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this.x+=(t.x-this.x)*e,this.y+=(t.y-this.y)*e,this}lerpVectors(t,e,n){return this.x=t.x+(e.x-t.x)*n,this.y=t.y+(e.y-t.y)*n,this}equals(t){return t.x===this.x&&t.y===this.y}fromArray(t,e=0){return this.x=t[e],this.y=t[e+1],this}toArray(t=[],e=0){return t[e]=this.x,t[e+1]=this.y,t}fromBufferAttribute(t,e,n){return void 0!==n&&console.warn(\\\\\\\"THREE.Vector2: offset has been removed from .fromBufferAttribute().\\\\\\\"),this.x=t.getX(e),this.y=t.getY(e),this}rotateAround(t,e){const n=Math.cos(e),i=Math.sin(e),r=this.x-t.x,s=this.y-t.y;return this.x=r*n-s*i+t.x,this.y=r*i+s*n+t.y,this}random(){return this.x=Math.random(),this.y=Math.random(),this}}vt.prototype.isVector2=!0;class yt{constructor(){this.elements=[1,0,0,0,1,0,0,0,1],arguments.length>0&&console.error(\\\\\\\"THREE.Matrix3: the constructor no longer reads arguments. use .set() instead.\\\\\\\")}set(t,e,n,i,r,s,a,o,l){const c=this.elements;return c[0]=t,c[1]=i,c[2]=a,c[3]=e,c[4]=r,c[5]=o,c[6]=n,c[7]=s,c[8]=l,this}identity(){return this.set(1,0,0,0,1,0,0,0,1),this}copy(t){const e=this.elements,n=t.elements;return e[0]=n[0],e[1]=n[1],e[2]=n[2],e[3]=n[3],e[4]=n[4],e[5]=n[5],e[6]=n[6],e[7]=n[7],e[8]=n[8],this}extractBasis(t,e,n){return t.setFromMatrix3Column(this,0),e.setFromMatrix3Column(this,1),n.setFromMatrix3Column(this,2),this}setFromMatrix4(t){const e=t.elements;return this.set(e[0],e[4],e[8],e[1],e[5],e[9],e[2],e[6],e[10]),this}multiply(t){return this.multiplyMatrices(this,t)}premultiply(t){return this.multiplyMatrices(t,this)}multiplyMatrices(t,e){const n=t.elements,i=e.elements,r=this.elements,s=n[0],a=n[3],o=n[6],l=n[1],c=n[4],h=n[7],u=n[2],d=n[5],p=n[8],m=i[0],f=i[3],g=i[6],v=i[1],y=i[4],x=i[7],_=i[2],M=i[5],b=i[8];return r[0]=s*m+a*v+o*_,r[3]=s*f+a*y+o*M,r[6]=s*g+a*x+o*b,r[1]=l*m+c*v+h*_,r[4]=l*f+c*y+h*M,r[7]=l*g+c*x+h*b,r[2]=u*m+d*v+p*_,r[5]=u*f+d*y+p*M,r[8]=u*g+d*x+p*b,this}multiplyScalar(t){const e=this.elements;return e[0]*=t,e[3]*=t,e[6]*=t,e[1]*=t,e[4]*=t,e[7]*=t,e[2]*=t,e[5]*=t,e[8]*=t,this}determinant(){const t=this.elements,e=t[0],n=t[1],i=t[2],r=t[3],s=t[4],a=t[5],o=t[6],l=t[7],c=t[8];return e*s*c-e*a*l-n*r*c+n*a*o+i*r*l-i*s*o}invert(){const t=this.elements,e=t[0],n=t[1],i=t[2],r=t[3],s=t[4],a=t[5],o=t[6],l=t[7],c=t[8],h=c*s-a*l,u=a*o-c*r,d=l*r-s*o,p=e*h+n*u+i*d;if(0===p)return this.set(0,0,0,0,0,0,0,0,0);const m=1/p;return t[0]=h*m,t[1]=(i*l-c*n)*m,t[2]=(a*n-i*s)*m,t[3]=u*m,t[4]=(c*e-i*o)*m,t[5]=(i*r-a*e)*m,t[6]=d*m,t[7]=(n*o-l*e)*m,t[8]=(s*e-n*r)*m,this}transpose(){let t;const e=this.elements;return t=e[1],e[1]=e[3],e[3]=t,t=e[2],e[2]=e[6],e[6]=t,t=e[5],e[5]=e[7],e[7]=t,this}getNormalMatrix(t){return this.setFromMatrix4(t).invert().transpose()}transposeIntoArray(t){const e=this.elements;return t[0]=e[0],t[1]=e[3],t[2]=e[6],t[3]=e[1],t[4]=e[4],t[5]=e[7],t[6]=e[2],t[7]=e[5],t[8]=e[8],this}setUvTransform(t,e,n,i,r,s,a){const o=Math.cos(r),l=Math.sin(r);return this.set(n*o,n*l,-n*(o*s+l*a)+s+t,-i*l,i*o,-i*(-l*s+o*a)+a+e,0,0,1),this}scale(t,e){const n=this.elements;return n[0]*=t,n[3]*=t,n[6]*=t,n[1]*=e,n[4]*=e,n[7]*=e,this}rotate(t){const e=Math.cos(t),n=Math.sin(t),i=this.elements,r=i[0],s=i[3],a=i[6],o=i[1],l=i[4],c=i[7];return i[0]=e*r+n*o,i[3]=e*s+n*l,i[6]=e*a+n*c,i[1]=-n*r+e*o,i[4]=-n*s+e*l,i[7]=-n*a+e*c,this}translate(t,e){const n=this.elements;return n[0]+=t*n[2],n[3]+=t*n[5],n[6]+=t*n[8],n[1]+=e*n[2],n[4]+=e*n[5],n[7]+=e*n[8],this}equals(t){const e=this.elements,n=t.elements;for(let t=0;t<9;t++)if(e[t]!==n[t])return!1;return!0}fromArray(t,e=0){for(let n=0;n<9;n++)this.elements[n]=t[n+e];return this}toArray(t=[],e=0){const n=this.elements;return t[e]=n[0],t[e+1]=n[1],t[e+2]=n[2],t[e+3]=n[3],t[e+4]=n[4],t[e+5]=n[5],t[e+6]=n[6],t[e+7]=n[7],t[e+8]=n[8],t}clone(){return(new this.constructor).fromArray(this.elements)}}let xt;yt.prototype.isMatrix3=!0;class _t{static getDataURL(t){if(/^data:/i.test(t.src))return t.src;if(\\\\\\\"undefined\\\\\\\"==typeof HTMLCanvasElement)return t.src;let e;if(t instanceof HTMLCanvasElement)e=t;else{void 0===xt&&(xt=document.createElementNS(\\\\\\\"http://www.w3.org/1999/xhtml\\\\\\\",\\\\\\\"canvas\\\\\\\")),xt.width=t.width,xt.height=t.height;const n=xt.getContext(\\\\\\\"2d\\\\\\\");t instanceof ImageData?n.putImageData(t,0,0):n.drawImage(t,0,0,t.width,t.height),e=xt}return e.width>2048||e.height>2048?(console.warn(\\\\\\\"THREE.ImageUtils.getDataURL: Image converted to jpg for performance reasons\\\\\\\",t),e.toDataURL(\\\\\\\"image/jpeg\\\\\\\",.6)):e.toDataURL(\\\\\\\"image/png\\\\\\\")}}let Mt=0;class bt extends rt{constructor(t=bt.DEFAULT_IMAGE,e=bt.DEFAULT_MAPPING,n=1001,i=1001,r=1006,s=1008,a=1023,o=1009,l=1,c=3e3){super(),Object.defineProperty(this,\\\\\\\"id\\\\\\\",{value:Mt++}),this.uuid=ct(),this.name=\\\\\\\"\\\\\\\",this.image=t,this.mipmaps=[],this.mapping=e,this.wrapS=n,this.wrapT=i,this.magFilter=r,this.minFilter=s,this.anisotropy=l,this.format=a,this.internalFormat=null,this.type=o,this.offset=new vt(0,0),this.repeat=new vt(1,1),this.center=new vt(0,0),this.rotation=0,this.matrixAutoUpdate=!0,this.matrix=new yt,this.generateMipmaps=!0,this.premultiplyAlpha=!1,this.flipY=!0,this.unpackAlignment=4,this.encoding=c,this.version=0,this.onUpdate=null,this.isRenderTargetTexture=!1}updateMatrix(){this.matrix.setUvTransform(this.offset.x,this.offset.y,this.repeat.x,this.repeat.y,this.rotation,this.center.x,this.center.y)}clone(){return(new this.constructor).copy(this)}copy(t){return this.name=t.name,this.image=t.image,this.mipmaps=t.mipmaps.slice(0),this.mapping=t.mapping,this.wrapS=t.wrapS,this.wrapT=t.wrapT,this.magFilter=t.magFilter,this.minFilter=t.minFilter,this.anisotropy=t.anisotropy,this.format=t.format,this.internalFormat=t.internalFormat,this.type=t.type,this.offset.copy(t.offset),this.repeat.copy(t.repeat),this.center.copy(t.center),this.rotation=t.rotation,this.matrixAutoUpdate=t.matrixAutoUpdate,this.matrix.copy(t.matrix),this.generateMipmaps=t.generateMipmaps,this.premultiplyAlpha=t.premultiplyAlpha,this.flipY=t.flipY,this.unpackAlignment=t.unpackAlignment,this.encoding=t.encoding,this}toJSON(t){const e=void 0===t||\\\\\\\"string\\\\\\\"==typeof t;if(!e&&void 0!==t.textures[this.uuid])return t.textures[this.uuid];const n={metadata:{version:4.5,type:\\\\\\\"Texture\\\\\\\",generator:\\\\\\\"Texture.toJSON\\\\\\\"},uuid:this.uuid,name:this.name,mapping:this.mapping,repeat:[this.repeat.x,this.repeat.y],offset:[this.offset.x,this.offset.y],center:[this.center.x,this.center.y],rotation:this.rotation,wrap:[this.wrapS,this.wrapT],format:this.format,type:this.type,encoding:this.encoding,minFilter:this.minFilter,magFilter:this.magFilter,anisotropy:this.anisotropy,flipY:this.flipY,premultiplyAlpha:this.premultiplyAlpha,unpackAlignment:this.unpackAlignment};if(void 0!==this.image){const i=this.image;if(void 0===i.uuid&&(i.uuid=ct()),!e&&void 0===t.images[i.uuid]){let e;if(Array.isArray(i)){e=[];for(let t=0,n=i.length;t<n;t++)i[t].isDataTexture?e.push(wt(i[t].image)):e.push(wt(i[t]))}else e=wt(i);t.images[i.uuid]={uuid:i.uuid,url:e}}n.image=i.uuid}return e||(t.textures[this.uuid]=n),n}dispose(){this.dispatchEvent({type:\\\\\\\"dispose\\\\\\\"})}transformUv(t){if(this.mapping!==i)return t;if(t.applyMatrix3(this.matrix),t.x<0||t.x>1)switch(this.wrapS){case h:t.x=t.x-Math.floor(t.x);break;case u:t.x=t.x<0?0:1;break;case d: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 h:t.y=t.y-Math.floor(t.y);break;case u:t.y=t.y<0?0:1;break;case d: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 wt(t){return\\\\\\\"undefined\\\\\\\"!=typeof HTMLImageElement&&t instanceof HTMLImageElement||\\\\\\\"undefined\\\\\\\"!=typeof HTMLCanvasElement&&t instanceof HTMLCanvasElement||\\\\\\\"undefined\\\\\\\"!=typeof ImageBitmap&&t instanceof ImageBitmap?_t.getDataURL(t):t.data?{data:Array.prototype.slice.call(t.data),width:t.width,height:t.height,type:t.data.constructor.name}:(console.warn(\\\\\\\"THREE.Texture: Unable to serialize Texture.\\\\\\\"),{})}bt.DEFAULT_IMAGE=void 0,bt.DEFAULT_MAPPING=i,bt.prototype.isTexture=!0;class St{constructor(t=0,e=0,n=0,i=1){this.x=t,this.y=e,this.z=n,this.w=i}get width(){return this.z}set width(t){this.z=t}get height(){return this.w}set height(t){this.w=t}set(t,e,n,i){return this.x=t,this.y=e,this.z=n,this.w=i,this}setScalar(t){return this.x=t,this.y=t,this.z=t,this.w=t,this}setX(t){return this.x=t,this}setY(t){return this.y=t,this}setZ(t){return this.z=t,this}setW(t){return this.w=t,this}setComponent(t,e){switch(t){case 0:this.x=e;break;case 1:this.y=e;break;case 2:this.z=e;break;case 3:this.w=e;break;default:throw new Error(\\\\\\\"index is out of range: \\\\\\\"+t)}return this}getComponent(t){switch(t){case 0:return this.x;case 1:return this.y;case 2:return this.z;case 3:return this.w;default:throw new Error(\\\\\\\"index is out of range: \\\\\\\"+t)}}clone(){return new this.constructor(this.x,this.y,this.z,this.w)}copy(t){return this.x=t.x,this.y=t.y,this.z=t.z,this.w=void 0!==t.w?t.w:1,this}add(t,e){return void 0!==e?(console.warn(\\\\\\\"THREE.Vector4: .add() now only accepts one argument. Use .addVectors( a, b ) instead.\\\\\\\"),this.addVectors(t,e)):(this.x+=t.x,this.y+=t.y,this.z+=t.z,this.w+=t.w,this)}addScalar(t){return this.x+=t,this.y+=t,this.z+=t,this.w+=t,this}addVectors(t,e){return this.x=t.x+e.x,this.y=t.y+e.y,this.z=t.z+e.z,this.w=t.w+e.w,this}addScaledVector(t,e){return this.x+=t.x*e,this.y+=t.y*e,this.z+=t.z*e,this.w+=t.w*e,this}sub(t,e){return void 0!==e?(console.warn(\\\\\\\"THREE.Vector4: .sub() now only accepts one argument. Use .subVectors( a, b ) instead.\\\\\\\"),this.subVectors(t,e)):(this.x-=t.x,this.y-=t.y,this.z-=t.z,this.w-=t.w,this)}subScalar(t){return this.x-=t,this.y-=t,this.z-=t,this.w-=t,this}subVectors(t,e){return this.x=t.x-e.x,this.y=t.y-e.y,this.z=t.z-e.z,this.w=t.w-e.w,this}multiply(t){return this.x*=t.x,this.y*=t.y,this.z*=t.z,this.w*=t.w,this}multiplyScalar(t){return this.x*=t,this.y*=t,this.z*=t,this.w*=t,this}applyMatrix4(t){const e=this.x,n=this.y,i=this.z,r=this.w,s=t.elements;return this.x=s[0]*e+s[4]*n+s[8]*i+s[12]*r,this.y=s[1]*e+s[5]*n+s[9]*i+s[13]*r,this.z=s[2]*e+s[6]*n+s[10]*i+s[14]*r,this.w=s[3]*e+s[7]*n+s[11]*i+s[15]*r,this}divideScalar(t){return this.multiplyScalar(1/t)}setAxisAngleFromQuaternion(t){this.w=2*Math.acos(t.w);const e=Math.sqrt(1-t.w*t.w);return e<1e-4?(this.x=1,this.y=0,this.z=0):(this.x=t.x/e,this.y=t.y/e,this.z=t.z/e),this}setAxisAngleFromRotationMatrix(t){let e,n,i,r;const s=.01,a=.1,o=t.elements,l=o[0],c=o[4],h=o[8],u=o[1],d=o[5],p=o[9],m=o[2],f=o[6],g=o[10];if(Math.abs(c-u)<s&&Math.abs(h-m)<s&&Math.abs(p-f)<s){if(Math.abs(c+u)<a&&Math.abs(h+m)<a&&Math.abs(p+f)<a&&Math.abs(l+d+g-3)<a)return this.set(1,0,0,0),this;e=Math.PI;const t=(l+1)/2,o=(d+1)/2,v=(g+1)/2,y=(c+u)/4,x=(h+m)/4,_=(p+f)/4;return t>o&&t>v?t<s?(n=0,i=.707106781,r=.707106781):(n=Math.sqrt(t),i=y/n,r=x/n):o>v?o<s?(n=.707106781,i=0,r=.707106781):(i=Math.sqrt(o),n=y/i,r=_/i):v<s?(n=.707106781,i=.707106781,r=0):(r=Math.sqrt(v),n=x/r,i=_/r),this.set(n,i,r,e),this}let v=Math.sqrt((f-p)*(f-p)+(h-m)*(h-m)+(u-c)*(u-c));return Math.abs(v)<.001&&(v=1),this.x=(f-p)/v,this.y=(h-m)/v,this.z=(u-c)/v,this.w=Math.acos((l+d+g-1)/2),this}min(t){return this.x=Math.min(this.x,t.x),this.y=Math.min(this.y,t.y),this.z=Math.min(this.z,t.z),this.w=Math.min(this.w,t.w),this}max(t){return this.x=Math.max(this.x,t.x),this.y=Math.max(this.y,t.y),this.z=Math.max(this.z,t.z),this.w=Math.max(this.w,t.w),this}clamp(t,e){return this.x=Math.max(t.x,Math.min(e.x,this.x)),this.y=Math.max(t.y,Math.min(e.y,this.y)),this.z=Math.max(t.z,Math.min(e.z,this.z)),this.w=Math.max(t.w,Math.min(e.w,this.w)),this}clampScalar(t,e){return this.x=Math.max(t,Math.min(e,this.x)),this.y=Math.max(t,Math.min(e,this.y)),this.z=Math.max(t,Math.min(e,this.z)),this.w=Math.max(t,Math.min(e,this.w)),this}clampLength(t,e){const n=this.length();return this.divideScalar(n||1).multiplyScalar(Math.max(t,Math.min(e,n)))}floor(){return this.x=Math.floor(this.x),this.y=Math.floor(this.y),this.z=Math.floor(this.z),this.w=Math.floor(this.w),this}ceil(){return this.x=Math.ceil(this.x),this.y=Math.ceil(this.y),this.z=Math.ceil(this.z),this.w=Math.ceil(this.w),this}round(){return this.x=Math.round(this.x),this.y=Math.round(this.y),this.z=Math.round(this.z),this.w=Math.round(this.w),this}roundToZero(){return this.x=this.x<0?Math.ceil(this.x):Math.floor(this.x),this.y=this.y<0?Math.ceil(this.y):Math.floor(this.y),this.z=this.z<0?Math.ceil(this.z):Math.floor(this.z),this.w=this.w<0?Math.ceil(this.w):Math.floor(this.w),this}negate(){return this.x=-this.x,this.y=-this.y,this.z=-this.z,this.w=-this.w,this}dot(t){return this.x*t.x+this.y*t.y+this.z*t.z+this.w*t.w}lengthSq(){return this.x*this.x+this.y*this.y+this.z*this.z+this.w*this.w}length(){return Math.sqrt(this.x*this.x+this.y*this.y+this.z*this.z+this.w*this.w)}manhattanLength(){return Math.abs(this.x)+Math.abs(this.y)+Math.abs(this.z)+Math.abs(this.w)}normalize(){return this.divideScalar(this.length()||1)}setLength(t){return this.normalize().multiplyScalar(t)}lerp(t,e){return this.x+=(t.x-this.x)*e,this.y+=(t.y-this.y)*e,this.z+=(t.z-this.z)*e,this.w+=(t.w-this.w)*e,this}lerpVectors(t,e,n){return this.x=t.x+(e.x-t.x)*n,this.y=t.y+(e.y-t.y)*n,this.z=t.z+(e.z-t.z)*n,this.w=t.w+(e.w-t.w)*n,this}equals(t){return t.x===this.x&&t.y===this.y&&t.z===this.z&&t.w===this.w}fromArray(t,e=0){return this.x=t[e],this.y=t[e+1],this.z=t[e+2],this.w=t[e+3],this}toArray(t=[],e=0){return t[e]=this.x,t[e+1]=this.y,t[e+2]=this.z,t[e+3]=this.w,t}fromBufferAttribute(t,e,n){return void 0!==n&&console.warn(\\\\\\\"THREE.Vector4: offset has been removed from .fromBufferAttribute().\\\\\\\"),this.x=t.getX(e),this.y=t.getY(e),this.z=t.getZ(e),this.w=t.getW(e),this}random(){return this.x=Math.random(),this.y=Math.random(),this.z=Math.random(),this.w=Math.random(),this}}St.prototype.isVector4=!0;class Tt extends rt{constructor(t,e,n={}){super(),this.width=t,this.height=e,this.depth=1,this.scissor=new St(0,0,t,e),this.scissorTest=!1,this.viewport=new St(0,0,t,e),this.texture=new bt(void 0,n.mapping,n.wrapS,n.wrapT,n.magFilter,n.minFilter,n.format,n.type,n.anisotropy,n.encoding),this.texture.isRenderTargetTexture=!0,this.texture.image={width:t,height:e,depth:1},this.texture.generateMipmaps=void 0!==n.generateMipmaps&&n.generateMipmaps,this.texture.minFilter=void 0!==n.minFilter?n.minFilter:g,this.depthBuffer=void 0===n.depthBuffer||n.depthBuffer,this.stencilBuffer=void 0!==n.stencilBuffer&&n.stencilBuffer,this.depthTexture=void 0!==n.depthTexture?n.depthTexture:null}setTexture(t){t.image={width:this.width,height:this.height,depth:this.depth},this.texture=t}setSize(t,e,n=1){this.width===t&&this.height===e&&this.depth===n||(this.width=t,this.height=e,this.depth=n,this.texture.image.width=t,this.texture.image.height=e,this.texture.image.depth=n,this.dispose()),this.viewport.set(0,0,t,e),this.scissor.set(0,0,t,e)}clone(){return(new this.constructor).copy(this)}copy(t){return this.width=t.width,this.height=t.height,this.depth=t.depth,this.viewport.copy(t.viewport),this.texture=t.texture.clone(),this.texture.image={...this.texture.image},this.depthBuffer=t.depthBuffer,this.stencilBuffer=t.stencilBuffer,this.depthTexture=t.depthTexture,this}dispose(){this.dispatchEvent({type:\\\\\\\"dispose\\\\\\\"})}}Tt.prototype.isWebGLRenderTarget=!0;class Et extends Tt{constructor(t,e,n){super(t,e);const i=this.texture;this.texture=[];for(let t=0;t<n;t++)this.texture[t]=i.clone()}setSize(t,e,n=1){if(this.width!==t||this.height!==e||this.depth!==n){this.width=t,this.height=e,this.depth=n;for(let i=0,r=this.texture.length;i<r;i++)this.texture[i].image.width=t,this.texture[i].image.height=e,this.texture[i].image.depth=n;this.dispose()}return this.viewport.set(0,0,t,e),this.scissor.set(0,0,t,e),this}copy(t){this.dispose(),this.width=t.width,this.height=t.height,this.depth=t.depth,this.viewport.set(0,0,this.width,this.height),this.scissor.set(0,0,this.width,this.height),this.depthBuffer=t.depthBuffer,this.stencilBuffer=t.stencilBuffer,this.depthTexture=t.depthTexture,this.texture.length=0;for(let e=0,n=t.texture.length;e<n;e++)this.texture[e]=t.texture[e].clone();return this}}Et.prototype.isWebGLMultipleRenderTargets=!0;class At extends Tt{constructor(t,e,n){super(t,e,n),this.samples=4}copy(t){return super.copy.call(this,t),this.samples=t.samples,this}}At.prototype.isWebGLMultisampleRenderTarget=!0;class Lt{constructor(t=0,e=0,n=0,i=1){this._x=t,this._y=e,this._z=n,this._w=i}static slerp(t,e,n,i){return console.warn(\\\\\\\"THREE.Quaternion: Static .slerp() has been deprecated. Use qm.slerpQuaternions( qa, qb, t ) instead.\\\\\\\"),n.slerpQuaternions(t,e,i)}static slerpFlat(t,e,n,i,r,s,a){let o=n[i+0],l=n[i+1],c=n[i+2],h=n[i+3];const u=r[s+0],d=r[s+1],p=r[s+2],m=r[s+3];if(0===a)return t[e+0]=o,t[e+1]=l,t[e+2]=c,void(t[e+3]=h);if(1===a)return t[e+0]=u,t[e+1]=d,t[e+2]=p,void(t[e+3]=m);if(h!==m||o!==u||l!==d||c!==p){let t=1-a;const e=o*u+l*d+c*p+h*m,n=e>=0?1:-1,i=1-e*e;if(i>Number.EPSILON){const r=Math.sqrt(i),s=Math.atan2(r,e*n);t=Math.sin(t*s)/r,a=Math.sin(a*s)/r}const r=a*n;if(o=o*t+u*r,l=l*t+d*r,c=c*t+p*r,h=h*t+m*r,t===1-a){const t=1/Math.sqrt(o*o+l*l+c*c+h*h);o*=t,l*=t,c*=t,h*=t}}t[e]=o,t[e+1]=l,t[e+2]=c,t[e+3]=h}static multiplyQuaternionsFlat(t,e,n,i,r,s){const a=n[i],o=n[i+1],l=n[i+2],c=n[i+3],h=r[s],u=r[s+1],d=r[s+2],p=r[s+3];return t[e]=a*p+c*h+o*d-l*u,t[e+1]=o*p+c*u+l*h-a*d,t[e+2]=l*p+c*d+a*u-o*h,t[e+3]=c*p-a*h-o*u-l*d,t}get x(){return this._x}set x(t){this._x=t,this._onChangeCallback()}get y(){return this._y}set y(t){this._y=t,this._onChangeCallback()}get z(){return this._z}set z(t){this._z=t,this._onChangeCallback()}get w(){return this._w}set w(t){this._w=t,this._onChangeCallback()}set(t,e,n,i){return this._x=t,this._y=e,this._z=n,this._w=i,this._onChangeCallback(),this}clone(){return new this.constructor(this._x,this._y,this._z,this._w)}copy(t){return this._x=t.x,this._y=t.y,this._z=t.z,this._w=t.w,this._onChangeCallback(),this}setFromEuler(t,e){if(!t||!t.isEuler)throw new Error(\\\\\\\"THREE.Quaternion: .setFromEuler() now expects an Euler rotation rather than a Vector3 and order.\\\\\\\");const n=t._x,i=t._y,r=t._z,s=t._order,a=Math.cos,o=Math.sin,l=a(n/2),c=a(i/2),h=a(r/2),u=o(n/2),d=o(i/2),p=o(r/2);switch(s){case\\\\\\\"XYZ\\\\\\\":this._x=u*c*h+l*d*p,this._y=l*d*h-u*c*p,this._z=l*c*p+u*d*h,this._w=l*c*h-u*d*p;break;case\\\\\\\"YXZ\\\\\\\":this._x=u*c*h+l*d*p,this._y=l*d*h-u*c*p,this._z=l*c*p-u*d*h,this._w=l*c*h+u*d*p;break;case\\\\\\\"ZXY\\\\\\\":this._x=u*c*h-l*d*p,this._y=l*d*h+u*c*p,this._z=l*c*p+u*d*h,this._w=l*c*h-u*d*p;break;case\\\\\\\"ZYX\\\\\\\":this._x=u*c*h-l*d*p,this._y=l*d*h+u*c*p,this._z=l*c*p-u*d*h,this._w=l*c*h+u*d*p;break;case\\\\\\\"YZX\\\\\\\":this._x=u*c*h+l*d*p,this._y=l*d*h+u*c*p,this._z=l*c*p-u*d*h,this._w=l*c*h-u*d*p;break;case\\\\\\\"XZY\\\\\\\":this._x=u*c*h-l*d*p,this._y=l*d*h-u*c*p,this._z=l*c*p+u*d*h,this._w=l*c*h+u*d*p;break;default:console.warn(\\\\\\\"THREE.Quaternion: .setFromEuler() encountered an unknown order: \\\\\\\"+s)}return!1!==e&&this._onChangeCallback(),this}setFromAxisAngle(t,e){const n=e/2,i=Math.sin(n);return this._x=t.x*i,this._y=t.y*i,this._z=t.z*i,this._w=Math.cos(n),this._onChangeCallback(),this}setFromRotationMatrix(t){const e=t.elements,n=e[0],i=e[4],r=e[8],s=e[1],a=e[5],o=e[9],l=e[2],c=e[6],h=e[10],u=n+a+h;if(u>0){const t=.5/Math.sqrt(u+1);this._w=.25/t,this._x=(c-o)*t,this._y=(r-l)*t,this._z=(s-i)*t}else if(n>a&&n>h){const t=2*Math.sqrt(1+n-a-h);this._w=(c-o)/t,this._x=.25*t,this._y=(i+s)/t,this._z=(r+l)/t}else if(a>h){const t=2*Math.sqrt(1+a-n-h);this._w=(r-l)/t,this._x=(i+s)/t,this._y=.25*t,this._z=(o+c)/t}else{const t=2*Math.sqrt(1+h-n-a);this._w=(s-i)/t,this._x=(r+l)/t,this._y=(o+c)/t,this._z=.25*t}return this._onChangeCallback(),this}setFromUnitVectors(t,e){let n=t.dot(e)+1;return n<Number.EPSILON?(n=0,Math.abs(t.x)>Math.abs(t.z)?(this._x=-t.y,this._y=t.x,this._z=0,this._w=n):(this._x=0,this._y=-t.z,this._z=t.y,this._w=n)):(this._x=t.y*e.z-t.z*e.y,this._y=t.z*e.x-t.x*e.z,this._z=t.x*e.y-t.y*e.x,this._w=n),this.normalize()}angleTo(t){return 2*Math.acos(Math.abs(ht(this.dot(t),-1,1)))}rotateTowards(t,e){const n=this.angleTo(t);if(0===n)return this;const i=Math.min(1,e/n);return this.slerp(t,i),this}identity(){return this.set(0,0,0,1)}invert(){return this.conjugate()}conjugate(){return this._x*=-1,this._y*=-1,this._z*=-1,this._onChangeCallback(),this}dot(t){return this._x*t._x+this._y*t._y+this._z*t._z+this._w*t._w}lengthSq(){return this._x*this._x+this._y*this._y+this._z*this._z+this._w*this._w}length(){return Math.sqrt(this._x*this._x+this._y*this._y+this._z*this._z+this._w*this._w)}normalize(){let t=this.length();return 0===t?(this._x=0,this._y=0,this._z=0,this._w=1):(t=1/t,this._x=this._x*t,this._y=this._y*t,this._z=this._z*t,this._w=this._w*t),this._onChangeCallback(),this}multiply(t,e){return void 0!==e?(console.warn(\\\\\\\"THREE.Quaternion: .multiply() now only accepts one argument. Use .multiplyQuaternions( a, b ) instead.\\\\\\\"),this.multiplyQuaternions(t,e)):this.multiplyQuaternions(this,t)}premultiply(t){return this.multiplyQuaternions(t,this)}multiplyQuaternions(t,e){const n=t._x,i=t._y,r=t._z,s=t._w,a=e._x,o=e._y,l=e._z,c=e._w;return this._x=n*c+s*a+i*l-r*o,this._y=i*c+s*o+r*a-n*l,this._z=r*c+s*l+n*o-i*a,this._w=s*c-n*a-i*o-r*l,this._onChangeCallback(),this}slerp(t,e){if(0===e)return this;if(1===e)return this.copy(t);const n=this._x,i=this._y,r=this._z,s=this._w;let a=s*t._w+n*t._x+i*t._y+r*t._z;if(a<0?(this._w=-t._w,this._x=-t._x,this._y=-t._y,this._z=-t._z,a=-a):this.copy(t),a>=1)return this._w=s,this._x=n,this._y=i,this._z=r,this;const o=1-a*a;if(o<=Number.EPSILON){const t=1-e;return this._w=t*s+e*this._w,this._x=t*n+e*this._x,this._y=t*i+e*this._y,this._z=t*r+e*this._z,this.normalize(),this._onChangeCallback(),this}const l=Math.sqrt(o),c=Math.atan2(l,a),h=Math.sin((1-e)*c)/l,u=Math.sin(e*c)/l;return this._w=s*h+this._w*u,this._x=n*h+this._x*u,this._y=i*h+this._y*u,this._z=r*h+this._z*u,this._onChangeCallback(),this}slerpQuaternions(t,e,n){this.copy(t).slerp(e,n)}equals(t){return t._x===this._x&&t._y===this._y&&t._z===this._z&&t._w===this._w}fromArray(t,e=0){return this._x=t[e],this._y=t[e+1],this._z=t[e+2],this._w=t[e+3],this._onChangeCallback(),this}toArray(t=[],e=0){return t[e]=this._x,t[e+1]=this._y,t[e+2]=this._z,t[e+3]=this._w,t}fromBufferAttribute(t,e){return this._x=t.getX(e),this._y=t.getY(e),this._z=t.getZ(e),this._w=t.getW(e),this}_onChange(t){return this._onChangeCallback=t,this}_onChangeCallback(){}}Lt.prototype.isQuaternion=!0;class Rt{constructor(t=0,e=0,n=0){this.x=t,this.y=e,this.z=n}set(t,e,n){return void 0===n&&(n=this.z),this.x=t,this.y=e,this.z=n,this}setScalar(t){return this.x=t,this.y=t,this.z=t,this}setX(t){return this.x=t,this}setY(t){return this.y=t,this}setZ(t){return this.z=t,this}setComponent(t,e){switch(t){case 0:this.x=e;break;case 1:this.y=e;break;case 2:this.z=e;break;default:throw new Error(\\\\\\\"index is out of range: \\\\\\\"+t)}return this}getComponent(t){switch(t){case 0:return this.x;case 1:return this.y;case 2:return this.z;default:throw new Error(\\\\\\\"index is out of range: \\\\\\\"+t)}}clone(){return new this.constructor(this.x,this.y,this.z)}copy(t){return this.x=t.x,this.y=t.y,this.z=t.z,this}add(t,e){return void 0!==e?(console.warn(\\\\\\\"THREE.Vector3: .add() now only accepts one argument. Use .addVectors( a, b ) instead.\\\\\\\"),this.addVectors(t,e)):(this.x+=t.x,this.y+=t.y,this.z+=t.z,this)}addScalar(t){return this.x+=t,this.y+=t,this.z+=t,this}addVectors(t,e){return this.x=t.x+e.x,this.y=t.y+e.y,this.z=t.z+e.z,this}addScaledVector(t,e){return this.x+=t.x*e,this.y+=t.y*e,this.z+=t.z*e,this}sub(t,e){return void 0!==e?(console.warn(\\\\\\\"THREE.Vector3: .sub() now only accepts one argument. Use .subVectors( a, b ) instead.\\\\\\\"),this.subVectors(t,e)):(this.x-=t.x,this.y-=t.y,this.z-=t.z,this)}subScalar(t){return this.x-=t,this.y-=t,this.z-=t,this}subVectors(t,e){return this.x=t.x-e.x,this.y=t.y-e.y,this.z=t.z-e.z,this}multiply(t,e){return void 0!==e?(console.warn(\\\\\\\"THREE.Vector3: .multiply() now only accepts one argument. Use .multiplyVectors( a, b ) instead.\\\\\\\"),this.multiplyVectors(t,e)):(this.x*=t.x,this.y*=t.y,this.z*=t.z,this)}multiplyScalar(t){return this.x*=t,this.y*=t,this.z*=t,this}multiplyVectors(t,e){return this.x=t.x*e.x,this.y=t.y*e.y,this.z=t.z*e.z,this}applyEuler(t){return t&&t.isEuler||console.error(\\\\\\\"THREE.Vector3: .applyEuler() now expects an Euler rotation rather than a Vector3 and order.\\\\\\\"),this.applyQuaternion(Pt.setFromEuler(t))}applyAxisAngle(t,e){return this.applyQuaternion(Pt.setFromAxisAngle(t,e))}applyMatrix3(t){const e=this.x,n=this.y,i=this.z,r=t.elements;return this.x=r[0]*e+r[3]*n+r[6]*i,this.y=r[1]*e+r[4]*n+r[7]*i,this.z=r[2]*e+r[5]*n+r[8]*i,this}applyNormalMatrix(t){return this.applyMatrix3(t).normalize()}applyMatrix4(t){const e=this.x,n=this.y,i=this.z,r=t.elements,s=1/(r[3]*e+r[7]*n+r[11]*i+r[15]);return this.x=(r[0]*e+r[4]*n+r[8]*i+r[12])*s,this.y=(r[1]*e+r[5]*n+r[9]*i+r[13])*s,this.z=(r[2]*e+r[6]*n+r[10]*i+r[14])*s,this}applyQuaternion(t){const e=this.x,n=this.y,i=this.z,r=t.x,s=t.y,a=t.z,o=t.w,l=o*e+s*i-a*n,c=o*n+a*e-r*i,h=o*i+r*n-s*e,u=-r*e-s*n-a*i;return this.x=l*o+u*-r+c*-a-h*-s,this.y=c*o+u*-s+h*-r-l*-a,this.z=h*o+u*-a+l*-s-c*-r,this}project(t){return this.applyMatrix4(t.matrixWorldInverse).applyMatrix4(t.projectionMatrix)}unproject(t){return this.applyMatrix4(t.projectionMatrixInverse).applyMatrix4(t.matrixWorld)}transformDirection(t){const e=this.x,n=this.y,i=this.z,r=t.elements;return this.x=r[0]*e+r[4]*n+r[8]*i,this.y=r[1]*e+r[5]*n+r[9]*i,this.z=r[2]*e+r[6]*n+r[10]*i,this.normalize()}divide(t){return this.x/=t.x,this.y/=t.y,this.z/=t.z,this}divideScalar(t){return this.multiplyScalar(1/t)}min(t){return this.x=Math.min(this.x,t.x),this.y=Math.min(this.y,t.y),this.z=Math.min(this.z,t.z),this}max(t){return this.x=Math.max(this.x,t.x),this.y=Math.max(this.y,t.y),this.z=Math.max(this.z,t.z),this}clamp(t,e){return this.x=Math.max(t.x,Math.min(e.x,this.x)),this.y=Math.max(t.y,Math.min(e.y,this.y)),this.z=Math.max(t.z,Math.min(e.z,this.z)),this}clampScalar(t,e){return this.x=Math.max(t,Math.min(e,this.x)),this.y=Math.max(t,Math.min(e,this.y)),this.z=Math.max(t,Math.min(e,this.z)),this}clampLength(t,e){const n=this.length();return this.divideScalar(n||1).multiplyScalar(Math.max(t,Math.min(e,n)))}floor(){return this.x=Math.floor(this.x),this.y=Math.floor(this.y),this.z=Math.floor(this.z),this}ceil(){return this.x=Math.ceil(this.x),this.y=Math.ceil(this.y),this.z=Math.ceil(this.z),this}round(){return this.x=Math.round(this.x),this.y=Math.round(this.y),this.z=Math.round(this.z),this}roundToZero(){return this.x=this.x<0?Math.ceil(this.x):Math.floor(this.x),this.y=this.y<0?Math.ceil(this.y):Math.floor(this.y),this.z=this.z<0?Math.ceil(this.z):Math.floor(this.z),this}negate(){return this.x=-this.x,this.y=-this.y,this.z=-this.z,this}dot(t){return this.x*t.x+this.y*t.y+this.z*t.z}lengthSq(){return this.x*this.x+this.y*this.y+this.z*this.z}length(){return Math.sqrt(this.x*this.x+this.y*this.y+this.z*this.z)}manhattanLength(){return Math.abs(this.x)+Math.abs(this.y)+Math.abs(this.z)}normalize(){return this.divideScalar(this.length()||1)}setLength(t){return this.normalize().multiplyScalar(t)}lerp(t,e){return this.x+=(t.x-this.x)*e,this.y+=(t.y-this.y)*e,this.z+=(t.z-this.z)*e,this}lerpVectors(t,e,n){return this.x=t.x+(e.x-t.x)*n,this.y=t.y+(e.y-t.y)*n,this.z=t.z+(e.z-t.z)*n,this}cross(t,e){return void 0!==e?(console.warn(\\\\\\\"THREE.Vector3: .cross() now only accepts one argument. Use .crossVectors( a, b ) instead.\\\\\\\"),this.crossVectors(t,e)):this.crossVectors(this,t)}crossVectors(t,e){const n=t.x,i=t.y,r=t.z,s=e.x,a=e.y,o=e.z;return this.x=i*o-r*a,this.y=r*s-n*o,this.z=n*a-i*s,this}projectOnVector(t){const e=t.lengthSq();if(0===e)return this.set(0,0,0);const n=t.dot(this)/e;return this.copy(t).multiplyScalar(n)}projectOnPlane(t){return Ct.copy(this).projectOnVector(t),this.sub(Ct)}reflect(t){return this.sub(Ct.copy(t).multiplyScalar(2*this.dot(t)))}angleTo(t){const e=Math.sqrt(this.lengthSq()*t.lengthSq());if(0===e)return Math.PI/2;const n=this.dot(t)/e;return Math.acos(ht(n,-1,1))}distanceTo(t){return Math.sqrt(this.distanceToSquared(t))}distanceToSquared(t){const e=this.x-t.x,n=this.y-t.y,i=this.z-t.z;return e*e+n*n+i*i}manhattanDistanceTo(t){return Math.abs(this.x-t.x)+Math.abs(this.y-t.y)+Math.abs(this.z-t.z)}setFromSpherical(t){return this.setFromSphericalCoords(t.radius,t.phi,t.theta)}setFromSphericalCoords(t,e,n){const i=Math.sin(e)*t;return this.x=i*Math.sin(n),this.y=Math.cos(e)*t,this.z=i*Math.cos(n),this}setFromCylindrical(t){return this.setFromCylindricalCoords(t.radius,t.theta,t.y)}setFromCylindricalCoords(t,e,n){return this.x=t*Math.sin(e),this.y=n,this.z=t*Math.cos(e),this}setFromMatrixPosition(t){const e=t.elements;return this.x=e[12],this.y=e[13],this.z=e[14],this}setFromMatrixScale(t){const e=this.setFromMatrixColumn(t,0).length(),n=this.setFromMatrixColumn(t,1).length(),i=this.setFromMatrixColumn(t,2).length();return this.x=e,this.y=n,this.z=i,this}setFromMatrixColumn(t,e){return this.fromArray(t.elements,4*e)}setFromMatrix3Column(t,e){return this.fromArray(t.elements,3*e)}equals(t){return t.x===this.x&&t.y===this.y&&t.z===this.z}fromArray(t,e=0){return this.x=t[e],this.y=t[e+1],this.z=t[e+2],this}toArray(t=[],e=0){return t[e]=this.x,t[e+1]=this.y,t[e+2]=this.z,t}fromBufferAttribute(t,e,n){return void 0!==n&&console.warn(\\\\\\\"THREE.Vector3: offset has been removed from .fromBufferAttribute().\\\\\\\"),this.x=t.getX(e),this.y=t.getY(e),this.z=t.getZ(e),this}random(){return this.x=Math.random(),this.y=Math.random(),this.z=Math.random(),this}}Rt.prototype.isVector3=!0;const Ct=new Rt,Pt=new Lt;class It{constructor(t=new Rt(1/0,1/0,1/0),e=new Rt(-1/0,-1/0,-1/0)){this.min=t,this.max=e}set(t,e){return this.min.copy(t),this.max.copy(e),this}setFromArray(t){let e=1/0,n=1/0,i=1/0,r=-1/0,s=-1/0,a=-1/0;for(let o=0,l=t.length;o<l;o+=3){const l=t[o],c=t[o+1],h=t[o+2];l<e&&(e=l),c<n&&(n=c),h<i&&(i=h),l>r&&(r=l),c>s&&(s=c),h>a&&(a=h)}return this.min.set(e,n,i),this.max.set(r,s,a),this}setFromBufferAttribute(t){let e=1/0,n=1/0,i=1/0,r=-1/0,s=-1/0,a=-1/0;for(let o=0,l=t.count;o<l;o++){const l=t.getX(o),c=t.getY(o),h=t.getZ(o);l<e&&(e=l),c<n&&(n=c),h<i&&(i=h),l>r&&(r=l),c>s&&(s=c),h>a&&(a=h)}return this.min.set(e,n,i),this.max.set(r,s,a),this}setFromPoints(t){this.makeEmpty();for(let e=0,n=t.length;e<n;e++)this.expandByPoint(t[e]);return this}setFromCenterAndSize(t,e){const n=Nt.copy(e).multiplyScalar(.5);return this.min.copy(t).sub(n),this.max.copy(t).add(n),this}setFromObject(t){return this.makeEmpty(),this.expandByObject(t)}clone(){return(new this.constructor).copy(this)}copy(t){return this.min.copy(t.min),this.max.copy(t.max),this}makeEmpty(){return this.min.x=this.min.y=this.min.z=1/0,this.max.x=this.max.y=this.max.z=-1/0,this}isEmpty(){return this.max.x<this.min.x||this.max.y<this.min.y||this.max.z<this.min.z}getCenter(t){return this.isEmpty()?t.set(0,0,0):t.addVectors(this.min,this.max).multiplyScalar(.5)}getSize(t){return this.isEmpty()?t.set(0,0,0):t.subVectors(this.max,this.min)}expandByPoint(t){return this.min.min(t),this.max.max(t),this}expandByVector(t){return this.min.sub(t),this.max.add(t),this}expandByScalar(t){return this.min.addScalar(-t),this.max.addScalar(t),this}expandByObject(t){t.updateWorldMatrix(!1,!1);const e=t.geometry;void 0!==e&&(null===e.boundingBox&&e.computeBoundingBox(),Bt.copy(e.boundingBox),Bt.applyMatrix4(t.matrixWorld),this.union(Bt));const n=t.children;for(let t=0,e=n.length;t<e;t++)this.expandByObject(n[t]);return this}containsPoint(t){return!(t.x<this.min.x||t.x>this.max.x||t.y<this.min.y||t.y>this.max.y||t.z<this.min.z||t.z>this.max.z)}containsBox(t){return this.min.x<=t.min.x&&t.max.x<=this.max.x&&this.min.y<=t.min.y&&t.max.y<=this.max.y&&this.min.z<=t.min.z&&t.max.z<=this.max.z}getParameter(t,e){return e.set((t.x-this.min.x)/(this.max.x-this.min.x),(t.y-this.min.y)/(this.max.y-this.min.y),(t.z-this.min.z)/(this.max.z-this.min.z))}intersectsBox(t){return!(t.max.x<this.min.x||t.min.x>this.max.x||t.max.y<this.min.y||t.min.y>this.max.y||t.max.z<this.min.z||t.min.z>this.max.z)}intersectsSphere(t){return this.clampPoint(t.center,Nt),Nt.distanceToSquared(t.center)<=t.radius*t.radius}intersectsPlane(t){let e,n;return t.normal.x>0?(e=t.normal.x*this.min.x,n=t.normal.x*this.max.x):(e=t.normal.x*this.max.x,n=t.normal.x*this.min.x),t.normal.y>0?(e+=t.normal.y*this.min.y,n+=t.normal.y*this.max.y):(e+=t.normal.y*this.max.y,n+=t.normal.y*this.min.y),t.normal.z>0?(e+=t.normal.z*this.min.z,n+=t.normal.z*this.max.z):(e+=t.normal.z*this.max.z,n+=t.normal.z*this.min.z),e<=-t.constant&&n>=-t.constant}intersectsTriangle(t){if(this.isEmpty())return!1;this.getCenter(kt),Vt.subVectors(this.max,kt),zt.subVectors(t.a,kt),Ft.subVectors(t.b,kt),Ot.subVectors(t.c,kt),Ut.subVectors(Ft,zt),Ht.subVectors(Ot,Ft),Gt.subVectors(zt,Ot);let e=[0,-Ut.z,Ut.y,0,-Ht.z,Ht.y,0,-Gt.z,Gt.y,Ut.z,0,-Ut.x,Ht.z,0,-Ht.x,Gt.z,0,-Gt.x,-Ut.y,Ut.x,0,-Ht.y,Ht.x,0,-Gt.y,Gt.x,0];return!!qt(e,zt,Ft,Ot,Vt)&&(e=[1,0,0,0,1,0,0,0,1],!!qt(e,zt,Ft,Ot,Vt)&&(Wt.crossVectors(Ut,Ht),e=[Wt.x,Wt.y,Wt.z],qt(e,zt,Ft,Ot,Vt)))}clampPoint(t,e){return e.copy(t).clamp(this.min,this.max)}distanceToPoint(t){return Nt.copy(t).clamp(this.min,this.max).sub(t).length()}getBoundingSphere(t){return this.getCenter(t.center),t.radius=.5*this.getSize(Nt).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()||(Dt[0].set(this.min.x,this.min.y,this.min.z).applyMatrix4(t),Dt[1].set(this.min.x,this.min.y,this.max.z).applyMatrix4(t),Dt[2].set(this.min.x,this.max.y,this.min.z).applyMatrix4(t),Dt[3].set(this.min.x,this.max.y,this.max.z).applyMatrix4(t),Dt[4].set(this.max.x,this.min.y,this.min.z).applyMatrix4(t),Dt[5].set(this.max.x,this.min.y,this.max.z).applyMatrix4(t),Dt[6].set(this.max.x,this.max.y,this.min.z).applyMatrix4(t),Dt[7].set(this.max.x,this.max.y,this.max.z).applyMatrix4(t),this.setFromPoints(Dt)),this}translate(t){return this.min.add(t),this.max.add(t),this}equals(t){return t.min.equals(this.min)&&t.max.equals(this.max)}}It.prototype.isBox3=!0;const Dt=[new Rt,new Rt,new Rt,new Rt,new Rt,new Rt,new Rt,new Rt],Nt=new Rt,Bt=new It,zt=new Rt,Ft=new Rt,Ot=new Rt,Ut=new Rt,Ht=new Rt,Gt=new Rt,kt=new Rt,Vt=new Rt,Wt=new Rt,jt=new Rt;function qt(t,e,n,i,r){for(let s=0,a=t.length-3;s<=a;s+=3){jt.fromArray(t,s);const a=r.x*Math.abs(jt.x)+r.y*Math.abs(jt.y)+r.z*Math.abs(jt.z),o=e.dot(jt),l=n.dot(jt),c=i.dot(jt);if(Math.max(-Math.max(o,l,c),Math.min(o,l,c))>a)return!1}return!0}const Xt=new It,Yt=new Rt,Jt=new Rt,Zt=new Rt;class Qt{constructor(t=new Rt,e=-1){this.center=t,this.radius=e}set(t,e){return this.center.copy(t),this.radius=e,this}setFromPoints(t,e){const n=this.center;void 0!==e?n.copy(e):Xt.setFromPoints(t).getCenter(n);let i=0;for(let e=0,r=t.length;e<r;e++)i=Math.max(i,n.distanceToSquared(t[e]));return this.radius=Math.sqrt(i),this}copy(t){return this.center.copy(t.center),this.radius=t.radius,this}isEmpty(){return this.radius<0}makeEmpty(){return this.center.set(0,0,0),this.radius=-1,this}containsPoint(t){return t.distanceToSquared(this.center)<=this.radius*this.radius}distanceToPoint(t){return t.distanceTo(this.center)-this.radius}intersectsSphere(t){const e=this.radius+t.radius;return t.center.distanceToSquared(this.center)<=e*e}intersectsBox(t){return t.intersectsSphere(this)}intersectsPlane(t){return Math.abs(t.distanceToPoint(this.center))<=this.radius}clampPoint(t,e){const n=this.center.distanceToSquared(t);return e.copy(t),n>this.radius*this.radius&&(e.sub(this.center).normalize(),e.multiplyScalar(this.radius).add(this.center)),e}getBoundingBox(t){return this.isEmpty()?(t.makeEmpty(),t):(t.set(this.center,this.center),t.expandByScalar(this.radius),t)}applyMatrix4(t){return this.center.applyMatrix4(t),this.radius=this.radius*t.getMaxScaleOnAxis(),this}translate(t){return this.center.add(t),this}expandByPoint(t){Zt.subVectors(t,this.center);const e=Zt.lengthSq();if(e>this.radius*this.radius){const t=Math.sqrt(e),n=.5*(t-this.radius);this.center.add(Zt.multiplyScalar(n/t)),this.radius+=n}return this}union(t){return Jt.subVectors(t.center,this.center).normalize().multiplyScalar(t.radius),this.expandByPoint(Yt.copy(t.center).add(Jt)),this.expandByPoint(Yt.copy(t.center).sub(Jt)),this}equals(t){return t.center.equals(this.center)&&t.radius===this.radius}clone(){return(new this.constructor).copy(this)}}const Kt=new Rt,$t=new Rt,te=new Rt,ee=new Rt,ne=new Rt,ie=new Rt,re=new Rt;class se{constructor(t=new Rt,e=new Rt(0,0,-1)){this.origin=t,this.direction=e}set(t,e){return this.origin.copy(t),this.direction.copy(e),this}copy(t){return this.origin.copy(t.origin),this.direction.copy(t.direction),this}at(t,e){return e.copy(this.direction).multiplyScalar(t).add(this.origin)}lookAt(t){return this.direction.copy(t).sub(this.origin).normalize(),this}recast(t){return this.origin.copy(this.at(t,Kt)),this}closestPointToPoint(t,e){e.subVectors(t,this.origin);const n=e.dot(this.direction);return n<0?e.copy(this.origin):e.copy(this.direction).multiplyScalar(n).add(this.origin)}distanceToPoint(t){return Math.sqrt(this.distanceSqToPoint(t))}distanceSqToPoint(t){const e=Kt.subVectors(t,this.origin).dot(this.direction);return e<0?this.origin.distanceToSquared(t):(Kt.copy(this.direction).multiplyScalar(e).add(this.origin),Kt.distanceToSquared(t))}distanceSqToSegment(t,e,n,i){$t.copy(t).add(e).multiplyScalar(.5),te.copy(e).sub(t).normalize(),ee.copy(this.origin).sub($t);const r=.5*t.distanceTo(e),s=-this.direction.dot(te),a=ee.dot(this.direction),o=-ee.dot(te),l=ee.lengthSq(),c=Math.abs(1-s*s);let h,u,d,p;if(c>0)if(h=s*o-a,u=s*a-o,p=r*c,h>=0)if(u>=-p)if(u<=p){const t=1/c;h*=t,u*=t,d=h*(h+s*u+2*a)+u*(s*h+u+2*o)+l}else u=r,h=Math.max(0,-(s*u+a)),d=-h*h+u*(u+2*o)+l;else u=-r,h=Math.max(0,-(s*u+a)),d=-h*h+u*(u+2*o)+l;else u<=-p?