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t=a*u,r=a*c,i=h*u,s=h*c;e[0]=t-s*o,e[4]=-n*c,e[8]=i+r*o,e[1]=r+i*o,e[5]=n*u,e[9]=s-t*o,e[2]=-n*h,e[6]=o,e[10]=n*a}else if(\\\\\\\"ZYX\\\\\\\"===t.order){const t=n*u,r=n*c,i=o*u,s=o*c;e[0]=a*u,e[4]=i*h-r,e[8]=t*h+s,e[1]=a*c,e[5]=s*h+t,e[9]=r*h-i,e[2]=-h,e[6]=o*a,e[10]=n*a}else if(\\\\\\\"YZX\\\\\\\"===t.order){const t=n*a,r=n*h,i=o*a,s=o*h;e[0]=a*u,e[4]=s-t*c,e[8]=i*c+r,e[1]=c,e[5]=n*u,e[9]=-o*u,e[2]=-h*u,e[6]=r*c+i,e[10]=t-s*c}else if(\\\\\\\"XZY\\\\\\\"===t.order){const t=n*a,r=n*h,i=o*a,s=o*h;e[0]=a*u,e[4]=-c,e[8]=h*u,e[1]=t*c+s,e[5]=n*u,e[9]=r*c-i,e[2]=i*c-r,e[6]=o*u,e[10]=s*c+t}return e[3]=0,e[7]=0,e[11]=0,e[12]=0,e[13]=0,e[14]=0,e[15]=1,this}makeRotationFromQuaternion(t){return this.compose(a,t,h)}lookAt(t,e,r){const i=this.elements;return 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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 r=t.elements,i=e.elements,s=this.elements,n=r[0],o=r[4],a=r[8],h=r[12],u=r[1],c=r[5],l=r[9],d=r[13],m=r[2],y=r[6],f=r[10],p=r[14],x=r[3],g=r[7],b=r[11],w=r[15],M=i[0],z=i[4],_=i[8],v=i[12],A=i[1],E=i[5],S=i[9],k=i[13],T=i[2],C=i[6],R=i[10],B=i[14],O=i[3],P=i[7],L=i[11],D=i[15];return s[0]=n*M+o*A+a*T+h*O,s[4]=n*z+o*E+a*C+h*P,s[8]=n*_+o*S+a*R+h*L,s[12]=n*v+o*k+a*B+h*D,s[1]=u*M+c*A+l*T+d*O,s[5]=u*z+c*E+l*C+d*P,s[9]=u*_+c*S+l*R+d*L,s[13]=u*v+c*k+l*B+d*D,s[2]=m*M+y*A+f*T+p*O,s[6]=m*z+y*E+f*C+p*P,s[10]=m*_+y*S+f*R+p*L,s[14]=m*v+y*k+f*B+p*D,s[3]=x*M+g*A+b*T+w*O,s[7]=x*z+g*E+b*C+w*P,s[11]=x*_+g*S+b*R+w*L,s[15]=x*v+g*k+b*B+w*D,this}multiplyScalar(t){const e=this.elements;return e[0]*=t,e[4]*=t,e[8]*=t,e[12]*=t,e[1]*=t,e[5]*=t,e[9]*=t,e[13]*=t,e[2]*=t,e[6]*=t,e[10]*=t,e[14]*=t,e[3]*=t,e[7]*=t,e[11]*=t,e[15]*=t,this}determinant(){const t=this.elements,e=t[0],r=t[4],i=t[8],s=t[12],n=t[1],o=t[5],a=t[9],h=t[13],u=t[2],c=t[6],l=t[10],d=t[14];return t[3]*(+s*a*c-i*h*c-s*o*l+r*h*l+i*o*d-r*a*d)+t[7]*(+e*a*d-e*h*l+s*n*l-i*n*d+i*h*u-s*a*u)+t[11]*(+e*h*c-e*o*d-s*n*c+r*n*d+s*o*u-r*h*u)+t[15]*(-i*o*u-e*a*c+e*o*l+i*n*c-r*n*l+r*a*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,r){const i=this.elements;return t.isVector3?(i[12]=t.x,i[13]=t.y,i[14]=t.z):(i[12]=t,i[13]=e,i[14]=r),this}invert(){const t=this.elements,e=t[0],r=t[1],i=t[2],s=t[3],n=t[4],o=t[5],a=t[6],h=t[7],u=t[8],c=t[9],l=t[10],d=t[11],m=t[12],y=t[13],f=t[14],p=t[15],x=c*f*h-y*l*h+y*a*d-o*f*d-c*a*p+o*l*p,g=m*l*h-u*f*h-m*a*d+n*f*d+u*a*p-n*l*p,b=u*y*h-m*c*h+m*o*d-n*y*d-u*o*p+n*c*p,w=m*c*a-u*y*a-m*o*l+n*y*l+u*o*f-n*c*f,M=e*x+r*g+i*b+s*w;if(0===M)return this.set(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0);const z=1/M;return