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Use .multiplyMatrices( a, b ) instead.\\\\\\\"),this.multiplyMatrices(t,e)):this.multiplyMatrices(this,t)}premultiply(t){return this.multiplyMatrices(t,this)}multiplyMatrices(t,e){const i=t.elements,r=e.elements,s=this.elements,n=i[0],a=i[4],o=i[8],h=i[12],u=i[1],c=i[5],l=i[9],d=i[13],p=i[2],f=i[6],m=i[10],y=i[14],g=i[3],x=i[7],b=i[11],v=i[15],w=r[0],M=r[4],T=r[8],S=r[12],_=r[1],E=r[5],A=r[9],z=r[13],R=r[2],O=r[6],I=r[10],k=r[14],P=r[3],B=r[7],L=r[11],N=r[15];return s[0]=n*w+a*_+o*R+h*P,s[4]=n*M+a*E+o*O+h*B,s[8]=n*T+a*A+o*I+h*L,s[12]=n*S+a*z+o*k+h*N,s[1]=u*w+c*_+l*R+d*P,s[5]=u*M+c*E+l*O+d*B,s[9]=u*T+c*A+l*I+d*L,s[13]=u*S+c*z+l*k+d*N,s[2]=p*w+f*_+m*R+y*P,s[6]=p*M+f*E+m*O+y*B,s[10]=p*T+f*A+m*I+y*L,s[14]=p*S+f*z+m*k+y*N,s[3]=g*w+x*_+b*R+v*P,s[7]=g*M+x*E+b*O+v*B,s[11]=g*T+x*A+b*I+v*L,s[15]=g*S+x*z+b*k+v*N,this}multiplyScalar(t){const e=this.elements;return e[0]*=t,e[4]*=t,e[8]*=t,e[12]*=t,e[1]*=t,e[5]*=t,e[9]*=t,e[13]*=t,e[2]*=t,e[6]*=t,e[10]*=t,e[14]*=t,e[3]*=t,e[7]*=t,e[11]*=t,e[15]*=t,this}determinant(){const t=this.elements,e=t[0],i=t[4],r=t[8],s=t[12],n=t[1],a=t[5],o=t[9],h=t[13],u=t[2],c=t[6],l=t[10],d=t[14];return t[3]*(+s*o*c-r*h*c-s*a*l+i*h*l+r*a*d-i*o*d)+t[7]*(+e*o*d-e*h*l+s*n*l-r*n*d+r*h*u-s*o*u)+t[11]*(+e*h*c-e*a*d-s*n*c+i*n*d+s*a*u-i*h*u)+t[15]*(-r*a*u-e*o*c+e*a*l+r*n*c-i*n*l+i*o*u)}transpose(){const t=this.elements;let e;return e=t[1],t[1]=t[4],t[4]=e,e=t[2],t[2]=t[8],t[8]=e,e=t[6],t[6]=t[9],t[9]=e,e=t[3],t[3]=t[12],t[12]=e,e=t[7],t[7]=t[13],t[13]=e,e=t[11],t[11]=t[14],t[14]=e,this}setPosition(t,e,i){const r=this.elements;return t.isVector3?(r[12]=t.x,r[13]=t.y,r[14]=t.z):(r[12]=t,r[13]=e,r[14]=i),this}invert(){const t=this.elements,e=t[0],i=t[1],r=t[2],s=t[3],n=t[4],a=t[5],o=t[6],h=t[7],u=t[8],c=t[9],l=t[10],d=t[11],p=t[12],f=t[13],m=t[14],y=t[15],g=c*m*h-f*l*h+f*o*d-a*m*d-c*o*y+a*l*y,x=p*l*h-u*m*h-p*o*d+n*m*d+u*o*y-n*l*y,b=u*f*h-p*c*h+p*a*d-n*f*d-u*a*y+n*c*y,v=p*c*o-u*f*o-p*a*l+n*f*l+u*a*m-n*c*m,w=e*g+i*x+r*b+s*v;if(0===w)return this.set(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0);const M=1/w;return t[0]=g*M,t[1]=(f*l*s-c*m*s-f*r*d+i*m*d+c*r*y-i*l*y)*M,t[2]=(a*m*s-f*o*s+f*r*h-i*m*h-a*r*y+i*o*y)*M,t[3]=(c*o*s-a*l*s-c*r*h+i*l*h+a*r*d-i*o*d)*M,t[4]=x*M,t[5]=(u*m*s-p*l*s+p*r*d-e*m*d-u*r*y+e*l*y)*M,t[6]=(p*o*s-n*m*s-p*r*h+e*m*h+n*r*y-e*o*y)*M,t[7]=(n*l*s-u*o*s+u*r*h-e*l*h-n*r*d+e*o*d)*M,t[8]=b*M,t[9]=(p*c*s-u*f*s-p*i*d+e*f*d+u*i*y-e*c*y)*M,t[10]=(n*f*s-p*a*s+p*i*h-e*f*h-n*i*y+e*a*y)*M,t[11]=(u*a*s-n*c*s-u*i*h+e*c*h+n*i*d-e*a*d)*M,t[12]=v*M,t[13]=(u*f*r-p*c*r+p*i*l-e*f*l-u*i*m+e*c*m)*M,t[14]=(p*a*r-n*f*r-p*i*o+e*f*o+n*i*m-e*a*m)*M,t[15]=(n*c*r-u*a*r+u*i*o-e*c*o-n*i*l+e*a*l)*M,this}scale(t){const e=this.elements,i=t.x,r=t.y,s=t.z;return e[0]*=i,e[4]*=r,e[8]*=s,e[1]*=i,e[5]*=r,e[9]*=s,e[2]*=i,e[6]*=r,e[10]*=s,e[3]*=i,e[7]*=r,e[11]*=s,this}getMaxScaleOnAxis(){const t=this.elements,e=t[0]*t[0]+t[1]*t[1]+t[2]*t[2],i=t[4]*t[4]+t[5]*t[5]+t[6]*t[6],r=t[8]*t[8]+t[9]*t[9]+t[10]*t[10];return Math.sqrt(Math.max(e,i,r))}makeTranslation(t,e,i){return this.set(1,0,0,t,0,1,0,e,0,0,1,i,0,0,0,1),this}makeRotationX(t){const e=Math.cos(t),i=Math.sin(t);return this.set(1,0,0,0,0,e,-i,0,0,i,e,0,0,0,0,1),this}makeRotationY(t){const e=Math.cos(t),i=Math.sin(t);return this.set(e,0,i,0,0,1,0,0,-i,0,e,0,0,0,0,1),this}makeRotationZ(t){const e=Math.cos(t),i=Math.sin(t);return this.set(e,-i,0,0,i,e,0,0,0,0,1,0,0,0,0,1),this}makeRotationAxis(t,e){const i=Math.cos(e),r=Math.sin(e),s=1-i,n=t.x,a=t.y,o=t.z,h=s*n,u=s*a;return this.set(h*n+i,h*a-r*o,h*o+r*a,0,h*a+r*o,u*a+i,u*o-r*n,0,h*o-r*a,u*o+r*n,s*o*o+i,0,0,0,0,1),this}makeScale(t,e,i){return this.set(t,0,0,0,0,e,0,0,0,0,i,0,0,0,0,1),this}makeShear(t,e,i){return this.set(1,e,i,0,t,1,i,0,t,e,1,0,0,0,0,1),this}compose(t,e,i){const r=this.elements,s=e._x,n=e._y,a=e._z,o=e._w,h=s+s,u=n+n,c=a+a,l=s*h,d=s*u,p=s*c,f=n*u,m=n*c,y=a*c,g=o*h,x=o*u,b=o*c,v=i.x,w=i.y,M=i.z;return r[0]=(1-(f+y))*v,r[1]=(d+b)*v,r[2]=(p-x)*v,r[3]=0,r[4]=(d-b)*w,r[5]=(1-(l+y))*w,r[6]=(m+g)*w,r[7]=0,r[8]=(p+x)*M,r[9]=(m-g)*M,r[10]=(1-(l+f))*M,r[11]=0,r[12]=t.x,r[13]=t.y,r[14]=t.z,r[15]=1,this}decompose(t,e,i){const r=this.elements;let s=n.set(r[0],r[1],r[2]).length();const o=n.set(r[4],r[5],r[6]).length(),h=n.set(r[8],r[9],r[10]).length();this.determinant()<0&&(s=-s),t.x=r[12],t.y=r[13],t.z=r[14],a.copy(this);const u=1/s,c=1/o,l=1/h;return a.elements[0]*=u,a.elements[1]*=u,a.elements[2]*=u,a.elements[4]*=c,a.elements[5]*=c,a.elements[6]*=c,a.elements[8]*=l,a.elements[9]*=l,a.elements[10]*=l,e.setFromRotationMatrix(a),i.x=s,i.y=o,i.z=h,this}makePerspective(t,e,i,r,s,n){void 0===n&&console.warn(\\\\\\\"THREE.Matrix4: .makePerspective() has been redefined and has a new signature. Please check the docs.\\\\\\\");const a=this.elements,o=2*s/(e-t),h=2*s/(i-r),u=(e+t)/(e-t),c=(i+r)/(i-r),l=-(n+s)/(n-s),d=-2*n*s/(n-s);return a[0]=o,a[4]=0,a[8]=u,a[12]=0,a[1]=0,a[5]=h,a[9]=c,a[13]=0,a[2]=0,a[6]=0,a[10]=l,a[14]=d,a[3]=0,a[7]=0,a[11]=-1,a[15]=0,this}makeOrthographic(t,e,i,r,s,n){const a=this.elements,o=1/(e-t),h=1/(i-r),u=1/(n-s),c=(e+t)*o,l=(i+r)*h,d=(n+s)*u;return a[0]=2*o,a[4]=0,a[8]=0,a[12]=-c,a[1]=0,a[5]=2*h,a[9]=0,a[13]=-l,a[2]=0,a[6]=0,a[10]=-2*u,a[14]=-d,a[3]=0,a[7]=0,a[11]=0,a[15]=1,this}equals(t){const e=this.elements,i=t.elements;for(let t=0;t<16;t++)if(e[t]!==i[t])return!1;return!0}fromArray(t,e=0){for(let i=0;i<16;i++)this.elements[i]=t[i+e];return this}toArray(t=[],e=0){const i=this.elements;return t[e]=i[0],t[e+1]=i[1],t[e+2]=i[2],t[e+3]=i[3],t[e+4]=i[4],t[e+5]=i[5],t[e+6]=i[6],t[e+7]=i[7],t[e+8]=i[8],t[e+9]=i[9],t[e+10]=i[10],t[e+11]=i[11],t[e+12]=i[12],t[e+13]=i[13],t[e+14]=i[14],t[e+15]=i[15],t}}s.prototype.isMatrix4=!0;const n=new r.a,a=new s,o=new r.a(0,0,0),h=new r.a(1,1,1),u=new r.a,c=new r.a,l=new r.a},function(t,e,i){\\\\\\\"use strict\\\\\\\";i.d(e,\\\\\\\"a\\\\\\\",(function(){return c}));var r=i(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},n={h:0,s:0,l:0},a={h:0,s:0,l:0};function o(t,e,i){return i<0&&(i+=1),i>1&&(i-=1),i<1/6?t+6*(e-t)*i:i<.5?e:i<2/3?t+6*(e-t)*(2/3-i):t}function h(t){return t<.04045?.0773993808*t:Math.pow(.9478672986*t+.0521327014,2.4)}function u(t){return t<.0031308?12.92*t:1.055*Math.pow(t,.41666)-.055}class c{constructor(t,e,i){return void 0===e&&void 0===i?this.set(t):this.setRGB(t,e,i)}set(t){return t&&t.isColor?this.copy(t):\\\\\\\"number\\\\\\\"==typeof t?this.setHex(t):\\\\\\\"string\\\\\\\"==typeof t&&this.setStyle(t),this}setScalar(t){return this.r=t,this.g=t,this.b=t,this}setHex(t){return t=Math.floor(t),this.r=(t>>16&255)/255,this.g=(t>>8&255)/255,this.b=(255&t)/255,this}setRGB(t,e,i){return this.r=t,this.g=e,this.b=i,this}setHSL(t,e,i){if(t=r.a.euclideanModulo(t,1),e=r.a.clamp(e,0,1),i=r.a.clamp(i,0,1),0===e)this.r=this.g=this.b=i;else{const r=i<=.5?i*(1+e):i+e-i*e,s=2*i-r;this.r=o(s,r,t+1/3),this.g=o(s,r,t),this.b=o(s,r,t-1/3)}return this}setStyle(t){function e(e){void 0!==e&&parseFloat(e)<1&&console.warn(\\\\\\\"THREE.Color: Alpha 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i=parseFloat(t[1])/360,r=parseInt(t[2],10)/100,s=parseInt(t[3],10)/100;return e(t[4]),this.setHSL(i,r,s)}}}else if(i=/^\\\\#([A-Fa-f\\\\d]+)$/.exec(t)){const t=i[1],e=t.length;if(3===e)return this.r=parseInt(t.charAt(0)+t.charAt(0),16)/255,this.g=parseInt(t.charAt(1)+t.charAt(1),16)/255,this.b=parseInt(t.charAt(2)+t.charAt(2),16)/255,this;if(6===e)return this.r=parseInt(t.charAt(0)+t.charAt(1),16)/255,this.g=parseInt(t.charAt(2)+t.charAt(3),16)/255,this.b=parseInt(t.charAt(4)+t.charAt(5),16)/255,this}return t&&t.length>0?this.setColorName(t):this}setColorName(t){const e=s[t];return void 0!==e?this.setHex(e):console.warn(\\\\\\\"THREE.Color: Unknown color \\\\\\\"+t),this}clone(){return new this.constructor(this.r,this.g,this.b)}copy(t){return this.r=t.r,this.g=t.g,this.b=t.b,this}copyGammaToLinear(t,e=2){return this.r=Math.pow(t.r,e),this.g=Math.pow(t.g,e),this.b=Math.pow(t.b,e),this}copyLinearToGamma(t,e=2){const i=e>0?1/e:1;return 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e=0;e65535?o.i:o.h)(t,1):this.index=t,this},getAttribute:function(t){return this.attributes[t]},setAttribute:function(t,e){return this.attributes[t]=e,this},deleteAttribute:function(t){return delete this.attributes[t],this},hasAttribute:function(t){return void 0!==this.attributes[t]},addGroup:function(t,e,i=0){this.groups.push({start:t,count:e,materialIndex:i})},clearGroups:function(){this.groups=[]},setDrawRange:function(t,e){this.drawRange.start=t,this.drawRange.count=e},applyMatrix4:function(t){const e=this.attributes.position;void 0!==e&&(e.applyMatrix4(t),e.needsUpdate=!0);const i=this.attributes.normal;if(void 0!==i){const e=(new l.a).getNormalMatrix(t);i.applyNormalMatrix(e),i.needsUpdate=!0}const r=this.attributes.tangent;return void 0!==r&&(r.transformDirection(t),r.needsUpdate=!0),null!==this.boundingBox&&this.computeBoundingBox(),null!==this.boundingSphere&&this.computeBoundingSphere(),this},rotateX:function(t){return m.makeRotationX(t),this.applyMatrix4(m),this},rotateY:function(t){return m.makeRotationY(t),this.applyMatrix4(m),this},rotateZ:function(t){return m.makeRotationZ(t),this.applyMatrix4(m),this},translate:function(t,e,i){return m.makeTranslation(t,e,i),this.applyMatrix4(m),this},scale:function(t,e,i){return m.makeScale(t,e,i),this.applyMatrix4(m),this},lookAt:function(t){return y.lookAt(t),y.updateMatrix(),this.applyMatrix4(y.matrix),this},center:function(){return this.computeBoundingBox(),this.boundingBox.getCenter(g).negate(),this.translate(g.x,g.y,g.z),this},setFromPoints:function(t){const e=[];for(let i=0,r=t.length;i0&&(t.userData=this.userData),void 0!==this.parameters){const e=this.parameters;for(const i in e)void 0!==e[i]&&(t[i]=e[i]);return t}t.data={attributes:{}};const e=this.index;null!==e&&(t.data.index={type:e.array.constructor.name,array:Array.prototype.slice.call(e.array)});const i=this.attributes;for(const e in i){const r=i[e];t.data.attributes[e]=r.toJSON(t.data)}const r={};let s=!1;for(const e in this.morphAttributes){const i=this.morphAttributes[e],n=[];for(let e=0,r=i.length;e0&&(r[e]=n,s=!0)}s&&(t.data.morphAttributes=r,t.data.morphTargetsRelative=this.morphTargetsRelative);const n=this.groups;n.length>0&&(t.data.groups=JSON.parse(JSON.stringify(n)));const a=this.boundingSphere;return null!==a&&(t.data.boundingSphere={center:a.center.toArray(),radius:a.radius}),t},clone:function(){return(new w).copy(this)},copy:function(t){this.index=null,this.attributes={},this.morphAttributes={},this.groups=[],this.boundingBox=null,this.boundingSphere=null;const e={};this.name=t.name;const i=t.index;null!==i&&this.setIndex(i.clone(e));const r=t.attributes;for(const t in r){const i=r[t];this.setAttribute(t,i.clone(e))}const s=t.morphAttributes;for(const t in s){const i=[],r=s[t];for(let t=0,s=r.length;t=0?1:-1,r=1-e*e;if(r>Number.EPSILON){const s=Math.sqrt(r),n=Math.atan2(s,e*i);t=Math.sin(t*n)/s,a=Math.sin(a*n)/s}const 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Euler rotation rather than a Vector3 and order.\\\\\\\");const i=t._x,r=t._y,s=t._z,n=t._order,a=Math.cos,o=Math.sin,h=a(i/2),u=a(r/2),c=a(s/2),l=o(i/2),d=o(r/2),p=o(s/2);switch(n){case\\\\\\\"XYZ\\\\\\\":this._x=l*u*c+h*d*p,this._y=h*d*c-l*u*p,this._z=h*u*p+l*d*c,this._w=h*u*c-l*d*p;break;case\\\\\\\"YXZ\\\\\\\":this._x=l*u*c+h*d*p,this._y=h*d*c-l*u*p,this._z=h*u*p-l*d*c,this._w=h*u*c+l*d*p;break;case\\\\\\\"ZXY\\\\\\\":this._x=l*u*c-h*d*p,this._y=h*d*c+l*u*p,this._z=h*u*p+l*d*c,this._w=h*u*c-l*d*p;break;case\\\\\\\"ZYX\\\\\\\":this._x=l*u*c-h*d*p,this._y=h*d*c+l*u*p,this._z=h*u*p-l*d*c,this._w=h*u*c+l*d*p;break;case\\\\\\\"YZX\\\\\\\":this._x=l*u*c+h*d*p,this._y=h*d*c+l*u*p,this._z=h*u*p-l*d*c,this._w=h*u*c-l*d*p;break;case\\\\\\\"XZY\\\\\\\":this._x=l*u*c-h*d*p,this._y=h*d*c-l*u*p,this._z=h*u*p+l*d*c,this._w=h*u*c+l*d*p;break;default:console.warn(\\\\\\\"THREE.Quaternion: .setFromEuler() encountered an unknown order: \\\\\\\"+n)}return!1!==e&&this._onChangeCallback(),this}setFromAxisAngle(t,e){const i=e/2,r=Math.sin(i);return this._x=t.x*r,this._y=t.y*r,this._z=t.z*r,this._w=Math.cos(i),this._onChangeCallback(),this}setFromRotationMatrix(t){const e=t.elements,i=e[0],r=e[4],s=e[8],n=e[1],a=e[5],o=e[9],h=e[2],u=e[6],c=e[10],l=i+a+c;if(l>0){const t=.5/Math.sqrt(l+1);this._w=.25/t,this._x=(u-o)*t,this._y=(s-h)*t,this._z=(n-r)*t}else if(i>a&&i>c){const t=2*Math.sqrt(1+i-a-c);this._w=(u-o)/t,this._x=.25*t,this._y=(r+n)/t,this._z=(s+h)/t}else if(a>c){const t=2*Math.sqrt(1+a-i-c);this._w=(s-h)/t,this._x=(r+n)/t,this._y=.25*t,this._z=(o+u)/t}else{const t=2*Math.sqrt(1+c-i-a);this._w=(n-r)/t,this._x=(s+h)/t,this._y=(o+u)/t,this._z=.25*t}return this._onChangeCallback(),this}setFromUnitVectors(t,e){let i=t.dot(e)+1;return iMath.abs(t.z)?(this._x=-t.y,this._y=t.x,this._z=0,this._w=i):(this._x=0,this._y=-t.z,this._z=t.y,this._w=i)):(this._x=t.y*e.z-t.z*e.y,this._y=t.z*e.x-t.x*e.z,this._z=t.x*e.y-t.y*e.x,this._w=i),this.normalize()}angleTo(t){return 2*Math.acos(Math.abs(r.a.clamp(this.dot(t),-1,1)))}rotateTowards(t,e){const i=this.angleTo(t);if(0===i)return this;const r=Math.min(1,e/i);return this.slerp(t,r),this}identity(){return this.set(0,0,0,1)}invert(){return this.conjugate()}conjugate(){return this._x*=-1,this._y*=-1,this._z*=-1,this._onChangeCallback(),this}dot(t){return this._x*t._x+this._y*t._y+this._z*t._z+this._w*t._w}lengthSq(){return this._x*this._x+this._y*this._y+this._z*this._z+this._w*this._w}length(){return Math.sqrt(this._x*this._x+this._y*this._y+this._z*this._z+this._w*this._w)}normalize(){let t=this.length();return 0===t?(this._x=0,this._y=0,this._z=0,this._w=1):(t=1/t,this._x=this._x*t,this._y=this._y*t,this._z=this._z*t,this._w=this._w*t),this._onChangeCallback(),this}multiply(t,e){return void 0!==e?(console.warn(\\\\\\\"THREE.Quaternion: .multiply() now only accepts one argument. 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e=r(t.textures),s=r(t.images);e.length>0&&(i.textures=e),s.length>0&&(i.images=s)}return i},clone:function(){return(new this.constructor).copy(this)},copy:function(t){this.name=t.name,this.fog=t.fog,this.blending=t.blending,this.side=t.side,this.vertexColors=t.vertexColors,this.opacity=t.opacity,this.transparent=t.transparent,this.blendSrc=t.blendSrc,this.blendDst=t.blendDst,this.blendEquation=t.blendEquation,this.blendSrcAlpha=t.blendSrcAlpha,this.blendDstAlpha=t.blendDstAlpha,this.blendEquationAlpha=t.blendEquationAlpha,this.depthFunc=t.depthFunc,this.depthTest=t.depthTest,this.depthWrite=t.depthWrite,this.stencilWriteMask=t.stencilWriteMask,this.stencilFunc=t.stencilFunc,this.stencilRef=t.stencilRef,this.stencilFuncMask=t.stencilFuncMask,this.stencilFail=t.stencilFail,this.stencilZFail=t.stencilZFail,this.stencilZPass=t.stencilZPass,this.stencilWrite=t.stencilWrite;const e=t.clippingPlanes;let i=null;if(null!==e){const t=e.length;i=new Array(t);for(let r=0;r!==t;++r)i[r]=e[r].clone()}return this.clippingPlanes=i,this.clipIntersection=t.clipIntersection,this.clipShadows=t.clipShadows,this.shadowSide=t.shadowSide,this.colorWrite=t.colorWrite,this.precision=t.precision,this.polygonOffset=t.polygonOffset,this.polygonOffsetFactor=t.polygonOffsetFactor,this.polygonOffsetUnits=t.polygonOffsetUnits,this.dithering=t.dithering,this.alphaTest=t.alphaTest,this.alphaToCoverage=t.alphaToCoverage,this.premultipliedAlpha=t.premultipliedAlpha,this.visible=t.visible,this.toneMapped=t.toneMapped,this.userData=JSON.parse(JSON.stringify(t.userData)),this},dispose:function(){this.dispatchEvent({type:\\\\\\\"dispose\\\\\\\"})}}),Object.defineProperty(o.prototype,\\\\\\\"needsUpdate\\\\\\\",{set:function(t){!0===t&&this.version++}})},function(t,e,i){\\\\\\\"use strict\\\\\\\";i.d(e,\\\\\\\"a\\\\\\\",(function(){return r}));class r{constructor(){this.elements=[1,0,0,0,1,0,0,0,1],arguments.length>0&&console.error(\\\\\\\"THREE.Matrix3: the constructor no 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s[0]=n*f+a*g+o*v,s[3]=n*m+a*x+o*w,s[6]=n*y+a*b+o*M,s[1]=h*f+u*g+c*v,s[4]=h*m+u*x+c*w,s[7]=h*y+u*b+c*M,s[2]=l*f+d*g+p*v,s[5]=l*m+d*x+p*w,s[8]=l*y+d*b+p*M,this}multiplyScalar(t){const e=this.elements;return e[0]*=t,e[3]*=t,e[6]*=t,e[1]*=t,e[4]*=t,e[7]*=t,e[2]*=t,e[5]*=t,e[8]*=t,this}determinant(){const t=this.elements,e=t[0],i=t[1],r=t[2],s=t[3],n=t[4],a=t[5],o=t[6],h=t[7],u=t[8];return e*n*u-e*a*h-i*s*u+i*a*o+r*s*h-r*n*o}invert(){const t=this.elements,e=t[0],i=t[1],r=t[2],s=t[3],n=t[4],a=t[5],o=t[6],h=t[7],u=t[8],c=u*n-a*h,l=a*o-u*s,d=h*s-n*o,p=e*c+i*l+r*d;if(0===p)return this.set(0,0,0,0,0,0,0,0,0);const f=1/p;return t[0]=c*f,t[1]=(r*h-u*i)*f,t[2]=(a*i-r*n)*f,t[3]=l*f,t[4]=(u*e-r*o)*f,t[5]=(r*s-a*e)*f,t[6]=d*f,t[7]=(i*o-h*e)*f,t[8]=(n*e-i*s)*f,this}transpose(){let t;const e=this.elements;return t=e[1],e[1]=e[3],e[3]=t,t=e[2],e[2]=e[6],e[6]=t,t=e[5],e[5]=e[7],e[7]=t,this}getNormalMatrix(t){return this.setFromMatrix4(t).invert().transpose()}transposeIntoArray(t){const 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t[e]=i[0],t[e+1]=i[1],t[e+2]=i[2],t[e+3]=i[3],t[e+4]=i[4],t[e+5]=i[5],t[e+6]=i[6],t[e+7]=i[7],t[e+8]=i[8],t}clone(){return(new this.constructor).fromArray(this.elements)}}r.prototype.isMatrix3=!0},function(t,e,i){\\\\\\\"use strict\\\\\\\";i.d(e,\\\\\\\"a\\\\\\\",(function(){return O}));var r=i(0),s=i(2),n=i(18),a=i(39),o=i(5),h=i(7),u=i(40),c=i(1),l=i(26),d=i(8);const p=new o.a,f=new a.a,m=new n.a,y=new r.a,g=new r.a,x=new r.a,b=new r.a,v=new r.a,w=new r.a,M=new r.a,T=new r.a,S=new r.a,_=new s.a,E=new s.a,A=new s.a,z=new r.a,R=new r.a;function O(t=new d.a,e=new l.a){h.a.call(this),this.type=\\\\\\\"Mesh\\\\\\\",this.geometry=t,this.material=e,this.updateMorphTargets()}function I(t,e,i,n,a,o,h,l,d,p,f,m){y.fromBufferAttribute(a,p),g.fromBufferAttribute(a,f),x.fromBufferAttribute(a,m);const O=t.morphTargetInfluences;if(e.morphTargets&&o&&O){M.set(0,0,0),T.set(0,0,0),S.set(0,0,0);for(let 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e=0,i=t.length;ethis.max.x||t.ythis.max.y||t.zthis.max.z)}containsBox(t){return this.min.x<=t.min.x&&t.max.x<=this.max.x&&this.min.y<=t.min.y&&t.max.y<=this.max.y&&this.min.z<=t.min.z&&t.max.z<=this.max.z}getParameter(t,e){return void 0===e&&(console.warn(\\\\\\\"THREE.Box3: .getParameter() target is now required\\\\\\\"),e=new r.a),e.set((t.x-this.min.x)/(this.max.x-this.min.x),(t.y-this.min.y)/(this.max.y-this.min.y),(t.z-this.min.z)/(this.max.z-this.min.z))}intersectsBox(t){return!