(h=Math.max(0,-(-s*r+a)),u=h>0?-r:Math.min(Math.max(-r,-o),r),d=-h*h+u*(u+2*o)+l):u<=p?(h=0,u=Math.min(Math.max(-r,-o),r),d=u*(u+2*o)+l):(h=Math.max(0,-(s*r+a)),u=h>0?r:Math.min(Math.max(-r,-o),r),d=-h*h+u*(u+2*o)+l);else u=s>0?-r:r,h=Math.max(0,-(s*u+a)),d=-h*h+u*(u+2*o)+l;return n&&n.copy(this.direction).multiplyScalar(h).add(this.origin),i&&i.copy(te).multiplyScalar(u).add($t),d}intersectSphere(t,e){Kt.subVectors(t.center,this.origin);const n=Kt.dot(this.direction),i=Kt.dot(Kt)-n*n,r=t.radius*t.radius;if(i>r)return null;const s=Math.sqrt(r-i),a=n-s,o=n+s;return a<0&&o<0?null:a<0?this.at(o,e):this.at(a,e)}intersectsSphere(t){return this.distanceSqToPoint(t.center)<=t.radius*t.radius}distanceToPlane(t){const e=t.normal.dot(this.direction);if(0===e)return 0===t.distanceToPoint(this.origin)?0:null;const n=-(this.origin.dot(t.normal)+t.constant)/e;return n>=0?n:null}intersectPlane(t,e){const n=this.distanceToPlane(t);return null===n?null:this.at(n,e)}intersectsPlane(t){const e=t.distanceToPoint(this.origin);if(0===e)return!0;return t.normal.dot(this.direction)*e<0}intersectBox(t,e){let n,i,r,s,a,o;const l=1/this.direction.x,c=1/this.direction.y,h=1/this.direction.z,u=this.origin;return l>=0?(n=(t.min.x-u.x)*l,i=(t.max.x-u.x)*l):(n=(t.max.x-u.x)*l,i=(t.min.x-u.x)*l),c>=0?(r=(t.min.y-u.y)*c,s=(t.max.y-u.y)*c):(r=(t.max.y-u.y)*c,s=(t.min.y-u.y)*c),n>s||r>i?null:((r>n||n!=n)&&(n=r),(s<i||i!=i)&&(i=s),h>=0?(a=(t.min.z-u.z)*h,o=(t.max.z-u.z)*h):(a=(t.max.z-u.z)*h,o=(t.min.z-u.z)*h),n>o||a>i?null:((a>n||n!=n)&&(n=a),(o<i||i!=i)&&(i=o),i<0?null:this.at(n>=0?n:i,e)))}intersectsBox(t){return null!==this.intersectBox(t,Kt)}intersectTriangle(t,e,n,i,r){ne.subVectors(e,t),ie.subVectors(n,t),re.crossVectors(ne,ie);let s,a=this.direction.dot(re);if(a>0){if(i)return null;s=1}else{if(!(a<0))return null;s=-1,a=-a}ee.subVectors(this.origin,t);const o=s*this.direction.dot(ie.crossVectors(ee,ie));if(o<0)return null;const l=s*this.direction.dot(ne.cross(ee));if(l<0)return null;if(o+l>a)return null;const c=-s*ee.dot(re);return c<0?null:this.at(c/a,r)}applyMatrix4(t){return this.origin.applyMatrix4(t),this.direction.transformDirection(t),this}equals(t){return t.origin.equals(this.origin)&&t.direction.equals(this.direction)}clone(){return(new this.constructor).copy(this)}}class ae{constructor(){this.elements=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1],arguments.length>0&&console.error(\\\\\\\"THREE.Matrix4: the constructor no longer reads arguments. use .set() instead.\\\\\\\")}set(t,e,n,i,r,s,a,o,l,c,h,u,d,p,m,f){const g=this.elements;return g[0]=t,g[4]=e,g[8]=n,g[12]=i,g[1]=r,g[5]=s,g[9]=a,g[13]=o,g[2]=l,g[6]=c,g[10]=h,g[14]=u,g[3]=d,g[7]=p,g[11]=m,g[15]=f,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 ae).fromArray(this.elements)}copy(t){const e=this.elements,n=t.elements;return e[0]=n[0],e[1]=n[1],e[2]=n[2],e[3]=n[3],e[4]=n[4],e[5]=n[5],e[6]=n[6],e[7]=n[7],e[8]=n[8],e[9]=n[9],e[10]=n[10],e[11]=n[11],e[12]=n[12],e[13]=n[13],e[14]=n[14],e[15]=n[15],this}copyPosition(t){const e=this.elements,n=t.elements;return e[12]=n[12],e[13]=n[13],e[14]=n[14],this}setFromMatrix3(t){const e=t.elements;return this.set(e[0],e[3],e[6],0,e[1],e[4],e[7],0,e[2],e[5],e[8],0,0,0,0,1),this}extractBasis(t,e,n){return t.setFromMatrixColumn(this,0),e.setFromMatrixColumn(this,1),n.setFromMatrixColumn(this,2),this}makeBasis(t,e,n){return this.set(t.x,e.x,n.x,0,t.y,e.y,n.y,0,t.z,e.z,n.z,0,0,0,0,1),this}extractRotation(t){const e=this.elements,n=t.elements,i=1/oe.setFromMatrixColumn(t,0).length(),r=1/oe.setFromMatrixColumn(t,1).length(),s=1/oe.setFromMatrixColumn(t,2).length();return e[0]=n[0]*i,e[1]=n[1]*i,e[2]=n[2]*i,e[3]=0,e[4]=n[4]*r,e[5]=n[5]*r,e[6]=n[6]*r,e[7]=0,e[8]=n[8]*s,e[9]=n[9]*s,e[10]=n[10]*s,e[11]=0,e[12]=0,e[13]=0,e[14]=0,e[15]=1,this}makeRotationFromEuler(t){t&&t.isEuler||console.error(\\\\\\\"THREE.Matrix4: .makeRotationFromEuler() now expects a Euler rotation rather than a Vector3 and order.\\\\\\\");const e=this.elements,n=t.x,i=t.y,r=t.z,s=Math.cos(n),a=Math.sin(n),o=Math.cos(i),l=Math.sin(i),c=Math.cos(r),h=Math.sin(r);if(\\\\\\\"XYZ\\\\\\\"===t.order){const t=s*c,n=s*h,i=a*c,r=a*h;e[0]=o*c,e[4]=-o*h,e[8]=l,e[1]=n+i*l,e[5]=t-r*l,e[9]=-a*o,e[2]=r-t*l,e[6]=i+n*l,e[10]=s*o}else if(\\\\\\\"YXZ\\\\\\\"===t.order){const t=o*c,n=o*h,i=l*c,r=l*h;e[0]=t+r*a,e[4]=i*a-n,e[8]=s*l,e[1]=s*h,e[5]=s*c,e[9]=-a,e[2]=n*a-i,e[6]=r+t*a,e[10]=s*o}else if(\\\\\\\"ZXY\\\\\\\"===t.order){const t=o*c,n=o*h,i=l*c,r=l*h;e[0]=t-r*a,e[4]=-s*h,e[8]=i+n*a,e[1]=n+i*a,e[5]=s*c,e[9]=r-t*a,e[2]=-s*l,e[6]=a,e[10]=s*o}else if(\\\\\\\"ZYX\\\\\\\"===t.order){const t=s*c,n=s*h,i=a*c,r=a*h;e[0]=o*c,e[4]=i*l-n,e[8]=t*l+r,e[1]=o*h,e[5]=r*l+t,e[9]=n*l-i,e[2]=-l,e[6]=a*o,e[10]=s*o}else if(\\\\\\\"YZX\\\\\\\"===t.order){const t=s*o,n=s*l,i=a*o,r=a*l;e[0]=o*c,e[4]=r-t*h,e[8]=i*h+n,e[1]=h,e[5]=s*c,e[9]=-a*c,e[2]=-l*c,e[6]=n*h+i,e[10]=t-r*h}else if(\\\\\\\"XZY\\\\\\\"===t.order){const t=s*o,n=s*l,i=a*o,r=a*l;e[0]=o*c,e[4]=-h,e[8]=l*c,e[1]=t*h+r,e[5]=s*c,e[9]=n*h-i,e[2]=i*h-n,e[6]=a*c,e[10]=r*h+t}return e[3]=0,e[7]=0,e[11]=0,e[12]=0,e[13]=0,e[14]=0,e[15]=1,this}makeRotationFromQuaternion(t){return this.compose(ce,t,he)}lookAt(t,e,n){const i=this.elements;return pe.subVectors(t,e),0===pe.lengthSq()&&(pe.z=1),pe.normalize(),ue.crossVectors(n,pe),0===ue.lengthSq()&&(1===Math.abs(n.z)?pe.x+=1e-4:pe.z+=1e-4,pe.normalize(),ue.crossVectors(n,pe)),ue.normalize(),de.crossVectors(pe,ue),i[0]=ue.x,i[4]=de.x,i[8]=pe.x,i[1]=ue.y,i[5]=de.y,i[9]=pe.y,i[2]=ue.z,i[6]=de.z,i[10]=pe.z,this}multiply(t,e){return void 0!==e?(console.warn(\\\\\\\"THREE.Matrix4: .multiply() now only accepts one argument. Use .multiplyMatrices( a, b ) instead.\\\\\\\"),this.multiplyMatrices(t,e)):this.multiplyMatrices(this,t)}premultiply(t){return this.multiplyMatrices(t,this)}multiplyMatrices(t,e){const n=t.elements,i=e.elements,r=this.elements,s=n[0],a=n[4],o=n[8],l=n[12],c=n[1],h=n[5],u=n[9],d=n[13],p=n[2],m=n[6],f=n[10],g=n[14],v=n[3],y=n[7],x=n[11],_=n[15],M=i[0],b=i[4],w=i[8],S=i[12],T=i[1],E=i[5],A=i[9],L=i[13],R=i[2],C=i[6],P=i[10],I=i[14],D=i[3],N=i[7],B=i[11],z=i[15];return r[0]=s*M+a*T+o*R+l*D,r[4]=s*b+a*E+o*C+l*N,r[8]=s*w+a*A+o*P+l*B,r[12]=s*S+a*L+o*I+l*z,r[1]=c*M+h*T+u*R+d*D,r[5]=c*b+h*E+u*C+d*N,r[9]=c*w+h*A+u*P+d*B,r[13]=c*S+h*L+u*I+d*z,r[2]=p*M+m*T+f*R+g*D,r[6]=p*b+m*E+f*C+g*N,r[10]=p*w+m*A+f*P+g*B,r[14]=p*S+m*L+f*I+g*z,r[3]=v*M+y*T+x*R+_*D,r[7]=v*b+y*E+x*C+_*N,r[11]=v*w+y*A+x*P+_*B,r[15]=v*S+y*L+x*I+_*z,this}multiplyScalar(t){const e=this.elements;return e[0]*=t,e[4]*=t,e[8]*=t,e[12]*=t,e[1]*=t,e[5]*=t,e[9]*=t,e[13]*=t,e[2]*=t,e[6]*=t,e[10]*=t,e[14]*=t,e[3]*=t,e[7]*=t,e[11]*=t,e[15]*=t,this}determinant(){const t=this.elements,e=t[0],n=t[4],i=t[8],r=t[12],s=t[1],a=t[5],o=t[9],l=t[13],c=t[2],h=t[6],u=t[10],d=t[14];return t[3]*(+r*o*h-i*l*h-r*a*u+n*l*u+i*a*d-n*o*d)+t[7]*(+e*o*d-e*l*u+r*s*u-i*s*d+i*l*c-r*o*c)+t[11]*(+e*l*h-e*a*d-r*s*h+n*s*d+r*a*c-n*l*c)+t[15]*(-i*a*c-e*o*h+e*a*u+i*s*h-n*s*u+n*o*c)}transpose(){const t=this.elements;let e;return e=t[1],t[1]=t[4],t[4]=e,e=t[2],t[2]=t[8],t[8]=e,e=t[6],t[6]=t[9],t[9]=e,e=t[3],t[3]=t[12],t[12]=e,e=t[7],t[7]=t[13],t[13]=e,e=t[11],t[11]=t[14],t[14]=e,this}setPosition(t,e,n){const i=this.elements;return t.isVector3?(i[12]=t.x,i[13]=t.y,i[14]=t.z):(i[12]=t,i[13]=e,i[14]=n),this}invert(){const t=this.elements,e=t[0],n=t[1],i=t[2],r=t[3],s=t[4],a=t[5],o=t[6],l=t[7],c=t[8],h=t[9],u=t[10],d=t[11],p=t[12],m=t[13],f=t[14],g=t[15],v=h*f*l-m*u*l+m*o*d-a*f*d-h*o*g+a*u*g,y=p*u*l-c*f*l-p*o*d+s*f*d+c*o*g-s*u*g,x=c*m*l-p*h*l+p*a*d-s*m*d-c*a*g+s*h*g,_=p*h*o-c*m*o-p*a*u+s*m*u+c*a*f-s*h*f,M=e*v+n*y+i*x+r*_;if(0===M)return this.set(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0);const b=1/M;return t[0]=v*b,t[1]=(m*u*r-h*f*r-m*i*d+n*f*d+h*i*g-n*u*g)*b,t[2]=(a*f*r-m*o*r+m*i*l-n*f*l-a*i*g+n*o*g)*b,t[3]=(h*o*r-a*u*r-h*i*l+n*u*l+a*i*d-n*o*d)*b,t[4]=y*b,t[5]=(c*f*r-p*u*r+p*i*d-e*f*d-c*i*g+e*u*g)*b,t[6]=(p*o*r-s*f*r-p*i*l+e*f*l+s*i*g-e*o*g)*b,t[7]=(s*u*r-c*o*r+c*i*l-e*u*l-s*i*d+e*o*d)*b,t[8]=x*b,t[9]=(p*h*r-c*m*r-p*n*d+e*m*d+c*n*g-e*h*g)*b,t[10]=(s*m*r-p*a*r+p*n*l-e*m*l-s*n*g+e*a*g)*b,t[11]=(c*a*r-s*h*r-c*n*l+e*h*l+s*n*d-e*a*d)*b,t[12]=_*b,t[13]=(c*m*i-p*h*i+p*n*u-e*m*u-c*n*f+e*h*f)*b,t[14]=(p*a*i-s*m*i-p*n*o+e*m*o+s*n*f-e*a*f)*b,t[15]=(s*h*i-c*a*i+c*n*o-e*h*o-s*n*u+e*a*u)*b,this}scale(t){const e=this.elements,n=t.x,i=t.y,r=t.z;return e[0]*=n,e[4]*=i,e[8]*=r,e[1]*=n,e[5]*=i,e[9]*=r,e[2]*=n,e[6]*=i,e[10]*=r,e[3]*=n,e[7]*=i,e[11]*=r,this}getMaxScaleOnAxis(){const t=this.elements,e=t[0]*t[0]+t[1]*t[1]+t[2]*t[2],n=t[4]*t[4]+t[5]*t[5]+t[6]*t[6],i=t[8]*t[8]+t[9]*t[9]+t[10]*t[10];return Math.sqrt(Math.max(e,n,i))}makeTranslation(t,e,n){return this.set(1,0,0,t,0,1,0,e,0,0,1,n,0,0,0,1),this}makeRotationX(t){const e=Math.cos(t),n=Math.sin(t);return this.set(1,0,0,0,0,e,-n,0,0,n,e,0,0,0,0,1),this}makeRotationY(t){const e=Math.cos(t),n=Math.sin(t);return this.set(e,0,n,0,0,1,0,0,-n,0,e,0,0,0,0,1),this}makeRotationZ(t){const e=Math.cos(t),n=Math.sin(t);return this.set(e,-n,0,0,n,e,0,0,0,0,1,0,0,0,0,1),this}makeRotationAxis(t,e){const n=Math.cos(e),i=Math.sin(e),r=1-n,s=t.x,a=t.y,o=t.z,l=r*s,c=r*a;return this.set(l*s+n,l*a-i*o,l*o+i*a,0,l*a+i*o,c*a+n,c*o-i*s,0,l*o-i*a,c*o+i*s,r*o*o+n,0,0,0,0,1),this}makeScale(t,e,n){return this.set(t,0,0,0,0,e,0,0,0,0,n,0,0,0,0,1),this}makeShear(t,e,n,i,r,s){return this.set(1,n,r,0,t,1,s,0,e,i,1,0,0,0,0,1),this}compose(t,e,n){const i=this.elements,r=e._x,s=e._y,a=e._z,o=e._w,l=r+r,c=s+s,h=a+a,u=r*l,d=r*c,p=r*h,m=s*c,f=s*h,g=a*h,v=o*l,y=o*c,x=o*h,_=n.x,M=n.y,b=n.z;return i[0]=(1-(m+g))*_,i[1]=(d+x)*_,i[2]=(p-y)*_,i[3]=0,i[4]=(d-x)*M,i[5]=(1-(u+g))*M,i[6]=(f+v)*M,i[7]=0,i[8]=(p+y)*b,i[9]=(f-v)*b,i[10]=(1-(u+m))*b,i[11]=0,i[12]=t.x,i[13]=t.y,i[14]=t.z,i[15]=1,this}decompose(t,e,n){const i=this.elements;let r=oe.set(i[0],i[1],i[2]).length();const s=oe.set(i[4],i[5],i[6]).length(),a=oe.set(i[8],i[9],i[10]).length();this.determinant()<0&&(r=-r),t.x=i[12],t.y=i[13],t.z=i[14],le.copy(this);const o=1/r,l=1/s,c=1/a;return le.elements[0]*=o,le.elements[1]*=o,le.elements[2]*=o,le.elements[4]*=l,le.elements[5]*=l,le.elements[6]*=l,le.elements[8]*=c,le.elements[9]*=c,le.elements[10]*=c,e.setFromRotationMatrix(le),n.x=r,n.y=s,n.z=a,this}makePerspective(t,e,n,i,r,s){void 0===s&&console.warn(\\\\\\\"THREE.Matrix4: .makePerspective() has been redefined and has a new signature. Please check the docs.\\\\\\\");const a=this.elements,o=2*r/(e-t),l=2*r/(n-i),c=(e+t)/(e-t),h=(n+i)/(n-i),u=-(s+r)/(s-r),d=-2*s*r/(s-r);return a[0]=o,a[4]=0,a[8]=c,a[12]=0,a[1]=0,a[5]=l,a[9]=h,a[13]=0,a[2]=0,a[6]=0,a[10]=u,a[14]=d,a[3]=0,a[7]=0,a[11]=-1,a[15]=0,this}makeOrthographic(t,e,n,i,r,s){const a=this.elements,o=1/(e-t),l=1/(n-i),c=1/(s-r),h=(e+t)*o,u=(n+i)*l,d=(s+r)*c;return a[0]=2*o,a[4]=0,a[8]=0,a[12]=-h,a[1]=0,a[5]=2*l,a[9]=0,a[13]=-u,a[2]=0,a[6]=0,a[10]=-2*c,a[14]=-d,a[3]=0,a[7]=0,a[11]=0,a[15]=1,this}equals(t){const e=this.elements,n=t.elements;for(let t=0;t<16;t++)if(e[t]!==n[t])return!1;return!0}fromArray(t,e=0){for(let n=0;n<16;n++)this.elements[n]=t[n+e];return this}toArray(t=[],e=0){const n=this.elements;return t[e]=n[0],t[e+1]=n[1],t[e+2]=n[2],t[e+3]=n[3],t[e+4]=n[4],t[e+5]=n[5],t[e+6]=n[6],t[e+7]=n[7],t[e+8]=n[8],t[e+9]=n[9],t[e+10]=n[10],t[e+11]=n[11],t[e+12]=n[12],t[e+13]=n[13],t[e+14]=n[14],t[e+15]=n[15],t}}ae.prototype.isMatrix4=!0;const oe=new Rt,le=new ae,ce=new Rt(0,0,0),he=new Rt(1,1,1),ue=new Rt,de=new Rt,pe=new Rt,me=new ae,fe=new Lt;class ge{constructor(t=0,e=0,n=0,i=ge.DefaultOrder){this._x=t,this._y=e,this._z=n,this._order=i}get x(){return this._x}set x(t){this._x=t,this._onChangeCallback()}get y(){return this._y}set y(t){this._y=t,this._onChangeCallback()}get z(){return this._z}set z(t){this._z=t,this._onChangeCallback()}get order(){return this._order}set order(t){this._order=t,this._onChangeCallback()}set(t,e,n,i=this._order){return this._x=t,this._y=e,this._z=n,this._order=i,this._onChangeCallback(),this}clone(){return new this.constructor(this._x,this._y,this._z,this._order)}copy(t){return this._x=t._x,this._y=t._y,this._z=t._z,this._order=t._order,this._onChangeCallback(),this}setFromRotationMatrix(t,e=this._order,n=!0){const i=t.elements,r=i[0],s=i[4],a=i[8],o=i[1],l=i[5],c=i[9],h=i[2],u=i[6],d=i[10];switch(e){case\\\\\\\"XYZ\\\\\\\":this._y=Math.asin(ht(a,-1,1)),Math.abs(a)<.9999999?(this._x=Math.atan2(-c,d),this._z=Math.atan2(-s,r)):(this._x=Math.atan2(u,l),this._z=0);break;case\\\\\\\"YXZ\\\\\\\":this._x=Math.asin(-ht(c,-1,1)),Math.abs(c)<.9999999?(this._y=Math.atan2(a,d),this._z=Math.atan2(o,l)):(this._y=Math.atan2(-h,r),this._z=0);break;case\\\\\\\"ZXY\\\\\\\":this._x=Math.asin(ht(u,-1,1)),Math.abs(u)<.9999999?(this._y=Math.atan2(-h,d),this._z=Math.atan2(-s,l)):(this._y=0,this._z=Math.atan2(o,r));break;case\\\\\\\"ZYX\\\\\\\":this._y=Math.asin(-ht(h,-1,1)),Math.abs(h)<.9999999?(this._x=Math.atan2(u,d),this._z=Math.atan2(o,r)):(this._x=0,this._z=Math.atan2(-s,l));break;case\\\\\\\"YZX\\\\\\\":this._z=Math.asin(ht(o,-1,1)),Math.abs(o)<.9999999?(this._x=Math.atan2(-c,l),this._y=Math.atan2(-h,r)):(this._x=0,this._y=Math.atan2(a,d));break;case\\\\\\\"XZY\\\\\\\":this._z=Math.asin(-ht(s,-1,1)),Math.abs(s)<.9999999?(this._x=Math.atan2(u,l),this._y=Math.atan2(a,r)):(this._x=Math.atan2(-c,d),this._y=0);break;default:console.warn(\\\\\\\"THREE.Euler: .setFromRotationMatrix() encountered an unknown order: \\\\\\\"+e)}return this._order=e,!0===n&&this._onChangeCallback(),this}setFromQuaternion(t,e,n){return me.makeRotationFromQuaternion(t),this.setFromRotationMatrix(me,e,n)}setFromVector3(t,e=this._order){return this.set(t.x,t.y,t.z,e)}reorder(t){return fe.setFromEuler(this),this.setFromQuaternion(fe,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 Rt(this._x,this._y,this._z)}_onChange(t){return this._onChangeCallback=t,this}_onChangeCallback(){}}ge.prototype.isEuler=!0,ge.DefaultOrder=\\\\\\\"XYZ\\\\\\\",ge.RotationOrders=[\\\\\\\"XYZ\\\\\\\",\\\\\\\"YZX\\\\\\\",\\\\\\\"ZXY\\\\\\\",\\\\\\\"XZY\\\\\\\",\\\\\\\"YXZ\\\\\\\",\\\\\\\"ZYX\\\\\\\"];class ve{constructor(){this.mask=1}set(t){this.mask=1<<t|0}enable(t){this.mask|=1<<t|0}enableAll(){this.mask=-1}toggle(t){this.mask^=1<<t|0}disable(t){this.mask&=~(1<<t|0)}disableAll(){this.mask=0}test(t){return 0!=(this.mask&t.mask)}}let ye=0;const xe=new Rt,_e=new Lt,Me=new ae,be=new Rt,we=new Rt,Se=new Rt,Te=new Lt,Ee=new Rt(1,0,0),Ae=new Rt(0,1,0),Le=new Rt(0,0,1),Re={type:\\\\\\\"added\\\\\\\"},Ce={type:\\\\\\\"removed\\\\\\\"};class Pe extends rt{constructor(){super(),Object.defineProperty(this,\\\\\\\"id\\\\\\\",{value:ye++}),this.uuid=ct(),this.name=\\\\\\\"\\\\\\\",this.type=\\\\\\\"Object3D\\\\\\\",this.parent=null,this.children=[],this.up=Pe.DefaultUp.clone();const t=new Rt,e=new ge,n=new Lt,i=new Rt(1,1,1);e._onChange((function(){n.setFromEuler(e,!1)})),n._onChange((function(){e.setFromQuaternion(n,void 0,!1)})),Object.defineProperties(this,{position:{configurable:!0,enumerable:!0,value:t},rotation:{configurable:!0,enumerable:!0,value:e},quaternion:{configurable:!0,enumerable:!0,value:n},scale:{configurable:!0,enumerable:!0,value:i},modelViewMatrix:{value:new ae},normalMatrix:{value:new yt}}),this.matrix=new ae,this.matrixWorld=new ae,this.matrixAutoUpdate=Pe.DefaultMatrixAutoUpdate,this.matrixWorldNeedsUpdate=!1,this.layers=new ve,this.visible=!0,this.castShadow=!1,this.receiveShadow=!1,this.frustumCulled=!0,this.renderOrder=0,this.animations=[],this.userData={}}onBeforeRender(){}onAfterRender(){}applyMatrix4(t){this.matrixAutoUpdate&&this.updateMatrix(),this.matrix.premultiply(t),this.matrix.decompose(this.position,this.quaternion,this.scale)}applyQuaternion(t){return this.quaternion.premultiply(t),this}setRotationFromAxisAngle(t,e){this.quaternion.setFromAxisAngle(t,e)}setRotationFromEuler(t){this.quaternion.setFromEuler(t,!0)}setRotationFromMatrix(t){this.quaternion.setFromRotationMatrix(t)}setRotationFromQuaternion(t){this.quaternion.copy(t)}rotateOnAxis(t,e){return _e.setFromAxisAngle(t,e),this.quaternion.multiply(_e),this}rotateOnWorldAxis(t,e){return _e.setFromAxisAngle(t,e),this.quaternion.premultiply(_e),this}rotateX(t){return this.rotateOnAxis(Ee,t)}rotateY(t){return this.rotateOnAxis(Ae,t)}rotateZ(t){return this.rotateOnAxis(Le,t)}translateOnAxis(t,e){return xe.copy(t).applyQuaternion(this.quaternion),this.position.add(xe.multiplyScalar(e)),this}translateX(t){return this.translateOnAxis(Ee,t)}translateY(t){return this.translateOnAxis(Ae,t)}translateZ(t){return this.translateOnAxis(Le,t)}localToWorld(t){return t.applyMatrix4(this.matrixWorld)}worldToLocal(t){return t.applyMatrix4(Me.copy(this.matrixWorld).invert())}lookAt(t,e,n){t.isVector3?be.copy(t):be.set(t,e,n);const i=this.parent;this.updateWorldMatrix(!0,!1),we.setFromMatrixPosition(this.matrixWorld),this.isCamera||this.isLight?Me.lookAt(we,be,this.up):Me.lookAt(be,we,this.up),this.quaternion.setFromRotationMatrix(Me),i&&(Me.extractRotation(i.matrixWorld),_e.setFromRotationMatrix(Me),this.quaternion.premultiply(_e.invert()))}add(t){if(arguments.length>1){for(let t=0;t<arguments.length;t++)this.add(arguments[t]);return this}return t===this?(console.error(\\\\\\\"THREE.Object3D.add: object can't be added as a child of itself.\\\\\\\",t),this):(t&&t.isObject3D?(null!==t.parent&&t.parent.remove(t),t.parent=this,this.children.push(t),t.dispatchEvent(Re)):console.error(\\\\\\\"THREE.Object3D.add: object not an instance of THREE.Object3D.\\\\\\\",t),this)}remove(t){if(arguments.length>1){for(let t=0;t<arguments.length;t++)this.remove(arguments[t]);return this}const e=this.children.indexOf(t);return-1!==e&&(t.parent=null,this.children.splice(e,1),t.dispatchEvent(Ce)),this}removeFromParent(){const t=this.parent;return null!==t&&t.remove(this),this}clear(){for(let t=0;t<this.children.length;t++){const e=this.children[t];e.parent=null,e.dispatchEvent(Ce)}return this.children.length=0,this}attach(t){return this.updateWorldMatrix(!0,!1),Me.copy(this.matrixWorld).invert(),null!==t.parent&&(t.parent.updateWorldMatrix(!0,!1),Me.multiply(t.parent.matrixWorld)),t.applyMatrix4(Me),this.add(t),t.updateWorldMatrix(!1,!0),this}getObjectById(t){return this.getObjectByProperty(\\\\\\\"id\\\\\\\",t)}getObjectByName(t){return this.getObjectByProperty(\\\\\\\"name\\\\\\\",t)}getObjectByProperty(t,e){if(this[t]===e)return this;for(let n=0,i=this.children.length;n<i;n++){const i=this.children[n].getObjectByProperty(t,e);if(void 0!==i)return i}}getWorldPosition(t){return this.updateWorldMatrix(!0,!1),t.setFromMatrixPosition(this.matrixWorld)}getWorldQuaternion(t){return this.updateWorldMatrix(!0,!1),this.matrixWorld.decompose(we,t,Se),t}getWorldScale(t){return this.updateWorldMatrix(!0,!1),this.matrixWorld.decompose(we,Te,t),t}getWorldDirection(t){this.updateWorldMatrix(!0,!1);const e=this.matrixWorld.elements;return t.set(e[8],e[9],e[10]).normalize()}raycast(){}traverse(t){t(this);const e=this.children;for(let n=0,i=e.length;n<i;n++)e[n].traverse(t)}traverseVisible(t){if(!1===this.visible)return;t(this);const e=this.children;for(let n=0,i=e.length;n<i;n++)e[n].traverseVisible(t)}traverseAncestors(t){const e=this.parent;null!==e&&(t(e),e.traverseAncestors(t))}updateMatrix(){this.matrix.compose(this.position,this.quaternion,this.scale),this.matrixWorldNeedsUpdate=!0}updateMatrixWorld(t){this.matrixAutoUpdate&&this.updateMatrix(),(this.matrixWorldNeedsUpdate||t)&&(null===this.parent?this.matrixWorld.copy(this.matrix):this.matrixWorld.multiplyMatrices(this.parent.matrixWorld,this.matrix),this.matrixWorldNeedsUpdate=!1,t=!0);const e=this.children;for(let n=0,i=e.length;n<i;n++)e[n].updateMatrixWorld(t)}updateWorldMatrix(t,e){const n=this.parent;if(!0===t&&null!==n&&n.updateWorldMatrix(!0,!1),this.matrixAutoUpdate&&this.updateMatrix(),null===this.parent?this.matrixWorld.copy(this.matrix):this.matrixWorld.multiplyMatrices(this.parent.matrixWorld,this.matrix),!0===e){const t=this.children;for(let e=0,n=t.length;e<n;e++)t[e].updateWorldMatrix(!1,!0)}}toJSON(t){const e=void 0===t||\\\\\\\"string\\\\\\\"==typeof t,n={};e&&(t={geometries:{},materials:{},textures:{},images:{},shapes:{},skeletons:{},animations:{}},n.metadata={version:4.5,type:\\\\\\\"Object\\\\\\\",generator:\\\\\\\"Object3D.toJSON\\\\\\\"});const i={};function r(e,n){return void 0===e[n.uuid]&&(e[n.uuid]=n.toJSON(t)),n.uuid}if(i.uuid=this.uuid,i.type=this.type,\\\\\\\"\\\\\\\"!==this.name&&(i.name=this.name),!0===this.castShadow&&(i.castShadow=!0),!0===this.receiveShadow&&(i.receiveShadow=!0),!1===this.visible&&(i.visible=!1),!1===this.frustumCulled&&(i.frustumCulled=!1),0!==this.renderOrder&&(i.renderOrder=this.renderOrder),\\\\\\\"{}\\\\\\\"!==JSON.stringify(this.userData)&&(i.userData=this.userData),i.layers=this.layers.mask,i.matrix=this.matrix.toArray(),!1===this.matrixAutoUpdate&&(i.matrixAutoUpdate=!1),this.isInstancedMesh&&(i.type=\\\\\\\"InstancedMesh\\\\\\\",i.count=this.count,i.instanceMatrix=this.instanceMatrix.toJSON(),null!==this.instanceColor&&(i.instanceColor=this.instanceColor.toJSON())),this.isScene)this.background&&(this.background.isColor?i.background=this.background.toJSON():this.background.isTexture&&(i.background=this.background.toJSON(t).uuid)),this.environment&&this.environment.isTexture&&(i.environment=this.environment.toJSON(t).uuid);else if(this.isMesh||this.isLine||this.isPoints){i.geometry=r(t.geometries,this.geometry);const e=this.geometry.parameters;if(void 0!==e&&void 0!==e.shapes){const n=e.shapes;if(Array.isArray(n))for(let e=0,i=n.length;e<i;e++){const i=n[e];r(t.shapes,i)}else r(t.shapes,n)}}if(this.isSkinnedMesh&&(i.bindMode=this.bindMode,i.bindMatrix=this.bindMatrix.toArray(),void 0!==this.skeleton&&(r(t.skeletons,this.skeleton),i.skeleton=this.skeleton.uuid)),void 0!==this.material)if(Array.isArray(this.material)){const e=[];for(let n=0,i=this.material.length;n<i;n++)e.push(r(t.materials,this.material[n]));i.material=e}else i.material=r(t.materials,this.material);if(this.children.length>0){i.children=[];for(let e=0;e<this.children.length;e++)i.children.push(this.children[e].toJSON(t).object)}if(this.animations.length>0){i.animations=[];for(let e=0;e<this.animations.length;e++){const n=this.animations[e];i.animations.push(r(t.animations,n))}}if(e){const e=s(t.geometries),i=s(t.materials),r=s(t.textures),a=s(t.images),o=s(t.shapes),l=s(t.skeletons),c=s(t.animations);e.length>0&&(n.geometries=e),i.length>0&&(n.materials=i),r.length>0&&(n.textures=r),a.length>0&&(n.images=a),o.length>0&&(n.shapes=o),l.length>0&&(n.skeletons=l),c.length>0&&(n.animations=c)}return n.object=i,n;function s(t){const e=[];for(const n in t){const i=t[n];delete i.metadata,e.push(i)}return e}}clone(t){return(new this.constructor).copy(this,t)}copy(t,e=!0){if(this.name=t.name,this.up.copy(t.up),this.position.copy(t.position),this.rotation.order=t.rotation.order,this.quaternion.copy(t.quaternion),this.scale.copy(t.scale),this.matrix.copy(t.matrix),this.matrixWorld.copy(t.matrixWorld),this.matrixAutoUpdate=t.matrixAutoUpdate,this.matrixWorldNeedsUpdate=t.matrixWorldNeedsUpdate,this.layers.mask=t.layers.mask,this.visible=t.visible,this.castShadow=t.castShadow,this.receiveShadow=t.receiveShadow,this.frustumCulled=t.frustumCulled,this.renderOrder=t.renderOrder,this.userData=JSON.parse(JSON.stringify(t.userData)),!0===e)for(let e=0;e<t.children.length;e++){const n=t.children[e];this.add(n.clone())}return this}}Pe.DefaultUp=new Rt(0,1,0),Pe.DefaultMatrixAutoUpdate=!0,Pe.prototype.isObject3D=!0;const Ie=new Rt,De=new Rt,Ne=new Rt,Be=new Rt,ze=new Rt,Fe=new Rt,Oe=new Rt,Ue=new Rt,He=new Rt,Ge=new Rt;class ke{constructor(t=new Rt,e=new Rt,n=new Rt){this.a=t,this.b=e,this.c=n}static getNormal(t,e,n,i){i.subVectors(n,e),Ie.subVectors(t,e),i.cross(Ie);const r=i.lengthSq();return r>0?i.multiplyScalar(1/Math.sqrt(r)):i.set(0,0,0)}static getBarycoord(t,e,n,i,r){Ie.subVectors(i,e),De.subVectors(n,e),Ne.subVectors(t,e);const s=Ie.dot(Ie),a=Ie.dot(De),o=Ie.dot(Ne),l=De.dot(De),c=De.dot(Ne),h=s*l-a*a;if(0===h)return r.set(-2,-1,-1);const u=1/h,d=(l*o-a*c)*u,p=(s*c-a*o)*u;return r.set(1-d-p,p,d)}static containsPoint(t,e,n,i){return this.getBarycoord(t,e,n,i,Be),Be.x>=0&&Be.y>=0&&Be.x+Be.y<=1}static getUV(t,e,n,i,r,s,a,o){return this.getBarycoord(t,e,n,i,Be),o.set(0,0),o.addScaledVector(r,Be.x),o.addScaledVector(s,Be.y),o.addScaledVector(a,Be.z),o}static isFrontFacing(t,e,n,i){return Ie.subVectors(n,e),De.subVectors(t,e),Ie.cross(De).dot(i)<0}set(t,e,n){return this.a.copy(t),this.b.copy(e),this.c.copy(n),this}setFromPointsAndIndices(t,e,n,i){return this.a.copy(t[e]),this.b.copy(t[n]),this.c.copy(t[i]),this}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 Ie.subVectors(this.c,this.b),De.subVectors(this.a,this.b),.5*Ie.cross(De).length()}getMidpoint(t){return t.addVectors(this.a,this.b).add(this.c).multiplyScalar(1/3)}getNormal(t){return ke.getNormal(this.a,this.b,this.c,t)}getPlane(t){return t.setFromCoplanarPoints(this.a,this.b,this.c)}getBarycoord(t,e){return ke.getBarycoord(t,this.a,this.b,this.c,e)}getUV(t,e,n,i,r){return ke.getUV(t,this.a,this.b,this.c,e,n,i,r)}containsPoint(t){return ke.containsPoint(t,this.a,this.b,this.c)}isFrontFacing(t){return ke.isFrontFacing(this.a,this.b,this.c,t)}intersectsBox(t){return t.intersectsTriangle(this)}closestPointToPoint(t,e){const n=this.a,i=this.b,r=this.c;let s,a;ze.subVectors(i,n),Fe.subVectors(r,n),Ue.subVectors(t,n);const o=ze.dot(Ue),l=Fe.dot(Ue);if(o<=0&&l<=0)return e.copy(n);He.subVectors(t,i);const c=ze.dot(He),h=Fe.dot(He);if(c>=0&&h<=c)return e.copy(i);const u=o*h-c*l;if(u<=0&&o>=0&&c<=0)return s=o/(o-c),e.copy(n).addScaledVector(ze,s);Ge.subVectors(t,r);const d=ze.dot(Ge),p=Fe.dot(Ge);if(p>=0&&d<=p)return e.copy(r);const m=d*l-o*p;if(m<=0&&l>=0&&p<=0)return a=l/(l-p),e.copy(n).addScaledVector(Fe,a);const f=c*p-d*h;if(f<=0&&h-c>=0&&d-p>=0)return Oe.subVectors(r,i),a=(h-c)/(h-c+(d-p)),e.copy(i).addScaledVector(Oe,a);const g=1/(f+m+u);return s=m*g,a=u*g,e.copy(n).addScaledVector(ze,s).addScaledVector(Fe,a)}equals(t){return t.a.equals(this.a)&&t.b.equals(this.b)&&t.c.equals(this.c)}}let Ve=0;class We extends rt{constructor(){super(),Object.defineProperty(this,\\\\\\\"id\\\\\\\",{value:Ve++}),this.uuid=ct(),this.name=\\\\\\\"\\\\\\\",this.type=\\\\\\\"Material\\\\\\\",this.fog=!0,this.blending=1,this.side=0,this.vertexColors=!1,this.opacity=1,this.transparent=!1,this.blendSrc=204,this.blendDst=205,this.blendEquation=n,this.blendSrcAlpha=null,this.blendDstAlpha=null,this.blendEquationAlpha=null,this.depthFunc=3,this.depthTest=!0,this.depthWrite=!0,this.stencilWriteMask=255,this.stencilFunc=519,this.stencilRef=0,this.stencilFuncMask=255,this.stencilFail=tt,this.stencilZFail=tt,this.stencilZPass=tt,this.stencilWrite=!1,this.clippingPlanes=null,this.clipIntersection=!1,this.clipShadows=!1,this.shadowSide=null,this.colorWrite=!0,this.precision=null,this.polygonOffset=!1,this.polygonOffsetFactor=0,this.polygonOffsetUnits=0,this.dithering=!1,this.alphaTest=0,this.alphaToCoverage=!1,this.premultipliedAlpha=!1,this.visible=!0,this.toneMapped=!0,this.userData={},this.version=0}onBuild(){}onBeforeCompile(){}customProgramCacheKey(){return this.onBeforeCompile.toString()}setValues(t){if(void 0!==t)for(const e in t){const n=t[e];if(void 0===n){console.warn(\\\\\\\"THREE.Material: '\\\\\\\"+e+\\\\\\\"' parameter is undefined.\\\\\\\");continue}if(\\\\\\\"shading\\\\\\\"===e){console.warn(\\\\\\\"THREE.\\\\\\\"+this.type+\\\\\\\": .shading has been removed. Use the boolean .flatShading instead.\\\\\\\"),this.flatShading=1===n;continue}const i=this[e];void 0!==i?i&&i.isColor?i.set(n):i&&i.isVector3&&n&&n.isVector3?i.copy(n):this[e]=n:console.warn(\\\\\\\"THREE.\\\\\\\"+this.type+\\\\\\\": '\\\\\\\"+e+\\\\\\\"' is not a property of this material.\\\\\\\")}}toJSON(t){const e=void 0===t||\\\\\\\"string\\\\\\\"==typeof t;e&&(t={textures:{},images:{}});const n={metadata:{version:4.5,type:\\\\\\\"Material\\\\\\\",generator:\\\\\\\"Material.toJSON\\\\\\\"}};function i(t){const e=[];for(const n in t){const i=t[n];delete i.metadata,e.push(i)}return e}if(n.uuid=this.uuid,n.type=this.type,\\\\\\\"\\\\\\\"!==this.name&&(n.name=this.name),this.color&&this.color.isColor&&(n.color=this.color.getHex()),void 0!==this.roughness&&(n.roughness=this.roughness),void 0!==this.metalness&&(n.metalness=this.metalness),this.sheen&&this.sheen.isColor&&(n.sheen=this.sheen.getHex()),this.emissive&&this.emissive.isColor&&(n.emissive=this.emissive.getHex()),this.emissiveIntensity&&1!==this.emissiveIntensity&&(n.emissiveIntensity=this.emissiveIntensity),this.specular&&this.specular.isColor&&(n.specular=this.specular.getHex()),void 0!==this.specularIntensity&&(n.specularIntensity=this.specularIntensity),this.specularTint&&this.specularTint.isColor&&(n.specularTint=this.specularTint.getHex()),void 0!==this.shininess&&(n.shininess=this.shininess),void 0!==this.clearcoat&&(n.clearcoat=this.clearcoat),void 0!==this.clearcoatRoughness&&(n.clearcoatRoughness=this.clearcoatRoughness),this.clearcoatMap&&this.clearcoatMap.isTexture&&(n.clearcoatMap=this.clearcoatMap.toJSON(t).uuid),this.clearcoatRoughnessMap&&this.clearcoatRoughnessMap.isTexture&&(n.clearcoatRoughnessMap=this.clearcoatRoughnessMap.toJSON(t).uuid),this.clearcoatNormalMap&&this.clearcoatNormalMap.isTexture&&(n.clearcoatNormalMap=this.clearcoatNormalMap.toJSON(t).uuid,n.clearcoatNormalScale=this.clearcoatNormalScale.toArray()),this.map&&this.map.isTexture&&(n.map=this.map.toJSON(t).uuid),this.matcap&&this.matcap.isTexture&&(n.matcap=this.matcap.toJSON(t).uuid),this.alphaMap&&this.alphaMap.isTexture&&(n.alphaMap=this.alphaMap.toJSON(t).uuid),this.lightMap&&this.lightMap.isTexture&&(n.lightMap=this.lightMap.toJSON(t).uuid,n.lightMapIntensity=this.lightMapIntensity),this.aoMap&&this.aoMap.isTexture&&(n.aoMap=this.aoMap.toJSON(t).uuid,n.aoMapIntensity=this.aoMapIntensity),this.bumpMap&&this.bumpMap.isTexture&&(n.bumpMap=this.bumpMap.toJSON(t).uuid,n.bumpScale=this.bumpScale),this.normalMap&&this.normalMap.isTexture&&(n.normalMap=this.normalMap.toJSON(t).uuid,n.normalMapType=this.normalMapType,n.normalScale=this.normalScale.toArray()),this.displacementMap&&this.displacementMap.isTexture&&(n.displacementMap=this.displacementMap.toJSON(t).uuid,n.displacementScale=this.displacementScale,n.displacementBias=this.displacementBias),this.roughnessMap&&this.roughnessMap.isTexture&&(n.roughnessMap=this.roughnessMap.toJSON(t).uuid),this.metalnessMap&&this.metalnessMap.isTexture&&(n.metalnessMap=this.metalnessMap.toJSON(t).uuid),this.emissiveMap&&this.emissiveMap.isTexture&&(n.emissiveMap=this.emissiveMap.toJSON(t).uuid),this.specularMap&&this.specularMap.isTexture&&(n.specularMap=this.specularMap.toJSON(t).uuid),this.specularIntensityMap&&this.specularIntensityMap.isTexture&&(n.specularIntensityMap=this.specularIntensityMap.toJSON(t).uuid),this.specularTintMap&&this.specularTintMap.isTexture&&(n.specularTintMap=this.specularTintMap.toJSON(t).uuid),this.envMap&&this.envMap.isTexture&&(n.envMap=this.envMap.toJSON(t).uuid,void 0!==this.combine&&(n.combine=this.combine)),void 0!==this.envMapIntensity&&(n.envMapIntensity=this.envMapIntensity),void 0!==this.reflectivity&&(n.reflectivity=this.reflectivity),void 0!==this.refractionRatio&&(n.refractionRatio=this.refractionRatio),this.gradientMap&&this.gradientMap.isTexture&&(n.gradientMap=this.gradientMap.toJSON(t).uuid),void 0!==this.transmission&&(n.transmission=this.transmission),this.transmissionMap&&this.transmissionMap.isTexture&&(n.transmissionMap=this.transmissionMap.toJSON(t).uuid),void 0!==this.thickness&&(n.thickness=this.thickness),this.thicknessMap&&this.thicknessMap.isTexture&&(n.thicknessMap=this.thicknessMap.toJSON(t).uuid),void 0!==this.attenuationDistance&&(n.attenuationDistance=this.attenuationDistance),void 0!==this.attenuationTint&&(n.attenuationTint=this.attenuationTint.getHex()),void 0!==this.size&&(n.size=this.size),null!==this.shadowSide&&(n.shadowSide=this.shadowSide),void 0!==this.sizeAttenuation&&(n.sizeAttenuation=this.sizeAttenuation),1!==this.blending&&(n.blending=this.blending),0!==this.side&&(n.side=this.side),this.vertexColors&&(n.vertexColors=!0),this.opacity<1&&(n.opacity=this.opacity),!0===this.transparent&&(n.transparent=this.transparent),n.depthFunc=this.depthFunc,n.depthTest=this.depthTest,n.depthWrite=this.depthWrite,n.colorWrite=this.colorWrite,n.stencilWrite=this.stencilWrite,n.stencilWriteMask=this.stencilWriteMask,n.stencilFunc=this.stencilFunc,n.stencilRef=this.stencilRef,n.stencilFuncMask=this.stencilFuncMask,n.stencilFail=this.stencilFail,n.stencilZFail=this.stencilZFail,n.stencilZPass=this.stencilZPass,this.rotation&&0!==this.rotation&&(n.rotation=this.rotation),!0===this.polygonOffset&&(n.polygonOffset=!0),0!==this.polygonOffsetFactor&&(n.polygonOffsetFactor=this.polygonOffsetFactor),0!==this.polygonOffsetUnits&&(n.polygonOffsetUnits=this.polygonOffsetUnits),this.linewidth&&1!==this.linewidth&&(n.linewidth=this.linewidth),void 0!==this.dashSize&&(n.dashSize=this.dashSize),void 0!==this.gapSize&&(n.gapSize=this.gapSize),void 0!==this.scale&&(n.scale=this.scale),!0===this.dithering&&(n.dithering=!0),this.alphaTest>0&&(n.alphaTest=this.alphaTest),!0===this.alphaToCoverage&&(n.alphaToCoverage=this.alphaToCoverage),!0===this.premultipliedAlpha&&(n.premultipliedAlpha=this.premultipliedAlpha),!0===this.wireframe&&(n.wireframe=this.wireframe),this.wireframeLinewidth>1&&(n.wireframeLinewidth=this.wireframeLinewidth),\\\\\\\"round\\\\\\\"!==this.wireframeLinecap&&(n.wireframeLinecap=this.wireframeLinecap),\\\\\\\"round\\\\\\\"!==this.wireframeLinejoin&&(n.wireframeLinejoin=this.wireframeLinejoin),!0===this.flatShading&&(n.flatShading=this.flatShading),!1===this.visible&&(n.visible=!1),!1===this.toneMapped&&(n.toneMapped=!1),\\\\\\\"{}\\\\\\\"!==JSON.stringify(this.userData)&&(n.userData=this.userData),e){const e=i(t.textures),r=i(t.images);e.length>0&&(n.textures=e),r.length>0&&(n.images=r)}return n}clone(){return(new this.constructor).copy(this)}copy(t){this.name=t.name,this.fog=t.fog,this.blending=t.blending,this.side=t.side,this.vertexColors=t.vertexColors,this.opacity=t.opacity,this.transparent=t.transparent,this.blendSrc=t.blendSrc,this.blendDst=t.blendDst,this.blendEquation=t.blendEquation,this.blendSrcAlpha=t.blendSrcAlpha,this.blendDstAlpha=t.blendDstAlpha,this.blendEquationAlpha=t.blendEquationAlpha,this.depthFunc=t.depthFunc,this.depthTest=t.depthTest,this.depthWrite=t.depthWrite,this.stencilWriteMask=t.stencilWriteMask,this.stencilFunc=t.stencilFunc,this.stencilRef=t.stencilRef,this.stencilFuncMask=t.stencilFuncMask,this.stencilFail=t.stencilFail,this.stencilZFail=t.stencilZFail,this.stencilZPass=t.stencilZPass,this.stencilWrite=t.stencilWrite;const e=t.clippingPlanes;let n=null;if(null!==e){const t=e.length;n=new Array(t);for(let i=0;i!==t;++i)n[i]=e[i].clone()}return this.clippingPlanes=n,this.clipIntersection=t.clipIntersection,this.clipShadows=t.clipShadows,this.shadowSide=t.shadowSide,this.colorWrite=t.colorWrite,this.precision=t.precision,this.polygonOffset=t.polygonOffset,this.polygonOffsetFactor=t.polygonOffsetFactor,this.polygonOffsetUnits=t.polygonOffsetUnits,this.dithering=t.dithering,this.alphaTest=t.alphaTest,this.alphaToCoverage=t.alphaToCoverage,this.premultipliedAlpha=t.premultipliedAlpha,this.visible=t.visible,this.toneMapped=t.toneMapped,this.userData=JSON.parse(JSON.stringify(t.userData)),this}dispose(){this.dispatchEvent({type:\\\\\\\"dispose\\\\\\\"})}set needsUpdate(t){!0===t&&this.version++}}We.prototype.isMaterial=!0;const je={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},qe={h:0,s:0,l:0},Xe={h:0,s:0,l:0};function Ye(t,e,n){return n<0&&(n+=1),n>1&&(n-=1),n<1/6?t+6*(e-t)*n:n<.5?e:n<2/3?t+6*(e-t)*(2/3-n):t}function Je(t){return t<.04045?.0773993808*t:Math.pow(.9478672986*t+.0521327014,2.4)}function Ze(t){return t<.0031308?12.92*t:1.055*Math.pow(t,.41666)-.055}class Qe{constructor(t,e,n){return void 0===e&&void 0===n?this.set(t):this.setRGB(t,e,n)}set(t){return t&&t.isColor?this.copy(t):\\\\\\\"number\\\\\\\"==typeof t?this.setHex(t):\\\\\\\"string\\\\\\\"==typeof t&&this.setStyle(t),this}setScalar(t){return this.r=t,this.g=t,this.b=t,this}setHex(t){return t=Math.floor(t),this.r=(t>>16&255)/255,this.g=(t>>8&255)/255,this.b=(255&t)/255,this}setRGB(t,e,n){return this.r=t,this.g=e,this.b=n,this}setHSL(t,e,n){if(t=ut(t,1),e=ht(e,0,1),n=ht(n,0,1),0===e)this.r=this.g=this.b=n;else{const i=n<=.5?n*(1+e):n+e-n*e,r=2*n-i;this.r=Ye(r,i,t+1/3),this.g=Ye(r,i,t),this.b=Ye(r,i,t-1/3)}return this}setStyle(t){function e(e){void 0!==e&&parseFloat(e)<1&&console.warn(\\\\\\\"THREE.Color: Alpha component of \\\\\\\"+t+\\\\\\\" will be ignored.\\\\\\\")}let n;if(n=/^((?:rgb|hsl)a?)\\\\(([^\\\\)]*)\\\\)/.exec(t)){let t;const i=n[1],r=n[2];switch(i){case\\\\\\\"rgb\\\\\\\":case\\\\\\\"rgba\\\\\\\":if(t=/^\\\\s*(\\\\d+)\\\\s*,\\\\s*(\\\\d+)\\\\s*,\\\\s*(\\\\d+)\\\\s*(?:,\\\\s*(\\\\d*\\\\.?\\\\d+)\\\\s*)?$/.exec(r))return this.r=Math.min(255,parseInt(t[1],10))/255,this.g=Math.min(255,parseInt(t[2],10))/255,this.b=Math.min(255,parseInt(t[3],10))/255,e(t[4]),this;if(t=/^\\\\s*(\\\\d+)\\\\%\\\\s*,\\\\s*(\\\\d+)\\\\%\\\\s*,\\\\s*(\\\\d+)\\\\%\\\\s*(?:,\\\\s*(\\\\d*\\\\.?\\\\d+)\\\\s*)?$/.exec(r))return this.r=Math.min(100,parseInt(t[1],10))/100,this.g=Math.min(100,parseInt(t[2],10))/100,this.b=Math.min(100,parseInt(t[3],10))/100,e(t[4]),this;break;case\\\\\\\"hsl\\\\\\\":case\\\\\\\"hsla\\\\\\\":if(t=/^\\\\s*(\\\\d*\\\\.?