t[0]=x*z,t[1]=(y*l*s-c*f*s-y*i*d+r*f*d+c*i*p-r*l*p)*z,t[2]=(o*f*s-y*a*s+y*i*h-r*f*h-o*i*p+r*a*p)*z,t[3]=(c*a*s-o*l*s-c*i*h+r*l*h+o*i*d-r*a*d)*z,t[4]=g*z,t[5]=(u*f*s-m*l*s+m*i*d-e*f*d-u*i*p+e*l*p)*z,t[6]=(m*a*s-n*f*s-m*i*h+e*f*h+n*i*p-e*a*p)*z,t[7]=(n*l*s-u*a*s+u*i*h-e*l*h-n*i*d+e*a*d)*z,t[8]=b*z,t[9]=(m*c*s-u*y*s-m*r*d+e*y*d+u*r*p-e*c*p)*z,t[10]=(n*y*s-m*o*s+m*r*h-e*y*h-n*r*p+e*o*p)*z,t[11]=(u*o*s-n*c*s-u*r*h+e*c*h+n*r*d-e*o*d)*z,t[12]=w*z,t[13]=(u*y*i-m*c*i+m*r*l-e*y*l-u*r*f+e*c*f)*z,t[14]=(m*o*i-n*y*i-m*r*a+e*y*a+n*r*f-e*o*f)*z,t[15]=(n*c*i-u*o*i+u*r*a-e*c*a-n*r*l+e*o*l)*z,this}scale(t){const e=this.elements,r=t.x,i=t.y,s=t.z;return e[0]*=r,e[4]*=i,e[8]*=s,e[1]*=r,e[5]*=i,e[9]*=s,e[2]*=r,e[6]*=i,e[10]*=s,e[3]*=r,e[7]*=i,e[11]*=s,this}getMaxScaleOnAxis(){const t=this.elements,e=t[0]*t[0]+t[1]*t[1]+t[2]*t[2],r=t[4]*t[4]+t[5]*t[5]+t[6]*t[6],i=t[8]*t[8]+t[9]*t[9]+t[10]*t[10];return Math.sqrt(Math.max(e,r,i))}makeTranslation(t,e,r){return this.set(1,0,0,t,0,1,0,e,0,0,1,r,0,0,0,1),this}makeRotationX(t){const e=Math.cos(t),r=Math.sin(t);return this.set(1,0,0,0,0,e,-r,0,0,r,e,0,0,0,0,1),this}makeRotationY(t){const e=Math.cos(t),r=Math.sin(t);return this.set(e,0,r,0,0,1,0,0,-r,0,e,0,0,0,0,1),this}makeRotationZ(t){const e=Math.cos(t),r=Math.sin(t);return this.set(e,-r,0,0,r,e,0,0,0,0,1,0,0,0,0,1),this}makeRotationAxis(t,e){const r=Math.cos(e),i=Math.sin(e),s=1-r,n=t.x,o=t.y,a=t.z,h=s*n,u=s*o;return this.set(h*n+r,h*o-i*a,h*a+i*o,0,h*o+i*a,u*o+r,u*a-i*n,0,h*a-i*o,u*a+i*n,s*a*a+r,0,0,0,0,1),this}makeScale(t,e,r){return this.set(t,0,0,0,0,e,0,0,0,0,r,0,0,0,0,1),this}makeShear(t,e,r){return this.set(1,e,r,0,t,1,r,0,t,e,1,0,0,0,0,1),this}compose(t,e,r){const i=this.elements,s=e._x,n=e._y,o=e._z,a=e._w,h=s+s,u=n+n,c=o+o,l=s*h,d=s*u,m=s*c,y=n*u,f=n*c,p=o*c,x=a*h,g=a*u,b=a*c,w=r.x,M=r.y,z=r.z;return i[0]=(1-(y+p))*w,i[1]=(d+b)*w,i[2]=(m-g)*w,i[3]=0,i[4]=(d-b)*M,i[5]=(1-(l+p))*M,i[6]=(f+x)*M,i[7]=0,i[8]=(m+g)*z,i[9]=(f-x)*z,i[10]=(1-(l+y))*z,i[11]=0,i[12]=t.x,i[13]=t.y,i[14]=t.z,i[15]=1,this}decompose(t,e,r){const i=this.elements;let s=n.set(i[0],i[1],i[2]).length();const a=n.set(i[4],i[5],i[6]).length(),h=n.set(i[8],i[9],i[10]).length();this.determinant()<0&&(s=-s),t.x=i[12],t.y=i[13],t.z=i[14],o.copy(this);const u=1/s,c=1/a,l=1/h;return o.elements[0]*=u,o.elements[1]*=u,o.elements[2]*=u,o.elements[4]*=c,o.elements[5]*=c,o.elements[6]*=c,o.elements[8]*=l,o.elements[9]*=l,o.elements[10]*=l,e.setFromRotationMatrix(o),r.x=s,r.y=a,r.z=h,this}makePerspective(t,e,r,i,s,n){void 0===n&&console.warn(\\\\\\\"THREE.Matrix4: .makePerspective() has been redefined and has a new signature. Please check the docs.