index()][s]=r.value}}for(let e=0;e<t.length;e++){const i=t[e],r=n[i.index()];i.setAttribValue(this.pv.name,r)}}}}_vector_by_attrib_size(t){switch(t){case 2:return new d.a(0,0);case 3:return new p.a(0,0,0);case 4:return new _.a(0,0,0,0)}}async add_string_attribute_to_points(t){const e=t.pointsFromGroup(this.pv.group),n=this.p.string,i=new Array(e.length);n.hasExpression()&&n.expressionController&&await n.expressionController.compute_expression_for_points(e,((t,e)=>{i[t.index()]=e}));const r=Wr.arrayToIndexedArrays(i),s=t.coreGeometry();s&&s.setIndexedAttribute(this.pv.name,r.values,r.indices)}async add_string_attribute_to_object(t){const e=this.p.string;e.hasExpression()&&e.expressionController&&await e.expressionController.compute_expression_for_objects(t,((t,e)=>{t.setAttribValue(this.pv.name,e)}))}_init_array_if_required(t,e,n){const i=t.uuid,r=e[i];return r?r.length<n&&(e[i]=new Array(n)):e[i]=new Array(n),e[i]}_is_using_expression(){switch(kr[this.pv.type]){case Dr.NUMERIC:return[this.p.value1,this.p.value2,this.p.value3,this.p.value4][this.pv.size-1].hasExpression();case Dr.STRING:return this.p.string.hasExpression()}}setType(t){this.p.type.set(kr.indexOf(t))}}const lq=new class extends aa{constructor(){super(...arguments),this.class=oa.INTEGER(Pr.VERTEX,{menu:{entries:Fr}}),this.name=oa.STRING(\\\\\\\"\\\\\\\")}};class cq extends gG{constructor(){super(...arguments),this.paramsConfig=lq}static type(){return\\\\\\\"attribDelete\\\\\\\"}static displayedInputNames(){return[\\\\\\\"geometry to delete attributes from\\\\\\\"]}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState(Qi.FROM_NODE),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.name])}))}))}cook(t){const e=t[0],n=e.attribNamesMatchingMask(this.pv.name);for(let t of n)switch(this.pv.class){case Pr.VERTEX:this.delete_vertex_attribute(e,t);case Pr.OBJECT:this.delete_object_attribute(e,t)}this.setCoreGroup(e)}delete_vertex_attribute(t,e){for(let n of t.objects())n.traverse((t=>{const n=t;if(n.geometry){new ps(n.geometry).deleteAttribute(e)}}))}delete_object_attribute(t,e){for(let n of t.objects()){let t=0;n.traverse((n=>{new vs(n,t).deleteAttribute(e),t++}))}}}class uq{set_attrib(t){const e=t.geometry,n=t.targetAttribSize;if(n<1||n>4)return;const i=t.add,r=t.mult,s=this._data_from_texture(t.texture);if(!s)return;const{data:o,resx:a,resy:l}=s,c=o.length/(a*l),u=e.getAttribute(t.uvAttribName).array,h=u.length/2,d=new Array(h*n);let p,_,m,f,g,v,y,x,b;const w=rs.clamp;for(v=0;v<h;v++)for(p=2*v,_=w(u[p],0,1),m=w(u[p+1],0,1),f=Math.floor((a-1)*_),g=Math.floor((l-1)*(1-m)),y=g*a+f,b=0;b<n;b++)x=o[c*y+b],d[v*n+b]=r*x+i;const T=Wr.remapName(t.targetAttribName),A=new Float32Array(d);e.setAttribute(T,new C.a(A,n))}_data_from_texture(t){if(t.image)return t.image.data?this._data_from_data_texture(t):this._data_from_default_texture(t)}_data_from_default_texture(t){const e=t.image.width,n=t.image.height;return{data:Rf.data_from_image(t.image).data,resx:e,resy:n}}_data_from_data_texture(t){return{data:t.image.data,resx:t.image.width,resy:t.image.height}}}class hq extends pG{static type(){return\\\\\\\"attribFromTexture\\\\\\\"}async cook(t,e){var n;const i=t[0],r=e.texture.nodeWithContext(Ki.COP,null===(n=this.states)||void 0===n?void 0:n.error);if(!r)return i;const s=(await r.compute()).texture();for(let t of i.coreObjects())this._set_position_from_data_texture(t,s,e);return i}_set_position_from_data_texture(t,e,n){var i,r;const s=null===(i=t.coreGeometry())||void 0===i?void 0:i.geometry();if(!s)return;if(null==s.getAttribute(n.uvAttrib))return void(null===(r=this.states)||void 0===r||r.error.set(`param '${n.uvAttrib} not found'`));(new uq).set_attrib({geometry:s,texture:e,uvAttribName:n.uvAttrib,targetAttribName:n.attrib,targetAttribSize:n.attribSize,add:n.add,mult:n.mult})}}hq.DEFAULT_PARAMS={texture:new vi(gi.EMPTY),uvAttrib:\\\\\\\"uv\\\\\\\",attrib:\\\\\\\"pscale\\\\\\\",attribSize:1,add:0,mult:1},hq.INPUT_CLONED_STATE=Qi.FROM_NODE;const dq=hq.DEFAULT_PARAMS;const pq=new class extends aa{constructor(){super(...arguments),this.texture=oa.NODE_PATH(dq.texture.path(),{nodeSelection:{context:Ki.COP}}),this.uvAttrib=oa.STRING(dq.uvAttrib),this.attrib=oa.STRING(dq.attrib),this.attribSize=oa.INTEGER(dq.attribSize,{range:[1,3],rangeLocked:[!0,!0]}),this.add=oa.FLOAT(dq.add),this.mult=oa.FLOAT(dq.mult)}};class _q extends gG{constructor(){super(...arguments),this.paramsConfig=pq}static type(){return\\\\\\\"attribFromTexture\\\\\\\"}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState(Qi.FROM_NODE),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.attrib])}))}))}async cook(t){this._operation=this._operation||new hq(this.scene(),this.states);const e=await this._operation.cook(t,this.pv);this.setCoreGroup(e)}}var mq;!function(t){t.MIN_MAX_TO_01=\\\\\\\"min/max to 0/1\\\\\\\",t.VECTOR_TO_LENGTH_1=\\\\\\\"vectors to length 1\\\\\\\"}(mq||(mq={}));const fq=[mq.MIN_MAX_TO_01,mq.VECTOR_TO_LENGTH_1];class gq extends pG{constructor(){super(...arguments),this.min3=new p.a,this.max3=new p.a,this._vec=new p.a}static type(){return\\\\\\\"attribNormalize\\\\\\\"}cook(t,e){const n=t[0],i=t[0].objectsWithGeo(),r=sr.attribNames(e.name);for(let t of i){const n=t.geometry;for(let t of r){const i=n.getAttribute(t);if(i){let t=i;e.changeName&&\\\\\\\"\\\\\\\"!=e.newName&&(t=n.getAttribute(e.newName),t&&(t.needsUpdate=!0),t=t||i.clone()),this._normalize_attribute(i,t,e)}}}return n}_normalize_attribute(t,e,n){switch(fq[n.mode]){case mq.MIN_MAX_TO_01:return this._normalize_from_min_max_to_01(t,e);case mq.VECTOR_TO_LENGTH_1:return this._normalize_vectors(t,e)}}_normalize_from_min_max_to_01(t,e){const n=t.itemSize,i=t.array,r=e.array;switch(n){case 1:{const t=Math.min(...i),e=Math.max(...i);for(let n=0;n<r.length;n++)r[n]=(i[n]-t)/(e-t);return}case 3:{const t=i.length/n,e=new Array(t),s=new Array(t),o=new Array(t);let a=0;for(let r=0;r<t;r++)a=r*n,e[r]=i[a+0],s[r]=i[a+1],o[r]=i[a+2];this.min3.set(Math.min(...e),Math.min(...s),Math.min(...o)),this.max3.set(Math.max(...e),Math.max(...s),Math.max(...o));for(let i=0;i<t;i++)a=i*n,r[a+0]=(e[i]-this.min3.x)/(this.max3.x-this.min3.x),r[a+1]=(s[i]-this.min3.y)/(this.max3.y-this.min3.y),r[a+2]=(o[i]-this.min3.z)/(this.max3.z-this.min3.z);return}}}_normalize_vectors(t,e){const n=t.array,i=e.array,r=n.length;if(3==t.itemSize)for(let t=0;t<r;t+=3)this._vec.fromArray(n,t),this._vec.normalize(),this._vec.toArray(i,t)}}gq.DEFAULT_PARAMS={mode:0,name:\\\\\\\"position\\\\\\\",changeName:!1,newName:\\\\\\\"\\\\\\\"},gq.INPUT_CLONED_STATE=Qi.FROM_NODE;const vq=gq.DEFAULT_PARAMS;const yq=new class extends aa{constructor(){super(...arguments),this.mode=oa.INTEGER(vq.mode,{menu:{entries:fq.map(((t,e)=>({name:t,value:e})))}}),this.name=oa.STRING(vq.name),this.changeName=oa.BOOLEAN(vq.changeName),this.newName=oa.STRING(vq.newName,{visibleIf:{changeName:1}})}};class xq extends gG{constructor(){super(...arguments),this.paramsConfig=yq}static type(){return\\\\\\\"attribNormalize\\\\\\\"}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState(gq.INPUT_CLONED_STATE),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.name])}))}))}set_mode(t){this.p.mode.set(fq.indexOf(t))}cook(t){this._operation=this._operation||new gq(this.scene(),this.states);const e=this._operation.cook(t,this.pv);this.setCoreGroup(e)}}var bq;!function(t){t[t.MIN=0]=\\\\\\\"MIN\\\\\\\",t[t.MAX=1]=\\\\\\\"MAX\\\\\\\",t[t.FIRST_FOUND=2]=\\\\\\\"FIRST_FOUND\\\\\\\"}(bq||(bq={}));class wq extends pG{constructor(){super(...arguments),this._values_per_attrib_name={},this._filtered_values_per_attrib_name={}}static type(){return\\\\\\\"attribPromote\\\\\\\"}cook(t,e){this._core_group=t[0],this._values_per_attrib_name={},this._filtered_values_per_attrib_name={};for(let t of this._core_group.coreObjects())this._core_object=t,this.find_values(e),this.filter_values(e),this.set_values(e);return this._core_group}find_values(t){const e=sr.attribNames(t.name);for(let n of e)this._find_values_for_attrib_name(n,t)}_find_values_for_attrib_name(t,e){switch(e.classFrom){case Pr.VERTEX:return this.find_values_from_points(t,e);case Pr.OBJECT:return this.find_values_from_object(t,e)}}find_values_from_points(t,e){if(this._core_object){const e=this._core_object.points(),n=e[0];if(n&&!n.isAttribIndexed(t)){const n=new Array(e.length);let i;for(let r=0;r<e.length;r++)i=e[r],n[r]=i.attribValue(t);this._values_per_attrib_name[t]=n}}}find_values_from_object(t,e){this._values_per_attrib_name[t]=[],this._core_object&&this._values_per_attrib_name[t].push(this._core_object.attribValue(t))}filter_values(t){const e=Object.keys(this._values_per_attrib_name);for(let n of e){const e=this._values_per_attrib_name[n];switch(t.mode){case bq.MIN:this._filtered_values_per_attrib_name[n]=f.min(e);break;case bq.MAX:this._filtered_values_per_attrib_name[n]=f.max(e);break;case bq.FIRST_FOUND:this._filtered_values_per_attrib_name[n]=e[0]}}}set_values(t){const e=Object.keys(this._filtered_values_per_attrib_name);for(let n of e){const e=this._filtered_values_per_attrib_name[n];if(null!=e)switch(t.classTo){case Pr.VERTEX:this.set_values_to_points(n,e,t);break;case Pr.OBJECT:this.set_values_to_object(n,e,t)}}}set_values_to_points(t,e,n){if(this._core_group&&this._core_object){if(!this._core_group.hasAttrib(t)){const n=Wr.attribSizeFromValue(e);n&&this._core_group.addNumericVertexAttrib(t,n,e)}const n=this._core_object.points();for(let i of n)i.setAttribValue(t,e)}}set_values_to_object(t,e,n){var i;null===(i=this._core_object)||void 0===i||i.setAttribValue(t,e)}}wq.DEFAULT_PARAMS={classFrom:Pr.VERTEX,classTo:Pr.OBJECT,mode:bq.FIRST_FOUND,name:\\\\\\\"\\\\\\\"},wq.INPUT_CLONED_STATE=Qi.FROM_NODE;const Tq=[{name:\\\\\\\"min\\\\\\\",value:bq.MIN},{name:\\\\\\\"max\\\\\\\",value:bq.MAX},{name:\\\\\\\"first_found\\\\\\\",value:bq.FIRST_FOUND}],Aq=wq.DEFAULT_PARAMS;const Eq=new class extends aa{constructor(){super(...arguments),this.classFrom=oa.INTEGER(Aq.classFrom,{menu:{entries:Fr}}),this.classTo=oa.INTEGER(Aq.classTo,{menu:{entries:Fr}}),this.mode=oa.INTEGER(Aq.mode,{menu:{entries:Tq}}),this.name=oa.STRING(Aq.name)}};class Mq extends gG{constructor(){super(...arguments),this.paramsConfig=Eq}static type(){return\\\\\\\"attribPromote\\\\\\\"}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState(wq.INPUT_CLONED_STATE),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.name,this.p.classFrom,this.p.classTo],(()=>{if(\\\\\\\"\\\\\\\"!=this.pv.name){const t=Fr.filter((t=>t.value==this.pv.classFrom))[0].name,e=Fr.filter((t=>t.value==this.pv.classTo))[0].name;return`${this.pv.name} (${t} -> ${e})`}return\\\\\\\"\\\\\\\"}))}))}))}cook(t){this._operation=this._operation||new wq(this.scene(),this.states);const e=this._operation.cook(t,this.pv);this.setCoreGroup(e)}}const Sq=new class extends aa{constructor(){super(...arguments),this.name=oa.STRING(),this.ramp=oa.RAMP(),this.changeName=oa.BOOLEAN(0),this.newName=oa.STRING(\\\\\\\"\\\\\\\",{visibleIf:{changeName:1}})}};class Cq extends gG{constructor(){super(...arguments),this.paramsConfig=Sq}static type(){return\\\\\\\"attribRemap\\\\\\\"}initializeNode(){this.io.inputs.setCount(1)}cook(t){const e=t[0];this._remap_attribute(e),this.setCoreGroup(e)}_remap_attribute(t){const e=t.points();if(0===e.length)return;if(\\\\\\\"\\\\\\\"===this.pv.name)return;const n=e[0].attribSize(this.pv.name),i=e.map((t=>t.attribValue(this.pv.name)));let r=new Array(e.length);this._get_remaped_values(n,i,r);let s=this.pv.name;this.pv.changeName&&(s=this.pv.newName,t.hasAttrib(s)||t.addNumericVertexAttrib(s,n,0));let o=0;for(let t of r){e[o].setAttribValue(s,t),o++}}_get_remaped_values(t,e,n){switch(t){case zr.FLOAT:return this._get_normalized_float(e,n);case zr.VECTOR2:return this._get_normalized_vector2(e,n);case zr.VECTOR3:return this._get_normalized_vector3(e,n);case zr.VECTOR4:return this._get_normalized_vector4(e,n)}ar.unreachable(t)}_get_normalized_float(t,e){const n=t,i=this.p.ramp;for(let t=0;t<n.length;t++){const r=n[t],s=i.value_at_position(r);e[t]=s}}_get_normalized_vector2(t,e){const n=t,i=this.p.ramp;for(let t=0;t<n.length;t++){const r=n[t],s=new d.a(i.value_at_position(r.x),i.value_at_position(r.y));e[t]=s}}_get_normalized_vector3(t,e){const n=t,i=this.p.ramp;for(let t=0;t<n.length;t++){const r=n[t],s=new p.a(i.value_at_position(r.x),i.value_at_position(r.y),i.value_at_position(r.z));e[t]=s}}_get_normalized_vector4(t,e){const n=t,i=this.p.ramp;for(let t=0;t<n.length;t++){const r=n[t],s=new _.a(i.value_at_position(r.x),i.value_at_position(r.y),i.value_at_position(r.z),i.value_at_position(r.w));e[t]=s}}}const Nq=new class extends aa{constructor(){super(...arguments),this.class=oa.INTEGER(Pr.VERTEX,{menu:{entries:Fr}}),this.oldName=oa.STRING(),this.newName=oa.STRING()}};class Lq extends gG{constructor(){super(...arguments),this.paramsConfig=Nq}static type(){return\\\\\\\"attribRename\\\\\\\"}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState(Qi.FROM_NODE),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.oldName,this.p.newName],(()=>\\\\\\\"\\\\\\\"!=this.pv.oldName&&\\\\\\\"\\\\\\\"!=this.pv.newName?`${this.pv.oldName} -> ${this.pv.newName}`:\\\\\\\"\\\\\\\"))}))}))}cook(t){const e=t[0];e.renameAttrib(this.pv.oldName,this.pv.newName,this.pv.class),this.setCoreGroup(e)}}var Oq=n(18);class Rq{constructor(t,e=0){this._bbox=t,this._level=e,this._leaves_by_octant={},this._points_by_octant_id={},this._leaves=[],this._bounding_boxes_by_octant={},this._bounding_boxes_by_octant_prepared=!1,this._center=this._bbox.max.clone().add(this._bbox.min).multiplyScalar(.5)}level(){return this._level}traverse(t){t(this);Object.values(this._leaves_by_octant).forEach((e=>{e.traverse(t)}))}intersects_sphere(t){return!!this._bbox&&this._bbox.intersectsSphere(t)}points_in_sphere(t,e){if(0==this._leaves.length){Object.values(this._points_by_octant_id).flat().filter((e=>t.containsPoint(e.position()))).forEach((t=>{e.push(t)}))}else{this._leaves.filter((e=>e.intersects_sphere(t))).forEach((n=>n.points_in_sphere(t,e)))}}bounding_box(){return this._bbox}set_points(t){this._points_by_octant_id={};for(let e of t)this.add_point(e);const e=Object.keys(this._points_by_octant_id);e.length>1&&e.forEach((t=>{this.create_leaf(t)}))}create_leaf(t){const e=this._leaf_bbox(t),n=new Rq(e,this._level+1);this._leaves_by_octant[t]=n,this._leaves.push(n),n.set_points(this._points_by_octant_id[t])}add_point(t){const e=this._octant_id(t.position());null==this._points_by_octant_id[e]&&(this._points_by_octant_id[e]=[]),this._points_by_octant_id[e].push(t)}_octant_id(t){return`${t.x>this._center.x?1:0}${t.y>this._center.y?1:0}${t.z>this._center.z?1:0}`}_leaf_bbox(t){return this._bounding_boxes_by_octant_prepared||(this._prepare_leaves_bboxes(),this._bounding_boxes_by_octant_prepared=!0),this._bounding_boxes_by_octant[t]}_bbox_center(t,e,n){const i=this._bbox.min.clone();return t&&(i.x=this._bbox.max.x),e&&(i.y=this._bbox.max.y),n&&(i.z=this._bbox.max.z),i.clone().add(this._center).multiplyScalar(.5)}_prepare_leaves_bboxes(){const t=[];t.push(this._bbox_center(0,0,0)),t.push(this._bbox_center(0,0,1)),t.push(this._bbox_center(0,1,0)),t.push(this._bbox_center(0,1,1)),t.push(this._bbox_center(1,0,0)),t.push(this._bbox_center(1,0,1)),t.push(this._bbox_center(1,1,0)),t.push(this._bbox_center(1,1,1));const e=this._bbox.max.clone().sub(this._bbox.min).multiplyScalar(.25);for(let n of t){const t=this._octant_id(n),i=new XB.a(n.clone().sub(e),n.clone().add(e));this._bounding_boxes_by_octant[t]=i}}}class Pq{constructor(t){this._root=new Rq(t)}set_points(t){this._root.set_points(t)}traverse(t){this._root.traverse(t)}find_points(t,e,n){const i=new Oq.a(t,e);let r=[];return this._root.intersects_sphere(i)&&this._root.points_in_sphere(i,r),null==n||r.length>n&&(r=f.sortBy(r,(e=>e.position().distanceTo(t))),r=r.slice(0,n)),r}}class Iq{constructor(t={}){this._array_index=0,this._count=0,this._current_count_index=0,this._resolve=null,this._max_time_per_chunk=t.max_time_per_chunk||10,this._check_every_interations=t.check_every_interations||100}async startWithCount(t,e){if(this._count=t,this._current_count_index=0,this._iteratee_method_count=e,this._bound_next_with_count=this.nextWithCount.bind(this),this._resolve)throw\\\\\\\"an iterator cannot be started twice\\\\\\\";return new Promise(((t,e)=>{this._resolve=t,this.nextWithCount()}))}nextWithCount(){const t=ai.performance.performanceManager(),e=t.now();if(this._iteratee_method_count&&this._bound_next_with_count)for(;this._current_count_index<this._count;)if(this._iteratee_method_count(this._current_count_index),this._current_count_index++,this._current_count_index%this._check_every_interations==0&&t.now()-e>this._max_time_per_chunk){setTimeout(this._bound_next_with_count,1);break}this._current_count_index>=this._count&&this._resolve&&this._resolve()}async startWithArray(t,e){if(this._array=t,this._array_index=0,this._iteratee_method_array=e,this._bound_next_with_array=this.nextWithArray.bind(this),this._resolve)throw\\\\\\\"an iterator cannot be started twice\\\\\\\";return new Promise(((t,e)=>{this._resolve=t,this.nextWithArray()}))}nextWithArray(){const t=ai.performance.performanceManager(),e=t.now();if(this._iteratee_method_array&&this._bound_next_with_array&&this._array)for(;this._current_array_element=this._array[this._array_index];)if(this._iteratee_method_array(this._current_array_element,this._array_index),this._array_index++,this._array_index%this._check_every_interations==0&&t.now()-e>this._max_time_per_chunk){setTimeout(this._bound_next_with_array,1);break}void 0===this._current_array_element&&this._resolve&&this._resolve()}}const Fq=new class extends aa{constructor(){super(...arguments),this.srcGroup=oa.STRING(),this.destGroup=oa.STRING(),this.name=oa.STRING(),this.maxSamplesCount=oa.INTEGER(1,{range:[1,10],rangeLocked:[!0,!1]}),this.distanceThreshold=oa.FLOAT(1),this.blendWidth=oa.FLOAT(0)}};class Dq extends gG{constructor(){super(...arguments),this.paramsConfig=Fq}static type(){return\\\\\\\"attribTransfer\\\\\\\"}static displayedInputNames(){return[\\\\\\\"geometry to transfer attributes to\\\\\\\",\\\\\\\"geometry to transfer attributes from\\\\\\\"]}initializeNode(){this.io.inputs.setCount(2),this.io.inputs.initInputsClonedState([Qi.FROM_NODE,Qi.NEVER])}async cook(t){this._core_group_dest=t[0];const e=this._core_group_dest.pointsFromGroup(this.pv.destGroup);this._core_group_src=t[1],this._attrib_names=this._core_group_src.attribNamesMatchingMask(this.pv.name),this._error_if_attribute_not_found_on_second_input(),this._build_octree_if_required(this._core_group_src),this._add_attribute_if_required(),await this._transfer_attributes(e),this.setCoreGroup(this._core_group_dest)}_error_if_attribute_not_found_on_second_input(){for(let t of this._attrib_names)this._core_group_src.hasAttrib(t)||this.states.error.set(`attribute '${t}' not found on second input`)}_build_octree_if_required(t){const e=null==this._octree_timestamp||this._octree_timestamp!==t.timestamp();if(this._prev_param_srcGroup!==this.pv.srcGroup||e){this._octree_timestamp=t.timestamp(),this._prev_param_srcGroup=this.pv.srcGroup;const e=this._core_group_src.pointsFromGroup(this.pv.srcGroup);this._octree=new Pq(this._core_group_src.boundingBox()),this._octree.set_points(e)}}_add_attribute_if_required(){for(let t of this._attrib_names)if(!this._core_group_dest.hasAttrib(t)){const e=this._core_group_src.attribSize(t);this._core_group_dest.addNumericVertexAttrib(t,e,0)}}async _transfer_attributes(t){const e=new Iq;await e.startWithArray(t,this._transfer_attributes_for_point.bind(this))}_transfer_attributes_for_point(t){var e;const n=this.pv.distanceThreshold+this.pv.blendWidth,i=(null===(e=this._octree)||void 0===e?void 0:e.find_points(t.position(),n,this.pv.maxSamplesCount))||[];for(let e of this._attrib_names)this._interpolate_points(t,i,e)}_interpolate_points(t,e,n){let i;i=class{static perform(t,e,n,i,r){switch(e.length){case 0:return t.attribValue(n);case 1:return this._interpolate_with_1_point(t,e[0],n,i,r);default:return this._interpolate_with_multiple_points(t,e,n,i,r)}}static _interpolate_with_1_point(t,e,n,i,r){const s=t.position(),o=e.position(),a=s.distanceTo(o),l=e.attribValue(n);return m.isNumber(l)?this._weighted_value_from_distance(t,l,n,a,i,r):(console.warn(\\\\\\\"value is not a number\\\\\\\",l),0)}static _weight_from_distance(t,e,n){return(t-e)/n}static _weighted_value_from_distance(t,e,n,i,r,s){if(i<=r)return e;{const o=t.attribValue(n);if(m.isNumber(o)){const t=this._weight_from_distance(i,r,s);return t*o+(1-t)*e}return console.warn(\\\\\\\"value is not a number\\\\\\\",o),0}}static _interpolate_with_multiple_points(t,e,n,i,r){const s=e.map((e=>this._interpolate_with_1_point(t,e,n,i,r)));return f.max(s)||0}static weights(t,e){switch(e.length){case 1:return 1;case 2:return this._weights_from_2(t,e);default:return e=e.slice(0,3),this._weights_from_3(t,e)}}static _weights_from_2(t,e){const n=e.map((e=>t.distanceTo(e))),i=f.sum(n);return[n[1]/i,n[0]/i]}static _weights_from_3(t,e){const n=e.map((e=>t.distanceTo(e))),i=f.sum([n[0]*n[1],n[0]*n[2],n[1]*n[2]]);return[n[1]*n[2]/i,n[0]*n[2]/i,n[0]*n[1]/i]}}.perform(t,e,n,this.pv.distanceThreshold,this.pv.blendWidth),null!=i&&t.setAttribValue(n,i)}}const kq=new class extends aa{constructor(){super(...arguments),this.stepSize=oa.FLOAT(.1)}};class Bq extends gG{constructor(){super(...arguments),this.paramsConfig=kq}static type(){return\\\\\\\"bboxScatter\\\\\\\"}static displayedInputNames(){return[\\\\\\\"geometry to create points from\\\\\\\"]}initializeNode(){this.io.inputs.setCount(1)}cook(t){const e=t[0],n=this.pv.stepSize,i=e.boundingBox(),r=i.min,s=i.max,o=[];for(let t=r.x;t<=s.x;t+=n)for(let e=r.y;e<=s.y;e+=n)for(let i=r.z;i<=s.z;i+=n)o.push(t),o.push(e),o.push(i);const a=new S.a;a.setAttribute(\\\\\\\"position\\\\\\\",new C.a(new Float32Array(o),3)),this.setGeometry(a,Sr.POINTS)}}const zq=new class extends aa{constructor(){super(...arguments),this.attribName=oa.STRING(\\\\\\\"position\\\\\\\"),this.blend=oa.FLOAT(.5,{range:[0,1],rangeLocked:[!0,!0]})}};class Uq extends gG{constructor(){super(...arguments),this.paramsConfig=zq}static type(){return\\\\\\\"blend\\\\\\\"}static displayedInputNames(){return[\\\\\\\"geometry to blend from\\\\\\\",\\\\\\\"geometry to blend to\\\\\\\"]}initializeNode(){this.io.inputs.setCount(2),this.io.inputs.initInputsClonedState([Qi.FROM_NODE,Qi.NEVER])}cook(t){const e=t[0],n=t[1],i=e.objects(),r=n.objects();let s,o;for(let t=0;t<i.length;t++)s=i[t],o=r[t],this.blend(s,o,this.pv.blend);this.setCoreGroup(e)}blend(t,e,n){const i=t.geometry,r=e.geometry;if(null==i||null==r)return;const s=i.getAttribute(this.pv.attribName),o=r.getAttribute(this.pv.attribName);if(null==s||null==o)return;const a=s.array,l=o.array;let c,u;for(let t=0;t<a.length;t++)c=a[t],u=l[t],null!=u&&(a[t]=(1-n)*c+n*u);i.computeVertexNormals()}}class Gq{constructor(){this.polygons=[]}clone(){let t=new Gq;return t.polygons=this.polygons.map((function(t){return t.clone()})),t}toPolygons(){return this.polygons}union(t){let e=new Yq(this.clone().polygons),n=new Yq(t.clone().polygons);return e.clipTo(n),n.clipTo(e),n.invert(),n.clipTo(e),n.invert(),e.build(n.allPolygons()),Gq.fromPolygons(e.allPolygons())}subtract(t){let e=new Yq(this.clone().polygons),n=new Yq(t.clone().polygons);return e.invert(),e.clipTo(n),n.clipTo(e),n.invert(),n.clipTo(e),n.invert(),e.build(n.allPolygons()),e.invert(),Gq.fromPolygons(e.allPolygons())}intersect(t){let e=new Yq(this.clone().polygons),n=new Yq(t.clone().polygons);return e.invert(),n.clipTo(e),n.invert(),e.clipTo(n),n.clipTo(e),e.build(n.allPolygons()),e.invert(),Gq.fromPolygons(e.allPolygons())}inverse(){let t=this.clone();return t.polygons.forEach((t=>t.flip())),t}}Gq.fromPolygons=function(t){let e=new Gq;return e.polygons=t,e};class Vq{constructor(t=0,e=0,n=0){this.x=t,this.y=e,this.z=n}copy(t){return this.x=t.x,this.y=t.y,this.z=t.z,this}clone(){return new Vq(this.x,this.y,this.z)}negate(){return this.x*=-1,this.y*=-1,this.z*=-1,this}add(t){return this.x+=t.x,this.y+=t.y,this.z+=t.z,this}sub(t){return this.x-=t.x,this.y-=t.y,this.z-=t.z,this}times(t){return this.x*=t,this.y*=t,this.z*=t,this}dividedBy(t){return this.x/=t,this.y/=t,this.z/=t,this}lerp(t,e){return this.add(Hq.copy(t).sub(this).times(e))}unit(){return this.dividedBy(this.length())}length(){return Math.sqrt(this.x**2+this.y**2+this.z**2)}normalize(){return this.unit()}cross(t){let e=this;const n=e.x,i=e.y,r=e.z,s=t.x,o=t.y,a=t.z;return this.x=i*a-r*o,this.y=r*s-n*a,this.z=n*o-i*s,this}dot(t){return this.x*t.x+this.y*t.y+this.z*t.z}}let Hq=new Vq,jq=new Vq;class Wq{constructor(t,e,n,i){this.pos=(new Vq).copy(t),this.normal=(new Vq).copy(e),this.uv=(new Vq).copy(n),this.uv.z=0,i&&(this.color=(new Vq).copy(i))}clone(){return new Wq(this.pos,this.normal,this.uv,this.color)}flip(){this.normal.negate()}interpolate(t,e){return new Wq(this.pos.clone().lerp(t.pos,e),this.normal.clone().lerp(t.normal,e),this.uv.clone().lerp(t.uv,e),this.color&&t.color&&this.color.clone().lerp(t.color,e))}}class qq{constructor(t,e){this.normal=t,this.w=e}clone(){return new qq(this.normal.clone(),this.w)}flip(){this.normal.negate(),this.w=-this.w}splitPolygon(t,e,n,i,r){let s=0,o=[];for(let e=0;e<t.vertices.length;e++){let n=this.normal.dot(t.vertices[e].pos)-this.w,i=n<-qq.EPSILON?2:n>qq.EPSILON?1:0;s|=i,o.push(i)}switch(s){case 0:(this.normal.dot(t.plane.normal)>0?e:n).push(t);break;case 1:i.push(t);break;case 2:r.push(t);break;case 3:let s=[],a=[];for(let e=0;e<t.vertices.length;e++){let n=(e+1)%t.vertices.length,i=o[e],r=o[n],l=t.vertices[e],c=t.vertices[n];if(2!=i&&s.push(l),1!=i&&a.push(2!=i?l.clone():l),3==(i|r)){let t=(this.w-this.normal.dot(l.pos))/this.normal.dot(Hq.copy(c.pos).sub(l.pos)),e=l.interpolate(c,t);s.push(e),a.push(e.clone())}}s.length>=3&&i.push(new Xq(s,t.shared)),a.length>=3&&r.push(new Xq(a,t.shared))}}}qq.EPSILON=1e-5,qq.fromPoints=function(t,e,n){let i=Hq.copy(e).sub(t).cross(jq.copy(n).sub(t)).normalize();return new qq(i.clone(),i.dot(t))};class Xq{constructor(t,e){this.vertices=t,this.shared=e,this.plane=qq.fromPoints(t[0].pos,t[1].pos,t[2].pos)}clone(){return new Xq(this.vertices.map((t=>t.clone())),this.shared)}flip(){this.vertices.reverse().map((t=>t.flip())),this.plane.flip()}}class Yq{constructor(t){this.plane=null,this.front=null,this.back=null,this.polygons=[],t&&this.build(t)}clone(){let t=new Yq;return t.plane=this.plane&&this.plane.clone(),t.front=this.front&&this.front.clone(),t.back=this.back&&this.back.clone(),t.polygons=this.polygons.map((t=>t.clone())),t}invert(){for(let t=0;t<this.polygons.length;t++)this.polygons[t].flip();this.plane&&this.plane.flip(),this.front&&this.front.invert(),this.back&&this.back.invert();let t=this.front;this.front=this.back,this.back=t}clipPolygons(t){if(!this.plane)return t.slice();let e=[],n=[];for(let i=0;i<t.length;i++)this.plane.splitPolygon(t[i],e,n,e,n);return this.front&&(e=this.front.clipPolygons(e)),n=this.back?this.back.clipPolygons(n):[],e.concat(n)}clipTo(t){this.polygons=t.clipPolygons(this.polygons),this.front&&this.front.clipTo(t),this.back&&this.back.clipTo(t)}allPolygons(){let t=this.polygons.slice();return this.front&&(t=t.concat(this.front.allPolygons())),this.back&&(t=t.concat(this.back.allPolygons())),t}build(t){if(!t.length)return;this.plane||(this.plane=t[0].plane.clone());let e=[],n=[];for(let i=0;i<t.length;i++)this.plane.splitPolygon(t[i],this.polygons,this.polygons,e,n);e.length&&(this.front||(this.front=new Yq),this.front.build(e)),n.length&&(this.back||(this.back=new Yq),this.back.build(n))}}Gq.fromJSON=function(t){return Gq.fromPolygons(t.polygons.map((t=>new Xq(t.vertices.map((t=>new Wq(t.pos,t.normal,t.uv))),t.shared))))},Gq.fromGeometry=function(t,e){let n=[];if(t.isGeometry){let i=t.faces,r=t.vertices,s=[\\\\\\\"a\\\\\\\",\\\\\\\"b\\\\\\\",\\\\\\\"c\\\\\\\"];for(let o=0;o<i.length;o++){let a=i[o],l=[];for(let e=0;e<3;e++)l.push(new Wq(r[a[s[e]]],a.vertexNormals[e],t.faceVertexUvs[0][o][e]));n.push(new Xq(l,e))}}else if(t.isBufferGeometry){let i,r=t.attributes.position,s=t.attributes.normal,o=t.attributes.uv,a=t.attributes.color;if(t.index)i=t.index.array;else{i=new Array(r.array.length/r.itemSize|0);for(let t=0;t<i.length;t++)i[t]=t}let l=i.length/3|0;n=new Array(l);for(let t=0,l=0,c=i.length;t<c;t+=3,l++){let c=new Array(3);for(let e=0;e<3;e++){let n=i[t+e],l=3*n,u=2*n,h=r.array[l],d=r.array[l+1],p=r.array[l+2],_=s.array[l],m=s.array[l+1],f=s.array[l+2],g=o.array[u],v=o.array[u+1];c[e]=new Wq({x:h,y:d,z:p},{x:_,y:m,z:f},{x:g,y:v,z:0},a&&{x:a.array[u],y:a.array[u+1],z:a.array[u+2]})}n[l]=new Xq(c,e)}}else console.error(\\\\\\\"Unsupported CSG input type:\\\\\\\"+t.type);return Gq.fromPolygons(n)};let $q=new p.a,Jq=new U.a;Gq.fromMesh=function(t,e){let n=Gq.fromGeometry(t.geometry,e);Jq.getNormalMatrix(t.matrix);for(let e=0;e<n.polygons.length;e++){let i=n.polygons[e];for(let e=0;e<i.vertices.length;e++){let n=i.vertices[e];n.pos.copy($q.copy(n.pos).applyMatrix4(t.matrix)),n.normal.copy($q.copy(n.normal).applyMatrix3(Jq))}}return n};let Zq=t=>({top:0,array:new Float32Array(t),write:function(t){this.array[this.top++]=t.x,this.array[this.top++]=t.y,this.array[this.top++]=t.z}}),Qq=t=>({top:0,array:new Float32Array(t),write:function(t){this.array[this.top++]=t.x,this.array[this.top++]=t.y}});var Kq;Gq.toMesh=function(t,e,n){let i,r,s=t.polygons;{let t=0;s.forEach((e=>t+=e.vertices.length-2)),i=new S.a;let e,n=Zq(3*t*3),o=Zq(3*t*3),a=Qq(2*t*3),l=[];if(s.forEach((i=>{let r=i.vertices,s=r.length;void 0!==i.shared&&(l[i.shared]||(l[i.shared]=[])),s&&void 0!==r[0].color&&(e||(e=Zq(3*t*3)));for(let t=3;t<=s;t++)void 0!==i.shared&&l[i.shared].push(n.top/3,n.top/3+1,n.top/3+2),n.write(r[0].pos),n.write(r[t-2].pos),n.write(r[t-1].pos),o.write(r[0].normal),o.write(r[t-2].normal),o.write(r[t-1].normal),a.write(r[0].uv),a.write(r[t-2].uv),a.write(r[t-1].uv),e&&(e.write(r[0].color)||e.write(r[t-2].color)||e.write(r[t-1].color))})),i.setAttribute(\\\\\\\"position\\\\\\\",new C.a(n.array,3)),i.setAttribute(\\\\\\\"normal\\\\\\\",new C.a(o.array,3)),i.setAttribute(\\\\\\\"uv\\\\\\\",new C.a(a.array,2)),e&&i.setAttribute(\\\\\\\"color\\\\\\\",new C.a(e.array,3)),l.length){let t=[],e=0;for(let n=0;n<l.length;n++)i.addGroup(e,l[n].length,n),e+=l[n].length,t=t.concat(l[n]);i.setIndex(t)}r=i}let o=(new A.a).copy(e).invert();i.applyMatrix4(o),i.computeBoundingSphere(),i.computeBoundingBox();let a=new k.a(i,n);return a.matrix.copy(e),a.matrix.decompose(a.position,a.quaternion,a.scale),a.rotation.setFromQuaternion(a.quaternion),a.updateMatrixWorld(),a.castShadow=a.receiveShadow=!0,a},function(t){t.INTERSECT=\\\\\\\"intersect\\\\\\\",t.SUBSTRACT=\\\\\\\"substract\\\\\\\",t.UNION=\\\\\\\"union\\\\\\\"}(Kq||(Kq={}));const tX=[Kq.INTERSECT,Kq.SUBSTRACT,Kq.UNION];class eX extends pG{static type(){return\\\\\\\"boolean\\\\\\\"}cook(t,e){const n=t[0].objectsWithGeo()[0],i=t[1].objectsWithGeo()[0],r=this._applyBooleaOperation(n,i,e);let s=n.material;if(e.useBothMaterials){s=m.isArray(s)?s[0]:s;let t=i.material;t=m.isArray(t)?t[0]:t,s=[s,t]}const o=Gq.toMesh(r,n.matrix,s);return this.createCoreGroupFromObjects([o])}_applyBooleaOperation(t,e,n){const i=tX[n.operation];let r=Gq.fromMesh(t,0),s=Gq.fromMesh(e,1);switch(i){case Kq.INTERSECT:return r.intersect(s);case Kq.SUBSTRACT:return r.subtract(s);case Kq.UNION:return r.union(s)}ar.unreachable(i)}}eX.DEFAULT_PARAMS={operation:tX.indexOf(Kq.INTERSECT),useBothMaterials:!0},eX.INPUT_CLONED_STATE=[Qi.FROM_NODE,Qi.NEVER];const nX=eX.DEFAULT_PARAMS;const iX=new class extends aa{constructor(){super(...arguments),this.operation=oa.INTEGER(nX.operation,{menu:{entries:tX.map(((t,e)=>({name:t,value:e})))}}),this.useBothMaterials=oa.BOOLEAN(nX.useBothMaterials)}};class rX extends gG{constructor(){super(...arguments),this.paramsConfig=iX}static type(){return\\\\\\\"boolean\\\\\\\"}initializeNode(){super.initializeNode(),this.io.inputs.setCount(2),this.io.inputs.initInputsClonedState(eX.INPUT_CLONED_STATE),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.operation],(()=>tX[this.pv.operation]))}))}))}setOperation(t){this.p.operation.set(tX.indexOf(t))}async cook(t){this._operation=this._operation||new eX(this.scene(),this.states,this);const e=await this._operation.cook(t,this.pv);this.setCoreGroup(e)}}class sX extends pG{constructor(){super(...arguments),this._core_transform=new Mz}static type(){return\\\\\\\"box\\\\\\\"}cook(t,e){const n=t[0],i=n?this._cook_with_input(n,e):this._cook_without_input(e);return this.createCoreGroupFromGeometry(i)}_cook_without_input(t){const e=t.divisions,n=t.size,i=new N(n,n,n,e,e,e);return i.translate(t.center.x,t.center.y,t.center.z),i.computeVertexNormals(),i}_cook_with_input(t,e){const n=e.divisions,i=t.boundingBox(),r=i.max.clone().sub(i.min),s=i.max.clone().add(i.min).multiplyScalar(.5),o=new N(r.x,r.y,r.z,n,n,n),a=this._core_transform.translation_matrix(s);return o.applyMatrix4(a),o}}sX.DEFAULT_PARAMS={size:1,divisions:1,center:new p.a(0,0,0)},sX.INPUT_CLONED_STATE=Qi.NEVER;const oX=sX.DEFAULT_PARAMS;const aX=new class extends aa{constructor(){super(...arguments),this.size=oa.FLOAT(oX.size),this.divisions=oa.INTEGER(oX.divisions,{range:[1,10],rangeLocked:[!0,!1]}),this.center=oa.VECTOR3(oX.center)}};class lX extends gG{constructor(){super(...arguments),this.paramsConfig=aX}static type(){return\\\\\\\"box\\\\\\\"}static displayedInputNames(){return[\\\\\\\"geometry to create bounding box from (optional)\\\\\\\"]}initializeNode(){this.io.inputs.setCount(0,1),this.io.inputs.initInputsClonedState(sX.INPUT_CLONED_STATE)}cook(t){this._operation=this._operation||new sX(this._scene,this.states);const e=this._operation.cook(t,this.pv);this.setCoreGroup(e)}}class cX extends C.a{constructor(t,e,n,i=1){\\\\\\\"number\\\\\\\"==typeof n&&(i=n,n=!1,console.error(\\\\\\\"THREE.InstancedBufferAttribute: The constructor now expects normalized as the third argument.\\\\\\\")),super(t,e,n),this.meshPerAttribute=i}copy(t){return super.copy(t),this.meshPerAttribute=t.meshPerAttribute,this}toJSON(){const t=super.toJSON();return t.meshPerAttribute=this.meshPerAttribute,t.isInstancedBufferAttribute=!0,t}}cX.prototype.isInstancedBufferAttribute=!0;const uX=new A.a,hX=new A.a,dX=[],pX=new k.a;class _X extends k.a{constructor(t,e,n){super(t,e),this.instanceMatrix=new cX(new Float32Array(16*n),16),this.instanceColor=null,this.count=n,this.frustumCulled=!1}copy(t){return super.copy(t),this.instanceMatrix.copy(t.instanceMatrix),null!==t.instanceColor&&(this.instanceColor=t.instanceColor.clone()),this.count=t.count,this}getColorAt(t,e){e.fromArray(this.instanceColor.array,3*t)}getMatrixAt(t,e){e.fromArray(this.instanceMatrix.array,16*t)}raycast(t,e){const n=this.matrixWorld,i=this.count;if(pX.geometry=this.geometry,pX.material=this.material,void 0!==pX.material)for(let r=0;r<i;r++){this.getMatrixAt(r,uX),hX.multiplyMatrices(n,uX),pX.matrixWorld=hX,pX.raycast(t,dX);for(let t=0,n=dX.length;t<n;t++){const n=dX[t];n.instanceId=r,n.object=this,e.push(n)}dX.length=0}}setColorAt(t,e){null===this.instanceColor&&(this.instanceColor=new cX(new Float32Array(3*this.instanceMatrix.count),3)),e.toArray(this.instanceColor.array,3*t)}setMatrixAt(t,e){e.toArray(this.instanceMatrix.array,16*t)}updateMorphTargets(){}dispose(){this.dispatchEvent({type:\\\\\\\"dispose\\\\\\\"})}}let mX;_X.prototype.isInstancedMesh=!0;const fX=new p.a,gX=new p.a,vX=new p.a,yX=new d.a,xX=new d.a,bX=new A.a,wX=new p.a,TX=new p.a,AX=new p.a,EX=new d.a,MX=new d.a,SX=new d.a;class CX extends Q.a{constructor(t){if(super(),this.type=\\\\\\\"Sprite\\\\\\\",void 0===mX){mX=new S.a;const t=new Float32Array([-.5,-.5,0,0,0,.5,-.5,0,1,0,.5,.5,0,1,1,-.5,.5,0,0,1]),e=new as.a(t,5);mX.setIndex([0,1,2,0,2,3]),mX.setAttribute(\\\\\\\"position\\\\\\\",new ls.a(e,3,0,!1)),mX.setAttribute(\\\\\\\"uv\\\\\\\",new ls.a(e,2,3,!1))}this.geometry=mX,this.material=void 0!==t?t:new zf,this.center=new d.a(.5,.5)}raycast(t,e){null===t.camera&&console.error('THREE.Sprite: \\\\\\\"Raycaster.camera\\\\\\\" needs to be set in order to raycast against sprites.'),gX.setFromMatrixScale(this.matrixWorld),bX.copy(t.camera.matrixWorld),this.modelViewMatrix.multiplyMatrices(t.camera.matrixWorldInverse,this.matrixWorld),vX.setFromMatrixPosition(this.modelViewMatrix),t.camera.isPerspectiveCamera&&!1===this.material.sizeAttenuation&&gX.multiplyScalar(-vX.z);const n=this.material.rotation;let i,r;0!==n&&(r=Math.cos(n),i=Math.sin(n));const s=this.center;NX(wX.set(-.5,-.5,0),vX,s,gX,i,r),NX(TX.set(.5,-.5,0),vX,s,gX,i,r),NX(AX.set(.5,.5,0),vX,s,gX,i,r),EX.set(0,0),MX.set(1,0),SX.set(1,1);let o=t.ray.intersectTriangle(wX,TX,AX,!1,fX);if(null===o&&(NX(TX.set(-.5,.5,0),vX,s,gX,i,r),MX.set(0,1),o=t.ray.intersectTriangle(wX,AX,TX,!1,fX),null===o))return;const a=t.ray.origin.distanceTo(fX);a<t.near||a>t.far||e.push({distance:a,point:fX.clone(),uv:Qr.a.getUV(fX,wX,TX,AX,EX,MX,SX,new d.a),face:null,object:this})}copy(t){return super.copy(t),void 0!==t.center&&this.center.copy(t.center),this.material=t.material,this}}function NX(t,e,n,i,r,s){yX.subVectors(t,n).addScalar(.5).multiply(i),void 0!==r?(xX.x=s*yX.x-r*yX.y,xX.y=r*yX.x+s*yX.y):xX.copy(yX),t.copy(e),t.x+=xX.x,t.y+=xX.y,t.applyMatrix4(bX)}CX.prototype.isSprite=!0;var LX=n(94),OX=n(81),RX=n(46);class PX{constructor(){this.coefficients=[];for(let t=0;t<9;t++)this.coefficients.push(new p.a)}set(t){for(let e=0;e<9;e++)this.coefficients[e].copy(t[e]);return this}zero(){for(let t=0;t<9;t++)this.coefficients[t].set(0,0,0);return this}getAt(t,e){const n=t.x,i=t.y,r=t.z,s=this.coefficients;return e.copy(s[0]).multiplyScalar(.282095),e.addScaledVector(s[1],.488603*i),e.addScaledVector(s[2],.488603*r),e.addScaledVector(s[3],.488603*n),e.addScaledVector(s[4],n*i*1.092548),e.addScaledVector(s[5],i*r*1.092548),e.addScaledVector(s[6],.315392*(3*r*r-1)),e.addScaledVector(s[7],n*r*1.092548),e.addScaledVector(s[8],.546274*(n*n-i*i)),e}getIrradianceAt(t,e){const n=t.x,i=t.y,r=t.z,s=this.coefficients;return e.copy(s[0]).multiplyScalar(.886227),e.addScaledVector(s[1],1.023328*i),e.addScaledVector(s[2],1.023328*r),e.addScaledVector(s[3],1.023328*n),e.addScaledVector(s[4],.858086*n*i),e.addScaledVector(s[5],.858086*i*r),e.addScaledVector(s[6],.743125*r*r-.247708),e.addScaledVector(s[7],.858086*n*r),e.addScaledVector(s[8],.429043*(n*n-i*i)),e}add(t){for(let e=0;e<9;e++)this.coefficients[e].add(t.coefficients[e]);return this}addScaledSH(t,e){for(let n=0;n<9;n++)this.coefficients[n].addScaledVector(t.coefficients[n],e);return this}scale(t){for(let e=0;e<9;e++)this.coefficients[e].multiplyScalar(t);return this}lerp(t,e){for(let n=0;n<9;n++)this.coefficients[n].lerp(t.coefficients[n],e);return this}equals(t){for(let e=0;e<9;e++)if(!this.coefficients[e].equals(t.coefficients[e]))return!1;return!0}copy(t){return this.set(t.coefficients)}clone(){return(new this.constructor).copy(this)}fromArray(t,e=0){const n=this.coefficients;for(let i=0;i<9;i++)n[i].fromArray(t,e+3*i);return this}toArray(t=[],e=0){const n=this.coefficients;for(let i=0;i<9;i++)n[i].toArray(t,e+3*i);return t}static getBasisAt(t,e){const n=t.x,i=t.y,r=t.z;e[0]=.282095,e[1]=.488603*i,e[2]=.488603*r,e[3]=.488603*n,e[4]=1.092548*n*i,e[5]=1.092548*i*r,e[6]=.315392*(3*r*r-1),e[7]=1.092548*n*r,e[8]=.546274*(n*n-i*i)}}PX.prototype.isSphericalHarmonics3=!0;class IX extends nv.a{constructor(t=new PX,e=1){super(void 0,e),this.sh=t}copy(t){return super.copy(t),this.sh.copy(t.sh),this}fromJSON(t){return this.intensity=t.intensity,this.sh.fromArray(t.sh),this}toJSON(t){const e=super.toJSON(t);return e.object.sh=this.sh.toArray(),e}}IX.prototype.isLightProbe=!0;var FX=n(62),DX=n(44);class kX extends S.a{constructor(){super(),this.type=\\\\\\\"InstancedBufferGeometry\\\\\\\",this.instanceCount=1/0}copy(t){return super.copy(t),this.instanceCount=t.instanceCount,this}clone(){return(new this.constructor).copy(this)}toJSON(){const t=super.toJSON(this);return t.instanceCount=this.instanceCount,t.isInstancedBufferGeometry=!0,t}}kX.prototype.isInstancedBufferGeometry=!0;class BX extends kf.a{constructor(t){super(t)}load(t,e,n,i){const r=this,s=new Df.a(r.manager);s.setPath(r.path),s.setRequestHeader(r.requestHeader),s.setWithCredentials(r.withCredentials),s.load(t,(function(n){try{e(r.parse(JSON.parse(n)))}catch(e){i?i(e):console.error(e),r.manager.itemError(t)}}),n,i)}parse(t){const e={},n={};function i(t,i){if(void 0!==e[i])return e[i];const r=t.interleavedBuffers[i],s=function(t,e){if(void 0!==n[e])return n[e];const i=t.arrayBuffers[e],r=new Uint32Array(i).buffer;return n[e]=r,r}(t,r.buffer),o=Object(Pt.c)(r.type,s),a=new as.a(o,r.stride);return a.uuid=r.uuid,e[i]=a,a}const r=t.isInstancedBufferGeometry?new kX:new S.a,s=t.data.index;if(void 0!==s){const t=Object(Pt.c)(s.type,s.array);r.setIndex(new C.a(t,1))}const o=t.data.attributes;for(const e in o){const n=o[e];let s;if(n.isInterleavedBufferAttribute){const e=i(t.data,n.data);s=new ls.a(e,n.itemSize,n.offset,n.normalized)}else{const t=Object(Pt.c)(n.type,n.array);s=new(n.isInstancedBufferAttribute?cX:C.a)(t,n.itemSize,n.normalized)}void 0!==n.name&&(s.name=n.name),void 0!==n.usage&&s.setUsage(n.usage),void 0!==n.updateRange&&(s.updateRange.offset=n.updateRange.offset,s.updateRange.count=n.updateRange.count),r.setAttribute(e,s)}const a=t.data.morphAttributes;if(a)for(const e in a){const n=a[e],s=[];for(let e=0,r=n.length;e<r;e++){const r=n[e];let o;if(r.isInterleavedBufferAttribute){const e=i(t.data,r.data);o=new ls.a(e,r.itemSize,r.offset,r.normalized)}else{const t=Object(Pt.c)(r.type,r.array);o=new C.a(t,r.itemSize,r.normalized)}void 0!==r.name&&(o.name=r.name),s.push(o)}r.morphAttributes[e]=s}t.data.morphTargetsRelative&&(r.morphTargetsRelative=!0);const l=t.data.groups||t.data.drawcalls||t.data.offsets;if(void 0!==l)for(let t=0,e=l.length;t!==e;++t){const e=l[t];r.addGroup(e.start,e.count,e.materialIndex)}const c=t.data.boundingSphere;if(void 0!==c){const t=new p.a;void 0!==c.center&&t.fromArray(c.center),r.boundingSphere=new Oq.a(t,c.radius)}return t.name&&(r.name=t.name),t.userData&&(r.userData=t.userData),r}}class zX extends S.a{constructor(t=1,e=8,n=0,i=2*Math.PI){super(),this.type=\\\\\\\"CircleGeometry\\\\\\\",this.parameters={radius:t,segments:e,thetaStart:n,thetaLength:i},e=Math.max(3,e);const r=[],s=[],o=[],a=[],l=new p.a,c=new d.a;s.push(0,0,0),o.push(0,0,1),a.push(.5,.5);for(let r=0,u=3;r<=e;r++,u+=3){const h=n+r/e*i;l.x=t*Math.cos(h),l.y=t*Math.sin(h),s.push(l.x,l.y,l.z),o.push(0,0,1),c.x=(s[u]/t+1)/2,c.y=(s[u+1]/t+1)/2,a.push(c.x,c.y)}for(let t=1;t<=e;t++)r.push(t,t+1,0);this.setIndex(r),this.setAttribute(\\\\\\\"position\\\\\\\",new C.c(s,3)),this.setAttribute(\\\\\\\"normal\\\\\\\",new C.c(o,3)),this.setAttribute(\\\\\\\"uv\\\\\\\",new C.c(a,2))}static fromJSON(t){return new zX(t.radius,t.segments,t.thetaStart,t.thetaLength)}}class UX extends Kz{constructor(t=1,e=0){const n=(1+Math.sqrt(5))/2,i=1/n;super([-1,-1,-1,-1,-1,1,-1,1,-1,-1,1,1,1,-1,-1,1,-1,1,1,1,-1,1,1,1,0,-i,-n,0,-i,n,0,i,-n,0,i,n,-i,-n,0,-i,n,0,i,-n,0,i,n,0,-n,0,-i,n,0,-i,-n,0,i,n,0,i],[3,11,7,3,7,15,3,15,13,7,19,17,7,17,6,7,6,15,17,4,8,17,8,10,17,10,6,8,0,16,8,16,2,8,2,10,0,12,1,0,1,18,0,18,16,6,10,2,6,2,13,6,13,15,2,16,18,2,18,3,2,3,13,18,1,9,18,9,11,18,11,3,4,14,12,4,12,0,4,0,8,11,9,5,11,5,19,11,19,7,19,5,14,19,14,4,19,4,17,1,12,14,1,14,5,1,5,9],t,e),this.type=\\\\\\\"DodecahedronGeometry\\\\\\\",this.parameters={radius:t,detail:e}}static fromJSON(t){return new UX(t.radius,t.detail)}}const GX=new p.a,VX=new p.a,HX=new p.a,jX=new Qr.a;class WX extends S.a{constructor(t=null,e=1){if(super(),this.type=\\\\\\\"EdgesGeometry\\\\\\\",this.parameters={geometry:t,thresholdAngle:e},null!==t){const n=4,i=Math.pow(10,n),r=Math.cos(Ln.a*e),s=t.getIndex(),o=t.getAttribute(\\\\\\\"position\\\\\\\"),a=s?s.count:o.count,l=[0,0,0],c=[\\\\\\\"a\\\\\\\",\\\\\\\"b\\\\\\\",\\\\\\\"c\\\\\\\"],u=new Array(3),h={},d=[];for(let t=0;t<a;t+=3){s?(l[0]=s.getX(t),l[1]=s.getX(t+1),l[2]=s.getX(t+2)):(l[0]=t,l[1]=t+1,l[2]=t+2);const{a:e,b:n,c:a}=jX;if(e.fromBufferAttribute(o,l[0]),n.fromBufferAttribute(o,l[1]),a.fromBufferAttribute(o,l[2]),jX.getNormal(HX),u[0]=`${Math.round(e.x*i)},${Math.round(e.y*i)},${Math.round(e.z*i)}`,u[1]=`${Math.round(n.x*i)},${Math.round(n.y*i)},${Math.round(n.z*i)}`,u[2]=`${Math.round(a.x*i)},${Math.round(a.y*i)},${Math.round(a.z*i)}`,u[0]!==u[1]&&u[1]!==u[2]&&u[2]!==u[0])for(let t=0;t<3;t++){const e=(t+1)%3,n=u[t],i=u[e],s=jX[c[t]],o=jX[c[e]],a=`${n}_${i}`,p=`${i}_${n}`;p in h&&h[p]?(HX.dot(h[p].normal)<=r&&(d.push(s.x,s.y,s.z),d.push(o.x,o.y,o.z)),h[p]=null):a in h||(h[a]={index0:l[t],index1:l[e],normal:HX.clone()})}}for(const t in h)if(h[t]){const{index0:e,index1:n}=h[t];GX.fromBufferAttribute(o,e),VX.fromBufferAttribute(o,n),d.push(GX.x,GX.y,GX.z),d.push(VX.x,VX.y,VX.z)}this.setAttribute(\\\\\\\"position\\\\\\\",new C.c(d,3))}}}var qX=n(79),XX=n(53);class YX extends S.a{constructor(t=new RX.a([new d.a(.5,.5),new d.a(-.5,.5),new d.a(-.5,-.5),new d.a(.5,-.5)]),e={}){super(),this.type=\\\\\\\"ExtrudeGeometry\\\\\\\",this.parameters={shapes:t,options:e},t=Array.isArray(t)?t:[t];const n=this,i=[],r=[];for(let e=0,n=t.length;e<n;e++){s(t[e])}function s(t){const s=[],o=void 0!==e.curveSegments?e.curveSegments:12,a=void 0!==e.steps?e.steps:1;let l=void 0!==e.depth?e.depth:1,c=void 0===e.bevelEnabled||e.bevelEnabled,u=void 0!==e.bevelThickness?e.bevelThickness:.2,h=void 0!==e.bevelSize?e.bevelSize:u-.1,_=void 0!==e.bevelOffset?e.bevelOffset:0,m=void 0!==e.bevelSegments?e.bevelSegments:3;const f=e.extrudePath,g=void 0!==e.UVGenerator?e.UVGenerator:$X;void 0!==e.amount&&(console.warn(\\\\\\\"THREE.ExtrudeBufferGeometry: amount has been renamed to depth.\\\\\\\"),l=e.amount);let v,y,x,b,w,T=!1;f&&(v=f.getSpacedPoints(a),T=!0,c=!1,y=f.computeFrenetFrames(a,!1),x=new p.a,b=new p.a,w=new p.a),c||(m=0,u=0,h=0,_=0);const A=t.extractPoints(o);let E=A.shape;const M=A.holes;if(!XX.a.isClockWise(E)){E=E.reverse();for(let t=0,e=M.length;t<e;t++){const e=M[t];XX.a.isClockWise(e)&&(M[t]=e.reverse())}}const S=XX.a.triangulateShape(E,M),C=E;for(let t=0,e=M.length;t<e;t++){const e=M[t];E=E.concat(e)}function N(t,e,n){return e||console.error(\\\\\\\"THREE.ExtrudeGeometry: vec does not exist\\\\\\\"),e.clone().multiplyScalar(n).add(t)}const L=E.length,O=S.length;function R(t,e,n){let i,r,s;const o=t.x-e.x,a=t.y-e.y,l=n.x-t.x,c=n.y-t.y,u=o*o+a*a,h=o*c-a*l;if(Math.abs(h)>Number.EPSILON){const h=Math.sqrt(u),p=Math.sqrt(l*l+c*c),_=e.x-a/h,m=e.y+o/h,f=((n.x-c/p-_)*c-(n.y+l/p-m)*l)/(o*c-a*l);i=_+o*f-t.x,r=m+a*f-t.y;const g=i*i+r*r;if(g<=2)return new d.a(i,r);s=Math.sqrt(g/2)}else{let t=!1;o>Number.EPSILON?l>Number.EPSILON&&(t=!0):o<-Number.EPSILON?l<-Number.EPSILON&&(t=!0):Math.sign(a)===Math.sign(c)&&(t=!0),t?(i=-a,r=o,s=Math.sqrt(u)):(i=o,r=a,s=Math.sqrt(u/2))}return new d.a(i/s,r/s)}const P=[];for(let t=0,e=C.length,n=e-1,i=t+1;t<e;t++,n++,i++)n===e&&(n=0),i===e&&(i=0),P[t]=R(C[t],C[n],C[i]);const I=[];let F,D=P.concat();for(let t=0,e=M.length;t<e;t++){const e=M[t];F=[];for(let t=0,n=e.length,i=n-1,r=t+1;t<n;t++,i++,r++)i===n&&(i=0),r===n&&(r=0),F[t]=R(e[t],e[i],e[r]);I.push(F),D=D.concat(F)}for(let t=0;t<m;t++){const e=t/m,n=u*Math.cos(e*Math.PI/2),i=h*Math.sin(e*Math.PI/2)+_;for(let t=0,e=C.length;t<e;t++){const e=N(C[t],P[t],i);z(e.x,e.y,-n)}for(let t=0,e=M.length;t<e;t++){const e=M[t];F=I[t];for(let t=0,r=e.length;t<r;t++){const r=N(e[t],F[t],i);z(r.x,r.y,-n)}}}const k=h+_;for(let t=0;t<L;t++){const e=c?N(E[t],D[t],k):E[t];T?(b.copy(y.normals[0]).multiplyScalar(e.x),x.copy(y.binormals[0]).multiplyScalar(e.y),w.copy(v[0]).add(b).add(x),z(w.x,w.y,w.z)):z(e.x,e.y,0)}for(let t=1;t<=a;t++)for(let e=0;e<L;e++){const n=c?N(E[e],D[e],k):E[e];T?(b.copy(y.normals[t]).multiplyScalar(n.x),x.copy(y.binormals[t]).multiplyScalar(n.y),w.copy(v[t]).add(b).add(x),z(w.x,w.y,w.z)):z(n.x,n.y,l/a*t)}for(let t=m-1;t>=0;t--){const e=t/m,n=u*Math.cos(e*Math.PI/2),i=h*Math.sin(e*Math.PI/2)+_;for(let t=0,e=C.length;t<e;t++){const e=N(C[t],P[t],i);z(e.x,e.y,l+n)}for(let t=0,e=M.length;t<e;t++){const e=M[t];F=I[t];for(let t=0,r=e.length;t<r;t++){const r=N(e[t],F[t],i);T?z(r.x,r.y+v[a-1].y,v[a-1].x+n):z(r.x,r.y,l+n)}}}function B(t,e){let n=t.length;for(;--n>=0;){const i=n;let r=n-1;r<0&&(r=t.length-1);for(let t=0,n=a+2*m;t<n;t++){const n=L*t,s=L*(t+1);G(e+i+n,e+r+n,e+r+s,e+i+s)}}}function z(t,e,n){s.push(t),s.push(e),s.push(n)}function U(t,e,r){V(t),V(e),V(r);const s=i.length/3,o=g.generateTopUV(n,i,s-3,s-2,s-1);H(o[0]),H(o[1]),H(o[2])}function G(t,e,r,s){V(t),V(e),V(s),V(e),V(r),V(s);const o=i.length/3,a=g.generateSideWallUV(n,i,o-6,o-3,o-2,o-1);H(a[0]),H(a[1]),H(a[3]),H(a[1]),H(a[2]),H(a[3])}function V(t){i.push(s[3*t+0]),i.push(s[3*t+1]),i.push(s[3*t+2])}function H(t){r.push(t.x),r.push(t.y)}!function(){const t=i.length/3;if(c){let t=0,e=L*t;for(let t=0;t<O;t++){const n=S[t];U(n[2]+e,n[1]+e,n[0]+e)}t=a+2*m,e=L*t;for(let t=0;t<O;t++){const n=S[t];U(n[0]+e,n[1]+e,n[2]+e)}}else{for(let t=0;t<O;t++){const e=S[t];U(e[2],e[1],e[0])}for(let t=0;t<O;t++){const e=S[t];U(e[0]+L*a,e[1]+L*a,e[2]+L*a)}}n.addGroup(t,i.length/3-t,0)}(),function(){const t=i.length/3;let e=0;B(C,e),e+=C.length;for(let t=0,n=M.length;t<n;t++){const n=M[t];B(n,e),e+=n.length}n.addGroup(t,i.length/3-t,1)}()}this.setAttribute(\\\\\\\"position\\\\\\\",new C.c(i,3)),this.setAttribute(\\\\\\\"uv\\\\\\\",new C.c(r,2)),this.computeVertexNormals()}toJSON(){const t=super.toJSON();return function(t,e,n){if(n.shapes=[],Array.isArray(t))for(let e=0,i=t.length;e<i;e++){const i=t[e];n.shapes.push(i.uuid)}else n.shapes.push(t.uuid);void 0!==e.extrudePath&&(n.options.extrudePath=e.extrudePath.toJSON());return n}(this.parameters.shapes,this.parameters.options,t)}static fromJSON(t,e){const n=[];for(let i=0,r=t.shapes.length;i<r;i++){const r=e[t.shapes[i]];n.push(r)}const i=t.options.extrudePath;return void 0!==i&&(t.options.extrudePath=(new qX[i.type]).fromJSON(i)),new YX(n,t.options)}}const $X={generateTopUV:function(t,e,n,i,r){const s=e[3*n],o=e[3*n+1],a=e[3*i],l=e[3*i+1],c=e[3*r],u=e[3*r+1];return[new d.a(s,o),new d.a(a,l),new d.a(c,u)]},generateSideWallUV:function(t,e,n,i,r,s){const o=e[3*n],a=e[3*n+1],l=e[3*n+2],c=e[3*i],u=e[3*i+1],h=e[3*i+2],p=e[3*r],_=e[3*r+1],m=e[3*r+2],f=e[3*s],g=e[3*s+1],v=e[3*s+2];return Math.abs(a-u)<Math.abs(o-c)?