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e=a*l,n=a*u,i=c*l,s=c*u;t[0]=e-s*o,t[4]=-r*u,t[8]=i+n*o,t[1]=n+i*o,t[5]=r*l,t[9]=s-e*o,t[2]=-r*c,t[6]=o,t[10]=r*a}else if(\\\\\\\"ZYX\\\\\\\"===e.order){const e=r*l,n=r*u,i=o*l,s=o*u;t[0]=a*l,t[4]=i*c-n,t[8]=e*c+s,t[1]=a*u,t[5]=s*c+e,t[9]=n*c-i,t[2]=-c,t[6]=o*a,t[10]=r*a}else if(\\\\\\\"YZX\\\\\\\"===e.order){const e=r*a,n=r*c,i=o*a,s=o*c;t[0]=a*l,t[4]=s-e*u,t[8]=i*u+n,t[1]=u,t[5]=r*l,t[9]=-o*l,t[2]=-c*l,t[6]=n*u+i,t[10]=e-s*u}else if(\\\\\\\"XZY\\\\\\\"===e.order){const e=r*a,n=r*c,i=o*a,s=o*c;t[0]=a*l,t[4]=-u,t[8]=c*l,t[1]=e*u+s,t[5]=r*l,t[9]=n*u-i,t[2]=i*u-n,t[6]=o*l,t[10]=s*u+e}return t[3]=0,t[7]=0,t[11]=0,t[12]=0,t[13]=0,t[14]=0,t[15]=1,this}makeRotationFromQuaternion(e){return this.compose(a,e,c)}lookAt(e,t,n){const i=this.elements;return 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Use .multiplyMatrices( a, b ) instead.\\\\\\\"),this.multiplyMatrices(e,t)):this.multiplyMatrices(this,e)}premultiply(e){return this.multiplyMatrices(e,this)}multiplyMatrices(e,t){const n=e.elements,i=t.elements,s=this.elements,r=n[0],o=n[4],a=n[8],c=n[12],l=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],A=i[8],T=i[12],E=i[1],C=i[5],M=i[9],N=i[13],S=i[2],O=i[6],L=i[10],P=i[14],R=i[3],I=i[7],F=i[11],D=i[15];return s[0]=r*b+o*E+a*S+c*R,s[4]=r*w+o*C+a*O+c*I,s[8]=r*A+o*M+a*L+c*F,s[12]=r*T+o*N+a*P+c*D,s[1]=l*b+u*E+h*S+d*R,s[5]=l*w+u*C+h*O+d*I,s[9]=l*A+u*M+h*L+d*F,s[13]=l*T+u*N+h*P+d*D,s[2]=p*b+_*E+m*S+f*R,s[6]=p*w+_*C+m*O+f*I,s[10]=p*A+_*M+m*L+f*F,s[14]=p*T+_*N+m*P+f*D,s[3]=g*b+v*E+y*S+x*R,s[7]=g*w+v*C+y*O+x*I,s[11]=g*A+v*M+y*L+x*F,s[15]=g*T+v*N+y*P+x*D,this}multiplyScalar(e){const t=this.elements;return t[0]*=e,t[4]*=e,t[8]*=e,t[12]*=e,t[1]*=e,t[5]*=e,t[9]*=e,t[13]*=e,t[2]*=e,t[6]*=e,t[10]*=e,t[14]*=e,t[3]*=e,t[7]*=e,t[11]*=e,t[15]*=e,this}determinant(){const e=this.elements,t=e[0],n=e[4],i=e[8],s=e[12],r=e[1],o=e[5],a=e[9],c=e[13],l=e[2],u=e[6],h=e[10],d=e[14];return e[3]*(+s*a*u-i*c*u-s*o*h+n*c*h+i*o*d-n*a*d)+e[7]*(+t*a*d-t*c*h+s*r*h-i*r*d+i*c*l-s*a*l)+e[11]*(+t*c*u-t*o*d-s*r*u+n*r*d+s*o*l-n*c*l)+e[15]*(-i*o*l-t*a*u+t*o*h+i*r*u-n*r*h+n*a*l)}transpose(){const e=this.elements;let t;return t=e[1],e[1]=e[4],e[4]=t,t=e[2],e[2]=e[8],e[8]=t,t=e[6],e[6]=e[9],e[9]=t,t=e[3],e[3]=e[12],e[12]=t,t=e[7],e[7]=e[13],e[13]=t,t=e[11],e[11]=e[14],e[14]=t,this}setPosition(e,t,n){const i=this.elements;return e.isVector3?(i[12]=e.x,i[13]=e.y,i[14]=e.z):(i[12]=e,i[13]=t,i[14]=n),this}invert(){const e=this.elements,t=e[0],n=e[1],i=e[2],s=e[3],r=e[4],o=e[5],a=e[6],c=e[7],l=e[8],u=e[9],h=e[10],d=e[11],p=e[12],_=e[13],m=e[14],f=e[15],g=u*m*c-_*h*c+_*a*d-o*m*d-u*a*f+o*h*f,v=p*h*c-l*m*c-p*a*d+r*m*d+l*a*f-r*h*f,y=l*_*c-p*u*c+p*o*d-r*_*d-l*o*f+r*u*f,x=p*u*a-l*_*a-p*o*h+r*_*h+l*o*m-r*u*m,b=t*g+n*v+i*y+s*x;if(0===b)return this.set(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0);const w=1/b;return e[0]=g*w,e[1]=(_*h*s-u*m*s-_*i*d+n*m*d+u*i*f-n*h*f)*w,e[2]=(o*m*s-_*a*s+_*i*c-n*m*c-o*i*f+n*a*f)*w,e[3]=(u*a*s-o*h*s-u*i*c+n*h*c+o*i*d-n*a*d)*w,e[4]=v*w,e[5]=(l*m*s-p*h*s+p*i*d-t*m*d-l*i*f+t*h*f)*w,e[6]=(p*a*s-r*m*s-p*i*c+t*m*c+r*i*f-t*a*f)*w,e[7]=(r*h*s-l*a*s+l*i*c-t*h*c-r*i*d+t*a*d)*w,e[8]=y*w,e[9]=(p*u*s-l*_*s-p*n*d+t*_*d+l*n*f-t*u*f)*w,e[10]=(r*_*s-p*o*s+p*n*c-t*_*c-r*n*f+t*o*f)*w,e[11]=(l*o*s-r*u*s-l*n*c+t*u*c+r*n*d-t*o*d)*w,e[12]=x*w,e[13]=(l*_*i-p*u*i+p*n*h-t*_*h-l*n*m+t*u*m)*w,e[14]=(p*o*i-r*_*i-p*n*a+t*_*a+r*n*m-t*o*m)*w,e[15]=(r*u*i-l*o*i+l*n*a-t*u*a-r*n*h+t*o*h)*w,this}scale(e){const t=this.elements,n=e.x,i=e.y,s=e.z;return t[0]*=n,t[4]*=i,t[8]*=s,t[1]*=n,t[5]*=i,t[9]*=s,t[2]*=n,t[6]*=i,t[10]*=s,t[3]*=n,t[7]*=i,t[11]*=s,this}getMaxScaleOnAxis(){const e=this.elements,t=e[0]*e[0]+e[1]*e[1]+e[2]*e[2],n=e[4]*e[4]+e[5]*e[5]+e[6]*e[6],i=e[8]*e[8]+e[9]*e[9]+e[10]*e[10];return Math.sqrt(Math.max(t,n,i))}makeTranslation(e,t,n){return this.set(1,0,0,e,0,1,0,t,0,0,1,n,0,0,0,1),this}makeRotationX(e){const t=Math.cos(e),n=Math.sin(e);return this.set(1,0,0,0,0,t,-n,0,0,n,t,0,0,0,0,1),this}makeRotationY(e){const t=Math.cos(e),n=Math.sin(e);return this.set(t,0,n,0,0,1,0,0,-n,0,t,0,0,0,0,1),this}makeRotationZ(e){const t=Math.cos(e),n=Math.sin(e);return this.set(t,-n,0,0,n,t,0,0,0,0,1,0,0,0,0,1),this}makeRotationAxis(e,t){const n=Math.cos(t),i=Math.sin(t),s=1-n,r=e.x,o=e.y,a=e.z,c=s*r,l=s*o;return this.set(c*r+n,c*o-i*a,c*a+i*o,0,c*o+i*a,l*o+n,l*a-i*r,0,c*a-i*o,l*a+i*r,s*a*a+n,0,0,0,0,1),this}makeScale(e,t,n){return this.set(e,0,0,0,0,t,0,0,0,0,n,0,0,0,0,1),this}makeShear(e,t,n){return this.set(1,t,n,0,e,1,n,0,e,t,1,0,0,0,0,1),this}compose(e,t,n){const i=this.elements,s=t._x,r=t._y,o=t._z,a=t._w,c=s+s,l=r+r,u=o+o,h=s*c,d=s*l,p=s*u,_=r*l,m=r*u,f=o*u,g=a*c,v=a*l,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]=e.x,i[13]=e.y,i[14]=e.z,i[15]=1,this}decompose(e,t,n){const i=this.elements;let s=r.set(i[0],i[1],i[2]).length();const a=r.set(i[4],i[5],i[6]).length(),c=r.set(i[8],i[9],i[10]).length();this.determinant()<0&&(s=-s),e.x=i[12],e.y=i[13],e.z=i[14],o.copy(this);const l=1/s,u=1/a,h=1/c;return o.elements[0]*=l,o.elements[1]*=l,o.elements[2]*=l,o.elements[4]*=u,o.elements[5]*=u,o.elements[6]*=u,o.elements[8]*=h,o.elements[9]*=h,o.elements[10]*=h,t.setFromRotationMatrix(o),n.x=s,n.y=a,n.z=c,this}makePerspective(e,t,n,i,s,r){void 0===r&&console.warn(\\\\\\\"THREE.Matrix4: .makePerspective() has been redefined and has a new signature. Please check the docs.\\\\\\\");const o=this.elements,a=2*s/(t-e),c=2*s/(n-i),l=(t+e)/(t-e),u=(n+i)/(n-i),h=-(r+s)/(r-s),d=-2*r*s/(r-s);return o[0]=a,o[4]=0,o[8]=l,o[12]=0,o[1]=0,o[5]=c,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(e,t,n,i,s,r){const o=this.elements,a=1/(t-e),c=1/(n-i),l=1/(r-s),u=(t+e)*a,h=(n+i)*c,d=(r+s)*l;return o[0]=2*a,o[4]=0,o[8]=0,o[12]=-u,o[1]=0,o[5]=2*c,o[9]=0,o[13]=-h,o[2]=0,o[6]=0,o[10]=-2*l,o[14]=-d,o[3]=0,o[7]=0,o[11]=0,o[15]=1,this}equals(e){const t=this.elements,n=e.elements;for(let e=0;e<16;e++)if(t[e]!==n[e])return!1;return!0}fromArray(e,t=0){for(let n=0;n<16;n++)this.elements[n]=e[n+t];return this}toArray(e=[],t=0){const n=this.elements;return e[t]=n[0],e[t+1]=n[1],e[t+2]=n[2],e[t+3]=n[3],e[t+4]=n[4],e[t+5]=n[5],e[t+6]=n[6],e[t+7]=n[7],e[t+8]=n[8],e[t+9]=n[9],e[t+10]=n[10],e[t+11]=n[11],e[t+12]=n[12],e[t+13]=n[13],e[t+14]=n[14],e[t+15]=n[15],e}}s.prototype.isMatrix4=!0;const r=new i.a,o=new s,a=new i.a(0,0,0),c=new i.a(1,1,1),l=new i.a,u=new i.a,h=new i.a},function(e,t,n){\\\\\\\"use strict\\\\\\\";n.d(t,\\\\\\\"a\\\\\\\",(function(){return u}));var i=n(4);const s={aliceblue:15792383,antiquewhite:16444375,aqua:65535,aquamarine:8388564,azure:15794175,beige:16119260,bisque:16770244,black:0,blanchedalmond:16772045,blue:255,blueviolet:9055202,brown:10824234,burlywood:14596231,cadetblue:6266528,chartreuse:8388352,chocolate:13789470,coral:16744272,cornflowerblue:6591981,cornsilk:16775388,crimson:14423100,cyan:65535,darkblue:139,darkcyan:35723,darkgoldenrod:12092939,darkgray:11119017,darkgreen:25600,darkgrey:11119017,darkkhaki:12433259,darkmagenta:9109643,darkolivegreen:5597999,darkorange:16747520,darkorchid:10040012,darkred:9109504,darksalmon:15308410,darkseagreen:9419919,darkslateblue:4734347,darkslategray:3100495,darkslategrey:3100495,darkturquoise:52945,darkviolet:9699539,deeppink:16716947,deepskyblue:49151,dimgray:6908265,dimgrey:6908265,dodgerblue:2003199,firebrick:11674146,floralwhite:16775920,forestgreen:2263842,fuchsia:16711935,gainsboro:14474460,ghostwhite:16316671,gold:16766720,goldenrod:14329120,gray:8421504,green:32768,greenyellow:11403055,grey:8421504,honeydew:15794160,hotpink:16738740,indianred:13458524,indigo:4915330,ivory:16777200,khaki:15787660,lavender:15132410,lavenderblush:16773365,lawngreen:8190976,lemonchiffon:16775885,lightblue:11393254,lightcoral:15761536,lightcyan:14745599,lightgoldenrodyellow:16448210,lightgray:13882323,lightgreen:9498256,lightgrey:13882323,lightpink:16758465,lightsalmon:16752762,lightseagreen:2142890,lightskyblue:8900346,lightslategray:7833753,lightslategrey:7833753,lightsteelblue:11584734,lightyellow:16777184,lime:65280,limegreen:3329330,linen:16445670,magenta:16711935,maroon:8388608,mediumaquamarine:6737322,mediumblue:205,mediumorchid:12211667,mediumpurple:9662683,mediumseagreen:3978097,mediumslateblue:8087790,mediumspringgreen:64154,mediumturquoise:4772300,mediumvioletred:13047173,midnightblue:1644912,mintcream:16121850,mistyrose:16770273,moccasin:16770229,navajowhite:16768685,navy:128,oldlace:16643558,olive:8421376,olivedrab:7048739,orange:16753920,orangered:16729344,orchid:14315734,palegoldenrod:15657130,palegreen:10025880,paleturquoise:11529966,palevioletred:14381203,papayawhip:16773077,peachpuff:16767673,peru:13468991,pink:16761035,plum:14524637,powderblue:11591910,purple:8388736,rebeccapurple:6697881,red:16711680,rosybrown:12357519,royalblue:4286945,saddlebrown:9127187,salmon:16416882,sandybrown:16032864,seagreen:3050327,seashell:16774638,sienna:10506797,silver:12632256,skyblue:8900331,slateblue:6970061,slategray:7372944,slategrey:7372944,snow:16775930,springgreen:65407,steelblue:4620980,tan:13808780,teal:32896,thistle:14204888,tomato:16737095,turquoise:4251856,violet:15631086,wheat:16113331,white:16777215,whitesmoke:16119285,yellow:16776960,yellowgreen:10145074},r={h:0,s:0,l:0},o={h:0,s:0,l:0};function a(e,t,n){return n<0&&(n+=1),n>1&&(n-=1),n<1/6?e+6*(t-e)*n:n<.5?t:n<2/3?e+6*(t-e)*(2/3-n):e}function c(e){return e<.04045?.0773993808*e:Math.pow(.9478672986*e+.0521327014,2.4)}function l(e){return e<.0031308?12.92*e:1.055*Math.pow(e,.41666)-.055}class u{constructor(e,t,n){return void 0===t&&void 0===n?this.set(e):this.setRGB(e,t,n)}set(e){return e&&e.isColor?this.copy(e):\\\\\\\"number\\\\\\\"==typeof e?this.setHex(e):\\\\\\\"string\\\\\\\"==typeof e&&this.setStyle(e),this}setScalar(e){return this.r=e,this.g=e,this.b=e,this}setHex(e){return e=Math.floor(e),this.r=(e>>16&255)/255,this.g=(e>>8&255)/255,this.b=(255&e)/255,this}setRGB(e,t,n){return this.r=e,this.g=t,this.b=n,this}setHSL(e,t,n){if(e=i.a.euclideanModulo(e,1),t=i.a.clamp(t,0,1),n=i.a.clamp(n,0,1),0===t)this.r=this.g=this.b=n;else{const i=n<=.5?n*(1+t):n+t-n*t,s=2*n-i;this.r=a(s,i,e+1/3),this.g=a(s,i,e),this.b=a(s,i,e-1/3)}return this}setStyle(e){function t(t){void 0!==t&&parseFloat(t)<1&&console.warn(\\\\\\\"THREE.Color: Alpha 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0===e&&(console.warn(\\\\\\\"THREE.Object3D: .getWorldDirection() target is now required\\\\\\\"),e=new s.a),this.updateWorldMatrix(!0,!1);const t=this.matrixWorld.elements;return e.set(t[8],t[9],t[10]).normalize()},raycast:function(){},traverse:function(e){e(this);const t=this.children;for(let n=0,i=t.length;n<i;n++)t[n].traverse(e)},traverseVisible:function(e){if(!1===this.visible)return;e(this);const t=this.children;for(let n=0,i=t.length;n<i;n++)t[n].traverseVisible(e)},traverseAncestors:function(e){const t=this.parent;null!==t&&(e(t),t.traverseAncestors(e))},updateMatrix:function(){this.matrix.compose(this.position,this.quaternion,this.scale),this.matrixWorldNeedsUpdate=!0},updateMatrixWorld:function(e){this.matrixAutoUpdate&&this.updateMatrix(),(this.matrixWorldNeedsUpdate||e)&&(null===this.parent?this.matrixWorld.copy(this.matrix):this.matrixWorld.multiplyMatrices(this.parent.matrixWorld,this.matrix),this.matrixWorldNeedsUpdate=!1,e=!0);const t=this.children;for(let n=0,i=t.length;n<i;n++)t[n].updateMatrixWorld(e)},updateWorldMatrix:function(e,t){const n=this.parent;if(!0===e&&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===t){const e=this.children;for(let t=0,n=e.length;t<n;t++)e[t].updateWorldMatrix(!1,!0)}},toJSON:function(e){const t=void 0===e||\\\\\\\"string\\\\\\\"==typeof e,n={};t&&(e={geometries:{},materials:{},textures:{},images:{},shapes:{},skeletons:{},animations:{}},n.metadata={version:4.5,type:\\\\\\\"Object\\\\\\\",generator:\\\\\\\"Object3D.toJSON\\\\\\\"});const i={};function s(t,n){return void 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m.makeScale(e,t,n),this.applyMatrix4(m),this},lookAt:function(e){return f.lookAt(e),f.updateMatrix(),this.applyMatrix4(f.matrix),this},center:function(){return this.computeBoundingBox(),this.boundingBox.getCenter(g).negate(),this.translate(g.x,g.y,g.z),this},setFromPoints:function(e){const t=[];for(let n=0,i=e.length;n<i;n++){const i=e[n];t.push(i.x,i.y,i.z||0)}return this.setAttribute(\\\\\\\"position\\\\\\\",new a.c(t,3)),this},computeBoundingBox:function(){null===this.boundingBox&&(this.boundingBox=new r.a);const e=this.attributes.position,t=this.morphAttributes.position;if(e&&e.isGLBufferAttribute)return console.error('THREE.BufferGeometry.computeBoundingBox(): GLBufferAttribute requires a manual bounding box. 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Alternatively set \\\\\\\"mesh.frustumCulled\\\\\\\" to \\\\\\\"false\\\\\\\".',this),void this.boundingSphere.set(new i.a,1/0);if(e){const n=this.boundingSphere.center;if(v.setFromBufferAttribute(e),t)for(let e=0,n=t.length;e<n;e++){const n=t[e];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 t=0,s=e.count;t<s;t++)x.fromBufferAttribute(e,t),i=Math.max(i,n.distanceToSquared(x));if(t)for(let s=0,r=t.length;s<r;s++){const r=t[s],o=this.morphTargetsRelative;for(let t=0,s=r.count;t<s;t++)x.fromBufferAttribute(r,t),o&&(g.fromBufferAttribute(e,t),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)}},computeFaceNormals:function(){},computeTangents:function(){const e=this.index,t=this.attributes;if(null===e||void 0===t.position||void 0===t.normal||void 0===t.uv)return void console.error(\\\\\\\"THREE.BufferGeometry: .computeTangents() failed. Missing required attributes (index, position, normal or uv)\\\\\\\");const n=e.array,r=t.position.array,o=t.normal.array,c=t.uv.array,l=r.length/3;void 0===t.tangent&&this.setAttribute(\\\\\\\"tangent\\\\\\\",new a.a(new Float32Array(4*l),4));const u=t.tangent.array,h=[],d=[];for(let e=0;e<l;e++)h[e]=new i.a,d[e]=new i.a;const p=new i.a,_=new i.a,m=new i.a,f=new s.a,g=new s.a,v=new s.a,y=new i.a,x=new i.a;function b(e,t,n){p.fromArray(r,3*e),_.fromArray(r,3*t),m.fromArray(r,3*n),f.fromArray(c,2*e),g.fromArray(c,2*t),v.fromArray(c,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[e].add(y),h[t].add(y),h[n].add(y),d[e].add(x),d[t].add(x),d[n].add(x))}let w=this.groups;0===w.length&&(w=[{start:0,count:n.length}]);for(let e=0,t=w.length;e<t;++e){const t=w[e],i=t.start;for(let e=i,s=i+t.count;e<s;e+=3)b(n[e+0],n[e+1],n[e+2])}const A=new i.a,T=new i.a,E=new i.a,C=new i.a;function M(e){E.fromArray(o,3*e),C.copy(E);const t=h[e];A.copy(t),A.sub(E.multiplyScalar(E.dot(t))).normalize(),T.crossVectors(C,t);const n=T.dot(d[e])<0?-1:1;u[4*e]=A.x,u[4*e+1]=A.y,u[4*e+2]=A.z,u[4*e+3]=n}for(let e=0,t=w.length;e<t;++e){const t=w[e],i=t.start;for(let e=i,s=i+t.count;e<s;e+=3)M(n[e+0]),M(n[e+1]),M(n[e+2])}},computeVertexNormals:function(){const e=this.index,t=this.getAttribute(\\\\\\\"position\\\\\\\");if(void 0!==t){let n=this.getAttribute(\\\\\\\"normal\\\\\\\");if(void 0===n)n=new a.a(new Float32Array(3*t.count),3),this.setAttribute(\\\\\\\"normal\\\\\\\",n);else for(let e=0,t=n.count;e<t;e++)n.setXYZ(e,0,0,0);const s=new i.a,r=new i.a,o=new i.a,c=new i.a,l=new i.a,u=new i.a,h=new i.a,d=new i.a;if(e)for(let i=0,a=e.count;i<a;i+=3){const a=e.getX(i+0),p=e.getX(i+1),_=e.getX(i+2);s.fromBufferAttribute(t,a),r.fromBufferAttribute(t,p),o.fromBufferAttribute(t,_),h.subVectors(o,r),d.subVectors(s,r),h.cross(d),c.fromBufferAttribute(n,a),l.fromBufferAttribute(n,p),u.fromBufferAttribute(n,_),c.add(h),l.add(h),u.add(h),n.setXYZ(a,c.x,c.y,c.z),n.setXYZ(p,l.x,l.y,l.z),n.setXYZ(_,u.x,u.y,u.z)}else for(let e=0,i=t.count;e<i;e+=3)s.fromBufferAttribute(t,e+0),r.fromBufferAttribute(t,e+1),o.fromBufferAttribute(t,e+2),h.subVectors(o,r),d.subVectors(s,r),h.cross(d),n.setXYZ(e+0,h.x,h.y,h.z),n.setXYZ(e+1,h.x,h.y,h.z),n.setXYZ(e+2,h.x,h.y,h.z);this.normalizeNormals(),n.needsUpdate=!0}},merge:function(e,t){if(!e||!e.isBufferGeometry)return void console.error(\\\\\\\"THREE.BufferGeometry.merge(): geometry not an instance of THREE.BufferGeometry.\\\\\\\",e);void 0===t&&(t=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===e.attributes[i])continue;const s=n[i].array,r=e.attributes[i],o=r.array,a=r.itemSize*t,c=Math.min(o.length,s.length-a);for(let e=0,t=a;e<c;e++,t++)s[t]=o[e]}return this},normalizeNormals:function(){const e=this.attributes.normal;for(let t=0,n=e.count;t<n;t++)x.fromBufferAttribute(e,t),x.normalize(),e.setXYZ(t,x.x,x.y,x.z)},toNonIndexed:function(){function e(e,t){const n=e.array,i=e.itemSize,s=e.normalized,r=new n.constructor(t.length*i);let o=0,c=0;for(let e=0,s=t.length;e<s;e++){o=t[e]*i;for(let e=0;e<i;e++)r[c++]=n[o++]}return new a.a(r,i,s)}if(null===this.index)return console.warn(\\\\\\\"THREE.BufferGeometry.toNonIndexed(): BufferGeometry is already non-indexed.\\\\\\\"),this;const t=new b,n=this.index.array,i=this.attributes;for(const s in i){const r=e(i[s],n);t.setAttribute(s,r)}const s=this.morphAttributes;for(const i in s){const r=[],o=s[i];for(let t=0,i=o.length;t<i;t++){const i=e(o[t],n);r.push(i)}t.morphAttributes[i]=r}t.morphTargetsRelative=this.morphTargetsRelative;const r=this.groups;for(let e=0,n=r.length;e<n;e++){const n=r[e];t.addGroup(n.start,n.count,n.materialIndex)}return t},toJSON:function(){const e={metadata:{version:4.5,type:\\\\\\\"BufferGeometry\\\\\\\",generator:\\\\\\\"BufferGeometry.toJSON\\\\\\\"}};if(e.uuid=this.uuid,e.type=this.type,\\\\\\\"\\\\\\\"!==this.name&&(e.name=this.name),Object.keys(this.userData).length>0&&(e.userData=this.userData),void 0!==this.parameters){const t=this.parameters;for(const n in t)void 0!==t[n]&&(e[n]=t[n]);return e}e.data={attributes:{}};const t=this.index;null!==t&&(e.data.index={type:t.array.constructor.name,array:Array.prototype.slice.call(t.array)});const n=this.attributes;for(const t in n){const i=n[t];e.data.attributes[t]=i.toJSON(e.data)}const i={};let s=!1;for(const t in this.morphAttributes){const n=this.morphAttributes[t],r=[];for(let t=0,i=n.length;t<i;t++){const i=n[t];r.push(i.toJSON(e.data))}r.length>0&&(i[t]=r,s=!0)}s&&(e.data.morphAttributes=i,e.data.morphTargetsRelative=this.morphTargetsRelative);const r=this.groups;r.length>0&&(e.data.groups=JSON.parse(JSON.stringify(r)));const o=this.boundingSphere;return null!==o&&(e.data.boundingSphere={center:o.center.toArray(),radius:o.radius}),e},clone:function(){return(new b).copy(this)},copy:function(e){this.index=null,this.attributes={},this.morphAttributes={},this.groups=[],this.boundingBox=null,this.boundingSphere=null;const t={};this.name=e.name;const n=e.index;null!==n&&this.setIndex(n.clone(t));const i=e.attributes;for(const e in i){const n=i[e];this.setAttribute(e,n.clone(t))}const s=e.morphAttributes;for(const e in s){const n=[],i=s[e];for(let e=0,s=i.length;e<s;e++)n.push(i[e].clone(t));this.morphAttributes[e]=n}this.morphTargetsRelative=e.morphTargetsRelative;const r=e.groups;for(let e=0,t=r.length;e<t;e++){const t=r[e];this.addGroup(t.start,t.count,t.materialIndex)}const o=e.boundingBox;null!==o&&(this.boundingBox=o.clone());const a=e.boundingSphere;return null!==a&&(this.boundingSphere=a.clone()),this.drawRange.start=e.drawRange.start,this.drawRange.count=e.drawRange.count,this.userData=e.userData,this},dispose:function(){this.dispatchEvent({type:\\\\\\\"dispose\\\\\\\"})}})},function(e,t,n){\\\\\\\"use strict\\\\\\\";n.d(t,\\\\\\\"a\\\\\\\",(function(){return s}));var i=n(4);class s{constructor(e=0,t=0,n=0,i=1){this._x=e,this._y=t,this._z=n,this._w=i}static slerp(e,t,n,i){return console.warn(\\\\\\\"THREE.Quaternion: Static .slerp() has been deprecated. Use qm.slerpQuaternions( qa, qb, t ) instead.\\\\\\\"),n.slerpQuaternions(e,t,i)}static slerpFlat(e,t,n,i,s,r,o){let a=n[i+0],c=n[i+1],l=n[i+2],u=n[i+3];const h=s[r+0],d=s[r+1],p=s[r+2],_=s[r+3];if(0===o)return e[t+0]=a,e[t+1]=c,e[t+2]=l,void(e[t+3]=u);if(1===o)return e[t+0]=h,e[t+1]=d,e[t+2]=p,void(e[t+3]=_);if(u!==_||a!==h||c!==d||l!==p){let e=1-o;const t=a*h+c*d+l*p+u*_,n=t>=0?1:-1,i=1-t*t;if(i>Number.EPSILON){const s=Math.sqrt(i),r=Math.atan2(s,t*n);e=Math.sin(e*r)/s,o=Math.sin(o*r)/s}const s=o*n;if(a=a*e+h*s,c=c*e+d*s,l=l*e+p*s,u=u*e+_*s,e===1-o){const e=1/Math.sqrt(a*a+c*c+l*l+u*u);a*=e,c*=e,l*=e,u*=e}}e[t]=a,e[t+1]=c,e[t+2]=l,e[t+3]=u}static multiplyQuaternionsFlat(e,t,n,i,s,r){const o=n[i],a=n[i+1],c=n[i+2],l=n[i+3],u=s[r],h=s[r+1],d=s[r+2],p=s[r+3];return e[t]=o*p+l*u+a*d-c*h,e[t+1]=a*p+l*h+c*u-o*d,e[t+2]=c*p+l*d+o*h-a*u,e[t+3]=l*p-o*u-a*h-c*d,e}get x(){return this._x}set x(e){this._x=e,this._onChangeCallback()}get y(){return this._y}set y(e){this._y=e,this._onChangeCallback()}get z(){return this._z}set z(e){this._z=e,this._onChangeCallback()}get w(){return this._w}set w(e){this._w=e,this._onChangeCallback()}set(e,t,n,i){return this._x=e,this._y=t,this._z=n,this._w=i,this._onChangeCallback(),this}clone(){return new this.constructor(this._x,this._y,this._z,this._w)}copy(e){return this._x=e.x,this._y=e.y,this._z=e.z,this._w=e.w,this._onChangeCallback(),this}setFromEuler(e,t){if(!e||!e.isEuler)throw new Error(\\\\\\\"THREE.Quaternion: .setFromEuler() now expects an Euler rotation rather than a Vector3 and order.\\\\\\\");const n=e._x,i=e._y,s=e._z,r=e._order,o=Math.cos,a=Math.sin,c=o(n/2),l=o(i/2),u=o(s/2),h=a(n/2),d=a(i/2),p=a(s/2);switch(r){case\\\\\\\"XYZ\\\\\\\":this._x=h*l*u+c*d*p,this._y=c*d*u-h*l*p,this._z=c*l*p+h*d*u,this._w=c*l*u-h*d*p;break;case\\\\\\\"YXZ\\\\\\\":this._x=h*l*u+c*d*p,this._y=c*d*u-h*l*p,this._z=c*l*p-h*d*u,this._w=c*l*u+h*d*p;break;case\\\\\\\"ZXY\\\\\\\":this._x=h*l*u-c*d*p,this._y=c*d*u+h*l*p,this._z=c*l*p+h*d*u,this._w=c*l*u-h*d*p;break;case\\\\\\\"ZYX\\\\\\\":this._x=h*l*u-c*d*p,this._y=c*d*u+h*l*p,this._z=c*l*p-h*d*u,this._w=c*l*u+h*d*p;break;case\\\\\\\"YZX\\\\\\\":this._x=h*l*u+c*d*p,this._y=c*d*u+h*l*p,this._z=c*l*p-h*d*u,this._w=c*l*u-h*d*p;break;case\\\\\\\"XZY\\\\\\\":this._x=h*l*u-c*d*p,this._y=c*d*u-h*l*p,this._z=c*l*p+h*d*u,this._w=c*l*u+h*d*p;break;default:console.warn(\\\\\\\"THREE.Quaternion: .setFromEuler() encountered an unknown order: \\\\\\\"+r)}return!1!==t&&this._onChangeCallback(),this}setFromAxisAngle(e,t){const n=t/2,i=Math.sin(n);return this._x=e.x*i,this._y=e.y*i,this._z=e.z*i,this._w=Math.cos(n),this._onChangeCallback(),this}setFromRotationMatrix(e){const t=e.elements,n=t[0],i=t[4],s=t[8],r=t[1],o=t[5],a=t[9],c=t[2],l=t[6],u=t[10],h=n+o+u;if(h>0){const e=.5/Math.sqrt(h+1);this._w=.25/e,this._x=(l-a)*e,this._y=(s-c)*e,this._z=(r-i)*e}else if(n>o&&n>u){const e=2*Math.sqrt(1+n-o-u);this._w=(l-a)/e,this._x=.25*e,this._y=(i+r)/e,this._z=(s+c)/e}else if(o>u){const e=2*Math.sqrt(1+o-n-u);this._w=(s-c)/e,this._x=(i+r)/e,this._y=.25*e,this._z=(a+l)/e}else{const e=2*Math.sqrt(1+u-n-o);this._w=(r-i)/e,this._x=(s+c)/e,this._y=(a+l)/e,this._z=.25*e}return this._onChangeCallback(),this}setFromUnitVectors(e,t){let n=e.dot(t)+1;return n<Number.EPSILON?(n=0,Math.abs(e.x)>Math.abs(e.z)?(this._x=-e.y,this._y=e.x,this._z=0,this._w=n):(this._x=0,this._y=-e.z,this._z=e.y,this._w=n)):(this._x=e.y*t.z-e.z*t.y,this._y=e.z*t.x-e.x*t.z,this._z=e.x*t.y-e.y*t.x,this._w=n),this.normalize()}angleTo(e){return 2*Math.acos(Math.abs(i.a.clamp(this.dot(e),-1,1)))}rotateTowards(e,t){const n=this.angleTo(e);if(0===n)return this;const i=Math.min(1,t/n);return this.slerp(e,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(e){return this._x*e._x+this._y*e._y+this._z*e._z+this._w*e._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 e=this.length();return 0===e?(this._x=0,this._y=0,this._z=0,this._w=1):(e=1/e,this._x=this._x*e,this._y=this._y*e,this._z=this._z*e,this._w=this._w*e),this._onChangeCallback(),this}multiply(e,t){return void 0!==t?(console.warn(\\\\\\\"THREE.Quaternion: .multiply() now only accepts one argument. Use .multiplyQuaternions( a, b ) instead.\\\\\\\"),this.multiplyQuaternions(e,t)):this.multiplyQuaternions(this,e)}premultiply(e){return this.multiplyQuaternions(e,this)}multiplyQuaternions(e,t){const n=e._x,i=e._y,s=e._z,r=e._w,o=t._x,a=t._y,c=t._z,l=t._w;return this._x=n*l+r*o+i*c-s*a,this._y=i*l+r*a+s*o-n*c,this._z=s*l+r*c+n*a-i*o,this._w=r*l-n*o-i*a-s*c,this._onChangeCallback(),this}slerp(e,t){if(0===t)return this;if(1===t)return this.copy(e);const n=this._x,i=this._y,s=this._z,r=this._w;let o=r*e._w+n*e._x+i*e._y+s*e._z;if(o<0?(this._w=-e._w,this._x=-e._x,this._y=-e._y,this._z=-e._z,o=-o):this.copy(e),o>=1)return this._w=r,this._x=n,this._y=i,this._z=s,this;const a=1-o*o;if(a<=Number.EPSILON){const e=1-t;return this._w=e*r+t*this._w,this._x=e*n+t*this._x,this._y=e*i+t*this._y,this._z=e*s+t*this._z,this.normalize(),this._onChangeCallback(),this}const c=Math.sqrt(a),l=Math.atan2(c,o),u=Math.sin((1-t)*l)/c,h=Math.sin(t*l)/c;return this._w=r*u+this._w*h,this._x=n*u+this._x*h,this._y=i*u+this._y*h,this._z=s*u+this._z*h,this._onChangeCallback(),this}slerpQuaternions(e,t,n){this.copy(e).slerp(t,n)}equals(e){return e._x===this._x&&e._y===this._y&&e._z===this._z&&e._w===this._w}fromArray(e,t=0){return this._x=e[t],this._y=e[t+1],this._z=e[t+2],this._w=e[t+3],this._onChangeCallback(),this}toArray(e=[],t=0){return e[t]=this._x,e[t+1]=this._y,e[t+2]=this._z,e[t+3]=this._w,e}fromBufferAttribute(e,t){return this._x=e.getX(t),this._y=e.getY(t),this._z=e.getZ(t),this._w=e.getW(t),this}_onChange(e){return this._onChangeCallback=e,this}_onChangeCallback(){}}s.prototype.isQuaternion=!0},function(e,t,n){\\\\\\\"use strict\\\\\\\";n.d(t,\\\\\\\"a\\\\\\\",(function(){return i}));class i{constructor(e=0,t=0,n=0,i=1){this.x=e,this.y=t,this.z=n,this.w=i}get width(){return this.z}set width(e){this.z=e}get height(){return this.w}set height(e){this.w=e}set(e,t,n,i){return this.x=e,this.y=t,this.z=n,this.w=i,this}setScalar(e){return this.x=e,this.y=e,this.z=e,this.w=e,this}setX(e){return this.x=e,this}setY(e){return this.y=e,this}setZ(e){return this.z=e,this}setW(e){return this.w=e,this}setComponent(e,t){switch(e){case 0:this.x=t;break;case 1:this.y=t;break;case 2:this.z=t;break;case 3:this.w=t;break;default:throw new Error(\\\\\\\"index is out of range: \\\\\\\"+e)}return this}getComponent(e){switch(e){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: \\\\\\\"+e)}}clone(){return new this.constructor(this.x,this.y,this.z,this.w)}copy(e){return this.x=e.x,this.y=e.y,this.z=e.z,this.w=void 0!==e.w?e.w:1,this}add(e,t){return void 0!==t?(console.warn(\\\\\\\"THREE.Vector4: .add() now only accepts one argument. Use .addVectors( a, b ) instead.\\\\\\\"),this.addVectors(e,t)):(this.x+=e.x,this.y+=e.y,this.z+=e.z,this.w+=e.w,this)}addScalar(e){return this.x+=e,this.y+=e,this.z+=e,this.w+=e,this}addVectors(e,t){return this.x=e.x+t.x,this.y=e.y+t.y,this.z=e.z+t.z,this.w=e.w+t.w,this}addScaledVector(e,t){return this.x+=e.x*t,this.y+=e.y*t,this.z+=e.z*t,this.w+=e.w*t,this}sub(e,t){return void 0!==t?(console.warn(\\\\\\\"THREE.Vector4: .sub() now only accepts one argument. 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this.onBeforeCompile.toString()},setValues:function(e){if(void 0!==e)for(const t in e){const n=e[t];if(void 0===n){console.warn(\\\\\\\"THREE.Material: '\\\\\\\"+t+\\\\\\\"' parameter is undefined.\\\\\\\");continue}if(\\\\\\\"shading\\\\\\\"===t){console.warn(\\\\\\\"THREE.\\\\\\\"+this.type+\\\\\\\": .shading has been removed. 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0!==this.metalness&&(n.metalness=this.metalness),this.sheen&&this.sheen.isColor&&(n.sheen=this.sheen.getHex()),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.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(e).uuid),this.clearcoatRoughnessMap&&this.clearcoatRoughnessMap.isTexture&&(n.clearcoatRoughnessMap=this.clearcoatRoughnessMap.toJSON(e).uuid),this.clearcoatNormalMap&&this.clearcoatNormalMap.isTexture&&(n.clearcoatNormalMap=this.clearcoatNormalMap.toJSON(e).uuid,n.clearcoatNormalScale=this.clearcoatNormalScale.toArray()),this.map&&this.map.isTexture&&(n.map=this.map.toJSON(e).uuid),this.matcap&&this.matcap.isTexture&&(n.matcap=this.matcap.toJSON(e).uuid),this.alphaMap&&this.alphaMap.isTexture&&(n.alphaMap=this.alphaMap.toJSON(e).uuid),this.lightMap&&this.lightMap.isTexture&&(n.lightMap=this.lightMap.toJSON(e).uuid,n.lightMapIntensity=this.lightMapIntensity),this.aoMap&&this.aoMap.isTexture&&(n.aoMap=this.aoMap.toJSON(e).uuid,n.aoMapIntensity=this.aoMapIntensity),this.bumpMap&&this.bumpMap.isTexture&&(n.bumpMap=this.bumpMap.toJSON(e).uuid,n.bumpScale=this.bumpScale),this.normalMap&&this.normalMap.isTexture&&(n.normalMap=this.normalMap.toJSON(e).uuid,n.normalMapType=this.normalMapType,n.normalScale=this.normalScale.toArray()),this.displacementMap&&this.displacementMap.isTexture&&(n.displacementMap=this.displacementMap.toJSON(e).uuid,n.displacementScale=this.displacementScale,n.displacementBias=this.displacementBias),this.roughnessMap&&this.roughnessMap.isTexture&&(n.roughnessMap=this.roughnessMap.toJSON(e).uuid),this.metalnessMap&&this.metalnessMap.isTexture&&(n.metalnessMap=this.metalnessMap.toJSON(e).uuid),this.emissiveMap&&this.emissiveMap.isTexture&&(n.emissiveMap=this.emissiveMap.toJSON(e).uuid),this.specularMap&&this.specularMap.isTexture&&(n.specularMap=this.specularMap.toJSON(e).uuid),this.envMap&&this.envMap.isTexture&&(n.envMap=this.envMap.toJSON(e).uuid,n.reflectivity=this.reflectivity,n.refractionRatio=this.refractionRatio,void 0!==this.combine&&(n.combine=this.combine),void 0!==this.envMapIntensity&&(n.envMapIntensity=this.envMapIntensity)),this.gradientMap&&this.gradientMap.isTexture&&(n.gradientMap=this.gradientMap.toJSON(e).uuid),void 0!==this.size&&(n.size=this.size),null!==this.shadowSide&&(n.shadowSide=this.shadowSide),void 0!==this.sizeAttenuation&&(n.sizeAttenuation=this.sizeAttenuation),this.blending!==s.xb&&(n.blending=this.blending),this.side!==s.H&&(n.side=this.side),this.vertexColors&&(n.vertexColors=!0),this.opacity<1&&(n.opacity=this.opacity),!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.morphTargets&&(n.morphTargets=!0),!0===this.morphNormals&&(n.morphNormals=!0),!0===this.skinning&&(n.skinning=!0),!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),t){const t=i(e.textures),s=i(e.images);t.length>0&&(n.textures=t),s.length>0&&(n.images=s)}return n},clone:function(){return(new this.constructor).copy(this)},copy:function(e){this.name=e.name,this.fog=e.fog,this.blending=e.blending,this.side=e.side,this.vertexColors=e.vertexColors,this.opacity=e.opacity,this.transparent=e.transparent,this.blendSrc=e.blendSrc,this.blendDst=e.blendDst,this.blendEquation=e.blendEquation,this.blendSrcAlpha=e.blendSrcAlpha,this.blendDstAlpha=e.blendDstAlpha,this.blendEquationAlpha=e.blendEquationAlpha,this.depthFunc=e.depthFunc,this.depthTest=e.depthTest,this.depthWrite=e.depthWrite,this.stencilWriteMask=e.stencilWriteMask,this.stencilFunc=e.stencilFunc,this.stencilRef=e.stencilRef,this.stencilFuncMask=e.stencilFuncMask,this.stencilFail=e.stencilFail,this.stencilZFail=e.stencilZFail,this.stencilZPass=e.stencilZPass,this.stencilWrite=e.stencilWrite;const t=e.clippingPlanes;let n=null;if(null!==t){const e=t.length;n=new Array(e);for(let i=0;i!==e;++i)n[i]=t[i].clone()}return this.clippingPlanes=n,this.clipIntersection=e.clipIntersection,this.clipShadows=e.clipShadows,this.shadowSide=e.shadowSide,this.colorWrite=e.colorWrite,this.precision=e.precision,this.polygonOffset=e.polygonOffset,this.polygonOffsetFactor=e.polygonOffsetFactor,this.polygonOffsetUnits=e.polygonOffsetUnits,this.dithering=e.dithering,this.alphaTest=e.alphaTest,this.alphaToCoverage=e.alphaToCoverage,this.premultipliedAlpha=e.premultipliedAlpha,this.visible=e.visible,this.toneMapped=e.toneMapped,this.userData=JSON.parse(JSON.stringify(e.userData)),this},dispose:function(){this.dispatchEvent({type:\\\\\\\"dispose\\\\\\\"})}}),Object.defineProperty(a.prototype,\\\\\\\"needsUpdate\\\\\\\",{set:function(e){!0===e&&this.version++}})},function(e,t,n){\\\\\\\"use strict\\\\\\\";n.d(t,\\\\\\\"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(e,t,n,i,s,r,o,a,c){const l=this.elements;return l[0]=e,l[1]=i,l[2]=o,l[3]=t,l[4]=s,l[5]=a,l[6]=n,l[7]=r,l[8]=c,this}identity(){return this.set(1,0,0,0,1,0,0,0,1),this}copy(e){const t=this.elements,n=e.elements;return t[0]=n[0],t[1]=n[1],t[2]=n[2],t[3]=n[3],t[4]=n[4],t[5]=n[5],t[6]=n[6],t[7]=n[7],t[8]=n[8],this}extractBasis(e,t,n){return e.setFromMatrix3Column(this,0),t.setFromMatrix3Column(this,1),n.setFromMatrix3Column(this,2),this}setFromMatrix4(e){const t=e.elements;return this.set(t[0],t[4],t[8],t[1],t[5],t[9],t[2],t[6],t[10]),this}multiply(e){return this.multiplyMatrices(this,e)}premultiply(e){return this.multiplyMatrices(e,this)}multiplyMatrices(e,t){const n=e.elements,i=t.elements,s=this.elements,r=n[0],o=n[3],a=n[6],c=n[1],l=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 s[0]=r*_+o*g+a*x,s[3]=r*m+o*v+a*b,s[6]=r*f+o*y+a*w,s[1]=c*_+l*g+u*x,s[4]=c*m+l*v+u*b,s[7]=c*f+l*y+u*w,s[2]=h*_+d*g+p*x,s[5]=h*m+d*v+p*b,s[8]=h*f+d*y+p*w,this}multiplyScalar(e){const t=this.elements;return t[0]*=e,t[3]*=e,t[6]*=e,t[1]*=e,t[4]*=e,t[7]*=e,t[2]*=e,t[5]*=e,t[8]*=e,this}determinant(){const e=this.elements,t=e[0],n=e[1],i=e[2],s=e[3],r=e[4],o=e[5],a=e[6],c=e[7],l=e[8];return t*r*l-t*o*c-n*s*l+n*o*a+i*s*c-i*r*a}invert(){const e=this.elements,t=e[0],n=e[1],i=e[2],s=e[3],r=e[4],o=e[5],a=e[6],c=e[7],l=e[8],u=l*r-o*c,h=o*a-l*s,d=c*s-r*a,p=t*u+n*h+i*d;if(0===p)return this.set(0,0,0,0,0,0,0,0,0);const _=1/p;return e[0]=u*_,e[1]=(i*c-l*n)*_,e[2]=(o*n-i*r)*_,e[3]=h*_,e[4]=(l*t-i*a)*_,e[5]=(i*s-o*t)*_,e[6]=d*_,e[7]=(n*a-c*t)*_,e[8]=(r*t-n*s)*_,this}transpose(){let e;const t=this.elements;return e=t[1],t[1]=t[3],t[3]=e,e=t[2],t[2]=t[6],t[6]=e,e=t[5],t[5]=t[7],t[7]=e,this}getNormalMatrix(e){return this.setFromMatrix4(e).invert().transpose()}transposeIntoArray(e){const 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0===this.distanceToPoint(e.start)?t.copy(e.start):null;const o=-(e.start.dot(this.normal)+this.constant)/i;return o<0||o>1?null:t.copy(n).multiplyScalar(o).add(e.start)}intersectsLine(e){const t=this.distanceToPoint(e.start),n=this.distanceToPoint(e.end);return t<0&&n>0||n<0&&t>0}intersectsBox(e){return e.intersectsPlane(this)}intersectsSphere(e){return e.intersectsPlane(this)}coplanarPoint(e){return void 0===e&&(console.warn(\\\\\\\"THREE.Plane: .coplanarPoint() target is now required\\\\\\\"),e=new s.a),e.copy(this.normal).multiplyScalar(-this.constant)}applyMatrix4(e,t){const n=t||a.getNormalMatrix(e),i=this.coplanarPoint(r).applyMatrix4(e),s=this.normal.applyMatrix3(n).normalize();return this.constant=-i.dot(s),this}translate(e){return this.constant-=e.dot(this.normal),this}equals(e){return e.normal.equals(this.normal)&&e.constant===this.constant}clone(){return(new this.constructor).copy(this)}}c.prototype.isPlane=!0},function(e,t,n){\\\\\\\"use strict\\\\\\\";n.d(t,\\\\\\\"a\\\\\\\",(function(){return l}));var i=n(9),s=n(0),r=n(5),o=n(4);const a=new r.a,c=new i.a;class l{constructor(e=0,t=0,n=0,i=l.DefaultOrder){this._x=e,this._y=t,this._z=n,this._order=i}get x(){return this._x}set x(e){this._x=e,this._onChangeCallback()}get y(){return this._y}set y(e){this._y=e,this._onChangeCallback()}get z(){return this._z}set z(e){this._z=e,this._onChangeCallback()}get order(){return this._order}set order(e){this._order=e,this._onChangeCallback()}set(e,t,n,i){return this._x=e,this._y=t,this._z=n,this._order=i||this._order,this._onChangeCallback(),this}clone(){return new this.constructor(this._x,this._y,this._z,this._order)}copy(e){return this._x=e._x,this._y=e._y,this._z=e._z,this._order=e._order,this._onChangeCallback(),this}setFromRotationMatrix(e,t,n){const i=o.a.clamp,s=e.elements,r=s[0],a=s[4],c=s[8],l=s[1],u=s[5],h=s[9],d=s[2],p=s[6],_=s[10];switch(t=t||this._order){case\\\\\\\"XYZ\\\\\\\":this._y=Math.asin(i(c,-1,1)),Math.abs(c)<.9999999?(this._x=Math.atan2(-h,_),this._z=Math.atan2(-a,r)):(this._x=Math.atan2(p,u),this._z=0);break;case\\\\\\\"YXZ\\\\\\\":this._x=Math.asin(-i(h,-1,1)),Math.abs(h)<.9999999?(this._y=Math.atan2(c,_),this._z=Math.atan2(l,u)):(this._y=Math.atan2(-d,r),this._z=0);break;case\\\\\\\"ZXY\\\\\\\":this._x=Math.asin(i(p,-1,1)),Math.abs(p)<.9999999?(this._y=Math.atan2(-d,_),this._z=Math.atan2(-a,u)):(this._y=0,this._z=Math.atan2(l,r));break;case\\\\\\\"ZYX\\\\\\\":this._y=Math.asin(-i(d,-1,1)),Math.abs(d)<.9999999?(this._x=Math.atan2(p,_),this._z=Math.atan2(l,r)):(this._x=0,this._z=Math.atan2(-a,u));break;case\\\\\\\"YZX\\\\\\\":this._z=Math.asin(i(l,-1,1)),Math.abs(l)<.9999999?(this._x=Math.atan2(-h,u),this._y=Math.atan2(-d,r)):(this._x=0,this._y=Math.atan2(c,_));break;case\\\\\\\"XZY\\\\\\\":this._z=Math.asin(-i(a,-1,1)),Math.abs(a)<.9999999?(this._x=Math.atan2(p,u),this._y=Math.atan2(c,r)):(this._x=Math.atan2(-h,_),this._y=0);break;default:console.warn(\\\\\\\"THREE.Euler: .setFromRotationMatrix() encountered an unknown order: \\\\\\\"+t)}return this._order=t,!1!==n&&this._onChangeCallback(),this}setFromQuaternion(e,t,n){return a.makeRotationFromQuaternion(e),this.setFromRotationMatrix(a,t,n)}setFromVector3(e,t){return this.set(e.x,e.y,e.z,t||this._order)}reorder(e){return c.setFromEuler(this),this.setFromQuaternion(c,e)}equals(e){return e._x===this._x&&e._y===this._y&&e._z===this._z&&e._order===this._order}fromArray(e){return this._x=e[0],this._y=e[1],this._z=e[2],void 0!==e[3]&&(this._order=e[3]),this._onChangeCallback(),this}toArray(e=[],t=0){return e[t]=this._x,e[t+1]=this._y,e[t+2]=this._z,e[t+3]=this._order,e}toVector3(e){return e?e.set(this._x,this._y,this._z):new s.a(this._x,this._y,this._z)}_onChange(e){return this._onChangeCallback=e,this}_onChangeCallback(){}}l.prototype.isEuler=!0,l.DefaultOrder=\\\\\\\"XYZ\\\\\\\",l.RotationOrders=[\\\\\\\"XYZ\\\\\\\",\\\\\\\"YZX\\\\\\\",\\\\\\\"ZXY\\\\\\\",\\\\\\\"XZY\\\\\\\",\\\\\\\"YXZ\\\\\\\",\\\\\\\"ZYX\\\\\\\"]},function(e,t,n){\\\\\\\"use strict\\\\\\\";function i(e,t,n,i){this.parameterPositions=e,this._cachedIndex=0,this.resultBuffer=void 0!==i?i:new t.constructor(n),this.sampleValues=t,this.valueSize=n}n.d(t,\\\\\\\"a\\\\\\\",(function(){return i})),Object.assign(i.prototype,{evaluate:function(e){const t=this.parameterPositions;let n=this._cachedIndex,i=t[n],s=t[n-1];e:{t:{let r;n:{i:if(!(e<i)){for(let r=n+2;;){if(void 0===i){if(e<s)break i;return n=t.length,this._cachedIndex=n,this.afterEnd_(n-1,e,s)}if(n===r)break;if(s=i,i=t[++n],e<i)break t}r=t.length;break n}if(e>=s)break e;{const o=t[1];e<o&&(n=2,s=o);for(let r=n-2;;){if(void 0===s)return this._cachedIndex=0,this.beforeStart_(0,e,i);if(n===r)break;if(i=s,s=t[--n-1],e>=s)break t}r=n,n=0}}for(;n<r;){const i=n+r>>>1;e<t[i]?r=i:n=i+1}if(i=t[n],s=t[n-1],void 0===s)return this._cachedIndex=0,this.beforeStart_(0,e,i);if(void 0===i)return n=t.length,this._cachedIndex=n,this.afterEnd_(n-1,s,e)}this._cachedIndex=n,this.intervalChanged_(n,s,i)}return this.interpolate_(n,s,e,i)},settings:null,DefaultSettings_:{},getSettings_:function(){return this.settings||this.DefaultSettings_},copySampleValue_:function(e){const t=this.resultBuffer,n=this.sampleValues,i=this.valueSize,s=e*i;for(let e=0;e!==i;++e)t[e]=n[s+e];return t},interpolate_:function(){throw new Error(\\\\\\\"call to abstract method\\\\\\\")},intervalChanged_:function(){}}),Object.assign(i.prototype,{beforeStart_:i.prototype.copySampleValue_,afterEnd_:i.prototype.copySampleValue_})},function(e,t,n){\\\\\\\"use strict\\\\\\\";n.d(t,\\\\\\\"a\\\\\\\",(function(){return h}));var i=n(16),s=n(1),r=n(4),o=n(2),a=n(13);let c;const l=function(e){if(/^data:/i.test(e.src))return e.src;if(\\\\\\\"undefined\\\\\\\"==typeof HTMLCanvasElement)return e.src;let t;if(e instanceof HTMLCanvasElement)t=e;else{void 0===c&&(c=document.createElementNS(\\\\\\\"http://www.w3.org/1999/xhtml\\\\\\\",\\\\\\\"canvas\\\\\\\")),c.width=e.width,c.height=e.height;const n=c.getContext(\\\\\\\"2d\\\\\\\");e instanceof ImageData?n.putImageData(e,0,0):n.drawImage(e,0,0,e.width,e.height),t=c}return t.width>2048||t.height>2048?(console.warn(\\\\\\\"THREE.ImageUtils.getDataURL: Image converted to jpg for performance reasons\\\\\\\",e),t.toDataURL(\\\\\\\"image/jpeg\\\\\\\",.6)):t.toDataURL(\\\\\\\"image/png\\\\\\\")};let u=0;class h extends i.a{constructor(e=h.DEFAULT_IMAGE,t=h.DEFAULT_MAPPING,n=s.n,i=s.n,c=s.V,l=s.Y,d=s.Ib,p=s.Zc,_=1,m=s.U){super(),Object.defineProperty(this,\\\\\\\"id\\\\\\\",{value:u++}),this.uuid=r.a.generateUUID(),this.name=\\\\\\\"\\\\\\\",this.image=e,this.mipmaps=[],this.mapping=t,this.wrapS=n,this.wrapT=i,this.magFilter=c,this.minFilter=l,this.anisotropy=_,this.format=d,this.internalFormat=null,this.type=p,this.offset=new o.a(0,0),this.repeat=new o.a(1,1),this.center=new o.a(0,0),this.rotation=0,this.matrixAutoUpdate=!0,this.matrix=new a.a,this.generateMipmaps=!0,this.premultiplyAlpha=!1,this.flipY=!0,this.unpackAlignment=4,this.encoding=m,this.version=0,this.onUpdate=null}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(e){return this.name=e.name,this.image=e.image,this.mipmaps=e.mipmaps.slice(0),this.mapping=e.mapping,this.wrapS=e.wrapS,this.wrapT=e.wrapT,this.magFilter=e.magFilter,this.minFilter=e.minFilter,this.anisotropy=e.anisotropy,this.format=e.format,this.internalFormat=e.internalFormat,this.type=e.type,this.offset.copy(e.offset),this.repeat.copy(e.repeat),this.center.copy(e.center),this.rotation=e.rotation,this.matrixAutoUpdate=e.matrixAutoUpdate,this.matrix.copy(e.matrix),this.generateMipmaps=e.generateMipmaps,this.premultiplyAlpha=e.premultiplyAlpha,this.flipY=e.flipY,this.unpackAlignment=e.unpackAlignment,this.encoding=e.encoding,this}toJSON(e){const t=void 0===e||\\\\\\\"string\\\\\\\"==typeof e;if(!t&&void 0!==e.textures[this.uuid])return e.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=r.a.generateUUID()),!t&&void 0===e.images[i.uuid]){let t;if(Array.isArray(i)){t=[];for(let e=0,n=i.length;e<n;e++)i[e].isDataTexture?t.push(d(i[e].image)):t.push(d(i[e]))}else t=d(i);e.images[i.uuid]={uuid:i.uuid,url:t}}n.image=i.uuid}return t||(e.textures[this.uuid]=n),n}dispose(){this.dispatchEvent({type:\\\\\\\"dispose\\\\\\\"})}transformUv(e){if(this.mapping!==s.Yc)return e;if(e.applyMatrix3(this.matrix),e.x<0||e.x>1)switch(this.wrapS){case s.wc:e.x=e.x-Math.floor(e.x);break;case s.n:e.x=e.x<0?0:1;break;case s.kb:1===Math.abs(Math.floor(e.x)%2)?e.x=Math.ceil(e.x)-e.x:e.x=e.x-Math.floor(e.x)}if(e.y<0||e.y>1)switch(this.wrapT){case s.wc:e.y=e.y-Math.floor(e.y);break;case s.n:e.y=e.y<0?0:1;break;case s.kb:1===Math.abs(Math.floor(e.y)%2)?e.y=Math.ceil(e.y)-e.y:e.y=e.y-Math.floor(e.y)}return this.flipY&&(e.y=1-e.y),e}set needsUpdate(e){!0===e&&this.version++}}function d(e){return\\\\\\\"undefined\\\\\\\"!=typeof HTMLImageElement&&e instanceof HTMLImageElement||\\\\\\\"undefined\\\\\\\"!=typeof HTMLCanvasElement&&e instanceof HTMLCanvasElement||\\\\\\\"undefined\\\\\\\"!=typeof ImageBitmap&&e instanceof ImageBitmap?l(e):e.data?{data:Array.prototype.slice.call(e.data),width:e.width,height:e.height,type:e.data.constructor.name}:(console.warn(\\\\\\\"THREE.Texture: Unable to serialize Texture.\\\\\\\"),{})}h.DEFAULT_IMAGE=void 0,h.DEFAULT_MAPPING=s.Yc,h.prototype.isTexture=!0},function(e,t,n){\\\\\\\"use strict\\\\\\\";n.d(t,\\\\\\\"a\\\\\\\",(function(){return o}));var i=n(42),s=n(7),r=n(4);function o(e=50,t=1,n=.1,s=2e3){i.a.call(this),this.type=\\\\\\\"PerspectiveCamera\\\\\\\",this.fov=e,this.zoom=1,this.near=n,this.far=s,this.focus=10,this.aspect=t,this.view=null,this.filmGauge=35,this.filmOffset=0,this.updateProjectionMatrix()}o.prototype=Object.assign(Object.create(i.a.prototype),{constructor:o,isPerspectiveCamera:!0,copy:function(e,t){return i.a.prototype.copy.call(this,e,t),this.fov=e.fov,this.zoom=e.zoom,this.near=e.near,this.far=e.far,this.focus=e.focus,this.aspect=e.aspect,this.view=null===e.view?null:Object.assign({},e.view),this.filmGauge=e.filmGauge,this.filmOffset=e.filmOffset,this},setFocalLength:function(e){const t=.5*this.getFilmHeight()/e;this.fov=2*r.a.RAD2DEG*Math.atan(t),this.updateProjectionMatrix()},getFocalLength:function(){const e=Math.tan(.5*r.a.DEG2RAD*this.fov);return.5*this.getFilmHeight()/e},getEffectiveFOV:function(){return 2*r.a.RAD2DEG*Math.atan(Math.tan(.5*r.a.DEG2RAD*this.fov)/this.zoom)},getFilmWidth:function(){return this.filmGauge*Math.min(this.aspect,1)},getFilmHeight:function(){return this.filmGauge/Math.max(this.aspect,1)},setViewOffset:function(e,t,n,i,s,r){this.aspect=e/t,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=e,this.view.fullHeight=t,this.view.offsetX=n,this.view.offsetY=i,this.view.width=s,this.view.height=r,this.updateProjectionMatrix()},clearViewOffset:function(){null!==this.view&&(this.view.enabled=!1),this.updateProjectionMatrix()},updateProjectionMatrix:function(){const e=this.near;let t=e*Math.tan(.5*r.a.DEG2RAD*this.fov)/this.zoom,n=2*t,i=this.aspect*n,s=-.5*i;const o=this.view;if(null!==this.view&&this.view.enabled){const e=o.fullWidth,r=o.fullHeight;s+=o.offsetX*i/e,t-=o.offsetY*n/r,i*=o.width/e,n*=o.height/r}const a=this.filmOffset;0!==a&&(s+=e*a/this.getFilmWidth()),this.projectionMatrix.makePerspective(s,s+i,t,t-n,e,this.far),this.projectionMatrixInverse.copy(this.projectionMatrix).invert()},toJSON:function(e){const t=s.a.prototype.toJSON.call(this,e);return t.object.fov=this.fov,t.object.zoom=this.zoom,t.object.near=this.near,t.object.far=this.far,t.object.focus=this.focus,t.object.aspect=this.aspect,null!==this.view&&(t.object.view=Object.assign({},this.view)),t.object.filmGauge=this.filmGauge,t.object.filmOffset=this.filmOffset,t}})},function(e,t,n){\\\\\\\"use strict\\\\\\\";function i(e){if(0===e.length)return-1/0;let t=e[0];for(let n=1,i=e.length;n<i;++n)e[n]>t&&(t=e[n]);return t}n.d(t,\\\\\\\"a\\\\\\\",(function(){return i})),n.d(t,\\\\\\\"b\\\\\\\",(function(){return r}));const s={Int8Array:Int8Array,Uint8Array:Uint8Array,Uint8ClampedArray:Uint8ClampedArray,Int16Array:Int16Array,Uint16Array:Uint16Array,Int32Array:Int32Array,Uint32Array:Uint32Array,Float32Array:Float32Array,Float64Array:Float64Array};function r(e,t){return new s[e](t)}},function(e,t,n){\\\\\\\"use strict\\\\\\\";function i(e,t,n,i,s){const r=.5*(i-t),o=.5*(s-n),a=e*e;return(2*n-2*i+r+o)*(e*a)+(-3*n+3*i-2*r-o)*a+r*e+n}function s(e,t,n,i){return function(e,t){const n=1-e;return n*n*t}(e,t)+function(e,t){return 2*(1-e)*e*t}(e,n)+function(e,t){return e*e*t}(e,i)}function r(e,t,n,i,s){return function(e,t){const n=1-e;return n*n*n*t}(e,t)+function(e,t){const n=1-e;return 3*n*n*e*t}(e,n)+function(e,t){return 3*(1-e)*e*e*t}(e,i)+function(e,t){return e*e*e*t}(e,s)}n.d(t,\\\\\\\"a\\\\\\\",(function(){return i})),n.d(t,\\\\\\\"c\\\\\\\",(function(){return s})),n.d(t,\\\\\\\"b\\\\\\\",(function(){return r}))},function(e,t,n){\\\\\\\"use strict\\\\\\\";n.d(t,\\\\\\\"a\\\\\\\",(function(){return r}));var i=n(7),s=n(6);class r extends i.a{constructor(e,t=1){super(),this.type=\\\\\\\"Light\\\\\\\",this.color=new s.a(e),this.intensity=t}copy(e){return super.copy(e),this.color.copy(e.color),this.intensity=e.intensity,this}toJSON(e){const t=super.toJSON(e);return t.object.color=this.color.getHex(),t.object.intensity=this.intensity,void 0!==this.groundColor&&(t.object.groundColor=this.groundColor.getHex()),void 0!==this.distance&&(t.object.distance=this.distance),void 0!==this.angle&&(t.object.angle=this.angle),void 0!==this.decay&&(t.object.decay=this.decay),void 0!==this.penumbra&&(t.object.penumbra=this.penumbra),void 0!==this.shadow&&(t.object.shadow=this.shadow.toJSON()),t}}r.prototype.isLight=!0},function(e,t,n){\\\\\\\"use strict\\\\\\\";n.d(t,\\\\\\\"a\\\\\\\",(function(){return f}));var i=n(18),s=n(39),r=n(5),o=n(7),a=n(0),c=n(23),l=n(8),u=n(3);const h=new a.a,d=new a.a,p=new r.a,_=new s.a,m=new i.a;function f(e=new l.a,t=new c.a){o.a.call(this),this.type=\\\\\\\"Line\\\\\\\",this.geometry=e,this.material=t,this.updateMorphTargets()}f.prototype=Object.assign(Object.create(o.a.prototype),{constructor:f,isLine:!0,copy:function(e){return o.a.prototype.copy.call(this,e),this.material=e.material,this.geometry=e.geometry,this},computeLineDistances:function(){const e=this.geometry;if(e.isBufferGeometry)if(null===e.index){const t=e.attributes.position,n=[0];for(let e=1,i=t.count;e<i;e++)h.fromBufferAttribute(t,e-1),d.fromBufferAttribute(t,e),n[e]=n[e-1],n[e]+=h.distanceTo(d);e.setAttribute(\\\\\\\"lineDistance\\\\\\\",new u.c(n,1))}else console.warn(\\\\\\\"THREE.Line.computeLineDistances(): Computation only possible with non-indexed BufferGeometry.\\\\\\\");else e.isGeometry&&console.error(\\\\\\\"THREE.Line.computeLineDistances() no longer supports THREE.Geometry. Use THREE.BufferGeometry instead.\\\\\\\");return this},raycast:function(e,t){const n=this.geometry,i=this.matrixWorld,s=e.params.Line.threshold,r=n.drawRange;if(null===n.boundingSphere&&n.computeBoundingSphere(),m.copy(n.boundingSphere),m.applyMatrix4(i),m.radius+=s,!1===e.ray.intersectsSphere(m))return;p.copy(i).invert(),_.copy(e.ray).applyMatrix4(p);const o=s/((this.scale.x+this.scale.y+this.scale.z)/3),c=o*o,l=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,s=n.attributes.position;if(null!==i){for(let n=Math.max(0,r.start),o=Math.min(i.count,r.start+r.count)-1;n<o;n+=f){const r=i.getX(n),o=i.getX(n+1);l.fromBufferAttribute(s,r),u.fromBufferAttribute(s,o);if(_.distanceSqToSegment(l,u,d,h)>c)continue;d.applyMatrix4(this.matrixWorld);const a=e.ray.origin.distanceTo(d);a<e.near||a>e.far||t.push({distance:a,point:h.clone().applyMatrix4(this.matrixWorld),index:n,face:null,faceIndex:null,object:this})}}else{for(let n=Math.max(0,r.start),i=Math.min(s.count,r.start+r.count)-1;n<i;n+=f){l.fromBufferAttribute(s,n),u.fromBufferAttribute(s,n+1);if(_.distanceSqToSegment(l,u,d,h)>c)continue;d.applyMatrix4(this.matrixWorld);const i=e.ray.origin.distanceTo(d);i<e.near||i>e.far||t.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:function(){const e=this.geometry;if(e.isBufferGeometry){const t=e.morphAttributes,n=Object.keys(t);if(n.length>0){const e=t[n[0]];if(void 0!==e){this.morphTargetInfluences=[],this.morphTargetDictionary={};for(let t=0,n=e.length;t<n;t++){const n=e[t].name||String(t);this.morphTargetInfluences.push(0),this.morphTargetDictionary[n]=t}}}}else{const t=e.morphTargets;void 0!==t&&t.length>0&&console.error(\\\\\\\"THREE.Line.updateMorphTargets() does not support THREE.Geometry. Use THREE.BufferGeometry instead.\\\\\\\")}}})},function(e,t,n){\\\\\\\"use strict\\\\\\\";n.d(t,\\\\\\\"a\\\\\\\",(function(){return c}));var i=n(35),s=n(0),r=n(3);const o=new s.a,a=new s.a;function c(e,t){i.a.call(this,e,t),this.type=\\\\\\\"LineSegments\\\\\\\"}c.prototype=Object.assign(Object.create(i.a.prototype),{constructor:c,isLineSegments:!0,computeLineDistances:function(){const e=this.geometry;if(e.isBufferGeometry)if(null===e.index){const t=e.attributes.position,n=[];for(let e=0,i=t.count;e<i;e+=2)o.fromBufferAttribute(t,e),a.fromBufferAttribute(t,e+1),n[e]=0===e?0:n[e-1],n[e+1]=n[e]+o.distanceTo(a);e.setAttribute(\\\\\\\"lineDistance\\\\\\\",new r.c(n,1))}else console.warn(\\\\\\\"THREE.LineSegments.computeLineDistances(): Computation only possible with non-indexed BufferGeometry.\\\\\\\");else e.isGeometry&&console.error(\\\\\\\"THREE.LineSegments.computeLineDistances() no longer supports THREE.Geometry. Use THREE.BufferGeometry instead.\\\\\\\");return this}})},function(e,t,n){\\\\\\\"use strict\\\\\\\";n.d(t,\\\\\\\"a\\\\\\\",(function(){return i}));class i{constructor(){this.mask=1}set(e){this.mask=1<<e|0}enable(e){this.mask|=1<<e|0}enableAll(){this.mask=-1}toggle(e){this.mask^=1<<e|0}disable(e){this.mask&=~(1<<e|0)}disableAll(){this.mask=0}test(e){return 0!=(this.mask&e.mask)}}},function(e,t,n){\\\\\\\"use strict\\\\\\\";n.d(t,\\\\\\\"a\\\\\\\",(function(){return r}));var i=n(42),s=n(7);class r extends i.a{constructor(e=-1,t=1,n=1,i=-1,s=.1,r=2e3){super(),this.type=\\\\\\\"OrthographicCamera\\\\\\\",this.zoom=1,this.view=null,this.left=e,this.right=t,this.top=n,this.bottom=i,this.near=s,this.far=r,this.updateProjectionMatrix()}copy(e,t){return super.copy(e,t),this.left=e.left,this.right=e.right,this.top=e.top,this.bottom=e.bottom,this.near=e.near,this.far=e.far,this.zoom=e.zoom,this.view=null===e.view?null:Object.assign({},e.view),this}setViewOffset(e,t,n,i,s,r){null===this.view&&(this.view={enabled:!0,fullWidth:1,fullHeight:1,offsetX:0,offsetY:0,width:1,height:1}),this.view.enabled=!0,this.view.fullWidth=e,this.view.fullHeight=t,this.view.offsetX=n,this.view.offsetY=i,this.view.width=s,this.view.height=r,this.updateProjectionMatrix()}clearViewOffset(){null!==this.view&&(this.view.enabled=!1),this.updateProjectionMatrix()}updateProjectionMatrix(){const e=(this.right-this.left)/(2*this.zoom),t=(this.top-this.bottom)/(2*this.zoom),n=(this.right+this.left)/2,i=(this.top+this.bottom)/2;let s=n-e,r=n+e,o=i+t,a=i-t;if(null!==this.view&&this.view.enabled){const e=(this.right-this.left)/this.view.fullWidth/this.zoom,t=(this.top-this.bottom)/this.view.fullHeight/this.zoom;s+=e*this.view.offsetX,r=s+e*this.view.width,o-=t*this.view.offsetY,a=o-t*this.view.height}this.projectionMatrix.makeOrthographic(s,r,o,a,this.near,this.far),this.projectionMatrixInverse.copy(this.projectionMatrix).invert()}toJSON(e){const t=s.a.prototype.toJSON.call(this,e);return t.object.zoom=this.zoom,t.object.left=this.left,t.object.right=this.right,t.object.top=this.top,t.object.bottom=this.bottom,t.object.near=this.near,t.object.far=this.far,null!==this.view&&(t.object.view=Object.assign({},this.view)),t}}r.prototype.isOrthographicCamera=!0},function(e,t,n){\\\\\\\"use strict\\\\\\\";n.d(t,\\\\\\\"a\\\\\\\",(function(){return h}));var i=n(0);const s=new i.a,r=new i.a,o=new i.a,a=new i.a,c=new i.a,l=new i.a,u=new i.a;class h{constructor(e=new i.a,t=new i.a(0,0,-1)){this.origin=e,this.direction=t}set(e,t){return this.origin.copy(e),this.direction.copy(t),this}copy(e){return this.origin.copy(e.origin),this.direction.copy(e.direction),this}at(e,t){return void 0===t&&(console.warn(\\\\\\\"THREE.Ray: .at() target is now required\\\\\\\"),t=new i.a),t.copy(this.direction).multiplyScalar(e).add(this.origin)}lookAt(e){return this.direction.copy(e).sub(this.origin).normalize(),this}recast(e){return this.origin.copy(this.at(e,s)),this}closestPointToPoint(e,t){void 0===t&&(console.warn(\\\\\\\"THREE.Ray: .closestPointToPoint() target is now required\\\\\\\"),t=new i.a),t.subVectors(e,this.origin);const n=t.dot(this.direction);return n<0?t.copy(this.origin):t.copy(this.direction).multiplyScalar(n).add(this.origin)}distanceToPoint(e){return Math.sqrt(this.distanceSqToPoint(e))}distanceSqToPoint(e){const t=s.subVectors(e,this.origin).dot(this.direction);return t<0?this.origin.distanceToSquared(e):(s.copy(this.direction).multiplyScalar(t).add(this.origin),s.distanceToSquared(e))}distanceSqToSegment(e,t,n,i){r.copy(e).add(t).multiplyScalar(.5),o.copy(t).sub(e).normalize(),a.copy(this.origin).sub(r);const s=.5*e.distanceTo(t),c=-this.direction.dot(o),l=a.dot(this.direction),u=-a.dot(o),h=a.lengthSq(),d=Math.abs(1-c*c);let p,_,m,f;if(d>0)if(p=c*u-l,_=c*l-u,f=s*d,p>=0)if(_>=-f)if(_<=f){const e=1/d;p*=e,_*=e,m=p*(p+c*_+2*l)+_*(c*p+_+2*u)+h}else _=s,p=Math.max(0,-(c*_+l)),m=-p*p+_*(_+2*u)+h;else _=-s,p=Math.max(0,-(c*_+l)),m=-p*p+_*(_+2*u)+h;else _<=-f?(p=Math.max(0,-(-c*s+l)),_=p>0?-s:Math.min(Math.max(-s,-u),s),m=-p*p+_*(_+2*u)+h):_<=f?(p=0,_=Math.min(Math.max(-s,-u),s),m=_*(_+2*u)+h):(p=Math.max(0,-(c*s+l)),_=p>0?s:Math.min(Math.max(-s,-u),s),m=-p*p+_*(_+2*u)+h);else _=c>0?-s:s,p=Math.max(0,-(c*_+l)),m=-p*p+_*(_+2*u)+h;return n&&n.copy(this.direction).multiplyScalar(p).add(this.origin),i&&i.copy(o).multiplyScalar(_).add(r),m}intersectSphere(e,t){s.subVectors(e.center,this.origin);const n=s.dot(this.direction),i=s.dot(s)-n*n,r=e.radius*e.radius;if(i>r)return null;const o=Math.sqrt(r-i),a=n-o,c=n+o;return a<0&&c<0?null:a<0?this.at(c,t):this.at(a,t)}intersectsSphere(e){return this.distanceSqToPoint(e.center)<=e.radius*e.radius}distanceToPlane(e){const t=e.normal.dot(this.direction);if(0===t)return 0===e.distanceToPoint(this.origin)?0:null;const n=-(this.origin.dot(e.normal)+e.constant)/t;return n>=0?n:null}intersectPlane(e,t){const n=this.distanceToPlane(e);return null===n?null:this.at(n,t)}intersectsPlane(e){const t=e.distanceToPoint(this.origin);if(0===t)return!0;return e.normal.dot(this.direction)*t<0}intersectBox(e,t){let n,i,s,r,o,a;const c=1/this.direction.x,l=1/this.direction.y,u=1/this.direction.z,h=this.origin;return c>=0?(n=(e.min.x-h.x)*c,i=(e.max.x-h.x)*c):(n=(e.max.x-h.x)*c,i=(e.min.x-h.x)*c),l>=0?(s=(e.min.y-h.y)*l,r=(e.max.y-h.y)*l):(s=(e.max.y-h.y)*l,r=(e.min.y-h.y)*l),n>r||s>i?null:((s>n||n!=n)&&(n=s),(r<i||i!=i)&&(i=r),u>=0?(o=(e.min.z-h.z)*u,a=(e.max.z-h.z)*u):(o=(e.max.z-h.z)*u,a=(e.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,t)))}intersectsBox(e){return null!==this.intersectBox(e,s)}intersectTriangle(e,t,n,i,s){c.subVectors(t,e),l.subVectors(n,e),u.crossVectors(c,l);let r,o=this.direction.dot(u);if(o>0){if(i)return null;r=1}else{if(!(o<0))return null;r=-1,o=-o}a.subVectors(this.origin,e);const h=r*this.direction.dot(l.crossVectors(a,l));if(h<0)return null;const d=r*this.direction.dot(c.cross(a));if(d<0)return null;if(h+d>o)return null;const p=-r*a.dot(u);return p<0?null:this.at(p/o,s)}applyMatrix4(e){return this.origin.applyMatrix4(e),this.direction.transformDirection(e),this}equals(e){return e.origin.equals(this.origin)&&e.direction.equals(this.direction)}clone(){return(new this.constructor).copy(this)}}},function(e,t,n){\\\\\\\"use strict\\\\\\\";n.d(t,\\\\\\\"a\\\\\\\",(function(){return m}));var i=n(0),s=n(27);const r=new i.a,o=new i.a,a=new i.a,c=new i.a,l=new i.a,u=new i.a,h=new i.a,d=new i.a,p=new i.a,_=new i.a;class m{constructor(e=new i.a,t=new i.a,n=new i.a){this.a=e,this.b=t,this.c=n}static getNormal(e,t,n,s){void 0===s&&(console.warn(\\\\\\\"THREE.Triangle: .getNormal() target is now required\\\\\\\"),s=new i.a),s.subVectors(n,t),r.subVectors(e,t),s.cross(r);const o=s.lengthSq();return o>0?s.multiplyScalar(1/Math.sqrt(o)):s.set(0,0,0)}static getBarycoord(e,t,n,s,c){r.subVectors(s,t),o.subVectors(n,t),a.subVectors(e,t);const l=r.dot(r),u=r.dot(o),h=r.dot(a),d=o.dot(o),p=o.dot(a),_=l*d-u*u;if(void 0===c&&(console.warn(\\\\\\\"THREE.Triangle: .getBarycoord() target is now required\\\\\\\"),c=new i.a),0===_)return c.set(-2,-1,-1);const m=1/_,f=(d*h-u*p)*m,g=(l*p-u*h)*m;return c.set(1-f-g,g,f)}static containsPoint(e,t,n,i){return this.getBarycoord(e,t,n,i,c),c.x>=0&&c.y>=0&&c.x+c.y<=1}static getUV(e,t,n,i,s,r,o,a){return this.getBarycoord(e,t,n,i,c),a.set(0,0),a.addScaledVector(s,c.x),a.addScaledVector(r,c.y),a.addScaledVector(o,c.z),a}static isFrontFacing(e,t,n,i){return r.subVectors(n,t),o.subVectors(e,t),r.cross(o).dot(i)<0}set(e,t,n){return this.a.copy(e),this.b.copy(t),this.c.copy(n),this}setFromPointsAndIndices(e,t,n,i){return this.a.copy(e[t]),this.b.copy(e[n]),this.c.copy(e[i]),this}clone(){return(new this.constructor).copy(this)}copy(e){return this.a.copy(e.a),this.b.copy(e.b),this.c.copy(e.c),this}getArea(){return r.subVectors(this.c,this.b),o.subVectors(this.a,this.b),.5*r.cross(o).length()}getMidpoint(e){return void 0===e&&(console.warn(\\\\\\\"THREE.Triangle: .getMidpoint() target is now required\\\\\\\"),e=new i.a),e.addVectors(this.a,this.b).add(this.c).multiplyScalar(1/3)}getNormal(e){return m.getNormal(this.a,this.b,this.c,e)}getPlane(e){return void 0===e&&(console.warn(\\\\\\\"THREE.Triangle: .getPlane() target is now required\\\\\\\"),e=new s.a),e.setFromCoplanarPoints(this.a,this.b,this.c)}getBarycoord(e,t){return m.getBarycoord(e,this.a,this.b,this.c,t)}getUV(e,t,n,i,s){return m.getUV(e,this.a,this.b,this.c,t,n,i,s)}containsPoint(e){return m.containsPoint(e,this.a,this.b,this.c)}isFrontFacing(e){return m.isFrontFacing(this.a,this.b,this.c,e)}intersectsBox(e){return e.intersectsTriangle(this)}closestPointToPoint(e,t){void 0===t&&(console.warn(\\\\\\\"THREE.Triangle: .closestPointToPoint() target is now required\\\\\\\"),t=new i.a);const n=this.a,s=this.b,r=this.c;let o,a;l.subVectors(s,n),u.subVectors(r,n),d.subVectors(e,n);const c=l.dot(d),m=u.dot(d);if(c<=0&&m<=0)return t.copy(n);p.subVectors(e,s);const f=l.dot(p),g=u.dot(p);if(f>=0&&g<=f)return t.copy(s);const v=c*g-f*m;if(v<=0&&c>=0&&f<=0)return o=c/(c-f),t.copy(n).addScaledVector(l,o);_.subVectors(e,r);const y=l.dot(_),x=u.dot(_);if(x>=0&&y<=x)return t.copy(r);const b=y*m-c*x;if(b<=0&&m>=0&&x<=0)return a=m/(m-x),t.copy(n).addScaledVector(u,a);const w=f*x-y*g;if(w<=0&&g-f>=0&&y-x>=0)return h.subVectors(r,s),a=(g-f)/(g-f+(y-x)),t.copy(s).addScaledVector(h,a);const A=1/(w+b+v);return o=b*A,a=v*A,t.copy(n).addScaledVector(l,o).addScaledVector(u,a)}equals(e){return e.a.equals(this.a)&&e.b.equals(this.b)&&e.c.equals(this.c)}}},function(e,t,n){\\\\\\\"use strict\\\\\\\";n.d(t,\\\\\\\"a\\\\\\\",(function(){return r}));var i=n(30),s=n(1);class r extends i.a{constructor(e,t,n,i,r,o,a,c,l,u,h,d){super(null,o,a,c,l,u,i,r,h,d),this.image={data:e||null,width:t||1,height:n||1},this.magFilter=void 0!==l?l:s.ob,this.minFilter=void 0!==u?u:s.ob,this.generateMipmaps=!1,this.flipY=!1,this.unpackAlignment=1,this.needsUpdate=!0}}r.prototype.isDataTexture=!0},function(e,t,n){\\\\\\\"use strict\\\\\\\";n.d(t,\\\\\\\"a\\\\\\\",(function(){return o}));var i=n(5),s=n(7),r=n(0);function o(){s.a.call(this),this.type=\\\\\\\"Camera\\\\\\\",this.matrixWorldInverse=new i.a,this.projectionMatrix=new i.a,this.projectionMatrixInverse=new i.a}o.prototype=Object.assign(Object.create(s.a.prototype),{constructor:o,isCamera:!0,copy:function(e,t){return s.a.prototype.copy.call(this,e,t),this.matrixWorldInverse.copy(e.matrixWorldInverse),this.projectionMatrix.copy(e.projectionMatrix),this.projectionMatrixInverse.copy(e.projectionMatrixInverse),this},getWorldDirection:function(e){void 0===e&&(console.warn(\\\\\\\"THREE.Camera: .getWorldDirection() target is now required\\\\\\\"),e=new r.a),this.updateWorldMatrix(!0,!1);const t=this.matrixWorld.elements;return e.set(-t[8],-t[9],-t[10]).normalize()},updateMatrixWorld:function(e){s.a.prototype.updateMatrixWorld.call(this,e),this.matrixWorldInverse.copy(this.matrixWorld).invert()},updateWorldMatrix:function(e,t){s.a.prototype.updateWorldMatrix.call(this,e,t),this.matrixWorldInverse.copy(this.matrixWorld).invert()},clone:function(){return(new this.constructor).copy(this)}})},function(e,t,n){\\\\\\\"use strict\\\\\\\";n.d(t,\\\\\\\"a\\\\\\\",(function(){return r}));var i=n(12),s=n(6);class r extends i.a{constructor(e){super(),this.type=\\\\\\\"PointsMaterial\\\\\\\",this.color=new s.a(16777215),this.map=null,this.alphaMap=null,this.size=1,this.sizeAttenuation=!0,this.morphTargets=!1,this.setValues(e)}copy(e){return super.copy(e),this.color.copy(e.color),this.map=e.map,this.alphaMap=e.alphaMap,this.size=e.size,this.sizeAttenuation=e.sizeAttenuation,this.morphTargets=e.morphTargets,this}}r.prototype.isPointsMaterial=!0},function(e,t,n){\\\\\\\"use strict\\\\\\\";n.d(t,\\\\\\\"a\\\\\\\",(function(){return a}));var i=n(1),s=n(12),r=n(2),o=n(6);function a(e){s.a.call(this),this.defines={STANDARD:\\\\\\\"\\\\\\\"},this.type=\\\\\\\"MeshStandardMaterial\\\\\\\",this.color=new o.a(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 o.a(0),this.emissiveIntensity=1,this.emissiveMap=null,this.bumpMap=null,this.bumpScale=1,this.normalMap=null,this.normalMapType=i.Uc,this.normalScale=new r.a(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.skinning=!1,this.morphTargets=!1,this.morphNormals=!1,this.flatShading=!1,this.vertexTangents=!1,this.setValues(e)}a.prototype=Object.create(s.a.prototype),a.prototype.constructor=a,a.prototype.isMeshStandardMaterial=!0,a.prototype.copy=function(e){return s.a.prototype.copy.call(this,e),this.defines={STANDARD:\\\\\\\"\\\\\\\"},this.color.copy(e.color),this.roughness=e.roughness,this.metalness=e.metalness,this.map=e.map,this.lightMap=e.lightMap,this.lightMapIntensity=e.lightMapIntensity,this.aoMap=e.aoMap,this.aoMapIntensity=e.aoMapIntensity,this.emissive.copy(e.emissive),this.emissiveMap=e.emissiveMap,this.emissiveIntensity=e.emissiveIntensity,this.bumpMap=e.bumpMap,this.bumpScale=e.bumpScale,this.normalMap=e.normalMap,this.normalMapType=e.normalMapType,this.normalScale.copy(e.normalScale),this.displacementMap=e.displacementMap,this.displacementScale=e.displacementScale,this.displacementBias=e.displacementBias,this.roughnessMap=e.roughnessMap,this.metalnessMap=e.metalnessMap,this.alphaMap=e.alphaMap,this.envMap=e.envMap,this.envMapIntensity=e.envMapIntensity,this.refractionRatio=e.refractionRatio,this.wireframe=e.wireframe,this.wireframeLinewidth=e.wireframeLinewidth,this.wireframeLinecap=e.wireframeLinecap,this.wireframeLinejoin=e.wireframeLinejoin,this.skinning=e.skinning,this.morphTargets=e.morphTargets,this.morphNormals=e.morphNormals,this.flatShading=e.flatShading,this.vertexTangents=e.vertexTangents,this}},function(e,t,n){\\\\\\\"use strict\\\\\\\";n.d(t,\\\\\\\"a\\\\\\\",(function(){return h}));var i=n(5),s=n(2),r=n(0),o=n(10),a=n(59);const c=new i.a,l=new r.a,u=new r.a;class h{constructor(e){this.camera=e,this.bias=0,this.normalBias=0,this.radius=1,this.mapSize=new s.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 s.a(1,1),this._viewportCount=1,this._viewports=[new o.a(0,0,1,1)]}getViewportCount(){return this._viewportCount}getFrustum(){return this._frustum}updateMatrices(e){const t=this.camera,n=this.matrix;l.setFromMatrixPosition(e.matrixWorld),t.position.copy(l),u.setFromMatrixPosition(e.target.matrixWorld),t.lookAt(u),t.updateMatrixWorld(),c.multiplyMatrices(t.projectionMatrix,t.matrixWorldInverse),this._frustum.setFromProjectionMatrix(c),n.set(.5,0,0,.5,0,.5,0,.5,0,0,.5,.5,0,0,0,1),n.multiply(t.projectionMatrix),n.multiply(t.matrixWorldInverse)}getViewport(e){return this._viewports[e]}getFrameExtents(){return this._frameExtents}copy(e){return this.camera=e.camera.clone(),this.bias=e.bias,this.radius=e.radius,this.mapSize.copy(e.mapSize),this}clone(){return(new this.constructor).copy(this)}toJSON(){const e={};return 0!==this.bias&&(e.bias=this.bias),0!==this.normalBias&&(e.normalBias=this.normalBias),1!==this.radius&&(e.radius=this.radius),512===this.mapSize.x&&512===this.mapSize.y||(e.mapSize=this.mapSize.toArray()),e.camera=this.camera.toJSON(!1).object,delete e.camera.matrix,e}}},function(e,t,n){\\\\\\\"use strict\\\\\\\";n.d(t,\\\\\\\"a\\\\\\\",(function(){return i}));const i={decodeText:function(e){if(\\\\\\\"undefined\\\\\\\"!=typeof TextDecoder)return(new TextDecoder).decode(e);let t=\\\\\\\"\\\\\\\";for(let n=0,i=e.length;n<i;n++)t+=String.fromCharCode(e[n]);try{return decodeURIComponent(escape(t))}catch(e){return t}},extractUrlBase:function(e){const t=e.lastIndexOf(\\\\\\\"/\\\\\\\");return-1===t?\\\\\\\"./\\\\\\\":e.substr(0,t+1)}}},function(e,t,n){\\\\\\\"use 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t=[];for(let n=0;n<=e;n++)t.push(this.getPoint(n/e));return this.autoClose&&t.push(t[0]),t}getPoints(e=12){const t=[];let n;for(let i=0,s=this.curves;i<s.length;i++){const r=s[i],o=r&&r.isEllipseCurve?2*e:r&&(r.isLineCurve||r.isLineCurve3)?1:r&&r.isSplineCurve?e*r.points.length:e,a=r.getPoints(o);for(let e=0;e<a.length;e++){const i=a[e];n&&n.equals(i)||(t.push(i),n=i)}}return this.autoClose&&t.length>1&&!t[t.length-1].equals(t[0])&&t.push(t[0]),t}copy(e){super.copy(e),this.curves=[];for(let t=0,n=e.curves.length;t<n;t++){const n=e.curves[t];this.curves.push(n.clone())}return this.autoClose=e.autoClose,this}toJSON(){const e=super.toJSON();e.autoClose=this.autoClose,e.curves=[];for(let t=0,n=this.curves.length;t<n;t++){const n=this.curves[t];e.curves.push(n.toJSON())}return e}fromJSON(e){super.fromJSON(e),this.autoClose=e.autoClose,this.curves=[];for(let t=0,n=e.curves.length;t<n;t++){const n=e.curves[t];this.curves.push((new o[n.type]).fromJSON(n))}return this}}var c=n(57),l=n(76),u=n(74),h=n(75);class d extends a{constructor(e){super(),this.type=\\\\\\\"Path\\\\\\\",this.currentPoint=new i.a,e&&this.setFromPoints(e)}setFromPoints(e){this.moveTo(e[0].x,e[0].y);for(let t=1,n=e.length;t<n;t++)this.lineTo(e[t].x,e[t].y);return this}moveTo(e,t){return this.currentPoint.set(e,t),this}lineTo(e,t){const n=new r.a(this.currentPoint.clone(),new i.a(e,t));return this.curves.push(n),this.currentPoint.set(e,t),this}quadraticCurveTo(e,t,n,s){const r=new h.a(this.currentPoint.clone(),new i.a(e,t),new i.a(n,s));return this.curves.push(r),this.currentPoint.set(n,s),this}bezierCurveTo(e,t,n,s,r,o){const a=new u.a(this.currentPoint.clone(),new i.a(e,t),new i.a(n,s),new i.a(r,o));return this.curves.push(a),this.currentPoint.set(r,o),this}splineThru(e){const t=[this.currentPoint.clone()].concat(e),n=new l.a(t);return this.curves.push(n),this.currentPoint.copy(e[e.length-1]),this}arc(e,t,n,i,s,r){const o=this.currentPoint.x,a=this.currentPoint.y;return this.absarc(e+o,t+a,n,i,s,r),this}absarc(e,t,n,i,s,r){return this.absellipse(e,t,n,n,i,s,r),this}ellipse(e,t,n,i,s,r,o,a){const c=this.currentPoint.x,l=this.currentPoint.y;return this.absellipse(e+c,t+l,n,i,s,r,o,a),this}absellipse(e,t,n,i,s,r,o,a){const l=new c.a(e,t,n,i,s,r,o,a);if(this.curves.length>0){const e=l.getPoint(0);e.equals(this.currentPoint)||this.lineTo(e.x,e.y)}this.curves.push(l);const u=l.getPoint(1);return this.currentPoint.copy(u),this}copy(e){return super.copy(e),this.currentPoint.copy(e.currentPoint),this}toJSON(){const e=super.toJSON();return e.currentPoint=this.currentPoint.toArray(),e}fromJSON(e){return super.fromJSON(e),this.currentPoint.fromArray(e.currentPoint),this}}},function(e,t,n){\\\\\\\"use strict\\\\\\\";n.d(t,\\\\\\\"a\\\\\\\",(function(){return a}));var i=n(6),s=n(47),r=n(55),o=n(53);class a{constructor(){this.type=\\\\\\\"ShapePath\\\\\\\",this.color=new i.a,this.subPaths=[],this.currentPath=null}moveTo(e,t){return this.currentPath=new 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e=super.toJSON();e.uuid=this.uuid,e.holes=[];for(let t=0,n=this.holes.length;t<n;t++){const n=this.holes[t];e.holes.push(n.toJSON())}return e}fromJSON(e){super.fromJSON(e),this.uuid=e.uuid,this.holes=[];for(let t=0,n=e.holes.length;t<n;t++){const n=e.holes[t];this.holes.push((new i.a).fromJSON(n))}return this}}},function(e,t,n){\\\\\\\"use strict\\\\\\\";n.d(t,\\\\\\\"a\\\\\\\",(function(){return a}));var i=n(1),s=n(12),r=n(2),o=n(6);class a extends s.a{constructor(e){super(),this.type=\\\\\\\"MeshPhongMaterial\\\\\\\",this.color=new o.a(16777215),this.specular=new o.a(1118481),this.shininess=30,this.map=null,this.lightMap=null,this.lightMapIntensity=1,this.aoMap=null,this.aoMapIntensity=1,this.emissive=new o.a(0),this.emissiveIntensity=1,this.emissiveMap=null,this.bumpMap=null,this.bumpScale=1,this.normalMap=null,this.normalMapType=i.Uc,this.normalScale=new 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4*this.intensity*Math.PI}set power(e){this.intensity=e/(4*Math.PI)}copy(e){return super.copy(e),this.distance=e.distance,this.decay=e.decay,this.shadow=e.shadow.clone(),this}}_.prototype.isPointLight=!0},function(e,t,n){\\\\\\\"use strict\\\\\\\";n.d(t,\\\\\\\"a\\\\\\\",(function(){return c}));var i=n(0),s=n(18),r=n(27);const o=new s.a,a=new i.a;class c{constructor(e=new r.a,t=new r.a,n=new r.a,i=new r.a,s=new r.a,o=new r.a){this.planes=[e,t,n,i,s,o]}set(e,t,n,i,s,r){const o=this.planes;return o[0].copy(e),o[1].copy(t),o[2].copy(n),o[3].copy(i),o[4].copy(s),o[5].copy(r),this}copy(e){const t=this.planes;for(let n=0;n<6;n++)t[n].copy(e.planes[n]);return this}setFromProjectionMatrix(e){const t=this.planes,n=e.elements,i=n[0],s=n[1],r=n[2],o=n[3],a=n[4],c=n[5],l=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 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super.copy(e),this.color.copy(e.color),this.map=e.map,this.lightMap=e.lightMap,this.lightMapIntensity=e.lightMapIntensity,this.aoMap=e.aoMap,this.aoMapIntensity=e.aoMapIntensity,this.emissive.copy(e.emissive),this.emissiveMap=e.emissiveMap,this.emissiveIntensity=e.emissiveIntensity,this.specularMap=e.specularMap,this.alphaMap=e.alphaMap,this.envMap=e.envMap,this.combine=e.combine,this.reflectivity=e.reflectivity,this.refractionRatio=e.refractionRatio,this.wireframe=e.wireframe,this.wireframeLinewidth=e.wireframeLinewidth,this.wireframeLinecap=e.wireframeLinecap,this.wireframeLinejoin=e.wireframeLinejoin,this.skinning=e.skinning,this.morphTargets=e.morphTargets,this.morphNormals=e.morphNormals,this}}o.prototype.isMeshLambertMaterial=!0},function(e,t,n){\\\\\\\"use strict\\\\\\\";n.d(t,\\\\\\\"a\\\\\\\",(function(){return p}));var i=n(19),s=n(24),r=n(1);class o extends 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t=[],n=e.tracks,i={name:e.name,duration:e.duration,tracks:t,uuid:e.uuid,blendMode:e.blendMode};for(let e=0,i=n.length;e!==i;++e)t.push(s.a.toJSON(n[e]));return i}static CreateFromMorphTargetSequence(e,t,n,s){const r=t.length,o=[];for(let e=0;e<r;e++){let a=[],l=[];a.push((e+r-1)%r,e,(e+1)%r),l.push(0,1,0);const u=i.a.getKeyframeOrder(a);a=i.a.sortedArray(a,1,u),l=i.a.sortedArray(l,1,u),s||0!==a[0]||(a.push(r),l.push(l[0])),o.push(new c.a(\\\\\\\".morphTargetInfluences[\\\\\\\"+t[e].name+\\\\\\\"]\\\\\\\",a,l).scale(1/n))}return new this(e,-1,o)}static findByName(e,t){let n=e;if(!Array.isArray(e)){const t=e;n=t.geometry&&t.geometry.animations||t.animations}for(let e=0;e<n.length;e++)if(n[e].name===t)return n[e];return null}static CreateClipsFromMorphTargetSequences(e,t,n){const i={},s=/^([\\\\w-]*?)([\\\\d]+)$/;for(let t=0,n=e.length;t<n;t++){const n=e[t],r=n.name.match(s);if(r&&r.length>1){const e=r[1];let t=i[e];t||(i[e]=t=[]),t.push(n)}}const r=[];for(const e in i)r.push(this.CreateFromMorphTargetSequence(e,i[e],t,n));return r}static parseAnimation(e,t){if(!e)return console.error(\\\\\\\"THREE.AnimationClip: No animation in JSONLoader data.\\\\\\\"),null;const n=function(e,t,n,s,r){if(0!==n.length){const o=[],a=[];i.a.flattenJSON(n,o,a,s),0!==o.length&&r.push(new e(t,o,a))}},s=[],r=e.name||\\\\\\\"default\\\\\\\",o=e.fps||30,a=e.blendMode;let u=e.length||-1;const d=e.hierarchy||[];for(let e=0;e<d.length;e++){const i=d[e].keys;if(i&&0!==i.length)if(i[0].morphTargets){const e={};let t;for(t=0;t<i.length;t++)if(i[t].morphTargets)for(let n=0;n<i[t].morphTargets.length;n++)e[i[t].morphTargets[n]]=-1;for(const n in e){const e=[],r=[];for(let s=0;s!==i[t].morphTargets.length;++s){const s=i[t];e.push(s.time),r.push(s.morphTarget===n?1:0)}s.push(new c.a(\\\\\\\".morphTargetInfluence[\\\\\\\"+n+\\\\\\\"]\\\\\\\",e,r))}u=e.length*(o||1)}else{const r=\\\\\\\".bones[\\\\\\\"+t[e].name+\\\\\\\"]\\\\\\\";n(h.a,r+\\\\\\\".position\\\\\\\",i,\\\\\\\"pos\\\\\\\",s),n(l.a,r+\\\\\\\".quaternion\\\\\\\",i,\\\\\\\"rot\\\\\\\",s),n(h.a,r+\\\\\\\".scale\\\\\\\",i,\\\\\\\"scl\\\\\\\",s)}}if(0===s.length)return null;return new this(r,u,s,a)}resetDuration(){let e=0;for(let t=0,n=this.tracks.length;t!==n;++t){const n=this.tracks[t];e=Math.max(e,n.times[n.times.length-1])}return this.duration=e,this}trim(){for(let e=0;e<this.tracks.length;e++)this.tracks[e].trim(0,this.duration);return this}validate(){let e=!0;for(let t=0;t<this.tracks.length;t++)e=e&&this.tracks[t].validate();return e}optimize(){for(let e=0;e<this.tracks.length;e++)this.tracks[e].optimize();return this}clone(){const e=[];for(let t=0;t<this.tracks.length;t++)e.push(this.tracks[t].clone());return new this.constructor(this.name,this.duration,e,this.blendMode)}toJSON(){return this.constructor.toJSON(this)}}function _(e){if(void 0===e.type)throw new Error(\\\\\\\"THREE.KeyframeTrack: track type undefined, can not parse\\\\\\\");const t=function(e){switch(e.toLowerCase()){case\\\\\\\"scalar\\\\\\\":case\\\\\\\"double\\\\\\\":case\\\\\\\"float\\\\\\\":case\\\\\\\"number\\\\\\\":case\\\\\\\"integer\\\\\\\":return c.a;case\\\\\\\"vector\\\\\\\":case\\\\\\\"vector2\\\\\\\":case\\\\\\\"vector3\\\\\\\":case\\\\\\\"vector4\\\\\\\":return h.a;case\\\\\\\"color\\\\\\\":return a;case\\\\\\\"quaternion\\\\\\\":return l.a;case\\\\\\\"bool\\\\\\\":case\\\\\\\"boolean\\\\\\\":return o;case\\\\\\\"string\\\\\\\":return u}throw new Error(\\\\\\\"THREE.KeyframeTrack: Unsupported typeName: \\\\\\\"+e)}(e.type);if(void 0===e.times){const t=[],n=[];i.a.flattenJSON(e.keys,t,n,\\\\\\\"value\\\\\\\"),e.times=t,e.values=n}return void 0!==t.parse?t.parse(e):new t(e.name,e.times,e.values,e.interpolation)}},function(e,t,n){\\\\\\\"use strict\\\\\\\";n.d(t,\\\\\\\"a\\\\\\\",(function(){return o}));var i=n(0),s=n(3);const r=new i.a;function o(e,t,n,i){this.name=\\\\\\\"\\\\\\\",this.data=e,this.itemSize=t,this.offset=n,this.normalized=!0===i}Object.defineProperties(o.prototype,{count:{get:function(){return this.data.count}},array:{get:function(){return this.data.array}},needsUpdate:{set:function(e){this.data.needsUpdate=e}}}),Object.assign(o.prototype,{isInterleavedBufferAttribute:!0,applyMatrix4:function(e){for(let t=0,n=this.data.count;t<n;t++)r.x=this.getX(t),r.y=this.getY(t),r.z=this.getZ(t),r.applyMatrix4(e),this.setXYZ(t,r.x,r.y,r.z);return this},applyNormalMatrix:function(e){for(let t=0,n=this.count;t<n;t++)r.x=this.getX(t),r.y=this.getY(t),r.z=this.getZ(t),r.applyNormalMatrix(e),this.setXYZ(t,r.x,r.y,r.z);return this},transformDirection:function(e){for(let t=0,n=this.count;t<n;t++)r.x=this.getX(t),r.y=this.getY(t),r.z=this.getZ(t),r.transformDirection(e),this.setXYZ(t,r.x,r.y,r.z);return this},setX:function(e,t){return this.data.array[e*this.data.stride+this.offset]=t,this},setY:function(e,t){return this.data.array[e*this.data.stride+this.offset+1]=t,this},setZ:function(e,t){return this.data.array[e*this.data.stride+this.offset+2]=t,this},setW:function(e,t){return this.data.array[e*this.data.stride+this.offset+3]=t,this},getX:function(e){return this.data.array[e*this.data.stride+this.offset]},getY:function(e){return this.data.array[e*this.data.stride+this.offset+1]},getZ:function(e){return this.data.array[e*this.data.stride+this.offset+2]},getW:function(e){return this.data.array[e*this.data.stride+this.offset+3]},setXY:function(e,t,n){return e=e*this.data.stride+this.offset,this.data.array[e+0]=t,this.data.array[e+1]=n,this},setXYZ:function(e,t,n,i){return e=e*this.data.stride+this.offset,this.data.array[e+0]=t,this.data.array[e+1]=n,this.data.array[e+2]=i,this},setXYZW:function(e,t,n,i,s){return e=e*this.data.stride+this.offset,this.data.array[e+0]=t,this.data.array[e+1]=n,this.data.array[e+2]=i,this.data.array[e+3]=s,this},clone:function(e){if(void 0===e){console.log(\\\\\\\"THREE.InterleavedBufferAttribute.clone(): Cloning an interlaved buffer attribute will deinterleave buffer data.\\\\\\\");const e=[];for(let t=0;t<this.count;t++){const n=t*this.data.stride+this.offset;for(let t=0;t<this.itemSize;t++)e.push(this.data.array[n+t])}return new s.a(new this.array.constructor(e),this.itemSize,this.normalized)}return void 0===e.interleavedBuffers&&(e.interleavedBuffers={}),void 0===e.interleavedBuffers[this.data.uuid]&&(e.interleavedBuffers[this.data.uuid]=this.data.clone(e)),new o(e.interleavedBuffers[this.data.uuid],this.itemSize,this.offset,this.normalized)},toJSON:function(e){if(void 0===e){console.log(\\\\\\\"THREE.InterleavedBufferAttribute.toJSON(): Serializing an interlaved buffer attribute will deinterleave buffer data.\\\\\\\");const e=[];for(let t=0;t<this.count;t++){const n=t*this.data.stride+this.offset;for(let t=0;t<this.itemSize;t++)e.push(this.data.array[n+t])}return{itemSize:this.itemSize,type:this.array.constructor.name,array:e,normalized:this.normalized}}return void 0===e.interleavedBuffers&&(e.interleavedBuffers={}),void 0===e.interleavedBuffers[this.data.uuid]&&(e.interleavedBuffers[this.data.uuid]=this.data.toJSON(e)),{isInterleavedBufferAttribute:!0,itemSize:this.itemSize,data:this.data.uuid,offset:this.offset,normalized:this.normalized}}})},function(e,t,n){\\\\\\\"use strict\\\\\\\";n.d(t,\\\\\\\"a\\\\\\\",(function(){return _}));const i=\\\\\\\"\\\\\\\\[\\\\\\\\]\\\\\\\\.:\\\\\\\\/\\\\\\\",s=new RegExp(\\\\\\\"[\\\\\\\\[\\\\\\\\]\\\\\\\\.:\\\\\\\\/]\\\\\\\",\\\\\\\"g\\\\\\\"),r=\\\\\\\"[^\\\\\\\\[\\\\\\\\]\\\\\\\\.:\\\\\\\\/]\\\\\\\",o=\\\\\\\"[^\\\\\\\"+i.replace(\\\\\\\"\\\\\\\\.\\\\\\\",\\\\\\\"\\\\\\\")+\\\\\\\"]\\\\\\\",a=/((?:WC+[\\\\/:])*)/.source.replace(\\\\\\\"WC\\\\\\\",r),c=/(WCOD+)?/.source.replace(\\\\\\\"WCOD\\\\\\\",o),l=/(?:\\\\.(WC+)(?:\\\\[(.+)\\\\])?)?/.source.replace(\\\\\\\"WC\\\\\\\",r),u=/\\\\.(WC+)(?:\\\\[(.+)\\\\])?/.source.replace(\\\\\\\"WC\\\\\\\",r),h=new RegExp(\\\\\\\"^\\\\\\\"+a+c+l+u+\\\\\\\"$\\\\\\\"),d=[\\\\\\\"material\\\\\\\",\\\\\\\"materials\\\\\\\",\\\\\\\"bones\\\\\\\"];function p(e,t,n){const i=n||_.parseTrackName(t);this._targetGroup=e,this._bindings=e.subscribe_(t,i)}function _(e,t,n){this.path=t,this.parsedPath=n||_.parseTrackName(t),this.node=_.findNode(e,this.parsedPath.nodeName)||e,this.rootNode=e}Object.assign(p.prototype,{getValue:function(e,t){this.bind();const n=this._targetGroup.nCachedObjects_,i=this._bindings[n];void 0!==i&&i.getValue(e,t)},setValue:function(e,t){const n=this._bindings;for(let i=this._targetGroup.nCachedObjects_,s=n.length;i!==s;++i)n[i].setValue(e,t)},bind:function(){const e=this._bindings;for(let t=this._targetGroup.nCachedObjects_,n=e.length;t!==n;++t)e[t].bind()},unbind:function(){const e=this._bindings;for(let t=this._targetGroup.nCachedObjects_,n=e.length;t!==n;++t)e[t].unbind()}}),Object.assign(_,{Composite:p,create:function(e,t,n){return e&&e.isAnimationObjectGroup?new _.Composite(e,t,n):new _(e,t,n)},sanitizeNodeName:function(e){return e.replace(/\\\\s/g,\\\\\\\"_\\\\\\\").replace(s,\\\\\\\"\\\\\\\")},parseTrackName:function(e){const t=h.exec(e);if(!t)throw new Error(\\\\\\\"PropertyBinding: Cannot parse trackName: \\\\\\\"+e);const n={nodeName:t[2],objectName:t[3],objectIndex:t[4],propertyName:t[5],propertyIndex:t[6]},i=n.nodeName&&n.nodeName.lastIndexOf(\\\\\\\".\\\\\\\");if(void 0!==i&&-1!==i){const e=n.nodeName.substring(i+1);-1!==d.indexOf(e)&&(n.nodeName=n.nodeName.substring(0,i),n.objectName=e)}if(null===n.propertyName||0===n.propertyName.length)throw new Error(\\\\\\\"PropertyBinding: can not parse propertyName from trackName: \\\\\\\"+e);return n},findNode:function(e,t){if(!t||\\\\\\\"\\\\\\\"===t||\\\\\\\".\\\\\\\"===t||-1===t||t===e.name||t===e.uuid)return e;if(e.skeleton){const n=e.skeleton.getBoneByName(t);if(void 0!==n)return n}if(e.children){const n=function(e){for(let i=0;i<e.length;i++){const s=e[i];if(s.name===t||s.uuid===t)return s;const r=n(s.children);if(r)return r}return null},i=n(e.children);if(i)return i}return null}}),Object.assign(_.prototype,{_getValue_unavailable:function(){},_setValue_unavailable:function(){},BindingType:{Direct:0,EntireArray:1,ArrayElement:2,HasFromToArray:3},Versioning:{None:0,NeedsUpdate:1,MatrixWorldNeedsUpdate:2},GetterByBindingType:[function(e,t){e[t]=this.node[this.propertyName]},function(e,t){const n=this.resolvedProperty;for(let i=0,s=n.length;i!==s;++i)e[t++]=n[i]},function(e,t){e[t]=this.resolvedProperty[this.propertyIndex]},function(e,t){this.resolvedProperty.toArray(e,t)}],SetterByBindingTypeAndVersioning:[[function(e,t){this.targetObject[this.propertyName]=e[t]},function(e,t){this.targetObject[this.propertyName]=e[t],this.targetObject.needsUpdate=!0},function(e,t){this.targetObject[this.propertyName]=e[t],this.targetObject.matrixWorldNeedsUpdate=!0}],[function(e,t){const n=this.resolvedProperty;for(let i=0,s=n.length;i!==s;++i)n[i]=e[t++]},function(e,t){const n=this.resolvedProperty;for(let i=0,s=n.length;i!==s;++i)n[i]=e[t++];this.targetObject.needsUpdate=!0},function(e,t){const n=this.resolvedProperty;for(let i=0,s=n.length;i!==s;++i)n[i]=e[t++];this.targetObject.matrixWorldNeedsUpdate=!0}],[function(e,t){this.resolvedProperty[this.propertyIndex]=e[t]},function(e,t){this.resolvedProperty[this.propertyIndex]=e[t],this.targetObject.needsUpdate=!0},function(e,t){this.resolvedProperty[this.propertyIndex]=e[t],this.targetObject.matrixWorldNeedsUpdate=!0}],[function(e,t){this.resolvedProperty.fromArray(e,t)},function(e,t){this.resolvedProperty.fromArray(e,t),this.targetObject.needsUpdate=!0},function(e,t){this.resolvedProperty.fromArray(e,t),this.targetObject.matrixWorldNeedsUpdate=!0}]],getValue:function(e,t){this.bind(),this.getValue(e,t)},setValue:function(e,t){this.bind(),this.setValue(e,t)},bind:function(){let e=this.node;const t=this.parsedPath,n=t.objectName,i=t.propertyName;let s=t.propertyIndex;if(e||(e=_.findNode(this.rootNode,t.nodeName)||this.rootNode,this.node=e),this.getValue=this._getValue_unavailable,this.setValue=this._setValue_unavailable,!e)return void console.error(\\\\\\\"THREE.PropertyBinding: Trying to update node for track: \\\\\\\"+this.path+\\\\\\\" but it wasn't found.\\\\\\\");if(n){let i=t.objectIndex;switch(n){case\\\\\\\"materials\\\\\\\":if(!e.material)return void console.error(\\\\\\\"THREE.PropertyBinding: Can not bind to material as node does not have a material.\\\\\\\",this);if(!e.material.materials)return void console.error(\\\\\\\"THREE.PropertyBinding: Can not bind to material.materials as node.material does not have a materials array.\\\\\\\",this);e=e.material.materials;break;case\\\\\\\"bones\\\\\\\":if(!e.skeleton)return void console.error(\\\\\\\"THREE.PropertyBinding: Can not bind to bones as node does not have a skeleton.\\\\\\\",this);e=e.skeleton.bones;for(let t=0;t<e.length;t++)if(e[t].name===i){i=t;break}break;default:if(void 0===e[n])return void console.error(\\\\\\\"THREE.PropertyBinding: Can not bind to objectName of node undefined.\\\\\\\",this);e=e[n]}if(void 0!==i){if(void 0===e[i])return void console.error(\\\\\\\"THREE.PropertyBinding: Trying to bind to objectIndex of objectName, but is undefined.\\\\\\\",this,e);e=e[i]}}const r=e[i];if(void 0===r){const n=t.nodeName;return void console.error(\\\\\\\"THREE.PropertyBinding: Trying to update property for track: \\\\\\\"+n+\\\\\\\".\\\\\\\"+i+\\\\\\\" but it wasn't found.\\\\\\\",e)}let o=this.Versioning.None;this.targetObject=e,void 0!==e.needsUpdate?o=this.Versioning.NeedsUpdate:void 0!==e.matrixWorldNeedsUpdate&&(o=this.Versioning.MatrixWorldNeedsUpdate);let a=this.BindingType.Direct;if(void 0!==s){if(\\\\\\\"morphTargetInfluences\\\\\\\"===i){if(!e.geometry)return void console.error(\\\\\\\"THREE.PropertyBinding: Can not bind to morphTargetInfluences because node does not have a geometry.\\\\\\\",this);if(!e.geometry.isBufferGeometry)return void console.error(\\\\\\\"THREE.PropertyBinding: Can not bind to morphTargetInfluences on THREE.Geometry. Use THREE.BufferGeometry instead.\\\\\\\",this);if(!e.geometry.morphAttributes)return void console.error(\\\\\\\"THREE.PropertyBinding: Can not bind to morphTargetInfluences because node does not have a geometry.morphAttributes.\\\\\\\",this);void 0!==e.morphTargetDictionary[s]&&(s=e.morphTargetDictionary[s])}a=this.BindingType.ArrayElement,this.resolvedProperty=r,this.propertyIndex=s}else void 0!==r.fromArray&&void 0!==r.toArray?(a=this.BindingType.HasFromToArray,this.resolvedProperty=r):Array.isArray(r)?(a=this.BindingType.EntireArray,this.resolvedProperty=r):this.propertyName=i;this.getValue=this.GetterByBindingType[a],this.setValue=this.SetterByBindingTypeAndVersioning[a][o]},unbind:function(){this.node=null,this.getValue=this._getValue_unbound,this.setValue=this._setValue_unbound}}),Object.assign(_.prototype,{_getValue_unbound:_.prototype.getValue,_setValue_unbound:_.prototype.setValue})},,function(e,t,n){var i=n(119),s=\\\\\\\"object\\\\\\\"==typeof self&&self&&self.Object===Object&&self,r=i||s||Function(\\\\\\\"return this\\\\\\\")();e.exports=r},function(e,t,n){\\\\\\\"use strict\\\\\\\";n.d(t,\\\\\\\"a\\\\\\\",(function(){return d}));var i=n(14),s=n(5),r=n(0),o=n(10);const a=new r.a,c=new o.a,l=new o.a,u=new r.a,h=new s.a;function d(e,t){i.a.call(this,e,t),this.type=\\\\\\\"SkinnedMesh\\\\\\\",this.bindMode=\\\\\\\"attached\\\\\\\",this.bindMatrix=new s.a,this.bindMatrixInverse=new s.a}d.prototype=Object.assign(Object.create(i.a.prototype),{constructor:d,isSkinnedMesh:!0,copy:function(e){return i.a.prototype.copy.call(this,e),this.bindMode=e.bindMode,this.bindMatrix.copy(e.bindMatrix),this.bindMatrixInverse.copy(e.bindMatrixInverse),this.skeleton=e.skeleton,this},bind:function(e,t){this.skeleton=e,void 0===t&&(this.updateMatrixWorld(!0),this.skeleton.calculateInverses(),t=this.matrixWorld),this.bindMatrix.copy(t),this.bindMatrixInverse.copy(t).invert()},pose:function(){this.skeleton.pose()},normalizeSkinWeights:function(){const e=new o.a,t=this.geometry.attributes.skinWeight;for(let n=0,i=t.count;n<i;n++){e.x=t.getX(n),e.y=t.getY(n),e.z=t.getZ(n),e.w=t.getW(n);const i=1/e.manhattanLength();i!==1/0?e.multiplyScalar(i):e.set(1,0,0,0),t.setXYZW(n,e.x,e.y,e.z,e.w)}},updateMatrixWorld:function(e){i.a.prototype.updateMatrixWorld.call(this,e),\\\\\\\"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:function(e,t){const n=this.skeleton,i=this.geometry;c.fromBufferAttribute(i.attributes.skinIndex,e),l.fromBufferAttribute(i.attributes.skinWeight,e),a.fromBufferAttribute(i.attributes.position,e).applyMatrix4(this.bindMatrix),t.set(0,0,0);for(let e=0;e<4;e++){const i=l.getComponent(e);if(0!==i){const s=c.getComponent(e);h.multiplyMatrices(n.bones[s].matrixWorld,n.boneInverses[s]),t.addScaledVector(u.copy(a).applyMatrix4(h),i)}}return t.applyMatrix4(this.bindMatrixInverse)}})},function(e,t,n){\\\\\\\"use strict\\\\\\\";n.d(t,\\\\\\\"a\\\\\\\",(function(){return r}));var i=n(1),s=n(29);function r(e,t,n,i){s.a.call(this,e,t,n,i),this._weightPrev=-0,this._offsetPrev=-0,this._weightNext=-0,this._offsetNext=-0}r.prototype=Object.assign(Object.create(s.a.prototype),{constructor:r,DefaultSettings_:{endingStart:i.id,endingEnd:i.id},intervalChanged_:function(e,t,n){const s=this.parameterPositions;let r=e-2,o=e+1,a=s[r],c=s[o];if(void 0===a)switch(this.getSettings_().endingStart){case i.kd:r=e,a=2*t-n;break;case i.hd:r=s.length-2,a=t+s[r]-s[r+1];break;default:r=e,a=n}if(void 0===c)switch(this.getSettings_().endingEnd){case i.kd:o=e,c=2*n-t;break;case i.hd:o=1,c=n+s[1]-s[0];break;default:o=e-1,c=t}const l=.5*(n-t),u=this.valueSize;this._weightPrev=l/(t-a),this._weightNext=l/(c-n),this._offsetPrev=r*u,this._offsetNext=o*u},interpolate_:function(e,t,n,i){const s=this.resultBuffer,r=this.sampleValues,o=this.valueSize,a=e*o,c=a-o,l=this._offsetPrev,u=this._offsetNext,h=this._weightPrev,d=this._weightNext,p=(n-t)/(i-t),_=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 e=0;e!==o;++e)s[e]=f*r[l+e]+g*r[c+e]+v*r[a+e]+y*r[u+e];return s}})},function(e,t,n){\\\\\\\"use strict\\\\\\\";n.d(t,\\\\\\\"a\\\\\\\",(function(){return r}));var i=n(17),s=n(11);class r extends s.a{constructor(e){super(e)}load(e,t,n,s){void 0!==this.path&&(e=this.path+e),e=this.manager.resolveURL(e);const r=this,o=i.a.get(e);if(void 0!==o)return r.manager.itemStart(e),setTimeout((function(){t&&t(o),r.manager.itemEnd(e)}),0),o;const a=document.createElementNS(\\\\\\\"http://www.w3.org/1999/xhtml\\\\\\\",\\\\\\\"img\\\\\\\");function c(){a.removeEventListener(\\\\\\\"load\\\\\\\",c,!1),a.removeEventListener(\\\\\\\"error\\\\\\\",l,!1),i.a.add(e,this),t&&t(this),r.manager.itemEnd(e)}function l(t){a.removeEventListener(\\\\\\\"load\\\\\\\",c,!1),a.removeEventListener(\\\\\\\"error\\\\\\\",l,!1),s&&s(t),r.manager.itemError(e),r.manager.itemEnd(e)}return a.addEventListener(\\\\\\\"load\\\\\\\",c,!1),a.addEventListener(\\\\\\\"error\\\\\\\",l,!1),\\\\\\\"data:\\\\\\\"!==e.substr(0,5)&&void 0!==this.crossOrigin&&(a.crossOrigin=this.crossOrigin),r.manager.itemStart(e),a.src=e,a}}},function(e,t,n){\\\\\\\"use strict\\\\\\\";n.d(t,\\\\\\\"a\\\\\\\",(function(){return s}));var i=n(29);function s(e,t,n,s){i.a.call(this,e,t,n,s)}s.prototype=Object.assign(Object.create(i.a.prototype),{constructor:s,interpolate_:function(e,t,n,i){const s=this.resultBuffer,r=this.sampleValues,o=this.valueSize,a=e*o,c=a-o,l=(n-t)/(i-t),u=1-l;for(let e=0;e!==o;++e)s[e]=r[c+e]*u+r[a+e]*l;return s}})},function(e,t,n){\\\\\\\"use 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super.copy(e),this.focus=e.focus,this}}a.prototype.isSpotLightShadow=!0;var c=n(7);class l extends i.a{constructor(e,t,n=0,i=Math.PI/3,s=0,r=1){super(e,t),this.type=\\\\\\\"SpotLight\\\\\\\",this.position.copy(c.a.DefaultUp),this.updateMatrix(),this.target=new c.a,this.distance=n,this.angle=i,this.penumbra=s,this.decay=r,this.shadow=new a}get power(){return this.intensity*Math.PI}set power(e){this.intensity=e/Math.PI}copy(e){return super.copy(e),this.distance=e.distance,this.angle=e.angle,this.penumbra=e.penumbra,this.decay=e.decay,this.target=e.target.clone(),this.shadow=e.shadow.clone(),this}}l.prototype.isSpotLight=!0},function(e,t,n){\\\\\\\"use strict\\\\\\\";n.d(t,\\\\\\\"a\\\\\\\",(function(){return r}));var i=n(2),s=n(22);class r extends s.a{constructor(e=new i.a,t=new i.a){super(),this.type=\\\\\\\"LineCurve\\\\\\\",this.v1=e,this.v2=t}getPoint(e,t=new i.a){const n=t;return 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e.v0=this.v0.toArray(),e.v1=this.v1.toArray(),e.v2=this.v2.toArray(),e}fromJSON(e){return super.fromJSON(e),this.v0.fromArray(e.v0),this.v1.fromArray(e.v1),this.v2.fromArray(e.v2),this}}o.prototype.isQuadraticBezierCurve=!0},function(e,t,n){\\\\\\\"use strict\\\\\\\";n.d(t,\\\\\\\"a\\\\\\\",(function(){return o}));var i=n(22),s=n(33),r=n(2);class o extends i.a{constructor(e=[]){super(),this.type=\\\\\\\"SplineCurve\\\\\\\",this.points=e}getPoint(e,t=new r.a){const n=t,i=this.points,o=(i.length-1)*e,a=Math.floor(o),c=o-a,l=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(s.a)(c,l.x,u.x,h.x,d.x),Object(s.a)(c,l.y,u.y,h.y,d.y)),n}copy(e){super.copy(e),this.points=[];for(let t=0,n=e.points.length;t<n;t++){const n=e.points[t];this.points.push(n.clone())}return this}toJSON(){const e=super.toJSON();e.points=[];for(let t=0,n=this.points.length;t<n;t++){const n=this.points[t];e.points.push(n.toArray())}return 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i=n(2),s=n(44),r=n(6),o=n(4);function a(e){s.a.call(this),this.defines={STANDARD:\\\\\\\"\\\\\\\",PHYSICAL:\\\\\\\"\\\\\\\"},this.type=\\\\\\\"MeshPhysicalMaterial\\\\\\\",this.clearcoat=0,this.clearcoatMap=null,this.clearcoatRoughness=0,this.clearcoatRoughnessMap=null,this.clearcoatNormalScale=new i.a(1,1),this.clearcoatNormalMap=null,this.reflectivity=.5,Object.defineProperty(this,\\\\\\\"ior\\\\\\\",{get:function(){return(1+.4*this.reflectivity)/(1-.4*this.reflectivity)},set:function(e){this.reflectivity=o.a.clamp(2.5*(e-1)/(e+1),0,1)}}),this.sheen=null,this.transmission=0,this.transmissionMap=null,this.setValues(e)}a.prototype=Object.create(s.a.prototype),a.prototype.constructor=a,a.prototype.isMeshPhysicalMaterial=!0,a.prototype.copy=function(e){return s.a.prototype.copy.call(this,e),this.defines={STANDARD:\\\\\\\"\\\\\\\",PHYSICAL:\\\\\\\"\\\\\\\"},this.clearcoat=e.clearcoat,this.clearcoatMap=e.clearcoatMap,this.clearcoatRoughness=e.clearcoatRoughness,this.clearcoatRoughnessMap=e.clearcoatRoughnessMap,this.clearcoatNormalMap=e.clearcoatNormalMap,this.clearcoatNormalScale.copy(e.clearcoatNormalScale),this.reflectivity=e.reflectivity,e.sheen?this.sheen=(this.sheen||new r.a).copy(e.sheen):this.sheen=null,this.transmission=e.transmission,this.transmissionMap=e.transmissionMap,this}},function(e,t,n){\\\\\\\"use strict\\\\\\\";n.d(t,\\\\\\\"a\\\\\\\",(function(){return a}));var i=n(1),s=n(69),r=n(30),o=n(11);function a(e){o.a.call(this,e)}a.prototype=Object.assign(Object.create(o.a.prototype),{constructor:a,load:function(e,t,n,o){const a=new r.a,c=new s.a(this.manager);return c.setCrossOrigin(this.crossOrigin),c.setPath(this.path),c.load(e,(function(n){a.image=n;const s=e.search(/\\\\.jpe?g($|\\\\?)/i)>0||0===e.search(/^data\\\\:image\\\\/jpeg/);a.format=s?i.ic:i.Ib,a.needsUpdate=!0,void 0!==t&&t(a)}),n,o),a}})},function(e,t,n){\\\\\\\"use strict\\\\\\\";n.d(t,\\\\\\\"a\\\\\\\",(function(){return c}));var i=n(52),s=n(5),r=n(4);const o=new s.a,a=new s.a;class c{constructor(e=[],t=[]){this.uuid=r.a.generateUUID(),this.bones=e.slice(0),this.boneInverses=t,this.boneMatrices=null,this.boneTexture=null,this.boneTextureSize=0,this.frame=-1,this.init()}init(){const e=this.bones,t=this.boneInverses;if(this.boneMatrices=new Float32Array(16*e.length),0===t.length)this.calculateInverses();else if(e.length!==t.length){console.warn(\\\\\\\"THREE.Skeleton: Number of inverse bone matrices does not match amount of bones.\\\\\\\"),this.boneInverses=[];for(let e=0,t=this.bones.length;e<t;e++)this.boneInverses.push(new s.a)}}calculateInverses(){this.boneInverses.length=0;for(let e=0,t=this.bones.length;e<t;e++){const t=new s.a;this.bones[e]&&t.copy(this.bones[e].matrixWorld).invert(),this.boneInverses.push(t)}}pose(){for(let e=0,t=this.bones.length;e<t;e++){const t=this.bones[e];t&&t.matrixWorld.copy(this.boneInverses[e]).invert()}for(let e=0,t=this.bones.length;e<t;e++){const t=this.bones[e];t&&(t.parent&&t.parent.isBone?(t.matrix.copy(t.parent.matrixWorld).invert(),t.matrix.multiply(t.matrixWorld)):t.matrix.copy(t.matrixWorld),t.matrix.decompose(t.position,t.quaternion,t.scale))}}update(){const e=this.bones,t=this.boneInverses,n=this.boneMatrices,i=this.boneTexture;for(let i=0,s=e.length;i<s;i++){const s=e[i]?e[i].matrixWorld:a;o.multiplyMatrices(s,t[i]),o.toArray(n,16*i)}null!==i&&(i.needsUpdate=!0)}clone(){return new c(this.bones,this.boneInverses)}getBoneByName(e){for(let t=0,n=this.bones.length;t<n;t++){const n=this.bones[t];if(n.name===e)return n}}dispose(){null!==this.boneTexture&&(this.boneTexture.dispose(),this.boneTexture=null)}fromJSON(e,t){this.uuid=e.uuid;for(let n=0,r=e.bones.length;n<r;n++){const r=e.bones[n];let o=t[r];void 0===o&&(console.warn(\\\\\\\"THREE.Skeleton: No bone found with UUID:\\\\\\\",r),o=new i.a),this.bones.push(o),this.boneInverses.push((new s.a).fromArray(e.boneInverses[n]))}return this.init(),this}toJSON(){const e={metadata:{version:4.5,type:\\\\\\\"Skeleton\\\\\\\",generator:\\\\\\\"Skeleton.toJSON\\\\\\\"},bones:[],boneInverses:[]};e.uuid=this.uuid;const t=this.bones,n=this.boneInverses;for(let i=0,s=t.length;i<s;i++){const s=t[i];e.bones.push(s.uuid);const r=n[i];e.boneInverses.push(r.toArray())}return e}}},function(e,t,n){\\\\\\\"use strict\\\\\\\";n.r(t),n.d(t,\\\\\\\"ArcCurve\\\\\\\",(function(){return s})),n.d(t,\\\\\\\"CatmullRomCurve3\\\\\\\",(function(){return r.a})),n.d(t,\\\\\\\"CubicBezierCurve\\\\\\\",(function(){return o.a})),n.d(t,\\\\\\\"CubicBezierCurve3\\\\\\\",(function(){return u})),n.d(t,\\\\\\\"EllipseCurve\\\\\\\",(function(){return i.a})),n.d(t,\\\\\\\"LineCurve\\\\\\\",(function(){return h.a})),n.d(t,\\\\\\\"LineCurve3\\\\\\\",(function(){return d})),n.d(t,\\\\\\\"QuadraticBezierCurve\\\\\\\",(function(){return p.a})),n.d(t,\\\\\\\"QuadraticBezierCurve3\\\\\\\",(function(){return _})),n.d(t,\\\\\\\"SplineCurve\\\\\\\",(function(){return m.a}));var i=n(57);class s extends i.a{constructor(e,t,n,i,s,r){super(e,t,n,n,i,s,r),this.type=\\\\\\\"ArcCurve\\\\\\\"}}s.prototype.isArcCurve=!0;var r=n(86),o=n(74),a=n(22),c=n(33),l=n(0);class u extends a.a{constructor(e=new l.a,t=new l.a,n=new l.a,i=new l.a){super(),this.type=\\\\\\\"CubicBezierCurve3\\\\\\\",this.v0=e,this.v1=t,this.v2=n,this.v3=i}getPoint(e,t=new l.a){const n=t,i=this.v0,s=this.v1,r=this.v2,o=this.v3;return n.set(Object(c.b)(e,i.x,s.x,r.x,o.x),Object(c.b)(e,i.y,s.y,r.y,o.y),Object(c.b)(e,i.z,s.z,r.z,o.z)),n}copy(e){return super.copy(e),this.v0.copy(e.v0),this.v1.copy(e.v1),this.v2.copy(e.v2),this.v3.copy(e.v3),this}toJSON(){const e=super.toJSON();return e.v0=this.v0.toArray(),e.v1=this.v1.toArray(),e.v2=this.v2.toArray(),e.v3=this.v3.toArray(),e}fromJSON(e){return super.fromJSON(e),this.v0.fromArray(e.v0),this.v1.fromArray(e.v1),this.v2.fromArray(e.v2),this.v3.fromArray(e.v3),this}}u.prototype.isCubicBezierCurve3=!0;var h=n(73);class d extends a.a{constructor(e=new l.a,t=new l.a){super(),this.type=\\\\\\\"LineCurve3\\\\\\\",this.isLineCurve3=!0,this.v1=e,this.v2=t}getPoint(e,t=new l.a){const n=t;return 1===e?n.copy(this.v2):(n.copy(this.v2).sub(this.v1),n.multiplyScalar(e).add(this.v1)),n}getPointAt(e,t){return this.getPoint(e,t)}copy(e){return super.copy(e),this.v1.copy(e.v1),this.v2.copy(e.v2),this}toJSON(){const e=super.toJSON();return e.v1=this.v1.toArray(),e.v2=this.v2.toArray(),e}fromJSON(e){return super.fromJSON(e),this.v1.fromArray(e.v1),this.v2.fromArray(e.v2),this}}var p=n(75);class _ extends a.a{constructor(e=new l.a,t=new l.a,n=new 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this.min.add(e),this.max.add(e),this}equals(e){return e.min.equals(this.min)&&e.max.equals(this.max)}}r.prototype.isBox2=!0;var o,a,c,l,u,h,d,p,_,m,f,g,v,y,x,b,w,A=n(8),T=n(21),E=n(3),C=n(11),M=n(13),N=n(47),S=n(55),O=n(48),L=n(53),P=n(0),R=function(e){C.a.call(this,e),this.defaultDPI=90,this.defaultUnit=\\\\\\\"px\\\\\\\"};R.prototype=Object.assign(Object.create(C.a.prototype),{constructor:R,load:function(e,t,n,i){var s=this,r=new T.a(s.manager);r.setPath(s.path),r.setRequestHeader(s.requestHeader),r.setWithCredentials(s.withCredentials),r.load(e,(function(n){try{t(s.parse(n))}catch(t){i?i(t):console.error(t),s.manager.itemError(e)}}),n,i)},parse:function(e){var t=this;function n(e,t,n,i,r,o,a,c){if(0!=t&&0!=n){i=i*Math.PI/180,t=Math.abs(t),n=Math.abs(n);var l=(a.x-c.x)/2,u=(a.y-c.y)/2,h=Math.cos(i)*l+Math.sin(i)*u,d=-Math.sin(i)*l+Math.cos(i)*u,p=t*t,_=n*n,m=h*h,f=d*d,g=m/p+f/_;if(g>1){var v=Math.sqrt(g);p=(t*=v)*t,_=(n*=v)*n}var y=p*f+_*m,x=(p*_-y)/y,b=Math.sqrt(Math.max(0,x));r===o&&(b=-b);var w=b*t*d/n,A=-b*n*h/t,T=Math.cos(i)*w-Math.sin(i)*A+(a.x+c.x)/2,E=Math.sin(i)*w+Math.cos(i)*A+(a.y+c.y)/2,C=s(1,0,(h-w)/t,(d-A)/n),M=s((h-w)/t,(d-A)/n,(-h-w)/t,(-d-A)/n)%(2*Math.PI);e.currentPath.absellipse(T,E,t,n,C,C+M,0===o,i)}else e.lineTo(c.x,c.y)}function s(e,t,n,i){var s=e*n+t*i,r=Math.sqrt(e*e+t*t)*Math.sqrt(n*n+i*i),o=Math.acos(Math.max(-1,Math.min(1,s/r)));return e*i-t*n<0&&(o=-o),o}function r(e,t){t=Object.assign({},t);var n={};if(e.hasAttribute(\\\\\\\"class\\\\\\\"))for(var i=e.getAttribute(\\\\\\\"class\\\\\\\").split(/\\\\s/).filter(Boolean).map((e=>e.trim())),s=0;s<i.length;s++)n=Object.assign(n,_[\\\\\\\".\\\\\\\"+i[s]]);function r(i,s,r){void 0===r&&(r=function(e){return e.startsWith(\\\\\\\"url\\\\\\\")&&console.warn(\\\\\\\"SVGLoader: url access in attributes is not 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h,d=0,p=e.length;for(d=0;d<p;d++)if(h=e[d],Array.isArray(t)&&t.includes(o.length%n)&&i.FLAGS.test(h))s=1,a=h,u();else{if(0===s){if(i.WHITESPACE.test(h))continue;if(i.DIGIT.test(h)||i.SIGN.test(h)){s=1,a=h;continue}if(i.POINT.test(h)){s=2,a=h;continue}i.COMMA.test(h)&&(r&&l(h,d,o),r=!0)}if(1===s){if(i.DIGIT.test(h)){a+=h;continue}if(i.POINT.test(h)){a+=h,s=2;continue}if(i.EXP.test(h)){s=3;continue}i.SIGN.test(h)&&1===a.length&&i.SIGN.test(a[0])&&l(h,d,o)}if(2===s){if(i.DIGIT.test(h)){a+=h;continue}if(i.EXP.test(h)){s=3;continue}i.POINT.test(h)&&\\\\\\\".\\\\\\\"===a[a.length-1]&&l(h,d,o)}if(3===s){if(i.DIGIT.test(h)){c+=h;continue}if(i.SIGN.test(h)){if(\\\\\\\"\\\\\\\"===c){c+=h;continue}1===c.length&&i.SIGN.test(c)&&l(h,d,o)}}i.WHITESPACE.test(h)?(u(),s=0,r=!1):i.COMMA.test(h)?(u(),s=0,r=!0):i.SIGN.test(h)?(u(),s=1,a=h):i.POINT.test(h)?(u(),s=2,a=h):l(h,d,o)}return u(),o}var c=[\\\\\\\"mm\\\\\\\",\\\\\\\"cm\\\\\\\",\\\\\\\"in\\\\\\\",\\\\\\\"pt\\\\\\\",\\\\\\\"pc\\\\\\\",\\\\\\\"px\\\\\\\"],l={mm:{mm:1,cm:.1,in:1/25.4,pt:72/25.4,pc:6/25.4,px:-1},cm:{mm:10,cm:1,in:1/2.54,pt:72/2.54,pc:6/2.54,px:-1},in:{mm:25.4,cm:2.54,in:1,pt:72,pc:6,px:-1},pt:{mm:25.4/72,cm:2.54/72,in:1/72,pt:1,pc:6/72,px:-1},pc:{mm:25.4/6,cm:2.54/6,in:1/6,pt:12,pc:1,px:-1},px:{px:1}};function u(e){var n=\\\\\\\"px\\\\\\\";if(\\\\\\\"string\\\\\\\"==typeof e||e instanceof String)for(var i=0,s=c.length;i<s;i++){var r=c[i];if(e.endsWith(r)){n=r,e=e.substring(0,e.length-r.length);break}}var o=void 0;return\\\\\\\"px\\\\\\\"===n&&\\\\\\\"px\\\\\\\"!==t.defaultUnit?o=l.in[t.defaultUnit]/t.defaultDPI:(o=l[n][t.defaultUnit])<0&&(o=l[n].in*t.defaultDPI),o*parseFloat(e)}function h(e){var t=e.elements;return Math.sqrt(t[0]*t[0]+t[1]*t[1])}function d(e){var t=e.elements;return Math.sqrt(t[3]*t[3]+t[4]*t[4])}var p=[],_={},m=[],f=new M.a,g=new M.a,v=new M.a,y=new M.a,x=new i.a,b=new P.a,w=new M.a,A=(new DOMParser).parseFromString(e,\\\\\\\"image/svg+xml\\\\\\\");return function e(t,s){if(1===t.nodeType){var c=function(e){if(!(e.hasAttribute(\\\\\\\"transform\\\\\\\")||\\\\\\\"use\\\\\\\"===e.nodeName&&(e.hasAttribute(\\\\\\\"x\\\\\\\")||e.hasAttribute(\\\\\\\"y\\\\\\\"))))return null;var t=function(e){var t=new M.a,n=f;if(\\\\\\\"use\\\\\\\"===e.nodeName&&(e.hasAttribute(\\\\\\\"x\\\\\\\")||e.hasAttribute(\\\\\\\"y\\\\\\\"))){var i=u(e.getAttribute(\\\\\\\"x\\\\\\\")),s=u(e.getAttribute(\\\\\\\"y\\\\\\\"));t.translate(i,s)}if(e.hasAttribute(\\\\\\\"transform\\\\\\\"))for(var r=e.getAttribute(\\\\\\\"transform\\\\\\\").split(\\\\\\\")\\\\\\\"),o=r.length-1;o>=0;o--){var c=r[o].trim();if(\\\\\\\"\\\\\\\"!==c){var l=c.indexOf(\\\\\\\"(\\\\\\\"),h=c.length;if(l>0&&l<h){var d=c.substr(0,l),p=a(c.substr(l+1,h-l-1));switch(n.identity(),d){case\\\\\\\"translate\\\\\\\":if(p.length>=1){s=i=p[0];p.length>=2&&(s=p[1]),n.translate(i,s)}break;case\\\\\\\"rotate\\\\\\\":if(p.length>=1){var _=0,m=0,x=0;_=-p[0]*Math.PI/180,p.length>=3&&(m=p[1],x=p[2]),g.identity().translate(-m,-x),v.identity().rotate(_),y.multiplyMatrices(v,g),g.identity().translate(m,x),n.multiplyMatrices(g,y)}break;case\\\\\\\"scale\\\\\\\":if(p.length>=1){var b=p[0],w=b;p.length>=2&&(w=p[1]),n.scale(b,w)}break;case\\\\\\\"skewX\\\\\\\":1===p.length&&n.set(1,Math.tan(p[0]*Math.PI/180),0,0,1,0,0,0,1);break;case\\\\\\\"skewY\\\\\\\":1===p.length&&n.set(1,0,0,Math.tan(p[0]*Math.PI/180),1,0,0,0,1);break;case\\\\\\\"matrix\\\\\\\":6===p.length&&n.set(p[0],p[2],p[4],p[1],p[3],p[5],0,0,1)}}t.premultiply(n)}}return t}(e);m.length>0&&t.premultiply(m[m.length-1]);return w.copy(t),m.push(t),t}(t),l=!0,A=null;switch(t.nodeName){case\\\\\\\"svg\\\\\\\":break;case\\\\\\\"style\\\\\\\":!function(e){if(!e.sheet||!e.sheet.cssRules||!e.sheet.cssRules.length)return;for(var t=0;t<e.sheet.cssRules.length;t++){var n=e.sheet.cssRules[t];if(1===n.type)for(var i=n.selectorText.split(/,/gm).filter(Boolean).map((e=>e.trim())),s=0;s<i.length;s++)_[i[s]]=Object.assign(_[i[s]]||{},n.style)}}(t);break;case\\\\\\\"g\\\\\\\":s=r(t,s);break;case\\\\\\\"path\\\\\\\":s=r(t,s),t.hasAttribute(\\\\\\\"d\\\\\\\")&&(A=function(e){for(var t=new O.a,s=new i.a,r=new i.a,c=new i.a,l=!0,u=!1,h=e.getAttribute(\\\\\\\"d\\\\\\\").match(/[a-df-z][^a-df-z]*/gi),d=0,p=h.length;d<p;d++){var _=h[d],m=_.charAt(0),f=_.substr(1).trim();switch(!0===l&&(u=!0,l=!1),m){case\\\\\\\"M\\\\\\\":for(var g=0,v=(w=a(f)).length;g<v;g+=2)s.x=w[g+0],s.y=w[g+1],r.x=s.x,r.y=s.y,0===g?t.moveTo(s.x,s.y):t.lineTo(s.x,s.y),0===g&&!0===u&&c.copy(s);break;case\\\\\\\"H\\\\\\\":for(g=0,v=(w=a(f)).length;g<v;g++)s.x=w[g],r.x=s.x,r.y=s.y,t.lineTo(s.x,s.y),0===g&&!0===u&&c.copy(s);break;case\\\\\\\"V\\\\\\\":for(g=0,v=(w=a(f)).length;g<v;g++)s.y=w[g],r.x=s.x,r.y=s.y,t.lineTo(s.x,s.y),0===g&&!0===u&&c.copy(s);break;case\\\\\\\"L\\\\\\\":for(g=0,v=(w=a(f)).length;g<v;g+=2)s.x=w[g+0],s.y=w[g+1],r.x=s.x,r.y=s.y,t.lineTo(s.x,s.y),0===g&&!0===u&&c.copy(s);break;case\\\\\\\"C\\\\\\\":for(g=0,v=(w=a(f)).length;g<v;g+=6)t.bezierCurveTo(w[g+0],w[g+1],w[g+2],w[g+3],w[g+4],w[g+5]),r.x=w[g+2],r.y=w[g+3],s.x=w[g+4],s.y=w[g+5],0===g&&!0===u&&c.copy(s);break;case\\\\\\\"S\\\\\\\":for(g=0,v=(w=a(f)).length;g<v;g+=4)t.bezierCurveTo(o(s.x,r.x),o(s.y,r.y),w[g+0],w[g+1],w[g+2],w[g+3]),r.x=w[g+0],r.y=w[g+1],s.x=w[g+2],s.y=w[g+3],0===g&&!0===u&&c.copy(s);break;case\\\\\\\"Q\\\\\\\":for(g=0,v=(w=a(f)).length;g<v;g+=4)t.quadraticCurveTo(w[g+0],w[g+1],w[g+2],w[g+3]),r.x=w[g+0],r.y=w[g+1],s.x=w[g+2],s.y=w[g+3],0===g&&!0===u&&c.copy(s);break;case\\\\\\\"T\\\\\\\":for(g=0,v=(w=a(f)).length;g<v;g+=2){var y=o(s.x,r.x),x=o(s.y,r.y);t.quadraticCurveTo(y,x,w[g+0],w[g+1]),r.x=y,r.y=x,s.x=w[g+0],s.y=w[g+1],0===g&&!0===u&&c.copy(s)}break;case\\\\\\\"A\\\\\\\":for(g=0,v=(w=a(f,[3,4],7)).length;g<v;g+=7)if(w[g+5]!=s.x||w[g+6]!=s.y){var 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n=i.get(t);n&&(e.deleteBuffer(n.buffer),i.delete(t))},update:function(t,s){if(t.isGLBufferAttribute){const e=i.get(t);return void((!e||e.version<t.version)&&i.set(t,{buffer:t.buffer,type:t.type,bytesPerElement:t.elementSize,version:t.version}))}t.isInterleavedBufferAttribute&&(t=t.data);const r=i.get(t);void 0===r?i.set(t,function(t,i){const s=t.array,r=t.usage,o=e.createBuffer();e.bindBuffer(i,o),e.bufferData(i,s,r),t.onUploadCallback();let a=e.FLOAT;return s instanceof Float32Array?a=e.FLOAT:s instanceof Float64Array?console.warn(\\\\\\\"THREE.WebGLAttributes: Unsupported data buffer format: Float64Array.\\\\\\\"):s instanceof Uint16Array?t.isFloat16BufferAttribute?n?a=e.HALF_FLOAT:console.warn(\\\\\\\"THREE.WebGLAttributes: Usage of Float16BufferAttribute requires WebGL2.\\\\\\\"):a=e.UNSIGNED_SHORT:s instanceof Int16Array?a=e.SHORT:s instanceof Uint32Array?a=e.UNSIGNED_INT:s instanceof Int32Array?a=e.INT:s instanceof Int8Array?a=e.BYTE:s instanceof Uint8Array&&(a=e.UNSIGNED_BYTE),{buffer:o,type:a,bytesPerElement:s.BYTES_PER_ELEMENT,version:t.version}}(t,s)):r.version<t.version&&(!function(t,i,s){const r=i.array,o=i.updateRange;e.bindBuffer(s,t),-1===o.count?e.bufferSubData(s,0,r):(n?e.bufferSubData(s,o.offset*r.BYTES_PER_ELEMENT,r,o.offset,o.count):e.bufferSubData(s,o.offset*r.BYTES_PER_ELEMENT,r.subarray(o.offset,o.offset+o.count)),o.count=-1)}(r.buffer,t,s),r.version=t.version)}}}var O=n(8),L=n(3);class P extends O.a{constructor(e=1,t=1,n=1,i=1,s=1,r=1){super(),this.type=\\\\\\\"BoxGeometry\\\\\\\",this.parameters={width:e,height:t,depth:n,widthSegments:i,heightSegments:s,depthSegments:r};const o=this;i=Math.floor(i),s=Math.floor(s),r=Math.floor(r);const a=[],c=[],l=[],u=[];let h=0,d=0;function _(e,t,n,i,s,r,_,m,f,g,v){const y=r/f,x=_/g,b=r/2,w=_/2,A=m/2,T=f+1,E=g+1;let C=0,M=0;const N=new p.a;for(let r=0;r<E;r++){const o=r*x-w;for(let a=0;a<T;a++){const h=a*y-b;N[e]=h*i,N[t]=o*s,N[n]=A,c.push(N.x,N.y,N.z),N[e]=0,N[t]=0,N[n]=m>0?1:-1,l.push(N.x,N.y,N.z),u.push(a/f),u.push(1-r/g),C+=1}}for(let e=0;e<g;e++)for(let t=0;t<f;t++){const n=h+t+T*e,i=h+t+T*(e+1),s=h+(t+1)+T*(e+1),r=h+(t+1)+T*e;a.push(n,i,r),a.push(i,s,r),M+=6}o.addGroup(d,M,v),d+=M,h+=C}_(\\\\\\\"z\\\\\\\",\\\\\\\"y\\\\\\\",\\\\\\\"x\\\\\\\",-1,-1,n,t,e,r,s,0),_(\\\\\\\"z\\\\\\\",\\\\\\\"y\\\\\\\",\\\\\\\"x\\\\\\\",1,-1,n,t,-e,r,s,1),_(\\\\\\\"x\\\\\\\",\\\\\\\"z\\\\\\\",\\\\\\\"y\\\\\\\",1,1,e,n,t,i,r,2),_(\\\\\\\"x\\\\\\\",\\\\\\\"z\\\\\\\",\\\\\\\"y\\\\\\\",1,-1,e,n,-t,i,r,3),_(\\\\\\\"x\\\\\\\",\\\\\\\"y\\\\\\\",\\\\\\\"z\\\\\\\",1,-1,e,t,n,i,s,4),_(\\\\\\\"x\\\\\\\",\\\\\\\"y\\\\\\\",\\\\\\\"z\\\\\\\",-1,-1,e,t,-n,i,s,5),this.setIndex(a),this.setAttribute(\\\\\\\"position\\\\\\\",new L.c(c,3)),this.setAttribute(\\\\\\\"normal\\\\\\\",new L.c(l,3)),this.setAttribute(\\\\\\\"uv\\\\\\\",new L.c(u,2))}}class R extends O.a{constructor(e=1,t=1,n=1,i=1){super(),this.type=\\\\\\\"PlaneGeometry\\\\\\\",this.parameters={width:e,height:t,widthSegments:n,heightSegments:i};const s=e/2,r=t/2,o=Math.floor(n),a=Math.floor(i),c=o+1,l=a+1,u=e/o,h=t/a,d=[],p=[],_=[],m=[];for(let e=0;e<l;e++){const t=e*h-r;for(let n=0;n<c;n++){const i=n*u-s;p.push(i,-t,0),_.push(0,0,1),m.push(n/o),m.push(1-e/a)}}for(let e=0;e<a;e++)for(let t=0;t<o;t++){const n=t+c*e,i=t+c*(e+1),s=t+1+c*(e+1),r=t+1+c*e;d.push(n,i,r),d.push(i,s,r)}this.setIndex(d),this.setAttribute(\\\\\\\"position\\\\\\\",new L.c(p,3)),this.setAttribute(\\\\\\\"normal\\\\\\\",new L.c(_,3)),this.setAttribute(\\\\\\\"uv\\\\\\\",new L.c(m,2))}}var I=n(12);function F(e){const t={};for(const n in e){t[n]={};for(const i in e[n]){const s=e[n][i];s&&(s.isColor||s.isMatrix3||s.isMatrix4||s.isVector2||s.isVector3||s.isVector4||s.isTexture||s.isQuaternion)?t[n][i]=s.clone():Array.isArray(s)?t[n][i]=s.slice():t[n][i]=s}}return t}function D(e){const t={};for(let n=0;n<e.length;n++){const i=F(e[n]);for(const e in i)t[e]=i[e]}return t}const k={clone:F,merge:D};function B(e){I.a.call(this),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.skinning=!1,this.morphTargets=!1,this.morphNormals=!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!==e&&(void 0!==e.attributes&&console.error(\\\\\\\"THREE.ShaderMaterial: attributes should now be defined in THREE.BufferGeometry instead.\\\\\\\"),this.setValues(e))}B.prototype=Object.create(I.a.prototype),B.prototype.constructor=B,B.prototype.isShaderMaterial=!0,B.prototype.copy=function(e){return I.a.prototype.copy.call(this,e),this.fragmentShader=e.fragmentShader,this.vertexShader=e.vertexShader,this.uniforms=F(e.uniforms),this.defines=Object.assign({},e.defines),this.wireframe=e.wireframe,this.wireframeLinewidth=e.wireframeLinewidth,this.lights=e.lights,this.clipping=e.clipping,this.skinning=e.skinning,this.morphTargets=e.morphTargets,this.morphNormals=e.morphNormals,this.extensions=Object.assign({},e.extensions),this.glslVersion=e.glslVersion,this},B.prototype.toJSON=function(e){const t=I.a.prototype.toJSON.call(this,e);t.glslVersion=this.glslVersion,t.uniforms={};for(const n in this.uniforms){const i=this.uniforms[n].value;i&&i.isTexture?t.uniforms[n]={type:\\\\\\\"t\\\\\\\",value:i.toJSON(e).uuid}:i&&i.isColor?t.uniforms[n]={type:\\\\\\\"c\\\\\\\",value:i.getHex()}:i&&i.isVector2?t.uniforms[n]={type:\\\\\\\"v2\\\\\\\",value:i.toArray()}:i&&i.isVector3?t.uniforms[n]={type:\\\\\\\"v3\\\\\\\",value:i.toArray()}:i&&i.isVector4?t.uniforms[n]={type:\\\\\\\"v4\\\\\\\",value:i.toArray()}:i&&i.isMatrix3?t.uniforms[n]={type:\\\\\\\"m3\\\\\\\",value:i.toArray()}:i&&i.isMatrix4?t.uniforms[n]={type:\\\\\\\"m4\\\\\\\",value:i.toArray()}:t.uniforms[n]={value:i}}Object.keys(this.defines).length>0&&(t.defines=this.defines),t.vertexShader=this.vertexShader,t.fragmentShader=this.fragmentShader;const n={};for(const e in this.extensions)!0===this.extensions[e]&&(n[e]=!0);return Object.keys(n).length>0&&(t.extensions=n),t};var z=n(14),U=\\\\\\\"\\\\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 G={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 ALPHATEST\\\\n\\\\n\\\\tif ( diffuseColor.a < ALPHATEST ) discard;\\\\n\\\\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.specularRoughness );\\\\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\\\\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 - environmentBRDF for GGX on mobile\\\\nvec2 integrateSpecularBRDF( const in float dotNV, const in float roughness ) {\\\\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\\\\treturn vec2( -1.04, 1.04 ) * a004 + r.zw;\\\\n\\\\n}\\\\n\\\\nfloat punctualLightIntensityToIrradianceFactor( const in float lightDistance, const in float cutoffDistance, const in float decayExponent ) {\\\\n\\\\n#if defined ( PHYSICALLY_CORRECT_LIGHTS )\\\\n\\\\n\\\\t// based upon Frostbite 3 Moving to Physically-based Rendering\\\\n\\\\t// page 32, equation 26: E[window1]\\\\n\\\\t// https://seblagarde.files.wordpress.com/2015/07/course_notes_moving_frostbite_to_pbr_v32.pdf\\\\n\\\\t// this is intended to be used on spot and point lights who are represented as luminous intensity\\\\n\\\\t// but who must be converted to luminous irradiance for surface lighting calculation\\\\n\\\\tfloat distanceFalloff = 1.0 / max( pow( lightDistance, decayExponent ), 0.01 );\\\\n\\\\n\\\\tif( cutoffDistance > 0.0 ) {\\\\n\\\\n\\\\t\\\\tdistanceFalloff *= pow2( saturate( 1.0 - pow4( lightDistance / cutoffDistance ) ) );\\\\n\\\\n\\\\t}\\\\n\\\\n\\\\treturn distanceFalloff;\\\\n\\\\n#else\\\\n\\\\n\\\\tif( cutoffDistance > 0.0 && decayExponent > 0.0 ) {\\\\n\\\\n\\\\t\\\\treturn pow( saturate( -lightDistance / cutoffDistance + 1.0 ), decayExponent );\\\\n\\\\n\\\\t}\\\\n\\\\n\\\\treturn 1.0;\\\\n\\\\n#endif\\\\n\\\\n}\\\\n\\\\nvec3 BRDF_Diffuse_Lambert( const in vec3 diffuseColor ) {\\\\n\\\\n\\\\treturn RECIPROCAL_PI * diffuseColor;\\\\n\\\\n} // validated\\\\n\\\\nvec3 F_Schlick( const in vec3 specularColor, const in float dotLH ) {\\\\n\\\\n\\\\t// Original approximation by Christophe Schlick \\\\'94\\\\n\\\\t// float fresnel = pow( 1.0 - dotLH, 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 * dotLH - 6.98316 ) * dotLH );\\\\n\\\\n\\\\treturn ( 1.0 - specularColor ) * fresnel + specularColor;\\\\n\\\\n} // validated\\\\n\\\\nvec3 F_Schlick_RoughnessDependent( const in vec3 F0, const in float dotNV, const in float roughness ) {\\\\n\\\\n\\\\t// See F_Schlick\\\\n\\\\tfloat fresnel = exp2( ( -5.55473 * dotNV - 6.98316 ) * dotNV );\\\\n\\\\tvec3 Fr = max( vec3( 1.0 - roughness ), F0 ) - F0;\\\\n\\\\n\\\\treturn Fr * fresnel + F0;\\\\n\\\\n}\\\\n\\\\n\\\\n// Microfacet Models for Refraction through Rough Surfaces - equation (34)\\\\n// http://graphicrants.blogspot.com/2013/08/specular-brdf-reference.html\\\\n// alpha is \\\\\\\"roughness squared\\\\\\\" in Disney’s reparameterization\\\\nfloat G_GGX_Smith( const in float alpha, const in float dotNL, const in float dotNV ) {\\\\n\\\\n\\\\t// geometry term (normalized) = G(l)⋅G(v) / 4(n⋅l)(n⋅v)\\\\n\\\\t// also see #12151\\\\n\\\\n\\\\tfloat a2 = pow2( alpha );\\\\n\\\\n\\\\tfloat gl = dotNL + sqrt( a2 + ( 1.0 - a2 ) * pow2( dotNL ) );\\\\n\\\\tfloat gv = dotNV + sqrt( a2 + ( 1.0 - a2 ) * pow2( dotNV ) );\\\\n\\\\n\\\\treturn 1.0 / ( gl * gv );\\\\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 G_GGX_SmithCorrelated( const in float alpha, const in float dotNL, const in float dotNV ) {\\\\n\\\\n\\\\tfloat a2 = pow2( alpha );\\\\n\\\\n\\\\t// dotNL and dotNV are explicitly swapped. This is not a mistake.\\\\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-Smith Visibility\\\\nvec3 BRDF_Specular_GGX( const in IncidentLight incidentLight, const in vec3 viewDir, const in vec3 normal, const in vec3 specularColor, const in float roughness ) {\\\\n\\\\n\\\\tfloat alpha = pow2( roughness ); // UE4\\\\'s roughness\\\\n\\\\n\\\\tvec3 halfDir = normalize( incidentLight.direction + viewDir );\\\\n\\\\n\\\\tfloat dotNL = saturate( dot( normal, incidentLight.direction ) );\\\\n\\\\tfloat dotNV = saturate( dot( normal, viewDir ) );\\\\n\\\\tfloat dotNH = saturate( dot( normal, halfDir ) );\\\\n\\\\tfloat dotLH = saturate( dot( incidentLight.direction, halfDir ) );\\\\n\\\\n\\\\tvec3 F = F_Schlick( specularColor, dotLH );\\\\n\\\\n\\\\tfloat G = G_GGX_SmithCorrelated( alpha, dotNL, dotNV );\\\\n\\\\n\\\\tfloat D = D_GGX( alpha, dotNH );\\\\n\\\\n\\\\treturn F * ( G * D );\\\\n\\\\n} // validated\\\\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// ref: https://www.unrealengine.com/blog/physically-based-shading-on-mobile - environmentBRDF for GGX on mobile\\\\nvec3 BRDF_Specular_GGX_Environment( const in vec3 viewDir, const in vec3 normal, const in vec3 specularColor, const in float roughness ) {\\\\n\\\\n\\\\tfloat dotNV = saturate( dot( normal, viewDir ) );\\\\n\\\\n\\\\tvec2 brdf = integrateSpecularBRDF( dotNV, roughness );\\\\n\\\\n\\\\treturn specularColor * brdf.x + brdf.y;\\\\n\\\\n} // validated\\\\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 BRDF_Specular_Multiscattering_Environment( const in GeometricContext geometry, const in vec3 specularColor, const in float roughness, inout vec3 singleScatter, inout vec3 multiScatter ) {\\\\n\\\\n\\\\tfloat dotNV = saturate( dot( geometry.normal, geometry.viewDir ) );\\\\n\\\\n\\\\tvec3 F = F_Schlick_RoughnessDependent( specularColor, dotNV, roughness );\\\\n\\\\tvec2 brdf = integrateSpecularBRDF( dotNV, roughness );\\\\n\\\\tvec3 FssEss = F * brdf.x + brdf.y;\\\\n\\\\n\\\\tfloat Ess = brdf.x + brdf.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\\\\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_Specular_BlinnPhong( const in IncidentLight incidentLight, const in GeometricContext geometry, const in vec3 specularColor, const in float shininess ) {\\\\n\\\\n\\\\tvec3 halfDir = normalize( incidentLight.direction + geometry.viewDir );\\\\n\\\\n\\\\t//float dotNL = saturate( dot( geometry.normal, incidentLight.direction ) );\\\\n\\\\t//float dotNV = saturate( dot( geometry.normal, geometry.viewDir ) );\\\\n\\\\tfloat dotNH = saturate( dot( geometry.normal, halfDir ) );\\\\n\\\\tfloat dotLH = saturate( dot( incidentLight.direction, halfDir ) );\\\\n\\\\n\\\\tvec3 F = F_Schlick( specularColor, dotLH );\\\\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// source: http://simonstechblog.blogspot.ca/2011/12/microfacet-brdf.html\\\\nfloat GGXRoughnessToBlinnExponent( const in float ggxRoughness ) {\\\\n\\\\treturn ( 2.0 / pow2( ggxRoughness + 0.0001 ) - 2.0 );\\\\n}\\\\n\\\\nfloat BlinnExponentToGGXRoughness( const in float blinnExponent ) {\\\\n\\\\treturn sqrt( 2.0 / ( blinnExponent + 2.0 ) );\\\\n}\\\\n\\\\n#if defined( USE_SHEEN )\\\\n\\\\n// https://github.com/google/filament/blob/master/shaders/src/brdf.fs#L94\\\\nfloat D_Charlie(float roughness, float NoH) {\\\\n\\\\t// Estevez and Kulla 2017, \\\\\\\"Production Friendly Microfacet Sheen BRDF\\\\\\\"\\\\n\\\\tfloat invAlpha = 1.0 / roughness;\\\\n\\\\tfloat cos2h = NoH * NoH;\\\\n\\\\tfloat sin2h = max(1.0 - cos2h, 0.0078125); // 2^(-14/2), so sin2h^2 > 0 in fp16\\\\n\\\\treturn (2.0 + invAlpha) * pow(sin2h, invAlpha * 0.5) / (2.0 * PI);\\\\n}\\\\n\\\\n// https://github.com/google/filament/blob/master/shaders/src/brdf.fs#L136\\\\nfloat V_Neubelt(float NoV, float NoL) {\\\\n\\\\t// Neubelt and Pettineo 2013, \\\\\\\"Crafting a Next-gen Material Pipeline for The Order: 1886\\\\\\\"\\\\n\\\\treturn saturate(1.0 / (4.0 * (NoL + NoV - NoL * NoV)));\\\\n}\\\\n\\\\nvec3 BRDF_Specular_Sheen( const in float roughness, const in vec3 L, const in GeometricContext geometry, vec3 specularColor ) {\\\\n\\\\n\\\\tvec3 N = geometry.normal;\\\\n\\\\tvec3 V = geometry.viewDir;\\\\n\\\\n\\\\tvec3 H = normalize( V + L );\\\\n\\\\tfloat dotNH = saturate( dot( N, H ) );\\\\n\\\\n\\\\treturn specularColor * D_Charlie( roughness, dotNH ) * V_Neubelt( dot(N, V), dot(N, L) );\\\\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 average( const in vec3 color ) { return dot( color, vec3( 0.3333 ) ); }\\\\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\\\\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\\\\n#ifdef HIGH_PRECISION\\\\n\\\\tfloat precisionSafeLength( vec3 v ) { return length( v ); }\\\\n#else\\\\n\\\\tfloat max3( vec3 v ) { return max( max( v.x, v.y ), v.z ); }\\\\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 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\\\\nvec3 projectOnPlane(in vec3 point, in vec3 pointOnPlane, in vec3 planeNormal ) {\\\\n\\\\n\\\\tfloat distance = dot( planeNormal, point - pointOnPlane );\\\\n\\\\n\\\\treturn - distance * planeNormal + point;\\\\n\\\\n}\\\\n\\\\nfloat sideOfPlane( in vec3 point, in vec3 pointOnPlane, in vec3 planeNormal ) {\\\\n\\\\n\\\\treturn sign( dot( point - pointOnPlane, planeNormal ) );\\\\n\\\\n}\\\\n\\\\nvec3 linePlaneIntersect( in vec3 pointOnLine, in vec3 lineDirection, in vec3 pointOnPlane, in vec3 planeNormal ) {\\\\n\\\\n\\\\treturn lineDirection * ( dot( planeNormal, pointOnPlane - pointOnLine ) / dot( planeNormal, lineDirection ) ) + pointOnLine;\\\\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#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#ifndef ENVMAP_TYPE_CUBE_UV\\\\n\\\\n\\\\t\\\\tenvColor = envMapTexelToLinear( envColor );\\\\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\\\\t\\\\tuniform float refractionRatio;\\\\n\\\\t#endif\\\\n\\\\n\\\\tvec3 getLightProbeIndirectIrradiance( /*const in SpecularLightProbe specularLightProbe,*/ const in GeometricContext geometry, const in int maxMIPLevel ) {\\\\n\\\\n\\\\t\\\\tvec3 worldNormal = inverseTransformDirection( geometry.normal, viewMatrix );\\\\n\\\\n\\\\t\\\\t#ifdef ENVMAP_TYPE_CUBE\\\\n\\\\n\\\\t\\\\t\\\\tvec3 queryVec = vec3( flipEnvMap * worldNormal.x, worldNormal.yz );\\\\n\\\\n\\\\t\\\\t\\\\t// TODO: replace with properly filtered cubemaps and access the irradiance LOD level, be it the last LOD level\\\\n\\\\t\\\\t\\\\t// of a specular cubemap, or just the default level of a specially created irradiance cubemap.\\\\n\\\\n\\\\t\\\\t\\\\t#ifdef TEXTURE_LOD_EXT\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tvec4 envMapColor = textureCubeLodEXT( envMap, queryVec, float( maxMIPLevel ) );\\\\n\\\\n\\\\t\\\\t\\\\t#else\\\\n\\\\n\\\\t\\\\t\\\\t\\\\t// force the bias high to get the last LOD level as it is the most blurred.\\\\n\\\\t\\\\t\\\\t\\\\tvec4 envMapColor = textureCube( envMap, queryVec, float( maxMIPLevel ) );\\\\n\\\\n\\\\t\\\\t\\\\t#endif\\\\n\\\\n\\\\t\\\\t\\\\tenvMapColor.rgb = envMapTexelToLinear( envMapColor ).rgb;\\\\n\\\\n\\\\t\\\\t#elif defined( ENVMAP_TYPE_CUBE_UV )\\\\n\\\\n\\\\t\\\\t\\\\tvec4 envMapColor = textureCubeUV( envMap, worldNormal, 1.0 );\\\\n\\\\n\\\\t\\\\t#else\\\\n\\\\n\\\\t\\\\t\\\\tvec4 envMapColor = vec4( 0.0 );\\\\n\\\\n\\\\t\\\\t#endif\\\\n\\\\n\\\\t\\\\treturn PI * envMapColor.rgb * envMapIntensity;\\\\n\\\\n\\\\t}\\\\n\\\\n\\\\t// Trowbridge-Reitz distribution to Mip level, following the logic of http://casual-effects.blogspot.ca/2011/08/plausible-environment-lighting-in-two.html\\\\n\\\\tfloat getSpecularMIPLevel( const in float roughness, const in int maxMIPLevel ) {\\\\n\\\\n\\\\t\\\\tfloat maxMIPLevelScalar = float( maxMIPLevel );\\\\n\\\\n\\\\t\\\\tfloat sigma = PI * roughness * roughness / ( 1.0 + roughness );\\\\n\\\\t\\\\tfloat desiredMIPLevel = maxMIPLevelScalar + log2( sigma );\\\\n\\\\n\\\\t\\\\t// clamp to allowable LOD ranges.\\\\n\\\\t\\\\treturn clamp( desiredMIPLevel, 0.0, maxMIPLevelScalar );\\\\n\\\\n\\\\t}\\\\n\\\\n\\\\tvec3 getLightProbeIndirectRadiance( /*const in SpecularLightProbe specularLightProbe,*/ const in vec3 viewDir, const in vec3 normal, const in float roughness, const in int maxMIPLevel ) {\\\\n\\\\n\\\\t\\\\t#ifdef ENVMAP_MODE_REFLECTION\\\\n\\\\n\\\\t\\\\t\\\\tvec3 reflectVec = reflect( -viewDir, normal );\\\\n\\\\n\\\\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\\\\treflectVec = normalize( mix( reflectVec, normal, roughness * roughness) );\\\\n\\\\n\\\\t\\\\t#else\\\\n\\\\n\\\\t\\\\t\\\\tvec3 reflectVec = refract( -viewDir, normal, refractionRatio );\\\\n\\\\n\\\\t\\\\t#endif\\\\n\\\\n\\\\t\\\\treflectVec = inverseTransformDirection( reflectVec, viewMatrix );\\\\n\\\\n\\\\t\\\\tfloat specularMIPLevel = getSpecularMIPLevel( roughness, maxMIPLevel );\\\\n\\\\n\\\\t\\\\t#ifdef ENVMAP_TYPE_CUBE\\\\n\\\\n\\\\t\\\\t\\\\tvec3 queryReflectVec = vec3( flipEnvMap * reflectVec.x, reflectVec.yz );\\\\n\\\\n\\\\t\\\\t\\\\t#ifdef TEXTURE_LOD_EXT\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tvec4 envMapColor = textureCubeLodEXT( envMap, queryReflectVec, specularMIPLevel );\\\\n\\\\n\\\\t\\\\t\\\\t#else\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tvec4 envMapColor = textureCube( envMap, queryReflectVec, specularMIPLevel );\\\\n\\\\n\\\\t\\\\t\\\\t#endif\\\\n\\\\n\\\\t\\\\t\\\\tenvMapColor.rgb = envMapTexelToLinear( envMapColor ).rgb;\\\\n\\\\n\\\\t\\\\t#elif defined( ENVMAP_TYPE_CUBE_UV )\\\\n\\\\n\\\\t\\\\t\\\\tvec4 envMapColor = textureCubeUV( envMap, reflectVec, roughness );\\\\n\\\\n\\\\t\\\\t#endif\\\\n\\\\n\\\\t\\\\treturn envMapColor.rgb * envMapIntensity;\\\\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\\\\tfogDepth = - mvPosition.z;\\\\n\\\\n#endif\\\\n\\\\\\\",fog_pars_vertex:\\\\\\\"\\\\n#ifdef USE_FOG\\\\n\\\\n\\\\tvarying float fogDepth;\\\\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 * fogDepth * fogDepth );\\\\n\\\\n\\\\t#else\\\\n\\\\n\\\\t\\\\tfloat fogFactor = smoothstep( fogNear, fogFar, fogDepth );\\\\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 fogDepth;\\\\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\\\\treflectedLight.indirectDiffuse += PI * lightMapTexelToLinear( lightMapTexel ).rgb * lightMapIntensity; // factor of PI should not be present; included here to prevent breakage\\\\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 );\\\\n\\\\n#ifdef DOUBLE_SIDED\\\\n\\\\n\\\\tvIndirectBack += getAmbientLightIrradiance( ambientLightColor );\\\\n\\\\n\\\\tvIndirectBack += getLightProbeIrradiance( lightProbe, backGeometry );\\\\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\\\\tgetPointDirectLightIrradiance( pointLights[ i ], geometry, directLight );\\\\n\\\\n\\\\t\\\\tdotNL = dot( geometry.normal, directLight.direction );\\\\n\\\\t\\\\tdirectLightColor_Diffuse = PI * 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\\\\tgetSpotDirectLightIrradiance( spotLights[ i ], geometry, directLight );\\\\n\\\\n\\\\t\\\\tdotNL = dot( geometry.normal, directLight.direction );\\\\n\\\\t\\\\tdirectLightColor_Diffuse = PI * 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/*\\\\n#if NUM_RECT_AREA_LIGHTS > 0\\\\n\\\\n\\\\tfor ( int i = 0; i < NUM_RECT_AREA_LIGHTS; i ++ ) {\\\\n\\\\n\\\\t\\\\t// TODO (abelnation): implement\\\\n\\\\n\\\\t}\\\\n\\\\n#endif\\\\n*/\\\\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\\\\tgetDirectionalDirectLightIrradiance( directionalLights[ i ], geometry, directLight );\\\\n\\\\n\\\\t\\\\tdotNL = dot( geometry.normal, directLight.direction );\\\\n\\\\t\\\\tdirectLightColor_Diffuse = PI * 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 );\\\\n\\\\n\\\\t\\\\t#ifdef DOUBLE_SIDED\\\\n\\\\n\\\\t\\\\t\\\\tvIndirectBack += getHemisphereLightIrradiance( hemisphereLights[ i ], backGeometry );\\\\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 GeometricContext geometry ) {\\\\n\\\\n\\\\tvec3 worldNormal = inverseTransformDirection( geometry.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\\\\t#ifndef PHYSICALLY_CORRECT_LIGHTS\\\\n\\\\n\\\\t\\\\tirradiance *= PI;\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\treturn irradiance;\\\\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 getDirectionalDirectLightIrradiance( const in DirectionalLight directionalLight, const in GeometricContext geometry, out IncidentLight directLight ) {\\\\n\\\\n\\\\t\\\\tdirectLight.color = directionalLight.color;\\\\n\\\\t\\\\tdirectLight.direction = directionalLight.direction;\\\\n\\\\t\\\\tdirectLight.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// directLight is an out parameter as having it as a return value caused compiler errors on some devices\\\\n\\\\tvoid getPointDirectLightIrradiance( const in PointLight pointLight, const in GeometricContext geometry, out IncidentLight directLight ) {\\\\n\\\\n\\\\t\\\\tvec3 lVector = pointLight.position - geometry.position;\\\\n\\\\t\\\\tdirectLight.direction = normalize( lVector );\\\\n\\\\n\\\\t\\\\tfloat lightDistance = length( lVector );\\\\n\\\\n\\\\t\\\\tdirectLight.color = pointLight.color;\\\\n\\\\t\\\\tdirectLight.color *= punctualLightIntensityToIrradianceFactor( lightDistance, pointLight.distance, pointLight.decay );\\\\n\\\\t\\\\tdirectLight.visible = ( directLight.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// directLight is an out parameter as having it as a return value caused compiler errors on some devices\\\\n\\\\tvoid getSpotDirectLightIrradiance( const in SpotLight spotLight, const in GeometricContext geometry, out IncidentLight directLight ) {\\\\n\\\\n\\\\t\\\\tvec3 lVector = spotLight.position - geometry.position;\\\\n\\\\t\\\\tdirectLight.direction = normalize( lVector );\\\\n\\\\n\\\\t\\\\tfloat lightDistance = length( lVector );\\\\n\\\\t\\\\tfloat angleCos = dot( directLight.direction, spotLight.direction );\\\\n\\\\n\\\\t\\\\tif ( angleCos > spotLight.coneCos ) {\\\\n\\\\n\\\\t\\\\t\\\\tfloat spotEffect = smoothstep( spotLight.coneCos, spotLight.penumbraCos, angleCos );\\\\n\\\\n\\\\t\\\\t\\\\tdirectLight.color = spotLight.color;\\\\n\\\\t\\\\t\\\\tdirectLight.color *= spotEffect * punctualLightIntensityToIrradianceFactor( lightDistance, spotLight.distance, spotLight.decay );\\\\n\\\\t\\\\t\\\\tdirectLight.visible = true;\\\\n\\\\n\\\\t\\\\t} else {\\\\n\\\\n\\\\t\\\\t\\\\tdirectLight.color = vec3( 0.0 );\\\\n\\\\t\\\\t\\\\tdirectLight.visible = false;\\\\n\\\\n\\\\t\\\\t}\\\\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 GeometricContext geometry ) {\\\\n\\\\n\\\\t\\\\tfloat dotNL = dot( geometry.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\\\\t#ifndef PHYSICALLY_CORRECT_LIGHTS\\\\n\\\\n\\\\t\\\\t\\\\tirradiance *= PI;\\\\n\\\\n\\\\t\\\\t#endif\\\\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\\\\n#ifndef FLAT_SHADED\\\\n\\\\n\\\\tvarying vec3 vNormal;\\\\n\\\\n#endif\\\\n\\\\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\\\\t#ifndef PHYSICALLY_CORRECT_LIGHTS\\\\n\\\\n\\\\t\\\\tirradiance *= PI; // punctual light\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\treflectedLight.directDiffuse += irradiance * BRDF_Diffuse_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_Diffuse_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\\\\n#ifndef FLAT_SHADED\\\\n\\\\n\\\\tvarying vec3 vNormal;\\\\n\\\\n#endif\\\\n\\\\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\\\\t#ifndef PHYSICALLY_CORRECT_LIGHTS\\\\n\\\\n\\\\t\\\\tirradiance *= PI; // punctual light\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\treflectedLight.directDiffuse += irradiance * BRDF_Diffuse_Lambert( material.diffuseColor );\\\\n\\\\n\\\\treflectedLight.directSpecular += irradiance * BRDF_Specular_BlinnPhong( directLight, geometry, 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_Diffuse_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.specularRoughness = max( roughnessFactor, 0.0525 );// 0.0525 corresponds to the base mip of a 256 cubemap.\\\\nmaterial.specularRoughness += geometryRoughness;\\\\nmaterial.specularRoughness = min( material.specularRoughness, 1.0 );\\\\n\\\\n#ifdef REFLECTIVITY\\\\n\\\\n\\\\tmaterial.specularColor = mix( vec3( MAXIMUM_SPECULAR_COEFFICIENT * pow2( reflectivity ) ), diffuseColor.rgb, metalnessFactor );\\\\n\\\\n#else\\\\n\\\\n\\\\tmaterial.specularColor = mix( vec3( DEFAULT_SPECULAR_COEFFICIENT ), diffuseColor.rgb, metalnessFactor );\\\\n\\\\n#endif\\\\n\\\\n#ifdef CLEARCOAT\\\\n\\\\n\\\\tmaterial.clearcoat = clearcoat;\\\\n\\\\tmaterial.clearcoatRoughness = clearcoatRoughness;\\\\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.sheenColor = sheen;\\\\n\\\\n#endif\\\\n\\\\\\\",lights_physical_pars_fragment:\\\\\\\"\\\\nstruct PhysicalMaterial {\\\\n\\\\n\\\\tvec3 diffuseColor;\\\\n\\\\tfloat specularRoughness;\\\\n\\\\tvec3 specularColor;\\\\n\\\\n#ifdef CLEARCOAT\\\\n\\\\tfloat clearcoat;\\\\n\\\\tfloat clearcoatRoughness;\\\\n#endif\\\\n#ifdef USE_SHEEN\\\\n\\\\tvec3 sheenColor;\\\\n#endif\\\\n\\\\n};\\\\n\\\\n#define MAXIMUM_SPECULAR_COEFFICIENT 0.16\\\\n#define DEFAULT_SPECULAR_COEFFICIENT 0.04\\\\n\\\\n// Clear coat directional hemishperical reflectance (this approximation should be improved)\\\\nfloat clearcoatDHRApprox( const in float roughness, const in float dotNL ) {\\\\n\\\\n\\\\treturn DEFAULT_SPECULAR_COEFFICIENT + ( 1.0 - DEFAULT_SPECULAR_COEFFICIENT ) * ( pow( 1.0 - dotNL, 5.0 ) * pow( 1.0 - roughness, 2.0 ) );\\\\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.specularRoughness;\\\\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#ifndef PHYSICALLY_CORRECT_LIGHTS\\\\n\\\\n\\\\t\\\\tirradiance *= PI; // punctual light\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\t#ifdef CLEARCOAT\\\\n\\\\n\\\\t\\\\tfloat ccDotNL = saturate( dot( geometry.clearcoatNormal, directLight.direction ) );\\\\n\\\\n\\\\t\\\\tvec3 ccIrradiance = ccDotNL * directLight.color;\\\\n\\\\n\\\\t\\\\t#ifndef PHYSICALLY_CORRECT_LIGHTS\\\\n\\\\n\\\\t\\\\t\\\\tccIrradiance *= PI; // punctual light\\\\n\\\\n\\\\t\\\\t#endif\\\\n\\\\n\\\\t\\\\tfloat clearcoatDHR = material.clearcoat * clearcoatDHRApprox( material.clearcoatRoughness, ccDotNL );\\\\n\\\\n\\\\t\\\\treflectedLight.directSpecular += ccIrradiance * material.clearcoat * BRDF_Specular_GGX( directLight, geometry.viewDir, geometry.clearcoatNormal, vec3( DEFAULT_SPECULAR_COEFFICIENT ), material.clearcoatRoughness );\\\\n\\\\n\\\\t#else\\\\n\\\\n\\\\t\\\\tfloat clearcoatDHR = 0.0;\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\t#ifdef USE_SHEEN\\\\n\\\\t\\\\treflectedLight.directSpecular += ( 1.0 - clearcoatDHR ) * irradiance * BRDF_Specular_Sheen(\\\\n\\\\t\\\\t\\\\tmaterial.specularRoughness,\\\\n\\\\t\\\\t\\\\tdirectLight.direction,\\\\n\\\\t\\\\t\\\\tgeometry,\\\\n\\\\t\\\\t\\\\tmaterial.sheenColor\\\\n\\\\t\\\\t);\\\\n\\\\t#else\\\\n\\\\t\\\\treflectedLight.directSpecular += ( 1.0 - clearcoatDHR ) * irradiance * BRDF_Specular_GGX( directLight, geometry.viewDir, geometry.normal, material.specularColor, material.specularRoughness);\\\\n\\\\t#endif\\\\n\\\\n\\\\treflectedLight.directDiffuse += ( 1.0 - clearcoatDHR ) * irradiance * BRDF_Diffuse_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_Diffuse_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 CLEARCOAT\\\\n\\\\n\\\\t\\\\tfloat ccDotNV = saturate( dot( geometry.clearcoatNormal, geometry.viewDir ) );\\\\n\\\\n\\\\t\\\\treflectedLight.indirectSpecular += clearcoatRadiance * material.clearcoat * BRDF_Specular_GGX_Environment( geometry.viewDir, geometry.clearcoatNormal, vec3( DEFAULT_SPECULAR_COEFFICIENT ), material.clearcoatRoughness );\\\\n\\\\n\\\\t\\\\tfloat ccDotNL = ccDotNV;\\\\n\\\\t\\\\tfloat clearcoatDHR = material.clearcoat * clearcoatDHRApprox( material.clearcoatRoughness, ccDotNL );\\\\n\\\\n\\\\t#else\\\\n\\\\n\\\\t\\\\tfloat clearcoatDHR = 0.0;\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\tfloat clearcoatInv = 1.0 - clearcoatDHR;\\\\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\\\\tBRDF_Specular_Multiscattering_Environment( geometry, material.specularColor, material.specularRoughness, singleScattering, multiScattering );\\\\n\\\\n\\\\tvec3 diffuse = material.diffuseColor * ( 1.0 - ( singleScattering + multiScattering ) );\\\\n\\\\n\\\\treflectedLight.indirectSpecular += clearcoatInv * 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 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\\\\tgetPointDirectLightIrradiance( 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\\\\tgetSpotDirectLightIrradiance( 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\\\\tgetDirectionalDirectLightIrradiance( 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 );\\\\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 );\\\\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; // factor of PI should not be present; included here to prevent breakage\\\\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 += getLightProbeIndirectIrradiance( /*lightProbe,*/ geometry, maxMipLevel );\\\\n\\\\n\\\\t#endif\\\\n\\\\n#endif\\\\n\\\\n#if defined( USE_ENVMAP ) && defined( RE_IndirectSpecular )\\\\n\\\\n\\\\tradiance += getLightProbeIndirectRadiance( /*specularLightProbe,*/ geometry.viewDir, geometry.normal, material.specularRoughness, maxMipLevel );\\\\n\\\\n\\\\t#ifdef CLEARCOAT\\\\n\\\\n\\\\t\\\\tclearcoatRadiance += getLightProbeIndirectRadiance( /*specularLightProbe,*/ geometry.viewDir, geometry.clearcoatNormal, material.clearcoatRoughness, maxMipLevel );\\\\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\\\\tobjectNormal += morphNormal0 * morphTargetInfluences[ 0 ];\\\\n\\\\tobjectNormal += morphNormal1 * morphTargetInfluences[ 1 ];\\\\n\\\\tobjectNormal += morphNormal2 * morphTargetInfluences[ 2 ];\\\\n\\\\tobjectNormal += morphNormal3 * morphTargetInfluences[ 3 ];\\\\n\\\\n#endif\\\\n\\\\\\\",morphtarget_pars_vertex:\\\\\\\"\\\\n#ifdef USE_MORPHTARGETS\\\\n\\\\n\\\\tuniform float morphTargetBaseInfluence;\\\\n\\\\n\\\\t#ifndef USE_MORPHNORMALS\\\\n\\\\n\\\\t\\\\tuniform float morphTargetInfluences[ 8 ];\\\\n\\\\n\\\\t#else\\\\n\\\\n\\\\t\\\\tuniform float morphTargetInfluences[ 4 ];\\\\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\\\\ttransformed += morphTarget0 * morphTargetInfluences[ 0 ];\\\\n\\\\ttransformed += morphTarget1 * morphTargetInfluences[ 1 ];\\\\n\\\\ttransformed += morphTarget2 * morphTargetInfluences[ 2 ];\\\\n\\\\ttransformed += morphTarget3 * morphTargetInfluences[ 3 ];\\\\n\\\\n\\\\t#ifndef USE_MORPHNORMALS\\\\n\\\\n\\\\t\\\\ttransformed += morphTarget4 * morphTargetInfluences[ 4 ];\\\\n\\\\t\\\\ttransformed += morphTarget5 * morphTargetInfluences[ 5 ];\\\\n\\\\t\\\\ttransformed += morphTarget6 * morphTargetInfluences[ 6 ];\\\\n\\\\t\\\\ttransformed += morphTarget7 * morphTargetInfluences[ 7 ];\\\\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\\\\\\\",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 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\\\\\\\",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}\\\\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\\\\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:U,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\\\\\\\",transmissionmap_fragment:\\\\\\\"\\\\n#ifdef USE_TRANSMISSIONMAP\\\\n\\\\n\\\\ttotalTransmission *= texture2D( transmissionMap, vUv ).r;\\\\n\\\\n#endif\\\\n\\\\\\\",transmissionmap_pars_fragment:\\\\\\\"\\\\n#ifdef USE_TRANSMISSIONMAP\\\\n\\\\n\\\\tuniform sampler2D transmissionMap;\\\\n\\\\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 )\\\\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_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\\\\\\\",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\\\\\\\",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\\\\\\\",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\\\\\\\",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 <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\\\\\\\",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\\\\\\\",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 <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\\\\\\\",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\\\\\\\",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\\\\\\\",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\\\\\\\",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\\\\tgl_FragColor = vec4( outgoingLight, diffuseColor.a );\\\\n\\\\n\\\\t#include <tonemapping_fragment>\\\\n\\\\t#include <encodings_fragment>\\\\n\\\\t#include <fog_fragment>\\\\n\\\\t#include <premultiplied_alpha_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\\\\\\\",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 <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\\\\t\\\\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\\\\tgl_FragColor = vec4( outgoingLight, diffuseColor.a );\\\\n\\\\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\\\\\\\",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\\\\t#include <skinbase_vertex>\\\\n\\\\n\\\\t#ifdef USE_ENVMAP\\\\n\\\\n\\\\t#include <beginnormal_vertex>\\\\n\\\\t#include <morphnormal_vertex>\\\\n\\\\t#include <skinnormal_vertex>\\\\n\\\\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\\\\n\\\\t#include <worldpos_vertex>\\\\n\\\\t#include <clipping_planes_vertex>\\\\n\\\\t#include <envmap_vertex>\\\\n\\\\t#include <fog_vertex>\\\\n\\\\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 <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_Diffuse_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_Diffuse_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\\\\tgl_FragColor = vec4( outgoingLight, diffuseColor.a );\\\\n\\\\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\\\\\\\",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\\\\\\\",meshmatcap_frag:\\\\\\\"\\\\n#define MATCAP\\\\n\\\\nuniform vec3 diffuse;\\\\nuniform float opacity;\\\\nuniform sampler2D matcap;\\\\n\\\\nvarying vec3 vViewPosition;\\\\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 <map_pars_fragment>\\\\n#include <alphamap_pars_fragment>\\\\n\\\\n#include <fog_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\\\\tgl_FragColor = vec4( outgoingLight, diffuseColor.a );\\\\n\\\\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\\\\\\\",meshmatcap_vert:\\\\\\\"\\\\n#define MATCAP\\\\n\\\\nvarying vec3 vViewPosition;\\\\n\\\\n#ifndef FLAT_SHADED\\\\n\\\\n\\\\tvarying vec3 vNormal;\\\\n\\\\n#endif\\\\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 <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\\\\n\\\\t#ifndef FLAT_SHADED // Normal computed with derivatives when FLAT_SHADED\\\\n\\\\n\\\\t\\\\tvNormal = normalize( transformedNormal );\\\\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\\\\n\\\\t#include <logdepthbuf_vertex>\\\\n\\\\t#include <clipping_planes_vertex>\\\\n\\\\t#include <fog_vertex>\\\\n\\\\n\\\\tvViewPosition = - mvPosition.xyz;\\\\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 <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 <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\\\\tgl_FragColor = vec4( outgoingLight, diffuseColor.a );\\\\n\\\\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#ifndef FLAT_SHADED\\\\n\\\\n\\\\tvarying vec3 vNormal;\\\\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 <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#ifndef FLAT_SHADED // Normal computed with derivatives when FLAT_SHADED\\\\n\\\\n\\\\tvNormal = normalize( transformedNormal );\\\\n\\\\n#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\\\\tvViewPosition = - mvPosition.xyz;\\\\n\\\\n\\\\t#include <worldpos_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 <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 <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\\\\n\\\\tgl_FragColor = vec4( outgoingLight, diffuseColor.a );\\\\n\\\\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\\\\\\\",meshphong_vert:\\\\\\\"\\\\n#define PHONG\\\\n\\\\nvarying vec3 vViewPosition;\\\\n\\\\n#ifndef FLAT_SHADED\\\\n\\\\n\\\\tvarying vec3 vNormal;\\\\n\\\\n#endif\\\\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 <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#ifndef FLAT_SHADED // Normal computed with derivatives when FLAT_SHADED\\\\n\\\\n\\\\tvNormal = normalize( transformedNormal );\\\\n\\\\n#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\\\\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\\\\\\\",meshphysical_frag:\\\\\\\"\\\\n#define STANDARD\\\\n\\\\n#ifdef PHYSICAL\\\\n\\\\t#define REFLECTIVITY\\\\n\\\\t#define CLEARCOAT\\\\n\\\\t#define TRANSMISSION\\\\n#endif\\\\n\\\\nuniform vec3 diffuse;\\\\nuniform vec3 emissive;\\\\nuniform float roughness;\\\\nuniform float metalness;\\\\nuniform float opacity;\\\\n\\\\n#ifdef TRANSMISSION\\\\n\\\\tuniform float transmission;\\\\n#endif\\\\n\\\\n#ifdef REFLECTIVITY\\\\n\\\\tuniform float reflectivity;\\\\n#endif\\\\n\\\\n#ifdef CLEARCOAT\\\\n\\\\tuniform float clearcoat;\\\\n\\\\tuniform float clearcoatRoughness;\\\\n#endif\\\\n\\\\n#ifdef USE_SHEEN\\\\n\\\\tuniform vec3 sheen;\\\\n#endif\\\\n\\\\nvarying vec3 vViewPosition;\\\\n\\\\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\\\\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 <aomap_pars_fragment>\\\\n#include <lightmap_pars_fragment>\\\\n#include <emissivemap_pars_fragment>\\\\n#include <transmissionmap_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 <lights_physical_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#ifdef TRANSMISSION\\\\n\\\\t\\\\tfloat totalTransmission = transmission;\\\\n\\\\t#endif\\\\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\\\\t#include <transmissionmap_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 outgoingLight = reflectedLight.directDiffuse + reflectedLight.indirectDiffuse + reflectedLight.directSpecular + reflectedLight.indirectSpecular + totalEmissiveRadiance;\\\\n\\\\n\\\\t// this is a stub for the transmission model\\\\n\\\\t#ifdef TRANSMISSION\\\\n\\\\t\\\\tdiffuseColor.a *= mix( saturate( 1. - totalTransmission + linearToRelativeLuminance( reflectedLight.directSpecular + reflectedLight.indirectSpecular ) ), 1.0, metalness );\\\\n\\\\t#endif\\\\n\\\\n\\\\tgl_FragColor = vec4( outgoingLight, diffuseColor.a );\\\\n\\\\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#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\\\\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 <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#ifndef FLAT_SHADED // Normal 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\\\\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\\\\\\\",normal_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#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\\\\n#include <packing>\\\\n#include <uv_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\\\\\\\",normal_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#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\\\\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\\\\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\\\\n#ifndef FLAT_SHADED // Normal 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\\\\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\\\\\\\",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 <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\\\\tgl_FragColor = vec4( outgoingLight, diffuseColor.a );\\\\n\\\\n\\\\t#include <tonemapping_fragment>\\\\n\\\\t#include <encodings_fragment>\\\\n\\\\t#include <fog_fragment>\\\\n\\\\t#include <premultiplied_alpha_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\\\\\\\",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\\\\\\\",shadow_vert:\\\\\\\"\\\\n#include <common>\\\\n#include <fog_pars_vertex>\\\\n#include <shadowmap_pars_vertex>\\\\n\\\\nvoid main() {\\\\n\\\\n\\\\t#include <begin_vertex>\\\\n\\\\t#include <project_vertex>\\\\n\\\\t#include <worldpos_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 <shadowmap_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 <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\\\\tgl_FragColor = vec4( outgoingLight, diffuseColor.a );\\\\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\\\\\\\"};var V=n(13);const j={common:{diffuse:{value:new M.a(15658734)},opacity:{value:1},map:{value:null},uvTransform:{value:new V.a},uv2Transform:{value:new V.a},alphaMap:{value:null}},specularmap:{specularMap:{value:null}},envmap:{envMap:{value:null},flipEnvMap:{value:-1},reflectivity:{value:1},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 M.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 M.a(15658734)},opacity:{value:1},size:{value:1},scale:{value:1},map:{value:null},alphaMap:{value:null},uvTransform:{value:new V.a}},sprite:{diffuse:{value:new M.a(15658734)},opacity:{value:1},center:{value:new d.a(.5,.5)},rotation:{value:0},map:{value:null},alphaMap:{value:null},uvTransform:{value:new V.a}}},H={basic:{uniforms:D([j.common,j.specularmap,j.envmap,j.aomap,j.lightmap,j.fog]),vertexShader:G.meshbasic_vert,fragmentShader:G.meshbasic_frag},lambert:{uniforms:D([j.common,j.specularmap,j.envmap,j.aomap,j.lightmap,j.emissivemap,j.fog,j.lights,{emissive:{value:new M.a(0)}}]),vertexShader:G.meshlambert_vert,fragmentShader:G.meshlambert_frag},phong:{uniforms:D([j.common,j.specularmap,j.envmap,j.aomap,j.lightmap,j.emissivemap,j.bumpmap,j.normalmap,j.displacementmap,j.fog,j.lights,{emissive:{value:new M.a(0)},specular:{value:new M.a(1118481)},shininess:{value:30}}]),vertexShader:G.meshphong_vert,fragmentShader:G.meshphong_frag},standard:{uniforms:D([j.common,j.envmap,j.aomap,j.lightmap,j.emissivemap,j.bumpmap,j.normalmap,j.displacementmap,j.roughnessmap,j.metalnessmap,j.fog,j.lights,{emissive:{value:new M.a(0)},roughness:{value:1},metalness:{value:0},envMapIntensity:{value:1}}]),vertexShader:G.meshphysical_vert,fragmentShader:G.meshphysical_frag},toon:{uniforms:D([j.common,j.aomap,j.lightmap,j.emissivemap,j.bumpmap,j.normalmap,j.displacementmap,j.gradientmap,j.fog,j.lights,{emissive:{value:new M.a(0)}}]),vertexShader:G.meshtoon_vert,fragmentShader:G.meshtoon_frag},matcap:{uniforms:D([j.common,j.bumpmap,j.normalmap,j.displacementmap,j.fog,{matcap:{value:null}}]),vertexShader:G.meshmatcap_vert,fragmentShader:G.meshmatcap_frag},points:{uniforms:D([j.points,j.fog]),vertexShader:G.points_vert,fragmentShader:G.points_frag},dashed:{uniforms:D([j.common,j.fog,{scale:{value:1},dashSize:{value:1},totalSize:{value:2}}]),vertexShader:G.linedashed_vert,fragmentShader:G.linedashed_frag},depth:{uniforms:D([j.common,j.displacementmap]),vertexShader:G.depth_vert,fragmentShader:G.depth_frag},normal:{uniforms:D([j.common,j.bumpmap,j.normalmap,j.displacementmap,{opacity:{value:1}}]),vertexShader:G.normal_vert,fragmentShader:G.normal_frag},sprite:{uniforms:D([j.sprite,j.fog]),vertexShader:G.sprite_vert,fragmentShader:G.sprite_frag},background:{uniforms:{uvTransform:{value:new V.a},t2D:{value:null}},vertexShader:G.background_vert,fragmentShader:G.background_frag},cube:{uniforms:D([j.envmap,{opacity:{value:1}}]),vertexShader:G.cube_vert,fragmentShader:G.cube_frag},equirect:{uniforms:{tEquirect:{value:null}},vertexShader:G.equirect_vert,fragmentShader:G.equirect_frag},distanceRGBA:{uniforms:D([j.common,j.displacementmap,{referencePosition:{value:new p.a},nearDistance:{value:1},farDistance:{value:1e3}}]),vertexShader:G.distanceRGBA_vert,fragmentShader:G.distanceRGBA_frag},shadow:{uniforms:D([j.lights,j.fog,{color:{value:new M.a(0)},opacity:{value:1}}]),vertexShader:G.shadow_vert,fragmentShader:G.shadow_frag}};function q(e,t,n,i,s){const r=new M.a(0);let o,a,c=0,l=null,u=0,h=null;function d(e,t){n.buffers.color.setClear(e.r,e.g,e.b,t,s)}return{getClearColor:function(){return r},setClearColor:function(e,t=1){r.set(e),c=t,d(r,c)},getClearAlpha:function(){return c},setClearAlpha:function(e){c=e,d(r,c)},render:function(n,s,p,_){let m=!0===s.isScene?s.background:null;m&&m.isTexture&&(m=t.get(m));const f=e.xr,g=f.getSession&&f.getSession();g&&\\\\\\\"additive\\\\\\\"===g.environmentBlendMode&&(m=null),null===m?d(r,c):m&&m.isColor&&(d(m,1),_=!0),(e.autoClear||_)&&e.clear(e.autoClearColor,e.autoClearDepth,e.autoClearStencil),m&&(m.isCubeTexture||m.mapping===w.q)?(void 0===a&&(a=new z.a(new P(1,1,1),new B({name:\\\\\\\"BackgroundCubeMaterial\\\\\\\",uniforms:F(H.cube.uniforms),vertexShader:H.cube.vertexShader,fragmentShader:H.cube.fragmentShader,side:w.i,depthTest:!1,depthWrite:!1,fog:!1})),a.geometry.deleteAttribute(\\\\\\\"normal\\\\\\\"),a.geometry.deleteAttribute(\\\\\\\"uv\\\\\\\"),a.onBeforeRender=function(e,t,n){this.matrixWorld.copyPosition(n.matrixWorld)},Object.defineProperty(a.material,\\\\\\\"envMap\\\\\\\",{get:function(){return 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i},getMaxPrecision:s,precision:o,logarithmicDepthBuffer:c,maxTextures:l,maxVertexTextures:u,maxTextureSize:h,maxCubemapSize:d,maxAttributes:p,maxVertexUniforms:_,maxVaryings:m,maxFragmentUniforms:f,vertexTextures:g,floatFragmentTextures:v,floatVertexTextures:g&&v,maxSamples:r?e.getParameter(e.MAX_SAMPLES):0}}H.physical={uniforms:D([H.standard.uniforms,{clearcoat:{value:0},clearcoatMap:{value:null},clearcoatRoughness:{value:0},clearcoatRoughnessMap:{value:null},clearcoatNormalScale:{value:new d.a(1,1)},clearcoatNormalMap:{value:null},sheen:{value:new M.a(0)},transmission:{value:0},transmissionMap:{value:null}}]),vertexShader:G.meshphysical_vert,fragmentShader:G.meshphysical_frag};var $=n(27);function Q(e){const t=this;let n=null,i=0,s=!1,r=!1;const o=new $.a,a=new V.a,c={value:null,needsUpdate:!1};function l(){c.value!==n&&(c.value=n,c.needsUpdate=i>0),t.numPlanes=i,t.numIntersection=0}function u(e,n,i,s){const r=null!==e?e.length:0;let l=null;if(0!==r){if(l=c.value,!0!==s||null===l){const t=i+4*r,s=n.matrixWorldInverse;a.getNormalMatrix(s),(null===l||l.length<t)&&(l=new Float32Array(t));for(let t=0,n=i;t!==r;++t,n+=4)o.copy(e[t]).applyMatrix4(s,a),o.normal.toArray(l,n),l[n+3]=o.constant}c.value=l,c.needsUpdate=!0}return t.numPlanes=r,t.numIntersection=0,l}this.uniform=c,this.numPlanes=0,this.numIntersection=0,this.init=function(e,t,r){const o=0!==e.length||t||0!==i||s;return s=t,n=u(e,r,0),i=e.length,o},this.beginShadows=function(){r=!0,u(null)},this.endShadows=function(){r=!1,l()},this.setState=function(t,o,a){const h=t.clippingPlanes,d=t.clipIntersection,p=t.clipShadows,_=e.get(t);if(!s||null===h||0===h.length||r&&!p)r?u(null):l();else{const e=r?0:i,t=4*e;let s=_.clippingState||null;c.value=s,s=u(h,o,t,a);for(let e=0;e!==t;++e)s[e]=n[e];_.clippingState=s,this.numIntersection=d?this.numPlanes:0,this.numPlanes+=e}}}var J=n(16),K=n(30);class Z extends J.a{constructor(e,t,n){super(),this.width=e,this.height=t,this.depth=1,this.scissor=new _.a(0,0,e,t),this.scissorTest=!1,this.viewport=new _.a(0,0,e,t),n=n||{},this.texture=new K.a(void 0,n.mapping,n.wrapS,n.wrapT,n.magFilter,n.minFilter,n.format,n.type,n.anisotropy,n.encoding),this.texture.image={},this.texture.image.width=e,this.texture.image.height=t,this.texture.image.depth=1,this.texture.generateMipmaps=void 0!==n.generateMipmaps&&n.generateMipmaps,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(e){e.image={width:this.width,height:this.height,depth:this.depth},this.texture=e}setSize(e,t,n=1){this.width===e&&this.height===t&&this.depth===n||(this.width=e,this.height=t,this.depth=n,this.texture.image.width=e,this.texture.image.height=t,this.texture.image.depth=n,this.dispose()),this.viewport.set(0,0,e,t),this.scissor.set(0,0,e,t)}clone(){return(new this.constructor).copy(this)}copy(e){return this.width=e.width,this.height=e.height,this.depth=e.depth,this.viewport.copy(e.viewport),this.texture=e.texture.clone(),this.depthBuffer=e.depthBuffer,this.stencilBuffer=e.stencilBuffer,this.depthTexture=e.depthTexture,this}dispose(){this.dispatchEvent({type:\\\\\\\"dispose\\\\\\\"})}}Z.prototype.isWebGLRenderTarget=!0;var ee=n(7),te=n(31);const ne=90;class ie extends ee.a{constructor(e,t,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 te.a(ne,1,e,t);i.layers=this.layers,i.up.set(0,-1,0),i.lookAt(new p.a(1,0,0)),this.add(i);const s=new te.a(ne,1,e,t);s.layers=this.layers,s.up.set(0,-1,0),s.lookAt(new p.a(-1,0,0)),this.add(s);const r=new te.a(ne,1,e,t);r.layers=this.layers,r.up.set(0,0,1),r.lookAt(new p.a(0,1,0)),this.add(r);const o=new te.a(ne,1,e,t);o.layers=this.layers,o.up.set(0,0,-1),o.lookAt(new p.a(0,-1,0)),this.add(o);const a=new te.a(ne,1,e,t);a.layers=this.layers,a.up.set(0,-1,0),a.lookAt(new p.a(0,0,1)),this.add(a);const c=new te.a(ne,1,e,t);c.layers=this.layers,c.up.set(0,-1,0),c.lookAt(new p.a(0,0,-1)),this.add(c)}update(e,t){null===this.parent&&this.updateMatrixWorld();const n=this.renderTarget,[i,s,r,o,a,c]=this.children,l=e.xr.enabled,u=e.getRenderTarget();e.xr.enabled=!1;const h=n.texture.generateMipmaps;n.texture.generateMipmaps=!1,e.setRenderTarget(n,0),e.render(t,i),e.setRenderTarget(n,1),e.render(t,s),e.setRenderTarget(n,2),e.render(t,r),e.setRenderTarget(n,3),e.render(t,o),e.setRenderTarget(n,4),e.render(t,a),n.texture.generateMipmaps=h,e.setRenderTarget(n,5),e.render(t,c),e.setRenderTarget(u),e.xr.enabled=l}}class se extends K.a{constructor(e,t,n,i,s,r,o,a,c,l){super(e=void 0!==e?e:[],t=void 0!==t?t:w.o,n,i,s,r,o=void 0!==o?o:w.ic,a,c,l),this._needsFlipEnvMap=!0,this.flipY=!1}get images(){return this.image}set images(e){this.image=e}}se.prototype.isCubeTexture=!0;class re extends Z{constructor(e,t,n){Number.isInteger(t)&&(console.warn(\\\\\\\"THREE.WebGLCubeRenderTarget: constructor signature is now WebGLCubeRenderTarget( size, options )\\\\\\\"),t=n),super(e,e,t),t=t||{},this.texture=new se(void 0,t.mapping,t.wrapS,t.wrapT,t.magFilter,t.minFilter,t.format,t.type,t.anisotropy,t.encoding),this.texture.generateMipmaps=void 0!==t.generateMipmaps&&t.generateMipmaps,this.texture.minFilter=void 0!==t.minFilter?t.minFilter:w.V,this.texture._needsFlipEnvMap=!1}fromEquirectangularTexture(e,t){this.texture.type=t.type,this.texture.format=w.Ib,this.texture.encoding=t.encoding,this.texture.generateMipmaps=t.generateMipmaps,this.texture.minFilter=t.minFilter,this.texture.magFilter=t.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 P(5,5,5),s=new B({name:\\\\\\\"CubemapFromEquirect\\\\\\\",uniforms:F(n.uniforms),vertexShader:n.vertexShader,fragmentShader:n.fragmentShader,side:w.i,blending:w.ub});s.uniforms.tEquirect.value=t;const r=new z.a(i,s),o=t.minFilter;t.minFilter===w.Y&&(t.minFilter=w.V);return new ie(1,10,this).update(e,r),t.minFilter=o,r.geometry.dispose(),r.material.dispose(),this}clear(e,t,n,i){const s=e.getRenderTarget();for(let s=0;s<6;s++)e.setRenderTarget(this,s),e.clear(t,n,i);e.setRenderTarget(s)}}function oe(e){let t=new WeakMap;function n(e,t){return t===w.D?e.mapping=w.o:t===w.E&&(e.mapping=w.p),e}function i(e){const n=e.target;n.removeEventListener(\\\\\\\"dispose\\\\\\\",i);const s=t.get(n);void 0!==s&&(t.delete(n),s.dispose())}return{get:function(s){if(s&&s.isTexture){const r=s.mapping;if(r===w.D||r===w.E){if(t.has(s)){return n(t.get(s).texture,s.mapping)}{const r=s.image;if(r&&r.height>0){const o=e.getRenderTarget(),a=new re(r.height/2);return a.fromEquirectangularTexture(e,s),t.set(s,a),e.setRenderTarget(o),s.addEventListener(\\\\\\\"dispose\\\\\\\",i),n(a.texture,s.mapping)}return null}}}return s},dispose:function(){t=new WeakMap}}}function ae(e){const t={};function n(n){if(void 0!==t[n])return t[n];let i;switch(n){case\\\\\\\"WEBGL_depth_texture\\\\\\\":i=e.getExtension(\\\\\\\"WEBGL_depth_texture\\\\\\\")||e.getExtension(\\\\\\\"MOZ_WEBGL_depth_texture\\\\\\\")||e.getExtension(\\\\\\\"WEBKIT_WEBGL_depth_texture\\\\\\\");break;case\\\\\\\"EXT_texture_filter_anisotropic\\\\\\\":i=e.getExtension(\\\\\\\"EXT_texture_filter_anisotropic\\\\\\\")||e.getExtension(\\\\\\\"MOZ_EXT_texture_filter_anisotropic\\\\\\\")||e.getExtension(\\\\\\\"WEBKIT_EXT_texture_filter_anisotropic\\\\\\\");break;case\\\\\\\"WEBGL_compressed_texture_s3tc\\\\\\\":i=e.getExtension(\\\\\\\"WEBGL_compressed_texture_s3tc\\\\\\\")||e.getExtension(\\\\\\\"MOZ_WEBGL_compressed_texture_s3tc\\\\\\\")||e.getExtension(\\\\\\\"WEBKIT_WEBGL_compressed_texture_s3tc\\\\\\\");break;case\\\\\\\"WEBGL_compressed_texture_pvrtc\\\\\\\":i=e.getExtension(\\\\\\\"WEBGL_compressed_texture_pvrtc\\\\\\\")||e.getExtension(\\\\\\\"WEBKIT_WEBGL_compressed_texture_pvrtc\\\\\\\");break;default:i=e.getExtension(n)}return t[n]=i,i}return{has:function(e){return null!==n(e)},init:function(e){e.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(e){const t=n(e);return null===t&&console.warn(\\\\\\\"THREE.WebGLRenderer: \\\\\\\"+e+\\\\\\\" extension not supported.\\\\\\\"),t}}}re.prototype.isWebGLCubeRenderTarget=!0;var ce=n(32);function le(e,t,n,i){const s={},r=new WeakMap;function o(e){const a=e.target;null!==a.index&&t.remove(a.index);for(const e in a.attributes)t.remove(a.attributes[e]);a.removeEventListener(\\\\\\\"dispose\\\\\\\",o),delete s[a.id];const c=r.get(a);c&&(t.remove(c),r.delete(a)),i.releaseStatesOfGeometry(a),!0===a.isInstancedBufferGeometry&&delete a._maxInstanceCount,n.memory.geometries--}function a(e){const n=[],i=e.index,s=e.attributes.position;let o=0;if(null!==i){const e=i.array;o=i.version;for(let t=0,i=e.length;t<i;t+=3){const i=e[t+0],s=e[t+1],r=e[t+2];n.push(i,s,s,r,r,i)}}else{const e=s.array;o=s.version;for(let t=0,i=e.length/3-1;t<i;t+=3){const e=t+0,i=t+1,s=t+2;n.push(e,i,i,s,s,e)}}const a=new(Object(ce.a)(n)>65535?L.i:L.h)(n,1);a.version=o;const c=r.get(e);c&&t.remove(c),r.set(e,a)}return{get:function(e,t){return!0===s[t.id]||(t.addEventListener(\\\\\\\"dispose\\\\\\\",o),s[t.id]=!0,n.memory.geometries++),t},update:function(n){const i=n.attributes;for(const n in i)t.update(i[n],e.ARRAY_BUFFER);const s=n.morphAttributes;for(const n in s){const i=s[n];for(let n=0,s=i.length;n<s;n++)t.update(i[n],e.ARRAY_BUFFER)}},getWireframeAttribute:function(e){const t=r.get(e);if(t){const n=e.index;null!==n&&t.version<n.version&&a(e)}else a(e);return r.get(e)}}}function ue(e,t,n,i){const s=i.isWebGL2;let r,o,a;this.setMode=function(e){r=e},this.setIndex=function(e){o=e.type,a=e.bytesPerElement},this.render=function(t,i){e.drawElements(r,i,o,t*a),n.update(i,r,1)},this.renderInstances=function(i,c,l){if(0===l)return;let u,h;if(s)u=e,h=\\\\\\\"drawElementsInstanced\\\\\\\";else if(u=t.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](r,c,o,i*a,l),n.update(c,r,l)}}function he(e){const t={frame:0,calls:0,triangles:0,points:0,lines:0};return{memory:{geometries:0,textures:0},render:t,programs:null,autoReset:!0,reset:function(){t.frame++,t.calls=0,t.triangles=0,t.points=0,t.lines=0},update:function(n,i,s){switch(t.calls++,i){case e.TRIANGLES:t.triangles+=s*(n/3);break;case e.LINES:t.lines+=s*(n/2);break;case e.LINE_STRIP:t.lines+=s*(n-1);break;case e.LINE_LOOP:t.lines+=s*n;break;case e.POINTS:t.points+=s*n;break;default:console.error(\\\\\\\"THREE.WebGLInfo: Unknown draw mode:\\\\\\\",i)}}}}function de(e,t){return e[0]-t[0]}function pe(e,t){return Math.abs(t[1])-Math.abs(e[1])}function _e(e){const t={},n=new Float32Array(8),i=[];for(let e=0;e<8;e++)i[e]=[e,0];return{update:function(s,r,o,a){const c=s.morphTargetInfluences,l=void 0===c?0:c.length;let u=t[r.id];if(void 0===u){u=[];for(let e=0;e<l;e++)u[e]=[e,0];t[r.id]=u}for(let e=0;e<l;e++){const t=u[e];t[0]=e,t[1]=c[e]}u.sort(pe);for(let e=0;e<8;e++)e<l&&u[e][1]?(i[e][0]=u[e][0],i[e][1]=u[e][1]):(i[e][0]=Number.MAX_SAFE_INTEGER,i[e][1]=0);i.sort(de);const h=o.morphTargets&&r.morphAttributes.position,d=o.morphNormals&&r.morphAttributes.normal;let p=0;for(let e=0;e<8;e++){const t=i[e],s=t[0],o=t[1];s!==Number.MAX_SAFE_INTEGER&&o?(h&&r.getAttribute(\\\\\\\"morphTarget\\\\\\\"+e)!==h[s]&&r.setAttribute(\\\\\\\"morphTarget\\\\\\\"+e,h[s]),d&&r.getAttribute(\\\\\\\"morphNormal\\\\\\\"+e)!==d[s]&&r.setAttribute(\\\\\\\"morphNormal\\\\\\\"+e,d[s]),n[e]=o,p+=o):(h&&!0===r.hasAttribute(\\\\\\\"morphTarget\\\\\\\"+e)&&r.deleteAttribute(\\\\\\\"morphTarget\\\\\\\"+e),d&&!0===r.hasAttribute(\\\\\\\"morphNormal\\\\\\\"+e)&&r.deleteAttribute(\\\\\\\"morphNormal\\\\\\\"+e),n[e]=0)}const _=r.morphTargetsRelative?1:1-p;a.getUniforms().setValue(e,\\\\\\\"morphTargetBaseInfluence\\\\\\\",_),a.getUniforms().setValue(e,\\\\\\\"morphTargetInfluences\\\\\\\",n)}}}function me(e,t,n,i){let s=new WeakMap;function r(e){const t=e.target;t.removeEventListener(\\\\\\\"dispose\\\\\\\",r),n.remove(t.instanceMatrix),null!==t.instanceColor&&n.remove(t.instanceColor)}return{update:function(o){const a=i.render.frame,c=o.geometry,l=t.get(o,c);return s.get(l)!==a&&(t.update(l),s.set(l,a)),o.isInstancedMesh&&(!1===o.hasEventListener(\\\\\\\"dispose\\\\\\\",r)&&o.addEventListener(\\\\\\\"dispose\\\\\\\",r),n.update(o.instanceMatrix,e.ARRAY_BUFFER),null!==o.instanceColor&&n.update(o.instanceColor,e.ARRAY_BUFFER)),l},dispose:function(){s=new WeakMap}}}class fe extends K.a{constructor(e=null,t=1,n=1,i=1){super(null),this.image={data:e,width:t,height:n,depth:i},this.magFilter=w.ob,this.minFilter=w.ob,this.wrapR=w.n,this.generateMipmaps=!1,this.flipY=!1,this.needsUpdate=!0}}fe.prototype.isDataTexture2DArray=!0;class ge extends K.a{constructor(e=null,t=1,n=1,i=1){super(null),this.image={data:e,width:t,height:n,depth:i},this.magFilter=w.ob,this.minFilter=w.ob,this.wrapR=w.n,this.generateMipmaps=!1,this.flipY=!1,this.needsUpdate=!0}}ge.prototype.isDataTexture3D=!0;const ve=new K.a,ye=new fe,xe=new ge,be=new se,we=[],Ae=[],Te=new Float32Array(16),Ee=new Float32Array(9),Ce=new Float32Array(4);function Me(e,t,n){const i=e[0];if(i<=0||i>0)return e;const s=t*n;let r=we[s];if(void 0===r&&(r=new Float32Array(s),we[s]=r),0!==t){i.toArray(r,0);for(let i=1,s=0;i!==t;++i)s+=n,e[i].toArray(r,s)}return r}function Ne(e,t){if(e.length!==t.length)return!1;for(let n=0,i=e.length;n<i;n++)if(e[n]!==t[n])return!1;return!0}function Se(e,t){for(let n=0,i=t.length;n<i;n++)e[n]=t[n]}function Oe(e,t){let n=Ae[t];void 0===n&&(n=new Int32Array(t),Ae[t]=n);for(let i=0;i!==t;++i)n[i]=e.allocateTextureUnit();return n}function Le(e,t){const n=this.cache;n[0]!==t&&(e.uniform1f(this.addr,t),n[0]=t)}function Pe(e,t){const n=this.cache;if(void 0!==t.x)n[0]===t.x&&n[1]===t.y||(e.uniform2f(this.addr,t.x,t.y),n[0]=t.x,n[1]=t.y);else{if(Ne(n,t))return;e.uniform2fv(this.addr,t),Se(n,t)}}function Re(e,t){const n=this.cache;if(void 0!==t.x)n[0]===t.x&&n[1]===t.y&&n[2]===t.z||(e.uniform3f(this.addr,t.x,t.y,t.z),n[0]=t.x,n[1]=t.y,n[2]=t.z);else if(void 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\\\\\\\",s.getError(),\\\\\\\"gl.VALIDATE_STATUS\\\\\\\",s.getProgramParameter(m,s.VALIDATE_STATUS),\\\\\\\"gl.getProgramInfoLog\\\\\\\",e,t,n)}else\\\\\\\"\\\\\\\"!==e?console.warn(\\\\\\\"THREE.WebGLProgram: gl.getProgramInfoLog()\\\\\\\",e):\\\\\\\"\\\\\\\"!==t&&\\\\\\\"\\\\\\\"!==n||(r=!1);r&&(this.diagnostics={runnable:i,programLog:e,vertexShader:{log:t,prefix:f},fragmentShader:{log:n,prefix:g}})}let T,E;return s.deleteShader(b),s.deleteShader(A),this.getUniforms=function(){return void 0===T&&(T=new yt(s,m)),T},this.getAttributes=function(){return void 0===E&&(E=function(e,t){const n={},i=e.getProgramParameter(t,e.ACTIVE_ATTRIBUTES);for(let s=0;s<i;s++){const i=e.getActiveAttrib(t,s).name;n[i]=e.getAttribLocation(t,i)}return n}(s,m)),E},this.destroy=function(){i.releaseStatesOfProgram(this),s.deleteProgram(m),this.program=void 0},this.name=n.shaderName,this.id=bt++,this.cacheKey=t,this.usedTimes=1,this.program=m,this.vertexShader=b,this.fragmentShader=A,this}function 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m(e){let t;return e&&e.isTexture?t=e.encoding:e&&e.isWebGLRenderTarget?(console.warn(\\\\\\\"THREE.WebGLPrograms.getTextureEncodingFromMap: don't use render targets as textures. 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C=e.getRenderTarget();return{isWebGL2:a,shaderID:b,shaderName:s.type,vertexShader:T,fragmentShader:E,defines:s.defines,isRawShaderMaterial:!0===s.isRawShaderMaterial,glslVersion:s.glslVersion,precision:d,instancing:!0===g.isInstancedMesh,instancingColor:!0===g.isInstancedMesh&&null!==g.instanceColor,supportsVertexTextures:h,outputEncoding:null!==C?m(C.texture):e.outputEncoding,map:!!s.map,mapEncoding:m(s.map),matcap:!!s.matcap,matcapEncoding:m(s.matcap),envMap:!!x,envMapMode:x&&x.mapping,envMapEncoding:m(x),envMapCubeUV:!!x&&(x.mapping===w.q||x.mapping===w.r),lightMap:!!s.lightMap,lightMapEncoding:m(s.lightMap),aoMap:!!s.aoMap,emissiveMap:!!s.emissiveMap,emissiveMapEncoding:m(s.emissiveMap),bumpMap:!!s.bumpMap,normalMap:!!s.normalMap,objectSpaceNormalMap:s.normalMapType===w.zb,tangentSpaceNormalMap:s.normalMapType===w.Uc,clearcoatMap:!!s.clearcoatMap,clearcoatRoughnessMap:!!s.clearcoatRoughnessMap,clearcoatNormalMap:!!s.clearcoatNormalMap,displacementMap:!!s.displacementMap,roughnessMap:!!s.roughnessMap,metalnessMap:!!s.metalnessMap,specularMap:!!s.specularMap,alphaMap:!!s.alphaMap,gradientMap:!!s.gradientMap,sheen:!!s.sheen,transmissionMap:!!s.transmissionMap,combine:s.combine,vertexTangents:s.normalMap&&s.vertexTangents,vertexColors:s.vertexColors,vertexAlphas:!0===s.vertexColors&&g.geometry.attributes.color&&4===g.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),uvsVertexOnly:!(s.map||s.bumpMap||s.normalMap||s.specularMap||s.alphaMap||s.emissiveMap||s.roughnessMap||s.metalnessMap||s.clearcoatNormalMap||s.transmissionMap||!s.displacementMap),fog:!!v,useFog:s.fog,fogExp2:v&&v.isFogExp2,flatShading:!!s.flatShading,sizeAttenuation:s.sizeAttenuation,logarithmicDepthBuffer:c,skinning:s.skinning&&A>0,maxBones:A,useVertexTexture:l,morphTargets:s.morphTargets,morphNormals:s.morphNormals,numDirLights:o.directional.length,numPointLights:o.point.length,numSpotLights:o.spot.length,numRectAreaLights:o.rectArea.length,numHemiLights:o.hemi.length,numDirLightShadows:o.directionalShadowMap.length,numPointLightShadows:o.pointShadowMap.length,numSpotLightShadows:o.spotShadowMap.length,numClippingPlanes:r.numPlanes,numClipIntersection:r.numIntersection,dithering:s.dithering,shadowMapEnabled:e.shadowMap.enabled&&_.length>0,shadowMapType:e.shadowMap.type,toneMapping:s.toneMapped?e.toneMapping:w.vb,physicallyCorrectLights:e.physicallyCorrectLights,premultipliedAlpha:s.premultipliedAlpha,alphaTest:s.alphaTest,doubleSided:s.side===w.z,flipSided:s.side===w.i,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:a||n.has(\\\\\\\"EXT_frag_depth\\\\\\\"),rendererExtensionDrawBuffers:a||n.has(\\\\\\\"WEBGL_draw_buffers\\\\\\\"),rendererExtensionShaderTextureLod:a||n.has(\\\\\\\"EXT_shader_texture_lod\\\\\\\"),customProgramCacheKey:s.customProgramCacheKey()}},getProgramCacheKey:function(t){const n=[];if(t.shaderID?n.push(t.shaderID):(n.push(t.fragmentShader),n.push(t.vertexShader)),void 0!==t.defines)for(const e in t.defines)n.push(e),n.push(t.defines[e]);if(!1===t.isRawShaderMaterial){for(let e=0;e<_.length;e++)n.push(t[_[e]]);n.push(e.outputEncoding),n.push(e.gammaFactor)}return n.push(t.customProgramCacheKey),n.join()},getUniforms:function(e){const t=p[e.type];let n;if(t){const e=H[t];n=k.clone(e.uniforms)}else n=e.uniforms;return n},acquireProgram:function(t,n){let i;for(let e=0,t=o.length;e<t;e++){const t=o[e];if(t.cacheKey===n){i=t,++i.usedTimes;break}}return void 0===i&&(i=new zt(e,n,t,s),o.push(i)),i},releaseProgram:function(e){if(0==--e.usedTimes){const t=o.indexOf(e);o[t]=o[o.length-1],o.pop(),e.destroy()}},programs:o}}function Gt(){let e=new WeakMap;return{get:function(t){let n=e.get(t);return void 0===n&&(n={},e.set(t,n)),n},remove:function(t){e.delete(t)},update:function(t,n,i){e.get(t)[n]=i},dispose:function(){e=new WeakMap}}}function Vt(e,t){return e.groupOrder!==t.groupOrder?e.groupOrder-t.groupOrder:e.renderOrder!==t.renderOrder?e.renderOrder-t.renderOrder:e.program!==t.program?e.program.id-t.program.id:e.material.id!==t.material.id?e.material.id-t.material.id:e.z!==t.z?e.z-t.z:e.id-t.id}function jt(e,t){return e.groupOrder!==t.groupOrder?e.groupOrder-t.groupOrder:e.renderOrder!==t.renderOrder?e.renderOrder-t.renderOrder:e.z!==t.z?t.z-e.z:e.id-t.id}function Ht(e){const t=[];let n=0;const i=[],s=[],r={id:-1};function o(i,s,o,a,c,l){let u=t[n];const h=e.get(o);return void 0===u?(u={id:i.id,object:i,geometry:s,material:o,program:h.program||r,groupOrder:a,renderOrder:i.renderOrder,z:c,group:l},t[n]=u):(u.id=i.id,u.object=i,u.geometry=s,u.material=o,u.program=h.program||r,u.groupOrder=a,u.renderOrder=i.renderOrder,u.z=c,u.group=l),n++,u}return{opaque:i,transparent:s,init:function(){n=0,i.length=0,s.length=0},push:function(e,t,n,r,a,c){const l=o(e,t,n,r,a,c);(!0===n.transparent?s:i).push(l)},unshift:function(e,t,n,r,a,c){const l=o(e,t,n,r,a,c);(!0===n.transparent?s:i).unshift(l)},finish:function(){for(let e=n,i=t.length;e<i;e++){const n=t[e];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(e,t){i.length>1&&i.sort(e||Vt),s.length>1&&s.sort(t||jt)}}}function qt(e){let t=new WeakMap;return{get:function(n,i){let s;return!1===t.has(n)?(s=new Ht(e),t.set(n,[s])):i>=t.get(n).length?(s=new Ht(e),t.get(n).push(s)):s=t.get(n)[i],s},dispose:function(){t=new WeakMap}}}function Wt(){const e={};return{get:function(t){if(void 0!==e[t.id])return e[t.id];let n;switch(t.type){case\\\\\\\"DirectionalLight\\\\\\\":n={direction:new p.a,color:new M.a};break;case\\\\\\\"SpotLight\\\\\\\":n={position:new p.a,direction:new p.a,color:new M.a,distance:0,coneCos:0,penumbraCos:0,decay:0};break;case\\\\\\\"PointLight\\\\\\\":n={position:new p.a,color:new M.a,distance:0,decay:0};break;case\\\\\\\"HemisphereLight\\\\\\\":n={direction:new p.a,skyColor:new M.a,groundColor:new M.a};break;case\\\\\\\"RectAreaLight\\\\\\\":n={color:new M.a,position:new p.a,halfWidth:new p.a,halfHeight:new p.a}}return e[t.id]=n,n}}}let Xt=0;function Yt(e,t){return(t.castShadow?1:0)-(e.castShadow?1:0)}function $t(e,t){const n=new Wt,i=function(){const e={};return{get:function(t){if(void 0!==e[t.id])return e[t.id];let n;switch(t.