\\\\\\\");const o=this.elements,a=2*s/(e-t),h=2*s/(r-i),u=(e+t)/(e-t),c=(r+i)/(r-i),l=-(n+s)/(n-s),d=-2*n*s/(n-s);return o[0]=a,o[4]=0,o[8]=u,o[12]=0,o[1]=0,o[5]=h,o[9]=c,o[13]=0,o[2]=0,o[6]=0,o[10]=l,o[14]=d,o[3]=0,o[7]=0,o[11]=-1,o[15]=0,this}makeOrthographic(t,e,r,i,s,n){const o=this.elements,a=1/(e-t),h=1/(r-i),u=1/(n-s),c=(e+t)*a,l=(r+i)*h,d=(n+s)*u;return o[0]=2*a,o[4]=0,o[8]=0,o[12]=-c,o[1]=0,o[5]=2*h,o[9]=0,o[13]=-l,o[2]=0,o[6]=0,o[10]=-2*u,o[14]=-d,o[3]=0,o[7]=0,o[11]=0,o[15]=1,this}equals(t){const e=this.elements,r=t.elements;for(let t=0;t<16;t++)if(e[t]!==r[t])return!1;return!0}fromArray(t,e=0){for(let r=0;r<16;r++)this.elements[r]=t[r+e];return this}toArray(t=[],e=0){const r=this.elements;return t[e]=r[0],t[e+1]=r[1],t[e+2]=r[2],t[e+3]=r[3],t[e+4]=r[4],t[e+5]=r[5],t[e+6]=r[6],t[e+7]=r[7],t[e+8]=r[8],t[e+9]=r[9],t[e+10]=r[10],t[e+11]=r[11],t[e+12]=r[12],t[e+13]=r[13],t[e+14]=r[14],t[e+15]=r[15],t}}s.prototype.isMatrix4=!0;const n=new i.a,o=new s,a=new i.a(0,0,0),h=new i.a(1,1,1),u=new i.a,c=new i.a,l=new i.a},6:function(t,e,r){\\\\\\\"use strict\\\\\\\";r.d(e,\\\\\\\"a\\\\\\\",(function(){return c}));var i=r(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},o={h:0,s:0,l:0};function a(t,e,r){return r<0&&(r+=1),r>1&&(r-=1),r<1/6?t+6*(e-t)*r:r<.5?e:r<2/3?t+6*(e-t)*(2/3-r):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,r){return void 0===e&&void 0===r?this.set(t):this.setRGB(t,e,r)}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,r){return this.r=t,this.g=e,this.b=r,this}setHSL(t,e,r){if(t=i.a.euclideanModulo(t,1),e=i.a.clamp(e,0,1),r=i.a.clamp(r,0,1),0===e)this.r=this.g=this.b=r;else{const i=r<=.5?r*(1+e):r+e-r*e,s=2*r-i;this.r=a(s,i,t+1/3),this.g=a(s,i,t),this.b=a(s,i,t-1/3)}return this}setStyle(t){function e(e){void 0!==e&&parseFloat(e)<1&&console.warn(\\\\\\\"THREE.Color: Alpha 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this.r=Math.pow(t.r,r),this.g=Math.pow(t.g,r),this.b=Math.pow(t.b,r),this}convertGammaToLinear(t){return this.copyGammaToLinear(this,t),this}convertLinearToGamma(t){return this.copyLinearToGamma(this,t),this}copySRGBToLinear(t){return this.r=h(t.r),this.g=h(t.g),this.b=h(t.b),this}copyLinearToSRGB(t){return this.r=u(t.r),this.g=u(t.g),this.b=u(t.b),this}convertSRGBToLinear(){return this.copySRGBToLinear(this),this}convertLinearToSRGB(){return this.copyLinearToSRGB(this),this}getHex(){return 255*this.r<<16^255*this.g<<8^255*this.b<<0}getHexString(){return(\\\\\\\"000000\\\\\\\"+this.getHex().toString(16)).slice(-6)}getHSL(t){void 0===t&&(console.warn(\\\\\\\"THREE.Color: .getHSL() target is now required\\\\\\\"),t={h:0,s:0,l:0});const e=this.r,r=this.g,i=this.b,s=Math.max(e,r,i),n=Math.min(e,r,i);let o,a;const h=(n+s)/2;if(n===s)o=0,a=0;else{const t=s-n;switch(a=h<=.5?t/(s+n):t/(2-s-n),s){case e:o=(r-i)/t+(r1){for(let t=0;t1){for(let t=0;t0){i.children=[];for(let e=0;e0){i.animations=[];for(let