\\\\d+)\\\\s*,\\\\s*(\\\\d+)\\\\%\\\\s*,\\\\s*(\\\\d+)\\\\%\\\\s*(?:,\\\\s*(\\\\d*\\\\.?\\\\d+)\\\\s*)?$/.exec(r)){const n=parseFloat(t[1])/360,i=parseInt(t[2],10)/100,r=parseInt(t[3],10)/100;return e(t[4]),this.setHSL(n,i,r)}}}else if(n=/^\\\\#([A-Fa-f\\\\d]+)$/.exec(t)){const t=n[1],e=t.length;if(3===e)return this.r=parseInt(t.charAt(0)+t.charAt(0),16)/255,this.g=parseInt(t.charAt(1)+t.charAt(1),16)/255,this.b=parseInt(t.charAt(2)+t.charAt(2),16)/255,this;if(6===e)return this.r=parseInt(t.charAt(0)+t.charAt(1),16)/255,this.g=parseInt(t.charAt(2)+t.charAt(3),16)/255,this.b=parseInt(t.charAt(4)+t.charAt(5),16)/255,this}return t&&t.length>0?this.setColorName(t):this}setColorName(t){const e=je[t.toLowerCase()];return void 0!==e?this.setHex(e):console.warn(\\\\\\\"THREE.Color: Unknown color \\\\\\\"+t),this}clone(){return new this.constructor(this.r,this.g,this.b)}copy(t){return this.r=t.r,this.g=t.g,this.b=t.b,this}copyGammaToLinear(t,e=2){return this.r=Math.pow(t.r,e),this.g=Math.pow(t.g,e),this.b=Math.pow(t.b,e),this}copyLinearToGamma(t,e=2){const n=e>0?1/e:1;return this.r=Math.pow(t.r,n),this.g=Math.pow(t.g,n),this.b=Math.pow(t.b,n),this}convertGammaToLinear(t){return this.copyGammaToLinear(this,t),this}convertLinearToGamma(t){return this.copyLinearToGamma(this,t),this}copySRGBToLinear(t){return this.r=Je(t.r),this.g=Je(t.g),this.b=Je(t.b),this}copyLinearToSRGB(t){return this.r=Ze(t.r),this.g=Ze(t.g),this.b=Ze(t.b),this}convertSRGBToLinear(){return this.copySRGBToLinear(this),this}convertLinearToSRGB(){return this.copyLinearToSRGB(this),this}getHex(){return 255*this.r<<16^255*this.g<<8^255*this.b<<0}getHexString(){return(\\\\\\\"000000\\\\\\\"+this.getHex().toString(16)).slice(-6)}getHSL(t){const e=this.r,n=this.g,i=this.b,r=Math.max(e,n,i),s=Math.min(e,n,i);let a,o;const l=(s+r)/2;if(s===r)a=0,o=0;else{const t=r-s;switch(o=l<=.5?t/(r+s):t/(2-r-s),r){case e:a=(n-i)/t+(n<i?6:0);break;case n:a=(i-e)/t+2;break;case i:a=(e-n)/t+4}a/=6}return t.h=a,t.s=o,t.l=l,t}getStyle(){return\\\\\\\"rgb(\\\\\\\"+(255*this.r|0)+\\\\\\\",\\\\\\\"+(255*this.g|0)+\\\\\\\",\\\\\\\"+(255*this.b|0)+\\\\\\\")\\\\\\\"}offsetHSL(t,e,n){return this.getHSL(qe),qe.h+=t,qe.s+=e,qe.l+=n,this.setHSL(qe.h,qe.s,qe.l),this}add(t){return this.r+=t.r,this.g+=t.g,this.b+=t.b,this}addColors(t,e){return this.r=t.r+e.r,this.g=t.g+e.g,this.b=t.b+e.b,this}addScalar(t){return this.r+=t,this.g+=t,this.b+=t,this}sub(t){return this.r=Math.max(0,this.r-t.r),this.g=Math.max(0,this.g-t.g),this.b=Math.max(0,this.b-t.b),this}multiply(t){return this.r*=t.r,this.g*=t.g,this.b*=t.b,this}multiplyScalar(t){return this.r*=t,this.g*=t,this.b*=t,this}lerp(t,e){return this.r+=(t.r-this.r)*e,this.g+=(t.g-this.g)*e,this.b+=(t.b-this.b)*e,this}lerpColors(t,e,n){return this.r=t.r+(e.r-t.r)*n,this.g=t.g+(e.g-t.g)*n,this.b=t.b+(e.b-t.b)*n,this}lerpHSL(t,e){this.getHSL(qe),t.getHSL(Xe);const n=dt(qe.h,Xe.h,e),i=dt(qe.s,Xe.s,e),r=dt(qe.l,Xe.l,e);return this.setHSL(n,i,r),this}equals(t){return t.r===this.r&&t.g===this.g&&t.b===this.b}fromArray(t,e=0){return this.r=t[e],this.g=t[e+1],this.b=t[e+2],this}toArray(t=[],e=0){return t[e]=this.r,t[e+1]=this.g,t[e+2]=this.b,t}fromBufferAttribute(t,e){return this.r=t.getX(e),this.g=t.getY(e),this.b=t.getZ(e),!0===t.normalized&&(this.r/=255,this.g/=255,this.b/=255),this}toJSON(){return this.getHex()}}Qe.NAMES=je,Qe.prototype.isColor=!0,Qe.prototype.r=1,Qe.prototype.g=1,Qe.prototype.b=1;class Ke extends We{constructor(t){super(),this.type=\\\\\\\"MeshBasicMaterial\\\\\\\",this.color=new Qe(16777215),this.map=null,this.lightMap=null,this.lightMapIntensity=1,this.aoMap=null,this.aoMapIntensity=1,this.specularMap=null,this.alphaMap=null,this.envMap=null,this.combine=0,this.reflectivity=1,this.refractionRatio=.98,this.wireframe=!1,this.wireframeLinewidth=1,this.wireframeLinecap=\\\\\\\"round\\\\\\\",this.wireframeLinejoin=\\\\\\\"round\\\\\\\",this.setValues(t)}copy(t){return super.copy(t),this.color.copy(t.color),this.map=t.map,this.lightMap=t.lightMap,this.lightMapIntensity=t.lightMapIntensity,this.aoMap=t.aoMap,this.aoMapIntensity=t.aoMapIntensity,this.specularMap=t.specularMap,this.alphaMap=t.alphaMap,this.envMap=t.envMap,this.combine=t.combine,this.reflectivity=t.reflectivity,this.refractionRatio=t.refractionRatio,this.wireframe=t.wireframe,this.wireframeLinewidth=t.wireframeLinewidth,this.wireframeLinecap=t.wireframeLinecap,this.wireframeLinejoin=t.wireframeLinejoin,this}}Ke.prototype.isMeshBasicMaterial=!0;const $e=new Rt,tn=new vt;class en{constructor(t,e,n){if(Array.isArray(t))throw new TypeError(\\\\\\\"THREE.BufferAttribute: array should be a Typed Array.\\\\\\\");this.name=\\\\\\\"\\\\\\\",this.array=t,this.itemSize=e,this.count=void 0!==t?t.length/e:0,this.normalized=!0===n,this.usage=et,this.updateRange={offset:0,count:-1},this.version=0}onUploadCallback(){}set needsUpdate(t){!0===t&&this.version++}setUsage(t){return this.usage=t,this}copy(t){return this.name=t.name,this.array=new t.array.constructor(t.array),this.itemSize=t.itemSize,this.count=t.count,this.normalized=t.normalized,this.usage=t.usage,this}copyAt(t,e,n){t*=this.itemSize,n*=e.itemSize;for(let i=0,r=this.itemSize;i<r;i++)this.array[t+i]=e.array[n+i];return this}copyArray(t){return this.array.set(t),this}copyColorsArray(t){const e=this.array;let n=0;for(let i=0,r=t.length;i<r;i++){let r=t[i];void 0===r&&(console.warn(\\\\\\\"THREE.BufferAttribute.copyColorsArray(): color is undefined\\\\\\\",i),r=new Qe),e[n++]=r.r,e[n++]=r.g,e[n++]=r.b}return this}copyVector2sArray(t){const e=this.array;let n=0;for(let i=0,r=t.length;i<r;i++){let r=t[i];void 0===r&&(console.warn(\\\\\\\"THREE.BufferAttribute.copyVector2sArray(): vector is undefined\\\\\\\",i),r=new vt),e[n++]=r.x,e[n++]=r.y}return this}copyVector3sArray(t){const e=this.array;let n=0;for(let i=0,r=t.length;i<r;i++){let r=t[i];void 0===r&&(console.warn(\\\\\\\"THREE.BufferAttribute.copyVector3sArray(): vector is undefined\\\\\\\",i),r=new Rt),e[n++]=r.x,e[n++]=r.y,e[n++]=r.z}return this}copyVector4sArray(t){const e=this.array;let n=0;for(let i=0,r=t.length;i<r;i++){let r=t[i];void 0===r&&(console.warn(\\\\\\\"THREE.BufferAttribute.copyVector4sArray(): vector is undefined\\\\\\\",i),r=new St),e[n++]=r.x,e[n++]=r.y,e[n++]=r.z,e[n++]=r.w}return this}applyMatrix3(t){if(2===this.itemSize)for(let e=0,n=this.count;e<n;e++)tn.fromBufferAttribute(this,e),tn.applyMatrix3(t),this.setXY(e,tn.x,tn.y);else if(3===this.itemSize)for(let e=0,n=this.count;e<n;e++)$e.fromBufferAttribute(this,e),$e.applyMatrix3(t),this.setXYZ(e,$e.x,$e.y,$e.z);return this}applyMatrix4(t){for(let e=0,n=this.count;e<n;e++)$e.x=this.getX(e),$e.y=this.getY(e),$e.z=this.getZ(e),$e.applyMatrix4(t),this.setXYZ(e,$e.x,$e.y,$e.z);return this}applyNormalMatrix(t){for(let e=0,n=this.count;e<n;e++)$e.x=this.getX(e),$e.y=this.getY(e),$e.z=this.getZ(e),$e.applyNormalMatrix(t),this.setXYZ(e,$e.x,$e.y,$e.z);return this}transformDirection(t){for(let e=0,n=this.count;e<n;e++)$e.x=this.getX(e),$e.y=this.getY(e),$e.z=this.getZ(e),$e.transformDirection(t),this.setXYZ(e,$e.x,$e.y,$e.z);return this}set(t,e=0){return this.array.set(t,e),this}getX(t){return this.array[t*this.itemSize]}setX(t,e){return this.array[t*this.itemSize]=e,this}getY(t){return this.array[t*this.itemSize+1]}setY(t,e){return this.array[t*this.itemSize+1]=e,this}getZ(t){return this.array[t*this.itemSize+2]}setZ(t,e){return this.array[t*this.itemSize+2]=e,this}getW(t){return this.array[t*this.itemSize+3]}setW(t,e){return this.array[t*this.itemSize+3]=e,this}setXY(t,e,n){return t*=this.itemSize,this.array[t+0]=e,this.array[t+1]=n,this}setXYZ(t,e,n,i){return t*=this.itemSize,this.array[t+0]=e,this.array[t+1]=n,this.array[t+2]=i,this}setXYZW(t,e,n,i,r){return t*=this.itemSize,this.array[t+0]=e,this.array[t+1]=n,this.array[t+2]=i,this.array[t+3]=r,this}onUpload(t){return this.onUploadCallback=t,this}clone(){return new this.constructor(this.array,this.itemSize).copy(this)}toJSON(){const t={itemSize:this.itemSize,type:this.array.constructor.name,array:Array.prototype.slice.call(this.array),normalized:this.normalized};return\\\\\\\"\\\\\\\"!==this.name&&(t.name=this.name),this.usage!==et&&(t.usage=this.usage),0===this.updateRange.offset&&-1===this.updateRange.count||(t.updateRange=this.updateRange),t}}en.prototype.isBufferAttribute=!0;class nn extends en{constructor(t,e,n){super(new Int8Array(t),e,n)}}class rn extends en{constructor(t,e,n){super(new Uint8Array(t),e,n)}}class sn extends en{constructor(t,e,n){super(new Uint8ClampedArray(t),e,n)}}class an extends en{constructor(t,e,n){super(new Int16Array(t),e,n)}}class on extends en{constructor(t,e,n){super(new Uint16Array(t),e,n)}}class ln extends en{constructor(t,e,n){super(new Int32Array(t),e,n)}}class cn extends en{constructor(t,e,n){super(new Uint32Array(t),e,n)}}class hn extends en{constructor(t,e,n){super(new Uint16Array(t),e,n)}}hn.prototype.isFloat16BufferAttribute=!0;class un extends en{constructor(t,e,n){super(new Float32Array(t),e,n)}}class dn extends en{constructor(t,e,n){super(new Float64Array(t),e,n)}}function pn(t){if(0===t.length)return-1/0;let e=t[0];for(let n=1,i=t.length;n<i;++n)t[n]>e&&(e=t[n]);return e}const mn={Int8Array:Int8Array,Uint8Array:Uint8Array,Uint8ClampedArray:Uint8ClampedArray,Int16Array:Int16Array,Uint16Array:Uint16Array,Int32Array:Int32Array,Uint32Array:Uint32Array,Float32Array:Float32Array,Float64Array:Float64Array};function fn(t,e){return new mn[t](e)}let gn=0;const vn=new ae,yn=new Pe,xn=new Rt,_n=new It,Mn=new It,bn=new Rt;class wn extends rt{constructor(){super(),Object.defineProperty(this,\\\\\\\"id\\\\\\\",{value:gn++}),this.uuid=ct(),this.name=\\\\\\\"\\\\\\\",this.type=\\\\\\\"BufferGeometry\\\\\\\",this.index=null,this.attributes={},this.morphAttributes={},this.morphTargetsRelative=!1,this.groups=[],this.boundingBox=null,this.boundingSphere=null,this.drawRange={start:0,count:1/0},this.userData={}}getIndex(){return this.index}setIndex(t){return Array.isArray(t)?this.index=new(pn(t)>65535?cn:on)(t,1):this.index=t,this}getAttribute(t){return this.attributes[t]}setAttribute(t,e){return this.attributes[t]=e,this}deleteAttribute(t){return delete this.attributes[t],this}hasAttribute(t){return void 0!==this.attributes[t]}addGroup(t,e,n=0){this.groups.push({start:t,count:e,materialIndex:n})}clearGroups(){this.groups=[]}setDrawRange(t,e){this.drawRange.start=t,this.drawRange.count=e}applyMatrix4(t){const e=this.attributes.position;void 0!==e&&(e.applyMatrix4(t),e.needsUpdate=!0);const n=this.attributes.normal;if(void 0!==n){const e=(new yt).getNormalMatrix(t);n.applyNormalMatrix(e),n.needsUpdate=!0}const i=this.attributes.tangent;return void 0!==i&&(i.transformDirection(t),i.needsUpdate=!0),null!==this.boundingBox&&this.computeBoundingBox(),null!==this.boundingSphere&&this.computeBoundingSphere(),this}applyQuaternion(t){return vn.makeRotationFromQuaternion(t),this.applyMatrix4(vn),this}rotateX(t){return vn.makeRotationX(t),this.applyMatrix4(vn),this}rotateY(t){return vn.makeRotationY(t),this.applyMatrix4(vn),this}rotateZ(t){return vn.makeRotationZ(t),this.applyMatrix4(vn),this}translate(t,e,n){return vn.makeTranslation(t,e,n),this.applyMatrix4(vn),this}scale(t,e,n){return vn.makeScale(t,e,n),this.applyMatrix4(vn),this}lookAt(t){return yn.lookAt(t),yn.updateMatrix(),this.applyMatrix4(yn.matrix),this}center(){return this.computeBoundingBox(),this.boundingBox.getCenter(xn).negate(),this.translate(xn.x,xn.y,xn.z),this}setFromPoints(t){const e=[];for(let n=0,i=t.length;n<i;n++){const i=t[n];e.push(i.x,i.y,i.z||0)}return this.setAttribute(\\\\\\\"position\\\\\\\",new un(e,3)),this}computeBoundingBox(){null===this.boundingBox&&(this.boundingBox=new It);const t=this.attributes.position,e=this.morphAttributes.position;if(t&&t.isGLBufferAttribute)return console.error('THREE.BufferGeometry.computeBoundingBox(): GLBufferAttribute requires a manual bounding box. Alternatively set \\\\\\\"mesh.frustumCulled\\\\\\\" to \\\\\\\"false\\\\\\\".',this),void this.boundingBox.set(new Rt(-1/0,-1/0,-1/0),new Rt(1/0,1/0,1/0));if(void 0!==t){if(this.boundingBox.setFromBufferAttribute(t),e)for(let t=0,n=e.length;t<n;t++){const n=e[t];_n.setFromBufferAttribute(n),this.morphTargetsRelative?(bn.addVectors(this.boundingBox.min,_n.min),this.boundingBox.expandByPoint(bn),bn.addVectors(this.boundingBox.max,_n.max),this.boundingBox.expandByPoint(bn)):(this.boundingBox.expandByPoint(_n.min),this.boundingBox.expandByPoint(_n.max))}}else this.boundingBox.makeEmpty();(isNaN(this.boundingBox.min.x)||isNaN(this.boundingBox.min.y)||isNaN(this.boundingBox.min.z))&&console.error('THREE.BufferGeometry.computeBoundingBox(): Computed min/max have NaN values. The \\\\\\\"position\\\\\\\" attribute is likely to have NaN values.',this)}computeBoundingSphere(){null===this.boundingSphere&&(this.boundingSphere=new Qt);const t=this.attributes.position,e=this.morphAttributes.position;if(t&&t.isGLBufferAttribute)return console.error('THREE.BufferGeometry.computeBoundingSphere(): GLBufferAttribute requires a manual bounding sphere. Alternatively set \\\\\\\"mesh.frustumCulled\\\\\\\" to \\\\\\\"false\\\\\\\".',this),void this.boundingSphere.set(new Rt,1/0);if(t){const n=this.boundingSphere.center;if(_n.setFromBufferAttribute(t),e)for(let t=0,n=e.length;t<n;t++){const n=e[t];Mn.setFromBufferAttribute(n),this.morphTargetsRelative?(bn.addVectors(_n.min,Mn.min),_n.expandByPoint(bn),bn.addVectors(_n.max,Mn.max),_n.expandByPoint(bn)):(_n.expandByPoint(Mn.min),_n.expandByPoint(Mn.max))}_n.getCenter(n);let i=0;for(let e=0,r=t.count;e<r;e++)bn.fromBufferAttribute(t,e),i=Math.max(i,n.distanceToSquared(bn));if(e)for(let r=0,s=e.length;r<s;r++){const s=e[r],a=this.morphTargetsRelative;for(let e=0,r=s.count;e<r;e++)bn.fromBufferAttribute(s,e),a&&(xn.fromBufferAttribute(t,e),bn.add(xn)),i=Math.max(i,n.distanceToSquared(bn))}this.boundingSphere.radius=Math.sqrt(i),isNaN(this.boundingSphere.radius)&&console.error('THREE.BufferGeometry.computeBoundingSphere(): Computed radius is NaN. The \\\\\\\"position\\\\\\\" attribute is likely to have NaN values.',this)}}computeFaceNormals(){}computeTangents(){const t=this.index,e=this.attributes;if(null===t||void 0===e.position||void 0===e.normal||void 0===e.uv)return void console.error(\\\\\\\"THREE.BufferGeometry: .computeTangents() failed. Missing required attributes (index, position, normal or uv)\\\\\\\");const n=t.array,i=e.position.array,r=e.normal.array,s=e.uv.array,a=i.length/3;void 0===e.tangent&&this.setAttribute(\\\\\\\"tangent\\\\\\\",new en(new Float32Array(4*a),4));const o=e.tangent.array,l=[],c=[];for(let t=0;t<a;t++)l[t]=new Rt,c[t]=new Rt;const h=new Rt,u=new Rt,d=new Rt,p=new vt,m=new vt,f=new vt,g=new Rt,v=new Rt;function y(t,e,n){h.fromArray(i,3*t),u.fromArray(i,3*e),d.fromArray(i,3*n),p.fromArray(s,2*t),m.fromArray(s,2*e),f.fromArray(s,2*n),u.sub(h),d.sub(h),m.sub(p),f.sub(p);const r=1/(m.x*f.y-f.x*m.y);isFinite(r)&&(g.copy(u).multiplyScalar(f.y).addScaledVector(d,-m.y).multiplyScalar(r),v.copy(d).multiplyScalar(m.x).addScaledVector(u,-f.x).multiplyScalar(r),l[t].add(g),l[e].add(g),l[n].add(g),c[t].add(v),c[e].add(v),c[n].add(v))}let x=this.groups;0===x.length&&(x=[{start:0,count:n.length}]);for(let t=0,e=x.length;t<e;++t){const e=x[t],i=e.start;for(let t=i,r=i+e.count;t<r;t+=3)y(n[t+0],n[t+1],n[t+2])}const _=new Rt,M=new Rt,b=new Rt,w=new Rt;function S(t){b.fromArray(r,3*t),w.copy(b);const e=l[t];_.copy(e),_.sub(b.multiplyScalar(b.dot(e))).normalize(),M.crossVectors(w,e);const n=M.dot(c[t])<0?-1:1;o[4*t]=_.x,o[4*t+1]=_.y,o[4*t+2]=_.z,o[4*t+3]=n}for(let t=0,e=x.length;t<e;++t){const e=x[t],i=e.start;for(let t=i,r=i+e.count;t<r;t+=3)S(n[t+0]),S(n[t+1]),S(n[t+2])}}computeVertexNormals(){const t=this.index,e=this.getAttribute(\\\\\\\"position\\\\\\\");if(void 0!==e){let n=this.getAttribute(\\\\\\\"normal\\\\\\\");if(void 0===n)n=new en(new Float32Array(3*e.count),3),this.setAttribute(\\\\\\\"normal\\\\\\\",n);else for(let t=0,e=n.count;t<e;t++)n.setXYZ(t,0,0,0);const i=new Rt,r=new Rt,s=new Rt,a=new Rt,o=new Rt,l=new Rt,c=new Rt,h=new Rt;if(t)for(let u=0,d=t.count;u<d;u+=3){const d=t.getX(u+0),p=t.getX(u+1),m=t.getX(u+2);i.fromBufferAttribute(e,d),r.fromBufferAttribute(e,p),s.fromBufferAttribute(e,m),c.subVectors(s,r),h.subVectors(i,r),c.cross(h),a.fromBufferAttribute(n,d),o.fromBufferAttribute(n,p),l.fromBufferAttribute(n,m),a.add(c),o.add(c),l.add(c),n.setXYZ(d,a.x,a.y,a.z),n.setXYZ(p,o.x,o.y,o.z),n.setXYZ(m,l.x,l.y,l.z)}else for(let t=0,a=e.count;t<a;t+=3)i.fromBufferAttribute(e,t+0),r.fromBufferAttribute(e,t+1),s.fromBufferAttribute(e,t+2),c.subVectors(s,r),h.subVectors(i,r),c.cross(h),n.setXYZ(t+0,c.x,c.y,c.z),n.setXYZ(t+1,c.x,c.y,c.z),n.setXYZ(t+2,c.x,c.y,c.z);this.normalizeNormals(),n.needsUpdate=!0}}merge(t,e){if(!t||!t.isBufferGeometry)return void console.error(\\\\\\\"THREE.BufferGeometry.merge(): geometry not an instance of THREE.BufferGeometry.\\\\\\\",t);void 0===e&&(e=0,console.warn(\\\\\\\"THREE.BufferGeometry.merge(): Overwriting original geometry, starting at offset=0. Use BufferGeometryUtils.mergeBufferGeometries() for lossless merge.\\\\\\\"));const n=this.attributes;for(const i in n){if(void 0===t.attributes[i])continue;const r=n[i].array,s=t.attributes[i],a=s.array,o=s.itemSize*e,l=Math.min(a.length,r.length-o);for(let t=0,e=o;t<l;t++,e++)r[e]=a[t]}return this}normalizeNormals(){const t=this.attributes.normal;for(let e=0,n=t.count;e<n;e++)bn.fromBufferAttribute(t,e),bn.normalize(),t.setXYZ(e,bn.x,bn.y,bn.z)}toNonIndexed(){function t(t,e){const n=t.array,i=t.itemSize,r=t.normalized,s=new n.constructor(e.length*i);let a=0,o=0;for(let r=0,l=e.length;r<l;r++){a=t.isInterleavedBufferAttribute?e[r]*t.data.stride+t.offset:e[r]*i;for(let t=0;t<i;t++)s[o++]=n[a++]}return new en(s,i,r)}if(null===this.index)return console.warn(\\\\\\\"THREE.BufferGeometry.toNonIndexed(): BufferGeometry is already non-indexed.\\\\\\\"),this;const e=new wn,n=this.index.array,i=this.attributes;for(const r in i){const s=t(i[r],n);e.setAttribute(r,s)}const r=this.morphAttributes;for(const i in r){const s=[],a=r[i];for(let e=0,i=a.length;e<i;e++){const i=t(a[e],n);s.push(i)}e.morphAttributes[i]=s}e.morphTargetsRelative=this.morphTargetsRelative;const s=this.groups;for(let t=0,n=s.length;t<n;t++){const n=s[t];e.addGroup(n.start,n.count,n.materialIndex)}return e}toJSON(){const t={metadata:{version:4.5,type:\\\\\\\"BufferGeometry\\\\\\\",generator:\\\\\\\"BufferGeometry.toJSON\\\\\\\"}};if(t.uuid=this.uuid,t.type=this.type,\\\\\\\"\\\\\\\"!==this.name&&(t.name=this.name),Object.keys(this.userData).length>0&&(t.userData=this.userData),void 0!==this.parameters){const e=this.parameters;for(const n in e)void 0!==e[n]&&(t[n]=e[n]);return t}t.data={attributes:{}};const e=this.index;null!==e&&(t.data.index={type:e.array.constructor.name,array:Array.prototype.slice.call(e.array)});const n=this.attributes;for(const e in n){const i=n[e];t.data.attributes[e]=i.toJSON(t.data)}const i={};let r=!1;for(const e in this.morphAttributes){const n=this.morphAttributes[e],s=[];for(let e=0,i=n.length;e<i;e++){const i=n[e];s.push(i.toJSON(t.data))}s.length>0&&(i[e]=s,r=!0)}r&&(t.data.morphAttributes=i,t.data.morphTargetsRelative=this.morphTargetsRelative);const s=this.groups;s.length>0&&(t.data.groups=JSON.parse(JSON.stringify(s)));const a=this.boundingSphere;return null!==a&&(t.data.boundingSphere={center:a.center.toArray(),radius:a.radius}),t}clone(){return(new wn).copy(this)}copy(t){this.index=null,this.attributes={},this.morphAttributes={},this.groups=[],this.boundingBox=null,this.boundingSphere=null;const e={};this.name=t.name;const n=t.index;null!==n&&this.setIndex(n.clone(e));const i=t.attributes;for(const t in i){const n=i[t];this.setAttribute(t,n.clone(e))}const r=t.morphAttributes;for(const t in r){const n=[],i=r[t];for(let t=0,r=i.length;t<r;t++)n.push(i[t].clone(e));this.morphAttributes[t]=n}this.morphTargetsRelative=t.morphTargetsRelative;const s=t.groups;for(let t=0,e=s.length;t<e;t++){const e=s[t];this.addGroup(e.start,e.count,e.materialIndex)}const a=t.boundingBox;null!==a&&(this.boundingBox=a.clone());const o=t.boundingSphere;return null!==o&&(this.boundingSphere=o.clone()),this.drawRange.start=t.drawRange.start,this.drawRange.count=t.drawRange.count,this.userData=t.userData,this}dispose(){this.dispatchEvent({type:\\\\\\\"dispose\\\\\\\"})}}wn.prototype.isBufferGeometry=!0;const Sn=new ae,Tn=new se,En=new Qt,An=new Rt,Ln=new Rt,Rn=new Rt,Cn=new Rt,Pn=new Rt,In=new Rt,Dn=new Rt,Nn=new Rt,Bn=new Rt,zn=new vt,Fn=new vt,On=new vt,Un=new Rt,Hn=new Rt;class Gn extends Pe{constructor(t=new wn,e=new Ke){super(),this.type=\\\\\\\"Mesh\\\\\\\",this.geometry=t,this.material=e,this.updateMorphTargets()}copy(t){return super.copy(t),void 0!==t.morphTargetInfluences&&(this.morphTargetInfluences=t.morphTargetInfluences.slice()),void 0!==t.morphTargetDictionary&&(this.morphTargetDictionary=Object.assign({},t.morphTargetDictionary)),this.material=t.material,this.geometry=t.geometry,this}updateMorphTargets(){const t=this.geometry;if(t.isBufferGeometry){const e=t.morphAttributes,n=Object.keys(e);if(n.length>0){const t=e[n[0]];if(void 0!==t){this.morphTargetInfluences=[],this.morphTargetDictionary={};for(let e=0,n=t.length;e<n;e++){const n=t[e].name||String(e);this.morphTargetInfluences.push(0),this.morphTargetDictionary[n]=e}}}}else{const e=t.morphTargets;void 0!==e&&e.length>0&&console.error(\\\\\\\"THREE.Mesh.updateMorphTargets() no longer supports THREE.Geometry. Use THREE.BufferGeometry instead.\\\\\\\")}}raycast(t,e){const n=this.geometry,i=this.material,r=this.matrixWorld;if(void 0===i)return;if(null===n.boundingSphere&&n.computeBoundingSphere(),En.copy(n.boundingSphere),En.applyMatrix4(r),!1===t.ray.intersectsSphere(En))return;if(Sn.copy(r).invert(),Tn.copy(t.ray).applyMatrix4(Sn),null!==n.boundingBox&&!1===Tn.intersectsBox(n.boundingBox))return;let s;if(n.isBufferGeometry){const r=n.index,a=n.attributes.position,o=n.morphAttributes.position,l=n.morphTargetsRelative,c=n.attributes.uv,h=n.attributes.uv2,u=n.groups,d=n.drawRange;if(null!==r)if(Array.isArray(i))for(let n=0,p=u.length;n<p;n++){const p=u[n],m=i[p.materialIndex];for(let n=Math.max(p.start,d.start),i=Math.min(p.start+p.count,d.start+d.count);n<i;n+=3){const i=r.getX(n),u=r.getX(n+1),d=r.getX(n+2);s=kn(this,m,t,Tn,a,o,l,c,h,i,u,d),s&&(s.faceIndex=Math.floor(n/3),s.face.materialIndex=p.materialIndex,e.push(s))}}else{for(let n=Math.max(0,d.start),u=Math.min(r.count,d.start+d.count);n<u;n+=3){const u=r.getX(n),d=r.getX(n+1),p=r.getX(n+2);s=kn(this,i,t,Tn,a,o,l,c,h,u,d,p),s&&(s.faceIndex=Math.floor(n/3),e.push(s))}}else if(void 0!==a)if(Array.isArray(i))for(let n=0,r=u.length;n<r;n++){const r=u[n],p=i[r.materialIndex];for(let n=Math.max(r.start,d.start),i=Math.min(r.start+r.count,d.start+d.count);n<i;n+=3){s=kn(this,p,t,Tn,a,o,l,c,h,n,n+1,n+2),s&&(s.faceIndex=Math.floor(n/3),s.face.materialIndex=r.materialIndex,e.push(s))}}else{for(let n=Math.max(0,d.start),r=Math.min(a.count,d.start+d.count);n<r;n+=3){s=kn(this,i,t,Tn,a,o,l,c,h,n,n+1,n+2),s&&(s.faceIndex=Math.floor(n/3),e.push(s))}}}else n.isGeometry&&console.error(\\\\\\\"THREE.Mesh.raycast() no longer supports THREE.Geometry. Use THREE.BufferGeometry instead.\\\\\\\")}}function kn(t,e,n,i,r,s,a,o,l,c,h,u){An.fromBufferAttribute(r,c),Ln.fromBufferAttribute(r,h),Rn.fromBufferAttribute(r,u);const d=t.morphTargetInfluences;if(s&&d){Dn.set(0,0,0),Nn.set(0,0,0),Bn.set(0,0,0);for(let t=0,e=s.length;t<e;t++){const e=d[t],n=s[t];0!==e&&(Cn.fromBufferAttribute(n,c),Pn.fromBufferAttribute(n,h),In.fromBufferAttribute(n,u),a?(Dn.addScaledVector(Cn,e),Nn.addScaledVector(Pn,e),Bn.addScaledVector(In,e)):(Dn.addScaledVector(Cn.sub(An),e),Nn.addScaledVector(Pn.sub(Ln),e),Bn.addScaledVector(In.sub(Rn),e)))}An.add(Dn),Ln.add(Nn),Rn.add(Bn)}t.isSkinnedMesh&&(t.boneTransform(c,An),t.boneTransform(h,Ln),t.boneTransform(u,Rn));const p=function(t,e,n,i,r,s,a,o){let l;if(l=1===e.side?i.intersectTriangle(a,s,r,!0,o):i.intersectTriangle(r,s,a,2!==e.side,o),null===l)return null;Hn.copy(o),Hn.applyMatrix4(t.matrixWorld);const c=n.ray.origin.distanceTo(Hn);return c<n.near||c>n.far?null:{distance:c,point:Hn.clone(),object:t}}(t,e,n,i,An,Ln,Rn,Un);if(p){o&&(zn.fromBufferAttribute(o,c),Fn.fromBufferAttribute(o,h),On.fromBufferAttribute(o,u),p.uv=ke.getUV(Un,An,Ln,Rn,zn,Fn,On,new vt)),l&&(zn.fromBufferAttribute(l,c),Fn.fromBufferAttribute(l,h),On.fromBufferAttribute(l,u),p.uv2=ke.getUV(Un,An,Ln,Rn,zn,Fn,On,new vt));const t={a:c,b:h,c:u,normal:new Rt,materialIndex:0};ke.getNormal(An,Ln,Rn,t.normal),p.face=t}return p}Gn.prototype.isMesh=!0;class Vn extends wn{constructor(t=1,e=1,n=1,i=1,r=1,s=1){super(),this.type=\\\\\\\"BoxGeometry\\\\\\\",this.parameters={width:t,height:e,depth:n,widthSegments:i,heightSegments:r,depthSegments:s};const a=this;i=Math.floor(i),r=Math.floor(r),s=Math.floor(s);const o=[],l=[],c=[],h=[];let u=0,d=0;function p(t,e,n,i,r,s,p,m,f,g,v){const y=s/f,x=p/g,_=s/2,M=p/2,b=m/2,w=f+1,S=g+1;let T=0,E=0;const A=new Rt;for(let s=0;s<S;s++){const a=s*x-M;for(let o=0;o<w;o++){const u=o*y-_;A[t]=u*i,A[e]=a*r,A[n]=b,l.push(A.x,A.y,A.z),A[t]=0,A[e]=0,A[n]=m>0?1:-1,c.push(A.x,A.y,A.z),h.push(o/f),h.push(1-s/g),T+=1}}for(let t=0;t<g;t++)for(let e=0;e<f;e++){const n=u+e+w*t,i=u+e+w*(t+1),r=u+(e+1)+w*(t+1),s=u+(e+1)+w*t;o.push(n,i,s),o.push(i,r,s),E+=6}a.addGroup(d,E,v),d+=E,u+=T}p(\\\\\\\"z\\\\\\\",\\\\\\\"y\\\\\\\",\\\\\\\"x\\\\\\\",-1,-1,n,e,t,s,r,0),p(\\\\\\\"z\\\\\\\",\\\\\\\"y\\\\\\\",\\\\\\\"x\\\\\\\",1,-1,n,e,-t,s,r,1),p(\\\\\\\"x\\\\\\\",\\\\\\\"z\\\\\\\",\\\\\\\"y\\\\\\\",1,1,t,n,e,i,s,2),p(\\\\\\\"x\\\\\\\",\\\\\\\"z\\\\\\\",\\\\\\\"y\\\\\\\",1,-1,t,n,-e,i,s,3),p(\\\\\\\"x\\\\\\\",\\\\\\\"y\\\\\\\",\\\\\\\"z\\\\\\\",1,-1,t,e,n,i,r,4),p(\\\\\\\"x\\\\\\\",\\\\\\\"y\\\\\\\",\\\\\\\"z\\\\\\\",-1,-1,t,e,-n,i,r,5),this.setIndex(o),this.setAttribute(\\\\\\\"position\\\\\\\",new un(l,3)),this.setAttribute(\\\\\\\"normal\\\\\\\",new un(c,3)),this.setAttribute(\\\\\\\"uv\\\\\\\",new un(h,2))}static fromJSON(t){return new Vn(t.width,t.height,t.depth,t.widthSegments,t.heightSegments,t.depthSegments)}}function Wn(t){const e={};for(const n in t){e[n]={};for(const i in t[n]){const r=t[n][i];r&&(r.isColor||r.isMatrix3||r.isMatrix4||r.isVector2||r.isVector3||r.isVector4||r.isTexture||r.isQuaternion)?e[n][i]=r.clone():Array.isArray(r)?e[n][i]=r.slice():e[n][i]=r}}return e}function jn(t){const e={};for(let n=0;n<t.length;n++){const i=Wn(t[n]);for(const t in i)e[t]=i[t]}return e}const qn={clone:Wn,merge:jn};class Xn extends We{constructor(t){super(),this.type=\\\\\\\"ShaderMaterial\\\\\\\",this.defines={},this.uniforms={},this.vertexShader=\\\\\\\"void main() {\\\\n\\\\tgl_Position = projectionMatrix * modelViewMatrix * vec4( position, 1.0 );\\\\n}\\\\\\\",this.fragmentShader=\\\\\\\"void main() {\\\\n\\\\tgl_FragColor = vec4( 1.0, 0.0, 0.0, 1.0 );\\\\n}\\\\\\\",this.linewidth=1,this.wireframe=!1,this.wireframeLinewidth=1,this.fog=!1,this.lights=!1,this.clipping=!1,this.extensions={derivatives:!1,fragDepth:!1,drawBuffers:!1,shaderTextureLOD:!1},this.defaultAttributeValues={color:[1,1,1],uv:[0,0],uv2:[0,0]},this.index0AttributeName=void 0,this.uniformsNeedUpdate=!1,this.glslVersion=null,void 0!==t&&(void 0!==t.attributes&&console.error(\\\\\\\"THREE.ShaderMaterial: attributes should now be defined in THREE.BufferGeometry instead.\\\\\\\"),this.setValues(t))}copy(t){return super.copy(t),this.fragmentShader=t.fragmentShader,this.vertexShader=t.vertexShader,this.uniforms=Wn(t.uniforms),this.defines=Object.assign({},t.defines),this.wireframe=t.wireframe,this.wireframeLinewidth=t.wireframeLinewidth,this.lights=t.lights,this.clipping=t.clipping,this.extensions=Object.assign({},t.extensions),this.glslVersion=t.glslVersion,this}toJSON(t){const e=super.toJSON(t);e.glslVersion=this.glslVersion,e.uniforms={};for(const n in this.uniforms){const i=this.uniforms[n].value;i&&i.isTexture?e.uniforms[n]={type:\\\\\\\"t\\\\\\\",value:i.toJSON(t).uuid}:i&&i.isColor?e.uniforms[n]={type:\\\\\\\"c\\\\\\\",value:i.getHex()}:i&&i.isVector2?e.uniforms[n]={type:\\\\\\\"v2\\\\\\\",value:i.toArray()}:i&&i.isVector3?e.uniforms[n]={type:\\\\\\\"v3\\\\\\\",value:i.toArray()}:i&&i.isVector4?e.uniforms[n]={type:\\\\\\\"v4\\\\\\\",value:i.toArray()}:i&&i.isMatrix3?e.uniforms[n]={type:\\\\\\\"m3\\\\\\\",value:i.toArray()}:i&&i.isMatrix4?e.uniforms[n]={type:\\\\\\\"m4\\\\\\\",value:i.toArray()}:e.uniforms[n]={value:i}}Object.keys(this.defines).length>0&&(e.defines=this.defines),e.vertexShader=this.vertexShader,e.fragmentShader=this.fragmentShader;const n={};for(const t in this.extensions)!0===this.extensions[t]&&(n[t]=!0);return Object.keys(n).length>0&&(e.extensions=n),e}}Xn.prototype.isShaderMaterial=!0;class Yn extends Pe{constructor(){super(),this.type=\\\\\\\"Camera\\\\\\\",this.matrixWorldInverse=new ae,this.projectionMatrix=new ae,this.projectionMatrixInverse=new ae}copy(t,e){return super.copy(t,e),this.matrixWorldInverse.copy(t.matrixWorldInverse),this.projectionMatrix.copy(t.projectionMatrix),this.projectionMatrixInverse.copy(t.projectionMatrixInverse),this}getWorldDirection(t){this.updateWorldMatrix(!0,!1);const e=this.matrixWorld.elements;return t.set(-e[8],-e[9],-e[10]).normalize()}updateMatrixWorld(t){super.updateMatrixWorld(t),this.matrixWorldInverse.copy(this.matrixWorld).invert()}updateWorldMatrix(t,e){super.updateWorldMatrix(t,e),this.matrixWorldInverse.copy(this.matrixWorld).invert()}clone(){return(new this.constructor).copy(this)}}Yn.prototype.isCamera=!0;class Jn extends Yn{constructor(t=50,e=1,n=.1,i=2e3){super(),this.type=\\\\\\\"PerspectiveCamera\\\\\\\",this.fov=t,this.zoom=1,this.near=n,this.far=i,this.focus=10,this.aspect=e,this.view=null,this.filmGauge=35,this.filmOffset=0,this.updateProjectionMatrix()}copy(t,e){return super.copy(t,e),this.fov=t.fov,this.zoom=t.zoom,this.near=t.near,this.far=t.far,this.focus=t.focus,this.aspect=t.aspect,this.view=null===t.view?null:Object.assign({},t.view),this.filmGauge=t.filmGauge,this.filmOffset=t.filmOffset,this}setFocalLength(t){const e=.5*this.getFilmHeight()/t;this.fov=2*lt*Math.atan(e),this.updateProjectionMatrix()}getFocalLength(){const t=Math.tan(.5*ot*this.fov);return.5*this.getFilmHeight()/t}getEffectiveFOV(){return 2*lt*Math.atan(Math.tan(.5*ot*this.fov)/this.zoom)}getFilmWidth(){return this.filmGauge*Math.min(this.aspect,1)}getFilmHeight(){return this.filmGauge/Math.max(this.aspect,1)}setViewOffset(t,e,n,i,r,s){this.aspect=t/e,null===this.view&&(this.view={enabled:!0,fullWidth:1,fullHeight:1,offsetX:0,offsetY:0,width:1,height:1}),this.view.enabled=!0,this.view.fullWidth=t,this.view.fullHeight=e,this.view.offsetX=n,this.view.offsetY=i,this.view.width=r,this.view.height=s,this.updateProjectionMatrix()}clearViewOffset(){null!==this.view&&(this.view.enabled=!1),this.updateProjectionMatrix()}updateProjectionMatrix(){const t=this.near;let e=t*Math.tan(.5*ot*this.fov)/this.zoom,n=2*e,i=this.aspect*n,r=-.5*i;const s=this.view;if(null!==this.view&&this.view.enabled){const t=s.fullWidth,a=s.fullHeight;r+=s.offsetX*i/t,e-=s.offsetY*n/a,i*=s.width/t,n*=s.height/a}const a=this.filmOffset;0!==a&&(r+=t*a/this.getFilmWidth()),this.projectionMatrix.makePerspective(r,r+i,e,e-n,t,this.far),this.projectionMatrixInverse.copy(this.projectionMatrix).invert()}toJSON(t){const e=super.toJSON(t);return e.object.fov=this.fov,e.object.zoom=this.zoom,e.object.near=this.near,e.object.far=this.far,e.object.focus=this.focus,e.object.aspect=this.aspect,null!==this.view&&(e.object.view=Object.assign({},this.view)),e.object.filmGauge=this.filmGauge,e.object.filmOffset=this.filmOffset,e}}Jn.prototype.isPerspectiveCamera=!0;const Zn=90;class Qn extends Pe{constructor(t,e,n){if(super(),this.type=\\\\\\\"CubeCamera\\\\\\\",!0!==n.isWebGLCubeRenderTarget)return void console.error(\\\\\\\"THREE.CubeCamera: The constructor now expects an instance of WebGLCubeRenderTarget as third parameter.\\\\\\\");this.renderTarget=n;const i=new Jn(Zn,1,t,e);i.layers=this.layers,i.up.set(0,-1,0),i.lookAt(new Rt(1,0,0)),this.add(i);const r=new Jn(Zn,1,t,e);r.layers=this.layers,r.up.set(0,-1,0),r.lookAt(new Rt(-1,0,0)),this.add(r);const s=new Jn(Zn,1,t,e);s.layers=this.layers,s.up.set(0,0,1),s.lookAt(new Rt(0,1,0)),this.add(s);const a=new Jn(Zn,1,t,e);a.layers=this.layers,a.up.set(0,0,-1),a.lookAt(new Rt(0,-1,0)),this.add(a);const o=new Jn(Zn,1,t,e);o.layers=this.layers,o.up.set(0,-1,0),o.lookAt(new Rt(0,0,1)),this.add(o);const l=new Jn(Zn,1,t,e);l.layers=this.layers,l.up.set(0,-1,0),l.lookAt(new Rt(0,0,-1)),this.add(l)}update(t,e){null===this.parent&&this.updateMatrixWorld();const n=this.renderTarget,[i,r,s,a,o,l]=this.children,c=t.xr.enabled,h=t.getRenderTarget();t.xr.enabled=!1;const u=n.texture.generateMipmaps;n.texture.generateMipmaps=!1,t.setRenderTarget(n,0),t.render(e,i),t.setRenderTarget(n,1),t.render(e,r),t.setRenderTarget(n,2),t.render(e,s),t.setRenderTarget(n,3),t.render(e,a),t.setRenderTarget(n,4),t.render(e,o),n.texture.generateMipmaps=u,t.setRenderTarget(n,5),t.render(e,l),t.setRenderTarget(h),t.xr.enabled=c}}class Kn extends bt{constructor(t,e,n,i,s,a,o,l,c,h){super(t=void 0!==t?t:[],e=void 0!==e?e:r,n,i,s,a,o=void 0!==o?o:T,l,c,h),this.flipY=!1}get images(){return this.image}set images(t){this.image=t}}Kn.prototype.isCubeTexture=!0;class $n extends Tt{constructor(t,e,n){Number.isInteger(e)&&(console.warn(\\\\\\\"THREE.WebGLCubeRenderTarget: constructor signature is now WebGLCubeRenderTarget( size, options )\\\\\\\"),e=n),super(t,t,e),e=e||{},this.texture=new Kn(void 0,e.mapping,e.wrapS,e.wrapT,e.magFilter,e.minFilter,e.format,e.type,e.anisotropy,e.encoding),this.texture.isRenderTargetTexture=!0,this.texture.generateMipmaps=void 0!==e.generateMipmaps&&e.generateMipmaps,this.texture.minFilter=void 0!==e.minFilter?e.minFilter:g,this.texture._needsFlipEnvMap=!1}fromEquirectangularTexture(t,e){this.texture.type=e.type,this.texture.format=E,this.texture.encoding=e.encoding,this.texture.generateMipmaps=e.generateMipmaps,this.texture.minFilter=e.minFilter,this.texture.magFilter=e.magFilter;const n={uniforms:{tEquirect:{value:null}},vertexShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tvarying vec3 vWorldDirection;\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tvec3 transformDirection( in vec3 dir, in mat4 matrix ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\t\\\\treturn normalize( ( matrix * vec4( dir, 0.0 ) ).xyz );\\\\n\\\\n\\\\t\\\\t\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tvWorldDirection = transformDirection( position, modelMatrix );\\\\n\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t#include <begin_vertex>\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t#include <project_vertex>\\\\n\\\\n\\\\t\\\\t\\\\t\\\\t}\\\\n\\\\t\\\\t\\\\t\\\\\\\",fragmentShader:\\\\\\\"\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tuniform sampler2D tEquirect;\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tvarying vec3 vWorldDirection;\\\\n\\\\n\\\\t\\\\t\\\\t\\\\t#include <common>\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tvec3 direction = normalize( vWorldDirection );\\\\n\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tvec2 sampleUV = equirectUv( direction );\\\\n\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tgl_FragColor = texture2D( tEquirect, sampleUV );\\\\n\\\\n\\\\t\\\\t\\\\t\\\\t}\\\\n\\\\t\\\\t\\\\t\\\\\\\"},i=new Vn(5,5,5),r=new Xn({name:\\\\\\\"CubemapFromEquirect\\\\\\\",uniforms:Wn(n.uniforms),vertexShader:n.vertexShader,fragmentShader:n.fragmentShader,side:1,blending:0});r.uniforms.tEquirect.value=e;const s=new Gn(i,r),a=e.minFilter;e.minFilter===y&&(e.minFilter=g);return new Qn(1,10,this).update(t,s),e.minFilter=a,s.geometry.dispose(),s.material.dispose(),this}clear(t,e,n,i){const r=t.getRenderTarget();for(let r=0;r<6;r++)t.setRenderTarget(this,r),t.clear(e,n,i);t.setRenderTarget(r)}}$n.prototype.isWebGLCubeRenderTarget=!0;const ti=new Rt,ei=new Rt,ni=new yt;class ii{constructor(t=new Rt(1,0,0),e=0){this.normal=t,this.constant=e}set(t,e){return this.normal.copy(t),this.constant=e,this}setComponents(t,e,n,i){return this.normal.set(t,e,n),this.constant=i,this}setFromNormalAndCoplanarPoint(t,e){return this.normal.copy(t),this.constant=-e.dot(this.normal),this}setFromCoplanarPoints(t,e,n){const i=ti.subVectors(n,e).cross(ei.subVectors(t,e)).normalize();return this.setFromNormalAndCoplanarPoint(i,t),this}copy(t){return this.normal.copy(t.normal),this.constant=t.constant,this}normalize(){const t=1/this.normal.length();return this.normal.multiplyScalar(t),this.constant*=t,this}negate(){return this.constant*=-1,this.normal.negate(),this}distanceToPoint(t){return this.normal.dot(t)+this.constant}distanceToSphere(t){return this.distanceToPoint(t.center)-t.radius}projectPoint(t,e){return e.copy(this.normal).multiplyScalar(-this.distanceToPoint(t)).add(t)}intersectLine(t,e){const n=t.delta(ti),i=this.normal.dot(n);if(0===i)return 0===this.distanceToPoint(t.start)?e.copy(t.start):null;const r=-(t.start.dot(this.normal)+this.constant)/i;return r<0||r>1?null:e.copy(n).multiplyScalar(r).add(t.start)}intersectsLine(t){const e=this.distanceToPoint(t.start),n=this.distanceToPoint(t.end);return e<0&&n>0||n<0&&e>0}intersectsBox(t){return t.intersectsPlane(this)}intersectsSphere(t){return t.intersectsPlane(this)}coplanarPoint(t){return t.copy(this.normal).multiplyScalar(-this.constant)}applyMatrix4(t,e){const n=e||ni.getNormalMatrix(t),i=this.coplanarPoint(ti).applyMatrix4(t),r=this.normal.applyMatrix3(n).normalize();return this.constant=-i.dot(r),this}translate(t){return this.constant-=t.dot(this.normal),this}equals(t){return t.normal.equals(this.normal)&&t.constant===this.constant}clone(){return(new this.constructor).copy(this)}}ii.prototype.isPlane=!0;const ri=new Qt,si=new Rt;class ai{constructor(t=new ii,e=new ii,n=new ii,i=new ii,r=new ii,s=new ii){this.planes=[t,e,n,i,r,s]}set(t,e,n,i,r,s){const a=this.planes;return a[0].copy(t),a[1].copy(e),a[2].copy(n),a[3].copy(i),a[4].copy(r),a[5].copy(s),this}copy(t){const e=this.planes;for(let n=0;n<6;n++)e[n].copy(t.planes[n]);return this}setFromProjectionMatrix(t){const e=this.planes,n=t.elements,i=n[0],r=n[1],s=n[2],a=n[3],o=n[4],l=n[5],c=n[6],h=n[7],u=n[8],d=n[9],p=n[10],m=n[11],f=n[12],g=n[13],v=n[14],y=n[15];return e[0].setComponents(a-i,h-o,m-u,y-f).normalize(),e[1].setComponents(a+i,h+o,m+u,y+f).normalize(),e[2].setComponents(a+r,h+l,m+d,y+g).normalize(),e[3].setComponents(a-r,h-l,m-d,y-g).normalize(),e[4].setComponents(a-s,h-c,m-p,y-v).normalize(),e[5].setComponents(a+s,h+c,m+p,y+v).normalize(),this}intersectsObject(t){const e=t.geometry;return null===e.boundingSphere&&e.computeBoundingSphere(),ri.copy(e.boundingSphere).applyMatrix4(t.matrixWorld),this.intersectsSphere(ri)}intersectsSprite(t){return ri.center.set(0,0,0),ri.radius=.7071067811865476,ri.applyMatrix4(t.matrixWorld),this.intersectsSphere(ri)}intersectsSphere(t){const e=this.planes,n=t.center,i=-t.radius;for(let t=0;t<6;t++){if(e[t].distanceToPoint(n)<i)return!1}return!0}intersectsBox(t){const e=this.planes;for(let n=0;n<6;n++){const i=e[n];if(si.x=i.normal.x>0?t.max.x:t.min.x,si.y=i.normal.y>0?t.max.y:t.min.y,si.z=i.normal.z>0?t.max.z:t.min.z,i.distanceToPoint(si)<0)return!1}return!0}containsPoint(t){const e=this.planes;for(let n=0;n<6;n++)if(e[n].distanceToPoint(t)<0)return!1;return!0}clone(){return(new this.constructor).copy(this)}}function oi(){let t=null,e=!1,n=null,i=null;function r(e,s){n(e,s),i=t.requestAnimationFrame(r)}return{start:function(){!0!