(t.max.xthis.max.x||t.max.ythis.max.y||t.max.zthis.max.z)}intersectsSphere(t){return this.clampPoint(t.center,a),a.distanceToSquared(t.center)<=t.radius*t.radius}intersectsPlane(t){let e,i;return t.normal.x>0?(e=t.normal.x*this.min.x,i=t.normal.x*this.max.x):(e=t.normal.x*this.max.x,i=t.normal.x*this.min.x),t.normal.y>0?(e+=t.normal.y*this.min.y,i+=t.normal.y*this.max.y):(e+=t.normal.y*this.max.y,i+=t.normal.y*this.min.y),t.normal.z>0?(e+=t.normal.z*this.min.z,i+=t.normal.z*this.max.z):(e+=t.normal.z*this.max.z,i+=t.normal.z*this.min.z),e<=-t.constant&&i>=-t.constant}intersectsTriangle(t){if(this.isEmpty())return!1;this.getCenter(f),m.subVectors(this.max,f),h.subVectors(t.a,f),u.subVectors(t.b,f),c.subVectors(t.c,f),l.subVectors(u,h),d.subVectors(c,u),p.subVectors(h,c);let e=[0,-l.z,l.y,0,-d.z,d.y,0,-p.z,p.y,l.z,0,-l.x,d.z,0,-d.x,p.z,0,-p.x,-l.y,l.x,0,-d.y,d.x,0,-p.y,p.x,0];return!!x(e,h,u,c,m)&&(e=[1,0,0,0,1,0,0,0,1],!!x(e,h,u,c,m)&&(y.crossVectors(l,d),e=[y.x,y.y,y.z],x(e,h,u,c,m)))}clampPoint(t,e){return void 0===e&&(console.warn(\\\\\\\"THREE.Box3: .clampPoint() target is now required\\\\\\\"),e=new r.a),e.copy(t).clamp(this.min,this.max)}distanceToPoint(t){return a.copy(t).clamp(this.min,this.max).sub(t).length()}getBoundingSphere(t){return void 0===t&&console.error(\\\\\\\"THREE.Box3: .getBoundingSphere() target is now required\\\\\\\"),this.getCenter(t.center),t.radius=.5*this.getSize(a).length(),t}intersect(t){return this.min.max(t.min),this.max.min(t.max),this.isEmpty()&&this.makeEmpty(),this}union(t){return this.min.min(t.min),this.max.max(t.max),this}applyMatrix4(t){return this.isEmpty()||(n[0].set(this.min.x,this.min.y,this.min.z).applyMatrix4(t),n[1].set(this.min.x,this.min.y,this.max.z).applyMatrix4(t),n[2].set(this.min.x,this.max.y,this.min.z).applyMatrix4(t),n[3].set(this.min.x,this.max.y,this.max.z).applyMatrix4(t),n[4].set(this.max.x,this.min.y,this.min.z).applyMatrix4(t),n[5].set(this.max.x,this.min.y,this.max.z).applyMatrix4(t),n[6].set(this.max.x,this.max.y,this.min.z).applyMatrix4(t),n[7].set(this.max.x,this.max.y,this.max.z).applyMatrix4(t),this.setFromPoints(n)),this}translate(t){return 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0!==r&&h.a.isTypedArray(r))for(let e=0,i=r.length;e!==i;++e){const i=r[e];if(isNaN(i)){console.error(\\\\\\\"THREE.KeyframeTrack: Value is not a valid number.\\\\\\\",this,e,i),t=!1;break}}return t}optimize(){const t=h.a.arraySlice(this.times),e=h.a.arraySlice(this.values),i=this.getValueSize(),s=this.getInterpolation()===r.Q,n=t.length-1;let a=1;for(let r=1;r0){t[a]=t[n];for(let t=n*i,r=a*i,s=0;s!==i;++s)e[r+s]=e[t+s];++a}return a!==t.length?(this.times=h.a.arraySlice(t,0,a),this.values=h.a.arraySlice(e,0,a*i)):(this.times=t,this.values=e),this}clone(){const t=h.a.arraySlice(this.times,0),e=h.a.arraySlice(this.values,0),i=new(0,this.constructor)(this.name,t,e);return i.createInterpolant=this.createInterpolant,i}}u.prototype.TimeBufferType=Float32Array,u.prototype.ValueBufferType=Float32Array,u.prototype.DefaultInterpolation=r.P},function(t,e,i){\\\\\\\"use strict\\\\\\\";function r(t,e,i){const r=this;let s,n=!1,a=0,o=0;const h=[];this.onStart=void 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s.a),t.copy(this.normal).multiplyScalar(-this.constant)}applyMatrix4(t,e){const i=e||o.getNormalMatrix(t),r=this.coplanarPoint(n).applyMatrix4(t),s=this.normal.applyMatrix3(i).normalize();return this.constant=-r.dot(s),this}translate(t){return this.constant-=t.dot(this.normal),this}equals(t){return t.normal.equals(this.normal)&&t.constant===this.constant}clone(){return(new this.constructor).copy(this)}}h.prototype.isPlane=!0},function(t,e,i){\\\\\\\"use strict\\\\\\\";i.d(e,\\\\\\\"a\\\\\\\",(function(){return u}));var r=i(9),s=i(0),n=i(5),a=i(4);const o=new n.a,h=new r.a;class u{constructor(t=0,e=0,i=0,r=u.DefaultOrder){this._x=t,this._y=e,this._z=i,this._order=r}get x(){return this._x}set x(t){this._x=t,this._onChangeCallback()}get y(){return this._y}set y(t){this._y=t,this._onChangeCallback()}get z(){return this._z}set z(t){this._z=t,this._onChangeCallback()}get order(){return this._order}set order(t){this._order=t,this._onChangeCallback()}set(t,e,i,r){return this._x=t,this._y=e,this._z=i,this._order=r||this._order,this._onChangeCallback(),this}clone(){return new this.constructor(this._x,this._y,this._z,this._order)}copy(t){return this._x=t._x,this._y=t._y,this._z=t._z,this._order=t._order,this._onChangeCallback(),this}setFromRotationMatrix(t,e,i){const r=a.a.clamp,s=t.elements,n=s[0],o=s[4],h=s[8],u=s[1],c=s[5],l=s[9],d=s[2],p=s[6],f=s[10];switch(e=e||this._order){case\\\\\\\"XYZ\\\\\\\":this._y=Math.asin(r(h,-1,1)),Math.abs(h)<.9999999?(this._x=Math.atan2(-l,f),this._z=Math.atan2(-o,n)):(this._x=Math.atan2(p,c),this._z=0);break;case\\\\\\\"YXZ\\\\\\\":this._x=Math.asin(-r(l,-1,1)),Math.abs(l)<.9999999?(this._y=Math.atan2(h,f),this._z=Math.atan2(u,c)):(this._y=Math.atan2(-d,n),this._z=0);break;case\\\\\\\"ZXY\\\\\\\":this._x=Math.asin(r(p,-1,1)),Math.abs(p)<.9999999?(this._y=Math.atan2(-d,f),this._z=Math.atan2(-o,c)):(this._y=0,this._z=Math.atan2(u,n));break;case\\\\\\\"ZYX\\\\\\\":this._y=Math.asin(-r(d,-1,1)),Math.abs(d)<.9999999?(this._x=Math.atan2(p,f),this._z=Math.atan2(u,n)):(this._x=0,this._z=Math.atan2(-o,c));break;case\\\\\\\"YZX\\\\\\\":this._z=Math.asin(r(u,-1,1)),Math.abs(u)<.9999999?(this._x=Math.atan2(-l,c),this._y=Math.atan2(-d,n)):(this._x=0,this._y=Math.atan2(h,f));break;case\\\\\\\"XZY\\\\\\\":this._z=Math.asin(-r(o,-1,1)),Math.abs(o)<.9999999?(this._x=Math.atan2(p,c),this._y=Math.atan2(h,n)):(this._x=Math.atan2(-l,f),this._y=0);break;default:console.warn(\\\\\\\"THREE.Euler: .setFromRotationMatrix() encountered an unknown order: \\\\\\\"+e)}return this._order=e,!1!==i&&this._onChangeCallback(),this}setFromQuaternion(t,e,i){return o.makeRotationFromQuaternion(t),this.setFromRotationMatrix(o,e,i)}setFromVector3(t,e){return this.set(t.x,t.y,t.z,e||this._order)}reorder(t){return h.setFromEuler(this),this.setFromQuaternion(h,t)}equals(t){return t._x===this._x&&t._y===this._y&&t._z===this._z&&t._order===this._order}fromArray(t){return this._x=t[0],this._y=t[1],this._z=t[2],void 0!==t[3]&&(this._order=t[3]),this._onChangeCallback(),this}toArray(t=[],e=0){return t[e]=this._x,t[e+1]=this._y,t[e+2]=this._z,t[e+3]=this._order,t}toVector3(t){return t?t.set(this._x,this._y,this._z):new s.a(this._x,this._y,this._z)}_onChange(t){return this._onChangeCallback=t,this}_onChangeCallback(){}}u.prototype.isEuler=!0,u.DefaultOrder=\\\\\\\"XYZ\\\\\\\",u.RotationOrders=[\\\\\\\"XYZ\\\\\\\",\\\\\\\"YZX\\\\\\\",\\\\\\\"ZXY\\\\\\\",\\\\\\\"XZY\\\\\\\",\\\\\\\"YXZ\\\\\\\",\\\\\\\"ZYX\\\\\\\"]},function(t,e,i){\\\\\\\"use strict\\\\\\\";function r(t,e,i,r){this.parameterPositions=t,this._cachedIndex=0,this.resultBuffer=void 0!==r?r:new e.constructor(i),this.sampleValues=e,this.valueSize=i}i.d(e,\\\\\\\"a\\\\\\\",(function(){return r})),Object.assign(r.prototype,{evaluate:function(t){const e=this.parameterPositions;let i=this._cachedIndex,r=e[i],s=e[i-1];t:{e:{let n;i:{r:if(!(t=s)break t;{const a=e[1];t=s)break e}n=i,i=0}}for(;i>>1;t2048||e.height>2048?(console.warn(\\\\\\\"THREE.ImageUtils.getDataURL: Image converted to jpg for performance reasons\\\\\\\",t),e.toDataURL(\\\\\\\"image/jpeg\\\\\\\",.6)):e.toDataURL(\\\\\\\"image/png\\\\\\\")};let c=0;class l extends r.a{constructor(t=l.DEFAULT_IMAGE,e=l.DEFAULT_MAPPING,i=s.n,r=s.n,h=s.V,u=s.Y,d=s.Ib,p=s.Zc,f=1,m=s.U){super(),Object.defineProperty(this,\\\\\\\"id\\\\\\\",{value:c++}),this.uuid=n.a.generateUUID(),this.name=\\\\\\\"\\\\\\\",this.image=t,this.mipmaps=[],this.mapping=e,this.wrapS=i,this.wrapT=r,this.magFilter=h,this.minFilter=u,this.anisotropy=f,this.format=d,this.internalFormat=null,this.type=p,this.offset=new a.a(0,0),this.repeat=new a.a(1,1),this.center=new a.a(0,0),this.rotation=0,this.matrixAutoUpdate=!0,this.matrix=new o.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(t){return this.name=t.name,this.image=t.image,this.mipmaps=t.mipmaps.slice(0),this.mapping=t.mapping,this.wrapS=t.wrapS,this.wrapT=t.wrapT,this.magFilter=t.magFilter,this.minFilter=t.minFilter,this.anisotropy=t.anisotropy,this.format=t.format,this.internalFormat=t.internalFormat,this.type=t.type,this.offset.copy(t.offset),this.repeat.copy(t.repeat),this.center.copy(t.center),this.rotation=t.rotation,this.matrixAutoUpdate=t.matrixAutoUpdate,this.matrix.copy(t.matrix),this.generateMipmaps=t.generateMipmaps,this.premultiplyAlpha=t.premultiplyAlpha,this.flipY=t.flipY,this.unpackAlignment=t.unpackAlignment,this.encoding=t.encoding,this}toJSON(t){const e=void 0===t||\\\\\\\"string\\\\\\\"==typeof t;if(!e&&void 0!==t.textures[this.uuid])return t.textures[this.uuid];const i={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 r=this.image;if(void 0===r.uuid&&(r.uuid=n.a.generateUUID()),!e&&void 0===t.images[r.uuid]){let e;if(Array.isArray(r)){e=[];for(let t=0,i=r.length;t1)switch(this.wrapS){case s.wc:t.x=t.x-Math.floor(t.x);break;case s.n:t.x=t.x<0?0:1;break;case s.kb:1===Math.abs(Math.floor(t.x)%2)?t.x=Math.ceil(t.x)-t.x:t.x=t.x-Math.floor(t.x)}if(t.y<0||t.y>1)switch(this.wrapT){case s.wc:t.y=t.y-Math.floor(t.y);break;case s.n:t.y=t.y<0?0:1;break;case s.kb:1===Math.abs(Math.floor(t.y)%2)?t.y=Math.ceil(t.y)-t.y:t.y=t.y-Math.floor(t.y)}return this.flipY&&(t.y=1-t.y),t}set needsUpdate(t){!0===t&&this.version++}}function d(t){return\\\\\\\"undefined\\\\\\\"!=typeof HTMLImageElement&&t instanceof HTMLImageElement||\\\\\\\"undefined\\\\\\\"!=typeof HTMLCanvasElement&&t instanceof HTMLCanvasElement||\\\\\\\"undefined\\\\\\\"!=typeof ImageBitmap&&t instanceof ImageBitmap?u(t):t.data?{data:Array.prototype.slice.call(t.data),width:t.width,height:t.height,type:t.data.constructor.name}:(console.warn(\\\\\\\"THREE.Texture: Unable to serialize Texture.\\\\\\\"),{})}l.DEFAULT_IMAGE=void 0,l.DEFAULT_MAPPING=s.Yc,l.prototype.isTexture=!0},function(t,e,i){\\\\\\\"use strict\\\\\\\";i.d(e,\\\\\\\"a\\\\\\\",(function(){return a}));var r=i(42),s=i(7),n=i(4);function a(t=50,e=1,i=.1,s=2e3){r.a.call(this),this.type=\\\\\\\"PerspectiveCamera\\\\\\\",this.fov=t,this.zoom=1,this.near=i,this.far=s,this.focus=10,this.aspect=e,this.view=null,this.filmGauge=35,this.filmOffset=0,this.updateProjectionMatrix()}a.prototype=Object.assign(Object.create(r.a.prototype),{constructor:a,isPerspectiveCamera:!0,copy:function(t,e){return r.a.prototype.copy.call(this,t,e),this.fov=t.fov,this.zoom=t.zoom,this.near=t.near,this.far=t.far,this.focus=t.focus,this.aspect=t.aspect,this.view=null===t.view?null:Object.assign({},t.view),this.filmGauge=t.filmGauge,this.filmOffset=t.filmOffset,this},setFocalLength:function(t){const e=.5*this.getFilmHeight()/t;this.fov=2*n.a.RAD2DEG*Math.atan(e),this.updateProjectionMatrix()},getFocalLength:function(){const t=Math.tan(.5*n.a.DEG2RAD*this.fov);return.5*this.getFilmHeight()/t},getEffectiveFOV:function(){return 2*n.a.RAD2DEG*Math.atan(Math.tan(.5*n.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(t,e,i,r,s,n){this.aspect=t/e,null===this.view&&(this.view={enabled:!0,fullWidth:1,fullHeight:1,offsetX:0,offsetY:0,width:1,height:1}),this.view.enabled=!0,this.view.fullWidth=t,this.view.fullHeight=e,this.view.offsetX=i,this.view.offsetY=r,this.view.width=s,this.view.height=n,this.updateProjectionMatrix()},clearViewOffset:function(){null!==this.view&&(this.view.enabled=!1),this.updateProjectionMatrix()},updateProjectionMatrix:function(){const t=this.near;let e=t*Math.tan(.5*n.a.DEG2RAD*this.fov)/this.zoom,i=2*e,r=this.aspect*i,s=-.5*r;const a=this.view;if(null!==this.view&&this.view.enabled){const t=a.fullWidth,n=a.fullHeight;s+=a.offsetX*r/t,e-=a.offsetY*i/n,r*=a.width/t,i*=a.height/n}const o=this.filmOffset;0!==o&&(s+=t*o/this.getFilmWidth()),this.projectionMatrix.makePerspective(s,s+r,e,e-i,t,this.far),this.projectionMatrixInverse.copy(this.projectionMatrix).invert()},toJSON:function(t){const e=s.a.prototype.toJSON.call(this,t);return e.object.fov=this.fov,e.object.zoom=this.zoom,e.object.near=this.near,e.object.far=this.far,e.object.focus=this.focus,e.object.aspect=this.aspect,null!==this.view&&(e.object.view=Object.assign({},this.view)),e.object.filmGauge=this.filmGauge,e.object.filmOffset=this.filmOffset,e}})},function(t,e,i){\\\\\\\"use strict\\\\\\\";function r(t){if(0===t.length)return-1/0;let e=t[0];for(let i=1,r=t.length;ie&&(e=t[i]);return e}i.d(e,\\\\\\\"a\\\\\\\",(function(){return r})),i.d(e,\\\\\\\"b\\\\\\\",(function(){return n}));const s={Int8Array:Int8Array,Uint8Array:Uint8Array,Uint8ClampedArray:Uint8ClampedArray,Int16Array:Int16Array,Uint16Array:Uint16Array,Int32Array:Int32Array,Uint32Array:Uint32Array,Float32Array:Float32Array,Float64Array:Float64Array};function n(t,e){return new s[t](e)}},,function(t,e,i){\\\\\\\"use strict\\\\\\\";i.d(e,\\\\\\\"a\\\\\\\",(function(){return n}));var r=i(7),s=i(6);class n extends r.a{constructor(t,e=1){super(),this.type=\\\\\\\"Light\\\\\\\",this.color=new s.a(t),this.intensity=e}copy(t){return super.copy(t),this.color.copy(t.color),this.intensity=t.intensity,this}toJSON(t){const e=super.toJSON(t);return e.object.color=this.color.getHex(),e.object.intensity=this.intensity,void 0!==this.groundColor&&(e.object.groundColor=this.groundColor.getHex()),void 0!==this.distance&&(e.object.distance=this.distance),void 0!==this.angle&&(e.object.angle=this.angle),void 0!==this.decay&&(e.object.decay=this.decay),void 0!==this.penumbra&&(e.object.penumbra=this.penumbra),void 0!==this.shadow&&(e.object.shadow=this.shadow.toJSON()),e}}n.prototype.isLight=!0},function(t,e,i){\\\\\\\"use strict\\\\\\\";i.d(e,\\\\\\\"a\\\\\\\",(function(){return y}));var r=i(18),s=i(39),n=i(5),a=i(7),o=i(0),h=i(23),u=i(8),c=i(3);const l=new o.a,d=new o.a,p=new n.a,f=new s.a,m=new r.a;function y(t=new u.a,e=new h.a){a.a.call(this),this.type=\\\\\\\"Line\\\\\\\",this.geometry=t,this.material=e,this.updateMorphTargets()}y.prototype=Object.assign(Object.create(a.a.prototype),{constructor:y,isLine:!0,copy:function(t){return a.a.prototype.copy.call(this,t),this.material=t.material,this.geometry=t.geometry,this},computeLineDistances:function(){const t=this.geometry;if(t.isBufferGeometry)if(null===t.index){const e=t.attributes.position,i=[0];for(let t=1,r=e.count;th)continue;d.applyMatrix4(this.matrixWorld);const o=t.ray.origin.distanceTo(d);ot.far||e.push({distance:o,point:l.clone().applyMatrix4(this.matrixWorld),index:i,face:null,faceIndex:null,object:this})}}else{for(let i=Math.max(0,n.start),r=Math.min(s.count,n.start+n.count)-1;ih)continue;d.applyMatrix4(this.matrixWorld);const r=t.ray.origin.distanceTo(d);rt.far||e.push({distance:r,point:l.clone().applyMatrix4(this.matrixWorld),index:i,face:null,faceIndex:null,object:this})}}}else i.isGeometry&&console.error(\\\\\\\"THREE.Line.raycast() no longer supports THREE.Geometry. Use THREE.BufferGeometry instead.\\\\\\\")},updateMorphTargets:function(){const t=this.geometry;if(t.isBufferGeometry){const e=t.morphAttributes,i=Object.keys(e);if(i.length>0){const t=e[i[0]];if(void 0!==t){this.morphTargetInfluences=[],this.morphTargetDictionary={};for(let e=0,i=t.length;e0&&console.error(\\\\\\\"THREE.Line.updateMorphTargets() does not support THREE.Geometry. Use THREE.BufferGeometry instead.\\\\\\\")}}})},function(t,e,i){\\\\\\\"use strict\\\\\\\";i.d(e,\\\\\\\"a\\\\\\\",(function(){return h}));var r=i(35),s=i(0),n=i(3);const a=new s.a,o=new s.a;function h(t,e){r.a.call(this,t,e),this.type=\\\\\\\"LineSegments\\\\\\\"}h.prototype=Object.assign(Object.create(r.a.prototype),{constructor:h,isLineSegments:!0,computeLineDistances:function(){const t=this.geometry;if(t.isBufferGeometry)if(null===t.index){const e=t.attributes.position,i=[];for(let t=0,r=e.count;t0)if(p=h*c-u,f=h*u-c,y=s*d,p>=0)if(f>=-y)if(f<=y){const t=1/d;p*=t,f*=t,m=p*(p+h*f+2*u)+f*(h*p+f+2*c)+l}else f=s,p=Math.max(0,-(h*f+u)),m=-p*p+f*(f+2*c)+l;else f=-s,p=Math.max(0,-(h*f+u)),m=-p*p+f*(f+2*c)+l;else f<=-y?(p=Math.max(0,-(-h*s+u)),f=p>0?-s:Math.min(Math.max(-s,-c),s),m=-p*p+f*(f+2*c)+l):f<=y?(p=0,f=Math.min(Math.max(-s,-c),s),m=f*(f+2*c)+l):(p=Math.max(0,-(h*s+u)),f=p>0?s:Math.min(Math.max(-s,-c),s),m=-p*p+f*(f+2*c)+l);else f=h>0?-s:s,p=Math.max(0,-(h*f+u)),m=-p*p+f*(f+2*c)+l;return i&&i.copy(this.direction).multiplyScalar(p).add(this.origin),r&&r.copy(a).multiplyScalar(f).add(n),m}intersectSphere(t,e){s.subVectors(t.center,this.origin);const i=s.dot(this.direction),r=s.dot(s)-i*i,n=t.radius*t.radius;if(r>n)return null;const a=Math.sqrt(n-r),o=i-a,h=i+a;return o<0&&h<0?null:o<0?this.at(h,e):this.at(o,e)}intersectsSphere(t){return this.distanceSqToPoint(t.center)<=t.radius*t.radius}distanceToPlane(t){const e=t.normal.dot(this.direction);if(0===e)return 0===t.distanceToPoint(this.origin)?0:null;const i=-(this.origin.dot(t.normal)+t.constant)/e;return i>=0?i:null}intersectPlane(t,e){const i=this.distanceToPlane(t);return null===i?null:this.at(i,e)}intersectsPlane(t){const e=t.distanceToPoint(this.origin);if(0===e)return!0;return t.normal.dot(this.direction)*e<0}intersectBox(t,e){let i,r,s,n,a,o;const h=1/this.direction.x,u=1/this.direction.y,c=1/this.direction.z,l=this.origin;return h>=0?(i=(t.min.x-l.x)*h,r=(t.max.x-l.x)*h):(i=(t.max.x-l.x)*h,r=(t.min.x-l.x)*h),u>=0?(s=(t.min.y-l.y)*u,n=(t.max.y-l.y)*u):(s=(t.max.y-l.y)*u,n=(t.min.y-l.y)*u),i>n||s>r?null:((s>i||i!=i)&&(i=s),(n=0?(a=(t.min.z-l.z)*c,o=(t.max.z-l.z)*c):(a=(t.max.z-l.z)*c,o=(t.min.z-l.z)*c),i>o||a>r?null:((a>i||i!=i)&&(i=a),(o=0?i:r,e)))}intersectsBox(t){return null!==this.intersectBox(t,s)}intersectTriangle(t,e,i,r,s){h.subVectors(e,t),u.subVectors(i,t),c.crossVectors(h,u);let n,a=this.direction.dot(c);if(a>0){if(r)return null;n=1}else{if(!