e=0;e0&&(r.geometries=e),i.length>0&&(r.materials=i),s.length>0&&(r.textures=s),o.length>0&&(r.images=o),a.length>0&&(r.shapes=a),h.length>0&&(r.skeletons=h),u.length>0&&(r.animations=u)}return r.object=i,r;function n(t){const e=[];for(const r in t){const i=t[r];delete i.metadata,e.push(i)}return e}},clone:function(t){return(new this.constructor).copy(this,t)},copy:function(t,e=!0){if(this.name=t.name,this.up.copy(t.up),this.position.copy(t.position),this.rotation.order=t.rotation.order,this.quaternion.copy(t.quaternion),this.scale.copy(t.scale),this.matrix.copy(t.matrix),this.matrixWorld.copy(t.matrixWorld),this.matrixAutoUpdate=t.matrixAutoUpdate,this.matrixWorldNeedsUpdate=t.matrixWorldNeedsUpdate,this.layers.mask=t.layers.mask,this.visible=t.visible,this.castShadow=t.castShadow,this.receiveShadow=t.receiveShadow,this.frustumCulled=t.frustumCulled,this.renderOrder=t.renderOrder,this.userData=JSON.parse(JSON.stringify(t.userData)),!0===e)for(let e=0;e65535?a.i:a.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,r=0){this.groups.push({start:t,count:e,materialIndex:r})},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 r=this.attributes.normal;if(void 0!==r){const e=(new l.a).getNormalMatrix(t);r.applyNormalMatrix(e),r.needsUpdate=!0}const i=this.attributes.tangent;return void 0!==i&&(i.transformDirection(t),i.needsUpdate=!0),null!==this.boundingBox&&this.computeBoundingBox(),null!==this.boundingSphere&&this.computeBoundingSphere(),this},rotateX:function(t){return f.makeRotationX(t),this.applyMatrix4(f),this},rotateY:function(t){return f.makeRotationY(t),this.applyMatrix4(f),this},rotateZ:function(t){return f.makeRotationZ(t),this.applyMatrix4(f),this},translate:function(t,e,r){return f.makeTranslation(t,e,r),this.applyMatrix4(f),this},scale:function(t,e,r){return f.makeScale(t,e,r),this.applyMatrix4(f),this},lookAt:function(t){return p.lookAt(t),p.updateMatrix(),this.applyMatrix4(p.matrix),this},center:function(){return this.computeBoundingBox(),this.boundingBox.getCenter(x).negate(),this.translate(x.x,x.y,x.z),this},setFromPoints:function(t){const e=[];for(let r=0,i=t.length;r0&&(t.userData=this.userData),void 0!==this.parameters){const e=this.parameters;for(const r in e)void 0!==e[r]&&(t[r]=e[r]);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 r=this.attributes;for(const e in r){const i=r[e];t.data.attributes[e]=i.toJSON(t.data)}const i={};let s=!1;for(const e in this.morphAttributes){const r=this.morphAttributes[e],n=[];for(let e=0,i=r.length;e0&&(i[e]=n,s=!0)}s&&(t.data.morphAttributes=i,t.data.morphTargetsRelative=this.morphTargetsRelative);const n=this.groups;n.length>0&&(t.data.groups=JSON.parse(JSON.stringify(n)));const o=this.boundingSphere;return null!