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 e[t.id]=n,n}}}(),s={version:0,hash:{directionalLength:-1,pointLength:-1,spotLength:-1,rectAreaLength:-1,hemiLength:-1,numDirectionalShadows:-1,numPointShadows:-1,numSpotShadows:-1},ambient:[0,0,0],probe:[],directional:[],directionalShadow:[],directionalShadowMap:[],directionalShadowMatrix:[],spot:[],spotShadow:[],spotShadowMap:[],spotShadowMatrix:[],rectArea:[],rectAreaLTC1:null,rectAreaLTC2:null,point:[],pointShadow:[],pointShadowMap:[],pointShadowMatrix:[],hemi:[]};for(let e=0;e<9;e++)s.probe.push(new p.a);const r=new p.a,o=new C.a,a=new C.a;return{setup:function(r){let o=0,a=0,c=0;for(let e=0;e<9;e++)s.probe[e].set(0,0,0);let l=0,u=0,h=0,d=0,p=0,_=0,m=0,f=0;r.sort(Yt);for(let e=0,t=r.length;e<t;e++){const t=r[e],g=t.color,v=t.intensity,y=t.distance,x=t.shadow&&t.shadow.map?t.shadow.map.texture:null;if(t.isAmbientLight)o+=g.r*v,a+=g.g*v,c+=g.b*v;else if(t.isLightProbe)for(let e=0;e<9;e++)s.probe[e].addScaledVector(t.sh.coefficients[e],v);else if(t.isDirectionalLight){const e=n.get(t);if(e.color.copy(t.color).multiplyScalar(t.intensity),t.castShadow){const e=t.shadow,n=i.get(t);n.shadowBias=e.bias,n.shadowNormalBias=e.normalBias,n.shadowRadius=e.radius,n.shadowMapSize=e.mapSize,s.directionalShadow[l]=n,s.directionalShadowMap[l]=x,s.directionalShadowMatrix[l]=t.shadow.matrix,_++}s.directional[l]=e,l++}else if(t.isSpotLight){const e=n.get(t);if(e.position.setFromMatrixPosition(t.matrixWorld),e.color.copy(g).multiplyScalar(v),e.distance=y,e.coneCos=Math.cos(t.angle),e.penumbraCos=Math.cos(t.angle*(1-t.penumbra)),e.decay=t.decay,t.castShadow){const e=t.shadow,n=i.get(t);n.shadowBias=e.bias,n.shadowNormalBias=e.normalBias,n.shadowRadius=e.radius,n.shadowMapSize=e.mapSize,s.spotShadow[h]=n,s.spotShadowMap[h]=x,s.spotShadowMatrix[h]=t.shadow.matrix,f++}s.spot[h]=e,h++}else if(t.isRectAreaLight){const e=n.get(t);e.color.copy(g).multiplyScalar(v),e.halfWidth.set(.5*t.width,0,0),e.halfHeight.set(0,.5*t.height,0),s.rectArea[d]=e,d++}else if(t.isPointLight){const e=n.get(t);if(e.color.copy(t.color).multiplyScalar(t.intensity),e.distance=t.distance,e.decay=t.decay,t.castShadow){const e=t.shadow,n=i.get(t);n.shadowBias=e.bias,n.shadowNormalBias=e.normalBias,n.shadowRadius=e.radius,n.shadowMapSize=e.mapSize,n.shadowCameraNear=e.camera.near,n.shadowCameraFar=e.camera.far,s.pointShadow[u]=n,s.pointShadowMap[u]=x,s.pointShadowMatrix[u]=t.shadow.matrix,m++}s.point[u]=e,u++}else if(t.isHemisphereLight){const e=n.get(t);e.skyColor.copy(t.color).multiplyScalar(v),e.groundColor.copy(t.groundColor).multiplyScalar(v),s.hemi[p]=e,p++}}d>0&&(t.isWebGL2||!0===e.has(\\\\\\\"OES_texture_float_linear\\\\\\\")?(s.rectAreaLTC1=j.LTC_FLOAT_1,s.rectAreaLTC2=j.LTC_FLOAT_2):!0===e.has(\\\\\\\"OES_texture_half_float_linear\\\\\\\")?(s.rectAreaLTC1=j.LTC_HALF_1,s.rectAreaLTC2=j.LTC_HALF_2):console.error(\\\\\\\"THREE.WebGLRenderer: Unable to use RectAreaLight. Missing WebGL extensions.\\\\\\\")),s.ambient[0]=o,s.ambient[1]=a,s.ambient[2]=c;const g=s.hash;g.directionalLength===l&&g.pointLength===u&&g.spotLength===h&&g.rectAreaLength===d&&g.hemiLength===p&&g.numDirectionalShadows===_&&g.numPointShadows===m&&g.numSpotShadows===f||(s.directional.length=l,s.spot.length=h,s.rectArea.length=d,s.point.length=u,s.hemi.length=p,s.directionalShadow.length=_,s.directionalShadowMap.length=_,s.pointShadow.length=m,s.pointShadowMap.length=m,s.spotShadow.length=f,s.spotShadowMap.length=f,s.directionalShadowMatrix.length=_,s.pointShadowMatrix.length=m,s.spotShadowMatrix.length=f,g.directionalLength=l,g.pointLength=u,g.spotLength=h,g.rectAreaLength=d,g.hemiLength=p,g.numDirectionalShadows=_,g.numPointShadows=m,g.numSpotShadows=f,s.version=Xt++)},setupView:function(e,t){let n=0,i=0,c=0,l=0,u=0;const h=t.matrixWorldInverse;for(let t=0,d=e.length;t<d;t++){const d=e[t];if(d.isDirectionalLight){const e=s.directional[n];e.direction.setFromMatrixPosition(d.matrixWorld),r.setFromMatrixPosition(d.target.matrixWorld),e.direction.sub(r),e.direction.transformDirection(h),n++}else if(d.isSpotLight){const e=s.spot[c];e.position.setFromMatrixPosition(d.matrixWorld),e.position.applyMatrix4(h),e.direction.setFromMatrixPosition(d.matrixWorld),r.setFromMatrixPosition(d.target.matrixWorld),e.direction.sub(r),e.direction.transformDirection(h),c++}else if(d.isRectAreaLight){const e=s.rectArea[l];e.position.setFromMatrixPosition(d.matrixWorld),e.position.applyMatrix4(h),a.identity(),o.copy(d.matrixWorld),o.premultiply(h),a.extractRotation(o),e.halfWidth.set(.5*d.width,0,0),e.halfHeight.set(0,.5*d.height,0),e.halfWidth.applyMatrix4(a),e.halfHeight.applyMatrix4(a),l++}else if(d.isPointLight){const e=s.point[i];e.position.setFromMatrixPosition(d.matrixWorld),e.position.applyMatrix4(h),i++}else if(d.isHemisphereLight){const e=s.hemi[u];e.direction.setFromMatrixPosition(d.matrixWorld),e.direction.transformDirection(h),e.direction.normalize(),u++}}},state:s}}function Qt(e,t){const n=new $t(e,t),i=[],s=[];return{init:function(){i.length=0,s.length=0},state:{lightsArray:i,shadowsArray:s,lights:n},setupLights:function(){n.setup(i)},setupLightsView:function(e){n.setupView(i,e)},pushLight:function(e){i.push(e)},pushShadow:function(e){s.push(e)}}}function Jt(e,t){let n=new WeakMap;return{get:function(i,s=0){let r;return!1===n.has(i)?(r=new Qt(e,t),n.set(i,[r])):s>=n.get(i).length?(r=new Qt(e,t),n.get(i).push(r)):r=n.get(i)[s],r},dispose:function(){n=new WeakMap}}}class Kt extends I.a{constructor(e){super(),this.type=\\\\\\\"MeshDepthMaterial\\\\\\\",this.depthPacking=w.j,this.skinning=!1,this.morphTargets=!1,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(e)}copy(e){return super.copy(e),this.depthPacking=e.depthPacking,this.skinning=e.skinning,this.morphTargets=e.morphTargets,this.map=e.map,this.alphaMap=e.alphaMap,this.displacementMap=e.displacementMap,this.displacementScale=e.displacementScale,this.displacementBias=e.displacementBias,this.wireframe=e.wireframe,this.wireframeLinewidth=e.wireframeLinewidth,this}}Kt.prototype.isMeshDepthMaterial=!0;class Zt extends I.a{constructor(e){super(),this.type=\\\\\\\"MeshDistanceMaterial\\\\\\\",this.referencePosition=new p.a,this.nearDistance=1,this.farDistance=1e3,this.skinning=!1,this.morphTargets=!1,this.map=null,this.alphaMap=null,this.displacementMap=null,this.displacementScale=1,this.displacementBias=0,this.fog=!1,this.setValues(e)}copy(e){return super.copy(e),this.referencePosition.copy(e.referencePosition),this.nearDistance=e.nearDistance,this.farDistance=e.farDistance,this.skinning=e.skinning,this.morphTargets=e.morphTargets,this.map=e.map,this.alphaMap=e.alphaMap,this.displacementMap=e.displacementMap,this.displacementScale=e.displacementScale,this.displacementBias=e.displacementBias,this}}Zt.prototype.isMeshDistanceMaterial=!0;function en(e,t,n){let i=new E.a;const s=new d.a,r=new d.a,o=new _.a,a=[],c=[],l={},u=n.maxTextureSize,h={0:w.i,1:w.H,2:w.z},p=new B({defines:{SAMPLE_RATE:2/8,HALF_SAMPLE_RATE:1/8},uniforms:{shadow_pass:{value:null},resolution:{value:new d.a},radius:{value:4}},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;\\\\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\\\\tfloat depth = unpackRGBAToDepth( texture2D( shadow_pass, ( gl_FragCoord.xy ) / resolution ) );\\\\n\\\\n\\\\tfor ( float i = -1.0; i < 1.0 ; i += SAMPLE_RATE) {\\\\n\\\\n\\\\t\\\\t#ifdef HORIZONTAL_PASS\\\\n\\\\n\\\\t\\\\t\\\\tvec2 distribution = unpackRGBATo2Half( texture2D( shadow_pass, ( gl_FragCoord.xy + vec2( i, 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, i ) * 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 * HALF_SAMPLE_RATE;\\\\n\\\\tsquared_mean = squared_mean * HALF_SAMPLE_RATE;\\\\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 O.a;f.setAttribute(\\\\\\\"position\\\\\\\",new L.a(new Float32Array([-1,-1,.5,3,-1,.5,-1,3,.5]),3));const g=new z.a(f,p),v=this;function y(n,i){const s=t.update(g);p.uniforms.shadow_pass.value=n.map.texture,p.uniforms.resolution.value=n.mapSize,p.uniforms.radius.value=n.radius,e.setRenderTarget(n.mapPass),e.clear(),e.renderBufferDirect(i,null,s,p,g,null),m.uniforms.shadow_pass.value=n.mapPass.texture,m.uniforms.resolution.value=n.mapSize,m.uniforms.radius.value=n.radius,e.setRenderTarget(n.map),e.clear(),e.renderBufferDirect(i,null,s,m,g,null)}function x(e,t,n){const i=e<<0|t<<1|n<<2;let s=a[i];return void 0===s&&(s=new Kt({depthPacking:w.Hb,morphTargets:e,skinning:t}),a[i]=s),s}function b(e,t,n){const i=e<<0|t<<1|n<<2;let s=c[i];return void 0===s&&(s=new Zt({morphTargets:e,skinning:t}),c[i]=s),s}function A(t,n,i,s,r,o,a){let c=null,u=x,d=t.customDepthMaterial;if(!0===s.isPointLight&&(u=b,d=t.customDistanceMaterial),void 0===d){let e=!1;!0===i.morphTargets&&(e=n.morphAttributes&&n.morphAttributes.position&&n.morphAttributes.position.length>0);let s=!1;!0===t.isSkinnedMesh&&(!0===i.skinning?s=!0:console.warn(\\\\\\\"THREE.WebGLShadowMap: THREE.SkinnedMesh with material.skinning set to false:\\\\\\\",t));c=u(e,s,!0===t.isInstancedMesh)}else c=d;if(e.localClippingEnabled&&!0===i.clipShadows&&0!==i.clippingPlanes.length){const e=c.uuid,t=i.uuid;let n=l[e];void 0===n&&(n={},l[e]=n);let s=n[t];void 0===s&&(s=c.clone(),n[t]=s),c=s}return c.visible=i.visible,c.wireframe=i.wireframe,a===w.gd?c.side=null!==i.shadowSide?i.shadowSide:i.side:c.side=null!==i.shadowSide?i.shadowSide:h[i.side],c.clipShadows=i.clipShadows,c.clippingPlanes=i.clippingPlanes,c.clipIntersection=i.clipIntersection,c.wireframeLinewidth=i.wireframeLinewidth,c.linewidth=i.linewidth,!0===s.isPointLight&&!0===c.isMeshDistanceMaterial&&(c.referencePosition.setFromMatrixPosition(s.matrixWorld),c.nearDistance=r,c.farDistance=o),c}function T(n,s,r,o,a){if(!1===n.visible)return;if(n.layers.test(s.layers)&&(n.isMesh||n.isLine||n.isPoints)&&(n.castShadow||n.receiveShadow&&a===w.gd)&&(!n.frustumCulled||i.intersectsObject(n))){n.modelViewMatrix.multiplyMatrices(r.matrixWorldInverse,n.matrixWorld);const i=t.update(n),s=n.material;if(Array.isArray(s)){const t=i.groups;for(let c=0,l=t.length;c<l;c++){const l=t[c],u=s[l.materialIndex];if(u&&u.visible){const t=A(n,i,u,o,r.near,r.far,a);e.renderBufferDirect(r,null,i,t,n,l)}}}else if(s.visible){const t=A(n,i,s,o,r.near,r.far,a);e.renderBufferDirect(r,null,i,t,n,null)}}const c=n.children;for(let e=0,t=c.length;e<t;e++)T(c[e],s,r,o,a)}this.enabled=!1,this.autoUpdate=!0,this.needsUpdate=!1,this.type=w.Fb,this.render=function(t,n,a){if(!1===v.enabled)return;if(!1===v.autoUpdate&&!1===v.needsUpdate)return;if(0===t.length)return;const c=e.getRenderTarget(),l=e.getActiveCubeFace(),h=e.getActiveMipmapLevel(),d=e.state;d.setBlending(w.ub),d.buffers.color.setClear(1,1,1,1),d.buffers.depth.setTest(!0),d.setScissorTest(!1);for(let c=0,l=t.length;c<l;c++){const l=t[c],h=l.shadow;if(void 0===h){console.warn(\\\\\\\"THREE.WebGLShadowMap:\\\\\\\",l,\\\\\\\"has no shadow.\\\\\\\");continue}if(!1===h.autoUpdate&&!1===h.needsUpdate)continue;s.copy(h.mapSize);const p=h.getFrameExtents();if(s.multiply(p),r.copy(h.mapSize),(s.x>u||s.y>u)&&(s.x>u&&(r.x=Math.floor(u/p.x),s.x=r.x*p.x,h.mapSize.x=r.x),s.y>u&&(r.y=Math.floor(u/p.y),s.y=r.y*p.y,h.mapSize.y=r.y)),null===h.map&&!h.isPointLightShadow&&this.type===w.gd){const e={minFilter:w.V,magFilter:w.V,format:w.Ib};h.map=new Z(s.x,s.y,e),h.map.texture.name=l.name+\\\\\\\".shadowMap\\\\\\\",h.mapPass=new Z(s.x,s.y,e),h.camera.updateProjectionMatrix()}if(null===h.map){const e={minFilter:w.ob,magFilter:w.ob,format:w.Ib};h.map=new Z(s.x,s.y,e),h.map.texture.name=l.name+\\\\\\\".shadowMap\\\\\\\",h.camera.updateProjectionMatrix()}e.setRenderTarget(h.map),e.clear();const _=h.getViewportCount();for(let e=0;e<_;e++){const t=h.getViewport(e);o.set(r.x*t.x,r.y*t.y,r.x*t.z,r.y*t.w),d.viewport(o),h.updateMatrices(l,e),i=h.getFrustum(),T(n,a,h.camera,l,this.type)}h.isPointLightShadow||this.type!==w.gd||y(h,a),h.needsUpdate=!1}v.needsUpdate=!1,e.setRenderTarget(c,l,h)}}function tn(e,t,n){const i=n.isWebGL2;const s=new function(){let t=!1;const n=new _.a;let i=null;const s=new _.a(0,0,0,0);return{setMask:function(n){i===n||t||(e.colorMask(n,n,n,n),i=n)},setLocked:function(e){t=e},setClear:function(t,i,r,o,a){!0===a&&(t*=o,i*=o,r*=o),n.set(t,i,r,o),!1===s.equals(n)&&(e.clearColor(t,i,r,o),s.copy(n))},reset:function(){t=!1,i=null,s.set(-1,0,0,0)}}},r=new function(){let t=!1,n=null,i=null,s=null;return{setTest:function(t){t?k(e.DEPTH_TEST):B(e.DEPTH_TEST)},setMask:function(i){n===i||t||(e.depthMask(i),n=i)},setFunc:function(t){if(i!==t){if(t)switch(t){case w.tb:e.depthFunc(e.NEVER);break;case w.g:e.depthFunc(e.ALWAYS);break;case w.S:e.depthFunc(e.LESS);break;case w.T:e.depthFunc(e.LEQUAL);break;case w.C:e.depthFunc(e.EQUAL);break;case w.L:e.depthFunc(e.GEQUAL);break;case w.K:e.depthFunc(e.GREATER);break;case w.yb:e.depthFunc(e.NOTEQUAL);break;default:e.depthFunc(e.LEQUAL)}else e.depthFunc(e.LEQUAL);i=t}},setLocked:function(e){t=e},setClear:function(t){s!==t&&(e.clearDepth(t),s=t)},reset:function(){t=!1,n=null,i=null,s=null}}},o=new function(){let t=!1,n=null,i=null,s=null,r=null,o=null,a=null,c=null,l=null;return{setTest:function(n){t||(n?k(e.STENCIL_TEST):B(e.STENCIL_TEST))},setMask:function(i){n===i||t||(e.stencilMask(i),n=i)},setFunc:function(t,n,o){i===t&&s===n&&r===o||(e.stencilFunc(t,n,o),i=t,s=n,r=o)},setOp:function(t,n,i){o===t&&a===n&&c===i||(e.stencilOp(t,n,i),o=t,a=n,c=i)},setLocked:function(e){t=e},setClear:function(t){l!==t&&(e.clearStencil(t),l=t)},reset:function(){t=!1,n=null,i=null,s=null,r=null,o=null,a=null,c=null,l=null}}};let a={},c=null,l={},u=null,h=!1,d=null,p=null,m=null,f=null,g=null,v=null,y=null,x=!1,b=null,A=null,T=null,E=null,C=null;const M=e.getParameter(e.MAX_COMBINED_TEXTURE_IMAGE_UNITS);let N=!1,S=0;const O=e.getParameter(e.VERSION);-1!==O.indexOf(\\\\\\\"WebGL\\\\\\\")?(S=parseFloat(/^WebGL (\\\\d)/.exec(O)[1]),N=S>=1):-1!==O.indexOf(\\\\\\\"OpenGL ES\\\\\\\")&&(S=parseFloat(/^OpenGL ES (\\\\d)/.exec(O)[1]),N=S>=2);let L=null,P={};const R=new _.a(0,0,e.canvas.width,e.canvas.height),I=new _.a(0,0,e.canvas.width,e.canvas.height);function F(t,n,i){const s=new Uint8Array(4),r=e.createTexture();e.bindTexture(t,r),e.texParameteri(t,e.TEXTURE_MIN_FILTER,e.NEAREST),e.texParameteri(t,e.TEXTURE_MAG_FILTER,e.NEAREST);for(let t=0;t<i;t++)e.texImage2D(n+t,0,e.RGBA,1,1,0,e.RGBA,e.UNSIGNED_BYTE,s);return r}const D={};function k(t){!0!==a[t]&&(e.enable(t),a[t]=!0)}function B(t){!1!==a[t]&&(e.disable(t),a[t]=!1)}D[e.TEXTURE_2D]=F(e.TEXTURE_2D,e.TEXTURE_2D,1),D[e.TEXTURE_CUBE_MAP]=F(e.TEXTURE_CUBE_MAP,e.TEXTURE_CUBE_MAP_POSITIVE_X,6),s.setClear(0,0,0,1),r.setClear(1),o.setClear(0),k(e.DEPTH_TEST),r.setFunc(w.T),V(!1),j(w.s),k(e.CULL_FACE),G(w.ub);const z={[w.b]:e.FUNC_ADD,[w.Rc]:e.FUNC_SUBTRACT,[w.xc]:e.FUNC_REVERSE_SUBTRACT};if(i)z[w.jb]=e.MIN,z[w.ib]=e.MAX;else{const e=t.get(\\\\\\\"EXT_blend_minmax\\\\\\\");null!==e&&(z[w.jb]=e.MIN_EXT,z[w.ib]=e.MAX_EXT)}const U={[w.jd]:e.ZERO,[w.Ab]:e.ONE,[w.Pc]:e.SRC_COLOR,[w.Nc]:e.SRC_ALPHA,[w.Oc]:e.SRC_ALPHA_SATURATE,[w.B]:e.DST_COLOR,[w.A]:e.DST_ALPHA,[w.Eb]:e.ONE_MINUS_SRC_COLOR,[w.Db]:e.ONE_MINUS_SRC_ALPHA,[w.Cb]:e.ONE_MINUS_DST_COLOR,[w.Bb]:e.ONE_MINUS_DST_ALPHA};function G(t,n,i,s,r,o,a,c){if(t!==w.ub){if(!1===h&&(k(e.BLEND),h=!0),t===w.v)r=r||n,o=o||i,a=a||s,n===p&&r===g||(e.blendEquationSeparate(z[n],z[r]),p=n,g=r),i===m&&s===f&&o===v&&a===y||(e.blendFuncSeparate(U[i],U[s],U[o],U[a]),m=i,f=s,v=o,y=a),d=t,x=null;else if(t!==d||c!==x){if(p===w.b&&g===w.b||(e.blendEquation(e.FUNC_ADD),p=w.b,g=w.b),c)switch(t){case w.xb:e.blendFuncSeparate(e.ONE,e.ONE_MINUS_SRC_ALPHA,e.ONE,e.ONE_MINUS_SRC_ALPHA);break;case w.e:e.blendFunc(e.ONE,e.ONE);break;case w.Sc:e.blendFuncSeparate(e.ZERO,e.ZERO,e.ONE_MINUS_SRC_COLOR,e.ONE_MINUS_SRC_ALPHA);break;case w.mb:e.blendFuncSeparate(e.ZERO,e.SRC_COLOR,e.ZERO,e.SRC_ALPHA);break;default:console.error(\\\\\\\"THREE.WebGLState: Invalid blending: \\\\\\\",t)}else switch(t){case w.xb:e.blendFuncSeparate(e.SRC_ALPHA,e.ONE_MINUS_SRC_ALPHA,e.ONE,e.ONE_MINUS_SRC_ALPHA);break;case w.e:e.blendFunc(e.SRC_ALPHA,e.ONE);break;case w.Sc:e.blendFunc(e.ZERO,e.ONE_MINUS_SRC_COLOR);break;case w.mb:e.blendFunc(e.ZERO,e.SRC_COLOR);break;default:console.error(\\\\\\\"THREE.WebGLState: Invalid blending: \\\\\\\",t)}m=null,f=null,v=null,y=null,d=t,x=c}}else!0===h&&(B(e.BLEND),h=!1)}function V(t){b!==t&&(t?e.frontFace(e.CW):e.frontFace(e.CCW),b=t)}function j(t){t!==w.u?(k(e.CULL_FACE),t!==A&&(t===w.s?e.cullFace(e.BACK):t===w.t?e.cullFace(e.FRONT):e.cullFace(e.FRONT_AND_BACK))):B(e.CULL_FACE),A=t}function H(t,n,i){t?(k(e.POLYGON_OFFSET_FILL),E===n&&C===i||(e.polygonOffset(n,i),E=n,C=i)):B(e.POLYGON_OFFSET_FILL)}function q(t){void 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a=t.stencilWrite;o.setTest(a),a&&(o.setMask(t.stencilWriteMask),o.setFunc(t.stencilFunc,t.stencilRef,t.stencilFuncMask),o.setOp(t.stencilFail,t.stencilZFail,t.stencilZPass)),H(t.polygonOffset,t.polygonOffsetFactor,t.polygonOffsetUnits),!0===t.alphaToCoverage?k(e.SAMPLE_ALPHA_TO_COVERAGE):B(e.SAMPLE_ALPHA_TO_COVERAGE)},setFlipSided:V,setCullFace:j,setLineWidth:function(t){t!==T&&(N&&e.lineWidth(t),T=t)},setPolygonOffset:H,setScissorTest:function(t){t?k(e.SCISSOR_TEST):B(e.SCISSOR_TEST)},activeTexture:q,bindTexture:function(t,n){null===L&&q();let i=P[L];void 0===i&&(i={type:void 0,texture:void 0},P[L]=i),i.type===t&&i.texture===n||(e.bindTexture(t,n||D[t]),i.type=t,i.texture=n)},unbindTexture:function(){const t=P[L];void 0!==t&&void 0!==t.type&&(e.bindTexture(t.type,null),t.type=void 0,t.texture=void 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t=0,s=b.length;t<s;t++)p=b[t],i.format!==w.Ib&&i.format!==w.ic?null!==d?n.compressedTexImage2D(e.TEXTURE_2D,t,m,p.width,p.height,0,p.data):console.warn(\\\\\\\"THREE.WebGLRenderer: Attempt to load unsupported compressed texture format in .uploadTexture()\\\\\\\"):n.texImage2D(e.TEXTURE_2D,t,m,p.width,p.height,0,d,_,p.data);t.__maxMipLevel=b.length-1}else if(i.isDataTexture2DArray)n.texImage3D(e.TEXTURE_2D_ARRAY,0,m,l.width,l.height,l.depth,0,d,_,l.data),t.__maxMipLevel=0;else if(i.isDataTexture3D)n.texImage3D(e.TEXTURE_3D,0,m,l.width,l.height,l.depth,0,d,_,l.data),t.__maxMipLevel=0;else if(b.length>0&&h){for(let t=0,i=b.length;t<i;t++)p=b[t],n.texImage2D(e.TEXTURE_2D,t,m,d,_,p);i.generateMipmaps=!1,t.__maxMipLevel=b.length-1}else n.texImage2D(e.TEXTURE_2D,0,m,d,_,l),t.__maxMipLevel=0;v(i,h)&&y(o,i,l.width,l.height),t.__version=i.version,i.onUpdate&&i.onUpdate(i)}function I(t,s,o,a){const c=s.texture,l=r.convert(c.format),u=r.convert(c.type),h=x(c.internalFormat,l,u);a===e.TEXTURE_3D||a===e.TEXTURE_2D_ARRAY?n.texImage3D(a,0,h,s.width,s.height,s.depth,0,l,u,null):n.texImage2D(a,0,h,s.width,s.height,0,l,u,null),n.bindFramebuffer(e.FRAMEBUFFER,t),e.framebufferTexture2D(e.FRAMEBUFFER,o,a,i.get(c).__webglTexture,0),n.bindFramebuffer(e.FRAMEBUFFER,null)}function F(t,n,i){if(e.bindRenderbuffer(e.RENDERBUFFER,t),n.depthBuffer&&!n.stencilBuffer){let s=e.DEPTH_COMPONENT16;if(i){const t=n.depthTexture;t&&t.isDepthTexture&&(t.type===w.G?s=e.DEPTH_COMPONENT32F:t.type===w.bd&&(s=e.DEPTH_COMPONENT24));const i=k(n);e.renderbufferStorageMultisample(e.RENDERBUFFER,i,s,n.width,n.height)}else e.renderbufferStorage(e.RENDERBUFFER,s,n.width,n.height);e.framebufferRenderbuffer(e.FRAMEBUFFER,e.DEPTH_ATTACHMENT,e.RENDERBUFFER,t)}else if(n.depthBuffer&&n.stencilBuffer){if(i){const t=k(n);e.renderbufferStorageMultisample(e.RENDERBUFFER,t,e.DEPTH24_STENCIL8,n.width,n.height)}else 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k(e){return a&&e.isWebGLMultisampleRenderTarget?Math.min(h,e.samples):0}let B=!1,z=!1;this.allocateTextureUnit=function(){const e=C;return e>=c&&console.warn(\\\\\\\"THREE.WebGLTextures: Trying to use \\\\\\\"+e+\\\\\\\" texture units while this GPU supports only \\\\\\\"+c),C+=1,e},this.resetTextureUnits=function(){C=0},this.setTexture2D=M,this.setTexture2DArray=function(t,s){const r=i.get(t);t.version>0&&r.__version!==t.version?R(r,t,s):(n.activeTexture(e.TEXTURE0+s),n.bindTexture(e.TEXTURE_2D_ARRAY,r.__webglTexture))},this.setTexture3D=function(t,s){const r=i.get(t);t.version>0&&r.__version!==t.version?R(r,t,s):(n.activeTexture(e.TEXTURE0+s),n.bindTexture(e.TEXTURE_3D,r.__webglTexture))},this.setTextureCube=N,this.setupRenderTarget=function(t){const s=t.texture,c=i.get(t),l=i.get(s);t.addEventListener(\\\\\\\"dispose\\\\\\\",E),l.__webglTexture=e.createTexture(),l.__version=s.version,o.memory.textures++;const u=!0===t.isWebGLCubeRenderTarget,h=!0===t.isWebGLMultisampleRenderTarget,d=s.isDataTexture3D||s.isDataTexture2DArray,p=g(t)||a;if(!a||s.format!==w.ic||s.type!==w.G&&s.type!==w.M||(s.format=w.Ib,console.warn(\\\\\\\"THREE.WebGLRenderer: Rendering to textures with RGB format is not supported. Using RGBA format instead.\\\\\\\")),u){c.__webglFramebuffer=[];for(let t=0;t<6;t++)c.__webglFramebuffer[t]=e.createFramebuffer()}else if(c.__webglFramebuffer=e.createFramebuffer(),h)if(a){c.__webglMultisampledFramebuffer=e.createFramebuffer(),c.__webglColorRenderbuffer=e.createRenderbuffer(),e.bindRenderbuffer(e.RENDERBUFFER,c.__webglColorRenderbuffer);const i=r.convert(s.format),o=r.convert(s.type),a=x(s.internalFormat,i,o),l=k(t);e.renderbufferStorageMultisample(e.RENDERBUFFER,l,a,t.width,t.height),n.bindFramebuffer(e.FRAMEBUFFER,c.__webglMultisampledFramebuffer),e.framebufferRenderbuffer(e.FRAMEBUFFER,e.COLOR_ATTACHMENT0,e.RENDERBUFFER,c.__webglColorRenderbuffer),e.bindRenderbuffer(e.RENDERBUFFER,null),t.depthBuffer&&(c.__webglDepthRenderbuffer=e.createRenderbuffer(),F(c.__webglDepthRenderbuffer,t,!0)),n.bindFramebuffer(e.FRAMEBUFFER,null)}else console.warn(\\\\\\\"THREE.WebGLRenderer: WebGLMultisampleRenderTarget can only be used with WebGL2.\\\\\\\");if(u){n.bindTexture(e.TEXTURE_CUBE_MAP,l.__webglTexture),L(e.TEXTURE_CUBE_MAP,s,p);for(let n=0;n<6;n++)I(c.__webglFramebuffer[n],t,e.COLOR_ATTACHMENT0,e.TEXTURE_CUBE_MAP_POSITIVE_X+n);v(s,p)&&y(e.TEXTURE_CUBE_MAP,s,t.width,t.height),n.bindTexture(e.TEXTURE_CUBE_MAP,null)}else{let i=e.TEXTURE_2D;if(d)if(a){i=s.isDataTexture3D?e.TEXTURE_3D:e.TEXTURE_2D_ARRAY}else console.warn(\\\\\\\"THREE.DataTexture3D and THREE.DataTexture2DArray only supported with WebGL2.\\\\\\\");n.bindTexture(i,l.__webglTexture),L(i,s,p),I(c.__webglFramebuffer,t,e.COLOR_ATTACHMENT0,i),v(s,p)&&y(e.TEXTURE_2D,s,t.width,t.height),n.bindTexture(e.TEXTURE_2D,null)}t.depthBuffer&&D(t)},this.updateRenderTargetMipmap=function(t){const s=t.texture;if(v(s,g(t)||a)){const r=t.isWebGLCubeRenderTarget?e.TEXTURE_CUBE_MAP:e.TEXTURE_2D,o=i.get(s).__webglTexture;n.bindTexture(r,o),y(r,s,t.width,t.height),n.bindTexture(r,null)}},this.updateMultisampleRenderTarget=function(t){if(t.isWebGLMultisampleRenderTarget)if(a){const s=i.get(t);n.bindFramebuffer(e.READ_FRAMEBUFFER,s.__webglMultisampledFramebuffer),n.bindFramebuffer(e.DRAW_FRAMEBUFFER,s.__webglFramebuffer);const r=t.width,o=t.height;let a=e.COLOR_BUFFER_BIT;t.depthBuffer&&(a|=e.DEPTH_BUFFER_BIT),t.stencilBuffer&&(a|=e.STENCIL_BUFFER_BIT),e.blitFramebuffer(0,0,r,o,0,0,r,o,a,e.NEAREST),n.bindFramebuffer(e.FRAMEBUFFER,s.__webglMultisampledFramebuffer)}else console.warn(\\\\\\\"THREE.WebGLRenderer: WebGLMultisampleRenderTarget can only be used with WebGL2.\\\\\\\")},this.safeSetTexture2D=function(e,t){e&&e.isWebGLRenderTarget&&(!1===B&&(console.warn(\\\\\\\"THREE.WebGLTextures.safeSetTexture2D: don't use render targets as textures. Use their .texture property instead.\\\\\\\"),B=!0),e=e.texture),M(e,t)},this.safeSetTextureCube=function(e,t){e&&e.isWebGLCubeRenderTarget&&(!1===z&&(console.warn(\\\\\\\"THREE.WebGLTextures.safeSetTextureCube: don't use cube render targets as textures. Use their .texture property instead.\\\\\\\"),z=!0),e=e.texture),N(e,t)}}function sn(e,t,n){const i=n.isWebGL2;return{convert:function(n){let s;if(n===w.Zc)return e.UNSIGNED_BYTE;if(n===w.cd)return e.UNSIGNED_SHORT_4_4_4_4;if(n===w.dd)return e.UNSIGNED_SHORT_5_5_5_1;if(n===w.ed)return e.UNSIGNED_SHORT_5_6_5;if(n===w.l)return e.BYTE;if(n===w.Mc)return e.SHORT;if(n===w.fd)return e.UNSIGNED_SHORT;if(n===w.N)return e.INT;if(n===w.bd)return e.UNSIGNED_INT;if(n===w.G)return e.FLOAT;if(n===w.M)return i?e.HALF_FLOAT:(s=t.get(\\\\\\\"OES_texture_half_float\\\\\\\"),null!==s?s.HALF_FLOAT_OES:null);if(n===w.f)return e.ALPHA;if(n===w.ic)return e.RGB;if(n===w.Ib)return e.RGBA;if(n===w.gb)return e.LUMINANCE;if(n===w.fb)return e.LUMINANCE_ALPHA;if(n===w.x)return e.DEPTH_COMPONENT;if(n===w.y)return e.DEPTH_STENCIL;if(n===w.tc)return e.RED;if(n===w.uc)return e.RED_INTEGER;if(n===w.rc)return e.RG;if(n===w.sc)return e.RG_INTEGER;if(n===w.jc)return e.RGB_INTEGER;if(n===w.Jb)return e.RGBA_INTEGER;if(n===w.qc||n===w.cc||n===w.dc||n===w.ec){if(s=t.get(\\\\\\\"WEBGL_compressed_texture_s3tc\\\\\\\"),null===s)return null;if(n===w.qc)return s.COMPRESSED_RGB_S3TC_DXT1_EXT;if(n===w.cc)return s.COMPRESSED_RGBA_S3TC_DXT1_EXT;if(n===w.dc)return s.COMPRESSED_RGBA_S3TC_DXT3_EXT;if(n===w.ec)return s.COMPRESSED_RGBA_S3TC_DXT5_EXT}if(n===w.pc||n===w.oc||n===w.bc||n===w.ac){if(s=t.get(\\\\\\\"WEBGL_compressed_texture_pvrtc\\\\\\\"),null===s)return null;if(n===w.pc)return s.COMPRESSED_RGB_PVRTC_4BPPV1_IMG;if(n===w.oc)return s.COMPRESSED_RGB_PVRTC_2BPPV1_IMG;if(n===w.bc)return s.COMPRESSED_RGBA_PVRTC_4BPPV1_IMG;if(n===w.ac)return s.COMPRESSED_RGBA_PVRTC_2BPPV1_IMG}if(n===w.mc)return s=t.get(\\\\\\\"WEBGL_compressed_texture_etc1\\\\\\\"),null!==s?s.COMPRESSED_RGB_ETC1_WEBGL:null;if((n===w.nc||n===w.Zb)&&(s=t.get(\\\\\\\"WEBGL_compressed_texture_etc\\\\\\\"),null!==s)){if(n===w.nc)return s.COMPRESSED_RGB8_ETC2;if(n===w.Zb)return s.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?(s=t.get(\\\\\\\"WEBGL_compressed_texture_astc\\\\\\\"),null!==s?n:null):n===w.Yb?(s=t.get(\\\\\\\"EXT_texture_compression_bptc\\\\\\\"),null!==s?n:null):n===w.ad?i?e.UNSIGNED_INT_24_8:(s=t.get(\\\\\\\"WEBGL_depth_texture\\\\\\\"),null!==s?s.UNSIGNED_INT_24_8_WEBGL:null):void 0}}}class rn extends te.a{constructor(e=[]){super(),this.cameras=e}}rn.prototype.isArrayCamera=!0;var on=n(20);function an(){this._targetRay=null,this._grip=null,this._hand=null}function cn(e,t){const n=this,i=e.state;let s=null,r=1,o=null,a=\\\\\\\"local-floor\\\\\\\",c=null;const l=[],u=new Map,h=new te.a;h.layers.enable(1),h.viewport=new _.a;const d=new te.a;d.layers.enable(2),d.viewport=new _.a;const m=[h,d],f=new rn;f.layers.enable(1),f.layers.enable(2);let g=null,v=null;function y(e){const t=u.get(e.inputSource);t&&t.dispatchEvent({type:e.type,data:e.inputSource})}function x(){u.forEach((function(e,t){e.disconnect(t)})),u.clear(),g=null,v=null,i.bindXRFramebuffer(null),e.setRenderTarget(e.getRenderTarget()),C.stop(),n.isPresenting=!1,n.dispatchEvent({type:\\\\\\\"sessionend\\\\\\\"})}function b(e){const t=s.inputSources;for(let e=0;e<l.length;e++)u.set(t[e],l[e]);for(let t=0;t<e.removed.length;t++){const n=e.removed[t],i=u.get(n);i&&(i.dispatchEvent({type:\\\\\\\"disconnected\\\\\\\",data:n}),u.delete(n))}for(let t=0;t<e.added.length;t++){const n=e.added[t],i=u.get(n);i&&i.dispatchEvent({type:\\\\\\\"connected\\\\\\\",data:n})}}this.enabled=!1,this.isPresenting=!1,this.getController=function(e){let t=l[e];return void 0===t&&(t=new an,l[e]=t),t.getTargetRaySpace()},this.getControllerGrip=function(e){let t=l[e];return void 0===t&&(t=new an,l[e]=t),t.getGripSpace()},this.getHand=function(e){let t=l[e];return void 0===t&&(t=new an,l[e]=t),t.getHandSpace()},this.setFramebufferScaleFactor=function(e){r=e,!0===n.isPresenting&&console.warn(\\\\\\\"THREE.WebXRManager: Cannot change framebuffer scale while presenting.\\\\\\\")},this.setReferenceSpaceType=function(e){a=e,!0===n.isPresenting&&console.warn(\\\\\\\"THREE.WebXRManager: Cannot change reference space type while presenting.\\\\\\\")},this.getReferenceSpace=function(){return o},this.getSession=function(){return s},this.setSession=async function(e){if(s=e,null!==s){s.addEventListener(\\\\\\\"select\\\\\\\",y),s.addEventListener(\\\\\\\"selectstart\\\\\\\",y),s.addEventListener(\\\\\\\"selectend\\\\\\\",y),s.addEventListener(\\\\\\\"squeeze\\\\\\\",y),s.addEventListener(\\\\\\\"squeezestart\\\\\\\",y),s.addEventListener(\\\\\\\"squeezeend\\\\\\\",y),s.addEventListener(\\\\\\\"end\\\\\\\",x),s.addEventListener(\\\\\\\"inputsourceschange\\\\\\\",b);const e=t.getContextAttributes();!0!==e.xrCompatible&&await t.makeXRCompatible();const i={antialias:e.antialias,alpha:e.alpha,depth:e.depth,stencil:e.stencil,framebufferScaleFactor:r},c=new XRWebGLLayer(s,t,i);s.updateRenderState({baseLayer:c}),o=await s.requestReferenceSpace(a),C.setContext(s),C.start(),n.isPresenting=!0,n.dispatchEvent({type:\\\\\\\"sessionstart\\\\\\\"})}};const w=new p.a,A=new p.a;function T(e,t){null===t?e.matrixWorld.copy(e.matrix):e.matrixWorld.multiplyMatrices(t.matrixWorld,e.matrix),e.matrixWorldInverse.copy(e.matrixWorld).invert()}this.getCamera=function(e){f.near=d.near=h.near=e.near,f.far=d.far=h.far=e.far,g===f.near&&v===f.far||(s.updateRenderState({depthNear:f.near,depthFar:f.far}),g=f.near,v=f.far);const t=e.parent,n=f.cameras;T(f,t);for(let e=0;e<n.length;e++)T(n[e],t);e.matrixWorld.copy(f.matrixWorld),e.matrix.copy(f.matrix),e.matrix.decompose(e.position,e.quaternion,e.scale);const i=e.children;for(let e=0,t=i.length;e<t;e++)i[e].updateMatrixWorld(!0);return 2===n.length?function(e,t,n){w.setFromMatrixPosition(t.matrixWorld),A.setFromMatrixPosition(n.matrixWorld);const i=w.distanceTo(A),s=t.projectionMatrix.elements,r=n.projectionMatrix.elements,o=s[14]/(s[10]-1),a=s[14]/(s[10]+1),c=(s[9]+1)/s[5],l=(s[9]-1)/s[5],u=(s[8]-1)/s[0],h=(r[8]+1)/r[0],d=o*u,p=o*h,_=i/(-u+h),m=_*-u;t.matrixWorld.decompose(e.position,e.quaternion,e.scale),e.translateX(m),e.translateZ(_),e.matrixWorld.compose(e.position,e.quaternion,e.scale),e.matrixWorldInverse.copy(e.matrixWorld).invert();const f=o+_,g=a+_,v=d-m,y=p+(i-m),x=c*a/g*f,b=l*a/g*f;e.projectionMatrix.makePerspective(v,y,x,b,f,g)}(f,h,d):f.projectionMatrix.copy(h.projectionMatrix),f};let E=null;const C=new N;C.setAnimationLoop((function(e,t){if(c=t.getViewerPose(o),null!==c){const e=c.views,t=s.renderState.baseLayer;i.bindXRFramebuffer(t.framebuffer);let n=!1;e.length!==f.cameras.length&&(f.cameras.length=0,n=!0);for(let i=0;i<e.length;i++){const s=e[i],r=t.getViewport(s),o=m[i];o.matrix.fromArray(s.transform.matrix),o.projectionMatrix.fromArray(s.projectionMatrix),o.viewport.set(r.x,r.y,r.width,r.height),0===i&&f.matrix.copy(o.matrix),!0===n&&f.cameras.push(o)}}const n=s.inputSources;for(let e=0;e<l.length;e++){const i=l[e],s=n[e];i.update(s,t,o)}E&&E(e,t)})),this.setAnimationLoop=function(e){E=e},this.dispose=function(){}}function ln(e){function t(t,n){t.opacity.value=n.opacity,n.color&&t.diffuse.value.copy(n.color),n.emissive&&t.emissive.value.copy(n.emissive).multiplyScalar(n.emissiveIntensity),n.map&&(t.map.value=n.map),n.alphaMap&&(t.alphaMap.value=n.alphaMap),n.specularMap&&(t.specularMap.value=n.specularMap);const i=e.get(n).envMap;if(i){t.envMap.value=i,t.flipEnvMap.value=i.isCubeTexture&&i._needsFlipEnvMap?-1:1,t.reflectivity.value=n.reflectivity,t.refractionRatio.value=n.refractionRatio;const s=e.get(i).__maxMipLevel;void 0!==s&&(t.maxMipLevel.value=s)}let s,r;n.lightMap&&(t.lightMap.value=n.lightMap,t.lightMapIntensity.value=n.lightMapIntensity),n.aoMap&&(t.aoMap.value=n.aoMap,t.aoMapIntensity.value=n.aoMapIntensity),n.map?s=n.map:n.specularMap?s=n.specularMap:n.displacementMap?s=n.displacementMap:n.normalMap?s=n.normalMap:n.bumpMap?s=n.bumpMap:n.roughnessMap?s=n.roughnessMap:n.metalnessMap?s=n.metalnessMap:n.alphaMap?s=n.alphaMap:n.emissiveMap?s=n.emissiveMap:n.clearcoatMap?s=n.clearcoatMap:n.clearcoatNormalMap?s=n.clearcoatNormalMap:n.clearcoatRoughnessMap&&(s=n.clearcoatRoughnessMap),void 0!==s&&(s.isWebGLRenderTarget&&(s=s.texture),!0===s.matrixAutoUpdate&&s.updateMatrix(),t.uvTransform.value.copy(s.matrix)),n.aoMap?r=n.aoMap:n.lightMap&&(r=n.lightMap),void 0!==r&&(r.isWebGLRenderTarget&&(r=r.texture),!0===r.matrixAutoUpdate&&r.updateMatrix(),t.uv2Transform.value.copy(r.matrix))}function n(t,n){t.roughness.value=n.roughness,t.metalness.value=n.metalness,n.roughnessMap&&(t.roughnessMap.value=n.roughnessMap),n.metalnessMap&&(t.metalnessMap.value=n.metalnessMap),n.emissiveMap&&(t.emissiveMap.value=n.emissiveMap),n.bumpMap&&(t.bumpMap.value=n.bumpMap,t.bumpScale.value=n.bumpScale,n.side===w.i&&(t.bumpScale.value*=-1)),n.normalMap&&(t.normalMap.value=n.normalMap,t.normalScale.value.copy(n.normalScale),n.side===w.i&&t.normalScale.value.negate()),n.displacementMap&&(t.displacementMap.value=n.displacementMap,t.displacementScale.value=n.displacementScale,t.displacementBias.value=n.displacementBias);e.get(n).envMap&&(t.envMapIntensity.value=n.envMapIntensity)}return{refreshFogUniforms:function(e,t){e.fogColor.value.copy(t.color),t.isFog?(e.fogNear.value=t.near,e.fogFar.value=t.far):t.isFogExp2&&(e.fogDensity.value=t.density)},refreshMaterialUniforms:function(e,i,s,r){i.isMeshBasicMaterial?t(e,i):i.isMeshLambertMaterial?(t(e,i),function(e,t){t.emissiveMap&&(e.emissiveMap.value=t.emissiveMap)}(e,i)):i.isMeshToonMaterial?(t(e,i),function(e,t){t.gradientMap&&(e.gradientMap.value=t.gradientMap);t.emissiveMap&&(e.emissiveMap.value=t.emissiveMap);t.bumpMap&&(e.bumpMap.value=t.bumpMap,e.bumpScale.value=t.bumpScale,t.side===w.i&&(e.bumpScale.value*=-1));t.normalMap&&(e.normalMap.value=t.normalMap,e.normalScale.value.copy(t.normalScale),t.side===w.i&&e.normalScale.value.negate());t.displacementMap&&(e.displacementMap.value=t.displacementMap,e.displacementScale.value=t.displacementScale,e.displacementBias.value=t.displacementBias)}(e,i)):i.isMeshPhongMaterial?(t(e,i),function(e,t){e.specular.value.copy(t.specular),e.shininess.value=Math.max(t.shininess,1e-4),t.emissiveMap&&(e.emissiveMap.value=t.emissiveMap);t.bumpMap&&(e.bumpMap.value=t.bumpMap,e.bumpScale.value=t.bumpScale,t.side===w.i&&(e.bumpScale.value*=-1));t.normalMap&&(e.normalMap.value=t.normalMap,e.normalScale.value.copy(t.normalScale),t.side===w.i&&e.normalScale.value.negate());t.displacementMap&&(e.displacementMap.value=t.displacementMap,e.displacementScale.value=t.displacementScale,e.displacementBias.value=t.displacementBias)}(e,i)):i.isMeshStandardMaterial?(t(e,i),i.isMeshPhysicalMaterial?function(e,t){n(e,t),e.reflectivity.value=t.reflectivity,e.clearcoat.value=t.clearcoat,e.clearcoatRoughness.value=t.clearcoatRoughness,t.sheen&&e.sheen.value.copy(t.sheen);t.clearcoatMap&&(e.clearcoatMap.value=t.clearcoatMap);t.clearcoatRoughnessMap&&(e.clearcoatRoughnessMap.value=t.clearcoatRoughnessMap);t.clearcoatNormalMap&&(e.clearcoatNormalScale.value.copy(t.clearcoatNormalScale),e.clearcoatNormalMap.value=t.clearcoatNormalMap,t.side===w.i&&e.clearcoatNormalScale.value.negate());e.transmission.value=t.transmission,t.transmissionMap&&(e.transmissionMap.value=t.transmissionMap)}(e,i):n(e,i)):i.isMeshMatcapMaterial?(t(e,i),function(e,t){t.matcap&&(e.matcap.value=t.matcap);t.bumpMap&&(e.bumpMap.value=t.bumpMap,e.bumpScale.value=t.bumpScale,t.side===w.i&&(e.bumpScale.value*=-1));t.normalMap&&(e.normalMap.value=t.normalMap,e.normalScale.value.copy(t.normalScale),t.side===w.i&&e.normalScale.value.negate());t.displacementMap&&(e.displacementMap.value=t.displacementMap,e.displacementScale.value=t.displacementScale,e.displacementBias.value=t.displacementBias)}(e,i)):i.isMeshDepthMaterial?(t(e,i),function(e,t){t.displacementMap&&(e.displacementMap.value=t.displacementMap,e.displacementScale.value=t.displacementScale,e.displacementBias.value=t.displacementBias)}(e,i)):i.isMeshDistanceMaterial?(t(e,i),function(e,t){t.displacementMap&&(e.displacementMap.value=t.displacementMap,e.displacementScale.value=t.displacementScale,e.displacementBias.value=t.displacementBias);e.referencePosition.value.copy(t.referencePosition),e.nearDistance.value=t.nearDistance,e.farDistance.value=t.farDistance}(e,i)):i.isMeshNormalMaterial?(t(e,i),function(e,t){t.bumpMap&&(e.bumpMap.value=t.bumpMap,e.bumpScale.value=t.bumpScale,t.side===w.i&&(e.bumpScale.value*=-1));t.normalMap&&(e.normalMap.value=t.normalMap,e.normalScale.value.copy(t.normalScale),t.side===w.i&&e.normalScale.value.negate());t.displacementMap&&(e.displacementMap.value=t.displacementMap,e.displacementScale.value=t.displacementScale,e.displacementBias.value=t.displacementBias)}(e,i)):i.isLineBasicMaterial?(function(e,t){e.diffuse.value.copy(t.color),e.opacity.value=t.opacity}(e,i),i.isLineDashedMaterial&&function(e,t){e.dashSize.value=t.dashSize,e.totalSize.value=t.dashSize+t.gapSize,e.scale.value=t.scale}(e,i)):i.isPointsMaterial?function(e,t,n,i){e.diffuse.value.copy(t.color),e.opacity.value=t.opacity,e.size.value=t.size*n,e.scale.value=.5*i,t.map&&(e.map.value=t.map);t.alphaMap&&(e.alphaMap.value=t.alphaMap);let s;t.map?s=t.map:t.alphaMap&&(s=t.alphaMap);void 0!==s&&(!0===s.matrixAutoUpdate&&s.updateMatrix(),e.uvTransform.value.copy(s.matrix))}(e,i,s,r):i.isSpriteMaterial?function(e,t){e.diffuse.value.copy(t.color),e.opacity.value=t.opacity,e.rotation.value=t.rotation,t.map&&(e.map.value=t.map);t.alphaMap&&(e.alphaMap.value=t.alphaMap);let n;t.map?n=t.map:t.alphaMap&&(n=t.alphaMap);void 0!==n&&(!0===n.matrixAutoUpdate&&n.updateMatrix(),e.uvTransform.value.copy(n.matrix))}(e,i):i.isShadowMaterial?(e.color.value.copy(i.color),e.opacity.value=i.opacity):i.isShaderMaterial&&(i.uniformsNeedUpdate=!1)}}}function un(e){const t=void 0!==(e=e||{}).canvas?e.canvas:function(){const e=document.createElementNS(\\\\\\\"http://www.w3.org/1999/xhtml\\\\\\\",\\\\\\\"canvas\\\\\\\");return e.style.display=\\\\\\\"block\\\\\\\",e}(),n=void 0!==e.context?e.context:null,i=void 0!==e.alpha&&e.alpha,s=void 0===e.depth||e.depth,r=void 0===e.stencil||e.stencil,o=void 0!==e.antialias&&e.antialias,a=void 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e.isMeshLambertMaterial||e.isMeshToonMaterial||e.isMeshPhongMaterial||e.isMeshStandardMaterial||e.isShadowMaterial||e.isShaderMaterial&&!0===e.lights}(e),i.lightsStateVersion=o,i.needsLights&&(h.ambientLightColor.value=s.state.ambient,h.lightProbe.value=s.state.probe,h.directionalLights.value=s.state.directional,h.directionalLightShadows.value=s.state.directionalShadow,h.spotLights.value=s.state.spot,h.spotLightShadows.value=s.state.spotShadow,h.rectAreaLights.value=s.state.rectArea,h.ltc_1.value=s.state.rectAreaLTC1,h.ltc_2.value=s.state.rectAreaLTC2,h.pointLights.value=s.state.point,h.pointLightShadows.value=s.state.pointShadow,h.hemisphereLights.value=s.state.hemi,h.directionalShadowMap.value=s.state.directionalShadowMap,h.directionalShadowMatrix.value=s.state.directionalShadowMatrix,h.spotShadowMap.value=s.state.spotShadowMap,h.spotShadowMatrix.value=s.state.spotShadowMatrix,h.pointShadowMap.value=s.state.pointShadowMap,h.pointShadowMatrix.value=s.state.pointShadowMatrix);const d=u.getUniforms(),p=yt.seqWithValue(d.seq,h);return i.currentProgram=u,i.uniformsList=p,u}function We(e,t){const n=ce.get(e);n.outputEncoding=t.outputEncoding,n.instancing=t.instancing,n.numClippingPlanes=t.numClippingPlanes,n.numIntersection=t.numClipIntersection,n.vertexAlphas=t.vertexAlphas}function Xe(e,t,n,i){!0!==t.isScene&&(t=ee),de.resetTextureUnits();const s=t.fog,r=n.isMeshStandardMaterial?t.environment:null,o=null===O?v.outputEncoding:O.texture.encoding,a=pe.get(n.envMap||r),c=!0===n.vertexColors&&i.geometry.attributes.color&&4===i.geometry.attributes.color.itemSize,l=ce.get(n),u=m.state.lights;if(!0===$&&(!0===J||e!==P)){const t=e===P&&n.id===L;Ae.setState(n,e,t)}let h=!1;n.version===l.__version?l.needsLights&&l.lightsStateVersion!==u.state.version||l.outputEncoding!==o||i.isInstancedMesh&&!1===l.instancing?h=!0:i.isInstancedMesh||!0!==l.instancing?l.envMap!==a||n.fog&&l.fog!==s?h=!0:void 0===l.numClippingPlanes||l.numClippingPlanes===Ae.numPlanes&&l.numIntersection===Ae.numIntersection?l.vertexAlphas!==c&&(h=!0):h=!0:h=!0:(h=!0,l.__version=n.version);let d=l.currentProgram;!0===h&&(d=qe(n,t,i));let p=!1,_=!1,f=!1;const g=d.getUniforms(),y=l.uniforms;if(se.useProgram(d.program)&&(p=!0,_=!0,f=!0),n.id!==L&&(L=n.id,_=!0),p||P!==e){if(g.setValue(Le,\\\\\\\"projectionMatrix\\\\\\\",e.projectionMatrix),ie.logarithmicDepthBuffer&&g.setValue(Le,\\\\\\\"logDepthBufFC\\\\\\\",2/(Math.log(e.far+1)/Math.LN2)),P!==e&&(P=e,_=!0,f=!0),n.isShaderMaterial||n.isMeshPhongMaterial||n.isMeshToonMaterial||n.isMeshStandardMaterial||n.envMap){const t=g.map.cameraPosition;void 0!==t&&t.setValue(Le,Z.setFromMatrixPosition(e.matrixWorld))}(n.isMeshPhongMaterial||n.isMeshToonMaterial||n.isMeshLambertMaterial||n.isMeshBasicMaterial||n.isMeshStandardMaterial||n.isShaderMaterial)&&g.setValue(Le,\\\\\\\"isOrthographic\\\\\\\",!0===e.isOrthographicCamera),(n.isMeshPhongMaterial||n.isMeshToonMaterial||n.isMeshLambertMaterial||n.isMeshBasicMaterial||n.isMeshStandardMaterial||n.isShaderMaterial||n.isShadowMaterial||n.skinning)&&g.setValue(Le,\\\\\\\"viewMatrix\\\\\\\",e.matrixWorldInverse)}if(n.skinning){g.setOptional(Le,i,\\\\\\\"bindMatrix\\\\\\\"),g.setOptional(Le,i,\\\\\\\"bindMatrixInverse\\\\\\\");const e=i.skeleton;if(e){const t=e.bones;if(ie.floatVertexTextures){if(null===e.boneTexture){let n=Math.sqrt(4*t.length);n=A.a.ceilPowerOfTwo(n),n=Math.max(n,4);const i=new Float32Array(n*n*4);i.set(e.boneMatrices);const s=new T.a(i,n,n,w.Ib,w.G);e.boneMatrices=i,e.boneTexture=s,e.boneTextureSize=n}g.setValue(Le,\\\\\\\"boneTexture\\\\\\\",e.boneTexture,de),g.setValue(Le,\\\\\\\"boneTextureSize\\\\\\\",e.boneTextureSize)}else g.setOptional(Le,e,\\\\\\\"boneMatrices\\\\\\\")}}var x,b;return(_||l.receiveShadow!==i.receiveShadow)&&(l.receiveShadow=i.receiveShadow,g.setValue(Le,\\\\\\\"receiveShadow\\\\\\\",i.receiveShadow)),_&&(g.setValue(Le,\\\\\\\"toneMappingExposure\\\\\\\",v.toneMappingExposure),l.needsLights&&(b=f,(x=y).ambientLightColor.needsUpdate=b,x.lightProbe.needsUpdate=b,x.directionalLights.needsUpdate=b,x.directionalLightShadows.needsUpdate=b,x.pointLights.needsUpdate=b,x.pointLightShadows.needsUpdate=b,x.spotLights.needsUpdate=b,x.spotLightShadows.needsUpdate=b,x.rectAreaLights.needsUpdate=b,x.hemisphereLights.needsUpdate=b),s&&n.fog&&xe.refreshFogUniforms(y,s),xe.refreshMaterialUniforms(y,n,B,k),yt.upload(Le,l.uniformsList,y,de)),n.isShaderMaterial&&!0===n.uniformsNeedUpdate&&(yt.upload(Le,l.uniformsList,y,de),n.uniformsNeedUpdate=!1),n.isSpriteMaterial&&g.setValue(Le,\\\\\\\"center\\\\\\\",i.center),g.setValue(Le,\\\\\\\"modelViewMatrix\\\\\\\",i.modelViewMatrix),g.setValue(Le,\\\\\\\"normalMatrix\\\\\\\",i.normalMatrix),g.setValue(Le,\\\\\\\"modelMatrix\\\\\\\",i.matrixWorld),d}Ge.setAnimationLoop((function(e){Be&&Be(e)})),\\\\\\\"undefined\\\\\\\"!=typeof window&&Ge.setContext(window),this.setAnimationLoop=function(e){Be=e,Ie.setAnimationLoop(e),null===e?Ge.stop():Ge.start()},Ie.addEventListener(\\\\\\\"sessionstart\\\\\\\",ze),Ie.addEventListener(\\\\\\\"sessionend\\\\\\\",Ue),this.render=function(e,t){let n,i;if(void 0!==arguments[2]&&(console.warn(\\\\\\\"THREE.WebGLRenderer.render(): the renderTarget argument has been removed. Use .setRenderTarget() instead.\\\\\\\"),n=arguments[2]),void 0!==arguments[3]&&(console.warn(\\\\\\\"THREE.WebGLRenderer.render(): the forceClear argument has been removed. Use .clear() instead.\\\\\\\"),i=arguments[3]),void 0!==t&&!0!==t.isCamera)return void console.error(\\\\\\\"THREE.WebGLRenderer.render: camera is not an instance of THREE.Camera.\\\\\\\");if(!0===y)return;!0===e.autoUpdate&&e.updateMatrixWorld(),null===t.parent&&t.updateMatrixWorld(),!0===Ie.enabled&&!0===Ie.isPresenting&&(t=Ie.getCamera(t)),!0===e.isScene&&e.onBeforeRender(v,e,t,n||O),m=we.get(e,g.length),m.init(),g.push(m),K.multiplyMatrices(t.projectionMatrix,t.matrixWorldInverse),H.setFromProjectionMatrix(K),J=this.localClippingEnabled,$=Ae.init(this.clippingPlanes,J,t),h=be.get(e,f.length),h.init(),f.push(h),Ve(e,t,0,v.sortObjects),h.finish(),!0===v.sortObjects&&h.sort(z,U),!0===$&&Ae.beginShadows();const s=m.state.shadowsArray;Te.render(s,e,t),m.setupLights(),m.setupLightsView(t),!0===$&&Ae.endShadows(),!0===this.info.autoReset&&this.info.reset(),void 0!==n&&this.setRenderTarget(n),Ee.render(h,e,t,i);const r=h.opaque,o=h.transparent;r.length>0&&je(r,e,t),o.length>0&&je(o,e,t),null!==O&&(de.updateRenderTargetMipmap(O),de.updateMultisampleRenderTarget(O)),!0===e.isScene&&e.onAfterRender(v,e,t),se.buffers.depth.setTest(!0),se.buffers.depth.setMask(!0),se.buffers.color.setMask(!0),se.setPolygonOffset(!1),Oe.resetDefaultState(),L=-1,P=null,g.pop(),m=g.length>0?g[g.length-1]:null,f.pop(),h=f.length>0?f[f.length-1]:null},this.getActiveCubeFace=function(){return x},this.getActiveMipmapLevel=function(){return b},this.getRenderTarget=function(){return O},this.setRenderTarget=function(e,t=0,n=0){O=e,x=t,b=n,e&&void 0===ce.get(e).__webglFramebuffer&&de.setupRenderTarget(e);let i=null,s=!1,r=!1;if(e){const n=e.texture;(n.isDataTexture3D||n.isDataTexture2DArray)&&(r=!0);const o=ce.get(e).__webglFramebuffer;e.isWebGLCubeRenderTarget?(i=o[t],s=!0):i=e.isWebGLMultisampleRenderTarget?ce.get(e).__webglMultisampledFramebuffer:o,R.copy(e.viewport),I.copy(e.scissor),F=e.scissorTest}else R.copy(G).multiplyScalar(B).floor(),I.copy(V).multiplyScalar(B).floor(),F=j;if(se.bindFramebuffer(Le.FRAMEBUFFER,i),se.viewport(R),se.scissor(I),se.setScissorTest(F),s){const i=ce.get(e.texture);Le.framebufferTexture2D(Le.FRAMEBUFFER,Le.COLOR_ATTACHMENT0,Le.TEXTURE_CUBE_MAP_POSITIVE_X+t,i.__webglTexture,n)}else if(r){const i=ce.get(e.texture),s=t||0;Le.framebufferTextureLayer(Le.FRAMEBUFFER,Le.COLOR_ATTACHMENT0,i.__webglTexture,n||0,s)}},this.readRenderTargetPixels=function(e,t,n,i,s,r,o){if(!e||!e.isWebGLRenderTarget)return void console.error(\\\\\\\"THREE.WebGLRenderer.readRenderTargetPixels: renderTarget is not THREE.WebGLRenderTarget.\\\\\\\");let a=ce.get(e).__webglFramebuffer;if(e.isWebGLCubeRenderTarget&&void 0!==o&&(a=a[o]),a){se.bindFramebuffer(Le.FRAMEBUFFER,a);try{const o=e.texture,a=o.format,c=o.type;if(a!==w.Ib&&Se.convert(a)!==Le.getParameter(Le.IMPLEMENTATION_COLOR_READ_FORMAT))return void console.error(\\\\\\\"THREE.WebGLRenderer.readRenderTargetPixels: renderTarget is not in RGBA or implementation defined format.\\\\\\\");const l=c===w.M&&(ne.has(\\\\\\\"EXT_color_buffer_half_float\\\\\\\")||ie.isWebGL2&&ne.has(\\\\\\\"EXT_color_buffer_float\\\\\\\"));if(!(c===w.Zc||Se.convert(c)===Le.getParameter(Le.IMPLEMENTATION_COLOR_READ_TYPE)||c===w.G&&(ie.isWebGL2||ne.has(\\\\\\\"OES_texture_float\\\\\\\")||ne.has(\\\\\\\"WEBGL_color_buffer_float\\\\\\\"))||l))return void console.error(\\\\\\\"THREE.WebGLRenderer.readRenderTargetPixels: renderTarget is not in UnsignedByteType or implementation defined type.\\\\\\\");Le.checkFramebufferStatus(Le.FRAMEBUFFER)===Le.FRAMEBUFFER_COMPLETE?t>=0&&t<=e.width-i&&n>=0&&n<=e.height-s&&Le.readPixels(t,n,i,s,Se.convert(a),Se.convert(c),r):console.error(\\\\\\\"THREE.WebGLRenderer.readRenderTargetPixels: readPixels from renderTarget failed. Framebuffer not complete.\\\\\\\")}finally{const e=null!==O?ce.get(O).__webglFramebuffer:null;se.bindFramebuffer(Le.FRAMEBUFFER,e)}}},this.copyFramebufferToTexture=function(e,t,n=0){const i=Math.pow(2,-n),s=Math.floor(t.image.width*i),r=Math.floor(t.image.height*i),o=Se.convert(t.format);de.setTexture2D(t,0),Le.copyTexImage2D(Le.TEXTURE_2D,n,o,e.x,e.y,s,r,0),se.unbindTexture()},this.copyTextureToTexture=function(e,t,n,i=0){const s=t.image.width,r=t.image.height,o=Se.convert(n.format),a=Se.convert(n.type);de.setTexture2D(n,0),Le.pixelStorei(Le.UNPACK_FLIP_Y_WEBGL,n.flipY),Le.pixelStorei(Le.UNPACK_PREMULTIPLY_ALPHA_WEBGL,n.premultiplyAlpha),Le.pixelStorei(Le.UNPACK_ALIGNMENT,n.unpackAlignment),t.isDataTexture?Le.texSubImage2D(Le.TEXTURE_2D,i,e.x,e.y,s,r,o,a,t.image.data):t.isCompressedTexture?Le.compressedTexSubImage2D(Le.TEXTURE_2D,i,e.x,e.y,t.mipmaps[0].width,t.mipmaps[0].height,o,t.mipmaps[0].data):Le.texSubImage2D(Le.TEXTURE_2D,i,e.x,e.y,o,a,t.image),0===i&&n.generateMipmaps&&Le.generateMipmap(Le.TEXTURE_2D),se.unbindTexture()},this.copyTextureToTexture3D=function(e,t,n,i,s=0){if(v.isWebGL1Renderer)return void console.warn(\\\\\\\"THREE.WebGLRenderer.copyTextureToTexture3D: can only be used with WebGL2.\\\\\\\");const{width:r,height:o,data:a}=n.image,c=Se.convert(i.format),l=Se.convert(i.type);let u;if(i.isDataTexture3D)de.setTexture3D(i,0),u=Le.TEXTURE_3D;else{if(!i.isDataTexture2DArray)return void console.warn(\\\\\\\"THREE.WebGLRenderer.copyTextureToTexture3D: only supports THREE.DataTexture3D and THREE.DataTexture2DArray.\\\\\\\");de.setTexture2DArray(i,0),u=Le.TEXTURE_2D_ARRAY}Le.pixelStorei(Le.UNPACK_FLIP_Y_WEBGL,i.flipY),Le.pixelStorei(Le.UNPACK_PREMULTIPLY_ALPHA_WEBGL,i.premultiplyAlpha),Le.pixelStorei(Le.UNPACK_ALIGNMENT,i.unpackAlignment);const h=Le.getParameter(Le.UNPACK_ROW_LENGTH),d=Le.getParameter(Le.UNPACK_IMAGE_HEIGHT),p=Le.getParameter(Le.UNPACK_SKIP_PIXELS),_=Le.getParameter(Le.UNPACK_SKIP_ROWS),m=Le.getParameter(Le.UNPACK_SKIP_IMAGES);Le.pixelStorei(Le.UNPACK_ROW_LENGTH,r),Le.pixelStorei(Le.UNPACK_IMAGE_HEIGHT,o),Le.pixelStorei(Le.UNPACK_SKIP_PIXELS,e.min.x),Le.pixelStorei(Le.UNPACK_SKIP_ROWS,e.min.y),Le.pixelStorei(Le.UNPACK_SKIP_IMAGES,e.min.z),Le.texSubImage3D(u,s,t.x,t.y,t.z,e.max.x-e.min.x+1,e.max.y-e.min.y+1,e.max.z-e.min.z+1,c,l,a),Le.pixelStorei(Le.UNPACK_ROW_LENGTH,h),Le.pixelStorei(Le.UNPACK_IMAGE_HEIGHT,d),Le.pixelStorei(Le.UNPACK_SKIP_PIXELS,p),Le.pixelStorei(Le.UNPACK_SKIP_ROWS,_),Le.pixelStorei(Le.UNPACK_SKIP_IMAGES,m),0===s&&i.generateMipmaps&&Le.generateMipmap(u),se.unbindTexture()},this.initTexture=function(e){de.setTexture2D(e,0),se.unbindTexture()},this.resetState=function(){x=0,b=0,O=null,se.reset(),Oe.reset()},\\\\\\\"undefined\\\\\\\"!=typeof __THREE_DEVTOOLS__&&__THREE_DEVTOOLS__.dispatchEvent(new CustomEvent(\\\\\\\"observe\\\\\\\",{detail:this}))}Object.assign(an.prototype,{constructor:an,getHandSpace:function(){return null===this._hand&&(this._hand=new on.a,this._hand.matrixAutoUpdate=!1,this._hand.visible=!1,this._hand.joints={},this._hand.inputState={pinching:!1}),this._hand},getTargetRaySpace:function(){return null===this._targetRay&&(this._targetRay=new on.a,this._targetRay.matrixAutoUpdate=!1,this._targetRay.visible=!1),this._targetRay},getGripSpace:function(){return null===this._grip&&(this._grip=new on.a,this._grip.matrixAutoUpdate=!1,this._grip.visible=!1),this._grip},dispatchEvent:function(e){return null!==this._targetRay&&this._targetRay.dispatchEvent(e),null!==this._grip&&this._grip.dispatchEvent(e),null!==this._hand&&this._hand.dispatchEvent(e),this},disconnect:function(e){return this.dispatchEvent({type:\\\\\\\"disconnected\\\\\\\",data:e}),null!==this._targetRay&&(this._targetRay.visible=!1),null!==this._grip&&(this._grip.visible=!1),null!==this._hand&&(this._hand.visible=!1),this},update:function(e,t,n){let i=null,s=null,r=null;const o=this._targetRay,a=this._grip,c=this._hand;if(e&&\\\\\\\"visible-blurred\\\\\\\"!==t.session.visibilityState)if(null!==o&&(i=t.getPose(e.targetRaySpace,n),null!==i&&(o.matrix.fromArray(i.transform.matrix),o.matrix.decompose(o.position,o.rotation,o.scale))),c&&e.hand){r=!0;for(const i of e.hand.values()){const e=t.getJointPose(i,n);if(void 0===c.joints[i.jointName]){const e=new on.a;e.matrixAutoUpdate=!1,e.visible=!1,c.joints[i.jointName]=e,c.add(e)}const s=c.joints[i.jointName];null!==e&&(s.matrix.fromArray(e.transform.matrix),s.matrix.decompose(s.position,s.rotation,s.scale),s.jointRadius=e.radius),s.visible=null!==e}const i=c.joints[\\\\\\\"index-finger-tip\\\\\\\"],s=c.joints[\\\\\\\"thumb-tip\\\\\\\"],o=i.position.distanceTo(s.position),a=.02,l=.005;c.inputState.pinching&&o>a+l?(c.inputState.pinching=!1,this.dispatchEvent({type:\\\\\\\"pinchend\\\\\\\",handedness:e.handedness,target:this})):!c.inputState.pinching&&o<=a-l&&(c.inputState.pinching=!0,this.dispatchEvent({type:\\\\\\\"pinchstart\\\\\\\",handedness:e.handedness,target:this}))}else null!==a&&e.gripSpace&&(s=t.getPose(e.gripSpace,n),null!==s&&(a.matrix.fromArray(s.transform.matrix),a.matrix.decompose(a.position,a.rotation,a.scale)));return null!==o&&(o.visible=null!==i),null!==a&&(a.visible=null!==s),null!==c&&(c.visible=null!==r),this}}),Object.assign(cn.prototype,J.a.prototype);class hn extends Z{constructor(e,t,n){super(e,t,n),this.samples=4}copy(e){return super.copy.call(this,e),this.samples=e.samples,this}}hn.prototype.isWebGLMultisampleRenderTarget=!0;const dn={};var pn,_n,mn;!function(e){e.WEBGL=\\\\\\\"webgl\\\\\\\",e.WEBGL2=\\\\\\\"webgl2\\\\\\\",e.EXPERIMENTAL_WEBGL=\\\\\\\"experimental-webgl\\\\\\\",e.EXPERIMENTAL_WEBGL2=\\\\\\\"experimental-webgl2\\\\\\\"}(pn||(pn={}));class fn{constructor(){this._next_renderer_id=0,this._renderers={},this._printDebug=!1,this._require_webgl2=!1,this._resolves=[]}setPrintDebug(e=!0){this._printDebug=e}printDebug(){return this._printDebug}printDebugMessage(e){this._printDebug&&console.warn(\\\\\\\"[Poly debug]\\\\\\\",e)}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 e=document.createElement(\\\\\\\"canvas\\\\\\\");return null!=(window.WebGL2RenderingContext&&e.getContext(pn.WEBGL2))}createWebGLRenderer(e){const t=new un(e);return this.printDebugMessage([\\\\\\\"create renderer:\\\\\\\",e]),t}createRenderingContext(e){let t=null;return this._require_webgl2&&(t=this._getRenderingContextWebgl(e,!0),t||console.warn(\\\\\\\"failed to create webgl2 context\\\\\\\")),t||(t=this._getRenderingContextWebgl(e,!1)),t}_getRenderingContextWebgl(e,t){let n;n=this.webgl2Available()||t?pn.WEBGL2:pn.WEBGL;let i=e.getContext(n,dn);return i?this.printDebugMessage(`create gl context: ${n}.`):(n=t?pn.EXPERIMENTAL_WEBGL2:pn.EXPERIMENTAL_WEBGL,this.printDebugMessage(`create gl context: ${n}.`),i=e.getContext(n,dn)),i}registerRenderer(e){if(e._polygon_id)throw new Error(\\\\\\\"render already registered\\\\\\\");e._polygon_id=this._next_renderer_id+=1,this._renderers[e._polygon_id]=e,1==Object.keys(this._renderers).length&&this.flush_callbacks_with_renderer(e)}deregisterRenderer(e){delete this._renderers[e._polygon_id],e.dispose()}firstRenderer(){const e=Object.keys(this._renderers)[0];return e?this._renderers[e]:null}renderers(){return Object.values(this._renderers)}flush_callbacks_with_renderer(e){let t;for(;t=this._resolves.pop();)t(e)}async waitForRenderer(){const e=this.firstRenderer();return e||new Promise(((e,t)=>{this._resolves.push(e)}))}renderTarget(e,t,n){return this.webgl2Available()?new hn(e,t,n):new Z(e,t,n)}}class gn{constructor(){this._root=\\\\\\\"/three/js/libs\\\\\\\",this._BASISPath=\\\\\\\"/basis\\\\\\\",this._DRACOPath=\\\\\\\"/draco\\\\\\\",this._DRACOGLTFPath=\\\\\\\"/draco/gltf\\\\\\\"}root(){return this._root}setRoot(e){this._root=e}BASISPath(){return this._BASISPath}DRACOPath(){return this._DRACOPath}DRACOGLTFPath(){return this._DRACOGLTFPath}}class vn{constructor(e){this.poly=e,this._node_register=new Map,this._node_register_categories=new Map,this._node_register_options=new Map}register(e,t,n){const i=e.context(),s=e.type().toLowerCase();let r=this._node_register.get(i);r||(r=new Map,this._node_register.set(i,r));if(r.get(s))console.error(`node ${i}/${s} already registered`);else{if(r.set(s,e),t){let e=this._node_register_categories.get(i);e||(e=new Map,this._node_register_categories.set(i,e)),e.set(s,t)}if(n){let e=this._node_register_options.get(i);e||(e=new Map,this._node_register_options.set(i,e)),e.set(s,n)}this.poly.pluginsRegister.registerNode(e)}}deregister(e,t){var n,i,s;null===(n=this._node_register.get(e))||void 0===n||n.delete(t),null===(i=this._node_register_categories.get(e))||void 0===i||i.delete(t),null===(s=this._node_register_options.get(e))||void 0===s||s.delete(t)}isRegistered(e,t){const n=this._node_register.get(e);return!!n&&null!=n.get(t)}registeredNodesForContextAndParentType(e,t){var n;if(this._node_register.get(e)){const i=[];return null===(n=this._node_register.get(e))||void 0===n||n.forEach(((e,t)=>{i.push(e)})),i.filter((n=>{var i;const s=n.type().toLowerCase(),r=null===(i=this._node_register_options.get(e))||void 0===i?void 0:i.get(s);if(r){const n=r.only,i=r.except,s=`${e}/${t}`;return n?n.includes(s):!i||!i.includes(s)}return!0}))}return[]}registeredNodes(e,t){const n={},i=this.registeredNodesForContextAndParentType(e,t);for(let e of i){n[e.type().toLowerCase()]=e}return n}registeredCategory(e,t){var n;return null===(n=this._node_register_categories.get(e))||void 0===n?void 0:n.get(t.toLowerCase())}map(){return this._node_register}}class yn{constructor(e){this.poly=e,this._operation_register=new Map}register(e){const t=e.context();let n=this._operation_register.get(t);n||(n=new Map,this._operation_register.set(t,n));const i=e.type().toLowerCase();if(n.get(i)){const e=`operation ${t}/${i} already registered`;console.error(e)}else n.set(i,e),this.poly.pluginsRegister.registerOperation(e)}registeredOperationsForContextAndParentType(e,t){var n;if(this._operation_register.get(e)){const t=[];return null===(n=this._operation_register.get(e))||void 0===n||n.forEach(((e,n)=>{t.push(e)})),t}return[]}registeredOperation(e,t){const n=this._operation_register.get(e);if(n)return n.get(t.toLowerCase())}}class xn extends class{constructor(){this._methods_names=[],this._methods_by_name=new Map}register(e,t){this._methods_names.push(t),this._methods_by_name.set(t,e)}getMethod(e){return this._methods_by_name.get(e)}availableMethods(){return this._methods_names}}{getMethod(e){return super.getMethod(e)}}!function(e){e.BasisTextureLoader=\\\\\\\"BasisTextureLoader\\\\\\\",e.DRACOLoader=\\\\\\\"DRACOLoader\\\\\\\",e.EXRLoader=\\\\\\\"EXRLoader\\\\\\\",e.FBXLoader=\\\\\\\"FBXLoader\\\\\\\",e.GLTFLoader=\\\\\\\"GLTFLoader\\\\\\\",e.OBJLoader=\\\\\\\"OBJLoader\\\\\\\",e.PDBLoader=\\\\\\\"PDBLoader\\\\\\\",e.PLYLoader=\\\\\\\"PLYLoader\\\\\\\",e.RGBELoader=\\\\\\\"RGBELoader\\\\\\\",e.SVGLoader=\\\\\\\"SVGLoader\\\\\\\",e.STLLoader=\\\\\\\"STLLoader\\\\\\\",e.TTFLoader=\\\\\\\"TTFLoader\\\\\\\"}(_n||(_n={}));class bn extends class{constructor(){this._module_by_name=new Map}register(e,t){this._module_by_name.set(e,t)}moduleNames(){const e=[];return this._module_by_name.forEach(((t,n)=>{e.push(n)})),e}module(e){return this._module_by_name.get(e)}}{}!function(e){e.GL_MESH_BASIC=\\\\\\\"GL_MESH_BASIC\\\\\\\",e.GL_MESH_LAMBERT=\\\\\\\"GL_MESH_LAMBERT\\\\\\\",e.GL_MESH_STANDARD=\\\\\\\"GL_MESH_STANDARD\\\\\\\",e.GL_MESH_PHONG=\\\\\\\"GL_MESH_PHONG\\\\\\\",e.GL_MESH_PHYSICAL=\\\\\\\"GL_MESH_PHYSICAL\\\\\\\",e.GL_PARTICLES=\\\\\\\"GL_PARTICLES\\\\\\\",e.GL_POINTS=\\\\\\\"GL_POINTS\\\\\\\",e.GL_LINE=\\\\\\\"GL_LINE\\\\\\\",e.GL_TEXTURE=\\\\\\\"GL_TEXTURE\\\\\\\",e.GL_VOLUME=\\\\\\\"GL_VOLUME\\\\\\\"}(mn||(mn={}));class wn extends class{constructor(){this._controller_assembler_by_name=new Map}register(e,t,n){this._controller_assembler_by_name.set(e,{controller:t,assembler:n})}unregister(e){this._controller_assembler_by_name.delete(e)}}{assembler(e,t){const n=this._controller_assembler_by_name.get(t);if(n){return new(0,n.controller)(e,n.assembler)}return n}unregister(e){const t=this._controller_assembler_by_name.get(e);return super.unregister(e),t}}class An{constructor(e){this.poly=e,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(e){this._current_plugin=e,this._plugins_by_name.set(e.name(),e),e.init(this.poly),this._current_plugin=void 0}pluginByName(e){return this._plugins_by_name.get(e)}registerNode(e){if(!this._current_plugin)return;const t=e.context(),n=e.type();let i=this._plugin_name_by_node_context_by_type.get(t);i||(i=new Map,this._plugin_name_by_node_context_by_type.set(t,i)),i.set(n,this._current_plugin.name())}registerOperation(e){if(!this._current_plugin)return;const t=e.context(),n=e.type();let i=this._plugin_name_by_operation_context_by_type.get(t);i||(i=new Map,this._plugin_name_by_operation_context_by_type.set(t,i)),i.set(n,this._current_plugin.name())}toJson(){const e={plugins:{},nodes:{},operations:{}};return this._plugins_by_name.forEach(((t,n)=>{e.plugins[n]=t.toJSON()})),this._plugin_name_by_node_context_by_type.forEach(((t,n)=>{e.nodes[n]={},t.forEach(((t,i)=>{e.nodes[n][i]=t}))})),this._plugin_name_by_operation_context_by_type.forEach(((t,n)=>{e.operations[n]={},t.forEach(((t,i)=>{e.operations[n][i]=t}))})),e}}class Tn{constructor(e){this._camera_types=[]}register(e){const t=e.type();this._camera_types.includes(t)||this._camera_types.push(t)}registeredTypes(){return this._camera_types}}var En=n(87);class Cn{constructor(){this._blobUrlsByStoredUrl=new Map,this._blobsByStoredUrl=new Map,this._blobDataByNodeId=new Map,this._globalBlobsByStoredUrl=new Map}registerBlobUrl(e){console.log(\\\\\\\"registerBlobUrl\\\\\\\",e,Rn.playerMode()),Rn.playerMode()&&this._blobUrlsByStoredUrl.set(e.storedUrl,e.blobUrl)}blobUrl(e){return this._blobUrlsByStoredUrl.get(e)}clear(){this._blobUrlsByStoredUrl.clear(),this._blobsByStoredUrl.clear(),this._blobDataByNodeId.clear()}_clearBlobForNode(e){const t=this._blobDataByNodeId.get(e.graphNodeId());t&&(this._blobsByStoredUrl.delete(t.storedUrl),this._blobUrlsByStoredUrl.delete(t.storedUrl)),this._blobDataByNodeId.delete(e.graphNodeId())}_assignBlobToNode(e,t){this._clearBlobForNode(e),this._blobDataByNodeId.set(e.graphNodeId(),{storedUrl:t.storedUrl,fullUrl:t.fullUrl})}async fetchBlobGlobal(e){if(Rn.playerMode())return{};try{if(this._blobUrlsByStoredUrl.get(e.storedUrl))return{};const t=Rn.assetUrls.remapedUrl(e.fullUrl),n=await fetch(t||e.fullUrl);if(n.ok){const t=await n.blob();return this._blobsByStoredUrl.set(e.storedUrl,t),this._blobUrlsByStoredUrl.set(e.storedUrl,this.createBlobUrl(t)),this._globalBlobsByStoredUrl.set(e.storedUrl,t),{blobData:{storedUrl:e.storedUrl,fullUrl:e.fullUrl}}}return{error:`failed to fetch ${e.fullUrl}`}}catch(t){return{error:`failed to fetch ${e.fullUrl}`}}}async fetchBlobForNode(e){if(Rn.playerMode())return{};try{if(this._blobUrlsByStoredUrl.get(e.storedUrl))return{};const t=Rn.assetUrls.remapedUrl(e.fullUrl),n=await fetch(t||e.fullUrl);if(n.ok){const t=await n.blob();return this._blobsByStoredUrl.set(e.storedUrl,t),this._blobUrlsByStoredUrl.set(e.storedUrl,this.createBlobUrl(t)),this._scene=e.node.scene(),this._assignBlobToNode(e.node,{storedUrl:e.storedUrl,fullUrl:e.fullUrl}),{blobData:{storedUrl:e.storedUrl,fullUrl:e.fullUrl}}}return{error:`failed to fetch ${e.fullUrl}`}}catch(t){return{error:`failed to fetch ${e.fullUrl}`}}}forEachBlob(e){this._blobDataByNodeId.forEach(((t,n)=>{if(this._scene){if(this._scene.graph.nodeFromId(n)){const{storedUrl:n}=t,i=this._blobsByStoredUrl.get(n);i&&e(i,n)}}}));let t=[];const n=new Map;this._globalBlobsByStoredUrl.forEach(((e,i)=>{t.push(i),n.set(i,e)})),t=t.sort(),t.forEach((t=>{const n=this._globalBlobsByStoredUrl.get(t);n&&e(n,t)}))}createBlobUrl(e){return Object(En.a)(e)}}class Mn{setMap(e){this._map=e}remapedUrl(e){if(!this._map)return;const t=e.split(\\\\\\\"?\\\\\\\"),n=t[0],i=t[1],s=this._map[n];return s?i?`${s}?${i}`:s:void 0}}var Nn=n(92),Sn=n(84);class On{markAsLoaded(e,t){this._sceneJsonImporterContructor=t,e()}load(e){if(!this._sceneJsonImporterContructor)return;const t=[];e.forEach(((e,n)=>{t.push(n)}));for(let n of t){const t=e.get(n);t&&(this._loadElement(n,t,this._sceneJsonImporterContructor),e.delete(n))}}async _loadElement(e,t,n){const{sceneData:i,assetsManifest:s,unzippedData:r}=t,o=Object.keys(s);for(let e of o){const t=r[`assets/${s[e]}`];if(!t)return void console.error(e,t);const n=new Blob([t]),i={storedUrl:e,blobUrl:Rn.blobs.createBlobUrl(n)};Rn.blobs.registerBlobUrl(i)}Rn.setPlayerMode(!0),Rn.libs.setRoot(null);const a=`${Math.random()}`.replace(\\\\\\\".\\\\\\\",\\\\\\\"_\\\\\\\"),c={Poly:`___POLY_polyConfig_configurePolygonjs_${a}`,scriptElementId:`___POLY_polyConfig_scriptElement_${a}`,loadSceneArgs:`___POLY_polyConfig_loadSceneArgs_${a}`};window[c.Poly]=Rn;const l={method:this._loadScene.bind(this),element:e,sceneData:i,sceneJsonImporterContructor:n};window[c.loadSceneArgs]=l;this._loadPolyConfig(c,r)||this._loadScene(e,i,n)}_loadPolyConfig(e,t){const n=t[Sn.a.POLY_CONFIG];if(!n)return!1;const i=this._createJsBlob(n,\\\\\\\"polyConfig\\\\\\\");let s=document.getElementById(e.scriptElementId);const r=[];return r.push(`import {configurePolygonjs, configureScene} from '${i}';`),r.push(`configurePolygonjs(window.${e.Poly});`),r.push(`window.${e.loadSceneArgs}.method(window.${e.loadSceneArgs}.element, window.${e.loadSceneArgs}.sceneData, window.${e.loadSceneArgs}.sceneJsonImporterContructor, configureScene);`),r.push(`delete window.${e.loadSceneArgs};`),s||(s=document.createElement(\\\\\\\"script\\\\\\\"),s.setAttribute(\\\\\\\"type\\\\\\\",\\\\\\\"module\\\\\\\"),s.text=r.join(\\\\\\\"\\\\n\\\\\\\"),document.body.append(s)),!0}async _loadScene(e,t,n,i){this._fadeOutPoster(e);const s=new n(t),r=await s.scene();i&&i(r);const o=r.mainCameraNode();if(!o)return void console.warn(\\\\\\\"no master camera found\\\\\\\");const a=o.createViewer(e);r.play(),e.scene=r,e.viewer=a}_fadeOutPoster(e){const t=e.firstElementChild;t&&(t.style.pointerEvents=\\\\\\\"none\\\\\\\",Nn.a.fadeOut(t).then((()=>{var e;null===(e=t.parentElement)||void 0===e||e.removeChild(t)})))}_createJsBlob(e,t){const n=new Blob([e]),i=new File([n],`${t}.js`,{type:\\\\\\\"application/javascript\\\\\\\"});return Object(En.a)(i)}}class Ln{setPerformanceManager(e){this._performanceManager=e}performanceManager(){return this._performanceManager||window.performance}}class Pn{constructor(){this.renderersController=new fn,this.nodesRegister=new vn(this),this.operationsRegister=new yn(this),this.expressionsRegister=new xn,this.modulesRegister=new bn,this.assemblersRegister=new wn,this.pluginsRegister=new An(this),this.camerasRegister=new Tn(this),this.blobs=new Cn,this.assetUrls=new Mn,this.selfContainedScenesLoader=new On,this.performance=new Ln,this.scenesByUuid={},this._player_mode=!0,this._logger=null}static _instance_(){if(window.__POLYGONJS_POLY_INSTANCE__)return window.__POLYGONJS_POLY_INSTANCE__;{const e=new Pn;return window.__POLYGONJS_POLY_INSTANCE__=e,window.__POLYGONJS_POLY_INSTANCE__}}setPlayerMode(e){this._player_mode=e}playerMode(){return this._player_mode}registerNode(e,t,n){this.nodesRegister.register(e,t,n)}registerOperation(e){this.operationsRegister.register(e)}registerCamera(e){this.camerasRegister.register(e)}registerPlugin(e){this.pluginsRegister.register(e)}registeredNodes(e,t){return this.nodesRegister.registeredNodes(e,t)}registeredOperation(e,t){return this.operationsRegister.registeredOperation(e,t)}registeredCameraTypes(){return this.camerasRegister.registeredTypes()}inWorkerThread(){return!1}desktopController(){}get libs(){return this._libs_controller=this._libs_controller||new gn}setEnv(e){this._env=e}env(){return this._env}setLogger(e){this._logger=e}get logger(){return this._logger}log(e,...t){var n;null===(n=this.logger)||void 0===n||n.log(e,...t)}warn(e,...t){var n;null===(n=this.logger)||void 0===n||n.warn(e,...t)}error(e,...t){var n;null===(n=this.logger)||void 0===n||n.error(e,...t)}}const Rn=Pn._instance_();class In{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(e,t){const n=Rn.performance.performanceManager(),i=n.now();t();const s=n.now()-i;console.log(`${e}: ${s}`)}start(){if(!this._started){this.reset(),this._started=!0;const e=Rn.performance.performanceManager();this._start_time=e.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(e,t){const n=e.graphNodeId();null==this._nodes_cook_data[n]&&(this._nodes_cook_data[n]=new l(e)),this._nodes_cook_data[n].update_cook_data(t)}record(e){this.started()||this.start();const t=performance.now();return null==this._durations_by_name[e]&&(this._durations_by_name[e]=0),this._durations_by_name[e]+=t-this._previous_timestamp,null==this._durations_count_by_name[e]&&(this._durations_count_by_name[e]=0),this._durations_count_by_name[e]+=1,this._previous_timestamp=t}print(){this.print_node_cook_data(),this.print_recordings()}print_node_cook_data(){let e=Object.values(this._nodes_cook_data);e=f.sortBy(e,(e=>e.total_cook_time()));const t=e.map((e=>e.print_object()));console.log(\\\\\\\"--------------- NODES COOK TIME -----------\\\\\\\");const n=[],i=f.sortBy(t,(e=>-e.total_cook_time));for(let e of i)n.push(e);return console.table(n),t}print_recordings(){const e=b.clone(this._durations_by_name),t=b.clone(this._durations_count_by_name),n=[],i={};for(let t of Object.keys(e)){const s=e[t];n.push(s),null==i[s]&&(i[s]=[]),i[s].push(t)}n.sort(((e,t)=>e-t));const s=f.uniq(n);console.log(\\\\\\\"--------------- PERF RECORDINGS -----------\\\\\\\");const r=[];for(let e of s){const n=i[e];for(let i of n){const n=t[i],s={duration:e,name:i,count:n,duration_per_iteration:e/n};r.push(s)}}return console.table(r),r}}class Fn{constructor(e){this.scene=e}setListener(e){this._events_listener?console.warn(\\\\\\\"scene already has a listener\\\\\\\"):(this._events_listener=e,this.run_on_add_listener_callbacks())}onAddListener(e){this._events_listener?e():(this._on_add_listener_callbacks=this._on_add_listener_callbacks||[],this._on_add_listener_callbacks.push(e))}run_on_add_listener_callbacks(){if(this._on_add_listener_callbacks){let e;for(;e=this._on_add_listener_callbacks.pop();)e();this._on_add_listener_callbacks=void 0}}get eventsListener(){return this._events_listener}dispatch(e,t,n){var i;null===(i=this._events_listener)||void 0===i||i.process_events(e,t,n)}emitAllowed(){return null!=this._events_listener&&this.scene.loadingController.loaded()&&this.scene.loadingController.autoUpdating()}}class Dn{constructor(){this._params_by_id=new Map}register_param(e){this._params_by_id.set(e.graphNodeId(),e)}deregister_param(e){this._params_by_id.delete(e.graphNodeId())}regenerate_referring_expressions(e){e.nameController.graph_node.setSuccessorsDirty(e)}}class kn{constructor(e){this.scene=e,this._lifecycle_on_create_allowed=!0}onCreateHookAllowed(){return this.scene.loadingController.loaded()&&this._lifecycle_on_create_allowed}onCreatePrevent(e){this._lifecycle_on_create_allowed=!1,e(),this._lifecycle_on_create_allowed=!0}}class Bn{constructor(e){this.dispatcher=e,this._nodes_by_graph_node_id=new Map,this._require_canvas_event_listeners=!1,this._activeEventDatas=[]}registerNode(e){this._nodes_by_graph_node_id.set(e.graphNodeId(),e),this.updateViewerEventListeners()}unregisterNode(e){this._nodes_by_graph_node_id.delete(e.graphNodeId()),this.updateViewerEventListeners()}processEvent(e){0!=this._activeEventDatas.length&&this._nodes_by_graph_node_id.forEach((t=>t.processEvent(e)))}updateViewerEventListeners(){this._update_active_event_types(),this._require_canvas_event_listeners&&this.dispatcher.scene.viewersRegister.traverseViewers((e=>{e.eventsController.updateEvents(this)}))}activeEventDatas(){return this._activeEventDatas}_update_active_event_types(){const e=new Map;this._nodes_by_graph_node_id.forEach((t=>{if(t.parent()){const n=t.activeEventDatas();for(let t of n)e.set(t,!0)}})),this._activeEventDatas=[],e.forEach(((e,t)=>{this._activeEventDatas.push(t)}))}}var zn;!function(e){e.LOADED=\\\\\\\"sceneLoaded\\\\\\\",e.PLAY=\\\\\\\"play\\\\\\\",e.PAUSE=\\\\\\\"pause\\\\\\\",e.TICK=\\\\\\\"tick\\\\\\\"}(zn||(zn={}));const Un=[zn.LOADED,zn.PLAY,zn.PAUSE,zn.TICK];class Gn extends Bn{type(){return\\\\\\\"scene\\\\\\\"}acceptedEventTypes(){return Un.map((e=>`${e}`))}}class Vn{constructor(e){this.scene=e,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(zn.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(e){this._loading_state=e,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(e){if(this._auto_updating!==e&&(this._auto_updating=e,this._auto_updating)){const e=this.scene.root();e&&await e.processQueue()}}on_first_object_loaded(){var e;if(!this._first_object_loaded){this._first_object_loaded=!0;const t=document.getElementById(\\\\\\\"scene_loading_container\\\\\\\");t&&(null===(e=t.parentElement)||void 0===e||e.removeChild(t))}}}const jn={EMPTY:\\\\\\\"\\\\\\\",UV:\\\\\\\"/COP/imageUv\\\\\\\",ENV_MAP:\\\\\\\"/COP/envMap\\\\\\\",CUBE_MAP:\\\\\\\"/COP/cubeCamera\\\\\\\"};class Hn{constructor(e=\\\\\\\"\\\\\\\"){this._path=e,this._node=null}set_path(e){this._path=e}set_node(e){this._node=e}path(){return this._path}node(){return this._node}resolve(e){this._node=Wn.findNode(e,this._path)}clone(){const e=new Hn(this._path);return e.set_node(this._node),e}nodeWithContext(e,t){const n=this.node();if(!n)return void(null==t||t.set(`no node found at ${this.path()}`));const i=n.context();return i==e?n:void(null==t||t.set(`expected ${e} node, but got a ${i}`))}}class qn{constructor(e=\\\\\\\"\\\\\\\"){this._path=e,this._param=null}set_path(e){this._path=e}set_param(e){this._param=e}path(){return this._path}param(){return this._param}resolve(e){this._param=Wn.findParam(e,this._path)}clone(){const e=new qn(this._path);return e.set_param(this._param),e}paramWithType(e,t){const n=this.param();if(n)return n.type()==e?n:void(null==t||t.set(`expected ${e} node, but got a ${n.type()}`));null==t||t.set(`no param found at ${this.path()}`)}}class Wn{static split_parent_child(e){const t=e.split(Wn.SEPARATOR).filter((e=>e.length>0)),n=t.pop();return{parent:t.join(Wn.SEPARATOR),child:n}}static findNode(e,t,n){if(!e)return null;const i=t.split(Wn.SEPARATOR).filter((e=>e.length>0)),s=i[0];let r=null;if(t[0]!==Wn.SEPARATOR){switch(s){case Wn.PARENT:null==n||n.add_path_element(s),r=e.parent();break;case Wn.CURRENT:null==n||n.add_path_element(s),r=e;break;default:r=e.node(s),r&&(null==n||n.add_node(s,r))}if(null!=r&&i.length>1){const e=i.slice(1).join(Wn.SEPARATOR);r=this.findNode(r,e,n)}return r}{const i=t.substr(1);r=this.findNode(e.root(),i,n)}return r}static findParam(e,t,n){if(!e)return null;const i=t.split(Wn.SEPARATOR);if(1===i.length)return e.params.get(i[0]);{const t=i.slice(0,+(i.length-2)+1||void 0).join(Wn.SEPARATOR),s=this.findNode(e,t,n);if(null!=s){const e=i[i.length-1],t=s.params.get(e);return n&&t&&n.add_node(e,t),t}return null}}static relativePath(e,t){const n=this.closestCommonParent(e,t);if(n){const i=this.distanceToParent(e,n);let s=\\\\\\\"\\\\\\\";if(i>0){let e=0;const t=[];for(;e++<i;)t.push(Wn.PARENT);s=t.join(Wn.SEPARATOR)+Wn.SEPARATOR}const r=n.path().split(Wn.SEPARATOR).filter((e=>e.length>0)),o=t.path().split(Wn.SEPARATOR).filter((e=>e.length>0)),a=[];let c=0;for(let e of o)r[c]||a.push(e),c++;return`${s}${a.join(Wn.SEPARATOR)}`}return t.path()}static closestCommonParent(e,t){const n=this.parents(e).reverse().concat([e]),i=this.parents(t).reverse().concat([t]),s=Math.min(n.length,i.length);let r=null;for(let e=0;e<s;e++)n[e].graphNodeId()==i[e].graphNodeId()&&(r=n[e]);return r}static parents(e){const t=[];let n=e.parent();for(;n;)t.push(n),n=n.parent();return t}static distanceToParent(e,t){let n=0,i=e;const s=t.graphNodeId();for(;i&&i.graphNodeId()!=s;)n+=1,i=i.parent();return i&&i.graphNodeId()==s?n:-1}static makeAbsolutePath(e,t){if(t[0]==Wn.SEPARATOR)return t;const n=t.split(Wn.SEPARATOR),i=n.shift();if(!i)return e.path();switch(i){case\\\\\\\"..\\\\\\\":{const t=e.parent();return t?this.makeAbsolutePath(t,n.join(Wn.SEPARATOR)):null}case\\\\\\\".\\\\\\\":return this.makeAbsolutePath(e,n.join(Wn.SEPARATOR));default:return[e.path(),t].join(Wn.SEPARATOR)}}}Wn.SEPARATOR=\\\\\\\"/\\\\\\\",Wn.DOT=\\\\\\\".\\\\\\\",Wn.CURRENT=Wn.DOT,Wn.PARENT=\\\\\\\"..\\\\\\\",Wn.CURRENT_WITH_SLASH=`${Wn.CURRENT}/`,Wn.PARENT_WITH_SLASH=`${Wn.PARENT}/`,Wn.NON_LETTER_PREFIXES=[Wn.SEPARATOR,Wn.DOT];class Xn{constructor(e,t){this.param=e,this.path=t}absolute_path(){return Wn.makeAbsolutePath(this.param.node,this.path)}matches_path(e){return this.absolute_path()==e}update_from_method_dependency_name_change(){var e;null===(e=this.param.expressionController)||void 0===e||e.update_from_method_dependency_name_change()}resolve_missing_dependencies(){const e=this.param.rawInputSerialized();this.param.set(this.param.defaultValue()),this.param.set(e)}}class Yn{constructor(e){this.scene=e,this.references=new Map}register(e,t,n){const i=new Xn(e,n);return u.pushOnArrayAtEntry(this.references,e.graphNodeId(),i),i}deregister_param(e){this.references.delete(e.graphNodeId())}resolve_missing_references(){const e=[];this.references.forEach((t=>{for(let n of t)this._is_reference_resolvable(n)&&e.push(n)}));for(let t of e)t.resolve_missing_dependencies()}_is_reference_resolvable(e){const t=e.absolute_path();if(t){if(this.scene.node(t))return!0;{const e=Wn.split_parent_child(t);if(e.child){const t=this.scene.node(e.parent);if(t){if(t.params.get(e.child))return!0}}}}}check_for_missing_references(e){this._check_for_missing_references_for_node(e);for(let t of e.params.all)this._check_for_missing_references_for_param(t)}_check_for_missing_references_for_node(e){const t=e.graphNodeId();this.references.forEach(((n,i)=>{let s=!1;for(let t of n)t.matches_path(e.path())&&(s=!0,t.resolve_missing_dependencies());s&&this.references.delete(t)}))}_check_for_missing_references_for_param(e){const t=e.graphNodeId();this.references.forEach(((n,i)=>{let s=!1;for(let t of n)t.matches_path(e.path())&&(s=!0,t.resolve_missing_dependencies());s&&this.references.delete(t)}))}}class $n{constructor(e){this.node=e,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(e,t){this._post_dirty_hook_names=this._post_dirty_hook_names||[],this._post_dirty_hooks=this._post_dirty_hooks||[],this._post_dirty_hook_names.includes(e)?console.warn(`hook with name ${e} already exists`,this.node):(this._post_dirty_hook_names.push(e),this._post_dirty_hooks.push(t))}removePostDirtyHook(e){if(this._post_dirty_hook_names&&this._post_dirty_hooks){const t=this._post_dirty_hook_names.indexOf(e);t>=0&&(this._post_dirty_hook_names.splice(t,1),this._post_dirty_hooks.splice(t,1))}}hasHook(e){return!!this._post_dirty_hook_names&&this._post_dirty_hook_names.includes(e)}removeDirtyState(){this._dirty=!1}setForbiddenTriggerNodes(e){this._forbidden_trigger_nodes=e.map((e=>e.graphNodeId()))}setDirty(e,t){if(null==t&&(t=!0),e&&this._forbidden_trigger_nodes&&this._forbidden_trigger_nodes.includes(e.graphNodeId()))return;null==e&&(e=this.node),this._dirty=!0;const n=Rn.performance.performanceManager();this._dirty_timestamp=n.now(),this._dirty_count+=1,this.runPostDirtyHooks(e),!0===t&&this.setSuccessorsDirty(e)}runPostDirtyHooks(e){if(this._post_dirty_hooks){const t=this.node.scene().cooker;if(t.blocked)t.enqueue(this.node,e);else for(let t of this._post_dirty_hooks)t(e)}}setSuccessorsDirty(e){this._cached_successors=this._cached_successors||this.node.graphAllSuccessors();for(let t of this._cached_successors)t.dirtyController.setDirty(e,false)}clearSuccessorsCache(){this._cached_successors=void 0}clearSuccessorsCacheWithPredecessors(){this.clearSuccessorsCache();for(let e of this.node.graphAllPredecessors())e.dirtyController.clearSuccessorsCache()}}class Qn{constructor(e,t){this._scene=e,this._name=t,this._dirty_controller=new $n(this),this._graph_node_id=e.graph.nextId(),e.graph.addNode(this),this._graph=e.graph}dispose(){this._dirty_controller.dispose(),this.graphRemove()}name(){return this._name}setName(e){this._name=e}scene(){return this._scene}graphNodeId(){return this._graph_node_id}get dirtyController(){return this._dirty_controller}setDirty(e){e=e||this,this._dirty_controller.setDirty(e)}setSuccessorsDirty(e){this._dirty_controller.setSuccessorsDirty(e)}removeDirtyState(){this._dirty_controller.removeDirtyState()}isDirty(){return this._dirty_controller.isDirty()}addPostDirtyHook(e,t){this._dirty_controller.addPostDirtyHook(e,t)}graphRemove(){this._graph.removeNode(this)}addGraphInput(e,t=!0){return this._graph.connect(e,this,t)}removeGraphInput(e){this._graph.disconnect(e,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 Jn;!function(e){e.CREATED=\\\\\\\"node_created\\\\\\\",e.DELETED=\\\\\\\"node_deleted\\\\\\\",e.NAME_UPDATED=\\\\\\\"node_name_update\\\\\\\",e.OVERRIDE_CLONABLE_STATE_UPDATE=\\\\\\\"node_override_clonable_state_update\\\\\\\",e.NAMED_OUTPUTS_UPDATED=\\\\\\\"node_named_outputs_updated\\\\\\\",e.NAMED_INPUTS_UPDATED=\\\\\\\"node_named_inputs_updated\\\\\\\",e.INPUTS_UPDATED=\\\\\\\"node_inputs_updated\\\\\\\",e.PARAMS_UPDATED=\\\\\\\"node_params_updated\\\\\\\",e.UI_DATA_POSITION_UPDATED=\\\\\\\"node_ui_data_position_updated\\\\\\\",e.UI_DATA_COMMENT_UPDATED=\\\\\\\"node_ui_data_comment_updated\\\\\\\",e.ERROR_UPDATED=\\\\\\\"node_error_updated\\\\\\\",e.FLAG_BYPASS_UPDATED=\\\\\\\"bypass_flag_updated\\\\\\\",e.FLAG_DISPLAY_UPDATED=\\\\\\\"display_flag_updated\\\\\\\",e.FLAG_OPTIMIZE_UPDATED=\\\\\\\"optimize_flag_updated\\\\\\\",e.SELECTION_UPDATED=\\\\\\\"selection_updated\\\\\\\"}(Jn||(Jn={}));class Kn{constructor(e,t=0,n=0){this.node=e,this._position=new d.a,this._width=50,this._color=new M.a(.75,.75,.75),this._layout_vertical=!0,this._json={x:0,y:0},this._position.x=t,this._position.y=n}setComment(e){this._comment=e,this.node.emit(Jn.UI_DATA_COMMENT_UPDATED)}comment(){return this._comment}setColor(e){this._color=e}color(){return this._color}setLayoutHorizontal(){this._layout_vertical=!1}isLayoutVertical(){return this._layout_vertical}copy(e){this._position.copy(e.position()),this._color.copy(e.color())}position(){return this._position}setPosition(e,t=0){if(m.isNumber(e)){const n=e;this._position.set(n,t)}else this._position.copy(e);this.node.emit(Jn.UI_DATA_POSITION_UPDATED)}translate(e,t=!1){this._position.add(e),t&&(this._position.x=Math.round(this._position.x),this._position.y=Math.round(this._position.y)),this.node.emit(Jn.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 Zn{constructor(e){this.node=e,this._state=!0,this._hooks=null}onUpdate(e){this._hooks=this._hooks||[],this._hooks.push(e)}_on_update(){}set(e){this._state!=e&&(this._state=e,this._on_update(),this.runHooks())}active(){return this._state}toggle(){this.set(!this._state)}runHooks(){if(this._hooks)for(let e of this._hooks)e()}}class ei extends Zn{constructor(){super(...arguments),this._state=!1}_on_update(){this.node.emit(Jn.FLAG_BYPASS_UPDATED),this.node.setDirty()}}class ti extends Zn{_on_update(){this.node.emit(Jn.FLAG_DISPLAY_UPDATED)}}class ni extends Zn{constructor(){super(...arguments),this._state=!1}_on_update(){this.node.emit(Jn.FLAG_OPTIMIZE_UPDATED)}}class ii{constructor(e){this.node=e}hasDisplay(){return!1}hasBypass(){return!1}hasOptimize(){return!1}}function si(e){return class extends e{constructor(){super(...arguments),this.display=new ti(this.node)}hasDisplay(){return!0}}}function ri(e){return class extends e{constructor(){super(...arguments),this.bypass=new ei(this.node)}hasBypass(){return!0}}}function oi(e){return class extends e{constructor(){super(...arguments),this.optimize=new ni(this.node)}hasOptimize(){return!0}}}class ai extends(si(ii)){}class ci extends(ri(ii)){}class li extends(ri(si(ii))){}class ui extends(oi(ri(ii))){}class hi extends(oi(ri(si(ii)))){}class di{constructor(e){this.node=e}}class pi extends di{active(){return this.paramsTimeDependent()||this.inputsTimeDependent()}paramsTimeDependent(){const e=this.node.params.names;for(let t of e){const e=this.node.params.get(t);if(e&&e.states.timeDependent.active())return!0}return!1}inputsTimeDependent(){const e=this.node.io.inputs.inputs();for(let t of e)if(t&&t.states.timeDependent.active())return!0;return!1}forceTimeDependent(){const e=this.node.graphPredecessors().map((e=>e.graphNodeId())),t=this.node.scene().timeController.graphNode;e.includes(t.graphNodeId())||this.node.addGraphInput(t,!1)}unforceTimeDependent(){const e=this.node.scene().timeController.graphNode;this.node.removeGraphInput(e)}}class _i extends di{set(e){this._message!=e&&(e&&Rn.warn(`[${this.node.path()}] error: '${e}'`),this._message=e,this.onUpdate())}message(){return this._message}clear(){this.set(void 0)}active(){return null!=this._message}onUpdate(){null!=this._message&&this.node._setContainer(null,`from error '${this._message}'`),this.node.emit(Jn.ERROR_UPDATED)}}class mi{constructor(e){this.node=e,this.timeDependent=new pi(this.node),this.error=new _i(this.node)}}class fi{constructor(e){this.node=e,this._graph_node=new Qn(e.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(e){let t=e.type();const n=t[t.length-1];return m.isNaN(parseInt(n))||(t+=\\\\\\\"_\\\\\\\"),`${t}1`}request_name_to_parent(e){const t=this.node.parent();t&&t.childrenAllowed()&&t.childrenController?t.childrenController.set_child_name(this.node,e):console.warn(\\\\\\\"request_name_to_parent failed, no parent found\\\\\\\")}setName(e){e!=this.node.name()&&this.request_name_to_parent(e)}update_name_from_parent(e){var t;if(this.node._set_core_name(e),this.post_setName(),this.run_post_set_fullPath_hooks(),this.node.childrenAllowed()){const e=null===(t=this.node.childrenController)||void 0===t?void 0:t.children();if(e)for(let t of e)t.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(Jn.NAME_UPDATED)}add_post_set_name_hook(e){this._on_set_name_hooks=this._on_set_name_hooks||[],this._on_set_name_hooks.push(e)}add_post_set_fullPath_hook(e){this._on_set_fullPath_hooks=this._on_set_fullPath_hooks||[],this._on_set_fullPath_hooks.push(e)}post_setName(){if(this._on_set_name_hooks)for(let e of this._on_set_name_hooks)e()}run_post_set_fullPath_hooks(){if(this._on_set_fullPath_hooks)for(let e of this._on_set_fullPath_hooks)e()}}class gi{constructor(e){this.node=e,this._parent=null}parent(){return this._parent}setParent(e){e!=this.node.parentController.parent()&&(this._parent=e,this._parent&&this.node.nameController.request_name_to_parent(fi.base_name(this.node)))}is_selected(){var e,t,n;return(null===(n=null===(t=null===(e=this.parent())||void 0===e?void 0:e.childrenController)||void 0===t?void 0:t.selection)||void 0===n?void 0:n.contains(this.node))||!1}path(e){const t=Wn.SEPARATOR;if(null!=this._parent){if(this._parent==e)return this.node.name();{const n=this._parent.path(e);return n===t?n+this.node.name():n+t+this.node.name()}}return t}onSetParent(){if(this._on_set_parent_hooks)for(let e of this._on_set_parent_hooks)e()}findNode(e){if(null==e)return null;if(e==Wn.CURRENT||e==Wn.CURRENT_WITH_SLASH)return this.node;if(e==Wn.PARENT||e==Wn.PARENT_WITH_SLASH)return this.node.parent();const t=Wn.SEPARATOR;if(e===t)return this.node.scene().root();if(e[0]===t)return e=e.substring(1,e.length),this.node.scene().root().node(e);if(e.split){const n=e.split(t);if(1===n.length){const e=n[0];return this.node.childrenController?this.node.childrenController.child_by_name(e):null}return Wn.findNode(this.node,e)}return console.error(\\\\\\\"unexpected path given:\\\\\\\",e),null}}const vi=/[, ]/,yi=/\\\\d+$/,xi=/^0+/,bi=/,| /,wi=/^-?\\\\d+\\\\.?\\\\d*$/;var Ai,Ti,Ei,Ci,Mi,Ni,Si,Oi;!function(e){e.TRUE=\\\\\\\"true\\\\\\\",e.FALSE=\\\\\\\"false\\\\\\\"}(Ai||(Ai={}));class Li{static isBoolean(e){return e==Ai.TRUE||e==Ai.FALSE}static toBoolean(e){return e==Ai.TRUE}static isNumber(e){return wi.test(e)}static tailDigits(e){const t=e.match(yi);return t?parseInt(t[0]):0}static increment(e){const t=e.match(yi);if(t){let n=t[0],i=\\\\\\\"\\\\\\\";const s=n.match(xi);s&&(i=s[0]);const r=parseInt(n);0==r&&i.length>0&&\\\\\\\"0\\\\\\\"==i[i.length-1]&&(i=i.slice(0,-1));return`${e.substring(0,e.length-t[0].length)}${i}${r+1}`}return`${e}1`}static pluralize(e){return\\\\\\\"s\\\\\\\"!==e[e.length-1]?`${e}s`:e}static camelCase(e){const t=e.replace(/_/g,\\\\\\\" \\\\\\\").split(\\\\\\\" \\\\\\\");let n=\\\\\\\"\\\\\\\";for(let e=0;e<t.length;e++){let i=t[e].toLowerCase();e>0&&(i=this.upperFirst(i)),n+=i}return n}static upperFirst(e){return e[0].toUpperCase()+e.substr(1)}static titleize(e){return e.split(/\\\\s|_/g).map((e=>this.upperFirst(e))).join(\\\\\\\" \\\\\\\")}static precision(e,t=2){t=Math.max(t,0);const n=`${e}`.split(\\\\\\\".\\\\\\\");if(t<=0)return n[0];let i=n[1];if(void 0!==i)return i.length>t&&(i=i.substring(0,t)),i=i.padEnd(t,\\\\\\\"0\\\\\\\"),`${n[0]}.${i}`;{const n=`${e}.`,i=n.length+t;return n.padEnd(i,\\\\\\\"0\\\\\\\")}}static ensureFloat(e){const t=`${e}`;return t.indexOf(\\\\\\\".\\\\\\\")>=0?t:`${t}.0`}static ensureInteger(e){const t=`${e}`;return t.indexOf(\\\\\\\".\\\\\\\")>=0?t.split(\\\\\\\".\\\\\\\")[0]:t}static matchMask(e,t){if(\\\\\\\"*\\\\\\\"===t)return!0;if(e==t)return!0;const n=t.split(\\\\\\\" \\\\\\\");if(n.length>1){for(let t of n){if(this.matchMask(e,t))return!0}return!1}t=`^${t=t.split(\\\\\\\"*\\\\\\\").join(\\\\\\\".*\\\\\\\")}$`;return new RegExp(t).test(e)}static matchesOneMask(e,t){let n=!1;for(let i of t)Li.matchMask(e,i)&&(n=!0);return n}static attribNames(e){const t=e.split(vi),n=new Set;for(let e of t)e=e.trim(),e.length>0&&n.add(e);const i=new Array(n.size);let s=0;return n.forEach((e=>{i[s]=e,s++})),i}static indices(e){const t=e.split(bi);if(t.length>1){const e=t.flatMap((e=>this.indices(e)));return f.uniq(e).sort(((e,t)=>e-t))}{const e=t[0];if(e){const t=\\\\\\\"-\\\\\\\";if(e.indexOf(t)>0){const n=e.split(t);return f.range(parseInt(n[0]),parseInt(n[1])+1)}{const t=parseInt(e);return m.isNumber(t)?[t]:[]}}return[]}}static escapeLineBreaks(e){return e.replace(/(\\\\r\\\\n|\\\\n|\\\\r)/gm,\\\\\\\"\\\\\\\\n\\\\\\\")}static sanitizeName(e){return e=(e=e.replace(/[^A-Za-z0-9]/g,\\\\\\\"_\\\\\\\")).replace(/^[0-9]/,\\\\\\\"_\\\\\\\")}}class Pi{constructor(e){this._node=e,this._node_ids=[],this._json=[]}node(){return this._node}nodes(){return this._node.scene().graph.nodesFromIds(this._node_ids)}contains(e){return this._node_ids.includes(e.graphNodeId())}equals(e){const t=e.map((e=>e.graphNodeId())).sort();return f.isEqual(t,this._node_ids)}clear(){this._node_ids=[],this.send_update_event()}set(e){this._node_ids=[],this.add(e)}add(e){const t=e.map((e=>e.graphNodeId()));this._node_ids=f.union(this._node_ids,t),this.send_update_event()}remove(e){const t=e.map((e=>e.graphNodeId()));this._node_ids=f.difference(this._node_ids,t),this.send_update_event()}send_update_event(){this._node.emit(Jn.SELECTION_UPDATED)}toJSON(){return this._json=this._json||[],this._json=this._node_ids.map((e=>e)),this._json}}!function(e){e.ALWAYS=\\\\\\\"always\\\\\\\",e.NEVER=\\\\\\\"never\\\\\\\",e.FROM_NODE=\\\\\\\"from_node\\\\\\\"}(Ti||(Ti={}));class Ri{static unreachable(e){throw new Error(\\\\\\\"Didn't expect to get here\\\\\\\")}}class Ii{constructor(e){this.inputs_controller=e,this._clone_required_states=[],this._overridden=!1}init_inputs_cloned_state(e){m.isArray(e)?this._cloned_states=e:this._cloned_state=e,this._update_clone_required_state()}override_cloned_state_allowed(){if(this._cloned_states)for(let e of this._cloned_states)if(e==Ti.FROM_NODE)return!0;return!!this._cloned_state&&this._cloned_state==Ti.FROM_NODE}clone_required_state(e){return this._clone_required_states[e]}clone_required_states(){return this._clone_required_states}_get_clone_required_state(e){const t=this._cloned_states;if(t){const n=t[e];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(e){switch(e){case Ti.ALWAYS:return!0;case Ti.NEVER:return!1;case Ti.FROM_NODE:return!this._overridden}return Ri.unreachable(e)}override_cloned_state(e){this._overridden=e,this._update_clone_required_state()}overriden(){return this._overridden}_update_clone_required_state(){if(this._cloned_states){const e=[];for(let t=0;t<this._cloned_states.length;t++)e[t]=this._get_clone_required_state(t);this._clone_required_states=e}else if(this._cloned_state){const e=this.inputs_controller.inputs_count(),t=[];for(let n=0;n<e;n++)t[n]=this._get_clone_required_state(n);this._clone_required_states=t}else;}}class Fi{constructor(e){this.operation_container=e}inputs_count(){return this.operation_container.inputs_count()}init_inputs_cloned_state(e){this._cloned_states_controller||(this._cloned_states_controller=new Ii(this),this._cloned_states_controller.init_inputs_cloned_state(e))}clone_required(e){var t;const n=null===(t=this._cloned_states_controller)||void 0===t?void 0:t.clone_required_state(e);return null==n||n}override_cloned_state(e){var t;null===(t=this._cloned_states_controller)||void 0===t||t.override_cloned_state(e)}}class Di extends class{constructor(e,t,n){this.operation=e,this.name=t,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(e){if(this._path_params)for(let t of this._path_params)t.resolve(e)}_apply_default_params(){const e=this.operation.constructor.DEFAULT_PARAMS,t=Object.keys(e);for(let n of t){const t=e[n],i=this._convert_param_data(n,t);null!=i&&(this.params[n]=i)}}_apply_init_params(e){const t=Object.keys(e);for(let n of t){const t=e[n];if(null!=t.simple_data){const e=t.simple_data,i=this._convert_export_param_data(n,e);null!=i&&(this.params[n]=i)}}}_convert_param_data(e,t){if(m.isNumber(t)||m.isBoolean(t)||m.isString(t))return t;if(t instanceof Hn){const e=t.clone();return this._path_params||(this._path_params=[]),this._path_params.push(e),e}return t instanceof M.a||t instanceof d.a||t instanceof p.a||t instanceof _.a?t.clone():void 0}_convert_export_param_data(e,t){const n=this.params[e];if(m.isBoolean(t))return t;if(m.isNumber(t))return m.isBoolean(n)?t>=1:t;if(m.isString(t)){if(n){if(n instanceof Hn)return n.set_path(t);if(n instanceof qn)return n.set_path(t)}return t}m.isArray(t)&&this.params[e].fromArray(t)}setInput(e,t){this._inputs=this._inputs||[],this._inputs[e]=t}inputs_count(){return this._inputs?this._inputs.length:0}inputsController(){return this._inputs_controller=this._inputs_controller||new Fi(this)}_init_cloned_states(){const e=this.operation.constructor.INPUT_CLONED_STATE;this.inputsController().init_inputs_cloned_state(e)}input_clone_required(e){return!this._inputs_controller||this._inputs_controller.clone_required(e)}override_input_clone_state(e){this.inputsController().override_cloned_state(e)}cook(e){return this.operation.cook(e,this.params)}}{constructor(e,t,n){super(e,t,n),this.operation=e,this.name=t,this.init_params=n,this._inputs=[],this._current_input_index=0,this._dirty=!0}add_input(e){super.setInput(this._current_input_index,e),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 e=0;e<this._inputs.length;e++){this._inputs[e].setDirty()}}}async compute(e,t){if(this._compute_result)return this._compute_result;const n=[],i=t.get(this);i&&i.forEach(((t,i)=>{n[i]=e[t]}));for(let i=0;i<this._inputs.length;i++){const s=this._inputs[i];let r=await s.compute(e,t);r&&(this.input_clone_required(i)&&(r=r.clone()),n[i]=r)}const s=this.operation.cook(n,this.params);return this._compute_result=s?s instanceof Promise?await s:s:void 0,this._dirty=!1,this._compute_result}}class ki{constructor(e,t){this.node=e,this._context=t,this._children={},this._children_by_type={},this._children_and_grandchildren_by_context={}}get selection(){return this._selection=this._selection||new Pi(this.node)}dispose(){const e=this.children();for(let t of e)this.node.removeNode(t);this._selection=void 0}get context(){return this._context}set_output_node_find_method(e){this._output_node_find_method=e}output_node(){if(this._output_node_find_method)return this._output_node_find_method()}set_child_name(e,t){let n;if(t=Li.sanitizeName(t),null!=(n=this._children[t])){if(e.name()===t&&n.graphNodeId()===e.graphNodeId())return;return t=Li.increment(t),this.set_child_name(e,t)}{const n=e.name();this._children[n]&&delete this._children[n],this._children[t]=e,e.nameController.update_name_from_parent(t),this._add_to_nodesByType(e),this.node.scene().nodesController.addToInstanciatedNode(e)}}node_context_signature(){return`${this.node.context()}/${this.node.type()}`}available_children_classes(){return Rn.registeredNodes(this._context,this.node.type())}is_valid_child_type(e){return null!=this.available_children_classes()[e]}createNode(e,t,n=\\\\\\\"\\\\\\\"){if(\\\\\\\"string\\\\\\\"==typeof e){const i=this._find_node_class(e);return this._create_and_init_node(i,t,n)}return this._create_and_init_node(e,t,n)}_create_and_init_node(e,t,n=\\\\\\\"\\\\\\\"){const i=new e(this.node.scene(),`child_node_${n}`,t);return i.initialize_base_and_node(),this.add_node(i),i.lifecycle.set_creation_completed(),i}_find_node_class(e){const t=this.available_children_classes()[e.toLowerCase()];if(null==t){const t=`child node type '${e}' not found for node '${this.node.path()}'. Available types are: ${Object.keys(this.available_children_classes()).join(\\\\\\\", \\\\\\\")}, ${this._context}, ${this.node.type()}`;throw console.error(t),t}return t}create_operation_container(e,t,n){const i=Rn.registeredOperation(this._context,e);if(null==i){const t=`no operation found with context ${this._context}/${e}`;throw console.error(t),t}{const e=new i(this.node.scene());return new Di(e,t,n||{})}}add_node(e){if(e.setParent(this.node),e.params.init(),e.parentController.onSetParent(),e.nameController.run_post_set_fullPath_hooks(),e.childrenAllowed()&&e.childrenController)for(let t of e.childrenController.children())t.nameController.run_post_set_fullPath_hooks();return this.node.emit(Jn.CREATED,{child_node_json:e.toJSON()}),this.node.scene().lifecycleController.onCreateHookAllowed()&&e.lifecycle.run_on_create_hooks(),e.lifecycle.run_on_add_hooks(),this.set_child_name(e,fi.base_name(e)),this.node.lifecycle.run_on_child_add_hooks(e),e.require_webgl2()&&this.node.scene().webgl_controller.set_require_webgl2(),this.node.scene().missingExpressionReferencesController.check_for_missing_references(e),e}removeNode(e){if(e.parent()!=this.node)return console.warn(`node ${e.name()} not under parent ${this.node.path()}`);{this.selection.contains(e)&&this.selection.remove([e]);const t=e.io.connections.firstInputConnection(),n=e.io.connections.inputConnections(),i=e.io.connections.outputConnections();if(n)for(let e of n)e&&e.disconnect({setInput:!0});if(i)for(let e of i)if(e&&(e.disconnect({setInput:!0}),t)){const n=t.node_src,i=e.output_index,s=e.node_dest,r=e.input_index;s.io.inputs.setInput(r,n,i)}e.setParent(null),delete this._children[e.name()],this._remove_from_nodesByType(e),this.node.scene().nodesController.removeFromInstanciatedNode(e),e.setSuccessorsDirty(this.node),e.graphDisconnectSuccessors(),this.node.lifecycle.run_on_child_remove_hooks(e),e.lifecycle.run_on_delete_hooks(),e.dispose(),e.emit(Jn.DELETED,{parent_id:this.node.graphNodeId()})}}_add_to_nodesByType(e){const t=e.graphNodeId(),n=e.type();this._children_by_type[n]=this._children_by_type[n]||[],this._children_by_type[n].includes(t)||this._children_by_type[n].push(t),this.add_to_children_and_grandchildren_by_context(e)}_remove_from_nodesByType(e){const t=e.graphNodeId(),n=e.type();if(this._children_by_type[n]){const e=this._children_by_type[n].indexOf(t);e>=0&&(this._children_by_type[n].splice(e,1),0==this._children_by_type[n].length&&delete this._children_by_type[n])}this.remove_from_children_and_grandchildren_by_context(e)}add_to_children_and_grandchildren_by_context(e){var t;const n=e.graphNodeId(),i=e.context();this._children_and_grandchildren_by_context[i]=this._children_and_grandchildren_by_context[i]||[],this._children_and_grandchildren_by_context[i].includes(n)||this._children_and_grandchildren_by_context[i].push(n);const s=this.node.parent();s&&s.childrenAllowed()&&(null===(t=s.childrenController)||void 0===t||t.add_to_children_and_grandchildren_by_context(e))}remove_from_children_and_grandchildren_by_context(e){var t;const n=e.graphNodeId(),i=e.context();if(this._children_and_grandchildren_by_context[i]){const e=this._children_and_grandchildren_by_context[i].indexOf(n);e>=0&&(this._children_and_grandchildren_by_context[i].splice(e,1),0==this._children_and_grandchildren_by_context[i].length&&delete this._children_and_grandchildren_by_context[i])}const s=this.node.parent();s&&s.childrenAllowed()&&(null===(t=s.childrenController)||void 0===t||t.remove_from_children_and_grandchildren_by_context(e))}nodesByType(e){const t=this._children_by_type[e]||[],n=this.node.scene().graph,i=[];for(let e of t){const t=n.nodeFromId(e);t&&i.push(t)}return i}child_by_name(e){return this._children[e]}has_children_and_grandchildren_with_context(e){return null!=this._children_and_grandchildren_by_context[e]}children(){return Object.values(this._children)}children_names(){return Object.keys(this._children).sort()}traverse_children(e){var t;for(let n of this.children())e(n),null===(t=n.childrenController)||void 0===t||t.traverse_children(e)}}class Bi{constructor(e){this.node=e,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(e){this._on_child_add_hooks=this._on_child_add_hooks||[],this._on_child_add_hooks.push(e)}run_on_child_add_hooks(e){this.execute_hooks_with_child_node(this._on_child_add_hooks,e)}add_on_child_remove_hook(e){this._on_child_remove_hooks=this._on_child_remove_hooks||[],this._on_child_remove_hooks.push(e)}run_on_child_remove_hooks(e){this.execute_hooks_with_child_node(this._on_child_remove_hooks,e)}add_on_create_hook(e){this._on_create_hooks=this._on_create_hooks||[],this._on_create_hooks.push(e)}run_on_create_hooks(){this.execute_hooks(this._on_create_hooks)}add_on_add_hook(e){this._on_add_hooks=this._on_add_hooks||[],this._on_add_hooks.push(e)}run_on_add_hooks(){this.execute_hooks(this._on_add_hooks)}add_delete_hook(e){this._on_delete_hooks=this._on_delete_hooks||[],this._on_delete_hooks.push(e)}run_on_delete_hooks(){this.execute_hooks(this._on_delete_hooks)}execute_hooks(e){if(e){let t;for(t of e)t()}}execute_hooks_with_child_node(e,t){if(e){let n;for(n of e)n(t)}}}!function(e){e.ANIM=\\\\\\\"anim\\\\\\\",e.COP=\\\\\\\"cop\\\\\\\",e.EVENT=\\\\\\\"event\\\\\\\",e.GL=\\\\\\\"gl\\\\\\\",e.JS=\\\\\\\"js\\\\\\\",e.MANAGER=\\\\\\\"manager\\\\\\\",e.MAT=\\\\\\\"mat\\\\\\\",e.OBJ=\\\\\\\"obj\\\\\\\",e.POST=\\\\\\\"post\\\\\\\",e.ROP=\\\\\\\"rop\\\\\\\",e.SOP=\\\\\\\"sop\\\\\\\"}(Ei||(Ei={})),function(e){e.ANIM=\\\\\\\"animationsNetwork\\\\\\\",e.COP=\\\\\\\"copNetwork\\\\\\\",e.EVENT=\\\\\\\"eventsNetwork\\\\\\\",e.MAT=\\\\\\\"materialsNetwork\\\\\\\",e.POST=\\\\\\\"postProcessNetwork\\\\\\\",e.ROP=\\\\\\\"renderersNetwork\\\\\\\"}(Ci||(Ci={})),function(e){e.INPUT=\\\\\\\"subnetInput\\\\\\\",e.OUTPUT=\\\\\\\"subnetOutput\\\\\\\"}(Mi||(Mi={})),function(e){e.PERSPECTIVE=\\\\\\\"perspectiveCamera\\\\\\\",e.ORTHOGRAPHIC=\\\\\\\"orthographicCamera\\\\\\\"}(Ni||(Ni={})),function(e){e.ATTRIBUTE=\\\\\\\"attribute\\\\\\\"}(Si||(Si={})),function(e){e.DEVICE_ORIENTATION=\\\\\\\"cameraDeviceOrientationControls\\\\\\\",e.MAP=\\\\\\\"cameraMapControls\\\\\\\",e.ORBIT=\\\\\\\"cameraOrbitControls\\\\\\\",e.FIRST_PERSON=\\\\\\\"firstPersonControls\\\\\\\"}(Oi||(Oi={}));const zi=[Oi.DEVICE_ORIENTATION,Oi.MAP,Oi.ORBIT,Oi.FIRST_PERSON];class Ui{constructor(e){this._node=e}set_node(e){this._node=e}node(){return this._node}set_content(e){this._content=e,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 Gi=n(67);class Vi extends ee.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(e,t){return super.copy(e,t),null!==e.background&&(this.background=e.background.clone()),null!==e.environment&&(this.environment=e.environment.clone()),null!==e.fog&&(this.fog=e.fog.clone()),null!==e.overrideMaterial&&(this.overrideMaterial=e.overrideMaterial.clone()),this.autoUpdate=e.autoUpdate,this.matrixAutoUpdate=e.matrixAutoUpdate,this}toJSON(e){const t=super.toJSON(e);return null!==this.background&&(t.object.background=this.background.toJSON(e)),null!==this.environment&&(t.object.environment=this.environment.toJSON(e)),null!==this.fog&&(t.object.fog=this.fog.toJSON()),t}}Vi.prototype.isScene=!0;var ji=n(49),Hi=n(52),qi=n(43),Wi=n(44),Xi=n(61),Yi=n(23),$i=n(36);const Qi=new p.a,Ji=new p.a;class Ki extends ee.a{constructor(){super(),this._currentLevel=0,this.type=\\\\\\\"LOD\\\\\\\",Object.defineProperties(this,{levels:{enumerable:!0,value:[]},isLOD:{value:!0}}),this.autoUpdate=!0}copy(e){super.copy(e,!1);const t=e.levels;for(let e=0,n=t.length;e<n;e++){const n=t[e];this.addLevel(n.object.clone(),n.distance)}return this.autoUpdate=e.autoUpdate,this}addLevel(e,t=0){t=Math.abs(t);const n=this.levels;let i;for(i=0;i<n.length&&!(t<n[i].distance);i++);return n.splice(i,0,{distance:t,object:e}),this.add(e),this}getCurrentLevel(){return this._currentLevel}getObjectForDistance(e){const t=this.levels;if(t.length>0){let n,i;for(n=1,i=t.length;n<i&&!(e<t[n].distance);n++);return t[n-1].object}return null}raycast(e,t){if(this.levels.length>0){Qi.setFromMatrixPosition(this.matrixWorld);const n=e.ray.origin.distanceTo(Qi);this.getObjectForDistance(n).raycast(e,t)}}update(e){const t=this.levels;if(t.length>1){Qi.setFromMatrixPosition(e.matrixWorld),Ji.setFromMatrixPosition(this.matrixWorld);const n=Qi.distanceTo(Ji)/e.zoom;let i,s;for(t[0].object.visible=!0,i=1,s=t.length;i<s&&n>=t[i].distance;i++)t[i-1].object.visible=!1,t[i].object.visible=!0;for(this._currentLevel=i-1;i<s;i++)t[i].object.visible=!1}}toJSON(e){const t=super.toJSON(e);!1===this.autoUpdate&&(t.object.autoUpdate=!1),t.object.levels=[];const n=this.levels;for(let e=0,i=n.length;e<i;e++){const i=n[e];t.object.levels.push({object:i.object.uuid,distance:i.distance})}return t}}var Zi;!function(e){e.OBJECT3D=\\\\\\\"Object3D\\\\\\\",e.MESH=\\\\\\\"Mesh\\\\\\\",e.POINTS=\\\\\\\"Points\\\\\\\",e.LINE_SEGMENTS=\\\\\\\"LineSegments\\\\\\\",e.LOD=\\\\\\\"LOD\\\\\\\"}(Zi||(Zi={}));const es={[Zi.MESH]:z.a,[Zi.POINTS]:ji.a,[Zi.LINE_SEGMENTS]:$i.a,[Zi.OBJECT3D]:ee.a,[Zi.LOD]:Ki};function ts(e){switch(e){case ee.a:return Zi.OBJECT3D;case z.a:return Zi.MESH;case ji.a:return Zi.POINTS;case $i.a:return Zi.LINE_SEGMENTS;case Ki:return Zi.LOD;default:return Rn.warn(\\\\\\\"object type not supported\\\\\\\",e),Zi.MESH}}const ns=[Zi.MESH,Zi.POINTS,Zi.LINE_SEGMENTS],is=[{name:\\\\\\\"Mesh\\\\\\\",value:ns.indexOf(Zi.MESH)},{name:\\\\\\\"Points\\\\\\\",value:ns.indexOf(Zi.POINTS)},{name:\\\\\\\"LineSegments\\\\\\\",value:ns.indexOf(Zi.LINE_SEGMENTS)}],ss={MeshStandard:new Wi.a({color:16777215,side:w.H,metalness:.5,roughness:.9}),[Zi.MESH]:new Xi.a({color:new M.a(1,1,1),side:w.H,vertexColors:!1,transparent:!0,depthTest:!0}),[Zi.POINTS]:new qi.a({color:16777215,size:.1,depthTest:!0}),[Zi.LINE_SEGMENTS]:new Yi.a({color:16777215,linewidth:1})};var rs;!function(e){e[e.VERTEX=0]=\\\\\\\"VERTEX\\\\\\\",e[e.OBJECT=1]=\\\\\\\"OBJECT\\\\\\\"}(rs||(rs={}));const os=[rs.VERTEX,rs.OBJECT],as=[{name:\\\\\\\"vertex\\\\\\\",value:rs.VERTEX},{name:\\\\\\\"object\\\\\\\",value:rs.OBJECT}];var cs;!function(e){e[e.NUMERIC=0]=\\\\\\\"NUMERIC\\\\\\\",e[e.STRING=1]=\\\\\\\"STRING\\\\\\\"}(cs||(cs={}));const ls=[cs.NUMERIC,cs.STRING],us=[{name:\\\\\\\"numeric\\\\\\\",value:cs.NUMERIC},{name:\\\\\\\"string\\\\\\\",value:cs.STRING}];var hs;!function(e){e[e.FLOAT=1]=\\\\\\\"FLOAT\\\\\\\",e[e.VECTOR2=2]=\\\\\\\"VECTOR2\\\\\\\",e[e.VECTOR3=3]=\\\\\\\"VECTOR3\\\\\\\",e[e.VECTOR4=4]=\\\\\\\"VECTOR4\\\\\\\"}(hs||(hs={}));const ds=[hs.FLOAT,hs.VECTOR2,hs.VECTOR3,hs.VECTOR4],ps=[hs.FLOAT,hs.VECTOR4],_s={ATTRIB_CLASS:{VERTEX:rs.VERTEX,OBJECT:rs.OBJECT},OBJECT_TYPES:ns,CONSTRUCTOR_NAMES_BY_CONSTRUCTOR_NAME:{[Vi.name]:\\\\\\\"Scene\\\\\\\",[on.a.name]:\\\\\\\"Group\\\\\\\",[ee.a.name]:\\\\\\\"Object3D\\\\\\\",[z.a.name]:\\\\\\\"Mesh\\\\\\\",[ji.a.name]:\\\\\\\"Points\\\\\\\",[$i.a.name]:\\\\\\\"LineSegments\\\\\\\",[Hi.a.name]:\\\\\\\"Bone\\\\\\\",[Gi.a.name]:\\\\\\\"SkinnedMesh\\\\\\\"},CONSTRUCTORS_BY_NAME:{[Zi.MESH]:z.a,[Zi.POINTS]:ji.a,[Zi.LINE_SEGMENTS]:$i.a},MATERIALS:ss};var ms;!function(e){e.POSITION=\\\\\\\"position\\\\\\\",e.NORMAL=\\\\\\\"normal\\\\\\\",e.TANGENT=\\\\\\\"tangent\\\\\\\"}(ms||(ms={}));const fs={P:\\\\\\\"position\\\\\\\",N:\\\\\\\"normal\\\\\\\",Cd:\\\\\\\"color\\\\\\\"};class gs{static remapName(e){return fs[e]||e}static arrayToIndexedArrays(e){const t={};let n=0;const i=[],s=[];let r=0;for(;r<e.length;){const o=e[r],a=t[o];null!=a?i.push(a):(s.push(o),i.push(n),t[o]=n,n+=1),r++}return{indices:i,values:s}}static default_value(e){switch(e){case 1:return 0;case 2:return new d.a(0,0);case 3:return new p.a(0,0,0);default:throw`size ${e} not yet implemented`}}static copy(e,t,n=!0){const i=null==e?void 0:e.array,s=null==t?void 0:t.array;if(i&&s){const e=Math.min(i.length,s.length);for(let t=0;t<e;t++)s[t]=i[t];n&&(t.needsUpdate=!0)}}static attribSizeFromValue(e){if(m.isString(e)||m.isNumber(e))return hs.FLOAT;if(m.isArray(e))return e.length;switch(e.constructor){case d.a:return hs.VECTOR2;case p.a:return hs.VECTOR3;case _.a:return hs.VECTOR4}return 0}}class vs{constructor(e){this._index=e}index(){return this._index}}const ys=\\\\\\\"position\\\\\\\",xs=\\\\\\\"normal\\\\\\\";var bs;!function(e){e.x=\\\\\\\"x\\\\\\\",e.y=\\\\\\\"y\\\\\\\",e.z=\\\\\\\"z\\\\\\\",e.w=\\\\\\\"w\\\\\\\",e.r=\\\\\\\"r\\\\\\\",e.g=\\\\\\\"g\\\\\\\",e.b=\\\\\\\"b\\\\\\\"}(bs||(bs={}));const ws={x:0,y:1,z:2,w:3,r:0,g:1,b:2};class As extends vs{constructor(e,t){super(t),this._core_geometry=e,this._geometry=this._core_geometry.geometry()}applyMatrix4(e){this.position().applyMatrix4(e)}core_geometry(){return this._core_geometry}geometry(){return this._geometry=this._geometry||this._core_geometry.geometry()}attribSize(e){return e=gs.remapName(e),this._geometry.getAttribute(e).itemSize}hasAttrib(e){const t=gs.remapName(e);return this._core_geometry.hasAttrib(t)}attribValue(e,t){if(\\\\\\\"ptnum\\\\\\\"===e)return this.index();{let n=null,i=null;\\\\\\\".\\\\\\\"===e[e.length-2]&&(n=e[e.length-1],i=ws[n],e=e.substring(0,e.length-2));const s=gs.remapName(e),r=this._geometry.getAttribute(s);if(!r){const t=`attrib ${e} not found. availables are: ${Object.keys(this._geometry.attributes||{}).join(\\\\\\\",\\\\\\\")}`;throw console.warn(t),t}{const{array:e}=r;if(this._core_geometry.isAttribIndexed(s))return this.indexedAttribValue(s);{const n=r.itemSize,s=this._index*n;if(null==i)switch(n){case 1:return e[s];case 2:return(t=t||new d.a).fromArray(e,s),t;case 3:return(t=t||new p.a).fromArray(e,s),t;case 4:return(t=t||new _.a).fromArray(e,s),t;default:throw`size not valid (${n})`}else switch(n){case 1:return e[s];default:return e[s+i]}}}}}indexedAttribValue(e){const t=this.attribValueIndex(e);return this._core_geometry.userDataAttrib(e)[t]}stringAttribValue(e){return this.indexedAttribValue(e)}attribValueIndex(e){return this._core_geometry.isAttribIndexed(e)?this._geometry.getAttribute(e).array[this._index]:-1}isAttribIndexed(e){return this._core_geometry.isAttribIndexed(e)}position(){return this._position||(this._position=this.getPosition(new p.a))}getPosition(e){const{array:t}=this._geometry.getAttribute(ys);return e.fromArray(t,3*this._index)}setPosition(e){this.setAttribValueVector3(ys,e)}normal(){return this._normal=this._normal||this.getNormal(new p.a)}getNormal(e){const{array:t}=this._geometry.getAttribute(xs);return e.fromArray(t,3*this._index)}setNormal(e){return this.setAttribValueVector3(xs,e)}setAttribValue(e,t){if(null==t)return;if(null==e)throw\\\\\\\"Point.set_attrib_value requires a name\\\\\\\";const n=this._geometry.getAttribute(e),i=n.array,s=n.itemSize;if(m.isArray(t))for(let e=0;e<s;e++)i[this._index*s+e]=t[e];else switch(s){case 1:i[this._index]=t;break;case 2:const e=t;i[2*this._index+0]=e.x,i[2*this._index+1]=e.y;break;case 3:if(null!=t.r){const e=t;i[3*this._index+0]=e.r,i[3*this._index+1]=e.g,i[3*this._index+2]=e.b}else{const e=t;i[3*this._index+0]=e.x,i[3*this._index+1]=e.y,i[3*this._index+2]=e.z}break;case 4:const n=t;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 ${s}`),`attrib size ${s} not implemented`}}setAttribValueVector3(e,t){if(null==t)return;if(null==e)throw\\\\\\\"Point.set_attrib_value requires a name\\\\\\\";const n=this._geometry.getAttribute(e).array,i=3*this._index;n[i]=t.x,n[i+1]=t.y,n[i+2]=t.z}setAttribIndex(e,t){return this._geometry.getAttribute(e).array[this._index]=t}}var Ts=n(40);const Es=function(e){return function(t){return Math.pow(t,e)}},Cs=function(e){return function(t){return 1-Math.abs(Math.pow(t-1,e))}},Ms=function(e){return function(t){return t<.5?Es(e)(2*t)/2:Cs(e)(2*t-1)/2+.5}},Ns={linear:Ms(1),ease_i:function(e,t){return Es(t)(e)},ease_o:function(e,t){return Cs(t)(e)},ease_io:function(e,t){return Ms(t)(e)},ease_i2:Es(2),ease_o2:Cs(2),ease_io2:Ms(2),ease_i3:Ms(3),ease_o3:Ms(3),ease_io3:Ms(3),ease_i4:Ms(4),ease_o4:Ms(4),ease_io4:Ms(4),ease_i_sin:function(e){return 1+Math.sin(Math.PI/2*e-Math.PI/2)},ease_o_sin:function(e){return Math.sin(Math.PI/2*e)},ease_io_sin:function(e){return(1+Math.sin(Math.PI*e-Math.PI/2))/2},ease_i_elastic:function(e){return(.04-.04/e)*Math.sin(25*e)+1},ease_o_elastic:function(e){return.04*e/--e*Math.sin(25*e)},ease_io_elastic:function(e){return(e-=.5)<0?(.02+.01/e)*Math.sin(50*e):(.02-.01/e)*Math.sin(50*e)+1}},Ss=Math.PI/180;class Os{static clamp(e,t,n){return e<t?t:e>n?n:e}static fit01(e,t,n){return this.fit(e,0,1,t,n)}static fit(e,t,n,i,s){return(e-t)/(n-t)*(s-i)+i}static blend(e,t,n){return(1-n)*e+n*t}static degrees_to_radians(e){return e*Ss}static radians_to_degrees(e){return e/Ss}static deg2rad(e){return this.degrees_to_radians(e)}static rad2deg(e){return this.radians_to_degrees(e)}static rand(e){return m.isNumber(e)?this.randFloat(e):this.randVec2(e)}static round(e,t){const n=e/t;return(e<0?Math.ceil(n):Math.floor(n))*t}static highest_even(e){return 2*Math.ceil(.5*e)}static randFloat(e,t=136574){return this._vec.x=e,this._vec.y=t,this.randVec2(this._vec)}static randVec2(e){const t=(12.9898*e.x+78.233*e.y)%Math.PI;return this.fract(43758.5453*Math.sin(t))}static geodesic_distance(e,t){var n=this.deg2rad(e.lat),i=this.deg2rad(t.lat),s=this.deg2rad(t.lat-e.lat),r=this.deg2rad(t.lng-e.lng),o=Math.sin(s/2)*Math.sin(s/2)+Math.cos(n)*Math.cos(i)*Math.sin(r/2)*Math.sin(r/2);return 6371e3*(2*Math.atan2(Math.sqrt(o),Math.sqrt(1-o)))}static expand_triangle(e,t){e.getMidpoint(this._triangle_mid),this._triangle_mid_to_corner.copy(e.a).sub(this._triangle_mid),this._triangle_mid_to_corner.normalize().multiplyScalar(t),e.a.add(this._triangle_mid_to_corner),this._triangle_mid_to_corner.copy(e.b).sub(this._triangle_mid),this._triangle_mid_to_corner.normalize().multiplyScalar(t),e.b.add(this._triangle_mid_to_corner),this._triangle_mid_to_corner.copy(e.c).sub(this._triangle_mid),this._triangle_mid_to_corner.normalize().multiplyScalar(t),e.c.add(this._triangle_mid_to_corner)}static nearestPower2(e){return Math.pow(2,Math.ceil(Math.log(e)/Math.log(2)))}}Os.Easing=Ns,Os.fract=e=>e-Math.floor(e),Os._vec={x:0,y:136574},Os._triangle_mid=new p.a,Os._triangle_mid_to_corner=new p.a;class Ls{constructor(e,t){this._core_geometry=e,this._index=t,this._geometry=this._core_geometry.geometry()}index(){return this._index}points(){return this._points=this._points||this._get_points()}applyMatrix4(e){for(let t of this.points())t.applyMatrix4(e)}_get_points(){var e;const t=(null===(e=this._geometry.index)||void 0===e?void 0:e.array)||[],n=3*this._index;return[new As(this._core_geometry,t[n+0]),new As(this._core_geometry,t[n+1]),new As(this._core_geometry,t[n+2])]}positions(){return this._positions=this._positions||this._get_positions()}_get_positions(){const e=this.points();return[e[0].position(),e[1].position(),e[2].position()]}triangle(){return this._triangle=this._triangle||this._get_triangle()}_get_triangle(){const e=this.positions();return new Ts.a(e[0],e[1],e[2])}deltas(){return this._deltas=this._deltas||this._get_deltas()}_get_deltas(){const e=this.positions();return[e[1].clone().sub(e[0]),e[2].clone().sub(e[0])]}area(){return this.triangle().getArea()}center(e){const t=this.positions();return e.x=(t[0].x+t[1].x+t[2].x)/3,e.y=(t[0].y+t[1].y+t[2].y)/3,e.z=(t[0].z+t[1].z+t[2].z)/3,e}random_position(e){let t=[Os.randFloat(e),Os.randFloat(6541*e)];return t[0]+t[1]>1&&(t[0]=1-t[0],t[1]=1-t[1]),this.positions()[0].clone().add(this.deltas()[0].clone().multiplyScalar(t[0])).add(this.deltas()[1].clone().multiplyScalar(t[1]))}attrib_value_at_position(e,t){const n=new p.a;this.triangle().getBarycoord(t,n);const i=n.toArray(),s=this._geometry.attributes[e].itemSize,r=this.points().map((t=>t.attribValue(e)));let o,a,c=0;switch(s){case 1:a=0;for(let e of r)a+=e*i[c],c++;o=a;break;default:for(let e of r){const t=e.multiplyScalar(i[c]);a?a.add(t):a=t,c++}o=a}return o}static interpolated_value(e,t,n,i){const s=[t.a,t.b,t.c],r=e.getAttribute(\\\\\\\"position\\\\\\\").array,o=s.map((e=>new p.a(r[3*e+0],r[3*e+1],r[3*e+2]))),a=i.itemSize,c=i.array;let l=[];switch(a){case 1:l=s.map((e=>c[e]));break;case 2:l=s.map((e=>new d.a(c[2*e+0],c[2*e+1])));break;case 3:l=s.map((e=>new p.a(c[3*e+0],c[3*e+1],c[3*e+2])))}const u=s.map(((e,t)=>n.distanceTo(o[t]))),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(s.map(((e,t)=>_[t]*l[t])));break;default:var g=s.map(((e,t)=>l[t].multiplyScalar(_[t])));m=null;for(let e of g)m?m.add(e):m=e}return m}}class Ps{from_points(e){e=this._filter_points(e);const t=new O.a,n=new Bs(t),i=e[0];if(null!=i){const s=i.geometry(),r=i.core_geometry(),o={};for(let t=0;t<e.length;t++)o[e[t].index()]=t;const a=this._indices_from_points(o,s);a&&t.setIndex(a);const{attributes:c}=s;for(let i of Object.keys(c)){if(null!=r.userDataAttribs()[i]){const s=f.uniq(e.map((e=>e.indexedAttribValue(i)))),r={};s.forEach(((e,t)=>r[e]=t)),n.userDataAttribs()[i]=s;const o=[];for(let t of e){const e=r[t.indexedAttribValue(i)];o.push(e)}t.setAttribute(i,new L.c(o,1))}else{const n=c[i].itemSize,s=new Array(e.length*n);switch(n){case 1:for(let t=0;t<e.length;t++)s[t]=e[t].attribValue(i);break;default:let t;for(let r=0;r<e.length;r++)t=e[r].attribValue(i),t.toArray(s,r*n)}t.setAttribute(i,new L.c(s,n))}}}return t}}var Rs=n(77),Is=n(63),Fs={computeTangents:function(e){e.computeTangents(),console.warn(\\\\\\\"THREE.BufferGeometryUtils: .computeTangents() has been removed. Use BufferGeometry.computeTangents() instead.\\\\\\\")},mergeBufferGeometries:function(e,t){for(var n=null!==e[0].index,i=new Set(Object.keys(e[0].attributes)),s=new Set(Object.keys(e[0].morphAttributes)),r={},o={},a=e[0].morphTargetsRelative,c=new O.a,l=0,u=0;u<e.length;++u){var h=e[u],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(var p in h.attributes){if(!i.has(p))return console.error(\\\\\\\"THREE.BufferGeometryUtils: .mergeBufferGeometries() failed with geometry at index \\\\\\\"+u+'. All geometries must have compatible attributes; make sure \\\\\\\"'+p+'\\\\\\\" attribute exists among all geometries, or in none of them.'),null;void 0===r[p]&&(r[p]=[]),r[p].push(h.attributes[p]),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(var p in h.morphAttributes){if(!s.has(p))return console.error(\\\\\\\"THREE.BufferGeometryUtils: .mergeBufferGeometries() failed with geometry at index \\\\\\\"+u+\\\\\\\".  .morphAttributes must be consistent throughout all geometries.\\\\\\\"),null;void 0===o[p]&&(o[p]=[]),o[p].push(h.morphAttributes[p])}if(c.userData.mergedUserData=c.userData.mergedUserData||[],c.userData.mergedUserData.push(h.userData),t){var _;if(n)_=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;_=h.attributes.position.count}c.addGroup(l,_,u),l+=_}}if(n){var m=0,f=[];for(u=0;u<e.length;++u){for(var g=e[u].index,v=0;v<g.count;++v)f.push(g.getX(v)+m);m+=e[u].attributes.position.count}c.setIndex(f)}for(var p in r){var y=this.mergeBufferAttributes(r[p]);if(!y)return console.error(\\\\\\\"THREE.BufferGeometryUtils: .mergeBufferGeometries() failed while trying to merge the \\\\\\\"+p+\\\\\\\" attribute.\\\\\\\"),null;c.setAttribute(p,y)}for(var p in o){var x=o[p][0].length;if(0===x)break;c.morphAttributes=c.morphAttributes||{},c.morphAttributes[p]=[];for(u=0;u<x;++u){var b=[];for(v=0;v<o[p].length;++v)b.push(o[p][v][u]);var w=this.mergeBufferAttributes(b);if(!w)return console.error(\\\\\\\"THREE.BufferGeometryUtils: .mergeBufferGeometries() failed while trying to merge the \\\\\\\"+p+\\\\\\\" morphAttribute.\\\\\\\"),null;c.morphAttributes[p].push(w)}}return c},mergeBufferAttributes:function(e){for(var t,n,i,s=0,r=0;r<e.length;++r){var o=e[r];if(o.isInterleavedBufferAttribute)return console.error(\\\\\\\"THREE.BufferGeometryUtils: .mergeBufferAttributes() failed. InterleavedBufferAttributes are not supported.\\\\\\\"),null;if(void 0===t&&(t=o.array.constructor),t!==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;s+=o.array.length}var a=new t(s),c=0;for(r=0;r<e.length;++r)a.set(e[r].array,c),c+=e[r].array.length;return new L.a(a,n,i)},interleaveAttributes:function(e){for(var t,n=0,i=0,s=0,r=e.length;s<r;++s){var o=e[s];if(void 0===t&&(t=o.array.constructor),t!==o.array.constructor)return console.error(\\\\\\\"AttributeBuffers of different types cannot be interleaved\\\\\\\"),null;n+=o.array.length,i+=o.itemSize}var a=new Rs.a(new t(n),i),c=0,l=[],u=[\\\\\\\"getX\\\\\\\",\\\\\\\"getY\\\\\\\",\\\\\\\"getZ\\\\\\\",\\\\\\\"getW\\\\\\\"],h=[\\\\\\\"setX\\\\\\\",\\\\\\\"setY\\\\\\\",\\\\\\\"setZ\\\\\\\",\\\\\\\"setW\\\\\\\"],d=0;for(r=e.length;d<r;d++){var p=(o=e[d]).itemSize,_=o.count,m=new Is.a(a,p,c,o.normalized);l.push(m),c+=p;for(var f=0;f<_;f++)for(var g=0;g<p;g++)m[h[g]](f,o[u[g]](f))}return l},estimateBytesUsed:function(e){var t=0;for(var n in e.attributes){var i=e.getAttribute(n);t+=i.count*i.itemSize*i.array.BYTES_PER_ELEMENT}var s=e.getIndex();return t+=s?s.count*s.itemSize*s.array.BYTES_PER_ELEMENT:0},mergeVertices:function(e,t=1e-4){t=Math.max(t,Number.EPSILON);for(var n={},i=e.getIndex(),s=e.getAttribute(\\\\\\\"position\\\\\\\"),r=i?i.count:s.count,o=0,a=Object.keys(e.attributes),c={},l={},u=[],h=[\\\\\\\"getX\\\\\\\",\\\\\\\"getY\\\\\\\",\\\\\\\"getZ\\\\\\\",\\\\\\\"getW\\\\\\\"],d=0,p=a.length;d<p;d++){c[y=a[d]]=[],(A=e.morphAttributes[y])&&(l[y]=new Array(A.length).fill().map((()=>[])))}var _=Math.log10(1/t),m=Math.pow(10,_);for(d=0;d<r;d++){var f=i?i.getX(d):d,g=\\\\\\\"\\\\\\\",v=0;for(p=a.length;v<p;v++)for(var y=a[v],x=(w=e.getAttribute(y)).itemSize,b=0;b<x;b++)g+=~~(w[h[b]](f)*m)+\\\\\\\",\\\\\\\";if(g in n)u.push(n[g]);else{for(v=0,p=a.length;v<p;v++){y=a[v];var w=e.getAttribute(y),A=e.morphAttributes[y],T=(x=w.itemSize,c[y]),E=l[y];for(b=0;b<x;b++){var C=h[b];if(T.push(w[C](f)),A)for(var M=0,N=A.length;M<N;M++)E[M].push(A[M][C](f))}}n[g]=o,u.push(o),o++}}const S=e.clone();for(d=0,p=a.length;d<p;d++){y=a[d];var O=e.getAttribute(y),P=new O.array.constructor(c[y]);w=new L.a(P,O.itemSize,O.normalized);if(S.setAttribute(y,w),y in l)for(v=0;v<l[y].length;v++){var R=e.morphAttributes[y][v],I=(P=new R.array.constructor(l[y][v]),new L.a(P,R.itemSize,R.normalized));S.morphAttributes[y][v]=I}}return S.setIndex(u),S},toTrianglesDrawMode:function(e,t){if(t===w.Xc)return console.warn(\\\\\\\"THREE.BufferGeometryUtils.toTrianglesDrawMode(): Geometry already defined as triangles.\\\\\\\"),e;if(t===w.Vc||t===w.Wc){var n=e.getIndex();if(null===n){var i=[],s=e.getAttribute(\\\\\\\"position\\\\\\\");if(void 0===s)return console.error(\\\\\\\"THREE.BufferGeometryUtils.toTrianglesDrawMode(): Undefined position attribute. Processing not possible.\\\\\\\"),e;for(var r=0;r<s.count;r++)i.push(r);e.setIndex(i),n=e.getIndex()}var o=n.count-2,a=[];if(t===w.Vc)for(r=1;r<=o;r++)a.push(n.getX(0)),a.push(n.getX(r)),a.push(n.getX(r+1));else for(r=0;r<o;r++)r%2==0?(a.push(n.getX(r)),a.push(n.getX(r+1)),a.push(n.getX(r+2))):(a.push(n.getX(r+2)),a.push(n.getX(r+1)),a.push(n.getX(r)));a.length/3!==o&&console.error(\\\\\\\"THREE.BufferGeometryUtils.toTrianglesDrawMode(): Unable to generate correct amount of triangles.\\\\\\\");var c=e.clone();return c.setIndex(a),c.clearGroups(),c}return console.error(\\\\\\\"THREE.BufferGeometryUtils.toTrianglesDrawMode(): Unknown draw mode:\\\\\\\",t),e},computeMorphedAttributes:function(e){if(!0!==e.geometry.isBufferGeometry)return console.error(\\\\\\\"THREE.BufferGeometryUtils: Geometry is not of type BufferGeometry.\\\\\\\"),null;var t=new p.a,n=new p.a,i=new p.a,s=new p.a,r=new p.a,o=new p.a,a=new p.a,c=new p.a,l=new p.a;function u(e,u,h,d,p,_,m,f,g){t.fromBufferAttribute(h,_),n.fromBufferAttribute(h,m),i.fromBufferAttribute(h,f);var v=e.morphTargetInfluences;if(u.morphTargets&&d&&v){a.set(0,0,0),c.set(0,0,0),l.set(0,0,0);for(var y=0,x=d.length;y<x;y++){var b=v[y],w=d[y];0!==b&&(s.fromBufferAttribute(w,_),r.fromBufferAttribute(w,m),o.fromBufferAttribute(w,f),p?(a.addScaledVector(s,b),c.addScaledVector(r,b),l.addScaledVector(o,b)):(a.addScaledVector(s.sub(t),b),c.addScaledVector(r.sub(n),b),l.addScaledVector(o.sub(i),b)))}t.add(a),n.add(c),i.add(l)}e.isSkinnedMesh&&(e.boneTransform(_,t),e.boneTransform(m,n),e.boneTransform(f,i)),g[3*_+0]=t.x,g[3*_+1]=t.y,g[3*_+2]=t.z,g[3*m+0]=n.x,g[3*m+1]=n.y,g[3*m+2]=n.z,g[3*f+0]=i.x,g[3*f+1]=i.y,g[3*f+2]=i.z}var h,d,_,m,f,g,v,y,x,b=e.geometry,w=e.material,A=b.index,T=b.attributes.position,E=b.morphAttributes.position,C=b.morphTargetsRelative,M=b.attributes.normal,N=b.morphAttributes.position,S=b.groups,O=b.drawRange,P=new Float32Array(T.count*T.itemSize),R=new Float32Array(M.count*M.itemSize);if(null!==A)if(Array.isArray(w))for(m=0,g=S.length;m<g;m++)for(x=w[(y=S[m]).materialIndex],f=Math.max(y.start,O.start),v=Math.min(y.start+y.count,O.start+O.count);f<v;f+=3)u(e,x,T,E,C,h=A.getX(f),d=A.getX(f+1),_=A.getX(f+2),P),u(e,x,M,N,C,h,d,_,R);else for(m=Math.max(0,O.start),g=Math.min(A.count,O.start+O.count);m<g;m+=3)u(e,w,T,E,C,h=A.getX(m),d=A.getX(m+1),_=A.getX(m+2),P),u(e,w,M,N,C,h,d,_,R);else if(void 0!==T)if(Array.isArray(w))for(m=0,g=S.length;m<g;m++)for(x=w[(y=S[m]).materialIndex],f=Math.max(y.start,O.start),v=Math.min(y.start+y.count,O.start+O.count);f<v;f+=3)u(e,x,T,E,C,h=f,d=f+1,_=f+2,P),u(e,x,M,N,C,h,d,_,R);else for(m=Math.max(0,O.start),g=Math.min(T.count,O.start+O.count);m<g;m+=3)u(e,w,T,E,C,h=m,d=m+1,_=m+2,P),u(e,w,M,N,C,h,d,_,R);return{positionAttribute:T,normalAttribute:M,morphedPositionAttribute:new L.c(P,3),morphedNormalAttribute:new L.c(R,3)}}};class Ds{static createIndexIfNone(e){if(!e.index){const t=e.getAttribute(\\\\\\\"position\\\\\\\");if(t){const n=t.array;e.setIndex(f.range(n.length/3))}}}}class ks{static merge(e){if(0===e.length)return;for(let t of e)Ds.createIndexIfNone(t);const t=e.map((e=>new Bs(e))),n=t[0].indexedAttributeNames(),i={};for(let e of n){const n={},s=[];for(let i of t){const t=i.points();for(let i of t){s.push(i);const t=i.indexedAttribValue(e);null!=n[t]?n[t]:n[t]=Object.keys(n).length}}const r=Object.keys(n);for(let t of s){const i=n[t.indexedAttribValue(e)];t.setAttribIndex(e,i)}i[e]=r}const s=Fs.mergeBufferGeometries(e),r=new Bs(s);return Object.keys(i).forEach((e=>{const t=i[e];r.setIndexedAttributeValues(e,t)})),s&&delete s.userData.mergedUserData,s}}class Bs{constructor(e){this._geometry=e}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(e){return!0===e.userData.isInstance}markedAsInstance(){return Bs.markedAsInstance(this._geometry)}positionAttribName(){let e=\\\\\\\"position\\\\\\\";return this.markedAsInstance()&&(e=\\\\\\\"instancePosition\\\\\\\"),e}computeVertexNormals(){this._geometry.computeVertexNormals()}userDataAttribs(){const e=\\\\\\\"indexed_attrib_values\\\\\\\";return this._geometry.userData[e]=this._geometry.userData[e]||{}}indexedAttributeNames(){return Object.keys(this.userDataAttribs()||{})}userDataAttrib(e){return e=gs.remapName(e),this.userDataAttribs()[e]}isAttribIndexed(e){return e=gs.remapName(e),null!=this.userDataAttrib(e)}hasAttrib(e){return\\\\\\\"ptnum\\\\\\\"===e||(e=gs.remapName(e),null!=this._geometry.attributes[e])}attribType(e){return this.isAttribIndexed(e)?cs.STRING:cs.NUMERIC}static attribNames(e){return Object.keys(e.attributes)}attribNames(){return Bs.attribNames(this._geometry)}static attribNamesMatchingMask(e,t){const n=Li.attribNames(t),i=[];for(let t of this.attribNames(e))for(let e of n)Li.matchMask(t,e)&&i.push(t);return f.uniq(i)}attribSizes(){const e={};for(let t of this.attribNames())e[t]=this._geometry.attributes[t].itemSize;return e}attribSize(e){let t;return e=gs.remapName(e),null!=(t=this._geometry.attributes[e])?t.itemSize:\\\\\\\"ptnum\\\\\\\"===e?1:0}setIndexedAttributeValues(e,t){this.userDataAttribs()[e]=t}setIndexedAttribute(e,t,n){this.setIndexedAttributeValues(e,t),this._geometry.setAttribute(e,new L.f(n,1))}addNumericAttrib(e,t=1,n=0){const i=[];let s=!1;if(m.isNumber(n)){for(let e=0;e<this.pointsCount();e++)for(let e=0;e<t;e++)i.push(n);s=!0}else if(t>1)if(m.isArray(n)){for(let e=0;e<this.pointsCount();e++)for(let e=0;e<t;e++)i.push(n[e]);s=!0}else{const e=n;if(2==t&&null!=e.x&&null!=e.y){for(let t=0;t<this.pointsCount();t++)i.push(e.x),i.push(e.y);s=!0}const r=n;if(3==t&&null!=r.x&&null!=r.y&&null!=r.z){for(let e=0;e<this.pointsCount();e++)i.push(r.x),i.push(r.y),i.push(r.z);s=!0}const o=n;if(3==t&&null!=o.r&&null!=o.g&&null!=o.b){for(let e=0;e<this.pointsCount();e++)i.push(o.r),i.push(o.g),i.push(o.b);s=!0}const a=n;if(4==t&&null!=a.x&&null!=a.y&&null!=a.z&&null!=a.w){for(let e=0;e<this.pointsCount();e++)i.push(a.x),i.push(a.y),i.push(a.z),i.push(a.w);s=!0}}if(!s)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(e.trim(),new L.c(i,t))}initPositionAttribute(e,t){const n=[];null==t&&(t=new p.a);for(let i=0;i<e;i++)n.push(t.x),n.push(t.y),n.push(t.z);return this._geometry.setAttribute(\\\\\\\"position\\\\\\\",new L.c(n,3))}addAttribute(e,t){switch(t.type()){case cs.STRING:return console.log(\\\\\\\"TODO: to implement\\\\\\\");case cs.NUMERIC:return this.addNumericAttrib(e,t.size())}}renameAttrib(e,t){this.isAttribIndexed(e)&&(this.userDataAttribs()[t]=b.clone(this.userDataAttribs()[e]),delete this.userDataAttribs()[e]);const n=this._geometry.getAttribute(e);return this._geometry.setAttribute(t.trim(),new L.c(n.array,n.itemSize)),this._geometry.deleteAttribute(e)}deleteAttribute(e){return this.isAttribIndexed(e)&&delete this.userDataAttribs()[e],this._geometry.deleteAttribute(e)}clone(){return Bs.clone(this._geometry)}static clone(e){let t;const n=e.clone();return null!=(t=e.userData)&&(n.userData=b.cloneDeep(t)),n}pointsCount(){return Bs.pointsCount(this._geometry)}static pointsCount(e){let t,n=0;let i=\\\\\\\"position\\\\\\\";if(new this(e).markedAsInstance()&&(i=\\\\\\\"instancePosition\\\\\\\"),null!=(t=e.getAttribute(i))){let e;null!=(e=t.array)&&(n=e.length/3)}return n}points(){return this.pointsFromGeometry()}pointsFromGeometry(){const e=[],t=this._geometry.getAttribute(this.positionAttribName());if(null!=t){const n=t.array.length/3;for(let t=0;t<n;t++){const n=new As(this,t);e.push(n)}}return e}static geometryFromPoints(e,t){switch(t){case Zi.MESH:return this._mesh_builder.from_points(e);case Zi.POINTS:return this._points_builder.from_points(e);case Zi.LINE_SEGMENTS:return this._lines_segment_builder.from_points(e);case Zi.OBJECT3D:case Zi.LOD:return null}Ri.unreachable(t)}static mergeGeometries(e){return ks.merge(e)}static merge_geometries(e){return ks.merge(e)}segments(){var e;const t=(null===(e=this.geometry().index)||void 0===e?void 0:e.array)||[];return f.chunk(t,2)}faces(){return this.facesFromGeometry()}facesFromGeometry(){var e;const t=((null===(e=this.geometry().index)||void 0===e?void 0:e.array)||[]).length/3;return f.range(t).map((e=>new Ls(this,e)))}}var zs;Bs._mesh_builder=new class extends Ps{_filter_points(e){var t;const n=e[0];if(n){const i=null===(t=n.geometry().getIndex())||void 0===t?void 0:t.array;if(i){const t={};for(let n of e)t[n.index()]=n;const n=[],s=i.length;let r,o,a;for(let e=0;e<s;e+=3)r=t[i[e+0]],o=t[i[e+1]],a=t[i[e+2]],r&&o&&a&&(n.push(r),n.push(o),n.push(a));return n}}return[]}_indices_from_points(e,t){const n=t.index;if(null!=n){const t=n.array,i=[];let s,r,o,a,c,l;for(let n=0;n<t.length;n+=3)s=t[n+0],r=t[n+1],o=t[n+2],a=e[s],c=e[r],l=e[o],null!=a&&null!=c&&null!=l&&(i.push(a),i.push(c),i.push(l));return i}}},Bs._points_builder=new class extends Ps{_filter_points(e){return e}_indices_from_points(e,t){const n=t.index;if(null!=n){const t=n.array,i=[];let s,r;for(let n=0;n<t.length;n++)s=t[n],r=e[s],null!=r&&i.push(r);return i}}},Bs._lines_segment_builder=new class extends Ps{_filter_points(e){var t;const n=e[0];if(n){const i=null===(t=n.geometry().getIndex())||void 0===t?void 0:t.array;if(i){const t={};for(let n of e)t[n.index()]=n;const n=[],s=i.length;let r,o;for(let e=0;e<s;e+=2)r=t[i[e+0]],o=t[i[e+1]],r&&o&&(n.push(r),n.push(o));return n}}return[]}_indices_from_points(e,t){const n=t.index;if(null!=n){const t=n.array,i=[];let s,r,o,a;for(let n=0;n<t.length;n+=2)s=t[n],r=t[n+1],o=e[s],a=e[r],null!=o&&null!=a&&(i.push(o),i.push(a));return i}}},function(e){e.customDistanceMaterial=\\\\\\\"customDistanceMaterial\\\\\\\",e.customDepthMaterial=\\\\\\\"customDepthMaterial\\\\\\\",e.customDepthDOFMaterial=\\\\\\\"customDepthDOFMaterial\\\\\\\"}(zs||(zs={}));const Us=(e,t,n,i,s,r)=>{};class Gs{static node(e,t){return e.node(t.name)}static clone(e){const t=e.clone(),n=e.uniforms;return n&&(t.uniforms=k.clone(n)),t}static add_user_data_render_hook(e,t){e.userData.POLY_render_hook=t}static apply_render_hook(e,t){if(t.userData){const n=t.userData.POLY_render_hook;if(n)return void(e.onBeforeRender=(t,i,s,r,o,a)=>{n(t,i,s,r,o,a,e)})}e.onBeforeRender=Us}static applyCustomMaterials(e,t){const n=t;if(n.customMaterials)for(let t of Object.keys(n.customMaterials)){const i=t,s=n.customMaterials[i];s&&(e[i]=s,s.needsUpdate=!0)}}static assign_custom_uniforms(e,t,n){const i=e;if(i.customMaterials)for(let e of Object.keys(i.customMaterials)){const s=e,r=i.customMaterials[s];r&&(r.uniforms[t].value=n)}}static init_custom_material_uniforms(e,t,n){const i=e;if(i.customMaterials)for(let e of Object.keys(i.customMaterials)){const s=e,r=i.customMaterials[s];r&&(r.uniforms[t]=r.uniforms[t]||n)}}}const Vs=\\\\\\\"name\\\\\\\";class js extends vs{constructor(e,t){super(t),this._object=e,null==this._object.userData.attributes&&(this._object.userData.attributes={})}object(){return this._object}geometry(){return this._object.geometry}coreGeometry(){const e=this.geometry();return e?new Bs(e):null}points(){var e;return(null===(e=this.coreGeometry())||void 0===e?void 0:e.points())||[]}pointsFromGroup(e){if(e){const t=Li.indices(e);if(t){const e=this.points();return t.map((t=>e[t]))}return[]}return this.points()}static isInGroup(e,t){const n=e.trim();if(0==n.length)return!0;const i=n.split(\\\\\\\"=\\\\\\\"),s=i[0];if(\\\\\\\"@\\\\\\\"==s[0]){const e=s.substr(1);return i[1]==this.attribValue(t,e)}return!1}computeVertexNormals(){var e;null===(e=this.coreGeometry())||void 0===e||e.computeVertexNormals()}static _convert_array_to_vector(e){switch(e.length){case 1:return e[0];case 2:return new d.a(e[0],e[1]);case 3:return new p.a(e[0],e[1],e[2]);case 4:return new _.a(e[0],e[1],e[2],e[3])}}static addAttribute(e,t,n){if(m.isArray(n)){if(!this._convert_array_to_vector(n)){const e=\\\\\\\"attribute_value invalid\\\\\\\";throw console.error(e,n),new Error(e)}}const i=n,s=e.userData;s.attributes=s.attributes||{},s.attributes[t]=i}addAttribute(e,t){js.addAttribute(this._object,e,t)}addNumericAttrib(e,t){this.addAttribute(e,t)}setAttribValue(e,t){this.addAttribute(e,t)}addNumericVertexAttrib(e,t,n){var i;null==n&&(n=gs.default_value(t)),null===(i=this.coreGeometry())||void 0===i||i.addNumericAttrib(e,t,n)}attributeNames(){return Object.keys(this._object.userData.attributes)}attribNames(){return this.attributeNames()}hasAttrib(e){return this.attributeNames().includes(e)}renameAttrib(e,t){const n=this.attribValue(e);null!=n?(this.addAttribute(t,n),this.deleteAttribute(e)):console.warn(`attribute ${e} not found`)}deleteAttribute(e){delete this._object.userData.attributes[e]}static attribValue(e,t,n=0,i){if(\\\\\\\"ptnum\\\\\\\"===t)return n;if(e.userData&&e.userData.attributes){const n=e.userData.attributes[t];if(null==n){if(t==Vs)return e.name}else if(m.isArray(n)&&i)return i.fromArray(n),i;return n}return t==Vs?e.name:void 0}static stringAttribValue(e,t,n=0){const i=this.attribValue(e,t,n);if(null!=i)return m.isString(i)?i:`${i}`}attribValue(e,t){return js.attribValue(this._object,e,this._index,t)}stringAttribValue(e){return js.stringAttribValue(this._object,e,this._index)}name(){return this.attribValue(Vs)}humanType(){return _s.CONSTRUCTOR_NAMES_BY_CONSTRUCTOR_NAME[this._object.constructor.name]}attribTypes(){const e={};for(let t of this.attribNames()){const n=this.attribType(t);null!=n&&(e[t]=n)}return e}attribType(e){const t=this.attribValue(e);return m.isString(t)?cs.STRING:cs.NUMERIC}attribSizes(){const e={};for(let t of this.attribNames()){const n=this.attribSize(t);null!=n&&(e[t]=n)}return e}attribSize(e){const t=this.attribValue(e);return null==t?null:gs.attribSizeFromValue(t)}clone(){return js.clone(this._object)}static clone(e){const t=e.clone();var n=new Map,i=new Map;return js.parallelTraverse(e,t,(function(e,t){n.set(t,e),i.set(e,t)})),t.traverse((function(t){const s=n.get(t),r=t;if(r.geometry){const e=s.geometry;r.geometry=Bs.clone(e);const t=r.geometry;t.userData&&(t.userData=b.cloneDeep(e.userData))}if(r.material){r.material=s.material,Gs.applyCustomMaterials(t,r.material);const e=r.material;null==e.color&&(e.color=new M.a(1,1,1))}e.userData&&(t.userData=b.cloneDeep(s.userData));const o=s;o.animations&&(t.animations=o.animations.map((e=>e.clone())));const a=t;if(a.isSkinnedMesh){var c=a,l=s,u=l.skeleton.bones;c.skeleton=l.skeleton.clone(),c.bindMatrix.copy(l.bindMatrix);const e=u.map((function(e){return i.get(e)}));c.skeleton.bones=e,c.bind(c.skeleton,c.bindMatrix)}})),t}static parallelTraverse(e,t,n){n(e,t);for(var i=0;i<e.children.length;i++)this.parallelTraverse(e.children[i],t.children[i],n)}}const Hs={[Ei.ANIM]:class extends Ui{set_content(e){super.set_content(e)}setTimelineBuilder(e){return this.set_content(e)}timeline_builder(){return this.content()}coreContentCloned(){if(this._content)return this._content.clone()}},[Ei.COP]:class extends Ui{set_content(e){super.set_content(e)}texture(){return this._content}coreContent(){return this._content}coreContentCloned(){var e;const t=null===(e=this._content)||void 0===e?void 0:e.clone();return t&&(t.needsUpdate=!0),t}object(){return this.texture()}infos(){if(null!=this._content)return[this._content]}resolution(){return this._content&&this._content.image?[this._content.image.width,this._content.image.height]:[-1,-1]}},[Ei.EVENT]:class extends Ui{set_content(e){super.set_content(e)}},[Ei.GL]:class extends Ui{object(){return this._content}},[Ei.JS]:class extends Ui{object(){return this._content}},[Ei.MANAGER]:class extends Ui{set_content(e){super.set_content(e)}},[Ei.MAT]:class extends Ui{set_content(e){super.set_content(e)}set_material(e){null!=this._content&&this._content.dispose(),this.set_content(e)}has_material(){return this.has_content()}material(){return this.content()}},[Ei.OBJ]:class extends Ui{set_content(e){super.set_content(e)}set_object(e){return this.set_content(e)}has_object(){return this.has_content()}object(){return this.content()}},[Ei.POST]:class extends Ui{set_content(e){super.set_content(e)}render_pass(){return this._content}object(e={}){return this.render_pass()}},[Ei.ROP]:class extends Ui{set_content(e){super.set_content(e)}renderer(){return this._content}},[Ei.SOP]:class extends Ui{coreContentCloned(){if(this._content)return this._content.clone()}set_content(e){super.set_content(e)}firstObject(){if(this._content)return this._content.objects()[0]}firstCoreObject(){const e=this.firstObject();if(e)return new js(e,0)}firstGeometry(){const e=this.firstObject();return e?e.geometry:null}objectsCount(){return this._content?this._content.objects().length:0}objectsVisibleCount(){return this._content,0}objectsCountByType(){const e={},t=this._content;if(this._content&&t)for(let n of t.coreObjects()){const t=n.humanType();null==e[t]&&(e[t]=0),e[t]+=1}return e}objectsNamesByType(){const e={},t=this._content;if(this._content&&t)for(let n of t.coreObjects()){const t=n.humanType();e[t]=e[t]||[],e[t].push(n.name())}return e}pointAttributeNames(){let e=[];const t=this.firstGeometry();return t&&(e=Object.keys(t.attributes)),e}pointAttributeSizesByName(){let e={};const t=this.firstGeometry();return t&&Object.keys(t.attributes).forEach((n=>{const i=t.attributes[n];e[n]=i.itemSize})),e}objectAttributeSizesByName(){let e={};const t=this.firstCoreObject();if(t){const n=t.attribNames();for(let i of n){const n=t.attribSize(i);null!=n&&(e[i]=n)}}return e}pointAttributeTypesByName(){let e={};const t=this.firstGeometry();if(t){const n=new Bs(t);Object.keys(t.attributes).forEach((t=>{e[t]=n.attribType(t)}))}return e}objectAttributeTypesByName(){let e={};const t=this.firstCoreObject();if(t)for(let n of t.attribNames())e[n]=t.attribType(n);return e}objectAttributeNames(){let e=[];const t=this.firstObject();return t&&(e=Object.keys(t.userData.attributes||{})),e}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 qs{constructor(e){this.node=e,this._callbacks=[],this._callbacks_tmp=[];const t=Hs[e.context()];this._container=new t(this.node)}container(){return this._container}async compute(){var e,t;if(null===(t=null===(e=this.node.flags)||void 0===e?void 0:e.bypass)||void 0===t?void 0:t.active()){const e=await this.requestInputContainer(0)||this._container;return this.node.cookController.endCook(),e}return this.node.isDirty()?new Promise(((e,t)=>{this._callbacks.push(e),this.node.cookController.cookMain()})):this._container}async requestInputContainer(e){const t=this.node.io.inputs.input(e);return t?await t.compute():(this.node.states.error.set(`input ${e} required`),this.notifyRequesters(),null)}notifyRequesters(e){let t;for(this._callbacks_tmp=this._callbacks.slice(),this._callbacks.splice(0,this._callbacks.length),e||(e=this.node.containerController.container());t=this._callbacks_tmp.pop();)t(e);this.node.scene().cookController.removeNode(this.node)}}const Ws=Rn.performance.performanceManager();class Xs{constructor(e){this.cookController=e,this._inputs_start=0,this._params_start=0,this._cook_start=0,this._cooksCount=0,this._data={inputsTime:0,paramsTime:0,cookTime:0}}cooksCount(){return this._cooksCount}data2(){return this._data}active(){return this.cookController.performanceRecordStarted()}recordInputsStart(){this.active()&&(this._inputs_start=Ws.now())}recordInputsEnd(){this.active()&&(this._data.inputsTime=Ws.now()-this._inputs_start)}recordParamsStart(){this.active()&&(this._params_start=Ws.now())}recordParamsEnd(){this.active()&&(this._data.paramsTime=Ws.now()-this._params_start)}recordCookStart(){this.active()&&(this._cook_start=Ws.now())}recordCookEnd(){this.active()&&(this._data.cookTime=Ws.now()-this._cook_start,this._cooksCount+=1)}}class Ys{constructor(e){this.node=e,this._cooking=!1,this._performanceController=new Xs(this),this._inputs_evaluation_required=!0,this._core_performance=this.node.scene().performance}performanceRecordStarted(){return this._core_performance.started()}disallowInputsEvaluation(){this._inputs_evaluation_required=!1}isCooking(){return!0===this._cooking}_start_cook_if_no_errors(e){if(this.node.states.error.active())this.endCook();else try{this._performanceController.recordCookStart(),this.node.cook(e)}catch(e){this.node.states.error.set(`node internal error: '${e}'.`),Rn.warn(e),this.endCook()}}async cookMain(){if(this.isCooking())return;let e;this._initCookingState(),this.node.states.error.clear(),this.node.scene().cookController.addNode(this.node),e=this._inputs_evaluation_required?await this._evaluateInputs():[],this.node.params.paramsEvalRequired()&&await this._evaluateParams(),this._start_cook_if_no_errors(e)}async cookMainWithoutInputs(){this.node.scene().cookController.addNode(this.node),this.isCooking()?Rn.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(e){this._finalizeCookPerformance();const t=this.node.dirtyController.dirtyTimestamp();null==t||t===this._cooking_dirty_timestamp?(this.node.removeDirtyState(),this._terminateCookProcess()):(Rn.log(\\\\\\\"COOK AGAIN\\\\\\\",t,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 e=[];const t=this.node.io.inputs;this._inputs_evaluation_required&&(e=t.is_any_input_dirty()?await t.eval_required_inputs():await t.containers_without_evaluation());const n=t.inputs(),i=[];let s;for(let r=0;r<n.length;r++)s=e[r],s&&(t.cloneRequired(r)?i[r]=s.coreContentCloned():i[r]=s.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(e,t){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(e),this._on_cook_complete_hooks.push(t)}deregisterOnCookEnd(e){var t;if(!this._on_cook_complete_hook_names||!this._on_cook_complete_hooks)return;const n=null===(t=this._on_cook_complete_hook_names)||void 0===t?void 0:t.indexOf(e);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 e of this._on_cook_complete_hooks)e()}onCookEndCallbackNames(){return this._on_cook_complete_hook_names}}class $s{constructor(e){this.node=e}toJSON(e=!1){var t,n,i,s,r,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(e),override_cloned_state_allowed:this.node.io.inputs.overrideClonedStateAllowed(),inputs_clone_required_states:this.node.io.inputs.cloneRequiredStates(),flags:{display:null===(n=null===(t=this.node.flags)||void 0===t?void 0:t.display)||void 0===n?void 0:n.active(),bypass:null===(s=null===(i=this.node.flags)||void 0===i?void 0:i.bypass)||void 0===s?void 0:s.active(),optimize:null===(o=null===(r=this.node.flags)||void 0===r?void 0:r.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((e=>e.graphNodeId()))}maxInputsCount(){return this.node.io.inputs.maxInputsCount()}inputIds(){return this.node.io.inputs.inputs().map((e=>null!=e?e.graphNodeId():void 0))}inputConnectionOutputIndices(){var e;return null===(e=this.node.io.connections.inputConnections())||void 0===e?void 0:e.map((e=>null!=e?e.output_index:void 0))}namedInputConnectionPoints(){return this.node.io.inputs.namedInputConnectionPoints().map((e=>e.toJSON()))}namedOutputConnectionPoints(){return this.node.io.outputs.namedOutputConnectionPoints().map((e=>e.toJSON()))}to_json_params_from_names(e,t=!1){return e.map((e=>this.node.params.get(e).graphNodeId()))}to_json_params(e=!1){return this.to_json_params_from_names(this.node.params.names,e)}}var Qs,Js;!function(e){e.BOOLEAN=\\\\\\\"boolean\\\\\\\",e.BUTTON=\\\\\\\"button\\\\\\\",e.COLOR=\\\\\\\"color\\\\\\\",e.FLOAT=\\\\\\\"float\\\\\\\",e.FOLDER=\\\\\\\"folder\\\\\\\",e.INTEGER=\\\\\\\"integer\\\\\\\",e.OPERATOR_PATH=\\\\\\\"operator_path\\\\\\\",e.PARAM_PATH=\\\\\\\"param_path\\\\\\\",e.NODE_PATH=\\\\\\\"node_path\\\\\\\",e.RAMP=\\\\\\\"ramp\\\\\\\",e.STRING=\\\\\\\"string\\\\\\\",e.VECTOR2=\\\\\\\"vector2\\\\\\\",e.VECTOR3=\\\\\\\"vector3\\\\\\\",e.VECTOR4=\\\\\\\"vector4\\\\\\\"}(Qs||(Qs={})),function(e){e.VISIBLE_UPDATED=\\\\\\\"param_visible_updated\\\\\\\",e.RAW_INPUT_UPDATED=\\\\\\\"raw_input_updated\\\\\\\",e.VALUE_UPDATED=\\\\\\\"param_value_updated\\\\\\\",e.EXPRESSION_UPDATED=\\\\\\\"param_expression_update\\\\\\\",e.ERROR_UPDATED=\\\\\\\"param_error_updated\\\\\\\",e.DELETED=\\\\\\\"param_deleted\\\\\\\"}(Js||(Js={}));const Ks=\\\\\\\"dependentOnFoundNode\\\\\\\",Zs=\\\\\\\"visibleIf\\\\\\\";var er,tr;!function(e){e.TYPESCRIPT=\\\\\\\"typescript\\\\\\\"}(er||(er={})),function(e){e.AUDIO=\\\\\\\"audio\\\\\\\",e.TEXTURE_IMAGE=\\\\\\\"texture_image\\\\\\\",e.TEXTURE_VIDEO=\\\\\\\"texture_video\\\\\\\",e.GEOMETRY=\\\\\\\"geometry\\\\\\\",e.FONT=\\\\\\\"font\\\\\\\",e.SVG=\\\\\\\"svg\\\\\\\",e.JSON=\\\\\\\"json\\\\\\\"}(tr||(tr={}));class nr{constructor(e){this._param=e,this._programatic_visible_state=!0,this._callbackAllowed=!1,this._updateVisibilityAndRemoveDirtyBound=this.updateVisibilityAndRemoveDirty.bind(this),this._ui_data_dependency_set=!1}dispose(){var e;try{this._options.callback=void 0,this._options.callbackString=void 0}catch(e){}null===(e=this._visibility_graph_node)||void 0===e||e.dispose()}set(e){this._default_options=e,this._options=b.cloneDeep(this._default_options),this.post_set_options()}copy(e){this._default_options=b.cloneDeep(e.default()),this._options=b.cloneDeep(e.current()),this.post_set_options()}setOption(e,t){if(this._options[e]=t,this._param.components)for(let n of this._param.components)n.options.setOption(e,t)}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 e={},t=Object.keys(this._options);for(let n of t)if(!b.isEqual(this._options[n],this._default_options[n])){const t=b.cloneDeep(this._options[n]);Object.assign(e,{[n]:t})}return e}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 e=this.getCallback();if(!e)return;if(!this.node().scene().loadingController.loaded())return;const t=this.param().parent_param;t?t.options.executeCallback():e(this.node(),this.param())}getCallback(){if(this.hasCallback())return this._options.callback=this._options.callback||this.createCallbackFromString()}createCallbackFromString(){const e=this._options.callbackString;if(e){const t=new Function(\\\\\\\"node\\\\\\\",\\\\\\\"scene\\\\\\\",\\\\\\\"window\\\\\\\",\\\\\\\"location\\\\\\\",e);return()=>{t(this.node(),this.node().scene(),null,null)}}}colorConversion(){return this._options.conversion}makesNodeDirtyWhenDirty(){let e;if(null!=this.param().parent_param)return!1;let t=!0;return null!=(e=this._options.cook)&&(t=e),t}fileBrowseOption(){return this._options.fileBrowse}fileBrowseAllowed(){return null!=this.fileBrowseOption()}fileBrowseType(){const e=this.fileBrowseOption();return e?e.type:null}separatorBefore(){return this._options.separatorBefore}separatorAfter(){return this._options.separatorAfter}isExpressionForEntities(){const e=this._options.expression;return e&&e.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 e=this.menuOptions()||this.menuStringOptions();return e?e.entries:[]}isMultiline(){return!0===this._options.multiline}language(){return this._options.language}isCode(){return null!=this.language()}nodeSelectionOptions(){return this._options.nodeSelection}nodeSelectionContext(){const e=this.nodeSelectionOptions();if(e)return e.context}nodeSelectionTypes(){const e=this.nodeSelectionOptions();if(e)return e.types}dependentOnFoundNode(){return!(Ks in this._options)||this._options.dependentOnFoundNode}isSelectingParam(){return null!=this.paramSelectionOptions()}paramSelectionOptions(){return this._options.paramSelection}paramSelectionType(){const e=this.paramSelectionOptions();if(e){const t=e;if(!m.isBoolean(t))return t}}range(){return this._options.range||[0,1]}step(){return this._options.step}rangeLocked(){return this._options.rangeLocked||[!1,!1]}ensureInRange(e){const t=this.range();return e>=t[0]&&e<=t[1]?e:e<t[0]?!0===this.rangeLocked()[0]?t[0]:e:!0===this.rangeLocked()[1]?t[1]:e}isSpare(){return this._options.spare||!1}textureOptions(){return this._options.texture}textureAsEnv(){const e=this.textureOptions();return null!=e&&!0===e.env}isHidden(){return!0===this._options.hidden||!1===this._programatic_visible_state}isVisible(){return!this.isHidden()}setVisibleState(e){this._options.hidden=!e,this.param().emit(Js.VISIBLE_UPDATED)}label(){return this._options.label}isLabelHidden(){const e=this.param().type();return e===Qs.BUTTON||e===Qs.BOOLEAN&&this.isFieldHidden()}isFieldHidden(){return!1===this._options.field}uiDataDependsOnOtherParams(){return Zs in this._options}visibilityPredecessors(){const e=this._options.visibleIf;if(!e)return[];let t=[];t=m.isArray(e)?f.uniq(e.map((e=>Object.keys(e))).flat()):Object.keys(e);const n=this.param().node;return f.compact(t.map((e=>{const t=n.params.get(e);if(t)return t;console.error(`param ${e} 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 e=this.visibilityPredecessors();if(e.length>0){this._visibility_graph_node=new Qn(this.param().scene(),\\\\\\\"param_visibility\\\\\\\");for(let t of e)this._visibility_graph_node.addGraphInput(t);this._visibility_graph_node.addPostDirtyHook(\\\\\\\"_update_visibility_and_remove_dirty\\\\\\\",this._updateVisibilityAndRemoveDirtyBound)}}updateVisibilityAndRemoveDirty(){this.updateVisibility(),this.param().removeDirtyState()}async updateVisibility(){const e=this._options.visibleIf;if(e){const t=this.visibilityPredecessors(),n=t.map((e=>{if(e.isDirty())return e.compute()}));if(this._programatic_visible_state=!1,await Promise.all(n),m.isArray(e))for(let n of e){t.filter((e=>e.value==n[e.name()])).length==t.length&&(this._programatic_visible_state=!0)}else{const n=t.filter((t=>t.value==e[t.name()]));this._programatic_visible_state=n.length==t.length}this.param().emit(Js.VISIBLE_UPDATED)}}}class ir{constructor(e){this.param=e,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 e of this.param.components)e.emitController.blockEmit();return!0}unblockEmit(){if(this._blocked_emit=!1,this.param.isMultiple()&&this.param.components)for(let e of this.param.components)e.emitController.unblockEmit();return!0}blockParentEmit(){return this._blocked_parent_emit=!0,!0}unblockParentEmit(){return this._blocked_parent_emit=!1,!0}incrementCount(e){this._count_by_event_name[e]=this._count_by_event_name[e]||0,this._count_by_event_name[e]+=1}eventsCount(e){return this._count_by_event_name[e]||0}emit(e){this.emitAllowed()&&(this.param.emit(e),null!=this.param.parent_param&&!0!==this._blocked_parent_emit&&this.param.parent_param.emit(e))}}class sr{constructor(e){this.param=e}toJSON(){const e={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&&(e.components=this.param.components.map((e=>e.graphNodeId()))),e}rawInput(){return this.param.rawInputSerialized()}value(){return this.param.valueSerialized()}value_pre_conversion(){return this.param.valuePreConversionSerialized()}expression(){var e;return this.param.hasExpression()?null===(e=this.param.expressionController)||void 0===e?void 0:e.expression():void 0}error_message(){return this.param.states.error.message()}is_visible(){return this.param.options.isVisible()}}class rr{constructor(e){this.param=e}active(){const e=this.param.scene().timeController.graphNode.graphNodeId();return this.param.graphPredecessorIds().includes(e)}}class or{constructor(e){this.param=e}set(e){this._message!=e&&(this._message=e,this._message&&Rn.warn(this.param.path(),this._message),this.param.emitController.emit(Js.ERROR_UPDATED))}message(){return this._message}clear(){this.set(void 0)}active(){return null!=this._message}}class ar{constructor(e){this.param=e,this.timeDependent=new rr(this.param),this.error=new or(this.param)}}class cr extends Qn{constructor(e,t,n){var i;super(e.scene(),\\\\\\\"MethodDependency\\\\\\\"),this.param=e,this.path_argument=t,this.decomposed_path=n,this._update_from_name_change_bound=this._update_from_name_change.bind(this),null===(i=e.expressionController)||void 0===i||i.registerMethodDependency(this),this.addPostDirtyHook(\\\\\\\"_update_from_name_change\\\\\\\",this._update_from_name_change_bound)}_update_from_name_change(e){if(e&&this.decomposed_path){const t=e;this.decomposed_path.update_from_name_change(t);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 e of this.decomposed_path.named_nodes())if(e){const t=e;t.nameController&&this.addGraphInput(t.nameController.graph_node)}}set_jsep_node(e){this.jsep_node=e}set_resolved_graph_node(e){this.resolved_graph_node=e}set_unresolved_path(e){this.unresolved_path=e}static create(e,t,n,i){const s=m.isNumber(t),r=new cr(e,t,i);if(n)r.set_resolved_graph_node(n);else if(!s){const e=t;r.set_unresolved_path(e)}return r}}const lr=[];class ur extends Qn{constructor(e,t){super(e,\\\\\\\"BaseParam\\\\\\\"),this._options=new nr(this),this._emit_controller=new ir(this),this._is_computing=!1,this._node=t,this.initialize_param()}get options(){return this._options=this._options||new nr(this)}get emitController(){return this._emit_controller=this._emit_controller||new ir(this)}get expressionController(){return this._expression_controller}get serializer(){return this._serializer=this._serializer||new sr(this)}get states(){return this._states=this._states||new ar(this)}dispose(){var e,t;const n=this.graphPredecessors();for(let e of n)e instanceof cr&&e.dispose();null===(e=this._expression_controller)||void 0===e||e.dispose(),super.dispose(),null===(t=this._options)||void 0===t||t.dispose()}initialize_param(){}static type(){return Qs.FLOAT}type(){return this.constructor.type()}isNumeric(){return!1}setName(e){super.setName(e)}get value(){return this._value}copy_value(e){e.type()==this.type()?this._copy_value(e):console.warn(`cannot copy value from ${e.type()} to ${this.type()}`)}_copy_value(e){throw\\\\\\\"abstract method param._copy_value\\\\\\\"}valuePreConversionSerialized(){}convert(e){return null}static are_raw_input_equal(e,t){return!1}is_raw_input_equal(e){return this.constructor.are_raw_input_equal(this._raw_input,e)}static are_values_equal(e,t){return!1}is_value_equal(e){return this.constructor.are_values_equal(this.value,e)}_clone_raw_input(e){return e}set(e){this._raw_input=this._clone_raw_input(this._prefilter_invalid_raw_input(e)),this.emitController.emit(Js.RAW_INPUT_UPDATED),this.processRawInput()}_prefilter_invalid_raw_input(e){return e}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 ${this.path()}`),this.isDirty()){if(this._is_computing)return new Promise(((e,t)=>{this._compute_resolves=this._compute_resolves||[],this._compute_resolves.push(e)}));if(this._is_computing=!0,await this.processComputation(),this._is_computing=!1,this._compute_resolves){let e;for(;e=this._compute_resolves.pop();)e()}}}async processComputation(){}setInitValue(e){this._default_value=this._clone_raw_input(this._prefilter_invalid_raw_input(e))}_setupNodeDependencies(e){var t,n;if(e?(this.options.allowCallback(),this.parent_param||(this.options.makesNodeDirtyWhenDirty()?null===(n=e.params.params_node)||void 0===n||n.addGraphInput(this,!1):this.dirtyController.addPostDirtyHook(\\\\\\\"run callback\\\\\\\",(async()=>{await this.compute(),this.options.executeCallback()})))):this._node&&(null===(t=this._node.params.params_node)||void 0===t||t.removeGraphInput(this)),this.components)for(let t of this.components)t._setupNodeDependencies(e)}get node(){return this._node}parent(){return this.node}set_parent_param(e){e.addGraphInput(this,!1),this._parent_param=e}get parent_param(){return this._parent_param}has_parent_param(){return null!=this._parent_param}path(){var e;return(null===(e=this.node)||void 0===e?void 0:e.path())+\\\\\\\"/\\\\\\\"+this.name()}pathRelativeTo(e){const t=Wn.relativePath(e,this.node);return t.length>0?`${t}${Wn.SEPARATOR}${this.name()}`:this.name()}emit(e){this.emitController.emitAllowed()&&(this.emitController.incrementCount(e),this.scene().dispatchController.dispatch(this,e))}get components(){return this._components}componentNames(){return lr}isMultiple(){return this.componentNames().length>0}initComponents(){}hasExpression(){return null!=this.expressionController&&this.expressionController.active()}toJSON(){return this.serializer.toJSON()}}var hr=n(94),dr=n.n(hr);dr.a.addUnaryOp(\\\\\\\"@\\\\\\\");dr.a.addBinaryOp(\\\\\\\"**\\\\\\\",10);class pr{constructor(){}parse_expression(e){try{this.reset(),this.node=dr()(e)}catch(t){const n=`could not parse the expression '${e}' (error: ${t})`;this.error_message=n}}parse_expression_for_string_param(e){try{this.reset();const t=pr.string_value_elements(e),n=[];for(let e=0;e<t.length;e++){const i=t[e];let s;if(e%2==1)s=dr()(i);else{const e=i.replace(/\\\\'/g,\\\\\\\"\\\\\\\\'\\\\\\\");s={type:\\\\\\\"Literal\\\\\\\",value:`'${e}'`,raw:`'${e}'`}}n.push(s)}this.node={type:\\\\\\\"CallExpression\\\\\\\",arguments:n,callee:{type:\\\\\\\"Identifier\\\\\\\",name:\\\\\\\"strConcat\\\\\\\"}}}catch(t){const n=`could not parse the expression '${e}' (error: ${t})`;this.error_message=n}}static string_value_elements(e){return null!=e&&m.isString(e)?e.split(\\\\\\\"`\\\\\\\"):[]}reset(){this.node=void 0,this.error_message=void 0}}class _r{constructor(e){this.param=e,this._set_error_from_error_bound=this._set_error_from_error.bind(this)}clear_error(){this._error_message=void 0}set_error(e){this._error_message=this._error_message||e}_set_error_from_error(e){m.isString(e)?this._error_message=e:this._error_message=e.message}is_errored(){return null!=this._error_message}error_message(){return this._error_message}reset(){this._error_message=void 0}traverse_node(e){const t=`traverse_${e.type}`;if(this[t])return this[t](e);this.set_error(`expression unknown node type: ${e.type}`)}traverse_BinaryExpression(e){return`${this.traverse_node(e.left)} ${e.operator} ${this.traverse_node(e.right)}`}traverse_LogicalExpression(e){return`${this.traverse_node(e.left)} ${e.operator} ${this.traverse_node(e.right)}`}traverse_MemberExpression(e){return`${this.traverse_node(e.object)}.${this.traverse_node(e.property)}`}traverse_ConditionalExpression(e){return`(${this.traverse_node(e.test)}) ? (${this.traverse_node(e.consequent)}) : (${this.traverse_node(e.alternate)})`}traverse_Compound(e){const t=e.body;let n=[];for(let e=0;e<t.length;e++){const i=t[e];\\\\\\\"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(e){return`${e.raw}`}}class mr{constructor(){}reset(){this._attribute_names&&this._attribute_names.clear()}assign_attributes_lines(){var e;if(this._attribute_names){const t=[];return null===(e=this._attribute_names)||void 0===e||e.forEach((e=>{t.push(mr.assign_attribute_line(e))})),t.join(\\\\\\\";\\\\n\\\\\\\")}return\\\\\\\"\\\\\\\"}assign_arrays_lines(){var e;if(this._attribute_names){const t=[];return null===(e=this._attribute_names)||void 0===e||e.forEach((e=>{t.push(mr.assign_item_size_line(e)),t.push(mr.assign_array_line(e))})),t.join(\\\\\\\";\\\\n\\\\\\\")}return\\\\\\\"\\\\\\\"}attribute_presence_check_line(){var e;if(this._attribute_names){const t=[];if(null===(e=this._attribute_names)||void 0===e||e.forEach((e=>{const n=mr.var_attribute(e);t.push(n)})),t.length>0)return t.join(\\\\\\\" && \\\\\\\")}return\\\\\\\"true\\\\\\\"}add(e){this._attribute_names=this._attribute_names||new Set,this._attribute_names.add(e)}static assign_attribute_line(e){return`const ${this.var_attribute(e)} = entities[0].geometry().attributes['${e}']`}static assign_item_size_line(e){const t=this.var_attribute(e);return`const ${this.var_attribute_size(e)} = ${t}.itemSize`}static assign_array_line(e){const t=this.var_attribute(e);return`const ${this.var_array(e)} = ${t}.array`}static var_attribute(e){return`attrib_${e}`}static var_attribute_size(e){return`attrib_size_${e}`}static var_array(e){return`array_${e}`}var_attribute_size(e){return mr.var_attribute_size(e)}var_array(e){return mr.var_array(e)}}const fr={math_random:\\\\\\\"random\\\\\\\"},gr=Object.keys(Ns),vr={};[\\\\\\\"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((e=>{vr[e]=`Math.${e}`})),[\\\\\\\"cbrt\\\\\\\",\\\\\\\"hypot\\\\\\\",\\\\\\\"log10\\\\\\\",\\\\\\\"trunc\\\\\\\"].forEach((e=>{vr[e]=`Math.${e}`})),Object.keys(fr).forEach((e=>{const t=fr[e];vr[e]=`Math.${t}`})),[\\\\\\\"fit\\\\\\\",\\\\\\\"fit01\\\\\\\",\\\\\\\"fract\\\\\\\",\\\\\\\"deg2rad\\\\\\\",\\\\\\\"rad2deg\\\\\\\",\\\\\\\"rand\\\\\\\",\\\\\\\"clamp\\\\\\\"].forEach((e=>{vr[e]=`Core.Math.${e}`})),gr.forEach((e=>{vr[e]=`Core.Math.Easing.${e}`})),[\\\\\\\"precision\\\\\\\"].forEach((e=>{vr[e]=`Core.String.${e}`}));const yr={if:class{static if(e){return`(${e[0]}) ? (${e[1]}) : (${e[2]})`}}.if},xr={};[\\\\\\\"E\\\\\\\",\\\\\\\"LN2\\\\\\\",\\\\\\\"LN10\\\\\\\",\\\\\\\"LOG10E\\\\\\\",\\\\\\\"LOG2E\\\\\\\",\\\\\\\"PI\\\\\\\",\\\\\\\"SQRT1_2\\\\\\\",\\\\\\\"SQRT2\\\\\\\"].forEach((e=>{xr[e]=`Math.${e}`}));const br={x:0,y:1,z:2,w:3,r:0,g:1,b:2};class wr extends _r{constructor(e){super(e),this.param=e,this._attribute_requirements_controller=new mr,this.methods=[],this.method_index=-1,this.method_dependencies=[],this.immutable_dependencies=[]}parse_tree(e){if(this.reset(),null==e.error_message){try{if(this._attribute_requirements_controller.reset(),e.node){const t=this.traverse_node(e.node);t&&!this.is_errored()&&(this.function_main_string=t)}else console.warn(\\\\\\\"no parsed_tree.node\\\\\\\")}catch(e){console.warn(`error in expression for param ${this.param.path()}`),console.warn(e)}if(this.function_main_string)try{this.function=new Function(\\\\\\\"Core\\\\\\\",\\\\\\\"param\\\\\\\",\\\\\\\"methods\\\\\\\",\\\\\\\"_set_error_from_error\\\\\\\",`\\\\n\\\\t\\\\t\\\\t\\\\t\\\\ttry {\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t${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(e){console.warn(e),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 = ${this.function_main_string}\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tresolve(value)\\\\n\\\\t\\\\t\\\\t\\\\t} catch(e) {\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t_set_error_from_error(e)\\\\n\\\\t\\\\t\\\\t\\\\t\\\\treject()\\\\n\\\\t\\\\t\\\\t\\\\t}\\\\n\\\\t\\\\t\\\\t})\\\\n\\\\t\\\\t\\\\t`}eval_allowed(){return null!=this.function}eval_function(){if(this.function){this.clear_error();const e={Math:Os,String:Li};return this.function(e,this.param,this.methods,this._set_error_from_error_bound)}}traverse_CallExpression(e){const t=e.arguments.map((e=>this.traverse_node(e))),n=e.callee.name;if(n){const i=yr[n];if(i)return i(t);const s=`${t.join(\\\\\\\", \\\\\\\")}`,r=vr[n];if(r)return`${r}(${s})`;const o=Rn.expressionsRegister;if(o.getMethod(n)){const i=e.arguments[0],r=`return ${t[0]}`;let o,a=[];try{o=new Function(r),a=o()}catch{}return this._create_method_and_dependencies(n,a,i),`(await methods[${this.method_index}].processArguments([${s}]))`}{const e=`method not found (${n}), available methods are: ${o.availableMethods().join(\\\\\\\", \\\\\\\")}`;Rn.warn(e)}}this.set_error(`unknown method: ${n}`)}traverse_BinaryExpression(e){return`(${this.traverse_node(e.left)} ${e.operator} ${this.traverse_node(e.right)})`}traverse_LogicalExpression(e){return`(${this.traverse_node(e.left)} ${e.operator} ${this.traverse_node(e.right)})`}traverse_MemberExpression(e){return`${this.traverse_node(e.object)}.${this.traverse_node(e.property)}`}traverse_UnaryExpression(e){if(\\\\\\\"@\\\\\\\"===e.operator){let t,n,i=e.argument;switch(i.type){case\\\\\\\"Identifier\\\\\\\":t=i.name;break;case\\\\\\\"MemberExpression\\\\\\\":{const e=i,s=e.object,r=e.property;t=s.name,n=r.name;break}}if(t){if(t=gs.remapName(t),\\\\\\\"ptnum\\\\\\\"==t)return\\\\\\\"((entity != null) ? entity.index() : 0)\\\\\\\";{const e=this._attribute_requirements_controller.var_attribute_size(t),i=this._attribute_requirements_controller.var_array(t);if(this._attribute_requirements_controller.add(t),n){return`${i}[entity.index()*${e}+${br[n]}]`}return`${i}[entity.index()*${e}]`}}return console.warn(\\\\\\\"attribute not found\\\\\\\"),\\\\\\\"\\\\\\\"}return`${e.operator}${this.traverse_node(e.argument)}`}traverse_Literal(e){return`${e.raw}`}traverse_Identifier(e){if(\\\\\\\"$\\\\\\\"!=e.name[0])return e.name;{const t=e.name.substr(1),n=xr[t];if(n)return n;const i=`traverse_Identifier_${t}`;if(this[i])return this[i]();this.set_error(`identifier unknown: ${e.name}`)}}traverse_Identifier_F(){return this.immutable_dependencies.push(this.param.scene().timeController.graphNode),\\\\\\\"param.scene().timeController.frame()\\\\\\\"}traverse_Identifier_T(){return this.immutable_dependencies.push(this.param.scene().timeController.graphNode),\\\\\\\"param.scene().timeController.time()\\\\\\\"}traverse_Identifier_OS(){return`'${this.param.node.name()}'`}traverse_Identifier_CH(){return`'${this.param.name()}'`}traverse_Identifier_CEX(){return this._method_centroid(\\\\\\\"x\\\\\\\")}traverse_Identifier_CEY(){return this._method_centroid(\\\\\\\"y\\\\\\\")}traverse_Identifier_CEZ(){return this._method_centroid(\\\\\\\"z\\\\\\\")}_method_centroid(e){const t=[0,`'${e}'`].join(\\\\\\\", \\\\\\\");return this._create_method_and_dependencies(\\\\\\\"centroid\\\\\\\",0),`(await methods[${this.method_index}].processArguments([${t}]))`}_create_method_and_dependencies(e,t,n){const i=Rn.expressionsRegister,s=i.getMethod(e);if(!s){const t=`method not found (${e}), available methods are: ${i.availableMethods().join(\\\\\\\", \\\\\\\")}`;return this.set_error(t),void Rn.warn(t)}const r=new s(this.param);if(this.method_index+=1,this.methods[this.method_index]=r,r.require_dependency()){const e=r.findDependency(t);e?(n&&e.set_jsep_node(n),this.method_dependencies.push(e)):n&&m.isString(t)&&this.param.scene().missingExpressionReferencesController.register(this.param,n,t)}}}class Ar extends _r{constructor(e){super(e),this.param=e}parse_tree(e){if(null==e.error_message&&e.node)try{return this.traverse_node(e.node)}catch(e){this.set_error(\\\\\\\"could not traverse tree\\\\\\\")}else this.set_error(\\\\\\\"cannot parse tree\\\\\\\")}traverse_CallExpression(e){const t=`${e.arguments.map((e=>this.traverse_node(e))).join(\\\\\\\", \\\\\\\")}`;return`${e.callee.name}(${t})`}traverse_UnaryExpression(e){return`${e.operator}${this.traverse_node(e.argument)}`}traverse_Identifier(e){return`${e.name}`}}class Tr{constructor(e){this.param=e,this.cyclic_graph_detected=!1,this.method_dependencies=[]}set_error(e){this.error_message=this.error_message||e}reset(){this.param.graphDisconnectPredecessors(),this.method_dependencies.forEach((e=>{e.reset()})),this.method_dependencies=[]}update(e){this.cyclic_graph_detected=!1,this.connect_immutable_dependencies(e),this.method_dependencies=e.method_dependencies,this.handle_method_dependencies(),this.listen_for_name_changes()}connect_immutable_dependencies(e){e.immutable_dependencies.forEach((e=>{if(0==this.cyclic_graph_detected&&0==this.param.addGraphInput(e))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((e=>{0==this.cyclic_graph_detected&&this.handle_method_dependency(e)}))}handle_method_dependency(e){const t=e.resolved_graph_node;if(t&&!this.param.addGraphInput(t))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((e=>{e.listen_for_name_changes()}))}}class Er{constructor(e){this.param=e,this.parse_completed=!1,this.parse_started=!1,this.parsed_tree=new pr,this.function_generator=new wr(this.param),this.dependencies_controller=new Tr(this.param)}parse_expression(e){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 pr,this.reset(),this.param.type()==Qs.STRING?this.parsed_tree.parse_expression_for_string_param(e):this.parsed_tree.parse_expression(e),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(((e,t)=>{e(null)}));try{return await this.function_generator.eval_function()}catch(e){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 Ar(this.param);const e=this.expression_string_generator.parse_tree(this.parsed_tree);e?this.param.set(e):console.warn(\\\\\\\"failed to regenerate expression\\\\\\\")}}class Cr{constructor(e){this.param=e}dispose(){this._resetMethodDependencies()}_resetMethodDependencies(){var e,t;null===(e=this._method_dependencies_by_graph_node_id)||void 0===e||e.forEach((e=>{e.dispose()})),null===(t=this._method_dependencies_by_graph_node_id)||void 0===t||t.clear()}registerMethodDependency(e){this._method_dependencies_by_graph_node_id=this._method_dependencies_by_graph_node_id||new Map,this._method_dependencies_by_graph_node_id.set(e.graphNodeId(),e)}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(e,t=!0){var n;this.param.scene().missingExpressionReferencesController.deregister_param(this.param),this.param.scene().expressionsController.deregister_param(this.param),this._expression!=e&&(this._resetMethodDependencies(),this._expression=e,this._expression?(this._manager=this._manager||new Er(this.param),this._manager.parse_expression(this._expression)):null===(n=this._manager)||void 0===n||n.reset(),t&&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(e,t){var n,i;this.set_entities(e,t),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: ${null===(i=this._manager)||void 0===i?void 0:i.error_message()}`),this.reset_entities()}compute_expression_for_points(e,t){return this.compute_expression_for_entities(e,t)}compute_expression_for_objects(e,t){return this.compute_expression_for_entities(e,t)}entities(){return this._entities}entity_callback(){return this._entity_callback}set_entities(e,t){this._entities=e,this._entity_callback=t}reset_entities(){this._entities=void 0,this._entity_callback=void 0}}class Mr extends ur{isNumeric(){return!0}isDefault(){return this._raw_input==this._default_value}_prefilter_invalid_raw_input(e){return m.isArray(e)?e[0]:e}processRawInput(){this.states.error.clear();const e=this.convert(this._raw_input);null!=e?(this._expression_controller&&(this._expression_controller.set_expression(void 0,!1),this.emitController.emit(Js.EXPRESSION_UPDATED)),e!=this._value&&(this._update_value(e),this.setSuccessorsDirty(this))):m.isString(this._raw_input)?(this._expression_controller=this._expression_controller||new Cr(this),this._raw_input!=this._expression_controller.expression()&&(this._expression_controller.set_expression(this._raw_input),this.emitController.emit(Js.EXPRESSION_UPDATED))):this.states.error.set(`param input is invalid (${this.path()})`)}async processComputation(){var e;if((null===(e=this.expressionController)||void 0===e?void 0:e.active())&&!this.expressionController.requires_entities()){const e=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 t=this.convert(e);null!=t?(this.states.error.active()&&this.states.error.clear(),this._update_value(t)):this.states.error.set(`expression returns an invalid type (${e}) (${this.expressionController.expression()})`)}}}_update_value(e){this._value=e,this.parent_param&&this.parent_param.set_value_from_components(),this.options.executeCallback(),this.emitController.emit(Js.VALUE_UPDATED),this.removeDirtyState()}}class Nr extends Mr{static type(){return Qs.FLOAT}defaultValueSerialized(){return this._default_value}rawInputSerialized(){return this._raw_input}valueSerialized(){return this.value}_copy_value(e){this.set(e.valueSerialized())}_prefilter_invalid_raw_input(e){return m.isArray(e)?e[0]:m.isString(e)&&Li.isNumber(e)?parseFloat(e):e}static are_raw_input_equal(e,t){return e==t}static are_values_equal(e,t){return e==t}static convert(e){if(m.isNumber(e))return e;if(m.isBoolean(e))return e?1:0;if(Li.isNumber(e)){const t=parseFloat(e);if(m.isNumber(t))return t}return null}convert(e){const t=Nr.convert(e);return t?this.options.ensureInRange(t):t}}class Sr extends ur{constructor(){super(...arguments),this._components_contructor=Nr}get components(){return this._components}isNumeric(){return!0}isDefault(){for(let e of this.components)if(!e.isDefault())return!1;return!0}rawInput(){return this._components.map((e=>e.rawInput()))}rawInputSerialized(){return this._components.map((e=>e.rawInputSerialized()))}_copy_value(e){for(let t=0;t<this.components.length;t++){const n=this.components[t],i=e.components[t];n.copy_value(i)}}initComponents(){if(null!=this._components)return;let e=0;this._components=new Array(this.componentNames().length);for(let t of this.componentNames()){const n=new this._components_contructor(this.scene(),this._node);let i;i=m.isArray(this._default_value)?this._default_value[e]:this._default_value[t],n.options.copy(this.options),n.setInitValue(i),n.setName(`${this.name()}${t}`),n.set_parent_param(this),this._components[e]=n,e++}}async processComputation(){await this.compute_components(),this.set_value_from_components()}set_value_from_components(){}hasExpression(){var e;for(let t of this.components)if(null===(e=t.expressionController)||void 0===e?void 0:e.active())return!0;return!1}async compute_components(){const e=this.components,t=[];for(let n of e)n.isDirty()&&t.push(n.compute());await Promise.all(t),this.removeDirtyState()}_prefilter_invalid_raw_input(e){if(m.isArray(e))return e;{const t=e;return this.componentNames().map((()=>t))}}processRawInput(){const e=this.scene().cooker;e.block();const t=this.components;for(let e of t)e.emitController.blockParentEmit();const n=this._raw_input;let i=0;if(m.isArray(n))for(let e=0;e<t.length;e++){let s=n[e];null==s&&(s=i),t[e].set(s),i=s}else for(let e=0;e<t.length;e++){let s=n[this.componentNames()[e]];null==s&&(s=i),t[e].set(s),i=s}e.unblock();for(let e=0;e<t.length;e++)t[e].emitController.unblockParentEmit();this.emitController.emit(Js.VALUE_UPDATED)}}var Or;!function(e){e.NONE=\\\\\\\"no conversion\\\\\\\",e.GAMMA_TO_LINEAR=\\\\\\\"gamma -> linear\\\\\\\",e.LINEAR_TO_GAMMA=\\\\\\\"linear -> gamma\\\\\\\",e.SRGB_TO_LINEAR=\\\\\\\"sRGB -> linear\\\\\\\",e.LINEAR_TO_SRGB=\\\\\\\"linear -> sRGB\\\\\\\"}(Or||(Or={}));Or.NONE,Or.GAMMA_TO_LINEAR,Or.LINEAR_TO_GAMMA,Or.SRGB_TO_LINEAR,Or.LINEAR_TO_SRGB;class Lr{static set_hsv(e,t,n,i){e=A.a.euclideanModulo(e,1),t=A.a.clamp(t,0,1),n=A.a.clamp(n,0,1),i.setHSL(e,t*n/((e=(2-t)*n)<1?e:2-e),.5*e)}}const Pr=[\\\\\\\"r\\\\\\\",\\\\\\\"g\\\\\\\",\\\\\\\"b\\\\\\\"];class Rr extends Mr{static type(){return Qs.INTEGER}defaultValueSerialized(){return this._default_value}rawInputSerialized(){return this._raw_input}valueSerialized(){return this.value}_copy_value(e){this.set(e.valueSerialized())}_prefilter_invalid_raw_input(e){return m.isArray(e)?e[0]:m.isString(e)&&Li.isNumber(e)?parseInt(e):e}static are_raw_input_equal(e,t){return e==t}static are_values_equal(e,t){return e==t}static convert(e){if(m.isNumber(e))return Math.round(e);if(m.isBoolean(e))return e?1:0;if(Li.isNumber(e)){const t=parseInt(e);if(m.isNumber(t))return t}return null}convert(e){const t=Rr.convert(e);return t?this.options.ensureInRange(t):t}}class Ir{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(e,t){this._index+=1,e==t.name()&&(this._named_nodes[this._index]=t),this._graph_node_ids[this._index]=t.graphNodeId(),this._node_element_by_graph_node_id.set(t.graphNodeId(),e)}add_path_element(e){this._index+=1,this._path_elements[this._index]=e}named_graph_nodes(){return this._named_nodes}named_nodes(){const e=[];for(let t of this._named_nodes)if(t){const n=t;n.nameController&&e.push(n)}return e}update_from_name_change(e){this._named_nodes.map((e=>null==e?void 0:e.graphNodeId())).includes(e.graphNodeId())&&this._node_element_by_graph_node_id.set(e.graphNodeId(),e.name())}to_path(){const e=new Array(this._index);for(let t=0;t<=this._index;t++){const n=this._named_nodes[t];if(n){const i=this._node_element_by_graph_node_id.get(n.graphNodeId());i&&(e[t]=i)}else{const n=this._path_elements[t];n&&(e[t]=n)}}let t=e.join(Wn.SEPARATOR);const n=t[0];return n&&(Wn.NON_LETTER_PREFIXES.includes(n)||(t=`${Wn.SEPARATOR}${t}`)),t}}class Fr extends ur{constructor(){super(...arguments),this.decomposed_path=new Ir}}var Dr;!function(e){e.NODE=\\\\\\\"NODE\\\\\\\",e.PARAM=\\\\\\\"PARAM\\\\\\\"}(Dr||(Dr={}));class kr extends Fr{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 Qs.OPERATOR_PATH}defaultValueSerialized(){return this._default_value}rawInputSerialized(){return`${this._raw_input}`}valueSerialized(){return`${this.value}`}_copy_value(e){this.set(e.valueSerialized())}static are_raw_input_equal(e,t){return e==t}static are_values_equal(e,t){return e==t}isDefault(){return this._value==this._default_value}setNode(e){this.set(e.path())}processRawInput(){this._value!