[new d.a(o,1-l),new d.a(c,1-h),new d.a(p,1-m),new d.a(f,1-v)]:[new d.a(a,1-l),new d.a(u,1-h),new d.a(_,1-m),new d.a(g,1-v)]}};class JX extends Kz{constructor(t=1,e=0){const n=(1+Math.sqrt(5))/2;super([-1,n,0,1,n,0,-1,-n,0,1,-n,0,0,-1,n,0,1,n,0,-1,-n,0,1,-n,n,0,-1,n,0,1,-n,0,-1,-n,0,1],[0,11,5,0,5,1,0,1,7,0,7,10,0,10,11,1,5,9,5,11,4,11,10,2,10,7,6,7,1,8,3,9,4,3,4,2,3,2,6,3,6,8,3,8,9,4,9,5,2,4,11,6,2,10,8,6,7,9,8,1],t,e),this.type=\\\\\\\"IcosahedronGeometry\\\\\\\",this.parameters={radius:t,detail:e}}static fromJSON(t){return new JX(t.radius,t.detail)}}class ZX extends S.a{constructor(t=[new d.a(0,.5),new d.a(.5,0),new d.a(0,-.5)],e=12,n=0,i=2*Math.PI){super(),this.type=\\\\\\\"LatheGeometry\\\\\\\",this.parameters={points:t,segments:e,phiStart:n,phiLength:i},e=Math.floor(e),i=Ln.d(i,0,2*Math.PI);const r=[],s=[],o=[],a=1/e,l=new p.a,c=new d.a;for(let r=0;r<=e;r++){const u=n+r*a*i,h=Math.sin(u),d=Math.cos(u);for(let n=0;n<=t.length-1;n++)l.x=t[n].x*h,l.y=t[n].y,l.z=t[n].x*d,s.push(l.x,l.y,l.z),c.x=r/e,c.y=n/(t.length-1),o.push(c.x,c.y)}for(let n=0;n<e;n++)for(let e=0;e<t.length-1;e++){const i=e+n*t.length,s=i,o=i+t.length,a=i+t.length+1,l=i+1;r.push(s,o,l),r.push(o,a,l)}if(this.setIndex(r),this.setAttribute(\\\\\\\"position\\\\\\\",new C.c(s,3)),this.setAttribute(\\\\\\\"uv\\\\\\\",new C.c(o,2)),this.computeVertexNormals(),i===2*Math.PI){const n=this.attributes.normal.array,i=new p.a,r=new p.a,s=new p.a,o=e*t.length*3;for(let e=0,a=0;e<t.length;e++,a+=3)i.x=n[a+0],i.y=n[a+1],i.z=n[a+2],r.x=n[o+a+0],r.y=n[o+a+1],r.z=n[o+a+2],s.addVectors(i,r).normalize(),n[a+0]=n[o+a+0]=s.x,n[a+1]=n[o+a+1]=s.y,n[a+2]=n[o+a+2]=s.z}}static fromJSON(t){return new ZX(t.points,t.segments,t.phiStart,t.phiLength)}}class QX extends S.a{constructor(t=.5,e=1,n=8,i=1,r=0,s=2*Math.PI){super(),this.type=\\\\\\\"RingGeometry\\\\\\\",this.parameters={innerRadius:t,outerRadius:e,thetaSegments:n,phiSegments:i,thetaStart:r,thetaLength:s},n=Math.max(3,n);const o=[],a=[],l=[],c=[];let u=t;const h=(e-t)/(i=Math.max(1,i)),_=new p.a,m=new d.a;for(let t=0;t<=i;t++){for(let t=0;t<=n;t++){const i=r+t/n*s;_.x=u*Math.cos(i),_.y=u*Math.sin(i),a.push(_.x,_.y,_.z),l.push(0,0,1),m.x=(_.x/e+1)/2,m.y=(_.y/e+1)/2,c.push(m.x,m.y)}u+=h}for(let t=0;t<i;t++){const e=t*(n+1);for(let t=0;t<n;t++){const i=t+e,r=i,s=i+n+1,a=i+n+2,l=i+1;o.push(r,s,l),o.push(s,a,l)}}this.setIndex(o),this.setAttribute(\\\\\\\"position\\\\\\\",new C.c(a,3)),this.setAttribute(\\\\\\\"normal\\\\\\\",new C.c(l,3)),this.setAttribute(\\\\\\\"uv\\\\\\\",new C.c(c,2))}static fromJSON(t){return new QX(t.innerRadius,t.outerRadius,t.thetaSegments,t.phiSegments,t.thetaStart,t.thetaLength)}}class KX extends S.a{constructor(t=new RX.a([new d.a(0,.5),new d.a(-.5,-.5),new d.a(.5,-.5)]),e=12){super(),this.type=\\\\\\\"ShapeGeometry\\\\\\\",this.parameters={shapes:t,curveSegments:e};const n=[],i=[],r=[],s=[];let o=0,a=0;if(!1===Array.isArray(t))l(t);else for(let e=0;e<t.length;e++)l(t[e]),this.addGroup(o,a,e),o+=a,a=0;function l(t){const o=i.length/3,l=t.extractPoints(e);let c=l.shape;const u=l.holes;!1===XX.a.isClockWise(c)&&(c=c.reverse());for(let t=0,e=u.length;t<e;t++){const e=u[t];!0===XX.a.isClockWise(e)&&(u[t]=e.reverse())}const h=XX.a.triangulateShape(c,u);for(let t=0,e=u.length;t<e;t++){const e=u[t];c=c.concat(e)}for(let t=0,e=c.length;t<e;t++){const e=c[t];i.push(e.x,e.y,0),r.push(0,0,1),s.push(e.x,e.y)}for(let t=0,e=h.length;t<e;t++){const e=h[t],i=e[0]+o,r=e[1]+o,s=e[2]+o;n.push(i,r,s),a+=3}}this.setIndex(n),this.setAttribute(\\\\\\\"position\\\\\\\",new C.c(i,3)),this.setAttribute(\\\\\\\"normal\\\\\\\",new C.c(r,3)),this.setAttribute(\\\\\\\"uv\\\\\\\",new C.c(s,2))}toJSON(){const t=super.toJSON();return function(t,e){if(e.shapes=[],Array.isArray(t))for(let n=0,i=t.length;n<i;n++){const i=t[n];e.shapes.push(i.uuid)}else e.shapes.push(t.uuid);return e}(this.parameters.shapes,t)}static fromJSON(t,e){const n=[];for(let i=0,r=t.shapes.length;i<r;i++){const r=e[t.shapes[i]];n.push(r)}return new KX(n,t.curveSegments)}}class tY extends Kz{constructor(t=1,e=0){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),this.type=\\\\\\\"TetrahedronGeometry\\\\\\\",this.parameters={radius:t,detail:e}}static fromJSON(t){return new tY(t.radius,t.detail)}}class eY extends S.a{constructor(t=1,e=.4,n=8,i=6,r=2*Math.PI){super(),this.type=\\\\\\\"TorusGeometry\\\\\\\",this.parameters={radius:t,tube:e,radialSegments:n,tubularSegments:i,arc:r},n=Math.floor(n),i=Math.floor(i);const s=[],o=[],a=[],l=[],c=new p.a,u=new p.a,h=new p.a;for(let s=0;s<=n;s++)for(let d=0;d<=i;d++){const p=d/i*r,_=s/n*Math.PI*2;u.x=(t+e*Math.cos(_))*Math.cos(p),u.y=(t+e*Math.cos(_))*Math.sin(p),u.z=e*Math.sin(_),o.push(u.x,u.y,u.z),c.x=t*Math.cos(p),c.y=t*Math.sin(p),h.subVectors(u,c).normalize(),a.push(h.x,h.y,h.z),l.push(d/i),l.push(s/n)}for(let t=1;t<=n;t++)for(let e=1;e<=i;e++){const n=(i+1)*t+e-1,r=(i+1)*(t-1)+e-1,o=(i+1)*(t-1)+e,a=(i+1)*t+e;s.push(n,r,a),s.push(r,o,a)}this.setIndex(s),this.setAttribute(\\\\\\\"position\\\\\\\",new C.c(o,3)),this.setAttribute(\\\\\\\"normal\\\\\\\",new C.c(a,3)),this.setAttribute(\\\\\\\"uv\\\\\\\",new C.c(l,2))}static fromJSON(t){return new eY(t.radius,t.tube,t.radialSegments,t.tubularSegments,t.arc)}}class nY extends S.a{constructor(t=1,e=.4,n=64,i=8,r=2,s=3){super(),this.type=\\\\\\\"TorusKnotGeometry\\\\\\\",this.parameters={radius:t,tube:e,tubularSegments:n,radialSegments:i,p:r,q:s},n=Math.floor(n),i=Math.floor(i);const o=[],a=[],l=[],c=[],u=new p.a,h=new p.a,d=new p.a,_=new p.a,m=new p.a,f=new p.a,g=new p.a;for(let o=0;o<=n;++o){const p=o/n*r*Math.PI*2;v(p,r,s,t,d),v(p+.01,r,s,t,_),f.subVectors(_,d),g.addVectors(_,d),m.crossVectors(f,g),g.crossVectors(m,f),m.normalize(),g.normalize();for(let t=0;t<=i;++t){const r=t/i*Math.PI*2,s=-e*Math.cos(r),p=e*Math.sin(r);u.x=d.x+(s*g.x+p*m.x),u.y=d.y+(s*g.y+p*m.y),u.z=d.z+(s*g.z+p*m.z),a.push(u.x,u.y,u.z),h.subVectors(u,d).normalize(),l.push(h.x,h.y,h.z),c.push(o/n),c.push(t/i)}}for(let t=1;t<=n;t++)for(let e=1;e<=i;e++){const n=(i+1)*(t-1)+(e-1),r=(i+1)*t+(e-1),s=(i+1)*t+e,a=(i+1)*(t-1)+e;o.push(n,r,a),o.push(r,s,a)}function v(t,e,n,i,r){const s=Math.cos(t),o=Math.sin(t),a=n/e*t,l=Math.cos(a);r.x=i*(2+l)*.5*s,r.y=i*(2+l)*o*.5,r.z=i*Math.sin(a)*.5}this.setIndex(o),this.setAttribute(\\\\\\\"position\\\\\\\",new C.c(a,3)),this.setAttribute(\\\\\\\"normal\\\\\\\",new C.c(l,3)),this.setAttribute(\\\\\\\"uv\\\\\\\",new C.c(c,2))}static fromJSON(t){return new nY(t.radius,t.tube,t.tubularSegments,t.radialSegments,t.p,t.q)}}var iY=n(92);class rY extends S.a{constructor(t=new iY.a(new p.a(-1,-1,0),new p.a(-1,1,0),new p.a(1,1,0)),e=64,n=1,i=8,r=!1){super(),this.type=\\\\\\\"TubeGeometry\\\\\\\",this.parameters={path:t,tubularSegments:e,radius:n,radialSegments:i,closed:r};const s=t.computeFrenetFrames(e,r);this.tangents=s.tangents,this.normals=s.normals,this.binormals=s.binormals;const o=new p.a,a=new p.a,l=new d.a;let c=new p.a;const u=[],h=[],_=[],m=[];function f(r){c=t.getPointAt(r/e,c);const l=s.normals[r],d=s.binormals[r];for(let t=0;t<=i;t++){const e=t/i*Math.PI*2,r=Math.sin(e),s=-Math.cos(e);a.x=s*l.x+r*d.x,a.y=s*l.y+r*d.y,a.z=s*l.z+r*d.z,a.normalize(),h.push(a.x,a.y,a.z),o.x=c.x+n*a.x,o.y=c.y+n*a.y,o.z=c.z+n*a.z,u.push(o.x,o.y,o.z)}}!function(){for(let t=0;t<e;t++)f(t);f(!1===r?e:0),function(){for(let t=0;t<=e;t++)for(let n=0;n<=i;n++)l.x=t/e,l.y=n/i,_.push(l.x,l.y)}(),function(){for(let t=1;t<=e;t++)for(let e=1;e<=i;e++){const n=(i+1)*(t-1)+(e-1),r=(i+1)*t+(e-1),s=(i+1)*t+e,o=(i+1)*(t-1)+e;m.push(n,r,o),m.push(r,s,o)}}()}(),this.setIndex(m),this.setAttribute(\\\\\\\"position\\\\\\\",new C.c(u,3)),this.setAttribute(\\\\\\\"normal\\\\\\\",new C.c(h,3)),this.setAttribute(\\\\\\\"uv\\\\\\\",new C.c(_,2))}toJSON(){const t=super.toJSON();return t.path=this.parameters.path.toJSON(),t}static fromJSON(t){return new rY((new qX[t.path.type]).fromJSON(t.path),t.tubularSegments,t.radius,t.radialSegments,t.closed)}}class sY extends S.a{constructor(t=null){if(super(),this.type=\\\\\\\"WireframeGeometry\\\\\\\",this.parameters={geometry:t},null!==t){const e=[],n=new Set,i=new p.a,r=new p.a;if(null!==t.index){const s=t.attributes.position,o=t.index;let a=t.groups;0===a.length&&(a=[{start:0,count:o.count,materialIndex:0}]);for(let t=0,l=a.length;t<l;++t){const l=a[t],c=l.start;for(let t=c,a=c+l.count;t<a;t+=3)for(let a=0;a<3;a++){const l=o.getX(t+a),c=o.getX(t+(a+1)%3);i.fromBufferAttribute(s,l),r.fromBufferAttribute(s,c),!0===oY(i,r,n)&&(e.push(i.x,i.y,i.z),e.push(r.x,r.y,r.z))}}}else{const s=t.attributes.position;for(let t=0,o=s.count/3;t<o;t++)for(let o=0;o<3;o++){const a=3*t+o,l=3*t+(o+1)%3;i.fromBufferAttribute(s,a),r.fromBufferAttribute(s,l),!0===oY(i,r,n)&&(e.push(i.x,i.y,i.z),e.push(r.x,r.y,r.z))}}this.setAttribute(\\\\\\\"position\\\\\\\",new C.c(e,3))}}}function oY(t,e,n){const i=`${t.x},${t.y},${t.z}-${e.x},${e.y},${e.z}`,r=`${e.x},${e.y},${e.z}-${t.x},${t.y},${t.z}`;return!0!==n.has(i)&&!0!==n.has(r)&&(n.add(i,r),!0)}class aY extends kf.a{constructor(t){super(t)}load(t,e,n,i){const r=this,s=\\\\\\\"\\\\\\\"===this.path?DX.a.extractUrlBase(t):this.path;this.resourcePath=this.resourcePath||s;const o=new Df.a(this.manager);o.setPath(this.path),o.setRequestHeader(this.requestHeader),o.setWithCredentials(this.withCredentials),o.load(t,(function(n){let s=null;try{s=JSON.parse(n)}catch(e){return void 0!==i&&i(e),void console.error(\\\\\\\"THREE:ObjectLoader: Can't parse \\\\\\\"+t+\\\\\\\".\\\\\\\",e.message)}const o=s.metadata;void 0!==o&&void 0!==o.type&&\\\\\\\"geometry\\\\\\\"!==o.type.toLowerCase()?r.parse(s,e):console.error(\\\\\\\"THREE.ObjectLoader: Can't load \\\\\\\"+t)}),n,i)}async loadAsync(t,e){const n=\\\\\\\"\\\\\\\"===this.path?DX.a.extractUrlBase(t):this.path;this.resourcePath=this.resourcePath||n;const i=new Df.a(this.manager);i.setPath(this.path),i.setRequestHeader(this.requestHeader),i.setWithCredentials(this.withCredentials);const r=await i.loadAsync(t,e),s=JSON.parse(r),o=s.metadata;if(void 0===o||void 0===o.type||\\\\\\\"geometry\\\\\\\"===o.type.toLowerCase())throw new Error(\\\\\\\"THREE.ObjectLoader: Can't load \\\\\\\"+t);return await this.parseAsync(s)}parse(t,e){const n=this.parseAnimations(t.animations),i=this.parseShapes(t.shapes),r=this.parseGeometries(t.geometries,i),s=this.parseImages(t.images,(function(){void 0!==e&&e(l)})),o=this.parseTextures(t.textures,s),a=this.parseMaterials(t.materials,o),l=this.parseObject(t.object,r,a,o,n),c=this.parseSkeletons(t.skeletons,l);if(this.bindSkeletons(l,c),void 0!==e){let t=!1;for(const e in s)if(s[e]instanceof HTMLImageElement){t=!0;break}!1===t&&e(l)}return l}async parseAsync(t){const e=this.parseAnimations(t.animations),n=this.parseShapes(t.shapes),i=this.parseGeometries(t.geometries,n),r=await this.parseImagesAsync(t.images),s=this.parseTextures(t.textures,r),o=this.parseMaterials(t.materials,s),a=this.parseObject(t.object,i,o,s,e),l=this.parseSkeletons(t.skeletons,a);return this.bindSkeletons(a,l),a}parseShapes(t){const e={};if(void 0!==t)for(let n=0,i=t.length;n<i;n++){const i=(new RX.a).fromJSON(t[n]);e[i.uuid]=i}return e}parseSkeletons(t,e){const n={},i={};if(e.traverse((function(t){t.isBone&&(i[t.uuid]=t)})),void 0!==t)for(let e=0,r=t.length;e<r;e++){const r=(new OX.a).fromJSON(t[e],i);n[r.uuid]=r}return n}parseGeometries(t,e){const n={};if(void 0!==t){const i=new BX;for(let s=0,o=t.length;s<o;s++){let o;const a=t[s];switch(a.type){case\\\\\\\"BufferGeometry\\\\\\\":case\\\\\\\"InstancedBufferGeometry\\\\\\\":o=i.parse(a);break;case\\\\\\\"Geometry\\\\\\\":console.error(\\\\\\\"THREE.ObjectLoader: The legacy Geometry type is no longer supported.\\\\\\\");break;default:a.type in r?o=r[a.type].fromJSON(a,e):console.warn(`THREE.ObjectLoader: Unsupported geometry type \\\\\\\"${a.type}\\\\\\\"`)}o.uuid=a.uuid,void 0!==a.name&&(o.name=a.name),!0===o.isBufferGeometry&&void 0!==a.userData&&(o.userData=a.userData),n[a.uuid]=o}}return n}parseMaterials(t,e){const n={},i={};if(void 0!==t){const r=new qf;r.setTextures(e);for(let e=0,s=t.length;e<s;e++){const s=t[e];if(\\\\\\\"MultiMaterial\\\\\\\"===s.type){const t=[];for(let e=0;e<s.materials.length;e++){const i=s.materials[e];void 0===n[i.uuid]&&(n[i.uuid]=r.parse(i)),t.push(n[i.uuid])}i[s.uuid]=t}else void 0===n[s.uuid]&&(n[s.uuid]=r.parse(s)),i[s.uuid]=n[s.uuid]}}return i}parseAnimations(t){const e={};if(void 0!==t)for(let n=0;n<t.length;n++){const i=t[n],r=kW.a.parse(i);e[r.uuid]=r}return e}parseImages(t,e){const n=this,i={};let r;function s(t){if(\\\\\\\"string\\\\\\\"==typeof t){const e=t;return function(t){return n.manager.itemStart(t),r.load(t,(function(){n.manager.itemEnd(t)}),void 0,(function(){n.manager.itemError(t),n.manager.itemEnd(t)}))}(/^(\\\\/\\\\/)|([a-z]+:(\\\\/\\\\/)?)/i.test(e)?e:n.resourcePath+e)}return t.data?{data:Object(Pt.c)(t.type,t.data),width:t.width,height:t.height}:null}if(void 0!==t&&t.length>0){const n=new Vg.b(e);r=new FX.a(n),r.setCrossOrigin(this.crossOrigin);for(let e=0,n=t.length;e<n;e++){const n=t[e],r=n.url;if(Array.isArray(r)){i[n.uuid]=[];for(let t=0,e=r.length;t<e;t++){const e=s(r[t]);null!==e&&(e instanceof HTMLImageElement?i[n.uuid].push(e):i[n.uuid].push(new mo.a(e.data,e.width,e.height)))}}else{const t=s(n.url);null!==t&&(i[n.uuid]=t)}}}return i}async parseImagesAsync(t){const e=this,n={};let i;async function r(t){if(\\\\\\\"string\\\\\\\"==typeof t){const n=t,r=/^(\\\\/\\\\/)|([a-z]+:(\\\\/\\\\/)?)/i.test(n)?n:e.resourcePath+n;return await i.loadAsync(r)}return t.data?{data:Object(Pt.c)(t.type,t.data),width:t.width,height:t.height}:null}if(void 0!==t&&t.length>0){i=new FX.a(this.manager),i.setCrossOrigin(this.crossOrigin);for(let e=0,i=t.length;e<i;e++){const i=t[e],s=i.url;if(Array.isArray(s)){n[i.uuid]=[];for(let t=0,e=s.length;t<e;t++){const e=s[t],o=await r(e);null!==o&&(o instanceof HTMLImageElement?n[i.uuid].push(o):n[i.uuid].push(new mo.a(o.data,o.width,o.height)))}}else{const t=await r(i.url);null!==t&&(n[i.uuid]=t)}}}return n}parseTextures(t,e){function n(t,e){return\\\\\\\"number\\\\\\\"==typeof t?t:(console.warn(\\\\\\\"THREE.ObjectLoader.parseTexture: Constant should be in numeric form.\\\\\\\",t),e[t])}const i={};if(void 0!==t)for(let r=0,s=t.length;r<s;r++){const s=t[r];let o;void 0===s.image&&console.warn('THREE.ObjectLoader: No \\\\\\\"image\\\\\\\" specified for',s.uuid),void 0===e[s.image]&&console.warn(\\\\\\\"THREE.ObjectLoader: Undefined image\\\\\\\",s.image);const a=e[s.image];Array.isArray(a)?(o=new nt(a),6===a.length&&(o.needsUpdate=!0)):(o=a&&a.data?new mo.a(a.data,a.width,a.height):new J.a(a),a&&(o.needsUpdate=!0)),o.uuid=s.uuid,void 0!==s.name&&(o.name=s.name),void 0!==s.mapping&&(o.mapping=n(s.mapping,lY)),void 0!==s.offset&&o.offset.fromArray(s.offset),void 0!==s.repeat&&o.repeat.fromArray(s.repeat),void 0!==s.center&&o.center.fromArray(s.center),void 0!==s.rotation&&(o.rotation=s.rotation),void 0!==s.wrap&&(o.wrapS=n(s.wrap[0],cY),o.wrapT=n(s.wrap[1],cY)),void 0!==s.format&&(o.format=s.format),void 0!==s.type&&(o.type=s.type),void 0!==s.encoding&&(o.encoding=s.encoding),void 0!==s.minFilter&&(o.minFilter=n(s.minFilter,uY)),void 0!==s.magFilter&&(o.magFilter=n(s.magFilter,uY)),void 0!==s.anisotropy&&(o.anisotropy=s.anisotropy),void 0!==s.flipY&&(o.flipY=s.flipY),void 0!==s.premultiplyAlpha&&(o.premultiplyAlpha=s.premultiplyAlpha),void 0!==s.unpackAlignment&&(o.unpackAlignment=s.unpackAlignment),i[s.uuid]=o}return i}parseObject(t,e,n,i,r){let s,o,a;function l(t){return void 0===e[t]&&console.warn(\\\\\\\"THREE.ObjectLoader: Undefined geometry\\\\\\\",t),e[t]}function c(t){if(void 0!==t){if(Array.isArray(t)){const e=[];for(let i=0,r=t.length;i<r;i++){const r=t[i];void 0===n[r]&&console.warn(\\\\\\\"THREE.ObjectLoader: Undefined material\\\\\\\",r),e.push(n[r])}return e}return void 0===n[t]&&console.warn(\\\\\\\"THREE.ObjectLoader: Undefined material\\\\\\\",t),n[t]}}function u(t){return void 0===i[t]&&console.warn(\\\\\\\"THREE.ObjectLoader: Undefined texture\\\\\\\",t),i[t]}switch(t.type){case\\\\\\\"Scene\\\\\\\":s=new fr,void 0!==t.background&&(Number.isInteger(t.background)?s.background=new D.a(t.background):s.background=u(t.background)),void 0!==t.environment&&(s.environment=u(t.environment)),void 0!==t.fog&&(\\\\\\\"Fog\\\\\\\"===t.fog.type?s.fog=new xa(t.fog.color,t.fog.near,t.fog.far):\\\\\\\"FogExp2\\\\\\\"===t.fog.type&&(s.fog=new ba(t.fog.color,t.fog.density)));break;case\\\\\\\"PerspectiveCamera\\\\\\\":s=new K.a(t.fov,t.aspect,t.near,t.far),void 0!==t.focus&&(s.focus=t.focus),void 0!==t.zoom&&(s.zoom=t.zoom),void 0!==t.filmGauge&&(s.filmGauge=t.filmGauge),void 0!==t.filmOffset&&(s.filmOffset=t.filmOffset),void 0!==t.view&&(s.view=Object.assign({},t.view));break;case\\\\\\\"OrthographicCamera\\\\\\\":s=new st.a(t.left,t.right,t.top,t.bottom,t.near,t.far),void 0!==t.zoom&&(s.zoom=t.zoom),void 0!==t.view&&(s.view=Object.assign({},t.view));break;case\\\\\\\"AmbientLight\\\\\\\":s=new uz.a(t.color,t.intensity);break;case\\\\\\\"DirectionalLight\\\\\\\":s=new Gz.a(t.color,t.intensity);break;case\\\\\\\"PointLight\\\\\\\":s=new sU.a(t.color,t.intensity,t.distance,t.decay);break;case\\\\\\\"RectAreaLight\\\\\\\":s=new vz(t.color,t.intensity,t.width,t.height);break;case\\\\\\\"SpotLight\\\\\\\":s=new hU.a(t.color,t.intensity,t.distance,t.angle,t.penumbra,t.decay);break;case\\\\\\\"HemisphereLight\\\\\\\":s=new Qz(t.color,t.groundColor,t.intensity);break;case\\\\\\\"LightProbe\\\\\\\":s=(new IX).fromJSON(t);break;case\\\\\\\"SkinnedMesh\\\\\\\":o=l(t.geometry),a=c(t.material),s=new mr.a(o,a),void 0!==t.bindMode&&(s.bindMode=t.bindMode),void 0!==t.bindMatrix&&s.bindMatrix.fromArray(t.bindMatrix),void 0!==t.skeleton&&(s.skeleton=t.skeleton);break;case\\\\\\\"Mesh\\\\\\\":o=l(t.geometry),a=c(t.material),s=new k.a(o,a);break;case\\\\\\\"InstancedMesh\\\\\\\":o=l(t.geometry),a=c(t.material);const e=t.count,n=t.instanceMatrix,i=t.instanceColor;s=new _X(o,a,e),s.instanceMatrix=new cX(new Float32Array(n.array),16),void 0!==i&&(s.instanceColor=new cX(new Float32Array(i.array),i.itemSize));break;case\\\\\\\"LOD\\\\\\\":s=new Mr;break;case\\\\\\\"Line\\\\\\\":s=new Pz.a(l(t.geometry),c(t.material));break;case\\\\\\\"LineLoop\\\\\\\":s=new LX.a(l(t.geometry),c(t.material));break;case\\\\\\\"LineSegments\\\\\\\":s=new Tr.a(l(t.geometry),c(t.material));break;case\\\\\\\"PointCloud\\\\\\\":case\\\\\\\"Points\\\\\\\":s=new gr.a(l(t.geometry),c(t.material));break;case\\\\\\\"Sprite\\\\\\\":s=new CX(c(t.material));break;case\\\\\\\"Group\\\\\\\":s=new In.a;break;case\\\\\\\"Bone\\\\\\\":s=new vr.a;break;default:s=new Q.a}if(s.uuid=t.uuid,void 0!==t.name&&(s.name=t.name),void 0!==t.matrix?(s.matrix.fromArray(t.matrix),void 0!==t.matrixAutoUpdate&&(s.matrixAutoUpdate=t.matrixAutoUpdate),s.matrixAutoUpdate&&s.matrix.decompose(s.position,s.quaternion,s.scale)):(void 0!==t.position&&s.position.fromArray(t.position),void 0!==t.rotation&&s.rotation.fromArray(t.rotation),void 0!==t.quaternion&&s.quaternion.fromArray(t.quaternion),void 0!==t.scale&&s.scale.fromArray(t.scale)),void 0!==t.castShadow&&(s.castShadow=t.castShadow),void 0!==t.receiveShadow&&(s.receiveShadow=t.receiveShadow),t.shadow&&(void 0!==t.shadow.bias&&(s.shadow.bias=t.shadow.bias),void 0!==t.shadow.normalBias&&(s.shadow.normalBias=t.shadow.normalBias),void 0!==t.shadow.radius&&(s.shadow.radius=t.shadow.radius),void 0!==t.shadow.mapSize&&s.shadow.mapSize.fromArray(t.shadow.mapSize),void 0!==t.shadow.camera&&(s.shadow.camera=this.parseObject(t.shadow.camera))),void 0!==t.visible&&(s.visible=t.visible),void 0!==t.frustumCulled&&(s.frustumCulled=t.frustumCulled),void 0!==t.renderOrder&&(s.renderOrder=t.renderOrder),void 0!==t.userData&&(s.userData=t.userData),void 0!==t.layers&&(s.layers.mask=t.layers),void 0!==t.children){const o=t.children;for(let t=0;t<o.length;t++)s.add(this.parseObject(o[t],e,n,i,r))}if(void 0!==t.animations){const e=t.animations;for(let t=0;t<e.length;t++){const n=e[t];s.animations.push(r[n])}}if(\\\\\\\"LOD\\\\\\\"===t.type){void 0!==t.autoUpdate&&(s.autoUpdate=t.autoUpdate);const e=t.levels;for(let t=0;t<e.length;t++){const n=e[t],i=s.getObjectByProperty(\\\\\\\"uuid\\\\\\\",n.object);void 0!==i&&s.addLevel(i,n.distance)}}return s}bindSkeletons(t,e){0!==Object.keys(e).length&&t.traverse((function(t){if(!0===t.isSkinnedMesh&&void 0!==t.skeleton){const n=e[t.skeleton];void 0===n?console.warn(\\\\\\\"THREE.ObjectLoader: No skeleton found with UUID:\\\\\\\",t.skeleton):t.bind(n,t.bindMatrix)}}))}setTexturePath(t){return console.warn(\\\\\\\"THREE.ObjectLoader: .setTexturePath() has been renamed to .setResourcePath().\\\\\\\"),this.setResourcePath(t)}}const lY={UVMapping:w.Yc,CubeReflectionMapping:w.o,CubeRefractionMapping:w.p,EquirectangularReflectionMapping:w.D,EquirectangularRefractionMapping:w.E,CubeUVReflectionMapping:w.q,CubeUVRefractionMapping:w.r},cY={RepeatWrapping:w.wc,ClampToEdgeWrapping:w.n,MirroredRepeatWrapping:w.kb},uY={NearestFilter:w.ob,NearestMipmapNearestFilter:w.sb,NearestMipmapLinearFilter:w.rb,LinearFilter:w.V,LinearMipmapNearestFilter:w.Z,LinearMipmapLinearFilter:w.Y};const hY=new class extends aa{constructor(){super(...arguments),this.cache=oa.STRING(\\\\\\\"\\\\\\\",{hidden:!0}),this.reset=oa.BUTTON(null,{callback:(t,e)=>{dY.PARAM_CALLBACK_reset(t,e)}})}};class dY extends gG{constructor(){super(...arguments),this.paramsConfig=hY}static type(){return\\\\\\\"cache\\\\\\\"}static displayedInputNames(){return[\\\\\\\"geometry to cache\\\\\\\"]}initializeNode(){this.io.inputs.setCount(0,1)}cook(t){const e=\\\\\\\"\\\\\\\"==this.pv.cache||null==this.pv.cache,n=t[0];if(e&&n){const t=[];for(let e of n.objects())t.push(e.toJSON());this.setCoreGroup(n),this.p.cache.set(JSON.stringify(t))}else if(this.pv.cache){const t=new aY,e=JSON.parse(this.pv.cache),n=[];for(let i of e){const e=t.parse(i);n.push(e)}this.setObjects(n)}else this.setObjects([])}static PARAM_CALLBACK_reset(t,e){t.param_callback_PARAM_CALLBACK_reset()}async param_callback_PARAM_CALLBACK_reset(){this.p.cache.set(\\\\\\\"\\\\\\\"),this.compute()}}const pY=[nr.ORTHOGRAPHIC,nr.PERSPECTIVE],_Y={direction:new p.a(0,1,0)},mY=[new d.a(-1,-1),new d.a(-1,1),new d.a(1,1),new d.a(1,-1)],fY=new p.a(0,0,1);const gY=new class extends aa{constructor(){super(...arguments),this.camera=oa.NODE_PATH(\\\\\\\"\\\\\\\",{nodeSelection:{context:Ki.OBJ,types:pY}}),this.direction=oa.VECTOR3(_Y.direction),this.offset=oa.FLOAT(0,{range:[-10,10],rangeLocked:[!1,!1]}),this.useSegmentsCount=oa.BOOLEAN(!0),this.stepSize=oa.FLOAT(1,{range:[.001,1],rangeLocked:[!1,!1],visibleIf:{useSegmentsCount:0}}),this.segments=oa.VECTOR2([10,10],{visibleIf:{useSegmentsCount:1}}),this.sizeMult=oa.FLOAT(1,{range:[0,2],rangeLocked:[!0,!1]}),this.updateOnWindowResize=oa.BOOLEAN(1),this.update=oa.BUTTON(null,{callback:t=>{vY.PARAM_CALLBACK_update(t)}})}};class vY extends gG{constructor(){super(...arguments),this.paramsConfig=gY,this._plane=new X.a,this._raycaster=new uL,this._planeCorners=[new p.a,new p.a,new p.a,new p.a],this._planeCenter=new p.a,this._core_transform=new Mz,this.segments_count=new d.a(1,1),this.planeSize=new d.a}static type(){return\\\\\\\"cameraPlane\\\\\\\"}cook(){this._updateWindowControllerDependency();const t=this.pv.camera.nodeWithContext(Ki.OBJ);if(!t)return this.states.error.set(\\\\\\\"no camera found\\\\\\\"),void this.cookController.endCook();if(!pY.includes(t.type()))return this.states.error.set(\\\\\\\"node found is not a camera\\\\\\\"),void this.cookController.endCook();const e=t.object;this._computePlaneParams(e)}_updateWindowControllerDependency(){this.pv.updateOnWindowResize?this.addGraphInput(this.scene().windowController.graphNode()):this.removeGraphInput(this.scene().windowController.graphNode())}_computePlaneParams(t){this._plane.normal.copy(this.pv.direction),this._plane.constant=this.pv.offset;let e=0;this._planeCenter.set(0,0,0);for(let n of mY){this._raycaster.setFromCamera(n,t);const i=this._planeCorners[e];this._raycaster.ray.intersectPlane(this._plane,i),this._planeCenter.add(i),e++}this._planeCenter.multiplyScalar(.25);const n=this._planeCorners[1].distanceTo(this._planeCorners[2]),i=this._planeCorners[0].distanceTo(this._planeCorners[3]),r=this._planeCorners[0].distanceTo(this._planeCorners[1]),s=this._planeCorners[2].distanceTo(this._planeCorners[3]),o=Math.max(n,i)*this.pv.sizeMult,a=Math.max(r,s)*this.pv.sizeMult;this.planeSize.set(o,a);const l=this._createPlane(this.planeSize);this._core_transform.rotate_geometry(l,fY,this.pv.direction);const c=this._core_transform.translation_matrix(this._planeCenter);l.applyMatrix4(c),this.setGeometry(l)}_createPlane(t){return t=t.clone(),this.pv.useSegmentsCount?(this.segments_count.x=Math.floor(this.pv.segments.x),this.segments_count.y=Math.floor(this.pv.segments.y)):this.pv.stepSize>0&&(this.segments_count.x=Math.floor(t.x/this.pv.stepSize),this.segments_count.y=Math.floor(t.y/this.pv.stepSize),t.x=this.segments_count.x*this.pv.stepSize,t.y=this.segments_count.y*this.pv.stepSize),new L(t.x,t.y,this.segments_count.x,this.segments_count.y)}static PARAM_CALLBACK_update(t){t._paramCallbackUpdate()}_paramCallbackUpdate(){this.setDirty()}}class yY extends pG{constructor(){super(...arguments),this._geo_center=new p.a}static type(){return\\\\\\\"center\\\\\\\"}cook(t,e){var n;const i=t[0].objectsWithGeo(),r=new Array(3*i.length);r.fill(0);for(let t=0;t<i.length;t++){const e=i[t],s=e.geometry;s.computeBoundingBox(),s.boundingBox&&(null===(n=s.boundingBox)||void 0===n||n.getCenter(this._geo_center),e.updateMatrixWorld(),this._geo_center.applyMatrix4(e.matrixWorld),this._geo_center.toArray(r,3*t))}const s=new S.a;s.setAttribute(\\\\\\\"position\\\\\\\",new C.a(new Float32Array(r),3));const o=this.createObject(s,Sr.POINTS);return this.createCoreGroupFromObjects([o])}}yY.DEFAULT_PARAMS={},yY.INPUT_CLONED_STATE=Qi.FROM_NODE;const xY=new class extends aa{};class bY extends gG{constructor(){super(...arguments),this.paramsConfig=xY}static type(){return\\\\\\\"center\\\\\\\"}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState(yY.INPUT_CLONED_STATE)}cook(t){this._operation=this._operation||new yY(this.scene(),this.states);const e=this._operation.cook(t,this.pv);this.setCoreGroup(e)}}class wY{static positions(t,e,n=360){const i=rs.degrees_to_radians(n)/e,r=[];for(let n=0;n<e;n++){const e=i*n,s=t*Math.cos(e),o=t*Math.sin(e);r.push(new d.a(s,o))}return r}static create(t,e,n=360){const i=this.positions(t,e,n),r=[],s=[];let o;for(let t=0;t<i.length;t++)o=i[t],r.push(o.x),r.push(o.y),r.push(0),t>0&&(s.push(t-1),s.push(t));s.push(e-1),s.push(0);const a=new S.a;return a.setAttribute(\\\\\\\"position\\\\\\\",new C.c(r,3)),a.setIndex(s),a}}const TY=new p.a(0,0,1);class AY extends pG{constructor(){super(...arguments),this._core_transform=new Mz}static type(){return\\\\\\\"circle\\\\\\\"}cook(t,e){return e.open?this._create_circle(e):this._create_disk(e)}_create_circle(t){const e=wY.create(t.radius,t.segments,t.arcAngle);return this._core_transform.rotate_geometry(e,TY,t.direction),this.createCoreGroupFromGeometry(e,Sr.LINE_SEGMENTS)}_create_disk(t){const e=new zX(t.radius,t.segments);return this._core_transform.rotate_geometry(e,TY,t.direction),this.createCoreGroupFromGeometry(e)}}AY.DEFAULT_PARAMS={radius:1,segments:12,open:!0,arcAngle:360,direction:new p.a(0,1,0)};const EY=AY.DEFAULT_PARAMS;const MY=new class extends aa{constructor(){super(...arguments),this.radius=oa.FLOAT(EY.radius),this.segments=oa.INTEGER(EY.segments,{range:[1,50],rangeLocked:[!0,!1]}),this.open=oa.BOOLEAN(EY.open),this.arcAngle=oa.FLOAT(EY.arcAngle,{range:[0,360],rangeLocked:[!1,!1],visibleIf:{open:1}}),this.direction=oa.VECTOR3(EY.direction)}};class SY extends gG{constructor(){super(...arguments),this.paramsConfig=MY}static type(){return\\\\\\\"circle\\\\\\\"}initializeNode(){}cook(){this._operation=this._operation||new AY(this._scene,this.states);const t=this._operation.cook([],this.pv);this.setCoreGroup(t)}}var CY;!function(t){t.SEGMENTS_COUNT=\\\\\\\"segments count\\\\\\\",t.SEGMENTS_LENGTH=\\\\\\\"segments length\\\\\\\"}(CY||(CY={}));const NY=[CY.SEGMENTS_COUNT,CY.SEGMENTS_LENGTH];var LY;!function(t){t.ABC=\\\\\\\"abc\\\\\\\",t.ACB=\\\\\\\"acb\\\\\\\",t.AB=\\\\\\\"ab\\\\\\\",t.BC=\\\\\\\"bc\\\\\\\",t.AC=\\\\\\\"ac\\\\\\\"}(LY||(LY={}));const OY=[LY.ABC,LY.ACB,LY.AB,LY.AC,LY.BC];class RY{constructor(t){this.params=t,this.a=new p.a,this.b=new p.a,this.c=new p.a,this.an=new p.a,this.bn=new p.a,this.cn=new p.a,this.ac=new p.a,this.ab=new p.a,this.ab_x_ac=new p.a,this.part0=new p.a,this.part1=new p.a,this.divider=1,this.a_center=new p.a,this.center=new p.a,this.normal=new p.a,this.radius=1,this.x=new p.a,this.y=new p.a,this.z=new p.a,this.angle_ab=1,this.angle_ac=1,this.angle_bc=1,this.angle=2*Math.PI,this.x_rotated=new p.a,this._created_geometries={}}created_geometries(){return this._created_geometries}create(t,e,n){this.a.copy(t),this.b.copy(e),this.c.copy(n),this._compute_axis(),this._create_arc(),this._create_center()}_create_arc(){this._compute_angle();const t=this._points_count(),e=new Array(3*t),n=new Array(t),i=this.angle/(t-1);this.x_rotated.copy(this.x).multiplyScalar(this.radius);let r=0;for(r=0;r<t;r++)this.x_rotated.copy(this.x).applyAxisAngle(this.normal,i*r).multiplyScalar(this.radius).add(this.center),this.x_rotated.toArray(e,3*r),r>0&&(n[2*(r-1)]=r-1,n[2*(r-1)+1]=r);this.params.full&&(n.push(r-1),n.push(0));const s=new S.a;if(s.setAttribute(\\\\\\\"position\\\\\\\",new C.a(new Float32Array(e),3)),s.setIndex(n),this.params.addIdAttribute||this.params.addIdnAttribute){const e=new Array(t);for(let t=0;t<e.length;t++)e[t]=t;this.params.addIdAttribute&&s.setAttribute(\\\\\\\"id\\\\\\\",new C.a(new Float32Array(e),1));const n=e.map((e=>e/(t-1)));this.params.addIdnAttribute&&s.setAttribute(\\\\\\\"idn\\\\\\\",new C.a(new Float32Array(n),1))}this._created_geometries.arc=s}_create_center(){if(!this.params.center)return;const t=new S.a,e=[this.center.x,this.center.y,this.center.z];t.setAttribute(\\\\\\\"position\\\\\\\",new C.a(new Float32Array(e),3)),this._created_geometries.center=t}_compute_axis(){this.ac.copy(this.c).sub(this.a),this.ab.copy(this.b).sub(this.a),this.ab_x_ac.copy(this.ab).cross(this.ac),this.divider=2*this.ab_x_ac.lengthSq(),this.part0.copy(this.ab_x_ac).cross(this.ab).multiplyScalar(this.ac.lengthSq()),this.part1.copy(this.ac).cross(this.ab_x_ac).multiplyScalar(this.ab.lengthSq()),this.a_center.copy(this.part0).add(this.part1).divideScalar(this.divider),this.radius=this.a_center.length(),this.normal.copy(this.ab_x_ac).normalize(),this.center.copy(this.a).add(this.a_center)}_compute_angle(){this.params.arc&&(this.params.full?(this.x.copy(this.a).sub(this.center).normalize(),this.angle=2*Math.PI):(this.an.copy(this.a).sub(this.center).normalize(),this.bn.copy(this.b).sub(this.center).normalize(),this.cn.copy(this.c).sub(this.center).normalize(),this._set_x_from_joinMode(),this.y.copy(this.normal),this.z.copy(this.x).cross(this.y).normalize(),this.angle_ab=this.an.angleTo(this.bn),this.angle_ac=this.an.angleTo(this.cn),this.angle_bc=this.bn.angleTo(this.cn),this._set_angle_from_joinMode()))}_points_count(){const t=this.params.pointsCountMode;switch(t){case CY.SEGMENTS_COUNT:return this.params.segmentsCount+1;case CY.SEGMENTS_LENGTH:{let t=Math.PI*this.radius*this.radius;return this.params.full||(t*=Math.abs(this.angle)/(2*Math.PI)),Math.ceil(t/this.params.segmentsLength)}}ar.unreachable(t)}_set_x_from_joinMode(){const t=this.params.joinMode;switch(this.x.copy(this.a).sub(this.center).normalize(),t){case LY.ABC:case LY.ACB:case LY.AB:case LY.AC:return this.x.copy(this.an);case LY.BC:return this.x.copy(this.bn)}ar.unreachable(t)}_set_angle_from_joinMode(){const t=this.params.joinMode;switch(t){case LY.ABC:return void(this.angle=this.angle_ab+this.angle_bc);case LY.ACB:return this.angle=this.angle_ac+this.angle_bc,void(this.angle*=-1);case LY.AB:return void(this.angle=this.angle_ab);case LY.AC:return this.angle=this.angle_ac,void(this.angle*=-1);case LY.BC:return void(this.angle=this.angle_bc)}ar.unreachable(t)}}const PY=new class extends aa{constructor(){super(...arguments),this.arc=oa.BOOLEAN(1),this.pointsCountMode=oa.INTEGER(NY.indexOf(CY.SEGMENTS_COUNT),{visibleIf:{arc:1},menu:{entries:NY.map(((t,e)=>({value:e,name:t})))}}),this.segmentsLength=oa.FLOAT(.1,{visibleIf:{arc:1,pointsCountMode:NY.indexOf(CY.SEGMENTS_LENGTH)},range:[0,1],rangeLocked:[!0,!1]}),this.segmentsCount=oa.INTEGER(100,{visibleIf:{arc:1,pointsCountMode:NY.indexOf(CY.SEGMENTS_COUNT)},range:[1,100],rangeLocked:[!0,!1]}),this.full=oa.BOOLEAN(1,{visibleIf:{arc:1}}),this.joinMode=oa.INTEGER(OY.indexOf(LY.ABC),{visibleIf:{arc:1,full:0},menu:{entries:OY.map(((t,e)=>({value:e,name:t})))}}),this.addIdAttribute=oa.BOOLEAN(1),this.addIdnAttribute=oa.BOOLEAN(1),this.center=oa.BOOLEAN(0)}};class IY extends gG{constructor(){super(...arguments),this.paramsConfig=PY,this.a=new p.a,this.b=new p.a,this.c=new p.a}static type(){return\\\\\\\"circle3Points\\\\\\\"}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState([Qi.NEVER])}cook(t){const e=t[0].points();e.length<3?this.states.error.set(`only ${e.length} points found, when 3 are required`):this._create_circle(e)}_create_circle(t){const e=new RY({arc:this.pv.arc,center:this.pv.center,pointsCountMode:NY[this.pv.pointsCountMode],segmentsLength:this.pv.segmentsLength,segmentsCount:this.pv.segmentsCount,full:this.pv.full,joinMode:OY[this.pv.joinMode],addIdAttribute:this.pv.addIdAttribute,addIdnAttribute:this.pv.addIdnAttribute});t[0].getPosition(this.a),t[1].getPosition(this.b),t[2].getPosition(this.c),e.create(this.a,this.b,this.c);const n=[],i=e.created_geometries();i.arc&&n.push(this.createObject(i.arc,Sr.LINE_SEGMENTS)),i.center&&n.push(this.createObject(i.center,Sr.POINTS)),this.setObjects(n)}}class FY extends pG{static type(){return\\\\\\\"color\\\\\\\"}cook(t,e){}}FY.DEFAULT_PARAMS={fromAttribute:!1,attribName:\\\\\\\"\\\\\\\",color:new D.a(1,1,1),asHsv:!1};const DY=new D.a(1,1,1),kY=\\\\\\\"color\\\\\\\",BY=FY.DEFAULT_PARAMS;const zY=new class extends aa{constructor(){super(...arguments),this.fromAttribute=oa.BOOLEAN(BY.fromAttribute),this.attribName=oa.STRING(BY.attribName,{visibleIf:{fromAttribute:1}}),this.color=oa.COLOR(BY.color,{visibleIf:{fromAttribute:0},expression:{forEntities:!0}}),this.asHsv=oa.BOOLEAN(BY.asHsv,{visibleIf:{fromAttribute:0}})}};class UY extends gG{constructor(){super(...arguments),this.paramsConfig=zY,this._r_arrays_by_geometry_uuid={},this._g_arrays_by_geometry_uuid={},this._b_arrays_by_geometry_uuid={}}static type(){return\\\\\\\"color\\\\\\\"}static displayedInputNames(){return[\\\\\\\"geometry to update color of\\\\\\\"]}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState(Qi.FROM_NODE)}async cook(t){const e=t[0],n=e.coreObjects();for(let t of n)if(this.pv.fromAttribute)this._set_fromAttribute(t);else{this.p.color.hasExpression()?await this._eval_expressions(t):this._eval_simple_values(t)}if(!this.io.inputs.cloneRequired(0)){const t=e.geometries();for(let e of t)e.getAttribute(kY).needsUpdate=!0}this.setCoreGroup(e)}_set_fromAttribute(t){const e=t.coreGeometry();if(!e)return;this._create_init_color(e,DY);const n=e.points(),i=e.attribSize(this.pv.attribName),r=e.geometry(),s=r.getAttribute(this.pv.attribName).array,o=r.getAttribute(kY).array;switch(i){case 1:for(let t=0;t<n.length;t++){const e=3*t;o[e+0]=s[t],o[e+1]=1-s[t],o[e+2]=0}break;case 2:for(let t=0;t<n.length;t++){const e=3*t,n=2*t;o[e+0]=s[n+0],o[e+1]=s[n+1],o[e+2]=0}break;case 3:for(let t=0;t<s.length;t++)o[t]=s[t];break;case 4:for(let t=0;t<n.length;t++){const e=3*t,n=4*t;o[e+0]=s[n+0],o[e+1]=s[n+1],o[e+2]=s[n+2]}}}_create_init_color(t,e){t.hasAttrib(kY)||t.addNumericAttrib(kY,3,DY)}_eval_simple_values(t){const e=t.coreGeometry();if(!e)return;let n;this._create_init_color(e,DY),this.pv.asHsv?(n=new D.a,oo.set_hsv(this.pv.color.r,this.pv.color.g,this.pv.color.b,n)):n=this.pv.color,e.addNumericAttrib(kY,3,n)}async _eval_expressions(t){const e=t.points(),n=t.object(),i=t.coreGeometry();i&&this._create_init_color(i,DY);const r=n.geometry;if(r){const t=r.getAttribute(kY).array,n=await this._update_from_param(r,t,e,0),i=await this._update_from_param(r,t,e,1),s=await this._update_from_param(r,t,e,2);if(n&&this._commit_tmp_values(n,t,0),i&&this._commit_tmp_values(i,t,1),s&&this._commit_tmp_values(s,t,2),this.pv.asHsv){let n,i=new D.a,r=new D.a;for(let s of e)n=3*s.index(),i.fromArray(t,n),oo.set_hsv(i.r,i.g,i.b,r),r.toArray(t,n)}}}async _update_from_param(t,e,n,i){const r=this.p.color.components[i],s=[this.pv.color.r,this.pv.color.g,this.pv.color.b][i],o=[this._r_arrays_by_geometry_uuid,this._g_arrays_by_geometry_uuid,this._b_arrays_by_geometry_uuid][i];let a;if(r.hasExpression()&&r.expressionController)a=this._init_array_if_required(t,o,n.length),await r.expressionController.compute_expression_for_points(n,((t,e)=>{a[t.index()]=e}));else for(let t of n)e[3*t.index()+i]=s;return a}_init_array_if_required(t,e,n){const i=t.uuid,r=e[i];return r?r.length<n&&(e[i]=new Array(n)):e[i]=new Array(n),e[i]}_commit_tmp_values(t,e,n){for(let i=0;i<t.length;i++)e[3*i+n]=t[i]}}const GY=new p.a(0,1,0);const VY=new class extends aa{constructor(){super(...arguments),this.radius=oa.FLOAT(1,{range:[0,1]}),this.height=oa.FLOAT(1,{range:[0,1]}),this.segmentsRadial=oa.INTEGER(12,{range:[3,20],rangeLocked:[!0,!1]}),this.segmentsHeight=oa.INTEGER(1,{range:[1,20],rangeLocked:[!0,!1]}),this.cap=oa.BOOLEAN(1),this.thetaStart=oa.FLOAT(1,{range:[0,2*Math.PI]}),this.thetaLength=oa.FLOAT(\\\\\\\"2*$PI\\\\\\\",{range:[0,2*Math.PI]}),this.center=oa.VECTOR3([0,0,0]),this.direction=oa.VECTOR3([0,0,1])}};class HY extends gG{constructor(){super(...arguments),this.paramsConfig=VY,this._core_transform=new Mz}static type(){return\\\\\\\"cone\\\\\\\"}cook(){const t=new _U(this.pv.radius,this.pv.height,this.pv.segmentsRadial,this.pv.segmentsHeight,!this.pv.cap,this.pv.thetaStart,this.pv.thetaLength);this._core_transform.rotate_geometry(t,GY,this.pv.direction),t.translate(this.pv.center.x,this.pv.center.y,this.pv.center.z),this.setGeometry(t)}}const jY={SCALE:new p.a(1,1,1),PSCALE:1,EYE:new p.a(0,0,0),UP:new p.a(0,1,0)},WY=new p.a(1,1,1),qY=new d.a(0,0),XY=\\\\\\\"color\\\\\\\";var YY,$Y;!function(t){t.POSITION=\\\\\\\"instancePosition\\\\\\\",t.SCALE=\\\\\\\"instanceScale\\\\\\\",t.ORIENTATION=\\\\\\\"instanceOrientation\\\\\\\",t.COLOR=\\\\\\\"instanceColor\\\\\\\",t.UV=\\\\\\\"instanceUv\\\\\\\"}(YY||(YY={}));class JY{constructor(t){this._group_wrapper=t,this._matrices={},this._point_scale=new p.a,this._point_normal=new p.a,this._point_up=new p.a,this._is_pscale_present=this._group_wrapper.hasAttrib(\\\\\\\"pscale\\\\\\\"),this._is_scale_present=this._group_wrapper.hasAttrib(\\\\\\\"scale\\\\\\\"),this._is_normal_present=this._group_wrapper.hasAttrib(\\\\\\\"normal\\\\\\\"),this._is_up_present=this._group_wrapper.hasAttrib(\\\\\\\"up\\\\\\\"),this._do_rotate_matrices=this._is_normal_present}matrices(){return this._matrices={},this._matrices.translate=new A.a,this._matrices.rotate=new A.a,this._matrices.scale=new A.a,this._group_wrapper.points().map((t=>{const e=new A.a;return this._matrix_from_point(t,e),e}))}_matrix_from_point(t,e){const n=t.position();this._is_scale_present?t.attribValue(\\\\\\\"scale\\\\\\\",this._point_scale):this._point_scale.copy(jY.SCALE);const i=this._is_pscale_present?t.attribValue(\\\\\\\"pscale\\\\\\\"):jY.PSCALE;this._point_scale.multiplyScalar(i);const r=this._matrices.scale;r.makeScale(this._point_scale.x,this._point_scale.y,this._point_scale.z);const s=this._matrices.translate;if(s.makeTranslation(n.x,n.y,n.z),e.multiply(s),this._do_rotate_matrices){const n=this._matrices.rotate,i=jY.EYE;t.attribValue(\\\\\\\"normal\\\\\\\",this._point_normal),this._point_normal.multiplyScalar(-1),this._is_up_present?t.attribValue(\\\\\\\"up\\\\\\\",this._point_up):this._point_up.copy(jY.UP),this._point_up.normalize(),n.lookAt(i,this._point_normal,this._point_up),e.multiply(n)}e.multiply(r)}static create_instance_buffer_geo(t,e,n){const i=e.points(),r=new kX;r.copy(t),r.instanceCount=1/0;const s=i.length,o=new Float32Array(3*s),a=new Float32Array(3*s),l=new Float32Array(3*s),c=new Float32Array(4*s),u=e.hasAttrib(XY),h=new p.a(0,0,0),d=new au.a,_=new p.a(1,1,1),f=new JY(e).matrices();i.forEach(((t,e)=>{const n=3*e,i=4*e;f[e].decompose(h,d,_),h.toArray(o,n),d.toArray(c,i),_.toArray(l,n);(u?t.attribValue(XY,this._point_color):WY).toArray(a,n)}));const g=e.hasAttrib(\\\\\\\"uv\\\\\\\");if(g){const t=new Float32Array(2*s);i.forEach(((e,n)=>{const i=2*n;(g?e.attribValue(\\\\\\\"uv\\\\\\\",this._point_uv):qY).toArray(t,i)})),r.setAttribute(YY.UV,new cX(t,2))}r.setAttribute(YY.POSITION,new cX(o,3)),r.setAttribute(YY.SCALE,new cX(l,3)),r.setAttribute(YY.ORIENTATION,new cX(c,4)),r.setAttribute(YY.COLOR,new cX(a,3));e.attribNamesMatchingMask(n).forEach((t=>{const n=e.attribSize(t),o=new Float32Array(s*n);i.forEach(((e,i)=>{const r=e.attribValue(t);m.isNumber(r)?o[i]=r:r.toArray(o,i*n)})),r.setAttribute(t,new cX(o,n))}));return new ps(r).markAsInstance(),r}}JY._point_color=new p.a,JY._point_uv=new d.a;class ZY extends ru{set_point(t){this._point=t,this.setDirty(),this.removeDirtyState()}value(t){return this._point?t?this._point.attribValue(t):this._point.index():this._global_index}}!function(t){t[t.OBJECT=0]=\\\\\\\"OBJECT\\\\\\\",t[t.GEOMETRY=1]=\\\\\\\"GEOMETRY\\\\\\\"}($Y||($Y={}));const QY=[$Y.OBJECT,$Y.GEOMETRY],KY=[{name:\\\\\\\"object\\\\\\\",value:$Y.OBJECT},{name:\\\\\\\"geometry\\\\\\\",value:$Y.GEOMETRY}];const t$=new class extends aa{constructor(){super(...arguments),this.count=oa.INTEGER(1,{range:[1,20],rangeLocked:[!0,!1]}),this.transformOnly=oa.BOOLEAN(0),this.transformMode=oa.INTEGER(0,{menu:{entries:KY}}),this.copyAttributes=oa.BOOLEAN(0),this.attributesToCopy=oa.STRING(\\\\\\\"\\\\\\\",{visibleIf:{copyAttributes:!0}}),this.useCopyExpr=oa.BOOLEAN(0)}};class e$ extends gG{constructor(){super(...arguments),this.paramsConfig=t$,this._attribute_names_to_copy=[],this._objects=[],this._object_position=new p.a}static type(){return\\\\\\\"copy\\\\\\\"}static displayedInputNames(){return[\\\\\\\"geometry to be copied\\\\\\\",\\\\\\\"points to copy to\\\\\\\"]}initializeNode(){this.io.inputs.setCount(1,2),this.io.inputs.initInputsClonedState([Qi.ALWAYS,Qi.NEVER])}async cook(t){const e=t[0];if(!this.io.inputs.has_input(1))return void await this.cook_without_template(e);const n=t[1];n?await this.cook_with_template(e,n):this.states.error.set(\\\\\\\"second input invalid\\\\\\\")}async cook_with_template(t,e){this._objects=[];const n=e.points();let i=new JY(e).matrices();const r=new p.a,s=new au.a,o=new p.a;i[0].decompose(r,s,o),this._attribute_names_to_copy=sr.attribNames(this.pv.attributesToCopy).filter((t=>e.hasAttrib(t))),await this._copy_moved_objects_on_template_points(t,i,n),this.setObjects(this._objects)}async _copy_moved_objects_on_template_points(t,e,n){for(let i=0;i<n.length;i++)await this._copy_moved_object_on_template_point(t,e,n,i)}async _copy_moved_object_on_template_point(t,e,n,i){const r=e[i],s=n[i];this.stamp_node.set_point(s);const o=await this._get_moved_objects_for_template_point(t,i);for(let t of o)this.pv.copyAttributes&&this._copyAttributes_from_template(t,s),this.pv.transformOnly?t.applyMatrix4(r):this._apply_matrix_to_object_or_geometry(t,r),this._objects.push(t)}_apply_matrix_to_object_or_geometry(t,e){const n=QY[this.pv.transformMode];switch(n){case $Y.OBJECT:return void this._apply_matrix_to_object(t,e);case $Y.GEOMETRY:{const n=t.geometry;return void(n&&n.applyMatrix4(e))}}ar.unreachable(n)}_apply_matrix_to_object(t,e){this._object_position.copy(t.position),t.position.multiplyScalar(0),t.updateMatrix(),t.applyMatrix4(e),t.position.add(this._object_position),t.updateMatrix()}async _get_moved_objects_for_template_point(t,e){const n=await this._stamp_instance_group_if_required(t);if(n){return this.pv.transformOnly?f.compact([n.objects()[e]]):n.clone().objects()}return[]}async _stamp_instance_group_if_required(t){if(!this.pv.useCopyExpr)return t;{const t=await this.containerController.requestInputContainer(0);if(t){const e=t.coreContent();return e||void 0}this.states.error.set(`input failed for index ${this.stamp_value()}`)}}async _copy_moved_objects_for_each_instance(t){for(let e=0;e<this.pv.count;e++)await this._copy_moved_objects_for_instance(t,e)}async _copy_moved_objects_for_instance(t,e){this.stamp_node.set_global_index(e);const n=await this._stamp_instance_group_if_required(t);n&&n.objects().forEach((t=>{const e=vs.clone(t);this._objects.push(e)}))}async cook_without_template(t){this._objects=[],await this._copy_moved_objects_for_each_instance(t),this.setObjects(this._objects)}_copyAttributes_from_template(t,e){this._attribute_names_to_copy.forEach(((n,i)=>{const r=e.attribValue(n);new vs(t,i).addAttribute(n,r)}))}stamp_value(t){return this.stamp_node.value(t)}get stamp_node(){return this._stamp_node=this._stamp_node||this.create_stamp_node()}create_stamp_node(){const t=new ZY(this.scene());return this.dirtyController.setForbiddenTriggerNodes([t]),t}dispose(){super.dispose(),this._stamp_node&&this._stamp_node.dispose()}}const n$=\\\\\\\"id\\\\\\\",i$=\\\\\\\"class\\\\\\\",r$=\\\\\\\"html\\\\\\\";class s$ extends pG{static type(){return\\\\\\\"CSS2DObject\\\\\\\"}cook(t,e){const n=t[0];if(n){const t=this._create_objects_from_input_points(n,e);return this.createCoreGroupFromObjects(t)}{const t=this._create_object_from_scratch(e);return this.createCoreGroupFromObjects([t])}}_create_objects_from_input_points(t,e){const n=t.points(),i=[];for(let t of n){const n=e.useIdAttrib?t.attribValue(n$):e.className,r=e.useClassAttrib?t.attribValue(i$):e.className,s=e.useHtmlAttrib?t.attribValue(r$):e.html,o=s$.create_css_object({id:n,className:r,html:s}),a=o.element;if(e.copyAttributes){const n=sr.attribNames(e.attributesToCopy);for(let e of n){const n=t.attribValue(e);m.isString(n)?a.setAttribute(e,n):m.isNumber(n)&&a.setAttribute(e,`${n}`)}}o.position.copy(t.position()),o.updateMatrix(),i.push(o)}return i}_create_object_from_scratch(t){return s$.create_css_object({id:t.id,className:t.className,html:t.html})}static create_css_object(t){const e=document.createElement(\\\\\\\"div\\\\\\\");e.id=t.id,e.className=t.className,e.innerHTML=t.html;const n=new vW(e);return n.matrixAutoUpdate=!1,n}}s$.DEFAULT_PARAMS={useIdAttrib:!1,id:\\\\\\\"my_css_object\\\\\\\",useClassAttrib:!1,className:\\\\\\\"CSS2DObject\\\\\\\",useHtmlAttrib:!1,html:\\\\\\\"<div>default html</div>\\\\\\\",copyAttributes:!1,attributesToCopy:\\\\\\\"\\\\\\\"},s$.INPUT_CLONED_STATE=Qi.FROM_NODE;const o$=s$.DEFAULT_PARAMS;const a$=new class extends aa{constructor(){super(...arguments),this.useIdAttrib=oa.BOOLEAN(o$.useIdAttrib),this.id=oa.STRING(o$.id,{visibleIf:{useIdAttrib:0}}),this.useClassAttrib=oa.BOOLEAN(o$.useClassAttrib),this.className=oa.STRING(o$.className,{visibleIf:{useClassAttrib:0}}),this.useHtmlAttrib=oa.BOOLEAN(o$.useHtmlAttrib),this.html=oa.STRING(o$.html,{visibleIf:{useHtmlAttrib:0},multiline:!0}),this.copyAttributes=oa.BOOLEAN(o$.copyAttributes),this.attributesToCopy=oa.STRING(o$.attributesToCopy,{visibleIf:{copyAttributes:!0}})}};class l$ extends gG{constructor(){super(...arguments),this.paramsConfig=a$}static type(){return\\\\\\\"CSS2DObject\\\\\\\"}initializeNode(){this.io.inputs.setCount(0,1)}cook(t){this._operation=this._operation||new s$(this.scene(),this.states);const e=this._operation.cook(t,this.pv);this.setCoreGroup(e)}}class c${constructor(t,e){this._size=t,this._type=e}size(){return this._size}type(){return this._type}static from_value(t){const e=m.isString(t)?Dr.STRING:Dr.NUMERIC;return new this(m.isArray(t)?t.length:1,e)}}class u${constructor(t={}){this._attribute_datas_by_name={},this._options={},this._options.dataKeysPrefix=t.dataKeysPrefix,this._options.skipEntries=t.skipEntries,this._options.doConvert=t.doConvert||!1,this._options.convertToNumeric=t.convertToNumeric}dataKeysPrefix(){return this._options.dataKeysPrefix}get_prefixed_json(t,e){if(0==e.length)return t;{const n=e.shift();if(n)return this.get_prefixed_json(t[n],e)}return[]}setJSON(t){return this._json=t}createObject(){const t=new S.a,e=new ps(t);if(null!=this._json){const n=this._json.length;e.initPositionAttribute(n),this._find_attributes();const i=sr.attribNames(this._options.convertToNumeric||\\\\\\\"\\\\\\\");for(let n of Object.keys(this._attribute_datas_by_name)){const r=Wr.remapName(n);let s=this._attribute_values_for_name(n).flat();const o=this._attribute_datas_by_name[n],a=o.size();if(o.type()===Dr.STRING)if(this._options.doConvert&&sr.matchesOneMask(n,i)){const e=s.map((t=>m.isString(t)?parseFloat(t)||0:t));t.setAttribute(r,new C.c(e,a))}else{const t=Wr.arrayToIndexedArrays(s);e.setIndexedAttribute(r,t.values,t.indices)}else{const e=s;t.setAttribute(r,new C.c(e,a))}}}return t}_find_attributes(){let t;const e=sr.attribNames(this._options.skipEntries||\\\\\\\"\\\\\\\");if(this._json&&null!=(t=this._json[0]))for(let n of Object.keys(t)){const i=t[n];if(this._value_has_subentries(i))for(let t of Object.keys(i)){const r=[n,t].join(\\\\\\\":\\\\\\\"),s=i[n];sr.matchesOneMask(r,e)||(this._attribute_datas_by_name[r]=c$.from_value(s))}else sr.matchesOneMask(n,e)||(this._attribute_datas_by_name[n]=c$.from_value(i))}}_attribute_values_for_name(t){return this._json?this._json.map((e=>{const n=t.split(\\\\\\\":\\\\\\\")[0],i=e[n];if(this._value_has_subentries(i)){return i[t.substring(n.length+1)]||0}return i||0})):[]}_value_has_subentries(t){return m.isObject(t)&&!m.isArray(t)}}const h$=JSON.stringify([{value:-40},{value:-30},{value:-20},{value:-10},{value:0},{value:10},{value:20},{value:30},{value:40},{value:50},{value:60},{value:70},{value:80}]);const d$=new class extends aa{constructor(){super(...arguments),this.data=oa.STRING(h$)}};class p$ extends gG{constructor(){super(...arguments),this.paramsConfig=d$}static type(){return\\\\\\\"data\\\\\\\"}cook(){let t=null;try{t=JSON.parse(this.pv.data)}catch(t){this.states.error.set(\\\\\\\"could not parse json\\\\\\\")}if(t)try{const e=new u$;e.setJSON(t);const n=e.createObject();this.setGeometry(n,Sr.POINTS)}catch(t){this.states.error.set(\\\\\\\"could not build geometry from json\\\\\\\")}else this.cookController.endCook()}}class _$ extends jg{constructor(t,e,n={},i){super(t,e,i),this._node=i,this._parser=new u$(n)}async load(t,e,n){const i=await this._urlToLoad();fetch(i).then((async e=>{let n=await e.json();const i=this._parser.dataKeysPrefix();null!=i&&\\\\\\\"\\\\\\\"!=i&&(n=this._parser.get_prefixed_json(n,i.split(\\\\\\\".