(a<0))return null;n=-1,a=-a}o.subVectors(this.origin,t);const l=n*this.direction.dot(u.crossVectors(o,u));if(l<0)return null;const d=n*this.direction.dot(h.cross(o));if(d<0)return null;if(l+d>a)return null;const p=-n*o.dot(c);return p<0?null:this.at(p/a,s)}applyMatrix4(t){return this.origin.applyMatrix4(t),this.direction.transformDirection(t),this}equals(t){return t.origin.equals(this.origin)&&t.direction.equals(this.direction)}clone(){return(new this.constructor).copy(this)}}},function(t,e,i){\\\\\\\"use strict\\\\\\\";i.d(e,\\\\\\\"a\\\\\\\",(function(){return m}));var r=i(0),s=i(27);const n=new r.a,a=new r.a,o=new r.a,h=new r.a,u=new r.a,c=new r.a,l=new r.a,d=new r.a,p=new r.a,f=new r.a;class m{constructor(t=new r.a,e=new r.a,i=new r.a){this.a=t,this.b=e,this.c=i}static getNormal(t,e,i,s){void 0===s&&(console.warn(\\\\\\\"THREE.Triangle: .getNormal() target is now required\\\\\\\"),s=new r.a),s.subVectors(i,e),n.subVectors(t,e),s.cross(n);const a=s.lengthSq();return a>0?s.multiplyScalar(1/Math.sqrt(a)):s.set(0,0,0)}static getBarycoord(t,e,i,s,h){n.subVectors(s,e),a.subVectors(i,e),o.subVectors(t,e);const u=n.dot(n),c=n.dot(a),l=n.dot(o),d=a.dot(a),p=a.dot(o),f=u*d-c*c;if(void 0===h&&(console.warn(\\\\\\\"THREE.Triangle: .getBarycoord() target is now required\\\\\\\"),h=new r.a),0===f)return h.set(-2,-1,-1);const m=1/f,y=(d*l-c*p)*m,g=(u*p-c*l)*m;return h.set(1-y-g,g,y)}static containsPoint(t,e,i,r){return this.getBarycoord(t,e,i,r,h),h.x>=0&&h.y>=0&&h.x+h.y<=1}static getUV(t,e,i,r,s,n,a,o){return this.getBarycoord(t,e,i,r,h),o.set(0,0),o.addScaledVector(s,h.x),o.addScaledVector(n,h.y),o.addScaledVector(a,h.z),o}static isFrontFacing(t,e,i,r){return n.subVectors(i,e),a.subVectors(t,e),n.cross(a).dot(r)<0}set(t,e,i){return this.a.copy(t),this.b.copy(e),this.c.copy(i),this}setFromPointsAndIndices(t,e,i,r){return this.a.copy(t[e]),this.b.copy(t[i]),this.c.copy(t[r]),this}clone(){return(new this.constructor).copy(this)}copy(t){return this.a.copy(t.a),this.b.copy(t.b),this.c.copy(t.c),this}getArea(){return n.subVectors(this.c,this.b),a.subVectors(this.a,this.b),.5*n.cross(a).length()}getMidpoint(t){return void 0===t&&(console.warn(\\\\\\\"THREE.Triangle: .getMidpoint() target is now required\\\\\\\"),t=new r.a),t.addVectors(this.a,this.b).add(this.c).multiplyScalar(1/3)}getNormal(t){return m.getNormal(this.a,this.b,this.c,t)}getPlane(t){return void 0===t&&(console.warn(\\\\\\\"THREE.Triangle: .getPlane() target is now required\\\\\\\"),t=new s.a),t.setFromCoplanarPoints(this.a,this.b,this.c)}getBarycoord(t,e){return m.getBarycoord(t,this.a,this.b,this.c,e)}getUV(t,e,i,r,s){return m.getUV(t,this.a,this.b,this.c,e,i,r,s)}containsPoint(t){return m.containsPoint(t,this.a,this.b,this.c)}isFrontFacing(t){return m.isFrontFacing(this.a,this.b,this.c,t)}intersectsBox(t){return t.intersectsTriangle(this)}closestPointToPoint(t,e){void 0===e&&(console.warn(\\\\\\\"THREE.Triangle: .closestPointToPoint() target is now required\\\\\\\"),e=new r.a);const i=this.a,s=this.b,n=this.c;let a,o;u.subVectors(s,i),c.subVectors(n,i),d.subVectors(t,i);const h=u.dot(d),m=c.dot(d);if(h<=0&&m<=0)return e.copy(i);p.subVectors(t,s);const y=u.dot(p),g=c.dot(p);if(y>=0&&g<=y)return e.copy(s);const x=h*g-y*m;if(x<=0&&h>=0&&y<=0)return a=h/(h-y),e.copy(i).addScaledVector(u,a);f.subVectors(t,n);const b=u.dot(f),v=c.dot(f);if(v>=0&&b<=v)return e.copy(n);const w=b*m-h*v;if(w<=0&&m>=0&&v<=0)return o=m/(m-v),e.copy(i).addScaledVector(c,o);const M=y*v-b*g;if(M<=0&&g-y>=0&&b-v>=0)return l.subVectors(n,s),o=(g-y)/(g-y+(b-v)),e.copy(s).addScaledVector(l,o);const T=1/(M+w+x);return a=w*T,o=x*T,e.copy(i).addScaledVector(u,a).addScaledVector(c,o)}equals(t){return t.a.equals(this.a)&&t.b.equals(this.b)&&t.c.equals(this.c)}}},,function(t,e,i){\\\\\\\"use strict\\\\\\\";i.d(e,\\\\\\\"a\\\\\\\",(function(){return a}));var r=i(5),s=i(7),n=i(0);function a(){s.a.call(this),this.type=\\\\\\\"Camera\\\\\\\",this.matrixWorldInverse=new r.a,this.projectionMatrix=new r.a,this.projectionMatrixInverse=new r.a}a.prototype=Object.assign(Object.create(s.a.prototype),{constructor:a,isCamera:!0,copy:function(t,e){return s.a.prototype.copy.call(this,t,e),this.matrixWorldInverse.copy(t.matrixWorldInverse),this.projectionMatrix.copy(t.projectionMatrix),this.projectionMatrixInverse.copy(t.projectionMatrixInverse),this},getWorldDirection:function(t){void 0===t&&(console.warn(\\\\\\\"THREE.Camera: .getWorldDirection() target is now required\\\\\\\"),t=new n.a),this.updateWorldMatrix(!0,!1);const e=this.matrixWorld.elements;return t.set(-e[8],-e[9],-e[10]).normalize()},updateMatrixWorld:function(t){s.a.prototype.updateMatrixWorld.call(this,t),this.matrixWorldInverse.copy(this.matrixWorld).invert()},updateWorldMatrix:function(t,e){s.a.prototype.updateWorldMatrix.call(this,t,e),this.matrixWorldInverse.copy(this.matrixWorld).invert()},clone:function(){return(new this.constructor).copy(this)}})},function(t,e,i){\\\\\\\"use strict\\\\\\\";i.d(e,\\\\\\\"a\\\\\\\",(function(){return n}));var r=i(12),s=i(6);class n extends r.a{constructor(t){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(t)}copy(t){return super.copy(t),this.color.copy(t.color),this.map=t.map,this.alphaMap=t.alphaMap,this.size=t.size,this.sizeAttenuation=t.sizeAttenuation,this.morphTargets=t.morphTargets,this}}n.prototype.isPointsMaterial=!0},function(t,e,i){\\\\\\\"use strict\\\\\\\";i.d(e,\\\\\\\"a\\\\\\\",(function(){return o}));var r=i(1),s=i(12),n=i(2),a=i(6);function o(t){s.a.call(this),this.defines={STANDARD:\\\\\\\"\\\\\\\"},this.type=\\\\\\\"MeshStandardMaterial\\\\\\\",this.color=new a.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 a.a(0),this.emissiveIntensity=1,this.emissiveMap=null,this.bumpMap=null,this.bumpScale=1,this.normalMap=null,this.normalMapType=r.Uc,this.normalScale=new n.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(t)}o.prototype=Object.create(s.a.prototype),o.prototype.constructor=o,o.prototype.isMeshStandardMaterial=!0,o.prototype.copy=function(t){return s.a.prototype.copy.call(this,t),this.defines={STANDARD:\\\\\\\"\\\\\\\"},this.color.copy(t.color),this.roughness=t.roughness,this.metalness=t.metalness,this.map=t.map,this.lightMap=t.lightMap,this.lightMapIntensity=t.lightMapIntensity,this.aoMap=t.aoMap,this.aoMapIntensity=t.aoMapIntensity,this.emissive.copy(t.emissive),this.emissiveMap=t.emissiveMap,this.emissiveIntensity=t.emissiveIntensity,this.bumpMap=t.bumpMap,this.bumpScale=t.bumpScale,this.normalMap=t.normalMap,this.normalMapType=t.normalMapType,this.normalScale.copy(t.normalScale),this.displacementMap=t.displacementMap,this.displacementScale=t.displacementScale,this.displacementBias=t.displacementBias,this.roughnessMap=t.roughnessMap,this.metalnessMap=t.metalnessMap,this.alphaMap=t.alphaMap,this.envMap=t.envMap,this.envMapIntensity=t.envMapIntensity,this.refractionRatio=t.refractionRatio,this.wireframe=t.wireframe,this.wireframeLinewidth=t.wireframeLinewidth,this.wireframeLinecap=t.wireframeLinecap,this.wireframeLinejoin=t.wireframeLinejoin,this.skinning=t.skinning,this.morphTargets=t.morphTargets,this.morphNormals=t.morphNormals,this.flatShading=t.flatShading,this.vertexTangents=t.vertexTangents,this}},function(t,e,i){\\\\\\\"use strict\\\\\\\";i.d(e,\\\\\\\"a\\\\\\\",(function(){return l}));var r=i(5),s=i(2),n=i(0),a=i(10),o=i(59);const h=new r.a,u=new n.a,c=new n.a;class l{constructor(t){this.camera=t,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 r.a,this.autoUpdate=!0,this.needsUpdate=!1,this._frustum=new o.a,this._frameExtents=new s.a(1,1),this._viewportCount=1,this._viewports=[new a.a(0,0,1,1)]}getViewportCount(){return this._viewportCount}getFrustum(){return this._frustum}updateMatrices(t){const e=this.camera,i=this.matrix;u.setFromMatrixPosition(t.matrixWorld),e.position.copy(u),c.setFromMatrixPosition(t.target.matrixWorld),e.lookAt(c),e.updateMatrixWorld(),h.multiplyMatrices(e.projectionMatrix,e.matrixWorldInverse),this._frustum.setFromProjectionMatrix(h),i.set(.5,0,0,.5,0,.5,0,.5,0,0,.5,.5,0,0,0,1),i.multiply(e.projectionMatrix),i.multiply(e.matrixWorldInverse)}getViewport(t){return this._viewports[t]}getFrameExtents(){return this._frameExtents}copy(t){return this.camera=t.camera.clone(),this.bias=t.bias,this.radius=t.radius,this.mapSize.copy(t.mapSize),this}clone(){return(new this.constructor).copy(this)}toJSON(){const t={};return 0!==this.bias&&(t.bias=this.bias),0!==this.normalBias&&(t.normalBias=this.normalBias),1!==this.radius&&(t.radius=this.radius),512===this.mapSize.x&&512===this.mapSize.y||(t.mapSize=this.mapSize.toArray()),t.camera=this.camera.toJSON(!1).object,delete t.camera.matrix,t}}},function(t,e,i){\\\\\\\"use strict\\\\\\\";i.d(e,\\\\\\\"a\\\\\\\",(function(){return r}));const r={decodeText:function(t){if(\\\\\\\"undefined\\\\\\\"!=typeof TextDecoder)return(new TextDecoder).decode(t);let e=\\\\\\\"\\\\\\\";for(let i=0,r=t.length;is.far)return;n.push({distance:u,distanceToRay:Math.sqrt(h),point:i,index:e,face:null,object:a})}}f.prototype=Object.assign(Object.create(a.a.prototype),{constructor:f,isPoints:!0,copy:function(t){return a.a.prototype.copy.call(this,t),this.material=t.material,this.geometry=t.geometry,this},raycast:function(t,e){const i=this.geometry,r=this.matrixWorld,s=t.params.Points.threshold,n=i.drawRange;if(null===i.boundingSphere&&i.computeBoundingSphere(),d.copy(i.boundingSphere),d.applyMatrix4(r),d.radius+=s,!1===t.ray.intersectsSphere(d))return;c.copy(r).invert(),l.copy(t.ray).applyMatrix4(c);const a=s/((this.scale.x+this.scale.y+this.scale.z)/3),o=a*a;if(i.isBufferGeometry){const s=i.index,a=i.attributes.position;if(null!==s){for(let i=Math.max(0,n.start),h=Math.min(s.count,n.start+n.count);i0){const t=e[i[0]];if(void 0!==t){this.morphTargetInfluences=[],this.morphTargetDictionary={};for(let e=0,i=t.length;e0&&console.error(\\\\\\\"THREE.Points.updateMorphTargets() does not support THREE.Geometry. Use THREE.BufferGeometry instead.\\\\\\\")}}})},function(t,e,i){\\\\\\\"use strict\\\\\\\";i.d(e,\\\\\\\"a\\\\\\\",(function(){return s}));var r=i(24);class s extends r.a{}s.prototype.ValueTypeName=\\\\\\\"number\\\\\\\"},function(t,e,i){\\\\\\\"use strict\\\\\\\";i.d(e,\\\\\\\"a\\\\\\\",(function(){return s}));var r=i(24);class s extends r.a{}s.prototype.ValueTypeName=\\\\\\\"vector\\\\\\\"},function(t,e,i){\\\\\\\"use strict\\\\\\\";i.d(e,\\\\\\\"a\\\\\\\",(function(){return s}));var r=i(7);function s(){r.a.call(this),this.type=\\\\\\\"Bone\\\\\\\"}s.prototype=Object.assign(Object.create(r.a.prototype),{constructor:s,isBone:!0})},,function(t,e,i){\\\\\\\"use strict\\\\\\\";i.d(e,\\\\\\\"a\\\\\\\",(function(){return h}));var r=i(1),s=i(24),n=i(29),a=i(9);function o(t,e,i,r){n.a.call(this,t,e,i,r)}o.prototype=Object.assign(Object.create(n.a.prototype),{constructor:o,interpolate_:function(t,e,i,r){const s=this.resultBuffer,n=this.sampleValues,o=this.valueSize,h=(i-e)/(r-e);let u=t*o;for(let t=u+o;u!==t;u+=4)a.a.slerpFlat(s,0,n,u-o,n,u,h);return s}});class h extends s.a{InterpolantFactoryMethodLinear(t){return new o(this.times,this.values,this.getValueSize(),t)}}h.prototype.ValueTypeName=\\\\\\\"quaternion\\\\\\\",h.prototype.DefaultInterpolation=r.P,h.prototype.InterpolantFactoryMethodSmooth=void 0},,,,function(t,e,i){\\\\\\\"use strict\\\\\\\";i.d(e,\\\\\\\"a\\\\\\\",(function(){return f}));var r=i(34),s=i(45),n=i(31),a=i(5),o=i(2),h=i(0),u=i(10);const c=new a.a,l=new h.a,d=new h.a;class p extends s.a{constructor(){super(new n.a(90,1,.5,500)),this._frameExtents=new o.a(4,2),this._viewportCount=6,this._viewports=[new u.a(2,1,1,1),new u.a(0,1,1,1),new u.a(3,1,1,1),new u.a(1,1,1,1),new u.a(3,0,1,1),new u.a(1,0,1,1)],this._cubeDirections=[new h.a(1,0,0),new h.a(-1,0,0),new h.a(0,0,1),new h.a(0,0,-1),new h.a(0,1,0),new h.a(0,-1,0)],this._cubeUps=[new h.a(0,1,0),new h.a(0,1,0),new h.a(0,1,0),new h.a(0,1,0),new h.a(0,0,1),new h.a(0,0,-1)]}updateMatrices(t,e=0){const i=this.camera,r=this.matrix,s=t.distance||i.far;s!==i.far&&(i.far=s,i.updateProjectionMatrix()),l.setFromMatrixPosition(t.matrixWorld),i.position.copy(l),d.copy(i.position),d.add(this._cubeDirections[e]),i.up.copy(this._cubeUps[e]),i.lookAt(d),i.updateMatrixWorld(),r.makeTranslation(-l.x,-l.y,-l.z),c.multiplyMatrices(i.projectionMatrix,i.matrixWorldInverse),this._frustum.setFromProjectionMatrix(c)}}p.prototype.isPointLightShadow=!0;class f extends r.a{constructor(t,e,i=0,r=1){super(t,e),this.type=\\\\\\\"PointLight\\\\\\\",this.distance=i,this.decay=r,this.shadow=new p}get power(){return 4*this.intensity*Math.PI}set power(t){this.intensity=t/(4*Math.PI)}copy(t){return super.copy(t),this.distance=t.distance,this.decay=t.decay,this.shadow=t.shadow.clone(),this}}f.prototype.isPointLight=!0},function(t,e,i){\\\\\\\"use strict\\\\\\\";i.d(e,\\\\\\\"a\\\\\\\",(function(){return h}));var r=i(0),s=i(18),n=i(27);const a=new s.a,o=new r.a;class h{constructor(t=new n.a,e=new n.a,i=new n.a,r=new n.a,s=new n.a,a=new n.a){this.planes=[t,e,i,r,s,a]}set(t,e,i,r,s,n){const a=this.planes;return a[0].copy(t),a[1].copy(e),a[2].copy(i),a[3].copy(r),a[4].copy(s),a[5].copy(n),this}copy(t){const e=this.planes;for(let i=0;i<6;i++)e[i].copy(t.planes[i]);return this}setFromProjectionMatrix(t){const e=this.planes,i=t.elements,r=i[0],s=i[1],n=i[2],a=i[3],o=i[4],h=i[5],u=i[6],c=i[7],l=i[8],d=i[9],p=i[10],f=i[11],m=i[12],y=i[13],g=i[14],x=i[15];return e[0].setComponents(a-r,c-o,f-l,x-m).normalize(),e[1].setComponents(a+r,c+o,f+l,x+m).normalize(),e[2].setComponents(a+s,c+h,f+d,x+y).normalize(),e[3].setComponents(a-s,c-h,f-d,x-y).normalize(),e[4].setComponents(a-n,c-u,f-p,x-g).normalize(),e[5].setComponents(a+n,c+u,f+p,x+g).normalize(),this}intersectsObject(t){const e=t.geometry;return null===e.boundingSphere&&e.computeBoundingSphere(),a.copy(e.boundingSphere).applyMatrix4(t.matrixWorld),this.intersectsSphere(a)}intersectsSprite(t){return a.center.set(0,0,0),a.radius=.7071067811865476,a.applyMatrix4(t.matrixWorld),this.intersectsSphere(a)}intersectsSphere(t){const e=this.planes,i=t.center,r=-t.radius;for(let t=0;t<6;t++){if(e[t].distanceToPoint(i)0?t.max.x:t.min.x,o.y=r.normal.y>0?t.max.y:t.min.y,o.z=r.normal.z>0?t.max.z:t.min.z,r.distanceToPoint(o)<0)return!1}return!0}containsPoint(t){const e=this.planes;for(let i=0;i<6;i++)if(e[i].distanceToPoint(t)<0)return!1;return!0}clone(){return(new this.constructor).copy(this)}}},,,function(t,e,i){\\\\\\\"use strict\\\\\\\";i.d(e,\\\\\\\"a\\\\\\\",(function(){return p}));var r=i(19),s=i(24),n=i(1);class a extends s.a{}a.prototype.ValueTypeName=\\\\\\\"bool\\\\\\\",a.prototype.ValueBufferType=Array,a.prototype.DefaultInterpolation=n.O,a.prototype.InterpolantFactoryMethodLinear=void 0,a.prototype.InterpolantFactoryMethodSmooth=void 0;class o extends s.a{}o.prototype.ValueTypeName=\\\\\\\"color\\\\\\\";var h=i(50),u=i(54);class c extends s.a{}c.prototype.ValueTypeName=\\\\\\\"string\\\\\\\",c.prototype.ValueBufferType=Array,c.prototype.DefaultInterpolation=n.O,c.prototype.InterpolantFactoryMethodLinear=void 0,c.prototype.InterpolantFactoryMethodSmooth=void 0;var l=i(51),d=i(4);class p{constructor(t,e=-1,i,r=n.wb){this.name=t,this.tracks=i,this.duration=e,this.blendMode=r,this.uuid=d.a.generateUUID(),this.duration<0&&this.resetDuration()}static parse(t){const e=[],i=t.tracks,r=1/(t.fps||1);for(let t=0,s=i.length;t!==s;++t)e.push(f(i[t]).scale(r));const s=new this(t.name,t.duration,e,t.blendMode);return s.uuid=t.uuid,s}static toJSON(t){const e=[],i=t.tracks,r={name:t.name,duration:t.duration,tracks:e,uuid:t.uuid,blendMode:t.blendMode};for(let t=0,r=i.length;t!==r;++t)e.push(s.a.toJSON(i[t]));return r}static CreateFromMorphTargetSequence(t,e,i,s){const n=e.length,a=[];for(let t=0;t1){const t=n[1];let e=r[t];e||(r[t]=e=[]),e.push(i)}}const n=[];for(const t in r)n.push(this.CreateFromMorphTargetSequence(t,r[t],e,i));return n}static parseAnimation(t,e){if(!t)return console.error(\\\\\\\"THREE.AnimationClip: No animation in JSONLoader data.\\\\\\\"),null;const i=function(t,e,i,s,n){if(0!==i.length){const a=[],o=[];r.a.flattenJSON(i,a,o,s),0!==a.length&&n.push(new t(e,a,o))}},s=[],n=t.name||\\\\\\\"default\\\\\\\",a=t.fps||30,o=t.blendMode;let c=t.length||-1;const d=t.hierarchy||[];for(let t=0;t0||0===t.search(/^data\\\\:image\\\\/jpeg/);o.format=s?r.ic:r.Ib,o.needsUpdate=!0,void 0!==e&&e(o)}),i,a),o}})},function(t,e,i){\\\\\\\"use strict\\\\\\\";i.d(e,\\\\\\\"a\\\\\\\",(function(){return h}));var r=i(52),s=i(5),n=i(4);const a=new s.a,o=new s.a;class h{constructor(t=[],e=[]){this.uuid=n.a.generateUUID(),this.bones=t.slice(0),this.boneInverses=e,this.boneMatrices=null,this.boneTexture=null,this.boneTextureSize=0,this.frame=-1,this.init()}init(){const t=this.bones,e=this.boneInverses;if(this.boneMatrices=new Float32Array(16*t.length),0===e.length)this.calculateInverses();else if(t.length!==e.length){console.warn(\\\\\\\"THREE.Skeleton: Number of inverse bone matrices does not match amount of bones.\\\\\\\"),this.boneInverses=[];for(let t=0,e=this.bones.length;t=2.0 are supported.\\\\\\\"));else{var u=new Pt(h,{path:e||this.resourcePath||\\\\\\\"\\\\\\\",crossOrigin:this.crossOrigin,requestHeader:this.requestHeader,manager:this.manager,ktx2Loader:this.ktx2Loader,meshoptDecoder:this.meshoptDecoder});u.fileLoader.setRequestHeader(this.requestHeader);for(var c=0;c=0&&void 0===o[d]&&console.warn('THREE.GLTFLoader: Unknown extension \\\\\\\"'+d+'\\\\\\\".')}}u.setExtensions(a),u.setPlugins(o),u.parse(r,s)}}});var i={KHR_BINARY_GLTF:\\\\\\\"KHR_binary_glTF\\\\\\\",KHR_DRACO_MESH_COMPRESSION:\\\\\\\"KHR_draco_mesh_compression\\\\\\\",KHR_LIGHTS_PUNCTUAL:\\\\\\\"KHR_lights_punctual\\\\\\\",KHR_MATERIALS_CLEARCOAT:\\\\\\\"KHR_materials_clearcoat\\\\\\\",KHR_MATERIALS_PBR_SPECULAR_GLOSSINESS:\\\\\\\"KHR_materials_pbrSpecularGlossiness\\\\\\\",KHR_MATERIALS_TRANSMISSION:\\\\\\\"KHR_materials_transmission\\\\\\\",KHR_MATERIALS_UNLIT:\\\\\\\"KHR_materials_unlit\\\\\\\",KHR_TEXTURE_BASISU:\\\\\\\"KHR_texture_basisu\\\\\\\",KHR_TEXTURE_TRANSFORM:\\\\\\\"KHR_texture_transform\\\\\\\",KHR_MESH_QUANTIZATION:\\\\\\\"KHR_mesh_quantization\\\\\\\",EXT_TEXTURE_WEBP:\\\\\\\"EXT_texture_webp\\\\\\\",EXT_MESHOPT_COMPRESSION:\\\\\\\"EXT_meshopt_compression\\\\\\\"};function f(t){this.parser=t,this.name=i.KHR_LIGHTS_PUNCTUAL,this.cache={refs:{},uses:{}}}function Z(){this.name=i.KHR_MATERIALS_UNLIT}function J(t){this.parser=t,this.name=i.KHR_MATERIALS_CLEARCOAT}function K(t){this.parser=t,this.name=i.KHR_MATERIALS_TRANSMISSION}function Q(t){this.parser=t,this.name=i.KHR_TEXTURE_BASISU}function $(t){this.parser=t,this.name=i.EXT_TEXTURE_WEBP,this.isSupported=null}function tt(t){this.name=i.EXT_MESHOPT_COMPRESSION,this.parser=t}f.prototype._markDefs=function(){for(var t=this.parser,e=this.parser.json.nodes||[],i=0,r=e.length;i=0)throw new Error(\\\\\\\"THREE.GLTFLoader: setKTX2Loader must be called before loading KTX2 textures\\\\\\\");return null}return e.loadTextureImage(t,n,a)},$.prototype.loadTexture=function(t){var e=this.name,i=this.parser,r=i.json,s=r.textures[t];if(!s.extensions||!s.extensions[e])return null;var n=s.extensions[e],a=r.images[n.source],o=i.textureLoader;if(a.uri){var h=i.options.manager.getHandler(a.uri);null!