==e&&null!==n&&(i=t.requestAnimationFrame(r),e=!0)},stop:function(){t.cancelAnimationFrame(i),e=!1},setAnimationLoop:function(t){n=t},setContext:function(e){t=e}}}function li(t,e){const n=e.isWebGL2,i=new WeakMap;return{get:function(t){return t.isInterleavedBufferAttribute&&(t=t.data),i.get(t)},remove:function(e){e.isInterleavedBufferAttribute&&(e=e.data);const n=i.get(e);n&&(t.deleteBuffer(n.buffer),i.delete(e))},update:function(e,r){if(e.isGLBufferAttribute){const t=i.get(e);return void((!t||t.version<e.version)&&i.set(e,{buffer:e.buffer,type:e.type,bytesPerElement:e.elementSize,version:e.version}))}e.isInterleavedBufferAttribute&&(e=e.data);const s=i.get(e);void 0===s?i.set(e,function(e,i){const r=e.array,s=e.usage,a=t.createBuffer();t.bindBuffer(i,a),t.bufferData(i,r,s),e.onUploadCallback();let o=5126;return r instanceof Float32Array?o=5126:r instanceof Float64Array?console.warn(\\\\\\\"THREE.WebGLAttributes: Unsupported data buffer format: Float64Array.\\\\\\\"):r instanceof Uint16Array?e.isFloat16BufferAttribute?n?o=5131:console.warn(\\\\\\\"THREE.WebGLAttributes: Usage of Float16BufferAttribute requires WebGL2.\\\\\\\"):o=5123:r instanceof Int16Array?o=5122:r instanceof Uint32Array?o=5125:r instanceof Int32Array?o=5124:r instanceof Int8Array?o=5120:(r instanceof Uint8Array||r instanceof Uint8ClampedArray)&&(o=5121),{buffer:a,type:o,bytesPerElement:r.BYTES_PER_ELEMENT,version:e.version}}(e,r)):s.version<e.version&&(!function(e,i,r){const s=i.array,a=i.updateRange;t.bindBuffer(r,e),-1===a.count?t.bufferSubData(r,0,s):(n?t.bufferSubData(r,a.offset*s.BYTES_PER_ELEMENT,s,a.offset,a.count):t.bufferSubData(r,a.offset*s.BYTES_PER_ELEMENT,s.subarray(a.offset,a.offset+a.count)),a.count=-1)}(s.buffer,e,r),s.version=e.version)}}}class ci extends wn{constructor(t=1,e=1,n=1,i=1){super(),this.type=\\\\\\\"PlaneGeometry\\\\\\\",this.parameters={width:t,height:e,widthSegments:n,heightSegments:i};const r=t/2,s=e/2,a=Math.floor(n),o=Math.floor(i),l=a+1,c=o+1,h=t/a,u=e/o,d=[],p=[],m=[],f=[];for(let t=0;t<c;t++){const e=t*u-s;for(let n=0;n<l;n++){const i=n*h-r;p.push(i,-e,0),m.push(0,0,1),f.push(n/a),f.push(1-t/o)}}for(let t=0;t<o;t++)for(let e=0;e<a;e++){const n=e+l*t,i=e+l*(t+1),r=e+1+l*(t+1),s=e+1+l*t;d.push(n,i,s),d.push(i,r,s)}this.setIndex(d),this.setAttribute(\\\\\\\"position\\\\\\\",new un(p,3)),this.setAttribute(\\\\\\\"normal\\\\\\\",new un(m,3)),this.setAttribute(\\\\\\\"uv\\\\\\\",new un(f,2))}static fromJSON(t){return new ci(t.width,t.height,t.widthSegments,t.heightSegments)}}const hi={alphamap_fragment:\\\\\\\"#ifdef USE_ALPHAMAP\\\\n\\\\tdiffuseColor.a *= texture2D( alphaMap, vUv ).g;\\\\n#endif\\\\\\\",alphamap_pars_fragment:\\\\\\\"#ifdef USE_ALPHAMAP\\\\n\\\\tuniform sampler2D alphaMap;\\\\n#endif\\\\\\\",alphatest_fragment:\\\\\\\"#ifdef ALPHATEST\\\\n\\\\tif ( diffuseColor.a < ALPHATEST ) discard;\\\\n#endif\\\\\\\",aomap_fragment:\\\\\\\"#ifdef USE_AOMAP\\\\n\\\\tfloat ambientOcclusion = ( texture2D( aoMap, vUv2 ).r - 1.0 ) * aoMapIntensity + 1.0;\\\\n\\\\treflectedLight.indirectDiffuse *= ambientOcclusion;\\\\n\\\\t#if defined( USE_ENVMAP ) && defined( STANDARD )\\\\n\\\\t\\\\tfloat dotNV = saturate( dot( geometry.normal, geometry.viewDir ) );\\\\n\\\\t\\\\treflectedLight.indirectSpecular *= computeSpecularOcclusion( dotNV, ambientOcclusion, material.specularRoughness );\\\\n\\\\t#endif\\\\n#endif\\\\\\\",aomap_pars_fragment:\\\\\\\"#ifdef USE_AOMAP\\\\n\\\\tuniform sampler2D aoMap;\\\\n\\\\tuniform float aoMapIntensity;\\\\n#endif\\\\\\\",begin_vertex:\\\\\\\"vec3 transformed = vec3( position );\\\\\\\",beginnormal_vertex:\\\\\\\"vec3 objectNormal = vec3( normal );\\\\n#ifdef USE_TANGENT\\\\n\\\\tvec3 objectTangent = vec3( tangent.xyz );\\\\n#endif\\\\\\\",bsdfs:\\\\\\\"vec2 integrateSpecularBRDF( const in float dotNV, const in float roughness ) {\\\\n\\\\tconst vec4 c0 = vec4( - 1, - 0.0275, - 0.572, 0.022 );\\\\n\\\\tconst vec4 c1 = vec4( 1, 0.0425, 1.04, - 0.04 );\\\\n\\\\tvec4 r = roughness * c0 + c1;\\\\n\\\\tfloat a004 = min( r.x * r.x, exp2( - 9.28 * dotNV ) ) * r.x + r.y;\\\\n\\\\treturn vec2( -1.04, 1.04 ) * a004 + r.zw;\\\\n}\\\\nfloat punctualLightIntensityToIrradianceFactor( const in float lightDistance, const in float cutoffDistance, const in float decayExponent ) {\\\\n#if defined ( PHYSICALLY_CORRECT_LIGHTS )\\\\n\\\\tfloat distanceFalloff = 1.0 / max( pow( lightDistance, decayExponent ), 0.01 );\\\\n\\\\tif( cutoffDistance > 0.0 ) {\\\\n\\\\t\\\\tdistanceFalloff *= pow2( saturate( 1.0 - pow4( lightDistance / cutoffDistance ) ) );\\\\n\\\\t}\\\\n\\\\treturn distanceFalloff;\\\\n#else\\\\n\\\\tif( cutoffDistance > 0.0 && decayExponent > 0.0 ) {\\\\n\\\\t\\\\treturn pow( saturate( -lightDistance / cutoffDistance + 1.0 ), decayExponent );\\\\n\\\\t}\\\\n\\\\treturn 1.0;\\\\n#endif\\\\n}\\\\nvec3 BRDF_Diffuse_Lambert( const in vec3 diffuseColor ) {\\\\n\\\\treturn RECIPROCAL_PI * diffuseColor;\\\\n}\\\\nvec3 F_Schlick( const in vec3 f0, const in vec3 f90, const in float dotVH ) {\\\\n\\\\tfloat fresnel = exp2( ( -5.55473 * dotVH - 6.98316 ) * dotVH );\\\\n\\\\treturn ( f90 - f0 ) * fresnel + f0;\\\\n}\\\\nvec3 F_Schlick_RoughnessDependent( const in vec3 F0, const in float dotNV, const in float roughness ) {\\\\n\\\\tfloat fresnel = exp2( ( -5.55473 * dotNV - 6.98316 ) * dotNV );\\\\n\\\\tvec3 Fr = max( vec3( 1.0 - roughness ), F0 ) - F0;\\\\n\\\\treturn Fr * fresnel + F0;\\\\n}\\\\nfloat G_GGX_Smith( const in float alpha, const in float dotNL, const in float dotNV ) {\\\\n\\\\tfloat a2 = pow2( alpha );\\\\n\\\\tfloat gl = dotNL + sqrt( a2 + ( 1.0 - a2 ) * pow2( dotNL ) );\\\\n\\\\tfloat gv = dotNV + sqrt( a2 + ( 1.0 - a2 ) * pow2( dotNV ) );\\\\n\\\\treturn 1.0 / ( gl * gv );\\\\n}\\\\nfloat G_GGX_SmithCorrelated( const in float alpha, const in float dotNL, const in float dotNV ) {\\\\n\\\\tfloat a2 = pow2( alpha );\\\\n\\\\tfloat gv = dotNL * sqrt( a2 + ( 1.0 - a2 ) * pow2( dotNV ) );\\\\n\\\\tfloat gl = dotNV * sqrt( a2 + ( 1.0 - a2 ) * pow2( dotNL ) );\\\\n\\\\treturn 0.5 / max( gv + gl, EPSILON );\\\\n}\\\\nfloat D_GGX( const in float alpha, const in float dotNH ) {\\\\n\\\\tfloat a2 = pow2( alpha );\\\\n\\\\tfloat denom = pow2( dotNH ) * ( a2 - 1.0 ) + 1.0;\\\\n\\\\treturn RECIPROCAL_PI * a2 / pow2( denom );\\\\n}\\\\nvec3 BRDF_Specular_GGX( const in IncidentLight incidentLight, const in vec3 viewDir, const in vec3 normal, const in vec3 f0, const in vec3 f90, const in float roughness ) {\\\\n\\\\tfloat alpha = pow2( roughness );\\\\n\\\\tvec3 halfDir = normalize( incidentLight.direction + viewDir );\\\\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\\\\tvec3 F = F_Schlick( f0, f90, dotLH );\\\\n\\\\tfloat G = G_GGX_SmithCorrelated( alpha, dotNL, dotNV );\\\\n\\\\tfloat D = D_GGX( alpha, dotNH );\\\\n\\\\treturn F * ( G * D );\\\\n}\\\\nvec2 LTC_Uv( const in vec3 N, const in vec3 V, const in float roughness ) {\\\\n\\\\tconst float LUT_SIZE = 64.0;\\\\n\\\\tconst float LUT_SCALE = ( LUT_SIZE - 1.0 ) / LUT_SIZE;\\\\n\\\\tconst float LUT_BIAS = 0.5 / LUT_SIZE;\\\\n\\\\tfloat dotNV = saturate( dot( N, V ) );\\\\n\\\\tvec2 uv = vec2( roughness, sqrt( 1.0 - dotNV ) );\\\\n\\\\tuv = uv * LUT_SCALE + LUT_BIAS;\\\\n\\\\treturn uv;\\\\n}\\\\nfloat LTC_ClippedSphereFormFactor( const in vec3 f ) {\\\\n\\\\tfloat l = length( f );\\\\n\\\\treturn max( ( l * l + f.z ) / ( l + 1.0 ), 0.0 );\\\\n}\\\\nvec3 LTC_EdgeVectorFormFactor( const in vec3 v1, const in vec3 v2 ) {\\\\n\\\\tfloat x = dot( v1, v2 );\\\\n\\\\tfloat y = abs( x );\\\\n\\\\tfloat a = 0.8543985 + ( 0.4965155 + 0.0145206 * y ) * y;\\\\n\\\\tfloat b = 3.4175940 + ( 4.1616724 + y ) * y;\\\\n\\\\tfloat v = a / b;\\\\n\\\\tfloat theta_sintheta = ( x > 0.0 ) ? v : 0.5 * inversesqrt( max( 1.0 - x * x, 1e-7 ) ) - v;\\\\n\\\\treturn cross( v1, v2 ) * theta_sintheta;\\\\n}\\\\nvec3 LTC_Evaluate( const in vec3 N, const in vec3 V, const in vec3 P, const in mat3 mInv, const in vec3 rectCoords[ 4 ] ) {\\\\n\\\\tvec3 v1 = rectCoords[ 1 ] - rectCoords[ 0 ];\\\\n\\\\tvec3 v2 = rectCoords[ 3 ] - rectCoords[ 0 ];\\\\n\\\\tvec3 lightNormal = cross( v1, v2 );\\\\n\\\\tif( dot( lightNormal, P - rectCoords[ 0 ] ) < 0.0 ) return vec3( 0.0 );\\\\n\\\\tvec3 T1, T2;\\\\n\\\\tT1 = normalize( V - N * dot( V, N ) );\\\\n\\\\tT2 = - cross( N, T1 );\\\\n\\\\tmat3 mat = mInv * transposeMat3( mat3( T1, T2, N ) );\\\\n\\\\tvec3 coords[ 4 ];\\\\n\\\\tcoords[ 0 ] = mat * ( rectCoords[ 0 ] - P );\\\\n\\\\tcoords[ 1 ] = mat * ( rectCoords[ 1 ] - P );\\\\n\\\\tcoords[ 2 ] = mat * ( rectCoords[ 2 ] - P );\\\\n\\\\tcoords[ 3 ] = mat * ( rectCoords[ 3 ] - P );\\\\n\\\\tcoords[ 0 ] = normalize( coords[ 0 ] );\\\\n\\\\tcoords[ 1 ] = normalize( coords[ 1 ] );\\\\n\\\\tcoords[ 2 ] = normalize( coords[ 2 ] );\\\\n\\\\tcoords[ 3 ] = normalize( coords[ 3 ] );\\\\n\\\\tvec3 vectorFormFactor = vec3( 0.0 );\\\\n\\\\tvectorFormFactor += LTC_EdgeVectorFormFactor( coords[ 0 ], coords[ 1 ] );\\\\n\\\\tvectorFormFactor += LTC_EdgeVectorFormFactor( coords[ 1 ], coords[ 2 ] );\\\\n\\\\tvectorFormFactor += LTC_EdgeVectorFormFactor( coords[ 2 ], coords[ 3 ] );\\\\n\\\\tvectorFormFactor += LTC_EdgeVectorFormFactor( coords[ 3 ], coords[ 0 ] );\\\\n\\\\tfloat result = LTC_ClippedSphereFormFactor( vectorFormFactor );\\\\n\\\\treturn vec3( result );\\\\n}\\\\nvec3 BRDF_Specular_GGX_Environment( const in vec3 viewDir, const in vec3 normal, const in vec3 specularColor, const in float roughness ) {\\\\n\\\\tfloat dotNV = saturate( dot( normal, viewDir ) );\\\\n\\\\tvec2 brdf = integrateSpecularBRDF( dotNV, roughness );\\\\n\\\\treturn specularColor * brdf.x + brdf.y;\\\\n}\\\\nvoid BRDF_Specular_Multiscattering_Environment( const in GeometricContext geometry, const in vec3 specularColor, const in float roughness, inout vec3 singleScatter, inout vec3 multiScatter ) {\\\\n\\\\tfloat dotNV = saturate( dot( geometry.normal, geometry.viewDir ) );\\\\n\\\\tvec3 F = F_Schlick_RoughnessDependent( specularColor, dotNV, roughness );\\\\n\\\\tvec2 brdf = integrateSpecularBRDF( dotNV, roughness );\\\\n\\\\tvec3 FssEss = F * brdf.x + brdf.y;\\\\n\\\\tfloat Ess = brdf.x + brdf.y;\\\\n\\\\tfloat Ems = 1.0 - Ess;\\\\n\\\\tvec3 Favg = specularColor + ( 1.0 - specularColor ) * 0.047619;\\\\tvec3 Fms = FssEss * Favg / ( 1.0 - Ems * Favg );\\\\n\\\\tsingleScatter += FssEss;\\\\n\\\\tmultiScatter += Fms * Ems;\\\\n}\\\\nfloat G_BlinnPhong_Implicit( ) {\\\\n\\\\treturn 0.25;\\\\n}\\\\nfloat D_BlinnPhong( const in float shininess, const in float dotNH ) {\\\\n\\\\treturn RECIPROCAL_PI * ( shininess * 0.5 + 1.0 ) * pow( dotNH, shininess );\\\\n}\\\\nvec3 BRDF_Specular_BlinnPhong( const in IncidentLight incidentLight, const in GeometricContext geometry, const in vec3 specularColor, const in float shininess ) {\\\\n\\\\tvec3 halfDir = normalize( incidentLight.direction + geometry.viewDir );\\\\n\\\\tfloat dotNH = saturate( dot( geometry.normal, halfDir ) );\\\\n\\\\tfloat dotLH = saturate( dot( incidentLight.direction, halfDir ) );\\\\n\\\\tvec3 F = F_Schlick( specularColor, vec3( 1.0 ), dotLH );\\\\n\\\\tfloat G = G_BlinnPhong_Implicit( );\\\\n\\\\tfloat D = D_BlinnPhong( shininess, dotNH );\\\\n\\\\treturn F * ( G * D );\\\\n}\\\\nfloat GGXRoughnessToBlinnExponent( const in float ggxRoughness ) {\\\\n\\\\treturn ( 2.0 / pow2( ggxRoughness + 0.0001 ) - 2.0 );\\\\n}\\\\nfloat BlinnExponentToGGXRoughness( const in float blinnExponent ) {\\\\n\\\\treturn sqrt( 2.0 / ( blinnExponent + 2.0 ) );\\\\n}\\\\n#if defined( USE_SHEEN )\\\\nfloat D_Charlie(float roughness, float NoH) {\\\\n\\\\tfloat invAlpha = 1.0 / roughness;\\\\n\\\\tfloat cos2h = NoH * NoH;\\\\n\\\\tfloat sin2h = max(1.0 - cos2h, 0.0078125);\\\\treturn (2.0 + invAlpha) * pow(sin2h, invAlpha * 0.5) / (2.0 * PI);\\\\n}\\\\nfloat V_Neubelt(float NoV, float NoL) {\\\\n\\\\treturn saturate(1.0 / (4.0 * (NoL + NoV - NoL * NoV)));\\\\n}\\\\nvec3 BRDF_Specular_Sheen( const in float roughness, const in vec3 L, const in GeometricContext geometry, vec3 specularColor ) {\\\\n\\\\tvec3 N = geometry.normal;\\\\n\\\\tvec3 V = geometry.viewDir;\\\\n\\\\tvec3 H = normalize( V + L );\\\\n\\\\tfloat dotNH = saturate( dot( N, H ) );\\\\n\\\\treturn specularColor * D_Charlie( roughness, dotNH ) * V_Neubelt( dot(N, V), dot(N, L) );\\\\n}\\\\n#endif\\\\\\\",bumpmap_pars_fragment:\\\\\\\"#ifdef USE_BUMPMAP\\\\n\\\\tuniform sampler2D bumpMap;\\\\n\\\\tuniform float bumpScale;\\\\n\\\\tvec2 dHdxy_fwd() {\\\\n\\\\t\\\\tvec2 dSTdx = dFdx( vUv );\\\\n\\\\t\\\\tvec2 dSTdy = dFdy( vUv );\\\\n\\\\t\\\\tfloat Hll = bumpScale * texture2D( bumpMap, vUv ).x;\\\\n\\\\t\\\\tfloat dBx = bumpScale * texture2D( bumpMap, vUv + dSTdx ).x - Hll;\\\\n\\\\t\\\\tfloat dBy = bumpScale * texture2D( bumpMap, vUv + dSTdy ).x - Hll;\\\\n\\\\t\\\\treturn vec2( dBx, dBy );\\\\n\\\\t}\\\\n\\\\tvec3 perturbNormalArb( vec3 surf_pos, vec3 surf_norm, vec2 dHdxy, float faceDirection ) {\\\\n\\\\t\\\\tvec3 vSigmaX = vec3( dFdx( surf_pos.x ), dFdx( surf_pos.y ), dFdx( surf_pos.z ) );\\\\n\\\\t\\\\tvec3 vSigmaY = vec3( dFdy( surf_pos.x ), dFdy( surf_pos.y ), dFdy( surf_pos.z ) );\\\\n\\\\t\\\\tvec3 vN = surf_norm;\\\\n\\\\t\\\\tvec3 R1 = cross( vSigmaY, vN );\\\\n\\\\t\\\\tvec3 R2 = cross( vN, vSigmaX );\\\\n\\\\t\\\\tfloat fDet = dot( vSigmaX, R1 ) * faceDirection;\\\\n\\\\t\\\\tvec3 vGrad = sign( fDet ) * ( dHdxy.x * R1 + dHdxy.y * R2 );\\\\n\\\\t\\\\treturn normalize( abs( fDet ) * surf_norm - vGrad );\\\\n\\\\t}\\\\n#endif\\\\\\\",clipping_planes_fragment:\\\\\\\"#if NUM_CLIPPING_PLANES > 0\\\\n\\\\tvec4 plane;\\\\n\\\\t#pragma unroll_loop_start\\\\n\\\\tfor ( int i = 0; i < UNION_CLIPPING_PLANES; i ++ ) {\\\\n\\\\t\\\\tplane = clippingPlanes[ i ];\\\\n\\\\t\\\\tif ( dot( vClipPosition, plane.xyz ) > plane.w ) discard;\\\\n\\\\t}\\\\n\\\\t#pragma unroll_loop_end\\\\n\\\\t#if UNION_CLIPPING_PLANES < NUM_CLIPPING_PLANES\\\\n\\\\t\\\\tbool clipped = true;\\\\n\\\\t\\\\t#pragma unroll_loop_start\\\\n\\\\t\\\\tfor ( int i = UNION_CLIPPING_PLANES; i < NUM_CLIPPING_PLANES; i ++ ) {\\\\n\\\\t\\\\t\\\\tplane = clippingPlanes[ i ];\\\\n\\\\t\\\\t\\\\tclipped = ( dot( vClipPosition, plane.xyz ) > plane.w ) && clipped;\\\\n\\\\t\\\\t}\\\\n\\\\t\\\\t#pragma unroll_loop_end\\\\n\\\\t\\\\tif ( clipped ) discard;\\\\n\\\\t#endif\\\\n#endif\\\\\\\",clipping_planes_pars_fragment:\\\\\\\"#if NUM_CLIPPING_PLANES > 0\\\\n\\\\tvarying vec3 vClipPosition;\\\\n\\\\tuniform vec4 clippingPlanes[ NUM_CLIPPING_PLANES ];\\\\n#endif\\\\\\\",clipping_planes_pars_vertex:\\\\\\\"#if NUM_CLIPPING_PLANES > 0\\\\n\\\\tvarying vec3 vClipPosition;\\\\n#endif\\\\\\\",clipping_planes_vertex:\\\\\\\"#if NUM_CLIPPING_PLANES > 0\\\\n\\\\tvClipPosition = - mvPosition.xyz;\\\\n#endif\\\\\\\",color_fragment:\\\\\\\"#if defined( USE_COLOR_ALPHA )\\\\n\\\\tdiffuseColor *= vColor;\\\\n#elif defined( USE_COLOR )\\\\n\\\\tdiffuseColor.rgb *= vColor;\\\\n#endif\\\\\\\",color_pars_fragment:\\\\\\\"#if defined( USE_COLOR_ALPHA )\\\\n\\\\tvarying vec4 vColor;\\\\n#elif defined( USE_COLOR )\\\\n\\\\tvarying vec3 vColor;\\\\n#endif\\\\\\\",color_pars_vertex:\\\\\\\"#if defined( USE_COLOR_ALPHA )\\\\n\\\\tvarying vec4 vColor;\\\\n#elif defined( USE_COLOR ) || defined( USE_INSTANCING_COLOR )\\\\n\\\\tvarying vec3 vColor;\\\\n#endif\\\\\\\",color_vertex:\\\\\\\"#if defined( USE_COLOR_ALPHA )\\\\n\\\\tvColor = vec4( 1.0 );\\\\n#elif defined( USE_COLOR ) || defined( USE_INSTANCING_COLOR )\\\\n\\\\tvColor = vec3( 1.0 );\\\\n#endif\\\\n#ifdef USE_COLOR\\\\n\\\\tvColor *= color;\\\\n#endif\\\\n#ifdef USE_INSTANCING_COLOR\\\\n\\\\tvColor.xyz *= instanceColor.xyz;\\\\n#endif\\\\\\\",common:\\\\\\\"#define PI 3.141592653589793\\\\n#define PI2 6.283185307179586\\\\n#define PI_HALF 1.5707963267948966\\\\n#define RECIPROCAL_PI 0.3183098861837907\\\\n#define RECIPROCAL_PI2 0.15915494309189535\\\\n#define EPSILON 1e-6\\\\n#ifndef saturate\\\\n#define saturate(a) clamp( a, 0.0, 1.0 )\\\\n#endif\\\\n#define whiteComplement(a) ( 1.0 - saturate( a ) )\\\\nfloat pow2( const in float x ) { return x*x; }\\\\nfloat pow3( const in float x ) { return x*x*x; }\\\\nfloat pow4( const in float x ) { float x2 = x*x; return x2*x2; }\\\\nfloat average( const in vec3 color ) { return dot( color, vec3( 0.3333 ) ); }\\\\nhighp float rand( const in vec2 uv ) {\\\\n\\\\tconst highp float a = 12.9898, b = 78.233, c = 43758.5453;\\\\n\\\\thighp float dt = dot( uv.xy, vec2( a,b ) ), sn = mod( dt, PI );\\\\n\\\\treturn fract(sin(sn) * c);\\\\n}\\\\n#ifdef HIGH_PRECISION\\\\n\\\\tfloat precisionSafeLength( vec3 v ) { return length( v ); }\\\\n#else\\\\n\\\\tfloat 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\\\\nstruct IncidentLight {\\\\n\\\\tvec3 color;\\\\n\\\\tvec3 direction;\\\\n\\\\tbool visible;\\\\n};\\\\nstruct ReflectedLight {\\\\n\\\\tvec3 directDiffuse;\\\\n\\\\tvec3 directSpecular;\\\\n\\\\tvec3 indirectDiffuse;\\\\n\\\\tvec3 indirectSpecular;\\\\n};\\\\nstruct GeometricContext {\\\\n\\\\tvec3 position;\\\\n\\\\tvec3 normal;\\\\n\\\\tvec3 viewDir;\\\\n#ifdef CLEARCOAT\\\\n\\\\tvec3 clearcoatNormal;\\\\n#endif\\\\n};\\\\nvec3 transformDirection( in vec3 dir, in mat4 matrix ) {\\\\n\\\\treturn normalize( ( matrix * vec4( dir, 0.0 ) ).xyz );\\\\n}\\\\nvec3 inverseTransformDirection( in vec3 dir, in mat4 matrix ) {\\\\n\\\\treturn normalize( ( vec4( dir, 0.0 ) * matrix ).xyz );\\\\n}\\\\nvec3 projectOnPlane(in vec3 point, in vec3 pointOnPlane, in vec3 planeNormal ) {\\\\n\\\\tfloat distance = dot( planeNormal, point - pointOnPlane );\\\\n\\\\treturn - distance * planeNormal + point;\\\\n}\\\\nfloat sideOfPlane( in vec3 point, in vec3 pointOnPlane, in vec3 planeNormal ) {\\\\n\\\\treturn sign( dot( point - pointOnPlane, planeNormal ) );\\\\n}\\\\nvec3 linePlaneIntersect( in vec3 pointOnLine, in vec3 lineDirection, in vec3 pointOnPlane, in vec3 planeNormal ) {\\\\n\\\\treturn lineDirection * ( dot( planeNormal, pointOnPlane - pointOnLine ) / dot( planeNormal, lineDirection ) ) + pointOnLine;\\\\n}\\\\nmat3 transposeMat3( const in mat3 m ) {\\\\n\\\\tmat3 tmp;\\\\n\\\\ttmp[ 0 ] = vec3( m[ 0 ].x, m[ 1 ].x, m[ 2 ].x );\\\\n\\\\ttmp[ 1 ] = vec3( m[ 0 ].y, m[ 1 ].y, m[ 2 ].y );\\\\n\\\\ttmp[ 2 ] = vec3( m[ 0 ].z, m[ 1 ].z, m[ 2 ].z );\\\\n\\\\treturn tmp;\\\\n}\\\\nfloat linearToRelativeLuminance( const in vec3 color ) {\\\\n\\\\tvec3 weights = vec3( 0.2126, 0.7152, 0.0722 );\\\\n\\\\treturn dot( weights, color.rgb );\\\\n}\\\\nbool isPerspectiveMatrix( mat4 m ) {\\\\n\\\\treturn m[ 2 ][ 3 ] == - 1.0;\\\\n}\\\\nvec2 equirectUv( in vec3 dir ) {\\\\n\\\\tfloat u = atan( dir.z, dir.x ) * RECIPROCAL_PI2 + 0.5;\\\\n\\\\tfloat v = asin( clamp( dir.y, - 1.0, 1.0 ) ) * RECIPROCAL_PI + 0.5;\\\\n\\\\treturn vec2( u, v );\\\\n}\\\\\\\",cube_uv_reflection_fragment:\\\\\\\"#ifdef ENVMAP_TYPE_CUBE_UV\\\\n\\\\t#define cubeUV_maxMipLevel 8.0\\\\n\\\\t#define cubeUV_minMipLevel 4.0\\\\n\\\\t#define cubeUV_maxTileSize 256.0\\\\n\\\\t#define cubeUV_minTileSize 16.0\\\\n\\\\tfloat getFace( vec3 direction ) {\\\\n\\\\t\\\\tvec3 absDirection = abs( direction );\\\\n\\\\t\\\\tfloat face = - 1.0;\\\\n\\\\t\\\\tif ( absDirection.x > absDirection.z ) {\\\\n\\\\t\\\\t\\\\tif ( absDirection.x > absDirection.y )\\\\n\\\\t\\\\t\\\\t\\\\tface = direction.x > 0.0 ? 0.0 : 3.0;\\\\n\\\\t\\\\t\\\\telse\\\\n\\\\t\\\\t\\\\t\\\\tface = direction.y > 0.0 ? 1.0 : 4.0;\\\\n\\\\t\\\\t} else {\\\\n\\\\t\\\\t\\\\tif ( absDirection.z > absDirection.y )\\\\n\\\\t\\\\t\\\\t\\\\tface = direction.z > 0.0 ? 2.0 : 5.0;\\\\n\\\\t\\\\t\\\\telse\\\\n\\\\t\\\\t\\\\t\\\\tface = direction.y > 0.0 ? 1.0 : 4.0;\\\\n\\\\t\\\\t}\\\\n\\\\t\\\\treturn face;\\\\n\\\\t}\\\\n\\\\tvec2 getUV( vec3 direction, float face ) {\\\\n\\\\t\\\\tvec2 uv;\\\\n\\\\t\\\\tif ( face == 0.0 ) {\\\\n\\\\t\\\\t\\\\tuv = vec2( direction.z, direction.y ) / abs( direction.x );\\\\n\\\\t\\\\t} else if ( face == 1.0 ) {\\\\n\\\\t\\\\t\\\\tuv = vec2( - direction.x, - direction.z ) / abs( direction.y );\\\\n\\\\t\\\\t} else if ( face == 2.0 ) {\\\\n\\\\t\\\\t\\\\tuv = vec2( - direction.x, direction.y ) / abs( direction.z );\\\\n\\\\t\\\\t} else if ( face == 3.0 ) {\\\\n\\\\t\\\\t\\\\tuv = vec2( - direction.z, direction.y ) / abs( direction.x );\\\\n\\\\t\\\\t} else if ( face == 4.0 ) {\\\\n\\\\t\\\\t\\\\tuv = vec2( - direction.x, direction.z ) / abs( direction.y );\\\\n\\\\t\\\\t} else {\\\\n\\\\t\\\\t\\\\tuv = vec2( direction.x, direction.y ) / abs( direction.z );\\\\n\\\\t\\\\t}\\\\n\\\\t\\\\treturn 0.5 * ( uv + 1.0 );\\\\n\\\\t}\\\\n\\\\tvec3 bilinearCubeUV( sampler2D envMap, vec3 direction, float mipInt ) {\\\\n\\\\t\\\\tfloat face = getFace( direction );\\\\n\\\\t\\\\tfloat filterInt = max( cubeUV_minMipLevel - mipInt, 0.0 );\\\\n\\\\t\\\\tmipInt = max( mipInt, cubeUV_minMipLevel );\\\\n\\\\t\\\\tfloat faceSize = exp2( mipInt );\\\\n\\\\t\\\\tfloat texelSize = 1.0 / ( 3.0 * cubeUV_maxTileSize );\\\\n\\\\t\\\\tvec2 uv = getUV( direction, face ) * ( faceSize - 1.0 );\\\\n\\\\t\\\\tvec2 f = fract( uv );\\\\n\\\\t\\\\tuv += 0.5 - f;\\\\n\\\\t\\\\tif ( face > 2.0 ) {\\\\n\\\\t\\\\t\\\\tuv.y += faceSize;\\\\n\\\\t\\\\t\\\\tface -= 3.0;\\\\n\\\\t\\\\t}\\\\n\\\\t\\\\tuv.x += face * faceSize;\\\\n\\\\t\\\\tif ( mipInt < cubeUV_maxMipLevel ) {\\\\n\\\\t\\\\t\\\\tuv.y += 2.0 * cubeUV_maxTileSize;\\\\n\\\\t\\\\t}\\\\n\\\\t\\\\tuv.y += filterInt * 2.0 * cubeUV_minTileSize;\\\\n\\\\t\\\\tuv.x += 3.0 * max( 0.0, cubeUV_maxTileSize - 2.0 * faceSize );\\\\n\\\\t\\\\tuv *= texelSize;\\\\n\\\\t\\\\tvec3 tl = envMapTexelToLinear( texture2D( envMap, uv ) ).rgb;\\\\n\\\\t\\\\tuv.x += texelSize;\\\\n\\\\t\\\\tvec3 tr = envMapTexelToLinear( texture2D( envMap, uv ) ).rgb;\\\\n\\\\t\\\\tuv.y += texelSize;\\\\n\\\\t\\\\tvec3 br = envMapTexelToLinear( texture2D( envMap, uv ) ).rgb;\\\\n\\\\t\\\\tuv.x -= texelSize;\\\\n\\\\t\\\\tvec3 bl = envMapTexelToLinear( texture2D( envMap, uv ) ).rgb;\\\\n\\\\t\\\\tvec3 tm = mix( tl, tr, f.x );\\\\n\\\\t\\\\tvec3 bm = mix( bl, br, f.x );\\\\n\\\\t\\\\treturn mix( tm, bm, f.y );\\\\n\\\\t}\\\\n\\\\t#define r0 1.0\\\\n\\\\t#define v0 0.339\\\\n\\\\t#define m0 - 2.0\\\\n\\\\t#define r1 0.8\\\\n\\\\t#define v1 0.276\\\\n\\\\t#define m1 - 1.0\\\\n\\\\t#define r4 0.4\\\\n\\\\t#define v4 0.046\\\\n\\\\t#define m4 2.0\\\\n\\\\t#define r5 0.305\\\\n\\\\t#define v5 0.016\\\\n\\\\t#define m5 3.0\\\\n\\\\t#define r6 0.21\\\\n\\\\t#define v6 0.0038\\\\n\\\\t#define m6 4.0\\\\n\\\\tfloat roughnessToMip( float roughness ) {\\\\n\\\\t\\\\tfloat mip = 0.0;\\\\n\\\\t\\\\tif ( roughness >= r1 ) {\\\\n\\\\t\\\\t\\\\tmip = ( r0 - roughness ) * ( m1 - m0 ) / ( r0 - r1 ) + m0;\\\\n\\\\t\\\\t} else if ( roughness >= r4 ) {\\\\n\\\\t\\\\t\\\\tmip = ( r1 - roughness ) * ( m4 - m1 ) / ( r1 - r4 ) + m1;\\\\n\\\\t\\\\t} else if ( roughness >= r5 ) {\\\\n\\\\t\\\\t\\\\tmip = ( r4 - roughness ) * ( m5 - m4 ) / ( r4 - r5 ) + m4;\\\\n\\\\t\\\\t} else if ( roughness >= r6 ) {\\\\n\\\\t\\\\t\\\\tmip = ( r5 - roughness ) * ( m6 - m5 ) / ( r5 - r6 ) + m5;\\\\n\\\\t\\\\t} else {\\\\n\\\\t\\\\t\\\\tmip = - 2.0 * log2( 1.16 * roughness );\\\\t\\\\t}\\\\n\\\\t\\\\treturn mip;\\\\n\\\\t}\\\\n\\\\tvec4 textureCubeUV( sampler2D envMap, vec3 sampleDir, float roughness ) {\\\\n\\\\t\\\\tfloat mip = clamp( roughnessToMip( roughness ), m0, cubeUV_maxMipLevel );\\\\n\\\\t\\\\tfloat mipF = fract( mip );\\\\n\\\\t\\\\tfloat mipInt = floor( mip );\\\\n\\\\t\\\\tvec3 color0 = bilinearCubeUV( envMap, sampleDir, mipInt );\\\\n\\\\t\\\\tif ( mipF == 0.0 ) {\\\\n\\\\t\\\\t\\\\treturn vec4( color0, 1.0 );\\\\n\\\\t\\\\t} else {\\\\n\\\\t\\\\t\\\\tvec3 color1 = bilinearCubeUV( envMap, sampleDir, mipInt + 1.0 );\\\\n\\\\t\\\\t\\\\treturn vec4( mix( color0, color1, mipF ), 1.0 );\\\\n\\\\t\\\\t}\\\\n\\\\t}\\\\n#endif\\\\\\\",defaultnormal_vertex:\\\\\\\"vec3 transformedNormal = objectNormal;\\\\n#ifdef USE_INSTANCING\\\\n\\\\tmat3 m = mat3( instanceMatrix );\\\\n\\\\ttransformedNormal /= vec3( dot( m[ 0 ], m[ 0 ] ), dot( m[ 1 ], m[ 1 ] ), dot( m[ 2 ], m[ 2 ] ) );\\\\n\\\\ttransformedNormal = m * transformedNormal;\\\\n#endif\\\\ntransformedNormal = normalMatrix * transformedNormal;\\\\n#ifdef FLIP_SIDED\\\\n\\\\ttransformedNormal = - transformedNormal;\\\\n#endif\\\\n#ifdef USE_TANGENT\\\\n\\\\tvec3 transformedTangent = ( modelViewMatrix * vec4( objectTangent, 0.0 ) ).xyz;\\\\n\\\\t#ifdef FLIP_SIDED\\\\n\\\\t\\\\ttransformedTangent = - transformedTangent;\\\\n\\\\t#endif\\\\n#endif\\\\\\\",displacementmap_pars_vertex:\\\\\\\"#ifdef USE_DISPLACEMENTMAP\\\\n\\\\tuniform sampler2D displacementMap;\\\\n\\\\tuniform float displacementScale;\\\\n\\\\tuniform float displacementBias;\\\\n#endif\\\\\\\",displacementmap_vertex:\\\\\\\"#ifdef USE_DISPLACEMENTMAP\\\\n\\\\ttransformed += normalize( objectNormal ) * ( texture2D( displacementMap, vUv ).x * displacementScale + displacementBias );\\\\n#endif\\\\\\\",emissivemap_fragment:\\\\\\\"#ifdef USE_EMISSIVEMAP\\\\n\\\\tvec4 emissiveColor = texture2D( emissiveMap, vUv );\\\\n\\\\temissiveColor.rgb = emissiveMapTexelToLinear( emissiveColor ).rgb;\\\\n\\\\ttotalEmissiveRadiance *= emissiveColor.rgb;\\\\n#endif\\\\\\\",emissivemap_pars_fragment:\\\\\\\"#ifdef USE_EMISSIVEMAP\\\\n\\\\tuniform sampler2D emissiveMap;\\\\n#endif\\\\\\\",encodings_fragment:\\\\\\\"gl_FragColor = linearToOutputTexel( gl_FragColor );\\\\\\\",encodings_pars_fragment:\\\\\\\"\\\\nvec4 LinearToLinear( in vec4 value ) {\\\\n\\\\treturn value;\\\\n}\\\\nvec4 GammaToLinear( in vec4 value, in float gammaFactor ) {\\\\n\\\\treturn vec4( pow( value.rgb, vec3( gammaFactor ) ), value.a );\\\\n}\\\\nvec4 LinearToGamma( in vec4 value, in float gammaFactor ) {\\\\n\\\\treturn vec4( pow( value.rgb, vec3( 1.0 / gammaFactor ) ), value.a );\\\\n}\\\\nvec4 sRGBToLinear( in vec4 value ) {\\\\n\\\\treturn vec4( mix( pow( value.rgb * 0.9478672986 + vec3( 0.0521327014 ), vec3( 2.4 ) ), value.rgb * 0.0773993808, vec3( lessThanEqual( value.rgb, vec3( 0.04045 ) ) ) ), value.a );\\\\n}\\\\nvec4 LinearTosRGB( in vec4 value ) {\\\\n\\\\treturn vec4( mix( pow( value.rgb, vec3( 0.41666 ) ) * 1.055 - vec3( 0.055 ), value.rgb * 12.92, vec3( lessThanEqual( value.rgb, vec3( 0.0031308 ) ) ) ), value.a );\\\\n}\\\\nvec4 RGBEToLinear( in vec4 value ) {\\\\n\\\\treturn vec4( value.rgb * exp2( value.a * 255.0 - 128.0 ), 1.0 );\\\\n}\\\\nvec4 LinearToRGBE( in vec4 value ) {\\\\n\\\\tfloat maxComponent = max( max( value.r, value.g ), value.b );\\\\n\\\\tfloat fExp = clamp( ceil( log2( maxComponent ) ), -128.0, 127.0 );\\\\n\\\\treturn vec4( value.rgb / exp2( fExp ), ( fExp + 128.0 ) / 255.0 );\\\\n}\\\\nvec4 RGBMToLinear( in vec4 value, in float maxRange ) {\\\\n\\\\treturn vec4( value.rgb * value.a * maxRange, 1.0 );\\\\n}\\\\nvec4 LinearToRGBM( in vec4 value, in float maxRange ) {\\\\n\\\\tfloat maxRGB = max( value.r, max( value.g, value.b ) );\\\\n\\\\tfloat M = clamp( maxRGB / maxRange, 0.0, 1.0 );\\\\n\\\\tM = ceil( M * 255.0 ) / 255.0;\\\\n\\\\treturn vec4( value.rgb / ( M * maxRange ), M );\\\\n}\\\\nvec4 RGBDToLinear( in vec4 value, in float maxRange ) {\\\\n\\\\treturn vec4( value.rgb * ( ( maxRange / 255.0 ) / value.a ), 1.0 );\\\\n}\\\\nvec4 LinearToRGBD( in vec4 value, in float maxRange ) {\\\\n\\\\tfloat maxRGB = max( value.r, max( value.g, value.b ) );\\\\n\\\\tfloat D = max( maxRange / maxRGB, 1.0 );\\\\n\\\\tD = clamp( floor( D ) / 255.0, 0.0, 1.0 );\\\\n\\\\treturn vec4( value.rgb * ( D * ( 255.0 / maxRange ) ), D );\\\\n}\\\\nconst mat3 cLogLuvM = mat3( 0.2209, 0.3390, 0.4184, 0.1138, 0.6780, 0.7319, 0.0102, 0.1130, 0.2969 );\\\\nvec4 LinearToLogLuv( in vec4 value ) {\\\\n\\\\tvec3 Xp_Y_XYZp = cLogLuvM * value.rgb;\\\\n\\\\tXp_Y_XYZp = max( Xp_Y_XYZp, vec3( 1e-6, 1e-6, 1e-6 ) );\\\\n\\\\tvec4 vResult;\\\\n\\\\tvResult.xy = Xp_Y_XYZp.xy / Xp_Y_XYZp.z;\\\\n\\\\tfloat Le = 2.0 * log2(Xp_Y_XYZp.y) + 127.0;\\\\n\\\\tvResult.w = fract( Le );\\\\n\\\\tvResult.z = ( Le - ( floor( vResult.w * 255.0 ) ) / 255.0 ) / 255.0;\\\\n\\\\treturn vResult;\\\\n}\\\\nconst mat3 cLogLuvInverseM = mat3( 6.0014, -2.7008, -1.7996, -1.3320, 3.1029, -5.7721, 0.3008, -1.0882, 5.6268 );\\\\nvec4 LogLuvToLinear( in vec4 value ) {\\\\n\\\\tfloat Le = value.z * 255.0 + value.w;\\\\n\\\\tvec3 Xp_Y_XYZp;\\\\n\\\\tXp_Y_XYZp.y = exp2( ( Le - 127.0 ) / 2.0 );\\\\n\\\\tXp_Y_XYZp.z = Xp_Y_XYZp.y / value.y;\\\\n\\\\tXp_Y_XYZp.x = value.x * Xp_Y_XYZp.z;\\\\n\\\\tvec3 vRGB = cLogLuvInverseM * Xp_Y_XYZp.rgb;\\\\n\\\\treturn vec4( max( vRGB, 0.0 ), 1.0 );\\\\n}\\\\\\\",envmap_fragment:\\\\\\\"#ifdef USE_ENVMAP\\\\n\\\\t#ifdef ENV_WORLDPOS\\\\n\\\\t\\\\tvec3 cameraToFrag;\\\\n\\\\t\\\\tif ( isOrthographic ) {\\\\n\\\\t\\\\t\\\\tcameraToFrag = normalize( vec3( - viewMatrix[ 0 ][ 2 ], - viewMatrix[ 1 ][ 2 ], - viewMatrix[ 2 ][ 2 ] ) );\\\\n\\\\t\\\\t} else {\\\\n\\\\t\\\\t\\\\tcameraToFrag = normalize( vWorldPosition - cameraPosition );\\\\n\\\\t\\\\t}\\\\n\\\\t\\\\tvec3 worldNormal = inverseTransformDirection( normal, viewMatrix );\\\\n\\\\t\\\\t#ifdef ENVMAP_MODE_REFLECTION\\\\n\\\\t\\\\t\\\\tvec3 reflectVec = reflect( cameraToFrag, worldNormal );\\\\n\\\\t\\\\t#else\\\\n\\\\t\\\\t\\\\tvec3 reflectVec = refract( cameraToFrag, worldNormal, refractionRatio );\\\\n\\\\t\\\\t#endif\\\\n\\\\t#else\\\\n\\\\t\\\\tvec3 reflectVec = vReflect;\\\\n\\\\t#endif\\\\n\\\\t#ifdef ENVMAP_TYPE_CUBE\\\\n\\\\t\\\\tvec4 envColor = textureCube( envMap, vec3( flipEnvMap * reflectVec.x, reflectVec.yz ) );\\\\n\\\\t#elif defined( ENVMAP_TYPE_CUBE_UV )\\\\n\\\\t\\\\tvec4 envColor = textureCubeUV( envMap, reflectVec, 0.0 );\\\\n\\\\t#else\\\\n\\\\t\\\\tvec4 envColor = vec4( 0.0 );\\\\n\\\\t#endif\\\\n\\\\t#ifndef ENVMAP_TYPE_CUBE_UV\\\\n\\\\t\\\\tenvColor = envMapTexelToLinear( envColor );\\\\n\\\\t#endif\\\\n\\\\t#ifdef ENVMAP_BLENDING_MULTIPLY\\\\n\\\\t\\\\toutgoingLight = mix( outgoingLight, outgoingLight * envColor.xyz, specularStrength * reflectivity );\\\\n\\\\t#elif defined( ENVMAP_BLENDING_MIX )\\\\n\\\\t\\\\toutgoingLight = mix( outgoingLight, envColor.xyz, specularStrength * reflectivity );\\\\n\\\\t#elif defined( ENVMAP_BLENDING_ADD )\\\\n\\\\t\\\\toutgoingLight += envColor.xyz * specularStrength * reflectivity;\\\\n\\\\t#endif\\\\n#endif\\\\\\\",envmap_common_pars_fragment:\\\\\\\"#ifdef USE_ENVMAP\\\\n\\\\tuniform float envMapIntensity;\\\\n\\\\tuniform float flipEnvMap;\\\\n\\\\tuniform int maxMipLevel;\\\\n\\\\t#ifdef ENVMAP_TYPE_CUBE\\\\n\\\\t\\\\tuniform samplerCube envMap;\\\\n\\\\t#else\\\\n\\\\t\\\\tuniform sampler2D envMap;\\\\n\\\\t#endif\\\\n\\\\t\\\\n#endif\\\\\\\",envmap_pars_fragment:\\\\\\\"#ifdef USE_ENVMAP\\\\n\\\\tuniform float reflectivity;\\\\n\\\\t#if defined( USE_BUMPMAP ) || defined( USE_NORMALMAP ) || defined( PHONG )\\\\n\\\\t\\\\t#define ENV_WORLDPOS\\\\n\\\\t#endif\\\\n\\\\t#ifdef ENV_WORLDPOS\\\\n\\\\t\\\\tvarying vec3 vWorldPosition;\\\\n\\\\t\\\\tuniform float refractionRatio;\\\\n\\\\t#else\\\\n\\\\t\\\\tvarying vec3 vReflect;\\\\n\\\\t#endif\\\\n#endif\\\\\\\",envmap_pars_vertex:\\\\\\\"#ifdef USE_ENVMAP\\\\n\\\\t#if defined( USE_BUMPMAP ) || defined( USE_NORMALMAP ) ||defined( PHONG )\\\\n\\\\t\\\\t#define ENV_WORLDPOS\\\\n\\\\t#endif\\\\n\\\\t#ifdef ENV_WORLDPOS\\\\n\\\\t\\\\t\\\\n\\\\t\\\\tvarying vec3 vWorldPosition;\\\\n\\\\t#else\\\\n\\\\t\\\\tvarying vec3 vReflect;\\\\n\\\\t\\\\tuniform float refractionRatio;\\\\n\\\\t#endif\\\\n#endif\\\\\\\",envmap_physical_pars_fragment:\\\\\\\"#if defined( USE_ENVMAP )\\\\n\\\\t#ifdef ENVMAP_MODE_REFRACTION\\\\n\\\\t\\\\tuniform float refractionRatio;\\\\n\\\\t#endif\\\\n\\\\tvec3 getLightProbeIndirectIrradiance( const in GeometricContext geometry, const in int maxMIPLevel ) {\\\\n\\\\t\\\\tvec3 worldNormal = inverseTransformDirection( geometry.normal, viewMatrix );\\\\n\\\\t\\\\t#ifdef ENVMAP_TYPE_CUBE\\\\n\\\\t\\\\t\\\\tvec3 queryVec = vec3( flipEnvMap * worldNormal.x, worldNormal.yz );\\\\n\\\\t\\\\t\\\\t#ifdef TEXTURE_LOD_EXT\\\\n\\\\t\\\\t\\\\t\\\\tvec4 envMapColor = textureCubeLodEXT( envMap, queryVec, float( maxMIPLevel ) );\\\\n\\\\t\\\\t\\\\t#else\\\\n\\\\t\\\\t\\\\t\\\\tvec4 envMapColor = textureCube( envMap, queryVec, float( maxMIPLevel ) );\\\\n\\\\t\\\\t\\\\t#endif\\\\n\\\\t\\\\t\\\\tenvMapColor.rgb = envMapTexelToLinear( envMapColor ).rgb;\\\\n\\\\t\\\\t#elif defined( ENVMAP_TYPE_CUBE_UV )\\\\n\\\\t\\\\t\\\\tvec4 envMapColor = textureCubeUV( envMap, worldNormal, 1.0 );\\\\n\\\\t\\\\t#else\\\\n\\\\t\\\\t\\\\tvec4 envMapColor = vec4( 0.0 );\\\\n\\\\t\\\\t#endif\\\\n\\\\t\\\\treturn PI * envMapColor.rgb * envMapIntensity;\\\\n\\\\t}\\\\n\\\\tfloat getSpecularMIPLevel( const in float roughness, const in int maxMIPLevel ) {\\\\n\\\\t\\\\tfloat maxMIPLevelScalar = float( maxMIPLevel );\\\\n\\\\t\\\\tfloat sigma = PI * roughness * roughness / ( 1.0 + roughness );\\\\n\\\\t\\\\tfloat desiredMIPLevel = maxMIPLevelScalar + log2( sigma );\\\\n\\\\t\\\\treturn clamp( desiredMIPLevel, 0.0, maxMIPLevelScalar );\\\\n\\\\t}\\\\n\\\\tvec3 getLightProbeIndirectRadiance( const in vec3 viewDir, const in vec3 normal, const in float roughness, const in int maxMIPLevel ) {\\\\n\\\\t\\\\t#ifdef ENVMAP_MODE_REFLECTION\\\\n\\\\t\\\\t\\\\tvec3 reflectVec = reflect( -viewDir, normal );\\\\n\\\\t\\\\t\\\\treflectVec = normalize( mix( reflectVec, normal, roughness * roughness) );\\\\n\\\\t\\\\t#else\\\\n\\\\t\\\\t\\\\tvec3 reflectVec = refract( -viewDir, normal, refractionRatio );\\\\n\\\\t\\\\t#endif\\\\n\\\\t\\\\treflectVec = inverseTransformDirection( reflectVec, viewMatrix );\\\\n\\\\t\\\\tfloat specularMIPLevel = getSpecularMIPLevel( roughness, maxMIPLevel );\\\\n\\\\t\\\\t#ifdef ENVMAP_TYPE_CUBE\\\\n\\\\t\\\\t\\\\tvec3 queryReflectVec = vec3( flipEnvMap * reflectVec.x, reflectVec.yz );\\\\n\\\\t\\\\t\\\\t#ifdef TEXTURE_LOD_EXT\\\\n\\\\t\\\\t\\\\t\\\\tvec4 envMapColor = textureCubeLodEXT( envMap, queryReflectVec, specularMIPLevel );\\\\n\\\\t\\\\t\\\\t#else\\\\n\\\\t\\\\t\\\\t\\\\tvec4 envMapColor = textureCube( envMap, queryReflectVec, specularMIPLevel );\\\\n\\\\t\\\\t\\\\t#endif\\\\n\\\\t\\\\t\\\\tenvMapColor.rgb = envMapTexelToLinear( envMapColor ).rgb;\\\\n\\\\t\\\\t#elif defined( ENVMAP_TYPE_CUBE_UV )\\\\n\\\\t\\\\t\\\\tvec4 envMapColor = textureCubeUV( envMap, reflectVec, roughness );\\\\n\\\\t\\\\t#endif\\\\n\\\\t\\\\treturn envMapColor.rgb * envMapIntensity;\\\\n\\\\t}\\\\n#endif\\\\\\\",envmap_vertex:\\\\\\\"#ifdef USE_ENVMAP\\\\n\\\\t#ifdef ENV_WORLDPOS\\\\n\\\\t\\\\tvWorldPosition = worldPosition.xyz;\\\\n\\\\t#else\\\\n\\\\t\\\\tvec3 cameraToVertex;\\\\n\\\\t\\\\tif ( isOrthographic ) {\\\\n\\\\t\\\\t\\\\tcameraToVertex = normalize( vec3( - viewMatrix[ 0 ][ 2 ], - viewMatrix[ 1 ][ 2 ], - viewMatrix[ 2 ][ 2 ] ) );\\\\n\\\\t\\\\t} else {\\\\n\\\\t\\\\t\\\\tcameraToVertex = normalize( worldPosition.xyz - cameraPosition );\\\\n\\\\t\\\\t}\\\\n\\\\t\\\\tvec3 worldNormal = inverseTransformDirection( transformedNormal, viewMatrix );\\\\n\\\\t\\\\t#ifdef ENVMAP_MODE_REFLECTION\\\\n\\\\t\\\\t\\\\tvReflect = reflect( cameraToVertex, worldNormal );\\\\n\\\\t\\\\t#else\\\\n\\\\t\\\\t\\\\tvReflect = refract( cameraToVertex, worldNormal, refractionRatio );\\\\n\\\\t\\\\t#endif\\\\n\\\\t#endif\\\\n#endif\\\\\\\",fog_vertex:\\\\\\\"#ifdef USE_FOG\\\\n\\\\tfogDepth = - mvPosition.z;\\\\n#endif\\\\\\\",fog_pars_vertex:\\\\\\\"#ifdef USE_FOG\\\\n\\\\tvarying float fogDepth;\\\\n#endif\\\\\\\",fog_fragment:\\\\\\\"#ifdef USE_FOG\\\\n\\\\t#ifdef FOG_EXP2\\\\n\\\\t\\\\tfloat fogFactor = 1.0 - exp( - fogDensity * fogDensity * fogDepth * fogDepth );\\\\n\\\\t#else\\\\n\\\\t\\\\tfloat fogFactor = smoothstep( fogNear, fogFar, fogDepth );\\\\n\\\\t#endif\\\\n\\\\tgl_FragColor.rgb = mix( gl_FragColor.rgb, fogColor, fogFactor );\\\\n#endif\\\\\\\",fog_pars_fragment:\\\\\\\"#ifdef USE_FOG\\\\n\\\\tuniform vec3 fogColor;\\\\n\\\\tvarying float fogDepth;\\\\n\\\\t#ifdef FOG_EXP2\\\\n\\\\t\\\\tuniform float fogDensity;\\\\n\\\\t#else\\\\n\\\\t\\\\tuniform