\\\\\\\"))),this._parser.setJSON(n);const r=this._parser.createObject();t(r)})).catch((t=>{ai.error(\\\\\\\"error\\\\\\\",t),n(t)}))}}const m$=\\\\\\\"position\\\\\\\";class f${constructor(t){this.attribute_names=t,this.attribute_names_from_first_line=!1,this.lines=[],this.points_count=0,this.attribute_values_by_name={},this.attribute_data_by_name={},this._loading=!1,this.attribute_names||(this.attribute_names_from_first_line=!0)}async load(t){if(this._loading)return void console.warn(\\\\\\\"is already loading\\\\\\\");this._loading=!0,this.points_count=0,await this.load_data(t),this.infer_types(),this.read_values();return this.create_points()}async load_data(t){const e=await fetch(t),n=await e.text();this.lines=n.split(\\\\\\\"\\\\n\\\\\\\"),this.attribute_names||(this.attribute_names=this.lines[0].split(f$.SEPARATOR)),this.attribute_names=this.attribute_names.map((t=>Wr.remapName(t)));for(let t of this.attribute_names)this.attribute_values_by_name[t]=[]}infer_types(){const t=this.attribute_names_from_first_line?1:0;let e=this.lines[t].split(f$.SEPARATOR);for(let t=0;t<e.length;t++){const n=this.attribute_names[t],i=e[t],r=this._value_from_line_element(i);this.attribute_data_by_name[n]=c$.from_value(r)}}_value_from_line_element(t){if(m.isString(t)){if(`${parseFloat(t)}`===t)return parseFloat(t);if(\\\\\\\"[\\\\\\\"===t[0]&&\\\\\\\"]\\\\\\\"===t[t.length-1]){return t.substring(1,t.length-1).split(f$.VECTOR_SEPARATOR).map((t=>parseFloat(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.lines.length;e++){t=this.lines[e];const n=t.split(f$.SEPARATOR);if(n.length>=this.attribute_names.length){for(let t=0;t<n.length;t++){const e=this.attribute_names[t];if(e){const i=n[t],r=this._value_from_line_element(i);this.attribute_values_by_name[e].push(r)}}this.points_count+=1}}if(!this.attribute_values_by_name.position){const t=new Array(3*this.points_count);t.fill(0),this.attribute_values_by_name.position=t,this.attribute_data_by_name.position=new c$(3,Dr.NUMERIC),this.attribute_names.push(m$)}}create_points(){if(!this.attribute_names)return;const t=new S.a,e=new ps(t);for(let n of this.attribute_names){const i=this.attribute_values_by_name[n].flat(),r=this.attribute_data_by_name[n].size();if(this.attribute_data_by_name[n].type()==Dr.STRING){const t=Wr.arrayToIndexedArrays(i);e.setIndexedAttribute(n,t.values,t.indices)}else t.setAttribute(n,new C.c(i,r))}const n=new Array(this.points_count);for(let t=0;t<this.points_count;t++)n.push(t);return t.setIndex(n),t}}var g$;f$.SEPARATOR=\\\\\\\",\\\\\\\",f$.VECTOR_SEPARATOR=\\\\\\\",\\\\\\\",function(t){t.JSON=\\\\\\\"json\\\\\\\",t.CSV=\\\\\\\"csv\\\\\\\"}(g$||(g$={}));const v$=[g$.JSON,g$.CSV],y$=`${Gg}/nodes/sop/DataUrl/basic.json`;const x$=new class extends aa{constructor(){super(...arguments),this.dataType=oa.INTEGER(v$.indexOf(g$.JSON),{menu:{entries:v$.map(((t,e)=>({name:t,value:e})))}}),this.url=oa.STRING(y$,{fileBrowse:{type:[Ls.JSON]}}),this.jsonDataKeysPrefix=oa.STRING(\\\\\\\"\\\\\\\",{visibleIf:{dataType:v$.indexOf(g$.JSON)}}),this.skipEntries=oa.STRING(\\\\\\\"\\\\\\\",{visibleIf:{dataType:v$.indexOf(g$.JSON)}}),this.convert=oa.BOOLEAN(0,{visibleIf:{dataType:v$.indexOf(g$.JSON)}}),this.convertToNumeric=oa.STRING(\\\\\\\"\\\\\\\",{visibleIf:{dataType:v$.indexOf(g$.JSON),convert:1}}),this.readAttribNamesFromFile=oa.BOOLEAN(1,{visibleIf:{dataType:v$.indexOf(g$.CSV)}}),this.attribNames=oa.STRING(\\\\\\\"height scale\\\\\\\",{visibleIf:{dataType:v$.indexOf(g$.CSV),readAttribNamesFromFile:0}}),this.reload=oa.BUTTON(null,{callback:(t,e)=>{b$.PARAM_CALLBACK_reload(t,e)}})}};class b$ extends gG{constructor(){super(...arguments),this.paramsConfig=x$}static type(){return\\\\\\\"dataUrl\\\\\\\"}initializeNode(){this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.url],(()=>{const t=this.p.url.rawInput();if(t){const e=t.split(\\\\\\\"/\\\\\\\");return e[e.length-1]}return\\\\\\\"\\\\\\\"}))}))}))}async cook(){switch(v$[this.pv.dataType]){case g$.JSON:return this._load_json();case g$.CSV:return this._load_csv()}}_url(){const t=this.scene().assets.root();return t?`${t}${this.pv.url}`:this.pv.url}_load_json(){new _$(this._url(),this.scene(),{dataKeysPrefix:this.pv.jsonDataKeysPrefix,skipEntries:this.pv.skipEntries,doConvert:this.pv.convert,convertToNumeric:this.pv.convertToNumeric},this).load(this._on_load.bind(this),void 0,this._on_error.bind(this))}_on_load(t){this.setGeometry(t,Sr.POINTS)}_on_error(t){this.states.error.set(`could not load geometry from ${this._url()} (${t})`),this.cookController.endCook()}async _load_csv(){const t=this.pv.readAttribNamesFromFile?void 0:this.pv.attribNames.split(\\\\\\\" \\\\\\\"),e=new f$(t),n=await e.load(this._url());n?this.setGeometry(n,Sr.POINTS):this.states.error.set(\\\\\\\"could not generate points\\\\\\\")}static PARAM_CALLBACK_reload(t,e){t.param_callback_reload()}param_callback_reload(){this.p.url.setDirty()}}class w$ extends S.a{constructor(t,e,n,i){super();const r=[],s=[],o=[],a=new p.a,l=new A.a;l.makeRotationFromEuler(n),l.setPosition(e);const c=new A.a;function u(e,n,i){n.applyMatrix4(t.matrixWorld),n.applyMatrix4(c),i.transformDirection(t.matrixWorld),e.push(new T$(n.clone(),i.clone()))}function h(t,e){const n=[],r=.5*Math.abs(i.dot(e));for(let i=0;i<t.length;i+=3){let s,o,a,l,c=0;const u=t[i+0].position.dot(e)-r>0,h=t[i+1].position.dot(e)-r>0,p=t[i+2].position.dot(e)-r>0;switch(c=(u?1:0)+(h?1:0)+(p?1:0),c){case 0:n.push(t[i]),n.push(t[i+1]),n.push(t[i+2]);break;case 1:if(u&&(s=t[i+1],o=t[i+2],a=d(t[i],s,e,r),l=d(t[i],o,e,r)),h){s=t[i],o=t[i+2],a=d(t[i+1],s,e,r),l=d(t[i+1],o,e,r),n.push(a),n.push(o.clone()),n.push(s.clone()),n.push(o.clone()),n.push(a.clone()),n.push(l);break}p&&(s=t[i],o=t[i+1],a=d(t[i+2],s,e,r),l=d(t[i+2],o,e,r)),n.push(s.clone()),n.push(o.clone()),n.push(a),n.push(l),n.push(a.clone()),n.push(o.clone());break;case 2:u||(s=t[i].clone(),o=d(s,t[i+1],e,r),a=d(s,t[i+2],e,r),n.push(s),n.push(o),n.push(a)),h||(s=t[i+1].clone(),o=d(s,t[i+2],e,r),a=d(s,t[i],e,r),n.push(s),n.push(o),n.push(a)),p||(s=t[i+2].clone(),o=d(s,t[i],e,r),a=d(s,t[i+1],e,r),n.push(s),n.push(o),n.push(a))}}return n}function d(t,e,n,i){const r=t.position.dot(n)-i,s=r/(r-(e.position.dot(n)-i));return new T$(new p.a(t.position.x+s*(e.position.x-t.position.x),t.position.y+s*(e.position.y-t.position.y),t.position.z+s*(e.position.z-t.position.z)),new p.a(t.normal.x+s*(e.normal.x-t.normal.x),t.normal.y+s*(e.normal.y-t.normal.y),t.normal.z+s*(e.normal.z-t.normal.z)))}c.copy(l).invert(),function(){let e=[];const n=new p.a,c=new p.a;if(!0===t.geometry.isGeometry)return void console.error(\\\\\\\"THREE.DecalGeometry no longer supports THREE.Geometry. Use BufferGeometry instead.\\\\\\\");const d=t.geometry,_=d.attributes.position,m=d.attributes.normal;if(null!==d.index){const t=d.index;for(let i=0;i<t.count;i++)n.fromBufferAttribute(_,t.getX(i)),c.fromBufferAttribute(m,t.getX(i)),u(e,n,c)}else for(let t=0;t<_.count;t++)n.fromBufferAttribute(_,t),c.fromBufferAttribute(m,t),u(e,n,c);e=h(e,a.set(1,0,0)),e=h(e,a.set(-1,0,0)),e=h(e,a.set(0,1,0)),e=h(e,a.set(0,-1,0)),e=h(e,a.set(0,0,1)),e=h(e,a.set(0,0,-1));for(let t=0;t<e.length;t++){const n=e[t];o.push(.5+n.position.x/i.x,.5+n.position.y/i.y),n.position.applyMatrix4(l),r.push(n.position.x,n.position.y,n.position.z),s.push(n.normal.x,n.normal.y,n.normal.z)}}(),this.setAttribute(\\\\\\\"position\\\\\\\",new C.c(r,3)),this.setAttribute(\\\\\\\"normal\\\\\\\",new C.c(s,3)),this.setAttribute(\\\\\\\"uv\\\\\\\",new C.c(o,2))}}class T${constructor(t,e){this.position=t,this.normal=e}clone(){return new this.constructor(this.position.clone(),this.normal.clone())}}class A$ extends pG{constructor(){super(...arguments),this._r=new p.a,this._rotation=new Wv.a(0,0,0),this._scale=new p.a(1,1,1)}static type(){return\\\\\\\"decal\\\\\\\"}cook(t,e){const n=t[0];this._r.copy(e.r).multiplyScalar(Ln.a),this._rotation.set(this._r.x,this._r.y,this._r.z),this._scale.copy(e.s).multiplyScalar(e.scale);const i=n.objectsWithGeo(),r=[];for(let t of i)if(t.isMesh){const n=new w$(t,e.t,this._rotation,this._scale),i=new k.a(n,t.material);r.push(i)}return this.createCoreGroupFromObjects(r)}}A$.DEFAULT_PARAMS={t:new p.a(0,0,0),r:new p.a(0,0,0),s:new p.a(1,1,1),scale:1},A$.INPUT_CLONED_STATE=Qi.NEVER;const E$=A$.DEFAULT_PARAMS;const M$=new class extends aa{constructor(){super(...arguments),this.t=oa.VECTOR3(E$.t),this.r=oa.VECTOR3(E$.r),this.s=oa.VECTOR3(E$.s),this.scale=oa.FLOAT(E$.scale)}};class S$ extends gG{constructor(){super(...arguments),this.paramsConfig=M$}static type(){return\\\\\\\"decal\\\\\\\"}static displayedInputNames(){return[\\\\\\\"geometry to create decal from\\\\\\\"]}initializeNode(){this.io.inputs.setCount(0,1),this.io.inputs.initInputsClonedState(A$.INPUT_CLONED_STATE)}cook(t){this._operation=this._operation||new A$(this._scene,this.states);const e=this._operation.cook(t,this.pv);this.setCoreGroup(e)}}const C$=new class extends aa{constructor(){super(...arguments),this.duration=oa.INTEGER(1e3,{range:[0,1e3],rangeLocked:[!0,!1]})}};class N$ extends gG{constructor(){super(...arguments),this.paramsConfig=C$}static type(){return\\\\\\\"delay\\\\\\\"}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState(Qi.ALWAYS)}cook(t){const e=t[0];setTimeout((()=>{this.setCoreGroup(e)}),Math.max(this.pv.duration,0))}}class L${constructor(t){this.node=t,this.selected_state=new Map,this._entities_count=0,this._selected_entities_count=0}init(t){this.selected_state.clear();for(let e of t)this.selected_state.set(e,!1);this._entities_count=t.length,this._selected_entities_count=0}select(t){const e=this.selected_state.get(t);null!=e&&0==e&&(this.selected_state.set(t,!0),this._selected_entities_count++)}entities_to_keep(){return this._entities_for_state(this.node.pv.invert)}entities_to_delete(){return this._entities_for_state(!this.node.pv.invert)}_entities_for_state(t){const e=!!t,n=t?this._selected_entities_count:this._entities_count-this._selected_entities_count;if(0==n)return[];{const t=new Array(n);let i=0;return this.selected_state.forEach(((n,r)=>{n==e&&(t[i]=r,i++)})),t}}}var O$;!function(t){t.EQUAL=\\\\\\\"==\\\\\\\",t.LESS_THAN=\\\\\\\"<\\\\\\\",t.EQUAL_OR_LESS_THAN=\\\\\\\"<=\\\\\\\",t.EQUAL_OR_GREATER_THAN=\\\\\\\">=\\\\\\\",t.GREATER_THAN=\\\\\\\">\\\\\\\",t.DIFFERENT=\\\\\\\"!=\\\\\\\"}(O$||(O$={}));const R$=[O$.EQUAL,O$.LESS_THAN,O$.EQUAL_OR_LESS_THAN,O$.EQUAL_OR_GREATER_THAN,O$.GREATER_THAN,O$.DIFFERENT],P$={[O$.EQUAL]:(t,e)=>t==e,[O$.LESS_THAN]:(t,e)=>t<e,[O$.EQUAL_OR_LESS_THAN]:(t,e)=>t<=e,[O$.EQUAL_OR_GREATER_THAN]:(t,e)=>t>=e,[O$.GREATER_THAN]:(t,e)=>t>e,[O$.DIFFERENT]:(t,e)=>t!=e},I$=R$.map(((t,e)=>({name:t,value:e})));class F${constructor(t){this.node=t}evalForEntities(t){const e=kr[this.node.pv.attribType];switch(e){case Dr.NUMERIC:return void this._eval_for_numeric(t);case Dr.STRING:return void this._eval_for_string(t)}ar.unreachable(e)}_eval_for_string(t){let e;for(let n of t)e=n.stringAttribValue(this.node.pv.attribName),e==this.node.pv.attrib_string&&this.node.entitySelectionHelper.select(n)}_eval_for_numeric(t){const e=Ur[this.node.pv.attribSize-1];switch(e){case zr.FLOAT:return this._eval_for_points_numeric_float(t);case zr.VECTOR2:return this._eval_for_points_numeric_vector2(t);case zr.VECTOR3:return this._eval_for_points_numeric_vector3(t);case zr.VECTOR4:return this._eval_for_points_numeric_vector4(t)}ar.unreachable(e)}_eval_for_points_numeric_float(t){let e=this.node.pv.attribName;const n=this.node.pv.attribValue1;let i;const r=R$[this.node.pv.attribComparisonOperator],s=P$[r];for(let r of t)i=r.attribValue(e),s(i,n)&&this.node.entitySelectionHelper.select(r)}_eval_for_points_numeric_vector2(t){let e=this.node.pv.attribName;const n=this.node.pv.attribValue2;let i=new d.a;for(let r of t){const t=r.attribValue(e,i);n.equals(t)&&this.node.entitySelectionHelper.select(r)}}_eval_for_points_numeric_vector3(t){let e=this.node.pv.attribName;const n=this.node.pv.attribValue3;let i=new p.a;for(let r of t){const t=r.attribValue(e,i);n.equals(t)&&this.node.entitySelectionHelper.select(r)}}_eval_for_points_numeric_vector4(t){let e=this.node.pv.attribName;const n=this.node.pv.attribValue4;let i=new _.a;for(let r of t){const t=r.attribValue(e,i);n.equals(t)&&this.node.entitySelectionHelper.select(r)}}}class D${constructor(t){this.node=t}async evalForEntities(t){const e=this.node.p.expression;this.node.p.expression.hasExpression()&&e.expressionController?await this.eval_expressions_for_points_with_expression(t):this.eval_expressions_without_expression(t)}async eval_expressions_for_points_with_expression(t){const e=this.node.p.expression;e.expressionController&&await e.expressionController.compute_expression_for_entities(t,((t,e)=>{e&&this.node.entitySelectionHelper.select(t)}))}eval_expressions_without_expression(t){if(this.node.pv.expression)for(let e of t)this.node.entitySelectionHelper.select(e)}}class k${constructor(t){this.node=t,this._point_position=new p.a}evalForPoints(t){const e=this._createBbox();for(let n of t){e.containsPoint(n.getPosition(this._point_position))&&this.node.entitySelectionHelper.select(n)}}_createBbox(){return new XB.a(this.node.pv.bboxCenter.clone().sub(this.node.pv.bboxSize.clone().multiplyScalar(.5)),this.node.pv.bboxCenter.clone().add(this.node.pv.bboxSize.clone().multiplyScalar(.5)))}}class B${constructor(t){this.node=t}eval_for_objects(t){const e=Lr[this.node.pv.objectType];for(let n of t){Nr(n.object().constructor)==e&&this.node.entitySelectionHelper.select(n)}}}class z${constructor(){this._sidePropertyByMaterial=new WeakMap,this._bound_setMat=this._setObjectMaterialDoubleSided.bind(this),this._bound_restoreMat=this._restoreObjectMaterialSide.bind(this)}setCoreGroupMaterialDoubleSided(t){const e=t.objects();for(let t of e)t.traverse(this._bound_setMat)}restoreMaterialSideProperty(t){const e=t.objects();for(let t of e)t.traverse(this._bound_restoreMat)}_setObjectMaterialDoubleSided(t){const e=t.material;if(e)if(m.isArray(e))for(let t of e)this._setMaterialDoubleSided(t);else this._setMaterialDoubleSided(e)}_restoreObjectMaterialSide(t){const e=t.material;if(e)if(m.isArray(e))for(let t of e)this._restoreMaterialDoubleSided(t);else this._restoreMaterialDoubleSided(e)}_setMaterialDoubleSided(t){this._sidePropertyByMaterial.set(t,t.side),t.side=w.z}_restoreMaterialDoubleSided(t){t.side=this._sidePropertyByMaterial.get(t)||w.z}}const U$=new p.a(0,1,0),G$=new p.a(0,-1,0);class V${constructor(t){this.node=t,this._matDoubleSideTmpSetter=new z$,this._point_position=new p.a,this._raycaster=new uL,this._intersections=[]}evalForPoints(t,e){if(!e)return;const n=null==e?void 0:e.objectsWithGeo()[0];if(!n)return;const i=n;if(!i.isMesh)return;this._matDoubleSideTmpSetter.setCoreGroupMaterialDoubleSided(e);const r=n.geometry;r.computeBoundingBox();const s=r.boundingBox;for(let e of t)e.getPosition(this._point_position),s.containsPoint(this._point_position)?this._isPositionInObject(this._point_position,i,U$)&&this._isPositionInObject(this._point_position,i,G$)&&this.node.entitySelectionHelper.select(e):this.node.entitySelectionHelper.select(e);this._matDoubleSideTmpSetter.restoreMaterialSideProperty(e)}_isPositionInObject(t,e,n){var i;this._raycaster.ray.direction.copy(n),this._raycaster.ray.origin.copy(t),this._intersections.length=0;const r=this._raycaster.intersectObject(e,!1,this._intersections);if(!r)return!1;if(0==r.length)return!1;const s=null===(i=r[0].face)||void 0===i?void 0:i.normal;if(!s)return!1;return this._raycaster.ray.direction.dot(s)>=0}}const H$=new class extends aa{constructor(){super(...arguments),this.class=oa.INTEGER(Ir.indexOf(Pr.VERTEX),{menu:{entries:Fr}}),this.invert=oa.BOOLEAN(0),this.byObjectType=oa.BOOLEAN(0,{visibleIf:{class:Ir.indexOf(Pr.OBJECT)}}),this.objectType=oa.INTEGER(Lr.indexOf(Sr.MESH),{menu:{entries:Or},visibleIf:{class:Ir.indexOf(Pr.OBJECT),byObjectType:!0},separatorAfter:!0}),this.byExpression=oa.BOOLEAN(0),this.expression=oa.BOOLEAN(\\\\\\\"@ptnum==0\\\\\\\",{visibleIf:{byExpression:!0},expression:{forEntities:!0},separatorAfter:!0}),this.byAttrib=oa.BOOLEAN(0),this.attribType=oa.INTEGER(kr.indexOf(Dr.NUMERIC),{menu:{entries:Br},visibleIf:{byAttrib:1}}),this.attribName=oa.STRING(\\\\\\\"\\\\\\\",{visibleIf:{byAttrib:1}}),this.attribSize=oa.INTEGER(1,{range:Gr,rangeLocked:[!0,!0],visibleIf:{byAttrib:1,attribType:kr.indexOf(Dr.NUMERIC)}}),this.attribComparisonOperator=oa.INTEGER(R$.indexOf(O$.EQUAL),{menu:{entries:I$},visibleIf:{byAttrib:!0,attribType:kr.indexOf(Dr.NUMERIC),attribSize:zr.FLOAT}}),this.attribValue1=oa.FLOAT(0,{visibleIf:{byAttrib:1,attribType:kr.indexOf(Dr.NUMERIC),attribSize:1}}),this.attribValue2=oa.VECTOR2([0,0],{visibleIf:{byAttrib:1,attribType:kr.indexOf(Dr.NUMERIC),attribSize:2}}),this.attribValue3=oa.VECTOR3([0,0,0],{visibleIf:{byAttrib:1,attribType:kr.indexOf(Dr.NUMERIC),attribSize:3}}),this.attribValue4=oa.VECTOR4([0,0,0,0],{visibleIf:{byAttrib:1,attribType:kr.indexOf(Dr.NUMERIC),attribSize:4}}),this.attribString=oa.STRING(\\\\\\\"\\\\\\\",{visibleIf:{byAttrib:1,attribType:kr.indexOf(Dr.STRING)},separatorAfter:!0}),this.byBbox=oa.BOOLEAN(0,{visibleIf:{class:Ir.indexOf(Pr.VERTEX)}}),this.bboxSize=oa.VECTOR3([1,1,1],{visibleIf:{class:Ir.indexOf(Pr.VERTEX),byBbox:!0}}),this.bboxCenter=oa.VECTOR3([0,0,0],{visibleIf:{class:Ir.indexOf(Pr.VERTEX),byBbox:!0},separatorAfter:!0}),this.byBoundingObject=oa.BOOLEAN(0,{visibleIf:{class:Ir.indexOf(Pr.VERTEX)}}),this.keepPoints=oa.BOOLEAN(0,{visibleIf:{class:Ir.indexOf(Pr.OBJECT)}})}};class j$ extends gG{constructor(){super(...arguments),this.paramsConfig=H$,this._marked_for_deletion_per_object_index=new Map,this.entitySelectionHelper=new L$(this),this.byExpressionHelper=new D$(this),this.byAttributeHelper=new F$(this),this.byObjectTypeHelper=new B$(this),this.byBboxHelper=new k$(this),this.byBoundingObjectHelper=new V$(this)}static type(){return\\\\\\\"delete\\\\\\\"}static displayedInputNames(){return[\\\\\\\"geometry to delete from\\\\\\\",\\\\\\\"points inside this geometry will be deleted (optional)\\\\\\\"]}initializeNode(){this.io.inputs.setCount(1,2),this.io.inputs.initInputsClonedState(Qi.FROM_NODE)}async cook(t){const e=t[0],n=t[1];switch(this.pv.class){case Pr.VERTEX:await this._eval_for_points(e,n);break;case Pr.OBJECT:await this._eval_for_objects(e)}}set_class(t){this.p.class.set(t)}async _eval_for_objects(t){const e=t.coreObjects();this.entitySelectionHelper.init(e),this._marked_for_deletion_per_object_index=new Map;for(let t of e)this._marked_for_deletion_per_object_index.set(t.index(),!1);this.pv.byExpression&&await this.byExpressionHelper.evalForEntities(e),this.pv.byObjectType&&this.byObjectTypeHelper.eval_for_objects(e),this.pv.byAttrib&&\\\\\\\"\\\\\\\"!=this.pv.attribName&&this.byAttributeHelper.evalForEntities(e);const n=this.entitySelectionHelper.entities_to_keep().map((t=>t.object()));if(this.pv.keepPoints){const t=this.entitySelectionHelper.entities_to_delete();for(let e of t){const t=this._point_object(e);t&&n.push(t)}}this.setObjects(n)}async _eval_for_points(t,e){const n=t.coreObjects();let i,r=[];for(let t=0;t<n.length;t++){i=n[t];let s=i.coreGeometry();if(s){const t=i.object(),n=s.pointsFromGeometry();this.entitySelectionHelper.init(n);const o=n.length;this.pv.byExpression&&await this.byExpressionHelper.evalForEntities(n),this.pv.byAttrib&&\\\\\\\"\\\\\\\"!=this.pv.attribName&&this.byAttributeHelper.evalForEntities(n),this.pv.byBbox&&this.byBboxHelper.evalForPoints(n),this.pv.byBoundingObject&&this.byBoundingObjectHelper.evalForPoints(n,e);const a=this.entitySelectionHelper.entities_to_keep();if(a.length==o)r.push(t);else if(s.geometry().dispose(),a.length>0){const e=ps.geometryFromPoints(a,Nr(t.constructor));e&&(t.geometry=e,r.push(t))}}}this.setObjects(r)}_point_object(t){const e=t.points(),n=ps.geometryFromPoints(e,Sr.POINTS);if(n)return this.createObject(n,Sr.POINTS)}}const W$=new class extends aa{constructor(){super(...arguments),this.start=oa.INTEGER(0,{range:[0,100],rangeLocked:[!0,!1]}),this.useCount=oa.BOOLEAN(0),this.count=oa.INTEGER(0,{range:[0,100],rangeLocked:[!0,!1],visibleIf:{useCount:1}})}};class q$ extends gG{constructor(){super(...arguments),this.paramsConfig=W$}static type(){return\\\\\\\"drawRange\\\\\\\"}initializeNode(){this.io.inputs.setCount(0,1),this.io.inputs.initInputsClonedState(Qi.FROM_NODE)}cook(t){const e=t[0],n=e.objects();for(let t of n){const e=t.geometry;if(e){const t=e.drawRange;t.start=this.pv.start,this.pv.useCount?t.count=this.pv.count:t.count=1/0}}this.setCoreGroup(e)}}class X${constructor(){this.pluginCallbacks=[],this.register((function(t){return new bJ(t)})),this.register((function(t){return new wJ(t)})),this.register((function(t){return new TJ(t)})),this.register((function(t){return new AJ(t)})),this.register((function(t){return new EJ(t)}))}register(t){return-1===this.pluginCallbacks.indexOf(t)&&this.pluginCallbacks.push(t),this}unregister(t){return-1!==this.pluginCallbacks.indexOf(t)&&this.pluginCallbacks.splice(this.pluginCallbacks.indexOf(t),1),this}parse(t,e,n){const i=new xJ,r=[];for(let t=0,e=this.pluginCallbacks.length;t<e;t++)r.push(this.pluginCallbacks[t](i));i.setPlugins(r),i.write(t,e,n)}}const 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c=this.processBufferView(t,o,n,i,l),u={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&&(u.normalized=!0),s.accessors||(s.accessors=[]),s.accessors.push(u)-1}processImage(t,e,n){const i=this,r=i.cache,s=i.json,o=i.options,a=i.pending;r.images.has(t)||r.images.set(t,{});const l=r.images.get(t),c=e===By?\\\\\\\"image/png\\\\\\\":\\\\\\\"image/jpeg\\\\\\\",u=c+\\\\\\\":flipY/\\\\\\\"+n.toString();if(void 0!==l[u])return l[u];s.images||(s.images=[]);const h={mimeType:c};if(o.embedImages){const r=yJ=yJ||document.createElement(\\\\\\\"canvas\\\\\\\");r.width=Math.min(t.width,o.maxTextureSize),r.height=Math.min(t.height,o.maxTextureSize);const s=r.getContext(\\\\\\\"2d\\\\\\\");if(!0===n&&(s.translate(0,r.height),s.scale(1,-1)),\\\\\\\"undefined\\\\\\\"!=typeof HTMLImageElement&&t instanceof HTMLImageElement||\\\\\\\"undefined\\\\\\\"!=typeof HTMLCanvasElement&&t instanceof HTMLCanvasElement||\\\\\\\"undefined\\\\\\\"!=typeof OffscreenCanvas&&t instanceof OffscreenCanvas||\\\\\\\"undefined\\\\\\\"!=typeof ImageBitmap&&t instanceof ImageBitmap)s.drawImage(t,0,0,r.width,r.height);else{e!==By&&e!==ky&&console.error(\\\\\\\"GLTFExporter: Only RGB and RGBA formats are supported.\\\\\\\"),(t.width>o.maxTextureSize||t.height>o.maxTextureSize)&&console.warn(\\\\\\\"GLTFExporter: Image size is bigger than maxTextureSize\\\\\\\",t);const n=new Uint8ClampedArray(t.height*t.width*4);if(e===By)for(let e=0;e<n.length;e+=4)n[e+0]=t.data[e+0],n[e+1]=t.data[e+1],n[e+2]=t.data[e+2],n[e+3]=t.data[e+3];else for(let e=0,i=0;e<n.length;e+=4,i+=3)n[e+0]=t.data[i+0],n[e+1]=t.data[i+1],n[e+2]=t.data[i+2],n[e+3]=255;s.putImageData(new ImageData(n,t.width,t.height),0,0)}!0===o.binary?a.push(new Promise((function(t){r.toBlob((function(e){i.processBufferViewImage(e).then((function(e){h.bufferView=e,t()}))}),c)}))):h.uri=r.toDataURL(c)}else h.uri=t.src;const d=s.images.push(h)-1;return l[u]=d,d}processSampler(t){const e=this.json;e.samplers||(e.samplers=[]);const n={magFilter:_J[t.magFilter],minFilter:_J[t.minFilter],wrapS:_J[t.wrapS],wrapT:_J[t.wrapT]};return e.samplers.push(n)-1}processTexture(t){const e=this.cache,n=this.json;if(e.textures.has(t))return e.textures.get(t);n.textures||(n.textures=[]);const i={sampler:this.processSampler(t),source:this.processImage(t.image,t.format,t.flipY)};t.name&&(i.name=t.name),this._invokeAll((function(e){e.writeTexture&&e.writeTexture(t,i)}));const r=n.textures.push(i)-1;return e.textures.set(t,r),r}processMaterial(t){const e=this.cache,n=this.json;if(e.materials.has(t))return e.materials.get(t);if(t.isShaderMaterial)return console.warn(\\\\\\\"GLTFExporter: THREE.ShaderMaterial not supported.\\\\\\\"),null;n.materials||(n.materials=[]);const i={pbrMetallicRoughness:{}};!0!==t.isMeshStandardMaterial&&!0!==t.isMeshBasicMaterial&&console.warn(\\\\\\\"GLTFExporter: Use MeshStandardMaterial or MeshBasicMaterial for best results.\\\\\\\");const r=t.color.toArray().concat([t.opacity]);if(fJ(r,[1,1,1,1])||(i.pbrMetallicRoughness.baseColorFactor=r),t.isMeshStandardMaterial?(i.pbrMetallicRoughness.metallicFactor=t.metalness,i.pbrMetallicRoughness.roughnessFactor=t.roughness):(i.pbrMetallicRoughness.metallicFactor=.5,i.pbrMetallicRoughness.roughnessFactor=.5),t.metalnessMap||t.roughnessMap)if(t.metalnessMap===t.roughnessMap){const e={index:this.processTexture(t.metalnessMap)};this.applyTextureTransform(e,t.metalnessMap),i.pbrMetallicRoughness.metallicRoughnessTexture=e}else console.warn(\\\\\\\"THREE.GLTFExporter: Ignoring metalnessMap and roughnessMap because they are not the same Texture.\\\\\\\");if(t.map){const e={index:this.processTexture(t.map)};this.applyTextureTransform(e,t.map),i.pbrMetallicRoughness.baseColorTexture=e}if(t.emissive){const e=t.emissive.clone().multiplyScalar(t.emissiveIntensity),n=Math.max(e.r,e.g,e.b);if(n>1&&(e.multiplyScalar(1/n),console.warn(\\\\\\\"THREE.GLTFExporter: Some emissive components exceed 1; emissive has been limited\\\\\\\")),n>0&&(i.emissiveFactor=e.toArray()),t.emissiveMap){const e={index:this.processTexture(t.emissiveMap)};this.applyTextureTransform(e,t.emissiveMap),i.emissiveTexture=e}}if(t.normalMap){const e={index:this.processTexture(t.normalMap)};t.normalScale&&1!==t.normalScale.x&&(e.scale=t.normalScale.x),this.applyTextureTransform(e,t.normalMap),i.normalTexture=e}if(t.aoMap){const e={index:this.processTexture(t.aoMap),texCoord:1};1!==t.aoMapIntensity&&(e.strength=t.aoMapIntensity),this.applyTextureTransform(e,t.aoMap),i.occlusionTexture=e}t.transparent?i.alphaMode=\\\\\\\"BLEND\\\\\\\":t.alphaTest>0&&(i.alphaMode=\\\\\\\"MASK\\\\\\\",i.alphaCutoff=t.alphaTest),2===t.side&&(i.doubleSided=!0),\\\\\\\"\\\\\\\"!==t.name&&(i.name=t.name),this.serializeUserData(t,i),this._invokeAll((function(e){e.writeMaterial&&e.writeMaterial(t,i)}));const s=n.materials.push(i)-1;return e.materials.set(t,s),s}processMesh(t){const e=this.cache,n=this.json,i=[t.geometry.uuid];if(Array.isArray(t.material))for(let e=0,n=t.material.length;e<n;e++)i.push(t.material[e].uuid);else i.push(t.material.uuid);const r=i.join(\\\\\\\":\\\\\\\");if(e.meshes.has(r))return e.meshes.get(r);const s=t.geometry;let o;if(o=t.isLineSegments?$$:t.isLineLoop?J$:t.isLine?Z$:t.isPoints?Y$:t.material.wireframe?$$:Q$,!0!==s.isBufferGeometry)throw new Error(\\\\\\\"THREE.GLTFExporter: Geometry is not of type THREE.BufferGeometry.\\\\\\\");const a={},l={},c=[],u=[],h={uv:\\\\\\\"TEXCOORD_0\\\\\\\",uv2:\\\\\\\"TEXCOORD_1\\\\\\\",color:\\\\\\\"COLOR_0\\\\\\\",skinWeight:\\\\\\\"WEIGHTS_0\\\\\\\",skinIndex:\\\\\\\"JOINTS_0\\\\\\\"},d=s.getAttribute(\\\\\\\"normal\\\\\\\");void 0===d||this.isNormalizedNormalAttribute(d)||(console.warn(\\\\\\\"THREE.GLTFExporter: Creating normalized normal attribute from the non-normalized one.\\\\\\\"),s.setAttribute(\\\\\\\"normal\\\\\\\",this.createNormalizedNormalAttribute(d)));let p=null;for(let t in s.attributes){if(\\\\\\\"morph\\\\\\\"===t.substr(0,5))continue;const n=s.attributes[t];t=h[t]||t.toUpperCase();if(/^(POSITION|NORMAL|TANGENT|TEXCOORD_\\\\d+|COLOR_\\\\d+|JOINTS_\\\\d+|WEIGHTS_\\\\d+)$/.test(t)||(t=\\\\\\\"_\\\\\\\"+t),e.attributes.has(this.getUID(n))){l[t]=e.attributes.get(this.getUID(n));continue}p=null;const i=n.array;\\\\\\\"JOINTS_0\\\\\\\"!==t||i instanceof Uint16Array||i instanceof Uint8Array||(console.warn('GLTFExporter: Attribute \\\\\\\"skinIndex\\\\\\\" converted to type UNSIGNED_SHORT.'),p=new ew(new Uint16Array(i),n.itemSize,n.normalized));const r=this.processAccessor(p||n,s);null!==r&&(l[t]=r,e.attributes.set(this.getUID(n),r))}if(void 0!==d&&s.setAttribute(\\\\\\\"normal\\\\\\\",d),0===Object.keys(l).length)return null;if(void 0!==t.morphTargetInfluences&&t.morphTargetInfluences.length>0){const n=[],i=[],r={};if(void 0!==t.morphTargetDictionary)for(const e in t.morphTargetDictionary)r[t.morphTargetDictionary[e]]=e;for(let o=0;o<t.morphTargetInfluences.length;++o){const a={};let l=!1;for(const t in s.morphAttributes){if(\\\\\\\"position\\\\\\\"!==t&&\\\\\\\"normal\\\\\\\"!==t){l||(console.warn(\\\\\\\"GLTFExporter: Only POSITION and NORMAL morph are supported.\\\\\\\"),l=!0);continue}const n=s.morphAttributes[t][o],i=t.toUpperCase(),r=s.attributes[t];if(e.attributes.has(this.getUID(n))){a[i]=e.attributes.get(this.getUID(n));continue}const c=n.clone();if(!s.morphTargetsRelative)for(let t=0,e=n.count;t<e;t++)c.setXYZ(t,n.getX(t)-r.getX(t),n.getY(t)-r.getY(t),n.getZ(t)-r.getZ(t));a[i]=this.processAccessor(c,s),e.attributes.set(this.getUID(r),a[i])}u.push(a),n.push(t.morphTargetInfluences[o]),void 0!==t.morphTargetDictionary&&i.push(r[o])}a.weights=n,i.length>0&&(a.extras={},a.extras.targetNames=i)}const _=Array.isArray(t.material);if(_&&0===s.groups.length)return null;const m=_?t.material:[t.material],f=_?s.groups:[{materialIndex:0,start:void 0,count:void 0}];for(let t=0,n=f.length;t<n;t++){const n={mode:o,attributes:l};if(this.serializeUserData(s,n),u.length>0&&(n.targets=u),null!==s.index){let i=this.getUID(s.index);void 0===f[t].start&&void 0===f[t].count||(i+=\\\\\\\":\\\\\\\"+f[t].start+\\\\\\\":\\\\\\\"+f[t].count),e.attributes.has(i)?n.indices=e.attributes.get(i):(n.indices=this.processAccessor(s.index,s,f[t].start,f[t].count),e.attributes.set(i,n.indices)),null===n.indices&&delete n.indices}const i=this.processMaterial(m[f[t].materialIndex]);null!==i&&(n.material=i),c.push(n)}a.primitives=c,n.meshes||(n.meshes=[]),this._invokeAll((function(e){e.writeMesh&&e.writeMesh(t,a)}));const g=n.meshes.push(a)-1;return e.meshes.set(r,g),g}processCamera(t){const e=this.json;e.cameras||(e.cameras=[]);const n=t.isOrthographicCamera,i={type:n?\\\\\\\"orthographic\\\\\\\":\\\\\\\"perspective\\\\\\\"};return n?i.orthographic={xmag:2*t.right,ymag:2*t.top,zfar:t.far<=0?.001:t.far,znear:t.near<0?0:t.near}:i.perspective={aspectRatio:t.aspect,yfov:mx.degToRad(t.fov),zfar:t.far<=0?.001:t.far,znear:t.near<0?0:t.near},\\\\\\\"\\\\\\\"!==t.name&&(i.name=t.type),e.cameras.push(i)-1}processAnimation(t,e){const n=this.json,i=this.nodeMap;n.animations||(n.animations=[]);const r=(t=X$.Utils.mergeMorphTargetTracks(t.clone(),e)).tracks,s=[],o=[];for(let t=0;t<r.length;++t){const n=r[t],a=KC.parseTrackName(n.name);let l=KC.findNode(e,a.nodeName);const c=mJ[a.propertyName];if(\\\\\\\"bones\\\\\\\"===a.objectName&&(l=!0===l.isSkinnedMesh?l.skeleton.getBoneByName(a.objectIndex):void 0),!l||!c)return console.warn('THREE.GLTFExporter: Could not export animation track \\\\\\\"%s\\\\\\\".',n.name),null;const u=1;let h,d=n.values.length/n.times.length;c===mJ.morphTargetInfluences&&(d/=l.morphTargetInfluences.length),!0===n.createInterpolant.isInterpolantFactoryMethodGLTFCubicSpline?(h=\\\\\\\"CUBICSPLINE\\\\\\\",d/=3):h=n.getInterpolation()===Gy?\\\\\\\"STEP\\\\\\\":\\\\\\\"LINEAR\\\\\\\",o.push({input:this.processAccessor(new ew(n.times,u)),output:this.processAccessor(new ew(n.values,d)),interpolation:h}),s.push({sampler:o.length-1,target:{node:i.get(l),path:c}})}return n.animations.push({name:t.name||\\\\\\\"clip_\\\\\\\"+n.animations.length,samplers:o,channels:s}),n.animations.length-1}processSkin(t){const e=this.json,n=this.nodeMap,i=e.nodes[n.get(t)],r=t.skeleton;if(void 0===r)return null;const s=t.skeleton.bones[0];if(void 0===s)return null;const o=[],a=new Float32Array(16*r.bones.length),l=new ob;for(let e=0;e<r.bones.length;++e)o.push(n.get(r.bones[e])),l.copy(r.boneInverses[e]),l.multiply(t.bindMatrix).toArray(a,16*e);void 0===e.skins&&(e.skins=[]),e.skins.push({inverseBindMatrices:this.processAccessor(new ew(a,16)),joints:o,skeleton:n.get(s)});return i.skin=e.skins.length-1}processNode(t){const e=this.json,n=this.options,i=this.nodeMap;e.nodes||(e.nodes=[]);const r={};if(n.trs){const e=t.quaternion.toArray(),n=t.position.toArray(),i=t.scale.toArray();fJ(e,[0,0,0,1])||(r.rotation=e),fJ(n,[0,0,0])||(r.translation=n),fJ(i,[1,1,1])||(r.scale=i)}else t.matrixAutoUpdate&&t.updateMatrix(),!1===fJ(t.matrix.elements,[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1])&&(r.matrix=t.matrix.elements);if(\\\\\\\"\\\\\\\"!==t.name&&(r.name=String(t.name)),this.serializeUserData(t,r),t.isMesh||t.isLine||t.isPoints){const e=this.processMesh(t);null!==e&&(r.mesh=e)}else t.isCamera&&(r.camera=this.processCamera(t));if(t.isSkinnedMesh&&this.skins.push(t),t.children.length>0){const e=[];for(let i=0,r=t.children.length;i<r;i++){const r=t.children[i];if(r.visible||!1===n.onlyVisible){const t=this.processNode(r);null!==t&&e.push(t)}}e.length>0&&(r.children=e)}this._invokeAll((function(e){e.writeNode&&e.writeNode(t,r)}));const s=e.nodes.push(r)-1;return i.set(t,s),s}processScene(t){const e=this.json,n=this.options;e.scenes||(e.scenes=[],e.scene=0);const i={};\\\\\\\"\\\\\\\"!==t.name&&(i.name=t.name),e.scenes.push(i);const r=[];for(let e=0,i=t.children.length;e<i;e++){const i=t.children[e];if(i.visible||!1===n.onlyVisible){const t=this.processNode(i);null!==t&&r.push(t)}}r.length>0&&(i.nodes=r),this.serializeUserData(t,i)}processObjects(t){const e=new UE;e.name=\\\\\\\"AuxScene\\\\\\\";for(let n=0;n<t.length;n++)e.children.push(t[n]);this.processScene(e)}processInput(t){const e=this.options;t=t instanceof Array?t:[t],this._invokeAll((function(e){e.beforeParse&&e.beforeParse(t)}));const n=[];for(let e=0;e<t.length;e++)t[e]instanceof UE?this.processScene(t[e]):n.push(t[e]);n.length>0&&this.processObjects(n);for(let t=0;t<this.skins.length;++t)this.processSkin(this.skins[t]);for(let n=0;n<e.animations.length;++n)this.processAnimation(e.animations[n],t[0]);this._invokeAll((function(e){e.afterParse&&e.afterParse(t)}))}_invokeAll(t){for(let e=0,n=this.plugins.length;e<n;e++)t(this.plugins[e])}}class bJ{constructor(t){this.writer=t,this.name=\\\\\\\"KHR_lights_punctual\\\\\\\"}writeNode(t,e){if(!t.isLight)return;if(!t.isDirectionalLight&&!t.isPointLight&&!t.isSpotLight)return void console.warn(\\\\\\\"THREE.GLTFExporter: Only directional, point, and spot lights are supported.\\\\\\\",t);const n=this.writer,i=n.json,r=n.extensionsUsed,s={};t.name&&(s.name=t.name),s.color=t.color.toArray(),s.intensity=t.intensity,t.isDirectionalLight?s.type=\\\\\\\"directional\\\\\\\":t.isPointLight?(s.type=\\\\\\\"point\\\\\\\",t.distance>0&&(s.range=t.distance)):t.isSpotLight&&(s.type=\\\\\\\"spot\\\\\\\",t.distance>0&&(s.range=t.distance),s.spot={},s.spot.innerConeAngle=(t.penumbra-1)*t.angle*-1,s.spot.outerConeAngle=t.angle),void 0!==t.decay&&2!==t.decay&&console.warn(\\\\\\\"THREE.GLTFExporter: Light decay may be lost. glTF is physically-based, and expects light.decay=2.\\\\\\\"),!t.target||t.target.parent===t&&0===t.target.position.x&&0===t.target.position.y&&-1===t.target.position.z||console.warn(\\\\\\\"THREE.GLTFExporter: Light direction may be lost. For best results, make light.target a child of the light with position 0,0,-1.\\\\\\\"),r[this.name]||(i.extensions=i.extensions||{},i.extensions[this.name]={lights:[]},r[this.name]=!0);const o=i.extensions[this.name].lights;o.push(s),e.extensions=e.extensions||{},e.extensions[this.name]={light:o.length-1}}}class wJ{constructor(t){this.writer=t,this.name=\\\\\\\"KHR_materials_unlit\\\\\\\"}writeMaterial(t,e){if(!t.isMeshBasicMaterial)return;const n=this.writer.extensionsUsed;e.extensions=e.extensions||{},e.extensions[this.name]={},n[this.name]=!0,e.pbrMetallicRoughness.metallicFactor=0,e.pbrMetallicRoughness.roughnessFactor=.9}}class TJ{constructor(t){this.writer=t,this.name=\\\\\\\"KHR_materials_pbrSpecularGlossiness\\\\\\\"}writeMaterial(t,e){if(!t.isGLTFSpecularGlossinessMaterial)return;const n=this.writer,i=n.extensionsUsed,r={};e.pbrMetallicRoughness.baseColorFactor&&(r.diffuseFactor=e.pbrMetallicRoughness.baseColorFactor);const s=[1,1,1];if(t.specular.toArray(s,0),r.specularFactor=s,r.glossinessFactor=t.glossiness,e.pbrMetallicRoughness.baseColorTexture&&(r.diffuseTexture=e.pbrMetallicRoughness.baseColorTexture),t.specularMap){const e={index:n.processTexture(t.specularMap)};n.applyTextureTransform(e,t.specularMap),r.specularGlossinessTexture=e}e.extensions=e.extensions||{},e.extensions[this.name]=r,i[this.name]=!0}}class AJ{constructor(t){this.writer=t,this.name=\\\\\\\"KHR_materials_transmission\\\\\\\"}writeMaterial(t,e){if(!t.isMeshPhysicalMaterial||0===t.transmission)return;const n=this.writer,i=n.extensionsUsed,r={};if(r.transmissionFactor=t.transmission,t.transmissionMap){const e={index:n.processTexture(t.transmissionMap)};n.applyTextureTransform(e,t.transmissionMap),r.transmissionTexture=e}e.extensions=e.extensions||{},e.extensions[this.name]=r,i[this.name]=!0}}class EJ{constructor(t){this.writer=t,this.name=\\\\\\\"KHR_materials_volume\\\\\\\"}writeMaterial(t,e){if(!t.isMeshPhysicalMaterial||0===t.thickness)return;const n=this.writer,i=n.extensionsUsed,r={};if(r.thicknessFactor=t.thickness,t.thicknessMap){const e={index:n.processTexture(t.thicknessMap)};n.applyTextureTransform(e,t.thicknessMap),r.thicknessTexture=e}r.attenuationDistance=t.attenuationDistance,r.attenuationColor=t.attenuationTint.toArray(),e.extensions=e.extensions||{},e.extensions[this.name]=r,i[this.name]=!0}}function MJ(t,e){const n=document.createElement(\\\\\\\"a\\\\\\\");n.style.display=\\\\\\\"none\\\\\\\",document.body.appendChild(n),n.href=URL.createObjectURL(t),n.download=e,n.click(),setTimeout((()=>{document.body.removeChild(n)}),10)}X$.Utils={insertKeyframe:function(t,e){const n=.001,i=t.getValueSize(),r=new t.TimeBufferType(t.times.length+1),s=new t.ValueBufferType(t.values.length+i),o=t.createInterpolant(new t.ValueBufferType(i));let a;if(0===t.times.length){r[0]=e;for(let t=0;t<i;t++)s[t]=0;a=0}else if(e<t.times[0]){if(Math.abs(t.times[0]-e)<n)return 0;r[0]=e,r.set(t.times,1),s.set(o.evaluate(e),0),s.set(t.values,i),a=0}else if(e>t.times[t.times.length-1]){if(Math.abs(t.times[t.times.length-1]-e)<n)return t.times.length-1;r[r.length-1]=e,r.set(t.times,0),s.set(t.values,0),s.set(o.evaluate(e),t.values.length),a=r.length-1}else for(let l=0;l<t.times.length;l++){if(Math.abs(t.times[l]-e)<n)return l;if(t.times[l]<e&&t.times[l+1]>e){r.set(t.times.slice(0,l+1),0),r[l+1]=e,r.set(t.times.slice(l+1),l+2),s.set(t.values.slice(0,(l+1)*i),0),s.set(o.evaluate(e),(l+1)*i),s.set(t.values.slice((l+1)*i),(l+2)*i),a=l+1;break}}return t.times=r,t.values=s,a},mergeMorphTargetTracks:function(t,e){const n=[],i={},r=t.tracks;for(let t=0;t<r.length;++t){let s=r[t];const o=KC.parseTrackName(s.name),a=KC.findNode(e,o.nodeName);if(\\\\\\\"morphTargetInfluences\\\\\\\"!==o.propertyName||void 0===o.propertyIndex){n.push(s);continue}if(s.createInterpolant!==s.InterpolantFactoryMethodDiscrete&&s.createInterpolant!==s.InterpolantFactoryMethodLinear){if(s.createInterpolant.isInterpolantFactoryMethodGLTFCubicSpline)throw new Error(\\\\\\\"THREE.GLTFExporter: Cannot merge tracks with glTF CUBICSPLINE interpolation.\\\\\\\");console.warn(\\\\\\\"THREE.GLTFExporter: Morph target interpolation mode not yet supported. Using LINEAR instead.\\\\\\\"),s=s.clone(),s.setInterpolation(Vy)}const l=a.morphTargetInfluences.length,c=a.morphTargetDictionary[o.propertyIndex];if(void 0===c)throw new Error(\\\\\\\"THREE.GLTFExporter: Morph target name not found: \\\\\\\"+o.propertyIndex);let u;if(void 0===i[a.uuid]){u=s.clone();const t=new u.ValueBufferType(l*u.times.length);for(let e=0;e<u.times.length;e++)t[e*l+c]=u.values[e];u.name=(o.nodeName||\\\\\\\"\\\\\\\")+\\\\\\\".morphTargetInfluences\\\\\\\",u.values=t,i[a.uuid]=u,n.push(u);continue}const h=s.createInterpolant(new s.ValueBufferType(1));u=i[a.uuid];for(let t=0;t<u.times.length;t++)u.values[t*l+c]=h.evaluate(u.times[t]);for(let t=0;t<s.times.length;t++){const e=this.insertKeyframe(u,s.times[t]);u.values[e*l+c]=s.values[t]}}return t.tracks=n,t}};const SJ=new class extends aa{constructor(){super(...arguments),this.export=oa.BUTTON(null,{callback:t=>{CJ.PARAM_CALLBACK_export(t)}})}};class CJ extends gG{constructor(){super(...arguments),this.paramsConfig=SJ}static type(){return\\\\\\\"exporter\\\\\\\"}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState(Qi.NEVER)}async cook(t){this.setCoreGroup(t[0])}static PARAM_CALLBACK_export(t){t._paramCallbackExport()}async _paramCallbackExport(){const t=(await this.compute()).coreContent();if(!t)return void console.error(\\\\\\\"input invalid\\\\\\\");const e=new WeakMap,n=t.objects();for(let t of n)e.set(t,t.parent);const i=new fr;for(let t of n)i.add(t);(new X$).parse(i,(t=>{if(t instanceof ArrayBuffer)i=\\\\\\\"scene.glb\\\\\\\",MJ(new Blob([t],{type:\\\\\\\"application/octet-stream\\\\\\\"}),i);else{!function(t,e){MJ(new Blob([t],{type:\\\\\\\"text/plain\\\\\\\"}),e)}(JSON.stringify(t,null,2),\\\\\\\"scene.gltf\\\\\\\")}var i;for(let t of n){const n=e.get(t);n&&n.add(t)}}),{embedImages:!0})}}const NJ=new class extends aa{constructor(){super(...arguments),this.makeFacesUnique=oa.BOOLEAN(0),this.addFaceCenterAttribute=oa.BOOLEAN(0,{visibleIf:{makeFacesUnique:1}}),this.addFaceId=oa.BOOLEAN(0,{visibleIf:{makeFacesUnique:1}}),this.transform=oa.BOOLEAN(0,{visibleIf:{makeFacesUnique:1}}),this.scale=oa.FLOAT(1,{visibleIf:{makeFacesUnique:1,transform:1}})}};class LJ extends gG{constructor(){super(...arguments),this.paramsConfig=NJ}static type(){return\\\\\\\"face\\\\\\\"}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState(Qi.FROM_NODE)}cook(t){const e=t[0];this.pv.makeFacesUnique&&(this._makeFacesUnique(e),this.pv.addFaceCenterAttribute&&this._addFaceCenterAttribute(e),this.pv.addFaceId&&this._addFaceId(e),this.pv.transform&&this._transform_faces(e)),this.setCoreGroup(e)}_makeFacesUnique(t){var e;for(let n of t.objects())if(n.isMesh){const t=n.geometry,i=f.chunk((null===(e=t.index)||void 0===e?void 0:e.array)||[],3),r=3*i.length;for(let e of Object.keys(t.attributes)){const n=t.attributes[e],s=n.itemSize,o=new Float32Array(r*s);let a=0;i.forEach((t=>{t.forEach((t=>{for(let e=0;e<s;e++){const i=n.array[t*s+e];o[a]=i,a+=1}}))})),t.setAttribute(e,new C.a(o,s))}const s=f.range(r);t.setIndex(s)}}_addFaceCenterAttribute(t){const e=\\\\\\\"face_center\\\\\\\",n=new p.a;let i,r,s,o;t.coreObjects().forEach((t=>{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<i.length;t++){r=i[t],r.center(n),s=r.points();for(let t=0;t<s.length;t++)o=s[t],o.setAttribValue(e,n)}}}))}_addFaceId(t){const e=\\\\\\\"face_id\\\\\\\";t.coreObjects().forEach((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<t.length;n++){const i=t[n].points();for(let t=0;t<i.length;t++){i[t].setAttribValue(e,n)}}}}))}_transform_faces(t){const e=\\\\\\\"position\\\\\\\",n=new p.a,i=new p.a,r=this.pv.scale;let s,o,a,l;t.coreObjects().forEach((t=>{const c=t.object(),u=t.coreGeometry();if(c.isMesh&&u){s=u.faces(),u.hasAttrib(e)||u.addNumericAttrib(e,3,-1);for(let t=0;t<s.length;t++){o=s[t],o.center(n),a=o.points();for(let t=0;t<a.length;t++){l=a[t];const s=l.position();i.x=s.x*r+n.x*(1-r),i.y=s.y*r+n.y*(1-r),i.z=s.z*r+n.z*(1-r),l.setAttribValue(e,i)}}}}))}}var OJ;!function(t){t.AUTO=\\\\\\\"auto\\\\\\\",t.DRC=\\\\\\\"drc\\\\\\\",t.FBX=\\\\\\\"fbx\\\\\\\",t.JSON=\\\\\\\"json\\\\\\\",t.GLTF=\\\\\\\"gltf\\\\\\\",t.GLTF_WITH_DRACO=\\\\\\\"gltf_with_draco\\\\\\\",t.OBJ=\\\\\\\"obj\\\\\\\",t.PDB=\\\\\\\"pdb\\\\\\\",t.PLY=\\\\\\\"ply\\\\\\\",t.STL=\\\\\\\"stl\\\\\\\"}(OJ||(OJ={}));const RJ=[OJ.AUTO,OJ.DRC,OJ.FBX,OJ.JSON,OJ.GLTF,OJ.GLTF_WITH_DRACO,OJ.OBJ,OJ.PDB,OJ.PLY,OJ.STL];var PJ;!function(t){t.DRC=\\\\\\\"drc\\\\\\\",t.FBX=\\\\\\\"fbx\\\\\\\",t.GLTF=\\\\\\\"gltf\\\\\\\",t.GLB=\\\\\\\"glb\\\\\\\",t.OBJ=\\\\\\\"obj\\\\\\\",t.PDB=\\\\\\\"pdb\\\\\\\",t.PLY=\\\\\\\"ply\\\\\\\",t.STL=\\\\\\\"stl\\\\\\\"}(PJ||(PJ={}));PJ.DRC,PJ.FBX,PJ.GLTF,PJ.GLB,PJ.OBJ,PJ.PDB,PJ.PLY,PJ.STL;class IJ extends jg{constructor(t,e,n){super(t.url,e,n),this._options=t,this._scene=e,this._node=n}load(t,e){this._load().then((e=>{t(e)})).catch((t=>{e(t)}))}_load(){return new Promise((async(t,e)=>{const n=await this._urlToLoad(),i=this.extension();if(i==OJ.JSON&&this._options.format==OJ.AUTO)IJ.increment_in_progress_loads_count(),await IJ.wait_for_max_concurrent_loads_queue_freed(),fetch(n).then((async e=>{const n=await e.json();new aY(this.loadingManager).parse(n,(e=>{IJ.decrement_in_progress_loads_count(),t(this.on_load_success(e.children[0]))}))})).catch((t=>{IJ.decrement_in_progress_loads_count(),e(t)}));else{const r=await this._loaderForFormat();if(r)IJ.increment_in_progress_loads_count(),await IJ.wait_for_max_concurrent_loads_queue_freed(),r.load(n,(e=>{this.on_load_success(e).then((e=>{IJ.decrement_in_progress_loads_count(),t(e)}))}),void 0,(t=>{ai.warn(\\\\\\\"error loading\\\\\\\",n,t),IJ.decrement_in_progress_loads_count(),e(t)}));else{e(`format not supported (${i})`)}}}))}async on_load_success(t){const e=this.extension();if(e==OJ.JSON)return[t];const n=t;if(n.isObject3D)switch(e){case PJ.PDB:return this.on_load_succes_pdb(t);case PJ.OBJ:default:return[n]}const i=t;if(i.isBufferGeometry)switch(e){case PJ.DRC:return this.on_load_succes_drc(i);default:return[new k.a(i)]}const r=t;if(null!=r.scene)switch(e){case PJ.GLTF:case PJ.GLB:return this.on_load_succes_gltf(r);default:return[n]}const s=t;if(s.geometryAtoms||s.geometryBonds)switch(e){case PJ.PDB:return this.on_load_succes_pdb(s);default:return[]}return[]}on_load_succes_drc(t){return[new k.a(t,IJ._default_mat_mesh)]}on_load_succes_gltf(t){const e=t.scene;return e.animations=t.animations,[e]}on_load_succes_pdb(t){return[new gr.a(t.geometryAtoms,IJ._default_mat_point),new Tr.a(t.geometryBonds,IJ._default_mat_line)]}static moduleNamesFromFormat(t,e){switch(t){case OJ.AUTO:return this.moduleNamesFromExt(e);case OJ.DRC:return[Vn.DRACOLoader];case OJ.FBX:return[Vn.FBXLoader];case OJ.JSON:return[];case OJ.GLTF:return[Vn.GLTFLoader];case OJ.GLTF_WITH_DRACO:return[Vn.GLTFLoader,Vn.DRACOLoader];case OJ.OBJ:return[Vn.OBJLoader];case OJ.PDB:return[Vn.PDBLoader];case OJ.PLY:return[Vn.PLYLoader];case OJ.STL:return[Vn.STLLoader]}ar.unreachable(t)}static moduleNamesFromExt(t){switch(t){case PJ.DRC:return[Vn.DRACOLoader];case PJ.FBX:return[Vn.FBXLoader];case PJ.GLTF:return[Vn.GLTFLoader];case PJ.GLB:return[Vn.GLTFLoader,Vn.DRACOLoader];case PJ.OBJ:return[Vn.OBJLoader];case PJ.PDB:return[Vn.PDBLoader];case PJ.PLY:return[Vn.PLYLoader];case PJ.STL:return[Vn.STLLoader]}}async _loaderForFormat(){const t=this._options.format;switch(t){case OJ.AUTO:return this._loaderForExt();case OJ.DRC:return this.loader_for_drc(this._node);case OJ.FBX:return this.loader_for_fbx();case OJ.JSON:return;case OJ.GLTF:return this.loader_for_gltf();case OJ.GLTF_WITH_DRACO:return this.loader_for_glb(this._node);case OJ.OBJ:return this.loader_for_obj();case OJ.PDB:return this.loader_for_pdb();case OJ.PLY:return this.loader_for_ply();case OJ.STL:return this.loader_for_stl()}ar.unreachable(t)}async _loaderForExt(){switch(this.extension().toLowerCase()){case PJ.DRC:return this.loader_for_drc(this._node);case PJ.FBX:return this.loader_for_fbx();case PJ.GLTF:return this.loader_for_gltf();case PJ.GLB:return this.loader_for_glb(this._node);case PJ.OBJ:return this.loader_for_obj();case PJ.PDB:return this.loader_for_pdb();case PJ.PLY:return this.loader_for_ply();case PJ.STL:return this.loader_for_stl()}}loader_for_fbx(){const t=ai.modulesRegister.module(Vn.FBXLoader);if(t)return new t(this.loadingManager)}loader_for_gltf(){const t=ai.modulesRegister.module(Vn.GLTFLoader);if(t)return new t(this.loadingManager)}static async loader_for_drc(t){const e=ai.modulesRegister.module(Vn.DRACOLoader);if(e){const n=new e(this.loadingManager),i=ai.libs.root(),r=ai.libs.DRACOPath();if(i||r){const e=`${i||\\\\\\\"\\\\\\\"}${r||\\\\\\\"\\\\\\\"}/`;if(t){const n=[\\\\\\\"draco_decoder.js\\\\\\\",\\\\\\\"draco_decoder.wasm\\\\\\\",\\\\\\\"draco_wasm_wrapper.js\\\\\\\"];await this._loadMultipleBlobGlobal({files:n.map((t=>({storedUrl:`${r}/${t}`,fullUrl:`${e}${t}`}))),node:t,error:\\\\\\\"failed to load draco libraries. Make sure to install them to load .glb files\\\\\\\"})}n.setDecoderPath(e)}else n.setDecoderPath(void 0);return n.setDecoderConfig({type:\\\\\\\"js\\\\\\\"}),n}}loader_for_drc(t){return IJ.loader_for_drc(t)}static async loader_for_glb(t){const e=ai.modulesRegister.module(Vn.GLTFLoader),n=ai.modulesRegister.module(Vn.DRACOLoader);if(e&&n){this.gltf_loader=this.gltf_loader||new e(this.loadingManager),this.draco_loader=this.draco_loader||new n(this.loadingManager);const i=ai.libs.root(),r=ai.libs.DRACOGLTFPath();if(i||r){const e=`${i||\\\\\\\"\\\\\\\"}${r||\\\\\\\"\\\\\\\"}/`;if(t){const n=[\\\\\\\"draco_decoder.js\\\\\\\",\\\\\\\"draco_decoder.wasm\\\\\\\",\\\\\\\"draco_wasm_wrapper.js\\\\\\\"];await this._loadMultipleBlobGlobal({files:n.map((t=>({storedUrl:`${r}/${t}`,fullUrl:`${e}${t}`}))),node:t,error:\\\\\\\"failed to load draco libraries. Make sure to install them to load .glb files\\\\\\\"})}this.draco_loader.setDecoderPath(e)}else this.draco_loader.setDecoderPath(void 0);return this.gltf_loader.setDRACOLoader(this.draco_loader),this.gltf_loader}}loader_for_glb(t){return IJ.loader_for_glb(t)}loader_for_obj(){const t=ai.modulesRegister.module(Vn.OBJLoader);if(t)return new t(this.loadingManager)}loader_for_pdb(){const t=ai.modulesRegister.module(Vn.PDBLoader);if(t)return new t(this.loadingManager)}loader_for_ply(){const t=ai.modulesRegister.module(Vn.PLYLoader);if(t)return new t(this.loadingManager)}loader_for_stl(){const t=ai.modulesRegister.module(Vn.STLLoader);if(t)return new t(this.loadingManager)}static setMaxConcurrentLoadsCount(t){this._maxConcurrentLoadsCountMethod=t}static _init_max_concurrent_loads_count(){return this._maxConcurrentLoadsCountMethod?this._maxConcurrentLoadsCountMethod():Zf.isChrome()?4:1}static _init_concurrent_loads_delay(){return Zf.isChrome()?1:10}static increment_in_progress_loads_count(){this.in_progress_loads_count++}static decrement_in_progress_loads_count(){this.in_progress_loads_count--;const t=this._queue.pop();if(t){const e=this.CONCURRENT_LOADS_DELAY;setTimeout((()=>{t()}),e)}}static async wait_for_max_concurrent_loads_queue_freed(){return this.in_progress_loads_count<=this.MAX_CONCURRENT_LOADS_COUNT?void 0:new Promise((t=>{this._queue.push(t)}))}}IJ._default_mat_mesh=new br.a,IJ._default_mat_point=new yr.a,IJ._default_mat_line=new wr.a,IJ.MAX_CONCURRENT_LOADS_COUNT=IJ._init_max_concurrent_loads_count(),IJ.CONCURRENT_LOADS_DELAY=IJ._init_concurrent_loads_delay(),IJ.in_progress_loads_count=0,IJ._queue=[];const FJ=`${Gg}/models/wolf.obj`;class DJ extends pG{static type(){return\\\\\\\"file\\\\\\\"}cook(t,e){const n=new IJ({url:e.url,format:e.format},this.scene(),this._node);return new Promise((t=>{n.load((e=>{const n=this._on_load(e);t(this.createCoreGroupFromObjects(n))}),(t=>{this._on_error(t,e)}))}))}_on_load(t){t=t.flat();for(let e of t)e.traverse((t=>{this._ensure_geometry_has_index(t),t.matrixAutoUpdate=!1}));return t}_on_error(t,e){var n;null===(n=this.states)||void 0===n||n.error.set(`could not load geometry from ${e.url} (${t})`)}_ensure_geometry_has_index(t){const e=t.geometry;e&&this.createIndexIfNone(e)}}DJ.DEFAULT_PARAMS={url:FJ,format:OJ.AUTO};const kJ=DJ.DEFAULT_PARAMS;const BJ=new class extends aa{constructor(){super(...arguments),this.url=oa.STRING(kJ.url,{fileBrowse:{type:[Ls.GEOMETRY]}}),this.format=oa.STRING(kJ.format,{menuString:{entries:RJ.map((t=>({name:t,value:t})))}}),this.reload=oa.BUTTON(null,{callback:t=>{zJ.PARAM_CALLBACK_reload(t)}})}};class zJ extends gG{constructor(){super(...arguments),this.paramsConfig=BJ}static type(){return\\\\\\\"file\\\\\\\"}async requiredModules(){for(let t of[this.p.url,this.p.format])t.isDirty()&&await t.compute();const t=jg.extension(this.pv.url||\\\\\\\"\\\\\\\"),e=this.pv.format;return IJ.moduleNamesFromFormat(e,t)}initializeNode(){this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.url],(()=>{const t=this.p.url.rawInput();if(t){const e=t.split(\\\\\\\"/\\\\\\\");return e[e.length-1]}return\\\\\\\"\\\\\\\"}))}))}))}async cook(t){this._operation=this._operation||new DJ(this.scene(),this.states,this);const e=await this._operation.cook(t,this.pv);this.setCoreGroup(e)}static PARAM_CALLBACK_reload(t){t._paramCallbackReload()}_paramCallbackReload(){this.p.url.setDirty()}}const UJ=new class extends aa{constructor(){super(...arguments),this.dist=oa.FLOAT(.1,{range:[0,1],rangeLocked:[!0,!1]})}};class GJ extends gG{constructor(){super(...arguments),this.paramsConfig=UJ}static type(){return\\\\\\\"fuse\\\\\\\"}static displayedInputNames(){return[\\\\\\\"points to fuse together\\\\\\\"]}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState(Qi.FROM_NODE)}cook(t){const e=t[0],n=[];let i;for(let t of e.coreObjects())i=this._fuse_core_object(t),i&&n.push(i);this.setObjects(n)}_fuse_core_object(t){const e=t.object();if(!e)return;const n=t.points(),i=this.pv.dist,r={};for(let t of n){const e=t.position(),n=new p.a(Math.round(e.x/i),Math.round(e.y/i),Math.round(e.z/i)).toArray().join(\\\\\\\"-\\\\\\\");r[n]=r[n]||[],r[n].push(t)}const s=[];if(Object.keys(r).forEach((t=>{s.push(r[t][0])})),e.geometry.dispose(),s.length>0){const t=ps.geometryFromPoints(s,Nr(e.constructor));return t&&(e.geometry=t),e}}}class VJ{constructor(t,e,n){this._param_size=t,this._param_hexagon_radius=e,this._param_points_only=n}process(){const t=this._param_hexagon_radius,e=.5*t,n=t,i=Math.cos(Math.PI/6)*this._param_hexagon_radius,r=Math.floor(this._param_size.x/n),s=Math.floor(this._param_size.y/i);let o=[],a=[];for(let t=0;t<s;t++)for(let s=0;s<r;s++)o.push([-.5*this._param_size.x+s*n+(t%2==0?e:0),0,-.5*this._param_size.y+t*i]),this._param_points_only||t>=1&&(0==s||s==r-1?0==s?a.push([s+1+(t-1)*r,s+(t-1)*r,s+t*r]):a.push([s+t*r,s+(t-1)*r,s-1+t*r]):(a.push([s+t*r,s+(t-1)*r,s-1+t*r]),a.push([s+t*r,s+1+(t-1)*r,s+(t-1)*r])));const l=new S.a;return l.setAttribute(\\\\\\\"position\\\\\\\",new C.a(new Float32Array(o.flat()),3)),this._param_points_only||(l.setIndex(a.flat()),l.computeVertexNormals()),l}}const HJ=new p.a(0,1,0);const jJ=new class extends aa{constructor(){super(...arguments),this.size=oa.VECTOR2([1,1]),this.hexagonRadius=oa.FLOAT(.1,{range:[.001,1],rangeLocked:[!1,!1]}),this.direction=oa.VECTOR3([0,1,0]),this.pointsOnly=oa.BOOLEAN(0)}};class WJ extends gG{constructor(){super(...arguments),this.paramsConfig=jJ,this._core_transform=new Mz}static type(){return\\\\\\\"hexagons\\\\\\\"}initializeNode(){}cook(){if(this.pv.hexagonRadius>0){const t=new VJ(this.pv.size,this.pv.hexagonRadius,this.pv.pointsOnly).process();this._core_transform.rotate_geometry(t,HJ,this.pv.direction),this.pv.pointsOnly?this.setGeometry(t,Sr.POINTS):this.setGeometry(t)}else this.setObjects([])}}var qJ;!function(t){t.ADD_PARENT=\\\\\\\"add_parent\\\\\\\",t.REMOVE_PARENT=\\\\\\\"remove_parent\\\\\\\",t.ADD_CHILD=\\\\\\\"add_child\\\\\\\"}(qJ||(qJ={}));const XJ=[qJ.ADD_PARENT,qJ.REMOVE_PARENT,qJ.ADD_CHILD];class YJ extends pG{static type(){return\\\\\\\"hierarchy\\\\\\\"}cook(t,e){const n=t[0],i=XJ[e.mode];switch(i){case qJ.ADD_PARENT:{const t=this._add_parent_to_core_group(n,e);return this.createCoreGroupFromObjects(t)}case qJ.REMOVE_PARENT:{const t=this._remove_parent_from_core_group(n,e);return this.createCoreGroupFromObjects(t)}case qJ.ADD_CHILD:{const i=this._add_child_to_core_group(n,t[1],e);return this.createCoreGroupFromObjects(i)}}ar.unreachable(i)}_add_parent_to_core_group(t,e){if(0==e.levels)return t.objects();return[this._add_parent_to_object(t.objects(),e)]}_add_parent_to_object(t,e){let n=new In.a;if(n.matrixAutoUpdate=!1,n.add(...t),e.levels>0)for(let t=0;t<e.levels-1;t++)n=this._add_new_parent(n,e);return n}_add_new_parent(t,e){const n=new In.a;return n.matrixAutoUpdate=!1,n.add(t),n}_remove_parent_from_core_group(t,e){if(0==e.levels)return t.objects();{const n=[];for(let i of t.objects()){const t=this._remove_parent_from_object(i,e);for(let e of t)n.push(e)}return n}}_remove_parent_from_object(t,e){let n=t.children;for(let t=0;t<e.levels-1;t++)n=this._get_children_from_objects(n,e);return n}_get_children_from_objects(t,e){let n;const i=[];for(;n=t.pop();)if(n.children)for(let t of n.children)i.push(t);return i}_add_child_to_core_group(t,e,n){var i,r;const s=t.objects();if(!e)return null===(i=this.states)||void 0===i||i.error.set(\\\\\\\"input 1 is invalid\\\\\\\"),[];const o=e.objects(),a=n.objectMask.trim(),l=\\\\\\\"\\\\\\\"!