==h&&(o=h)}return this.detectSupport().then((function(s){if(s)return i.loadTextureImage(t,a,o);if(r.extensionsRequired&&r.extensionsRequired.indexOf(e)>=0)throw new Error(\\\\\\\"THREE.GLTFLoader: WebP required by asset but unsupported.\\\\\\\");return i.loadTexture(t)}))},$.prototype.detectSupport=function(){return this.isSupported||(this.isSupported=new Promise((function(t){var e=new Image;e.src=\\\\\\\"data:image/webp;base64,UklGRiIAAABXRUJQVlA4IBYAAAAwAQCdASoBAAEADsD+JaQAA3AAAAAA\\\\\\\",e.onload=e.onerror=function(){t(1===e.height)}}))),this.isSupported},tt.prototype.loadBufferView=function(t){var e=this.parser.json,i=e.bufferViews[t];if(i.extensions&&i.extensions[this.name]){var r=i.extensions[this.name],s=this.parser.getDependency(\\\\\\\"buffer\\\\\\\",r.buffer),n=this.parser.options.meshoptDecoder;if(!n||!n.supported){if(e.extensionsRequired&&e.extensionsRequired.indexOf(this.name)>=0)throw new Error(\\\\\\\"THREE.GLTFLoader: setMeshoptDecoder must be called before loading compressed files\\\\\\\");return null}return Promise.all([s,n.ready]).then((function(t){var e=r.byteOffset||0,i=r.byteLength||0,s=r.count,a=r.byteStride,o=new ArrayBuffer(s*a),h=new Uint8Array(t[0],e,i);return n.decodeGltfBuffer(new Uint8Array(o),s,a,h,r.mode,r.filter),o}))}return null};var et=\\\\\\\"glTF\\\\\\\",it=1313821514,rt=5130562;function st(t){this.name=i.KHR_BINARY_GLTF,this.content=null,this.body=null;var e=new DataView(t,0,12);if(this.header={magic:S.a.decodeText(new Uint8Array(t.slice(0,4))),version:e.getUint32(4,!0),length:e.getUint32(8,!0)},this.header.magic!==et)throw new Error(\\\\\\\"THREE.GLTFLoader: Unsupported glTF-Binary header.\\\\\\\");if(this.header.version<2)throw new Error(\\\\\\\"THREE.GLTFLoader: Legacy binary file detected.\\\\\\\");for(var r=this.header.length-12,s=new DataView(t,12),n=0;n\\\\\\\",e).replace(\\\\\\\"#include \\\\\\\",i).replace(\\\\\\\"#include \\\\\\\",r).replace(\\\\\\\"#include \\\\\\\",s).replace(\\\\\\\"#include \\\\\\\",n)},Object.defineProperties(this,{specular:{get:function(){return a.specular.value},set:function(t){a.specular.value=t}},specularMap:{get:function(){return a.specularMap.value},set:function(t){a.specularMap.value=t,t?this.defines.USE_SPECULARMAP=\\\\\\\"\\\\\\\":delete this.defines.USE_SPECULARMAP}},glossiness:{get:function(){return a.glossiness.value},set:function(t){a.glossiness.value=t}},glossinessMap:{get:function(){return a.glossinessMap.value},set:function(t){a.glossinessMap.value=t,t?(this.defines.USE_GLOSSINESSMAP=\\\\\\\"\\\\\\\",this.defines.USE_UV=\\\\\\\"\\\\\\\"):(delete this.defines.USE_GLOSSINESSMAP,delete this.defines.USE_UV)}}}),delete this.metalness,delete this.roughness,delete this.metalnessMap,delete this.roughnessMap,this.setValues(t)}function ht(){return{name:i.KHR_MATERIALS_PBR_SPECULAR_GLOSSINESS,specularGlossinessParams:[\\\\\\\"color\\\\\\\",\\\\\\\"map\\\\\\\",\\\\\\\"lightMap\\\\\\\",\\\\\\\"lightMapIntensity\\\\\\\",\\\\\\\"aoMap\\\\\\\",\\\\\\\"aoMapIntensity\\\\\\\",\\\\\\\"emissive\\\\\\\",\\\\\\\"emissiveIntensity\\\\\\\",\\\\\\\"emissiveMap\\\\\\\",\\\\\\\"bumpMap\\\\\\\",\\\\\\\"bumpScale\\\\\\\",\\\\\\\"normalMap\\\\\\\",\\\\\\\"normalMapType\\\\\\\",\\\\\\\"displacementMap\\\\\\\",\\\\\\\"displacementScale\\\\\\\",\\\\\\\"displacementBias\\\\\\\",\\\\\\\"specularMap\\\\\\\",\\\\\\\"specular\\\\\\\",\\\\\\\"glossinessMap\\\\\\\",\\\\\\\"glossiness\\\\\\\",\\\\\\\"alphaMap\\\\\\\",\\\\\\\"envMap\\\\\\\",\\\\\\\"envMapIntensity\\\\\\\",\\\\\\\"refractionRatio\\\\\\\"],getMaterialType:function(){return ot},extendParams:function(t,e,i){var r=e.extensions[this.name];t.color=new c.a(1,1,1),t.opacity=1;var s=[];if(Array.isArray(r.diffuseFactor)){var n=r.diffuseFactor;t.color.fromArray(n),t.opacity=n[3]}if(void 0!==r.diffuseTexture&&s.push(i.assignTexture(t,\\\\\\\"map\\\\\\\",r.diffuseTexture)),t.emissive=new c.a(0,0,0),t.glossiness=void 0!==r.glossinessFactor?r.glossinessFactor:1,t.specular=new c.a(1,1,1),Array.isArray(r.specularFactor)&&t.specular.fromArray(r.specularFactor),void 0!==r.specularGlossinessTexture){var a=r.specularGlossinessTexture;s.push(i.assignTexture(t,\\\\\\\"glossinessMap\\\\\\\",a)),s.push(i.assignTexture(t,\\\\\\\"specularMap\\\\\\\",a))}return Promise.all(s)},createMaterial:function(t){var e=new ot(t);return e.fog=!0,e.color=t.color,e.map=void 0===t.map?null:t.map,e.lightMap=null,e.lightMapIntensity=1,e.aoMap=void 0===t.aoMap?null:t.aoMap,e.aoMapIntensity=1,e.emissive=t.emissive,e.emissiveIntensity=1,e.emissiveMap=void 0===t.emissiveMap?null:t.emissiveMap,e.bumpMap=void 0===t.bumpMap?null:t.bumpMap,e.bumpScale=1,e.normalMap=void 0===t.normalMap?null:t.normalMap,e.normalMapType=u.Uc,t.normalScale&&(e.normalScale=t.normalScale),e.displacementMap=null,e.displacementScale=1,e.displacementBias=0,e.specularMap=void 0===t.specularMap?null:t.specularMap,e.specular=t.specular,e.glossinessMap=void 0===t.glossinessMap?null:t.glossinessMap,e.glossiness=t.glossiness,e.alphaMap=null,e.envMap=void 0===t.envMap?null:t.envMap,e.envMapIntensity=1,e.refractionRatio=.98,e}}}function ut(){this.name=i.KHR_MESH_QUANTIZATION}function ct(t,e,i,r){b.a.call(this,t,e,i,r)}nt.prototype.decodePrimitive=function(t,e){var i=this.json,r=this.dracoLoader,s=t.extensions[this.name].bufferView,n=t.extensions[this.name].attributes,a={},o={},h={};for(var u in n){var c=Mt[u]||u.toLowerCase();a[c]=n[u]}for(u in t.attributes){c=Mt[u]||u.toLowerCase();if(void 0!==n[u]){var l=i.accessors[t.attributes[u]],d=xt[l.componentType];h[c]=d,o[c]=!0===l.normalized}}return e.getDependency(\\\\\\\"bufferView\\\\\\\",s).then((function(t){return new Promise((function(e){r.decodeDracoFile(t,(function(t){for(var i in t.attributes){var r=t.attributes[i],s=o[i];void 0!==s&&(r.normalized=s)}e(t)}),a,h)}))}))},at.prototype.extendTexture=function(t,e){return t=t.clone(),void 0!==e.offset&&t.offset.fromArray(e.offset),void 0!==e.rotation&&(t.rotation=e.rotation),void 0!==e.scale&&t.repeat.fromArray(e.scale),void 0!==e.texCoord&&console.warn('THREE.GLTFLoader: Custom UV sets in \\\\\\\"'+this.name+'\\\\\\\" extension not yet supported.'),t.needsUpdate=!0,t},ot.prototype=Object.create(I.a.prototype),ot.prototype.constructor=ot,ot.prototype.copy=function(t){return I.a.prototype.copy.call(this,t),this.specularMap=t.specularMap,this.specular.copy(t.specular),this.glossinessMap=t.glossinessMap,this.glossiness=t.glossiness,delete this.metalness,delete this.roughness,delete this.metalnessMap,delete this.roughnessMap,this},ct.prototype=Object.create(b.a.prototype),ct.prototype.constructor=ct,ct.prototype.copySampleValue_=function(t){for(var e=this.resultBuffer,i=this.sampleValues,r=this.valueSize,s=t*r*3+r,n=0;n!==r;n++)e[n]=i[s+n];return e},ct.prototype.beforeStart_=ct.prototype.copySampleValue_,ct.prototype.afterEnd_=ct.prototype.copySampleValue_,ct.prototype.interpolate_=function(t,e,i,r){for(var s=this.resultBuffer,n=this.sampleValues,a=this.valueSize,o=2*a,h=3*a,u=r-e,c=(i-e)/u,l=c*c,d=l*c,p=t*h,f=p-h,m=-2*d+3*l,y=d-l,g=1-m,x=y-l+c,b=0;b!==a;b++){var v=n[f+b+a],w=n[f+b+o]*u,M=n[p+b+a],T=n[p+b]*u;s[b]=g*v+x*w+m*M+y*T}return s};var lt=0,dt=1,pt=2,ft=3,mt=4,yt=5,gt=6,xt={5120:Int8Array,5121:Uint8Array,5122:Int16Array,5123:Uint16Array,5125:Uint32Array,5126:Float32Array},bt={9728:u.ob,9729:u.V,9984:u.sb,9985:u.Z,9986:u.rb,9987:u.Y},vt={33071:u.n,33648:u.kb,10497:u.wc},wt={SCALAR:1,VEC2:2,VEC3:3,VEC4:4,MAT2:4,MAT3:9,MAT4:16},Mt={POSITION:\\\\\\\"position\\\\\\\",NORMAL:\\\\\\\"normal\\\\\\\",TANGENT:\\\\\\\"tangent\\\\\\\",TEXCOORD_0:\\\\\\\"uv\\\\\\\",TEXCOORD_1:\\\\\\\"uv2\\\\\\\",COLOR_0:\\\\\\\"color\\\\\\\",WEIGHTS_0:\\\\\\\"skinWeight\\\\\\\",JOINTS_0:\\\\\\\"skinIndex\\\\\\\"},Tt={scale:\\\\\\\"scale\\\\\\\",translation:\\\\\\\"position\\\\\\\",rotation:\\\\\\\"quaternion\\\\\\\",weights:\\\\\\\"morphTargetInfluences\\\\\\\"},St={CUBICSPLINE:void 0,LINEAR:u.P,STEP:u.O},_t=\\\\\\\"OPAQUE\\\\\\\",Et=\\\\\\\"MASK\\\\\\\",At=\\\\\\\"BLEND\\\\\\\";function zt(t,e){return\\\\\\\"string\\\\\\\"!=typeof t||\\\\\\\"\\\\\\\"===t?\\\\\\\"\\\\\\\":(/^https?:\\\\/\\\\//i.test(e)&&/^\\\\//.test(t)&&(e=e.replace(/(^https?:\\\\/\\\\/[^\\\\/]+).*/i,\\\\\\\"$1\\\\\\\")),/^(https?:)?\\\\/\\\\//i.test(t)||/^data:.*,.*$/i.test(t)||/^blob:.*$/i.test(t)?t:e+t)}function Rt(t,e,i){for(var r in i.extensions)void 0===t[r]&&(e.userData.gltfExtensions=e.userData.gltfExtensions||{},e.userData.gltfExtensions[r]=i.extensions[r])}function Ot(t,e){void 0!==e.extras&&(\\\\\\\"object\\\\\\\"==typeof e.extras?Object.assign(t.userData,e.extras):console.warn(\\\\\\\"THREE.GLTFLoader: Ignoring primitive type .extras, \\\\\\\"+e.extras))}function It(t,e){if(t.updateMorphTargets(),void 0!==e.weights)for(var i=0,r=e.weights.length;i=2&&n.setY(z,_[E*h+1]),h>=3&&n.setZ(z,_[E*h+2]),h>=4&&n.setW(z,_[E*h+3]),h>=5)throw new Error(\\\\\\\"THREE.GLTFLoader: Unsupported itemSize in sparse BufferAttribute.\\\\\\\")}}return n}))},Pt.prototype.loadTexture=function(t){var e=this.json,i=this.options,r=e.textures[t],s=e.images[r.source],n=this.textureLoader;if(s.uri){var a=i.manager.getHandler(s.uri);null!==a&&(n=a)}return this.loadTextureImage(t,s,n)},Pt.prototype.loadTextureImage=function(t,e,i){var r=this,s=this.json,n=this.options,a=s.textures[t],o=self.URL||self.webkitURL,c=e.uri,l=!1,d=!0;if(\\\\\\\"image/jpeg\\\\\\\"===e.mimeType&&(d=!1),void 0!==e.bufferView)c=r.getDependency(\\\\\\\"bufferView\\\\\\\",e.bufferView).then((function(t){if(\\\\\\\"image/png\\\\\\\"===e.mimeType){var i=new DataView(t,25,1).getUint8(0,!1);d=6===i||4===i||3===i}l=!0;var r=new Blob([t],{type:e.mimeType});return c=o.createObjectURL(r)}));else if(void 0===e.uri)throw new Error(\\\\\\\"THREE.GLTFLoader: Image \\\\\\\"+t+\\\\\\\" is missing URI and bufferView\\\\\\\");return Promise.resolve(c).then((function(t){return new Promise((function(e,r){var s=e;!0===i.isImageBitmapLoader&&(s=function(t){e(new h.a(t))}),i.load(zt(t,n.path),s,void 0,r)}))})).then((function(e){!0===l&&o.revokeObjectURL(c),e.flipY=!1,a.name&&(e.name=a.name),d||(e.format=u.ic);var i=(s.samplers||{})[a.sampler]||{};return e.magFilter=bt[i.magFilter]||u.V,e.minFilter=bt[i.minFilter]||u.Y,e.wrapS=vt[i.wrapS]||u.wc,e.wrapT=vt[i.wrapT]||u.wc,r.associations.set(e,{type:\\\\\\\"textures\\\\\\\",index:t}),e}))},Pt.prototype.assignTexture=function(t,e,r){var s=this;return this.getDependency(\\\\\\\"texture\\\\\\\",r.index).then((function(n){if(void 0===r.texCoord||0==r.texCoord||\\\\\\\"aoMap\\\\\\\"===e&&1==r.texCoord||console.warn(\\\\\\\"THREE.GLTFLoader: Custom UV set \\\\\\\"+r.texCoord+\\\\\\\" for texture \\\\\\\"+e+\\\\\\\" not yet supported.\\\\\\\"),s.extensions[i.KHR_TEXTURE_TRANSFORM]){var a=void 0!==r.extensions?r.extensions[i.KHR_TEXTURE_TRANSFORM]:void 0;if(a){var o=s.associations.get(n);n=s.extensions[i.KHR_TEXTURE_TRANSFORM].extendTexture(n,a),s.associations.set(n,o)}}t[e]=n}))},Pt.prototype.assignFinalMaterial=function(t){var e=t.geometry,i=t.material,r=void 0!==e.attributes.tangent,s=void 0!==e.attributes.color,n=void 0===e.attributes.normal,a=!0===t.isSkinnedMesh,o=Object.keys(e.morphAttributes).length>0,h=o&&void 0!==e.morphAttributes.normal;if(t.isPoints){var u=\\\\\\\"PointsMaterial:\\\\\\\"+i.uuid,c=this.cache.get(u);c||(c=new F.a,_.a.prototype.copy.call(c,i),c.color.copy(i.color),c.map=i.map,c.sizeAttenuation=!1,this.cache.add(u,c)),i=c}else if(t.isLine){u=\\\\\\\"LineBasicMaterial:\\\\\\\"+i.uuid;var l=this.cache.get(u);l||(l=new w.a,_.a.prototype.copy.call(l,i),l.color.copy(i.color),this.cache.add(u,l)),i=l}if(r||s||n||a||o){u=\\\\\\\"ClonedMaterial:\\\\\\\"+i.uuid+\\\\\\\":\\\\\\\";i.isGLTFSpecularGlossinessMaterial&&(u+=\\\\\\\"specular-glossiness:\\\\\\\"),a&&(u+=\\\\\\\"skinning:\\\\\\\"),r&&(u+=\\\\\\\"vertex-tangents:\\\\\\\"),s&&(u+=\\\\\\\"vertex-colors:\\\\\\\"),n&&(u+=\\\\\\\"flat-shading:\\\\\\\"),o&&(u+=\\\\\\\"morph-targets:\\\\\\\"),h&&(u+=\\\\\\\"morph-normals:\\\\\\\");var d=this.cache.get(u);d||(d=i.clone(),a&&(d.skinning=!0),s&&(d.vertexColors=!0),n&&(d.flatShading=!0),o&&(d.morphTargets=!0),h&&(d.morphNormals=!0),r&&(d.vertexTangents=!0,d.normalScale&&(d.normalScale.y*=-1),d.clearcoatNormalScale&&(d.clearcoatNormalScale.y*=-1)),this.cache.add(u,d),this.associations.set(d,this.associations.get(i))),i=d}i.aoMap&&void 0===e.attributes.uv2&&void 0!==e.attributes.uv&&e.setAttribute(\\\\\\\"uv2\\\\\\\",e.attributes.uv),t.material=i},Pt.prototype.getMaterialType=function(){return I.a},Pt.prototype.loadMaterial=function(t){var e,r=this,s=this.json,n=this.extensions,a=s.materials[t],o={},h=a.extensions||{},l=[];if(h[i.KHR_MATERIALS_PBR_SPECULAR_GLOSSINESS]){var d=n[i.KHR_MATERIALS_PBR_SPECULAR_GLOSSINESS];e=d.getMaterialType(),l.push(d.extendParams(o,a,r))}else if(h[i.KHR_MATERIALS_UNLIT]){var p=n[i.KHR_MATERIALS_UNLIT];e=p.getMaterialType(),l.push(p.extendParams(o,a,r))}else{var f=a.pbrMetallicRoughness||{};if(o.color=new c.a(1,1,1),o.opacity=1,Array.isArray(f.baseColorFactor)){var m=f.baseColorFactor;o.color.fromArray(m),o.opacity=m[3]}void 0!==f.baseColorTexture&&l.push(r.assignTexture(o,\\\\\\\"map\\\\\\\",f.baseColorTexture)),o.metalness=void 0!==f.metallicFactor?f.metallicFactor:1,o.roughness=void 0!==f.roughnessFactor?f.roughnessFactor:1,void 0!==f.metallicRoughnessTexture&&(l.push(r.assignTexture(o,\\\\\\\"metalnessMap\\\\\\\",f.metallicRoughnessTexture)),l.push(r.assignTexture(o,\\\\\\\"roughnessMap\\\\\\\",f.metallicRoughnessTexture))),e=this._invokeOne((function(e){return e.getMaterialType&&e.getMaterialType(t)})),l.push(Promise.all(this._invokeAll((function(e){return e.extendMaterialParams&&e.extendMaterialParams(t,o)}))))}!0===a.doubleSided&&(o.side=u.z);var y=a.alphaMode||_t;return y===At?(o.transparent=!0,o.depthWrite=!1):(o.transparent=!1,y===Et&&(o.alphaTest=void 0!==a.alphaCutoff?a.alphaCutoff:.5)),void 0!==a.normalTexture&&e!==R.a&&(l.push(r.assignTexture(o,\\\\\\\"normalMap\\\\\\\",a.normalTexture)),o.normalScale=new W.a(1,-1),void 0!==a.normalTexture.scale&&o.normalScale.set(a.normalTexture.scale,-a.normalTexture.scale)),void 0!==a.occlusionTexture&&e!==R.a&&(l.push(r.assignTexture(o,\\\\\\\"aoMap\\\\\\\",a.occlusionTexture)),void 0!==a.occlusionTexture.strength&&(o.aoMapIntensity=a.occlusionTexture.strength)),void 0!==a.emissiveFactor&&e!==R.a&&(o.emissive=(new c.a).fromArray(a.emissiveFactor)),void 0!==a.emissiveTexture&&e!==R.a&&l.push(r.assignTexture(o,\\\\\\\"emissiveMap\\\\\\\",a.emissiveTexture)),Promise.all(l).then((function(){var s;return s=e===ot?n[i.KHR_MATERIALS_PBR_SPECULAR_GLOSSINESS].createMaterial(o):new e(o),a.name&&(s.name=a.name),s.map&&(s.map.encoding=u.ld),s.emissiveMap&&(s.emissiveMap.encoding=u.ld),Ot(s,a),r.associations.set(s,{type:\\\\\\\"materials\\\\\\\",index:t}),a.extensions&&Rt(n,s,a),s}))},Pt.prototype.createUniqueName=function(t){for(var e=j.a.sanitizeNodeName(t||\\\\\\\"\\\\\\\"),i=e,r=1;this.nodeNamesUsed[i];++r)i=e+\\\\\\\"_\\\\\\\"+r;return this.nodeNamesUsed[i]=!0,i},Pt.prototype.loadGeometries=function(t){var e=this,r=this.extensions,s=this.primitiveCache;function n(t){return r[i.KHR_DRACO_MESH_COMPRESSION].decodePrimitive(t,e).then((function(i){return Bt(i,t,e)}))}for(var a,h,u=[],c=0,l=t.length;c0&&It(d,n),d.name=i.createUniqueName(n.name||\\\\\\\"mesh_\\\\\\\"+t),Ot(d,n),m.extensions&&Rt(s,d,m),i.assignFinalMaterial(d),h.push(d)}if(1===h.length)return h[0];var g=new p.a;for(c=0,l=h.length;c1?new p.a:1===e.length?e[0]:new P.a)!==e[0])for(var h=0,u=e.length;h>8&255]+i[e>>16&255]+i[e>>24&255]+\\\\\\\"-\\\\\\\"+i[255&t]+i[t>>8&255]+\\\\\\\"-\\\\\\\"+i[t>>16&15|64]+i[t>>24&255]+\\\\\\\"-\\\\\\\"+i[63&n|128]+i[n>>8&255]+\\\\\\\"-\\\\\\\"+i[n>>16&255]+i[n>>24&255]+i[255&s]+i[s>>8&255]+i[s>>16&255]+i[s>>24&255]).toUpperCase()},clamp:function(e,t,n){return Math.max(t,Math.min(n,e))},euclideanModulo:function(e,t){return(e%t+t)%t},mapLinear:function(e,t,n,i,s){return i+(e-t)*(s-i)/(n-t)},inverseLerp:function(e,t,n){return e!==t?(n-e)/(t-e):0},lerp:function(e,t,n){return(1-n)*e+n*t},damp:function(e,t,n,i){return r.lerp(e,t,1-Math.exp(-n*i))},pingpong:function(e,t=1){return t-Math.abs(r.euclideanModulo(e,2*t)-t)},smoothstep:function(e,t,n){return e<=t?0:e>=n?1:(e=(e-t)/(n-t))*e*(3-2*e)},smootherstep:function(e,t,n){return e<=t?0:e>=n?1:(e=(e-t)/(n-t))*e*e*(e*(6*e-15)+10)},randInt:function(e,t){return e+Math.floor(Math.random()*(t-e+1))},randFloat:function(e,t){return e+Math.random()*(t-e)},randFloatSpread:function(e){return e*(.5-Math.random())},seededRandom:function(e){return void 0!==e&&(s=e%2147483647),s=16807*s%2147483647,(s-1)/2147483646},degToRad:function(e){return e*r.DEG2RAD},radToDeg:function(e){return e*r.RAD2DEG},isPowerOfTwo:function(e){return 0==(e&e-1)&&0!==e},ceilPowerOfTwo:function(e){return Math.pow(2,Math.ceil(Math.log(e)/Math.LN2))},floorPowerOfTwo:function(e){return Math.pow(2,Math.floor(Math.log(e)/Math.LN2))},setQuaternionFromProperEuler:function(e,t,n,i,s){const r=Math.cos,o=Math.sin,a=r(n/2),c=o(n/2),l=r((t+i)/2),u=o((t+i)/2),h=r((t-i)/2),d=o((t-i)/2),p=r((i-t)/2),_=o((i-t)/2);switch(s){case\\\\\\\"XYX\\\\\\\":e.set(a*u,c*h,c*d,a*l);break;case\\\\\\\"YZY\\\\\\\":e.set(c*d,a*u,c*h,a*l);break;case\\\\\\\"ZXZ\\\\\\\":e.set(c*h,c*d,a*u,a*l);break;case\\\\\\\"XZX\\\\\\\":e.set(a*u,c*_,c*p,a*l);break;case\\\\\\\"YXY\\\\\\\":e.set(c*p,a*u,c*_,a*l);break;case\\\\\\\"ZYZ\\\\\\\":e.set(c*_,c*p,a*u,a*l);break;default:console.warn(\\\\\\\"THREE.MathUtils: .setQuaternionFromProperEuler() encountered an unknown order: \\\\\\\"+s)}}}},function(e,t,n){\\\\\\\"use strict\\\\\\\";n.d(t,\\\\\\\"a\\\\\\\",(function(){return s}));var i=n(0);class s{constructor(){this.elements=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1],arguments.length>0&&console.error(\\\\\\\"THREE.Matrix4: the constructor no longer reads arguments. use .set() instead.