=this._raw_input&&(this._value=this._raw_input,this.setDirty(),this.emitController.emit(Js.VALUE_UPDATED))}async processComputation(){this.find_target()}find_target(){if(!this.node)return;const e=this._value;let t=null,n=null;const i=null!=e&&\\\\\\\"\\\\\\\"!==e,s=this.options.paramSelectionOptions()?Dr.PARAM:Dr.NODE;this.scene().referencesController.reset_reference_from_param(this),this.decomposed_path.reset(),i&&(s==Dr.PARAM?n=Wn.findParam(this.node,e,this.decomposed_path):t=Wn.findNode(this.node,e,this.decomposed_path));const r=s==Dr.PARAM?this._found_param:this._found_node,o=s==Dr.PARAM?n:t;if(this.scene().referencesController.set_named_nodes_from_param(this),t&&this.scene().referencesController.set_reference_from_param(this,t),(null==r?void 0:r.graphNodeId())!==(null==o?void 0:o.graphNodeId())){const e=this.options.dependentOnFoundNode();this._found_node&&e&&this.removeGraphInput(this._found_node),s==Dr.PARAM?(this._found_param=n,this._found_node=null):(this._found_node=t,this._found_param=null),t&&this._assign_found_node(t),n&&this._assign_found_param(n),this.options.executeCallback()}this.removeDirtyState()}_assign_found_node(e){const t=this.options.dependentOnFoundNode();this._is_node_expected_context(e)?this._is_node_expected_type(e)?(this._found_node_with_expected_type=e,t&&this.addGraphInput(e)):this.states.error.set(`node type is ${e.type()} but the params expects one of ${(this._expected_node_types()||[]).join(\\\\\\\", \\\\\\\")}`):this.states.error.set(`node context is ${e.context()} but the params expects a ${this._expected_context()}`)}_assign_found_param(e){this._is_param_expected_type(e)?this._found_param_with_expected_type=e:this.states.error.set(`param type is ${e.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(e){return this._found_node_with_expected_type}found_node_with_context_and_type(e,t){const n=this.found_node_with_context(e);if(n)if(m.isArray(t)){for(let e of t)if(n.type()==e)return n;this.states.error.set(`expected node type to be ${t.join(\\\\\\\", \\\\\\\")}, but was instead ${n.type()}`)}else{const e=t;if(n.type()==e)return n;this.states.error.set(`expected node type to be ${e}, but was instead ${n.type()}`)}}found_param_with_type(e){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(e){var t,n;const i=this._expected_context();if(null==i)return!0;return i==(null===(n=null===(t=e.parent())||void 0===t?void 0:t.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(e){const t=this._expected_node_types();return null==t||(null==t?void 0:t.includes(e.type()))}_is_param_expected_type(e){const t=this._expected_node_types();return null==t||t.includes(e.type())}notify_path_rebuild_required(e){this.decomposed_path.update_from_name_change(e);const t=this.decomposed_path.to_path();this.set(t)}notify_target_param_owner_params_updated(e){this.setDirty()}}var Br,zr=n(68);class Ur{constructor(e=0,t=0){this._position=e,this._value=t}toJSON(){return{position:this._position,value:this._value}}get position(){return this._position}get value(){return this._value}copy(e){this._position=e.position,this._value=e.value}clone(){const e=new Ur;return e.copy(this),e}is_equal(e){return this._position==e.position&&this._value==e.value}is_equal_json(e){return this._position==e.position&&this._value==e.value}from_json(e){this._position=e.position,this._value=e.value}static are_equal_json(e,t){return e.position==t.position&&e.value==t.value}static from_json(e){return new Ur(e.position,e.value)}}!function(e){e.LINEAR=\\\\\\\"linear\\\\\\\"}(Br||(Br={}));class Gr{constructor(e=Br.LINEAR,t=[]){this._interpolation=e,this._points=t,this._uuid=A.a.generateUUID()}get uuid(){return this._uuid}get interpolation(){return this._interpolation}get points(){return this._points}static from_json(e){const t=[];for(let n of e.points)t.push(Ur.from_json(n));return new Gr(e.interpolation,t)}toJSON(){return{interpolation:this._interpolation,points:this._points.map((e=>e.toJSON()))}}clone(){const e=new Gr;return e.copy(this),e}copy(e){this._interpolation=e.interpolation;let t=0;for(let n of e.points){const e=this._points[t];e?e.copy(n):this._points.push(n.clone()),t+=1}}is_equal(e){if(this._interpolation!=e.interpolation)return!1;const t=e.points;if(this._points.length!=t.length)return!1;let n=0;for(let e of this._points){const i=t[n];if(!e.is_equal(i))return!1;n+=1}return!0}is_equal_json(e){if(this._interpolation!=e.interpolation)return!1;if(this._points.length!=e.points.length)return!1;let t=0;for(let n of this._points){const i=e.points[t];if(!n.is_equal_json(i))return!1;t+=1}return!0}static are_json_equal(e,t){if(e.interpolation!=t.interpolation)return!1;if(e.points.length!=t.points.length)return!1;let n=0;for(let i of e.points){const e=t.points[n];if(!Ur.are_equal_json(i,e))return!1;n+=1}return!0}from_json(e){this._interpolation=e.interpolation;let t=0;for(let n of e.points){const e=this._points[t];e?e.from_json(n):this._points.push(Ur.from_json(n)),t+=1}}}const Vr=1024;class jr extends ur{constructor(){super(...arguments),this._texture_data=new Uint8Array(3072),this._ramp_texture=new T.a(this._texture_data,Vr,1,w.ic)}static type(){return Qs.RAMP}defaultValueSerialized(){return this._default_value instanceof Gr?this._default_value.toJSON():this._default_value}_clone_raw_input(e){return e instanceof Gr?e.clone():Gr.from_json(e).toJSON()}rawInputSerialized(){return this._raw_input instanceof Gr?this._raw_input.toJSON():Gr.from_json(this._raw_input).toJSON()}valueSerialized(){return this.value.toJSON()}_copy_value(e){this.set(e.valueSerialized())}static are_raw_input_equal(e,t){return e instanceof Gr?t instanceof Gr?e.is_equal(t):e.is_equal_json(t):t instanceof Gr?t.is_equal_json(e):Gr.are_json_equal(e,t)}static are_values_equal(e,t){return e.is_equal(t)}isDefault(){return this._default_value instanceof Gr?this.value.is_equal(this._default_value):this.value.is_equal_json(this._default_value)}processRawInput(){this._raw_input instanceof Gr?this._value?this._value.copy(this._raw_input):this._value=this._raw_input:this._value?this._value.from_json(this._raw_input):this._value=Gr.from_json(this._raw_input),this._reset_ramp_interpolant(),this._update_rampTexture(),this.options.executeCallback(),this.emitController.emit(Js.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 e=0,t=0,n=0;for(var i=0;i<1024;i++)e=3*i,t=i/Vr,n=this.value_at_position(t),this._texture_data[e]=255*n}static create_interpolant(e,t){const n=new Float32Array(1);return new zr.a(e,t,1,n)}interpolant(){return this._ramp_interpolant=this._ramp_interpolant||this._create_interpolant()}_create_interpolant(){const e=this.value.points,t=f.sortBy(e,(e=>e.position)),n=new Float32Array(t.length),i=new Float32Array(t.length);let s=0;for(let e of t)n[s]=e.position,i[s]=e.value,s++;return jr.create_interpolant(n,i)}value_at_position(e){return this.interpolant().evaluate(e)[0]}}jr.DEFAULT_VALUE=new Gr(Br.LINEAR,[new Ur(0,0),new Ur(1,1)]),jr.DEFAULT_VALUE_JSON=jr.DEFAULT_VALUE.toJSON();class Hr extends ur{static type(){return Qs.STRING}defaultValueSerialized(){return this._default_value}_clone_raw_input(e){return`${e}`}rawInputSerialized(){return`${this._raw_input}`}valueSerialized(){return`${this.value}`}_copy_value(e){this.set(e.value)}static are_raw_input_equal(e,t){return e==t}static are_values_equal(e,t){return e==t}isDefault(){return this._raw_input==this._default_value}convert(e){return m.isString(e)?e:`${e}`}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 Cr(this),this._raw_input!=this._expression_controller.expression()&&(this._expression_controller.set_expression(this._raw_input),this.setDirty(),this.emitController.emit(Js.EXPRESSION_UPDATED))):this._raw_input!=this._value&&(this._value=this._raw_input,this.removeDirtyState(),this.setSuccessorsDirty(this),this.emitController.emit(Js.VALUE_UPDATED),this.options.executeCallback(),this._expression_controller&&(this._expression_controller.set_expression(void 0,!1),this.emitController.emit(Js.EXPRESSION_UPDATED)))}async processComputation(){var e;if((null===(e=this.expressionController)||void 0===e?void 0:e.active())&&!this.expressionController.requires_entities()){const e=await this.expressionController.compute_expression();if(this.expressionController.is_errored())this.states.error.set(`expression error: ${this.expressionController.error_message()}`);else{const t=this.convert(e);null!=t?(this._value=t,this.emitController.emit(Js.VALUE_UPDATED),this.options.executeCallback()):this.states.error.set(`expression returns an invalid type (${e})`),this.removeDirtyState()}}}_value_elements(e){return pr.string_value_elements(e)}}const qr=[\\\\\\\"x\\\\\\\",\\\\\\\"y\\\\\\\"];const Wr=[\\\\\\\"x\\\\\\\",\\\\\\\"y\\\\\\\",\\\\\\\"z\\\\\\\"];const Xr=[\\\\\\\"x\\\\\\\",\\\\\\\"y\\\\\\\",\\\\\\\"z\\\\\\\",\\\\\\\"w\\\\\\\"];const Yr={[Qs.BOOLEAN]:class extends Mr{static type(){return Qs.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(e){this.set(e.value)}static are_raw_input_equal(e,t){return e==t}static are_values_equal(e,t){return e==t}convert(e){if(m.isBoolean(e))return e;if(m.isNumber(e))return e>=1;if(m.isString(e)){if(Li.isBoolean(e))return Li.toBoolean(e);if(Li.isNumber(e)){return parseFloat(e)>=1}}return null}},[Qs.BUTTON]:class extends ur{static type(){return Qs.BUTTON}defaultValueSerialized(){return this._default_value}rawInputSerialized(){return this._raw_input}valueSerialized(){return this.value}_copy_value(e){}static are_raw_input_equal(e,t){return!0}static are_values_equal(e,t){return!0}async pressButton(){(this.node.isDirty()||this.node.cookController.isCooking())&&await this.node.compute(),this.options.executeCallback()}},[Qs.COLOR]:class extends Sr{constructor(){super(...arguments),this._value=new M.a,this._value_pre_conversion=new M.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 Qs.COLOR}componentNames(){return Pr}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(e){e.value.toArray(this._copied_value),this.set(this._copied_value)}_clone_raw_input(e){if(e instanceof M.a)return e.clone();{const t=[e[0],e[1],e[2]];return null==t[0]&&(t[0]=t[0]||0),null==t[1]&&(t[1]=t[1]||t[0]),null==t[2]&&(t[2]=t[2]||t[1]),t}}static are_raw_input_equal(e,t){return e instanceof M.a?t instanceof M.a?e.equals(t):e.r==t[0]&&e.g==t[1]&&e.b==t[2]:t instanceof M.a?e[0]==t.r&&e[1]==t.g&&e[2]==t.b:e[0]==t[0]&&e[1]==t[1]&&e[2]==t[2]}static are_values_equal(e,t){return e.equals(t)}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 e=this.options.colorConversion();if(null!=e&&e!=Or.NONE){switch(e){case Or.GAMMA_TO_LINEAR:return void this._value.convertGammaToLinear();case Or.LINEAR_TO_GAMMA:return void this._value.convertLinearToGamma();case Or.SRGB_TO_LINEAR:return void this._value.convertSRGBToLinear();case Or.LINEAR_TO_SRGB:return void this._value.convertLinearToSRGB()}Ri.unreachable(e)}this._value_serialized_dirty=!0}},[Qs.FLOAT]:Nr,[Qs.FOLDER]:class extends ur{static type(){return Qs.FOLDER}defaultValueSerialized(){return this._default_value}rawInputSerialized(){return this._raw_input}valueSerialized(){return this.value}_copy_value(e){}static are_raw_input_equal(e,t){return!0}static are_values_equal(e,t){return!0}},[Qs.INTEGER]:Rr,[Qs.OPERATOR_PATH]:kr,[Qs.PARAM_PATH]:class extends Fr{static type(){return Qs.PARAM_PATH}initialize_param(){this._value=new qn}defaultValueSerialized(){return this._default_value}rawInputSerialized(){return`${this._raw_input}`}valueSerialized(){return`${this.value}`}_copy_value(e){this.set(e.valueSerialized())}static are_raw_input_equal(e,t){return e==t}static are_values_equal(e,t){return e==t}isDefault(){return this._raw_input==this._default_value}setParam(e){this.set(e.path())}processRawInput(){this._value.path()!=this._raw_input&&(this._value.set_path(this._raw_input),this.find_target(),this.setDirty(),this.emitController.emit(Js.VALUE_UPDATED))}async processComputation(){this.find_target()}find_target(){if(!this.node)return;const e=this._raw_input;let t=null;const n=null!=e&&\\\\\\\"\\\\\\\"!==e;this.scene().referencesController.reset_reference_from_param(this),this.decomposed_path.reset(),n&&(t=Wn.findParam(this.node,e,this.decomposed_path));const i=this._value.param(),s=t;if(this.scene().referencesController.set_named_nodes_from_param(this),t&&this.scene().referencesController.set_reference_from_param(this,t),(null==i?void 0:i.graphNodeId())!==(null==s?void 0:s.graphNodeId())){const e=this.options.dependentOnFoundNode(),n=this._value.param();n&&e&&this.removeGraphInput(n),t?this._assign_found_node(t):this._value.set_param(null),this.options.executeCallback()}this.removeDirtyState()}_assign_found_node(e){const t=this.options.dependentOnFoundNode();this._value.set_param(e),t&&this.addGraphInput(e)}notify_path_rebuild_required(e){this.decomposed_path.update_from_name_change(e);const t=this.decomposed_path.to_path();this.set(t)}notify_target_param_owner_params_updated(e){this.setDirty()}},[Qs.NODE_PATH]:class extends Fr{static type(){return Qs.NODE_PATH}initialize_param(){this._value=new Hn}defaultValueSerialized(){return this._default_value}rawInputSerialized(){return`${this._raw_input}`}valueSerialized(){return`${this.value}`}_copy_value(e){this.set(e.valueSerialized())}static are_raw_input_equal(e,t){return e==t}static are_values_equal(e,t){return e==t}isDefault(){return this._raw_input==this._default_value}setNode(e){this.set(e.path())}processRawInput(){this._value.path()!=this._raw_input&&(this._value.set_path(this._raw_input),this._findTarget(),this.setDirty(),this.emitController.emit(Js.VALUE_UPDATED))}async processComputation(){this._findTarget()}_findTarget(){if(!this.node)return;const e=this._raw_input;let t=null;const n=null!=e&&\\\\\\\"\\\\\\\"!==e;this.scene().referencesController.reset_reference_from_param(this),this.decomposed_path.reset(),n&&(t=Wn.findNode(this.node,e,this.decomposed_path));const i=this._value.node(),s=t;if(this.scene().referencesController.set_named_nodes_from_param(this),t&&this.scene().referencesController.set_reference_from_param(this,t),(null==i?void 0:i.graphNodeId())!==(null==s?void 0:s.graphNodeId())){const e=this.options.dependentOnFoundNode(),n=this._value.node();n&&e&&this.removeGraphInput(n),t?this._assign_found_node(t):this._value.set_node(null),this.options.executeCallback()}n&&!t&&this.scene().loadingController.loaded()&&n&&this.states.error.set(`no node found at path '${e}'`),this.removeDirtyState()}_assign_found_node(e){const t=this.options.dependentOnFoundNode();this._isNodeExpectedContext(e)?this._is_node_expected_type(e)?(this.states.error.clear(),this._value.set_node(e),t&&this.addGraphInput(e)):this.states.error.set(`node type is ${e.type()} but the params expects one of ${(this._expected_node_types()||[]).join(\\\\\\\", \\\\\\\")}`):this.states.error.set(`node context is ${e.context()} but the params expects a ${this._expectedContext()}`)}_expectedContext(){return this.options.nodeSelectionContext()}_isNodeExpectedContext(e){var t,n;const i=this._expectedContext();if(null==i)return!0;return i==(null===(n=null===(t=e.parent())||void 0===t?void 0:t.childrenController)||void 0===n?void 0:n.context)}_expected_node_types(){return this.options.nodeSelectionTypes()}_is_node_expected_type(e){const t=this._expected_node_types();return null==t||(null==t?void 0:t.includes(e.type()))}notify_path_rebuild_required(e){this.decomposed_path.update_from_name_change(e);const t=this.decomposed_path.to_path();this.set(t)}notify_target_param_owner_params_updated(e){this.setDirty()}},[Qs.RAMP]:jr,[Qs.STRING]:Hr,[Qs.VECTOR2]:class extends Sr{constructor(){super(...arguments),this._value=new d.a,this._copied_value=[0,0]}static type(){return Qs.VECTOR2}componentNames(){return qr}defaultValueSerialized(){return m.isArray(this._default_value)?this._default_value:this._default_value.toArray()}valueSerialized(){return this.value.toArray()}_copy_value(e){e.value.toArray(this._copied_value),this.set(this._copied_value)}_clone_raw_input(e){if(e instanceof d.a)return e.clone();{const t=[e[0],e[1]];return null==t[0]&&(t[0]=t[0]||0),null==t[1]&&(t[1]=t[1]||t[0]),t}}static are_raw_input_equal(e,t){return e instanceof d.a?t instanceof d.a?e.equals(t):e.x==t[0]&&e.y==t[1]:t instanceof d.a?e[0]==t.x&&e[1]==t.y:e[0]==t[0]&&e[1]==t[1]}static are_values_equal(e,t){return e.equals(t)}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}},[Qs.VECTOR3]:class extends Sr{constructor(){super(...arguments),this._value=new p.a,this._copied_value=[0,0,0]}static type(){return Qs.VECTOR3}componentNames(){return Wr}defaultValueSerialized(){return m.isArray(this._default_value)?this._default_value:this._default_value.toArray()}valueSerialized(){return this.value.toArray()}_copy_value(e){e.value.toArray(this._copied_value),this.set(this._copied_value)}_clone_raw_input(e){if(e instanceof p.a)return e.clone();{const t=[e[0],e[1],e[2]];return null==t[0]&&(t[0]=t[0]||0),null==t[1]&&(t[1]=t[1]||t[0]),null==t[2]&&(t[2]=t[2]||t[1]),t}}static are_raw_input_equal(e,t){return e instanceof p.a?t instanceof p.a?e.equals(t):e.x==t[0]&&e.y==t[1]&&e.z==t[2]:t instanceof p.a?e[0]==t.x&&e[1]==t.y&&e[2]==t.z:e[0]==t[0]&&e[1]==t[1]&&e[2]==t[2]}static are_values_equal(e,t){return e.equals(t)}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}},[Qs.VECTOR4]:class extends Sr{constructor(){super(...arguments),this._value=new _.a,this._copied_value=[0,0,0,0]}static type(){return Qs.VECTOR4}componentNames(){return Xr}defaultValueSerialized(){return m.isArray(this._default_value)?this._default_value:this._default_value.toArray()}valueSerialized(){return this.value.toArray()}_copy_value(e){e.value.toArray(this._copied_value),this.set(this._copied_value)}_clone_raw_input(e){if(e instanceof _.a)return e.clone();{const t=[e[0],e[1],e[2],e[3]];return null==t[0]&&(t[0]=t[0]||0),null==t[1]&&(t[1]=t[1]||t[0]),null==t[2]&&(t[2]=t[2]||t[1]),null==t[3]&&(t[3]=t[3]||t[2]),t}}static are_raw_input_equal(e,t){return e instanceof _.a?t instanceof _.a?e.equals(t):e.x==t[0]&&e.y==t[1]&&e.z==t[2]&&e.w==t[3]:t instanceof _.a?e[0]==t.x&&e[1]==t.y&&e[2]==t.z&&e[3]==t.w:e[0]==t[0]&&e[1]==t[1]&&e[2]==t[2]&&e[3]==t[3]}static are_values_equal(e,t){return e.equals(t)}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 $r{dispose(){this._callback=void 0}params(){return this._params}callback(){return this._callback}init(e,t){if(this._params=e,t)this._callback=t;else{const e=this._params[0];switch(e.type()){case Qs.STRING:return this._handle_string_param(e);case Qs.OPERATOR_PATH:return this._handle_operator_path_param(e);case Qs.NODE_PATH:return this._handle_node_path_param(e);case Qs.PARAM_PATH:return this._handle_param_path_param(e);case Qs.FLOAT:case Qs.INTEGER:return this._handle_number_param(e)}}}_handle_string_param(e){this._callback=()=>e.value}_handle_operator_path_param(e){this._callback=()=>e.value}_handle_node_path_param(e){this._callback=()=>e.value.path()}_handle_param_path_param(e){this._callback=()=>e.value.path()}_handle_number_param(e){this._callback=()=>`${e.value}`}}class Qr{constructor(e){this.node=e,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 $r}hasLabelController(){return null!=this._label_controller}dispose(){var e;this._params_node&&this._params_node.dispose();for(let e of this.all)e.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===(e=this._label_controller)||void 0===e||e.dispose()}initDependencyNode(){this._params_node||(this._params_node=new Qn(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(Jn.PARAMS_UPDATED)}updateParams(e){let t=!1,n=!1;if(e.namesToDelete)for(let t of e.namesToDelete)this.has(t)&&(this._deleteParam(t),n=!0);if(e.toAdd)for(let n of e.toAdd){const e=this.addParam(n.type,n.name,n.init_value,n.options);e&&(null!=n.raw_input&&e.set(n.raw_input),t=!0)}(n||t)&&this.postCreateSpareParams()}_initFromParamsConfig(){const e=this.node.paramsConfig;let t=!1;if(e)for(let n of Object.keys(e)){const i=e[n];let s;this.node.params_init_value_overrides&&(s=this.node.params_init_value_overrides[n],t=!0),this.addParam(i.type,n,i.init_value,i.options,s)}t&&this.node.setDirty(),this.node.params_init_value_overrides=void 0}_initParamAccessors(){let e=Object.getOwnPropertyNames(this.node.pv);this._removeUnneededAccessors(e),e=Object.getOwnPropertyNames(this.node.pv);for(let t of this.all){const n=t.options.isSpare();(!e.includes(t.name())||n)&&(Object.defineProperty(this.node.pv,t.name(),{get:()=>t.value,configurable:n}),Object.defineProperty(this.node.p,t.name(),{get:()=>t,configurable:n}))}}_removeUnneededAccessors(e){const t=this._param_names,n=[];for(let i of e)t.includes(i)||n.push(i);for(let e of n)Object.defineProperty(this.node.pv,e,{get:()=>{},configurable:!0}),Object.defineProperty(this.node.p,e,{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(e,t,n){const i=this.param_with_type(e,n);i?i.set(t):Rn.warn(`param ${e} not found with type ${n}`)}set_float(e,t){this.set_with_type(e,t,Qs.FLOAT)}set_vector3(e,t){this.set_with_type(e,t,Qs.VECTOR3)}has_param(e){return null!=this._params_by_name[e]}has(e){return this.has_param(e)}get(e){return this.param(e)}param_with_type(e,t){const n=this.param(e);if(n&&n.type()==t)return n}get_float(e){return this.param_with_type(e,Qs.FLOAT)}get_operator_path(e){return this.param_with_type(e,Qs.OPERATOR_PATH)}value(e){var t;return null===(t=this.param(e))||void 0===t?void 0:t.value}value_with_type(e,t){var n;return null===(n=this.param_with_type(e,t))||void 0===n?void 0:n.value}boolean(e){return this.value_with_type(e,Qs.BOOLEAN)}float(e){return this.value_with_type(e,Qs.FLOAT)}integer(e){return this.value_with_type(e,Qs.INTEGER)}string(e){return this.value_with_type(e,Qs.STRING)}vector2(e){return this.value_with_type(e,Qs.VECTOR2)}vector3(e){return this.value_with_type(e,Qs.VECTOR3)}color(e){return this.value_with_type(e,Qs.COLOR)}param(e){const t=this._params_by_name[e];return null!=t?t:(Rn.warn(`tried to access param '${e}' in node ${this.node.path()}, but existing params are: ${this.names} on node ${this.node.path()}`),null)}_deleteParam(e){const t=this._params_by_name[e];if(!t)throw new Error(`param '${e}' does not exist on node ${this.node.path()}`);if(this._params_node&&this._params_node.removeGraphInput(this._params_by_name[e]),t._setupNodeDependencies(null),delete this._params_by_name[e],t.isMultiple()&&t.components)for(let e of t.components){const t=e.name();delete this._params_by_name[t]}}addParam(e,t,n,i={},s){const r=i.spare||!1;!1!==this._param_create_mode||r||Rn.warn(`node ${this.node.path()} (${this.node.type()}) param '${t}' cannot be created outside of create_params`),null==this.node.scene()&&Rn.warn(`node ${this.node.path()} (${this.node.type()}) has no scene assigned`);const o=Yr[e];if(null!=o){const a=this._params_by_name[t];a&&(r?a.type()!=e&&this._deleteParam(a.name()):Rn.warn(`a param named ${t} already exists`,this.node));const c=new o(this.node.scene(),this.node);if(c.options.set(i),c.setName(t),c.setInitValue(n),c.initComponents(),null==s)c.set(n);else if(c.options.isExpressionForEntities()&&c.set(n),null!=s.raw_input)c.set(s.raw_input);else if(null!=s.simple_data)c.set(s.simple_data);else if(null!=s.complex_data){const e=s.complex_data.raw_input;e?c.set(e):c.set(n);const t=s.complex_data.overriden_options;if(null!=t){const e=Object.keys(t);for(let n of e)c.options.setOption(n,t[n])}}if(c._setupNodeDependencies(this.node),this._params_by_name[c.name()]=c,c.isMultiple()&&c.components)for(let e of c.components)this._params_by_name[e.name()]=e;return this._params_added_since_last_params_eval=!0,c}}_updateCaches(){this._params_list=Object.values(this._params_by_name),this._param_names=Object.keys(this._params_by_name),this._non_spare_params=Object.values(this._params_by_name).filter((e=>!e.options.isSpare())),this._spare_params=Object.values(this._params_by_name).filter((e=>e.options.isSpare())),this._non_spare_param_names=Object.values(this._params_by_name).filter((e=>!e.options.isSpare())).map((e=>e.name())),this._spare_param_names=Object.values(this._params_by_name).filter((e=>e.options.isSpare())).map((e=>e.name()))}async _evalParam(e){e.isDirty()&&(await e.compute(),e.states.error.active()&&this.node.states.error.set(`param '${e.name()}' error: ${e.states.error.message()}`))}async evalParams(e){const t=[];for(let n of e)n.isDirty()&&t.push(this._evalParam(n));await Promise.all(t),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 e;this.paramsEvalRequired()&&(await this.evalParams(this._params_list),null===(e=this._params_node)||void 0===e||e.removeDirtyState(),this._params_added_since_last_params_eval=!1)}onParamsCreated(e,t){if(this._params_created)t();else{if(this._post_create_params_hook_names&&this._post_create_params_hook_names.includes(e))return void Rn.error(`hook name ${e} already exists`);this._post_create_params_hook_names=this._post_create_params_hook_names||[],this._post_create_params_hook_names.push(e),this._post_create_params_hooks=this._post_create_params_hooks||[],this._post_create_params_hooks.push(t)}}addOnSceneLoadHook(e,t){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(e)?Rn.warn(`hook with name ${e} already exists`,this.node):(this._on_scene_load_hook_names.push(e),this._on_scene_load_hooks.push(t))}_runPostCreateParamsHooks(){if(this._post_create_params_hooks)for(let e of this._post_create_params_hooks)e()}runOnSceneLoadHooks(){if(this._on_scene_load_hooks)for(let e of this._on_scene_load_hooks)e()}}class Jr{constructor(){}}class Kr{constructor(e,t,n=0,i=0){if(this._node_src=e,this._node_dest=t,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=Kr._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 e=this._node_src,t=this._output_index;return e.io.outputs.namedOutputConnectionPoints()[t]}dest_connection_point(){const e=this._node_dest,t=this._input_index;return e.io.inputs.namedInputConnectionPoints()[t]}disconnect(e={}){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===e.setInput&&this._node_dest.io.inputs.setInput(this._input_index,null)}}Kr._next_id=0;class Zr{constructor(e){this.inputs_controller=e,this._clone_required_states=[],this._overridden=!1,this.node=e.node}initInputsClonedState(e){m.isArray(e)?this._cloned_states=e:this._cloned_state=e,this._update_clone_required_state()}overrideClonedStateAllowed(){if(this._cloned_states)for(let e of this._cloned_states)if(e==Ti.FROM_NODE)return!0;return!!this._cloned_state&&this._cloned_state==Ti.FROM_NODE}cloneRequiredState(e){return this._clone_required_states[e]}cloneRequiredStates(){return this._clone_required_states}_get_clone_required_state(e){const t=this._cloned_states;if(t){const n=t[e];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(e){switch(e){case Ti.ALWAYS:return!0;case Ti.NEVER:return!1;case Ti.FROM_NODE:return!this._overridden}return Ri.unreachable(e)}overrideClonedState(e){this._overridden=e,this._update_clone_required_state(),this.node.emit(Jn.OVERRIDE_CLONABLE_STATE_UPDATE),this.node.setDirty()}overriden(){return this._overridden}_update_clone_required_state(){if(this._cloned_states){const e=[];for(let t=0;t<this._cloned_states.length;t++)e[t]=this._get_clone_required_state(t);this._clone_required_states=e}else if(this._cloned_state){const e=this.inputs_controller.maxInputsCount(),t=[];for(let n=0;n<e;n++)t[n]=this._get_clone_required_state(n);this._clone_required_states=t}else;}}class eo{constructor(e){this.node=e,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 e of this._graph_node_inputs)e&&e.dispose();this._on_update_hooks=void 0,this._on_update_hook_names=void 0}set_depends_on_inputs(e){this._depends_on_inputs=e}set_min_inputs_count(e){this._min_inputs_count=e}set_max_inputs_count(e){0==this._max_inputs_count&&(this._maxInputsCountOnInput=e),this._max_inputs_count=e,this.init_graph_node_inputs()}namedInputConnectionPointsByName(e){if(this._named_input_connection_points)for(let t of this._named_input_connection_points)if(t&&t.name()==e)return t}setNamedInputConnectionPoints(e){this._has_named_inputs=!0;const t=this.node.io.connections.inputConnections();if(t)for(let n of t)n&&n.input_index>=e.length&&n.disconnect({setInput:!0});this._named_input_connection_points=e,this.set_min_inputs_count(0),this.set_max_inputs_count(e.length),this.init_graph_node_inputs(),this.node.emit(Jn.NAMED_INPUTS_UPDATED)}hasNamedInputs(){return this._has_named_inputs}namedInputConnectionPoints(){return this._named_input_connection_points||[]}init_graph_node_inputs(){for(let e=0;e<this._max_inputs_count;e++)this._graph_node_inputs[e]=this._graph_node_inputs[e]||this._create_graph_node_input(e)}_create_graph_node_input(e){const t=new Qn(this.node.scene(),`input_${e}`);return this._graph_node||(this._graph_node=new Qn(this.node.scene(),\\\\\\\"inputs\\\\\\\"),this.node.addGraphInput(this._graph_node,!1)),this._graph_node.addGraphInput(t,!1),t}maxInputsCount(){return this._max_inputs_count||0}maxInputsCountOverriden(){return this._max_inputs_count!=this._maxInputsCountOnInput}input_graph_node(e){return this._graph_node_inputs[e]}setCount(e,t){null==t&&(t=e),this.set_min_inputs_count(e),this.set_max_inputs_count(t),this.init_connections_controller_inputs()}init_connections_controller_inputs(){this.node.io.connections.initInputs()}is_any_input_dirty(){var e;return(null===(e=this._graph_node)||void 0===e?void 0:e.isDirty())||!1}async containers_without_evaluation(){const e=[];for(let t=0;t<this._inputs.length;t++){const n=this._inputs[t];let i;n&&(i=await n.compute()),e.push(i)}return e}existing_input_indices(){const e=[];if(this._max_inputs_count>0)for(let t=0;t<this._inputs.length;t++)this._inputs[t]&&e.push(t);return e}async eval_required_inputs(){var e;let t=[];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 e=0;e<this._inputs.length;e++)i=this._inputs[e],i&&n.push(this.eval_required_input(e));t=await Promise.all(n),null===(e=this._graph_node)||void 0===e||e.removeDirtyState()}}return t}async eval_required_input(e){let t;const n=this.input(e);if(n&&(t=await n.compute(),this._graph_node_inputs[e].removeDirtyState()),t&&t.coreContent());else{const t=this.input(e);if(t){const n=t.states.error.message();n&&this.node.states.error.set(`input ${e} is invalid (error: ${n})`)}}return t}get_named_input_index(e){var t;if(this._named_input_connection_points)for(let n=0;n<this._named_input_connection_points.length;n++)if((null===(t=this._named_input_connection_points[n])||void 0===t?void 0:t.name())==e)return n;return-1}get_input_index(e){if(m.isString(e)){if(this.hasNamedInputs())return this.get_named_input_index(e);throw new Error(`node ${this.node.path()} has no named inputs`)}return e}setInput(e,t,n=0){const i=this.get_input_index(e)||0;if(i<0){const t=`invalid input (${e}) for node ${this.node.path()}`;throw console.warn(t),new Error(t)}let s=0;if(t&&t.io.outputs.hasNamedOutputs()&&(s=t.io.outputs.getOutputIndex(n),null==s||s<0)){const e=t.io.outputs.namedOutputConnectionPoints().map((e=>e.name()));return void console.warn(`node ${t.path()} does not have an output named ${n}. inputs are: ${e.join(\\\\\\\", \\\\\\\")}`)}const r=this._graph_node_inputs[i];if(null==r){const e=`graph_input_node not found at index ${i}`;throw console.warn(e),new Error(e)}if(t&&this.node.parent()!=t.parent())return;const o=this._inputs[i];let a,c=null;this.node.io.connections&&(a=this.node.io.connections.inputConnection(i)),a&&(c=a.output_index),t===o&&s==c||(null!=o&&this._depends_on_inputs&&r.removeGraphInput(o),null!=t?r.addGraphInput(t)?(this._depends_on_inputs||r.removeGraphInput(t),a&&a.disconnect({setInput:!1}),this._inputs[i]=t,new Kr(t,this.node,s,i)):console.warn(`cannot connect ${t.path()} to ${this.node.path()}`):(this._inputs[i]=null,a&&a.disconnect({setInput:!1})),this._run_on_set_input_hooks(),r.setSuccessorsDirty(),this.node.emit(Jn.INPUTS_UPDATED))}remove_input(e){const t=this.inputs();let n;for(let i=0;i<t.length;i++)n=t[i],null!=n&&null!=e&&n.graphNodeId()===e.graphNodeId()&&this.setInput(i,null)}input(e){return this._inputs[e]}named_input(e){if(this.hasNamedInputs()){const t=this.get_input_index(e);return this._inputs[t]}return null}named_input_connection_point(e){if(this.hasNamedInputs()&&this._named_input_connection_points){const t=this.get_input_index(e);return this._named_input_connection_points[t]}}has_named_input(e){return this.get_named_input_index(e)>=0}has_input(e){return null!=this._inputs[e]}inputs(){return this._inputs}initInputsClonedState(e){this._cloned_states_controller||(this._cloned_states_controller=new Zr(this),this._cloned_states_controller.initInputsClonedState(e))}overrideClonedStateAllowed(){var e;return(null===(e=this._cloned_states_controller)||void 0===e?void 0:e.overrideClonedStateAllowed())||!1}overrideClonedState(e){var t;null===(t=this._cloned_states_controller)||void 0===t||t.overrideClonedState(e)}clonedStateOverriden(){var e;return(null===(e=this._cloned_states_controller)||void 0===e?void 0:e.overriden())||!1}cloneRequired(e){var t;const n=null===(t=this._cloned_states_controller)||void 0===t?void 0:t.cloneRequiredState(e);return null==n||n}cloneRequiredStates(){var e;const t=null===(e=this._cloned_states_controller)||void 0===e?void 0:e.cloneRequiredStates();return null==t||t}add_on_set_input_hook(e,t){this._on_update_hooks=this._on_update_hooks||[],this._on_update_hook_names=this._on_update_hook_names||[],this._on_update_hook_names.includes(e)?console.warn(`hook with name ${e} already exists`,this.node):(this._on_update_hooks.push(t),this._on_update_hook_names.push(e))}_run_on_set_input_hooks(){if(this._on_update_hooks)for(let e of this._on_update_hooks)e()}}class to{constructor(e){this.node=e,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(e){return this.getNamedOutputIndex(e)>=0}namedOutputConnectionPoints(){return this._named_output_connection_points||[]}namedOutputConnection(e){if(this._named_output_connection_points)return this._named_output_connection_points[e]}getNamedOutputIndex(e){var t;if(this._named_output_connection_points)for(let n=0;n<this._named_output_connection_points.length;n++)if((null===(t=this._named_output_connection_points[n])||void 0===t?void 0:t.name())==e)return n;return-1}getOutputIndex(e){return null!=e?m.isString(e)?this.hasNamedOutputs()?this.getNamedOutputIndex(e):(console.warn(`node ${this.node.path()} has no named outputs`),-1):e:-1}namedOutputConnectionPointsByName(e){if(this._named_output_connection_points)for(let t of this._named_output_connection_points)if((null==t?void 0:t.name())==e)return t}setNamedOutputConnectionPoints(e,t=!0){this._has_named_outputs=!0;const n=this.node.io.connections.outputConnections();if(n)for(let t of n)t&&t.output_index>=e.length&&t.disconnect({setInput:!0});this._named_output_connection_points=e,t&&this.node.scene()&&this.node.setDirty(this.node),this.node.emit(Jn.NAMED_OUTPUTS_UPDATED)}used_output_names(){var e;const t=this.node.io.connections;if(t){let n=t.outputConnections().map((e=>e?e.output_index:null));n=f.uniq(n);const i=[];n.forEach((e=>{m.isNumber(e)&&i.push(e)}));const s=[];for(let t of i){const n=null===(e=this.namedOutputConnectionPoints()[t])||void 0===e?void 0:e.name();n&&s.push(n)}return s}return[]}}class no{constructor(e){this._node=e,this._output_connections=new Map}initInputs(){const e=this._node.io.inputs.maxInputsCount();for(this._input_connections=this._input_connections||new Array(e);this._input_connections.length<e;)this._input_connections.push(void 0)}addInputConnection(e){this._input_connections?this._input_connections[e.input_index]=e:console.warn(\\\\\\\"input connections array not initialized\\\\\\\")}removeInputConnection(e){if(this._input_connections)if(e.input_index<this._input_connections.length){this._input_connections[e.input_index]=void 0;let t=!0;for(let n=e.input_index;n<this._input_connections.length;n++)this._input_connections[n]&&(t=!1);t&&(this._input_connections=this._input_connections.slice(0,e.input_index))}else console.warn(`attempt to remove an input connection at index ${e.input_index}`);else console.warn(\\\\\\\"input connections array not initialized\\\\\\\")}inputConnection(e){if(this._input_connections)return this._input_connections[e]}firstInputConnection(){return this._input_connections?f.compact(this._input_connections)[0]:null}inputConnections(){return this._input_connections}existingInputConnections(){const e=this._input_connections;if(e)for(;e.length>1&&void 0===e[e.length-1];)e.pop();return e}addOutputConnection(e){const t=e.output_index,n=e.id;let i=this._output_connections.get(t);i||(i=new Map,this._output_connections.set(t,i)),i.set(n,e)}removeOutputConnection(e){const t=e.output_index,n=e.id;let i=this._output_connections.get(t);i&&i.delete(n)}outputConnections(){let e=[];return this._output_connections.forEach(((t,n)=>{t.forEach(((t,n)=>{t&&e.push(t)}))})),e}}class io{constructor(e){this._node=e}set_in(e){this._in=e}set_out(e){this._out=e}clear(){this._in=void 0,this._out=void 0}in(){return this._in}out(){return this._out}}class so{constructor(e,t,n){this._name=e,this._type=t,this._init_value=n}get init_value(){return this._init_value}name(){return this._name}type(){return this._type}are_types_matched(e,t){return!0}toJSON(){return this._json=this._json||this._create_json()}_create_json(){return{name:this._name,type:this._type}}}var ro;!function(e){e.BOOL=\\\\\\\"bool\\\\\\\",e.INT=\\\\\\\"int\\\\\\\",e.FLOAT=\\\\\\\"float\\\\\\\",e.VEC2=\\\\\\\"vec2\\\\\\\",e.VEC3=\\\\\\\"vec3\\\\\\\",e.VEC4=\\\\\\\"vec4\\\\\\\",e.SAMPLER_2D=\\\\\\\"sampler2D\\\\\\\",e.SSS_MODEL=\\\\\\\"SSSModel\\\\\\\"}(ro||(ro={}));const oo=[ro.BOOL,ro.INT,ro.FLOAT,ro.VEC2,ro.VEC3,ro.VEC4],ao={[ro.BOOL]:Qs.BOOLEAN,[ro.INT]:Qs.INTEGER,[ro.FLOAT]:Qs.FLOAT,[ro.VEC2]:Qs.VECTOR2,[ro.VEC3]:Qs.VECTOR3,[ro.VEC4]:Qs.VECTOR4,[ro.SAMPLER_2D]:Qs.RAMP,[ro.SSS_MODEL]:Qs.STRING},co={[Qs.BOOLEAN]:ro.BOOL,[Qs.COLOR]:ro.VEC3,[Qs.INTEGER]:ro.INT,[Qs.FLOAT]:ro.FLOAT,[Qs.FOLDER]:void 0,[Qs.VECTOR2]:ro.VEC2,[Qs.VECTOR3]:ro.VEC3,[Qs.VECTOR4]:ro.VEC4,[Qs.BUTTON]:void 0,[Qs.OPERATOR_PATH]:void 0,[Qs.PARAM_PATH]:void 0,[Qs.NODE_PATH]:void 0,[Qs.RAMP]:void 0,[Qs.STRING]:void 0},lo={[ro.BOOL]:!1,[ro.INT]:0,[ro.FLOAT]:0,[ro.VEC2]:[0,0],[ro.VEC3]:[0,0,0],[ro.VEC4]:[0,0,0,0],[ro.SAMPLER_2D]:jr.DEFAULT_VALUE_JSON,[ro.SSS_MODEL]:\\\\\\\"SSSModel()\\\\\\\"},uo={[ro.BOOL]:1,[ro.INT]:1,[ro.FLOAT]:1,[ro.VEC2]:2,[ro.VEC3]:3,[ro.VEC4]:4,[ro.SAMPLER_2D]:1,[ro.SSS_MODEL]:1};class ho extends so{constructor(e,t,n){super(e,t),this._name=e,this._type=t,this._init_value=n,this._init_value=this._init_value||lo[this._type]}type(){return this._type}are_types_matched(e,t){return e==t}get param_type(){return ao[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 po;!function(e){e.BOOL=\\\\\\\"bool\\\\\\\",e.INT=\\\\\\\"int\\\\\\\",e.FLOAT=\\\\\\\"float\\\\\\\",e.VEC2=\\\\\\\"vec2\\\\\\\",e.VEC3=\\\\\\\"vec3\\\\\\\",e.VEC4=\\\\\\\"vec4\\\\\\\"}(po||(po={}));const _o=[po.BOOL,po.INT,po.FLOAT,po.VEC2,po.VEC3,po.VEC4],mo={[po.BOOL]:Qs.BOOLEAN,[po.INT]:Qs.INTEGER,[po.FLOAT]:Qs.FLOAT,[po.VEC2]:Qs.VECTOR2,[po.VEC3]:Qs.VECTOR3,[po.VEC4]:Qs.VECTOR4},fo={[Qs.BOOLEAN]:po.BOOL,[Qs.COLOR]:po.VEC3,[Qs.INTEGER]:po.INT,[Qs.FLOAT]:po.FLOAT,[Qs.FOLDER]:void 0,[Qs.VECTOR2]:po.VEC2,[Qs.VECTOR3]:po.VEC3,[Qs.VECTOR4]:po.VEC4,[Qs.BUTTON]:void 0,[Qs.OPERATOR_PATH]:void 0,[Qs.PARAM_PATH]:void 0,[Qs.NODE_PATH]:void 0,[Qs.RAMP]:void 0,[Qs.STRING]:void 0},go={[po.BOOL]:!1,[po.INT]:0,[po.FLOAT]:0,[po.VEC2]:[0,0],[po.VEC3]:[0,0,0],[po.VEC4]:[0,0,0,0]};po.BOOL,po.INT,po.FLOAT,po.VEC2,po.VEC3,po.VEC4;class vo extends so{constructor(e,t){super(e,t),this._name=e,this._type=t,this._init_value=go[this._type]}type(){return this._type}are_types_matched(e,t){return e==t}get param_type(){return mo[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 yo;!function(e){e.BASE=\\\\\\\"base\\\\\\\",e.DRAG=\\\\\\\"drag\\\\\\\",e.KEYBOARD=\\\\\\\"keyboard\\\\\\\",e.MOUSE=\\\\\\\"mouse\\\\\\\",e.POINTER=\\\\\\\"pointer\\\\\\\"}(yo||(yo={}));class xo extends so{constructor(e,t,n){super(e,t),this._name=e,this._type=t,this._event_listener=n}type(){return this._type}get param_type(){return Qs.FLOAT}are_types_matched(e,t){return t==yo.BASE||e==t}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 bo={[Ei.ANIM]:void 0,[Ei.COP]:void 0,[Ei.EVENT]:yo.BASE,[Ei.GL]:ro.FLOAT,[Ei.JS]:po.FLOAT,[Ei.MANAGER]:void 0,[Ei.MAT]:void 0,[Ei.OBJ]:void 0,[Ei.POST]:void 0,[Ei.ROP]:void 0,[Ei.SOP]:void 0};function wo(e,t,n){switch(e){case Ei.EVENT:return new xo(t,n);case Ei.GL:return new ho(t,n);case Ei.JS:return new vo(t,n);default:return}}class Ao{constructor(e,t){this.node=e,this._context=t,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 e=function(e){switch(e){case Ei.EVENT:return;case Ei.GL:return co;case Ei.JS:return fo;default:return}}(this._context);if(!e)return;const t=[];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=e[i.type()];if(n){const e=wo(this._context,i.name(),n);e&&t.push(e)}}}}this.node.io.inputs.setNamedInputConnectionPoints(t)}set_inputless_param_names(e){return this._inputless_param_names=e}createSpareParameters(){if(this.node.scene().loadingController.isLoading())return;const e=this.node.params.spare_names,t={};for(let n of e)if(this.node.params.has(n)){const e=this.node.params.get(n);e&&(this._raw_input_serialized_by_param_name.set(n,e.rawInputSerialized()),this._default_value_serialized_by_param_name.set(n,e.defaultValueSerialized()),t.namesToDelete=t.namesToDelete||[],t.namesToDelete.push(n))}for(let e of this.node.io.inputs.namedInputConnectionPoints())if(e){const n=e.name(),i=e.param_type;let s=e.init_value;const r=this._default_value_serialized_by_param_name.get(n);let o=this.node.paramDefaultValue(n);if(s=null!=o?o:null!=r?r:e.init_value,m.isArray(e.init_value))if(m.isNumber(s)){const t=new Array(e.init_value.length);t.fill(s),s=t}else m.isArray(s)&&s.length==e.init_value.length&&null!=r&&(s=e.init_value);null!=s&&(t.toAdd=t.toAdd||[],t.toAdd.push({name:n,type:i,init_value:b.clone(s),raw_input:b.clone(s),options:{spare:!0}}))}this.node.params.updateParams(t);for(let e of this.node.params.spare)if(!e.parent_param){const t=this._raw_input_serialized_by_param_name.get(e.name());t&&e.set(t)}}}class To{constructor(e,t){this.node=e,this._context=t,this._create_spare_params_from_inputs=!0,this._functions_overridden=!1,this._input_name_function=e=>`in${e}`,this._output_name_function=e=>0==e?\\\\\\\"val\\\\\\\":`val${e}`,this._expected_input_types_function=()=>{const e=this.first_input_connection_type()||this.default_connection_type();return[e,e]},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 Ao(this.node,this._context)}default_connection_type(){return bo[this._context]}create_connection_point(e,t){return wo(this._context,e,t)}functions_overridden(){return this._functions_overridden}initialized(){return this._initialized}set_create_spare_params_from_inputs(e){this._create_spare_params_from_inputs=e}set_input_name_function(e){this._initialize_if_required(),this._input_name_function=e}set_output_name_function(e){this._initialize_if_required(),this._output_name_function=e}set_expected_input_types_function(e){this._initialize_if_required(),this._functions_overridden=!0,this._expected_input_types_function=e}set_expected_output_types_function(e){this._initialize_if_required(),this._functions_overridden=!0,this._expected_output_types_function=e}input_name(e){return this._wrapped_input_name_function(e)}output_name(e){return this._wrapped_output_name_function(e)}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(e){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 e=this.node.graphAllSuccessors();if(this.node.childrenAllowed()){const t=this.node.nodesByType(Mi.INPUT),n=this.node.nodesByType(Mi.OUTPUT);for(let n of t)e.push(n);for(let t of n)e.push(t)}for(let t of e){const e=t;e.io&&e.io.has_connection_points_controller&&e.io.connection_points.initialized()&&e.io.connection_points.update_signature_if_required(this.node)}}update_connection_types(){const e=this._wrapped_expected_input_types_function(),t=this._wrapped_expected_output_types_function(),n=[];for(let t=0;t<e.length;t++){const i=e[t],s=this.create_connection_point(this._wrapped_input_name_function(t),i);n.push(s)}const i=[];for(let e=0;e<t.length;e++){const n=t[e],s=this.create_connection_point(this._wrapped_output_name_function(e),n);i.push(s)}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 e=this.node.io.inputs.namedInputConnectionPoints().map((e=>null==e?void 0:e.type())),t=this.node.io.outputs.namedOutputConnectionPoints().map((e=>null==e?void 0:e.type())),n=this._wrapped_expected_input_types_function(),i=this._wrapped_expected_output_types_function();if(n.length!=e.length)return!1;if(i.length!=t.length)return!1;for(let t=0;t<e.length;t++)if(e[t]!=n[t])return!1;for(let e=0;e<t.length;e++)if(t[e]!=i[e])return!1;return!0}_wrapped_expected_input_types_function(){if(this.node.scene().loadingController.isLoading()){const e=this.node.io.saved_connection_points_data.in();if(e)return e.map((e=>e.type))}return this._expected_input_types_function()}_wrapped_expected_output_types_function(){if(this.node.scene().loadingController.isLoading()){const e=this.node.io.saved_connection_points_data.out();if(e)return e.map((e=>e.type))}return this._expected_output_types_function()}_wrapped_input_name_function(e){if(this.node.scene().loadingController.isLoading()){const t=this.node.io.saved_connection_points_data.in();if(t)return t[e].name}return this._input_name_function(e)}_wrapped_output_name_function(e){if(this.node.scene().loadingController.isLoading()){const t=this.node.io.saved_connection_points_data.out();if(t)return t[e].name}return this._output_name_function(e)}first_input_connection_type(){return this.input_connection_type(0)}input_connection_type(e){const t=this.node.io.connections.inputConnections();if(t){const n=t[e];if(n)return n.src_connection_point().type()}}}class Eo{constructor(e){this.node=e,this._connections=new no(this.node)}get connections(){return this._connections}get inputs(){return this._inputs=this._inputs||new eo(this.node)}has_inputs(){return null!=this._inputs}get outputs(){return this._outputs=this._outputs||new to(this.node)}has_outputs(){return null!=this._outputs}get connection_points(){return this._connection_points=this._connection_points||new To(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 Co{constructor(){}}class Mo extends Qn{constructor(e,t=\\\\\\\"BaseNode\\\\\\\",n){super(e,t),this.params_init_value_overrides=n,this.containerController=new qs(this),this.pv=new Jr,this.p=new Co,this._initialized=!1}copy_param_values(e){const t=this.params.non_spare;for(let n of t){const t=e.params.get(n.name());t&&n.copy_value(t)}}get parentController(){return this._parent_controller=this._parent_controller||new gi(this)}static displayedInputNames(){return[]}get childrenControllerContext(){return this._children_controller_context}_create_children_controller(){if(this._children_controller_context)return new ki(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 Kn(this)}get states(){return this._states=this._states||new mi(this)}get lifecycle(){return this._lifecycle=this._lifecycle||new Bi(this)}get serializer(){return this._serializer=this._serializer||new $s(this)}get cookController(){return this._cook_controller=this._cook_controller||new Ys(this)}get io(){return this._io=this._io||new Eo(this)}get nameController(){return this._name_controller=this._name_controller||new fi(this)}setName(e){this.nameController.setName(e)}_set_core_name(e){this._name=e}get params(){return this._params_controller=this._params_controller||new Qr(this)}initialize_base_and_node(){var e;this._initialized?console.warn(\\\\\\\"node already initialized\\\\\\\"):(this._initialized=!0,null===(e=this.displayNodeController)||void 0===e||e.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(e){this.parentController.setParent(e)}parent(){return this.parentController.parent()}root(){return this._scene.root()}path(e){return this.parentController.path(e)}createParams(){}addParam(e,t,n,i){var s;return null===(s=this._params_controller)||void 0===s?void 0:s.addParam(e,t,n,i)}paramDefaultValue(e){return null}cook(e){return null}onCookEnd(e,t){this.cookController.registerOnCookEnd(e,t)}async compute(){var e,t;return this.isDirty()||(null===(t=null===(e=this.flags)||void 0===e?void 0:e.bypass)||void 0===t?void 0:t.active())?await this.containerController.compute():this.containerController.container()}_setContainer(e,t=null){this.containerController.container().set_content(e),null!=e&&(e.name||(e.name=this.path()),e.node||(e.node=this)),this.cookController.endCook(t)}createNode(e,t){var n;return null===(n=this.childrenController)||void 0===n?void 0:n.createNode(e,t)}create_operation_container(e,t,n){var i;return null===(i=this.childrenController)||void 0===i?void 0:i.create_operation_container(e,t,n)}removeNode(e){var t;null===(t=this.childrenController)||void 0===t||t.removeNode(e)}dispose(){var e,t;super.dispose(),this.setParent(null),this.io.inputs.dispose(),this.lifecycle.dispose(),null===(e=this.displayNodeController)||void 0===e||e.dispose(),this.nameController.dispose(),null===(t=this.childrenController)||void 0===t||t.dispose(),this.params.dispose()}children(){var e;return(null===(e=this.childrenController)||void 0===e?void 0:e.children())||[]}node(e){var t;return(null===(t=this.parentController)||void 0===t?void 0:t.findNode(e))||null}nodeSibbling(e){var t;const n=this.parent();if(n){const i=null===(t=n.childrenController)||void 0===t?void 0:t.child_by_name(e);if(i)return i}return null}nodesByType(e){var t;return(null===(t=this.childrenController)||void 0===t?void 0:t.nodesByType(e))||[]}setInput(e,t,n=0){this.io.inputs.setInput(e,t,n)}emit(e,t=null){this.scene().dispatchController.dispatch(this,e,t)}toJSON(e=!1){return this.serializer.toJSON(e)}async requiredModules(){}usedAssembler(){}integrationData(){}}class No extends Mo{static context(){return Ei.MANAGER}}class So{constructor(e,t,n){this.type=e,this.init_value=t,this.options=n}}class Oo{static BUTTON(e,t){return new So(Qs.BUTTON,e,t)}static BOOLEAN(e,t){return new So(Qs.BOOLEAN,e,t)}static COLOR(e,t){return e instanceof M.a&&(e=e.toArray()),new So(Qs.COLOR,e,t)}static FLOAT(e,t){return new So(Qs.FLOAT,e,t)}static FOLDER(e=null,t){return new So(Qs.FOLDER,e,t)}static INTEGER(e,t){return new So(Qs.INTEGER,e,t)}static RAMP(e=jr.DEFAULT_VALUE,t){return new So(Qs.RAMP,e,t)}static STRING(e=\\\\\\\"\\\\\\\",t){return new So(Qs.STRING,e,t)}static VECTOR2(e,t){return e instanceof d.a&&(e=e.toArray()),new So(Qs.VECTOR2,e,t)}static VECTOR3(e,t){return e instanceof p.a&&(e=e.toArray()),new So(Qs.VECTOR3,e,t)}static VECTOR4(e,t){return e instanceof _.a&&(e=e.toArray()),new So(Qs.VECTOR4,e,t)}static OPERATOR_PATH(e,t){return new So(Qs.OPERATOR_PATH,e,t)}static NODE_PATH(e,t){return new So(Qs.NODE_PATH,e,t)}static PARAM_PATH(e,t){return new So(Qs.PARAM_PATH,e,t)}}class Lo{}class Po{constructor(e){this.scene=e}findObjectByMask(e){return this.findObjectByMaskInObject(e,this.scene.threejsScene())}findObjectByMaskInObject(e,t,n=\\\\\\\"\\\\\\\"){for(let i of t.children){const t=this._removeTrailingOrHeadingSlash(i.name),s=`${n=this._removeTrailingOrHeadingSlash(n)}/${t}`;if(Li.matchMask(s,e))return i;const r=this.findObjectByMaskInObject(e,i,s);if(r)return r}}objectsByMask(e){return this.objectsByMaskInObject(e,this.scene.threejsScene(),[],\\\\\\\"\\\\\\\")}objectsByMaskInObject(e,t,n=[],i=\\\\\\\"\\\\\\\"){for(let s of t.children){const t=this._removeTrailingOrHeadingSlash(s.name),r=`${i=this._removeTrailingOrHeadingSlash(i)}/${t}`;Li.matchMask(r,e)&&n.push(s),this.objectsByMaskInObject(e,s,n,r)}return n}_removeTrailingOrHeadingSlash(e){return\\\\\\\"/\\\\\\\"==e[0]&&(e=e.substr(1)),\\\\\\\"/\\\\\\\"==e[e.length-1]&&(e=e.substr(0,e.length-1)),e}}const Ro={computeOnDirty:!1,callback:e=>{Fo.update(e)}};function Io(e){return class extends e{constructor(){super(...arguments),this.autoUpdate=Oo.BOOLEAN(1,Ro)}}}Io(Lo);class Fo{constructor(e){this.node=e}async update(){const e=this.node.object,t=this.node.pv;t.autoUpdate!=e.autoUpdate&&(e.autoUpdate=t.autoUpdate)}static async update(e){e.sceneAutoUpdateController.update()}}var Do;!function(e){e.NONE=\\\\\\\"none\\\\\\\",e.COLOR=\\\\\\\"color\\\\\\\",e.TEXTURE=\\\\\\\"texture\\\\\\\"}(Do||(Do={}));const ko=[Do.NONE,Do.COLOR,Do.TEXTURE],Bo={computeOnDirty:!1,callback:e=>{Uo.update(e)}};function zo(e){return class extends e{constructor(){super(...arguments),this.backgroundMode=Oo.INTEGER(ko.indexOf(Do.NONE),{menu:{entries:ko.map(((e,t)=>({name:e,value:t})))},...Bo}),this.bgColor=Oo.COLOR([0,0,0],{visibleIf:{backgroundMode:ko.indexOf(Do.COLOR)},...Bo}),this.bgTexture=Oo.NODE_PATH(\\\\\\\"\\\\\\\",{visibleIf:{backgroundMode:ko.indexOf(Do.TEXTURE)},nodeSelection:{context:Ei.COP},dependentOnFoundNode:!1,...Bo})}}}zo(Lo);class Uo{constructor(e){this.node=e}update(){const e=this.node.object,t=this.node.pv;if(t.backgroundMode==ko.indexOf(Do.NONE))e.background=null;else if(t.backgroundMode==ko.indexOf(Do.COLOR))e.background=t.bgColor;else{const n=t.bgTexture.nodeWithContext(Ei.COP);n?n.compute().then((t=>{e.background=t.texture()})):this.node.states.error.set(\\\\\\\"bgTexture node not found\\\\\\\")}}static update(e){e.sceneBackgroundController.update()}}const Go={computeOnDirty:!1,callback:e=>{jo.update(e)}};function Vo(e){return class extends e{constructor(){super(...arguments),this.useEnvironment=Oo.BOOLEAN(0,Go),this.environment=Oo.NODE_PATH(\\\\\\\"\\\\\\\",{visibleIf:{useEnvironment:1},nodeSelection:{context:Ei.COP},dependentOnFoundNode:!1,...Go})}}}Vo(Lo);class jo{constructor(e){this.node=e}async update(){const e=this.node.object,t=this.node.pv;if(t.useEnvironment){const n=t.environment.nodeWithContext(Ei.COP);n?n.compute().then((t=>{e.environment=t.texture()})):this.node.states.error.set(\\\\\\\"bgTexture node not found\\\\\\\")}else e.environment=null}static async update(e){e.sceneEnvController.update()}}class Ho{constructor(e,t=1,n=1e3){this.name=\\\\\\\"\\\\\\\",this.color=new M.a(e),this.near=t,this.far=n}clone(){return new Ho(this.color,this.near,this.far)}toJSON(){return{type:\\\\\\\"Fog\\\\\\\",color:this.color.getHex(),near:this.near,far:this.far}}}Ho.prototype.isFog=!0;class qo{constructor(e,t=25e-5){this.name=\\\\\\\"\\\\\\\",this.color=new M.a(e),this.density=t}clone(){return new qo(this.color,this.density)}toJSON(){return{type:\\\\\\\"FogExp2\\\\\\\",color:this.color.getHex(),density:this.density}}}qo.prototype.isFogExp2=!0;const Wo={computeOnDirty:!1,callback:e=>{Qo.update(e)}};var Xo;!function(e){e.LINEAR=\\\\\\\"linear\\\\\\\",e.EXPONENTIAL=\\\\\\\"exponential\\\\\\\"}(Xo||(Xo={}));const Yo=[Xo.LINEAR,Xo.EXPONENTIAL];function $o(e){return class extends e{constructor(){super(...arguments),this.useFog=Oo.BOOLEAN(0,Wo),this.fogType=Oo.INTEGER(Yo.indexOf(Xo.EXPONENTIAL),{visibleIf:{useFog:1},menu:{entries:Yo.map(((e,t)=>({name:e,value:t})))},...Wo}),this.fogColor=Oo.COLOR([1,1,1],{visibleIf:{useFog:1},...Wo}),this.fogNear=Oo.FLOAT(1,{range:[0,100],rangeLocked:[!0,!1],visibleIf:{useFog:1,fogType:Yo.indexOf(Xo.LINEAR)},...Wo}),this.fogFar=Oo.FLOAT(100,{range:[0,100],rangeLocked:[!0,!1],visibleIf:{useFog:1,fogType:Yo.indexOf(Xo.LINEAR)},...Wo}),this.fogDensity=Oo.FLOAT(25e-5,{visibleIf:{useFog:1,fogType:Yo.indexOf(Xo.EXPONENTIAL)},...Wo})}}}$o(Lo);class Qo{constructor(e){this.node=e}async update(){const e=this.node.object,t=this.node.pv;if(t.useFog)if(t.fogType==Yo.indexOf(Xo.LINEAR)){const n=this.fog2(t);e.fog=n,n.color=t.fogColor,n.near=t.fogNear,n.far=t.fogFar}else{const n=this.fogExp2(t);e.fog=this.fogExp2(t),n.color=t.fogColor,n.density=t.fogDensity}else{e.fog&&(e.fog=null)}}fog2(e){return this._fog=this._fog||new Ho(16777215,e.fogNear,e.fogFar)}fogExp2(e){return this._fogExp2=this._fogExp2||new qo(16777215,e.fogDensity)}static async update(e){e.sceneFogController.update()}}const Jo={computeOnDirty:!1,callback:e=>{Zo.update(e)}};function Ko(e){return class extends e{constructor(){super(...arguments),this.useOverrideMaterial=Oo.BOOLEAN(0,Jo),this.overrideMaterial=Oo.NODE_PATH(\\\\\\\"\\\\\\\",{visibleIf:{useOverrideMaterial:1},nodeSelection:{context:Ei.MAT},dependentOnFoundNode:!1,...Jo})}}}Ko(Lo);class Zo{constructor(e){this.node=e}async update(){const e=this.node.object,t=this.node.pv;if(t.useOverrideMaterial){const n=t.overrideMaterial.nodeWithContext(Ei.MAT);n?n.compute().then((t=>{e.overrideMaterial=t.material()})):this.node.states.error.set(\\\\\\\"bgTexture node not found\\\\\\\")}else e.overrideMaterial=null}static async update(e){e.SceneMaterialOverrideController.update()}}class ea extends(Ko(Vo($o(zo(Io(Lo)))))){}const ta=new ea;class na extends No{constructor(){super(...arguments),this.paramsConfig=ta,this._object=this._createScene(),this._queued_nodes_by_id=new Map,this.sceneAutoUpdateController=new Fo(this),this.sceneBackgroundController=new Uo(this),this.sceneEnvController=new jo(this),this.sceneFogController=new Qo(this),this.sceneMaterialOverrideController=new Zo(this),this._children_controller_context=Ei.OBJ}static type(){return\\\\\\\"obj\\\\\\\"}initializeNode(){this._object.matrixAutoUpdate=!1,this.lifecycle.add_on_child_add_hook(this._on_child_add.bind(this)),this.lifecycle.add_on_child_remove_hook(this._on_child_remove.bind(this))}_createScene(){const e=new Vi;return e.name=\\\\\\\"/\\\\\\\",e.matrixAutoUpdate=!1,e}get object(){return this._object}createNode(e,t){return super.createNode(e,t)}children(){return super.children()}nodesByType(e){return super.nodesByType(e)}_updateScene(){this.sceneAutoUpdateController.update(),this.sceneBackgroundController.update(),this.sceneEnvController.update(),this.sceneFogController.update(),this.sceneMaterialOverrideController.update()}_addToQueue(e){const t=e.graphNodeId();return this._queued_nodes_by_id.has(t)||this._queued_nodes_by_id.set(t,e),e}async processQueue(){this._updateScene();const e=new Map,t=[];this._queued_nodes_by_id.forEach(((n,i)=>{const s=`_____${n.renderOrder}__${n.path()}`;t.push(s),e.set(s,n)})),this._queued_nodes_by_id.clear();for(let n of t){const t=e.get(n);t&&(e.delete(n),this._addToScene(t))}}_update_object(e){return this.scene().loadingController.autoUpdating()?this._addToScene(e):this._addToQueue(e)}getParentForNode(e){if(e.attachableToHierarchy()){const t=e.io.inputs.input(0);return t?t.children_group:this._object}return null}_addToScene(e){var t;if(e.attachableToHierarchy()){const n=this.getParentForNode(e);n&&(e.usedInScene()?(null===(t=e.childrenDisplayController)||void 0===t||t.request_display_node_container(),e.addObjectToParent(n)):e.removeObjectFromParent())}}_removeFromScene(e){e.removeObjectFromParent()}areChildrenCooking(){const e=this.children();for(let t of e)if(t.cookController.isCooking()||t.isDisplayNodeCooking())return!0;return!1}addToParentTransform(e){this._update_object(e)}removeFromParentTransform(e){this._update_object(e)}_on_child_add(e){e&&this._update_object(e)}_on_child_remove(e){e&&this._removeFromScene(e)}}class ia{constructor(e){this.scene=e,this._node_context_signatures={},this._instanciated_nodes_by_context_and_type={}}init(){this._root=new na(this.scene),this._root.initialize_base_and_node(),this._root.params.init(),this._root._set_core_name(\\\\\\\"RootNode\\\\\\\")}root(){return this._root}_traverseNode(e,t){const n=e.children();if(n&&0!=n.length)for(let e of n)t(e),e.childrenController&&this._traverseNode(e,t)}clear(){var e;const t=this.root().children();for(let n of t)null===(e=this.root().childrenController)||void 0===e||e.removeNode(n)}node(e){return\\\\\\\"/\\\\\\\"===e?this.root():this.root().node(e)}allNodes(){let e=[this.root()],t=[this.root()],n=0;for(;t.length>0&&n<10;){const i=t.map((e=>e.childrenAllowed()?e.children():[])).flat();e=e.concat(i),t=i,n+=1}return e.flat()}nodesFromMask(e){const t=this.allNodes(),n=[];for(let i of t){const t=i.path();Li.matchMask(t,e)&&n.push(i)}return n}reset_node_context_signatures(){this._node_context_signatures={}}register_node_context_signature(e){e.childrenAllowed()&&e.childrenController&&(this._node_context_signatures[e.childrenController.node_context_signature()]=!0)}node_context_signatures(){return Object.keys(this._node_context_signatures).sort().map((e=>e.toLowerCase()))}addToInstanciatedNode(e){const t=e.context(),n=e.type();this._instanciated_nodes_by_context_and_type[t]=this._instanciated_nodes_by_context_and_type[t]||{},this._instanciated_nodes_by_context_and_type[t][n]=this._instanciated_nodes_by_context_and_type[t][n]||{},this._instanciated_nodes_by_context_and_type[t][n][e.graphNodeId()]=e}removeFromInstanciatedNode(e){const t=e.context(),n=e.type();delete this._instanciated_nodes_by_context_and_type[t][n][e.graphNodeId()]}nodesByType(e){const t=[];return this._traverseNode(this.scene.root(),(n=>{n.type()==e&&t.push(n)})),t}nodesByContextAndType(e,t){const n=[],i=this._instanciated_nodes_by_context_and_type[e];if(i){const e=i[t];if(e)for(let t of Object.keys(e))n.push(e[t])}return n}}class sa{constructor(e){this.scene=e}toJSON(e=!1){const t={},n={};for(let i of this.scene.nodesController.allNodes()){const s=new $s(i);t[i.graphNodeId()]=s.toJSON(e);const r=i.params.all;for(let e of r)n[e.graphNodeId()]=e.toJSON()}return{nodes_by_graph_node_id:t,params_by_graph_node_id:n}}}var ra;!function(e){e.auxclick=\\\\\\\"auxclick\\\\\\\",e.click=\\\\\\\"click\\\\\\\",e.contextmenu=\\\\\\\"contextmenu\\\\\\\",e.dblclick=\\\\\\\"dblclick\\\\\\\",e.mousedown=\\\\\\\"mousedown\\\\\\\",e.mouseenter=\\\\\\\"mouseenter\\\\\\\",e.mouseleave=\\\\\\\"mouseleave\\\\\\\",e.mousemove=\\\\\\\"mousemove\\\\\\\",e.mouseover=\\\\\\\"mouseover\\\\\\\",e.mouseout=\\\\\\\"mouseout\\\\\\\",e.mouseup=\\\\\\\"mouseup\\\\\\\",e.pointerlockchange=\\\\\\\"pointerlockchange\\\\\\\",e.pointerlockerror=\\\\\\\"pointerlockerror\\\\\\\",e.select=\\\\\\\"select\\\\\\\",e.wheel=\\\\\\\"wheel\\\\\\\"}(ra||(ra={}));const oa=[ra.auxclick,ra.click,ra.contextmenu,ra.dblclick,ra.mousedown,ra.mouseenter,ra.mouseleave,ra.mousemove,ra.mouseover,ra.mouseout,ra.mouseup,ra.pointerlockchange,ra.pointerlockerror,ra.select,ra.wheel];class aa extends Bn{constructor(){super(...arguments),this._require_canvas_event_listeners=!0}type(){return\\\\\\\"mouse\\\\\\\"}acceptedEventTypes(){return oa.map((e=>`${e}`))}}class ca extends Mo{constructor(){super(...arguments),this._cook_without_inputs_bound=this._cook_without_inputs.bind(this)}static context(){return Ei.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(e,t){t.event_listener?t.event_listener(e):this.processEvent(e)}processEvent(e){}async dispatchEventToOutput(e,t){this.run_on_dispatch_hook(e,t);const n=this.io.outputs.getOutputIndex(e);if(n>=0){const e=this.io.connections.outputConnections().filter((e=>e.output_index==n));let i;for(let n of e){i=n.node_dest;const e=i.io.inputs.namedInputConnectionPoints()[n.input_index];i.processEventViaConnectionPoint(t,e)}}else console.warn(`requested output '${e}' does not exist on node '${this.path()}'`)}onDispatch(e,t){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,e,t)}run_on_dispatch_hook(e,t){if(this._on_dispatch_hooks_by_output_name){const n=this._on_dispatch_hooks_by_output_name.get(e);if(n)for(let e of n)e(t)}}}var la;!function(e){e.CANVAS=\\\\\\\"canvas\\\\\\\",e.DOCUMENT=\\\\\\\"document\\\\\\\"}(la||(la={}));const ua=[la.CANVAS,la.DOCUMENT];class ha{constructor(e){this.viewer=e,this._bound_listener_map_by_event_controller_type=new Map}updateEvents(e){const t=this.canvas();if(!t)return;const n=e.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(((e,n)=>{this._eventOwner(e.data,t).removeEventListener(n,e.listener)})),i.clear();const s=t=>{this.processEvent(t,e)};for(let n of e.activeEventDatas()){this._eventOwner(n,t).addEventListener(n.type,s),i.set(n.type,{listener:s,data:n})}}_eventOwner(e,t){return\\\\\\\"resize\\\\\\\"==e.type?window:e.emitter==la.CANVAS?t:document}cameraNode(){return this.viewer.camerasController.cameraNode()}canvas(){return this.viewer.canvas()}init(){this.canvas&&this.viewer.scene().eventsDispatcher.traverseControllers((e=>{this.updateEvents(e)}))}registeredEventTypes(){const e=[];return this._bound_listener_map_by_event_controller_type.forEach((t=>{t.forEach(((t,n)=>{e.push(n)}))})),e}dispose(){const e=this.canvas();this._bound_listener_map_by_event_controller_type.forEach((t=>{e&&t.forEach(((t,n)=>{this._eventOwner(t.data,e).removeEventListener(n,t.listener)}))}))}processEvent(e,t){if(!this.canvas())return;const n={viewer:this.viewer,event:e,cameraNode:this.cameraNode()};t.processEvent(n)}}const da={visibleIf:{active:1},callback:e=>{_a.PARAM_CALLBACK_updateRegister(e)}};class pa extends ca{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(e){this.pv.active&&e.event&&this.dispatchEventToOutput(e.event.type,e)}static PARAM_CALLBACK_updateRegister(e){e._updateRegister()}_updateRegister(){this._updateActiveEventDatas(),this.scene().eventsDispatcher.updateViewerEventListeners(this)}_updateActiveEventDatas(){if(this._activeEventDatas=[],this.pv.active){const e=this.acceptedEventTypes();for(let t of e){const e=this.params.get(t);e&&e.value&&this._activeEventDatas.push({type:t,emitter:ua[this.pv.element]})}}}activeEventDatas(){return this._activeEventDatas}}class _a extends pa{acceptedEventTypes(){return[]}}const ma=new class extends Lo{constructor(){super(...arguments),this.active=Oo.BOOLEAN(!0,{callback:e=>{fa.PARAM_CALLBACK_updateRegister(e)},separatorAfter:!0}),this.element=Oo.INTEGER(ua.indexOf(la.CANVAS),{menu:{entries:ua.map(((e,t)=>({name:e,value:t})))},separatorAfter:!0}),this.auxclick=Oo.BOOLEAN(0,da),this.click=Oo.BOOLEAN(0,da),this.contextmenu=Oo.BOOLEAN(0,da),this.dblclick=Oo.BOOLEAN(0,da),this.mousedown=Oo.BOOLEAN(1,da),this.mouseenter=Oo.BOOLEAN(0,da),this.mouseleave=Oo.BOOLEAN(0,da),this.mousemove=Oo.BOOLEAN(1,da),this.mouseover=Oo.BOOLEAN(0,da),this.mouseout=Oo.BOOLEAN(0,da),this.mouseup=Oo.BOOLEAN(1,da),this.pointerlockchange=Oo.BOOLEAN(0,da),this.pointerlockerror=Oo.BOOLEAN(0,da),this.select=Oo.BOOLEAN(0,da),this.wheel=Oo.BOOLEAN(0,da),this.ctrlKey=Oo.BOOLEAN(0,{...da,separatorBefore:!0}),this.altKey=Oo.BOOLEAN(0,da),this.shiftKey=Oo.BOOLEAN(0,da),this.metaKey=Oo.BOOLEAN(0,da)}};class fa extends pa{constructor(){super(...arguments),this.paramsConfig=ma}static type(){return\\\\\\\"mouse\\\\\\\"}acceptedEventTypes(){return oa.map((e=>`${e}`))}initializeNode(){this.io.outputs.setNamedOutputConnectionPoints(oa.map((e=>new xo(e,yo.MOUSE)))),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{const e=[this.p.auxclick,this.p.click,this.p.dblclick,this.p.mousedown,this.p.mouseenter,this.p.mouseleave,this.p.mousemove,this.p.mouseout,this.p.mouseout,this.p.mouseup,this.p.pointerlockchange,this.p.pointerlockerror,this.p.select,this.p.wheel];this.params.label.init(e,(()=>e.map((e=>e.value?e.name():void 0)).filter((e=>e)).join(\\\\\\\", \\\\\\\")))}))}))}processEvent(e){if(!this.pv.active)return;if(!e.event)return;const t=e.event;t.ctrlKey==this.pv.ctrlKey&&t.shiftKey==this.pv.shiftKey&&t.altKey==this.pv.altKey&&t.metaKey==this.pv.metaKey&&this.dispatchEventToOutput(e.event.type,e)}}var ga;!function(e){e.pointerdown=\\\\\\\"pointerdown\\\\\\\",e.pointermove=\\\\\\\"pointermove\\\\\\\",e.pointerup=\\\\\\\"pointerup\\\\\\\"}(ga||(ga={}));const va=[ga.pointerdown,ga.pointermove,ga.pointerup];class ya extends Bn{constructor(){super(...arguments),this._require_canvas_event_listeners=!0}type(){return\\\\\\\"pointer\\\\\\\"}acceptedEventTypes(){return va.map((e=>`${e}`))}}const xa=new class extends Lo{constructor(){super(...arguments),this.active=Oo.BOOLEAN(!0,{callback:e=>{ba.PARAM_CALLBACK_updateRegister(e)},separatorAfter:!0}),this.element=Oo.INTEGER(ua.indexOf(la.CANVAS),{menu:{entries:ua.map(((e,t)=>({name:e,value:t})))},separatorAfter:!0}),this.pointerdown=Oo.BOOLEAN(1,da),this.pointermove=Oo.BOOLEAN(0,da),this.pointerup=Oo.BOOLEAN(0,da),this.ctrlKey=Oo.BOOLEAN(0,{...da,separatorBefore:!0}),this.altKey=Oo.BOOLEAN(0,da),this.shiftKey=Oo.BOOLEAN(0,da),this.metaKey=Oo.BOOLEAN(0,da)}};class ba extends pa{constructor(){super(...arguments),this.paramsConfig=xa}static type(){return\\\\\\\"pointer\\\\\\\"}acceptedEventTypes(){return va.map((e=>`${e}`))}initializeNode(){this.io.outputs.setNamedOutputConnectionPoints(va.map((e=>new xo(e,yo.POINTER)))),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{const e=[this.p.pointerdown,this.p.pointermove,this.p.pointerup];this.params.label.init(e,(()=>e.map((e=>e.value?e.name():void 0)).filter((e=>e)).join(\\\\\\\", \\\\\\\")))}))}))}processEvent(e){if(!this.pv.active)return;if(!e.event)return;const t=e.event;t.ctrlKey==this.pv.ctrlKey&&t.shiftKey==this.pv.shiftKey&&t.altKey==this.pv.altKey&&t.metaKey==this.pv.metaKey&&this.dispatchEventToOutput(e.event.type,e)}}var wa,Aa;!function(e){e.SET_FRAME=\\\\\\\"setFrame\\\\\\\"}(wa||(wa={})),function(e){e.TIME_REACHED=\\\\\\\"timeReached\\\\\\\"}(Aa||(Aa={}));const Ta=new class extends Lo{constructor(){super(...arguments),this.active=Oo.BOOLEAN(!0,{callback:(e,t)=>{Ea.PARAM_CALLBACK_updateRegister(e)},separatorAfter:!0}),this.element=Oo.INTEGER(0,{hidden:!0}),this.sceneLoaded=Oo.BOOLEAN(1,da),this.play=Oo.BOOLEAN(1,da),this.pause=Oo.BOOLEAN(1,da),this.tick=Oo.BOOLEAN(1,{separatorAfter:!0,...da}),this.treachedTime=Oo.BOOLEAN(0,{callback:e=>{Ea.PARAM_CALLBACK_update_time_dependency(e)}}),this.reachedTime=Oo.INTEGER(10,{visibleIf:{treachedTime:1},range:[0,100],separatorAfter:!0}),this.setFrameValue=Oo.INTEGER(1,{range:[0,100]}),this.setFrame=Oo.BUTTON(null,{callback:e=>{Ea.PARAM_CALLBACK_setFrame(e)}})}};class Ea extends pa{constructor(){super(...arguments),this.paramsConfig=Ta}static type(){return\\\\\\\"scene\\\\\\\"}acceptedEventTypes(){return Un.map((e=>`${e}`))}dispose(){var e;null===(e=this.graph_node)||void 0===e||e.dispose(),super.dispose()}initializeNode(){this.io.inputs.setNamedInputConnectionPoints([new xo(wa.SET_FRAME,yo.BASE,this.onSetFrame.bind(this))]);const e=Un.map((e=>new xo(e,yo.BASE)));e.push(new xo(Aa.TIME_REACHED,yo.BASE)),this.io.outputs.setNamedOutputConnectionPoints(e),this.params.onParamsCreated(\\\\\\\"update_time_dependency\\\\\\\",(()=>{this.update_time_dependency()}))}onSetFrame(e){this.scene().setFrame(this.pv.setFrameValue)}on_frame_update(){this.scene().time()>=this.pv.reachedTime&&this.dispatchEventToOutput(Aa.TIME_REACHED,{})}update_time_dependency(){this.pv.treachedTime?(this.graph_node=this.graph_node||new Qn(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(e){e.onSetFrame({})}static PARAM_CALLBACK_update_time_dependency(e){e.update_time_dependency()}}var Ca;!function(e){e.keydown=\\\\\\\"keydown\\\\\\\",e.keypress=\\\\\\\"keypress\\\\\\\",e.keyup=\\\\\\\"keyup\\\\\\\"}(Ca||(Ca={}));const Ma=[Ca.keydown,Ca.keypress,Ca.keyup];class Na extends Bn{constructor(){super(...arguments),this._require_canvas_event_listeners=!0}type(){return\\\\\\\"keyboard\\\\\\\"}acceptedEventTypes(){return Ma.map((e=>`${e}`))}}const Sa=new class extends Lo{constructor(){super(...arguments),this.active=Oo.BOOLEAN(!0,{callback:(e,t)=>{Oa.PARAM_CALLBACK_updateRegister(e)},separatorAfter:!0}),this.element=Oo.INTEGER(ua.indexOf(la.CANVAS),{menu:{entries:ua.map(((e,t)=>({name:e,value:t})))},separatorAfter:!0}),this.keydown=Oo.BOOLEAN(1,da),this.keypress=Oo.BOOLEAN(0,da),this.keyup=Oo.BOOLEAN(0,da),this.keyCodes=Oo.STRING(\\\\\\\"Digit1 KeyE ArrowDown\\\\\\\",da),this.ctrlKey=Oo.BOOLEAN(0,da),this.altKey=Oo.BOOLEAN(0,da),this.shiftKey=Oo.BOOLEAN(0,da),this.metaKey=Oo.BOOLEAN(0,da)}};class Oa extends pa{constructor(){super(...arguments),this.paramsConfig=Sa}static type(){return\\\\\\\"keyboard\\\\\\\"}acceptedEventTypes(){return Ma.map((e=>`${e}`))}initializeNode(){this.io.outputs.setNamedOutputConnectionPoints(Ma.map((e=>new xo(e,yo.KEYBOARD)))),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{const e=[this.p.keydown,this.p.keypress,this.p.keyup];this.params.label.init(e.concat([this.p.keyCodes]),(()=>`${e.map((e=>e.value?e.name():void 0)).filter((e=>e)).join(\\\\\\\", \\\\\\\")} (${this.pv.keyCodes})`))}))}))}processEvent(e){if(!this.pv.active)return;if(!e.event)return;const t=e.event;if(t.ctrlKey!=this.pv.ctrlKey)return;if(t.shiftKey!=this.pv.shiftKey)return;if(t.altKey!=this.pv.altKey)return;if(t.metaKey!=this.pv.metaKey)return;if(this.pv.keyCodes.trim().length>0){if(!this.pv.keyCodes.split(\\\\\\\" \\\\\\\").includes(t.code))return}this.dispatchEventToOutput(e.event.type,e)}}var La;!function(e){e.resize=\\\\\\\"resize\\\\\\\"}(La||(La={}));const Pa=[La.resize];class Ra extends Bn{constructor(){super(...arguments),this._require_canvas_event_listeners=!0}type(){return\\\\\\\"window\\\\\\\"}acceptedEventTypes(){return Pa.map((e=>`${e}`))}}const Ia=new class extends Lo{constructor(){super(...arguments),this.active=Oo.BOOLEAN(!0,{callback:e=>{Fa.PARAM_CALLBACK_updateRegister(e)},separatorAfter:!0}),this.element=Oo.INTEGER(0,{hidden:!0}),this.resize=Oo.BOOLEAN(1,da)}};class Fa extends pa{constructor(){super(...arguments),this.paramsConfig=Ia}static type(){return\\\\\\\"window\\\\\\\"}acceptedEventTypes(){return Pa.map((e=>`${e}`))}initializeNode(){this.io.outputs.setNamedOutputConnectionPoints(Pa.map((e=>new xo(e,yo.POINTER)))),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{const e=[this.p.resize];this.params.label.init(e,(()=>e.map((e=>e.value?e.name():void 0)).filter((e=>e)).join(\\\\\\\", \\\\\\\")))}))}))}processEvent(e){this.pv.active&&e.event&&this.dispatchEventToOutput(e.event.type,e)}}var Da;!function(e){e.dragover=\\\\\\\"dragover\\\\\\\"}(Da||(Da={}));const ka=[Da.dragover];class Ba extends Bn{constructor(){super(...arguments),this._require_canvas_event_listeners=!0}type(){return\\\\\\\"drag\\\\\\\"}acceptedEventTypes(){return ka.map((e=>`${e}`))}}var za;!function(e){e.touchstart=\\\\\\\"touchstart\\\\\\\",e.touchmove=\\\\\\\"touchmove\\\\\\\",e.touchend=\\\\\\\"touchend\\\\\\\"}(za||(za={}));const Ua=[za.touchstart,za.touchmove,za.touchend];class Ga extends Bn{constructor(){super(...arguments),this._require_canvas_event_listeners=!0}type(){return\\\\\\\"touch\\\\\\\"}acceptedEventTypes(){return Ua.map((e=>`${e}`))}}const Va=new class extends Lo{constructor(){super(...arguments),this.active=Oo.BOOLEAN(!0,{callback:e=>{ja.PARAM_CALLBACK_updateRegister(e)},separatorAfter:!0}),this.element=Oo.INTEGER(ua.indexOf(la.CANVAS),{menu:{entries:ua.map(((e,t)=>({name:e,value:t})))},separatorAfter:!0}),this.dragover=Oo.BOOLEAN(1,da),this.ctrlKey=Oo.BOOLEAN(0,{...da,separatorBefore:!0}),this.altKey=Oo.BOOLEAN(0,da),this.shiftKey=Oo.BOOLEAN(0,da),this.metaKey=Oo.BOOLEAN(0,da)}};class ja extends pa{constructor(){super(...arguments),this.paramsConfig=Va}static type(){return\\\\\\\"drag\\\\\\\"}acceptedEventTypes(){return ka.map((e=>`${e}`))}initializeNode(){this.io.outputs.setNamedOutputConnectionPoints(ka.map((e=>new xo(e,yo.DRAG)))),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{const e=[this.p.dragover];this.params.label.init(e,(()=>e.map((e=>e.value?e.name():void 0)).filter((e=>e)).join(\\\\\\\", \\\\\\\")))}))}))}processEvent(e){if(!this.pv.active)return;if(!e.event)return;const t=e.event;t.ctrlKey==this.pv.ctrlKey&&t.shiftKey==this.pv.shiftKey&&t.altKey==this.pv.altKey&&t.metaKey==this.pv.metaKey&&this.dispatchEventToOutput(e.event.type,e)}}const Ha=new class extends Lo{constructor(){super(...arguments),this.active=Oo.BOOLEAN(!0,{callback:e=>{qa.PARAM_CALLBACK_updateRegister(e)},separatorAfter:!0}),this.element=Oo.INTEGER(ua.indexOf(la.CANVAS),{menu:{entries:ua.map(((e,t)=>({name:e,value:t})))},separatorAfter:!0}),this.touchstart=Oo.BOOLEAN(1,da),this.touchmove=Oo.BOOLEAN(0,da),this.touchend=Oo.BOOLEAN(0,da)}};class qa extends pa{constructor(){super(...arguments),this.paramsConfig=Ha}static type(){return\\\\\\\"touch\\\\\\\"}acceptedEventTypes(){return Ua.map((e=>`${e}`))}initializeNode(){this.io.outputs.setNamedOutputConnectionPoints(Ua.map((e=>new xo(e,yo.DRAG)))),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{const e=[this.p.touchstart,this.p.touchmove,this.p.touchend];this.params.label.init(e,(()=>e.map((e=>e.value?e.name():void 0)).filter((e=>e)).join(\\\\\\\", \\\\\\\")))}))}))}processEvent(e){this.pv.active&&e.event&&this.dispatchEventToOutput(e.event.type,e)}}class Wa{constructor(e){this.scene=e,this._controllers=[]}registerEventNode(e){const t=this._find_or_create_controller_for_node(e);t&&t.registerNode(e)}unregisterEventNode(e){const t=this._find_or_create_controller_for_node(e);t&&t.unregisterNode(e)}updateViewerEventListeners(e){const t=this._find_or_create_controller_for_node(e);t&&t.updateViewerEventListeners()}traverseControllers(e){for(let t of this._controllers)e(t)}_find_or_create_controller_for_node(e){switch(e.type()){case Oa.type():return this.keyboardEventsController;case fa.type():return this.mouseEventsController;case ja.type():return this.dragEventsController;case ba.type():return this.pointerEventsController;case Ea.type():return this.sceneEventsController;case qa.type():return this.touchEventsController;case Fa.type():return this.windowEventsController}}get keyboardEventsController(){return this._keyboard_events_controller=this._keyboard_events_controller||this._create_controller(Na)}get mouseEventsController(){return this._mouse_events_controller=this._mouse_events_controller||this._create_controller(aa)}get dragEventsController(){return this._drag_events_controller=this._drag_events_controller||this._create_controller(Ba)}get pointerEventsController(){return this._pointer_events_controller=this._pointer_events_controller||this._create_controller(ya)}get sceneEventsController(){return this._scene_events_controller=this._scene_events_controller||this._create_controller(Gn)}get windowEventsController(){return this._window_events_controller=this._window_events_controller||this._create_controller(Ra)}get touchEventsController(){return this._touch_events_controller=this._touch_events_controller||this._create_controller(Ga)}_create_controller(e){const t=new e(this);return this._controllers.includes(t)||this._controllers.push(t),t}}class Xa{constructor(e){this.scene=e,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(e,t){this._referenced_nodes_by_src_param_id.set(e.graphNodeId(),t),u.pushOnArrayAtEntry(this._referencing_params_by_referenced_node_id,t.graphNodeId(),e)}set_named_nodes_from_param(e){const t=e.decomposed_path.named_nodes();for(let n of t)u.pushOnArrayAtEntry(this._referencing_params_by_all_named_node_ids,n.graphNodeId(),e)}reset_reference_from_param(e){const t=this._referenced_nodes_by_src_param_id.get(e.graphNodeId());if(t){u.popFromArrayAtEntry(this._referencing_params_by_referenced_node_id,t.graphNodeId(),e);const n=e.decomposed_path.named_nodes();for(let t of n)u.popFromArrayAtEntry(this._referencing_params_by_all_named_node_ids,t.graphNodeId(),e);this._referenced_nodes_by_src_param_id.delete(e.graphNodeId())}}referencing_params(e){return this._referencing_params_by_referenced_node_id.get(e.graphNodeId())}referencing_nodes(e){const t=this._referencing_params_by_referenced_node_id.get(e.graphNodeId());if(t){const e=new Map;for(let n of t){const t=n.node;e.set(t.graphNodeId(),t)}const n=[];return e.forEach((e=>{n.push(e)})),n}}nodes_referenced_by(e){const t=new Set([Qs.OPERATOR_PATH,Qs.NODE_PATH]),n=[];for(let i of e.params.all)t.has(i.type())&&n.push(i);const i=new Map,s=[];for(let e of n)this._check_param(e,i,s);for(let e of s)i.set(e.node.graphNodeId(),e.node);const r=[];return i.forEach((e=>{r.push(e)})),r}_check_param(e,t,n){if(e instanceof kr){const i=e.found_node(),s=e.found_param();return i&&t.set(i.graphNodeId(),i),void(s&&n.push(s))}}notify_name_updated(e){const t=this._referencing_params_by_all_named_node_ids.get(e.graphNodeId());if(t)for(let n of t)n.notify_path_rebuild_required(e)}notify_params_updated(e){const t=this._referencing_params_by_all_named_node_ids.get(e.graphNodeId());if(t)for(let n of t)n.options.isSelectingParam()&&n.notify_target_param_owner_params_updated(e)}}var Ya;!function(e){e.MAX_FRAME_UPDATED=\\\\\\\"scene_maxFrameUpdated\\\\\\\",e.REALTIME_STATUS_UPDATED=\\\\\\\"scene_realtime_status_updated\\\\\\\",e.FRAME_UPDATED=\\\\\\\"scene_frame_updated\\\\\\\",e.PLAY_STATE_UPDATED=\\\\\\\"scene_play_state_updated\\\\\\\"}(Ya||(Ya={}));const $a=Rn.performance.performanceManager();class Qa{constructor(e){this.scene=e,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 Qn(e,\\\\\\\"time controller\\\\\\\")}get PLAY_EVENT_CONTEXT(){return this._PLAY_EVENT_CONTEXT=this._PLAY_EVENT_CONTEXT||{event:new Event(zn.PLAY)}}get PAUSE_EVENT_CONTEXT(){return this._PAUSE_EVENT_CONTEXT=this._PAUSE_EVENT_CONTEXT||{event:new Event(zn.PAUSE)}}get TICK_EVENT_CONTEXT(){return this._TICK_EVENT_CONTEXT=this._TICK_EVENT_CONTEXT||{event:new Event(zn.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(e){this._maxFrame=Math.floor(e),this.scene.dispatchController.dispatch(this._graph_node,Ya.MAX_FRAME_UPDATED)}setMaxFrameLocked(e){this._maxFrameLocked=e,this.scene.dispatchController.dispatch(this._graph_node,Ya.MAX_FRAME_UPDATED)}setRealtimeState(e){this._realtimeState=e,this.scene.dispatchController.dispatch(this._graph_node,Ya.REALTIME_STATUS_UPDATED)}setTime(e,t=!0){if(e!=this._time){if(this._time=e,this._onBeforeTickCallbacks)for(let e of this._onBeforeTickCallbacks)e();if(t){const e=Math.floor(60*this._time),t=this._ensureFrameWithinBounds(e);e!=t?this.setFrame(t,!0):this._frame=e}if(this.scene.dispatchController.dispatch(this._graph_node,Ya.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 e of this._onAfterTickCallbacks)e()}}setFrame(e,t=!0){e!=this._frame&&(e=this._ensureFrameWithinBounds(e))!=this._frame&&(this._frame=e,t&&this.setTime(this._frame/60,!1))}setFrameToStart(){this.setFrame(Qa.START_FRAME,!0)}incrementTimeIfPlaying(){this._playing&&(this.scene.root().areChildrenCooking()||this.incrementTime())}incrementTime(){if(this._realtimeState){const e=$a.now(),t=(e-this._prev_performance_now)/1e3,n=this._time+t;this._prev_performance_now=e,this.setTime(n)}else this.setFrame(this.frame()+1)}_ensureFrameWithinBounds(e){if(this._playing){if(this._maxFrameLocked&&e>this._maxFrame)return Qa.START_FRAME}else{if(this._maxFrameLocked&&e>this._maxFrame)return this._maxFrame;if(e<Qa.START_FRAME)return Qa.START_FRAME}return e}playing(){return!0===this._playing}pause(){1==this._playing&&(this._playing=!1,this.scene.dispatchController.dispatch(this._graph_node,Ya.PLAY_STATE_UPDATED),this.scene.eventsDispatcher.sceneEventsController.processEvent(this.PAUSE_EVENT_CONTEXT))}play(){!0!==this._playing&&(this._playing=!0,this._prev_performance_now=$a.now(),this.scene.dispatchController.dispatch(this._graph_node,Ya.PLAY_STATE_UPDATED),this.scene.eventsDispatcher.sceneEventsController.processEvent(this.PLAY_EVENT_CONTEXT))}togglePlayPause(){this.playing()?this.pause():this.play()}registerOnBeforeTick(e,t){this._onBeforeTickCallbackNames=this._onBeforeTickCallbackNames||[],this._onBeforeTickCallbacks=this._onBeforeTickCallbacks||[],this._registerCallback(e,t,this._onBeforeTickCallbackNames,this._onBeforeTickCallbacks)}unRegisterOnBeforeTick(e){this._unregisterCallback(e,this._onBeforeTickCallbackNames,this._onBeforeTickCallbacks)}registeredBeforeTickCallbackNames(){return this._onBeforeTickCallbackNames}registerOnAfterTick(e,t){this._onAfterTickCallbacks=this._onAfterTickCallbacks||[],this._onAfterTickCallbackNames=this._onAfterTickCallbackNames||[],this._registerCallback(e,t,this._onAfterTickCallbackNames,this._onAfterTickCallbacks)}unRegisterOnAfterTick(e){this._unregisterCallback(e,this._onAfterTickCallbackNames,this._onAfterTickCallbacks)}registeredAfterTickCallbackNames(){return this._onAfterTickCallbackNames}_registerCallback(e,t,n,i){(null==n?void 0:n.includes(e))?console.warn(`callback ${e} already registered`):(i.push(t),n.push(e))}_unregisterCallback(e,t,n){if(!t||!n)return;const i=t.indexOf(e);t.splice(i,1),n.splice(i,1)}}Qa.START_FRAME=0;class Ja{constructor(e){this.scene=e,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(e,t){this._time_dependent_uniform_owners[e]=t,this._time_dependent_uniform_owners_ids||(this._time_dependent_uniform_owners_ids=[]),this._time_dependent_uniform_owners_ids.includes(e)||this._time_dependent_uniform_owners_ids.push(e)}removeTimeDependentUniformOwner(e){if(delete this._time_dependent_uniform_owners[e],this._time_dependent_uniform_owners_ids){const t=this._time_dependent_uniform_owners_ids.indexOf(e);t>=0&&this._time_dependent_uniform_owners_ids.splice(t,1)}}updateTimeDependentUniformOwners(){const e=this.scene.time();if(this._time_dependent_uniform_owners_ids)for(let t of this._time_dependent_uniform_owners_ids){this._time_dependent_uniform_owners[t].time.value=e}}addResolutionDependentUniformOwner(e,t){this._resolution_dependent_uniform_owners[e]=t,this._resolution_dependent_uniform_owners_ids||(this._resolution_dependent_uniform_owners_ids=[]),this._resolution_dependent_uniform_owners_ids.includes(e)||this._resolution_dependent_uniform_owners_ids.push(e),this._resolution&&this.updateResolutionDependentUniforms(t)}removeResolutionDependentUniformOwner(e){if(delete this._resolution_dependent_uniform_owners[e],this._resolution_dependent_uniform_owners_ids){const t=this._resolution_dependent_uniform_owners_ids.indexOf(e);t>=0&&this._resolution_dependent_uniform_owners_ids.splice(t,1)}}updateResolutionDependentUniformOwners(e){this._resolution.copy(e);for(let e of this._resolution_dependent_uniform_owners_ids){const t=this._resolution_dependent_uniform_owners[e];this.updateResolutionDependentUniforms(t)}}updateResolutionDependentUniforms(e){e.resolution.value.x=this._resolution.x,e.resolution.value.y=this._resolution.y}}class Ka{constructor(e){this.scene=e,this._viewers_by_id=new Map}registerViewer(e){this._viewers_by_id.set(e.id(),e)}unregisterViewer(e){this._viewers_by_id.delete(e.id())}traverseViewers(e){this._viewers_by_id.forEach(e)}}class Za{constructor(){this._require_webgl2=!1}require_webgl2(){return this._require_webgl2}set_require_webgl2(){this._require_webgl2||(this._require_webgl2=!0,Rn.renderersController.setRequireWebGL2())}}class ec{constructor(e){this._scene=e,this._onWindowResizeBound=this._onWindowResize.bind(this)}graphNode(){return this._coreGraphNode=this._coreGraphNode||this._createGraphNode()}_createGraphNode(){const e=new Qn(this._scene,\\\\\\\"SceneWindowController\\\\\\\");return window.addEventListener(\\\\\\\"resize\\\\\\\",this._onWindowResizeBound),e}_onWindowResize(){this.graphNode().setSuccessorsDirty()}dispose(){window.removeEventListener(\\\\\\\"resize\\\\\\\",this._onWindowResizeBound)}}class tc{constructor(){this._params_by_id=new Map,this._assets_root=null}register_param(e){this._params_by_id.set(e.graphNodeId(),e)}deregister_param(e){this._params_by_id.delete(e.graphNodeId())}traverse_params(e){this._params_by_id.forEach(((t,n)=>{e(t)}))}root(){return this._assets_root}setRoot(e){\\\\\\\"\\\\\\\"==e&&(e=null),this._assets_root=e}}class nc{constructor(){this._cameras_controller=new r(this),this._cooker=new o(this),this.cookController=new a,this._graph=new c,this._missing_expression_references_controller=new Yn(this),this._expressions_controller=new Dn,this._nodes_controller=new ia(this),this._objects_controller=new Po(this),this._references_controller=new Xa(this),this._time_controller=new Qa(this),this._read_only=!1,this._graph.setScene(this),this.nodesController.init()}threejsScene(){return this.root().object}setUuid(e){return this._uuid=e}get uuid(){return this._uuid}setName(e){return e=Li.sanitizeName(e),this._name=e}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 tc}async waitForCooksCompleted(){return this.cookController.waitForCooksCompleted()}get dispatchController(){return this._dispatch_controller=this._dispatch_controller||new Fn(this)}get eventsDispatcher(){return this._events_dispatcher=this._events_dispatcher||new Wa(this)}get graph(){return this._graph}get lifecycleController(){return this._lifecycle_controller=this._lifecycle_controller||new kn(this)}get loadingController(){return this._loading_controller=this._loading_controller||new Vn(this)}get missingExpressionReferencesController(){return this._missing_expression_references_controller}get expressionsController(){return this._expressions_controller}get nodesController(){return this._nodes_controller}createNode(e,t){return this.root().createNode(e,t)}nodesByType(e){return this.nodesController.nodesByType(e)}get objectsController(){return this._objects_controller}findObjectByMask(e){return this._objects_controller.findObjectByMask(e)}objectsByMask(e){return this._objects_controller.objectsByMask(e)}get referencesController(){return this._references_controller}get performance(){return this._performance=this._performance||new In}get viewersRegister(){return this._viewers_register=this._viewers_register||new Ka(this)}get timeController(){return this._time_controller}setFrame(e){this.timeController.setFrame(e)}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 sa(this)}toJSON(){return this.serializer.toJSON()}markAsReadOnly(e){this._read_only||(this._read_only_requester=e,this._read_only=!0)}readOnly(){return this._read_only}readOnlyRequester(){return this._read_only_requester}get uniformsController(){return this._uniformsController=this._uniformsController||new Ja(this)}get webgl_controller(){return this._webgl_controller=this._webgl_controller||new Za}get windowController(){return this._windowController=this._windowController||new ec(this)}dispose(){var e;null===(e=this._windowController)||void 0===e||e.dispose()}batchUpdates(e){this._cooker.block(),e(),this._cooker.unblock()}node(e){return this.nodesController.node(e)}root(){return this.nodesController.root()}registerOnBeforeTick(e,t){this.timeController.registerOnBeforeTick(e,t)}unRegisterOnBeforeTick(e){this.timeController.unRegisterOnBeforeTick(e)}registeredBeforeTickCallbackNames(){return this.timeController.registeredBeforeTickCallbackNames()}registerOnAfterTick(e,t){this.timeController.registerOnAfterTick(e,t)}unRegisterOnAfterTick(e){this.timeController.unRegisterOnAfterTick(e)}registeredAfterTickCallbackNames(){return this.timeController.registeredAfterTickCallbackNames()}}class ic{constructor(e){this._param=e}process_data(e){const t=e.raw_input;void 0!==t&&this._param.set(t),this.add_main(e)}add_main(e){}static spare_params_data(e){return this.params_data(!0,e)}static non_spare_params_data_value(e){return this.params_data_value(!1,e)}static params_data(e,t){let n;if(t){n={};const e=Object.keys(t);let i;for(let s of e)i=t[s],i&&(n[s]=t)}return n}static params_data_value(e,t){let n;if(t){n={};const i=Object.keys(t);let s;for(let r of i)if(s=t[r],null!=s){const t=s.options,i=s.overriden_options;if(t||i){const o=s;t&&t.spare==e?null!=o.raw_input&&(n[r]={complex_data:o}):i&&(n[r]={complex_data:o})}else{const e=s;(i||null!=e)&&(n[r]={simple_data:e})}}}return n}}const sc=\\\\\\\"operationsComposer\\\\\\\";class rc{constructor(e,t,n){this._scene=e,this.states=t,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(e,t){}}rc.DEFAULT_PARAMS={},rc.INPUT_CLONED_STATE=[];class oc{constructor(e){this._node=e,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(e,t){var n,i,s;if(!t)return;if(!this._node.childrenAllowed()||!this._node.childrenController)return;const{optimized_names:r}=oc.child_names_by_optimized_state(t);this._nodes=[],this._optimized_root_node_names=new Set;for(let e of r)oc.is_optimized_root_node(t,e)&&this._optimized_root_node_names.add(e);for(let r of this._optimized_root_node_names){const o=t[r],a=this._node.createNode(sc);if(a){a.setName(r),this._nodes.push(a),(null===(n=o.flags)||void 0===n?void 0:n.display)&&(null===(s=null===(i=a.flags)||void 0===i?void 0:i.display)||void 0===s||s.set(!0));const t=this._create_operation_container(e,a,o,a.name());a.set_output_operation_container(t)}}for(let n of this._nodes){const i=n.output_operation_container();if(i){this._node_inputs=[],this._add_optimized_node_inputs(e,n,t,n.name(),i),n.io.inputs.setCount(this._node_inputs.length);for(let e=0;e<this._node_inputs.length;e++)n.setInput(e,this._node_inputs[e])}}}_add_optimized_node_inputs(e,t,n,i,s){var r;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(oc.is_node_optimized(o)&&!this._optimized_root_node_names.has(i)){let r=this._operation_containers_by_name.get(i);r||(r=this._create_operation_container(e,t,o,i),r&&this._add_optimized_node_inputs(e,t,n,i,r)),s.add_input(r)}else{const e=null===(r=t.parent())||void 0===r?void 0:r.node(i);if(e){this._node_inputs.push(e);const n=this._node_inputs.length-1;t.add_input_config(s,{operation_input_index:s.current_input_index(),node_input_index:n}),s.increment_input_index()}}}1==o.cloned_state_overriden&&s.override_input_clone_state(o.cloned_state_overriden)}}static child_names_by_optimized_state(e){const t=Object.keys(e),n=[],i=[];for(let s of t){const t=e[s];Rn.playerMode()&&this.is_node_optimized(t)?n.push(s):i.push(s)}return{optimized_names:n,non_optimized_names:i}}static is_optimized_root_node_generic(e){return 0==e.outputs_count||e.non_optimized_count>0}static is_optimized_root_node(e,t){const n=this.node_outputs(e,t);let i=0;return n.forEach((t=>{const n=e[t];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(e){var t,n,i,s;if(!(null===(n=null===(t=e.flags)||void 0===t?void 0:t.optimize)||void 0===n?void 0:n.active()))return!1;const r=e.io.connections.outputConnections().map((e=>e.node_dest));let o=0;for(let e of r)(null===(s=null===(i=e.flags)||void 0===i?void 0:i.optimize)||void 0===s?void 0:s.active())||o++;return this.is_optimized_root_node_generic({outputs_count:r.length,non_optimized_count:o})}static node_outputs(e,t){const n=Object.keys(e),i=new Set;for(let s of n)if(s!=t){const n=e[s].inputs;if(n)for(let e of n)if(m.isString(e)){e==t&&i.add(s)}}return i}_create_operation_container(e,t,n,i){const s=ic.non_spare_params_data_value(n.params),r=oc.operation_type(n),o=this._node.create_operation_container(r,i,s);return o&&(this._operation_containers_by_name.set(i,o),o.path_param_resolve_required()&&(t.add_operation_container_with_path_param_resolve_required(o),e.add_operations_composer_node_with_path_param_resolve_required(t))),o}static operation_type(e){return oc.is_node_bypassed(e)?\\\\\\\"null\\\\\\\":e.type}static is_node_optimized(e){const t=e.flags;return!(!t||!t.optimize)}static is_node_bypassed(e){const t=e.flags;return!(!t||!t.bypass)}}class ac{constructor(e){this._node=e}process_data(e,t){var n;if(!t)return;if(!this._node.childrenAllowed()||!this._node.childrenController)return;const{optimized_names:i,non_optimized_names:s}=oc.child_names_by_optimized_state(t),r=[];for(let n of s){const i=t[n],s=i.type.toLowerCase(),o=ic.non_spare_params_data_value(i.params);try{const e=this._node.createNode(s,o);e&&(e.setName(n),r.push(e))}catch(t){console.error(`error importing node: cannot create with type ${s}`,t);const i=Li.camelCase(s);try{const e=this._node.createNode(i,o);e&&(e.setName(n),r.push(e))}catch(t){const a=`${s}Network`;try{const e=this._node.createNode(a,o);e&&(e.setName(n),r.push(e))}catch(t){const n=`failed to create node with type '${s}', '${i}' or '${a}'`;e.report.addWarning(n),Rn.warn(n,t)}}}}if(i.length>0){const i=new oc(this._node);if(i.process_data(e,t),this._node.childrenController.context==Ei.SOP){const e=Object.keys(t);let s;for(let i of e){(null===(n=t[i].flags)||void 0===n?void 0:n.display)&&(s=i)}if(s){const e=r.map((e=>e.name())),t=i.nodes();for(let n of t)e.push(n.name());if(!e.includes(s)){const e=`node '${`${this._node.path()}/${s}`}' with display flag has been optimized and does not exist in player mode`;console.error(e)}}}}const o=new Map;for(let n of r){if(t[n.name()]){const i=mc.dispatch_node(n);o.set(n.name(),i),i.process_data(e,t[n.name()])}else Rn.warn(`possible import error for node ${n.name()}`)}for(let e of r){const n=o.get(e.name());n&&n.process_inputs_data(t[e.name()])}}}const cc=[\\\\\\\"overriden_options\\\\\\\",\\\\\\\"type\\\\\\\"];class lc{constructor(e){this._node=e}process_data(e,t){if(this.set_connection_points(t.connection_points),this._node.childrenAllowed()&&this.create_nodes(e,t.nodes),this.set_selection(t.selection),this._node.io.inputs.overrideClonedStateAllowed()){const e=t.cloned_state_overriden;e&&this._node.io.inputs.overrideClonedState(e)}this.set_flags(t),this.set_params(t.params),t.persisted_config&&this.set_persisted_config(t.persisted_config),this.from_data_custom(t)}process_inputs_data(e){const t=e.maxInputsCount;null!=t&&this._node.io.inputs.setCount(1,t),this.setInputs(e.inputs)}process_ui_data(e,t){if(!t)return;if(Rn.playerMode())return;const n=this._node.uiData,i=t.pos;if(i){const e=(new d.a).fromArray(i);n.setPosition(e)}const s=t.comment;s&&n.setComment(s),this._node.childrenAllowed()&&this.process_nodes_ui_data(e,t.nodes)}create_nodes(e,t){if(!t)return;new ac(this._node).process_data(e,t)}set_selection(e){if(this._node.childrenAllowed()&&this._node.childrenController&&e&&e.length>0){const t=[];e.forEach((e=>{const n=this._node.node(e);n&&t.push(n)})),this._node.childrenController.selection.set(t)}}set_flags(e){var t,n,i,s,r,o;const a=e.flags;if(a){const e=a.bypass;null!=e&&(null===(n=null===(t=this._node.flags)||void 0===t?void 0:t.bypass)||void 0===n||n.set(e));const c=a.display;null!=c&&(null===(s=null===(i=this._node.flags)||void 0===i?void 0:i.display)||void 0===s||s.set(c));const l=a.optimize;null!=l&&(null===(o=null===(r=this._node.flags)||void 0===r?void 0:r.optimize)||void 0===o||o.set(l))}}set_connection_points(e){e&&(e.in&&this._node.io.saved_connection_points_data.set_in(e.in),e.out&&this._node.io.saved_connection_points_data.set_out(e.out),this._node.io.has_connection_points_controller&&this._node.io.connection_points.update_signature_if_required())}setInputs(e){if(!e)return;let t;for(let n=0;n<e.length;n++)if(t=e[n],t&&this._node.parent())if(m.isString(t)){const e=t,i=this._node.nodeSibbling(e);this._node.setInput(n,i)}else{const e=this._node.nodeSibbling(t.node),n=t.index;this._node.setInput(n,e,t.output)}}process_nodes_ui_data(e,t){if(!t)return;if(Rn.playerMode())return;const n=Object.keys(t);for(let i of n){const n=this._node.node(i);if(n){const s=t[i];mc.dispatch_node(n).process_ui_data(e,s)}}}set_params(e){if(!e)return;const t=Object.keys(e),n={};for(let i of t){const t=e[i],s=t.options;0;const r=t.type;let o,a=!1;this._node.params.has_param(i)&&(o=this._node.params.get(i),(o&&o.type()==r||null==r)&&(a=!0)),a?this._is_param_data_complex(t)?this._process_param_data_complex(i,t):this._process_param_data_simple(i,t):(n.namesToDelete=n.namesToDelete||[],n.namesToDelete.push(i),n.toAdd=n.toAdd||[],n.toAdd.push({name:i,type:r,init_value:t.default_value,raw_input:t.raw_input,options:s}))}const i=n.namesToDelete&&n.namesToDelete.length>0,s=n.toAdd&&n.toAdd.length>0;if(i||s){this._node.params.updateParams(n);for(let t of this._node.params.spare){const n=e[t.name()];!t.parent_param&&n&&(this._is_param_data_complex(n)?this._process_param_data_complex(t.name(),n):this._process_param_data_simple(t.name(),n))}}this._node.params.runOnSceneLoadHooks()}_process_param_data_simple(e,t){var n;null===(n=this._node.params.get(e))||void 0===n||n.set(t)}_process_param_data_complex(e,t){const n=this._node.params.get(e);n&&mc.dispatch_param(n).process_data(t)}_is_param_data_complex(e){if(m.isString(e)||m.isNumber(e)||m.isArray(e)||m.isBoolean(e))return!1;if(m.isObject(e)){const t=Object.keys(e);for(let e of cc)if(t.includes(e))return!0}return!1}set_persisted_config(e){this._node.persisted_config&&this._node.persisted_config.load(e)}from_data_custom(e){}}class uc extends ic{add_main(e){}}const hc=/\\\\\\\\n+/g;class dc extends ic{add_main(e){let t=e.raw_input;void 0!==t&&(t=t.replace(hc,\\\\\\\"\\\\n\\\\\\\"),this._param.set(t))}}class pc extends ic{add_main(e){const t=e.raw_input;t&&this._param.set(t)}}class _c extends lc{create_nodes(e,t){const n=this._node.polyNodeController;n&&n.createChildNodesFromDefinition()}}class mc{static dispatch_node(e){return e.polyNodeController?new _c(e):new lc(e)}static dispatch_param(e){return e instanceof Sr?new uc(e):e instanceof Hr?new dc(e):e instanceof jr?new pc(e):new ic(e)}}class fc{constructor(e){this._warnings=[]}warnings(){return this._warnings}reset(){this._warnings=[]}addWarning(e){this._warnings.push(e)}}class gc{constructor(e){this._data=e,this.report=new fc(this)}static async loadData(e){const t=new gc(e);return await t.scene()}async scene(){const e=new nc;e.loadingController.markAsLoading();const t=this._data.properties;if(t){const n=t.maxFrame||600;e.timeController.setMaxFrame(n);const i=t.maxFrameLocked;i&&e.timeController.setMaxFrameLocked(i);const s=t.realtimeState;null!=s&&e.timeController.setRealtimeState(s),e.setFrame(t.frame||Qa.START_FRAME),t.mainCameraNodePath&&e.camerasController.setMainCameraNodePath(t.mainCameraNodePath)}e.cooker.block(),this._base_operations_composer_nodes_with_resolve_required=void 0;const n=mc.dispatch_node(e.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 e.loadingController.markAsLoaded(),e.cooker.unblock(),e}add_operations_composer_node_with_path_param_resolve_required(e){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(e)}_resolve_operation_containers_with_path_param_resolve(){if(this._base_operations_composer_nodes_with_resolve_required)for(let e of this._base_operations_composer_nodes_with_resolve_required)e.resolve_operation_containers_path_params()}}class vc{static async importSceneData(e){null==e.editorMode&&(e.editorMode=!1);const{manifest:t,urlPrefix:n}=e,i=Object.keys(t.nodes),s=[];for(let e of i){const i=`${n}/root/${e}.json?t=${t.nodes[e]}`;s.push(i)}const r=[`${n}/root.json?t=${t.root}`,`${n}/properties.json?t=${t.properties}`];if(e.editorMode){const e=Date.now();r.push(`${n}/ui.json?t=${e}`)}for(let e of s)r.push(e);const o=r.map((e=>fetch(e))),a=await Promise.all(o),c=[];for(let e of a)c.push(await e.json());const l={root:c[0],properties:c[1]};let u=2;e.editorMode&&(l.ui=c[2],u+=1);const h={},d=Object.keys(t.nodes);for(let e=0;e<d.length;e++){const t=d[e],n=c[e+u];h[t]=n}return this.assemble(l,d,h)}static async assemble(e,t,n){const i={root:e.root,properties:e.properties,ui:e.ui};for(let e=0;e<t.length;e++){const s=t[e],r=n[s];this.insert_child_data(i.root,s,r)}return i}static insert_child_data(e,t,n){const i=t.split(\\\\\\\"/\\\\\\\");if(1==i.length)e.nodes||(e.nodes={}),e.nodes[t]=n;else{const t=i.shift(),s=i.join(\\\\\\\"/\\\\\\\"),r=e.nodes[t];this.insert_child_data(r,s,n)}}}async function yc(e){const t=e.scenesSrcRoot||\\\\\\\"/src/polygonjs/scenes\\\\\\\",n=e.scenesSrcRoot||\\\\\\\"/public/polygonjs/scenes\\\\\\\",i=e.sceneName;const s=await async function(){const e=await fetch(`${t}/${i}/manifest.json`);return await e.json()}(),r=await async function(e){return await vc.importSceneData({manifest:e,urlPrefix:`${n}/${i}`})}(s);return await async function(t){const n=new gc(t),i=await n.scene(),s=i.mainCameraNode();if(!s)return void console.warn(\\\\\\\"no master camera found\\\\\\\");const r=m.isString(e.domElement)?document.getElementById(e.domElement):e.domElement;if(!r)return void console.warn(\\\\\\\"no element to mount the viewer onto\\\\\\\");const o=s.createViewer(r);return{scene:i,cameraNode:s,viewer:o}}(r)}const xc=\\\\\\\"networks\\\\\\\",bc=\\\\\\\"misc\\\\\\\",wc=\\\\\\\"modifiers\\\\\\\",Ac=xc,Tc=\\\\\\\"prop\\\\\\\",Ec=\\\\\\\"timing\\\\\\\",Cc=\\\\\\\"advanced\\\\\\\",Mc=\\\\\\\"inputs\\\\\\\",Nc=\\\\\\\"misc\\\\\\\",Sc=xc,Oc=\\\\\\\"cameras\\\\\\\",Lc=\\\\\\\"inputs\\\\\\\",Pc=\\\\\\\"misc\\\\\\\",Rc=\\\\\\\"scene\\\\\\\",Ic=xc,Fc=\\\\\\\"color\\\\\\\",Dc=\\\\\\\"conversion\\\\\\\",kc=\\\\\\\"geometry\\\\\\\",Bc=\\\\\\\"globals\\\\\\\",zc=\\\\\\\"lighting\\\\\\\",Uc=\\\\\\\"logic\\\\\\\",Gc=\\\\\\\"math\\\\\\\",Vc=\\\\\\\"physics\\\\\\\",jc=\\\\\\\"quat\\\\\\\",Hc=\\\\\\\"trigo\\\\\\\",qc=\\\\\\\"util\\\\\\\",Wc=\\\\\\\"globals\\\\\\\",Xc=\\\\\\\"advanced\\\\\\\",Yc=\\\\\\\"lines\\\\\\\",$c=\\\\\\\"meshes\\\\\\\",Qc=xc,Jc=\\\\\\\"points\\\\\\\",Kc=\\\\\\\"volumes\\\\\\\",Zc=\\\\\\\"advanced\\\\\\\",el=\\\\\\\"audio\\\\\\\",tl=\\\\\\\"cameras\\\\\\\",nl=\\\\\\\"geometries\\\\\\\",il=\\\\\\\"lights\\\\\\\",sl=xc,rl=\\\\\\\"transform\\\\\\\",ol=\\\\\\\"css\\\\\\\",al=xc,cl=\\\\\\\"webgl\\\\\\\",ll=\\\\\\\"advanced\\\\\\\",ul=\\\\\\\"animation\\\\\\\",hl=\\\\\\\"attributes\\\\\\\",dl=\\\\\\\"dynamics\\\\\\\",pl=\\\\\\\"inputs\\\\\\\",_l=\\\\\\\"lights\\\\\\\",ml=\\\\\\\"misc\\\\\\\",fl=\\\\\\\"modifiers\\\\\\\",gl=xc,vl=\\\\\\\"primitives\\\\\\\",yl=\\\\\\\"render\\\\\\\",xl=\\\\\\\"blur\\\\\\\",bl=\\\\\\\"color\\\\\\\",wl=\\\\\\\"effect\\\\\\\",Al=\\\\\\\"misc\\\\\\\",Tl=xc,El=\\\\\\\"input 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Yd(this._pt,m?g:E,h,c,_?_*a:a-c,m||\\\\\\\"px\\\\\\\"!==p&&\\\\\\\"zIndex\\\\\\\"!==h||!1===t.autoRound?_p:gp),this._pt.u=p||0,d!==p&&(this._pt.b=r,this._pt.r=fp);else if(h in E)Vp.call(this,e,h,r,o);else{if(!(h in e)){Vu(h,o);continue}this.add(e,h,e[h],o,i,s)}T.push(h)}y&&Xd(this)},get:Gp,aliases:pp,getSetter:function(e,t,n){var i=pp[t];return i&&i.indexOf(\\\\\\\",\\\\\\\")<0&&(t=i),t in op&&t!==Mp&&(e._gsap.x||Gp(e,\\\\\\\"x\\\\\\\"))?n&&sp===n?\\\\\\\"scale\\\\\\\"===t?Ap:wp:(sp=n||{})&&(\\\\\\\"scale\\\\\\\"===t?Tp:Ep):e.style&&!Eu(e.style[t])?xp:~t.indexOf(\\\\\\\"-\\\\\\\")?bp:zd(e,t)},core:{_removeProperty:kp,_getMatrix:Qp}};Kd.utils.checkPrefix=Lp,d_=ih((u_=\\\\\\\"x,y,z,scale,scaleX,scaleY,xPercent,yPercent\\\\\\\")+\\\\\\\",\\\\\\\"+(h_=\\\\\\\"rotation,rotationX,rotationY,skewX,skewY\\\\\\\")+\\\\\\\",transform,transformOrigin,svgOrigin,force3D,smoothOrigin,transformPerspective\\\\\\\",(function(e){op[e]=1})),ih(h_,(function(e){du.units[e]=\\\\\\\"deg\\\\\\\",Xp[e]=1})),pp[d_[13]]=u_+\\\\\\\",\\\\\\\"+h_,ih(\\\\\\\"0:translateX,1:translateY,2:translateZ,8:rotate,8:rotationZ,8:rotateZ,9:rotateX,10:rotateY\\\\\\\",(function(e){var t=e.split(\\\\\\\":\\\\\\\");pp[t[1]]=d_[t[0]]})),ih(\\\\\\\"x,y,z,top,right,bottom,left,width,height,fontSize,padding,margin,perspective\\\\\\\",(function(e){du.units[e]=\\\\\\\"px\\\\\\\"})),Kd.registerPlugin(p_);var __,m_=Kd.registerPlugin(p_)||Kd;m_.core.Tween;!function(e){e.SET=\\\\\\\"set\\\\\\\",e.ADD=\\\\\\\"add\\\\\\\",e.SUBSTRACT=\\\\\\\"substract\\\\\\\"}(__||(__={}));const f_=[__.SET,__.ADD,__.SUBSTRACT];class g_{constructor(){this._timeline_builders=[],this._duration=1,this._operation=__.SET,this._delay=0,this._debug=!1}setDebug(e){this._debug=e}_printDebug(e){this._debug&&console.log(e)}addTimelineBuilder(e){this._timeline_builders.push(e),e.setParent(this)}timelineBuilders(){return this._timeline_builders}setParent(e){this._parent=e}parent(){return this._parent}setTarget(e){this._target=e;for(let t of this._timeline_builders)t.setTarget(e)}target(){return this._target}setDuration(e){if(e>=0){this._duration=e;for(let t of this._timeline_builders)t.setDuration(e)}}duration(){return this._duration}setEasing(e){this._easing=e;for(let t of this._timeline_builders)t.setEasing(e)}easing(){return this._easing}setOperation(e){this._operation=e;for(let t of this._timeline_builders)t.setOperation(e)}operation(){return this._operation}setRepeatParams(e){this._repeat_params=e;for(let t of this._timeline_builders)t.setRepeatParams(e)}repeatParams(){return this._repeat_params}setDelay(e){this._delay=e;for(let t of this._timeline_builders)t.setDelay(e)}delay(){return this._delay}setPosition(e){this._position=e}position(){return this._position}setUpdateCallback(e){this._update_callback=e}updateCallback(){return this._update_callback}clone(){const e=new g_;if(e.setDuration(this._duration),e.setOperation(this._operation),e.setDelay(this._delay),this._target&&e.setTarget(this._target.clone()),this._easing&&e.setEasing(this._easing),this._delay&&e.setDelay(this._delay),this._update_callback&&e.setUpdateCallback(this._update_callback.clone()),this._repeat_params&&e.setRepeatParams({count:this._repeat_params.count,delay:this._repeat_params.delay,yoyo:this._repeat_params.yoyo}),this._property){const t=this._property.name();t&&e.setPropertyName(t);const n=this._property.targetValue();null!=n&&e.setPropertyValue(n)}this._position&&e.setPosition(this._position.clone());for(let t of this._timeline_builders){const n=t.clone();e.addTimelineBuilder(n)}return e}setPropertyName(e){this.property().setName(e)}property(){return this._property=this._property||new Fl}propertyName(){return this.property().name()}setPropertyValue(e){this.property().setTargetValue(e)}populate(e){var t;this._printDebug([\\\\\\\"populate\\\\\\\",this,e]);for(let n of this._timeline_builders){const i=m_.timeline();n.setDebug(this._debug),n.populate(i);const s=(null===(t=n.position())||void 0===t?void 0:t.toParameter())||void 0;e.add(i,s)}this._property&&this._target&&(this._property.setDebug(this._debug),this._property.addToTimeline(this,e,this._target))}}const v_=new class extends Lo{constructor(){super(...arguments),this.count=Oo.INTEGER(1,{range:[1,20],rangeLocked:[!0,!1]})}};class y_ extends Ml{constructor(){super(...arguments),this.paramsConfig=v_}static type(){return\\\\\\\"copy\\\\\\\"}initializeNode(){this.io.inputs.setCount(1)}async cook(e){const t=new g_;for(let e=0;e<this.pv.count;e++){this.stampNode().set_global_index(e);const n=await this.containerController.requestInputContainer(0);if(n){const e=n.coreContentCloned();e&&t.addTimelineBuilder(e)}}this.setTimelineBuilder(t)}stamp_value(e){return this.stampNode().value(e)}stampNode(){return this._stamp_node=this._stamp_node||this.create_stamp_node()}create_stamp_node(){const e=new Sl(this.scene());return this.dirtyController.setForbiddenTriggerNodes([e]),e}}const x_=new class extends Lo{constructor(){super(...arguments),this.delay=Oo.FLOAT(1)}};class b_ extends Ml{constructor(){super(...arguments),this.paramsConfig=x_}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(e){const t=e[0]||new g_;t.setDelay(this.pv.delay),this.setTimelineBuilder(t)}}const w_=new class extends Lo{constructor(){super(...arguments),this.duration=Oo.FLOAT(1,{range:[0,10],rangeLocked:[!0,!1]})}};class A_ extends Ml{constructor(){super(...arguments),this.paramsConfig=w_}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(e){const t=e[0]||new g_;t.setDuration(this.pv.duration),this.setTimelineBuilder(t)}}const T_=new class extends Lo{constructor(){super(...arguments),this.name=Oo.INTEGER(Pl.indexOf(Ol.POWER4),{menu:{entries:Pl.map(((e,t)=>({name:e,value:t})))}}),this.inOut=Oo.INTEGER(Il.indexOf(Rl.OUT),{menu:{entries:Il.map(((e,t)=>({name:e,value:t})))}})}};class E_ extends Ml{constructor(){super(...arguments),this.paramsConfig=T_}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 e=Pl[this.pv.name];if(e==Ol.NONE)return e;return`${e}.${Il[this.pv.inOut]}`}cook(e){const t=e[0]||new g_,n=this.easing_full_name();t.setEasing(n),this.setTimelineBuilder(t)}}var C_;!function(e){e.RELATIVE=\\\\\\\"relative\\\\\\\",e.ABSOLUTE=\\\\\\\"absolute\\\\\\\"}(C_||(C_={}));const M_=[C_.RELATIVE,C_.ABSOLUTE];var N_;!function(e){e.START=\\\\\\\"start\\\\\\\",e.END=\\\\\\\"end\\\\\\\"}(N_||(N_={}));const S_=[N_.START,N_.END];class O_{constructor(){this._mode=C_.RELATIVE,this._relativeTo=N_.END,this._offset=0}clone(){const e=new O_;return e.setMode(this._mode),e.setRelativeTo(this._relativeTo),e.setOffset(this._offset),e}setMode(e){this._mode=e}mode(){return this._mode}setRelativeTo(e){this._relativeTo=e}relativeTo(){return this._relativeTo}setOffset(e){this._offset=e}offset(){return this._offset}toParameter(){switch(this._mode){case C_.RELATIVE:return this._relative_position_param();case C_.ABSOLUTE:return this._absolutePositionParam()}Ri.unreachable(this._mode)}_relative_position_param(){switch(this._relativeTo){case N_.END:return this._offsetString();case N_.START:return`<${this._offset}`}Ri.unreachable(this._relativeTo)}_absolutePositionParam(){return this._offset}_offsetString(){return this._offset>0?`+=${this._offset}`:`-=${Math.abs(this._offset)}`}}var L_;!function(e){e.ALL_TOGETHER=\\\\\\\"play all together\\\\\\\",e.ONE_AT_A_TIME=\\\\\\\"play one at a time\\\\\\\"}(L_||(L_={}));const P_=[L_.ALL_TOGETHER,L_.ONE_AT_A_TIME];const R_=new class extends Lo{constructor(){super(...arguments),this.mode=Oo.INTEGER(0,{menu:{entries:P_.map(((e,t)=>({name:e,value:t})))}}),this.offset=Oo.FLOAT(0,{range:[-1,1]}),this.overridePositions=Oo.BOOLEAN(0),this.inputsCount=Oo.INTEGER(4,{range:[1,32],rangeLocked:[!0,!1],callback:e=>{I_.PARAM_CALLBACK_setInputsCount(e)}})}};class I_ extends Ml{constructor(){super(...arguments),this.paramsConfig=R_}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],(()=>P_[this.pv.mode]))})),this.params.addOnSceneLoadHook(\\\\\\\"update inputs\\\\\\\",(()=>{this._callbackUpdateInputsCount()}))}))}cook(e){const t=new g_;let n=0;for(let i of e)i&&(n>0&&this._update_timeline_builder(i),t.addTimelineBuilder(i),n++);this.setTimelineBuilder(t)}_update_timeline_builder(e){const t=P_[this.pv.mode];switch(t){case L_.ALL_TOGETHER:return this._set_play_all_together(e);case L_.ONE_AT_A_TIME:return this._set_play_one_at_a_time(e)}Ri.unreachable(t)}_set_play_all_together(e){let t=e.position();t&&!this.pv.overridePositions||(t=new O_,t.setMode(C_.RELATIVE),t.setRelativeTo(N_.START),t.setOffset(this.pv.offset),e.setPosition(t))}_set_play_one_at_a_time(e){let t=e.position();t&&!this.pv.overridePositions||(t=new O_,t.setMode(C_.RELATIVE),t.setRelativeTo(N_.END),t.setOffset(this.pv.offset),e.setPosition(t))}_callbackUpdateInputsCount(){this.io.inputs.setCount(1,this.pv.inputsCount),this.emit(Jn.INPUTS_UPDATED)}static PARAM_CALLBACK_setInputsCount(e){e._callbackUpdateInputsCount()}}const F_=new class extends Lo{constructor(){super(...arguments),this.play=Oo.BUTTON(null,{callback:e=>{D_.PARAM_CALLBACK_play(e)}}),this.pause=Oo.BUTTON(null,{callback:e=>{D_.PARAM_CALLBACK_pause(e)}}),this.debug=Oo.BOOLEAN(0)}};class D_ extends Ml{constructor(){super(...arguments),this.paramsConfig=F_}static type(){return\\\\\\\"null\\\\\\\"}initializeNode(){this.io.inputs.setCount(0,1),this.io.inputs.initInputsClonedState(Ti.FROM_NODE)}cook(e){const t=e[0]||new g_;this.setTimelineBuilder(t)}async play(){return new Promise((async e=>{const t=await this.compute();t&&(this._timeline_builder=t.coreContent(),this._timeline_builder&&(this._timeline&&this._timeline.kill(),this._timeline=m_.timeline({onComplete:e}),this.pv.debug&&console.log(`play from '${this.path()}'`),this._timeline_builder.setDebug(this.pv.debug),this._timeline_builder.populate(this._timeline)))}))}async pause(){this._timeline&&this._timeline.pause()}static PARAM_CALLBACK_play(e){e.play()}static PARAM_CALLBACK_pause(e){e.pause()}}const k_=new class extends Lo{constructor(){super(...arguments),this.operation=Oo.INTEGER(0,{menu:{entries:f_.map(((e,t)=>({value:t,name:e})))}})}};class B_ extends Ml{constructor(){super(...arguments),this.paramsConfig=k_}static type(){return\\\\\\\"operation\\\\\\\"}initializeNode(){this.io.inputs.setCount(0,1),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.operation],(()=>f_[this.pv.operation]))}))}))}cook(e){const t=e[0]||new g_;t.setOperation(f_[this.pv.operation]),this.setTimelineBuilder(t)}}const z_=new class extends Lo{constructor(){super(...arguments),this.mode=Oo.INTEGER(0,{menu:{entries:M_.map(((e,t)=>({name:e,value:t})))}}),this.relativeTo=Oo.INTEGER(0,{menu:{entries:S_.map(((e,t)=>({name:e,value:t})))}}),this.offset=Oo.FLOAT(0)}};class U_ extends Ml{constructor(){super(...arguments),this.paramsConfig=z_}static type(){return\\\\\\\"position\\\\\\\"}initializeNode(){this.io.inputs.setCount(0,1),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.mode,this.p.relativeTo,this.p.offset],(()=>{switch(M_[this.pv.mode]){case C_.RELATIVE:return this._relative_label();case C_.ABSOLUTE:return this._absolute_label()}}))}))}))}_relative_label(){const e=this.pv.offset>0?\\\\\\\"after\\\\\\\":\\\\\\\"before\\\\\\\",t=S_[this.pv.relativeTo];return`${Math.abs(this.pv.offset)} ${e} ${t}`}_absolute_label(){return\\\\\\\"absolute\\\\\\\"}cook(e){const t=e[0]||new g_,n=new O_;n.setMode(M_[this.pv.mode]),n.setRelativeTo(S_[this.pv.relativeTo]),n.setOffset(this.pv.offset),t.setPosition(n),this.setTimelineBuilder(t)}}const G_=new class extends Lo{constructor(){super(...arguments),this.name=Oo.STRING(\\\\\\\"position\\\\\\\")}};class V_ extends Ml{constructor(){super(...arguments),this.paramsConfig=G_}static type(){return\\\\\\\"propertyName\\\\\\\"}initializeNode(){this.io.inputs.setCount(0,1),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.name])}))}))}cook(e){const t=e[0]||new g_;t.setPropertyName(this.pv.name),this.setTimelineBuilder(t)}}var j_;!function(e){e.CUSTOM=\\\\\\\"custom\\\\\\\",e.FROM_SCENE_GRAPH=\\\\\\\"from scene graph\\\\\\\",e.FROM_NODE=\\\\\\\"from node\\\\\\\"}(j_||(j_={}));const H_=[j_.CUSTOM,j_.FROM_SCENE_GRAPH,j_.FROM_NODE],q_=H_.indexOf(j_.CUSTOM),W_=H_.indexOf(j_.FROM_SCENE_GRAPH),X_=H_.indexOf(j_.FROM_NODE);const Y_=new class extends Lo{constructor(){super(...arguments),this.mode=Oo.INTEGER(q_,{menu:{entries:H_.map(((e,t)=>({name:e,value:t})))}}),this.nodePath=Oo.NODE_PATH(\\\\\\\"\\\\\\\",{visibleIf:{mode:X_}}),this.objectMask=Oo.STRING(\\\\\\\"*geo1\\\\\\\",{visibleIf:{mode:W_}}),this.printResolve=Oo.BUTTON(null,{visibleIf:{mode:W_},callback:e=>{$_.PARAM_CALLBACK_print_resolve(e)}}),this.overridePropertyName=Oo.BOOLEAN(0,{visibleIf:[{mode:W_},{mode:X_}]}),this.propertyName=Oo.STRING(\\\\\\\"\\\\\\\",{visibleIf:[{overridePropertyName:!0,mode:W_},{overridePropertyName:!0,mode:X_}]}),this.size=Oo.INTEGER(3,{range:[1,4],rangeLocked:[!0,!0],visibleIf:{mode:q_}}),this.value1=Oo.FLOAT(0,{visibleIf:{mode:q_,size:1}}),this.value2=Oo.VECTOR2([0,0],{visibleIf:{mode:q_,size:2}}),this.value3=Oo.VECTOR3([0,0,0],{visibleIf:{mode:q_,size:3}}),this.value4=Oo.VECTOR4([0,0,0,0],{visibleIf:{mode:q_,size:4}})}};class $_ extends Ml{constructor(){super(...arguments),this.paramsConfig=Y_}static type(){return\\\\\\\"propertyValue\\\\\\\"}initializeNode(){this.io.inputs.setCount(0,1)}async cook(e){const t=e[0]||new g_;await this._prepare_timeline_builder(t),this.setTimelineBuilder(t)}setMode(e){this.p.mode.set(H_.indexOf(e))}async _prepare_timeline_builder(e){const t=H_[this.pv.mode];switch(t){case j_.CUSTOM:return this._prepare_timebuilder_custom(e);case j_.FROM_SCENE_GRAPH:return this._prepare_timebuilder_from_scene_graph(e);case j_.FROM_NODE:return await this._prepare_timebuilder_from_node(e)}Ri.unreachable(t)}_prepare_timebuilder_custom(e){const t=[this.pv.value1,this.pv.value2.clone(),this.pv.value3.clone(),this.pv.value4.clone()][this.pv.size-1];e.setPropertyValue(t)}_prepare_timebuilder_from_scene_graph(e){const t=this.pv.overridePropertyName?this.pv.propertyName:e.propertyName();if(!t)return;const n=this._foundObjectFromSceneGraph();if(n){const i=n[t];i&&(m.isNumber(i)||m.isVector(i)||i instanceof Ll.a)&&e.setPropertyValue(i)}}async _prepare_timebuilder_from_node(e){const t=this.pv.overridePropertyName?this.pv.propertyName:e.propertyName();if(!t)return;const n=this.pv.nodePath.node();if(!n)return;const i=n.params.get(t);if(!i)return;i.isDirty()&&await i.compute();const s=i.value;s&&(m.isNumber(s)||m.isVector(s))&&e.setPropertyValue(s)}static PARAM_CALLBACK_print_resolve(e){e.printResolve()}_foundObjectFromSceneGraph(){return this.scene().findObjectByMask(this.pv.objectMask)}printResolve(){const e=this._foundObjectFromSceneGraph();console.log(e)}}const Q_=new class extends Lo{constructor(){super(...arguments),this.unlimited=Oo.BOOLEAN(0),this.count=Oo.INTEGER(1,{range:[0,10],visibleIf:{unlimited:0}}),this.delay=Oo.FLOAT(0),this.yoyo=Oo.BOOLEAN(0)}};class J_ extends Ml{constructor(){super(...arguments),this.paramsConfig=Q_}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(e){const t=e[0]||new g_;t.setRepeatParams(this._repeat_params()),this.setTimelineBuilder(t)}}const K_=new class extends Lo{constructor(){super(...arguments),this.input=Oo.INTEGER(0,{range:[0,3],rangeLocked:[!0,!0]})}};class Z_ extends Ml{constructor(){super(...arguments),this.paramsConfig=K_}static type(){return\\\\\\\"switch\\\\\\\"}initializeNode(){this.io.inputs.setCount(0,4)}cook(e){const t=e[this.pv.input];t?this.setTimelineBuilder(t):this.states.error.set(`input ${this.pv.input} is not valid`)}}class em{constructor(e,t){this._scene=e,this._options=t}clone(){return new em(this._scene,this._options)}objects(){const e=this._options.objectMask;if(e)return this._scene.objectsByMask(e)}node(){if(!this._options.node)return;const e=this._options.node;return e.relativeTo.node(e.path)}}class tm{constructor(){this._update_matrix=!1}clone(){const e=new tm;return e.setUpdateMatrix(this._update_matrix),e}setUpdateMatrix(e){this._update_matrix=e}updateMatrix(){return this._update_matrix}}var nm;!function(e){e.SCENE_GRAPH=\\\\\\\"scene graph\\\\\\\",e.NODE=\\\\\\\"node\\\\\\\"}(nm||(nm={}));const im=[nm.SCENE_GRAPH,nm.NODE],sm=im.indexOf(nm.SCENE_GRAPH),rm=im.indexOf(nm.NODE);const om=new class extends Lo{constructor(){super(...arguments),this.type=Oo.INTEGER(sm,{menu:{entries:im.map(((e,t)=>({name:e,value:t})))}}),this.nodePath=Oo.OPERATOR_PATH(\\\\\\\"\\\\\\\",{visibleIf:{type:rm}}),this.objectMask=Oo.STRING(\\\\\\\"/geo*\\\\\\\",{visibleIf:{type:sm}}),this.updateMatrix=Oo.BOOLEAN(0,{visibleIf:{type:sm}}),this.printResolve=Oo.BUTTON(null,{callback:(e,t)=>{am.PARAM_CALLBACK_print_resolve(e)}})}};class am extends Ml{constructor(){super(...arguments),this.paramsConfig=om}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 e=im[this.pv.type];switch(e){case nm.NODE:return this.pv.nodePath;case nm.SCENE_GRAPH:return this.pv.objectMask}Ri.unreachable(e)}))}))}))}cook(e){const t=e[0]||new g_,n=this._create_target(t);t.setTarget(n),this._set_update_callback(t),this.setTimelineBuilder(t)}setTargetType(e){this.p.type.set(im.indexOf(e))}_create_target(e){const t=im[this.pv.type];switch(t){case nm.NODE:return new em(this.scene(),{node:{path:this.pv.nodePath,relativeTo:this}});case nm.SCENE_GRAPH:return new em(this.scene(),{objectMask:this.pv.objectMask})}Ri.unreachable(t)}_set_update_callback(e){const t=im[this.pv.type];let n=e.updateCallback();switch(t){case nm.NODE:return;case nm.SCENE_GRAPH:return void(this.pv.updateMatrix&&(n=n||new tm,n.setUpdateMatrix(this.pv.updateMatrix),e.setUpdateCallback(n)))}Ri.unreachable(t)}static PARAM_CALLBACK_print_resolve(e){e.print_resolve()}print_resolve(){const e=im[this.pv.type],t=new g_,n=this._create_target(t);switch(e){case nm.NODE:return console.log(n.node());case nm.SCENE_GRAPH:return console.log(n.objects())}}}class cm extends Mo{static context(){return Ei.ANIM}cook(){this.cookController.endCook()}}class lm extends cm{}class um extends lm{constructor(){super(...arguments),this._children_controller_context=Ei.ANIM}static type(){return Ci.ANIM}createNode(e,t){return super.createNode(e,t)}children(){return super.children()}nodesByType(e){return super.nodesByType(e)}}class hm extends lm{constructor(){super(...arguments),this._children_controller_context=Ei.COP}static type(){return Ci.COP}createNode(e,t){return super.createNode(e,t)}children(){return super.children()}nodesByType(e){return super.nodesByType(e)}}class dm extends lm{constructor(){super(...arguments),this._children_controller_context=Ei.EVENT}static type(){return Ci.EVENT}createNode(e,t){return super.createNode(e,t)}children(){return super.children()}nodesByType(e){return super.nodesByType(e)}}class pm extends lm{constructor(){super(...arguments),this._children_controller_context=Ei.MAT}static type(){return Ci.MAT}createNode(e,t){return super.createNode(e,t)}children(){return super.children()}nodesByType(e){return super.nodesByType(e)}}const _m={dependsOnDisplayNode:!0};class mm{constructor(e,t,n=_m){this.node=e,this.options=n,this._initialized=!1,this._display_node=void 0,this._graph_node=new Qn(e.scene(),\\\\\\\"DisplayNodeController\\\\\\\"),this._graph_node.node=e,this._on_display_node_remove_callback=t.onDisplayNodeRemove,this._on_display_node_set_callback=t.onDisplayNodeSet,this._on_display_node_update_callback=t.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((e=>{var t,n;this._display_node||null===(n=null===(t=e.flags)||void 0===t?void 0:t.display)||void 0===n||n.set(!0)})),this.node.lifecycle.add_on_child_remove_hook((e=>{var t,n,i;if(e.graphNodeId()==(null===(t=this._display_node)||void 0===t?void 0:t.graphNodeId())){const e=this.node.children(),t=e[e.length-1];t?null===(i=null===(n=t.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(e){if(this._initialized||console.error(\\\\\\\"display node controller not initialized\\\\\\\",this.node),this._display_node!=e){const t=this._display_node;t&&(t.flags.display.set(!1),this.options.dependsOnDisplayNode&&this._graph_node.removeGraphInput(t),this._on_display_node_remove_callback&&this._on_display_node_remove_callback()),this._display_node=e,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 fm{constructor(e){this.autoStart=void 0===e||e,this.startTime=0,this.oldTime=0,this.elapsedTime=0,this.running=!1}start(){this.startTime=gm(),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 e=0;if(this.autoStart&&!this.running)return this.start(),0;if(this.running){const t=gm();e=(t-this.oldTime)/1e3,this.oldTime=t,this.elapsedTime+=e}return e}}function gm(){return(\\\\\\\"undefined\\\\\\\"==typeof performance?Date:performance).now()}var vm=n(38),ym={uniforms:{tDiffuse:{value:null},opacity:{value:1}},vertexShader:[\\\\\\\"varying vec2 vUv;\\\\\\\",\\\\\\\"void main() {\\\\\\\",\\\\\\\"\\\\tvUv = uv;\\\\\\\",\\\\\\\"\\\\tgl_Position = projectionMatrix * modelViewMatrix * vec4( position, 1.0 );\\\\\\\",\\\\\\\"}\\\\\\\"].join(\\\\\\\"\\\\n\\\\\\\"),fragmentShader:[\\\\\\\"uniform float opacity;\\\\\\\",\\\\\\\"uniform sampler2D tDiffuse;\\\\\\\",\\\\\\\"varying vec2 vUv;\\\\\\\",\\\\\\\"void main() {\\\\\\\",\\\\\\\"\\\\tvec4 texel = texture2D( tDiffuse, vUv );\\\\\\\",\\\\\\\"\\\\tgl_FragColor = opacity * texel;\\\\\\\",\\\\\\\"}\\\\\\\"].join(\\\\\\\"\\\\n\\\\\\\")};function xm(){this.enabled=!0,this.needsSwap=!0,this.clear=!1,this.renderToScreen=!1}Object.assign(xm.prototype,{setSize:function(){},render:function(){console.error(\\\\\\\"THREE.Pass: .render() must be implemented in derived pass.\\\\\\\")}}),xm.FullScreenQuad=function(){var e=new vm.a(-1,1,1,-1,0,1),t=new O.a;t.setAttribute(\\\\\\\"position\\\\\\\",new L.c([-1,3,0,-1,-1,0,3,-1,0],3)),t.setAttribute(\\\\\\\"uv\\\\\\\",new L.c([0,2,0,0,2,0],2));var n=function(e){this._mesh=new z.a(t,e)};return Object.defineProperty(n.prototype,\\\\\\\"material\\\\\\\",{get:function(){return this._mesh.material},set:function(e){this._mesh.material=e}}),Object.assign(n.prototype,{dispose:function(){this._mesh.geometry.dispose()},render:function(t){t.render(this._mesh,e)}}),n}();var bm=function(e,t){xm.call(this),this.textureID=void 0!==t?t:\\\\\\\"tDiffuse\\\\\\\",e instanceof B?(this.uniforms=e.uniforms,this.material=e):e&&(this.uniforms=k.clone(e.uniforms),this.material=new B({defines:Object.assign({},e.defines),uniforms:this.uniforms,vertexShader:e.vertexShader,fragmentShader:e.fragmentShader})),this.fsQuad=new xm.FullScreenQuad(this.material)};bm.prototype=Object.assign(Object.create(xm.prototype),{constructor:bm,render:function(e,t,n){this.uniforms[this.textureID]&&(this.uniforms[this.textureID].value=n.texture),this.fsQuad.material=this.material,this.renderToScreen?(e.setRenderTarget(null),this.fsQuad.render(e)):(e.setRenderTarget(t),this.clear&&e.clear(e.autoClearColor,e.autoClearDepth,e.autoClearStencil),this.fsQuad.render(e))}});var wm=function(e,t){xm.call(this),this.scene=e,this.camera=t,this.clear=!0,this.needsSwap=!1,this.inverse=!1};wm.prototype=Object.assign(Object.create(xm.prototype),{constructor:wm,render:function(e,t,n){var i,s,r=e.getContext(),o=e.state;o.buffers.color.setMask(!1),o.buffers.depth.setMask(!1),o.buffers.color.setLocked(!0),o.buffers.depth.setLocked(!0),this.inverse?(i=0,s=1):(i=1,s=0),o.buffers.stencil.setTest(!0),o.buffers.stencil.setOp(r.REPLACE,r.REPLACE,r.REPLACE),o.buffers.stencil.setFunc(r.ALWAYS,i,4294967295),o.buffers.stencil.setClear(s),o.buffers.stencil.setLocked(!0),e.setRenderTarget(n),this.clear&&e.clear(),e.render(this.scene,this.camera),e.setRenderTarget(t),this.clear&&e.clear(),e.render(this.scene,this.camera),o.buffers.color.setLocked(!1),o.buffers.depth.setLocked(!1),o.buffers.stencil.setLocked(!1),o.buffers.stencil.setFunc(r.EQUAL,1,4294967295),o.buffers.stencil.setOp(r.KEEP,r.KEEP,r.KEEP),o.buffers.stencil.setLocked(!0)}});var Am=function(){xm.call(this),this.needsSwap=!1};Am.prototype=Object.create(xm.prototype),Object.assign(Am.prototype,{render:function(e){e.state.buffers.stencil.setLocked(!1),e.state.buffers.stencil.setTest(!1)}});var Tm=function(e,t){if(this.renderer=e,void 0===t){var n={minFilter:w.V,magFilter:w.V,format:w.Ib},i=e.getSize(new d.a);this._pixelRatio=e.getPixelRatio(),this._width=i.width,this._height=i.height,(t=new Z(this._width*this._pixelRatio,this._height*this._pixelRatio,n)).texture.name=\\\\\\\"EffectComposer.rt1\\\\\\\"}else this._pixelRatio=1,this._width=t.width,this._height=t.height;this.renderTarget1=t,this.renderTarget2=t.clone(),this.renderTarget2.texture.name=\\\\\\\"EffectComposer.rt2\\\\\\\",this.writeBuffer=this.renderTarget1,this.readBuffer=this.renderTarget2,this.renderToScreen=!0,this.passes=[],void 0===ym&&console.error(\\\\\\\"THREE.EffectComposer relies on CopyShader\\\\\\\"),void 0===bm&&console.error(\\\\\\\"THREE.EffectComposer relies on ShaderPass\\\\\\\"),this.copyPass=new bm(ym),this.clock=new fm};Object.assign(Tm.prototype,{swapBuffers:function(){var e=this.readBuffer;this.readBuffer=this.writeBuffer,this.writeBuffer=e},addPass:function(e){this.passes.push(e),e.setSize(this._width*this._pixelRatio,this._height*this._pixelRatio)},insertPass:function(e,t){this.passes.splice(t,0,e),e.setSize(this._width*this._pixelRatio,this._height*this._pixelRatio)},removePass:function(e){const t=this.passes.indexOf(e);-1!==t&&this.passes.splice(t,1)},isLastEnabledPass:function(e){for(var t=e+1;t<this.passes.length;t++)if(this.passes[t].enabled)return!1;return!0},render:function(e){void 0===e&&(e=this.clock.getDelta());var t,n,i=this.renderer.getRenderTarget(),s=!1,r=this.passes.length;for(n=0;n<r;n++)if(!1!==(t=this.passes[n]).enabled){if(t.renderToScreen=this.renderToScreen&&this.isLastEnabledPass(n),t.render(this.renderer,this.writeBuffer,this.readBuffer,e,s),t.needsSwap){if(s){var o=this.renderer.getContext(),a=this.renderer.state.buffers.stencil;a.setFunc(o.NOTEQUAL,1,4294967295),this.copyPass.render(this.renderer,this.writeBuffer,this.readBuffer,e),a.setFunc(o.EQUAL,1,4294967295)}this.swapBuffers()}void 0!==wm&&(t instanceof wm?s=!0:t instanceof Am&&(s=!1))}this.renderer.setRenderTarget(i)},reset:function(e){if(void 0===e){var t=this.renderer.getSize(new d.a);this._pixelRatio=this.renderer.getPixelRatio(),this._width=t.width,this._height=t.height,(e=this.renderTarget1.clone()).setSize(this._width*this._pixelRatio,this._height*this._pixelRatio)}this.renderTarget1.dispose(),this.renderTarget2.dispose(),this.renderTarget1=e,this.renderTarget2=e.clone(),this.writeBuffer=this.renderTarget1,this.readBuffer=this.renderTarget2},setSize:function(e,t){this._width=e,this._height=t;var n=this._width*this._pixelRatio,i=this._height*this._pixelRatio;this.renderTarget1.setSize(n,i),this.renderTarget2.setSize(n,i);for(var s=0;s<this.passes.length;s++)this.passes[s].setSize(n,i)},setPixelRatio:function(e){this._pixelRatio=e,this.setSize(this._width,this._height)}});var Em=function(){this.enabled=!0,this.needsSwap=!0,this.clear=!1,this.renderToScreen=!1};Object.assign(Em.prototype,{setSize:function(){},render:function(){console.error(\\\\\\\"THREE.Pass: .render() must be implemented in derived pass.\\\\\\\")}}),Em.FullScreenQuad=function(){var e=new vm.a(-1,1,1,-1,0,1),t=new O.a;t.setAttribute(\\\\\\\"position\\\\\\\",new L.c([-1,3,0,-1,-1,0,3,-1,0],3)),t.setAttribute(\\\\\\\"uv\\\\\\\",new L.c([0,2,0,0,2,0],2));var n=function(e){this._mesh=new z.a(t,e)};return Object.defineProperty(n.prototype,\\\\\\\"material\\\\\\\",{get:function(){return this._mesh.material},set:function(e){this._mesh.material=e}}),Object.assign(n.prototype,{dispose:function(){this._mesh.geometry.dispose()},render:function(t){t.render(this._mesh,e)}}),n}();var Cm=function(e,t,n,i,s){xm.call(this),this.scene=e,this.camera=t,this.overrideMaterial=n,this.clearColor=i,this.clearAlpha=void 0!==s?s:0,this.clear=!0,this.clearDepth=!1,this.needsSwap=!1,this._oldClearColor=new M.a};Cm.prototype=Object.assign(Object.create(xm.prototype),{constructor:Cm,render:function(e,t,n){var i,s,r=e.autoClear;e.autoClear=!1,void 0!==this.overrideMaterial&&(s=this.scene.overrideMaterial,this.scene.overrideMaterial=this.overrideMaterial),this.clearColor&&(e.getClearColor(this._oldClearColor),i=e.getClearAlpha(),e.setClearColor(this.clearColor,this.clearAlpha)),this.clearDepth&&e.clearDepth(),e.setRenderTarget(this.renderToScreen?null:n),this.clear&&e.clear(e.autoClearColor,e.autoClearDepth,e.autoClearStencil),e.render(this.scene,this.camera),this.clearColor&&e.setClearColor(this._oldClearColor,i),void 0!==this.overrideMaterial&&(this.scene.overrideMaterial=s),e.autoClear=r}});const Mm=[{LinearFilter:w.V},{NearestFilter:w.ob}],Nm=[{NearestFilter:w.ob},{NearestMipMapNearestFilter:w.qb},{NearestMipMapLinearFilter:w.pb},{LinearFilter:w.V},{LinearMipMapNearestFilter:w.X},{LinearMipMapLinearFilter:w.W}],Sm=Object.values(Mm[0])[0],Om=Object.values(Nm[5])[0],Lm=Mm.map((e=>({name:Object.keys(e)[0],value:Object.values(e)[0]}))),Pm=Nm.map((e=>({name:Object.keys(e)[0],value:Object.values(e)[0]})));class Rm extends Lo{constructor(){super(...arguments),this.prependRenderPass=Oo.BOOLEAN(1),this.useRenderTarget=Oo.BOOLEAN(1),this.tmagFilter=Oo.BOOLEAN(0,{visibleIf:{useRenderTarget:1}}),this.magFilter=Oo.INTEGER(Sm,{visibleIf:{useRenderTarget:1,tmagFilter:1},menu:{entries:Lm}}),this.tminFilter=Oo.BOOLEAN(0,{visibleIf:{useRenderTarget:1}}),this.minFilter=Oo.INTEGER(Om,{visibleIf:{useRenderTarget:1,tminFilter:1},menu:{entries:Pm}}),this.stencilBuffer=Oo.BOOLEAN(0,{visibleIf:{useRenderTarget:1}}),this.sampling=Oo.INTEGER(1,{range:[1,4],rangeLocked:[!0,!1]})}}class Im{constructor(e){this.node=e,this._renderer_size=new d.a}displayNodeControllerCallbacks(){return{onDisplayNodeRemove:()=>{},onDisplayNodeSet:()=>{this.node.setDirty()},onDisplayNodeUpdate:()=>{this.node.setDirty()}}}createEffectsComposer(e){const t=e.renderer;let n;if(this.node.pv.useRenderTarget){const e=this._create_render_target(t);n=new Tm(t,e)}else n=new Tm(t);return n.setPixelRatio(window.devicePixelRatio*this.node.pv.sampling),this._build_passes(n,e),n}_create_render_target(e){let t;e.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),e.getDrawingBufferSize(this._renderer_size),t=Rn.renderersController.renderTarget(this._renderer_size.x,this._renderer_size.y,n),t}_build_passes(e,t){if(this.node.pv.prependRenderPass){const n=new Cm(t.scene,t.camera);e.addPass(n)}const n=this.node.displayNodeController.displayNode();n&&n.setupComposer({composer:e,camera:t.camera,resolution:t.resolution,camera_node:t.camera_node,scene:t.scene,requester:t.requester})}}class Fm extends cm{constructor(){super(...arguments),this.paramsConfig=new Rm,this.effectsComposerController=new Im(this),this.displayNodeController=new mm(this,this.effectsComposerController.displayNodeControllerCallbacks()),this._children_controller_context=Ei.POST}static type(){return Ci.POST}createNode(e,t){return super.createNode(e,t)}children(){return super.children()}nodesByType(e){return super.nodesByType(e)}}class Dm extends lm{constructor(){super(...arguments),this._children_controller_context=Ei.ROP}static type(){return Ci.ROP}createNode(e,t){return super.createNode(e,t)}children(){return super.children()}nodesByType(e){return super.nodesByType(e)}}var km=n(42);const Bm=\\\\\\\"input texture\\\\\\\",zm=[Bm,Bm,Bm,Bm];for(var Um=new Uint16Array(32),Gm=0;Gm<32;Gm++)Um[Gm]=28898;const Vm=new T.a(Um,32,1,w.gb,w.M);class jm extends Mo{constructor(e){super(e,\\\\\\\"BaseCopNode\\\\\\\"),this.flags=new ui(this)}static context(){return Ei.COP}static displayedInputNames(){return zm}initializeBaseNode(){this.io.outputs.setHasOneOutput()}setTexture(e){e.name=this.path();const t=this.containerController.container().texture();if(t){if(t.uuid!=e.uuid){const n=Object.keys(e);for(let i of n)t[i]=e[i];t.needsUpdate=!0}this._setContainer(t)}else this._setContainer(e)}_clearTexture(){this._setContainer(Vm)}}class Hm extends jm{}class qm{constructor(){this._id=qm.__next_id++}id(){return this._id}handle_globals_node(e,t,n){}}qm.__next_id=0;class Wm{static any(e){return m.isString(e)?e:m.isBoolean(e)?`${e}`:m.isNumber(e)?`${Li.ensureFloat(e)}`:m.isArray(e)?this.numeric_array(e):e instanceof d.a||e instanceof p.a||e instanceof _.a||e instanceof M.a?this.numeric_array(e.toArray()):`ThreeToGl error: unknown value type '${e}'`}static numeric_array(e){const t=new Array(e.length);for(let n=0;n<e.length;n++)t[n]=`${Li.ensureFloat(e[n])}`;return`${`vec${e.length}`}(${t.join(\\\\\\\", \\\\\\\")})`}static vector4(e){if(m.isString(e))return e;return`vec4(${e.toArray().map((e=>`${Li.ensureFloat(e)}`)).join(\\\\\\\", \\\\\\\")})`}static vector3(e){if(m.isString(e))return e;return`vec3(${e.toArray().map((e=>`${Li.ensureFloat(e)}`)).join(\\\\\\\", \\\\\\\")})`}static vector2(e){if(m.isString(e))return e;return`vec2(${e.toArray().map((e=>`${Li.ensureFloat(e)}`)).join(\\\\\\\", \\\\\\\")})`}static vector3_float(e,t){return m.isNumber(t)&&(t=Li.ensureFloat(t)),`vec4(${this.vector3(e)}, ${t})`}static float4(e,t,n,i){return m.isNumber(e)&&(e=Li.ensureFloat(e)),m.isNumber(t)&&(t=Li.ensureFloat(t)),m.isNumber(n)&&(n=Li.ensureFloat(n)),m.isNumber(i)&&(i=Li.ensureFloat(i)),`vec4(${e}, ${t}, ${n}, ${i})`}static float3(e,t,n){return m.isNumber(e)&&(e=Li.ensureFloat(e)),m.isNumber(t)&&(t=Li.ensureFloat(t)),m.isNumber(n)&&(n=Li.ensureFloat(n)),`vec3(${e}, ${t}, ${n})`}static float2(e,t){return m.isNumber(e)&&(e=Li.ensureFloat(e)),m.isNumber(t)&&(t=Li.ensureFloat(t)),`vec2(${e}, ${t})`}static float(e){if(m.isNumber(e))return Li.ensureFloat(e);{const t=parseFloat(e);return m.isNaN(t)?e:Li.ensureFloat(t)}}static integer(e){if(m.isNumber(e))return Li.ensureInteger(e);{const t=parseInt(e);return m.isNaN(t)?e:Li.ensureInteger(t)}}static bool(e){return m.isBoolean(e)?`${e}`:e}}const Xm=/\\\\/+/g;class Ym extends Mo{static context(){return Ei.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 e,t;null===(t=null===(e=this.material_node)||void 0===e?void 0:e.assemblerController)||void 0===t||t.set_compilation_required_and_dirty(this)}get material_node(){var e;const t=this.parent();if(t)return t.context()==Ei.GL?null===(e=t)||void 0===e?void 0:e.material_node:t}glVarName(e){return`v_POLY_${this.path(this.material_node).replace(Xm,\\\\\\\"_\\\\\\\")}_${e}`}variableForInputParam(e){return this.variableForInput(e.name())}variableForInput(e){var t;const n=this.io.inputs.get_input_index(e),i=this.io.connections.inputConnection(n);if(i){const t=i.node_src,n=t.io.outputs.namedOutputConnectionPoints()[i.output_index];if(n){const e=n.name();return t.glVarName(e)}throw console.warn(`no output called '${e}' for gl node ${t.path()}`),\\\\\\\"variable_for_input ERROR\\\\\\\"}if(this.params.has(e))return Wm.any(null===(t=this.params.get(e))||void 0===t?void 0:t.value);{const e=this.io.inputs.namedInputConnectionPoints()[n];return Wm.any(e.init_value)}}setLines(e){}reset_code(){var e;null===(e=this._param_configs_controller)||void 0===e||e.reset()}setParamConfigs(){}param_configs(){var e;return null===(e=this._param_configs_controller)||void 0===e?void 0:e.list()}paramsGenerating(){return!1}paramDefaultValue(e){return null}}const $m=new class extends Lo{};class Qm extends Ym{constructor(){super(...arguments),this.paramsConfig=$m}}const Jm=[ro.FLOAT,ro.VEC2,ro.VEC3,ro.VEC4];const Km=new class extends Lo{constructor(){super(...arguments),this.name=Oo.STRING(\\\\\\\"\\\\\\\"),this.type=Oo.INTEGER(0,{menu:{entries:Jm.map(((e,t)=>({name:e,value:t})))}}),this.texportWhenConnected=Oo.BOOLEAN(0,{hidden:!0}),this.exportWhenConnected=Oo.BOOLEAN(0,{visibleIf:{texportWhenConnected:1}})}};class Zm extends Ym{constructor(){super(...arguments),this.paramsConfig=Km,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 Si.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 e,t;return(null===(t=null===(e=this.material_node)||void 0===e?void 0:e.assemblerController)||void 0===t?void 0:t.allow_attribute_exports())?[Jm[this.pv.type]]:[]})),this.io.connection_points.set_input_name_function((e=>Zm.INPUT_NAME)),this.io.connection_points.set_expected_output_types_function((()=>[Jm[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?`${this.pv.name} (EXPORTED)`:this.pv.name))}))})),this.lifecycle.add_on_add_hook(this._bound_setExportWhenConnectedStatus),this.params.addOnSceneLoadHook(\\\\\\\"prepare params\\\\\\\",this._bound_setExportWhenConnectedStatus)}_setExportWhenConnectedStatus(){var e,t;(null===(t=null===(e=this.material_node)||void 0===e?void 0:e.assemblerController)||void 0===t?void 0:t.allow_attribute_exports())&&this.p.texportWhenConnected.set(1)}setAttribSize(e){this.p.type.set(e-1)}get input_name(){return Zm.INPUT_NAME}get output_name(){return Zm.OUTPUT_NAME}setLines(e){e.assembler().set_node_lines_attribute(this,e)}get attribute_name(){return this.pv.name.trim()}gl_type(){return this.io.outputs.namedOutputConnectionPoints()[0].type()}set_gl_type(e){this.p.type.set(Jm.indexOf(e))}connected_input_node(){return this.io.inputs.named_input(Zm.INPUT_NAME)}connected_input_connection_point(){return this.io.inputs.named_input_connection_point(Zm.INPUT_NAME)}output_connection_point(){return this.io.outputs.namedOutputConnectionPointsByName(this.output_name)}isImporting(){return this.io.outputs.used_output_names().length>0}isExporting(){if(this.pv.exportWhenConnected){return null!=this.io.inputs.named_input(Zm.INPUT_NAME)}return!1}_set_mat_to_recompile_if_is_exporting(){this.isExporting()&&this._set_mat_to_recompile()}_on_create_set_name_if_none(){\\\\\\\"\\\\\\\"==this.pv.name&&this.p.name.set(this.name())}}Zm.INPUT_NAME=\\\\\\\"in\\\\\\\",Zm.OUTPUT_NAME=\\\\\\\"val\\\\\\\";class ef{constructor(e=[]){this._definitions=e,this._errored=!1}get errored(){return this._errored}get error_message(){return this._error_message}uniq(){const e=new Map,t=[];for(let n of this._definitions)if(!this._errored){const i=n.name(),s=e.get(i);s?s.data_type!=n.data_type&&(this._errored=!0,this._error_message=`attempt to create '${n.name()}' with types '${n.data_type}' by node '${n.node.path()}', when there is already an existing with type ${s.data_type} from node '${s.node.path()}'`,console.warn(\\\\\\\"emitting error message:\\\\\\\",this._error_message)):(e.set(i,n),t.push(i))}const n=[];for(let i of t){const t=e.get(i);t&&n.push(t)}return n}}var tf,nf;!function(e){e.ATTRIBUTE=\\\\\\\"attribute\\\\\\\",e.FUNCTION=\\\\\\\"function\\\\\\\",e.UNIFORM=\\\\\\\"uniform\\\\\\\",e.VARYING=\\\\\\\"varying\\\\\\\"}(tf||(tf={}));class sf{constructor(e,t,n,i){this._definition_type=e,this._data_type=t,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 ef}}class rf extends sf{constructor(e,t,n){super(tf.ATTRIBUTE,t,e,n),this._node=e,this._data_type=t,this._name=n}get line(){return`attribute ${this.data_type} ${this.name()}`}}class of extends sf{constructor(e,t){super(tf.FUNCTION,ro.FLOAT,e,t),this._node=e,this._name=t}get line(){return this.name()}}class af extends sf{constructor(e,t,n){super(tf.UNIFORM,t,e,n),this._node=e,this._data_type=t,this._name=n}get line(){return`uniform ${this.data_type} ${this.name()}`}}class cf extends sf{constructor(e,t,n){super(tf.VARYING,t,e,n),this._node=e,this._data_type=t,this._name=n}get line(){return`varying ${this.data_type} ${this.name()}`}}!function(e){e.VERTEX=\\\\\\\"vertex\\\\\\\",e.FRAGMENT=\\\\\\\"fragment\\\\\\\",e.LEAVES_FROM_NODES_SHADER=\\\\\\\"leaves_from_nodes_shader\\\\\\\"}(nf||(nf={}));const lf={position:\\\\\\\"vec3( position )\\\\\\\"};class uf extends qm{handle_globals_node(e,t,n){var i,s;const r=e.io.outputs.namedOutputConnectionPointsByName(t);if(!r)return;const o=e.glVarName(t),a=r.type(),c=new cf(e,a,o);n.addDefinitions(e,[c]);const l=null===(s=null===(i=e.material_node)||void 0===i?void 0:i.assemblerController)||void 0===s?void 0:s.assembler;if(!l)return;const u=l.shader_config(n.current_shader_name);if(!u)return;const h=u.dependencies(),d=[],p=`${o} = modelMatrix * vec4( position, 1.0 )`,_=`${o} = normalize( mat3( modelMatrix[0].xyz, modelMatrix[1].xyz, modelMatrix[2].xyz ) * normal )`;switch(t){case\\\\\\\"worldPosition\\\\\\\":d.push(p);break;case\\\\\\\"worldNormal\\\\\\\":d.push(_);break;default:d.push(`${o} = ${a}(${t})`)}for(let t of h)n.addDefinitions(e,[c],t),n.addBodyLines(e,d,t);0==h.length&&n.addBodyLines(e,d)}static variable_config_default(e){return lf[e]}variable_config_default(e){return uf.variable_config_default(e)}read_attribute(e,t,n,i){return uf.read_attribute(e,t,n,i)}static read_attribute(e,t,n,i){var s,r;uf.PRE_DEFINED_ATTRIBUTES.indexOf(n)<0&&i.addDefinitions(e,[new rf(e,t,n)],nf.VERTEX);const o=i.current_shader_name;switch(o){case nf.VERTEX:return n;case nf.FRAGMENT:{if(!(e instanceof Zm))return;const a=\\\\\\\"varying_\\\\\\\"+e.glVarName(e.output_name),c=new cf(e,t,a),l=new Map;l.set(nf.FRAGMENT,[]);const h=new Map;h.set(nf.FRAGMENT,[]),u.pushOnArrayAtEntry(l,o,c);const d=`${a} = ${t}(${n})`,p=null===(r=null===(s=e.material_node)||void 0===s?void 0:s.assemblerController)||void 0===r?void 0:r.assembler.shader_config(o);if(p){const t=p.dependencies();for(let e of t)u.pushOnArrayAtEntry(l,e,c),u.pushOnArrayAtEntry(h,e,d);l.forEach(((t,n)=>{i.addDefinitions(e,t,n)})),h.forEach(((t,n)=>{i.addBodyLines(e,t,n)}))}return a}}}handle_attribute_node(e,t,n,i){return uf.read_attribute(e,t,n,i)}}uf.PRE_DEFINED_ATTRIBUTES=[\\\\\\\"position\\\\\\\",\\\\\\\"color\\\\\\\",\\\\\\\"normal\\\\\\\",\\\\\\\"uv\\\\\\\",\\\\\\\"uv2\\\\\\\",\\\\\\\"morphTarget0\\\\\\\",\\\\\\\"morphTarget1\\\\\\\",\\\\\\\"morphTarget2\\\\\\\",\\\\\\\"morphTarget3\\\\\\\",\\\\\\\"skinIndex\\\\\\\",\\\\\\\"skinWeight\\\\\\\"],uf.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 hf=[ro.FLOAT,ro.VEC2,ro.VEC3,ro.VEC4];const df=new class extends Lo{constructor(){super(...arguments),this.name=Oo.STRING(\\\\\\\"\\\\\\\"),this.type=Oo.INTEGER(0,{menu:{entries:hf.map(((e,t)=>({name:e,value:t})))}})}};class pf extends Ym{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\\\\\\\"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((()=>[hf[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 pf.INPUT_NAME}setLines(e){if(e.current_shader_name==nf.VERTEX){const t=this.gl_type();if(!t)return;const n=this.pv.name,i=new cf(this,t,n),s=`${n} = ${Wm.any(this.variableForInput(pf.INPUT_NAME))}`;e.addDefinitions(this,[i],nf.VERTEX),e.addBodyLines(this,[s],nf.VERTEX)}}get attribute_name(){return this.pv.name.trim()}gl_type(){const e=this.io.inputs.namedInputConnectionPoints()[0];if(e)return e.type()}set_gl_type(e){this.p.type.set(hf.indexOf(e))}_on_create_set_name_if_none(){\\\\\\\"\\\\\\\"==this.pv.name&&this.p.name.set(this.name())}}pf.INPUT_NAME=\\\\\\\"vertex\\\\\\\";class _f{static findOutputNodes(e){return e.nodesByType(\\\\\\\"output\\\\\\\")}static findParamGeneratingNodes(e){var t;const n=[];return null===(t=e.childrenController)||void 0===t||t.traverse_children((e=>{const t=e;t.paramsGenerating()&&n.push(t)})),n}static findVaryingNodes(e){return e.nodesByType(pf.type())}static findAttributeExportNodes(e){return e.nodesByType(Zm.type()).filter((e=>e.isExporting()))}}class mf{static overlay(e,t){return new Promise(((n,i)=>{let s=document.createElement(\\\\\\\"canvas\\\\\\\");s.width=Math.max(e.width,t.width),s.height=Math.max(e.height,t.height);let r=s.getContext(\\\\\\\"2d\\\\\\\");r.drawImage(e,0,0,e.width,e.height),r.drawImage(t,0,0,t.width,t.height);const o=s.toDataURL(\\\\\\\"image/png\\\\\\\"),a=new Image;a.onload=()=>{n(a)},a.src=o}))}static create_white_image(e,t){return new Promise(((n,i)=>{let s=document.createElement(\\\\\\\"canvas\\\\\\\");s.width=e,s.height=t;let r=s.getContext(\\\\\\\"2d\\\\\\\");r.beginPath(),r.rect(0,0,e,t),r.fillStyle=\\\\\\\"white\\\\\\\",r.fill();const o=s.toDataURL(\\\\\\\"image/png\\\\\\\"),a=new Image;a.onload=()=>{n(a)},a.src=o}))}static make_square(e){return new Promise(((t,n)=>{let i=document.createElement(\\\\\\\"canvas\\\\\\\");const s=Math.min(e.width,e.height),r=e.width/e.height;i.width=s,i.height=s;let o=i.getContext(\\\\\\\"2d\\\\\\\");const a=r>1,c=a?(e.width-s)/2:(e.height-s)/2;a?o.drawImage(e,c,0,s,s,0,0,s,s):o.drawImage(e,0,c,s,s,0,0,s,s);const l=i.toDataURL(\\\\\\\"image/png\\\\\\\"),u=new Image;u.onload=()=>{t(u)},u.src=l}))}static async image_to_blob(e){return new Promise((function(t,n){try{let i=new XMLHttpRequest;i.open(\\\\\\\"GET\\\\\\\",e.src),i.responseType=\\\\\\\"blob\\\\\\\",i.onerror=function(){n(\\\\\\\"Network error.\\\\\\\")},i.onload=function(){200===i.status?t(i.response):n(\\\\\\\"Loading error:\\\\\\\"+i.statusText)},i.send()}catch(e){n(e.message)}}))}static data_from_url(e){return new Promise(((t,n)=>{const i=new Image;i.crossOrigin=\\\\\\\"Anonymous\\\\\\\",i.onload=()=>{const e=this.data_from_image(i);t(e)},i.src=e}))}static data_from_image(e){const t=document.createElement(\\\\\\\"canvas\\\\\\\");t.width=e.width,t.height=e.height;const n=t.getContext(\\\\\\\"2d\\\\\\\");return n.drawImage(e,0,0,e.width,e.height),n.getImageData(0,0,e.width,e.height)}}var ff;!function(e){e.Uint8Array=\\\\\\\"Uint8Array\\\\\\\",e.Uint8ClampedArray=\\\\\\\"Uint8ClampedArray\\\\\\\",e.Float32Array=\\\\\\\"Float32Array\\\\\\\"}(ff||(ff={}));class gf{constructor(e){this.buffer_type=e}from_render_target(e,t){return this._data_texture&&this._same_dimensions(t.texture)||(this._data_texture=this._create_data_texture(t.texture)),this._copy_to_data_texture(e,t),this._data_texture}from_texture(e){const t=mf.data_from_image(e.image);this._data_texture&&this._same_dimensions(e)||(this._data_texture=this._create_data_texture(e));const n=t.width*t.height,i=t.data,s=this._data_texture.image.data,r=4*n;for(let e=0;e<r;e++)s[e]=i[e];return this._data_texture}get data_texture(){return this._data_texture}reset(){this._data_texture=void 0}_copy_to_data_texture(e,t){const n=t.texture.image;this._data_texture=this._data_texture||this._create_data_texture(t.texture),e.readRenderTargetPixels(t,0,0,n.width,n.height,this._data_texture.image.data),this._data_texture.needsUpdate=!0}_create_data_texture(e){const t=e.image,n=this._create_pixel_buffer(t.width,t.height);return new T.a(n,t.width,t.height,e.format,e.type,e.mapping,e.wrapS,e.wrapT,e.magFilter,e.minFilter,e.anisotropy,e.encoding)}_create_pixel_buffer(e,t){const n=e*t*4;switch(this.buffer_type){case ff.Uint8Array:return new Uint8Array(n);case ff.Uint8ClampedArray:return new Uint8ClampedArray(n);case ff.Float32Array:return new Float32Array(n)}Ri.unreachable(this.buffer_type)}_same_dimensions(e){if(this._data_texture){const t=this._data_texture.image.width==e.image.width,n=this._data_texture.image.height==e.image.height;return t&&n}return!0}}new class extends Lo{};class vf{constructor(e){this.node=e}async renderer(){return await this.cameraRenderer()}reset(){var e;null===(e=this._renderer)||void 0===e||e.dispose(),this._renderer=void 0}async cameraRenderer(){let e=Rn.renderersController.firstRenderer();return e||await Rn.renderersController.waitForRenderer()}save_state(){this.make_linear()}make_linear(){}restore_state(){}}var yf=n(21),xf=n(11);class bf extends I.a{constructor(e){super(),this.type=\\\\\\\"ShadowMaterial\\\\\\\",this.color=new M.a(0),this.transparent=!0,this.setValues(e)}copy(e){return super.copy(e),this.color.copy(e.color),this}}bf.prototype.isShadowMaterial=!0;class wf extends I.a{constructor(e){super(),this.type=\\\\\\\"SpriteMaterial\\\\\\\",this.color=new M.a(16777215),this.map=null,this.alphaMap=null,this.rotation=0,this.sizeAttenuation=!0,this.transparent=!0,this.setValues(e)}copy(e){return super.copy(e),this.color.copy(e.color),this.map=e.map,this.alphaMap=e.alphaMap,this.rotation=e.rotation,this.sizeAttenuation=e.sizeAttenuation,this}}wf.prototype.isSpriteMaterial=!0;class Af extends B{constructor(e){super(e),this.type=\\\\\\\"RawShaderMaterial\\\\\\\"}}Af.prototype.isRawShaderMaterial=!0;var Tf=n(79),Ef=n(56);class Cf extends I.a{constructor(e){super(),this.defines={TOON:\\\\\\\"\\\\\\\"},this.type=\\\\\\\"MeshToonMaterial\\\\\\\",this.color=new M.a(16777215),this.map=null,this.gradientMap=null,this.lightMap=null,this.lightMapIntensity=1,this.aoMap=null,this.aoMapIntensity=1,this.emissive=new M.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.skinning=!1,this.morphTargets=!1,this.morphNormals=!1,this.setValues(e)}copy(e){return super.copy(e),this.color.copy(e.color),this.map=e.map,this.gradientMap=e.gradientMap,this.lightMap=e.lightMap,this.lightMapIntensity=e.lightMapIntensity,this.aoMap=e.aoMap,this.aoMapIntensity=e.aoMapIntensity,this.emissive.copy(e.emissive),this.emissiveMap=e.emissiveMap,this.emissiveIntensity=e.emissiveIntensity,this.bumpMap=e.bumpMap,this.bumpScale=e.bumpScale,this.normalMap=e.normalMap,this.normalMapType=e.normalMapType,this.normalScale.copy(e.normalScale),this.displacementMap=e.displacementMap,this.displacementScale=e.displacementScale,this.displacementBias=e.displacementBias,this.alphaMap=e.alphaMap,this.wireframe=e.wireframe,this.wireframeLinewidth=e.wireframeLinewidth,this.wireframeLinecap=e.wireframeLinecap,this.wireframeLinejoin=e.wireframeLinejoin,this.skinning=e.skinning,this.morphTargets=e.morphTargets,this.morphNormals=e.morphNormals,this}}Cf.prototype.isMeshToonMaterial=!0;class Mf extends I.a{constructor(e){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.skinning=!1,this.morphTargets=!1,this.morphNormals=!1,this.flatShading=!1,this.setValues(e)}copy(e){return super.copy(e),this.bumpMap=e.bumpMap,this.bumpScale=e.bumpScale,this.normalMap=e.normalMap,this.normalMapType=e.normalMapType,this.normalScale.copy(e.normalScale),this.displacementMap=e.displacementMap,this.displacementScale=e.displacementScale,this.displacementBias=e.displacementBias,this.wireframe=e.wireframe,this.wireframeLinewidth=e.wireframeLinewidth,this.skinning=e.skinning,this.morphTargets=e.morphTargets,this.morphNormals=e.morphNormals,this.flatShading=e.flatShading,this}}Mf.prototype.isMeshNormalMaterial=!0;var Nf=n(26);class Sf extends I.a{constructor(e){super(),this.defines={MATCAP:\\\\\\\"\\\\\\\"},this.type=\\\\\\\"MeshMatcapMaterial\\\\\\\",this.color=new M.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.skinning=!1,this.morphTargets=!1,this.morphNormals=!1,this.flatShading=!1,this.setValues(e)}copy(e){return super.copy(e),this.defines={MATCAP:\\\\\\\"\\\\\\\"},this.color.copy(e.color),this.matcap=e.matcap,this.map=e.map,this.bumpMap=e.bumpMap,this.bumpScale=e.bumpScale,this.normalMap=e.normalMap,this.normalMapType=e.normalMapType,this.normalScale.copy(e.normalScale),this.displacementMap=e.displacementMap,this.displacementScale=e.displacementScale,this.displacementBias=e.displacementBias,this.alphaMap=e.alphaMap,this.skinning=e.skinning,this.morphTargets=e.morphTargets,this.morphNormals=e.morphNormals,this.flatShading=e.flatShading,this}}Sf.prototype.isMeshMatcapMaterial=!0;class Of extends Yi.a{constructor(e){super(),this.type=\\\\\\\"LineDashedMaterial\\\\\\\",this.scale=1,this.dashSize=3,this.gapSize=1,this.setValues(e)}copy(e){return super.copy(e),this.scale=e.scale,this.dashSize=e.dashSize,this.gapSize=e.gapSize,this}}Of.prototype.isLineDashedMaterial=!0;class Lf extends xf.a{constructor(e){super(e),this.textures={}}load(e,t,n,i){const s=this,r=new yf.a(s.manager);r.setPath(s.path),r.setRequestHeader(s.requestHeader),r.setWithCredentials(s.withCredentials),r.load(e,(function(n){try{t(s.parse(JSON.parse(n)))}catch(t){i?i(t):console.error(t),s.manager.itemError(e)}}),n,i)}parse(e){const t=this.textures;function n(e){return void 0===t[e]&&console.warn(\\\\\\\"THREE.MaterialLoader: Undefined texture\\\\\\\",e),t[e]}const s=new i[e.type];if(void 0!==e.uuid&&(s.uuid=e.uuid),void 0!==e.name&&(s.name=e.name),void 0!==e.color&&void 0!==s.color&&s.color.setHex(e.color),void 0!==e.roughness&&(s.roughness=e.roughness),void 0!==e.metalness&&(s.metalness=e.metalness),void 0!==e.sheen&&(s.sheen=(new M.a).setHex(e.sheen)),void 0!==e.emissive&&void 0!==s.emissive&&s.emissive.setHex(e.emissive),void 0!==e.specular&&void 0!==s.specular&&s.specular.setHex(e.specular),void 0!==e.shininess&&(s.shininess=e.shininess),void 0!==e.clearcoat&&(s.clearcoat=e.clearcoat),void 0!==e.clearcoatRoughness&&(s.clearcoatRoughness=e.clearcoatRoughness),void 0!==e.fog&&(s.fog=e.fog),void 0!==e.flatShading&&(s.flatShading=e.flatShading),void 0!==e.blending&&(s.blending=e.blending),void 0!==e.combine&&(s.combine=e.combine),void 0!==e.side&&(s.side=e.side),void 0!==e.shadowSide&&(s.shadowSide=e.shadowSide),void 0!==e.opacity&&(s.opacity=e.opacity),void 0!==e.transparent&&(s.transparent=e.transparent),void 0!==e.alphaTest&&(s.alphaTest=e.alphaTest),void 0!==e.depthTest&&(s.depthTest=e.depthTest),void 0!==e.depthWrite&&(s.depthWrite=e.depthWrite),void 0!==e.colorWrite&&(s.colorWrite=e.colorWrite),void 0!==e.stencilWrite&&(s.stencilWrite=e.stencilWrite),void 0!==e.stencilWriteMask&&(s.stencilWriteMask=e.stencilWriteMask),void 0!==e.stencilFunc&&(s.stencilFunc=e.stencilFunc),void 0!==e.stencilRef&&(s.stencilRef=e.stencilRef),void 0!==e.stencilFuncMask&&(s.stencilFuncMask=e.stencilFuncMask),void 0!==e.stencilFail&&(s.stencilFail=e.stencilFail),void 0!==e.stencilZFail&&(s.stencilZFail=e.stencilZFail),void 0!==e.stencilZPass&&(s.stencilZPass=e.stencilZPass),void 0!==e.wireframe&&(s.wireframe=e.wireframe),void 0!==e.wireframeLinewidth&&(s.wireframeLinewidth=e.wireframeLinewidth),void 0!==e.wireframeLinecap&&(s.wireframeLinecap=e.wireframeLinecap),void 0!==e.wireframeLinejoin&&(s.wireframeLinejoin=e.wireframeLinejoin),void 0!==e.rotation&&(s.rotation=e.rotation),1!==e.linewidth&&(s.linewidth=e.linewidth),void 0!==e.dashSize&&(s.dashSize=e.dashSize),void 0!==e.gapSize&&(s.gapSize=e.gapSize),void 0!==e.scale&&(s.scale=e.scale),void 0!==e.polygonOffset&&(s.polygonOffset=e.polygonOffset),void 0!==e.polygonOffsetFactor&&(s.polygonOffsetFactor=e.polygonOffsetFactor),void 0!==e.polygonOffsetUnits&&(s.polygonOffsetUnits=e.polygonOffsetUnits),void 0!==e.skinning&&(s.skinning=e.skinning),void 0!==e.morphTargets&&(s.morphTargets=e.morphTargets),void 0!==e.morphNormals&&(s.morphNormals=e.morphNormals),void 0!==e.dithering&&(s.dithering=e.dithering),void 0!==e.alphaToCoverage&&(s.alphaToCoverage=e.alphaToCoverage),void 0!==e.premultipliedAlpha&&(s.premultipliedAlpha=e.premultipliedAlpha),void 0!==e.vertexTangents&&(s.vertexTangents=e.vertexTangents),void 0!==e.visible&&(s.visible=e.visible),void 0!==e.toneMapped&&(s.toneMapped=e.toneMapped),void 0!==e.userData&&(s.userData=e.userData),void 0!==e.vertexColors&&(\\\\\\\"number\\\\\\\"==typeof e.vertexColors?s.vertexColors=e.vertexColors>0:s.vertexColors=e.vertexColors),void 0!==e.uniforms)for(const t in e.uniforms){const i=e.uniforms[t];switch(s.uniforms[t]={},i.type){case\\\\\\\"t\\\\\\\":s.uniforms[t].value=n(i.value);break;case\\\\\\\"c\\\\\\\":s.uniforms[t].value=(new M.a).setHex(i.value);break;case\\\\\\\"v2\\\\\\\":s.uniforms[t].value=(new d.a).fromArray(i.value);break;case\\\\\\\"v3\\\\\\\":s.uniforms[t].value=(new p.a).fromArray(i.value);break;case\\\\\\\"v4\\\\\\\":s.uniforms[t].value=(new _.a).fromArray(i.value);break;case\\\\\\\"m3\\\\\\\":s.uniforms[t].value=(new V.a).fromArray(i.value);break;case\\\\\\\"m4\\\\\\\":s.uniforms[t].value=(new C.a).fromArray(i.value);break;default:s.uniforms[t].value=i.value}}if(void 0!==e.defines&&(s.defines=e.defines),void 0!==e.vertexShader&&(s.vertexShader=e.vertexShader),void 0!==e.fragmentShader&&(s.fragmentShader=e.fragmentShader),void 0!==e.extensions)for(const t in e.extensions)s.extensions[t]=e.extensions[t];if(void 0!==e.shading&&(s.flatShading=1===e.shading),void 0!==e.size&&(s.size=e.size),void 0!==e.sizeAttenuation&&(s.sizeAttenuation=e.sizeAttenuation),void 0!==e.map&&(s.map=n(e.map)),void 0!==e.matcap&&(s.matcap=n(e.matcap)),void 0!==e.alphaMap&&(s.alphaMap=n(e.alphaMap)),void 0!==e.bumpMap&&(s.bumpMap=n(e.bumpMap)),void 0!==e.bumpScale&&(s.bumpScale=e.bumpScale),void 0!==e.normalMap&&(s.normalMap=n(e.normalMap)),void 0!==e.normalMapType&&(s.normalMapType=e.normalMapType),void 0!==e.normalScale){let t=e.normalScale;!1===Array.isArray(t)&&(t=[t,t]),s.normalScale=(new d.a).fromArray(t)}return void 0!==e.displacementMap&&(s.displacementMap=n(e.displacementMap)),void 0!==e.displacementScale&&(s.displacementScale=e.displacementScale),void 0!==e.displacementBias&&(s.displacementBias=e.displacementBias),void 0!==e.roughnessMap&&(s.roughnessMap=n(e.roughnessMap)),void 0!==e.metalnessMap&&(s.metalnessMap=n(e.metalnessMap)),void 0!==e.emissiveMap&&(s.emissiveMap=n(e.emissiveMap)),void 0!==e.emissiveIntensity&&(s.emissiveIntensity=e.emissiveIntensity),void 0!==e.specularMap&&(s.specularMap=n(e.specularMap)),void 0!==e.envMap&&(s.envMap=n(e.envMap)),void 0!==e.envMapIntensity&&(s.envMapIntensity=e.envMapIntensity),void 0!==e.reflectivity&&(s.reflectivity=e.reflectivity),void 0!==e.refractionRatio&&(s.refractionRatio=e.refractionRatio),void 0!==e.lightMap&&(s.lightMap=n(e.lightMap)),void 0!==e.lightMapIntensity&&(s.lightMapIntensity=e.lightMapIntensity),void 0!==e.aoMap&&(s.aoMap=n(e.aoMap)),void 0!==e.aoMapIntensity&&(s.aoMapIntensity=e.aoMapIntensity),void 0!==e.gradientMap&&(s.gradientMap=n(e.gradientMap)),void 0!==e.clearcoatMap&&(s.clearcoatMap=n(e.clearcoatMap)),void 0!==e.clearcoatRoughnessMap&&(s.clearcoatRoughnessMap=n(e.clearcoatRoughnessMap)),void 0!==e.clearcoatNormalMap&&(s.clearcoatNormalMap=n(e.clearcoatNormalMap)),void 0!==e.clearcoatNormalScale&&(s.clearcoatNormalScale=(new d.a).fromArray(e.clearcoatNormalScale)),void 0!==e.transmission&&(s.transmission=e.transmission),void 0!==e.transmissionMap&&(s.transmissionMap=n(e.transmissionMap)),s}setTextures(e){return this.textures=e,this}}class Pf{constructor(e){this.node=e,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(e){}_materialToJson(e,t){let n;this._unassignTextures(e);try{n=e.toJSON({}),n&&(n.shadowSide=e.shadowSide,n.colorWrite=e.colorWrite)}catch(t){console.error(\\\\\\\"failed to save material data\\\\\\\"),console.log(e)}return n&&null!=e.lights&&(n.lights=e.lights),n&&(n.uuid=`${t.node.path()}-${t.suffix}`),this._reassignTextures(e),n}_unassignTextures(e){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 t=e.uniforms,n=Object.keys(t);for(let e of n){const n=t[e].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(e,i.uuid),t[e].value=null}}const i=Object.keys(e);for(let t of i){const n=e[t];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(t,i.uuid),e[t]=null}}}_reassignTextures(e){const t=[],n=[];this._found_uniform_textures_id_by_uniform_name.forEach(((e,n)=>{t.push(n)})),this._found_param_textures_id_by_uniform_name.forEach(((e,t)=>{n.push(t)}));const i=e.uniforms;for(let e of t){const t=this._found_uniform_textures_id_by_uniform_name.get(e);if(t){const n=this._found_uniform_texture_by_id.get(t);n&&(i[e].value=n)}}for(let t of n){const n=this._found_param_textures_id_by_uniform_name.get(t);if(n){const i=this._found_param_texture_by_id.get(n);i&&(e[t]=i)}}}_loadMaterial(e){e.color=void 0;const t=(new Lf).parse(e);e.shadowSide&&(t.shadowSide=e.shadowSide),null!=e.lights&&(t.lights=e.lights);const n=t.uniforms.uv2Transform;n&&this.mat4ToMat3(n);const i=t.uniforms.uvTransform;return i&&this.mat4ToMat3(i),t}mat4ToMat3(e){const t=e.value;if(null==t.elements[t.elements.length-1]){const n=new V.a;for(let e=0;e<n.elements.length;e++)n.elements[e]=t.elements[e];e.value=n}}}class Rf{constructor(e,t,n){this._type=e,this._name=t,this._default_value=n}static from_param(e){return new Rf(e.type(),e.name(),e.defaultValue())}type(){return this._type}name(){return this._name}get default_value(){return this._default_value}get param_options(){const e=this._callback.bind(this);switch(this._type){case Qs.OPERATOR_PATH:return{callback:e,nodeSelection:{context:Ei.COP}};default:return{callback:e}}}_callback(e,t){}}class If extends Rf{constructor(e,t,n,i){super(e,t,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 If.uniform_by_type(this._type)}execute_callback(e,t){this._callback(e,t)}_callback(e,t){If.callback(t,this.uniform)}static callback(e,t){switch(e.type()){case Qs.RAMP:return void(t.value=e.rampTexture());case Qs.OPERATOR_PATH:return void If.set_uniform_value_from_texture(e,t);case Qs.NODE_PATH:return void If.set_uniform_value_from_texture_from_node_path_param(e,t);default:t.value=e.value}}static uniform_by_type(e){switch(e){case Qs.BOOLEAN:case Qs.BUTTON:return{value:0};case Qs.COLOR:return{value:new M.a(0,0,0)};case Qs.FLOAT:case Qs.FOLDER:case Qs.INTEGER:case Qs.OPERATOR_PATH:case Qs.NODE_PATH:case Qs.PARAM_PATH:return{value:0};case Qs.RAMP:case Qs.STRING:return{value:null};case Qs.VECTOR2:return{value:new d.a(0,0)};case Qs.VECTOR3:return{value:new p.a(0,0,0)};case Qs.VECTOR4:return{value:new _.a(0,0,0,0)}}Ri.unreachable(e)}static set_uniform_value_from_texture(e,t){const n=e.found_node();if(n)if(n.isDirty())n.compute().then((e=>{const n=e.texture();t.value=n}));else{const e=n.containerController.container().texture();t.value=e}else t.value=null}static async set_uniform_value_from_texture_from_node_path_param(e,t){e.isDirty()&&await e.compute();const n=e.value.nodeWithContext(Ei.COP);if(n)if(n.isDirty())n.compute().then((e=>{const n=e.texture();t.value=n}));else{const e=n.containerController.container().texture();t.value=e}else t.value=null}set_uniform_value_from_ramp(e,t){t.value=e.rampTexture()}}class Ff extends Pf{constructor(e){super(e),this.node=e}toJSON(){const e=this.node.assemblerController;if(!e)return;const t=[],n=e.assembler.param_configs();for(let e of n)t.push([e.name(),e.uniform_name]);return{fragment_shader:this.node.texture_material.fragmentShader,uniforms:this.node.texture_material.uniforms,param_uniform_pairs:t,uniforms_time_dependent:e.assembler.uniformsTimeDependent(),uniforms_resolution_dependent:e.assembler.uniforms_resolution_dependent()}}load(e){this.node.texture_material.fragmentShader=e.fragment_shader,this.node.texture_material.uniforms=e.uniforms,zf.handle_dependencies(this.node,e.uniforms_time_dependent||!1,e.uniforms);for(let t of e.param_uniform_pairs){const n=this.node.params.get(t[0]),i=e.uniforms[t[1]];n&&i&&n.options.set({callback:()=>{If.callback(n,i)}})}}}class Df{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 e=document.createElement(\\\\\\\"div\\\\\\\");return e.setAttribute(\\\\\\\"ongesturestart\\\\\\\",\\\\\\\"return;\\\\\\\"),\\\\\\\"function\\\\\\\"==typeof e.ongesturestart}}const kf=[256,256];const Bf=new class extends Lo{constructor(){super(...arguments),this.resolution=Oo.VECTOR2(kf),this.useCameraRenderer=Oo.BOOLEAN(0)}};class zf extends jm{constructor(){super(...arguments),this.paramsConfig=Bf,this.persisted_config=new Ff(this),this._assembler_controller=this._create_assembler_controller(),this._texture_mesh=new z.a(new R(2,2)),this.texture_material=new B({uniforms:{},vertexShader:\\\\\\\"\\\\nvoid main()\\\\t{\\\\n\\\\tgl_Position = vec4( position, 1.0 );\\\\n}\\\\n\\\\\\\",fragmentShader:\\\\\\\"\\\\\\\"}),this._texture_scene=new Vi,this._texture_camera=new km.a,this._children_controller_context=Ei.GL,this._cook_main_without_inputs_when_dirty_bound=this._cook_main_without_inputs_when_dirty.bind(this)}static type(){return\\\\\\\"builder\\\\\\\"}usedAssembler(){return mn.GL_TEXTURE}_create_assembler_controller(){const e=Rn.assemblersRegister.assembler(this,this.usedAssembler());if(e){const t=new uf;return e.set_assembler_globals_handler(t),e}}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(e,t){return super.createNode(e,t)}children(){return super.children()}nodesByType(e){return super.nodesByType(e)}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 e;(null===(e=this.assemblerController)||void 0===e?void 0:e.compileRequired())&&this.compile()}compile(){const e=this.assemblerController;if(!e)return;const t=_f.findOutputNodes(this);if(t.length>1)return void this.states.error.set(\\\\\\\"only one output node allowed\\\\\\\");if(t[0]){const n=t;e.assembler.set_root_nodes(n),e.assembler.update_fragment_shader();const i=e.assembler.fragment_shader(),s=e.assembler.uniforms();i&&s&&(this._fragment_shader=i,this._uniforms=s),zf.handle_dependencies(this,e.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}),e.post_compile()}static handle_dependencies(e,t,n){const i=e.scene(),s=`${e.graphNodeId()}`;t?(e.states.timeDependent.forceTimeDependent(),n&&i.uniformsController.addTimeDependentUniformOwner(s,n)):(e.states.timeDependent.unforceTimeDependent(),i.uniformsController.removeTimeDependentUniformOwner(s))}async renderOnTarget(){if(this.createRenderTargetIfRequired(),!this._render_target)return;this._renderer_controller=this._renderer_controller||new vf(this);const e=await this._renderer_controller.renderer(),t=e.getRenderTarget();if(e.setRenderTarget(this._render_target),e.clear(),e.render(this._texture_scene,this._texture_camera),e.setRenderTarget(t),this._render_target.texture)if(this.pv.useCameraRenderer)this.setTexture(this._render_target.texture);else{this._data_texture_controller=this._data_texture_controller||new gf(ff.Float32Array);const t=this._data_texture_controller.from_render_target(e,this._render_target);this.setTexture(t)}else this.cookController.endCook()}renderTarget(){return this._render_target=this._render_target||this._createRenderTarget(this.pv.resolution.x,this.pv.resolution.y)}createRenderTargetIfRequired(){var e;this._render_target&&this._renderTargetResolutionValid()||(this._render_target=this._createRenderTarget(this.pv.resolution.x,this.pv.resolution.y),null===(e=this._data_texture_controller)||void 0===e||e.reset())}_renderTargetResolutionValid(){if(this._render_target){const e=this._render_target.texture.image;return e.width==this.pv.resolution.x&&e.height==this.pv.resolution.y}return!1}_createRenderTarget(e,t){if(this._render_target){const n=this._render_target.texture.image;if(n.width==e&&n.height==t)return this._render_target}const n=w.n,i=w.n,s=w.V,r=w.ob;var o=new Z(e,t,{wrapS:n,wrapT:i,minFilter:s,magFilter:r,format:w.Ib,type:Df.isiOS()?w.M:w.G,stencilBuffer:!1,depthBuffer:!1});return Rn.warn(\\\\\\\"created render target\\\\\\\",this.path(),e,t),o}}const Uf=[{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}],Gf=[{ClampToEdgeWrapping:w.n},{RepeatWrapping:w.wc},{MirroredRepeatWrapping:w.kb}],Vf=[{UVMapping:w.Yc},{CubeReflectionMapping:w.o},{CubeRefractionMapping:w.p},{EquirectangularReflectionMapping:w.D},{EquirectangularRefractionMapping:w.E},{CubeUVReflectionMapping:w.q},{CubeUVRefractionMapping:w.r}],jf=[{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}],Hf=[{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 qf(e){return{cook:!1,callback:t=>{ag[e](t)}}}const Wf={ENCODING:w.U,FORMAT:w.Ib,MAPPING:w.Yc,MIN_FILTER:w.V,MAG_FILTER:w.V,TYPE:w.Zc,WRAPPING:w.wc},Xf=qf(\\\\\\\"PARAM_CALLBACK_update_encoding\\\\\\\"),Yf=qf(\\\\\\\"PARAM_CALLBACK_update_mapping\\\\\\\"),$f=qf(\\\\\\\"PARAM_CALLBACK_update_wrap\\\\\\\"),Qf=qf(\\\\\\\"PARAM_CALLBACK_update_filter\\\\\\\"),Jf=qf(\\\\\\\"PARAM_CALLBACK_update_anisotropy\\\\\\\"),Kf=qf(\\\\\\\"PARAM_CALLBACK_update_flipY\\\\\\\"),Zf=qf(\\\\\\\"PARAM_CALLBACK_update_transform\\\\\\\"),eg=qf(\\\\\\\"PARAM_CALLBACK_update_repeat\\\\\\\"),tg=qf(\\\\\\\"PARAM_CALLBACK_update_offset\\\\\\\"),ng=qf(\\\\\\\"PARAM_CALLBACK_update_rotation\\\\\\\"),ig=qf(\\\\\\\"PARAM_CALLBACK_update_center\\\\\\\"),sg=qf(\\\\\\\"PARAM_CALLBACK_update_advanced\\\\\\\");function rg(e){return class extends e{constructor(){super(...arguments),this.tencoding=Oo.BOOLEAN(0,{...Xf}),this.encoding=Oo.INTEGER(Wf.ENCODING,{visibleIf:{tencoding:1},menu:{entries:Uf.map((e=>({name:Object.keys(e)[0],value:Object.values(e)[0]})))},...Xf}),this.tmapping=Oo.BOOLEAN(0,{...Yf}),this.mapping=Oo.INTEGER(Wf.MAPPING,{visibleIf:{tmapping:1},menu:{entries:Vf.map((e=>({name:Object.keys(e)[0],value:Object.values(e)[0]})))},...Yf}),this.twrap=Oo.BOOLEAN(0,{...$f}),this.wrapS=Oo.INTEGER(Wf.WRAPPING,{visibleIf:{twrap:1},menu:{entries:Gf.map((e=>({name:Object.keys(e)[0],value:Object.values(e)[0]})))},...$f}),this.wrapT=Oo.INTEGER(Wf.WRAPPING,{visibleIf:{twrap:1},menu:{entries:Gf.map((e=>({name:Object.keys(e)[0],value:Object.values(e)[0]})))},separatorAfter:!0,...$f}),this.tminFilter=Oo.BOOLEAN(0,{...Qf}),this.minFilter=Oo.INTEGER(Om,{visibleIf:{tminFilter:1},menu:{entries:Pm},...Qf}),this.tmagFilter=Oo.BOOLEAN(0,{...Qf}),this.magFilter=Oo.INTEGER(Sm,{visibleIf:{tmagFilter:1},menu:{entries:Lm},...Qf}),this.tanisotropy=Oo.BOOLEAN(0,{...Jf}),this.useRendererMaxAnisotropy=Oo.BOOLEAN(0,{visibleIf:{tanisotropy:1},...Jf}),this.anisotropy=Oo.INTEGER(2,{visibleIf:{tanisotropy:1,useRendererMaxAnisotropy:0},range:[0,32],rangeLocked:[!0,!1],...Jf}),this.tflipY=Oo.BOOLEAN(0,{...Kf}),this.flipY=Oo.BOOLEAN(0,{visibleIf:{tflipY:1},...Kf}),this.ttransform=Oo.BOOLEAN(0,{...Zf}),this.offset=Oo.VECTOR2([0,0],{visibleIf:{ttransform:1},...tg}),this.repeat=Oo.VECTOR2([1,1],{visibleIf:{ttransform:1},...eg}),this.rotation=Oo.FLOAT(0,{range:[-1,1],visibleIf:{ttransform:1},...ng}),this.center=Oo.VECTOR2([0,0],{visibleIf:{ttransform:1},...ig}),this.tadvanced=Oo.BOOLEAN(0,{...sg}),this.tformat=Oo.BOOLEAN(0,{visibleIf:{tadvanced:1},...sg}),this.format=Oo.INTEGER(Wf.FORMAT,{visibleIf:{tadvanced:1,tformat:1},menu:{entries:Hf.map((e=>({name:Object.keys(e)[0],value:Object.values(e)[0]})))},...sg}),this.ttype=Oo.BOOLEAN(0,{visibleIf:{tadvanced:1},...sg}),this.type=Oo.INTEGER(Wf.TYPE,{visibleIf:{tadvanced:1,ttype:1},menu:{entries:jf.map((e=>({name:Object.keys(e)[0],value:Object.values(e)[0]})))},...sg})}}}class og extends(rg(Lo)){}new og;class ag{constructor(e){this.node=e}async update(e){const t=this.node.pv;this._updateEncoding(e,t),this._updateAdvanced(e,t),this._updateMapping(e,t),this._updateWrap(e,t),this._updateFilter(e,t),this._updateFlip(e,t),await this._updateAnisotropy(e,t),this._updateTransform(e)}_updateEncoding(e,t){t.tencoding?e.encoding=t.encoding:e.encoding=Wf.ENCODING,e.needsUpdate=!0}_updateAdvanced(e,t){t.tadvanced&&(t.tformat?e.format=t.format:e.format=Wf.FORMAT,t.ttype?e.type=t.type:e.type=Wf.TYPE),e.needsUpdate=!0}_updateMapping(e,t){t.tmapping?e.mapping=t.mapping:e.mapping=Wf.MAPPING,e.needsUpdate=!0}_updateWrap(e,t){t.twrap?