=a?this._findObjectsByMaskFromObjects(a,s):s;n.debugObjectMask&&console.log(l);for(let t=0;t<l.length;t++){const e=l[t],n=o[t]||o[0];if(!n)return null===(r=this.states)||void 0===r||r.error.set(\\\\\\\"no objects found in input 1\\\\\\\"),[];e.add(n)}return s}_findObjectsByMaskFromObjects(t,e){const n=[];for(let i of e)this.scene().objectsController.objectsByMaskInObject(t,i,n);return n}}YJ.DEFAULT_PARAMS={mode:0,levels:1,objectMask:\\\\\\\"\\\\\\\",debugObjectMask:!1},YJ.INPUT_CLONED_STATE=Qi.FROM_NODE;const $J=[qJ.ADD_PARENT,qJ.REMOVE_PARENT],JJ=YJ.DEFAULT_PARAMS;const ZJ=new class extends aa{constructor(){super(...arguments),this.mode=oa.INTEGER(JJ.mode,{menu:{entries:XJ.map(((t,e)=>({name:t,value:e})))}}),this.levels=oa.INTEGER(JJ.levels,{range:[0,5],visibleIf:[{mode:XJ.indexOf(qJ.ADD_PARENT)},{mode:XJ.indexOf(qJ.REMOVE_PARENT)}]}),this.objectMask=oa.STRING(\\\\\\\"\\\\\\\",{visibleIf:{mode:XJ.indexOf(qJ.ADD_CHILD)}}),this.debugObjectMask=oa.BOOLEAN(0,{visibleIf:{mode:XJ.indexOf(qJ.ADD_CHILD)}})}};class QJ extends gG{constructor(){super(...arguments),this.paramsConfig=ZJ}static type(){return\\\\\\\"hierarchy\\\\\\\"}static displayedInputNames(){return[\\\\\\\"geometry to add or remove parents to/from\\\\\\\",\\\\\\\"objects to use as parent or children (optional)\\\\\\\"]}initializeNode(){this.io.inputs.setCount(1,2),this.io.inputs.initInputsClonedState(YJ.INPUT_CLONED_STATE),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.mode,this.p.levels,this.p.objectMask],(()=>{const t=XJ[this.pv.mode];return $J.includes(t)?`${t} ${this.pv.levels}`:`${t} (with mask: ${this.pv.objectMask})`}))}))}))}cook(t){this._operation=this._operation||new YJ(this._scene,this.states);const e=this._operation.cook(t,this.pv);this.setCoreGroup(e)}}const KJ=new class extends aa{constructor(){super(...arguments),this.texture=oa.OPERATOR_PATH(gi.UV,{nodeSelection:{context:Ki.COP}}),this.mult=oa.FLOAT(1)}};class tZ extends gG{constructor(){super(...arguments),this.paramsConfig=KJ}static type(){return\\\\\\\"heightMap\\\\\\\"}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState(Qi.FROM_NODE)}async cook(t){const e=t[0],n=this.p.texture.found_node();if(n){if(n.context()==Ki.COP){const t=n,i=(await t.compute()).texture();for(let t of e.coreObjects())this._set_position_from_data_texture(t,i)}else this.states.error.set(\\\\\\\"found node is not a texture\\\\\\\")}e.computeVertexNormals(),this.setCoreGroup(e)}_set_position_from_data_texture(t,e){var n;const i=this._data_from_texture(e);if(!i)return;const{data:r,resx:s,resy:o}=i,a=r.length/(s*o),l=null===(n=t.coreGeometry())||void 0===n?void 0:n.geometry();if(!l)return;const c=l.getAttribute(\\\\\\\"position\\\\\\\").array,u=l.getAttribute(\\\\\\\"uv\\\\\\\"),h=l.getAttribute(\\\\\\\"normal\\\\\\\");if(null==u)return void this.states.error.set(\\\\\\\"uvs are required\\\\\\\");if(null==h)return void this.states.error.set(\\\\\\\"normals are required\\\\\\\");const d=u.array,p=h.array,_=c.length/3;let m,f,g,v,y,x,b,w=0;for(let t=0;t<_;t++)m=2*t,f=d[m],g=d[m+1],v=Math.floor((s-1)*f),y=Math.floor((o-1)*(1-g)),x=y*s+v,b=r[a*x],w=3*t,c[w+0]+=p[w+0]*b*this.pv.mult,c[w+1]+=p[w+1]*b*this.pv.mult,c[w+2]+=p[w+2]*b*this.pv.mult}_data_from_texture(t){if(t.image)return t.image.data?this._data_from_data_texture(t):this._data_from_default_texture(t)}_data_from_default_texture(t){const e=t.image.width,n=t.image.height;return{data:Rf.data_from_image(t.image).data,resx:e,resy:n}}_data_from_data_texture(t){return{data:t.image.data,resx:t.image.width,resy:t.image.height}}}function eZ(t){return Math.atan2(-t.y,Math.sqrt(t.x*t.x+t.z*t.z))}class nZ extends S.a{constructor(t,e,n,i,r){super(),this.type=\\\\\\\"PolyhedronBufferGeometry\\\\\\\",this.parameters={vertices:t,indices:e,radius:n,detail:i},n=n||1,i=i||0;const s=[],o=[],a=new Map;function l(t,e,n,i){const r=i+1,s=[];for(let i=0;i<=r;i++){s[i]=[];const o=t.clone().lerp(n,i/r),a=e.clone().lerp(n,i/r),l=r-i;for(let t=0;t<=l;t++)s[i][t]=0===t&&i===r?o:o.clone().lerp(a,t/l)}for(let t=0;t<r;t++)for(let e=0;e<2*(r-t)-1;e++){const n=Math.floor(e/2);e%2==0?(c(s[t][n+1]),c(s[t+1][n]),c(s[t][n])):(c(s[t][n+1]),c(s[t+1][n+1]),c(s[t+1][n]))}}function c(t){if(r){let e=a.get(t.x);if(e){const n=e.get(t.y);if(n&&n.has(t.z))return}e||(e=new Map,a.set(t.x,e));let n=e.get(t.y);n||(n=new Set,e.set(t.y,n)),n.add(t.z)}s.push(t.x,t.y,t.z)}function u(e,n){const i=3*e;n.x=t[i+0],n.y=t[i+1],n.z=t[i+2]}!function(t){const n=new p.a,i=new p.a,r=new p.a;for(let s=0;s<e.length;s+=3)u(e[s+0],n),u(e[s+1],i),u(e[s+2],r),l(n,i,r,t)}(i),function(t){const e=new p.a;for(let n=0;n<s.length;n+=3)e.x=s[n+0],e.y=s[n+1],e.z=s[n+2],e.normalize().multiplyScalar(t),s[n+0]=e.x,s[n+1]=e.y,s[n+2]=e.z}(n),function(){const t=new p.a;for(let n=0;n<s.length;n+=3){t.x=s[n+0],t.y=s[n+1],t.z=s[n+2];const i=(e=t,Math.atan2(e.z,-e.x)/2/Math.PI+.5),r=eZ(t)/Math.PI+.5;o.push(i,1-r)}var e}(),this.setAttribute(\\\\\\\"position\\\\\\\",new C.c(s,3)),this.setAttribute(\\\\\\\"uv\\\\\\\",new C.c(o,2)),r||(this.setAttribute(\\\\\\\"normal\\\\\\\",new C.c(s.slice(),3)),0===i?this.computeVertexNormals():this.normalizeNormals())}}class iZ extends nZ{constructor(t,e,n){const i=(1+Math.sqrt(5))/2;super([-1,i,0,1,i,0,-1,-i,0,1,-i,0,0,-1,i,0,1,i,0,-1,-i,0,1,-i,i,0,-1,i,0,1,-i,0,-1,-i,0,1],[0,11,5,0,5,1,0,1,7,0,7,10,0,10,11,1,5,9,5,11,4,11,10,2,10,7,6,7,1,8,3,9,4,3,4,2,3,2,6,3,6,8,3,8,9,4,9,5,2,4,11,6,2,10,8,6,7,9,8,1],t,e,n),this.type=\\\\\\\"IcosahedronBufferGeometry\\\\\\\",this.parameters={radius:t,detail:e}}}class rZ extends pG{static type(){return\\\\\\\"icosahedron\\\\\\\"}cook(t,e){const n=e.pointsOnly,i=new iZ(e.radius,e.detail,n);if(i.translate(e.center.x,e.center.y,e.center.z),n){const t=this.createObject(i,Sr.POINTS);return this.createCoreGroupFromObjects([t])}return i.computeVertexNormals(),this.createCoreGroupFromGeometry(i)}}rZ.DEFAULT_PARAMS={radius:1,detail:0,pointsOnly:!1,center:new p.a(0,0,0)};const sZ=rZ.DEFAULT_PARAMS;const oZ=new class extends aa{constructor(){super(...arguments),this.radius=oa.FLOAT(sZ.radius),this.detail=oa.INTEGER(sZ.detail,{range:[0,10],rangeLocked:[!0,!1]}),this.pointsOnly=oa.BOOLEAN(sZ.pointsOnly),this.center=oa.VECTOR3(sZ.center)}};class aZ extends gG{constructor(){super(...arguments),this.paramsConfig=oZ}static type(){return\\\\\\\"icosahedron\\\\\\\"}cook(){this._operation=this._operation||new rZ(this._scene,this.states);const t=this._operation.cook([],this.pv);this.setCoreGroup(t)}}class lZ extends pG{static type(){return\\\\\\\"instance\\\\\\\"}async cook(t,e){const n=t[0];this._geometry=void 0;const i=n.objectsWithGeo()[0];if(i){const n=i.geometry;if(n){const i=t[1];this._create_instance(n,i,e)}}if(this._geometry){const t=(r=i)instanceof k.a?Sr.MESH:r instanceof Tr.a?Sr.LINE_SEGMENTS:r instanceof gr.a?Sr.POINTS:r instanceof Q.a?Sr.OBJECT3D:void ai.warn(\\\\\\\"ObjectTypeByObject received an unknown object type\\\\\\\",r);if(t){const n=this.createObject(this._geometry,t);if(e.applyMaterial){const t=await this._get_material(e);t&&await this._applyMaterial(n,t)}return this.createCoreGroupFromObjects([n])}}var r;return this.createCoreGroupFromObjects([])}async _get_material(t){var e;if(t.applyMaterial){const n=t.material.nodeWithContext(Ki.MAT,null===(e=this.states)||void 0===e?void 0:e.error);if(n){this._globals_handler=this._globals_handler||new Sf;const t=n.assemblerController;t&&t.set_assembler_globals_handler(this._globals_handler);return(await n.compute()).material()}}}async _applyMaterial(t,e){t.material=e,fs.applyCustomMaterials(t,e)}_create_instance(t,e,n){this._geometry=JY.create_instance_buffer_geo(t,e,n.attributesToCopy)}}lZ.DEFAULT_PARAMS={attributesToCopy:\\\\\\\"instance*\\\\\\\",applyMaterial:!0,material:new vi(\\\\\\\"\\\\\\\")},lZ.INPUT_CLONED_STATE=[Qi.ALWAYS,Qi.NEVER];const cZ=lZ.DEFAULT_PARAMS;const uZ=new class extends aa{constructor(){super(...arguments),this.attributesToCopy=oa.STRING(cZ.attributesToCopy),this.applyMaterial=oa.BOOLEAN(cZ.applyMaterial),this.material=oa.NODE_PATH(cZ.material.path(),{visibleIf:{applyMaterial:1},nodeSelection:{context:Ki.MAT},dependentOnFoundNode:!1})}};class hZ extends gG{constructor(){super(...arguments),this.paramsConfig=uZ}static type(){return\\\\\\\"instance\\\\\\\"}static displayedInputNames(){return[\\\\\\\"geometry to be instanciated\\\\\\\",\\\\\\\"points to instance to\\\\\\\"]}initializeNode(){super.initializeNode(),this.io.inputs.setCount(2),this.io.inputs.initInputsClonedState(lZ.INPUT_CLONED_STATE)}async cook(t){this._operation=this._operation||new lZ(this.scene(),this.states);const e=await this._operation.cook(t,this.pv);this.setCoreGroup(e)}}const dZ=new class extends aa{constructor(){super(...arguments),this.useMax=oa.BOOLEAN(0),this.max=oa.INTEGER(1,{range:[0,100],rangeLocked:[!0,!1],visibleIf:{useMax:1}})}};class pZ extends gG{constructor(){super(...arguments),this.paramsConfig=dZ}static type(){return\\\\\\\"instancesCount\\\\\\\"}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState(Qi.FROM_NODE)}async cook(t){const e=t[0],n=e.objectsWithGeo();for(let t of n){const e=t.geometry;e&&e instanceof kX&&(this.pv.useMax?e.instanceCount=this.pv.max:e.instanceCount=1/0)}this.setCoreGroup(e)}}class _Z extends pG{static type(){return\\\\\\\"jitter\\\\\\\"}cook(t,e){const n=t[0],i=n.points();let r;for(let t=0;t<i.length;t++){r=i[t];const n=new p.a(2*e.mult.x*(rs.randFloat(75*t+764+e.seed)-.5),2*e.mult.y*(rs.randFloat(5678*t+3653+e.seed)-.5),2*e.mult.z*(rs.randFloat(657*t+48464+e.seed)-.5));n.normalize(),n.multiplyScalar(e.amount*rs.randFloat(78*t+54+e.seed));const s=r.position().clone().add(n);r.setPosition(s)}return n}}_Z.DEFAULT_PARAMS={amount:1,mult:new p.a(1,1,1),seed:1},_Z.INPUT_CLONED_STATE=Qi.FROM_NODE;const mZ=_Z.DEFAULT_PARAMS;const fZ=new class extends aa{constructor(){super(...arguments),this.amount=oa.FLOAT(mZ.amount),this.mult=oa.VECTOR3(mZ.mult),this.seed=oa.INTEGER(mZ.seed,{range:[0,100]})}};class gZ extends gG{constructor(){super(...arguments),this.paramsConfig=fZ}static type(){return\\\\\\\"jitter\\\\\\\"}static displayedInputNames(){return[\\\\\\\"geometry to jitter points of\\\\\\\"]}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState(_Z.INPUT_CLONED_STATE)}cook(t){this._operation=this._operation||new _Z(this.scene(),this.states);const e=this._operation.cook(t,this.pv);this.setCoreGroup(e)}}new class extends aa{};const vZ=new class extends aa{constructor(){super(...arguments),this.layer=oa.INTEGER(0,{range:[0,31],rangeLocked:[!0,!0]})}};class yZ extends gG{constructor(){super(...arguments),this.paramsConfig=vZ}static type(){return\\\\\\\"layer\\\\\\\"}static displayedInputNames(){return[\\\\\\\"objects to change layers of\\\\\\\"]}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState(Qi.FROM_NODE),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.layer])}))}))}cook(t){const e=t[0];for(let t of e.objects())t.layers.set(this.pv.layer);this.setCoreGroup(e)}}const xZ=new class extends aa{constructor(){super(...arguments),this.length=oa.FLOAT(1,{range:[0,10]}),this.pointsCount=oa.INTEGER(1,{range:[2,100],rangeLocked:[!0,!1]}),this.origin=oa.VECTOR3([0,0,0]),this.direction=oa.VECTOR3([0,1,0])}};class bZ extends gG{constructor(){super(...arguments),this.paramsConfig=xZ}static type(){return\\\\\\\"line\\\\\\\"}initializeNode(){}cook(){const t=Math.max(2,this.pv.pointsCount),e=new Array(3*t),n=new Array(t),i=this.pv.direction.clone().normalize().multiplyScalar(this.pv.length);for(let r=0;r<t;r++){const s=r/(t-1),o=i.clone().multiplyScalar(s);o.add(this.pv.origin),o.toArray(e,3*r),r>0&&(n[2*(r-1)]=r-1,n[2*(r-1)+1]=r)}const r=new S.a;r.setAttribute(\\\\\\\"position\\\\\\\",new C.c(e,3)),r.setIndex(n),this.setGeometry(r,Sr.LINE_SEGMENTS)}}const wZ=new class extends aa{constructor(){super(...arguments),this.distance0=oa.FLOAT(1),this.distance1=oa.FLOAT(2),this.autoUpdate=oa.BOOLEAN(1),this.update=oa.BUTTON(null,{callback:t=>{TZ.PARAM_CALLBACK_update(t)}}),this.camera=oa.OPERATOR_PATH(\\\\\\\"/perspective_camera1\\\\\\\",{visibleIf:{autoUpdate:0},dependentOnFoundNode:!1})}};class TZ extends gG{constructor(){super(...arguments),this.paramsConfig=wZ,this._lod=this._create_LOD()}static type(){return\\\\\\\"lod\\\\\\\"}static displayedInputNames(){return[\\\\\\\"high res\\\\\\\",\\\\\\\"mid res\\\\\\\",\\\\\\\"low res\\\\\\\"]}initializeNode(){this.io.inputs.setCount(1,3),this.io.inputs.initInputsClonedState(Qi.FROM_NODE)}_create_LOD(){const t=new Mr;return t.matrixAutoUpdate=!1,t}cook(t){this._clear_lod(),this._add_level(t[0],0),this._add_level(t[1],this.pv.distance0),this._add_level(t[2],this.pv.distance1),this._lod.autoUpdate=this.pv.autoUpdate,this.setObject(this._lod)}_add_level(t,e){if(t){const n=t.objects();let i;for(let t=0;t<n.length;t++)i=n[t],i.visible=!0,this._lod.addLevel(i,e),0==e&&0==t&&(this._lod.matrix.copy(i.matrix),Mz.decompose_matrix(this._lod)),i.matrix.identity(),Mz.decompose_matrix(i)}}_clear_lod(){let t;for(;t=this._lod.children[0];)this._lod.remove(t),t.matrix.multiply(this._lod.matrix),Mz.decompose_matrix(t);for(;this._lod.levels.pop(););}static PARAM_CALLBACK_update(t){t._update_lod()}async _update_lod(){if(this.p.autoUpdate)return;const t=this.p.camera;t.isDirty()&&await t.compute();let e=t.found_node_with_context_and_type(Ki.OBJ,nr.PERSPECTIVE)||t.found_node_with_context_and_type(Ki.OBJ,nr.ORTHOGRAPHIC);if(e){const t=e.object;this._lod.update(t)}else this.states.error.set(\\\\\\\"no camera node found\\\\\\\")}}class AZ extends pG{constructor(){super(...arguments),this._globals_handler=new Sf,this._old_mat_by_old_new_id=new Map,this._materials_by_uuid=new Map}static type(){return\\\\\\\"material\\\\\\\"}async cook(t,e){const n=t[0];return this._old_mat_by_old_new_id.clear(),await this._apply_materials(n,e),this._swap_textures(n,e),n}async _apply_materials(t,e){var n,i,r;if(!e.assignMat)return;const s=e.material.nodeWithContext(Ki.MAT,null===(n=this.states)||void 0===n?void 0:n.error);if(s){const n=s.material,r=s.assemblerController;if(r&&r.set_assembler_globals_handler(this._globals_handler),await s.compute(),n){if(e.applyToChildren)for(let i of t.objects())i.traverse((t=>{this._apply_material(t,n,e)}));else for(let i of t.objectsFromGroup(e.group))this._apply_material(i,n,e);return t}null===(i=this.states)||void 0===i||i.error.set(`material invalid. (error: '${s.states.error.message()}')`)}else null===(r=this.states)||void 0===r||r.error.set(\\\\\\\"no material node found\\\\\\\")}_swap_textures(t,e){if(e.swapCurrentTex){this._materials_by_uuid.clear();for(let n of t.objectsFromGroup(e.group))if(e.applyToChildren)n.traverse((t=>{const e=n.material;this._materials_by_uuid.set(e.uuid,e)}));else{const t=n.material;this._materials_by_uuid.set(t.uuid,t)}this._materials_by_uuid.forEach(((t,n)=>{this._swap_texture(t,e)}))}}_apply_material(t,e,n){if(n.group&&!vs.isInGroup(n.group,t))return;const i=n.cloneMat?fs.clone(e):e;if(e instanceof F&&i instanceof F)for(let t in e.uniforms)i.uniforms[t]=e.uniforms[t];const r=t;this._old_mat_by_old_new_id.set(i.uuid,r.material),r.material=i,fs.apply_render_hook(t,i),fs.applyCustomMaterials(t,i)}_swap_texture(t,e){if(\\\\\\\"\\\\\\\"==e.texSrc0||\\\\\\\"\\\\\\\"==e.texDest0)return;let n=this._old_mat_by_old_new_id.get(t.uuid);n=n||t;const i=n[e.texSrc0];if(i){t[e.texDest0]=i;const n=t.uniforms;if(n){n[e.texDest0]&&(n[e.texDest0]={value:i})}}}}AZ.DEFAULT_PARAMS={group:\\\\\\\"\\\\\\\",assignMat:!0,material:new vi(\\\\\\\"\\\\\\\"),applyToChildren:!0,cloneMat:!1,shareUniforms:!0,swapCurrentTex:!1,texSrc0:\\\\\\\"emissiveMap\\\\\\\",texDest0:\\\\\\\"map\\\\\\\"},AZ.INPUT_CLONED_STATE=Qi.FROM_NODE;const EZ=AZ.DEFAULT_PARAMS;const MZ=new class extends aa{constructor(){super(...arguments),this.group=oa.STRING(EZ.group),this.assignMat=oa.BOOLEAN(EZ.assignMat),this.material=oa.NODE_PATH(EZ.material.path(),{nodeSelection:{context:Ki.MAT},dependentOnFoundNode:!1,visibleIf:{assignMat:1}}),this.applyToChildren=oa.BOOLEAN(EZ.applyToChildren,{visibleIf:{assignMat:1}}),this.cloneMat=oa.BOOLEAN(EZ.cloneMat,{visibleIf:{assignMat:1}}),this.shareUniforms=oa.BOOLEAN(EZ.shareUniforms,{visibleIf:{assignMat:1,cloneMat:1}}),this.swapCurrentTex=oa.BOOLEAN(EZ.swapCurrentTex),this.texSrc0=oa.STRING(EZ.texSrc0,{visibleIf:{swapCurrentTex:1}}),this.texDest0=oa.STRING(EZ.texDest0,{visibleIf:{swapCurrentTex:1}})}};class SZ extends gG{constructor(){super(...arguments),this.paramsConfig=MZ}static type(){return\\\\\\\"material\\\\\\\"}static displayedInputNames(){return[\\\\\\\"objects to assign material to\\\\\\\"]}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState(AZ.INPUT_CLONED_STATE),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.material],(()=>this.p.material.rawInput()))}))}))}async cook(t){this._operation=this._operation||new AZ(this._scene,this.states);const e=await this._operation.cook(t,this.pv);this.setCoreGroup(e)}}var CZ=n(88);class NZ{static sleep(t){return new Promise(((e,n)=>{setTimeout((()=>{e()}),t)}))}}const LZ=[Lg.VIDEO,Lg.WEB_CAM];const OZ=new class extends aa{constructor(){super(...arguments),this.webcam=oa.NODE_PATH(\\\\\\\"\\\\\\\",{nodeSelection:{context:Ki.COP,types:LZ}}),this.scale=oa.FLOAT(2,{range:[0,2],rangeLocked:[!0,!1]}),this.selfieMode=oa.BOOLEAN(0),this.minDetectionConfidence=oa.FLOAT(.5,{range:[0,1],rangeLocked:[!0,!0]}),this.maxDetectionConfidence=oa.FLOAT(.5,{range:[0,1],rangeLocked:[!0,!0]}),this.frames=oa.INTEGER(1,{range:[1,100],rangeLocked:[!0,!1]}),this.fps=oa.INTEGER(24,{range:[1,60],rangeLocked:[!0,!1]}),this.capture=oa.BUTTON(null,{callback:t=>{RZ.PARAM_CALLBACK_capture(t)}}),this.showAllMeshes=oa.BOOLEAN(1)}};class RZ extends gG{constructor(){super(...arguments),this.paramsConfig=OZ,this._faceMesh=this._createFaceMesh(),this._currentCapturedFrame=9999999,this._webcamSnapshotCanvases=[],this._webcamSnapshots=[],this._faceMeshObjects=[]}static type(){return\\\\\\\"mediapipeFaceMesh\\\\\\\"}initializeNode(){this._forceTimeDependent()}_forceTimeDependent(){this._graph_node||(this._graph_node=new Ai(this.scene(),\\\\\\\"facemesh_update_object\\\\\\\"),this._graph_node.addGraphInput(this.scene().timeController.graphNode),this._graph_node.addPostDirtyHook(\\\\\\\"on_time_change\\\\\\\",this.setDirty.bind(this)))}async cook(){if(this.pv.showAllMeshes)this.setObjects(this._faceMeshObjects);else{const t=this.scene().frame()%this._faceMeshObjects.length,e=this._faceMeshObjects[t];e?this.setObject(e):this.setObjects([])}}_createFaceMesh(){const t=new CZ.FaceMesh({locateFile:t=>`https://cdn.jsdelivr.net/npm/@mediapipe/face_mesh@0.4/${t}`});return t.onResults(this._onResults.bind(this)),t}async _getHTMLVideoElement(){const t=this.pv.webcam.nodeWithContext(Ki.COP);if(!t)return this.states.error.set(\\\\\\\"node is not a COP node\\\\\\\"),void this.cookController.endCook();if(!LZ.includes(t.type()))return this.states.error.set(`node type '${t.type()}' is not accepted by MediapipeFaceMesh (${LZ.join(\\\\\\\", \\\\\\\")})`),void this.cookController.endCook();const e=t;await e.compute();const n=e.HTMLVideoElement();if(n)return{videoElement:n};console.log(\\\\\\\"no video element found\\\\\\\")}_videoSnapshotCanvas(t){const e=document.createElement(\\\\\\\"canvas\\\\\\\");e.width=t.videoWidth,e.height=t.videoHeight;return e.getContext(\\\\\\\"2d\\\\\\\").drawImage(t,0,0,e.width,e.height),e}static _canvasToTexture(t,e){return new Promise((n=>{const i=t.toDataURL(\\\\\\\"image/png\\\\\\\"),r=new Image;console.log(`start ${e}`),r.onload=()=>{const t=new J.a(r);t.name=`facemesh-texture-${e}`,t.encoding=w.ld,t.needsUpdate=!0,console.log(`done ${e}`),n({texture:t,image:r})},r.src=i}))}async _initActiveHTMLVideoElement(){const t=await this._getHTMLVideoElement();if(!t)return;const{videoElement:e}=t;for(;e.paused;)console.log(\\\\\\\"video is paused\\\\\\\"),await NZ.sleep(500);this._activeHTMLVideoElement=e}_captureAllowed(){return this._currentCapturedFrame<this.pv.frames}async _capture(){console.log(\\\\\\\"capture start\\\\\\\"),await this._initActiveHTMLVideoElement(),this._currentCapturedFrame=0,await this._captureWebCamSnapshots(),console.log(\\\\\\\"capture complete\\\\\\\"),this._initFaceMeshObjects(),this._currentCapturedFrame=0,await this._sendToFaceMeshAll(),console.log(\\\\\\\"facemesh generation completed\\\\\\\")}_captureWebCamSnapshots(){return new Promise((t=>{this._webcamSnapshotCanvases=[],this._webcamSnapshots=[],this._captureSingleWebCamSnapshot(t)}))}async _captureSingleWebCamSnapshot(t){if(this._activeHTMLVideoElement)if(this._captureAllowed()){const e=this._videoSnapshotCanvas(this._activeHTMLVideoElement);this._webcamSnapshotCanvases.push(e),this._currentCapturedFrame++,console.log(\\\\\\\"capture\\\\\\\",this._currentCapturedFrame),setTimeout((()=>{this._captureSingleWebCamSnapshot(t)}),1e3/this.pv.fps)}else{console.log(\\\\\\\"converting snapshots to images and textures...\\\\\\\");let e=0;for(let t of this._webcamSnapshotCanvases){const n=await RZ._canvasToTexture(t,e);this._webcamSnapshots.push(n),e++}t()}else console.log(\\\\\\\"no video found\\\\\\\")}_initFaceMeshObjects(){this._faceMeshObjects=[];const t=this.pv.frames;for(let e=0;e<t;e++){const t=this._createFaceMeshObject(e);this._faceMeshObjects.push(t)}}async _sendToFaceMeshAll(){this._faceMesh.setOptions({enableFaceGeometry:!0,selfieMode:this.pv.selfieMode,maxNumFaces:1,minDetectionConfidence:this.pv.minDetectionConfidence,minTrackingConfidence:this.pv.maxDetectionConfidence});for(let t of this._webcamSnapshots)await this._sendToFaceMeshSingle(t)}_sendToFaceMeshSingle(t){return new Promise((e=>{this._onSendToFaceMeshSingleResolve=e,this._faceMesh.send({image:t.image})}))}_onResults(t){if(!this._captureAllowed())return;console.log(this._currentCapturedFrame);const e=t.multiFaceLandmarks[0];if(!e)return console.error(`no landmark found (${this._currentCapturedFrame})`),void console.log(\\\\\\\"results\\\\\\\",t);const n=this._faceMeshObjects[this._currentCapturedFrame];let i=0;const r=n.geometry.getAttribute(\\\\\\\"position\\\\\\\"),s=n.geometry.getAttribute(\\\\\\\"uv\\\\\\\"),o=r.array,a=s.array,l=this.pv.scale;for(let t of e)o[3*i+0]=(1-t.x)*l,o[3*i+1]=(1-t.y)*l,o[3*i+2]=t.z*l,a[2*i+0]=t.x,a[2*i+1]=1-t.y,i++;r.needsUpdate=!0,s.needsUpdate=!0,this._updateMaterial(this._currentCapturedFrame),this._currentCapturedFrame++,this._onSendToFaceMeshSingleResolve&&this._onSendToFaceMeshSingleResolve()}_updateMaterial(t){const e=this._faceMeshObjects[t].material;if(!m.isArray(e)){const n=e,i=this._webcamSnapshots[t].texture;n.map=i,n.needsUpdate=!0}}_createFaceMeshObject(t){const e=new S.a,n=[],i=[];for(let t=0;t<468;t++)n.push(t),n.push(t),n.push(t),i.push(t),i.push(t);const r=[],s=CZ.FACEMESH_TESSELATION.length/3;for(let t=0;t<s;t++)r.push(CZ.FACEMESH_TESSELATION[3*t+0][0]),r.push(CZ.FACEMESH_TESSELATION[3*t+1][0]),r.push(CZ.FACEMESH_TESSELATION[3*t+2][0]);e.setAttribute(\\\\\\\"position\\\\\\\",new C.a(new Float32Array(n),3)),e.setAttribute(\\\\\\\"uv\\\\\\\",new C.a(new Float32Array(i),2)),e.setIndex(r);const o=this.createObject(e,Sr.MESH,new at.a);return o.name=`${this.path()}-${t}`,o}static PARAM_CALLBACK_capture(t){t._paramCallbackCapture()}_paramCallbackCapture(){this._capture()}}class PZ extends pG{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._make_compact(n));for(let t of n)t.traverse((t=>{t.matrixAutoUpdate=!1}));return this.createCoreGroupFromObjects(n)}_make_compact(t){const e=new Map,n=new Map,i=[];for(let r of t)r.traverse((t=>{if(t instanceof In.a)return;const r=t;if(r.geometry){const t=Nr(r.constructor);if(i.includes(t)||i.push(t),t){e.get(t)||e.set(t,r.material),u.pushOnArrayAtEntry(n,t,r)}}}));const r=[];return i.forEach((t=>{var i,s;const o=n.get(t);if(o){const n=[];for(let t of o){const e=t.geometry;e.applyMatrix4(t.matrix),n.push(e)}try{const s=ps.mergeGeometries(n);if(s){const n=e.get(t),i=this.createObject(s,t,n);r.push(i)}else null===(i=this.states)||void 0===i||i.error.set(\\\\\\\"merge failed, check that input geometries have the same attributes\\\\\\\")}catch(t){null===(s=this.states)||void 0===s||s.error.set(t.message)}}})),r}}PZ.DEFAULT_PARAMS={compact:!1},PZ.INPUT_CLONED_STATE=Qi.FROM_NODE;const IZ=\\\\\\\"geometry to merge\\\\\\\",FZ=PZ.DEFAULT_PARAMS;const DZ=new class extends aa{constructor(){super(...arguments),this.compact=oa.BOOLEAN(FZ.compact),this.inputsCount=oa.INTEGER(4,{range:[1,32],rangeLocked:[!0,!1],callback:t=>{kZ.PARAM_CALLBACK_setInputsCount(t)}})}};class kZ extends gG{constructor(){super(...arguments),this.paramsConfig=DZ}static type(){return\\\\\\\"merge\\\\\\\"}static displayedInputNames(){return[IZ,IZ,IZ,IZ]}setCompactMode(t){this.p.compact.set(t)}initializeNode(){this.io.inputs.setCount(1,4),this.io.inputs.initInputsClonedState(PZ.INPUT_CLONED_STATE),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.compact],(()=>this.pv.compact?\\\\\\\"compact\\\\\\\":\\\\\\\"separate objects\\\\\\\"))})),this.params.addOnSceneLoadHook(\\\\\\\"update inputs\\\\\\\",(()=>{this._callbackUpdateInputsCount()}))}))}cook(t){this._operation=this._operation||new PZ(this.scene(),this.states);const e=this._operation.cook(t,this.pv);this.setCoreGroup(e)}_callbackUpdateInputsCount(){this.io.inputs.setCount(1,this.pv.inputsCount),this.emit(Ei.INPUTS_UPDATED)}static PARAM_CALLBACK_setInputsCount(t){t._callbackUpdateInputsCount()}}class BZ{constructor(t=Math){this.grad3=[[1,1,0],[-1,1,0],[1,-1,0],[-1,-1,0],[1,0,1],[-1,0,1],[1,0,-1],[-1,0,-1],[0,1,1],[0,-1,1],[0,1,-1],[0,-1,-1]],this.grad4=[[0,1,1,1],[0,1,1,-1],[0,1,-1,1],[0,1,-1,-1],[0,-1,1,1],[0,-1,1,-1],[0,-1,-1,1],[0,-1,-1,-1],[1,0,1,1],[1,0,1,-1],[1,0,-1,1],[1,0,-1,-1],[-1,0,1,1],[-1,0,1,-1],[-1,0,-1,1],[-1,0,-1,-1],[1,1,0,1],[1,1,0,-1],[1,-1,0,1],[1,-1,0,-1],[-1,1,0,1],[-1,1,0,-1],[-1,-1,0,1],[-1,-1,0,-1],[1,1,1,0],[1,1,-1,0],[1,-1,1,0],[1,-1,-1,0],[-1,1,1,0],[-1,1,-1,0],[-1,-1,1,0],[-1,-1,-1,0]],this.p=[];for(let e=0;e<256;e++)this.p[e]=Math.floor(256*t.random());this.perm=[];for(let t=0;t<512;t++)this.perm[t]=this.p[255&t];this.simplex=[[0,1,2,3],[0,1,3,2],[0,0,0,0],[0,2,3,1],[0,0,0,0],[0,0,0,0],[0,0,0,0],[1,2,3,0],[0,2,1,3],[0,0,0,0],[0,3,1,2],[0,3,2,1],[0,0,0,0],[0,0,0,0],[0,0,0,0],[1,3,2,0],[0,0,0,0],[0,0,0,0],[0,0,0,0],[0,0,0,0],[0,0,0,0],[0,0,0,0],[0,0,0,0],[0,0,0,0],[1,2,0,3],[0,0,0,0],[1,3,0,2],[0,0,0,0],[0,0,0,0],[0,0,0,0],[2,3,0,1],[2,3,1,0],[1,0,2,3],[1,0,3,2],[0,0,0,0],[0,0,0,0],[0,0,0,0],[2,0,3,1],[0,0,0,0],[2,1,3,0],[0,0,0,0],[0,0,0,0],[0,0,0,0],[0,0,0,0],[0,0,0,0],[0,0,0,0],[0,0,0,0],[0,0,0,0],[2,0,1,3],[0,0,0,0],[0,0,0,0],[0,0,0,0],[3,0,1,2],[3,0,2,1],[0,0,0,0],[3,1,2,0],[2,1,0,3],[0,0,0,0],[0,0,0,0],[0,0,0,0],[3,1,0,2],[0,0,0,0],[3,2,0,1],[3,2,1,0]]}dot(t,e,n){return t[0]*e+t[1]*n}dot3(t,e,n,i){return t[0]*e+t[1]*n+t[2]*i}dot4(t,e,n,i,r){return t[0]*e+t[1]*n+t[2]*i+t[3]*r}noise(t,e){let n,i,r;const s=(t+e)*(.5*(Math.sqrt(3)-1)),o=Math.floor(t+s),a=Math.floor(e+s),l=(3-Math.sqrt(3))/6,c=(o+a)*l,u=t-(o-c),h=e-(a-c);let d,p;u>h?(d=1,p=0):(d=0,p=1);const _=u-d+l,m=h-p+l,f=u-1+2*l,g=h-1+2*l,v=255&o,y=255&a,x=this.perm[v+this.perm[y]]%12,b=this.perm[v+d+this.perm[y+p]]%12,w=this.perm[v+1+this.perm[y+1]]%12;let T=.5-u*u-h*h;T<0?n=0:(T*=T,n=T*T*this.dot(this.grad3[x],u,h));let A=.5-_*_-m*m;A<0?i=0:(A*=A,i=A*A*this.dot(this.grad3[b],_,m));let E=.5-f*f-g*g;return E<0?r=0:(E*=E,r=E*E*this.dot(this.grad3[w],f,g)),70*(n+i+r)}noise3d(t,e,n){let i,r,s,o;const a=(t+e+n)*(1/3),l=Math.floor(t+a),c=Math.floor(e+a),u=Math.floor(n+a),h=1/6,d=(l+c+u)*h,p=t-(l-d),_=e-(c-d),m=n-(u-d);let f,g,v,y,x,b;p>=_?_>=m?(f=1,g=0,v=0,y=1,x=1,b=0):p>=m?(f=1,g=0,v=0,y=1,x=0,b=1):(f=0,g=0,v=1,y=1,x=0,b=1):_<m?(f=0,g=0,v=1,y=0,x=1,b=1):p<m?(f=0,g=1,v=0,y=0,x=1,b=1):(f=0,g=1,v=0,y=1,x=1,b=0);const w=p-f+h,T=_-g+h,A=m-v+h,E=p-y+2*h,M=_-x+2*h,S=m-b+2*h,C=p-1+.5,N=_-1+.5,L=m-1+.5,O=255&l,R=255&c,P=255&u,I=this.perm[O+this.perm[R+this.perm[P]]]%12,F=this.perm[O+f+this.perm[R+g+this.perm[P+v]]]%12,D=this.perm[O+y+this.perm[R+x+this.perm[P+b]]]%12,k=this.perm[O+1+this.perm[R+1+this.perm[P+1]]]%12;let B=.6-p*p-_*_-m*m;B<0?i=0:(B*=B,i=B*B*this.dot3(this.grad3[I],p,_,m));let z=.6-w*w-T*T-A*A;z<0?r=0:(z*=z,r=z*z*this.dot3(this.grad3[F],w,T,A));let U=.6-E*E-M*M-S*S;U<0?s=0:(U*=U,s=U*U*this.dot3(this.grad3[D],E,M,S));let G=.6-C*C-N*N-L*L;return G<0?o=0:(G*=G,o=G*G*this.dot3(this.grad3[k],C,N,L)),32*(i+r+s+o)}noise4d(t,e,n,i){const r=this.grad4,s=this.simplex,o=this.perm,a=(Math.sqrt(5)-1)/4,l=(5-Math.sqrt(5))/20;let c,u,h,d,p;const _=(t+e+n+i)*a,m=Math.floor(t+_),f=Math.floor(e+_),g=Math.floor(n+_),v=Math.floor(i+_),y=(m+f+g+v)*l,x=t-(m-y),b=e-(f-y),w=n-(g-y),T=i-(v-y),A=(x>b?32:0)+(x>w?16:0)+(b>w?8:0)+(x>T?4:0)+(b>T?2:0)+(w>T?1:0),E=s[A][0]>=3?1:0,M=s[A][1]>=3?1:0,S=s[A][2]>=3?1:0,C=s[A][3]>=3?1:0,N=s[A][0]>=2?1:0,L=s[A][1]>=2?1:0,O=s[A][2]>=2?1:0,R=s[A][3]>=2?1:0,P=s[A][0]>=1?1:0,I=s[A][1]>=1?1:0,F=s[A][2]>=1?1:0,D=s[A][3]>=1?1:0,k=x-E+l,B=b-M+l,z=w-S+l,U=T-C+l,G=x-N+2*l,V=b-L+2*l,H=w-O+2*l,j=T-R+2*l,W=x-P+3*l,q=b-I+3*l,X=w-F+3*l,Y=T-D+3*l,$=x-1+4*l,J=b-1+4*l,Z=w-1+4*l,Q=T-1+4*l,K=255&m,tt=255&f,et=255&g,nt=255&v,it=o[K+o[tt+o[et+o[nt]]]]%32,rt=o[K+E+o[tt+M+o[et+S+o[nt+C]]]]%32,st=o[K+N+o[tt+L+o[et+O+o[nt+R]]]]%32,ot=o[K+P+o[tt+I+o[et+F+o[nt+D]]]]%32,at=o[K+1+o[tt+1+o[et+1+o[nt+1]]]]%32;let lt=.6-x*x-b*b-w*w-T*T;lt<0?c=0:(lt*=lt,c=lt*lt*this.dot4(r[it],x,b,w,T));let ct=.6-k*k-B*B-z*z-U*U;ct<0?u=0:(ct*=ct,u=ct*ct*this.dot4(r[rt],k,B,z,U));let ut=.6-G*G-V*V-H*H-j*j;ut<0?h=0:(ut*=ut,h=ut*ut*this.dot4(r[st],G,V,H,j));let ht=.6-W*W-q*q-X*X-Y*Y;ht<0?d=0:(ht*=ht,d=ht*ht*this.dot4(r[ot],W,q,X,Y));let dt=.6-$*$-J*J-Z*Z-Q*Q;return dt<0?p=0:(dt*=dt,p=dt*dt*this.dot4(r[at],$,J,Z,Q)),27*(c+u+h+d+p)}}var zZ;!function(t){t.ADD=\\\\\\\"add\\\\\\\",t.SET=\\\\\\\"set\\\\\\\",t.MULT=\\\\\\\"mult\\\\\\\",t.SUBSTRACT=\\\\\\\"substract\\\\\\\",t.DIVIDE=\\\\\\\"divide\\\\\\\"}(zZ||(zZ={}));const UZ=[zZ.ADD,zZ.SET,zZ.MULT,zZ.SUBSTRACT,zZ.DIVIDE];const GZ=new class extends aa{constructor(){super(...arguments),this.amplitude=oa.FLOAT(1),this.tamplitudeAttrib=oa.BOOLEAN(0),this.amplitudeAttrib=oa.STRING(\\\\\\\"amp\\\\\\\",{visibleIf:{tamplitudeAttrib:!0}}),this.freq=oa.VECTOR3([1,1,1]),this.offset=oa.VECTOR3([0,0,0]),this.octaves=oa.INTEGER(3,{range:[1,8],rangeLocked:[!0,!1]}),this.ampAttenuation=oa.FLOAT(.5,{range:[0,1]}),this.freqIncrease=oa.FLOAT(2,{range:[0,10]}),this.seed=oa.INTEGER(0,{range:[0,100],separatorAfter:!0}),this.useNormals=oa.BOOLEAN(0),this.attribName=oa.STRING(\\\\\\\"position\\\\\\\"),this.useRestAttributes=oa.BOOLEAN(0),this.restP=oa.STRING(\\\\\\\"restP\\\\\\\",{visibleIf:{useRestAttributes:!0}}),this.restN=oa.STRING(\\\\\\\"restN\\\\\\\",{visibleIf:{useRestAttributes:!0}}),this.operation=oa.INTEGER(UZ.indexOf(zZ.ADD),{menu:{entries:UZ.map((t=>({name:t,value:UZ.indexOf(t)})))}}),this.computeNormals=oa.BOOLEAN(1)}};class VZ extends gG{constructor(){super(...arguments),this.paramsConfig=GZ,this._simplex_by_seed=new Map,this._rest_pos=new p.a,this._rest_value2=new d.a,this._noise_value_v=new p.a}static type(){return\\\\\\\"noise\\\\\\\"}static displayedInputNames(){return[\\\\\\\"geometry to add noise to\\\\\\\"]}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState([Qi.FROM_NODE])}setOperation(t){this.p.operation.set(UZ.indexOf(t))}async cook(t){const e=t[0],n=e.points(),i=this.pv.attribName;if(!e.hasAttrib(i))return this.states.error.set(`attribute ${i} not found`),void this.cookController.endCook();if(e.attribType(i)!=Dr.NUMERIC)return this.states.error.set(`attribute ${i} is not a numeric attribute`),void this.cookController.endCook();const r=this._get_simplex(),s=this.pv.useNormals&&e.hasAttrib(\\\\\\\"normal\\\\\\\"),o=e.attribSize(this.pv.attribName),a=UZ[this.pv.operation],l=this.pv.useRestAttributes,c=this.pv.amplitude,u=this.pv.tamplitudeAttrib;let h,f,g=new p.a;for(let t=0;t<n.length;t++){const e=n[t];f=e.attribValue(i),l?(g=e.attribValue(this.pv.restP),h=s?e.attribValue(this.pv.restN):void 0):(e.getPosition(g),h=s?e.attribValue(\\\\\\\"normal\\\\\\\"):void 0);const v=u?this._amplitude_from_attrib(e,c):c,y=this._noise_value(s,r,v,g,h),x=this._make_noise_value_correct_size(y,o);if(m.isNumber(f)&&m.isNumber(x)){const t=this._new_attrib_value_from_float(a,f,x);e.setAttribValue(i,t)}else if(f instanceof d.a&&x instanceof d.a){const t=this._new_attrib_value_from_vector2(a,f,x);e.setAttribValue(i,t)}else if(f instanceof p.a&&x instanceof p.a){const t=this._new_attrib_value_from_vector3(a,f,x);e.setAttribValue(i,t)}else if(f instanceof _.a&&x instanceof _.a){const t=this._new_attrib_value_from_vector4(a,f,x);e.setAttribValue(i,t)}}if(!this.io.inputs.cloneRequired(0))for(let t of e.geometries())t.getAttribute(i).needsUpdate=!0;this.pv.computeNormals&&e.computeVertexNormals(),this.setCoreGroup(e)}_noise_value(t,e,n,i,r){if(this._rest_pos.copy(i).add(this.pv.offset).multiply(this.pv.freq),t&&r){const t=n*this._fbm(e,this._rest_pos.x,this._rest_pos.y,this._rest_pos.z);return this._noise_value_v.copy(r),this._noise_value_v.multiplyScalar(t)}return this._noise_value_v.set(n*this._fbm(e,this._rest_pos.x+545,this._rest_pos.y+125454,this._rest_pos.z+2142),n*this._fbm(e,this._rest_pos.x-425,this._rest_pos.y-25746,this._rest_pos.z+95242),n*this._fbm(e,this._rest_pos.x+765132,this._rest_pos.y+21,this._rest_pos.z-9245)),this._noise_value_v}_make_noise_value_correct_size(t,e){switch(e){case 1:return t.x;case 2:return this._rest_value2.set(t.x,t.y),this._rest_value2;case 3:default:return t}}_new_attrib_value_from_float(t,e,n){switch(t){case zZ.ADD:return e+n;case zZ.SET:return n;case zZ.MULT:return e*n;case zZ.DIVIDE:return e/n;case zZ.SUBSTRACT:return e-n}ar.unreachable(t)}_new_attrib_value_from_vector2(t,e,n){switch(t){case zZ.ADD:return e.add(n);case zZ.SET:return n;case zZ.MULT:return e.multiply(n);case zZ.DIVIDE:return e.divide(n);case zZ.SUBSTRACT:return e.sub(n)}ar.unreachable(t)}_new_attrib_value_from_vector3(t,e,n){switch(t){case zZ.ADD:return e.add(n);case zZ.SET:return n;case zZ.MULT:return e.multiply(n);case zZ.DIVIDE:return e.divide(n);case zZ.SUBSTRACT:return e.sub(n)}ar.unreachable(t)}_new_attrib_value_from_vector4(t,e,n){switch(t){case zZ.ADD:return e.add(n);case zZ.SET:return n;case zZ.MULT:return e.multiplyScalar(n.x);case zZ.DIVIDE:return e.divideScalar(n.x);case zZ.SUBSTRACT:return e.sub(n)}ar.unreachable(t)}_amplitude_from_attrib(t,e){const n=t.attribValue(this.pv.amplitudeAttrib);return m.isNumber(n)?n*e:n instanceof d.a||n instanceof p.a||n instanceof _.a?n.x*e:1}_fbm(t,e,n,i){let r=0,s=1;for(let o=0;o<this.pv.octaves;o++)r+=s*t.noise3d(e,n,i),e*=this.pv.freqIncrease,n*=this.pv.freqIncrease,i*=this.pv.freqIncrease,s*=this.pv.ampAttenuation;return r}_get_simplex(){const t=this._simplex_by_seed.get(this.pv.seed);if(t)return t;{const t=this._create_simplex();return this._simplex_by_seed.set(this.pv.seed,t),t}}_create_simplex(){const t=this.pv.seed,e=new BZ({random:function(){return rs.randFloat(t)}});return this._simplex_by_seed.delete(t),e}}const HZ=new class extends aa{constructor(){super(...arguments),this.edit=oa.BOOLEAN(0),this.updateX=oa.BOOLEAN(0,{visibleIf:{edit:1}}),this.x=oa.FLOAT(\\\\\\\"@N.x\\\\\\\",{visibleIf:{updateX:1,edit:1},expression:{forEntities:!0}}),this.updateY=oa.BOOLEAN(0,{visibleIf:{edit:1}}),this.y=oa.FLOAT(\\\\\\\"@N.y\\\\\\\",{visibleIf:{updateY:1,edit:1},expression:{forEntities:!0}}),this.updateZ=oa.BOOLEAN(0,{visibleIf:{edit:1}}),this.z=oa.FLOAT(\\\\\\\"@N.z\\\\\\\",{visibleIf:{updateZ:1,edit:1},expression:{forEntities:!0}}),this.recompute=oa.BOOLEAN(1,{visibleIf:{edit:0}}),this.invert=oa.BOOLEAN(0)}};class jZ extends gG{constructor(){super(...arguments),this.paramsConfig=HZ}static type(){return\\\\\\\"normals\\\\\\\"}static displayedInputNames(){return[\\\\\\\"geometry to update normals of\\\\\\\"]}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState(Qi.FROM_NODE)}async cook(t){const e=t[0];this.pv.edit?await this._eval_expressions_for_core_group(e):this.pv.recompute&&e.computeVertexNormals(),this.pv.invert&&this._invert_normals(e),this.setCoreGroup(e)}async _eval_expressions_for_core_group(t){const e=t.coreObjects();for(let t=0;t<e.length;t++)await this._eval_expressions_for_core_object(e[t])}async _eval_expressions_for_core_object(t){const e=t.object().geometry,n=t.points();let i=e.getAttribute(Hr.NORMAL);if(!i){new ps(e).addNumericAttrib(Hr.NORMAL,3,0),i=e.getAttribute(Hr.NORMAL)}const r=i.array;if(this.pv.updateX)if(this.p.x.hasExpression()&&this.p.x.expressionController)await this.p.x.expressionController.compute_expression_for_points(n,((t,e)=>{r[3*t.index()+0]=e}));else{let t;for(let e=0;e<n.length;e++)t=n[e],r[3*t.index()+0]=this.pv.x}if(this.pv.updateY)if(this.p.y.hasExpression()&&this.p.y.expressionController)await this.p.y.expressionController.compute_expression_for_points(n,((t,e)=>{r[3*t.index()+1]=e}));else{let t;for(let e=0;e<n.length;e++)t=n[e],r[3*t.index()+1]=this.pv.y}if(this.pv.updateZ)if(this.p.z.hasExpression()&&this.p.z.expressionController)await this.p.z.expressionController.compute_expression_for_points(n,((t,e)=>{r[3*t.index()+2]=e}));else{let t;for(let e=0;e<n.length;e++)t=n[e],r[3*t.index()+2]=this.pv.z}}_invert_normals(t){var e;for(let n of t.coreObjects()){const t=null===(e=n.coreGeometry())||void 0===e?void 0:e.geometry();if(t){const e=t.attributes[Hr.NORMAL];if(e){const t=e.array;for(let e=0;e<t.length;e++)t[e]*=-1}}}}}class WZ extends pG{static type(){return\\\\\\\"null\\\\\\\"}cook(t,e){const n=t[0];return n||this.createCoreGroupFromObjects([])}}WZ.DEFAULT_PARAMS={},WZ.INPUT_CLONED_STATE=Qi.FROM_NODE;const qZ=new class extends aa{};class XZ extends gG{constructor(){super(...arguments),this.paramsConfig=qZ}static type(){return\\\\\\\"null\\\\\\\"}initializeNode(){this.io.inputs.setCount(0,1),this.io.inputs.initInputsClonedState(WZ.INPUT_CLONED_STATE)}cook(t){this._operation=this._operation||new WZ(this.scene(),this.states);const e=this._operation.cook(t,this.pv);this.setCoreGroup(e)}}const YZ=new class extends aa{constructor(){super(...arguments),this.geometry=oa.OPERATOR_PATH(\\\\\\\"\\\\\\\",{nodeSelection:{context:Ki.SOP}})}};class $Z extends gG{constructor(){super(...arguments),this.paramsConfig=YZ}static type(){return\\\\\\\"objectMerge\\\\\\\"}initializeNode(){}async cook(t){const e=this.p.geometry.found_node();if(e)if(e.context()==Ki.SOP){const t=await e.compute();this.import_input(e,t)}else this.states.error.set(\\\\\\\"found node is not a geometry\\\\\\\");else this.states.error.set(`node not found at path '${this.pv.geometry}'`)}import_input(t,e){let n;null!=(n=e.coreContentCloned())?this.setCoreGroup(n):this.states.error.set(\\\\\\\"invalid target\\\\\\\")}}class JZ extends pG{static type(){return\\\\\\\"objectProperties\\\\\\\"}cook(t,e){const n=t[0];for(let t of n.objects())e.applyToChildren?t.traverse((t=>{this._update_object(t,e)})):this._update_object(t,e);return n}_update_object(t,e){e.tname&&(t.name=e.name),e.trenderOrder&&(t.renderOrder=e.renderOrder),e.tfrustumCulled&&(t.frustumCulled=e.frustumCulled),e.tmatrixAutoUpdate&&(t.matrixAutoUpdate=e.matrixAutoUpdate),e.tvisible&&(t.visible=e.visible),e.tcastShadow&&(t.castShadow=e.castShadow),e.treceiveShadow&&(t.receiveShadow=e.receiveShadow)}}JZ.DEFAULT_PARAMS={applyToChildren:!0,tname:!1,name:\\\\\\\"\\\\\\\",trenderOrder:!1,renderOrder:0,tfrustumCulled:!1,frustumCulled:!0,tmatrixAutoUpdate:!1,matrixAutoUpdate:!1,tvisible:!1,visible:!0,tcastShadow:!1,castShadow:!0,treceiveShadow:!1,receiveShadow:!0},JZ.INPUT_CLONED_STATE=Qi.FROM_NODE;const ZZ=JZ.DEFAULT_PARAMS;const QZ=new class extends aa{constructor(){super(...arguments),this.applyToChildren=oa.BOOLEAN(ZZ.applyToChildren,{separatorAfter:!0}),this.tname=oa.BOOLEAN(ZZ.tname),this.name=oa.STRING(ZZ.name,{visibleIf:{tname:!0},separatorAfter:!0}),this.trenderOrder=oa.BOOLEAN(ZZ.trenderOrder),this.renderOrder=oa.INTEGER(ZZ.renderOrder,{visibleIf:{trenderOrder:!0},range:[0,10],rangeLocked:[!1,!1],separatorAfter:!0}),this.tfrustumCulled=oa.BOOLEAN(ZZ.tfrustumCulled),this.frustumCulled=oa.BOOLEAN(ZZ.frustumCulled,{visibleIf:{tfrustumCulled:!0},separatorAfter:!0}),this.tmatrixAutoUpdate=oa.BOOLEAN(ZZ.tmatrixAutoUpdate),this.matrixAutoUpdate=oa.BOOLEAN(ZZ.matrixAutoUpdate,{visibleIf:{tmatrixAutoUpdate:!0},separatorAfter:!0}),this.tvisible=oa.BOOLEAN(ZZ.tvisible),this.visible=oa.BOOLEAN(ZZ.visible,{visibleIf:{tvisible:!0},separatorAfter:!0}),this.tcastShadow=oa.BOOLEAN(ZZ.tcastShadow),this.castShadow=oa.BOOLEAN(ZZ.castShadow,{visibleIf:{tcastShadow:!0},separatorAfter:!0}),this.treceiveShadow=oa.BOOLEAN(ZZ.treceiveShadow),this.receiveShadow=oa.BOOLEAN(ZZ.receiveShadow,{visibleIf:{treceiveShadow:!0}})}};class KZ extends gG{constructor(){super(...arguments),this.paramsConfig=QZ}static type(){return\\\\\\\"objectProperties\\\\\\\"}static displayedInputNames(){return[\\\\\\\"objects to change properties of\\\\\\\"]}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState(JZ.INPUT_CLONED_STATE)}async cook(t){this._operation=this._operation||new JZ(this.scene(),this.states);const e=this._operation.cook(t,this.pv);this.setCoreGroup(e)}}const tQ=new class extends aa{};class eQ extends gG{constructor(){super(...arguments),this.paramsConfig=tQ,this._input_configs_by_operation_container=new WeakMap}static type(){return Il}initializeNode(){this.io.inputs.initInputsClonedState(Qi.FROM_NODE)}set_output_operation_container(t){this._output_operation_container=t}output_operation_container(){return this._output_operation_container}add_input_config(t,e){let n=this._input_configs_by_operation_container.get(t);n||(n=new Map,this._input_configs_by_operation_container.set(t,n)),n.set(e.operation_input_index,e.node_input_index)}add_operation_container_with_path_param_resolve_required(t){this._operation_containers_requiring_resolve||(this._operation_containers_requiring_resolve=[]),this._operation_containers_requiring_resolve.push(t)}resolve_operation_containers_path_params(){if(this._operation_containers_requiring_resolve)for(let t of this._operation_containers_requiring_resolve)t.resolve_path_params(this)}async cook(t){if(this._output_operation_container){this._output_operation_container.setDirty();const e=await this._output_operation_container.compute(t,this._input_configs_by_operation_container);e&&this.setCoreGroup(e)}}}class nQ extends cf{constructor(t){super(),this._uv_name=t}set_texture_allocations_controller(t){this._texture_allocations_controller=t}handle_globals_node(t,e,n){if(!this._texture_allocations_controller)return;const i=t.io.outputs.namedOutputConnectionPointsByName(e),r=t.glVarName(e);if(this._texture_allocations_controller.variable(e)&&i){const s=i.type(),o=`${s} ${r} = ${this.read_attribute(t,s,e,n)}`;n.addBodyLines(t,[o])}else this.globals_geometry_handler=this.globals_geometry_handler||new Sf,this.globals_geometry_handler.handle_globals_node(t,e,n)}read_attribute(t,e,n,i){if(!this._texture_allocations_controller)return;const r=this._texture_allocations_controller.variable(n);if(!r)return Sf.read_attribute(t,e,n,i);{this.add_particles_sim_uv_attribute(t,i);const e=r.component(),n=r.allocation();if(n){const r=n.textureName(),s=new Af(t,Do.SAMPLER_2D,r);i.addDefinitions(t,[s]);return`texture2D( ${r}, ${this._uv_name} ).${e}`}}}add_particles_sim_uv_attribute(t,e){const n=new wf(t,Do.VEC2,nQ.UV_ATTRIB),i=new Ef(t,Do.VEC2,nQ.UV_VARYING);e.addDefinitions(t,[n,i],xf.VERTEX),e.addDefinitions(t,[i],xf.FRAGMENT),e.addBodyLines(t,[`${nQ.UV_VARYING} = ${nQ.UV_ATTRIB}`],xf.VERTEX)}}nQ.UV_ATTRIB=\\\\\\\"particles_sim_uv_attrib\\\\\\\",nQ.UV_VARYING=\\\\\\\"particles_sim_uv_varying\\\\\\\",nQ.PARTICLE_SIM_UV=\\\\\\\"particleUV\\\\\\\";class iQ{constructor(t){this.node=t,this._particles_group_objects=[],this._all_shader_names=[],this._all_uniform_names=[],this.globals_handler=new nQ(nQ.UV_VARYING)}setShadersByName(t){this._shaders_by_name=t,this._all_shader_names=[],this._all_uniform_names=[],this._shaders_by_name.forEach(((t,e)=>{this._all_shader_names.push(e),this._all_uniform_names.push(`texture_${e}`)})),this.reset_render_material()}assign_render_material(){if(this._render_material){for(let t of this._particles_group_objects){const e=t;e.geometry&&(e.material=this._render_material,fs.applyCustomMaterials(e,this._render_material),e.matrixAutoUpdate=!1,e.updateMatrix())}this._render_material.needsUpdate=!0,this.update_render_material_uniforms()}}update_render_material_uniforms(){var t;if(!this._render_material)return;let e,n;for(let i=0;i<this._all_shader_names.length;i++){n=this._all_shader_names[i],e=this._all_uniform_names[i];const r=null===(t=this.node.gpuController.getCurrentRenderTarget(n))||void 0===t?void 0:t.texture;r&&(this._render_material.uniforms[e].value=r,fs.assign_custom_uniforms(this._render_material,e,r))}}reset_render_material(){this._render_material=void 0,this._particles_group_objects=[]}material(){return this._render_material}initialized(){return null!=this._render_material}init_core_group(t){for(let e of t.objectsWithGeo())this._particles_group_objects.push(e)}async init_render_material(){var t;const e=null===(t=this.node.assemblerController)||void 0===t?void 0:t.assembler;if(this._render_material)return;this.node.p.material.isDirty()&&await this.node.p.material.compute();const n=this.node.p.material.found_node();if(n){if(e){const t=e.textureAllocationsController().toJSON(this.node.scene()),i=n.assemblerController;i&&(this.globals_handler.set_texture_allocations_controller(e.textureAllocationsController()),i.set_assembler_globals_handler(this.globals_handler)),this._texture_allocations_json&&JSON.stringify(this._texture_allocations_json)==JSON.stringify(t)||(this._texture_allocations_json=b.cloneDeep(t),i&&i.set_compilation_required_and_dirty())}const t=await n.compute();this._render_material=t.material()}else this.node.states.error.set(\\\\\\\"render material not valid\\\\\\\");if(this._render_material){const t=this._render_material.uniforms;for(let e of this._all_uniform_names){const n={value:null};t[e]=n,this._render_material&&fs.init_custom_material_uniforms(this._render_material,e,n)}}this.assign_render_material()}}var rQ,sQ=function(t,e,n){this.variables=[],this.currentTextureIndex=0;var i=w.G,r=new fr;r.matrixAutoUpdate=!1;var s=new tf.a;s.position.z=1,s.matrixAutoUpdate=!1,s.updateMatrix();var o={passThruTexture:{value:null}},a=u(\\\\\\\"uniform sampler2D passThruTexture;\\\\n\\\\nvoid main() {\\\\n\\\\n\\\\tvec2 uv = gl_FragCoord.xy / resolution.xy;\\\\n\\\\n\\\\tgl_FragColor = texture2D( passThruTexture, uv );\\\\n\\\\n}\\\\n\\\\\\\",o),l=new k.a(new L(2,2),a);function c(n){n.defines.resolution=\\\\\\\"vec2( \\\\\\\"+t.toFixed(1)+\\\\\\\", \\\\\\\"+e.toFixed(1)+\\\\\\\" )\\\\\\\"}function u(t,e){var n=new F({uniforms:e=e||{},vertexShader:\\\\\\\"void main()\\\\t{\\\\n\\\\n\\\\tgl_Position = vec4( position, 1.0 );\\\\n\\\\n}\\\\n\\\\\\\",fragmentShader:t});return c(n),n}l.matrixAutoUpdate=!1,l.updateMatrix(),r.add(l),this.setDataType=function(t){return i=t,this},this.addVariable=function(t,e,n){var i={name:t,initialValueTexture:n,material:this.createShaderMaterial(e),dependencies:null,renderTargets:[],wrapS:null,wrapT:null,minFilter:w.ob,magFilter:w.ob};return this.variables.push(i),i},this.setVariableDependencies=function(t,e){t.dependencies=e},this.init=function(){if(!1===n.capabilities.isWebGL2&&!1===n.extensions.has(\\\\\\\"OES_texture_float\\\\\\\"))return\\\\\\\"No OES_texture_float support for float textures.\\\\\\\";if(0===n.capabilities.maxVertexTextures)return\\\\\\\"No support for vertex shader textures.\\\\\\\";for(var i=0;i<this.variables.length;i++){var r=this.variables[i];r.renderTargets[0]=this.createRenderTarget(t,e,r.wrapS,r.wrapT,r.minFilter,r.magFilter),r.renderTargets[1]=this.createRenderTarget(t,e,r.wrapS,r.wrapT,r.minFilter,r.magFilter),this.renderTexture(r.initialValueTexture,r.renderTargets[0]),this.renderTexture(r.initialValueTexture,r.renderTargets[1]);var s=r.material.uniforms;if(null!==r.dependencies)for(var o=0;o<r.dependencies.length;o++){var a=r.dependencies[o];if(a.name!==r.name){for(var l=!1,c=0;c<this.variables.length;c++)if(a.name===this.variables[c].name){l=!0;break}if(!l)return\\\\\\\"Variable dependency not found. Variable=\\\\\\\"+r.name+\\\\\\\", dependency=\\\\\\\"+a.name}s[a.name]={value:null}}}return this.currentTextureIndex=0,null},this.compute=function(){for(var t=this.currentTextureIndex,e=0===this.currentTextureIndex?1:0,n=0,i=this.variables.length;n<i;n++){var r=this.variables[n];if(null!