\\\\\\\")}set(e,t,n,i,s,r,o,a,c,l,u,h,d,p,_,m){const f=this.elements;return f[0]=e,f[4]=t,f[8]=n,f[12]=i,f[1]=s,f[5]=r,f[9]=o,f[13]=a,f[2]=c,f[6]=l,f[10]=u,f[14]=h,f[3]=d,f[7]=p,f[11]=_,f[15]=m,this}identity(){return this.set(1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1),this}clone(){return(new s).fromArray(this.elements)}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],t[9]=n[9],t[10]=n[10],t[11]=n[11],t[12]=n[12],t[13]=n[13],t[14]=n[14],t[15]=n[15],this}copyPosition(e){const t=this.elements,n=e.elements;return t[12]=n[12],t[13]=n[13],t[14]=n[14],this}setFromMatrix3(e){const t=e.elements;return this.set(t[0],t[3],t[6],0,t[1],t[4],t[7],0,t[2],t[5],t[8],0,0,0,0,1),this}extractBasis(e,t,n){return e.setFromMatrixColumn(this,0),t.setFromMatrixColumn(this,1),n.setFromMatrixColumn(this,2),this}makeBasis(e,t,n){return this.set(e.x,t.x,n.x,0,e.y,t.y,n.y,0,e.z,t.z,n.z,0,0,0,0,1),this}extractRotation(e){const t=this.elements,n=e.elements,i=1/r.setFromMatrixColumn(e,0).length(),s=1/r.setFromMatrixColumn(e,1).length(),o=1/r.setFromMatrixColumn(e,2).length();return t[0]=n[0]*i,t[1]=n[1]*i,t[2]=n[2]*i,t[3]=0,t[4]=n[4]*s,t[5]=n[5]*s,t[6]=n[6]*s,t[7]=0,t[8]=n[8]*o,t[9]=n[9]*o,t[10]=n[10]*o,t[11]=0,t[12]=0,t[13]=0,t[14]=0,t[15]=1,this}makeRotationFromEuler(e){e&&e.isEuler||console.error(\\\\\\\"THREE.Matrix4: .makeRotationFromEuler() now expects a Euler rotation rather than a Vector3 and order.\\\\\\\");const t=this.elements,n=e.x,i=e.y,s=e.z,r=Math.cos(n),o=Math.sin(n),a=Math.cos(i),c=Math.sin(i),l=Math.cos(s),u=Math.sin(s);if(\\\\\\\"XYZ\\\\\\\"===e.order){const e=r*l,n=r*u,i=o*l,s=o*u;t[0]=a*l,t[4]=-a*u,t[8]=c,t[1]=n+i*c,t[5]=e-s*c,t[9]=-o*a,t[2]=s-e*c,t[6]=i+n*c,t[10]=r*a}else if(\\\\\\\"YXZ\\\\\\\"===e.order){const e=a*l,n=a*u,i=c*l,s=c*u;t[0]=e+s*o,t[4]=i*o-n,t[8]=r*c,t[1]=r*u,t[5]=r*l,t[9]=-o,t[2]=n*o-i,t[6]=s+e*o,t[10]=r*a}else if(\\\\\\\"ZXY\\\\\\\"===e.order){const 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 h.subVectors(e,t),0===h.lengthSq()&&(h.z=1),h.normalize(),l.crossVectors(n,h),0===l.lengthSq()&&(1===Math.abs(n.z)?h.x+=1e-4:h.z+=1e-4,h.normalize(),l.crossVectors(n,h)),l.normalize(),u.crossVectors(h,l),i[0]=l.x,i[4]=u.x,i[8]=h.x,i[1]=l.y,i[5]=u.y,i[9]=h.y,i[2]=l.z,i[6]=u.z,i[10]=h.z,this}multiply(e,t){return void 0!==t?(console.warn(\\\\\\\"THREE.Matrix4: .multiply() now only accepts one argument. 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 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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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n=parseFloat(e[1])/360,i=parseInt(e[2],10)/100,s=parseInt(e[3],10)/100;return t(e[4]),this.setHSL(n,i,s)}}}else if(n=/^\\\\#([A-Fa-f\\\\d]+)$/.exec(e)){const e=n[1],t=e.length;if(3===t)return this.r=parseInt(e.charAt(0)+e.charAt(0),16)/255,this.g=parseInt(e.charAt(1)+e.charAt(1),16)/255,this.b=parseInt(e.charAt(2)+e.charAt(2),16)/255,this;if(6===t)return this.r=parseInt(e.charAt(0)+e.charAt(1),16)/255,this.g=parseInt(e.charAt(2)+e.charAt(3),16)/255,this.b=parseInt(e.charAt(4)+e.charAt(5),16)/255,this}return e&&e.length>0?this.setColorName(e):this}setColorName(e){const t=s[e];return void 0!==t?this.setHex(t):console.warn(\\\\\\\"THREE.Color: Unknown color \\\\\\\"+e),this}clone(){return new this.constructor(this.r,this.g,this.b)}copy(e){return this.r=e.r,this.g=e.g,this.b=e.b,this}copyGammaToLinear(e,t=2){return this.r=Math.pow(e.r,t),this.g=Math.pow(e.g,t),this.b=Math.pow(e.b,t),this}copyLinearToGamma(e,t=2){const n=t>0?1/t:1;return this.r=Math.pow(e.r,n),this.g=Math.pow(e.g,n),this.b=Math.pow(e.b,n),this}convertGammaToLinear(e){return this.copyGammaToLinear(this,e),this}convertLinearToGamma(e){return this.copyLinearToGamma(this,e),this}copySRGBToLinear(e){return this.r=c(e.r),this.g=c(e.g),this.b=c(e.b),this}copyLinearToSRGB(e){return this.r=l(e.r),this.g=l(e.g),this.b=l(e.b),this}convertSRGBToLinear(){return this.copySRGBToLinear(this),this}convertLinearToSRGB(){return this.copyLinearToSRGB(this),this}getHex(){return 255*this.r<<16^255*this.g<<8^255*this.b<<0}getHexString(){return(\\\\\\\"000000\\\\\\\"+this.getHex().toString(16)).slice(-6)}getHSL(e){void 0===e&&(console.warn(\\\\\\\"THREE.Color: .getHSL() target is now required\\\\\\\"),e={h:0,s:0,l:0});const t=this.r,n=this.g,i=this.b,s=Math.max(t,n,i),r=Math.min(t,n,i);let o,a;const c=(r+s)/2;if(r===s)o=0,a=0;else{const e=s-r;switch(a=c<=.5?e/(s+r):e/(2-s-r),s){case t:o=(n-i)/e+(n1){for(let e=0;e1){for(let e=0;e0){i.children=[];for(let t=0;t0){i.animations=[];for(let t=0;t0&&(n.geometries=t),i.length>0&&(n.materials=i),s.length>0&&(n.textures=s),o.length>0&&(n.images=o),a.length>0&&(n.shapes=a),c.length>0&&(n.skeletons=c),l.length>0&&(n.animations=l)}return n.object=i,n;function r(e){const t=[];for(const n in e){const i=e[n];delete i.metadata,t.push(i)}return t}},clone:function(e){return(new this.constructor).copy(this,e)},copy:function(e,t=!0){if(this.name=e.name,this.up.copy(e.up),this.position.copy(e.position),this.rotation.order=e.rotation.order,this.quaternion.copy(e.quaternion),this.scale.copy(e.scale),this.matrix.copy(e.matrix),this.matrixWorld.copy(e.matrixWorld),this.matrixAutoUpdate=e.matrixAutoUpdate,this.matrixWorldNeedsUpdate=e.matrixWorldNeedsUpdate,this.layers.mask=e.layers.mask,this.visible=e.visible,this.castShadow=e.castShadow,this.receiveShadow=e.receiveShadow,this.frustumCulled=e.frustumCulled,this.renderOrder=e.renderOrder,this.userData=JSON.parse(JSON.stringify(e.userData)),!0===t)for(let t=0;t65535?a.i:a.h)(e,1):this.index=e,this},getAttribute:function(e){return this.attributes[e]},setAttribute:function(e,t){return this.attributes[e]=t,this},deleteAttribute:function(e){return delete this.attributes[e],this},hasAttribute:function(e){return void 0!==this.attributes[e]},addGroup:function(e,t,n=0){this.groups.push({start:e,count:t,materialIndex:n})},clearGroups:function(){this.groups=[]},setDrawRange:function(e,t){this.drawRange.start=e,this.drawRange.count=t},applyMatrix4:function(e){const t=this.attributes.position;void 0!==t&&(t.applyMatrix4(e),t.needsUpdate=!0);const n=this.attributes.normal;if(void 0!==n){const t=(new h.a).getNormalMatrix(e);n.applyNormalMatrix(t),n.needsUpdate=!0}const i=this.attributes.tangent;return void 0!==i&&(i.transformDirection(e),i.needsUpdate=!0),null!==this.boundingBox&&this.computeBoundingBox(),null!==this.boundingSphere&&this.computeBoundingSphere(),this},rotateX:function(e){return m.makeRotationX(e),this.applyMatrix4(m),this},rotateY:function(e){return m.makeRotationY(e),this.applyMatrix4(m),this},rotateZ:function(e){return m.makeRotationZ(e),this.applyMatrix4(m),this},translate:function(e,t,n){return m.makeTranslation(e,t,n),this.applyMatrix4(m),this},scale:function(e,t,n){return 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;n0&&(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;t0&&(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=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 nMath.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. Use .subVectors( a, b ) instead.\\\\\\\"),this.subVectors(e,t)):(this.x-=e.x,this.y-=e.y,this.z-=e.z,this.w-=e.w,this)}subScalar(e){return this.x-=e,this.y-=e,this.z-=e,this.w-=e,this}subVectors(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}multiply(e){return this.x*=e.x,this.y*=e.y,this.z*=e.z,this.w*=e.w,this}multiplyScalar(e){return this.x*=e,this.y*=e,this.z*=e,this.w*=e,this}applyMatrix4(e){const t=this.x,n=this.y,i=this.z,s=this.w,r=e.elements;return this.x=r[0]*t+r[4]*n+r[8]*i+r[12]*s,this.y=r[1]*t+r[5]*n+r[9]*i+r[13]*s,this.z=r[2]*t+r[6]*n+r[10]*i+r[14]*s,this.w=r[3]*t+r[7]*n+r[11]*i+r[15]*s,this}divideScalar(e){return this.multiplyScalar(1/e)}setAxisAngleFromQuaternion(e){this.w=2*Math.acos(e.w);const t=Math.sqrt(1-e.w*e.w);return t<1e-4?(this.x=1,this.y=0,this.z=0):(this.x=e.x/t,this.y=e.y/t,this.z=e.z/t),this}setAxisAngleFromRotationMatrix(e){let t,n,i,s;const r=.01,o=.1,a=e.elements,c=a[0],l=a[4],u=a[8],h=a[1],d=a[5],p=a[9],_=a[2],m=a[6],f=a[10];if(Math.abs(l-h)a&&e>g?eg?a0&&(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 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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 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e.arcLengthDivisions=this.arcLengthDivisions,e.type=this.type,e},fromJSON:function(e){return this.arcLengthDivisions=e.arcLengthDivisions,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=\\\\\\\"LineBasicMaterial\\\\\\\",this.color=new s.a(16777215),this.linewidth=1,this.linecap=\\\\\\\"round\\\\\\\",this.linejoin=\\\\\\\"round\\\\\\\",this.morphTargets=!1,this.setValues(e)}copy(e){return super.copy(e),this.color.copy(e.color),this.linewidth=e.linewidth,this.linecap=e.linecap,this.linejoin=e.linejoin,this.morphTargets=e.morphTargets,this}}r.prototype.isLineBasicMaterial=!0},function(e,t,n){\\\\\\\"use strict\\\\\\\";n.d(t,\\\\\\\"a\\\\\\\",(function(){return l}));var i=n(1),s=n(68),r=n(70),o=n(29);function a(e,t,n,i){o.a.call(this,e,t,n,i)}a.prototype=Object.assign(Object.create(o.a.prototype),{constructor:a,interpolate_:function(e){return 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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: 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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=s)break e;{const o=t[1];e=s)break t}r=n,n=0}}for(;n>>1;e2048||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 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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;e1)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 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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;nt&&(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;ec)continue;d.applyMatrix4(this.matrixWorld);const a=e.ray.origin.distanceTo(d);ae.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;nc)continue;d.applyMatrix4(this.matrixWorld);const i=e.ray.origin.distanceTo(d);ie.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;t0&&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;e0)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=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=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 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c{constructor(){this._next_id=0,this._successors=new Map,this._predecessors=new Map,this._nodes_by_id=new Map,this._nodesCount=0,this._debugging=!1,this._addedNodesDuringDebugging=new Map}startDebugging(){this._debugging=!0,console.log(\\\\\\\"CoreGraph.startDebugging\\\\\\\",this._next_id)}stopDebugging(){this._debugging=!1,console.log(\\\\\\\"CoreGraph.stopDebugging\\\\\\\",this._next_id)}printDebug(){this._addedNodesDuringDebugging.forEach(((e,t)=>{console.log(t,e,e.graphPredecessors(),e.graphSuccessors())}))}setScene(e){this._scene=e}scene(){return this._scene}nextId(){return this._next_id+=1,this._next_id}nodesFromIds(e){const t=[];for(let n of e){const e=this.nodeFromId(n);e&&t.push(e)}return t}nodeFromId(e){return this._nodes_by_id.get(e)}hasNode(e){return null!=this._nodes_by_id.get(e.graphNodeId())}addNode(e){this._nodes_by_id.set(e.graphNodeId(),e),this._nodesCount+=1,this._debugging&&this._addedNodesDuringDebugging.set(e.graphNodeId(),e)}removeNode(e){this._nodes_by_id.delete(e.graphNodeId()),this._successors.delete(e.graphNodeId()),this._predecessors.delete(e.graphNodeId()),this._nodesCount-=1,this._debugging&&this._addedNodesDuringDebugging.delete(e.graphNodeId())}nodesCount(){return this._nodesCount}connect(e,t,n=!0){const i=e.graphNodeId(),s=t.graphNodeId();if(this.hasNode(e)&&this.hasNode(t)){if(n){n=!(!this._scene||this._scene.loadingController.isLoading())}let t=!1;return n&&(t=this._hasPredecessor(i,s)),!t&&(this._createConnection(i,s),e.dirtyController.clearSuccessorsCacheWithPredecessors(),!0)}return console.warn(`attempt to connect non existing node ${i} or ${s}`),!1}disconnect(e,t){this._remove_connection(e.graphNodeId(),t.graphNodeId()),e.dirtyController.clearSuccessorsCacheWithPredecessors()}disconnectPredecessors(e){const t=this.predecessors(e);for(let n of t)this.disconnect(n,e)}disconnectSuccessors(e){const t=this.successors(e);for(let n of t)this.disconnect(e,n)}predecessorIds(e){const t=this._predecessors.get(e);if(t){const e=[];return t.forEach(((t,n)=>{e.push(n)})),e}return[]}predecessors(e){const t=this.predecessorIds(e.graphNodeId());return this.nodesFromIds(t)}successorIds(e){const t=this._successors.get(e);if(t){const e=[];return t.forEach(((t,n)=>{e.push(n)})),e}return[]}successors(e){const t=this.successorIds(e.graphNodeId())||[];return this.nodesFromIds(t)}allPredecessorIds(e){return this.allNextIds(e,\\\\\\\"predecessorIds\\\\\\\")}allSuccessorIds(e){return this.allNextIds(e,\\\\\\\"successorIds\\\\\\\")}allPredecessors(e){const t=this.allPredecessorIds(e);return this.nodesFromIds(t)}allSuccessors(e){const 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h.intersection(this.toSet(e),this.toSet(t)).forEach((e=>n.push(e))),n}static difference(e,t){const n=[];return h.difference(this.toSet(e),this.toSet(t)).forEach((e=>n.push(e))),n}static toSet(e){const t=new Set;for(let n of e)t.add(n);return t}static isEqual(e,t){if(e.length!=t.length)return!1;const n=e.length;for(let i=0;i{s[r]=e,r++})),m.isString(s[0])?s.sort():s.sort(((e,t)=>e-t));const o=new Array(e.length);r=0;for(let e of s){const t=n.get(e);if(t)for(let e of t)o[r]=e,r++}return o}static range(e,t,n=1){null==t&&(t=e,e=0);const i=Math.floor((t-e)/n),s=new Array(i);for(let t=0;t0?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;e0&&(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// 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// 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 \\\\n\\\\t#include \\\\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 \\\\nuniform float opacity;\\\\n\\\\nvarying vec3 vWorldDirection;\\\\n\\\\n#include \\\\n\\\\nvoid main() {\\\\n\\\\n\\\\tvec3 vReflect = vWorldDirection;\\\\n\\\\t#include \\\\n\\\\n\\\\tgl_FragColor = envColor;\\\\n\\\\tgl_FragColor.a *= opacity;\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n}\\\\n\\\\\\\",cube_vert:\\\\\\\"\\\\nvarying vec3 vWorldDirection;\\\\n\\\\n#include \\\\n\\\\nvoid main() {\\\\n\\\\n\\\\tvWorldDirection = transformDirection( position, modelMatrix );\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n\\\\tgl_Position.z = gl_Position.w; // set z to camera.far\\\\n\\\\n}\\\\n\\\\\\\",depth_frag:\\\\\\\"\\\\n#if DEPTH_PACKING == 3200\\\\n\\\\n\\\\tuniform float opacity;\\\\n\\\\n#endif\\\\n\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n\\\\nvarying vec2 vHighPrecisionZW;\\\\n\\\\nvoid main() {\\\\n\\\\n\\\\t#include \\\\n\\\\n\\\\tvec4 diffuseColor = vec4( 1.0 );\\\\n\\\\n\\\\t#if DEPTH_PACKING == 3200\\\\n\\\\n\\\\t\\\\tdiffuseColor.a = opacity;\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n\\\\t#include \\\\n\\\\n\\\\t// Higher precision equivalent of gl_FragCoord.z. This assumes depthRange has been left to its default values.\\\\n\\\\tfloat fragCoordZ = 0.5 * vHighPrecisionZW[0] / vHighPrecisionZW[1] + 0.5;\\\\n\\\\n\\\\t#if DEPTH_PACKING == 3200\\\\n\\\\n\\\\t\\\\tgl_FragColor = vec4( vec3( 1.0 - fragCoordZ ), opacity );\\\\n\\\\n\\\\t#elif DEPTH_PACKING == 3201\\\\n\\\\n\\\\t\\\\tgl_FragColor = packDepthToRGBA( fragCoordZ );\\\\n\\\\n\\\\t#endif\\\\n\\\\n}\\\\n\\\\\\\",depth_vert:\\\\\\\"\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n\\\\n// This is used for computing an equivalent of gl_FragCoord.z that is as high precision as possible.\\\\n// Some platforms compute gl_FragCoord at a lower precision which makes the manually computed value better for\\\\n// depth-based postprocessing effects. Reproduced on iPad with A10 processor / iPadOS 13.3.1.