(e.wrapS=t.wrapS,e.wrapT=t.wrapT):(e.wrapS=Wf.WRAPPING,e.wrapT=Wf.WRAPPING),e.needsUpdate=!0}_updateFilter(e,t){t.tminFilter?e.minFilter=t.minFilter:e.minFilter=w.V,t.tmagFilter?e.magFilter=t.magFilter:e.magFilter=w.V,e.needsUpdate=!0}_updateFlip(e,t){e.flipY=t.tflipY&&t.flipY,e.needsUpdate=!0}async _updateAnisotropy(e,t){if(t.tanisotropy){if(t.useRendererMaxAnisotropy)e.anisotropy=await this._maxRendererAnisotropy();else{const n=t.anisotropy;e.anisotropy=n<=2?n:Math.min(n,await this._maxRendererAnisotropy())}e.needsUpdate=!0}else e.anisotropy=1}async _maxRendererAnisotropy(){this._renderer_controller=this._renderer_controller||new vf(this.node);return(await this._renderer_controller.renderer()).capabilities.getMaxAnisotropy()}_updateTransform(e){if(!this.node.pv.ttransform)return e.offset.set(0,0),e.rotation=0,e.repeat.set(1,1),void e.center.set(0,0);this._updateTransformOffset(e,!1),this._updateTransformRepeat(e,!1),this._updateTransformRotation(e,!1),this._updateTransformCenter(e,!1),e.updateMatrix()}async _updateTransformOffset(e,t){e.offset.copy(this.node.pv.offset),t&&e.updateMatrix()}async _updateTransformRepeat(e,t){e.repeat.copy(this.node.pv.repeat),t&&e.updateMatrix()}async _updateTransformRotation(e,t){e.rotation=this.node.pv.rotation,t&&e.updateMatrix()}async _updateTransformCenter(e,t){e.center.copy(this.node.pv.center),t&&e.updateMatrix()}static PARAM_CALLBACK_update_encoding(e){const t=e.containerController.container().texture();t&&e.textureParamsController._updateEncoding(t,e.pv)}static PARAM_CALLBACK_update_mapping(e){const t=e.containerController.container().texture();t&&e.textureParamsController._updateMapping(t,e.pv)}static PARAM_CALLBACK_update_wrap(e){const t=e.containerController.container().texture();t&&e.textureParamsController._updateWrap(t,e.pv)}static PARAM_CALLBACK_update_filter(e){const t=e.containerController.container().texture();t&&e.textureParamsController._updateFilter(t,e.pv)}static PARAM_CALLBACK_update_anisotropy(e){const t=e.containerController.container().texture();t&&e.textureParamsController._updateAnisotropy(t,e.pv)}static PARAM_CALLBACK_update_flipY(e){const t=e.containerController.container().texture();t&&e.textureParamsController._updateFlip(t,e.pv)}static PARAM_CALLBACK_update_transform(e){const t=e.containerController.container().texture();t&&e.textureParamsController._updateTransform(t)}static PARAM_CALLBACK_update_offset(e){const t=e.containerController.container().texture();t&&e.textureParamsController._updateTransformOffset(t,!0)}static PARAM_CALLBACK_update_repeat(e){const t=e.containerController.container().texture();t&&e.textureParamsController._updateTransformRepeat(t,!0)}static PARAM_CALLBACK_update_rotation(e){const t=e.containerController.container().texture();t&&e.textureParamsController._updateTransformRotation(t,!0)}static PARAM_CALLBACK_update_center(e){const t=e.containerController.container().texture();t&&e.textureParamsController._updateTransformCenter(t,!0)}static PARAM_CALLBACK_update_advanced(e){const t=e.containerController.container().texture();t&&e.textureParamsController._updateAdvanced(t,e.pv)}static copyTextureAttributes(e,t){e.encoding=t.encoding,e.mapping=t.mapping,e.wrapS=t.wrapS,e.wrapT=t.wrapT,e.minFilter=t.minFilter,e.magFilter=t.magFilter,e.magFilter=t.magFilter,e.anisotropy=t.anisotropy,e.flipY=t.flipY,e.repeat.copy(t.repeat),e.offset.copy(t.offset),e.center.copy(t.center),e.rotation=t.rotation,e.type=t.type,e.format=t.format,e.needsUpdate=!0}paramLabelsParams(){const e=this.node.p;return[e.tencoding,e.encoding,e.tmapping,e.mapping,e.twrap,e.wrapS,e.wrapT,e.tminFilter,e.minFilter,e.tmagFilter,e.magFilter,e.tflipY,e.flipY]}paramLabels(){const e=[],t=this.node.pv;if(t.tencoding)for(let n of Uf){const i=Object.keys(n)[0];n[i]==t.encoding&&e.push(`encoding: ${i}`)}if(t.tmapping)for(let n of Vf){const i=Object.keys(n)[0];n[i]==t.mapping&&e.push(`mapping: ${i}`)}if(t.twrap){function n(n){for(let i of Gf){const s=Object.keys(i)[0];i[s]==t[n]&&e.push(`${n}: ${s}`)}}n(\\\\\\\"wrapS\\\\\\\"),n(\\\\\\\"wrapT\\\\\\\")}if(t.tminFilter)for(let n of Nm){const i=Object.keys(n)[0];n[i]==t.minFilter&&e.push(`minFilter: ${i}`)}if(t.tmagFilter)for(let n of Mm){const i=Object.keys(n)[0];n[i]==t.magFilter&&e.push(`magFilter: ${i}`)}return t.tflipY&&e.push(`flipY: ${t.flipY}`),e}}var cg=n(88);class lg extends(rg(function(e){return class extends e{constructor(){super(...arguments),this.canvasId=Oo.STRING(\\\\\\\"canvas-id\\\\\\\"),this.update=Oo.BUTTON(null,{cook:!1,callback:e=>{hg.PARAM_CALLBACK_update(e)}})}}}(Lo))){}const ug=new lg;class hg extends jm{constructor(){super(...arguments),this.paramsConfig=ug,this.textureParamsController=new ag(this)}static type(){return\\\\\\\"canvas\\\\\\\"}async cook(){const e=this.pv.canvasId,t=document.getElementById(e);if(!t)return this.states.error.set(`element with id '${e}' not found`),void this.cookController.endCook();if(!(t instanceof HTMLCanvasElement))return this.states.error.set(\\\\\\\"element found is not a canvas\\\\\\\"),void this.cookController.endCook();const n=new cg.a(t);await this.textureParamsController.update(n),this.setTexture(n)}static PARAM_CALLBACK_update(e){e.markTextureNeedsUpdate()}markTextureNeedsUpdate(){const e=this.containerController.container().texture();e&&(e.needsUpdate=!0)}}const dg=new class extends Lo{constructor(){super(...arguments),this.resolution=Oo.VECTOR2([256,256],{callback:e=>{pg.PARAM_CALLBACK_reset(e)}}),this.color=Oo.COLOR([1,1,1])}};class pg extends jm{constructor(){super(...arguments),this.paramsConfig=dg}static type(){return\\\\\\\"color\\\\\\\"}cook(){const e=this.pv.resolution.x,t=this.pv.resolution.y;this._data_texture=this._data_texture||this._create_data_texture(e,t);const n=t*e,i=this.pv.color.toArray(),s=255*i[0],r=255*i[1],o=255*i[2],a=this._data_texture.image.data;for(let e=0;e<n;e++)a[4*e+0]=s,a[4*e+1]=r,a[4*e+2]=o,a[4*e+3]=255;this._data_texture.needsUpdate=!0,this.setTexture(this._data_texture)}_create_data_texture(e,t){const n=this._create_pixel_buffer(e,t);return new T.a(n,e,t)}_create_pixel_buffer(e,t){return new Uint8Array(e*t*4)}static PARAM_CALLBACK_reset(e){e._reset()}_reset(){this._data_texture=void 0}}var _g,mg,fg;!function(e){e.CUBE_CAMERA=\\\\\\\"cubeCamera\\\\\\\",e.AUDIO_LISTENER=\\\\\\\"audioListener\\\\\\\",e.POSITIONAL_AUDIO=\\\\\\\"positionalAudio\\\\\\\"}(_g||(_g={})),function(e){e.CUBE_CAMERA=\\\\\\\"cubeCamera\\\\\\\"}(mg||(mg={})),function(e){e.REFLECTION=\\\\\\\"reflection\\\\\\\",e.REFRACTION=\\\\\\\"refraction\\\\\\\"}(fg||(fg={}));const gg=[fg.REFLECTION,fg.REFRACTION];const vg=new class extends Lo{constructor(){super(...arguments),this.cubeCamera=Oo.NODE_PATH(\\\\\\\"\\\\\\\",{nodeSelection:{context:Ei.OBJ,types:[_g.CUBE_CAMERA]}}),this.mode=Oo.INTEGER(0,{menu:{entries:gg.map(((e,t)=>({name:e,value:t})))}})}};class yg extends jm{constructor(){super(...arguments),this.paramsConfig=vg}static type(){return mg.CUBE_CAMERA}async cook(){const e=this.pv.cubeCamera.nodeWithContext(Ei.OBJ,this.states.error);if(!e)return this.states.error.set(`cubeCamera not found at '${this.pv.cubeCamera.path()}'`),this.cookController.endCook();const t=e.renderTarget();if(!t)return this.states.error.set(\\\\\\\"cubeCamera has no render target'\\\\\\\"),this.cookController.endCook();const n=t.texture;gg[this.pv.mode]==fg.REFLECTION?n.mapping=w.o:n.mapping=w.p,this.setTexture(n)}}const xg=Math.pow(2,8),bg=[.125,.215,.35,.446,.526,.582],wg=5+bg.length,Ag=20,Tg={[w.U]:0,[w.ld]:1,[w.gc]:2,[w.lc]:3,[w.kc]:4,[w.fc]:5,[w.J]:6},Eg=new Nf.a({side:w.i,depthWrite:!1,depthTest:!1}),Cg=new z.a(new P,Eg),Mg=new vm.a,{_lodPlanes:Ng,_sizeLods:Sg,_sigmas:Og}=zg(),Lg=new M.a;let Pg=null;const Rg=(1+Math.sqrt(5))/2,Ig=1/Rg,Fg=[new p.a(1,1,1),new p.a(-1,1,1),new p.a(1,1,-1),new p.a(-1,1,-1),new p.a(0,Rg,Ig),new p.a(0,Rg,-Ig),new p.a(Ig,0,Rg),new p.a(-Ig,0,Rg),new p.a(Rg,Ig,0),new p.a(-Rg,Ig,0)];function Dg(e){const t=Math.max(e.r,e.g,e.b),n=Math.min(Math.max(Math.ceil(Math.log2(t)),-128),127);e.multiplyScalar(Math.pow(2,-n));return(n+128)/255}class kg{constructor(e){this._renderer=e,this._pingPongRenderTarget=null,this._blurMaterial=function(e){const t=new Float32Array(e),n=new p.a(0,1,0);return new Af({name:\\\\\\\"SphericalGaussianBlur\\\\\\\",defines:{n:e},uniforms:{envMap:{value:null},samples:{value:1},weights:{value:t},latitudinal:{value:!1},dTheta:{value:0},mipInt:{value:0},poleAxis:{value:n},inputEncoding:{value:Tg[w.U]},outputEncoding:{value:Tg[w.U]}},vertexShader:Hg(),fragmentShader:`\\\\n\\\\n\\\\t\\\\t\\\\tprecision mediump float;\\\\n\\\\t\\\\t\\\\tprecision mediump int;\\\\n\\\\n\\\\t\\\\t\\\\tvarying vec3 vOutputDirection;\\\\n\\\\n\\\\t\\\\t\\\\tuniform sampler2D envMap;\\\\n\\\\t\\\\t\\\\tuniform int samples;\\\\n\\\\t\\\\t\\\\tuniform float weights[ n ];\\\\n\\\\t\\\\t\\\\tuniform bool latitudinal;\\\\n\\\\t\\\\t\\\\tuniform float dTheta;\\\\n\\\\t\\\\t\\\\tuniform float mipInt;\\\\n\\\\t\\\\t\\\\tuniform vec3 poleAxis;\\\\n\\\\n\\\\t\\\\t\\\\t${qg()}\\\\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})}(Ag),this._equirectShader=null,this._cubemapShader=null,this._compileMaterial(this._blurMaterial)}fromScene(e,t=0,n=.1,i=100){Pg=this._renderer.getRenderTarget();const s=this._allocateTargets();return this._sceneToCubeUV(e,n,i,s),t>0&&this._blur(s,0,0,t),this._applyPMREM(s),this._cleanup(s),s}fromEquirectangular(e){return this._fromTexture(e)}fromCubemap(e){return this._fromTexture(e)}compileCubemapShader(){null===this._cubemapShader&&(this._cubemapShader=jg(),this._compileMaterial(this._cubemapShader))}compileEquirectangularShader(){null===this._equirectShader&&(this._equirectShader=Vg(),this._compileMaterial(this._equirectShader))}dispose(){this._blurMaterial.dispose(),null!==this._cubemapShader&&this._cubemapShader.dispose(),null!==this._equirectShader&&this._equirectShader.dispose();for(let e=0;e<Ng.length;e++)Ng[e].dispose()}_cleanup(e){this._pingPongRenderTarget.dispose(),this._renderer.setRenderTarget(Pg),e.scissorTest=!1,Gg(e,0,0,e.width,e.height)}_fromTexture(e){Pg=this._renderer.getRenderTarget();const t=this._allocateTargets(e);return this._textureToCubeUV(e,t),this._applyPMREM(t),this._cleanup(t),t}_allocateTargets(e){const t={magFilter:w.ob,minFilter:w.ob,generateMipmaps:!1,type:w.Zc,format:w.hc,encoding:Bg(e)?e.encoding:w.gc,depthBuffer:!1},n=Ug(t);return n.depthBuffer=!e,this._pingPongRenderTarget=Ug(t),n}_compileMaterial(e){const t=new z.a(Ng[0],e);this._renderer.compile(t,Mg)}_sceneToCubeUV(e,t,n,i){const s=new te.a(90,1,t,n),r=[1,-1,1,1,1,1],o=[1,1,1,-1,-1,-1],a=this._renderer,c=a.autoClear,l=a.outputEncoding,u=a.toneMapping;a.getClearColor(Lg),a.toneMapping=w.vb,a.outputEncoding=w.U,a.autoClear=!1;let h=!1;const d=e.background;if(d){if(d.isColor){Eg.color.copy(d).convertSRGBToLinear(),e.background=null;const t=Dg(Eg.color);Eg.opacity=t,h=!0}}else{Eg.color.copy(Lg).convertSRGBToLinear();const e=Dg(Eg.color);Eg.opacity=e,h=!0}for(let t=0;t<6;t++){const n=t%3;0==n?(s.up.set(0,r[t],0),s.lookAt(o[t],0,0)):1==n?(s.up.set(0,0,r[t]),s.lookAt(0,o[t],0)):(s.up.set(0,r[t],0),s.lookAt(0,0,o[t])),Gg(i,n*xg,t>2?xg:0,xg,xg),a.setRenderTarget(i),h&&a.render(Cg,s),a.render(e,s)}a.toneMapping=u,a.outputEncoding=l,a.autoClear=c}_textureToCubeUV(e,t){const n=this._renderer;e.isCubeTexture?null==this._cubemapShader&&(this._cubemapShader=jg()):null==this._equirectShader&&(this._equirectShader=Vg());const i=e.isCubeTexture?this._cubemapShader:this._equirectShader,s=new z.a(Ng[0],i),r=i.uniforms;r.envMap.value=e,e.isCubeTexture||r.texelSize.value.set(1/e.image.width,1/e.image.height),r.inputEncoding.value=Tg[e.encoding],r.outputEncoding.value=Tg[t.texture.encoding],Gg(t,0,0,3*xg,2*xg),n.setRenderTarget(t),n.render(s,Mg)}_applyPMREM(e){const t=this._renderer,n=t.autoClear;t.autoClear=!1;for(let t=1;t<wg;t++){const n=Math.sqrt(Og[t]*Og[t]-Og[t-1]*Og[t-1]),i=Fg[(t-1)%Fg.length];this._blur(e,t-1,t,n,i)}t.autoClear=n}_blur(e,t,n,i,s){const r=this._pingPongRenderTarget;this._halfBlur(e,r,t,n,i,\\\\\\\"latitudinal\\\\\\\",s),this._halfBlur(r,e,n,n,i,\\\\\\\"longitudinal\\\\\\\",s)}_halfBlur(e,t,n,i,s,r,o){const a=this._renderer,c=this._blurMaterial;\\\\\\\"latitudinal\\\\\\\"!==r&&\\\\\\\"longitudinal\\\\\\\"!==r&&console.error(\\\\\\\"blur direction must be either latitudinal or longitudinal!\\\\\\\");const l=new z.a(Ng[i],c),u=c.uniforms,h=Sg[n]-1,d=isFinite(s)?Math.PI/(2*h):2*Math.PI/39,p=s/d,_=isFinite(s)?1+Math.floor(3*p):Ag;_>Ag&&console.warn(`sigmaRadians, ${s}, is too large and will clip, as it requested ${_} samples when the maximum is set to 20`);const m=[];let f=0;for(let e=0;e<Ag;++e){const t=e/p,n=Math.exp(-t*t/2);m.push(n),0==e?f+=n:e<_&&(f+=2*n)}for(let e=0;e<m.length;e++)m[e]=m[e]/f;u.envMap.value=e.texture,u.samples.value=_,u.weights.value=m,u.latitudinal.value=\\\\\\\"latitudinal\\\\\\\"===r,o&&(u.poleAxis.value=o),u.dTheta.value=d,u.mipInt.value=8-n,u.inputEncoding.value=Tg[e.texture.encoding],u.outputEncoding.value=Tg[e.texture.encoding];const g=Sg[i];Gg(t,3*Math.max(0,xg-2*g),(0===i?0:2*xg)+2*g*(i>4?i-8+4:0),3*g,2*g),a.setRenderTarget(t),a.render(l,Mg)}}function Bg(e){return void 0!==e&&e.type===w.Zc&&(e.encoding===w.U||e.encoding===w.ld||e.encoding===w.J)}function zg(){const e=[],t=[],n=[];let i=8;for(let s=0;s<wg;s++){const r=Math.pow(2,i);t.push(r);let o=1/r;s>4?o=bg[s-8+4-1]:0==s&&(o=0),n.push(o);const a=1/(r-1),c=-a/2,l=1+a/2,u=[c,c,l,c,l,l,c,c,l,l,c,l],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 e=0;e<h;e++){const t=e%3*2/3-1,n=e>2?0:-1,i=[t,n,0,t+2/3,n,0,t+2/3,n+1,0,t,n,0,t+2/3,n+1,0,t,n+1,0];f.set(i,p*d*e),g.set(u,_*d*e);const s=[e,e,e,e,e,e];v.set(s,m*d*e)}const y=new O.a;y.setAttribute(\\\\\\\"position\\\\\\\",new L.a(f,p)),y.setAttribute(\\\\\\\"uv\\\\\\\",new L.a(g,_)),y.setAttribute(\\\\\\\"faceIndex\\\\\\\",new L.a(v,m)),e.push(y),i>4&&i--}return{_lodPlanes:e,_sizeLods:t,_sigmas:n}}function Ug(e){const t=new Z(3*xg,3*xg,e);return t.texture.mapping=w.q,t.texture.name=\\\\\\\"PMREM.cubeUv\\\\\\\",t.scissorTest=!0,t}function Gg(e,t,n,i,s){e.viewport.set(t,n,i,s),e.scissor.set(t,n,i,s)}function Vg(){const e=new d.a(1,1);return new Af({name:\\\\\\\"EquirectangularToCubeUV\\\\\\\",uniforms:{envMap:{value:null},texelSize:{value:e},inputEncoding:{value:Tg[w.U]},outputEncoding:{value:Tg[w.U]}},vertexShader:Hg(),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${qg()}\\\\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 jg(){return new Af({name:\\\\\\\"CubemapToCubeUV\\\\\\\",uniforms:{envMap:{value:null},inputEncoding:{value:Tg[w.U]},outputEncoding:{value:Tg[w.U]}},vertexShader:Hg(),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${qg()}\\\\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 Hg(){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 qg(){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\\\\\\\"}var Wg;!function(e){e.REFLECTION=\\\\\\\"reflection\\\\\\\",e.REFRACTION=\\\\\\\"refraction\\\\\\\"}(Wg||(Wg={}));const Xg=[Wg.REFLECTION,Wg.REFRACTION];const Yg=new class extends Lo{constructor(){super(...arguments),this.useCameraRenderer=Oo.BOOLEAN(1),this.mode=Oo.INTEGER(0,{menu:{entries:Xg.map(((e,t)=>({name:e,value:t})))}})}};class $g extends jm{constructor(){super(...arguments),this.paramsConfig=Yg}static type(){return\\\\\\\"envMap\\\\\\\"}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState(Ti.NEVER)}async cook(e){const t=e[0];this.convert_texture_to_env_map(t)}async convert_texture_to_env_map(e){this._renderer_controller=this._renderer_controller||new vf(this);const t=await this._renderer_controller.renderer();if(t){const n=new kg(t).fromEquirectangular(e);if(this.pv.useCameraRenderer)this._set_mapping(n.texture),this.setTexture(n.texture);else{this._data_texture_controller=this._data_texture_controller||new gf(ff.Uint8Array);const e=this._data_texture_controller.from_render_target(t,n);this._set_mapping(e),this.setTexture(e)}}else this.states.error.set(\\\\\\\"no renderer found to convert the texture to an env map\\\\\\\"),this.cookController.endCook()}_set_mapping(e){Xg[this.pv.mode]==Wg.REFLECTION?e.mapping=w.q:e.mapping=w.r}}class Qg extends K.a{constructor(e,t,n,i,s,r,o,a,c){super(e,t,n,i,s,r,o,a,c),this.format=void 0!==o?o:w.ic,this.minFilter=void 0!==r?r:w.V,this.magFilter=void 0!==s?s:w.V,this.generateMipmaps=!1;const l=this;\\\\\\\"requestVideoFrameCallback\\\\\\\"in e&&e.requestVideoFrameCallback((function t(){l.needsUpdate=!0,e.requestVideoFrameCallback(t)}))}clone(){return new this.constructor(this.image).copy(this)}update(){const e=this.image;!1===\\\\\\\"requestVideoFrameCallback\\\\\\\"in e&&e.readyState>=e.HAVE_CURRENT_DATA&&(this.needsUpdate=!0)}}Qg.prototype.isVideoTexture=!0;var Jg=n(80);const Kg=\\\\\\\"https://raw.githubusercontent.com/polygonjs/polygonjs-assets/master/\\\\\\\";var Zg=n(25);const ev=new Zg.b;ev.setURLModifier((e=>{const t=Rn.assetUrls.remapedUrl(e);if(t)return t;const n=Rn.blobs.blobUrl(e);return n||e}));class tv{constructor(e,t,n){this._url=e,this._scene=t,this._node=n,this.loadingManager=ev}static extension(e){let t=null;try{t=new URL(e).searchParams.get(\\\\\\\"ext\\\\\\\")}catch(e){}if(!t){const n=e.split(\\\\\\\"?\\\\\\\")[0].split(\\\\\\\".\\\\\\\");t=n[n.length-1].toLowerCase()}return t}extension(){return tv.extension(this._url)}async _urlToLoad(){let e=this._url;const t=this._url.split(\\\\\\\"?\\\\\\\")[0];if(\\\\\\\"h\\\\\\\"!=e[0]){const t=this._scene.assets.root();t&&(e=`${t}${e}`)}this._node&&await Rn.blobs.fetchBlobForNode({storedUrl:t,fullUrl:e,node:this._node});return Rn.blobs.blobUrl(t)||e}static async _loadMultipleBlobGlobal(e){const t=[];for(let n of e.files){const i=n.storedUrl,s=n.fullUrl,r=e.node;t.push(Rn.blobs.fetchBlobGlobal({storedUrl:i,fullUrl:s,node:r}))}const n=await Promise.all(t);for(let t of n)t.error&&e.node.states.error.set(e.error)}}var nv;tv.loadingManager=ev,function(e){e.JPG=\\\\\\\"jpg\\\\\\\",e.JPEG=\\\\\\\"jpeg\\\\\\\",e.PNG=\\\\\\\"png\\\\\\\",e.EXR=\\\\\\\"exr\\\\\\\",e.BASIS=\\\\\\\"basis\\\\\\\",e.HDR=\\\\\\\"hdr\\\\\\\"}(nv||(nv={}));nv.JPEG,nv.JPG,nv.PNG,nv.EXR,nv.BASIS,nv.HDR;class iv extends tv{constructor(e,t,n,i){super(e,i,n),this._param=t,this._node=n,this._scene=i}static onTextureLoaded(e){this._onTextureLoadedCallback=e}async load_texture_from_url_or_op(e){let t=null,n=null;if(\\\\\\\"op:\\\\\\\"==this._url.substring(0,3)){const e=this._url.substring(3);if(n=Wn.findNode(this._node,e),n)if(n instanceof Hm){t=(await n.compute()).texture()}else this._node.states.error.set(\\\\\\\"found node is not a texture node\\\\\\\");else this._node.states.error.set(`no node found in path '${e}'`)}else t=await this._loadUrl(e),t||this._node.states.error.set(`could not load texture ${this._url}`);return n&&this._param.graphPredecessors()[0]!=n&&(this._param.graphDisconnectPredecessors(),this._param.addGraphInput(n)),t}async _loadUrl(e){return new Promise((async(t,n)=>{const i=this.extension(),s=await this._urlToLoad();if(iv.VIDEO_EXTENSIONS.includes(i)){const e=await this._load_as_video(s);t(e)}else this.loader_for_ext(i,e).then((async e=>{e?(iv.increment_in_progress_loads_count(),await iv.wait_for_max_concurrent_loads_queue_freed(),e.load(s,(e=>{iv.decrement_in_progress_loads_count();const n=iv._onTextureLoadedCallback;n&&n(s,e),t(e)}),void 0,(e=>{iv.decrement_in_progress_loads_count(),Rn.warn(\\\\\\\"error\\\\\\\",e),n()}))):n()}))}))}static module_names(e){switch(e){case nv.EXR:return[_n.EXRLoader];case nv.HDR:return[_n.RGBELoader];case nv.BASIS:return[_n.BasisTextureLoader]}}async loader_for_ext(e,t){switch(e.toLowerCase()){case nv.EXR:return await this._exr_loader(t);case nv.HDR:return await this._hdr_loader(t);case nv.BASIS:return await iv._basis_loader(this._node)}return new Jg.a(this.loadingManager)}async _exr_loader(e){const t=await Rn.modulesRegister.module(_n.EXRLoader);if(t){const n=new t(this.loadingManager);return e.tdataType&&n.setDataType(e.dataType),n}}async _hdr_loader(e){const t=await Rn.modulesRegister.module(_n.RGBELoader);if(t){const n=new t(this.loadingManager);return e.tdataType&&n.setDataType(e.dataType),n}}static async _basis_loader(e){const t=await Rn.modulesRegister.module(_n.BasisTextureLoader);if(t){const n=new t(this.loadingManager),i=Rn.libs.root(),s=Rn.libs.BASISPath();if(i||s){const t=`${i||\\\\\\\"\\\\\\\"}${s||\\\\\\\"\\\\\\\"}/`;if(e){const n=[\\\\\\\"basis_transcoder.js\\\\\\\",\\\\\\\"basis_transcoder.wasm\\\\\\\"];await this._loadMultipleBlobGlobal({files:n.map((e=>({storedUrl:`${s}/${e}`,fullUrl:`${t}${e}`}))),node:e,error:\\\\\\\"failed to load basis libraries. Make sure to install them to load .basis files\\\\\\\"})}n.setTranscoderPath(t)}else n.setTranscoderPath(void 0);const r=await Rn.renderersController.waitForRenderer();return r?n.detectSupport(r):Rn.warn(\\\\\\\"texture loader found no renderer for basis texture loader\\\\\\\"),n}}_load_as_video(e){return new Promise(((t,n)=>{const i=document.createElement(\\\\\\\"video\\\\\\\");i.setAttribute(\\\\\\\"crossOrigin\\\\\\\",\\\\\\\"anonymous\\\\\\\"),i.setAttribute(\\\\\\\"autoplay\\\\\\\",\\\\\\\"true\\\\\\\"),i.setAttribute(\\\\\\\"loop\\\\\\\",\\\\\\\"true\\\\\\\"),i.onloadedmetadata=function(){i.pause();const e=new Qg(i);t(e)};const s=document.createElement(\\\\\\\"source\\\\\\\"),r=tv.extension(e);let o=iv.VIDEO_SOURCE_TYPE_BY_EXT[r];o=o||iv._default_video_source_type(e),s.setAttribute(\\\\\\\"type\\\\\\\",o),s.setAttribute(\\\\\\\"src\\\\\\\",e),i.appendChild(s);let a=e;a=\\\\\\\"mp4\\\\\\\"==r?iv.replaceExtension(e,\\\\\\\"ogv\\\\\\\"):iv.replaceExtension(e,\\\\\\\"mp4\\\\\\\");const c=document.createElement(\\\\\\\"source\\\\\\\"),l=tv.extension(a);o=iv.VIDEO_SOURCE_TYPE_BY_EXT[l],o=o||iv._default_video_source_type(e),c.setAttribute(\\\\\\\"type\\\\\\\",o),c.setAttribute(\\\\\\\"src\\\\\\\",e),i.appendChild(c)}))}static _default_video_source_type(e){return`video/${tv.extension(e)}`}static pixel_data(e){const t=e.image,n=document.createElement(\\\\\\\"canvas\\\\\\\");n.width=t.width,n.height=t.height;const i=n.getContext(\\\\\\\"2d\\\\\\\");if(i)return i.drawImage(t,0,0,t.width,t.height),i.getImageData(0,0,t.width,t.height)}static replaceExtension(e,t){const n=e.split(\\\\\\\"?\\\\\\\"),i=n[0].split(\\\\\\\".\\\\\\\");return i.pop(),i.push(t),[i.join(\\\\\\\".\\\\\\\"),n[1]].join(\\\\\\\"?\\\\\\\")}static setMaxConcurrentLoadsCount(e){this._maxConcurrentLoadsCountMethod=e}static _init_max_concurrent_loads_count(){return this._maxConcurrentLoadsCountMethod?this._maxConcurrentLoadsCountMethod():Df.isChrome()?10:4}static _init_concurrent_loads_delay(){return Df.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 e=this._queue.pop();if(e){const t=this.CONCURRENT_LOADS_DELAY;setTimeout((()=>{e()}),t)}}static async wait_for_max_concurrent_loads_queue_freed(){return this.in_progress_loads_count<=this.MAX_CONCURRENT_LOADS_COUNT?void 0:new Promise((e=>{this._queue.push(e)}))}}iv.PARAM_DEFAULT=`${Kg}/textures/uv.jpg`,iv.PARAM_ENV_DEFAULT=`${Kg}/textures/piz_compressed.exr`,iv.VIDEO_EXTENSIONS=[\\\\\\\"mp4\\\\\\\",\\\\\\\"webm\\\\\\\",\\\\\\\"ogv\\\\\\\"],iv.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\\\\\\\"'},iv.MAX_CONCURRENT_LOADS_COUNT=iv._init_max_concurrent_loads_count(),iv.CONCURRENT_LOADS_DELAY=iv._init_concurrent_loads_delay(),iv.in_progress_loads_count=0,iv._queue=[];var sv=n(114);class rv extends(rg(function(e){return class extends e{constructor(){super(...arguments),this.url=Oo.STRING(iv.PARAM_DEFAULT,{fileBrowse:{type:[tr.TEXTURE_IMAGE]}}),this.reload=Oo.BUTTON(null,{callback:(e,t)=>{av.PARAM_CALLBACK_reload(e)}}),this.play=Oo.BOOLEAN(1,{cook:!1,callback:e=>{av.PARAM_CALLBACK_gifUpdatePlay(e)}}),this.gifFrame=Oo.INTEGER(0,{cook:!1,range:[0,100],rangeLocked:[!0,!1],callback:e=>{av.PARAM_CALLBACK_gifUpdateFrameIndex(e)}})}}}(Lo))){}const ov=new rv;class av extends jm{constructor(){super(...arguments),this.paramsConfig=ov,this.textureParamsController=new ag(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 e=tv.extension(this.pv.url||\\\\\\\"\\\\\\\");return iv.module_names(e)}static displayedInputNames(){return[\\\\\\\"optional texture to copy attributes from\\\\\\\"]}initializeNode(){this.io.inputs.setCount(0,1),this.io.inputs.initInputsClonedState(Ti.NEVER),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{let e=[this.p.url];e=e.concat(this.textureParamsController.paramLabelsParams()),this.params.label.init(e,(()=>{const e=this.p.url.rawInput();if(e){const t=e.split(\\\\\\\"/\\\\\\\"),n=t[t.length-1],i=this.textureParamsController.paramLabels();return[n].concat(i)}return\\\\\\\"\\\\\\\"}))}))}))}async cook(e){const t=await fetch(this.pv.url),n=await t.arrayBuffer(),i=await Object(sv.parseGIF)(n);this._parsedFrames=await Object(sv.decompressFrames)(i,!0);const s=this._parsedFrames[0];if(this._frameDelay=s.delay,this._frameIndex=this.pv.gifFrame-1,this._createCanvas(),this._gifCanvasElement){const e=new cg.a(this._gifCanvasElement);await this.textureParamsController.update(e),this.setTexture(e)}else this.states.error.set(\\\\\\\"failed to create canvas\\\\\\\")}_createCanvas(){const e=this._parsedFrames[0];this._gifCanvasElement=document.createElement(\\\\\\\"canvas\\\\\\\"),this._tmpCanvasElement=document.createElement(\\\\\\\"canvas\\\\\\\"),this._gifCanvasElement.width=e.dims.width,this._gifCanvasElement.height=e.dims.height,this._tmpCanvasElement.width=e.dims.width,this._tmpCanvasElement.height=e.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 e=this._parsedFrames[this._frameIndex];if(e||(console.warn(`no frame at index ${this._frameIndex}, using last frame`),e=this._parsedFrames[this._parsedFrames.length-1]),e){const t=e.dims;this._frameImageData&&t.width==this._frameImageData.width&&t.height==this._frameImageData.height||(this._tmpCanvasElement.width=t.width,this._tmpCanvasElement.height=t.height,this._frameImageData=this._tmpCanvasContext.createImageData(t.width,t.height)),this._frameImageData.data.set(e.patch),this._tmpCanvasContext.putImageData(this._frameImageData,0,0),this._gifCanvasContext.drawImage(this._tmpCanvasElement,t.left,t.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(e){e.paramCallbackReload()}paramCallbackReload(){this.p.url.setDirty()}static PARAM_CALLBACK_gifUpdatePlay(e){e.gifUpdatePlay()}gifUpdatePlay(){this.pv.play&&this._drawNextFrame()}static PARAM_CALLBACK_gifUpdateFrameIndex(e){e.gifUpdateFrameIndex()}}const cv=[\\\\\\\"mp4\\\\\\\",\\\\\\\"ogv\\\\\\\"];class lv{static isStaticImageUrl(e){const t=e.split(\\\\\\\"?\\\\\\\")[0].split(\\\\\\\".\\\\\\\"),n=t[t.length-1];return!cv.includes(n)}}class uv extends(rg(function(e){return class extends e{constructor(){super(...arguments),this.url=Oo.STRING(iv.PARAM_DEFAULT,{fileBrowse:{type:[tr.TEXTURE_IMAGE]}}),this.reload=Oo.BUTTON(null,{callback:(e,t)=>{dv.PARAM_CALLBACK_reload(e,t)}})}}}(Lo))){}const hv=new uv;class dv extends jm{constructor(){super(...arguments),this.paramsConfig=hv,this.textureParamsController=new ag(this)}static type(){return\\\\\\\"image\\\\\\\"}async requiredModules(){this.p.url.isDirty()&&await this.p.url.compute();const e=tv.extension(this.pv.url||\\\\\\\"\\\\\\\");return iv.module_names(e)}static displayedInputNames(){return[\\\\\\\"optional texture to copy attributes from\\\\\\\"]}initializeNode(){this.io.inputs.setCount(0,1),this.io.inputs.initInputsClonedState(Ti.NEVER),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{let e=[this.p.url];e=e.concat(this.textureParamsController.paramLabelsParams()),this.params.label.init(e,(()=>{const e=this.p.url.rawInput();if(e){const t=e.split(\\\\\\\"/\\\\\\\"),n=t[t.length-1],i=this.textureParamsController.paramLabels();return[n].concat(i)}return\\\\\\\"\\\\\\\"}))}))}))}async cook(e){if(lv.isStaticImageUrl(this.pv.url)){const t=await this._loadTexture(this.pv.url);if(t){const n=e[0];n&&ag.copyTextureAttributes(t,n),await this.textureParamsController.update(t),this.setTexture(t)}else this._clearTexture()}else this.states.error.set(\\\\\\\"input is not an image\\\\\\\")}static PARAM_CALLBACK_reload(e,t){e.paramCallbackReload()}paramCallbackReload(){this.p.url.setDirty()}async _loadTexture(e){let t=null;const n=this.p.url,i=new iv(e,n,this,this.scene());try{t=await i.load_texture_from_url_or_op({tdataType:this.pv.ttype&&this.pv.tadvanced,dataType:this.pv.type}),t&&(t.matrixAutoUpdate=!1)}catch(e){}return t||this.states.error.set(`could not load texture '${e}'`),t}}var pv=n(34);const _v=.005;class mv{constructor(e,t=1024){this.renderer=e,this.res=t,this.objectTargets=[],this.lights=[],this.scene=new Vi,this.buffer1Active=!1,this._params={lightRadius:1,iterations:1,iterationBlend:_v,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 Ll.a,this._s=new p.a;const n=Df.isAndroid()||Df.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(e){this._params.lightRadius=e.lightRadius,this._params.iterations=e.iterations,this._params.iterationBlend=e.iterationBlend,this._params.blur=e.blur,this._params.blurAmount=e.blurAmount}init(e,t){this._setObjects(e),this._setLights(t)}_setObjects(e){this.objectTargets=[];for(let t of e)null==this.blurringPlane&&this._initializeBlurPlane(this.res,this.progressiveLightMap1),this.objectTargets.push(t);this._saveObjectsState()}_setLights(e){this.lights=e;for(let t of e)this._saveLightHierarchyState(t),this.scene.attach(t),this._saveLightMatrixState(t)}_saveLightHierarchyState(e){this._lightHierarchyStateByLight.set(e,{parent:e.parent,matrixAutoUpdate:e.matrixAutoUpdate}),e.matrixAutoUpdate=!0}_saveLightMatrixState(e){e.updateMatrix(),e.matrix.decompose(this._t,this._q,this._s),this._lightMatrixStateByLight.set(e,{matrix:e.matrix.clone(),position:this._t.clone()})}_saveObjectsState(){let e=0;for(let t of this.objectTargets)this._objectStateByObject.set(t,{frustumCulled:t.frustumCulled,material:t.material,parent:t.parent,castShadow:t.castShadow,receiveShadow:t.receiveShadow}),t.material=this.uvMat,t.frustumCulled=!1,t.castShadow=!0,t.receiveShadow=!0,t.renderOrder=1e3+e,this.scene.attach(t),e++;this._previousRenderTarget=this.renderer.getRenderTarget()}_moveLights(){const e=this._params.lightRadius;for(let t of this.lights){const n=this._lightMatrixStateByLight.get(t);if(n){const i=n.position;t.position.x=i.x+e*(Math.random()-.5),t.position.y=i.y+e*(Math.random()-.5),t.position.z=i.z+e*(Math.random()-.5)}}}restoreState(){this._restoreObjectsState(),this._restoreLightsState(),this.renderer.setRenderTarget(this._previousRenderTarget)}_restoreObjectsState(){for(let e of this.objectTargets){const t=this._objectStateByObject.get(e);if(t){e.frustumCulled=t.frustumCulled,e.castShadow=t.castShadow,e.receiveShadow=t.receiveShadow,e.material=t.material;const n=t.parent;n&&n.add(e)}}}_restoreLightsState(){var e;for(let t of this.lights){const n=this._lightHierarchyStateByLight.get(t),i=this._lightMatrixStateByLight.get(t);n&&i&&(t.matrixAutoUpdate=n.matrixAutoUpdate,t.matrix.copy(i.matrix),t.matrix.decompose(t.position,t.quaternion,t.scale),t.updateMatrix(),null===(e=n.parent)||void 0===e||e.attach(t))}}runUpdates(e){if(!this.blurMaterial)return;if(null==this.blurringPlane)return;const t=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(e);for(let n=0;n<t;n++)this._moveLights(),this._update(e)}_clear(e){this.scene.visible=!1,this._update(e),this._update(e),this.scene.visible=!0}_update(e){if(!this.blurMaterial)return;const t=this.buffer1Active?this.progressiveLightMap1:this.progressiveLightMap2,n=this.buffer1Active?this.progressiveLightMap2:this.progressiveLightMap1;this.renderer.setRenderTarget(t),this.uvMat.uniforms.previousShadowMap.value=n.texture,this.blurMaterial.uniforms.previousShadowMap.value=n.texture,this.buffer1Active=!this.buffer1Active,this.renderer.render(this.scene,e)}_initializeBlurPlane(e,t){this.blurMaterial=this._createBlurPlaneMaterial(e,t),this.blurringPlane=new z.a(new R(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(e,t){const n=new Nf.a;return n.uniforms={previousShadowMap:{value:null},pixelOffset:{value:1/e}},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 s=i.fragmentShader.indexOf(\\\\\\\"void main() {\\\\\\\");i.fragmentShader=\\\\\\\"#define USE_UV\\\\n\\\\\\\"+i.fragmentShader.slice(0,s)+\\\\\\\"\\\\tuniform sampler2D previousShadowMap;\\\\n\\\\tuniform float pixelOffset;\\\\n\\\\\\\"+i.fragmentShader.slice(s-1,-2)+\\\\\\\"\\\\tgl_FragColor.rgb = (\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\ttexture2D(previousShadowMap, vUv + vec2( pixelOffset,  0.0        )).rgb +\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\ttexture2D(previousShadowMap, vUv + vec2( 0.0        ,  pixelOffset)).rgb +\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\ttexture2D(previousShadowMap, vUv + vec2( 0.0        , -pixelOffset)).rgb +\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\ttexture2D(previousShadowMap, vUv + vec2(-pixelOffset,  0.0        )).rgb +\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\ttexture2D(previousShadowMap, vUv + vec2( pixelOffset,  pixelOffset)).rgb +\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\ttexture2D(previousShadowMap, vUv + vec2(-pixelOffset,  pixelOffset)).rgb +\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\ttexture2D(previousShadowMap, vUv + vec2( pixelOffset, -pixelOffset)).rgb +\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\ttexture2D(previousShadowMap, vUv + vec2(-pixelOffset, -pixelOffset)).rgb)/8.0;\\\\n\\\\t\\\\t\\\\t\\\\t}\\\\\\\";const r={previousShadowMap:{value:t.texture},pixelOffset:{value:.5/e}};i.uniforms.previousShadowMap=r.previousShadowMap,i.uniforms.pixelOffset=r.pixelOffset,n.uniforms.previousShadowMap=r.previousShadowMap,n.uniforms.pixelOffset=r.pixelOffset,n.userData.shader=i},n}_createUVMat(){const e=new Ef.a;return e.uniforms={previousShadowMap:{value:null},iterationBlend:{value:_v}},e.name=\\\\\\\"uvMat\\\\\\\",e.onBeforeCompile=t=>{t.vertexShader=\\\\\\\"#define USE_LIGHTMAP\\\\n\\\\\\\"+t.vertexShader.slice(0,-2)+\\\\\\\"\\\\tgl_Position = vec4((uv2 - 0.5) * 2.0, 1.0, 1.0); }\\\\\\\";const n=t.fragmentShader.indexOf(\\\\\\\"void main() {\\\\\\\");t.fragmentShader=\\\\\\\"varying vec2 vUv2;\\\\n\\\\\\\"+t.fragmentShader.slice(0,n)+\\\\\\\"\\\\tuniform sampler2D previousShadowMap;\\\\n\\\\tuniform float iterationBlend;\\\\n\\\\\\\"+t.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:_v}};t.uniforms.previousShadowMap=i.previousShadowMap,t.uniforms.iterationBlend=i.iterationBlend,e.uniforms.previousShadowMap=i.previousShadowMap,e.uniforms.iterationBlend=i.iterationBlend,e.userData.shader=t},e}}const fv=new class extends Lo{constructor(){super(...arguments),this.update=Oo.BUTTON(null,{callback:e=>{gv.PARAM_CALLBACK_updateManual(e)}}),this.useCameraRenderer=Oo.BOOLEAN(1),this.lightMapRes=Oo.INTEGER(1024,{range:[1,2048],rangeLocked:[!0,!1]}),this.iterations=Oo.INTEGER(512,{range:[1,2048],rangeLocked:[!0,!1]}),this.iterationBlend=Oo.FLOAT(_v,{range:[0,1],rangeLocked:[!0,!0]}),this.blur=Oo.BOOLEAN(1),this.blurAmount=Oo.FLOAT(1,{visibleIf:{blur:1},range:[0,1],rangeLocked:[!0,!1]}),this.lightRadius=Oo.FLOAT(1,{range:[0,10]}),this.objectsMask=Oo.STRING(\\\\\\\"\\\\\\\"),this.lightsMask=Oo.STRING(\\\\\\\"*\\\\\\\"),this.printResolveObjectsList=Oo.BUTTON(null,{callback:e=>{gv.PARAM_CALLBACK_printResolveObjectsList(e)}})}};class gv extends jm{constructor(){super(...arguments),this.paramsConfig=fv,this._includedObjects=[],this._includedLights=[]}static type(){return\\\\\\\"lightMap\\\\\\\"}async cook(){this._updateManual()}async _createLightMapController(){const e=await Rn.renderersController.firstRenderer();if(!e)return void console.warn(\\\\\\\"no renderer found\\\\\\\");return new mv(e,this.pv.lightMapRes)}static PARAM_CALLBACK_update_updateMode(e){}async _updateManual(){if(this.lightMapController=this.lightMapController||await this._createLightMapController(),!this.lightMapController)return;const e=this.scene().mainCameraNode();if(!e)return;this._updateObjectsAndLightsList(),this.lightMapController.init(this._includedObjects,this._includedLights);const t=e.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(t),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 gf(ff.Float32Array),this._renderer_controller=this._renderer_controller||new vf(this);const e=await this._renderer_controller.renderer(),t=this._data_texture_controller.from_render_target(e,n);this.setTexture(t)}}static PARAM_CALLBACK_updateManual(e){e._updateManual()}_updateObjectsAndLightsList(){let e=[],t=[];this._includedLights=[],this._includedObjects=[];const n=new WeakSet;if(\\\\\\\"\\\\\\\"!=this.pv.lightsMask){t=this.scene().objectsByMask(this.pv.lightsMask);for(let e of t)e instanceof pv.a&&(this._includedLights.push(e),n.add(e))}if(\\\\\\\"\\\\\\\"!=this.pv.objectsMask){e=this.scene().objectsByMask(this.pv.objectsMask);for(let t of e)t instanceof pv.a||!n.has(t)&&t instanceof z.a&&this._includedObjects.push(t)}}static PARAM_CALLBACK_printResolveObjectsList(e){e._printResolveObjectsList()}_printResolveObjectsList(){this._updateObjectsAndLightsList(),console.log(\\\\\\\"included objects:\\\\\\\"),console.log(this._includedObjects),console.log(\\\\\\\"included lights:\\\\\\\"),console.log(this._includedLights)}}const vv=new Lo;class yv extends jm{constructor(){super(...arguments),this.paramsConfig=vv}static type(){return\\\\\\\"null\\\\\\\"}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState(Ti.NEVER)}async cook(e){const t=e[0];this.setTexture(t)}}const xv=[Ni.ORTHOGRAPHIC,Ni.PERSPECTIVE];class bv extends(rg(function(e){return class extends e{constructor(){super(...arguments),this.camera=Oo.NODE_PATH(\\\\\\\"\\\\\\\",{nodeSelection:{context:Ei.OBJ,types:xv}}),this.resolution=Oo.VECTOR2([1024,1024]),this.useCameraRenderer=Oo.BOOLEAN(1),this.render=Oo.BUTTON(null,{callback:e=>{Av.PARAM_CALLBACK_render(e)}})}}}(Lo))){}const wv=new bv;class Av extends jm{constructor(){super(...arguments),this.paramsConfig=wv,this.textureParamsController=new ag(this)}static type(){return\\\\\\\"render\\\\\\\"}async cook(){this._texture_scene=this.scene().threejsScene(),this._camera_node=this.pv.camera.nodeWithContext(Ei.OBJ),this._camera_node&&xv.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 vf(this);const e=await this._renderer_controller.renderer(),t=e.getRenderTarget();if(e.setRenderTarget(this._render_target),e.clear(),e.render(this._texture_scene,this._texture_camera),e.setRenderTarget(t),this._render_target.texture)if(this.pv.useCameraRenderer)this.setTexture(this._render_target.texture);else{this._data_texture_controller=this._data_texture_controller||new gf(ff.Float32Array);const t=this._data_texture_controller.from_render_target(e,this._render_target);await this.textureParamsController.update(t),this.setTexture(t)}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 e;this._render_target&&this._renderTargetResolutionValid()||(this._render_target=await this._createRenderTarget(this.pv.resolution.x,this.pv.resolution.y),null===(e=this._data_texture_controller)||void 0===e||e.reset())}_renderTargetResolutionValid(){if(this._render_target){const e=this._render_target.texture.image;return e.width==this.pv.resolution.x&&e.height==this.pv.resolution.y}return!1}async _createRenderTarget(e,t){if(this._render_target){const n=this._render_target.texture.image;if(n.width==e&&n.height==t)return this._render_target}const n=w.n,i=w.n,s=w.V,r=w.ob;var o=new Z(e,t,{wrapS:n,wrapT:i,minFilter:s,magFilter:r,format:w.Ib,generateMipmaps:!0,type:Df.isiOS()?w.M:w.G,stencilBuffer:!1,depthBuffer:!1});return await this.textureParamsController.update(o.texture),Rn.warn(\\\\\\\"created render target\\\\\\\",this.path(),e,t),o}static PARAM_CALLBACK_render(e){e.renderOnTarget()}}const Tv=new class extends Lo{constructor(){super(...arguments),this.input=Oo.INTEGER(0,{range:[0,3],rangeLocked:[!0,!0]})}};class Ev extends jm{constructor(){super(...arguments),this.paramsConfig=Tv}static type(){return\\\\\\\"switch\\\\\\\"}initializeNode(){this.io.inputs.setCount(0,4),this.io.inputs.initInputsClonedState(Ti.NEVER),this.cookController.disallowInputsEvaluation()}async cook(){const e=this.pv.input;if(this.io.inputs.has_input(e)){const t=await this.containerController.requestInputContainer(e);if(t)return void this.setTexture(t.texture())}else this.states.error.set(`no input ${e}`);this.cookController.endCook()}}class Cv extends(rg(Lo)){}const Mv=new Cv;class Nv extends jm{constructor(){super(...arguments),this.paramsConfig=Mv,this.textureParamsController=new ag(this)}static type(){return\\\\\\\"textureProperties\\\\\\\"}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState([Ti.FROM_NODE])}async cook(e){const t=e[0];this.textureParamsController.update(t),this.setTexture(t)}}class Sv extends(rg(function(e){return class extends e{constructor(){super(...arguments),this.url=Oo.STRING(iv.PARAM_DEFAULT,{fileBrowse:{type:[tr.TEXTURE_VIDEO]}}),this.reload=Oo.BUTTON(null,{callback:(e,t)=>{Lv.PARAM_CALLBACK_reload(e,t)}}),this.play=Oo.BOOLEAN(1,{cook:!1,callback:e=>{Lv.PARAM_CALLBACK_video_update_play(e)}}),this.muted=Oo.BOOLEAN(1,{cook:!1,callback:e=>{Lv.PARAM_CALLBACK_video_update_muted(e)}}),this.loop=Oo.BOOLEAN(1,{cook:!1,callback:e=>{Lv.PARAM_CALLBACK_video_update_loop(e)}}),this.videoTime=Oo.FLOAT(0,{cook:!1}),this.setVideoTime=Oo.BUTTON(null,{cook:!1,callback:e=>{Lv.PARAM_CALLBACK_video_update_time(e)}})}}}(Lo))){}const Ov=new Sv;class Lv extends jm{constructor(){super(...arguments),this.paramsConfig=Ov,this.textureParamsController=new ag(this)}static type(){return\\\\\\\"video\\\\\\\"}async requiredModules(){this.p.url.isDirty()&&await this.p.url.compute();const e=tv.extension(this.pv.url||\\\\\\\"\\\\\\\");return iv.module_names(e)}static displayedInputNames(){return[\\\\\\\"optional texture to copy attributes from\\\\\\\"]}initializeNode(){this.io.inputs.setCount(0,1),this.io.inputs.initInputsClonedState(Ti.NEVER),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.url],(()=>{const e=this.p.url.rawInput();if(e){const t=e.split(\\\\\\\"/\\\\\\\");return t[t.length-1]}return\\\\\\\"\\\\\\\"}))}))}))}async cook(e){if(lv.isStaticImageUrl(this.pv.url))this.states.error.set(\\\\\\\"input is not a video\\\\\\\");else{const t=await this._load_texture(this.pv.url);if(t){this._video=t.image;const n=e[0];n&&ag.copyTextureAttributes(t,n),this.video_update_loop(),this.video_update_muted(),this.video_update_play(),this.video_update_time(),await this.textureParamsController.update(t),this.setTexture(t)}else this.cookController.endCook()}}static PARAM_CALLBACK_video_update_time(e){e.video_update_time()}static PARAM_CALLBACK_video_update_play(e){e.video_update_play()}static PARAM_CALLBACK_video_update_muted(e){e.video_update_muted()}static PARAM_CALLBACK_video_update_loop(e){e.video_update_loop()}async video_update_time(){if(this._video){const e=this.p.videoTime;e.isDirty()&&await e.compute(),this._video.currentTime=e.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(e,t){e.paramCallbackReload()}paramCallbackReload(){this.p.url.setDirty()}async _load_texture(e){let t=null;const n=this.p.url;this._texture_loader=this._texture_loader||new iv(e,n,this,this.scene());try{t=await this._texture_loader.load_texture_from_url_or_op({tdataType:this.pv.ttype&&this.pv.tadvanced,dataType:this.pv.type}),t&&(t.matrixAutoUpdate=!1)}catch(e){}return t||this.states.error.set(`could not load texture '${e}'`),t}}class Pv extends(rg(function(e){return class extends e{constructor(){super(...arguments),this.res=Oo.VECTOR2([1024,1024])}}}(Lo))){}const Rv=new Pv;class Iv extends jm{constructor(){super(...arguments),this.paramsConfig=Rv,this.textureParamsController=new ag(this)}static type(){return\\\\\\\"webCam\\\\\\\"}HTMLVideoElement(){return this._video}static displayedInputNames(){return[\\\\\\\"optional texture to copy attributes from\\\\\\\"]}initializeNode(){this.io.inputs.setCount(0,1),this.io.inputs.initInputsClonedState(Ti.NEVER)}_createHTMLVideoElement(){this._video&&document.body.removeChild(this._video);const e=document.createElement(\\\\\\\"video\\\\\\\");return e.style.display=\\\\\\\"none\\\\\\\",e.width=this.pv.res.x,e.height=this.pv.res.y,e.autoplay=!0,e.setAttribute(\\\\\\\"autoplay\\\\\\\",\\\\\\\"true\\\\\\\"),e.setAttribute(\\\\\\\"muted\\\\\\\",\\\\\\\"true\\\\\\\"),e.setAttribute(\\\\\\\"playsinline\\\\\\\",\\\\\\\"true\\\\\\\"),document.body.appendChild(e),e}async cook(e){this._video=this._createHTMLVideoElement();const t=new Qg(this._video),n=e[0];if(n&&ag.copyTextureAttributes(t,n),await this.textureParamsController.update(t),navigator&&navigator.mediaDevices&&navigator.mediaDevices.getUserMedia){const e={video:{width:this.pv.res.x,height:this.pv.res.y,facingMode:\\\\\\\"user\\\\\\\"}};navigator.mediaDevices.getUserMedia(e).then((e=>{this._video&&(this._video.srcObject=e,this._video.play(),this.setTexture(t))})).catch((e=>{this.states.error.set(\\\\\\\"Unable to access the camera/webcam\\\\\\\")}))}else this.states.error.set(\\\\\\\"MediaDevices interface not available.\\\\\\\")}}class Fv extends Mo{static context(){return Ei.COP}cook(){this.cookController.endCook()}}class Dv extends Fv{}class kv extends Dv{constructor(){super(...arguments),this._children_controller_context=Ei.ANIM}static type(){return Ci.ANIM}createNode(e,t){return super.createNode(e,t)}children(){return super.children()}nodesByType(e){return super.nodesByType(e)}}class Bv extends Dv{constructor(){super(...arguments),this._children_controller_context=Ei.EVENT}static type(){return Ci.EVENT}createNode(e,t){return super.createNode(e,t)}children(){return super.children()}nodesByType(e){return super.nodesByType(e)}}class zv extends Dv{constructor(){super(...arguments),this._children_controller_context=Ei.COP}static type(){return Ci.COP}createNode(e,t){return super.createNode(e,t)}children(){return super.children()}nodesByType(e){return super.nodesByType(e)}}class Uv extends Dv{constructor(){super(...arguments),this._children_controller_context=Ei.MAT}static type(){return Ci.MAT}createNode(e,t){return super.createNode(e,t)}children(){return super.children()}nodesByType(e){return super.nodesByType(e)}}class Gv extends Fv{constructor(){super(...arguments),this.paramsConfig=new Rm,this.effectsComposerController=new Im(this),this.displayNodeController=new mm(this,this.effectsComposerController.displayNodeControllerCallbacks()),this._children_controller_context=Ei.POST}static type(){return Ci.POST}createNode(e,t){return super.createNode(e,t)}children(){return super.children()}nodesByType(e){return super.nodesByType(e)}}class Vv extends Dv{constructor(){super(...arguments),this._children_controller_context=Ei.ROP}static type(){return Ci.ROP}createNode(e,t){return super.createNode(e,t)}children(){return super.children()}nodesByType(e){return super.nodesByType(e)}}var jv,Hv;!function(e){e.START=\\\\\\\"start\\\\\\\",e.STOP=\\\\\\\"stop\\\\\\\",e.UPDATE=\\\\\\\"update\\\\\\\"}(jv||(jv={})),function(e){e.START=\\\\\\\"start\\\\\\\",e.COMPLETE=\\\\\\\"completed\\\\\\\"}(Hv||(Hv={}));const qv=new class extends Lo{constructor(){super(...arguments),this.animation=Oo.NODE_PATH(\\\\\\\"\\\\\\\",{nodeSelection:{context:Ei.ANIM},dependentOnFoundNode:!1}),this.play=Oo.BUTTON(null,{callback:e=>{Wv.PARAM_CALLBACK_play(e)}}),this.pause=Oo.BUTTON(null,{callback:e=>{Wv.PARAM_CALLBACK_pause(e)}})}};class Wv extends ca{constructor(){super(...arguments),this.paramsConfig=qv}static type(){return\\\\\\\"animation\\\\\\\"}initializeNode(){this.io.inputs.setNamedInputConnectionPoints([new xo(jv.START,yo.BASE,this._play.bind(this)),new xo(jv.STOP,yo.BASE,this._pause.bind(this))]),this.io.outputs.setNamedOutputConnectionPoints([new xo(Hv.START,yo.BASE),new xo(Hv.COMPLETE,yo.BASE)]),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.animation],(()=>this.pv.animation.path()))}))}))}processEvent(e){}static PARAM_CALLBACK_play(e){e._play({})}static PARAM_CALLBACK_pause(e){e._pause()}async _play(e){const t=this.p.animation;t.isDirty()&&await t.compute();const n=t.value.nodeWithContext(Ei.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=m_.timeline(),this._timeline_builder.populate(this._timeline),this._timeline.vars.onStart=()=>{this.trigger_animation_started(e)},this._timeline.vars.onComplete=()=>{this._timeline&&this._timeline.kill(),this.trigger_animation_completed(e)}))}_pause(){this._timeline&&this._timeline.pause()}trigger_animation_started(e){this.dispatchEventToOutput(Hv.START,e)}trigger_animation_completed(e){this.dispatchEventToOutput(Hv.COMPLETE,e)}}const Xv=\\\\\\\"event\\\\\\\";const Yv=new class extends Lo{constructor(){super(...arguments),this.active=Oo.BOOLEAN(1),this.inputsCount=Oo.INTEGER(5,{range:[1,10],rangeLocked:[!0,!1]})}};class $v extends ca{constructor(){super(...arguments),this.paramsConfig=Yv}static type(){return\\\\\\\"any\\\\\\\"}initializeNode(){this.io.connection_points.set_expected_input_types_function(this._expected_input_types.bind(this)),this.io.connection_points.set_input_name_function(this._input_name.bind(this)),this.io.connection_points.set_output_name_function((()=>Xv)),this.io.connection_points.set_expected_output_types_function((()=>[yo.BASE]))}_expected_input_types(){const e=new Array(this.pv.inputsCount);return e.fill(yo.BASE),e}_input_name(e){return`trigger${e}`}async processEvent(e){this.p.active.isDirty()&&await this.p.active.compute(),this.pv.active&&this.dispatchEventToOutput(Xv,e)}}const Qv=new class extends Lo{constructor(){super(...arguments),this.blocking=Oo.BOOLEAN(1)}};class Jv extends ca{constructor(){super(...arguments),this.paramsConfig=Qv}static type(){return\\\\\\\"block\\\\\\\"}initializeNode(){this.io.inputs.setNamedInputConnectionPoints([new xo(\\\\\\\"in\\\\\\\",yo.BASE,this._process_incoming_event.bind(this))]),this.io.outputs.setNamedOutputConnectionPoints([new xo(Jv.OUTPUT,yo.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(e){this.dispatchEventToOutput(Jv.OUTPUT,e)}_process_incoming_event(e){this.pv.blocking||this.trigger_output(e)}}var Kv;Jv.OUTPUT=\\\\\\\"output\\\\\\\",function(e){e.OUT=\\\\\\\"out\\\\\\\"}(Kv||(Kv={}));const Zv=new class extends Lo{constructor(){super(...arguments),this.dispatch=Oo.BUTTON(null,{callback:e=>{ey.PARAM_CALLBACK_execute(e)}})}};class ey extends ca{constructor(){super(...arguments),this.paramsConfig=Zv}static type(){return\\\\\\\"button\\\\\\\"}initializeNode(){this.io.outputs.setNamedOutputConnectionPoints([new xo(Kv.OUT,yo.BASE)])}processEvent(e){}process_event_execute(e){this.dispatchEventToOutput(Kv.OUT,e)}static PARAM_CALLBACK_execute(e){e.process_event_execute({})}}class ty extends ca{constructor(){super(...arguments),this._controls_by_viewer=new Map}async apply_controls(e,t){var n;null===(n=t.controlsController)||void 0===n||n.dispose_controls();const i=t.canvas();if(!i)return;const s=await this.create_controls_instance(e,i),r=this._controls_by_viewer.get(t);r&&r.dispose(),this._controls_by_viewer.set(t,s);const o=Rn.performance.performanceManager().now();return s.name=`${this.path()}:${e.name}:${o}:${this.controls_id()}`,await this.params.evalAll(),this.setup_controls(s),s}controls_id(){return JSON.stringify(this.params.all.map((e=>e.valueSerialized())))}}var ny=n(28),iy=function(e){!1===window.isSecureContext&&console.error(\\\\\\\"THREE.DeviceOrientationControls: DeviceOrientationEvent is only available in secure contexts (https)\\\\\\\");var t=this,n={type:\\\\\\\"change\\\\\\\"};this.object=e,this.object.rotation.reorder(\\\\\\\"YXZ\\\\\\\"),this.enabled=!0,this.deviceOrientation={},this.screenOrientation=0,this.alphaOffset=0;var i,s,r,o,a,c=function(e){t.deviceOrientation=e},l=function(){t.screenOrientation=window.orientation||0},u=(i=new p.a(0,0,1),s=new ny.a,r=new Ll.a,o=new Ll.a(-Math.sqrt(.5),0,0,Math.sqrt(.5)),function(e,t,n,a,c){s.set(n,t,-a,\\\\\\\"YXZ\\\\\\\"),e.setFromEuler(s),e.multiply(o),e.multiply(r.setFromAxisAngle(i,-c))});this.connect=function(){l(),void 0!==window.DeviceOrientationEvent&&\\\\\\\"function\\\\\\\"==typeof window.DeviceOrientationEvent.requestPermission?window.DeviceOrientationEvent.requestPermission().then((function(e){\\\\\\\"granted\\\\\\\"==e&&(window.addEventListener(\\\\\\\"orientationchange\\\\\\\",l),window.addEventListener(\\\\\\\"deviceorientation\\\\\\\",c))})).catch((function(e){console.error(\\\\\\\"THREE.DeviceOrientationControls: Unable to use DeviceOrientation API:\\\\\\\",e)})):(window.addEventListener(\\\\\\\"orientationchange\\\\\\\",l),window.addEventListener(\\\\\\\"deviceorientation\\\\\\\",c)),t.enabled=!0},this.disconnect=function(){window.removeEventListener(\\\\\\\"orientationchange\\\\\\\",l),window.removeEventListener(\\\\\\\"deviceorientation\\\\\\\",c),t.enabled=!1},this.update=(a=new Ll.a,function(){if(!1!==t.enabled){var e=t.deviceOrientation;if(e){var i=e.alpha?A.a.degToRad(e.alpha)+t.alphaOffset:0,s=e.beta?A.a.degToRad(e.beta):0,r=e.gamma?A.a.degToRad(e.gamma):0,o=t.screenOrientation?A.a.degToRad(t.screenOrientation):0;u(t.object.quaternion,i,s,r,o),8*(1-a.dot(t.object.quaternion))>1e-6&&(a.copy(t.object.quaternion),t.dispatchEvent(n))}}}),this.dispose=function(){t.disconnect()},this.connect()};(iy.prototype=Object.create(J.a.prototype)).constructor=iy;const sy=new class extends Lo{constructor(){super(...arguments),this.enabled=Oo.BOOLEAN(1)}};class ry extends ty{constructor(){super(...arguments),this.paramsConfig=sy,this._controls_by_element_id=new Map}static type(){return Oi.DEVICE_ORIENTATION}endEventName(){return\\\\\\\"end\\\\\\\"}async create_controls_instance(e,t){const n=new iy(e);return this._controls_by_element_id.set(t.id,n),n}setup_controls(e){e.enabled=this.pv.enabled}update_required(){return!0}dispose_controls_for_html_element_id(e){const t=this._controls_by_element_id.get(e);t&&(t.dispose(),this._controls_by_element_id.delete(e))}}class oy{constructor(e=1,t=0,n=0){return this.radius=e,this.phi=t,this.theta=n,this}set(e,t,n){return this.radius=e,this.phi=t,this.theta=n,this}copy(e){return this.radius=e.radius,this.phi=e.phi,this.theta=e.theta,this}makeSafe(){const e=1e-6;return this.phi=Math.max(e,Math.min(Math.PI-e,this.phi)),this}setFromVector3(e){return this.setFromCartesianCoords(e.x,e.y,e.z)}setFromCartesianCoords(e,t,n){return this.radius=Math.sqrt(e*e+t*t+n*n),0===this.radius?(this.theta=0,this.phi=0):(this.theta=Math.atan2(e,n),this.phi=Math.acos(A.a.clamp(t/this.radius,-1,1))),this}clone(){return(new this.constructor).copy(this)}}var ay=function(e,t){var n,i,s,r,o,a,c;void 0===t&&console.warn('THREE.OrbitControls: The second parameter \\\\\\\"domElement\\\\\\\" is now mandatory.'),t===document&&console.error('THREE.OrbitControls: \\\\\\\"document\\\\\\\" should not be used as the target \\\\\\\"domElement\\\\\\\". Please use \\\\\\\"renderer.domElement\\\\\\\" instead.'),this.object=e,this.domElement=t,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:37,UP:38,RIGHT:39,BOTTOM:40},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.getPolarAngle=function(){return v.phi},this.getAzimuthalAngle=function(){return v.theta},this.saveState=function(){l.target0.copy(l.target),l.position0.copy(l.object.position),l.zoom0=l.object.zoom},this.reset=function(){l.target.copy(l.target0),l.object.position.copy(l.position0),l.object.zoom=l.zoom0,l.object.updateProjectionMatrix(),l.dispatchEvent(u),l.update(),f=m.NONE},this.update=(n=new p.a,i=(new Ll.a).setFromUnitVectors(e.up,new p.a(0,1,0)),s=i.clone().invert(),r=new p.a,o=new Ll.a,a=2*Math.PI,c=!1,function(){var e=l.object.position;if(n.copy(e).sub(l.target),n.applyQuaternion(i),v.setFromVector3(n),l.autoRotate&&f===m.NONE&&I(2*Math.PI/60/60*l.autoRotateSpeed),l.enableDamping){const e=y.theta*l.dampingFactor,t=y.phi*l.dampingFactor;e<g&&t<g?c||(l.dispatchEvent(_),c=!0):c=!1,v.theta+=e,v.phi+=t}else v.theta+=y.theta,v.phi+=y.phi;var t=l.minAzimuthAngle,h=l.maxAzimuthAngle;return isFinite(t)&&isFinite(h)&&(t<-Math.PI?t+=a:t>Math.PI&&(t-=a),h<-Math.PI?h+=a:h>Math.PI&&(h-=a),v.theta=t<h?Math.max(t,Math.min(h,v.theta)):v.theta>(t+h)/2?Math.max(t,v.theta):Math.min(h,v.theta)),v.phi=Math.max(l.minPolarAngle,Math.min(l.maxPolarAngle,v.phi)),v.makeSafe(),v.radius*=x,v.radius=Math.max(l.minDistance,Math.min(l.maxDistance,v.radius)),!0===l.enableDamping?l.target.addScaledVector(b,l.dampingFactor):l.target.add(b),n.setFromSpherical(v),n.applyQuaternion(s),e.copy(l.target).add(n),l.object.lookAt(l.target),!0===l.enableDamping?(y.theta*=1-l.dampingFactor,y.phi*=1-l.dampingFactor,b.multiplyScalar(1-l.dampingFactor)):(y.set(0,0,0),b.set(0,0,0)),x=1,!!(A||r.distanceToSquared(l.object.position)>g||8*(1-o.dot(l.object.quaternion))>g)&&(l.dispatchEvent(u),r.copy(l.object.position),o.copy(l.object.quaternion),A=!1,!0)}),this.dispose=function(){l.domElement.removeEventListener(\\\\\\\"contextmenu\\\\\\\",se,!1),l.domElement.removeEventListener(\\\\\\\"pointerdown\\\\\\\",Q,!1),l.domElement.removeEventListener(\\\\\\\"wheel\\\\\\\",Z,!1),l.domElement.removeEventListener(\\\\\\\"touchstart\\\\\\\",te,!1),l.domElement.removeEventListener(\\\\\\\"touchend\\\\\\\",ie,!1),l.domElement.removeEventListener(\\\\\\\"touchmove\\\\\\\",ne,!1),l.domElement.ownerDocument.removeEventListener(\\\\\\\"pointermove\\\\\\\",J,!1),l.domElement.ownerDocument.removeEventListener(\\\\\\\"pointerup\\\\\\\",K,!1),l.domElement.removeEventListener(\\\\\\\"keydown\\\\\\\",ee,!1)};var l=this,u={type:\\\\\\\"change\\\\\\\"},h={type:\\\\\\\"start\\\\\\\"},_={type:\\\\\\\"end\\\\\\\"},m={NONE:-1,ROTATE:0,DOLLY:1,PAN:2,TOUCH_ROTATE:3,TOUCH_PAN:4,TOUCH_DOLLY_PAN:5,TOUCH_DOLLY_ROTATE:6},f=m.NONE,g=1e-6,v=new oy,y=new oy,x=1,b=new p.a,A=!1,T=new d.a,E=new d.a,C=new d.a,M=new d.a,N=new d.a,S=new d.a,O=new d.a,L=new d.a,P=new d.a;function R(){return Math.pow(.95,l.zoomSpeed)}function I(e){y.theta-=e}function F(e){y.phi-=e}var D,k=(D=new p.a,function(e,t){D.setFromMatrixColumn(t,0),D.multiplyScalar(-e),b.add(D)}),B=function(){var e=new p.a;return function(t,n){!0===l.screenSpacePanning?e.setFromMatrixColumn(n,1):(e.setFromMatrixColumn(n,0),e.crossVectors(l.object.up,e)),e.multiplyScalar(t),b.add(e)}}(),z=function(){var e=new p.a;return function(t,n){var i=l.domElement;if(l.object.isPerspectiveCamera){var s=l.object.position;e.copy(s).sub(l.target);var r=e.length();r*=Math.tan(l.object.fov/2*Math.PI/180),k(2*t*r/i.clientHeight,l.object.matrix),B(2*n*r/i.clientHeight,l.object.matrix)}else l.object.isOrthographicCamera?(k(t*(l.object.right-l.object.left)/l.object.zoom/i.clientWidth,l.object.matrix),B(n*(l.object.top-l.object.bottom)/l.object.zoom/i.clientHeight,l.object.matrix)):(console.warn(\\\\\\\"WARNING: OrbitControls.js encountered an unknown camera type - pan disabled.\\\\\\\"),l.enablePan=!1)}}();function U(e){l.object.isPerspectiveCamera?x/=e:l.object.isOrthographicCamera?(l.object.zoom=Math.max(l.minZoom,Math.min(l.maxZoom,l.object.zoom*e)),l.object.updateProjectionMatrix(),A=!0):(console.warn(\\\\\\\"WARNING: OrbitControls.js encountered an unknown camera type - dolly/zoom disabled.\\\\\\\"),l.enableZoom=!1)}function G(e){l.object.isPerspectiveCamera?x*=e:l.object.isOrthographicCamera?(l.object.zoom=Math.max(l.minZoom,Math.min(l.maxZoom,l.object.zoom/e)),l.object.updateProjectionMatrix(),A=!0):(console.warn(\\\\\\\"WARNING: OrbitControls.js encountered an unknown camera type - dolly/zoom disabled.\\\\\\\"),l.enableZoom=!1)}function V(e){T.set(e.clientX,e.clientY)}function j(e){M.set(e.clientX,e.clientY)}function H(e){if(1==e.touches.length)T.set(e.touches[0].pageX,e.touches[0].pageY);else{var t=.5*(e.touches[0].pageX+e.touches[1].pageX),n=.5*(e.touches[0].pageY+e.touches[1].pageY);T.set(t,n)}}function q(e){if(1==e.touches.length)M.set(e.touches[0].pageX,e.touches[0].pageY);else{var t=.5*(e.touches[0].pageX+e.touches[1].pageX),n=.5*(e.touches[0].pageY+e.touches[1].pageY);M.set(t,n)}}function W(e){var t=e.touches[0].pageX-e.touches[1].pageX,n=e.touches[0].pageY-e.touches[1].pageY,i=Math.sqrt(t*t+n*n);O.set(0,i)}function X(e){if(1==e.touches.length)E.set(e.touches[0].pageX,e.touches[0].pageY);else{var t=.5*(e.touches[0].pageX+e.touches[1].pageX),n=.5*(e.touches[0].pageY+e.touches[1].pageY);E.set(t,n)}C.subVectors(E,T).multiplyScalar(l.rotateSpeed);var i=l.domElement;I(2*Math.PI*C.x/i.clientHeight),F(2*Math.PI*C.y/i.clientHeight),T.copy(E)}function Y(e){if(1==e.touches.length)N.set(e.touches[0].pageX,e.touches[0].pageY);else{var t=.5*(e.touches[0].pageX+e.touches[1].pageX),n=.5*(e.touches[0].pageY+e.touches[1].pageY);N.set(t,n)}S.subVectors(N,M).multiplyScalar(l.panSpeed),z(S.x,S.y),M.copy(N)}function $(e){var t=e.touches[0].pageX-e.touches[1].pageX,n=e.touches[0].pageY-e.touches[1].pageY,i=Math.sqrt(t*t+n*n);L.set(0,i),P.set(0,Math.pow(L.y/O.y,l.zoomSpeed)),U(P.y),O.copy(L)}function Q(e){if(!1!==l.enabled)switch(e.pointerType){case\\\\\\\"mouse\\\\\\\":case\\\\\\\"pen\\\\\\\":!function(e){var t;switch(e.preventDefault(),l.domElement.focus?l.domElement.focus():window.focus(),e.button){case 0:t=l.mouseButtons.LEFT;break;case 1:t=l.mouseButtons.MIDDLE;break;case 2:t=l.mouseButtons.RIGHT;break;default:t=-1}switch(t){case w.hb.DOLLY:if(!1===l.enableZoom)return;!function(e){O.set(e.clientX,e.clientY)}(e),f=m.DOLLY;break;case w.hb.ROTATE:if(e.ctrlKey||e.metaKey||e.shiftKey){if(!1===l.enablePan)return;j(e),f=m.PAN}else{if(!1===l.enableRotate)return;V(e),f=m.ROTATE}break;case w.hb.PAN:if(e.ctrlKey||e.metaKey||e.shiftKey){if(!1===l.enableRotate)return;V(e),f=m.ROTATE}else{if(!1===l.enablePan)return;j(e),f=m.PAN}break;default:f=m.NONE}f!==m.NONE&&(l.domElement.ownerDocument.addEventListener(\\\\\\\"pointermove\\\\\\\",J,!1),l.domElement.ownerDocument.addEventListener(\\\\\\\"pointerup\\\\\\\",K,!1),l.dispatchEvent(h))}(e)}}function J(e){if(!1!==l.enabled)switch(e.pointerType){case\\\\\\\"mouse\\\\\\\":case\\\\\\\"pen\\\\\\\":!function(e){if(!1===l.enabled)return;switch(e.preventDefault(),f){case m.ROTATE:if(!1===l.enableRotate)return;!function(e){E.set(e.clientX,e.clientY),C.subVectors(E,T).multiplyScalar(l.rotateSpeed);var t=l.domElement;I(2*Math.PI*C.x/t.clientHeight),F(2*Math.PI*C.y/t.clientHeight),T.copy(E),l.update()}(e);break;case m.DOLLY:if(!1===l.enableZoom)return;!function(e){L.set(e.clientX,e.clientY),P.subVectors(L,O),P.y>0?U(R()):P.y<0&&G(R()),O.copy(L),l.update()}(e);break;case m.PAN:if(!1===l.enablePan)return;!function(e){N.set(e.clientX,e.clientY),S.subVectors(N,M).multiplyScalar(l.panSpeed),z(S.x,S.y),M.copy(N),l.update()}(e)}}(e)}}function K(e){if(!1!==l.enabled)switch(e.pointerType){case\\\\\\\"mouse\\\\\\\":case\\\\\\\"pen\\\\\\\":!function(e){if(!1===l.enabled)return;l.domElement.ownerDocument.removeEventListener(\\\\\\\"pointermove\\\\\\\",J,!1),l.domElement.ownerDocument.removeEventListener(\\\\\\\"pointerup\\\\\\\",K,!1),l.dispatchEvent(_),f=m.NONE}()}}function Z(e){!1===l.enabled||!1===l.enableZoom||f!==m.NONE&&f!==m.ROTATE||(e.preventDefault(),e.stopPropagation(),l.dispatchEvent(h),function(e){e.deltaY<0?G(R()):e.deltaY>0&&U(R()),l.update()}(e),l.dispatchEvent(_))}function ee(e){!1!==l.enabled&&!1!==l.enableKeys&&(\\\\\\\"pan\\\\\\\"==l.keyMode&&!1===l.enablePan||\\\\\\\"pan\\\\\\\"!=l.keyMode&&!1===l.enableRotate||function(e){var t=!1;if(\\\\\\\"pan\\\\\\\"==l.keyMode)switch(e.keyCode){case l.keys.UP:z(0,l.keyPanSpeed),t=!0;break;case l.keys.BOTTOM:z(0,-l.keyPanSpeed),t=!0;break;case l.keys.LEFT:z(l.keyPanSpeed,0),t=!0;break;case l.keys.RIGHT:z(-l.keyPanSpeed,0),t=!0}else switch(e.keyCode){case l.keys.UP:F(l.keyRotateSpeedVertical),t=!0;break;case l.keys.BOTTOM:F(-l.keyRotateSpeedVertical),t=!0;break;case l.keys.LEFT:I(l.keyRotateSpeedHorizontal),t=!0;break;case l.keys.RIGHT:I(-l.keyRotateSpeedHorizontal),t=!0}t&&(e.preventDefault(),l.update())}(e))}function te(e){if(!1!==l.enabled){switch(e.preventDefault(),e.touches.length){case 1:switch(l.touches.ONE){case w.Tc.ROTATE:if(!1===l.enableRotate)return;H(e),f=m.TOUCH_ROTATE;break;case w.Tc.PAN:if(!1===l.enablePan)return;q(e),f=m.TOUCH_PAN;break;default:f=m.NONE}break;case 2:switch(l.touches.TWO){case w.Tc.DOLLY_PAN:if(!1===l.enableZoom&&!1===l.enablePan)return;!function(e){l.enableZoom&&W(e),l.enablePan&&q(e)}(e),f=m.TOUCH_DOLLY_PAN;break;case w.Tc.DOLLY_ROTATE:if(!1===l.enableZoom&&!1===l.enableRotate)return;!function(e){l.enableZoom&&W(e),l.enableRotate&&H(e)}(e),f=m.TOUCH_DOLLY_ROTATE;break;default:f=m.NONE}break;default:f=m.NONE}f!==m.NONE&&l.dispatchEvent(h)}}function ne(e){if(!1!==l.enabled)switch(e.preventDefault(),e.stopPropagation(),f){case m.TOUCH_ROTATE:if(!1===l.enableRotate)return;X(e),l.update();break;case m.TOUCH_PAN:if(!1===l.enablePan)return;Y(e),l.update();break;case m.TOUCH_DOLLY_PAN:if(!1===l.enableZoom&&!1===l.enablePan)return;!function(e){l.enableZoom&&$(e),l.enablePan&&Y(e)}(e),l.update();break;case m.TOUCH_DOLLY_ROTATE:if(!1===l.enableZoom&&!1===l.enableRotate)return;!function(e){l.enableZoom&&$(e),l.enableRotate&&X(e)}(e),l.update();break;default:f=m.NONE}}function ie(e){!1!==l.enabled&&(l.dispatchEvent(_),f=m.NONE)}function se(e){!1!==l.enabled&&e.preventDefault()}l.domElement.addEventListener(\\\\\\\"contextmenu\\\\\\\",se,!1),l.domElement.addEventListener(\\\\\\\"pointerdown\\\\\\\",Q,!1),l.domElement.addEventListener(\\\\\\\"wheel\\\\\\\",Z,!1),l.domElement.addEventListener(\\\\\\\"touchstart\\\\\\\",te,!1),l.domElement.addEventListener(\\\\\\\"touchend\\\\\\\",ie,!1),l.domElement.addEventListener(\\\\\\\"touchmove\\\\\\\",ne,!1),l.domElement.addEventListener(\\\\\\\"keydown\\\\\\\",ee,!1),-1===l.domElement.tabIndex&&(l.domElement.tabIndex=0),this.update()};(ay.prototype=Object.create(J.a.prototype)).constructor=ay;var cy=function(e,t){ay.call(this,e,t),this.screenSpacePanning=!1,this.mouseButtons.LEFT=w.hb.PAN,this.mouseButtons.RIGHT=w.hb.ROTATE,this.touches.ONE=w.Tc.PAN,this.touches.TWO=w.Tc.DOLLY_ROTATE};(cy.prototype=Object.create(J.a.prototype)).constructor=cy;const ly=\\\\\\\"start\\\\\\\",uy=\\\\\\\"change\\\\\\\";var hy;!function(e){e.PAN=\\\\\\\"pan\\\\\\\",e.ROTATE=\\\\\\\"rotate\\\\\\\"}(hy||(hy={}));const dy=[hy.PAN,hy.ROTATE];const py=new class extends Lo{constructor(){super(...arguments),this.enabled=Oo.BOOLEAN(1),this.allowPan=Oo.BOOLEAN(1),this.allowRotate=Oo.BOOLEAN(1),this.allowZoom=Oo.BOOLEAN(1),this.tdamping=Oo.BOOLEAN(1),this.damping=Oo.FLOAT(.1,{visibleIf:{tdamping:!0}}),this.screenSpacePanning=Oo.BOOLEAN(1),this.rotateSpeed=Oo.FLOAT(.5),this.minDistance=Oo.FLOAT(1,{range:[0,100],rangeLocked:[!0,!1]}),this.maxDistance=Oo.FLOAT(50,{range:[0,100],rangeLocked:[!0,!1]}),this.limitAzimuthAngle=Oo.BOOLEAN(0),this.azimuthAngleRange=Oo.VECTOR2([\\\\\\\"-2*$PI\\\\\\\",\\\\\\\"2*$PI\\\\\\\"],{visibleIf:{limitAzimuthAngle:1}}),this.polarAngleRange=Oo.VECTOR2([0,\\\\\\\"$PI\\\\\\\"]),this.target=Oo.VECTOR3([0,0,0],{cook:!1,computeOnDirty:!0,callback:e=>{_y.PARAM_CALLBACK_update_target(e)}}),this.enableKeys=Oo.BOOLEAN(0),this.keysMode=Oo.INTEGER(dy.indexOf(hy.PAN),{visibleIf:{enableKeys:1},menu:{entries:dy.map(((e,t)=>({name:e,value:t})))}}),this.keysPanSpeed=Oo.FLOAT(7,{range:[0,10],rangeLocked:[!1,!1],visibleIf:{enableKeys:1,keysMode:dy.indexOf(hy.PAN)}}),this.keysRotateSpeedVertical=Oo.FLOAT(1,{range:[0,1],rangeLocked:[!1,!1],visibleIf:{enableKeys:1,keysMode:dy.indexOf(hy.ROTATE)}}),this.keysRotateSpeedHorizontal=Oo.FLOAT(1,{range:[0,1],rangeLocked:[!1,!1],visibleIf:{enableKeys:1,keysMode:dy.indexOf(hy.ROTATE)}})}};class _y extends ty{constructor(){super(...arguments),this.paramsConfig=py,this._controls_by_element_id=new Map,this._target_array=[0,0,0]}static type(){return Oi.ORBIT}endEventName(){return\\\\\\\"end\\\\\\\"}initializeNode(){this.io.outputs.setNamedOutputConnectionPoints([new xo(ly,yo.BASE),new xo(uy,yo.BASE),new xo(\\\\\\\"end\\\\\\\",yo.BASE)])}async create_controls_instance(e,t){const n=new ay(e,t);return n.addEventListener(\\\\\\\"end\\\\\\\",(()=>{this._on_controls_end(n)})),this._controls_by_element_id.set(t.id,n),this._bind_listeners_to_controls_instance(n),n}_bind_listeners_to_controls_instance(e){e.addEventListener(\\\\\\\"start\\\\\\\",(()=>{this.dispatchEventToOutput(ly,{})})),e.addEventListener(\\\\\\\"change\\\\\\\",(()=>{this.dispatchEventToOutput(uy,{})})),e.addEventListener(\\\\\\\"end\\\\\\\",(()=>{this.dispatchEventToOutput(\\\\\\\"end\\\\\\\",{})}))}setup_controls(e){e.enabled=this.pv.enabled,e.enablePan=this.pv.allowPan,e.enableRotate=this.pv.allowRotate,e.enableZoom=this.pv.allowZoom,e.enableDamping=this.pv.tdamping,e.dampingFactor=this.pv.damping,e.rotateSpeed=this.pv.rotateSpeed,e.screenSpacePanning=this.pv.screenSpacePanning,e.minDistance=this.pv.minDistance,e.maxDistance=this.pv.maxDistance,this._set_azimuth_angle(e),e.minPolarAngle=this.pv.polarAngleRange.x,e.maxPolarAngle=this.pv.polarAngleRange.y,e.target.copy(this.pv.target),e.enabled&&e.update(),e.enableKeys=this.pv.enableKeys,e.enableKeys&&(e.keyMode=dy[this.pv.keysMode],e.keyRotateSpeedVertical=this.pv.keysRotateSpeedVertical,e.keyRotateSpeedHorizontal=this.pv.keysRotateSpeedHorizontal,e.keyPanSpeed=this.pv.keysPanSpeed)}_set_azimuth_angle(e){this.pv.limitAzimuthAngle?(e.minAzimuthAngle=this.pv.azimuthAngleRange.x,e.maxAzimuthAngle=this.pv.azimuthAngleRange.y):(e.minAzimuthAngle=1/0,e.maxAzimuthAngle=1/0)}update_required(){return this.pv.tdamping}_on_controls_end(e){this.pv.allowPan&&(e.target.toArray(this._target_array),this.p.target.set(this._target_array))}static PARAM_CALLBACK_update_target(e){e._update_target()}_update_target(){const e=this.pv.target;this._controls_by_element_id.forEach(((t,n)=>{const i=t.target;i.equals(e)||(i.copy(e),t.update())}))}dispose_controls_for_html_element_id(e){this._controls_by_element_id.get(e)&&this._controls_by_element_id.delete(e)}}class my extends _y{static type(){return Oi.MAP}async create_controls_instance(e,t){const n=new cy(e,t);return this._bind_listeners_to_controls_instance(n),n}}const fy=new class extends Lo{constructor(){super(...arguments),this.delay=Oo.INTEGER(1e3,{range:[0,1e3],rangeLocked:[!0,!1]})}};class gy extends ca{constructor(){super(...arguments),this.paramsConfig=fy}static type(){return\\\\\\\"delay\\\\\\\"}initializeNode(){this.io.inputs.setNamedInputConnectionPoints([new xo(\\\\\\\"in\\\\\\\",yo.BASE,this._process_input.bind(this))]),this.io.outputs.setNamedOutputConnectionPoints([new xo(\\\\\\\"out\\\\\\\",yo.BASE)])}_process_input(e){setTimeout((()=>{this.dispatchEventToOutput(\\\\\\\"out\\\\\\\",e)}),this.pv.delay)}}var vy,yy;!function(e){e.TRIGGER=\\\\\\\"trigger\\\\\\\",e.RESET=\\\\\\\"reset\\\\\\\"}(vy||(vy={})),function(e){e.OUT=\\\\\\\"out\\\\\\\",e.LAST=\\\\\\\"last\\\\\\\"}(yy||(yy={}));const xy=new class extends Lo{constructor(){super(...arguments),this.maxCount=Oo.INTEGER(5,{range:[0,10],rangeLocked:[!0,!1]}),this.reset=Oo.BUTTON(null,{callback:e=>{by.PARAM_CALLBACK_reset(e)}})}};class by extends ca{constructor(){super(...arguments),this.paramsConfig=xy,this._process_count=0,this._last_dispatched=!1}static type(){return\\\\\\\"limit\\\\\\\"}initializeNode(){this.io.inputs.setNamedInputConnectionPoints([new xo(vy.TRIGGER,yo.BASE,this.process_event_trigger.bind(this)),new xo(vy.RESET,yo.BASE,this.process_event_reset.bind(this))]),this.io.outputs.setNamedOutputConnectionPoints([new xo(yy.OUT,yo.BASE),new xo(yy.LAST,yo.BASE)])}processEvent(e){}process_event_trigger(e){this._process_count<this.pv.maxCount?(this._process_count+=1,this.dispatchEventToOutput(yy.OUT,e)):this._last_dispatched||(this._last_dispatched=!0,this.dispatchEventToOutput(yy.LAST,e))}process_event_reset(e){this._process_count=0,this._last_dispatched=!1}static PARAM_CALLBACK_reset(e){e.process_event_reset({})}}const wy=new class extends Lo{constructor(){super(...arguments),this.alert=Oo.BOOLEAN(0),this.console=Oo.BOOLEAN(1)}};class Ay extends ca{constructor(){super(...arguments),this.paramsConfig=wy}static type(){return\\\\\\\"message\\\\\\\"}initializeNode(){this.io.inputs.setNamedInputConnectionPoints([new xo(\\\\\\\"trigger\\\\\\\",yo.BASE,this._process_trigger_event.bind(this))]),this.io.outputs.setNamedOutputConnectionPoints([new xo(Ay.OUTPUT,yo.BASE)])}trigger_output(e){this.dispatchEventToOutput(Ay.OUTPUT,e)}_process_trigger_event(e){this.pv.alert&&alert(e),this.pv.console&&console.log(this.path(),Date.now(),e),this.trigger_output(e)}}var Ty;Ay.OUTPUT=\\\\\\\"output\\\\\\\",function(e){e.ALL_TOGETHER=\\\\\\\"all together\\\\\\\",e.BATCH=\\\\\\\"batch\\\\\\\"}(Ty||(Ty={}));const Ey=[Ty.ALL_TOGETHER,Ty.BATCH];const Cy=new class extends Lo{constructor(){super(...arguments),this.mask=Oo.STRING(\\\\\\\"/geo*\\\\\\\",{callback:e=>{My.PARAM_CALLBACK_update_resolved_nodes(e)}}),this.force=Oo.BOOLEAN(0),this.cookMode=Oo.INTEGER(Ey.indexOf(Ty.ALL_TOGETHER),{menu:{entries:Ey.map(((e,t)=>({name:e,value:t})))}}),this.batchSize=Oo.INTEGER(1,{visibleIf:{cookMode:Ey.indexOf(Ty.BATCH)},separatorAfter:!0}),this.updateResolve=Oo.BUTTON(null,{callback:(e,t)=>{My.PARAM_CALLBACK_update_resolve(e)}}),this.printResolve=Oo.BUTTON(null,{callback:(e,t)=>{My.PARAM_CALLBACK_print_resolve(e)}})}};class My extends ca{constructor(){super(...arguments),this.paramsConfig=Cy,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 xo(My.INPUT_TRIGGER,yo.BASE,this.process_event_trigger.bind(this))]),this.io.outputs.setNamedOutputConnectionPoints([new xo(My.OUTPUT_FIRST_NODE,yo.BASE),new xo(My.OUTPUT_EACH_NODE,yo.BASE),new xo(My.OUTPUT_ALL_NODES,yo.BASE)])}trigger(){this.process_event_trigger({})}cook(){this._update_resolved_nodes(),this.cookController.endCook()}dispose(){super.dispose(),this._reset()}process_event_trigger(e){this._cook_nodes_with_mode()}_cook_nodes_with_mode(){this._update_resolved_nodes();const e=Ey[this.pv.cookMode];switch(e){case Ty.ALL_TOGETHER:return this._cook_nodes_all_together();case Ty.BATCH:return this._cook_nodes_batch()}Ri.unreachable(e)}_cook_nodes_all_together(){this._cook_nodes(this._resolved_nodes)}async _cook_nodes_batch(){const e=this.pv.batchSize,t=Math.ceil(this._resolved_nodes.length/e);for(let n=0;n<t;n++){const t=n*e,i=(n+1)*e,s=this._resolved_nodes.slice(t,i);await this._cook_nodes(s)}}async _cook_nodes(e){const t=[];for(let n of e)t.push(this._cook_node(n));return await Promise.all(t)}_cook_node(e){return this.pv.force&&e.setDirty(this),e.compute()}static PARAM_CALLBACK_update_resolved_nodes(e){e._update_resolved_nodes()}_update_resolved_nodes(){this._reset(),this._resolved_nodes=this.scene().nodesController.nodesFromMask(this.pv.mask||\\\\\\\"\\\\\\\");for(let e of this._resolved_nodes)e.cookController.registerOnCookEnd(this._callbackNameForNode(e),(()=>{this._on_node_cook_complete(e)})),this._cook_state_by_node_id.set(e.graphNodeId(),!1)}_callbackNameForNode(e){return`owner-${this.graphNodeId()}-target-${e.graphNodeId()}`}_reset(){this._dispatched_first_node_cooked=!1,this._cook_state_by_node_id.clear();for(let e of this._resolved_nodes)e.cookController.deregisterOnCookEnd(this._callbackNameForNode(e));this._resolved_nodes=[]}_all_nodes_have_cooked(){for(let e of this._resolved_nodes){if(!this._cook_state_by_node_id.get(e.graphNodeId()))return!1}return!0}_on_node_cook_complete(e){const t={value:{node:e}};this._dispatched_first_node_cooked||(this._dispatched_first_node_cooked=!0,this.dispatchEventToOutput(My.OUTPUT_FIRST_NODE,t)),this._cook_state_by_node_id.get(e.graphNodeId())||this.dispatchEventToOutput(My.OUTPUT_EACH_NODE,t),this._cook_state_by_node_id.set(e.graphNodeId(),!0),this._dispatched_all_nodes_cooked||this._all_nodes_have_cooked()&&(this._dispatched_all_nodes_cooked=!0,this.dispatchEventToOutput(My.OUTPUT_ALL_NODES,{}))}static PARAM_CALLBACK_update_resolve(e){e._update_resolved_nodes()}static PARAM_CALLBACK_print_resolve(e){e.print_resolve()}print_resolve(){console.log(this._resolved_nodes)}}var Ny,Sy;My.INPUT_TRIGGER=\\\\\\\"trigger\\\\\\\",My.OUTPUT_FIRST_NODE=\\\\\\\"first\\\\\\\",My.OUTPUT_EACH_NODE=\\\\\\\"each\\\\\\\",My.OUTPUT_ALL_NODES=\\\\\\\"all\\\\\\\",function(e){e.TRIGGER=\\\\\\\"trigger\\\\\\\"}(Ny||(Ny={})),function(e){e.OUT=\\\\\\\"out\\\\\\\"}(Sy||(Sy={}));const Oy=new class extends Lo{};class Ly extends ca{constructor(){super(...arguments),this.paramsConfig=Oy}static type(){return\\\\\\\"null\\\\\\\"}initializeNode(){this.io.inputs.setNamedInputConnectionPoints([new xo(Ny.TRIGGER,yo.BASE,this.process_event_trigger.bind(this))]),this.io.outputs.setNamedOutputConnectionPoints([new xo(Sy.OUT,yo.BASE)])}processEvent(e){}process_event_trigger(e){this.dispatchEventToOutput(Sy.OUT,e)}}var Py=function(e,t){void 0===t&&(console.warn('THREE.PointerLockControls: The second parameter \\\\\\\"domElement\\\\\\\" is now mandatory.'),t=document.body),this.domElement=t,this.isLocked=!1,this.minPolarAngle=0,this.maxPolarAngle=Math.PI,this.speed=1;var n=this,i={type:\\\\\\\"change\\\\\\\"},s={type:\\\\\\\"lock\\\\\\\"},r={type:\\\\\\\"unlock\\\\\\\"},o=new ny.a(0,0,0,\\\\\\\"YXZ\\\\\\\"),a=Math.PI/2,c=new p.a;function l(t){if(!1!==n.isLocked){var s=t.movementX||t.mozMovementX||t.webkitMovementX||0,r=t.movementY||t.mozMovementY||t.webkitMovementY||0;o.setFromQuaternion(e.quaternion),o.y-=.002*s,o.x-=.002*r,o.x=Math.max(a-n.maxPolarAngle,Math.min(a-n.minPolarAngle,o.x)),e.quaternion.setFromEuler(o),n.dispatchEvent(i)}}function u(){n.domElement.ownerDocument.pointerLockElement===n.domElement?(n.dispatchEvent(s),n.isLocked=!0):(n.dispatchEvent(r),n.isLocked=!1)}function h(){console.error(\\\\\\\"THREE.PointerLockControls: Unable to use Pointer Lock API\\\\\\\")}this.connect=function(){n.domElement.ownerDocument.addEventListener(\\\\\\\"mousemove\\\\\\\",l),n.domElement.ownerDocument.addEventListener(\\\\\\\"pointerlockchange\\\\\\\",u),n.domElement.ownerDocument.addEventListener(\\\\\\\"pointerlockerror\\\\\\\",h)},this.disconnect=function(){n.domElement.ownerDocument.removeEventListener(\\\\\\\"mousemove\\\\\\\",l),n.domElement.ownerDocument.removeEventListener(\\\\\\\"pointerlockchange\\\\\\\",u),n.domElement.ownerDocument.removeEventListener(\\\\\\\"pointerlockerror\\\\\\\",h)},this.dispose=function(){this.disconnect()},this.getObject=function(){return e},this.getDirection=function(){var t=new p.a(0,0,-1);return function(n){return n.copy(t).applyQuaternion(e.quaternion)}}(),this.moveForward=function(t){c.setFromMatrixColumn(e.matrix,0),c.crossVectors(e.up,c),e.position.addScaledVector(c,t)},this.moveRight=function(t){c.setFromMatrixColumn(e.matrix,0),e.position.addScaledVector(c,t)},this.lock=function(){this.domElement.requestPointerLock()},this.unlock=function(){n.domElement.ownerDocument.exitPointerLock()};let d=new p.a,_=new p.a,m=!1,f=!1,g=!1,v=!1;this.setMoveForward=function(e){m=e},this.setMoveBackward=function(e){f=e},this.setMoveLeft=function(e){g=e},this.setMoveRight=function(e){v=e};let y=0;this.update=function(){const e=performance.now();if(!0===this.isLocked){const t=(e-y)/1e3;d.x-=10*d.x*t,d.z-=10*d.z*t,d.y-=9.8*100*t,_.z=Number(m)-Number(f),_.x=Number(v)-Number(g),_.normalize(),(m||f)&&(d.z-=400*_.z*t*this.speed),(g||v)&&(d.x-=400*_.x*t*this.speed),this.moveRight(-d.x*t),this.moveForward(-d.z*t)}y=e},this.connect()};(Py.prototype=Object.create(J.a.prototype)).constructor=Py;const Ry=\\\\\\\"lock\\\\\\\",Iy=\\\\\\\"change\\\\\\\",Fy=\\\\\\\"unlock\\\\\\\";const Dy=new class extends Lo{constructor(){super(...arguments),this.lock=Oo.BUTTON(null,{callback:e=>{ky.PARAM_CALLBACK_lock_controls(e)}}),this.minPolarAngle=Oo.FLOAT(0,{range:[0,Math.PI],rangeLocked:[!0,!0]}),this.maxPolarAngle=Oo.FLOAT(Math.PI,{range:[0,Math.PI],rangeLocked:[!0,!0]}),this.speed=Oo.FLOAT(1)}};class ky extends ty{constructor(){super(...arguments),this.paramsConfig=Dy,this._controls_by_element_id=new Map}static type(){return Oi.FIRST_PERSON}endEventName(){return\\\\\\\"unlock\\\\\\\"}initializeNode(){this.io.inputs.setNamedInputConnectionPoints([new xo(Ry,yo.BASE,this.lock_controls.bind(this))]),this.io.outputs.setNamedOutputConnectionPoints([new xo(Ry,yo.BASE),new xo(Iy,yo.BASE),new xo(Fy,yo.BASE)])}async create_controls_instance(e,t){const n=new Py(e,t);return this._controls_by_element_id.set(t.id,n),this._bind_listeners_to_controls_instance(n),n}_bind_listeners_to_controls_instance(e){e.addEventListener(Ry,(()=>{this._createKeysEvents(e),this.dispatchEventToOutput(Ry,{})})),e.addEventListener(Iy,(()=>{this.dispatchEventToOutput(Iy,{})})),e.addEventListener(Fy,(()=>{this._removeKeysEvents(),this.dispatchEventToOutput(Fy,{})}))}_onKeyDown(e,t){switch(e.code){case\\\\\\\"ArrowUp\\\\\\\":case\\\\\\\"KeyW\\\\\\\":t.setMoveForward(!0);break;case\\\\\\\"ArrowLeft\\\\\\\":case\\\\\\\"KeyA\\\\\\\":t.setMoveLeft(!0);break;case\\\\\\\"ArrowDown\\\\\\\":case\\\\\\\"KeyS\\\\\\\":t.setMoveBackward(!0);break;case\\\\\\\"ArrowRight\\\\\\\":case\\\\\\\"KeyD\\\\\\\":t.setMoveRight(!0)}}_onKeyUp(e,t){switch(e.code){case\\\\\\\"ArrowUp\\\\\\\":case\\\\\\\"KeyW\\\\\\\":t.setMoveForward(!1);break;case\\\\\\\"ArrowLeft\\\\\\\":case\\\\\\\"KeyA\\\\\\\":t.setMoveLeft(!1);break;case\\\\\\\"ArrowDown\\\\\\\":case\\\\\\\"KeyS\\\\\\\":t.setMoveBackward(!1);break;case\\\\\\\"ArrowRight\\\\\\\":case\\\\\\\"KeyD\\\\\\\":t.setMoveRight(!1)}}_createKeysEvents(e){this._onKeyDownBound=t=>{this._onKeyDown(t,e)},this._onKeyUpBound=t=>{this._onKeyUp(t,e)},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))}update_required(){return!0}setup_controls(e){e.minPolarAngle=this.pv.minPolarAngle,e.maxPolarAngle=this.pv.maxPolarAngle,e.speed=this.pv.speed}dispose_controls_for_html_element_id(e){this._controls_by_element_id.get(e)&&this._controls_by_element_id.delete(e)}lock_controls(){let e;this._controls_by_element_id.forEach(((t,n)=>{e=e||t})),e&&e.lock()}static PARAM_CALLBACK_lock_controls(e){e.lock_controls()}}var By,zy=n(39),Uy=n(37);function Gy(e,t,n=0,i=1/0){this.ray=new zy.a(e,t),this.near=n,this.far=i,this.camera=null,this.layers=new Uy.a,this.params={Mesh:{},Line:{threshold:1},LOD:{},Points:{threshold:1},Sprite:{}},Object.defineProperties(this.params,{PointCloud:{get:function(){return console.warn(\\\\\\\"THREE.Raycaster: params.PointCloud has been renamed to params.Points.\\\\\\\"),this.Points}}})}function Vy(e,t){return e.distance-t.distance}function jy(e,t,n,i){if(e.layers.test(t.layers)&&e.raycast(t,n),!0===i){const i=e.children;for(let e=0,s=i.length;e<s;e++)jy(i[e],t,n,!0)}}Object.assign(Gy.prototype,{set:function(e,t){this.ray.set(e,t)},setFromCamera:function(e,t){t&&t.isPerspectiveCamera?(this.ray.origin.setFromMatrixPosition(t.matrixWorld),this.ray.direction.set(e.x,e.y,.5).unproject(t).sub(this.ray.origin).normalize(),this.camera=t):t&&t.isOrthographicCamera?(this.ray.origin.set(e.x,e.y,(t.near+t.far)/(t.near-t.far)).unproject(t),this.ray.direction.set(0,0,-1).transformDirection(t.matrixWorld),this.camera=t):console.error(\\\\\\\"THREE.Raycaster: Unsupported camera type: \\\\\\\"+t.type)},intersectObject:function(e,t=!1,n=[]){return jy(e,this,n,t),n.sort(Vy),n},intersectObjects:function(e,t=!1,n=[]){for(let i=0,s=e.length;i<s;i++)jy(e[i],this,n,t);return n.sort(Vy),n}}),function(e){e.GEOMETRY=\\\\\\\"geometry\\\\\\\",e.PLANE=\\\\\\\"plane\\\\\\\"}(By||(By={}));By.GEOMETRY,By.PLANE;class Hy{constructor(e){this._node=e,this._set_pos_timestamp=-1,this._hit_velocity=new p.a(0,0,0),this._hit_velocity_array=[0,0,0]}process(e){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(e);const t=Rn.performance.performanceManager().now(),n=t-this._set_pos_timestamp;if(this._set_pos_timestamp=t,this._hit_velocity.copy(e).sub(this._prev_position).divideScalar(n).multiplyScalar(1e3),this._hit_velocity.toArray(this._hit_velocity_array),this._node.pv.tvelocityTarget){if(Rn.playerMode())this._found_velocity_target_param=this._found_velocity_target_param||this._node.pv.velocityTarget.paramWithType(Qs.VECTOR3);else{const e=this._node.pv.velocityTarget;this._found_velocity_target_param=e.paramWithType(Qs.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(e)}reset(){this._prev_position=void 0}}var qy;!function(e){e.GEOMETRY=\\\\\\\"geometry\\\\\\\",e.PLANE=\\\\\\\"plane\\\\\\\"}(qy||(qy={}));const Wy=[qy.GEOMETRY,qy.PLANE];function Xy(e,t,n){var i=t.getBoundingClientRect();n.offsetX=e.pageX-i.left,n.offsetY=e.pageY-i.top}class Yy{constructor(e){this._node=e,this._offset={offsetX:0,offsetY:0},this._mouse=new d.a,this._mouse_array=[0,0],this._raycaster=new Gy,this._plane=new $.a,this._plane_intersect_target=new p.a,this._intersections=[],this._hit_position_array=[0,0,0],this.velocity_controller=new Hy(this._node)}updateMouse(e){var t;const n=null===(t=e.viewer)||void 0===t?void 0:t.canvas(),i=e.cameraNode;if(!n||!i)return;const s=e.event;if((s instanceof MouseEvent||s instanceof DragEvent||s instanceof PointerEvent)&&Xy(s,n,this._offset),s instanceof TouchEvent){Xy(s.touches[0],n,this._offset)}(e=>{this._mouse.x=e.offsetX/n.offsetWidth*2-1,this._mouse.y=-e.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(e){this._prepareRaycaster(e);const t=Wy[this._node.pv.intersectWith];switch(t){case qy.GEOMETRY:return this._intersect_with_geometry(e);case qy.PLANE:return this._intersect_with_plane(e)}Ri.unreachable(t)}_intersect_with_plane(e){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(e)}_intersect_with_geometry(e){if(this._resolved_targets||this.update_target(),this._resolved_targets){this._intersections.length=0;const t=this._raycaster.intersectObjects(this._resolved_targets,this._node.pv.traverseChildren,this._intersections)[0];t?(this._set_position_param(t.point),this._node.pv.geoAttribute&&this._resolve_geometry_attribute(t),e.value={intersect:t},this._node.trigger_hit(e)):this._node.trigger_miss(e)}}_resolve_geometry_attribute(e){const t=ls[this._node.pv.geoAttributeType],n=Yy.resolve_geometry_attribute(e,this._node.pv.geoAttributeName,t);if(null!=n){switch(t){case cs.NUMERIC:return void this._node.p.geoAttributeValue1.set(n);case cs.STRING:return void(m.isString(n)&&this._node.p.geoAttributeValues.set(n))}Ri.unreachable(t)}}static resolve_geometry_attribute(e,t,n){switch(ts(e.object.constructor)){case Zi.MESH:return this.resolve_geometry_attribute_for_mesh(e,t,n);case Zi.POINTS:return this.resolve_geometry_attribute_for_point(e,t,n)}}static resolve_geometry_attribute_for_mesh(e,t,n){const i=e.object.geometry;if(i){const s=i.getAttribute(t);if(s){switch(n){case cs.NUMERIC:{const t=i.getAttribute(\\\\\\\"position\\\\\\\");return e.face?(this._vA.fromBufferAttribute(t,e.face.a),this._vB.fromBufferAttribute(t,e.face.b),this._vC.fromBufferAttribute(t,e.face.c),this._uvA.fromBufferAttribute(s,e.face.a),this._uvB.fromBufferAttribute(s,e.face.b),this._uvC.fromBufferAttribute(s,e.face.c),e.uv=Ts.a.getUV(e.point,this._vA,this._vB,this._vC,this._uvA,this._uvB,this._uvC,this._hitUV),this._hitUV.x):void 0}case cs.STRING:{const e=new Bs(i).points()[0];return e?e.stringAttribValue(t):void 0}}Ri.unreachable(n)}}}static resolve_geometry_attribute_for_point(e,t,n){const i=e.object.geometry;if(i&&null!=e.index){switch(n){case cs.NUMERIC:{const n=i.getAttribute(t);return n?n.array[e.index]:void 0}case cs.STRING:{const n=new Bs(i).points()[e.index];return n?n.stringAttribValue(t):void 0}}Ri.unreachable(n)}}_set_position_param(e){if(e.toArray(this._hit_position_array),this._node.pv.tpositionTarget){if(Rn.playerMode())this._found_position_target_param=this._found_position_target_param||this._node.pv.positionTarget.paramWithType(Qs.VECTOR3);else{const e=this._node.pv.positionTarget;this._found_position_target_param=e.paramWithType(Qs.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(e)}_prepareRaycaster(e){const t=this._raycaster.params.Points;t&&(t.threshold=this._node.pv.pointsThreshold);let n=e.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 e=this._node.p.camera.found_node_with_context(Ei.OBJ);e&&(n=e)}n&&!this._node.pv.overrideRay&&n.prepareRaycaster(this._mouse,this._raycaster)}update_target(){const e=sx[this._node.pv.targetType];switch(e){case ix.NODE:return this._update_target_from_node();case ix.SCENE_GRAPH:return this._update_target_from_scene_graph()}Ri.unreachable(e)}_update_target_from_node(){const e=this._node.p.targetNode.value.nodeWithContext(Ei.OBJ);if(e){const t=this._node.pv.traverseChildren?e.object:e.childrenDisplayController.sopGroup();this._resolved_targets=t?[t]:void 0}else this._node.states.error.set(\\\\\\\"node is not an object\\\\\\\")}_update_target_from_scene_graph(){const e=this._node.scene().objectsByMask(this._node.pv.objectMask);e.length>0?this._resolved_targets=e: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(e){e.cpuController.update_target()}static PARAM_CALLBACK_print_resolve(e){e.cpuController.print_resolve()}print_resolve(){this.update_target(),console.log(this._resolved_targets)}}Yy._vA=new p.a,Yy._vB=new p.a,Yy._vC=new p.a,Yy._uvA=new d.a,Yy._uvB=new d.a,Yy._uvC=new d.a,Yy._hitUV=new d.a;class $y{constructor(e){this._node=e,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(e){var t;const n=null===(t=e.viewer)||void 0===t?void 0:t.canvas();n&&e.event&&(e.event instanceof MouseEvent||e.event instanceof DragEvent||e.event instanceof PointerEvent?(this._mouse.x=e.event.offsetX/n.offsetWidth,this._mouse.y=1-e.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(e){var t;const n=null===(t=e.viewer)||void 0===t?void 0:t.canvas();if(!n||!e.cameraNode)return;const i=e.cameraNode,s=i.renderController;if(s){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 t=i,r=s.resolved_scene||i.scene().threejsScene(),o=s.renderer(n);this._modify_scene_and_renderer(r,o),o.setRenderTarget(this._render_target),o.clear(),o.render(r,t.object),o.setRenderTarget(null),this._restore_scene_and_renderer(r,o),o.readRenderTargetPixels(this._render_target,Math.round(this._mouse.x*n.offsetWidth),Math.round(this._mouse.y*n.offsetHeight),1,1,this._read),this._param_read[0]=this._read[0],this._param_read[1]=this._read[1],this._param_read[2]=this._read[2],this._param_read[3]=this._read[3],this._node.p.pixelValue.set(this._param_read),this._node.pv.pixelValue.x>this._node.pv.hitThreshold?this._node.trigger_hit(e):this._node.trigger_miss(e)}}_modify_scene_and_renderer(e,t){this._restore_context.scene.overrideMaterial=e.overrideMaterial,this._restore_context.renderer.outputEncoding=t.outputEncoding,this._restore_context.renderer.toneMapping=t.toneMapping,e.overrideMaterial=this._resolved_material,t.toneMapping=w.vb,t.outputEncoding=w.U}_restore_scene_and_renderer(e,t){e.overrideMaterial=this._restore_context.scene.overrideMaterial,t.outputEncoding=this._restore_context.renderer.outputEncoding,t.toneMapping=this._restore_context.renderer.toneMapping}update_material(){const e=this._node.p.material.found_node();e?e.context()==Ei.MAT?this._resolved_material=e.material:this._node.states.error.set(\\\\\\\"target is not an obj\\\\\\\"):this._node.states.error.set(\\\\\\\"no target found\\\\\\\")}static PARAM_CALLBACK_update_material(e){e.gpuController.update_material()}}const Qy=1e3/60;var Jy;!function(e){e.CPU=\\\\\\\"cpu\\\\\\\",e.GPU=\\\\\\\"gpu\\\\\\\"}(Jy||(Jy={}));const Ky=[Jy.CPU,Jy.GPU];function Zy(e={}){return e.mode=Ky.indexOf(Jy.CPU),{visibleIf:e}}function ex(e={}){return e.mode=Ky.indexOf(Jy.CPU),e.intersectWith=Wy.indexOf(qy.GEOMETRY),{visibleIf:e}}function tx(e={}){return e.mode=Ky.indexOf(Jy.CPU),e.intersectWith=Wy.indexOf(qy.PLANE),{visibleIf:e}}function nx(e={}){return e.mode=Ky.indexOf(Jy.GPU),{visibleIf:e}}var ix;!function(e){e.SCENE_GRAPH=\\\\\\\"scene graph\\\\\\\",e.NODE=\\\\\\\"node\\\\\\\"}(ix||(ix={}));const sx=[ix.SCENE_GRAPH,ix.NODE];const rx=new class extends Lo{constructor(){super(...arguments),this.mode=Oo.INTEGER(Ky.indexOf(Jy.CPU),{menu:{entries:Ky.map(((e,t)=>({name:e,value:t})))}}),this.mouse=Oo.VECTOR2([0,0],{cook:!1}),this.overrideCamera=Oo.BOOLEAN(0),this.overrideRay=Oo.BOOLEAN(0,{visibleIf:{mode:Ky.indexOf(Jy.CPU),overrideCamera:1}}),this.camera=Oo.OPERATOR_PATH(\\\\\\\"/perspective_camera1\\\\\\\",{nodeSelection:{context:Ei.OBJ},dependentOnFoundNode:!1,visibleIf:{overrideCamera:1,overrideRay:0}}),this.rayOrigin=Oo.VECTOR3([0,0,0],{visibleIf:{overrideCamera:1,overrideRay:1}}),this.rayDirection=Oo.VECTOR3([0,0,1],{visibleIf:{overrideCamera:1,overrideRay:1}}),this.material=Oo.OPERATOR_PATH(\\\\\\\"/MAT/mesh_basic_builder1\\\\\\\",{nodeSelection:{context:Ei.MAT},dependentOnFoundNode:!1,callback:(e,t)=>{$y.PARAM_CALLBACK_update_material(e)},...nx()}),this.pixelValue=Oo.VECTOR4([0,0,0,0],{cook:!1,...nx()}),this.hitThreshold=Oo.FLOAT(.5,{cook:!1,...nx()}),this.intersectWith=Oo.INTEGER(Wy.indexOf(qy.GEOMETRY),{menu:{entries:Wy.map(((e,t)=>({name:e,value:t})))},...Zy()}),this.pointsThreshold=Oo.FLOAT(1,{range:[0,100],rangeLocked:[!0,!1],...Zy()}),this.planeDirection=Oo.VECTOR3([0,1,0],{...tx()}),this.planeOffset=Oo.FLOAT(0,{...tx()}),this.targetType=Oo.INTEGER(0,{menu:{entries:sx.map(((e,t)=>({name:e,value:t})))},...ex()}),this.targetNode=Oo.NODE_PATH(\\\\\\\"\\\\\\\",{nodeSelection:{context:Ei.OBJ},dependentOnFoundNode:!1,callback:(e,t)=>{Yy.PARAM_CALLBACK_update_target(e)},...ex({targetType:sx.indexOf(ix.NODE)})}),this.objectMask=Oo.STRING(\\\\\\\"*geo1*\\\\\\\",{callback:(e,t)=>{Yy.PARAM_CALLBACK_update_target(e)},...ex({targetType:sx.indexOf(ix.SCENE_GRAPH)})}),this.printFoundObjectsFromMask=Oo.BUTTON(null,{callback:(e,t)=>{Yy.PARAM_CALLBACK_print_resolve(e)},...ex({targetType:sx.indexOf(ix.SCENE_GRAPH)})}),this.traverseChildren=Oo.BOOLEAN(!0,{callback:(e,t)=>{Yy.PARAM_CALLBACK_update_target(e)},...ex(),separatorAfter:!0}),this.tpositionTarget=Oo.BOOLEAN(0,{cook:!1,...Zy()}),this.position=Oo.VECTOR3([0,0,0],{cook:!1,...Zy({tpositionTarget:0})}),this.positionTarget=Oo.PARAM_PATH(\\\\\\\"\\\\\\\",{cook:!1,...Zy({tpositionTarget:1}),paramSelection:Qs.VECTOR3,computeOnDirty:!0}),this.tvelocity=Oo.BOOLEAN(0,{cook:!1}),this.tvelocityTarget=Oo.BOOLEAN(0,{cook:!1,...Zy({tvelocity:1})}),this.velocity=Oo.VECTOR3([0,0,0],{cook:!1,...Zy({tvelocity:1,tvelocityTarget:0})}),this.velocityTarget=Oo.PARAM_PATH(\\\\\\\"\\\\\\\",{cook:!1,...Zy({tvelocity:1,tvelocityTarget:1}),paramSelection:Qs.VECTOR3,computeOnDirty:!0}),this.geoAttribute=Oo.BOOLEAN(0,ex()),this.geoAttributeName=Oo.STRING(\\\\\\\"id\\\\\\\",{cook:!1,...ex({geoAttribute:1})}),this.geoAttributeType=Oo.INTEGER(ls.indexOf(cs.NUMERIC),{menu:{entries:us},...ex({geoAttribute:1})}),this.geoAttributeValue1=Oo.FLOAT(0,{cook:!1,...ex({geoAttribute:1,geoAttributeType:ls.indexOf(cs.NUMERIC)})}),this.geoAttributeValues=Oo.STRING(\\\\\\\"\\\\\\\",{...ex({geoAttribute:1,geoAttributeType:ls.indexOf(cs.STRING)})})}};class ox extends ca{constructor(){super(...arguments),this.paramsConfig=rx,this.cpuController=new Yy(this),this.gpuController=new $y(this),this._last_event_processed_at=-1}static type(){return\\\\\\\"raycast\\\\\\\"}initializeNode(){this.io.inputs.setNamedInputConnectionPoints([new xo(ox.INPUT_TRIGGER,yo.BASE,this._process_trigger_event_throttled.bind(this)),new xo(ox.INPUT_MOUSE,yo.MOUSE,this._process_mouse_event.bind(this)),new xo(ox.INPUT_UPDATE_OBJECTS,yo.BASE,this._process_trigger_update_objects.bind(this)),new xo(ox.INPUT_TRIGGER_VEL_RESET,yo.BASE,this._process_trigger_vel_reset.bind(this))]),this.io.outputs.setNamedOutputConnectionPoints([new xo(ox.OUTPUT_HIT,yo.BASE),new xo(ox.OUTPUT_MISS,yo.BASE)])}trigger_hit(e){this.dispatchEventToOutput(ox.OUTPUT_HIT,e)}trigger_miss(e){this.dispatchEventToOutput(ox.OUTPUT_MISS,e)}_process_mouse_event(e){this.pv.mode==Ky.indexOf(Jy.CPU)?this.cpuController.updateMouse(e):this.gpuController.updateMouse(e)}_process_trigger_event_throttled(e){const t=this._last_event_processed_at,n=Rn.performance.performanceManager().now();this._last_event_processed_at=n;const i=n-t;i<Qy?setTimeout((()=>{this._process_trigger_event(e)}),Qy-i):this._process_trigger_event(e)}_process_trigger_event(e){this.pv.mode==Ky.indexOf(Jy.CPU)?this.cpuController.processEvent(e):this.gpuController.processEvent(e)}_process_trigger_update_objects(e){this.pv.mode==Ky.indexOf(Jy.CPU)&&this.cpuController.update_target()}_process_trigger_vel_reset(e){this.pv.mode==Ky.indexOf(Jy.CPU)&&this.cpuController.velocity_controller.reset()}}var ax;ox.INPUT_TRIGGER=\\\\\\\"trigger\\\\\\\",ox.INPUT_MOUSE=\\\\\\\"mouse\\\\\\\",ox.INPUT_UPDATE_OBJECTS=\\\\\\\"updateObjects\\\\\\\",ox.INPUT_TRIGGER_VEL_RESET=\\\\\\\"triggerVelReset\\\\\\\",ox.OUTPUT_HIT=\\\\\\\"hit\\\\\\\",ox.OUTPUT_MISS=\\\\\\\"miss\\\\\\\",function(e){e.SET=\\\\\\\"set\\\\\\\",e.TOGGLE=\\\\\\\"toggle\\\\\\\"}(ax||(ax={}));const cx=[ax.SET,ax.TOGGLE];const lx=new class extends Lo{constructor(){super(...arguments),this.mask=Oo.STRING(\\\\\\\"/geo*\\\\\\\",{separatorAfter:!0}),this.tdisplay=Oo.BOOLEAN(0),this.displayMode=Oo.INTEGER(cx.indexOf(ax.SET),{visibleIf:{tdisplay:1},menu:{entries:cx.map(((e,t)=>({name:e,value:t})))}}),this.display=Oo.BOOLEAN(0,{visibleIf:{tdisplay:1,displayMode:cx.indexOf(ax.SET)},separatorAfter:!0}),this.tbypass=Oo.BOOLEAN(0),this.bypassMode=Oo.INTEGER(cx.indexOf(ax.SET),{visibleIf:{tbypass:1},menu:{entries:cx.map(((e,t)=>({name:e,value:t})))}}),this.bypass=Oo.BOOLEAN(0,{visibleIf:{tbypass:1,displayMode:cx.indexOf(ax.SET)}}),this.execute=Oo.BUTTON(null,{callback:e=>{ux.PARAM_CALLBACK_execute(e)}})}};class ux extends ca{constructor(){super(...arguments),this.paramsConfig=lx}static type(){return\\\\\\\"setFlag\\\\\\\"}initializeNode(){this.io.inputs.setNamedInputConnectionPoints([new xo(\\\\\\\"trigger\\\\\\\",yo.BASE)])}async processEvent(e){let t=this.pv.mask;if(e.value){const n=e.value.node;if(n){const e=n.parent();e&&(t=`${e.path()}/${t}`)}}const n=this.scene().nodesController.nodesFromMask(t);for(let e of n)this._update_node_flags(e)}_update_node_flags(e){this._update_node_display_flag(e),this._update_node_bypass_flag(e)}_update_node_display_flag(e){var t;if(!this.pv.tdisplay)return;if(!(null===(t=e.flags)||void 0===t?void 0:t.hasDisplay()))return;const n=e.flags.display;if(!n)return;const i=cx[this.pv.displayMode];switch(i){case ax.SET:return void n.set(this.pv.display);case ax.TOGGLE:return void n.set(!n.active())}Ri.unreachable(i)}_update_node_bypass_flag(e){var t;if(!this.pv.tbypass)return;if(!(null===(t=e.flags)||void 0===t?void 0:t.hasBypass()))return;const n=e.flags.bypass;if(!n)return;const i=cx[this.pv.bypassMode];switch(i){case ax.SET:return void n.set(this.pv.bypass);case ax.TOGGLE:return void n.set(!n.active())}Ri.unreachable(i)}static PARAM_CALLBACK_execute(e){e.processEvent({})}}var hx;!function(e){e.BOOLEAN=\\\\\\\"boolean\\\\\\\",e.BUTTON=\\\\\\\"button\\\\\\\",e.NUMBER=\\\\\\\"number\\\\\\\",e.VECTOR2=\\\\\\\"vector2\\\\\\\",e.VECTOR3=\\\\\\\"vector3\\\\\\\",e.VECTOR4=\\\\\\\"vector4\\\\\\\",e.STRING=\\\\\\\"string\\\\\\\"}(hx||(hx={}));const dx=[hx.BOOLEAN,hx.BUTTON,hx.NUMBER,hx.VECTOR2,hx.VECTOR3,hx.VECTOR4,hx.STRING],px=dx.indexOf(hx.BOOLEAN),_x=dx.indexOf(hx.NUMBER),mx=dx.indexOf(hx.VECTOR2),fx=dx.indexOf(hx.VECTOR3),gx=dx.indexOf(hx.VECTOR4),vx=dx.indexOf(hx.STRING),yx=\\\\\\\"output\\\\\\\";const xx=new class extends Lo{constructor(){super(...arguments),this.param=Oo.PARAM_PATH(\\\\\\\"\\\\\\\",{paramSelection:!0,computeOnDirty:!0}),this.type=Oo.INTEGER(_x,{menu:{entries:dx.map(((e,t)=>({name:e,value:t})))}}),this.toggle=Oo.BOOLEAN(0,{visibleIf:{type:px}}),this.boolean=Oo.BOOLEAN(0,{visibleIf:{type:px,toggle:0}}),this.number=Oo.FLOAT(0,{visibleIf:{type:_x}}),this.vector2=Oo.VECTOR2([0,0],{visibleIf:{type:mx}}),this.vector3=Oo.VECTOR3([0,0,0],{visibleIf:{type:fx}}),this.vector4=Oo.VECTOR4([0,0,0,0],{visibleIf:{type:gx}}),this.increment=Oo.BOOLEAN(0,{visibleIf:[{type:_x},{type:mx},{type:fx},{type:gx}]}),this.string=Oo.STRING(\\\\\\\"\\\\\\\",{visibleIf:{type:vx}}),this.execute=Oo.BUTTON(null,{callback:e=>{bx.PARAM_CALLBACK_execute(e)}})}};class bx extends ca{constructor(){super(...arguments),this.paramsConfig=xx,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 xo(\\\\\\\"trigger\\\\\\\",yo.BASE)]),this.io.outputs.setNamedOutputConnectionPoints([new xo(yx,yo.BASE)]),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.param])}))}))}async processEvent(e){this.p.param.isDirty()&&await this.p.param.compute();const t=this.p.param.value.param();if(t){const e=await this._new_param_value(t);null!=e&&t.set(e)}else this.states.error.set(\\\\\\\"target param not found\\\\\\\");this.dispatchEventToOutput(yx,e)}async _new_param_value(e){const t=dx[this.pv.type];switch(t){case hx.BOOLEAN:return await this._compute_params_if_dirty([this.p.toggle]),this.pv.toggle?e.value?0:1:this.pv.boolean?1:0;case hx.BUTTON:return e.options.executeCallback();case hx.NUMBER:return await this._compute_params_if_dirty([this.p.increment,this.p.number]),this.pv.increment?e.type()==Qs.FLOAT?e.value+this.pv.number:e.value:this.pv.number;case hx.VECTOR2:return await this._compute_params_if_dirty([this.p.increment,this.p.vector2]),this.pv.increment?e.type()==Qs.VECTOR2?(this._tmp_vector2.copy(e.value),this._tmp_vector2.add(this.pv.vector2),this._tmp_vector2.toArray(this._tmp_array2)):e.value.toArray(this._tmp_array2):this.pv.vector2.toArray(this._tmp_array2),this._tmp_array2;case hx.VECTOR3:return await this._compute_params_if_dirty([this.p.increment,this.p.vector3]),this.pv.increment?e.type()==Qs.VECTOR3?(this._tmp_vector3.copy(e.value),this._tmp_vector3.add(this.pv.vector3),this._tmp_vector3.toArray(this._tmp_array3)):e.value.toArray(this._tmp_array3):this.pv.vector3.toArray(this._tmp_array3),this._tmp_array3;case hx.VECTOR4:return await this._compute_params_if_dirty([this.p.increment,this.p.vector4]),this.pv.increment?e.type()==Qs.VECTOR4?(this._tmp_vector4.copy(e.value),this._tmp_vector4.add(this.pv.vector4),this._tmp_vector4.toArray(this._tmp_array4)):e.value.toArray(this._tmp_array4):this.pv.vector4.toArray(this._tmp_array4),this._tmp_array4;case hx.STRING:return await this._compute_params_if_dirty([this.p.string]),this.pv.string}Ri.unreachable(t)}static PARAM_CALLBACK_execute(e){e.processEvent({})}async _compute_params_if_dirty(e){const t=[];for(let n of e)n.isDirty()&&t.push(n);const n=[];for(let e of t)n.push(e.compute());return await Promise.all(n)}}const wx=new class extends Lo{constructor(){super(...arguments),this.outputsCount=Oo.INTEGER(5,{range:[1,10],rangeLocked:[!0,!1]})}};class Ax extends ca{constructor(){super(...arguments),this.paramsConfig=wx}static type(){return\\\\\\\"sequence\\\\\\\"}initializeNode(){this.io.connection_points.set_input_name_function((()=>\\\\\\\"trigger\\\\\\\")),this.io.connection_points.set_expected_input_types_function((()=>[yo.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 e=new Array(this.pv.outputsCount);return e.fill(yo.BASE),e}_output_name(e){return`out${e}`}processEvent(e){const t=this.pv.outputsCount;for(let n=0;n<t;n++){const t=this.io.outputs.namedOutputConnectionPoints()[n];this.dispatchEventToOutput(t.name(),e)}}}const Tx=\\\\\\\"tick\\\\\\\";const Ex=new class extends Lo{constructor(){super(...arguments),this.period=Oo.INTEGER(1e3),this.count=Oo.INTEGER(-1)}};class Cx extends ca{constructor(){super(...arguments),this.paramsConfig=Ex,this._timer_active=!1,this._current_count=0}static type(){return\\\\\\\"timer\\\\\\\"}initializeNode(){this.io.inputs.setNamedInputConnectionPoints([new xo(\\\\\\\"start\\\\\\\",yo.BASE,this._start_timer.bind(this)),new xo(\\\\\\\"stop\\\\\\\",yo.BASE,this._stop_timer.bind(this))]),this.io.outputs.setNamedOutputConnectionPoints([new xo(Tx,yo.BASE)])}_start_timer(e){this._timer_active||(this._timer_active=!0,this._current_count=0),this._run_timer(e)}_stop_timer(){this._timer_active=!1}_run_timer(e){setTimeout((()=>{this._timer_active&&(this.pv.count<=0||this._current_count<this.pv.count?(this.dispatchEventToOutput(Tx,e),this._current_count+=1,this._run_timer(e)):this._stop_timer())}),this.pv.period)}}const Mx=new class extends Lo{constructor(){super(...arguments),this.className=Oo.STRING(\\\\\\\"active\\\\\\\")}};class Nx extends ca{constructor(){super(...arguments),this.paramsConfig=Mx}static type(){return\\\\\\\"viewer\\\\\\\"}initializeNode(){this.io.inputs.setNamedInputConnectionPoints([new xo(\\\\\\\"setCss\\\\\\\",yo.BASE,this._process_trigger_setClass.bind(this)),new xo(\\\\\\\"unSetCss\\\\\\\",yo.BASE,this._process_trigger_unsetClass.bind(this)),new xo(\\\\\\\"createControls\\\\\\\",yo.BASE,this._process_trigger_createControls.bind(this)),new xo(\\\\\\\"disposeControls\\\\\\\",yo.BASE,this._process_trigger_disposeControls.bind(this))])}_process_trigger_setClass(e){var t;const n=null===(t=e.viewer)||void 0===t?void 0:t.canvas();n&&n.classList.add(this.pv.className)}_process_trigger_unsetClass(e){var t;const n=null===(t=e.viewer)||void 0===t?void 0:t.canvas();n&&n.classList.remove(this.pv.className)}_process_trigger_createControls(e){this.scene().viewersRegister.traverseViewers((e=>{var t;null===(t=e.controlsController)||void 0===t||t.create_controls()}))}_process_trigger_disposeControls(e){this.scene().viewersRegister.traverseViewers((e=>{var t;null===(t=e.controlsController)||void 0===t||t.dispose_controls()}))}}class Sx extends Mo{static context(){return Ei.EVENT}cook(){this.cookController.endCook()}}class Ox extends Sx{}class Lx extends Ox{constructor(){super(...arguments),this._children_controller_context=Ei.ANIM}static type(){return Ci.ANIM}createNode(e,t){return super.createNode(e,t)}children(){return super.children()}nodesByType(e){return super.nodesByType(e)}}class Px extends Ox{constructor(){super(...arguments),this._children_controller_context=Ei.COP}static type(){return Ci.COP}createNode(e,t){return super.createNode(e,t)}children(){return super.children()}nodesByType(e){return super.nodesByType(e)}}class Rx extends Ox{constructor(){super(...arguments),this._children_controller_context=Ei.EVENT}static type(){return Ci.EVENT}createNode(e,t){return super.createNode(e,t)}children(){return super.children()}nodesByType(e){return super.nodesByType(e)}}class Ix extends Ox{constructor(){super(...arguments),this._children_controller_context=Ei.MAT}static type(){return Ci.MAT}createNode(e,t){return super.createNode(e,t)}children(){return super.children()}nodesByType(e){return super.nodesByType(e)}}class Fx extends Sx{constructor(){super(...arguments),this.paramsConfig=new Rm,this.effectsComposerController=new Im(this),this.displayNodeController=new mm(this,this.effectsComposerController.displayNodeControllerCallbacks()),this._children_controller_context=Ei.POST}static type(){return Ci.POST}createNode(e,t){return super.createNode(e,t)}children(){return super.children()}nodesByType(e){return super.nodesByType(e)}}class Dx extends Ox{constructor(){super(...arguments),this._children_controller_context=Ei.ROP}static type(){return Ci.ROP}createNode(e,t){return super.createNode(e,t)}children(){return super.children()}nodesByType(e){return super.nodesByType(e)}}const kx=\\\\\\\"int\\\\\\\";const Bx=new class extends Lo{constructor(){super(...arguments),this.float=Oo.FLOAT(0)}};class zx extends Ym{constructor(){super(...arguments),this.paramsConfig=Bx}static type(){return\\\\\\\"floatToInt\\\\\\\"}initializeNode(){this.io.outputs.setNamedOutputConnectionPoints([new ho(kx,ro.INT)])}setLines(e){const t=this.variableForInputParam(this.p.float),n=`int ${this.glVarName(kx)} = int(${Wm.float(t)})`;e.addBodyLines(this,[n])}}const Ux=\\\\\\\"float\\\\\\\";const Gx=new class extends Lo{constructor(){super(...arguments),this.int=Oo.INTEGER(0)}};class Vx extends Ym{constructor(){super(...arguments),this.paramsConfig=Gx}static type(){return\\\\\\\"intToFloat\\\\\\\"}initializeNode(){this.io.outputs.setNamedOutputConnectionPoints([new ho(Ux,ro.FLOAT)])}setLines(e){const t=this.variableForInputParam(this.p.int),n=`float ${this.glVarName(Ux)} = float(${Wm.integer(t)})`;e.addBodyLines(this,[n])}}const jx=\\\\\\\"bool\\\\\\\";const Hx=new class extends Lo{constructor(){super(...arguments),this.int=Oo.INTEGER(0)}};class qx extends Ym{constructor(){super(...arguments),this.paramsConfig=Hx}static type(){return\\\\\\\"intToBool\\\\\\\"}initializeNode(){this.io.outputs.setNamedOutputConnectionPoints([new ho(jx,ro.BOOL)])}setLines(e){const t=this.variableForInputParam(this.p.int),n=`bool ${this.glVarName(jx)} = bool(${Wm.integer(t)})`;e.addBodyLines(this,[n])}}const Wx=new class extends Lo{constructor(){super(...arguments),this.bool=Oo.BOOLEAN(0)}};class Xx extends Ym{constructor(){super(...arguments),this.paramsConfig=Wx}static type(){return\\\\\\\"boolToInt\\\\\\\"}initializeNode(){this.io.outputs.setNamedOutputConnectionPoints([new ho(kx,ro.INT)])}setLines(e){const t=this.variableForInputParam(this.p.bool),n=`int ${this.glVarName(kx)} = int(${Wm.bool(t)})`;e.addBodyLines(this,[n])}}const Yx=new class extends Lo{constructor(){super(...arguments),this.x=Oo.FLOAT(0),this.y=Oo.FLOAT(0)}};class $x extends Ym{constructor(){super(...arguments),this.paramsConfig=Yx}static type(){return\\\\\\\"floatToVec2\\\\\\\"}initializeNode(){this.io.outputs.setNamedOutputConnectionPoints([new ho($x.OUTPUT_NAME,ro.VEC2)])}setLines(e){const t=this.variableForInputParam(this.p.x),n=this.variableForInputParam(this.p.y),i=`vec2 ${this.glVarName($x.OUTPUT_NAME)} = ${Wm.float2(t,n)}`;e.addBodyLines(this,[i])}}$x.OUTPUT_NAME=\\\\\\\"vec2\\\\\\\";const Qx=new class extends Lo{constructor(){super(...arguments),this.x=Oo.FLOAT(0),this.y=Oo.FLOAT(0),this.z=Oo.FLOAT(0)}};class Jx extends Ym{constructor(){super(...arguments),this.paramsConfig=Qx}static type(){return\\\\\\\"floatToVec3\\\\\\\"}initializeNode(){this.io.outputs.setNamedOutputConnectionPoints([new ho(Jx.OUTPUT_NAME,ro.VEC3)])}setLines(e){const t=this.variableForInputParam(this.p.x),n=this.variableForInputParam(this.p.y),i=this.variableForInputParam(this.p.z),s=`vec3 ${this.glVarName(Jx.OUTPUT_NAME)} = ${Wm.float3(t,n,i)}`;e.addBodyLines(this,[s])}}Jx.OUTPUT_NAME=\\\\\\\"vec3\\\\\\\";const Kx=new class extends Lo{constructor(){super(...arguments),this.x=Oo.FLOAT(0),this.y=Oo.FLOAT(0),this.z=Oo.FLOAT(0),this.w=Oo.FLOAT(0)}};class Zx extends Ym{constructor(){super(...arguments),this.paramsConfig=Kx}static type(){return\\\\\\\"floatToVec4\\\\\\\"}initializeNode(){this.io.outputs.setNamedOutputConnectionPoints([new ho(Zx.OUTPUT_NAME,ro.VEC4)])}setLines(e){const t=this.variableForInputParam(this.p.x),n=this.variableForInputParam(this.p.y),i=this.variableForInputParam(this.p.z),s=this.variableForInputParam(this.p.w),r=`vec4 ${this.glVarName(Zx.OUTPUT_NAME)} = ${Wm.float4(t,n,i,s)}`;e.addBodyLines(this,[r])}}Zx.OUTPUT_NAME=\\\\\\\"vec4\\\\\\\";const eb=new class extends Lo{};class tb extends Ym{constructor(){super(...arguments),this.paramsConfig=eb}}function nb(e,t){const n=t.components,i=t.param_type;return class extends tb{static type(){return e}initializeNode(){this.io.outputs.setNamedOutputConnectionPoints(n.map((e=>new ho(e,ro.FLOAT))))}createParams(){this.addParam(i,\\\\\\\"vec\\\\\\\",n.map((e=>0)))}setLines(e){const t=[],n=this.variableForInput(\\\\\\\"vec\\\\\\\");this.io.outputs.used_output_names().forEach((e=>{const i=this.glVarName(e);t.push(`float ${i} = ${n}.${e}`)})),e.addBodyLines(this,t)}}}const ib=[\\\\\\\"x\\\\\\\",\\\\\\\"y\\\\\\\"],sb=[\\\\\\\"x\\\\\\\",\\\\\\\"y\\\\\\\",\\\\\\\"z\\\\\\\"],rb=[\\\\\\\"x\\\\\\\",\\\\\\\"y\\\\\\\",\\\\\\\"z\\\\\\\",\\\\\\\"w\\\\\\\"];class ob extends(nb(\\\\\\\"vec2ToFloat\\\\\\\",{components:[\\\\\\\"x\\\\\\\",\\\\\\\"y\\\\\\\"],param_type:Qs.VECTOR2})){}class ab extends(nb(\\\\\\\"vec3ToFloat\\\\\\\",{components:[\\\\\\\"x\\\\\\\",\\\\\\\"y\\\\\\\",\\\\\\\"z\\\\\\\"],param_type:Qs.VECTOR3})){}class cb extends(nb(\\\\\\\"vec4ToFloat\\\\\\\",{components:rb,param_type:Qs.VECTOR4})){}class lb extends tb{static type(){return\\\\\\\"vec4ToVec3\\\\\\\"}initializeNode(){this.io.outputs.setNamedOutputConnectionPoints([new ho(lb.OUTPUT_NAME_VEC3,ro.VEC3),new ho(lb.OUTPUT_NAME_W,ro.FLOAT)])}createParams(){this.addParam(Qs.VECTOR4,lb.INPUT_NAME_VEC4,rb.map((e=>0)))}setLines(e){const t=[],n=lb.INPUT_NAME_VEC4,i=lb.OUTPUT_NAME_VEC3,s=lb.OUTPUT_NAME_W,r=this.variableForInput(n),o=this.io.outputs.used_output_names();if(o.indexOf(i)>=0){const e=this.glVarName(i);t.push(`vec3 ${e} = ${r}.xyz`)}if(o.indexOf(s)>=0){const e=this.glVarName(s);t.push(`float ${e} = ${r}.w`)}e.addBodyLines(this,t)}}lb.INPUT_NAME_VEC4=\\\\\\\"vec4\\\\\\\",lb.OUTPUT_NAME_VEC3=\\\\\\\"vec3\\\\\\\",lb.OUTPUT_NAME_W=\\\\\\\"w\\\\\\\";class ub extends tb{static type(){return\\\\\\\"vec3ToVec2\\\\\\\"}initializeNode(){this.io.outputs.setNamedOutputConnectionPoints([new ho(ub.OUTPUT_NAME_VEC2,ro.VEC2),new ho(ub.OUTPUT_NAME_Z,ro.FLOAT)])}createParams(){this.addParam(Qs.VECTOR3,ub.INPUT_NAME_VEC3,sb.map((e=>0)))}setLines(e){const t=[],n=ub.INPUT_NAME_VEC3,i=ub.OUTPUT_NAME_VEC2,s=ub.OUTPUT_NAME_Z,r=this.variableForInput(n),o=this.io.outputs.used_output_names();if(o.indexOf(i)>=0){const e=this.glVarName(i);t.push(`vec2 ${e} = ${r}.xy`)}if(o.indexOf(s)>=0){const e=this.glVarName(s);t.push(`float ${e} = ${r}.z`)}e.addBodyLines(this,t)}}ub.INPUT_NAME_VEC3=\\\\\\\"vec3\\\\\\\",ub.OUTPUT_NAME_VEC2=\\\\\\\"vec2\\\\\\\",ub.OUTPUT_NAME_Z=\\\\\\\"z\\\\\\\";class hb extends tb{static type(){return\\\\\\\"vec2ToVec3\\\\\\\"}initializeNode(){this.io.outputs.setNamedOutputConnectionPoints([new ho(hb.OUTPUT_NAME_VEC3,ro.VEC3)])}createParams(){this.addParam(Qs.VECTOR2,hb.INPUT_NAME_VEC2,ib.map((e=>0))),this.addParam(Qs.FLOAT,hb.INPUT_NAME_Z,0)}setLines(e){const t=[],n=hb.INPUT_NAME_VEC2,i=hb.INPUT_NAME_Z,s=hb.OUTPUT_NAME_VEC3,r=this.variableForInput(n),o=this.variableForInput(i),a=this.glVarName(s);t.push(`vec3 ${a} = vec3(${r}.xy, ${o})`),e.addBodyLines(this,t)}}hb.INPUT_NAME_VEC2=\\\\\\\"vec3\\\\\\\",hb.INPUT_NAME_Z=\\\\\\\"z\\\\\\\",hb.OUTPUT_NAME_VEC3=\\\\\\\"vec3\\\\\\\";class db extends tb{static type(){return\\\\\\\"vec3ToVec4\\\\\\\"}initializeNode(){this.io.outputs.setNamedOutputConnectionPoints([new ho(db.OUTPUT_NAME_VEC4,ro.VEC4)])}createParams(){this.addParam(Qs.VECTOR3,db.INPUT_NAME_VEC3,sb.map((e=>0))),this.addParam(Qs.FLOAT,db.INPUT_NAME_W,0)}setLines(e){const t=[],n=db.INPUT_NAME_VEC3,i=db.INPUT_NAME_W,s=db.OUTPUT_NAME_VEC4,r=this.variableForInput(n),o=this.variableForInput(i),a=this.glVarName(s);t.push(`vec4 ${a} = vec4(${r}.xyz, ${o})`),e.addBodyLines(this,t)}}db.INPUT_NAME_VEC3=\\\\\\\"vec3\\\\\\\",db.INPUT_NAME_W=\\\\\\\"w\\\\\\\",db.OUTPUT_NAME_VEC4=\\\\\\\"vec4\\\\\\\";const pb=new class extends Lo{};class _b extends Ym{constructor(){super(...arguments),this.paramsConfig=pb}gl_method_name(){return\\\\\\\"\\\\\\\"}gl_function_definitions(){return[]}initializeNode(){super.initializeNode(),this.io.connection_points.set_expected_input_types_function(this._expected_input_types.bind(this)),this.io.connection_points.set_expected_output_types_function(this._expected_output_types.bind(this)),this.io.connection_points.set_input_name_function(this._gl_input_name.bind(this))}_expected_input_types(){const e=this.io.connection_points.first_input_connection_type()||ro.FLOAT;if(this.io.connections.firstInputConnection()){const t=this.io.connections.inputConnections();if(t){let n=Math.max(f.compact(t).length+1,2);return f.range(n).map((t=>e))}return[]}return f.range(2).map((t=>e))}_expected_output_types(){return[this._expected_input_types()[0]]}_gl_input_name(e){return\\\\\\\"in\\\\\\\"}setLines(e){const t=this.io.outputs.namedOutputConnectionPoints()[0].type(),n=this.io.inputs.namedInputConnectionPoints().map(((e,t)=>{const n=e.name();return Wm.any(this.variableForInput(n))})).join(\\\\\\\", \\\\\\\"),i=`${t} ${this.glVarName(this.io.connection_points.output_name(0))} = ${this.gl_method_name()}(${n})`;e.addBodyLines(this,[i]),e.addDefinitions(this,this.gl_function_definitions())}}class mb extends _b{_gl_input_name(e){return\\\\\\\"in\\\\\\\"}_expected_input_types(){return[this.io.connection_points.first_input_connection_type()||ro.FLOAT]}}class fb extends _b{_expected_input_types(){const e=this.io.connection_points.first_input_connection_type()||ro.FLOAT;return[e,e]}}class gb extends _b{_expected_input_types(){const e=this.io.connection_points.first_input_connection_type()||ro.FLOAT;return[e,e,e]}}class vb extends _b{_expected_input_types(){const e=this.io.connection_points.first_input_connection_type()||ro.FLOAT;return[e,e,e,e]}}class yb extends _b{_expected_input_types(){const e=this.io.connection_points.first_input_connection_type()||ro.FLOAT;return[e,e,e,e,e]}}function xb(e,t={}){const n=t.method||e,i=t.out||\\\\\\\"val\\\\\\\",s=t.in||\\\\\\\"in\\\\\\\";return class extends mb{static type(){return e}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(e){return s}_gl_output_name(e){return i}gl_method_name(){return n}}}class bb extends(xb(\\\\\\\"abs\\\\\\\")){}class wb extends(xb(\\\\\\\"acos\\\\\\\",{out:\\\\\\\"radians\\\\\\\"})){}class Ab extends(xb(\\\\\\\"asin\\\\\\\",{out:\\\\\\\"radians\\\\\\\"})){}class Tb extends(xb(\\\\\\\"atan\\\\\\\",{out:\\\\\\\"radians\\\\\\\"})){}class Eb extends(xb(\\\\\\\"ceil\\\\\\\")){}class Cb extends(xb(\\\\\\\"cos\\\\\\\",{in:\\\\\\\"radians\\\\\\\"})){}class Mb extends(xb(\\\\\\\"degrees\\\\\\\",{in:\\\\\\\"radians\\\\\\\",out:\\\\\\\"degrees\\\\\\\"})){}class Nb extends(xb(\\\\\\\"exp\\\\\\\")){}class Sb extends(xb(\\\\\\\"exp2\\\\\\\")){}class Ob extends(xb(\\\\\\\"floor\\\\\\\")){}class Lb extends(xb(\\\\\\\"fract\\\\\\\")){}class Pb extends(xb(\\\\\\\"inverseSqrt\\\\\\\",{method:\\\\\\\"inversesqrt\\\\\\\"})){}class Rb extends(xb(\\\\\\\"log\\\\\\\")){}class Ib extends(xb(\\\\\\\"log2\\\\\\\")){}class Fb extends(xb(\\\\\\\"normalize\\\\\\\",{out:\\\\\\\"normalized\\\\\\\"})){}class Db extends(xb(\\\\\\\"radians\\\\\\\",{in:\\\\\\\"degrees\\\\\\\",out:\\\\\\\"radians\\\\\\\"})){}class kb extends(xb(\\\\\\\"sign\\\\\\\")){}class Bb extends(xb(\\\\\\\"sin\\\\\\\",{in:\\\\\\\"radians\\\\\\\"})){}class zb extends(xb(\\\\\\\"sqrt\\\\\\\")){}class Ub extends(xb(\\\\\\\"tan\\\\\\\")){}function Gb(e,t={}){const n=t.method||e,i=t.out||\\\\\\\"val\\\\\\\",s=t.in||[\\\\\\\"in0\\\\\\\",\\\\\\\"in1\\\\\\\"],r=t.default_in_type,o=t.allowed_in_types,a=t.out_type,c=t.functions||[];return class extends fb{static type(){return e}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(e){return s[e]}_gl_output_name(e){return i}gl_method_name(){return n}gl_function_definitions(){return c?c.map((e=>new of(this,e))):[]}_expected_input_types(){let e=this.io.connection_points.first_input_connection_type();if(e&&o&&!o.includes(e)){const t=this.io.inputs.namedInputConnectionPoints()[0];e=t?t.type():r}const t=e||r||ro.FLOAT;return[t,t]}}}class Vb extends(Gb(\\\\\\\"distance\\\\\\\",{in:[\\\\\\\"p0\\\\\\\",\\\\\\\"p1\\\\\\\"],default_in_type:ro.VEC3,allowed_in_types:[ro.VEC2,ro.VEC3,ro.VEC4],out_type:ro.FLOAT})){}class jb extends(Gb(\\\\\\\"dot\\\\\\\",{in:[\\\\\\\"vec0\\\\\\\",\\\\\\\"vec1\\\\\\\"],default_in_type:ro.VEC3,allowed_in_types:[ro.VEC2,ro.VEC3,ro.VEC4],out_type:ro.FLOAT})){}class Hb extends(Gb(\\\\\\\"max\\\\\\\")){}class qb extends(Gb(\\\\\\\"min\\\\\\\")){}class Wb extends(Gb(\\\\\\\"mod\\\\\\\")){paramDefaultValue(e){return{in1:1}[e]}_expected_input_types(){const e=ro.FLOAT;return[e,e]}}class Xb extends(Gb(\\\\\\\"pow\\\\\\\",{in:[\\\\\\\"x\\\\\\\",\\\\\\\"y\\\\\\\"]})){}class Yb extends(Gb(\\\\\\\"reflect\\\\\\\",{in:[\\\\\\\"I\\\\\\\",\\\\\\\"N\\\\\\\"],default_in_type:ro.VEC3})){}class $b extends(Gb(\\\\\\\"step\\\\\\\",{in:[\\\\\\\"edge\\\\\\\",\\\\\\\"x\\\\\\\"]})){}function Qb(e,t={}){const n=t.method||e,i=t.out||\\\\\\\"val\\\\\\\",s=t.in||[\\\\\\\"in0\\\\\\\",\\\\\\\"in1\\\\\\\",\\\\\\\"in2\\\\\\\"],r=t.default||{},o=t.out_type||ro.FLOAT,a=t.functions||[];return class extends gb{static type(){return e}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(e){return s[e]}_gl_output_name(e){return i}gl_method_name(){return n}_expected_output_types(){return[o]}paramDefaultValue(e){return r[e]}gl_function_definitions(){return a.map((e=>new of(this,e)))}}}class Jb extends(Qb(\\\\\\\"clamp\\\\\\\",{in:[\\\\\\\"value\\\\\\\",\\\\\\\"min\\\\\\\",\\\\\\\"max\\\\\\\"],default:{max:1}})){_expected_output_types(){return[this._expected_input_types()[0]]}}class Kb extends(Qb(\\\\\\\"faceForward\\\\\\\",{in:[\\\\\\\"N\\\\\\\",\\\\\\\"I\\\\\\\",\\\\\\\"Nref\\\\\\\"]})){}class Zb extends(Qb(\\\\\\\"smoothstep\\\\\\\",{in:[\\\\\\\"edge0\\\\\\\",\\\\\\\"edge1\\\\\\\",\\\\\\\"x\\\\\\\"],default:{edge1:1}})){_expected_output_types(){return[this._expected_input_types()[0]]}}function ew(e,t){const n=t.in_prefix||e,i=t.out||\\\\\\\"val\\\\\\\",s=t.operation,r=t.allowed_in_types;return class extends fb{static type(){return e}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(e){const t=this.io.outputs.namedOutputConnectionPoints()[0].type(),n=this.io.inputs.namedInputConnectionPoints().map(((e,t)=>{const n=e.name(),i=this.variableForInput(n);if(i)return Wm.any(i)})).join(` ${this.gl_operation()} `),i=`${t} ${this.glVarName(this.io.connection_points.output_name(0))} = ${this.gl_method_name()}(${n})`;e.addBodyLines(this,[i])}_gl_input_name(e){return`${n}${e}`}_gl_output_name(e){return i}gl_operation(){return s}_expected_input_types(){let e=this.io.connection_points.first_input_connection_type();if(e&&r&&!r.includes(e)){const t=this.io.inputs.namedInputConnectionPoints()[0];t&&(e=t.type())}const t=e||ro.FLOAT,n=this.io.connections.existingInputConnections(),i=n?Math.max(n.length+1,2):2,s=[];for(let e=0;e<i;e++)s.push(t);return s}_expected_output_types(){const e=this._expected_input_types();return[e[1]||e[0]||ro.FLOAT]}}}class tw extends(ew(\\\\\\\"add\\\\\\\",{in_prefix:\\\\\\\"add\\\\\\\",out:\\\\\\\"sum\\\\\\\",operation:\\\\\\\"+\\\\\\\"})){}class nw extends(ew(\\\\\\\"divide\\\\\\\",{in_prefix:\\\\\\\"div\\\\\\\",out:\\\\\\\"divide\\\\\\\",operation:\\\\\\\"/\\\\\\\"})){paramDefaultValue(e){return 1}}class iw extends(ew(\\\\\\\"substract\\\\\\\",{in_prefix:\\\\\\\"sub\\\\\\\",out:\\\\\\\"substract\\\\\\\",operation:\\\\\\\"-\\\\\\\"})){}class sw extends(ew(\\\\\\\"mult\\\\\\\",{in_prefix:\\\\\\\"mult\\\\\\\",out:\\\\\\\"product\\\\\\\",operation:\\\\\\\"*\\\\\\\"})){static type(){return\\\\\\\"mult\\\\\\\"}paramDefaultValue(e){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 e=this._expected_input_types();return[e[e.length-1]]}_expected_input_types(){const e=this.io.connections.existingInputConnections();if(e){const t=e[0];if(t){const n=t.node_src.io.outputs.namedOutputConnectionPoints()[t.output_index].type(),i=Math.max(e.length+1,2),s=new Array(i);if(n==ro.FLOAT){const t=e[1];if(t){const e=t.node_src.io.outputs.namedOutputConnectionPoints()[t.output_index].type();return e==ro.FLOAT?s.fill(n):[n,e]}return[n,n]}return s.fill(n)}}return[ro.FLOAT,ro.FLOAT]}}class rw extends fb{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[ro.BOOL,ro.BOOL]}_expected_output_types(){return[ro.BOOL]}setLines(e){const t=this.io.inputs.namedInputConnectionPoints().map(((e,t)=>{const n=e.name();return Wm.any(this.variableForInput(n))})).join(` ${this.boolean_operation()} `),n=`bool ${this.glVarName(this.io.connection_points.output_name(0))} = ${t}`;e.addBodyLines(this,[n])}}function ow(e,t){return class extends rw{static type(){return e}initializeNode(){super.initializeNode(),this.io.connection_points.set_input_name_function(this._gl_input_name.bind(this)),this.io.connection_points.set_output_name_function(this._gl_output_name.bind(this))}boolean_operation(){return t.op}_gl_output_name(t){return e}_gl_input_name(t=0){return`${e}${t}`}}}class aw extends(ow(\\\\\\\"and\\\\\\\",{op:\\\\\\\"&&\\\\\\\"})){}class cw extends(ow(\\\\\\\"or\\\\\\\",{op:\\\\\\\"||\\\\\\\"})){}var lw;!function(e){e.TIME=\\\\\\\"time\\\\\\\",e.DELTA_TIME=\\\\\\\"delta_time\\\\\\\"}(lw||(lw={}));var uw,hw;!function(e){e.POSITION=\\\\\\\"position\\\\\\\",e.VELOCITY=\\\\\\\"velocity\\\\\\\",e.MASS=\\\\\\\"mass\\\\\\\",e.FORCE=\\\\\\\"force\\\\\\\"}(uw||(uw={})),function(e){e.POSITION=\\\\\\\"position\\\\\\\",e.VELOCITY=\\\\\\\"velocity\\\\\\\"}(hw||(hw={}));const dw=[uw.POSITION,uw.VELOCITY,uw.MASS,uw.FORCE],pw=[hw.POSITION,hw.VELOCITY],_w={[uw.POSITION]:[0,0,0],[uw.VELOCITY]:[0,0,0],[uw.MASS]:1,[uw.FORCE]:[0,-9.8,0]};const mw=new class extends Lo{};class fw extends Ym{constructor(){super(...arguments),this.paramsConfig=mw}static type(){return\\\\\\\"acceleration\\\\\\\"}initializeNode(){super.initializeNode(),this.io.outputs.setNamedOutputConnectionPoints([new ho(hw.POSITION,ro.VEC3),new ho(hw.VELOCITY,ro.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 e=this.io.connection_points.first_input_connection_type()||ro.VEC3;return[e,e,ro.FLOAT,e]}_expected_output_types(){const e=this._expected_input_types()[0];return[e,e]}_gl_input_name(e){return dw[e]}_gl_output_name(e){return pw[e]}paramDefaultValue(e){return _w[e]}setLines(e){const t=this.io.outputs.namedOutputConnectionPoints()[0].type(),n=new af(this,ro.FLOAT,lw.DELTA_TIME),i=new of(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}\\\\\\\");e.addDefinitions(this,[n,i]);const s=Wm.any(this.variableForInput(uw.POSITION)),r=Wm.any(this.variableForInput(uw.VELOCITY)),o=Wm.float(this.variableForInput(uw.MASS)),a=Wm.any(this.variableForInput(uw.FORCE)),c=this.glVarName(hw.POSITION),l=this.glVarName(hw.VELOCITY),u=`${t} ${l} = compute_velocity_from_acceleration(${[r,a,o,lw.DELTA_TIME].join(\\\\\\\", \\\\\\\")})`,h=`${t} ${c} = compute_position_from_velocity(${[s,l,lw.DELTA_TIME].join(\\\\\\\", \\\\\\\")})`;e.addBodyLines(this,[u,h])}}var gw,vw='\\\\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(e){e.DIR=\\\\\\\"dir\\\\\\\",e.UP=\\\\\\\"up\\\\\\\"}(gw||(gw={}));const yw=[gw.DIR,gw.UP],xw={[gw.DIR]:[0,0,1],[gw.UP]:[0,1,0]};class bw extends fb{static type(){return\\\\\\\"align\\\\\\\"}initializeNode(){super.initializeNode(),this.io.connection_points.set_input_name_function((e=>yw[e])),this.io.connection_points.set_expected_input_types_function((()=>[ro.VEC3,ro.VEC3])),this.io.connection_points.set_expected_output_types_function((()=>[ro.VEC4]))}paramDefaultValue(e){return xw[e]}gl_method_name(){return\\\\\\\"align\\\\\\\"}gl_function_definitions(){return[new of(this,vw)]}}var ww;!function(e){e.LINEAR=\\\\\\\"Linear\\\\\\\",e.GAMMA=\\\\\\\"Gamma\\\\\\\",e.SRGB=\\\\\\\"sRGB\\\\\\\",e.RGBE=\\\\\\\"RGBE\\\\\\\",e.RGBM=\\\\\\\"RGBM\\\\\\\",e.RGBD=\\\\\\\"RGBD\\\\\\\",e.LogLuv=\\\\\\\"LogLuv\\\\\\\"}(ww||(ww={}));const Aw=[ww.LINEAR,ww.GAMMA,ww.SRGB,ww.RGBE,ww.RGBM,ww.RGBD,ww.LogLuv];const Tw=new class extends Lo{constructor(){super(...arguments),this.color=Oo.VECTOR4([1,1,1,1]),this.from=Oo.INTEGER(Aw.indexOf(ww.LINEAR),{menu:{entries:Aw.map(((e,t)=>({name:e,value:t})))}}),this.to=Oo.INTEGER(Aw.indexOf(ww.GAMMA),{menu:{entries:Aw.map(((e,t)=>({name:e,value:t})))}}),this.gammaFactor=Oo.FLOAT(2.2)}};class Ew extends Ym{constructor(){super(...arguments),this.paramsConfig=Tw}static type(){return\\\\\\\"colorCorrect\\\\\\\"}initializeNode(){this.io.connection_points.spare_params.set_inputless_param_names([\\\\\\\"to\\\\\\\",\\\\\\\"from\\\\\\\"]),this.io.outputs.setNamedOutputConnectionPoints([new ho(Ew.OUTPUT_NAME,ro.VEC4)])}setLines(e){const t=Aw[this.pv.from],n=Aw[this.pv.to],i=this.glVarName(Ew.OUTPUT_NAME),s=Wm.any(this.variableForInput(Ew.INPUT_NAME)),r=[];if(t!=n){const e=`${t}To${n}`,o=[];if(o.push(s),t==ww.GAMMA||n==ww.GAMMA){const e=Wm.any(this.variableForInputParam(this.p.gammaFactor));o.push(e)}r.push(`vec4 ${i} = ${e}(${o.join(\\\\\\\", \\\\\\\")})`)}else r.push(`vec4 ${i} = ${s}`);e.addBodyLines(this,r)}}var Cw,Mw;Ew.INPUT_NAME=\\\\\\\"color\\\\\\\",Ew.INPUT_GAMMA_FACTOR=\\\\\\\"gammaFactor\\\\\\\",Ew.OUTPUT_NAME=\\\\\\\"out\\\\\\\",function(e){e.EQUAL=\\\\\\\"Equal\\\\\\\",e.LESS_THAN=\\\\\\\"Less Than\\\\\\\",e.GREATER_THAN=\\\\\\\"Greater Than\\\\\\\",e.LESS_THAN_OR_EQUAL=\\\\\\\"Less Than Or Equal\\\\\\\",e.GREATER_THAN_OR_EQUAL=\\\\\\\"Greater Than Or Equal\\\\\\\",e.NOT_EQUAL=\\\\\\\"Not Equal\\\\\\\"}(Cw||(Cw={})),function(e){e.EQUAL=\\\\\\\"==\\\\\\\",e.LESS_THAN=\\\\\\\"<\\\\\\\",e.GREATER_THAN=\\\\\\\">\\\\\\\",e.LESS_THAN_OR_EQUAL=\\\\\\\"<=\\\\\\\",e.GREATER_THAN_OR_EQUAL=\\\\\\\">=\\\\\\\",e.NOT_EQUAL=\\\\\\\"!=\\\\\\\"}(Mw||(Mw={}));const Nw=[Cw.EQUAL,Cw.LESS_THAN,Cw.GREATER_THAN,Cw.LESS_THAN_OR_EQUAL,Cw.GREATER_THAN_OR_EQUAL,Cw.NOT_EQUAL],Sw=[Mw.EQUAL,Mw.LESS_THAN,Mw.GREATER_THAN,Mw.LESS_THAN_OR_EQUAL,Mw.GREATER_THAN_OR_EQUAL,Mw.NOT_EQUAL],Ow=[\\\\\\\"x\\\\\\\",\\\\\\\"y\\\\\\\",\\\\\\\"z\\\\\\\",\\\\\\\"w\\\\\\\"];const Lw=new class extends Lo{constructor(){super(...arguments),this.test=Oo.INTEGER(0,{menu:{entries:Nw.map(((e,t)=>({name:`${Sw[t].padEnd(2,\\\\\\\" \\\\\\\")} (${e})`,value:t})))}})}};class Pw extends Ym{constructor(){super(...arguments),this.paramsConfig=Lw}static type(){return\\\\\\\"compare\\\\\\\"}initializeNode(){super.initializeNode(),this.io.connection_points.spare_params.set_inputless_param_names([\\\\\\\"test\\\\\\\"]),this.io.connection_points.initializeNode(),this.io.connection_points.set_input_name_function(this._gl_input_name.bind(this)),this.io.connection_points.set_output_name_function((e=>\\\\\\\"val\\\\\\\")),this.io.connection_points.set_expected_input_types_function(this._expected_input_type.bind(this)),this.io.connection_points.set_expected_output_types_function((()=>[ro.BOOL]))}set_test_name(e){this.p.test.set(Nw.indexOf(e))}_gl_input_name(e){return[\\\\\\\"value0\\\\\\\",\\\\\\\"value1\\\\\\\"][e]}_expected_input_type(){const e=this.io.connection_points.first_input_connection_type()||ro.FLOAT;return[e,e]}setLines(e){const t=[],n=this.glVarName(\\\\\\\"val\\\\\\\"),i=Sw[this.pv.test],s=Wm.any(this.variableForInput(this._gl_input_name(0))),r=Wm.any(this.variableForInput(this._gl_input_name(1))),o=this.io.inputs.namedInputConnectionPoints()[0];let a=1;if(o&&(a=uo[o.type()]||1),a>1){let e=[];for(let n=0;n<a;n++){const o=this.glVarName(`tmp_value_${n}`),a=Ow[n];e.push(o),t.push(`bool ${o} = (${s}.${a} ${i} ${r}.${a})`)}t.push(`bool ${n} = (${e.join(\\\\\\\" && \\\\\\\")})`)}else t.push(`bool ${n} = (${s} ${i} ${r})`);e.addBodyLines(this,t)}}class Rw extends mb{static type(){return\\\\\\\"complement\\\\\\\"}gl_method_name(){return\\\\\\\"complement\\\\\\\"}gl_function_definitions(){return[new of(this,\\\\\\\"float complement(float x){return 1.0-x;}\\\\nvec2 complement(vec2 x){return vec2(1.0-x.x, 1.0-x.y);}\\\\nvec3 complement(vec3 x){return vec3(1.0-x.x, 1.0-x.y, 1.0-x.z);}\\\\nvec4 complement(vec4 x){return vec4(1.0-x.x, 1.0-x.y, 1.0-x.z, 1.0-x.w);}\\\\n\\\\\\\")]}}function Iw(e){return{visibleIf:{type:oo.indexOf(e)}}}const Fw=new class extends Lo{constructor(){super(...arguments),this.type=Oo.INTEGER(oo.indexOf(ro.FLOAT),{menu:{entries:oo.map(((e,t)=>({name:e,value:t})))}}),this.bool=Oo.BOOLEAN(0,Iw(ro.BOOL)),this.int=Oo.INTEGER(0,Iw(ro.INT)),this.float=Oo.FLOAT(0,Iw(ro.FLOAT)),this.vec2=Oo.VECTOR2([0,0],Iw(ro.VEC2)),this.vec3=Oo.VECTOR3([0,0,0],Iw(ro.VEC3)),this.vec4=Oo.VECTOR4([0,0,0,0],Iw(ro.VEC4))}};class Dw extends Ym{constructor(){super(...arguments),this.paramsConfig=Fw,this._allow_inputs_created_from_params=!1}static type(){return\\\\\\\"constant\\\\\\\"}initializeNode(){this.io.connection_points.set_output_name_function((e=>Dw.OUTPUT_NAME)),this.io.connection_points.set_expected_input_types_function((()=>[])),this.io.connection_points.set_expected_output_types_function((()=>[this._current_connection_type]))}setLines(e){const t=this._current_param;if(t){const n=this._current_connection_type;let i=Wm.any(t.value);t.name()==this.p.int.name()&&m.isNumber(t.value)&&(i=Wm.integer(t.value));const s=`${n} ${this._current_var_name} = ${i}`;e.addBodyLines(this,[s])}else console.warn(`no param found for constant node for type '${this.pv.type}'`)}get _current_connection_type(){null==this.pv.type&&console.warn(\\\\\\\"constant gl node type if not valid\\\\\\\");const e=oo[this.pv.type];return null==e&&console.warn(\\\\\\\"constant gl node type if not valid\\\\\\\"),e}get _current_param(){this._params_by_type=this._params_by_type||new Map([[ro.BOOL,this.p.bool],[ro.INT,this.p.int],[ro.FLOAT,this.p.float],[ro.VEC2,this.p.vec2],[ro.VEC3,this.p.vec3],[ro.VEC4,this.p.vec4]]);const e=oo[this.pv.type];return this._params_by_type.get(e)}get _current_var_name(){return this.glVarName(Dw.OUTPUT_NAME)}set_gl_type(e){this.p.type.set(oo.indexOf(e))}}Dw.OUTPUT_NAME=\\\\\\\"val\\\\\\\";const kw=\\\\\\\"cross\\\\\\\";const Bw=new class extends Lo{constructor(){super(...arguments),this.x=Oo.VECTOR3([0,0,1]),this.y=Oo.VECTOR3([0,1,0])}};class zw extends Ym{constructor(){super(...arguments),this.paramsConfig=Bw}static type(){return\\\\\\\"cross\\\\\\\"}initializeNode(){super.initializeNode(),this.io.outputs.setNamedOutputConnectionPoints([new ho(kw,ro.VEC3)])}setLines(e){const t=Wm.float(this.variableForInputParam(this.p.x)),n=Wm.float(this.variableForInputParam(this.p.y)),i=`vec3 ${this.glVarName(kw)} = cross(${t}, ${n})`;e.addBodyLines(this,[i])}}class Uw extends(Qb(\\\\\\\"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 Gw=\\\\\\\"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 Vw=new class extends Lo{constructor(){super(...arguments),this.position=Oo.VECTOR2([0,0]),this.center=Oo.VECTOR2([0,0]),this.radius=Oo.FLOAT(1),this.feather=Oo.FLOAT(.1)}};class jw extends Ym{constructor(){super(...arguments),this.paramsConfig=Vw}static type(){return\\\\\\\"disk\\\\\\\"}initializeNode(){super.initializeNode(),this.io.outputs.setNamedOutputConnectionPoints([new ho(\\\\\\\"float\\\\\\\",ro.FLOAT)])}setLines(e){const t=Wm.vector2(this.variableForInputParam(this.p.position)),n=Wm.vector2(this.variableForInputParam(this.p.center)),i=Wm.float(this.variableForInputParam(this.p.radius)),s=Wm.float(this.variableForInputParam(this.p.feather)),r=`float ${this.glVarName(\\\\\\\"float\\\\\\\")} = disk2d(${t}, ${n}, ${i}, ${s})`;e.addBodyLines(this,[r]),e.addDefinitions(this,[new of(this,Gw)])}}var Hw=\\\\\\\"\\\\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 qw=[\\\\\\\"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\\\\\\\"],Ww={\\\\\\\"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\\\\\\\":Hw,\\\\\\\"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\\\\\\\"},Xw={\\\\\\\"bounce-in\\\\\\\":[Hw],\\\\\\\"bounce-in-out\\\\\\\":[Hw]},Yw={\\\\\\\"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\\\\\\\"},$w=qw.indexOf(\\\\\\\"sine-in-out\\\\\\\");const Qw=new class extends Lo{constructor(){super(...arguments),this.type=Oo.INTEGER($w,{menu:{entries:qw.map(((e,t)=>({name:e,value:t})))}}),this.input=Oo.FLOAT(0)}};class Jw extends Ym{constructor(){super(...arguments),this.paramsConfig=Qw}static type(){return\\\\\\\"easing\\\\\\\"}initializeNode(){super.initializeNode(),this.io.connection_points.spare_params.set_inputless_param_names([\\\\\\\"type\\\\\\\"]),this.io.outputs.setNamedOutputConnectionPoints([new ho(\\\\\\\"out\\\\\\\",ro.FLOAT)])}setLines(e){const t=qw[this.pv.type],n=Yw[t];let i=[new of(this,Ww[t])];const s=(Xw[t]||[]).map((e=>new of(this,e)));s&&(i=s.concat(i));const r=Wm.float(this.variableForInputParam(this.p.input)),o=`float ${this.glVarName(\\\\\\\"out\\\\\\\")} = ${n}(${r})`;e.addDefinitions(this,i),e.addBodyLines(this,[o])}}var Kw=\\\\\\\"//\\\\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 Zw={srcMin:0,srcMax:1,destMin:0,destMax:1};class eA extends yb{static type(){return\\\\\\\"fit\\\\\\\"}_gl_input_name(e){return[\\\\\\\"val\\\\\\\",\\\\\\\"srcMin\\\\\\\",\\\\\\\"srcMax\\\\\\\",\\\\\\\"destMin\\\\\\\",\\\\\\\"destMax\\\\\\\"][e]}paramDefaultValue(e){return Zw[e]}gl_method_name(){return\\\\\\\"fit\\\\\\\"}gl_function_definitions(){return[new of(this,Kw)]}}const tA={srcMin:0,srcMax:1};class nA extends gb{static type(){return\\\\\\\"fitTo01\\\\\\\"}_gl_input_name(e){return[\\\\\\\"val\\\\\\\",\\\\\\\"srcMin\\\\\\\",\\\\\\\"srcMax\\\\\\\"][e]}paramDefaultValue(e){return tA[e]}gl_method_name(){return\\\\\\\"fitTo01\\\\\\\"}gl_function_definitions(){return[new of(this,Kw)]}}const iA={destMin:0,destMax:1};class sA extends gb{static type(){return\\\\\\\"fitFrom01\\\\\\\"}_gl_input_name(e){return[\\\\\\\"val\\\\\\\",\\\\\\\"destMin\\\\\\\",\\\\\\\"destMax\\\\\\\"][e]}paramDefaultValue(e){return iA[e]}gl_method_name(){return\\\\\\\"fitFrom01\\\\\\\"}gl_function_definitions(){return[new of(this,Kw)]}}const rA={center:.5,variance:.5};class oA extends gb{static type(){return\\\\\\\"fitFrom01ToVariance\\\\\\\"}_gl_input_name(e){return[\\\\\\\"val\\\\\\\",\\\\\\\"center\\\\\\\",\\\\\\\"variance\\\\\\\"][e]}paramDefaultValue(e){return rA[e]}gl_method_name(){return\\\\\\\"fitFrom01ToVariance\\\\\\\"}gl_function_definitions(){return[new of(this,Kw)]}}const aA=\\\\\\\"color\\\\\\\";const cA=new class extends Lo{constructor(){super(...arguments),this.mvPosition=Oo.VECTOR4([0,0,0,0]),this.baseColor=Oo.COLOR([0,0,0]),this.fogColor=Oo.COLOR([1,1,1]),this.near=Oo.FLOAT(0),this.far=Oo.FLOAT(0)}};class lA extends Ym{constructor(){super(...arguments),this.paramsConfig=cA}static type(){return\\\\\\\"fog\\\\\\\"}initializeNode(){super.initializeNode(),this.io.outputs.setNamedOutputConnectionPoints([new ho(aA,ro.VEC3)])}setLines(e){if(e.current_shader_name==nf.FRAGMENT){const t=this.glVarName(this.name()),n=new cf(this,ro.VEC4,t),i=`${t} = modelViewMatrix * vec4(position, 1.0)`;e.addDefinitions(this,[n],nf.VERTEX),e.addBodyLines(this,[i],nf.VERTEX);const s=new of(this,\\\\\\\"vec3 compute_fog(vec4 mvPosition, vec3 base_color, vec3 fog_color, float near, float far) {\\\\n\\\\tfloat blend = (-mvPosition.z - near) / (far - near);\\\\n\\\\tblend = clamp(blend, 0.0, 1.0);\\\\n\\\\treturn blend * fog_color + (1.0 - blend) * base_color;\\\\n}\\\\\\\"),r=Wm.vector4(this.variableForInputParam(this.p.mvPosition)),o=Wm.vector3(this.variableForInputParam(this.p.baseColor)),a=Wm.vector3(this.variableForInputParam(this.p.fogColor)),c=Wm.vector3(this.variableForInputParam(this.p.near)),l=Wm.vector3(this.variableForInputParam(this.p.far)),u=`vec3 ${this.glVarName(aA)} = compute_fog(${[r,o,a,c,l].join(\\\\\\\", \\\\\\\")})`;e.addDefinitions(this,[n,s]),e.addBodyLines(this,[u])}}}const uA=new class extends Lo{};class hA extends Ym{constructor(){super(...arguments),this.paramsConfig=uA}static type(){return Mi.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 e;null===(e=this.parent())||void 0===e||e.setDirty(this)}))}parent(){return super.parent()}_expected_input_name(e){const t=this.parent();return(null==t?void 0:t.child_expected_output_connection_point_name(e))||`in${e}`}_expected_input_types(){const e=this.parent();return(null==e?void 0:e.child_expected_output_connection_point_types())||[]}setLines(e){const t=this.parent();if(!t)return;const n=[],i=this.io.connections.inputConnections();if(i)for(let e of i)if(e){const i=e.dest_connection_point(),s=Wm.any(this.variableForInput(i.name())),r=`\\\\t${t.glVarName(i.name())} = ${s}`;n.push(r)}e.addBodyLines(this,n),t.set_lines_block_end(e,this)}}class dA extends Ym{constructor(){super(...arguments),this._children_controller_context=Ei.GL}initializeNode(){var e;null===(e=this.childrenController)||void 0===e||e.set_output_node_find_method((()=>this.nodesByType(hA.type())[0])),this.io.connection_points.set_input_name_function(this._expected_input_name.bind(this)),this.io.connection_points.set_output_name_function(this._expected_output_name.bind(this)),this.io.connection_points.set_expected_input_types_function(this._expected_input_types.bind(this)),this.io.connection_points.set_expected_output_types_function(this._expected_output_types.bind(this))}_expected_inputs_count(){const e=this.io.connections.inputConnections();return e?e.length+1:1}_expected_input_types(){const e=[],t=ro.FLOAT,n=this.io.connections.inputConnections(),i=this._expected_inputs_count();for(let s=0;s<i;s++)if(n){const i=n[s];if(i){const t=i.src_connection_point().type();e.push(t)}else e.push(t)}else e.push(t);return e}_expected_output_types(){const e=[],t=this._expected_input_types();for(let n=0;n<t.length;n++)e.push(t[n]);return e}_expected_input_name(e){const t=this.io.connections.inputConnection(e);if(t){return t.src_connection_point().name()}return`in${e}`}_expected_output_name(e){return this._expected_input_name(e)}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(e){return this._expected_input_name(e)}child_expected_output_connection_point_name(e){return this._expected_output_name(e)}createNode(e,t){return super.createNode(e,t)}children(){return super.children()}nodesByType(e){return super.nodesByType(e)}set_lines_block_start(e,t){const n=[],i=this.io.inputs.namedInputConnectionPoints();for(let e=0;e<i.length;e++){const t=i[e],s=`${t.type()} ${this.glVarName(t.name())} = ${Wm.any(this.variableForInput(t.name()))}`;n.push(s)}n.push(\\\\\\\"if(true){\\\\\\\");const s=this.io.connections.inputConnections();if(s)for(let e of s)if(e){const i=e.dest_connection_point(),s=Wm.any(this.variableForInput(i.name())),r=`\\\\t${i.type()} ${t.glVarName(i.name())} = ${s}`;n.push(r)}e.addBodyLines(t,n)}set_lines_block_end(e,t){e.addBodyLines(t,[\\\\\\\"}\\\\\\\"])}setLines(e){}}const pA=new class extends Lo{};class _A extends dA{constructor(){super(...arguments),this.paramsConfig=pA}static type(){return\\\\\\\"subnet\\\\\\\"}}var mA;!function(e){e.START_INDEX=\\\\\\\"i\\\\\\\",e.MAX=\\\\\\\"max\\\\\\\",e.STEP=\\\\\\\"step\\\\\\\"}(mA||(mA={}));const fA={[mA.START_INDEX]:0,[mA.MAX]:10,[mA.STEP]:1};const gA=new class extends Lo{constructor(){super(...arguments),this.start=Oo.FLOAT(0),this.max=Oo.FLOAT(10,{range:[0,100],rangeLocked:[!1,!1]}),this.step=Oo.FLOAT(1)}};class vA extends dA{constructor(){super(...arguments),this.paramsConfig=gA}static type(){return\\\\\\\"forLoop\\\\\\\"}paramDefaultValue(e){return fA[e]}_expected_inputs_count(){const e=this.io.connections.inputConnections();return e?e.length+1:1}_expected_input_types(){const e=[],t=ro.FLOAT,n=this.io.connections.inputConnections(),i=this._expected_inputs_count();for(let s=0;s<i;s++)if(n){const i=n[s];if(i){const t=i.src_connection_point().type();e.push(t)}else e.push(t)}else e.push(t);return e}_expected_output_types(){const e=[],t=this._expected_input_types();for(let n=0;n<t.length;n++)e.push(t[n]);return e}_expected_input_name(e){const t=this.io.connections.inputConnection(e);if(t){return t.src_connection_point().name()}return`in${e}`}_expected_output_name(e){return this._expected_input_name(e+0)}child_expected_input_connection_point_types(){return this._expected_input_types()}child_expected_input_connection_point_name(e){return this._expected_input_name(e)}child_expected_output_connection_point_types(){return this._expected_output_types()}child_expected_output_connection_point_name(e){return this._expected_output_name(e)}set_lines_block_start(e,t){const n=[],i=this.io.inputs.namedInputConnectionPoints();for(let e=0;e<i.length;e++){const t=i[e],s=`${t.type()} ${this.glVarName(t.name())} = ${Wm.any(this.variableForInput(t.name()))}`;n.push(s)}const s=this.io.connections.inputConnections();if(s)for(let e of s)if(e&&e.input_index>=0){const t=e.dest_connection_point(),i=Wm.any(this.variableForInput(t.name())),s=`${t.type()} ${this.glVarName(t.name())} = ${i}`;n.push(s)}const r=this.pv.start,o=this.pv.max,a=this.pv.step,c=Wm.float(r),l=Wm.float(o),u=Wm.float(a),h=this.glVarName(\\\\\\\"i\\\\\\\"),d=`for(float ${h} = ${c}; ${h} < ${l}; ${h}+= ${u}){`;n.push(d);const p=`\\\\tfloat ${t.glVarName(mA.START_INDEX)} = ${h}`;if(n.push(p),s)for(let e of s)if(e&&e.input_index>=0){const i=e.dest_connection_point(),s=this.glVarName(i.name()),r=`\\\\t${i.type()} ${t.glVarName(i.name())} = ${s}`;n.push(r)}e.addBodyLines(t,n)}setLines(e){}}const yA=new class extends Lo{};class xA extends Ym{constructor(){super(...arguments),this.paramsConfig=yA}static type(){return\\\\\\\"globals\\\\\\\"}initializeNode(){super.initializeNode(),this.lifecycle.add_on_add_hook((()=>{var e,t;null===(t=null===(e=this.material_node)||void 0===e?void 0:e.assemblerController)||void 0===t||t.add_globals_outputs(this)}))}setLines(e){e.assembler().set_node_lines_globals(this,e)}}const bA=new class extends Lo{constructor(){super(...arguments),this.hsluv=Oo.VECTOR3([1,1,1])}};class wA extends Ym{constructor(){super(...arguments),this.paramsConfig=bA}static type(){return\\\\\\\"hsluvToRgb\\\\\\\"}initializeNode(){super.initializeNode(),this.io.outputs.setNamedOutputConnectionPoints([new ho(\\\\\\\"rgb\\\\\\\",ro.VEC3)])}setLines(e){const t=[],n=[];t.push(new of(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=Wm.vector3(this.variableForInputParam(this.p.hsluv)),s=this.glVarName(\\\\\\\"rgb\\\\\\\");n.push(`vec3 ${s} = hsluvToRgb(${i}.x * 360.0, ${i}.y * 100.0, ${i}.z * 100.0)`),e.addDefinitions(this,t),e.addBodyLines(this,n)}}const AA=new class extends Lo{constructor(){super(...arguments),this.hsv=Oo.VECTOR3([1,1,1])}};class TA extends Ym{constructor(){super(...arguments),this.paramsConfig=AA}static type(){return\\\\\\\"hsvToRgb\\\\\\\"}initializeNode(){super.initializeNode(),this.io.outputs.setNamedOutputConnectionPoints([new ho(\\\\\\\"rgb\\\\\\\",ro.VEC3)])}setLines(e){const t=[],n=[];t.push(new of(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=Wm.vector3(this.variableForInputParam(this.p.hsv)),s=this.glVarName(\\\\\\\"rgb\\\\\\\");n.push(`vec3 ${s} = hsv2rgb(${i})`),e.addDefinitions(this,t),e.addBodyLines(this,n)}}const EA=\\\\\\\"condition\\\\\\\";const CA=new class extends Lo{};class MA extends _A{constructor(){super(...arguments),this.paramsConfig=CA}static type(){return\\\\\\\"ifThen\\\\\\\"}_expected_inputs_count(){const e=this.io.connections.inputConnections();return e?Math.max(e.length+1,2):2}_expected_input_types(){const e=[ro.BOOL],t=ro.FLOAT,n=this.io.connections.inputConnections(),i=this._expected_inputs_count();for(let s=1;s<i;s++)if(n){const i=n[s];if(i){const t=i.src_connection_point().type();e.push(t)}else e.push(t)}else e.push(t);return e}_expected_output_types(){const e=[],t=this._expected_input_types();for(let n=1;n<t.length;n++)e.push(t[n]);return e}_expected_input_name(e){if(0==e)return EA;{const t=this.io.connections.inputConnection(e);if(t){return t.src_connection_point().name()}return`in${e}`}}_expected_output_name(e){return this._expected_input_name(e+1)}child_expected_input_connection_point_types(){return this._expected_output_types()}child_expected_input_connection_point_name(e){return this._expected_output_name(e)}child_expected_output_connection_point_types(){return this._expected_output_types()}child_expected_output_connection_point_name(e){return this._expected_output_name(e)}set_lines_block_start(e,t){const n=[],i=this.io.inputs.namedInputConnectionPoints();for(let e=1;e<i.length;e++){const t=i[e],s=`${t.type()} ${this.glVarName(t.name())} = ${Wm.any(this.variableForInput(t.name()))}`;n.push(s)}const s=`if(${Wm.any(this.variableForInput(EA))}){`;n.push(s);const r=this.io.connections.inputConnections();if(r)for(let e of r)if(e&&0!=e.input_index){const i=e.dest_connection_point(),s=Wm.any(this.variableForInput(i.name())),r=`\\\\t${i.type()} ${t.glVarName(i.name())} = ${s}`;n.push(r)}e.addBodyLines(t,n)}setLines(e){}}const NA=new class extends Lo{constructor(){super(...arguments),this.center=Oo.VECTOR3([0,0,0]),this.cameraPos=Oo.VECTOR3([0,0,0]),this.uv=Oo.VECTOR2([0,0]),this.tilesCount=Oo.INTEGER(8,{range:[0,32],rangeLocked:[!0,!1]}),this.offset=Oo.FLOAT(0)}};class SA extends Ym{constructor(){super(...arguments),this.paramsConfig=NA}static type(){return\\\\\\\"impostorUv\\\\\\\"}initializeNode(){super.initializeNode(),this.io.outputs.setNamedOutputConnectionPoints([new ho(\\\\\\\"uv\\\\\\\",ro.VEC2)])}setLines(e){const t=[];e.addDefinitions(this,[new of(this,vw),new of(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=Wm.vector3(this.variableForInputParam(this.p.center)),i=Wm.vector3(this.variableForInputParam(this.p.cameraPos)),s=Wm.vector2(this.variableForInputParam(this.p.uv)),r=Wm.float(this.variableForInputParam(this.p.tilesCount)),o=Wm.float(this.variableForInputParam(this.p.offset)),a=this.glVarName(\\\\\\\"uv\\\\\\\"),c=[n,i,s,r,o].join(\\\\\\\", \\\\\\\");t.push(`vec2 ${a} = impostor_uv(${c})`),e.addBodyLines(this,t)}}const OA=\\\\\\\"position\\\\\\\",LA=\\\\\\\"normal\\\\\\\",PA=\\\\\\\"instancePosition\\\\\\\",RA=\\\\\\\"instanceOrientation\\\\\\\",IA=\\\\\\\"instanceScale\\\\\\\";const FA=new class extends Lo{constructor(){super(...arguments),this.position=Oo.VECTOR3([0,0,0]),this.normal=Oo.VECTOR3([0,0,1]),this.instancePosition=Oo.VECTOR3([0,0,0]),this.instanceOrientation=Oo.VECTOR4([0,0,0,0]),this.instanceScale=Oo.VECTOR3([1,1,1])}};class DA extends Ym{constructor(){super(...arguments),this.paramsConfig=FA}static type(){return\\\\\\\"instanceTransform\\\\\\\"}initializeNode(){super.initializeNode(),this.io.outputs.setNamedOutputConnectionPoints([new ho(this.gl_output_name_position(),ro.VEC3),new ho(this.gl_output_name_normal(),ro.VEC3)])}setLines(e){const t=[],n=[];n.push(new of(this,vw));const i=this.io.inputs.named_input(this.p.position.name())?Wm.float(this.variableForInputParam(this.p.position)):this._default_position(),s=this.io.inputs.named_input(this.p.normal.name())?Wm.float(this.variableForInputParam(this.p.normal)):this._default_normal(),r=this.io.inputs.named_input(this.p.instancePosition.name())?Wm.float(this.variableForInputParam(this.p.instancePosition)):this._default_instancePosition(e),o=this.io.inputs.named_input(this.p.instanceOrientation.name())?Wm.float(this.variableForInputParam(this.p.instanceOrientation)):this._default_input_instanceOrientation(e),a=this.io.inputs.named_input(this.p.instanceScale.name())?Wm.float(this.variableForInputParam(this.p.instanceScale)):this._default_input_instanceScale(e),c=this.glVarName(this.gl_output_name_position()),l=this.glVarName(this.gl_output_name_normal());t.push(`vec3 ${c} = vec3(${i})`),t.push(`${c} *= ${a}`),t.push(`${c} = rotateWithQuat( ${c}, ${o} )`),t.push(`${c} += ${r}`),t.push(`vec3 ${l} = vec3(${s})`),t.push(`${l} = rotateWithQuat( ${l}, ${o} )`),e.addBodyLines(this,t),e.addDefinitions(this,n)}gl_output_name_position(){return\\\\\\\"position\\\\\\\"}gl_output_name_normal(){return\\\\\\\"normal\\\\\\\"}_default_position(){return OA}_default_normal(){return LA}_default_instancePosition(e){var t;return null===(t=e.assembler().globals_handler)||void 0===t?void 0:t.read_attribute(this,ro.VEC3,PA,e)}_default_input_instanceOrientation(e){var t;return null===(t=e.assembler().globals_handler)||void 0===t?void 0:t.read_attribute(this,ro.VEC4,RA,e)}_default_input_instanceScale(e){var t;return null===(t=e.assembler().globals_handler)||void 0===t?void 0:t.read_attribute(this,ro.VEC3,IA,e)}}class kA extends mb{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(e){return[\\\\\\\"x\\\\\\\"][e]}gl_method_name(){return\\\\\\\"length\\\\\\\"}_expected_output_types(){return[ro.FLOAT]}}const BA=new class extends Lo{constructor(){super(...arguments),this.color=Oo.VECTOR3([1,1,1])}};class zA extends Ym{constructor(){super(...arguments),this.paramsConfig=BA}static type(){return\\\\\\\"luminance\\\\\\\"}initializeNode(){super.initializeNode(),this.io.outputs.setNamedOutputConnectionPoints([new ho(\\\\\\\"lum\\\\\\\",ro.FLOAT)])}setLines(e){const t=Wm.vector3(this.variableForInputParam(this.p.color)),n=`float ${this.glVarName(\\\\\\\"lum\\\\\\\")} = linearToRelativeLuminance(${t})`;e.addBodyLines(this,[n])}}const UA={max:1};class GA extends fb{static type(){return\\\\\\\"maxLength\\\\\\\"}_expected_input_types(){return[this.io.connection_points.first_input_connection_type()||ro.VEC3,ro.FLOAT]}_gl_input_name(e){return[\\\\\\\"val\\\\\\\",\\\\\\\"max\\\\\\\"][e]}paramDefaultValue(e){return UA[e]}gl_method_name(){return\\\\\\\"maxLength\\\\\\\"}gl_function_definitions(){return[new of(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 VA={blend:.5};class jA extends _b{static type(){return\\\\\\\"mix\\\\\\\"}gl_method_name(){return\\\\\\\"mix\\\\\\\"}paramDefaultValue(e){return VA[e]}initializeNode(){super.initializeNode(),this.io.connection_points.set_input_name_function((e=>[\\\\\\\"value0\\\\\\\",\\\\\\\"value1\\\\\\\",\\\\\\\"blend\\\\\\\"][e])),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 e=this.io.connection_points.first_input_connection_type()||ro.FLOAT;return[e,e,ro.FLOAT]}_expected_output_types(){return[this._expected_input_types()[0]]}}const HA=\\\\\\\"mvMult\\\\\\\";const qA=new class extends Lo{constructor(){super(...arguments),this.vector=Oo.VECTOR3([0,0,0])}};class WA extends Ym{constructor(){super(...arguments),this.paramsConfig=qA}static type(){return\\\\\\\"modelViewMatrixMult\\\\\\\"}initializeNode(){super.initializeNode(),this.io.outputs.setNamedOutputConnectionPoints([new ho(HA,ro.VEC4)])}setLines(e){if(e.current_shader_name==nf.VERTEX){const t=Wm.vector3(this.variableForInputParam(this.p.vector)),n=`vec4 ${this.glVarName(HA)} = modelViewMatrix * vec4(${t}, 1.0)`;e.addBodyLines(this,[n],nf.VERTEX)}}}const XA={mult:1};var YA;!function(e){e.VALUE=\\\\\\\"value\\\\\\\",e.PRE_ADD=\\\\\\\"preAdd\\\\\\\",e.MULT=\\\\\\\"mult\\\\\\\",e.POST_ADD=\\\\\\\"postAdd\\\\\\\"}(YA||(YA={}));class $A extends vb{static type(){return\\\\\\\"multAdd\\\\\\\"}_gl_input_name(e){return[YA.VALUE,YA.PRE_ADD,YA.MULT,YA.POST_ADD][e]}paramDefaultValue(e){return XA[e]}setLines(e){const t=Wm.any(this.variableForInput(YA.VALUE)),n=Wm.any(this.variableForInput(YA.PRE_ADD)),i=Wm.any(this.variableForInput(YA.MULT)),s=Wm.any(this.variableForInput(YA.POST_ADD)),r=this._expected_output_types()[0],o=this.io.outputs.namedOutputConnectionPoints()[0].name(),a=`${r} ${this.glVarName(o)} = (${i}*(${t} + ${n})) + ${s}`;e.addBodyLines(this,[a])}}class QA extends mb{static type(){return\\\\\\\"negate\\\\\\\"}initializeNode(){super.initializeNode(),this.io.connection_points.set_input_name_function((e=>[\\\\\\\"in\\\\\\\"][e]))}_gl_input_name(e){return[\\\\\\\"in\\\\\\\"][e]}setLines(e){const t=Wm.any(this.variableForInput(this._gl_input_name(0))),n=`${this.io.inputs.namedInputConnectionPoints()[0].type()} ${this.glVarName(this.io.connection_points.output_name(0))} = -1.0 * ${t}`;e.addBodyLines(this,[n])}}var JA;!function(e){e.CLASSIC_PERLIN_2D=\\\\\\\"Classic Perlin 2D\\\\\\\",e.CLASSIC_PERLIN_3D=\\\\\\\"Classic Perlin 3D\\\\\\\",e.CLASSIC_PERLIN_4D=\\\\\\\"Classic Perlin 4D\\\\\\\",e.NOISE_2D=\\\\\\\"noise2D\\\\\\\",e.NOISE_3D=\\\\\\\"noise3D\\\\\\\",e.NOISE_4D=\\\\\\\"noise4D\\\\\\\"}(JA||(JA={}));const KA=[JA.CLASSIC_PERLIN_2D,JA.CLASSIC_PERLIN_3D,JA.CLASSIC_PERLIN_4D,JA.NOISE_2D,JA.NOISE_3D,JA.NOISE_4D],ZA={[JA.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',[JA.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',[JA.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',[JA.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\\\\\\\",[JA.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\\\\\\\",[JA.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\\\\\\\"},eT={[JA.CLASSIC_PERLIN_2D]:ro.VEC2,[JA.CLASSIC_PERLIN_3D]:ro.VEC3,[JA.CLASSIC_PERLIN_4D]:ro.VEC4,[JA.NOISE_2D]:ro.VEC2,[JA.NOISE_3D]:ro.VEC3,[JA.NOISE_4D]:ro.VEC4},tT={[JA.CLASSIC_PERLIN_2D]:ro.FLOAT,[JA.CLASSIC_PERLIN_3D]:ro.FLOAT,[JA.CLASSIC_PERLIN_4D]:ro.FLOAT,[JA.NOISE_2D]:ro.FLOAT,[JA.NOISE_3D]:ro.FLOAT,[JA.NOISE_4D]:ro.FLOAT},nT={[JA.CLASSIC_PERLIN_2D]:\\\\\\\"cnoise\\\\\\\",[JA.CLASSIC_PERLIN_3D]:\\\\\\\"cnoise\\\\\\\",[JA.CLASSIC_PERLIN_4D]:\\\\\\\"cnoise\\\\\\\",[JA.NOISE_2D]:\\\\\\\"snoise\\\\\\\",[JA.NOISE_3D]:\\\\\\\"snoise\\\\\\\",[JA.NOISE_4D]:\\\\\\\"snoise\\\\\\\"};var iT;!function(e){e[e.NoChange=0]=\\\\\\\"NoChange\\\\\\\",e[e.Float=1]=\\\\\\\"Float\\\\\\\",e[e.Vec2=2]=\\\\\\\"Vec2\\\\\\\",e[e.Vec3=3]=\\\\\\\"Vec3\\\\\\\",e[e.Vec4=4]=\\\\\\\"Vec4\\\\\\\"}(iT||(iT={}));const sT=[iT.NoChange,iT.Float,iT.Vec2,iT.Vec3,iT.Vec4],rT={[iT.NoChange]:\\\\\\\"Same as noise\\\\\\\",[iT.Float]:\\\\\\\"Float\\\\\\\",[iT.Vec2]:\\\\\\\"Vec2\\\\\\\",[iT.Vec3]:\\\\\\\"Vec3\\\\\\\",[iT.Vec4]:\\\\\\\"Vec4\\\\\\\"},oT={[iT.NoChange]:ro.FLOAT,[iT.Float]:ro.FLOAT,[iT.Vec2]:ro.VEC2,[iT.Vec3]:ro.VEC3,[iT.Vec4]:ro.VEC4},aT=[\\\\\\\"x\\\\\\\",\\\\\\\"y\\\\\\\",\\\\\\\"z\\\\\\\",\\\\\\\"w\\\\\\\"],cT=\\\\\\\"noise\\\\\\\",lT=KA.indexOf(JA.NOISE_3D),uT=iT.NoChange,hT={amp:1,freq:1};var dT;!function(e){e.AMP=\\\\\\\"amp\\\\\\\",e.POSITION=\\\\\\\"position\\\\\\\",e.FREQ=\\\\\\\"freq\\\\\\\",e.OFFSET=\\\\\\\"offset\\\\\\\"}(dT||(dT={}));const pT=new class extends Lo{constructor(){super(...arguments),this.type=Oo.INTEGER(lT,{menu:{entries:KA.map(((e,t)=>({name:`${e} (output: ${tT[e]})`,value:t})))}}),this.outputType=Oo.INTEGER(uT,{menu:{entries:sT.map((e=>{const t=sT[e];return{name:rT[t],value:t}}))}}),this.octaves=Oo.INTEGER(3,{range:[1,10],rangeLocked:[!0,!1]}),this.ampAttenuation=Oo.FLOAT(.5,{range:[0,1]}),this.freqIncrease=Oo.FLOAT(2,{range:[0,10],separatorAfter:!0})}};class _T extends Ym{constructor(){super(...arguments),this.paramsConfig=pT}static type(){return\\\\\\\"noise\\\\\\\"}initializeNode(){super.initializeNode(),this.io.connection_points.initializeNode(),this.io.connection_points.spare_params.set_inputless_param_names([\\\\\\\"octaves\\\\\\\",\\\\\\\"ampAttenuation\\\\\\\",\\\\\\\"freqIncrease\\\\\\\"]),this.io.outputs.setNamedOutputConnectionPoints([new ho(cT,ro.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((()=>cT))}_gl_input_name(e){return[dT.AMP,dT.POSITION,dT.FREQ,dT.OFFSET][e]}paramDefaultValue(e){return hT[e]}_expected_input_types(){const e=KA[this.pv.type],t=this._expected_output_types()[0],n=eT[e];return[t,n,n,n]}_expected_output_types(){const e=KA[this.pv.type],t=sT[this.pv.outputType];return t==iT.NoChange?[eT[e]]:[oT[t]]}setLines(e){const t=[],n=[],i=KA[this.pv.type],s=ZA[i],r=tT[i];t.push(new of(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}\\\\\\\")),t.push(new of(this,s)),t.push(new of(this,this.fbm_function()));const o=this._expected_output_types()[0];if(o==r){const e=this.single_noise_line();n.push(e)}else{const e=uo[o],t=[],s=this.glVarName(\\\\\\\"noise\\\\\\\");for(let r=0;r<e;r++){const e=aT[r];t.push(`${s}${e}`);const o=eT[i],a=uo[o],c=`${o}(${f.range(a).map((e=>Wm.float(1e3*r))).join(\\\\\\\", \\\\\\\")})`,l=this.single_noise_line(e,e,c);n.push(l)}const r=`vec${e} ${s} = vec${e}(${t.join(\\\\\\\", \\\\\\\")})`;n.push(r)}e.addDefinitions(this,t),e.addBodyLines(this,n)}fbm_method_name(){const e=KA[this.pv.type];return`fbm_${nT[e]}_${this.name()}`}fbm_function(){const e=KA[this.pv.type],t=nT[e],n=eT[e];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 < ${Wm.integer(this.pv.octaves)}; i++) {\\\\n\\\\t\\\\tvalue += amplitude * ${t}(st);\\\\n\\\\t\\\\tst *= ${Wm.float(this.pv.freqIncrease)};\\\\n\\\\t\\\\tamplitude *= ${Wm.float(this.pv.ampAttenuation)};\\\\n\\\\t}\\\\n\\\\treturn value;\\\\n}\\\\n`}single_noise_line(e,t,n){const i=this.fbm_method_name(),s=Wm.any(this.variableForInput(dT.AMP)),r=Wm.any(this.variableForInput(dT.POSITION)),o=Wm.any(this.variableForInput(dT.FREQ));let a=Wm.any(this.variableForInput(dT.OFFSET));n&&(a=`(${a}+${n})`);const c=[`(${r}*${o})+${a}`].join(\\\\\\\", \\\\\\\"),l=this.glVarName(cT),u=`${s}*${i}(${c})`;if(t)return`float ${l}${e} = (${u}).${t}`;return`${this.io.outputs.namedOutputConnectionPoints()[0].type()} ${l} = ${u}`}}class mT extends mb{static type(){return\\\\\\\"null\\\\\\\"}setLines(e){const t=Wm.any(this.variableForInput(this._gl_input_name(0))),n=this.io.outputs.namedOutputConnectionPoints()[0],i=`${n.type()} ${this.glVarName(n.name())} = ${t}`;e.addBodyLines(this,[i])}}const fT=new class extends Lo{};class gT extends Ym{constructor(){super(...arguments),this.paramsConfig=fT}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 e,t;null===(t=null===(e=this.material_node)||void 0===e?void 0:e.assemblerController)||void 0===t||t.add_output_inputs(this)}))}setLines(e){e.assembler().set_node_lines_output(this,e)}}class vT{constructor(){this._param_configs=[]}reset(){this._param_configs=[]}push(e){this._param_configs.push(e)}list(){return this._param_configs}}const yT=new class extends Lo{constructor(){super(...arguments),this.name=Oo.STRING(\\\\\\\"\\\\\\\"),this.type=Oo.INTEGER(oo.indexOf(ro.FLOAT),{menu:{entries:oo.map(((e,t)=>({name:e,value:t})))}}),this.asColor=Oo.BOOLEAN(0,{visibleIf:{type:oo.indexOf(ro.VEC3)}})}};class xT extends Ym{constructor(){super(...arguments),this.paramsConfig=yT,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((()=>[oo[this.pv.type]])),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.name])}))}))}setLines(e){const t=[],n=oo[this.pv.type],i=this.uniform_name();t.push(new af(this,n,i)),e.addDefinitions(this,t)}paramsGenerating(){return!0}setParamConfigs(){const e=oo[this.pv.type],t=lo[e];let n=ao[e];if(this._param_configs_controller=this._param_configs_controller||new vT,this._param_configs_controller.reset(),n==Qs.VECTOR3&&this.p.asColor.value&&m.isArray(t)&&3==t.length){const e=new If(Qs.COLOR,this.pv.name,t,this.uniform_name());this._param_configs_controller.push(e)}else{const e=new If(n,this.pv.name,t,this.uniform_name());this._param_configs_controller.push(e)}}uniform_name(){const e=this.io.outputs.namedOutputConnectionPoints()[0];return this.glVarName(e.name())}set_gl_type(e){const t=oo.indexOf(e);this.p.type.set(t)}_on_create_set_name_if_none(){\\\\\\\"\\\\\\\"==this.pv.name&&this.p.name.set(this.name())}}class bT extends _b{static type(){return\\\\\\\"refract\\\\\\\"}initializeNode(){super.initializeNode(),this.io.connection_points.set_input_name_function((e=>[\\\\\\\"I\\\\\\\",\\\\\\\"N\\\\\\\",\\\\\\\"eta\\\\\\\"][e])),this.io.connection_points.set_output_name_function((e=>\\\\\\\"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 e=this.io.connection_points.first_input_connection_type()||ro.VEC3;return[e,e,ro.FLOAT]}_expected_output_types(){return[this._expected_input_types()[0]]}}const wT=\\\\\\\"SSSModel\\\\\\\";const AT=new class extends Lo{constructor(){super(...arguments),this.color=Oo.COLOR([1,1,1]),this.thickness=Oo.FLOAT(.1),this.power=Oo.FLOAT(2),this.scale=Oo.FLOAT(16),this.distortion=Oo.FLOAT(.1),this.ambient=Oo.FLOAT(.4),this.attenuation=Oo.FLOAT(.8)}};class TT extends Ym{constructor(){super(...arguments),this.paramsConfig=AT}static type(){return\\\\\\\"SSSModel\\\\\\\"}initializeNode(){this.io.outputs.setNamedOutputConnectionPoints([new ho(wT,ro.SSS_MODEL)])}setLines(e){const t=[],n=this.glVarName(wT);t.push(`SSSModel ${n}`),t.push(`${n}.isActive = true;`),t.push(this._paramLineFloat(n,this.p.color)),t.push(this._paramLineFloat(n,this.p.thickness)),t.push(this._paramLineFloat(n,this.p.power)),t.push(this._paramLineFloat(n,this.p.scale)),t.push(this._paramLineFloat(n,this.p.distortion)),t.push(this._paramLineFloat(n,this.p.ambient)),t.push(this._paramLineFloat(n,this.p.attenuation)),e.addBodyLines(this,t)}_paramLineFloat(e,t){return`${e}.${t.name()} = ${Wm.vector3(this.variableForInputParam(t))};`}}class ET extends mb{static type(){return\\\\\\\"quatMult\\\\\\\"}initializeNode(){super.initializeNode(),this.io.connection_points.set_input_name_function((e=>[\\\\\\\"quat0\\\\\\\",\\\\\\\"quat1\\\\\\\"][e])),this.io.connection_points.set_expected_input_types_function((()=>[ro.VEC4,ro.VEC4])),this.io.connection_points.set_expected_output_types_function((()=>[ro.VEC4]))}gl_method_name(){return\\\\\\\"quatMult\\\\\\\"}gl_function_definitions(){return[new of(this,vw)]}}var CT;!function(e){e.AXIS=\\\\\\\"axis\\\\\\\",e.ANGLE=\\\\\\\"angle\\\\\\\"}(CT||(CT={}));const MT=[CT.AXIS,CT.ANGLE],NT={[CT.AXIS]:[0,0,1],[CT.ANGLE]:0};class ST extends fb{static type(){return\\\\\\\"quatFromAxisAngle\\\\\\\"}initializeNode(){super.initializeNode(),this.io.connection_points.set_input_name_function((e=>MT[e])),this.io.connection_points.set_expected_input_types_function((()=>[ro.VEC3,ro.FLOAT])),this.io.connection_points.set_expected_output_types_function((()=>[ro.VEC4]))}paramDefaultValue(e){return NT[e]}gl_method_name(){return\\\\\\\"quatFromAxisAngle\\\\\\\"}gl_function_definitions(){return[new of(this,vw)]}}class OT extends mb{static type(){return\\\\\\\"quatToAngle\\\\\\\"}initializeNode(){super.initializeNode(),this.io.connection_points.set_input_name_function((e=>[\\\\\\\"quat\\\\\\\"][e])),this.io.connection_points.set_expected_input_types_function((()=>[ro.VEC4])),this.io.connection_points.set_expected_output_types_function((()=>[ro.FLOAT]))}gl_method_name(){return\\\\\\\"quatToAngle\\\\\\\"}gl_function_definitions(){return[new of(this,vw)]}}class LT extends mb{static type(){return\\\\\\\"quatToAxis\\\\\\\"}initializeNode(){super.initializeNode(),this.io.connection_points.set_input_name_function((e=>[\\\\\\\"quat\\\\\\\"][e])),this.io.connection_points.set_expected_input_types_function((()=>[ro.VEC4])),this.io.connection_points.set_expected_output_types_function((()=>[ro.VEC3]))}gl_method_name(){return\\\\\\\"quatToAxis\\\\\\\"}gl_function_definitions(){return[new of(this,vw)]}}const PT=\\\\\\\"val\\\\\\\";const RT=new class extends Lo{constructor(){super(...arguments),this.name=Oo.STRING(\\\\\\\"ramp\\\\\\\"),this.input=Oo.FLOAT(0)}};class IT extends Ym{constructor(){super(...arguments),this.paramsConfig=RT}static type(){return\\\\\\\"ramp\\\\\\\"}initializeNode(){super.initializeNode(),this.addPostDirtyHook(\\\\\\\"_set_mat_to_recompile\\\\\\\",this._set_mat_to_recompile.bind(this)),this.io.outputs.setNamedOutputConnectionPoints([new ho(PT,ro.FLOAT)]),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.name])}))}))}setLines(e){const t=ro.FLOAT,n=this._uniform_name(),i=this.glVarName(PT),s=new af(this,ro.SAMPLER_2D,n);e.addDefinitions(this,[s]);const r=this.variableForInputParam(this.p.input),o=`${t} ${i} = texture2D(${this._uniform_name()}, vec2(${r}, 0.0)).x`;e.addBodyLines(this,[o])}paramsGenerating(){return!0}setParamConfigs(){this._param_configs_controller=this._param_configs_controller||new vT,this._param_configs_controller.reset();const e=new If(Qs.RAMP,this.pv.name,jr.DEFAULT_VALUE,this._uniform_name());this._param_configs_controller.push(e)}_uniform_name(){return\\\\\\\"ramp_texture_\\\\\\\"+this.glVarName(PT)}}const FT=\\\\\\\"rand\\\\\\\";const DT=new class extends Lo{constructor(){super(...arguments),this.seed=Oo.VECTOR2([1,1])}};class kT extends Ym{constructor(){super(...arguments),this.paramsConfig=DT}static type(){return\\\\\\\"random\\\\\\\"}initializeNode(){super.initializeNode(),this.io.outputs.setNamedOutputConnectionPoints([new ho(FT,ro.FLOAT)])}setLines(e){const t=this.io.inputs.namedInputConnectionPoints()[0].name(),n=Wm.vector2(this.variableForInput(t)),i=`float ${this.glVarName(FT)} = rand(${n})`;e.addBodyLines(this,[i])}}const BT=new class extends Lo{constructor(){super(...arguments),this.rgb=Oo.VECTOR3([1,1,1])}};class zT extends Ym{constructor(){super(...arguments),this.paramsConfig=BT}static type(){return\\\\\\\"rgbToHsv\\\\\\\"}initializeNode(){this.io.outputs.setNamedOutputConnectionPoints([new ho(\\\\\\\"hsv\\\\\\\",ro.VEC3)])}setLines(e){const t=[],n=[];t.push(new of(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=Wm.vector3(this.variableForInputParam(this.p.rgb)),s=this.glVarName(\\\\\\\"hsv\\\\\\\");n.push(`vec3 ${s} = rgb2hsv(${i})`),e.addDefinitions(this,t),e.addBodyLines(this,n)}}var UT;!function(e){e[e.AXIS=0]=\\\\\\\"AXIS\\\\\\\",e[e.QUAT=1]=\\\\\\\"QUAT\\\\\\\"}(UT||(UT={}));const GT=[UT.AXIS,UT.QUAT],VT={[UT.AXIS]:\\\\\\\"from axis + angle\\\\\\\",[UT.QUAT]:\\\\\\\"from quaternion\\\\\\\"},jT={[UT.AXIS]:[\\\\\\\"vector\\\\\\\",\\\\\\\"axis\\\\\\\",\\\\\\\"angle\\\\\\\"],[UT.QUAT]:[\\\\\\\"vector\\\\\\\",\\\\\\\"quat\\\\\\\"]},HT={[UT.AXIS]:\\\\\\\"rotateWithAxisAngle\\\\\\\",[UT.QUAT]:\\\\\\\"rotateWithQuat\\\\\\\"},qT={[UT.AXIS]:[ro.VEC3,ro.VEC3,ro.FLOAT],[UT.QUAT]:[ro.VEC3,ro.VEC4]},WT={vector:[0,0,1],axis:[0,1,0]};const XT=new class extends Lo{constructor(){super(...arguments),this.signature=Oo.INTEGER(UT.AXIS,{menu:{entries:GT.map(((e,t)=>({name:VT[e],value:t})))}})}};class YT extends Ym{constructor(){super(...arguments),this.paramsConfig=XT}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(e){const t=GT.indexOf(e);this.p.signature.set(t)}_gl_input_name(e){const t=GT[this.pv.signature];return jT[t][e]}paramDefaultValue(e){return WT[e]}gl_method_name(){const e=GT[this.pv.signature];return HT[e]}_expected_input_types(){const e=GT[this.pv.signature];return qT[e]}_expected_output_types(){return[ro.VEC3]}gl_function_definitions(){return[new of(this,vw)]}setLines(e){const t=this.io.outputs.namedOutputConnectionPoints()[0].type(),n=this.io.inputs.namedInputConnectionPoints().map(((e,t)=>{const n=e.name();return Wm.any(this.variableForInput(n))})).join(\\\\\\\", \\\\\\\"),i=`${t} ${this.glVarName(this.io.connection_points.output_name(0))} = ${this.gl_method_name()}(${n})`;e.addBodyLines(this,[i]),e.addDefinitions(this,this.gl_function_definitions())}}const $T=[\\\\\\\"x\\\\\\\",\\\\\\\"y\\\\\\\",\\\\\\\"z\\\\\\\",\\\\\\\"w\\\\\\\"];class QT extends mb{static type(){return\\\\\\\"round\\\\\\\"}setLines(e){const t=this.io.inputs.namedInputConnectionPoints()[0],n=Wm.vector2(this.variableForInput(t.name())),i=this.io.outputs.namedOutputConnectionPoints()[0],s=this.glVarName(i.name()),r=[];if(1==uo[i.type()])r.push(`${i.type()} ${s} = ${this._simple_line(n)}`);else{const e=$T.map((e=>this._simple_line(`${n}.${e}`)));r.push(`${i.type()} ${s} = ${i.type()}(${e.join(\\\\\\\",\\\\\\\")})`)}e.addBodyLines(this,r)}_simple_line(e){return`sign(${e})*floor(abs(${e})+0.5)`}}const JT=new class extends Lo{constructor(){super(...arguments),this.position=Oo.VECTOR3([0,0,0]),this.center=Oo.VECTOR3([0,0,0]),this.radius=Oo.FLOAT(1),this.feather=Oo.FLOAT(.1)}};class KT extends Ym{constructor(){super(...arguments),this.paramsConfig=JT}static type(){return\\\\\\\"sphere\\\\\\\"}initializeNode(){super.initializeNode(),this.io.outputs.setNamedOutputConnectionPoints([new ho(\\\\\\\"float\\\\\\\",ro.FLOAT)])}setLines(e){const t=Wm.vector2(this.variableForInputParam(this.p.position)),n=Wm.vector2(this.variableForInputParam(this.p.center)),i=Wm.float(this.variableForInputParam(this.p.radius)),s=Wm.float(this.variableForInputParam(this.p.feather)),r=`float ${this.glVarName(\\\\\\\"float\\\\\\\")} = disk3d(${t}, ${n}, ${i}, ${s})`;e.addBodyLines(this,[r]),e.addDefinitions(this,[new of(this,Gw)])}}const ZT=new class extends Lo{};class eE extends Ym{constructor(){super(...arguments),this.paramsConfig=ZT}static type(){return Mi.INPUT}initializeNode(){this.io.connection_points.set_output_name_function(this._expected_output_names.bind(this)),this.io.connection_points.set_expected_input_types_function((()=>[])),this.io.connection_points.set_expected_output_types_function(this._expected_output_types.bind(this))}parent(){return super.parent()}_expected_output_names(e){const t=this.parent();return(null==t?void 0:t.child_expected_input_connection_point_name(e))||`out${e}`}_expected_output_types(){const e=this.parent();return(null==e?void 0:e.child_expected_input_connection_point_types())||[]}setLines(e){const t=this.parent();t&&t.set_lines_block_start(e,this)}}const tE=new class extends Lo{};class nE extends Ym{constructor(){super(...arguments),this.paramsConfig=tE}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(e){return 0==e?nE.INPUT_INDEX:\\\\\\\"in\\\\\\\"+(e-1)}_expected_input_types(){const e=this.io.connection_points.input_connection_type(1)||ro.FLOAT,t=this.io.connections.inputConnections(),n=t?Os.clamp(t.length,2,16):2,i=[ro.INT];for(let t=0;t<n;t++)i.push(e);return i}_expected_output_types(){return[this._expected_input_types()[1]||ro.FLOAT]}setLines(e){const t=this.io.outputs.namedOutputConnectionPoints()[0].type(),n=this.glVarName(this.io.connection_points.output_name(0)),i=this.io.connection_points.input_name(0),s=Wm.integer(this.variableForInput(i)),r=this.glVarName(\\\\\\\"index\\\\\\\"),o=[`${t} ${n};`,`int ${r} = ${s}`],a=this._expected_input_types().length-1;for(let e=0;e<a;e++){const t=0==e?\\\\\\\"if\\\\\\\":\\\\\\\"else if\\\\\\\",i=`${r} == ${e}`,s=this.io.connection_points.input_name(e+1),a=`${t}(${i}){${`${n} = ${Wm.any(this.variableForInput(s))};`}}`;o.push(a)}e.addBodyLines(this,o)}}nE.INPUT_INDEX=\\\\\\\"index\\\\\\\";const iE=new class extends Lo{constructor(){super(...arguments),this.paramName=Oo.STRING(\\\\\\\"textureMap\\\\\\\"),this.defaultValue=Oo.STRING(jn.UV),this.uv=Oo.VECTOR2([0,0])}};class sE extends Ym{constructor(){super(...arguments),this.paramsConfig=iE}static type(){return\\\\\\\"texture\\\\\\\"}initializeNode(){this.addPostDirtyHook(\\\\\\\"_set_mat_to_recompile\\\\\\\",this._set_mat_to_recompile.bind(this)),this.io.outputs.setNamedOutputConnectionPoints([new ho(sE.OUTPUT_NAME,ro.VEC4)]),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.paramName])}))}))}setLines(e){const t=Wm.vector2(this.variableForInputParam(this.p.uv)),n=this.glVarName(sE.OUTPUT_NAME),i=this._uniform_name(),s=new af(this,ro.SAMPLER_2D,i),r=`vec4 ${n} = texture2D(${i}, ${t})`;e.addDefinitions(this,[s]),e.addBodyLines(this,[r])}paramsGenerating(){return!0}setParamConfigs(){this._param_configs_controller=this._param_configs_controller||new vT,this._param_configs_controller.reset();const e=new If(Qs.OPERATOR_PATH,this.pv.paramName,this.pv.defaultValue,this._uniform_name());this._param_configs_controller.push(e)}_uniform_name(){return this.glVarName(this.pv.paramName)}}var rE;sE.OUTPUT_NAME=\\\\\\\"rgba\\\\\\\",function(e){e.POSITION=\\\\\\\"position\\\\\\\",e.DIR_VEC=\\\\\\\"direction vector\\\\\\\"}(rE||(rE={}));const oE=[rE.POSITION,rE.DIR_VEC];const aE=new class extends Lo{constructor(){super(...arguments),this.vec=Oo.VECTOR3([0,0,0]),this.interpretation=Oo.INTEGER(0,{menu:{entries:oE.map(((e,t)=>({name:e,value:t})))}})}};class cE extends Ym{constructor(){super(...arguments),this.paramsConfig=aE}static type(){return\\\\\\\"toWorldSpace\\\\\\\"}initializeNode(){this.io.connection_points.spare_params.set_inputless_param_names([\\\\\\\"interpretation\\\\\\\"]),this.io.outputs.setNamedOutputConnectionPoints([new ho(\\\\\\\"out\\\\\\\",ro.VEC3)])}setLines(e){const t=[],n=Wm.vector3(this.variableForInputParam(this.p.vec)),i=this.glVarName(\\\\\\\"out\\\\\\\");switch(oE[this.pv.interpretation]){case rE.POSITION:t.push(`vec3 ${i} = (modelMatrix * vec4( ${n}, 1.0 )).xyz`);break;case rE.DIR_VEC:t.push(`vec3 ${i} = normalize( mat3( modelMatrix[0].xyz, modelMatrix[1].xyz, modelMatrix[2].xyz ) * ${n} )`)}e.addBodyLines(this,t)}}var lE;!function(e){e.CONDITION=\\\\\\\"condition\\\\\\\",e.IF_TRUE=\\\\\\\"ifTrue\\\\\\\",e.IF_FALSE=\\\\\\\"ifFalse\\\\\\\"}(lE||(lE={}));const uE=[lE.CONDITION,lE.IF_TRUE,lE.IF_FALSE];class hE extends Qm{static type(){return\\\\\\\"twoWaySwitch\\\\\\\"}initializeNode(){super.initializeNode(),this.io.connection_points.initializeNode(),this.io.connection_points.set_expected_input_types_function(this._expected_input_types.bind(this)),this.io.connection_points.set_expected_output_types_function(this._expected_output_types.bind(this)),this.io.connection_points.set_input_name_function(this._gl_input_name.bind(this)),this.io.connection_points.set_output_name_function(this._gl_output_name.bind(this))}_gl_input_name(e){return uE[e]}_gl_output_name(){return\\\\\\\"val\\\\\\\"}_expected_input_types(){const e=this.io.connections.inputConnection(1)||this.io.connections.inputConnection(2),t=e?e.src_connection_point().type():ro.FLOAT;return[ro.BOOL,t,t]}_expected_output_types(){return[this._expected_input_types()[1]]}setLines(e){const t=[],n=this.glVarName(\\\\\\\"val\\\\\\\"),i=Wm.bool(this.variableForInput(lE.CONDITION)),s=Wm.any(this.variableForInput(lE.IF_TRUE)),r=Wm.any(this.variableForInput(lE.IF_FALSE)),o=this._expected_output_types()[0];t.push(`${o} ${n}`),t.push(`if(${i}){`),t.push(`${n} = ${s}`),t.push(\\\\\\\"} else {\\\\\\\"),t.push(`${n} = ${r}`),t.push(\\\\\\\"}\\\\\\\"),e.addBodyLines(this,t)}}const dE=[ro.FLOAT,ro.VEC2,ro.VEC3,ro.VEC4];const pE=new class extends Lo{constructor(){super(...arguments),this.name=Oo.STRING(\\\\\\\"\\\\\\\"),this.type=Oo.INTEGER(0,{menu:{entries:dE.map(((e,t)=>({name:e,value:t})))}})}};class _E extends Ym{constructor(){super(...arguments),this.paramsConfig=pE,this._on_create_set_name_if_none_bound=this._on_create_set_name_if_none.bind(this)}static type(){return\\\\\\\"varyingRead\\\\\\\"}initializeNode(){this.addPostDirtyHook(\\\\\\\"_set_mat_to_recompile\\\\\\\",this._set_mat_to_recompile.bind(this)),this.lifecycle.add_on_create_hook(this._on_create_set_name_if_none_bound),this.io.connection_points.initializeNode(),this.io.connection_points.set_output_name_function((()=>this.output_name)),this.io.connection_points.set_expected_input_types_function((()=>[])),this.io.connection_points.set_expected_output_types_function((()=>[dE[this.pv.type]])),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.name])}))}))}get output_name(){return _E.OUTPUT_NAME}setLines(e){if(e.current_shader_name==nf.FRAGMENT){const t=this.pv.name,n=new cf(this,this.gl_type(),t),i=this.glVarName(_E.OUTPUT_NAME),s=`${this.gl_type()} ${i} = ${t}`;e.addDefinitions(this,[n]),e.addBodyLines(this,[s])}}get attribute_name(){return this.pv.name.trim()}gl_type(){return this.io.outputs.namedOutputConnectionPoints()[0].type()}set_gl_type(e){this.p.type.set(dE.indexOf(e))}_on_create_set_name_if_none(){\\\\\\\"\\\\\\\"==this.pv.name&&this.p.name.set(this.name())}}_E.OUTPUT_NAME=\\\\\\\"fragment\\\\\\\";const mE={start:[0,0,1],end:[1,0,0],up:[0,1,0]};class fE extends(Qb(\\\\\\\"vectorAlign\\\\\\\",{in:[\\\\\\\"start\\\\\\\",\\\\\\\"end\\\\\\\",\\\\\\\"up\\\\\\\"],method:\\\\\\\"vectorAlignWithUp\\\\\\\",functions:[vw]})){_expected_input_types(){const e=ro.VEC3;return[e,e,e]}_expected_output_types(){return[ro.VEC4]}paramDefaultValue(e){return mE[e]}}const gE={start:[0,0,1],end:[1,0,0]};class vE extends(Gb(\\\\\\\"vectorAngle\\\\\\\",{in:[\\\\\\\"start\\\\\\\",\\\\\\\"end\\\\\\\"],method:\\\\\\\"vectorAngle\\\\\\\",functions:[vw]})){_expected_input_types(){const e=ro.VEC3;return[e,e]}_expected_output_types(){return[ro.FLOAT]}paramDefaultValue(e){return gE[e]}}const yE={only:[`${MA.context()}/${MA.type()}`,`${_A.context()}/${_A.type()}`,`${vA.context()}/${vA.type()}`]};class xE extends Mo{static context(){return Ei.JS}initializeBaseNode(){this.uiData.setLayoutHorizontal(),this.io.connection_points.initializeNode()}cook(){console.warn(\\\\\\\"js nodes should never cook\\\\\\\")}_set_function_node_to_recompile(){var e;null===(e=this.function_node)||void 0===e||e.assembler_controller.set_compilation_required_and_dirty(this)}get function_node(){var e;const t=this.parent();if(t)return t.type()==this.type()?null===(e=t)||void 0===e?void 0:e.function_node:t}js_var_name(e){return`v_POLY_${this.name()}_${e}`}variableForInput(e){const t=this.io.inputs.get_input_index(e),n=this.io.connections.inputConnection(t);if(n){const t=n.node_src,i=t.io.outputs.namedOutputConnectionPoints()[n.output_index];if(i){const e=i.name();return t.js_var_name(e)}throw console.warn(`no output called '${e}' for gl node ${t.path()}`),\\\\\\\"variable_for_input ERROR\\\\\\\"}return\\\\\\\"to debug...