float fogNear;\\\\n\\\\t\\\\tuniform float fogFar;\\\\n\\\\t#endif\\\\n#endif\\\\\\\",gradientmap_pars_fragment:\\\\\\\"#ifdef USE_GRADIENTMAP\\\\n\\\\tuniform sampler2D gradientMap;\\\\n#endif\\\\nvec3 getGradientIrradiance( vec3 normal, vec3 lightDirection ) {\\\\n\\\\tfloat dotNL = dot( normal, lightDirection );\\\\n\\\\tvec2 coord = vec2( dotNL * 0.5 + 0.5, 0.0 );\\\\n\\\\t#ifdef USE_GRADIENTMAP\\\\n\\\\t\\\\treturn texture2D( gradientMap, coord ).rgb;\\\\n\\\\t#else\\\\n\\\\t\\\\treturn ( coord.x < 0.7 ) ? vec3( 0.7 ) : vec3( 1.0 );\\\\n\\\\t#endif\\\\n}\\\\\\\",lightmap_fragment:\\\\\\\"#ifdef USE_LIGHTMAP\\\\n\\\\tvec4 lightMapTexel= texture2D( lightMap, vUv2 );\\\\n\\\\treflectedLight.indirectDiffuse += PI * lightMapTexelToLinear( lightMapTexel ).rgb * lightMapIntensity;\\\\n#endif\\\\\\\",lightmap_pars_fragment:\\\\\\\"#ifdef USE_LIGHTMAP\\\\n\\\\tuniform sampler2D lightMap;\\\\n\\\\tuniform float lightMapIntensity;\\\\n#endif\\\\\\\",lights_lambert_vertex:\\\\\\\"vec3 diffuse = vec3( 1.0 );\\\\nGeometricContext geometry;\\\\ngeometry.position = mvPosition.xyz;\\\\ngeometry.normal = normalize( transformedNormal );\\\\ngeometry.viewDir = ( isOrthographic ) ? vec3( 0, 0, 1 ) : normalize( -mvPosition.xyz );\\\\nGeometricContext backGeometry;\\\\nbackGeometry.position = geometry.position;\\\\nbackGeometry.normal = -geometry.normal;\\\\nbackGeometry.viewDir = geometry.viewDir;\\\\nvLightFront = vec3( 0.0 );\\\\nvIndirectFront = vec3( 0.0 );\\\\n#ifdef DOUBLE_SIDED\\\\n\\\\tvLightBack = vec3( 0.0 );\\\\n\\\\tvIndirectBack = vec3( 0.0 );\\\\n#endif\\\\nIncidentLight directLight;\\\\nfloat dotNL;\\\\nvec3 directLightColor_Diffuse;\\\\nvIndirectFront += getAmbientLightIrradiance( ambientLightColor );\\\\nvIndirectFront += getLightProbeIrradiance( lightProbe, geometry );\\\\n#ifdef DOUBLE_SIDED\\\\n\\\\tvIndirectBack += getAmbientLightIrradiance( ambientLightColor );\\\\n\\\\tvIndirectBack += getLightProbeIrradiance( lightProbe, backGeometry );\\\\n#endif\\\\n#if NUM_POINT_LIGHTS > 0\\\\n\\\\t#pragma unroll_loop_start\\\\n\\\\tfor ( int i = 0; i < NUM_POINT_LIGHTS; i ++ ) {\\\\n\\\\t\\\\tgetPointDirectLightIrradiance( pointLights[ i ], geometry, directLight );\\\\n\\\\t\\\\tdotNL = dot( geometry.normal, directLight.direction );\\\\n\\\\t\\\\tdirectLightColor_Diffuse = PI * directLight.color;\\\\n\\\\t\\\\tvLightFront += saturate( dotNL ) * directLightColor_Diffuse;\\\\n\\\\t\\\\t#ifdef DOUBLE_SIDED\\\\n\\\\t\\\\t\\\\tvLightBack += saturate( -dotNL ) * directLightColor_Diffuse;\\\\n\\\\t\\\\t#endif\\\\n\\\\t}\\\\n\\\\t#pragma unroll_loop_end\\\\n#endif\\\\n#if NUM_SPOT_LIGHTS > 0\\\\n\\\\t#pragma unroll_loop_start\\\\n\\\\tfor ( int i = 0; i < NUM_SPOT_LIGHTS; i ++ ) {\\\\n\\\\t\\\\tgetSpotDirectLightIrradiance( spotLights[ i ], geometry, directLight );\\\\n\\\\t\\\\tdotNL = dot( geometry.normal, directLight.direction );\\\\n\\\\t\\\\tdirectLightColor_Diffuse = PI * directLight.color;\\\\n\\\\t\\\\tvLightFront += saturate( dotNL ) * directLightColor_Diffuse;\\\\n\\\\t\\\\t#ifdef DOUBLE_SIDED\\\\n\\\\t\\\\t\\\\tvLightBack += saturate( -dotNL ) * directLightColor_Diffuse;\\\\n\\\\t\\\\t#endif\\\\n\\\\t}\\\\n\\\\t#pragma unroll_loop_end\\\\n#endif\\\\n#if NUM_DIR_LIGHTS > 0\\\\n\\\\t#pragma unroll_loop_start\\\\n\\\\tfor ( int i = 0; i < NUM_DIR_LIGHTS; i ++ ) {\\\\n\\\\t\\\\tgetDirectionalDirectLightIrradiance( directionalLights[ i ], geometry, directLight );\\\\n\\\\t\\\\tdotNL = dot( geometry.normal, directLight.direction );\\\\n\\\\t\\\\tdirectLightColor_Diffuse = PI * directLight.color;\\\\n\\\\t\\\\tvLightFront += saturate( dotNL ) * directLightColor_Diffuse;\\\\n\\\\t\\\\t#ifdef DOUBLE_SIDED\\\\n\\\\t\\\\t\\\\tvLightBack += saturate( -dotNL ) * directLightColor_Diffuse;\\\\n\\\\t\\\\t#endif\\\\n\\\\t}\\\\n\\\\t#pragma unroll_loop_end\\\\n#endif\\\\n#if NUM_HEMI_LIGHTS > 0\\\\n\\\\t#pragma unroll_loop_start\\\\n\\\\tfor ( int i = 0; i < NUM_HEMI_LIGHTS; i ++ ) {\\\\n\\\\t\\\\tvIndirectFront += getHemisphereLightIrradiance( hemisphereLights[ i ], geometry );\\\\n\\\\t\\\\t#ifdef DOUBLE_SIDED\\\\n\\\\t\\\\t\\\\tvIndirectBack += getHemisphereLightIrradiance( hemisphereLights[ i ], backGeometry );\\\\n\\\\t\\\\t#endif\\\\n\\\\t}\\\\n\\\\t#pragma unroll_loop_end\\\\n#endif\\\\\\\",lights_pars_begin:\\\\\\\"uniform bool receiveShadow;\\\\nuniform vec3 ambientLightColor;\\\\nuniform vec3 lightProbe[ 9 ];\\\\nvec3 shGetIrradianceAt( in vec3 normal, in vec3 shCoefficients[ 9 ] ) {\\\\n\\\\tfloat x = normal.x, y = normal.y, z = normal.z;\\\\n\\\\tvec3 result = shCoefficients[ 0 ] * 0.886227;\\\\n\\\\tresult += shCoefficients[ 1 ] * 2.0 * 0.511664 * y;\\\\n\\\\tresult += shCoefficients[ 2 ] * 2.0 * 0.511664 * z;\\\\n\\\\tresult += shCoefficients[ 3 ] * 2.0 * 0.511664 * x;\\\\n\\\\tresult += shCoefficients[ 4 ] * 2.0 * 0.429043 * x * y;\\\\n\\\\tresult += shCoefficients[ 5 ] * 2.0 * 0.429043 * y * z;\\\\n\\\\tresult += shCoefficients[ 6 ] * ( 0.743125 * z * z - 0.247708 );\\\\n\\\\tresult += shCoefficients[ 7 ] * 2.0 * 0.429043 * x * z;\\\\n\\\\tresult += shCoefficients[ 8 ] * 0.429043 * ( x * x - y * y );\\\\n\\\\treturn result;\\\\n}\\\\nvec3 getLightProbeIrradiance( const in vec3 lightProbe[ 9 ], const in GeometricContext geometry ) {\\\\n\\\\tvec3 worldNormal = inverseTransformDirection( geometry.normal, viewMatrix );\\\\n\\\\tvec3 irradiance = shGetIrradianceAt( worldNormal, lightProbe );\\\\n\\\\treturn irradiance;\\\\n}\\\\nvec3 getAmbientLightIrradiance( const in vec3 ambientLightColor ) {\\\\n\\\\tvec3 irradiance = ambientLightColor;\\\\n\\\\t#ifndef PHYSICALLY_CORRECT_LIGHTS\\\\n\\\\t\\\\tirradiance *= PI;\\\\n\\\\t#endif\\\\n\\\\treturn irradiance;\\\\n}\\\\n#if NUM_DIR_LIGHTS > 0\\\\n\\\\tstruct DirectionalLight {\\\\n\\\\t\\\\tvec3 direction;\\\\n\\\\t\\\\tvec3 color;\\\\n\\\\t};\\\\n\\\\tuniform DirectionalLight directionalLights[ NUM_DIR_LIGHTS ];\\\\n\\\\tvoid getDirectionalDirectLightIrradiance( const in DirectionalLight directionalLight, const in GeometricContext geometry, out IncidentLight directLight ) {\\\\n\\\\t\\\\tdirectLight.color = directionalLight.color;\\\\n\\\\t\\\\tdirectLight.direction = directionalLight.direction;\\\\n\\\\t\\\\tdirectLight.visible = true;\\\\n\\\\t}\\\\n#endif\\\\n#if NUM_POINT_LIGHTS > 0\\\\n\\\\tstruct PointLight {\\\\n\\\\t\\\\tvec3 position;\\\\n\\\\t\\\\tvec3 color;\\\\n\\\\t\\\\tfloat distance;\\\\n\\\\t\\\\tfloat decay;\\\\n\\\\t};\\\\n\\\\tuniform PointLight pointLights[ NUM_POINT_LIGHTS ];\\\\n\\\\tvoid getPointDirectLightIrradiance( const in PointLight pointLight, const in GeometricContext geometry, out IncidentLight directLight ) {\\\\n\\\\t\\\\tvec3 lVector = pointLight.position - geometry.position;\\\\n\\\\t\\\\tdirectLight.direction = normalize( lVector );\\\\n\\\\t\\\\tfloat lightDistance = length( lVector );\\\\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\\\\t}\\\\n#endif\\\\n#if NUM_SPOT_LIGHTS > 0\\\\n\\\\tstruct SpotLight {\\\\n\\\\t\\\\tvec3 position;\\\\n\\\\t\\\\tvec3 direction;\\\\n\\\\t\\\\tvec3 color;\\\\n\\\\t\\\\tfloat distance;\\\\n\\\\t\\\\tfloat decay;\\\\n\\\\t\\\\tfloat coneCos;\\\\n\\\\t\\\\tfloat penumbraCos;\\\\n\\\\t};\\\\n\\\\tuniform SpotLight spotLights[ NUM_SPOT_LIGHTS ];\\\\n\\\\tvoid getSpotDirectLightIrradiance( const in SpotLight spotLight, const in GeometricContext geometry, out IncidentLight directLight ) {\\\\n\\\\t\\\\tvec3 lVector = spotLight.position - geometry.position;\\\\n\\\\t\\\\tdirectLight.direction = normalize( lVector );\\\\n\\\\t\\\\tfloat lightDistance = length( lVector );\\\\n\\\\t\\\\tfloat angleCos = dot( directLight.direction, spotLight.direction );\\\\n\\\\t\\\\tif ( angleCos > spotLight.coneCos ) {\\\\n\\\\t\\\\t\\\\tfloat spotEffect = smoothstep( spotLight.coneCos, spotLight.penumbraCos, angleCos );\\\\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\\\\t\\\\t} else {\\\\n\\\\t\\\\t\\\\tdirectLight.color = vec3( 0.0 );\\\\n\\\\t\\\\t\\\\tdirectLight.visible = false;\\\\n\\\\t\\\\t}\\\\n\\\\t}\\\\n#endif\\\\n#if NUM_RECT_AREA_LIGHTS > 0\\\\n\\\\tstruct RectAreaLight {\\\\n\\\\t\\\\tvec3 color;\\\\n\\\\t\\\\tvec3 position;\\\\n\\\\t\\\\tvec3 halfWidth;\\\\n\\\\t\\\\tvec3 halfHeight;\\\\n\\\\t};\\\\n\\\\tuniform sampler2D ltc_1;\\\\tuniform sampler2D ltc_2;\\\\n\\\\tuniform RectAreaLight rectAreaLights[ NUM_RECT_AREA_LIGHTS ];\\\\n#endif\\\\n#if NUM_HEMI_LIGHTS > 0\\\\n\\\\tstruct HemisphereLight {\\\\n\\\\t\\\\tvec3 direction;\\\\n\\\\t\\\\tvec3 skyColor;\\\\n\\\\t\\\\tvec3 groundColor;\\\\n\\\\t};\\\\n\\\\tuniform HemisphereLight hemisphereLights[ NUM_HEMI_LIGHTS ];\\\\n\\\\tvec3 getHemisphereLightIrradiance( const in HemisphereLight hemiLight, const in GeometricContext geometry ) {\\\\n\\\\t\\\\tfloat dotNL = dot( geometry.normal, hemiLight.direction );\\\\n\\\\t\\\\tfloat hemiDiffuseWeight = 0.5 * dotNL + 0.5;\\\\n\\\\t\\\\tvec3 irradiance = mix( hemiLight.groundColor, hemiLight.skyColor, hemiDiffuseWeight );\\\\n\\\\t\\\\t#ifndef PHYSICALLY_CORRECT_LIGHTS\\\\n\\\\t\\\\t\\\\tirradiance *= PI;\\\\n\\\\t\\\\t#endif\\\\n\\\\t\\\\treturn irradiance;\\\\n\\\\t}\\\\n#endif\\\\\\\",lights_toon_fragment:\\\\\\\"ToonMaterial material;\\\\nmaterial.diffuseColor = diffuseColor.rgb;\\\\\\\",lights_toon_pars_fragment:\\\\\\\"varying vec3 vViewPosition;\\\\n#ifndef FLAT_SHADED\\\\n\\\\tvarying vec3 vNormal;\\\\n#endif\\\\nstruct ToonMaterial {\\\\n\\\\tvec3 diffuseColor;\\\\n};\\\\nvoid RE_Direct_Toon( const in IncidentLight directLight, const in GeometricContext geometry, const in ToonMaterial material, inout ReflectedLight reflectedLight ) {\\\\n\\\\tvec3 irradiance = getGradientIrradiance( geometry.normal, directLight.direction ) * directLight.color;\\\\n\\\\t#ifndef PHYSICALLY_CORRECT_LIGHTS\\\\n\\\\t\\\\tirradiance *= PI;\\\\n\\\\t#endif\\\\n\\\\treflectedLight.directDiffuse += irradiance * BRDF_Diffuse_Lambert( material.diffuseColor );\\\\n}\\\\nvoid RE_IndirectDiffuse_Toon( const in vec3 irradiance, const in GeometricContext geometry, const in ToonMaterial material, inout ReflectedLight reflectedLight ) {\\\\n\\\\treflectedLight.indirectDiffuse += irradiance * BRDF_Diffuse_Lambert( material.diffuseColor );\\\\n}\\\\n#define RE_Direct\\\\t\\\\t\\\\t\\\\tRE_Direct_Toon\\\\n#define RE_IndirectDiffuse\\\\t\\\\tRE_IndirectDiffuse_Toon\\\\n#define Material_LightProbeLOD( material )\\\\t(0)\\\\\\\",lights_phong_fragment:\\\\\\\"BlinnPhongMaterial material;\\\\nmaterial.diffuseColor = diffuseColor.rgb;\\\\nmaterial.specularColor = specular;\\\\nmaterial.specularShininess = shininess;\\\\nmaterial.specularStrength = specularStrength;\\\\\\\",lights_phong_pars_fragment:\\\\\\\"varying vec3 vViewPosition;\\\\n#ifndef FLAT_SHADED\\\\n\\\\tvarying vec3 vNormal;\\\\n#endif\\\\nstruct BlinnPhongMaterial {\\\\n\\\\tvec3 diffuseColor;\\\\n\\\\tvec3 specularColor;\\\\n\\\\tfloat specularShininess;\\\\n\\\\tfloat specularStrength;\\\\n};\\\\nvoid RE_Direct_BlinnPhong( const in IncidentLight directLight, const in GeometricContext geometry, const in BlinnPhongMaterial material, inout ReflectedLight reflectedLight ) {\\\\n\\\\tfloat dotNL = saturate( dot( geometry.normal, directLight.direction ) );\\\\n\\\\tvec3 irradiance = dotNL * directLight.color;\\\\n\\\\t#ifndef PHYSICALLY_CORRECT_LIGHTS\\\\n\\\\t\\\\tirradiance *= PI;\\\\n\\\\t#endif\\\\n\\\\treflectedLight.directDiffuse += irradiance * BRDF_Diffuse_Lambert( material.diffuseColor );\\\\n\\\\treflectedLight.directSpecular += irradiance * BRDF_Specular_BlinnPhong( directLight, geometry, material.specularColor, material.specularShininess ) * material.specularStrength;\\\\n}\\\\nvoid RE_IndirectDiffuse_BlinnPhong( const in vec3 irradiance, const in GeometricContext geometry, const in BlinnPhongMaterial material, inout ReflectedLight reflectedLight ) {\\\\n\\\\treflectedLight.indirectDiffuse += irradiance * BRDF_Diffuse_Lambert( material.diffuseColor );\\\\n}\\\\n#define RE_Direct\\\\t\\\\t\\\\t\\\\tRE_Direct_BlinnPhong\\\\n#define RE_IndirectDiffuse\\\\t\\\\tRE_IndirectDiffuse_BlinnPhong\\\\n#define Material_LightProbeLOD( material )\\\\t(0)\\\\\\\",lights_physical_fragment:\\\\\\\"PhysicalMaterial material;\\\\nmaterial.diffuseColor = diffuseColor.rgb * ( 1.0 - metalnessFactor );\\\\nvec3 dxy = max( abs( dFdx( geometryNormal ) ), abs( dFdy( geometryNormal ) ) );\\\\nfloat geometryRoughness = max( max( dxy.x, dxy.y ), dxy.z );\\\\nmaterial.specularRoughness = max( roughnessFactor, 0.0525 );material.specularRoughness += geometryRoughness;\\\\nmaterial.specularRoughness = min( material.specularRoughness, 1.0 );\\\\n#ifdef REFLECTIVITY\\\\n\\\\t#ifdef SPECULAR\\\\n\\\\t\\\\tvec3 specularIntensityFactor = vec3( specularIntensity );\\\\n\\\\t\\\\tvec3 specularTintFactor = specularTint;\\\\n\\\\t\\\\t#ifdef USE_SPECULARINTENSITYMAP\\\\n\\\\t\\\\t\\\\tspecularIntensityFactor *= texture2D( specularIntensityMap, vUv ).a;\\\\n\\\\t\\\\t#endif\\\\n\\\\t\\\\t#ifdef USE_SPECULARTINTMAP\\\\n\\\\t\\\\t\\\\tspecularTintFactor *= specularTintMapTexelToLinear( texture2D( specularTintMap, vUv ) ).rgb;\\\\n\\\\t\\\\t#endif\\\\n\\\\t\\\\tmaterial.specularColorF90 = mix( specularIntensityFactor, vec3( 1.0 ), metalnessFactor );\\\\n\\\\t#else\\\\n\\\\t\\\\tvec3 specularIntensityFactor = vec3( 1.0 );\\\\n\\\\t\\\\tvec3 specularTintFactor = vec3( 1.0 );\\\\n\\\\t\\\\tmaterial.specularColorF90 = vec3( 1.0 );\\\\n\\\\t#endif\\\\n\\\\tmaterial.specularColor = mix( min( vec3( MAXIMUM_SPECULAR_COEFFICIENT * pow2( reflectivity ) ) * specularTintFactor, vec3( 1.0 ) ) * specularIntensityFactor, diffuseColor.rgb, metalnessFactor );\\\\n#else\\\\n\\\\tmaterial.specularColor = mix( vec3( DEFAULT_SPECULAR_COEFFICIENT ), diffuseColor.rgb, metalnessFactor );\\\\n\\\\tmaterial.specularColorF90 = vec3( 1.0 );\\\\n#endif\\\\n#ifdef CLEARCOAT\\\\n\\\\tmaterial.clearcoat = clearcoat;\\\\n\\\\tmaterial.clearcoatRoughness = clearcoatRoughness;\\\\n\\\\t#ifdef USE_CLEARCOATMAP\\\\n\\\\t\\\\tmaterial.clearcoat *= texture2D( clearcoatMap, vUv ).x;\\\\n\\\\t#endif\\\\n\\\\t#ifdef USE_CLEARCOAT_ROUGHNESSMAP\\\\n\\\\t\\\\tmaterial.clearcoatRoughness *= texture2D( clearcoatRoughnessMap, vUv ).y;\\\\n\\\\t#endif\\\\n\\\\tmaterial.clearcoat = saturate( material.clearcoat );\\\\tmaterial.clearcoatRoughness = max( material.clearcoatRoughness, 0.0525 );\\\\n\\\\tmaterial.clearcoatRoughness += geometryRoughness;\\\\n\\\\tmaterial.clearcoatRoughness = min( material.clearcoatRoughness, 1.0 );\\\\n#endif\\\\n#ifdef USE_SHEEN\\\\n\\\\tmaterial.sheenColor = sheen;\\\\n#endif\\\\\\\",lights_physical_pars_fragment:\\\\\\\"struct PhysicalMaterial {\\\\n\\\\tvec3 diffuseColor;\\\\n\\\\tfloat specularRoughness;\\\\n\\\\tvec3 specularColor;\\\\n\\\\tvec3 specularColorF90;\\\\n#ifdef CLEARCOAT\\\\n\\\\tfloat clearcoat;\\\\n\\\\tfloat clearcoatRoughness;\\\\n#endif\\\\n#ifdef USE_SHEEN\\\\n\\\\tvec3 sheenColor;\\\\n#endif\\\\n};\\\\n#define MAXIMUM_SPECULAR_COEFFICIENT 0.16\\\\n#define DEFAULT_SPECULAR_COEFFICIENT 0.04\\\\nfloat clearcoatDHRApprox( const in float roughness, const in float dotNL ) {\\\\n\\\\treturn DEFAULT_SPECULAR_COEFFICIENT + ( 1.0 - DEFAULT_SPECULAR_COEFFICIENT ) * ( pow( 1.0 - dotNL, 5.0 ) * pow( 1.0 - roughness, 2.0 ) );\\\\n}\\\\n#if NUM_RECT_AREA_LIGHTS > 0\\\\n\\\\tvoid RE_Direct_RectArea_Physical( const in RectAreaLight rectAreaLight, const in GeometricContext geometry, const in PhysicalMaterial material, inout ReflectedLight reflectedLight ) {\\\\n\\\\t\\\\tvec3 normal = geometry.normal;\\\\n\\\\t\\\\tvec3 viewDir = geometry.viewDir;\\\\n\\\\t\\\\tvec3 position = geometry.position;\\\\n\\\\t\\\\tvec3 lightPos = rectAreaLight.position;\\\\n\\\\t\\\\tvec3 halfWidth = rectAreaLight.halfWidth;\\\\n\\\\t\\\\tvec3 halfHeight = rectAreaLight.halfHeight;\\\\n\\\\t\\\\tvec3 lightColor = rectAreaLight.color;\\\\n\\\\t\\\\tfloat roughness = material.specularRoughness;\\\\n\\\\t\\\\tvec3 rectCoords[ 4 ];\\\\n\\\\t\\\\trectCoords[ 0 ] = lightPos + halfWidth - halfHeight;\\\\t\\\\trectCoords[ 1 ] = lightPos - halfWidth - halfHeight;\\\\n\\\\t\\\\trectCoords[ 2 ] = lightPos - halfWidth + halfHeight;\\\\n\\\\t\\\\trectCoords[ 3 ] = lightPos + halfWidth + halfHeight;\\\\n\\\\t\\\\tvec2 uv = LTC_Uv( normal, viewDir, roughness );\\\\n\\\\t\\\\tvec4 t1 = texture2D( ltc_1, uv );\\\\n\\\\t\\\\tvec4 t2 = texture2D( ltc_2, uv );\\\\n\\\\t\\\\tmat3 mInv = mat3(\\\\n\\\\t\\\\t\\\\tvec3( t1.x, 0, t1.y ),\\\\n\\\\t\\\\t\\\\tvec3(\\\\t\\\\t0, 1,\\\\t\\\\t0 ),\\\\n\\\\t\\\\t\\\\tvec3( t1.z, 0, t1.w )\\\\n\\\\t\\\\t);\\\\n\\\\t\\\\tvec3 fresnel = ( material.specularColor * t2.x + ( vec3( 1.0 ) - material.specularColor ) * t2.y );\\\\n\\\\t\\\\treflectedLight.directSpecular += lightColor * fresnel * LTC_Evaluate( normal, viewDir, position, mInv, rectCoords );\\\\n\\\\t\\\\treflectedLight.directDiffuse += lightColor * material.diffuseColor * LTC_Evaluate( normal, viewDir, position, mat3( 1.0 ), rectCoords );\\\\n\\\\t}\\\\n#endif\\\\nvoid RE_Direct_Physical( const in IncidentLight directLight, const in GeometricContext geometry, const in PhysicalMaterial material, inout ReflectedLight reflectedLight ) {\\\\n\\\\tfloat dotNL = saturate( dot( geometry.normal, directLight.direction ) );\\\\n\\\\tvec3 irradiance = dotNL * directLight.color;\\\\n\\\\t#ifndef PHYSICALLY_CORRECT_LIGHTS\\\\n\\\\t\\\\tirradiance *= PI;\\\\n\\\\t#endif\\\\n\\\\t#ifdef CLEARCOAT\\\\n\\\\t\\\\tfloat ccDotNL = saturate( dot( geometry.clearcoatNormal, directLight.direction ) );\\\\n\\\\t\\\\tvec3 ccIrradiance = ccDotNL * directLight.color;\\\\n\\\\t\\\\t#ifndef PHYSICALLY_CORRECT_LIGHTS\\\\n\\\\t\\\\t\\\\tccIrradiance *= PI;\\\\n\\\\t\\\\t#endif\\\\n\\\\t\\\\tfloat clearcoatDHR = material.clearcoat * clearcoatDHRApprox( material.clearcoatRoughness, ccDotNL );\\\\n\\\\t\\\\treflectedLight.directSpecular += ccIrradiance * material.clearcoat * BRDF_Specular_GGX( directLight, geometry.viewDir, geometry.clearcoatNormal, vec3( DEFAULT_SPECULAR_COEFFICIENT ), vec3( 1.0 ), material.clearcoatRoughness );\\\\n\\\\t#else\\\\n\\\\t\\\\tfloat clearcoatDHR = 0.0;\\\\n\\\\t#endif\\\\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.specularColorF90, material.specularRoughness);\\\\n\\\\t#endif\\\\n\\\\treflectedLight.directDiffuse += ( 1.0 - clearcoatDHR ) * irradiance * BRDF_Diffuse_Lambert( material.diffuseColor );\\\\n}\\\\nvoid RE_IndirectDiffuse_Physical( const in vec3 irradiance, const in GeometricContext geometry, const in PhysicalMaterial material, inout ReflectedLight reflectedLight ) {\\\\n\\\\treflectedLight.indirectDiffuse += irradiance * BRDF_Diffuse_Lambert( material.diffuseColor );\\\\n}\\\\nvoid RE_IndirectSpecular_Physical( const in vec3 radiance, const in vec3 irradiance, const in vec3 clearcoatRadiance, const in GeometricContext geometry, const in PhysicalMaterial material, inout ReflectedLight reflectedLight) {\\\\n\\\\t#ifdef CLEARCOAT\\\\n\\\\t\\\\tfloat ccDotNV = saturate( dot( geometry.clearcoatNormal, geometry.viewDir ) );\\\\n\\\\t\\\\treflectedLight.indirectSpecular += clearcoatRadiance * material.clearcoat * BRDF_Specular_GGX_Environment( geometry.viewDir, geometry.clearcoatNormal, vec3( DEFAULT_SPECULAR_COEFFICIENT ), material.clearcoatRoughness );\\\\n\\\\t\\\\tfloat ccDotNL = ccDotNV;\\\\n\\\\t\\\\tfloat clearcoatDHR = material.clearcoat * clearcoatDHRApprox( material.clearcoatRoughness, ccDotNL );\\\\n\\\\t#else\\\\n\\\\t\\\\tfloat clearcoatDHR = 0.0;\\\\n\\\\t#endif\\\\n\\\\tfloat clearcoatInv = 1.0 - clearcoatDHR;\\\\n\\\\tvec3 singleScattering = vec3( 0.0 );\\\\n\\\\tvec3 multiScattering = vec3( 0.0 );\\\\n\\\\tvec3 cosineWeightedIrradiance = irradiance * RECIPROCAL_PI;\\\\n\\\\tBRDF_Specular_Multiscattering_Environment( geometry, material.specularColor, material.specularRoughness, singleScattering, multiScattering );\\\\n\\\\tvec3 diffuse = material.diffuseColor * ( 1.0 - ( singleScattering + multiScattering ) );\\\\n\\\\treflectedLight.indirectSpecular += clearcoatInv * radiance * singleScattering;\\\\n\\\\treflectedLight.indirectSpecular += multiScattering * cosineWeightedIrradiance;\\\\n\\\\treflectedLight.indirectDiffuse += diffuse * cosineWeightedIrradiance;\\\\n}\\\\n#define RE_Direct\\\\t\\\\t\\\\t\\\\tRE_Direct_Physical\\\\n#define RE_Direct_RectArea\\\\t\\\\tRE_Direct_RectArea_Physical\\\\n#define RE_IndirectDiffuse\\\\t\\\\tRE_IndirectDiffuse_Physical\\\\n#define RE_IndirectSpecular\\\\t\\\\tRE_IndirectSpecular_Physical\\\\nfloat computeSpecularOcclusion( const in float dotNV, const in float ambientOcclusion, const in float roughness ) {\\\\n\\\\treturn saturate( pow( dotNV + ambientOcclusion, exp2( - 16.0 * roughness - 1.0 ) ) - 1.0 + ambientOcclusion );\\\\n}\\\\\\\",lights_fragment_begin:\\\\\\\"\\\\nGeometricContext geometry;\\\\ngeometry.position = - vViewPosition;\\\\ngeometry.normal = normal;\\\\ngeometry.viewDir = ( isOrthographic ) ? vec3( 0, 0, 1 ) : normalize( vViewPosition );\\\\n#ifdef CLEARCOAT\\\\n\\\\tgeometry.clearcoatNormal = clearcoatNormal;\\\\n#endif\\\\nIncidentLight directLight;\\\\n#if ( NUM_POINT_LIGHTS > 0 ) && defined( RE_Direct )\\\\n\\\\tPointLight pointLight;\\\\n\\\\t#if defined( USE_SHADOWMAP ) && NUM_POINT_LIGHT_SHADOWS > 0\\\\n\\\\tPointLightShadow pointLightShadow;\\\\n\\\\t#endif\\\\n\\\\t#pragma unroll_loop_start\\\\n\\\\tfor ( int i = 0; i < NUM_POINT_LIGHTS; i ++ ) {\\\\n\\\\t\\\\tpointLight = pointLights[ i ];\\\\n\\\\t\\\\tgetPointDirectLightIrradiance( pointLight, geometry, directLight );\\\\n\\\\t\\\\t#if defined( USE_SHADOWMAP ) && ( UNROLLED_LOOP_INDEX < NUM_POINT_LIGHT_SHADOWS )\\\\n\\\\t\\\\tpointLightShadow = pointLightShadows[ i ];\\\\n\\\\t\\\\tdirectLight.color *= all( bvec2( directLight.visible, receiveShadow ) ) ? getPointShadow( pointShadowMap[ i ], pointLightShadow.shadowMapSize, pointLightShadow.shadowBias, pointLightShadow.shadowRadius, vPointShadowCoord[ i ], pointLightShadow.shadowCameraNear, pointLightShadow.shadowCameraFar ) : 1.0;\\\\n\\\\t\\\\t#endif\\\\n\\\\t\\\\tRE_Direct( directLight, geometry, material, reflectedLight );\\\\n\\\\t}\\\\n\\\\t#pragma unroll_loop_end\\\\n#endif\\\\n#if ( NUM_SPOT_LIGHTS > 0 ) && defined( RE_Direct )\\\\n\\\\tSpotLight spotLight;\\\\n\\\\t#if defined( USE_SHADOWMAP ) && NUM_SPOT_LIGHT_SHADOWS > 0\\\\n\\\\tSpotLightShadow spotLightShadow;\\\\n\\\\t#endif\\\\n\\\\t#pragma unroll_loop_start\\\\n\\\\tfor ( int i = 0; i < NUM_SPOT_LIGHTS; i ++ ) {\\\\n\\\\t\\\\tspotLight = spotLights[ i ];\\\\n\\\\t\\\\tgetSpotDirectLightIrradiance( spotLight, geometry, directLight );\\\\n\\\\t\\\\t#if defined( USE_SHADOWMAP ) && ( UNROLLED_LOOP_INDEX < NUM_SPOT_LIGHT_SHADOWS )\\\\n\\\\t\\\\tspotLightShadow = spotLightShadows[ i ];\\\\n\\\\t\\\\tdirectLight.color *= all( bvec2( directLight.visible, receiveShadow ) ) ? getShadow( spotShadowMap[ i ], spotLightShadow.shadowMapSize, spotLightShadow.shadowBias, spotLightShadow.shadowRadius, vSpotShadowCoord[ i ] ) : 1.0;\\\\n\\\\t\\\\t#endif\\\\n\\\\t\\\\tRE_Direct( directLight, geometry, material, reflectedLight );\\\\n\\\\t}\\\\n\\\\t#pragma unroll_loop_end\\\\n#endif\\\\n#if ( NUM_DIR_LIGHTS > 0 ) && defined( RE_Direct )\\\\n\\\\tDirectionalLight directionalLight;\\\\n\\\\t#if defined( USE_SHADOWMAP ) && NUM_DIR_LIGHT_SHADOWS > 0\\\\n\\\\tDirectionalLightShadow directionalLightShadow;\\\\n\\\\t#endif\\\\n\\\\t#pragma unroll_loop_start\\\\n\\\\tfor ( int i = 0; i < NUM_DIR_LIGHTS; i ++ ) {\\\\n\\\\t\\\\tdirectionalLight = directionalLights[ i ];\\\\n\\\\t\\\\tgetDirectionalDirectLightIrradiance( directionalLight, geometry, directLight );\\\\n\\\\t\\\\t#if defined( USE_SHADOWMAP ) && ( UNROLLED_LOOP_INDEX < NUM_DIR_LIGHT_SHADOWS )\\\\n\\\\t\\\\tdirectionalLightShadow = directionalLightShadows[ i ];\\\\n\\\\t\\\\tdirectLight.color *= all( bvec2( directLight.visible, receiveShadow ) ) ? getShadow( directionalShadowMap[ i ], directionalLightShadow.shadowMapSize, directionalLightShadow.shadowBias, directionalLightShadow.shadowRadius, vDirectionalShadowCoord[ i ] ) : 1.0;\\\\n\\\\t\\\\t#endif\\\\n\\\\t\\\\tRE_Direct( directLight, geometry, material, reflectedLight );\\\\n\\\\t}\\\\n\\\\t#pragma unroll_loop_end\\\\n#endif\\\\n#if ( NUM_RECT_AREA_LIGHTS > 0 ) && defined( RE_Direct_RectArea )\\\\n\\\\tRectAreaLight rectAreaLight;\\\\n\\\\t#pragma unroll_loop_start\\\\n\\\\tfor ( int i = 0; i < NUM_RECT_AREA_LIGHTS; i ++ ) {\\\\n\\\\t\\\\trectAreaLight = rectAreaLights[ i ];\\\\n\\\\t\\\\tRE_Direct_RectArea( rectAreaLight, geometry, material, reflectedLight );\\\\n\\\\t}\\\\n\\\\t#pragma unroll_loop_end\\\\n#endif\\\\n#if defined( RE_IndirectDiffuse )\\\\n\\\\tvec3 iblIrradiance = vec3( 0.0 );\\\\n\\\\tvec3 irradiance = getAmbientLightIrradiance( ambientLightColor );\\\\n\\\\tirradiance += getLightProbeIrradiance( lightProbe, geometry );\\\\n\\\\t#if ( NUM_HEMI_LIGHTS > 0 )\\\\n\\\\t\\\\t#pragma unroll_loop_start\\\\n\\\\t\\\\tfor ( int i = 0; i < NUM_HEMI_LIGHTS; i ++ ) {\\\\n\\\\t\\\\t\\\\tirradiance += getHemisphereLightIrradiance( hemisphereLights[ i ], geometry );\\\\n\\\\t\\\\t}\\\\n\\\\t\\\\t#pragma unroll_loop_end\\\\n\\\\t#endif\\\\n#endif\\\\n#if defined( RE_IndirectSpecular )\\\\n\\\\tvec3 radiance = vec3( 0.0 );\\\\n\\\\tvec3 clearcoatRadiance = vec3( 0.0 );\\\\n#endif\\\\\\\",lights_fragment_maps:\\\\\\\"#if defined( RE_IndirectDiffuse )\\\\n\\\\t#ifdef USE_LIGHTMAP\\\\n\\\\t\\\\tvec4 lightMapTexel= texture2D( lightMap, vUv2 );\\\\n\\\\t\\\\tvec3 lightMapIrradiance = lightMapTexelToLinear( lightMapTexel ).rgb * lightMapIntensity;\\\\n\\\\t\\\\t#ifndef PHYSICALLY_CORRECT_LIGHTS\\\\n\\\\t\\\\t\\\\tlightMapIrradiance *= PI;\\\\n\\\\t\\\\t#endif\\\\n\\\\t\\\\tirradiance += lightMapIrradiance;\\\\n\\\\t#endif\\\\n\\\\t#if defined( USE_ENVMAP ) && defined( STANDARD ) && defined( ENVMAP_TYPE_CUBE_UV )\\\\n\\\\t\\\\tiblIrradiance += getLightProbeIndirectIrradiance( geometry, maxMipLevel );\\\\n\\\\t#endif\\\\n#endif\\\\n#if defined( USE_ENVMAP ) && defined( RE_IndirectSpecular )\\\\n\\\\tradiance += getLightProbeIndirectRadiance( geometry.viewDir, geometry.normal, material.specularRoughness, maxMipLevel );\\\\n\\\\t#ifdef CLEARCOAT\\\\n\\\\t\\\\tclearcoatRadiance += getLightProbeIndirectRadiance( geometry.viewDir, geometry.clearcoatNormal, material.clearcoatRoughness, maxMipLevel );\\\\n\\\\t#endif\\\\n#endif\\\\\\\",lights_fragment_end:\\\\\\\"#if defined( RE_IndirectDiffuse )\\\\n\\\\tRE_IndirectDiffuse( irradiance, geometry, material, reflectedLight );\\\\n#endif\\\\n#if defined( RE_IndirectSpecular )\\\\n\\\\tRE_IndirectSpecular( radiance, iblIrradiance, clearcoatRadiance, geometry, material, reflectedLight );\\\\n#endif\\\\\\\",logdepthbuf_fragment:\\\\\\\"#if defined( USE_LOGDEPTHBUF ) && defined( USE_LOGDEPTHBUF_EXT )\\\\n\\\\tgl_FragDepthEXT = vIsPerspective == 0.0 ? gl_FragCoord.z : log2( vFragDepth ) * logDepthBufFC * 0.5;\\\\n#endif\\\\\\\",logdepthbuf_pars_fragment:\\\\\\\"#if defined( USE_LOGDEPTHBUF ) && defined( USE_LOGDEPTHBUF_EXT )\\\\n\\\\tuniform float logDepthBufFC;\\\\n\\\\tvarying float vFragDepth;\\\\n\\\\tvarying float vIsPerspective;\\\\n#endif\\\\\\\",logdepthbuf_pars_vertex:\\\\\\\"#ifdef USE_LOGDEPTHBUF\\\\n\\\\t#ifdef USE_LOGDEPTHBUF_EXT\\\\n\\\\t\\\\tvarying float vFragDepth;\\\\n\\\\t\\\\tvarying float vIsPerspective;\\\\n\\\\t#else\\\\n\\\\t\\\\tuniform float logDepthBufFC;\\\\n\\\\t#endif\\\\n#endif\\\\\\\",logdepthbuf_vertex:\\\\\\\"#ifdef USE_LOGDEPTHBUF\\\\n\\\\t#ifdef USE_LOGDEPTHBUF_EXT\\\\n\\\\t\\\\tvFragDepth = 1.0 + gl_Position.w;\\\\n\\\\t\\\\tvIsPerspective = float( isPerspectiveMatrix( projectionMatrix ) );\\\\n\\\\t#else\\\\n\\\\t\\\\tif ( isPerspectiveMatrix( projectionMatrix ) ) {\\\\n\\\\t\\\\t\\\\tgl_Position.z = log2( max( EPSILON, gl_Position.w + 1.0 ) ) * logDepthBufFC - 1.0;\\\\n\\\\t\\\\t\\\\tgl_Position.z *= gl_Position.w;\\\\n\\\\t\\\\t}\\\\n\\\\t#endif\\\\n#endif\\\\\\\",map_fragment:\\\\\\\"#ifdef USE_MAP\\\\n\\\\tvec4 texelColor = texture2D( map, vUv );\\\\n\\\\ttexelColor = mapTexelToLinear( texelColor );\\\\n\\\\tdiffuseColor *= texelColor;\\\\n#endif\\\\\\\",map_pars_fragment:\\\\\\\"#ifdef USE_MAP\\\\n\\\\tuniform sampler2D map;\\\\n#endif\\\\\\\",map_particle_fragment:\\\\\\\"#if defined( USE_MAP ) || defined( USE_ALPHAMAP )\\\\n\\\\tvec2 uv = ( uvTransform * vec3( gl_PointCoord.x, 1.0 - gl_PointCoord.y, 1 ) ).xy;\\\\n#endif\\\\n#ifdef USE_MAP\\\\n\\\\tvec4 mapTexel = texture2D( map, uv );\\\\n\\\\tdiffuseColor *= mapTexelToLinear( mapTexel );\\\\n#endif\\\\n#ifdef USE_ALPHAMAP\\\\n\\\\tdiffuseColor.a *= texture2D( alphaMap, uv ).g;\\\\n#endif\\\\\\\",map_particle_pars_fragment:\\\\\\\"#if defined( USE_MAP ) || defined( USE_ALPHAMAP )\\\\n\\\\tuniform mat3 uvTransform;\\\\n#endif\\\\n#ifdef USE_MAP\\\\n\\\\tuniform sampler2D map;\\\\n#endif\\\\n#ifdef USE_ALPHAMAP\\\\n\\\\tuniform sampler2D alphaMap;\\\\n#endif\\\\\\\",metalnessmap_fragment:\\\\\\\"float metalnessFactor = metalness;\\\\n#ifdef USE_METALNESSMAP\\\\n\\\\tvec4 texelMetalness = texture2D( metalnessMap, vUv );\\\\n\\\\tmetalnessFactor *= texelMetalness.b;\\\\n#endif\\\\\\\",metalnessmap_pars_fragment:\\\\\\\"#ifdef USE_METALNESSMAP\\\\n\\\\tuniform sampler2D metalnessMap;\\\\n#endif\\\\\\\",morphnormal_vertex:\\\\\\\"#ifdef USE_MORPHNORMALS\\\\n\\\\tobjectNormal *= morphTargetBaseInfluence;\\\\n\\\\tobjectNormal += morphNormal0 * morphTargetInfluences[ 0 ];\\\\n\\\\tobjectNormal += morphNormal1 * morphTargetInfluences[ 1 ];\\\\n\\\\tobjectNormal += morphNormal2 * morphTargetInfluences[ 2 ];\\\\n\\\\tobjectNormal += morphNormal3 * morphTargetInfluences[ 3 ];\\\\n#endif\\\\\\\",morphtarget_pars_vertex:\\\\\\\"#ifdef USE_MORPHTARGETS\\\\n\\\\tuniform float morphTargetBaseInfluence;\\\\n\\\\t#ifndef USE_MORPHNORMALS\\\\n\\\\t\\\\tuniform float morphTargetInfluences[ 8 ];\\\\n\\\\t#else\\\\n\\\\t\\\\tuniform float morphTargetInfluences[ 4 ];\\\\n\\\\t#endif\\\\n#endif\\\\\\\",morphtarget_vertex:\\\\\\\"#ifdef USE_MORPHTARGETS\\\\n\\\\ttransformed *= morphTargetBaseInfluence;\\\\n\\\\ttransformed += morphTarget0 * morphTargetInfluences[ 0 ];\\\\n\\\\ttransformed += morphTarget1 * morphTargetInfluences[ 1 ];\\\\n\\\\ttransformed += morphTarget2 * morphTargetInfluences[ 2 ];\\\\n\\\\ttransformed += morphTarget3 * morphTargetInfluences[ 3 ];\\\\n\\\\t#ifndef USE_MORPHNORMALS\\\\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\\\\t#endif\\\\n#endif\\\\\\\",normal_fragment_begin:\\\\\\\"float faceDirection = gl_FrontFacing ? 1.0 : - 1.0;\\\\n#ifdef FLAT_SHADED\\\\n\\\\tvec3 fdx = vec3( dFdx( vViewPosition.x ), dFdx( vViewPosition.y ), dFdx( vViewPosition.z ) );\\\\n\\\\tvec3 fdy = vec3( dFdy( vViewPosition.x ), dFdy( vViewPosition.y ), dFdy( vViewPosition.z ) );\\\\n\\\\tvec3 normal = normalize( cross( fdx, fdy ) );\\\\n#else\\\\n\\\\tvec3 normal = normalize( vNormal );\\\\n\\\\t#ifdef DOUBLE_SIDED\\\\n\\\\t\\\\tnormal = normal * faceDirection;\\\\n\\\\t#endif\\\\n\\\\t#ifdef USE_TANGENT\\\\n\\\\t\\\\tvec3 tangent = normalize( vTangent );\\\\n\\\\t\\\\tvec3 bitangent = normalize( vBitangent );\\\\n\\\\t\\\\t#ifdef DOUBLE_SIDED\\\\n\\\\t\\\\t\\\\ttangent = tangent * faceDirection;\\\\n\\\\t\\\\t\\\\tbitangent = bitangent * faceDirection;\\\\n\\\\t\\\\t#endif\\\\n\\\\t\\\\t#if defined( TANGENTSPACE_NORMALMAP ) || defined( USE_CLEARCOAT_NORMALMAP )\\\\n\\\\t\\\\t\\\\tmat3 vTBN = mat3( tangent, bitangent, normal );\\\\n\\\\t\\\\t#endif\\\\n\\\\t#endif\\\\n#endif\\\\nvec3 geometryNormal = normal;\\\\\\\",normal_fragment_maps:\\\\\\\"#ifdef OBJECTSPACE_NORMALMAP\\\\n\\\\tnormal = texture2D( normalMap, vUv ).xyz * 2.0 - 1.0;\\\\n\\\\t#ifdef FLIP_SIDED\\\\n\\\\t\\\\tnormal = - normal;\\\\n\\\\t#endif\\\\n\\\\t#ifdef DOUBLE_SIDED\\\\n\\\\t\\\\tnormal = normal * faceDirection;\\\\n\\\\t#endif\\\\n\\\\tnormal = normalize( normalMatrix * normal );\\\\n#elif defined( TANGENTSPACE_NORMALMAP )\\\\n\\\\tvec3 mapN = texture2D( normalMap, vUv ).xyz * 2.0 - 1.0;\\\\n\\\\tmapN.xy *= normalScale;\\\\n\\\\t#ifdef USE_TANGENT\\\\n\\\\t\\\\tnormal = normalize( vTBN * mapN );\\\\n\\\\t#else\\\\n\\\\t\\\\tnormal = perturbNormal2Arb( -vViewPosition, normal, mapN, faceDirection );\\\\n\\\\t#endif\\\\n#elif defined( USE_BUMPMAP )\\\\n\\\\tnormal = perturbNormalArb( -vViewPosition, normal, dHdxy_fwd(), faceDirection );\\\\n#endif\\\\\\\",normalmap_pars_fragment:\\\\\\\"#ifdef USE_NORMALMAP\\\\n\\\\tuniform sampler2D normalMap;\\\\n\\\\tuniform vec2 normalScale;\\\\n#endif\\\\n#ifdef OBJECTSPACE_NORMALMAP\\\\n\\\\tuniform mat3 normalMatrix;\\\\n#endif\\\\n#if ! defined ( USE_TANGENT ) && ( defined ( TANGENTSPACE_NORMALMAP ) || defined ( USE_CLEARCOAT_NORMALMAP ) )\\\\n\\\\tvec3 perturbNormal2Arb( vec3 eye_pos, vec3 surf_norm, vec3 mapN, float faceDirection ) {\\\\n\\\\t\\\\tvec3 q0 = vec3( dFdx( eye_pos.x ), dFdx( eye_pos.y ), dFdx( eye_pos.z ) );\\\\n\\\\t\\\\tvec3 q1 = vec3( dFdy( eye_pos.x ), dFdy( eye_pos.y ), dFdy( eye_pos.z ) );\\\\n\\\\t\\\\tvec2 st0 = dFdx( vUv.st );\\\\n\\\\t\\\\tvec2 st1 = dFdy( vUv.st );\\\\n\\\\t\\\\tvec3 N = surf_norm;\\\\n\\\\t\\\\tvec3 q1perp = cross( q1, N );\\\\n\\\\t\\\\tvec3 q0perp = cross( N, q0 );\\\\n\\\\t\\\\tvec3 T = q1perp * st0.x + q0perp * st1.x;\\\\n\\\\t\\\\tvec3 B = q1perp * st0.y + q0perp * st1.y;\\\\n\\\\t\\\\tfloat det = max( dot( T, T ), dot( B, B ) );\\\\n\\\\t\\\\tfloat scale = ( det == 0.0 ) ? 