==o&&(t.data.boundingSphere={center:o.center.toArray(),radius:o.radius}),t},clone:function(){return(new M).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 r=t.index;null!==r&&this.setIndex(r.clone(e));const i=t.attributes;for(const t in i){const r=i[t];this.setAttribute(t,r.clone(e))}const s=t.morphAttributes;for(const t in s){const r=[],i=s[t];for(let t=0,s=i.length;t=0?1:-1,i=1-e*e;if(i>Number.EPSILON){const s=Math.sqrt(i),n=Math.atan2(s,e*r);t=Math.sin(t*n)/s,o=Math.sin(o*n)/s}const 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\\\\\\\"+n)}return!1!==e&&this._onChangeCallback(),this}setFromAxisAngle(t,e){const r=e/2,i=Math.sin(r);return this._x=t.x*i,this._y=t.y*i,this._z=t.z*i,this._w=Math.cos(r),this._onChangeCallback(),this}setFromRotationMatrix(t){const e=t.elements,r=e[0],i=e[4],s=e[8],n=e[1],o=e[5],a=e[9],h=e[2],u=e[6],c=e[10],l=r+o+c;if(l>0){const t=.5/Math.sqrt(l+1);this._w=.25/t,this._x=(u-a)*t,this._y=(s-h)*t,this._z=(n-i)*t}else if(r>o&&r>c){const t=2*Math.sqrt(1+r-o-c);this._w=(u-a)/t,this._x=.25*t,this._y=(i+n)/t,this._z=(s+h)/t}else if(o>c){const t=2*Math.sqrt(1+o-r-c);this._w=(s-h)/t,this._x=(i+n)/t,this._y=.25*t,this._z=(a+u)/t}else{const t=2*Math.sqrt(1+c-r-o);this._w=(n-i)/t,this._x=(s+h)/t,this._y=(a+u)/t,this._z=.25*t}return this._onChangeCallback(),this}setFromUnitVectors(t,e){let r=t.dot(e)+1;return rMath.abs(t.z)?(this._x=-t.y,this._y=t.x,this._z=0,this._w=r):(this._x=0,this._y=-t.z,this._z=t.y,this._w=r)):(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=r),this.normalize()}angleTo(t){return 2*Math.acos(Math.abs(i.a.clamp(this.dot(t),-1,1)))}rotateTowards(t,e){const r=this.angleTo(t);if(0===r)return this;const i=Math.min(1,e/r);return this.slerp(t,i),this}identity(){return this.set(0,0,0,1)}invert(){return this.conjugate()}conjugate(){return this._x*=-1,this._y*=-1,this._z*=-1,this._onChangeCallback(),this}dot(t){return this._x*t._x+this._y*t._y+this._z*t._z+this._w*t._w}lengthSq(){return this._x*this._x+this._y*this._y+this._z*this._z+this._w*this._w}length(){return Math.sqrt(this._x*this._x+this._y*this._y+this._z*this._z+this._w*this._w)}normalize(){let t=this.length();return 0===t?(this._x=0,this._y=0,this._z=0,this._w=1):(t=1/t,this._x=this._x*t,this._y=this._y*t,this._z=this._z*t,this._w=this._w*t),this._onChangeCallback(),this}multiply(t,e){return void 0!==e?(console.warn(\\\\\\\"THREE.Quaternion: .multiply() now only accepts one argument. 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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 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run(e){e.registerCamera(GP),e.registerCamera(qP)}}.run(Rn),class{static 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