\\\\\\\"}setLines(e){}reset_code(){var e;null===(e=this._param_configs_controller)||void 0===e||e.reset()}setParamConfigs(){}param_configs(){var e;return null===(e=this._param_configs_controller)||void 0===e?void 0:e.list()}js_input_default_value(e){return null}}new class extends Lo{};const bE=[po.FLOAT,po.VEC2,po.VEC3,po.VEC4];const wE=new class extends Lo{constructor(){super(...arguments),this.name=Oo.STRING(\\\\\\\"\\\\\\\"),this.type=Oo.INTEGER(0,{menu:{entries:bE.map(((e,t)=>({name:e,value:t})))}})}};class AE extends xE{constructor(){super(...arguments),this.paramsConfig=wE,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((()=>[bE[this.pv.type]]))}get input_name(){return AE.INPUT_NAME}get output_name(){return AE.OUTPUT_NAME}setLines(e){var t;null===(t=this.function_node)||void 0===t||t.assembler_controller.assembler.set_node_lines_attribute(this,e)}get attribute_name(){return this.pv.name.trim()}gl_type(){return this.io.outputs.namedOutputConnectionPoints()[0].type()}set_gl_type(e){this.p.type.set(bE.indexOf(e))}connected_input_node(){return this.io.inputs.named_input(AE.INPUT_NAME)}connected_input_connection_point(){return this.io.inputs.named_input_connection_point(AE.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())}}AE.INPUT_NAME=\\\\\\\"export\\\\\\\",AE.OUTPUT_NAME=\\\\\\\"val\\\\\\\";const TE=new class extends Lo{};class EE extends xE{constructor(){super(...arguments),this.paramsConfig=TE}static type(){return\\\\\\\"globals\\\\\\\"}createParams(){var e;null===(e=this.function_node)||void 0===e||e.assembler_controller.add_globals_outputs(this)}setLines(e){var t,n;null===(n=null===(t=this.function_node)||void 0===t?void 0:t.assembler_controller)||void 0===n||n.assembler.set_node_lines_globals(this,e)}}const CE=new class extends Lo{};class ME extends xE{constructor(){super(...arguments),this.paramsConfig=CE}static type(){return\\\\\\\"output\\\\\\\"}initializeNode(){super.initializeNode(),this.addPostDirtyHook(\\\\\\\"_set_mat_to_recompile\\\\\\\",this._set_function_node_to_recompile.bind(this))}createParams(){var e;null===(e=this.function_node)||void 0===e||e.assembler_controller.add_output_inputs(this)}setLines(e){var t;null===(t=this.function_node)||void 0===t||t.assembler_controller.assembler.set_node_lines_output(this,e)}}class NE{constructor(e=[]){this._definitions=e,this._errored=!1}get errored(){return this._errored}get error_message(){return this._error_message}uniq(){const e=new Map,t=[];for(let n of this._definitions)if(!this._errored){const i=n.name(),s=e.get(i);s?s.data_type!=n.data_type&&(this._errored=!0,this._error_message=`attempt to create '${n.name()}' with types '${n.data_type}' by node '${n.node.path()}', when there is already an existing with type ${s.data_type} from node '${s.node.path()}'`,console.warn(\\\\\\\"emitting error message:\\\\\\\",this._error_message)):(e.set(i,n),t.push(i))}const n=[];for(let i of t){const t=e.get(i);t&&n.push(t)}return n}}var SE;!function(e){e.ATTRIBUTE=\\\\\\\"attribute\\\\\\\",e.FUNCTION=\\\\\\\"function\\\\\\\",e.UNIFORM=\\\\\\\"uniform\\\\\\\"}(SE||(SE={}));class OE{constructor(e,t,n,i){this._definition_type=e,this._data_type=t,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 NE}}class LE extends OE{constructor(e,t,n){super(SE.UNIFORM,t,e,n),this._node=e,this._data_type=t,this._name=n}get line(){return`uniform ${this.data_type} ${this.name()}`}}class PE extends Rf{constructor(e,t,n,i){super(e,t,n),this._uniform_name=i}get uniform_name(){return this._uniform_name}static uniform_by_type(e){switch(e){case Qs.BOOLEAN:case Qs.BUTTON:return{value:0};case Qs.COLOR:return{value:new M.a(0,0,0)};case Qs.FLOAT:case Qs.FOLDER:case Qs.INTEGER:case Qs.OPERATOR_PATH:case Qs.NODE_PATH:case Qs.PARAM_PATH:return{value:0};case Qs.RAMP:case Qs.STRING:return{value:null};case Qs.VECTOR2:return{value:new d.a(0,0)};case Qs.VECTOR3:return{value:new p.a(0,0,0)};case Qs.VECTOR4:return{value:new _.a(0,0,0,0)}}Ri.unreachable(e)}}const RE=new class extends Lo{constructor(){super(...arguments),this.name=Oo.STRING(\\\\\\\"\\\\\\\"),this.type=Oo.INTEGER(_o.indexOf(po.FLOAT),{menu:{entries:_o.map(((e,t)=>({name:e,value:t})))}}),this.asColor=Oo.BOOLEAN(0,{visibleIf:{type:_o.indexOf(po.VEC3)}})}};class IE extends xE{constructor(){super(...arguments),this.paramsConfig=RE,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((()=>[_o[this.pv.type]]))}setLines(e){const t=[],n=_o[this.pv.type],i=this.uniform_name();t.push(new LE(this,n,i)),e.addDefinitions(this,t)}setParamConfigs(){const e=_o[this.pv.type],t=go[e];let n=mo[e];if(this._param_configs_controller=this._param_configs_controller||new vT,this._param_configs_controller.reset(),n==Qs.VECTOR3&&this.p.asColor.value&&m.isArray(t)&&3==t.length){const e=new PE(Qs.COLOR,this.pv.name,t,this.uniform_name());this._param_configs_controller.push(e)}else{const e=new PE(n,this.pv.name,t,this.uniform_name());this._param_configs_controller.push(e)}}uniform_name(){const e=this.io.outputs.namedOutputConnectionPoints()[0];return this.js_var_name(e.name())}set_gl_type(e){const t=_o.indexOf(e);this.p.type.set(t)}_on_create_set_name_if_none(){\\\\\\\"\\\\\\\"==this.pv.name&&this.p.name.set(this.name())}}class FE extends Mo{constructor(){super(...arguments),this._cook_main_without_inputs_when_dirty_bound=this._cook_main_without_inputs_when_dirty.bind(this)}static context(){return Ei.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(e){this._setContainer(e)}}class DE{constructor(e){this.node=e}add_params(){}update(){}get material(){return this.node.material}}const kE={NoBlending:w.ub,NormalBlending:w.xb,AdditiveBlending:w.e,SubtractiveBlending:w.Sc,MultiplyBlending:w.mb},BE=Object.keys(kE);function zE(e){return class extends e{constructor(){super(...arguments),this.doubleSided=Oo.BOOLEAN(0),this.front=Oo.BOOLEAN(1,{visibleIf:{doubleSided:!1}}),this.overrideShadowSide=Oo.BOOLEAN(0),this.shadowDoubleSided=Oo.BOOLEAN(0,{visibleIf:{overrideShadowSide:!0}}),this.shadowFront=Oo.BOOLEAN(1,{visibleIf:{overrideShadowSide:!0,shadowDoubleSided:!1}}),this.colorWrite=Oo.BOOLEAN(1,{separatorBefore:!0,cook:!1,callback:(e,t)=>{UE.update(e)}}),this.depthWrite=Oo.BOOLEAN(1,{cook:!1,callback:(e,t)=>{UE.update(e)}}),this.depthTest=Oo.BOOLEAN(1,{cook:!1,callback:(e,t)=>{UE.update(e)}}),this.premultipliedAlpha=Oo.BOOLEAN(!1,{separatorAfter:!0}),this.blending=Oo.INTEGER(w.xb,{menu:{entries:BE.map((e=>({name:e,value:kE[e]})))}}),this.dithering=Oo.BOOLEAN(0),this.polygonOffset=Oo.BOOLEAN(!1,{separatorBefore:!0}),this.polygonOffsetFactor=Oo.INTEGER(0,{range:[0,1e3],visibleIf:{polygonOffset:1}}),this.polygonOffsetUnits=Oo.INTEGER(0,{range:[0,1e3],visibleIf:{polygonOffset:1}})}}}zE(Lo);class UE extends DE{constructor(e){super(e),this.node=e}initializeNode(){}async update(){const e=this.node.material,t=this.node.pv;this._updateSides(e,t),e.colorWrite=t.colorWrite,e.depthWrite=t.depthWrite,e.depthTest=t.depthTest,e.blending=t.blending,e.premultipliedAlpha=t.premultipliedAlpha,e.dithering=t.dithering,e.polygonOffset=t.polygonOffset,e.polygonOffset&&(e.polygonOffsetFactor=t.polygonOffsetFactor,e.polygonOffsetUnits=t.polygonOffsetUnits,e.needsUpdate=!0)}_updateSides(e,t){const n=t.front?w.H:w.i,i=t.doubleSided?w.z:n;if(i!=e.side&&(e.side=i,e.needsUpdate=!0),t.overrideShadowSide){const e=t.shadowFront?w.H:w.i,n=t.shadowDoubleSided?w.z:e,i=this.node.material;n!=i.shadowSide&&(i.shadowSide=n,i.needsUpdate=!0)}else e.shadowSide=null;const s=e.customMaterials;if(s){const e=Object.keys(s);for(let n of e){const e=s[n];e&&this._updateSides(e,t)}}}static async update(e){e.controllers.advancedCommon.update()}}class GE extends(zE(Lo)){constructor(){super(...arguments),this.color=Oo.COLOR([1,1,1]),this.lineWidth=Oo.FLOAT(1,{range:[1,10],rangeLocked:[!0,!1]})}}const VE=new GE;class jE extends FE{constructor(){super(...arguments),this.paramsConfig=VE,this.controllers={advancedCommon:new UE(this)},this.controllerNames=Object.keys(this.controllers)}static type(){return\\\\\\\"lineBasic\\\\\\\"}createMaterial(){return new Yi.a({color:16777215,linewidth:1})}initializeNode(){this.params.onParamsCreated(\\\\\\\"init controllers\\\\\\\",(()=>{for(let e of this.controllerNames)this.controllers[e].initializeNode()}))}async cook(){for(let e of this.controllerNames)this.controllers[e].update();this.material.color.copy(this.pv.color),this.material.linewidth=this.pv.lineWidth,this.setMaterial(this.material)}}function HE(e){return class extends e{constructor(){super(...arguments),this.transparent=Oo.BOOLEAN(0),this.opacity=Oo.FLOAT(1),this.alphaTest=Oo.FLOAT(0)}}}HE(Lo);class qE extends DE{constructor(e){super(e),this.node=e}static update(e){const t=e.material,n=e.pv;this._updateTransparency(t,n)}static _updateTransparency(e,t){e.transparent=t.transparent,this._updateCommon(e,t)}static _updateCommon(e,t){e.uniforms.opacity&&(e.uniforms.opacity.value=t.opacity),e.opacity=t.opacity,e.alphaTest=t.alphaTest;const n=e.customMaterials;if(n){const e=Object.keys(n);for(let i of e){const e=n[i];e&&this._updateCommon(e,t)}}}}function WE(e){return class extends e{constructor(){super(...arguments),this.skinning=Oo.BOOLEAN(0,{separatorBefore:!0})}}}I.a;WE(Lo);class XE extends DE{constructor(e){super(e),this.node=e}static update(e){const t=e.pv.skinning;t!=e.material.skinning&&(e.material.skinning=t,e.material.needsUpdate=!0)}}class YE extends Pf{constructor(e){super(e),this.node=e}toJSON(){const e=this.node.assemblerController;if(!e)return;const t={},n=this.node.material.customMaterials;if(n){const e=Object.keys(n);for(let i of e){const e=n[i];if(e){const n=this._materialToJson(e,{node:this.node,suffix:i});n&&(t[i]=n)}}}const i=[],s=e.assembler.param_configs();for(let e of s)i.push([e.name(),e.uniform_name]);const r=this._materialToJson(this.node.material,{node:this.node,suffix:\\\\\\\"main\\\\\\\"});r||console.warn(\\\\\\\"failed to save material from node\\\\\\\",this.node.path());return{material:r||{},uniforms_time_dependent:e.assembler.uniformsTimeDependent(),uniforms_resolution_dependent:e.assembler.uniforms_resolution_dependent(),param_uniform_pairs:i,customMaterials:t}}load(e){if(this._material=this._loadMaterial(e.material),this._material){if(this._material.customMaterials=this._material.customMaterials||{},e.customMaterials){const t=Object.keys(e.customMaterials);for(let n of t){const t=e.customMaterials[n],i=this._loadMaterial(t);i&&(this._material.customMaterials[n]=i)}}if(e.uniforms_time_dependent&&this.node.scene().uniformsController.addTimeDependentUniformOwner(this._material.uuid,this._material.uniforms),e.uniforms_resolution_dependent&&this.node.scene().uniformsController.addResolutionDependentUniformOwner(this._material.uuid,this._material.uniforms),e.param_uniform_pairs)for(let t of e.param_uniform_pairs){const e=t[0],n=t[1],i=this.node.params.get(e),s=this._material.uniforms[n],r=Object.keys(this._material.customMaterials);let o;for(let e of r){const t=this._material.customMaterials[e],i=null==t?void 0:t.uniforms[n];i&&(o=o||[],o.push(i))}i&&(s||o)&&i.options.setOption(\\\\\\\"callback\\\\\\\",(()=>{if(s&&If.callback(i,s),o)for(let e of o)If.callback(i,e)}))}}}material(){if(Rn.playerMode())return this._material}}function $E(e){return class extends e{constructor(){super(...arguments),this.setBuilderNode=Oo.BOOLEAN(0,{callback:e=>{QE.PARAM_CALLBACK_setCompileRequired(e)}}),this.builderNode=Oo.NODE_PATH(\\\\\\\"\\\\\\\",{visibleIf:{setBuilderNode:!0},callback:e=>{QE.PARAM_CALLBACK_setCompileRequired(e)}})}}}$E(Lo);class QE extends FE{constructor(){super(...arguments),this._children_controller_context=Ei.GL,this.persisted_config=new YE(this)}createMaterial(){var e;let t;return this.persisted_config&&(t=this.persisted_config.material()),t||(t=null===(e=this.assemblerController)||void 0===e?void 0:e.assembler.createMaterial()),t}get assemblerController(){return this._assembler_controller=this._assembler_controller||this._create_assembler_controller()}createNode(e,t){return super.createNode(e,t)}children(){return super.children()}nodesByType(e){return super.nodesByType(e)}childrenAllowed(){return this.assemblerController?super.childrenAllowed():(this.scene().markAsReadOnly(this),!1)}compileIfRequired(){var e;(null===(e=this.assemblerController)||void 0===e?void 0:e.compileRequired())&&this._compile()}_compile(){const e=this.assemblerController;this.material&&e&&(e.assembler.setGlParentNode(this),this._setAssemblerGlParentNode(e),e.assembler.compileMaterial(this.material),e.post_compile())}_setAssemblerGlParentNode(e){if(!this.pv.setBuilderNode)return;const t=this.pv.builderNode.nodeWithContext(Ei.MAT);if(!t)return;const n=t;n.assemblerController?n.type()==this.type()?e.assembler.setGlParentNode(n):this.states.error.set(`resolved node '${t.path()}' does not have the same type '${t.type()}' as current node '${this.type()}'`):this.states.error.set(`resolved node '${t.path()}' is not a builder node`)}static PARAM_CALLBACK_setCompileRequired(e){e.PARAM_CALLBACK_setCompileRequired()}PARAM_CALLBACK_setCompileRequired(){var e;null===(e=this.assemblerController)||void 0===e||e.setCompilationRequired(!0)}}function JE(e){return class extends e{constructor(){super(...arguments),this.useFog=Oo.BOOLEAN(0)}}}JE(Lo);class KE extends DE{constructor(e){super(e),this.node=e}static update(e){const t=e.material,n=e.pv;t.fog=n.useFog}}function ZE(e){return class extends e{constructor(){super(...arguments),this.default=Oo.FOLDER(null)}}}function eC(e){return class extends e{constructor(){super(...arguments),this.advanced=Oo.FOLDER(null)}}}class tC extends(JE(WE(zE($E(eC(HE(ZE(Lo)))))))){constructor(){super(...arguments),this.linewidth=Oo.FLOAT(1,{range:[0,10],rangeLocked:[!0,!1]})}}const nC=new tC;class iC extends QE{constructor(){super(...arguments),this.paramsConfig=nC,this.controllers={advancedCommon:new UE(this)},this.controllerNames=Object.keys(this.controllers)}static type(){return\\\\\\\"lineBasicBuilder\\\\\\\"}usedAssembler(){return mn.GL_LINE}_create_assembler_controller(){return Rn.assemblersRegister.assembler(this,this.usedAssembler())}initializeNode(){this.params.onParamsCreated(\\\\\\\"init controllers\\\\\\\",(()=>{for(let e of this.controllerNames)this.controllers[e].initializeNode()}))}async cook(){for(let e of this.controllerNames)this.controllers[e].update();qE.update(this),KE.update(this),XE.update(this),this.compileIfRequired(),this.material.linewidth=this.pv.linewidth,this.setMaterial(this.material)}}function sC(e){return class extends e{constructor(){super(...arguments),this.color=Oo.COLOR([1,1,1],{conversion:Or.SRGB_TO_LINEAR}),this.useVertexColors=Oo.BOOLEAN(0,{separatorAfter:!0}),this.transparent=Oo.BOOLEAN(0),this.opacity=Oo.FLOAT(1),this.alphaTest=Oo.FLOAT(0)}}}I.a;sC(Lo);class rC extends DE{constructor(e){super(e),this.node=e}static update(e){const t=e.material,n=e.pv;t.color.copy(n.color);const i=n.useVertexColors;i!=t.vertexColors&&(t.vertexColors=i,t.needsUpdate=!0),t.opacity=n.opacity,t.transparent=n.transparent,t.alphaTest=n.alphaTest}}function oC(e){return class extends e{constructor(){super(...arguments),this.useFog=Oo.BOOLEAN(0)}}}oC(Lo);class aC extends DE{constructor(e){super(e),this.node=e}static update(e){const t=e.material,n=e.pv;t.fog=n.useFog}}function cC(e){return{cook:!1,callback:(t,n)=>{e.update(t)}}}function lC(e,t,n){return{visibleIf:{[t]:1},nodeSelection:{context:Ei.COP,types:null==n?void 0:n.types},cook:!1,callback:(t,n)=>{e.update(t)}}}class uC extends DE{constructor(e,t){super(e),this.node=e,this._update_options=t}add_hooks(e,t){e.addPostDirtyHook(\\\\\\\"TextureController\\\\\\\",(()=>{this.update()})),t.addPostDirtyHook(\\\\\\\"TextureController\\\\\\\",(()=>{this.update()}))}static update(e){}async _update(e,t,n,i){if(this._update_options.uniforms){const s=e,r=t;await this._update_texture_on_uniforms(s,r,n,i)}if(this._update_options.directParams){const s=e,r=t;await this._update_texture_on_material(s,r,n,i)}}async _update_texture_on_uniforms(e,t,n,i){this._update_required_attribute(e,e.uniforms,t,n,i,this._apply_texture_on_uniforms.bind(this),this._remove_texture_from_uniforms.bind(this))}_apply_texture_on_uniforms(e,t,n,i){const s=null!=t[n]&&null!=t[n].value;let r=!1;if(s){t[n].value.uuid!=i.uuid&&(r=!0)}if(!s||r){t[n]&&(t[n].value=i),this._apply_texture_on_material(e,e,n,i),e.needsUpdate=!0;const s=e.customMaterials;if(s){const e=Object.keys(s);for(let t of e){const e=s[t];e&&this._apply_texture_on_uniforms(e,e.uniforms,n,i)}}}}_remove_texture_from_uniforms(e,t,n){if(t[n]){if(t[n].value){t[n].value=null,this._remove_texture_from_material(e,e,n),e.needsUpdate=!0;const i=e.customMaterials;if(i){const e=Object.keys(i);for(let t of e){const e=i[t];e&&this._remove_texture_from_uniforms(e,e.uniforms,n)}}}}else Rn.warn(`'${n}' uniform not found. existing uniforms are:`,Object.keys(t).sort())}async _update_texture_on_material(e,t,n,i){this._update_required_attribute(e,e,t,n,i,this._apply_texture_on_material.bind(this),this._remove_texture_from_material.bind(this))}_apply_texture_on_material(e,t,n,i){const s=null!=t[n];let r=!1;if(s){t[n].uuid!=i.uuid&&(r=!0)}s&&!r||(t[n]=i,e.needsUpdate=!0)}_remove_texture_from_material(e,t,n){t[n]&&(t[n]=null,e.needsUpdate=!0)}async _update_required_attribute(e,t,n,i,s,r,o){i.isDirty()&&await i.compute();if(i.value){s.isDirty()&&await s.compute();const i=s.value.nodeWithContext(Ei.COP);if(i){const s=(await i.compute()).texture();if(s)return void r(e,t,n,s)}}o(e,t,n)}}function hC(e){return class extends e{constructor(){super(...arguments),this.useMap=Oo.BOOLEAN(0,cC(dC)),this.map=Oo.NODE_PATH(jn.EMPTY,lC(dC,\\\\\\\"useMap\\\\\\\"))}}}I.a;hC(Lo);class dC extends uC{constructor(e,t){super(e,t),this.node=e}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(e){e.controllers.map.update()}}function pC(e){return class extends e{constructor(){super(...arguments),this.useAlphaMap=Oo.BOOLEAN(0,{separatorBefore:!0,...cC(_C)}),this.alphaMap=Oo.NODE_PATH(jn.EMPTY,lC(_C,\\\\\\\"useAlphaMap\\\\\\\"))}}}I.a;pC(Lo);class _C extends uC{constructor(e,t){super(e,t),this.node=e}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(e){e.controllers.alphaMap.update()}}function mC(e){return class extends e{constructor(){super(...arguments),this.useAOMap=Oo.BOOLEAN(0,{separatorBefore:!0,...cC(fC)}),this.aoMap=Oo.NODE_PATH(jn.EMPTY,lC(fC,\\\\\\\"useAOMap\\\\\\\")),this.aoMapIntensity=Oo.FLOAT(1,{range:[0,1],rangeLocked:[!1,!1],visibleIf:{useAOMap:1}})}}}I.a;mC(Lo);class fC extends uC{constructor(e,t){super(e,t),this.node=e}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(e){e.controllers.aoMap.update()}}var gC;!function(e){e.MULT=\\\\\\\"mult\\\\\\\",e.ADD=\\\\\\\"add\\\\\\\",e.MIX=\\\\\\\"mix\\\\\\\"}(gC||(gC={}));const vC=[gC.MULT,gC.ADD,gC.MIX],yC={[gC.MULT]:w.nb,[gC.ADD]:w.c,[gC.MIX]:w.lb};function xC(e){return class extends e{constructor(){super(...arguments),this.useEnvMap=Oo.BOOLEAN(0,cC(bC)),this.envMap=Oo.NODE_PATH(jn.EMPTY,lC(bC,\\\\\\\"useEnvMap\\\\\\\",{types:[mg.CUBE_CAMERA]})),this.combine=Oo.INTEGER(0,{visibleIf:{useEnvMap:1},menu:{entries:vC.map(((e,t)=>({name:e,value:t})))}}),this.reflectivity=Oo.FLOAT(1,{visibleIf:{useEnvMap:1}}),this.refractionRatio=Oo.FLOAT(.98,{range:[-1,1],rangeLocked:[!1,!1],visibleIf:{useEnvMap:1}})}}}xC(Lo);class bC extends uC{constructor(e,t){super(e,t),this.node=e}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 e=yC[vC[this.node.pv.combine]];if(this._update_options.uniforms){const e=this.node.material;e.uniforms.reflectivity.value=this.node.pv.reflectivity,e.uniforms.refractionRatio.value=this.node.pv.refractionRatio}if(this._update_options.directParams){const t=this.node.material;t.combine=e,t.reflectivity=this.node.pv.reflectivity,t.refractionRatio=this.node.pv.refractionRatio}}static async update(e){e.controllers.envMap.update()}}function wC(e){return class extends e{constructor(){super(...arguments),this.useLightMap=Oo.BOOLEAN(0,{separatorBefore:!0,...cC(AC)}),this.lightMap=Oo.NODE_PATH(jn.EMPTY,lC(AC,\\\\\\\"useLightMap\\\\\\\")),this.lightMapIntensity=Oo.FLOAT(1,{visibleIf:{useLightMap:1}})}}}I.a;wC(Lo);class AC extends uC{constructor(e,t){super(e,t),this.node=e}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(e){e.controllers.lightMap.update()}}var TC;!function(e){e.ROUND=\\\\\\\"round\\\\\\\",e.BUTT=\\\\\\\"butt\\\\\\\",e.SQUARE=\\\\\\\"square\\\\\\\"}(TC||(TC={}));const EC=[TC.ROUND,TC.BUTT,TC.SQUARE];var CC;!function(e){e.ROUND=\\\\\\\"round\\\\\\\",e.BEVEL=\\\\\\\"bevel\\\\\\\",e.MITER=\\\\\\\"miter\\\\\\\"}(CC||(CC={}));const MC=[CC.ROUND,CC.BEVEL,CC.MITER];function NC(e){return class extends e{constructor(){super(...arguments),this.wireframe=Oo.BOOLEAN(0,{separatorBefore:!0}),this.wireframeLinecap=Oo.INTEGER(0,{menu:{entries:EC.map(((e,t)=>({name:e,value:t})))},visibleIf:{wireframe:1}}),this.wireframeLinejoin=Oo.INTEGER(0,{menu:{entries:MC.map(((e,t)=>({name:e,value:t})))},visibleIf:{wireframe:1}})}}}I.a;NC(Lo);class SC extends DE{constructor(e){super(e),this.node=e}static update(e){const t=e.material,n=e.pv;t.wireframe=n.wireframe,t.wireframeLinecap=EC[n.wireframeLinecap],t.wireframeLinejoin=MC[n.wireframeLinejoin],t.needsUpdate=!0}}function OC(e){return class extends e{constructor(){super(...arguments),this.textures=Oo.FOLDER(null)}}}const LC={directParams:!0};class PC extends(oC(WE(NC(zE(eC(wC(xC(mC(pC(hC(OC(sC(ZE(Lo)))))))))))))){}const RC=new PC;class IC extends FE{constructor(){super(...arguments),this.paramsConfig=RC,this.controllers={advancedCommon:new UE(this),alphaMap:new _C(this,LC),aoMap:new fC(this,LC),envMap:new bC(this,LC),lightMap:new AC(this,LC),map:new dC(this,LC)},this.controllerNames=Object.keys(this.controllers)}static type(){return\\\\\\\"meshBasic\\\\\\\"}createMaterial(){return new Nf.a({vertexColors:!1,side:w.H,color:16777215,opacity:1})}initializeNode(){this.params.onParamsCreated(\\\\\\\"init controllers\\\\\\\",(()=>{for(let e of this.controllerNames)this.controllers[e].initializeNode()}))}async cook(){for(let e of this.controllerNames)this.controllers[e].update();rC.update(this),aC.update(this),XE.update(this),SC.update(this),this.setMaterial(this.material)}}function FC(e){return class extends e{constructor(){super(...arguments),this.wireframe=Oo.BOOLEAN(0)}}}FC(Lo);class DC extends DE{constructor(e){super(e),this.node=e}static update(e){const t=e.material,n=e.pv;t.wireframe=n.wireframe,t.needsUpdate=!0}}const kC={uniforms:!0};class BC extends(JE(WE(FC(zE($E(eC(xC(mC(pC(hC(OC(HE(ZE(Lo)))))))))))))){}const zC=new BC;class UC extends QE{constructor(){super(...arguments),this.paramsConfig=zC,this.controllers={advancedCommon:new UE(this),alphaMap:new _C(this,kC),aoMap:new fC(this,kC),envMap:new bC(this,kC),map:new dC(this,kC)},this.controllerNames=Object.keys(this.controllers)}static type(){return\\\\\\\"meshBasicBuilder\\\\\\\"}usedAssembler(){return mn.GL_MESH_BASIC}_create_assembler_controller(){return Rn.assemblersRegister.assembler(this,this.usedAssembler())}initializeNode(){this.params.onParamsCreated(\\\\\\\"init controllers\\\\\\\",(()=>{for(let e of this.controllerNames)this.controllers[e].initializeNode()}))}async cook(){for(let e of this.controllerNames)this.controllers[e].update();qE.update(this),KE.update(this),XE.update(this),DC.update(this),this.compileIfRequired(),this.setMaterial(this.material)}}function GC(e){return class extends e{constructor(){super(...arguments),this.emissive=Oo.COLOR([0,0,0],{separatorBefore:!0}),this.useEmissiveMap=Oo.BOOLEAN(0,cC(VC)),this.emissiveMap=Oo.NODE_PATH(jn.EMPTY,lC(VC,\\\\\\\"useEmissiveMap\\\\\\\")),this.emissiveIntensity=Oo.FLOAT(1)}}}I.a;GC(Lo);class VC extends uC{constructor(e,t){super(e,t),this.node=e}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 e=this.node.material;e.emissive.copy(this.node.pv.emissive),e.emissiveIntensity=this.node.pv.emissiveIntensity}}static async update(e){e.controllers.emissiveMap.update()}}const jC={directParams:!0};class HC extends(oC(WE(NC(zE(eC(wC(xC(GC(mC(pC(hC(OC(sC(ZE(Lo))))))))))))))){}const qC=new HC;class WC extends FE{constructor(){super(...arguments),this.paramsConfig=qC,this.controllers={advancedCommon:new UE(this),alphaMap:new _C(this,jC),aoMap:new fC(this,jC),emissiveMap:new VC(this,jC),envMap:new bC(this,jC),lightMap:new AC(this,jC),map:new dC(this,jC)},this.controllerNames=Object.keys(this.controllers)}static type(){return\\\\\\\"meshLambert\\\\\\\"}createMaterial(){return new Xi.a({vertexColors:!1,side:w.H,color:16777215,opacity:1})}initializeNode(){this.params.onParamsCreated(\\\\\\\"init controllers\\\\\\\",(()=>{for(let e of this.controllerNames)this.controllers[e].initializeNode()}))}async cook(){for(let e of this.controllerNames)this.controllers[e].update();rC.update(this),aC.update(this),XE.update(this),SC.update(this),this.setMaterial(this.material)}}function XC(e){return class extends e{constructor(){super(...arguments),this.shadowPCSS=Oo.BOOLEAN(0,{callback:e=>{YC.PARAM_CALLBACK_setRecompileRequired(e)},separatorBefore:!0}),this.shadowPCSSSamplesCount=Oo.INTEGER(16,{visibleIf:{shadowPCSS:1},range:[0,128],rangeLocked:[!0,!1]}),this.shadowPCSSFilterSize=Oo.FLOAT(1,{visibleIf:{shadowPCSS:1},range:[0,10],rangeLocked:[!0,!1]})}}}XC(Lo);class YC extends DE{constructor(e){super(e),this.node=e}initializeNode(){}static filterFragmentShader(e,t){const n=`\\\\n#define NUM_SAMPLES ${Wm.integer(e.pv.shadowPCSSSamplesCount)}\\\\n#define PCSS_FILTER_SIZE ${Wm.float(e.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=U;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 )\\\\\\\"),t=t.replace(\\\\\\\"#include <shadowmap_pars_fragment>\\\\\\\",i)}async update(){const e=this.node;if(!e.assemblerController)return;const t=\\\\\\\"PCSS\\\\\\\";this.node.pv.shadowPCSS?e.assemblerController.addFilterFragmentShaderCallback(t,(e=>YC.filterFragmentShader(this.node,e))):e.assemblerController.removeFilterFragmentShaderCallback(t)}static async update(e){e.controllers.PCSS.update()}static PARAM_CALLBACK_setRecompileRequired(e){e.controllers.PCSS.update()}}const $C={uniforms:!0};class QC extends(XC(JE(WE(FC(zE($E(eC(wC(xC(GC(mC(pC(hC(OC(HE(ZE(Lo))))))))))))))))){}const JC=new QC;class KC extends QE{constructor(){super(...arguments),this.paramsConfig=JC,this.controllers={advancedCommon:new UE(this),alphaMap:new _C(this,$C),aoMap:new fC(this,$C),emissiveMap:new VC(this,$C),envMap:new bC(this,$C),lightMap:new AC(this,$C),map:new dC(this,$C),PCSS:new YC(this)},this.controllerNames=Object.keys(this.controllers)}static type(){return\\\\\\\"meshLambertBuilder\\\\\\\"}usedAssembler(){return mn.GL_MESH_LAMBERT}_create_assembler_controller(){return Rn.assemblersRegister.assembler(this,this.usedAssembler())}initializeNode(){this.params.onParamsCreated(\\\\\\\"init controllers\\\\\\\",(()=>{for(let e of this.controllerNames)this.controllers[e].initializeNode()}))}async cook(){for(let e of this.controllerNames)this.controllers[e].update();qE.update(this),KE.update(this),XE.update(this),DC.update(this),this.compileIfRequired(),this.setMaterial(this.material)}}function ZC(e){return class extends e{constructor(){super(...arguments),this.useBumpMap=Oo.BOOLEAN(0,{separatorBefore:!0,...cC(eM)}),this.bumpMap=Oo.NODE_PATH(\\\\\\\"\\\\\\\",lC(eM,\\\\\\\"useBumpMap\\\\\\\")),this.bumpScale=Oo.FLOAT(1,{range:[0,1],rangeLocked:[!1,!1],...lC(eM,\\\\\\\"useBumpMap\\\\\\\")}),this.bumpBias=Oo.FLOAT(0,{range:[0,1],rangeLocked:[!1,!1],...lC(eM,\\\\\\\"useBumpMap\\\\\\\")})}}}I.a;ZC(Lo);class eM extends uC{constructor(e,t){super(e,t),this.node=e}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(e){e.controllers.bumpMap.update()}}var tM;!function(e){e.TANGENT=\\\\\\\"tangent\\\\\\\",e.OBJECT=\\\\\\\"object\\\\\\\"}(tM||(tM={}));const nM=[tM.TANGENT,tM.OBJECT],iM={[tM.TANGENT]:w.Uc,[tM.OBJECT]:w.zb};function sM(e){return class extends e{constructor(){super(...arguments),this.useNormalMap=Oo.BOOLEAN(0,{separatorBefore:!0,...cC(rM)}),this.normalMap=Oo.NODE_PATH(jn.EMPTY,lC(rM,\\\\\\\"useNormalMap\\\\\\\")),this.normalMapType=Oo.INTEGER(0,{visibleIf:{useNormalMap:1},menu:{entries:nM.map(((e,t)=>({name:e,value:t})))}}),this.normalScale=Oo.VECTOR2([1,1],{visibleIf:{useNormalMap:1}})}}}I.a;sM(Lo);class rM extends uC{constructor(e,t){super(e,t),this.node=e}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 e=iM[nM[this.node.pv.normalMapType]];if(this._update_options.uniforms){this.node.material.uniforms.normalScale.value.copy(this.node.pv.normalScale)}const t=this.node.material;t.normalMapType=e,this._update_options.directParams&&t.normalScale.copy(this.node.pv.normalScale)}static async update(e){e.controllers.normalMap.update()}}function oM(e){return class extends e{constructor(){super(...arguments),this.useDisplacementMap=Oo.BOOLEAN(0,{separatorBefore:!0,...cC(aM)}),this.displacementMap=Oo.NODE_PATH(\\\\\\\"\\\\\\\",lC(aM,\\\\\\\"useDisplacementMap\\\\\\\")),this.displacementScale=Oo.FLOAT(1,{range:[0,1],rangeLocked:[!1,!1],...lC(aM,\\\\\\\"useDisplacementMap\\\\\\\")}),this.displacementBias=Oo.FLOAT(0,{range:[0,1],rangeLocked:[!1,!1],...lC(aM,\\\\\\\"useDisplacementMap\\\\\\\")})}}}I.a;oM(Lo);class aM extends uC{constructor(e,t){super(e,t),this.node=e}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 e=this.node.material;e.uniforms.displacementScale.value=this.node.pv.displacementScale,e.uniforms.displacementBias.value=this.node.pv.displacementBias}if(this._update_options.directParams){const e=this.node.material;e.displacementScale=this.node.pv.displacementScale,e.displacementBias=this.node.pv.displacementBias}}static async update(e){e.controllers.displacementMap.update()}}function cM(e){return class extends e{constructor(){super(...arguments),this.useMatcapMap=Oo.BOOLEAN(0,cC(lM)),this.matcapMap=Oo.NODE_PATH(jn.EMPTY,lC(lM,\\\\\\\"useMatcapMap\\\\\\\"))}}}I.a;cM(Lo);class lM extends uC{constructor(e,t){super(e,t),this.node=e}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(e){e.controllers.matcap.update()}}const uM={directParams:!0};class hM extends(oC(WE(zE(eC(sM(oM(ZC(pC(hC(cM(OC(sC(ZE(Lo)))))))))))))){}const dM=new hM;class pM extends FE{constructor(){super(...arguments),this.paramsConfig=dM,this.controllers={advancedCommon:new UE(this),alphaMap:new _C(this,uM),bumpMap:new eM(this,uM),displacementMap:new aM(this,uM),map:new dC(this,uM),matcap:new lM(this,uM),normalMap:new rM(this,uM)},this.controllerNames=Object.keys(this.controllers)}static type(){return\\\\\\\"meshMatcap\\\\\\\"}createMaterial(){return new Sf({vertexColors:!1,side:w.H,color:16777215,opacity:1})}initializeNode(){this.params.onParamsCreated(\\\\\\\"init controllers\\\\\\\",(()=>{for(let e of this.controllerNames)this.controllers[e].initializeNode()}))}async cook(){for(let e of this.controllerNames)this.controllers[e].update();rC.update(this),aC.update(this),XE.update(this),this.setMaterial(this.material)}}function _M(e){return class extends e{constructor(){super(...arguments),this.useSpecularMap=Oo.BOOLEAN(0,cC(mM)),this.specularMap=Oo.NODE_PATH(jn.EMPTY,lC(mM,\\\\\\\"useSpecularMap\\\\\\\"))}}}I.a;_M(Lo);class mM extends uC{constructor(e,t){super(e,t),this.node=e}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(e){e.controllers.specularMap.update()}}const fM={directParams:!0};class gM extends(oC(WE(NC(zE(eC(_M(sM(wC(xC(GC(oM(ZC(mC(pC(hC(OC(sC(ZE(Lo))))))))))))))))))){constructor(){super(...arguments),this.flatShading=Oo.BOOLEAN(0)}}const vM=new gM;class yM extends FE{constructor(){super(...arguments),this.paramsConfig=vM,this.controllers={advancedCommon:new UE(this),alphaMap:new _C(this,fM),aoMap:new fC(this,fM),bumpMap:new eM(this,fM),displacementMap:new aM(this,fM),emissiveMap:new VC(this,fM),envMap:new bC(this,fM),lightMap:new AC(this,fM),map:new dC(this,fM),normalMap:new rM(this,fM),specularMap:new mM(this,fM)},this.controllerNames=Object.keys(this.controllers)}static type(){return\\\\\\\"meshPhong\\\\\\\"}createMaterial(){return new Ef.a({vertexColors:!1,side:w.H,color:16777215,opacity:1})}initializeNode(){this.params.onParamsCreated(\\\\\\\"init controllers\\\\\\\",(()=>{for(let e of this.controllerNames)this.controllers[e].initializeNode()}))}async cook(){for(let e of this.controllerNames)this.controllers[e].update();rC.update(this),aC.update(this),XE.update(this),SC.update(this),this.material.flatShading!=this.pv.flatShading&&(this.material.flatShading=this.pv.flatShading,this.material.needsUpdate=!0),this.setMaterial(this.material)}}const xM={uniforms:!0};class bM extends(XC(JE(WE(FC(zE($E(eC(_M(sM(wC(xC(GC(oM(ZC(mC(pC(hC(OC(HE(ZE(Lo))))))))))))))))))))){}const wM=new bM;class AM extends QE{constructor(){super(...arguments),this.paramsConfig=wM,this.controllers={advancedCommon:new UE(this),alphaMap:new _C(this,xM),aoMap:new fC(this,xM),bumpMap:new eM(this,xM),displacementMap:new aM(this,xM),emissiveMap:new VC(this,xM),envMap:new bC(this,xM),lightMap:new AC(this,xM),map:new dC(this,xM),normalMap:new rM(this,xM),specularMap:new mM(this,xM),PCSS:new YC(this)},this.controllerNames=Object.keys(this.controllers)}static type(){return\\\\\\\"meshPhongBuilder\\\\\\\"}usedAssembler(){return mn.GL_MESH_PHONG}_create_assembler_controller(){return Rn.assemblersRegister.assembler(this,this.usedAssembler())}initializeNode(){this.params.onParamsCreated(\\\\\\\"init controllers\\\\\\\",(()=>{for(let e of this.controllerNames)this.controllers[e].initializeNode()}))}async cook(){for(let e of this.controllerNames)this.controllers[e].update();qE.update(this),KE.update(this),XE.update(this),DC.update(this),this.compileIfRequired(),this.setMaterial(this.material)}}function TM(e){return class extends e{constructor(){super(...arguments),this.useEnvMap=Oo.BOOLEAN(0,{separatorBefore:!0,...cC(EM)}),this.envMap=Oo.NODE_PATH(jn.EMPTY,lC(EM,\\\\\\\"useEnvMap\\\\\\\")),this.envMapIntensity=Oo.FLOAT(1,{visibleIf:{useEnvMap:1}}),this.refractionRatio=Oo.FLOAT(.98,{range:[-1,1],rangeLocked:[!1,!1],visibleIf:{useEnvMap:1}})}}}TM(Lo);class EM extends uC{constructor(e,t){super(e,t),this.node=e}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 e=this.node.material;e.uniforms.envMapIntensity.value=this.node.pv.envMapIntensity,e.uniforms.refractionRatio.value=this.node.pv.refractionRatio}if(this._update_options.directParams){const e=this.node.material;e.envMapIntensity=this.node.pv.envMapIntensity,e.refractionRatio=this.node.pv.refractionRatio}}static async update(e){e.controllers.envMap.update()}}function CM(e){return class extends e{constructor(){super(...arguments),this.useMetalnessMap=Oo.BOOLEAN(0,{separatorBefore:!0,...cC(MM)}),this.metalnessMap=Oo.NODE_PATH(jn.EMPTY,lC(MM,\\\\\\\"useMetalnessMap\\\\\\\")),this.metalness=Oo.FLOAT(1),this.useRoughnessMap=Oo.BOOLEAN(0,{separatorBefore:!0,...cC(MM)}),this.roughnessMap=Oo.NODE_PATH(jn.EMPTY,lC(MM,\\\\\\\"useRoughnessMap\\\\\\\")),this.roughness=Oo.FLOAT(.5)}}}I.a;CM(Lo);class MM extends uC{constructor(e,t){super(e,t),this.node=e}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(e){e.controllers.metalnessRoughnessMap.update()}}function NM(e){return class extends e{constructor(){super(...arguments),this.clearcoat=Oo.FLOAT(0,{separatorBefore:!0}),this.useClearCoatMap=Oo.BOOLEAN(0,cC(SM)),this.clearcoatMap=Oo.NODE_PATH(jn.EMPTY,lC(SM,\\\\\\\"useClearCoatMap\\\\\\\")),this.useClearCoatNormalMap=Oo.BOOLEAN(0,cC(SM)),this.clearcoatNormalMap=Oo.NODE_PATH(jn.EMPTY,lC(SM,\\\\\\\"useClearCoatNormalMap\\\\\\\")),this.clearcoatNormalScale=Oo.VECTOR2([1,1],{visibleIf:{useClearCoatNormalMap:1}}),this.clearcoatRoughness=Oo.FLOAT(0),this.useClearCoatRoughnessMap=Oo.BOOLEAN(0,cC(SM)),this.clearcoatRoughnessMap=Oo.NODE_PATH(jn.EMPTY,lC(SM,\\\\\\\"useClearCoatRoughnessMap\\\\\\\")),this.reflectivity=Oo.FLOAT(.5,{range:[0,1],rangeLocked:[!0,!0]}),this.useSheen=Oo.BOOLEAN(0),this.sheen=Oo.COLOR([1,1,1],{visibleIf:{useSheen:1}}),this.transmission=Oo.FLOAT(0,{range:[0,1]}),this.useTransmissionMap=Oo.BOOLEAN(0),this.transmissionMap=Oo.NODE_PATH(jn.EMPTY,{visibleIf:{useTransmissionMap:1}})}}}NM(Lo);class SM extends uC{constructor(e,t){super(e,t),this.node=e,this._sheenClone=new M.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)}async update(){if(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_options.uniforms){const e=this.node.material;e.uniforms.clearcoat.value=this.node.pv.clearcoat,e.uniforms.clearcoatNormalScale.value.copy(this.node.pv.clearcoatNormalScale),e.uniforms.clearcoatRoughness.value=this.node.pv.clearcoatRoughness,e.uniforms.reflectivity.value=this.node.pv.reflectivity,e.uniforms.transmission.value=this.node.pv.transmission,this.node.pv.useSheen?(this._sheenClone.copy(this.node.pv.sheen),e.uniforms.sheen.value=this._sheenClone):e.uniforms.sheen.value=null}if(this._update_options.directParams){const e=this.node.material;e.clearcoat=this.node.pv.clearcoat,e.clearcoatNormalScale.copy(this.node.pv.clearcoatNormalScale),e.clearcoatRoughness=this.node.pv.clearcoatRoughness,e.reflectivity=this.node.pv.reflectivity,this.node.pv.useSheen?(this._sheenClone.copy(this.node.pv.sheen),e.sheen=this._sheenClone):e.sheen=null,e.transmission=this.node.pv.transmission}}static async update(e){e.controllers.physical.update()}}const OM={directParams:!0};class LM extends(oC(WE(NC(zE(eC(NM(CM(sM(wC(TM(GC(oM(ZC(mC(pC(hC(OC(sC(ZE(Lo)))))))))))))))))))){}const PM=new LM;class RM extends FE{constructor(){super(...arguments),this.paramsConfig=PM,this.controllers={advancedCommon:new UE(this),alphaMap:new _C(this,OM),aoMap:new fC(this,OM),bumpMap:new eM(this,OM),displacementMap:new aM(this,OM),emissiveMap:new VC(this,OM),envMap:new EM(this,OM),lightMap:new AC(this,OM),map:new dC(this,OM),metalnessRoughnessMap:new MM(this,OM),normalMap:new rM(this,OM),physical:new SM(this,OM)},this.controllerNames=Object.keys(this.controllers)}static type(){return\\\\\\\"meshPhysical\\\\\\\"}createMaterial(){return new Tf.a({vertexColors:!1,side:w.H,color:16777215,opacity:1,metalness:1,roughness:0})}initializeNode(){this.params.onParamsCreated(\\\\\\\"init controllers\\\\\\\",(()=>{for(let e of this.controllerNames)this.controllers[e].initializeNode()}))}async cook(){for(let e of this.controllerNames)this.controllers[e].update();rC.update(this),aC.update(this),XE.update(this),SC.update(this),this.setMaterial(this.material)}}const IM={uniforms:!0};class FM extends(function(e){return class extends(XC(JE(WE(FC(zE($E(e))))))){}}(eC(NM(CM(sM(wC(TM(GC(oM(ZC(mC(pC(hC(OC(HE(ZE(Lo))))))))))))))))){}const DM=new FM;class kM extends QE{constructor(){super(...arguments),this.paramsConfig=DM,this.controllers={advancedCommon:new UE(this),alphaMap:new _C(this,IM),aoMap:new fC(this,IM),bumpMap:new eM(this,IM),displacementMap:new aM(this,IM),emissiveMap:new VC(this,IM),envMap:new EM(this,IM),lightMap:new AC(this,IM),map:new dC(this,IM),metalnessRoughnessMap:new MM(this,IM),normalMap:new rM(this,IM),physical:new SM(this,IM),PCSS:new YC(this)},this.controllerNames=Object.keys(this.controllers)}static type(){return\\\\\\\"meshPhysicalBuilder\\\\\\\"}usedAssembler(){return mn.GL_MESH_PHYSICAL}_create_assembler_controller(){return Rn.assemblersRegister.assembler(this,this.usedAssembler())}initializeNode(){this.params.onParamsCreated(\\\\\\\"init controllers\\\\\\\",(()=>{for(let e of this.controllerNames)this.controllers[e].initializeNode()}))}async cook(){for(let e of this.controllerNames)this.controllers[e].update();qE.update(this),KE.update(this),XE.update(this),DC.update(this),this.compileIfRequired(),this.setMaterial(this.material)}}const BM={directParams:!0};class zM extends(oC(WE(NC(zE(eC(CM(sM(wC(TM(GC(oM(ZC(mC(pC(hC(OC(sC(ZE(Lo))))))))))))))))))){}const UM=new zM;class GM extends FE{constructor(){super(...arguments),this.paramsConfig=UM,this.controllers={advancedCommon:new UE(this),alphaMap:new _C(this,BM),aoMap:new fC(this,BM),bumpMap:new eM(this,BM),displacementMap:new aM(this,BM),emissiveMap:new VC(this,BM),envMap:new EM(this,BM),lightMap:new AC(this,BM),map:new dC(this,BM),metalnessRoughnessMap:new MM(this,BM),normalMap:new rM(this,BM)},this.controllerNames=Object.keys(this.controllers)}static type(){return\\\\\\\"meshStandard\\\\\\\"}createMaterial(){return new Wi.a({vertexColors:!1,side:w.H,color:16777215,opacity:1,metalness:1,roughness:0})}initializeNode(){this.params.onParamsCreated(\\\\\\\"init controllers\\\\\\\",(()=>{for(let e of this.controllerNames)this.controllers[e].initializeNode()}))}async cook(){for(let e of this.controllerNames)this.controllers[e].update();rC.update(this),aC.update(this),XE.update(this),SC.update(this),this.setMaterial(this.material)}}const VM={uniforms:!0};class jM extends(XC(JE(WE(FC(zE($E(eC(CM(sM(wC(TM(GC(oM(ZC(mC(pC(hC(OC(HE(ZE(Lo))))))))))))))))))))){}const HM=new jM;class qM extends QE{constructor(){super(...arguments),this.paramsConfig=HM,this.controllers={advancedCommon:new UE(this),alphaMap:new _C(this,VM),aoMap:new fC(this,VM),bumpMap:new eM(this,VM),displacementMap:new aM(this,VM),emissiveMap:new VC(this,VM),envMap:new EM(this,VM),lightMap:new AC(this,VM),map:new dC(this,VM),metalnessRoughnessMap:new MM(this,VM),normalMap:new rM(this,VM),PCSS:new YC(this)},this.controllerNames=Object.keys(this.controllers)}static type(){return\\\\\\\"meshStandardBuilder\\\\\\\"}usedAssembler(){return mn.GL_MESH_STANDARD}_create_assembler_controller(){return Rn.assemblersRegister.assembler(this,this.usedAssembler())}initializeNode(){this.params.onParamsCreated(\\\\\\\"init controllers\\\\\\\",(()=>{for(let e of this.controllerNames)this.controllers[e].initializeNode()}))}async cook(){for(let e of this.controllerNames)this.controllers[e].update();qE.update(this),KE.update(this),XE.update(this),DC.update(this),this.compileIfRequired(),this.setMaterial(this.material)}}var WM,XM,YM,$M=G.meshphong_frag.slice(0,G.meshphong_frag.indexOf(\\\\\\\"void main() {\\\\\\\")),QM=G.meshphong_frag.slice(G.meshphong_frag.indexOf(\\\\\\\"void main() {\\\\\\\")),JM={uniforms:k.merge([H.phong.uniforms,{thicknessMap:{value:null},thicknessColor:{value:new M.a(16777215)},thicknessDistortion:{value:.1},thicknessAmbient:{value:0},thicknessAttenuation:{value:.1},thicknessPower:{value:2},thicknessScale:{value:10}}]),vertexShader:[\\\\\\\"#define USE_UV\\\\\\\",G.meshphong_vert].join(\\\\\\\"\\\\n\\\\\\\"),fragmentShader:[\\\\\\\"#define USE_UV\\\\\\\",\\\\\\\"#define SUBSURFACE\\\\\\\",$M,\\\\\\\"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;\\\\\\\",\\\\\\\"}\\\\\\\",QM.replace(\\\\\\\"#include <lights_fragment_begin>\\\\\\\",(WM=G.lights_fragment_begin,XM=\\\\\\\"RE_Direct( directLight, geometry, material, reflectedLight );\\\\\\\",YM=[\\\\\\\"RE_Direct( directLight, geometry, material, reflectedLight );\\\\\\\",\\\\\\\"#if defined( SUBSURFACE ) && defined( USE_UV )\\\\\\\",\\\\\\\" RE_Direct_Scattering(directLight, vUv, geometry, reflectedLight);\\\\\\\",\\\\\\\"#endif\\\\\\\"].join(\\\\\\\"\\\\n\\\\\\\"),WM.split(XM).join(YM)))].join(\\\\\\\"\\\\n\\\\\\\")};function KM(e){return{cook:!1,callback:(t,n)=>{iN.PARAM_CALLBACK_update_uniformColor(t,n,e)}}}function ZM(e){return{cook:!1,callback:(t,n)=>{iN.PARAM_CALLBACK_update_uniformN(t,n,e)}}}const eN={uniforms:!0};class tN extends(oC(WE(FC(zE(eC(pC(hC(OC(function(e){return class extends e{constructor(){var e;super(...arguments),this.diffuse=Oo.COLOR([1,1,1],{...KM(\\\\\\\"diffuse\\\\\\\")}),this.shininess=Oo.FLOAT(1,{range:[0,1e3]}),this.thicknessMap=Oo.NODE_PATH(jn.EMPTY,{nodeSelection:{context:Ei.COP},...(e=\\\\\\\"thicknessMap\\\\\\\",{cook:!1,callback:(t,n)=>{iN.PARAM_CALLBACK_update_uniformTexture(t,n,e)}})}),this.thicknessColor=Oo.COLOR([.5,.3,0],{...KM(\\\\\\\"thicknessColor\\\\\\\")}),this.thicknessDistortion=Oo.FLOAT(.1,{...ZM(\\\\\\\"thicknessDistortion\\\\\\\")}),this.thicknessAmbient=Oo.FLOAT(.4,{...ZM(\\\\\\\"thicknessAmbient\\\\\\\")}),this.thicknessAttenuation=Oo.FLOAT(.8,{...ZM(\\\\\\\"thicknessAttenuation\\\\\\\")}),this.thicknessPower=Oo.FLOAT(2,{range:[0,10],...ZM(\\\\\\\"thicknessPower\\\\\\\")}),this.thicknessScale=Oo.FLOAT(16,{range:[0,100],...ZM(\\\\\\\"thicknessScale\\\\\\\")})}}}(ZE(Lo))))))))))){}const nN=new tN;class iN extends FE{constructor(){super(...arguments),this.paramsConfig=nN,this.controllers={advancedCommon:new UE(this),alphaMap:new _C(this,eN),map:new dC(this,eN)},this.controllerNames=Object.keys(this.controllers)}static type(){return\\\\\\\"meshSubsurfaceScattering\\\\\\\"}createMaterial(){const e=new B({uniforms:k.clone(JM.uniforms),vertexShader:JM.vertexShader,fragmentShader:JM.fragmentShader,lights:!0});return e.extensions.derivatives=!0,e}initializeNode(){this.params.onParamsCreated(\\\\\\\"init controllers\\\\\\\",(()=>{for(let e of this.controllerNames)this.controllers[e].initializeNode()}))}async cook(){for(let e of this.controllerNames)this.controllers[e].update();aC.update(this),XE.update(this),DC.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(e,t,n){e.material.uniforms[n].value=t.value}static PARAM_CALLBACK_update_uniformColor(e,t,n){t.parent_param&&e.material.uniforms[n].value.copy(t.parent_param.value)}static PARAM_CALLBACK_update_uniformTexture(e,t,n){e.update_map(t,n)}async update_map(e,t){const n=e.value.nodeWithContext(Ei.COP);n||(this.material.uniforms[t].value=null);const i=n,s=await i.compute();this.material.uniforms[t].value=s.texture()}}function sN(e){return class extends e{constructor(){super(...arguments),this.useGradientMap=Oo.BOOLEAN(0,cC(rN)),this.gradientMap=Oo.NODE_PATH(jn.EMPTY,lC(rN,\\\\\\\"useGradientMap\\\\\\\"))}}}I.a;sN(Lo);class rN extends uC{constructor(e,t){super(e,t),this.node=e}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(e){e.controllers.gradientMap.update()}}const oN={directParams:!0};class aN extends(oC(WE(NC(zE(eC(sM(wC(sN(GC(oM(ZC(mC(pC(hC(OC(sC(ZE(Lo)))))))))))))))))){}const cN=new aN;class lN extends FE{constructor(){super(...arguments),this.paramsConfig=cN,this.controllers={advancedCommon:new UE(this),alphaMap:new _C(this,oN),aoMap:new fC(this,oN),bumpMap:new eM(this,oN),displacementMap:new aM(this,oN),emissiveMap:new VC(this,oN),gradientMap:new rN(this,oN),lightMap:new AC(this,oN),map:new dC(this,oN),normalMap:new rM(this,oN)},this.controllerNames=Object.keys(this.controllers)}static type(){return\\\\\\\"meshToon\\\\\\\"}createMaterial(){return new Cf({vertexColors:!1,side:w.H,color:16777215,opacity:1})}initializeNode(){this.params.onParamsCreated(\\\\\\\"init controllers\\\\\\\",(()=>{for(let e of this.controllerNames)this.controllers[e].initializeNode()}))}async cook(){for(let e of this.controllerNames)this.controllers[e].update();rC.update(this),aC.update(this),XE.update(this),SC.update(this),this.setMaterial(this.material)}}const uN={directParams:!0};class hN extends(JE(zE(eC(pC(hC(OC(sC(function(e){return class extends e{constructor(){super(...arguments),this.size=Oo.FLOAT(1),this.sizeAttenuation=Oo.BOOLEAN(1)}}}(ZE(Lo)))))))))){}const dN=new hN;class pN extends FE{constructor(){super(...arguments),this.paramsConfig=dN,this.controllers={advancedCommon:new UE(this),alphaMap:new _C(this,uN),map:new dC(this,uN)},this.controllerNames=Object.keys(this.controllers)}static type(){return\\\\\\\"points\\\\\\\"}createMaterial(){return new qi.a({vertexColors:!1,side:w.H,color:16777215,opacity:1})}initializeNode(){this.params.onParamsCreated(\\\\\\\"init controllers\\\\\\\",(()=>{for(let e of this.controllerNames)this.controllers[e].initializeNode()}))}async cook(){for(let e of this.controllerNames)this.controllers[e].update();rC.update(this),KE.update(this),this.material.size=this.pv.size,this.material.sizeAttenuation=this.pv.sizeAttenuation,this.setMaterial(this.material)}}class _N extends(JE(WE(zE($E(eC(HE(ZE(Lo)))))))){}const mN=new _N;class fN extends QE{constructor(){super(...arguments),this.paramsConfig=mN,this.controllers={advancedCommon:new UE(this)},this.controllerNames=Object.keys(this.controllers)}static type(){return\\\\\\\"pointsBuilder\\\\\\\"}usedAssembler(){return mn.GL_POINTS}_create_assembler_controller(){return Rn.assemblersRegister.assembler(this,this.usedAssembler())}initializeNode(){this.params.onParamsCreated(\\\\\\\"init controllers\\\\\\\",(()=>{for(let e of this.controllerNames)this.controllers[e].initializeNode()}))}async cook(){for(let e of this.controllerNames)this.controllers[e].update();qE.update(this),KE.update(this),XE.update(this),this.compileIfRequired(),this.setMaterial(this.material)}}class gN extends(zE(sC(Lo))){}const vN=new gN;class yN extends FE{constructor(){super(...arguments),this.paramsConfig=vN,this.controllers={advancedCommon:new UE(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 e of this.controllerNames)this.controllers[e].initializeNode()}))}async cook(){for(let e of this.controllerNames)this.controllers[e].update();rC.update(this),this.setMaterial(this.material)}}var xN=function(){var e=xN.SkyShader,t=new B({name:\\\\\\\"SkyShader\\\\\\\",fragmentShader:e.fragmentShader,vertexShader:e.vertexShader,uniforms:k.clone(e.uniforms),side:w.i,depthWrite:!1});z.a.call(this,new P(1,1,1),t)};xN.prototype=Object.create(z.a.prototype),xN.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:[\\\\\\\"uniform vec3 sunPosition;\\\\\\\",\\\\\\\"uniform float rayleigh;\\\\\\\",\\\\\\\"uniform float turbidity;\\\\\\\",\\\\\\\"uniform float mieCoefficient;\\\\\\\",\\\\\\\"uniform vec3 up;\\\\\\\",\\\\\\\"varying vec3 vWorldPosition;\\\\\\\",\\\\\\\"varying vec3 vSunDirection;\\\\\\\",\\\\\\\"varying float vSunfade;\\\\\\\",\\\\\\\"varying vec3 vBetaR;\\\\\\\",\\\\\\\"varying vec3 vBetaM;\\\\\\\",\\\\\\\"varying float vSunE;\\\\\\\",\\\\\\\"const float e = 2.71828182845904523536028747135266249775724709369995957;\\\\\\\",\\\\\\\"const float pi = 3.141592653589793238462643383279502884197169;\\\\\\\",\\\\\\\"const vec3 lambda = vec3( 680E-9, 550E-9, 450E-9 );\\\\\\\",\\\\\\\"const vec3 totalRayleigh = vec3( 5.804542996261093E-6, 1.3562911419845635E-5, 3.0265902468824876E-5 );\\\\\\\",\\\\\\\"const float v = 4.0;\\\\\\\",\\\\\\\"const vec3 K = vec3( 0.686, 0.678, 0.666 );\\\\\\\",\\\\\\\"const vec3 MieConst = vec3( 1.8399918514433978E14, 2.7798023919660528E14, 4.0790479543861094E14 );\\\\\\\",\\\\\\\"const float cutoffAngle = 1.6110731556870734;\\\\\\\",\\\\\\\"const float steepness = 1.5;\\\\\\\",\\\\\\\"const float EE = 1000.0;\\\\\\\",\\\\\\\"float sunIntensity( float zenithAngleCos ) {\\\\\\\",\\\\\\\"\\\\tzenithAngleCos = clamp( zenithAngleCos, -1.0, 1.0 );\\\\\\\",\\\\\\\"\\\\treturn EE * max( 0.0, 1.0 - pow( e, -( ( cutoffAngle - acos( zenithAngleCos ) ) / steepness ) ) );\\\\\\\",\\\\\\\"}\\\\\\\",\\\\\\\"vec3 totalMie( float T ) {\\\\\\\",\\\\\\\"\\\\tfloat c = ( 0.2 * T ) * 10E-18;\\\\\\\",\\\\\\\"\\\\treturn 0.434 * c * MieConst;\\\\\\\",\\\\\\\"}\\\\\\\",\\\\\\\"void main() {\\\\\\\",\\\\\\\"\\\\tvec4 worldPosition = modelMatrix * vec4( position, 1.0 );\\\\\\\",\\\\\\\"\\\\tvWorldPosition = worldPosition.xyz;\\\\\\\",\\\\\\\"\\\\tgl_Position = projectionMatrix * modelViewMatrix * vec4( position, 1.0 );\\\\\\\",\\\\\\\"\\\\tgl_Position.z = gl_Position.w;\\\\\\\",\\\\\\\"\\\\tvSunDirection = normalize( sunPosition );\\\\\\\",\\\\\\\"\\\\tvSunE = sunIntensity( dot( vSunDirection, up ) );\\\\\\\",\\\\\\\"\\\\tvSunfade = 1.0 - clamp( 1.0 - exp( ( sunPosition.y / 450000.0 ) ), 0.0, 1.0 );\\\\\\\",\\\\\\\"\\\\tfloat rayleighCoefficient = rayleigh - ( 1.0 * ( 1.0 - vSunfade ) );\\\\\\\",\\\\\\\"\\\\tvBetaR = totalRayleigh * rayleighCoefficient;\\\\\\\",\\\\\\\"\\\\tvBetaM = totalMie( turbidity ) * mieCoefficient;\\\\\\\",\\\\\\\"}\\\\\\\"].join(\\\\\\\"\\\\n\\\\\\\"),fragmentShader:[\\\\\\\"varying vec3 vWorldPosition;\\\\\\\",\\\\\\\"varying vec3 vSunDirection;\\\\\\\",\\\\\\\"varying float vSunfade;\\\\\\\",\\\\\\\"varying vec3 vBetaR;\\\\\\\",\\\\\\\"varying vec3 vBetaM;\\\\\\\",\\\\\\\"varying float vSunE;\\\\\\\",\\\\\\\"uniform float mieDirectionalG;\\\\\\\",\\\\\\\"uniform vec3 up;\\\\\\\",\\\\\\\"const vec3 cameraPos = vec3( 0.0, 0.0, 0.0 );\\\\\\\",\\\\\\\"const float pi = 3.141592653589793238462643383279502884197169;\\\\\\\",\\\\\\\"const float n = 1.0003;\\\\\\\",\\\\\\\"const float N = 2.545E25;\\\\\\\",\\\\\\\"const float rayleighZenithLength = 8.4E3;\\\\\\\",\\\\\\\"const float mieZenithLength = 1.25E3;\\\\\\\",\\\\\\\"const float sunAngularDiameterCos = 0.999956676946448443553574619906976478926848692873900859324;\\\\\\\",\\\\\\\"const float THREE_OVER_SIXTEENPI = 0.05968310365946075;\\\\\\\",\\\\\\\"const float ONE_OVER_FOURPI = 0.07957747154594767;\\\\\\\",\\\\\\\"float rayleighPhase( float cosTheta ) {\\\\\\\",\\\\\\\"\\\\treturn THREE_OVER_SIXTEENPI * ( 1.0 + pow( cosTheta, 2.0 ) );\\\\\\\",\\\\\\\"}\\\\\\\",\\\\\\\"float hgPhase( float cosTheta, float g ) {\\\\\\\",\\\\\\\"\\\\tfloat g2 = pow( g, 2.0 );\\\\\\\",\\\\\\\"\\\\tfloat inverse = 1.0 / pow( 1.0 - 2.0 * g * cosTheta + g2, 1.5 );\\\\\\\",\\\\\\\"\\\\treturn ONE_OVER_FOURPI * ( ( 1.0 - g2 ) * inverse );\\\\\\\",\\\\\\\"}\\\\\\\",\\\\\\\"void main() {\\\\\\\",\\\\\\\"\\\\tvec3 direction = normalize( vWorldPosition - cameraPos );\\\\\\\",\\\\\\\"\\\\tfloat zenithAngle = acos( max( 0.0, dot( up, direction ) ) );\\\\\\\",\\\\\\\"\\\\tfloat inverse = 1.0 / ( cos( zenithAngle ) + 0.15 * pow( 93.885 - ( ( zenithAngle * 180.0 ) / pi ), -1.253 ) );\\\\\\\",\\\\\\\"\\\\tfloat sR = rayleighZenithLength * inverse;\\\\\\\",\\\\\\\"\\\\tfloat sM = mieZenithLength * inverse;\\\\\\\",\\\\\\\"\\\\tvec3 Fex = exp( -( vBetaR * sR + vBetaM * sM ) );\\\\\\\",\\\\\\\"\\\\tfloat cosTheta = dot( direction, vSunDirection );\\\\\\\",\\\\\\\"\\\\tfloat rPhase = rayleighPhase( cosTheta * 0.5 + 0.5 );\\\\\\\",\\\\\\\"\\\\tvec3 betaRTheta = vBetaR * rPhase;\\\\\\\",\\\\\\\"\\\\tfloat mPhase = hgPhase( cosTheta, mieDirectionalG );\\\\\\\",\\\\\\\"\\\\tvec3 betaMTheta = vBetaM * mPhase;\\\\\\\",\\\\\\\"\\\\tvec3 Lin = pow( vSunE * ( ( betaRTheta + betaMTheta ) / ( vBetaR + vBetaM ) ) * ( 1.0 - Fex ), vec3( 1.5 ) );\\\\\\\",\\\\\\\"\\\\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 ) );\\\\\\\",\\\\\\\"\\\\tfloat theta = acos( direction.y ); // elevation --\\\\x3e y-axis, [-pi/2, pi/2]\\\\\\\",\\\\\\\"\\\\tfloat phi = atan( direction.z, direction.x ); // azimuth --\\\\x3e x-axis [-pi/2, pi/2]\\\\\\\",\\\\\\\"\\\\tvec2 uv = vec2( phi, theta ) / vec2( 2.0 * pi, pi ) + vec2( 0.5, 0.0 );\\\\\\\",\\\\\\\"\\\\tvec3 L0 = vec3( 0.1 ) * Fex;\\\\\\\",\\\\\\\"\\\\tfloat sundisk = smoothstep( sunAngularDiameterCos, sunAngularDiameterCos + 0.00002, cosTheta );\\\\\\\",\\\\\\\"\\\\tL0 += ( vSunE * 19000.0 * Fex ) * sundisk;\\\\\\\",\\\\\\\"\\\\tvec3 texColor = ( Lin + L0 ) * 0.04 + vec3( 0.0, 0.0003, 0.00075 );\\\\\\\",\\\\\\\"\\\\tvec3 retColor = pow( texColor, vec3( 1.0 / ( 1.2 + ( 1.2 * vSunfade ) ) ) );\\\\\\\",\\\\\\\"\\\\tgl_FragColor = vec4( retColor, 1.0 );\\\\\\\",\\\\\\\"#include <tonemapping_fragment>\\\\\\\",\\\\\\\"#include <encodings_fragment>\\\\\\\",\\\\\\\"}\\\\\\\"].join(\\\\\\\"\\\\n\\\\\\\")};const bN=new class extends Lo{constructor(){super(...arguments),this.turbidity=Oo.FLOAT(2,{range:[0,20]}),this.rayleigh=Oo.FLOAT(1,{range:[0,4]}),this.mieCoefficient=Oo.FLOAT(.005),this.mieDirectional=Oo.FLOAT(.8),this.inclination=Oo.FLOAT(.5),this.azimuth=Oo.FLOAT(.25),this.up=Oo.VECTOR3([0,1,0])}};class wN extends FE{constructor(){super(...arguments),this.paramsConfig=bN}static type(){return\\\\\\\"sky\\\\\\\"}createMaterial(){const e=(new xN).material;return e.depthWrite=!0,e}async cook(){const e=this.material.uniforms;e.turbidity.value=this.pv.turbidity,e.rayleigh.value=this.pv.rayleigh,e.mieCoefficient.value=this.pv.mieCoefficient,e.mieDirectionalG.value=this.pv.mieDirectional,e.up.value.copy(this.pv.up);const t=Math.PI*(this.pv.inclination-.5),n=2*Math.PI*(this.pv.azimuth-.5);e.sunPosition.value.x=Math.cos(n),e.sunPosition.value.y=Math.sin(n)*Math.sin(t),e.sunPosition.value.z=Math.sin(n)*Math.cos(t),this.setMaterial(this.material)}}var AN=\\\\\\\"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}\\\\\\\",TN=\\\\\\\"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 EN={u_Color:{value:new M.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 CN=n(15);function MN(e){return class extends e{constructor(){super(...arguments),this.color=Oo.COLOR([1,1,1]),this.stepSize=Oo.FLOAT(.01),this.density=Oo.FLOAT(1),this.shadowDensity=Oo.FLOAT(1),this.lightDir=Oo.VECTOR3([-1,-1,-1])}}}MN(Lo);class NN{constructor(e){this.node=e}static render_hook(e,t,n,i,s,r,o){if(o){this._object_bbox.setFromObject(o);const e=s;e.uniforms.u_BoundingBoxMin.value.copy(this._object_bbox.min),e.uniforms.u_BoundingBoxMax.value.copy(this._object_bbox.max)}}update_uniforms_from_params(){const e=this.node.material.uniforms;e.u_Color.value.copy(this.node.pv.color),e.u_StepSize.value=this.node.pv.stepSize,e.u_VolumeDensity.value=this.node.pv.density,e.u_ShadowDensity.value=this.node.pv.shadowDensity;const t=e.u_DirectionalLightDirection.value,n=this.node.pv.lightDir;t&&(t.x=n.x,t.y=n.y,t.z=n.z)}}NN._object_bbox=new CN.a;class SN extends(MN(Lo)){}const ON=new SN;class LN extends FE{constructor(){super(...arguments),this.paramsConfig=ON,this._volume_controller=new NN(this)}static type(){return\\\\\\\"volume\\\\\\\"}createMaterial(){const e=new B({vertexShader:AN,fragmentShader:TN,side:w.H,transparent:!0,depthTest:!0,uniforms:k.clone(EN)});return Gs.add_user_data_render_hook(e,NN.render_hook.bind(NN)),e}initializeNode(){}async cook(){this._volume_controller.update_uniforms_from_params(),this.setMaterial(this.material)}}class PN extends($E(MN(Lo))){}const RN=new PN;class IN extends QE{constructor(){super(...arguments),this.paramsConfig=RN,this._volume_controller=new NN(this)}static type(){return\\\\\\\"volumeBuilder\\\\\\\"}usedAssembler(){return mn.GL_VOLUME}_create_assembler_controller(){return Rn.assemblersRegister.assembler(this,this.usedAssembler())}initializeNode(){}async cook(){this._volume_controller.update_uniforms_from_params(),this.compileIfRequired(),this.setMaterial(this.material)}}class FN extends Mo{static context(){return Ei.MAT}cook(){this.cookController.endCook()}}class DN extends FN{}class kN extends DN{constructor(){super(...arguments),this._children_controller_context=Ei.ANIM}static type(){return Ci.ANIM}createNode(e,t){return super.createNode(e,t)}children(){return super.children()}nodesByType(e){return super.nodesByType(e)}}class BN extends DN{constructor(){super(...arguments),this._children_controller_context=Ei.COP}static type(){return Ci.COP}createNode(e,t){return super.createNode(e,t)}children(){return super.children()}nodesByType(e){return super.nodesByType(e)}}class zN extends DN{constructor(){super(...arguments),this._children_controller_context=Ei.EVENT}static type(){return Ci.EVENT}createNode(e,t){return super.createNode(e,t)}children(){return super.children()}nodesByType(e){return super.nodesByType(e)}}class UN extends DN{constructor(){super(...arguments),this._children_controller_context=Ei.MAT}static type(){return Ci.MAT}createNode(e,t){return super.createNode(e,t)}children(){return super.children()}nodesByType(e){return super.nodesByType(e)}}class GN extends FN{constructor(){super(...arguments),this.paramsConfig=new Rm,this.effectsComposerController=new Im(this),this.displayNodeController=new mm(this,this.effectsComposerController.displayNodeControllerCallbacks()),this._children_controller_context=Ei.POST}static type(){return Ci.POST}createNode(e,t){return super.createNode(e,t)}children(){return super.children()}nodesByType(e){return super.nodesByType(e)}}class VN extends DN{constructor(){super(...arguments),this._children_controller_context=Ei.ROP}static type(){return Ci.ROP}createNode(e,t){return super.createNode(e,t)}children(){return super.children()}nodesByType(e){return super.nodesByType(e)}}var jN=n(89);const HN=\\\\\\\"parent object\\\\\\\",qN=[HN,HN,HN,HN];var WN;!function(e){e[e.MANAGER=0]=\\\\\\\"MANAGER\\\\\\\",e[e.CAMERA=2]=\\\\\\\"CAMERA\\\\\\\",e[e.LIGHT=3]=\\\\\\\"LIGHT\\\\\\\"}(WN||(WN={}));class XN extends Mo{constructor(){super(...arguments),this.renderOrder=WN.MANAGER,this._children_group=this._create_children_group(),this._attachableToHierarchy=!0,this._used_in_scene=!0}static context(){return Ei.OBJ}static displayedInputNames(){return qN}_create_children_group(){const e=new on.a;return e.matrixAutoUpdate=!1,e}attachableToHierarchy(){return this._attachableToHierarchy}usedInScene(){return this._used_in_scene}addObjectToParent(e){this.attachableToHierarchy()&&e.add(this.object)}removeObjectFromParent(){if(this.attachableToHierarchy()){const e=this.object.parent;e&&e.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 e=this.createObject();return e.node=this,e.add(this._children_group),e}set_object_name(){this._object&&(this._object.name=this.path(),this._children_group.name=`${this.path()}:parented_outputs`)}createObject(){const e=new ee.a;return e.matrixAutoUpdate=!1,e}isDisplayNodeCooking(){if(this.displayNodeController){const e=this.displayNodeController.displayNode();if(e)return e.cookController.isCooking()}return!1}isDisplayed(){var e,t;return(null===(t=null===(e=this.flags)||void 0===e?void 0:e.display)||void 0===t?void 0:t.active())||!1}}class YN extends XN{constructor(){super(...arguments),this.flags=new ai(this),this.renderOrder=WN.LIGHT,this._color_with_intensity=new M.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 $N=new class extends Lo{constructor(){super(...arguments),this.color=Oo.COLOR([1,1,1],{conversion:Or.SRGB_TO_LINEAR}),this.intensity=Oo.FLOAT(1)}};class QN extends YN{constructor(){super(...arguments),this.paramsConfig=$N}static type(){return\\\\\\\"ambientLight\\\\\\\"}createLight(){const e=new jN.a;return e.matrixAutoUpdate=!1,e}initializeNode(){this.io.inputs.setCount(0,1)}updateLightParams(){this.light.color=this.pv.color,this.light.intensity=this.pv.intensity}}class JN extends pv.a{constructor(e,t,n=10,i=10){super(e,t),this.type=\\\\\\\"RectAreaLight\\\\\\\",this.width=n,this.height=i}copy(e){return super.copy(e),this.width=e.width,this.height=e.height,this}toJSON(e){const t=super.toJSON(e);return t.object.width=this.width,t.object.height=this.height,t}}JN.prototype.isRectAreaLight=!0;var KN,ZN=n(60),eS={init:function(){var 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aS=\\\\\\\"_cook_main_without_inputs_when_dirty\\\\\\\";class cS{constructor(e){this.node=e,this._cook_main_without_inputs_when_dirty_bound=this._cook_main_without_inputs_when_dirty.bind(this),this._core_transform=new rS,this._keep_pos_when_parenting_m_object=new C.a,this._keep_pos_when_parenting_m_new_parent_inv=new C.a}initializeNode(){this.node.dirtyController.hasHook(aS)||this.node.dirtyController.addPostDirtyHook(aS,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(e){const t=this.node.object;null==e||e.equals(t.matrix)?this._update_matrix_from_params_with_core_transform():(t.matrix.copy(e),t.dispatchEvent({type:\\\\\\\"change\\\\\\\"}))}_update_matrix_from_params_with_core_transform(){const e=this.node.object;let t=e.matrixAutoUpdate;t&&(e.matrixAutoUpdate=!1);const n=this._core_transform.matrix(this.node.pv.t,this.node.pv.r,this.node.pv.s,this.node.pv.scale,iS[this.node.pv.rotationOrder]);e.matrix.identity(),e.applyMatrix4(n),this._apply_look_at(),e.updateMatrix(),t&&(e.matrixAutoUpdate=!0),e.dispatchEvent({type:\\\\\\\"change\\\\\\\"})}_apply_look_at(){}set_params_from_matrix(e,t={}){rS.set_params_from_matrix(e,this.node,t)}static update_node_transform_params_if_required(e,t){e.transformController.update_node_transform_params_if_required(t)}update_node_transform_params_if_required(e){if(!this.node.pv.keepPosWhenParenting)return;if(!this.node.scene().loadingController.loaded())return;if(e==this.node.object.parent)return;const t=this.node.object;t.updateMatrixWorld(!0),e.updateMatrixWorld(!0),this._keep_pos_when_parenting_m_object.copy(t.matrixWorld),this._keep_pos_when_parenting_m_new_parent_inv.copy(e.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),rS.set_params_from_matrix(this._keep_pos_when_parenting_m_object,this.node,{scale:!0})}update_node_transform_params_from_object(e=!1){const t=this.node.object;e&&t.updateMatrix(),rS.set_params_from_matrix(t.matrix,this.node,{scale:!0})}static PARAM_CALLBACK_update_transform_from_object(e){e.transformController.update_node_transform_params_from_object()}}class lS{constructor(e){this.node=e}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(e){const t=e.root().getParentForNode(e);e.transformController&&t&&cS.update_node_transform_params_if_required(e,t),null!=e.io.inputs.input(0)?e.root().addToParentTransform(e):e.root().removeFromParentTransform(e)}on_input_updated(){lS.on_input_updated(this.node)}}oS(Lo);class uS extends YN{constructor(){super(...arguments),this.flags=new ai(this),this.hierarchyController=new lS(this),this.transformController=new cS(this)}initializeBaseNode(){super.initializeBaseNode(),this.hierarchyController.initializeNode(),this.transformController.initializeNode()}cook(){this.transformController.update(),this.updateLightParams(),this.updateShadowParams(),this.cookController.endCook()}}class hS{constructor(e,t,n){this.node=e,this._helperConstructor=t,this._name=n}initializeNode(){this.node.flags.display.onUpdate((()=>{this.update()}))}visible(){return this.node.flags.display.active()&&this.node.pv.showHelper}_createHelper(){const e=new this._helperConstructor(this.node,this._name);return e.build(),e}update(){this.visible()?(this._helper||(this._helper=this._createHelper()),this._helper&&(this.node.light.add(this._helper.object),this._helper.update())):this._helper&&this.node.light.remove(this._helper.object)}}var dS=n(35);function pS(e,t){this.light=e,this.color=t;var n=new O.a;n.setAttribute(\\\\\\\"position\\\\\\\",new L.c([1,1,0,-1,1,0,-1,-1,0,1,-1,0,1,1,0],3)),n.computeBoundingSphere();var i=new Yi.a({fog:!1});dS.a.call(this,n,i),this.type=\\\\\\\"RectAreaLightHelper\\\\\\\";var s=new O.a;s.setAttribute(\\\\\\\"position\\\\\\\",new L.c([1,1,0,-1,1,0,-1,-1,0,1,1,0,-1,-1,0,1,-1,0],3)),s.computeBoundingSphere(),this.add(new z.a(s,new 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Nf.a({wireframe:!0,fog:!1})}build(){this._object.matrixAutoUpdate=!1,this._object.name=this._name,this.buildHelper()}get object(){return this._object}}class fS extends mS{createObject(){return new pS(this.node.light)}buildHelper(){}update(){this._object.updateMatrixWorld()}}class gS extends(function(e){return class extends e{constructor(){super(...arguments),this.light=Oo.FOLDER(),this.color=Oo.COLOR([1,1,1],{conversion:Or.SRGB_TO_LINEAR}),this.intensity=Oo.FLOAT(1,{range:[0,10]}),this.width=Oo.FLOAT(1,{range:[0,10]}),this.height=Oo.FLOAT(1,{range:[0,10]}),this.showHelper=Oo.BOOLEAN(0)}}}(oS(Lo))){}const vS=new gS;class yS extends uS{constructor(){super(...arguments),this.paramsConfig=vS,this._helperController=new hS(this,fS,\\\\\\\"RectAreaLightObjNodeHelper\\\\\\\")}static type(){return\\\\\\\"areaLight\\\\\\\"}initializeNode(){this._helperController.initializeNode()}createLight(){const e=new JN(16777215,1,1,1);return e.matrixAutoUpdate=!1,eS.initialized||(eS.init(),eS.initialized=!0),e}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 xS=n(71);const bS=new p.a,wS=new km.a;class AS extends $i.a{constructor(e){const t=new O.a,n=new Yi.a({color:16777215,vertexColors:!0,toneMapped:!1}),i=[],s=[],r={},o=new M.a(16755200),a=new M.a(16711680),c=new M.a(43775),l=new M.a(16777215),u=new M.a(3355443);function h(e,t,n){d(e,n),d(t,n)}function d(e,t){i.push(0,0,0),s.push(t.r,t.g,t.b),void 0===r[e]&&(r[e]=[]),r[e].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\\\\\\\",c),h(\\\\\\\"u2\\\\\\\",\\\\\\\"u3\\\\\\\",c),h(\\\\\\\"u3\\\\\\\",\\\\\\\"u1\\\\\\\",c),h(\\\\\\\"c\\\\\\\",\\\\\\\"t\\\\\\\",l),h(\\\\\\\"p\\\\\\\",\\\\\\\"c\\\\\\\",u),h(\\\\\\\"cn1\\\\\\\",\\\\\\\"cn2\\\\\\\",u),h(\\\\\\\"cn3\\\\\\\",\\\\\\\"cn4\\\\\\\",u),h(\\\\\\\"cf1\\\\\\\",\\\\\\\"cf2\\\\\\\",u),h(\\\\\\\"cf3\\\\\\\",\\\\\\\"cf4\\\\\\\",u),t.setAttribute(\\\\\\\"position\\\\\\\",new L.c(i,3)),t.setAttribute(\\\\\\\"color\\\\\\\",new L.c(s,3)),super(t,n),this.type=\\\\\\\"CameraHelper\\\\\\\",this.camera=e,this.camera.updateProjectionMatrix&&this.camera.updateProjectionMatrix(),this.matrixAutoUpdate=!1,this.pointMap=r,this.update()}update(){const e=this.geometry,t=this.pointMap;wS.projectionMatrixInverse.copy(this.camera.projectionMatrixInverse),TS(\\\\\\\"c\\\\\\\",t,e,wS,0,0,-1),TS(\\\\\\\"t\\\\\\\",t,e,wS,0,0,1),TS(\\\\\\\"n1\\\\\\\",t,e,wS,-1,-1,-1),TS(\\\\\\\"n2\\\\\\\",t,e,wS,1,-1,-1),TS(\\\\\\\"n3\\\\\\\",t,e,wS,-1,1,-1),TS(\\\\\\\"n4\\\\\\\",t,e,wS,1,1,-1),TS(\\\\\\\"f1\\\\\\\",t,e,wS,-1,-1,1),TS(\\\\\\\"f2\\\\\\\",t,e,wS,1,-1,1),TS(\\\\\\\"f3\\\\\\\",t,e,wS,-1,1,1),TS(\\\\\\\"f4\\\\\\\",t,e,wS,1,1,1),TS(\\\\\\\"u1\\\\\\\",t,e,wS,.7,1.1,-1),TS(\\\\\\\"u2\\\\\\\",t,e,wS,-.7,1.1,-1),TS(\\\\\\\"u3\\\\\\\",t,e,wS,0,2,-1),TS(\\\\\\\"cf1\\\\\\\",t,e,wS,-1,0,1),TS(\\\\\\\"cf2\\\\\\\",t,e,wS,1,0,1),TS(\\\\\\\"cf3\\\\\\\",t,e,wS,0,-1,1),TS(\\\\\\\"cf4\\\\\\\",t,e,wS,0,1,1),TS(\\\\\\\"cn1\\\\\\\",t,e,wS,-1,0,-1),TS(\\\\\\\"cn2\\\\\\\",t,e,wS,1,0,-1),TS(\\\\\\\"cn3\\\\\\\",t,e,wS,0,-1,-1),TS(\\\\\\\"cn4\\\\\\\",t,e,wS,0,1,-1),e.getAttribute(\\\\\\\"position\\\\\\\").needsUpdate=!0}}function TS(e,t,n,i,s,r,o){bS.set(s,r,o).unproject(i);const a=t[e];if(void 0!==a){const e=n.getAttribute(\\\\\\\"position\\\\\\\");for(let t=0,n=a.length;t<n;t++)e.setXYZ(a[t],bS.x,bS.y,bS.z)}}class ES extends mS{constructor(){super(...arguments),this._square=new dS.a,this._line_material=new Yi.a({fog:!1})}createObject(){return new z.a}buildHelper(){const e=new O.a;e.setAttribute(\\\\\\\"position\\\\\\\",new L.c([-1,1,0,1,1,0,1,-1,0,-1,-1,0,-1,1,0],3)),this._square.geometry=e,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 AS(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 CS,MS;!function(e){e.DIRECTIONAL=\\\\\\\"directionalLight\\\\\\\",e.HEMISPHERE=\\\\\\\"hemisphereLight\\\\\\\",e.POINT=\\\\\\\"pointLight\\\\\\\",e.SPOT=\\\\\\\"spotLight\\\\\\\"}(CS||(CS={})),function(e){e.DIRECTIONAL=\\\\\\\"DirectionalLight\\\\\\\",e.HEMISPHERE=\\\\\\\"HemisphereLight\\\\\\\",e.POINT=\\\\\\\"PointLight\\\\\\\",e.SPOT=\\\\\\\"SpotLight\\\\\\\"}(MS||(MS={}));class NS extends(function(e){return class extends e{constructor(){super(...arguments),this.light=Oo.FOLDER(),this.color=Oo.COLOR([1,1,1],{conversion:Or.SRGB_TO_LINEAR}),this.intensity=Oo.FLOAT(1),this.distance=Oo.FLOAT(100,{range:[0,100]}),this.showHelper=Oo.BOOLEAN(0),this.shadow=Oo.FOLDER(),this.castShadow=Oo.BOOLEAN(1),this.shadowRes=Oo.VECTOR2([1024,1024],{visibleIf:{castShadow:!0}}),this.shadowSize=Oo.VECTOR2([2,2],{visibleIf:{castShadow:!0}}),this.shadowBias=Oo.FLOAT(.001,{visibleIf:{castShadow:!0}}),this.shadowRadius=Oo.FLOAT(0,{visibleIf:{castShadow:1},range:[0,10],rangeLocked:[!0,!1]})}}}(oS(Lo))){}const SS=new NS;class OS extends uS{constructor(){super(...arguments),this.paramsConfig=SS,this._helperController=new hS(this,ES,\\\\\\\"DirectionalLightHelper\\\\\\\")}static type(){return CS.DIRECTIONAL}initializeNode(){this._helperController.initializeNode()}createLight(){const e=new xS.a;return e.matrixAutoUpdate=!1,e.castShadow=!0,e.shadow.bias=-.001,e.shadow.mapSize.x=1024,e.shadow.mapSize.y=1024,e.shadow.camera.near=.1,this._target_target=e.target,this._target_target.name=\\\\\\\"DirectionalLight Default Target\\\\\\\",this.object.add(this._target_target),e}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 e=this.light.shadow.camera,t=this.pv.shadowSize;e.left=.5*-t.x,e.right=.5*t.x,e.top=.5*t.y,e.bottom=.5*-t.y,this.light.shadow.camera.updateProjectionMatrix(),this._helperController.update()}}class LS extends pv.a{constructor(e,t,n){super(e,n),this.type=\\\\\\\"HemisphereLight\\\\\\\",this.position.copy(ee.a.DefaultUp),this.updateMatrix(),this.groundColor=new M.a(t)}copy(e){return pv.a.prototype.copy.call(this,e),this.groundColor.copy(e.groundColor),this}}LS.prototype.isHemisphereLight=!0;class PS extends O.a{constructor(e,t,n=1,i=0){super(),this.type=\\\\\\\"PolyhedronGeometry\\\\\\\",this.parameters={vertices:e,indices:t,radius:n,detail:i};const s=[],r=[];function o(e,t,n,i){const s=i+1,r=[];for(let i=0;i<=s;i++){r[i]=[];const o=e.clone().lerp(n,i/s),a=t.clone().lerp(n,i/s),c=s-i;for(let e=0;e<=c;e++)r[i][e]=0===e&&i===s?o:o.clone().lerp(a,e/c)}for(let e=0;e<s;e++)for(let t=0;t<2*(s-e)-1;t++){const n=Math.floor(t/2);t%2==0?(a(r[e][n+1]),a(r[e+1][n]),a(r[e][n])):(a(r[e][n+1]),a(r[e+1][n+1]),a(r[e+1][n]))}}function a(e){s.push(e.x,e.y,e.z)}function c(t,n){const i=3*t;n.x=e[i+0],n.y=e[i+1],n.z=e[i+2]}function l(e,t,n,i){i<0&&1===e.x&&(r[t]=e.x-1),0===n.x&&0===n.z&&(r[t]=i/2/Math.PI+.5)}function u(e){return Math.atan2(e.z,-e.x)}!function(e){const n=new p.a,i=new p.a,s=new p.a;for(let r=0;r<t.length;r+=3)c(t[r+0],n),c(t[r+1],i),c(t[r+2],s),o(n,i,s,e)}(i),function(e){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],t.normalize().multiplyScalar(e),s[n+0]=t.x,s[n+1]=t.y,s[n+2]=t.z}(n),function(){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];const i=u(e)/2/Math.PI+.5,o=(t=e,Math.atan2(-t.y,Math.sqrt(t.x*t.x+t.z*t.z))/Math.PI+.5);r.push(i,1-o)}var t;(function(){const e=new p.a,t=new p.a,n=new p.a,i=new p.a,o=new d.a,a=new d.a,c=new d.a;for(let h=0,d=0;h<s.length;h+=9,d+=6){e.set(s[h+0],s[h+1],s[h+2]),t.set(s[h+3],s[h+4],s[h+5]),n.set(s[h+6],s[h+7],s[h+8]),o.set(r[d+0],r[d+1]),a.set(r[d+2],r[d+3]),c.set(r[d+4],r[d+5]),i.copy(e).add(t).add(n).divideScalar(3);const p=u(i);l(o,d+0,e,p),l(a,d+2,t,p),l(c,d+4,n,p)}})(),function(){for(let e=0;e<r.length;e+=6){const t=r[e+0],n=r[e+2],i=r[e+4],s=Math.max(t,n,i),o=Math.min(t,n,i);s>.9&&o<.1&&(t<.2&&(r[e+0]+=1),n<.2&&(r[e+2]+=1),i<.2&&(r[e+4]+=1))}}()}(),this.setAttribute(\\\\\\\"position\\\\\\\",new L.c(s,3)),this.setAttribute(\\\\\\\"normal\\\\\\\",new L.c(s.slice(),3)),this.setAttribute(\\\\\\\"uv\\\\\\\",new L.c(r,2)),0===i?this.computeVertexNormals():this.normalizeNormals()}}class RS extends PS{constructor(e=1,t=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],e,t),this.type=\\\\\\\"OctahedronGeometry\\\\\\\",this.parameters={radius:e,detail:t}}}class IS extends mS{constructor(){super(...arguments),this._geometry=new RS(1),this._quat=new Ll.a,this._default_position=new p.a(0,1,0),this._color1=new M.a,this._color2=new M.a}createObject(){return new z.a}buildHelper(){this._geometry.rotateZ(.5*Math.PI),this._material.vertexColors=!0;const e=this._geometry.getAttribute(\\\\\\\"position\\\\\\\"),t=new Float32Array(3*e.count);this._geometry.setAttribute(\\\\\\\"color\\\\\\\",new L.a(t,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 e=this._geometry.getAttribute(\\\\\\\"color\\\\\\\");this._color1.copy(this.node.light.color),this._color2.copy(this.node.light.groundColor);for(let t=0,n=e.count;t<n;t++){const i=t<n/2?this._color1:this._color2;e.setXYZ(t,i.r,i.g,i.b)}e.needsUpdate=!0}}const FS={skyColor:new M.a(1,1,1),groundColor:new M.a(0,0,0)};const DS=new class extends Lo{constructor(){super(...arguments),this.skyColor=Oo.COLOR(FS.skyColor,{conversion:Or.SRGB_TO_LINEAR}),this.groundColor=Oo.COLOR(FS.groundColor,{conversion:Or.SRGB_TO_LINEAR}),this.intensity=Oo.FLOAT(1),this.position=Oo.VECTOR3([0,1,0]),this.showHelper=Oo.BOOLEAN(0),this.helperSize=Oo.FLOAT(1,{visibleIf:{showHelper:1}})}};class kS extends YN{constructor(){super(...arguments),this.paramsConfig=DS,this._helperController=new hS(this,IS,\\\\\\\"HemisphereLightHelper\\\\\\\")}static type(){return CS.HEMISPHERE}createLight(){const e=new LS;return e.matrixAutoUpdate=!1,e.color.copy(FS.skyColor),e.groundColor.copy(FS.groundColor),e}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 BS=n(58);class zS extends O.a{constructor(e=1,t=8,n=6,i=0,s=2*Math.PI,r=0,o=Math.PI){super(),this.type=\\\\\\\"SphereGeometry\\\\\\\",this.parameters={radius:e,widthSegments:t,heightSegments:n,phiStart:i,phiLength:s,thetaStart:r,thetaLength:o},t=Math.max(3,Math.floor(t)),n=Math.max(2,Math.floor(n));const a=Math.min(r+o,Math.PI);let c=0;const l=[],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==r?v=.5/t:d==n&&a==Math.PI&&(v=-.5/t);for(let n=0;n<=t;n++){const a=n/t;u.x=-e*Math.cos(i+a*s)*Math.sin(r+g*o),u.y=e*Math.cos(r+g*o),u.z=e*Math.sin(i+a*s)*Math.sin(r+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(c++)}l.push(p)}for(let e=0;e<n;e++)for(let i=0;i<t;i++){const t=l[e][i+1],s=l[e][i],o=l[e+1][i],c=l[e+1][i+1];(0!==e||r>0)&&d.push(t,s,c),(e!==n-1||a<Math.PI)&&d.push(s,o,c)}this.setIndex(d),this.setAttribute(\\\\\\\"position\\\\\\\",new L.c(_,3)),this.setAttribute(\\\\\\\"normal\\\\\\\",new L.c(m,3)),this.setAttribute(\\\\\\\"uv\\\\\\\",new L.c(f,2))}}class US extends mS{constructor(){super(...arguments),this._matrix_scale=new p.a(1,1,1)}createObject(){return new z.a}buildHelper(){this._object.geometry=new zS(1,4,2),this._object.matrixAutoUpdate=!1,this._object.material=this._material}update(){const e=this.node.pv.helperSize;this._matrix_scale.set(e,e,e),this._object.matrix.identity(),this._object.matrix.scale(this._matrix_scale),this._material.color.copy(this.node.light.color)}}class GS extends(oS(Lo)){constructor(){super(...arguments),this.light=Oo.FOLDER(),this.color=Oo.COLOR([1,1,1],{conversion:Or.SRGB_TO_LINEAR}),this.intensity=Oo.FLOAT(1),this.decay=Oo.FLOAT(.1),this.distance=Oo.FLOAT(100),this.castShadows=Oo.BOOLEAN(1),this.shadowRes=Oo.VECTOR2([1024,1024],{visibleIf:{castShadows:1}}),this.shadowBias=Oo.FLOAT(.001,{visibleIf:{castShadows:1}}),this.shadowNear=Oo.FLOAT(1,{visibleIf:{castShadows:1}}),this.shadowFar=Oo.FLOAT(100,{visibleIf:{castShadows:1}}),this.showHelper=Oo.BOOLEAN(0),this.helperSize=Oo.FLOAT(1,{visibleIf:{showHelper:1}})}}const VS=new GS;class jS extends uS{constructor(){super(...arguments),this.paramsConfig=VS,this._helperController=new hS(this,US,\\\\\\\"PointLightHelper\\\\\\\")}static type(){return CS.POINT}initializeNode(){this._helperController.initializeNode()}createLight(){const e=new BS.a;return e.matrixAutoUpdate=!1,e.castShadow=!0,e.shadow.bias=-.001,e.shadow.mapSize.x=1024,e.shadow.mapSize.y=1024,e.shadow.camera.near=.1,e}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 HS=n(72);class qS extends mS{constructor(){super(...arguments),this._cone=new $i.a,this._line_material=new Yi.a({fog:!1})}createObject(){return new z.a}static buildConeGeometry(){const e=new O.a,t=[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 e=0,n=1,i=32;e<i;e++,n++){const s=e/i*Math.PI*2,r=n/i*Math.PI*2;t.push(Math.cos(s),Math.sin(s),1,Math.cos(r),Math.sin(r),1)}return e.setAttribute(\\\\\\\"position\\\\\\\",new L.c(t,3)),e}static updateConeObject(e,t){const n=(t.distance?t.distance:1e3)*t.sizeMult,i=n*Math.tan(t.angle);this._matrix_scale.set(i,i,n),e.matrix.identity(),e.matrix.makeRotationX(.5*Math.PI),e.matrix.scale(this._matrix_scale)}buildHelper(){this._cone.geometry=qS.buildConeGeometry(),this._cone.material=this._line_material,this._cone.matrixAutoUpdate=!1,this.object.add(this._cone)}update(){qS.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)}}qS._matrix_scale=new p.a;class WS extends O.a{constructor(e=1,t=1,n=1,i=8,s=1,r=!1,o=0,a=2*Math.PI){super(),this.type=\\\\\\\"CylinderGeometry\\\\\\\",this.parameters={radiusTop:e,radiusBottom:t,height:n,radialSegments:i,heightSegments:s,openEnded:r,thetaStart:o,thetaLength:a};const c=this;i=Math.floor(i),s=Math.floor(s);const l=[],u=[],h=[],_=[];let m=0;const f=[],g=n/2;let v=0;function y(n){const s=m,r=new d.a,f=new p.a;let y=0;const x=!0===n?e:t,b=!0===n?1:-1;for(let e=1;e<=i;e++)u.push(0,g*b,0),h.push(0,b,0),_.push(.5,.5),m++;const w=m;for(let e=0;e<=i;e++){const t=e/i*a+o,n=Math.cos(t),s=Math.sin(t);f.x=x*s,f.y=g*b,f.z=x*n,u.push(f.x,f.y,f.z),h.push(0,b,0),r.x=.5*n+.5,r.y=.5*s*b+.5,_.push(r.x,r.y),m++}for(let e=0;e<i;e++){const t=s+e,i=w+e;!0===n?l.push(i,i+1,t):l.push(i+1,i,t),y+=3}c.addGroup(v,y,!0===n?1:2),v+=y}!function(){const r=new p.a,d=new p.a;let y=0;const x=(t-e)/n;for(let c=0;c<=s;c++){const l=[],p=c/s,v=p*(t-e)+e;for(let e=0;e<=i;e++){const t=e/i,s=t*a+o,c=Math.sin(s),f=Math.cos(s);d.x=v*c,d.y=-p*n+g,d.z=v*f,u.push(d.x,d.y,d.z),r.set(c,x,f).normalize(),h.push(r.x,r.y,r.z),_.push(t,1-p),l.push(m++)}f.push(l)}for(let e=0;e<i;e++)for(let t=0;t<s;t++){const n=f[t][e],i=f[t+1][e],s=f[t+1][e+1],r=f[t][e+1];l.push(n,i,r),l.push(i,s,r),y+=6}c.addGroup(v,y,0),v+=y}(),!1===r&&(e>0&&y(!0),t>0&&y(!1)),this.setIndex(l),this.setAttribute(\\\\\\\"position\\\\\\\",new L.c(u,3)),this.setAttribute(\\\\\\\"normal\\\\\\\",new L.c(h,3)),this.setAttribute(\\\\\\\"uv\\\\\\\",new L.c(_,2))}}class XS extends WS{constructor(e=1,t=1,n=8,i=1,s=!1,r=0,o=2*Math.PI){super(0,e,t,n,i,s,r,o),this.type=\\\\\\\"ConeGeometry\\\\\\\",this.parameters={radius:e,height:t,radialSegments:n,heightSegments:i,openEnded:s,thetaStart:r,thetaLength:o}}}class YS{constructor(e){this.node=e}update(){const e=this.node.pv;if(e.tvolumetric){const t=this.object(),n=this.node.light;qS.updateConeObject(t,{sizeMult:e.helperSize,distance:n.distance,angle:n.angle});const i=t.material.uniforms;i.lightColor.value.copy(n.color),i.attenuation.value=e.volAttenuation,i.anglePower.value=e.volAnglePower,this.node.light.add(t)}else this._mesh&&this.node.light.remove(this._mesh)}object(){return this._mesh=this._mesh||this._createMesh()}_createMesh(){const e=new XS(1,1,256,1);e.applyMatrix4((new C.a).makeTranslation(0,-.5,0)),e.applyMatrix4((new C.a).makeRotationX(-Math.PI/2));const t=this._createMaterial(),n=new z.a(e,t);return n.matrixAutoUpdate=!1,n.name=\\\\\\\"Volumetric\\\\\\\",t.uniforms.lightColor.value.set(\\\\\\\"white\\\\\\\"),n}_createMaterial(){return new B({uniforms:{attenuation:{value:5},anglePower:{value:1.2},lightColor:{value:new M.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 $S extends(oS(Lo)){constructor(){super(...arguments),this.light=Oo.FOLDER(),this.color=Oo.COLOR([1,1,1],{conversion:Or.SRGB_TO_LINEAR}),this.intensity=Oo.FLOAT(1),this.angle=Oo.FLOAT(45,{range:[0,180]}),this.penumbra=Oo.FLOAT(.1),this.decay=Oo.FLOAT(.1,{range:[0,1]}),this.distance=Oo.FLOAT(100,{range:[0,100]}),this.showHelper=Oo.BOOLEAN(0),this.helperSize=Oo.FLOAT(1,{visibleIf:{showHelper:1}}),this.shadow=Oo.FOLDER(),this.castShadow=Oo.BOOLEAN(1),this.shadowAutoUpdate=Oo.BOOLEAN(1,{visibleIf:{castShadow:1}}),this.shadowUpdateOnNextRender=Oo.BOOLEAN(0,{visibleIf:{castShadow:1,shadowAutoUpdate:0}}),this.shadowRes=Oo.VECTOR2([256,256],{visibleIf:{castShadow:1}}),this.shadowBias=Oo.FLOAT(.001,{visibleIf:{castShadow:1},range:[-.01,.01],rangeLocked:[!1,!1]}),this.shadowNear=Oo.FLOAT(.1,{visibleIf:{castShadow:1},range:[0,100],rangeLocked:[!0,!1]}),this.shadowFar=Oo.FLOAT(100,{visibleIf:{castShadow:1},range:[0,100],rangeLocked:[!0,!1]}),this.shadowRadius=Oo.FLOAT(0,{visibleIf:{castShadow:1},range:[0,10],rangeLocked:[!0,!1]}),this.volumetric=Oo.FOLDER(),this.tvolumetric=Oo.BOOLEAN(0),this.volAttenuation=Oo.FLOAT(5,{range:[0,10],rangeLocked:[!0,!1]}),this.volAnglePower=Oo.FLOAT(10,{range:[0,20],rangeLocked:[!0,!1]})}}const QS=new $S;class JS extends uS{constructor(){super(...arguments),this.paramsConfig=QS,this._helperController=new hS(this,qS,\\\\\\\"SpotLightHelper\\\\\\\"),this._volumetricController=new YS(this)}static type(){return CS.SPOT}initializeNode(){this._helperController.initializeNode()}createLight(){const e=new HS.a;return e.matrixAutoUpdate=!1,e.castShadow=!0,e.shadow.bias=-.001,e.shadow.mapSize.x=256,e.shadow.mapSize.y=256,e.shadow.camera.near=.1,this._target_target=e.target,this._target_target.name=\\\\\\\"SpotLight Default Target\\\\\\\",this._target_target.matrixAutoUpdate=!1,this.object.add(this._target_target),e}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 KS;const ZS=function(){return void 0===KS&&(KS=new(window.AudioContext||window.webkitAudioContext)),KS},eO=new p.a,tO=new Ll.a,nO=new p.a,iO=new p.a;class sO extends ee.a{constructor(){super(),this.type=\\\\\\\"AudioListener\\\\\\\",this.context=ZS(),this.gain=this.context.createGain(),this.gain.connect(this.context.destination),this.filter=null,this.timeDelta=0,this._clock=new fm}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(e){return null!==this.filter?(this.gain.disconnect(this.filter),this.filter.disconnect(this.context.destination)):this.gain.disconnect(this.context.destination),this.filter=e,this.gain.connect(this.filter),this.filter.connect(this.context.destination),this}getMasterVolume(){return this.gain.gain.value}setMasterVolume(e){return this.gain.gain.setTargetAtTime(e,this.context.currentTime,.01),this}updateMatrixWorld(e){super.updateMatrixWorld(e);const t=this.context.listener,n=this.up;if(this.timeDelta=this._clock.getDelta(),this.matrixWorld.decompose(eO,tO,nO),iO.set(0,0,-1).applyQuaternion(tO),t.positionX){const e=this.context.currentTime+this.timeDelta;t.positionX.linearRampToValueAtTime(eO.x,e),t.positionY.linearRampToValueAtTime(eO.y,e),t.positionZ.linearRampToValueAtTime(eO.z,e),t.forwardX.linearRampToValueAtTime(iO.x,e),t.forwardY.linearRampToValueAtTime(iO.y,e),t.forwardZ.linearRampToValueAtTime(iO.z,e),t.upX.linearRampToValueAtTime(n.x,e),t.upY.linearRampToValueAtTime(n.y,e),t.upZ.linearRampToValueAtTime(n.z,e)}else t.setPosition(eO.x,eO.y,eO.z),t.setOrientation(iO.x,iO.y,iO.z,n.x,n.y,n.z)}}class rO extends(oS(Lo)){}const oO=new rO;class aO extends XN{constructor(){super(...arguments),this.paramsConfig=oO,this.hierarchyController=new lS(this),this.transformController=new cS(this),this.flags=new ai(this)}static type(){return _g.AUDIO_LISTENER}createObject(){const e=new sO;return e.matrixAutoUpdate=!1,e}initializeNode(){this.hierarchyController.initializeNode(),this.transformController.initializeNode()}cook(){this.transformController.update(),this.cookController.endCook()}}class cO extends $i.a{constructor(e=1){const t=[0,0,0,e,0,0,0,0,0,0,e,0,0,0,0,0,0,e],n=new O.a;n.setAttribute(\\\\\\\"position\\\\\\\",new L.c(t,3)),n.setAttribute(\\\\\\\"color\\\\\\\",new L.c([1,0,0,1,.6,0,0,1,0,.6,1,0,0,0,1,0,.6,1],3));super(n,new Yi.a({vertexColors:!0,toneMapped:!1})),this.type=\\\\\\\"AxesHelper\\\\\\\"}}var lO;!function(e){e.TOGETHER=\\\\\\\"translate + rotate together\\\\\\\",e.SEPARATELY=\\\\\\\"translate + rotate separately\\\\\\\"}(lO||(lO={}));const uO=[lO.TOGETHER,lO.SEPARATELY];const hO=new class extends Lo{constructor(){super(...arguments),this.object0=Oo.OPERATOR_PATH(\\\\\\\"/geo1\\\\\\\",{nodeSelection:{context:Ei.OBJ}}),this.object1=Oo.OPERATOR_PATH(\\\\\\\"/geo2\\\\\\\",{nodeSelection:{context:Ei.OBJ}}),this.mode=Oo.INTEGER(uO.indexOf(lO.TOGETHER),{menu:{entries:uO.map(((e,t)=>({name:e,value:t})))}}),this.blend=Oo.FLOAT(0,{visibleIf:{mode:uO.indexOf(lO.TOGETHER)},range:[0,1],rangeLocked:[!1,!1]}),this.blendT=Oo.FLOAT(0,{visibleIf:{mode:uO.indexOf(lO.SEPARATELY)},range:[0,1],rangeLocked:[!1,!1]}),this.blendR=Oo.FLOAT(0,{visibleIf:{mode:uO.indexOf(lO.SEPARATELY)},range:[0,1],rangeLocked:[!1,!1]})}};class dO extends XN{constructor(){super(...arguments),this.paramsConfig=hO,this.hierarchyController=new lS(this),this.flags=new ai(this),this._helper=new cO(1),this._t0=new p.a,this._q0=new Ll.a,this._s0=new p.a,this._t1=new p.a,this._q1=new Ll.a,this._s1=new p.a}static type(){return\\\\\\\"blend\\\\\\\"}createObject(){const e=new on.a;return e.matrixAutoUpdate=!1,e}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 e=this.p.object0.found_node_with_context(Ei.OBJ),t=this.p.object1.found_node_with_context(Ei.OBJ);e&&t&&this._blend(e.object,t.object),this.cookController.endCook()}_blend(e,t){const n=uO[this.pv.mode];switch(n){case lO.TOGETHER:return this._blend_together(e,t);case lO.SEPARATELY:return this._blend_separately(e,t)}Ri.unreachable(n)}_blend_together(e,t){this._decompose_matrices(e,t),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(e,t){this._decompose_matrices(e,t),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(e,t){e.matrixWorld.decompose(this._t0,this._q0,this._s0),t.matrixWorld.decompose(this._t1,this._q1,this._s1)}}var pO={uniforms:{tDiffuse:{value:null},h:{value:1/512}},vertexShader:[\\\\\\\"varying vec2 vUv;\\\\\\\",\\\\\\\"void main() {\\\\\\\",\\\\\\\"\\\\tvUv = uv;\\\\\\\",\\\\\\\"\\\\tgl_Position = projectionMatrix * modelViewMatrix * vec4( position, 1.0 );\\\\\\\",\\\\\\\"}\\\\\\\"].join(\\\\\\\"\\\\n\\\\\\\"),fragmentShader:[\\\\\\\"uniform sampler2D tDiffuse;\\\\\\\",\\\\\\\"uniform float h;\\\\\\\",\\\\\\\"varying vec2 vUv;\\\\\\\",\\\\\\\"void main() {\\\\\\\",\\\\\\\"\\\\tvec4 sum = vec4( 0.0 );\\\\\\\",\\\\\\\"\\\\tsum += texture2D( tDiffuse, vec2( vUv.x - 4.0 * h, vUv.y ) ) * 0.051;\\\\\\\",\\\\\\\"\\\\tsum += texture2D( tDiffuse, vec2( vUv.x - 3.0 * h, vUv.y ) ) * 0.0918;\\\\\\\",\\\\\\\"\\\\tsum += texture2D( tDiffuse, vec2( vUv.x - 2.0 * h, vUv.y ) ) * 0.12245;\\\\\\\",\\\\\\\"\\\\tsum += texture2D( tDiffuse, vec2( vUv.x - 1.0 * h, vUv.y ) ) * 0.1531;\\\\\\\",\\\\\\\"\\\\tsum += texture2D( tDiffuse, vec2( vUv.x, vUv.y ) ) * 0.1633;\\\\\\\",\\\\\\\"\\\\tsum += texture2D( tDiffuse, vec2( vUv.x + 1.0 * h, vUv.y ) ) * 0.1531;\\\\\\\",\\\\\\\"\\\\tsum += texture2D( tDiffuse, vec2( vUv.x + 2.0 * h, vUv.y ) ) * 0.12245;\\\\\\\",\\\\\\\"\\\\tsum += texture2D( tDiffuse, vec2( vUv.x + 3.0 * h, vUv.y ) ) * 0.0918;\\\\\\\",\\\\\\\"\\\\tsum += texture2D( tDiffuse, vec2( vUv.x + 4.0 * h, vUv.y ) ) * 0.051;\\\\\\\",\\\\\\\"\\\\tgl_FragColor = sum;\\\\\\\",\\\\\\\"}\\\\\\\"].join(\\\\\\\"\\\\n\\\\\\\")},_O={uniforms:{tDiffuse:{value:null},v:{value:1/512}},vertexShader:[\\\\\\\"varying vec2 vUv;\\\\\\\",\\\\\\\"void main() {\\\\\\\",\\\\\\\"\\\\tvUv = uv;\\\\\\\",\\\\\\\"\\\\tgl_Position = projectionMatrix * modelViewMatrix * vec4( position, 1.0 );\\\\\\\",\\\\\\\"}\\\\\\\"].join(\\\\\\\"\\\\n\\\\\\\"),fragmentShader:[\\\\\\\"uniform sampler2D tDiffuse;\\\\\\\",\\\\\\\"uniform float v;\\\\\\\",\\\\\\\"varying vec2 vUv;\\\\\\\",\\\\\\\"void main() {\\\\\\\",\\\\\\\"\\\\tvec4 sum = vec4( 0.0 );\\\\\\\",\\\\\\\"\\\\tsum += texture2D( tDiffuse, vec2( vUv.x, vUv.y - 4.0 * v ) ) * 0.051;\\\\\\\",\\\\\\\"\\\\tsum += texture2D( tDiffuse, vec2( vUv.x, vUv.y - 3.0 * v ) ) * 0.0918;\\\\\\\",\\\\\\\"\\\\tsum += texture2D( tDiffuse, vec2( vUv.x, vUv.y - 2.0 * v ) ) * 0.12245;\\\\\\\",\\\\\\\"\\\\tsum += texture2D( tDiffuse, vec2( vUv.x, vUv.y - 1.0 * v ) ) * 0.1531;\\\\\\\",\\\\\\\"\\\\tsum += texture2D( tDiffuse, vec2( vUv.x, vUv.y ) ) * 0.1633;\\\\\\\",\\\\\\\"\\\\tsum += texture2D( tDiffuse, vec2( vUv.x, vUv.y + 1.0 * v ) ) * 0.1531;\\\\\\\",\\\\\\\"\\\\tsum += texture2D( tDiffuse, vec2( vUv.x, vUv.y + 2.0 * v ) ) * 0.12245;\\\\\\\",\\\\\\\"\\\\tsum += texture2D( tDiffuse, vec2( vUv.x, vUv.y + 3.0 * v ) ) * 0.0918;\\\\\\\",\\\\\\\"\\\\tsum += texture2D( tDiffuse, vec2( vUv.x, vUv.y + 4.0 * v ) ) * 0.051;\\\\\\\",\\\\\\\"\\\\tgl_FragColor = sum;\\\\\\\",\\\\\\\"}\\\\\\\"].join(\\\\\\\"\\\\n\\\\\\\")};const mO=1/256e3;class fO{constructor(e){this._renderTargetBlur=this._createRenderTarget(e),this._camera=this._createCamera(),this._blurPlane=this._createBlurPlane(),this._horizontalBlurMaterial=new B(pO),this._horizontalBlurMaterial.depthTest=!1,this._verticalBlurMaterial=new B(_O),this._verticalBlurMaterial.depthTest=!1}setSize(e,t){this._renderTargetBlur.setSize(e,t)}_createRenderTarget(e){const t=new Z(e.x,e.y);return t.texture.generateMipmaps=!1,t}_createCamera(){const e=new vm.a(-.5,.5,.5,-.5,0,1);return e.position.z=.5,e}_createBlurPlane(){const e=new R(1,1);return new z.a(e)}applyBlur(e,t,n,i){const s=Math.max(this._renderTargetBlur.width,this._renderTargetBlur.height);this._horizontalBlurMaterial.uniforms.tDiffuse.value=e.texture,this._horizontalBlurMaterial.uniforms.h.value=n*s*mO,this._blurPlane.material=this._horizontalBlurMaterial,t.setRenderTarget(this._renderTargetBlur),t.render(this._blurPlane,this._camera),this._verticalBlurMaterial.uniforms.tDiffuse.value=this._renderTargetBlur.texture,this._verticalBlurMaterial.uniforms.v.value=i*s*mO,this._blurPlane.material=this._verticalBlurMaterial,t.setRenderTarget(e),t.render(this._blurPlane,this._camera)}}var gO;!function(e){e.ON_RENDER=\\\\\\\"On Every Render\\\\\\\",e.MANUAL=\\\\\\\"Manual\\\\\\\"}(gO||(gO={}));const vO=[gO.ON_RENDER,gO.MANUAL];class yO extends(oS(Lo)){constructor(){super(...arguments),this.shadow=Oo.FOLDER(),this.dist=Oo.FLOAT(1,{range:[0,10],rangeLocked:[!0,!1]}),this.planeSize=Oo.VECTOR2([1,1]),this.shadowRes=Oo.VECTOR2([256,256]),this.blur=Oo.FLOAT(1,{range:[0,10],rangeLocked:[!0,!1]}),this.tblur2=Oo.BOOLEAN(1),this.blur2=Oo.FLOAT(1,{range:[0,10],rangeLocked:[!0,!1],visibleIf:{tblur2:1}}),this.darkness=Oo.FLOAT(1),this.opacity=Oo.FLOAT(1),this.showHelper=Oo.BOOLEAN(0),this.updateMode=Oo.INTEGER(vO.indexOf(gO.ON_RENDER),{callback:e=>{wO.PARAM_CALLBACK_update_updateMode(e)},menu:{entries:vO.map(((e,t)=>({name:e,value:t})))}}),this.update=Oo.BUTTON(null,{callback:e=>{wO.PARAM_CALLBACK_updateManual(e)},visibleIf:{updateMode:vO.indexOf(gO.MANUAL)}}),this.scene=Oo.FOLDER(),this.include=Oo.STRING(\\\\\\\"\\\\\\\"),this.exclude=Oo.STRING(\\\\\\\"\\\\\\\"),this.updateObjectsList=Oo.BUTTON(null,{callback:e=>{wO.PARAM_CALLBACK_updateObjectsList(e)}}),this.printResolveObjectsList=Oo.BUTTON(null,{callback:e=>{wO.PARAM_CALLBACK_printResolveObjectsList(e)}})}}const xO=new yO,bO=new d.a(256,256);class wO extends XN{constructor(){super(...arguments),this.paramsConfig=xO,this.hierarchyController=new lS(this),this.flags=new ai(this),this._renderTarget=this._createRenderTarget(bO),this._coreRenderBlur=this._createCoreRenderBlur(bO),this._includedObjects=[],this._includedAncestors=[],this._excludedObjects=[],this.transformController=new cS(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(e){const t=new Z(e.x,e.y);return t.texture.generateMipmaps=!1,t}_createCoreRenderBlur(e){return new fO(e)}createObject(){const e=new on.a;this._shadowGroup=new on.a,e.add(this._shadowGroup),this._shadowGroup.name=\\\\\\\"shadowGroup\\\\\\\";const t=new R(1,1).rotateX(-Math.PI/2),n=t.getAttribute(\\\\\\\"uv\\\\\\\").array;for(let e of[1,3,5,7])n[e]=1-n[e];return this._planeMaterial=new Nf.a({map:this._renderTarget.texture,opacity:1,transparent:!0,depthWrite:!1}),this._plane=new z.a(t,this._planeMaterial),this._plane.renderOrder=1,this._plane.matrixAutoUpdate=!1,this._shadowGroup.add(this._plane),this._createDepthCamera(this._shadowGroup),this._createMaterials(),e}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(e){this._shadowCamera=new vm.a(-.5,.5,.5,-.5,0,1),this._shadowCamera.rotation.x=Math.PI/2,e.add(this._shadowCamera),this._helper=new AS(this._shadowCamera),this._helper.visible=!1,this._shadowCamera.add(this._helper)}_createMaterials(){this._depthMaterial=new Kt,this._depthMaterial.onBeforeCompile=e=>{e.uniforms.darkness=this._darknessUniform,e.fragmentShader=`\\\\n\\\\t\\\\t\\\\tuniform float darkness;\\\\n\\\\t\\\\t\\\\t${e.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(e,t){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=t.background,s=this._helper.visible;t.background=null,this._plane.onBeforeRender=this._emptyOnBeforeRender,this._helper.visible=!1,t.overrideMaterial=this._depthMaterial,this._initVisibility(t),e.setRenderTarget(this._renderTarget),e.render(t,this._shadowCamera),this._coreRenderBlur.applyBlur(this._renderTarget,e,this.pv.blur,this.pv.blur),this.pv.tblur2&&this._coreRenderBlur.applyBlur(this._renderTarget,e,this.pv.blur2,this.pv.blur2),this._restoreVisibility(t),t.overrideMaterial=null,this._helper.visible=s,e.setRenderTarget(null),t.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 e=vO[this.pv.updateMode];switch(e){case gO.ON_RENDER:return this._addRenderHook();case gO.MANUAL:return this._removeRenderHook()}Ri.unreachable(e)}_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(e,t,n,i,s,r){e&&t?this._renderShadow(e,t):console.log(\\\\\\\"no renderer or scene\\\\\\\")}_updateManual(){const e=Rn.renderersController.firstRenderer();if(!e)return void console.log(\\\\\\\"no renderer found\\\\\\\");const t=this.scene().threejsScene();this._renderShadow(e,t)}static PARAM_CALLBACK_update_updateMode(e){e._updateRenderHook()}static PARAM_CALLBACK_updateManual(e){e._updateManual()}static PARAM_CALLBACK_updateObjectsList(e){e._updateObjectsList()}_updateObjectsList(){\\\\\\\"\\\\\\\"!=this.pv.include?this._includedObjects=this.scene().objectsByMask(this.pv.include):this._includedObjects=[];const e=new Map;for(let t of this._includedObjects)t.traverseAncestors((t=>{e.set(t.uuid,t)}));this._includedAncestors=[],e.forEach(((e,t)=>{this._includedAncestors.push(e)})),\\\\\\\"\\\\\\\"!=this.pv.exclude?this._excludedObjects=this.scene().objectsByMask(this.pv.exclude):this._excludedObjects=[]}static PARAM_CALLBACK_printResolveObjectsList(e){e._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(e){this._includedObjects.length>0?e.traverse((e=>{this._initialVisibilityState.set(e,e.visible),e.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(e){for(let t of e)this._initialVisibilityState.set(t,t.visible)}_setObjectsVisibility(e,t){for(let n of e)n.visible=t}_restoreVisibility(e){this._includedObjects.length>0?e.traverse((e=>{const t=this._initialVisibilityState.get(e);t&&(e.visible=t)})):(this._restoreObjectsVisibility(this._includedObjects),this._restoreObjectsVisibility(this._includedAncestors),this._restoreObjectsVisibility(this._excludedObjects))}_restoreObjectsVisibility(e){for(let t of e){const e=this._initialVisibilityState.get(t);e&&(t.visible=e)}}}const AO=\\\\\\\"display\\\\\\\";class TO{constructor(e){this.node=e,this._children_uuids_dict=new Map,this._children_length=0,this._sop_group=this._create_sop_group()}_create_sop_group(){const e=new on.a;return e.matrixAutoUpdate=!1,e}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 e;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 t=null===(e=this.node.flags)||void 0===e?void 0:e.display;t&&t.onUpdate((()=>{this._updateSopGroupHierarchy(),t.active()&&this.request_display_node_container()}))}_updateSopGroupHierarchy(){var e;if(null===(e=this.node.flags)||void 0===e?void 0:e.display){const e=this.sopGroup();this.usedInScene()?(e.visible=!0,this.node.object.add(e),e.updateMatrix()):(e.visible=!1,this.node.object.remove(e))}}usedInScene(){var e,t;const n=this.node.params.has(AO),i=this.node.params.boolean(AO),s=this.node.usedInScene(),r=(null===(t=null===(e=this.node.flags)||void 0===e?void 0:e.display)||void 0===t?void 0:t.active())||!1;return s&&r&&(!n||i)}async request_display_node_container(){this.node.scene().loadingController.loaded()&&this.usedInScene()&&await this._set_content_under_sop_group()}remove_children(){if(0==this._sop_group.children.length)return;let e;for(;e=this._sop_group.children[0];)this._sop_group.remove(e);this._children_uuids_dict.clear(),this._children_length=0}async _set_content_under_sop_group(){var e;const t=this.node.displayNodeController.displayNode();if(t&&(null===(e=t.parent())||void 0===e?void 0:e.graphNodeId())==this.node.graphNodeId()){const e=(await t.compute()).coreContent();if(e){const t=e.objects();let n=t.length!=this._children_length;if(!n)for(let e of t)this._children_uuids_dict.get(e.uuid)||(n=!0);if(n){this.remove_children();for(let e of t)this._sop_group.add(e),e.updateMatrix(),this._children_uuids_dict.set(e.uuid,!0);this._children_length=t.length}return}}this.remove_children()}}class EO extends(oS(Lo)){constructor(){super(...arguments),this.display=Oo.BOOLEAN(1),this.renderOrder=Oo.INTEGER(0,{range:[0,10],rangeLocked:[!0,!1]})}}const CO=new EO;class MO extends XN{constructor(){super(...arguments),this.paramsConfig=CO,this.hierarchyController=new lS(this),this.transformController=new cS(this),this.flags=new ai(this),this.childrenDisplayController=new TO(this),this.displayNodeController=new mm(this,this.childrenDisplayController.displayNodeControllerCallbacks()),this._children_controller_context=Ei.SOP,this._onChildAddBound=this._onChildAdd.bind(this)}static type(){return\\\\\\\"geo\\\\\\\"}createObject(){const e=new on.a;return e.matrixAutoUpdate=!1,e}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 e=this.displayNodeController.displayNode();return!!e&&e.isDirty()}return!1}createNode(e,t){return super.createNode(e,t)}children(){return super.children()}nodesByType(e){return super.nodesByType(e)}_onChildAdd(e){var t,n;this.scene().loadingController.loaded()&&1==this.children().length&&(null===(n=null===(t=e.flags)||void 0===t?void 0:t.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 NO extends(oS(Lo)){}const SO=new NO;class OO extends XN{constructor(){super(...arguments),this.paramsConfig=SO,this.hierarchyController=new lS(this),this.transformController=new cS(this),this.flags=new ai(this),this._helper=new cO(1)}static type(){return\\\\\\\"null\\\\\\\"}createObject(){const e=new on.a;return e.matrixAutoUpdate=!1,e}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 LO=new class extends Lo{constructor(){super(...arguments),this.center=Oo.VECTOR3([0,0,0]),this.longitude=Oo.FLOAT(0,{range:[0,360]}),this.latitude=Oo.FLOAT(0,{range:[-180,180]}),this.depth=Oo.FLOAT(1,{range:[0,10]})}},PO=\\\\\\\"_cook_main_without_inputs_when_dirty\\\\\\\",RO=new p.a(0,1,0),IO=new p.a(-1,0,0);class FO extends XN{constructor(){super(...arguments),this.paramsConfig=LO,this.hierarchyController=new lS(this),this.flags=new ai(this),this._helper=new cO(1),this._cook_main_without_inputs_when_dirty_bound=this._cook_main_without_inputs_when_dirty.bind(this),this._centerMatrix=new C.a,this._longitudeMatrix=new C.a,this._latitudeMatrix=new C.a,this._depthMatrix=new C.a,this._fullMatrix=new C.a,this._decomposed={t:new p.a,q:new Ll.a,s:new p.a}}static type(){return\\\\\\\"polarTransform\\\\\\\"}createObject(){const e=new on.a;return e.matrixAutoUpdate=!1,e}initializeNode(){this.hierarchyController.initializeNode(),this.dirtyController.hasHook(PO)||this.dirtyController.addPostDirtyHook(PO,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 e=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(RO,A.a.degToRad(this.pv.longitude)),this._latitudeMatrix.makeRotationAxis(IO,A.a.degToRad(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),e.position.copy(this._decomposed.t),e.quaternion.copy(this._decomposed.q),e.scale.copy(this._decomposed.s),e.updateMatrix(),this.cookController.endCook()}}class DO{constructor(e){this._scene=e,this._data={}}data(e){this._scene.nodesController.reset_node_context_signatures();const t=HO.dispatch_node(this._scene.root()),n=t.data(),i=t.ui_data();return this._data={properties:{frame:this._scene.frame()||Qa.START_FRAME,maxFrame:this._scene.maxFrame(),maxFrameLocked:this._scene.timeController.maxFrameLocked(),realtimeState:this._scene.timeController.realtimeState(),mainCameraNodePath:this._scene.camerasController.mainCameraNodePath(),versions:e},root:n,ui:i},this._data}static sanitize_string(e){return e=e.replace(/'/g,\\\\\\\"'\\\\\\\"),e=Li.escapeLineBreaks(e)}}class kO{constructor(e){this._node=e}data(e={}){var t,n,i,s,r,o,a;this.is_root()||this._node.scene().nodesController.register_node_context_signature(this._node),this._data={type:this._node.type()};const c=this.nodes_data(e);Object.keys(c).length>0&&(this._data.nodes=c);const l=this.params_data();if(Object.keys(l).length>0&&(this._data.params=l),!this.is_root()){this._node.io.inputs.maxInputsCountOverriden()&&(this._data.maxInputsCount=this._node.io.inputs.maxInputsCount());const e=this.inputs_data();e.length>0&&(this._data.inputs=e);const t=this.connection_points_data();t&&(this._data.connection_points=t)}if(this._node.flags){const e={};(this._node.flags.hasBypass()||this._node.flags.hasDisplay()||this._node.flags.hasOptimize())&&(this._node.flags.hasBypass()&&(null===(t=this._node.flags.bypass)||void 0===t?void 0:t.active())&&(e.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)||(e.display=null===(s=this._node.flags.display)||void 0===s?void 0:s.active())),this._node.flags.hasOptimize()&&(null===(r=this._node.flags.optimize)||void 0===r?void 0:r.active())&&(e.optimize=null===(o=this._node.flags.optimize)||void 0===o?void 0:o.active())),Object.keys(e).length>0&&(this._data.flags=e)}if(this._node.childrenAllowed()){const e=null===(a=this._node.childrenController)||void 0===a?void 0:a.selection;if(e&&this._node.children().length>0){const t=[],n={};for(let t of e.nodes())n[t.graphNodeId()]=!0;for(let e of this._node.children())e.graphNodeId()in n&&t.push(e);const i=t.map((e=>e.name()));i.length>0&&(this._data.selection=i)}}if(this._node.io.inputs.overrideClonedStateAllowed()){const e=this._node.io.inputs.clonedStateOverriden();e&&(this._data.cloned_state_overriden=e)}if(this._node.persisted_config){const e=this._node.persisted_config.toJSON();e&&(this._data.persisted_config=e)}return this.add_custom(),this._data}ui_data(e={}){const t=this.ui_data_without_children(),n=this._node.children();return n.length>0&&(t.nodes={},n.forEach((n=>{const i=HO.dispatch_node(n);t.nodes[n.name()]=i.ui_data(e)}))),t}ui_data_without_children(){const e={};if(!this.is_root()){const t=this._node.uiData;e.pos=t.position().toArray();const n=t.comment();n&&(e.comment=DO.sanitize_string(n))}return e}is_root(){return null===this._node.parent()&&this._node.graphNodeId()==this._node.root().graphNodeId()}inputs_data(){const e=[];return this._node.io.inputs.inputs().forEach(((t,n)=>{var i;if(t){const s=this._node.io.connections.inputConnection(n);if(this._node.io.inputs.hasNamedInputs()){const r=s.output_index,o=null===(i=t.io.outputs.namedOutputConnectionPoints()[r])||void 0===i?void 0:i.name();o&&(e[n]={index:n,node:t.name(),output:o})}else e[n]=t.name()}})),e}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 e={};if(this._node.io.inputs.hasNamedInputs()){e.in=[];for(let t of this._node.io.inputs.namedInputConnectionPoints())t&&e.in.push(t.toJSON())}if(this._node.io.outputs.hasNamedOutputs()){e.out=[];for(let t of this._node.io.outputs.namedOutputConnectionPoints())t&&e.out.push(t.toJSON())}return e}}params_data(){const e={};for(let t of this._node.params.names){const n=this._node.params.get(t);if(n&&!n.parent_param){const t=HO.dispatch_param(n);if(t.required()){const i=t.data();e[n.name()]=i}}}return e}nodes_data(e={}){const t={};for(let n of this._node.children()){const i=HO.dispatch_node(n);t[n.name()]=i.data(e)}return t}add_custom(){}}class BO{constructor(e){this._param=e,this._complex_data={}}required(){const e=this._param.options.isSpare()&&!this._param.parent_param,t=!this._param.isDefault();return e||t||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 e={},t=this._param.options.overriddenOptions();for(let n of Object.keys(t)){const i=t[n];m.isString(i)||m.isNumber(i)?e[n]=i:e[n]=JSON.stringify(i)}this._complex_data.overriden_options=e}return this._complex_data}_require_data_complex(){return!!this._param.options.isSpare()||!!this._param.options.hasOptionsOverridden()}add_main(){}}class zO extends BO{add_main(){if(!this._require_data_complex())return this._param.rawInputSerialized();this._complex_data.raw_input=this._param.rawInputSerialized()}}class UO extends BO{add_main(){let e=this._param.rawInput();if(e=DO.sanitize_string(e),!this._require_data_complex())return e;this._complex_data.raw_input=e}}class GO extends BO{add_main(){let e=this._param.rawInput();if(e=DO.sanitize_string(e),!this._require_data_complex())return e;this._complex_data.raw_input=e}}class VO extends BO{add_main(){if(!this._require_data_complex())return this._param.rawInputSerialized();this._complex_data.raw_input=this._param.rawInputSerialized()}}class jO extends kO{nodes_data(e={}){return e.showPolyNodesData?super.nodes_data(e):{}}ui_data(e={}){return e.showPolyNodesData?super.ui_data(e):this.ui_data_without_children()}}class HO{static dispatch_node(e){return e.polyNodeController?new jO(e):new kO(e)}static dispatch_param(e){return e instanceof Mr?new zO(e):e instanceof kr?new UO(e):e instanceof Hr?new GO(e):e instanceof jr?new VO(e):new BO(e)}}class qO{constructor(){this._objects=[],this._objects_with_geo=[],this.touch()}timestamp(){return this._timestamp}touch(){const e=Rn.performance.performanceManager();this._timestamp=e.now(),this.reset()}reset(){this._bounding_box=void 0,this._core_geometries=void 0,this._core_objects=void 0}clone(){const e=new qO;if(this._objects){const t=[];for(let e of this._objects)t.push(js.clone(e));e.setObjects(t)}return e}setObjects(e){this._objects=e,this._objects_with_geo=e.filter((e=>null!=e.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(((e,t)=>new js(e,t))):[]}objectsData(){return this._objects?this._objects.map((e=>this._objectData(e))):[]}_objectData(e){let t=0;return e.geometry&&(t=Bs.pointsCount(e.geometry)),{type:ts(e.constructor),name:e.name,children_count:e.children.length,points_count:t}}geometries(){const e=[];for(let t of this.coreObjects()){const n=t.object().geometry;n&&e.push(n)}return e}coreGeometries(){return this._core_geometries=this._core_geometries||this._createCoreGeometries()}_createCoreGeometries(){const e=[];for(let t of this.geometries())e.push(new Bs(t));return e}static geometryFromObject(e){return e.isMesh||e.isLine||e.isPoints?e.geometry:null}faces(){const e=[];for(let t of this.objectsWithGeo())if(t.geometry){const n=new Bs(t.geometry).faces();for(let i of n)i.applyMatrix4(t.matrix),e.push(i)}return e}points(){return this.coreGeometries().map((e=>e.points())).flat()}pointsCount(){return f.sum(this.coreGeometries().map((e=>e.pointsCount())))}totalPointsCount(){if(this._objects){let e=0;for(let t of this._objects)t.traverse((t=>{const n=t.geometry;n&&(e+=Bs.pointsCount(n))}));return e}return 0}pointsFromGroup(e){if(e){const t=Li.indices(e),n=this.points();return f.compact(t.map((e=>n[e])))}return this.points()}static _fromObjects(e){const t=new qO;return t.setObjects(e),t}objectsFromGroup(e){return this.coreObjectsFromGroup(e).map((e=>e.object()))}coreObjectsFromGroup(e){if(\\\\\\\"\\\\\\\"!==(e=e.trim())){const t=parseInt(e);return m.isNaN(t)?this.coreObjects().filter((t=>Li.matchMask(e,t.name()))):f.compact([this.coreObjects()[t]])}return this.coreObjects()}boundingBox(){return this._bounding_box=this._bounding_box||this._compute_bounding_box()}center(){const e=new p.a;return this.boundingBox().getCenter(e),e}size(){const e=new p.a;return this.boundingBox().getSize(e),e}_compute_bounding_box(){let e;if(this._objects)for(let t of this._objects){const n=t.geometry;n&&(n.computeBoundingBox(),e?e.expandByObject(t):n.boundingBox&&(e=n.boundingBox.clone()))}return e=e||new CN.a(new p.a(-1,-1,-1),new p.a(1,1,1)),e}computeVertexNormals(){for(let e of this.coreObjects())e.computeVertexNormals()}hasAttrib(e){let t;return null!=(t=this.coreGeometries()[0])&&t.hasAttrib(e)}attribType(e){const t=this.coreGeometries()[0];return null!=t?t.attribType(e):null}objectAttribType(e){const t=this.coreObjects()[0];return null!=t?t.attribType(e):null}renameAttrib(e,t,n){switch(n){case _s.ATTRIB_CLASS.VERTEX:if(this.hasAttrib(e)&&this._objects)for(let n of this._objects)n.traverse((n=>{const i=qO.geometryFromObject(n);if(i){new Bs(i).renameAttrib(e,t)}}));break;case _s.ATTRIB_CLASS.OBJECT:if(this.hasAttrib(e)&&this._objects)for(let n of this._objects)n.traverse((n=>{new js(n,0).renameAttrib(e,t)}))}}attribNames(){let e;return null!=(e=this.coreGeometries()[0])?e.attribNames():[]}objectAttribNames(){let e;return null!=(e=this.coreObjects()[0])?e.attribNames():[]}attribNamesMatchingMask(e){const t=Li.attribNames(e),n=[];for(let e of this.attribNames())for(let i of t)if(Li.matchMask(e,i))n.push(e);else{e==gs.remapName(i)&&n.push(e)}return f.uniq(n)}attribSizes(){let e;return null!=(e=this.coreGeometries()[0])?e.attribSizes():{}}objectAttribSizes(){let e;return null!=(e=this.coreObjects()[0])?e.attribSizes():{}}attribSize(e){let t;return null!=(t=this.coreGeometries()[0])?t.attribSize(e):0}addNumericVertexAttrib(e,t,n){null==n&&(n=gs.default_value(t));for(let i of this.coreGeometries())i.addNumericAttrib(e,t,n)}static clone(e){const t=new on.a;return e.children.forEach((e=>{const n=js.clone(e);t.add(n)})),t}}class WO extends rc{static context(){return Ei.SOP}cook(e,t){}createCoreGroupFromObjects(e){const t=new qO;return t.setObjects(e),t}createCoreGroupFromGeometry(e,t=Zi.MESH){const n=WO.createObject(e,t);return this.createCoreGroupFromObjects([n])}createObject(e,t,n){return WO.createObject(e,t,n)}static createObject(e,t,n){this.createIndexIfNone(e);const i=new(0,es[t])(e,n=n||_s.MATERIALS[t].clone());return i.castShadow=!0,i.receiveShadow=!0,i.frustumCulled=!1,i.matrixAutoUpdate=!1,i}createIndexIfNone(e){WO.createIndexIfNone(e)}static createIndexIfNone(e){Ds.createIndexIfNone(e)}}var XO;!function(e){e.FROM_SET_CORE_GROUP=\\\\\\\"from set_core_group\\\\\\\",e.FROM_SET_GROUP=\\\\\\\"from set_group\\\\\\\",e.FROM_SET_OBJECTS=\\\\\\\"from set_objects\\\\\\\",e.FROM_SET_OBJECT=\\\\\\\"from set_object\\\\\\\",e.FROM_SET_GEOMETRIES=\\\\\\\"from set_geometries\\\\\\\",e.FROM_SET_GEOMETRY=\\\\\\\"from set_geometry\\\\\\\"}(XO||(XO={}));const YO=\\\\\\\"input geometry\\\\\\\",$O=[YO,YO,YO,YO];class QO extends Mo{constructor(){super(...arguments),this.flags=new hi(this)}static context(){return Ei.SOP}static displayedInputNames(){return $O}initializeBaseNode(){this.flags.display.set(!1),this.flags.display.onUpdate((()=>{if(this.flags.display.active()){const e=this.parent();e&&e.displayNodeController&&e.displayNodeController.setDisplayNode(this)}})),this.io.outputs.setHasOneOutput()}setCoreGroup(e){this._setContainer(e,XO.FROM_SET_CORE_GROUP)}setObject(e){this._setContainerObjects([e],XO.FROM_SET_OBJECT)}setObjects(e){this._setContainerObjects(e,XO.FROM_SET_OBJECTS)}setGeometry(e,t=Zi.MESH){const n=this.createObject(e,t);this._setContainerObjects([n],XO.FROM_SET_GEOMETRY)}setGeometries(e,t=Zi.MESH){const n=[];let i;for(let s of e)i=this.createObject(s,t),n.push(i);this._setContainerObjects(n,XO.FROM_SET_GEOMETRIES)}_setContainerObjects(e,t){const n=this.containerController.container().coreContent()||new qO;n.setObjects(e),n.touch(),this._setContainer(n)}static createObject(e,t,n){return WO.createObject(e,t,n)}createObject(e,t,n){return QO.createObject(e,t,n)}static createIndexIfNone(e){WO.createIndexIfNone(e)}_createIndexIfNone(e){QO.createIndexIfNone(e)}}const JO=new class extends Lo{};class KO extends QO{constructor(){super(...arguments),this.paramsConfig=JO}static type(){return Mi.OUTPUT}initializeNode(){this.io.inputs.setCount(1),this.io.outputs.setHasNoOutput(),this.io.inputs.initInputsClonedState(Ti.NEVER)}cook(e){this.setCoreGroup(e[0])}}class ZO extends QO{constructor(){super(...arguments),this.childrenDisplayController=new tL(this),this.displayNodeController=new mm(this,this.childrenDisplayController.displayNodeControllerCallbacks()),this._children_controller_context=Ei.SOP}initializeBaseNode(){super.initializeBaseNode(),this.childrenDisplayController.initializeNode(),this.cookController.disallowInputsEvaluation()}createNode(e,t){return super.createNode(e,t)}children(){return super.children()}nodesByType(e){return super.nodesByType(e)}async cook(e){const t=this.childrenDisplayController.output_node();if(t){const e=(await t.compute()).coreContent();e?this.setCoreGroup(e):t.states.error.active()?this.states.error.set(t.states.error.message()):this.setObjects([])}else this.states.error.set(\\\\\\\"no output node found inside subnet\\\\\\\")}}const eL={dependsOnDisplayNode:!0};class tL{constructor(e,t=eL){this.node=e,this.options=t,this._output_node_needs_update=!0}dispose(){var e;null===(e=this._graph_node)||void 0===e||e.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 e;const t=null===(e=this.node.flags)||void 0===e?void 0:e.display;t&&t.onUpdate((()=>{t.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 e=this.node.nodesByType(KO.type())[0];null!=this._output_node&&null!=e&&this._output_node.graphNodeId()==e.graphNodeId()||(this._graph_node&&this._output_node&&this._graph_node.removeGraphInput(this._output_node),this._output_node=e,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 e=new Qn(this.node.scene(),\\\\\\\"subnetChildrenDisplayController\\\\\\\");return e.addPostDirtyHook(\\\\\\\"subnetChildrenDisplayController\\\\\\\",(()=>{this.node.setDirty()})),e}}function nL(e,t){const n=new class extends Lo{constructor(){super(...arguments),this.template=Oo.OPERATOR_PATH(\\\\\\\"../template\\\\\\\"),this.debug=Oo.BUTTON(null,{callback:e=>{i.PARAM_CALLBACK_debug(e)}})}};class i extends ZO{constructor(){super(...arguments),this.paramsConfig=n,this.polyNodeController=new rL(this,t)}static type(){return e}static PARAM_CALLBACK_debug(e){e._debug()}_debug(){this.polyNodeController.debug(this.p.template)}}return i}const iL=nL(\\\\\\\"poly\\\\\\\",{nodeContext:Ei.SOP,inputs:[0,4]});class sL extends iL{}class rL{constructor(e,t){this.node=e,this._definition=t}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 e=this._definition.inputs;e&&this.node.io.inputs.setCount(e[0],e[1])}create_params_from_definition(){const e=this._definition.params;if(e){for(let t of e)t.options=t.options||{},t.options.spare=!0;this.node.params.updateParams({toAdd:e})}}createChildNodesFromDefinition(){const e=this._definition.nodes;if(!e)return;const t=this.node.scene().loadingController.loaded();t&&this.node.scene().loadingController.markAsLoading();const n=new gc({}),i=new lc(this.node);i.create_nodes(n,e);const s=this._definition.ui;s&&i.process_nodes_ui_data(n,s),t&&this.node.scene().loadingController.markAsLoaded()}debug(e){const t=e.found_node();if(t){const e=HO.dispatch_node(t),n=e.data({showPolyNodesData:!0}),i=e.ui_data({showPolyNodesData:!0}),s={nodeContext:t.context(),inputs:[0,0],params:[],nodes:n.nodes,ui:i.nodes};console.log(JSON.stringify(s))}}static createNodeClass(e,t,n){switch(t){case Ei.SOP:return nL(e,n);case Ei.OBJ:return oL(e,n)}}}function oL(e,t){const n=new class extends Lo{constructor(){super(...arguments),this.display=Oo.BOOLEAN(1),this.template=Oo.OPERATOR_PATH(\\\\\\\"../template\\\\\\\"),this.debug=Oo.BUTTON(null,{callback:e=>{i.PARAM_CALLBACK_debug(e)}})}};class i extends XN{constructor(){super(...arguments),this.paramsConfig=n,this.hierarchyController=new lS(this),this.flags=new ai(this),this.childrenDisplayController=new TO(this),this.displayNodeController=new mm(this,this.childrenDisplayController.displayNodeControllerCallbacks()),this._children_controller_context=Ei.SOP,this.polyNodeController=new rL(this,t)}static type(){return e}createObject(){const e=new on.a;return e.matrixAutoUpdate=!1,e}initializeNode(){this.hierarchyController.initializeNode(),this.childrenDisplayController.initializeNode()}isDisplayNodeCooking(){if(this.flags.display.active()){const e=this.displayNodeController.displayNode();return!!e&&e.isDirty()}return!1}createNode(e,t){return super.createNode(e,t)}children(){return super.children()}nodesByType(e){return super.nodesByType(e)}cook(){this.object.visible=this.pv.display,this.cookController.endCook()}static PARAM_CALLBACK_debug(e){e._debug()}_debug(){this.polyNodeController.debug(this.p.template)}}return i}const aL=oL(\\\\\\\"poly\\\\\\\",{nodeContext:Ei.OBJ});class cL extends aL{}class lL extends ee.a{constructor(e){super(),this.type=\\\\\\\"Audio\\\\\\\",this.listener=e,this.context=e.context,this.gain=this.context.createGain(),this.gain.connect(e.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(e){return this.hasPlaybackControl=!1,this.sourceType=\\\\\\\"audioNode\\\\\\\",this.source=e,this.connect(),this}setMediaElementSource(e){return this.hasPlaybackControl=!1,this.sourceType=\\\\\\\"mediaNode\\\\\\\",this.source=this.context.createMediaElementSource(e),this.connect(),this}setMediaStreamSource(e){return this.hasPlaybackControl=!1,this.sourceType=\\\\\\\"mediaStreamNode\\\\\\\",this.source=this.context.createMediaStreamSource(e),this.connect(),this}setBuffer(e){return this.buffer=e,this.sourceType=\\\\\\\"buffer\\\\\\\",this.autoplay&&this.play(),this}play(e=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+e;const t=this.context.createBufferSource();return t.buffer=this.buffer,t.loop=this.loop,t.loopStart=this.loopStart,t.loopEnd=this.loopEnd,t.onended=this.onEnded.bind(this),t.start(this._startedAt,this._progress+this.offset,this.duration),this.isPlaying=!0,this.source=t,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 e=1,t=this.filters.length;e<t;e++)this.filters[e-1].connect(this.filters[e]);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 e=1,t=this.filters.length;e<t;e++)this.filters[e-1].disconnect(this.filters[e]);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(e){return e||(e=[]),!0===this._connected?(this.disconnect(),this.filters=e.slice(),this.connect()):this.filters=e.slice(),this}setDetune(e){if(this.detune=e,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(e){return this.setFilters(e?[e]:[])}setPlaybackRate(e){if(!1!==this.hasPlaybackControl)return this.playbackRate=e,!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(e){if(!1!==this.hasPlaybackControl)return this.loop=e,!0===this.isPlaying&&(this.source.loop=this.loop),this;console.warn(\\\\\\\"THREE.Audio: this Audio has no playback control.\\\\\\\")}setLoopStart(e){return this.loopStart=e,this}setLoopEnd(e){return this.loopEnd=e,this}getVolume(){return this.gain.gain.value}setVolume(e){return this.gain.gain.setTargetAtTime(e,this.context.currentTime,.01),this}}const uL=new p.a,hL=new Ll.a,dL=new p.a,pL=new p.a;class _L extends lL{constructor(e){super(e),this.panner=this.context.createPanner(),this.panner.panningModel=\\\\\\\"HRTF\\\\\\\",this.panner.connect(this.gain)}getOutput(){return this.panner}getRefDistance(){return this.panner.refDistance}setRefDistance(e){return this.panner.refDistance=e,this}getRolloffFactor(){return this.panner.rolloffFactor}setRolloffFactor(e){return this.panner.rolloffFactor=e,this}getDistanceModel(){return this.panner.distanceModel}setDistanceModel(e){return this.panner.distanceModel=e,this}getMaxDistance(){return this.panner.maxDistance}setMaxDistance(e){return this.panner.maxDistance=e,this}setDirectionalCone(e,t,n){return this.panner.coneInnerAngle=e,this.panner.coneOuterAngle=t,this.panner.coneOuterGain=n,this}updateMatrixWorld(e){if(super.updateMatrixWorld(e),!0===this.hasPlaybackControl&&!1===this.isPlaying)return;this.matrixWorld.decompose(uL,hL,dL),pL.set(0,0,1).applyQuaternion(hL);const t=this.panner;if(t.positionX){const e=this.context.currentTime+this.listener.timeDelta;t.positionX.linearRampToValueAtTime(uL.x,e),t.positionY.linearRampToValueAtTime(uL.y,e),t.positionZ.linearRampToValueAtTime(uL.z,e),t.orientationX.linearRampToValueAtTime(pL.x,e),t.orientationY.linearRampToValueAtTime(pL.y,e),t.orientationZ.linearRampToValueAtTime(pL.z,e)}else t.setPosition(uL.x,uL.y,uL.z),t.setOrientation(pL.x,pL.y,pL.z)}}function mL(e,t,n,i){this.audio=e,this.range=t||1,this.divisionsInnerAngle=n||16,this.divisionsOuterAngle=i||2;var s=new O.a,r=this.divisionsInnerAngle+2*this.divisionsOuterAngle,o=new Float32Array(3*(3*r+3));s.setAttribute(\\\\\\\"position\\\\\\\",new L.a(o,3));var a=new Yi.a({color:65280}),c=new Yi.a({color:16776960});dS.a.call(this,s,[c,a]),this.type=\\\\\\\"PositionalAudioHelper\\\\\\\",this.update()}mL.prototype=Object.create(dS.a.prototype),mL.prototype.constructor=mL,mL.prototype.update=function(){var e,t,n=this.audio,i=this.range,s=this.divisionsInnerAngle,r=this.divisionsOuterAngle,o=A.a.degToRad(n.panner.coneInnerAngle),a=A.a.degToRad(n.panner.coneOuterAngle),c=o/2,l=a/2,u=0,h=0,d=this.geometry,p=d.attributes.position;function _(n,s,r,o){var a=(s-n)/r;for(p.setXYZ(u,0,0,0),h++,e=n;e<s;e+=a)t=u+h,p.setXYZ(t,Math.sin(e)*i,0,Math.cos(e)*i),p.setXYZ(t+1,Math.sin(Math.min(e+a,s))*i,0,Math.cos(Math.min(e+a,s))*i),p.setXYZ(t+2,0,0,0),h+=3;d.addGroup(u,h,o),u+=h,h=0}d.clearGroups(),_(-l,-c,r,0),_(-c,c,s,1),_(c,l,r,0),p.needsUpdate=!0,o===a&&(this.material[0].visible=!1)},mL.prototype.dispose=function(){this.geometry.dispose(),this.material[0].dispose(),this.material[1].dispose()};class fL extends xf.a{constructor(e){super(e)}load(e,t,n,i){const s=this,r=new yf.a(this.manager);r.setResponseType(\\\\\\\"arraybuffer\\\\\\\"),r.setPath(this.path),r.setRequestHeader(this.requestHeader),r.setWithCredentials(this.withCredentials),r.load(e,(function(n){try{const e=n.slice(0);ZS().decodeAudioData(e,(function(e){t(e)}))}catch(t){i?i(t):console.error(t),s.manager.itemError(e)}}),n,i)}}var gL;!function(e){e.MP3=\\\\\\\"mp3\\\\\\\",e.WAV=\\\\\\\"wav\\\\\\\"}(gL||(gL={}));gL.MP3,gL.WAV;class vL extends tv{async load(){const e=new fL(this.loadingManager),t=await this._urlToLoad();return new Promise((n=>{e.load(t,(function(e){n(e)}))}))}}var yL;!function(e){e.LINEAR=\\\\\\\"linear\\\\\\\",e.INVERSE=\\\\\\\"inverse\\\\\\\",e.EXPONENTIAL=\\\\\\\"exponential\\\\\\\"}(yL||(yL={}));const xL=[yL.LINEAR,yL.INVERSE,yL.EXPONENTIAL];class bL extends(oS(Lo)){constructor(){super(...arguments),this.audio=Oo.FOLDER(),this.listener=Oo.NODE_PATH(\\\\\\\"\\\\\\\",{nodeSelection:{context:Ei.OBJ,types:[_g.AUDIO_LISTENER]}}),this.url=Oo.STRING(\\\\\\\"\\\\\\\",{fileBrowse:{type:[tr.AUDIO]}}),this.volume=Oo.FLOAT(1),this.loop=Oo.BOOLEAN(1,{separatorBefore:!0}),this.loopStart=Oo.FLOAT(0,{visibleIf:{loop:1}}),this.loopEnd=Oo.FLOAT(0,{visibleIf:{loop:1},separatorAfter:!0}),this.refDistance=Oo.FLOAT(10,{range:[0,10],rangeLocked:[!0,!1]}),this.rolloffFactor=Oo.FLOAT(10,{range:[0,10],rangeLocked:[!0,!1]}),this.maxDistance=Oo.FLOAT(100,{range:[.001,100],rangeLocked:[!0,!1]}),this.distanceModel=Oo.INTEGER(xL.indexOf(yL.LINEAR),{menu:{entries:xL.map(((e,t)=>({name:e,value:t})))}}),this.coneInnerAngle=Oo.FLOAT(180,{range:[0,360],rangeLocked:[!0,!0]}),this.coneOuterAngle=Oo.FLOAT(230,{range:[0,360],rangeLocked:[!0,!0]}),this.coneOuterGain=Oo.FLOAT(.1,{range:[0,1],rangeLocked:[!0,!0]}),this.autoplay=Oo.BOOLEAN(1),this.showHelper=Oo.BOOLEAN(0),this.play=Oo.BUTTON(null,{callback:e=>{AL.PARAM_CALLBACK_play(e)}}),this.pause=Oo.BUTTON(null,{callback:e=>{AL.PARAM_CALLBACK_pause(e)}})}}const wL=new bL;class AL extends XN{constructor(){super(...arguments),this.paramsConfig=wL,this.hierarchyController=new lS(this),this.transformController=new cS(this),this.flags=new ai(this)}static type(){return _g.POSITIONAL_AUDIO}createObject(){const e=new on.a;return e.matrixAutoUpdate=!1,e}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 e=this.pv.url;if(this._loadedUrl!=e)try{await this._createPositionalAudio()}catch(e){this.states.error.set(`error when creating audio: ${e}`)}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(xL[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(e){const t=new mL(e);return t.matrixAutoUpdate=!1,t}async _createPositionalAudio(){const e=this.pv.listener.nodeWithContext(Ei.OBJ);if(!e)return;const t=e.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 _L(t),this._positionalAudio.matrixAutoUpdate=!1;const n=new vL(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(e){e.PARAM_CALLBACK_play()}static PARAM_CALLBACK_pause(e){e.PARAM_CALLBACK_pause()}PARAM_CALLBACK_play(){this._positionalAudio&&(this.isPlaying()||this._positionalAudio.play())}PARAM_CALLBACK_pause(){this._positionalAudio&&this.isPlaying()&&this._positionalAudio.pause()}}var TL;!function(e){e.ON_RENDER=\\\\\\\"On Every Render\\\\\\\",e.MANUAL=\\\\\\\"Manual\\\\\\\"}(TL||(TL={}));const EL=[TL.ON_RENDER,TL.MANUAL];const CL=new class extends Lo{constructor(){super(...arguments),this.object=Oo.OPERATOR_PATH(\\\\\\\"\\\\\\\",{nodeSelection:{context:Ei.OBJ},dependentOnFoundNode:!1,computeOnDirty:!0,callback:e=>{ML.PARAM_CALLBACK_update_resolved_object(e)}}),this.pointIndex=Oo.INTEGER(0,{range:[0,100]}),this.updateMode=Oo.INTEGER(EL.indexOf(TL.ON_RENDER),{callback:e=>{ML.PARAM_CALLBACK_update_updateMode(e)},menu:{entries:EL.map(((e,t)=>({name:e,value:t})))}}),this.update=Oo.BUTTON(null,{callback:e=>{ML.PARAM_CALLBACK_update(e)},visibleIf:{updateMode:EL.indexOf(TL.MANUAL)}})}};class ML extends XN{constructor(){super(...arguments),this.paramsConfig=CL,this.hierarchyController=new lS(this),this.flags=new ai(this),this._helper=new cO(1),this._found_point_post=new p.a,this._on_object_before_render_bound=this._update.bind(this)}static type(){return\\\\\\\"rivet\\\\\\\"}createObject(){const e=new z.a;return e.matrixAutoUpdate=!1,e}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 e=EL[this.pv.updateMode];switch(e){case TL.ON_RENDER:return this._add_render_hook();case TL.MANUAL:return this._remove_render_hook()}Ri.unreachable(e)}_add_render_hook(){this.object.onBeforeRender=this._on_object_before_render_bound,this.object.frustumCulled=!1}_remove_render_hook(){this.object.onBeforeRender=()=>{}}_update(e,t,n,i,s,r){const o=this._resolved_object();if(o){const e=o.geometry;if(e){const t=e.attributes.position;if(t){const e=t.array;this._found_point_post.fromArray(e,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(e){e._update_resolved_object()}async _update_resolved_object(){this.p.object.isDirty()&&await this.p.object.compute();const e=this.p.object.found_node();if(e)if(e.context()==Ei.OBJ&&e.type()==MO.type()){const t=e;this._resolved_sop_group=t.childrenDisplayController.sopGroup()}else this.states.error.set(\\\\\\\"found node is not a geo node\\\\\\\")}_resolved_object(){if(!this._resolved_sop_group)return;const e=this._resolved_sop_group.children[0];return e||void 0}static PARAM_CALLBACK_update_updateMode(e){e._update_render_hook()}static PARAM_CALLBACK_update(e){e._update()}}class NL extends(Ko($o(Vo(zo(Io(Lo)))))){}const SL=new NL;class OL extends XN{constructor(){super(...arguments),this.paramsConfig=SL,this.hierarchyController=new lS(this),this.SceneAutoUpdateController=new Fo(this),this.sceneBackgroundController=new Uo(this),this.SceneEnvController=new jo(this),this.sceneFogController=new Qo(this),this.sceneMaterialOverrideController=new Zo(this)}static type(){return\\\\\\\"scene\\\\\\\"}createObject(){const e=new Vi;return e.matrixAutoUpdate=!1,e}initializeNode(){this.hierarchyController.initializeNode()}cook(){this.SceneAutoUpdateController.update(),this.sceneBackgroundController.update(),this.SceneEnvController.update(),this.sceneFogController.update(),this.sceneMaterialOverrideController.update(),this.cookController.endCook()}}class LL{constructor(e,t,n){this._camera_node_id=e,this._controls_node=t,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(e){return e.camera_node_id==this._camera_node_id&&e.controls_node.graphNodeId()==this._controls_node.graphNodeId()}}const PL=\\\\\\\"controls\\\\\\\";class RL{constructor(e){this.node=e,this._applied_controls_by_element_id=new Map,this._controls_node=null}controls_param(){return this.node.params.has(PL)?this.node.params.get(PL):null}async controls_node(){const e=this.node.p.controls,t=e.rawInput();if(t&&\\\\\\\"\\\\\\\"!=t){e.isDirty()&&await e.compute();const t=e.value.node();if(t){if(zi.includes(t.type()))return t;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 e=await this.controls_node();e&&this._controls_node!=e&&this._dispose_control_refs(),this._controls_node=e}async apply_controls(e){const t=e.canvas();if(!t)return;const n=await this.controls_node();if(n){this._controlsEndEventName=n.endEventName();const i=n.controls_id();let s=!1,r=this._applied_controls_by_element_id.get(t.id);if(r&&r.get(i)&&(s=!0),!s){r=new Map,this._applied_controls_by_element_id.set(t.id,r),r.set(i,n);const s=await n.apply_controls(this.node.object,e);if(!s)return;const o=new LL(this.node.graphNodeId(),n,s);return this.set_controls_events(s),o}}}_dispose_control_refs(){this._applied_controls_by_element_id.forEach(((e,t)=>{this._dispose_controls_for_element_id(t)})),this._applied_controls_by_element_id.clear(),this._controlsEndEventName=void 0}_dispose_controls_for_element_id(e){const t=this._applied_controls_by_element_id.get(e);t&&t.forEach(((t,n)=>{t.dispose_controls_for_html_element_id(e)})),this._applied_controls_by_element_id.delete(e)}async dispose_controls(e){this._dispose_controls_for_element_id(e.id)}set_controls_events(e){const t=EP[this.node.pv.updateFromControlsMode];switch(t){case TP.ON_END:return this._set_controls_events_to_update_on_end(e);case TP.ALWAYS:return this._set_controls_events_to_update_always(e);case TP.NEVER:return this._reset(e)}Ri.unreachable(t)}_reset(e){this.controls_change_listener&&(e.removeEventListener(\\\\\\\"change\\\\\\\",this.controls_change_listener),this.controls_change_listener=void 0),this.controls_end_listener&&this._controlsEndEventName&&(e.removeEventListener(this._controlsEndEventName,this.controls_end_listener),this.controls_end_listener=void 0)}_set_controls_events_to_update_on_end(e){this._reset(e),this._controlsEndEventName&&(this.controls_end_listener=()=>{this.node.update_transform_params_from_object()},e.addEventListener(this._controlsEndEventName,this.controls_end_listener))}_set_controls_events_to_update_always(e){this._reset(e),this.controls_change_listener=()=>{this.node.update_transform_params_from_object()},e.addEventListener(\\\\\\\"change\\\\\\\",this.controls_change_listener)}}function IL(e){return class extends e{constructor(){super(...arguments),this.layer=Oo.INTEGER(0,{range:[0,31],rangeLocked:[!0,!0]})}}}class FL{constructor(e){this.node=e}update(){const e=this.node.object;e.layers.set(0),e.layers.enable(this.node.params.integer(\\\\\\\"layer\\\\\\\"))}}const DL={callback:e=>{IP.PARAM_CALLBACK_reset_effects_composer(e)}};function kL(e){return class extends e{constructor(){super(...arguments),this.doPostProcess=Oo.BOOLEAN(0),this.postProcessNode=Oo.NODE_PATH(\\\\\\\"\\\\\\\",{visibleIf:{doPostProcess:1},nodeSelection:{types:[Ci.POST]},...DL})}}}class BL{constructor(e){this.node=e,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(e,t){const n=this.composer(e);n&&(t&&n.setSize(t.x,t.y),n.render())}reset(){const e=Object.keys(this._composers_by_canvas_id);for(let t of e)delete this._composers_by_canvas_id[t]}composer(e){return this._composers_by_canvas_id[e.id]=this._composers_by_canvas_id[e.id]||this._create_composer(e)}_create_composer(e){const t=this.node.renderController.renderer(e);if(t){const n=this.node.renderController.resolved_scene||this.node.scene().threejsScene(),i=this.node.object,s=this.node.p.postProcessNode.value.node();if(s){if(s.type()==Ci.POST){const r=s,o=this.node.renderController.canvas_resolution(e);return r.effectsComposerController.createEffectsComposer({renderer:t,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 zL extends Mo{constructor(){super(...arguments),this.flags=new ii(this)}static context(){return Ei.ROP}initializeBaseNode(){this.dirtyController.addPostDirtyHook(\\\\\\\"cook_immediately\\\\\\\",(()=>{this.cookController.cookMainWithoutInputs()}))}cook(){this.cookController.endCook()}}var UL,GL,VL,jL;!function(e){e.CSS2D=\\\\\\\"CSS2DRenderer\\\\\\\",e.CSS3D=\\\\\\\"CSS3DRenderer\\\\\\\",e.WEBGL=\\\\\\\"WebGLRenderer\\\\\\\"}(UL||(UL={})),function(e){e.Linear=\\\\\\\"Linear\\\\\\\",e.sRGB=\\\\\\\"sRGB\\\\\\\",e.Gamma=\\\\\\\"Gamma\\\\\\\",e.RGBE=\\\\\\\"RGBE\\\\\\\",e.LogLuv=\\\\\\\"LogLuv\\\\\\\",e.RGBM7=\\\\\\\"RGBM7\\\\\\\",e.RGBM16=\\\\\\\"RGBM16\\\\\\\",e.RGBD=\\\\\\\"RGBD\\\\\\\"}(GL||(GL={})),(jL=VL||(VL={}))[jL.Linear=w.U]=\\\\\\\"Linear\\\\\\\",jL[jL.sRGB=w.ld]=\\\\\\\"sRGB\\\\\\\",jL[jL.Gamma=w.J]=\\\\\\\"Gamma\\\\\\\",jL[jL.RGBE=w.gc]=\\\\\\\"RGBE\\\\\\\",jL[jL.LogLuv=w.bb]=\\\\\\\"LogLuv\\\\\\\",jL[jL.RGBM7=w.lc]=\\\\\\\"RGBM7\\\\\\\",jL[jL.RGBM16=w.kc]=\\\\\\\"RGBM16\\\\\\\",jL[jL.RGBD=w.fc]=\\\\\\\"RGBD\\\\\\\";const HL=[GL.Linear,GL.sRGB,GL.Gamma,GL.RGBE,GL.LogLuv,GL.RGBM7,GL.RGBM16,GL.RGBD],qL=[VL.Linear,VL.sRGB,VL.Gamma,VL.RGBE,VL.LogLuv,VL.RGBM7,VL.RGBM16,VL.RGBD],WL=VL.sRGB;var XL,YL,$L;!function(e){e.No=\\\\\\\"No\\\\\\\",e.Linear=\\\\\\\"Linear\\\\\\\",e.Reinhard=\\\\\\\"Reinhard\\\\\\\",e.Cineon=\\\\\\\"Cineon\\\\\\\",e.ACESFilmic=\\\\\\\"ACESFilmic\\\\\\\"}(XL||(XL={})),($L=YL||(YL={}))[$L.No=w.vb]=\\\\\\\"No\\\\\\\",$L[$L.Linear=w.ab]=\\\\\\\"Linear\\\\\\\",$L[$L.Reinhard=w.vc]=\\\\\\\"Reinhard\\\\\\\",$L[$L.Cineon=w.m]=\\\\\\\"Cineon\\\\\\\",$L[$L.ACESFilmic=w.a]=\\\\\\\"ACESFilmic\\\\\\\";const QL=[XL.No,XL.Linear,XL.Reinhard,XL.Cineon,XL.ACESFilmic],JL=[YL.No,YL.Linear,YL.Reinhard,YL.Cineon,YL.ACESFilmic],KL=YL.ACESFilmic,ZL=QL.map(((e,t)=>({name:e,value:JL[t]})));var eP;!function(e){e.HIGH=\\\\\\\"highp\\\\\\\",e.MEDIUM=\\\\\\\"mediump\\\\\\\",e.LOW=\\\\\\\"lowp\\\\\\\"}(eP||(eP={}));const tP=[eP.HIGH,eP.MEDIUM,eP.LOW];var nP;!function(e){e.HIGH=\\\\\\\"high-performance\\\\\\\",e.LOW=\\\\\\\"low-power\\\\\\\",e.DEFAULT=\\\\\\\"default\\\\\\\"}(nP||(nP={}));const iP=[nP.HIGH,nP.LOW,nP.DEFAULT];var sP,rP,oP;!function(e){e.Basic=\\\\\\\"Basic\\\\\\\",e.PCF=\\\\\\\"PCF\\\\\\\",e.PCFSoft=\\\\\\\"PCFSoft\\\\\\\",e.VSM=\\\\\\\"VSM\\\\\\\"}(sP||(sP={})),(oP=rP||(rP={}))[oP.Basic=w.k]=\\\\\\\"Basic\\\\\\\",oP[oP.PCF=w.Fb]=\\\\\\\"PCF\\\\\\\",oP[oP.PCFSoft=w.Gb]=\\\\\\\"PCFSoft\\\\\\\",oP[oP.VSM=w.gd]=\\\\\\\"VSM\\\\\\\";const aP=[sP.Basic,sP.PCF,sP.PCFSoft,sP.VSM],cP=[rP.Basic,rP.PCF,rP.PCFSoft,rP.VSM],lP=(w.k,w.Fb,w.Gb,w.gd,rP.PCFSoft),uP={alpha:!1,precision:eP.HIGH,premultipliedAlpha:!0,antialias:!1,stencil:!0,preserveDrawingBuffer:!1,powerPreference:nP.DEFAULT,depth:!0,logarithmicDepthBuffer:!1};const hP=new class extends Lo{constructor(){super(...arguments),this.tprecision=Oo.BOOLEAN(0),this.precision=Oo.INTEGER(tP.indexOf(eP.HIGH),{visibleIf:{tprecision:1},menu:{entries:tP.map(((e,t)=>({value:t,name:e})))}}),this.tpowerPreference=Oo.BOOLEAN(0),this.powerPreference=Oo.INTEGER(iP.indexOf(nP.DEFAULT),{visibleIf:{tpowerPreference:1},menu:{entries:iP.map(((e,t)=>({value:t,name:e})))}}),this.alpha=Oo.BOOLEAN(1),this.premultipliedAlpha=Oo.BOOLEAN(1),this.antialias=Oo.BOOLEAN(1),this.stencil=Oo.BOOLEAN(1),this.depth=Oo.BOOLEAN(1),this.logarithmicDepthBuffer=Oo.BOOLEAN(0),this.toneMapping=Oo.INTEGER(KL,{menu:{entries:ZL}}),this.toneMappingExposure=Oo.FLOAT(1,{range:[0,2]}),this.outputEncoding=Oo.INTEGER(WL,{menu:{entries:HL.map(((e,t)=>({name:e,value:qL[t]})))}}),this.physicallyCorrectLights=Oo.BOOLEAN(1),this.sortObjects=Oo.BOOLEAN(1),this.tpixelRatio=Oo.BOOLEAN(0),this.pixelRatio=Oo.INTEGER(2,{visibleIf:{tpixelRatio:!0},range:[1,4],rangeLocked:[!0,!1]}),this.tshadowMap=Oo.BOOLEAN(1),this.shadowMapAutoUpdate=Oo.BOOLEAN(1,{visibleIf:{tshadowMap:1}}),this.shadowMapNeedsUpdate=Oo.BOOLEAN(0,{visibleIf:{tshadowMap:1}}),this.shadowMapType=Oo.INTEGER(lP,{visibleIf:{tshadowMap:1},menu:{entries:aP.map(((e,t)=>({name:e,value:cP[t]})))}})}};class dP extends zL{constructor(){super(...arguments),this.paramsConfig=hP,this._renderers_by_canvas_id={}}static type(){return UL.WEBGL}createRenderer(e,t){const n={},i=Object.keys(uP);let s;for(s of i)n[s]=uP[s];if(this.pv.tprecision){const e=tP[this.pv.precision];n.precision=e}if(this.pv.tpowerPreference){const e=iP[this.pv.powerPreference];n.powerPreference=e}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=e,n.context=t;const r=Rn.renderersController.createWebGLRenderer(n);return Rn.renderersController.printDebug()&&(Rn.renderersController.printDebugMessage(`create renderer from node '${this.path()}'`),Rn.renderersController.printDebugMessage({params:n})),this._update_renderer(r),this._renderers_by_canvas_id[e.id]=r,r}cook(){const e=Object.keys(this._renderers_by_canvas_id);for(let t of e){const e=this._renderers_by_canvas_id[t];this._update_renderer(e)}this._traverse_scene_and_update_materials(),this.cookController.endCook()}_update_renderer(e){e.physicallyCorrectLights=this.pv.physicallyCorrectLights,e.outputEncoding=this.pv.outputEncoding,e.toneMapping=this.pv.toneMapping,e.toneMappingExposure=this.pv.toneMappingExposure,e.shadowMap.enabled=this.pv.tshadowMap,e.shadowMap.autoUpdate=this.pv.shadowMapAutoUpdate,e.shadowMap.needsUpdate=this.pv.shadowMapNeedsUpdate,e.shadowMap.type=this.pv.shadowMapType,e.sortObjects=this.pv.sortObjects;const t=this.pv.tpixelRatio?this.pv.pixelRatio:_P.defaultPixelRatio();Rn.renderersController.printDebug()&&(Rn.renderersController.printDebugMessage(`set renderer pixelRatio from '${this.path()}'`),Rn.renderersController.printDebugMessage({pixelRatio:t})),e.setPixelRatio(t)}_traverse_scene_and_update_materials(){this.scene().threejsScene().traverse((e=>{const t=e.material;if(t)if(m.isArray(t))for(let e of t)e.needsUpdate=!0;else t.needsUpdate=!0}))}}function pP(e){return class extends e{constructor(){super(...arguments),this.render=Oo.FOLDER(),this.setScene=Oo.BOOLEAN(0),this.scene=Oo.OPERATOR_PATH(\\\\\\\"\\\\\\\",{visibleIf:{setScene:1},nodeSelection:{context:Ei.OBJ,types:[OL.type()]}}),this.setRenderer=Oo.BOOLEAN(0),this.renderer=Oo.OPERATOR_PATH(\\\\\\\"\\\\\\\",{visibleIf:{setRenderer:1},nodeSelection:{context:Ei.ROP,types:[dP.type()]}}),this.setCSSRenderer=Oo.BOOLEAN(0),this.CSSRenderer=Oo.OPERATOR_PATH(\\\\\\\"\\\\\\\",{visibleIf:{setCSSRenderer:1},nodeSelection:{context:Ei.ROP,types:[UL.CSS2D,UL.CSS3D]}})}}}class _P{constructor(e){this.node=e,this._renderers_by_canvas_id={},this._resolution_by_canvas_id={},this._super_sampling_size=new d.a}render(e,t,n){if(this.node.pv.doPostProcess?this.node.postProcessController.render(e,t):this.render_with_renderer(e),this._resolved_cssRenderer_rop&&this._resolved_scene&&this.node.pv.setCSSRenderer){const t=this.cssRenderer(e);t&&t.render(this._resolved_scene,this.node.object)}}render_with_renderer(e){const t=this.renderer(e);t&&this._resolved_scene&&t.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 e=this.node.p.scene;e.isDirty()&&e.find_target();const t=e.found_node_with_context_and_type(Ei.OBJ,OL.type());t&&(t.isDirty()&&t.cookController.cookMainWithoutInputs(),this._resolved_scene=t.object)}else this._resolved_scene=this.node.scene().threejsScene()}update_renderer(){if(this.node.pv.setRenderer){const e=this.node.p.renderer;e.isDirty()&&e.find_target(),this._resolved_renderer_rop=e.found_node_with_context_and_type(Ei.ROP,UL.WEBGL)}else this._resolved_renderer_rop=void 0}update_cssRenderer(){if(this.node.pv.setCSSRenderer){const e=this.node.p.CSSRenderer;e.isDirty()&&e.find_target(),this._resolved_cssRenderer_rop=e.found_node_with_context_and_type(Ei.ROP,[UL.CSS2D,UL.CSS3D])}else this._resolved_cssRenderer_rop,this._resolved_cssRenderer_rop=void 0}renderer(e){return this._renderers_by_canvas_id[e.id]}cssRenderer(e){if(this._resolved_cssRenderer_rop&&this.node.pv.setCSSRenderer)return this._resolved_cssRenderer_rop.renderer(e)}createRenderer(e,t){const n=Rn.renderersController.createRenderingContext(e);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(e,n))),i||(i=_P._createDefaultRenderer(e,n)),Rn.renderersController.registerRenderer(i),this._renderers_by_canvas_id[e.id]=i,this._super_sampling_size.copy(t),this.set_renderer_size(e,this._super_sampling_size),i}static defaultPixelRatio(){return Df.isMobile()?1:Math.max(2,window.devicePixelRatio)}static _createDefaultRenderer(e,t){const n={canvas:e,antialias:!1,alpha:!1,context:t},i=Rn.renderersController.createWebGLRenderer(n),s=this.defaultPixelRatio();return i.setPixelRatio(s),i.shadowMap.enabled=!0,i.shadowMap.type=lP,i.physicallyCorrectLights=!0,i.toneMapping=KL,i.toneMappingExposure=1,i.outputEncoding=WL,Rn.renderersController.printDebug()&&(Rn.renderersController.printDebugMessage(\\\\\\\"create default renderer\\\\\\\"),Rn.renderersController.printDebugMessage({params:n,pixelRatio:s})),i}delete_renderer(e){const t=this.renderer(e);t&&Rn.renderersController.deregisterRenderer(t)}canvas_resolution(e){return this._resolution_by_canvas_id[e.id]}set_renderer_size(e,t){this._resolution_by_canvas_id[e.id]=this._resolution_by_canvas_id[e.id]||new d.a,this._resolution_by_canvas_id[e.id].copy(t);const n=this.renderer(e);if(n){const e=!1;n.setSize(t.x,t.y,e)}if(this._resolved_cssRenderer_rop){const n=this.cssRenderer(e);n&&n.setSize(t.x,t.y)}}}class mP{constructor(e){this.viewer=e,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 e;this.dispose_controls();this.viewer.canvas()&&(this._config=await(null===(e=this.viewer.cameraControlsController)||void 0===e?void 0:e.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 e;null===(e=this._graph_node)||void 0===e||e.graphDisconnectPredecessors(),this.dispose_controls()}dispose_controls(){var e;if(this._controls){const t=this.viewer.canvas();t&&(null===(e=this.viewer)||void 0===e||e.cameraControlsController.dispose_controls(t)),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 e=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(e))}_create_graph_node(){const e=new Qn(this.viewer.cameraNode().scene(),\\\\\\\"viewer-controls\\\\\\\");return e.addPostDirtyHook(\\\\\\\"this.viewer.controls_controller\\\\\\\",(async()=>{await this.viewer.controlsController.create_controls()})),e}}class fP{constructor(e){this._viewer=e,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 e;this.updateCameraAspect(),await(null===(e=this._viewer.controlsController)||void 0===e?void 0:e.create_controls())}}class gP{constructor(e){this.viewer=e}init(){const e=this.viewer.canvas();e&&(e.onwebglcontextlost=this._on_webglcontextlost.bind(this),e.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 vP=\\\\\\\"hovered\\\\\\\";class yP{constructor(e,t,n){this._container=e,this._scene=t,this._camera_node=n,this._active=!1,this._id=yP._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 fP(this)}get controlsController(){return this._controls_controller}get eventsController(){return this._events_controller=this._events_controller||new ha(this)}get webglController(){return this._webgl_controller=this._webgl_controller||new gP(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 e;for(this._scene.viewersRegister.unregisterViewer(this),this.eventsController.dispose();e=this._container.children[0];)this._container.removeChild(e)}resetContainerClass(){this.domElement().classList.remove(vP)}setContainerClassHovered(){this.domElement().classList.add(vP)}registerOnBeforeTick(e,t){this._onBeforeTickCallbackNames=this._onBeforeTickCallbackNames||[],this._onBeforeTickCallbacks=this._onBeforeTickCallbacks||[],this._registerCallback(e,t,this._onBeforeTickCallbackNames,this._onBeforeTickCallbacks)}unRegisterOnBeforeTick(e){this._unregisterCallback(e,this._onBeforeTickCallbackNames,this._onBeforeTickCallbacks)}registeredBeforeTickCallbackNames(){return this._onBeforeTickCallbackNames}registerOnAfterTick(e,t){this._onAfterTickCallbacks=this._onAfterTickCallbacks||[],this._onAfterTickCallbackNames=this._onAfterTickCallbackNames||[],this._registerCallback(e,t,this._onAfterTickCallbackNames,this._onAfterTickCallbacks)}unRegisterOnAfterTick(e){this._unregisterCallback(e,this._onAfterTickCallbackNames,this._onAfterTickCallbacks)}registeredAfterTickCallbackNames(){return this._onAfterTickCallbackNames}registerOnBeforeRender(e,t){this._onBeforeRenderCallbackNames=this._onBeforeRenderCallbackNames||[],this._onBeforeRenderCallbacks=this._onBeforeRenderCallbacks||[],this._registerCallback(e,t,this._onBeforeRenderCallbackNames,this._onBeforeRenderCallbacks)}unRegisterOnBeforeRender(e){this._unregisterCallback(e,this._onBeforeRenderCallbackNames,this._onBeforeRenderCallbacks)}registeredBeforeRenderCallbackNames(){return this._onBeforeRenderCallbackNames}registerOnAfterRender(e,t){this._onAfterRenderCallbackNames=this._onAfterRenderCallbackNames||[],this._onAfterRenderCallbacks=this._onAfterRenderCallbacks||[],this._registerCallback(e,t,this._onAfterRenderCallbackNames,this._onAfterRenderCallbacks)}unRegisterOnAfterRender(e){this._unregisterCallback(e,this._onAfterRenderCallbackNames,this._onAfterRenderCallbacks)}registeredAfterRenderCallbackNames(){return this._onAfterRenderCallbackNames}_registerCallback(e,t,n,i){(null==n?void 0:n.includes(e))?console.warn(`callback ${e} already registered`):(i.push(t),n.push(e))}_unregisterCallback(e,t,n){if(!t||!n)return;const i=t.indexOf(e);t.splice(i,1),n.splice(i,1)}}yP._next_viewer_id=0;class xP extends yP{constructor(e,t,n,i){super(e,t,n),this._scene=t,this._camera_node=n,this._properties=i,this._do_render=!0,this._animate_method=this.animate.bind(this),this._onResizeBound=this.onResize.bind(this),this._do_render=null==this._properties||this._properties.autoRender,this._canvas=document.createElement(\\\\\\\"canvas\\\\\\\"),this._canvas.id=`canvas_id_${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 mP(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 e=this.canvas();e&&(this.camerasController.computeSizeAndAspect(),this._camera_node.renderController.set_renderer_size(e,this.camerasController.size),this.camerasController.updateCameraAspect())}_init_display(){if(!this._canvas)return void console.warn(\\\\\\\"no canvas found for viewer\\\\\\\");this.camerasController.computeSizeAndAspect();const e=this.camerasController.size;this._camera_node.renderController.createRenderer(this._canvas,e),this.camerasController.prepareCurrentCamera(),this.animate()}setAutoRender(e=!0){this._do_render=e,this._do_render&&this.animate()}animate(){var e;if(this._do_render){if(this._request_animation_frame_id=requestAnimationFrame(this._animate_method),this._onBeforeTickCallbacks)for(let e of this._onBeforeTickCallbacks)e();if(this._scene.timeController.incrementTimeIfPlaying(),this._onAfterTickCallbacks)for(let e of this._onAfterTickCallbacks)e();this.render(),null===(e=this._controls_controller)||void 0===e||e.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 e of this._onBeforeRenderCallbacks)e();const e=this.camerasController.size,t=this.camerasController.aspect;if(this._camera_node.renderController.render(this._canvas,e,t),this._onAfterRenderCallbacks)for(let e of this._onAfterRenderCallbacks)e()}else console.warn(\\\\\\\"no camera to render with\\\\\\\")}renderer(){if(this._canvas)return this._camera_node.renderController.renderer(this._canvas)}}const bP={type:\\\\\\\"change\\\\\\\"},wP=1,AP=100;var TP;!function(e){e.ON_END=\\\\\\\"on move end\\\\\\\",e.ALWAYS=\\\\\\\"always\\\\\\\",e.NEVER=\\\\\\\"never\\\\\\\"}(TP||(TP={}));const EP=[TP.ON_END,TP.ALWAYS,TP.NEVER];function CP(e){return class extends e{constructor(){super(...arguments),this.setMainCamera=Oo.BUTTON(null,{callback:(e,t)=>{RP.PARAM_CALLBACK_setMasterCamera(e)}})}}}function MP(e){return class extends e{constructor(){super(...arguments),this.camera=Oo.FOLDER(),this.controls=Oo.NODE_PATH(\\\\\\\"\\\\\\\",{nodeSelection:{context:Ei.EVENT}}),this.updateFromControlsMode=Oo.INTEGER(EP.indexOf(TP.ON_END),{menu:{entries:EP.map(((e,t)=>({name:e,value:t})))}}),this.near=Oo.FLOAT(wP,{range:[0,100],cook:!1,computeOnDirty:!0,callback:(e,t)=>{IP.PARAM_CALLBACK_update_near_far_from_param(e,t)}}),this.far=Oo.FLOAT(AP,{range:[0,100],cook:!1,computeOnDirty:!0,callback:(e,t)=>{IP.PARAM_CALLBACK_update_near_far_from_param(e,t)}}),this.display=Oo.BOOLEAN(1),this.showHelper=Oo.BOOLEAN(0)}}}var NP;!function(e){e.DEFAULT=\\\\\\\"default\\\\\\\",e.COVER=\\\\\\\"cover\\\\\\\",e.CONTAIN=\\\\\\\"contain\\\\\\\"}(NP||(NP={}));const SP=[NP.DEFAULT,NP.COVER,NP.CONTAIN];function OP(e){return class extends e{constructor(){super(...arguments),this.fovAdjustMode=Oo.INTEGER(SP.indexOf(NP.DEFAULT),{menu:{entries:SP.map(((e,t)=>({name:e,value:t})))}}),this.expectedAspectRatio=Oo.FLOAT(\\\\\\\"16/9\\\\\\\",{visibleIf:[{fovAdjustMode:SP.indexOf(NP.COVER)},{fovAdjustMode:SP.indexOf(NP.CONTAIN)}],range:[0,2],rangeLocked:[!0,!1]})}}}CP(Lo);kL(pP(oS(IL(MP(CP(Lo))))));class LP extends XN{constructor(){super(...arguments),this.renderOrder=WN.CAMERA,this._aspect=-1}get object(){return this._object}async cook(){this.updateCamera(),this._object.dispatchEvent(bP),this.cookController.endCook()}on_create(){}on_delete(){}prepareRaycaster(e,t){}camera(){return this._object}updateCamera(){}static PARAM_CALLBACK_setMasterCamera(e){e.set_as_master_camera()}set_as_master_camera(){this.scene().camerasController.setMainCameraNodePath(this.path())}setupForAspectRatio(e){}_updateForAspectRatio(){}update_transform_params_from_object(){rS.set_params_from_object(this._object,this)}static PARAM_CALLBACK_update_from_param(e,t){e.object[t.name()]=e.pv[t.name()]}}class PP extends LP{constructor(){super(...arguments),this.flags=new ai(this),this.hierarchyController=new lS(this),this.transformController=new cS(this),this.childrenDisplayController=new TO(this),this.displayNodeController=new mm(this,this.childrenDisplayController.displayNodeControllerCallbacks()),this._children_controller_context=Ei.SOP}get controls_controller(){return this._controls_controller=this._controls_controller||new RL(this)}get layers_controller(){return this._layers_controller=this._layers_controller||new FL(this)}get renderController(){return this._render_controller=this._render_controller||new _P(this)}get postProcessController(){return this._post_process_controller=this._post_process_controller||new BL(this)}initializeBaseNode(){super.initializeBaseNode(),this.io.outputs.setHasOneOutput(),this.hierarchyController.initializeNode(),this.transformController.initializeNode(),this.childrenDisplayController.initializeNode(),this.initHelperHook()}createNode(e,t){return super.createNode(e,t)}children(){return super.children()}nodesByType(e){return super.nodesByType(e)}prepareRaycaster(e,t){t.setFromCamera(e,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(bP),this.cookController.endCook()}static PARAM_CALLBACK_update_near_far_from_param(e,t){e.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(e){m.isNaN(e)||e&&this._aspect!=e&&(this._aspect=e,this._updateForAspectRatio())}createViewer(e,t){return new xP(e,this.scene(),this,t)}static PARAM_CALLBACK_reset_effects_composer(e){e.postProcessController.reset()}initHelperHook(){this.flags.display.onUpdate((()=>{this._updateHelper()}))}helperVisible(){return this.flags.display.active()&&this.pv.showHelper}_createHelper(){const e=new AS(this.object);return e.update(),e}_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 RP extends LP{}class IP extends PP{PARAM_CALLBACK_update_effects_composer(e){}}const FP=-.5,DP=.5,kP=.5,BP=-.5;class zP extends(kL(pP(IL(CP(OP(function(e){return class extends e{constructor(){super(...arguments),this.size=Oo.FLOAT(1)}}}(MP(oS(Lo,{matrixAutoUpdate:!0}))))))))){}const UP=new zP;class GP extends PP{constructor(){super(...arguments),this.paramsConfig=UP}static type(){return Ni.ORTHOGRAPHIC}createObject(){return new vm.a(2*FP,2*DP,2*kP,2*BP,wP,AP)}updateCamera(){this._updateForAspectRatio()}_updateForAspectRatio(){this._aspect&&(this._adjustFOVFromMode(),this._object.updateProjectionMatrix())}_adjustFOVFromMode(){const e=SP[this.pv.fovAdjustMode];switch(e){case NP.DEFAULT:return this._adjustFOVFromModeDefault();case NP.COVER:return this._adjustFOVFromModeCover();case NP.CONTAIN:return this._adjustFOVFromModeContain()}Ri.unreachable(e)}_adjustFOVFromModeDefault(){this._adjustFOVFromSize(this.pv.size||1)}_adjustFOVFromModeCover(){const e=this.pv.size||1;this._aspect>this.pv.expectedAspectRatio?this._adjustFOVFromSize(this.pv.expectedAspectRatio*e/this._aspect):this._adjustFOVFromSize(e)}_adjustFOVFromModeContain(){const e=this.pv.size||1;this._aspect>this.pv.expectedAspectRatio?this._adjustFOVFromSize(e):this._adjustFOVFromSize(this.pv.expectedAspectRatio*e/this._aspect)}_adjustFOVFromSize(e){const t=e*this._aspect;this._object.left=FP*t*1,this._object.right=DP*t*1,this._object.top=kP*e*1,this._object.bottom=BP*e*1}}const VP=50;class jP extends(kL(pP(IL(CP(OP(function(e){return class extends e{constructor(){super(...arguments),this.fov=Oo.FLOAT(VP,{range:[0,100]})}}}(MP(oS(Lo,{matrixAutoUpdate:!0}))))))))){}const HP=new jP;class qP extends PP{constructor(){super(...arguments),this.paramsConfig=HP}static type(){return Ni.PERSPECTIVE}createObject(){return new te.a(VP,1,wP,AP)}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 e=SP[this.pv.fovAdjustMode];switch(e){case NP.DEFAULT:return this._adjustFOVFromModeDefault();case NP.COVER:return this._adjustFOVFromModeCover();case NP.CONTAIN:return this._adjustFOVFromModeContain()}Ri.unreachable(e)}_adjustFOVFromModeDefault(){this._object.fov=this.pv.fov}_adjustFOVFromModeCover(){if(this._object.aspect>this.pv.expectedAspectRatio){const e=Math.tan(A.a.degToRad(this.pv.fov/2))/(this._object.aspect/this.pv.expectedAspectRatio);this._object.fov=2*A.a.radToDeg(Math.atan(e))}else this._object.fov=this.pv.fov}_adjustFOVFromModeContain(){if(this._object.aspect>this.pv.expectedAspectRatio)this._object.fov=this.pv.fov;else{const e=Math.tan(A.a.degToRad(this.pv.fov/2))/(this._object.aspect/this.pv.expectedAspectRatio);this._object.fov=2*A.a.radToDeg(Math.atan(e))}}}class WP extends(function(e){return class extends e{constructor(){super(...arguments),this.main=Oo.FOLDER(),this.resolution=Oo.INTEGER(256),this.excludedObjects=Oo.STRING(\\\\\\\"*`$OS`\\\\\\\"),this.printResolve=Oo.BUTTON(null,{callback:e=>{YP.PARAM_CALLBACK_printResolve(e)}}),this.near=Oo.FLOAT(1),this.far=Oo.FLOAT(100),this.render=Oo.BUTTON(null,{callback:e=>{YP.PARAM_CALLBACK_render(e)}}),this.renderTarget=Oo.FOLDER(),this.tencoding=Oo.BOOLEAN(0),this.encoding=Oo.INTEGER(w.ld,{visibleIf:{tencoding:1},menu:{entries:Uf.map((e=>({name:Object.keys(e)[0],value:Object.values(e)[0]})))}}),this.tminFilter=Oo.BOOLEAN(0),this.minFilter=Oo.INTEGER(Om,{visibleIf:{tminFilter:1},menu:{entries:Pm}}),this.tmagFilter=Oo.BOOLEAN(0),this.magFilter=Oo.INTEGER(Sm,{visibleIf:{tmagFilter:1},menu:{entries:Lm}})}}}(oS(Lo))){}const XP=new WP;class YP extends XN{constructor(){super(...arguments),this.paramsConfig=XP,this.hierarchyController=new lS(this),this.transformController=new cS(this),this.flags=new ai(this),this._excludedObjects=[],this._previousVisibleStateByUuid=new Map,this._helper=new cO(1)}static type(){return _g.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 e=new on.a;return e.matrixAutoUpdate=!0,e}cook(){this.transformController.update(),this._resolveObjects();const e=this._setupCubeCamera();this._cubeCamera&&!e||this._createCubeCamera(),this.cookController.endCook()}_updateHelperHierarchy(){this.flags.display.active()?this.object.add(this._helper):this.object.remove(this._helper)}_setupCubeCamera(){let e=!1;if(this._cubeCamera){const t=this._cubeCamera.children[0],n=t.near,i=t.far,s=this._cubeCamera.renderTarget.width;n==this.pv.near&&i==this.pv.far&&s==this.pv.resolution||(e=!0),e&&this.object.remove(this._cubeCamera)}return e}_createCubeCamera(){const e=new re(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 ie(this.pv.near,this.pv.far,e),this._cubeCamera.matrixAutoUpdate=!0,this.object.add(this._cubeCamera)}renderTarget(){if(this._cubeCamera)return this._cubeCamera.renderTarget}render(){const e=Rn.renderersController.firstRenderer();if(e)if(this._cubeCamera){for(let e of this._excludedObjects)this._previousVisibleStateByUuid.set(e.uuid,e.visible),e.visible=!1;this._cubeCamera.update(e,this.scene().threejsScene());for(let e of this._excludedObjects){const t=this._previousVisibleStateByUuid.get(e.uuid);t&&(e.visible=t)}this._previousVisibleStateByUuid.clear()}else console.warn(`no cubeCamera for ${this.path()}`);else console.warn(`no renderer found for ${this.path()}`)}_resolveObjects(){const e=this.scene().objectsByMask(this.pv.excludedObjects),t=new Map;for(let n of e)t.set(n.uuid,n);this._excludedObjects=[];for(let n of e){const e=n.parent;e&&(t.get(e.uuid)||this._excludedObjects.push(n))}}static PARAM_CALLBACK_printResolve(e){e.param_callback_printResolve()}param_callback_printResolve(){this._resolveObjects(),console.log(this._excludedObjects)}static PARAM_CALLBACK_render(e){e.param_callback_render()}param_callback_render(){this.render()}}class $P extends XN{constructor(){super(...arguments),this._attachableToHierarchy=!1}createObject(){const e=new on.a;return e.matrixAutoUpdate=!1,e}cook(){this.cookController.endCook()}}class QP extends $P{}class JP extends QP{constructor(){super(...arguments),this._children_controller_context=Ei.ANIM}static type(){return Ci.ANIM}createNode(e,t){return super.createNode(e,t)}children(){return super.children()}nodesByType(e){return super.nodesByType(e)}}class KP extends JP{constructor(){super(...arguments),this.renderOrder=WN.MANAGER}}class ZP extends QP{constructor(){super(...arguments),this._children_controller_context=Ei.COP}static type(){return Ci.COP}createNode(e,t){return super.createNode(e,t)}children(){return super.children()}nodesByType(e){return super.nodesByType(e)}}class eR extends QP{constructor(){super(...arguments),this.renderOrder=WN.MANAGER,this._children_controller_context=Ei.EVENT}static type(){return Ci.EVENT}createNode(e,t){return super.createNode(e,t)}children(){return super.children()}nodesByType(e){return super.nodesByType(e)}}class tR extends QP{constructor(){super(...arguments),this.renderOrder=WN.MANAGER,this._children_controller_context=Ei.MAT}static type(){return Ci.MAT}createNode(e,t){return super.createNode(e,t)}children(){return super.children()}nodesByType(e){return super.nodesByType(e)}}class nR extends $P{constructor(){super(...arguments),this.paramsConfig=new Rm,this.effectsComposerController=new Im(this),this.displayNodeController=new mm(this,this.effectsComposerController.displayNodeControllerCallbacks()),this._children_controller_context=Ei.POST}static type(){return Ci.POST}createNode(e,t){return super.createNode(e,t)}children(){return super.children()}nodesByType(e){return super.nodesByType(e)}}class iR extends QP{constructor(){super(...arguments),this.renderOrder=WN.MANAGER,this._children_controller_context=Ei.ROP}static type(){return Ci.ROP}createNode(e,t){return super.createNode(e,t)}children(){return super.children()}nodesByType(e){return super.nodesByType(e)}}const sR=[\\\\\\\"input pass\\\\\\\"];const rR={cook:!1,callback:function(e,t){oR.PARAM_CALLBACK_updatePasses(e)},computeOnDirty:!0};class oR extends Mo{constructor(){super(...arguments),this.flags=new li(this),this._passes_by_requester_id=new Map,this._update_pass_bound=this.updatePass.bind(this)}static context(){return Ei.POST}static displayedInputNames(){return sR}initializeNode(){this.flags.display.set(!1),this.flags.display.onUpdate((()=>{if(this.flags.display.active()){const e=this.parent();e&&e.displayNodeController&&e.displayNodeController.setDisplayNode(this)}})),this.io.inputs.setCount(0,1),this.io.outputs.setHasOneOutput()}cook(){this.cookController.endCook()}setupComposer(e){if(this._addPassFromInput(0,e),!this.flags.bypass.active()){let t=this._passes_by_requester_id.get(e.requester.graphNodeId());t||(t=this._createPass(e),t&&this._passes_by_requester_id.set(e.requester.graphNodeId(),t)),t&&e.composer.addPass(t)}}_addPassFromInput(e,t){const n=this.io.inputs.input(e);n&&n.setupComposer(t)}_createPass(e){}static PARAM_CALLBACK_updatePasses(e){e._updatePasses()}_updatePasses(){this._passes_by_requester_id.forEach(this._update_pass_bound)}updatePass(e){}}var aR={uniforms:{tDiffuse:{value:null}},vertexShader:[\\\\\\\"varying vec2 vUv;\\\\\\\",\\\\\\\"void main() {\\\\\\\",\\\\\\\"\\\\tvUv = uv;\\\\\\\",\\\\\\\"\\\\tgl_Position = projectionMatrix * modelViewMatrix * vec4( position, 1.0 );\\\\\\\",\\\\\\\"}\\\\\\\"].join(\\\\\\\"\\\\n\\\\\\\"),fragmentShader:[\\\\\\\"#include <common>\\\\\\\",\\\\\\\"uniform sampler2D tDiffuse;\\\\\\\",\\\\\\\"varying vec2 vUv;\\\\\\\",\\\\\\\"void main() {\\\\\\\",\\\\\\\"\\\\tvec4 texel = texture2D( tDiffuse, vUv );\\\\\\\",\\\\\\\"\\\\tfloat l = linearToRelativeLuminance( texel.rgb );\\\\\\\",\\\\\\\"\\\\tgl_FragColor = vec4( l, l, l, texel.w );\\\\\\\",\\\\\\\"}\\\\\\\"].join(\\\\\\\"\\\\n\\\\\\\")},cR={uniforms:{tDiffuse:{value:null},averageLuminance:{value:1},luminanceMap:{value:null},maxLuminance:{value:16},minLuminance:{value:.01},middleGrey:{value:.6}},vertexShader:[\\\\\\\"varying vec2 vUv;\\\\\\\",\\\\\\\"void main() {\\\\\\\",\\\\\\\"\\\\tvUv = uv;\\\\\\\",\\\\\\\"\\\\tgl_Position = projectionMatrix * modelViewMatrix * vec4( position, 1.0 );\\\\\\\",\\\\\\\"}\\\\\\\"].join(\\\\\\\"\\\\n\\\\\\\"),fragmentShader:[\\\\\\\"#include <common>\\\\\\\",\\\\\\\"uniform sampler2D tDiffuse;\\\\\\\",\\\\\\\"varying vec2 vUv;\\\\\\\",\\\\\\\"uniform float middleGrey;\\\\\\\",\\\\\\\"uniform float minLuminance;\\\\\\\",\\\\\\\"uniform float maxLuminance;\\\\\\\",\\\\\\\"#ifdef ADAPTED_LUMINANCE\\\\\\\",\\\\\\\"\\\\tuniform sampler2D luminanceMap;\\\\\\\",\\\\\\\"#else\\\\\\\",\\\\\\\"\\\\tuniform float averageLuminance;\\\\\\\",\\\\\\\"#endif\\\\\\\",\\\\\\\"vec3 ToneMap( vec3 vColor ) {\\\\\\\",\\\\\\\"\\\\t#ifdef ADAPTED_LUMINANCE\\\\\\\",\\\\\\\"\\\\t\\\\tfloat fLumAvg = texture2D(luminanceMap, vec2(0.5, 0.5)).r;\\\\\\\",\\\\\\\"\\\\t#else\\\\\\\",\\\\\\\"\\\\t\\\\tfloat fLumAvg = averageLuminance;\\\\\\\",\\\\\\\"\\\\t#endif\\\\\\\",\\\\\\\"\\\\tfloat fLumPixel = linearToRelativeLuminance( vColor );\\\\\\\",\\\\\\\"\\\\tfloat fLumScaled = (fLumPixel * middleGrey) / max( minLuminance, fLumAvg );\\\\\\\",\\\\\\\"\\\\tfloat fLumCompressed = (fLumScaled * (1.0 + (fLumScaled / (maxLuminance * maxLuminance)))) / (1.0 + fLumScaled);\\\\\\\",\\\\\\\"\\\\treturn fLumCompressed * vColor;\\\\\\\",\\\\\\\"}\\\\\\\",\\\\\\\"void main() {\\\\\\\",\\\\\\\"\\\\tvec4 texel = texture2D( tDiffuse, vUv );\\\\\\\",\\\\\\\"\\\\tgl_FragColor = vec4( ToneMap( texel.xyz ), texel.w );\\\\\\\",\\\\\\\"}\\\\\\\"].join(\\\\\\\"\\\\n\\\\\\\")},lR=function(e,t){xm.call(this),this.resolution=void 0!==t?t:256,this.needsInit=!0,this.adaptive=void 0===e||!!e,this.luminanceRT=null,this.previousLuminanceRT=null,this.currentLuminanceRT=null,void 0===ym&&console.error(\\\\\\\"THREE.AdaptiveToneMappingPass relies on CopyShader\\\\\\\");var n=ym;this.copyUniforms=k.clone(n.uniforms),this.materialCopy=new B({uniforms:this.copyUniforms,vertexShader:n.vertexShader,fragmentShader:n.fragmentShader,blending:w.ub,depthTest:!1}),void 0===aR&&console.error(\\\\\\\"THREE.AdaptiveToneMappingPass relies on LuminosityShader\\\\\\\"),this.materialLuminance=new B({uniforms:k.clone(aR.uniforms),vertexShader:aR.vertexShader,fragmentShader:aR.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;\\\\\\\",\\\\\\\"void main() {\\\\\\\",\\\\\\\"\\\\tvUv = uv;\\\\\\\",\\\\\\\"\\\\tgl_Position = projectionMatrix * modelViewMatrix * vec4( position, 1.0 );\\\\\\\",\\\\\\\"}\\\\\\\"].join(\\\\\\\"\\\\n\\\\\\\"),fragmentShader:[\\\\\\\"varying vec2 vUv;\\\\\\\",\\\\\\\"uniform sampler2D lastLum;\\\\\\\",\\\\\\\"uniform sampler2D currentLum;\\\\\\\",\\\\\\\"uniform float minLuminance;\\\\\\\",\\\\\\\"uniform float delta;\\\\\\\",\\\\\\\"uniform float tau;\\\\\\\",\\\\\\\"void main() {\\\\\\\",\\\\\\\"\\\\tvec4 lastLum = texture2D( lastLum, vUv, MIP_LEVEL_1X1 );\\\\\\\",\\\\\\\"\\\\tvec4 currentLum = texture2D( currentLum, vUv, MIP_LEVEL_1X1 );\\\\\\\",\\\\\\\"\\\\tfloat fLastLum = max( minLuminance, lastLum.r );\\\\\\\",\\\\\\\"\\\\tfloat fCurrentLum = max( minLuminance, currentLum.r );\\\\\\\",\\\\\\\"\\\\tfCurrentLum *= fCurrentLum;\\\\\\\",\\\\\\\"\\\\tfloat fAdaptedLum = fLastLum + (fCurrentLum - fLastLum) * (1.0 - exp(-delta * tau));\\\\\\\",\\\\\\\"\\\\tgl_FragColor.r = fAdaptedLum;\\\\\\\",\\\\\\\"}\\\\\\\"].join(\\\\\\\"\\\\n\\\\\\\")},this.materialAdaptiveLum=new B({uniforms:k.clone(this.adaptLuminanceShader.uniforms),vertexShader:this.adaptLuminanceShader.vertexShader,fragmentShader:this.adaptLuminanceShader.fragmentShader,defines:Object.assign({},this.adaptLuminanceShader.defines),blending:w.ub}),void 0===cR&&console.error(\\\\\\\"THREE.AdaptiveToneMappingPass relies on ToneMapShader\\\\\\\"),this.materialToneMap=new B({uniforms:k.clone(cR.uniforms),vertexShader:cR.vertexShader,fragmentShader:cR.fragmentShader,blending:w.ub}),this.fsQuad=new xm.FullScreenQuad(null)};lR.prototype=Object.assign(Object.create(xm.prototype),{constructor:lR,render:function(e,t,n,i){this.needsInit&&(this.reset(e),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,e.setRenderTarget(this.currentLuminanceRT),this.fsQuad.render(e),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,e.setRenderTarget(this.luminanceRT),this.fsQuad.render(e),this.fsQuad.material=this.materialCopy,this.copyUniforms.tDiffuse.value=this.luminanceRT.texture,e.setRenderTarget(this.previousLuminanceRT),this.fsQuad.render(e)),this.fsQuad.material=this.materialToneMap,this.materialToneMap.uniforms.tDiffuse.value=n.texture,this.renderToScreen?(e.setRenderTarget(null),this.fsQuad.render(e)):(e.setRenderTarget(t),this.clear&&e.clear(),this.fsQuad.render(e))},reset:function(){this.luminanceRT&&this.luminanceRT.dispose(),this.currentLuminanceRT&&this.currentLuminanceRT.dispose(),this.previousLuminanceRT&&this.previousLuminanceRT.dispose();var e={minFilter:w.V,magFilter:w.V,format:w.Ib};this.luminanceRT=new Z(this.resolution,this.resolution,e),this.luminanceRT.texture.name=\\\\\\\"AdaptiveToneMappingPass.l\\\\\\\",this.luminanceRT.texture.generateMipmaps=!1,this.previousLuminanceRT=new Z(this.resolution,this.resolution,e),this.previousLuminanceRT.texture.name=\\\\\\\"AdaptiveToneMappingPass.pl\\\\\\\",this.previousLuminanceRT.texture.generateMipmaps=!1,e.minFilter=w.Y,e.generateMipmaps=!0,this.currentLuminanceRT=new Z(this.resolution,this.resolution,e),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 Nf.a({color:7829367}),this.materialLuminance.needsUpdate=!0,this.materialAdaptiveLum.needsUpdate=!0,this.materialToneMap.needsUpdate=!0},setAdaptive:function(e){e?(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:function(e){e&&(this.materialAdaptiveLum.uniforms.tau.value=Math.abs(e))},setMinLuminance:function(e){e&&(this.materialToneMap.uniforms.minLuminance.value=e,this.materialAdaptiveLum.uniforms.minLuminance.value=e)},setMaxLuminance:function(e){e&&(this.materialToneMap.uniforms.maxLuminance.value=e)},setAverageLuminance:function(e){e&&(this.materialToneMap.uniforms.averageLuminance.value=e)},setMiddleGrey:function(e){e&&(this.materialToneMap.uniforms.middleGrey.value=e)},dispose:function(){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 uR=new class extends Lo{constructor(){super(...arguments),this.adaptive=Oo.BOOLEAN(1,{...rR}),this.averageLuminance=Oo.FLOAT(.7,{...rR}),this.midGrey=Oo.FLOAT(.04,{...rR}),this.maxLuminance=Oo.FLOAT(16,{range:[0,20],...rR}),this.adaptiveRange=Oo.FLOAT(2,{range:[0,10],...rR})}};class hR extends oR{constructor(){super(...arguments),this.paramsConfig=uR}static type(){return\\\\\\\"adaptiveToneMapping\\\\\\\"}_createPass(e){const t=new lR(this.pv.adaptive,e.resolution.x);return this.updatePass(t),t}updatePass(e){e.setMaxLuminance(this.pv.maxLuminance),e.setMiddleGrey(this.pv.midGrey),e.setAverageLuminance(this.pv.averageLuminance)}}var dR={uniforms:{damp:{value:.96},tOld:{value:null},tNew:{value:null}},vertexShader:[\\\\\\\"varying vec2 vUv;\\\\\\\",\\\\\\\"void main() {\\\\\\\",\\\\\\\"\\\\tvUv = uv;\\\\\\\",\\\\\\\"\\\\tgl_Position = projectionMatrix * modelViewMatrix * vec4( position, 1.0 );\\\\\\\",\\\\\\\"}\\\\\\\"].join(\\\\\\\"\\\\n\\\\\\\"),fragmentShader:[\\\\\\\"uniform float damp;\\\\\\\",\\\\\\\"uniform sampler2D tOld;\\\\\\\",\\\\\\\"uniform sampler2D tNew;\\\\\\\",\\\\\\\"varying vec2 vUv;\\\\\\\",\\\\\\\"vec4 when_gt( vec4 x, float y ) {\\\\\\\",\\\\\\\"\\\\treturn max( sign( x - y ), 0.0 );\\\\\\\",\\\\\\\"}\\\\\\\",\\\\\\\"void main() {\\\\\\\",\\\\\\\"\\\\tvec4 texelOld = texture2D( tOld, vUv );\\\\\\\",\\\\\\\"\\\\tvec4 texelNew = texture2D( tNew, vUv );\\\\\\\",\\\\\\\"\\\\ttexelOld *= damp * when_gt( texelOld, 0.1 );\\\\\\\",\\\\\\\"\\\\tgl_FragColor = max(texelNew, texelOld);\\\\\\\",\\\\\\\"}\\\\\\\"].join(\\\\\\\"\\\\n\\\\\\\")},pR=function(e){xm.call(this),void 0===dR&&console.error(\\\\\\\"THREE.AfterimagePass relies on AfterimageShader\\\\\\\"),this.shader=dR,this.uniforms=k.clone(this.shader.uniforms),this.uniforms.damp.value=void 0!==e?e:.96,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 B({uniforms:this.uniforms,vertexShader:this.shader.vertexShader,fragmentShader:this.shader.fragmentShader}),this.compFsQuad=new xm.FullScreenQuad(this.shaderMaterial);var t=new Nf.a;this.copyFsQuad=new xm.FullScreenQuad(t)};pR.prototype=Object.assign(Object.create(xm.prototype),{constructor:pR,render:function(e,t,n){this.uniforms.tOld.value=this.textureOld.texture,this.uniforms.tNew.value=n.texture,e.setRenderTarget(this.textureComp),this.compFsQuad.render(e),this.copyFsQuad.material.map=this.textureComp.texture,this.renderToScreen?