\\\\nvarying vec2 vHighPrecisionZW;\\\\n\\\\nvoid main() {\\\\n\\\\n\\\\t#include \\\\n\\\\n\\\\t#include \\\\n\\\\n\\\\t#ifdef USE_DISPLACEMENTMAP\\\\n\\\\n\\\\t\\\\t#include \\\\n\\\\t\\\\t#include \\\\n\\\\t\\\\t#include \\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n\\\\tvHighPrecisionZW = gl_Position.zw;\\\\n\\\\n}\\\\n\\\\\\\",distanceRGBA_frag:\\\\\\\"\\\\n#define DISTANCE\\\\n\\\\nuniform vec3 referencePosition;\\\\nuniform float nearDistance;\\\\nuniform float farDistance;\\\\nvarying vec3 vWorldPosition;\\\\n\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n\\\\nvoid main () {\\\\n\\\\n\\\\t#include \\\\n\\\\n\\\\tvec4 diffuseColor = vec4( 1.0 );\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n\\\\tfloat dist = length( vWorldPosition - referencePosition );\\\\n\\\\tdist = ( dist - nearDistance ) / ( farDistance - nearDistance );\\\\n\\\\tdist = saturate( dist ); // clamp to [ 0, 1 ]\\\\n\\\\n\\\\tgl_FragColor = packDepthToRGBA( dist );\\\\n\\\\n}\\\\n\\\\\\\",distanceRGBA_vert:\\\\\\\"\\\\n#define DISTANCE\\\\n\\\\nvarying vec3 vWorldPosition;\\\\n\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n\\\\nvoid main() {\\\\n\\\\n\\\\t#include \\\\n\\\\n\\\\t#include \\\\n\\\\n\\\\t#ifdef USE_DISPLACEMENTMAP\\\\n\\\\n\\\\t\\\\t#include \\\\n\\\\t\\\\t#include \\\\n\\\\t\\\\t#include \\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n\\\\tvWorldPosition = worldPosition.xyz;\\\\n\\\\n}\\\\n\\\\\\\",equirect_frag:\\\\\\\"\\\\nuniform sampler2D tEquirect;\\\\n\\\\nvarying vec3 vWorldDirection;\\\\n\\\\n#include \\\\n\\\\nvoid main() {\\\\n\\\\n\\\\tvec3 direction = normalize( vWorldDirection );\\\\n\\\\n\\\\tvec2 sampleUV = equirectUv( direction );\\\\n\\\\n\\\\tvec4 texColor = texture2D( tEquirect, sampleUV );\\\\n\\\\n\\\\tgl_FragColor = mapTexelToLinear( texColor );\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n}\\\\n\\\\\\\",equirect_vert:\\\\\\\"\\\\nvarying vec3 vWorldDirection;\\\\n\\\\n#include \\\\n\\\\nvoid main() {\\\\n\\\\n\\\\tvWorldDirection = transformDirection( position, modelMatrix );\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n}\\\\n\\\\\\\",linedashed_frag:\\\\\\\"\\\\nuniform vec3 diffuse;\\\\nuniform float opacity;\\\\n\\\\nuniform float dashSize;\\\\nuniform float totalSize;\\\\n\\\\nvarying float vLineDistance;\\\\n\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n\\\\nvoid main() {\\\\n\\\\n\\\\t#include \\\\n\\\\n\\\\tif ( mod( vLineDistance, totalSize ) > dashSize ) {\\\\n\\\\n\\\\t\\\\tdiscard;\\\\n\\\\n\\\\t}\\\\n\\\\n\\\\tvec3 outgoingLight = vec3( 0.0 );\\\\n\\\\tvec4 diffuseColor = vec4( diffuse, opacity );\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n\\\\toutgoingLight = diffuseColor.rgb; // simple shader\\\\n\\\\n\\\\tgl_FragColor = vec4( outgoingLight, diffuseColor.a );\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n}\\\\n\\\\\\\",linedashed_vert:\\\\\\\"\\\\nuniform float scale;\\\\nattribute float lineDistance;\\\\n\\\\nvarying float vLineDistance;\\\\n\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n\\\\nvoid main() {\\\\n\\\\n\\\\tvLineDistance = scale * lineDistance;\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n}\\\\n\\\\\\\",meshbasic_frag:\\\\\\\"\\\\nuniform vec3 diffuse;\\\\nuniform float opacity;\\\\n\\\\n#ifndef FLAT_SHADED\\\\n\\\\n\\\\tvarying vec3 vNormal;\\\\n\\\\n#endif\\\\n\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n\\\\nvoid main() {\\\\n\\\\n\\\\t#include \\\\n\\\\n\\\\tvec4 diffuseColor = vec4( diffuse, opacity );\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n\\\\tReflectedLight reflectedLight = ReflectedLight( vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ) );\\\\n\\\\n\\\\t// accumulation (baked indirect lighting only)\\\\n\\\\t#ifdef USE_LIGHTMAP\\\\n\\\\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 \\\\n\\\\n\\\\treflectedLight.indirectDiffuse *= diffuseColor.rgb;\\\\n\\\\n\\\\tvec3 outgoingLight = reflectedLight.indirectDiffuse;\\\\n\\\\n\\\\t#include \\\\n\\\\n\\\\tgl_FragColor = vec4( outgoingLight, diffuseColor.a );\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n}\\\\n\\\\\\\",meshbasic_vert:\\\\\\\"\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n\\\\nvoid main() {\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n\\\\t#ifdef USE_ENVMAP\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n}\\\\n\\\\\\\",meshlambert_frag:\\\\\\\"\\\\nuniform vec3 diffuse;\\\\nuniform vec3 emissive;\\\\nuniform float opacity;\\\\n\\\\nvarying vec3 vLightFront;\\\\nvarying vec3 vIndirectFront;\\\\n\\\\n#ifdef DOUBLE_SIDED\\\\n\\\\tvarying vec3 vLightBack;\\\\n\\\\tvarying vec3 vIndirectBack;\\\\n#endif\\\\n\\\\n\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n\\\\nvoid main() {\\\\n\\\\n\\\\t#include \\\\n\\\\n\\\\tvec4 diffuseColor = vec4( diffuse, opacity );\\\\n\\\\tReflectedLight reflectedLight = ReflectedLight( vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ) );\\\\n\\\\tvec3 totalEmissiveRadiance = emissive;\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n\\\\t// accumulation\\\\n\\\\n\\\\t#ifdef DOUBLE_SIDED\\\\n\\\\n\\\\t\\\\treflectedLight.indirectDiffuse += ( gl_FrontFacing ) ? vIndirectFront : vIndirectBack;\\\\n\\\\n\\\\t#else\\\\n\\\\n\\\\t\\\\treflectedLight.indirectDiffuse += vIndirectFront;\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\t#include \\\\n\\\\n\\\\treflectedLight.indirectDiffuse *= BRDF_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 \\\\n\\\\n\\\\tvec3 outgoingLight = reflectedLight.directDiffuse + reflectedLight.indirectDiffuse + totalEmissiveRadiance;\\\\n\\\\n\\\\t#include \\\\n\\\\n\\\\tgl_FragColor = vec4( outgoingLight, diffuseColor.a );\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n}\\\\n\\\\\\\",meshlambert_vert:\\\\\\\"\\\\n#define LAMBERT\\\\n\\\\nvarying vec3 vLightFront;\\\\nvarying vec3 vIndirectFront;\\\\n\\\\n#ifdef DOUBLE_SIDED\\\\n\\\\tvarying vec3 vLightBack;\\\\n\\\\tvarying vec3 vIndirectBack;\\\\n#endif\\\\n\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n\\\\nvoid main() {\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n}\\\\n\\\\\\\",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 \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n\\\\nvoid main() {\\\\n\\\\n\\\\t#include \\\\n\\\\n\\\\tvec4 diffuseColor = vec4( diffuse, opacity );\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n\\\\tvec3 viewDir = normalize( vViewPosition );\\\\n\\\\tvec3 x = normalize( vec3( viewDir.z, 0.0, - viewDir.x ) );\\\\n\\\\tvec3 y = cross( viewDir, x );\\\\n\\\\tvec2 uv = vec2( dot( x, normal ), dot( y, normal ) ) * 0.495 + 0.5; // 0.495 to remove artifacts caused by undersized matcap disks\\\\n\\\\n\\\\t#ifdef USE_MATCAP\\\\n\\\\n\\\\t\\\\tvec4 matcapColor = texture2D( matcap, uv );\\\\n\\\\t\\\\tmatcapColor = matcapTexelToLinear( matcapColor );\\\\n\\\\n\\\\t#else\\\\n\\\\n\\\\t\\\\tvec4 matcapColor = vec4( 1.0 );\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\tvec3 outgoingLight = diffuseColor.rgb * matcapColor.rgb;\\\\n\\\\n\\\\tgl_FragColor = vec4( outgoingLight, diffuseColor.a );\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\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 \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n\\\\n#include \\\\n#include \\\\n\\\\nvoid main() {\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\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 \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n\\\\tvViewPosition = - mvPosition.xyz;\\\\n\\\\n}\\\\n\\\\\\\",meshtoon_frag:\\\\\\\"\\\\n#define TOON\\\\n\\\\nuniform vec3 diffuse;\\\\nuniform vec3 emissive;\\\\nuniform float opacity;\\\\n\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n\\\\nvoid main() {\\\\n\\\\n\\\\t#include \\\\n\\\\n\\\\tvec4 diffuseColor = vec4( diffuse, opacity );\\\\n\\\\tReflectedLight reflectedLight = ReflectedLight( vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ) );\\\\n\\\\tvec3 totalEmissiveRadiance = emissive;\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n\\\\t// accumulation\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n\\\\t// modulation\\\\n\\\\t#include \\\\n\\\\n\\\\tvec3 outgoingLight = reflectedLight.directDiffuse + reflectedLight.indirectDiffuse + totalEmissiveRadiance;\\\\n\\\\n\\\\tgl_FragColor = vec4( outgoingLight, diffuseColor.a );\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n}\\\\n\\\\\\\",meshtoon_vert:\\\\\\\"\\\\n#define TOON\\\\n\\\\nvarying vec3 vViewPosition;\\\\n\\\\n#ifndef FLAT_SHADED\\\\n\\\\n\\\\tvarying vec3 vNormal;\\\\n\\\\n#endif\\\\n\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n\\\\nvoid main() {\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n#ifndef FLAT_SHADED // Normal computed with derivatives when FLAT_SHADED\\\\n\\\\n\\\\tvNormal = normalize( transformedNormal );\\\\n\\\\n#endif\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n\\\\tvViewPosition = - mvPosition.xyz;\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n}\\\\n\\\\\\\",meshphong_frag:\\\\\\\"\\\\n#define PHONG\\\\n\\\\nuniform vec3 diffuse;\\\\nuniform vec3 emissive;\\\\nuniform vec3 specular;\\\\nuniform float shininess;\\\\nuniform float opacity;\\\\n\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n\\\\nvoid main() {\\\\n\\\\n\\\\t#include \\\\n\\\\n\\\\tvec4 diffuseColor = vec4( diffuse, opacity );\\\\n\\\\tReflectedLight reflectedLight = ReflectedLight( vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ) );\\\\n\\\\tvec3 totalEmissiveRadiance = emissive;\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n\\\\t// accumulation\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n\\\\t// modulation\\\\n\\\\t#include \\\\n\\\\n\\\\tvec3 outgoingLight = reflectedLight.directDiffuse + reflectedLight.indirectDiffuse + reflectedLight.directSpecular + reflectedLight.indirectSpecular + totalEmissiveRadiance;\\\\n\\\\n\\\\t#include \\\\n\\\\n\\\\tgl_FragColor = vec4( outgoingLight, diffuseColor.a );\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\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 \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n\\\\nvoid main() {\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n#ifndef FLAT_SHADED // Normal computed with derivatives when FLAT_SHADED\\\\n\\\\n\\\\tvNormal = normalize( transformedNormal );\\\\n\\\\n#endif\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n\\\\tvViewPosition = - mvPosition.xyz;\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\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 \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n\\\\nvoid main() {\\\\n\\\\n\\\\t#include \\\\n\\\\n\\\\tvec4 diffuseColor = vec4( diffuse, opacity );\\\\n\\\\tReflectedLight reflectedLight = ReflectedLight( vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ) );\\\\n\\\\tvec3 totalEmissiveRadiance = emissive;\\\\n\\\\n\\\\t#ifdef TRANSMISSION\\\\n\\\\t\\\\tfloat totalTransmission = transmission;\\\\n\\\\t#endif\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n\\\\t// accumulation\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n\\\\t// modulation\\\\n\\\\t#include \\\\n\\\\n\\\\tvec3 outgoingLight = reflectedLight.directDiffuse + reflectedLight.indirectDiffuse + reflectedLight.directSpecular + reflectedLight.indirectSpecular + totalEmissiveRadiance;\\\\n\\\\n\\\\t// 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 \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n}\\\\n\\\\\\\",meshphysical_vert:\\\\\\\"\\\\n#define STANDARD\\\\n\\\\nvarying vec3 vViewPosition;\\\\n\\\\n#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 \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n\\\\nvoid main() {\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n#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 \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n\\\\tvViewPosition = - mvPosition.xyz;\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n}\\\\n\\\\\\\",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 \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n\\\\nvoid main() {\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n\\\\tgl_FragColor = vec4( packNormalToRGB( normal ), opacity );\\\\n\\\\n}\\\\n\\\\\\\",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 \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n\\\\nvoid main() {\\\\n\\\\n\\\\t#include \\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\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 \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n#if defined( FLAT_SHADED ) || defined( USE_BUMPMAP ) || defined( TANGENTSPACE_NORMALMAP )\\\\n\\\\n\\\\tvViewPosition = - mvPosition.xyz;\\\\n\\\\n#endif\\\\n\\\\n}\\\\n\\\\\\\",points_frag:\\\\\\\"\\\\nuniform vec3 diffuse;\\\\nuniform float opacity;\\\\n\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n\\\\nvoid main() {\\\\n\\\\n\\\\t#include \\\\n\\\\n\\\\tvec3 outgoingLight = vec3( 0.0 );\\\\n\\\\tvec4 diffuseColor = vec4( diffuse, opacity );\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n\\\\toutgoingLight = diffuseColor.rgb;\\\\n\\\\n\\\\tgl_FragColor = vec4( outgoingLight, diffuseColor.a );\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n}\\\\n\\\\\\\",points_vert:\\\\\\\"\\\\nuniform float size;\\\\nuniform float scale;\\\\n\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n\\\\nvoid main() {\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n\\\\tgl_PointSize = size;\\\\n\\\\n\\\\t#ifdef USE_SIZEATTENUATION\\\\n\\\\n\\\\t\\\\tbool isPerspective = isPerspectiveMatrix( projectionMatrix );\\\\n\\\\n\\\\t\\\\tif ( isPerspective ) gl_PointSize *= ( scale / - mvPosition.z );\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n}\\\\n\\\\\\\",shadow_frag:\\\\\\\"\\\\nuniform vec3 color;\\\\nuniform float opacity;\\\\n\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n\\\\nvoid main() {\\\\n\\\\n\\\\tgl_FragColor = vec4( color, opacity * ( 1.0 - getShadowMask() ) );\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n}\\\\n\\\\\\\",shadow_vert:\\\\\\\"\\\\n#include \\\\n#include \\\\n#include \\\\n\\\\nvoid main() {\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n}\\\\n\\\\\\\",sprite_frag:\\\\\\\"\\\\nuniform vec3 diffuse;\\\\nuniform float opacity;\\\\n\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n\\\\nvoid main() {\\\\n\\\\n\\\\t#include \\\\n\\\\n\\\\tvec3 outgoingLight = vec3( 0.0 );\\\\n\\\\tvec4 diffuseColor = vec4( diffuse, opacity );\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n\\\\toutgoingLight = diffuseColor.rgb;\\\\n\\\\n\\\\tgl_FragColor = vec4( outgoingLight, diffuseColor.a );\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n}\\\\n\\\\\\\",sprite_vert:\\\\\\\"\\\\nuniform float rotation;\\\\nuniform vec2 center;\\\\n\\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n#include \\\\n\\\\nvoid main() {\\\\n\\\\n\\\\t#include \\\\n\\\\n\\\\tvec4 mvPosition = modelViewMatrix * vec4( 0.0, 0.0, 0.0, 1.0 );\\\\n\\\\n\\\\tvec2 scale;\\\\n\\\\tscale.x = length( vec3( modelMatrix[ 0 ].x, modelMatrix[ 0 ].y, modelMatrix[ 0 ].z ) );\\\\n\\\\tscale.y = length( vec3( modelMatrix[ 1 ].x, modelMatrix[ 1 ].y, modelMatrix[ 1 ].z ) );\\\\n\\\\n\\\\t#ifndef USE_SIZEATTENUATION\\\\n\\\\n\\\\t\\\\tbool isPerspective = isPerspectiveMatrix( projectionMatrix );\\\\n\\\\n\\\\t\\\\tif ( isPerspective ) scale *= - mvPosition.z;\\\\n\\\\n\\\\t#endif\\\\n\\\\n\\\\tvec2 alignedPosition = ( position.xy - ( center - vec2( 0.5 ) ) ) * scale;\\\\n\\\\n\\\\tvec2 rotatedPosition;\\\\n\\\\trotatedPosition.x = cos( rotation ) * alignedPosition.x - sin( rotation ) * alignedPosition.y;\\\\n\\\\trotatedPosition.y = sin( rotation ) * alignedPosition.x + cos( rotation ) * alignedPosition.y;\\\\n\\\\n\\\\tmvPosition.xy += rotatedPosition;\\\\n\\\\n\\\\tgl_Position = projectionMatrix * mvPosition;\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n}\\\\n\\\\\\\"};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 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isOrthographic;\\\\\\\",\\\\\\\"#ifdef USE_INSTANCING\\\\\\\",\\\\\\\"\\\\tattribute mat4 instanceMatrix;\\\\\\\",\\\\\\\"#endif\\\\\\\",\\\\\\\"#ifdef USE_INSTANCING_COLOR\\\\\\\",\\\\\\\"\\\\tattribute vec3 instanceColor;\\\\\\\",\\\\\\\"#endif\\\\\\\",\\\\\\\"attribute vec3 position;\\\\\\\",\\\\\\\"attribute vec3 normal;\\\\\\\",\\\\\\\"attribute vec2 uv;\\\\\\\",\\\\\\\"#ifdef USE_TANGENT\\\\\\\",\\\\\\\"\\\\tattribute vec4 tangent;\\\\\\\",\\\\\\\"#endif\\\\\\\",\\\\\\\"#if defined( USE_COLOR_ALPHA )\\\\\\\",\\\\\\\"\\\\tattribute vec4 color;\\\\\\\",\\\\\\\"#elif defined( USE_COLOR )\\\\\\\",\\\\\\\"\\\\tattribute vec3 color;\\\\\\\",\\\\\\\"#endif\\\\\\\",\\\\\\\"#ifdef USE_MORPHTARGETS\\\\\\\",\\\\\\\"\\\\tattribute vec3 morphTarget0;\\\\\\\",\\\\\\\"\\\\tattribute vec3 morphTarget1;\\\\\\\",\\\\\\\"\\\\tattribute vec3 morphTarget2;\\\\\\\",\\\\\\\"\\\\tattribute vec3 morphTarget3;\\\\\\\",\\\\\\\"\\\\t#ifdef USE_MORPHNORMALS\\\\\\\",\\\\\\\"\\\\t\\\\tattribute vec3 morphNormal0;\\\\\\\",\\\\\\\"\\\\t\\\\tattribute vec3 morphNormal1;\\\\\\\",\\\\\\\"\\\\t\\\\tattribute vec3 morphNormal2;\\\\\\\",\\\\\\\"\\\\t\\\\tattribute vec3 morphNormal3;\\\\\\\",\\\\\\\"\\\\t#else\\\\\\\",\\\\\\\"\\\\t\\\\tattribute vec3 morphTarget4;\\\\\\\",\\\\\\\"\\\\t\\\\tattribute vec3 morphTarget5;\\\\\\\",\\\\\\\"\\\\t\\\\tattribute vec3 morphTarget6;\\\\\\\",\\\\\\\"\\\\t\\\\tattribute vec3 morphTarget7;\\\\\\\",\\\\\\\"\\\\t#endif\\\\\\\",\\\\\\\"#endif\\\\\\\",\\\\\\\"#ifdef USE_SKINNING\\\\\\\",\\\\\\\"\\\\tattribute vec4 skinIndex;\\\\\\\",\\\\\\\"\\\\tattribute vec4 skinWeight;\\\\\\\",\\\\\\\"#endif\\\\\\\",\\\\\\\"\\\\n\\\\\\\"].filter(Mt).join(\\\\\\\"\\\\n\\\\\\\"),g=[p,Bt(n),\\\\\\\"#define SHADER_NAME \\\\\\\"+n.shaderName,_,n.alphaTest?\\\\\\\"#define ALPHATEST \\\\\\\"+n.alphaTest+(n.alphaTest%1?\\\\\\\"\\\\\\\":\\\\\\\".0\\\\\\\"):\\\\\\\"\\\\\\\",\\\\\\\"#define GAMMA_FACTOR \\\\\\\"+d,n.useFog&&n.fog?\\\\\\\"#define USE_FOG\\\\\\\":\\\\\\\"\\\\\\\",n.useFog&&n.fogExp2?\\\\\\\"#define FOG_EXP2\\\\\\\":\\\\\\\"\\\\\\\",n.map?\\\\\\\"#define USE_MAP\\\\\\\":\\\\\\\"\\\\\\\",n.matcap?\\\\\\\"#define USE_MATCAP\\\\\\\":\\\\\\\"\\\\\\\",n.envMap?\\\\\\\"#define USE_ENVMAP\\\\\\\":\\\\\\\"\\\\\\\",n.envMap?\\\\\\\"#define \\\\\\\"+l:\\\\\\\"\\\\\\\",n.envMap?\\\\\\\"#define \\\\\\\"+u:\\\\\\\"\\\\\\\",n.envMap?\\\\\\\"#define \\\\\\\"+h:\\\\\\\"\\\\\\\",n.lightMap?\\\\\\\"#define USE_LIGHTMAP\\\\\\\":\\\\\\\"\\\\\\\",n.aoMap?\\\\\\\"#define USE_AOMAP\\\\\\\":\\\\\\\"\\\\\\\",n.emissiveMap?\\\\\\\"#define USE_EMISSIVEMAP\\\\\\\":\\\\\\\"\\\\\\\",n.bumpMap?\\\\\\\"#define USE_BUMPMAP\\\\\\\":\\\\\\\"\\\\\\\",n.normalMap?\\\\\\\"#define USE_NORMALMAP\\\\\\\":\\\\\\\"\\\\\\\",n.normalMap&&n.objectSpaceNormalMap?\\\\\\\"#define OBJECTSPACE_NORMALMAP\\\\\\\":\\\\\\\"\\\\\\\",n.normalMap&&n.tangentSpaceNormalMap?\\\\\\\"#define TANGENTSPACE_NORMALMAP\\\\\\\":\\\\\\\"\\\\\\\",n.clearcoatMap?\\\\\\\"#define USE_CLEARCOATMAP\\\\\\\":\\\\\\\"\\\\\\\",n.clearcoatRoughnessMap?\\\\\\\"#define USE_CLEARCOAT_ROUGHNESSMAP\\\\\\\":\\\\\\\"\\\\\\\",n.clearcoatNormalMap?\\\\\\\"#define USE_CLEARCOAT_NORMALMAP\\\\\\\":\\\\\\\"\\\\\\\",n.specularMap?\\\\\\\"#define USE_SPECULARMAP\\\\\\\":\\\\\\\"\\\\\\\",n.roughnessMap?\\\\\\\"#define USE_ROUGHNESSMAP\\\\\\\":\\\\\\\"\\\\\\\",n.metalnessMap?\\\\\\\"#define USE_METALNESSMAP\\\\\\\":\\\\\\\"\\\\\\\",n.alphaMap?\\\\\\\"#define USE_ALPHAMAP\\\\\\\":\\\\\\\"\\\\\\\",n.sheen?\\\\\\\"#define USE_SHEEN\\\\\\\":\\\\\\\"\\\\\\\",n.transmissionMap?\\\\\\\"#define USE_TRANSMISSIONMAP\\\\\\\":\\\\\\\"\\\\\\\",n.vertexTangents?\\\\\\\"#define USE_TANGENT\\\\\\\":\\\\\\\"\\\\\\\",n.vertexColors||n.instancingColor?\\\\\\\"#define USE_COLOR\\\\\\\":\\\\\\\"\\\\\\\",n.vertexAlphas?\\\\\\\"#define USE_COLOR_ALPHA\\\\\\\":\\\\\\\"\\\\\\\",n.vertexUvs?\\\\\\\"#define USE_UV\\\\\\\":\\\\\\\"\\\\\\\",n.uvsVertexOnly?\\\\\\\"#define UVS_VERTEX_ONLY\\\\\\\":\\\\\\\"\\\\\\\",n.gradientMap?\\\\\\\"#define USE_GRADIENTMAP\\\\\\\":\\\\\\\"\\\\\\\",n.flatShading?\\\\\\\"#define FLAT_SHADED\\\\\\\":\\\\\\\"\\\\\\\",n.doubleSided?\\\\\\\"#define DOUBLE_SIDED\\\\\\\":\\\\\\\"\\\\\\\",n.flipSided?\\\\\\\"#define FLIP_SIDED\\\\\\\":\\\\\\\"\\\\\\\",n.shadowMapEnabled?\\\\\\\"#define USE_SHADOWMAP\\\\\\\":\\\\\\\"\\\\\\\",n.shadowMapEnabled?\\\\\\\"#define \\\\\\\"+c:\\\\\\\"\\\\\\\",n.premultipliedAlpha?\\\\\\\"#define PREMULTIPLIED_ALPHA\\\\\\\":\\\\\\\"\\\\\\\",n.physicallyCorrectLights?\\\\\\\"#define PHYSICALLY_CORRECT_LIGHTS\\\\\\\":\\\\\\\"\\\\\\\",n.logarithmicDepthBuffer?\\\\\\\"#define USE_LOGDEPTHBUF\\\\\\\":\\\\\\\"\\\\\\\",n.logarithmicDepthBuffer&&n.rendererExtensionFragDepth?\\\\\\\"#define USE_LOGDEPTHBUF_EXT\\\\\\\":\\\\\\\"\\\\\\\",(n.extensionShaderTextureLOD||n.envMap)&&n.rendererExtensionShaderTextureLod?