==r.dependencies)for(var s=r.material.uniforms,o=0,a=r.dependencies.length;o<a;o++){var l=r.dependencies[o];s[l.name].value=l.renderTargets[t].texture}this.doRenderTarget(r.material,r.renderTargets[e])}this.currentTextureIndex=e},this.getCurrentRenderTarget=function(t){return t.renderTargets[this.currentTextureIndex]},this.getAlternateRenderTarget=function(t){return t.renderTargets[0===this.currentTextureIndex?1:0]},this.addResolutionDefine=c,this.createShaderMaterial=u,this.createRenderTarget=function(n,r,s,o,a,l){return n=n||t,r=r||e,s=s||w.n,o=o||w.n,a=a||w.ob,l=l||w.ob,new Z(n,r,{wrapS:s,wrapT:o,minFilter:a,magFilter:l,format:w.Ib,type:i,depthBuffer:!1})},this.createTexture=function(){var n=new Float32Array(t*e*4);return new mo.a(n,t,e,w.Ib,w.G)},this.renderTexture=function(t,e){o.passThruTexture.value=t,this.doRenderTarget(a,e),o.passThruTexture.value=null},this.doRenderTarget=function(t,e){var i=n.getRenderTarget();l.material=t,n.setRenderTarget(e),n.render(r,s),l.material=a,n.setRenderTarget(i)}};!function(t){t.FLOAT=\\\\\\\"float\\\\\\\",t.HALF_FLOAT=\\\\\\\"half\\\\\\\"}(rQ||(rQ={}));const oQ=[rQ.FLOAT,rQ.HALF_FLOAT],aQ={[rQ.FLOAT]:w.G,[rQ.HALF_FLOAT]:w.M};class lQ{constructor(t){this.node=t,this._simulation_restart_required=!1,this._points=[],this.variables_by_name=new Map,this._all_variables=[],this._created_textures_by_name=new Map,this._delta_time=0,this._used_textures_size=new d.a}dispose(){this._graph_node&&this._graph_node.dispose()}set_persisted_texture_allocation_controller(t){this._persisted_texture_allocations_controller=t}setShadersByName(t){this._shaders_by_name=t,this.reset_gpu_compute()}allVariables(){return this._all_variables}async init(t){this.init_particle_group_points(t),await this.create_gpu_compute()}getCurrentRenderTarget(t){var e;const n=this.variables_by_name.get(t);if(n)return null===(e=this._gpu_compute)||void 0===e?void 0:e.getCurrentRenderTarget(n)}init_particle_group_points(t){this.reset_gpu_compute(),t&&(this._particles_core_group=t,this._points=this._get_points()||[])}compute_similation_if_required(){const t=this.node.scene().frame(),e=this.node.pv.startFrame;t>=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;e<t;e++)this._gpu_compute.compute();this.node.renderController.update_render_material_uniforms(),this._last_simulated_frame=this.node.scene().frame();const e=this.node.scene().time();this._delta_time=e-this._last_simulated_time,this._last_simulated_time=e}_data_type(){const t=oQ[this.node.pv.dataType];return aQ[t]}_textureNameForShaderName(t){return`texture_${t}`}async create_gpu_compute(){var t,e;if(this.node.pv.autoTexturesSize){const t=rs.nearestPower2(Math.sqrt(this._points.length));this._used_textures_size.x=Math.min(t,this.node.pv.maxTexturesSize.x),this._used_textures_size.y=Math.min(t,this.node.pv.maxTexturesSize.y)}else{if(!Object(Ln.i)(this.node.pv.texturesSize.x)||!Object(Ln.i)(this.node.pv.texturesSize.y))return void this.node.states.error.set(\\\\\\\"texture size must be a power of 2\\\\\\\");const t=this.node.pv.texturesSize.x*this.node.pv.texturesSize.y;if(this._points.length>t)return void this.node.states.error.set(`max particles is set to (${this.node.pv.texturesSize.x}x${this.node.pv.texturesSize.y}=) ${t}`);this._used_textures_size.copy(this.node.pv.texturesSize)}this._forceTimeDependent(),this._init_particles_uvs(),this.node.renderController.reset_render_material();const n=await ai.renderersController.waitForRenderer();if(n?this._renderer=n:this.node.states.error.set(\\\\\\\"no renderer found\\\\\\\"),!this._renderer)return;const i=new sQ(this._used_textures_size.x,this._used_textures_size.y,this._renderer);if(i.setDataType(this._data_type()),this._gpu_compute=i,this._gpu_compute){this._last_simulated_frame=void 0,this.variables_by_name.forEach(((t,e)=>{t.renderTargets[0].dispose(),t.renderTargets[1].dispose(),this.variables_by_name.delete(e)})),this._all_variables=[],null===(t=this._shaders_by_name)||void 0===t||t.forEach(((t,e)=>{if(this._gpu_compute){const n=this._gpu_compute.addVariable(this._textureNameForShaderName(e),t,this._created_textures_by_name.get(e));this.variables_by_name.set(e,n),this._all_variables.push(n)}})),null===(e=this.variables_by_name)||void 0===e||e.forEach(((t,e)=>{this._gpu_compute&&this._gpu_compute.setVariableDependencies(t,this._all_variables)})),this._create_texture_render_targets(),this._fill_textures(),this.create_simulation_material_uniforms();var r=this._gpu_compute.init();null!==r&&(console.error(r),this.node.states.error.set(r))}else this.node.states.error.set(\\\\\\\"failed to create the GPUComputationRenderer\\\\\\\")}_forceTimeDependent(){this._graph_node||(this._graph_node=new Ai(this.node.scene(),\\\\\\\"gpu_compute\\\\\\\"),this._graph_node.addGraphInput(this.node.scene().timeController.graphNode),this._graph_node.addPostDirtyHook(\\\\\\\"on_time_change\\\\\\\",this._on_graph_node_dirty.bind(this)))}_on_graph_node_dirty(){this.node.is_on_frame_start()?this.node.setDirty():this.compute_similation_if_required()}materials(){const t=[];return this.variables_by_name.forEach(((e,n)=>{t.push(e.material)})),t}create_simulation_material_uniforms(){const t=this.node.assemblerController,e=null==t?void 0:t.assembler;if(!e&&!this._persisted_texture_allocations_controller)return;const n=[];this.variables_by_name.forEach(((t,e)=>{n.push(t.material)}));const i=this._readonlyAllocations();for(let t of n)t.uniforms[RR.TIME]={value:this.node.scene().time()},t.uniforms[RR.DELTA_TIME]={value:this.node.scene().time()},i&&this._assignReadonlyTextures(t,i);if(e)for(let t of n)for(let n of e.param_configs())t.uniforms[n.uniform_name]=n.uniform;else{const t=this.node.persisted_config.loaded_data();if(t){const e=this.node.persisted_config.uniforms();if(e){const r=t.param_uniform_pairs;for(let t of r){const r=t[0],s=t[1],o=this.node.params.get(r),a=e[s];for(let t of n)t.uniforms[s]=a,i&&this._assignReadonlyTextures(t,i);o&&a&&o.options.setOption(\\\\\\\"callback\\\\\\\",(()=>{for(let t of n)$f.callback(o,t.uniforms[s])}))}}}}}_assignReadonlyTextures(t,e){for(let n of e){const e=n.shaderName(),i=this._created_textures_by_name.get(e);if(i){const n=this._textureNameForShaderName(e);t.uniforms[n]={value:i}}}}update_simulation_material_uniforms(){for(let t of this._all_variables)t.material.uniforms[RR.TIME].value=this.node.scene().time(),t.material.uniforms[RR.DELTA_TIME].value=this._delta_time}_init_particles_uvs(){var t=new Float32Array(2*this._points.length);let e=0;for(var n=0,i=0;i<this._used_textures_size.x;i++)for(var r=0;r<this._used_textures_size.y&&(t[e++]=r/(this._used_textures_size.x-1),t[e++]=i/(this._used_textures_size.y-1),!((n+=2)>=t.length));r++);const s=nQ.UV_ATTRIB;if(this._particles_core_group)for(let e of this._particles_core_group.coreGeometries()){const n=e.geometry(),i=e.markedAsInstance()?cX:C.a;n.setAttribute(s,new i(t,2))}}createdTexturesByName(){return this._created_textures_by_name}_fill_textures(){const t=this._textureAllocationsController();t&&this._created_textures_by_name.forEach(((e,n)=>{const i=t.allocationForShaderName(n);if(!i)return void console.warn(`no allocation found for shader ${n}`);const r=i.variables();if(!r)return void console.warn(\\\\\\\"allocation has no variables\\\\\\\");const s=e.image.data;for(let t of r){const e=t.position();let n=t.name();const i=this._points[0];if(i){if(i.hasAttrib(n)){const t=i.attribSize(n);let r=e;for(let e of this._points){if(1==t){const t=e.attribValue(n);s[r]=t}else e.attribValue(n).toArray(s,r);r+=4}}}}}))}reset_gpu_compute(){this._gpu_compute=void 0,this._simulation_restart_required=!0}set_restart_not_required(){this._simulation_restart_required=!1}reset_gpu_compute_and_set_dirty(){this.reset_gpu_compute(),this.node.setDirty()}reset_particle_groups(){this._particles_core_group=void 0}initialized(){return null!=this._particles_core_group&&null!=this._gpu_compute}_create_texture_render_targets(){this._created_textures_by_name.forEach(((t,e)=>{t.dispose()})),this._created_textures_by_name.clear(),this.variables_by_name.forEach(((t,e)=>{this._gpu_compute&&this._created_textures_by_name.set(e,this._gpu_compute.createTexture())}));const t=this._readonlyAllocations();if(t&&this._gpu_compute)for(let e of t)this._created_textures_by_name.set(e.shaderName(),this._gpu_compute.createTexture())}_textureAllocationsController(){var t;return(null===(t=this.node.assemblerController)||void 0===t?void 0:t.assembler.textureAllocationsController())||this._persisted_texture_allocations_controller}_readonlyAllocations(){var t;return null===(t=this._textureAllocationsController())||void 0===t?void 0:t.readonlyAllocations()}restart_simulation_if_required(){this._simulation_restart_required&&this._restart_simulation()}_restart_simulation(){this._last_simulated_time=void 0,this._create_texture_render_targets();this._get_points()&&(this._fill_textures(),this.variables_by_name.forEach(((t,e)=>{const n=this._created_textures_by_name.get(e);this._gpu_compute&&n&&(this._gpu_compute.renderTexture(n,t.renderTargets[0]),this._gpu_compute.renderTexture(n,t.renderTargets[1]))})))}_get_points(){if(!this._particles_core_group)return;let t=this._particles_core_group.coreGeometries();const e=t[0];if(e){const n=e.markedAsInstance(),i=[];for(let e of t)e.markedAsInstance()==n&&i.push(e);const r=[];for(let t of i)for(let e of t.points())r.push(e);return r}return[]}}class cQ{constructor(t,e){if(this._name=t,this._size=e,this._position=-1,this._readonly=!1,!t)throw\\\\\\\"TextureVariable requires a name\\\\\\\"}merge(t){var e;t.readonly()||this.setReadonly(!1),null===(e=t.graphNodeIds())||void 0===e||e.forEach(((t,e)=>{this.addGraphNodeId(e)}))}setReadonly(t){this._readonly=t}readonly(){return this._readonly}setAllocation(t){this._allocation=t}allocation(){return this._allocation}graphNodeIds(){return this._graph_node_ids}addGraphNodeId(t){this._graph_node_ids=this._graph_node_ids||new Map,this._graph_node_ids.set(t,!0)}name(){return this._name}size(){return this._size}setPosition(t){this._position=t}position(){return this._position}component(){return\\\\\\\"xyzw\\\\\\\".split(\\\\\\\"\\\\\\\").splice(this._position,this._size).join(\\\\\\\"\\\\\\\")}static fromJSON(t){return new cQ(t.name,t.size)}toJSON(t){const e=[];return this._graph_node_ids&&this._graph_node_ids.forEach(((n,i)=>{const r=t.graph.nodeFromId(i);if(r){const t=r.path();t&&e.push(t)}})),{name:this.name(),size:this.size(),nodes:e}}}class uQ{constructor(){this._size=0}addVariable(t){this._variables=this._variables||[],this._variables.push(t),t.setPosition(this._size),t.setAllocation(this),this._size+=t.size()}hasSpaceForVariable(t){return this._size+t.size()<=4}shaderName(){var t;return((null===(t=this.variables())||void 0===t?void 0:t.map((t=>t.name())))||[\\\\\\\"no_variables_allocated\\\\\\\"]).join(\\\\\\\"_SEPARATOR_\\\\\\\")}textureName(){return`texture_${this.shaderName()}`}variables(){return this._variables}variablesForInputNode(t){var e;return null===(e=this._variables)||void 0===e?void 0:e.filter((e=>{var n;return(null===(n=e.graphNodeIds())||void 0===n?void 0:n.has(t.graphNodeId()))||!1}))}inputNamesForNode(t){const e=this.variablesForInputNode(t);if(e)return t.type()==ir.ATTRIBUTE?[gf.INPUT_NAME]:e.map((t=>t.name()))}variable(t){if(this._variables)for(let e of this._variables)if(e.name()==t)return e}static fromJSON(t){const e=new uQ;for(let n of t){const t=cQ.fromJSON(n);e.addVariable(t)}return e}toJSON(t){return this._variables?this._variables.map((e=>e.toJSON(t))):[]}}const hQ=[\\\\\\\"position\\\\\\\",\\\\\\\"normal\\\\\\\",\\\\\\\"color\\\\\\\",\\\\\\\"uv\\\\\\\"];class dQ{constructor(){this._writableAllocations=[],this._readonlyAllocations=[]}static _sortNodes(t){const e=t.filter((t=>t.type()==UI.type())),n=t.filter((t=>t.type()!=UI.type())),i=n.map((t=>t.name())).sort(),r=new Map;for(let t of n)r.set(t.name(),t);for(let t of i){const n=r.get(t);n&&e.push(n)}return e}allocateConnectionsFromRootNodes(t,e){const n=[];t=dQ._sortNodes(t),e=dQ._sortNodes(e);for(let e of t){const t=e.graphNodeId();switch(e.type()){case UI.type():for(let i of e.io.inputs.namedInputConnectionPoints()){if(e.io.inputs.named_input(i.name())){const e=new cQ(i.name(),Go[i.type()]);e.addGraphNodeId(t),n.push(e)}}break;case gf.type():{const i=e,r=i.connected_input_node(),s=i.connected_input_connection_point();if(r&&s){const e=new cQ(i.attribute_name,Go[s.type()]);e.addGraphNodeId(t),n.push(e)}break}}}for(let t of e){const e=t.graphNodeId();switch(t.type()){case HP.type():{const i=t;for(let t of i.io.outputs.used_output_names()){if(hQ.includes(t)){const r=i.io.outputs.namedOutputConnectionPointsByName(t);if(r){const i=r.type(),s=new cQ(t,Go[i]);s.addGraphNodeId(e),n.push(s)}}}break}case gf.type():{const i=t,r=i.output_connection_point();if(r){const t=new cQ(i.attribute_name,Go[r.type()]);i.isExporting()||t.setReadonly(!0),t.addGraphNodeId(e),n.push(t)}break}}}this._allocateVariables(n)}_allocateVariables(t){const e=f.sortBy(t,(t=>-t.size())),n=this._ensureVariablesAreUnique(e);for(let t of n)t.readonly()?this._allocateVariable(t,this._readonlyAllocations):this._allocateVariable(t,this._writableAllocations)}_ensureVariablesAreUnique(t){const e=new Map;for(let n of t)u.pushOnArrayAtEntry(e,n.name(),n);const n=[];return e.forEach(((t,e)=>{const i=t[0];n.push(i);for(let e=1;e<t.length;e++){const n=t[e];i.merge(n)}})),n}_allocateVariable(t,e){let n=this.hasVariable(t.name());if(n)throw\\\\\\\"no variable should be allocated since they have been made unique before\\\\\\\";if(!n)for(let i of e)!n&&i.hasSpaceForVariable(t)&&(i.addVariable(t),n=!0);if(!n){const n=new uQ;e.push(n),n.addVariable(t)}}_addWritableAllocation(t){this._writableAllocations.push(t)}_addReadonlyAllocation(t){this._readonlyAllocations.push(t)}readonlyAllocations(){return this._readonlyAllocations}shaderNames(){const t=this._writableAllocations.map((t=>t.shaderName()));return f.uniq(t)}createShaderConfigs(){return[]}allocationForShaderName(t){const e=this._writableAllocations.filter((e=>e.shaderName()==t))[0];return e||this._readonlyAllocations.filter((e=>e.shaderName()==t))[0]}inputNamesForShaderName(t,e){const n=this.allocationForShaderName(e);if(n)return n.inputNamesForNode(t)}variable(t){for(let e of this._writableAllocations){const n=e.variable(t);if(n)return n}for(let e of this._readonlyAllocations){const n=e.variable(t);if(n)return n}}variables(){const t=this._writableAllocations.map((t=>t.variables()||[])).flat(),e=this._writableAllocations.map((t=>t.variables()||[])).flat();return t.concat(e)}hasVariable(t){return this.variables().map((t=>t.name())).includes(t)}static fromJSON(t){const e=new dQ;for(let n of t.writable){const t=n[Object.keys(n)[0]],i=uQ.fromJSON(t);e._addWritableAllocation(i)}for(let n of t.readonly){const t=n[Object.keys(n)[0]],i=uQ.fromJSON(t);e._addReadonlyAllocation(i)}return e}toJSON(t){return{writable:this._writableAllocations.map((e=>({[e.shaderName()]:e.toJSON(t)}))),readonly:this._readonlyAllocations.map((e=>({[e.shaderName()]:e.toJSON(t)})))}}print(t){console.warn(JSON.stringify(this.toJSON(t),[\\\\\\\"\\\\\\\"],2))}}class pQ extends Xf{constructor(t){super(t),this.node=t}toJSON(){const t=this.node.assemblerController;if(!t)return;const e={};this.node.shaders_by_name().forEach(((t,n)=>{e[n]=t}));const n=t.assembler.textureAllocationsController().toJSON(this.node.scene()),i=[],r=new F,s=t.assembler.param_configs();for(let t of s)i.push([t.name(),t.uniform_name]),r.uniforms[t.uniform_name]=t.uniform;const o=this._materialToJson(r,{node:this.node,suffix:\\\\\\\"main\\\\\\\"});return{shaders_by_name:e,texture_allocations:n,param_uniform_pairs:i,uniforms_owner:o||{}}}load(t){ai.playerMode()&&(this._loaded_data=t,this.node.init_with_persisted_config())}loaded_data(){return this._loaded_data}shaders_by_name(){if(this._loaded_data){const t=new Map,e=Object.keys(this._loaded_data.shaders_by_name);for(let n of e)t.set(n,this._loaded_data.shaders_by_name[n]);return t}}texture_allocations_controller(){if(this._loaded_data)return dQ.fromJSON(this._loaded_data.texture_allocations)}uniforms(){if(this._loaded_data){const t=this._loadMaterial(this._loaded_data.uniforms_owner);return(null==t?void 0:t.uniforms)||{}}}}const _Q=new class extends aa{constructor(){super(...arguments),this.startFrame=oa.FLOAT(Ml.START_FRAME,{range:[0,1e3],rangeLocked:[!0,!1]}),this.autoTexturesSize=oa.BOOLEAN(1),this.maxTexturesSize=oa.VECTOR2([1024,1024],{visibleIf:{autoTexturesSize:1}}),this.texturesSize=oa.VECTOR2([64,64],{visibleIf:{autoTexturesSize:0}}),this.dataType=oa.INTEGER(0,{menu:{entries:oQ.map(((t,e)=>({value:e,name:t})))}}),this.reset=oa.BUTTON(null,{callback:(t,e)=>{mQ.PARAM_CALLBACK_reset(t)}}),this.material=oa.OPERATOR_PATH(\\\\\\\"\\\\\\\",{nodeSelection:{context:Ki.MAT},dependentOnFoundNode:!1})}};class mQ extends gG{constructor(){super(...arguments),this.paramsConfig=_Q,this._assembler_controller=this._create_assembler_controller(),this.persisted_config=new pQ(this),this.globals_handler=new nQ(nQ.PARTICLE_SIM_UV),this._shaders_by_name=new Map,this.gpuController=new lQ(this),this.renderController=new iQ(this),this._reset_material_if_dirty_bound=this._reset_material_if_dirty.bind(this),this._children_controller_context=Ki.GL}static type(){return\\\\\\\"particlesSystemGpu\\\\\\\"}dispose(){super.dispose(),this.gpuController.dispose()}get assemblerController(){return this._assembler_controller}usedAssembler(){return Hn.GL_PARTICLES}_create_assembler_controller(){return ai.assemblersRegister.assembler(this,this.usedAssembler())}shaders_by_name(){return this._shaders_by_name}static require_webgl2(){return!0}static PARAM_CALLBACK_reset(t){t.PARAM_CALLBACK_reset()}PARAM_CALLBACK_reset(){this.gpuController.reset_gpu_compute_and_set_dirty()}static displayedInputNames(){return[\\\\\\\"points to emit particles from\\\\\\\"]}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState(Qi.NEVER),this.addPostDirtyHook(\\\\\\\"_reset_material_if_dirty\\\\\\\",this._reset_material_if_dirty_bound)}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}childrenAllowed(){return this.assemblerController?super.childrenAllowed():(this.scene().markAsReadOnly(this),!1)}async _reset_material_if_dirty(){this.p.material.isDirty()&&(this.renderController.reset_render_material(),this.is_on_frame_start()||await this.renderController.init_render_material())}is_on_frame_start(){return this.scene().frame()==this.pv.startFrame}async cook(t){this.gpuController.set_restart_not_required();const e=t[0];this.compileIfRequired(),this.is_on_frame_start()&&this.gpuController.reset_particle_groups(),this.gpuController.initialized()||await this.gpuController.init(e),this.renderController.initialized()||(this.renderController.init_core_group(e),await this.renderController.init_render_material()),this.gpuController.restart_simulation_if_required(),this.gpuController.compute_similation_if_required(),this.is_on_frame_start()?this.setCoreGroup(e):this.cookController.endCook()}async compileIfRequired(){var t;(null===(t=this.assemblerController)||void 0===t?void 0:t.compileRequired())&&await this.run_assembler()}async run_assembler(){const t=this.assemblerController;if(!t)return;const e=this._find_export_nodes();if(e.length>0){const n=e;t.set_assembler_globals_handler(this.globals_handler),t.assembler.set_root_nodes(n),t.assembler.compile(),t.post_compile()}const n=t.assembler.shaders_by_name();this._setShaderNames(n)}_setShaderNames(t){this._shaders_by_name=t,this.gpuController.setShadersByName(this._shaders_by_name),this.renderController.setShadersByName(this._shaders_by_name),this.gpuController.reset_gpu_compute(),this.gpuController.reset_particle_groups()}init_with_persisted_config(){const t=this.persisted_config.shaders_by_name(),e=this.persisted_config.texture_allocations_controller();t&&e&&(this._setShaderNames(t),this.gpuController.set_persisted_texture_allocation_controller(e))}_find_export_nodes(){const t=Of.findAttributeExportNodes(this),e=Of.findOutputNodes(this);if(e.length>1)return this.states.error.set(\\\\\\\"only one output node is allowed\\\\\\\"),[];const n=e[0];return n&&t.push(n),t}}class fQ extends pG{static type(){return\\\\\\\"peak\\\\\\\"}cook(t,e){const n=t[0];let i,r;for(let t of n.objects())t.traverse((t=>{let n;if(null!=(n=t.geometry)){for(r of(i=new ps(n),i.points())){const t=r.normal(),n=r.position().clone().add(t.multiplyScalar(e.amount));r.setPosition(n)}i.geometry().getAttribute(\\\\\\\"position\\\\\\\").needsUpdate=!0}}));return t[0]}}fQ.DEFAULT_PARAMS={amount:0};const gQ=fQ.DEFAULT_PARAMS;const vQ=new class extends aa{constructor(){super(...arguments),this.amount=oa.FLOAT(gQ.amount,{range:[-1,1]})}};class yQ extends gG{constructor(){super(...arguments),this.paramsConfig=vQ}static type(){return\\\\\\\"peak\\\\\\\"}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState(Qi.FROM_NODE)}cook(t){this._operation=this._operation||new fQ(this.scene(),this.states);const e=this._operation.cook(t,this.pv);this.setCoreGroup(e)}}const xQ=new p.a(0,0,1),bQ=new p.a(0,0,1),wQ=new p.a(0,1,0);class TQ extends pG{constructor(){super(...arguments),this._core_transform=new Mz,this._size=new p.a,this._center=new p.a,this._segmentsCount=new d.a(1,1)}static type(){return\\\\\\\"plane\\\\\\\"}cook(t,e){const n=t[0];return n?this._cook_with_input(n,e):this._cook_without_input(e)}_cook_without_input(t){const e=this._create_plane(t.size,t);this._core_transform.rotate_geometry(e,xQ,t.direction);const n=this._core_transform.translation_matrix(t.center);return e.applyMatrix4(n),this.createCoreGroupFromGeometry(e)}_cook_with_input(t,e){const n=t.boundingBox();n.getSize(this._size),n.getCenter(this._center);const i=new d.a(this._size.x,this._size.z),r=this._create_plane(i,e);this._core_transform.rotate_geometry(r,bQ,wQ);const s=this._core_transform.translation_matrix(this._center);return r.applyMatrix4(s),this.createCoreGroupFromGeometry(r)}_create_plane(t,e){return t=t.clone(),e.useSegmentsCount?(this._segmentsCount.x=Math.floor(e.segments.x),this._segmentsCount.y=Math.floor(e.segments.y)):e.stepSize>0&&(this._segmentsCount.x=Math.floor(t.x/e.stepSize),this._segmentsCount.y=Math.floor(t.y/e.stepSize),t.x=this._segmentsCount.x*e.stepSize,t.y=this._segmentsCount.y*e.stepSize),new L(t.x,t.y,this._segmentsCount.x,this._segmentsCount.y)}}TQ.DEFAULT_PARAMS={size:new d.a(1,1),useSegmentsCount:!1,stepSize:1,segments:new d.a(1,1),direction:new p.a(0,1,0),center:new p.a(0,0,0)},TQ.INPUT_CLONED_STATE=Qi.NEVER;const AQ=TQ.DEFAULT_PARAMS;const EQ=new class extends aa{constructor(){super(...arguments),this.size=oa.VECTOR2(AQ.size),this.useSegmentsCount=oa.BOOLEAN(AQ.useSegmentsCount),this.stepSize=oa.FLOAT(AQ.stepSize,{range:[.001,1],rangeLocked:[!1,!1],visibleIf:{useSegmentsCount:0}}),this.segments=oa.VECTOR2(AQ.segments,{visibleIf:{useSegmentsCount:1}}),this.direction=oa.VECTOR3(AQ.direction),this.center=oa.VECTOR3(AQ.center)}};class MQ extends gG{constructor(){super(...arguments),this.paramsConfig=EQ}static type(){return\\\\\\\"plane\\\\\\\"}static displayedInputNames(){return[\\\\\\\"geometry to create plane from (optional)\\\\\\\"]}initializeNode(){this.io.inputs.setCount(0,1),this.io.inputs.initInputsClonedState(TQ.INPUT_CLONED_STATE)}cook(t){this._operation=this._operation||new TQ(this.scene(),this.states);const e=this._operation.cook(t,this.pv);this.setCoreGroup(e)}}const SQ=\\\\\\\"position\\\\\\\";const CQ=new class extends aa{constructor(){super(...arguments),this.updateX=oa.BOOLEAN(0),this.x=oa.FLOAT(\\\\\\\"@P.x\\\\\\\",{visibleIf:{updateX:1},expression:{forEntities:!0}}),this.updateY=oa.BOOLEAN(0),this.y=oa.FLOAT(\\\\\\\"@P.y\\\\\\\",{visibleIf:{updateY:1},expression:{forEntities:!0}}),this.updateZ=oa.BOOLEAN(0),this.z=oa.FLOAT(\\\\\\\"@P.z\\\\\\\",{visibleIf:{updateZ:1},expression:{forEntities:!0}}),this.updateNormals=oa.BOOLEAN(1)}};class NQ extends gG{constructor(){super(...arguments),this.paramsConfig=CQ,this._x_arrays_by_geometry_uuid=new Map,this._y_arrays_by_geometry_uuid=new Map,this._z_arrays_by_geometry_uuid=new Map}static type(){return\\\\\\\"point\\\\\\\"}static displayedInputNames(){return[\\\\\\\"points to move\\\\\\\"]}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState(Qi.FROM_NODE)}async cook(t){const e=t[0];await this._eval_expressions_for_core_group(e)}async _eval_expressions_for_core_group(t){const e=t.coreObjects();for(let t=0;t<e.length;t++)await this._eval_expressions_for_core_object(e[t]);this.pv.updateNormals&&t.computeVertexNormals();const n=t.geometries();for(let t of n)t.computeBoundingBox();if(!this.io.inputs.cloneRequired(0)){const e=t.geometries();for(let t of e){t.getAttribute(SQ).needsUpdate=!0}}this.setCoreGroup(t)}async _eval_expressions_for_core_object(t){const e=t.object().geometry,n=t.points(),i=e.getAttribute(SQ).array,r=await this._update_from_param(e,i,n,this.p.updateX,this.p.x,this.pv.x,this._x_arrays_by_geometry_uuid,0),s=await this._update_from_param(e,i,n,this.p.updateY,this.p.y,this.pv.y,this._y_arrays_by_geometry_uuid,1),o=await this._update_from_param(e,i,n,this.p.updateZ,this.p.z,this.pv.z,this._z_arrays_by_geometry_uuid,2);r&&this._commit_tmp_values(r,i,0),s&&this._commit_tmp_values(s,i,1),o&&this._commit_tmp_values(o,i,2)}async _update_from_param(t,e,n,i,r,s,o,a){const l=i,c=r;let u=this._init_array_if_required(t,o,n.length,a);if(l.value)if(c.hasExpression()&&c.expressionController)await c.expressionController.compute_expression_for_points(n,((t,e)=>{u[t.index()]=e}));else{let t;for(let e=0;e<n.length;e++)t=n[e],u[t.index()]=s}return u}_init_array_if_required(t,e,n,i){const r=t.uuid,s=e.get(r);if(s){if(s.length<n){const s=this._array_for_component(t,n,i);return e.set(r,s),s}return s}{const s=this._array_for_component(t,n,i);return e.set(r,s),s}}_array_for_component(t,e,n){const i=new Array(e),r=t.getAttribute(SQ).array;for(let t=0;t<i.length;t++)i[t]=r[3*t+n];return i}_commit_tmp_values(t,e,n){for(let i=0;i<t.length;i++)e[3*i+n]=t[i]}}class LQ extends pG{static type(){return\\\\\\\"pointLight\\\\\\\"}cook(t,e){const n=new sU.a;return n.matrixAutoUpdate=!1,n.castShadow=!0,n.shadow.bias=-.001,n.shadow.mapSize.x=1024,n.shadow.mapSize.y=1024,n.shadow.camera.near=.1,n.color=e.color,n.intensity=e.intensity,n.decay=e.decay,n.distance=e.distance,n.castShadow=e.castShadows,n.shadow.mapSize.copy(e.shadowRes),n.shadow.camera.near=e.shadowNear,n.shadow.camera.far=e.shadowFar,n.shadow.bias=e.shadowBias,this.createCoreGroupFromObjects([n])}}LQ.DEFAULT_PARAMS={color:new D.a(1,1,1),intensity:1,decay:.1,distance:100,castShadows:!1,shadowRes:new d.a(1024,1024),shadowBias:.001,shadowNear:1,shadowFar:100},LQ.INPUT_CLONED_STATE=Qi.NEVER;const OQ=LQ.DEFAULT_PARAMS;const RQ=new class extends aa{constructor(){super(...arguments),this.light=oa.FOLDER(),this.color=oa.COLOR(OQ.color.toArray(),{conversion:so.SRGB_TO_LINEAR}),this.intensity=oa.FLOAT(OQ.intensity),this.decay=oa.FLOAT(OQ.decay),this.distance=oa.FLOAT(OQ.distance),this.castShadows=oa.BOOLEAN(OQ.castShadows),this.shadowRes=oa.VECTOR2(OQ.shadowRes.toArray(),{visibleIf:{castShadows:1}}),this.shadowBias=oa.FLOAT(OQ.shadowBias,{visibleIf:{castShadows:1}}),this.shadowNear=oa.FLOAT(OQ.shadowNear,{visibleIf:{castShadows:1}}),this.shadowFar=oa.FLOAT(OQ.shadowFar,{visibleIf:{castShadows:1}})}};class PQ extends gG{constructor(){super(...arguments),this.paramsConfig=RQ}static type(){return\\\\\\\"pointLight\\\\\\\"}initializeNode(){this.io.inputs.setCount(0)}cook(t){this._operation=this._operation||new LQ(this._scene,this.states);const e=this._operation.cook(t,this.pv);this.setCoreGroup(e)}}const IQ=new p.a(0,1,0),FQ=new p.a(-1,0,0);class DQ extends pG{constructor(){super(...arguments),this._centerMatrix=new A.a,this._longitudeMatrix=new A.a,this._latitudeMatrix=new A.a,this._depthMatrix=new A.a,this._fullMatrix=new A.a,this._decomposed={t:new p.a,q:new au.a,s:new p.a}}static type(){return\\\\\\\"polarTransform\\\\\\\"}cook(t,e){const n=t[0].objects(),i=this.matrix(e);return this._apply_transform(n,e,i),t[0]}_apply_transform(t,e,n){const i=wz[e.applyOn];switch(i){case yz.GEOMETRIES:return this._apply_matrix_to_geometries(t,n);case yz.OBJECTS:return this._apply_matrix_to_objects(t,n)}ar.unreachable(i)}_apply_matrix_to_geometries(t,e){for(let n of t){const t=n.geometry;t&&t.applyMatrix4(e)}}_apply_matrix_to_objects(t,e){for(let n of t)e.decompose(this._decomposed.t,this._decomposed.q,this._decomposed.s),n.position.copy(this._decomposed.t),n.quaternion.copy(this._decomposed.q),n.scale.copy(this._decomposed.s),n.updateMatrix()}matrix(t){return this._centerMatrix.identity(),this._longitudeMatrix.identity(),this._latitudeMatrix.identity(),this._depthMatrix.identity(),this._centerMatrix.makeTranslation(t.center.x,t.center.y,t.center.z),this._longitudeMatrix.makeRotationAxis(IQ,Object(Ln.e)(t.longitude)),this._latitudeMatrix.makeRotationAxis(FQ,Object(Ln.e)(t.latitude)),this._depthMatrix.makeTranslation(0,0,t.depth),this._fullMatrix.copy(this._centerMatrix).multiply(this._longitudeMatrix).multiply(this._latitudeMatrix).multiply(this._depthMatrix),this._fullMatrix}}DQ.DEFAULT_PARAMS={applyOn:wz.indexOf(yz.GEOMETRIES),center:new p.a(0,0,0),longitude:0,latitude:0,depth:1},DQ.INPUT_CLONED_STATE=Qi.FROM_NODE;const kQ=DQ.DEFAULT_PARAMS;const BQ=new class extends aa{constructor(){super(...arguments),this.applyOn=oa.INTEGER(kQ.applyOn,{menu:{entries:wz.map(((t,e)=>({name:t,value:e})))}}),this.center=oa.VECTOR3(kQ.center.toArray()),this.longitude=oa.FLOAT(kQ.longitude,{range:[0,360]}),this.latitude=oa.FLOAT(kQ.latitude,{range:[-180,180]}),this.depth=oa.FLOAT(kQ.depth,{range:[0,10]})}};class zQ extends gG{constructor(){super(...arguments),this.paramsConfig=BQ}static type(){return\\\\\\\"polarTransform\\\\\\\"}static displayedInputNames(){return[\\\\\\\"geometries or objects to transform\\\\\\\"]}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState(DQ.INPUT_CLONED_STATE)}cook(t){this._operation=this._operation||new DQ(this.scene(),this.states);const e=this._operation.cook(t,this.pv);this.setCoreGroup(e)}}class UQ{static accumulated_curve_point_indices(t){let e=[];const n=[];let i,r=null;for(let s=0;s<t.length;s++)if(s%2==1){i=t[s];const o=t[s-1];null==r||o===r?(0===e.length&&e.push(o),e.push(i),r=i):(n.push(e),e=[o,i],r=i)}return n.push(e),n}static create_line_segment_geometry(t,e,n,i){const r=[],s={};n.forEach((t=>{s[t]=[]})),e.forEach(((e,o)=>{const a=t[e];n.forEach((t=>{const e=a.attribValue(t);let n;n=i[t]>1?e.toArray():[e],n.forEach((e=>{s[t].push(e)}))})),o>0&&(r.push(o-1),r.push(o))}));const o=new S.a;return n.forEach((t=>{const e=i[t],n=s[t];o.setAttribute(t,new C.c(n,e))})),o.setIndex(r),o}static line_segment_to_geometries(t){var e;const n=[],i=new ps(t),r=i.attribNames(),s=i.points(),o=(null===(e=t.getIndex())||void 0===e?void 0:e.array)||[],a=this.accumulated_curve_point_indices(o);if(a.length>0){const e=i.attribSizes();a.forEach(((i,o)=>{t=this.create_line_segment_geometry(s,i,r,e),n.push(t)}))}return n}}class GQ{constructor(t,e,n){this.geometry=t,this.geometry1=e,this.geometry0=n}process(){const t=new ps(this.geometry0),e=new ps(this.geometry1),n=t.segments(),i=e.segments();if(0===n.length||0===i.length)return;const r=n.length<i.length?[t,e]:[e,t],s=r[0],o=r[1],a=s.segments(),l=o.segments(),c=s.points(),u=o.points(),h=c.length,d=c.concat(u),p=[];a.forEach(((t,e)=>{const n=l[e];p.push(t[0]),p.push(t[1]),p.push(n[0]+h),p.push(t[1]),p.push(n[1]+h),p.push(n[0]+h)}));f.intersection(s.attribNames(),o.attribNames()).forEach((t=>{const e=s.attribSize(t);let n,i=d.map((e=>e.attribValue(t)));n=1==e?i:i.map((t=>t.toArray())).flat(),this.geometry.setAttribute(t,new C.c(n,e))})),this.geometry.setIndex(p),this.geometry.computeVertexNormals()}}const VQ=new p.a(0,0,0),HQ=new p.a(1,1,1);const jQ=new class extends aa{constructor(){super(...arguments),this.radius=oa.FLOAT(1),this.segmentsRadial=oa.INTEGER(8,{range:[3,20],rangeLocked:[!0,!1]}),this.closed=oa.BOOLEAN(0)}};class WQ extends gG{constructor(){super(...arguments),this.paramsConfig=jQ,this._core_transform=new Mz,this._geometries=[]}static type(){return\\\\\\\"polywire\\\\\\\"}static displayedInputNames(){return[\\\\\\\"lines to create tubes from\\\\\\\"]}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState(Qi.NEVER)}cook(t){const e=t[0];this._geometries=[];for(let t of e.objects())t instanceof Tr.a&&this._create_tube(t);const n=ps.mergeGeometries(this._geometries);for(let t of this._geometries)t.dispose();if(n){const t=this.createObject(n,Sr.MESH);this.setObject(t)}else this.setObjects([])}_create_tube(t){var e;const n=t.geometry,i=new ps(n).points(),r=null===(e=n.getIndex())||void 0===e?void 0:e.array,s=UQ.accumulated_curve_point_indices(r);for(let t of s){const e=t.map((t=>i[t]));this._create_tube_from_points(e)}}_create_tube_from_points(t){if(t.length<=1)return;const e=t.map((t=>t.attribValue(\\\\\\\"position\\\\\\\"))),n=wY.create(this.pv.radius,this.pv.segmentsRadial),i=[];for(let t of e){const e=t,r=this._core_transform.matrix(e,VQ,HQ,1,Ez),s=n.clone();s.applyMatrix4(r),i.push(s)}for(let t=0;t<i.length;t++)if(t>0){const e=i[t],n=i[t-1],r=this._skin(n,e);this._geometries.push(r)}}_skin(t,e){const n=new S.a;return new GQ(n,t,e).process(),n}}const qQ=\\\\\\\"dist\\\\\\\";class XQ extends pG{constructor(){super(...arguments),this._matDoubleSideTmpSetter=new z$,this._raycaster=new uL,this._pointPos=new p.a,this._pointNormal=new p.a}static type(){return\\\\\\\"ray\\\\\\\"}cook(t,e){const n=t[0],i=t[1];return this._ray(n,i,e)}_ray(t,e,n){let i,r;this._matDoubleSideTmpSetter.setCoreGroupMaterialDoubleSided(e),n.addDistAttribute&&(t.hasAttrib(qQ)||t.addNumericVertexAttrib(qQ,1,-1));const s=t.points();for(let t of s)if(t.getPosition(this._pointPos),i=n.direction,n.useNormals&&(t.getNormal(this._pointNormal),i=this._pointNormal),this._raycaster.set(this._pointPos,i),r=this._raycaster.intersectObjects(e.objects(),!0)[0],r){if(n.transformPoints&&t.setPosition(r.point),n.addDistAttribute){const e=this._pointPos.distanceTo(r.point);console.log(e),t.setAttribValue(qQ,e)}n.transferFaceNormals&&r.face&&t.setNormal(r.face.normal)}return this._matDoubleSideTmpSetter.restoreMaterialSideProperty(e),t}}XQ.DEFAULT_PARAMS={useNormals:!0,direction:new p.a(0,-1,0),transformPoints:!0,transferFaceNormals:!0,addDistAttribute:!1},XQ.INPUT_CLONED_STATE=[Qi.FROM_NODE,Qi.ALWAYS];const YQ=XQ.DEFAULT_PARAMS;const $Q=new class extends aa{constructor(){super(...arguments),this.useNormals=oa.BOOLEAN(YQ.useNormals),this.direction=oa.VECTOR3(YQ.direction.toArray(),{visibleIf:{useNormals:0}}),this.transformPoints=oa.BOOLEAN(YQ.transformPoints),this.transferFaceNormals=oa.BOOLEAN(YQ.transferFaceNormals),this.addDistAttribute=oa.BOOLEAN(YQ.addDistAttribute)}};class JQ extends gG{constructor(){super(...arguments),this.paramsConfig=$Q}static type(){return\\\\\\\"ray\\\\\\\"}static displayedInputNames(){return[\\\\\\\"geometry to move\\\\\\\",\\\\\\\"geometry to ray onto\\\\\\\"]}initializeNode(){this.io.inputs.setCount(2),this.io.inputs.initInputsClonedState([Qi.FROM_NODE,Qi.ALWAYS])}cook(t){this._operation=this._operation||new XQ(this._scene,this.states);const e=this._operation.cook(t,this.pv);this.setCoreGroup(e)}}const ZQ={color:{value:null},tDiffuse:{value:null},textureMatrix:{value:null},opacity:{value:.5}},QQ=\\\\\\\"uniform mat4 textureMatrix;\\\\nvarying vec4 vUv;\\\\n\\\\nvoid main() {\\\\n\\\\n\\\\tvUv = textureMatrix * vec4( position, 1.0 );\\\\n\\\\tgl_Position = projectionMatrix * modelViewMatrix * vec4( position, 1.0 );\\\\n\\\\n}\\\\\\\",KQ=\\\\\\\"uniform vec3 color;\\\\nuniform sampler2D tDiffuse;\\\\nvarying vec4 vUv;\\\\nuniform float opacity;\\\\n\\\\nfloat blendOverlay( float base, float blend ) {\\\\n\\\\n\\\\treturn( base < 0.5 ? ( 2.0 * base * blend ) : ( 1.0 - 2.0 * ( 1.0 - base ) * ( 1.0 - blend ) ) );\\\\n\\\\n}\\\\n\\\\nvec3 blendOverlay( vec3 base, vec3 blend ) {\\\\n\\\\n\\\\treturn vec3( blendOverlay( base.r, blend.r ), blendOverlay( base.g, blend.g ), blendOverlay( base.b, blend.b ) );\\\\n\\\\n}\\\\n\\\\nvoid main() {\\\\n\\\\n\\\\tvec4 base = texture2DProj( tDiffuse, vUv );\\\\n\\\\tgl_FragColor = vec4( blendOverlay( base.rgb, color ), opacity );\\\\n\\\\n}\\\\\\\",tK={minFilter:w.V,magFilter:w.V,format:w.ic};class eK extends k.a{constructor(t,e){super(),this.geometry=t,this._options=e,this.type=\\\\\\\"Reflector\\\\\\\",this.reflectorPlane=new X.a,this.normal=new p.a,this.reflectorWorldPosition=new p.a,this.cameraWorldPosition=new p.a,this.rotationMatrix=new A.a,this.lookAtPosition=new p.a(0,0,-1),this.clipPlane=new _.a,this.view=new p.a,this.target=new p.a,this.q=new _.a,this.textureMatrix=new A.a,this.virtualCamera=new K.a,this.onBeforeRender=this._onBeforeRender.bind(this),this._onWindowResizeBound=this._onWindowResize.bind(this);const{width:n,height:i}=this._getRendererSize(this._options.renderer);this.renderTarget=new Z(n,i,tK),Object(Ln.i)(n)&&Object(Ln.i)(i)||(this.renderTarget.texture.generateMipmaps=!1),this._coreRenderBlur=new kU(new d.a(n,i)),this.material=new F({uniforms:I.clone(ZQ),fragmentShader:KQ,vertexShader:QQ}),this.material.uniforms.tDiffuse.value=this.renderTarget.texture,this.material.uniforms.color.value=this._options.color,this.material.uniforms.textureMatrix.value=this.textureMatrix,this.material.uniforms.opacity.value=this._options.opacity,this.material.transparent=this._options.opacity<1,this._addWindowResizeEvent()}_addWindowResizeEvent(){window.addEventListener(\\\\\\\"resize\\\\\\\",this._onWindowResizeBound.bind(this),!1)}_removeWindowResizeEvent(){window.removeEventListener(\\\\\\\"resize\\\\\\\",this._onWindowResizeBound.bind(this),!1)}_onWindowResize(){this.traverseAncestors((t=>{t.parent||t.uuid!=this._options.scene.uuid&&this._removeWindowResizeEvent()}));const{width:t,height:e}=this._getRendererSize(this._options.renderer);this.renderTarget.setSize(t,e),this._coreRenderBlur.setSize(t,e)}_getRendererSize(t){const e=t.domElement;return{width:e.width*this._options.pixelRatio,height:e.height*this._options.pixelRatio}}_onBeforeRender(t,e,n,i,r,s){if(!this._options.active)return;const o=n;if(this.reflectorWorldPosition.setFromMatrixPosition(this.matrixWorld),this.cameraWorldPosition.setFromMatrixPosition(o.matrixWorld),this.rotationMatrix.extractRotation(this.matrixWorld),this.normal.set(0,0,1),this.normal.applyMatrix4(this.rotationMatrix),this.view.subVectors(this.reflectorWorldPosition,this.cameraWorldPosition),!(this.view.dot(this.normal)>0)){this.view.reflect(this.normal).negate(),this.view.add(this.reflectorWorldPosition),this.rotationMatrix.extractRotation(o.matrixWorld),this.lookAtPosition.set(0,0,-1),this.lookAtPosition.applyMatrix4(this.rotationMatrix),this.lookAtPosition.add(this.cameraWorldPosition),this.target.subVectors(this.reflectorWorldPosition,this.lookAtPosition),this.target.reflect(this.normal).negate(),this.target.add(this.reflectorWorldPosition),this.virtualCamera.position.copy(this.view),this.virtualCamera.up.set(0,1,0),this.virtualCamera.up.applyMatrix4(this.rotationMatrix),this.virtualCamera.up.reflect(this.normal),this.virtualCamera.lookAt(this.target),this.virtualCamera.far=o.far,this.virtualCamera.updateMatrixWorld(),this.virtualCamera.projectionMatrix.copy(o.projectionMatrix),this.textureMatrix.set(.5,0,0,.5,0,.5,0,.5,0,0,.5,.5,0,0,0,1),this.textureMatrix.multiply(this.virtualCamera.projectionMatrix),this.textureMatrix.multiply(this.virtualCamera.matrixWorldInverse),this.textureMatrix.multiply(this.matrixWorld),this.reflectorPlane.setFromNormalAndCoplanarPoint(this.normal,this.reflectorWorldPosition),this.reflectorPlane.applyMatrix4(this.virtualCamera.matrixWorldInverse),this.clipPlane.set(this.reflectorPlane.normal.x,this.reflectorPlane.normal.y,this.reflectorPlane.normal.z,this.reflectorPlane.constant);var a=this.virtualCamera.projectionMatrix;this.q.x=(Math.sign(this.clipPlane.x)+a.elements[8])/a.elements[0],this.q.y=(Math.sign(this.clipPlane.y)+a.elements[9])/a.elements[5],this.q.z=-1,this.q.w=(1+a.elements[10])/a.elements[14],this.clipPlane.multiplyScalar(2/this.clipPlane.dot(this.q)),a.elements[2]=this.clipPlane.x,a.elements[6]=this.clipPlane.y,a.elements[10]=this.clipPlane.z+1-this._options.clipBias,a.elements[14]=this.clipPlane.w,this.renderTarget.texture.encoding=t.outputEncoding,this.visible=!1;var l=t.getRenderTarget(),c=t.xr.enabled,u=t.shadowMap.autoUpdate;if(t.xr.enabled=!1,t.shadowMap.autoUpdate=!1,t.setRenderTarget(this.renderTarget),t.state.buffers.depth.setMask(!0),!1===t.autoClear&&t.clear(),t.render(e,this.virtualCamera),this._options.tblur){const e=this._options.blur*this._options.pixelRatio,n=e*this._options.verticalBlurMult;if(this._coreRenderBlur.applyBlur(this.renderTarget,t,e,n),this._options.tblur2){const e=this._options.blur2*this._options.pixelRatio,n=e*this._options.verticalBlur2Mult;this._coreRenderBlur.applyBlur(this.renderTarget,t,e,n)}}t.xr.enabled=c,t.shadowMap.autoUpdate=u,t.setRenderTarget(l);var h=o.viewport;void 0!==h&&t.state.viewport(h),this.visible=!0}}}class nK extends pG{static type(){return\\\\\\\"reflector\\\\\\\"}async cook(t,e){const n=t[0],i=[],r=await ai.renderersController.firstRenderer();if(!r)return this.createCoreGroupFromObjects(i);const s=n.objectsWithGeo();for(let t of s){const n=new eK(t.geometry,{clipBias:e.clipBias,renderer:r,scene:this.scene().threejsScene(),pixelRatio:e.pixelRatio,color:e.color,opacity:e.opacity,active:e.active,tblur:e.tblur,blur:e.blur,verticalBlurMult:e.verticalBlurMult,tblur2:e.tblur2,blur2:e.blur2,verticalBlur2Mult:e.verticalBlur2Mult});n.position.copy(t.position),n.rotation.copy(t.rotation),n.scale.copy(t.scale),n.updateMatrix(),i.push(n)}return this.createCoreGroupFromObjects(i)}}nK.DEFAULT_PARAMS={active:!0,clipBias:.003,color:new D.a(1,1,1),opacity:1,pixelRatio:1,tblur:!1,blur:1,verticalBlurMult:1,tblur2:!1,blur2:1,verticalBlur2Mult:1},nK.INPUT_CLONED_STATE=Qi.NEVER;const iK=nK.DEFAULT_PARAMS;const rK=new class extends aa{constructor(){super(...arguments),this.active=oa.BOOLEAN(iK.active),this.clipBias=oa.FLOAT(iK.clipBias),this.color=oa.COLOR(iK.color.toArray()),this.opacity=oa.FLOAT(iK.opacity),this.pixelRatio=oa.INTEGER(iK.pixelRatio,{range:[1,4],rangeLocked:[!0,!1]}),this.tblur=oa.BOOLEAN(iK.tblur),this.blur=oa.FLOAT(iK.blur,{visibleIf:{tblur:1}}),this.verticalBlurMult=oa.FLOAT(iK.verticalBlurMult,{visibleIf:{tblur:1}}),this.tblur2=oa.BOOLEAN(iK.tblur2,{visibleIf:{tblur:1}}),this.blur2=oa.FLOAT(iK.blur2,{visibleIf:{tblur:1,tblur2:1}}),this.verticalBlur2Mult=oa.FLOAT(iK.verticalBlur2Mult,{visibleIf:{tblur:1,tblur2:1}})}};class sK extends gG{constructor(){super(...arguments),this.paramsConfig=rK}static type(){return\\\\\\\"reflector\\\\\\\"}static displayedInputNames(){return[\\\\\\\"geometry to create a reflector from\\\\\\\"]}initializeNode(){this.io.inputs.setCount(0,1),this.io.inputs.initInputsClonedState(nK.INPUT_CLONED_STATE)}async cook(t){this._operation=this._operation||new nK(this._scene,this.states);const e=await this._operation.cook(t,this.pv);this.setCoreGroup(e)}}var oK=n(85);var aK;!function(t){t.POINTS_COUNT=\\\\\\\"pointsCount\\\\\\\",t.SEGMENT_LENGTH=\\\\\\\"segmentLength\\\\\\\"}(aK||(aK={}));const lK=[aK.POINTS_COUNT,aK.SEGMENT_LENGTH];var cK;!function(t){t.CENTRIPETAL=\\\\\\\"centripetal\\\\\\\",t.CHORDAL=\\\\\\\"chordal\\\\\\\",t.CATMULLROM=\\\\\\\"catmullrom\\\\\\\"}(cK||(cK={}));const uK=[cK.CENTRIPETAL,cK.CHORDAL,cK.CATMULLROM];const hK=new class extends aa{constructor(){super(...arguments),this.method=oa.INTEGER(lK.indexOf(aK.POINTS_COUNT),{menu:{entries:lK.map(((t,e)=>({name:t,value:e})))}}),this.curveType=oa.INTEGER(uK.indexOf(cK.CATMULLROM),{range:[0,2],rangeLocked:[!0,!0],menu:{entries:uK.map(((t,e)=>({name:t,value:e})))}}),this.tension=oa.FLOAT(.01,{range:[0,1],rangeLocked:[!0,!0]}),this.pointsCount=oa.INTEGER(100,{visibleIf:{method:lK.indexOf(aK.POINTS_COUNT)},range:[1,1e3],rangeLocked:[!0,!1]}),this.segmentLength=oa.FLOAT(1,{visibleIf:{method:lK.indexOf(aK.SEGMENT_LENGTH)}})}};class dK extends gG{constructor(){super(...arguments),this.paramsConfig=hK}static type(){return\\\\\\\"resample\\\\\\\"}initializeNode(){this.io.inputs.setCount(1)}cook(t){const e=t[0],n=[];if(this.pv.pointsCount>=2){const t=e.coreObjects();for(let e=0;e<t.length;e++){const i=t[e].object();if(i instanceof Tr.a){const t=this._resample(i);n.push(t)}}}this.setObjects(n)}_resample(t){var e;const n=t.geometry,i=new ps(n).points(),r=null===(e=n.getIndex())||void 0===e?void 0:e.array,s=UQ.accumulated_curve_point_indices(r),o=[];for(let t=0;t<s.length;t++){const e=s[t].map((t=>i[t])),n=this._create_curve_from_points(e);n&&o.push(n)}const a=cs(o);return this.createObject(a,Sr.LINE_SEGMENTS)}_create_curve_from_points(t){if(t.length<=1)return;const e=t.map((t=>t.attribValue(\\\\\\\"position\\\\\\\"))),n=uK[this.pv.curveType],i=this.pv.tension,r=new oK.a(e,!1,n,i),s=this._get_points_from_curve(r);let o=[];const a=[];for(let t=0;t<s.length;t++){const e=s[t].toArray();o.push(e),t>0&&(a.push(t-1),a.push(t))}const l=new S.a;return l.setAttribute(\\\\\\\"position\\\\\\\",new C.c(o.flat(),3)),l.setIndex(a),l}_get_points_from_curve(t){const e=lK[this.pv.method];switch(e){case aK.POINTS_COUNT:return t.getSpacedPoints(Math.max(2,this.pv.pointsCount));case aK.SEGMENT_LENGTH:var n=t.getLength(),i=0!==this.pv.segmentLength?1+n/this.pv.segmentLength:2;return i=Math.max(2,i),t.getSpacedPoints(i)}ar.unreachable(e)}}class pK extends pG{static type(){return\\\\\\\"restAttributes\\\\\\\"}cook(t,e){const n=t[0].objectsWithGeo();return e.tposition&&this._create_rest_attribute(n,e.position,e.restP),e.tnormal&&this._create_rest_attribute(n,e.normal,e.restN),this.createCoreGroupFromObjects(n)}_create_rest_attribute(t,e,n){for(let i of t){const t=i.geometry;if(t){const i=t.getAttribute(e);i&&t.setAttribute(n,i.clone())}}}}pK.DEFAULT_PARAMS={tposition:!0,position:\\\\\\\"position\\\\\\\",restP:\\\\\\\"restP\\\\\\\",tnormal:!0,normal:\\\\\\\"normal\\\\\\\",restN:\\\\\\\"restN\\\\\\\"};const _K=pK.DEFAULT_PARAMS;const mK=new class extends aa{constructor(){super(...arguments),this.tposition=oa.BOOLEAN(_K.tposition),this.position=oa.STRING(_K.position,{visibleIf:{tposition:!0}}),this.restP=oa.STRING(_K.restP,{visibleIf:{tposition:!0}}),this.tnormal=oa.BOOLEAN(_K.tnormal),this.normal=oa.STRING(_K.normal,{visibleIf:{tnormal:!0}}),this.restN=oa.STRING(_K.restN,{visibleIf:{tnormal:!0}})}};class fK extends gG{constructor(){super(...arguments),this.paramsConfig=mK}static type(){return\\\\\\\"restAttributes\\\\\\\"}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState([Qi.FROM_NODE])}cook(t){this._operation=this._operation||new pK(this.scene(),this.states);const e=this._operation.cook(t,this.pv);this.setCoreGroup(e)}}const gK=new p.a;function vK(t,e,n,i,r,s){const o=2*Math.PI*r/4,a=Math.max(s-2*r,0),l=Math.PI/4;gK.copy(e),gK[i]=0,gK.normalize();const c=.5*o/(o+a),u=1-gK.angleTo(t)/l;if(1===Math.sign(gK[n]))return u*c;return a/(o+a)+c+c*(1-u)}class yK extends N{constructor(t=1,e=1,n=1,i=2,r=.1){if(i=2*i+1,r=Math.min(t/2,e/2,n/2,r),super(1,1,1,i,i,i),1===i)return;const s=this.toNonIndexed();this.index=null,this.attributes.position=s.attributes.position,this.attributes.normal=s.attributes.normal,this.attributes.uv=s.attributes.uv;const o=new p.a,a=new p.a,l=new p.a(t,e,n).divideScalar(2).subScalar(r),c=this.attributes.position.array,u=this.attributes.normal.array,h=this.attributes.uv.array,d=c.length/6,_=new p.a,m=.5/i;for(let i=0,s=0;i<c.length;i+=3,s+=2){o.fromArray(c,i),a.copy(o),a.x-=Math.sign(a.x)*m,a.y-=Math.sign(a.y)*m,a.z-=Math.sign(a.z)*m,a.normalize(),c[i+0]=l.x*Math.sign(o.x)+a.x*r,c[i+1]=l.y*Math.sign(o.y)+a.y*r,c[i+2]=l.z*Math.sign(o.z)+a.z*r,u[i+0]=a.x,u[i+1]=a.y,u[i+2]=a.z;switch(Math.floor(i/d)){case 0:_.set(1,0,0),h[s+0]=vK(_,a,\\\\\\\"z\\\\\\\",\\\\\\\"y\\\\\\\",r,n),h[s+1]=1-vK(_,a,\\\\\\\"y\\\\\\\",\\\\\\\"z\\\\\\\",r,e);break;case 1:_.set(-1,0,0),h[s+0]=1-vK(_,a,\\\\\\\"z\\\\\\\",\\\\\\\"y\\\\\\\",r,n),h[s+1]=1-vK(_,a,\\\\\\\"y\\\\\\\",\\\\\\\"z\\\\\\\",r,e);break;case 2:_.set(0,1,0),h[s+0]=1-vK(_,a,\\\\\\\"x\\\\\\\",\\\\\\\"z\\\\\\\",r,t),h[s+1]=vK(_,a,\\\\\\\"z\\\\\\\",\\\\\\\"x\\\\\\\",r,n);break;case 3:_.set(0,-1,0),h[s+0]=1-vK(_,a,\\\\\\\"x\\\\\\\",\\\\\\\"z\\\\\\\",r,t),h[s+1]=1-vK(_,a,\\\\\\\"z\\\\\\\",\\\\\\\"x\\\\\\\",r,n);break;case 4:_.set(0,0,1),h[s+0]=1-vK(_,a,\\\\\\\"x\\\\\\\",\\\\\\\"y\\\\\\\",r,t),h[s+1]=1-vK(_,a,\\\\\\\"y\\\\\\\",\\\\\\\"x\\\\\\\",r,e);break;case 5:_.set(0,0,-1),h[s+0]=vK(_,a,\\\\\\\"x\\\\\\\",\\\\\\\"y\\\\\\\",r,t),h[s+1]=1-vK(_,a,\\\\\\\"y\\\\\\\",\\\\\\\"x\\\\\\\",r,e)}}}}class xK extends pG{constructor(){super(...arguments),this._core_transform=new Mz}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 yK(e,e,e,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(),r=i.max.clone().sub(i.min),s=i.max.clone().add(i.min).multiplyScalar(.5),o=new yK(r.x,r.y,r.z,n,e.bevel),a=this._core_transform.translation_matrix(s);return o.applyMatrix4(a),o}}xK.DEFAULT_PARAMS={size:1,divisions:2,bevel:.1,center:new p.a(0,0,0)},xK.INPUT_CLONED_STATE=Qi.NEVER;const bK=xK.DEFAULT_PARAMS;const wK=new class extends aa{constructor(){super(...arguments),this.size=oa.FLOAT(bK.size),this.divisions=oa.INTEGER(bK.divisions,{range:[1,10],rangeLocked:[!0,!1]}),this.bevel=oa.FLOAT(bK.bevel,{range:[0,1],rangeLocked:[!0,!1]}),this.center=oa.VECTOR3(bK.center)}};class TK extends gG{constructor(){super(...arguments),this.paramsConfig=wK}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(xK.INPUT_CLONED_STATE)}cook(t){this._operation=this._operation||new xK(this._scene,this.states);const e=this._operation.cook(t,this.pv);this.setCoreGroup(e)}}class AK extends pG{static type(){return\\\\\\\"scatter\\\\\\\"}async cook(t,e){const n=t[0];let i=n.faces();const r=[];let s=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)s+=o.get(t.index()),r[l]=s,l++;const c=[];let u=[];e.transferAttributes&&(u=n.attribNamesMatchingMask(e.attributesToTransfer));const h=new Map,d=new Map;for(let t of u)h.set(t,[]),d.set(t,n.attribSize(t));const p=new Iq,_=2454*e.seed%Number.MAX_SAFE_INTEGER;await p.startWithCount(e.pointsCount,(t=>{const e=rs.randFloat(_+t)*s;for(let t=0;t<r.length;t++){if(e<=r[t]){const n=a[t],i=n.random_position(e);i.toArray(c,c.length);for(let t of u){const e=n.attrib_value_at_position(t,i);e&&(m.isNumber(e)?h.get(t).push(e):e.toArray(h.get(t),h.get(t).length))}break}}}));const g=new S.a;g.setAttribute(\\\\\\\"position\\\\\\\",new C.a(new Float32Array(c),3));for(let t of u)g.setAttribute(t,new C.a(new Float32Array(h.get(t)),d.get(t)));if(e.addIdAttribute||e.addIdnAttribute){const t=e.pointsCount,n=f.range(t);e.addIdAttribute&&g.setAttribute(\\\\\\\"id\\\\\\\",new C.a(new Float32Array(n),1));const i=n.map((e=>e/(t-1)));e.addIdnAttribute&&g.setAttribute(\\\\\\\"idn\\\\\\\",new C.a(new Float32Array(i),1))}const v=this.createObject(g,Sr.POINTS);return this.createCoreGroupFromObjects([v])}}AK.DEFAULT_PARAMS={pointsCount:100,seed:0,transferAttributes:!0,attributesToTransfer:\\\\\\\"normal\\\\\\\",addIdAttribute:!0,addIdnAttribute:!0},AK.INPUT_CLONED_STATE=Qi.FROM_NODE;const EK=AK.DEFAULT_PARAMS;const MK=new class extends aa{constructor(){super(...arguments),this.pointsCount=oa.INTEGER(EK.pointsCount,{range:[0,100],rangeLocked:[!0,!1]}),this.seed=oa.INTEGER(EK.seed,{range:[0,100],rangeLocked:[!1,!1]}),this.transferAttributes=oa.BOOLEAN(EK.transferAttributes),this.attributesToTransfer=oa.STRING(EK.attributesToTransfer,{visibleIf:{transferAttributes:1}}),this.addIdAttribute=oa.BOOLEAN(EK.addIdAttribute),this.addIdnAttribute=oa.BOOLEAN(EK.addIdnAttribute)}};class SK extends gG{constructor(){super(...arguments),this.paramsConfig=MK}static type(){return\\\\\\\"scatter\\\\\\\"}static displayedInputNames(){return[\\\\\\\"geometry to scatter points onto\\\\\\\"]}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState(Qi.NEVER)}async cook(t){this._operation=this._operation||new AK(this.scene(),this.states);const e=await this._operation.cook(t,this.pv);this.setCoreGroup(e)}}var CK;!function(t){t.MATRIX=\\\\\\\"matrix\\\\\\\",t.AXIS=\\\\\\\"axis\\\\\\\"}(CK||(CK={}));const NK=[CK.MATRIX,CK.AXIS];var LK;!function(t){t.BBOX_CENTER=\\\\\\\"bbox center\\\\\\\",t.BBOX_CENTER_OFFSET=\\\\\\\"bbox center offset\\\\\\\",t.CUSTOM=\\\\\\\"custom\\\\\\\"}(LK||(LK={}));const OK=[LK.BBOX_CENTER,LK.BBOX_CENTER_OFFSET,LK.CUSTOM];class RK extends pG{constructor(){super(...arguments),this._m4=new A.a,this._axisNormalized=new p.a,this._center=new p.a,this._pointPos=new p.a,this._axisPlane=new X.a,this._pointOnPlane=new p.a,this._delta=new p.a,this._deltaNormalized=new p.a,this._offset=new p.a}static type(){return\\\\\\\"shear\\\\\\\"}cook(t,e){const n=t[0].objects();return this._applyShear(n,e),t[0]}_applyShear(t,e){const n=NK[e.mode];switch(n){case CK.MATRIX:return this._applyMatrixShear(t,e);case CK.AXIS:return this._applyAxisShear(t,e)}ar.unreachable(n)}_applyMatrixShear(t,e){this._m4.makeShear(e.xy,e.xz,e.yx,e.yz,e.zx,e.zy);for(let e of t){const t=e.geometry;t&&t.applyMatrix4(this._m4)}}_applyAxisShear(t,e){this._axisNormalized.copy(e.axis),this._axisNormalized.normalize();for(let n of t){const t=n.geometry;if(t){this._getAxisModeCenter(t,e),this._axisPlane.setFromNormalAndCoplanarPoint(e.planeAxis,this._center);const n=new ps(t).points();for(let t of n){t.getPosition(this._pointPos),this._axisPlane.projectPoint(this._pointPos,this._pointOnPlane),this._delta.copy(this._pointOnPlane).sub(this._pointPos);const n=this._delta.length();this._deltaNormalized.copy(this._delta).normalize(),this._offset.copy(this._axisNormalized).multiplyScalar(e.axisAmount*n),this._delta.dot(e.planeAxis)>0&&this._offset.multiplyScalar(-1),this._pointPos.add(this._offset),t.setPosition(this._pointPos)}}}}_getAxisModeCenter(t,e){const n=OK[e.centerMode];switch(n){case LK.BBOX_CENTER:return this._getAxisModeCenterBbox(t,e);case LK.BBOX_CENTER_OFFSET:return this._getAxisModeCenterBboxOffset(t,e);case LK.CUSTOM:return this._getAxisModeCenterCustom(e)}ar.unreachable(n)}_getAxisModeCenterBbox(t,e){t.computeBoundingBox();const n=t.boundingBox;n?n.getCenter(this._center):this._center.set(0,0,0)}_getAxisModeCenterBboxOffset(t,e){this._getAxisModeCenterBbox(t,e),this._center.add(e.centerOffset)}_getAxisModeCenterCustom(t){return this._center.copy(t.center)}}var PK;RK.DEFAULT_PARAMS={mode:NK.indexOf(CK.AXIS),xy:0,xz:0,yx:0,yz:0,zx:0,zy:0,centerMode:OK.indexOf(LK.BBOX_CENTER),centerOffset:new p.a(0,0,0),center:new p.a(0,0,0),planeAxis:new p.a(0,0,1),axis:new p.a(0,1,0),axisAmount:0},RK.INPUT_CLONED_STATE=Qi.FROM_NODE,function(t){t.SHEAR=\\\\\\\"shear\\\\\\\",t.TRANSFORM=\\\\\\\"transform\\\\\\\",t.UV_LAYOUT=\\\\\\\"uvLayout\\\\\\\",t.UV_TRANSFORM=\\\\\\\"uvTransform\\\\\\\",t.UV_UNWRAP=\\\\\\\"uvUnwrap\\\\\\\"}(PK||(PK={}));const IK=RK.DEFAULT_PARAMS;const FK=new class extends aa{constructor(){super(...arguments),this.mode=oa.INTEGER(IK.mode,{menu:{entries:NK.map(((t,e)=>({name:t,value:e})))}}),this.xy=oa.FLOAT(IK.xy,{visibleIf:{mode:NK.indexOf(CK.MATRIX)}}),this.xz=oa.FLOAT(IK.xz,{visibleIf:{mode:NK.indexOf(CK.MATRIX)}}),this.yx=oa.FLOAT(IK.yx,{visibleIf:{mode:NK.indexOf(CK.MATRIX)}}),this.yz=oa.FLOAT(IK.yz,{visibleIf:{mode:NK.indexOf(CK.MATRIX)}}),this.zx=oa.FLOAT(IK.zx,{visibleIf:{mode:NK.indexOf(CK.MATRIX)}}),this.zy=oa.FLOAT(IK.zy,{visibleIf:{mode:NK.indexOf(CK.MATRIX)}}),this.centerMode=oa.INTEGER(IK.centerMode,{visibleIf:{mode:NK.indexOf(CK.AXIS)},menu:{entries:OK.map(((t,e)=>({name:t,value:e})))}}),this.centerOffset=oa.VECTOR3(IK.centerOffset.toArray(),{visibleIf:{mode:NK.indexOf(CK.AXIS),centerMode:OK.indexOf(LK.BBOX_CENTER_OFFSET)}}),this.center=oa.VECTOR3(IK.center.toArray(),{visibleIf:{mode:NK.indexOf(CK.AXIS),centerMode:OK.indexOf(LK.CUSTOM)}}),this.planeAxis=oa.VECTOR3(IK.planeAxis.toArray(),{visibleIf:{mode:NK.indexOf(CK.AXIS)}}),this.axis=oa.VECTOR3(IK.axis.toArray(),{visibleIf:{mode:NK.indexOf(CK.AXIS)}}),this.axisAmount=oa.FLOAT(IK.axisAmount,{range:[-1,1],visibleIf:{mode:NK.indexOf(CK.AXIS)}})}};class DK extends gG{constructor(){super(...arguments),this.paramsConfig=FK}static type(){return PK.SHEAR}static displayedInputNames(){return[\\\\\\\"geometries or objects to transform\\\\\\\"]}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState(RK.INPUT_CLONED_STATE)}setMode(t){this.p.mode.set(NK.indexOf(t))}cook(t){this._operation=this._operation||new RK(this.scene(),this.states);const e=this._operation.cook(t,this.pv);this.setCoreGroup(e)}}const kK=new class extends aa{};class BK extends gG{constructor(){super(...arguments),this.paramsConfig=kK}static type(){return\\\\\\\"skin\\\\\\\"}static displayedInputNames(){return[\\\\\\\"lines to create polygons from\\\\\\\",\\\\\\\"if used, lines from both inputs will be used\\\\\\\"]}initializeNode(){this.io.inputs.setCount(1,2)}cook(t){switch(f.compact(this.io.inputs.inputs()).length){case 1:return this.process_one_input(t);case 2:return this.process_two_inputs(t);default:return this.states.error.set(\\\\\\\"inputs count not valid\\\\\\\")}}process_one_input(t){const e=t[0],n=this._get_line_segments(e),i=[];if(n){const t=n[0];if(t){const e=UQ.line_segment_to_geometries(t.geometry);e.forEach(((t,n)=>{if(n>0){const r=e[n-1],s=this._skin(r,t);i.push(s)}}))}}this.setGeometries(i)}process_two_inputs(t){const e=t[0],n=t[1],i=this._get_line_segments(e),r=this._get_line_segments(n),s=f.sortBy([i,r],(t=>-t.length)),o=s[0],a=s[1],l=[];o.forEach(((t,e)=>{const n=a[e];if(null!