(e.setRenderTarget(null),this.copyFsQuad.render(e)):(e.setRenderTarget(t),this.clear&&e.clear(),this.copyFsQuad.render(e));var i=this.textureOld;this.textureOld=this.textureComp,this.textureComp=i},setSize:function(e,t){this.textureComp.setSize(e,t),this.textureOld.setSize(e,t)}});const _R=new class extends Lo{constructor(){super(...arguments),this.damp=Oo.FLOAT(.96,{range:[0,1],rangeLocked:[!0,!0],...rR})}};class mR extends oR{constructor(){super(...arguments),this.paramsConfig=_R}static type(){return\\\\\\\"afterImage\\\\\\\"}_createPass(e){const t=new pR;return this.updatePass(t),t}updatePass(e){e.uniforms.damp.value=this.pv.damp}}var fR={uniforms:{tDiffuse:{value:null},opacity:{value:1}},vertexShader:[\\\\\\\"varying vec2 vUv;\\\\\\\",\\\\\\\"void main() {\\\\\\\",\\\\\\\"\\\\tvUv = uv;\\\\\\\",\\\\\\\"\\\\tgl_Position = projectionMatrix * modelViewMatrix * vec4( position, 1.0 );\\\\\\\",\\\\\\\"}\\\\\\\"].join(\\\\\\\"\\\\n\\\\\\\"),fragmentShader:[\\\\\\\"uniform float opacity;\\\\\\\",\\\\\\\"uniform sampler2D tDiffuse;\\\\\\\",\\\\\\\"varying vec2 vUv;\\\\\\\",\\\\\\\"void main() {\\\\\\\",\\\\\\\"\\\\tvec4 base = texture2D( tDiffuse, vUv );\\\\\\\",\\\\\\\"\\\\tvec3 lumCoeff = vec3( 0.25, 0.65, 0.1 );\\\\\\\",\\\\\\\"\\\\tfloat lum = dot( lumCoeff, base.rgb );\\\\\\\",\\\\\\\"\\\\tvec3 blend = vec3( lum );\\\\\\\",\\\\\\\"\\\\tfloat L = min( 1.0, max( 0.0, 10.0 * ( lum - 0.45 ) ) );\\\\\\\",\\\\\\\"\\\\tvec3 result1 = 2.0 * base.rgb * blend;\\\\\\\",\\\\\\\"\\\\tvec3 result2 = 1.0 - 2.0 * ( 1.0 - blend ) * ( 1.0 - base.rgb );\\\\\\\",\\\\\\\"\\\\tvec3 newColor = mix( result1, result2, L );\\\\\\\",\\\\\\\"\\\\tfloat A2 = opacity * base.a;\\\\\\\",\\\\\\\"\\\\tvec3 mixRGB = A2 * newColor.rgb;\\\\\\\",\\\\\\\"\\\\tmixRGB += ( ( 1.0 - A2 ) * base.rgb );\\\\\\\",\\\\\\\"\\\\tgl_FragColor = vec4( mixRGB, base.a );\\\\\\\",\\\\\\\"}\\\\\\\"].join(\\\\\\\"\\\\n\\\\\\\")};const gR=new class extends Lo{constructor(){super(...arguments),this.opacity=Oo.FLOAT(.95,{range:[-5,5],rangeLocked:[!0,!0],...rR})}};class vR extends oR{constructor(){super(...arguments),this.paramsConfig=gR}static type(){return\\\\\\\"bleach\\\\\\\"}_createPass(e){const t=new bm(fR);return this.updatePass(t),t}updatePass(e){e.uniforms.opacity.value=this.pv.opacity}}var yR={uniforms:{tDiffuse:{value:null},brightness:{value:0},contrast:{value:0}},vertexShader:[\\\\\\\"varying vec2 vUv;\\\\\\\",\\\\\\\"void main() {\\\\\\\",\\\\\\\"\\\\tvUv = uv;\\\\\\\",\\\\\\\"\\\\tgl_Position = projectionMatrix * modelViewMatrix * vec4( position, 1.0 );\\\\\\\",\\\\\\\"}\\\\\\\"].join(\\\\\\\"\\\\n\\\\\\\"),fragmentShader:[\\\\\\\"uniform sampler2D tDiffuse;\\\\\\\",\\\\\\\"uniform float brightness;\\\\\\\",\\\\\\\"uniform float contrast;\\\\\\\",\\\\\\\"varying vec2 vUv;\\\\\\\",\\\\\\\"void main() {\\\\\\\",\\\\\\\"\\\\tgl_FragColor = texture2D( tDiffuse, vUv );\\\\\\\",\\\\\\\"\\\\tgl_FragColor.rgb += brightness;\\\\\\\",\\\\\\\"\\\\tif (contrast > 0.0) {\\\\\\\",\\\\\\\"\\\\t\\\\tgl_FragColor.rgb = (gl_FragColor.rgb - 0.5) / (1.0 - contrast) + 0.5;\\\\\\\",\\\\\\\"\\\\t} else {\\\\\\\",\\\\\\\"\\\\t\\\\tgl_FragColor.rgb = (gl_FragColor.rgb - 0.5) * (1.0 + contrast) + 0.5;\\\\\\\",\\\\\\\"\\\\t}\\\\\\\",\\\\\\\"}\\\\\\\"].join(\\\\\\\"\\\\n\\\\\\\")};const xR=new class extends Lo{constructor(){super(...arguments),this.brightness=Oo.FLOAT(0,{range:[-1,1],rangeLocked:[!1,!1],...rR}),this.contrast=Oo.FLOAT(0,{range:[-1,1],rangeLocked:[!1,!1],...rR}),this.transparent=Oo.BOOLEAN(1,rR)}};class bR extends oR{constructor(){super(...arguments),this.paramsConfig=xR}static type(){return\\\\\\\"brightnessContrast\\\\\\\"}_createPass(e){const t=new bm(yR);return console.log(\\\\\\\"brightness\\\\\\\",t),t.fsQuad.material.transparent=!0,this.updatePass(t),t}updatePass(e){e.uniforms.brightness.value=this.pv.brightness,e.uniforms.contrast.value=this.pv.contrast,e.material.transparent=this.pv.transparent}}var wR=function(e,t){xm.call(this),this.needsSwap=!1,this.clearColor=void 0!==e?e:0,this.clearAlpha=void 0!==t?t:0,this._oldClearColor=new M.a};wR.prototype=Object.assign(Object.create(xm.prototype),{constructor:wR,render:function(e,t,n){var i;this.clearColor&&(e.getClearColor(this._oldClearColor),i=e.getClearAlpha(),e.setClearColor(this.clearColor,this.clearAlpha)),e.setRenderTarget(this.renderToScreen?null:n),e.clear(),this.clearColor&&e.setClearColor(this._oldClearColor,i)}});const AR=new class extends Lo{};class TR extends oR{constructor(){super(...arguments),this.paramsConfig=AR}static type(){return\\\\\\\"clear\\\\\\\"}_createPass(e){const t=new wR;return this.updatePass(t),t}updatePass(e){}}const ER=new class extends Lo{};class CR extends oR{constructor(){super(...arguments),this.paramsConfig=ER}static type(){return\\\\\\\"clearMask\\\\\\\"}_createPass(e){const t=new Am;return this.updatePass(t),t}updatePass(e){}}var MR={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:[\\\\\\\"varying vec2 vUv;\\\\\\\",\\\\\\\"void main() {\\\\\\\",\\\\\\\"\\\\tvUv = uv;\\\\\\\",\\\\\\\"\\\\tgl_Position = projectionMatrix * modelViewMatrix * vec4( position, 1.0 );\\\\\\\",\\\\\\\"}\\\\\\\"].join(\\\\\\\"\\\\n\\\\\\\"),fragmentShader:[\\\\\\\"uniform sampler2D tDiffuse;\\\\\\\",\\\\\\\"uniform vec3 powRGB;\\\\\\\",\\\\\\\"uniform vec3 mulRGB;\\\\\\\",\\\\\\\"uniform vec3 addRGB;\\\\\\\",\\\\\\\"varying vec2 vUv;\\\\\\\",\\\\\\\"void main() {\\\\\\\",\\\\\\\"\\\\tgl_FragColor = texture2D( tDiffuse, vUv );\\\\\\\",\\\\\\\"\\\\tgl_FragColor.rgb = mulRGB * pow( ( gl_FragColor.rgb + addRGB ), powRGB );\\\\\\\",\\\\\\\"}\\\\\\\"].join(\\\\\\\"\\\\n\\\\\\\")};const NR=new class extends Lo{constructor(){super(...arguments),this.pow=Oo.VECTOR3([2,2,2],{...rR}),this.mult=Oo.COLOR([1,1,1],{...rR}),this.add=Oo.COLOR([0,0,0],{...rR})}};class SR extends oR{constructor(){super(...arguments),this.paramsConfig=NR}static type(){return\\\\\\\"colorCorrection\\\\\\\"}_createPass(e){const t=new bm(MR);return this.updatePass(t),t}updatePass(e){e.uniforms.powRGB.value.copy(this.pv.pow),e.uniforms.mulRGB.value.set(this.pv.mult.r,this.pv.mult.g,this.pv.mult.b),e.uniforms.addRGB.value.set(this.pv.add.r,this.pv.add.g,this.pv.add.b)}}const OR=new class extends Lo{constructor(){super(...arguments),this.opacity=Oo.FLOAT(1,{range:[0,1],rangeLocked:[!0,!0],...rR}),this.transparent=Oo.BOOLEAN(1,rR)}};class LR extends oR{constructor(){super(...arguments),this.paramsConfig=OR}static type(){return\\\\\\\"copy\\\\\\\"}_createPass(e){const t=new bm(ym);return this.updatePass(t),t}updatePass(e){e.uniforms.opacity.value=this.pv.opacity,e.material.transparent=this.pv.transparent}}var PR={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:[\\\\\\\"varying vec2 vUv;\\\\\\\",\\\\\\\"void main() {\\\\\\\",\\\\\\\"\\\\tvUv = uv;\\\\\\\",\\\\\\\"\\\\tgl_Position = projectionMatrix * modelViewMatrix * vec4( position, 1.0 );\\\\\\\",\\\\\\\"}\\\\\\\"].join(\\\\\\\"\\\\n\\\\\\\"),fragmentShader:[\\\\\\\"#include <common>\\\\\\\",\\\\\\\"varying vec2 vUv;\\\\\\\",\\\\\\\"uniform sampler2D tColor;\\\\\\\",\\\\\\\"uniform sampler2D tDepth;\\\\\\\",\\\\\\\"uniform float textureWidth;\\\\\\\",\\\\\\\"uniform float textureHeight;\\\\\\\",\\\\\\\"uniform float focalDepth;  //focal distance value in meters, but you may use autofocus option below\\\\\\\",\\\\\\\"uniform float focalLength; //focal length in mm\\\\\\\",\\\\\\\"uniform float fstop; //f-stop value\\\\\\\",\\\\\\\"uniform bool showFocus; //show debug focus point and focal range (red = focal point, green = focal range)\\\\\\\",\\\\\\\"/*\\\\\\\",\\\\\\\"make sure that these two values are the same for your camera, otherwise distances will be wrong.\\\\\\\",\\\\\\\"*/\\\\\\\",\\\\\\\"uniform float znear; // camera clipping start\\\\\\\",\\\\\\\"uniform float zfar; // camera clipping end\\\\\\\",\\\\\\\"//------------------------------------------\\\\\\\",\\\\\\\"//user variables\\\\\\\",\\\\\\\"const int samples = SAMPLES; //samples on the first ring\\\\\\\",\\\\\\\"const int rings = RINGS; //ring count\\\\\\\",\\\\\\\"const int maxringsamples = rings * samples;\\\\\\\",\\\\\\\"uniform bool manualdof; // manual dof calculation\\\\\\\",\\\\\\\"float ndofstart = 1.0; // near dof blur start\\\\\\\",\\\\\\\"float ndofdist = 2.0; // near dof blur falloff distance\\\\\\\",\\\\\\\"float fdofstart = 1.0; // far dof blur start\\\\\\\",\\\\\\\"float fdofdist = 3.0; // far dof blur falloff distance\\\\\\\",\\\\\\\"float CoC = 0.03; //circle of confusion size in mm (35mm film = 0.03mm)\\\\\\\",\\\\\\\"uniform bool vignetting; // use optical lens vignetting\\\\\\\",\\\\\\\"float vignout = 1.3; // vignetting outer border\\\\\\\",\\\\\\\"float vignin = 0.0; // vignetting inner border\\\\\\\",\\\\\\\"float vignfade = 22.0; // f-stops till vignete fades\\\\\\\",\\\\\\\"uniform bool shaderFocus;\\\\\\\",\\\\\\\"// disable if you use external focalDepth value\\\\\\\",\\\\\\\"uniform vec2 focusCoords;\\\\\\\",\\\\\\\"// autofocus point on screen (0.0,0.0 - left lower corner, 1.0,1.0 - upper right)\\\\\\\",\\\\\\\"// if center of screen use vec2(0.5, 0.5);\\\\\\\",\\\\\\\"uniform float maxblur;\\\\\\\",\\\\\\\"//clamp value of max blur (0.0 = no blur, 1.0 default)\\\\\\\",\\\\\\\"uniform float threshold; // highlight threshold;\\\\\\\",\\\\\\\"uniform float gain; // highlight gain;\\\\\\\",\\\\\\\"uniform float bias; // bokeh edge bias\\\\\\\",\\\\\\\"uniform float fringe; // bokeh chromatic aberration / fringing\\\\\\\",\\\\\\\"uniform bool noise; //use noise instead of pattern for sample dithering\\\\\\\",\\\\\\\"uniform float dithering;\\\\\\\",\\\\\\\"uniform bool depthblur; // blur the depth buffer\\\\\\\",\\\\\\\"float dbsize = 1.25; // depth blur size\\\\\\\",\\\\\\\"/*\\\\\\\",\\\\\\\"next part is experimental\\\\\\\",\\\\\\\"not looking good with small sample and ring count\\\\\\\",\\\\\\\"looks okay starting from samples = 4, rings = 4\\\\\\\",\\\\\\\"*/\\\\\\\",\\\\\\\"uniform bool pentagon; //use pentagon as bokeh shape?\\\\\\\",\\\\\\\"float feather = 0.4; //pentagon shape feather\\\\\\\",\\\\\\\"//------------------------------------------\\\\\\\",\\\\\\\"float penta(vec2 coords) {\\\\\\\",\\\\\\\"\\\\t//pentagonal shape\\\\\\\",\\\\\\\"\\\\tfloat scale = float(rings) - 1.3;\\\\\\\",\\\\\\\"\\\\tvec4  HS0 = vec4( 1.0,         0.0,         0.0,  1.0);\\\\\\\",\\\\\\\"\\\\tvec4  HS1 = vec4( 0.309016994, 0.951056516, 0.0,  1.0);\\\\\\\",\\\\\\\"\\\\tvec4  HS2 = vec4(-0.809016994, 0.587785252, 0.0,  1.0);\\\\\\\",\\\\\\\"\\\\tvec4  HS3 = vec4(-0.809016994,-0.587785252, 0.0,  1.0);\\\\\\\",\\\\\\\"\\\\tvec4  HS4 = vec4( 0.309016994,-0.951056516, 0.0,  1.0);\\\\\\\",\\\\\\\"\\\\tvec4  HS5 = vec4( 0.0        ,0.0         , 1.0,  1.0);\\\\\\\",\\\\\\\"\\\\tvec4  one = vec4( 1.0 );\\\\\\\",\\\\\\\"\\\\tvec4 P = vec4((coords),vec2(scale, scale));\\\\\\\",\\\\\\\"\\\\tvec4 dist = vec4(0.0);\\\\\\\",\\\\\\\"\\\\tfloat inorout = -4.0;\\\\\\\",\\\\\\\"\\\\tdist.x = dot( P, HS0 );\\\\\\\",\\\\\\\"\\\\tdist.y = dot( P, HS1 );\\\\\\\",\\\\\\\"\\\\tdist.z = dot( P, HS2 );\\\\\\\",\\\\\\\"\\\\tdist.w = dot( P, HS3 );\\\\\\\",\\\\\\\"\\\\tdist = smoothstep( -feather, feather, dist );\\\\\\\",\\\\\\\"\\\\tinorout += dot( dist, one );\\\\\\\",\\\\\\\"\\\\tdist.x = dot( P, HS4 );\\\\\\\",\\\\\\\"\\\\tdist.y = HS5.w - abs( P.z );\\\\\\\",\\\\\\\"\\\\tdist = smoothstep( -feather, feather, dist );\\\\\\\",\\\\\\\"\\\\tinorout += dist.x;\\\\\\\",\\\\\\\"\\\\treturn clamp( inorout, 0.0, 1.0 );\\\\\\\",\\\\\\\"}\\\\\\\",\\\\\\\"float bdepth(vec2 coords) {\\\\\\\",\\\\\\\"\\\\t// Depth buffer blur\\\\\\\",\\\\\\\"\\\\tfloat d = 0.0;\\\\\\\",\\\\\\\"\\\\tfloat kernel[9];\\\\\\\",\\\\\\\"\\\\tvec2 offset[9];\\\\\\\",\\\\\\\"\\\\tvec2 wh = vec2(1.0/textureWidth,1.0/textureHeight) * dbsize;\\\\\\\",\\\\\\\"\\\\toffset[0] = vec2(-wh.x,-wh.y);\\\\\\\",\\\\\\\"\\\\toffset[1] = vec2( 0.0, -wh.y);\\\\\\\",\\\\\\\"\\\\toffset[2] = vec2( wh.x -wh.y);\\\\\\\",\\\\\\\"\\\\toffset[3] = vec2(-wh.x,  0.0);\\\\\\\",\\\\\\\"\\\\toffset[4] = vec2( 0.0,   0.0);\\\\\\\",\\\\\\\"\\\\toffset[5] = vec2( wh.x,  0.0);\\\\\\\",\\\\\\\"\\\\toffset[6] = vec2(-wh.x, wh.y);\\\\\\\",\\\\\\\"\\\\toffset[7] = vec2( 0.0,  wh.y);\\\\\\\",\\\\\\\"\\\\toffset[8] = vec2( wh.x, wh.y);\\\\\\\",\\\\\\\"\\\\tkernel[0] = 1.0/16.0;   kernel[1] = 2.0/16.0;   kernel[2] = 1.0/16.0;\\\\\\\",\\\\\\\"\\\\tkernel[3] = 2.0/16.0;   kernel[4] = 4.0/16.0;   kernel[5] = 2.0/16.0;\\\\\\\",\\\\\\\"\\\\tkernel[6] = 1.0/16.0;   kernel[7] = 2.0/16.0;   kernel[8] = 1.0/16.0;\\\\\\\",\\\\\\\"\\\\tfor( int i=0; i<9; i++ ) {\\\\\\\",\\\\\\\"\\\\t\\\\tfloat tmp = texture2D(tDepth, coords + offset[i]).r;\\\\\\\",\\\\\\\"\\\\t\\\\td += tmp * kernel[i];\\\\\\\",\\\\\\\"\\\\t}\\\\\\\",\\\\\\\"\\\\treturn d;\\\\\\\",\\\\\\\"}\\\\\\\",\\\\\\\"vec3 color(vec2 coords,float blur) {\\\\\\\",\\\\\\\"\\\\t//processing the sample\\\\\\\",\\\\\\\"\\\\tvec3 col = vec3(0.0);\\\\\\\",\\\\\\\"\\\\tvec2 texel = vec2(1.0/textureWidth,1.0/textureHeight);\\\\\\\",\\\\\\\"\\\\tcol.r = texture2D(tColor,coords + vec2(0.0,1.0)*texel*fringe*blur).r;\\\\\\\",\\\\\\\"\\\\tcol.g = texture2D(tColor,coords + vec2(-0.866,-0.5)*texel*fringe*blur).g;\\\\\\\",\\\\\\\"\\\\tcol.b = texture2D(tColor,coords + vec2(0.866,-0.5)*texel*fringe*blur).b;\\\\\\\",\\\\\\\"\\\\tvec3 lumcoeff = vec3(0.299,0.587,0.114);\\\\\\\",\\\\\\\"\\\\tfloat lum = dot(col.rgb, lumcoeff);\\\\\\\",\\\\\\\"\\\\tfloat thresh = max((lum-threshold)*gain, 0.0);\\\\\\\",\\\\\\\"\\\\treturn col+mix(vec3(0.0),col,thresh*blur);\\\\\\\",\\\\\\\"}\\\\\\\",\\\\\\\"vec3 debugFocus(vec3 col, float blur, float depth) {\\\\\\\",\\\\\\\"\\\\tfloat edge = 0.002*depth; //distance based edge smoothing\\\\\\\",\\\\\\\"\\\\tfloat m = clamp(smoothstep(0.0,edge,blur),0.0,1.0);\\\\\\\",\\\\\\\"\\\\tfloat e = clamp(smoothstep(1.0-edge,1.0,blur),0.0,1.0);\\\\\\\",\\\\\\\"\\\\tcol = mix(col,vec3(1.0,0.5,0.0),(1.0-m)*0.6);\\\\\\\",\\\\\\\"\\\\tcol = mix(col,vec3(0.0,0.5,1.0),((1.0-e)-(1.0-m))*0.2);\\\\\\\",\\\\\\\"\\\\treturn col;\\\\\\\",\\\\\\\"}\\\\\\\",\\\\\\\"float linearize(float depth) {\\\\\\\",\\\\\\\"\\\\treturn -zfar * znear / (depth * (zfar - znear) - zfar);\\\\\\\",\\\\\\\"}\\\\\\\",\\\\\\\"float vignette() {\\\\\\\",\\\\\\\"\\\\tfloat dist = distance(vUv.xy, vec2(0.5,0.5));\\\\\\\",\\\\\\\"\\\\tdist = smoothstep(vignout+(fstop/vignfade), vignin+(fstop/vignfade), dist);\\\\\\\",\\\\\\\"\\\\treturn clamp(dist,0.0,1.0);\\\\\\\",\\\\\\\"}\\\\\\\",\\\\\\\"float gather(float i, float j, int ringsamples, inout vec3 col, float w, float h, float blur) {\\\\\\\",\\\\\\\"\\\\tfloat rings2 = float(rings);\\\\\\\",\\\\\\\"\\\\tfloat step = PI*2.0 / float(ringsamples);\\\\\\\",\\\\\\\"\\\\tfloat pw = cos(j*step)*i;\\\\\\\",\\\\\\\"\\\\tfloat ph = sin(j*step)*i;\\\\\\\",\\\\\\\"\\\\tfloat p = 1.0;\\\\\\\",\\\\\\\"\\\\tif (pentagon) {\\\\\\\",\\\\\\\"\\\\t\\\\tp = penta(vec2(pw,ph));\\\\\\\",\\\\\\\"\\\\t}\\\\\\\",\\\\\\\"\\\\tcol += color(vUv.xy + vec2(pw*w,ph*h), blur) * mix(1.0, i/rings2, bias) * p;\\\\\\\",\\\\\\\"\\\\treturn 1.0 * mix(1.0, i /rings2, bias) * p;\\\\\\\",\\\\\\\"}\\\\\\\",\\\\\\\"void main() {\\\\\\\",\\\\\\\"\\\\t//scene depth calculation\\\\\\\",\\\\\\\"\\\\tfloat depth = linearize(texture2D(tDepth,vUv.xy).x);\\\\\\\",\\\\\\\"\\\\t// Blur depth?\\\\\\\",\\\\\\\"\\\\tif ( depthblur ) {\\\\\\\",\\\\\\\"\\\\t\\\\tdepth = linearize(bdepth(vUv.xy));\\\\\\\",\\\\\\\"\\\\t}\\\\\\\",\\\\\\\"\\\\t//focal plane calculation\\\\\\\",\\\\\\\"\\\\tfloat fDepth = focalDepth;\\\\\\\",\\\\\\\"\\\\tif (shaderFocus) {\\\\\\\",\\\\\\\"\\\\t\\\\tfDepth = linearize(texture2D(tDepth,focusCoords).x);\\\\\\\",\\\\\\\"\\\\t}\\\\\\\",\\\\\\\"\\\\t// dof blur factor calculation\\\\\\\",\\\\\\\"\\\\tfloat blur = 0.0;\\\\\\\",\\\\\\\"\\\\tif (manualdof) {\\\\\\\",\\\\\\\"\\\\t\\\\tfloat a = depth-fDepth; // Focal plane\\\\\\\",\\\\\\\"\\\\t\\\\tfloat b = (a-fdofstart)/fdofdist; // Far DoF\\\\\\\",\\\\\\\"\\\\t\\\\tfloat c = (-a-ndofstart)/ndofdist; // Near Dof\\\\\\\",\\\\\\\"\\\\t\\\\tblur = (a>0.0) ? b : c;\\\\\\\",\\\\\\\"\\\\t} else {\\\\\\\",\\\\\\\"\\\\t\\\\tfloat f = focalLength; // focal length in mm\\\\\\\",\\\\\\\"\\\\t\\\\tfloat d = fDepth*1000.0; // focal plane in mm\\\\\\\",\\\\\\\"\\\\t\\\\tfloat o = depth*1000.0; // depth in mm\\\\\\\",\\\\\\\"\\\\t\\\\tfloat a = (o*f)/(o-f);\\\\\\\",\\\\\\\"\\\\t\\\\tfloat b = (d*f)/(d-f);\\\\\\\",\\\\\\\"\\\\t\\\\tfloat c = (d-f)/(d*fstop*CoC);\\\\\\\",\\\\\\\"\\\\t\\\\tblur = abs(a-b)*c;\\\\\\\",\\\\\\\"\\\\t}\\\\\\\",\\\\\\\"\\\\tblur = clamp(blur,0.0,1.0);\\\\\\\",\\\\\\\"\\\\t// calculation of pattern for dithering\\\\\\\",\\\\\\\"\\\\tvec2 noise = vec2(rand(vUv.xy), rand( vUv.xy + vec2( 0.4, 0.6 ) ) )*dithering*blur;\\\\\\\",\\\\\\\"\\\\t// getting blur x and y step factor\\\\\\\",\\\\\\\"\\\\tfloat w = (1.0/textureWidth)*blur*maxblur+noise.x;\\\\\\\",\\\\\\\"\\\\tfloat h = (1.0/textureHeight)*blur*maxblur+noise.y;\\\\\\\",\\\\\\\"\\\\t// calculation of final color\\\\\\\",\\\\\\\"\\\\tvec3 col = vec3(0.0);\\\\\\\",\\\\\\\"\\\\tif(blur < 0.05) {\\\\\\\",\\\\\\\"\\\\t\\\\t//some optimization thingy\\\\\\\",\\\\\\\"\\\\t\\\\tcol = texture2D(tColor, vUv.xy).rgb;\\\\\\\",\\\\\\\"\\\\t} else {\\\\\\\",\\\\\\\"\\\\t\\\\tcol = texture2D(tColor, vUv.xy).rgb;\\\\\\\",\\\\\\\"\\\\t\\\\tfloat s = 1.0;\\\\\\\",\\\\\\\"\\\\t\\\\tint ringsamples;\\\\\\\",\\\\\\\"\\\\t\\\\tfor (int i = 1; i <= rings; i++) {\\\\\\\",\\\\\\\"\\\\t\\\\t\\\\t/*unboxstart*/\\\\\\\",\\\\\\\"\\\\t\\\\t\\\\tringsamples = i * samples;\\\\\\\",\\\\\\\"\\\\t\\\\t\\\\tfor (int j = 0 ; j < maxringsamples ; j++) {\\\\\\\",\\\\\\\"\\\\t\\\\t\\\\t\\\\tif (j >= ringsamples) break;\\\\\\\",\\\\\\\"\\\\t\\\\t\\\\t\\\\ts += gather(float(i), float(j), ringsamples, col, w, h, blur);\\\\\\\",\\\\\\\"\\\\t\\\\t\\\\t}\\\\\\\",\\\\\\\"\\\\t\\\\t\\\\t/*unboxend*/\\\\\\\",\\\\\\\"\\\\t\\\\t}\\\\\\\",\\\\\\\"\\\\t\\\\tcol /= s; //divide by sample count\\\\\\\",\\\\\\\"\\\\t}\\\\\\\",\\\\\\\"\\\\tif (showFocus) {\\\\\\\",\\\\\\\"\\\\t\\\\tcol = debugFocus(col, blur, depth);\\\\\\\",\\\\\\\"\\\\t}\\\\\\\",\\\\\\\"\\\\tif (vignetting) {\\\\\\\",\\\\\\\"\\\\t\\\\tcol *= vignette();\\\\\\\",\\\\\\\"\\\\t}\\\\\\\",\\\\\\\"\\\\tgl_FragColor.rgb = col;\\\\\\\",\\\\\\\"\\\\tgl_FragColor.a = 1.0;\\\\\\\",\\\\\\\"} \\\\\\\"].join(\\\\\\\"\\\\n\\\\\\\")},RR={uniforms:{mNear:{value:1},mFar:{value:1e3}},vertexShader:[\\\\\\\"varying float vViewZDepth;\\\\\\\",\\\\\\\"void main() {\\\\\\\",\\\\\\\"\\\\t#include <begin_vertex>\\\\\\\",\\\\\\\"\\\\t#include <project_vertex>\\\\\\\",\\\\\\\"\\\\tvViewZDepth = - mvPosition.z;\\\\\\\",\\\\\\\"}\\\\\\\"].join(\\\\\\\"\\\\n\\\\\\\"),fragmentShader:[\\\\\\\"uniform float mNear;\\\\\\\",\\\\\\\"uniform float mFar;\\\\\\\",\\\\\\\"varying float vViewZDepth;\\\\\\\",\\\\\\\"void main() {\\\\\\\",\\\\\\\"\\\\tfloat color = 1.0 - smoothstep( mNear, mFar, vViewZDepth );\\\\\\\",\\\\\\\"\\\\tgl_FragColor = vec4( vec3( color ), 1.0 );\\\\\\\",\\\\\\\"} \\\\\\\"].join(\\\\\\\"\\\\n\\\\\\\")};class IR{constructor(e){this._scene=e}scene(){return this._scene}with_overriden_material(e,t,n,i){const s={};let r;this._scene.traverse((i=>{const o=i;if(o.material){const i=o.geometry;if(i){const a=o.customDepthDOFMaterial;if(a){if(r=a,r.uniforms)for(let e of Object.keys(n))r.uniforms[e].value=n[e].value}else r=Bs.markedAsInstance(i)?t:e;r&&(s[o.uuid]=o.material,o.material=r)}}})),i(),this._scene.traverse((e=>{const t=e;if(t.material){t.geometry&&(t.material=s[t.uuid])}}));for(let e of Object.keys(s))delete s[e]}}class FR{constructor(e,t,n,i){this._depth_of_field_node=e,this._scene=t,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 Vi,this.clear_color=new M.a(1,1,1),this._prev_clear_color=new M.a,this._core_scene=new IR(this._scene);const s=3,r=4;this._processing_camera=new vm.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=PR;a||console.error(\\\\\\\"BokehPass relies on BokehShader\\\\\\\"),this.bokeh_uniforms=k.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 B({uniforms:this.bokeh_uniforms,vertexShader:a.vertexShader,fragmentShader:a.fragmentShader,defines:{RINGS:s,SAMPLES:r}}),this._quad=new z.a(new R(this._resolution.x,this._resolution.y),this.bokeh_material),this._quad.position.z=-500,this._processing_scene.add(this._quad);var c=RR;c||console.error(\\\\\\\"BokehPass relies on BokehDepthShader\\\\\\\"),this.materialDepth=new B({uniforms:c.uniforms,vertexShader:c.vertexShader,fragmentShader:c.fragmentShader}),this.materialDepthInstance=new B({uniforms:c.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:c.fragmentShader}),this.update_camera_uniforms_with_node(this._depth_of_field_node,this._camera)}setSize(e,t){this._rtTextureDepth.setSize(e,t),this._rtTextureColor.setSize(e,t),this.bokeh_uniforms.textureWidth.value=e,this.bokeh_uniforms.textureHeight.value=t}dispose(){this._rtTextureDepth.dispose(),this._rtTextureColor.dispose()}render(e,t,n){e.getClearColor(this._prev_clear_color),e.setClearColor(this.clear_color),e.clear(),e.setRenderTarget(this._rtTextureColor),e.clear(),e.render(this._scene,this._camera),e.setClearColor(0),this._core_scene.with_overriden_material(this.materialDepth,this.materialDepthInstance,this._camera_uniforms,(()=>{e.setRenderTarget(this._rtTextureDepth),e.clear(),e.render(this._scene,this._camera)})),e.setRenderTarget(null),e.clear(),e.render(this._processing_scene,this._processing_camera),e.setClearColor(this._prev_clear_color)}update_camera_uniforms_with_node(e,t){this.bokeh_uniforms.focalLength.value=t.getFocalLength(),this.bokeh_uniforms.znear.value=t.near,this.bokeh_uniforms.zfar.value=t.far;var n=kR.smoothstep(t.near,t.far,e.pv.focalDepth),i=kR.linearize(1-n,t.near,t.far);this.bokeh_uniforms.focalDepth.value=i,this._camera_uniforms={mNear:{value:t.near},mFar:{value:t.far}};for(let e of[this.materialDepth,this.materialDepthInstance])e.uniforms.mNear.value=this._camera_uniforms.mNear.value,e.uniforms.mFar.value=this._camera_uniforms.mFar.value}}const DR=new class extends Lo{constructor(){super(...arguments),this.focalDepth=Oo.FLOAT(10,{range:[0,50],rangeLocked:[!0,!1],step:.001,...rR}),this.fStep=Oo.FLOAT(10,{range:[.1,22],rangeLocked:[!0,!0],...rR}),this.maxBlur=Oo.FLOAT(2,{range:[0,10],rangeLocked:[!0,!1],...rR}),this.vignetting=Oo.BOOLEAN(0,{...rR}),this.depthBlur=Oo.BOOLEAN(0,{...rR}),this.threshold=Oo.FLOAT(.5,{range:[0,1],rangeLocked:[!0,!0],step:.001,...rR}),this.gain=Oo.FLOAT(1,{range:[0,100],rangeLocked:[!0,!0],step:.001,...rR}),this.bias=Oo.FLOAT(1,{range:[0,3],rangeLocked:[!0,!0],step:.001,...rR}),this.fringe=Oo.FLOAT(.7,{range:[0,5],rangeLocked:[!0,!1],step:.001,...rR}),this.noise=Oo.BOOLEAN(0,{...rR}),this.dithering=Oo.FLOAT(0,{range:[0,.001],rangeLocked:[!0,!0],step:1e-4,...rR}),this.pentagon=Oo.BOOLEAN(0,{...rR}),this.rings=Oo.INTEGER(3,{range:[1,8],rangeLocked:[!0,!0],...rR}),this.samples=Oo.INTEGER(4,{range:[1,13],rangeLocked:[!0,!0],...rR}),this.clearColor=Oo.COLOR([1,1,1],{...rR})}};class kR extends oR{constructor(){super(...arguments),this.paramsConfig=DR}static type(){return\\\\\\\"depthOfField\\\\\\\"}static saturate(e){return Math.max(0,Math.min(1,e))}static linearize(e,t,n){return-n*t/(e*(n-t)-n)}static smoothstep(e,t,n){var i=this.saturate((n-e)/(t-e));return i*i*(3-2*i)}_createPass(e){if(e.camera.isPerspectiveCamera){const t=e.camera_node;if(t){const n=new FR(this,e.scene,t.object,e.resolution);this.updatePass(n);const i=new Qn(this.scene(),\\\\\\\"DOF\\\\\\\");return i.addGraphInput(t.p.near),i.addGraphInput(t.p.far),i.addGraphInput(t.p.fov),i.addGraphInput(this.p.focalDepth),i.addPostDirtyHook(\\\\\\\"post/DOF\\\\\\\",(()=>{this.update_pass_from_camera_node(n,t)})),n}}}update_pass_from_camera_node(e,t){e.update_camera_uniforms_with_node(this,t.object)}updatePass(e){e.bokeh_uniforms.fstop.value=this.pv.fStep,e.bokeh_uniforms.maxblur.value=this.pv.maxBlur,e.bokeh_uniforms.threshold.value=this.pv.threshold,e.bokeh_uniforms.gain.value=this.pv.gain,e.bokeh_uniforms.bias.value=this.pv.bias,e.bokeh_uniforms.fringe.value=this.pv.fringe,e.bokeh_uniforms.dithering.value=this.pv.dithering,e.bokeh_uniforms.noise.value=this.pv.noise?1:0,e.bokeh_uniforms.pentagon.value=this.pv.pentagon?1:0,e.bokeh_uniforms.vignetting.value=this.pv.vignetting?1:0,e.bokeh_uniforms.depthblur.value=this.pv.depthBlur?1:0,e.bokeh_uniforms.shaderFocus.value=0,e.bokeh_uniforms.showFocus.value=0,e.bokeh_uniforms.manualdof.value=0,e.bokeh_uniforms.focusCoords.value.set(.5,.5),e.bokeh_material.defines.RINGS=this.pv.rings,e.bokeh_material.defines.SAMPLES=this.pv.samples,e.bokeh_material.needsUpdate=!0,e.clear_color.copy(this.pv.clearColor)}}var BR={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:[\\\\\\\"varying vec2 vUv;\\\\\\\",\\\\\\\"void main() {\\\\\\\",\\\\\\\"\\\\tvUv = uv;\\\\\\\",\\\\\\\"\\\\tgl_Position = projectionMatrix * modelViewMatrix * vec4( position, 1.0 );\\\\\\\",\\\\\\\"}\\\\\\\"].join(\\\\\\\"\\\\n\\\\\\\"),fragmentShader:[\\\\\\\"uniform vec2 center;\\\\\\\",\\\\\\\"uniform float angle;\\\\\\\",\\\\\\\"uniform float scale;\\\\\\\",\\\\\\\"uniform vec2 tSize;\\\\\\\",\\\\\\\"uniform sampler2D tDiffuse;\\\\\\\",\\\\\\\"varying vec2 vUv;\\\\\\\",\\\\\\\"float pattern() {\\\\\\\",\\\\\\\"\\\\tfloat s = sin( angle ), c = cos( angle );\\\\\\\",\\\\\\\"\\\\tvec2 tex = vUv * tSize - center;\\\\\\\",\\\\\\\"\\\\tvec2 point = vec2( c * tex.x - s * tex.y, s * tex.x + c * tex.y ) * scale;\\\\\\\",\\\\\\\"\\\\treturn ( sin( point.x ) * sin( point.y ) ) * 4.0;\\\\\\\",\\\\\\\"}\\\\\\\",\\\\\\\"void main() {\\\\\\\",\\\\\\\"\\\\tvec4 color = texture2D( tDiffuse, vUv );\\\\\\\",\\\\\\\"\\\\tfloat average = ( color.r + color.g + color.b ) / 3.0;\\\\\\\",\\\\\\\"\\\\tgl_FragColor = vec4( vec3( average * 10.0 - 5.0 + pattern() ), color.a );\\\\\\\",\\\\\\\"}\\\\\\\"].join(\\\\\\\"\\\\n\\\\\\\")};const zR=new class extends Lo{constructor(){super(...arguments),this.center=Oo.VECTOR2([.5,.5],{...rR}),this.angle=Oo.FLOAT(\\\\\\\"$PI*0.5\\\\\\\",{range:[0,10],rangeLocked:[!1,!1],...rR}),this.scale=Oo.FLOAT(1,{range:[0,1],rangeLocked:[!1,!1],...rR})}};class UR extends oR{constructor(){super(...arguments),this.paramsConfig=zR}static type(){return\\\\\\\"dotScreen\\\\\\\"}_createPass(e){const t=new bm(BR);return this.updatePass(t),t}updatePass(e){e.uniforms.center.value=this.pv.center,e.uniforms.angle.value=this.pv.angle,e.uniforms.scale.value=this.pv.scale}}var GR={uniforms:{tDiffuse:{value:null},time:{value:0},nIntensity:{value:.5},sIntensity:{value:.05},sCount:{value:4096},grayscale:{value:1}},vertexShader:[\\\\\\\"varying vec2 vUv;\\\\\\\",\\\\\\\"void main() {\\\\\\\",\\\\\\\"\\\\tvUv = uv;\\\\\\\",\\\\\\\"\\\\tgl_Position = projectionMatrix * modelViewMatrix * vec4( position, 1.0 );\\\\\\\",\\\\\\\"}\\\\\\\"].join(\\\\\\\"\\\\n\\\\\\\"),fragmentShader:[\\\\\\\"#include <common>\\\\\\\",\\\\\\\"uniform float time;\\\\\\\",\\\\\\\"uniform bool grayscale;\\\\\\\",\\\\\\\"uniform float nIntensity;\\\\\\\",\\\\\\\"uniform float sIntensity;\\\\\\\",\\\\\\\"uniform float sCount;\\\\\\\",\\\\\\\"uniform sampler2D tDiffuse;\\\\\\\",\\\\\\\"varying vec2 vUv;\\\\\\\",\\\\\\\"void main() {\\\\\\\",\\\\\\\"\\\\tvec4 cTextureScreen = texture2D( tDiffuse, vUv );\\\\\\\",\\\\\\\"\\\\tfloat dx = rand( vUv + time );\\\\\\\",\\\\\\\"\\\\tvec3 cResult = cTextureScreen.rgb + cTextureScreen.rgb * clamp( 0.1 + dx, 0.0, 1.0 );\\\\\\\",\\\\\\\"\\\\tvec2 sc = vec2( sin( vUv.y * sCount ), cos( vUv.y * sCount ) );\\\\\\\",\\\\\\\"\\\\tcResult += cTextureScreen.rgb * vec3( sc.x, sc.y, sc.x ) * sIntensity;\\\\\\\",\\\\\\\"\\\\tcResult = cTextureScreen.rgb + clamp( nIntensity, 0.0,1.0 ) * ( cResult - cTextureScreen.rgb );\\\\\\\",\\\\\\\"\\\\tif( grayscale ) {\\\\\\\",\\\\\\\"\\\\t\\\\tcResult = vec3( cResult.r * 0.3 + cResult.g * 0.59 + cResult.b * 0.11 );\\\\\\\",\\\\\\\"\\\\t}\\\\\\\",\\\\\\\"\\\\tgl_FragColor =  vec4( cResult, cTextureScreen.a );\\\\\\\",\\\\\\\"}\\\\\\\"].join(\\\\\\\"\\\\n\\\\\\\")},VR=function(e,t,n,i){xm.call(this),void 0===GR&&console.error(\\\\\\\"THREE.FilmPass relies on FilmShader\\\\\\\");var s=GR;this.uniforms=k.clone(s.uniforms),this.material=new B({uniforms:this.uniforms,vertexShader:s.vertexShader,fragmentShader:s.fragmentShader}),void 0!==i&&(this.uniforms.grayscale.value=i),void 0!==e&&(this.uniforms.nIntensity.value=e),void 0!==t&&(this.uniforms.sIntensity.value=t),void 0!==n&&(this.uniforms.sCount.value=n),this.fsQuad=new xm.FullScreenQuad(this.material)};VR.prototype=Object.assign(Object.create(xm.prototype),{constructor:VR,render:function(e,t,n,i){this.uniforms.tDiffuse.value=n.texture,this.uniforms.time.value+=i,this.renderToScreen?(e.setRenderTarget(null),this.fsQuad.render(e)):(e.setRenderTarget(t),this.clear&&e.clear(),this.fsQuad.render(e))}});const jR=new class extends Lo{constructor(){super(...arguments),this.noiseIntensity=Oo.FLOAT(.5,{range:[0,1],rangeLocked:[!1,!1],...rR}),this.scanlinesIntensity=Oo.FLOAT(.05,{range:[0,1],rangeLocked:[!0,!1],...rR}),this.scanlinesCount=Oo.FLOAT(4096,{range:[0,4096],rangeLocked:[!0,!1],...rR}),this.grayscale=Oo.BOOLEAN(1,{...rR})}};class HR extends oR{constructor(){super(...arguments),this.paramsConfig=jR}static type(){return\\\\\\\"film\\\\\\\"}_createPass(e){const t=new VR(this.pv.noiseIntensity,this.pv.scanlinesIntensity,this.pv.scanlinesCount,this.pv.grayscale?1:0);return this.updatePass(t),t}updatePass(e){e.uniforms.nIntensity.value=this.pv.noiseIntensity,e.uniforms.sIntensity.value=this.pv.scanlinesIntensity,e.uniforms.sCount.value=this.pv.scanlinesCount,e.uniforms.grayscale.value=this.pv.grayscale?1:0}}var qR={uniforms:{tDiffuse:{value:null},resolution:{value:new d.a(1/1024,1/512)}},vertexShader:[\\\\\\\"varying vec2 vUv;\\\\\\\",\\\\\\\"void main() {\\\\\\\",\\\\\\\"\\\\tvUv = uv;\\\\\\\",\\\\\\\"\\\\tgl_Position = projectionMatrix * modelViewMatrix * vec4( position, 1.0 );\\\\\\\",\\\\\\\"}\\\\\\\"].join(\\\\\\\"\\\\n\\\\\\\"),fragmentShader:[\\\\\\\"precision highp float;\\\\\\\",\\\\\\\"\\\\\\\",\\\\\\\"uniform sampler2D tDiffuse;\\\\\\\",\\\\\\\"\\\\\\\",\\\\\\\"uniform vec2 resolution;\\\\\\\",\\\\\\\"\\\\\\\",\\\\\\\"varying vec2 vUv;\\\\\\\",\\\\\\\"\\\\\\\",\\\\\\\"// FXAA 3.11 implementation by NVIDIA, ported to WebGL by Agost Biro (biro@archilogic.com)\\\\\\\",\\\\\\\"\\\\\\\",\\\\\\\"//----------------------------------------------------------------------------------\\\\\\\",\\\\\\\"// File:        es3-keplerFXAAassetsshaders/FXAA_DefaultES.frag\\\\\\\",\\\\\\\"// SDK Version: v3.00\\\\\\\",\\\\\\\"// Email:       gameworks@nvidia.com\\\\\\\",\\\\\\\"// Site:        http://developer.nvidia.com/\\\\\\\",\\\\\\\"//\\\\\\\",\\\\\\\"// Copyright (c) 2014-2015, NVIDIA CORPORATION. All rights reserved.\\\\\\\",\\\\\\\"//\\\\\\\",\\\\\\\"// Redistribution and use in source and binary forms, with or without\\\\\\\",\\\\\\\"// modification, are permitted provided that the following conditions\\\\\\\",\\\\\\\"// are met:\\\\\\\",\\\\\\\"//  * Redistributions of source code must retain the above copyright\\\\\\\",\\\\\\\"//    notice, this list of conditions and the following disclaimer.\\\\\\\",\\\\\\\"//  * Redistributions in binary form must reproduce the above copyright\\\\\\\",\\\\\\\"//    notice, this list of conditions and the following disclaimer in the\\\\\\\",\\\\\\\"//    documentation and/or other materials provided with the distribution.\\\\\\\",\\\\\\\"//  * Neither the name of NVIDIA CORPORATION nor the names of its\\\\\\\",\\\\\\\"//    contributors may be used to endorse or promote products derived\\\\\\\",\\\\\\\"//    from this software without specific prior written permission.\\\\\\\",\\\\\\\"//\\\\\\\",\\\\\\\"// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY\\\\\\\",\\\\\\\"// EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE\\\\\\\",\\\\\\\"// IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR\\\\\\\",\\\\\\\"// PURPOSE ARE DISCLAIMED.  IN NO EVENT SHALL THE COPYRIGHT OWNER OR\\\\\\\",\\\\\\\"// CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,\\\\\\\",\\\\\\\"// EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,\\\\\\\",\\\\\\\"// PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR\\\\\\\",\\\\\\\"// PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY\\\\\\\",\\\\\\\"// OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT\\\\\\\",\\\\\\\"// (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE\\\\\\\",\\\\\\\"// OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.\\\\\\\",\\\\\\\"//\\\\\\\",\\\\\\\"//----------------------------------------------------------------------------------\\\\\\\",\\\\\\\"\\\\\\\",\\\\\\\"#define FXAA_PC 1\\\\\\\",\\\\\\\"#define FXAA_GLSL_100 1\\\\\\\",\\\\\\\"#define FXAA_QUALITY_PRESET 12\\\\\\\",\\\\\\\"\\\\\\\",\\\\\\\"#define FXAA_GREEN_AS_LUMA 1\\\\\\\",\\\\\\\"\\\\\\\",\\\\\\\"/*--------------------------------------------------------------------------*/\\\\\\\",\\\\\\\"#ifndef FXAA_PC_CONSOLE\\\\\\\",\\\\\\\"    //\\\\\\\",\\\\\\\"    // The console algorithm for PC is included\\\\\\\",\\\\\\\"    // for developers targeting really low spec machines.\\\\\\\",\\\\\\\"    // Likely better to just run FXAA_PC, and use a really low preset.\\\\\\\",\\\\\\\"    //\\\\\\\",\\\\\\\"    #define FXAA_PC_CONSOLE 0\\\\\\\",\\\\\\\"#endif\\\\\\\",\\\\\\\"/*--------------------------------------------------------------------------*/\\\\\\\",\\\\\\\"#ifndef FXAA_GLSL_120\\\\\\\",\\\\\\\"    #define FXAA_GLSL_120 0\\\\\\\",\\\\\\\"#endif\\\\\\\",\\\\\\\"/*--------------------------------------------------------------------------*/\\\\\\\",\\\\\\\"#ifndef FXAA_GLSL_130\\\\\\\",\\\\\\\"    #define FXAA_GLSL_130 0\\\\\\\",\\\\\\\"#endif\\\\\\\",\\\\\\\"/*--------------------------------------------------------------------------*/\\\\\\\",\\\\\\\"#ifndef FXAA_HLSL_3\\\\\\\",\\\\\\\"    #define FXAA_HLSL_3 0\\\\\\\",\\\\\\\"#endif\\\\\\\",\\\\\\\"/*--------------------------------------------------------------------------*/\\\\\\\",\\\\\\\"#ifndef FXAA_HLSL_4\\\\\\\",\\\\\\\"    #define FXAA_HLSL_4 0\\\\\\\",\\\\\\\"#endif\\\\\\\",\\\\\\\"/*--------------------------------------------------------------------------*/\\\\\\\",\\\\\\\"#ifndef FXAA_HLSL_5\\\\\\\",\\\\\\\"    #define FXAA_HLSL_5 0\\\\\\\",\\\\\\\"#endif\\\\\\\",\\\\\\\"/*==========================================================================*/\\\\\\\",\\\\\\\"#ifndef FXAA_GREEN_AS_LUMA\\\\\\\",\\\\\\\"    //\\\\\\\",\\\\\\\"    // For those using non-linear color,\\\\\\\",\\\\\\\"    // and either not able to get luma in alpha, or not wanting to,\\\\\\\",\\\\\\\"    // this enables FXAA to run using green as a proxy for luma.\\\\\\\",\\\\\\\"    // So with this enabled, no need to pack luma in alpha.\\\\\\\",\\\\\\\"    //\\\\\\\",\\\\\\\"    // This will turn off AA on anything which lacks some amount of green.\\\\\\\",\\\\\\\"    // Pure red and blue or combination of only R and B, will get no AA.\\\\\\\",\\\\\\\"    //\\\\\\\",\\\\\\\"    // Might want to lower the settings for both,\\\\\\\",\\\\\\\"    //    fxaaConsoleEdgeThresholdMin\\\\\\\",\\\\\\\"    //    fxaaQualityEdgeThresholdMin\\\\\\\",\\\\\\\"    // In order to insure AA does not get turned off on colors\\\\\\\",\\\\\\\"    // which contain a minor amount of green.\\\\\\\",\\\\\\\"    //\\\\\\\",\\\\\\\"    // 1 = On.\\\\\\\",\\\\\\\"    // 0 = Off.\\\\\\\",\\\\\\\"    //\\\\\\\",\\\\\\\"    #define FXAA_GREEN_AS_LUMA 0\\\\\\\",\\\\\\\"#endif\\\\\\\",\\\\\\\"/*--------------------------------------------------------------------------*/\\\\\\\",\\\\\\\"#ifndef FXAA_EARLY_EXIT\\\\\\\",\\\\\\\"    //\\\\\\\",\\\\\\\"    // Controls algorithm's early exit path.\\\\\\\",\\\\\\\"    // On PS3 turning this ON adds 2 cycles to the shader.\\\\\\\",\\\\\\\"    // On 360 turning this OFF adds 10ths of a millisecond to the shader.\\\\\\\",\\\\\\\"    // Turning this off on console will result in a more blurry image.\\\\\\\",\\\\\\\"    // So this defaults to on.\\\\\\\",\\\\\\\"    //\\\\\\\",\\\\\\\"    // 1 = On.\\\\\\\",\\\\\\\"    // 0 = Off.\\\\\\\",\\\\\\\"    //\\\\\\\",\\\\\\\"    #define FXAA_EARLY_EXIT 1\\\\\\\",\\\\\\\"#endif\\\\\\\",\\\\\\\"/*--------------------------------------------------------------------------*/\\\\\\\",\\\\\\\"#ifndef FXAA_DISCARD\\\\\\\",\\\\\\\"    //\\\\\\\",\\\\\\\"    // Only valid for PC OpenGL currently.\\\\\\\",\\\\\\\"    // Probably will not work when FXAA_GREEN_AS_LUMA = 1.\\\\\\\",\\\\\\\"    //\\\\\\\",\\\\\\\"    // 1 = Use discard on pixels which don't need AA.\\\\\\\",\\\\\\\"    //     For APIs which enable concurrent TEX+ROP from same surface.\\\\\\\",\\\\\\\"    // 0 = Return unchanged color on pixels which don't need AA.\\\\\\\",\\\\\\\"    //\\\\\\\",\\\\\\\"    #define FXAA_DISCARD 0\\\\\\\",\\\\\\\"#endif\\\\\\\",\\\\\\\"/*--------------------------------------------------------------------------*/\\\\\\\",\\\\\\\"#ifndef FXAA_FAST_PIXEL_OFFSET\\\\\\\",\\\\\\\"    //\\\\\\\",\\\\\\\"    // Used for GLSL 120 only.\\\\\\\",\\\\\\\"    //\\\\\\\",\\\\\\\"    // 1 = GL API supports fast pixel offsets\\\\\\\",\\\\\\\"    // 0 = do not use fast pixel offsets\\\\\\\",\\\\\\\"    //\\\\\\\",\\\\\\\"    #ifdef GL_EXT_gpu_shader4\\\\\\\",\\\\\\\"        #define FXAA_FAST_PIXEL_OFFSET 1\\\\\\\",\\\\\\\"    #endif\\\\\\\",\\\\\\\"    #ifdef GL_NV_gpu_shader5\\\\\\\",\\\\\\\"        #define FXAA_FAST_PIXEL_OFFSET 1\\\\\\\",\\\\\\\"    #endif\\\\\\\",\\\\\\\"    #ifdef GL_ARB_gpu_shader5\\\\\\\",\\\\\\\"        #define FXAA_FAST_PIXEL_OFFSET 1\\\\\\\",\\\\\\\"    #endif\\\\\\\",\\\\\\\"    #ifndef FXAA_FAST_PIXEL_OFFSET\\\\\\\",\\\\\\\"        #define FXAA_FAST_PIXEL_OFFSET 0\\\\\\\",\\\\\\\"    #endif\\\\\\\",\\\\\\\"#endif\\\\\\\",\\\\\\\"/*--------------------------------------------------------------------------*/\\\\\\\",\\\\\\\"#ifndef FXAA_GATHER4_ALPHA\\\\\\\",\\\\\\\"    //\\\\\\\",\\\\\\\"    // 1 = API supports gather4 on alpha channel.\\\\\\\",\\\\\\\"    // 0 = API does not support gather4 on alpha channel.\\\\\\\",\\\\\\\"    //\\\\\\\",\\\\\\\"    #if (FXAA_HLSL_5 == 1)\\\\\\\",\\\\\\\"        #define FXAA_GATHER4_ALPHA 1\\\\\\\",\\\\\\\"    #endif\\\\\\\",\\\\\\\"    #ifdef GL_ARB_gpu_shader5\\\\\\\",\\\\\\\"        #define FXAA_GATHER4_ALPHA 1\\\\\\\",\\\\\\\"    #endif\\\\\\\",\\\\\\\"    #ifdef GL_NV_gpu_shader5\\\\\\\",\\\\\\\"        #define FXAA_GATHER4_ALPHA 1\\\\\\\",\\\\\\\"    #endif\\\\\\\",\\\\\\\"    #ifndef FXAA_GATHER4_ALPHA\\\\\\\",\\\\\\\"        #define FXAA_GATHER4_ALPHA 0\\\\\\\",\\\\\\\"    #endif\\\\\\\",\\\\\\\"#endif\\\\\\\",\\\\\\\"\\\\\\\",\\\\\\\"\\\\\\\",\\\\\\\"/*============================================================================\\\\\\\",\\\\\\\"                        FXAA QUALITY - TUNING KNOBS\\\\\\\",\\\\\\\"------------------------------------------------------------------------------\\\\\\\",\\\\\\\"NOTE the other tuning knobs are now in the shader function inputs!\\\\\\\",\\\\\\\"============================================================================*/\\\\\\\",\\\\\\\"#ifndef FXAA_QUALITY_PRESET\\\\\\\",\\\\\\\"    //\\\\\\\",\\\\\\\"    // Choose the quality preset.\\\\\\\",\\\\\\\"    // This needs to be compiled into the shader as it effects code.\\\\\\\",\\\\\\\"    // Best option to include multiple presets is to\\\\\\\",\\\\\\\"    // in each shader define the preset, then include this file.\\\\\\\",\\\\\\\"    //\\\\\\\",\\\\\\\"    // OPTIONS\\\\\\\",\\\\\\\"    // -----------------------------------------------------------------------\\\\\\\",\\\\\\\"    // 10 to 15 - default medium dither (10=fastest, 15=highest quality)\\\\\\\",\\\\\\\"    // 20 to 29 - less dither, more expensive (20=fastest, 29=highest quality)\\\\\\\",\\\\\\\"    // 39       - no dither, very expensive\\\\\\\",\\\\\\\"    //\\\\\\\",\\\\\\\"    // NOTES\\\\\\\",\\\\\\\"    // -----------------------------------------------------------------------\\\\\\\",\\\\\\\"    // 12 = slightly faster then FXAA 3.9 and higher edge quality (default)\\\\\\\",\\\\\\\"    // 13 = about same speed as FXAA 3.9 and better than 12\\\\\\\",\\\\\\\"    // 23 = closest to FXAA 3.9 visually and performance wise\\\\\\\",\\\\\\\"    //  _ = the lowest digit is directly related to performance\\\\\\\",\\\\\\\"    // _  = the highest digit is directly related to style\\\\\\\",\\\\\\\"    //\\\\\\\",\\\\\\\"    #define FXAA_QUALITY_PRESET 12\\\\\\\",\\\\\\\"#endif\\\\\\\",\\\\\\\"\\\\\\\",\\\\\\\"\\\\\\\",\\\\\\\"/*============================================================================\\\\\\\",\\\\\\\"\\\\\\\",\\\\\\\"                           FXAA QUALITY - PRESETS\\\\\\\",\\\\\\\"\\\\\\\",\\\\\\\"============================================================================*/\\\\\\\",\\\\\\\"\\\\\\\",\\\\\\\"/*============================================================================\\\\\\\",\\\\\\\"                     FXAA QUALITY - MEDIUM DITHER PRESETS\\\\\\\",\\\\\\\"============================================================================*/\\\\\\\",\\\\\\\"#if (FXAA_QUALITY_PRESET == 10)\\\\\\\",\\\\\\\"    #define FXAA_QUALITY_PS 3\\\\\\\",\\\\\\\"    #define FXAA_QUALITY_P0 1.5\\\\\\\",\\\\\\\"    #define FXAA_QUALITY_P1 3.0\\\\\\\",\\\\\\\"    #define FXAA_QUALITY_P2 12.0\\\\\\\",\\\\\\\"#endif\\\\\\\",\\\\\\\"/*--------------------------------------------------------------------------*/\\\\\\\",\\\\\\\"#if (FXAA_QUALITY_PRESET == 11)\\\\\\\",\\\\\\\"    #define FXAA_QUALITY_PS 4\\\\\\\",\\\\\\\"    #define FXAA_QUALITY_P0 1.0\\\\\\\",\\\\\\\"    #define FXAA_QUALITY_P1 1.5\\\\\\\",\\\\\\\"    #define FXAA_QUALITY_P2 3.0\\\\\\\",\\\\\\\"    #define FXAA_QUALITY_P3 12.0\\\\\\\",\\\\\\\"#endif\\\\\\\",\\\\\\\"/*--------------------------------------------------------------------------*/\\\\\\\",\\\\\\\"#if (FXAA_QUALITY_PRESET == 12)\\\\\\\",\\\\\\\"    #define FXAA_QUALITY_PS 5\\\\\\\",\\\\\\\"    #define FXAA_QUALITY_P0 1.0\\\\\\\",\\\\\\\"    #define FXAA_QUALITY_P1 1.5\\\\\\\",\\\\\\\"    #define FXAA_QUALITY_P2 2.0\\\\\\\",\\\\\\\"    #define FXAA_QUALITY_P3 4.0\\\\\\\",\\\\\\\"    #define FXAA_QUALITY_P4 12.0\\\\\\\",\\\\\\\"#endif\\\\\\\",\\\\\\\"/*--------------------------------------------------------------------------*/\\\\\\\",\\\\\\\"#if (FXAA_QUALITY_PRESET == 13)\\\\\\\",\\\\\\\"    #define FXAA_QUALITY_PS 6\\\\\\\",\\\\\\\"    #define FXAA_QUALITY_P0 1.0\\\\\\\",\\\\\\\"    #define FXAA_QUALITY_P1 1.5\\\\\\\",\\\\\\\"    #define FXAA_QUALITY_P2 2.0\\\\\\\",\\\\\\\"    #define FXAA_QUALITY_P3 2.0\\\\\\\",\\\\\\\"    #define FXAA_QUALITY_P4 4.0\\\\\\\",\\\\\\\"    #define FXAA_QUALITY_P5 12.0\\\\\\\",\\\\\\\"#endif\\\\\\\",\\\\\\\"/*--------------------------------------------------------------------------*/\\\\\\\",\\\\\\\"#if (FXAA_QUALITY_PRESET == 14)\\\\\\\",\\\\\\\"    #define FXAA_QUALITY_PS 7\\\\\\\",\\\\\\\"    #define FXAA_QUALITY_P0 1.0\\\\\\\",\\\\\\\"    #define FXAA_QUALITY_P1 1.5\\\\\\\",\\\\\\\"    #define FXAA_QUALITY_P2 2.0\\\\\\\",\\\\\\\"    #define FXAA_QUALITY_P3 2.0\\\\\\\",\\\\\\\"    #define FXAA_QUALITY_P4 2.0\\\\\\\",\\\\\\\"    #define FXAA_QUALITY_P5 4.0\\\\\\\",\\\\\\\"    #define FXAA_QUALITY_P6 12.0\\\\\\\",\\\\\\\"#endif\\\\\\\",\\\\\\\"/*--------------------------------------------------------------------------*/\\\\\\\",\\\\\\\"#if (FXAA_QUALITY_PRESET == 15)\\\\\\\",\\\\\\\"    #define FXAA_QUALITY_PS 8\\\\\\\",\\\\\\\"    #define FXAA_QUALITY_P0 1.0\\\\\\\",\\\\\\\"    #define FXAA_QUALITY_P1 1.5\\\\\\\",\\\\\\\"    #define FXAA_QUALITY_P2 2.0\\\\\\\",\\\\\\\"    #define FXAA_QUALITY_P3 2.0\\\\\\\",\\\\\\\"    #define FXAA_QUALITY_P4 2.0\\\\\\\",\\\\\\\"    #define FXAA_QUALITY_P5 2.0\\\\\\\",\\\\\\\"    #define FXAA_QUALITY_P6 4.0\\\\\\\",\\\\\\\"    #define FXAA_QUALITY_P7 12.0\\\\\\\",\\\\\\\"#endif\\\\\\\",\\\\\\\"\\\\\\\",\\\\\\\"/*============================================================================\\\\\\\",\\\\\\\"                     FXAA QUALITY - LOW DITHER PRESETS\\\\\\\",\\\\\\\"============================================================================*/\\\\\\\",\\\\\\\"#if (FXAA_QUALITY_PRESET == 20)\\\\\\\",\\\\\\\"    #define FXAA_QUALITY_PS 3\\\\\\\",\\\\\\\"    #define FXAA_QUALITY_P0 1.5\\\\\\\",\\\\\\\"    #define FXAA_QUALITY_P1 2.0\\\\\\\",\\\\\\\"    #define FXAA_QUALITY_P2 8.0\\\\\\\",\\\\\\\"#endif\\\\\\\",\\\\\\\"/*--------------------------------------------------------------------------*/\\\\\\\",\\\\\\\"#if (FXAA_QUALITY_PRESET == 21)\\\\\\\",\\\\\\\"    #define FXAA_QUALITY_PS 4\\\\\\\",\\\\\\\"    #define FXAA_QUALITY_P0 1.0\\\\\\\",\\\\\\\"    #define FXAA_QUALITY_P1 1.5\\\\\\\",\\\\\\\"    #define FXAA_QUALITY_P2 2.0\\\\\\\",\\\\\\\"    #define FXAA_QUALITY_P3 8.0\\\\\\\",\\\\\\\"#endif\\\\\\\",\\\\\\\"/*--------------------------------------------------------------------------*/\\\\\\\",\\\\\\\"#if (FXAA_QUALITY_PRESET == 22)\\\\\\\",\\\\\\\"    #define FXAA_QUALITY_PS 5\\\\\\\",\\\\\\\"    #define FXAA_QUALITY_P0 1.0\\\\\\\",\\\\\\\"    #define FXAA_QUALITY_P1 1.5\\\\\\\",\\\\\\\"    #define FXAA_QUALITY_P2 2.0\\\\\\\",\\\\\\\"    #define FXAA_QUALITY_P3 2.0\\\\\\\",\\\\\\\"    #define FXAA_QUALITY_P4 8.0\\\\\\\",\\\\\\\"#endif\\\\\\\",\\\\\\\"/*--------------------------------------------------------------------------*/\\\\\\\",\\\\\\\"#if (FXAA_QUALITY_PRESET == 23)\\\\\\\",\\\\\\\"    #define FXAA_QUALITY_PS 6\\\\\\\",\\\\\\\"    #define FXAA_QUALITY_P0 1.0\\\\\\\",\\\\\\\"    #define FXAA_QUALITY_P1 1.5\\\\\\\",\\\\\\\"    #define FXAA_QUALITY_P2 2.0\\\\\\\",\\\\\\\"    #define FXAA_QUALITY_P3 2.0\\\\\\\",\\\\\\\"    #define FXAA_QUALITY_P4 2.0\\\\\\\",\\\\\\\"    #define FXAA_QUALITY_P5 8.0\\\\\\\",\\\\\\\"#endif\\\\\\\",\\\\\\\"/*--------------------------------------------------------------------------*/\\\\\\\",\\\\\\\"#if (FXAA_QUALITY_PRESET == 24)\\\\\\\",\\\\\\\"    #define FXAA_QUALITY_PS 7\\\\\\\",\\\\\\\"    #define FXAA_QUALITY_P0 1.0\\\\\\\",\\\\\\\"    #define FXAA_QUALITY_P1 1.5\\\\\\\",\\\\\\\"    #define FXAA_QUALITY_P2 2.0\\\\\\\",\\\\\\\"    #define FXAA_QUALITY_P3 2.0\\\\\\\",\\\\\\\"    #define FXAA_QUALITY_P4 2.0\\\\\\\",\\\\\\\"    #define FXAA_QUALITY_P5 3.0\\\\\\\",\\\\\\\"    #define FXAA_QUALITY_P6 8.0\\\\\\\",\\\\\\\"#endif\\\\\\\",\\\\\\\"/*--------------------------------------------------------------------------*/\\\\\\\",\\\\\\\"#if (FXAA_QUALITY_PRESET == 25)\\\\\\\",\\\\\\\"    #define FXAA_QUALITY_PS 8\\\\\\\",\\\\\\\"    #define FXAA_QUALITY_P0 1.0\\\\\\\",\\\\\\\"    #define FXAA_QUALITY_P1 1.5\\\\\\\",\\\\\\\"    #define FXAA_QUALITY_P2 2.0\\\\\\\",\\\\\\\"    #define FXAA_QUALITY_P3 2.0\\\\\\\",\\\\\\\"    #define FXAA_QUALITY_P4 2.0\\\\\\\",\\\\\\\"    #define FXAA_QUALITY_P5 2.0\\\\\\\",\\\\\\\"    #define FXAA_QUALITY_P6 4.0\\\\\\\",\\\\\\\"    #define FXAA_QUALITY_P7 8.0\\\\\\\",\\\\\\\"#endif\\\\\\\",\\\\\\\"/*--------------------------------------------------------------------------*/\\\\\\\",\\\\\\\"#if (FXAA_QUALITY_PRESET == 26)\\\\\\\",\\\\\\\"    #define FXAA_QUALITY_PS 9\\\\\\\",\\\\\\\"    #define FXAA_QUALITY_P0 1.0\\\\\\\",\\\\\\\"    #define FXAA_QUALITY_P1 1.5\\\\\\\",\\\\\\\"    #define FXAA_QUALITY_P2 2.0\\\\\\\",\\\\\\\"    #define FXAA_QUALITY_P3 2.0\\\\\\\",\\\\\\\"    #define FXAA_QUALITY_P4 2.0\\\\\\\",\\\\\\\"    #define FXAA_QUALITY_P5 2.0\\\\\\\",\\\\\\\"    #define FXAA_QUALITY_P6 2.0\\\\\\\",\\\\\\\"    #define FXAA_QUALITY_P7 4.0\\\\\\\",\\\\\\\"    #define FXAA_QUALITY_P8 8.0\\\\\\\",\\\\\\\"#endif\\\\\\\",\\\\\\\"/*--------------------------------------------------------------------------*/\\\\\\\",\\\\\\\"#if (FXAA_QUALITY_PRESET == 27)\\\\\\\",\\\\\\\"    #define FXAA_QUALITY_PS 10\\\\\\\",\\\\\\\"    #define FXAA_QUALITY_P0 1.0\\\\\\\",\\\\\\\"    #define FXAA_QUALITY_P1 1.5\\\\\\\",\\\\\\\"    #define FXAA_QUALITY_P2 2.0\\\\\\\",\\\\\\\"    #define FXAA_QUALITY_P3 2.0\\\\\\\",\\\\\\\"    #define FXAA_QUALITY_P4 2.0\\\\\\\",\\\\\\\"    #define FXAA_QUALITY_P5 2.0\\\\\\\",\\\\\\\"    #define FXAA_QUALITY_P6 2.0\\\\\\\",\\\\\\\"    #define FXAA_QUALITY_P7 2.0\\\\\\\",\\\\\\\"    #define FXAA_QUALITY_P8 4.0\\\\\\\",\\\\\\\"    #define FXAA_QUALITY_P9 8.0\\\\\\\",\\\\\\\"#endif\\\\\\\",\\\\\\\"/*--------------------------------------------------------------------------*/\\\\\\\",\\\\\\\"#if (FXAA_QUALITY_PRESET == 28)\\\\\\\",\\\\\\\"    #define FXAA_QUALITY_PS 11\\\\\\\",\\\\\\\"    #define FXAA_QUALITY_P0 1.0\\\\\\\",\\\\\\\"    #define FXAA_QUALITY_P1 1.5\\\\\\\",\\\\\\\"    #define FXAA_QUALITY_P2 2.0\\\\\\\",\\\\\\\"    #define FXAA_QUALITY_P3 2.0\\\\\\\",\\\\\\\"    #define FXAA_QUALITY_P4 2.0\\\\\\\",\\\\\\\"    #define FXAA_QUALITY_P5 2.0\\\\\\\",\\\\\\\"    #define FXAA_QUALITY_P6 2.0\\\\\\\",\\\\\\\"    #define FXAA_QUALITY_P7 2.0\\\\\\\",\\\\\\\"    #define FXAA_QUALITY_P8 2.0\\\\\\\",\\\\\\\"    #define FXAA_QUALITY_P9 4.0\\\\\\\",\\\\\\\"    #define FXAA_QUALITY_P10 8.0\\\\\\\",\\\\\\\"#endif\\\\\\\",\\\\\\\"/*--------------------------------------------------------------------------*/\\\\\\\",\\\\\\\"#if (FXAA_QUALITY_PRESET == 29)\\\\\\\",\\\\\\\"    #define FXAA_QUALITY_PS 12\\\\\\\",\\\\\\\"    #define FXAA_QUALITY_P0 1.0\\\\\\\",\\\\\\\"    #define FXAA_QUALITY_P1 1.5\\\\\\\",\\\\\\\"    #define FXAA_QUALITY_P2 2.0\\\\\\\",\\\\\\\"    #define FXAA_QUALITY_P3 2.0\\\\\\\",\\\\\\\"    #define FXAA_QUALITY_P4 2.0\\\\\\\",\\\\\\\"    #define FXAA_QUALITY_P5 2.0\\\\\\\",\\\\\\\"    #define FXAA_QUALITY_P6 2.0\\\\\\\",\\\\\\\"    #define FXAA_QUALITY_P7 2.0\\\\\\\",\\\\\\\"    #define FXAA_QUALITY_P8 2.0\\\\\\\",\\\\\\\"    #define FXAA_QUALITY_P9 2.0\\\\\\\",\\\\\\\"    #define FXAA_QUALITY_P10 4.0\\\\\\\",\\\\\\\"    #define FXAA_QUALITY_P11 8.0\\\\\\\",\\\\\\\"#endif\\\\\\\",\\\\\\\"\\\\\\\",\\\\\\\"/*============================================================================\\\\\\\",\\\\\\\"                     FXAA QUALITY - EXTREME QUALITY\\\\\\\",\\\\\\\"============================================================================*/\\\\\\\",\\\\\\\"#if (FXAA_QUALITY_PRESET == 39)\\\\\\\",\\\\\\\"    #define FXAA_QUALITY_PS 12\\\\\\\",\\\\\\\"    #define FXAA_QUALITY_P0 1.0\\\\\\\",\\\\\\\"    #define FXAA_QUALITY_P1 1.0\\\\\\\",\\\\\\\"    #define FXAA_QUALITY_P2 1.0\\\\\\\",\\\\\\\"    #define FXAA_QUALITY_P3 1.0\\\\\\\",\\\\\\\"    #define FXAA_QUALITY_P4 1.0\\\\\\\",\\\\\\\"    #define FXAA_QUALITY_P5 1.5\\\\\\\",\\\\\\\"    #define FXAA_QUALITY_P6 2.0\\\\\\\",\\\\\\\"    #define FXAA_QUALITY_P7 2.0\\\\\\\",\\\\\\\"    #define FXAA_QUALITY_P8 2.0\\\\\\\",\\\\\\\"    #define FXAA_QUALITY_P9 2.0\\\\\\\",\\\\\\\"    #define FXAA_QUALITY_P10 4.0\\\\\\\",\\\\\\\"    #define FXAA_QUALITY_P11 8.0\\\\\\\",\\\\\\\"#endif\\\\\\\",\\\\\\\"\\\\\\\",\\\\\\\"\\\\\\\",\\\\\\\"\\\\\\\",\\\\\\\"/*============================================================================\\\\\\\",\\\\\\\"\\\\\\\",\\\\\\\"                                API PORTING\\\\\\\",\\\\\\\"\\\\\\\",\\\\\\\"============================================================================*/\\\\\\\",\\\\\\\"#if (FXAA_GLSL_100 == 1) || (FXAA_GLSL_120 == 1) || (FXAA_GLSL_130 == 1)\\\\\\\",\\\\\\\"    #define FxaaBool bool\\\\\\\",\\\\\\\"    #define FxaaDiscard discard\\\\\\\",\\\\\\\"    #define FxaaFloat float\\\\\\\",\\\\\\\"    #define FxaaFloat2 vec2\\\\\\\",\\\\\\\"    #define FxaaFloat3 vec3\\\\\\\",\\\\\\\"    #define FxaaFloat4 vec4\\\\\\\",\\\\\\\"    #define FxaaHalf float\\\\\\\",\\\\\\\"    #define FxaaHalf2 vec2\\\\\\\",\\\\\\\"    #define FxaaHalf3 vec3\\\\\\\",\\\\\\\"    #define FxaaHalf4 vec4\\\\\\\",\\\\\\\"    #define FxaaInt2 ivec2\\\\\\\",\\\\\\\"    #define FxaaSat(x) clamp(x, 0.0, 1.0)\\\\\\\",\\\\\\\"    #define FxaaTex sampler2D\\\\\\\",\\\\\\\"#else\\\\\\\",\\\\\\\"    #define FxaaBool bool\\\\\\\",\\\\\\\"    #define FxaaDiscard clip(-1)\\\\\\\",\\\\\\\"    #define FxaaFloat float\\\\\\\",\\\\\\\"    #define FxaaFloat2 float2\\\\\\\",\\\\\\\"    #define FxaaFloat3 float3\\\\\\\",\\\\\\\"    #define FxaaFloat4 float4\\\\\\\",\\\\\\\"    #define FxaaHalf half\\\\\\\",\\\\\\\"    #define FxaaHalf2 half2\\\\\\\",\\\\\\\"    #define FxaaHalf3 half3\\\\\\\",\\\\\\\"    #define FxaaHalf4 half4\\\\\\\",\\\\\\\"    #define FxaaSat(x) saturate(x)\\\\\\\",\\\\\\\"#endif\\\\\\\",\\\\\\\"/*--------------------------------------------------------------------------*/\\\\\\\",\\\\\\\"#if (FXAA_GLSL_100 == 1)\\\\\\\",\\\\\\\"  #define FxaaTexTop(t, p) texture2D(t, p, 0.0)\\\\\\\",\\\\\\\"  #define FxaaTexOff(t, p, o, r) texture2D(t, p + (o * r), 0.0)\\\\\\\",\\\\\\\"#endif\\\\\\\",\\\\\\\"/*--------------------------------------------------------------------------*/\\\\\\\",\\\\\\\"#if (FXAA_GLSL_120 == 1)\\\\\\\",\\\\\\\"    // Requires,\\\\\\\",\\\\\\\"    //  #version 120\\\\\\\",\\\\\\\"    // And at least,\\\\\\\",\\\\\\\"    //  #extension GL_EXT_gpu_shader4 : enable\\\\\\\",\\\\\\\"    //  (or set FXAA_FAST_PIXEL_OFFSET 1 to work like DX9)\\\\\\\",\\\\\\\"    #define FxaaTexTop(t, p) texture2DLod(t, p, 0.0)\\\\\\\",\\\\\\\"    #if (FXAA_FAST_PIXEL_OFFSET == 1)\\\\\\\",\\\\\\\"        #define FxaaTexOff(t, p, o, r) texture2DLodOffset(t, p, 0.0, o)\\\\\\\",\\\\\\\"    #else\\\\\\\",\\\\\\\"        #define FxaaTexOff(t, p, o, r) texture2DLod(t, p + (o * r), 0.0)\\\\\\\",\\\\\\\"    #endif\\\\\\\",\\\\\\\"    #if (FXAA_GATHER4_ALPHA == 1)\\\\\\\",\\\\\\\"        // use #extension GL_ARB_gpu_shader5 : enable\\\\\\\",\\\\\\\"        #define FxaaTexAlpha4(t, p) textureGather(t, p, 3)\\\\\\\",\\\\\\\"        #define FxaaTexOffAlpha4(t, p, o) textureGatherOffset(t, p, o, 3)\\\\\\\",\\\\\\\"        #define FxaaTexGreen4(t, p) textureGather(t, p, 1)\\\\\\\",\\\\\\\"        #define FxaaTexOffGreen4(t, p, o) textureGatherOffset(t, p, o, 1)\\\\\\\",\\\\\\\"    #endif\\\\\\\",\\\\\\\"#endif\\\\\\\",\\\\\\\"/*--------------------------------------------------------------------------*/\\\\\\\",\\\\\\\"#if (FXAA_GLSL_130 == 1)\\\\\\\",'    // Requires \\\\\\\"#version 130\\\\\\\" or better',\\\\\\\"    #define FxaaTexTop(t, p) textureLod(t, p, 0.0)\\\\\\\",\\\\\\\"    #define FxaaTexOff(t, p, o, r) textureLodOffset(t, p, 0.0, o)\\\\\\\",\\\\\\\"    #if (FXAA_GATHER4_ALPHA == 1)\\\\\\\",\\\\\\\"        // use #extension GL_ARB_gpu_shader5 : enable\\\\\\\",\\\\\\\"        #define FxaaTexAlpha4(t, p) textureGather(t, p, 3)\\\\\\\",\\\\\\\"        #define FxaaTexOffAlpha4(t, p, o) textureGatherOffset(t, p, o, 3)\\\\\\\",\\\\\\\"        #define FxaaTexGreen4(t, p) textureGather(t, p, 1)\\\\\\\",\\\\\\\"        #define FxaaTexOffGreen4(t, p, o) textureGatherOffset(t, p, o, 1)\\\\\\\",\\\\\\\"    #endif\\\\\\\",\\\\\\\"#endif\\\\\\\",\\\\\\\"/*--------------------------------------------------------------------------*/\\\\\\\",\\\\\\\"#if (FXAA_HLSL_3 == 1)\\\\\\\",\\\\\\\"    #define FxaaInt2 float2\\\\\\\",\\\\\\\"    #define FxaaTex sampler2D\\\\\\\",\\\\\\\"    #define FxaaTexTop(t, p) tex2Dlod(t, float4(p, 0.0, 0.0))\\\\\\\",\\\\\\\"    #define FxaaTexOff(t, p, o, r) tex2Dlod(t, float4(p + (o * r), 0, 0))\\\\\\\",\\\\\\\"#endif\\\\\\\",\\\\\\\"/*--------------------------------------------------------------------------*/\\\\\\\",\\\\\\\"#if (FXAA_HLSL_4 == 1)\\\\\\\",\\\\\\\"    #define FxaaInt2 int2\\\\\\\",\\\\\\\"    struct FxaaTex { SamplerState smpl; Texture2D tex; };\\\\\\\",\\\\\\\"    #define FxaaTexTop(t, p) t.tex.SampleLevel(t.smpl, p, 0.0)\\\\\\\",\\\\\\\"    #define FxaaTexOff(t, p, o, r) t.tex.SampleLevel(t.smpl, p, 0.0, o)\\\\\\\",\\\\\\\"#endif\\\\\\\",\\\\\\\"/*--------------------------------------------------------------------------*/\\\\\\\",\\\\\\\"#if (FXAA_HLSL_5 == 1)\\\\\\\",\\\\\\\"    #define FxaaInt2 int2\\\\\\\",\\\\\\\"    struct FxaaTex { SamplerState smpl; Texture2D tex; };\\\\\\\",\\\\\\\"    #define FxaaTexTop(t, p) t.tex.SampleLevel(t.smpl, p, 0.0)\\\\\\\",\\\\\\\"    #define FxaaTexOff(t, p, o, r) t.tex.SampleLevel(t.smpl, p, 0.0, o)\\\\\\\",\\\\\\\"    #define FxaaTexAlpha4(t, p) t.tex.GatherAlpha(t.smpl, p)\\\\\\\",\\\\\\\"    #define FxaaTexOffAlpha4(t, p, o) t.tex.GatherAlpha(t.smpl, p, o)\\\\\\\",\\\\\\\"    #define FxaaTexGreen4(t, p) t.tex.GatherGreen(t.smpl, p)\\\\\\\",\\\\\\\"    #define FxaaTexOffGreen4(t, p, o) t.tex.GatherGreen(t.smpl, p, o)\\\\\\\",\\\\\\\"#endif\\\\\\\",\\\\\\\"\\\\\\\",\\\\\\\"\\\\\\\",\\\\\\\"/*============================================================================\\\\\\\",\\\\\\\"                   GREEN AS LUMA OPTION SUPPORT FUNCTION\\\\\\\",\\\\\\\"============================================================================*/\\\\\\\",\\\\\\\"#if (FXAA_GREEN_AS_LUMA == 0)\\\\\\\",\\\\\\\"    FxaaFloat FxaaLuma(FxaaFloat4 rgba) { return rgba.w; }\\\\\\\",\\\\\\\"#else\\\\\\\",\\\\\\\"    FxaaFloat FxaaLuma(FxaaFloat4 rgba) { return rgba.y; }\\\\\\\",\\\\\\\"#endif\\\\\\\",\\\\\\\"\\\\\\\",\\\\\\\"\\\\\\\",\\\\\\\"\\\\\\\",\\\\\\\"\\\\\\\",\\\\\\\"/*============================================================================\\\\\\\",\\\\\\\"\\\\\\\",\\\\\\\"                             FXAA3 QUALITY - PC\\\\\\\",\\\\\\\"\\\\\\\",\\\\\\\"============================================================================*/\\\\\\\",\\\\\\\"#if (FXAA_PC == 1)\\\\\\\",\\\\\\\"/*--------------------------------------------------------------------------*/\\\\\\\",\\\\\\\"FxaaFloat4 FxaaPixelShader(\\\\\\\",\\\\\\\"    //\\\\\\\",\\\\\\\"    // Use noperspective interpolation here (turn off perspective interpolation).\\\\\\\",\\\\\\\"    // {xy} = center of pixel\\\\\\\",\\\\\\\"    FxaaFloat2 pos,\\\\\\\",\\\\\\\"    //\\\\\\\",\\\\\\\"    // Used only for FXAA Console, and not used on the 360 version.\\\\\\\",\\\\\\\"    // Use noperspective interpolation here (turn off perspective interpolation).\\\\\\\",\\\\\\\"    // {xy_} = upper left of pixel\\\\\\\",\\\\\\\"    // {_zw} = lower right of pixel\\\\\\\",\\\\\\\"    FxaaFloat4 fxaaConsolePosPos,\\\\\\\",\\\\\\\"    //\\\\\\\",\\\\\\\"    // Input color texture.\\\\\\\",\\\\\\\"    // {rgb_} = color in linear or perceptual color space\\\\\\\",\\\\\\\"    // if (FXAA_GREEN_AS_LUMA == 0)\\\\\\\",\\\\\\\"    //     {__a} = luma in perceptual color space (not linear)\\\\\\\",\\\\\\\"    FxaaTex tex,\\\\\\\",\\\\\\\"    //\\\\\\\",\\\\\\\"    // Only used on the optimized 360 version of FXAA Console.\\\\\\\",'    // For everything but 360, just use the same input here as for \\\\\\\"tex\\\\\\\".',\\\\\\\"    // For 360, same texture, just alias with a 2nd sampler.\\\\\\\",\\\\\\\"    // This sampler needs to have an exponent bias of -1.\\\\\\\",\\\\\\\"    FxaaTex fxaaConsole360TexExpBiasNegOne,\\\\\\\",\\\\\\\"    //\\\\\\\",\\\\\\\"    // Only used on the optimized 360 version of FXAA Console.\\\\\\\",'    // For everything but 360, just use the same input here as for \\\\\\\"tex\\\\\\\".',\\\\\\\"    // For 360, same texture, just alias with a 3nd sampler.\\\\\\\",\\\\\\\"    // This sampler needs to have an exponent bias of -2.\\\\\\\",\\\\\\\"    FxaaTex fxaaConsole360TexExpBiasNegTwo,\\\\\\\",\\\\\\\"    //\\\\\\\",\\\\\\\"    // Only used on FXAA Quality.\\\\\\\",\\\\\\\"    // This must be from a constant/uniform.\\\\\\\",\\\\\\\"    // {x_} = 1.0/screenWidthInPixels\\\\\\\",\\\\\\\"    // {_y} = 1.0/screenHeightInPixels\\\\\\\",\\\\\\\"    FxaaFloat2 fxaaQualityRcpFrame,\\\\\\\",\\\\\\\"    //\\\\\\\",\\\\\\\"    // Only used on FXAA Console.\\\\\\\",\\\\\\\"    // This must be from a constant/uniform.\\\\\\\",\\\\\\\"    // This effects sub-pixel AA quality and inversely sharpness.\\\\\\\",\\\\\\\"    //   Where N ranges between,\\\\\\\",\\\\\\\"    //     N = 0.50 (default)\\\\\\\",\\\\\\\"    //     N = 0.33 (sharper)\\\\\\\",\\\\\\\"    // {x__} = -N/screenWidthInPixels\\\\\\\",\\\\\\\"    // {_y_} = -N/screenHeightInPixels\\\\\\\",\\\\\\\"    // {_z_} =  N/screenWidthInPixels\\\\\\\",\\\\\\\"    // {__w} =  N/screenHeightInPixels\\\\\\\",\\\\\\\"    FxaaFloat4 fxaaConsoleRcpFrameOpt,\\\\\\\",\\\\\\\"    //\\\\\\\",\\\\\\\"    // Only used on FXAA Console.\\\\\\\",\\\\\\\"    // Not used on 360, but used on PS3 and PC.\\\\\\\",\\\\\\\"    // This must be from a constant/uniform.\\\\\\\",\\\\\\\"    // {x__} = -2.0/screenWidthInPixels\\\\\\\",\\\\\\\"    // {_y_} = -2.0/screenHeightInPixels\\\\\\\",\\\\\\\"    // {_z_} =  2.0/screenWidthInPixels\\\\\\\",\\\\\\\"    // {__w} =  2.0/screenHeightInPixels\\\\\\\",\\\\\\\"    FxaaFloat4 fxaaConsoleRcpFrameOpt2,\\\\\\\",\\\\\\\"    //\\\\\\\",\\\\\\\"    // Only used on FXAA Console.\\\\\\\",\\\\\\\"    // Only used on 360 in place of fxaaConsoleRcpFrameOpt2.\\\\\\\",\\\\\\\"    // This must be from a constant/uniform.\\\\\\\",\\\\\\\"    // {x__} =  8.0/screenWidthInPixels\\\\\\\",\\\\\\\"    // {_y_} =  8.0/screenHeightInPixels\\\\\\\",\\\\\\\"    // {_z_} = -4.0/screenWidthInPixels\\\\\\\",\\\\\\\"    // {__w} = -4.0/screenHeightInPixels\\\\\\\",\\\\\\\"    FxaaFloat4 fxaaConsole360RcpFrameOpt2,\\\\\\\",\\\\\\\"    //\\\\\\\",\\\\\\\"    // Only used on FXAA Quality.\\\\\\\",\\\\\\\"    // This used to be the FXAA_QUALITY_SUBPIX define.\\\\\\\",\\\\\\\"    // It is here now to allow easier tuning.\\\\\\\",\\\\\\\"    // Choose the amount of sub-pixel aliasing removal.\\\\\\\",\\\\\\\"    // This can effect sharpness.\\\\\\\",\\\\\\\"    //   1.00 - upper limit (softer)\\\\\\\",\\\\\\\"    //   0.75 - default amount of filtering\\\\\\\",\\\\\\\"    //   0.50 - lower limit (sharper, less sub-pixel aliasing removal)\\\\\\\",\\\\\\\"    //   0.25 - almost off\\\\\\\",\\\\\\\"    //   0.00 - completely off\\\\\\\",\\\\\\\"    FxaaFloat fxaaQualitySubpix,\\\\\\\",\\\\\\\"    //\\\\\\\",\\\\\\\"    // Only used on FXAA Quality.\\\\\\\",\\\\\\\"    // This used to be the FXAA_QUALITY_EDGE_THRESHOLD define.\\\\\\\",\\\\\\\"    // It is here now to allow easier tuning.\\\\\\\",\\\\\\\"    // The minimum amount of local contrast required to apply algorithm.\\\\\\\",\\\\\\\"    //   0.333 - too little (faster)\\\\\\\",\\\\\\\"    //   0.250 - low quality\\\\\\\",\\\\\\\"    //   0.166 - default\\\\\\\",\\\\\\\"    //   0.125 - high quality\\\\\\\",\\\\\\\"    //   0.063 - overkill (slower)\\\\\\\",\\\\\\\"    FxaaFloat fxaaQualityEdgeThreshold,\\\\\\\",\\\\\\\"    //\\\\\\\",\\\\\\\"    // Only used on FXAA Quality.\\\\\\\",\\\\\\\"    // This used to be the FXAA_QUALITY_EDGE_THRESHOLD_MIN define.\\\\\\\",\\\\\\\"    // It is here now to allow easier tuning.\\\\\\\",\\\\\\\"    // Trims the algorithm from processing darks.\\\\\\\",\\\\\\\"    //   0.0833 - upper limit (default, the start of visible unfiltered edges)\\\\\\\",\\\\\\\"    //   0.0625 - high quality (faster)\\\\\\\",\\\\\\\"    //   0.0312 - visible limit (slower)\\\\\\\",\\\\\\\"    // Special notes when using FXAA_GREEN_AS_LUMA,\\\\\\\",\\\\\\\"    //   Likely want to set this to zero.\\\\\\\",\\\\\\\"    //   As colors that are mostly not-green\\\\\\\",\\\\\\\"    //   will appear very dark in the green channel!\\\\\\\",\\\\\\\"    //   Tune by looking at mostly non-green content,\\\\\\\",\\\\\\\"    //   then start at zero and increase until aliasing is a problem.\\\\\\\",\\\\\\\"    FxaaFloat fxaaQualityEdgeThresholdMin,\\\\\\\",\\\\\\\"    //\\\\\\\",\\\\\\\"    // Only used on FXAA Console.\\\\\\\",\\\\\\\"    // This used to be the FXAA_CONSOLE_EDGE_SHARPNESS define.\\\\\\\",\\\\\\\"    // It is here now to allow easier tuning.\\\\\\\",\\\\\\\"    // This does not effect PS3, as this needs to be compiled in.\\\\\\\",\\\\\\\"    //   Use FXAA_CONSOLE_PS3_EDGE_SHARPNESS for PS3.\\\\\\\",\\\\\\\"    //   Due to the PS3 being ALU bound,\\\\\\\",\\\\\\\"    //   there are only three safe values here: 2 and 4 and 8.\\\\\\\",\\\\\\\"    //   These options use the shaders ability to a free *|/ by 2|4|8.\\\\\\\",\\\\\\\"    // For all other platforms can be a non-power of two.\\\\\\\",\\\\\\\"    //   8.0 is sharper (default!!!)\\\\\\\",\\\\\\\"    //   4.0 is softer\\\\\\\",\\\\\\\"    //   2.0 is really soft (good only for vector graphics inputs)\\\\\\\",\\\\\\\"    FxaaFloat fxaaConsoleEdgeSharpness,\\\\\\\",\\\\\\\"    //\\\\\\\",\\\\\\\"    // Only used on FXAA Console.\\\\\\\",\\\\\\\"    // This used to be the FXAA_CONSOLE_EDGE_THRESHOLD define.\\\\\\\",\\\\\\\"    // It is here now to allow easier tuning.\\\\\\\",\\\\\\\"    // This does not effect PS3, as this needs to be compiled in.\\\\\\\",\\\\\\\"    //   Use FXAA_CONSOLE_PS3_EDGE_THRESHOLD for PS3.\\\\\\\",\\\\\\\"    //   Due to the PS3 being ALU bound,\\\\\\\",\\\\\\\"    //   there are only two safe values here: 1/4 and 1/8.\\\\\\\",\\\\\\\"    //   These options use the shaders ability to a free *|/ by 2|4|8.\\\\\\\",\\\\\\\"    // The console setting has a different mapping than the quality setting.\\\\\\\",\\\\\\\"    // Other platforms can use other values.\\\\\\\",\\\\\\\"    //   0.125 leaves less aliasing, but is softer (default!!!)\\\\\\\",\\\\\\\"    //   0.25 leaves more aliasing, and is sharper\\\\\\\",\\\\\\\"    FxaaFloat fxaaConsoleEdgeThreshold,\\\\\\\",\\\\\\\"    //\\\\\\\",\\\\\\\"    // Only used on FXAA Console.\\\\\\\",\\\\\\\"    // This used to be the FXAA_CONSOLE_EDGE_THRESHOLD_MIN define.\\\\\\\",\\\\\\\"    // It is here now to allow easier tuning.\\\\\\\",\\\\\\\"    // Trims the algorithm from processing darks.\\\\\\\",\\\\\\\"    // The console setting has a different mapping than the quality setting.\\\\\\\",\\\\\\\"    // This only applies when FXAA_EARLY_EXIT is 1.\\\\\\\",\\\\\\\"    // This does not apply to PS3,\\\\\\\",\\\\\\\"    // PS3 was simplified to avoid more shader instructions.\\\\\\\",\\\\\\\"    //   0.06 - faster but more aliasing in darks\\\\\\\",\\\\\\\"    //   0.05 - default\\\\\\\",\\\\\\\"    //   0.04 - slower and less aliasing in darks\\\\\\\",\\\\\\\"    // Special notes when using FXAA_GREEN_AS_LUMA,\\\\\\\",\\\\\\\"    //   Likely want to set this to zero.\\\\\\\",\\\\\\\"    //   As colors that are mostly not-green\\\\\\\",\\\\\\\"    //   will appear very dark in the green channel!\\\\\\\",\\\\\\\"    //   Tune by looking at mostly non-green content,\\\\\\\",\\\\\\\"    //   then start at zero and increase until aliasing is a problem.\\\\\\\",\\\\\\\"    FxaaFloat fxaaConsoleEdgeThresholdMin,\\\\\\\",\\\\\\\"    //\\\\\\\",\\\\\\\"    // Extra constants for 360 FXAA Console only.\\\\\\\",\\\\\\\"    // Use zeros or anything else for other platforms.\\\\\\\",\\\\\\\"    // These must be in physical constant registers and NOT immediates.\\\\\\\",\\\\\\\"    // Immediates will result in compiler un-optimizing.\\\\\\\",\\\\\\\"    // {xyzw} = float4(1.0, -1.0, 0.25, -0.25)\\\\\\\",\\\\\\\"    FxaaFloat4 fxaaConsole360ConstDir\\\\\\\",\\\\\\\") {\\\\\\\",\\\\\\\"/*--------------------------------------------------------------------------*/\\\\\\\",\\\\\\\"    FxaaFloat2 posM;\\\\\\\",\\\\\\\"    posM.x = pos.x;\\\\\\\",\\\\\\\"    posM.y = pos.y;\\\\\\\",\\\\\\\"    #if (FXAA_GATHER4_ALPHA == 1)\\\\\\\",\\\\\\\"        #if (FXAA_DISCARD == 0)\\\\\\\",\\\\\\\"            FxaaFloat4 rgbyM = FxaaTexTop(tex, posM);\\\\\\\",\\\\\\\"            #if (FXAA_GREEN_AS_LUMA == 0)\\\\\\\",\\\\\\\"                #define lumaM rgbyM.w\\\\\\\",\\\\\\\"            #else\\\\\\\",\\\\\\\"                #define lumaM rgbyM.y\\\\\\\",\\\\\\\"            #endif\\\\\\\",\\\\\\\"        #endif\\\\\\\",\\\\\\\"        #if (FXAA_GREEN_AS_LUMA == 0)\\\\\\\",\\\\\\\"            FxaaFloat4 luma4A = FxaaTexAlpha4(tex, posM);\\\\\\\",\\\\\\\"            FxaaFloat4 luma4B = FxaaTexOffAlpha4(tex, posM, FxaaInt2(-1, -1));\\\\\\\",\\\\\\\"        #else\\\\\\\",\\\\\\\"            FxaaFloat4 luma4A = FxaaTexGreen4(tex, posM);\\\\\\\",\\\\\\\"            FxaaFloat4 luma4B = FxaaTexOffGreen4(tex, posM, FxaaInt2(-1, -1));\\\\\\\",\\\\\\\"        #endif\\\\\\\",\\\\\\\"        #if (FXAA_DISCARD == 1)\\\\\\\",\\\\\\\"            #define lumaM luma4A.w\\\\\\\",\\\\\\\"        #endif\\\\\\\",\\\\\\\"        #define lumaE luma4A.z\\\\\\\",\\\\\\\"        #define lumaS luma4A.x\\\\\\\",\\\\\\\"        #define lumaSE luma4A.y\\\\\\\",\\\\\\\"        #define lumaNW luma4B.w\\\\\\\",\\\\\\\"        #define lumaN luma4B.z\\\\\\\",\\\\\\\"        #define lumaW luma4B.x\\\\\\\",\\\\\\\"    #else\\\\\\\",\\\\\\\"        FxaaFloat4 rgbyM = FxaaTexTop(tex, posM);\\\\\\\",\\\\\\\"        #if (FXAA_GREEN_AS_LUMA == 0)\\\\\\\",\\\\\\\"            #define lumaM rgbyM.w\\\\\\\",\\\\\\\"        #else\\\\\\\",\\\\\\\"            #define lumaM rgbyM.y\\\\\\\",\\\\\\\"        #endif\\\\\\\",\\\\\\\"        #if (FXAA_GLSL_100 == 1)\\\\\\\",\\\\\\\"          FxaaFloat lumaS = FxaaLuma(FxaaTexOff(tex, posM, FxaaFloat2( 0.0, 1.0), fxaaQualityRcpFrame.xy));\\\\\\\",\\\\\\\"          FxaaFloat lumaE = FxaaLuma(FxaaTexOff(tex, posM, FxaaFloat2( 1.0, 0.0), fxaaQualityRcpFrame.xy));\\\\\\\",\\\\\\\"          FxaaFloat lumaN = FxaaLuma(FxaaTexOff(tex, posM, FxaaFloat2( 0.0,-1.0), fxaaQualityRcpFrame.xy));\\\\\\\",\\\\\\\"          FxaaFloat lumaW = FxaaLuma(FxaaTexOff(tex, posM, FxaaFloat2(-1.0, 0.0), fxaaQualityRcpFrame.xy));\\\\\\\",\\\\\\\"        #else\\\\\\\",\\\\\\\"          FxaaFloat lumaS = FxaaLuma(FxaaTexOff(tex, posM, FxaaInt2( 0, 1), fxaaQualityRcpFrame.xy));\\\\\\\",\\\\\\\"          FxaaFloat lumaE = FxaaLuma(FxaaTexOff(tex, posM, FxaaInt2( 1, 0), fxaaQualityRcpFrame.xy));\\\\\\\",\\\\\\\"          FxaaFloat lumaN = FxaaLuma(FxaaTexOff(tex, posM, FxaaInt2( 0,-1), fxaaQualityRcpFrame.xy));\\\\\\\",\\\\\\\"          FxaaFloat lumaW = FxaaLuma(FxaaTexOff(tex, posM, FxaaInt2(-1, 0), fxaaQualityRcpFrame.xy));\\\\\\\",\\\\\\\"        #endif\\\\\\\",\\\\\\\"    #endif\\\\\\\",\\\\\\\"/*--------------------------------------------------------------------------*/\\\\\\\",\\\\\\\"    FxaaFloat maxSM = max(lumaS, lumaM);\\\\\\\",\\\\\\\"    FxaaFloat minSM = min(lumaS, lumaM);\\\\\\\",\\\\\\\"    FxaaFloat maxESM = max(lumaE, maxSM);\\\\\\\",\\\\\\\"    FxaaFloat minESM = min(lumaE, minSM);\\\\\\\",\\\\\\\"    FxaaFloat maxWN = max(lumaN, lumaW);\\\\\\\",\\\\\\\"    FxaaFloat minWN = min(lumaN, lumaW);\\\\\\\",\\\\\\\"    FxaaFloat rangeMax = max(maxWN, maxESM);\\\\\\\",\\\\\\\"    FxaaFloat rangeMin = min(minWN, minESM);\\\\\\\",\\\\\\\"    FxaaFloat rangeMaxScaled = rangeMax * fxaaQualityEdgeThreshold;\\\\\\\",\\\\\\\"    FxaaFloat range = rangeMax - rangeMin;\\\\\\\",\\\\\\\"    FxaaFloat rangeMaxClamped = max(fxaaQualityEdgeThresholdMin, rangeMaxScaled);\\\\\\\",\\\\\\\"    FxaaBool earlyExit = range < rangeMaxClamped;\\\\\\\",\\\\\\\"/*--------------------------------------------------------------------------*/\\\\\\\",\\\\\\\"    if(earlyExit)\\\\\\\",\\\\\\\"        #if (FXAA_DISCARD == 1)\\\\\\\",\\\\\\\"            FxaaDiscard;\\\\\\\",\\\\\\\"        #else\\\\\\\",\\\\\\\"            return rgbyM;\\\\\\\",\\\\\\\"        #endif\\\\\\\",\\\\\\\"/*--------------------------------------------------------------------------*/\\\\\\\",\\\\\\\"    #if (FXAA_GATHER4_ALPHA == 0)\\\\\\\",\\\\\\\"        #if (FXAA_GLSL_100 == 1)\\\\\\\",\\\\\\\"          FxaaFloat lumaNW = FxaaLuma(FxaaTexOff(tex, posM, FxaaFloat2(-1.0,-1.0), fxaaQualityRcpFrame.xy));\\\\\\\",\\\\\\\"          FxaaFloat lumaSE = FxaaLuma(FxaaTexOff(tex, posM, FxaaFloat2( 1.0, 1.0), fxaaQualityRcpFrame.xy));\\\\\\\",\\\\\\\"          FxaaFloat lumaNE = FxaaLuma(FxaaTexOff(tex, posM, FxaaFloat2( 1.0,-1.0), fxaaQualityRcpFrame.xy));\\\\\\\",\\\\\\\"          FxaaFloat lumaSW = FxaaLuma(FxaaTexOff(tex, posM, FxaaFloat2(-1.0, 1.0), fxaaQualityRcpFrame.xy));\\\\\\\",\\\\\\\"        #else\\\\\\\",\\\\\\\"          FxaaFloat lumaNW = FxaaLuma(FxaaTexOff(tex, posM, FxaaInt2(-1,-1), fxaaQualityRcpFrame.xy));\\\\\\\",\\\\\\\"          FxaaFloat lumaSE = FxaaLuma(FxaaTexOff(tex, posM, FxaaInt2( 1, 1), fxaaQualityRcpFrame.xy));\\\\\\\",\\\\\\\"          FxaaFloat lumaNE = FxaaLuma(FxaaTexOff(tex, posM, FxaaInt2( 1,-1), fxaaQualityRcpFrame.xy));\\\\\\\",\\\\\\\"          FxaaFloat lumaSW = FxaaLuma(FxaaTexOff(tex, posM, FxaaInt2(-1, 1), fxaaQualityRcpFrame.xy));\\\\\\\",\\\\\\\"        #endif\\\\\\\",\\\\\\\"    #else\\\\\\\",\\\\\\\"        FxaaFloat lumaNE = FxaaLuma(FxaaTexOff(tex, posM, FxaaInt2(1, -1), fxaaQualityRcpFrame.xy));\\\\\\\",\\\\\\\"        FxaaFloat lumaSW = FxaaLuma(FxaaTexOff(tex, posM, FxaaInt2(-1, 1), fxaaQualityRcpFrame.xy));\\\\\\\",\\\\\\\"    #endif\\\\\\\",\\\\\\\"/*--------------------------------------------------------------------------*/\\\\\\\",\\\\\\\"    FxaaFloat lumaNS = lumaN + lumaS;\\\\\\\",\\\\\\\"    FxaaFloat lumaWE = lumaW + lumaE;\\\\\\\",\\\\\\\"    FxaaFloat subpixRcpRange = 1.0/range;\\\\\\\",\\\\\\\"    FxaaFloat subpixNSWE = lumaNS + lumaWE;\\\\\\\",\\\\\\\"    FxaaFloat edgeHorz1 = (-2.0 * lumaM) + lumaNS;\\\\\\\",\\\\\\\"    FxaaFloat edgeVert1 = (-2.0 * lumaM) + lumaWE;\\\\\\\",\\\\\\\"/*--------------------------------------------------------------------------*/\\\\\\\",\\\\\\\"    FxaaFloat lumaNESE = lumaNE + lumaSE;\\\\\\\",\\\\\\\"    FxaaFloat lumaNWNE = lumaNW + lumaNE;\\\\\\\",\\\\\\\"    FxaaFloat edgeHorz2 = (-2.0 * lumaE) + lumaNESE;\\\\\\\",\\\\\\\"    FxaaFloat edgeVert2 = (-2.0 * lumaN) + lumaNWNE;\\\\\\\",\\\\\\\"/*--------------------------------------------------------------------------*/\\\\\\\",\\\\\\\"    FxaaFloat lumaNWSW = lumaNW + lumaSW;\\\\\\\",\\\\\\\"    FxaaFloat lumaSWSE = lumaSW + lumaSE;\\\\\\\",\\\\\\\"    FxaaFloat edgeHorz4 = (abs(edgeHorz1) * 2.0) + abs(edgeHorz2);\\\\\\\",\\\\\\\"    FxaaFloat edgeVert4 = (abs(edgeVert1) * 2.0) + abs(edgeVert2);\\\\\\\",\\\\\\\"    FxaaFloat edgeHorz3 = (-2.0 * lumaW) + lumaNWSW;\\\\\\\",\\\\\\\"    FxaaFloat edgeVert3 = (-2.0 * lumaS) + lumaSWSE;\\\\\\\",\\\\\\\"    FxaaFloat edgeHorz = abs(edgeHorz3) + edgeHorz4;\\\\\\\",\\\\\\\"    FxaaFloat edgeVert = abs(edgeVert3) + edgeVert4;\\\\\\\",\\\\\\\"/*--------------------------------------------------------------------------*/\\\\\\\",\\\\\\\"    FxaaFloat subpixNWSWNESE = lumaNWSW + lumaNESE;\\\\\\\",\\\\\\\"    FxaaFloat lengthSign = fxaaQualityRcpFrame.x;\\\\\\\",\\\\\\\"    FxaaBool horzSpan = edgeHorz >= edgeVert;\\\\\\\",\\\\\\\"    FxaaFloat subpixA = subpixNSWE * 2.0 + subpixNWSWNESE;\\\\\\\",\\\\\\\"/*--------------------------------------------------------------------------*/\\\\\\\",\\\\\\\"    if(!horzSpan) lumaN = lumaW;\\\\\\\",\\\\\\\"    if(!horzSpan) lumaS = lumaE;\\\\\\\",\\\\\\\"    if(horzSpan) lengthSign = fxaaQualityRcpFrame.y;\\\\\\\",\\\\\\\"    FxaaFloat subpixB = (subpixA * (1.0/12.0)) - lumaM;\\\\\\\",\\\\\\\"/*--------------------------------------------------------------------------*/\\\\\\\",\\\\\\\"    FxaaFloat gradientN = lumaN - lumaM;\\\\\\\",\\\\\\\"    FxaaFloat gradientS = lumaS - lumaM;\\\\\\\",\\\\\\\"    FxaaFloat lumaNN = lumaN + lumaM;\\\\\\\",\\\\\\\"    FxaaFloat lumaSS = lumaS + lumaM;\\\\\\\",\\\\\\\"    FxaaBool pairN = abs(gradientN) >= abs(gradientS);\\\\\\\",\\\\\\\"    FxaaFloat gradient = max(abs(gradientN), abs(gradientS));\\\\\\\",\\\\\\\"    if(pairN) lengthSign = -lengthSign;\\\\\\\",\\\\\\\"    FxaaFloat subpixC = FxaaSat(abs(subpixB) * subpixRcpRange);\\\\\\\",\\\\\\\"/*--------------------------------------------------------------------------*/\\\\\\\",\\\\\\\"    FxaaFloat2 posB;\\\\\\\",\\\\\\\"    posB.x = posM.x;\\\\\\\",\\\\\\\"    posB.y = posM.y;\\\\\\\",\\\\\\\"    FxaaFloat2 offNP;\\\\\\\",\\\\\\\"    offNP.x = (!horzSpan) ? 0.0 : fxaaQualityRcpFrame.x;\\\\\\\",\\\\\\\"    offNP.y = ( horzSpan) ? 0.0 : fxaaQualityRcpFrame.y;\\\\\\\",\\\\\\\"    if(!horzSpan) posB.x += lengthSign * 0.5;\\\\\\\",\\\\\\\"    if( horzSpan) posB.y += lengthSign * 0.5;\\\\\\\",\\\\\\\"/*--------------------------------------------------------------------------*/\\\\\\\",\\\\\\\"    FxaaFloat2 posN;\\\\\\\",\\\\\\\"    posN.x = posB.x - offNP.x * FXAA_QUALITY_P0;\\\\\\\",\\\\\\\"    posN.y = posB.y - offNP.y * FXAA_QUALITY_P0;\\\\\\\",\\\\\\\"    FxaaFloat2 posP;\\\\\\\",\\\\\\\"    posP.x = posB.x + offNP.x * FXAA_QUALITY_P0;\\\\\\\",\\\\\\\"    posP.y = posB.y + offNP.y * FXAA_QUALITY_P0;\\\\\\\",\\\\\\\"    FxaaFloat subpixD = ((-2.0)*subpixC) + 3.0;\\\\\\\",\\\\\\\"    FxaaFloat lumaEndN = FxaaLuma(FxaaTexTop(tex, posN));\\\\\\\",\\\\\\\"    FxaaFloat subpixE = subpixC * subpixC;\\\\\\\",\\\\\\\"    FxaaFloat lumaEndP = FxaaLuma(FxaaTexTop(tex, posP));\\\\\\\",\\\\\\\"/*--------------------------------------------------------------------------*/\\\\\\\",\\\\\\\"    if(!pairN) lumaNN = lumaSS;\\\\\\\",\\\\\\\"    FxaaFloat gradientScaled = gradient * 1.0/4.0;\\\\\\\",\\\\\\\"    FxaaFloat lumaMM = lumaM - lumaNN * 0.5;\\\\\\\",\\\\\\\"    FxaaFloat subpixF = subpixD * subpixE;\\\\\\\",\\\\\\\"    FxaaBool lumaMLTZero = lumaMM < 0.0;\\\\\\\",\\\\\\\"/*--------------------------------------------------------------------------*/\\\\\\\",\\\\\\\"    lumaEndN -= lumaNN * 0.5;\\\\\\\",\\\\\\\"    lumaEndP -= lumaNN * 0.5;\\\\\\\",\\\\\\\"    FxaaBool doneN = abs(lumaEndN) >= gradientScaled;\\\\\\\",\\\\\\\"    FxaaBool doneP = abs(lumaEndP) >= gradientScaled;\\\\\\\",\\\\\\\"    if(!doneN) posN.x -= offNP.x * FXAA_QUALITY_P1;\\\\\\\",\\\\\\\"    if(!doneN) posN.y -= offNP.y * FXAA_QUALITY_P1;\\\\\\\",\\\\\\\"    FxaaBool doneNP = (!doneN) || (!doneP);\\\\\\\",\\\\\\\"    if(!doneP) posP.x += offNP.x * FXAA_QUALITY_P1;\\\\\\\",\\\\\\\"    if(!doneP) posP.y += offNP.y * FXAA_QUALITY_P1;\\\\\\\",\\\\\\\"/*--------------------------------------------------------------------------*/\\\\\\\",\\\\\\\"    if(doneNP) {\\\\\\\",\\\\\\\"        if(!doneN) lumaEndN = FxaaLuma(FxaaTexTop(tex, posN.xy));\\\\\\\",\\\\\\\"        if(!doneP) lumaEndP = FxaaLuma(FxaaTexTop(tex, posP.xy));\\\\\\\",\\\\\\\"        if(!doneN) lumaEndN = lumaEndN - lumaNN * 0.5;\\\\\\\",\\\\\\\"        if(!doneP) lumaEndP = lumaEndP - lumaNN * 0.5;\\\\\\\",\\\\\\\"        doneN = abs(lumaEndN) >= gradientScaled;\\\\\\\",\\\\\\\"        doneP = abs(lumaEndP) >= gradientScaled;\\\\\\\",\\\\\\\"        if(!doneN) posN.x -= offNP.x * FXAA_QUALITY_P2;\\\\\\\",\\\\\\\"        if(!doneN) posN.y -= offNP.y * FXAA_QUALITY_P2;\\\\\\\",\\\\\\\"        doneNP = (!doneN) || (!doneP);\\\\\\\",\\\\\\\"        if(!doneP) posP.x += offNP.x * FXAA_QUALITY_P2;\\\\\\\",\\\\\\\"        if(!doneP) posP.y += offNP.y * FXAA_QUALITY_P2;\\\\\\\",\\\\\\\"/*--------------------------------------------------------------------------*/\\\\\\\",\\\\\\\"        #if (FXAA_QUALITY_PS > 3)\\\\\\\",\\\\\\\"        if(doneNP) {\\\\\\\",\\\\\\\"            if(!doneN) lumaEndN = FxaaLuma(FxaaTexTop(tex, posN.xy));\\\\\\\",\\\\\\\"            if(!doneP) lumaEndP = FxaaLuma(FxaaTexTop(tex, posP.xy));\\\\\\\",\\\\\\\"            if(!doneN) lumaEndN = lumaEndN - lumaNN * 0.5;\\\\\\\",\\\\\\\"            if(!doneP) lumaEndP = lumaEndP - lumaNN * 0.5;\\\\\\\",\\\\\\\"            doneN = abs(lumaEndN) >= gradientScaled;\\\\\\\",\\\\\\\"            doneP = abs(lumaEndP) >= gradientScaled;\\\\\\\",\\\\\\\"            if(!doneN) posN.x -= offNP.x * FXAA_QUALITY_P3;\\\\\\\",\\\\\\\"            if(!doneN) posN.y -= offNP.y * FXAA_QUALITY_P3;\\\\\\\",\\\\\\\"            doneNP = (!doneN) || (!doneP);\\\\\\\",\\\\\\\"            if(!doneP) posP.x += offNP.x * FXAA_QUALITY_P3;\\\\\\\",\\\\\\\"            if(!doneP) posP.y += offNP.y * FXAA_QUALITY_P3;\\\\\\\",\\\\\\\"/*--------------------------------------------------------------------------*/\\\\\\\",\\\\\\\"            #if (FXAA_QUALITY_PS > 4)\\\\\\\",\\\\\\\"            if(doneNP) {\\\\\\\",\\\\\\\"                if(!doneN) lumaEndN = FxaaLuma(FxaaTexTop(tex, posN.xy));\\\\\\\",\\\\\\\"                if(!doneP) lumaEndP = FxaaLuma(FxaaTexTop(tex, posP.xy));\\\\\\\",\\\\\\\"                if(!doneN) lumaEndN = lumaEndN - lumaNN * 0.5;\\\\\\\",\\\\\\\"                if(!doneP) lumaEndP = lumaEndP - lumaNN * 0.5;\\\\\\\",\\\\\\\"                doneN = abs(lumaEndN) >= gradientScaled;\\\\\\\",\\\\\\\"                doneP = abs(lumaEndP) >= gradientScaled;\\\\\\\",\\\\\\\"                if(!doneN) posN.x -= offNP.x * FXAA_QUALITY_P4;\\\\\\\",\\\\\\\"                if(!doneN) posN.y -= offNP.y * FXAA_QUALITY_P4;\\\\\\\",\\\\\\\"                doneNP = (!doneN) || (!doneP);\\\\\\\",\\\\\\\"                if(!doneP) posP.x += offNP.x * FXAA_QUALITY_P4;\\\\\\\",\\\\\\\"                if(!doneP) posP.y += offNP.y * FXAA_QUALITY_P4;\\\\\\\",\\\\\\\"/*--------------------------------------------------------------------------*/\\\\\\\",\\\\\\\"                #if (FXAA_QUALITY_PS > 5)\\\\\\\",\\\\\\\"                if(doneNP) {\\\\\\\",\\\\\\\"                    if(!doneN) lumaEndN = FxaaLuma(FxaaTexTop(tex, posN.xy));\\\\\\\",\\\\\\\"                    if(!doneP) lumaEndP = FxaaLuma(FxaaTexTop(tex, posP.xy));\\\\\\\",\\\\\\\"                    if(!doneN) lumaEndN = lumaEndN - lumaNN * 0.5;\\\\\\\",\\\\\\\"                    if(!doneP) lumaEndP = lumaEndP - lumaNN * 0.5;\\\\\\\",\\\\\\\"                    doneN = abs(lumaEndN) >= gradientScaled;\\\\\\\",\\\\\\\"                    doneP = abs(lumaEndP) >= gradientScaled;\\\\\\\",\\\\\\\"                    if(!doneN) posN.x -= offNP.x * FXAA_QUALITY_P5;\\\\\\\",\\\\\\\"                    if(!doneN) posN.y -= offNP.y * FXAA_QUALITY_P5;\\\\\\\",\\\\\\\"                    doneNP = (!doneN) || (!doneP);\\\\\\\",\\\\\\\"                    if(!doneP) posP.x += offNP.x * FXAA_QUALITY_P5;\\\\\\\",\\\\\\\"                    if(!doneP) posP.y += offNP.y * FXAA_QUALITY_P5;\\\\\\\",\\\\\\\"/*--------------------------------------------------------------------------*/\\\\\\\",\\\\\\\"                    #if (FXAA_QUALITY_PS > 6)\\\\\\\",\\\\\\\"                    if(doneNP) {\\\\\\\",\\\\\\\"                        if(!doneN) lumaEndN = FxaaLuma(FxaaTexTop(tex, posN.xy));\\\\\\\",\\\\\\\"                        if(!doneP) lumaEndP = FxaaLuma(FxaaTexTop(tex, posP.xy));\\\\\\\",\\\\\\\"                        if(!doneN) lumaEndN = lumaEndN - lumaNN * 0.5;\\\\\\\",\\\\\\\"                        if(!doneP) lumaEndP = lumaEndP - lumaNN * 0.5;\\\\\\\",\\\\\\\"                        doneN = abs(lumaEndN) >= gradientScaled;\\\\\\\",\\\\\\\"                        doneP = abs(lumaEndP) >= gradientScaled;\\\\\\\",\\\\\\\"                        if(!doneN) posN.x -= offNP.x * FXAA_QUALITY_P6;\\\\\\\",\\\\\\\"                        if(!doneN) posN.y -= offNP.y * FXAA_QUALITY_P6;\\\\\\\",\\\\\\\"                        doneNP = (!doneN) || (!doneP);\\\\\\\",\\\\\\\"                        if(!doneP) posP.x += offNP.x * FXAA_QUALITY_P6;\\\\\\\",\\\\\\\"                        if(!doneP) posP.y += offNP.y * FXAA_QUALITY_P6;\\\\\\\",\\\\\\\"/*--------------------------------------------------------------------------*/\\\\\\\",\\\\\\\"                        #if (FXAA_QUALITY_PS > 7)\\\\\\\",\\\\\\\"                        if(doneNP) {\\\\\\\",\\\\\\\"                            if(!doneN) lumaEndN = FxaaLuma(FxaaTexTop(tex, posN.xy));\\\\\\\",\\\\\\\"                            if(!doneP) lumaEndP = FxaaLuma(FxaaTexTop(tex, posP.xy));\\\\\\\",\\\\\\\"                            if(!doneN) lumaEndN = lumaEndN - lumaNN * 0.5;\\\\\\\",\\\\\\\"                            if(!doneP) lumaEndP = lumaEndP - lumaNN * 0.5;\\\\\\\",\\\\\\\"                            doneN = abs(lumaEndN) >= gradientScaled;\\\\\\\",\\\\\\\"                            doneP = abs(lumaEndP) >= gradientScaled;\\\\\\\",\\\\\\\"                            if(!doneN) posN.x -= offNP.x * FXAA_QUALITY_P7;\\\\\\\",\\\\\\\"                            if(!doneN) posN.y -= offNP.y * FXAA_QUALITY_P7;\\\\\\\",\\\\\\\"                            doneNP = (!doneN) || (!doneP);\\\\\\\",\\\\\\\"                            if(!doneP) posP.x += offNP.x * FXAA_QUALITY_P7;\\\\\\\",\\\\\\\"                            if(!doneP) posP.y += offNP.y * FXAA_QUALITY_P7;\\\\\\\",\\\\\\\"/*--------------------------------------------------------------------------*/\\\\\\\",\\\\\\\"    #if (FXAA_QUALITY_PS > 8)\\\\\\\",\\\\\\\"    if(doneNP) {\\\\\\\",\\\\\\\"        if(!doneN) lumaEndN = FxaaLuma(FxaaTexTop(tex, posN.xy));\\\\\\\",\\\\\\\"        if(!doneP) lumaEndP = FxaaLuma(FxaaTexTop(tex, posP.xy));\\\\\\\",\\\\\\\"        if(!doneN) lumaEndN = lumaEndN - lumaNN * 0.5;\\\\\\\",\\\\\\\"        if(!doneP) lumaEndP = lumaEndP - lumaNN * 0.5;\\\\\\\",\\\\\\\"        doneN = abs(lumaEndN) >= gradientScaled;\\\\\\\",\\\\\\\"        doneP = abs(lumaEndP) >= gradientScaled;\\\\\\\",\\\\\\\"        if(!doneN) posN.x -= offNP.x * FXAA_QUALITY_P8;\\\\\\\",\\\\\\\"        if(!doneN) posN.y -= offNP.y * FXAA_QUALITY_P8;\\\\\\\",\\\\\\\"        doneNP = (!doneN) || (!doneP);\\\\\\\",\\\\\\\"        if(!doneP) posP.x += offNP.x * FXAA_QUALITY_P8;\\\\\\\",\\\\\\\"        if(!doneP) posP.y += offNP.y * FXAA_QUALITY_P8;\\\\\\\",\\\\\\\"/*--------------------------------------------------------------------------*/\\\\\\\",\\\\\\\"        #if (FXAA_QUALITY_PS > 9)\\\\\\\",\\\\\\\"        if(doneNP) {\\\\\\\",\\\\\\\"            if(!doneN) lumaEndN = FxaaLuma(FxaaTexTop(tex, posN.xy));\\\\\\\",\\\\\\\"            if(!doneP) lumaEndP = FxaaLuma(FxaaTexTop(tex, posP.xy));\\\\\\\",\\\\\\\"            if(!doneN) lumaEndN = lumaEndN - lumaNN * 0.5;\\\\\\\",\\\\\\\"            if(!doneP) lumaEndP = lumaEndP - lumaNN * 0.5;\\\\\\\",\\\\\\\"            doneN = abs(lumaEndN) >= gradientScaled;\\\\\\\",\\\\\\\"            doneP = abs(lumaEndP) >= gradientScaled;\\\\\\\",\\\\\\\"            if(!doneN) posN.x -= offNP.x * FXAA_QUALITY_P9;\\\\\\\",\\\\\\\"            if(!doneN) posN.y -= offNP.y * FXAA_QUALITY_P9;\\\\\\\",\\\\\\\"            doneNP = (!doneN) || (!doneP);\\\\\\\",\\\\\\\"            if(!doneP) posP.x += offNP.x * FXAA_QUALITY_P9;\\\\\\\",\\\\\\\"            if(!doneP) posP.y += offNP.y * FXAA_QUALITY_P9;\\\\\\\",\\\\\\\"/*--------------------------------------------------------------------------*/\\\\\\\",\\\\\\\"            #if (FXAA_QUALITY_PS > 10)\\\\\\\",\\\\\\\"            if(doneNP) {\\\\\\\",\\\\\\\"                if(!doneN) lumaEndN = FxaaLuma(FxaaTexTop(tex, posN.xy));\\\\\\\",\\\\\\\"                if(!doneP) lumaEndP = FxaaLuma(FxaaTexTop(tex, posP.xy));\\\\\\\",\\\\\\\"                if(!doneN) lumaEndN = lumaEndN - lumaNN * 0.5;\\\\\\\",\\\\\\\"                if(!doneP) lumaEndP = lumaEndP - lumaNN * 0.5;\\\\\\\",\\\\\\\"                doneN = abs(lumaEndN) >= gradientScaled;\\\\\\\",\\\\\\\"                doneP = abs(lumaEndP) >= gradientScaled;\\\\\\\",\\\\\\\"                if(!doneN) posN.x -= offNP.x * FXAA_QUALITY_P10;\\\\\\\",\\\\\\\"                if(!doneN) posN.y -= offNP.y * FXAA_QUALITY_P10;\\\\\\\",\\\\\\\"                doneNP = (!doneN) || (!doneP);\\\\\\\",\\\\\\\"                if(!doneP) posP.x += offNP.x * FXAA_QUALITY_P10;\\\\\\\",\\\\\\\"                if(!doneP) posP.y += offNP.y * FXAA_QUALITY_P10;\\\\\\\",\\\\\\\"/*--------------------------------------------------------------------------*/\\\\\\\",\\\\\\\"                #if (FXAA_QUALITY_PS > 11)\\\\\\\",\\\\\\\"                if(doneNP) {\\\\\\\",\\\\\\\"                    if(!doneN) lumaEndN = FxaaLuma(FxaaTexTop(tex, posN.xy));\\\\\\\",\\\\\\\"                    if(!doneP) lumaEndP = FxaaLuma(FxaaTexTop(tex, posP.xy));\\\\\\\",\\\\\\\"                    if(!doneN) lumaEndN = lumaEndN - lumaNN * 0.5;\\\\\\\",\\\\\\\"                    if(!doneP) lumaEndP = lumaEndP - lumaNN * 0.5;\\\\\\\",\\\\\\\"                    doneN = abs(lumaEndN) >= gradientScaled;\\\\\\\",\\\\\\\"                    doneP = abs(lumaEndP) >= gradientScaled;\\\\\\\",\\\\\\\"                    if(!doneN) posN.x -= offNP.x * FXAA_QUALITY_P11;\\\\\\\",\\\\\\\"                    if(!doneN) posN.y -= offNP.y * FXAA_QUALITY_P11;\\\\\\\",\\\\\\\"                    doneNP = (!doneN) || (!doneP);\\\\\\\",\\\\\\\"                    if(!doneP) posP.x += offNP.x * FXAA_QUALITY_P11;\\\\\\\",\\\\\\\"                    if(!doneP) posP.y += offNP.y * FXAA_QUALITY_P11;\\\\\\\",\\\\\\\"/*--------------------------------------------------------------------------*/\\\\\\\",\\\\\\\"                    #if (FXAA_QUALITY_PS > 12)\\\\\\\",\\\\\\\"                    if(doneNP) {\\\\\\\",\\\\\\\"                        if(!doneN) lumaEndN = FxaaLuma(FxaaTexTop(tex, posN.xy));\\\\\\\",\\\\\\\"                        if(!doneP) lumaEndP = FxaaLuma(FxaaTexTop(tex, posP.xy));\\\\\\\",\\\\\\\"                        if(!doneN) lumaEndN = lumaEndN - lumaNN * 0.5;\\\\\\\",\\\\\\\"                        if(!doneP) lumaEndP = lumaEndP - lumaNN * 0.5;\\\\\\\",\\\\\\\"                        doneN = abs(lumaEndN) >= gradientScaled;\\\\\\\",\\\\\\\"                        doneP = abs(lumaEndP) >= gradientScaled;\\\\\\\",\\\\\\\"                        if(!doneN) posN.x -= offNP.x * FXAA_QUALITY_P12;\\\\\\\",\\\\\\\"                        if(!doneN) posN.y -= offNP.y * FXAA_QUALITY_P12;\\\\\\\",\\\\\\\"                        doneNP = (!doneN) || (!doneP);\\\\\\\",\\\\\\\"                        if(!doneP) posP.x += offNP.x * FXAA_QUALITY_P12;\\\\\\\",\\\\\\\"                        if(!doneP) posP.y += offNP.y * FXAA_QUALITY_P12;\\\\\\\",\\\\\\\"/*--------------------------------------------------------------------------*/\\\\\\\",\\\\\\\"                    }\\\\\\\",\\\\\\\"                    #endif\\\\\\\",\\\\\\\"/*--------------------------------------------------------------------------*/\\\\\\\",\\\\\\\"                }\\\\\\\",\\\\\\\"                #endif\\\\\\\",\\\\\\\"/*--------------------------------------------------------------------------*/\\\\\\\",\\\\\\\"            }\\\\\\\",\\\\\\\"            #endif\\\\\\\",\\\\\\\"/*--------------------------------------------------------------------------*/\\\\\\\",\\\\\\\"        }\\\\\\\",\\\\\\\"        #endif\\\\\\\",\\\\\\\"/*--------------------------------------------------------------------------*/\\\\\\\",\\\\\\\"    }\\\\\\\",\\\\\\\"    #endif\\\\\\\",\\\\\\\"/*--------------------------------------------------------------------------*/\\\\\\\",\\\\\\\"                        }\\\\\\\",\\\\\\\"                        #endif\\\\\\\",\\\\\\\"/*--------------------------------------------------------------------------*/\\\\\\\",\\\\\\\"                    }\\\\\\\",\\\\\\\"                    #endif\\\\\\\",\\\\\\\"/*--------------------------------------------------------------------------*/\\\\\\\",\\\\\\\"                }\\\\\\\",\\\\\\\"                #endif\\\\\\\",\\\\\\\"/*--------------------------------------------------------------------------*/\\\\\\\",\\\\\\\"            }\\\\\\\",\\\\\\\"            #endif\\\\\\\",\\\\\\\"/*--------------------------------------------------------------------------*/\\\\\\\",\\\\\\\"        }\\\\\\\",\\\\\\\"        #endif\\\\\\\",\\\\\\\"/*--------------------------------------------------------------------------*/\\\\\\\",\\\\\\\"    }\\\\\\\",\\\\\\\"/*--------------------------------------------------------------------------*/\\\\\\\",\\\\\\\"    FxaaFloat dstN = posM.x - posN.x;\\\\\\\",\\\\\\\"    FxaaFloat dstP = posP.x - posM.x;\\\\\\\",\\\\\\\"    if(!horzSpan) dstN = posM.y - posN.y;\\\\\\\",\\\\\\\"    if(!horzSpan) dstP = posP.y - posM.y;\\\\\\\",\\\\\\\"/*--------------------------------------------------------------------------*/\\\\\\\",\\\\\\\"    FxaaBool goodSpanN = (lumaEndN < 0.0) != lumaMLTZero;\\\\\\\",\\\\\\\"    FxaaFloat spanLength = (dstP + dstN);\\\\\\\",\\\\\\\"    FxaaBool goodSpanP = (lumaEndP < 0.0) != lumaMLTZero;\\\\\\\",\\\\\\\"    FxaaFloat spanLengthRcp = 1.0/spanLength;\\\\\\\",\\\\\\\"/*--------------------------------------------------------------------------*/\\\\\\\",\\\\\\\"    FxaaBool directionN = dstN < dstP;\\\\\\\",\\\\\\\"    FxaaFloat dst = min(dstN, dstP);\\\\\\\",\\\\\\\"    FxaaBool goodSpan = directionN ? goodSpanN : goodSpanP;\\\\\\\",\\\\\\\"    FxaaFloat subpixG = subpixF * subpixF;\\\\\\\",\\\\\\\"    FxaaFloat pixelOffset = (dst * (-spanLengthRcp)) + 0.5;\\\\\\\",\\\\\\\"    FxaaFloat subpixH = subpixG * fxaaQualitySubpix;\\\\\\\",\\\\\\\"/*--------------------------------------------------------------------------*/\\\\\\\",\\\\\\\"    FxaaFloat pixelOffsetGood = goodSpan ? pixelOffset : 0.0;\\\\\\\",\\\\\\\"    FxaaFloat pixelOffsetSubpix = max(pixelOffsetGood, subpixH);\\\\\\\",\\\\\\\"    if(!horzSpan) posM.x += pixelOffsetSubpix * lengthSign;\\\\\\\",\\\\\\\"    if( horzSpan) posM.y += pixelOffsetSubpix * lengthSign;\\\\\\\",\\\\\\\"    #if (FXAA_DISCARD == 1)\\\\\\\",\\\\\\\"        return FxaaTexTop(tex, posM);\\\\\\\",\\\\\\\"    #else\\\\\\\",\\\\\\\"        return FxaaFloat4(FxaaTexTop(tex, posM).xyz, lumaM);\\\\\\\",\\\\\\\"    #endif\\\\\\\",\\\\\\\"}\\\\\\\",\\\\\\\"/*==========================================================================*/\\\\\\\",\\\\\\\"#endif\\\\\\\",\\\\\\\"\\\\\\\",\\\\\\\"void main() {\\\\\\\",\\\\\\\"  gl_FragColor = FxaaPixelShader(\\\\\\\",\\\\\\\"    vUv,\\\\\\\",\\\\\\\"    vec4(0.0),\\\\\\\",\\\\\\\"    tDiffuse,\\\\\\\",\\\\\\\"    tDiffuse,\\\\\\\",\\\\\\\"    tDiffuse,\\\\\\\",\\\\\\\"    resolution,\\\\\\\",\\\\\\\"    vec4(0.0),\\\\\\\",\\\\\\\"    vec4(0.0),\\\\\\\",\\\\\\\"    vec4(0.0),\\\\\\\",\\\\\\\"    0.75,\\\\\\\",\\\\\\\"    0.166,\\\\\\\",\\\\\\\"    0.0833,\\\\\\\",\\\\\\\"    0.0,\\\\\\\",\\\\\\\"    0.0,\\\\\\\",\\\\\\\"    0.0,\\\\\\\",\\\\\\\"    vec4(0.0)\\\\\\\",\\\\\\\"  );\\\\\\\",\\\\\\\"\\\\\\\",\\\\\\\"  // TODO avoid querying texture twice for same texel\\\\\\\",\\\\\\\"  gl_FragColor.a = texture2D(tDiffuse, vUv).a;\\\\\\\",\\\\\\\"}\\\\\\\"].join(\\\\\\\"\\\\n\\\\\\\")};const WR=new class extends Lo{constructor(){super(...arguments),this.transparent=Oo.BOOLEAN(1,rR)}};class XR extends oR{constructor(){super(...arguments),this.paramsConfig=WR}static type(){return\\\\\\\"FXAA\\\\\\\"}_createPass(e){const t=new bm(qR);return t.uniforms.resolution.value.set(1/e.resolution.x,1/e.resolution.y),t.material.transparent=!0,this.updatePass(t),t}updatePass(e){e.material.transparent=this.pv.transparent}}var YR={uniforms:{tDiffuse:{value:null}},vertexShader:[\\\\\\\"varying vec2 vUv;\\\\\\\",\\\\\\\"void main() {\\\\\\\",\\\\\\\"\\\\tvUv = uv;\\\\\\\",\\\\\\\"\\\\tgl_Position = projectionMatrix * modelViewMatrix * vec4( position, 1.0 );\\\\\\\",\\\\\\\"}\\\\\\\"].join(\\\\\\\"\\\\n\\\\\\\"),fragmentShader:[\\\\\\\"uniform sampler2D tDiffuse;\\\\\\\",\\\\\\\"varying vec2 vUv;\\\\\\\",\\\\\\\"void main() {\\\\\\\",\\\\\\\"\\\\tvec4 tex = texture2D( tDiffuse, vUv );\\\\\\\",\\\\\\\"\\\\tgl_FragColor = LinearTosRGB( tex );\\\\\\\",\\\\\\\"}\\\\\\\"].join(\\\\\\\"\\\\n\\\\\\\")};const $R=new class extends Lo{};class QR extends oR{constructor(){super(...arguments),this.paramsConfig=$R}static type(){return\\\\\\\"gammaCorrection\\\\\\\"}_createPass(e){const t=new bm(YR);return this.updatePass(t),t}updatePass(e){}}const JR=new class extends Lo{constructor(){super(...arguments),this.amount=Oo.FLOAT(2,{range:[0,10],rangeLocked:[!0,!1],step:.01,...rR}),this.transparent=Oo.BOOLEAN(1,rR)}};class KR extends oR{constructor(){super(...arguments),this.paramsConfig=JR}static type(){return\\\\\\\"horizontalBlur\\\\\\\"}_createPass(e){const t=new bm(pO);return t.resolution_x=e.resolution.x,this.updatePass(t),t}updatePass(e){e.uniforms.h.value=this.pv.amount/(e.resolution_x*window.devicePixelRatio),e.material.transparent=this.pv.transparent}}const ZR=new class extends Lo{constructor(){super(...arguments),this.map=Oo.OPERATOR_PATH(jn.UV,{nodeSelection:{context:Ei.COP},...rR}),this.darkness=Oo.FLOAT(0,{range:[0,2],rangeLocked:[!0,!1],...rR}),this.offset=Oo.FLOAT(0,{range:[0,2],rangeLocked:[!0,!1],...rR})}};class eI extends oR{constructor(){super(...arguments),this.paramsConfig=ZR}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(e){const t=new bm(eI._create_shader());return this.updatePass(t),t}updatePass(e){e.uniforms.darkness.value=this.pv.darkness,e.uniforms.offset.value=this.pv.offset,this._update_map(e)}async _update_map(e){this.p.map.isDirty()&&await this.p.map.compute();const t=this.p.map.found_node();if(t)if(t.context()==Ei.COP){const n=t,i=(await n.compute()).coreContent();e.uniforms.map.value=i}else this.states.error.set(\\\\\\\"node is not COP\\\\\\\");else this.states.error.set(\\\\\\\"no map found\\\\\\\")}}const tI={tDiffuse:{value:null},texture1:{value:null},texture2:{value:null},h:{value:1/512}},nI=\\\\\\\"varying vec2 vUv;\\\\n\\\\nvoid main() {\\\\n\\\\n\\\\tvUv = uv;\\\\n\\\\tgl_Position = projectionMatrix * modelViewMatrix * vec4( position, 1.0 );\\\\n\\\\n}\\\\\\\",iI=\\\\\\\"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 sI extends xm{constructor(e,t){super(),this._composer1=e,this._composer2=t,this.uniforms=k.clone(tI),this.material=new B({uniforms:this.uniforms,vertexShader:nI,fragmentShader:iI,transparent:!0}),this.fsQuad=new xm.FullScreenQuad(this.material)}render(e,t){this._composer1.render(),this._composer2.render(),this.uniforms.texture1.value=this._composer1.readBuffer.texture,this.uniforms.texture2.value=this._composer2.readBuffer.texture,this.renderToScreen?(e.setRenderTarget(null),this.fsQuad.render(e)):(e.setRenderTarget(t),this.clear&&e.clear(e.autoClearColor,e.autoClearDepth,e.autoClearStencil),this.fsQuad.render(e))}}const rI=new class extends Lo{};class oI extends oR{constructor(){super(...arguments),this.paramsConfig=rI}static type(){return\\\\\\\"layer\\\\\\\"}initializeNode(){super.initializeNode(),this.io.inputs.setCount(2)}setupComposer(e){const t=e.composer.renderer,n={minFilter:w.V,magFilter:w.V,format:w.Ib,stencilBuffer:!0},i=Rn.renderersController.renderTarget(t.domElement.offsetWidth,t.domElement.offsetHeight,n),s=Rn.renderersController.renderTarget(t.domElement.offsetWidth,t.domElement.offsetHeight,n),r=new Tm(t,i),o=new Tm(t,s);r.renderToScreen=!1,o.renderToScreen=!1;const a={...e},c={...e};a.composer=r,c.composer=o,this._addPassFromInput(0,a),this._addPassFromInput(1,c);const l=new sI(r,o);this.updatePass(l),e.composer.addPass(l)}updatePass(e){}}const aI=new class extends Lo{constructor(){super(...arguments),this.overrideScene=Oo.BOOLEAN(0,rR),this.scene=Oo.OPERATOR_PATH(\\\\\\\"/scene1\\\\\\\",{visibleIf:{overrideScene:1},nodeSelection:{context:Ei.OBJ,types:[OL.type()]},...rR}),this.overrideCamera=Oo.BOOLEAN(0,rR),this.camera=Oo.OPERATOR_PATH(\\\\\\\"/perspective_camera1\\\\\\\",{visibleIf:{overrideCamera:1},nodeSelection:{context:Ei.OBJ},...rR}),this.inverse=Oo.BOOLEAN(0,rR)}};class cI extends oR{constructor(){super(...arguments),this.paramsConfig=aI}static type(){return\\\\\\\"mask\\\\\\\"}_createPass(e){const t=new wm(e.scene,e.camera);return t.context={scene:e.scene,camera:e.camera},this.updatePass(t),t}updatePass(e){e.inverse=this.pv.inverse,this._update_scene(e),this._updateCamera(e)}async _update_scene(e){if(this.pv.overrideScene){this.p.scene.isDirty()&&await this.p.scene.compute();const t=this.p.scene.found_node_with_expected_type();if(t)return void(e.scene=t.object)}e.scene=e.context.scene}async _updateCamera(e){if(this.pv.overrideCamera){this.p.camera.isDirty()&&await this.p.camera.compute();const t=this.p.camera.found_node_with_expected_type();if(t)return void(e.camera=t.object)}e.camera=e.context.camera}}const lI=new class extends Lo{};class uI extends oR{constructor(){super(...arguments),this.paramsConfig=lI}static type(){return\\\\\\\"null\\\\\\\"}}var hI=function(e,t,n,i){this.renderScene=t,this.renderCamera=n,this.selectedObjects=void 0!==i?i:[],this.visibleEdgeColor=new M.a(1,1,1),this.hiddenEdgeColor=new M.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,xm.call(this),this.resolution=void 0!==e?new d.a(e.x,e.y):new d.a(256,256);var s={minFilter:w.V,magFilter:w.V,format:w.Ib},r=Math.round(this.resolution.x/this.downSampleRatio),o=Math.round(this.resolution.y/this.downSampleRatio);this.maskBufferMaterial=new Nf.a({color:16777215}),this.maskBufferMaterial.side=w.z,this.renderTargetMaskBuffer=new Z(this.resolution.x,this.resolution.y,s),this.renderTargetMaskBuffer.texture.name=\\\\\\\"OutlinePass.mask\\\\\\\",this.renderTargetMaskBuffer.texture.generateMipmaps=!1,this.depthMaterial=new Kt,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(e,t){var n=t.isPerspectiveCamera?\\\\\\\"perspective\\\\\\\":\\\\\\\"orthographic\\\\\\\";return e.replace(/DEPTH_TO_VIEW_Z/g,n+\\\\\\\"DepthToViewZ\\\\\\\")}(this.prepareMaskMaterial.fragmentShader,this.renderCamera),this.renderTargetDepthBuffer=new Z(this.resolution.x,this.resolution.y,s),this.renderTargetDepthBuffer.texture.name=\\\\\\\"OutlinePass.depth\\\\\\\",this.renderTargetDepthBuffer.texture.generateMipmaps=!1,this.renderTargetMaskDownSampleBuffer=new Z(r,o,s),this.renderTargetMaskDownSampleBuffer.texture.name=\\\\\\\"OutlinePass.depthDownSample\\\\\\\",this.renderTargetMaskDownSampleBuffer.texture.generateMipmaps=!1,this.renderTargetBlurBuffer1=new Z(r,o,s),this.renderTargetBlurBuffer1.texture.name=\\\\\\\"OutlinePass.blur1\\\\\\\",this.renderTargetBlurBuffer1.texture.generateMipmaps=!1,this.renderTargetBlurBuffer2=new Z(Math.round(r/2),Math.round(o/2),s),this.renderTargetBlurBuffer2.texture.name=\\\\\\\"OutlinePass.blur2\\\\\\\",this.renderTargetBlurBuffer2.texture.generateMipmaps=!1,this.edgeDetectionMaterial=this.getEdgeDetectionMaterial(),this.renderTargetEdgeBuffer1=new Z(r,o,s),this.renderTargetEdgeBuffer1.texture.name=\\\\\\\"OutlinePass.edge1\\\\\\\",this.renderTargetEdgeBuffer1.texture.generateMipmaps=!1,this.renderTargetEdgeBuffer2=new Z(Math.round(r/2),Math.round(o/2),s),this.renderTargetEdgeBuffer2.texture.name=\\\\\\\"OutlinePass.edge2\\\\\\\",this.renderTargetEdgeBuffer2.texture.generateMipmaps=!1;this.separableBlurMaterial1=this.getSeperableBlurMaterial(4),this.separableBlurMaterial1.uniforms.texSize.value.set(r,o),this.separableBlurMaterial1.uniforms.kernelRadius.value=1,this.separableBlurMaterial2=this.getSeperableBlurMaterial(4),this.separableBlurMaterial2.uniforms.texSize.value.set(Math.round(r/2),Math.round(o/2)),this.separableBlurMaterial2.uniforms.kernelRadius.value=4,this.overlayMaterial=this.getOverlayMaterial(),void 0===ym&&console.error(\\\\\\\"THREE.OutlinePass relies on CopyShader\\\\\\\");var a=ym;this.copyUniforms=k.clone(a.uniforms),this.copyUniforms.opacity.value=1,this.materialCopy=new B({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 M.a,this.oldClearAlpha=1,this.fsQuad=new xm.FullScreenQuad(null),this.tempPulseColor1=new M.a,this.tempPulseColor2=new M.a,this.textureMatrix=new C.a};hI.prototype=Object.assign(Object.create(xm.prototype),{constructor:hI,dispose:function(){this.renderTargetMaskBuffer.dispose(),this.renderTargetDepthBuffer.dispose(),this.renderTargetMaskDownSampleBuffer.dispose(),this.renderTargetBlurBuffer1.dispose(),this.renderTargetBlurBuffer2.dispose(),this.renderTargetEdgeBuffer1.dispose(),this.renderTargetEdgeBuffer2.dispose()},setSize:function(e,t){this.renderTargetMaskBuffer.setSize(e,t),this.renderTargetDepthBuffer.setSize(e,t);var n=Math.round(e/this.downSampleRatio),i=Math.round(t/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:function(e){var t=this._visibilityCache;function n(n){n.isMesh&&(!0===e?n.visible=t.get(n):(t.set(n,n.visible),n.visible=e))}for(var i=0;i<this.selectedObjects.length;i++){this.selectedObjects[i].traverse(n)}},changeVisibilityOfNonSelectedObjects:function(e){var t=this._visibilityCache,n=[];function i(e){e.isMesh&&n.push(e)}for(var s=0;s<this.selectedObjects.length;s++){this.selectedObjects[s].traverse(i)}this.renderScene.traverse((function(i){if(i.isMesh||i.isSprite){for(var s=!1,r=0;r<n.length;r++){if(n[r].id===i.id){s=!0;break}}if(!1===s){var o=i.visible;!1!==e&&!0!==t.get(i)||(i.visible=e),t.set(i,o)}}else(i.isPoints||i.isLine)&&(!0===e?i.visible=t.get(i):(t.set(i,i.visible),i.visible=e))}))},updateTextureMatrix:function(){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:function(e,t,n,i,s){if(this.selectedObjects.length>0){e.getClearColor(this._oldClearColor),this.oldClearAlpha=e.getClearAlpha();var r=e.autoClear;e.autoClear=!1,s&&e.state.buffers.stencil.setTest(!1),e.setClearColor(16777215,1),this.changeVisibilityOfSelectedObjects(!1);var o=this.renderScene.background;if(this.renderScene.background=null,this.renderScene.overrideMaterial=this.depthMaterial,e.setRenderTarget(this.renderTargetDepthBuffer),e.clear(),e.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,e.setRenderTarget(this.renderTargetMaskBuffer),e.clear(),e.render(this.renderScene,this.renderCamera),this.renderScene.overrideMaterial=null,this.changeVisibilityOfNonSelectedObjects(!0),this._visibilityCache.clear(),this.renderScene.background=o,this.fsQuad.material=this.materialCopy,this.copyUniforms.tDiffuse.value=this.renderTargetMaskBuffer.texture,e.setRenderTarget(this.renderTargetMaskDownSampleBuffer),e.clear(),this.fsQuad.render(e),this.tempPulseColor1.copy(this.visibleEdgeColor),this.tempPulseColor2.copy(this.hiddenEdgeColor),this.pulsePeriod>0){var a=.625+.75*Math.cos(.01*performance.now()/this.pulsePeriod)/2;this.tempPulseColor1.multiplyScalar(a),this.tempPulseColor2.multiplyScalar(a)}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,e.setRenderTarget(this.renderTargetEdgeBuffer1),e.clear(),this.fsQuad.render(e),this.fsQuad.material=this.separableBlurMaterial1,this.separableBlurMaterial1.uniforms.colorTexture.value=this.renderTargetEdgeBuffer1.texture,this.separableBlurMaterial1.uniforms.direction.value=hI.BlurDirectionX,this.separableBlurMaterial1.uniforms.kernelRadius.value=this.edgeThickness,e.setRenderTarget(this.renderTargetBlurBuffer1),e.clear(),this.fsQuad.render(e),this.separableBlurMaterial1.uniforms.colorTexture.value=this.renderTargetBlurBuffer1.texture,this.separableBlurMaterial1.uniforms.direction.value=hI.BlurDirectionY,e.setRenderTarget(this.renderTargetEdgeBuffer1),e.clear(),this.fsQuad.render(e),this.fsQuad.material=this.separableBlurMaterial2,this.separableBlurMaterial2.uniforms.colorTexture.value=this.renderTargetEdgeBuffer1.texture,this.separableBlurMaterial2.uniforms.direction.value=hI.BlurDirectionX,e.setRenderTarget(this.renderTargetBlurBuffer2),e.clear(),this.fsQuad.render(e),this.separableBlurMaterial2.uniforms.colorTexture.value=this.renderTargetBlurBuffer2.texture,this.separableBlurMaterial2.uniforms.direction.value=hI.BlurDirectionY,e.setRenderTarget(this.renderTargetEdgeBuffer2),e.clear(),this.fsQuad.render(e),this.fsQuad.material=this.overlayMaterial,this.overlayMaterial.uniforms.maskTexture.value=this.renderTargetMaskBuffer.texture,this.overlayMaterial.uniforms.edgeTexture1.value=this.renderTargetEdgeBuffer1.texture,this.overlayMaterial.uniforms.edgeTexture2.value=this.renderTargetEdgeBuffer2.texture,this.overlayMaterial.uniforms.patternTexture.value=this.patternTexture,this.overlayMaterial.uniforms.edgeStrength.value=this.edgeStrength,this.overlayMaterial.uniforms.edgeGlow.value=this.edgeGlow,this.overlayMaterial.uniforms.usePatternTexture.value=this.usePatternTexture,s&&e.state.buffers.stencil.setTest(!0),e.setRenderTarget(n),this.fsQuad.render(e),e.setClearColor(this._oldClearColor,this.oldClearAlpha),e.autoClear=r}this.renderToScreen&&(this.fsQuad.material=this.materialCopy,this.copyUniforms.tDiffuse.value=n.texture,e.setRenderTarget(null),this.fsQuad.render(e))},getPrepareMaskMaterial:function(){return new B({uniforms:{depthTexture:{value:null},cameraNearFar:{value:new d.a(.5,.5)},textureMatrix:{value:null}},vertexShader:[\\\\\\\"#include <morphtarget_pars_vertex>\\\\\\\",\\\\\\\"#include <skinning_pars_vertex>\\\\\\\",\\\\\\\"varying vec4 projTexCoord;\\\\\\\",\\\\\\\"varying vec4 vPosition;\\\\\\\",\\\\\\\"uniform mat4 textureMatrix;\\\\\\\",\\\\\\\"void main() {\\\\\\\",\\\\\\\"\\\\t#include <skinbase_vertex>\\\\\\\",\\\\\\\"\\\\t#include <begin_vertex>\\\\\\\",\\\\\\\"\\\\t#include <morphtarget_vertex>\\\\\\\",\\\\\\\"\\\\t#include <skinning_vertex>\\\\\\\",\\\\\\\"\\\\t#include <project_vertex>\\\\\\\",\\\\\\\"\\\\tvPosition = mvPosition;\\\\\\\",\\\\\\\"\\\\tvec4 worldPosition = modelMatrix * vec4( position, 1.0 );\\\\\\\",\\\\\\\"\\\\tprojTexCoord = textureMatrix * worldPosition;\\\\\\\",\\\\\\\"}\\\\\\\"].join(\\\\\\\"\\\\n\\\\\\\"),fragmentShader:[\\\\\\\"#include <packing>\\\\\\\",\\\\\\\"varying vec4 vPosition;\\\\\\\",\\\\\\\"varying vec4 projTexCoord;\\\\\\\",\\\\\\\"uniform sampler2D depthTexture;\\\\\\\",\\\\\\\"uniform vec2 cameraNearFar;\\\\\\\",\\\\\\\"void main() {\\\\\\\",\\\\\\\"\\\\tfloat depth = unpackRGBAToDepth(texture2DProj( depthTexture, projTexCoord ));\\\\\\\",\\\\\\\"\\\\tfloat viewZ = - DEPTH_TO_VIEW_Z( depth, cameraNearFar.x, cameraNearFar.y );\\\\\\\",\\\\\\\"\\\\tfloat depthTest = (-vPosition.z > viewZ) ? 1.0 : 0.0;\\\\\\\",\\\\\\\"\\\\tgl_FragColor = vec4(0.0, depthTest, 1.0, 1.0);\\\\\\\",\\\\\\\"}\\\\\\\"].join(\\\\\\\"\\\\n\\\\\\\")})},getEdgeDetectionMaterial:function(){return new B({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\\\\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;\\\\t\\\\t\\\\t\\\\tuniform sampler2D maskTexture;\\\\t\\\\t\\\\t\\\\tuniform vec2 texSize;\\\\t\\\\t\\\\t\\\\tuniform vec3 visibleEdgeColor;\\\\t\\\\t\\\\t\\\\tuniform vec3 hiddenEdgeColor;\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tvoid main() {\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tvec2 invSize = 1.0 / texSize;\\\\t\\\\t\\\\t\\\\t\\\\tvec4 uvOffset = vec4(1.0, 0.0, 0.0, 1.0) * vec4(invSize, invSize);\\\\t\\\\t\\\\t\\\\t\\\\tvec4 c1 = texture2D( maskTexture, vUv + uvOffset.xy);\\\\t\\\\t\\\\t\\\\t\\\\tvec4 c2 = texture2D( maskTexture, vUv - uvOffset.xy);\\\\t\\\\t\\\\t\\\\t\\\\tvec4 c3 = texture2D( maskTexture, vUv + uvOffset.yw);\\\\t\\\\t\\\\t\\\\t\\\\tvec4 c4 = texture2D( maskTexture, vUv - uvOffset.yw);\\\\t\\\\t\\\\t\\\\t\\\\tfloat diff1 = (c1.r - c2.r)*0.5;\\\\t\\\\t\\\\t\\\\t\\\\tfloat diff2 = (c3.r - c4.r)*0.5;\\\\t\\\\t\\\\t\\\\t\\\\tfloat d = length( vec2(diff1, diff2) );\\\\t\\\\t\\\\t\\\\t\\\\tfloat a1 = min(c1.g, c2.g);\\\\t\\\\t\\\\t\\\\t\\\\tfloat a2 = min(c3.g, c4.g);\\\\t\\\\t\\\\t\\\\t\\\\tfloat visibilityFactor = min(a1, a2);\\\\t\\\\t\\\\t\\\\t\\\\tvec3 edgeColor = 1.0 - visibilityFactor > 0.001 ? visibleEdgeColor : hiddenEdgeColor;\\\\t\\\\t\\\\t\\\\t\\\\tgl_FragColor = vec4(edgeColor, 1.0) * vec4(d);\\\\t\\\\t\\\\t\\\\t}\\\\\\\"})},getSeperableBlurMaterial:function(e){return new B({defines:{MAX_RADIUS:e},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\\\\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>\\\\t\\\\t\\\\t\\\\tvarying vec2 vUv;\\\\t\\\\t\\\\t\\\\tuniform sampler2D colorTexture;\\\\t\\\\t\\\\t\\\\tuniform vec2 texSize;\\\\t\\\\t\\\\t\\\\tuniform vec2 direction;\\\\t\\\\t\\\\t\\\\tuniform float kernelRadius;\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tfloat gaussianPdf(in float x, in float sigma) {\\\\t\\\\t\\\\t\\\\t\\\\treturn 0.39894 * exp( -0.5 * x * x/( sigma * sigma))/sigma;\\\\t\\\\t\\\\t\\\\t}\\\\t\\\\t\\\\t\\\\tvoid main() {\\\\t\\\\t\\\\t\\\\t\\\\tvec2 invSize = 1.0 / texSize;\\\\t\\\\t\\\\t\\\\t\\\\tfloat weightSum = gaussianPdf(0.0, kernelRadius);\\\\t\\\\t\\\\t\\\\t\\\\tvec4 diffuseSum = texture2D( colorTexture, vUv) * weightSum;\\\\t\\\\t\\\\t\\\\t\\\\tvec2 delta = direction * invSize * kernelRadius/float(MAX_RADIUS);\\\\t\\\\t\\\\t\\\\t\\\\tvec2 uvOffset = delta;\\\\t\\\\t\\\\t\\\\t\\\\tfor( int i = 1; i <= MAX_RADIUS; i ++ ) {\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tfloat w = gaussianPdf(uvOffset.x, kernelRadius);\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tvec4 sample1 = texture2D( colorTexture, vUv + uvOffset);\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tvec4 sample2 = texture2D( colorTexture, vUv - uvOffset);\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tdiffuseSum += ((sample1 + sample2) * w);\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tweightSum += (2.0 * w);\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tuvOffset += delta;\\\\t\\\\t\\\\t\\\\t\\\\t}\\\\t\\\\t\\\\t\\\\t\\\\tgl_FragColor = diffuseSum/weightSum;\\\\t\\\\t\\\\t\\\\t}\\\\\\\"})},getOverlayMaterial:function(){return new B({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\\\\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;\\\\t\\\\t\\\\t\\\\tuniform sampler2D maskTexture;\\\\t\\\\t\\\\t\\\\tuniform sampler2D edgeTexture1;\\\\t\\\\t\\\\t\\\\tuniform sampler2D edgeTexture2;\\\\t\\\\t\\\\t\\\\tuniform sampler2D patternTexture;\\\\t\\\\t\\\\t\\\\tuniform float edgeStrength;\\\\t\\\\t\\\\t\\\\tuniform float edgeGlow;\\\\t\\\\t\\\\t\\\\tuniform bool usePatternTexture;\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tvoid main() {\\\\t\\\\t\\\\t\\\\t\\\\tvec4 edgeValue1 = texture2D(edgeTexture1, vUv);\\\\t\\\\t\\\\t\\\\t\\\\tvec4 edgeValue2 = texture2D(edgeTexture2, vUv);\\\\t\\\\t\\\\t\\\\t\\\\tvec4 maskColor = texture2D(maskTexture, vUv);\\\\t\\\\t\\\\t\\\\t\\\\tvec4 patternColor = texture2D(patternTexture, 6.0 * vUv);\\\\t\\\\t\\\\t\\\\t\\\\tfloat visibilityFactor = 1.0 - maskColor.g > 0.0 ? 1.0 : 0.5;\\\\t\\\\t\\\\t\\\\t\\\\tvec4 edgeValue = edgeValue1 + edgeValue2 * edgeGlow;\\\\t\\\\t\\\\t\\\\t\\\\tvec4 finalColor = edgeStrength * maskColor.r * edgeValue;\\\\t\\\\t\\\\t\\\\t\\\\tif(usePatternTexture)\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tfinalColor += + visibilityFactor * (1.0 - maskColor.r) * (1.0 - patternColor.r);\\\\t\\\\t\\\\t\\\\t\\\\tgl_FragColor = finalColor;\\\\t\\\\t\\\\t\\\\t}\\\\\\\",blending:w.e,depthTest:!1,depthWrite:!1,transparent:!0})}}),hI.BlurDirectionX=new d.a(1,0),hI.BlurDirectionY=new d.a(0,1);const dI=new class extends Lo{constructor(){super(...arguments),this.objectsMask=Oo.STRING(\\\\\\\"*outlined*\\\\\\\",{...rR}),this.refreshObjects=Oo.BUTTON(null,{...rR}),this.edgeStrength=Oo.FLOAT(3,{range:[0,10],rangeLocked:[!0,!1],...rR}),this.edgeThickness=Oo.FLOAT(0,{range:[0,4],rangeLocked:[!0,!1],...rR}),this.edgeGlow=Oo.FLOAT(0,{range:[0,1],rangeLocked:[!0,!1],...rR}),this.pulsePeriod=Oo.FLOAT(0,{range:[0,5],rangeLocked:[!0,!1],...rR}),this.visibleEdgeColor=Oo.COLOR([1,1,1],{...rR}),this.hiddenEdgeColor=Oo.COLOR([0,0,0],{...rR})}};class pI extends oR{constructor(){super(...arguments),this.paramsConfig=dI}static type(){return\\\\\\\"outline\\\\\\\"}_createPass(e){const t=new hI(new d.a(e.resolution.x,e.resolution.y),e.scene,e.camera,e.scene.children);return this.updatePass(t),t}updatePass(e){e.edgeStrength=this.pv.edgeStrength,e.edgeThickness=this.pv.edgeThickness,e.edgeGlow=this.pv.edgeGlow,e.pulsePeriod=this.pv.pulsePeriod,e.visibleEdgeColor=this.pv.visibleEdgeColor,e.hiddenEdgeColor=this.pv.hiddenEdgeColor,this._set_selected_objects(e)}_set_selected_objects(e){e.selectedObjects=this.scene().objectsByMask(this.pv.objectsMask)}}var _I={uniforms:{tDiffuse:{value:null},resolution:{value:null},pixelSize:{value:1}},vertexShader:[\\\\\\\"varying highp vec2 vUv;\\\\\\\",\\\\\\\"void main() {\\\\\\\",\\\\\\\"vUv = uv;\\\\\\\",\\\\\\\"gl_Position = projectionMatrix * modelViewMatrix * vec4( position, 1.0 );\\\\\\\",\\\\\\\"}\\\\\\\"].join(\\\\\\\"\\\\n\\\\\\\"),fragmentShader:[\\\\\\\"uniform sampler2D tDiffuse;\\\\\\\",\\\\\\\"uniform float pixelSize;\\\\\\\",\\\\\\\"uniform vec2 resolution;\\\\\\\",\\\\\\\"varying highp vec2 vUv;\\\\\\\",\\\\\\\"void main(){\\\\\\\",\\\\\\\"vec2 dxy = pixelSize / resolution;\\\\\\\",\\\\\\\"vec2 coord = dxy * floor( vUv / dxy );\\\\\\\",\\\\\\\"gl_FragColor = texture2D(tDiffuse, coord);\\\\\\\",\\\\\\\"}\\\\\\\"].join(\\\\\\\"\\\\n\\\\\\\")};const mI=new class extends Lo{constructor(){super(...arguments),this.pixelSize=Oo.INTEGER(16,{range:[1,50],rangeLocked:[!0,!1],...rR})}};class fI extends oR{constructor(){super(...arguments),this.paramsConfig=mI}static type(){return\\\\\\\"pixel\\\\\\\"}_createPass(e){const t=new bm(_I);return t.uniforms.resolution.value=e.resolution,t.uniforms.resolution.value.multiplyScalar(window.devicePixelRatio),this.updatePass(t),t}updatePass(e){e.uniforms.pixelSize.value=this.pv.pixelSize}}const gI=new class extends Lo{constructor(){super(...arguments),this.overrideScene=Oo.BOOLEAN(0,rR),this.scene=Oo.OPERATOR_PATH(\\\\\\\"/scene1\\\\\\\",{visibleIf:{overrideScene:1},nodeSelection:{context:Ei.OBJ,types:[OL.type()]},...rR}),this.overrideCamera=Oo.BOOLEAN(0,rR),this.camera=Oo.OPERATOR_PATH(\\\\\\\"/perspective_camera1\\\\\\\",{visibleIf:{overrideCamera:1},nodeSelection:{context:Ei.OBJ},...rR})}};class vI extends oR{constructor(){super(...arguments),this.paramsConfig=gI}static type(){return\\\\\\\"render\\\\\\\"}_createPass(e){const t=new Cm(e.scene,e.camera);return t.context={camera:e.camera,scene:e.scene},this.updatePass(t),t}updatePass(e){this._updateCamera(e),this._update_scene(e)}async _updateCamera(e){if(this.pv.overrideCamera){this.p.camera.isDirty()&&await this.p.camera.compute();const t=this.p.camera.found_node_with_context(Ei.OBJ);if(t&&(t.type()==Ni.PERSPECTIVE||t.type()==Ni.ORTHOGRAPHIC)){const n=t.object;e.camera=n}}else e.camera=e.context.camera}async _update_scene(e){if(this.pv.overrideScene){this.p.camera.isDirty()&&await this.p.scene.compute();const t=this.p.scene.found_node_with_context(Ei.OBJ);if(t&&t.type()==OL.type()){const n=t.object;e.scene=n}}else e.scene=e.context.scene}}var yI={uniforms:{tDiffuse:{value:null},amount:{value:.005},angle:{value:0}},vertexShader:[\\\\\\\"varying vec2 vUv;\\\\\\\",\\\\\\\"void main() {\\\\\\\",\\\\\\\"\\\\tvUv = uv;\\\\\\\",\\\\\\\"\\\\tgl_Position = projectionMatrix * modelViewMatrix * vec4( position, 1.0 );\\\\\\\",\\\\\\\"}\\\\\\\"].join(\\\\\\\"\\\\n\\\\\\\"),fragmentShader:[\\\\\\\"uniform sampler2D tDiffuse;\\\\\\\",\\\\\\\"uniform float amount;\\\\\\\",\\\\\\\"uniform float angle;\\\\\\\",\\\\\\\"varying vec2 vUv;\\\\\\\",\\\\\\\"void main() {\\\\\\\",\\\\\\\"\\\\tvec2 offset = amount * vec2( cos(angle), sin(angle));\\\\\\\",\\\\\\\"\\\\tvec4 cr = texture2D(tDiffuse, vUv + offset);\\\\\\\",\\\\\\\"\\\\tvec4 cga = texture2D(tDiffuse, vUv);\\\\\\\",\\\\\\\"\\\\tvec4 cb = texture2D(tDiffuse, vUv - offset);\\\\\\\",\\\\\\\"\\\\tgl_FragColor = vec4(cr.r, cga.g, cb.b, cga.a);\\\\\\\",\\\\\\\"}\\\\\\\"].join(\\\\\\\"\\\\n\\\\\\\")};const xI=new class extends Lo{constructor(){super(...arguments),this.amount=Oo.FLOAT(.005,{range:[0,1],rangeLocked:[!0,!1],...rR}),this.angle=Oo.FLOAT(0,{range:[0,10],rangeLocked:[!0,!1],...rR})}};class bI extends oR{constructor(){super(...arguments),this.paramsConfig=xI}static type(){return\\\\\\\"RGBShift\\\\\\\"}_createPass(e){const t=new bm(yI);return this.updatePass(t),t}updatePass(e){e.uniforms.amount.value=this.pv.amount,e.uniforms.angle.value=this.pv.angle}}var wI={uniforms:{tDiffuse:{value:null},amount:{value:1}},vertexShader:[\\\\\\\"varying vec2 vUv;\\\\\\\",\\\\\\\"void main() {\\\\\\\",\\\\\\\"\\\\tvUv = uv;\\\\\\\",\\\\\\\"\\\\tgl_Position = projectionMatrix * modelViewMatrix * vec4( position, 1.0 );\\\\\\\",\\\\\\\"}\\\\\\\"].join(\\\\\\\"\\\\n\\\\\\\"),fragmentShader:[\\\\\\\"uniform float amount;\\\\\\\",\\\\\\\"uniform sampler2D tDiffuse;\\\\\\\",\\\\\\\"varying vec2 vUv;\\\\\\\",\\\\\\\"void main() {\\\\\\\",\\\\\\\"\\\\tvec4 color = texture2D( tDiffuse, vUv );\\\\\\\",\\\\\\\"\\\\tvec3 c = color.rgb;\\\\\\\",\\\\\\\"\\\\tcolor.r = dot( c, vec3( 1.0 - 0.607 * amount, 0.769 * amount, 0.189 * amount ) );\\\\\\\",\\\\\\\"\\\\tcolor.g = dot( c, vec3( 0.349 * amount, 1.0 - 0.314 * amount, 0.168 * amount ) );\\\\\\\",\\\\\\\"\\\\tcolor.b = dot( c, vec3( 0.272 * amount, 0.534 * amount, 1.0 - 0.869 * amount ) );\\\\\\\",\\\\\\\"\\\\tgl_FragColor = vec4( min( vec3( 1.0 ), color.rgb ), color.a );\\\\\\\",\\\\\\\"}\\\\\\\"].join(\\\\\\\"\\\\n\\\\\\\")};const AI=new class extends Lo{constructor(){super(...arguments),this.amount=Oo.FLOAT(.5,{range:[0,2],rangeLocked:[!1,!1],...rR})}};class TI extends oR{constructor(){super(...arguments),this.paramsConfig=AI}static type(){return\\\\\\\"sepia\\\\\\\"}_createPass(e){const t=new bm(wI);return this.updatePass(t),t}updatePass(e){e.uniforms.amount.value=this.pv.amount}}const EI=new class extends Lo{};class CI extends oR{constructor(){super(...arguments),this.paramsConfig=EI}static type(){return\\\\\\\"sequence\\\\\\\"}initializeNode(){super.initializeNode(),this.io.inputs.setCount(0,4)}setupComposer(e){this._addPassFromInput(0,e),this._addPassFromInput(1,e),this._addPassFromInput(2,e),this._addPassFromInput(3,e)}}const MI=k.clone(pO.uniforms);MI.delta={value:new d.a};const NI={uniforms:MI,vertexShader:pO.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 SI=new class extends Lo{constructor(){super(...arguments),this.delta=Oo.VECTOR2([2,2],{...rR})}};class OI extends oR{constructor(){super(...arguments),this.paramsConfig=SI}static type(){return\\\\\\\"triangleBlur\\\\\\\"}_createPass(e){const t=new bm(NI);return t.resolution=e.resolution.clone(),this.updatePass(t),t}updatePass(e){e.uniforms.delta.value.copy(this.pv.delta).divide(e.resolution).multiplyScalar(window.devicePixelRatio)}}var LI={shaderID:\\\\\\\"luminosityHighPass\\\\\\\",uniforms:{tDiffuse:{value:null},luminosityThreshold:{value:1},smoothWidth:{value:1},defaultColor:{value:new M.a(0)},defaultOpacity:{value:0}},vertexShader:[\\\\\\\"varying vec2 vUv;\\\\\\\",\\\\\\\"void main() {\\\\\\\",\\\\\\\"\\\\tvUv = uv;\\\\\\\",\\\\\\\"\\\\tgl_Position = projectionMatrix * modelViewMatrix * vec4( position, 1.0 );\\\\\\\",\\\\\\\"}\\\\\\\"].join(\\\\\\\"\\\\n\\\\\\\"),fragmentShader:[\\\\\\\"uniform sampler2D tDiffuse;\\\\\\\",\\\\\\\"uniform vec3 defaultColor;\\\\\\\",\\\\\\\"uniform float defaultOpacity;\\\\\\\",\\\\\\\"uniform float luminosityThreshold;\\\\\\\",\\\\\\\"uniform float smoothWidth;\\\\\\\",\\\\\\\"varying vec2 vUv;\\\\\\\",\\\\\\\"void main() {\\\\\\\",\\\\\\\"\\\\tvec4 texel = texture2D( tDiffuse, vUv );\\\\\\\",\\\\\\\"\\\\tvec3 luma = vec3( 0.299, 0.587, 0.114 );\\\\\\\",\\\\\\\"\\\\tfloat v = dot( texel.xyz, luma );\\\\\\\",\\\\\\\"\\\\tvec4 outputColor = vec4( defaultColor.rgb, defaultOpacity );\\\\\\\",\\\\\\\"\\\\tfloat alpha = smoothstep( luminosityThreshold, luminosityThreshold + smoothWidth, v );\\\\\\\",\\\\\\\"\\\\tgl_FragColor = mix( outputColor, texel, alpha );\\\\\\\",\\\\\\\"}\\\\\\\"].join(\\\\\\\"\\\\n\\\\\\\")},PI=function(e,t,n,i){xm.call(this),this.strength=void 0!==t?t:1,this.radius=n,this.threshold=i,this.resolution=void 0!==e?new d.a(e.x,e.y):new d.a(256,256),this.clearColor=new M.a(0,0,0);var s={minFilter:w.V,magFilter:w.V,format:w.Ib};this.renderTargetsHorizontal=[],this.renderTargetsVertical=[],this.nMips=5;var r=Math.round(this.resolution.x/2),o=Math.round(this.resolution.y/2);this.renderTargetBright=new Z(r,o,s),this.renderTargetBright.texture.name=\\\\\\\"UnrealBloomPass.bright\\\\\\\",this.renderTargetBright.texture.generateMipmaps=!1;for(var a=0;a<this.nMips;a++){var c=new Z(r,o,s);c.texture.name=\\\\\\\"UnrealBloomPass.h\\\\\\\"+a,c.texture.generateMipmaps=!1,this.renderTargetsHorizontal.push(c);var l=new Z(r,o,s);l.texture.name=\\\\\\\"UnrealBloomPass.v\\\\\\\"+a,l.texture.generateMipmaps=!1,this.renderTargetsVertical.push(l),r=Math.round(r/2),o=Math.round(o/2)}void 0===LI&&console.error(\\\\\\\"THREE.UnrealBloomPass relies on LuminosityHighPassShader\\\\\\\");var u=LI;this.highPassUniforms=k.clone(u.uniforms),this.highPassUniforms.luminosityThreshold.value=i,this.highPassUniforms.smoothWidth.value=.01,this.materialHighPassFilter=new B({uniforms:this.highPassUniforms,vertexShader:u.vertexShader,fragmentShader:u.fragmentShader,defines:{}}),this.separableBlurMaterials=[];var h=[3,5,7,9,11];for(r=Math.round(this.resolution.x/2),o=Math.round(this.resolution.y/2),a=0;a<this.nMips;a++)this.separableBlurMaterials.push(this.getSeperableBlurMaterial(h[a])),this.separableBlurMaterials[a].uniforms.texSize.value=new d.a(r,o),r=Math.round(r/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=t,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===ym&&console.error(\\\\\\\"THREE.UnrealBloomPass relies on CopyShader\\\\\\\");var _=ym;this.copyUniforms=k.clone(_.uniforms),this.copyUniforms.opacity.value=1,this.materialCopy=new B({uniforms:this.copyUniforms,vertexShader:_.vertexShader,fragmentShader:_.fragmentShader,blending:w.e,depthTest:!1,depthWrite:!1,transparent:!0}),this.enabled=!0,this.needsSwap=!1,this._oldClearColor=new M.a,this.oldClearAlpha=1,this.basic=new Nf.a,this.fsQuad=new xm.FullScreenQuad(null)};PI.prototype=Object.assign(Object.create(xm.prototype),{constructor:PI,dispose:function(){for(var e=0;e<this.renderTargetsHorizontal.length;e++)this.renderTargetsHorizontal[e].dispose();for(e=0;e<this.renderTargetsVertical.length;e++)this.renderTargetsVertical[e].dispose();this.renderTargetBright.dispose()},setSize:function(e,t){var n=Math.round(e/2),i=Math.round(t/2);this.renderTargetBright.setSize(n,i);for(var s=0;s<this.nMips;s++)this.renderTargetsHorizontal[s].setSize(n,i),this.renderTargetsVertical[s].setSize(n,i),this.separableBlurMaterials[s].uniforms.texSize.value=new d.a(n,i),n=Math.round(n/2),i=Math.round(i/2)},render:function(e,t,n,i,s){e.getClearColor(this._oldClearColor),this.oldClearAlpha=e.getClearAlpha();var r=e.autoClear;e.autoClear=!1,e.setClearColor(this.clearColor,0),s&&e.state.buffers.stencil.setTest(!1),this.renderToScreen&&(this.fsQuad.material=this.basic,this.basic.map=n.texture,e.setRenderTarget(null),e.clear(),this.fsQuad.render(e)),this.highPassUniforms.tDiffuse.value=n.texture,this.highPassUniforms.luminosityThreshold.value=this.threshold,this.fsQuad.material=this.materialHighPassFilter,e.setRenderTarget(this.renderTargetBright),e.clear(),this.fsQuad.render(e);for(var o=this.renderTargetBright,a=0;a<this.nMips;a++)this.fsQuad.material=this.separableBlurMaterials[a],this.separableBlurMaterials[a].uniforms.colorTexture.value=o.texture,this.separableBlurMaterials[a].uniforms.direction.value=PI.BlurDirectionX,e.setRenderTarget(this.renderTargetsHorizontal[a]),e.clear(),this.fsQuad.render(e),this.separableBlurMaterials[a].uniforms.colorTexture.value=this.renderTargetsHorizontal[a].texture,this.separableBlurMaterials[a].uniforms.direction.value=PI.BlurDirectionY,e.setRenderTarget(this.renderTargetsVertical[a]),e.clear(),this.fsQuad.render(e),o=this.renderTargetsVertical[a];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,e.setRenderTarget(this.renderTargetsHorizontal[0]),e.clear(),this.fsQuad.render(e),this.fsQuad.material=this.materialCopy,this.copyUniforms.tDiffuse.value=this.renderTargetsHorizontal[0].texture,s&&e.state.buffers.stencil.setTest(!0),this.renderToScreen?(e.setRenderTarget(null),this.fsQuad.render(e)):(e.setRenderTarget(n),this.fsQuad.render(e)),e.setClearColor(this._oldClearColor,this.oldClearAlpha),e.autoClear=r},getSeperableBlurMaterial:function(e){return new B({defines:{KERNEL_RADIUS:e,SIGMA:e},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>\\\\t\\\\t\\\\t\\\\tvarying vec2 vUv;\\\\n\\\\t\\\\t\\\\t\\\\tuniform sampler2D colorTexture;\\\\n\\\\t\\\\t\\\\t\\\\tuniform vec2 texSize;\\\\t\\\\t\\\\t\\\\tuniform vec2 direction;\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tfloat gaussianPdf(in float x, in float sigma) {\\\\t\\\\t\\\\t\\\\t\\\\treturn 0.39894 * exp( -0.5 * x * x/( sigma * sigma))/sigma;\\\\t\\\\t\\\\t\\\\t}\\\\t\\\\t\\\\t\\\\tvoid main() {\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tvec2 invSize = 1.0 / texSize;\\\\t\\\\t\\\\t\\\\t\\\\tfloat fSigma = float(SIGMA);\\\\t\\\\t\\\\t\\\\t\\\\tfloat weightSum = gaussianPdf(0.0, fSigma);\\\\t\\\\t\\\\t\\\\t\\\\tvec3 diffuseSum = texture2D( colorTexture, vUv).rgb * weightSum;\\\\t\\\\t\\\\t\\\\t\\\\tfor( int i = 1; i < KERNEL_RADIUS; i ++ ) {\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tfloat x = float(i);\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tfloat w = gaussianPdf(x, fSigma);\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tvec2 uvOffset = direction * invSize * x;\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tvec3 sample1 = texture2D( colorTexture, vUv + uvOffset).rgb;\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tvec3 sample2 = texture2D( colorTexture, vUv - uvOffset).rgb;\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tdiffuseSum += (sample1 + sample2) * w;\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tweightSum += 2.0 * w;\\\\t\\\\t\\\\t\\\\t\\\\t}\\\\t\\\\t\\\\t\\\\t\\\\tgl_FragColor = vec4(diffuseSum/weightSum, 1.0);\\\\n\\\\t\\\\t\\\\t\\\\t}\\\\\\\"})},getCompositeMaterial:function(e){return new B({defines:{NUM_MIPS:e},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;\\\\t\\\\t\\\\t\\\\tuniform sampler2D blurTexture1;\\\\t\\\\t\\\\t\\\\tuniform sampler2D blurTexture2;\\\\t\\\\t\\\\t\\\\tuniform sampler2D blurTexture3;\\\\t\\\\t\\\\t\\\\tuniform sampler2D blurTexture4;\\\\t\\\\t\\\\t\\\\tuniform sampler2D blurTexture5;\\\\t\\\\t\\\\t\\\\tuniform sampler2D dirtTexture;\\\\t\\\\t\\\\t\\\\tuniform float bloomStrength;\\\\t\\\\t\\\\t\\\\tuniform float bloomRadius;\\\\t\\\\t\\\\t\\\\tuniform float bloomFactors[NUM_MIPS];\\\\t\\\\t\\\\t\\\\tuniform vec3 bloomTintColors[NUM_MIPS];\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tfloat lerpBloomFactor(const in float factor) { \\\\t\\\\t\\\\t\\\\t\\\\tfloat mirrorFactor = 1.2 - factor;\\\\t\\\\t\\\\t\\\\t\\\\treturn mix(factor, mirrorFactor, bloomRadius);\\\\t\\\\t\\\\t\\\\t}\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tvoid main() {\\\\t\\\\t\\\\t\\\\t\\\\tgl_FragColor = bloomStrength * ( lerpBloomFactor(bloomFactors[0]) * vec4(bloomTintColors[0], 1.0) * texture2D(blurTexture1, vUv) + \\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t lerpBloomFactor(bloomFactors[1]) * vec4(bloomTintColors[1], 1.0) * texture2D(blurTexture2, vUv) + \\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t lerpBloomFactor(bloomFactors[2]) * vec4(bloomTintColors[2], 1.0) * texture2D(blurTexture3, vUv) + \\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t lerpBloomFactor(bloomFactors[3]) * vec4(bloomTintColors[3], 1.0) * texture2D(blurTexture4, vUv) + \\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t lerpBloomFactor(bloomFactors[4]) * vec4(bloomTintColors[4], 1.0) * texture2D(blurTexture5, vUv) );\\\\t\\\\t\\\\t\\\\t}\\\\\\\"})}}),PI.BlurDirectionX=new d.a(1,0),PI.BlurDirectionY=new d.a(0,1);const RI=new class extends Lo{constructor(){super(...arguments),this.strength=Oo.FLOAT(1.5,{range:[0,3],rangeLocked:[!0,!1],...rR}),this.radius=Oo.FLOAT(1,{...rR}),this.threshold=Oo.FLOAT(0,{...rR})}};class II extends oR{constructor(){super(...arguments),this.paramsConfig=RI}static type(){return\\\\\\\"unrealBloom\\\\\\\"}_createPass(e){return new PI(new d.a(e.resolution.x,e.resolution.y),this.pv.strength,this.pv.radius,this.pv.threshold)}updatePass(e){e.strength=this.pv.strength,e.radius=this.pv.radius,e.threshold=this.pv.threshold}}const FI=new class extends Lo{constructor(){super(...arguments),this.amount=Oo.FLOAT(2,{range:[0,10],rangeLocked:[!0,!1],step:.01,...rR}),this.transparent=Oo.BOOLEAN(1,rR)}};class DI extends oR{constructor(){super(...arguments),this.paramsConfig=FI}static type(){return\\\\\\\"verticalBlur\\\\\\\"}_createPass(e){const t=new bm(_O);return t.resolution_y=e.resolution.y,this.updatePass(t),t}updatePass(e){e.uniforms.v.value=this.pv.amount/(e.resolution_y*window.devicePixelRatio),e.material.transparent=this.pv.transparent}}var kI={uniforms:{tDiffuse:{value:null},offset:{value:1},darkness:{value:1}},vertexShader:[\\\\\\\"varying vec2 vUv;\\\\\\\",\\\\\\\"void main() {\\\\\\\",\\\\\\\"\\\\tvUv = uv;\\\\\\\",\\\\\\\"\\\\tgl_Position = projectionMatrix * modelViewMatrix * vec4( position, 1.0 );\\\\\\\",\\\\\\\"}\\\\\\\"].join(\\\\\\\"\\\\n\\\\\\\"),fragmentShader:[\\\\\\\"uniform float offset;\\\\\\\",\\\\\\\"uniform float darkness;\\\\\\\",\\\\\\\"uniform sampler2D tDiffuse;\\\\\\\",\\\\\\\"varying vec2 vUv;\\\\\\\",\\\\\\\"void main() {\\\\\\\",\\\\\\\"\\\\tvec4 texel = texture2D( tDiffuse, vUv );\\\\\\\",\\\\\\\"\\\\tvec2 uv = ( vUv - vec2( 0.5 ) ) * vec2( offset );\\\\\\\",\\\\\\\"\\\\tgl_FragColor = vec4( mix( texel.rgb, vec3( 1.0 - darkness ), dot( uv, uv ) ), texel.a );\\\\\\\",\\\\\\\"}\\\\\\\"].join(\\\\\\\"\\\\n\\\\\\\")};const BI=new class extends Lo{constructor(){super(...arguments),this.offset=Oo.FLOAT(1,{range:[0,1],rangeLocked:[!1,!1],...rR}),this.darkness=Oo.FLOAT(1,{range:[0,2],rangeLocked:[!0,!1],...rR})}};class zI extends oR{constructor(){super(...arguments),this.paramsConfig=BI}static type(){return\\\\\\\"vignette\\\\\\\"}_createPass(e){const t=new bm(kI);return this.updatePass(t),t}updatePass(e){e.uniforms.offset.value=this.pv.offset,e.uniforms.darkness.value=this.pv.darkness}}class UI extends Mo{static context(){return Ei.POST}cook(){this.cookController.endCook()}}class GI extends UI{}class VI extends GI{constructor(){super(...arguments),this._children_controller_context=Ei.ANIM}static type(){return Ci.ANIM}createNode(e,t){return super.createNode(e,t)}children(){return super.children()}nodesByType(e){return super.nodesByType(e)}}class jI extends GI{constructor(){super(...arguments),this._children_controller_context=Ei.COP}static type(){return Ci.COP}createNode(e,t){return super.createNode(e,t)}children(){return super.children()}nodesByType(e){return super.nodesByType(e)}}class HI extends GI{constructor(){super(...arguments),this._children_controller_context=Ei.EVENT}static type(){return Ci.EVENT}createNode(e,t){return super.createNode(e,t)}children(){return super.children()}nodesByType(e){return super.nodesByType(e)}}class qI extends GI{constructor(){super(...arguments),this._children_controller_context=Ei.MAT}static type(){return Ci.MAT}createNode(e,t){return super.createNode(e,t)}children(){return super.children()}nodesByType(e){return super.nodesByType(e)}}class WI extends UI{constructor(){super(...arguments),this.paramsConfig=new Rm,this.effectsComposerController=new Im(this),this.displayNodeController=new mm(this,this.effectsComposerController.displayNodeControllerCallbacks()),this._children_controller_context=Ei.POST}static type(){return Ci.POST}createNode(e,t){return super.createNode(e,t)}children(){return super.children()}nodesByType(e){return super.nodesByType(e)}}class XI extends GI{constructor(){super(...arguments),this._children_controller_context=Ei.ROP}static type(){return Ci.ROP}createNode(e,t){return super.createNode(e,t)}children(){return super.children()}nodesByType(e){return super.nodesByType(e)}}class YI extends Mo{static context(){return Ei.ROP}cook(){this.cookController.endCook()}}class $I extends YI{}class QI extends $I{constructor(){super(...arguments),this._children_controller_context=Ei.COP}static type(){return Ci.COP}createNode(e,t){return super.createNode(e,t)}children(){return super.children()}nodesByType(e){return super.nodesByType(e)}}class JI extends ee.a{constructor(e){super(),this._element=e,this._element.style.position=\\\\\\\"absolute\\\\\\\",this.addEventListener(\\\\\\\"removed\\\\\\\",this._on_removed.bind(this))}_on_removed(){this.traverse((function(e){e instanceof JI&&e.element instanceof Element&&null!==e.element.parentNode&&e.element.parentNode.removeChild(e.element)}))}get element(){return this._element}copy(e,t){return ee.a.prototype.copy.call(this,e,t),this._element=e.element.cloneNode(!0),this.matrixAutoUpdate=e.matrixAutoUpdate,this}}class KI{constructor(){this._width=0,this._height=0,this._widthHalf=0,this._heightHalf=0,this.vector=new p.a,this.viewMatrix=new C.a,this.viewProjectionMatrix=new C.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(e,t){this._width=e,this._height=t,this._widthHalf=this._width/2,this._heightHalf=this._height/2,this.domElement.style.width=e+\\\\\\\"px\\\\\\\",this.domElement.style.height=t+\\\\\\\"px\\\\\\\"}renderObject(e,t,n){if(e instanceof JI){this.vector.setFromMatrixPosition(e.matrixWorld),this.vector.applyMatrix4(this.viewProjectionMatrix);var i=e.element,s=\\\\\\\"translate(-50%,-50%) translate(\\\\\\\"+(this.vector.x*this._widthHalf+this._widthHalf)+\\\\\\\"px,\\\\\\\"+(-this.vector.y*this._heightHalf+this._heightHalf)+\\\\\\\"px)\\\\\\\";if(i.style.webkitTransform=s,i.style.transform=s,i.style.display=e.visible&&this.vector.z>=-1&&this.vector.z<=1?\\\\\\\"\\\\\\\":\\\\\\\"none\\\\\\\",this._sort_objects||this._use_fog){const t=this.getDistanceToSquared(n,e);if(this._use_fog){const e=Math.sqrt(t),n=Os.fit(e,this._fog_near,this._fog_far,0,1),s=Os.clamp(1-n,0,1);i.style.opacity=`${s}`,0==s&&(i.style.display=\\\\\\\"none\\\\\\\")}this.cache_distanceToCameraSquared.set(e,t)}i.parentNode!==this.domElement&&this.domElement.appendChild(i)}for(var r=0,o=e.children.length;r<o;r++)this.renderObject(e.children[r],t,n)}getDistanceToSquared(e,t){return this.a.setFromMatrixPosition(e.matrixWorld),this.b.setFromMatrixPosition(t.matrixWorld),this.a.distanceToSquared(this.b)}filterAndFlatten(e){const t=[];return e.traverse((function(e){e instanceof JI&&t.push(e)})),t}render(e,t){!0===e.autoUpdate&&e.updateMatrixWorld(),null===t.parent&&t.updateMatrixWorld(),this.viewMatrix.copy(t.matrixWorldInverse),this.viewProjectionMatrix.multiplyMatrices(t.projectionMatrix,this.viewMatrix),this.renderObject(e,e,t),this._sort_objects&&this.zOrder(e)}set_sorting(e){this._sort_objects=e}zOrder(e){const t=this.filterAndFlatten(e).sort(((e,t)=>{const n=this.cache_distanceToCameraSquared.get(e),i=this.cache_distanceToCameraSquared.get(t);return null!=n&&null!=i?n-i:0})),n=t.length;for(let e=0,i=t.length;e<i;e++)t[e].element.style.zIndex=\\\\\\\"\\\\\\\"+(n-e)}set_use_fog(e){this._use_fog=e}set_fog_range(e,t){this._fog_near=e,this._fog_far=t}}const ZI=new class extends Lo{constructor(){super(...arguments),this.css=Oo.STRING(\\\\\\\"\\\\\\\",{multiline:!0}),this.sortObjects=Oo.BOOLEAN(0),this.useFog=Oo.BOOLEAN(0),this.fogNear=Oo.FLOAT(1,{range:[0,100],rangeLocked:[!0,!1],visibleIf:{useFog:1}}),this.fogFar=Oo.FLOAT(100,{range:[0,100],rangeLocked:[!0,!1],visibleIf:{useFog:1}})}};class eF extends zL{constructor(){super(...arguments),this.paramsConfig=ZI,this._renderers_by_canvas_id=new Map}static type(){return UL.CSS2D}createRenderer(e){const t=new KI;this._renderers_by_canvas_id.set(e.id,t);const n=e.parentElement;n&&(n.prepend(t.domElement),n.style.position=\\\\\\\"relative\\\\\\\"),t.domElement.style.position=\\\\\\\"absolute\\\\\\\",t.domElement.style.top=\\\\\\\"0px\\\\\\\",t.domElement.style.left=\\\\\\\"0px\\\\\\\",t.domElement.style.pointerEvents=\\\\\\\"none\\\\\\\";const i=e.getBoundingClientRect();return t.setSize(i.width,i.height),this._update_renderer(t),t}renderer(e){return this._renderers_by_canvas_id.get(e.id)||this.createRenderer(e)}cook(){this._update_css(),this._renderers_by_canvas_id.forEach((e=>{this._update_renderer(e)})),this.cookController.endCook()}_update_renderer(e){e.set_sorting(this.pv.sortObjects),e.set_use_fog(this.pv.useFog),e.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 e=document.createElement(\\\\\\\"style\\\\\\\");return e.appendChild(document.createTextNode(\\\\\\\"\\\\\\\")),document.head.appendChild(e),e.id=this._css_element_id(),e}_css_element_id(){return`css_2d_renderer-${this.graphNodeId()}`}}class tF extends $I{constructor(){super(...arguments),this._children_controller_context=Ei.ANIM}static type(){return Ci.ANIM}createNode(e,t){return super.createNode(e,t)}children(){return super.children()}nodesByType(e){return super.nodesByType(e)}}class nF extends $I{constructor(){super(...arguments),this._children_controller_context=Ei.EVENT}static type(){return Ci.EVENT}createNode(e,t){return super.createNode(e,t)}children(){return super.children()}nodesByType(e){return super.nodesByType(e)}}class iF extends $I{constructor(){super(...arguments),this._children_controller_context=Ei.MAT}static type(){return Ci.MAT}createNode(e,t){return super.createNode(e,t)}children(){return super.children()}nodesByType(e){return super.nodesByType(e)}}class sF extends YI{constructor(){super(...arguments),this.paramsConfig=new Rm,this.effectsComposerController=new Im(this),this.displayNodeController=new mm(this,this.effectsComposerController.displayNodeControllerCallbacks()),this._children_controller_context=Ei.POST}static type(){return Ci.POST}createNode(e,t){return super.createNode(e,t)}children(){return super.children()}nodesByType(e){return super.nodesByType(e)}}class rF extends $I{constructor(){super(...arguments),this._children_controller_context=Ei.ROP}static type(){return Ci.ROP}createNode(e,t){return super.createNode(e,t)}children(){return super.children()}nodesByType(e){return super.nodesByType(e)}}class oF extends WO{static type(){return\\\\\\\"add\\\\\\\"}cook(e,t){const n=[];return this._create_point(n,t),this._create_polygon(e[0],n,t),this.createCoreGroupFromObjects(n)}_create_point(e,t){if(!t.createPoint)return;const n=new O.a,i=[];for(let e=0;e<t.pointsCount;e++)t.position.toArray(i,3*e);n.setAttribute(\\\\\\\"position\\\\\\\",new L.a(new Float32Array(i),3));const s=this.createObject(n,Zi.POINTS);e&&e.push(s)}_create_polygon(e,t,n){if(!n.connectInputPoints)return;e.points().length>0&&this._create_polygon_open(e,t,n)}_create_polygon_open(e,t,n){const i=e.points();let s=[];const r=[];let o;for(let e=0;e<i.length;e++)o=i[e],o.position().toArray(s,3*e),e>0&&(r.push(e-1),r.push(e));if(i.length>2&&n.connectToLastPoint){i[0].position().toArray(s,s.length);const e=r[r.length-1];r.push(e),r.push(0)}const a=new O.a;a.setAttribute(\\\\\\\"position\\\\\\\",new L.c(s,3)),a.setIndex(r);const c=this.createObject(a,Zi.LINE_SEGMENTS);t.push(c)}}oF.DEFAULT_PARAMS={createPoint:!0,pointsCount:1,position:new p.a(0,0,0),connectInputPoints:!1,connectToLastPoint:!1};const aF=oF.DEFAULT_PARAMS;const cF=new class extends Lo{constructor(){super(...arguments),this.createPoint=Oo.BOOLEAN(aF.createPoint),this.pointsCount=Oo.INTEGER(aF.pointsCount,{range:[1,100],rangeLocked:[!0,!1],visibleIf:{createPoint:!0}}),this.position=Oo.VECTOR3(aF.position,{visibleIf:{createPoint:!0}}),this.connectInputPoints=Oo.BOOLEAN(aF.connectInputPoints),this.connectToLastPoint=Oo.BOOLEAN(aF.connectToLastPoint)}};class lF extends QO{constructor(){super(...arguments),this.paramsConfig=cF}static type(){return\\\\\\\"add\\\\\\\"}static displayedInputNames(){return[\\\\\\\"geometry to create polygons from (optional)\\\\\\\"]}initializeNode(){this.io.inputs.setCount(0,1)}cook(e){this._operation=this._operation||new oF(this.scene(),this.states);const t=this._operation.cook(e,this.pv);this.setCoreGroup(t)}}const uF=new class extends Lo{};class hF extends QO{constructor(){super(...arguments),this.paramsConfig=uF}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([Ti.FROM_NODE,Ti.NEVER])}cook(e){const t=e[0],n=e[1].objects()[0],i=t.objects()[0],s=n.animations;s?(i.animations=s.map((e=>e.clone())),this.setCoreGroup(t)):this.states.error.set(\\\\\\\"no animation found\\\\\\\")}}class dF{constructor(e,t,n=null,i=t.blendMode){this._mixer=e,this._clip=t,this._localRoot=n,this.blendMode=i;const s=t.tracks,r=s.length,o=new Array(r),a={endingStart:w.id,endingEnd:w.id};for(let e=0;e!==r;++e){const t=s[e].createInterpolant(null);o[e]=t,t.settings=a}this._interpolantSettings=a,this._interpolants=o,this._propertyBindings=new Array(r),this._cacheIndex=null,this._byClipCacheIndex=null,this._timeScaleInterpolant=null,this._weightInterpolant=null,this.loop=w.eb,this._loopCount=-1,this._startTime=null,this.time=0,this.timeScale=1,this._effectiveTimeScale=1,this.weight=1,this._effectiveWeight=1,this.repetitions=1/0,this.paused=!1,this.enabled=!0,this.clampWhenFinished=!1,this.zeroSlopeAtStart=!0,this.zeroSlopeAtEnd=!0}play(){return this._mixer._activateAction(this),this}stop(){return this._mixer._deactivateAction(this),this.reset()}reset(){return this.paused=!1,this.enabled=!0,this.time=0,this._loopCount=-1,this._startTime=null,this.stopFading().stopWarping()}isRunning(){return this.enabled&&!this.paused&&0!==this.timeScale&&null===this._startTime&&this._mixer._isActiveAction(this)}isScheduled(){return this._mixer._isActiveAction(this)}startAt(e){return this._startTime=e,this}setLoop(e,t){return this.loop=e,this.repetitions=t,this}setEffectiveWeight(e){return this.weight=e,this._effectiveWeight=this.enabled?e:0,this.stopFading()}getEffectiveWeight(){return this._effectiveWeight}fadeIn(e){return this._scheduleFading(e,0,1)}fadeOut(e){return this._scheduleFading(e,1,0)}crossFadeFrom(e,t,n){if(e.fadeOut(t),this.fadeIn(t),n){const n=this._clip.duration,i=e._clip.duration,s=i/n,r=n/i;e.warp(1,s,t),this.warp(r,1,t)}return this}crossFadeTo(e,t,n){return e.crossFadeFrom(this,t,n)}stopFading(){const e=this._weightInterpolant;return null!==e&&(this._weightInterpolant=null,this._mixer._takeBackControlInterpolant(e)),this}setEffectiveTimeScale(e){return this.timeScale=e,this._effectiveTimeScale=this.paused?0:e,this.stopWarping()}getEffectiveTimeScale(){return this._effectiveTimeScale}setDuration(e){return this.timeScale=this._clip.duration/e,this.stopWarping()}syncWith(e){return this.time=e.time,this.timeScale=e.timeScale,this.stopWarping()}halt(e){return this.warp(this._effectiveTimeScale,0,e)}warp(e,t,n){const i=this._mixer,s=i.time,r=this.timeScale;let o=this._timeScaleInterpolant;null===o&&(o=i._lendControlInterpolant(),this._timeScaleInterpolant=o);const a=o.parameterPositions,c=o.sampleValues;return a[0]=s,a[1]=s+n,c[0]=e/r,c[1]=t/r,this}stopWarping(){const e=this._timeScaleInterpolant;return null!==e&&(this._timeScaleInterpolant=null,this._mixer._takeBackControlInterpolant(e)),this}getMixer(){return this._mixer}getClip(){return this._clip}getRoot(){return this._localRoot||this._mixer._root}_update(e,t,n,i){if(!this.enabled)return void this._updateWeight(e);const s=this._startTime;if(null!==s){const i=(e-s)*n;if(i<0||0===n)return;this._startTime=null,t=n*i}t*=this._updateTimeScale(e);const r=this._updateTime(t),o=this._updateWeight(e);if(o>0){const e=this._interpolants,t=this._propertyBindings;switch(this.blendMode){case w.d:for(let n=0,i=e.length;n!==i;++n)e[n].evaluate(r),t[n].accumulateAdditive(o);break;case w.wb:default:for(let n=0,s=e.length;n!==s;++n)e[n].evaluate(r),t[n].accumulate(i,o)}}}_updateWeight(e){let t=0;if(this.enabled){t=this.weight;const n=this._weightInterpolant;if(null!==n){const i=n.evaluate(e)[0];t*=i,e>n.parameterPositions[1]&&(this.stopFading(),0===i&&(this.enabled=!1))}}return this._effectiveWeight=t,t}_updateTimeScale(e){let t=0;if(!this.paused){t=this.timeScale;const n=this._timeScaleInterpolant;if(null!==n){t*=n.evaluate(e)[0],e>n.parameterPositions[1]&&(this.stopWarping(),0===t?this.paused=!0:this.timeScale=t)}}return this._effectiveTimeScale=t,t}_updateTime(e){const t=this._clip.duration,n=this.loop;let i=this.time+e,s=this._loopCount;const r=n===w.db;if(0===e)return-1===s?i:r&&1==(1&s)?t-i:i;if(n===w.cb){-1===s&&(this._loopCount=0,this._setEndings(!0,!0,!1));e:{if(i>=t)i=t;else{if(!(i<0)){this.time=i;break e}i=0}this.clampWhenFinished?this.paused=!0:this.enabled=!1,this.time=i,this._mixer.dispatchEvent({type:\\\\\\\"finished\\\\\\\",action:this,direction:e<0?-1:1})}}else{if(-1===s&&(e>=0?(s=0,this._setEndings(!0,0===this.repetitions,r)):this._setEndings(0===this.repetitions,!0,r)),i>=t||i<0){const n=Math.floor(i/t);i-=t*n,s+=Math.abs(n);const o=this.repetitions-s;if(o<=0)this.clampWhenFinished?this.paused=!0:this.enabled=!1,i=e>0?t:0,this.time=i,this._mixer.dispatchEvent({type:\\\\\\\"finished\\\\\\\",action:this,direction:e>0?1:-1});else{if(1===o){const t=e<0;this._setEndings(t,!t,r)}else this._setEndings(!1,!1,r);this._loopCount=s,this.time=i,this._mixer.dispatchEvent({type:\\\\\\\"loop\\\\\\\",action:this,loopDelta:n})}}else this.time=i;if(r&&1==(1&s))return t-i}return i}_setEndings(e,t,n){const i=this._interpolantSettings;n?(i.endingStart=w.kd,i.endingEnd=w.kd):(i.endingStart=e?this.zeroSlopeAtStart?w.kd:w.id:w.hd,i.endingEnd=t?this.zeroSlopeAtEnd?w.kd:w.id:w.hd)}_scheduleFading(e,t,n){const i=this._mixer,s=i.time;let r=this._weightInterpolant;null===r&&(r=i._lendControlInterpolant(),this._weightInterpolant=r);const o=r.parameterPositions,a=r.sampleValues;return o[0]=s,a[0]=t,o[1]=s+e,a[1]=n,this}}var pF=n(70),_F=n(64);class mF{constructor(e,t,n){let i,s,r;switch(this.binding=e,this.valueSize=n,t){case\\\\\\\"quaternion\\\\\\\":i=this._slerp,s=this._slerpAdditive,r=this._setAdditiveIdentityQuaternion,this.buffer=new Float64Array(6*n),this._workIndex=5;break;case\\\\\\\"string\\\\\\\":case\\\\\\\"bool\\\\\\\":i=this._select,s=this._select,r=this._setAdditiveIdentityOther,this.buffer=new Array(5*n);break;default:i=this._lerp,s=this._lerpAdditive,r=this._setAdditiveIdentityNumeric,this.buffer=new Float64Array(5*n)}this._mixBufferRegion=i,this._mixBufferRegionAdditive=s,this._setIdentity=r,this._origIndex=3,this._addIndex=4,this.cumulativeWeight=0,this.cumulativeWeightAdditive=0,this.useCount=0,this.referenceCount=0}accumulate(e,t){const n=this.buffer,i=this.valueSize,s=e*i+i;let r=this.cumulativeWeight;if(0===r){for(let e=0;e!==i;++e)n[s+e]=n[e];r=t}else{r+=t;const e=t/r;this._mixBufferRegion(n,s,0,e,i)}this.cumulativeWeight=r}accumulateAdditive(e){const t=this.buffer,n=this.valueSize,i=n*this._addIndex;0===this.cumulativeWeightAdditive&&this._setIdentity(),this._mixBufferRegionAdditive(t,i,0,e,n),this.cumulativeWeightAdditive+=e}apply(e){const t=this.valueSize,n=this.buffer,i=e*t+t,s=this.cumulativeWeight,r=this.cumulativeWeightAdditive,o=this.binding;if(this.cumulativeWeight=0,this.cumulativeWeightAdditive=0,s<1){const e=t*this._origIndex;this._mixBufferRegion(n,i,e,1-s,t)}r>0&&this._mixBufferRegionAdditive(n,i,this._addIndex*t,1,t);for(let e=t,s=t+t;e!==s;++e)if(n[e]!==n[e+t]){o.setValue(n,i);break}}saveOriginalState(){const e=this.binding,t=this.buffer,n=this.valueSize,i=n*this._origIndex;e.getValue(t,i);for(let e=n,s=i;e!==s;++e)t[e]=t[i+e%n];this._setIdentity(),this.cumulativeWeight=0,this.cumulativeWeightAdditive=0}restoreOriginalState(){const e=3*this.valueSize;this.binding.setValue(this.buffer,e)}_setAdditiveIdentityNumeric(){const e=this._addIndex*this.valueSize,t=e+this.valueSize;for(let n=e;n<t;n++)this.buffer[n]=0}_setAdditiveIdentityQuaternion(){this._setAdditiveIdentityNumeric(),this.buffer[this._addIndex*this.valueSize+3]=1}_setAdditiveIdentityOther(){const e=this._origIndex*this.valueSize,t=this._addIndex*this.valueSize;for(let n=0;n<this.valueSize;n++)this.buffer[t+n]=this.buffer[e+n]}_select(e,t,n,i,s){if(i>=.5)for(let i=0;i!==s;++i)e[t+i]=e[n+i]}_slerp(e,t,n,i){Ll.a.slerpFlat(e,t,e,t,e,n,i)}_slerpAdditive(e,t,n,i,s){const r=this._workIndex*s;Ll.a.multiplyQuaternionsFlat(e,r,e,t,e,n),Ll.a.slerpFlat(e,t,e,t,e,r,i)}_lerp(e,t,n,i,s){const r=1-i;for(let o=0;o!==s;++o){const s=t+o;e[s]=e[s]*r+e[n+o]*i}}_lerpAdditive(e,t,n,i,s){for(let r=0;r!==s;++r){const s=t+r;e[s]=e[s]+e[n+r]*i}}}var fF=n(62);class gF extends J.a{constructor(e){super(),this._root=e,this._initMemoryManager(),this._accuIndex=0,this.time=0,this.timeScale=1}_bindAction(e,t){const n=e._localRoot||this._root,i=e._clip.tracks,s=i.length,r=e._propertyBindings,o=e._interpolants,a=n.uuid,c=this._bindingsByRootAndName;let l=c[a];void 0===l&&(l={},c[a]=l);for(let e=0;e!==s;++e){const s=i[e],c=s.name;let u=l[c];if(void 0!==u)r[e]=u;else{if(u=r[e],void 0!==u){null===u._cacheIndex&&(++u.referenceCount,this._addInactiveBinding(u,a,c));continue}const i=t&&t._propertyBindings[e].binding.parsedPath;u=new mF(_F.a.create(n,c,i),s.ValueTypeName,s.getValueSize()),++u.referenceCount,this._addInactiveBinding(u,a,c),r[e]=u}o[e].resultBuffer=u.buffer}}_activateAction(e){if(!this._isActiveAction(e)){if(null===e._cacheIndex){const t=(e._localRoot||this._root).uuid,n=e._clip.uuid,i=this._actionsByClip[n];this._bindAction(e,i&&i.knownActions[0]),this._addInactiveAction(e,n,t)}const t=e._propertyBindings;for(let e=0,n=t.length;e!==n;++e){const n=t[e];0==n.useCount++&&(this._lendBinding(n),n.saveOriginalState())}this._lendAction(e)}}_deactivateAction(e){if(this._isActiveAction(e)){const t=e._propertyBindings;for(let e=0,n=t.length;e!==n;++e){const n=t[e];0==--n.useCount&&(n.restoreOriginalState(),this._takeBackBinding(n))}this._takeBackAction(e)}}_initMemoryManager(){this._actions=[],this._nActiveActions=0,this._actionsByClip={},this._bindings=[],this._nActiveBindings=0,this._bindingsByRootAndName={},this._controlInterpolants=[],this._nActiveControlInterpolants=0;const e=this;this.stats={actions:{get total(){return e._actions.length},get inUse(){return e._nActiveActions}},bindings:{get total(){return e._bindings.length},get inUse(){return e._nActiveBindings}},controlInterpolants:{get total(){return e._controlInterpolants.length},get inUse(){return e._nActiveControlInterpolants}}}}_isActiveAction(e){const t=e._cacheIndex;return null!==t&&t<this._nActiveActions}_addInactiveAction(e,t,n){const i=this._actions,s=this._actionsByClip;let r=s[t];if(void 0===r)r={knownActions:[e],actionByRoot:{}},e._byClipCacheIndex=0,s[t]=r;else{const t=r.knownActions;e._byClipCacheIndex=t.length,t.push(e)}e._cacheIndex=i.length,i.push(e),r.actionByRoot[n]=e}_removeInactiveAction(e){const t=this._actions,n=t[t.length-1],i=e._cacheIndex;n._cacheIndex=i,t[i]=n,t.pop(),e._cacheIndex=null;const s=e._clip.uuid,r=this._actionsByClip,o=r[s],a=o.knownActions,c=a[a.length-1],l=e._byClipCacheIndex;c._byClipCacheIndex=l,a[l]=c,a.pop(),e._byClipCacheIndex=null;delete o.actionByRoot[(e._localRoot||this._root).uuid],0===a.length&&delete r[s],this._removeInactiveBindingsForAction(e)}_removeInactiveBindingsForAction(e){const t=e._propertyBindings;for(let e=0,n=t.length;e!==n;++e){const n=t[e];0==--n.referenceCount&&this._removeInactiveBinding(n)}}_lendAction(e){const t=this._actions,n=e._cacheIndex,i=this._nActiveActions++,s=t[i];e._cacheIndex=i,t[i]=e,s._cacheIndex=n,t[n]=s}_takeBackAction(e){const t=this._actions,n=e._cacheIndex,i=--this._nActiveActions,s=t[i];e._cacheIndex=i,t[i]=e,s._cacheIndex=n,t[n]=s}_addInactiveBinding(e,t,n){const i=this._bindingsByRootAndName,s=this._bindings;let r=i[t];void 0===r&&(r={},i[t]=r),r[n]=e,e._cacheIndex=s.length,s.push(e)}_removeInactiveBinding(e){const t=this._bindings,n=e.binding,i=n.rootNode.uuid,s=n.path,r=this._bindingsByRootAndName,o=r[i],a=t[t.length-1],c=e._cacheIndex;a._cacheIndex=c,t[c]=a,t.pop(),delete o[s],0===Object.keys(o).length&&delete r[i]}_lendBinding(e){const t=this._bindings,n=e._cacheIndex,i=this._nActiveBindings++,s=t[i];e._cacheIndex=i,t[i]=e,s._cacheIndex=n,t[n]=s}_takeBackBinding(e){const t=this._bindings,n=e._cacheIndex,i=--this._nActiveBindings,s=t[i];e._cacheIndex=i,t[i]=e,s._cacheIndex=n,t[n]=s}_lendControlInterpolant(){const e=this._controlInterpolants,t=this._nActiveControlInterpolants++;let n=e[t];return void 0===n&&(n=new pF.a(new Float32Array(2),new Float32Array(2),1,this._controlInterpolantsResultBuffer),n.__cacheIndex=t,e[t]=n),n}_takeBackControlInterpolant(e){const t=this._controlInterpolants,n=e.__cacheIndex,i=--this._nActiveControlInterpolants,s=t[i];e.__cacheIndex=i,t[i]=e,s.__cacheIndex=n,t[n]=s}clipAction(e,t,n){const i=t||this._root,s=i.uuid;let r=\\\\\\\"string\\\\\\\"==typeof e?fF.a.findByName(i,e):e;const o=null!==r?r.uuid:e,a=this._actionsByClip[o];let c=null;if(void 0===n&&(n=null!==r?r.blendMode:w.wb),void 0!==a){const e=a.actionByRoot[s];if(void 0!==e&&e.blendMode===n)return e;c=a.knownActions[0],null===r&&(r=c._clip)}if(null===r)return null;const l=new dF(this,r,t,n);return this._bindAction(l,c),this._addInactiveAction(l,o,s),l}existingAction(e,t){const n=t||this._root,i=n.uuid,s=\\\\\\\"string\\\\\\\"==typeof e?fF.a.findByName(n,e):e,r=s?s.uuid:e,o=this._actionsByClip[r];return void 0!==o&&o.actionByRoot[i]||null}stopAllAction(){const e=this._actions;for(let t=this._nActiveActions-1;t>=0;--t)e[t].stop();return this}update(e){e*=this.timeScale;const t=this._actions,n=this._nActiveActions,i=this.time+=e,s=Math.sign(e),r=this._accuIndex^=1;for(let o=0;o!==n;++o){t[o]._update(i,e,s,r)}const o=this._bindings,a=this._nActiveBindings;for(let e=0;e!==a;++e)o[e].apply(r);return this}setTime(e){this.time=0;for(let e=0;e<this._actions.length;e++)this._actions[e].time=0;return this.update(e)}getRoot(){return this._root}uncacheClip(e){const t=this._actions,n=e.uuid,i=this._actionsByClip,s=i[n];if(void 0!==s){const e=s.knownActions;for(let n=0,i=e.length;n!==i;++n){const i=e[n];this._deactivateAction(i);const s=i._cacheIndex,r=t[t.length-1];i._cacheIndex=null,i._byClipCacheIndex=null,r._cacheIndex=s,t[s]=r,t.pop(),this._removeInactiveBindingsForAction(i)}delete i[n]}}uncacheRoot(e){const t=e.uuid,n=this._actionsByClip;for(const e in n){const i=n[e].actionByRoot[t];void 0!==i&&(this._deactivateAction(i),this._removeInactiveAction(i))}const i=this._bindingsByRootAndName[t];if(void 0!==i)for(const e in i){const t=i[e];t.restoreOriginalState(),this._removeInactiveBinding(t)}}uncacheAction(e,t){const n=this.existingAction(e,t);null!==n&&(this._deactivateAction(n),this._removeInactiveAction(n))}}gF.prototype._controlInterpolantsResultBuffer=new Float32Array(1);const vF=new class extends Lo{constructor(){super(...arguments),this.time=Oo.FLOAT(\\\\\\\"$T\\\\\\\",{range:[0,10]}),this.clip=Oo.OPERATOR_PATH(\\\\\\\"/ANIM/OUT\\\\\\\",{nodeSelection:{context:Ei.ANIM},dependentOnFoundNode:!1}),this.reset=Oo.BUTTON(null,{callback:(e,t)=>{yF.PARAM_CALLBACK_reset(e,t)}})}};class yF extends QO{constructor(){super(...arguments),this.paramsConfig=vF}static type(){return\\\\\\\"animationMixer\\\\\\\"}static displayedInputNames(){return[\\\\\\\"geometry to be animated\\\\\\\"]}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState(Ti.NEVER)}async cook(e){const t=e[0].objects()[0];t&&(await this.create_mixer_if_required(t),this._update_mixer()),this.setObjects([t])}async create_mixer_if_required(e){if(!this._mixer){const t=await this._create_mixer(e);t&&(this._mixer=t)}}async _create_mixer(e){this.p.clip.isDirty()&&await this.p.clip.compute();if(this.p.clip.found_node_with_context(Ei.ANIM)){return new gF(e)}}_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(e,t){t.setDirty(),e.reset_animation_mixer()}async reset_animation_mixer(){this._mixer=void 0,this._previous_time=void 0,this.setDirty()}}class xF extends WO{static type(){return\\\\\\\"attribAddMult\\\\\\\"}cook(e,t){const n=e[0],i=n.attribNamesMatchingMask(t.name);for(let e of i){const i=n.geometries();for(let n of i)this._update_attrib(e,n,t)}return n}_update_attrib(e,t,n){const i=t.getAttribute(e);if(i){const e=i.array,t=n.preAdd,s=n.mult,r=n.postAdd;for(let n=0;n<e.length;n++){const i=e[n];e[n]=(i+t)*s+r}i.needsUpdate=!0}}}xF.DEFAULT_PARAMS={name:\\\\\\\"\\\\\\\",preAdd:0,mult:1,postAdd:0},xF.INPUT_CLONED_STATE=Ti.FROM_NODE;const bF=xF.DEFAULT_PARAMS;const wF=new class extends Lo{constructor(){super(...arguments),this.name=Oo.STRING(bF.name),this.preAdd=Oo.FLOAT(bF.preAdd,{range:[0,1]}),this.mult=Oo.FLOAT(bF.mult,{range:[0,1]}),this.postAdd=Oo.FLOAT(bF.postAdd,{range:[0,1]})}};class AF extends QO{constructor(){super(...arguments),this.paramsConfig=wF}static type(){return\\\\\\\"attribAddMult\\\\\\\"}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState(xF.INPUT_CLONED_STATE)}cook(e){this._operation=this._operation||new xF(this.scene(),this.states);const t=this._operation.cook(e,this.pv);this.setCoreGroup(t)}}var TF;!function(e){e.Float64BufferAttribute=\\\\\\\"Float64BufferAttribute\\\\\\\",e.Float32BufferAttribute=\\\\\\\"Float32BufferAttribute\\\\\\\",e.Float16BufferAttribute=\\\\\\\"Float16BufferAttribute\\\\\\\",e.Uint32BufferAttribute=\\\\\\\"Uint32BufferAttribute\\\\\\\",e.Int32BufferAttribute=\\\\\\\"Int32BufferAttribute\\\\\\\",e.Uint16BufferAttribute=\\\\\\\"Uint16BufferAttribute\\\\\\\",e.Int16BufferAttribute=\\\\\\\"Int16BufferAttribute\\\\\\\",e.Uint8ClampedBufferAttribute=\\\\\\\"Uint8ClampedBufferAttribute\\\\\\\",e.Uint8BufferAttribute=\\\\\\\"Uint8BufferAttribute\\\\\\\",e.Int8BufferAttribute=\\\\\\\"Int8BufferAttribute\\\\\\\"}(TF||(TF={}));const EF=[TF.Float64BufferAttribute,TF.Float32BufferAttribute,TF.Float16BufferAttribute,TF.Uint32BufferAttribute,TF.Int32BufferAttribute,TF.Uint16BufferAttribute,TF.Int16BufferAttribute,TF.Uint8ClampedBufferAttribute,TF.Uint8BufferAttribute,TF.Int8BufferAttribute],CF={[TF.Float64BufferAttribute]:L.d,[TF.Float32BufferAttribute]:L.c,[TF.Float16BufferAttribute]:L.b,[TF.Uint32BufferAttribute]:L.i,[TF.Int32BufferAttribute]:L.f,[TF.Uint16BufferAttribute]:L.h,[TF.Int16BufferAttribute]:L.e,[TF.Uint8ClampedBufferAttribute]:L.k,[TF.Uint8BufferAttribute]:L.j,[TF.Int8BufferAttribute]:L.g},MF={[TF.Float64BufferAttribute]:Float64Array,[TF.Float32BufferAttribute]:Float32Array,[TF.Float16BufferAttribute]:Uint16Array,[TF.Uint32BufferAttribute]:Uint32Array,[TF.Int32BufferAttribute]:Int32Array,[TF.Uint16BufferAttribute]:Uint16Array,[TF.Int16BufferAttribute]:Int16Array,[TF.Uint8ClampedBufferAttribute]:Uint8Array,[TF.Uint8BufferAttribute]:Uint8Array,[TF.Int8BufferAttribute]:Int8Array};class NF extends WO{static type(){return\\\\\\\"attribCast\\\\\\\"}cook(e,t){const n=e[0],i=n.objectsWithGeo();for(let e of i)this._castGeoAttributes(e.geometry,t);return n}_castGeoAttributes(e,t){const n=EF[t.type],i=CF[n],s=MF[n];if(t.castAttributes){const n=Bs.attribNamesMatchingMask(e,t.mask);for(let t of n){const n=e.attributes[t],r=n.array,o=new s(n.count*n.itemSize);for(let e=0;e<r.length;e++)o[e]=r[e];const a=new i(o,1);e.setAttribute(t,a)}}if(t.castIndex){const t=e.getIndex();if(t){const n=t.array,r=new s(t.count*1);for(let e=0;e<n.length;e++)r[e]=n[e];const o=new i(r,1);e.setIndex(o)}}}}NF.DEFAULT_PARAMS={castAttributes:!0,mask:\\\\\\\"*\\\\\\\",castIndex:!1,type:EF.indexOf(TF.Float32BufferAttribute)},NF.INPUT_CLONED_STATE=Ti.FROM_NODE;const SF=NF.DEFAULT_PARAMS;const OF=new class extends Lo{constructor(){super(...arguments),this.castAttributes=Oo.BOOLEAN(SF.castAttributes),this.mask=Oo.STRING(SF.mask,{visibleIf:{castAttributes:1}}),this.castIndex=Oo.BOOLEAN(SF.castIndex),this.type=Oo.INTEGER(SF.type,{menu:{entries:EF.map(((e,t)=>({name:e,value:t})))}})}};class LF extends QO{constructor(){super(...arguments),this.paramsConfig=OF}static type(){return\\\\\\\"attribCast\\\\\\\"}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState(NF.INPUT_CLONED_STATE),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.type],(()=>EF[this.pv.type]))}))}))}cook(e){this._operation=this._operation||new NF(this.scene(),this.states);const t=this._operation.cook(e,this.pv);this.setCoreGroup(t)}}class PF extends WO{static type(){return\\\\\\\"attribCopy\\\\\\\"}cook(e,t){const n=e[0],i=e[1]||n,s=i.attribNamesMatchingMask(t.name);for(let e of s)this.copy_vertex_attribute_between_core_groups(n,i,e,t);return n}copy_vertex_attribute_between_core_groups(e,t,n,i){var s;const r=t.objectsWithGeo(),o=e.objectsWithGeo();if(o.length>r.length)null===(s=this.states)||void 0===s||s.error.set(\\\\\\\"second input does not have enough objects to copy attributes from\\\\\\\");else for(let e=0;e<o.length;e++){const t=o[e].geometry,s=r[e].geometry;this.copy_vertex_attribute_between_geometries(t,s,n,i)}}copy_vertex_attribute_between_geometries(e,t,n,i){var s,r;const o=t.getAttribute(n);if(o){const r=o.itemSize,a=t.getAttribute(\\\\\\\"position\\\\\\\").array.length/3,c=e.getAttribute(\\\\\\\"position\\\\\\\").array.length/3;c>a&&(null===(s=this.states)||void 0===s||s.error.set(\\\\\\\"not enough points in second input\\\\\\\"));const l=i.tnewName?i.newName:n;let u=e.getAttribute(l);if(u)this._fill_dest_array(u,o,i),u.needsUpdate=!0;else{const t=o.array.slice(0,c*r);e.setAttribute(l,new L.c(t,r))}}else null===(r=this.states)||void 0===r||r.error.set(`attribute '${n}' does not exist on second input`)}_fill_dest_array(e,t,n){const i=e.array,s=t.array,r=i.length,o=e.itemSize,a=t.itemSize,c=n.srcOffset,l=n.destOffset;if(e.itemSize==t.itemSize){e.copyArray(t.array);for(let e=0;e<r;e++)i[e]=s[e]}else{const e=i.length/o;if(o<a)for(let t=0;t<e;t++)for(let e=0;e<o;e++)i[t*o+e+l]=s[t*a+e+c];else for(let t=0;t<e;t++)for(let e=0;e<a;e++)i[t*o+e+l]=s[t*a+e+c]}}}PF.DEFAULT_PARAMS={name:\\\\\\\"\\\\\\\",tnewName:!1,newName:\\\\\\\"\\\\\\\",srcOffset:0,destOffset:0},PF.INPUT_CLONED_STATE=[Ti.FROM_NODE,Ti.NEVER];const RF=PF.DEFAULT_PARAMS;const IF=new class extends Lo{constructor(){super(...arguments),this.name=Oo.STRING(RF.name),this.tnewName=Oo.BOOLEAN(RF.tnewName),this.newName=Oo.STRING(RF.newName,{visibleIf:{tnewName:1}}),this.srcOffset=Oo.INTEGER(RF.srcOffset,{range:[0,3],rangeLocked:[!0,!0]}),this.destOffset=Oo.INTEGER(RF.destOffset,{range:[0,3],rangeLocked:[!0,!0]})}};class FF extends QO{constructor(){super(...arguments),this.paramsConfig=IF}static type(){return\\\\\\\"attribCopy\\\\\\\"}static displayedInputNames(){return[\\\\\\\"geometry to copy attributes to\\\\\\\",\\\\\\\"geometry to copy attributes from\\\\\\\"]}initializeNode(){this.io.inputs.setCount(1,2),this.io.inputs.initInputsClonedState(PF.INPUT_CLONED_STATE),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.name,this.p.tnewName,this.p.newName],(()=>this.pv.tnewName?`${this.pv.name} -> ${this.pv.newName}`:this.pv.name))}))}))}cook(e){this._operation=this._operation||new PF(this.scene(),this.states);const t=this._operation.cook(e,this.pv);this.setCoreGroup(t)}}class DF extends WO{static type(){return\\\\\\\"attribCreate\\\\\\\"}cook(e,t){var n;const i=e[0];return t.name&&\\\\\\\"\\\\\\\"!=t.name.trim()?this._add_attribute(os[t.class],i,t):null===(n=this.states)||void 0===n||n.error.set(\\\\\\\"attribute name is not valid\\\\\\\"),i}async _add_attribute(e,t,n){const i=ls[n.type];switch(e){case rs.VERTEX:return void await this.add_point_attribute(i,t,n);case rs.OBJECT:return void await this.add_object_attribute(i,t,n)}Ri.unreachable(e)}async add_point_attribute(e,t,n){const i=t.coreObjects();switch(e){case cs.NUMERIC:for(let e=0;e<i.length;e++)await this.add_numeric_attribute_to_points(i[e],n);return;case cs.STRING:for(let e=0;e<i.length;e++)await this.add_string_attribute_to_points(i[e],n);return}Ri.unreachable(e)}async add_object_attribute(e,t,n){const i=t.coreObjectsFromGroup(n.group);switch(e){case cs.NUMERIC:return void await this.add_numeric_attribute_to_object(i,n);case cs.STRING:return void await this.add_string_attribute_to_object(i,n)}Ri.unreachable(e)}async add_numeric_attribute_to_points(e,t){if(!e.coreGeometry())return;const n=[t.value1,t.value2,t.value3,t.value4][t.size-1];e.addNumericVertexAttrib(t.name,t.size,n)}async add_numeric_attribute_to_object(e,t){const n=[t.value1,t.value2,t.value3,t.value4][t.size-1];for(let i of e)i.setAttribValue(t.name,n)}async add_string_attribute_to_points(e,t){const n=e.pointsFromGroup(t.group),i=t.string,s=new Array(n.length);for(let e=0;e<n.length;e++)s[e]=i;const r=gs.arrayToIndexedArrays(s),o=e.coreGeometry();o&&o.setIndexedAttribute(t.name,r.values,r.indices)}async add_string_attribute_to_object(e,t){const n=t.string;for(let i of e)i.setAttribValue(t.name,n)}}DF.DEFAULT_PARAMS={group:\\\\\\\"\\\\\\\",class:os.indexOf(rs.VERTEX),type:ls.indexOf(cs.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:\\\\\\\"\\\\\\\"},DF.INPUT_CLONED_STATE=Ti.FROM_NODE;const kF=[\\\\\\\"x\\\\\\\",\\\\\\\"y\\\\\\\",\\\\\\\"z\\\\\\\",\\\\\\\"w\\\\\\\"],BF=DF.DEFAULT_PARAMS;const zF=new class extends Lo{constructor(){super(...arguments),this.group=Oo.STRING(BF.group),this.class=Oo.INTEGER(BF.class,{menu:{entries:as}}),this.type=Oo.INTEGER(BF.type,{menu:{entries:us}}),this.name=Oo.STRING(BF.name),this.size=Oo.INTEGER(BF.size,{range:[1,4],rangeLocked:[!0,!0],visibleIf:{type:cs.NUMERIC}}),this.value1=Oo.FLOAT(BF.value1,{visibleIf:{type:cs.NUMERIC,size:1},expression:{forEntities:!0}}),this.value2=Oo.VECTOR2(BF.value2,{visibleIf:{type:cs.NUMERIC,size:2},expression:{forEntities:!0}}),this.value3=Oo.VECTOR3(BF.value3,{visibleIf:{type:cs.NUMERIC,size:3},expression:{forEntities:!0}}),this.value4=Oo.VECTOR4(BF.value4,{visibleIf:{type:cs.NUMERIC,size:4},expression:{forEntities:!0}}),this.string=Oo.STRING(BF.string,{visibleIf:{type:cs.STRING},expression:{forEntities:!0}})}};class UF extends QO{constructor(){super(...arguments),this.paramsConfig=zF,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(DF.INPUT_CLONED_STATE),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.name])}))}))}cook(e){if(this._is_using_expression())this.pv.name&&\\\\\\\"\\\\\\\"!=this.pv.name.trim()?this._add_attribute(os[this.pv.class],e[0]):this.states.error.set(\\\\\\\"attribute name is not valid\\\\\\\");else{this._operation=this._operation||new DF(this.scene(),this.states);const t=this._operation.cook(e,this.pv);this.setCoreGroup(t)}}async _add_attribute(e,t){const n=ls[this.pv.type];switch(e){case rs.VERTEX:return await this.add_point_attribute(n,t),this.setCoreGroup(t);case rs.OBJECT:return await this.add_object_attribute(n,t),this.setCoreGroup(t)}Ri.unreachable(e)}async add_point_attribute(e,t){const n=t.coreObjects();switch(e){case cs.NUMERIC:for(let e=0;e<n.length;e++)await this.add_numeric_attribute_to_points(n[e]);return;case cs.STRING:for(let e=0;e<n.length;e++)await this.add_string_attribute_to_points(n[e]);return}Ri.unreachable(e)}async add_object_attribute(e,t){const n=t.coreObjectsFromGroup(this.pv.group);switch(e){case cs.NUMERIC:return void await this.add_numeric_attribute_to_object(n);case cs.STRING:return void await this.add_string_attribute_to_object(n)}Ri.unreachable(e)}async add_numeric_attribute_to_points(e){const t=e.coreGeometry();if(!t)return;const n=e.pointsFromGroup(this.pv.group),i=[this.p.value1,this.p.value2,this.p.value3,this.p.value4][this.pv.size-1];if(i.hasExpression()){t.hasAttrib(this.pv.name)||t.addNumericAttrib(this.pv.name,this.pv.size,i.value);const e=t.geometry(),s=e.getAttribute(this.pv.name).array;if(1==this.pv.size)this.p.value1.expressionController&&await this.p.value1.expressionController.compute_expression_for_points(n,((e,t)=>{s[e.index()*this.pv.size+0]=t}));else{let t=[this.p.value2,this.p.value3,this.p.value4][this.pv.size-2].components;const i=new Array(t.length);let r;const o=[this._x_arrays_by_geometry_uuid,this._y_arrays_by_geometry_uuid,this._z_arrays_by_geometry_uuid,this._w_arrays_by_geometry_uuid];for(let a=0;a<t.length;a++)if(r=t[a],r.hasExpression()&&r.expressionController)i[a]=this._init_array_if_required(e,o[a],n.length),await r.expressionController.compute_expression_for_points(n,((e,t)=>{i[a][e.index()]=t}));else{const e=r.value;for(let t of n)s[t.index()*this.pv.size+a]=e}for(let e=0;e<i.length;e++){const t=i[e];if(t)for(let n=0;n<t.length;n++)s[n*this.pv.size+e]=t[n]}}}}async add_numeric_attribute_to_object(e){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(e,((e,t)=>{e.setAttribValue(this.pv.name,t)}));else{let t=[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 t of e)n[t.index()]=i;for(let i=0;i<t.length;i++){const s=t[i],r=kF[i];if(s.hasExpression()&&s.expressionController)await s.expressionController.compute_expression_for_objects(e,((e,t)=>{n[e.index()][r]=t}));else for(let t of e){n[t.index()][r]=s.value}}for(let t=0;t<e.length;t++){const i=e[t],s=n[i.index()];i.setAttribValue(this.pv.name,s)}}}}_vector_by_attrib_size(e){switch(e){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(e){const t=e.pointsFromGroup(this.pv.group),n=this.p.string,i=new Array(t.length);n.hasExpression()&&n.expressionController&&await n.expressionController.compute_expression_for_points(t,((e,t)=>{i[e.index()]=t}));const s=gs.arrayToIndexedArrays(i),r=e.coreGeometry();r&&r.setIndexedAttribute(this.pv.name,s.values,s.indices)}async add_string_attribute_to_object(e){const t=this.p.string;t.hasExpression()&&t.expressionController&&await t.expressionController.compute_expression_for_objects(e,((e,t)=>{e.setAttribValue(this.pv.name,t)}))}_init_array_if_required(e,t,n){const i=e.uuid,s=t[i];return s?s.length<n&&(t[i]=new Array(n)):t[i]=new Array(n),t[i]}_is_using_expression(){switch(ls[this.pv.type]){case cs.NUMERIC:return[this.p.value1,this.p.value2,this.p.value3,this.p.value4][this.pv.size-1].hasExpression();case cs.STRING:return this.p.string.hasExpression()}}setType(e){this.p.type.set(ls.indexOf(e))}}const GF=new class extends Lo{constructor(){super(...arguments),this.class=Oo.INTEGER(rs.VERTEX,{menu:{entries:as}}),this.name=Oo.STRING(\\\\\\\"\\\\\\\")}};class VF extends QO{constructor(){super(...arguments),this.paramsConfig=GF}static type(){return\\\\\\\"attribDelete\\\\\\\"}static displayedInputNames(){return[\\\\\\\"geometry to delete attributes from\\\\\\\"]}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState(Ti.FROM_NODE),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.name])}))}))}cook(e){const t=e[0],n=t.attribNamesMatchingMask(this.pv.name);for(let e of n)switch(this.pv.class){case rs.VERTEX:this.delete_vertex_attribute(t,e);case rs.OBJECT:this.delete_object_attribute(t,e)}this.setCoreGroup(t)}delete_vertex_attribute(e,t){for(let n of e.objects())n.traverse((e=>{const n=e;if(n.geometry){new Bs(n.geometry).deleteAttribute(t)}}))}delete_object_attribute(e,t){for(let n of e.objects()){let e=0;n.traverse((n=>{new js(n,e).deleteAttribute(t),e++}))}}}class jF{set_attrib(e){const t=e.geometry,n=e.targetAttribSize;if(n<1||n>4)return;const i=e.add,s=e.mult,r=this._data_from_texture(e.texture);if(!r)return;const{data:o,resx:a,resy:c}=r,l=o.length/(a*c),u=t.getAttribute(e.uvAttribName).array,h=u.length/2,d=new Array(h*n);let p,_,m,f,g,v,y,x,b;const w=Os.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((c-1)*(1-m)),y=g*a+f,b=0;b<n;b++)x=o[l*y+b],d[v*n+b]=s*x+i;const A=gs.remapName(e.targetAttribName),T=new Float32Array(d);t.setAttribute(A,new L.a(T,n))}_data_from_texture(e){if(e.image)return e.image.data?this._data_from_data_texture(e):this._data_from_default_texture(e)}_data_from_default_texture(e){const t=e.image.width,n=e.image.height;return{data:mf.data_from_image(e.image).data,resx:t,resy:n}}_data_from_data_texture(e){return{data:e.image.data,resx:e.image.width,resy:e.image.height}}}class HF extends WO{static type(){return\\\\\\\"attribFromTexture\\\\\\\"}async cook(e,t){var n;const i=e[0],s=t.texture.nodeWithContext(Ei.COP,null===(n=this.states)||void 0===n?void 0:n.error);if(!s)return i;const r=(await s.compute()).texture();for(let e of i.coreObjects())this._set_position_from_data_texture(e,r,t);return i}_set_position_from_data_texture(e,t,n){var i,s;const r=null===(i=e.coreGeometry())||void 0===i?void 0:i.geometry();if(!r)return;if(null==r.getAttribute(n.uvAttrib))return void(null===(s=this.states)||void 0===s||s.error.set(`param '${n.uvAttrib} not found'`));(new jF).set_attrib({geometry:r,texture:t,uvAttribName:n.uvAttrib,targetAttribName:n.attrib,targetAttribSize:n.attribSize,add:n.add,mult:n.mult})}}HF.DEFAULT_PARAMS={texture:new Hn(jn.EMPTY),uvAttrib:\\\\\\\"uv\\\\\\\",attrib:\\\\\\\"pscale\\\\\\\",attribSize:1,add:0,mult:1},HF.INPUT_CLONED_STATE=Ti.FROM_NODE;const qF=HF.DEFAULT_PARAMS;const WF=new class extends Lo{constructor(){super(...arguments),this.texture=Oo.NODE_PATH(qF.texture.path(),{nodeSelection:{context:Ei.COP}}),this.uvAttrib=Oo.STRING(qF.uvAttrib),this.attrib=Oo.STRING(qF.attrib),this.attribSize=Oo.INTEGER(qF.attribSize,{range:[1,3],rangeLocked:[!0,!0]}),this.add=Oo.FLOAT(qF.add),this.mult=Oo.FLOAT(qF.mult)}};class XF extends QO{constructor(){super(...arguments),this.paramsConfig=WF}static type(){return\\\\\\\"attribFromTexture\\\\\\\"}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState(Ti.FROM_NODE),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.attrib])}))}))}async cook(e){this._operation=this._operation||new HF(this.scene(),this.states);const t=await this._operation.cook(e,this.pv);this.setCoreGroup(t)}}var YF;!function(e){e.MIN_MAX_TO_01=\\\\\\\"min/max to 0/1\\\\\\\",e.VECTOR_TO_LENGTH_1=\\\\\\\"vectors to length 1\\\\\\\"}(YF||(YF={}));const $F=[YF.MIN_MAX_TO_01,YF.VECTOR_TO_LENGTH_1];class QF extends WO{constructor(){super(...arguments),this.min3=new p.a,this.max3=new p.a,this._vec=new p.a}static type(){return\\\\\\\"attribNormalize\\\\\\\"}cook(e,t){const n=e[0],i=e[0].objectsWithGeo(),s=Li.attribNames(t.name);for(let e of i){const n=e.geometry;for(let e of s){const i=n.getAttribute(e);if(i){let e=i;t.changeName&&\\\\\\\"\\\\\\\"!=t.newName&&(e=n.getAttribute(t.newName),e&&(e.needsUpdate=!0),e=e||i.clone()),this._normalize_attribute(i,e,t)}}}return n}_normalize_attribute(e,t,n){switch($F[n.mode]){case YF.MIN_MAX_TO_01:return this._normalize_from_min_max_to_01(e,t);case YF.VECTOR_TO_LENGTH_1:return this._normalize_vectors(e,t)}}_normalize_from_min_max_to_01(e,t){const n=e.itemSize,i=e.array,s=t.array;switch(n){case 1:{const e=Math.min(...i),t=Math.max(...i);for(let n=0;n<s.length;n++)s[n]=(i[n]-e)/(t-e);return}case 3:{const e=i.length/n,t=new Array(e),r=new Array(e),o=new Array(e);let a=0;for(let s=0;s<e;s++)a=s*n,t[s]=i[a+0],r[s]=i[a+1],o[s]=i[a+2];this.min3.set(Math.min(...t),Math.min(...r),Math.min(...o)),this.max3.set(Math.max(...t),Math.max(...r),Math.max(...o));for(let i=0;i<e;i++)a=i*n,s[a+0]=(t[i]-this.min3.x)/(this.max3.x-this.min3.x),s[a+1]=(r[i]-this.min3.y)/(this.max3.y-this.min3.y),s[a+2]=(o[i]-this.min3.z)/(this.max3.z-this.min3.z);return}}}_normalize_vectors(e,t){const n=e.array,i=t.array,s=n.length;if(3==e.itemSize)for(let e=0;e<s;e+=3)this._vec.fromArray(n,e),this._vec.normalize(),this._vec.toArray(i,e)}}QF.DEFAULT_PARAMS={mode:0,name:\\\\\\\"position\\\\\\\",changeName:!1,newName:\\\\\\\"\\\\\\\"},QF.INPUT_CLONED_STATE=Ti.FROM_NODE;const JF=QF.DEFAULT_PARAMS;const KF=new class extends Lo{constructor(){super(...arguments),this.mode=Oo.INTEGER(JF.mode,{menu:{entries:$F.map(((e,t)=>({name:e,value:t})))}}),this.name=Oo.STRING(JF.name),this.changeName=Oo.BOOLEAN(JF.changeName),this.newName=Oo.STRING(JF.newName,{visibleIf:{changeName:1}})}};class ZF extends QO{constructor(){super(...arguments),this.paramsConfig=KF}static type(){return\\\\\\\"attribNormalize\\\\\\\"}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState(QF.INPUT_CLONED_STATE),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.name])}))}))}set_mode(e){this.p.mode.set($F.indexOf(e))}cook(e){this._operation=this._operation||new QF(this.scene(),this.states);const t=this._operation.cook(e,this.pv);this.setCoreGroup(t)}}var eD;!function(e){e[e.MIN=0]=\\\\\\\"MIN\\\\\\\",e[e.MAX=1]=\\\\\\\"MAX\\\\\\\",e[e.FIRST_FOUND=2]=\\\\\\\"FIRST_FOUND\\\\\\\"}(eD||(eD={}));class tD extends WO{constructor(){super(...arguments),this._values_per_attrib_name={},this._filtered_values_per_attrib_name={}}static type(){return\\\\\\\"attribPromote\\\\\\\"}cook(e,t){this._core_group=e[0],this._values_per_attrib_name={},this._filtered_values_per_attrib_name={};for(let e of this._core_group.coreObjects())this._core_object=e,this.find_values(t),this.filter_values(t),this.set_values(t);return this._core_group}find_values(e){const t=Li.attribNames(e.name);for(let n of t)this._find_values_for_attrib_name(n,e)}_find_values_for_attrib_name(e,t){switch(t.classFrom){case rs.VERTEX:return this.find_values_from_points(e,t);case rs.OBJECT:return this.find_values_from_object(e,t)}}find_values_from_points(e,t){if(this._core_object){const t=this._core_object.points(),n=t[0];if(n&&!n.isAttribIndexed(e)){const n=new Array(t.length);let i;for(let s=0;s<t.length;s++)i=t[s],n[s]=i.attribValue(e);this._values_per_attrib_name[e]=n}}}find_values_from_object(e,t){this._values_per_attrib_name[e]=[],this._core_object&&this._values_per_attrib_name[e].push(this._core_object.attribValue(e))}filter_values(e){const t=Object.keys(this._values_per_attrib_name);for(let n of t){const t=this._values_per_attrib_name[n];switch(e.mode){case eD.MIN:this._filtered_values_per_attrib_name[n]=f.min(t);break;case eD.MAX:this._filtered_values_per_attrib_name[n]=f.max(t);break;case eD.FIRST_FOUND:this._filtered_values_per_attrib_name[n]=t[0]}}}set_values(e){const t=Object.keys(this._filtered_values_per_attrib_name);for(let n of t){const t=this._filtered_values_per_attrib_name[n];if(null!