\\\\\\\"#define TEXTURE_LOD_EXT\\\\\\\":\\\\\\\"\\\\\\\",\\\\\\\"uniform mat4 viewMatrix;\\\\\\\",\\\\\\\"uniform vec3 cameraPosition;\\\\\\\",\\\\\\\"uniform bool isOrthographic;\\\\\\\",n.toneMapping!==w.vb?\\\\\\\"#define TONE_MAPPING\\\\\\\":\\\\\\\"\\\\\\\",n.toneMapping!==w.vb?G.tonemapping_pars_fragment:\\\\\\\"\\\\\\\",n.toneMapping!==w.vb?Ct(\\\\\\\"toneMapping\\\\\\\",n.toneMapping):\\\\\\\"\\\\\\\",n.dithering?\\\\\\\"#define DITHERING\\\\\\\":\\\\\\\"\\\\\\\",G.encodings_pars_fragment,n.map?Tt(\\\\\\\"mapTexelToLinear\\\\\\\",n.mapEncoding):\\\\\\\"\\\\\\\",n.matcap?Tt(\\\\\\\"matcapTexelToLinear\\\\\\\",n.matcapEncoding):\\\\\\\"\\\\\\\",n.envMap?Tt(\\\\\\\"envMapTexelToLinear\\\\\\\",n.envMapEncoding):\\\\\\\"\\\\\\\",n.emissiveMap?Tt(\\\\\\\"emissiveMapTexelToLinear\\\\\\\",n.emissiveMapEncoding):\\\\\\\"\\\\\\\",n.lightMap?Tt(\\\\\\\"lightMapTexelToLinear\\\\\\\",n.lightMapEncoding):\\\\\\\"\\\\\\\",Et(\\\\\\\"linearToOutputTexel\\\\\\\",n.outputEncoding),n.depthPacking?\\\\\\\"#define DEPTH_PACKING \\\\\\\"+n.depthPacking:\\\\\\\"\\\\\\\",\\\\\\\"\\\\n\\\\\\\"].filter(Mt).join(\\\\\\\"\\\\n\\\\\\\")),o=Lt(o),o=Nt(o,n),o=St(o,n),a=Lt(a),a=Nt(a,n),a=St(a,n),o=Ft(o),a=Ft(a),n.isWebGL2&&!0!==n.isRawShaderMaterial&&(v=\\\\\\\"#version 300 es\\\\n\\\\\\\",f=[\\\\\\\"#define attribute in\\\\\\\",\\\\\\\"#define varying out\\\\\\\",\\\\\\\"#define texture2D texture\\\\\\\"].join(\\\\\\\"\\\\n\\\\\\\")+\\\\\\\"\\\\n\\\\\\\"+f,g=[\\\\\\\"#define varying in\\\\\\\",n.glslVersion===w.I?\\\\\\\"\\\\\\\":\\\\\\\"out highp vec4 pc_fragColor;\\\\\\\",n.glslVersion===w.I?\\\\\\\"\\\\\\\":\\\\\\\"#define gl_FragColor pc_fragColor\\\\\\\",\\\\\\\"#define gl_FragDepthEXT gl_FragDepth\\\\\\\",\\\\\\\"#define texture2D texture\\\\\\\",\\\\\\\"#define textureCube texture\\\\\\\",\\\\\\\"#define texture2DProj textureProj\\\\\\\",\\\\\\\"#define texture2DLodEXT textureLod\\\\\\\",\\\\\\\"#define texture2DProjLodEXT textureProjLod\\\\\\\",\\\\\\\"#define textureCubeLodEXT textureLod\\\\\\\",\\\\\\\"#define texture2DGradEXT textureGrad\\\\\\\",\\\\\\\"#define texture2DProjGradEXT textureProjGrad\\\\\\\",\\\\\\\"#define textureCubeGradEXT textureGrad\\\\\\\"].join(\\\\\\\"\\\\n\\\\\\\")+\\\\\\\"\\\\n\\\\\\\"+g);const y=v+f+o,x=v+g+a,b=xt(s,s.VERTEX_SHADER,y),A=xt(s,s.FRAGMENT_SHADER,x);if(s.attachShader(m,b),s.attachShader(m,A),void 0!==n.index0AttributeName?s.bindAttribLocation(m,0,n.index0AttributeName):!0===n.morphTargets&&s.bindAttribLocation(m,0,\\\\\\\"position\\\\\\\"),s.linkProgram(m),e.debug.checkShaderErrors){const e=s.getProgramInfoLog(m).trim(),t=s.getShaderInfoLog(b).trim(),n=s.getShaderInfoLog(A).trim();let i=!0,r=!0;if(!1===s.getProgramParameter(m,s.LINK_STATUS)){i=!1;const t=At(s,b,\\\\\\\"vertex\\\\\\\"),n=At(s,A,\\\\\\\"fragment\\\\\\\");console.error(\\\\\\\"THREE.WebGLProgram: shader error: \\\\\\\",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;s0,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;e1&&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;e0&&(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=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 \\\\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 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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;e0&&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 ${this.path()}`)}}class Wn{static split_parent_child(e){const t=e.split(Wn.SEPARATOR).filter((e=>e.length>0)),n=t.pop();return{parent:t.join(Wn.SEPARATOR),child:n}}static findNode(e,t,n){if(!e)return null;const i=t.split(Wn.SEPARATOR).filter((e=>e.length>0)),s=i[0];let r=null;if(t[0]!==Wn.SEPARATOR){switch(s){case Wn.PARENT:null==n||n.add_path_element(s),r=e.parent();break;case Wn.CURRENT:null==n||n.add_path_element(s),r=e;break;default:r=e.node(s),r&&(null==n||n.add_node(s,r))}if(null!=r&&i.length>1){const e=i.slice(1).join(Wn.SEPARATOR);r=this.findNode(r,e,n)}return r}{const i=t.substr(1);r=this.findNode(e.root(),i,n)}return r}static findParam(e,t,n){if(!e)return null;const i=t.split(Wn.SEPARATOR);if(1===i.length)return e.params.get(i[0]);{const t=i.slice(0,+(i.length-2)+1||void 0).join(Wn.SEPARATOR),s=this.findNode(e,t,n);if(null!=s){const e=i[i.length-1],t=s.params.get(e);return n&&t&&n.add_node(e,t),t}return null}}static relativePath(e,t){const n=this.closestCommonParent(e,t);if(n){const i=this.distanceToParent(e,n);let s=\\\\\\\"\\\\\\\";if(i>0){let e=0;const t=[];for(;e++e.length>0)),o=t.path().split(Wn.SEPARATOR).filter((e=>e.length>0)),a=[];let c=0;for(let e of o)r[c]||a.push(e),c++;return`${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{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;e0&&(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=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{n[i]=e[t]}));for(let i=0;i=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;e0){let n,i;for(n=1,i=t.length;n0){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=t[i].distance;i++)t[i-1].object.visible=!1,t[i].object.visible=!0;for(this._currentLevel=i-1;in?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;te.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[])))}var _=Math.log10(1/t),m=Math.pow(10,_);for(d=0;dnew 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;e1)if(m.isArray(n)){for(let e=0;enew 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{};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{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;re.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:eObject.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.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;tt))}}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 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=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;e0)for(let t=0;t0){const n=this.existing_input_indices();if(n.length0){const n=[];let i;for(let e=0;ee.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=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=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.length1&&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;tnull==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;te.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=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;e0}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;n0,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{r.matrixAutoUpdate=!0},l.onComplete=()=>{r.matrixAutoUpdate=e,r.matrixAutoUpdate||r.updateMatrix()}}if(o instanceof Ll.a&&this._target_value instanceof Ll.a){const e={value:0},t=o,n=(new Ll.a).copy(o),i=this._target_value;l.onUpdate=()=>{t.slerpQuaternions(n,i,e.value)},a=e,l.value=1}if(m.isNumber(this._target_value)){if(m.isNumber(o))for(let e of c)l[e]=this.withOp(o,this._target_value,i)}else if(!m.isNumber(o))for(let e of c)l[e]=this.withOp(o[e],this._target_value[e],i);a&&this._startTimeline(t,n,l,a)}}}_sceneGraphProps(e,t){const n=t.split(\\\\\\\".\\\\\\\");if(!(n.length>1)){const n=e[t];let i=null;const s=[];return m.isNumber(n)?(i=e,s.push(t)):(i=n,this._target_value instanceof d.a&&s.push(\\\\\\\"x\\\\\\\",\\\\\\\"y\\\\\\\"),this._target_value instanceof p.a&&s.push(\\\\\\\"x\\\\\\\",\\\\\\\"y\\\\\\\",\\\\\\\"z\\\\\\\"),this._target_value instanceof _.a&&s.push(\\\\\\\"x\\\\\\\",\\\\\\\"y\\\\\\\",\\\\\\\"z\\\\\\\",\\\\\\\"w\\\\\\\"),this._target_value,Ll.a),{target_property:n,to_target:i,property_names:s}}{const t=e[n.shift()];if(t){const e=n.join(\\\\\\\".\\\\\\\");return this._sceneGraphProps(t,e)}}}_populateWithNode(e,t,n){this._printDebug([\\\\\\\"_populateWithNode\\\\\\\",e]);const i=e.p[this._property_name];this._printDebug([\\\\\\\"target_param\\\\\\\",i]),i?i&&this._populateVarsForParam(i,t,n):Rn.warn(`${this._property_name} not found on node ${e.path()}`)}_populateVarsForParam(e,t,n){switch(this._printDebug([\\\\\\\"_populateVarsForParam\\\\\\\",e]),e.type()){case Qs.INTEGER:return this._populateVarsForParamInteger(e,t,n);case Qs.FLOAT:return this._populateVarsForParamFloat(e,t,n);case Qs.VECTOR2:return this._populateVarsForParamVector2(e,t,n);case Qs.VECTOR3:return this._populateVarsForParamVector3(e,t,n);case Qs.VECTOR4:return this._populateVarsForParamVector4(e,t,n)}this._printDebug(`param type cannot be animated (yet): '${e.type()}' '${e.path()}'`)}_populateVarsForParamInteger(e,t,n){if(!m.isNumber(this._target_value))return void Rn.warn(\\\\\\\"value is not a numbber\\\\\\\",this._target_value);const 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o=t.operation();i.x=this.withOp(e.value.x,this._target_value.x,o),i.y=this.withOp(e.value.y,this._target_value.y,o),i.z=this.withOp(e.value.z,this._target_value.z,o),this._startTimeline(t,n,i,s)}_populateVarsForParamVector4(e,t,n){if(!(this._target_value instanceof _.a))return;const i=this._commonVars(t),s=e.value.clone(),r=[0,0,0,0];i.onUpdate=()=>{s.toArray(r),e.set(r)};const o=t.operation();i.x=this.withOp(e.value.x,this._target_value.x,o),i.y=this.withOp(e.value.y,this._target_value.y,o),i.z=this.withOp(e.value.z,this._target_value.z,o),i.w=this.withOp(e.value.w,this._target_value.w,o),this._startTimeline(t,n,i,s)}withOp(e,t,n){switch(n){case __.SET:return t;case __.ADD:return e+t;case __.SUBSTRACT:return e-t}Ri.unreachable(n)}_commonVars(e){const t={duration:e.duration()},n=e.easing()||Ol.NONE;n&&(t.ease=n);const i=e.delay();null!=i&&(t.delay=i);const s=e.repeatParams();return s&&(t.repeat=s.count,t.repeatDelay=s.delay,t.yoyo=s.yoyo),t}_startTimeline(e,t,n,i){const 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type(){return\\\\\\\"copy\\\\\\\"}initializeNode(){this.io.inputs.setCount(1)}async cook(e){const t=new g_;for(let e=0;e{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 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type(){return\\\\\\\"position\\\\\\\"}initializeNode(){this.io.inputs.setCount(0,1),this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.mode,this.p.relativeTo,this.p.offset],(()=>{switch(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({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`${Li.ensureFloat(e)}`)).join(\\\\\\\", \\\\\\\")})`}static vector3(e){if(m.isString(e))return e;return`vec3(${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;e0: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{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({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 \\\\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;e2?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;tAg&&console.warn(`sigmaRadians, ${s}, is too large and will clip, as it requested ${_} samples when the maximum is set to 20`);const m=[];let f=0;for(let e=0;e4?i-8+4:0),3*g,2*g),a.setRenderTarget(t),a.render(l,Mg)}}function Bg(e){return void 0!==e&&e.type===w.Zc&&(e.encoding===w.U||e.encoding===w.ld||e.encoding===w.J)}function zg(){const e=[],t=[],n=[];let i=8;for(let s=0;s4?o=bg[s-8+4-1]:0==s&&(o=0),n.push(o);const a=1/(r-1),c=-a/2,l=1+a/2,u=[c,c,l,c,l,l,c,c,l,l,c,l],h=6,d=6,p=3,_=2,m=1,f=new Float32Array(p*d*h),g=new Float32Array(_*d*h),v=new Float32Array(m*d*h);for(let e=0;e2?0:-1,i=[t,n,0,t+2/3,n,0,t+2/3,n+1,0,t,n,0,t+2/3,n+1,0,t,n+1,0];f.set(i,p*d*e),g.set(u,_*d*e);const s=[e,e,e,e,e,e];v.set(s,m*d*e)}const y=new O.a;y.setAttribute(\\\\\\\"position\\\\\\\",new L.a(f,p)),y.setAttribute(\\\\\\\"uv\\\\\\\",new L.a(g,_)),y.setAttribute(\\\\\\\"faceIndex\\\\\\\",new L.a(v,m)),e.push(y),i>4&&i--}return{_lodPlanes:e,_sizeLods:t,_sigmas:n}}function Ug(e){const t=new Z(3*xg,3*xg,e);return t.texture.mapping=w.q,t.texture.name=\\\\\\\"PMREM.cubeUv\\\\\\\",t.scissorTest=!0,t}function Gg(e,t,n,i,s){e.viewport.set(t,n,i,s),e.scissor.set(t,n,i,s)}function Vg(){const e=new d.a(1,1);return new Af({name:\\\\\\\"EquirectangularToCubeUV\\\\\\\",uniforms:{envMap:{value:null},texelSize:{value:e},inputEncoding:{value:Tg[w.U]},outputEncoding:{value:Tg[w.U]}},vertexShader:Hg(),fragmentShader:`\\\\n\\\\n\\\\t\\\\t\\\\tprecision mediump float;\\\\n\\\\t\\\\t\\\\tprecision mediump int;\\\\n\\\\n\\\\t\\\\t\\\\tvarying vec3 vOutputDirection;\\\\n\\\\n\\\\t\\\\t\\\\tuniform sampler2D envMap;\\\\n\\\\t\\\\t\\\\tuniform vec2 texelSize;\\\\n\\\\n\\\\t\\\\t\\\\t${qg()}\\\\n\\\\n\\\\t\\\\t\\\\t#include \\\\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 \\\\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{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;eMath.PI&&(t-=a),h<-Math.PI?h+=a:h>Math.PI&&(h-=a),v.theta=t(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{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{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{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{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{this._timer_active&&(this.pv.count<=0||this._current_count{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{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({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())} = ${s}`;n.push(r)}e.addBodyLines(this,n),t.set_lines_block_end(e,this)}}class dA extends Ym{constructor(){super(...arguments),this._children_controller_context=Ei.GL}initializeNode(){var e;null===(e=this.childrenController)||void 0===e||e.set_output_node_find_method((()=>this.nodesByType(hA.type())[0])),this.io.connection_points.set_input_name_function(this._expected_input_name.bind(this)),this.io.connection_points.set_output_name_function(this._expected_output_name.bind(this)),this.io.connection_points.set_expected_input_types_function(this._expected_input_types.bind(this)),this.io.connection_points.set_expected_output_types_function(this._expected_output_types.bind(this))}_expected_inputs_count(){const e=this.io.connections.inputConnections();return e?e.length+1:1}_expected_input_types(){const e=[],t=ro.FLOAT,n=this.io.connections.inputConnections(),i=this._expected_inputs_count();for(let s=0;s=0){const t=e.dest_connection_point(),i=Wm.any(this.variableForInput(t.name())),s=`${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[\\\\\\\"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;rWm.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{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 \\\\\\\",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 \\\\\\\",(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 \\\\\\\",\\\\\\\"#include \\\\\\\",\\\\\\\"}\\\\\\\"].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 \\\\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 \\\\n\\\\n#define DIR_LIGHTS_COUNT 1\\\\n#define MAX_STEPS_COUNT 4096\\\\n\\\\nuniform vec3 u_Color;\\\\nuniform float u_VolumeDensity;\\\\nuniform float u_ShadowDensity;\\\\nuniform float u_StepSize;\\\\nuniform vec3 u_BoundingBoxMin;\\\\nuniform vec3 u_BoundingBoxMax;\\\\n//const int u_PointsCount = 3;\\\\n//uniform vec3 u_Points[3];\\\\nuniform sampler2D u_Map;\\\\n\\\\n//const int u_DirectionalLightsCount = 1;\\\\nuniform vec3 u_DirectionalLightDirection; //[DIR_LIGHTS_COUNT];\\\\n\\\\nvarying vec3 vPw;\\\\n// varying vec3 vN;\\\\n// varying vec2 vUV;\\\\n//varying vec3 vPCameraSpace;\\\\n// varying vec4 vCd;\\\\n\\\\nvec3 normalize_in_bbox(vec3 point){\\\\n\\\\n\\\\tvec3 min = u_BoundingBoxMin;\\\\n\\\\tvec3 max = u_BoundingBoxMax;\\\\n\\\\n\\\\treturn vec3(\\\\n\\\\t\\\\t(point.x - min.x) / (max.x - min.x),\\\\n\\\\t\\\\t(point.y - min.y) / (max.y - min.y),\\\\n\\\\t\\\\t(point.z - min.z) / (max.z - min.z)\\\\n\\\\t);\\\\n}\\\\n\\\\nbool is_inside_bbox(vec3 Pw){\\\\n\\\\n\\\\tvec3 min = u_BoundingBoxMin;\\\\n\\\\tvec3 max = u_BoundingBoxMax;\\\\n\\\\n\\\\treturn (\\\\n\\\\t\\\\tPw.x > min.x &&\\\\n\\\\t\\\\tPw.y > min.y &&\\\\n\\\\t\\\\tPw.z > min.z &&\\\\n\\\\n\\\\t\\\\tPw.x < max.x &&\\\\n\\\\t\\\\tPw.y < max.y &&\\\\n\\\\t\\\\tPw.z < max.z\\\\n\\\\t\\\\t);\\\\n}\\\\n\\\\nfloat density_to_opacity(float density, float step_size){\\\\n\\\\tfloat curent_density = density;\\\\n\\\\tcurent_density = max(0.0, curent_density);\\\\n\\\\n\\\\tfloat opacity = (1.0-exp(-curent_density * step_size));\\\\n\\\\treturn max(opacity,0.0);\\\\n}\\\\n\\\\nfloat density_function(vec3 position_for_step){\\\\n\\\\tfloat density = 1.0;\\\\n\\\\t// start builder body code\\\\n\\\\n\\\\treturn density;\\\\n}\\\\n\\\\nvec4 raymarch_light(vec3 ray_dir, vec3 start_pos){\\\\n\\\\n\\\\tfloat step_size = u_StepSize;\\\\n\\\\tvec3 step_vector = ray_dir * step_size;\\\\n\\\\n\\\\tvec3 current_pos = start_pos + step_vector*rand(start_pos.x*ray_dir.xy);\\\\n\\\\tfloat opacity = 0.0;\\\\n\\\\tfor(int i=0; i= 0.99){ break; }\\\\n\\\\n\\\\t\\\\tif( is_inside_bbox(current_pos) ){\\\\n\\\\n\\\\t\\\\t\\\\tfloat density = density_function(current_pos) * u_ShadowDensity;\\\\n\\\\t\\\\t\\\\topacity += density_to_opacity(density, step_size);\\\\n\\\\t\\\\t\\\\tcurrent_pos += step_vector;\\\\n\\\\n\\\\t\\\\t}else{\\\\n\\\\t\\\\t\\\\tbreak;\\\\n\\\\t\\\\t}\\\\n\\\\t}\\\\n\\\\n\\\\tvec3 light_color = vec3(1.0, 1.0, 1.0) * u_Color;\\\\n\\\\tlight_color *= (1.0-opacity);\\\\n\\\\treturn vec4(light_color, 1.0-opacity);\\\\n}\\\\n\\\\nvec4 raymarch_bbox(vec3 start_pos, vec3 ray_dir){\\\\n\\\\n\\\\tfloat step_size = u_StepSize;\\\\n\\\\tvec3 step_vector = ray_dir * step_size;\\\\n\\\\n\\\\tvec3 current_pos = start_pos - step_vector*rand(ray_dir.xz);\\\\n\\\\tfloat opacity = 0.0;\\\\n\\\\tvec3 color = vec3(0.0, 0.0, 0.0);\\\\n\\\\tfloat steps_count = 0.0;\\\\n\\\\tbool was_inside_bbox = false;\\\\n\\\\tfor(int i=0; i= 0.99){ break; }\\\\n\\\\n\\\\t\\\\tif( i==0 || is_inside_bbox(current_pos) ){\\\\n\\\\t\\\\t\\\\twas_inside_bbox = true;\\\\n\\\\n\\\\t\\\\t\\\\tfloat density = density_function(current_pos) * u_VolumeDensity;\\\\n\\\\t\\\\t\\\\topacity += density_to_opacity(density, step_size);\\\\n\\\\n\\\\t\\\\t\\\\tvec4 light_color = vec4(0.0,0.0,0.0,1.0); //vec4(1.0,1.0,1.0,1.0);\\\\n\\\\t\\\\t\\\\t// vec3 directional_light_direction;\\\\n\\\\t\\\\t\\\\t// for ( int l = 0; l < DIR_LIGHTS_COUNT; l++ ) {\\\\n\\\\t\\\\t\\\\t// directional_light_direction = u_DirectionalLightsDirection[ l ];\\\\n\\\\t\\\\t\\\\tlight_color += raymarch_light(-u_DirectionalLightDirection, current_pos);\\\\n\\\\t\\\\t\\\\t// }\\\\n\\\\t\\\\t\\\\tfloat blend = 1.0-opacity;\\\\n\\\\t\\\\t\\\\tcolor = mix( color.xyz, light_color.xyz, vec3(blend, blend, blend) );\\\\n\\\\t\\\\t\\\\tsteps_count += 1.0;\\\\n\\\\n\\\\t\\\\t}else{\\\\n\\\\t\\\\t\\\\tif (was_inside_bbox) { break; }\\\\n\\\\t\\\\t}\\\\n\\\\t\\\\tcurrent_pos += step_vector;\\\\n\\\\t}\\\\n\\\\n\\\\treturn vec4(color, opacity);\\\\n\\\\t// steps_count = steps_count / 5.0;\\\\n\\\\t// return vec4(vec3(steps_count, steps_count, steps_count), 1.0);\\\\n}\\\\n\\\\nvoid main()\\\\t{\\\\n\\\\n\\\\tvec3 eye = normalize(vPw - cameraPosition);\\\\n\\\\t// we can start from the bbox, as we are front facing\\\\n\\\\tvec3 start_pos = vPw;\\\\n\\\\n\\\\tvec4 color = raymarch_bbox(start_pos, eye);\\\\n\\\\tgl_FragColor = color;\\\\n\\\\n}\\\\\\\";const 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.