0.0 : faceDirection * inversesqrt( det );\\\\n\\\\t\\\\treturn normalize( T * ( mapN.x * scale ) + B * ( mapN.y * scale ) + N * mapN.z );\\\\n\\\\t}\\\\n#endif\\\\\\\",clearcoat_normal_fragment_begin:\\\\\\\"#ifdef CLEARCOAT\\\\n\\\\tvec3 clearcoatNormal = geometryNormal;\\\\n#endif\\\\\\\",clearcoat_normal_fragment_maps:\\\\\\\"#ifdef USE_CLEARCOAT_NORMALMAP\\\\n\\\\tvec3 clearcoatMapN = texture2D( clearcoatNormalMap, vUv ).xyz * 2.0 - 1.0;\\\\n\\\\tclearcoatMapN.xy *= clearcoatNormalScale;\\\\n\\\\t#ifdef USE_TANGENT\\\\n\\\\t\\\\tclearcoatNormal = normalize( vTBN * clearcoatMapN );\\\\n\\\\t#else\\\\n\\\\t\\\\tclearcoatNormal = perturbNormal2Arb( - vViewPosition, clearcoatNormal, clearcoatMapN, faceDirection );\\\\n\\\\t#endif\\\\n#endif\\\\\\\",clearcoat_pars_fragment:\\\\\\\"#ifdef USE_CLEARCOATMAP\\\\n\\\\tuniform sampler2D clearcoatMap;\\\\n#endif\\\\n#ifdef USE_CLEARCOAT_ROUGHNESSMAP\\\\n\\\\tuniform sampler2D clearcoatRoughnessMap;\\\\n#endif\\\\n#ifdef USE_CLEARCOAT_NORMALMAP\\\\n\\\\tuniform sampler2D clearcoatNormalMap;\\\\n\\\\tuniform vec2 clearcoatNormalScale;\\\\n#endif\\\\\\\",packing:\\\\\\\"vec3 packNormalToRGB( const in vec3 normal ) {\\\\n\\\\treturn normalize( normal ) * 0.5 + 0.5;\\\\n}\\\\nvec3 unpackRGBToNormal( const in vec3 rgb ) {\\\\n\\\\treturn 2.0 * rgb.xyz - 1.0;\\\\n}\\\\nconst float PackUpscale = 256. / 255.;const float UnpackDownscale = 255. / 256.;\\\\nconst vec3 PackFactors = vec3( 256. * 256. * 256., 256. * 256., 256. );\\\\nconst vec4 UnpackFactors = UnpackDownscale / vec4( PackFactors, 1. );\\\\nconst float ShiftRight8 = 1. / 256.;\\\\nvec4 packDepthToRGBA( const in float v ) {\\\\n\\\\tvec4 r = vec4( fract( v * PackFactors ), v );\\\\n\\\\tr.yzw -= r.xyz * ShiftRight8;\\\\treturn r * PackUpscale;\\\\n}\\\\nfloat unpackRGBAToDepth( const in vec4 v ) {\\\\n\\\\treturn dot( v, UnpackFactors );\\\\n}\\\\nvec4 pack2HalfToRGBA( vec2 v ) {\\\\n\\\\tvec4 r = vec4( v.x, fract( v.x * 255.0 ), v.y, fract( v.y * 255.0 ));\\\\n\\\\treturn vec4( r.x - r.y / 255.0, r.y, r.z - r.w / 255.0, r.w);\\\\n}\\\\nvec2 unpackRGBATo2Half( vec4 v ) {\\\\n\\\\treturn vec2( v.x + ( v.y / 255.0 ), v.z + ( v.w / 255.0 ) );\\\\n}\\\\nfloat viewZToOrthographicDepth( const in float viewZ, const in float near, const in float far ) {\\\\n\\\\treturn ( viewZ + near ) / ( near - far );\\\\n}\\\\nfloat orthographicDepthToViewZ( const in float linearClipZ, const in float near, const in float far ) {\\\\n\\\\treturn linearClipZ * ( near - far ) - near;\\\\n}\\\\nfloat viewZToPerspectiveDepth( const in float viewZ, const in float near, const in float far ) {\\\\n\\\\treturn (( near + viewZ ) * far ) / (( far - near ) * viewZ );\\\\n}\\\\nfloat perspectiveDepthToViewZ( const in float invClipZ, const in float near, const in float far ) {\\\\n\\\\treturn ( near * far ) / ( ( far - near ) * invClipZ - far );\\\\n}\\\\\\\",premultiplied_alpha_fragment:\\\\\\\"#ifdef PREMULTIPLIED_ALPHA\\\\n\\\\tgl_FragColor.rgb *= gl_FragColor.a;\\\\n#endif\\\\\\\",project_vertex:\\\\\\\"vec4 mvPosition = vec4( transformed, 1.0 );\\\\n#ifdef USE_INSTANCING\\\\n\\\\tmvPosition = instanceMatrix * mvPosition;\\\\n#endif\\\\nmvPosition = modelViewMatrix * mvPosition;\\\\ngl_Position = projectionMatrix * mvPosition;\\\\\\\",dithering_fragment:\\\\\\\"#ifdef DITHERING\\\\n\\\\tgl_FragColor.rgb = dithering( gl_FragColor.rgb );\\\\n#endif\\\\\\\",dithering_pars_fragment:\\\\\\\"#ifdef DITHERING\\\\n\\\\tvec3 dithering( vec3 color ) {\\\\n\\\\t\\\\tfloat grid_position = rand( gl_FragCoord.xy );\\\\n\\\\t\\\\tvec3 dither_shift_RGB = vec3( 0.25 / 255.0, -0.25 / 255.0, 0.25 / 255.0 );\\\\n\\\\t\\\\tdither_shift_RGB = mix( 2.0 * dither_shift_RGB, -2.0 * dither_shift_RGB, grid_position );\\\\n\\\\t\\\\treturn color + dither_shift_RGB;\\\\n\\\\t}\\\\n#endif\\\\\\\",roughnessmap_fragment:\\\\\\\"float roughnessFactor = roughness;\\\\n#ifdef USE_ROUGHNESSMAP\\\\n\\\\tvec4 texelRoughness = texture2D( roughnessMap, vUv );\\\\n\\\\troughnessFactor *= texelRoughness.g;\\\\n#endif\\\\\\\",roughnessmap_pars_fragment:\\\\\\\"#ifdef USE_ROUGHNESSMAP\\\\n\\\\tuniform sampler2D roughnessMap;\\\\n#endif\\\\\\\",shadowmap_pars_fragment:\\\\\\\"#ifdef USE_SHADOWMAP\\\\n\\\\t#if NUM_DIR_LIGHT_SHADOWS > 0\\\\n\\\\t\\\\tuniform sampler2D directionalShadowMap[ NUM_DIR_LIGHT_SHADOWS ];\\\\n\\\\t\\\\tvarying vec4 vDirectionalShadowCoord[ NUM_DIR_LIGHT_SHADOWS ];\\\\n\\\\t\\\\tstruct DirectionalLightShadow {\\\\n\\\\t\\\\t\\\\tfloat shadowBias;\\\\n\\\\t\\\\t\\\\tfloat shadowNormalBias;\\\\n\\\\t\\\\t\\\\tfloat shadowRadius;\\\\n\\\\t\\\\t\\\\tvec2 shadowMapSize;\\\\n\\\\t\\\\t};\\\\n\\\\t\\\\tuniform DirectionalLightShadow directionalLightShadows[ NUM_DIR_LIGHT_SHADOWS ];\\\\n\\\\t#endif\\\\n\\\\t#if NUM_SPOT_LIGHT_SHADOWS > 0\\\\n\\\\t\\\\tuniform sampler2D spotShadowMap[ NUM_SPOT_LIGHT_SHADOWS ];\\\\n\\\\t\\\\tvarying vec4 vSpotShadowCoord[ NUM_SPOT_LIGHT_SHADOWS ];\\\\n\\\\t\\\\tstruct SpotLightShadow {\\\\n\\\\t\\\\t\\\\tfloat shadowBias;\\\\n\\\\t\\\\t\\\\tfloat shadowNormalBias;\\\\n\\\\t\\\\t\\\\tfloat shadowRadius;\\\\n\\\\t\\\\t\\\\tvec2 shadowMapSize;\\\\n\\\\t\\\\t};\\\\n\\\\t\\\\tuniform SpotLightShadow spotLightShadows[ NUM_SPOT_LIGHT_SHADOWS ];\\\\n\\\\t#endif\\\\n\\\\t#if NUM_POINT_LIGHT_SHADOWS > 0\\\\n\\\\t\\\\tuniform sampler2D pointShadowMap[ NUM_POINT_LIGHT_SHADOWS ];\\\\n\\\\t\\\\tvarying vec4 vPointShadowCoord[ NUM_POINT_LIGHT_SHADOWS ];\\\\n\\\\t\\\\tstruct PointLightShadow {\\\\n\\\\t\\\\t\\\\tfloat shadowBias;\\\\n\\\\t\\\\t\\\\tfloat shadowNormalBias;\\\\n\\\\t\\\\t\\\\tfloat shadowRadius;\\\\n\\\\t\\\\t\\\\tvec2 shadowMapSize;\\\\n\\\\t\\\\t\\\\tfloat shadowCameraNear;\\\\n\\\\t\\\\t\\\\tfloat shadowCameraFar;\\\\n\\\\t\\\\t};\\\\n\\\\t\\\\tuniform PointLightShadow pointLightShadows[ NUM_POINT_LIGHT_SHADOWS ];\\\\n\\\\t#endif\\\\n\\\\tfloat texture2DCompare( sampler2D depths, vec2 uv, float compare ) {\\\\n\\\\t\\\\treturn step( compare, unpackRGBAToDepth( texture2D( depths, uv ) ) );\\\\n\\\\t}\\\\n\\\\tvec2 texture2DDistribution( sampler2D shadow, vec2 uv ) {\\\\n\\\\t\\\\treturn unpackRGBATo2Half( texture2D( shadow, uv ) );\\\\n\\\\t}\\\\n\\\\tfloat VSMShadow (sampler2D shadow, vec2 uv, float compare ){\\\\n\\\\t\\\\tfloat occlusion = 1.0;\\\\n\\\\t\\\\tvec2 distribution = texture2DDistribution( shadow, uv );\\\\n\\\\t\\\\tfloat hard_shadow = step( compare , distribution.x );\\\\n\\\\t\\\\tif (hard_shadow != 1.0 ) {\\\\n\\\\t\\\\t\\\\tfloat distance = compare - distribution.x ;\\\\n\\\\t\\\\t\\\\tfloat variance = max( 0.00000, distribution.y * distribution.y );\\\\n\\\\t\\\\t\\\\tfloat softness_probability = variance / (variance + distance * distance );\\\\t\\\\t\\\\tsoftness_probability = clamp( ( softness_probability - 0.3 ) / ( 0.95 - 0.3 ), 0.0, 1.0 );\\\\t\\\\t\\\\tocclusion = clamp( max( hard_shadow, softness_probability ), 0.0, 1.0 );\\\\n\\\\t\\\\t}\\\\n\\\\t\\\\treturn occlusion;\\\\n\\\\t}\\\\n\\\\tfloat getShadow( sampler2D shadowMap, vec2 shadowMapSize, float shadowBias, float shadowRadius, vec4 shadowCoord ) {\\\\n\\\\t\\\\tfloat shadow = 1.0;\\\\n\\\\t\\\\tshadowCoord.xyz /= shadowCoord.w;\\\\n\\\\t\\\\tshadowCoord.z += shadowBias;\\\\n\\\\t\\\\tbvec4 inFrustumVec = bvec4 ( shadowCoord.x >= 0.0, shadowCoord.x <= 1.0, shadowCoord.y >= 0.0, shadowCoord.y <= 1.0 );\\\\n\\\\t\\\\tbool inFrustum = all( inFrustumVec );\\\\n\\\\t\\\\tbvec2 frustumTestVec = bvec2( inFrustum, shadowCoord.z <= 1.0 );\\\\n\\\\t\\\\tbool frustumTest = all( frustumTestVec );\\\\n\\\\t\\\\tif ( frustumTest ) {\\\\n\\\\t\\\\t#if defined( SHADOWMAP_TYPE_PCF )\\\\n\\\\t\\\\t\\\\tvec2 texelSize = vec2( 1.0 ) / shadowMapSize;\\\\n\\\\t\\\\t\\\\tfloat dx0 = - texelSize.x * shadowRadius;\\\\n\\\\t\\\\t\\\\tfloat dy0 = - texelSize.y * shadowRadius;\\\\n\\\\t\\\\t\\\\tfloat dx1 = + texelSize.x * shadowRadius;\\\\n\\\\t\\\\t\\\\tfloat dy1 = + texelSize.y * shadowRadius;\\\\n\\\\t\\\\t\\\\tfloat dx2 = dx0 / 2.0;\\\\n\\\\t\\\\t\\\\tfloat dy2 = dy0 / 2.0;\\\\n\\\\t\\\\t\\\\tfloat dx3 = dx1 / 2.0;\\\\n\\\\t\\\\t\\\\tfloat dy3 = dy1 / 2.0;\\\\n\\\\t\\\\t\\\\tshadow = (\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx0, dy0 ), shadowCoord.z ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( 0.0, dy0 ), shadowCoord.z ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx1, dy0 ), shadowCoord.z ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx2, dy2 ), shadowCoord.z ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( 0.0, dy2 ), shadowCoord.z ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx3, dy2 ), shadowCoord.z ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx0, 0.0 ), shadowCoord.z ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx2, 0.0 ), shadowCoord.z ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, shadowCoord.xy, shadowCoord.z ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx3, 0.0 ), shadowCoord.z ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx1, 0.0 ), shadowCoord.z ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx2, dy3 ), shadowCoord.z ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( 0.0, dy3 ), shadowCoord.z ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx3, dy3 ), shadowCoord.z ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx0, dy1 ), shadowCoord.z ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( 0.0, dy1 ), shadowCoord.z ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx1, dy1 ), shadowCoord.z )\\\\n\\\\t\\\\t\\\\t) * ( 1.0 / 17.0 );\\\\n\\\\t\\\\t#elif defined( SHADOWMAP_TYPE_PCF_SOFT )\\\\n\\\\t\\\\t\\\\tvec2 texelSize = vec2( 1.0 ) / shadowMapSize;\\\\n\\\\t\\\\t\\\\tfloat dx = texelSize.x;\\\\n\\\\t\\\\t\\\\tfloat dy = texelSize.y;\\\\n\\\\t\\\\t\\\\tvec2 uv = shadowCoord.xy;\\\\n\\\\t\\\\t\\\\tvec2 f = fract( uv * shadowMapSize + 0.5 );\\\\n\\\\t\\\\t\\\\tuv -= f * texelSize;\\\\n\\\\t\\\\t\\\\tshadow = (\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, uv, shadowCoord.z ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, uv + vec2( dx, 0.0 ), shadowCoord.z ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, uv + vec2( 0.0, dy ), shadowCoord.z ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, uv + texelSize, shadowCoord.z ) +\\\\n\\\\t\\\\t\\\\t\\\\tmix( texture2DCompare( shadowMap, uv + vec2( -dx, 0.0 ), shadowCoord.z ), \\\\n\\\\t\\\\t\\\\t\\\\t\\\\t texture2DCompare( shadowMap, uv + vec2( 2.0 * dx, 0.0 ), shadowCoord.z ),\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t f.x ) +\\\\n\\\\t\\\\t\\\\t\\\\tmix( texture2DCompare( shadowMap, uv + vec2( -dx, dy ), shadowCoord.z ), \\\\n\\\\t\\\\t\\\\t\\\\t\\\\t texture2DCompare( shadowMap, uv + vec2( 2.0 * dx, dy ), shadowCoord.z ),\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t f.x ) +\\\\n\\\\t\\\\t\\\\t\\\\tmix( texture2DCompare( shadowMap, uv + vec2( 0.0, -dy ), shadowCoord.z ), \\\\n\\\\t\\\\t\\\\t\\\\t\\\\t texture2DCompare( shadowMap, uv + vec2( 0.0, 2.0 * dy ), shadowCoord.z ),\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t f.y ) +\\\\n\\\\t\\\\t\\\\t\\\\tmix( texture2DCompare( shadowMap, uv + vec2( dx, -dy ), shadowCoord.z ), \\\\n\\\\t\\\\t\\\\t\\\\t\\\\t texture2DCompare( shadowMap, uv + vec2( dx, 2.0 * dy ), shadowCoord.z ),\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t f.y ) +\\\\n\\\\t\\\\t\\\\t\\\\tmix( mix( texture2DCompare( shadowMap, uv + vec2( -dx, -dy ), shadowCoord.z ), \\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, uv + vec2( 2.0 * dx, -dy ), shadowCoord.z ),\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tf.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\\\\ttexture2DCompare( shadowMap, uv + vec2( 2.0 * dx, 2.0 * dy ), shadowCoord.z ),\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tf.x ),\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t f.y )\\\\n\\\\t\\\\t\\\\t) * ( 1.0 / 9.0 );\\\\n\\\\t\\\\t#elif defined( SHADOWMAP_TYPE_VSM )\\\\n\\\\t\\\\t\\\\tshadow = VSMShadow( shadowMap, shadowCoord.xy, shadowCoord.z );\\\\n\\\\t\\\\t#else\\\\n\\\\t\\\\t\\\\tshadow = texture2DCompare( shadowMap, shadowCoord.xy, shadowCoord.z );\\\\n\\\\t\\\\t#endif\\\\n\\\\t\\\\t}\\\\n\\\\t\\\\treturn shadow;\\\\n\\\\t}\\\\n\\\\tvec2 cubeToUV( vec3 v, float texelSizeY ) {\\\\n\\\\t\\\\tvec3 absV = abs( v );\\\\n\\\\t\\\\tfloat scaleToCube = 1.0 / max( absV.x, max( absV.y, absV.z ) );\\\\n\\\\t\\\\tabsV *= scaleToCube;\\\\n\\\\t\\\\tv *= scaleToCube * ( 1.0 - 2.0 * texelSizeY );\\\\n\\\\t\\\\tvec2 planar = v.xy;\\\\n\\\\t\\\\tfloat almostATexel = 1.5 * texelSizeY;\\\\n\\\\t\\\\tfloat almostOne = 1.0 - almostATexel;\\\\n\\\\t\\\\tif ( absV.z >= almostOne ) {\\\\n\\\\t\\\\t\\\\tif ( v.z > 0.0 )\\\\n\\\\t\\\\t\\\\t\\\\tplanar.x = 4.0 - v.x;\\\\n\\\\t\\\\t} else if ( absV.x >= almostOne ) {\\\\n\\\\t\\\\t\\\\tfloat signX = sign( v.x );\\\\n\\\\t\\\\t\\\\tplanar.x = v.z * signX + 2.0 * signX;\\\\n\\\\t\\\\t} else if ( absV.y >= almostOne ) {\\\\n\\\\t\\\\t\\\\tfloat signY = sign( v.y );\\\\n\\\\t\\\\t\\\\tplanar.x = v.x + 2.0 * signY + 2.0;\\\\n\\\\t\\\\t\\\\tplanar.y = v.z * signY - 2.0;\\\\n\\\\t\\\\t}\\\\n\\\\t\\\\treturn vec2( 0.125, 0.25 ) * planar + vec2( 0.375, 0.75 );\\\\n\\\\t}\\\\n\\\\tfloat getPointShadow( sampler2D shadowMap, vec2 shadowMapSize, float shadowBias, float shadowRadius, vec4 shadowCoord, float shadowCameraNear, float shadowCameraFar ) {\\\\n\\\\t\\\\tvec2 texelSize = vec2( 1.0 ) / ( shadowMapSize * vec2( 4.0, 2.0 ) );\\\\n\\\\t\\\\tvec3 lightToPosition = shadowCoord.xyz;\\\\n\\\\t\\\\tfloat dp = ( length( lightToPosition ) - shadowCameraNear ) / ( shadowCameraFar - shadowCameraNear );\\\\t\\\\tdp += shadowBias;\\\\n\\\\t\\\\tvec3 bd3D = normalize( lightToPosition );\\\\n\\\\t\\\\t#if defined( SHADOWMAP_TYPE_PCF ) || defined( SHADOWMAP_TYPE_PCF_SOFT ) || defined( SHADOWMAP_TYPE_VSM )\\\\n\\\\t\\\\t\\\\tvec2 offset = vec2( - 1, 1 ) * shadowRadius * texelSize.y;\\\\n\\\\t\\\\t\\\\treturn (\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, cubeToUV( bd3D + offset.xyy, texelSize.y ), dp ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, cubeToUV( bd3D + offset.yyy, texelSize.y ), dp ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, cubeToUV( bd3D + offset.xyx, texelSize.y ), dp ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, cubeToUV( bd3D + offset.yyx, texelSize.y ), dp ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, cubeToUV( bd3D, texelSize.y ), dp ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, cubeToUV( bd3D + offset.xxy, texelSize.y ), dp ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, cubeToUV( bd3D + offset.yxy, texelSize.y ), dp ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, cubeToUV( bd3D + offset.xxx, texelSize.y ), dp ) +\\\\n\\\\t\\\\t\\\\t\\\\ttexture2DCompare( shadowMap, cubeToUV( bd3D + offset.yxx, texelSize.y ), dp )\\\\n\\\\t\\\\t\\\\t) * ( 1.0 / 9.0 );\\\\n\\\\t\\\\t#else\\\\n\\\\t\\\\t\\\\treturn texture2DCompare( shadowMap, cubeToUV( bd3D, texelSize.y ), dp );\\\\n\\\\t\\\\t#endif\\\\n\\\\t}\\\\n#endif\\\\\\\",shadowmap_pars_vertex:\\\\\\\"#ifdef USE_SHADOWMAP\\\\n\\\\t#if NUM_DIR_LIGHT_SHADOWS > 0\\\\n\\\\t\\\\tuniform mat4 directionalShadowMatrix[ NUM_DIR_LIGHT_SHADOWS ];\\\\n\\\\t\\\\tvarying vec4 vDirectionalShadowCoord[ NUM_DIR_LIGHT_SHADOWS ];\\\\n\\\\t\\\\tstruct DirectionalLightShadow {\\\\n\\\\t\\\\t\\\\tfloat shadowBias;\\\\n\\\\t\\\\t\\\\tfloat shadowNormalBias;\\\\n\\\\t\\\\t\\\\tfloat shadowRadius;\\\\n\\\\t\\\\t\\\\tvec2 shadowMapSize;\\\\n\\\\t\\\\t};\\\\n\\\\t\\\\tuniform DirectionalLightShadow directionalLightShadows[ NUM_DIR_LIGHT_SHADOWS ];\\\\n\\\\t#endif\\\\n\\\\t#if NUM_SPOT_LIGHT_SHADOWS > 0\\\\n\\\\t\\\\tuniform mat4 spotShadowMatrix[ NUM_SPOT_LIGHT_SHADOWS ];\\\\n\\\\t\\\\tvarying vec4 vSpotShadowCoord[ NUM_SPOT_LIGHT_SHADOWS ];\\\\n\\\\t\\\\tstruct SpotLightShadow {\\\\n\\\\t\\\\t\\\\tfloat shadowBias;\\\\n\\\\t\\\\t\\\\tfloat shadowNormalBias;\\\\n\\\\t\\\\t\\\\tfloat shadowRadius;\\\\n\\\\t\\\\t\\\\tvec2 shadowMapSize;\\\\n\\\\t\\\\t};\\\\n\\\\t\\\\tuniform SpotLightShadow spotLightShadows[ NUM_SPOT_LIGHT_SHADOWS ];\\\\n\\\\t#endif\\\\n\\\\t#if NUM_POINT_LIGHT_SHADOWS > 0\\\\n\\\\t\\\\tuniform mat4 pointShadowMatrix[ NUM_POINT_LIGHT_SHADOWS ];\\\\n\\\\t\\\\tvarying vec4 vPointShadowCoord[ NUM_POINT_LIGHT_SHADOWS ];\\\\n\\\\t\\\\tstruct PointLightShadow {\\\\n\\\\t\\\\t\\\\tfloat shadowBias;\\\\n\\\\t\\\\t\\\\tfloat shadowNormalBias;\\\\n\\\\t\\\\t\\\\tfloat shadowRadius;\\\\n\\\\t\\\\t\\\\tvec2 shadowMapSize;\\\\n\\\\t\\\\t\\\\tfloat shadowCameraNear;\\\\n\\\\t\\\\t\\\\tfloat shadowCameraFar;\\\\n\\\\t\\\\t};\\\\n\\\\t\\\\tuniform PointLightShadow pointLightShadows[ NUM_POINT_LIGHT_SHADOWS ];\\\\n\\\\t#endif\\\\n#endif\\\\\\\",shadowmap_vertex:\\\\\\\"#ifdef USE_SHADOWMAP\\\\n\\\\t#if NUM_DIR_LIGHT_SHADOWS > 0 || NUM_SPOT_LIGHT_SHADOWS > 0 || NUM_POINT_LIGHT_SHADOWS > 0\\\\n\\\\t\\\\tvec3 shadowWorldNormal = inverseTransformDirection( transformedNormal, viewMatrix );\\\\n\\\\t\\\\tvec4 shadowWorldPosition;\\\\n\\\\t#endif\\\\n\\\\t#if NUM_DIR_LIGHT_SHADOWS > 0\\\\n\\\\t#pragma unroll_loop_start\\\\n\\\\tfor ( int i = 0; i < NUM_DIR_LIGHT_SHADOWS; i ++ ) {\\\\n\\\\t\\\\tshadowWorldPosition = worldPosition + vec4( shadowWorldNormal * directionalLightShadows[ i ].shadowNormalBias, 0 );\\\\n\\\\t\\\\tvDirectionalShadowCoord[ i ] = directionalShadowMatrix[ i ] * shadowWorldPosition;\\\\n\\\\t}\\\\n\\\\t#pragma unroll_loop_end\\\\n\\\\t#endif\\\\n\\\\t#if NUM_SPOT_LIGHT_SHADOWS > 0\\\\n\\\\t#pragma unroll_loop_start\\\\n\\\\tfor ( int i = 0; i < NUM_SPOT_LIGHT_SHADOWS; i ++ ) {\\\\n\\\\t\\\\tshadowWorldPosition = worldPosition + vec4( shadowWorldNormal * spotLightShadows[ i ].shadowNormalBias, 0 );\\\\n\\\\t\\\\tvSpotShadowCoord[ i ] = spotShadowMatrix[ i ] * shadowWorldPosition;\\\\n\\\\t}\\\\n\\\\t#pragma unroll_loop_end\\\\n\\\\t#endif\\\\n\\\\t#if NUM_POINT_LIGHT_SHADOWS > 0\\\\n\\\\t#pragma unroll_loop_start\\\\n\\\\tfor ( int i = 0; i < NUM_POINT_LIGHT_SHADOWS; i ++ ) {\\\\n\\\\t\\\\tshadowWorldPosition = worldPosition + vec4( shadowWorldNormal * pointLightShadows[ i ].shadowNormalBias, 0 );\\\\n\\\\t\\\\tvPointShadowCoord[ i ] = pointShadowMatrix[ i ] * shadowWorldPosition;\\\\n\\\\t}\\\\n\\\\t#pragma unroll_loop_end\\\\n\\\\t#endif\\\\n#endif\\\\\\\",shadowmask_pars_fragment:\\\\\\\"float getShadowMask() {\\\\n\\\\tfloat shadow = 1.0;\\\\n\\\\t#ifdef USE_SHADOWMAP\\\\n\\\\t#if NUM_DIR_LIGHT_SHADOWS > 0\\\\n\\\\tDirectionalLightShadow directionalLight;\\\\n\\\\t#pragma unroll_loop_start\\\\n\\\\tfor ( int i = 0; i < NUM_DIR_LIGHT_SHADOWS; i ++ ) {\\\\n\\\\t\\\\tdirectionalLight = directionalLightShadows[ i ];\\\\n\\\\t\\\\tshadow *= receiveShadow ? getShadow( directionalShadowMap[ i ], directionalLight.shadowMapSize, directionalLight.shadowBias, directionalLight.shadowRadius, vDirectionalShadowCoord[ i ] ) : 1.0;\\\\n\\\\t}\\\\n\\\\t#pragma unroll_loop_end\\\\n\\\\t#endif\\\\n\\\\t#if NUM_SPOT_LIGHT_SHADOWS > 0\\\\n\\\\tSpotLightShadow spotLight;\\\\n\\\\t#pragma unroll_loop_start\\\\n\\\\tfor ( int i = 0; i < NUM_SPOT_LIGHT_SHADOWS; i ++ ) {\\\\n\\\\t\\\\tspotLight = spotLightShadows[ i ];\\\\n\\\\t\\\\tshadow *= receiveShadow ? getShadow( spotShadowMap[ i ], spotLight.shadowMapSize, spotLight.shadowBias, spotLight.shadowRadius, vSpotShadowCoord[ i ] ) : 1.0;\\\\n\\\\t}\\\\n\\\\t#pragma unroll_loop_end\\\\n\\\\t#endif\\\\n\\\\t#if NUM_POINT_LIGHT_SHADOWS > 0\\\\n\\\\tPointLightShadow pointLight;\\\\n\\\\t#pragma unroll_loop_start\\\\n\\\\tfor ( int i = 0; i < NUM_POINT_LIGHT_SHADOWS; i ++ ) {\\\\n\\\\t\\\\tpointLight = pointLightShadows[ i ];\\\\n\\\\t\\\\tshadow *= receiveShadow ? getPointShadow( pointShadowMap[ i ], pointLight.shadowMapSize, pointLight.shadowBias, pointLight.shadowRadius, vPointShadowCoord[ i ], pointLight.shadowCameraNear, pointLight.shadowCameraFar ) : 1.0;\\\\n\\\\t}\\\\n\\\\t#pragma unroll_loop_end\\\\n\\\\t#endif\\\\n\\\\t#endif\\\\n\\\\treturn shadow;\\\\n}\\\\\\\",skinbase_vertex:\\\\\\\"#ifdef USE_SKINNING\\\\n\\\\tmat4 boneMatX = getBoneMatrix( skinIndex.x );\\\\n\\\\tmat4 boneMatY = getBoneMatrix( skinIndex.y );\\\\n\\\\tmat4 boneMatZ = getBoneMatrix( skinIndex.z );\\\\n\\\\tmat4 boneMatW = getBoneMatrix( skinIndex.w );\\\\n#endif\\\\\\\",skinning_pars_vertex:\\\\\\\"#ifdef USE_SKINNING\\\\n\\\\tuniform mat4 bindMatrix;\\\\n\\\\tuniform mat4 bindMatrixInverse;\\\\n\\\\t#ifdef BONE_TEXTURE\\\\n\\\\t\\\\tuniform highp sampler2D boneTexture;\\\\n\\\\t\\\\tuniform int boneTextureSize;\\\\n\\\\t\\\\tmat4 getBoneMatrix( const in float i ) {\\\\n\\\\t\\\\t\\\\tfloat j = i * 4.0;\\\\n\\\\t\\\\t\\\\tfloat x = mod( j, float( boneTextureSize ) );\\\\n\\\\t\\\\t\\\\tfloat y = floor( j / float( boneTextureSize ) );\\\\n\\\\t\\\\t\\\\tfloat dx = 1.0 / float( boneTextureSize );\\\\n\\\\t\\\\t\\\\tfloat dy = 1.0 / float( boneTextureSize );\\\\n\\\\t\\\\t\\\\ty = dy * ( y + 0.5 );\\\\n\\\\t\\\\t\\\\tvec4 v1 = texture2D( boneTexture, vec2( dx * ( x + 0.5 ), y ) );\\\\n\\\\t\\\\t\\\\tvec4 v2 = texture2D( boneTexture, vec2( dx * ( x + 1.5 ), y ) );\\\\n\\\\t\\\\t\\\\tvec4 v3 = texture2D( boneTexture, vec2( dx * ( x + 2.5 ), y ) );\\\\n\\\\t\\\\t\\\\tvec4 v4 = texture2D( boneTexture, vec2( dx * ( x + 3.5 ), y ) );\\\\n\\\\t\\\\t\\\\tmat4 bone = mat4( v1, v2, v3, v4 );\\\\n\\\\t\\\\t\\\\treturn bone;\\\\n\\\\t\\\\t}\\\\n\\\\t#else\\\\n\\\\t\\\\tuniform mat4 boneMatrices[ MAX_BONES ];\\\\n\\\\t\\\\tmat4 getBoneMatrix( const in float i ) {\\\\n\\\\t\\\\t\\\\tmat4 bone = boneMatrices[ int(i) ];\\\\n\\\\t\\\\t\\\\treturn bone;\\\\n\\\\t\\\\t}\\\\n\\\\t#endif\\\\n#endif\\\\\\\",skinning_vertex:\\\\\\\"#ifdef USE_SKINNING\\\\n\\\\tvec4 skinVertex = bindMatrix * vec4( transformed, 1.0 );\\\\n\\\\tvec4 skinned = vec4( 0.0 );\\\\n\\\\tskinned += boneMatX * skinVertex * skinWeight.x;\\\\n\\\\tskinned += boneMatY * skinVertex * skinWeight.y;\\\\n\\\\tskinned += boneMatZ * skinVertex * skinWeight.z;\\\\n\\\\tskinned += boneMatW * skinVertex * skinWeight.w;\\\\n\\\\ttransformed = ( bindMatrixInverse * skinned ).xyz;\\\\n#endif\\\\\\\",skinnormal_vertex:\\\\\\\"#ifdef USE_SKINNING\\\\n\\\\tmat4 skinMatrix = mat4( 0.0 );\\\\n\\\\tskinMatrix += skinWeight.x * boneMatX;\\\\n\\\\tskinMatrix += skinWeight.y * boneMatY;\\\\n\\\\tskinMatrix += skinWeight.z * boneMatZ;\\\\n\\\\tskinMatrix += skinWeight.w * boneMatW;\\\\n\\\\tskinMatrix = bindMatrixInverse * skinMatrix * bindMatrix;\\\\n\\\\tobjectNormal = vec4( skinMatrix * vec4( objectNormal, 0.0 ) ).xyz;\\\\n\\\\t#ifdef USE_TANGENT\\\\n\\\\t\\\\tobjectTangent = vec4( skinMatrix * vec4( objectTangent, 0.0 ) ).xyz;\\\\n\\\\t#endif\\\\n#endif\\\\\\\",specularmap_fragment:\\\\\\\"float specularStrength;\\\\n#ifdef USE_SPECULARMAP\\\\n\\\\tvec4 texelSpecular = texture2D( specularMap, vUv );\\\\n\\\\tspecularStrength = texelSpecular.r;\\\\n#else\\\\n\\\\tspecularStrength = 1.0;\\\\n#endif\\\\\\\",specularmap_pars_fragment:\\\\\\\"#ifdef USE_SPECULARMAP\\\\n\\\\tuniform sampler2D specularMap;\\\\n#endif\\\\\\\",tonemapping_fragment:\\\\\\\"#if defined( TONE_MAPPING )\\\\n\\\\tgl_FragColor.rgb = toneMapping( gl_FragColor.rgb );\\\\n#endif\\\\\\\",tonemapping_pars_fragment:\\\\\\\"#ifndef saturate\\\\n#define saturate(a) clamp( a, 0.0, 1.0 )\\\\n#endif\\\\nuniform float toneMappingExposure;\\\\nvec3 LinearToneMapping( vec3 color ) {\\\\n\\\\treturn toneMappingExposure * color;\\\\n}\\\\nvec3 ReinhardToneMapping( vec3 color ) {\\\\n\\\\tcolor *= toneMappingExposure;\\\\n\\\\treturn saturate( color / ( vec3( 1.0 ) + color ) );\\\\n}\\\\nvec3 OptimizedCineonToneMapping( vec3 color ) {\\\\n\\\\tcolor *= toneMappingExposure;\\\\n\\\\tcolor = max( vec3( 0.0 ), color - 0.004 );\\\\n\\\\treturn pow( ( color * ( 6.2 * color + 0.5 ) ) / ( color * ( 6.2 * color + 1.7 ) + 0.06 ), vec3( 2.2 ) );\\\\n}\\\\nvec3 RRTAndODTFit( vec3 v ) {\\\\n\\\\tvec3 a = v * ( v + 0.0245786 ) - 0.000090537;\\\\n\\\\tvec3 b = v * ( 0.983729 * v + 0.4329510 ) + 0.238081;\\\\n\\\\treturn a / b;\\\\n}\\\\nvec3 ACESFilmicToneMapping( vec3 color ) {\\\\n\\\\tconst mat3 ACESInputMat = mat3(\\\\n\\\\t\\\\tvec3( 0.59719, 0.07600, 0.02840 ),\\\\t\\\\tvec3( 0.35458, 0.90834, 0.13383 ),\\\\n\\\\t\\\\tvec3( 0.04823, 0.01566, 0.83777 )\\\\n\\\\t);\\\\n\\\\tconst mat3 ACESOutputMat = mat3(\\\\n\\\\t\\\\tvec3(\\\\t1.60475, -0.10208, -0.00327 ),\\\\t\\\\tvec3( -0.53108,\\\\t1.10813, -0.07276 ),\\\\n\\\\t\\\\tvec3( -0.07367, -0.00605,\\\\t1.07602 )\\\\n\\\\t);\\\\n\\\\tcolor *= toneMappingExposure / 0.6;\\\\n\\\\tcolor = ACESInputMat * color;\\\\n\\\\tcolor = RRTAndODTFit( color );\\\\n\\\\tcolor = ACESOutputMat * color;\\\\n\\\\treturn saturate( color );\\\\n}\\\\nvec3 CustomToneMapping( vec3 color ) { return color; }\\\\\\\",transmission_fragment:\\\\\\\"#ifdef USE_TRANSMISSION\\\\n\\\\tfloat transmissionFactor = transmission;\\\\n\\\\tfloat thicknessFactor = thickness;\\\\n\\\\t#ifdef USE_TRANSMISSIONMAP\\\\n\\\\t\\\\ttransmissionFactor *= texture2D( transmissionMap, vUv ).r;\\\\n\\\\t#endif\\\\n\\\\t#ifdef USE_THICKNESSMAP\\\\n\\\\t\\\\tthicknessFactor *= texture2D( thicknessMap, vUv ).g;\\\\n\\\\t#endif\\\\n\\\\tvec3 pos = vWorldPosition.xyz / vWorldPosition.w;\\\\n\\\\tvec3 v = normalize( cameraPosition - pos );\\\\n\\\\tvec3 n = inverseTransformDirection( normal, viewMatrix );\\\\n\\\\tfloat ior = ( 1.0 + 0.4 * reflectivity ) / ( 1.0 - 0.4 * reflectivity );\\\\n\\\\tvec3 transmission = transmissionFactor * getIBLVolumeRefraction(\\\\n\\\\t\\\\tn, v, roughnessFactor, material.diffuseColor, material.specularColor,\\\\n\\\\t\\\\tpos, modelMatrix, viewMatrix, projectionMatrix, ior, thicknessFactor,\\\\n\\\\t\\\\tattenuationTint, attenuationDistance );\\\\n\\\\ttotalDiffuse = mix( totalDiffuse, transmission, transmissionFactor );\\\\n#endif\\\\\\\",transmission_pars_fragment:\\\\\\\"#ifdef USE_TRANSMISSION\\\\n\\\\t#ifdef USE_TRANSMISSIONMAP\\\\n\\\\t\\\\tuniform sampler2D transmissionMap;\\\\n\\\\t#endif\\\\n\\\\t#ifdef USE_THICKNESSMAP\\\\n\\\\t\\\\tuniform sampler2D thicknessMap;\\\\n\\\\t#endif\\\\n\\\\tuniform vec2 transmissionSamplerSize;\\\\n\\\\tuniform sampler2D transmissionSamplerMap;\\\\n\\\\tuniform mat4 modelMatrix;\\\\n\\\\tuniform mat4 projectionMatrix;\\\\n\\\\tvarying vec4 vWorldPosition;\\\\n\\\\tvec3 getVolumeTransmissionRay(vec3 n, vec3 v, float thickness, float ior, mat4 modelMatrix) {\\\\n\\\\t\\\\tvec3 refractionVector = refract(-v, normalize(n), 1.0 / ior);\\\\n\\\\t\\\\tvec3 modelScale;\\\\n\\\\t\\\\tmodelScale.x = length(vec3(modelMatrix[0].xyz));\\\\n\\\\t\\\\tmodelScale.y = length(vec3(modelMatrix[1].xyz));\\\\n\\\\t\\\\tmodelScale.z = length(vec3(modelMatrix[2].xyz));\\\\n\\\\t\\\\treturn normalize(refractionVector) * thickness * modelScale;\\\\n\\\\t}\\\\n\\\\tfloat applyIorToRoughness(float roughness, float ior) {\\\\n\\\\t\\\\treturn roughness * clamp(ior * 2.0 - 2.0, 0.0, 1.0);\\\\n\\\\t}\\\\n\\\\tvec3 getTransmissionSample(vec2 fragCoord, float roughness, float ior) {\\\\n\\\\t\\\\tfloat framebufferLod = log2(transmissionSamplerSize.x) * applyIorToRoughness(roughness, ior);\\\\n\\\\t\\\\treturn texture2DLodEXT(transmissionSamplerMap, fragCoord.xy, framebufferLod).rgb;\\\\n\\\\t}\\\\n\\\\tvec3 applyVolumeAttenuation(vec3 radiance, float transmissionDistance, vec3 attenuationColor, float attenuationDistance) {\\\\n\\\\t\\\\tif (attenuationDistance == 0.0) {\\\\n\\\\t\\\\t\\\\treturn radiance;\\\\n\\\\t\\\\t} else {\\\\n\\\\t\\\\t\\\\tvec3 attenuationCoefficient = -log(attenuationColor) / attenuationDistance;\\\\n\\\\t\\\\t\\\\tvec3 transmittance = exp(-attenuationCoefficient * transmissionDistance);\\\\t\\\\t\\\\treturn transmittance * radiance;\\\\n\\\\t\\\\t}\\\\n\\\\t}\\\\n\\\\tvec3 getIBLVolumeRefraction(vec3 n, vec3 v, float perceptualRoughness, vec3 baseColor, vec3 specularColor,\\\\n\\\\t\\\\tvec3 position, mat4 modelMatrix, mat4 viewMatrix, mat4 projMatrix, float ior, float thickness,\\\\n\\\\t\\\\tvec3 attenuationColor, float attenuationDistance) {\\\\n\\\\t\\\\tvec3 transmissionRay = getVolumeTransmissionRay(n, v, thickness, ior, modelMatrix);\\\\n\\\\t\\\\tvec3 refractedRayExit = position + transmissionRay;\\\\n\\\\t\\\\tvec4 ndcPos = projMatrix * viewMatrix * vec4(refractedRayExit, 1.0);\\\\n\\\\t\\\\tvec2 refractionCoords = ndcPos.xy / ndcPos.w;\\\\n\\\\t\\\\trefractionCoords += 1.0;\\\\n\\\\t\\\\trefractionCoords /= 2.0;\\\\n\\\\t\\\\tvec3 transmittedLight = getTransmissionSample(refractionCoords, perceptualRoughness, ior);\\\\n\\\\t\\\\tvec3 attenuatedColor = applyVolumeAttenuation(transmittedLight, length(transmissionRay), attenuationColor, attenuationDistance);\\\\n\\\\t\\\\treturn (1.0 - specularColor) * attenuatedColor * baseColor;\\\\n\\\\t}\\\\n#endif\\\\\\\",uv_pars_fragment:\\\\\\\"#if ( defined( USE_UV ) && ! defined( UVS_VERTEX_ONLY ) )\\\\n\\\\tvarying vec2 vUv;\\\\n#endif\\\\\\\",uv_pars_vertex:\\\\\\\"#ifdef USE_UV\\\\n\\\\t#ifdef UVS_VERTEX_ONLY\\\\n\\\\t\\\\tvec2 vUv;\\\\n\\\\t#else\\\\n\\\\t\\\\tvarying vec2 vUv;\\\\n\\\\t#endif\\\\n\\\\tuniform mat3 uvTransform;\\\\n#endif\\\\\\\",uv_vertex:\\\\\\\"#ifdef USE_UV\\\\n\\\\tvUv = ( uvTransform * vec3( uv, 1 ) ).xy;\\\\n#endif\\\\\\\",uv2_pars_fragment:\\\\\\\"#if defined( USE_LIGHTMAP ) || defined( USE_AOMAP )\\\\n\\\\tvarying vec2 vUv2;\\\\n#endif\\\\\\\",uv2_pars_vertex:\\\\\\\"#if defined( USE_LIGHTMAP ) || defined( USE_AOMAP )\\\\n\\\\tattribute vec2 uv2;\\\\n\\\\tvarying vec2 vUv2;\\\\n\\\\tuniform mat3 uv2Transform;\\\\n#endif\\\\\\\",uv2_vertex:\\\\\\\"#if defined( USE_LIGHTMAP ) || defined( USE_AOMAP )\\\\n\\\\tvUv2 = ( uv2Transform * vec3( uv2, 1 ) ).xy;\\\\n#endif\\\\\\\",worldpos_vertex:\\\\\\\"#if defined( USE_ENVMAP ) || defined( DISTANCE ) || defined ( USE_SHADOWMAP ) || defined ( USE_TRANSMISSION )\\\\n\\\\tvec4 worldPosition = vec4( transformed, 1.0 );\\\\n\\\\t#ifdef USE_INSTANCING\\\\n\\\\t\\\\tworldPosition = instanceMatrix * worldPosition;\\\\n\\\\t#endif\\\\n\\\\tworldPosition = modelMatrix * worldPosition;\\\\n#endif\\\\\\\",background_frag:\\\\\\\"uniform sampler2D t2D;\\\\nvarying vec2 vUv;\\\\nvoid main() {\\\\n\\\\tvec4 texColor = texture2D( t2D, vUv );\\\\n\\\\tgl_FragColor = mapTexelToLinear( texColor );\\\\n\\\\t#include <tonemapping_fragment>\\\\n\\\\t#include <encodings_fragment>\\\\n}\\\\\\\",background_vert:\\\\\\\"varying vec2 vUv;\\\\nuniform mat3 uvTransform;\\\\nvoid main() {\\\\n\\\\tvUv = ( uvTransform * vec3( uv, 1 ) ).xy;\\\\n\\\\tgl_Position = vec4( position.xy, 1.0, 1.0 );\\\\n}\\\\\\\",cube_frag:\\\\\\\"#include <envmap_common_pars_fragment>\\\\nuniform float opacity;\\\\nvarying vec3 vWorldDirection;\\\\n#include <cube_uv_reflection_fragment>\\\\nvoid main() {\\\\n\\\\tvec3 vReflect = vWorldDirection;\\\\n\\\\t#include <envmap_fragment>\\\\n\\\\tgl_FragColor = envColor;\\\\n\\\\tgl_FragColor.a *= opacity;\\\\n\\\\t#include <tonemapping_fragment>\\\\n\\\\t#include <encodings_fragment>\\\\n}\\\\\\\",cube_vert:\\\\\\\"varying vec3 vWorldDirection;\\\\n#include <common>\\\\nvoid main() {\\\\n\\\\tvWorldDirection = transformDirection( position, modelMatrix );\\\\n\\\\t#include <begin_vertex>\\\\n\\\\t#include <project_vertex>\\\\n\\\\tgl_Position.z = gl_Position.w;\\\\n}\\\\\\\",depth_frag:\\\\\\\"#if DEPTH_PACKING == 3200\\\\n\\\\tuniform float opacity;\\\\n#endif\\\\n#include <common>\\\\n#include <packing>\\\\n#include <uv_pars_fragment>\\\\n#include <map_pars_fragment>\\\\n#include <alphamap_pars_fragment>\\\\n#include <logdepthbuf_pars_fragment>\\\\n#include <clipping_planes_pars_fragment>\\\\nvarying vec2 vHighPrecisionZW;\\\\nvoid main() {\\\\n\\\\t#include <clipping_planes_fragment>\\\\n\\\\tvec4 diffuseColor = vec4( 1.0 );\\\\n\\\\t#if DEPTH_PACKING == 3200\\\\n\\\\t\\\\tdiffuseColor.a = opacity;\\\\n\\\\t#endif\\\\n\\\\t#include <map_fragment>\\\\n\\\\t#include <alphamap_fragment>\\\\n\\\\t#include <alphatest_fragment>\\\\n\\\\t#include <logdepthbuf_fragment>\\\\n\\\\tfloat fragCoordZ = 0.5 * vHighPrecisionZW[0] / vHighPrecisionZW[1] + 0.5;\\\\n\\\\t#if DEPTH_PACKING == 3200\\\\n\\\\t\\\\tgl_FragColor = vec4( vec3( 1.0 - fragCoordZ ), opacity );\\\\n\\\\t#elif DEPTH_PACKING == 3201\\\\n\\\\t\\\\tgl_FragColor = packDepthToRGBA( fragCoordZ );\\\\n\\\\t#endif\\\\n}\\\\\\\",depth_vert:\\\\\\\"#include <common>\\\\n#include <uv_pars_vertex>\\\\n#include <displacementmap_pars_vertex>\\\\n#include <morphtarget_pars_vertex>\\\\n#include <skinning_pars_vertex>\\\\n#include <logdepthbuf_pars_vertex>\\\\n#include <clipping_planes_pars_vertex>\\\\nvarying vec2 vHighPrecisionZW;\\\\nvoid main() {\\\\n\\\\t#include <uv_vertex>\\\\n\\\\t#include <skinbase_vertex>\\\\n\\\\t#ifdef USE_DISPLACEMENTMAP\\\\n\\\\t\\\\t#include <beginnormal_vertex>\\\\n\\\\t\\\\t#include <morphnormal_vertex>\\\\n\\\\t\\\\t#include <skinnormal_vertex>\\\\n\\\\t#endif\\\\n\\\\t#include <begin_vertex>\\\\n\\\\t#include <morphtarget_vertex>\\\\n\\\\t#include <skinning_vertex>\\\\n\\\\t#include <displacementmap_vertex>\\\\n\\\\t#include <project_vertex>\\\\n\\\\t#include <logdepthbuf_vertex>\\\\n\\\\t#include <clipping_planes_vertex>\\\\n\\\\tvHighPrecisionZW = gl_Position.zw;\\\\n}\\\\\\\",distanceRGBA_frag:\\\\\\\"#define DISTANCE\\\\nuniform vec3 referencePosition;\\\\nuniform float nearDistance;\\\\nuniform float farDistance;\\\\nvarying vec3 vWorldPosition;\\\\n#include <common>\\\\n#include <packing>\\\\n#include <uv_pars_fragment>\\\\n#include <map_pars_fragment>\\\\n#include <alphamap_pars_fragment>\\\\n#include <clipping_planes_pars_fragment>\\\\nvoid main () {\\\\n\\\\t#include <clipping_planes_fragment>\\\\n\\\\tvec4 diffuseColor = vec4( 1.0 );\\\\n\\\\t#include <map_fragment>\\\\n\\\\t#include <alphamap_fragment>\\\\n\\\\t#include <alphatest_fragment>\\\\n\\\\tfloat dist = length( vWorldPosition - referencePosition );\\\\n\\\\tdist = ( dist - nearDistance ) / ( farDistance - nearDistance );\\\\n\\\\tdist = saturate( dist );\\\\n\\\\tgl_FragColor = packDepthToRGBA( dist );\\\\n}\\\\\\\",distanceRGBA_vert:\\\\\\\"#define DISTANCE\\\\nvarying vec3 vWorldPosition;\\\\n#include <common>\\\\n#include <uv_pars_vertex>\\\\n#include <displacementmap_pars_vertex>\\\\n#include <morphtarget_pars_vertex>\\\\n#include <skinning_pars_vertex>\\\\n#include <clipping_planes_pars_vertex>\\\\nvoid main() {\\\\n\\\\t#include <uv_vertex>\\\\n\\\\t#include <skinbase_vertex>\\\\n\\\\t#ifdef USE_DISPLACEMENTMAP\\\\n\\\\t\\\\t#include <beginnormal_vertex>\\\\n\\\\t\\\\t#include <morphnormal_vertex>\\\\n\\\\t\\\\t#include <skinnormal_vertex>\\\\n\\\\t#endif\\\\n\\\\t#include <begin_vertex>\\\\n\\\\t#include <morphtarget_vertex>\\\\n\\\\t#include <skinning_vertex>\\\\n\\\\t#include <displacementmap_vertex>\\\\n\\\\t#include <project_vertex>\\\\n\\\\t#include <worldpos_vertex>\\\\n\\\\t#include <clipping_planes_vertex>\\\\n\\\\tvWorldPosition = worldPosition.xyz;\\\\n}\\\\\\\",equirect_frag:\\\\\\\"uniform sampler2D tEquirect;\\\\nvarying vec3 vWorldDirection;\\\\n#include <common>\\\\nvoid main() {\\\\n\\\\tvec3 direction = normalize( vWorldDirection );\\\\n\\\\tvec2 sampleUV = equirectUv( direction );\\\\n\\\\tvec4 texColor = texture2D( tEquirect, sampleUV );\\\\n\\\\tgl_FragColor = mapTexelToLinear( texColor );\\\\n\\\\t#include <tonemapping_fragment>\\\\n\\\\t#include <encodings_fragment>\\\\n}\\\\\\\",equirect_vert:\\\\\\\"varying vec3 vWorldDirection;\\\\n#include <common>\\\\nvoid main() {\\\\n\\\\tvWorldDirection = transformDirection( position, modelMatrix );\\\\n\\\\t#include <begin_vertex>\\\\n\\\\t#include <project_vertex>\\\\n}\\\\\\\",linedashed_frag:\\\\\\\"uniform vec3 diffuse;\\\\nuniform float opacity;\\\\nuniform float dashSize;\\\\nuniform float totalSize;\\\\nvarying float vLineDistance;\\\\n#include <common>\\\\n#include <color_pars_fragment>\\\\n#include <fog_pars_fragment>\\\\n#include <logdepthbuf_pars_fragment>\\\\n#include <clipping_planes_pars_fragment>\\\\nvoid main() {\\\\n\\\\t#include <clipping_planes_fragment>\\\\n\\\\tif ( mod( vLineDistance, totalSize ) > dashSize ) {\\\\n\\\\t\\\\tdiscard;\\\\n\\\\t}\\\\n\\\\tvec3 outgoingLight = vec3( 0.0 );\\\\n\\\\tvec4 diffuseColor = vec4( diffuse, opacity );\\\\n\\\\t#include <logdepthbuf_fragment>\\\\n\\\\t#include <color_fragment>\\\\n\\\\toutgoingLight = diffuseColor.rgb;\\\\n\\\\tgl_FragColor = vec4( outgoingLight, diffuseColor.a );\\\\n\\\\t#include <tonemapping_fragment>\\\\n\\\\t#include <encodings_fragment>\\\\n\\\\t#include <fog_fragment>\\\\n\\\\t#include <premultiplied_alpha_fragment>\\\\n}\\\\\\\",linedashed_vert:\\\\\\\"uniform float scale;\\\\nattribute float lineDistance;\\\\nvarying float vLineDistance;\\\\n#include <common>\\\\n#include <color_pars_vertex>\\\\n#include <fog_pars_vertex>\\\\n#include <morphtarget_pars_vertex>\\\\n#include <logdepthbuf_pars_vertex>\\\\n#include <clipping_planes_pars_vertex>\\\\nvoid main() {\\\\n\\\\tvLineDistance = scale * lineDistance;\\\\n\\\\t#include <color_vertex>\\\\n\\\\t#include <begin_vertex>\\\\n\\\\t#include <morphtarget_vertex>\\\\n\\\\t#include <project_vertex>\\\\n\\\\t#include <logdepthbuf_vertex>\\\\n\\\\t#include <clipping_planes_vertex>\\\\n\\\\t#include <fog_vertex>\\\\n}\\\\\\\",meshbasic_frag:\\\\\\\"uniform vec3 diffuse;\\\\nuniform float opacity;\\\\n#ifndef FLAT_SHADED\\\\n\\\\tvarying vec3 vNormal;\\\\n#endif\\\\n#include <common>\\\\n#include <dithering_pars_fragment>\\\\n#include <color_pars_fragment>\\\\n#include <uv_pars_fragment>\\\\n#include <uv2_pars_fragment>\\\\n#include <map_pars_fragment>\\\\n#include <alphamap_pars_fragment>\\\\n#include <aomap_pars_fragment>\\\\n#include <lightmap_pars_fragment>\\\\n#include <envmap_common_pars_fragment>\\\\n#include <envmap_pars_fragment>\\\\n#include <cube_uv_reflection_fragment>\\\\n#include <fog_pars_fragment>\\\\n#include <specularmap_pars_fragment>\\\\n#include <logdepthbuf_pars_fragment>\\\\n#include <clipping_planes_pars_fragment>\\\\nvoid main() {\\\\n\\\\t#include <clipping_planes_fragment>\\\\n\\\\tvec4 diffuseColor = vec4( diffuse, opacity );\\\\n\\\\t#include <logdepthbuf_fragment>\\\\n\\\\t#include <map_fragment>\\\\n\\\\t#include <color_fragment>\\\\n\\\\t#include <alphamap_fragment>\\\\n\\\\t#include <alphatest_fragment>\\\\n\\\\t#include <specularmap_fragment>\\\\n\\\\tReflectedLight reflectedLight = ReflectedLight( vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ) );\\\\n\\\\t#ifdef USE_LIGHTMAP\\\\n\\\\t\\\\n\\\\t\\\\tvec4 lightMapTexel= texture2D( lightMap, vUv2 );\\\\n\\\\t\\\\treflectedLight.indirectDiffuse += lightMapTexelToLinear( lightMapTexel ).rgb * lightMapIntensity;\\\\n\\\\t#else\\\\n\\\\t\\\\treflectedLight.indirectDiffuse += vec3( 1.0 );\\\\n\\\\t#endif\\\\n\\\\t#include <aomap_fragment>\\\\n\\\\treflectedLight.indirectDiffuse *= diffuseColor.rgb;\\\\n\\\\tvec3 outgoingLight = reflectedLight.indirectDiffuse;\\\\n\\\\t#include <envmap_fragment>\\\\n\\\\tgl_FragColor = vec4( outgoingLight, diffuseColor.a );\\\\n\\\\t#include <tonemapping_fragment>\\\\n\\\\t#include <encodings_fragment>\\\\n\\\\t#include <fog_fragment>\\\\n\\\\t#include <premultiplied_alpha_fragment>\\\\n\\\\t#include <dithering_fragment>\\\\n}\\\\\\\",meshbasic_vert:\\\\\\\"#include <common>\\\\n#include <uv_pars_vertex>\\\\n#include <uv2_pars_vertex>\\\\n#include <envmap_pars_vertex>\\\\n#include <color_pars_vertex>\\\\n#include <fog_pars_vertex>\\\\n#include <morphtarget_pars_vertex>\\\\n#include <skinning_pars_vertex>\\\\n#include <logdepthbuf_pars_vertex>\\\\n#include <clipping_planes_pars_vertex>\\\\nvoid main() {\\\\n\\\\t#include <uv_vertex>\\\\n\\\\t#include <uv2_vertex>\\\\n\\\\t#include <color_vertex>\\\\n\\\\t#if defined ( USE_ENVMAP ) || defined ( USE_SKINNING )\\\\n\\\\t\\\\t#include <beginnormal_vertex>\\\\n\\\\t\\\\t#include <morphnormal_vertex>\\\\n\\\\t\\\\t#include <skinbase_vertex>\\\\n\\\\t\\\\t#include <skinnormal_vertex>\\\\n\\\\t\\\\t#include <defaultnormal_vertex>\\\\n\\\\t#endif\\\\n\\\\t#include <begin_vertex>\\\\n\\\\t#include <morphtarget_vertex>\\\\n\\\\t#include <skinning_vertex>\\\\n\\\\t#include <project_vertex>\\\\n\\\\t#include <logdepthbuf_vertex>\\\\n\\\\t#include <clipping_planes_vertex>\\\\n\\\\t#include <worldpos_vertex>\\\\n\\\\t#include <envmap_vertex>\\\\n\\\\t#include <fog_vertex>\\\\n}\\\\\\\",meshlambert_frag:\\\\\\\"uniform vec3 diffuse;\\\\nuniform vec3 emissive;\\\\nuniform float opacity;\\\\nvarying vec3 vLightFront;\\\\nvarying vec3 vIndirectFront;\\\\n#ifdef DOUBLE_SIDED\\\\n\\\\tvarying vec3 vLightBack;\\\\n\\\\tvarying vec3 vIndirectBack;\\\\n#endif\\\\n#include <common>\\\\n#include <packing>\\\\n#include <dithering_pars_fragment>\\\\n#include <color_pars_fragment>\\\\n#include <uv_pars_fragment>\\\\n#include <uv2_pars_fragment>\\\\n#include <map_pars_fragment>\\\\n#include <alphamap_pars_fragment>\\\\n#include <aomap_pars_fragment>\\\\n#include <lightmap_pars_fragment>\\\\n#include <emissivemap_pars_fragment>\\\\n#include <envmap_common_pars_fragment>\\\\n#include <envmap_pars_fragment>\\\\n#include <cube_uv_reflection_fragment>\\\\n#include <bsdfs>\\\\n#include <lights_pars_begin>\\\\n#include <fog_pars_fragment>\\\\n#include <shadowmap_pars_fragment>\\\\n#include <shadowmask_pars_fragment>\\\\n#include <specularmap_pars_fragment>\\\\n#include <logdepthbuf_pars_fragment>\\\\n#include <clipping_planes_pars_fragment>\\\\nvoid main() {\\\\n\\\\t#include <clipping_planes_fragment>\\\\n\\\\tvec4 diffuseColor = vec4( diffuse, opacity );\\\\n\\\\tReflectedLight reflectedLight = ReflectedLight( vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ) );\\\\n\\\\tvec3 totalEmissiveRadiance = emissive;\\\\n\\\\t#include <logdepthbuf_fragment>\\\\n\\\\t#include <map_fragment>\\\\n\\\\t#include <color_fragment>\\\\n\\\\t#include <alphamap_fragment>\\\\n\\\\t#include <alphatest_fragment>\\\\n\\\\t#include <specularmap_fragment>\\\\n\\\\t#include <emissivemap_fragment>\\\\n\\\\t#ifdef DOUBLE_SIDED\\\\n\\\\t\\\\treflectedLight.indirectDiffuse += ( gl_FrontFacing ) ? vIndirectFront : vIndirectBack;\\\\n\\\\t#else\\\\n\\\\t\\\\treflectedLight.indirectDiffuse += vIndirectFront;\\\\n\\\\t#endif\\\\n\\\\t#include <lightmap_fragment>\\\\n\\\\treflectedLight.indirectDiffuse *= BRDF_Diffuse_Lambert( diffuseColor.rgb );\\\\n\\\\t#ifdef DOUBLE_SIDED\\\\n\\\\t\\\\treflectedLight.directDiffuse = ( gl_FrontFacing ) ? vLightFront : vLightBack;\\\\n\\\\t#else\\\\n\\\\t\\\\treflectedLight.directDiffuse = vLightFront;\\\\n\\\\t#endif\\\\n\\\\treflectedLight.directDiffuse *= BRDF_Diffuse_Lambert( diffuseColor.rgb ) * getShadowMask();\\\\n\\\\t#include <aomap_fragment>\\\\n\\\\tvec3 outgoingLight = reflectedLight.directDiffuse + reflectedLight.indirectDiffuse + totalEmissiveRadiance;\\\\n\\\\t#include <envmap_fragment>\\\\n\\\\tgl_FragColor = vec4( outgoingLight, diffuseColor.a );\\\\n\\\\t#include <tonemapping_fragment>\\\\n\\\\t#include <encodings_fragment>\\\\n\\\\t#include <fog_fragment>\\\\n\\\\t#include <premultiplied_alpha_fragment>\\\\n\\\\t#include <dithering_fragment>\\\\n}\\\\\\\",meshlambert_vert:\\\\\\\"#define LAMBERT\\\\nvarying vec3 vLightFront;\\\\nvarying vec3 vIndirectFront;\\\\n#ifdef DOUBLE_SIDED\\\\n\\\\tvarying vec3 vLightBack;\\\\n\\\\tvarying vec3 vIndirectBack;\\\\n#endif\\\\n#include <common>\\\\n#include <uv_pars_vertex>\\\\n#include <uv2_pars_vertex>\\\\n#include <envmap_pars_vertex>\\\\n#include <bsdfs>\\\\n#include <lights_pars_begin>\\\\n#include <color_pars_vertex>\\\\n#include <fog_pars_vertex>\\\\n#include <morphtarget_pars_vertex>\\\\n#include <skinning_pars_vertex>\\\\n#include <shadowmap_pars_vertex>\\\\n#include <logdepthbuf_pars_vertex>\\\\n#include <clipping_planes_pars_vertex>\\\\nvoid main() {\\\\n\\\\t#include <uv_vertex>\\\\n\\\\t#include <uv2_vertex>\\\\n\\\\t#include <color_vertex>\\\\n\\\\t#include <beginnormal_vertex>\\\\n\\\\t#include <morphnormal_vertex>\\\\n\\\\t#include <skinbase_vertex>\\\\n\\\\t#include <skinnormal_vertex>\\\\n\\\\t#include <defaultnormal_vertex>\\\\n\\\\t#include <begin_vertex>\\\\n\\\\t#include <morphtarget_vertex>\\\\n\\\\t#include <skinning_vertex>\\\\n\\\\t#include <project_vertex>\\\\n\\\\t#include <logdepthbuf_vertex>\\\\n\\\\t#include <clipping_planes_vertex>\\\\n\\\\t#include <worldpos_vertex>\\\\n\\\\t#include <envmap_vertex>\\\\n\\\\t#include <lights_lambert_vertex>\\\\n\\\\t#include <shadowmap_vertex>\\\\n\\\\t#include <fog_vertex>\\\\n}\\\\\\\",meshmatcap_frag:\\\\\\\"#define MATCAP\\\\nuniform vec3 diffuse;\\\\nuniform float opacity;\\\\nuniform sampler2D matcap;\\\\nvarying vec3 vViewPosition;\\\\n#ifndef FLAT_SHADED\\\\n\\\\tvarying vec3 vNormal;\\\\n\\\\t#ifdef USE_TANGENT\\\\n\\\\t\\\\tvarying vec3 vTangent;\\\\n\\\\t\\\\tvarying vec3 vBitangent;\\\\n\\\\t#endif\\\\n#endif\\\\n#include <common>\\\\n#include <dithering_pars_fragment>\\\\n#include <color_pars_fragment>\\\\n#include <uv_pars_fragment>\\\\n#include <map_pars_fragment>\\\\n#include <alphamap_pars_fragment>\\\\n#include <fog_pars_fragment>\\\\n#include <bumpmap_pars_fragment>\\\\n#include <normalmap_pars_fragment>\\\\n#include <logdepthbuf_pars_fragment>\\\\n#include <clipping_planes_pars_fragment>\\\\nvoid main() {\\\\n\\\\t#include <clipping_planes_fragment>\\\\n\\\\tvec4 diffuseColor = vec4( diffuse, opacity );\\\\n\\\\t#include <logdepthbuf_fragment>\\\\n\\\\t#include <map_fragment>\\\\n\\\\t#include <color_fragment>\\\\n\\\\t#include <alphamap_fragment>\\\\n\\\\t#include <alphatest_fragment>\\\\n\\\\t#include <normal_fragment_begin>\\\\n\\\\t#include <normal_fragment_maps>\\\\n\\\\tvec3 viewDir = normalize( vViewPosition );\\\\n\\\\tvec3 x = normalize( vec3( viewDir.z, 0.0, - viewDir.x ) );\\\\n\\\\tvec3 y = cross( viewDir, x );\\\\n\\\\tvec2 uv = vec2( dot( x, normal ), dot( y, normal ) ) * 0.495 + 0.5;\\\\n\\\\t#ifdef USE_MATCAP\\\\n\\\\t\\\\tvec4 matcapColor = texture2D( matcap, uv );\\\\n\\\\t\\\\tmatcapColor = matcapTexelToLinear( matcapColor );\\\\n\\\\t#else\\\\n\\\\t\\\\tvec4 matcapColor = vec4( 1.0 );\\\\n\\\\t#endif\\\\n\\\\tvec3 outgoingLight = diffuseColor.rgb * matcapColor.rgb;\\\\n\\\\tgl_FragColor = vec4( outgoingLight, diffuseColor.a );\\\\n\\\\t#include <tonemapping_fragment>\\\\n\\\\t#include <encodings_fragment>\\\\n\\\\t#include <fog_fragment>\\\\n\\\\t#include <premultiplied_alpha_fragment>\\\\n\\\\t#include <dithering_fragment>\\\\n}\\\\\\\",meshmatcap_vert:\\\\\\\"#define MATCAP\\\\nvarying vec3 vViewPosition;\\\\n#ifndef FLAT_SHADED\\\\n\\\\tvarying vec3 vNormal;\\\\n\\\\t#ifdef USE_TANGENT\\\\n\\\\t\\\\tvarying vec3 vTangent;\\\\n\\\\t\\\\tvarying vec3 vBitangent;\\\\n\\\\t#endif\\\\n#endif\\\\n#include <common>\\\\n#include <uv_pars_vertex>\\\\n#include <color_pars_vertex>\\\\n#include <displacementmap_pars_vertex>\\\\n#include <fog_pars_vertex>\\\\n#include <morphtarget_pars_vertex>\\\\n#include <skinning_pars_vertex>\\\\n#include <logdepthbuf_pars_vertex>\\\\n#include <clipping_planes_pars_vertex>\\\\nvoid main() {\\\\n\\\\t#include <uv_vertex>\\\\n\\\\t#include <color_vertex>\\\\n\\\\t#include <beginnormal_vertex>\\\\n\\\\t#include <morphnormal_vertex>\\\\n\\\\t#include <skinbase_vertex>\\\\n\\\\t#include <skinnormal_vertex>\\\\n\\\\t#include <defaultnormal_vertex>\\\\n\\\\t#ifndef FLAT_SHADED\\\\n\\\\t\\\\tvNormal = normalize( transformedNormal );\\\\n\\\\t\\\\t#ifdef USE_TANGENT\\\\n\\\\t\\\\t\\\\tvTangent = normalize( transformedTangent );\\\\n\\\\t\\\\t\\\\tvBitangent = normalize( cross( vNormal, vTangent ) * tangent.w );\\\\n\\\\t\\\\t#endif\\\\n\\\\t#endif\\\\n\\\\t#include <begin_vertex>\\\\n\\\\t#include <morphtarget_vertex>\\\\n\\\\t#include <skinning_vertex>\\\\n\\\\t#include <displacementmap_vertex>\\\\n\\\\t#include <project_vertex>\\\\n\\\\t#include <logdepthbuf_vertex>\\\\n\\\\t#include <clipping_planes_vertex>\\\\n\\\\t#include <fog_vertex>\\\\n\\\\tvViewPosition = - mvPosition.xyz;\\\\n}\\\\\\\",meshtoon_frag:\\\\\\\"#define TOON\\\\nuniform vec3 diffuse;\\\\nuniform vec3 emissive;\\\\nuniform float opacity;\\\\n#include <common>\\\\n#include <packing>\\\\n#include <dithering_pars_fragment>\\\\n#include <color_pars_fragment>\\\\n#include <uv_pars_fragment>\\\\n#include <uv2_pars_fragment>\\\\n#include <map_pars_fragment>\\\\n#include <alphamap_pars_fragment>\\\\n#include <aomap_pars_fragment>\\\\n#include <lightmap_pars_fragment>\\\\n#include <emissivemap_pars_fragment>\\\\n#include <gradientmap_pars_fragment>\\\\n#include <fog_pars_fragment>\\\\n#include <bsdfs>\\\\n#include <lights_pars_begin>\\\\n#include <lights_toon_pars_fragment>\\\\n#include <shadowmap_pars_fragment>\\\\n#include <bumpmap_pars_fragment>\\\\n#include <normalmap_pars_fragment>\\\\n#include <logdepthbuf_pars_fragment>\\\\n#include <clipping_planes_pars_fragment>\\\\nvoid main() {\\\\n\\\\t#include <clipping_planes_fragment>\\\\n\\\\tvec4 diffuseColor = vec4( diffuse, opacity );\\\\n\\\\tReflectedLight reflectedLight = ReflectedLight( vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ) );\\\\n\\\\tvec3 totalEmissiveRadiance = emissive;\\\\n\\\\t#include <logdepthbuf_fragment>\\\\n\\\\t#include <map_fragment>\\\\n\\\\t#include <color_fragment>\\\\n\\\\t#include <alphamap_fragment>\\\\n\\\\t#include <alphatest_fragment>\\\\n\\\\t#include <normal_fragment_begin>\\\\n\\\\t#include <normal_fragment_maps>\\\\n\\\\t#include <emissivemap_fragment>\\\\n\\\\t#include <lights_toon_fragment>\\\\n\\\\t#include <lights_fragment_begin>\\\\n\\\\t#include <lights_fragment_maps>\\\\n\\\\t#include <lights_fragment_end>\\\\n\\\\t#include <aomap_fragment>\\\\n\\\\tvec3 outgoingLight = reflectedLight.directDiffuse + reflectedLight.indirectDiffuse + totalEmissiveRadiance;\\\\n\\\\tgl_FragColor = vec4( outgoingLight, diffuseColor.a );\\\\n\\\\t#include <tonemapping_fragment>\\\\n\\\\t#include <encodings_fragment>\\\\n\\\\t#include <fog_fragment>\\\\n\\\\t#include <premultiplied_alpha_fragment>\\\\n\\\\t#include <dithering_fragment>\\\\n}\\\\\\\",meshtoon_vert:\\\\\\\"#define TOON\\\\nvarying vec3 vViewPosition;\\\\n#ifndef FLAT_SHADED\\\\n\\\\tvarying vec3 vNormal;\\\\n#endif\\\\n#include <common>\\\\n#include <uv_pars_vertex>\\\\n#include <uv2_pars_vertex>\\\\n#include <displacementmap_pars_vertex>\\\\n#include <color_pars_vertex>\\\\n#include <fog_pars_vertex>\\\\n#include <morphtarget_pars_vertex>\\\\n#include <skinning_pars_vertex>\\\\n#include <shadowmap_pars_vertex>\\\\n#include <logdepthbuf_pars_vertex>\\\\n#include <clipping_planes_pars_vertex>\\\\nvoid main() {\\\\n\\\\t#include <uv_vertex>\\\\n\\\\t#include <uv2_vertex>\\\\n\\\\t#include <color_vertex>\\\\n\\\\t#include <beginnormal_vertex>\\\\n\\\\t#include <morphnormal_vertex>\\\\n\\\\t#include <skinbase_vertex>\\\\n\\\\t#include <skinnormal_vertex>\\\\n\\\\t#include <defaultnormal_vertex>\\\\n#ifndef FLAT_SHADED\\\\n\\\\tvNormal = normalize( transformedNormal );\\\\n#endif\\\\n\\\\t#include <begin_vertex>\\\\n\\\\t#include <morphtarget_vertex>\\\\n\\\\t#include <skinning_vertex>\\\\n\\\\t#include <displacementmap_vertex>\\\\n\\\\t#include <project_vertex>\\\\n\\\\t#include <logdepthbuf_vertex>\\\\n\\\\t#include <clipping_planes_vertex>\\\\n\\\\tvViewPosition = - mvPosition.xyz;\\\\n\\\\t#include <worldpos_vertex>\\\\n\\\\t#include <shadowmap_vertex>\\\\n\\\\t#include <fog_vertex>\\\\n}\\\\\\\",meshphong_frag:\\\\\\\"#define PHONG\\\\nuniform vec3 diffuse;\\\\nuniform vec3 emissive;\\\\nuniform vec3 specular;\\\\nuniform float shininess;\\\\nuniform float opacity;\\\\n#include <common>\\\\n#include <packing>\\\\n#include <dithering_pars_fragment>\\\\n#include <color_pars_fragment>\\\\n#include <uv_pars_fragment>\\\\n#include <uv2_pars_fragment>\\\\n#include <map_pars_fragment>\\\\n#include <alphamap_pars_fragment>\\\\n#include <aomap_pars_fragment>\\\\n#include <lightmap_pars_fragment>\\\\n#include <emissivemap_pars_fragment>\\\\n#include <envmap_common_pars_fragment>\\\\n#include <envmap_pars_fragment>\\\\n#include <cube_uv_reflection_fragment>\\\\n#include <fog_pars_fragment>\\\\n#include <bsdfs>\\\\n#include <lights_pars_begin>\\\\n#include <lights_phong_pars_fragment>\\\\n#include <shadowmap_pars_fragment>\\\\n#include <bumpmap_pars_fragment>\\\\n#include <normalmap_pars_fragment>\\\\n#include <specularmap_pars_fragment>\\\\n#include <logdepthbuf_pars_fragment>\\\\n#include <clipping_planes_pars_fragment>\\\\nvoid main() {\\\\n\\\\t#include <clipping_planes_fragment>\\\\n\\\\tvec4 diffuseColor = vec4( diffuse, opacity );\\\\n\\\\tReflectedLight reflectedLight = ReflectedLight( vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ) );\\\\n\\\\tvec3 totalEmissiveRadiance = emissive;\\\\n\\\\t#include <logdepthbuf_fragment>\\\\n\\\\t#include <map_fragment>\\\\n\\\\t#include <color_fragment>\\\\n\\\\t#include <alphamap_fragment>\\\\n\\\\t#include <alphatest_fragment>\\\\n\\\\t#include <specularmap_fragment>\\\\n\\\\t#include <normal_fragment_begin>\\\\n\\\\t#include <normal_fragment_maps>\\\\n\\\\t#include <emissivemap_fragment>\\\\n\\\\t#include <lights_phong_fragment>\\\\n\\\\t#include <lights_fragment_begin>\\\\n\\\\t#include <lights_fragment_maps>\\\\n\\\\t#include <lights_fragment_end>\\\\n\\\\t#include <aomap_fragment>\\\\n\\\\tvec3 outgoingLight = reflectedLight.directDiffuse + reflectedLight.indirectDiffuse + reflectedLight.directSpecular + reflectedLight.indirectSpecular + totalEmissiveRadiance;\\\\n\\\\t#include <envmap_fragment>\\\\n\\\\tgl_FragColor = vec4( outgoingLight, diffuseColor.a );\\\\n\\\\t#include <tonemapping_fragment>\\\\n\\\\t#include <encodings_fragment>\\\\n\\\\t#include <fog_fragment>\\\\n\\\\t#include <premultiplied_alpha_fragment>\\\\n\\\\t#include <dithering_fragment>\\\\n}\\\\\\\",meshphong_vert:\\\\\\\"#define PHONG\\\\nvarying vec3 vViewPosition;\\\\n#ifndef FLAT_SHADED\\\\n\\\\tvarying vec3 vNormal;\\\\n#endif\\\\n#include <common>\\\\n#include <uv_pars_vertex>\\\\n#include <uv2_pars_vertex>\\\\n#include <displacementmap_pars_vertex>\\\\n#include <envmap_pars_vertex>\\\\n#include <color_pars_vertex>\\\\n#include <fog_pars_vertex>\\\\n#include <morphtarget_pars_vertex>\\\\n#include <skinning_pars_vertex>\\\\n#include <shadowmap_pars_vertex>\\\\n#include <logdepthbuf_pars_vertex>\\\\n#include <clipping_planes_pars_vertex>\\\\nvoid main() {\\\\n\\\\t#include <uv_vertex>\\\\n\\\\t#include <uv2_vertex>\\\\n\\\\t#include <color_vertex>\\\\n\\\\t#include <beginnormal_vertex>\\\\n\\\\t#include <morphnormal_vertex>\\\\n\\\\t#include <skinbase_vertex>\\\\n\\\\t#include <skinnormal_vertex>\\\\n\\\\t#include <defaultnormal_vertex>\\\\n#ifndef FLAT_SHADED\\\\n\\\\tvNormal = normalize( transformedNormal );\\\\n#endif\\\\n\\\\t#include <begin_vertex>\\\\n\\\\t#include <morphtarget_vertex>\\\\n\\\\t#include <skinning_vertex>\\\\n\\\\t#include <displacementmap_vertex>\\\\n\\\\t#include <project_vertex>\\\\n\\\\t#include <logdepthbuf_vertex>\\\\n\\\\t#include <clipping_planes_vertex>\\\\n\\\\tvViewPosition = - mvPosition.xyz;\\\\n\\\\t#include <worldpos_vertex>\\\\n\\\\t#include <envmap_vertex>\\\\n\\\\t#include <shadowmap_vertex>\\\\n\\\\t#include <fog_vertex>\\\\n}\\\\\\\",meshphysical_frag:\\\\\\\"#define STANDARD\\\\n#ifdef PHYSICAL\\\\n\\\\t#define REFLECTIVITY\\\\n\\\\t#define CLEARCOAT\\\\n\\\\t#define SPECULAR\\\\n#endif\\\\nuniform vec3 diffuse;\\\\nuniform vec3 emissive;\\\\nuniform float roughness;\\\\nuniform float metalness;\\\\nuniform float opacity;\\\\n#ifdef USE_TRANSMISSION\\\\n\\\\tuniform float transmission;\\\\n\\\\tuniform float thickness;\\\\n\\\\tuniform float attenuationDistance;\\\\n\\\\tuniform vec3 attenuationTint;\\\\n#endif\\\\n#ifdef REFLECTIVITY\\\\n\\\\tuniform float reflectivity;\\\\n#endif\\\\n#ifdef SPECULAR\\\\n\\\\tuniform float specularIntensity;\\\\n\\\\tuniform vec3 specularTint;\\\\n\\\\t#ifdef USE_SPECULARINTENSITYMAP\\\\n\\\\t\\\\tuniform sampler2D specularIntensityMap;\\\\n\\\\t#endif\\\\n\\\\t#ifdef USE_SPECULARTINTMAP\\\\n\\\\t\\\\tuniform sampler2D specularTintMap;\\\\n\\\\t#endif\\\\n#endif\\\\n#ifdef CLEARCOAT\\\\n\\\\tuniform float clearcoat;\\\\n\\\\tuniform float clearcoatRoughness;\\\\n#endif\\\\n#ifdef USE_SHEEN\\\\n\\\\tuniform vec3 sheen;\\\\n#endif\\\\nvarying vec3 vViewPosition;\\\\n#ifndef FLAT_SHADED\\\\n\\\\tvarying vec3 vNormal;\\\\n\\\\t#ifdef USE_TANGENT\\\\n\\\\t\\\\tvarying vec3 vTangent;\\\\n\\\\t\\\\tvarying vec3 vBitangent;\\\\n\\\\t#endif\\\\n#endif\\\\n#include <common>\\\\n#include <packing>\\\\n#include <dithering_pars_fragment>\\\\n#include <color_pars_fragment>\\\\n#include <uv_pars_fragment>\\\\n#include <uv2_pars_fragment>\\\\n#include <map_pars_fragment>\\\\n#include <alphamap_pars_fragment>\\\\n#include <aomap_pars_fragment>\\\\n#include <lightmap_pars_fragment>\\\\n#include <emissivemap_pars_fragment>\\\\n#include <bsdfs>\\\\n#include <transmission_pars_fragment>\\\\n#include <cube_uv_reflection_fragment>\\\\n#include <envmap_common_pars_fragment>\\\\n#include <envmap_physical_pars_fragment>\\\\n#include <fog_pars_fragment>\\\\n#include <lights_pars_begin>\\\\n#include <lights_physical_pars_fragment>\\\\n#include <shadowmap_pars_fragment>\\\\n#include <bumpmap_pars_fragment>\\\\n#include <normalmap_pars_fragment>\\\\n#include <clearcoat_pars_fragment>\\\\n#include <roughnessmap_pars_fragment>\\\\n#include <metalnessmap_pars_fragment>\\\\n#include <logdepthbuf_pars_fragment>\\\\n#include <clipping_planes_pars_fragment>\\\\nvoid main() {\\\\n\\\\t#include <clipping_planes_fragment>\\\\n\\\\tvec4 diffuseColor = vec4( diffuse, opacity );\\\\n\\\\tReflectedLight reflectedLight = ReflectedLight( vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ) );\\\\n\\\\tvec3 totalEmissiveRadiance = emissive;\\\\n\\\\t#include <logdepthbuf_fragment>\\\\n\\\\t#include <map_fragment>\\\\n\\\\t#include <color_fragment>\\\\n\\\\t#include <alphamap_fragment>\\\\n\\\\t#include <alphatest_fragment>\\\\n\\\\t#include <roughnessmap_fragment>\\\\n\\\\t#include <metalnessmap_fragment>\\\\n\\\\t#include <normal_fragment_begin>\\\\n\\\\t#include <normal_fragment_maps>\\\\n\\\\t#include <clearcoat_normal_fragment_begin>\\\\n\\\\t#include <clearcoat_normal_fragment_maps>\\\\n\\\\t#include <emissivemap_fragment>\\\\n\\\\t#include <lights_physical_fragment>\\\\n\\\\t#include <lights_fragment_begin>\\\\n\\\\t#include <lights_fragment_maps>\\\\n\\\\t#include <lights_fragment_end>\\\\n\\\\t#include <aomap_fragment>\\\\n\\\\tvec3 totalDiffuse = reflectedLight.directDiffuse + reflectedLight.indirectDiffuse;\\\\n\\\\tvec3 totalSpecular = reflectedLight.directSpecular + reflectedLight.indirectSpecular;\\\\n\\\\t#include <transmission_fragment>\\\\n\\\\tvec3 outgoingLight = totalDiffuse + totalSpecular + totalEmissiveRadiance;\\\\n\\\\tgl_FragColor = vec4( outgoingLight, diffuseColor.a );\\\\n\\\\t#include <tonemapping_fragment>\\\\n\\\\t#include <encodings_fragment>\\\\n\\\\t#include <fog_fragment>\\\\n\\\\t#include <premultiplied_alpha_fragment>\\\\n\\\\t#include <dithering_fragment>\\\\n}\\\\\\\",meshphysical_vert:\\\\\\\"#define STANDARD\\\\nvarying vec3 vViewPosition;\\\\n#ifndef FLAT_SHADED\\\\n\\\\tvarying vec3 vNormal;\\\\n\\\\t#ifdef USE_TANGENT\\\\n\\\\t\\\\tvarying vec3 vTangent;\\\\n\\\\t\\\\tvarying vec3 vBitangent;\\\\n\\\\t#endif\\\\n#endif\\\\n#ifdef USE_TRANSMISSION\\\\n\\\\tvarying vec4 vWorldPosition;\\\\n#endif\\\\n#include <common>\\\\n#include <uv_pars_vertex>\\\\n#include <uv2_pars_vertex>\\\\n#include <displacementmap_pars_vertex>\\\\n#include <color_pars_vertex>\\\\n#include <fog_pars_vertex>\\\\n#include <morphtarget_pars_vertex>\\\\n#include <skinning_pars_vertex>\\\\n#include <shadowmap_pars_vertex>\\\\n#include <logdepthbuf_pars_vertex>\\\\n#include <clipping_planes_pars_vertex>\\\\nvoid main() {\\\\n\\\\t#include <uv_vertex>\\\\n\\\\t#include <uv2_vertex>\\\\n\\\\t#include <color_vertex>\\\\n\\\\t#include <beginnormal_vertex>\\\\n\\\\t#include <morphnormal_vertex>\\\\n\\\\t#include <skinbase_vertex>\\\\n\\\\t#include <skinnormal_vertex>\\\\n\\\\t#include <defaultnormal_vertex>\\\\n#ifndef FLAT_SHADED\\\\n\\\\tvNormal = normalize( transformedNormal );\\\\n\\\\t#ifdef USE_TANGENT\\\\n\\\\t\\\\tvTangent = normalize( transformedTangent );\\\\n\\\\t\\\\tvBitangent = normalize( cross( vNormal, vTangent ) * tangent.w );\\\\n\\\\t#endif\\\\n#endif\\\\n\\\\t#include <begin_vertex>\\\\n\\\\t#include <morphtarget_vertex>\\\\n\\\\t#include <skinning_vertex>\\\\n\\\\t#include <displacementmap_vertex>\\\\n\\\\t#include <project_vertex>\\\\n\\\\t#include <logdepthbuf_vertex>\\\\n\\\\t#include <clipping_planes_vertex>\\\\n\\\\tvViewPosition = - mvPosition.xyz;\\\\n\\\\t#include <worldpos_vertex>\\\\n\\\\t#include <shadowmap_vertex>\\\\n\\\\t#include <fog_vertex>\\\\n#ifdef USE_TRANSMISSION\\\\n\\\\tvWorldPosition = worldPosition;\\\\n#endif\\\\n}\\\\\\\",normal_frag:\\\\\\\"#define NORMAL\\\\nuniform float opacity;\\\\n#if defined( FLAT_SHADED ) || defined( USE_BUMPMAP ) || defined( TANGENTSPACE_NORMALMAP )\\\\n\\\\tvarying vec3 vViewPosition;\\\\n#endif\\\\n#ifndef FLAT_SHADED\\\\n\\\\tvarying vec3 vNormal;\\\\n\\\\t#ifdef USE_TANGENT\\\\n\\\\t\\\\tvarying vec3 vTangent;\\\\n\\\\t\\\\tvarying vec3 vBitangent;\\\\n\\\\t#endif\\\\n#endif\\\\n#include <packing>\\\\n#include <uv_pars_fragment>\\\\n#include <bumpmap_pars_fragment>\\\\n#include <normalmap_pars_fragment>\\\\n#include <logdepthbuf_pars_fragment>\\\\n#include <clipping_planes_pars_fragment>\\\\nvoid main() {\\\\n\\\\t#include <clipping_planes_fragment>\\\\n\\\\t#include <logdepthbuf_fragment>\\\\n\\\\t#include <normal_fragment_begin>\\\\n\\\\t#include <normal_fragment_maps>\\\\n\\\\tgl_FragColor = vec4( packNormalToRGB( normal ), opacity );\\\\n}\\\\\\\",normal_vert:\\\\\\\"#define NORMAL\\\\n#if defined( FLAT_SHADED ) || defined( USE_BUMPMAP ) || defined( TANGENTSPACE_NORMALMAP )\\\\n\\\\tvarying vec3 vViewPosition;\\\\n#endif\\\\n#ifndef FLAT_SHADED\\\\n\\\\tvarying vec3 vNormal;\\\\n\\\\t#ifdef USE_TANGENT\\\\n\\\\t\\\\tvarying vec3 vTangent;\\\\n\\\\t\\\\tvarying vec3 vBitangent;\\\\n\\\\t#endif\\\\n#endif\\\\n#include <common>\\\\n#include <uv_pars_vertex>\\\\n#include <displacementmap_pars_vertex>\\\\n#include <morphtarget_pars_vertex>\\\\n#include <skinning_pars_vertex>\\\\n#include <logdepthbuf_pars_vertex>\\\\n#include <clipping_planes_pars_vertex>\\\\nvoid main() {\\\\n\\\\t#include <uv_vertex>\\\\n\\\\t#include <beginnormal_vertex>\\\\n\\\\t#include <morphnormal_vertex>\\\\n\\\\t#include <skinbase_vertex>\\\\n\\\\t#include <skinnormal_vertex>\\\\n\\\\t#include <defaultnormal_vertex>\\\\n#ifndef FLAT_SHADED\\\\n\\\\tvNormal = normalize( transformedNormal );\\\\n\\\\t#ifdef USE_TANGENT\\\\n\\\\t\\\\tvTangent = normalize( transformedTangent );\\\\n\\\\t\\\\tvBitangent = normalize( cross( vNormal, vTangent ) * tangent.w );\\\\n\\\\t#endif\\\\n#endif\\\\n\\\\t#include <begin_vertex>\\\\n\\\\t#include <morphtarget_vertex>\\\\n\\\\t#include <skinning_vertex>\\\\n\\\\t#include <displacementmap_vertex>\\\\n\\\\t#include <project_vertex>\\\\n\\\\t#include <logdepthbuf_vertex>\\\\n\\\\t#include <clipping_planes_vertex>\\\\n#if defined( FLAT_SHADED ) || defined( USE_BUMPMAP ) || defined( TANGENTSPACE_NORMALMAP )\\\\n\\\\tvViewPosition = - mvPosition.xyz;\\\\n#endif\\\\n}\\\\\\\",points_frag:\\\\\\\"uniform vec3 diffuse;\\\\nuniform float opacity;\\\\n#include <common>\\\\n#include <color_pars_fragment>\\\\n#include <map_particle_pars_fragment>\\\\n#include <fog_pars_fragment>\\\\n#include <logdepthbuf_pars_fragment>\\\\n#include <clipping_planes_pars_fragment>\\\\nvoid main() {\\\\n\\\\t#include <clipping_planes_fragment>\\\\n\\\\tvec3 outgoingLight = vec3( 0.0 );\\\\n\\\\tvec4 diffuseColor = vec4( diffuse, opacity );\\\\n\\\\t#include <logdepthbuf_fragment>\\\\n\\\\t#include <map_particle_fragment>\\\\n\\\\t#include <color_fragment>\\\\n\\\\t#include <alphatest_fragment>\\\\n\\\\toutgoingLight = diffuseColor.rgb;\\\\n\\\\tgl_FragColor = vec4( outgoingLight, diffuseColor.a );\\\\n\\\\t#include <tonemapping_fragment>\\\\n\\\\t#include <encodings_fragment>\\\\n\\\\t#include <fog_fragment>\\\\n\\\\t#include <premultiplied_alpha_fragment>\\\\n}\\\\\\\",points_vert:\\\\\\\"uniform float size;\\\\nuniform float scale;\\\\n#include <common>\\\\n#include <color_pars_vertex>\\\\n#include <fog_pars_vertex>\\\\n#include <morphtarget_pars_vertex>\\\\n#include <logdepthbuf_pars_vertex>\\\\n#include <clipping_planes_pars_vertex>\\\\nvoid main() {\\\\n\\\\t#include <color_vertex>\\\\n\\\\t#include <begin_vertex>\\\\n\\\\t#include <morphtarget_vertex>\\\\n\\\\t#include <project_vertex>\\\\n\\\\tgl_PointSize = size;\\\\n\\\\t#ifdef USE_SIZEATTENUATION\\\\n\\\\t\\\\tbool isPerspective = isPerspectiveMatrix( projectionMatrix );\\\\n\\\\t\\\\tif ( isPerspective ) gl_PointSize *= ( scale / - mvPosition.z );\\\\n\\\\t#endif\\\\n\\\\t#include <logdepthbuf_vertex>\\\\n\\\\t#include <clipping_planes_vertex>\\\\n\\\\t#include <worldpos_vertex>\\\\n\\\\t#include <fog_vertex>\\\\n}\\\\\\\",shadow_frag:\\\\\\\"uniform vec3 color;\\\\nuniform float opacity;\\\\n#include <common>\\\\n#include <packing>\\\\n#include <fog_pars_fragment>\\\\n#include <bsdfs>\\\\n#include <lights_pars_begin>\\\\n#include <shadowmap_pars_fragment>\\\\n#include <shadowmask_pars_fragment>\\\\nvoid main() {\\\\n\\\\tgl_FragColor = vec4( color, opacity * ( 1.0 - getShadowMask() ) );\\\\n\\\\t#include <tonemapping_fragment>\\\\n\\\\t#include <encodings_fragment>\\\\n\\\\t#include <fog_fragment>\\\\n}\\\\\\\",shadow_vert:\\\\\\\"#include <common>\\\\n#include <fog_pars_vertex>\\\\n#include <shadowmap_pars_vertex>\\\\nvoid main() {\\\\n\\\\t#include <begin_vertex>\\\\n\\\\t#include <project_vertex>\\\\n\\\\t#include <worldpos_vertex>\\\\n\\\\t#include <beginnormal_vertex>\\\\n\\\\t#include <morphnormal_vertex>\\\\n\\\\t#include <skinbase_vertex>\\\\n\\\\t#include <skinnormal_vertex>\\\\n\\\\t#include <defaultnormal_vertex>\\\\n\\\\t#include <shadowmap_vertex>\\\\n\\\\t#include <fog_vertex>\\\\n}\\\\\\\",sprite_frag:\\\\\\\"uniform vec3 diffuse;\\\\nuniform float opacity;\\\\n#include <common>\\\\n#include <uv_pars_fragment>\\\\n#include <map_pars_fragment>\\\\n#include <alphamap_pars_fragment>\\\\n#include <fog_pars_fragment>\\\\n#include <logdepthbuf_pars_fragment>\\\\n#include <clipping_planes_pars_fragment>\\\\nvoid main() {\\\\n\\\\t#include <clipping_planes_fragment>\\\\n\\\\tvec3 outgoingLight = vec3( 0.0 );\\\\n\\\\tvec4 diffuseColor = vec4( diffuse, opacity );\\\\n\\\\t#include <logdepthbuf_fragment>\\\\n\\\\t#include <map_fragment>\\\\n\\\\t#include <alphamap_fragment>\\\\n\\\\t#include <alphatest_fragment>\\\\n\\\\toutgoingLight = diffuseColor.rgb;\\\\n\\\\tgl_FragColor = vec4( outgoingLight, diffuseColor.a );\\\\n\\\\t#include <tonemapping_fragment>\\\\n\\\\t#include <encodings_fragment>\\\\n\\\\t#include <fog_fragment>\\\\n}\\\\\\\",sprite_vert:\\\\\\\"uniform float rotation;\\\\nuniform vec2 center;\\\\n#include <common>\\\\n#include <uv_pars_vertex>\\\\n#include <fog_pars_vertex>\\\\n#include <logdepthbuf_pars_vertex>\\\\n#include <clipping_planes_pars_vertex>\\\\nvoid main() {\\\\n\\\\t#include <uv_vertex>\\\\n\\\\tvec4 mvPosition = modelViewMatrix * vec4( 0.0, 0.0, 0.0, 1.0 );\\\\n\\\\tvec2 scale;\\\\n\\\\tscale.x = length( vec3( modelMatrix[ 0 ].x, modelMatrix[ 0 ].y, modelMatrix[ 0 ].z ) );\\\\n\\\\tscale.y = length( vec3( modelMatrix[ 1 ].x, modelMatrix[ 1 ].y, modelMatrix[ 1 ].z ) );\\\\n\\\\t#ifndef USE_SIZEATTENUATION\\\\n\\\\t\\\\tbool isPerspective = isPerspectiveMatrix( projectionMatrix );\\\\n\\\\t\\\\tif ( isPerspective ) scale *= - mvPosition.z;\\\\n\\\\t#endif\\\\n\\\\tvec2 alignedPosition = ( position.xy - ( center - vec2( 0.5 ) ) ) * scale;\\\\n\\\\tvec2 rotatedPosition;\\\\n\\\\trotatedPosition.x = cos( rotation ) * alignedPosition.x - sin( rotation ) * alignedPosition.y;\\\\n\\\\trotatedPosition.y = sin( rotation ) * alignedPosition.x + cos( rotation ) * alignedPosition.y;\\\\n\\\\tmvPosition.xy += rotatedPosition;\\\\n\\\\tgl_Position = projectionMatrix * mvPosition;\\\\n\\\\t#include <logdepthbuf_vertex>\\\\n\\\\t#include <clipping_planes_vertex>\\\\n\\\\t#include <fog_vertex>\\\\n}\\\\\\\"},ui={common:{diffuse:{value:new Qe(16777215)},opacity:{value:1},map:{value:null},uvTransform:{value:new yt},uv2Transform:{value:new yt},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 vt(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 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instanceof WebGL2RenderingContext||\\\\\\\"undefined\\\\\\\"!=typeof WebGL2ComputeRenderingContext&&t instanceof WebGL2ComputeRenderingContext;let a=void 0!==n.precision?n.precision:\\\\\\\"highp\\\\\\\";const o=r(a);o!==a&&(console.warn(\\\\\\\"THREE.WebGLRenderer:\\\\\\\",a,\\\\\\\"not supported, using\\\\\\\",o,\\\\\\\"instead.\\\\\\\"),a=o);const l=s||e.has(\\\\\\\"WEBGL_draw_buffers\\\\\\\"),c=!0===n.logarithmicDepthBuffer,h=t.getParameter(34930),u=t.getParameter(35660),d=t.getParameter(3379),p=t.getParameter(34076),m=t.getParameter(34921),f=t.getParameter(36347),g=t.getParameter(36348),v=t.getParameter(36349),y=u>0,x=s||e.has(\\\\\\\"OES_texture_float\\\\\\\");return{isWebGL2:s,drawBuffers:l,getMaxAnisotropy:function(){if(void 0!==i)return i;if(!0===e.has(\\\\\\\"EXT_texture_filter_anisotropic\\\\\\\")){const n=e.get(\\\\\\\"EXT_texture_filter_anisotropic\\\\\\\");i=t.getParameter(n.MAX_TEXTURE_MAX_ANISOTROPY_EXT)}else i=0;return i},getMaxPrecision:r,precision:a,logarithmicDepthBuffer:c,maxTextures:h,maxVertexTextures:u,maxTextureSize:d,maxCubemapSize:p,maxAttributes:m,maxVertexUniforms:f,maxVaryings:g,maxFragmentUniforms:v,vertexTextures:y,floatFragmentTextures:x,floatVertexTextures:y&&x,maxSamples:s?t.getParameter(36183):0}}function vi(t){const e=this;let n=null,i=0,r=!1,s=!1;const a=new ii,o=new yt,l={value:null,needsUpdate:!1};function c(){l.value!==n&&(l.value=n,l.needsUpdate=i>0),e.numPlanes=i,e.numIntersection=0}function h(t,n,i,r){const s=null!==t?t.length:0;let c=null;if(0!==s){if(c=l.value,!0!==r||null===c){const e=i+4*s,r=n.matrixWorldInverse;o.getNormalMatrix(r),(null===c||c.length<e)&&(c=new Float32Array(e));for(let e=0,n=i;e!==s;++e,n+=4)a.copy(t[e]).applyMatrix4(r,o),a.normal.toArray(c,n),c[n+3]=a.constant}l.value=c,l.needsUpdate=!0}return e.numPlanes=s,e.numIntersection=0,c}this.uniform=l,this.numPlanes=0,this.numIntersection=0,this.init=function(t,e,s){const a=0!==t.length||e||0!==i||r;return r=e,n=h(t,s,0),i=t.length,a},this.beginShadows=function(){s=!0,h(null)},this.endShadows=function(){s=!1,c()},this.setState=function(e,a,o){const u=e.clippingPlanes,d=e.clipIntersection,p=e.clipShadows,m=t.get(e);if(!r||null===u||0===u.length||s&&!p)s?h(null):c();else{const t=s?0:i,e=4*t;let r=m.clippingState||null;l.value=r,r=h(u,a,e,o);for(let t=0;t!==e;++t)r[t]=n[t];m.clippingState=r,this.numIntersection=d?this.numPlanes:0,this.numPlanes+=t}}}function yi(t){let e=new WeakMap;function n(t,e){return e===a?t.mapping=r:e===o&&(t.mapping=s),t}function i(t){const n=t.target;n.removeEventListener(\\\\\\\"dispose\\\\\\\",i);const r=e.get(n);void 0!==r&&(e.delete(n),r.dispose())}return{get:function(r){if(r&&r.isTexture&&!1===r.isRenderTargetTexture){const s=r.mapping;if(s===a||s===o){if(e.has(r)){return n(e.get(r).texture,r.mapping)}{const s=r.image;if(s&&s.height>0){const a=t.getRenderTarget(),o=new $n(s.height/2);return o.fromEquirectangularTexture(t,r),e.set(r,o),t.setRenderTarget(a),r.addEventListener(\\\\\\\"dispose\\\\\\\",i),n(o.texture,r.mapping)}return null}}}return r},dispose:function(){e=new WeakMap}}}di.physical={uniforms:jn([di.standard.uniforms,{clearcoat:{value:0},clearcoatMap:{value:null},clearcoatRoughness:{value:0},clearcoatRoughnessMap:{value:null},clearcoatNormalScale:{value:new vt(1,1)},clearcoatNormalMap:{value:null},sheen:{value:new Qe(0)},transmission:{value:0},transmissionMap:{value:null},transmissionSamplerSize:{value:new vt},transmissionSamplerMap:{value:null},thickness:{value:0},thicknessMap:{value:null},attenuationDistance:{value:0},attenuationTint:{value:new Qe(0)},specularIntensity:{value:0},specularIntensityMap:{value:null},specularTint:{value:new Qe(1,1,1)},specularTintMap:{value:null}}]),vertexShader:hi.meshphysical_vert,fragmentShader:hi.meshphysical_frag};class xi extends Yn{constructor(t=-1,e=1,n=1,i=-1,r=.1,s=2e3){super(),this.type=\\\\\\\"OrthographicCamera\\\\\\\",this.zoom=1,this.view=null,this.left=t,this.right=e,this.top=n,this.bottom=i,this.near=r,this.far=s,this.updateProjectionMatrix()}copy(t,e){return super.copy(t,e),this.left=t.left,this.right=t.right,this.top=t.top,this.bottom=t.bottom,this.near=t.near,this.far=t.far,this.zoom=t.zoom,this.view=null===t.view?null:Object.assign({},t.view),this}setViewOffset(t,e,n,i,r,s){null===this.view&&(this.view={enabled:!0,fullWidth:1,fullHeight:1,offsetX:0,offsetY:0,width:1,height:1}),this.view.enabled=!0,this.view.fullWidth=t,this.view.fullHeight=e,this.view.offsetX=n,this.view.offsetY=i,this.view.width=r,this.view.height=s,this.updateProjectionMatrix()}clearViewOffset(){null!==this.view&&(this.view.enabled=!1),this.updateProjectionMatrix()}updateProjectionMatrix(){const t=(this.right-this.left)/(2*this.zoom),e=(this.top-this.bottom)/(2*this.zoom),n=(this.right+this.left)/2,i=(this.top+this.bottom)/2;let r=n-t,s=n+t,a=i+e,o=i-e;if(null!==this.view&&this.view.enabled){const t=(this.right-this.left)/this.view.fullWidth/this.zoom,e=(this.top-this.bottom)/this.view.fullHeight/this.zoom;r+=t*this.view.offsetX,s=r+t*this.view.width,a-=e*this.view.offsetY,o=a-e*this.view.height}this.projectionMatrix.makeOrthographic(r,s,a,o,this.near,this.far),this.projectionMatrixInverse.copy(this.projectionMatrix).invert()}toJSON(t){const e=super.toJSON(t);return e.object.zoom=this.zoom,e.object.left=this.left,e.object.right=this.right,e.object.top=this.top,e.object.bottom=this.bottom,e.object.near=this.near,e.object.far=this.far,null!==this.view&&(e.object.view=Object.assign({},this.view)),e}}xi.prototype.isOrthographicCamera=!0;class _i extends Xn{constructor(t){super(t),this.type=\\\\\\\"RawShaderMaterial\\\\\\\"}}_i.prototype.isRawShaderMaterial=!0;const Mi=Math.pow(2,8),bi=[.125,.215,.35,.446,.526,.582],wi=5+bi.length,Si=20,Ti={[X]:0,[Y]:1,[Z]:2,[Q]:3,[K]:4,[$]:5,[J]:6},Ei=new Ke({side:1,depthWrite:!1,depthTest:!1}),Ai=new Gn(new Vn,Ei),Li=new xi,{_lodPlanes:Ri,_sizeLods:Ci,_sigmas:Pi}=Hi(),Ii=new Qe;let Di=null;const Ni=(1+Math.sqrt(5))/2,Bi=1/Ni,zi=[new Rt(1,1,1),new Rt(-1,1,1),new Rt(1,1,-1),new Rt(-1,1,-1),new Rt(0,Ni,Bi),new Rt(0,Ni,-Bi),new Rt(Bi,0,Ni),new Rt(-Bi,0,Ni),new Rt(Ni,Bi,0),new Rt(-Ni,Bi,0)];function Fi(t){const e=Math.max(t.r,t.g,t.b),n=Math.min(Math.max(Math.ceil(Math.log2(e)),-128),127);t.multiplyScalar(Math.pow(2,-n));return(n+128)/255}class Oi{constructor(t){this._renderer=t,this._pingPongRenderTarget=null,this._blurMaterial=function(t){const e=new Float32Array(t),n=new Rt(0,1,0);return new _i({name:\\\\\\\"SphericalGaussianBlur\\\\\\\",defines:{n:t},uniforms:{envMap:{value:null},samples:{value:1},weights:{value:e},latitudinal:{value:!1},dTheta:{value:0},mipInt:{value:0},poleAxis:{value:n},inputEncoding:{value:Ti[3e3]},outputEncoding:{value:Ti[3e3]}},vertexShader:ji(),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${qi()}\\\\n\\\\n\\\\t\\\\t\\\\t#define ENVMAP_TYPE_CUBE_UV\\\\n\\\\t\\\\t\\\\t#include <cube_uv_reflection_fragment>\\\\n\\\\n\\\\t\\\\t\\\\tvec3 getSample( float theta, vec3 axis ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tfloat cosTheta = cos( theta );\\\\n\\\\t\\\\t\\\\t\\\\t// Rodrigues' axis-angle rotation\\\\n\\\\t\\\\t\\\\t\\\\tvec3 sampleDirection = vOutputDirection * cosTheta\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t+ cross( axis, vOutputDirection ) * sin( theta )\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t+ axis * dot( axis, vOutputDirection ) * ( 1.0 - cosTheta );\\\\n\\\\n\\\\t\\\\t\\\\t\\\\treturn bilinearCubeUV( envMap, sampleDirection, mipInt );\\\\n\\\\n\\\\t\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tvec3 axis = latitudinal ? poleAxis : cross( poleAxis, vOutputDirection );\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tif ( all( equal( axis, vec3( 0.0 ) ) ) ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\t\\\\taxis = vec3( vOutputDirection.z, 0.0, - vOutputDirection.x );\\\\n\\\\n\\\\t\\\\t\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\t\\\\t\\\\taxis = normalize( axis );\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tgl_FragColor = vec4( 0.0, 0.0, 0.0, 1.0 );\\\\n\\\\t\\\\t\\\\t\\\\tgl_FragColor.rgb += weights[ 0 ] * getSample( 0.0, axis );\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tfor ( int i = 1; i < n; i++ ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tif ( i >= samples ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tbreak;\\\\n\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tfloat theta = dTheta * float( i );\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tgl_FragColor.rgb += weights[ i ] * getSample( -1.0 * theta, axis );\\\\n\\\\t\\\\t\\\\t\\\\t\\\\tgl_FragColor.rgb += weights[ i ] * getSample( theta, axis );\\\\n\\\\n\\\\t\\\\t\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tgl_FragColor = linearToOutputTexel( gl_FragColor );\\\\n\\\\n\\\\t\\\\t\\\\t}\\\\n\\\\t\\\\t`,blending:0,depthTest:!1,depthWrite:!1})}(Si),this._equirectShader=null,this._cubemapShader=null,this._compileMaterial(this._blurMaterial)}fromScene(t,e=0,n=.1,i=100){Di=this._renderer.getRenderTarget();const r=this._allocateTargets();return this._sceneToCubeUV(t,n,i,r),e>0&&this._blur(r,0,0,e),this._applyPMREM(r),this._cleanup(r),r}fromEquirectangular(t){return this._fromTexture(t)}fromCubemap(t){return this._fromTexture(t)}compileCubemapShader(){null===this._cubemapShader&&(this._cubemapShader=Wi(),this._compileMaterial(this._cubemapShader))}compileEquirectangularShader(){null===this._equirectShader&&(this._equirectShader=Vi(),this._compileMaterial(this._equirectShader))}dispose(){this._blurMaterial.dispose(),null!==this._cubemapShader&&this._cubemapShader.dispose(),null!==this._equirectShader&&this._equirectShader.dispose();for(let t=0;t<Ri.length;t++)Ri[t].dispose()}_cleanup(t){this._pingPongRenderTarget.dispose(),this._renderer.setRenderTarget(Di),t.scissorTest=!1,ki(t,0,0,t.width,t.height)}_fromTexture(t){Di=this._renderer.getRenderTarget();const e=this._allocateTargets(t);return this._textureToCubeUV(t,e),this._applyPMREM(e),this._cleanup(e),e}_allocateTargets(t){const e={magFilter:p,minFilter:p,generateMipmaps:!1,type:x,format:1023,encoding:Ui(t)?t.encoding:Z,depthBuffer:!1},n=Gi(e);return n.depthBuffer=!t,this._pingPongRenderTarget=Gi(e),n}_compileMaterial(t){const e=new Gn(Ri[0],t);this._renderer.compile(e,Li)}_sceneToCubeUV(t,e,n,i){const r=new Jn(90,1,e,n),s=[1,-1,1,1,1,1],a=[1,1,1,-1,-1,-1],o=this._renderer,l=o.autoClear,c=o.outputEncoding,h=o.toneMapping;o.getClearColor(Ii),o.toneMapping=0,o.outputEncoding=X,o.autoClear=!1;let u=!1;const d=t.background;if(d){if(d.isColor){Ei.color.copy(d).convertSRGBToLinear(),t.background=null;const e=Fi(Ei.color);Ei.opacity=e,u=!0}}else{Ei.color.copy(Ii).convertSRGBToLinear();const t=Fi(Ei.color);Ei.opacity=t,u=!0}for(let e=0;e<6;e++){const n=e%3;0==n?(r.up.set(0,s[e],0),r.lookAt(a[e],0,0)):1==n?(r.up.set(0,0,s[e]),r.lookAt(0,a[e],0)):(r.up.set(0,s[e],0),r.lookAt(0,0,a[e])),ki(i,n*Mi,e>2?Mi:0,Mi,Mi),o.setRenderTarget(i),u&&o.render(Ai,r),o.render(t,r)}o.toneMapping=h,o.outputEncoding=c,o.autoClear=l}_textureToCubeUV(t,e){const n=this._renderer;t.isCubeTexture?null==this._cubemapShader&&(this._cubemapShader=Wi()):null==this._equirectShader&&(this._equirectShader=Vi());const i=t.isCubeTexture?this._cubemapShader:this._equirectShader,r=new Gn(Ri[0],i),s=i.uniforms;s.envMap.value=t,t.isCubeTexture||s.texelSize.value.set(1/t.image.width,1/t.image.height),s.inputEncoding.value=Ti[t.encoding],s.outputEncoding.value=Ti[e.texture.encoding],ki(e,0,0,3*Mi,2*Mi),n.setRenderTarget(e),n.render(r,Li)}_applyPMREM(t){const e=this._renderer,n=e.autoClear;e.autoClear=!1;for(let e=1;e<wi;e++){const n=Math.sqrt(Pi[e]*Pi[e]-Pi[e-1]*Pi[e-1]),i=zi[(e-1)%zi.length];this._blur(t,e-1,e,n,i)}e.autoClear=n}_blur(t,e,n,i,r){const s=this._pingPongRenderTarget;this._halfBlur(t,s,e,n,i,\\\\\\\"latitudinal\\\\\\\",r),this._halfBlur(s,t,n,n,i,\\\\\\\"longitudinal\\\\\\\",r)}_halfBlur(t,e,n,i,r,s,a){const o=this._renderer,l=this._blurMaterial;\\\\\\\"latitudinal\\\\\\\"!