=t)switch(e.classTo){case rs.VERTEX:this.set_values_to_points(n,t,e);break;case rs.OBJECT:this.set_values_to_object(n,t,e)}}}set_values_to_points(e,t,n){if(this._core_group&&this._core_object){if(!this._core_group.hasAttrib(e)){const n=gs.attribSizeFromValue(t);n&&this._core_group.addNumericVertexAttrib(e,n,t)}const n=this._core_object.points();for(let i of n)i.setAttribValue(e,t)}}set_values_to_object(e,t,n){var i;null===(i=this._core_object)||void 0===i||i.setAttribValue(e,t)}}tD.DEFAULT_PARAMS={classFrom:rs.VERTEX,classTo:rs.OBJECT,mode:eD.FIRST_FOUND,name:\\\\\\\"\\\\\\\"},tD.INPUT_CLONED_STATE=Ti.FROM_NODE;const nD=[{name:\\\\\\\"min\\\\\\\",value:eD.MIN},{name:\\\\\\\"max\\\\\\\",value:eD.MAX},{name:\\\\\\\"first_found\\\\\\\",value:eD.FIRST_FOUND}],iD=tD.DEFAULT_PARAMS;const sD=new class extends Lo{constructor(){super(...arguments),this.classFrom=Oo.INTEGER(iD.classFrom,{menu:{entries:as}}),this.classTo=Oo.INTEGER(iD.classTo,{menu:{entries:as}}),this.mode=Oo.INTEGER(iD.mode,{menu:{entries:nD}}),this.name=Oo.STRING(iD.name)}};class rD extends QO{constructor(){super(...arguments),this.paramsConfig=sD}static type(){return\\\\\\\"attribPromote\\\\\\\"}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState(tD.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 e=as.filter((e=>e.value==this.pv.classFrom))[0].name,t=as.filter((e=>e.value==this.pv.classTo))[0].name;return`${this.pv.name} (${e} -> ${t})`}return\\\\\\\"\\\\\\\"}))}))}))}cook(e){this._operation=this._operation||new tD(this.scene(),this.states);const t=this._operation.cook(e,this.pv);this.setCoreGroup(t)}}const oD=new class extends Lo{constructor(){super(...arguments),this.name=Oo.STRING(),this.ramp=Oo.RAMP(),this.changeName=Oo.BOOLEAN(0),this.newName=Oo.STRING(\\\\\\\"\\\\\\\",{visibleIf:{changeName:1}})}};class aD extends QO{constructor(){super(...arguments),this.paramsConfig=oD}static type(){return\\\\\\\"attribRemap\\\\\\\"}initializeNode(){this.io.inputs.setCount(1)}cook(e){const t=e[0];this._remap_attribute(t),this.setCoreGroup(t)}_remap_attribute(e){const t=e.points();if(0===t.length)return;if(\\\\\\\"\\\\\\\"===this.pv.name)return;const n=t[0].attribSize(this.pv.name),i=t.map((e=>e.attribValue(this.pv.name)));let s=new Array(t.length);this._get_remaped_values(n,i,s);let r=this.pv.name;this.pv.changeName&&(r=this.pv.newName,e.hasAttrib(r)||e.addNumericVertexAttrib(r,n,0));let o=0;for(let e of s){t[o].setAttribValue(r,e),o++}}_get_remaped_values(e,t,n){switch(e){case hs.FLOAT:return this._get_normalized_float(t,n);case hs.VECTOR2:return this._get_normalized_vector2(t,n);case hs.VECTOR3:return this._get_normalized_vector3(t,n);case hs.VECTOR4:return this._get_normalized_vector4(t,n)}Ri.unreachable(e)}_get_normalized_float(e,t){const n=e,i=this.p.ramp;for(let e=0;e<n.length;e++){const s=n[e],r=i.value_at_position(s);t[e]=r}}_get_normalized_vector2(e,t){const n=e,i=this.p.ramp;for(let e=0;e<n.length;e++){const s=n[e],r=new d.a(i.value_at_position(s.x),i.value_at_position(s.y));t[e]=r}}_get_normalized_vector3(e,t){const n=e,i=this.p.ramp;for(let e=0;e<n.length;e++){const s=n[e],r=new p.a(i.value_at_position(s.x),i.value_at_position(s.y),i.value_at_position(s.z));t[e]=r}}_get_normalized_vector4(e,t){const n=e,i=this.p.ramp;for(let e=0;e<n.length;e++){const s=n[e],r=new _.a(i.value_at_position(s.x),i.value_at_position(s.y),i.value_at_position(s.z),i.value_at_position(s.w));t[e]=r}}}const cD=new class extends Lo{constructor(){super(...arguments),this.class=Oo.INTEGER(rs.VERTEX,{menu:{entries:as}}),this.oldName=Oo.STRING(),this.newName=Oo.STRING()}};class lD extends QO{constructor(){super(...arguments),this.paramsConfig=cD}static type(){return\\\\\\\"attribRename\\\\\\\"}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState(Ti.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} -> ${this.pv.newName}`:\\\\\\\"\\\\\\\"))}))}))}cook(e){const t=e[0];t.renameAttrib(this.pv.oldName,this.pv.newName,this.pv.class),this.setCoreGroup(t)}}var uD=n(18);class hD{constructor(e,t=0){this._bbox=e,this._level=t,this._leaves_by_octant={},this._points_by_octant_id={},this._leaves=[],this._bounding_boxes_by_octant={},this._bounding_boxes_by_octant_prepared=!1,this._center=this._bbox.max.clone().add(this._bbox.min).multiplyScalar(.5)}level(){return this._level}traverse(e){e(this);Object.values(this._leaves_by_octant).forEach((t=>{t.traverse(e)}))}intersects_sphere(e){return!!this._bbox&&this._bbox.intersectsSphere(e)}points_in_sphere(e,t){if(0==this._leaves.length){Object.values(this._points_by_octant_id).flat().filter((t=>e.containsPoint(t.position()))).forEach((e=>{t.push(e)}))}else{this._leaves.filter((t=>t.intersects_sphere(e))).forEach((n=>n.points_in_sphere(e,t)))}}bounding_box(){return this._bbox}set_points(e){this._points_by_octant_id={};for(let t of e)this.add_point(t);const t=Object.keys(this._points_by_octant_id);t.length>1&&t.forEach((e=>{this.create_leaf(e)}))}create_leaf(e){const t=this._leaf_bbox(e),n=new hD(t,this._level+1);this._leaves_by_octant[e]=n,this._leaves.push(n),n.set_points(this._points_by_octant_id[e])}add_point(e){const t=this._octant_id(e.position());null==this._points_by_octant_id[t]&&(this._points_by_octant_id[t]=[]),this._points_by_octant_id[t].push(e)}_octant_id(e){return`${e.x>this._center.x?1:0}${e.y>this._center.y?1:0}${e.z>this._center.z?1:0}`}_leaf_bbox(e){return this._bounding_boxes_by_octant_prepared||(this._prepare_leaves_bboxes(),this._bounding_boxes_by_octant_prepared=!0),this._bounding_boxes_by_octant[e]}_bbox_center(e,t,n){const i=this._bbox.min.clone();return e&&(i.x=this._bbox.max.x),t&&(i.y=this._bbox.max.y),n&&(i.z=this._bbox.max.z),i.clone().add(this._center).multiplyScalar(.5)}_prepare_leaves_bboxes(){const e=[];e.push(this._bbox_center(0,0,0)),e.push(this._bbox_center(0,0,1)),e.push(this._bbox_center(0,1,0)),e.push(this._bbox_center(0,1,1)),e.push(this._bbox_center(1,0,0)),e.push(this._bbox_center(1,0,1)),e.push(this._bbox_center(1,1,0)),e.push(this._bbox_center(1,1,1));const t=this._bbox.max.clone().sub(this._bbox.min).multiplyScalar(.25);for(let n of e){const e=this._octant_id(n),i=new CN.a(n.clone().sub(t),n.clone().add(t));this._bounding_boxes_by_octant[e]=i}}}class dD{constructor(e){this._root=new hD(e)}set_points(e){this._root.set_points(e)}traverse(e){this._root.traverse(e)}find_points(e,t,n){const i=new uD.a(e,t);let s=[];return this._root.intersects_sphere(i)&&this._root.points_in_sphere(i,s),null==n||s.length>n&&(s=f.sortBy(s,(t=>t.position().distanceTo(e))),s=s.slice(0,n)),s}}class pD{constructor(e={}){this._array_index=0,this._count=0,this._current_count_index=0,this._resolve=null,this._max_time_per_chunk=e.max_time_per_chunk||10,this._check_every_interations=e.check_every_interations||100}async startWithCount(e,t){if(this._count=e,this._current_count_index=0,this._iteratee_method_count=t,this._bound_next_with_count=this.nextWithCount.bind(this),this._resolve)throw\\\\\\\"an iterator cannot be started twice\\\\\\\";return new Promise(((e,t)=>{this._resolve=e,this.nextWithCount()}))}nextWithCount(){const e=Rn.performance.performanceManager(),t=e.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&&e.now()-t>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(e,t){if(this._array=e,this._array_index=0,this._iteratee_method_array=t,this._bound_next_with_array=this.nextWithArray.bind(this),this._resolve)throw\\\\\\\"an iterator cannot be started twice\\\\\\\";return new Promise(((e,t)=>{this._resolve=e,this.nextWithArray()}))}nextWithArray(){const e=Rn.performance.performanceManager(),t=e.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&&e.now()-t>this._max_time_per_chunk){setTimeout(this._bound_next_with_array,1);break}void 0===this._current_array_element&&this._resolve&&this._resolve()}}const _D=new class extends Lo{constructor(){super(...arguments),this.srcGroup=Oo.STRING(),this.destGroup=Oo.STRING(),this.name=Oo.STRING(),this.maxSamplesCount=Oo.INTEGER(1,{range:[1,10],rangeLocked:[!0,!1]}),this.distanceThreshold=Oo.FLOAT(1),this.blendWidth=Oo.FLOAT(0)}};class mD extends QO{constructor(){super(...arguments),this.paramsConfig=_D}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([Ti.FROM_NODE,Ti.NEVER])}async cook(e){this._core_group_dest=e[0];const t=this._core_group_dest.pointsFromGroup(this.pv.destGroup);this._core_group_src=e[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(t),this.setCoreGroup(this._core_group_dest)}_error_if_attribute_not_found_on_second_input(){for(let e of this._attrib_names)this._core_group_src.hasAttrib(e)||this.states.error.set(`attribute '${e}' not found on second input`)}_build_octree_if_required(e){const t=null==this._octree_timestamp||this._octree_timestamp!==e.timestamp();if(this._prev_param_srcGroup!==this.pv.srcGroup||t){this._octree_timestamp=e.timestamp(),this._prev_param_srcGroup=this.pv.srcGroup;const t=this._core_group_src.pointsFromGroup(this.pv.srcGroup);this._octree=new dD(this._core_group_src.boundingBox()),this._octree.set_points(t)}}_add_attribute_if_required(){for(let e of this._attrib_names)if(!this._core_group_dest.hasAttrib(e)){const t=this._core_group_src.attribSize(e);this._core_group_dest.addNumericVertexAttrib(e,t,0)}}async _transfer_attributes(e){const t=new pD;await t.startWithArray(e,this._transfer_attributes_for_point.bind(this))}_transfer_attributes_for_point(e){var t;const n=this.pv.distanceThreshold+this.pv.blendWidth,i=(null===(t=this._octree)||void 0===t?void 0:t.find_points(e.position(),n,this.pv.maxSamplesCount))||[];for(let t of this._attrib_names)this._interpolate_points(e,i,t)}_interpolate_points(e,t,n){let i;i=class{static perform(e,t,n,i,s){switch(t.length){case 0:return e.attribValue(n);case 1:return this._interpolate_with_1_point(e,t[0],n,i,s);default:return this._interpolate_with_multiple_points(e,t,n,i,s)}}static _interpolate_with_1_point(e,t,n,i,s){const r=e.position(),o=t.position(),a=r.distanceTo(o),c=t.attribValue(n);return m.isNumber(c)?this._weighted_value_from_distance(e,c,n,a,i,s):(console.warn(\\\\\\\"value is not a number\\\\\\\",c),0)}static _weight_from_distance(e,t,n){return(e-t)/n}static _weighted_value_from_distance(e,t,n,i,s,r){if(i<=s)return t;{const o=e.attribValue(n);if(m.isNumber(o)){const e=this._weight_from_distance(i,s,r);return e*o+(1-e)*t}return console.warn(\\\\\\\"value is not a number\\\\\\\",o),0}}static _interpolate_with_multiple_points(e,t,n,i,s){const r=t.map((t=>this._interpolate_with_1_point(e,t,n,i,s)));return f.max(r)||0}static weights(e,t){switch(t.length){case 1:return 1;case 2:return this._weights_from_2(e,t);default:return t=t.slice(0,3),this._weights_from_3(e,t)}}static _weights_from_2(e,t){const n=t.map((t=>e.distanceTo(t))),i=f.sum(n);return[n[1]/i,n[0]/i]}static _weights_from_3(e,t){const n=t.map((t=>e.distanceTo(t))),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(e,t,n,this.pv.distanceThreshold,this.pv.blendWidth),null!=i&&e.setAttribValue(n,i)}}const fD=new class extends Lo{constructor(){super(...arguments),this.stepSize=Oo.FLOAT(.1)}};class gD extends QO{constructor(){super(...arguments),this.paramsConfig=fD}static type(){return\\\\\\\"bboxScatter\\\\\\\"}static displayedInputNames(){return[\\\\\\\"geometry to create points from\\\\\\\"]}initializeNode(){this.io.inputs.setCount(1)}cook(e){const t=e[0],n=this.pv.stepSize,i=t.boundingBox(),s=i.min,r=i.max,o=[];for(let e=s.x;e<=r.x;e+=n)for(let t=s.y;t<=r.y;t+=n)for(let i=s.z;i<=r.z;i+=n)o.push(e),o.push(t),o.push(i);const a=new O.a;a.setAttribute(\\\\\\\"position\\\\\\\",new L.a(new Float32Array(o),3)),this.setGeometry(a,Zi.POINTS)}}const vD=new class extends Lo{constructor(){super(...arguments),this.attribName=Oo.STRING(\\\\\\\"position\\\\\\\"),this.blend=Oo.FLOAT(.5,{range:[0,1],rangeLocked:[!0,!0]})}};class yD extends QO{constructor(){super(...arguments),this.paramsConfig=vD}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([Ti.FROM_NODE,Ti.NEVER])}cook(e){const t=e[0],n=e[1],i=t.objects(),s=n.objects();let r,o;for(let e=0;e<i.length;e++)r=i[e],o=s[e],this.blend(r,o,this.pv.blend);this.setCoreGroup(t)}blend(e,t,n){const i=e.geometry,s=t.geometry;if(null==i||null==s)return;const r=i.getAttribute(this.pv.attribName),o=s.getAttribute(this.pv.attribName);if(null==r||null==o)return;const a=r.array,c=o.array;let l,u;for(let e=0;e<a.length;e++)l=a[e],u=c[e],null!=u&&(a[e]=(1-n)*l+n*u);i.computeVertexNormals()}}class xD{constructor(){this.polygons=[]}clone(){let e=new xD;return e.polygons=this.polygons.map((function(e){return e.clone()})),e}toPolygons(){return this.polygons}union(e){let t=new MD(this.clone().polygons),n=new MD(e.clone().polygons);return t.clipTo(n),n.clipTo(t),n.invert(),n.clipTo(t),n.invert(),t.build(n.allPolygons()),xD.fromPolygons(t.allPolygons())}subtract(e){let t=new MD(this.clone().polygons),n=new MD(e.clone().polygons);return t.invert(),t.clipTo(n),n.clipTo(t),n.invert(),n.clipTo(t),n.invert(),t.build(n.allPolygons()),t.invert(),xD.fromPolygons(t.allPolygons())}intersect(e){let t=new MD(this.clone().polygons),n=new MD(e.clone().polygons);return t.invert(),n.clipTo(t),n.invert(),t.clipTo(n),n.clipTo(t),t.build(n.allPolygons()),t.invert(),xD.fromPolygons(t.allPolygons())}inverse(){let e=this.clone();return e.polygons.forEach((e=>e.flip())),e}}xD.fromPolygons=function(e){let t=new xD;return t.polygons=e,t};class bD{constructor(e=0,t=0,n=0){this.x=e,this.y=t,this.z=n}copy(e){return this.x=e.x,this.y=e.y,this.z=e.z,this}clone(){return new bD(this.x,this.y,this.z)}negate(){return this.x*=-1,this.y*=-1,this.z*=-1,this}add(e){return this.x+=e.x,this.y+=e.y,this.z+=e.z,this}sub(e){return this.x-=e.x,this.y-=e.y,this.z-=e.z,this}times(e){return this.x*=e,this.y*=e,this.z*=e,this}dividedBy(e){return this.x/=e,this.y/=e,this.z/=e,this}lerp(e,t){return this.add(wD.copy(e).sub(this).times(t))}unit(){return this.dividedBy(this.length())}length(){return Math.sqrt(this.x**2+this.y**2+this.z**2)}normalize(){return this.unit()}cross(e){let t=this;const n=t.x,i=t.y,s=t.z,r=e.x,o=e.y,a=e.z;return this.x=i*a-s*o,this.y=s*r-n*a,this.z=n*o-i*r,this}dot(e){return this.x*e.x+this.y*e.y+this.z*e.z}}let wD=new bD,AD=new bD;class TD{constructor(e,t,n,i){this.pos=(new bD).copy(e),this.normal=(new bD).copy(t),this.uv=(new bD).copy(n),this.uv.z=0,i&&(this.color=(new bD).copy(i))}clone(){return new TD(this.pos,this.normal,this.uv,this.color)}flip(){this.normal.negate()}interpolate(e,t){return new TD(this.pos.clone().lerp(e.pos,t),this.normal.clone().lerp(e.normal,t),this.uv.clone().lerp(e.uv,t),this.color&&e.color&&this.color.clone().lerp(e.color,t))}}class ED{constructor(e,t){this.normal=e,this.w=t}clone(){return new ED(this.normal.clone(),this.w)}flip(){this.normal.negate(),this.w=-this.w}splitPolygon(e,t,n,i,s){let r=0,o=[];for(let t=0;t<e.vertices.length;t++){let n=this.normal.dot(e.vertices[t].pos)-this.w,i=n<-ED.EPSILON?2:n>ED.EPSILON?1:0;r|=i,o.push(i)}switch(r){case 0:(this.normal.dot(e.plane.normal)>0?t:n).push(e);break;case 1:i.push(e);break;case 2:s.push(e);break;case 3:let r=[],a=[];for(let t=0;t<e.vertices.length;t++){let n=(t+1)%e.vertices.length,i=o[t],s=o[n],c=e.vertices[t],l=e.vertices[n];if(2!=i&&r.push(c),1!=i&&a.push(2!=i?c.clone():c),3==(i|s)){let e=(this.w-this.normal.dot(c.pos))/this.normal.dot(wD.copy(l.pos).sub(c.pos)),t=c.interpolate(l,e);r.push(t),a.push(t.clone())}}r.length>=3&&i.push(new CD(r,e.shared)),a.length>=3&&s.push(new CD(a,e.shared))}}}ED.EPSILON=1e-5,ED.fromPoints=function(e,t,n){let i=wD.copy(t).sub(e).cross(AD.copy(n).sub(e)).normalize();return new ED(i.clone(),i.dot(e))};class CD{constructor(e,t){this.vertices=e,this.shared=t,this.plane=ED.fromPoints(e[0].pos,e[1].pos,e[2].pos)}clone(){return new CD(this.vertices.map((e=>e.clone())),this.shared)}flip(){this.vertices.reverse().map((e=>e.flip())),this.plane.flip()}}class MD{constructor(e){this.plane=null,this.front=null,this.back=null,this.polygons=[],e&&this.build(e)}clone(){let e=new MD;return e.plane=this.plane&&this.plane.clone(),e.front=this.front&&this.front.clone(),e.back=this.back&&this.back.clone(),e.polygons=this.polygons.map((e=>e.clone())),e}invert(){for(let e=0;e<this.polygons.length;e++)this.polygons[e].flip();this.plane&&this.plane.flip(),this.front&&this.front.invert(),this.back&&this.back.invert();let e=this.front;this.front=this.back,this.back=e}clipPolygons(e){if(!this.plane)return e.slice();let t=[],n=[];for(let i=0;i<e.length;i++)this.plane.splitPolygon(e[i],t,n,t,n);return this.front&&(t=this.front.clipPolygons(t)),n=this.back?this.back.clipPolygons(n):[],t.concat(n)}clipTo(e){this.polygons=e.clipPolygons(this.polygons),this.front&&this.front.clipTo(e),this.back&&this.back.clipTo(e)}allPolygons(){let e=this.polygons.slice();return this.front&&(e=e.concat(this.front.allPolygons())),this.back&&(e=e.concat(this.back.allPolygons())),e}build(e){if(!e.length)return;this.plane||(this.plane=e[0].plane.clone());let t=[],n=[];for(let i=0;i<e.length;i++)this.plane.splitPolygon(e[i],this.polygons,this.polygons,t,n);t.length&&(this.front||(this.front=new MD),this.front.build(t)),n.length&&(this.back||(this.back=new MD),this.back.build(n))}}xD.fromJSON=function(e){return xD.fromPolygons(e.polygons.map((e=>new CD(e.vertices.map((e=>new TD(e.pos,e.normal,e.uv))),e.shared))))},xD.fromGeometry=function(e,t){let n=[];if(e.isGeometry){let i=e.faces,s=e.vertices,r=[\\\\\\\"a\\\\\\\",\\\\\\\"b\\\\\\\",\\\\\\\"c\\\\\\\"];for(let o=0;o<i.length;o++){let a=i[o],c=[];for(let t=0;t<3;t++)c.push(new TD(s[a[r[t]]],a.vertexNormals[t],e.faceVertexUvs[0][o][t]));n.push(new CD(c,t))}}else if(e.isBufferGeometry){let i,s=e.attributes.position,r=e.attributes.normal,o=e.attributes.uv,a=e.attributes.color;if(e.index)i=e.index.array;else{i=new Array(s.array.length/s.itemSize|0);for(let e=0;e<i.length;e++)i[e]=e}let c=i.length/3|0;n=new Array(c);for(let e=0,c=0,l=i.length;e<l;e+=3,c++){let l=new Array(3);for(let t=0;t<3;t++){let n=i[e+t],c=3*n,u=2*n,h=s.array[c],d=s.array[c+1],p=s.array[c+2],_=r.array[c],m=r.array[c+1],f=r.array[c+2],g=o.array[u],v=o.array[u+1];l[t]=new TD({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[c]=new CD(l,t)}}else console.error(\\\\\\\"Unsupported CSG input type:\\\\\\\"+e.type);return xD.fromPolygons(n)};let ND=new p.a,SD=new V.a;xD.fromMesh=function(e,t){let n=xD.fromGeometry(e.geometry,t);SD.getNormalMatrix(e.matrix);for(let t=0;t<n.polygons.length;t++){let i=n.polygons[t];for(let t=0;t<i.vertices.length;t++){let n=i.vertices[t];n.pos.copy(ND.copy(n.pos).applyMatrix4(e.matrix)),n.normal.copy(ND.copy(n.normal).applyMatrix3(SD))}}return n};let OD=e=>({top:0,array:new Float32Array(e),write:function(e){this.array[this.top++]=e.x,this.array[this.top++]=e.y,this.array[this.top++]=e.z}}),LD=e=>({top:0,array:new Float32Array(e),write:function(e){this.array[this.top++]=e.x,this.array[this.top++]=e.y}});var PD;xD.toMesh=function(e,t,n){let i,s,r=e.polygons;{let e=0;r.forEach((t=>e+=t.vertices.length-2)),i=new O.a;let t,n=OD(3*e*3),o=OD(3*e*3),a=LD(2*e*3),c=[];if(r.forEach((i=>{let s=i.vertices,r=s.length;void 0!==i.shared&&(c[i.shared]||(c[i.shared]=[])),r&&void 0!==s[0].color&&(t||(t=OD(3*e*3)));for(let e=3;e<=r;e++)void 0!==i.shared&&c[i.shared].push(n.top/3,n.top/3+1,n.top/3+2),n.write(s[0].pos),n.write(s[e-2].pos),n.write(s[e-1].pos),o.write(s[0].normal),o.write(s[e-2].normal),o.write(s[e-1].normal),a.write(s[0].uv),a.write(s[e-2].uv),a.write(s[e-1].uv),t&&(t.write(s[0].color)||t.write(s[e-2].color)||t.write(s[e-1].color))})),i.setAttribute(\\\\\\\"position\\\\\\\",new L.a(n.array,3)),i.setAttribute(\\\\\\\"normal\\\\\\\",new L.a(o.array,3)),i.setAttribute(\\\\\\\"uv\\\\\\\",new L.a(a.array,2)),t&&i.setAttribute(\\\\\\\"color\\\\\\\",new L.a(t.array,3)),c.length){let e=[],t=0;for(let n=0;n<c.length;n++)i.addGroup(t,c[n].length,n),t+=c[n].length,e=e.concat(c[n]);i.setIndex(e)}s=i}let o=(new C.a).copy(t).invert();i.applyMatrix4(o),i.computeBoundingSphere(),i.computeBoundingBox();let a=new z.a(i,n);return a.matrix.copy(t),a.matrix.decompose(a.position,a.quaternion,a.scale),a.rotation.setFromQuaternion(a.quaternion),a.updateMatrixWorld(),a.castShadow=a.receiveShadow=!0,a},function(e){e.INTERSECT=\\\\\\\"intersect\\\\\\\",e.SUBSTRACT=\\\\\\\"substract\\\\\\\",e.UNION=\\\\\\\"union\\\\\\\"}(PD||(PD={}));const RD=[PD.INTERSECT,PD.SUBSTRACT,PD.UNION];class ID extends WO{static type(){return\\\\\\\"boolean\\\\\\\"}cook(e,t){const n=e[0].objectsWithGeo()[0],i=e[1].objectsWithGeo()[0],s=this._applyBooleaOperation(n,i,t);let r=n.material;if(t.useBothMaterials){r=m.isArray(r)?r[0]:r;let e=i.material;e=m.isArray(e)?e[0]:e,r=[r,e]}const o=xD.toMesh(s,n.matrix,r);return this.createCoreGroupFromObjects([o])}_applyBooleaOperation(e,t,n){const i=RD[n.operation];let s=xD.fromMesh(e,0),r=xD.fromMesh(t,1);switch(i){case PD.INTERSECT:return s.intersect(r);case PD.SUBSTRACT:return s.subtract(r);case PD.UNION:return s.union(r)}Ri.unreachable(i)}}ID.DEFAULT_PARAMS={operation:RD.indexOf(PD.INTERSECT),useBothMaterials:!0},ID.INPUT_CLONED_STATE=[Ti.FROM_NODE,Ti.NEVER];const FD=ID.DEFAULT_PARAMS;const DD=new class extends Lo{constructor(){super(...arguments),this.operation=Oo.INTEGER(FD.operation,{menu:{entries:RD.map(((e,t)=>({name:e,value:t})))}}),this.useBothMaterials=Oo.BOOLEAN(FD.useBothMaterials)}};class kD extends QO{constructor(){super(...arguments),this.paramsConfig=DD}static type(){return\\\\\\\"boolean\\\\\\\"}initializeNode(){super.initializeNode(),this.io.inputs.setCount(2),this.io.inputs.initInputsClonedState(ID.INPUT_CLONED_STATE),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.operation],(()=>RD[this.pv.operation]))}))}))}setOperation(e){this.p.operation.set(RD.indexOf(e))}async cook(e){this._operation=this._operation||new ID(this.scene(),this.states,this);const t=await this._operation.cook(e,this.pv);this.setCoreGroup(t)}}class BD extends WO{constructor(){super(...arguments),this._core_transform=new rS}static type(){return\\\\\\\"box\\\\\\\"}cook(e,t){const n=e[0],i=n?this._cook_with_input(n,t):this._cook_without_input(t);return this.createCoreGroupFromGeometry(i)}_cook_without_input(e){const t=e.divisions,n=e.size,i=new P(n,n,n,t,t,t);return i.translate(e.center.x,e.center.y,e.center.z),i.computeVertexNormals(),i}_cook_with_input(e,t){const n=t.divisions,i=e.boundingBox(),s=i.max.clone().sub(i.min),r=i.max.clone().add(i.min).multiplyScalar(.5),o=new P(s.x,s.y,s.z,n,n,n),a=this._core_transform.translation_matrix(r);return o.applyMatrix4(a),o}}BD.DEFAULT_PARAMS={size:1,divisions:1,center:new p.a(0,0,0)},BD.INPUT_CLONED_STATE=Ti.NEVER;const zD=BD.DEFAULT_PARAMS;const UD=new class extends Lo{constructor(){super(...arguments),this.size=Oo.FLOAT(zD.size),this.divisions=Oo.INTEGER(zD.divisions,{range:[1,10],rangeLocked:[!0,!1]}),this.center=Oo.VECTOR3(zD.center)}};class GD extends QO{constructor(){super(...arguments),this.paramsConfig=UD}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(BD.INPUT_CLONED_STATE)}cook(e){this._operation=this._operation||new BD(this._scene,this.states);const t=this._operation.cook(e,this.pv);this.setCoreGroup(t)}}const VD=new C.a,jD=new C.a,HD=[],qD=new z.a;function WD(e,t,n){z.a.call(this,e,t),this.instanceMatrix=new L.a(new Float32Array(16*n),16),this.instanceColor=null,this.count=n,this.frustumCulled=!1}let XD;WD.prototype=Object.assign(Object.create(z.a.prototype),{constructor:WD,isInstancedMesh:!0,copy:function(e){return z.a.prototype.copy.call(this,e),this.instanceMatrix.copy(e.instanceMatrix),null!==e.instanceColor&&(this.instanceColor=e.instanceColor.clone()),this.count=e.count,this},getColorAt:function(e,t){t.fromArray(this.instanceColor.array,3*e)},getMatrixAt:function(e,t){t.fromArray(this.instanceMatrix.array,16*e)},raycast:function(e,t){const n=this.matrixWorld,i=this.count;if(qD.geometry=this.geometry,qD.material=this.material,void 0!==qD.material)for(let s=0;s<i;s++){this.getMatrixAt(s,VD),jD.multiplyMatrices(n,VD),qD.matrixWorld=jD,qD.raycast(e,HD);for(let e=0,n=HD.length;e<n;e++){const n=HD[e];n.instanceId=s,n.object=this,t.push(n)}HD.length=0}},setColorAt:function(e,t){null===this.instanceColor&&(this.instanceColor=new L.a(new Float32Array(3*this.count),3)),t.toArray(this.instanceColor.array,3*e)},setMatrixAt:function(e,t){t.toArray(this.instanceMatrix.array,16*e)},updateMorphTargets:function(){},dispose:function(){this.dispatchEvent({type:\\\\\\\"dispose\\\\\\\"})}});const YD=new p.a,$D=new p.a,QD=new p.a,JD=new d.a,KD=new d.a,ZD=new C.a,ek=new p.a,tk=new p.a,nk=new p.a,ik=new d.a,sk=new d.a,rk=new d.a;class ok extends ee.a{constructor(e){if(super(),this.type=\\\\\\\"Sprite\\\\\\\",void 0===XD){XD=new O.a;const e=new Float32Array([-.5,-.5,0,0,0,.5,-.5,0,1,0,.5,.5,0,1,1,-.5,.5,0,0,1]),t=new Rs.a(e,5);XD.setIndex([0,1,2,0,2,3]),XD.setAttribute(\\\\\\\"position\\\\\\\",new Is.a(t,3,0,!1)),XD.setAttribute(\\\\\\\"uv\\\\\\\",new Is.a(t,2,3,!1))}this.geometry=XD,this.material=void 0!==e?e:new wf,this.center=new d.a(.5,.5)}raycast(e,t){null===e.camera&&console.error('THREE.Sprite: \\\\\\\"Raycaster.camera\\\\\\\" needs to be set in order to raycast against sprites.'),$D.setFromMatrixScale(this.matrixWorld),ZD.copy(e.camera.matrixWorld),this.modelViewMatrix.multiplyMatrices(e.camera.matrixWorldInverse,this.matrixWorld),QD.setFromMatrixPosition(this.modelViewMatrix),e.camera.isPerspectiveCamera&&!1===this.material.sizeAttenuation&&$D.multiplyScalar(-QD.z);const n=this.material.rotation;let i,s;0!==n&&(s=Math.cos(n),i=Math.sin(n));const r=this.center;ak(ek.set(-.5,-.5,0),QD,r,$D,i,s),ak(tk.set(.5,-.5,0),QD,r,$D,i,s),ak(nk.set(.5,.5,0),QD,r,$D,i,s),ik.set(0,0),sk.set(1,0),rk.set(1,1);let o=e.ray.intersectTriangle(ek,tk,nk,!1,YD);if(null===o&&(ak(tk.set(-.5,.5,0),QD,r,$D,i,s),sk.set(0,1),o=e.ray.intersectTriangle(ek,nk,tk,!1,YD),null===o))return;const a=e.ray.origin.distanceTo(YD);a<e.near||a>e.far||t.push({distance:a,point:YD.clone(),uv:Ts.a.getUV(YD,ek,tk,nk,ik,sk,rk,new d.a),face:null,object:this})}copy(e){return super.copy(e),void 0!==e.center&&this.center.copy(e.center),this.material=e.material,this}}function ak(e,t,n,i,s,r){JD.subVectors(e,n).addScalar(.5).multiply(i),void 0!==s?(KD.x=r*JD.x-s*JD.y,KD.y=s*JD.x+r*JD.y):KD.copy(JD),e.copy(t),e.x+=KD.x,e.y+=KD.y,e.applyMatrix4(ZD)}ok.prototype.isSprite=!0;var ck=n(93),lk=n(81),uk=n(55);class hk{constructor(){this.coefficients=[];for(let e=0;e<9;e++)this.coefficients.push(new p.a)}set(e){for(let t=0;t<9;t++)this.coefficients[t].copy(e[t]);return this}zero(){for(let e=0;e<9;e++)this.coefficients[e].set(0,0,0);return this}getAt(e,t){const n=e.x,i=e.y,s=e.z,r=this.coefficients;return t.copy(r[0]).multiplyScalar(.282095),t.addScaledVector(r[1],.488603*i),t.addScaledVector(r[2],.488603*s),t.addScaledVector(r[3],.488603*n),t.addScaledVector(r[4],n*i*1.092548),t.addScaledVector(r[5],i*s*1.092548),t.addScaledVector(r[6],.315392*(3*s*s-1)),t.addScaledVector(r[7],n*s*1.092548),t.addScaledVector(r[8],.546274*(n*n-i*i)),t}getIrradianceAt(e,t){const n=e.x,i=e.y,s=e.z,r=this.coefficients;return t.copy(r[0]).multiplyScalar(.886227),t.addScaledVector(r[1],1.023328*i),t.addScaledVector(r[2],1.023328*s),t.addScaledVector(r[3],1.023328*n),t.addScaledVector(r[4],.858086*n*i),t.addScaledVector(r[5],.858086*i*s),t.addScaledVector(r[6],.743125*s*s-.247708),t.addScaledVector(r[7],.858086*n*s),t.addScaledVector(r[8],.429043*(n*n-i*i)),t}add(e){for(let t=0;t<9;t++)this.coefficients[t].add(e.coefficients[t]);return this}addScaledSH(e,t){for(let n=0;n<9;n++)this.coefficients[n].addScaledVector(e.coefficients[n],t);return this}scale(e){for(let t=0;t<9;t++)this.coefficients[t].multiplyScalar(e);return this}lerp(e,t){for(let n=0;n<9;n++)this.coefficients[n].lerp(e.coefficients[n],t);return this}equals(e){for(let t=0;t<9;t++)if(!this.coefficients[t].equals(e.coefficients[t]))return!1;return!0}copy(e){return this.set(e.coefficients)}clone(){return(new this.constructor).copy(this)}fromArray(e,t=0){const n=this.coefficients;for(let i=0;i<9;i++)n[i].fromArray(e,t+3*i);return this}toArray(e=[],t=0){const n=this.coefficients;for(let i=0;i<9;i++)n[i].toArray(e,t+3*i);return e}static getBasisAt(e,t){const n=e.x,i=e.y,s=e.z;t[0]=.282095,t[1]=.488603*i,t[2]=.488603*s,t[3]=.488603*n,t[4]=1.092548*n*i,t[5]=1.092548*i*s,t[6]=.315392*(3*s*s-1),t[7]=1.092548*n*s,t[8]=.546274*(n*n-i*i)}}hk.prototype.isSphericalHarmonics3=!0;class dk extends pv.a{constructor(e=new hk,t=1){super(void 0,t),this.sh=e}copy(e){return super.copy(e),this.sh.copy(e.sh),this}fromJSON(e){return this.intensity=e.intensity,this.sh.fromArray(e.sh),this}toJSON(e){const t=super.toJSON(e);return t.object.sh=this.sh.toArray(),t}}dk.prototype.isLightProbe=!0;var pk=n(69),_k=n(46);function mk(){O.a.call(this),this.type=\\\\\\\"InstancedBufferGeometry\\\\\\\",this.instanceCount=1/0}function fk(e,t,n,i){\\\\\\\"number\\\\\\\"==typeof n&&(i=n,n=!1,console.error(\\\\\\\"THREE.InstancedBufferAttribute: The constructor now expects normalized as the third argument.\\\\\\\")),L.a.call(this,e,t,n),this.meshPerAttribute=i||1}mk.prototype=Object.assign(Object.create(O.a.prototype),{constructor:mk,isInstancedBufferGeometry:!0,copy:function(e){return O.a.prototype.copy.call(this,e),this.instanceCount=e.instanceCount,this},clone:function(){return(new this.constructor).copy(this)},toJSON:function(){const e=O.a.prototype.toJSON.call(this);return e.instanceCount=this.instanceCount,e.isInstancedBufferGeometry=!0,e}}),fk.prototype=Object.assign(Object.create(L.a.prototype),{constructor:fk,isInstancedBufferAttribute:!0,copy:function(e){return L.a.prototype.copy.call(this,e),this.meshPerAttribute=e.meshPerAttribute,this},toJSON:function(){const e=L.a.prototype.toJSON.call(this);return e.meshPerAttribute=this.meshPerAttribute,e.isInstancedBufferAttribute=!0,e}});class gk extends xf.a{constructor(e){super(e)}load(e,t,n,i){const s=this,r=new yf.a(s.manager);r.setPath(s.path),r.setRequestHeader(s.requestHeader),r.setWithCredentials(s.withCredentials),r.load(e,(function(n){try{t(s.parse(JSON.parse(n)))}catch(t){i?i(t):console.error(t),s.manager.itemError(e)}}),n,i)}parse(e){const t={},n={};function i(e,i){if(void 0!==t[i])return t[i];const s=e.interleavedBuffers[i],r=function(e,t){if(void 0!==n[t])return n[t];const i=e.arrayBuffers[t],s=new Uint32Array(i).buffer;return n[t]=s,s}(e,s.buffer),o=Object(ce.b)(s.type,r),a=new Rs.a(o,s.stride);return a.uuid=s.uuid,t[i]=a,a}const s=e.isInstancedBufferGeometry?new mk:new O.a,r=e.data.index;if(void 0!==r){const e=Object(ce.b)(r.type,r.array);s.setIndex(new L.a(e,1))}const o=e.data.attributes;for(const t in o){const n=o[t];let r;if(n.isInterleavedBufferAttribute){const t=i(e.data,n.data);r=new Is.a(t,n.itemSize,n.offset,n.normalized)}else{const e=Object(ce.b)(n.type,n.array);r=new(n.isInstancedBufferAttribute?fk:L.a)(e,n.itemSize,n.normalized)}void 0!==n.name&&(r.name=n.name),void 0!==n.usage&&r.setUsage(n.usage),void 0!==n.updateRange&&(r.updateRange.offset=n.updateRange.offset,r.updateRange.count=n.updateRange.count),s.setAttribute(t,r)}const a=e.data.morphAttributes;if(a)for(const t in a){const n=a[t],r=[];for(let t=0,s=n.length;t<s;t++){const s=n[t];let o;if(s.isInterleavedBufferAttribute){const t=i(e.data,s.data);o=new Is.a(t,s.itemSize,s.offset,s.normalized)}else{const e=Object(ce.b)(s.type,s.array);o=new L.a(e,s.itemSize,s.normalized)}void 0!==s.name&&(o.name=s.name),r.push(o)}s.morphAttributes[t]=r}e.data.morphTargetsRelative&&(s.morphTargetsRelative=!0);const c=e.data.groups||e.data.drawcalls||e.data.offsets;if(void 0!==c)for(let e=0,t=c.length;e!==t;++e){const t=c[e];s.addGroup(t.start,t.count,t.materialIndex)}const l=e.data.boundingSphere;if(void 0!==l){const e=new p.a;void 0!==l.center&&e.fromArray(l.center),s.boundingSphere=new uD.a(e,l.radius)}return e.name&&(s.name=e.name),e.userData&&(s.userData=e.userData),s}}class vk extends O.a{constructor(e=1,t=8,n=0,i=2*Math.PI){super(),this.type=\\\\\\\"CircleGeometry\\\\\\\",this.parameters={radius:e,segments:t,thetaStart:n,thetaLength:i},t=Math.max(3,t);const s=[],r=[],o=[],a=[],c=new p.a,l=new d.a;r.push(0,0,0),o.push(0,0,1),a.push(.5,.5);for(let s=0,u=3;s<=t;s++,u+=3){const h=n+s/t*i;c.x=e*Math.cos(h),c.y=e*Math.sin(h),r.push(c.x,c.y,c.z),o.push(0,0,1),l.x=(r[u]/e+1)/2,l.y=(r[u+1]/e+1)/2,a.push(l.x,l.y)}for(let e=1;e<=t;e++)s.push(e,e+1,0);this.setIndex(s),this.setAttribute(\\\\\\\"position\\\\\\\",new L.c(r,3)),this.setAttribute(\\\\\\\"normal\\\\\\\",new L.c(o,3)),this.setAttribute(\\\\\\\"uv\\\\\\\",new L.c(a,2))}}class yk extends PS{constructor(e=1,t=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],e,t),this.type=\\\\\\\"DodecahedronGeometry\\\\\\\",this.parameters={radius:e,detail:t}}}const xk=new p.a,bk=new p.a,wk=new p.a,Ak=new Ts.a;class Tk extends O.a{constructor(e,t){if(super(),this.type=\\\\\\\"EdgesGeometry\\\\\\\",this.parameters={thresholdAngle:t},t=void 0!==t?t:1,!0===e.isGeometry)return void console.error(\\\\\\\"THREE.EdgesGeometry no longer supports THREE.Geometry. Use THREE.BufferGeometry instead.\\\\\\\");const n=Math.pow(10,4),i=Math.cos(A.a.DEG2RAD*t),s=e.getIndex(),r=e.getAttribute(\\\\\\\"position\\\\\\\"),o=s?s.count:r.count,a=[0,0,0],c=[\\\\\\\"a\\\\\\\",\\\\\\\"b\\\\\\\",\\\\\\\"c\\\\\\\"],l=new Array(3),u={},h=[];for(let e=0;e<o;e+=3){s?(a[0]=s.getX(e),a[1]=s.getX(e+1),a[2]=s.getX(e+2)):(a[0]=e,a[1]=e+1,a[2]=e+2);const{a:t,b:o,c:d}=Ak;if(t.fromBufferAttribute(r,a[0]),o.fromBufferAttribute(r,a[1]),d.fromBufferAttribute(r,a[2]),Ak.getNormal(wk),l[0]=`${Math.round(t.x*n)},${Math.round(t.y*n)},${Math.round(t.z*n)}`,l[1]=`${Math.round(o.x*n)},${Math.round(o.y*n)},${Math.round(o.z*n)}`,l[2]=`${Math.round(d.x*n)},${Math.round(d.y*n)},${Math.round(d.z*n)}`,l[0]!==l[1]&&l[1]!==l[2]&&l[2]!==l[0])for(let e=0;e<3;e++){const t=(e+1)%3,n=l[e],s=l[t],r=Ak[c[e]],o=Ak[c[t]],d=`${n}_${s}`,p=`${s}_${n}`;p in u&&u[p]?(wk.dot(u[p].normal)<=i&&(h.push(r.x,r.y,r.z),h.push(o.x,o.y,o.z)),u[p]=null):d in u||(u[d]={index0:a[e],index1:a[t],normal:wk.clone()})}}for(const e in u)if(u[e]){const{index0:t,index1:n}=u[e];xk.fromBufferAttribute(r,t),bk.fromBufferAttribute(r,n),h.push(xk.x,xk.y,xk.z),h.push(bk.x,bk.y,bk.z)}this.setAttribute(\\\\\\\"position\\\\\\\",new L.c(h,3))}}var Ek=n(53);class Ck extends O.a{constructor(e,t){super(),this.type=\\\\\\\"ExtrudeGeometry\\\\\\\",this.parameters={shapes:e,options:t},e=Array.isArray(e)?e:[e];const n=this,i=[],s=[];for(let t=0,n=e.length;t<n;t++){r(e[t])}function r(e){const r=[],o=void 0!==t.curveSegments?t.curveSegments:12,a=void 0!==t.steps?t.steps:1;let c=void 0!==t.depth?t.depth:100,l=void 0===t.bevelEnabled||t.bevelEnabled,u=void 0!==t.bevelThickness?t.bevelThickness:6,h=void 0!==t.bevelSize?t.bevelSize:u-2,_=void 0!==t.bevelOffset?t.bevelOffset:0,m=void 0!==t.bevelSegments?t.bevelSegments:3;const f=t.extrudePath,g=void 0!==t.UVGenerator?t.UVGenerator:Mk;void 0!==t.amount&&(console.warn(\\\\\\\"THREE.ExtrudeBufferGeometry: amount has been renamed to depth.\\\\\\\"),c=t.amount);let v,y,x,b,w,A=!1;f&&(v=f.getSpacedPoints(a),A=!0,l=!1,y=f.computeFrenetFrames(a,!1),x=new p.a,b=new p.a,w=new p.a),l||(m=0,u=0,h=0,_=0);const T=e.extractPoints(o);let E=T.shape;const C=T.holes;if(!Ek.a.isClockWise(E)){E=E.reverse();for(let e=0,t=C.length;e<t;e++){const t=C[e];Ek.a.isClockWise(t)&&(C[e]=t.reverse())}}const M=Ek.a.triangulateShape(E,C),N=E;for(let e=0,t=C.length;e<t;e++){const t=C[e];E=E.concat(t)}function S(e,t,n){return t||console.error(\\\\\\\"THREE.ExtrudeGeometry: vec does not exist\\\\\\\"),t.clone().multiplyScalar(n).add(e)}const O=E.length,L=M.length;function P(e,t,n){let i,s,r;const o=e.x-t.x,a=e.y-t.y,c=n.x-e.x,l=n.y-e.y,u=o*o+a*a,h=o*l-a*c;if(Math.abs(h)>Number.EPSILON){const h=Math.sqrt(u),p=Math.sqrt(c*c+l*l),_=t.x-a/h,m=t.y+o/h,f=((n.x-l/p-_)*l-(n.y+c/p-m)*c)/(o*l-a*c);i=_+o*f-e.x,s=m+a*f-e.y;const g=i*i+s*s;if(g<=2)return new d.a(i,s);r=Math.sqrt(g/2)}else{let e=!1;o>Number.EPSILON?c>Number.EPSILON&&(e=!0):o<-Number.EPSILON?c<-Number.EPSILON&&(e=!0):Math.sign(a)===Math.sign(l)&&(e=!0),e?(i=-a,s=o,r=Math.sqrt(u)):(i=o,s=a,r=Math.sqrt(u/2))}return new d.a(i/r,s/r)}const R=[];for(let e=0,t=N.length,n=t-1,i=e+1;e<t;e++,n++,i++)n===t&&(n=0),i===t&&(i=0),R[e]=P(N[e],N[n],N[i]);const I=[];let F,D=R.concat();for(let e=0,t=C.length;e<t;e++){const t=C[e];F=[];for(let e=0,n=t.length,i=n-1,s=e+1;e<n;e++,i++,s++)i===n&&(i=0),s===n&&(s=0),F[e]=P(t[e],t[i],t[s]);I.push(F),D=D.concat(F)}for(let e=0;e<m;e++){const t=e/m,n=u*Math.cos(t*Math.PI/2),i=h*Math.sin(t*Math.PI/2)+_;for(let e=0,t=N.length;e<t;e++){const t=S(N[e],R[e],i);z(t.x,t.y,-n)}for(let e=0,t=C.length;e<t;e++){const t=C[e];F=I[e];for(let e=0,s=t.length;e<s;e++){const s=S(t[e],F[e],i);z(s.x,s.y,-n)}}}const k=h+_;for(let e=0;e<O;e++){const t=l?S(E[e],D[e],k):E[e];A?(b.copy(y.normals[0]).multiplyScalar(t.x),x.copy(y.binormals[0]).multiplyScalar(t.y),w.copy(v[0]).add(b).add(x),z(w.x,w.y,w.z)):z(t.x,t.y,0)}for(let e=1;e<=a;e++)for(let t=0;t<O;t++){const n=l?S(E[t],D[t],k):E[t];A?(b.copy(y.normals[e]).multiplyScalar(n.x),x.copy(y.binormals[e]).multiplyScalar(n.y),w.copy(v[e]).add(b).add(x),z(w.x,w.y,w.z)):z(n.x,n.y,c/a*e)}for(let e=m-1;e>=0;e--){const t=e/m,n=u*Math.cos(t*Math.PI/2),i=h*Math.sin(t*Math.PI/2)+_;for(let e=0,t=N.length;e<t;e++){const t=S(N[e],R[e],i);z(t.x,t.y,c+n)}for(let e=0,t=C.length;e<t;e++){const t=C[e];F=I[e];for(let e=0,s=t.length;e<s;e++){const s=S(t[e],F[e],i);A?z(s.x,s.y+v[a-1].y,v[a-1].x+n):z(s.x,s.y,c+n)}}}function B(e,t){let n=e.length;for(;--n>=0;){const i=n;let s=n-1;s<0&&(s=e.length-1);for(let e=0,n=a+2*m;e<n;e++){const n=O*e,r=O*(e+1);G(t+i+n,t+s+n,t+s+r,t+i+r)}}}function z(e,t,n){r.push(e),r.push(t),r.push(n)}function U(e,t,s){V(e),V(t),V(s);const r=i.length/3,o=g.generateTopUV(n,i,r-3,r-2,r-1);j(o[0]),j(o[1]),j(o[2])}function G(e,t,s,r){V(e),V(t),V(r),V(t),V(s),V(r);const o=i.length/3,a=g.generateSideWallUV(n,i,o-6,o-3,o-2,o-1);j(a[0]),j(a[1]),j(a[3]),j(a[1]),j(a[2]),j(a[3])}function V(e){i.push(r[3*e+0]),i.push(r[3*e+1]),i.push(r[3*e+2])}function j(e){s.push(e.x),s.push(e.y)}!function(){const e=i.length/3;if(l){let e=0,t=O*e;for(let e=0;e<L;e++){const n=M[e];U(n[2]+t,n[1]+t,n[0]+t)}e=a+2*m,t=O*e;for(let e=0;e<L;e++){const n=M[e];U(n[0]+t,n[1]+t,n[2]+t)}}else{for(let e=0;e<L;e++){const t=M[e];U(t[2],t[1],t[0])}for(let e=0;e<L;e++){const t=M[e];U(t[0]+O*a,t[1]+O*a,t[2]+O*a)}}n.addGroup(e,i.length/3-e,0)}(),function(){const e=i.length/3;let t=0;B(N,t),t+=N.length;for(let e=0,n=C.length;e<n;e++){const n=C[e];B(n,t),t+=n.length}n.addGroup(e,i.length/3-e,1)}()}this.setAttribute(\\\\\\\"position\\\\\\\",new L.c(i,3)),this.setAttribute(\\\\\\\"uv\\\\\\\",new L.c(s,2)),this.computeVertexNormals()}toJSON(){const e=O.a.prototype.toJSON.call(this);return function(e,t,n){if(n.shapes=[],Array.isArray(e))for(let t=0,i=e.length;t<i;t++){const i=e[t];n.shapes.push(i.uuid)}else n.shapes.push(e.uuid);void 0!==t.extrudePath&&(n.options.extrudePath=t.extrudePath.toJSON());return n}(this.parameters.shapes,this.parameters.options,e)}}const Mk={generateTopUV:function(e,t,n,i,s){const r=t[3*n],o=t[3*n+1],a=t[3*i],c=t[3*i+1],l=t[3*s],u=t[3*s+1];return[new d.a(r,o),new d.a(a,c),new d.a(l,u)]},generateSideWallUV:function(e,t,n,i,s,r){const o=t[3*n],a=t[3*n+1],c=t[3*n+2],l=t[3*i],u=t[3*i+1],h=t[3*i+2],p=t[3*s],_=t[3*s+1],m=t[3*s+2],f=t[3*r],g=t[3*r+1],v=t[3*r+2];return Math.abs(a-u)<.01?[new d.a(o,1-c),new d.a(l,1-h),new d.a(p,1-m),new d.a(f,1-v)]:[new d.a(a,1-c),new d.a(u,1-h),new d.a(_,1-m),new d.a(g,1-v)]}};class Nk extends PS{constructor(e=1,t=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],e,t),this.type=\\\\\\\"IcosahedronGeometry\\\\\\\",this.parameters={radius:e,detail:t}}}class Sk extends O.a{constructor(e,t=12,n=0,i=2*Math.PI){super(),this.type=\\\\\\\"LatheGeometry\\\\\\\",this.parameters={points:e,segments:t,phiStart:n,phiLength:i},t=Math.floor(t),i=A.a.clamp(i,0,2*Math.PI);const s=[],r=[],o=[],a=1/t,c=new p.a,l=new d.a;for(let s=0;s<=t;s++){const u=n+s*a*i,h=Math.sin(u),d=Math.cos(u);for(let n=0;n<=e.length-1;n++)c.x=e[n].x*h,c.y=e[n].y,c.z=e[n].x*d,r.push(c.x,c.y,c.z),l.x=s/t,l.y=n/(e.length-1),o.push(l.x,l.y)}for(let n=0;n<t;n++)for(let t=0;t<e.length-1;t++){const i=t+n*e.length,r=i,o=i+e.length,a=i+e.length+1,c=i+1;s.push(r,o,c),s.push(o,a,c)}if(this.setIndex(s),this.setAttribute(\\\\\\\"position\\\\\\\",new L.c(r,3)),this.setAttribute(\\\\\\\"uv\\\\\\\",new L.c(o,2)),this.computeVertexNormals(),i===2*Math.PI){const n=this.attributes.normal.array,i=new p.a,s=new p.a,r=new p.a,o=t*e.length*3;for(let t=0,a=0;t<e.length;t++,a+=3)i.x=n[a+0],i.y=n[a+1],i.z=n[a+2],s.x=n[o+a+0],s.y=n[o+a+1],s.z=n[o+a+2],r.addVectors(i,s).normalize(),n[a+0]=n[o+a+0]=r.x,n[a+1]=n[o+a+1]=r.y,n[a+2]=n[o+a+2]=r.z}}}function Ok(e,t,n){O.a.call(this),this.type=\\\\\\\"ParametricGeometry\\\\\\\",this.parameters={func:e,slices:t,stacks:n};const i=[],s=[],r=[],o=[],a=1e-5,c=new p.a,l=new p.a,u=new p.a,h=new p.a,d=new p.a;e.length<3&&console.error(\\\\\\\"THREE.ParametricGeometry: Function must now modify a Vector3 as third parameter.\\\\\\\");const _=t+1;for(let i=0;i<=n;i++){const p=i/n;for(let n=0;n<=t;n++){const i=n/t;e(i,p,l),s.push(l.x,l.y,l.z),i-a>=0?(e(i-a,p,u),h.subVectors(l,u)):(e(i+a,p,u),h.subVectors(u,l)),p-a>=0?(e(i,p-a,u),d.subVectors(l,u)):(e(i,p+a,u),d.subVectors(u,l)),c.crossVectors(h,d).normalize(),r.push(c.x,c.y,c.z),o.push(i,p)}}for(let e=0;e<n;e++)for(let n=0;n<t;n++){const t=e*_+n,s=e*_+n+1,r=(e+1)*_+n+1,o=(e+1)*_+n;i.push(t,s,o),i.push(s,r,o)}this.setIndex(i),this.setAttribute(\\\\\\\"position\\\\\\\",new L.c(s,3)),this.setAttribute(\\\\\\\"normal\\\\\\\",new L.c(r,3)),this.setAttribute(\\\\\\\"uv\\\\\\\",new L.c(o,2))}Ok.prototype=Object.create(O.a.prototype),Ok.prototype.constructor=Ok;class Lk extends O.a{constructor(e=.5,t=1,n=8,i=1,s=0,r=2*Math.PI){super(),this.type=\\\\\\\"RingGeometry\\\\\\\",this.parameters={innerRadius:e,outerRadius:t,thetaSegments:n,phiSegments:i,thetaStart:s,thetaLength:r},n=Math.max(3,n);const o=[],a=[],c=[],l=[];let u=e;const h=(t-e)/(i=Math.max(1,i)),_=new p.a,m=new d.a;for(let e=0;e<=i;e++){for(let e=0;e<=n;e++){const i=s+e/n*r;_.x=u*Math.cos(i),_.y=u*Math.sin(i),a.push(_.x,_.y,_.z),c.push(0,0,1),m.x=(_.x/t+1)/2,m.y=(_.y/t+1)/2,l.push(m.x,m.y)}u+=h}for(let e=0;e<i;e++){const t=e*(n+1);for(let e=0;e<n;e++){const i=e+t,s=i,r=i+n+1,a=i+n+2,c=i+1;o.push(s,r,c),o.push(r,a,c)}}this.setIndex(o),this.setAttribute(\\\\\\\"position\\\\\\\",new L.c(a,3)),this.setAttribute(\\\\\\\"normal\\\\\\\",new L.c(c,3)),this.setAttribute(\\\\\\\"uv\\\\\\\",new L.c(l,2))}}class Pk extends O.a{constructor(e,t=12){super(),this.type=\\\\\\\"ShapeGeometry\\\\\\\",this.parameters={shapes:e,curveSegments:t};const n=[],i=[],s=[],r=[];let o=0,a=0;if(!1===Array.isArray(e))c(e);else for(let t=0;t<e.length;t++)c(e[t]),this.addGroup(o,a,t),o+=a,a=0;function c(e){const o=i.length/3,c=e.extractPoints(t);let l=c.shape;const u=c.holes;!1===Ek.a.isClockWise(l)&&(l=l.reverse());for(let e=0,t=u.length;e<t;e++){const t=u[e];!0===Ek.a.isClockWise(t)&&(u[e]=t.reverse())}const h=Ek.a.triangulateShape(l,u);for(let e=0,t=u.length;e<t;e++){const t=u[e];l=l.concat(t)}for(let e=0,t=l.length;e<t;e++){const t=l[e];i.push(t.x,t.y,0),s.push(0,0,1),r.push(t.x,t.y)}for(let e=0,t=h.length;e<t;e++){const t=h[e],i=t[0]+o,s=t[1]+o,r=t[2]+o;n.push(i,s,r),a+=3}}this.setIndex(n),this.setAttribute(\\\\\\\"position\\\\\\\",new L.c(i,3)),this.setAttribute(\\\\\\\"normal\\\\\\\",new L.c(s,3)),this.setAttribute(\\\\\\\"uv\\\\\\\",new L.c(r,2))}toJSON(){const e=O.a.prototype.toJSON.call(this);return function(e,t){if(t.shapes=[],Array.isArray(e))for(let n=0,i=e.length;n<i;n++){const i=e[n];t.shapes.push(i.uuid)}else t.shapes.push(e.uuid);return t}(this.parameters.shapes,e)}}class Rk extends PS{constructor(e=1,t=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],e,t),this.type=\\\\\\\"TetrahedronGeometry\\\\\\\",this.parameters={radius:e,detail:t}}}class Ik extends Ck{constructor(e,t={}){const n=t.font;if(!n||!n.isFont)return console.error(\\\\\\\"THREE.TextGeometry: font parameter is not an instance of THREE.Font.\\\\\\\"),new O.a;const i=n.generateShapes(e,t.size);t.depth=void 0!==t.height?t.height:50,void 0===t.bevelThickness&&(t.bevelThickness=10),void 0===t.bevelSize&&(t.bevelSize=8),void 0===t.bevelEnabled&&(t.bevelEnabled=!1),super(i,t),this.type=\\\\\\\"TextGeometry\\\\\\\"}}class Fk extends O.a{constructor(e=1,t=.4,n=8,i=6,s=2*Math.PI){super(),this.type=\\\\\\\"TorusGeometry\\\\\\\",this.parameters={radius:e,tube:t,radialSegments:n,tubularSegments:i,arc:s},n=Math.floor(n),i=Math.floor(i);const r=[],o=[],a=[],c=[],l=new p.a,u=new p.a,h=new p.a;for(let r=0;r<=n;r++)for(let d=0;d<=i;d++){const p=d/i*s,_=r/n*Math.PI*2;u.x=(e+t*Math.cos(_))*Math.cos(p),u.y=(e+t*Math.cos(_))*Math.sin(p),u.z=t*Math.sin(_),o.push(u.x,u.y,u.z),l.x=e*Math.cos(p),l.y=e*Math.sin(p),h.subVectors(u,l).normalize(),a.push(h.x,h.y,h.z),c.push(d/i),c.push(r/n)}for(let e=1;e<=n;e++)for(let t=1;t<=i;t++){const n=(i+1)*e+t-1,s=(i+1)*(e-1)+t-1,o=(i+1)*(e-1)+t,a=(i+1)*e+t;r.push(n,s,a),r.push(s,o,a)}this.setIndex(r),this.setAttribute(\\\\\\\"position\\\\\\\",new L.c(o,3)),this.setAttribute(\\\\\\\"normal\\\\\\\",new L.c(a,3)),this.setAttribute(\\\\\\\"uv\\\\\\\",new L.c(c,2))}}class Dk extends O.a{constructor(e=1,t=.4,n=64,i=8,s=2,r=3){super(),this.type=\\\\\\\"TorusKnotGeometry\\\\\\\",this.parameters={radius:e,tube:t,tubularSegments:n,radialSegments:i,p:s,q:r},n=Math.floor(n),i=Math.floor(i);const o=[],a=[],c=[],l=[],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*s*Math.PI*2;v(p,s,r,e,d),v(p+.01,s,r,e,_),f.subVectors(_,d),g.addVectors(_,d),m.crossVectors(f,g),g.crossVectors(m,f),m.normalize(),g.normalize();for(let e=0;e<=i;++e){const s=e/i*Math.PI*2,r=-t*Math.cos(s),p=t*Math.sin(s);u.x=d.x+(r*g.x+p*m.x),u.y=d.y+(r*g.y+p*m.y),u.z=d.z+(r*g.z+p*m.z),a.push(u.x,u.y,u.z),h.subVectors(u,d).normalize(),c.push(h.x,h.y,h.z),l.push(o/n),l.push(e/i)}}for(let e=1;e<=n;e++)for(let t=1;t<=i;t++){const n=(i+1)*(e-1)+(t-1),s=(i+1)*e+(t-1),r=(i+1)*e+t,a=(i+1)*(e-1)+t;o.push(n,s,a),o.push(s,r,a)}function v(e,t,n,i,s){const r=Math.cos(e),o=Math.sin(e),a=n/t*e,c=Math.cos(a);s.x=i*(2+c)*.5*r,s.y=i*(2+c)*o*.5,s.z=i*Math.sin(a)*.5}this.setIndex(o),this.setAttribute(\\\\\\\"position\\\\\\\",new L.c(a,3)),this.setAttribute(\\\\\\\"normal\\\\\\\",new L.c(c,3)),this.setAttribute(\\\\\\\"uv\\\\\\\",new L.c(l,2))}}class kk extends O.a{constructor(e,t=64,n=1,i=8,s=!1){super(),this.type=\\\\\\\"TubeGeometry\\\\\\\",this.parameters={path:e,tubularSegments:t,radius:n,radialSegments:i,closed:s};const r=e.computeFrenetFrames(t,s);this.tangents=r.tangents,this.normals=r.normals,this.binormals=r.binormals;const o=new p.a,a=new p.a,c=new d.a;let l=new p.a;const u=[],h=[],_=[],m=[];function f(s){l=e.getPointAt(s/t,l);const c=r.normals[s],d=r.binormals[s];for(let e=0;e<=i;e++){const t=e/i*Math.PI*2,s=Math.sin(t),r=-Math.cos(t);a.x=r*c.x+s*d.x,a.y=r*c.y+s*d.y,a.z=r*c.z+s*d.z,a.normalize(),h.push(a.x,a.y,a.z),o.x=l.x+n*a.x,o.y=l.y+n*a.y,o.z=l.z+n*a.z,u.push(o.x,o.y,o.z)}}!function(){for(let e=0;e<t;e++)f(e);f(!1===s?t:0),function(){for(let e=0;e<=t;e++)for(let n=0;n<=i;n++)c.x=e/t,c.y=n/i,_.push(c.x,c.y)}(),function(){for(let e=1;e<=t;e++)for(let t=1;t<=i;t++){const n=(i+1)*(e-1)+(t-1),s=(i+1)*e+(t-1),r=(i+1)*e+t,o=(i+1)*(e-1)+t;m.push(n,s,o),m.push(s,r,o)}}()}(),this.setIndex(m),this.setAttribute(\\\\\\\"position\\\\\\\",new L.c(u,3)),this.setAttribute(\\\\\\\"normal\\\\\\\",new L.c(h,3)),this.setAttribute(\\\\\\\"uv\\\\\\\",new L.c(_,2))}toJSON(){const e=O.a.prototype.toJSON.call(this);return e.path=this.parameters.path.toJSON(),e}}class Bk extends O.a{constructor(e){if(super(),this.type=\\\\\\\"WireframeGeometry\\\\\\\",!0===e.isGeometry)return void console.error(\\\\\\\"THREE.WireframeGeometry no longer supports THREE.Geometry. Use THREE.BufferGeometry instead.\\\\\\\");const t=[],n=[0,0],i={},s=new p.a;if(null!==e.index){const r=e.attributes.position,o=e.index;let a=e.groups;0===a.length&&(a=[{start:0,count:o.count,materialIndex:0}]);for(let e=0,t=a.length;e<t;++e){const t=a[e],s=t.start;for(let e=s,r=s+t.count;e<r;e+=3)for(let t=0;t<3;t++){const s=o.getX(e+t),r=o.getX(e+(t+1)%3);n[0]=Math.min(s,r),n[1]=Math.max(s,r);const a=n[0]+\\\\\\\",\\\\\\\"+n[1];void 0===i[a]&&(i[a]={index1:n[0],index2:n[1]})}}for(const e in i){const n=i[e];s.fromBufferAttribute(r,n.index1),t.push(s.x,s.y,s.z),s.fromBufferAttribute(r,n.index2),t.push(s.x,s.y,s.z)}}else{const n=e.attributes.position;for(let e=0,i=n.count/3;e<i;e++)for(let i=0;i<3;i++){const r=3*e+i;s.fromBufferAttribute(n,r),t.push(s.x,s.y,s.z);const o=3*e+(i+1)%3;s.fromBufferAttribute(n,o),t.push(s.x,s.y,s.z)}}this.setAttribute(\\\\\\\"position\\\\\\\",new L.c(t,3))}}var zk=n(82);class Uk extends xf.a{constructor(e){super(e)}load(e,t,n,i){const s=this,r=\\\\\\\"\\\\\\\"===this.path?_k.a.extractUrlBase(e):this.path;this.resourcePath=this.resourcePath||r;const o=new yf.a(this.manager);o.setPath(this.path),o.setRequestHeader(this.requestHeader),o.setWithCredentials(this.withCredentials),o.load(e,(function(n){let r=null;try{r=JSON.parse(n)}catch(t){return void 0!==i&&i(t),void console.error(\\\\\\\"THREE:ObjectLoader: Can't parse \\\\\\\"+e+\\\\\\\".\\\\\\\",t.message)}const o=r.metadata;void 0!==o&&void 0!==o.type&&\\\\\\\"geometry\\\\\\\"!==o.type.toLowerCase()?s.parse(r,t):console.error(\\\\\\\"THREE.ObjectLoader: Can't load \\\\\\\"+e)}),n,i)}parse(e,t){const n=this.parseAnimations(e.animations),i=this.parseShapes(e.shapes),s=this.parseGeometries(e.geometries,i),r=this.parseImages(e.images,(function(){void 0!==t&&t(c)})),o=this.parseTextures(e.textures,r),a=this.parseMaterials(e.materials,o),c=this.parseObject(e.object,s,a,n),l=this.parseSkeletons(e.skeletons,c);if(this.bindSkeletons(c,l),void 0!==t){let e=!1;for(const t in r)if(r[t]instanceof HTMLImageElement){e=!0;break}!1===e&&t(c)}return c}parseShapes(e){const t={};if(void 0!==e)for(let n=0,i=e.length;n<i;n++){const i=(new uk.a).fromJSON(e[n]);t[i.uuid]=i}return t}parseSkeletons(e,t){const n={},i={};if(t.traverse((function(e){e.isBone&&(i[e.uuid]=e)})),void 0!==e)for(let t=0,s=e.length;t<s;t++){const s=(new lk.a).fromJSON(e[t],i);n[s.uuid]=s}return n}parseGeometries(e,t){const n={};let i;if(void 0!==e){const r=new gk;for(let o=0,a=e.length;o<a;o++){let a;const c=e[o];switch(c.type){case\\\\\\\"PlaneGeometry\\\\\\\":case\\\\\\\"PlaneBufferGeometry\\\\\\\":a=new s[c.type](c.width,c.height,c.widthSegments,c.heightSegments);break;case\\\\\\\"BoxGeometry\\\\\\\":case\\\\\\\"BoxBufferGeometry\\\\\\\":a=new s[c.type](c.width,c.height,c.depth,c.widthSegments,c.heightSegments,c.depthSegments);break;case\\\\\\\"CircleGeometry\\\\\\\":case\\\\\\\"CircleBufferGeometry\\\\\\\":a=new s[c.type](c.radius,c.segments,c.thetaStart,c.thetaLength);break;case\\\\\\\"CylinderGeometry\\\\\\\":case\\\\\\\"CylinderBufferGeometry\\\\\\\":a=new s[c.type](c.radiusTop,c.radiusBottom,c.height,c.radialSegments,c.heightSegments,c.openEnded,c.thetaStart,c.thetaLength);break;case\\\\\\\"ConeGeometry\\\\\\\":case\\\\\\\"ConeBufferGeometry\\\\\\\":a=new s[c.type](c.radius,c.height,c.radialSegments,c.heightSegments,c.openEnded,c.thetaStart,c.thetaLength);break;case\\\\\\\"SphereGeometry\\\\\\\":case\\\\\\\"SphereBufferGeometry\\\\\\\":a=new s[c.type](c.radius,c.widthSegments,c.heightSegments,c.phiStart,c.phiLength,c.thetaStart,c.thetaLength);break;case\\\\\\\"DodecahedronGeometry\\\\\\\":case\\\\\\\"DodecahedronBufferGeometry\\\\\\\":case\\\\\\\"IcosahedronGeometry\\\\\\\":case\\\\\\\"IcosahedronBufferGeometry\\\\\\\":case\\\\\\\"OctahedronGeometry\\\\\\\":case\\\\\\\"OctahedronBufferGeometry\\\\\\\":case\\\\\\\"TetrahedronGeometry\\\\\\\":case\\\\\\\"TetrahedronBufferGeometry\\\\\\\":a=new s[c.type](c.radius,c.detail);break;case\\\\\\\"RingGeometry\\\\\\\":case\\\\\\\"RingBufferGeometry\\\\\\\":a=new s[c.type](c.innerRadius,c.outerRadius,c.thetaSegments,c.phiSegments,c.thetaStart,c.thetaLength);break;case\\\\\\\"TorusGeometry\\\\\\\":case\\\\\\\"TorusBufferGeometry\\\\\\\":a=new s[c.type](c.radius,c.tube,c.radialSegments,c.tubularSegments,c.arc);break;case\\\\\\\"TorusKnotGeometry\\\\\\\":case\\\\\\\"TorusKnotBufferGeometry\\\\\\\":a=new s[c.type](c.radius,c.tube,c.tubularSegments,c.radialSegments,c.p,c.q);break;case\\\\\\\"TubeGeometry\\\\\\\":case\\\\\\\"TubeBufferGeometry\\\\\\\":a=new s[c.type]((new zk[c.path.type]).fromJSON(c.path),c.tubularSegments,c.radius,c.radialSegments,c.closed);break;case\\\\\\\"LatheGeometry\\\\\\\":case\\\\\\\"LatheBufferGeometry\\\\\\\":a=new s[c.type](c.points,c.segments,c.phiStart,c.phiLength);break;case\\\\\\\"PolyhedronGeometry\\\\\\\":case\\\\\\\"PolyhedronBufferGeometry\\\\\\\":a=new s[c.type](c.vertices,c.indices,c.radius,c.details);break;case\\\\\\\"ShapeGeometry\\\\\\\":case\\\\\\\"ShapeBufferGeometry\\\\\\\":i=[];for(let e=0,n=c.shapes.length;e<n;e++){const n=t[c.shapes[e]];i.push(n)}a=new s[c.type](i,c.curveSegments);break;case\\\\\\\"ExtrudeGeometry\\\\\\\":case\\\\\\\"ExtrudeBufferGeometry\\\\\\\":i=[];for(let e=0,n=c.shapes.length;e<n;e++){const n=t[c.shapes[e]];i.push(n)}const e=c.options.extrudePath;void 0!==e&&(c.options.extrudePath=(new zk[e.type]).fromJSON(e)),a=new s[c.type](i,c.options);break;case\\\\\\\"BufferGeometry\\\\\\\":case\\\\\\\"InstancedBufferGeometry\\\\\\\":a=r.parse(c);break;case\\\\\\\"Geometry\\\\\\\":console.error('THREE.ObjectLoader: Loading \\\\\\\"Geometry\\\\\\\" is not supported anymore.');break;default:console.warn('THREE.ObjectLoader: Unsupported geometry type \\\\\\\"'+c.type+'\\\\\\\"');continue}a.uuid=c.uuid,void 0!==c.name&&(a.name=c.name),!0===a.isBufferGeometry&&void 0!==c.userData&&(a.userData=c.userData),n[c.uuid]=a}}return n}parseMaterials(e,t){const n={},i={};if(void 0!==e){const s=new Lf;s.setTextures(t);for(let t=0,r=e.length;t<r;t++){const r=e[t];if(\\\\\\\"MultiMaterial\\\\\\\"===r.type){const e=[];for(let t=0;t<r.materials.length;t++){const i=r.materials[t];void 0===n[i.uuid]&&(n[i.uuid]=s.parse(i)),e.push(n[i.uuid])}i[r.uuid]=e}else void 0===n[r.uuid]&&(n[r.uuid]=s.parse(r)),i[r.uuid]=n[r.uuid]}}return i}parseAnimations(e){const t={};if(void 0!==e)for(let n=0;n<e.length;n++){const i=e[n],s=fF.a.parse(i);t[s.uuid]=s}return t}parseImages(e,t){const n=this,i={};let s;function r(e){if(\\\\\\\"string\\\\\\\"==typeof e){const t=e;return function(e){return n.manager.itemStart(e),s.load(e,(function(){n.manager.itemEnd(e)}),void 0,(function(){n.manager.itemError(e),n.manager.itemEnd(e)}))}(/^(\\\\/\\\\/)|([a-z]+:(\\\\/\\\\/)?)/i.test(t)?t:n.resourcePath+t)}return e.data?{data:Object(ce.b)(e.type,e.data),width:e.width,height:e.height}:null}if(void 0!==e&&e.length>0){const n=new Zg.b(t);s=new pk.a(n),s.setCrossOrigin(this.crossOrigin);for(let t=0,n=e.length;t<n;t++){const n=e[t],s=n.url;if(Array.isArray(s)){i[n.uuid]=[];for(let e=0,t=s.length;e<t;e++){const t=r(s[e]);null!==t&&(t instanceof HTMLImageElement?i[n.uuid].push(t):i[n.uuid].push(new T.a(t.data,t.width,t.height)))}}else{const e=r(n.url);null!==e&&(i[n.uuid]=e)}}}return i}parseTextures(e,t){function n(e,t){return\\\\\\\"number\\\\\\\"==typeof e?e:(console.warn(\\\\\\\"THREE.ObjectLoader.parseTexture: Constant should be in numeric form.\\\\\\\",e),t[e])}const i={};if(void 0!==e)for(let s=0,r=e.length;s<r;s++){const r=e[s];let o;void 0===r.image&&console.warn('THREE.ObjectLoader: No \\\\\\\"image\\\\\\\" specified for',r.uuid),void 0===t[r.image]&&console.warn(\\\\\\\"THREE.ObjectLoader: Undefined image\\\\\\\",r.image);const a=t[r.image];Array.isArray(a)?(o=new se(a),6===a.length&&(o.needsUpdate=!0)):(o=a&&a.data?new T.a(a.data,a.width,a.height):new K.a(a),a&&(o.needsUpdate=!0)),o.uuid=r.uuid,void 0!==r.name&&(o.name=r.name),void 0!==r.mapping&&(o.mapping=n(r.mapping,Gk)),void 0!==r.offset&&o.offset.fromArray(r.offset),void 0!==r.repeat&&o.repeat.fromArray(r.repeat),void 0!==r.center&&o.center.fromArray(r.center),void 0!==r.rotation&&(o.rotation=r.rotation),void 0!==r.wrap&&(o.wrapS=n(r.wrap[0],Vk),o.wrapT=n(r.wrap[1],Vk)),void 0!==r.format&&(o.format=r.format),void 0!==r.type&&(o.type=r.type),void 0!==r.encoding&&(o.encoding=r.encoding),void 0!==r.minFilter&&(o.minFilter=n(r.minFilter,jk)),void 0!==r.magFilter&&(o.magFilter=n(r.magFilter,jk)),void 0!==r.anisotropy&&(o.anisotropy=r.anisotropy),void 0!==r.flipY&&(o.flipY=r.flipY),void 0!==r.premultiplyAlpha&&(o.premultiplyAlpha=r.premultiplyAlpha),void 0!==r.unpackAlignment&&(o.unpackAlignment=r.unpackAlignment),i[r.uuid]=o}return i}parseObject(e,t,n,i){let s,r,o;function a(e){return void 0===t[e]&&console.warn(\\\\\\\"THREE.ObjectLoader: Undefined geometry\\\\\\\",e),t[e]}function c(e){if(void 0!==e){if(Array.isArray(e)){const t=[];for(let i=0,s=e.length;i<s;i++){const s=e[i];void 0===n[s]&&console.warn(\\\\\\\"THREE.ObjectLoader: Undefined material\\\\\\\",s),t.push(n[s])}return t}return void 0===n[e]&&console.warn(\\\\\\\"THREE.ObjectLoader: Undefined material\\\\\\\",e),n[e]}}switch(e.type){case\\\\\\\"Scene\\\\\\\":s=new Vi,void 0!==e.background&&Number.isInteger(e.background)&&(s.background=new M.a(e.background)),void 0!==e.fog&&(\\\\\\\"Fog\\\\\\\"===e.fog.type?s.fog=new Ho(e.fog.color,e.fog.near,e.fog.far):\\\\\\\"FogExp2\\\\\\\"===e.fog.type&&(s.fog=new qo(e.fog.color,e.fog.density)));break;case\\\\\\\"PerspectiveCamera\\\\\\\":s=new te.a(e.fov,e.aspect,e.near,e.far),void 0!==e.focus&&(s.focus=e.focus),void 0!==e.zoom&&(s.zoom=e.zoom),void 0!==e.filmGauge&&(s.filmGauge=e.filmGauge),void 0!==e.filmOffset&&(s.filmOffset=e.filmOffset),void 0!==e.view&&(s.view=Object.assign({},e.view));break;case\\\\\\\"OrthographicCamera\\\\\\\":s=new vm.a(e.left,e.right,e.top,e.bottom,e.near,e.far),void 0!==e.zoom&&(s.zoom=e.zoom),void 0!==e.view&&(s.view=Object.assign({},e.view));break;case\\\\\\\"AmbientLight\\\\\\\":s=new jN.a(e.color,e.intensity);break;case\\\\\\\"DirectionalLight\\\\\\\":s=new xS.a(e.color,e.intensity);break;case\\\\\\\"PointLight\\\\\\\":s=new BS.a(e.color,e.intensity,e.distance,e.decay);break;case\\\\\\\"RectAreaLight\\\\\\\":s=new JN(e.color,e.intensity,e.width,e.height);break;case\\\\\\\"SpotLight\\\\\\\":s=new HS.a(e.color,e.intensity,e.distance,e.angle,e.penumbra,e.decay);break;case\\\\\\\"HemisphereLight\\\\\\\":s=new LS(e.color,e.groundColor,e.intensity);break;case\\\\\\\"LightProbe\\\\\\\":s=(new dk).fromJSON(e);break;case\\\\\\\"SkinnedMesh\\\\\\\":r=a(e.geometry),o=c(e.material),s=new Gi.a(r,o),void 0!==e.bindMode&&(s.bindMode=e.bindMode),void 0!==e.bindMatrix&&s.bindMatrix.fromArray(e.bindMatrix),void 0!==e.skeleton&&(s.skeleton=e.skeleton);break;case\\\\\\\"Mesh\\\\\\\":r=a(e.geometry),o=c(e.material),s=new z.a(r,o);break;case\\\\\\\"InstancedMesh\\\\\\\":r=a(e.geometry),o=c(e.material);const t=e.count,n=e.instanceMatrix,i=e.instanceColor;s=new WD(r,o,t),s.instanceMatrix=new L.a(new Float32Array(n.array),16),void 0!==i&&(s.instanceColor=new L.a(new Float32Array(i.array),i.itemSize));break;case\\\\\\\"LOD\\\\\\\":s=new Ki;break;case\\\\\\\"Line\\\\\\\":s=new dS.a(a(e.geometry),c(e.material));break;case\\\\\\\"LineLoop\\\\\\\":s=new ck.a(a(e.geometry),c(e.material));break;case\\\\\\\"LineSegments\\\\\\\":s=new $i.a(a(e.geometry),c(e.material));break;case\\\\\\\"PointCloud\\\\\\\":case\\\\\\\"Points\\\\\\\":s=new ji.a(a(e.geometry),c(e.material));break;case\\\\\\\"Sprite\\\\\\\":s=new ok(c(e.material));break;case\\\\\\\"Group\\\\\\\":s=new on.a;break;case\\\\\\\"Bone\\\\\\\":s=new Hi.a;break;default:s=new ee.a}if(s.uuid=e.uuid,void 0!==e.name&&(s.name=e.name),void 0!==e.matrix?(s.matrix.fromArray(e.matrix),void 0!==e.matrixAutoUpdate&&(s.matrixAutoUpdate=e.matrixAutoUpdate),s.matrixAutoUpdate&&s.matrix.decompose(s.position,s.quaternion,s.scale)):(void 0!==e.position&&s.position.fromArray(e.position),void 0!==e.rotation&&s.rotation.fromArray(e.rotation),void 0!==e.quaternion&&s.quaternion.fromArray(e.quaternion),void 0!==e.scale&&s.scale.fromArray(e.scale)),void 0!==e.castShadow&&(s.castShadow=e.castShadow),void 0!==e.receiveShadow&&(s.receiveShadow=e.receiveShadow),e.shadow&&(void 0!==e.shadow.bias&&(s.shadow.bias=e.shadow.bias),void 0!==e.shadow.normalBias&&(s.shadow.normalBias=e.shadow.normalBias),void 0!==e.shadow.radius&&(s.shadow.radius=e.shadow.radius),void 0!==e.shadow.mapSize&&s.shadow.mapSize.fromArray(e.shadow.mapSize),void 0!==e.shadow.camera&&(s.shadow.camera=this.parseObject(e.shadow.camera))),void 0!==e.visible&&(s.visible=e.visible),void 0!==e.frustumCulled&&(s.frustumCulled=e.frustumCulled),void 0!==e.renderOrder&&(s.renderOrder=e.renderOrder),void 0!==e.userData&&(s.userData=e.userData),void 0!==e.layers&&(s.layers.mask=e.layers),void 0!==e.children){const r=e.children;for(let e=0;e<r.length;e++)s.add(this.parseObject(r[e],t,n,i))}if(void 0!==e.animations){const t=e.animations;for(let e=0;e<t.length;e++){const n=t[e];s.animations.push(i[n])}}if(\\\\\\\"LOD\\\\\\\"===e.type){void 0!==e.autoUpdate&&(s.autoUpdate=e.autoUpdate);const t=e.levels;for(let e=0;e<t.length;e++){const n=t[e],i=s.getObjectByProperty(\\\\\\\"uuid\\\\\\\",n.object);void 0!==i&&s.addLevel(i,n.distance)}}return s}bindSkeletons(e,t){0!==Object.keys(t).length&&e.traverse((function(e){if(!0===e.isSkinnedMesh&&void 0!==e.skeleton){const n=t[e.skeleton];void 0===n?console.warn(\\\\\\\"THREE.ObjectLoader: No skeleton found with UUID:\\\\\\\",e.skeleton):e.bind(n,e.bindMatrix)}}))}setTexturePath(e){return console.warn(\\\\\\\"THREE.ObjectLoader: .setTexturePath() has been renamed to .setResourcePath().\\\\\\\"),this.setResourcePath(e)}}const Gk={UVMapping:w.Yc,CubeReflectionMapping:w.o,CubeRefractionMapping:w.p,EquirectangularReflectionMapping:w.D,EquirectangularRefractionMapping:w.E,CubeUVReflectionMapping:w.q,CubeUVRefractionMapping:w.r},Vk={RepeatWrapping:w.wc,ClampToEdgeWrapping:w.n,MirroredRepeatWrapping:w.kb},jk={NearestFilter:w.ob,NearestMipmapNearestFilter:w.sb,NearestMipmapLinearFilter:w.rb,LinearFilter:w.V,LinearMipmapNearestFilter:w.Z,LinearMipmapLinearFilter:w.Y};const Hk=new class extends Lo{constructor(){super(...arguments),this.cache=Oo.STRING(\\\\\\\"\\\\\\\",{hidden:!0}),this.reset=Oo.BUTTON(null,{callback:(e,t)=>{qk.PARAM_CALLBACK_reset(e,t)}})}};class qk extends QO{constructor(){super(...arguments),this.paramsConfig=Hk}static type(){return\\\\\\\"cache\\\\\\\"}static displayedInputNames(){return[\\\\\\\"geometry to cache\\\\\\\"]}initializeNode(){this.io.inputs.setCount(0,1)}cook(e){const t=\\\\\\\"\\\\\\\"==this.pv.cache||null==this.pv.cache,n=e[0];if(t&&n){const e=[];for(let t of n.objects())e.push(t.toJSON());this.setCoreGroup(n),this.p.cache.set(JSON.stringify(e))}else if(this.pv.cache){const e=new Uk,t=JSON.parse(this.pv.cache),n=[];for(let i of t){const t=e.parse(i);n.push(t)}this.setObjects(n)}else this.setObjects([])}static PARAM_CALLBACK_reset(e,t){e.param_callback_PARAM_CALLBACK_reset()}async param_callback_PARAM_CALLBACK_reset(){this.p.cache.set(\\\\\\\"\\\\\\\"),this.compute()}}const Wk=[Ni.ORTHOGRAPHIC,Ni.PERSPECTIVE],Xk={direction:new p.a(0,1,0)},Yk=[new d.a(-1,-1),new d.a(-1,1),new d.a(1,1),new d.a(1,-1)],$k=new p.a(0,0,1);const Qk=new class extends Lo{constructor(){super(...arguments),this.camera=Oo.NODE_PATH(\\\\\\\"\\\\\\\",{nodeSelection:{context:Ei.OBJ,types:Wk}}),this.direction=Oo.VECTOR3(Xk.direction),this.offset=Oo.FLOAT(0,{range:[-10,10],rangeLocked:[!1,!1]}),this.useSegmentsCount=Oo.BOOLEAN(!0),this.stepSize=Oo.FLOAT(1,{range:[.001,1],rangeLocked:[!1,!1],visibleIf:{useSegmentsCount:0}}),this.segments=Oo.VECTOR2([10,10],{visibleIf:{useSegmentsCount:1}}),this.sizeMult=Oo.FLOAT(1,{range:[0,2],rangeLocked:[!0,!1]}),this.updateOnWindowResize=Oo.BOOLEAN(1),this.update=Oo.BUTTON(null,{callback:e=>{Jk.PARAM_CALLBACK_update(e)}})}};class Jk extends QO{constructor(){super(...arguments),this.paramsConfig=Qk,this._plane=new $.a,this._raycaster=new Gy,this._planeCorners=[new p.a,new p.a,new p.a,new p.a],this._planeCenter=new p.a,this._core_transform=new rS,this.segments_count=new d.a(1,1),this.planeSize=new d.a}static type(){return\\\\\\\"cameraPlane\\\\\\\"}cook(){this._updateWindowControllerDependency();const e=this.pv.camera.nodeWithContext(Ei.OBJ);if(!e)return this.states.error.set(\\\\\\\"no camera found\\\\\\\"),void this.cookController.endCook();if(!Wk.includes(e.type()))return this.states.error.set(\\\\\\\"node found is not a camera\\\\\\\"),void this.cookController.endCook();const t=e.object;this._computePlaneParams(t)}_updateWindowControllerDependency(){this.pv.updateOnWindowResize?this.addGraphInput(this.scene().windowController.graphNode()):this.removeGraphInput(this.scene().windowController.graphNode())}_computePlaneParams(e){this._plane.normal.copy(this.pv.direction),this._plane.constant=this.pv.offset;let t=0;this._planeCenter.set(0,0,0);for(let n of Yk){this._raycaster.setFromCamera(n,e);const i=this._planeCorners[t];this._raycaster.ray.intersectPlane(this._plane,i),this._planeCenter.add(i),t++}this._planeCenter.multiplyScalar(.25);const n=this._planeCorners[1].distanceTo(this._planeCorners[2]),i=this._planeCorners[0].distanceTo(this._planeCorners[3]),s=this._planeCorners[0].distanceTo(this._planeCorners[1]),r=this._planeCorners[2].distanceTo(this._planeCorners[3]),o=Math.max(n,i)*this.pv.sizeMult,a=Math.max(s,r)*this.pv.sizeMult;this.planeSize.set(o,a);const c=this._createPlane(this.planeSize);this._core_transform.rotate_geometry(c,$k,this.pv.direction);const l=this._core_transform.translation_matrix(this._planeCenter);c.applyMatrix4(l),this.setGeometry(c)}_createPlane(e){return e=e.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(e.x/this.pv.stepSize),this.segments_count.y=Math.floor(e.y/this.pv.stepSize),e.x=this.segments_count.x*this.pv.stepSize,e.y=this.segments_count.y*this.pv.stepSize),new R(e.x,e.y,this.segments_count.x,this.segments_count.y)}static PARAM_CALLBACK_update(e){e._paramCallbackUpdate()}_paramCallbackUpdate(){this.setDirty()}}class Kk extends WO{constructor(){super(...arguments),this._geo_center=new p.a}static type(){return\\\\\\\"center\\\\\\\"}cook(e,t){var n;const i=e[0].objectsWithGeo(),s=new Array(3*i.length);s.fill(0);for(let e=0;e<i.length;e++){const t=i[e],r=t.geometry;r.computeBoundingBox(),r.boundingBox&&(null===(n=r.boundingBox)||void 0===n||n.getCenter(this._geo_center),t.updateMatrixWorld(),this._geo_center.applyMatrix4(t.matrixWorld),this._geo_center.toArray(s,3*e))}const r=new O.a;r.setAttribute(\\\\\\\"position\\\\\\\",new L.a(new Float32Array(s),3));const o=this.createObject(r,Zi.POINTS);return this.createCoreGroupFromObjects([o])}}Kk.DEFAULT_PARAMS={},Kk.INPUT_CLONED_STATE=Ti.FROM_NODE;const Zk=new class extends Lo{};class eB extends QO{constructor(){super(...arguments),this.paramsConfig=Zk}static type(){return\\\\\\\"center\\\\\\\"}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState(Kk.INPUT_CLONED_STATE)}cook(e){this._operation=this._operation||new Kk(this.scene(),this.states);const t=this._operation.cook(e,this.pv);this.setCoreGroup(t)}}class tB{static positions(e,t,n=360){const i=Os.degrees_to_radians(n)/t,s=[];for(let n=0;n<t;n++){const t=i*n,r=e*Math.cos(t),o=e*Math.sin(t);s.push(new d.a(r,o))}return s}static create(e,t,n=360){const i=this.positions(e,t,n),s=[],r=[];let o;for(let e=0;e<i.length;e++)o=i[e],s.push(o.x),s.push(o.y),s.push(0),e>0&&(r.push(e-1),r.push(e));r.push(t-1),r.push(0);const a=new O.a;return a.setAttribute(\\\\\\\"position\\\\\\\",new L.c(s,3)),a.setIndex(r),a}}const nB=new p.a(0,0,1);class iB extends WO{constructor(){super(...arguments),this._core_transform=new rS}static type(){return\\\\\\\"circle\\\\\\\"}cook(e,t){return t.open?this._create_circle(t):this._create_disk(t)}_create_circle(e){const t=tB.create(e.radius,e.segments,e.arcAngle);return this._core_transform.rotate_geometry(t,nB,e.direction),this.createCoreGroupFromGeometry(t,Zi.LINE_SEGMENTS)}_create_disk(e){const t=new vk(e.radius,e.segments);return this._core_transform.rotate_geometry(t,nB,e.direction),this.createCoreGroupFromGeometry(t)}}iB.DEFAULT_PARAMS={radius:1,segments:12,open:!0,arcAngle:360,direction:new p.a(0,1,0)};const sB=iB.DEFAULT_PARAMS;const rB=new class extends Lo{constructor(){super(...arguments),this.radius=Oo.FLOAT(sB.radius),this.segments=Oo.INTEGER(sB.segments,{range:[1,50],rangeLocked:[!0,!1]}),this.open=Oo.BOOLEAN(sB.open),this.arcAngle=Oo.FLOAT(sB.arcAngle,{range:[0,360],rangeLocked:[!1,!1],visibleIf:{open:1}}),this.direction=Oo.VECTOR3(sB.direction)}};class oB extends QO{constructor(){super(...arguments),this.paramsConfig=rB}static type(){return\\\\\\\"circle\\\\\\\"}initializeNode(){}cook(){this._operation=this._operation||new iB(this._scene,this.states);const e=this._operation.cook([],this.pv);this.setCoreGroup(e)}}var aB;!function(e){e.SEGMENTS_COUNT=\\\\\\\"segments count\\\\\\\",e.SEGMENTS_LENGTH=\\\\\\\"segments length\\\\\\\"}(aB||(aB={}));const cB=[aB.SEGMENTS_COUNT,aB.SEGMENTS_LENGTH];var lB;!function(e){e.ABC=\\\\\\\"abc\\\\\\\",e.ACB=\\\\\\\"acb\\\\\\\",e.AB=\\\\\\\"ab\\\\\\\",e.BC=\\\\\\\"bc\\\\\\\",e.AC=\\\\\\\"ac\\\\\\\"}(lB||(lB={}));const uB=[lB.ABC,lB.ACB,lB.AB,lB.AC,lB.BC];class hB{constructor(e){this.params=e,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(e,t,n){this.a.copy(e),this.b.copy(t),this.c.copy(n),this._compute_axis(),this._create_arc(),this._create_center()}_create_arc(){this._compute_angle();const e=this._points_count(),t=new Array(3*e),n=new Array(e),i=this.angle/(e-1);this.x_rotated.copy(this.x).multiplyScalar(this.radius);let s=0;for(s=0;s<e;s++)this.x_rotated.copy(this.x).applyAxisAngle(this.normal,i*s).multiplyScalar(this.radius).add(this.center),this.x_rotated.toArray(t,3*s),s>0&&(n[2*(s-1)]=s-1,n[2*(s-1)+1]=s);this.params.full&&(n.push(s-1),n.push(0));const r=new O.a;if(r.setAttribute(\\\\\\\"position\\\\\\\",new L.a(new Float32Array(t),3)),r.setIndex(n),this.params.addIdAttribute||this.params.addIdnAttribute){const t=new Array(e);for(let e=0;e<t.length;e++)t[e]=e;this.params.addIdAttribute&&r.setAttribute(\\\\\\\"id\\\\\\\",new L.a(new Float32Array(t),1));const n=t.map((t=>t/(e-1)));this.params.addIdnAttribute&&r.setAttribute(\\\\\\\"idn\\\\\\\",new L.a(new Float32Array(n),1))}this._created_geometries.arc=r}_create_center(){if(!this.params.center)return;const e=new O.a,t=[this.center.x,this.center.y,this.center.z];e.setAttribute(\\\\\\\"position\\\\\\\",new L.a(new Float32Array(t),3)),this._created_geometries.center=e}_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 e=this.params.pointsCountMode;switch(e){case aB.SEGMENTS_COUNT:return this.params.segmentsCount+1;case aB.SEGMENTS_LENGTH:{let e=Math.PI*this.radius*this.radius;return this.params.full||(e*=Math.abs(this.angle)/(2*Math.PI)),Math.ceil(e/this.params.segmentsLength)}}Ri.unreachable(e)}_set_x_from_joinMode(){const e=this.params.joinMode;switch(this.x.copy(this.a).sub(this.center).normalize(),e){case lB.ABC:case lB.ACB:case lB.AB:case lB.AC:return this.x.copy(this.an);case lB.BC:return this.x.copy(this.bn)}Ri.unreachable(e)}_set_angle_from_joinMode(){const e=this.params.joinMode;switch(e){case lB.ABC:return void(this.angle=this.angle_ab+this.angle_bc);case lB.ACB:return this.angle=this.angle_ac+this.angle_bc,void(this.angle*=-1);case lB.AB:return void(this.angle=this.angle_ab);case lB.AC:return this.angle=this.angle_ac,void(this.angle*=-1);case lB.BC:return void(this.angle=this.angle_bc)}Ri.unreachable(e)}}const dB=new class extends Lo{constructor(){super(...arguments),this.arc=Oo.BOOLEAN(1),this.pointsCountMode=Oo.INTEGER(cB.indexOf(aB.SEGMENTS_COUNT),{visibleIf:{arc:1},menu:{entries:cB.map(((e,t)=>({value:t,name:e})))}}),this.segmentsLength=Oo.FLOAT(.1,{visibleIf:{arc:1,pointsCountMode:cB.indexOf(aB.SEGMENTS_LENGTH)},range:[0,1],rangeLocked:[!0,!1]}),this.segmentsCount=Oo.INTEGER(100,{visibleIf:{arc:1,pointsCountMode:cB.indexOf(aB.SEGMENTS_COUNT)},range:[1,100],rangeLocked:[!0,!1]}),this.full=Oo.BOOLEAN(1,{visibleIf:{arc:1}}),this.joinMode=Oo.INTEGER(uB.indexOf(lB.ABC),{visibleIf:{arc:1,full:0},menu:{entries:uB.map(((e,t)=>({value:t,name:e})))}}),this.addIdAttribute=Oo.BOOLEAN(1),this.addIdnAttribute=Oo.BOOLEAN(1),this.center=Oo.BOOLEAN(0)}};class pB extends QO{constructor(){super(...arguments),this.paramsConfig=dB,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([Ti.NEVER])}cook(e){const t=e[0].points();t.length<3?this.states.error.set(`only ${t.length} points found, when 3 are required`):this._create_circle(t)}_create_circle(e){const t=new hB({arc:this.pv.arc,center:this.pv.center,pointsCountMode:cB[this.pv.pointsCountMode],segmentsLength:this.pv.segmentsLength,segmentsCount:this.pv.segmentsCount,full:this.pv.full,joinMode:uB[this.pv.joinMode],addIdAttribute:this.pv.addIdAttribute,addIdnAttribute:this.pv.addIdnAttribute});e[0].getPosition(this.a),e[1].getPosition(this.b),e[2].getPosition(this.c),t.create(this.a,this.b,this.c);const n=[],i=t.created_geometries();i.arc&&n.push(this.createObject(i.arc,Zi.LINE_SEGMENTS)),i.center&&n.push(this.createObject(i.center,Zi.POINTS)),this.setObjects(n)}}class _B extends WO{static type(){return\\\\\\\"color\\\\\\\"}cook(e,t){}}_B.DEFAULT_PARAMS={fromAttribute:!1,attribName:\\\\\\\"\\\\\\\",color:new M.a(1,1,1),asHsv:!1};const mB=new M.a(1,1,1),fB=\\\\\\\"color\\\\\\\",gB=_B.DEFAULT_PARAMS;const vB=new class extends Lo{constructor(){super(...arguments),this.fromAttribute=Oo.BOOLEAN(gB.fromAttribute),this.attribName=Oo.STRING(gB.attribName,{visibleIf:{fromAttribute:1}}),this.color=Oo.COLOR(gB.color,{visibleIf:{fromAttribute:0},expression:{forEntities:!0}}),this.asHsv=Oo.BOOLEAN(gB.asHsv,{visibleIf:{fromAttribute:0}})}};class yB extends QO{constructor(){super(...arguments),this.paramsConfig=vB,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(Ti.FROM_NODE)}async cook(e){const t=e[0],n=t.coreObjects();for(let e of n)if(this.pv.fromAttribute)this._set_fromAttribute(e);else{this.p.color.hasExpression()?await this._eval_expressions(e):this._eval_simple_values(e)}if(!this.io.inputs.cloneRequired(0)){const e=t.geometries();for(let t of e)t.getAttribute(fB).needsUpdate=!0}this.setCoreGroup(t)}_set_fromAttribute(e){const t=e.coreGeometry();if(!t)return;this._create_init_color(t,mB);const n=t.points(),i=t.attribSize(this.pv.attribName),s=t.geometry(),r=s.getAttribute(this.pv.attribName).array,o=s.getAttribute(fB).array;switch(i){case 1:for(let e=0;e<n.length;e++){const t=3*e;o[t+0]=r[e],o[t+1]=1-r[e],o[t+2]=0}break;case 2:for(let e=0;e<n.length;e++){const t=3*e,n=2*e;o[t+0]=r[n+0],o[t+1]=r[n+1],o[t+2]=0}break;case 3:for(let e=0;e<r.length;e++)o[e]=r[e];break;case 4:for(let e=0;e<n.length;e++){const t=3*e,n=4*e;o[t+0]=r[n+0],o[t+1]=r[n+1],o[t+2]=r[n+2]}}}_create_init_color(e,t){e.hasAttrib(fB)||e.addNumericAttrib(fB,3,mB)}_eval_simple_values(e){const t=e.coreGeometry();if(!t)return;let n;this._create_init_color(t,mB),this.pv.asHsv?(n=new M.a,Lr.set_hsv(this.pv.color.r,this.pv.color.g,this.pv.color.b,n)):n=this.pv.color,t.addNumericAttrib(fB,3,n)}async _eval_expressions(e){const t=e.points(),n=e.object(),i=e.coreGeometry();i&&this._create_init_color(i,mB);const s=n.geometry;if(s){const e=s.getAttribute(fB).array,n=await this._update_from_param(s,e,t,0),i=await this._update_from_param(s,e,t,1),r=await this._update_from_param(s,e,t,2);if(n&&this._commit_tmp_values(n,e,0),i&&this._commit_tmp_values(i,e,1),r&&this._commit_tmp_values(r,e,2),this.pv.asHsv){let n,i=new M.a,s=new M.a;for(let r of t)n=3*r.index(),i.fromArray(e,n),Lr.set_hsv(i.r,i.g,i.b,s),s.toArray(e,n)}}}async _update_from_param(e,t,n,i){const s=this.p.color.components[i],r=[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(s.hasExpression()&&s.expressionController)a=this._init_array_if_required(e,o,n.length),await s.expressionController.compute_expression_for_points(n,((e,t)=>{a[e.index()]=t}));else for(let e of n)t[3*e.index()+i]=r;return a}_init_array_if_required(e,t,n){const i=e.uuid,s=t[i];return s?s.length<n&&(t[i]=new Array(n)):t[i]=new Array(n),t[i]}_commit_tmp_values(e,t,n){for(let i=0;i<e.length;i++)t[3*i+n]=e[i]}}const xB=new p.a(0,1,0);const bB=new class extends Lo{constructor(){super(...arguments),this.radius=Oo.FLOAT(1,{range:[0,1]}),this.height=Oo.FLOAT(1,{range:[0,1]}),this.segmentsRadial=Oo.INTEGER(12,{range:[3,20],rangeLocked:[!0,!1]}),this.segmentsHeight=Oo.INTEGER(1,{range:[1,20],rangeLocked:[!0,!1]}),this.cap=Oo.BOOLEAN(1),this.thetaStart=Oo.FLOAT(1,{range:[0,2*Math.PI]}),this.thetaLength=Oo.FLOAT(\\\\\\\"2*$PI\\\\\\\",{range:[0,2*Math.PI]}),this.center=Oo.VECTOR3([0,0,0]),this.direction=Oo.VECTOR3([0,0,1])}};class wB extends QO{constructor(){super(...arguments),this.paramsConfig=bB,this._core_transform=new rS}static type(){return\\\\\\\"cone\\\\\\\"}cook(){const e=new XS(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(e,xB,this.pv.direction),e.translate(this.pv.center.x,this.pv.center.y,this.pv.center.z),this.setGeometry(e)}}const AB={SCALE:new p.a(1,1,1),PSCALE:1,EYE:new p.a(0,0,0),UP:new p.a(0,1,0)},TB=new p.a(1,1,1),EB=new d.a(0,0),CB=\\\\\\\"color\\\\\\\";var MB,NB;!function(e){e.POSITION=\\\\\\\"instancePosition\\\\\\\",e.SCALE=\\\\\\\"instanceScale\\\\\\\",e.ORIENTATION=\\\\\\\"instanceOrientation\\\\\\\",e.COLOR=\\\\\\\"instanceColor\\\\\\\",e.UV=\\\\\\\"instanceUv\\\\\\\"}(MB||(MB={}));class SB{constructor(e){this._group_wrapper=e,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 C.a,this._matrices.rotate=new C.a,this._matrices.scale=new C.a,this._group_wrapper.points().map((e=>{const t=new C.a;return this._matrix_from_point(e,t),t}))}_matrix_from_point(e,t){const n=e.position();this._is_scale_present?e.attribValue(\\\\\\\"scale\\\\\\\",this._point_scale):this._point_scale.copy(AB.SCALE);const i=this._is_pscale_present?e.attribValue(\\\\\\\"pscale\\\\\\\"):AB.PSCALE;this._point_scale.multiplyScalar(i);const s=this._matrices.scale;s.makeScale(this._point_scale.x,this._point_scale.y,this._point_scale.z);const r=this._matrices.translate;if(r.makeTranslation(n.x,n.y,n.z),t.multiply(r),this._do_rotate_matrices){const n=this._matrices.rotate,i=AB.EYE;e.attribValue(\\\\\\\"normal\\\\\\\",this._point_normal),this._point_normal.multiplyScalar(-1),this._is_up_present?e.attribValue(\\\\\\\"up\\\\\\\",this._point_up):this._point_up.copy(AB.UP),this._point_up.normalize(),n.lookAt(i,this._point_normal,this._point_up),t.multiply(n)}t.multiply(s)}static create_instance_buffer_geo(e,t,n){const i=t.points(),s=new mk;s.copy(e),s.instanceCount=1/0;const r=i.length,o=new Float32Array(3*r),a=new Float32Array(3*r),c=new Float32Array(3*r),l=new Float32Array(4*r),u=t.hasAttrib(CB),h=new p.a(0,0,0),d=new Ll.a,_=new p.a(1,1,1),f=new SB(t).matrices();i.forEach(((e,t)=>{const n=3*t,i=4*t;f[t].decompose(h,d,_),h.toArray(o,n),d.toArray(l,i),_.toArray(c,n);(u?e.attribValue(CB,this._point_color):TB).toArray(a,n)}));const g=t.hasAttrib(\\\\\\\"uv\\\\\\\");if(g){const e=new Float32Array(2*r);i.forEach(((t,n)=>{const i=2*n;(g?t.attribValue(\\\\\\\"uv\\\\\\\",this._point_uv):EB).toArray(e,i)})),s.setAttribute(MB.UV,new fk(e,2))}s.setAttribute(MB.POSITION,new fk(o,3)),s.setAttribute(MB.SCALE,new fk(c,3)),s.setAttribute(MB.ORIENTATION,new fk(l,4)),s.setAttribute(MB.COLOR,new fk(a,3));t.attribNamesMatchingMask(n).forEach((e=>{const n=t.attribSize(e),o=new Float32Array(r*n);i.forEach(((t,i)=>{const s=t.attribValue(e);m.isNumber(s)?o[i]=s:s.toArray(o,i*n)})),s.setAttribute(e,new fk(o,n))}));return new Bs(s).markAsInstance(),s}}SB._point_color=new p.a,SB._point_uv=new d.a;class OB extends Nl{set_point(e){this._point=e,this.setDirty(),this.removeDirtyState()}value(e){return this._point?e?this._point.attribValue(e):this._point.index():this._global_index}}!function(e){e[e.OBJECT=0]=\\\\\\\"OBJECT\\\\\\\",e[e.GEOMETRY=1]=\\\\\\\"GEOMETRY\\\\\\\"}(NB||(NB={}));const LB=[NB.OBJECT,NB.GEOMETRY],PB=[{name:\\\\\\\"object\\\\\\\",value:NB.OBJECT},{name:\\\\\\\"geometry\\\\\\\",value:NB.GEOMETRY}];const RB=new class extends Lo{constructor(){super(...arguments),this.count=Oo.INTEGER(1,{range:[1,20],rangeLocked:[!0,!1]}),this.transformOnly=Oo.BOOLEAN(0),this.transformMode=Oo.INTEGER(0,{menu:{entries:PB}}),this.copyAttributes=Oo.BOOLEAN(0),this.attributesToCopy=Oo.STRING(\\\\\\\"\\\\\\\",{visibleIf:{copyAttributes:!0}}),this.useCopyExpr=Oo.BOOLEAN(0)}};class IB extends QO{constructor(){super(...arguments),this.paramsConfig=RB,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([Ti.ALWAYS,Ti.NEVER])}async cook(e){const t=e[0];if(!this.io.inputs.has_input(1))return void await this.cook_without_template(t);const n=e[1];n?await this.cook_with_template(t,n):this.states.error.set(\\\\\\\"second input invalid\\\\\\\")}async cook_with_template(e,t){this._objects=[];const n=t.points();let i=new SB(t).matrices();const s=new p.a,r=new Ll.a,o=new p.a;i[0].decompose(s,r,o),this._attribute_names_to_copy=Li.attribNames(this.pv.attributesToCopy).filter((e=>t.hasAttrib(e))),await this._copy_moved_objects_on_template_points(e,i,n),this.setObjects(this._objects)}async _copy_moved_objects_on_template_points(e,t,n){for(let i=0;i<n.length;i++)await this._copy_moved_object_on_template_point(e,t,n,i)}async _copy_moved_object_on_template_point(e,t,n,i){const s=t[i],r=n[i];this.stamp_node.set_point(r);const o=await this._get_moved_objects_for_template_point(e,i);for(let e of o)this.pv.copyAttributes&&this._copyAttributes_from_template(e,r),this.pv.transformOnly?e.applyMatrix4(s):this._apply_matrix_to_object_or_geometry(e,s),this._objects.push(e)}_apply_matrix_to_object_or_geometry(e,t){const n=LB[this.pv.transformMode];switch(n){case NB.OBJECT:return void this._apply_matrix_to_object(e,t);case NB.GEOMETRY:{const n=e.geometry;return void(n&&n.applyMatrix4(t))}}Ri.unreachable(n)}_apply_matrix_to_object(e,t){this._object_position.copy(e.position),e.position.multiplyScalar(0),e.updateMatrix(),e.applyMatrix4(t),e.position.add(this._object_position),e.updateMatrix()}async _get_moved_objects_for_template_point(e,t){const n=await this._stamp_instance_group_if_required(e);if(n){return this.pv.transformOnly?f.compact([n.objects()[t]]):n.clone().objects()}return[]}async _stamp_instance_group_if_required(e){if(!this.pv.useCopyExpr)return e;{const e=await this.containerController.requestInputContainer(0);if(e){const t=e.coreContent();return t||void 0}this.states.error.set(`input failed for index ${this.stamp_value()}`)}}async _copy_moved_objects_for_each_instance(e){for(let t=0;t<this.pv.count;t++)await this._copy_moved_objects_for_instance(e,t)}async _copy_moved_objects_for_instance(e,t){this.stamp_node.set_global_index(t);const n=await this._stamp_instance_group_if_required(e);n&&n.objects().forEach((e=>{const t=js.clone(e);this._objects.push(t)}))}async cook_without_template(e){this._objects=[],await this._copy_moved_objects_for_each_instance(e),this.setObjects(this._objects)}_copyAttributes_from_template(e,t){this._attribute_names_to_copy.forEach(((n,i)=>{const s=t.attribValue(n);new js(e,i).addAttribute(n,s)}))}stamp_value(e){return this.stamp_node.value(e)}get stamp_node(){return this._stamp_node=this._stamp_node||this.create_stamp_node()}create_stamp_node(){const e=new OB(this.scene());return this.dirtyController.setForbiddenTriggerNodes([e]),e}dispose(){super.dispose(),this._stamp_node&&this._stamp_node.dispose()}}const FB=\\\\\\\"id\\\\\\\",DB=\\\\\\\"class\\\\\\\",kB=\\\\\\\"html\\\\\\\";class BB extends WO{static type(){return\\\\\\\"CSS2DObject\\\\\\\"}cook(e,t){const n=e[0];if(n){const e=this._create_objects_from_input_points(n,t);return this.createCoreGroupFromObjects(e)}{const e=this._create_object_from_scratch(t);return this.createCoreGroupFromObjects([e])}}_create_objects_from_input_points(e,t){const n=e.points(),i=[];for(let e of n){const n=t.useIdAttrib?e.attribValue(FB):t.className,s=t.useClassAttrib?e.attribValue(DB):t.className,r=t.useHtmlAttrib?e.attribValue(kB):t.html,o=BB.create_css_object({id:n,className:s,html:r}),a=o.element;if(t.copyAttributes){const n=Li.attribNames(t.attributesToCopy);for(let t of n){const n=e.attribValue(t);m.isString(n)?a.setAttribute(t,n):m.isNumber(n)&&a.setAttribute(t,`${n}`)}}o.position.copy(e.position()),o.updateMatrix(),i.push(o)}return i}_create_object_from_scratch(e){return BB.create_css_object({id:e.id,className:e.className,html:e.html})}static create_css_object(e){const t=document.createElement(\\\\\\\"div\\\\\\\");t.id=e.id,t.className=e.className,t.innerHTML=e.html;const n=new JI(t);return n.matrixAutoUpdate=!1,n}}BB.DEFAULT_PARAMS={useIdAttrib:!1,id:\\\\\\\"my_css_object\\\\\\\",useClassAttrib:!1,className:\\\\\\\"CSS2DObject\\\\\\\",useHtmlAttrib:!1,html:\\\\\\\"<div>default html</div>\\\\\\\",copyAttributes:!1,attributesToCopy:\\\\\\\"\\\\\\\"},BB.INPUT_CLONED_STATE=Ti.FROM_NODE;const zB=BB.DEFAULT_PARAMS;const UB=new class extends Lo{constructor(){super(...arguments),this.useIdAttrib=Oo.BOOLEAN(zB.useIdAttrib),this.id=Oo.STRING(zB.id,{visibleIf:{useIdAttrib:0}}),this.useClassAttrib=Oo.BOOLEAN(zB.useClassAttrib),this.className=Oo.STRING(zB.className,{visibleIf:{useClassAttrib:0}}),this.useHtmlAttrib=Oo.BOOLEAN(zB.useHtmlAttrib),this.html=Oo.STRING(zB.html,{visibleIf:{useHtmlAttrib:0},multiline:!0}),this.copyAttributes=Oo.BOOLEAN(zB.copyAttributes),this.attributesToCopy=Oo.STRING(zB.attributesToCopy,{visibleIf:{copyAttributes:!0}})}};class GB extends QO{constructor(){super(...arguments),this.paramsConfig=UB}static type(){return\\\\\\\"CSS2DObject\\\\\\\"}initializeNode(){this.io.inputs.setCount(0,1)}cook(e){this._operation=this._operation||new BB(this.scene(),this.states);const t=this._operation.cook(e,this.pv);this.setCoreGroup(t)}}class VB{constructor(e,t){this._size=e,this._type=t}size(){return this._size}type(){return this._type}static from_value(e){const t=m.isString(e)?cs.STRING:cs.NUMERIC;return new this(m.isArray(e)?e.length:1,t)}}class jB{constructor(e={}){this._attribute_datas_by_name={},this._options={},this._options.dataKeysPrefix=e.dataKeysPrefix,this._options.skipEntries=e.skipEntries,this._options.doConvert=e.doConvert||!1,this._options.convertToNumeric=e.convertToNumeric}dataKeysPrefix(){return this._options.dataKeysPrefix}get_prefixed_json(e,t){if(0==t.length)return e;{const n=t.shift();if(n)return this.get_prefixed_json(e[n],t)}return[]}setJSON(e){return this._json=e}createObject(){const e=new O.a,t=new Bs(e);if(null!=this._json){const n=this._json.length;t.initPositionAttribute(n),this._find_attributes();const i=Li.attribNames(this._options.convertToNumeric||\\\\\\\"\\\\\\\");for(let n of Object.keys(this._attribute_datas_by_name)){const s=gs.remapName(n);let r=this._attribute_values_for_name(n).flat();const o=this._attribute_datas_by_name[n],a=o.size();if(o.type()===cs.STRING)if(this._options.doConvert&&Li.matchesOneMask(n,i)){const t=r.map((e=>m.isString(e)?parseFloat(e)||0:e));e.setAttribute(s,new L.c(t,a))}else{const e=gs.arrayToIndexedArrays(r);t.setIndexedAttribute(s,e.values,e.indices)}else{const t=r;e.setAttribute(s,new L.c(t,a))}}}return e}_find_attributes(){let e;const t=Li.attribNames(this._options.skipEntries||\\\\\\\"\\\\\\\");if(this._json&&null!=(e=this._json[0]))for(let n of Object.keys(e)){const i=e[n];if(this._value_has_subentries(i))for(let e of Object.keys(i)){const s=[n,e].join(\\\\\\\":\\\\\\\"),r=i[n];Li.matchesOneMask(s,t)||(this._attribute_datas_by_name[s]=VB.from_value(r))}else Li.matchesOneMask(n,t)||(this._attribute_datas_by_name[n]=VB.from_value(i))}}_attribute_values_for_name(e){return this._json?this._json.map((t=>{const n=e.split(\\\\\\\":\\\\\\\")[0],i=t[n];if(this._value_has_subentries(i)){return i[e.substring(n.length+1)]||0}return i||0})):[]}_value_has_subentries(e){return m.isObject(e)&&!m.isArray(e)}}const HB=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 qB=new class extends Lo{constructor(){super(...arguments),this.data=Oo.STRING(HB)}};class WB extends QO{constructor(){super(...arguments),this.paramsConfig=qB}static type(){return\\\\\\\"data\\\\\\\"}cook(){let e=null;try{e=JSON.parse(this.pv.data)}catch(e){this.states.error.set(\\\\\\\"could not parse json\\\\\\\")}if(e)try{const t=new jB;t.setJSON(e);const n=t.createObject();this.setGeometry(n,Zi.POINTS)}catch(e){this.states.error.set(\\\\\\\"could not build geometry from json\\\\\\\")}else this.cookController.endCook()}}class XB extends tv{constructor(e,t,n={},i){super(e,t,i),this._node=i,this._parser=new jB(n)}async load(e,t,n){const i=await this._urlToLoad();fetch(i).then((async t=>{let n=await t.json();const i=this._parser.dataKeysPrefix();null!=i&&\\\\\\\"\\\\\\\"!=i&&(n=this._parser.get_prefixed_json(n,i.split(\\\\\\\".\\\\\\\"))),this._parser.setJSON(n);const s=this._parser.createObject();e(s)})).catch((e=>{Rn.error(\\\\\\\"error\\\\\\\",e),n(e)}))}}const YB=\\\\\\\"position\\\\\\\";class $B{constructor(e){this.attribute_names=e,this.attribute_names_from_first_line=!1,this.lines=[],this.points_count=0,this.attribute_values_by_name={},this.attribute_data_by_name={},this._loading=!1,this.attribute_names||(this.attribute_names_from_first_line=!0)}async load(e){if(this._loading)return void console.warn(\\\\\\\"is already loading\\\\\\\");this._loading=!0,this.points_count=0,await this.load_data(e),this.infer_types(),this.read_values();return this.create_points()}async load_data(e){const t=await fetch(e),n=await t.text();this.lines=n.split(\\\\\\\"\\\\n\\\\\\\"),this.attribute_names||(this.attribute_names=this.lines[0].split($B.SEPARATOR)),this.attribute_names=this.attribute_names.map((e=>gs.remapName(e)));for(let e of this.attribute_names)this.attribute_values_by_name[e]=[]}infer_types(){const e=this.attribute_names_from_first_line?1:0;let t=this.lines[e].split($B.SEPARATOR);for(let e=0;e<t.length;e++){const n=this.attribute_names[e],i=t[e],s=this._value_from_line_element(i);this.attribute_data_by_name[n]=VB.from_value(s)}}_value_from_line_element(e){if(m.isString(e)){if(`${parseFloat(e)}`===e)return parseFloat(e);if(\\\\\\\"[\\\\\\\"===e[0]&&\\\\\\\"]\\\\\\\"===e[e.length-1]){return e.substring(1,e.length-1).split($B.VECTOR_SEPARATOR).map((e=>parseFloat(e)))}return e}return e}read_values(){if(!this.attribute_names)return;let e;for(let t=this.attribute_names_from_first_line?1:0;t<this.lines.length;t++){e=this.lines[t];const n=e.split($B.SEPARATOR);if(n.length>=this.attribute_names.length){for(let e=0;e<n.length;e++){const t=this.attribute_names[e];if(t){const i=n[e],s=this._value_from_line_element(i);this.attribute_values_by_name[t].push(s)}}this.points_count+=1}}if(!this.attribute_values_by_name.position){const e=new Array(3*this.points_count);e.fill(0),this.attribute_values_by_name.position=e,this.attribute_data_by_name.position=new VB(3,cs.NUMERIC),this.attribute_names.push(YB)}}create_points(){if(!this.attribute_names)return;const e=new O.a,t=new Bs(e);for(let n of this.attribute_names){const i=this.attribute_values_by_name[n].flat(),s=this.attribute_data_by_name[n].size();if(this.attribute_data_by_name[n].type()==cs.STRING){const e=gs.arrayToIndexedArrays(i);t.setIndexedAttribute(n,e.values,e.indices)}else e.setAttribute(n,new L.c(i,s))}const n=new Array(this.points_count);for(let e=0;e<this.points_count;e++)n.push(e);return e.setIndex(n),e}}var QB;$B.SEPARATOR=\\\\\\\",\\\\\\\",$B.VECTOR_SEPARATOR=\\\\\\\",\\\\\\\",function(e){e.JSON=\\\\\\\"json\\\\\\\",e.CSV=\\\\\\\"csv\\\\\\\"}(QB||(QB={}));const JB=[QB.JSON,QB.CSV],KB=`${Kg}/nodes/sop/DataUrl/basic.json`;const ZB=new class extends Lo{constructor(){super(...arguments),this.dataType=Oo.INTEGER(JB.indexOf(QB.JSON),{menu:{entries:JB.map(((e,t)=>({name:e,value:t})))}}),this.url=Oo.STRING(KB,{fileBrowse:{type:[tr.JSON]}}),this.jsonDataKeysPrefix=Oo.STRING(\\\\\\\"\\\\\\\",{visibleIf:{dataType:JB.indexOf(QB.JSON)}}),this.skipEntries=Oo.STRING(\\\\\\\"\\\\\\\",{visibleIf:{dataType:JB.indexOf(QB.JSON)}}),this.convert=Oo.BOOLEAN(0,{visibleIf:{dataType:JB.indexOf(QB.JSON)}}),this.convertToNumeric=Oo.STRING(\\\\\\\"\\\\\\\",{visibleIf:{dataType:JB.indexOf(QB.JSON),convert:1}}),this.readAttribNamesFromFile=Oo.BOOLEAN(1,{visibleIf:{dataType:JB.indexOf(QB.CSV)}}),this.attribNames=Oo.STRING(\\\\\\\"height scale\\\\\\\",{visibleIf:{dataType:JB.indexOf(QB.CSV),readAttribNamesFromFile:0}}),this.reload=Oo.BUTTON(null,{callback:(e,t)=>{ez.PARAM_CALLBACK_reload(e,t)}})}};class ez extends QO{constructor(){super(...arguments),this.paramsConfig=ZB}static type(){return\\\\\\\"dataUrl\\\\\\\"}initializeNode(){this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.url],(()=>{const e=this.p.url.rawInput();if(e){const t=e.split(\\\\\\\"/\\\\\\\");return t[t.length-1]}return\\\\\\\"\\\\\\\"}))}))}))}async cook(){switch(JB[this.pv.dataType]){case QB.JSON:return this._load_json();case QB.CSV:return this._load_csv()}}_url(){const e=this.scene().assets.root();return e?`${e}${this.pv.url}`:this.pv.url}_load_json(){new XB(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(e){this.setGeometry(e,Zi.POINTS)}_on_error(e){this.states.error.set(`could not load geometry from ${this._url()} (${e})`),this.cookController.endCook()}async _load_csv(){const e=this.pv.readAttribNamesFromFile?void 0:this.pv.attribNames.split(\\\\\\\" \\\\\\\"),t=new $B(e),n=await t.load(this._url());n?this.setGeometry(n,Zi.POINTS):this.states.error.set(\\\\\\\"could not generate points\\\\\\\")}static PARAM_CALLBACK_reload(e,t){e.param_callback_reload()}param_callback_reload(){this.p.url.setDirty()}}var tz=function(e,t,n,i){O.a.call(this);var s=[],r=[],o=[],a=new p.a,c=new C.a;c.makeRotationFromEuler(n),c.setPosition(t);var l=new C.a;function u(t,n,i){n.applyMatrix4(e.matrixWorld),n.applyMatrix4(l),i.transformDirection(e.matrixWorld),t.push(new nz(n.clone(),i.clone()))}function h(e,t){for(var n=[],s=.5*Math.abs(i.dot(t)),r=0;r<e.length;r+=3){var o,a,c,l,u,h,p;switch(((o=e[r+0].position.dot(t)-s>0)?1:0)+((a=e[r+1].position.dot(t)-s>0)?1:0)+((c=e[r+2].position.dot(t)-s>0)?1:0)){case 0:n.push(e[r]),n.push(e[r+1]),n.push(e[r+2]);break;case 1:if(o&&(l=e[r+1],u=e[r+2],h=d(e[r],l,t,s),p=d(e[r],u,t,s)),a){l=e[r],u=e[r+2],h=d(e[r+1],l,t,s),p=d(e[r+1],u,t,s),n.push(h),n.push(u.clone()),n.push(l.clone()),n.push(u.clone()),n.push(h.clone()),n.push(p);break}c&&(l=e[r],u=e[r+1],h=d(e[r+2],l,t,s),p=d(e[r+2],u,t,s)),n.push(l.clone()),n.push(u.clone()),n.push(h),n.push(p),n.push(h.clone()),n.push(u.clone());break;case 2:o||(u=d(l=e[r].clone(),e[r+1],t,s),h=d(l,e[r+2],t,s),n.push(l),n.push(u),n.push(h)),a||(u=d(l=e[r+1].clone(),e[r+2],t,s),h=d(l,e[r],t,s),n.push(l),n.push(u),n.push(h)),c||(u=d(l=e[r+2].clone(),e[r],t,s),h=d(l,e[r+1],t,s),n.push(l),n.push(u),n.push(h))}}return n}function d(e,t,n,i){var s=e.position.dot(n)-i,r=s/(s-(t.position.dot(n)-i));return new nz(new p.a(e.position.x+r*(t.position.x-e.position.x),e.position.y+r*(t.position.y-e.position.y),e.position.z+r*(t.position.z-e.position.z)),new p.a(e.normal.x+r*(t.normal.x-e.normal.x),e.normal.y+r*(t.normal.y-e.normal.y),e.normal.z+r*(t.normal.z-e.normal.z)))}l.copy(c).invert(),function(){var t,n=[],l=new p.a,d=new p.a;if(!0===e.geometry.isGeometry)return void console.error(\\\\\\\"THREE.DecalGeometry no longer supports THREE.Geometry. Use BufferGeometry instead.\\\\\\\");var _=e.geometry,m=_.attributes.position,f=_.attributes.normal;if(null!==_.index){var g=_.index;for(t=0;t<g.count;t++)l.fromBufferAttribute(m,g.getX(t)),d.fromBufferAttribute(f,g.getX(t)),u(n,l,d)}else for(t=0;t<m.count;t++)l.fromBufferAttribute(m,t),d.fromBufferAttribute(f,t),u(n,l,d);for(n=h(n,a.set(1,0,0)),n=h(n,a.set(-1,0,0)),n=h(n,a.set(0,1,0)),n=h(n,a.set(0,-1,0)),n=h(n,a.set(0,0,1)),n=h(n,a.set(0,0,-1)),t=0;t<n.length;t++){var v=n[t];o.push(.5+v.position.x/i.x,.5+v.position.y/i.y),v.position.applyMatrix4(c),s.push(v.position.x,v.position.y,v.position.z),r.push(v.normal.x,v.normal.y,v.normal.z)}}(),this.setAttribute(\\\\\\\"position\\\\\\\",new L.c(s,3)),this.setAttribute(\\\\\\\"normal\\\\\\\",new L.c(r,3)),this.setAttribute(\\\\\\\"uv\\\\\\\",new L.c(o,2))};(tz.prototype=Object.create(O.a.prototype)).constructor=tz;var nz=function(e,t){this.position=e,this.normal=t};nz.prototype.clone=function(){return new this.constructor(this.position.clone(),this.normal.clone())};class iz extends WO{constructor(){super(...arguments),this._r=new p.a,this._rotation=new ny.a(0,0,0),this._scale=new p.a(1,1,1)}static type(){return\\\\\\\"decal\\\\\\\"}cook(e,t){const n=e[0];this._r.copy(t.r).multiplyScalar(A.a.DEG2RAD),this._rotation.set(this._r.x,this._r.y,this._r.z),this._scale.copy(t.s).multiplyScalar(t.scale);const i=n.objectsWithGeo(),s=[];for(let e of i)if(e.isMesh){const n=new tz(e,t.t,this._rotation,this._scale),i=new z.a(n,e.material);s.push(i)}return this.createCoreGroupFromObjects(s)}}iz.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},iz.INPUT_CLONED_STATE=Ti.NEVER;const sz=iz.DEFAULT_PARAMS;const rz=new class extends Lo{constructor(){super(...arguments),this.t=Oo.VECTOR3(sz.t),this.r=Oo.VECTOR3(sz.r),this.s=Oo.VECTOR3(sz.s),this.scale=Oo.FLOAT(sz.scale)}};class oz extends QO{constructor(){super(...arguments),this.paramsConfig=rz}static type(){return\\\\\\\"decal\\\\\\\"}static displayedInputNames(){return[\\\\\\\"geometry to create decal from\\\\\\\"]}initializeNode(){this.io.inputs.setCount(0,1),this.io.inputs.initInputsClonedState(iz.INPUT_CLONED_STATE)}cook(e){this._operation=this._operation||new iz(this._scene,this.states);const t=this._operation.cook(e,this.pv);this.setCoreGroup(t)}}const az=new class extends Lo{constructor(){super(...arguments),this.duration=Oo.INTEGER(1e3,{range:[0,1e3],rangeLocked:[!0,!1]})}};class cz extends QO{constructor(){super(...arguments),this.paramsConfig=az}static type(){return\\\\\\\"delay\\\\\\\"}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState(Ti.ALWAYS)}cook(e){const t=e[0];setTimeout((()=>{this.setCoreGroup(t)}),Math.max(this.pv.duration,0))}}class lz{constructor(e){this.node=e,this.selected_state=new Map,this._entities_count=0,this._selected_entities_count=0}init(e){this.selected_state.clear();for(let t of e)this.selected_state.set(t,!1);this._entities_count=e.length,this._selected_entities_count=0}select(e){const t=this.selected_state.get(e);null!=t&&0==t&&(this.selected_state.set(e,!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(e){const t=!!e,n=e?this._selected_entities_count:this._entities_count-this._selected_entities_count;if(0==n)return[];{const e=new Array(n);let i=0;return this.selected_state.forEach(((n,s)=>{n==t&&(e[i]=s,i++)})),e}}}var uz;!function(e){e.EQUAL=\\\\\\\"==\\\\\\\",e.LESS_THAN=\\\\\\\"<\\\\\\\",e.EQUAL_OR_LESS_THAN=\\\\\\\"<=\\\\\\\",e.EQUAL_OR_GREATER_THAN=\\\\\\\">=\\\\\\\",e.GREATER_THAN=\\\\\\\">\\\\\\\",e.DIFFERENT=\\\\\\\"!=\\\\\\\"}(uz||(uz={}));const hz=[uz.EQUAL,uz.LESS_THAN,uz.EQUAL_OR_LESS_THAN,uz.EQUAL_OR_GREATER_THAN,uz.GREATER_THAN,uz.DIFFERENT],dz={[uz.EQUAL]:(e,t)=>e==t,[uz.LESS_THAN]:(e,t)=>e<t,[uz.EQUAL_OR_LESS_THAN]:(e,t)=>e<=t,[uz.EQUAL_OR_GREATER_THAN]:(e,t)=>e>=t,[uz.GREATER_THAN]:(e,t)=>e>t,[uz.DIFFERENT]:(e,t)=>e!=t},pz=hz.map(((e,t)=>({name:e,value:t})));class _z{constructor(e){this.node=e}evalForEntities(e){const t=ls[this.node.pv.attribType];switch(t){case cs.NUMERIC:return void this._eval_for_numeric(e);case cs.STRING:return void this._eval_for_string(e)}Ri.unreachable(t)}_eval_for_string(e){let t;for(let n of e)t=n.stringAttribValue(this.node.pv.attribName),t==this.node.pv.attrib_string&&this.node.entitySelectionHelper.select(n)}_eval_for_numeric(e){const t=ds[this.node.pv.attribSize-1];switch(t){case hs.FLOAT:return this._eval_for_points_numeric_float(e);case hs.VECTOR2:return this._eval_for_points_numeric_vector2(e);case hs.VECTOR3:return this._eval_for_points_numeric_vector3(e);case hs.VECTOR4:return this._eval_for_points_numeric_vector4(e)}Ri.unreachable(t)}_eval_for_points_numeric_float(e){let t=this.node.pv.attribName;const n=this.node.pv.attribValue1;let i;const s=hz[this.node.pv.attribComparisonOperator],r=dz[s];for(let s of e)i=s.attribValue(t),r(i,n)&&this.node.entitySelectionHelper.select(s)}_eval_for_points_numeric_vector2(e){let t=this.node.pv.attribName;const n=this.node.pv.attribValue2;let i=new d.a;for(let s of e){const e=s.attribValue(t,i);n.equals(e)&&this.node.entitySelectionHelper.select(s)}}_eval_for_points_numeric_vector3(e){let t=this.node.pv.attribName;const n=this.node.pv.attribValue3;let i=new p.a;for(let s of e){const e=s.attribValue(t,i);n.equals(e)&&this.node.entitySelectionHelper.select(s)}}_eval_for_points_numeric_vector4(e){let t=this.node.pv.attribName;const n=this.node.pv.attribValue4;let i=new _.a;for(let s of e){const e=s.attribValue(t,i);n.equals(e)&&this.node.entitySelectionHelper.select(s)}}}class mz{constructor(e){this.node=e}async evalForEntities(e){const t=this.node.p.expression;this.node.p.expression.hasExpression()&&t.expressionController?await this.eval_expressions_for_points_with_expression(e):this.eval_expressions_without_expression(e)}async eval_expressions_for_points_with_expression(e){const t=this.node.p.expression;t.expressionController&&await t.expressionController.compute_expression_for_entities(e,((e,t)=>{t&&this.node.entitySelectionHelper.select(e)}))}eval_expressions_without_expression(e){if(this.node.pv.expression)for(let t of e)this.node.entitySelectionHelper.select(t)}}class fz{constructor(e){this.node=e,this._point_position=new p.a}evalForPoints(e){const t=this._createBbox();for(let n of e){t.containsPoint(n.getPosition(this._point_position))&&this.node.entitySelectionHelper.select(n)}}_createBbox(){return new CN.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 gz{constructor(e){this.node=e}eval_for_objects(e){const t=ns[this.node.pv.objectType];for(let n of e){ts(n.object().constructor)==t&&this.node.entitySelectionHelper.select(n)}}}class vz{constructor(){this._sidePropertyByMaterial=new WeakMap,this._bound_setMat=this._setObjectMaterialDoubleSided.bind(this),this._bound_restoreMat=this._restoreObjectMaterialSide.bind(this)}setCoreGroupMaterialDoubleSided(e){const t=e.objects();for(let e of t)e.traverse(this._bound_setMat)}restoreMaterialSideProperty(e){const t=e.objects();for(let e of t)e.traverse(this._bound_restoreMat)}_setObjectMaterialDoubleSided(e){const t=e.material;if(t)if(m.isArray(t))for(let e of t)this._setMaterialDoubleSided(e);else this._setMaterialDoubleSided(t)}_restoreObjectMaterialSide(e){const t=e.material;if(t)if(m.isArray(t))for(let e of t)this._restoreMaterialDoubleSided(e);else this._restoreMaterialDoubleSided(t)}_setMaterialDoubleSided(e){this._sidePropertyByMaterial.set(e,e.side),e.side=w.z}_restoreMaterialDoubleSided(e){e.side=this._sidePropertyByMaterial.get(e)||w.z}}const yz=new p.a(0,1,0),xz=new p.a(0,-1,0);class bz{constructor(e){this.node=e,this._matDoubleSideTmpSetter=new vz,this._point_position=new p.a,this._raycaster=new Gy,this._intersections=[]}evalForPoints(e,t){if(!t)return;const n=null==t?void 0:t.objectsWithGeo()[0];if(!n)return;const i=n;if(!i.isMesh)return;this._matDoubleSideTmpSetter.setCoreGroupMaterialDoubleSided(t);const s=n.geometry;s.computeBoundingBox();const r=s.boundingBox;for(let t of e)t.getPosition(this._point_position),r.containsPoint(this._point_position)?this._isPositionInObject(this._point_position,i,yz)&&this._isPositionInObject(this._point_position,i,xz)&&this.node.entitySelectionHelper.select(t):this.node.entitySelectionHelper.select(t);this._matDoubleSideTmpSetter.restoreMaterialSideProperty(t)}_isPositionInObject(e,t,n){var i;this._raycaster.ray.direction.copy(n),this._raycaster.ray.origin.copy(e),this._intersections.length=0;const s=this._raycaster.intersectObject(t,!1,this._intersections);if(!s)return!1;if(0==s.length)return!1;const r=null===(i=s[0].face)||void 0===i?void 0:i.normal;if(!r)return!1;return this._raycaster.ray.direction.dot(r)>=0}}const wz=new class extends Lo{constructor(){super(...arguments),this.class=Oo.INTEGER(os.indexOf(rs.VERTEX),{menu:{entries:as}}),this.invert=Oo.BOOLEAN(0),this.byObjectType=Oo.BOOLEAN(0,{visibleIf:{class:os.indexOf(rs.OBJECT)}}),this.objectType=Oo.INTEGER(ns.indexOf(Zi.MESH),{menu:{entries:is},visibleIf:{class:os.indexOf(rs.OBJECT),byObjectType:!0},separatorAfter:!0}),this.byExpression=Oo.BOOLEAN(0),this.expression=Oo.BOOLEAN(\\\\\\\"@ptnum==0\\\\\\\",{visibleIf:{byExpression:!0},expression:{forEntities:!0},separatorAfter:!0}),this.byAttrib=Oo.BOOLEAN(0),this.attribType=Oo.INTEGER(ls.indexOf(cs.NUMERIC),{menu:{entries:us},visibleIf:{byAttrib:1}}),this.attribName=Oo.STRING(\\\\\\\"\\\\\\\",{visibleIf:{byAttrib:1}}),this.attribSize=Oo.INTEGER(1,{range:ps,rangeLocked:[!0,!0],visibleIf:{byAttrib:1,attribType:ls.indexOf(cs.NUMERIC)}}),this.attribComparisonOperator=Oo.INTEGER(hz.indexOf(uz.EQUAL),{menu:{entries:pz},visibleIf:{byAttrib:!0,attribType:ls.indexOf(cs.NUMERIC),attribSize:hs.FLOAT}}),this.attribValue1=Oo.FLOAT(0,{visibleIf:{byAttrib:1,attribType:ls.indexOf(cs.NUMERIC),attribSize:1}}),this.attribValue2=Oo.VECTOR2([0,0],{visibleIf:{byAttrib:1,attribType:ls.indexOf(cs.NUMERIC),attribSize:2}}),this.attribValue3=Oo.VECTOR3([0,0,0],{visibleIf:{byAttrib:1,attribType:ls.indexOf(cs.NUMERIC),attribSize:3}}),this.attribValue4=Oo.VECTOR4([0,0,0,0],{visibleIf:{byAttrib:1,attribType:ls.indexOf(cs.NUMERIC),attribSize:4}}),this.attribString=Oo.STRING(\\\\\\\"\\\\\\\",{visibleIf:{byAttrib:1,attribType:ls.indexOf(cs.STRING)},separatorAfter:!0}),this.byBbox=Oo.BOOLEAN(0,{visibleIf:{class:os.indexOf(rs.VERTEX)}}),this.bboxSize=Oo.VECTOR3([1,1,1],{visibleIf:{class:os.indexOf(rs.VERTEX),byBbox:!0}}),this.bboxCenter=Oo.VECTOR3([0,0,0],{visibleIf:{class:os.indexOf(rs.VERTEX),byBbox:!0},separatorAfter:!0}),this.byBoundingObject=Oo.BOOLEAN(0,{visibleIf:{class:os.indexOf(rs.VERTEX)}}),this.keepPoints=Oo.BOOLEAN(0,{visibleIf:{class:os.indexOf(rs.OBJECT)}})}};class Az extends QO{constructor(){super(...arguments),this.paramsConfig=wz,this._marked_for_deletion_per_object_index=new Map,this.entitySelectionHelper=new lz(this),this.byExpressionHelper=new mz(this),this.byAttributeHelper=new _z(this),this.byObjectTypeHelper=new gz(this),this.byBboxHelper=new fz(this),this.byBoundingObjectHelper=new bz(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(Ti.FROM_NODE)}async cook(e){const t=e[0],n=e[1];switch(this.pv.class){case rs.VERTEX:await this._eval_for_points(t,n);break;case rs.OBJECT:await this._eval_for_objects(t)}}set_class(e){this.p.class.set(e)}async _eval_for_objects(e){const t=e.coreObjects();this.entitySelectionHelper.init(t),this._marked_for_deletion_per_object_index=new Map;for(let e of t)this._marked_for_deletion_per_object_index.set(e.index(),!1);this.pv.byExpression&&await this.byExpressionHelper.evalForEntities(t),this.pv.byObjectType&&this.byObjectTypeHelper.eval_for_objects(t),this.pv.byAttrib&&\\\\\\\"\\\\\\\"!=this.pv.attribName&&this.byAttributeHelper.evalForEntities(t);const n=this.entitySelectionHelper.entities_to_keep().map((e=>e.object()));if(this.pv.keepPoints){const e=this.entitySelectionHelper.entities_to_delete();for(let t of e){const e=this._point_object(t);e&&n.push(e)}}this.setObjects(n)}async _eval_for_points(e,t){const n=e.coreObjects();let i,s=[];for(let e=0;e<n.length;e++){i=n[e];let r=i.coreGeometry();if(r){const e=i.object(),n=r.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,t);const a=this.entitySelectionHelper.entities_to_keep();if(a.length==o)s.push(e);else if(r.geometry().dispose(),a.length>0){const t=Bs.geometryFromPoints(a,ts(e.constructor));t&&(e.geometry=t,s.push(e))}}}this.setObjects(s)}_point_object(e){const t=e.points(),n=Bs.geometryFromPoints(t,Zi.POINTS);if(n)return this.createObject(n,Zi.POINTS)}}const Tz=new class extends Lo{constructor(){super(...arguments),this.start=Oo.INTEGER(0,{range:[0,100],rangeLocked:[!0,!1]}),this.useCount=Oo.BOOLEAN(0),this.count=Oo.INTEGER(0,{range:[0,100],rangeLocked:[!0,!1],visibleIf:{useCount:1}})}};class Ez extends QO{constructor(){super(...arguments),this.paramsConfig=Tz}static type(){return\\\\\\\"drawRange\\\\\\\"}initializeNode(){this.io.inputs.setCount(0,1),this.io.inputs.initInputsClonedState(Ti.FROM_NODE)}cook(e){const t=e[0],n=t.objects();for(let e of n){const t=e.geometry;if(t){const e=t.drawRange;e.start=this.pv.start,this.pv.useCount?e.count=this.pv.count:e.count=1/0}}this.setCoreGroup(t)}}const Cz=new class extends Lo{constructor(){super(...arguments),this.makeFacesUnique=Oo.BOOLEAN(0),this.addFaceCenterAttribute=Oo.BOOLEAN(0,{visibleIf:{makeFacesUnique:1}}),this.addFaceId=Oo.BOOLEAN(0,{visibleIf:{makeFacesUnique:1}}),this.transform=Oo.BOOLEAN(0,{visibleIf:{makeFacesUnique:1}}),this.scale=Oo.FLOAT(1,{visibleIf:{makeFacesUnique:1,transform:1}})}};class Mz extends QO{constructor(){super(...arguments),this.paramsConfig=Cz}static type(){return\\\\\\\"face\\\\\\\"}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState(Ti.FROM_NODE)}cook(e){const t=e[0];this.pv.makeFacesUnique&&(this._makeFacesUnique(t),this.pv.addFaceCenterAttribute&&this._addFaceCenterAttribute(t),this.pv.addFaceId&&this._addFaceId(t),this.pv.transform&&this._transform_faces(t)),this.setCoreGroup(t)}_makeFacesUnique(e){var t;for(let n of e.objects())if(n.isMesh){const e=n.geometry,i=f.chunk((null===(t=e.index)||void 0===t?void 0:t.array)||[],3),s=3*i.length;for(let t of Object.keys(e.attributes)){const n=e.attributes[t],r=n.itemSize,o=new Float32Array(s*r);let a=0;i.forEach((e=>{e.forEach((e=>{for(let t=0;t<r;t++){const i=n.array[e*r+t];o[a]=i,a+=1}}))})),e.setAttribute(t,new L.a(o,r))}const r=f.range(s);e.setIndex(r)}}_addFaceCenterAttribute(e){const t=\\\\\\\"face_center\\\\\\\",n=new p.a;let i,s,r,o;e.coreObjects().forEach((e=>{const a=e.object(),c=e.coreGeometry();if(a.isMesh&&c){i=c.faces(),c.hasAttrib(t)||c.addNumericAttrib(t,3,-1);for(let e=0;e<i.length;e++){s=i[e],s.center(n),r=s.points();for(let e=0;e<r.length;e++)o=r[e],o.setAttribValue(t,n)}}}))}_addFaceId(e){const t=\\\\\\\"face_id\\\\\\\";e.coreObjects().forEach((e=>{const n=e.object(),i=e.coreGeometry();if(n.isMesh&&i){const e=i.faces();i.hasAttrib(t)||i.addNumericAttrib(t,1,-1);for(let n=0;n<e.length;n++){const i=e[n].points();for(let e=0;e<i.length;e++){i[e].setAttribValue(t,n)}}}}))}_transform_faces(e){const t=\\\\\\\"position\\\\\\\",n=new p.a,i=new p.a,s=this.pv.scale;let r,o,a,c;e.coreObjects().forEach((e=>{const l=e.object(),u=e.coreGeometry();if(l.isMesh&&u){r=u.faces(),u.hasAttrib(t)||u.addNumericAttrib(t,3,-1);for(let e=0;e<r.length;e++){o=r[e],o.center(n),a=o.points();for(let e=0;e<a.length;e++){c=a[e];const r=c.position();i.x=r.x*s+n.x*(1-s),i.y=r.y*s+n.y*(1-s),i.z=r.z*s+n.z*(1-s),c.setAttribValue(t,i)}}}}))}}var Nz;!function(e){e.AUTO=\\\\\\\"auto\\\\\\\",e.DRC=\\\\\\\"drc\\\\\\\",e.FBX=\\\\\\\"fbx\\\\\\\",e.JSON=\\\\\\\"json\\\\\\\",e.GLTF=\\\\\\\"gltf\\\\\\\",e.GLTF_WITH_DRACO=\\\\\\\"gltf_with_draco\\\\\\\",e.OBJ=\\\\\\\"obj\\\\\\\",e.PDB=\\\\\\\"pdb\\\\\\\",e.PLY=\\\\\\\"ply\\\\\\\",e.STL=\\\\\\\"stl\\\\\\\"}(Nz||(Nz={}));const Sz=[Nz.AUTO,Nz.DRC,Nz.FBX,Nz.JSON,Nz.GLTF,Nz.GLTF_WITH_DRACO,Nz.OBJ,Nz.PDB,Nz.PLY,Nz.STL];var Oz;!function(e){e.DRC=\\\\\\\"drc\\\\\\\",e.FBX=\\\\\\\"fbx\\\\\\\",e.GLTF=\\\\\\\"gltf\\\\\\\",e.GLB=\\\\\\\"glb\\\\\\\",e.OBJ=\\\\\\\"obj\\\\\\\",e.PDB=\\\\\\\"pdb\\\\\\\",e.PLY=\\\\\\\"ply\\\\\\\",e.STL=\\\\\\\"stl\\\\\\\"}(Oz||(Oz={}));Oz.DRC,Oz.FBX,Oz.GLTF,Oz.GLB,Oz.OBJ,Oz.PDB,Oz.PLY,Oz.STL;class Lz extends tv{constructor(e,t,n){super(e.url,t,n),this._options=e,this._scene=t,this._node=n}load(e,t){this._load().then((t=>{e(t)})).catch((e=>{t(e)}))}_load(){return new Promise((async(e,t)=>{const n=await this._urlToLoad(),i=this.extension();if(i==Nz.JSON&&this._options.format==Nz.AUTO)Lz.increment_in_progress_loads_count(),await Lz.wait_for_max_concurrent_loads_queue_freed(),fetch(n).then((async t=>{const n=await t.json();new Uk(this.loadingManager).parse(n,(t=>{Lz.decrement_in_progress_loads_count(),e(this.on_load_success(t.children[0]))}))})).catch((e=>{Lz.decrement_in_progress_loads_count(),t(e)}));else{const s=await this._loaderForFormat();if(s)Lz.increment_in_progress_loads_count(),await Lz.wait_for_max_concurrent_loads_queue_freed(),s.load(n,(t=>{this.on_load_success(t).then((t=>{Lz.decrement_in_progress_loads_count(),e(t)}))}),void 0,(e=>{Rn.warn(\\\\\\\"error loading\\\\\\\",n,e),Lz.decrement_in_progress_loads_count(),t(e)}));else{t(`format not supported (${i})`)}}}))}async on_load_success(e){const t=this.extension();if(t==Nz.JSON)return[e];const n=e;if(n.isObject3D)switch(t){case Oz.PDB:return this.on_load_succes_pdb(e);case Oz.OBJ:default:return[n]}const i=e;if(i.isBufferGeometry)switch(t){case Oz.DRC:return this.on_load_succes_drc(i);default:return[new z.a(i)]}const s=e;if(null!=s.scene)switch(t){case Oz.GLTF:case Oz.GLB:return this.on_load_succes_gltf(s);default:return[n]}const r=e;if(r.geometryAtoms||r.geometryBonds)switch(t){case Oz.PDB:return this.on_load_succes_pdb(r);default:return[]}return[]}on_load_succes_drc(e){return[new z.a(e,Lz._default_mat_mesh)]}on_load_succes_gltf(e){const t=e.scene;return t.animations=e.animations,[t]}on_load_succes_pdb(e){return[new ji.a(e.geometryAtoms,Lz._default_mat_point),new $i.a(e.geometryBonds,Lz._default_mat_line)]}static moduleNamesFromFormat(e,t){switch(e){case Nz.AUTO:return this.moduleNamesFromExt(t);case Nz.DRC:return[_n.DRACOLoader];case Nz.FBX:return[_n.FBXLoader];case Nz.JSON:return[];case Nz.GLTF:return[_n.GLTFLoader];case Nz.GLTF_WITH_DRACO:return[_n.GLTFLoader,_n.DRACOLoader];case Nz.OBJ:return[_n.OBJLoader];case Nz.PDB:return[_n.PDBLoader];case Nz.PLY:return[_n.PLYLoader];case Nz.STL:return[_n.STLLoader]}Ri.unreachable(e)}static moduleNamesFromExt(e){switch(e){case Oz.DRC:return[_n.DRACOLoader];case Oz.FBX:return[_n.FBXLoader];case Oz.GLTF:return[_n.GLTFLoader];case Oz.GLB:return[_n.GLTFLoader,_n.DRACOLoader];case Oz.OBJ:return[_n.OBJLoader];case Oz.PDB:return[_n.PDBLoader];case Oz.PLY:return[_n.PLYLoader];case Oz.STL:return[_n.STLLoader]}}async _loaderForFormat(){const e=this._options.format;switch(e){case Nz.AUTO:return this._loaderForExt();case Nz.DRC:return this.loader_for_drc(this._node);case Nz.FBX:return this.loader_for_fbx();case Nz.JSON:return;case Nz.GLTF:return this.loader_for_gltf();case Nz.GLTF_WITH_DRACO:return this.loader_for_glb(this._node);case Nz.OBJ:return this.loader_for_obj();case Nz.PDB:return this.loader_for_pdb();case Nz.PLY:return this.loader_for_ply();case Nz.STL:return this.loader_for_stl()}Ri.unreachable(e)}async _loaderForExt(){switch(this.extension().toLowerCase()){case Oz.DRC:return this.loader_for_drc(this._node);case Oz.FBX:return this.loader_for_fbx();case Oz.GLTF:return this.loader_for_gltf();case Oz.GLB:return this.loader_for_glb(this._node);case Oz.OBJ:return this.loader_for_obj();case Oz.PDB:return this.loader_for_pdb();case Oz.PLY:return this.loader_for_ply();case Oz.STL:return this.loader_for_stl()}}loader_for_fbx(){const e=Rn.modulesRegister.module(_n.FBXLoader);if(e)return new e(this.loadingManager)}loader_for_gltf(){const e=Rn.modulesRegister.module(_n.GLTFLoader);if(e)return new e(this.loadingManager)}static async loader_for_drc(e){const t=Rn.modulesRegister.module(_n.DRACOLoader);if(t){const n=new t(this.loadingManager),i=Rn.libs.root(),s=Rn.libs.DRACOPath();if(i||s){const t=`${i||\\\\\\\"\\\\\\\"}${s||\\\\\\\"\\\\\\\"}/`;if(e){const n=[\\\\\\\"draco_decoder.js\\\\\\\",\\\\\\\"draco_decoder.wasm\\\\\\\",\\\\\\\"draco_wasm_wrapper.js\\\\\\\"];await this._loadMultipleBlobGlobal({files:n.map((e=>({storedUrl:`${s}/${e}`,fullUrl:`${t}${e}`}))),node:e,error:\\\\\\\"failed to load draco libraries. Make sure to install them to load .glb files\\\\\\\"})}n.setDecoderPath(t)}else n.setDecoderPath(void 0);return n.setDecoderConfig({type:\\\\\\\"js\\\\\\\"}),n}}loader_for_drc(e){return Lz.loader_for_drc(e)}static async loader_for_glb(e){const t=Rn.modulesRegister.module(_n.GLTFLoader),n=Rn.modulesRegister.module(_n.DRACOLoader);if(t&&n){this.gltf_loader=this.gltf_loader||new t(this.loadingManager),this.draco_loader=this.draco_loader||new n(this.loadingManager);const i=Rn.libs.root(),s=Rn.libs.DRACOGLTFPath();if(i||s){const t=`${i||\\\\\\\"\\\\\\\"}${s||\\\\\\\"\\\\\\\"}/`;if(e){const n=[\\\\\\\"draco_decoder.js\\\\\\\",\\\\\\\"draco_decoder.wasm\\\\\\\",\\\\\\\"draco_wasm_wrapper.js\\\\\\\"];await this._loadMultipleBlobGlobal({files:n.map((e=>({storedUrl:`${s}/${e}`,fullUrl:`${t}${e}`}))),node:e,error:\\\\\\\"failed to load draco libraries. Make sure to install them to load .glb files\\\\\\\"})}this.draco_loader.setDecoderPath(t)}else this.draco_loader.setDecoderPath(void 0);return this.gltf_loader.setDRACOLoader(this.draco_loader),this.gltf_loader}}loader_for_glb(e){return Lz.loader_for_glb(e)}loader_for_obj(){const e=Rn.modulesRegister.module(_n.OBJLoader);if(e)return new e(this.loadingManager)}loader_for_pdb(){const e=Rn.modulesRegister.module(_n.PDBLoader);if(e)return new e(this.loadingManager)}loader_for_ply(){const e=Rn.modulesRegister.module(_n.PLYLoader);if(e)return new e(this.loadingManager)}loader_for_stl(){const e=Rn.modulesRegister.module(_n.STLLoader);if(e)return new e(this.loadingManager)}static setMaxConcurrentLoadsCount(e){this._maxConcurrentLoadsCountMethod=e}static _init_max_concurrent_loads_count(){return this._maxConcurrentLoadsCountMethod?this._maxConcurrentLoadsCountMethod():Df.isChrome()?4:1}static _init_concurrent_loads_delay(){return Df.isChrome()?1:10}static increment_in_progress_loads_count(){this.in_progress_loads_count++}static decrement_in_progress_loads_count(){this.in_progress_loads_count--;const e=this._queue.pop();if(e){const t=this.CONCURRENT_LOADS_DELAY;setTimeout((()=>{e()}),t)}}static async wait_for_max_concurrent_loads_queue_freed(){return this.in_progress_loads_count<=this.MAX_CONCURRENT_LOADS_COUNT?void 0:new Promise((e=>{this._queue.push(e)}))}}Lz._default_mat_mesh=new Xi.a,Lz._default_mat_point=new qi.a,Lz._default_mat_line=new Yi.a,Lz.MAX_CONCURRENT_LOADS_COUNT=Lz._init_max_concurrent_loads_count(),Lz.CONCURRENT_LOADS_DELAY=Lz._init_concurrent_loads_delay(),Lz.in_progress_loads_count=0,Lz._queue=[];const Pz=`${Kg}/models/wolf.obj`;class Rz extends WO{static type(){return\\\\\\\"file\\\\\\\"}cook(e,t){const n=new Lz({url:t.url,format:t.format},this.scene(),this._node);return new Promise((e=>{n.load((t=>{const n=this._on_load(t);e(this.createCoreGroupFromObjects(n))}),(e=>{this._on_error(e,t)}))}))}_on_load(e){e=e.flat();for(let t of e)t.traverse((e=>{this._ensure_geometry_has_index(e),e.matrixAutoUpdate=!1}));return e}_on_error(e,t){var n;null===(n=this.states)||void 0===n||n.error.set(`could not load geometry from ${t.url} (${e})`)}_ensure_geometry_has_index(e){const t=e.geometry;t&&this.createIndexIfNone(t)}}Rz.DEFAULT_PARAMS={url:Pz,format:Nz.AUTO};const Iz=Rz.DEFAULT_PARAMS;const Fz=new class extends Lo{constructor(){super(...arguments),this.url=Oo.STRING(Iz.url,{fileBrowse:{type:[tr.GEOMETRY]}}),this.format=Oo.STRING(Iz.format,{menuString:{entries:Sz.map((e=>({name:e,value:e})))}}),this.reload=Oo.BUTTON(null,{callback:e=>{Dz.PARAM_CALLBACK_reload(e)}})}};class Dz extends QO{constructor(){super(...arguments),this.paramsConfig=Fz}static type(){return\\\\\\\"file\\\\\\\"}async requiredModules(){for(let e of[this.p.url,this.p.format])e.isDirty()&&await e.compute();const e=tv.extension(this.pv.url||\\\\\\\"\\\\\\\"),t=this.pv.format;return Lz.moduleNamesFromFormat(t,e)}initializeNode(){this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.url],(()=>{const e=this.p.url.rawInput();if(e){const t=e.split(\\\\\\\"/\\\\\\\");return t[t.length-1]}return\\\\\\\"\\\\\\\"}))}))}))}async cook(e){this._operation=this._operation||new Rz(this.scene(),this.states,this);const t=await this._operation.cook(e,this.pv);this.setCoreGroup(t)}static PARAM_CALLBACK_reload(e){e._paramCallbackReload()}_paramCallbackReload(){this.p.url.setDirty()}}const kz=new class extends Lo{constructor(){super(...arguments),this.dist=Oo.FLOAT(.1,{range:[0,1],rangeLocked:[!0,!1]})}};class Bz extends QO{constructor(){super(...arguments),this.paramsConfig=kz}static type(){return\\\\\\\"fuse\\\\\\\"}static displayedInputNames(){return[\\\\\\\"points to fuse together\\\\\\\"]}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState(Ti.FROM_NODE)}cook(e){const t=e[0],n=[];let i;for(let e of t.coreObjects())i=this._fuse_core_object(e),i&&n.push(i);this.setObjects(n)}_fuse_core_object(e){const t=e.object();if(!t)return;const n=e.points(),i=this.pv.dist,s={};for(let e of n){const t=e.position(),n=new p.a(Math.round(t.x/i),Math.round(t.y/i),Math.round(t.z/i)).toArray().join(\\\\\\\"-\\\\\\\");s[n]=s[n]||[],s[n].push(e)}const r=[];if(Object.keys(s).forEach((e=>{r.push(s[e][0])})),t.geometry.dispose(),r.length>0){const e=Bs.geometryFromPoints(r,ts(t.constructor));return e&&(t.geometry=e),t}}}class zz{constructor(e,t,n){this._param_size=e,this._param_hexagon_radius=t,this._param_points_only=n}process(){const e=this._param_hexagon_radius,t=.5*e,n=e,i=Math.cos(Math.PI/6)*this._param_hexagon_radius,s=Math.floor(this._param_size.x/n),r=Math.floor(this._param_size.y/i);let o=[],a=[];for(let e=0;e<r;e++)for(let r=0;r<s;r++)o.push([-.5*this._param_size.x+r*n+(e%2==0?t:0),0,-.5*this._param_size.y+e*i]),this._param_points_only||e>=1&&(0==r||r==s-1?0==r?a.push([r+1+(e-1)*s,r+(e-1)*s,r+e*s]):a.push([r+e*s,r+(e-1)*s,r-1+e*s]):(a.push([r+e*s,r+(e-1)*s,r-1+e*s]),a.push([r+e*s,r+1+(e-1)*s,r+(e-1)*s])));const c=new O.a;return c.setAttribute(\\\\\\\"position\\\\\\\",new L.a(new Float32Array(o.flat()),3)),this._param_points_only||(c.setIndex(a.flat()),c.computeVertexNormals()),c}}const Uz=new p.a(0,1,0);const Gz=new class extends Lo{constructor(){super(...arguments),this.size=Oo.VECTOR2([1,1]),this.hexagonRadius=Oo.FLOAT(.1,{range:[.001,1],rangeLocked:[!1,!1]}),this.direction=Oo.VECTOR3([0,1,0]),this.pointsOnly=Oo.BOOLEAN(0)}};class Vz extends QO{constructor(){super(...arguments),this.paramsConfig=Gz,this._core_transform=new rS}static type(){return\\\\\\\"hexagons\\\\\\\"}initializeNode(){}cook(){if(this.pv.hexagonRadius>0){const e=new zz(this.pv.size,this.pv.hexagonRadius,this.pv.pointsOnly).process();this._core_transform.rotate_geometry(e,Uz,this.pv.direction),this.pv.pointsOnly?this.setGeometry(e,Zi.POINTS):this.setGeometry(e)}else this.setObjects([])}}var jz;!function(e){e.ADD_PARENT=\\\\\\\"add_parent\\\\\\\",e.REMOVE_PARENT=\\\\\\\"remove_parent\\\\\\\",e.ADD_CHILD=\\\\\\\"add_child\\\\\\\"}(jz||(jz={}));const Hz=[jz.ADD_PARENT,jz.REMOVE_PARENT,jz.ADD_CHILD];class qz extends WO{static type(){return\\\\\\\"hierarchy\\\\\\\"}cook(e,t){const n=e[0],i=Hz[t.mode];switch(i){case jz.ADD_PARENT:{const e=this._add_parent_to_core_group(n,t);return this.createCoreGroupFromObjects(e)}case jz.REMOVE_PARENT:{const e=this._remove_parent_from_core_group(n,t);return this.createCoreGroupFromObjects(e)}case jz.ADD_CHILD:{const i=this._add_child_to_core_group(n,e[1],t);return this.createCoreGroupFromObjects(i)}}Ri.unreachable(i)}_add_parent_to_core_group(e,t){if(0==t.levels)return e.objects();return[this._add_parent_to_object(e.objects(),t)]}_add_parent_to_object(e,t){let n=new on.a;if(n.matrixAutoUpdate=!1,n.add(...e),t.levels>0)for(let e=0;e<t.levels-1;e++)n=this._add_new_parent(n,t);return n}_add_new_parent(e,t){const n=new on.a;return n.matrixAutoUpdate=!1,n.add(e),n}_remove_parent_from_core_group(e,t){if(0==t.levels)return e.objects();{const n=[];for(let i of e.objects()){const e=this._remove_parent_from_object(i,t);for(let t of e)n.push(t)}return n}}_remove_parent_from_object(e,t){let n=e.children;for(let e=0;e<t.levels-1;e++)n=this._get_children_from_objects(n,t);return n}_get_children_from_objects(e,t){let n;const i=[];for(;n=e.pop();)if(n.children)for(let e of n.children)i.push(e);return i}_add_child_to_core_group(e,t,n){var i,s;const r=e.objects();if(!t)return null===(i=this.states)||void 0===i||i.error.set(\\\\\\\"input 1 is invalid\\\\\\\"),[];const o=t.objects(),a=n.objectMask.trim(),c=\\\\\\\"\\\\\\\"!=a?this._findObjectsByMaskFromObjects(a,r):r;n.debugObjectMask&&console.log(c);for(let e=0;e<c.length;e++){const t=c[e],n=o[e]||o[0];if(!n)return null===(s=this.states)||void 0===s||s.error.set(\\\\\\\"no objects found in input 1\\\\\\\"),[];t.add(n)}return r}_findObjectsByMaskFromObjects(e,t){const n=[];for(let i of t)this.scene().objectsController.objectsByMaskInObject(e,i,n);return n}}qz.DEFAULT_PARAMS={mode:0,levels:1,objectMask:\\\\\\\"\\\\\\\",debugObjectMask:!1},qz.INPUT_CLONED_STATE=Ti.FROM_NODE;const Wz=[jz.ADD_PARENT,jz.REMOVE_PARENT],Xz=qz.DEFAULT_PARAMS;const Yz=new class extends Lo{constructor(){super(...arguments),this.mode=Oo.INTEGER(Xz.mode,{menu:{entries:Hz.map(((e,t)=>({name:e,value:t})))}}),this.levels=Oo.INTEGER(Xz.levels,{range:[0,5],visibleIf:[{mode:Hz.indexOf(jz.ADD_PARENT)},{mode:Hz.indexOf(jz.REMOVE_PARENT)}]}),this.objectMask=Oo.STRING(\\\\\\\"\\\\\\\",{visibleIf:{mode:Hz.indexOf(jz.ADD_CHILD)}}),this.debugObjectMask=Oo.BOOLEAN(0,{visibleIf:{mode:Hz.indexOf(jz.ADD_CHILD)}})}};class $z extends QO{constructor(){super(...arguments),this.paramsConfig=Yz}static type(){return\\\\\\\"hierarchy\\\\\\\"}static displayedInputNames(){return[\\\\\\\"geometry to add or remove parents to/from\\\\\\\",\\\\\\\"objects to use as parent or children (optional)\\\\\\\"]}initializeNode(){this.io.inputs.setCount(1,2),this.io.inputs.initInputsClonedState(qz.INPUT_CLONED_STATE),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.mode,this.p.levels,this.p.objectMask],(()=>{const e=Hz[this.pv.mode];return Wz.includes(e)?`${e} ${this.pv.levels}`:`${e} (with mask: ${this.pv.objectMask})`}))}))}))}cook(e){this._operation=this._operation||new qz(this._scene,this.states);const t=this._operation.cook(e,this.pv);this.setCoreGroup(t)}}const Qz=new class extends Lo{constructor(){super(...arguments),this.texture=Oo.OPERATOR_PATH(jn.UV,{nodeSelection:{context:Ei.COP}}),this.mult=Oo.FLOAT(1)}};class Jz extends QO{constructor(){super(...arguments),this.paramsConfig=Qz}static type(){return\\\\\\\"heightMap\\\\\\\"}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState(Ti.FROM_NODE)}async cook(e){const t=e[0],n=this.p.texture.found_node();if(n){if(n.context()==Ei.COP){const e=n,i=(await e.compute()).texture();for(let e of t.coreObjects())this._set_position_from_data_texture(e,i)}else this.states.error.set(\\\\\\\"found node is not a texture\\\\\\\")}t.computeVertexNormals(),this.setCoreGroup(t)}_set_position_from_data_texture(e,t){var n;const i=this._data_from_texture(t);if(!i)return;const{data:s,resx:r,resy:o}=i,a=s.length/(r*o),c=null===(n=e.coreGeometry())||void 0===n?void 0:n.geometry();if(!c)return;const l=c.getAttribute(\\\\\\\"position\\\\\\\").array,u=c.getAttribute(\\\\\\\"uv\\\\\\\"),h=c.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,_=l.length/3;let m,f,g,v,y,x,b,w=0;for(let e=0;e<_;e++)m=2*e,f=d[m],g=d[m+1],v=Math.floor((r-1)*f),y=Math.floor((o-1)*(1-g)),x=y*r+v,b=s[a*x],w=3*e,l[w+0]+=p[w+0]*b*this.pv.mult,l[w+1]+=p[w+1]*b*this.pv.mult,l[w+2]+=p[w+2]*b*this.pv.mult}_data_from_texture(e){if(e.image)return e.image.data?this._data_from_data_texture(e):this._data_from_default_texture(e)}_data_from_default_texture(e){const t=e.image.width,n=e.image.height;return{data:mf.data_from_image(e.image).data,resx:t,resy:n}}_data_from_data_texture(e){return{data:e.image.data,resx:e.image.width,resy:e.image.height}}}function Kz(e){return Math.atan2(-e.y,Math.sqrt(e.x*e.x+e.z*e.z))}class Zz extends O.a{constructor(e,t,n,i,s){super(),this.type=\\\\\\\"PolyhedronBufferGeometry\\\\\\\",this.parameters={vertices:e,indices:t,radius:n,detail:i},n=n||1,i=i||0;const r=[],o=[],a=new Map;function c(e,t,n,i){const s=i+1,r=[];for(let i=0;i<=s;i++){r[i]=[];const o=e.clone().lerp(n,i/s),a=t.clone().lerp(n,i/s),c=s-i;for(let e=0;e<=c;e++)r[i][e]=0===e&&i===s?o:o.clone().lerp(a,e/c)}for(let e=0;e<s;e++)for(let t=0;t<2*(s-e)-1;t++){const n=Math.floor(t/2);t%2==0?(l(r[e][n+1]),l(r[e+1][n]),l(r[e][n])):(l(r[e][n+1]),l(r[e+1][n+1]),l(r[e+1][n]))}}function l(e){if(s){let t=a.get(e.x);if(t){const n=t.get(e.y);if(n&&n.has(e.z))return}t||(t=new Map,a.set(e.x,t));let n=t.get(e.y);n||(n=new Set,t.set(e.y,n)),n.add(e.z)}r.push(e.x,e.y,e.z)}function u(t,n){const i=3*t;n.x=e[i+0],n.y=e[i+1],n.z=e[i+2]}!function(e){const n=new p.a,i=new p.a,s=new p.a;for(let r=0;r<t.length;r+=3)u(t[r+0],n),u(t[r+1],i),u(t[r+2],s),c(n,i,s,e)}(i),function(e){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],t.normalize().multiplyScalar(e),r[n+0]=t.x,r[n+1]=t.y,r[n+2]=t.z}(n),function(){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];const i=(t=e,Math.atan2(t.z,-t.x)/2/Math.PI+.5),s=Kz(e)/Math.PI+.5;o.push(i,1-s)}var t}(),this.setAttribute(\\\\\\\"position\\\\\\\",new L.c(r,3)),this.setAttribute(\\\\\\\"uv\\\\\\\",new L.c(o,2)),s||(this.setAttribute(\\\\\\\"normal\\\\\\\",new L.c(r.slice(),3)),0===i?this.computeVertexNormals():this.normalizeNormals())}}class eU extends Zz{constructor(e,t,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],e,t,n),this.type=\\\\\\\"IcosahedronBufferGeometry\\\\\\\",this.parameters={radius:e,detail:t}}}class tU extends WO{static type(){return\\\\\\\"icosahedron\\\\\\\"}cook(e,t){const n=t.pointsOnly,i=new eU(t.radius,t.detail,n);if(i.translate(t.center.x,t.center.y,t.center.z),n){const e=this.createObject(i,Zi.POINTS);return this.createCoreGroupFromObjects([e])}return i.computeVertexNormals(),this.createCoreGroupFromGeometry(i)}}tU.DEFAULT_PARAMS={radius:1,detail:0,pointsOnly:!1,center:new p.a(0,0,0)};const nU=tU.DEFAULT_PARAMS;const iU=new class extends Lo{constructor(){super(...arguments),this.radius=Oo.FLOAT(nU.radius),this.detail=Oo.INTEGER(nU.detail,{range:[0,10],rangeLocked:[!0,!1]}),this.pointsOnly=Oo.BOOLEAN(nU.pointsOnly),this.center=Oo.VECTOR3(nU.center)}};class sU extends QO{constructor(){super(...arguments),this.paramsConfig=iU}static type(){return\\\\\\\"icosahedron\\\\\\\"}cook(){this._operation=this._operation||new tU(this._scene,this.states);const e=this._operation.cook([],this.pv);this.setCoreGroup(e)}}class rU extends WO{static type(){return\\\\\\\"instance\\\\\\\"}async cook(e,t){const n=e[0];this._geometry=void 0;const i=n.objectsWithGeo()[0];if(i){const n=i.geometry;if(n){const i=e[1];this._create_instance(n,i,t)}}if(this._geometry){const e=(s=i)instanceof z.a?Zi.MESH:s instanceof $i.a?Zi.LINE_SEGMENTS:s instanceof ji.a?Zi.POINTS:s instanceof ee.a?Zi.OBJECT3D:void Rn.warn(\\\\\\\"ObjectTypeByObject received an unknown object type\\\\\\\",s);if(e){const n=this.createObject(this._geometry,e);if(t.applyMaterial){const e=await this._get_material(t);e&&await this._applyMaterial(n,e)}return this.createCoreGroupFromObjects([n])}}var s;return this.createCoreGroupFromObjects([])}async _get_material(e){var t;if(e.applyMaterial){const n=e.material.nodeWithContext(Ei.MAT,null===(t=this.states)||void 0===t?void 0:t.error);if(n){this._globals_handler=this._globals_handler||new uf;const e=n.assemblerController;e&&e.set_assembler_globals_handler(this._globals_handler);return(await n.compute()).material()}}}async _applyMaterial(e,t){e.material=t,Gs.applyCustomMaterials(e,t)}_create_instance(e,t,n){this._geometry=SB.create_instance_buffer_geo(e,t,n.attributesToCopy)}}rU.DEFAULT_PARAMS={attributesToCopy:\\\\\\\"instance*\\\\\\\",applyMaterial:!0,material:new Hn(\\\\\\\"\\\\\\\")},rU.INPUT_CLONED_STATE=[Ti.ALWAYS,Ti.NEVER];const oU=rU.DEFAULT_PARAMS;const aU=new class extends Lo{constructor(){super(...arguments),this.attributesToCopy=Oo.STRING(oU.attributesToCopy),this.applyMaterial=Oo.BOOLEAN(oU.applyMaterial),this.material=Oo.NODE_PATH(oU.material.path(),{visibleIf:{applyMaterial:1},nodeSelection:{context:Ei.MAT},dependentOnFoundNode:!1})}};class cU extends QO{constructor(){super(...arguments),this.paramsConfig=aU}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(rU.INPUT_CLONED_STATE)}async cook(e){this._operation=this._operation||new rU(this.scene(),this.states);const t=await this._operation.cook(e,this.pv);this.setCoreGroup(t)}}const lU=new class extends Lo{constructor(){super(...arguments),this.useMax=Oo.BOOLEAN(0),this.max=Oo.INTEGER(1,{range:[0,100],rangeLocked:[!0,!1],visibleIf:{useMax:1}})}};class uU extends QO{constructor(){super(...arguments),this.paramsConfig=lU}static type(){return\\\\\\\"instancesCount\\\\\\\"}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState(Ti.FROM_NODE)}async cook(e){const t=e[0],n=t.objectsWithGeo();for(let e of n){const t=e.geometry;t&&t instanceof mk&&(this.pv.useMax?t.instanceCount=this.pv.max:t.instanceCount=1/0)}this.setCoreGroup(t)}}class hU extends WO{static type(){return\\\\\\\"jitter\\\\\\\"}cook(e,t){const n=e[0],i=n.points();let s;for(let e=0;e<i.length;e++){s=i[e];const n=new p.a(2*t.mult.x*(Os.randFloat(75*e+764+t.seed)-.5),2*t.mult.y*(Os.randFloat(5678*e+3653+t.seed)-.5),2*t.mult.z*(Os.randFloat(657*e+48464+t.seed)-.5));n.normalize(),n.multiplyScalar(t.amount*Os.randFloat(78*e+54+t.seed));const r=s.position().clone().add(n);s.setPosition(r)}return n}}hU.DEFAULT_PARAMS={amount:1,mult:new p.a(1,1,1),seed:1},hU.INPUT_CLONED_STATE=Ti.FROM_NODE;const dU=hU.DEFAULT_PARAMS;const pU=new class extends Lo{constructor(){super(...arguments),this.amount=Oo.FLOAT(dU.amount),this.mult=Oo.VECTOR3(dU.mult),this.seed=Oo.INTEGER(dU.seed,{range:[0,100]})}};class _U extends QO{constructor(){super(...arguments),this.paramsConfig=pU}static type(){return\\\\\\\"jitter\\\\\\\"}static displayedInputNames(){return[\\\\\\\"geometry to jitter points of\\\\\\\"]}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState(hU.INPUT_CLONED_STATE)}cook(e){this._operation=this._operation||new hU(this.scene(),this.states);const t=this._operation.cook(e,this.pv);this.setCoreGroup(t)}}new class extends Lo{};const mU=new class extends Lo{constructor(){super(...arguments),this.layer=Oo.INTEGER(0,{range:[0,31],rangeLocked:[!0,!0]})}};class fU extends QO{constructor(){super(...arguments),this.paramsConfig=mU}static type(){return\\\\\\\"layer\\\\\\\"}static displayedInputNames(){return[\\\\\\\"objects to change layers of\\\\\\\"]}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState(Ti.FROM_NODE),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.layer])}))}))}cook(e){const t=e[0];for(let e of t.objects())e.layers.set(this.pv.layer);this.setCoreGroup(t)}}const gU=new class extends Lo{constructor(){super(...arguments),this.length=Oo.FLOAT(1,{range:[0,10]}),this.pointsCount=Oo.INTEGER(1,{range:[2,100],rangeLocked:[!0,!1]}),this.origin=Oo.VECTOR3([0,0,0]),this.direction=Oo.VECTOR3([0,1,0])}};class vU extends QO{constructor(){super(...arguments),this.paramsConfig=gU}static type(){return\\\\\\\"line\\\\\\\"}initializeNode(){}cook(){const e=Math.max(2,this.pv.pointsCount),t=new Array(3*e),n=new Array(e),i=this.pv.direction.clone().normalize().multiplyScalar(this.pv.length);for(let s=0;s<e;s++){const r=s/(e-1),o=i.clone().multiplyScalar(r);o.add(this.pv.origin),o.toArray(t,3*s),s>0&&(n[2*(s-1)]=s-1,n[2*(s-1)+1]=s)}const s=new O.a;s.setAttribute(\\\\\\\"position\\\\\\\",new L.c(t,3)),s.setIndex(n),this.setGeometry(s,Zi.LINE_SEGMENTS)}}const yU=new class extends Lo{constructor(){super(...arguments),this.distance0=Oo.FLOAT(1),this.distance1=Oo.FLOAT(2),this.autoUpdate=Oo.BOOLEAN(1),this.update=Oo.BUTTON(null,{callback:e=>{xU.PARAM_CALLBACK_update(e)}}),this.camera=Oo.OPERATOR_PATH(\\\\\\\"/perspective_camera1\\\\\\\",{visibleIf:{autoUpdate:0},dependentOnFoundNode:!1})}};class xU extends QO{constructor(){super(...arguments),this.paramsConfig=yU,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(Ti.FROM_NODE)}_create_LOD(){const e=new Ki;return e.matrixAutoUpdate=!1,e}cook(e){this._clear_lod(),this._add_level(e[0],0),this._add_level(e[1],this.pv.distance0),this._add_level(e[2],this.pv.distance1),this._lod.autoUpdate=this.pv.autoUpdate,this.setObject(this._lod)}_add_level(e,t){if(e){const n=e.objects();let i;for(let e=0;e<n.length;e++)i=n[e],i.visible=!0,this._lod.addLevel(i,t),0==t&&0==e&&(this._lod.matrix.copy(i.matrix),rS.decompose_matrix(this._lod)),i.matrix.identity(),rS.decompose_matrix(i)}}_clear_lod(){let e;for(;e=this._lod.children[0];)this._lod.remove(e),e.matrix.multiply(this._lod.matrix),rS.decompose_matrix(e);for(;this._lod.levels.pop(););}static PARAM_CALLBACK_update(e){e._update_lod()}async _update_lod(){if(this.p.autoUpdate)return;const e=this.p.camera;e.isDirty()&&await e.compute();let t=e.found_node_with_context_and_type(Ei.OBJ,Ni.PERSPECTIVE)||e.found_node_with_context_and_type(Ei.OBJ,Ni.ORTHOGRAPHIC);if(t){const e=t.object;this._lod.update(e)}else this.states.error.set(\\\\\\\"no camera node found\\\\\\\")}}class bU extends WO{constructor(){super(...arguments),this._globals_handler=new uf,this._old_mat_by_old_new_id=new Map,this._materials_by_uuid=new Map}static type(){return\\\\\\\"material\\\\\\\"}async cook(e,t){const n=e[0];return this._old_mat_by_old_new_id.clear(),await this._apply_materials(n,t),this._swap_textures(n,t),n}async _apply_materials(e,t){var n,i,s;if(!t.assignMat)return;const r=t.material.nodeWithContext(Ei.MAT,null===(n=this.states)||void 0===n?void 0:n.error);if(r){const n=r.material,s=r.assemblerController;if(s&&s.set_assembler_globals_handler(this._globals_handler),await r.compute(),n){if(t.applyToChildren)for(let i of e.objects())i.traverse((e=>{this._apply_material(e,n,t)}));else for(let i of e.objectsFromGroup(t.group))this._apply_material(i,n,t);return e}null===(i=this.states)||void 0===i||i.error.set(`material invalid. (error: '${r.states.error.message()}')`)}else null===(s=this.states)||void 0===s||s.error.set(\\\\\\\"no material node found\\\\\\\")}_swap_textures(e,t){if(t.swapCurrentTex){this._materials_by_uuid.clear();for(let n of e.objectsFromGroup(t.group))if(t.applyToChildren)n.traverse((e=>{const t=n.material;this._materials_by_uuid.set(t.uuid,t)}));else{const e=n.material;this._materials_by_uuid.set(e.uuid,e)}this._materials_by_uuid.forEach(((e,n)=>{this._swap_texture(e,t)}))}}_apply_material(e,t,n){if(n.group&&!js.isInGroup(n.group,e))return;const i=n.cloneMat?Gs.clone(t):t;if(t instanceof B&&i instanceof B)for(let e in t.uniforms)i.uniforms[e]=t.uniforms[e];const s=e;this._old_mat_by_old_new_id.set(i.uuid,s.material),s.material=i,Gs.apply_render_hook(e,i),Gs.applyCustomMaterials(e,i)}_swap_texture(e,t){if(\\\\\\\"\\\\\\\"==t.texSrc0||\\\\\\\"\\\\\\\"==t.texDest0)return;let n=this._old_mat_by_old_new_id.get(e.uuid);n=n||e;const i=n[t.texSrc0];if(i){e[t.texDest0]=i;const n=e.uniforms;if(n){n[t.texDest0]&&(n[t.texDest0]={value:i})}}}}bU.DEFAULT_PARAMS={group:\\\\\\\"\\\\\\\",assignMat:!0,material:new Hn(\\\\\\\"\\\\\\\"),applyToChildren:!0,cloneMat:!1,shareUniforms:!0,swapCurrentTex:!1,texSrc0:\\\\\\\"emissiveMap\\\\\\\",texDest0:\\\\\\\"map\\\\\\\"},bU.INPUT_CLONED_STATE=Ti.FROM_NODE;const wU=bU.DEFAULT_PARAMS;const AU=new class extends Lo{constructor(){super(...arguments),this.group=Oo.STRING(wU.group),this.assignMat=Oo.BOOLEAN(wU.assignMat),this.material=Oo.NODE_PATH(wU.material.path(),{nodeSelection:{context:Ei.MAT},dependentOnFoundNode:!1,visibleIf:{assignMat:1}}),this.applyToChildren=Oo.BOOLEAN(wU.applyToChildren,{visibleIf:{assignMat:1}}),this.cloneMat=Oo.BOOLEAN(wU.cloneMat,{visibleIf:{assignMat:1}}),this.shareUniforms=Oo.BOOLEAN(wU.shareUniforms,{visibleIf:{assignMat:1,cloneMat:1}}),this.swapCurrentTex=Oo.BOOLEAN(wU.swapCurrentTex),this.texSrc0=Oo.STRING(wU.texSrc0,{visibleIf:{swapCurrentTex:1}}),this.texDest0=Oo.STRING(wU.texDest0,{visibleIf:{swapCurrentTex:1}})}};class TU extends QO{constructor(){super(...arguments),this.paramsConfig=AU}static type(){return\\\\\\\"material\\\\\\\"}static displayedInputNames(){return[\\\\\\\"objects to assign material to\\\\\\\"]}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState(bU.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(e){this._operation=this._operation||new bU(this._scene,this.states);const t=await this._operation.cook(e,this.pv);this.setCoreGroup(t)}}class EU extends WO{static type(){return\\\\\\\"merge\\\\\\\"}cook(e,t){let n=[];for(let i of e)if(i){const e=i.objects();if(t.compact)for(let t of e)t.traverse((e=>{n.push(e)}));else for(let e of i.objects())n.push(e)}t.compact&&(n=this._make_compact(n));for(let e of n)e.traverse((e=>{e.matrixAutoUpdate=!1}));return this.createCoreGroupFromObjects(n)}_make_compact(e){const t=new Map,n=new Map,i=[];for(let s of e)s.traverse((e=>{if(e instanceof on.a)return;const s=e;if(s.geometry){const e=ts(s.constructor);if(i.includes(e)||i.push(e),e){t.get(e)||t.set(e,s.material),u.pushOnArrayAtEntry(n,e,s)}}}));const s=[];return i.forEach((e=>{var i,r;const o=n.get(e);if(o){const n=[];for(let e of o){const t=e.geometry;t.applyMatrix4(e.matrix),n.push(t)}try{const r=Bs.mergeGeometries(n);if(r){const n=t.get(e),i=this.createObject(r,e,n);s.push(i)}else null===(i=this.states)||void 0===i||i.error.set(\\\\\\\"merge failed, check that input geometries have the same attributes\\\\\\\")}catch(e){null===(r=this.states)||void 0===r||r.error.set(e)}}})),s}}EU.DEFAULT_PARAMS={compact:!1},EU.INPUT_CLONED_STATE=Ti.FROM_NODE;const CU=\\\\\\\"geometry to merge\\\\\\\",MU=EU.DEFAULT_PARAMS;const NU=new class extends Lo{constructor(){super(...arguments),this.compact=Oo.BOOLEAN(MU.compact),this.inputsCount=Oo.INTEGER(4,{range:[1,32],rangeLocked:[!0,!1],callback:e=>{SU.PARAM_CALLBACK_setInputsCount(e)}})}};class SU extends QO{constructor(){super(...arguments),this.paramsConfig=NU}static type(){return\\\\\\\"merge\\\\\\\"}static displayedInputNames(){return[CU,CU,CU,CU]}setCompactMode(e){this.p.compact.set(e)}initializeNode(){this.io.inputs.setCount(1,4),this.io.inputs.initInputsClonedState(EU.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(e){this._operation=this._operation||new EU(this.scene(),this.states);const t=this._operation.cook(e,this.pv);this.setCoreGroup(t)}_callbackUpdateInputsCount(){this.io.inputs.setCount(1,this.pv.inputsCount),this.emit(Jn.INPUTS_UPDATED)}static PARAM_CALLBACK_setInputsCount(e){e._callbackUpdateInputsCount()}}var OU,LU=function(e){null==e&&(e=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(var t=0;t<256;t++)this.p[t]=Math.floor(256*e.random());this.perm=[];for(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]]};LU.prototype.dot=function(e,t,n){return e[0]*t+e[1]*n},LU.prototype.dot3=function(e,t,n,i){return e[0]*t+e[1]*n+e[2]*i},LU.prototype.dot4=function(e,t,n,i,s){return e[0]*t+e[1]*n+e[2]*i+e[3]*s},LU.prototype.noise=function(e,t){var n,i,s=(e+t)*(.5*(Math.sqrt(3)-1)),r=Math.floor(e+s),o=Math.floor(t+s),a=(3-Math.sqrt(3))/6,c=(r+o)*a,l=e-(r-c),u=t-(o-c);l>u?(n=1,i=0):(n=0,i=1);var h=l-n+a,d=u-i+a,p=l-1+2*a,_=u-1+2*a,m=255&r,f=255&o,g=this.perm[m+this.perm[f]]%12,v=this.perm[m+n+this.perm[f+i]]%12,y=this.perm[m+1+this.perm[f+1]]%12,x=.5-l*l-u*u,b=.5-h*h-d*d,w=.5-p*p-_*_;return 70*((x<0?0:(x*=x)*x*this.dot(this.grad3[g],l,u))+(b<0?0:(b*=b)*b*this.dot(this.grad3[v],h,d))+(w<0?0:(w*=w)*w*this.dot(this.grad3[y],p,_)))},LU.prototype.noise3d=function(e,t,n){var i,s,r,o,a,c,l=(e+t+n)*(1/3),u=Math.floor(e+l),h=Math.floor(t+l),d=Math.floor(n+l),p=1/6,_=(u+h+d)*p,m=e-(u-_),f=t-(h-_),g=n-(d-_);m>=f?f>=g?(i=1,s=0,r=0,o=1,a=1,c=0):m>=g?(i=1,s=0,r=0,o=1,a=0,c=1):(i=0,s=0,r=1,o=1,a=0,c=1):f<g?(i=0,s=0,r=1,o=0,a=1,c=1):m<g?(i=0,s=1,r=0,o=0,a=1,c=1):(i=0,s=1,r=0,o=1,a=1,c=0);var v=m-i+p,y=f-s+p,x=g-r+p,b=m-o+2*p,w=f-a+2*p,A=g-c+2*p,T=m-1+.5,E=f-1+.5,C=g-1+.5,M=255&u,N=255&h,S=255&d,O=this.perm[M+this.perm[N+this.perm[S]]]%12,L=this.perm[M+i+this.perm[N+s+this.perm[S+r]]]%12,P=this.perm[M+o+this.perm[N+a+this.perm[S+c]]]%12,R=this.perm[M+1+this.perm[N+1+this.perm[S+1]]]%12,I=.6-m*m-f*f-g*g,F=.6-v*v-y*y-x*x,D=.6-b*b-w*w-A*A,k=.6-T*T-E*E-C*C;return 32*((I<0?0:(I*=I)*I*this.dot3(this.grad3[O],m,f,g))+(F<0?0:(F*=F)*F*this.dot3(this.grad3[L],v,y,x))+(D<0?0:(D*=D)*D*this.dot3(this.grad3[P],b,w,A))+(k<0?0:(k*=k)*k*this.dot3(this.grad3[R],T,E,C)))},LU.prototype.noise4d=function(e,t,n,i){var s,r,o,a,c,l,u,h,d,p,_,m,f=this.grad4,g=this.simplex,v=this.perm,y=(Math.sqrt(5)-1)/4,x=(5-Math.sqrt(5))/20,b=(e+t+n+i)*y,w=Math.floor(e+b),A=Math.floor(t+b),T=Math.floor(n+b),E=Math.floor(i+b),C=(w+A+T+E)*x,M=e-(w-C),N=t-(A-C),S=n-(T-C),O=i-(E-C),L=(M>N?32:0)+(M>S?16:0)+(N>S?8:0)+(M>O?4:0)+(N>O?2:0)+(S>O?1:0),P=M-(s=g[L][0]>=3?1:0)+x,R=N-(r=g[L][1]>=3?1:0)+x,I=S-(o=g[L][2]>=3?1:0)+x,F=O-(a=g[L][3]>=3?1:0)+x,D=M-(c=g[L][0]>=2?1:0)+2*x,k=N-(l=g[L][1]>=2?1:0)+2*x,B=S-(u=g[L][2]>=2?1:0)+2*x,z=O-(h=g[L][3]>=2?1:0)+2*x,U=M-(d=g[L][0]>=1?1:0)+3*x,G=N-(p=g[L][1]>=1?1:0)+3*x,V=S-(_=g[L][2]>=1?1:0)+3*x,j=O-(m=g[L][3]>=1?1:0)+3*x,H=M-1+4*x,q=N-1+4*x,W=S-1+4*x,X=O-1+4*x,Y=255&w,$=255&A,Q=255&T,J=255&E,K=v[Y+v[$+v[Q+v[J]]]]%32,Z=v[Y+s+v[$+r+v[Q+o+v[J+a]]]]%32,ee=v[Y+c+v[$+l+v[Q+u+v[J+h]]]]%32,te=v[Y+d+v[$+p+v[Q+_+v[J+m]]]]%32,ne=v[Y+1+v[$+1+v[Q+1+v[J+1]]]]%32,ie=.6-M*M-N*N-S*S-O*O,se=.6-P*P-R*R-I*I-F*F,re=.6-D*D-k*k-B*B-z*z,oe=.6-U*U-G*G-V*V-j*j,ae=.6-H*H-q*q-W*W-X*X;return 27*((ie<0?0:(ie*=ie)*ie*this.dot4(f[K],M,N,S,O))+(se<0?0:(se*=se)*se*this.dot4(f[Z],P,R,I,F))+(re<0?0:(re*=re)*re*this.dot4(f[ee],D,k,B,z))+(oe<0?0:(oe*=oe)*oe*this.dot4(f[te],U,G,V,j))+(ae<0?0:(ae*=ae)*ae*this.dot4(f[ne],H,q,W,X)))},function(e){e.ADD=\\\\\\\"add\\\\\\\",e.SET=\\\\\\\"set\\\\\\\",e.MULT=\\\\\\\"mult\\\\\\\",e.SUBSTRACT=\\\\\\\"substract\\\\\\\",e.DIVIDE=\\\\\\\"divide\\\\\\\"}(OU||(OU={}));const PU=[OU.ADD,OU.SET,OU.MULT,OU.SUBSTRACT,OU.DIVIDE];const RU=new class extends Lo{constructor(){super(...arguments),this.amplitude=Oo.FLOAT(1),this.tamplitudeAttrib=Oo.BOOLEAN(0),this.amplitudeAttrib=Oo.STRING(\\\\\\\"amp\\\\\\\",{visibleIf:{tamplitudeAttrib:!0}}),this.freq=Oo.VECTOR3([1,1,1]),this.offset=Oo.VECTOR3([0,0,0]),this.octaves=Oo.INTEGER(3,{range:[1,8],rangeLocked:[!0,!1]}),this.ampAttenuation=Oo.FLOAT(.5,{range:[0,1]}),this.freqIncrease=Oo.FLOAT(2,{range:[0,10]}),this.seed=Oo.INTEGER(0,{range:[0,100],separatorAfter:!0}),this.useNormals=Oo.BOOLEAN(0),this.attribName=Oo.STRING(\\\\\\\"position\\\\\\\"),this.useRestAttributes=Oo.BOOLEAN(0),this.restP=Oo.STRING(\\\\\\\"restP\\\\\\\",{visibleIf:{useRestAttributes:!0}}),this.restN=Oo.STRING(\\\\\\\"restN\\\\\\\",{visibleIf:{useRestAttributes:!0}}),this.operation=Oo.INTEGER(PU.indexOf(OU.ADD),{menu:{entries:PU.map((e=>({name:e,value:PU.indexOf(e)})))}}),this.computeNormals=Oo.BOOLEAN(1)}};class IU extends QO{constructor(){super(...arguments),this.paramsConfig=RU,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([Ti.FROM_NODE])}setOperation(e){this.p.operation.set(PU.indexOf(e))}async cook(e){const t=e[0],n=t.points(),i=this.pv.attribName;if(!t.hasAttrib(i))return this.states.error.set(`attribute ${i} not found`),void this.cookController.endCook();if(t.attribType(i)!=cs.NUMERIC)return this.states.error.set(`attribute ${i} is not a numeric attribute`),void this.cookController.endCook();const s=this._get_simplex(),r=this.pv.useNormals&&t.hasAttrib(\\\\\\\"normal\\\\\\\"),o=t.attribSize(this.pv.attribName),a=PU[this.pv.operation],c=this.pv.useRestAttributes,l=this.pv.amplitude,u=this.pv.tamplitudeAttrib;let h,f,g=new p.a;for(let e=0;e<n.length;e++){const t=n[e];f=t.attribValue(i),c?(g=t.attribValue(this.pv.restP),h=r?t.attribValue(this.pv.restN):void 0):(t.getPosition(g),h=r?t.attribValue(\\\\\\\"normal\\\\\\\"):void 0);const v=u?this._amplitude_from_attrib(t,l):l,y=this._noise_value(r,s,v,g,h),x=this._make_noise_value_correct_size(y,o);if(m.isNumber(f)&&m.isNumber(x)){const e=this._new_attrib_value_from_float(a,f,x);t.setAttribValue(i,e)}else if(f instanceof d.a&&x instanceof d.a){const e=this._new_attrib_value_from_vector2(a,f,x);t.setAttribValue(i,e)}else if(f instanceof p.a&&x instanceof p.a){const e=this._new_attrib_value_from_vector3(a,f,x);t.setAttribValue(i,e)}else if(f instanceof _.a&&x instanceof _.a){const e=this._new_attrib_value_from_vector4(a,f,x);t.setAttribValue(i,e)}}if(!this.io.inputs.cloneRequired(0))for(let e of t.geometries())e.getAttribute(i).needsUpdate=!0;this.pv.computeNormals&&t.computeVertexNormals(),this.setCoreGroup(t)}_noise_value(e,t,n,i,s){if(this._rest_pos.copy(i).add(this.pv.offset).multiply(this.pv.freq),e&&s){const e=n*this._fbm(t,this._rest_pos.x,this._rest_pos.y,this._rest_pos.z);return this._noise_value_v.copy(s),this._noise_value_v.multiplyScalar(e)}return this._noise_value_v.set(n*this._fbm(t,this._rest_pos.x+545,this._rest_pos.y+125454,this._rest_pos.z+2142),n*this._fbm(t,this._rest_pos.x-425,this._rest_pos.y-25746,this._rest_pos.z+95242),n*this._fbm(t,this._rest_pos.x+765132,this._rest_pos.y+21,this._rest_pos.z-9245)),this._noise_value_v}_make_noise_value_correct_size(e,t){switch(t){case 1:return e.x;case 2:return this._rest_value2.set(e.x,e.y),this._rest_value2;case 3:default:return e}}_new_attrib_value_from_float(e,t,n){switch(e){case OU.ADD:return t+n;case OU.SET:return n;case OU.MULT:return t*n;case OU.DIVIDE:return t/n;case OU.SUBSTRACT:return t-n}Ri.unreachable(e)}_new_attrib_value_from_vector2(e,t,n){switch(e){case OU.ADD:return t.add(n);case OU.SET:return n;case OU.MULT:return t.multiply(n);case OU.DIVIDE:return t.divide(n);case OU.SUBSTRACT:return t.sub(n)}Ri.unreachable(e)}_new_attrib_value_from_vector3(e,t,n){switch(e){case OU.ADD:return t.add(n);case OU.SET:return n;case OU.MULT:return t.multiply(n);case OU.DIVIDE:return t.divide(n);case OU.SUBSTRACT:return t.sub(n)}Ri.unreachable(e)}_new_attrib_value_from_vector4(e,t,n){switch(e){case OU.ADD:return t.add(n);case OU.SET:return n;case OU.MULT:return t.multiplyScalar(n.x);case OU.DIVIDE:return t.divideScalar(n.x);case OU.SUBSTRACT:return t.sub(n)}Ri.unreachable(e)}_amplitude_from_attrib(e,t){const n=e.attribValue(this.pv.amplitudeAttrib);return m.isNumber(n)?n*t:n instanceof d.a||n instanceof p.a||n instanceof _.a?n.x*t:1}_fbm(e,t,n,i){let s=0,r=1;for(let o=0;o<this.pv.octaves;o++)s+=r*e.noise3d(t,n,i),t*=this.pv.freqIncrease,n*=this.pv.freqIncrease,i*=this.pv.freqIncrease,r*=this.pv.ampAttenuation;return s}_get_simplex(){const e=this._simplex_by_seed.get(this.pv.seed);if(e)return e;{const e=this._create_simplex();return this._simplex_by_seed.set(this.pv.seed,e),e}}_create_simplex(){const e=this.pv.seed,t=new LU({random:function(){return Os.randFloat(e)}});return this._simplex_by_seed.delete(e),t}}const FU=new class extends Lo{constructor(){super(...arguments),this.edit=Oo.BOOLEAN(0),this.updateX=Oo.BOOLEAN(0,{visibleIf:{edit:1}}),this.x=Oo.FLOAT(\\\\\\\"@N.x\\\\\\\",{visibleIf:{updateX:1,edit:1},expression:{forEntities:!0}}),this.updateY=Oo.BOOLEAN(0,{visibleIf:{edit:1}}),this.y=Oo.FLOAT(\\\\\\\"@N.y\\\\\\\",{visibleIf:{updateY:1,edit:1},expression:{forEntities:!0}}),this.updateZ=Oo.BOOLEAN(0,{visibleIf:{edit:1}}),this.z=Oo.FLOAT(\\\\\\\"@N.z\\\\\\\",{visibleIf:{updateZ:1,edit:1},expression:{forEntities:!0}}),this.recompute=Oo.BOOLEAN(1,{visibleIf:{edit:0}}),this.invert=Oo.BOOLEAN(0)}};class DU extends QO{constructor(){super(...arguments),this.paramsConfig=FU}static type(){return\\\\\\\"normals\\\\\\\"}static displayedInputNames(){return[\\\\\\\"geometry to update normals of\\\\\\\"]}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState(Ti.FROM_NODE)}async cook(e){const t=e[0];this.pv.edit?await this._eval_expressions_for_core_group(t):this.pv.recompute&&t.computeVertexNormals(),this.pv.invert&&this._invert_normals(t),this.setCoreGroup(t)}async _eval_expressions_for_core_group(e){const t=e.coreObjects();for(let e=0;e<t.length;e++)await this._eval_expressions_for_core_object(t[e])}async _eval_expressions_for_core_object(e){const t=e.object().geometry,n=e.points();let i=t.getAttribute(ms.NORMAL);if(!i){new Bs(t).addNumericAttrib(ms.NORMAL,3,0),i=t.getAttribute(ms.NORMAL)}const s=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,((e,t)=>{s[3*e.index()+0]=t}));else{let e;for(let t=0;t<n.length;t++)e=n[t],s[3*e.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,((e,t)=>{s[3*e.index()+1]=t}));else{let e;for(let t=0;t<n.length;t++)e=n[t],s[3*e.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,((e,t)=>{s[3*e.index()+2]=t}));else{let e;for(let t=0;t<n.length;t++)e=n[t],s[3*e.index()+2]=this.pv.z}}_invert_normals(e){var t;for(let n of e.coreObjects()){const e=null===(t=n.coreGeometry())||void 0===t?void 0:t.geometry();if(e){const t=e.attributes[ms.NORMAL];if(t){const e=t.array;for(let t=0;t<e.length;t++)e[t]*=-1}}}}}class kU extends WO{static type(){return\\\\\\\"null\\\\\\\"}cook(e,t){const n=e[0];return n||this.createCoreGroupFromObjects([])}}kU.DEFAULT_PARAMS={},kU.INPUT_CLONED_STATE=Ti.FROM_NODE;const BU=new class extends Lo{};class zU extends QO{constructor(){super(...arguments),this.paramsConfig=BU}static type(){return\\\\\\\"null\\\\\\\"}initializeNode(){this.io.inputs.setCount(0,1),this.io.inputs.initInputsClonedState(kU.INPUT_CLONED_STATE)}cook(e){this._operation=this._operation||new kU(this.scene(),this.states);const t=this._operation.cook(e,this.pv);this.setCoreGroup(t)}}const UU=new class extends Lo{constructor(){super(...arguments),this.geometry=Oo.OPERATOR_PATH(\\\\\\\"\\\\\\\",{nodeSelection:{context:Ei.SOP}})}};class GU extends QO{constructor(){super(...arguments),this.paramsConfig=UU}static type(){return\\\\\\\"objectMerge\\\\\\\"}initializeNode(){}async cook(e){const t=this.p.geometry.found_node();if(t)if(t.context()==Ei.SOP){const e=await t.compute();this.import_input(t,e)}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(e,t){let n;null!=(n=t.coreContentCloned())?this.setCoreGroup(n):this.states.error.set(\\\\\\\"invalid target\\\\\\\")}}class VU extends WO{static type(){return\\\\\\\"objectProperties\\\\\\\"}cook(e,t){const n=e[0];for(let e of n.objects())t.applyToChildren?e.traverse((e=>{this._update_object(e,t)})):this._update_object(e,t);return n}_update_object(e,t){t.tname&&(e.name=t.name),t.trenderOrder&&(e.renderOrder=t.renderOrder),t.tfrustumCulled&&(e.frustumCulled=t.frustumCulled),t.tmatrixAutoUpdate&&(e.matrixAutoUpdate=t.matrixAutoUpdate),t.tvisible&&(e.visible=t.visible),t.tcastShadow&&(e.castShadow=t.castShadow),t.treceiveShadow&&(e.receiveShadow=t.receiveShadow)}}VU.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},VU.INPUT_CLONED_STATE=Ti.FROM_NODE;const jU=VU.DEFAULT_PARAMS;const HU=new class extends Lo{constructor(){super(...arguments),this.applyToChildren=Oo.BOOLEAN(jU.applyToChildren,{separatorAfter:!0}),this.tname=Oo.BOOLEAN(jU.tname),this.name=Oo.STRING(jU.name,{visibleIf:{tname:!0},separatorAfter:!0}),this.trenderOrder=Oo.BOOLEAN(jU.trenderOrder),this.renderOrder=Oo.INTEGER(jU.renderOrder,{visibleIf:{trenderOrder:!0},range:[0,10],rangeLocked:[!1,!1],separatorAfter:!0}),this.tfrustumCulled=Oo.BOOLEAN(jU.tfrustumCulled),this.frustumCulled=Oo.BOOLEAN(jU.frustumCulled,{visibleIf:{tfrustumCulled:!0},separatorAfter:!0}),this.tmatrixAutoUpdate=Oo.BOOLEAN(jU.tmatrixAutoUpdate),this.matrixAutoUpdate=Oo.BOOLEAN(jU.matrixAutoUpdate,{visibleIf:{tmatrixAutoUpdate:!0},separatorAfter:!0}),this.tvisible=Oo.BOOLEAN(jU.tvisible),this.visible=Oo.BOOLEAN(jU.visible,{visibleIf:{tvisible:!0},separatorAfter:!0}),this.tcastShadow=Oo.BOOLEAN(jU.tcastShadow),this.castShadow=Oo.BOOLEAN(jU.castShadow,{visibleIf:{tcastShadow:!0},separatorAfter:!0}),this.treceiveShadow=Oo.BOOLEAN(jU.treceiveShadow),this.receiveShadow=Oo.BOOLEAN(jU.receiveShadow,{visibleIf:{treceiveShadow:!0}})}};class qU extends QO{constructor(){super(...arguments),this.paramsConfig=HU}static type(){return\\\\\\\"objectProperties\\\\\\\"}static displayedInputNames(){return[\\\\\\\"objects to change properties of\\\\\\\"]}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState(VU.INPUT_CLONED_STATE)}async cook(e){this._operation=this._operation||new VU(this.scene(),this.states);const t=this._operation.cook(e,this.pv);this.setCoreGroup(t)}}const WU=new class extends Lo{};class XU extends QO{constructor(){super(...arguments),this.paramsConfig=WU,this._input_configs_by_operation_container=new WeakMap}static type(){return sc}initializeNode(){this.io.inputs.initInputsClonedState(Ti.FROM_NODE)}set_output_operation_container(e){this._output_operation_container=e}output_operation_container(){return this._output_operation_container}add_input_config(e,t){let n=this._input_configs_by_operation_container.get(e);n||(n=new Map,this._input_configs_by_operation_container.set(e,n)),n.set(t.operation_input_index,t.node_input_index)}add_operation_container_with_path_param_resolve_required(e){this._operation_containers_requiring_resolve||(this._operation_containers_requiring_resolve=[]),this._operation_containers_requiring_resolve.push(e)}resolve_operation_containers_path_params(){if(this._operation_containers_requiring_resolve)for(let e of this._operation_containers_requiring_resolve)e.resolve_path_params(this)}async cook(e){if(this._output_operation_container){this._output_operation_container.setDirty();const t=await this._output_operation_container.compute(e,this._input_configs_by_operation_container);t&&this.setCoreGroup(t)}}}class YU extends qm{constructor(e){super(),this._uv_name=e}set_texture_allocations_controller(e){this._texture_allocations_controller=e}handle_globals_node(e,t,n){if(!this._texture_allocations_controller)return;const i=e.io.outputs.namedOutputConnectionPointsByName(t),s=e.glVarName(t);if(this._texture_allocations_controller.variable(t)&&i){const r=i.type(),o=`${r} ${s} = ${this.read_attribute(e,r,t,n)}`;n.addBodyLines(e,[o])}else this.globals_geometry_handler=this.globals_geometry_handler||new uf,this.globals_geometry_handler.handle_globals_node(e,t,n)}read_attribute(e,t,n,i){if(!this._texture_allocations_controller)return;const s=this._texture_allocations_controller.variable(n);if(!s)return uf.read_attribute(e,t,n,i);{this.add_particles_sim_uv_attribute(e,i);const t=s.component(),n=s.allocation();if(n){const s=n.textureName(),r=new af(e,ro.SAMPLER_2D,s);i.addDefinitions(e,[r]);return`texture2D( ${s}, ${this._uv_name} ).${t}`}}}add_particles_sim_uv_attribute(e,t){const n=new rf(e,ro.VEC2,YU.UV_ATTRIB),i=new cf(e,ro.VEC2,YU.UV_VARYING);t.addDefinitions(e,[n,i],nf.VERTEX),t.addDefinitions(e,[i],nf.FRAGMENT),t.addBodyLines(e,[`${YU.UV_VARYING} = ${YU.UV_ATTRIB}`],nf.VERTEX)}}YU.UV_ATTRIB=\\\\\\\"particles_sim_uv_attrib\\\\\\\",YU.UV_VARYING=\\\\\\\"particles_sim_uv_varying\\\\\\\",YU.PARTICLE_SIM_UV=\\\\\\\"particleUV\\\\\\\";class $U{constructor(e){this.node=e,this._particles_group_objects=[],this._all_shader_names=[],this._all_uniform_names=[],this.globals_handler=new YU(YU.UV_VARYING)}setShadersByName(e){this._shaders_by_name=e,this._all_shader_names=[],this._all_uniform_names=[],this._shaders_by_name.forEach(((e,t)=>{this._all_shader_names.push(t),this._all_uniform_names.push(`texture_${t}`)})),this.reset_render_material()}assign_render_material(){if(this._render_material){for(let e of this._particles_group_objects){const t=e;t.geometry&&(t.material=this._render_material,Gs.applyCustomMaterials(t,this._render_material),t.matrixAutoUpdate=!1,t.updateMatrix())}this._render_material.needsUpdate=!0,this.update_render_material_uniforms()}}update_render_material_uniforms(){var e;if(!this._render_material)return;let t,n;for(let i=0;i<this._all_shader_names.length;i++){n=this._all_shader_names[i],t=this._all_uniform_names[i];const s=null===(e=this.node.gpuController.getCurrentRenderTarget(n))||void 0===e?void 0:e.texture;s&&(this._render_material.uniforms[t].value=s,Gs.assign_custom_uniforms(this._render_material,t,s))}}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(e){for(let t of e.objectsWithGeo())this._particles_group_objects.push(t)}async init_render_material(){var e;const t=null===(e=this.node.assemblerController)||void 0===e?void 0:e.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(t){const e=t.textureAllocationsController().toJSON(this.node.scene()),i=n.assemblerController;i&&(this.globals_handler.set_texture_allocations_controller(t.textureAllocationsController()),i.set_assembler_globals_handler(this.globals_handler)),this._texture_allocations_json&&JSON.stringify(this._texture_allocations_json)==JSON.stringify(e)||(this._texture_allocations_json=b.cloneDeep(e),i&&i.set_compilation_required_and_dirty())}const e=await n.compute();this._render_material=e.material()}else this.node.states.error.set(\\\\\\\"render material not valid\\\\\\\");if(this._render_material){const e=this._render_material.uniforms;for(let t of this._all_uniform_names){const n={value:null};e[t]=n,this._render_material&&Gs.init_custom_material_uniforms(this._render_material,t,n)}}this.assign_render_material()}}var QU,JU=function(e,t,n){this.variables=[],this.currentTextureIndex=0;var i=w.G,s=new Vi;s.matrixAutoUpdate=!1;var r=new km.a;r.position.z=1,r.matrixAutoUpdate=!1,r.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),c=new z.a(new R(2,2),a);function l(n){n.defines.resolution=\\\\\\\"vec2( \\\\\\\"+e.toFixed(1)+\\\\\\\", \\\\\\\"+t.toFixed(1)+\\\\\\\" )\\\\\\\"}function u(e,t){var n=new B({uniforms:t=t||{},vertexShader:\\\\\\\"void main()\\\\t{\\\\n\\\\n\\\\tgl_Position = vec4( position, 1.0 );\\\\n\\\\n}\\\\n\\\\\\\",fragmentShader:e});return l(n),n}c.matrixAutoUpdate=!1,c.updateMatrix(),s.add(c),this.setDataType=function(e){return i=e,this},this.addVariable=function(e,t,n){var i={name:e,initialValueTexture:n,material:this.createShaderMaterial(t),dependencies:null,renderTargets:[],wrapS:null,wrapT:null,minFilter:w.ob,magFilter:w.ob};return this.variables.push(i),i},this.setVariableDependencies=function(e,t){e.dependencies=t},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 s=this.variables[i];s.renderTargets[0]=this.createRenderTarget(e,t,s.wrapS,s.wrapT,s.minFilter,s.magFilter),s.renderTargets[1]=this.createRenderTarget(e,t,s.wrapS,s.wrapT,s.minFilter,s.magFilter),this.renderTexture(s.initialValueTexture,s.renderTargets[0]),this.renderTexture(s.initialValueTexture,s.renderTargets[1]);var r=s.material.uniforms;if(null!==s.dependencies)for(var o=0;o<s.dependencies.length;o++){var a=s.dependencies[o];if(a.name!==s.name){for(var c=!1,l=0;l<this.variables.length;l++)if(a.name===this.variables[l].name){c=!0;break}if(!c)return\\\\\\\"Variable dependency not found. Variable=\\\\\\\"+s.name+\\\\\\\", dependency=\\\\\\\"+a.name}r[a.name]={value:null}}}return this.currentTextureIndex=0,null},this.compute=function(){for(var e=this.currentTextureIndex,t=0===this.currentTextureIndex?1:0,n=0,i=this.variables.length;n<i;n++){var s=this.variables[n];if(null!==s.dependencies)for(var r=s.material.uniforms,o=0,a=s.dependencies.length;o<a;o++){var c=s.dependencies[o];r[c.name].value=c.renderTargets[e].texture}this.doRenderTarget(s.material,s.renderTargets[t])}this.currentTextureIndex=t},this.getCurrentRenderTarget=function(e){return e.renderTargets[this.currentTextureIndex]},this.getAlternateRenderTarget=function(e){return e.renderTargets[0===this.currentTextureIndex?1:0]},this.addResolutionDefine=l,this.createShaderMaterial=u,this.createRenderTarget=function(n,s,r,o,a,c){return n=n||e,s=s||t,r=r||w.n,o=o||w.n,a=a||w.ob,c=c||w.ob,new Z(n,s,{wrapS:r,wrapT:o,minFilter:a,magFilter:c,format:w.Ib,type:i,depthBuffer:!1})},this.createTexture=function(){var n=new Float32Array(e*t*4);return new T.a(n,e,t,w.Ib,w.G)},this.renderTexture=function(e,t){o.passThruTexture.value=e,this.doRenderTarget(a,t),o.passThruTexture.value=null},this.doRenderTarget=function(e,t){var i=n.getRenderTarget();c.material=e,n.setRenderTarget(t),n.render(s,r),c.material=a,n.setRenderTarget(i)}};!function(e){e.FLOAT=\\\\\\\"float\\\\\\\",e.HALF_FLOAT=\\\\\\\"half\\\\\\\"}(QU||(QU={}));const KU=[QU.FLOAT,QU.HALF_FLOAT],ZU={[QU.FLOAT]:w.G,[QU.HALF_FLOAT]:w.M};class eG{constructor(e){this.node=e,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(e){this._persisted_texture_allocations_controller=e}setShadersByName(e){this._shaders_by_name=e,this.reset_gpu_compute()}allVariables(){return this._all_variables}async init(e){this.init_particle_group_points(e),await this.create_gpu_compute()}getCurrentRenderTarget(e){var t;const n=this.variables_by_name.get(e);if(n)return null===(t=this._gpu_compute)||void 0===t?void 0:t.getCurrentRenderTarget(n)}init_particle_group_points(e){this.reset_gpu_compute(),e&&(this._particles_core_group=e,this._points=this._get_points()||[])}compute_similation_if_required(){const e=this.node.scene().frame(),t=this.node.pv.startFrame;e>=t&&(null==this._last_simulated_frame&&(this._last_simulated_frame=t-1),null==this._last_simulated_time&&(this._last_simulated_time=this.node.scene().time()),e>this._last_simulated_frame&&this._compute_simulation(e-this._last_simulated_frame))}_compute_simulation(e=1){if(!this._gpu_compute||null==this._last_simulated_time)return;this.update_simulation_material_uniforms();for(let t=0;t<e;t++)this._gpu_compute.compute();this.node.renderController.update_render_material_uniforms(),this._last_simulated_frame=this.node.scene().frame();const t=this.node.scene().time();this._delta_time=t-this._last_simulated_time,this._last_simulated_time=t}_data_type(){const e=KU[this.node.pv.dataType];return ZU[e]}_textureNameForShaderName(e){return`texture_${e}`}async create_gpu_compute(){var e,t;if(this.node.pv.autoTexturesSize){const e=Os.nearestPower2(Math.sqrt(this._points.length));this._used_textures_size.x=Math.min(e,this.node.pv.maxTexturesSize.x),this._used_textures_size.y=Math.min(e,this.node.pv.maxTexturesSize.y)}else{if(!A.a.isPowerOfTwo(this.node.pv.texturesSize.x)||!A.a.isPowerOfTwo(this.node.pv.texturesSize.y))return void this.node.states.error.set(\\\\\\\"texture size must be a power of 2\\\\\\\");const e=this.node.pv.texturesSize.x*this.node.pv.texturesSize.y;if(this._points.length>e)return void this.node.states.error.set(`max particles is set to (${this.node.pv.texturesSize.x}x${this.node.pv.texturesSize.y}=) ${e}`);this._used_textures_size.copy(this.node.pv.texturesSize)}this._forceTimeDependent(),this._init_particles_uvs(),this.node.renderController.reset_render_material();const n=await Rn.renderersController.waitForRenderer();if(n?this._renderer=n:this.node.states.error.set(\\\\\\\"no renderer found\\\\\\\"),!this._renderer)return;const i=new JU(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(((e,t)=>{e.renderTargets[0].dispose(),e.renderTargets[1].dispose(),this.variables_by_name.delete(t)})),this._all_variables=[],null===(e=this._shaders_by_name)||void 0===e||e.forEach(((e,t)=>{if(this._gpu_compute){const n=this._gpu_compute.addVariable(this._textureNameForShaderName(t),e,this._created_textures_by_name.get(t));this.variables_by_name.set(t,n),this._all_variables.push(n)}})),null===(t=this.variables_by_name)||void 0===t||t.forEach(((e,t)=>{this._gpu_compute&&this._gpu_compute.setVariableDependencies(e,this._all_variables)})),this._create_texture_render_targets(),this._fill_textures(),this.create_simulation_material_uniforms();var s=this._gpu_compute.init();null!==s&&(console.error(s),this.node.states.error.set(s))}else this.node.states.error.set(\\\\\\\"failed to create the GPUComputationRenderer\\\\\\\")}_forceTimeDependent(){this._graph_node||(this._graph_node=new Qn(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 e=[];return this.variables_by_name.forEach(((t,n)=>{e.push(t.material)})),e}create_simulation_material_uniforms(){const e=this.node.assemblerController,t=null==e?void 0:e.assembler;if(!t&&!this._persisted_texture_allocations_controller)return;const n=[];this.variables_by_name.forEach(((e,t)=>{n.push(e.material)}));const i=this._readonlyAllocations();for(let e of n)e.uniforms[lw.TIME]={value:this.node.scene().time()},e.uniforms[lw.DELTA_TIME]={value:this.node.scene().time()},i&&this._assignReadonlyTextures(e,i);if(t)for(let e of n)for(let n of t.param_configs())e.uniforms[n.uniform_name]=n.uniform;else{const e=this.node.persisted_config.loaded_data();if(e){const t=this.node.persisted_config.uniforms();if(t){const s=e.param_uniform_pairs;for(let e of s){const s=e[0],r=e[1],o=this.node.params.get(s),a=t[r];for(let e of n)e.uniforms[r]=a,i&&this._assignReadonlyTextures(e,i);o&&a&&o.options.setOption(\\\\\\\"callback\\\\\\\",(()=>{for(let e of n)If.callback(o,e.uniforms[r])}))}}}}}_assignReadonlyTextures(e,t){for(let n of t){const t=n.shaderName(),i=this._created_textures_by_name.get(t);if(i){const n=this._textureNameForShaderName(t);e.uniforms[n]={value:i}}}}update_simulation_material_uniforms(){for(let e of this._all_variables)e.material.uniforms[lw.TIME].value=this.node.scene().time(),e.material.uniforms[lw.DELTA_TIME].value=this._delta_time}_init_particles_uvs(){var e=new Float32Array(2*this._points.length);let t=0;for(var n=0,i=0;i<this._used_textures_size.x;i++)for(var s=0;s<this._used_textures_size.y&&(e[t++]=s/(this._used_textures_size.x-1),e[t++]=i/(this._used_textures_size.y-1),!((n+=2)>=e.length));s++);const r=YU.UV_ATTRIB;if(this._particles_core_group)for(let t of this._particles_core_group.coreGeometries()){const n=t.geometry(),i=t.markedAsInstance()?fk:L.a;n.setAttribute(r,new i(e,2))}}createdTexturesByName(){return this._created_textures_by_name}_fill_textures(){const e=this._textureAllocationsController();e&&this._created_textures_by_name.forEach(((t,n)=>{const i=e.allocationForShaderName(n);if(!i)return void console.warn(`no allocation found for shader ${n}`);const s=i.variables();if(!s)return void console.warn(\\\\\\\"allocation has no variables\\\\\\\");const r=t.image.data;for(let e of s){const t=e.position();let n=e.name();const i=this._points[0];if(i){if(i.hasAttrib(n)){const e=i.attribSize(n);let s=t;for(let t of this._points){if(1==e){const e=t.attribValue(n);r[s]=e}else t.attribValue(n).toArray(r,s);s+=4}}}}}))}reset_gpu_compute(){this._gpu_compute=void 0,this._simulation_restart_required=!0}set_restart_not_required(){this._simulation_restart_required=!1}reset_gpu_compute_and_set_dirty(){this.reset_gpu_compute(),this.node.setDirty()}reset_particle_groups(){this._particles_core_group=void 0}initialized(){return null!=this._particles_core_group&&null!=this._gpu_compute}_create_texture_render_targets(){this._created_textures_by_name.forEach(((e,t)=>{e.dispose()})),this._created_textures_by_name.clear(),this.variables_by_name.forEach(((e,t)=>{this._gpu_compute&&this._created_textures_by_name.set(t,this._gpu_compute.createTexture())}));const e=this._readonlyAllocations();if(e&&this._gpu_compute)for(let t of e)this._created_textures_by_name.set(t.shaderName(),this._gpu_compute.createTexture())}_textureAllocationsController(){var e;return(null===(e=this.node.assemblerController)||void 0===e?void 0:e.assembler.textureAllocationsController())||this._persisted_texture_allocations_controller}_readonlyAllocations(){var e;return null===(e=this._textureAllocationsController())||void 0===e?void 0:e.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(((e,t)=>{const n=this._created_textures_by_name.get(t);this._gpu_compute&&n&&(this._gpu_compute.renderTexture(n,e.renderTargets[0]),this._gpu_compute.renderTexture(n,e.renderTargets[1]))})))}_get_points(){if(!this._particles_core_group)return;let e=this._particles_core_group.coreGeometries();const t=e[0];if(t){const n=t.markedAsInstance(),i=[];for(let t of e)t.markedAsInstance()==n&&i.push(t);const s=[];for(let e of i)for(let t of e.points())s.push(t);return s}return[]}}class tG{constructor(e,t){if(this._name=e,this._size=t,this._position=-1,this._readonly=!1,!e)throw\\\\\\\"TextureVariable requires a name\\\\\\\"}merge(e){var t;e.readonly()||this.setReadonly(!1),null===(t=e.graphNodeIds())||void 0===t||t.forEach(((e,t)=>{this.addGraphNodeId(t)}))}setReadonly(e){this._readonly=e}readonly(){return this._readonly}setAllocation(e){this._allocation=e}allocation(){return this._allocation}graphNodeIds(){return this._graph_node_ids}addGraphNodeId(e){this._graph_node_ids=this._graph_node_ids||new Map,this._graph_node_ids.set(e,!0)}name(){return this._name}size(){return this._size}setPosition(e){this._position=e}position(){return this._position}component(){return\\\\\\\"xyzw\\\\\\\".split(\\\\\\\"\\\\\\\").splice(this._position,this._size).join(\\\\\\\"\\\\\\\")}static fromJSON(e){return new tG(e.name,e.size)}toJSON(e){const t=[];return this._graph_node_ids&&this._graph_node_ids.forEach(((n,i)=>{const s=e.graph.nodeFromId(i);if(s){const e=s.path();e&&t.push(e)}})),{name:this.name(),size:this.size(),nodes:t}}}class nG{constructor(){this._size=0}addVariable(e){this._variables=this._variables||[],this._variables.push(e),e.setPosition(this._size),e.setAllocation(this),this._size+=e.size()}hasSpaceForVariable(e){return this._size+e.size()<=4}shaderName(){var e;return((null===(e=this.variables())||void 0===e?void 0:e.map((e=>e.name())))||[\\\\\\\"no_variables_allocated\\\\\\\"]).join(\\\\\\\"_SEPARATOR_\\\\\\\")}textureName(){return`texture_${this.shaderName()}`}variables(){return this._variables}variablesForInputNode(e){var t;return null===(t=this._variables)||void 0===t?void 0:t.filter((t=>{var n;return(null===(n=t.graphNodeIds())||void 0===n?void 0:n.has(e.graphNodeId()))||!1}))}inputNamesForNode(e){const t=this.variablesForInputNode(e);if(t)return e.type()==Si.ATTRIBUTE?[Zm.INPUT_NAME]:t.map((e=>e.name()))}variable(e){if(this._variables)for(let t of this._variables)if(t.name()==e)return t}static fromJSON(e){const t=new nG;for(let n of e){const e=tG.fromJSON(n);t.addVariable(e)}return t}toJSON(e){return this._variables?this._variables.map((t=>t.toJSON(e))):[]}}const iG=[\\\\\\\"position\\\\\\\",\\\\\\\"normal\\\\\\\",\\\\\\\"color\\\\\\\",\\\\\\\"uv\\\\\\\"];class sG{constructor(){this._writableAllocations=[],this._readonlyAllocations=[]}static _sortNodes(e){const t=e.filter((e=>e.type()==gT.type())),n=e.filter((e=>e.type()!