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;t0)&&d.push(t,s,c),(e!==n-1||a0&&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;e0){this.source.disconnect(this.filters[0]);for(let e=1,t=this.filters.length;e{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 \\\\\\\",\\\\\\\"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 \\\\\\\",\\\\\\\"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 \\\\\\\",\\\\\\\"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 \\\\\\\",\\\\\\\"\\\\t#include \\\\\\\",\\\\\\\"\\\\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 \\\\n\\\\nvec3 rotate_with_quat( vec3 v, vec4 q )\\\\n{\\\\n\\\\treturn v + 2.0 * cross( q.xyz, cross( q.xyz, v ) + q.w * v );\\\\n}\\\\n\\\\n\\\\nattribute vec4 instanceOrientation;\\\\nattribute vec3 instancePosition;\\\\nattribute vec3 instanceScale;\\\\nvarying float vViewZDepth;\\\\n\\\\n\\\\n\\\\nvoid main() {\\\\n\\\\n\\\\tvec3 v_POLYGON_instance_transform1_position = vec3(position);\\\\n\\\\tv_POLYGON_instance_transform1_position *= instanceScale;\\\\n\\\\tv_POLYGON_instance_transform1_position = rotate_with_quat( v_POLYGON_instance_transform1_position, instanceOrientation );\\\\n\\\\tv_POLYGON_instance_transform1_position += instancePosition;\\\\n\\\\t\\\\n\\\\t// replaces #include \\\\n\\\\tvec3 transformed = v_POLYGON_instance_transform1_position;\\\\n\\\\n\\\\n\\\\t#include \\\\n\\\\n\\\\tvViewZDepth = - mvPosition.z;\\\\n}\\\\\\\",fragmentShader: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 \\\\\\\",\\\\\\\"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;i0){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 \\\\\\\",\\\\\\\"#include \\\\\\\",\\\\\\\"varying vec4 projTexCoord;\\\\\\\",\\\\\\\"varying vec4 vPosition;\\\\\\\",\\\\\\\"uniform mat4 textureMatrix;\\\\\\\",\\\\\\\"void main() {\\\\\\\",\\\\\\\"\\\\t#include \\\\\\\",\\\\\\\"\\\\t#include \\\\\\\",\\\\\\\"\\\\t#include \\\\\\\",\\\\\\\"\\\\t#include \\\\\\\",\\\\\\\"\\\\t#include \\\\\\\",\\\\\\\"\\\\tvPosition = mvPosition;\\\\\\\",\\\\\\\"\\\\tvec4 worldPosition = modelMatrix * vec4( position, 1.0 );\\\\\\\",\\\\\\\"\\\\tprojTexCoord = textureMatrix * worldPosition;\\\\\\\",\\\\\\\"}\\\\\\\"].join(\\\\\\\"\\\\n\\\\\\\"),fragmentShader:[\\\\\\\"#include \\\\\\\",\\\\\\\"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 \\\\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 \\\\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\\\\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{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{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;e0&&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;e0&&(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=.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=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{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({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;ea&&(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{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{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{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{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{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{n[e.index()][r]=t}));else for(let t of e){n[t.index()][r]=s.value}}for(let t=0;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{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{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({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($F.indexOf(e))}cook(e){this._operation=this._operation||new QF(this.scene(),this.states);const t=this._operation.cook(e,this.pv);this.setCoreGroup(t)}}var eD;!function(e){e[e.MIN=0]=\\\\\\\"MIN\\\\\\\",e[e.MAX=1]=\\\\\\\"MAX\\\\\\\",e[e.FIRST_FOUND=2]=\\\\\\\"FIRST_FOUND\\\\\\\"}(eD||(eD={}));class tD extends WO{constructor(){super(...arguments),this._values_per_attrib_name={},this._filtered_values_per_attrib_name={}}static type(){return\\\\\\\"attribPromote\\\\\\\"}cook(e,t){this._core_group=e[0],this._values_per_attrib_name={},this._filtered_values_per_attrib_name={};for(let e of this._core_group.coreObjects())this._core_object=e,this.find_values(t),this.filter_values(t),this.set_values(t);return this._core_group}find_values(e){const t=Li.attribNames(e.name);for(let n of t)this._find_values_for_attrib_name(n,e)}_find_values_for_attrib_name(e,t){switch(t.classFrom){case rs.VERTEX:return this.find_values_from_points(e,t);case rs.OBJECT:return this.find_values_from_object(e,t)}}find_values_from_points(e,t){if(this._core_object){const t=this._core_object.points(),n=t[0];if(n&&!n.isAttribIndexed(e)){const n=new Array(t.length);let i;for(let s=0;s{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.name,this.p.classFrom,this.p.classTo],(()=>{if(\\\\\\\"\\\\\\\"!=this.pv.name){const e=as.filter((e=>e.value==this.pv.classFrom))[0].name,t=as.filter((e=>e.value==this.pv.classTo))[0].name;return`${this.pv.name} (${e} -> ${t})`}return\\\\\\\"\\\\\\\"}))}))}))}cook(e){this._operation=this._operation||new tD(this.scene(),this.states);const t=this._operation.cook(e,this.pv);this.setCoreGroup(t)}}const oD=new class extends Lo{constructor(){super(...arguments),this.name=Oo.STRING(),this.ramp=Oo.RAMP(),this.changeName=Oo.BOOLEAN(0),this.newName=Oo.STRING(\\\\\\\"\\\\\\\",{visibleIf:{changeName:1}})}};class aD extends QO{constructor(){super(...arguments),this.paramsConfig=oD}static type(){return\\\\\\\"attribRemap\\\\\\\"}initializeNode(){this.io.inputs.setCount(1)}cook(e){const t=e[0];this._remap_attribute(t),this.setCoreGroup(t)}_remap_attribute(e){const t=e.points();if(0===t.length)return;if(\\\\\\\"\\\\\\\"===this.pv.name)return;const n=t[0].attribSize(this.pv.name),i=t.map((e=>e.attribValue(this.pv.name)));let s=new Array(t.length);this._get_remaped_values(n,i,s);let r=this.pv.name;this.pv.changeName&&(r=this.pv.newName,e.hasAttrib(r)||e.addNumericVertexAttrib(r,n,0));let o=0;for(let e of s){t[o].setAttribValue(r,e),o++}}_get_remaped_values(e,t,n){switch(e){case hs.FLOAT:return this._get_normalized_float(t,n);case hs.VECTOR2:return this._get_normalized_vector2(t,n);case hs.VECTOR3:return this._get_normalized_vector3(t,n);case hs.VECTOR4:return this._get_normalized_vector4(t,n)}Ri.unreachable(e)}_get_normalized_float(e,t){const n=e,i=this.p.ramp;for(let e=0;e{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.oldName,this.p.newName],(()=>\\\\\\\"\\\\\\\"!=this.pv.oldName&&\\\\\\\"\\\\\\\"!=this.pv.newName?`${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_indexthis._max_time_per_chunk){setTimeout(this._bound_next_with_count,1);break}this._current_count_index>=this._count&&this._resolve&&this._resolve()}async startWithArray(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;ee.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;tED.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=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;enew 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({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({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;se.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;tNumber.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=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=0;){const i=n;let s=n-1;s<0&&(s=e.length-1);for(let e=0,n=a+2*m;e=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;e0){const n=new Zg.b(t);s=new pk.a(n),s.setCrossOrigin(this.crossOrigin);for(let t=0,n=e.length;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;e0&&(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;s0&&(n[2*(s-1)]=s-1,n[2*(s-1)+1]=s);this.params.full&&(n.push(s-1),n.push(0));const r=new 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;et/(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{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{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{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:\\\\\\\"
default html
\\\\\\\",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($B.SEPARATOR);for(let e=0;eparseFloat(e)))}return e}return e}read_values(){if(!this.attribute_names)return;let e;for(let t=this.attribute_names_from_first_line?1:0;t=this.attribute_names.length){for(let e=0;e({name:e,value:t})))}}),this.url=Oo.STRING(KB,{fileBrowse:{type:[tr.JSON]}}),this.jsonDataKeysPrefix=Oo.STRING(\\\\\\\"\\\\\\\",{visibleIf:{dataType:JB.indexOf(QB.JSON)}}),this.skipEntries=Oo.STRING(\\\\\\\"\\\\\\\",{visibleIf:{dataType:JB.indexOf(QB.JSON)}}),this.convert=Oo.BOOLEAN(0,{visibleIf:{dataType:JB.indexOf(QB.JSON)}}),this.convertToNumeric=Oo.STRING(\\\\\\\"\\\\\\\",{visibleIf:{dataType:JB.indexOf(QB.JSON),convert:1}}),this.readAttribNamesFromFile=Oo.BOOLEAN(1,{visibleIf:{dataType:JB.indexOf(QB.CSV)}}),this.attribNames=Oo.STRING(\\\\\\\"height scale\\\\\\\",{visibleIf:{dataType:JB.indexOf(QB.CSV),readAttribNamesFromFile:0}}),this.reload=Oo.BUTTON(null,{callback:(e,t)=>{ez.PARAM_CALLBACK_reload(e,t)}})}};class ez extends QO{constructor(){super(...arguments),this.paramsConfig=ZB}static type(){return\\\\\\\"dataUrl\\\\\\\"}initializeNode(){this.scene().dispatchController.onAddListener((()=>{this.params.onParamsCreated(\\\\\\\"params_label\\\\\\\",(()=>{this.params.label.init([this.p.url],(()=>{const e=this.p.url.rawInput();if(e){const t=e.split(\\\\\\\"/\\\\\\\");return t[t.length-1]}return\\\\\\\"\\\\\\\"}))}))}))}async cook(){switch(JB[this.pv.dataType]){case QB.JSON:return this._load_json();case QB.CSV:return this._load_csv()}}_url(){const e=this.scene().assets.root();return e?`${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;r0)?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{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)=>ee<=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;e0){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{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{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{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{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=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({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{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;s0&&(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{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):fN?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{s[3*e.index()+0]=t}));else{let e;for(let t=0;t{s[3*e.index()+1]=t}));else{let e;for(let t=0;t{s[3*e.index()+2]=t}));else{let e;for(let t=0;t{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=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;te)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=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;te.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{u[e.index()]=t}));else{let e;for(let t=0;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{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{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;e0){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;ti[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;e0&&(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;io.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;et/(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 ${t}`),null!=t)for(let n=0;n({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({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;t0)for(let e=0;e0&&(this.normalsNeedUpdate=!0)},computeFlatVertexNormals:function(){this.computeFaceNormals();for(let e=0,t=this.faces.length;e0&&(this.normalsNeedUpdate=!0)},computeMorphNormals:function(){for(let e=0,t=this.faces.length;e=0;e--){const t=r[e];this.faces.splice(t,1);for(let e=0,n=this.faceVertexUvs.length;e0,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<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;e0){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;e0){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;i0,r=i[1]&&i[1].length>0,o=e.morphTargets,a=o.length;let c;if(a>0){c=[];for(let e=0;e0){h=[];for(let e=0;e0&&0===t.length&&console.error(\\\\\\\"THREE.DirectGeometry: Faceless geometries are not supported.\\\\\\\");for(let e=0;e0;)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;z3&&(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{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{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{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;s0&&t0)for(let t=0;t{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({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({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;et.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=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;s0){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 \\\\\\\"],[nf.FRAGMENT,\\\\\\\"#include \\\\\\\"]]),Tq=new Map([[nf.VERTEX,\\\\\\\"#include \\\\\\\"],[nf.FRAGMENT,\\\\\\\"vec4 diffuseColor = vec4( diffuse, opacity );\\\\\\\"]]),Eq=new Map([[nf.VERTEX,[\\\\\\\"#include \\\\\\\",\\\\\\\"#include \\\\\\\"]],[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 \\\\\\\"],[nf.FRAGMENT,\\\\\\\"vec4 diffuseColor = vec4( 1.0 );\\\\\\\"]]);const Pq=new Map([[nf.VERTEX,\\\\\\\"#include \\\\\\\"],[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 \\\\n\\\\nvarying float vViewZDepth;\\\\n\\\\n// INSERT DEFINES\\\\n\\\\n\\\\n\\\\nvoid main() {\\\\n\\\\n\\\\t// INSERT BODY\\\\n\\\\n\\\\n\\\\t#include \\\\n\\\\n\\\\tvViewZDepth = - mvPosition.z;\\\\n}\\\\\\\",fragmentShader: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 \\\\\\\",\\\\\\\"float metalnessFactor = metalness * POLY_metalness;\\\\n\\\\n#ifdef USE_METALNESSMAP\\\\n\\\\n\\\\tvec4 texelMetalness = texture2D( metalnessMap, vUv );\\\\n\\\\n\\\\t// reads channel B, compatible with a combined OcclusionRoughnessMetallic (RGB) texture\\\\n\\\\tmetalnessFactor *= texelMetalness.b;\\\\n\\\\n#endif\\\\n\\\\\\\")).replace(\\\\\\\"#include \\\\\\\",\\\\\\\"float roughnessFactor = roughness * POLY_roughness;\\\\n\\\\n#ifdef USE_ROUGHNESSMAP\\\\n\\\\n\\\\tvec4 texelRoughness = texture2D( roughnessMap, vUv );\\\\n\\\\n\\\\t// reads channel G, compatible with a combined OcclusionRoughnessMetallic (RGB) texture\\\\n\\\\troughnessFactor *= texelRoughness.g;\\\\n\\\\n#endif\\\\n\\\\\\\")).replace(\\\\\\\"vec3 totalEmissiveRadiance = emissive;\\\\\\\",\\\\\\\"vec3 totalEmissiveRadiance = emissive * POLY_emissive;\\\\\\\"),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 \\\\\\\",\\\\\\\"#include \\\\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 \\\\\\\",\\\\\\\"gl_PointSize = size;\\\\\\\"]],[nf.FRAGMENT,[]]]),$q=new Map;$q.set(Mq.DISTANCE,class extends Oq{templateShader(){const e=H.distanceRGBA,t=k.clone(e.uniforms);return t.size={value:1},t.scale={value:1},{vertexShader:\\\\\\\"uniform float size;\\\\nuniform float scale;\\\\n#define DISTANCE\\\\nvarying vec3 vWorldPosition;\\\\n#include \\\\n#include \\\\nvarying float vViewZDepth;\\\\n\\\\n// INSERT DEFINES\\\\n\\\\n\\\\n\\\\nvoid main() {\\\\n\\\\n\\\\t// INSERT BODY\\\\n\\\\n\\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\t#include \\\\n\\\\n\\\\t#ifdef USE_SIZEATTENUATION\\\\n\\\\t\\\\tbool isPerspective = ( projectionMatrix[ 2 ][ 3 ] == - 1.0 );\\\\n\\\\t\\\\tif ( isPerspective ) gl_PointSize *= ( scale / - mvPosition.z );\\\\n\\\\t#endif\\\\n\\\\tvWorldPosition = worldPosition.xyz;\\\\n}\\\\n\\\\n// #define DISTANCE\\\\n// varying vec3 vWorldPosition;\\\\n// #include \\\\n// #include \\\\n// #include \\\\n// #include \\\\n// #include \\\\n// #include \\\\n// void main() {\\\\n// \\\\t#include \\\\n// \\\\t#include \\\\n// \\\\t#ifdef USE_DISPLACEMENTMAP\\\\n// \\\\t\\\\t#include \\\\n// \\\\t\\\\t#include \\\\n// \\\\t\\\\t#include \\\\n// \\\\t#endif\\\\n// \\\\t#include \\\\n// \\\\t#include \\\\n// \\\\t#include \\\\n// \\\\t#include \\\\n// \\\\t#include \\\\n// \\\\t#include \\\\n// \\\\t#include \\\\n// \\\\tvWorldPosition = worldPosition.xyz;\\\\n// }\\\\n\\\\n\\\\n\\\\\\\",fragmentShader:e.fragmentShader,uniforms:t}}insert_define_after(e){return Hq.get(e)}insert_body_after(e){return qq.get(e)}createMaterial(){const e=this.templateShader();return new B({defines:{USE_SIZEATTENUATION:1,DEPTH_PACKING:[w.Hb,w.j][0]},uniforms:k.clone(e.uniforms),vertexShader:e.vertexShader,fragmentShader:e.fragmentShader})}}),$q.set(Mq.DEPTH_DOF,class extends Oq{templateShader(){return{vertexShader:\\\\\\\"uniform float size;\\\\nuniform float scale;\\\\n#include \\\\n\\\\nvarying float vViewZDepth;\\\\n\\\\n// INSERT DEFINES\\\\n\\\\n\\\\n\\\\nvoid main() {\\\\n\\\\n\\\\t// INSERT BODY\\\\n\\\\n\\\\n\\\\t#include \\\\n\\\\n\\\\tvViewZDepth = - mvPosition.z;\\\\n\\\\t#ifdef USE_SIZEATTENUATION\\\\n\\\\t\\\\tbool isPerspective = ( projectionMatrix[ 2 ][ 3 ] == - 1.0 );\\\\n\\\\t\\\\tif ( isPerspective ) gl_PointSize *= ( scale / - mvPosition.z );\\\\n\\\\t#endif\\\\n\\\\n}\\\\n\\\\n\\\\\\\",fragmentShader: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 \\\\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 \\\\\\\",\\\\\\\"#include \\\\\\\"]],[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 \\\\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 \\\\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 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