=t&&null!=n){const e=t.geometry,i=n.geometry,r=this._skin(e,i);l.push(r)}})),this.setGeometries(l)}_get_line_segments(t){return t.objects().filter((t=>t.isLineSegments))}_skin(t,e){const n=new S.a;return new GQ(n,t,e).process(),n}}var zK;!function(t){t.X=\\\\\\\"x\\\\\\\",t.Y=\\\\\\\"y\\\\\\\",t.Z=\\\\\\\"z\\\\\\\"}(zK||(zK={}));const UK=[zK.X,zK.Y,zK.Z];class GK extends pG{constructor(){super(...arguments),this._pointPos=new p.a,this._positions=[],this._indicesByPos=new Map,this._indexDest=new Map,this._debugActive=!1}static type(){return\\\\\\\"sort\\\\\\\"}cook(t,e){const n=t[0],i=n.objectsWithGeo();for(let t of i)this._sortObject(t,e);return n}_debug(t){this._debugActive}_sortObject(t,e){const n=new vs(t,0).points(),i=t.geometry.getIndex();if(!i)return void console.warn(\\\\\\\"geometry cannot be sorted since it has no index\\\\\\\");const r=i.array;this._positions=new Array(n.length),this._indicesByPos.clear(),this._indexDest.clear();const s=UK[e.axis];let o=0,a=0;for(let t of n){switch(t.getPosition(this._pointPos),s){case zK.X:o=this._pointPos.x;break;case zK.Y:o=this._pointPos.y;break;case zK.Z:o=this._pointPos.z}this._positions[a]=o,u.pushOnArrayAtEntry(this._indicesByPos,o,t.index()),a++}let l=this._positions.sort(((t,e)=>t-e));e.invert&&l.reverse();const c=new Array(n.length);a=0;const h=f.uniq(l);for(let t of h){const e=this._indicesByPos.get(t);if(e)for(let t of e)c[a]=t,this._indexDest.set(t,a),a++}const d=new Array(r.length);for(let t=0;t<r.length;t++){const e=r[t],n=this._indexDest.get(e);d[t]=n}t.geometry.setIndex(d);const p=ps.attribNames(t.geometry);for(let e of p){\\\\\\\"id\\\\\\\"==e&&(this._debugActive=!0);const n=t.geometry.getAttribute(e);this._updateAttribute(n,c),this._debugActive=!1}}_updateAttribute(t,e){const n=t.clone(),i=t.array,r=n.array,s=n.itemSize;this._debug(e);for(let t of e){const e=this._indexDest.get(t);if(this._debug(`${t} -> ${e}`),null!=e)for(let n=0;n<s;n++)r[e*s+n]=i[t*s+n];else console.warn(\\\\\\\"no old index found\\\\\\\")}t.array=r,t.needsUpdate=!0}}GK.DEFAULT_PARAMS={axis:UK.indexOf(zK.X),invert:!1},GK.INPUT_CLONED_STATE=Qi.FROM_NODE;const VK=GK.DEFAULT_PARAMS;const HK=new class extends aa{constructor(){super(...arguments),this.axis=oa.INTEGER(VK.axis,{menu:{entries:UK.map(((t,e)=>({name:t,value:e})))}}),this.invert=oa.BOOLEAN(VK.invert)}};class jK extends gG{constructor(){super(...arguments),this.paramsConfig=HK}static type(){return\\\\\\\"sort\\\\\\\"}static displayedInputNames(){return[\\\\\\\"geometry to sort\\\\\\\"]}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState([Qi.FROM_NODE])}cook(t){this._operation=this._operation||new GK(this._scene,this.states);const e=this._operation.cook(t,this.pv);this.setCoreGroup(e)}}const WK=new class extends aa{constructor(){super(...arguments),this.startFrame=oa.INTEGER(Ml.START_FRAME)}};class qK extends xG{constructor(){super(...arguments),this.paramsConfig=WK,this._last_simulated_frame=null,this.childrenDisplayController=new wG(this,{dependsOnDisplayNode:!1}),this.displayNodeController=new Lm(this,{onDisplayNodeRemove:()=>{},onDisplayNodeSet:()=>{},onDisplayNodeUpdate:()=>{}},{dependsOnDisplayNode:!1})}static type(){return\\\\\\\"solver\\\\\\\"}initializeNode(){this.io.inputs.setCount(0,4),this.io.inputs.initInputsClonedState(Qi.NEVER),this.addGraphInput(this.scene().timeController.graphNode)}previousFrameCoreGroup(){return this._previousFrameCoreGroup}async cook(t){this.pv.startFrame==this.scene().frame()&&this._reset(),this.computeSolverIfRequired()}_reset(){this._previousFrameCoreGroup=void 0,this._last_simulated_frame=null}computeSolverIfRequired(){const t=this.scene().frame(),e=this.pv.startFrame;t>=e&&(null==this._last_simulated_frame&&(this._last_simulated_frame=e-1),t>this._last_simulated_frame&&this._computeSolverMultipleTimes(t-this._last_simulated_frame))}_computeSolverMultipleTimes(t=1){for(let e=0;e<t;e++)this.computeSolver();this._last_simulated_frame=this.scene().frame()}async computeSolver(){const t=this.childrenDisplayController.output_node();if(t){const e=(await t.compute()).coreContent();e?(this._previousFrameCoreGroup=e,this.setCoreGroup(e)):t.states.error.active()?this.states.error.set(t.states.error.message()):(this._previousFrameCoreGroup=void 0,this.setObjects([]))}else this.states.error.set(\\\\\\\"no output node found inside subnet\\\\\\\")}isOnFrameStart(){return this.scene().frame()==this.pv.startFrame}}const XK=new class extends aa{};class YK extends gG{constructor(){super(...arguments),this.paramsConfig=XK}static type(){return\\\\\\\"solverPreviousFrame\\\\\\\"}initializeNode(){this.addGraphInput(this.scene().timeController.graphNode)}async cook(){const t=this.parent();(null==t?void 0:t.type())!=qK.type()&&(this.states.error.set(`the parent is not a '${qK.type()}'`),this.cookController.endCook());const e=t.previousFrameCoreGroup();e?this.setCoreGroup(e):this.setObjects([])}}var $K;!function(t){t.DEFAULT=\\\\\\\"default\\\\\\\",t.ISOCAHEDRON=\\\\\\\"isocahedron\\\\\\\"}($K||($K={}));const JK={default:0,isocahedron:1},ZK=[$K.DEFAULT,$K.ISOCAHEDRON];class QK extends pG{static type(){return\\\\\\\"sphere\\\\\\\"}cook(t,e){const n=t[0];return n?this._cook_with_input(n,e):this._cook_without_input(e)}_cook_without_input(t){const e=this._create_required_geometry(t);return e.translate(t.center.x,t.center.y,t.center.z),this.createCoreGroupFromGeometry(e)}_cook_with_input(t,e){const n=t.boundingBox(),i=n.max.clone().sub(n.min),r=n.max.clone().add(n.min).multiplyScalar(.5),s=this._create_required_geometry(e);return s.translate(e.center.x,e.center.y,e.center.z),s.translate(r.x,r.y,r.z),s.scale(i.x,i.y,i.z),this.createCoreGroupFromGeometry(s)}_create_required_geometry(t){return t.type==JK.default?this._create_default_sphere(t):this._create_default_isocahedron(t)}_create_default_sphere(t){return t.open?new oU(t.radius,t.resolution.x,t.resolution.y,t.phiStart,t.phiLength,t.thetaStart,t.thetaLength):new oU(t.radius,t.resolution.x,t.resolution.y)}_create_default_isocahedron(t){return new JX(t.radius,t.detail)}}QK.DEFAULT_PARAMS={type:JK.default,radius:1,resolution:new d.a(30,30),open:!1,phiStart:0,phiLength:2*Math.PI,thetaStart:0,thetaLength:Math.PI,detail:1,center:new p.a(0,0,0)},QK.INPUT_CLONED_STATE=Qi.FROM_NODE;const KK=QK.DEFAULT_PARAMS;const t0=new class extends aa{constructor(){super(...arguments),this.type=oa.INTEGER(KK.type,{menu:{entries:ZK.map((t=>({name:t,value:JK[t]})))}}),this.radius=oa.FLOAT(KK.radius,{visibleIf:{type:JK.default}}),this.resolution=oa.VECTOR2(KK.resolution,{visibleIf:{type:JK.default}}),this.open=oa.BOOLEAN(KK.open,{visibleIf:{type:JK.default}}),this.phiStart=oa.FLOAT(KK.phiStart,{range:[0,2*Math.PI],visibleIf:{type:JK.default,open:!0}}),this.phiLength=oa.FLOAT(\\\\\\\"$PI*2\\\\\\\",{range:[0,2*Math.PI],visibleIf:{type:JK.default,open:!0}}),this.thetaStart=oa.FLOAT(KK.thetaStart,{range:[0,Math.PI],visibleIf:{type:JK.default,open:!0}}),this.thetaLength=oa.FLOAT(\\\\\\\"$PI\\\\\\\",{range:[0,Math.PI],visibleIf:{type:JK.default,open:!0}}),this.detail=oa.INTEGER(KK.detail,{range:[0,5],rangeLocked:[!0,!1],visibleIf:{type:JK.isocahedron}}),this.center=oa.VECTOR3(KK.center)}};class e0 extends gG{constructor(){super(...arguments),this.paramsConfig=t0}static type(){return\\\\\\\"sphere\\\\\\\"}initializeNode(){this.io.inputs.setCount(0,1),this.io.inputs.initInputsClonedState(QK.INPUT_CLONED_STATE)}cook(t){this._operation=this._operation||new QK(this.scene(),this.states);const e=this._operation.cook(t,this.pv);this.setCoreGroup(e)}}const n0=new class extends aa{constructor(){super(...arguments),this.attribType=oa.INTEGER(kr.indexOf(Dr.NUMERIC),{menu:{entries:Br}}),this.attribName=oa.STRING(\\\\\\\"\\\\\\\")}};class i0 extends gG{constructor(){super(...arguments),this.paramsConfig=n0,this._new_objects=[]}static type(){return\\\\\\\"split\\\\\\\"}static displayedInputNames(){return[\\\\\\\"geometry to split in multiple objects\\\\\\\"]}initializeNode(){this.io.inputs.setCount(1)}async cook(t){const e=t[0];this._new_objects=[],\\\\\\\"\\\\\\\"!=this.pv.attribName&&this._split_core_group(e),this.setObjects(this._new_objects)}async _split_core_group(t){const e=t.coreObjects();for(let t of e)this._split_core_object(t)}_split_core_object(t){let e=t.coreGeometry(),n=this.pv.attribName,i=new Map;if(e){const r=t.object(),s=e.pointsFromGeometry(),o=s[0];if(o){if(o.attribSize(n)!=zr.FLOAT&&!o.isAttribIndexed(n))return void this.states.error.set(`attrib '${n}' must be a float or a string`);let t;if(o.isAttribIndexed(n))for(let e of s)t=e.indexedAttribValue(n),u.pushOnArrayAtEntry(i,t,e);else for(let e of s)t=e.attribValue(n),u.pushOnArrayAtEntry(i,t,e)}const a=Nr(r.constructor);i.forEach(((t,e)=>{const i=ps.geometryFromPoints(t,a);if(i){const t=this.createObject(i,a);vs.addAttribute(t,n,e),this._new_objects.push(t)}}))}}}const r0=new A.a,s0=new Q.a,o0=new p.a;class a0 extends $.a{constructor(){super(),this.uuid=Ln.h(),this.name=\\\\\\\"\\\\\\\",this.type=\\\\\\\"Geometry\\\\\\\",this.vertices=[],this.colors=[],this.faces=[],this.faceVertexUvs=[[]],this.morphTargets=[],this.morphNormals=[],this.skinWeights=[],this.skinIndices=[],this.lineDistances=[],this.boundingBox=null,this.boundingSphere=null,this.elementsNeedUpdate=!1,this.verticesNeedUpdate=!1,this.uvsNeedUpdate=!1,this.normalsNeedUpdate=!1,this.colorsNeedUpdate=!1,this.lineDistancesNeedUpdate=!1,this.groupsNeedUpdate=!1}applyMatrix4(t){const e=(new U.a).getNormalMatrix(t);for(let e=0,n=this.vertices.length;e<n;e++){this.vertices[e].applyMatrix4(t)}for(let t=0,n=this.faces.length;t<n;t++){const n=this.faces[t];n.normal.applyMatrix3(e).normalize();for(let t=0,i=n.vertexNormals.length;t<i;t++)n.vertexNormals[t].applyMatrix3(e).normalize()}return null!==this.boundingBox&&this.computeBoundingBox(),null!==this.boundingSphere&&this.computeBoundingSphere(),this.verticesNeedUpdate=!0,this.normalsNeedUpdate=!0,this}rotateX(t){return r0.makeRotationX(t),this.applyMatrix4(r0),this}rotateY(t){return r0.makeRotationY(t),this.applyMatrix4(r0),this}rotateZ(t){return r0.makeRotationZ(t),this.applyMatrix4(r0),this}translate(t,e,n){return r0.makeTranslation(t,e,n),this.applyMatrix4(r0),this}scale(t,e,n){return r0.makeScale(t,e,n),this.applyMatrix4(r0),this}lookAt(t){return s0.lookAt(t),s0.updateMatrix(),this.applyMatrix4(s0.matrix),this}fromBufferGeometry(t){const e=this,n=null!==t.index?t.index:void 0,i=t.attributes;if(void 0===i.position)return console.error(\\\\\\\"THREE.Geometry.fromBufferGeometry(): Position attribute required for conversion.\\\\\\\"),this;const r=i.position,s=i.normal,o=i.color,a=i.uv,l=i.uv2;void 0!==l&&(this.faceVertexUvs[1]=[]);for(let t=0;t<r.count;t++)e.vertices.push((new p.a).fromBufferAttribute(r,t)),void 0!==o&&e.colors.push((new D.a).fromBufferAttribute(o,t));function c(t,n,i,r){const c=void 0===o?[]:[e.colors[t].clone(),e.colors[n].clone(),e.colors[i].clone()],u=void 0===s?[]:[(new p.a).fromBufferAttribute(s,t),(new p.a).fromBufferAttribute(s,n),(new p.a).fromBufferAttribute(s,i)],h=new c0(t,n,i,u,c,r);e.faces.push(h),void 0!==a&&e.faceVertexUvs[0].push([(new d.a).fromBufferAttribute(a,t),(new d.a).fromBufferAttribute(a,n),(new d.a).fromBufferAttribute(a,i)]),void 0!==l&&e.faceVertexUvs[1].push([(new d.a).fromBufferAttribute(l,t),(new d.a).fromBufferAttribute(l,n),(new d.a).fromBufferAttribute(l,i)])}const u=t.groups;if(u.length>0)for(let t=0;t<u.length;t++){const e=u[t],i=e.start;for(let t=i,r=i+e.count;t<r;t+=3)void 0!==n?c(n.getX(t),n.getX(t+1),n.getX(t+2),e.materialIndex):c(t,t+1,t+2,e.materialIndex)}else if(void 0!==n)for(let t=0;t<n.count;t+=3)c(n.getX(t),n.getX(t+1),n.getX(t+2));else for(let t=0;t<r.count;t+=3)c(t,t+1,t+2);return this.computeFaceNormals(),null!==t.boundingBox&&(this.boundingBox=t.boundingBox.clone()),null!==t.boundingSphere&&(this.boundingSphere=t.boundingSphere.clone()),this}center(){return this.computeBoundingBox(),this.boundingBox.getCenter(o0).negate(),this.translate(o0.x,o0.y,o0.z),this}normalize(){this.computeBoundingSphere();const t=this.boundingSphere.center,e=this.boundingSphere.radius,n=0===e?1:1/e,i=new A.a;return i.set(n,0,0,-n*t.x,0,n,0,-n*t.y,0,0,n,-n*t.z,0,0,0,1),this.applyMatrix4(i),this}computeFaceNormals(){const t=new p.a,e=new p.a;for(let n=0,i=this.faces.length;n<i;n++){const i=this.faces[n],r=this.vertices[i.a],s=this.vertices[i.b],o=this.vertices[i.c];t.subVectors(o,s),e.subVectors(r,s),t.cross(e),t.normalize(),i.normal.copy(t)}}computeVertexNormals(t=!0){const e=new Array(this.vertices.length);for(let t=0,n=this.vertices.length;t<n;t++)e[t]=new p.a;if(t){const t=new p.a,n=new p.a;for(let i=0,r=this.faces.length;i<r;i++){const r=this.faces[i],s=this.vertices[r.a],o=this.vertices[r.b],a=this.vertices[r.c];t.subVectors(a,o),n.subVectors(s,o),t.cross(n),e[r.a].add(t),e[r.b].add(t),e[r.c].add(t)}}else{this.computeFaceNormals();for(let t=0,n=this.faces.length;t<n;t++){const n=this.faces[t];e[n.a].add(n.normal),e[n.b].add(n.normal),e[n.c].add(n.normal)}}for(let t=0,n=this.vertices.length;t<n;t++)e[t].normalize();for(let t=0,n=this.faces.length;t<n;t++){const n=this.faces[t],i=n.vertexNormals;3===i.length?(i[0].copy(e[n.a]),i[1].copy(e[n.b]),i[2].copy(e[n.c])):(i[0]=e[n.a].clone(),i[1]=e[n.b].clone(),i[2]=e[n.c].clone())}this.faces.length>0&&(this.normalsNeedUpdate=!0)}computeFlatVertexNormals(){this.computeFaceNormals();for(let t=0,e=this.faces.length;t<e;t++){const e=this.faces[t],n=e.vertexNormals;3===n.length?(n[0].copy(e.normal),n[1].copy(e.normal),n[2].copy(e.normal)):(n[0]=e.normal.clone(),n[1]=e.normal.clone(),n[2]=e.normal.clone())}this.faces.length>0&&(this.normalsNeedUpdate=!0)}computeMorphNormals(){for(let t=0,e=this.faces.length;t<e;t++){const e=this.faces[t];e.__originalFaceNormal?e.__originalFaceNormal.copy(e.normal):e.__originalFaceNormal=e.normal.clone(),e.__originalVertexNormals||(e.__originalVertexNormals=[]);for(let t=0,n=e.vertexNormals.length;t<n;t++)e.__originalVertexNormals[t]?e.__originalVertexNormals[t].copy(e.vertexNormals[t]):e.__originalVertexNormals[t]=e.vertexNormals[t].clone()}const t=new a0;t.faces=this.faces;for(let e=0,n=this.morphTargets.length;e<n;e++){if(!this.morphNormals[e]){this.morphNormals[e]={},this.morphNormals[e].faceNormals=[],this.morphNormals[e].vertexNormals=[];const t=this.morphNormals[e].faceNormals,n=this.morphNormals[e].vertexNormals;for(let e=0,i=this.faces.length;e<i;e++){const e=new p.a,i={a:new p.a,b:new p.a,c:new p.a};t.push(e),n.push(i)}}const n=this.morphNormals[e];t.vertices=this.morphTargets[e].vertices,t.computeFaceNormals(),t.computeVertexNormals();for(let t=0,e=this.faces.length;t<e;t++){const e=this.faces[t],i=n.faceNormals[t],r=n.vertexNormals[t];i.copy(e.normal),r.a.copy(e.vertexNormals[0]),r.b.copy(e.vertexNormals[1]),r.c.copy(e.vertexNormals[2])}}for(let t=0,e=this.faces.length;t<e;t++){const e=this.faces[t];e.normal=e.__originalFaceNormal,e.vertexNormals=e.__originalVertexNormals}}computeBoundingBox(){null===this.boundingBox&&(this.boundingBox=new XB.a),this.boundingBox.setFromPoints(this.vertices)}computeBoundingSphere(){null===this.boundingSphere&&(this.boundingSphere=new Oq.a),this.boundingSphere.setFromPoints(this.vertices)}merge(t,e,n=0){if(!t||!t.isGeometry)return void console.error(\\\\\\\"THREE.Geometry.merge(): geometry not an instance of THREE.Geometry.\\\\\\\",t);let i;const r=this.vertices.length,s=this.vertices,o=t.vertices,a=this.faces,l=t.faces,c=this.colors,u=t.colors;void 0!==e&&(i=(new U.a).getNormalMatrix(e));for(let t=0,n=o.length;t<n;t++){const n=o[t].clone();void 0!==e&&n.applyMatrix4(e),s.push(n)}for(let t=0,e=u.length;t<e;t++)c.push(u[t].clone());for(let t=0,e=l.length;t<e;t++){const e=l[t];let s,o;const c=e.vertexNormals,u=e.vertexColors,h=new c0(e.a+r,e.b+r,e.c+r);h.normal.copy(e.normal),void 0!==i&&h.normal.applyMatrix3(i).normalize();for(let t=0,e=c.length;t<e;t++)s=c[t].clone(),void 0!==i&&s.applyMatrix3(i).normalize(),h.vertexNormals.push(s);h.color.copy(e.color);for(let t=0,e=u.length;t<e;t++)o=u[t],h.vertexColors.push(o.clone());h.materialIndex=e.materialIndex+n,a.push(h)}for(let e=0,n=t.faceVertexUvs.length;e<n;e++){const n=t.faceVertexUvs[e];void 0===this.faceVertexUvs[e]&&(this.faceVertexUvs[e]=[]);for(let t=0,i=n.length;t<i;t++){const i=n[t],r=[];for(let t=0,e=i.length;t<e;t++)r.push(i[t].clone());this.faceVertexUvs[e].push(r)}}}mergeMesh(t){t&&t.isMesh?(t.matrixAutoUpdate&&t.updateMatrix(),this.merge(t.geometry,t.matrix)):console.error(\\\\\\\"THREE.Geometry.mergeMesh(): mesh not an instance of THREE.Mesh.\\\\\\\",t)}mergeVertices(t=4){const e={},n=[],i=[],r=Math.pow(10,t);for(let t=0,s=this.vertices.length;t<s;t++){const s=this.vertices[t],o=Math.round(s.x*r)+\\\\\\\"_\\\\\\\"+Math.round(s.y*r)+\\\\\\\"_\\\\\\\"+Math.round(s.z*r);void 0===e[o]?(e[o]=t,n.push(this.vertices[t]),i[t]=n.length-1):i[t]=i[e[o]]}const s=[];for(let t=0,e=this.faces.length;t<e;t++){const e=this.faces[t];e.a=i[e.a],e.b=i[e.b],e.c=i[e.c];const n=[e.a,e.b,e.c];for(let e=0;e<3;e++)if(n[e]===n[(e+1)%3]){s.push(t);break}}for(let t=s.length-1;t>=0;t--){const e=s[t];this.faces.splice(e,1);for(let t=0,n=this.faceVertexUvs.length;t<n;t++)this.faceVertexUvs[t].splice(e,1)}const o=this.vertices.length-n.length;return this.vertices=n,o}setFromPoints(t){this.vertices=[];for(let e=0,n=t.length;e<n;e++){const n=t[e];this.vertices.push(new p.a(n.x,n.y,n.z||0))}return this}sortFacesByMaterialIndex(){const t=this.faces,e=t.length;for(let n=0;n<e;n++)t[n]._id=n;t.sort((function(t,e){return t.materialIndex-e.materialIndex}));const n=this.faceVertexUvs[0],i=this.faceVertexUvs[1];let r,s;n&&n.length===e&&(r=[]),i&&i.length===e&&(s=[]);for(let o=0;o<e;o++){const e=t[o]._id;r&&r.push(n[e]),s&&s.push(i[e])}r&&(this.faceVertexUvs[0]=r),s&&(this.faceVertexUvs[1]=s)}toJSON(){const t={metadata:{version:4.5,type:\\\\\\\"Geometry\\\\\\\",generator:\\\\\\\"Geometry.toJSON\\\\\\\"}};if(t.uuid=this.uuid,t.type=this.type,\\\\\\\"\\\\\\\"!==this.name&&(t.name=this.name),void 0!==this.parameters){const e=this.parameters;for(const n in e)void 0!==e[n]&&(t[n]=e[n]);return t}const e=[];for(let t=0;t<this.vertices.length;t++){const n=this.vertices[t];e.push(n.x,n.y,n.z)}const n=[],i=[],r={},s=[],o={},a=[],l={};for(let t=0;t<this.faces.length;t++){const e=this.faces[t],i=!0,r=!1,s=void 0!==this.faceVertexUvs[0][t],o=e.normal.length()>0,a=e.vertexNormals.length>0,l=1!==e.color.r||1!==e.color.g||1!==e.color.b,p=e.vertexColors.length>0;let _=0;if(_=c(_,0,0),_=c(_,1,i),_=c(_,2,r),_=c(_,3,s),_=c(_,4,o),_=c(_,5,a),_=c(_,6,l),_=c(_,7,p),n.push(_),n.push(e.a,e.b,e.c),n.push(e.materialIndex),s){const e=this.faceVertexUvs[0][t];n.push(d(e[0]),d(e[1]),d(e[2]))}if(o&&n.push(u(e.normal)),a){const t=e.vertexNormals;n.push(u(t[0]),u(t[1]),u(t[2]))}if(l&&n.push(h(e.color)),p){const t=e.vertexColors;n.push(h(t[0]),h(t[1]),h(t[2]))}}function c(t,e,n){return n?t|1<<e:t&~(1<<e)}function u(t){const e=t.x.toString()+t.y.toString()+t.z.toString();return void 0!==r[e]||(r[e]=i.length/3,i.push(t.x,t.y,t.z)),r[e]}function h(t){const e=t.r.toString()+t.g.toString()+t.b.toString();return void 0!==o[e]||(o[e]=s.length,s.push(t.getHex())),o[e]}function d(t){const e=t.x.toString()+t.y.toString();return void 0!==l[e]||(l[e]=a.length/2,a.push(t.x,t.y)),l[e]}return t.data={},t.data.vertices=e,t.data.normals=i,s.length>0&&(t.data.colors=s),a.length>0&&(t.data.uvs=[a]),t.data.faces=n,t}clone(){return(new a0).copy(this)}copy(t){this.vertices=[],this.colors=[],this.faces=[],this.faceVertexUvs=[[]],this.morphTargets=[],this.morphNormals=[],this.skinWeights=[],this.skinIndices=[],this.lineDistances=[],this.boundingBox=null,this.boundingSphere=null,this.name=t.name;const e=t.vertices;for(let t=0,n=e.length;t<n;t++)this.vertices.push(e[t].clone());const n=t.colors;for(let t=0,e=n.length;t<e;t++)this.colors.push(n[t].clone());const i=t.faces;for(let t=0,e=i.length;t<e;t++)this.faces.push(i[t].clone());for(let e=0,n=t.faceVertexUvs.length;e<n;e++){const n=t.faceVertexUvs[e];void 0===this.faceVertexUvs[e]&&(this.faceVertexUvs[e]=[]);for(let t=0,i=n.length;t<i;t++){const i=n[t],r=[];for(let t=0,e=i.length;t<e;t++){const e=i[t];r.push(e.clone())}this.faceVertexUvs[e].push(r)}}const r=t.morphTargets;for(let t=0,e=r.length;t<e;t++){const e={};if(e.name=r[t].name,void 0!==r[t].vertices){e.vertices=[];for(let n=0,i=r[t].vertices.length;n<i;n++)e.vertices.push(r[t].vertices[n].clone())}if(void 0!==r[t].normals){e.normals=[];for(let n=0,i=r[t].normals.length;n<i;n++)e.normals.push(r[t].normals[n].clone())}this.morphTargets.push(e)}const s=t.morphNormals;for(let t=0,e=s.length;t<e;t++){const e={};if(void 0!==s[t].vertexNormals){e.vertexNormals=[];for(let n=0,i=s[t].vertexNormals.length;n<i;n++){const i=s[t].vertexNormals[n],r={};r.a=i.a.clone(),r.b=i.b.clone(),r.c=i.c.clone(),e.vertexNormals.push(r)}}if(void 0!==s[t].faceNormals){e.faceNormals=[];for(let n=0,i=s[t].faceNormals.length;n<i;n++)e.faceNormals.push(s[t].faceNormals[n].clone())}this.morphNormals.push(e)}const o=t.skinWeights;for(let t=0,e=o.length;t<e;t++)this.skinWeights.push(o[t].clone());const a=t.skinIndices;for(let t=0,e=a.length;t<e;t++)this.skinIndices.push(a[t].clone());const l=t.lineDistances;for(let t=0,e=l.length;t<e;t++)this.lineDistances.push(l[t]);const c=t.boundingBox;null!==c&&(this.boundingBox=c.clone());const u=t.boundingSphere;return null!==u&&(this.boundingSphere=u.clone()),this.elementsNeedUpdate=t.elementsNeedUpdate,this.verticesNeedUpdate=t.verticesNeedUpdate,this.uvsNeedUpdate=t.uvsNeedUpdate,this.normalsNeedUpdate=t.normalsNeedUpdate,this.colorsNeedUpdate=t.colorsNeedUpdate,this.lineDistancesNeedUpdate=t.lineDistancesNeedUpdate,this.groupsNeedUpdate=t.groupsNeedUpdate,this}toBufferGeometry(){const t=(new l0).fromGeometry(this),e=new S.a,n=new Float32Array(3*t.vertices.length);if(e.setAttribute(\\\\\\\"position\\\\\\\",new C.a(n,3).copyVector3sArray(t.vertices)),t.normals.length>0){const n=new Float32Array(3*t.normals.length);e.setAttribute(\\\\\\\"normal\\\\\\\",new C.a(n,3).copyVector3sArray(t.normals))}if(t.colors.length>0){const n=new Float32Array(3*t.colors.length);e.setAttribute(\\\\\\\"color\\\\\\\",new C.a(n,3).copyColorsArray(t.colors))}if(t.uvs.length>0){const n=new Float32Array(2*t.uvs.length);e.setAttribute(\\\\\\\"uv\\\\\\\",new C.a(n,2).copyVector2sArray(t.uvs))}if(t.uvs2.length>0){const n=new Float32Array(2*t.uvs2.length);e.setAttribute(\\\\\\\"uv2\\\\\\\",new C.a(n,2).copyVector2sArray(t.uvs2))}e.groups=t.groups;for(const n in t.morphTargets){const i=[],r=t.morphTargets[n];for(let t=0,e=r.length;t<e;t++){const e=r[t],n=new C.c(3*e.data.length,3);n.name=e.name,i.push(n.copyVector3sArray(e.data))}e.morphAttributes[n]=i}if(t.skinIndices.length>0){const n=new C.c(4*t.skinIndices.length,4);e.setAttribute(\\\\\\\"skinIndex\\\\\\\",n.copyVector4sArray(t.skinIndices))}if(t.skinWeights.length>0){const n=new C.c(4*t.skinWeights.length,4);e.setAttribute(\\\\\\\"skinWeight\\\\\\\",n.copyVector4sArray(t.skinWeights))}return null!==t.boundingSphere&&(e.boundingSphere=t.boundingSphere.clone()),null!==t.boundingBox&&(e.boundingBox=t.boundingBox.clone()),e}computeTangents(){console.error(\\\\\\\"THREE.Geometry: .computeTangents() has been removed.\\\\\\\")}computeLineDistances(){console.error(\\\\\\\"THREE.Geometry: .computeLineDistances() has been removed. Use THREE.Line.computeLineDistances() instead.\\\\\\\")}applyMatrix(t){return console.warn(\\\\\\\"THREE.Geometry: .applyMatrix() has been renamed to .applyMatrix4().\\\\\\\"),this.applyMatrix4(t)}dispose(){this.dispatchEvent({type:\\\\\\\"dispose\\\\\\\"})}static createBufferGeometryFromObject(t){let e=new S.a;const n=t.geometry;if(t.isPoints||t.isLine){const t=new C.c(3*n.vertices.length,3),i=new C.c(3*n.colors.length,3);if(e.setAttribute(\\\\\\\"position\\\\\\\",t.copyVector3sArray(n.vertices)),e.setAttribute(\\\\\\\"color\\\\\\\",i.copyColorsArray(n.colors)),n.lineDistances&&n.lineDistances.length===n.vertices.length){const t=new C.c(n.lineDistances.length,1);e.setAttribute(\\\\\\\"lineDistance\\\\\\\",t.copyArray(n.lineDistances))}null!==n.boundingSphere&&(e.boundingSphere=n.boundingSphere.clone()),null!==n.boundingBox&&(e.boundingBox=n.boundingBox.clone())}else t.isMesh&&(e=n.toBufferGeometry());return e}}a0.prototype.isGeometry=!0;class l0{constructor(){this.vertices=[],this.normals=[],this.colors=[],this.uvs=[],this.uvs2=[],this.groups=[],this.morphTargets={},this.skinWeights=[],this.skinIndices=[],this.boundingBox=null,this.boundingSphere=null,this.verticesNeedUpdate=!1,this.normalsNeedUpdate=!1,this.colorsNeedUpdate=!1,this.uvsNeedUpdate=!1,this.groupsNeedUpdate=!1}computeGroups(t){const e=[];let n,i,r;const s=t.faces;for(i=0;i<s.length;i++){const t=s[i];t.materialIndex!==r&&(r=t.materialIndex,void 0!==n&&(n.count=3*i-n.start,e.push(n)),n={start:3*i,materialIndex:r})}void 0!==n&&(n.count=3*i-n.start,e.push(n)),this.groups=e}fromGeometry(t){const e=t.faces,n=t.vertices,i=t.faceVertexUvs,r=i[0]&&i[0].length>0,s=i[1]&&i[1].length>0,o=t.morphTargets,a=o.length;let l;if(a>0){l=[];for(let t=0;t<a;t++)l[t]={name:o[t].name,data:[]};this.morphTargets.position=l}const c=t.morphNormals,u=c.length;let h;if(u>0){h=[];for(let t=0;t<u;t++)h[t]={name:c[t].name,data:[]};this.morphTargets.normal=h}const p=t.skinIndices,_=t.skinWeights,m=p.length===n.length,f=_.length===n.length;n.length>0&&0===e.length&&console.error(\\\\\\\"THREE.DirectGeometry: Faceless geometries are not supported.\\\\\\\");for(let t=0;t<e.length;t++){const g=e[t];this.vertices.push(n[g.a],n[g.b],n[g.c]);const v=g.vertexNormals;if(3===v.length)this.normals.push(v[0],v[1],v[2]);else{const t=g.normal;this.normals.push(t,t,t)}const y=g.vertexColors;if(3===y.length)this.colors.push(y[0],y[1],y[2]);else{const t=g.color;this.colors.push(t,t,t)}if(!0===r){const e=i[0][t];void 0!==e?this.uvs.push(e[0],e[1],e[2]):(console.warn(\\\\\\\"THREE.DirectGeometry.fromGeometry(): Undefined vertexUv \\\\\\\",t),this.uvs.push(new d.a,new d.a,new d.a))}if(!0===s){const e=i[1][t];void 0!==e?this.uvs2.push(e[0],e[1],e[2]):(console.warn(\\\\\\\"THREE.DirectGeometry.fromGeometry(): Undefined vertexUv2 \\\\\\\",t),this.uvs2.push(new d.a,new d.a,new d.a))}for(let t=0;t<a;t++){const e=o[t].vertices;l[t].data.push(e[g.a],e[g.b],e[g.c])}for(let e=0;e<u;e++){const n=c[e].vertexNormals[t];h[e].data.push(n.a,n.b,n.c)}m&&this.skinIndices.push(p[g.a],p[g.b],p[g.c]),f&&this.skinWeights.push(_[g.a],_[g.b],_[g.c])}return this.computeGroups(t),this.verticesNeedUpdate=t.verticesNeedUpdate,this.normalsNeedUpdate=t.normalsNeedUpdate,this.colorsNeedUpdate=t.colorsNeedUpdate,this.uvsNeedUpdate=t.uvsNeedUpdate,this.groupsNeedUpdate=t.groupsNeedUpdate,null!==t.boundingSphere&&(this.boundingSphere=t.boundingSphere.clone()),null!==t.boundingBox&&(this.boundingBox=t.boundingBox.clone()),this}}class c0{constructor(t,e,n,i,r,s=0){this.a=t,this.b=e,this.c=n,this.normal=i&&i.isVector3?i:new p.a,this.vertexNormals=Array.isArray(i)?i:[],this.color=r&&r.isColor?r:new D.a,this.vertexColors=Array.isArray(r)?r:[],this.materialIndex=s}clone(){return(new this.constructor).copy(this)}copy(t){this.a=t.a,this.b=t.b,this.c=t.c,this.normal.copy(t.normal),this.color.copy(t.color),this.materialIndex=t.materialIndex;for(let e=0,n=t.vertexNormals.length;e<n;e++)this.vertexNormals[e]=t.vertexNormals[e].clone();for(let e=0,n=t.vertexColors.length;e<n;e++)this.vertexColors[e]=t.vertexColors[e].clone();return this}}var u0=function(t){this.subdivisions=void 0===t?1:t};u0.prototype.modify=function(t){var e=t.isBufferGeometry;(t=e?(new a0).fromBufferGeometry(t):t.clone()).mergeVertices(6);for(var n=this.subdivisions;n-- >0;)this.smooth(t);return t.computeFaceNormals(),t.computeVertexNormals(),e?t.toBufferGeometry():t},function(){var t=[\\\\\\\"a\\\\\\\",\\\\\\\"b\\\\\\\",\\\\\\\"c\\\\\\\"];function e(t,e,n){return n[Math.min(t,e)+\\\\\\\"_\\\\\\\"+Math.max(t,e)]}function n(t,e,n,i,r,s){var o,a=Math.min(t,e),l=Math.max(t,e),c=a+\\\\\\\"_\\\\\\\"+l;c in i?o=i[c]:(o={a:n[a],b:n[l],newEdge:null,faces:[]},i[c]=o);o.faces.push(r),s[t].edges.push(o),s[e].edges.push(o)}function i(t,e,n,i,r){t.push(new c0(e,n,i,void 0,void 0,r))}function r(t,e){return Math.abs(e-t)/2+Math.min(t,e)}function s(t,e,n,i){t.push([e.clone(),n.clone(),i.clone()])}u0.prototype.smooth=function(o){var a,l,c,u,h,_,m,f,g,v,y,x,b,w=new p.a,T=[];a=o.vertices,l=o.faces;var A,E,M,S,C,N,L,O,R,P,I,F,D,k,B=void 0!==(c=o.faceVertexUvs)[0]&&c[0].length>0;if(B)for(var z=0;z<c.length;z++)T.push([]);for(m in function(t,e,i,r){var s,o,a;for(s=0,o=t.length;s<o;s++)i[s]={edges:[]};for(s=0,o=e.length;s<o;s++)n((a=e[s]).a,a.b,t,r,a,i),n(a.b,a.c,t,r,a,i),n(a.c,a.a,t,r,a,i)}(a,l,v=new Array(a.length),y={}),x=[],y){for(E=y[m],M=new p.a,C=3/8,N=1/8,2!=(L=E.faces.length)&&(C=.5,N=0),M.addVectors(E.a,E.b).multiplyScalar(C),w.set(0,0,0),z=0;z<L;z++){for(S=E.faces[z],g=0;g<3&&((A=a[S[t[g]]])===E.a||A===E.b);g++);w.add(A)}w.multiplyScalar(N),M.add(w),E.newEdge=x.length,x.push(M)}for(b=[],m=0,f=a.length;m<f;m++){for(D=a[m],3==(_=(F=v[m].edges).length)?O=3/16:_>3&&(O=3/(8*_)),R=1-_*O,P=O,_<=2&&2==_&&(R=3/4,P=1/8),k=D.clone().multiplyScalar(R),w.set(0,0,0),z=0;z<_;z++)A=(I=F[z]).a!==D?I.a:I.b,w.add(A);w.multiplyScalar(P),k.add(w),b.push(k)}u=b.concat(x);var U,G,V,H,j,W,q,X=b.length;h=[];var Y=new d.a,$=new d.a,J=new d.a;for(m=0,f=l.length;m<f;m++)if(i(h,U=e((S=l[m]).a,S.b,y).newEdge+X,G=e(S.b,S.c,y).newEdge+X,V=e(S.c,S.a,y).newEdge+X,S.materialIndex),i(h,S.a,U,V,S.materialIndex),i(h,S.b,G,U,S.materialIndex),i(h,S.c,V,G,S.materialIndex),B)for(z=0;z<c.length;z++)j=(H=c[z][m])[0],W=H[1],q=H[2],Y.set(r(j.x,W.x),r(j.y,W.y)),$.set(r(W.x,q.x),r(W.y,q.y)),J.set(r(j.x,q.x),r(j.y,q.y)),s(T[z],Y,$,J),s(T[z],j,Y,J),s(T[z],W,$,Y),s(T[z],q,J,$);o.vertices=u,o.faces=h,B&&(o.faceVertexUvs=T)}}();class h0 extends pG{static type(){return\\\\\\\"subdivide\\\\\\\"}cook(t,e){const n=t[0],i=new u0(e.subdivisions);for(let t of n.objects()){const e=t.geometry;if(e){const n=i.modify(e);t.geometry=n}}return n}}h0.DEFAULT_PARAMS={subdivisions:1};const d0=h0.DEFAULT_PARAMS;const p0=new class extends aa{constructor(){super(...arguments),this.subdivisions=oa.INTEGER(d0.subdivisions,{range:[0,5],rangeLocked:[!0,!1]})}};class _0 extends gG{constructor(){super(...arguments),this.paramsConfig=p0}static type(){return\\\\\\\"subdivide\\\\\\\"}initializeNode(){this.io.inputs.setCount(1)}cook(t){this._operation=this._operation||new h0(this.scene(),this.states);const e=this._operation.cook(t,this.pv);this.setCoreGroup(e)}}const m0=new class extends aa{};class f0 extends xG{constructor(){super(...arguments),this.paramsConfig=m0}static type(){return\\\\\\\"subnet\\\\\\\"}initializeNode(){this.io.inputs.setCount(0,4),this.io.inputs.initInputsClonedState(Qi.NEVER)}}const g0=new class extends aa{constructor(){super(...arguments),this.input=oa.INTEGER(0,{range:[0,3],rangeLocked:[!0,!0],callback:t=>{v0.PARAM_CALLBACK_reset(t)}})}};class v0 extends gG{constructor(){super(...arguments),this.paramsConfig=g0}static type(){return er.INPUT}initializeNode(){this.io.inputs.setCount(0),this.lifecycle.add_on_add_hook((()=>{this.set_parent_input_dependency()}))}async cook(){const t=this.pv.input,e=this.parent();if(e){if(e.io.inputs.has_input(t)){const n=await e.containerController.requestInputContainer(t);if(n){const t=n.coreContent();if(t)return void this.setCoreGroup(t)}}else this.states.error.set(`parent has no input ${t}`);this.cookController.endCook()}else this.states.error.set(\\\\\\\"subnet input has no parent\\\\\\\")}static PARAM_CALLBACK_reset(t){t.set_parent_input_dependency()}set_parent_input_dependency(){this._current_parent_input_graph_node&&this.removeGraphInput(this._current_parent_input_graph_node);const t=this.parent();t&&(this._current_parent_input_graph_node=t.io.inputs.input_graph_node(this.pv.input),this.addGraphInput(this._current_parent_input_graph_node))}}var y0=n(82);class x0 extends jg{constructor(t,e,n){super(t,e,n)}load(t){return new Promise((async(e,n)=>{const i=new y0.a(this.loadingManager),r=await this._urlToLoad();i.load(r,(i=>{try{const n=this._onLoaded(i,t);e(n)}catch(t){n([])}}))}))}parse(t,e){const n=new y0.a(this.loadingManager).parse(t);return this._onLoaded(n,e)}_onLoaded(t,e){const n=t.paths,i=new In.a;for(let t=0;t<n.length;t++){const r=n[t],s=r.userData,o=s.style.fill;e.drawFillShapes&&void 0!==o&&\\\\\\\"none\\\\\\\"!==o&&this._drawShapes(i,r,e);const a=s.style.stroke;e.drawStrokes&&void 0!==a&&\\\\\\\"none\\\\\\\"!==a&&this._drawStrokes(i,r,e)}return i}_drawShapes(t,e,n){const i=e.userData,r=new at.a({color:(new D.a).setStyle(i.style.fill),opacity:i.style.fillOpacity,transparent:i.style.fillOpacity<1,side:w.z,depthWrite:!1,wireframe:n.fillShapesWireframe}),s=e.toShapes(!0);for(let e=0;e<s.length;e++){const n=s[e],i=new KX(n),o=new k.a(i,r);t.add(o)}}_drawStrokes(t,e,n){const i=e.userData;if(n.strokesWireframe){const n=new wr.a({color:(new D.a).setStyle(i.style.stroke),opacity:i.style.strokeOpacity,transparent:i.style.strokeOpacity<1,side:w.z,depthWrite:!1});for(let r=0,s=e.subPaths.length;r<s;r++){const s=e.subPaths[r],o=y0.a.pointsToStroke(s.getPoints(),i.style);if(o){const e=new Tr.a(o,n);t.add(e)}}}else{const n=new at.a({color:(new D.a).setStyle(i.style.stroke),opacity:i.style.strokeOpacity,transparent:i.style.strokeOpacity<1,side:w.z,depthWrite:!1});for(let r=0,s=e.subPaths.length;r<s;r++){const s=e.subPaths[r],o=y0.a.pointsToStroke(s.getPoints(),i.style);if(o){const e=new k.a(o,n);t.add(e)}}}}}const b0=`${Gg}/models/svg/tiger.svg`;class w0 extends pG{static type(){return\\\\\\\"svg\\\\\\\"}cook(t,e){const n=new x0(e.url,this.scene(),this._node);return new Promise((async t=>{const i=await n.load(e);for(let t of i.children)this._ensure_geometry_has_index(t);t(this.createCoreGroupFromObjects(i.children))}))}_ensure_geometry_has_index(t){const e=t.geometry;e&&this.createIndexIfNone(e)}}w0.DEFAULT_PARAMS={url:b0,drawFillShapes:!0,fillShapesWireframe:!1,drawStrokes:!0,strokesWireframe:!1};const T0=w0.DEFAULT_PARAMS;const A0=new class extends aa{constructor(){super(...arguments),this.url=oa.STRING(T0.url,{fileBrowse:{type:[Ls.SVG]}}),this.reload=oa.BUTTON(null,{callback:(t,e)=>{E0.PARAM_CALLBACK_reload(t)}}),this.drawFillShapes=oa.BOOLEAN(T0.drawFillShapes),this.fillShapesWireframe=oa.BOOLEAN(T0.fillShapesWireframe),this.drawStrokes=oa.BOOLEAN(T0.drawStrokes),this.strokesWireframe=oa.BOOLEAN(T0.strokesWireframe)}};class E0 extends gG{constructor(){super(...arguments),this.paramsConfig=A0}static type(){return\\\\\\\"svg\\\\\\\"}async requiredModules(){return[Vn.SVGLoader]}initializeNode(){this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.url],(()=>{const t=this.pv.url;if(t){const e=t.split(\\\\\\\"/\\\\\\\");return e[e.length-1]}return\\\\\\\"\\\\\\\"}))}))}))}async cook(t){this._operation=this._operation||new w0(this.scene(),this.states,this);const e=await this._operation.cook(t,this.pv);this.setCoreGroup(e)}static PARAM_CALLBACK_reload(t){t.param_callback_reload()}param_callback_reload(){this.p.url.setDirty()}}const M0=\\\\\\\"geometry to switch to\\\\\\\";const S0=new class extends aa{constructor(){super(...arguments),this.input=oa.INTEGER(0,{range:[0,3],rangeLocked:[!0,!0]})}};class C0 extends gG{constructor(){super(...arguments),this.paramsConfig=S0}static type(){return\\\\\\\"switch\\\\\\\"}static displayedInputNames(){return[M0,M0,M0,M0]}initializeNode(){this.io.inputs.setCount(0,4),this.io.inputs.initInputsClonedState(Qi.NEVER),this.cookController.disallowInputsEvaluation()}async cook(){const t=this.pv.input;if(this.io.inputs.has_input(t)){const e=await this.containerController.requestInputContainer(t);if(e){const t=e.coreContent();if(t)return void this.setCoreGroup(t)}}else this.states.error.set(`no input ${t}`);this.cookController.endCook()}}class N0 extends nZ{constructor(t,e,n){super([1,1,1,-1,-1,1,-1,1,-1,1,-1,-1],[2,1,0,0,3,2,1,3,0,2,3,1],t,e,n),this.type=\\\\\\\"TetrahedronBufferGeometry\\\\\\\",this.parameters={radius:t,detail:e}}}const L0=new class extends aa{constructor(){super(...arguments),this.radius=oa.FLOAT(1),this.detail=oa.INTEGER(0,{range:[0,10],rangeLocked:[!0,!1]}),this.pointsOnly=oa.BOOLEAN(0),this.center=oa.VECTOR3([0,0,0])}};class O0 extends gG{constructor(){super(...arguments),this.paramsConfig=L0}static type(){return\\\\\\\"tetrahedron\\\\\\\"}cook(){const t=this.pv.pointsOnly,e=new N0(this.pv.radius,this.pv.detail,t);if(e.translate(this.pv.center.x,this.pv.center.y,this.pv.center.z),t){const t=this.createObject(e,Sr.POINTS);this.setObject(t)}else e.computeVertexNormals(),this.setGeometry(e)}}class R0 extends YX{constructor(t,e={}){const n=e.font;if(!n||!n.isFont)return new S.a;const i=n.generateShapes(t,e.size);e.depth=void 0!==e.height?e.height:50,void 0===e.bevelThickness&&(e.bevelThickness=10),void 0===e.bevelSize&&(e.bevelSize=8),void 0===e.bevelEnabled&&(e.bevelEnabled=!1),super(i,e),this.type=\\\\\\\"TextGeometry\\\\\\\"}}var P0=n(48);class I0 extends kf.a{constructor(t){super(t)}load(t,e,n,i){const r=this,s=new Df.a(this.manager);s.setPath(this.path),s.setRequestHeader(this.requestHeader),s.setWithCredentials(r.withCredentials),s.load(t,(function(t){let n;try{n=JSON.parse(t)}catch(e){console.warn(\\\\\\\"THREE.FontLoader: typeface.js support is being deprecated. Use typeface.json instead.\\\\\\\"),n=JSON.parse(t.substring(65,t.length-2))}const i=r.parse(n);e&&e(i)}),n,i)}parse(t){return new F0(t)}}class F0{constructor(t){this.type=\\\\\\\"Font\\\\\\\",this.data=t}generateShapes(t,e=100){const n=[],i=function(t,e,n){const i=Array.from(t),r=e/n.resolution,s=(n.boundingBox.yMax-n.boundingBox.yMin+n.underlineThickness)*r,o=[];let a=0,l=0;for(let t=0;t<i.length;t++){const e=i[t];if(\\\\\\\"\\\\n\\\\\\\"===e)a=0,l-=s;else{const t=D0(e,r,a,l,n);a+=t.offsetX,o.push(t.path)}}return o}(t,e,this.data);for(let t=0,e=i.length;t<e;t++)Array.prototype.push.apply(n,i[t].toShapes());return n}}function D0(t,e,n,i,r){const s=r.glyphs[t]||r.glyphs[\\\\\\\"?\\\\\\\"];if(!s)return void console.error('THREE.Font: character \\\\\\\"'+t+'\\\\\\\" does not exists in font family '+r.familyName+\\\\\\\".