=gT.type())),i=n.map((e=>e.name())).sort(),s=new Map;for(let e of n)s.set(e.name(),e);for(let e of i){const n=s.get(e);n&&t.push(n)}return t}allocateConnectionsFromRootNodes(e,t){const n=[];e=sG._sortNodes(e),t=sG._sortNodes(t);for(let t of e){const e=t.graphNodeId();switch(t.type()){case gT.type():for(let i of t.io.inputs.namedInputConnectionPoints()){if(t.io.inputs.named_input(i.name())){const t=new tG(i.name(),uo[i.type()]);t.addGraphNodeId(e),n.push(t)}}break;case Zm.type():{const i=t,s=i.connected_input_node(),r=i.connected_input_connection_point();if(s&&r){const t=new tG(i.attribute_name,uo[r.type()]);t.addGraphNodeId(e),n.push(t)}break}}}for(let e of t){const t=e.graphNodeId();switch(e.type()){case xA.type():{const i=e;for(let e of i.io.outputs.used_output_names()){if(iG.includes(e)){const s=i.io.outputs.namedOutputConnectionPointsByName(e);if(s){const i=s.type(),r=new tG(e,uo[i]);r.addGraphNodeId(t),n.push(r)}}}break}case Zm.type():{const i=e,s=i.output_connection_point();if(s){const e=new tG(i.attribute_name,uo[s.type()]);i.isExporting()||e.setReadonly(!0),e.addGraphNodeId(t),n.push(e)}break}}}this._allocateVariables(n)}_allocateVariables(e){const t=f.sortBy(e,(e=>-e.size())),n=this._ensureVariablesAreUnique(t);for(let e of n)e.readonly()?this._allocateVariable(e,this._readonlyAllocations):this._allocateVariable(e,this._writableAllocations)}_ensureVariablesAreUnique(e){const t=new Map;for(let n of e)u.pushOnArrayAtEntry(t,n.name(),n);const n=[];return t.forEach(((e,t)=>{const i=e[0];n.push(i);for(let t=1;t<e.length;t++){const n=e[t];i.merge(n)}})),n}_allocateVariable(e,t){let n=this.hasVariable(e.name());if(n)throw\\\\\\\"no variable should be allocated since they have been made unique before\\\\\\\";if(!n)for(let i of t)!n&&i.hasSpaceForVariable(e)&&(i.addVariable(e),n=!0);if(!n){const n=new nG;t.push(n),n.addVariable(e)}}_addWritableAllocation(e){this._writableAllocations.push(e)}_addReadonlyAllocation(e){this._readonlyAllocations.push(e)}readonlyAllocations(){return this._readonlyAllocations}shaderNames(){const e=this._writableAllocations.map((e=>e.shaderName()));return f.uniq(e)}createShaderConfigs(){return[]}allocationForShaderName(e){const t=this._writableAllocations.filter((t=>t.shaderName()==e))[0];return t||this._readonlyAllocations.filter((t=>t.shaderName()==e))[0]}inputNamesForShaderName(e,t){const n=this.allocationForShaderName(t);if(n)return n.inputNamesForNode(e)}variable(e){for(let t of this._writableAllocations){const n=t.variable(e);if(n)return n}for(let t of this._readonlyAllocations){const n=t.variable(e);if(n)return n}}variables(){const e=this._writableAllocations.map((e=>e.variables()||[])).flat(),t=this._writableAllocations.map((e=>e.variables()||[])).flat();return e.concat(t)}hasVariable(e){return this.variables().map((e=>e.name())).includes(e)}static fromJSON(e){const t=new sG;for(let n of e.writable){const e=n[Object.keys(n)[0]],i=nG.fromJSON(e);t._addWritableAllocation(i)}for(let n of e.readonly){const e=n[Object.keys(n)[0]],i=nG.fromJSON(e);t._addReadonlyAllocation(i)}return t}toJSON(e){return{writable:this._writableAllocations.map((t=>({[t.shaderName()]:t.toJSON(e)}))),readonly:this._readonlyAllocations.map((t=>({[t.shaderName()]:t.toJSON(e)})))}}print(e){console.warn(JSON.stringify(this.toJSON(e),[\\\\\\\"\\\\\\\"],2))}}class rG extends Pf{constructor(e){super(e),this.node=e}toJSON(){const e=this.node.assemblerController;if(!e)return;const t={};this.node.shaders_by_name().forEach(((e,n)=>{t[n]=e}));const n=e.assembler.textureAllocationsController().toJSON(this.node.scene()),i=[],s=new B,r=e.assembler.param_configs();for(let e of r)i.push([e.name(),e.uniform_name]),s.uniforms[e.uniform_name]=e.uniform;const o=this._materialToJson(s,{node:this.node,suffix:\\\\\\\"main\\\\\\\"});return{shaders_by_name:t,texture_allocations:n,param_uniform_pairs:i,uniforms_owner:o||{}}}load(e){Rn.playerMode()&&(this._loaded_data=e,this.node.init_with_persisted_config())}loaded_data(){return this._loaded_data}shaders_by_name(){if(this._loaded_data){const e=new Map,t=Object.keys(this._loaded_data.shaders_by_name);for(let n of t)e.set(n,this._loaded_data.shaders_by_name[n]);return e}}texture_allocations_controller(){if(this._loaded_data)return sG.fromJSON(this._loaded_data.texture_allocations)}uniforms(){if(this._loaded_data){const e=this._loadMaterial(this._loaded_data.uniforms_owner);return(null==e?void 0:e.uniforms)||{}}}}const oG=new class extends Lo{constructor(){super(...arguments),this.startFrame=Oo.FLOAT(Qa.START_FRAME,{range:[0,1e3],rangeLocked:[!0,!1]}),this.autoTexturesSize=Oo.BOOLEAN(1),this.maxTexturesSize=Oo.VECTOR2([1024,1024],{visibleIf:{autoTexturesSize:1}}),this.texturesSize=Oo.VECTOR2([64,64],{visibleIf:{autoTexturesSize:0}}),this.dataType=Oo.INTEGER(0,{menu:{entries:KU.map(((e,t)=>({value:t,name:e})))}}),this.reset=Oo.BUTTON(null,{callback:(e,t)=>{aG.PARAM_CALLBACK_reset(e)}}),this.material=Oo.OPERATOR_PATH(\\\\\\\"\\\\\\\",{nodeSelection:{context:Ei.MAT},dependentOnFoundNode:!1})}};class aG extends QO{constructor(){super(...arguments),this.paramsConfig=oG,this._assembler_controller=this._create_assembler_controller(),this.persisted_config=new rG(this),this.globals_handler=new YU(YU.PARTICLE_SIM_UV),this._shaders_by_name=new Map,this.gpuController=new eG(this),this.renderController=new $U(this),this._reset_material_if_dirty_bound=this._reset_material_if_dirty.bind(this),this._children_controller_context=Ei.GL}static type(){return\\\\\\\"particlesSystemGpu\\\\\\\"}dispose(){super.dispose(),this.gpuController.dispose()}get assemblerController(){return this._assembler_controller}usedAssembler(){return mn.GL_PARTICLES}_create_assembler_controller(){return Rn.assemblersRegister.assembler(this,this.usedAssembler())}shaders_by_name(){return this._shaders_by_name}static require_webgl2(){return!0}static PARAM_CALLBACK_reset(e){e.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(Ti.NEVER),this.addPostDirtyHook(\\\\\\\"_reset_material_if_dirty\\\\\\\",this._reset_material_if_dirty_bound)}createNode(e,t){return super.createNode(e,t)}children(){return super.children()}nodesByType(e){return super.nodesByType(e)}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(e){this.gpuController.set_restart_not_required();const t=e[0];this.compileIfRequired(),this.is_on_frame_start()&&this.gpuController.reset_particle_groups(),this.gpuController.initialized()||await this.gpuController.init(t),this.renderController.initialized()||(this.renderController.init_core_group(t),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(t):this.cookController.endCook()}async compileIfRequired(){var e;(null===(e=this.assemblerController)||void 0===e?void 0:e.compileRequired())&&await this.run_assembler()}async run_assembler(){const e=this.assemblerController;if(!e)return;const t=this._find_export_nodes();if(t.length>0){const n=t;e.set_assembler_globals_handler(this.globals_handler),e.assembler.set_root_nodes(n),e.assembler.compile(),e.post_compile()}const n=e.assembler.shaders_by_name();this._setShaderNames(n)}_setShaderNames(e){this._shaders_by_name=e,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 e=this.persisted_config.shaders_by_name(),t=this.persisted_config.texture_allocations_controller();e&&t&&(this._setShaderNames(e),this.gpuController.set_persisted_texture_allocation_controller(t))}_find_export_nodes(){const e=_f.findAttributeExportNodes(this),t=_f.findOutputNodes(this);if(t.length>1)return this.states.error.set(\\\\\\\"only one output node is allowed\\\\\\\"),[];const n=t[0];return n&&e.push(n),e}}class cG extends WO{static type(){return\\\\\\\"peak\\\\\\\"}cook(e,t){const n=e[0];let i,s;for(let e of n.objects())e.traverse((e=>{let n;if(null!=(n=e.geometry)){for(s of(i=new Bs(n),i.points())){const e=s.normal(),n=s.position().clone().add(e.multiplyScalar(t.amount));s.setPosition(n)}i.geometry().getAttribute(\\\\\\\"position\\\\\\\").needsUpdate=!0}}));return e[0]}}cG.DEFAULT_PARAMS={amount:0};const lG=cG.DEFAULT_PARAMS;const uG=new class extends Lo{constructor(){super(...arguments),this.amount=Oo.FLOAT(lG.amount,{range:[-1,1]})}};class hG extends QO{constructor(){super(...arguments),this.paramsConfig=uG}static type(){return\\\\\\\"peak\\\\\\\"}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState(Ti.FROM_NODE)}cook(e){this._operation=this._operation||new cG(this.scene(),this.states);const t=this._operation.cook(e,this.pv);this.setCoreGroup(t)}}const dG=new p.a(0,0,1),pG=new p.a(0,0,1),_G=new p.a(0,1,0);class mG extends WO{constructor(){super(...arguments),this._core_transform=new rS,this._size=new p.a,this._center=new p.a,this._segmentsCount=new d.a(1,1)}static type(){return\\\\\\\"plane\\\\\\\"}cook(e,t){const n=e[0];return n?this._cook_with_input(n,t):this._cook_without_input(t)}_cook_without_input(e){const t=this._create_plane(e.size,e);this._core_transform.rotate_geometry(t,dG,e.direction);const n=this._core_transform.translation_matrix(e.center);return t.applyMatrix4(n),this.createCoreGroupFromGeometry(t)}_cook_with_input(e,t){const n=e.boundingBox();n.getSize(this._size),n.getCenter(this._center);const i=new d.a(this._size.x,this._size.z),s=this._create_plane(i,t);this._core_transform.rotate_geometry(s,pG,_G);const r=this._core_transform.translation_matrix(this._center);return s.applyMatrix4(r),this.createCoreGroupFromGeometry(s)}_create_plane(e,t){return e=e.clone(),t.useSegmentsCount?(this._segmentsCount.x=Math.floor(t.segments.x),this._segmentsCount.y=Math.floor(t.segments.y)):t.stepSize>0&&(this._segmentsCount.x=Math.floor(e.x/t.stepSize),this._segmentsCount.y=Math.floor(e.y/t.stepSize),e.x=this._segmentsCount.x*t.stepSize,e.y=this._segmentsCount.y*t.stepSize),new R(e.x,e.y,this._segmentsCount.x,this._segmentsCount.y)}}mG.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)},mG.INPUT_CLONED_STATE=Ti.NEVER;const fG=mG.DEFAULT_PARAMS;const gG=new class extends Lo{constructor(){super(...arguments),this.size=Oo.VECTOR2(fG.size),this.useSegmentsCount=Oo.BOOLEAN(fG.useSegmentsCount),this.stepSize=Oo.FLOAT(fG.stepSize,{range:[.001,1],rangeLocked:[!1,!1],visibleIf:{useSegmentsCount:0}}),this.segments=Oo.VECTOR2(fG.segments,{visibleIf:{useSegmentsCount:1}}),this.direction=Oo.VECTOR3(fG.direction),this.center=Oo.VECTOR3(fG.center)}};class vG extends QO{constructor(){super(...arguments),this.paramsConfig=gG}static type(){return\\\\\\\"plane\\\\\\\"}static displayedInputNames(){return[\\\\\\\"geometry to create plane from (optional)\\\\\\\"]}initializeNode(){this.io.inputs.setCount(0,1),this.io.inputs.initInputsClonedState(mG.INPUT_CLONED_STATE)}cook(e){this._operation=this._operation||new mG(this.scene(),this.states);const t=this._operation.cook(e,this.pv);this.setCoreGroup(t)}}const yG=\\\\\\\"position\\\\\\\";const xG=new class extends Lo{constructor(){super(...arguments),this.updateX=Oo.BOOLEAN(0),this.x=Oo.FLOAT(\\\\\\\"@P.x\\\\\\\",{visibleIf:{updateX:1},expression:{forEntities:!0}}),this.updateY=Oo.BOOLEAN(0),this.y=Oo.FLOAT(\\\\\\\"@P.y\\\\\\\",{visibleIf:{updateY:1},expression:{forEntities:!0}}),this.updateZ=Oo.BOOLEAN(0),this.z=Oo.FLOAT(\\\\\\\"@P.z\\\\\\\",{visibleIf:{updateZ:1},expression:{forEntities:!0}}),this.updateNormals=Oo.BOOLEAN(1)}};class bG extends QO{constructor(){super(...arguments),this.paramsConfig=xG,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(Ti.FROM_NODE)}async cook(e){const t=e[0];await this._eval_expressions_for_core_group(t)}async _eval_expressions_for_core_group(e){const t=e.coreObjects();for(let e=0;e<t.length;e++)await this._eval_expressions_for_core_object(t[e]);this.pv.updateNormals&&e.computeVertexNormals();const n=e.geometries();for(let e of n)e.computeBoundingBox();if(!this.io.inputs.cloneRequired(0)){const t=e.geometries();for(let e of t){e.getAttribute(yG).needsUpdate=!0}}this.setCoreGroup(e)}async _eval_expressions_for_core_object(e){const t=e.object().geometry,n=e.points(),i=t.getAttribute(yG).array,s=await this._update_from_param(t,i,n,this.p.updateX,this.p.x,this.pv.x,this._x_arrays_by_geometry_uuid,0),r=await this._update_from_param(t,i,n,this.p.updateY,this.p.y,this.pv.y,this._y_arrays_by_geometry_uuid,1),o=await this._update_from_param(t,i,n,this.p.updateZ,this.p.z,this.pv.z,this._z_arrays_by_geometry_uuid,2);s&&this._commit_tmp_values(s,i,0),r&&this._commit_tmp_values(r,i,1),o&&this._commit_tmp_values(o,i,2)}async _update_from_param(e,t,n,i,s,r,o,a){const c=i,l=s;let u=this._init_array_if_required(e,o,n.length,a);if(c.value)if(l.hasExpression()&&l.expressionController)await l.expressionController.compute_expression_for_points(n,((e,t)=>{u[e.index()]=t}));else{let e;for(let t=0;t<n.length;t++)e=n[t],u[e.index()]=r}return u}_init_array_if_required(e,t,n,i){const s=e.uuid,r=t.get(s);if(r){if(r.length<n){const r=this._array_for_component(e,n,i);return t.set(s,r),r}return r}{const r=this._array_for_component(e,n,i);return t.set(s,r),r}}_array_for_component(e,t,n){const i=new Array(t),s=e.getAttribute(yG).array;for(let e=0;e<i.length;e++)i[e]=s[3*e+n];return i}_commit_tmp_values(e,t,n){for(let i=0;i<e.length;i++)t[3*i+n]=e[i]}}class wG extends WO{static type(){return\\\\\\\"pointLight\\\\\\\"}cook(e,t){const n=new BS.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=t.color,n.intensity=t.intensity,n.decay=t.decay,n.distance=t.distance,n.castShadow=t.castShadows,n.shadow.mapSize.copy(t.shadowRes),n.shadow.camera.near=t.shadowNear,n.shadow.camera.far=t.shadowFar,n.shadow.bias=t.shadowBias,this.createCoreGroupFromObjects([n])}}wG.DEFAULT_PARAMS={color:new M.a(1,1,1),intensity:1,decay:.1,distance:100,castShadows:!1,shadowRes:new d.a(1024,1024),shadowBias:.001,shadowNear:1,shadowFar:100},wG.INPUT_CLONED_STATE=Ti.NEVER;const AG=wG.DEFAULT_PARAMS;const TG=new class extends Lo{constructor(){super(...arguments),this.light=Oo.FOLDER(),this.color=Oo.COLOR(AG.color.toArray(),{conversion:Or.SRGB_TO_LINEAR}),this.intensity=Oo.FLOAT(AG.intensity),this.decay=Oo.FLOAT(AG.decay),this.distance=Oo.FLOAT(AG.distance),this.castShadows=Oo.BOOLEAN(AG.castShadows),this.shadowRes=Oo.VECTOR2(AG.shadowRes.toArray(),{visibleIf:{castShadows:1}}),this.shadowBias=Oo.FLOAT(AG.shadowBias,{visibleIf:{castShadows:1}}),this.shadowNear=Oo.FLOAT(AG.shadowNear,{visibleIf:{castShadows:1}}),this.shadowFar=Oo.FLOAT(AG.shadowFar,{visibleIf:{castShadows:1}})}};class EG extends QO{constructor(){super(...arguments),this.paramsConfig=TG}static type(){return\\\\\\\"pointLight\\\\\\\"}initializeNode(){this.io.inputs.setCount(0)}cook(e){this._operation=this._operation||new wG(this._scene,this.states);const t=this._operation.cook(e,this.pv);this.setCoreGroup(t)}}const CG=new p.a(0,1,0),MG=new p.a(-1,0,0);class NG extends WO{constructor(){super(...arguments),this._centerMatrix=new C.a,this._longitudeMatrix=new C.a,this._latitudeMatrix=new C.a,this._depthMatrix=new C.a,this._fullMatrix=new C.a,this._decomposed={t:new p.a,q:new Ll.a,s:new p.a}}static type(){return\\\\\\\"polarTransform\\\\\\\"}cook(e,t){const n=e[0].objects(),i=this.matrix(t);return this._apply_transform(n,t,i),e[0]}_apply_transform(e,t,n){const i=tS[t.applyOn];switch(i){case KN.GEOMETRIES:return this._apply_matrix_to_geometries(e,n);case KN.OBJECTS:return this._apply_matrix_to_objects(e,n)}Ri.unreachable(i)}_apply_matrix_to_geometries(e,t){for(let n of e){const e=n.geometry;e&&e.applyMatrix4(t)}}_apply_matrix_to_objects(e,t){for(let n of e)t.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(e){return this._centerMatrix.identity(),this._longitudeMatrix.identity(),this._latitudeMatrix.identity(),this._depthMatrix.identity(),this._centerMatrix.makeTranslation(e.center.x,e.center.y,e.center.z),this._longitudeMatrix.makeRotationAxis(CG,A.a.degToRad(e.longitude)),this._latitudeMatrix.makeRotationAxis(MG,A.a.degToRad(e.latitude)),this._depthMatrix.makeTranslation(0,0,e.depth),this._fullMatrix.copy(this._centerMatrix).multiply(this._longitudeMatrix).multiply(this._latitudeMatrix).multiply(this._depthMatrix),this._fullMatrix}}NG.DEFAULT_PARAMS={applyOn:tS.indexOf(KN.GEOMETRIES),center:new p.a(0,0,0),longitude:0,latitude:0,depth:1},NG.INPUT_CLONED_STATE=Ti.FROM_NODE;const SG=NG.DEFAULT_PARAMS;const OG=new class extends Lo{constructor(){super(...arguments),this.applyOn=Oo.INTEGER(SG.applyOn,{menu:{entries:tS.map(((e,t)=>({name:e,value:t})))}}),this.center=Oo.VECTOR3(SG.center.toArray()),this.longitude=Oo.FLOAT(SG.longitude,{range:[0,360]}),this.latitude=Oo.FLOAT(SG.latitude,{range:[-180,180]}),this.depth=Oo.FLOAT(SG.depth,{range:[0,10]})}};class LG extends QO{constructor(){super(...arguments),this.paramsConfig=OG}static type(){return\\\\\\\"polarTransform\\\\\\\"}static displayedInputNames(){return[\\\\\\\"geometries or objects to transform\\\\\\\"]}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState(NG.INPUT_CLONED_STATE)}cook(e){this._operation=this._operation||new NG(this.scene(),this.states);const t=this._operation.cook(e,this.pv);this.setCoreGroup(t)}}class PG{static accumulated_curve_point_indices(e){let t=[];const n=[];let i,s=null;for(let r=0;r<e.length;r++)if(r%2==1){i=e[r];const o=e[r-1];null==s||o===s?(0===t.length&&t.push(o),t.push(i),s=i):(n.push(t),t=[o,i],s=i)}return n.push(t),n}static create_line_segment_geometry(e,t,n,i){const s=[],r={};n.forEach((e=>{r[e]=[]})),t.forEach(((t,o)=>{const a=e[t];n.forEach((e=>{const t=a.attribValue(e);let n;n=i[e]>1?t.toArray():[t],n.forEach((t=>{r[e].push(t)}))})),o>0&&(s.push(o-1),s.push(o))}));const o=new O.a;return n.forEach((e=>{const t=i[e],n=r[e];o.setAttribute(e,new L.c(n,t))})),o.setIndex(s),o}static line_segment_to_geometries(e){var t;const n=[],i=new Bs(e),s=i.attribNames(),r=i.points(),o=(null===(t=e.getIndex())||void 0===t?void 0:t.array)||[],a=this.accumulated_curve_point_indices(o);if(a.length>0){const t=i.attribSizes();a.forEach(((i,o)=>{e=this.create_line_segment_geometry(r,i,s,t),n.push(e)}))}return n}}class RG{constructor(e,t,n){this.geometry=e,this.geometry1=t,this.geometry0=n}process(){const e=new Bs(this.geometry0),t=new Bs(this.geometry1),n=e.segments(),i=t.segments();if(0===n.length||0===i.length)return;const s=n.length<i.length?[e,t]:[t,e],r=s[0],o=s[1],a=r.segments(),c=o.segments(),l=r.points(),u=o.points(),h=l.length,d=l.concat(u),p=[];a.forEach(((e,t)=>{const n=c[t];p.push(e[0]),p.push(e[1]),p.push(n[0]+h),p.push(e[1]),p.push(n[1]+h),p.push(n[0]+h)}));f.intersection(r.attribNames(),o.attribNames()).forEach((e=>{const t=r.attribSize(e);let n,i=d.map((t=>t.attribValue(e)));n=1==t?i:i.map((e=>e.toArray())).flat(),this.geometry.setAttribute(e,new L.c(n,t))})),this.geometry.setIndex(p),this.geometry.computeVertexNormals()}}const IG=new p.a(0,0,0),FG=new p.a(1,1,1);const DG=new class extends Lo{constructor(){super(...arguments),this.radius=Oo.FLOAT(1),this.segmentsRadial=Oo.INTEGER(8,{range:[3,20],rangeLocked:[!0,!1]}),this.closed=Oo.BOOLEAN(0)}};class kG extends QO{constructor(){super(...arguments),this.paramsConfig=DG,this._core_transform=new rS,this._geometries=[]}static type(){return\\\\\\\"polywire\\\\\\\"}static displayedInputNames(){return[\\\\\\\"lines to create tubes from\\\\\\\"]}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState(Ti.NEVER)}cook(e){const t=e[0];this._geometries=[];for(let e of t.objects())e instanceof $i.a&&this._create_tube(e);const n=Bs.mergeGeometries(this._geometries);for(let e of this._geometries)e.dispose();if(n){const e=this.createObject(n,Zi.MESH);this.setObject(e)}else this.setObjects([])}_create_tube(e){var t;const n=e.geometry,i=new Bs(n).points(),s=null===(t=n.getIndex())||void 0===t?void 0:t.array,r=PG.accumulated_curve_point_indices(s);for(let e of r){const t=e.map((e=>i[e]));this._create_tube_from_points(t)}}_create_tube_from_points(e){if(e.length<=1)return;const t=e.map((e=>e.attribValue(\\\\\\\"position\\\\\\\"))),n=tB.create(this.pv.radius,this.pv.segmentsRadial),i=[];for(let e of t){const t=e,s=this._core_transform.matrix(t,IG,FG,1,sS),r=n.clone();r.applyMatrix4(s),i.push(r)}for(let e=0;e<i.length;e++)if(e>0){const t=i[e],n=i[e-1],s=this._skin(n,t);this._geometries.push(s)}}_skin(e,t){const n=new O.a;return new RG(n,e,t).process(),n}}const BG=\\\\\\\"dist\\\\\\\";class zG extends WO{constructor(){super(...arguments),this._matDoubleSideTmpSetter=new vz,this._raycaster=new Gy,this._pointPos=new p.a,this._pointNormal=new p.a}static type(){return\\\\\\\"ray\\\\\\\"}cook(e,t){const n=e[0],i=e[1];return this._ray(n,i,t)}_ray(e,t,n){let i,s;this._matDoubleSideTmpSetter.setCoreGroupMaterialDoubleSided(t),n.addDistAttribute&&(e.hasAttrib(BG)||e.addNumericVertexAttrib(BG,1,-1));const r=e.points();for(let e of r)if(e.getPosition(this._pointPos),i=n.direction,n.useNormals&&(e.getNormal(this._pointNormal),i=this._pointNormal),this._raycaster.set(this._pointPos,i),s=this._raycaster.intersectObjects(t.objects(),!0)[0],s){if(n.transformPoints&&e.setPosition(s.point),n.addDistAttribute){const t=this._pointPos.distanceTo(s.point);console.log(t),e.setAttribValue(BG,t)}n.transferFaceNormals&&s.face&&e.setNormal(s.face.normal)}return this._matDoubleSideTmpSetter.restoreMaterialSideProperty(t),e}}zG.DEFAULT_PARAMS={useNormals:!0,direction:new p.a(0,-1,0),transformPoints:!0,transferFaceNormals:!0,addDistAttribute:!1},zG.INPUT_CLONED_STATE=[Ti.FROM_NODE,Ti.ALWAYS];const UG=zG.DEFAULT_PARAMS;const GG=new class extends Lo{constructor(){super(...arguments),this.useNormals=Oo.BOOLEAN(UG.useNormals),this.direction=Oo.VECTOR3(UG.direction.toArray(),{visibleIf:{useNormals:0}}),this.transformPoints=Oo.BOOLEAN(UG.transformPoints),this.transferFaceNormals=Oo.BOOLEAN(UG.transferFaceNormals),this.addDistAttribute=Oo.BOOLEAN(UG.addDistAttribute)}};class VG extends QO{constructor(){super(...arguments),this.paramsConfig=GG}static type(){return\\\\\\\"ray\\\\\\\"}static displayedInputNames(){return[\\\\\\\"geometry to move\\\\\\\",\\\\\\\"geometry to ray onto\\\\\\\"]}initializeNode(){this.io.inputs.setCount(2),this.io.inputs.initInputsClonedState([Ti.FROM_NODE,Ti.ALWAYS])}cook(e){this._operation=this._operation||new zG(this._scene,this.states);const t=this._operation.cook(e,this.pv);this.setCoreGroup(t)}}const jG={color:{value:null},tDiffuse:{value:null},textureMatrix:{value:null},opacity:{value:.5}},HG=\\\\\\\"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}\\\\\\\",qG=\\\\\\\"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}\\\\\\\",WG={minFilter:w.V,magFilter:w.V,format:w.ic};class XG extends z.a{constructor(e,t){super(),this.geometry=e,this._options=t,this.type=\\\\\\\"Reflector\\\\\\\",this.reflectorPlane=new $.a,this.normal=new p.a,this.reflectorWorldPosition=new p.a,this.cameraWorldPosition=new p.a,this.rotationMatrix=new C.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 C.a,this.virtualCamera=new te.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,WG),A.a.isPowerOfTwo(n)&&A.a.isPowerOfTwo(i)||(this.renderTarget.texture.generateMipmaps=!1),this._coreRenderBlur=new fO(new d.a(n,i)),this.material=new B({uniforms:k.clone(jG),fragmentShader:qG,vertexShader:HG}),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((e=>{e.parent||e.uuid!=this._options.scene.uuid&&this._removeWindowResizeEvent()}));const{width:e,height:t}=this._getRendererSize(this._options.renderer);this.renderTarget.setSize(e,t),this._coreRenderBlur.setSize(e,t)}_getRendererSize(e){const t=e.domElement;return{width:t.width*this._options.pixelRatio,height:t.height*this._options.pixelRatio}}_onBeforeRender(e,t,n,i,s,r){if(!this._options.active)return;const o=n;if(this.reflectorWorldPosition.setFromMatrixPosition(this.matrixWorld),this.cameraWorldPosition.setFromMatrixPosition(o.matrixWorld),this.rotationMatrix.extractRotation(this.matrixWorld),this.normal.set(0,0,1),this.normal.applyMatrix4(this.rotationMatrix),this.view.subVectors(this.reflectorWorldPosition,this.cameraWorldPosition),!(this.view.dot(this.normal)>0)){this.view.reflect(this.normal).negate(),this.view.add(this.reflectorWorldPosition),this.rotationMatrix.extractRotation(o.matrixWorld),this.lookAtPosition.set(0,0,-1),this.lookAtPosition.applyMatrix4(this.rotationMatrix),this.lookAtPosition.add(this.cameraWorldPosition),this.target.subVectors(this.reflectorWorldPosition,this.lookAtPosition),this.target.reflect(this.normal).negate(),this.target.add(this.reflectorWorldPosition),this.virtualCamera.position.copy(this.view),this.virtualCamera.up.set(0,1,0),this.virtualCamera.up.applyMatrix4(this.rotationMatrix),this.virtualCamera.up.reflect(this.normal),this.virtualCamera.lookAt(this.target),this.virtualCamera.far=o.far,this.virtualCamera.updateMatrixWorld(),this.virtualCamera.projectionMatrix.copy(o.projectionMatrix),this.textureMatrix.set(.5,0,0,.5,0,.5,0,.5,0,0,.5,.5,0,0,0,1),this.textureMatrix.multiply(this.virtualCamera.projectionMatrix),this.textureMatrix.multiply(this.virtualCamera.matrixWorldInverse),this.textureMatrix.multiply(this.matrixWorld),this.reflectorPlane.setFromNormalAndCoplanarPoint(this.normal,this.reflectorWorldPosition),this.reflectorPlane.applyMatrix4(this.virtualCamera.matrixWorldInverse),this.clipPlane.set(this.reflectorPlane.normal.x,this.reflectorPlane.normal.y,this.reflectorPlane.normal.z,this.reflectorPlane.constant);var a=this.virtualCamera.projectionMatrix;this.q.x=(Math.sign(this.clipPlane.x)+a.elements[8])/a.elements[0],this.q.y=(Math.sign(this.clipPlane.y)+a.elements[9])/a.elements[5],this.q.z=-1,this.q.w=(1+a.elements[10])/a.elements[14],this.clipPlane.multiplyScalar(2/this.clipPlane.dot(this.q)),a.elements[2]=this.clipPlane.x,a.elements[6]=this.clipPlane.y,a.elements[10]=this.clipPlane.z+1-this._options.clipBias,a.elements[14]=this.clipPlane.w,this.renderTarget.texture.encoding=e.outputEncoding,this.visible=!1;var c=e.getRenderTarget(),l=e.xr.enabled,u=e.shadowMap.autoUpdate;if(e.xr.enabled=!1,e.shadowMap.autoUpdate=!1,e.setRenderTarget(this.renderTarget),e.state.buffers.depth.setMask(!0),!1===e.autoClear&&e.clear(),e.render(t,this.virtualCamera),this._options.tblur){const t=this._options.blur*this._options.pixelRatio,n=t*this._options.verticalBlurMult;if(this._coreRenderBlur.applyBlur(this.renderTarget,e,t,n),this._options.tblur2){const t=this._options.blur2*this._options.pixelRatio,n=t*this._options.verticalBlur2Mult;this._coreRenderBlur.applyBlur(this.renderTarget,e,t,n)}}e.xr.enabled=l,e.shadowMap.autoUpdate=u,e.setRenderTarget(c);var h=o.viewport;void 0!==h&&e.state.viewport(h),this.visible=!0}}}class YG extends WO{static type(){return\\\\\\\"reflector\\\\\\\"}async cook(e,t){const n=e[0],i=[],s=await Rn.renderersController.firstRenderer();if(!s)return this.createCoreGroupFromObjects(i);const r=n.objectsWithGeo();for(let e of r){const n=new XG(e.geometry,{clipBias:t.clipBias,renderer:s,scene:this.scene().threejsScene(),pixelRatio:t.pixelRatio,color:t.color,opacity:t.opacity,active:t.active,tblur:t.tblur,blur:t.blur,verticalBlurMult:t.verticalBlurMult,tblur2:t.tblur2,blur2:t.blur2,verticalBlur2Mult:t.verticalBlur2Mult});n.position.copy(e.position),n.rotation.copy(e.rotation),n.scale.copy(e.scale),n.updateMatrix(),i.push(n)}return this.createCoreGroupFromObjects(i)}}YG.DEFAULT_PARAMS={active:!0,clipBias:.003,color:new M.a(1,1,1),opacity:1,pixelRatio:1,tblur:!1,blur:1,verticalBlurMult:1,tblur2:!1,blur2:1,verticalBlur2Mult:1},YG.INPUT_CLONED_STATE=Ti.NEVER;const $G=YG.DEFAULT_PARAMS;const QG=new class extends Lo{constructor(){super(...arguments),this.active=Oo.BOOLEAN($G.active),this.clipBias=Oo.FLOAT($G.clipBias),this.color=Oo.COLOR($G.color.toArray()),this.opacity=Oo.FLOAT($G.opacity),this.pixelRatio=Oo.INTEGER($G.pixelRatio,{range:[1,4],rangeLocked:[!0,!1]}),this.tblur=Oo.BOOLEAN($G.tblur),this.blur=Oo.FLOAT($G.blur,{visibleIf:{tblur:1}}),this.verticalBlurMult=Oo.FLOAT($G.verticalBlurMult,{visibleIf:{tblur:1}}),this.tblur2=Oo.BOOLEAN($G.tblur2,{visibleIf:{tblur:1}}),this.blur2=Oo.FLOAT($G.blur2,{visibleIf:{tblur:1,tblur2:1}}),this.verticalBlur2Mult=Oo.FLOAT($G.verticalBlur2Mult,{visibleIf:{tblur:1,tblur2:1}})}};class JG extends QO{constructor(){super(...arguments),this.paramsConfig=QG}static type(){return\\\\\\\"reflector\\\\\\\"}static displayedInputNames(){return[\\\\\\\"geometry to create a reflector from\\\\\\\"]}initializeNode(){this.io.inputs.setCount(0,1),this.io.inputs.initInputsClonedState(YG.INPUT_CLONED_STATE)}async cook(e){this._operation=this._operation||new YG(this._scene,this.states);const t=await this._operation.cook(e,this.pv);this.setCoreGroup(t)}}var KG=n(86);var ZG;!function(e){e.POINTS_COUNT=\\\\\\\"pointsCount\\\\\\\",e.SEGMENT_LENGTH=\\\\\\\"segmentLength\\\\\\\"}(ZG||(ZG={}));const eV=[ZG.POINTS_COUNT,ZG.SEGMENT_LENGTH];var tV;!function(e){e.CENTRIPETAL=\\\\\\\"centripetal\\\\\\\",e.CHORDAL=\\\\\\\"chordal\\\\\\\",e.CATMULLROM=\\\\\\\"catmullrom\\\\\\\"}(tV||(tV={}));const nV=[tV.CENTRIPETAL,tV.CHORDAL,tV.CATMULLROM];const iV=new class extends Lo{constructor(){super(...arguments),this.method=Oo.INTEGER(eV.indexOf(ZG.POINTS_COUNT),{menu:{entries:eV.map(((e,t)=>({name:e,value:t})))}}),this.curveType=Oo.INTEGER(nV.indexOf(tV.CATMULLROM),{range:[0,2],rangeLocked:[!0,!0],menu:{entries:nV.map(((e,t)=>({name:e,value:t})))}}),this.tension=Oo.FLOAT(.01,{range:[0,1],rangeLocked:[!0,!0]}),this.pointsCount=Oo.INTEGER(100,{visibleIf:{method:eV.indexOf(ZG.POINTS_COUNT)},range:[1,1e3],rangeLocked:[!0,!1]}),this.segmentLength=Oo.FLOAT(1,{visibleIf:{method:eV.indexOf(ZG.SEGMENT_LENGTH)}})}};class sV extends QO{constructor(){super(...arguments),this.paramsConfig=iV}static type(){return\\\\\\\"resample\\\\\\\"}initializeNode(){this.io.inputs.setCount(1)}cook(e){const t=e[0],n=[];if(this.pv.pointsCount>=2){const e=t.coreObjects();for(let t=0;t<e.length;t++){const i=e[t].object();if(i instanceof $i.a){const e=this._resample(i);n.push(e)}}}this.setObjects(n)}_resample(e){var t;const n=e.geometry,i=new Bs(n).points(),s=null===(t=n.getIndex())||void 0===t?void 0:t.array,r=PG.accumulated_curve_point_indices(s),o=[];for(let e=0;e<r.length;e++){const t=r[e].map((e=>i[e])),n=this._create_curve_from_points(t);n&&o.push(n)}const a=Fs.mergeBufferGeometries(o);return this.createObject(a,Zi.LINE_SEGMENTS)}_create_curve_from_points(e){if(e.length<=1)return;const t=e.map((e=>e.attribValue(\\\\\\\"position\\\\\\\"))),n=nV[this.pv.curveType],i=this.pv.tension,s=new KG.a(t,!1,n,i),r=this._get_points_from_curve(s);let o=[];const a=[];for(let e=0;e<r.length;e++){const t=r[e].toArray();o.push(t),e>0&&(a.push(e-1),a.push(e))}const c=new O.a;return c.setAttribute(\\\\\\\"position\\\\\\\",new L.c(o.flat(),3)),c.setIndex(a),c}_get_points_from_curve(e){const t=eV[this.pv.method];switch(t){case ZG.POINTS_COUNT:return e.getSpacedPoints(Math.max(2,this.pv.pointsCount));case ZG.SEGMENT_LENGTH:var n=e.getLength(),i=0!==this.pv.segmentLength?1+n/this.pv.segmentLength:2;return i=Math.max(2,i),e.getSpacedPoints(i)}Ri.unreachable(t)}}class rV extends WO{static type(){return\\\\\\\"restAttributes\\\\\\\"}cook(e,t){const n=e[0].objectsWithGeo();return t.tposition&&this._create_rest_attribute(n,t.position,t.restP),t.tnormal&&this._create_rest_attribute(n,t.normal,t.restN),this.createCoreGroupFromObjects(n)}_create_rest_attribute(e,t,n){for(let i of e){const e=i.geometry;if(e){const i=e.getAttribute(t);i&&e.setAttribute(n,i.clone())}}}}rV.DEFAULT_PARAMS={tposition:!0,position:\\\\\\\"position\\\\\\\",restP:\\\\\\\"restP\\\\\\\",tnormal:!0,normal:\\\\\\\"normal\\\\\\\",restN:\\\\\\\"restN\\\\\\\"};const oV=rV.DEFAULT_PARAMS;const aV=new class extends Lo{constructor(){super(...arguments),this.tposition=Oo.BOOLEAN(oV.tposition),this.position=Oo.STRING(oV.position,{visibleIf:{tposition:!0}}),this.restP=Oo.STRING(oV.restP,{visibleIf:{tposition:!0}}),this.tnormal=Oo.BOOLEAN(oV.tnormal),this.normal=Oo.STRING(oV.normal,{visibleIf:{tnormal:!0}}),this.restN=Oo.STRING(oV.restN,{visibleIf:{tnormal:!0}})}};class cV extends QO{constructor(){super(...arguments),this.paramsConfig=aV}static type(){return\\\\\\\"restAttributes\\\\\\\"}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState([Ti.FROM_NODE])}cook(e){this._operation=this._operation||new rV(this.scene(),this.states);const t=this._operation.cook(e,this.pv);this.setCoreGroup(t)}}const lV=new p.a;function uV(e,t,n,i,s,r){const o=2*Math.PI*s/4,a=Math.max(r-2*s,0),c=Math.PI/4;lV.copy(t),lV[i]=0,lV.normalize();const l=.5*o/(o+a),u=1-lV.angleTo(e)/c;if(1===Math.sign(lV[n]))return u*l;return a/(o+a)+l+l*(1-u)}class hV extends P{constructor(e=1,t=1,n=1,i=2,s=.1){if(i=2*i+1,s=Math.min(e/2,t/2,n/2,s),super(1,1,1,i,i,i),1===i)return;const r=this.toNonIndexed();this.index=null,this.attributes.position=r.attributes.position,this.attributes.normal=r.attributes.normal,this.attributes.uv=r.attributes.uv;const o=new p.a,a=new p.a,c=new p.a(e,t,n).divideScalar(2).subScalar(s),l=this.attributes.position.array,u=this.attributes.normal.array,h=this.attributes.uv.array,d=l.length/6,_=new p.a,m=.5/i;for(let i=0,r=0;i<l.length;i+=3,r+=2){o.fromArray(l,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(),l[i+0]=c.x*Math.sign(o.x)+a.x*s,l[i+1]=c.y*Math.sign(o.y)+a.y*s,l[i+2]=c.z*Math.sign(o.z)+a.z*s,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[r+0]=uV(_,a,\\\\\\\"z\\\\\\\",\\\\\\\"y\\\\\\\",s,n),h[r+1]=1-uV(_,a,\\\\\\\"y\\\\\\\",\\\\\\\"z\\\\\\\",s,t);break;case 1:_.set(-1,0,0),h[r+0]=1-uV(_,a,\\\\\\\"z\\\\\\\",\\\\\\\"y\\\\\\\",s,n),h[r+1]=1-uV(_,a,\\\\\\\"y\\\\\\\",\\\\\\\"z\\\\\\\",s,t);break;case 2:_.set(0,1,0),h[r+0]=1-uV(_,a,\\\\\\\"x\\\\\\\",\\\\\\\"z\\\\\\\",s,e),h[r+1]=uV(_,a,\\\\\\\"z\\\\\\\",\\\\\\\"x\\\\\\\",s,n);break;case 3:_.set(0,-1,0),h[r+0]=1-uV(_,a,\\\\\\\"x\\\\\\\",\\\\\\\"z\\\\\\\",s,e),h[r+1]=1-uV(_,a,\\\\\\\"z\\\\\\\",\\\\\\\"x\\\\\\\",s,n);break;case 4:_.set(0,0,1),h[r+0]=1-uV(_,a,\\\\\\\"x\\\\\\\",\\\\\\\"y\\\\\\\",s,e),h[r+1]=1-uV(_,a,\\\\\\\"y\\\\\\\",\\\\\\\"x\\\\\\\",s,t);break;case 5:_.set(0,0,-1),h[r+0]=uV(_,a,\\\\\\\"x\\\\\\\",\\\\\\\"y\\\\\\\",s,e),h[r+1]=1-uV(_,a,\\\\\\\"y\\\\\\\",\\\\\\\"x\\\\\\\",s,t)}}}}class dV extends WO{constructor(){super(...arguments),this._core_transform=new rS}static type(){return\\\\\\\"roundedBox\\\\\\\"}cook(e,t){const n=e[0],i=n?this._cook_with_input(n,t):this._cook_without_input(t);return this.createCoreGroupFromGeometry(i)}_cook_without_input(e){const t=e.size,n=new hV(t,t,t,e.divisions,e.bevel);return n.translate(e.center.x,e.center.y,e.center.z),n.computeVertexNormals(),n}_cook_with_input(e,t){const n=t.divisions,i=e.boundingBox(),s=i.max.clone().sub(i.min),r=i.max.clone().add(i.min).multiplyScalar(.5),o=new hV(s.x,s.y,s.z,n,t.bevel),a=this._core_transform.translation_matrix(r);return o.applyMatrix4(a),o}}dV.DEFAULT_PARAMS={size:1,divisions:2,bevel:.1,center:new p.a(0,0,0)},dV.INPUT_CLONED_STATE=Ti.NEVER;const pV=dV.DEFAULT_PARAMS;const _V=new class extends Lo{constructor(){super(...arguments),this.size=Oo.FLOAT(pV.size),this.divisions=Oo.INTEGER(pV.divisions,{range:[1,10],rangeLocked:[!0,!1]}),this.bevel=Oo.FLOAT(pV.bevel,{range:[0,1],rangeLocked:[!0,!1]}),this.center=Oo.VECTOR3(pV.center)}};class mV extends QO{constructor(){super(...arguments),this.paramsConfig=_V}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(dV.INPUT_CLONED_STATE)}cook(e){this._operation=this._operation||new dV(this._scene,this.states);const t=this._operation.cook(e,this.pv);this.setCoreGroup(t)}}class fV extends WO{static type(){return\\\\\\\"scatter\\\\\\\"}async cook(e,t){const n=e[0];let i=n.faces();const s=[];let r=0;const o=new Map;for(let e of i){const t=e.area();o.set(e.index(),t)}const a=f.sortBy(i,(e=>o.get(e.index())||-1));let c=0;for(let e of a)r+=o.get(e.index()),s[c]=r,c++;const l=[];let u=[];t.transferAttributes&&(u=n.attribNamesMatchingMask(t.attributesToTransfer));const h=new Map,d=new Map;for(let e of u)h.set(e,[]),d.set(e,n.attribSize(e));const p=new pD,_=2454*t.seed%Number.MAX_SAFE_INTEGER;await p.startWithCount(t.pointsCount,(e=>{const t=Os.randFloat(_+e)*r;for(let e=0;e<s.length;e++){if(t<=s[e]){const n=a[e],i=n.random_position(t);i.toArray(l,l.length);for(let e of u){const t=n.attrib_value_at_position(e,i);t&&(m.isNumber(t)?h.get(e).push(t):t.toArray(h.get(e),h.get(e).length))}break}}}));const g=new O.a;g.setAttribute(\\\\\\\"position\\\\\\\",new L.a(new Float32Array(l),3));for(let e of u)g.setAttribute(e,new L.a(new Float32Array(h.get(e)),d.get(e)));if(t.addIdAttribute||t.addIdnAttribute){const e=t.pointsCount,n=f.range(e);t.addIdAttribute&&g.setAttribute(\\\\\\\"id\\\\\\\",new L.a(new Float32Array(n),1));const i=n.map((t=>t/(e-1)));t.addIdnAttribute&&g.setAttribute(\\\\\\\"idn\\\\\\\",new L.a(new Float32Array(i),1))}const v=this.createObject(g,Zi.POINTS);return this.createCoreGroupFromObjects([v])}}fV.DEFAULT_PARAMS={pointsCount:100,seed:0,transferAttributes:!0,attributesToTransfer:\\\\\\\"normal\\\\\\\",addIdAttribute:!0,addIdnAttribute:!0},fV.INPUT_CLONED_STATE=Ti.FROM_NODE;const gV=fV.DEFAULT_PARAMS;const vV=new class extends Lo{constructor(){super(...arguments),this.pointsCount=Oo.INTEGER(gV.pointsCount,{range:[0,100],rangeLocked:[!0,!1]}),this.seed=Oo.INTEGER(gV.seed,{range:[0,100],rangeLocked:[!1,!1]}),this.transferAttributes=Oo.BOOLEAN(gV.transferAttributes),this.attributesToTransfer=Oo.STRING(gV.attributesToTransfer,{visibleIf:{transferAttributes:1}}),this.addIdAttribute=Oo.BOOLEAN(gV.addIdAttribute),this.addIdnAttribute=Oo.BOOLEAN(gV.addIdnAttribute)}};class yV extends QO{constructor(){super(...arguments),this.paramsConfig=vV}static type(){return\\\\\\\"scatter\\\\\\\"}static displayedInputNames(){return[\\\\\\\"geometry to scatter points onto\\\\\\\"]}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState(Ti.NEVER)}async cook(e){this._operation=this._operation||new fV(this.scene(),this.states);const t=await this._operation.cook(e,this.pv);this.setCoreGroup(t)}}var xV;!function(e){e.MATRIX=\\\\\\\"matrix\\\\\\\",e.AXIS=\\\\\\\"axis\\\\\\\"}(xV||(xV={}));const bV=[xV.MATRIX,xV.AXIS];var wV;!function(e){e.BBOX_CENTER=\\\\\\\"bbox center\\\\\\\",e.BBOX_CENTER_OFFSET=\\\\\\\"bbox center offset\\\\\\\",e.CUSTOM=\\\\\\\"custom\\\\\\\"}(wV||(wV={}));const AV=[wV.BBOX_CENTER,wV.BBOX_CENTER_OFFSET,wV.CUSTOM];class TV extends WO{constructor(){super(...arguments),this._m4=new C.a,this._axisNormalized=new p.a,this._center=new p.a,this._pointPos=new p.a,this._axisPlane=new $.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(e,t){const n=e[0].objects();return this._applyShear(n,t),e[0]}_applyShear(e,t){const n=bV[t.mode];switch(n){case xV.MATRIX:return this._applyMatrixShear(e,t);case xV.AXIS:return this._applyAxisShear(e,t)}Ri.unreachable(n)}_applyMatrixShear(e,t){this._m4.makeShear(t.matrixAmount.x,t.matrixAmount.y,t.matrixAmount.z);for(let t of e){const e=t.geometry;e&&e.applyMatrix4(this._m4)}}_applyAxisShear(e,t){this._axisNormalized.copy(t.axis),this._axisNormalized.normalize();for(let n of e){const e=n.geometry;if(e){this._getAxisModeCenter(e,t),this._axisPlane.setFromNormalAndCoplanarPoint(t.planeAxis,this._center);const n=new Bs(e).points();for(let e of n){e.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(t.axisAmount*n),this._delta.dot(t.planeAxis)>0&&this._offset.multiplyScalar(-1),this._pointPos.add(this._offset),e.setPosition(this._pointPos)}}}}_getAxisModeCenter(e,t){const n=AV[t.centerMode];switch(n){case wV.BBOX_CENTER:return this._getAxisModeCenterBbox(e,t);case wV.BBOX_CENTER_OFFSET:return this._getAxisModeCenterBboxOffset(e,t);case wV.CUSTOM:return this._getAxisModeCenterCustom(t)}Ri.unreachable(n)}_getAxisModeCenterBbox(e,t){e.computeBoundingBox();const n=e.boundingBox;n?n.getCenter(this._center):this._center.set(0,0,0)}_getAxisModeCenterBboxOffset(e,t){this._getAxisModeCenterBbox(e,t),this._center.add(t.centerOffset)}_getAxisModeCenterCustom(e){return this._center.copy(e.center)}}var EV;TV.DEFAULT_PARAMS={mode:bV.indexOf(xV.AXIS),matrixAmount:new p.a(0,0,0),centerMode:AV.indexOf(wV.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},TV.INPUT_CLONED_STATE=Ti.FROM_NODE,function(e){e.SHEAR=\\\\\\\"shear\\\\\\\",e.TRANSFORM=\\\\\\\"transform\\\\\\\",e.UV_LAYOUT=\\\\\\\"uvLayout\\\\\\\",e.UV_TRANSFORM=\\\\\\\"uvTransform\\\\\\\",e.UV_UNWRAP=\\\\\\\"uvUnwrap\\\\\\\"}(EV||(EV={}));const CV=TV.DEFAULT_PARAMS;const MV=new class extends Lo{constructor(){super(...arguments),this.mode=Oo.INTEGER(CV.mode,{menu:{entries:bV.map(((e,t)=>({name:e,value:t})))}}),this.matrixAmount=Oo.VECTOR3(CV.matrixAmount.toArray(),{visibleIf:{mode:bV.indexOf(xV.MATRIX)}}),this.centerMode=Oo.INTEGER(CV.centerMode,{visibleIf:{mode:bV.indexOf(xV.AXIS)},menu:{entries:AV.map(((e,t)=>({name:e,value:t})))}}),this.centerOffset=Oo.VECTOR3(CV.centerOffset.toArray(),{visibleIf:{mode:bV.indexOf(xV.AXIS),centerMode:AV.indexOf(wV.BBOX_CENTER_OFFSET)}}),this.center=Oo.VECTOR3(CV.center.toArray(),{visibleIf:{mode:bV.indexOf(xV.AXIS),centerMode:AV.indexOf(wV.CUSTOM)}}),this.planeAxis=Oo.VECTOR3(CV.planeAxis.toArray(),{visibleIf:{mode:bV.indexOf(xV.AXIS)}}),this.axis=Oo.VECTOR3(CV.axis.toArray(),{visibleIf:{mode:bV.indexOf(xV.AXIS)}}),this.axisAmount=Oo.FLOAT(CV.axisAmount,{range:[-1,1],visibleIf:{mode:bV.indexOf(xV.AXIS)}})}};class NV extends QO{constructor(){super(...arguments),this.paramsConfig=MV}static type(){return EV.SHEAR}static displayedInputNames(){return[\\\\\\\"geometries or objects to transform\\\\\\\"]}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState(TV.INPUT_CLONED_STATE)}setMode(e){this.p.mode.set(bV.indexOf(e))}cook(e){this._operation=this._operation||new TV(this.scene(),this.states);const t=this._operation.cook(e,this.pv);this.setCoreGroup(t)}}const SV=new class extends Lo{};class OV extends QO{constructor(){super(...arguments),this.paramsConfig=SV}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(e){switch(f.compact(this.io.inputs.inputs()).length){case 1:return this.process_one_input(e);case 2:return this.process_two_inputs(e);default:return this.states.error.set(\\\\\\\"inputs count not valid\\\\\\\")}}process_one_input(e){const t=e[0],n=this._get_line_segments(t),i=[];if(n){const e=n[0];if(e){const t=PG.line_segment_to_geometries(e.geometry);t.forEach(((e,n)=>{if(n>0){const s=t[n-1],r=this._skin(s,e);i.push(r)}}))}}this.setGeometries(i)}process_two_inputs(e){const t=e[0],n=e[1],i=this._get_line_segments(t),s=this._get_line_segments(n),r=f.sortBy([i,s],(e=>-e.length)),o=r[0],a=r[1],c=[];o.forEach(((e,t)=>{const n=a[t];if(null!=e&&null!=n){const t=e.geometry,i=n.geometry,s=this._skin(t,i);c.push(s)}})),this.setGeometries(c)}_get_line_segments(e){return e.objects().filter((e=>e.isLineSegments))}_skin(e,t){const n=new O.a;return new RG(n,e,t).process(),n}}var LV;!function(e){e.X=\\\\\\\"x\\\\\\\",e.Y=\\\\\\\"y\\\\\\\",e.Z=\\\\\\\"z\\\\\\\"}(LV||(LV={}));const PV=[LV.X,LV.Y,LV.Z];class RV extends WO{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(e,t){const n=e[0],i=n.objectsWithGeo();for(let e of i)this._sortObject(e,t);return n}_debug(e){this._debugActive}_sortObject(e,t){const n=new js(e,0).points(),i=e.geometry.getIndex();if(!i)return void console.warn(\\\\\\\"geometry cannot be sorted since it has no index\\\\\\\");const s=i.array;this._positions=new Array(n.length),this._indicesByPos.clear(),this._indexDest.clear();const r=PV[t.axis];let o=0,a=0;for(let e of n){switch(e.getPosition(this._pointPos),r){case LV.X:o=this._pointPos.x;break;case LV.Y:o=this._pointPos.y;break;case LV.Z:o=this._pointPos.z}this._positions[a]=o,u.pushOnArrayAtEntry(this._indicesByPos,o,e.index()),a++}let c=this._positions.sort(((e,t)=>e-t));t.invert&&c.reverse();const l=new Array(n.length);a=0;const h=f.uniq(c);for(let e of h){const t=this._indicesByPos.get(e);if(t)for(let e of t)l[a]=e,this._indexDest.set(e,a),a++}const d=new Array(s.length);for(let e=0;e<s.length;e++){const t=s[e],n=this._indexDest.get(t);d[e]=n}e.geometry.setIndex(d);const p=Bs.attribNames(e.geometry);for(let t of p){\\\\\\\"id\\\\\\\"==t&&(this._debugActive=!0);const n=e.geometry.getAttribute(t);this._updateAttribute(n,l),this._debugActive=!1}}_updateAttribute(e,t){const n=e.clone(),i=e.array,s=n.array,r=n.itemSize;this._debug(t);for(let e of t){const t=this._indexDest.get(e);if(this._debug(`${e} -> ${t}`),null!=t)for(let n=0;n<r;n++)s[t*r+n]=i[e*r+n];else console.warn(\\\\\\\"no old index found\\\\\\\")}e.array=s,e.needsUpdate=!0}}RV.DEFAULT_PARAMS={axis:PV.indexOf(LV.X),invert:!1},RV.INPUT_CLONED_STATE=Ti.FROM_NODE;const IV=RV.DEFAULT_PARAMS;const FV=new class extends Lo{constructor(){super(...arguments),this.axis=Oo.INTEGER(IV.axis,{menu:{entries:PV.map(((e,t)=>({name:e,value:t})))}}),this.invert=Oo.BOOLEAN(IV.invert)}};class DV extends QO{constructor(){super(...arguments),this.paramsConfig=FV}static type(){return\\\\\\\"sort\\\\\\\"}static displayedInputNames(){return[\\\\\\\"geometry to sort\\\\\\\"]}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState([Ti.FROM_NODE])}cook(e){this._operation=this._operation||new RV(this._scene,this.states);const t=this._operation.cook(e,this.pv);this.setCoreGroup(t)}}const kV=new class extends Lo{constructor(){super(...arguments),this.startFrame=Oo.INTEGER(Qa.START_FRAME)}};class BV extends ZO{constructor(){super(...arguments),this.paramsConfig=kV,this._last_simulated_frame=null,this.childrenDisplayController=new tL(this,{dependsOnDisplayNode:!1}),this.displayNodeController=new mm(this,{onDisplayNodeRemove:()=>{},onDisplayNodeSet:()=>{},onDisplayNodeUpdate:()=>{}},{dependsOnDisplayNode:!1})}static type(){return\\\\\\\"solver\\\\\\\"}initializeNode(){this.io.inputs.setCount(0,4),this.io.inputs.initInputsClonedState(Ti.NEVER),this.addGraphInput(this.scene().timeController.graphNode)}previousFrameCoreGroup(){return this._previousFrameCoreGroup}async cook(e){this.pv.startFrame==this.scene().frame()&&this._reset(),this.computeSolverIfRequired()}_reset(){this._previousFrameCoreGroup=void 0,this._last_simulated_frame=null}computeSolverIfRequired(){const e=this.scene().frame(),t=this.pv.startFrame;e>=t&&(null==this._last_simulated_frame&&(this._last_simulated_frame=t-1),e>this._last_simulated_frame&&this._computeSolverMultipleTimes(e-this._last_simulated_frame))}_computeSolverMultipleTimes(e=1){for(let t=0;t<e;t++)this.computeSolver();this._last_simulated_frame=this.scene().frame()}async computeSolver(){const e=this.childrenDisplayController.output_node();if(e){const t=(await e.compute()).coreContent();t?(this._previousFrameCoreGroup=t,this.setCoreGroup(t)):e.states.error.active()?this.states.error.set(e.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 zV=new class extends Lo{};class UV extends QO{constructor(){super(...arguments),this.paramsConfig=zV}static type(){return\\\\\\\"solverPreviousFrame\\\\\\\"}initializeNode(){this.addGraphInput(this.scene().timeController.graphNode)}async cook(){const e=this.parent();(null==e?void 0:e.type())!=BV.type()&&(this.states.error.set(`the parent is not a '${BV.type()}'`),this.cookController.endCook());const t=e.previousFrameCoreGroup();t?this.setCoreGroup(t):this.setObjects([])}}var GV;!function(e){e.DEFAULT=\\\\\\\"default\\\\\\\",e.ISOCAHEDRON=\\\\\\\"isocahedron\\\\\\\"}(GV||(GV={}));const VV={default:0,isocahedron:1},jV=[GV.DEFAULT,GV.ISOCAHEDRON];class HV extends WO{static type(){return\\\\\\\"sphere\\\\\\\"}cook(e,t){const n=e[0];return n?this._cook_with_input(n,t):this._cook_without_input(t)}_cook_without_input(e){const t=this._create_required_geometry(e);return t.translate(e.center.x,e.center.y,e.center.z),this.createCoreGroupFromGeometry(t)}_cook_with_input(e,t){const n=e.boundingBox(),i=n.max.clone().sub(n.min),s=n.max.clone().add(n.min).multiplyScalar(.5),r=this._create_required_geometry(t);return r.translate(t.center.x,t.center.y,t.center.z),r.translate(s.x,s.y,s.z),r.scale(i.x,i.y,i.z),this.createCoreGroupFromGeometry(r)}_create_required_geometry(e){return e.type==VV.default?this._create_default_sphere(e):this._create_default_isocahedron(e)}_create_default_sphere(e){return e.open?new zS(e.radius,e.resolution.x,e.resolution.y,e.phiStart,e.phiLength,e.thetaStart,e.thetaLength):new zS(e.radius,e.resolution.x,e.resolution.y)}_create_default_isocahedron(e){return new Nk(e.radius,e.detail)}}HV.DEFAULT_PARAMS={type:VV.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)},HV.INPUT_CLONED_STATE=Ti.FROM_NODE;const qV=HV.DEFAULT_PARAMS;const WV=new class extends Lo{constructor(){super(...arguments),this.type=Oo.INTEGER(qV.type,{menu:{entries:jV.map((e=>({name:e,value:VV[e]})))}}),this.radius=Oo.FLOAT(qV.radius,{visibleIf:{type:VV.default}}),this.resolution=Oo.VECTOR2(qV.resolution,{visibleIf:{type:VV.default}}),this.open=Oo.BOOLEAN(qV.open,{visibleIf:{type:VV.default}}),this.phiStart=Oo.FLOAT(qV.phiStart,{range:[0,2*Math.PI],visibleIf:{type:VV.default,open:!0}}),this.phiLength=Oo.FLOAT(\\\\\\\"$PI*2\\\\\\\",{range:[0,2*Math.PI],visibleIf:{type:VV.default,open:!0}}),this.thetaStart=Oo.FLOAT(qV.thetaStart,{range:[0,Math.PI],visibleIf:{type:VV.default,open:!0}}),this.thetaLength=Oo.FLOAT(\\\\\\\"$PI\\\\\\\",{range:[0,Math.PI],visibleIf:{type:VV.default,open:!0}}),this.detail=Oo.INTEGER(qV.detail,{range:[0,5],rangeLocked:[!0,!1],visibleIf:{type:VV.isocahedron}}),this.center=Oo.VECTOR3(qV.center)}};class XV extends QO{constructor(){super(...arguments),this.paramsConfig=WV}static type(){return\\\\\\\"sphere\\\\\\\"}initializeNode(){this.io.inputs.setCount(0,1),this.io.inputs.initInputsClonedState(HV.INPUT_CLONED_STATE)}cook(e){this._operation=this._operation||new HV(this.scene(),this.states);const t=this._operation.cook(e,this.pv);this.setCoreGroup(t)}}const YV=new class extends Lo{constructor(){super(...arguments),this.attribType=Oo.INTEGER(ls.indexOf(cs.NUMERIC),{menu:{entries:us}}),this.attribName=Oo.STRING(\\\\\\\"\\\\\\\")}};class $V extends QO{constructor(){super(...arguments),this.paramsConfig=YV,this._new_objects=[]}static type(){return\\\\\\\"split\\\\\\\"}static displayedInputNames(){return[\\\\\\\"geometry to split in multiple objects\\\\\\\"]}initializeNode(){this.io.inputs.setCount(1)}async cook(e){const t=e[0];this._new_objects=[],\\\\\\\"\\\\\\\"!=this.pv.attribName&&this._split_core_group(t),this.setObjects(this._new_objects)}async _split_core_group(e){const t=e.coreObjects();for(let e of t)this._split_core_object(e)}_split_core_object(e){let t=e.coreGeometry(),n=this.pv.attribName,i=new Map;if(t){const s=e.object(),r=t.pointsFromGeometry(),o=r[0];if(o){if(o.attribSize(n)!=hs.FLOAT&&!o.isAttribIndexed(n))return void this.states.error.set(`attrib '${n}' must be a float or a string`);let e;if(o.isAttribIndexed(n))for(let t of r)e=t.indexedAttribValue(n),u.pushOnArrayAtEntry(i,e,t);else for(let t of r)e=t.attribValue(n),u.pushOnArrayAtEntry(i,e,t)}const a=ts(s.constructor);i.forEach(((e,t)=>{const i=Bs.geometryFromPoints(e,a);if(i){const e=this.createObject(i,a);js.addAttribute(e,n,t),this._new_objects.push(e)}}))}}}const QV=new C.a,JV=new ee.a,KV=new p.a;function ZV(){this.uuid=A.a.generateUUID(),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}ZV.prototype=Object.assign(Object.create(J.a.prototype),{constructor:ZV,isGeometry:!0,applyMatrix4:function(e){const t=(new V.a).getNormalMatrix(e);for(let t=0,n=this.vertices.length;t<n;t++){this.vertices[t].applyMatrix4(e)}for(let e=0,n=this.faces.length;e<n;e++){const n=this.faces[e];n.normal.applyMatrix3(t).normalize();for(let e=0,i=n.vertexNormals.length;e<i;e++)n.vertexNormals[e].applyMatrix3(t).normalize()}return null!==this.boundingBox&&this.computeBoundingBox(),null!==this.boundingSphere&&this.computeBoundingSphere(),this.verticesNeedUpdate=!0,this.normalsNeedUpdate=!0,this},rotateX:function(e){return QV.makeRotationX(e),this.applyMatrix4(QV),this},rotateY:function(e){return QV.makeRotationY(e),this.applyMatrix4(QV),this},rotateZ:function(e){return QV.makeRotationZ(e),this.applyMatrix4(QV),this},translate:function(e,t,n){return QV.makeTranslation(e,t,n),this.applyMatrix4(QV),this},scale:function(e,t,n){return QV.makeScale(e,t,n),this.applyMatrix4(QV),this},lookAt:function(e){return JV.lookAt(e),JV.updateMatrix(),this.applyMatrix4(JV.matrix),this},fromBufferGeometry:function(e){const t=this,n=null!==e.index?e.index:void 0,i=e.attributes;if(void 0===i.position)return console.error(\\\\\\\"THREE.Geometry.fromBufferGeometry(): Position attribute required for conversion.\\\\\\\"),this;const s=i.position,r=i.normal,o=i.color,a=i.uv,c=i.uv2;void 0!==c&&(this.faceVertexUvs[1]=[]);for(let e=0;e<s.count;e++)t.vertices.push((new p.a).fromBufferAttribute(s,e)),void 0!==o&&t.colors.push((new M.a).fromBufferAttribute(o,e));function l(e,n,i,s){const l=void 0===o?[]:[t.colors[e].clone(),t.colors[n].clone(),t.colors[i].clone()],u=void 0===r?[]:[(new p.a).fromBufferAttribute(r,e),(new p.a).fromBufferAttribute(r,n),(new p.a).fromBufferAttribute(r,i)],h=new tj(e,n,i,u,l,s);t.faces.push(h),void 0!==a&&t.faceVertexUvs[0].push([(new d.a).fromBufferAttribute(a,e),(new d.a).fromBufferAttribute(a,n),(new d.a).fromBufferAttribute(a,i)]),void 0!==c&&t.faceVertexUvs[1].push([(new d.a).fromBufferAttribute(c,e),(new d.a).fromBufferAttribute(c,n),(new d.a).fromBufferAttribute(c,i)])}const u=e.groups;if(u.length>0)for(let e=0;e<u.length;e++){const t=u[e],i=t.start;for(let e=i,s=i+t.count;e<s;e+=3)void 0!==n?l(n.getX(e),n.getX(e+1),n.getX(e+2),t.materialIndex):l(e,e+1,e+2,t.materialIndex)}else if(void 0!==n)for(let e=0;e<n.count;e+=3)l(n.getX(e),n.getX(e+1),n.getX(e+2));else for(let e=0;e<s.count;e+=3)l(e,e+1,e+2);return this.computeFaceNormals(),null!==e.boundingBox&&(this.boundingBox=e.boundingBox.clone()),null!==e.boundingSphere&&(this.boundingSphere=e.boundingSphere.clone()),this},center:function(){return this.computeBoundingBox(),this.boundingBox.getCenter(KV).negate(),this.translate(KV.x,KV.y,KV.z),this},normalize:function(){this.computeBoundingSphere();const e=this.boundingSphere.center,t=this.boundingSphere.radius,n=0===t?1:1/t,i=new C.a;return i.set(n,0,0,-n*e.x,0,n,0,-n*e.y,0,0,n,-n*e.z,0,0,0,1),this.applyMatrix4(i),this},computeFaceNormals:function(){const e=new p.a,t=new p.a;for(let n=0,i=this.faces.length;n<i;n++){const i=this.faces[n],s=this.vertices[i.a],r=this.vertices[i.b],o=this.vertices[i.c];e.subVectors(o,r),t.subVectors(s,r),e.cross(t),e.normalize(),i.normal.copy(e)}},computeVertexNormals:function(e=!0){const t=new Array(this.vertices.length);for(let e=0,n=this.vertices.length;e<n;e++)t[e]=new p.a;if(e){const e=new p.a,n=new p.a;for(let i=0,s=this.faces.length;i<s;i++){const s=this.faces[i],r=this.vertices[s.a],o=this.vertices[s.b],a=this.vertices[s.c];e.subVectors(a,o),n.subVectors(r,o),e.cross(n),t[s.a].add(e),t[s.b].add(e),t[s.c].add(e)}}else{this.computeFaceNormals();for(let e=0,n=this.faces.length;e<n;e++){const n=this.faces[e];t[n.a].add(n.normal),t[n.b].add(n.normal),t[n.c].add(n.normal)}}for(let e=0,n=this.vertices.length;e<n;e++)t[e].normalize();for(let e=0,n=this.faces.length;e<n;e++){const n=this.faces[e],i=n.vertexNormals;3===i.length?(i[0].copy(t[n.a]),i[1].copy(t[n.b]),i[2].copy(t[n.c])):(i[0]=t[n.a].clone(),i[1]=t[n.b].clone(),i[2]=t[n.c].clone())}this.faces.length>0&&(this.normalsNeedUpdate=!0)},computeFlatVertexNormals:function(){this.computeFaceNormals();for(let e=0,t=this.faces.length;e<t;e++){const t=this.faces[e],n=t.vertexNormals;3===n.length?(n[0].copy(t.normal),n[1].copy(t.normal),n[2].copy(t.normal)):(n[0]=t.normal.clone(),n[1]=t.normal.clone(),n[2]=t.normal.clone())}this.faces.length>0&&(this.normalsNeedUpdate=!0)},computeMorphNormals:function(){for(let e=0,t=this.faces.length;e<t;e++){const t=this.faces[e];t.__originalFaceNormal?t.__originalFaceNormal.copy(t.normal):t.__originalFaceNormal=t.normal.clone(),t.__originalVertexNormals||(t.__originalVertexNormals=[]);for(let e=0,n=t.vertexNormals.length;e<n;e++)t.__originalVertexNormals[e]?t.__originalVertexNormals[e].copy(t.vertexNormals[e]):t.__originalVertexNormals[e]=t.vertexNormals[e].clone()}const e=new ZV;e.faces=this.faces;for(let t=0,n=this.morphTargets.length;t<n;t++){if(!this.morphNormals[t]){this.morphNormals[t]={},this.morphNormals[t].faceNormals=[],this.morphNormals[t].vertexNormals=[];const e=this.morphNormals[t].faceNormals,n=this.morphNormals[t].vertexNormals;for(let t=0,i=this.faces.length;t<i;t++){const t=new p.a,i={a:new p.a,b:new p.a,c:new p.a};e.push(t),n.push(i)}}const n=this.morphNormals[t];e.vertices=this.morphTargets[t].vertices,e.computeFaceNormals(),e.computeVertexNormals();for(let e=0,t=this.faces.length;e<t;e++){const t=this.faces[e],i=n.faceNormals[e],s=n.vertexNormals[e];i.copy(t.normal),s.a.copy(t.vertexNormals[0]),s.b.copy(t.vertexNormals[1]),s.c.copy(t.vertexNormals[2])}}for(let e=0,t=this.faces.length;e<t;e++){const t=this.faces[e];t.normal=t.__originalFaceNormal,t.vertexNormals=t.__originalVertexNormals}},computeBoundingBox:function(){null===this.boundingBox&&(this.boundingBox=new CN.a),this.boundingBox.setFromPoints(this.vertices)},computeBoundingSphere:function(){null===this.boundingSphere&&(this.boundingSphere=new uD.a),this.boundingSphere.setFromPoints(this.vertices)},merge:function(e,t,n=0){if(!e||!e.isGeometry)return void console.error(\\\\\\\"THREE.Geometry.merge(): geometry not an instance of THREE.Geometry.\\\\\\\",e);let i;const s=this.vertices.length,r=this.vertices,o=e.vertices,a=this.faces,c=e.faces,l=this.colors,u=e.colors;void 0!==t&&(i=(new V.a).getNormalMatrix(t));for(let e=0,n=o.length;e<n;e++){const n=o[e].clone();void 0!==t&&n.applyMatrix4(t),r.push(n)}for(let e=0,t=u.length;e<t;e++)l.push(u[e].clone());for(let e=0,t=c.length;e<t;e++){const t=c[e];let r,o;const l=t.vertexNormals,u=t.vertexColors,h=new tj(t.a+s,t.b+s,t.c+s);h.normal.copy(t.normal),void 0!==i&&h.normal.applyMatrix3(i).normalize();for(let e=0,t=l.length;e<t;e++)r=l[e].clone(),void 0!==i&&r.applyMatrix3(i).normalize(),h.vertexNormals.push(r);h.color.copy(t.color);for(let e=0,t=u.length;e<t;e++)o=u[e],h.vertexColors.push(o.clone());h.materialIndex=t.materialIndex+n,a.push(h)}for(let t=0,n=e.faceVertexUvs.length;t<n;t++){const n=e.faceVertexUvs[t];void 0===this.faceVertexUvs[t]&&(this.faceVertexUvs[t]=[]);for(let e=0,i=n.length;e<i;e++){const i=n[e],s=[];for(let e=0,t=i.length;e<t;e++)s.push(i[e].clone());this.faceVertexUvs[t].push(s)}}},mergeMesh:function(e){e&&e.isMesh?(e.matrixAutoUpdate&&e.updateMatrix(),this.merge(e.geometry,e.matrix)):console.error(\\\\\\\"THREE.Geometry.mergeMesh(): mesh not an instance of THREE.Mesh.\\\\\\\",e)},mergeVertices:function(e=4){const t={},n=[],i=[],s=Math.pow(10,e);for(let e=0,r=this.vertices.length;e<r;e++){const r=this.vertices[e],o=Math.round(r.x*s)+\\\\\\\"_\\\\\\\"+Math.round(r.y*s)+\\\\\\\"_\\\\\\\"+Math.round(r.z*s);void 0===t[o]?(t[o]=e,n.push(this.vertices[e]),i[e]=n.length-1):i[e]=i[t[o]]}const r=[];for(let e=0,t=this.faces.length;e<t;e++){const t=this.faces[e];t.a=i[t.a],t.b=i[t.b],t.c=i[t.c];const n=[t.a,t.b,t.c];for(let t=0;t<3;t++)if(n[t]===n[(t+1)%3]){r.push(e);break}}for(let e=r.length-1;e>=0;e--){const t=r[e];this.faces.splice(t,1);for(let e=0,n=this.faceVertexUvs.length;e<n;e++)this.faceVertexUvs[e].splice(t,1)}const o=this.vertices.length-n.length;return this.vertices=n,o},setFromPoints:function(e){this.vertices=[];for(let t=0,n=e.length;t<n;t++){const n=e[t];this.vertices.push(new p.a(n.x,n.y,n.z||0))}return this},sortFacesByMaterialIndex:function(){const e=this.faces,t=e.length;for(let n=0;n<t;n++)e[n]._id=n;e.sort((function(e,t){return e.materialIndex-t.materialIndex}));const n=this.faceVertexUvs[0],i=this.faceVertexUvs[1];let s,r;n&&n.length===t&&(s=[]),i&&i.length===t&&(r=[]);for(let o=0;o<t;o++){const t=e[o]._id;s&&s.push(n[t]),r&&r.push(i[t])}s&&(this.faceVertexUvs[0]=s),r&&(this.faceVertexUvs[1]=r)},toJSON:function(){const e={metadata:{version:4.5,type:\\\\\\\"Geometry\\\\\\\",generator:\\\\\\\"Geometry.toJSON\\\\\\\"}};if(e.uuid=this.uuid,e.type=this.type,\\\\\\\"\\\\\\\"!==this.name&&(e.name=this.name),void 0!==this.parameters){const t=this.parameters;for(const n in t)void 0!==t[n]&&(e[n]=t[n]);return e}const t=[];for(let e=0;e<this.vertices.length;e++){const n=this.vertices[e];t.push(n.x,n.y,n.z)}const n=[],i=[],s={},r=[],o={},a=[],c={};for(let e=0;e<this.faces.length;e++){const t=this.faces[e],i=!0,s=!1,r=void 0!==this.faceVertexUvs[0][e],o=t.normal.length()>0,a=t.vertexNormals.length>0,c=1!==t.color.r||1!==t.color.g||1!==t.color.b,p=t.vertexColors.length>0;let _=0;if(_=l(_,0,0),_=l(_,1,i),_=l(_,2,s),_=l(_,3,r),_=l(_,4,o),_=l(_,5,a),_=l(_,6,c),_=l(_,7,p),n.push(_),n.push(t.a,t.b,t.c),n.push(t.materialIndex),r){const t=this.faceVertexUvs[0][e];n.push(d(t[0]),d(t[1]),d(t[2]))}if(o&&n.push(u(t.normal)),a){const e=t.vertexNormals;n.push(u(e[0]),u(e[1]),u(e[2]))}if(c&&n.push(h(t.color)),p){const e=t.vertexColors;n.push(h(e[0]),h(e[1]),h(e[2]))}}function l(e,t,n){return n?e|1<<t:e&~(1<<t)}function u(e){const t=e.x.toString()+e.y.toString()+e.z.toString();return void 0!==s[t]||(s[t]=i.length/3,i.push(e.x,e.y,e.z)),s[t]}function h(e){const t=e.r.toString()+e.g.toString()+e.b.toString();return void 0!==o[t]||(o[t]=r.length,r.push(e.getHex())),o[t]}function d(e){const t=e.x.toString()+e.y.toString();return void 0!==c[t]||(c[t]=a.length/2,a.push(e.x,e.y)),c[t]}return e.data={},e.data.vertices=t,e.data.normals=i,r.length>0&&(e.data.colors=r),a.length>0&&(e.data.uvs=[a]),e.data.faces=n,e},clone:function(){return(new ZV).copy(this)},copy:function(e){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=e.name;const t=e.vertices;for(let e=0,n=t.length;e<n;e++)this.vertices.push(t[e].clone());const n=e.colors;for(let e=0,t=n.length;e<t;e++)this.colors.push(n[e].clone());const i=e.faces;for(let e=0,t=i.length;e<t;e++)this.faces.push(i[e].clone());for(let t=0,n=e.faceVertexUvs.length;t<n;t++){const n=e.faceVertexUvs[t];void 0===this.faceVertexUvs[t]&&(this.faceVertexUvs[t]=[]);for(let e=0,i=n.length;e<i;e++){const i=n[e],s=[];for(let e=0,t=i.length;e<t;e++){const t=i[e];s.push(t.clone())}this.faceVertexUvs[t].push(s)}}const s=e.morphTargets;for(let e=0,t=s.length;e<t;e++){const t={};if(t.name=s[e].name,void 0!==s[e].vertices){t.vertices=[];for(let n=0,i=s[e].vertices.length;n<i;n++)t.vertices.push(s[e].vertices[n].clone())}if(void 0!==s[e].normals){t.normals=[];for(let n=0,i=s[e].normals.length;n<i;n++)t.normals.push(s[e].normals[n].clone())}this.morphTargets.push(t)}const r=e.morphNormals;for(let e=0,t=r.length;e<t;e++){const t={};if(void 0!==r[e].vertexNormals){t.vertexNormals=[];for(let n=0,i=r[e].vertexNormals.length;n<i;n++){const i=r[e].vertexNormals[n],s={};s.a=i.a.clone(),s.b=i.b.clone(),s.c=i.c.clone(),t.vertexNormals.push(s)}}if(void 0!==r[e].faceNormals){t.faceNormals=[];for(let n=0,i=r[e].faceNormals.length;n<i;n++)t.faceNormals.push(r[e].faceNormals[n].clone())}this.morphNormals.push(t)}const o=e.skinWeights;for(let e=0,t=o.length;e<t;e++)this.skinWeights.push(o[e].clone());const a=e.skinIndices;for(let e=0,t=a.length;e<t;e++)this.skinIndices.push(a[e].clone());const c=e.lineDistances;for(let e=0,t=c.length;e<t;e++)this.lineDistances.push(c[e]);const l=e.boundingBox;null!==l&&(this.boundingBox=l.clone());const u=e.boundingSphere;return null!==u&&(this.boundingSphere=u.clone()),this.elementsNeedUpdate=e.elementsNeedUpdate,this.verticesNeedUpdate=e.verticesNeedUpdate,this.uvsNeedUpdate=e.uvsNeedUpdate,this.normalsNeedUpdate=e.normalsNeedUpdate,this.colorsNeedUpdate=e.colorsNeedUpdate,this.lineDistancesNeedUpdate=e.lineDistancesNeedUpdate,this.groupsNeedUpdate=e.groupsNeedUpdate,this},toBufferGeometry:function(){const e=(new ej).fromGeometry(this),t=new O.a,n=new Float32Array(3*e.vertices.length);if(t.setAttribute(\\\\\\\"position\\\\\\\",new L.a(n,3).copyVector3sArray(e.vertices)),e.normals.length>0){const n=new Float32Array(3*e.normals.length);t.setAttribute(\\\\\\\"normal\\\\\\\",new L.a(n,3).copyVector3sArray(e.normals))}if(e.colors.length>0){const n=new Float32Array(3*e.colors.length);t.setAttribute(\\\\\\\"color\\\\\\\",new L.a(n,3).copyColorsArray(e.colors))}if(e.uvs.length>0){const n=new Float32Array(2*e.uvs.length);t.setAttribute(\\\\\\\"uv\\\\\\\",new L.a(n,2).copyVector2sArray(e.uvs))}if(e.uvs2.length>0){const n=new Float32Array(2*e.uvs2.length);t.setAttribute(\\\\\\\"uv2\\\\\\\",new L.a(n,2).copyVector2sArray(e.uvs2))}t.groups=e.groups;for(const n in e.morphTargets){const i=[],s=e.morphTargets[n];for(let e=0,t=s.length;e<t;e++){const t=s[e],n=new L.c(3*t.data.length,3);n.name=t.name,i.push(n.copyVector3sArray(t.data))}t.morphAttributes[n]=i}if(e.skinIndices.length>0){const n=new L.c(4*e.skinIndices.length,4);t.setAttribute(\\\\\\\"skinIndex\\\\\\\",n.copyVector4sArray(e.skinIndices))}if(e.skinWeights.length>0){const n=new L.c(4*e.skinWeights.length,4);t.setAttribute(\\\\\\\"skinWeight\\\\\\\",n.copyVector4sArray(e.skinWeights))}return null!==e.boundingSphere&&(t.boundingSphere=e.boundingSphere.clone()),null!==e.boundingBox&&(t.boundingBox=e.boundingBox.clone()),t},computeTangents:function(){console.error(\\\\\\\"THREE.Geometry: .computeTangents() has been removed.\\\\\\\")},computeLineDistances:function(){console.error(\\\\\\\"THREE.Geometry: .computeLineDistances() has been removed. Use THREE.Line.computeLineDistances() instead.\\\\\\\")},applyMatrix:function(e){return console.warn(\\\\\\\"THREE.Geometry: .applyMatrix() has been renamed to .applyMatrix4().\\\\\\\"),this.applyMatrix4(e)},dispose:function(){this.dispatchEvent({type:\\\\\\\"dispose\\\\\\\"})}}),ZV.createBufferGeometryFromObject=function(e){let t=new O.a;const n=e.geometry;if(e.isPoints||e.isLine){const e=new L.c(3*n.vertices.length,3),i=new L.c(3*n.colors.length,3);if(t.setAttribute(\\\\\\\"position\\\\\\\",e.copyVector3sArray(n.vertices)),t.setAttribute(\\\\\\\"color\\\\\\\",i.copyColorsArray(n.colors)),n.lineDistances&&n.lineDistances.length===n.vertices.length){const e=new L.c(n.lineDistances.length,1);t.setAttribute(\\\\\\\"lineDistance\\\\\\\",e.copyArray(n.lineDistances))}null!==n.boundingSphere&&(t.boundingSphere=n.boundingSphere.clone()),null!==n.boundingBox&&(t.boundingBox=n.boundingBox.clone())}else e.isMesh&&(t=n.toBufferGeometry());return t};class ej{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(e){const t=[];let n,i,s;const r=e.faces;for(i=0;i<r.length;i++){const e=r[i];e.materialIndex!==s&&(s=e.materialIndex,void 0!==n&&(n.count=3*i-n.start,t.push(n)),n={start:3*i,materialIndex:s})}void 0!==n&&(n.count=3*i-n.start,t.push(n)),this.groups=t}fromGeometry(e){const t=e.faces,n=e.vertices,i=e.faceVertexUvs,s=i[0]&&i[0].length>0,r=i[1]&&i[1].length>0,o=e.morphTargets,a=o.length;let c;if(a>0){c=[];for(let e=0;e<a;e++)c[e]={name:o[e].name,data:[]};this.morphTargets.position=c}const l=e.morphNormals,u=l.length;let h;if(u>0){h=[];for(let e=0;e<u;e++)h[e]={name:l[e].name,data:[]};this.morphTargets.normal=h}const p=e.skinIndices,_=e.skinWeights,m=p.length===n.length,f=_.length===n.length;n.length>0&&0===t.length&&console.error(\\\\\\\"THREE.DirectGeometry: Faceless geometries are not supported.\\\\\\\");for(let e=0;e<t.length;e++){const g=t[e];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 e=g.normal;this.normals.push(e,e,e)}const y=g.vertexColors;if(3===y.length)this.colors.push(y[0],y[1],y[2]);else{const e=g.color;this.colors.push(e,e,e)}if(!0===s){const t=i[0][e];void 0!==t?this.uvs.push(t[0],t[1],t[2]):(console.warn(\\\\\\\"THREE.DirectGeometry.fromGeometry(): Undefined vertexUv \\\\\\\",e),this.uvs.push(new d.a,new d.a,new d.a))}if(!0===r){const t=i[1][e];void 0!==t?this.uvs2.push(t[0],t[1],t[2]):(console.warn(\\\\\\\"THREE.DirectGeometry.fromGeometry(): Undefined vertexUv2 \\\\\\\",e),this.uvs2.push(new d.a,new d.a,new d.a))}for(let e=0;e<a;e++){const t=o[e].vertices;c[e].data.push(t[g.a],t[g.b],t[g.c])}for(let t=0;t<u;t++){const n=l[t].vertexNormals[e];h[t].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(e),this.verticesNeedUpdate=e.verticesNeedUpdate,this.normalsNeedUpdate=e.normalsNeedUpdate,this.colorsNeedUpdate=e.colorsNeedUpdate,this.uvsNeedUpdate=e.uvsNeedUpdate,this.groupsNeedUpdate=e.groupsNeedUpdate,null!==e.boundingSphere&&(this.boundingSphere=e.boundingSphere.clone()),null!==e.boundingBox&&(this.boundingBox=e.boundingBox.clone()),this}}class tj{constructor(e,t,n,i,s,r=0){this.a=e,this.b=t,this.c=n,this.normal=i&&i.isVector3?i:new p.a,this.vertexNormals=Array.isArray(i)?i:[],this.color=s&&s.isColor?s:new M.a,this.vertexColors=Array.isArray(s)?s:[],this.materialIndex=r}clone(){return(new this.constructor).copy(this)}copy(e){this.a=e.a,this.b=e.b,this.c=e.c,this.normal.copy(e.normal),this.color.copy(e.color),this.materialIndex=e.materialIndex;for(let t=0,n=e.vertexNormals.length;t<n;t++)this.vertexNormals[t]=e.vertexNormals[t].clone();for(let t=0,n=e.vertexColors.length;t<n;t++)this.vertexColors[t]=e.vertexColors[t].clone();return this}}var nj=function(e){this.subdivisions=void 0===e?1:e};nj.prototype.modify=function(e){var t=e.isBufferGeometry;(e=t?(new ZV).fromBufferGeometry(e):e.clone()).mergeVertices(6);for(var n=this.subdivisions;n-- >0;)this.smooth(e);return e.computeFaceNormals(),e.computeVertexNormals(),t?e.toBufferGeometry():e},function(){var e=[\\\\\\\"a\\\\\\\",\\\\\\\"b\\\\\\\",\\\\\\\"c\\\\\\\"];function t(e,t,n){return n[Math.min(e,t)+\\\\\\\"_\\\\\\\"+Math.max(e,t)]}function n(e,t,n,i,s,r){var o,a=Math.min(e,t),c=Math.max(e,t),l=a+\\\\\\\"_\\\\\\\"+c;l in i?o=i[l]:(o={a:n[a],b:n[c],newEdge:null,faces:[]},i[l]=o);o.faces.push(s),r[e].edges.push(o),r[t].edges.push(o)}function i(e,t,n,i,s){e.push(new tj(t,n,i,void 0,void 0,s))}function s(e,t){return Math.abs(t-e)/2+Math.min(e,t)}function r(e,t,n,i){e.push([t.clone(),n.clone(),i.clone()])}nj.prototype.smooth=function(o){var a,c,l,u,h,_,m,f,g,v,y,x,b,w=new p.a,A=[];a=o.vertices,c=o.faces;var T,E,C,M,N,S,O,L,P,R,I,F,D,k,B=void 0!==(l=o.faceVertexUvs)[0]&&l[0].length>0;if(B)for(var z=0;z<l.length;z++)A.push([]);for(m in function(e,t,i,s){var r,o,a;for(r=0,o=e.length;r<o;r++)i[r]={edges:[]};for(r=0,o=t.length;r<o;r++)n((a=t[r]).a,a.b,e,s,a,i),n(a.b,a.c,e,s,a,i),n(a.c,a.a,e,s,a,i)}(a,c,v=new Array(a.length),y={}),x=[],y){for(E=y[m],C=new p.a,N=3/8,S=1/8,2!=(O=E.faces.length)&&(N=.5,S=0),C.addVectors(E.a,E.b).multiplyScalar(N),w.set(0,0,0),z=0;z<O;z++){for(M=E.faces[z],g=0;g<3&&((T=a[M[e[g]]])===E.a||T===E.b);g++);w.add(T)}w.multiplyScalar(S),C.add(w),E.newEdge=x.length,x.push(C)}for(b=[],m=0,f=a.length;m<f;m++){for(D=a[m],3==(_=(F=v[m].edges).length)?L=3/16:_>3&&(L=3/(8*_)),P=1-_*L,R=L,_<=2&&2==_&&(P=3/4,R=1/8),k=D.clone().multiplyScalar(P),w.set(0,0,0),z=0;z<_;z++)T=(I=F[z]).a!==D?I.a:I.b,w.add(T);w.multiplyScalar(R),k.add(w),b.push(k)}u=b.concat(x);var U,G,V,j,H,q,W,X=b.length;h=[];var Y=new d.a,$=new d.a,Q=new d.a;for(m=0,f=c.length;m<f;m++)if(i(h,U=t((M=c[m]).a,M.b,y).newEdge+X,G=t(M.b,M.c,y).newEdge+X,V=t(M.c,M.a,y).newEdge+X,M.materialIndex),i(h,M.a,U,V,M.materialIndex),i(h,M.b,G,U,M.materialIndex),i(h,M.c,V,G,M.materialIndex),B)for(z=0;z<l.length;z++)H=(j=l[z][m])[0],q=j[1],W=j[2],Y.set(s(H.x,q.x),s(H.y,q.y)),$.set(s(q.x,W.x),s(q.y,W.y)),Q.set(s(H.x,W.x),s(H.y,W.y)),r(A[z],Y,$,Q),r(A[z],H,Y,Q),r(A[z],q,$,Y),r(A[z],W,Q,$);o.vertices=u,o.faces=h,B&&(o.faceVertexUvs=A)}}();class ij extends WO{static type(){return\\\\\\\"subdivide\\\\\\\"}cook(e,t){const n=e[0],i=new nj(t.subdivisions);for(let e of n.objects()){const t=e.geometry;if(t){const n=i.modify(t);e.geometry=n}}return n}}ij.DEFAULT_PARAMS={subdivisions:1};const sj=ij.DEFAULT_PARAMS;const rj=new class extends Lo{constructor(){super(...arguments),this.subdivisions=Oo.INTEGER(sj.subdivisions,{range:[0,5],rangeLocked:[!0,!1]})}};class oj extends QO{constructor(){super(...arguments),this.paramsConfig=rj}static type(){return\\\\\\\"subdivide\\\\\\\"}initializeNode(){this.io.inputs.setCount(1)}cook(e){this._operation=this._operation||new ij(this.scene(),this.states);const t=this._operation.cook(e,this.pv);this.setCoreGroup(t)}}const aj=new class extends Lo{};class cj extends ZO{constructor(){super(...arguments),this.paramsConfig=aj}static type(){return\\\\\\\"subnet\\\\\\\"}initializeNode(){this.io.inputs.setCount(0,4),this.io.inputs.initInputsClonedState(Ti.NEVER)}}const lj=new class extends Lo{constructor(){super(...arguments),this.input=Oo.INTEGER(0,{range:[0,3],rangeLocked:[!0,!0],callback:e=>{uj.PARAM_CALLBACK_reset(e)}})}};class uj extends QO{constructor(){super(...arguments),this.paramsConfig=lj}static type(){return Mi.INPUT}initializeNode(){this.io.inputs.setCount(0),this.lifecycle.add_on_add_hook((()=>{this.set_parent_input_dependency()}))}async cook(){const e=this.pv.input,t=this.parent();if(t){if(t.io.inputs.has_input(e)){const n=await t.containerController.requestInputContainer(e);if(n){const e=n.coreContent();if(e)return void this.setCoreGroup(e)}}else this.states.error.set(`parent has no input ${e}`);this.cookController.endCook()}else this.states.error.set(\\\\\\\"subnet input has no parent\\\\\\\")}static PARAM_CALLBACK_reset(e){e.set_parent_input_dependency()}set_parent_input_dependency(){this._current_parent_input_graph_node&&this.removeGraphInput(this._current_parent_input_graph_node);const e=this.parent();e&&(this._current_parent_input_graph_node=e.io.inputs.input_graph_node(this.pv.input),this.addGraphInput(this._current_parent_input_graph_node))}}var hj=n(83);class dj extends tv{constructor(e,t,n){super(e,t,n)}load(e){return new Promise((async(t,n)=>{const i=new hj.a(this.loadingManager),s=await this._urlToLoad();i.load(s,(i=>{try{const n=this._onLoaded(i,e);t(n)}catch(e){n([])}}))}))}parse(e,t){const n=new hj.a(this.loadingManager).parse(e);return this._onLoaded(n,t)}_onLoaded(e,t){const n=e.paths,i=new on.a;for(let e=0;e<n.length;e++){const s=n[e],r=s.userData,o=r.style.fill;t.drawFillShapes&&void 0!==o&&\\\\\\\"none\\\\\\\"!==o&&this._drawShapes(i,s,t);const a=r.style.stroke;t.drawStrokes&&void 0!==a&&\\\\\\\"none\\\\\\\"!==a&&this._drawStrokes(i,s,t)}return i}_drawShapes(e,t,n){const i=t.userData,s=new Nf.a({color:(new M.a).setStyle(i.style.fill),opacity:i.style.fillOpacity,transparent:i.style.fillOpacity<1,side:w.z,depthWrite:!1,wireframe:n.fillShapesWireframe}),r=t.toShapes(!0);for(let t=0;t<r.length;t++){const n=r[t],i=new Pk(n),o=new z.a(i,s);e.add(o)}}_drawStrokes(e,t,n){const i=t.userData;if(n.strokesWireframe){const n=new Yi.a({color:(new M.a).setStyle(i.style.stroke),opacity:i.style.strokeOpacity,transparent:i.style.strokeOpacity<1,side:w.z,depthWrite:!1});for(let s=0,r=t.subPaths.length;s<r;s++){const r=t.subPaths[s],o=hj.a.pointsToStroke(r.getPoints(),i.style);if(o){const t=new $i.a(o,n);e.add(t)}}}else{const n=new Nf.a({color:(new M.a).setStyle(i.style.stroke),opacity:i.style.strokeOpacity,transparent:i.style.strokeOpacity<1,side:w.z,depthWrite:!1});for(let s=0,r=t.subPaths.length;s<r;s++){const r=t.subPaths[s],o=hj.a.pointsToStroke(r.getPoints(),i.style);if(o){const t=new z.a(o,n);e.add(t)}}}}}const pj=`${Kg}/models/svg/tiger.svg`;class _j extends WO{static type(){return\\\\\\\"svg\\\\\\\"}cook(e,t){const n=new dj(t.url,this.scene(),this._node);return new Promise((async e=>{const i=await n.load(t);for(let e of i.children)this._ensure_geometry_has_index(e);e(this.createCoreGroupFromObjects(i.children))}))}_ensure_geometry_has_index(e){const t=e.geometry;t&&this.createIndexIfNone(t)}}_j.DEFAULT_PARAMS={url:pj,drawFillShapes:!0,fillShapesWireframe:!1,drawStrokes:!0,strokesWireframe:!1};const mj=_j.DEFAULT_PARAMS;const fj=new class extends Lo{constructor(){super(...arguments),this.url=Oo.STRING(mj.url,{fileBrowse:{type:[tr.SVG]}}),this.reload=Oo.BUTTON(null,{callback:(e,t)=>{gj.PARAM_CALLBACK_reload(e)}}),this.drawFillShapes=Oo.BOOLEAN(mj.drawFillShapes),this.fillShapesWireframe=Oo.BOOLEAN(mj.fillShapesWireframe),this.drawStrokes=Oo.BOOLEAN(mj.drawStrokes),this.strokesWireframe=Oo.BOOLEAN(mj.strokesWireframe)}};class gj extends QO{constructor(){super(...arguments),this.paramsConfig=fj}static type(){return\\\\\\\"svg\\\\\\\"}async requiredModules(){return[_n.SVGLoader]}initializeNode(){this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.url],(()=>{const e=this.pv.url;if(e){const t=e.split(\\\\\\\"/\\\\\\\");return t[t.length-1]}return\\\\\\\"\\\\\\\"}))}))}))}async cook(e){this._operation=this._operation||new _j(this.scene(),this.states,this);const t=await this._operation.cook(e,this.pv);this.setCoreGroup(t)}static PARAM_CALLBACK_reload(e){e.param_callback_reload()}param_callback_reload(){this.p.url.setDirty()}}const vj=\\\\\\\"geometry to switch to\\\\\\\";const yj=new class extends Lo{constructor(){super(...arguments),this.input=Oo.INTEGER(0,{range:[0,3],rangeLocked:[!0,!0]})}};class xj extends QO{constructor(){super(...arguments),this.paramsConfig=yj}static type(){return\\\\\\\"switch\\\\\\\"}static displayedInputNames(){return[vj,vj,vj,vj]}initializeNode(){this.io.inputs.setCount(0,4),this.io.inputs.initInputsClonedState(Ti.NEVER),this.cookController.disallowInputsEvaluation()}async cook(){const e=this.pv.input;if(this.io.inputs.has_input(e)){const t=await this.containerController.requestInputContainer(e);if(t){const e=t.coreContent();if(e)return void this.setCoreGroup(e)}}else this.states.error.set(`no input ${e}`);this.cookController.endCook()}}class bj extends Zz{constructor(e,t,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],e,t,n),this.type=\\\\\\\"TetrahedronBufferGeometry\\\\\\\",this.parameters={radius:e,detail:t}}}const wj=new class extends Lo{constructor(){super(...arguments),this.radius=Oo.FLOAT(1),this.detail=Oo.INTEGER(0,{range:[0,10],rangeLocked:[!0,!1]}),this.pointsOnly=Oo.BOOLEAN(0),this.center=Oo.VECTOR3([0,0,0])}};class Aj extends QO{constructor(){super(...arguments),this.paramsConfig=wj}static type(){return\\\\\\\"tetrahedron\\\\\\\"}cook(){const e=this.pv.pointsOnly,t=new bj(this.pv.radius,this.pv.detail,e);if(t.translate(this.pv.center.x,this.pv.center.y,this.pv.center.z),e){const e=this.createObject(t,Zi.POINTS);this.setObject(e)}else t.computeVertexNormals(),this.setGeometry(t)}}var Tj=n(48);class Ej{constructor(e){this.type=\\\\\\\"Font\\\\\\\",this.data=e}generateShapes(e,t=100){const n=[],i=function(e,t,n){const i=Array.from(e),s=t/n.resolution,r=(n.boundingBox.yMax-n.boundingBox.yMin+n.underlineThickness)*s,o=[];let a=0,c=0;for(let e=0;e<i.length;e++){const t=i[e];if(\\\\\\\"\\\\n\\\\\\\"===t)a=0,c-=r;else{const e=Cj(t,s,a,c,n);a+=e.offsetX,o.push(e.path)}}return o}(e,t,this.data);for(let e=0,t=i.length;e<t;e++)Array.prototype.push.apply(n,i[e].toShapes());return n}}function Cj(e,t,n,i,s){const r=s.glyphs[e]||s.glyphs[\\\\\\\"?\\\\\\\"];if(!r)return void console.error('THREE.Font: character \\\\\\\"'+e+'\\\\\\\" does not exists in font family '+s.familyName+\\\\\\\".\\\\\\\");const o=new Tj.a;let a,c,l,u,h,d,p,_;if(r.o){const e=r._cachedOutline||(r._cachedOutline=r.o.split(\\\\\\\" \\\\\\\"));for(let s=0,r=e.length;s<r;){switch(e[s++]){case\\\\\\\"m\\\\\\\":a=e[s++]*t+n,c=e[s++]*t+i,o.moveTo(a,c);break;case\\\\\\\"l\\\\\\\":a=e[s++]*t+n,c=e[s++]*t+i,o.lineTo(a,c);break;case\\\\\\\"q\\\\\\\":l=e[s++]*t+n,u=e[s++]*t+i,h=e[s++]*t+n,d=e[s++]*t+i,o.quadraticCurveTo(h,d,l,u);break;case\\\\\\\"b\\\\\\\":l=e[s++]*t+n,u=e[s++]*t+i,h=e[s++]*t+n,d=e[s++]*t+i,p=e[s++]*t+n,_=e[s++]*t+i,o.bezierCurveTo(h,d,p,_,l,u)}}}return{offsetX:r.ha*t,path:o}}Ej.prototype.isFont=!0;class Mj extends xf.a{constructor(e){super(e)}load(e,t,n,i){const s=this,r=new yf.a(this.manager);r.setPath(this.path),r.setRequestHeader(this.requestHeader),r.setWithCredentials(s.withCredentials),r.load(e,(function(e){let n;try{n=JSON.parse(e)}catch(t){console.warn(\\\\\\\"THREE.FontLoader: typeface.js support is being deprecated. Use typeface.json instead.\\\\\\\"),n=JSON.parse(e.substring(65,e.length-2))}const i=s.parse(n);t&&t(i)}),n,i)}parse(e){return new Ej(e)}}class Nj extends tv{constructor(e,t,n){super(e,t,n),this._font_loader=new Mj(this.loadingManager)}async load(){const e=this.extension(),t=await this._urlToLoad();switch(e){case\\\\\\\"ttf\\\\\\\":return this._loadTTF(t);case\\\\\\\"json\\\\\\\":return this._loadJSON(t);default:return null}}static requiredModules(e){switch(this.extension(e)){case\\\\\\\"ttf\\\\\\\":return[_n.TTFLoader];case\\\\\\\"json\\\\\\\":return[_n.SVGLoader]}}_loadTTF(e){return new Promise((async(t,n)=>{const i=await this._loadTTFLoader();i&&i.load(e,(e=>{const n=this._font_loader.parse(e);t(n)}),void 0,(()=>{n()}))}))}_loadJSON(e){return new Promise(((t,n)=>{this._font_loader.load(e,(e=>{t(e)}),void 0,(()=>{n()}))}))}async _loadTTFLoader(){const e=await Rn.modulesRegister.module(_n.TTFLoader);if(e)return new e(this.loadingManager)}static async loadSVGLoader(){const e=await Rn.modulesRegister.module(_n.SVGLoader);if(e)return e}}var Sj;!function(e){e.MESH=\\\\\\\"mesh\\\\\\\",e.FLAT=\\\\\\\"flat\\\\\\\",e.LINE=\\\\\\\"line\\\\\\\",e.STROKE=\\\\\\\"stroke\\\\\\\"}(Sj||(Sj={}));const Oj=[Sj.MESH,Sj.FLAT,Sj.LINE,Sj.STROKE],Lj=\\\\\\\"failed to generate geometry. Try to remove some characters\\\\\\\";const Pj=new class extends Lo{constructor(){super(...arguments),this.font=Oo.STRING(\\\\\\\"https://raw.githubusercontent.com/polygonjs/polygonjs-assets/master/fonts/droid_sans_regular.typeface.json\\\\\\\",{fileBrowse:{type:[tr.FONT]}}),this.text=Oo.STRING(\\\\\\\"polygonjs\\\\\\\",{multiline:!0}),this.type=Oo.INTEGER(0,{menu:{entries:Oj.map(((e,t)=>({name:e,value:t})))}}),this.size=Oo.FLOAT(1,{range:[0,1],rangeLocked:[!0,!1]}),this.extrude=Oo.FLOAT(.1,{visibleIf:{type:Oj.indexOf(Sj.MESH)}}),this.segments=Oo.INTEGER(1,{range:[1,20],rangeLocked:[!0,!1],visibleIf:{type:Oj.indexOf(Sj.MESH)}}),this.strokeWidth=Oo.FLOAT(.02,{visibleIf:{type:Oj.indexOf(Sj.STROKE)}})}};class Rj extends QO{constructor(){super(...arguments),this.paramsConfig=Pj,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(e){return void this.states.error.set(`count not load font (${this.pv.font})`)}const e=this._loaded_fonts[this.pv.font];if(e)switch(Oj[this.pv.type]){case Sj.MESH:return this._create_geometry_from_type_mesh(e);case Sj.FLAT:return this._create_geometry_from_type_flat(e);case Sj.LINE:return this._create_geometry_from_type_line(e);case Sj.STROKE:return this._create_geometry_from_type_stroke(e);default:console.warn(\\\\\\\"type is not valid\\\\\\\")}}_create_geometry_from_type_mesh(e){const t=this.displayed_text(),n={font:e,size:this.pv.size,height:this.pv.extrude,curveSegments:this.pv.segments};try{const e=new Ik(t,n);if(!e.index){const t=e.getAttribute(\\\\\\\"position\\\\\\\").array;e.setIndex(f.range(t.length/3))}this.setGeometry(e)}catch(e){this.states.error.set(Lj)}}_create_geometry_from_type_flat(e){const t=this._get_shapes(e);if(t){var n=new Pk(t);this.setGeometry(n)}}_create_geometry_from_type_line(e){const t=this.shapes_from_font(e);if(t){const e=[],n=[];let i=0;for(let s=0;s<t.length;s++){const r=t[s].getPoints();for(let t=0;t<r.length;t++){const s=r[t];e.push(s.x),e.push(s.y),e.push(0),n.push(i),t>0&&t<r.length-1&&n.push(i),i+=1}}const s=new O.a;s.setAttribute(\\\\\\\"position\\\\\\\",new L.c(e,3)),s.setIndex(n),this.setGeometry(s,Zi.LINE_SEGMENTS)}}async _create_geometry_from_type_stroke(e){const t=this.shapes_from_font(e);if(t){const e=await Nj.loadSVGLoader();if(!e)return;var n=e.getStrokeStyle(this.pv.strokeWidth,\\\\\\\"white\\\\\\\",\\\\\\\"miter\\\\\\\",\\\\\\\"butt\\\\\\\",4);const i=[];for(let s=0;s<t.length;s++){const r=t[s].getPoints(),o=12,a=.001,c=e.pointsToStroke(r,n,o,a);i.push(c)}const s=Fs.mergeBufferGeometries(i);this.setGeometry(s)}}shapes_from_font(e){const t=this._get_shapes(e);if(t){const e=[];for(let n=0;n<t.length;n++){const i=t[n];if(i.holes&&i.holes.length>0)for(let t=0;t<i.holes.length;t++){const n=i.holes[t];e.push(n)}}return t.push.apply(t,e),t}}_get_shapes(e){const t=this.displayed_text();try{return e.generateShapes(t,this.pv.size)}catch(e){this.states.error.set(Lj)}}displayed_text(){return this.pv.text||\\\\\\\"\\\\\\\"}_loadFont(){return new Nj(this.pv.font,this.scene(),this).load()}async requiredModules(){return this.p.font.isDirty()&&await this.p.font.compute(),Nj.requiredModules(this.pv.font)}}class Ij extends WO{static type(){return\\\\\\\"TextureCopy\\\\\\\"}async cook(e,t){const n=e[0],i=e[1];let s;for(let e of i.objects())e.traverse((e=>{const n=e.material;n&&(m.isArray(n)||s||(s=n[t.textureName]))}));if(s)for(let e of n.objects())e.traverse((e=>{const n=e.material;if(n&&!m.isArray(n)){n[t.textureName]=s;const e=n.uniforms;if(e){const n=e[t.textureName];n&&(n.value=s)}n.needsUpdate=!0}}));return n}}Ij.DEFAULT_PARAMS={textureName:\\\\\\\"map\\\\\\\"},Ij.INPUT_CLONED_STATE=[Ti.FROM_NODE,Ti.NEVER];const Fj=Ij.DEFAULT_PARAMS;const Dj=new class extends Lo{constructor(){super(...arguments),this.textureName=Oo.STRING(Fj.textureName)}};class kj extends QO{constructor(){super(...arguments),this.paramsConfig=Dj}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(Ij.INPUT_CLONED_STATE)}async cook(e){this._operation=this._operation||new Ij(this.scene(),this.states);const t=await this._operation.cook(e,this.pv);this.setCoreGroup(t)}}class Bj extends WO{static type(){return\\\\\\\"textureProperties\\\\\\\"}async cook(e,t){const n=e[0],i=[];for(let e of n.objects())t.applyToChildren?e.traverse((e=>{i.push(e)})):i.push(e);const s=i.map((e=>this._update_object(e,t)));return await Promise.all(s),n}async _update_object(e,t){const n=e.material;n&&await this._update_material(n,t)}async _update_material(e,t){let n=e.map;n&&await this._update_texture(n,t)}async _update_texture(e,t){this._updateEncoding(e,t),this._updateMapping(e,t),this._updateWrap(e,t),await this._updateAnisotropy(e,t),this._updateFilter(e,t)}_updateEncoding(e,t){t.tencoding&&(e.encoding=t.encoding,e.needsUpdate=!0)}_updateMapping(e,t){t.tmapping&&(e.mapping=t.mapping)}_updateWrap(e,t){t.twrap&&(e.wrapS=t.wrapS,e.wrapT=t.wrapT)}async _updateAnisotropy(e,t){if(t.tanisotropy)if(t.useRendererMaxAnisotropy){const t=await Rn.renderersController.firstRenderer();t&&(e.anisotropy=t.capabilities.getMaxAnisotropy())}else e.anisotropy=t.anisotropy}_updateFilter(e,t){t.tminFilter&&(e.minFilter=t.minFilter),t.tmagFilter&&(e.magFilter=t.magFilter)}}Bj.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:Om,tmagFilter:!1,magFilter:Sm},Bj.INPUT_CLONED_STATE=Ti.FROM_NODE;const zj=Bj.DEFAULT_PARAMS;const Uj=new class extends Lo{constructor(){super(...arguments),this.applyToChildren=Oo.BOOLEAN(zj.applyToChildren,{separatorAfter:!0}),this.tencoding=Oo.BOOLEAN(zj.tencoding),this.encoding=Oo.INTEGER(zj.encoding,{visibleIf:{tencoding:1},menu:{entries:Uf.map((e=>({name:Object.keys(e)[0],value:Object.values(e)[0]})))}}),this.tmapping=Oo.BOOLEAN(zj.tmapping),this.mapping=Oo.INTEGER(zj.mapping,{visibleIf:{tmapping:1},menu:{entries:Vf.map((e=>({name:Object.keys(e)[0],value:Object.values(e)[0]})))}}),this.twrap=Oo.BOOLEAN(zj.twrap),this.wrapS=Oo.INTEGER(zj.wrapS,{visibleIf:{twrap:1},menu:{entries:Gf.map((e=>({name:Object.keys(e)[0],value:Object.values(e)[0]})))}}),this.wrapT=Oo.INTEGER(zj.wrapT,{visibleIf:{twrap:1},menu:{entries:Gf.map((e=>({name:Object.keys(e)[0],value:Object.values(e)[0]})))},separatorAfter:!0}),this.tanisotropy=Oo.BOOLEAN(zj.tanisotropy),this.useRendererMaxAnisotropy=Oo.BOOLEAN(zj.useRendererMaxAnisotropy,{visibleIf:{tanisotropy:1}}),this.anisotropy=Oo.INTEGER(zj.anisotropy,{visibleIf:{tanisotropy:1,useRendererMaxAnisotropy:0},range:[0,32],rangeLocked:[!0,!1]}),this.tminFilter=Oo.BOOLEAN(0),this.minFilter=Oo.INTEGER(zj.minFilter,{visibleIf:{tminFilter:1},menu:{entries:Pm}}),this.tmagFilter=Oo.BOOLEAN(0),this.magFilter=Oo.INTEGER(zj.magFilter,{visibleIf:{tmagFilter:1},menu:{entries:Lm}})}};class Gj extends QO{constructor(){super(...arguments),this.paramsConfig=Uj}static type(){return\\\\\\\"textureProperties\\\\\\\"}static displayedInputNames(){return[\\\\\\\"objects with textures to change properties of\\\\\\\"]}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState(Bj.INPUT_CLONED_STATE)}async cook(e){this._operation=this._operation||new Bj(this.scene(),this.states);const t=await this._operation.cook(e,this.pv);this.setCoreGroup(t)}}const Vj=new p.a(0,0,1);class jj extends WO{constructor(){super(...arguments),this._core_transform=new rS}static type(){return\\\\\\\"torus\\\\\\\"}cook(e,t){const n=t.radius,i=t.radiusTube,s=t.segmentsRadial,r=t.segmentsTube,o=new Fk(n,i,s,r);return o.translate(t.center.x,t.center.y,t.center.z),this._core_transform.rotate_geometry(o,Vj,t.direction),this.createCoreGroupFromGeometry(o)}}jj.DEFAULT_PARAMS={radius:1,radiusTube:1,segmentsRadial:20,segmentsTube:12,direction:new p.a(0,1,0),center:new p.a(0,0,0)},jj.INPUT_CLONED_STATE=Ti.FROM_NODE;const Hj=jj.DEFAULT_PARAMS;const qj=new class extends Lo{constructor(){super(...arguments),this.radius=Oo.FLOAT(Hj.radius,{range:[0,1]}),this.radiusTube=Oo.FLOAT(Hj.radiusTube,{range:[0,1]}),this.segmentsRadial=Oo.INTEGER(Hj.segmentsRadial,{range:[1,50],rangeLocked:[!0,!1]}),this.segmentsTube=Oo.INTEGER(Hj.segmentsTube,{range:[1,50],rangeLocked:[!0,!1]}),this.direction=Oo.VECTOR3(Hj.direction),this.center=Oo.VECTOR3(Hj.center)}};class Wj extends QO{constructor(){super(...arguments),this.paramsConfig=qj}static type(){return\\\\\\\"torus\\\\\\\"}cook(e){this._operation=this._operation||new jj(this.scene(),this.states);const t=this._operation.cook(e,this.pv);this.setCoreGroup(t)}}class Xj extends WO{static type(){return\\\\\\\"torusKnot\\\\\\\"}cook(e,t){const n=t.radius,i=t.radiusTube,s=t.segmentsRadial,r=t.segmentsTube,o=t.p,a=t.q,c=new Dk(n,i,s,r,o,a);return c.translate(t.center.x,t.center.y,t.center.z),this.createCoreGroupFromGeometry(c)}}Xj.DEFAULT_PARAMS={radius:1,radiusTube:1,segmentsRadial:64,segmentsTube:8,p:2,q:3,center:new p.a(0,0,0)},Xj.INPUT_CLONED_STATE=Ti.FROM_NODE;const Yj=Xj.DEFAULT_PARAMS;const $j=new class extends Lo{constructor(){super(...arguments),this.radius=Oo.FLOAT(Yj.radius),this.radiusTube=Oo.FLOAT(Yj.radiusTube),this.segmentsRadial=Oo.INTEGER(Yj.segmentsRadial,{range:[1,128]}),this.segmentsTube=Oo.INTEGER(Yj.segmentsTube,{range:[1,32]}),this.p=Oo.INTEGER(Yj.p,{range:[1,10]}),this.q=Oo.INTEGER(Yj.q,{range:[1,10]}),this.center=Oo.VECTOR3(Yj.center)}};class Qj extends QO{constructor(){super(...arguments),this.paramsConfig=$j}static type(){return\\\\\\\"torusKnot\\\\\\\"}initializeNode(){}cook(e){this._operation=this._operation||new Xj(this.scene(),this.states);const t=this._operation.cook(e,this.pv);this.setCoreGroup(t)}}var Jj;!function(e){e.SET_PARAMS=\\\\\\\"set params\\\\\\\",e.UPDATE_MATRIX=\\\\\\\"update matrix\\\\\\\"}(Jj||(Jj={}));const Kj=[Jj.SET_PARAMS,Jj.UPDATE_MATRIX];class Zj extends WO{constructor(){super(...arguments),this._core_transform=new rS,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(e,t){const n=e[0].objects();return this._apply_transform(n,t),e[0]}_apply_transform(e,t){const n=tS[t.applyOn];switch(n){case KN.GEOMETRIES:return this._update_geometries(e,t);case KN.OBJECTS:return this._update_objects(e,t)}Ri.unreachable(n)}_update_geometries(e,t){const n=this._matrix(t);if(\\\\\\\"\\\\\\\"===t.group.trim())for(let i of e){const e=i.geometry;e&&(e.translate(-t.pivot.x,-t.pivot.y,-t.pivot.z),e.applyMatrix4(n),e.translate(t.pivot.x,t.pivot.y,t.pivot.z))}else{const i=qO._fromObjects(e).pointsFromGroup(t.group);for(let e of i){const i=e.getPosition(this._point_pos).sub(t.pivot);i.applyMatrix4(n),e.setPosition(i.add(t.pivot))}}}_update_objects(e,t){const n=Kj[t.objectMode];switch(n){case Jj.SET_PARAMS:return this._update_objects_params(e,t);case Jj.UPDATE_MATRIX:return this._update_objects_matrix(e,t)}Ri.unreachable(n)}_update_objects_params(e,t){for(let n of e){n.position.copy(t.t);const e=iS[t.rotationOrder];this._r.copy(t.r).multiplyScalar(A.a.DEG2RAD),n.rotation.set(this._r.x,this._r.y,this._r.z,e),this._object_scale.copy(t.s).multiplyScalar(t.scale),n.scale.copy(this._object_scale),n.updateMatrix()}}_update_objects_matrix(e,t){const n=this._matrix(t);for(let t of e)this._object_position.copy(t.position),t.position.multiplyScalar(0),t.updateMatrix(),t.applyMatrix4(n),t.position.add(this._object_position),t.updateMatrix()}_matrix(e){return this._core_transform.matrix(e.t,e.r,e.s,e.scale,iS[e.rotationOrder])}}Zj.DEFAULT_PARAMS={applyOn:tS.indexOf(KN.GEOMETRIES),objectMode:Kj.indexOf(Jj.SET_PARAMS),group:\\\\\\\"\\\\\\\",rotationOrder:iS.indexOf(nS.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)},Zj.INPUT_CLONED_STATE=Ti.FROM_NODE;const eH=Zj.DEFAULT_PARAMS;const tH=new class extends Lo{constructor(){super(...arguments),this.applyOn=Oo.INTEGER(eH.applyOn,{menu:{entries:tS.map(((e,t)=>({name:e,value:t})))}}),this.objectMode=Oo.INTEGER(eH.objectMode,{visibleIf:{applyOn:tS.indexOf(KN.OBJECTS)},menu:{entries:Kj.map(((e,t)=>({name:e,value:t})))}}),this.group=Oo.STRING(eH.group,{visibleIf:{applyOn:tS.indexOf(KN.GEOMETRIES)}}),this.rotationOrder=Oo.INTEGER(eH.rotationOrder,{menu:{entries:iS.map(((e,t)=>({name:e,value:t})))}}),this.t=Oo.VECTOR3(eH.t),this.r=Oo.VECTOR3(eH.r),this.s=Oo.VECTOR3(eH.s),this.scale=Oo.FLOAT(eH.scale,{range:[0,10]}),this.pivot=Oo.VECTOR3(eH.pivot,{visibleIf:{applyOn:tS.indexOf(KN.GEOMETRIES)}})}};class nH extends QO{constructor(){super(...arguments),this.paramsConfig=tH}static type(){return EV.TRANSFORM}static displayedInputNames(){return[\\\\\\\"geometries or objects to transform\\\\\\\"]}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState(Zj.INPUT_CLONED_STATE),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.applyOn],(()=>tS[this.pv.applyOn]))}))}))}setApplyOn(e){this.p.applyOn.set(tS.indexOf(e))}setObjectMode(e){this.p.objectMode.set(Kj.indexOf(e))}cook(e){this._operation=this._operation||new Zj(this.scene(),this.states);const t=this._operation.cook(e,this.pv);this.setCoreGroup(t)}}const iH=new class extends Lo{constructor(){super(...arguments),this.useSecondInput=Oo.BOOLEAN(1),this.reference=Oo.OPERATOR_PATH(\\\\\\\"\\\\\\\",{nodeSelection:{context:Ei.SOP},visibleIf:{useSecondInput:0}})}};class sH extends QO{constructor(){super(...arguments),this.paramsConfig=iH}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([Ti.FROM_NODE,Ti.NEVER])}cook(e){this.pv.useSecondInput&&e[1]?this._copy_from_src_objects(e[0].objects(),e[1].objects()):this._copy_from_found_node(e[0].objects())}_copy_from_src_objects(e,t){let n,i;for(let s=0;s<e.length;s++)n=e[s],i=t[s],i.updateMatrix(),n.matrix.copy(i.matrix),n.matrix.decompose(n.position,n.quaternion,n.scale);this.setObjects(e)}async _copy_from_found_node(e){const t=this.p.reference.found_node_with_context(Ei.SOP);if(t){const n=(await t.compute()).coreContent();if(n){const t=n.objects();return void this._copy_from_src_objects(e,t)}}this.setObjects(e)}}const rH=iS.indexOf(nS.XYZ),oH={menu:{entries:iS.map(((e,t)=>({name:e,value:t})))}};function aH(e){const t=[];for(let n=e+1;n<=6;n++)t.push({count:n});return{visibleIf:t}}const cH=new class extends Lo{constructor(){super(...arguments),this.applyOn=Oo.INTEGER(tS.indexOf(KN.GEOMETRIES),{menu:{entries:tS.map(((e,t)=>({name:e,value:t})))}}),this.count=Oo.INTEGER(2,{range:[0,6],rangeLocked:[!0,!0]}),this.rotationOrder0=Oo.INTEGER(rH,{separatorBefore:!0,...oH,...aH(0)}),this.r0=Oo.VECTOR3([0,0,0],{...aH(0)}),this.rotationOrder1=Oo.INTEGER(rH,{separatorBefore:!0,...oH,...aH(1)}),this.r1=Oo.VECTOR3([0,0,0],{...aH(1)}),this.rotationOrder2=Oo.INTEGER(rH,{separatorBefore:!0,...oH,...aH(2)}),this.r2=Oo.VECTOR3([0,0,0],{...aH(2)}),this.rotationOrder3=Oo.INTEGER(rH,{separatorBefore:!0,...oH,...aH(3)}),this.r3=Oo.VECTOR3([0,0,0],{...aH(3)}),this.rotationOrder4=Oo.INTEGER(rH,{separatorBefore:!0,...oH,...aH(4)}),this.r4=Oo.VECTOR3([0,0,0],{...aH(4)}),this.rotationOrder5=Oo.INTEGER(rH,{separatorBefore:!0,...oH,...aH(5)}),this.r5=Oo.VECTOR3([0,0,0],{...aH(5)})}};class lH extends QO{constructor(){super(...arguments),this.paramsConfig=cH,this._core_transform=new rS,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([Ti.FROM_NODE,Ti.NEVER]),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.applyOn],(()=>tS[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(e){const t=e[0].objectsWithGeo(),n=e[1]?e[1].objectsWithGeo()[0]:void 0;this._apply_transforms(t,n),this.setObjects(t)}_apply_transforms(e,t){const n=tS[this.pv.applyOn];switch(n){case KN.GEOMETRIES:return this._apply_matrix_to_geometries(e,t);case KN.OBJECTS:return this._apply_matrix_to_objects(e,t)}Ri.unreachable(n)}_apply_matrix_to_geometries(e,t){if(!this._rot_and_index_pairs)return;if(t){const n=t.geometry;if(n){const t=[ms.POSITION,ms.NORMAL,ms.TANGENT];for(let i of t){const t=n.attributes[i];for(let n of e){const e=n.geometry.attributes[i];t&&e&&gs.copy(t,e)}}}}let n;for(let t=0;t<this.pv.count;t++){n=this._rot_and_index_pairs[t];const i=this._matrix(n[0].value,n[1].value);for(let t of e)t.geometry.applyMatrix4(i)}}_apply_matrix_to_objects(e,t){if(!this._rot_and_index_pairs)return;if(t)for(let n of e)n.matrix.copy(t.matrix),n.matrix.decompose(n.position,n.quaternion,n.scale);let n;for(let t=0;t<this.pv.count;t++){n=this._rot_and_index_pairs[t];const i=this._matrix(n[0].value,n[1].value);for(let t of e)t.applyMatrix4(i)}}_matrix(e,t){return this._core_transform.matrix(this._t,e,this._s,this._scale,iS[t])}}var uH;!function(e){e.RESET_OBJECT=\\\\\\\"reset objects transform\\\\\\\",e.CENTER_GEO=\\\\\\\"center geometries\\\\\\\",e.PROMOTE_GEO_TO_OBJECT=\\\\\\\"center geometry and transform object\\\\\\\"}(uH||(uH={}));const hH=[uH.RESET_OBJECT,uH.CENTER_GEO,uH.PROMOTE_GEO_TO_OBJECT];const dH=new class extends Lo{constructor(){super(...arguments),this.mode=Oo.INTEGER(hH.indexOf(uH.RESET_OBJECT),{menu:{entries:hH.map(((e,t)=>({name:e,value:t})))}})}};class pH extends QO{constructor(){super(...arguments),this.paramsConfig=dH,this._bbox_center=new p.a,this._translate_matrix=new C.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(Ti.FROM_NODE)}setMode(e){this.p.mode.set(hH.indexOf(e))}cook(e){const t=hH[this.pv.mode];this._select_mode(t,e)}_select_mode(e,t){switch(e){case uH.RESET_OBJECT:return this._reset_objects(t);case uH.CENTER_GEO:return this._center_geos(t,!1);case uH.PROMOTE_GEO_TO_OBJECT:return this._center_geos(t,!0)}Ri.unreachable(e)}_reset_objects(e){const t=e[0],n=t.objects();for(let e of n)e.matrix.identity(),rS.decompose_matrix(e);this.setCoreGroup(t)}_center_geos(e,t){const n=e[0],i=n.objectsWithGeo();let s=i;const r=e[1];r&&(s=r.objectsWithGeo());for(let e=0;e<i.length;e++){const n=i[e],r=s[e]||s[s.length-1],o=n.geometry,a=r.geometry;if(o&&a){a.computeBoundingBox();const e=a.boundingBox;e&&(e.getCenter(this._bbox_center),r.updateMatrixWorld(),this._bbox_center.applyMatrix4(r.matrixWorld),t&&(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),rS.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 _H=new p.a(0,1,0);const mH=new class extends Lo{constructor(){super(...arguments),this.radius=Oo.FLOAT(1,{range:[0,1]}),this.height=Oo.FLOAT(1,{range:[0,1]}),this.segmentsRadial=Oo.INTEGER(12,{range:[3,20],rangeLocked:[!0,!1]}),this.segmentsHeight=Oo.INTEGER(1,{range:[1,20],rangeLocked:[!0,!1]}),this.cap=Oo.BOOLEAN(1),this.center=Oo.VECTOR3([0,0,0]),this.direction=Oo.VECTOR3([0,0,1])}};class fH extends QO{constructor(){super(...arguments),this.paramsConfig=mH,this._core_transform=new rS}static type(){return\\\\\\\"tube\\\\\\\"}cook(){const e=new WS(this.pv.radius,this.pv.radius,this.pv.height,this.pv.segmentsRadial,this.pv.segmentsHeight,!this.pv.cap);this._core_transform.rotate_geometry(e,_H,this.pv.direction),e.translate(this.pv.center.x,this.pv.center.y,this.pv.center.z),this.setGeometry(e)}}function gH(e){return function(e){let t=0,n=0;for(const i of e)t+=i.w*i.h,n=Math.max(n,i.w);e.sort(((e,t)=>t.h-e.h));const i=[{x:0,y:0,w:Math.max(Math.ceil(Math.sqrt(t/.95)),n),h:1/0}];let s=0,r=0;for(const t of e)for(let e=i.length-1;e>=0;e--){const n=i[e];if(!(t.w>n.w||t.h>n.h)){if(t.x=n.x,t.y=n.y,r=Math.max(r,t.y+t.h),s=Math.max(s,t.x+t.w),t.w===n.w&&t.h===n.h){const t=i.pop();e<i.length&&(i[e]=t)}else t.h===n.h?(n.x+=t.w,n.w-=t.w):t.w===n.w?(n.y+=t.h,n.h-=t.h):(i.push({x:n.x+t.w,y:n.y,w:n.w-t.w,h:t.h}),n.y+=t.h,n.h-=t.h);break}}return{w:s,h:r,fill:t/(s*r)||0}}(e)}class vH extends WO{static type(){return EV.UV_LAYOUT}cook(e,t){const n=e[0].objectsWithGeo(),i=[];for(let e of n){const t=e;t.isMesh&&i.push(t)}return this._layoutUVs(i,t),e[0]}_layoutUVs(e,t){var n;const i=[],s=new WeakMap,r=t.padding/t.res;let o=0;for(let a of e){a.geometry.hasAttribute(t.uv)||null===(n=this.states)||void 0===n||n.error.set(`attribute ${t.uv} not found`);const e={w:1+2*r,h:1+2*r};i.push(e),s.set(e,o),o++}const a=gH(i);for(let n of i){const i=n,o=s.get(n);if(null!=o){const n=e[o],s=n.geometry.getAttribute(t.uv).clone(),c=s.array;for(let e=0;e<s.array.length;e+=s.itemSize)c[e]=(s.array[e]+i.x+r)/a.w,c[e+1]=(s.array[e+1]+i.y+r)/a.h;n.geometry.setAttribute(t.uv2,s),n.geometry.getAttribute(t.uv2).needsUpdate=!0}}}}vH.DEFAULT_PARAMS={res:1024,padding:3,uv:\\\\\\\"uv\\\\\\\",uv2:\\\\\\\"uv2\\\\\\\"},vH.INPUT_CLONED_STATE=Ti.FROM_NODE;const yH=new class extends Lo{constructor(){super(...arguments),this.res=Oo.INTEGER(1024),this.padding=Oo.INTEGER(3),this.uv=Oo.STRING(\\\\\\\"uv\\\\\\\"),this.uv2=Oo.STRING(\\\\\\\"uv2\\\\\\\")}};class xH extends QO{constructor(){super(...arguments),this.paramsConfig=yH}static type(){return EV.UV_LAYOUT}static displayedInputNames(){return[\\\\\\\"geometries to unwrap UVs\\\\\\\"]}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState(vH.INPUT_CLONED_STATE)}cook(e){this._operation=this._operation||new vH(this.scene(),this.states);const t=this._operation.cook(e,this.pv);this.setCoreGroup(t)}}var bH;!function(e){e.CHANGE=\\\\\\\"change\\\\\\\",e.MOVEEND=\\\\\\\"moveend\\\\\\\"}(bH||(bH={}));class wH{constructor(e){this._callback=e,this._updateAlways=!0,this._listenerAdded=!1,this._listener=this._executeCallback.bind(this)}removeTarget(){this.setTarget(void 0)}setTarget(e){e||this._removeCameraEvent();const t=this._target;this._target=e,null!=this._target&&this._executeCallback(),(null!=this._target?this._target.uuid:void 0)!==(null!=t?t.uuid:void 0)&&this._addCameraEvent()}setUpdateAlways(e){this._removeCameraEvent(),this._updateAlways=e,this._addCameraEvent()}_currentEventName(){return this._updateAlways?bH.CHANGE:bH.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 AH=new class extends Lo{constructor(){super(...arguments),this.camera=Oo.OPERATOR_PATH(\\\\\\\"/perspective_camera1\\\\\\\",{nodeSelection:{context:Ei.OBJ}})}};class TH extends QO{constructor(){super(...arguments),this.paramsConfig=AH,this._cameraController=new wH(this._updateUVsFromCamera.bind(this))}static type(){return\\\\\\\"uvProject\\\\\\\"}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState(Ti.FROM_NODE)}cook(e){this._processed_core_group=e[0];const t=this.p.camera.found_node();null!=t?(this._camera_object=t.object,this._cameraController.setTarget(this._camera_object)):(this._camera_object=void 0,this._cameraController.removeTarget()),this.setCoreGroup(this._processed_core_group)}_updateUVsFromCamera(e){const t=this.parent();if(this._processed_core_group&&t){const e=this._processed_core_group.points(),n=t.object.matrixWorld;for(let t of e){const e=t.position(),i=this._vectorInCameraSpace(e,n);if(i){const e={x:1-(.5*i[0]+.5),y:.5*i[1]+.5};t.setAttribValue(\\\\\\\"uv\\\\\\\",e)}}}}_vectorInCameraSpace(e,t){if(this._camera_object)return e.applyMatrix4(t),e.project(this._camera_object).toArray()}}class EH extends WO{static type(){return EV.UV_TRANSFORM}cook(e,t){const n=e[0].objectsWithGeo();for(let e of n){const n=e.geometry.getAttribute(t.attribName),i=n.array,s=i.length/2;for(let e=0;e<s;e++)i[2*e+0]=t.t.x+t.pivot.x+t.s.x*(i[2*e+0]-t.pivot.x),i[2*e+1]=t.t.y+t.pivot.y+t.s.y*(i[2*e+1]-t.pivot.y);n.needsUpdate=!0}return e[0]}}EH.DEFAULT_PARAMS={attribName:\\\\\\\"uv\\\\\\\",t:new d.a(0,0),s:new d.a(1,1),pivot:new d.a(0,0)},EH.INPUT_CLONED_STATE=Ti.FROM_NODE;const CH=EH.DEFAULT_PARAMS;const MH=new class extends Lo{constructor(){super(...arguments),this.attribName=Oo.STRING(CH.attribName),this.t=Oo.VECTOR2(CH.t.toArray()),this.s=Oo.VECTOR2(CH.s.toArray()),this.pivot=Oo.VECTOR2(CH.pivot.toArray())}};class NH extends QO{constructor(){super(...arguments),this.paramsConfig=MH}static type(){return EV.UV_TRANSFORM}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState(EH.INPUT_CLONED_STATE)}async cook(e){this._operation=this._operation||new EH(this.scene(),this.states,this);const t=await this._operation.cook(e,this.pv);this.setCoreGroup(t)}}class SH extends WO{static type(){return EV.UV_UNWRAP}cook(e,t){const n=e[0].objectsWithGeo();for(let e of n){const n=e;n.isMesh&&this._unwrapUVs(n,t)}return e[0]}_unwrapUVs(e,t){var n,i,s;const r=[],o=e.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 c=null===(s=o.attributes[t.uv])||void 0===s?void 0:s.array;if(!c)return;const l=a.length/3;for(let e=0;e<l;e++)r.push({w:1,h:1});const u=gH(r),h=new Array(c.length);for(let e=0;e<l;e++){const t=r[e],n=t.x/u.w,i=t.y/u.h,s=t.w/u.w,o=t.h/u.h,c=2*a[3*e+0],l=2*a[3*e+1],d=2*a[3*e+2];h[c]=n,h[c+1]=i,h[l]=n+s,h[l+1]=i,h[d]=n,h[d+1]=i+o}o.setAttribute(t.uv,new L.c(h,2))}}SH.DEFAULT_PARAMS={uv:\\\\\\\"uv\\\\\\\"},SH.INPUT_CLONED_STATE=Ti.FROM_NODE;const OH=new class extends Lo{constructor(){super(...arguments),this.uv=Oo.STRING(\\\\\\\"uv\\\\\\\")}};class LH extends QO{constructor(){super(...arguments),this.paramsConfig=OH}static type(){return EV.UV_UNWRAP}static displayedInputNames(){return[\\\\\\\"geometries to unwrap UVs\\\\\\\"]}initializeNode(){this.io.inputs.setCount(1),this.io.inputs.initInputsClonedState(SH.INPUT_CLONED_STATE)}cook(e){this._operation=this._operation||new SH(this.scene(),this.states);const t=this._operation.cook(e,this.pv);this.setCoreGroup(t)}}class PH extends Mo{static context(){return Ei.SOP}cook(){this.cookController.endCook()}}class RH extends PH{}class IH extends RH{constructor(){super(...arguments),this._children_controller_context=Ei.ANIM}static type(){return Ci.ANIM}createNode(e,t){return super.createNode(e,t)}children(){return super.children()}nodesByType(e){return super.nodesByType(e)}}class FH extends RH{constructor(){super(...arguments),this._children_controller_context=Ei.COP}static type(){return Ci.COP}createNode(e,t){return super.createNode(e,t)}children(){return super.children()}nodesByType(e){return super.nodesByType(e)}}class DH extends RH{constructor(){super(...arguments),this._children_controller_context=Ei.EVENT}static type(){return Ci.EVENT}createNode(e,t){return super.createNode(e,t)}children(){return super.children()}nodesByType(e){return super.nodesByType(e)}}class kH extends RH{constructor(){super(...arguments),this._children_controller_context=Ei.MAT}static type(){return Ci.MAT}createNode(e,t){return super.createNode(e,t)}children(){return super.children()}nodesByType(e){return super.nodesByType(e)}}class BH extends PH{constructor(){super(...arguments),this.paramsConfig=new Rm,this.effectsComposerController=new Im(this),this.displayNodeController=new mm(this,this.effectsComposerController.displayNodeControllerCallbacks()),this._children_controller_context=Ei.POST}static type(){return Ci.POST}createNode(e,t){return super.createNode(e,t)}children(){return super.children()}nodesByType(e){return super.nodesByType(e)}}class zH extends RH{constructor(){super(...arguments),this._children_controller_context=Ei.ROP}static type(){return Ci.ROP}createNode(e,t){return super.createNode(e,t)}children(){return super.children()}nodesByType(e){return super.nodesByType(e)}}class UH{constructor(e){this.param=e,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(e){return e>=this.minAllowedArgumentsCount()&&e<=this.maxAllowedArgumentsCount()}processArguments(e){throw\\\\\\\"Expression.Method._Base.process_arguments virtual method call. Please override\\\\\\\"}async getReferencedNodeContainer(e){const t=this.getReferencedNode(e);if(t){let e;if(e=t.isDirty()?await t.compute():t.containerController.container(),e){if(e.coreContent())return e}throw`referenced node invalid: ${t.path()}`}throw`invalid input (${e})`}getReferencedParam(e,t){const n=this.node();return n?Wn.findParam(n,e,t):null}findReferencedGraphNode(e,t){if(!m.isNumber(e)){const n=e;return this.getReferencedNode(n,t)}{const t=e,n=this.node();if(n){return n.io.inputs.input_graph_node(t)}}return null}getReferencedNode(e,t){let n=null;const i=this.node();if(m.isString(e)){if(i){const s=e;n=Wn.findNode(i,s,t)}}else if(i){const t=e;n=i.io.inputs.input(t)}return n||null}findDependency(e){return null}createDependencyFromIndexOrPath(e){const t=new Ir,n=this.findReferencedGraphNode(e,t);return n?this.createDependency(n,e,t):(Rn.warn(\\\\\\\"node not found for path\\\\\\\",e),null)}createDependency(e,t,n){return cr.create(this.param,t,e,n)}}class GH extends UH{constructor(){super(...arguments),this._require_dependency=!0}static requiredArguments(){return[[\\\\\\\"string\\\\\\\",\\\\\\\"arguments list\\\\\\\"],[\\\\\\\"number\\\\\\\",\\\\\\\"index\\\\\\\"]]}processArguments(e){return new Promise(((t,n)=>{if(2==e.length){const n=e[0],i=e[1];t(n.split(\\\\\\\" \\\\\\\")[i])}else t(0)}))}}class VH extends UH{constructor(){super(...arguments),this._require_dependency=!0}static requiredArguments(){return[[\\\\\\\"string\\\\\\\",\\\\\\\"arguments list\\\\\\\"]]}processArguments(e){return new Promise(((t,n)=>{if(1==e.length){t(e[0].split(\\\\\\\" \\\\\\\").length)}else t(0)}))}}const jH=[\\\\\\\"min\\\\\\\",\\\\\\\"max\\\\\\\",\\\\\\\"size\\\\\\\",\\\\\\\"center\\\\\\\"],HH=[\\\\\\\"x\\\\\\\",\\\\\\\"y\\\\\\\",\\\\\\\"z\\\\\\\"];class qH extends UH{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(e){return this.createDependencyFromIndexOrPath(e)}processArguments(e){let t=0;return new Promise((async(n,i)=>{if(e.length>=1){const s=e[0],r=e[1],o=e[2];let a=null;try{a=await this.getReferencedNodeContainer(s)}catch(e){i(e)}a&&(t=this._get_value_from_container(a,r,o),n(t))}else n(0)}))}_get_value_from_container(e,t,n){const i=e.boundingBox();if(!t)return i;if(jH.indexOf(t)>=0){let e=new p.a;switch(t){case\\\\\\\"size\\\\\\\":i.getSize(e);break;case\\\\\\\"center\\\\\\\":i.getCenter(e);break;default:e=i[t]}return n?HH.indexOf(n)>=0?e[n]:-1:e}return-1}}class WH extends UH{constructor(){super(...arguments),this._require_dependency=!0}static requiredArguments(){return[[\\\\\\\"string\\\\\\\",\\\\\\\"path to node\\\\\\\"],[\\\\\\\"string\\\\\\\",\\\\\\\"component_name, x,y or z\\\\\\\"]]}findDependency(e){return this.createDependencyFromIndexOrPath(e)}processArguments(e){return new Promise((async(t,n)=>{if(e.length>=1){const i=e[0],s=e[1];let r=null;try{r=await this.getReferencedNodeContainer(i)}catch(e){n(e)}if(r){const e=r.boundingBox(),n=e.min.clone().add(e.max).multiplyScalar(.5);if(s){const e=n[s];t(null!=e?e:0)}else t(n)}}else t(0)}))}}class XH extends UH{constructor(){super(...arguments),this._require_dependency=!0}static requiredArguments(){return[[\\\\\\\"string\\\\\\\",\\\\\\\"path to param\\\\\\\"]]}findDependency(e){const t=new Ir,n=this.getReferencedParam(e,t);return n?this.createDependency(n,e,t):null}async processArguments(e){return new Promise((async(t,n)=>{let i=0;if(1==e.length){const s=e[0],r=this.getReferencedParam(s);if(r){r.isDirty()&&await r.compute();const e=r.value;null!=e&&(i=e,t(i))}else n(0)}}))}}class YH extends UH{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(e){const t=this.findReferencedGraphNode(e);if(t&&\\\\\\\"copy\\\\\\\"==t.type()){const n=t.stamp_node;return this.createDependency(n,e)}return null}processArguments(e){return new Promise(((t,n)=>{if(2==e.length||3==e.length){const n=e[0],i=e[1],s=e[2],r=this.node(),o=r?Wn.findNode(r,n):null;let a;o&&o.type()==IB.type()&&(a=o.stamp_value(s)),null==a&&(a=i),t(a)}else t(0)}))}}class $H extends UH{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(e){return this.createDependencyFromIndexOrPath(e)}async processArguments(e){if(1==e.length||2==e.length){const t=e[0],n=e[1],i=await this.getReferencedNodeContainer(t);if(i){const e=i.resolution();if(!n)return this._resolution.set(e[0],e[1]),this._resolution;if([0,\\\\\\\"0\\\\\\\",\\\\\\\"x\\\\\\\"].includes(n))return e[0];if([1,\\\\\\\"1\\\\\\\",\\\\\\\"y\\\\\\\"].includes(n))return e[1]}}return-1}}class QH extends UH{constructor(){super(...arguments),this._require_dependency=!0}static requiredArguments(){return[]}async processArguments(e){return new Promise((async(e,t)=>{e(Df.isMobile())}))}}class JH extends UH{constructor(){super(...arguments),this._require_dependency=!0}static requiredArguments(){return[]}async processArguments(e){return new Promise((async(e,t)=>{e(Df.isTouchDevice())}))}}class KH extends UH{static requiredArguments(){return[[\\\\\\\"string\\\\\\\",\\\\\\\"javascript expression\\\\\\\"]]}async processArguments(e){let t=0;if(1==e.length){const n=e[0];if(this._function=this._function||this._create_function(n),this._function)try{t=this._function(this.param.scene(),this.param.node,this.param)}catch(e){console.warn(\\\\\\\"expression error\\\\\\\"),console.warn(e)}}return t}_create_function(e){return new Function(\\\\\\\"scene\\\\\\\",\\\\\\\"node\\\\\\\",\\\\\\\"param\\\\\\\",`return ${e}`)}}class ZH extends UH{constructor(){super(...arguments),this._require_dependency=!0}static requiredArguments(){return[[\\\\\\\"string\\\\\\\",\\\\\\\"path to node\\\\\\\"],[\\\\\\\"string\\\\\\\",\\\\\\\"attribute name\\\\\\\"],[\\\\\\\"index\\\\\\\",\\\\\\\"object index\\\\\\\"]]}findDependency(e){return this.createDependencyFromIndexOrPath(e)}processArguments(e){return new Promise((async(t,n)=>{if(3==e.length){const i=e[0],s=e[1],r=e[2];let o=null;try{o=await this.getReferencedNodeContainer(i)}catch(e){n(e)}if(o){t(this._get_value_from_container(o,s,r))}}else console.warn(`${e.length} given when expected 3`),t(0)}))}_get_value_from_container(e,t,n){const i=e.coreContent();if(i){const e=i.coreObjects()[n];return e?e.attribValue(t):0}return null}}class eq extends UH{constructor(){super(...arguments),this._require_dependency=!0}static requiredArguments(){return[[\\\\\\\"string\\\\\\\",\\\\\\\"path to node\\\\\\\"]]}findDependency(e){return this.createDependencyFromIndexOrPath(e)}processArguments(e){return new Promise((async(t,n)=>{if(1==e.length){const i=e[0];let s;try{s=await this.getReferencedNodeContainer(i)}catch(e){return void n(e)}if(s){t(s.objectsCount())}}else t(0)}))}}class tq extends UH{constructor(){super(...arguments),this._require_dependency=!0}static requiredArguments(){return[[\\\\\\\"string\\\\\\\",\\\\\\\"path to node\\\\\\\"]]}findDependency(e){const t=this.findReferencedGraphNode(e);if(t){const n=t;if(n.nameController){const t=n.nameController.graph_node;return this.createDependency(t,e)}}return null}processArguments(e){return new Promise(((t,n)=>{if(1==e.length){const n=e[0],i=this.getReferencedNode(n);if(i){const e=i.name();t(Li.tailDigits(e))}else t(0)}else t(0)}))}}class nq extends UH{constructor(){super(...arguments),this._require_dependency=!0}static requiredArguments(){return[[\\\\\\\"string\\\\\\\",\\\\\\\"path to node\\\\\\\"]]}findDependency(e){const t=this.findReferencedGraphNode(e);if(t){const n=t;if(n.nameController){const t=n.nameController.graph_node;return this.createDependency(t,e)}}return null}processArguments(e){return new Promise(((t,n)=>{if(1==e.length){const n=e[0],i=this.getReferencedNode(n);if(i){t(i.name())}else t(0)}else t(0)}))}}class iq extends UH{static requiredArguments(){return[[\\\\\\\"string\\\\\\\",\\\\\\\"number\\\\\\\"]]}processArguments(e){return new Promise((t=>{const n=e[0]||2;t(`${e[1]||0}`.padStart(n,\\\\\\\"0\\\\\\\"))}))}}class sq extends UH{constructor(){super(...arguments),this._require_dependency=!0}static requiredArguments(){return[[\\\\\\\"string\\\\\\\",\\\\\\\"path to node\\\\\\\"],[\\\\\\\"string\\\\\\\",\\\\\\\"attribute name\\\\\\\"],[\\\\\\\"index\\\\\\\",\\\\\\\"point index\\\\\\\"]]}findDependency(e){return this.createDependencyFromIndexOrPath(e)}processArguments(e){return new Promise((async(t,n)=>{if(3==e.length){const i=e[0],s=e[1],r=e[2];let o=null;try{o=await this.getReferencedNodeContainer(i)}catch(e){n(e)}if(o){t(this._get_value_from_container(o,s,r))}}else console.warn(`${e.length} given when expected 3`),t(0)}))}_get_value_from_container(e,t,n){const i=e.coreContent();if(i){const e=i.points()[n];return e?e.attribValue(t):0}return null}}class rq extends UH{constructor(){super(...arguments),this._require_dependency=!0}static requiredArguments(){return[[\\\\\\\"string\\\\\\\",\\\\\\\"path to node\\\\\\\"]]}findDependency(e){return this.createDependencyFromIndexOrPath(e)}processArguments(e){return new Promise((async(t,n)=>{if(1==e.length){const i=e[0];let s;try{s=await this.getReferencedNodeContainer(i)}catch(e){return void n(e)}if(s){t(s.pointsCount())}}else t(0)}))}}class oq extends UH{static requiredArguments(){return[[\\\\\\\"string\\\\\\\",\\\\\\\"string to count characters of\\\\\\\"]]}async processArguments(e){let t=0;if(1==e.length){t=e[0].length}return t}}class aq extends UH{static requiredArguments(){return[]}async processArguments(e){let t=\\\\\\\"\\\\\\\";for(let n of e)null==n&&(n=\\\\\\\"\\\\\\\"),t+=`${n}`;return t}}class cq extends UH{static requiredArguments(){return[[\\\\\\\"string\\\\\\\",\\\\\\\"string to get index from\\\\\\\"],[\\\\\\\"string\\\\\\\",\\\\\\\"char to find index of\\\\\\\"]]}async processArguments(e){let t=-1;if(2==e.length){const n=e[0],i=e[1];t=n.indexOf(i)}return t}}class lq extends UH{static requiredArguments(){return[[\\\\\\\"string\\\\\\\",\\\\\\\"string to get range from\\\\\\\"],[\\\\\\\"integer\\\\\\\",\\\\\\\"range start\\\\\\\"],[\\\\\\\"integer\\\\\\\",\\\\\\\"range size\\\\\\\"]]}async processArguments(e){let t=\\\\\\\"\\\\\\\";const n=e[0],i=e[1]||0;let s=e[2]||1;return n&&(t=n.substr(i,s)),t}}class uq extends UH{constructor(){super(...arguments),this._require_dependency=!0,this._windowSize=new d.a}static requiredArguments(){return[[]]}findDependency(e){return this.param.addGraphInput(this.param.scene().windowController.graphNode()),null}processArguments(e){return new Promise((e=>{this._windowSize.set(window.innerWidth,window.innerHeight),e(this._windowSize)}))}}class hq{constructor(e,t){this._controller=e,this._node=t,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 e;const t={},n=this.assembler.param_configs(),i=n.map((e=>e.name())),s=b.clone(i);if(0==this._validateNames(s))return;b.clone(this._created_spare_param_names).concat(s).forEach((e=>{const n=this._node.params.get(e);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 e=HO.dispatch_param(n);if(e.required()){const t=e.data();this._deleted_params_data.set(n.name(),t)}}t.namesToDelete=t.namesToDelete||[],t.namesToDelete.push(e)}));for(let e of n)if(s.indexOf(e.name())>=0){const n=b.clone(e.param_options),i={spare:!0,computeOnDirty:!0,cook:!1},s=b.merge(n,i);let r=this._init_value_serialized_by_param_name.get(e.name());null==r&&(r=e.default_value);let o=this._raw_input_serialized_by_param_name.get(e.name());null==o&&(o=e.default_value),t.toAdd=t.toAdd||[],t.toAdd.push({name:e.name(),type:e.type(),init_value:r,raw_input:o,options:s})}this._node.params.updateParams(t),this._created_spare_param_names=(null===(e=t.toAdd)||void 0===e?void 0:e.map((e=>e.name)))||[];for(let e of n){const t=this._node.params.get(e.name());t&&(e.execute_callback(this._node,t),t.type()==Qs.OPERATOR_PATH&&setTimeout((async()=>{t.isDirty()&&await t.compute(),t.options.executeCallback()}),200))}}_validateNames(e){const t=b.clone(this._node.params.non_spare_names),n=f.intersection(e,t);if(n.length>0){const e=`${this._node.path()} attempts to create spare params called '${n.join(\\\\\\\", \\\\\\\")}' with same name as params`;return this._node.states.error.set(e),!1}return!0}}class dq{constructor(e,t){this.node=e,this._globals_handler=new uf,this._compile_required=!0,this._assembler=new t(this.node),this._spare_params_controller=new hq(this,this.node)}set_assembler_globals_handler(e){(this._globals_handler?this._globals_handler.id():null)!=(e?e.id():null)&&(this._globals_handler=e,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(e){this._assembler.add_output_inputs(e)}add_globals_outputs(e){this._assembler.add_globals_outputs(e)}allow_attribute_exports(){return this._assembler.allow_attribute_exports()}setCompilationRequired(e=!0){this._compile_required=e}set_compilation_required_and_dirty(e){this.setCompilationRequired(),this.node.setDirty(e)}compileRequired(){return this._compile_required}post_compile(){this.createSpareParameters(),this.setCompilationRequired(!1)}createSpareParameters(){this._spare_params_controller.createSpareParameters()}addFilterFragmentShaderCallback(e,t){this.assembler._addFilterFragmentShaderCallback(e,t),this.setCompilationRequired()}removeFilterFragmentShaderCallback(e){this.assembler._removeFilterFragmentShaderCallback(e),this.setCompilationRequired()}}var pq;!function(e){e.FUNCTION_DECLARATION=\\\\\\\"function_declaration\\\\\\\",e.DEFINE=\\\\\\\"define\\\\\\\",e.BODY=\\\\\\\"body\\\\\\\"}(pq||(pq={}));class _q{constructor(e,t,n){this._name=e,this._input_names=t,this._dependencies=n}name(){return this._name}input_names(){return this._input_names}dependencies(){return this._dependencies}}class mq{constructor(e,t={}){this._name=e,this._options=t}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 fq{constructor(e){this._shader_name=e,this._definitions_by_node_id=new Map,this._body_lines_by_node_id=new Map}get shader_name(){return this._shader_name}addDefinitions(e,t){for(let n of t)u.pushOnArrayAtEntry(this._definitions_by_node_id,e.graphNodeId(),n)}definitions(e){return this._definitions_by_node_id.get(e.graphNodeId())}addBodyLines(e,t){for(let n of t)u.pushOnArrayAtEntry(this._body_lines_by_node_id,e.graphNodeId(),n)}body_lines(e){return this._body_lines_by_node_id.get(e.graphNodeId())}}class gq{constructor(e,t,n){this._shader_names=e,this._current_shader_name=t,this._assembler=n,this._lines_controller_by_shader_name=new Map;for(let e of this._shader_names)this._lines_controller_by_shader_name.set(e,new fq(e))}assembler(){return this._assembler}shaderNames(){return this._shader_names}set_current_shader_name(e){this._current_shader_name=e}get current_shader_name(){return this._current_shader_name}addDefinitions(e,t,n){if(0==t.length)return;n=n||this._current_shader_name;const i=this._lines_controller_by_shader_name.get(n);i&&i.addDefinitions(e,t)}definitions(e,t){const n=this._lines_controller_by_shader_name.get(e);if(n)return n.definitions(t)}addBodyLines(e,t,n){if(0==t.length)return;n=n||this._current_shader_name;const i=this._lines_controller_by_shader_name.get(n);i&&i.addBodyLines(e,t)}body_lines(e,t){const n=this._lines_controller_by_shader_name.get(e);if(n)return n.body_lines(t)}}const vq={[pq.FUNCTION_DECLARATION]:\\\\\\\"\\\\\\\",[pq.DEFINE]:\\\\\\\";\\\\\\\",[pq.BODY]:\\\\\\\";\\\\\\\"},yq={[pq.FUNCTION_DECLARATION]:\\\\\\\"\\\\\\\",[pq.DEFINE]:\\\\\\\"\\\\\\\",[pq.BODY]:\\\\\\\"\\\\t\\\\\\\"};class xq{static node_comment(e,t){let n=`// ${e.path()}`,i=yq[t];if(t==pq.BODY){let t=this.node_distance_to_material(e);e.type()==Mi.OUTPUT&&(t+=1),i=i.repeat(t)}return t==pq.BODY&&(n=`${i}${n}`),n}static line_wrap(e,t,n){let i=!0;0!=t.indexOf(\\\\\\\"#if\\\\\\\")&&0!=t.indexOf(\\\\\\\"#endif\\\\\\\")||(i=!1);let s=yq[n];if(n==pq.BODY&&(s=s.repeat(this.node_distance_to_material(e))),t=`${s}${t}`,i){const e=t[t.length-1],i=vq[n];e!=i&&\\\\\\\"{\\\\\\\"!=e&&\\\\\\\"}\\\\\\\"!=e&&(t+=i)}return t}static post_line_separator(e){return e==pq.BODY?\\\\\\\"\\\\t\\\\\\\":\\\\\\\"\\\\\\\"}static node_distance_to_material(e){const t=e.parent();if(!t)return 0;if(t.context()!=e.context())return 1;{let n=1;return e.type()!=Mi.INPUT&&e.type()!=Mi.OUTPUT||(n=0),n+this.node_distance_to_material(t)}}}class bq{constructor(e,t,n){this._node_traverser=e,this._root_nodes_for_shader_method=t,this._assembler=n,this._param_configs_controller=new vT,this._param_configs_set_allowed=!0,this._lines=new Map}shaderNames(){return this._node_traverser.shaderNames()}buildFromNodes(e,t){this._node_traverser.traverse(e);const n=new Map;for(let e of this.shaderNames()){const t=this._node_traverser.nodes_for_shader_name(e);n.set(e,t)}const i=this._node_traverser.sorted_nodes();for(let e of this.shaderNames()){const t=this._root_nodes_for_shader_method(e);for(let i of t)u.pushOnArrayAtEntry(n,e,i)}const s=new Map;for(let e of i)s.set(e.graphNodeId(),!0);for(let t of e)s.get(t.graphNodeId())||(i.push(t),s.set(t.graphNodeId(),!0));for(let e of i)e.reset_code();for(let e of t)e.reset_code();this._shaders_collection_controller=new gq(this.shaderNames(),this.shaderNames()[0],this._assembler),this.reset();for(let e of this.shaderNames()){let t=n.get(e)||[];if(t=f.uniq(t),this._shaders_collection_controller.set_current_shader_name(e),t)for(let e of t)e.setLines(this._shaders_collection_controller)}if(this._param_configs_set_allowed){for(let e of t)e.setParamConfigs();this.setParamConfigs(t)}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 e of this.shaderNames()){const t=new Map;this._lines.set(e,t)}}param_configs(){return this._param_configs_controller.list()||[]}lines(e,t){var n;return(null===(n=this._lines.get(e))||void 0===n?void 0:n.get(t))||[]}all_lines(){return this._lines}setParamConfigs(e){this._param_configs_controller.reset();for(let t of e){const e=t.param_configs();if(e)for(let t of e)this._param_configs_controller.push(t)}}set_code_lines(e){for(let t of this.shaderNames())this.add_code_lines(e,t)}add_code_lines(e,t){this.addDefinitions(e,t,tf.FUNCTION,pq.FUNCTION_DECLARATION),this.addDefinitions(e,t,tf.UNIFORM,pq.DEFINE),this.addDefinitions(e,t,tf.VARYING,pq.DEFINE),this.addDefinitions(e,t,tf.ATTRIBUTE,pq.DEFINE),this.add_code_line_for_nodes_and_line_type(e,t,pq.BODY)}addDefinitions(e,t,n,i){if(!this._shaders_collection_controller)return;const s=[];for(let i of e){let e=this._shaders_collection_controller.definitions(t,i);if(e){e=e.filter((e=>e.definition_type==n));for(let t of e)s.push(t)}}if(s.length>0){const e=new ef(s),n=e.uniq();if(e.errored)throw`code builder error: ${e.error_message}`;const r=new Map,o=new Map;for(let e of n){const t=e.node.graphNodeId();o.has(t)||o.set(t,!0),u.pushOnArrayAtEntry(r,t,e)}const a=this._lines.get(t);o.forEach(((e,t)=>{const n=r.get(t);if(n){const e=n[0];if(e){const t=xq.node_comment(e.node,i);u.pushOnArrayAtEntry(a,i,t);for(let t of n){const n=xq.line_wrap(e.node,t.line,i);u.pushOnArrayAtEntry(a,i,n)}const s=xq.post_line_separator(i);u.pushOnArrayAtEntry(a,i,s)}}}))}}add_code_line_for_nodes_and_line_type(e,t,n){var i=(e=e.filter((e=>{if(this._shaders_collection_controller){const n=this._shaders_collection_controller.body_lines(t,e);return n&&n.length>0}}))).length;for(let s=0;s<i;s++){const i=s==e.length-1;this.add_code_line_for_node_and_line_type(e[s],t,n,i)}}add_code_line_for_node_and_line_type(e,t,n,i){if(!this._shaders_collection_controller)return;const s=this._shaders_collection_controller.body_lines(t,e);if(s&&s.length>0){const r=this._lines.get(t),o=xq.node_comment(e,n);if(u.pushOnArrayAtEntry(r,n,o),f.uniq(s).forEach((t=>{t=xq.line_wrap(e,t,n),u.pushOnArrayAtEntry(r,n,t)})),n!=pq.BODY||!i){const e=xq.post_line_separator(n);u.pushOnArrayAtEntry(r,n,e)}}}}class wq{constructor(e,t,n){this._parent_node=e,this._shader_names=t,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((e=>{this._graph_ids_by_shader_name.set(e,new Map)}))}shaderNames(){return this._shader_names}input_names_for_shader_name(e,t){return this._input_names_for_shader_name_method(e,t)}traverse(e){this.reset();for(let e of this.shaderNames())this._leaves_graph_id.set(e,new Map);for(let t of this.shaderNames()){this._shader_name=t;for(let t of e)this.find_leaves_from_root_node(t),this.set_nodes_depth()}this._depth_by_graph_id.forEach(((e,t)=>{null!=e&&u.pushOnArrayAtEntry(this._graph_id_by_depth,e,t)}))}leaves_from_nodes(e){var t;this._shader_name=nf.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 t of e)this.find_leaves(t);const n=[];return null===(t=this._leaves_graph_id.get(this._shader_name))||void 0===t||t.forEach(((e,t)=>{n.push(t)})),this._graph.nodesFromIds(n)}nodes_for_shader_name(e){const t=[];this._graph_id_by_depth.forEach(((e,n)=>{t.push(n)})),t.sort(((e,t)=>e-t));const n=[],i=new Map;return t.forEach((t=>{const s=this._graph_id_by_depth.get(t);s&&s.forEach((t=>{var s;if(null===(s=this._graph_ids_by_shader_name.get(e))||void 0===s?void 0:s.get(t)){const s=this._graph.nodeFromId(t);this.add_nodes_with_children(s,i,n,e)}}))})),n}sorted_nodes(){const e=[];this._graph_id_by_depth.forEach(((t,n)=>{e.push(n)})),e.sort(((e,t)=>e-t));const t=[],n=new Map;return e.forEach((e=>{const i=this._graph_id_by_depth.get(e);if(i)for(let e of i){const i=this._graph.nodeFromId(e);i&&this.add_nodes_with_children(i,n,t)}})),t}add_nodes_with_children(e,t,n,i){if(t.get(e.graphNodeId())||(n.push(e),t.set(e.graphNodeId(),!0)),e.type()==Mi.INPUT){const s=e.parent();if(s){const r=this.sorted_nodes_for_shader_name_for_parent(s,i);for(let s of r)s.graphNodeId()!=e.graphNodeId()&&this.add_nodes_with_children(s,t,n,i)}}}sorted_nodes_for_shader_name_for_parent(e,t){const n=[];this._graph_id_by_depth.forEach(((e,t)=>{n.push(t)})),n.sort(((e,t)=>e-t));const i=[];n.forEach((n=>{const s=this._graph_id_by_depth.get(n);s&&s.forEach((n=>{var s;if(!t||(null===(s=this._graph_ids_by_shader_name.get(t))||void 0===s?void 0:s.get(n))){const t=this._graph.nodeFromId(n);t.parent()==e&&i.push(t)}}))}));const s=i[0];return e.context()==s.context()&&i.push(e),i}find_leaves_from_root_node(e){var t;null===(t=this._graph_ids_by_shader_name.get(this._shader_name))||void 0===t||t.set(e.graphNodeId(),!0);const n=this.input_names_for_shader_name(e,this._shader_name);if(n)for(let t of n){const n=e.io.inputs.named_input(t);n&&(u.pushOnArrayAtEntry(this._outputs_by_graph_id,n.graphNodeId(),e.graphNodeId()),this.find_leaves(n))}this._outputs_by_graph_id.forEach(((e,t)=>{this._outputs_by_graph_id.set(t,f.uniq(e))}))}find_leaves(e){var t;null===(t=this._graph_ids_by_shader_name.get(this._shader_name))||void 0===t||t.set(e.graphNodeId(),!0);const n=this._find_inputs_or_children(e),i=f.compact(n),s=f.uniq(i.map((e=>e.graphNodeId()))).map((e=>this._graph.nodeFromId(e)));if(s.length>0)for(let t of s)u.pushOnArrayAtEntry(this._outputs_by_graph_id,t.graphNodeId(),e.graphNodeId()),this.find_leaves(t);else this._leaves_graph_id.get(this._shader_name).set(e.graphNodeId(),!0)}_find_inputs_or_children(e){var t,n;if(e.type()==Mi.INPUT)return(null===(t=e.parent())||void 0===t?void 0:t.io.inputs.inputs())||[];if(e.childrenAllowed()){return[null===(n=e.childrenController)||void 0===n?void 0:n.output_node()]}return e.io.inputs.inputs()}set_nodes_depth(){this._leaves_graph_id.forEach(((e,t)=>{e.forEach(((e,t)=>{this.set_node_depth(t)}))}))}set_node_depth(e,t=0){const n=this._depth_by_graph_id.get(e);null!=n?this._depth_by_graph_id.set(e,Math.max(n,t)):this._depth_by_graph_id.set(e,t);const i=this._outputs_by_graph_id.get(e);i&&i.forEach((e=>{this.set_node_depth(e,t+1)}))}}const Aq=new Map([[nf.VERTEX,\\\\\\\"#include <common>\\\\\\\"],[nf.FRAGMENT,\\\\\\\"#include <common>\\\\\\\"]]),Tq=new Map([[nf.VERTEX,\\\\\\\"#include <color_vertex>\\\\\\\"],[nf.FRAGMENT,\\\\\\\"vec4 diffuseColor = vec4( diffuse, opacity );\\\\\\\"]]),Eq=new Map([[nf.VERTEX,[\\\\\\\"#include <begin_vertex>\\\\\\\",\\\\\\\"#include <beginnormal_vertex>\\\\\\\"]],[nf.FRAGMENT,[]]]);class Cq extends class{}{constructor(e){super(),this._gl_parent_node=e,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(e){this._overriden_gl_parent_node=e}currentGlParentNode(){return this._overriden_gl_parent_node||this._gl_parent_node}compile(){}_template_shader_for_shader_name(e){var t,n;switch(e){case nf.VERTEX:return null===(t=this.templateShader())||void 0===t?void 0:t.vertexShader;case nf.FRAGMENT:return null===(n=this.templateShader())||void 0===n?void 0:n.fragmentShader}}get globals_handler(){var e;return null===(e=this.currentGlParentNode().assemblerController)||void 0===e?void 0:e.globals_handler}compileAllowed(){var e;return null!=(null===(e=this.currentGlParentNode().assemblerController)||void 0===e?void 0:e.globals_handler)}shaders_by_name(){return this._shaders_by_name}_build_lines(){for(let e of this.shaderNames()){const t=this._template_shader_for_shader_name(e);t&&this._replace_template(t,e)}}set_root_nodes(e){this._root_nodes=e}templateShader(){}addUniforms(e){for(let t of this.param_configs())e[t.uniform_name]=t.uniform;this.uniformsTimeDependent()&&(e.time={value:this.currentGlParentNode().scene().time()}),this.uniforms_resolution_dependent()&&(e.resolution={value:new d.a(1e3,1e3)})}root_nodes_by_shader_name(e){const t=[];for(let e of this._root_nodes)switch(e.type()){case gT.type():case xT.type():t.push(e);break;case Zm.type():case pf.type():t.push(e)}return t}leaf_nodes_by_shader_name(e){const t=[];for(let e of this._leaf_nodes)switch(e.type()){case xA.type():t.push(e);break;case Zm.type():}return t}set_node_lines_globals(e,t){}set_node_lines_output(e,t){}set_node_lines_attribute(e,t){}codeBuilder(){return this._code_builder=this._code_builder||this._create_code_builder()}_resetCodeBuilder(){this._code_builder=void 0}_create_code_builder(){const e=new wq(this.currentGlParentNode(),this.shaderNames(),((e,t)=>this.input_names_for_shader_name(e,t)));return new bq(e,(e=>this.root_nodes_by_shader_name(e)),this)}build_code_from_nodes(e){const t=_f.findParamGeneratingNodes(this.currentGlParentNode());this.codeBuilder().buildFromNodes(e,t)}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(e,t){return this.codeBuilder().lines(e,t)}all_builder_lines(){return this.codeBuilder().all_lines()}param_configs(){return(this._param_config_owner||this.codeBuilder()).param_configs()}set_param_configs_owner(e){this._param_config_owner=e,this._param_config_owner?this.codeBuilder().disallow_new_param_configs():this.codeBuilder().allow_new_param_configs()}static output_input_connection_points(){return[new ho(\\\\\\\"position\\\\\\\",ro.VEC3),new ho(\\\\\\\"normal\\\\\\\",ro.VEC3),new ho(\\\\\\\"color\\\\\\\",ro.VEC3),new ho(\\\\\\\"alpha\\\\\\\",ro.FLOAT),new ho(\\\\\\\"uv\\\\\\\",ro.VEC2)]}add_output_inputs(e){e.io.inputs.setNamedInputConnectionPoints(Cq.output_input_connection_points())}static create_globals_node_output_connections(){return[new ho(\\\\\\\"position\\\\\\\",ro.VEC3),new ho(\\\\\\\"normal\\\\\\\",ro.VEC3),new ho(\\\\\\\"color\\\\\\\",ro.VEC3),new ho(\\\\\\\"uv\\\\\\\",ro.VEC2),new ho(\\\\\\\"mvPosition\\\\\\\",ro.VEC4),new ho(\\\\\\\"worldPosition\\\\\\\",ro.VEC4),new ho(\\\\\\\"worldNormal\\\\\\\",ro.VEC3),new ho(\\\\\\\"gl_Position\\\\\\\",ro.VEC4),new ho(\\\\\\\"gl_FragCoord\\\\\\\",ro.VEC4),new ho(\\\\\\\"cameraPosition\\\\\\\",ro.VEC3),new ho(\\\\\\\"resolution\\\\\\\",ro.VEC2),new ho(\\\\\\\"time\\\\\\\",ro.FLOAT)]}create_globals_node_output_connections(){return Cq.create_globals_node_output_connections()}add_globals_outputs(e){e.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(e){this._shader_configs=e}shaderNames(){var e;return(null===(e=this.shaderConfigs())||void 0===e?void 0:e.map((e=>e.name())))||[]}_reset_shader_configs(){this._shader_configs=void 0}create_shader_configs(){return[new _q(nf.VERTEX,[\\\\\\\"position\\\\\\\",\\\\\\\"normal\\\\\\\",\\\\\\\"uv\\\\\\\",pf.INPUT_NAME],[]),new _q(nf.FRAGMENT,[\\\\\\\"color\\\\\\\",\\\\\\\"alpha\\\\\\\"],[nf.VERTEX])]}shader_config(e){var t;return null===(t=this.shaderConfigs())||void 0===t?void 0:t.filter((t=>t.name()==e))[0]}variable_configs(){return this._variable_configs=this._variable_configs||this.create_variable_configs()}set_variable_configs(e){this._variable_configs=e}variable_config(e){return this.variable_configs().filter((t=>t.name()==e))[0]}static create_variable_configs(){return[new mq(\\\\\\\"position\\\\\\\",{default_from_attribute:!0,prefix:\\\\\\\"vec3 transformed = \\\\\\\"}),new mq(\\\\\\\"normal\\\\\\\",{default_from_attribute:!0,prefix:\\\\\\\"vec3 objectNormal = \\\\\\\",postLines:[\\\\\\\"#ifdef USE_TANGENT\\\\\\\",\\\\\\\"\\\\tvec3 objectTangent = vec3( tangent.xyz );\\\\\\\",\\\\\\\"#endif\\\\\\\"]}),new mq(\\\\\\\"color\\\\\\\",{prefix:\\\\\\\"diffuseColor.xyz = \\\\\\\"}),new mq(\\\\\\\"alpha\\\\\\\",{prefix:\\\\\\\"diffuseColor.a = \\\\\\\"}),new mq(\\\\\\\"uv\\\\\\\",{prefix:\\\\\\\"vUv = \\\\\\\",if:uf.IF_RULE.uv})]}create_variable_configs(){return Cq.create_variable_configs()}_reset_variable_configs(){this._variable_configs=void 0,this.variable_configs()}input_names_for_shader_name(e,t){var n;return(null===(n=this.shader_config(t))||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(e){return Aq.get(e)}insert_body_after(e){return Tq.get(e)}lines_to_remove(e){return Eq.get(e)}_replace_template(e,t){const n=this.builder_lines(t,pq.FUNCTION_DECLARATION),i=this.builder_lines(t,pq.DEFINE),s=this.builder_lines(t,pq.BODY);let r=e.split(\\\\\\\"\\\\n\\\\\\\");const o=[],a=this.insert_define_after(t),c=this.insert_body_after(t),l=this.lines_to_remove(t);let u=!1,h=!1;for(let e of r){1==u&&(n&&this._insert_lines(o,n),i&&this._insert_lines(o,i),u=!1),1==h&&(s&&this._insert_lines(o,s),h=!1);let t=!1;if(l)for(let n of l)e.indexOf(n)>=0&&(t=!0);t?(o.push(\\\\\\\"// removed:\\\\\\\"),o.push(`//${e}`)):o.push(e),a&&e.indexOf(a)>=0&&(u=!0),c&&e.indexOf(c)>=0&&(h=!0)}this._lines.set(t,o)}_insert_lines(e,t){if(t.length>0){for(let t=0;t<3;t++)e.push(\\\\\\\"\\\\\\\");for(let n of t)e.push(n);for(let t=0;t<3;t++)e.push(\\\\\\\"\\\\\\\")}}_addFilterFragmentShaderCallback(e,t){}_removeFilterFragmentShaderCallback(e){}getCustomMaterials(){return new Map}static expandShader(e){return function e(t){return t.replace(/^[ \\\\t]*#include +<([\\\\w\\\\d./]+)>/gm,(function(t,n){var i=G[n];if(void 0===i)throw new Error(\\\\\\\"Can not resolve #include <\\\\\\\"+n+\\\\\\\">\\\\\\\");return e(i)}))}(e)}}var Mq,Nq;!function(e){e.DISTANCE=\\\\\\\"customDistanceMaterial\\\\\\\",e.DEPTH=\\\\\\\"customDepthMaterial\\\\\\\",e.DEPTH_DOF=\\\\\\\"customDepthDOFMaterial\\\\\\\"}(Mq||(Mq={})),function(e){e.TIME=\\\\\\\"time\\\\\\\",e.RESOLUTION=\\\\\\\"resolution\\\\\\\",e.MV_POSITION=\\\\\\\"mvPosition\\\\\\\",e.GL_POSITION=\\\\\\\"gl_Position\\\\\\\",e.GL_FRAGCOORD=\\\\\\\"gl_FragCoord\\\\\\\",e.GL_POINTCOORD=\\\\\\\"gl_PointCoord\\\\\\\"}(Nq||(Nq={}));const Sq=[Nq.GL_FRAGCOORD,Nq.GL_POINTCOORD];class Oq extends Cq{constructor(){super(...arguments),this._assemblers_by_custom_name=new Map,this._filterFragmentShaderCallbacks=new Map}createMaterial(){return new B}custom_assembler_class_by_custom_name(){}_addCustomMaterials(e){const t=this.custom_assembler_class_by_custom_name();t&&t.forEach(((t,n)=>{this._add_custom_material(e,n,t)}))}_add_custom_material(e,t,n){let i=this._assemblers_by_custom_name.get(t);i||(i=new n(this.currentGlParentNode()),this._assemblers_by_custom_name.set(t,i)),e.customMaterials=e.customMaterials||{};const s=i.createMaterial();s.name=t,e.customMaterials[t]=s}compileCustomMaterials(e){const t=this.custom_assembler_class_by_custom_name();t&&t.forEach(((t,n)=>{if(this._code_builder){let i=this._assemblers_by_custom_name.get(n);i||(i=new t(this.currentGlParentNode()),this._assemblers_by_custom_name.set(n,i)),i.set_root_nodes(this._root_nodes),i.set_param_configs_owner(this._code_builder),i.set_shader_configs(this.shaderConfigs()),i.set_variable_configs(this.variable_configs());const s=e.customMaterials[n];s&&(i.setFilterFragmentShaderMethodOwner(this),i.compileMaterial(s),i.setFilterFragmentShaderMethodOwner(void 0))}}))}_resetFilterFragmentShaderCallbacks(){this._filterFragmentShaderCallbacks.clear()}_addFilterFragmentShaderCallback(e,t){this._filterFragmentShaderCallbacks.set(e,t)}_removeFilterFragmentShaderCallback(e){this._filterFragmentShaderCallbacks.delete(e)}setFilterFragmentShaderMethodOwner(e){this._filterFragmentShaderMethodOwner=e}filterFragmentShader(e){return this._filterFragmentShaderCallbacks.forEach(((t,n)=>{e=t(e)})),e}processFilterFragmentShader(e){return this._filterFragmentShaderMethodOwner?this._filterFragmentShaderMethodOwner.filterFragmentShader(e):this.filterFragmentShader(e)}compileMaterial(e){if(!this.compileAllowed())return;const t=_f.findOutputNodes(this.currentGlParentNode());t.length>1&&this.currentGlParentNode().states.error.set(\\\\\\\"only one output node allowed\\\\\\\");const n=_f.findVaryingNodes(this.currentGlParentNode()),i=t.concat(n);this.set_root_nodes(i),this._update_shaders();const s=this._shaders_by_name.get(nf.VERTEX),r=this._shaders_by_name.get(nf.FRAGMENT);s&&r&&(e.vertexShader=s,e.fragmentShader=this.processFilterFragmentShader(r),this.addUniforms(e.uniforms),e.needsUpdate=!0);const o=this.currentGlParentNode().scene();this.uniformsTimeDependent()?o.uniformsController.addTimeDependentUniformOwner(e.uuid,e.uniforms):o.uniformsController.removeTimeDependentUniformOwner(e.uuid),this.uniforms_resolution_dependent()?o.uniformsController.addResolutionDependentUniformOwner(e.uuid,e.uniforms):o.uniformsController.removeResolutionDependentUniformOwner(e.uuid),e.customMaterials&&this.compileCustomMaterials(e)}_update_shaders(){this._shaders_by_name=new Map,this._lines=new Map;for(let e of this.shaderNames()){const t=this._template_shader_for_shader_name(e);t&&this._lines.set(e,t.split(\\\\\\\"\\\\n\\\\\\\"))}this._root_nodes.length>0&&(this.build_code_from_nodes(this._root_nodes),this._build_lines());for(let e of this.shaderNames()){const t=this._lines.get(e);t&&this._shaders_by_name.set(e,t.join(\\\\\\\"\\\\n\\\\\\\"))}}shadow_assembler_class_by_custom_name(){return{}}add_output_body_line(e,t,n){var i;const s=e.io.inputs.named_input(n),r=e.variableForInput(n),o=this.variable_config(n);let a=null;if(s)a=Wm.vector3(r);else if(o.default_from_attribute()){const s=e.io.inputs.namedInputConnectionPointsByName(n);if(s){const r=s.type(),o=null===(i=this.globals_handler)||void 0===i?void 0:i.read_attribute(e,r,n,t);o&&(a=o)}}else{const e=o.default();e&&(a=e)}if(a){const n=o.prefix(),i=o.suffix(),s=o.if_condition();s&&t.addBodyLines(e,[`#if ${s}`]),t.addBodyLines(e,[`${n}${a}${i}`]);const r=o.postLines();r&&t.addBodyLines(e,r),s&&t.addBodyLines(e,[\\\\\\\"#endif\\\\\\\"])}}set_node_lines_output(e,t){var n;const i=t.current_shader_name,s=null===(n=this.shader_config(i))||void 0===n?void 0:n.input_names();if(s)for(let n of s)e.io.inputs.has_named_input(n)&&this.add_output_body_line(e,t,n)}set_node_lines_attribute(e,t){var n;const i=e.gl_type(),s=null===(n=this.globals_handler)||void 0===n?void 0:n.read_attribute(e,i,e.attribute_name,t),r=e.glVarName(e.output_name);t.addBodyLines(e,[`${i} ${r} = ${s}`])}handle_globals_output_name(e){var t;switch(e.output_name){case Nq.TIME:return void this.handleTime(e);case Nq.RESOLUTION:return void this.handle_resolution(e);case Nq.MV_POSITION:return void this.handle_mvPosition(e);case Nq.GL_POSITION:return void this.handle_gl_Position(e);case Nq.GL_FRAGCOORD:return void this.handle_gl_FragCoord(e);case Nq.GL_POINTCOORD:return void this.handle_gl_PointCoord(e);default:null===(t=this.globals_handler)||void 0===t||t.handle_globals_node(e.globals_node,e.output_name,e.shaders_collection_controller)}}handleTime(e){const t=new af(e.globals_node,ro.FLOAT,e.output_name);e.globals_shader_name&&u.pushOnArrayAtEntry(e.definitions_by_shader_name,e.globals_shader_name,t);const n=`float ${e.var_name} = ${e.output_name}`;for(let i of e.dependencies)u.pushOnArrayAtEntry(e.definitions_by_shader_name,i,t),u.pushOnArrayAtEntry(e.body_lines_by_shader_name,i,n);e.body_lines.push(n),this.setUniformsTimeDependent()}handle_resolution(e){e.body_lines.push(`vec2 ${e.var_name} = resolution`);const t=new af(e.globals_node,ro.VEC2,e.output_name);e.globals_shader_name&&u.pushOnArrayAtEntry(e.definitions_by_shader_name,e.globals_shader_name,t);for(let n of e.dependencies)u.pushOnArrayAtEntry(e.definitions_by_shader_name,n,t);this.set_uniforms_resolution_dependent()}handle_mvPosition(e){if(e.shader_name==nf.FRAGMENT){const t=e.globals_node,n=e.shaders_collection_controller,i=new cf(t,ro.VEC4,e.var_name),s=`${e.var_name} = modelViewMatrix * vec4(position, 1.0)`;n.addDefinitions(t,[i],nf.VERTEX),n.addBodyLines(t,[s],nf.VERTEX),n.addDefinitions(t,[i])}}handle_gl_Position(e){if(e.shader_name==nf.FRAGMENT){const t=e.globals_node,n=e.shaders_collection_controller,i=new cf(t,ro.VEC4,e.var_name),s=`${e.var_name} = projectionMatrix * modelViewMatrix * vec4(position, 1.0)`;n.addDefinitions(t,[i],nf.VERTEX),n.addBodyLines(t,[s],nf.VERTEX),n.addDefinitions(t,[i])}}handle_gl_FragCoord(e){e.shader_name==nf.FRAGMENT&&e.body_lines.push(`vec4 ${e.var_name} = gl_FragCoord`)}handle_gl_PointCoord(e){e.shader_name==nf.FRAGMENT?e.body_lines.push(`vec2 ${e.var_name} = gl_PointCoord`):e.body_lines.push(`vec2 ${e.var_name} = vec2(0.0, 0.0)`)}set_node_lines_globals(e,t){const n=[],i=t.current_shader_name,s=this.shader_config(i);if(!s)return;const r=s.dependencies(),o=new Map,a=new Map,c=this.used_output_names_for_shader(e,i);for(let s of c){const c=e.glVarName(s),l=t.current_shader_name,u={globals_node:e,shaders_collection_controller:t,output_name:s,globals_shader_name:l,definitions_by_shader_name:o,body_lines:n,var_name:c,shader_name:i,dependencies:r,body_lines_by_shader_name:a};this.handle_globals_output_name(u)}o.forEach(((n,i)=>{t.addDefinitions(e,n,i)})),a.forEach(((n,i)=>{t.addBodyLines(e,n,i)})),t.addBodyLines(e,n)}used_output_names_for_shader(e,t){const n=e.io.outputs.used_output_names(),i=[];for(let e of n)t==nf.VERTEX&&Sq.includes(e)||i.push(e);return i}}const Lq=new Map([[nf.VERTEX,\\\\\\\"#include <begin_vertex>\\\\\\\"],[nf.FRAGMENT,\\\\\\\"vec4 diffuseColor = vec4( 1.0 );\\\\\\\"]]);const Pq=new Map([[nf.VERTEX,\\\\\\\"#include <begin_vertex>\\\\\\\"],[nf.FRAGMENT,\\\\\\\"vec4 diffuseColor = vec4( 1.0 );\\\\\\\"]]);var Rq=\\\\\\\"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 Iq=new Map([[nf.VERTEX,\\\\\\\"// INSERT DEFINES\\\\\\\"]]),Fq=new Map([[nf.VERTEX,\\\\\\\"// INSERT BODY\\\\\\\"]]);const Dq=new Map([]);Dq.set(Mq.DISTANCE,class extends Oq{templateShader(){const e=H.distanceRGBA;return{vertexShader:e.vertexShader,fragmentShader:e.fragmentShader,uniforms:e.uniforms}}insert_body_after(e){return Lq.get(e)}createMaterial(){const e=this.templateShader();return new B({defines:{DEPTH_PACKING:[w.Hb,w.j][0]},uniforms:k.clone(e.uniforms),vertexShader:e.vertexShader,fragmentShader:e.fragmentShader})}}),Dq.set(Mq.DEPTH,class extends Oq{templateShader(){const e=H.depth;return{vertexShader:e.vertexShader,fragmentShader:e.fragmentShader,uniforms:e.uniforms}}insert_body_after(e){return Pq.get(e)}createMaterial(){const e=this.templateShader();return new B({defines:{DEPTH_PACKING:[w.Hb,w.j][0]},uniforms:k.clone(e.uniforms),vertexShader:e.vertexShader,fragmentShader:e.fragmentShader})}}),Dq.set(Mq.DEPTH_DOF,class extends Oq{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:Rq,uniforms:{mNear:{value:0},mFar:{value:10}}}}insert_define_after(e){return Iq.get(e)}insert_body_after(e){return Fq.get(e)}createMaterial(){const e=this.templateShader();return new B({uniforms:k.clone(e.uniforms),vertexShader:e.vertexShader,fragmentShader:e.fragmentShader})}});class kq extends Oq{custom_assembler_class_by_custom_name(){return Dq}}class Bq extends kq{templateShader(){const e=H.basic;return{vertexShader:e.vertexShader,fragmentShader:e.fragmentShader,uniforms:e.uniforms}}createMaterial(){const e=this.templateShader(),t=new B({lights:!1,uniforms:k.clone(e.uniforms),vertexShader:e.vertexShader,fragmentShader:e.fragmentShader});return this._addCustomMaterials(t),t}}class zq extends kq{templateShader(){const e=H.lambert;return{vertexShader:e.vertexShader,fragmentShader:e.fragmentShader,uniforms:e.uniforms}}createMaterial(){const e=this.templateShader(),t=new B({lights:!0,uniforms:k.clone(e.uniforms),vertexShader:e.vertexShader,fragmentShader:e.fragmentShader});return this._addCustomMaterials(t),t}}class Uq extends kq{templateShader(){const e=H.phong;return{vertexShader:e.vertexShader,fragmentShader:e.fragmentShader,uniforms:e.uniforms}}createMaterial(){const e=this.templateShader(),t=new B({lights:!0,uniforms:k.clone(e.uniforms),vertexShader:e.vertexShader,fragmentShader:e.fragmentShader});return this._addCustomMaterials(t),t}}var Gq=\\\\\\\"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 Vq extends kq{constructor(e){super(e),this._gl_parent_node=e,this._addFilterFragmentShaderCallback(\\\\\\\"MeshStandardBuilderMatNode\\\\\\\",Vq.filterFragmentShader)}isPhysical(){return!1}templateShader(){const e=this.isPhysical()?H.physical:H.standard;return{vertexShader:e.vertexShader,fragmentShader:e.fragmentShader,uniforms:e.uniforms}}static filterFragmentShader(e){return e=(e=(e=e.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;\\\\\\\"),Vq.USE_SSS&&(e=(e=e.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\\\\\\\")),e}createMaterial(){const e=this.templateShader(),t=new B({lights:!0,extensions:{derivatives:!0},uniforms:k.clone(e.uniforms),vertexShader:e.vertexShader,fragmentShader:e.fragmentShader});return this.isPhysical()&&(t.defines.PHYSICAL=!0),this._addCustomMaterials(t),t}add_output_inputs(e){const t=Cq.output_input_connection_points();t.push(new ho(\\\\\\\"metalness\\\\\\\",ro.FLOAT,1)),t.push(new ho(\\\\\\\"roughness\\\\\\\",ro.FLOAT,1)),t.push(new ho(\\\\\\\"emissive\\\\\\\",ro.VEC3,[1,1,1])),Vq.USE_SSS&&t.push(new ho(\\\\\\\"SSSModel\\\\\\\",ro.SSS_MODEL,Gq)),e.io.inputs.setNamedInputConnectionPoints(t)}create_shader_configs(){return[new _q(nf.VERTEX,[\\\\\\\"position\\\\\\\",\\\\\\\"normal\\\\\\\",\\\\\\\"uv\\\\\\\"],[]),new _q(nf.FRAGMENT,[\\\\\\\"color\\\\\\\",\\\\\\\"alpha\\\\\\\",\\\\\\\"metalness\\\\\\\",\\\\\\\"roughness\\\\\\\",\\\\\\\"emissive\\\\\\\",\\\\\\\"SSSModel\\\\\\\"],[nf.VERTEX])]}create_variable_configs(){const e=Cq.create_variable_configs();return e.push(new mq(\\\\\\\"metalness\\\\\\\",{default:\\\\\\\"1.0\\\\\\\",prefix:\\\\\\\"float POLY_metalness = \\\\\\\"})),e.push(new mq(\\\\\\\"roughness\\\\\\\",{default:\\\\\\\"1.0\\\\\\\",prefix:\\\\\\\"float POLY_roughness = \\\\\\\"})),e.push(new mq(\\\\\\\"emissive\\\\\\\",{default:\\\\\\\"vec3(1.0, 1.0, 1.0)\\\\\\\",prefix:\\\\\\\"vec3 POLY_emissive = \\\\\\\"})),Vq.USE_SSS&&e.push(new mq(\\\\\\\"SSSModel\\\\\\\",{default:Gq,prefix:\\\\\\\"SSSModel POLY_SSSModel = \\\\\\\"})),e}}Vq.USE_SSS=!0;class jq extends Vq{isPhysical(){return!0}}const Hq=new Map([[nf.VERTEX,\\\\\\\"// INSERT DEFINES\\\\\\\"]]),qq=new Map([[nf.VERTEX,\\\\\\\"// INSERT BODY\\\\\\\"]]);const Wq=new Map([[nf.VERTEX,\\\\\\\"// INSERT DEFINES\\\\\\\"]]),Xq=new Map([[nf.VERTEX,\\\\\\\"// INSERT BODY\\\\\\\"]]);const Yq=new Map([[nf.VERTEX,[\\\\\\\"#include <begin_vertex>\\\\\\\",\\\\\\\"gl_PointSize = size;\\\\\\\"]],[nf.FRAGMENT,[]]]),$q=new Map;$q.set(Mq.DISTANCE,class extends Oq{templateShader(){const e=H.distanceRGBA,t=k.clone(e.uniforms);return t.size={value:1},t.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:e.fragmentShader,uniforms:t}}insert_define_after(e){return Hq.get(e)}insert_body_after(e){return qq.get(e)}createMaterial(){const e=this.templateShader();return new B({defines:{USE_SIZEATTENUATION:1,DEPTH_PACKING:[w.Hb,w.j][0]},uniforms:k.clone(e.uniforms),vertexShader:e.vertexShader,fragmentShader:e.fragmentShader})}}),$q.set(Mq.DEPTH_DOF,class extends Oq{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:Rq,uniforms:{size:{value:1},scale:{value:1},mNear:{value:0},mFar:{value:10}}}}insert_define_after(e){return Wq.get(e)}insert_body_after(e){return Xq.get(e)}createMaterial(){const e=this.templateShader();return new B({depthTest:!0,defines:{USE_SIZEATTENUATION:1},uniforms:k.clone(e.uniforms),vertexShader:e.vertexShader,fragmentShader:e.fragmentShader})}});class Qq extends Oq{custom_assembler_class_by_custom_name(){return $q}templateShader(){const e=H.points;return{vertexShader:e.vertexShader,fragmentShader:e.fragmentShader,uniforms:e.uniforms}}createMaterial(){const e=this.templateShader(),t=new B({transparent:!0,fog:!0,defines:{USE_SIZEATTENUATION:1},uniforms:k.clone(e.uniforms),vertexShader:e.vertexShader,fragmentShader:e.fragmentShader});return this._addCustomMaterials(t),t}add_output_inputs(e){const t=Cq.output_input_connection_points();t.push(new ho(\\\\\\\"gl_PointSize\\\\\\\",ro.FLOAT)),e.io.inputs.setNamedInputConnectionPoints(t)}create_globals_node_output_connections(){return Cq.create_globals_node_output_connections().concat([new ho(Nq.GL_POINTCOORD,ro.VEC2)])}create_shader_configs(){return[new _q(nf.VERTEX,[\\\\\\\"position\\\\\\\",\\\\\\\"normal\\\\\\\",\\\\\\\"uv\\\\\\\",\\\\\\\"gl_PointSize\\\\\\\"],[]),new _q(nf.FRAGMENT,[\\\\\\\"color\\\\\\\",\\\\\\\"alpha\\\\\\\"],[nf.VERTEX])]}create_variable_configs(){return Cq.create_variable_configs().concat([new mq(\\\\\\\"gl_PointSize\\\\\\\",{default:\\\\\\\"1.0\\\\\\\",prefix:\\\\\\\"gl_PointSize = \\\\\\\",suffix:\\\\\\\" * size * 10.0\\\\\\\"})])}lines_to_remove(e){return Yq.get(e)}}const Jq=new Map([[nf.VERTEX,\\\\\\\"// INSERT DEFINES\\\\\\\"]]),Kq=new Map([[nf.VERTEX,\\\\\\\"// INSERT BODY\\\\\\\"]]);const Zq=new Map([]);Zq.set(Mq.DEPTH_DOF,class extends Oq{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:Rq,uniforms:{scale:{value:1},mNear:{value:0},mFar:{value:10}}}}insert_define_after(e){return Jq.get(e)}insert_body_after(e){return Kq.get(e)}createMaterial(){const e=this.templateShader();return new B({depthTest:!0,linewidth:100,uniforms:k.clone(e.uniforms),vertexShader:e.vertexShader,fragmentShader:e.fragmentShader})}});const eW=new Map([[nf.VERTEX,[\\\\\\\"#include <begin_vertex>\\\\\\\",\\\\\\\"#include <project_vertex>\\\\\\\"]],[nf.FRAGMENT,[]]]);class tW extends Oq{templateShader(){const e=H.dashed;return{vertexShader:e.vertexShader,fragmentShader:e.fragmentShader,uniforms:e.uniforms}}createMaterial(){const e=this.templateShader(),t=new B({depthTest:!0,alphaTest:.5,linewidth:1,uniforms:k.clone(e.uniforms),vertexShader:e.vertexShader,fragmentShader:e.fragmentShader});return this._addCustomMaterials(t),t}custom_assembler_class_by_custom_name(){return console.log(\\\\\\\"custom_assembler_class_by_custom_name\\\\\\\",Zq),Zq}create_shader_configs(){return[new _q(nf.VERTEX,[\\\\\\\"position\\\\\\\",\\\\\\\"uv\\\\\\\"],[]),new _q(nf.FRAGMENT,[\\\\\\\"color\\\\\\\",\\\\\\\"alpha\\\\\\\"],[nf.VERTEX])]}static output_input_connection_points(){return[new ho(\\\\\\\"position\\\\\\\",ro.VEC3),new ho(\\\\\\\"color\\\\\\\",ro.VEC3),new ho(\\\\\\\"alpha\\\\\\\",ro.FLOAT),new ho(\\\\\\\"uv\\\\\\\",ro.VEC2)]}add_output_inputs(e){e.io.inputs.setNamedInputConnectionPoints(tW.output_input_connection_points())}static create_globals_node_output_connections(){return[new ho(\\\\\\\"position\\\\\\\",ro.VEC3),new ho(\\\\\\\"color\\\\\\\",ro.VEC3),new ho(\\\\\\\"uv\\\\\\\",ro.VEC2),new ho(\\\\\\\"gl_FragCoord\\\\\\\",ro.VEC4),new ho(\\\\\\\"resolution\\\\\\\",ro.VEC2),new ho(\\\\\\\"time\\\\\\\",ro.FLOAT)]}create_globals_node_output_connections(){return tW.create_globals_node_output_connections()}create_variable_configs(){return[new mq(\\\\\\\"position\\\\\\\",{default:\\\\\\\"vec3( position )\\\\\\\",prefix:\\\\\\\"vec3 transformed = \\\\\\\",suffix:\\\\\\\";vec4 mvPosition = vec4( transformed, 1.0 ); gl_Position = projectionMatrix * modelViewMatrix * mvPosition;\\\\\\\"}),new mq(\\\\\\\"color\\\\\\\",{prefix:\\\\\\\"diffuseColor.xyz = \\\\\\\"}),new mq(\\\\\\\"alpha\\\\\\\",{prefix:\\\\\\\"diffuseColor.w = \\\\\\\"}),new mq(\\\\\\\"uv\\\\\\\",{prefix:\\\\\\\"vUv = \\\\\\\",if:uf.IF_RULE.uv})]}lines_to_remove(e){return eW.get(e)}}class nW extends Cq{templateShader(){}_template_shader_for_shader_name(e){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(e){var t,n;const i=[];for(let s of this._root_nodes)switch(s.type()){case gT.type():i.push(s);break;case Zm.type():{const r=s.attribute_name,o=null===(t=this._texture_allocations_controller)||void 0===t?void 0:t.variable(r);if(o&&o.allocation()){(null===(n=o.allocation())||void 0===n?void 0:n.shaderName())==e&&i.push(s)}break}}return i}leaf_nodes_by_shader_name(e){var t,n;const i=[];for(let s of this._leaf_nodes)switch(s.type()){case xA.type():i.push(s);break;case Zm.type():{const r=s.attribute_name,o=null===(t=this._texture_allocations_controller)||void 0===t?void 0:t.variable(r);if(o&&o.allocation()){(null===(n=o.allocation())||void 0===n?void 0:n.shaderName())==e&&i.push(s)}break}}return i}setup_shader_names_and_variables(){var e;const t=new wq(this.currentGlParentNode(),this.shaderNames(),((e,t)=>this.input_names_for_shader_name(e,t)));this._leaf_nodes=t.leaves_from_nodes(this._root_nodes),this._texture_allocations_controller=new sG,this._texture_allocations_controller.allocateConnectionsFromRootNodes(this._root_nodes,this._leaf_nodes),this.globals_handler&&(null===(e=this.globals_handler)||void 0===e||e.set_texture_allocations_controller(this._texture_allocations_controller)),this._reset_shader_configs()}update_shaders(){this._shaders_by_name.clear(),this._lines.clear();for(let e of this.shaderNames()){const t=this._template_shader_for_shader_name(e);this._lines.set(e,t.split(\\\\\\\"\\\\n\\\\\\\"))}this._root_nodes.length>0&&(this._resetCodeBuilder(),this.build_code_from_nodes(this._root_nodes),this._build_lines());for(let e of this.shaderNames()){const t=this._lines.get(e);t&&this._shaders_by_name.set(e,t.join(\\\\\\\"\\\\n\\\\\\\"))}}add_output_inputs(e){e.io.inputs.setNamedInputConnectionPoints([new ho(\\\\\\\"position\\\\\\\",ro.VEC3),new ho(\\\\\\\"velocity\\\\\\\",ro.VEC3)])}add_globals_outputs(e){e.io.outputs.setNamedOutputConnectionPoints([new ho(\\\\\\\"position\\\\\\\",ro.VEC3),new ho(\\\\\\\"velocity\\\\\\\",ro.VEC3),new ho(\\\\\\\"time\\\\\\\",ro.FLOAT)])}allow_attribute_exports(){return!0}textureAllocationsController(){return this._texture_allocations_controller=this._texture_allocations_controller||new sG}create_shader_configs(){var e;return(null===(e=this._texture_allocations_controller)||void 0===e?void 0:e.createShaderConfigs())||[]}create_variable_configs(){return[]}shaderNames(){return this.textureAllocationsController().shaderNames()||[]}input_names_for_shader_name(e,t){return this.textureAllocationsController().inputNamesForShaderName(e,t)||[]}insert_define_after(e){return\\\\\\\"// INSERT DEFINE\\\\\\\"}insert_body_after(e){return\\\\\\\"// INSERT BODY\\\\\\\"}lines_to_remove(e){return[\\\\\\\"// INSERT DEFINE\\\\\\\",\\\\\\\"// INSERT BODY\\\\\\\"]}add_export_body_line(e,t,n,i,s){var r;if(n){const n=e.variableForInput(t),o=Wm.vector3(n);if(o){const t=this.textureAllocationsController().variable(i),n=s.current_shader_name;if(t&&(null===(r=t.allocation())||void 0===r?void 0:r.shaderName())==n){const i=`gl_FragColor.${t.component()} = ${o}`;s.addBodyLines(e,[i],n)}}}}set_node_lines_output(e,t){const n=t.current_shader_name,i=this.textureAllocationsController().inputNamesForShaderName(e,n);if(i)for(let n of i){const i=e.io.inputs.named_input(n);if(i){const s=n;this.add_export_body_line(e,n,i,s,t)}}}set_node_lines_attribute(e,t){var n,i;if(e.isImporting()){const s=e.gl_type(),r=e.attribute_name,o=null===(n=this.globals_handler)||void 0===n?void 0:n.read_attribute(e,s,r,t),a=e.glVarName(e.output_name),c=`${s} ${a} = ${o}`;t.addBodyLines(e,[c]);const l=this.textureAllocationsController().variable(r),u=t.current_shader_name;if(l&&(null===(i=l.allocation())||void 0===i?void 0:i.shaderName())==u){const n=this.textureAllocationsController().variable(r);if(n){const i=`gl_FragColor.${n.component()} = ${a}`;t.addBodyLines(e,[i])}}}if(e.isExporting()){const n=e.connected_input_node();if(n){const i=e.attribute_name;this.add_export_body_line(e,e.input_name,n,i,t)}}}set_node_lines_globals(e,t){for(let n of e.io.outputs.used_output_names())switch(n){case\\\\\\\"time\\\\\\\":this._handle_globals_time(e,n,t);break;default:this._handle_globals_default(e,n,t)}}_handle_globals_time(e,t,n){const i=new af(e,ro.FLOAT,t);n.addDefinitions(e,[i]);const s=`float ${e.glVarName(t)} = ${t}`;n.addBodyLines(e,[s]),this.setUniformsTimeDependent()}_handle_globals_default(e,t,n){var i;const s=e.io.outputs.namedOutputConnectionPointsByName(t);if(s){const r=s.type(),o=null===(i=this.globals_handler)||void 0===i?void 0:i.read_attribute(e,r,t,n);if(o){const i=`${r} ${e.glVarName(t)} = ${o}`;n.addBodyLines(e,[i])}}}}class iW extends Cq{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(nf.FRAGMENT)}uniforms(){return this._uniforms}update_fragment_shader(){this._lines=new Map,this._shaders_by_name=new Map;for(let e of this.shaderNames())if(e==nf.FRAGMENT){const t=this.templateShader().fragmentShader;this._lines.set(e,t.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 e of this.shaderNames()){const t=this._lines.get(e);t&&this._shaders_by_name.set(e,t.join(\\\\\\\"\\\\n\\\\\\\"))}zf.handle_dependencies(this.currentGlParentNode(),this.uniformsTimeDependent(),this._uniforms)}add_output_inputs(e){e.io.inputs.setNamedInputConnectionPoints([new ho(\\\\\\\"color\\\\\\\",ro.VEC3),new ho(\\\\\\\"alpha\\\\\\\",ro.FLOAT)])}add_globals_outputs(e){e.io.outputs.setNamedOutputConnectionPoints([new ho(\\\\\\\"gl_FragCoord\\\\\\\",ro.VEC2),new ho(\\\\\\\"time\\\\\\\",ro.FLOAT)])}create_shader_configs(){return[new _q(nf.FRAGMENT,[\\\\\\\"color\\\\\\\",\\\\\\\"alpha\\\\\\\"],[])]}create_variable_configs(){return[new mq(\\\\\\\"color\\\\\\\",{prefix:\\\\\\\"diffuseColor.xyz = \\\\\\\"}),new mq(\\\\\\\"alpha\\\\\\\",{prefix:\\\\\\\"diffuseColor.a = \\\\\\\",default:\\\\\\\"1.0\\\\\\\"})]}insert_define_after(e){return\\\\\\\"// INSERT DEFINE\\\\\\\"}insert_body_after(e){return\\\\\\\"// INSERT BODY\\\\\\\"}lines_to_remove(e){return[\\\\\\\"// INSERT DEFINE\\\\\\\",\\\\\\\"// INSERT BODY\\\\\\\"]}handle_gl_FragCoord(e,t,n){\\\\\\\"fragment\\\\\\\"==t&&e.push(`vec2 ${n} = vec2(gl_FragCoord.x / resolution.x, gl_FragCoord.y / resolution.y)`)}set_node_lines_output(e,t){const n=this.input_names_for_shader_name(e,t.current_shader_name);if(n)for(let i of n){if(e.io.inputs.named_input(i)){const n=e.variableForInput(i);let s;\\\\\\\"color\\\\\\\"==i&&(s=`diffuseColor.xyz = ${Wm.any(n)}`),\\\\\\\"alpha\\\\\\\"==i&&(s=`diffuseColor.a = ${Wm.any(n)}`),s&&t.addBodyLines(e,[s])}}}set_node_lines_globals(e,t){const n=t.current_shader_name;if(!this.shader_config(n))return;const i=[],s=[];for(let t of e.io.outputs.used_output_names()){const r=e.glVarName(t);switch(t){case\\\\\\\"time\\\\\\\":s.push(new af(e,ro.FLOAT,t)),i.push(`float ${r} = ${t}`),this.setUniformsTimeDependent();break;case\\\\\\\"gl_FragCoord\\\\\\\":this.handle_gl_FragCoord(i,n,r)}}t.addDefinitions(e,s,n),t.addBodyLines(e,i)}}const sW=new Map([]);class rW extends Oq{custom_assembler_class_by_custom_name(){return sW}}const oW=new Map([[nf.VERTEX,\\\\\\\"// start builder body code\\\\\\\"],[nf.FRAGMENT,\\\\\\\"// start builder body code\\\\\\\"]]),aW=new Map([[nf.FRAGMENT,[]]]);class cW extends rW{templateShader(){return{vertexShader:AN,fragmentShader:TN,uniforms:k.clone(EN)}}createMaterial(){const e=this.templateShader(),t=new B({vertexShader:e.vertexShader,fragmentShader:e.fragmentShader,side:w.H,transparent:!0,depthTest:!0,uniforms:k.clone(e.uniforms)});return Gs.add_user_data_render_hook(t,NN.render_hook.bind(NN)),this._addCustomMaterials(t),t}add_output_inputs(e){e.io.inputs.setNamedInputConnectionPoints([new ho(\\\\\\\"density\\\\\\\",ro.FLOAT,1)])}static create_globals_node_output_connections(){return[new ho(\\\\\\\"position\\\\\\\",ro.VEC3),new ho(\\\\\\\"pos_normalized\\\\\\\",ro.VEC3),new ho(\\\\\\\"time\\\\\\\",ro.FLOAT)]}create_globals_node_output_connections(){return cW.create_globals_node_output_connections()}insert_body_after(e){return oW.get(e)}lines_to_remove(e){return aW.get(e)}create_shader_configs(){return[new _q(nf.VERTEX,[],[]),new _q(nf.FRAGMENT,[\\\\\\\"density\\\\\\\"],[nf.VERTEX])]}static create_variable_configs(){return[new mq(\\\\\\\"position\\\\\\\",{}),new mq(\\\\\\\"density\\\\\\\",{prefix:\\\\\\\"density *= \\\\\\\"})]}create_variable_configs(){return cW.create_variable_configs()}set_node_lines_globals(e,t){const n=[],i=t.current_shader_name,s=this.shader_config(i);if(!s)return;const r=s.dependencies(),o=new Map,a=new Map;let c,l;for(let s of e.io.outputs.used_output_names()){const h=e.glVarName(s),d=t.current_shader_name;switch(s){case\\\\\\\"time\\\\\\\":c=new af(e,ro.FLOAT,s),d&&u.pushOnArrayAtEntry(o,d,c),l=`float ${h} = ${s}`;for(let e of r)u.pushOnArrayAtEntry(o,e,c),u.pushOnArrayAtEntry(a,e,l);n.push(l),this.setUniformsTimeDependent();break;case\\\\\\\"position\\\\\\\":i==nf.FRAGMENT&&n.push(`vec3 ${h} = position_for_step`);break;case\\\\\\\"pos_normalized\\\\\\\":i==nf.FRAGMENT&&n.push(`vec3 ${h} = (position_for_step - u_BoundingBoxMax) / (u_BoundingBoxMax - u_BoundingBoxMin)`)}}o.forEach(((n,i)=>{t.addDefinitions(e,n,i)})),a.forEach(((n,i)=>{t.addBodyLines(e,n,i)})),t.addBodyLines(e,n)}}class lW{static async run(){this._started||(this._started=!0,class{static async run(e){(class{static run(e){e.registerNode(y_,wc),e.registerNode(b_,Ec),e.registerNode(A_,wc),e.registerNode(E_,wc),e.registerNode(I_,wc),e.registerNode(D_,bc),e.registerNode(B_,wc),e.registerNode(U_,Ec),e.registerNode(V_,Tc),e.registerNode($_,Tc),e.registerNode(J_,wc),e.registerNode(Z_,bc),e.registerNode(am,Tc),e.registerNode(um,Ac),e.registerNode(hm,Ac),e.registerNode(dm,Ac),e.registerNode(pm,Ac),e.registerNode(Fm,Ac),e.registerNode(Dm,Ac)}}).run(e),class{static run(e){e.registerNode(zf,Cc),e.registerNode(hg,Mc),e.registerNode(pg,Mc),e.registerNode(yg,Mc),e.registerNode($g,Mc),e.registerNode(av,Mc),e.registerNode(dv,Mc),e.registerNode(gv,Nc),e.registerNode(yv,Nc),e.registerNode(Av,Nc),e.registerNode(Ev,Nc),e.registerNode(Nv,Cc),e.registerNode(Lv,Mc),e.registerNode(Iv,Cc),e.registerNode(kv,Sc),e.registerNode(zv,Sc),e.registerNode(Bv,Sc),e.registerNode(Uv,Sc),e.registerNode(Gv,Sc),e.registerNode(Vv,Sc)}}.run(e),class{static run(e){e.registerNode(Wv,Rc),e.registerNode($v,Pc),e.registerNode(Jv,Pc),e.registerNode(ey,Pc),e.registerNode(ry,Oc),e.registerNode(my,Oc),e.registerNode(_y,Oc),e.registerNode(gy,Pc),e.registerNode(ja,Lc),e.registerNode(Oa,Lc),e.registerNode(by,Pc),e.registerNode(Ay,Pc),e.registerNode(fa,Lc),e.registerNode(My,Rc),e.registerNode(Ly,Pc),e.registerNode(ba,Lc),e.registerNode(ky,Oc),e.registerNode(ox,Pc),e.registerNode(Ea,Rc),e.registerNode(ux,Rc),e.registerNode(bx,Rc),e.registerNode(Ax,Pc),e.registerNode(Cx,Pc),e.registerNode(qa,Lc),e.registerNode(Nx,Pc),e.registerNode(Fa,Lc),e.registerNode(Lx,Ic),e.registerNode(Px,Ic),e.registerNode(Rx,Ic),e.registerNode(Ix,Ic),e.registerNode(Fx,Ic),e.registerNode(Dx,Ic)}}.run(e),class{static run(e){e.registerNode(bb,Gc),e.registerNode(fw,Vc),e.registerNode(wb,Hc),e.registerNode(tw,Gc),e.registerNode(bw,Hc),e.registerNode(aw,Uc),e.registerNode(Ab,Hc),e.registerNode(Tb,Hc),e.registerNode(Zm,Bc,{except:[`${Ei.COP}/builder`]}),e.registerNode(Xx,Dc),e.registerNode(Eb,Gc),e.registerNode(Jb,Gc),e.registerNode(Ew,Fc),e.registerNode(Pw,Uc),e.registerNode(Rw,Gc),e.registerNode(Dw,Bc),e.registerNode(Cb,Hc),e.registerNode(zw,kc),e.registerNode(Uw,Gc),e.registerNode(Mb,Dc),e.registerNode(jw,kc),e.registerNode(Vb,kc),e.registerNode(nw,Gc),e.registerNode(jb,kc),e.registerNode(Jw,Gc),e.registerNode(Nb,Gc),e.registerNode(Sb,Gc),e.registerNode(Kb,kc),e.registerNode(eA,Gc),e.registerNode(nA,Gc),e.registerNode(sA,Gc),e.registerNode(oA,Gc),e.registerNode(zx,Dc),e.registerNode($x,Dc),e.registerNode(Jx,Dc),e.registerNode(Zx,Dc),e.registerNode(Ob,Gc),e.registerNode(lA,Fc),e.registerNode(vA,Uc),e.registerNode(Lb,Gc),e.registerNode(xA,Bc),e.registerNode(wA,Fc),e.registerNode(TA,Fc),e.registerNode(MA,Uc),e.registerNode(SA,qc),e.registerNode(qx,Dc),e.registerNode(Vx,Dc),e.registerNode(Pb,Gc),e.registerNode(DA,kc),e.registerNode(kA,kc),e.registerNode(zA,Fc),e.registerNode(Rb,Gc),e.registerNode(Ib,Gc),e.registerNode(Hb,Gc),e.registerNode(GA,Gc),e.registerNode(qb,Gc),e.registerNode(Wb,Gc),e.registerNode(WA,Gc),e.registerNode(jA,Gc),e.registerNode(sw,Gc),e.registerNode($A,Gc),e.registerNode(QA,Gc),e.registerNode(mT,qc),e.registerNode(_T,kc),e.registerNode(Fb,Gc),e.registerNode(cw,Uc),e.registerNode(gT,Bc),e.registerNode(xT,Bc),e.registerNode(Xb,Gc),e.registerNode(ET,jc),e.registerNode(ST,jc),e.registerNode(OT,jc),e.registerNode(LT,jc),e.registerNode(IT,Bc),e.registerNode(kT,Bc),e.registerNode(Db,Dc),e.registerNode(Yb,kc),e.registerNode(bT,kc),e.registerNode(zT,Fc),e.registerNode(YT,kc),e.registerNode(QT,Gc),e.registerNode(kb,Gc),e.registerNode(Bb,Hc),e.registerNode(Zb,Gc),e.registerNode(KT,kc),e.registerNode(zb,Gc),e.registerNode(TT,zc),e.registerNode($b,kc),e.registerNode(_A,Uc),e.registerNode(eE,Uc,yE),e.registerNode(hA,Uc,yE),e.registerNode(iw,Gc),e.registerNode(nE,Uc),e.registerNode(Ub,Hc),e.registerNode(sE,Fc),e.registerNode(cE,Bc),e.registerNode(hE,Uc),e.registerNode(pf,Bc),e.registerNode(_E,Bc),e.registerNode(ob,Dc),e.registerNode(hb,Dc),e.registerNode(ab,Dc),e.registerNode(ub,Dc),e.registerNode(db,Dc),e.registerNode(cb,Dc),e.registerNode(lb,Dc),e.registerNode(fE,kc),e.registerNode(vE,kc)}}.run(e),class{static run(e){e.registerNode(AE,Wc),e.registerNode(EE,Wc),e.registerNode(ME,Wc),e.registerNode(IE,Wc)}}.run(e),class{static run(e){e.registerNode(jE,Yc),e.registerNode(iC,Yc),e.registerNode(IC,$c),e.registerNode(UC,Xc),e.registerNode(WC,$c),e.registerNode(KC,Xc),e.registerNode(pM,$c),e.registerNode(yM,$c),e.registerNode(AM,Xc),e.registerNode(RM,$c),e.registerNode(kM,Xc),e.registerNode(GM,$c),e.registerNode(qM,Xc),e.registerNode(iN,$c),e.registerNode(lN,$c),e.registerNode(pN,Jc),e.registerNode(fN,Xc),e.registerNode(yN,Xc),e.registerNode(wN,$c),e.registerNode(LN,Kc),e.registerNode(IN,Kc),e.registerNode(kN,Qc),e.registerNode(BN,Qc),e.registerNode(zN,Qc),e.registerNode(UN,Qc),e.registerNode(GN,Qc),e.registerNode(VN,Qc)}}.run(e),class{static run(e){e.registerNode(QN,il),e.registerNode(yS,il),e.registerNode(OS,il),e.registerNode(kS,il),e.registerNode(jS,il),e.registerNode(JS,il),e.registerNode(aO,el),e.registerNode(dO,rl),e.registerNode(wO,Zc),e.registerNode(MO,nl),e.registerNode(OO,rl),e.registerNode(FO,rl),e.registerNode(cL,Zc),e.registerNode(AL,el),e.registerNode(ML,rl),e.registerNode(OL,Zc),e.registerNode(GP,tl),e.registerNode(qP,tl),e.registerNode(YP,tl),e.registerNode(KP,sl),e.registerNode(ZP,sl),e.registerNode(eR,sl),e.registerNode(tR,sl),e.registerNode(nR,sl),e.registerNode(iR,sl)}}.run(e),class{static run(e){e.registerNode(hR,wl),e.registerNode(mR,wl),e.registerNode(vR,bl),e.registerNode(bR,bl),e.registerNode(TR,Al),e.registerNode(CR,Al),e.registerNode(SR,Al),e.registerNode(LR,Al),e.registerNode(UR,wl),e.registerNode(kR,wl),e.registerNode(HR,wl),e.registerNode(XR,Al),e.registerNode(QR,bl),e.registerNode(KR,xl),e.registerNode(eI,Al),e.registerNode(oI,Al),e.registerNode(cI,Al),e.registerNode(uI,Al),e.registerNode(pI,wl),e.registerNode(fI,wl),e.registerNode(vI,Al),e.registerNode(bI,wl),e.registerNode(TI,bl),e.registerNode(CI,Al),e.registerNode(OI,xl),e.registerNode(II,wl),e.registerNode(DI,xl),e.registerNode(zI,wl),e.registerNode(VI,Tl),e.registerNode(jI,Tl),e.registerNode(HI,Tl),e.registerNode(qI,Tl),e.registerNode(WI,Tl),e.registerNode(XI,Tl)}}.run(e),class{static run(e){e.registerNode(eF,ol),e.registerNode(dP,cl),e.registerNode(tF,al),e.registerNode(QI,al),e.registerNode(nF,al),e.registerNode(iF,al),e.registerNode(sF,al),e.registerNode(rF,al)}}.run(e),class{static run(e){e.registerOperation(oF),e.registerOperation(xF),e.registerOperation(NF),e.registerOperation(PF),e.registerOperation(DF),e.registerOperation(QF),e.registerOperation(HF),e.registerOperation(tD),e.registerOperation(ID),e.registerOperation(BD),e.registerOperation(Kk),e.registerOperation(iB),e.registerOperation(BB),e.registerOperation(iz),e.registerOperation(Rz),e.registerOperation(qz),e.registerOperation(tU),e.registerOperation(rU),e.registerOperation(hU),e.registerOperation(EU),e.registerOperation(bU),e.registerOperation(kU),e.registerOperation(VU),e.registerOperation(cG),e.registerOperation(mG),e.registerOperation(wG),e.registerOperation(NG),e.registerOperation(zG),e.registerOperation(YG),e.registerOperation(rV),e.registerOperation(dV),e.registerOperation(fV),e.registerOperation(TV),e.registerOperation(RV),e.registerOperation(HV),e.registerOperation(ij),e.registerOperation(_j),e.registerOperation(Ij),e.registerOperation(Bj),e.registerOperation(jj),e.registerOperation(Xj),e.registerOperation(Zj),e.registerOperation(vH),e.registerOperation(EH),e.registerOperation(SH),e.registerNode(lF,pl),e.registerNode(hF,ul),e.registerNode(yF,ul),e.registerNode(AF,hl),e.registerNode(LF,hl),e.registerNode(FF,hl),e.registerNode(UF,hl),e.registerNode(VF,hl),e.registerNode(XF,hl),e.registerNode(ZF,hl),e.registerNode(rD,hl),e.registerNode(aD,hl),e.registerNode(lD,hl),e.registerNode(mD,hl),e.registerNode(gD,fl),e.registerNode(yD,fl),e.registerNode(kD,fl),e.registerNode(GD,vl),e.registerNode(qk,ml),e.registerNode(Jk,vl),e.registerNode(eB,vl),e.registerNode(oB,vl),e.registerNode(pB,vl),e.registerNode(yB,fl),e.registerNode(wB,vl),e.registerNode(IB,fl),e.registerNode(GB,vl),e.registerNode(WB,pl),e.registerNode(ez,pl),e.registerNode(oz,ml),e.registerNode(cz,ml),e.registerNode(Az,fl),e.registerNode(Ez,fl),e.registerNode(Mz,fl),e.registerNode(Dz,pl),e.registerNode(Bz,fl),e.registerNode(Vz,vl),e.registerNode(Jz,fl),e.registerNode($z,ml),e.registerNode(sU,vl),e.registerNode(cU,yl),e.registerNode(uU,yl),e.registerNode(_U,fl),e.registerNode(fU,fl),e.registerNode(vU,vl),e.registerNode(xU,ll),e.registerNode(TU,yl),e.registerNode(SU,ml),e.registerNode(IU,ml),e.registerNode(DU,fl),e.registerNode(zU,ml),e.registerNode(GU,pl),e.registerNode(qU,fl),e.registerNode(XU,ll,{userAllowed:!1}),e.registerNode(aG,dl),e.registerNode(hG,fl),e.registerNode(vG,vl),e.registerNode(LG,fl),e.registerNode(bG,fl),e.registerNode(EG,_l),e.registerNode(sL,ll),e.registerNode(kG,fl),e.registerNode(VG,fl),e.registerNode(JG,yl),e.registerNode(sV,fl),e.registerNode(cV,hl),e.registerNode(mV,pl),e.registerNode(yV,fl),e.registerNode(OV,fl),e.registerNode(NV,fl),e.registerNode(BV,ll),e.registerNode(UV,ll),e.registerNode(DV,fl),e.registerNode(XV,vl),e.registerNode($V,fl),e.registerNode(oj,fl),e.registerNode(cj,ml),e.registerNode(uj,ml),e.registerNode(KO,ml),e.registerNode(gj,pl),e.registerNode(xj,ml),e.registerNode(Aj,vl),e.registerNode(Rj,vl),e.registerNode(kj,fl),e.registerNode(Gj,fl),e.registerNode(Wj,vl),e.registerNode(Qj,vl),e.registerNode(nH,fl),e.registerNode(sH,fl),e.registerNode(lH,fl),e.registerNode(pH,fl),e.registerNode(fH,vl),e.registerNode(xH,fl),e.registerNode(TH,fl),e.registerNode(NH,fl),e.registerNode(LH,fl),e.registerNode(IH,gl),e.registerNode(FH,gl),e.registerNode(DH,gl),e.registerNode(kH,gl),e.registerNode(BH,gl),e.registerNode(zH,gl)}}.run(e)}}.run(Rn),class{static run(e){e.registerCamera(GP),e.registerCamera(qP)}}.run(Rn),class{static run(e){e.expressionsRegister.register(GH,\\\\\\\"arg\\\\\\\"),e.expressionsRegister.register(VH,\\\\\\\"argc\\\\\\\"),e.expressionsRegister.register(qH,\\\\\\\"bbox\\\\\\\"),e.expressionsRegister.register(WH,\\\\\\\"centroid\\\\\\\"),e.expressionsRegister.register(XH,\\\\\\\"ch\\\\\\\"),e.expressionsRegister.register(YH,\\\\\\\"copy\\\\\\\"),e.expressionsRegister.register($H,\\\\\\\"copRes\\\\\\\"),e.expressionsRegister.register(QH,\\\\\\\"isDeviceMobile\\\\\\\"),e.expressionsRegister.register(JH,\\\\\\\"isDeviceTouch\\\\\\\"),e.expressionsRegister.register(KH,\\\\\\\"js\\\\\\\"),e.expressionsRegister.register(ZH,\\\\\\\"object\\\\\\\"),e.expressionsRegister.register(eq,\\\\\\\"objectsCount\\\\\\\"),e.expressionsRegister.register(tq,\\\\\\\"opdigits\\\\\\\"),e.expressionsRegister.register(nq,\\\\\\\"opname\\\\\\\"),e.expressionsRegister.register(iq,\\\\\\\"padzero\\\\\\\"),e.expressionsRegister.register(sq,\\\\\\\"point\\\\\\\"),e.expressionsRegister.register(rq,\\\\\\\"pointsCount\\\\\\\"),e.expressionsRegister.register(oq,\\\\\\\"strCharsCount\\\\\\\"),e.expressionsRegister.register(aq,\\\\\\\"strConcat\\\\\\\"),e.expressionsRegister.register(cq,\\\\\\\"strIndex\\\\\\\"),e.expressionsRegister.register(lq,\\\\\\\"strSub\\\\\\\"),e.expressionsRegister.register(uq,\\\\\\\"windowSize\\\\\\\")}}.run(Rn),class{static run(e){e.assemblersRegister.register(mn.GL_MESH_BASIC,dq,Bq),e.assemblersRegister.register(mn.GL_MESH_LAMBERT,dq,zq),e.assemblersRegister.register(mn.GL_MESH_PHONG,dq,Uq),e.assemblersRegister.register(mn.GL_MESH_STANDARD,dq,Vq),e.assemblersRegister.register(mn.GL_MESH_PHYSICAL,dq,jq),e.assemblersRegister.register(mn.GL_PARTICLES,dq,nW),e.assemblersRegister.register(mn.GL_POINTS,dq,Qq),e.assemblersRegister.register(mn.GL_LINE,dq,tW),e.assemblersRegister.register(mn.GL_TEXTURE,dq,iW),e.assemblersRegister.register(mn.GL_VOLUME,dq,cW)}}.run(Rn))}}lW._started=!1,lW.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":"2152907"}},"type":2,"external":true,"timestamp":1723864404844},{"data":{"url":"blob:https://ipfs.arkivo.art/4159f601-a464-4238-8bb2-bc60d5a52300","host":"","path":"https://ipfs.arkivo.art/4159f601-a464-4238-8bb2-bc60d5a52300","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":1723864404845},{"data":{"url":"blob:https://ipfs.arkivo.art/4159f601-a464-4238-8bb2-bc60d5a52300","body":"\"\\n\\t\\tconsole.log('no poly plugins');\\n\\t\\tfunction configurePolygonjs(){}\\n\\t\\tfunction configureScene(){}\\n\\t\\texport {configurePolygonjs, configureScene};\\n\\t\\t\"","status":200,"headers":{"content-type":"application/javascript","content-length":"148"}},"type":2,"external":true,"timestamp":1723864414905}],"browser":{"name":"chromium","version":"119.0.6045.9"},"viewport":{"width":2000,"height":2000},"screenshot":"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","consoleMessages":[{"text":"Unrecognized 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