\\\\\\\");const o=new P0.a;let a,l,c,u,h,d,p,_;if(s.o){const t=s._cachedOutline||(s._cachedOutline=s.o.split(\\\\\\\" \\\\\\\"));for(let r=0,s=t.length;r<s;){switch(t[r++]){case\\\\\\\"m\\\\\\\":a=t[r++]*e+n,l=t[r++]*e+i,o.moveTo(a,l);break;case\\\\\\\"l\\\\\\\":a=t[r++]*e+n,l=t[r++]*e+i,o.lineTo(a,l);break;case\\\\\\\"q\\\\\\\":c=t[r++]*e+n,u=t[r++]*e+i,h=t[r++]*e+n,d=t[r++]*e+i,o.quadraticCurveTo(h,d,c,u);break;case\\\\\\\"b\\\\\\\":c=t[r++]*e+n,u=t[r++]*e+i,h=t[r++]*e+n,d=t[r++]*e+i,p=t[r++]*e+n,_=t[r++]*e+i,o.bezierCurveTo(h,d,p,_,c,u)}}}return{offsetX:s.ha*e,path:o}}F0.prototype.isFont=!0;class k0 extends jg{constructor(t,e,n){super(t,e,n),this._font_loader=new I0(this.loadingManager)}async load(){const t=this.extension(),e=await this._urlToLoad();switch(t){case\\\\\\\"ttf\\\\\\\":return this._loadTTF(e);case\\\\\\\"json\\\\\\\":return this._loadJSON(e);default:return null}}static requiredModules(t){switch(this.extension(t)){case\\\\\\\"ttf\\\\\\\":return[Vn.TTFLoader];case\\\\\\\"json\\\\\\\":return[Vn.SVGLoader]}}_loadTTF(t){return new Promise((async(e,n)=>{const i=await this._loadTTFLoader();i&&i.load(t,(t=>{const n=this._font_loader.parse(t);e(n)}),void 0,(()=>{n()}))}))}_loadJSON(t){return new Promise(((e,n)=>{this._font_loader.load(t,(t=>{e(t)}),void 0,(()=>{n()}))}))}async _loadTTFLoader(){const t=await ai.modulesRegister.module(Vn.TTFLoader);if(t)return new t(this.loadingManager)}static async loadSVGLoader(){const t=await ai.modulesRegister.module(Vn.SVGLoader);if(t)return t}}var B0;!function(t){t.MESH=\\\\\\\"mesh\\\\\\\",t.FLAT=\\\\\\\"flat\\\\\\\",t.LINE=\\\\\\\"line\\\\\\\",t.STROKE=\\\\\\\"stroke\\\\\\\"}(B0||(B0={}));const z0=[B0.MESH,B0.FLAT,B0.LINE,B0.STROKE],U0=\\\\\\\"failed to generate geometry. Try to remove some characters\\\\\\\";const G0=new class extends aa{constructor(){super(...arguments),this.font=oa.STRING(\\\\\\\"https://raw.githubusercontent.com/polygonjs/polygonjs-assets/master/fonts/droid_sans_regular.typeface.json\\\\\\\",{fileBrowse:{type:[Ls.FONT]}}),this.text=oa.STRING(\\\\\\\"polygonjs\\\\\\\",{multiline:!0}),this.type=oa.INTEGER(0,{menu:{entries:z0.map(((t,e)=>({name:t,value:e})))}}),this.size=oa.FLOAT(1,{range:[0,1],rangeLocked:[!0,!1]}),this.extrude=oa.FLOAT(.1,{visibleIf:{type:z0.indexOf(B0.MESH)}}),this.segments=oa.INTEGER(1,{range:[1,20],rangeLocked:[!0,!1],visibleIf:{type:z0.indexOf(B0.MESH)}}),this.strokeWidth=oa.FLOAT(.02,{visibleIf:{type:z0.indexOf(B0.STROKE)}})}};class V0 extends gG{constructor(){super(...arguments),this.paramsConfig=G0,this._loaded_fonts={}}static type(){return\\\\\\\"text\\\\\\\"}initializeNode(){this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.text],(()=>this.p.text.rawInput()))}))}))}async cook(){try{this._loaded_fonts[this.pv.font]=this._loaded_fonts[this.pv.font]||await this._loadFont()}catch(t){return void this.states.error.set(`count not load font (${this.pv.font})`)}const t=this._loaded_fonts[this.pv.font];if(t)switch(z0[this.pv.type]){case B0.MESH:return this._create_geometry_from_type_mesh(t);case B0.FLAT:return this._create_geometry_from_type_flat(t);case B0.LINE:return this._create_geometry_from_type_line(t);case B0.STROKE:return this._create_geometry_from_type_stroke(t);default:console.warn(\\\\\\\"type is not valid\\\\\\\")}}_create_geometry_from_type_mesh(t){const e=this.displayed_text(),n={font:t,size:this.pv.size,height:this.pv.extrude,curveSegments:this.pv.segments};try{const t=new R0(e,n);if(!t.index){const e=t.getAttribute(\\\\\\\"position\\\\\\\").array;t.setIndex(f.range(e.length/3))}this.setGeometry(t)}catch(t){this.states.error.set(U0)}}_create_geometry_from_type_flat(t){const e=this._get_shapes(t);if(e){var n=new KX(e);this.setGeometry(n)}}_create_geometry_from_type_line(t){const e=this.shapes_from_font(t);if(e){const t=[],n=[];let i=0;for(let r=0;r<e.length;r++){const s=e[r].getPoints();for(let e=0;e<s.length;e++){const r=s[e];t.push(r.x),t.push(r.y),t.push(0),n.push(i),e>0&&e<s.length-1&&n.push(i),i+=1}}const r=new S.a;r.setAttribute(\\\\\\\"position\\\\\\\",new C.c(t,3)),r.setIndex(n),this.setGeometry(r,Sr.LINE_SEGMENTS)}}async _create_geometry_from_type_stroke(t){const e=this.shapes_from_font(t);if(e){const t=await k0.loadSVGLoader();if(!t)return;var n=t.getStrokeStyle(this.pv.strokeWidth,\\\\\\\"white\\\\\\\",\\\\\\\"miter\\\\\\\",\\\\\\\"butt\\\\\\\",4);const i=[];for(let r=0;r<e.length;r++){const s=e[r].getPoints(),o=12,a=.001,l=t.pointsToStroke(s,n,o,a);i.push(l)}const r=cs(i);this.setGeometry(r)}}shapes_from_font(t){const e=this._get_shapes(t);if(e){const t=[];for(let n=0;n<e.length;n++){const i=e[n];if(i.holes&&i.holes.length>0)for(let e=0;e<i.holes.length;e++){const n=i.holes[e];t.push(n)}}return e.push.apply(e,t),e}}_get_shapes(t){const e=this.displayed_text();try{return t.generateShapes(e,this.pv.size)}catch(t){this.states.error.set(U0)}}displayed_text(){return this.pv.text||\\\\\\\"\\\\\\\"}_loadFont(){return new k0(this.pv.font,this.scene(),this).load()}async requiredModules(){return this.p.font.isDirty()&&await this.p.font.compute(),k0.requiredModules(this.pv.font)}}class H0 extends pG{static type(){return\\\\\\\"TextureCopy\\\\\\\"}async cook(t,e){const n=t[0],i=t[1];let r;for(let t of i.objects())t.traverse((t=>{const n=t.material;n&&(m.isArray(n)||r||(r=n[e.textureName]))}));if(r)for(let t of n.objects())t.traverse((t=>{const n=t.material;if(n&&!m.isArray(n)){n[e.textureName]=r;const t=n.uniforms;if(t){const n=t[e.textureName];n&&(n.value=r)}n.needsUpdate=!0}}));return n}}H0.DEFAULT_PARAMS={textureName:\\\\\\\"map\\\\\\\"},H0.INPUT_CLONED_STATE=[Qi.FROM_NODE,Qi.NEVER];const j0=H0.DEFAULT_PARAMS;const W0=new class extends aa{constructor(){super(...arguments),this.textureName=oa.STRING(j0.textureName)}};class q0 extends gG{constructor(){super(...arguments),this.paramsConfig=W0}static type(){return\\\\\\\"TextureCopy\\\\\\\"}static displayedInputNames(){return[\\\\\\\"objects to copy textures to\\\\\\\",\\\\\\\"objects to copy textures from\\\\\\\"]}initializeNode(){this.io.inputs.setCount(2),this.io.inputs.initInputsClonedState(H0.INPUT_CLONED_STATE)}async cook(t){this._operation=this._operation||new H0(this.scene(),this.states);const e=await this._operation.cook(t,this.pv);this.setCoreGroup(e)}}class X0 extends pG{static type(){return\\\\\\\"textureProperties\\\\\\\"}async cook(t,e){const n=t[0],i=[];for(let t of n.objects())e.applyToChildren?t.traverse((t=>{i.push(t)})):i.push(t);const r=i.map((t=>this._update_object(t,e)));return await Promise.all(r),n}async _update_object(t,e){const n=t.material;n&&await this._update_material(n,e)}async _update_material(t,e){let n=t.map;n&&await this._update_texture(n,e)}async _update_texture(t,e){this._updateEncoding(t,e),this._updateMapping(t,e),this._updateWrap(t,e),await this._updateAnisotropy(t,e),this._updateFilter(t,e)}_updateEncoding(t,e){e.tencoding&&(t.encoding=e.encoding,t.needsUpdate=!0)}_updateMapping(t,e){e.tmapping&&(t.mapping=e.mapping)}_updateWrap(t,e){e.twrap&&(t.wrapS=e.wrapS,t.wrapT=e.wrapT)}async _updateAnisotropy(t,e){if(e.tanisotropy)if(e.useRendererMaxAnisotropy){const e=await ai.renderersController.firstRenderer();e&&(t.anisotropy=e.capabilities.getMaxAnisotropy())}else t.anisotropy=e.anisotropy}_updateFilter(t,e){e.tminFilter&&(t.minFilter=e.minFilter),e.tmagFilter&&(t.magFilter=e.magFilter)}}X0.DEFAULT_PARAMS={applyToChildren:!1,tencoding:!1,encoding:w.U,tmapping:!1,mapping:w.Yc,twrap:!1,wrapS:w.wc,wrapT:w.wc,tanisotropy:!1,useRendererMaxAnisotropy:!1,anisotropy:2,tminFilter:!1,minFilter:Xm,tmagFilter:!1,magFilter:qm},X0.INPUT_CLONED_STATE=Qi.FROM_NODE;const Y0=X0.DEFAULT_PARAMS;const $0=new class extends aa{constructor(){super(...arguments),this.applyToChildren=oa.BOOLEAN(Y0.applyToChildren,{separatorAfter:!0}),this.tencoding=oa.BOOLEAN(Y0.tencoding),this.encoding=oa.INTEGER(Y0.encoding,{visibleIf:{tencoding:1},menu:{entries:eg.map((t=>({name:Object.keys(t)[0],value:Object.values(t)[0]})))}}),this.tmapping=oa.BOOLEAN(Y0.tmapping),this.mapping=oa.INTEGER(Y0.mapping,{visibleIf:{tmapping:1},menu:{entries:ig.map((t=>({name:Object.keys(t)[0],value:Object.values(t)[0]})))}}),this.twrap=oa.BOOLEAN(Y0.twrap),this.wrapS=oa.INTEGER(Y0.wrapS,{visibleIf:{twrap:1},menu:{entries:ng.map((t=>({name:Object.keys(t)[0],value:Object.values(t)[0]})))}}),this.wrapT=oa.INTEGER(Y0.wrapT,{visibleIf:{twrap:1},menu:{entries:ng.map((t=>({name:Object.keys(t)[0],value:Object.values(t)[0]})))},separatorAfter:!0}),this.tanisotropy=oa.BOOLEAN(Y0.tanisotropy),this.useRendererMaxAnisotropy=oa.BOOLEAN(Y0.useRendererMaxAnisotropy,{visibleIf:{tanisotropy:1}}),this.anisotropy=oa.INTEGER(Y0.anisotropy,{visibleIf:{tanisotropy:1,useRendererMaxAnisotropy:0},range:[0,32],rangeLocked:[!0,!1]}),this.tminFilter=oa.BOOLEAN(0),this.minFilter=oa.INTEGER(Y0.minFilter,{visibleIf:{tminFilter:1},menu:{entries:$m}}),this.tmagFilter=oa.BOOLEAN(0),this.magFilter=oa.INTEGER(Y0.magFilter,{visibleIf:{tmagFilter:1},menu:{entries:Ym}})}};class J0 extends gG{constructor(){super(...arguments),this.paramsConfig=$0}static type(){return\\\\\\\"textureProperties\\\\\\\"}static displayedInputNames(){return[\\\\\\\"objects with textures to change properties of\\\\\\\"]}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState(X0.INPUT_CLONED_STATE)}async cook(t){this._operation=this._operation||new X0(this.scene(),this.states);const e=await this._operation.cook(t,this.pv);this.setCoreGroup(e)}}const Z0=new p.a(0,0,1);class Q0 extends pG{constructor(){super(...arguments),this._core_transform=new Mz}static type(){return\\\\\\\"torus\\\\\\\"}cook(t,e){const n=e.radius,i=e.radiusTube,r=e.segmentsRadial,s=e.segmentsTube,o=new eY(n,i,r,s);return o.translate(e.center.x,e.center.y,e.center.z),this._core_transform.rotate_geometry(o,Z0,e.direction),this.createCoreGroupFromGeometry(o)}}Q0.DEFAULT_PARAMS={radius:1,radiusTube:1,segmentsRadial:20,segmentsTube:12,direction:new p.a(0,1,0),center:new p.a(0,0,0)},Q0.INPUT_CLONED_STATE=Qi.FROM_NODE;const K0=Q0.DEFAULT_PARAMS;const t1=new class extends aa{constructor(){super(...arguments),this.radius=oa.FLOAT(K0.radius,{range:[0,1]}),this.radiusTube=oa.FLOAT(K0.radiusTube,{range:[0,1]}),this.segmentsRadial=oa.INTEGER(K0.segmentsRadial,{range:[1,50],rangeLocked:[!0,!1]}),this.segmentsTube=oa.INTEGER(K0.segmentsTube,{range:[1,50],rangeLocked:[!0,!1]}),this.direction=oa.VECTOR3(K0.direction),this.center=oa.VECTOR3(K0.center)}};class e1 extends gG{constructor(){super(...arguments),this.paramsConfig=t1}static type(){return\\\\\\\"torus\\\\\\\"}cook(t){this._operation=this._operation||new Q0(this.scene(),this.states);const e=this._operation.cook(t,this.pv);this.setCoreGroup(e)}}class n1 extends pG{static type(){return\\\\\\\"torusKnot\\\\\\\"}cook(t,e){const n=e.radius,i=e.radiusTube,r=e.segmentsRadial,s=e.segmentsTube,o=e.p,a=e.q,l=new nY(n,i,r,s,o,a);return l.translate(e.center.x,e.center.y,e.center.z),this.createCoreGroupFromGeometry(l)}}n1.DEFAULT_PARAMS={radius:1,radiusTube:1,segmentsRadial:64,segmentsTube:8,p:2,q:3,center:new p.a(0,0,0)},n1.INPUT_CLONED_STATE=Qi.FROM_NODE;const i1=n1.DEFAULT_PARAMS;const r1=new class extends aa{constructor(){super(...arguments),this.radius=oa.FLOAT(i1.radius),this.radiusTube=oa.FLOAT(i1.radiusTube),this.segmentsRadial=oa.INTEGER(i1.segmentsRadial,{range:[1,128]}),this.segmentsTube=oa.INTEGER(i1.segmentsTube,{range:[1,32]}),this.p=oa.INTEGER(i1.p,{range:[1,10]}),this.q=oa.INTEGER(i1.q,{range:[1,10]}),this.center=oa.VECTOR3(i1.center)}};class s1 extends gG{constructor(){super(...arguments),this.paramsConfig=r1}static type(){return\\\\\\\"torusKnot\\\\\\\"}initializeNode(){}cook(t){this._operation=this._operation||new n1(this.scene(),this.states);const e=this._operation.cook(t,this.pv);this.setCoreGroup(e)}}var o1;!function(t){t.SET_PARAMS=\\\\\\\"set params\\\\\\\",t.UPDATE_MATRIX=\\\\\\\"update matrix\\\\\\\"}(o1||(o1={}));const a1=[o1.SET_PARAMS,o1.UPDATE_MATRIX];class l1 extends pG{constructor(){super(...arguments),this._core_transform=new Mz,this._point_pos=new p.a,this._object_scale=new p.a,this._r=new p.a,this._object_position=new p.a}static type(){return\\\\\\\"transform\\\\\\\"}cook(t,e){const n=t[0].objects();return this._apply_transform(n,e),t[0]}_apply_transform(t,e){const n=wz[e.applyOn];switch(n){case yz.GEOMETRIES:return this._update_geometries(t,e);case yz.OBJECTS:return this._update_objects(t,e)}ar.unreachable(n)}_update_geometries(t,e){const n=this._matrix(e);if(\\\\\\\"\\\\\\\"===e.group.trim())for(let i of t){const t=i.geometry;t&&(t.translate(-e.pivot.x,-e.pivot.y,-e.pivot.z),t.applyMatrix4(n),t.translate(e.pivot.x,e.pivot.y,e.pivot.z))}else{const i=dG._fromObjects(t).pointsFromGroup(e.group);for(let t of i){const i=t.getPosition(this._point_pos).sub(e.pivot);i.applyMatrix4(n),t.setPosition(i.add(e.pivot))}}}_update_objects(t,e){const n=a1[e.objectMode];switch(n){case o1.SET_PARAMS:return this._update_objects_params(t,e);case o1.UPDATE_MATRIX:return this._update_objects_matrix(t,e)}ar.unreachable(n)}_update_objects_params(t,e){for(let n of t){n.position.copy(e.t);const t=Az[e.rotationOrder];this._r.copy(e.r).multiplyScalar(Ln.a),n.rotation.set(this._r.x,this._r.y,this._r.z,t),this._object_scale.copy(e.s).multiplyScalar(e.scale),n.scale.copy(this._object_scale),n.updateMatrix()}}_update_objects_matrix(t,e){const n=this._matrix(e);for(let e of t)this._object_position.copy(e.position),e.position.multiplyScalar(0),e.updateMatrix(),e.applyMatrix4(n),e.position.add(this._object_position),e.updateMatrix()}_matrix(t){return this._core_transform.matrix(t.t,t.r,t.s,t.scale,Az[t.rotationOrder])}}l1.DEFAULT_PARAMS={applyOn:wz.indexOf(yz.GEOMETRIES),objectMode:a1.indexOf(o1.SET_PARAMS),group:\\\\\\\"\\\\\\\",rotationOrder:Az.indexOf(Tz.XYZ),t:new p.a(0,0,0),r:new p.a(0,0,0),s:new p.a(1,1,1),scale:1,pivot:new p.a(0,0,0)},l1.INPUT_CLONED_STATE=Qi.FROM_NODE;const c1=l1.DEFAULT_PARAMS;const u1=new class extends aa{constructor(){super(...arguments),this.applyOn=oa.INTEGER(c1.applyOn,{menu:{entries:wz.map(((t,e)=>({name:t,value:e})))}}),this.objectMode=oa.INTEGER(c1.objectMode,{visibleIf:{applyOn:wz.indexOf(yz.OBJECTS)},menu:{entries:a1.map(((t,e)=>({name:t,value:e})))}}),this.group=oa.STRING(c1.group,{visibleIf:{applyOn:wz.indexOf(yz.GEOMETRIES)}}),this.rotationOrder=oa.INTEGER(c1.rotationOrder,{menu:{entries:Az.map(((t,e)=>({name:t,value:e})))}}),this.t=oa.VECTOR3(c1.t),this.r=oa.VECTOR3(c1.r),this.s=oa.VECTOR3(c1.s),this.scale=oa.FLOAT(c1.scale,{range:[0,10]}),this.pivot=oa.VECTOR3(c1.pivot,{visibleIf:{applyOn:wz.indexOf(yz.GEOMETRIES)}})}};class h1 extends gG{constructor(){super(...arguments),this.paramsConfig=u1}static type(){return PK.TRANSFORM}static displayedInputNames(){return[\\\\\\\"geometries or objects to transform\\\\\\\"]}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState(l1.INPUT_CLONED_STATE),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.applyOn],(()=>wz[this.pv.applyOn]))}))}))}setApplyOn(t){this.p.applyOn.set(wz.indexOf(t))}setObjectMode(t){this.p.objectMode.set(a1.indexOf(t))}cook(t){this._operation=this._operation||new l1(this.scene(),this.states);const e=this._operation.cook(t,this.pv);this.setCoreGroup(e)}}const d1=new class extends aa{constructor(){super(...arguments),this.useSecondInput=oa.BOOLEAN(1),this.reference=oa.OPERATOR_PATH(\\\\\\\"\\\\\\\",{nodeSelection:{context:Ki.SOP},visibleIf:{useSecondInput:0}})}};class p1 extends gG{constructor(){super(...arguments),this.paramsConfig=d1}static type(){return\\\\\\\"transformCopy\\\\\\\"}static displayedInputNames(){return[\\\\\\\"objects to transform\\\\\\\",\\\\\\\"objects to copy transform from\\\\\\\"]}initializeNode(){this.io.inputs.setCount(1,2),this.io.inputs.initInputsClonedState([Qi.FROM_NODE,Qi.NEVER])}cook(t){this.pv.useSecondInput&&t[1]?this._copy_from_src_objects(t[0].objects(),t[1].objects()):this._copy_from_found_node(t[0].objects())}_copy_from_src_objects(t,e){let n,i;for(let r=0;r<t.length;r++)n=t[r],i=e[r],i.updateMatrix(),n.matrix.copy(i.matrix),n.matrix.decompose(n.position,n.quaternion,n.scale);this.setObjects(t)}async _copy_from_found_node(t){const e=this.p.reference.found_node_with_context(Ki.SOP);if(e){const n=(await e.compute()).coreContent();if(n){const e=n.objects();return void this._copy_from_src_objects(t,e)}}this.setObjects(t)}}const _1=Az.indexOf(Tz.XYZ),m1={menu:{entries:Az.map(((t,e)=>({name:t,value:e})))}};function f1(t){const e=[];for(let n=t+1;n<=6;n++)e.push({count:n});return{visibleIf:e}}const g1=new class extends aa{constructor(){super(...arguments),this.applyOn=oa.INTEGER(wz.indexOf(yz.GEOMETRIES),{menu:{entries:wz.map(((t,e)=>({name:t,value:e})))}}),this.count=oa.INTEGER(2,{range:[0,6],rangeLocked:[!0,!0]}),this.rotationOrder0=oa.INTEGER(_1,{separatorBefore:!0,...m1,...f1(0)}),this.r0=oa.VECTOR3([0,0,0],{...f1(0)}),this.rotationOrder1=oa.INTEGER(_1,{separatorBefore:!0,...m1,...f1(1)}),this.r1=oa.VECTOR3([0,0,0],{...f1(1)}),this.rotationOrder2=oa.INTEGER(_1,{separatorBefore:!0,...m1,...f1(2)}),this.r2=oa.VECTOR3([0,0,0],{...f1(2)}),this.rotationOrder3=oa.INTEGER(_1,{separatorBefore:!0,...m1,...f1(3)}),this.r3=oa.VECTOR3([0,0,0],{...f1(3)}),this.rotationOrder4=oa.INTEGER(_1,{separatorBefore:!0,...m1,...f1(4)}),this.r4=oa.VECTOR3([0,0,0],{...f1(4)}),this.rotationOrder5=oa.INTEGER(_1,{separatorBefore:!0,...m1,...f1(5)}),this.r5=oa.VECTOR3([0,0,0],{...f1(5)})}};class v1 extends gG{constructor(){super(...arguments),this.paramsConfig=g1,this._core_transform=new Mz,this._t=new p.a(0,0,0),this._s=new p.a(1,1,1),this._scale=1}static type(){return\\\\\\\"transformMulti\\\\\\\"}static displayedInputNames(){return[\\\\\\\"objects to transform\\\\\\\",\\\\\\\"objects to copy initial transform from\\\\\\\"]}initializeNode(){this.io.inputs.setCount(1,2),this.io.inputs.initInputsClonedState([Qi.FROM_NODE,Qi.NEVER]),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.applyOn],(()=>wz[this.pv.applyOn]))}))})),this.params.onParamsCreated(\\\\\\\"cache param pairs\\\\\\\",(()=>{this._rot_and_index_pairs=[[this.p.r0,this.p.rotationOrder0],[this.p.r1,this.p.rotationOrder1],[this.p.r2,this.p.rotationOrder2],[this.p.r3,this.p.rotationOrder3],[this.p.r4,this.p.rotationOrder4],[this.p.r5,this.p.rotationOrder5]]}))}cook(t){const e=t[0].objectsWithGeo(),n=t[1]?t[1].objectsWithGeo()[0]:void 0;this._apply_transforms(e,n),this.setObjects(e)}_apply_transforms(t,e){const n=wz[this.pv.applyOn];switch(n){case yz.GEOMETRIES:return this._apply_matrix_to_geometries(t,e);case yz.OBJECTS:return this._apply_matrix_to_objects(t,e)}ar.unreachable(n)}_apply_matrix_to_geometries(t,e){if(!this._rot_and_index_pairs)return;if(e){const n=e.geometry;if(n){const e=[Hr.POSITION,Hr.NORMAL,Hr.TANGENT];for(let i of e){const e=n.attributes[i];for(let n of t){const t=n.geometry.attributes[i];e&&t&&Wr.copy(e,t)}}}}let n;for(let e=0;e<this.pv.count;e++){n=this._rot_and_index_pairs[e];const i=this._matrix(n[0].value,n[1].value);for(let e of t)e.geometry.applyMatrix4(i)}}_apply_matrix_to_objects(t,e){if(!this._rot_and_index_pairs)return;if(e)for(let n of t)n.matrix.copy(e.matrix),n.matrix.decompose(n.position,n.quaternion,n.scale);let n;for(let e=0;e<this.pv.count;e++){n=this._rot_and_index_pairs[e];const i=this._matrix(n[0].value,n[1].value);for(let e of t)e.applyMatrix4(i)}}_matrix(t,e){return this._core_transform.matrix(this._t,t,this._s,this._scale,Az[e])}}var y1;!function(t){t.RESET_OBJECT=\\\\\\\"reset objects transform\\\\\\\",t.CENTER_GEO=\\\\\\\"center geometries\\\\\\\",t.PROMOTE_GEO_TO_OBJECT=\\\\\\\"center geometry and transform object\\\\\\\"}(y1||(y1={}));const x1=[y1.RESET_OBJECT,y1.CENTER_GEO,y1.PROMOTE_GEO_TO_OBJECT];const b1=new class extends aa{constructor(){super(...arguments),this.mode=oa.INTEGER(x1.indexOf(y1.RESET_OBJECT),{menu:{entries:x1.map(((t,e)=>({name:t,value:e})))}})}};class w1 extends gG{constructor(){super(...arguments),this.paramsConfig=b1,this._bbox_center=new p.a,this._translate_matrix=new A.a}static type(){return\\\\\\\"transformReset\\\\\\\"}static displayedInputNames(){return[\\\\\\\"objects to reset transform\\\\\\\",\\\\\\\"optional reference for center\\\\\\\"]}initializeNode(){this.io.inputs.setCount(1,2),this.io.inputs.initInputsClonedState(Qi.FROM_NODE)}setMode(t){this.p.mode.set(x1.indexOf(t))}cook(t){const e=x1[this.pv.mode];this._select_mode(e,t)}_select_mode(t,e){switch(t){case y1.RESET_OBJECT:return this._reset_objects(e);case y1.CENTER_GEO:return this._center_geos(e,!1);case y1.PROMOTE_GEO_TO_OBJECT:return this._center_geos(e,!0)}ar.unreachable(t)}_reset_objects(t){const e=t[0],n=e.objects();for(let t of n)t.matrix.identity(),Mz.decompose_matrix(t);this.setCoreGroup(e)}_center_geos(t,e){const n=t[0],i=n.objectsWithGeo();let r=i;const s=t[1];s&&(r=s.objectsWithGeo());for(let t=0;t<i.length;t++){const n=i[t],s=r[t]||r[r.length-1],o=n.geometry,a=s.geometry;if(o&&a){a.computeBoundingBox();const t=a.boundingBox;t&&(t.getCenter(this._bbox_center),s.updateMatrixWorld(),this._bbox_center.applyMatrix4(s.matrixWorld),e&&(this._translate_matrix.identity(),this._translate_matrix.makeTranslation(this._bbox_center.x,this._bbox_center.y,this._bbox_center.z),n.matrix.multiply(this._translate_matrix),Mz.decompose_matrix(n),n.updateWorldMatrix(!1,!1)),this._translate_matrix.identity(),this._translate_matrix.makeTranslation(-this._bbox_center.x,-this._bbox_center.y,-this._bbox_center.z),o.applyMatrix4(this._translate_matrix))}}this.setCoreGroup(n)}}const T1=new p.a(0,1,0);const A1=new class extends aa{constructor(){super(...arguments),this.radius=oa.FLOAT(1,{range:[0,1]}),this.height=oa.FLOAT(1,{range:[0,1]}),this.segmentsRadial=oa.INTEGER(12,{range:[3,20],rangeLocked:[!0,!1]}),this.segmentsHeight=oa.INTEGER(1,{range:[1,20],rangeLocked:[!0,!1]}),this.cap=oa.BOOLEAN(1),this.center=oa.VECTOR3([0,0,0]),this.direction=oa.VECTOR3([0,0,1])}};class E1 extends gG{constructor(){super(...arguments),this.paramsConfig=A1,this._core_transform=new Mz}static type(){return\\\\\\\"tube\\\\\\\"}cook(){const t=new pU(this.pv.radius,this.pv.radius,this.pv.height,this.pv.segmentsRadial,this.pv.segmentsHeight,!this.pv.cap);this._core_transform.rotate_geometry(t,T1,this.pv.direction),t.translate(this.pv.center.x,this.pv.center.y,this.pv.center.z),this.setGeometry(t)}}function M1(t){return function(t){let e=0,n=0;for(const i of t)e+=i.w*i.h,n=Math.max(n,i.w);t.sort(((t,e)=>e.h-t.h));const i=[{x:0,y:0,w:Math.max(Math.ceil(Math.sqrt(e/.95)),n),h:1/0}];let r=0,s=0;for(const e of t)for(let t=i.length-1;t>=0;t--){const n=i[t];if(!(e.w>n.w||e.h>n.h)){if(e.x=n.x,e.y=n.y,s=Math.max(s,e.y+e.h),r=Math.max(r,e.x+e.w),e.w===n.w&&e.h===n.h){const e=i.pop();t<i.length&&(i[t]=e)}else e.h===n.h?(n.x+=e.w,n.w-=e.w):e.w===n.w?(n.y+=e.h,n.h-=e.h):(i.push({x:n.x+e.w,y:n.y,w:n.w-e.w,h:e.h}),n.y+=e.h,n.h-=e.h);break}}return{w:r,h:s,fill:e/(r*s)||0}}(t)}class S1 extends pG{static type(){return PK.UV_LAYOUT}cook(t,e){const n=t[0].objectsWithGeo(),i=[];for(let t of n){const e=t;e.isMesh&&i.push(e)}return this._layoutUVs(i,e),t[0]}_layoutUVs(t,e){var n;const i=[],r=new WeakMap,s=e.padding/e.res;let o=0;for(let a of t){a.geometry.hasAttribute(e.uv)||null===(n=this.states)||void 0===n||n.error.set(`attribute ${e.uv} not found`);const t={w:1+2*s,h:1+2*s};i.push(t),r.set(t,o),o++}const a=M1(i);for(let n of i){const i=n,o=r.get(n);if(null!=o){const n=t[o],r=n.geometry.getAttribute(e.uv).clone(),l=r.array;for(let t=0;t<r.array.length;t+=r.itemSize)l[t]=(r.array[t]+i.x+s)/a.w,l[t+1]=(r.array[t+1]+i.y+s)/a.h;n.geometry.setAttribute(e.uv2,r),n.geometry.getAttribute(e.uv2).needsUpdate=!0}}}}S1.DEFAULT_PARAMS={res:1024,padding:3,uv:\\\\\\\"uv\\\\\\\",uv2:\\\\\\\"uv2\\\\\\\"},S1.INPUT_CLONED_STATE=Qi.FROM_NODE;const C1=new class extends aa{constructor(){super(...arguments),this.res=oa.INTEGER(1024),this.padding=oa.INTEGER(3),this.uv=oa.STRING(\\\\\\\"uv\\\\\\\"),this.uv2=oa.STRING(\\\\\\\"uv2\\\\\\\")}};class N1 extends gG{constructor(){super(...arguments),this.paramsConfig=C1}static type(){return PK.UV_LAYOUT}static displayedInputNames(){return[\\\\\\\"geometries to unwrap UVs\\\\\\\"]}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState(S1.INPUT_CLONED_STATE)}cook(t){this._operation=this._operation||new S1(this.scene(),this.states);const e=this._operation.cook(t,this.pv);this.setCoreGroup(e)}}var L1;!function(t){t.CHANGE=\\\\\\\"change\\\\\\\",t.MOVEEND=\\\\\\\"moveend\\\\\\\"}(L1||(L1={}));class O1{constructor(t){this._callback=t,this._updateAlways=!0,this._listenerAdded=!1,this._listener=this._executeCallback.bind(this)}removeTarget(){this.setTarget(void 0)}setTarget(t){t||this._removeCameraEvent();const e=this._target;this._target=t,null!=this._target&&this._executeCallback(),(null!=this._target?this._target.uuid:void 0)!==(null!=e?e.uuid:void 0)&&this._addCameraEvent()}setUpdateAlways(t){this._removeCameraEvent(),this._updateAlways=t,this._addCameraEvent()}_currentEventName(){return this._updateAlways?L1.CHANGE:L1.MOVEEND}_addCameraEvent(){this._listenerAdded||null!=this._target&&(this._target.addEventListener(this._currentEventName(),this._listener),this._listenerAdded=!0)}_removeCameraEvent(){!0===this._listenerAdded&&null!=this._target&&(this._target.removeEventListener(this._currentEventName(),this._listener),this._listenerAdded=!1)}_executeCallback(){null!=this._target&&this._callback(this._target)}}const R1=new class extends aa{constructor(){super(...arguments),this.camera=oa.OPERATOR_PATH(\\\\\\\"/perspective_camera1\\\\\\\",{nodeSelection:{context:Ki.OBJ}})}};class P1 extends gG{constructor(){super(...arguments),this.paramsConfig=R1,this._cameraController=new O1(this._updateUVsFromCamera.bind(this))}static type(){return\\\\\\\"uvProject\\\\\\\"}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState(Qi.FROM_NODE)}cook(t){this._processed_core_group=t[0];const e=this.p.camera.found_node();null!=e?(this._camera_object=e.object,this._cameraController.setTarget(this._camera_object)):(this._camera_object=void 0,this._cameraController.removeTarget()),this.setCoreGroup(this._processed_core_group)}_updateUVsFromCamera(t){const e=this.parent();if(this._processed_core_group&&e){const t=this._processed_core_group.points(),n=e.object.matrixWorld;for(let e of t){const t=e.position(),i=this._vectorInCameraSpace(t,n);if(i){const t={x:1-(.5*i[0]+.5),y:.5*i[1]+.5};e.setAttribValue(\\\\\\\"uv\\\\\\\",t)}}}}_vectorInCameraSpace(t,e){if(this._camera_object)return t.applyMatrix4(e),t.project(this._camera_object).toArray()}}class I1 extends pG{static type(){return PK.UV_TRANSFORM}cook(t,e){const n=t[0].objectsWithGeo();for(let t of n){const n=t.geometry.getAttribute(e.attribName),i=n.array,r=i.length/2;for(let t=0;t<r;t++)i[2*t+0]=e.t.x+e.pivot.x+e.s.x*(i[2*t+0]-e.pivot.x),i[2*t+1]=e.t.y+e.pivot.y+e.s.y*(i[2*t+1]-e.pivot.y);n.needsUpdate=!0}return t[0]}}I1.DEFAULT_PARAMS={attribName:\\\\\\\"uv\\\\\\\",t:new d.a(0,0),s:new d.a(1,1),pivot:new d.a(0,0)},I1.INPUT_CLONED_STATE=Qi.FROM_NODE;const F1=I1.DEFAULT_PARAMS;const D1=new class extends aa{constructor(){super(...arguments),this.attribName=oa.STRING(F1.attribName),this.t=oa.VECTOR2(F1.t.toArray()),this.s=oa.VECTOR2(F1.s.toArray()),this.pivot=oa.VECTOR2(F1.pivot.toArray())}};class k1 extends gG{constructor(){super(...arguments),this.paramsConfig=D1}static type(){return PK.UV_TRANSFORM}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState(I1.INPUT_CLONED_STATE)}async cook(t){this._operation=this._operation||new I1(this.scene(),this.states,this);const e=await this._operation.cook(t,this.pv);this.setCoreGroup(e)}}class B1 extends pG{static type(){return PK.UV_UNWRAP}cook(t,e){const n=t[0].objectsWithGeo();for(let t of n){const n=t;n.isMesh&&this._unwrapUVs(n,e)}return t[0]}_unwrapUVs(t,e){var n,i,r;const s=[],o=t.geometry,a=null===(n=o.getIndex())||void 0===n?void 0:n.array;if(!a)return;if(!(null===(i=o.attributes.position)||void 0===i?void 0:i.array))return;const l=null===(r=o.attributes[e.uv])||void 0===r?void 0:r.array;if(!l)return;const c=a.length/3;for(let t=0;t<c;t++)s.push({w:1,h:1});const u=M1(s),h=new Array(l.length);for(let t=0;t<c;t++){const e=s[t],n=e.x/u.w,i=e.y/u.h,r=e.w/u.w,o=e.h/u.h,l=2*a[3*t+0],c=2*a[3*t+1],d=2*a[3*t+2];h[l]=n,h[l+1]=i,h[c]=n+r,h[c+1]=i,h[d]=n,h[d+1]=i+o}o.setAttribute(e.uv,new C.c(h,2))}}B1.DEFAULT_PARAMS={uv:\\\\\\\"uv\\\\\\\"},B1.INPUT_CLONED_STATE=Qi.FROM_NODE;const z1=new class extends aa{constructor(){super(...arguments),this.uv=oa.STRING(\\\\\\\"uv\\\\\\\")}};class U1 extends gG{constructor(){super(...arguments),this.paramsConfig=z1}static type(){return PK.UV_UNWRAP}static displayedInputNames(){return[\\\\\\\"geometries to unwrap UVs\\\\\\\"]}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState(B1.INPUT_CLONED_STATE)}cook(t){this._operation=this._operation||new B1(this.scene(),this.states);const e=this._operation.cook(t,this.pv);this.setCoreGroup(e)}}class G1 extends ia{static context(){return Ki.SOP}cook(){this.cookController.endCook()}}class V1 extends G1{}class H1 extends V1{constructor(){super(...arguments),this._children_controller_context=Ki.ANIM}static type(){return tr.ANIM}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class j1 extends V1{constructor(){super(...arguments),this._children_controller_context=Ki.COP}static type(){return tr.COP}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class W1 extends V1{constructor(){super(...arguments),this._children_controller_context=Ki.EVENT}static type(){return tr.EVENT}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class q1 extends V1{constructor(){super(...arguments),this._children_controller_context=Ki.MAT}static type(){return tr.MAT}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class X1 extends G1{constructor(){super(...arguments),this.paramsConfig=new Jm,this.effectsComposerController=new Zm(this),this.displayNodeController=new Lm(this,this.effectsComposerController.displayNodeControllerCallbacks()),this._children_controller_context=Ki.POST}static type(){return tr.POST}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class Y1 extends V1{constructor(){super(...arguments),this._children_controller_context=Ki.ROP}static type(){return tr.ROP}createNode(t,e){return super.createNode(t,e)}children(){return super.children()}nodesByType(t){return super.nodesByType(t)}}class $1{constructor(t){this.param=t,this._require_dependency=!1}require_dependency(){return this._require_dependency}node(){return this._node=this._node||this.param.node}static requiredArguments(){return console.warn(\\\\\\\"Expression.Method._Base.required_arguments virtual method call. Please override\\\\\\\"),[]}static optionalArguments(){return[]}static minAllowedArgumentsCount(){return this.requiredArguments().length}static maxAllowedArgumentsCount(){return this.minAllowedArgumentsCount()+this.optionalArguments().length}static allowedArgumentsCount(t){return t>=this.minAllowedArgumentsCount()&&t<=this.maxAllowedArgumentsCount()}processArguments(t){throw\\\\\\\"Expression.Method._Base.process_arguments virtual method call. Please override\\\\\\\"}async getReferencedNodeContainer(t){const e=this.getReferencedNode(t);if(e){let t;if(t=e.isDirty()?await e.compute():e.containerController.container(),t){if(t.coreContent())return t}throw`referenced node invalid: ${e.path()}`}throw`invalid input (${t})`}getReferencedParam(t,e){const n=this.node();return n?xi.findParam(n,t,e):null}findReferencedGraphNode(t,e){if(!m.isNumber(t)){const n=t;return this.getReferencedNode(n,e)}{const e=t,n=this.node();if(n){return n.io.inputs.input_graph_node(e)}}return null}getReferencedNode(t,e){let n=null;const i=this.node();if(m.isString(t)){if(i){const r=t;n=xi.findNode(i,r,e)}}else if(i){const e=t;n=i.io.inputs.input(e)}return n||null}findDependency(t){return null}createDependencyFromIndexOrPath(t){const e=new co,n=this.findReferencedGraphNode(t,e);return n?this.createDependency(n,t,e):(ai.warn(\\\\\\\"node not found for path\\\\\\\",t),null)}createDependency(t,e,n){return ks.create(this.param,e,t,n)}}class J1 extends $1{constructor(){super(...arguments),this._require_dependency=!0}static requiredArguments(){return[[\\\\\\\"string\\\\\\\",\\\\\\\"arguments list\\\\\\\"],[\\\\\\\"number\\\\\\\",\\\\\\\"index\\\\\\\"]]}processArguments(t){return new Promise(((e,n)=>{if(2==t.length){const n=t[0],i=t[1];e(n.split(\\\\\\\" \\\\\\\")[i])}else e(0)}))}}class Z1 extends $1{constructor(){super(...arguments),this._require_dependency=!0}static requiredArguments(){return[[\\\\\\\"string\\\\\\\",\\\\\\\"arguments list\\\\\\\"]]}processArguments(t){return new Promise(((e,n)=>{if(1==t.length){e(t[0].split(\\\\\\\" \\\\\\\").length)}else e(0)}))}}const Q1=[\\\\\\\"min\\\\\\\",\\\\\\\"max\\\\\\\",\\\\\\\"size\\\\\\\",\\\\\\\"center\\\\\\\"],K1=[\\\\\\\"x\\\\\\\",\\\\\\\"y\\\\\\\",\\\\\\\"z\\\\\\\"];class t2 extends $1{constructor(){super(...arguments),this._require_dependency=!0}static requiredArguments(){return[[\\\\\\\"string\\\\\\\",\\\\\\\"path to node\\\\\\\"],[\\\\\\\"string\\\\\\\",\\\\\\\"vector name, min, max, size or center\\\\\\\"],[\\\\\\\"string\\\\\\\",\\\\\\\"component_name, x,y or z\\\\\\\"]]}findDependency(t){return this.createDependencyFromIndexOrPath(t)}processArguments(t){let e=0;return new Promise((async(n,i)=>{if(t.length>=1){const r=t[0],s=t[1],o=t[2];let a=null;try{a=await this.getReferencedNodeContainer(r)}catch(t){i(t)}a&&(e=this._get_value_from_container(a,s,o),n(e))}else n(0)}))}_get_value_from_container(t,e,n){const i=t.boundingBox();if(!e)return i;if(Q1.indexOf(e)>=0){let t=new p.a;switch(e){case\\\\\\\"size\\\\\\\":i.getSize(t);break;case\\\\\\\"center\\\\\\\":i.getCenter(t);break;default:t=i[e]}return n?K1.indexOf(n)>=0?t[n]:-1:t}return-1}}class e2 extends $1{constructor(){super(...arguments),this._require_dependency=!0}static requiredArguments(){return[[\\\\\\\"string\\\\\\\",\\\\\\\"path to node\\\\\\\"],[\\\\\\\"string\\\\\\\",\\\\\\\"component_name, x,y or z\\\\\\\"]]}findDependency(t){return this.createDependencyFromIndexOrPath(t)}processArguments(t){return new Promise((async(e,n)=>{if(t.length>=1){const i=t[0],r=t[1];let s=null;try{s=await this.getReferencedNodeContainer(i)}catch(t){n(t)}if(s){const t=s.boundingBox(),n=t.min.clone().add(t.max).multiplyScalar(.5);if(r){const t=n[r];e(null!=t?t:0)}else e(n)}}else e(0)}))}}class n2 extends $1{constructor(){super(...arguments),this._require_dependency=!0}static requiredArguments(){return[[\\\\\\\"string\\\\\\\",\\\\\\\"path to param\\\\\\\"]]}findDependency(t){const e=new co,n=this.getReferencedParam(t,e);return n?this.createDependency(n,t,e):null}async processArguments(t){return new Promise((async(e,n)=>{let i=0;if(1==t.length){const r=t[0],s=this.getReferencedParam(r);if(s){s.isDirty()&&await s.compute();const t=s.value;null!=t&&(i=t,e(i))}else n(0)}}))}}class i2 extends $1{constructor(){super(...arguments),this._require_dependency=!0}static requiredArguments(){return[[\\\\\\\"string\\\\\\\",\\\\\\\"path to copy\\\\\\\"],[\\\\\\\"integer\\\\\\\",\\\\\\\"default value\\\\\\\"]]}static optionalArguments(){return[[\\\\\\\"string\\\\\\\",\\\\\\\"attribute name (optional)\\\\\\\"]]}findDependency(t){const e=this.findReferencedGraphNode(t);if(e&&\\\\\\\"copy\\\\\\\"==e.type()){const n=e.stamp_node;return this.createDependency(n,t)}return null}processArguments(t){return new Promise(((e,n)=>{if(2==t.length||3==t.length){const n=t[0],i=t[1],r=t[2],s=this.node(),o=s?xi.findNode(s,n):null;let a;o&&o.type()==e$.type()&&(a=o.stamp_value(r)),null==a&&(a=i),e(a)}else e(0)}))}}class r2 extends $1{constructor(){super(...arguments),this._require_dependency=!0,this._resolution=new d.a}static requiredArguments(){return[[\\\\\\\"string\\\\\\\",\\\\\\\"path to node\\\\\\\"],[\\\\\\\"string\\\\\\\",\\\\\\\"component_name: x or y\\\\\\\"]]}findDependency(t){return this.createDependencyFromIndexOrPath(t)}async processArguments(t){if(1==t.length||2==t.length){const e=t[0],n=t[1],i=await this.getReferencedNodeContainer(e);if(i){const t=i.resolution();if(!n)return this._resolution.set(t[0],t[1]),this._resolution;if([0,\\\\\\\"0\\\\\\\",\\\\\\\"x\\\\\\\"].includes(n))return t[0];if([1,\\\\\\\"1\\\\\\\",\\\\\\\"y\\\\\\\"].includes(n))return t[1]}}return-1}}class s2 extends $1{constructor(){super(...arguments),this._require_dependency=!0}static requiredArguments(){return[]}async processArguments(t){return new Promise((async(t,e)=>{t(Zf.isMobile())}))}}class o2 extends $1{constructor(){super(...arguments),this._require_dependency=!0}static requiredArguments(){return[]}async processArguments(t){return new Promise((async(t,e)=>{t(Zf.isTouchDevice())}))}}class a2 extends $1{static requiredArguments(){return[[\\\\\\\"string\\\\\\\",\\\\\\\"javascript expression\\\\\\\"]]}async processArguments(t){let e=0;if(1==t.length){const n=t[0];if(this._function=this._function||this._create_function(n),this._function)try{e=this._function(this.param.scene(),this.param.node,this.param)}catch(t){console.warn(\\\\\\\"expression error\\\\\\\"),console.warn(t)}}return e}_create_function(t){return new Function(\\\\\\\"scene\\\\\\\",\\\\\\\"node\\\\\\\",\\\\\\\"param\\\\\\\",`return ${t}`)}}class l2 extends $1{constructor(){super(...arguments),this._require_dependency=!0}static requiredArguments(){return[[\\\\\\\"string\\\\\\\",\\\\\\\"path to node\\\\\\\"],[\\\\\\\"string\\\\\\\",\\\\\\\"attribute name\\\\\\\"],[\\\\\\\"index\\\\\\\",\\\\\\\"object index\\\\\\\"]]}findDependency(t){return this.createDependencyFromIndexOrPath(t)}processArguments(t){return new Promise((async(e,n)=>{if(3==t.length){const i=t[0],r=t[1],s=t[2];let o=null;try{o=await this.getReferencedNodeContainer(i)}catch(t){n(t)}if(o){e(this._get_value_from_container(o,r,s))}}else console.warn(`${t.length} given when expected 3`),e(0)}))}_get_value_from_container(t,e,n){const i=t.coreContent();if(i){const t=i.coreObjects()[n];return t?t.attribValue(e):0}return null}}class c2 extends $1{constructor(){super(...arguments),this._require_dependency=!0}static requiredArguments(){return[[\\\\\\\"string\\\\\\\",\\\\\\\"path to node\\\\\\\"]]}findDependency(t){return this.createDependencyFromIndexOrPath(t)}processArguments(t){return new Promise((async(e,n)=>{if(1==t.length){const i=t[0];let r;try{r=await this.getReferencedNodeContainer(i)}catch(t){return void n(t)}if(r){e(r.objectsCount())}}else e(0)}))}}class u2 extends $1{constructor(){super(...arguments),this._require_dependency=!0}static requiredArguments(){return[[\\\\\\\"string\\\\\\\",\\\\\\\"path to node\\\\\\\"]]}findDependency(t){const e=this.findReferencedGraphNode(t);if(e){const n=e;if(n.nameController){const e=n.nameController.graph_node;return this.createDependency(e,t)}}return null}processArguments(t){return new Promise(((e,n)=>{if(1==t.length){const n=t[0],i=this.getReferencedNode(n);if(i){const t=i.name();e(sr.tailDigits(t))}else e(0)}else e(0)}))}}class h2 extends $1{constructor(){super(...arguments),this._require_dependency=!0}static requiredArguments(){return[[\\\\\\\"string\\\\\\\",\\\\\\\"path to node\\\\\\\"]]}findDependency(t){const e=this.findReferencedGraphNode(t);if(e){const n=e;if(n.nameController){const e=n.nameController.graph_node;return this.createDependency(e,t)}}return null}processArguments(t){return new Promise(((e,n)=>{if(1==t.length){const n=t[0],i=this.getReferencedNode(n);if(i){e(i.name())}else e(0)}else e(0)}))}}class d2 extends $1{static requiredArguments(){return[[\\\\\\\"string\\\\\\\",\\\\\\\"number\\\\\\\"]]}processArguments(t){return new Promise((e=>{const n=t[0]||2;e(`${t[1]||0}`.padStart(n,\\\\\\\"0\\\\\\\"))}))}}class p2 extends $1{constructor(){super(...arguments),this._require_dependency=!0}static requiredArguments(){return[[\\\\\\\"string\\\\\\\",\\\\\\\"path to node\\\\\\\"],[\\\\\\\"string\\\\\\\",\\\\\\\"attribute name\\\\\\\"],[\\\\\\\"index\\\\\\\",\\\\\\\"point index\\\\\\\"]]}findDependency(t){return this.createDependencyFromIndexOrPath(t)}processArguments(t){return new Promise((async(e,n)=>{if(3==t.length){const i=t[0],r=t[1],s=t[2];let o=null;try{o=await this.getReferencedNodeContainer(i)}catch(t){n(t)}if(o){e(this._get_value_from_container(o,r,s))}}else console.warn(`${t.length} given when expected 3`),e(0)}))}_get_value_from_container(t,e,n){const i=t.coreContent();if(i){const t=i.points()[n];return t?t.attribValue(e):0}return null}}class _2 extends $1{constructor(){super(...arguments),this._require_dependency=!0}static requiredArguments(){return[[\\\\\\\"string\\\\\\\",\\\\\\\"path to node\\\\\\\"]]}findDependency(t){return this.createDependencyFromIndexOrPath(t)}processArguments(t){return new Promise((async(e,n)=>{if(1==t.length){const i=t[0];let r;try{r=await this.getReferencedNodeContainer(i)}catch(t){return void n(t)}if(r){e(r.pointsCount())}}else e(0)}))}}class m2 extends $1{static requiredArguments(){return[[\\\\\\\"string\\\\\\\",\\\\\\\"string to count characters of\\\\\\\"]]}async processArguments(t){let e=0;if(1==t.length){e=t[0].length}return e}}class f2 extends $1{static requiredArguments(){return[]}async processArguments(t){let e=\\\\\\\"\\\\\\\";for(let n of t)null==n&&(n=\\\\\\\"\\\\\\\"),e+=`${n}`;return e}}class g2 extends $1{static requiredArguments(){return[[\\\\\\\"string\\\\\\\",\\\\\\\"string to get index from\\\\\\\"],[\\\\\\\"string\\\\\\\",\\\\\\\"char to find index of\\\\\\\"]]}async processArguments(t){let e=-1;if(2==t.length){const n=t[0],i=t[1];e=n.indexOf(i)}return e}}class v2 extends $1{static requiredArguments(){return[[\\\\\\\"string\\\\\\\",\\\\\\\"string to get range from\\\\\\\"],[\\\\\\\"integer\\\\\\\",\\\\\\\"range start\\\\\\\"],[\\\\\\\"integer\\\\\\\",\\\\\\\"range size\\\\\\\"]]}async processArguments(t){let e=\\\\\\\"\\\\\\\";const n=t[0],i=t[1]||0;let r=t[2]||1;return n&&(e=n.substr(i,r)),e}}class y2 extends $1{constructor(){super(...arguments),this._require_dependency=!0,this._windowSize=new d.a}static requiredArguments(){return[[]]}findDependency(t){return this.param.addGraphInput(this.param.scene().windowController.graphNode()),null}processArguments(t){return new Promise((t=>{this._windowSize.set(window.innerWidth,window.innerHeight),t(this._windowSize)}))}}class x2{constructor(t,e){this._controller=t,this._node=e,this._deleted_params_data=new Map,this._created_spare_param_names=[],this._raw_input_serialized_by_param_name=new Map,this._init_value_serialized_by_param_name=new Map}get assembler(){return this._controller.assembler}createSpareParameters(){var t;const e={},n=this.assembler.param_configs(),i=n.map((t=>t.name())),r=b.clone(i);if(0==this._validateNames(r))return;b.clone(this._created_spare_param_names).concat(r).forEach((t=>{const n=this._node.params.get(t);if(n){this._raw_input_serialized_by_param_name.set(n.name(),n.rawInputSerialized()),this._init_value_serialized_by_param_name.set(n.name(),n.defaultValueSerialized());const t=hG.dispatch_param(n);if(t.required()){const e=t.data();this._deleted_params_data.set(n.name(),e)}}e.namesToDelete=e.namesToDelete||[],e.namesToDelete.push(t)}));for(let t of n)if(r.indexOf(t.name())>=0){const n=b.clone(t.param_options),i={spare:!0,computeOnDirty:!0,cook:!1},r=b.merge(n,i);let s=this._init_value_serialized_by_param_name.get(t.name());null==s&&(s=t.default_value);let o=this._raw_input_serialized_by_param_name.get(t.name());null==o&&(o=t.default_value),e.toAdd=e.toAdd||[],e.toAdd.push({name:t.name(),type:t.type(),init_value:s,raw_input:o,options:r})}this._node.params.updateParams(e),this._created_spare_param_names=(null===(t=e.toAdd)||void 0===t?void 0:t.map((t=>t.name)))||[];for(let t of n){const e=this._node.params.get(t.name());e&&(t.execute_callback(this._node,e),e.type()==Es.OPERATOR_PATH&&setTimeout((async()=>{e.isDirty()&&await e.compute(),e.options.executeCallback()}),200))}}_validateNames(t){const e=b.clone(this._node.params.non_spare_names),n=f.intersection(t,e);if(n.length>0){const t=`${this._node.path()} attempts to create spare params called '${n.join(\\\\\\\", \\\\\\\")}' with same name as params`;return this._node.states.error.set(t),!1}return!0}}class b2{constructor(t,e){this.node=t,this._globals_handler=new Sf,this._compile_required=!0,this._assembler=new e(this.node),this._spare_params_controller=new x2(this,this.node)}set_assembler_globals_handler(t){(this._globals_handler?this._globals_handler.id():null)!=(t?t.id():null)&&(this._globals_handler=t,this.set_compilation_required_and_dirty(),this._assembler.reset_configs())}get assembler(){return this._assembler}get globals_handler(){return this._globals_handler}add_output_inputs(t){this._assembler.add_output_inputs(t)}add_globals_outputs(t){this._assembler.add_globals_outputs(t)}allow_attribute_exports(){return this._assembler.allow_attribute_exports()}setCompilationRequired(t=!0){this._compile_required=t}set_compilation_required_and_dirty(t){this.setCompilationRequired(),this.node.setDirty(t)}compileRequired(){return this._compile_required}post_compile(){this.createSpareParameters(),this.setCompilationRequired(!1)}createSpareParameters(){this._spare_params_controller.createSpareParameters()}addFilterFragmentShaderCallback(t,e){this.assembler._addFilterFragmentShaderCallback(t,e),this.setCompilationRequired()}removeFilterFragmentShaderCallback(t){this.assembler._removeFilterFragmentShaderCallback(t),this.setCompilationRequired()}}var w2;!function(t){t.FUNCTION_DECLARATION=\\\\\\\"function_declaration\\\\\\\",t.DEFINE=\\\\\\\"define\\\\\\\",t.BODY=\\\\\\\"body\\\\\\\"}(w2||(w2={}));class T2{constructor(t,e,n){this._name=t,this._input_names=e,this._dependencies=n}name(){return this._name}input_names(){return this._input_names}dependencies(){return this._dependencies}}class A2{constructor(t,e={}){this._name=t,this._options=e}name(){return this._name}default_from_attribute(){return this._options.default_from_attribute||!1}default(){return this._options.default}if_condition(){return this._options.if}prefix(){return this._options.prefix||\\\\\\\"\\\\\\\"}suffix(){return this._options.suffix||\\\\\\\"\\\\\\\"}postLines(){return this._options.postLines}}class E2{constructor(t){this._shader_name=t,this._definitions_by_node_id=new Map,this._body_lines_by_node_id=new Map}get shader_name(){return this._shader_name}addDefinitions(t,e){for(let n of e)u.pushOnArrayAtEntry(this._definitions_by_node_id,t.graphNodeId(),n)}definitions(t){return this._definitions_by_node_id.get(t.graphNodeId())}addBodyLines(t,e){for(let n of e)u.pushOnArrayAtEntry(this._body_lines_by_node_id,t.graphNodeId(),n)}body_lines(t){return this._body_lines_by_node_id.get(t.graphNodeId())}}class M2{constructor(t,e,n){this._shader_names=t,this._current_shader_name=e,this._assembler=n,this._lines_controller_by_shader_name=new Map;for(let t of this._shader_names)this._lines_controller_by_shader_name.set(t,new E2(t))}assembler(){return this._assembler}shaderNames(){return this._shader_names}set_current_shader_name(t){this._current_shader_name=t}get current_shader_name(){return this._current_shader_name}addDefinitions(t,e,n){if(0==e.length)return;n=n||this._current_shader_name;const i=this._lines_controller_by_shader_name.get(n);i&&i.addDefinitions(t,e)}definitions(t,e){const n=this._lines_controller_by_shader_name.get(t);if(n)return n.definitions(e)}addBodyLines(t,e,n){if(0==e.length)return;n=n||this._current_shader_name;const i=this._lines_controller_by_shader_name.get(n);i&&i.addBodyLines(t,e)}body_lines(t,e){const n=this._lines_controller_by_shader_name.get(t);if(n)return n.body_lines(e)}}const S2={[w2.FUNCTION_DECLARATION]:\\\\\\\"\\\\\\\",[w2.DEFINE]:\\\\\\\";\\\\\\\",[w2.BODY]:\\\\\\\";\\\\\\\"},C2={[w2.FUNCTION_DECLARATION]:\\\\\\\"\\\\\\\",[w2.DEFINE]:\\\\\\\"\\\\\\\",[w2.BODY]:\\\\\\\"\\\\t\\\\\\\"};class N2{static node_comment(t,e){let n=`// ${t.path()}`,i=C2[e];if(e==w2.BODY){let e=this.node_distance_to_material(t);t.type()==er.OUTPUT&&(e+=1),i=i.repeat(e)}return e==w2.BODY&&(n=`${i}${n}`),n}static line_wrap(t,e,n){let i=!0;0!=e.indexOf(\\\\\\\"#if\\\\\\\")&&0!=e.indexOf(\\\\\\\"#endif\\\\\\\")||(i=!1);let r=C2[n];if(n==w2.BODY&&(r=r.repeat(this.node_distance_to_material(t))),e=`${r}${e}`,i){const t=e[e.length-1],i=S2[n];t!=i&&\\\\\\\"{\\\\\\\"!=t&&\\\\\\\"}\\\\\\\"!=t&&(e+=i)}return e}static post_line_separator(t){return t==w2.BODY?\\\\\\\"\\\\t\\\\\\\":\\\\\\\"\\\\\\\"}static node_distance_to_material(t){const e=t.parent();if(!e)return 0;if(e.context()!=t.context())return 1;{let n=1;return t.type()!=er.INPUT&&t.type()!=er.OUTPUT||(n=0),n+this.node_distance_to_material(e)}}}class L2{constructor(t,e,n){this._node_traverser=t,this._root_nodes_for_shader_method=e,this._assembler=n,this._param_configs_controller=new GI,this._param_configs_set_allowed=!0,this._lines=new Map}shaderNames(){return this._node_traverser.shaderNames()}buildFromNodes(t,e){this._node_traverser.traverse(t);const n=new Map;for(let t of this.shaderNames()){const e=this._node_traverser.nodes_for_shader_name(t);n.set(t,e)}const i=this._node_traverser.sorted_nodes();for(let t of this.shaderNames()){const e=this._root_nodes_for_shader_method(t);for(let i of e)u.pushOnArrayAtEntry(n,t,i)}const r=new Map;for(let t of i)r.set(t.graphNodeId(),!0);for(let e of t)r.get(e.graphNodeId())||(i.push(e),r.set(e.graphNodeId(),!0));for(let t of i)t.reset_code();for(let t of e)t.reset_code();this._shaders_collection_controller=new M2(this.shaderNames(),this.shaderNames()[0],this._assembler),this.reset();for(let t of this.shaderNames()){let e=n.get(t)||[];if(e=f.uniq(e),this._shaders_collection_controller.set_current_shader_name(t),e)for(let t of e)t.setLines(this._shaders_collection_controller)}if(this._param_configs_set_allowed){for(let t of e)t.setParamConfigs();this.setParamConfigs(e)}this.set_code_lines(i)}shaders_collection_controller(){return this._shaders_collection_controller}disallow_new_param_configs(){this._param_configs_set_allowed=!1}allow_new_param_configs(){this._param_configs_set_allowed=!0}reset(){for(let t of this.shaderNames()){const e=new Map;this._lines.set(t,e)}}param_configs(){return this._param_configs_controller.list()||[]}lines(t,e){var n;return(null===(n=this._lines.get(t))||void 0===n?void 0:n.get(e))||[]}all_lines(){return this._lines}setParamConfigs(t){this._param_configs_controller.reset();for(let e of t){const t=e.param_configs();if(t)for(let e of t)this._param_configs_controller.push(e)}}set_code_lines(t){for(let e of this.shaderNames())this.add_code_lines(t,e)}add_code_lines(t,e){this.addDefinitions(t,e,yf.FUNCTION,w2.FUNCTION_DECLARATION),this.addDefinitions(t,e,yf.UNIFORM,w2.DEFINE),this.addDefinitions(t,e,yf.VARYING,w2.DEFINE),this.addDefinitions(t,e,yf.ATTRIBUTE,w2.DEFINE),this.add_code_line_for_nodes_and_line_type(t,e,w2.BODY)}addDefinitions(t,e,n,i){if(!this._shaders_collection_controller)return;const r=[];for(let i of t){let t=this._shaders_collection_controller.definitions(e,i);if(t){t=t.filter((t=>t.definition_type==n));for(let e of t)r.push(e)}}if(r.length>0){const t=new vf(r),n=t.uniq();if(t.errored)throw`code builder error: ${t.error_message}`;const s=new Map,o=new Map;for(let t of n){const e=t.node.graphNodeId();o.has(e)||o.set(e,!0),u.pushOnArrayAtEntry(s,e,t)}const a=this._lines.get(e);o.forEach(((t,e)=>{const n=s.get(e);if(n){const t=n[0];if(t){const e=N2.node_comment(t.node,i);u.pushOnArrayAtEntry(a,i,e);for(let e of n){const n=N2.line_wrap(t.node,e.line,i);u.pushOnArrayAtEntry(a,i,n)}const r=N2.post_line_separator(i);u.pushOnArrayAtEntry(a,i,r)}}}))}}add_code_line_for_nodes_and_line_type(t,e,n){var i=(t=t.filter((t=>{if(this._shaders_collection_controller){const n=this._shaders_collection_controller.body_lines(e,t);return n&&n.length>0}}))).length;for(let r=0;r<i;r++){const i=r==t.length-1;this.add_code_line_for_node_and_line_type(t[r],e,n,i)}}add_code_line_for_node_and_line_type(t,e,n,i){if(!this._shaders_collection_controller)return;const r=this._shaders_collection_controller.body_lines(e,t);if(r&&r.length>0){const s=this._lines.get(e),o=N2.node_comment(t,n);if(u.pushOnArrayAtEntry(s,n,o),f.uniq(r).forEach((e=>{e=N2.line_wrap(t,e,n),u.pushOnArrayAtEntry(s,n,e)})),n!=w2.BODY||!i){const t=N2.post_line_separator(n);u.pushOnArrayAtEntry(s,n,t)}}}}class O2{constructor(t,e,n){this._parent_node=t,this._shader_names=e,this._input_names_for_shader_name_method=n,this._leaves_graph_id=new Map,this._graph_ids_by_shader_name=new Map,this._outputs_by_graph_id=new Map,this._depth_by_graph_id=new Map,this._graph_id_by_depth=new Map,this._graph=this._parent_node.scene().graph}reset(){this._leaves_graph_id.clear(),this._graph_ids_by_shader_name.clear(),this._outputs_by_graph_id.clear(),this._depth_by_graph_id.clear(),this._graph_id_by_depth.clear(),this._shader_names.forEach((t=>{this._graph_ids_by_shader_name.set(t,new Map)}))}shaderNames(){return this._shader_names}input_names_for_shader_name(t,e){return this._input_names_for_shader_name_method(t,e)}traverse(t){this.reset();for(let t of this.shaderNames())this._leaves_graph_id.set(t,new Map);for(let e of this.shaderNames()){this._shader_name=e;for(let e of t)this.find_leaves_from_root_node(e),this.set_nodes_depth()}this._depth_by_graph_id.forEach(((t,e)=>{null!=t&&u.pushOnArrayAtEntry(this._graph_id_by_depth,t,e)}))}leaves_from_nodes(t){var e;this._shader_name=xf.LEAVES_FROM_NODES_SHADER,this._graph_ids_by_shader_name.set(this._shader_name,new Map),this._leaves_graph_id.set(this._shader_name,new Map);for(let e of t)this.find_leaves(e);const n=[];return null===(e=this._leaves_graph_id.get(this._shader_name))||void 0===e||e.forEach(((t,e)=>{n.push(e)})),this._graph.nodesFromIds(n)}nodes_for_shader_name(t){const e=[];this._graph_id_by_depth.forEach(((t,n)=>{e.push(n)})),e.sort(((t,e)=>t-e));const n=[],i=new Map;return e.forEach((e=>{const r=this._graph_id_by_depth.get(e);r&&r.forEach((e=>{var r;if(null===(r=this._graph_ids_by_shader_name.get(t))||void 0===r?void 0:r.get(e)){const r=this._graph.nodeFromId(e);this.add_nodes_with_children(r,i,n,t)}}))})),n}sorted_nodes(){const t=[];this._graph_id_by_depth.forEach(((e,n)=>{t.push(n)})),t.sort(((t,e)=>t-e));const e=[],n=new Map;return t.forEach((t=>{const i=this._graph_id_by_depth.get(t);if(i)for(let t of i){const i=this._graph.nodeFromId(t);i&&this.add_nodes_with_children(i,n,e)}})),e}add_nodes_with_children(t,e,n,i){if(e.get(t.graphNodeId())||(n.push(t),e.set(t.graphNodeId(),!0)),t.type()==er.INPUT){const r=t.parent();if(r){const s=this.sorted_nodes_for_shader_name_for_parent(r,i);for(let r of s)r.graphNodeId()!=t.graphNodeId()&&this.add_nodes_with_children(r,e,n,i)}}}sorted_nodes_for_shader_name_for_parent(t,e){const n=[];this._graph_id_by_depth.forEach(((t,e)=>{n.push(e)})),n.sort(((t,e)=>t-e));const i=[];n.forEach((n=>{const r=this._graph_id_by_depth.get(n);r&&r.forEach((n=>{var r;if(!e||(null===(r=this._graph_ids_by_shader_name.get(e))||void 0===r?void 0:r.get(n))){const e=this._graph.nodeFromId(n);e.parent()==t&&i.push(e)}}))}));const r=i[0];return t.context()==r.context()&&i.push(t),i}find_leaves_from_root_node(t){var e;null===(e=this._graph_ids_by_shader_name.get(this._shader_name))||void 0===e||e.set(t.graphNodeId(),!0);const n=this.input_names_for_shader_name(t,this._shader_name);if(n)for(let e of n){const n=t.io.inputs.named_input(e);n&&(u.pushOnArrayAtEntry(this._outputs_by_graph_id,n.graphNodeId(),t.graphNodeId()),this.find_leaves(n))}this._outputs_by_graph_id.forEach(((t,e)=>{this._outputs_by_graph_id.set(e,f.uniq(t))}))}find_leaves(t){var e;null===(e=this._graph_ids_by_shader_name.get(this._shader_name))||void 0===e||e.set(t.graphNodeId(),!0);const n=this._find_inputs_or_children(t),i=f.compact(n),r=f.uniq(i.map((t=>t.graphNodeId()))).map((t=>this._graph.nodeFromId(t)));if(r.length>0)for(let e of r)u.pushOnArrayAtEntry(this._outputs_by_graph_id,e.graphNodeId(),t.graphNodeId()),this.find_leaves(e);else this._leaves_graph_id.get(this._shader_name).set(t.graphNodeId(),!0)}_find_inputs_or_children(t){var e,n;if(t.type()==er.INPUT)return(null===(e=t.parent())||void 0===e?void 0:e.io.inputs.inputs())||[];if(t.childrenAllowed()){return[null===(n=t.childrenController)||void 0===n?void 0:n.output_node()]}return t.io.inputs.inputs()}set_nodes_depth(){this._leaves_graph_id.forEach(((t,e)=>{t.forEach(((t,e)=>{this.set_node_depth(e)}))}))}set_node_depth(t,e=0){const n=this._depth_by_graph_id.get(t);null!=n?this._depth_by_graph_id.set(t,Math.max(n,e)):this._depth_by_graph_id.set(t,e);const i=this._outputs_by_graph_id.get(t);i&&i.forEach((t=>{this.set_node_depth(t,e+1)}))}}const R2=new Map([[xf.VERTEX,\\\\\\\"#include <common>\\\\\\\"],[xf.FRAGMENT,\\\\\\\"#include <common>\\\\\\\"]]),P2=new Map([[xf.VERTEX,\\\\\\\"#include <color_vertex>\\\\\\\"],[xf.FRAGMENT,\\\\\\\"vec4 diffuseColor = vec4( diffuse, opacity );\\\\\\\"]]),I2=new Map([[xf.VERTEX,[\\\\\\\"#include <begin_vertex>\\\\\\\",\\\\\\\"#include <beginnormal_vertex>\\\\\\\"]],[xf.FRAGMENT,[]]]);class F2 extends class{}{constructor(t){super(),this._gl_parent_node=t,this._shaders_by_name=new Map,this._lines=new Map,this._root_nodes=[],this._leaf_nodes=[],this._uniforms_time_dependent=!1,this._uniforms_resolution_dependent=!1}setGlParentNode(t){this._overriden_gl_parent_node=t}currentGlParentNode(){return this._overriden_gl_parent_node||this._gl_parent_node}compile(){}_template_shader_for_shader_name(t){var e,n;switch(t){case xf.VERTEX:return null===(e=this.templateShader())||void 0===e?void 0:e.vertexShader;case xf.FRAGMENT:return null===(n=this.templateShader())||void 0===n?void 0:n.fragmentShader}}get globals_handler(){var t;return null===(t=this.currentGlParentNode().assemblerController)||void 0===t?void 0:t.globals_handler}compileAllowed(){var t;return null!=(null===(t=this.currentGlParentNode().assemblerController)||void 0===t?void 0:t.globals_handler)}shaders_by_name(){return this._shaders_by_name}_build_lines(){for(let t of this.shaderNames()){const e=this._template_shader_for_shader_name(t);e&&this._replace_template(e,t)}}set_root_nodes(t){this._root_nodes=t}templateShader(){}addUniforms(t){for(let e of this.param_configs())t[e.uniform_name]=e.uniform;this.uniformsTimeDependent()&&(t.time={value:this.currentGlParentNode().scene().time()}),this.uniforms_resolution_dependent()&&(t.resolution={value:new d.a(1e3,1e3)})}root_nodes_by_shader_name(t){const e=[];for(let t of this._root_nodes)switch(t.type()){case UI.type():case HI.type():e.push(t);break;case gf.type():case Lf.type():e.push(t)}return e}leaf_nodes_by_shader_name(t){const e=[];for(let t of this._leaf_nodes)switch(t.type()){case HP.type():e.push(t);break;case gf.type():}return e}set_node_lines_globals(t,e){}set_node_lines_output(t,e){}set_node_lines_attribute(t,e){}codeBuilder(){return this._code_builder=this._code_builder||this._create_code_builder()}_resetCodeBuilder(){this._code_builder=void 0}_create_code_builder(){const t=new O2(this.currentGlParentNode(),this.shaderNames(),((t,e)=>this.input_names_for_shader_name(t,e)));return new L2(t,(t=>this.root_nodes_by_shader_name(t)),this)}build_code_from_nodes(t){const e=Of.findParamGeneratingNodes(this.currentGlParentNode());this.codeBuilder().buildFromNodes(t,e)}allow_new_param_configs(){this.codeBuilder().allow_new_param_configs()}disallow_new_param_configs(){this.codeBuilder().disallow_new_param_configs()}builder_param_configs(){return this.codeBuilder().param_configs()}builder_lines(t,e){return this.codeBuilder().lines(t,e)}all_builder_lines(){return this.codeBuilder().all_lines()}param_configs(){return(this._param_config_owner||this.codeBuilder()).param_configs()}set_param_configs_owner(t){this._param_config_owner=t,this._param_config_owner?this.codeBuilder().disallow_new_param_configs():this.codeBuilder().allow_new_param_configs()}static output_input_connection_points(){return[new Vo(\\\\\\\"position\\\\\\\",Do.VEC3),new Vo(\\\\\\\"normal\\\\\\\",Do.VEC3),new Vo(\\\\\\\"color\\\\\\\",Do.VEC3),new Vo(\\\\\\\"alpha\\\\\\\",Do.FLOAT),new Vo(\\\\\\\"uv\\\\\\\",Do.VEC2)]}add_output_inputs(t){t.io.inputs.setNamedInputConnectionPoints(F2.output_input_connection_points())}static create_globals_node_output_connections(){return[new Vo(\\\\\\\"position\\\\\\\",Do.VEC3),new Vo(\\\\\\\"normal\\\\\\\",Do.VEC3),new Vo(\\\\\\\"color\\\\\\\",Do.VEC3),new Vo(\\\\\\\"uv\\\\\\\",Do.VEC2),new Vo(\\\\\\\"mvPosition\\\\\\\",Do.VEC4),new Vo(\\\\\\\"worldPosition\\\\\\\",Do.VEC4),new Vo(\\\\\\\"worldNormal\\\\\\\",Do.VEC3),new Vo(\\\\\\\"gl_Position\\\\\\\",Do.VEC4),new Vo(\\\\\\\"gl_FragCoord\\\\\\\",Do.VEC4),new Vo(\\\\\\\"cameraPosition\\\\\\\",Do.VEC3),new Vo(\\\\\\\"resolution\\\\\\\",Do.VEC2),new Vo(\\\\\\\"time\\\\\\\",Do.FLOAT)]}create_globals_node_output_connections(){return F2.create_globals_node_output_connections()}add_globals_outputs(t){t.io.outputs.setNamedOutputConnectionPoints(this.create_globals_node_output_connections())}allow_attribute_exports(){return!1}reset_configs(){this._reset_shader_configs(),this._reset_variable_configs(),this._resetUniformsTimeDependency(),this._reset_uniforms_resolution_dependency()}shaderConfigs(){return this._shader_configs=this._shader_configs||this.create_shader_configs()}set_shader_configs(t){this._shader_configs=t}shaderNames(){var t;return(null===(t=this.shaderConfigs())||void 0===t?void 0:t.map((t=>t.name())))||[]}_reset_shader_configs(){this._shader_configs=void 0}create_shader_configs(){return[new T2(xf.VERTEX,[\\\\\\\"position\\\\\\\",\\\\\\\"normal\\\\\\\",\\\\\\\"uv\\\\\\\",Lf.INPUT_NAME],[]),new T2(xf.FRAGMENT,[\\\\\\\"color\\\\\\\",\\\\\\\"alpha\\\\\\\"],[xf.VERTEX])]}shader_config(t){var e;return null===(e=this.shaderConfigs())||void 0===e?void 0:e.filter((e=>e.name()==t))[0]}variable_configs(){return this._variable_configs=this._variable_configs||this.create_variable_configs()}set_variable_configs(t){this._variable_configs=t}variable_config(t){return this.variable_configs().filter((e=>e.name()==t))[0]}static create_variable_configs(){return[new A2(\\\\\\\"position\\\\\\\",{default_from_attribute:!0,prefix:\\\\\\\"vec3 transformed = \\\\\\\"}),new A2(\\\\\\\"normal\\\\\\\",{default_from_attribute:!0,prefix:\\\\\\\"vec3 objectNormal = \\\\\\\",postLines:[\\\\\\\"#ifdef USE_TANGENT\\\\\\\",\\\\\\\"\\\\tvec3 objectTangent = vec3( tangent.xyz );\\\\\\\",\\\\\\\"#endif\\\\\\\"]}),new A2(\\\\\\\"color\\\\\\\",{prefix:\\\\\\\"diffuseColor.xyz = \\\\\\\"}),new A2(\\\\\\\"alpha\\\\\\\",{prefix:\\\\\\\"diffuseColor.a = \\\\\\\"}),new A2(\\\\\\\"uv\\\\\\\",{prefix:\\\\\\\"vUv = \\\\\\\",if:Sf.IF_RULE.uv})]}create_variable_configs(){return F2.create_variable_configs()}_reset_variable_configs(){this._variable_configs=void 0,this.variable_configs()}input_names_for_shader_name(t,e){var n;return(null===(n=this.shader_config(e))||void 0===n?void 0:n.input_names())||[]}_resetUniformsTimeDependency(){this._uniforms_time_dependent=!1}setUniformsTimeDependent(){this._uniforms_time_dependent=!0}uniformsTimeDependent(){return this._uniforms_time_dependent}_reset_uniforms_resolution_dependency(){this._uniforms_resolution_dependent=!1}set_uniforms_resolution_dependent(){this._uniforms_resolution_dependent=!0}uniforms_resolution_dependent(){return this._uniforms_resolution_dependent}insert_define_after(t){return R2.get(t)}insert_body_after(t){return P2.get(t)}lines_to_remove(t){return I2.get(t)}_replace_template(t,e){const n=this.builder_lines(e,w2.FUNCTION_DECLARATION),i=this.builder_lines(e,w2.DEFINE),r=this.builder_lines(e,w2.BODY);let s=t.split(\\\\\\\"\\\\n\\\\\\\");const o=[],a=this.insert_define_after(e),l=this.insert_body_after(e),c=this.lines_to_remove(e);let u=!1,h=!1;for(let t of s){1==u&&(n&&this._insert_lines(o,n),i&&this._insert_lines(o,i),u=!1),1==h&&(r&&this._insert_lines(o,r),h=!1);let e=!1;if(c)for(let n of c)t.indexOf(n)>=0&&(e=!0);e?(o.push(\\\\\\\"// removed:\\\\\\\"),o.push(`//${t}`)):o.push(t),a&&t.indexOf(a)>=0&&(u=!0),l&&t.indexOf(l)>=0&&(h=!0)}this._lines.set(e,o)}_insert_lines(t,e){if(e.length>0){for(let e=0;e<3;e++)t.push(\\\\\\\"\\\\\\\");for(let n of e)t.push(n);for(let e=0;e<3;e++)t.push(\\\\\\\"\\\\\\\")}}_addFilterFragmentShaderCallback(t,e){}_removeFilterFragmentShaderCallback(t){}getCustomMaterials(){return new Map}static expandShader(t){return function t(e){return e.replace(/^[ \\\\t]*#include +<([\\\\w\\\\d./]+)>/gm,(function(e,n){var i=z[n];if(void 0===i)throw new Error(\\\\\\\"Can not resolve #include <\\\\\\\"+n+\\\\\\\">\\\\\\\");return t(i)}))}(t)}}var D2,k2;!function(t){t.DISTANCE=\\\\\\\"customDistanceMaterial\\\\\\\",t.DEPTH=\\\\\\\"customDepthMaterial\\\\\\\",t.DEPTH_DOF=\\\\\\\"customDepthDOFMaterial\\\\\\\"}(D2||(D2={})),function(t){t.TIME=\\\\\\\"time\\\\\\\",t.RESOLUTION=\\\\\\\"resolution\\\\\\\",t.MV_POSITION=\\\\\\\"mvPosition\\\\\\\",t.GL_POSITION=\\\\\\\"gl_Position\\\\\\\",t.GL_FRAGCOORD=\\\\\\\"gl_FragCoord\\\\\\\",t.GL_POINTCOORD=\\\\\\\"gl_PointCoord\\\\\\\"}(k2||(k2={}));const B2=[k2.GL_FRAGCOORD,k2.GL_POINTCOORD];class z2 extends F2{constructor(){super(...arguments),this._assemblers_by_custom_name=new Map,this._filterFragmentShaderCallbacks=new Map}createMaterial(){return new F}custom_assembler_class_by_custom_name(){}_addCustomMaterials(t){const e=this.custom_assembler_class_by_custom_name();e&&e.forEach(((e,n)=>{this._add_custom_material(t,n,e)}))}_add_custom_material(t,e,n){let i=this._assemblers_by_custom_name.get(e);i||(i=new n(this.currentGlParentNode()),this._assemblers_by_custom_name.set(e,i)),t.customMaterials=t.customMaterials||{};const r=i.createMaterial();r.name=e,t.customMaterials[e]=r}compileCustomMaterials(t){const e=this.custom_assembler_class_by_custom_name();e&&e.forEach(((e,n)=>{if(this._code_builder){let i=this._assemblers_by_custom_name.get(n);i||(i=new e(this.currentGlParentNode()),this._assemblers_by_custom_name.set(n,i)),i.set_root_nodes(this._root_nodes),i.set_param_configs_owner(this._code_builder),i.set_shader_configs(this.shaderConfigs()),i.set_variable_configs(this.variable_configs());const r=t.customMaterials[n];r&&(i.setFilterFragmentShaderMethodOwner(this),i.compileMaterial(r),i.setFilterFragmentShaderMethodOwner(void 0))}}))}_resetFilterFragmentShaderCallbacks(){this._filterFragmentShaderCallbacks.clear()}_addFilterFragmentShaderCallback(t,e){this._filterFragmentShaderCallbacks.set(t,e)}_removeFilterFragmentShaderCallback(t){this._filterFragmentShaderCallbacks.delete(t)}setFilterFragmentShaderMethodOwner(t){this._filterFragmentShaderMethodOwner=t}filterFragmentShader(t){return this._filterFragmentShaderCallbacks.forEach(((e,n)=>{t=e(t)})),t}processFilterFragmentShader(t){return this._filterFragmentShaderMethodOwner?this._filterFragmentShaderMethodOwner.filterFragmentShader(t):this.filterFragmentShader(t)}compileMaterial(t){if(!this.compileAllowed())return;const e=Of.findOutputNodes(this.currentGlParentNode());e.length>1&&this.currentGlParentNode().states.error.set(\\\\\\\"only one output node allowed\\\\\\\");const n=Of.findVaryingNodes(this.currentGlParentNode()),i=e.concat(n);this.set_root_nodes(i),this._update_shaders();const r=this._shaders_by_name.get(xf.VERTEX),s=this._shaders_by_name.get(xf.FRAGMENT);r&&s&&(t.vertexShader=r,t.fragmentShader=this.processFilterFragmentShader(s),this.addUniforms(t.uniforms),t.needsUpdate=!0);const o=this.currentGlParentNode().scene();this.uniformsTimeDependent()?o.uniformsController.addTimeDependentUniformOwner(t.uuid,t.uniforms):o.uniformsController.removeTimeDependentUniformOwner(t.uuid),this.uniforms_resolution_dependent()?o.uniformsController.addResolutionDependentUniformOwner(t.uuid,t.uniforms):o.uniformsController.removeResolutionDependentUniformOwner(t.uuid),t.customMaterials&&this.compileCustomMaterials(t)}_update_shaders(){this._shaders_by_name=new Map,this._lines=new Map;for(let t of this.shaderNames()){const e=this._template_shader_for_shader_name(t);e&&this._lines.set(t,e.split(\\\\\\\"\\\\n\\\\\\\"))}this._root_nodes.length>0&&(this.build_code_from_nodes(this._root_nodes),this._build_lines());for(let t of this.shaderNames()){const e=this._lines.get(t);e&&this._shaders_by_name.set(t,e.join(\\\\\\\"\\\\n\\\\\\\"))}}shadow_assembler_class_by_custom_name(){return{}}add_output_body_line(t,e,n){var i;const r=t.io.inputs.named_input(n),s=t.variableForInput(n),o=this.variable_config(n);let a=null;if(r)a=uf.vector3(s);else if(o.default_from_attribute()){const r=t.io.inputs.namedInputConnectionPointsByName(n);if(r){const s=r.type(),o=null===(i=this.globals_handler)||void 0===i?void 0:i.read_attribute(t,s,n,e);o&&(a=o)}}else{const t=o.default();t&&(a=t)}if(a){const n=o.prefix(),i=o.suffix(),r=o.if_condition();r&&e.addBodyLines(t,[`#if ${r}`]),e.addBodyLines(t,[`${n}${a}${i}`]);const s=o.postLines();s&&e.addBodyLines(t,s),r&&e.addBodyLines(t,[\\\\\\\"#endif\\\\\\\"])}}set_node_lines_output(t,e){var n;const i=e.current_shader_name,r=null===(n=this.shader_config(i))||void 0===n?void 0:n.input_names();if(r)for(let n of r)t.io.inputs.has_named_input(n)&&this.add_output_body_line(t,e,n)}set_node_lines_attribute(t,e){var n;const i=t.gl_type(),r=null===(n=this.globals_handler)||void 0===n?void 0:n.read_attribute(t,i,t.attribute_name,e),s=t.glVarName(t.output_name);e.addBodyLines(t,[`${i} ${s} = ${r}`])}handle_globals_output_name(t){var e;switch(t.output_name){case k2.TIME:return void this.handleTime(t);case k2.RESOLUTION:return void this.handle_resolution(t);case k2.MV_POSITION:return void this.handle_mvPosition(t);case k2.GL_POSITION:return void this.handle_gl_Position(t);case k2.GL_FRAGCOORD:return void this.handle_gl_FragCoord(t);case k2.GL_POINTCOORD:return void this.handle_gl_PointCoord(t);default:null===(e=this.globals_handler)||void 0===e||e.handle_globals_node(t.globals_node,t.output_name,t.shaders_collection_controller)}}handleTime(t){const e=new Af(t.globals_node,Do.FLOAT,t.output_name);t.globals_shader_name&&u.pushOnArrayAtEntry(t.definitions_by_shader_name,t.globals_shader_name,e);const n=`float ${t.var_name} = ${t.output_name}`;for(let i of t.dependencies)u.pushOnArrayAtEntry(t.definitions_by_shader_name,i,e),u.pushOnArrayAtEntry(t.body_lines_by_shader_name,i,n);t.body_lines.push(n),this.setUniformsTimeDependent()}handle_resolution(t){t.body_lines.push(`vec2 ${t.var_name} = resolution`);const e=new Af(t.globals_node,Do.VEC2,t.output_name);t.globals_shader_name&&u.pushOnArrayAtEntry(t.definitions_by_shader_name,t.globals_shader_name,e);for(let n of t.dependencies)u.pushOnArrayAtEntry(t.definitions_by_shader_name,n,e);this.set_uniforms_resolution_dependent()}handle_mvPosition(t){if(t.shader_name==xf.FRAGMENT){const e=t.globals_node,n=t.shaders_collection_controller,i=new Ef(e,Do.VEC4,t.var_name),r=`${t.var_name} = modelViewMatrix * vec4(position, 1.0)`;n.addDefinitions(e,[i],xf.VERTEX),n.addBodyLines(e,[r],xf.VERTEX),n.addDefinitions(e,[i])}}handle_gl_Position(t){if(t.shader_name==xf.FRAGMENT){const e=t.globals_node,n=t.shaders_collection_controller,i=new Ef(e,Do.VEC4,t.var_name),r=`${t.var_name} = projectionMatrix * modelViewMatrix * vec4(position, 1.0)`;n.addDefinitions(e,[i],xf.VERTEX),n.addBodyLines(e,[r],xf.VERTEX),n.addDefinitions(e,[i])}}handle_gl_FragCoord(t){t.shader_name==xf.FRAGMENT&&t.body_lines.push(`vec4 ${t.var_name} = gl_FragCoord`)}handle_gl_PointCoord(t){t.shader_name==xf.FRAGMENT?t.body_lines.push(`vec2 ${t.var_name} = gl_PointCoord`):t.body_lines.push(`vec2 ${t.var_name} = vec2(0.0, 0.0)`)}set_node_lines_globals(t,e){const n=[],i=e.current_shader_name,r=this.shader_config(i);if(!r)return;const s=r.dependencies(),o=new Map,a=new Map,l=this.used_output_names_for_shader(t,i);for(let r of l){const l=t.glVarName(r),c=e.current_shader_name,u={globals_node:t,shaders_collection_controller:e,output_name:r,globals_shader_name:c,definitions_by_shader_name:o,body_lines:n,var_name:l,shader_name:i,dependencies:s,body_lines_by_shader_name:a};this.handle_globals_output_name(u)}o.forEach(((n,i)=>{e.addDefinitions(t,n,i)})),a.forEach(((n,i)=>{e.addBodyLines(t,n,i)})),e.addBodyLines(t,n)}used_output_names_for_shader(t,e){const n=t.io.outputs.used_output_names(),i=[];for(let t of n)e==xf.VERTEX&&B2.includes(t)||i.push(t);return i}}const U2=new Map([[xf.VERTEX,\\\\\\\"#include <begin_vertex>\\\\\\\"],[xf.FRAGMENT,\\\\\\\"vec4 diffuseColor = vec4( 1.0 );\\\\\\\"]]);const G2=new Map([[xf.VERTEX,\\\\\\\"#include <begin_vertex>\\\\\\\"],[xf.FRAGMENT,\\\\\\\"vec4 diffuseColor = vec4( 1.0 );\\\\\\\"]]);var V2=\\\\\\\"uniform float mNear;\\\\nuniform float mFar;\\\\n\\\\nvarying float vViewZDepth;\\\\n\\\\nvoid main() {\\\\n\\\\n\\\\tfloat color = 1.0 - smoothstep( mNear, mFar, vViewZDepth );\\\\n\\\\tgl_FragColor = vec4( vec3( color ), 1.0 );\\\\n\\\\n}\\\\n\\\\\\\";const H2=new Map([[xf.VERTEX,\\\\\\\"// INSERT DEFINES\\\\\\\"]]),j2=new Map([[xf.VERTEX,\\\\\\\"// INSERT BODY\\\\\\\"]]);const W2=new Map([]);W2.set(D2.DISTANCE,class extends z2{templateShader(){const t=V.distanceRGBA;return{vertexShader:t.vertexShader,fragmentShader:t.fragmentShader,uniforms:t.uniforms}}insert_body_after(t){return U2.get(t)}createMaterial(){const t=this.templateShader();return new F({defines:{DEPTH_PACKING:[w.Hb,w.j][0]},uniforms:I.clone(t.uniforms),vertexShader:t.vertexShader,fragmentShader:t.fragmentShader})}}),W2.set(D2.DEPTH,class extends z2{templateShader(){const t=V.depth;return{vertexShader:t.vertexShader,fragmentShader:t.fragmentShader,uniforms:t.uniforms}}insert_body_after(t){return G2.get(t)}createMaterial(){const t=this.templateShader();return new F({defines:{DEPTH_PACKING:[w.Hb,w.j][0]},uniforms:I.clone(t.uniforms),vertexShader:t.vertexShader,fragmentShader:t.fragmentShader})}}),W2.set(D2.DEPTH_DOF,class extends z2{templateShader(){return{vertexShader:\\\\\\\"#include <common>\\\\n\\\\nvarying float vViewZDepth;\\\\n\\\\n// INSERT DEFINES\\\\n\\\\n\\\\n\\\\nvoid main() {\\\\n\\\\n\\\\t// INSERT BODY\\\\n\\\\n\\\\n\\\\t#include <project_vertex>\\\\n\\\\n\\\\tvViewZDepth = - mvPosition.z;\\\\n}\\\\\\\",fragmentShader:V2,uniforms:{mNear:{value:0},mFar:{value:10}}}}insert_define_after(t){return H2.get(t)}insert_body_after(t){return j2.get(t)}createMaterial(){const t=this.templateShader();return new F({uniforms:I.clone(t.uniforms),vertexShader:t.vertexShader,fragmentShader:t.fragmentShader})}});class q2 extends z2{custom_assembler_class_by_custom_name(){return W2}}class X2 extends q2{templateShader(){const t=V.basic;return{vertexShader:t.vertexShader,fragmentShader:t.fragmentShader,uniforms:t.uniforms}}createMaterial(){const t=this.templateShader(),e=new F({lights:!1,uniforms:I.clone(t.uniforms),vertexShader:t.vertexShader,fragmentShader:t.fragmentShader});return this._addCustomMaterials(e),e}}class Y2 extends q2{templateShader(){const t=V.lambert;return{vertexShader:t.vertexShader,fragmentShader:t.fragmentShader,uniforms:t.uniforms}}createMaterial(){const t=this.templateShader(),e=new F({lights:!0,uniforms:I.clone(t.uniforms),vertexShader:t.vertexShader,fragmentShader:t.fragmentShader});return this._addCustomMaterials(e),e}}class $2 extends q2{templateShader(){const t=V.phong;return{vertexShader:t.vertexShader,fragmentShader:t.fragmentShader,uniforms:t.uniforms}}createMaterial(){const t=this.templateShader(),e=new F({lights:!0,uniforms:I.clone(t.uniforms),vertexShader:t.vertexShader,fragmentShader:t.fragmentShader});return this._addCustomMaterials(e),e}}var J2=\\\\\\\"SSSModel(/*isActive*/false,/*color*/vec3(1.0, 1.0, 1.0), /*thickness*/0.1, /*power*/2.0, /*scale*/16.0, /*distortion*/0.1,/*ambient*/0.4,/*attenuation*/0.8 )\\\\\\\";class Z2 extends q2{constructor(t){super(t),this._gl_parent_node=t,this._addFilterFragmentShaderCallback(\\\\\\\"MeshStandardBuilderMatNode\\\\\\\",Z2.filterFragmentShader)}isPhysical(){return!1}templateShader(){const t=this.isPhysical()?V.physical:V.standard;return{vertexShader:t.vertexShader,fragmentShader:t.fragmentShader,uniforms:t.uniforms}}static filterFragmentShader(t){return t=(t=(t=t.replace(\\\\\\\"#include <metalnessmap_fragment>\\\\\\\",\\\\\\\"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 <roughnessmap_fragment>\\\\\\\",\\\\\\\"float roughnessFactor = roughness * POLY_roughness;\\\\n\\\\n#ifdef USE_ROUGHNESSMAP\\\\n\\\\n\\\\tvec4 texelRoughness = texture2D( roughnessMap, vUv );\\\\n\\\\n\\\\t// reads channel G, compatible with a combined OcclusionRoughnessMetallic (RGB) texture\\\\n\\\\troughnessFactor *= texelRoughness.g;\\\\n\\\\n#endif\\\\n\\\\\\\")).replace(\\\\\\\"vec3 totalEmissiveRadiance = emissive;\\\\\\\",\\\\\\\"vec3 totalEmissiveRadiance = emissive * POLY_emissive;\\\\\\\"),Z2.USE_SSS&&(t=(t=t.replace(/void main\\\\s?\\\\(\\\\) {/,\\\\\\\"struct SSSModel {\\\\n\\\\tbool isActive;\\\\n\\\\tvec3 color;\\\\n\\\\tfloat thickness;\\\\n\\\\tfloat power;\\\\n\\\\tfloat scale;\\\\n\\\\tfloat distortion;\\\\n\\\\tfloat ambient;\\\\n\\\\tfloat attenuation;\\\\n};\\\\n\\\\nvoid RE_Direct_Scattering(\\\\n\\\\tconst in IncidentLight directLight,\\\\n\\\\tconst in GeometricContext geometry,\\\\n\\\\tconst in SSSModel sssModel,\\\\n\\\\tinout ReflectedLight reflectedLight\\\\n\\\\t){\\\\n\\\\tvec3 scatteringHalf = normalize(directLight.direction + (geometry.normal * sssModel.distortion));\\\\n\\\\tfloat scatteringDot = pow(saturate(dot(geometry.viewDir, -scatteringHalf)), sssModel.power) * sssModel.scale;\\\\n\\\\tvec3 scatteringIllu = (scatteringDot + sssModel.ambient) * (sssModel.color * (1.0-sssModel.thickness));\\\\n\\\\treflectedLight.directDiffuse += scatteringIllu * sssModel.attenuation * directLight.color;\\\\n}\\\\n\\\\nvoid main() {\\\\\\\")).replace(\\\\\\\"#include <lights_fragment_begin>\\\\\\\",\\\\\\\"#include <lights_fragment_begin>\\\\nif(POLY_SSSModel.isActive){\\\\n\\\\tRE_Direct_Scattering(directLight, geometry, POLY_SSSModel, reflectedLight);\\\\n}\\\\n\\\\n\\\\\\\")),t}createMaterial(){const t=this.templateShader(),e={lights:!0,extensions:{derivatives:!0},uniforms:I.clone(t.uniforms),vertexShader:t.vertexShader,fragmentShader:t.fragmentShader},n=new F(e);return this.isPhysical()&&(n.defines.PHYSICAL=!0),this._addCustomMaterials(n),n}add_output_inputs(t){const e=F2.output_input_connection_points();e.push(new Vo(\\\\\\\"metalness\\\\\\\",Do.FLOAT,1)),e.push(new Vo(\\\\\\\"roughness\\\\\\\",Do.FLOAT,1)),e.push(new Vo(\\\\\\\"emissive\\\\\\\",Do.VEC3,[1,1,1])),Z2.USE_SSS&&e.push(new Vo(\\\\\\\"SSSModel\\\\\\\",Do.SSS_MODEL,J2)),t.io.inputs.setNamedInputConnectionPoints(e)}create_shader_configs(){return[new T2(xf.VERTEX,[\\\\\\\"position\\\\\\\",\\\\\\\"normal\\\\\\\",\\\\\\\"uv\\\\\\\"],[]),new T2(xf.FRAGMENT,[\\\\\\\"color\\\\\\\",\\\\\\\"alpha\\\\\\\",\\\\\\\"metalness\\\\\\\",\\\\\\\"roughness\\\\\\\",\\\\\\\"emissive\\\\\\\",\\\\\\\"SSSModel\\\\\\\"],[xf.VERTEX])]}create_variable_configs(){const t=F2.create_variable_configs();return t.push(new A2(\\\\\\\"metalness\\\\\\\",{default:\\\\\\\"1.0\\\\\\\",prefix:\\\\\\\"float POLY_metalness = \\\\\\\"})),t.push(new A2(\\\\\\\"roughness\\\\\\\",{default:\\\\\\\"1.0\\\\\\\",prefix:\\\\\\\"float POLY_roughness = \\\\\\\"})),t.push(new A2(\\\\\\\"emissive\\\\\\\",{default:\\\\\\\"vec3(1.0, 1.0, 1.0)\\\\\\\",prefix:\\\\\\\"vec3 POLY_emissive = \\\\\\\"})),Z2.USE_SSS&&t.push(new A2(\\\\\\\"SSSModel\\\\\\\",{default:J2,prefix:\\\\\\\"SSSModel POLY_SSSModel = \\\\\\\"})),t}}Z2.USE_SSS=!0;class Q2 extends Z2{isPhysical(){return!0}}const K2=new Map([[xf.VERTEX,\\\\\\\"// INSERT DEFINES\\\\\\\"]]),t3=new Map([[xf.VERTEX,\\\\\\\"// INSERT BODY\\\\\\\"]]);const e3=new Map([[xf.VERTEX,\\\\\\\"// INSERT DEFINES\\\\\\\"]]),n3=new Map([[xf.VERTEX,\\\\\\\"// INSERT BODY\\\\\\\"]]);const i3=new Map([[xf.VERTEX,[\\\\\\\"#include <begin_vertex>\\\\\\\",\\\\\\\"gl_PointSize = size;\\\\\\\"]],[xf.FRAGMENT,[]]]),r3=new Map;r3.set(D2.DISTANCE,class extends z2{templateShader(){const t=V.distanceRGBA,e=I.clone(t.uniforms);return e.size={value:1},e.scale={value:1},{vertexShader:\\\\\\\"uniform float size;\\\\nuniform float scale;\\\\n#define DISTANCE\\\\nvarying vec3 vWorldPosition;\\\\n#include <common>\\\\n#include <clipping_planes_pars_vertex>\\\\nvarying float vViewZDepth;\\\\n\\\\n// INSERT DEFINES\\\\n\\\\n\\\\n\\\\nvoid main() {\\\\n\\\\n\\\\t// INSERT BODY\\\\n\\\\n\\\\n\\\\t#include <project_vertex>\\\\n\\\\t#include <worldpos_vertex>\\\\n\\\\t#include <clipping_planes_vertex>\\\\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 <common>\\\\n// #include <uv_pars_vertex>\\\\n// #include <displacementmap_pars_vertex>\\\\n// #include <morphtarget_pars_vertex>\\\\n// #include <skinning_pars_vertex>\\\\n// #include <clipping_planes_pars_vertex>\\\\n// void main() {\\\\n// \\\\t#include <uv_vertex>\\\\n// \\\\t#include <skinbase_vertex>\\\\n// \\\\t#ifdef USE_DISPLACEMENTMAP\\\\n// \\\\t\\\\t#include <beginnormal_vertex>\\\\n// \\\\t\\\\t#include <morphnormal_vertex>\\\\n// \\\\t\\\\t#include <skinnormal_vertex>\\\\n// \\\\t#endif\\\\n// \\\\t#include <begin_vertex>\\\\n// \\\\t#include <morphtarget_vertex>\\\\n// \\\\t#include <skinning_vertex>\\\\n// \\\\t#include <displacementmap_vertex>\\\\n// \\\\t#include <project_vertex>\\\\n// \\\\t#include <worldpos_vertex>\\\\n// \\\\t#include <clipping_planes_vertex>\\\\n// \\\\tvWorldPosition = worldPosition.xyz;\\\\n// }\\\\n\\\\n\\\\n\\\\\\\",fragmentShader:t.fragmentShader,uniforms:e}}insert_define_after(t){return K2.get(t)}insert_body_after(t){return t3.get(t)}createMaterial(){const t=this.templateShader();return new F({defines:{USE_SIZEATTENUATION:1,DEPTH_PACKING:[w.Hb,w.j][0]},uniforms:I.clone(t.uniforms),vertexShader:t.vertexShader,fragmentShader:t.fragmentShader})}}),r3.set(D2.DEPTH_DOF,class extends z2{templateShader(){return{vertexShader:\\\\\\\"uniform float size;\\\\nuniform float scale;\\\\n#include <common>\\\\n\\\\nvarying float vViewZDepth;\\\\n\\\\n// INSERT DEFINES\\\\n\\\\n\\\\n\\\\nvoid main() {\\\\n\\\\n\\\\t// INSERT BODY\\\\n\\\\n\\\\n\\\\t#include <project_vertex>\\\\n\\\\n\\\\tvViewZDepth = - mvPosition.z;\\\\n\\\\t#ifdef USE_SIZEATTENUATION\\\\n\\\\t\\\\tbool isPerspective = ( projectionMatrix[ 2 ][ 3 ] == - 1.0 );\\\\n\\\\t\\\\tif ( isPerspective ) gl_PointSize *= ( scale / - mvPosition.z );\\\\n\\\\t#endif\\\\n\\\\n}\\\\n\\\\n\\\\\\\",fragmentShader:V2,uniforms:{size:{value:1},scale:{value:1},mNear:{value:0},mFar:{value:10}}}}insert_define_after(t){return e3.get(t)}insert_body_after(t){return n3.get(t)}createMaterial(){const t=this.templateShader();return new F({depthTest:!0,defines:{USE_SIZEATTENUATION:1},uniforms:I.clone(t.uniforms),vertexShader:t.vertexShader,fragmentShader:t.fragmentShader})}});class s3 extends z2{custom_assembler_class_by_custom_name(){return r3}templateShader(){const t=V.points;return{vertexShader:t.vertexShader,fragmentShader:t.fragmentShader,uniforms:t.uniforms}}createMaterial(){const t=this.templateShader(),e=new F({transparent:!0,fog:!0,defines:{USE_SIZEATTENUATION:1},uniforms:I.clone(t.uniforms),vertexShader:t.vertexShader,fragmentShader:t.fragmentShader});return this._addCustomMaterials(e),e}add_output_inputs(t){const e=F2.output_input_connection_points();e.push(new Vo(\\\\\\\"gl_PointSize\\\\\\\",Do.FLOAT)),t.io.inputs.setNamedInputConnectionPoints(e)}create_globals_node_output_connections(){return F2.create_globals_node_output_connections().concat([new Vo(k2.GL_POINTCOORD,Do.VEC2)])}create_shader_configs(){return[new T2(xf.VERTEX,[\\\\\\\"position\\\\\\\",\\\\\\\"normal\\\\\\\",\\\\\\\"uv\\\\\\\",\\\\\\\"gl_PointSize\\\\\\\"],[]),new T2(xf.FRAGMENT,[\\\\\\\"color\\\\\\\",\\\\\\\"alpha\\\\\\\"],[xf.VERTEX])]}create_variable_configs(){return F2.create_variable_configs().concat([new A2(\\\\\\\"gl_PointSize\\\\\\\",{default:\\\\\\\"1.0\\\\\\\",prefix:\\\\\\\"gl_PointSize = \\\\\\\",suffix:\\\\\\\" * size * 10.0\\\\\\\"})])}lines_to_remove(t){return i3.get(t)}}const o3=new Map([[xf.VERTEX,\\\\\\\"// INSERT DEFINES\\\\\\\"]]),a3=new Map([[xf.VERTEX,\\\\\\\"// INSERT BODY\\\\\\\"]]);const l3=new Map([]);l3.set(D2.DEPTH_DOF,class extends z2{templateShader(){return{vertexShader:\\\\\\\"uniform float scale;\\\\nattribute float lineDistance;\\\\nvarying float vLineDistance;\\\\n#include <common>\\\\n\\\\nvarying float vViewZDepth;\\\\n\\\\n// INSERT DEFINES\\\\n\\\\n\\\\n\\\\nvoid main() {\\\\n\\\\n\\\\t// INSERT BODY\\\\n\\\\n\\\\n\\\\tvLineDistance = scale * lineDistance;\\\\n\\\\tgl_Position = projectionMatrix * mvPosition;\\\\n\\\\n\\\\tvViewZDepth = - mvPosition.z;\\\\n\\\\n\\\\n}\\\\n\\\\n\\\\n\\\\n\\\\\\\",fragmentShader:V2,uniforms:{scale:{value:1},mNear:{value:0},mFar:{value:10}}}}insert_define_after(t){return o3.get(t)}insert_body_after(t){return a3.get(t)}createMaterial(){const t=this.templateShader();return new F({depthTest:!0,linewidth:100,uniforms:I.clone(t.uniforms),vertexShader:t.vertexShader,fragmentShader:t.fragmentShader})}});const c3=new Map([[xf.VERTEX,[\\\\\\\"#include <begin_vertex>\\\\\\\",\\\\\\\"#include <project_vertex>\\\\\\\"]],[xf.FRAGMENT,[]]]);class u3 extends z2{templateShader(){const t=V.dashed;return{vertexShader:t.vertexShader,fragmentShader:t.fragmentShader,uniforms:t.uniforms}}createMaterial(){const t=this.templateShader(),e=new F({depthTest:!0,alphaTest:.5,linewidth:1,uniforms:I.clone(t.uniforms),vertexShader:t.vertexShader,fragmentShader:t.fragmentShader});return this._addCustomMaterials(e),e}custom_assembler_class_by_custom_name(){return console.log(\\\\\\\"custom_assembler_class_by_custom_name\\\\\\\",l3),l3}create_shader_configs(){return[new T2(xf.VERTEX,[\\\\\\\"position\\\\\\\",\\\\\\\"uv\\\\\\\"],[]),new T2(xf.FRAGMENT,[\\\\\\\"color\\\\\\\",\\\\\\\"alpha\\\\\\\"],[xf.VERTEX])]}static output_input_connection_points(){return[new Vo(\\\\\\\"position\\\\\\\",Do.VEC3),new Vo(\\\\\\\"color\\\\\\\",Do.VEC3),new Vo(\\\\\\\"alpha\\\\\\\",Do.FLOAT),new Vo(\\\\\\\"uv\\\\\\\",Do.VEC2)]}add_output_inputs(t){t.io.inputs.setNamedInputConnectionPoints(u3.output_input_connection_points())}static create_globals_node_output_connections(){return[new Vo(\\\\\\\"position\\\\\\\",Do.VEC3),new Vo(\\\\\\\"color\\\\\\\",Do.VEC3),new Vo(\\\\\\\"uv\\\\\\\",Do.VEC2),new Vo(\\\\\\\"gl_FragCoord\\\\\\\",Do.VEC4),new Vo(\\\\\\\"resolution\\\\\\\",Do.VEC2),new Vo(\\\\\\\"time\\\\\\\",Do.FLOAT)]}create_globals_node_output_connections(){return u3.create_globals_node_output_connections()}create_variable_configs(){return[new A2(\\\\\\\"position\\\\\\\",{default:\\\\\\\"vec3( position )\\\\\\\",prefix:\\\\\\\"vec3 transformed = \\\\\\\",suffix:\\\\\\\";vec4 mvPosition = vec4( transformed, 1.0 ); gl_Position = projectionMatrix * modelViewMatrix * mvPosition;\\\\\\\"}),new A2(\\\\\\\"color\\\\\\\",{prefix:\\\\\\\"diffuseColor.xyz = \\\\\\\"}),new A2(\\\\\\\"alpha\\\\\\\",{prefix:\\\\\\\"diffuseColor.w = \\\\\\\"}),new A2(\\\\\\\"uv\\\\\\\",{prefix:\\\\\\\"vUv = \\\\\\\",if:Sf.IF_RULE.uv})]}lines_to_remove(t){return c3.get(t)}}class h3 extends F2{templateShader(){}_template_shader_for_shader_name(t){return\\\\\\\"#include <common>\\\\n\\\\n// INSERT DEFINE\\\\n\\\\nvoid main() {\\\\n\\\\n\\\\tvec2 particleUV = (gl_FragCoord.xy / resolution.xy);\\\\n\\\\n\\\\t// INSERT BODY\\\\n\\\\n}\\\\\\\"}compile(){this.setup_shader_names_and_variables(),this.update_shaders()}root_nodes_by_shader_name(t){var e,n;const i=[];for(let r of this._root_nodes)switch(r.type()){case UI.type():i.push(r);break;case gf.type():{const s=r.attribute_name,o=null===(e=this._texture_allocations_controller)||void 0===e?void 0:e.variable(s);if(o&&o.allocation()){(null===(n=o.allocation())||void 0===n?void 0:n.shaderName())==t&&i.push(r)}break}}return i}leaf_nodes_by_shader_name(t){var e,n;const i=[];for(let r of this._leaf_nodes)switch(r.type()){case HP.type():i.push(r);break;case gf.type():{const s=r.attribute_name,o=null===(e=this._texture_allocations_controller)||void 0===e?void 0:e.variable(s);if(o&&o.allocation()){(null===(n=o.allocation())||void 0===n?void 0:n.shaderName())==t&&i.push(r)}break}}return i}setup_shader_names_and_variables(){var t;const e=new O2(this.currentGlParentNode(),this.shaderNames(),((t,e)=>this.input_names_for_shader_name(t,e)));this._leaf_nodes=e.leaves_from_nodes(this._root_nodes),this._texture_allocations_controller=new dQ,this._texture_allocations_controller.allocateConnectionsFromRootNodes(this._root_nodes,this._leaf_nodes),this.globals_handler&&(null===(t=this.globals_handler)||void 0===t||t.set_texture_allocations_controller(this._texture_allocations_controller)),this._reset_shader_configs()}update_shaders(){this._shaders_by_name.clear(),this._lines.clear();for(let t of this.shaderNames()){const e=this._template_shader_for_shader_name(t);this._lines.set(t,e.split(\\\\\\\"\\\\n\\\\\\\"))}this._root_nodes.length>0&&(this._resetCodeBuilder(),this.build_code_from_nodes(this._root_nodes),this._build_lines());for(let t of this.shaderNames()){const e=this._lines.get(t);e&&this._shaders_by_name.set(t,e.join(\\\\\\\"\\\\n\\\\\\\"))}}add_output_inputs(t){t.io.inputs.setNamedInputConnectionPoints([new Vo(\\\\\\\"position\\\\\\\",Do.VEC3),new Vo(\\\\\\\"velocity\\\\\\\",Do.VEC3)])}add_globals_outputs(t){t.io.outputs.setNamedOutputConnectionPoints([new Vo(\\\\\\\"position\\\\\\\",Do.VEC3),new Vo(\\\\\\\"velocity\\\\\\\",Do.VEC3),new Vo(\\\\\\\"time\\\\\\\",Do.FLOAT)])}allow_attribute_exports(){return!0}textureAllocationsController(){return this._texture_allocations_controller=this._texture_allocations_controller||new dQ}create_shader_configs(){var t;return(null===(t=this._texture_allocations_controller)||void 0===t?void 0:t.createShaderConfigs())||[]}create_variable_configs(){return[]}shaderNames(){return this.textureAllocationsController().shaderNames()||[]}input_names_for_shader_name(t,e){return this.textureAllocationsController().inputNamesForShaderName(t,e)||[]}insert_define_after(t){return\\\\\\\"// INSERT DEFINE\\\\\\\"}insert_body_after(t){return\\\\\\\"// INSERT BODY\\\\\\\"}lines_to_remove(t){return[\\\\\\\"// INSERT DEFINE\\\\\\\",\\\\\\\"// INSERT BODY\\\\\\\"]}add_export_body_line(t,e,n,i,r){var s;if(n){const n=t.variableForInput(e),o=uf.vector3(n);if(o){const e=this.textureAllocationsController().variable(i),n=r.current_shader_name;if(e&&(null===(s=e.allocation())||void 0===s?void 0:s.shaderName())==n){const i=`gl_FragColor.${e.component()} = ${o}`;r.addBodyLines(t,[i],n)}}}}set_node_lines_output(t,e){const n=e.current_shader_name,i=this.textureAllocationsController().inputNamesForShaderName(t,n);if(i)for(let n of i){const i=t.io.inputs.named_input(n);if(i){const r=n;this.add_export_body_line(t,n,i,r,e)}}}set_node_lines_attribute(t,e){var n,i;if(t.isImporting()){const r=t.gl_type(),s=t.attribute_name,o=null===(n=this.globals_handler)||void 0===n?void 0:n.read_attribute(t,r,s,e),a=t.glVarName(t.output_name),l=`${r} ${a} = ${o}`;e.addBodyLines(t,[l]);const c=this.textureAllocationsController().variable(s),u=e.current_shader_name;if(c&&(null===(i=c.allocation())||void 0===i?void 0:i.shaderName())==u){const n=this.textureAllocationsController().variable(s);if(n){const i=`gl_FragColor.${n.component()} = ${a}`;e.addBodyLines(t,[i])}}}if(t.isExporting()){const n=t.connected_input_node();if(n){const i=t.attribute_name;this.add_export_body_line(t,t.input_name,n,i,e)}}}set_node_lines_globals(t,e){for(let n of t.io.outputs.used_output_names())switch(n){case\\\\\\\"time\\\\\\\":this._handle_globals_time(t,n,e);break;default:this._handle_globals_default(t,n,e)}}_handle_globals_time(t,e,n){const i=new Af(t,Do.FLOAT,e);n.addDefinitions(t,[i]);const r=`float ${t.glVarName(e)} = ${e}`;n.addBodyLines(t,[r]),this.setUniformsTimeDependent()}_handle_globals_default(t,e,n){var i;const r=t.io.outputs.namedOutputConnectionPointsByName(e);if(r){const s=r.type(),o=null===(i=this.globals_handler)||void 0===i?void 0:i.read_attribute(t,s,e,n);if(o){const i=`${s} ${t.glVarName(e)} = ${o}`;n.addBodyLines(t,[i])}}}}class d3 extends F2{templateShader(){return{fragmentShader:\\\\\\\"#include <common>\\\\n\\\\nuniform vec2 resolution;\\\\n\\\\n// INSERT DEFINE\\\\n\\\\nvoid main() {\\\\n\\\\n\\\\tvec4 diffuseColor = vec4(0.0,0.0,0.0,1.0);\\\\n\\\\n\\\\n\\\\t// INSERT BODY\\\\n\\\\n\\\\tgl_FragColor = vec4( diffuseColor );\\\\n}\\\\\\\",vertexShader:void 0,uniforms:void 0}}fragment_shader(){return this._shaders_by_name.get(xf.FRAGMENT)}uniforms(){return this._uniforms}update_fragment_shader(){this._lines=new Map,this._shaders_by_name=new Map;for(let t of this.shaderNames())if(t==xf.FRAGMENT){const e=this.templateShader().fragmentShader;this._lines.set(t,e.split(\\\\\\\"\\\\n\\\\\\\"))}this._root_nodes.length>0&&(this.build_code_from_nodes(this._root_nodes),this._build_lines()),this._uniforms=this._uniforms||{},this.addUniforms(this._uniforms);for(let t of this.shaderNames()){const e=this._lines.get(t);e&&this._shaders_by_name.set(t,e.join(\\\\\\\"\\\\n\\\\\\\"))}tg.handle_dependencies(this.currentGlParentNode(),this.uniformsTimeDependent(),this._uniforms)}add_output_inputs(t){t.io.inputs.setNamedInputConnectionPoints([new Vo(\\\\\\\"color\\\\\\\",Do.VEC3),new Vo(\\\\\\\"alpha\\\\\\\",Do.FLOAT)])}add_globals_outputs(t){t.io.outputs.setNamedOutputConnectionPoints([new Vo(\\\\\\\"gl_FragCoord\\\\\\\",Do.VEC2),new Vo(\\\\\\\"time\\\\\\\",Do.FLOAT)])}create_shader_configs(){return[new T2(xf.FRAGMENT,[\\\\\\\"color\\\\\\\",\\\\\\\"alpha\\\\\\\"],[])]}create_variable_configs(){return[new A2(\\\\\\\"color\\\\\\\",{prefix:\\\\\\\"diffuseColor.xyz = \\\\\\\"}),new A2(\\\\\\\"alpha\\\\\\\",{prefix:\\\\\\\"diffuseColor.a = \\\\\\\",default:\\\\\\\"1.0\\\\\\\"})]}insert_define_after(t){return\\\\\\\"// INSERT DEFINE\\\\\\\"}insert_body_after(t){return\\\\\\\"// INSERT BODY\\\\\\\"}lines_to_remove(t){return[\\\\\\\"// INSERT DEFINE\\\\\\\",\\\\\\\"// INSERT BODY\\\\\\\"]}handle_gl_FragCoord(t,e,n){\\\\\\\"fragment\\\\\\\"==e&&t.push(`vec2 ${n} = vec2(gl_FragCoord.x / resolution.x, gl_FragCoord.y / resolution.y)`)}set_node_lines_output(t,e){const n=this.input_names_for_shader_name(t,e.current_shader_name);if(n)for(let i of n){if(t.io.inputs.named_input(i)){const n=t.variableForInput(i);let r;\\\\\\\"color\\\\\\\"==i&&(r=`diffuseColor.xyz = ${uf.any(n)}`),\\\\\\\"alpha\\\\\\\"==i&&(r=`diffuseColor.a = ${uf.any(n)}`),r&&e.addBodyLines(t,[r])}}}set_node_lines_globals(t,e){const n=e.current_shader_name;if(!this.shader_config(n))return;const i=[],r=[];for(let e of t.io.outputs.used_output_names()){const s=t.glVarName(e);switch(e){case\\\\\\\"time\\\\\\\":r.push(new Af(t,Do.FLOAT,e)),i.push(`float ${s} = ${e}`),this.setUniformsTimeDependent();break;case\\\\\\\"gl_FragCoord\\\\\\\":this.handle_gl_FragCoord(i,n,s)}}e.addDefinitions(t,r,n),e.addBodyLines(t,i)}}const p3=new Map([]);class _3 extends z2{custom_assembler_class_by_custom_name(){return p3}}const m3=new Map([[xf.VERTEX,\\\\\\\"// start builder body code\\\\\\\"],[xf.FRAGMENT,\\\\\\\"// start builder body code\\\\\\\"]]),f3=new Map([[xf.FRAGMENT,[]]]);class g3 extends _3{templateShader(){return{vertexShader:jB,fragmentShader:WB,uniforms:I.clone(qB)}}createMaterial(){const t=this.templateShader(),e=new F({vertexShader:t.vertexShader,fragmentShader:t.fragmentShader,side:w.H,transparent:!0,depthTest:!0,uniforms:I.clone(t.uniforms)});return fs.add_user_data_render_hook(e,$B.render_hook.bind($B)),this._addCustomMaterials(e),e}add_output_inputs(t){t.io.inputs.setNamedInputConnectionPoints([new Vo(\\\\\\\"density\\\\\\\",Do.FLOAT,1)])}static create_globals_node_output_connections(){return[new Vo(\\\\\\\"position\\\\\\\",Do.VEC3),new Vo(\\\\\\\"pos_normalized\\\\\\\",Do.VEC3),new Vo(\\\\\\\"time\\\\\\\",Do.FLOAT)]}create_globals_node_output_connections(){return g3.create_globals_node_output_connections()}insert_body_after(t){return m3.get(t)}lines_to_remove(t){return f3.get(t)}create_shader_configs(){return[new T2(xf.VERTEX,[],[]),new T2(xf.FRAGMENT,[\\\\\\\"density\\\\\\\"],[xf.VERTEX])]}static create_variable_configs(){return[new A2(\\\\\\\"position\\\\\\\",{}),new A2(\\\\\\\"density\\\\\\\",{prefix:\\\\\\\"density *= \\\\\\\"})]}create_variable_configs(){return g3.create_variable_configs()}set_node_lines_globals(t,e){const n=[],i=e.current_shader_name,r=this.shader_config(i);if(!r)return;const s=r.dependencies(),o=new Map,a=new Map;let l,c;for(let r of t.io.outputs.used_output_names()){const h=t.glVarName(r),d=e.current_shader_name;switch(r){case\\\\\\\"time\\\\\\\":l=new Af(t,Do.FLOAT,r),d&&u.pushOnArrayAtEntry(o,d,l),c=`float ${h} = ${r}`;for(let t of s)u.pushOnArrayAtEntry(o,t,l),u.pushOnArrayAtEntry(a,t,c);n.push(c),this.setUniformsTimeDependent();break;case\\\\\\\"position\\\\\\\":i==xf.FRAGMENT&&n.push(`vec3 ${h} = position_for_step`);break;case\\\\\\\"pos_normalized\\\\\\\":i==xf.FRAGMENT&&n.push(`vec3 ${h} = (position_for_step - u_BoundingBoxMax) / (u_BoundingBoxMax - u_BoundingBoxMin)`)}}o.forEach(((n,i)=>{e.addDefinitions(t,n,i)})),a.forEach(((n,i)=>{e.addBodyLines(t,n,i)})),e.addBodyLines(t,n)}}class v3{static async run(){this._started||(this._started=!0,class{static async run(t){(class{static run(t){t.registerNode(I_,Ql),t.registerNode(D_,ec),t.registerNode(B_,Ql),t.registerNode(U_,Ql),t.registerNode($_,Ql),t.registerNode(Z_,Zl),t.registerNode(K_,Ql),t.registerNode(em,ec),t.registerNode(im,tc),t.registerNode(um,tc),t.registerNode(dm,Ql),t.registerNode(_m,Zl),t.registerNode(wm,tc),t.registerNode(Em,Kl),t.registerNode(Mm,Kl),t.registerNode(Sm,Kl),t.registerNode(Cm,Kl),t.registerNode(Qm,Kl),t.registerNode(Km,Kl)}}).run(t),class{static run(t){t.registerNode(tg,nc),t.registerNode(Mg,ic),t.registerNode(Cg,ic),t.registerNode(Ig,ic),t.registerNode(Bg,ic),t.registerNode(Jg,ic),t.registerNode(ev,ic),t.registerNode(ov,rc),t.registerNode(lv,rc),t.registerNode(dv,rc),t.registerNode(_v,rc),t.registerNode(gv,nc),t.registerNode(xv,ic),t.registerNode(Tv,nc),t.registerNode(Mv,sc),t.registerNode(Cv,sc),t.registerNode(Sv,sc),t.registerNode(Nv,sc),t.registerNode(Lv,sc),t.registerNode(Ov,sc)}}.run(t),class{static run(t){t.registerNode(Fv,cc),t.registerNode(Bv,lc),t.registerNode(Uv,lc),t.registerNode(Hv,lc),t.registerNode(Kv,oc),t.registerNode(dy,oc),t.registerNode(hy,oc),t.registerNode(_y,lc),t.registerNode(yl,ac),t.registerNode(DN,oc),t.registerNode(ol,ac),t.registerNode(UN,lc),t.registerNode(VN,lc),t.registerNode(QN,oc),t.registerNode(qa,ac),t.registerNode(nL,cc),t.registerNode(oL,lc),t.registerNode(Za,ac),t.registerNode(NL,lc),t.registerNode(el,cc),t.registerNode(PL,cc),t.registerNode(jL,cc),t.registerNode(qL,lc),t.registerNode($L,lc),t.registerNode(bl,ac),t.registerNode(ZL,lc),t.registerNode(hl,ac),t.registerNode(tO,uc),t.registerNode(eO,uc),t.registerNode(nO,uc),t.registerNode(iO,uc),t.registerNode(rO,uc),t.registerNode(sO,uc)}}.run(t),class{static run(t){t.registerNode(jO,gc),t.registerNode(zR,vc),t.registerNode(WO,xc),t.registerNode(AR,gc),t.registerNode(jR,xc),t.registerNode(LR,fc),t.registerNode(qO,xc),t.registerNode(XO,xc),t.registerNode(gf,_c,{except:[`${Ki.COP}/builder`]}),t.registerNode(fO,dc),t.registerNode(YO,gc),t.registerNode(xR,gc),t.registerNode(YR,hc),t.registerNode(eP,fc),t.registerNode(nP,gc),t.registerNode(sP,_c),t.registerNode($O,xc),t.registerNode(lP,pc),t.registerNode(cP,gc),t.registerNode(JO,dc),t.registerNode(dP,pc),t.registerNode(hR,pc),t.registerNode(ER,gc),t.registerNode(dR,pc),t.registerNode(xP,gc),t.registerNode(ZO,gc),t.registerNode(QO,gc),t.registerNode(bR,pc),t.registerNode(TP,gc),t.registerNode(EP,gc),t.registerNode(SP,gc),t.registerNode(NP,gc),t.registerNode(lO,dc),t.registerNode(vO,dc),t.registerNode(xO,dc),t.registerNode(wO,dc),t.registerNode(KO,gc),t.registerNode(RP,hc),t.registerNode(GP,fc),t.registerNode(tR,gc),t.registerNode(HP,_c),t.registerNode(WP,hc),t.registerNode(XP,hc),t.registerNode(JP,fc),t.registerNode(QP,bc),t.registerNode(_O,dc),t.registerNode(hO,dc),t.registerNode(eR,gc),t.registerNode(sI,pc),t.registerNode(oI,pc),t.registerNode(lI,hc),t.registerNode(nR,gc),t.registerNode(iR,gc),t.registerNode(pR,gc),t.registerNode(uI,gc),t.registerNode(_R,gc),t.registerNode(mR,gc),t.registerNode(mI,gc),t.registerNode(dI,gc),t.registerNode(SR,gc),t.registerNode(vI,gc),t.registerNode(yI,gc),t.registerNode(BI,bc),t.registerNode(kI,pc),t.registerNode(rR,gc),t.registerNode(OR,fc),t.registerNode(UI,_c),t.registerNode(HI,_c),t.registerNode(fR,gc),t.registerNode(YI,yc),t.registerNode(QI,yc),t.registerNode(KI,yc),t.registerNode(tF,yc),t.registerNode(iF,_c),t.registerNode(oF,_c),t.registerNode(sR,dc),t.registerNode(gR,pc),t.registerNode(jI,pc),t.registerNode(lF,hc),t.registerNode(gF,pc),t.registerNode(yF,gc),t.registerNode(oR,gc),t.registerNode(aR,xc),t.registerNode(wR,gc),t.registerNode(bF,pc),t.registerNode(lR,gc),t.registerNode(XI,mc),t.registerNode(vR,pc),t.registerNode(kP,fc),t.registerNode(TF,fc,VF),t.registerNode(IP,fc,VF),t.registerNode(MR,gc),t.registerNode(EF,fc),t.registerNode(cR,xc),t.registerNode(SF,hc),t.registerNode(OF,_c),t.registerNode(IF,fc),t.registerNode(Lf,_c),t.registerNode(kF,_c),t.registerNode(NO,dc),t.registerNode(IO,dc),t.registerNode(LO,dc),t.registerNode(PO,dc),t.registerNode(FO,dc),t.registerNode(OO,dc),t.registerNode(RO,dc),t.registerNode(zF,pc),t.registerNode(GF,pc)}}.run(t),class{static run(t){t.registerNode(qF,wc),t.registerNode(YF,wc),t.registerNode(JF,wc),t.registerNode(iD,wc)}}.run(t),class{static run(t){t.registerNode(dD,Ac),t.registerNode(AD,Ac),t.registerNode(ek,Ec),t.registerNode(ak,Tc),t.registerNode(pk,Ec),t.registerNode(yk,Tc),t.registerNode(Ik,Ec),t.registerNode(Uk,Ec),t.registerNode(jk,Tc),t.registerNode(tB,Ec),t.registerNode(rB,Tc),t.registerNode(lB,Ec),t.registerNode(dB,Tc),t.registerNode(AB,Ec),t.registerNode(LB,Ec),t.registerNode(IB,Sc),t.registerNode(kB,Tc),t.registerNode(UB,Tc),t.registerNode(HB,Ec),t.registerNode(QB,Cc),t.registerNode(ez,Cc),t.registerNode(rz,Mc),t.registerNode(sz,Mc),t.registerNode(oz,Mc),t.registerNode(az,Mc),t.registerNode(lz,Mc),t.registerNode(cz,Mc)}}.run(t),class{static run(t){t.registerNode(gz,Pc),t.registerNode(Uz,Pc),t.registerNode(Zz,Pc),t.registerNode(rU,Pc),t.registerNode(uU,Pc),t.registerNode(vU,Pc),t.registerNode(CU,Lc),t.registerNode(PU,Fc),t.registerNode(HU,Nc),t.registerNode(YU,Rc),t.registerNode(ZU,Fc),t.registerNode(nG,Fc),t.registerNode(NG,Nc),t.registerNode(jG,Lc),t.registerNode(YG,Fc),t.registerNode(ZG,Nc),t.registerNode(lH,Oc),t.registerNode(dH,Oc),t.registerNode(mH,Oc),t.registerNode(yH,Ic),t.registerNode(xH,Ic),t.registerNode(bH,Ic),t.registerNode(wH,Ic),t.registerNode(TH,Ic),t.registerNode(AH,Ic)}}.run(t),class{static run(t){t.registerNode(RH,Qc),t.registerNode(DH,Qc),t.registerNode(zH,Zc),t.registerNode(VH,Zc),t.registerNode(WH,Kc),t.registerNode(XH,Kc),t.registerNode(JH,Kc),t.registerNode(QH,Kc),t.registerNode(aj,Qc),t.registerNode(rj,Qc),t.registerNode(hj,Qc),t.registerNode(_j,Kc),t.registerNode(gj,Zc),t.registerNode(yj,Jc),t.registerNode(bj,Kc),t.registerNode(Sj,Kc),t.registerNode(Nj,Kc),t.registerNode(Oj,Kc),t.registerNode(Ij,Qc),t.registerNode(kj,Qc),t.registerNode(zj,Kc),t.registerNode(Vj,Qc),t.registerNode(Wj,Zc),t.registerNode(Xj,Kc),t.registerNode(Zj,Jc),t.registerNode(eW,Qc),t.registerNode(iW,Jc),t.registerNode(oW,Qc),t.registerNode(cW,tu),t.registerNode(uW,tu),t.registerNode(hW,tu),t.registerNode(dW,tu),t.registerNode(pW,tu),t.registerNode(_W,tu)}}.run(t),class{static run(t){t.registerNode(bW,Dc),t.registerNode(PV,Bc),t.registerNode(wW,kc),t.registerNode(gW,kc),t.registerNode(TW,kc),t.registerNode(AW,kc),t.registerNode(EW,kc),t.registerNode(MW,kc)}}.run(t),class{static run(t){t.registerOperation(SW),t.registerOperation(GW),t.registerOperation($W),t.registerOperation(KW),t.registerOperation(iq),t.registerOperation(gq),t.registerOperation(hq),t.registerOperation(wq),t.registerOperation(eX),t.registerOperation(sX),t.registerOperation(yY),t.registerOperation(AY),t.registerOperation(s$),t.registerOperation(A$),t.registerOperation(DJ),t.registerOperation(YJ),t.registerOperation(rZ),t.registerOperation(lZ),t.registerOperation(_Z),t.registerOperation(PZ),t.registerOperation(AZ),t.registerOperation(WZ),t.registerOperation(JZ),t.registerOperation(fQ),t.registerOperation(TQ),t.registerOperation(LQ),t.registerOperation(DQ),t.registerOperation(XQ),t.registerOperation(nK),t.registerOperation(pK),t.registerOperation(xK),t.registerOperation(AK),t.registerOperation(RK),t.registerOperation(GK),t.registerOperation(QK),t.registerOperation(h0),t.registerOperation(w0),t.registerOperation(H0),t.registerOperation(X0),t.registerOperation(Q0),t.registerOperation(n1),t.registerOperation(l1),t.registerOperation(S1),t.registerOperation(I1),t.registerOperation(B1),t.registerNode(LW,Hc),t.registerNode(RW,Uc),t.registerNode(UW,Uc),t.registerNode(jW,Gc),t.registerNode(QW,Gc),t.registerNode(nq,Gc),t.registerNode(aq,Gc),t.registerNode(cq,Gc),t.registerNode(_q,Gc),t.registerNode(xq,Gc),t.registerNode(Mq,Gc),t.registerNode(Cq,Gc),t.registerNode(Lq,Gc),t.registerNode(Dq,Gc),t.registerNode(Bq,qc),t.registerNode(Uq,qc),t.registerNode(rX,qc),t.registerNode(lX,Yc),t.registerNode(dY,Wc),t.registerNode(vY,Yc),t.registerNode(bY,Yc),t.registerNode(SY,Yc),t.registerNode(IY,Yc),t.registerNode(UY,qc),t.registerNode(HY,Yc),t.registerNode(e$,qc),t.registerNode(l$,Yc),t.registerNode(p$,Hc),t.registerNode(b$,Hc),t.registerNode(S$,Wc),t.registerNode(N$,Wc),t.registerNode(j$,qc),t.registerNode(q$,qc),t.registerNode(CJ,zc),t.registerNode(LJ,qc),t.registerNode(zJ,Hc),t.registerNode(GJ,qc),t.registerNode(WJ,Yc),t.registerNode(tZ,qc),t.registerNode(QJ,Wc),t.registerNode(aZ,Yc),t.registerNode(hZ,$c),t.registerNode(pZ,$c),t.registerNode(gZ,qc),t.registerNode(yZ,qc),t.registerNode(bZ,Yc),t.registerNode(TZ,zc),t.registerNode(SZ,$c),t.registerNode(RZ,zc),t.registerNode(kZ,Wc),t.registerNode(VZ,Wc),t.registerNode(jZ,qc),t.registerNode(XZ,Wc),t.registerNode($Z,Hc),t.registerNode(KZ,qc),t.registerNode(eQ,zc,{userAllowed:!1}),t.registerNode(mQ,Vc),t.registerNode(yQ,qc),t.registerNode(MQ,Yc),t.registerNode(zQ,qc),t.registerNode(NQ,qc),t.registerNode(PQ,jc),t.registerNode(EG,zc),t.registerNode(WQ,qc),t.registerNode(JQ,qc),t.registerNode(sK,$c),t.registerNode(dK,qc),t.registerNode(fK,Gc),t.registerNode(TK,Hc),t.registerNode(SK,qc),t.registerNode(BK,qc),t.registerNode(DK,qc),t.registerNode(qK,zc),t.registerNode(YK,zc),t.registerNode(jK,qc),t.registerNode(e0,Yc),t.registerNode(i0,qc),t.registerNode(_0,qc),t.registerNode(f0,Wc),t.registerNode(v0,Wc),t.registerNode(yG,Wc),t.registerNode(E0,Hc),t.registerNode(C0,Wc),t.registerNode(O0,Yc),t.registerNode(V0,Yc),t.registerNode(q0,qc),t.registerNode(J0,qc),t.registerNode(e1,Yc),t.registerNode(s1,Yc),t.registerNode(h1,qc),t.registerNode(p1,qc),t.registerNode(v1,qc),t.registerNode(w1,qc),t.registerNode(E1,Yc),t.registerNode(N1,qc),t.registerNode(P1,qc),t.registerNode(k1,qc),t.registerNode(U1,qc),t.registerNode(H1,Xc),t.registerNode(j1,Xc),t.registerNode(W1,Xc),t.registerNode(q1,Xc),t.registerNode(X1,Xc),t.registerNode(Y1,Xc)}}.run(t)}}.run(ai),class{static run(t){t.registerCamera(lH),t.registerCamera(dH)}}.run(ai),class{static run(t){t.expressionsRegister.register(J1,\\\\\\\"arg\\\\\\\"),t.expressionsRegister.register(Z1,\\\\\\\"argc\\\\\\\"),t.expressionsRegister.register(t2,\\\\\\\"bbox\\\\\\\"),t.expressionsRegister.register(e2,\\\\\\\"centroid\\\\\\\"),t.expressionsRegister.register(n2,\\\\\\\"ch\\\\\\\"),t.expressionsRegister.register(i2,\\\\\\\"copy\\\\\\\"),t.expressionsRegister.register(r2,\\\\\\\"copRes\\\\\\\"),t.expressionsRegister.register(s2,\\\\\\\"isDeviceMobile\\\\\\\"),t.expressionsRegister.register(o2,\\\\\\\"isDeviceTouch\\\\\\\"),t.expressionsRegister.register(a2,\\\\\\\"js\\\\\\\"),t.expressionsRegister.register(l2,\\\\\\\"object\\\\\\\"),t.expressionsRegister.register(c2,\\\\\\\"objectsCount\\\\\\\"),t.expressionsRegister.register(u2,\\\\\\\"opdigits\\\\\\\"),t.expressionsRegister.register(h2,\\\\\\\"opname\\\\\\\"),t.expressionsRegister.register(d2,\\\\\\\"padzero\\\\\\\"),t.expressionsRegister.register(p2,\\\\\\\"point\\\\\\\"),t.expressionsRegister.register(_2,\\\\\\\"pointsCount\\\\\\\"),t.expressionsRegister.register(m2,\\\\\\\"strCharsCount\\\\\\\"),t.expressionsRegister.register(f2,\\\\\\\"strConcat\\\\\\\"),t.expressionsRegister.register(g2,\\\\\\\"strIndex\\\\\\\"),t.expressionsRegister.register(v2,\\\\\\\"strSub\\\\\\\"),t.expressionsRegister.register(y2,\\\\\\\"windowSize\\\\\\\")}}.run(ai),class{static run(t){t.assemblersRegister.register(Hn.GL_MESH_BASIC,b2,X2),t.assemblersRegister.register(Hn.GL_MESH_LAMBERT,b2,Y2),t.assemblersRegister.register(Hn.GL_MESH_PHONG,b2,$2),t.assemblersRegister.register(Hn.GL_MESH_STANDARD,b2,Z2),t.assemblersRegister.register(Hn.GL_MESH_PHYSICAL,b2,Q2),t.assemblersRegister.register(Hn.GL_PARTICLES,b2,h3),t.assemblersRegister.register(Hn.GL_POINTS,b2,s3),t.assemblersRegister.register(Hn.GL_LINE,b2,u3),t.assemblersRegister.register(Hn.GL_TEXTURE,b2,d3),t.assemblersRegister.register(Hn.GL_VOLUME,b2,g3)}}.run(ai))}}v3._started=!1,v3.run()}]);void 0===POLY&&console.error(\\\\\\\"esm-webpack-plugin: nothing exported!\\\\\\\");const _POLY$PolyScene=POLY.PolyScene,_POLY$Poly=POLY.Poly,_POLY$SceneJsonImporter=POLY.SceneJsonImporter,_POLY$SceneDataManifestImporter=POLY.SceneDataManifestImporter,_POLY$mountScene=POLY.mountScene;export{_POLY$PolyScene as PolyScene,_POLY$Poly as Poly,_POLY$SceneJsonImporter as SceneJsonImporter,_POLY$SceneDataManifestImporter as SceneDataManifestImporter,_POLY$mountScene as mountScene};\\n//# sourceMappingURL=all.js.map\"","status":200,"headers":{"content-type":"application/javascript","content-length":"2809283"}},"type":2,"external":true,"timestamp":1723952569313},{"data":{"url":"blob:https://ipfs.arkivo.art/7bb61b61-a71c-4e74-bfec-9c5c8f6dc7a4","host":"","path":"https://ipfs.arkivo.art/7bb61b61-a71c-4e74-bfec-9c5c8f6dc7a4","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":1723952569314},{"data":{"url":"blob:https://ipfs.arkivo.art/7bb61b61-a71c-4e74-bfec-9c5c8f6dc7a4","body":"\"// 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":"155"}},"type":2,"external":true,"timestamp":1723952576610}],"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":1723952568206},{"text":"brightness Bm","level":"log","timestamp":1723952569546},{"text":"[.WebGL-0x1b20013caa00]GL Driver Message (OpenGL, Performance, GL_CLOSE_PATH_NV, High): GPU stall due to ReadPixels","level":"warning","timestamp":1723952579502},{"text":"[.WebGL-0x1b20013caa00]GL Driver Message (OpenGL, Performance, GL_CLOSE_PATH_NV, High): GPU stall due to ReadPixels","level":"warning","timestamp":1723952579502},{"text":"[.WebGL-0x1b20013caa00]GL Driver Message (OpenGL, Performance, GL_CLOSE_PATH_NV, High): GPU stall due to ReadPixels","level":"warning","timestamp":1723952579502},{"text":"[.WebGL-0x1b20013caa00]GL Driver Message (OpenGL, Performance, GL_CLOSE_PATH_NV, High): GPU stall due to ReadPixels (this message will no longer repeat)","level":"warning","timestamp":1723952579502}],"screenshotDelay":10000},"timestamp":1723952567611},"created_at":"2024-08-18T03:43:20.607+00:00","updated_at":"2024-08-18T03:43:20.607+00:00"}