==s&&\\\\\\\"longitudinal\\\\\\\"!==s&&console.error(\\\\\\\"blur direction must be either latitudinal or longitudinal!\\\\\\\");const c=new Gn(Ri[i],l),h=l.uniforms,u=Ci[n]-1,d=isFinite(r)?Math.PI/(2*u):2*Math.PI/39,p=r/d,m=isFinite(r)?1+Math.floor(3*p):Si;m>Si&&console.warn(`sigmaRadians, ${r}, is too large and will clip, as it requested ${m} samples when the maximum is set to 20`);const f=[];let g=0;for(let t=0;t<Si;++t){const e=t/p,n=Math.exp(-e*e/2);f.push(n),0==t?g+=n:t<m&&(g+=2*n)}for(let t=0;t<f.length;t++)f[t]=f[t]/g;h.envMap.value=t.texture,h.samples.value=m,h.weights.value=f,h.latitudinal.value=\\\\\\\"latitudinal\\\\\\\"===s,a&&(h.poleAxis.value=a),h.dTheta.value=d,h.mipInt.value=8-n,h.inputEncoding.value=Ti[t.texture.encoding],h.outputEncoding.value=Ti[t.texture.encoding];const v=Ci[i];ki(e,3*Math.max(0,Mi-2*v),(0===i?0:2*Mi)+2*v*(i>4?i-8+4:0),3*v,2*v),o.setRenderTarget(e),o.render(c,Li)}}function Ui(t){return void 0!==t&&t.type===x&&(t.encoding===X||t.encoding===Y||t.encoding===J)}function Hi(){const t=[],e=[],n=[];let i=8;for(let r=0;r<wi;r++){const s=Math.pow(2,i);e.push(s);let a=1/s;r>4?a=bi[r-8+4-1]:0==r&&(a=0),n.push(a);const o=1/(s-1),l=-o/2,c=1+o/2,h=[l,l,c,l,c,c,l,l,c,c,l,c],u=6,d=6,p=3,m=2,f=1,g=new Float32Array(p*d*u),v=new Float32Array(m*d*u),y=new Float32Array(f*d*u);for(let t=0;t<u;t++){const e=t%3*2/3-1,n=t>2?0:-1,i=[e,n,0,e+2/3,n,0,e+2/3,n+1,0,e,n,0,e+2/3,n+1,0,e,n+1,0];g.set(i,p*d*t),v.set(h,m*d*t);const r=[t,t,t,t,t,t];y.set(r,f*d*t)}const x=new wn;x.setAttribute(\\\\\\\"position\\\\\\\",new en(g,p)),x.setAttribute(\\\\\\\"uv\\\\\\\",new en(v,m)),x.setAttribute(\\\\\\\"faceIndex\\\\\\\",new en(y,f)),t.push(x),i>4&&i--}return{_lodPlanes:t,_sizeLods:e,_sigmas:n}}function Gi(t){const e=new Tt(3*Mi,3*Mi,t);return e.texture.mapping=l,e.texture.name=\\\\\\\"PMREM.cubeUv\\\\\\\",e.scissorTest=!0,e}function ki(t,e,n,i,r){t.viewport.set(e,n,i,r),t.scissor.set(e,n,i,r)}function Vi(){const t=new vt(1,1);return new _i({name:\\\\\\\"EquirectangularToCubeUV\\\\\\\",uniforms:{envMap:{value:null},texelSize:{value:t},inputEncoding:{value:Ti[3e3]},outputEncoding:{value:Ti[3e3]}},vertexShader:ji(),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${qi()}\\\\n\\\\n\\\\t\\\\t\\\\t#include <common>\\\\n\\\\n\\\\t\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tgl_FragColor = vec4( 0.0, 0.0, 0.0, 1.0 );\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tvec3 outputDirection = normalize( vOutputDirection );\\\\n\\\\t\\\\t\\\\t\\\\tvec2 uv = equirectUv( outputDirection );\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tvec2 f = fract( uv / texelSize - 0.5 );\\\\n\\\\t\\\\t\\\\t\\\\tuv -= f * texelSize;\\\\n\\\\t\\\\t\\\\t\\\\tvec3 tl = envMapTexelToLinear( texture2D ( envMap, uv ) ).rgb;\\\\n\\\\t\\\\t\\\\t\\\\tuv.x += texelSize.x;\\\\n\\\\t\\\\t\\\\t\\\\tvec3 tr = envMapTexelToLinear( texture2D ( envMap, uv ) ).rgb;\\\\n\\\\t\\\\t\\\\t\\\\tuv.y += texelSize.y;\\\\n\\\\t\\\\t\\\\t\\\\tvec3 br = envMapTexelToLinear( texture2D ( envMap, uv ) ).rgb;\\\\n\\\\t\\\\t\\\\t\\\\tuv.x -= texelSize.x;\\\\n\\\\t\\\\t\\\\t\\\\tvec3 bl = envMapTexelToLinear( texture2D ( envMap, uv ) ).rgb;\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tvec3 tm = mix( tl, tr, f.x );\\\\n\\\\t\\\\t\\\\t\\\\tvec3 bm = mix( bl, br, f.x );\\\\n\\\\t\\\\t\\\\t\\\\tgl_FragColor.rgb = mix( tm, bm, f.y );\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tgl_FragColor = linearToOutputTexel( gl_FragColor );\\\\n\\\\n\\\\t\\\\t\\\\t}\\\\n\\\\t\\\\t`,blending:0,depthTest:!1,depthWrite:!1})}function Wi(){return new _i({name:\\\\\\\"CubemapToCubeUV\\\\\\\",uniforms:{envMap:{value:null},inputEncoding:{value:Ti[3e3]},outputEncoding:{value:Ti[3e3]}},vertexShader:ji(),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${qi()}\\\\n\\\\n\\\\t\\\\t\\\\tvoid main() {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\tgl_FragColor = vec4( 0.0, 0.0, 0.0, 1.0 );\\\\n\\\\t\\\\t\\\\t\\\\tgl_FragColor.rgb = envMapTexelToLinear( textureCube( envMap, vec3( - vOutputDirection.x, vOutputDirection.yz ) ) ).rgb;\\\\n\\\\t\\\\t\\\\t\\\\tgl_FragColor = linearToOutputTexel( gl_FragColor );\\\\n\\\\n\\\\t\\\\t\\\\t}\\\\n\\\\t\\\\t`,blending:0,depthTest:!1,depthWrite:!1})}function ji(){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 qi(){return\\\\\\\"\\\\n\\\\n\\\\t\\\\tuniform int inputEncoding;\\\\n\\\\t\\\\tuniform int outputEncoding;\\\\n\\\\n\\\\t\\\\t#include <encodings_pars_fragment>\\\\n\\\\n\\\\t\\\\tvec4 inputTexelToLinear( vec4 value ) {\\\\n\\\\n\\\\t\\\\t\\\\tif ( inputEncoding == 0 ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\treturn value;\\\\n\\\\n\\\\t\\\\t\\\\t} else if ( inputEncoding == 1 ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\treturn sRGBToLinear( value );\\\\n\\\\n\\\\t\\\\t\\\\t} else if ( inputEncoding == 2 ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\treturn RGBEToLinear( value );\\\\n\\\\n\\\\t\\\\t\\\\t} else if ( inputEncoding == 3 ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\treturn RGBMToLinear( value, 7.0 );\\\\n\\\\n\\\\t\\\\t\\\\t} else if ( inputEncoding == 4 ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\treturn RGBMToLinear( value, 16.0 );\\\\n\\\\n\\\\t\\\\t\\\\t} else if ( inputEncoding == 5 ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\treturn RGBDToLinear( value, 256.0 );\\\\n\\\\n\\\\t\\\\t\\\\t} else {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\treturn GammaToLinear( value, 2.2 );\\\\n\\\\n\\\\t\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\tvec4 linearToOutputTexel( vec4 value ) {\\\\n\\\\n\\\\t\\\\t\\\\tif ( outputEncoding == 0 ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\treturn value;\\\\n\\\\n\\\\t\\\\t\\\\t} else if ( outputEncoding == 1 ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\treturn LinearTosRGB( value );\\\\n\\\\n\\\\t\\\\t\\\\t} else if ( outputEncoding == 2 ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\treturn LinearToRGBE( value );\\\\n\\\\n\\\\t\\\\t\\\\t} else if ( outputEncoding == 3 ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\treturn LinearToRGBM( value, 7.0 );\\\\n\\\\n\\\\t\\\\t\\\\t} else if ( outputEncoding == 4 ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\treturn LinearToRGBM( value, 16.0 );\\\\n\\\\n\\\\t\\\\t\\\\t} else if ( outputEncoding == 5 ) {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\treturn LinearToRGBD( value, 256.0 );\\\\n\\\\n\\\\t\\\\t\\\\t} else {\\\\n\\\\n\\\\t\\\\t\\\\t\\\\treturn LinearToGamma( value, 2.2 );\\\\n\\\\n\\\\t\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\t}\\\\n\\\\n\\\\t\\\\tvec4 envMapTexelToLinear( vec4 color ) {\\\\n\\\\n\\\\t\\\\t\\\\treturn inputTexelToLinear( color );\\\\n\\\\n\\\\t\\\\t}\\\\n\\\\t\\\\\\\"}function Xi(t){let e=new WeakMap,n=null;function i(t){const n=t.target;n.removeEventListener(\\\\\\\"dispose\\\\\\\",i);const r=e.get(n);void 0!==r&&(r.delete(n),r.dispose())}return{get:function(l){if(l&&l.isTexture&&!1===l.isRenderTargetTexture){const c=l.mapping,h=c===a||c===o,u=c===r||c===s;if(h||u){if(e.has(l))return e.get(l).texture;{const r=l.image;if(h&&r&&r.height>0||u&&r&&function(t){let e=0;const n=6;for(let i=0;i<n;i++)void 0!==t[i]&&e++;return e===n}(r)){const r=t.getRenderTarget();null===n&&(n=new Oi(t));const s=h?n.fromEquirectangular(l):n.fromCubemap(l);return e.set(l,s),t.setRenderTarget(r),l.addEventListener(\\\\\\\"dispose\\\\\\\",i),s.texture}return null}}}return l},dispose:function(){e=new WeakMap,null!==n&&(n.dispose(),n=null)}}}function Yi(t){const e={};function n(n){if(void 0!==e[n])return e[n];let i;switch(n){case\\\\\\\"WEBGL_depth_texture\\\\\\\":i=t.getExtension(\\\\\\\"WEBGL_depth_texture\\\\\\\")||t.getExtension(\\\\\\\"MOZ_WEBGL_depth_texture\\\\\\\")||t.getExtension(\\\\\\\"WEBKIT_WEBGL_depth_texture\\\\\\\");break;case\\\\\\\"EXT_texture_filter_anisotropic\\\\\\\":i=t.getExtension(\\\\\\\"EXT_texture_filter_anisotropic\\\\\\\")||t.getExtension(\\\\\\\"MOZ_EXT_texture_filter_anisotropic\\\\\\\")||t.getExtension(\\\\\\\"WEBKIT_EXT_texture_filter_anisotropic\\\\\\\");break;case\\\\\\\"WEBGL_compressed_texture_s3tc\\\\\\\":i=t.getExtension(\\\\\\\"WEBGL_compressed_texture_s3tc\\\\\\\")||t.getExtension(\\\\\\\"MOZ_WEBGL_compressed_texture_s3tc\\\\\\\")||t.getExtension(\\\\\\\"WEBKIT_WEBGL_compressed_texture_s3tc\\\\\\\");break;case\\\\\\\"WEBGL_compressed_texture_pvrtc\\\\\\\":i=t.getExtension(\\\\\\\"WEBGL_compressed_texture_pvrtc\\\\\\\")||t.getExtension(\\\\\\\"WEBKIT_WEBGL_compressed_texture_pvrtc\\\\\\\");break;default:i=t.getExtension(n)}return e[n]=i,i}return{has:function(t){return null!==n(t)},init:function(t){t.isWebGL2?n(\\\\\\\"EXT_color_buffer_float\\\\\\\"):(n(\\\\\\\"WEBGL_depth_texture\\\\\\\"),n(\\\\\\\"OES_texture_float\\\\\\\"),n(\\\\\\\"OES_texture_half_float\\\\\\\"),n(\\\\\\\"OES_texture_half_float_linear\\\\\\\"),n(\\\\\\\"OES_standard_derivatives\\\\\\\"),n(\\\\\\\"OES_element_index_uint\\\\\\\"),n(\\\\\\\"OES_vertex_array_object\\\\\\\"),n(\\\\\\\"ANGLE_instanced_arrays\\\\\\\")),n(\\\\\\\"OES_texture_float_linear\\\\\\\"),n(\\\\\\\"EXT_color_buffer_half_float\\\\\\\")},get:function(t){const e=n(t);return null===e&&console.warn(\\\\\\\"THREE.WebGLRenderer: \\\\\\\"+t+\\\\\\\" extension not supported.\\\\\\\"),e}}}function Ji(t,e,n,i){const r={},s=new WeakMap;function a(t){const o=t.target;null!==o.index&&e.remove(o.index);for(const t in o.attributes)e.remove(o.attributes[t]);o.removeEventListener(\\\\\\\"dispose\\\\\\\",a),delete r[o.id];const l=s.get(o);l&&(e.remove(l),s.delete(o)),i.releaseStatesOfGeometry(o),!0===o.isInstancedBufferGeometry&&delete o._maxInstanceCount,n.memory.geometries--}function o(t){const n=[],i=t.index,r=t.attributes.position;let a=0;if(null!==i){const t=i.array;a=i.version;for(let e=0,i=t.length;e<i;e+=3){const i=t[e+0],r=t[e+1],s=t[e+2];n.push(i,r,r,s,s,i)}}else{const t=r.array;a=r.version;for(let e=0,i=t.length/3-1;e<i;e+=3){const t=e+0,i=e+1,r=e+2;n.push(t,i,i,r,r,t)}}const o=new(pn(n)>65535?cn:on)(n,1);o.version=a;const l=s.get(t);l&&e.remove(l),s.set(t,o)}return{get:function(t,e){return!0===r[e.id]||(e.addEventListener(\\\\\\\"dispose\\\\\\\",a),r[e.id]=!0,n.memory.geometries++),e},update:function(t){const n=t.attributes;for(const t in n)e.update(n[t],34962);const i=t.morphAttributes;for(const t in i){const n=i[t];for(let t=0,i=n.length;t<i;t++)e.update(n[t],34962)}},getWireframeAttribute:function(t){const e=s.get(t);if(e){const n=t.index;null!==n&&e.version<n.version&&o(t)}else o(t);return s.get(t)}}}function Zi(t,e,n,i){const r=i.isWebGL2;let s,a,o;this.setMode=function(t){s=t},this.setIndex=function(t){a=t.type,o=t.bytesPerElement},this.render=function(e,i){t.drawElements(s,i,a,e*o),n.update(i,s,1)},this.renderInstances=function(i,l,c){if(0===c)return;let h,u;if(r)h=t,u=\\\\\\\"drawElementsInstanced\\\\\\\";else if(h=e.get(\\\\\\\"ANGLE_instanced_arrays\\\\\\\"),u=\\\\\\\"drawElementsInstancedANGLE\\\\\\\",null===h)return void console.error(\\\\\\\"THREE.WebGLIndexedBufferRenderer: using THREE.InstancedBufferGeometry but hardware does not support extension ANGLE_instanced_arrays.\\\\\\\");h[u](s,l,a,i*o,c),n.update(l,s,c)}}function Qi(t){const e={frame:0,calls:0,triangles:0,points:0,lines:0};return{memory:{geometries:0,textures:0},render:e,programs:null,autoReset:!0,reset:function(){e.frame++,e.calls=0,e.triangles=0,e.points=0,e.lines=0},update:function(t,n,i){switch(e.calls++,n){case 4:e.triangles+=i*(t/3);break;case 1:e.lines+=i*(t/2);break;case 3:e.lines+=i*(t-1);break;case 2:e.lines+=i*t;break;case 0:e.points+=i*t;break;default:console.error(\\\\\\\"THREE.WebGLInfo: Unknown draw mode:\\\\\\\",n)}}}}function Ki(t,e){return t[0]-e[0]}function $i(t,e){return Math.abs(e[1])-Math.abs(t[1])}function tr(t){const e={},n=new Float32Array(8),i=[];for(let t=0;t<8;t++)i[t]=[t,0];return{update:function(r,s,a,o){const l=r.morphTargetInfluences,c=void 0===l?0:l.length;let h=e[s.id];if(void 0===h||h.length!==c){h=[];for(let t=0;t<c;t++)h[t]=[t,0];e[s.id]=h}for(let t=0;t<c;t++){const e=h[t];e[0]=t,e[1]=l[t]}h.sort($i);for(let t=0;t<8;t++)t<c&&h[t][1]?(i[t][0]=h[t][0],i[t][1]=h[t][1]):(i[t][0]=Number.MAX_SAFE_INTEGER,i[t][1]=0);i.sort(Ki);const u=s.morphAttributes.position,d=s.morphAttributes.normal;let p=0;for(let t=0;t<8;t++){const e=i[t],r=e[0],a=e[1];r!==Number.MAX_SAFE_INTEGER&&a?(u&&s.getAttribute(\\\\\\\"morphTarget\\\\\\\"+t)!==u[r]&&s.setAttribute(\\\\\\\"morphTarget\\\\\\\"+t,u[r]),d&&s.getAttribute(\\\\\\\"morphNormal\\\\\\\"+t)!==d[r]&&s.setAttribute(\\\\\\\"morphNormal\\\\\\\"+t,d[r]),n[t]=a,p+=a):(u&&!0===s.hasAttribute(\\\\\\\"morphTarget\\\\\\\"+t)&&s.deleteAttribute(\\\\\\\"morphTarget\\\\\\\"+t),d&&!0===s.hasAttribute(\\\\\\\"morphNormal\\\\\\\"+t)&&s.deleteAttribute(\\\\\\\"morphNormal\\\\\\\"+t),n[t]=0)}const m=s.morphTargetsRelative?1:1-p;o.getUniforms().setValue(t,\\\\\\\"morphTargetBaseInfluence\\\\\\\",m),o.getUniforms().setValue(t,\\\\\\\"morphTargetInfluences\\\\\\\",n)}}}function er(t,e,n,i){let r=new WeakMap;function s(t){const e=t.target;e.removeEventListener(\\\\\\\"dispose\\\\\\\",s),n.remove(e.instanceMatrix),null!==e.instanceColor&&n.remove(e.instanceColor)}return{update:function(t){const a=i.render.frame,o=t.geometry,l=e.get(t,o);return r.get(l)!==a&&(e.update(l),r.set(l,a)),t.isInstancedMesh&&(!1===t.hasEventListener(\\\\\\\"dispose\\\\\\\",s)&&t.addEventListener(\\\\\\\"dispose\\\\\\\",s),n.update(t.instanceMatrix,34962),null!==t.instanceColor&&n.update(t.instanceColor,34962)),l},dispose:function(){r=new WeakMap}}}class nr extends bt{constructor(t=null,e=1,n=1,i=1){super(null),this.image={data:t,width:e,height:n,depth:i},this.magFilter=p,this.minFilter=p,this.wrapR=u,this.generateMipmaps=!1,this.flipY=!1,this.unpackAlignment=1,this.needsUpdate=!0}}nr.prototype.isDataTexture2DArray=!0;class ir extends bt{constructor(t=null,e=1,n=1,i=1){super(null),this.image={data:t,width:e,height:n,depth:i},this.magFilter=p,this.minFilter=p,this.wrapR=u,this.generateMipmaps=!1,this.flipY=!1,this.unpackAlignment=1,this.needsUpdate=!0}}ir.prototype.isDataTexture3D=!0;const rr=new bt,sr=new nr,ar=new ir,or=new Kn,lr=[],cr=[],hr=new Float32Array(16),ur=new Float32Array(9),dr=new Float32Array(4);function pr(t,e,n){const i=t[0];if(i<=0||i>0)return t;const r=e*n;let s=lr[r];if(void 0===s&&(s=new Float32Array(r),lr[r]=s),0!==e){i.toArray(s,0);for(let i=1,r=0;i!==e;++i)r+=n,t[i].toArray(s,r)}return s}function mr(t,e){if(t.length!==e.length)return!1;for(let n=0,i=t.length;n<i;n++)if(t[n]!==e[n])return!1;return!0}function fr(t,e){for(let n=0,i=e.length;n<i;n++)t[n]=e[n]}function gr(t,e){let n=cr[e];void 0===n&&(n=new Int32Array(e),cr[e]=n);for(let i=0;i!==e;++i)n[i]=t.allocateTextureUnit();return n}function vr(t,e){const n=this.cache;n[0]!==e&&(t.uniform1f(this.addr,e),n[0]=e)}function yr(t,e){const n=this.cache;if(void 0!==e.x)n[0]===e.x&&n[1]===e.y||(t.uniform2f(this.addr,e.x,e.y),n[0]=e.x,n[1]=e.y);else{if(mr(n,e))return;t.uniform2fv(this.addr,e),fr(n,e)}}function xr(t,e){const n=this.cache;if(void 0!==e.x)n[0]===e.x&&n[1]===e.y&&n[2]===e.z||(t.uniform3f(this.addr,e.x,e.y,e.z),n[0]=e.x,n[1]=e.y,n[2]=e.z);else if(void 0!==e.r)n[0]===e.r&&n[1]===e.g&&n[2]===e.b||(t.uniform3f(this.addr,e.r,e.g,e.b),n[0]=e.r,n[1]=e.g,n[2]=e.b);else{if(mr(n,e))return;t.uniform3fv(this.addr,e),fr(n,e)}}function _r(t,e){const n=this.cache;if(void 0!==e.x)n[0]===e.x&&n[1]===e.y&&n[2]===e.z&&n[3]===e.w||(t.uniform4f(this.addr,e.x,e.y,e.z,e.w),n[0]=e.x,n[1]=e.y,n[2]=e.z,n[3]=e.w);else{if(mr(n,e))return;t.uniform4fv(this.addr,e),fr(n,e)}}function Mr(t,e){const n=this.cache,i=e.elements;if(void 0===i){if(mr(n,e))return;t.uniformMatrix2fv(this.addr,!1,e),fr(n,e)}else{if(mr(n,i))return;dr.set(i),t.uniformMatrix2fv(this.addr,!1,dr),fr(n,i)}}function br(t,e){const n=this.cache,i=e.elements;if(void 0===i){if(mr(n,e))return;t.uniformMatrix3fv(this.addr,!1,e),fr(n,e)}else{if(mr(n,i))return;ur.set(i),t.uniformMatrix3fv(this.addr,!1,ur),fr(n,i)}}function wr(t,e){const n=this.cache,i=e.elements;if(void 0===i){if(mr(n,e))return;t.uniformMatrix4fv(this.addr,!1,e),fr(n,e)}else{if(mr(n,i))return;hr.set(i),t.uniformMatrix4fv(this.addr,!1,hr),fr(n,i)}}function Sr(t,e){const n=this.cache;n[0]!==e&&(t.uniform1i(this.addr,e),n[0]=e)}function Tr(t,e){const n=this.cache;mr(n,e)||(t.uniform2iv(this.addr,e),fr(n,e))}function Er(t,e){const n=this.cache;mr(n,e)||(t.uniform3iv(this.addr,e),fr(n,e))}function Ar(t,e){const n=this.cache;mr(n,e)||(t.uniform4iv(this.addr,e),fr(n,e))}function Lr(t,e){const n=this.cache;n[0]!==e&&(t.uniform1ui(this.addr,e),n[0]=e)}function Rr(t,e){const n=this.cache;mr(n,e)||(t.uniform2uiv(this.addr,e),fr(n,e))}function Cr(t,e){const n=this.cache;mr(n,e)||(t.uniform3uiv(this.addr,e),fr(n,e))}function Pr(t,e){const n=this.cache;mr(n,e)||(t.uniform4uiv(this.addr,e),fr(n,e))}function Ir(t,e,n){const i=this.cache,r=n.allocateTextureUnit();i[0]!==r&&(t.uniform1i(this.addr,r),i[0]=r),n.safeSetTexture2D(e||rr,r)}function Dr(t,e,n){const i=this.cache,r=n.allocateTextureUnit();i[0]!==r&&(t.uniform1i(this.addr,r),i[0]=r),n.setTexture3D(e||ar,r)}function Nr(t,e,n){const i=this.cache,r=n.allocateTextureUnit();i[0]!==r&&(t.uniform1i(this.addr,r),i[0]=r),n.safeSetTextureCube(e||or,r)}function Br(t,e,n){const i=this.cache,r=n.allocateTextureUnit();i[0]!==r&&(t.uniform1i(this.addr,r),i[0]=r),n.setTexture2DArray(e||sr,r)}function zr(t,e){t.uniform1fv(this.addr,e)}function Fr(t,e){const n=pr(e,this.size,2);t.uniform2fv(this.addr,n)}function Or(t,e){const n=pr(e,this.size,3);t.uniform3fv(this.addr,n)}function Ur(t,e){const n=pr(e,this.size,4);t.uniform4fv(this.addr,n)}function Hr(t,e){const n=pr(e,this.size,4);t.uniformMatrix2fv(this.addr,!1,n)}function Gr(t,e){const n=pr(e,this.size,9);t.uniformMatrix3fv(this.addr,!1,n)}function kr(t,e){const n=pr(e,this.size,16);t.uniformMatrix4fv(this.addr,!1,n)}function Vr(t,e){t.uniform1iv(this.addr,e)}function Wr(t,e){t.uniform2iv(this.addr,e)}function jr(t,e){t.uniform3iv(this.addr,e)}function qr(t,e){t.uniform4iv(this.addr,e)}function Xr(t,e){t.uniform1uiv(this.addr,e)}function Yr(t,e){t.uniform2uiv(this.addr,e)}function Jr(t,e){t.uniform3uiv(this.addr,e)}function Zr(t,e){t.uniform4uiv(this.addr,e)}function Qr(t,e,n){const i=e.length,r=gr(n,i);t.uniform1iv(this.addr,r);for(let t=0;t!==i;++t)n.safeSetTexture2D(e[t]||rr,r[t])}function Kr(t,e,n){const i=e.length,r=gr(n,i);t.uniform1iv(this.addr,r);for(let t=0;t!==i;++t)n.safeSetTextureCube(e[t]||or,r[t])}function $r(t,e,n){this.id=t,this.addr=n,this.cache=[],this.setValue=function(t){switch(t){case 5126:return vr;case 35664:return yr;case 35665:return xr;case 35666:return _r;case 35674:return Mr;case 35675:return br;case 35676:return wr;case 5124:case 35670:return Sr;case 35667:case 35671:return Tr;case 35668:case 35672:return Er;case 35669:case 35673:return Ar;case 5125:return Lr;case 36294:return Rr;case 36295:return Cr;case 36296:return Pr;case 35678:case 36198:case 36298:case 36306:case 35682:return Ir;case 35679:case 36299:case 36307:return Dr;case 35680:case 36300:case 36308:case 36293:return Nr;case 36289:case 36303:case 36311:case 36292:return Br}}(e.type)}function ts(t,e,n){this.id=t,this.addr=n,this.cache=[],this.size=e.size,this.setValue=function(t){switch(t){case 5126:return zr;case 35664:return Fr;case 35665:return Or;case 35666:return Ur;case 35674:return Hr;case 35675:return Gr;case 35676:return kr;case 5124:case 35670:return Vr;case 35667:case 35671:return Wr;case 35668:case 35672:return jr;case 35669:case 35673:return qr;case 5125:return Xr;case 36294:return Yr;case 36295:return Jr;case 36296:return Zr;case 35678:case 36198:case 36298:case 36306:case 35682:return Qr;case 35680:case 36300:case 36308:case 36293:return Kr}}(e.type)}function es(t){this.id=t,this.seq=[],this.map={}}ts.prototype.updateCache=function(t){const e=this.cache;t instanceof Float32Array&&e.length!==t.length&&(this.cache=new Float32Array(t.length)),fr(e,t)},es.prototype.setValue=function(t,e,n){const i=this.seq;for(let r=0,s=i.length;r!==s;++r){const s=i[r];s.setValue(t,e[s.id],n)}};const ns=/(\\\\w+)(\\\\])?(\\\\[|\\\\.)?/g;function is(t,e){t.seq.push(e),t.map[e.id]=e}function rs(t,e,n){const i=t.name,r=i.length;for(ns.lastIndex=0;;){const s=ns.exec(i),a=ns.lastIndex;let o=s[1];const l=\\\\\\\"]\\\\\\\"===s[2],c=s[3];if(l&&(o|=0),void 0===c||\\\\\\\"[\\\\\\\"===c&&a+2===r){is(n,void 0===c?new $r(o,t,e):new ts(o,t,e));break}{let t=n.map[o];void 0===t&&(t=new es(o),is(n,t)),n=t}}}function ss(t,e){this.seq=[],this.map={};const n=t.getProgramParameter(e,35718);for(let i=0;i<n;++i){const n=t.getActiveUniform(e,i);rs(n,t.getUniformLocation(e,n.name),this)}}function as(t,e,n){const i=t.createShader(e);return t.shaderSource(i,n),t.compileShader(i),i}ss.prototype.setValue=function(t,e,n,i){const r=this.map[e];void 0!==r&&r.setValue(t,n,i)},ss.prototype.setOptional=function(t,e,n){const i=e[n];void 0!==i&&this.setValue(t,n,i)},ss.upload=function(t,e,n,i){for(let r=0,s=e.length;r!==s;++r){const s=e[r],a=n[s.id];!1!==a.needsUpdate&&s.setValue(t,a.value,i)}},ss.seqWithValue=function(t,e){const n=[];for(let i=0,r=t.length;i!==r;++i){const r=t[i];r.id in e&&n.push(r)}return n};let os=0;function ls(t){switch(t){case X:return[\\\\\\\"Linear\\\\\\\",\\\\\\\"( value )\\\\\\\"];case Y:return[\\\\\\\"sRGB\\\\\\\",\\\\\\\"( value )\\\\\\\"];case Z:return[\\\\\\\"RGBE\\\\\\\",\\\\\\\"( value )\\\\\\\"];case Q:return[\\\\\\\"RGBM\\\\\\\",\\\\\\\"( value, 7.0 )\\\\\\\"];case K:return[\\\\\\\"RGBM\\\\\\\",\\\\\\\"( value, 16.0 )\\\\\\\"];case $:return[\\\\\\\"RGBD\\\\\\\",\\\\\\\"( value, 256.0 )\\\\\\\"];case J:return[\\\\\\\"Gamma\\\\\\\",\\\\\\\"( value, float( GAMMA_FACTOR ) )\\\\\\\"];case 3003:return[\\\\\\\"LogLuv\\\\\\\",\\\\\\\"( value )\\\\\\\"];default:return console.warn(\\\\\\\"THREE.WebGLProgram: Unsupported encoding:\\\\\\\",t),[\\\\\\\"Linear\\\\\\\",\\\\\\\"( value )\\\\\\\"]}}function cs(t,e,n){const i=t.getShaderParameter(e,35713),r=t.getShaderInfoLog(e).trim();if(i&&\\\\\\\"\\\\\\\"===r)return\\\\\\\"\\\\\\\";return\\\\\\\"THREE.WebGLShader: gl.getShaderInfoLog() \\\\\\\"+n+\\\\\\\"\\\\n\\\\\\\"+r+function(t){const e=t.split(\\\\\\\"\\\\n\\\\\\\");for(let t=0;t<e.length;t++)e[t]=t+1+\\\\\\\": \\\\\\\"+e[t];return e.join(\\\\\\\"\\\\n\\\\\\\")}(t.getShaderSource(e))}function hs(t,e){const n=ls(e);return\\\\\\\"vec4 \\\\\\\"+t+\\\\\\\"( vec4 value ) { return \\\\\\\"+n[0]+\\\\\\\"ToLinear\\\\\\\"+n[1]+\\\\\\\"; }\\\\\\\"}function us(t,e){const n=ls(e);return\\\\\\\"vec4 \\\\\\\"+t+\\\\\\\"( vec4 value ) { return LinearTo\\\\\\\"+n[0]+n[1]+\\\\\\\"; }\\\\\\\"}function ds(t,e){let n;switch(e){case 1:n=\\\\\\\"Linear\\\\\\\";break;case 2:n=\\\\\\\"Reinhard\\\\\\\";break;case 3:n=\\\\\\\"OptimizedCineon\\\\\\\";break;case 4:n=\\\\\\\"ACESFilmic\\\\\\\";break;case 5:n=\\\\\\\"Custom\\\\\\\";break;default:console.warn(\\\\\\\"THREE.WebGLProgram: 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<\\\\\\\"+e+\\\\\\\">\\\\\\\");return vs(n)}const xs=/#pragma unroll_loop[\\\\s]+?for \\\\( int i \\\\= (\\\\d+)\\\\; i < (\\\\d+)\\\\; i \\\\+\\\\+ \\\\) \\\\{([\\\\s\\\\S]+?)(?=\\\\})\\\\}/g,_s=/#pragma unroll_loop_start\\\\s+for\\\\s*\\\\(\\\\s*int\\\\s+i\\\\s*=\\\\s*(\\\\d+)\\\\s*;\\\\s*i\\\\s*<\\\\s*(\\\\d+)\\\\s*;\\\\s*i\\\\s*\\\\+\\\\+\\\\s*\\\\)\\\\s*{([\\\\s\\\\S]+?)}\\\\s+#pragma unroll_loop_end/g;function Ms(t){return t.replace(_s,ws).replace(xs,bs)}function bs(t,e,n,i){return console.warn(\\\\\\\"WebGLProgram: #pragma unroll_loop shader syntax is deprecated. 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1===t.shadowMapType?e=\\\\\\\"SHADOWMAP_TYPE_PCF\\\\\\\":2===t.shadowMapType?e=\\\\\\\"SHADOWMAP_TYPE_PCF_SOFT\\\\\\\":3===t.shadowMapType&&(e=\\\\\\\"SHADOWMAP_TYPE_VSM\\\\\\\"),e}(n),p=function(t){let e=\\\\\\\"ENVMAP_TYPE_CUBE\\\\\\\";if(t.envMap)switch(t.envMapMode){case r:case s:e=\\\\\\\"ENVMAP_TYPE_CUBE\\\\\\\";break;case l:case c:e=\\\\\\\"ENVMAP_TYPE_CUBE_UV\\\\\\\"}return e}(n),m=function(t){let e=\\\\\\\"ENVMAP_MODE_REFLECTION\\\\\\\";if(t.envMap)switch(t.envMapMode){case s:case c:e=\\\\\\\"ENVMAP_MODE_REFRACTION\\\\\\\"}return e}(n),f=function(t){let e=\\\\\\\"ENVMAP_BLENDING_NONE\\\\\\\";if(t.envMap)switch(t.combine){case 0:e=\\\\\\\"ENVMAP_BLENDING_MULTIPLY\\\\\\\";break;case 1:e=\\\\\\\"ENVMAP_BLENDING_MIX\\\\\\\";break;case 2:e=\\\\\\\"ENVMAP_BLENDING_ADD\\\\\\\"}return e}(n),g=t.gammaFactor>0?t.gammaFactor:1,v=n.isWebGL2?\\\\\\\"\\\\\\\":function(t){return[t.extensionDerivatives||t.envMapCubeUV||t.bumpMap||t.tangentSpaceNormalMap||t.clearcoatNormalMap||t.flatShading||\\\\\\\"physical\\\\\\\"===t.shaderID?\\\\\\\"#extension GL_OES_standard_derivatives : enable\\\\\\\":\\\\\\\"\\\\\\\",(t.extensionFragDepth||t.logarithmicDepthBuffer)&&t.rendererExtensionFragDepth?\\\\\\\"#extension GL_EXT_frag_depth : enable\\\\\\\":\\\\\\\"\\\\\\\",t.extensionDrawBuffers&&t.rendererExtensionDrawBuffers?\\\\\\\"#extension GL_EXT_draw_buffers : require\\\\\\\":\\\\\\\"\\\\\\\",(t.extensionShaderTextureLOD||t.envMap||t.transmission>0)&&t.rendererExtensionShaderTextureLod?\\\\\\\"#extension GL_EXT_shader_texture_lod : enable\\\\\\\":\\\\\\\"\\\\\\\"].filter(ps).join(\\\\\\\"\\\\n\\\\\\\")}(n),y=function(t){const e=[];for(const n in t){const i=t[n];!1!==i&&e.push(\\\\\\\"#define \\\\\\\"+n+\\\\\\\" \\\\\\\"+i)}return e.join(\\\\\\\"\\\\n\\\\\\\")}(o),x=a.createProgram();let _,M,b=n.glslVersion?\\\\\\\"#version \\\\\\\"+n.glslVersion+\\\\\\\"\\\\n\\\\\\\":\\\\\\\"\\\\\\\";n.isRawShaderMaterial?(_=[y].filter(ps).join(\\\\\\\"\\\\n\\\\\\\"),_.length>0&&(_+=\\\\\\\"\\\\n\\\\\\\"),M=[v,y].filter(ps).join(\\\\\\\"\\\\n\\\\\\\"),M.length>0&&(M+=\\\\\\\"\\\\n\\\\\\\")):(_=[Ss(n),\\\\\\\"#define SHADER_NAME \\\\\\\"+n.shaderName,y,n.instancing?\\\\\\\"#define USE_INSTANCING\\\\\\\":\\\\\\\"\\\\\\\",n.instancingColor?\\\\\\\"#define USE_INSTANCING_COLOR\\\\\\\":\\\\\\\"\\\\\\\",n.supportsVertexTextures?\\\\\\\"#define VERTEX_TEXTURES\\\\\\\":\\\\\\\"\\\\\\\",\\\\\\\"#define GAMMA_FACTOR \\\\\\\"+g,\\\\\\\"#define MAX_BONES \\\\\\\"+n.maxBones,n.useFog&&n.fog?\\\\\\\"#define USE_FOG\\\\\\\":\\\\\\\"\\\\\\\",n.useFog&&n.fogExp2?\\\\\\\"#define FOG_EXP2\\\\\\\":\\\\\\\"\\\\\\\",n.map?\\\\\\\"#define USE_MAP\\\\\\\":\\\\\\\"\\\\\\\",n.envMap?\\\\\\\"#define 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\\\\\\\"+n.shaderName,y,n.alphaTest?\\\\\\\"#define ALPHATEST \\\\\\\"+n.alphaTest+(n.alphaTest%1?\\\\\\\"\\\\\\\":\\\\\\\".0\\\\\\\"):\\\\\\\"\\\\\\\",\\\\\\\"#define GAMMA_FACTOR \\\\\\\"+g,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 \\\\\\\"+p:\\\\\\\"\\\\\\\",n.envMap?\\\\\\\"#define \\\\\\\"+m:\\\\\\\"\\\\\\\",n.envMap?\\\\\\\"#define \\\\\\\"+f:\\\\\\\"\\\\\\\",n.lightMap?\\\\\\\"#define USE_LIGHTMAP\\\\\\\":\\\\\\\"\\\\\\\",n.aoMap?\\\\\\\"#define USE_AOMAP\\\\\\\":\\\\\\\"\\\\\\\",n.emissiveMap?\\\\\\\"#define USE_EMISSIVEMAP\\\\\\\":\\\\\\\"\\\\\\\",n.bumpMap?\\\\\\\"#define USE_BUMPMAP\\\\\\\":\\\\\\\"\\\\\\\",n.normalMap?\\\\\\\"#define 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t.groupOrder!==e.groupOrder?t.groupOrder-e.groupOrder:t.renderOrder!==e.renderOrder?t.renderOrder-e.renderOrder:t.z!==e.z?e.z-t.z:t.id-e.id}function Cs(t){const e=[];let n=0;const i=[],r=[],s=[],a={id:-1};function o(i,r,s,o,l,c){let h=e[n];const u=t.get(s);return void 0===h?(h={id:i.id,object:i,geometry:r,material:s,program:u.program||a,groupOrder:o,renderOrder:i.renderOrder,z:l,group:c},e[n]=h):(h.id=i.id,h.object=i,h.geometry=r,h.material=s,h.program=u.program||a,h.groupOrder=o,h.renderOrder=i.renderOrder,h.z=l,h.group=c),n++,h}return{opaque:i,transmissive:r,transparent:s,init:function(){n=0,i.length=0,r.length=0,s.length=0},push:function(t,e,n,a,l,c){const h=o(t,e,n,a,l,c);n.transmission>0?r.push(h):!0===n.transparent?s.push(h):i.push(h)},unshift:function(t,e,n,a,l,c){const h=o(t,e,n,a,l,c);n.transmission>0?r.unshift(h):!0===n.transparent?s.unshift(h):i.unshift(h)},finish:function(){for(let t=n,i=e.length;t<i;t++){const n=e[t];if(null===n.id)break;n.id=null,n.object=null,n.geometry=null,n.material=null,n.program=null,n.group=null}},sort:function(t,e){i.length>1&&i.sort(t||Ls),r.length>1&&r.sort(e||Rs),s.length>1&&s.sort(e||Rs)}}}function Ps(t){let e=new WeakMap;return{get:function(n,i){let r;return!1===e.has(n)?(r=new Cs(t),e.set(n,[r])):i>=e.get(n).length?(r=new Cs(t),e.get(n).push(r)):r=e.get(n)[i],r},dispose:function(){e=new WeakMap}}}function Is(){const t={};return{get:function(e){if(void 0!==t[e.id])return t[e.id];let n;switch(e.type){case\\\\\\\"DirectionalLight\\\\\\\":n={direction:new Rt,color:new Qe};break;case\\\\\\\"SpotLight\\\\\\\":n={position:new Rt,direction:new Rt,color:new Qe,distance:0,coneCos:0,penumbraCos:0,decay:0};break;case\\\\\\\"PointLight\\\\\\\":n={position:new Rt,color:new Qe,distance:0,decay:0};break;case\\\\\\\"HemisphereLight\\\\\\\":n={direction:new Rt,skyColor:new Qe,groundColor:new Qe};break;case\\\\\\\"RectAreaLight\\\\\\\":n={color:new Qe,position:new Rt,halfWidth:new Rt,halfHeight:new Rt}}return t[e.id]=n,n}}}let Ds=0;function Ns(t,e){return(e.castShadow?1:0)-(t.castShadow?1:0)}function Bs(t,e){const n=new Is,i=function(){const t={};return{get:function(e){if(void 0!==t[e.id])return t[e.id];let n;switch(e.type){case\\\\\\\"DirectionalLight\\\\\\\":case\\\\\\\"SpotLight\\\\\\\":n={shadowBias:0,shadowNormalBias:0,shadowRadius:1,shadowMapSize:new vt};break;case\\\\\\\"PointLight\\\\\\\":n={shadowBias:0,shadowNormalBias:0,shadowRadius:1,shadowMapSize:new vt,shadowCameraNear:1,shadowCameraFar:1e3}}return t[e.id]=n,n}}}(),r={version:0,hash:{directionalLength:-1,pointLength:-1,spotLength:-1,rectAreaLength:-1,hemiLength:-1,numDirectionalShadows:-1,numPointShadows:-1,numSpotShadows:-1},ambient:[0,0,0],probe:[],directional:[],directionalShadow:[],directionalShadowMap:[],directionalShadowMatrix:[],spot:[],spotShadow:[],spotShadowMap:[],spotShadowMatrix:[],rectArea:[],rectAreaLTC1:null,rectAreaLTC2:null,point:[],pointShadow:[],pointShadowMap:[],pointShadowMatrix:[],hemi:[]};for(let t=0;t<9;t++)r.probe.push(new Rt);const s=new Rt,a=new ae,o=new ae;return{setup:function(s){let a=0,o=0,l=0;for(let t=0;t<9;t++)r.probe[t].set(0,0,0);let c=0,h=0,u=0,d=0,p=0,m=0,f=0,g=0;s.sort(Ns);for(let t=0,e=s.length;t<e;t++){const e=s[t],v=e.color,y=e.intensity,x=e.distance,_=e.shadow&&e.shadow.map?e.shadow.map.texture:null;if(e.isAmbientLight)a+=v.r*y,o+=v.g*y,l+=v.b*y;else if(e.isLightProbe)for(let t=0;t<9;t++)r.probe[t].addScaledVector(e.sh.coefficients[t],y);else if(e.isDirectionalLight){const t=n.get(e);if(t.color.copy(e.color).multiplyScalar(e.intensity),e.castShadow){const t=e.shadow,n=i.get(e);n.shadowBias=t.bias,n.shadowNormalBias=t.normalBias,n.shadowRadius=t.radius,n.shadowMapSize=t.mapSize,r.directionalShadow[c]=n,r.directionalShadowMap[c]=_,r.directionalShadowMatrix[c]=e.shadow.matrix,m++}r.directional[c]=t,c++}else if(e.isSpotLight){const t=n.get(e);if(t.position.setFromMatrixPosition(e.matrixWorld),t.color.copy(v).multiplyScalar(y),t.distance=x,t.coneCos=Math.cos(e.angle),t.penumbraCos=Math.cos(e.angle*(1-e.penumbra)),t.decay=e.decay,e.castShadow){const t=e.shadow,n=i.get(e);n.shadowBias=t.bias,n.shadowNormalBias=t.normalBias,n.shadowRadius=t.radius,n.shadowMapSize=t.mapSize,r.spotShadow[u]=n,r.spotShadowMap[u]=_,r.spotShadowMatrix[u]=e.shadow.matrix,g++}r.spot[u]=t,u++}else if(e.isRectAreaLight){const t=n.get(e);t.color.copy(v).multiplyScalar(y),t.halfWidth.set(.5*e.width,0,0),t.halfHeight.set(0,.5*e.height,0),r.rectArea[d]=t,d++}else if(e.isPointLight){const t=n.get(e);if(t.color.copy(e.color).multiplyScalar(e.intensity),t.distance=e.distance,t.decay=e.decay,e.castShadow){const t=e.shadow,n=i.get(e);n.shadowBias=t.bias,n.shadowNormalBias=t.normalBias,n.shadowRadius=t.radius,n.shadowMapSize=t.mapSize,n.shadowCameraNear=t.camera.near,n.shadowCameraFar=t.camera.far,r.pointShadow[h]=n,r.pointShadowMap[h]=_,r.pointShadowMatrix[h]=e.shadow.matrix,f++}r.point[h]=t,h++}else if(e.isHemisphereLight){const t=n.get(e);t.skyColor.copy(e.color).multiplyScalar(y),t.groundColor.copy(e.groundColor).multiplyScalar(y),r.hemi[p]=t,p++}}d>0&&(e.isWebGL2||!0===t.has(\\\\\\\"OES_texture_float_linear\\\\\\\")?(r.rectAreaLTC1=ui.LTC_FLOAT_1,r.rectAreaLTC2=ui.LTC_FLOAT_2):!0===t.has(\\\\\\\"OES_texture_half_float_linear\\\\\\\")?(r.rectAreaLTC1=ui.LTC_HALF_1,r.rectAreaLTC2=ui.LTC_HALF_2):console.error(\\\\\\\"THREE.WebGLRenderer: Unable to use RectAreaLight. 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t=r.directional[n];t.direction.setFromMatrixPosition(d.matrixWorld),s.setFromMatrixPosition(d.target.matrixWorld),t.direction.sub(s),t.direction.transformDirection(u),n++}else if(d.isSpotLight){const t=r.spot[l];t.position.setFromMatrixPosition(d.matrixWorld),t.position.applyMatrix4(u),t.direction.setFromMatrixPosition(d.matrixWorld),s.setFromMatrixPosition(d.target.matrixWorld),t.direction.sub(s),t.direction.transformDirection(u),l++}else if(d.isRectAreaLight){const t=r.rectArea[c];t.position.setFromMatrixPosition(d.matrixWorld),t.position.applyMatrix4(u),o.identity(),a.copy(d.matrixWorld),a.premultiply(u),o.extractRotation(a),t.halfWidth.set(.5*d.width,0,0),t.halfHeight.set(0,.5*d.height,0),t.halfWidth.applyMatrix4(o),t.halfHeight.applyMatrix4(o),c++}else if(d.isPointLight){const t=r.point[i];t.position.setFromMatrixPosition(d.matrixWorld),t.position.applyMatrix4(u),i++}else if(d.isHemisphereLight){const t=r.hemi[h];t.direction.setFromMatrixPosition(d.matrixWorld),t.direction.transformDirection(u),t.direction.normalize(),h++}}},state:r}}function zs(t,e){const n=new Bs(t,e),i=[],r=[];return{init:function(){i.length=0,r.length=0},state:{lightsArray:i,shadowsArray:r,lights:n},setupLights:function(){n.setup(i)},setupLightsView:function(t){n.setupView(i,t)},pushLight:function(t){i.push(t)},pushShadow:function(t){r.push(t)}}}function Fs(t,e){let n=new WeakMap;return{get:function(i,r=0){let s;return!1===n.has(i)?(s=new zs(t,e),n.set(i,[s])):r>=n.get(i).length?(s=new zs(t,e),n.get(i).push(s)):s=n.get(i)[r],s},dispose:function(){n=new WeakMap}}}class Os extends We{constructor(t){super(),this.type=\\\\\\\"MeshDepthMaterial\\\\\\\",this.depthPacking=3200,this.map=null,this.alphaMap=null,this.displacementMap=null,this.displacementScale=1,this.displacementBias=0,this.wireframe=!1,this.wireframeLinewidth=1,this.fog=!1,this.setValues(t)}copy(t){return super.copy(t),this.depthPacking=t.depthPacking,this.map=t.map,this.alphaMap=t.alphaMap,this.displacementMap=t.displacementMap,this.displacementScale=t.displacementScale,this.displacementBias=t.displacementBias,this.wireframe=t.wireframe,this.wireframeLinewidth=t.wireframeLinewidth,this}}Os.prototype.isMeshDepthMaterial=!0;class Us extends We{constructor(t){super(),this.type=\\\\\\\"MeshDistanceMaterial\\\\\\\",this.referencePosition=new Rt,this.nearDistance=1,this.farDistance=1e3,this.map=null,this.alphaMap=null,this.displacementMap=null,this.displacementScale=1,this.displacementBias=0,this.fog=!1,this.setValues(t)}copy(t){return super.copy(t),this.referencePosition.copy(t.referencePosition),this.nearDistance=t.nearDistance,this.farDistance=t.farDistance,this.map=t.map,this.alphaMap=t.alphaMap,this.displacementMap=t.displacementMap,this.displacementScale=t.displacementScale,this.displacementBias=t.displacementBias,this}}Us.prototype.isMeshDistanceMaterial=!0;function Hs(t,e,n){let i=new ai;const r=new vt,s=new vt,a=new St,o=new Os({depthPacking:3201}),l=new Us,c={},h=n.maxTextureSize,u={0:1,1:0,2:2},d=new Xn({defines:{SAMPLE_RATE:2/8,HALF_SAMPLE_RATE:1/8},uniforms:{shadow_pass:{value:null},resolution:{value:new vt},radius:{value:4}},vertexShader:\\\\\\\"void main() {\\\\n\\\\tgl_Position = vec4( position, 1.0 );\\\\n}\\\\\\\",fragmentShader:\\\\\\\"uniform sampler2D shadow_pass;\\\\nuniform vec2 resolution;\\\\nuniform float radius;\\\\n#include <packing>\\\\nvoid main() {\\\\n\\\\tfloat mean = 0.0;\\\\n\\\\tfloat squared_mean = 0.0;\\\\n\\\\tfloat depth = unpackRGBAToDepth( texture2D( shadow_pass, ( gl_FragCoord.xy ) / resolution ) );\\\\n\\\\tfor ( float i = -1.0; i < 1.0 ; i += SAMPLE_RATE) {\\\\n\\\\t\\\\t#ifdef HORIZONTAL_PASS\\\\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\\\\t\\\\t#else\\\\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\\\\t\\\\t#endif\\\\n\\\\t}\\\\n\\\\tmean = mean * HALF_SAMPLE_RATE;\\\\n\\\\tsquared_mean = squared_mean * HALF_SAMPLE_RATE;\\\\n\\\\tfloat std_dev = sqrt( squared_mean - mean * mean );\\\\n\\\\tgl_FragColor = pack2HalfToRGBA( vec2( mean, std_dev ) );\\\\n}\\\\\\\"}),m=d.clone();m.defines.HORIZONTAL_PASS=1;const f=new wn;f.setAttribute(\\\\\\\"position\\\\\\\",new en(new Float32Array([-1,-1,.5,3,-1,.5,-1,3,.5]),3));const v=new Gn(f,d),y=this;function x(n,i){const r=e.update(v);d.uniforms.shadow_pass.value=n.map.texture,d.uniforms.resolution.value=n.mapSize,d.uniforms.radius.value=n.radius,t.setRenderTarget(n.mapPass),t.clear(),t.renderBufferDirect(i,null,r,d,v,null),m.uniforms.shadow_pass.value=n.mapPass.texture,m.uniforms.resolution.value=n.mapSize,m.uniforms.radius.value=n.radius,t.setRenderTarget(n.map),t.clear(),t.renderBufferDirect(i,null,r,m,v,null)}function _(e,n,i,r,s,a,h){let d=null;const p=!0===r.isPointLight?e.customDistanceMaterial:e.customDepthMaterial;if(d=void 0!==p?p:!0===r.isPointLight?l:o,t.localClippingEnabled&&!0===i.clipShadows&&0!==i.clippingPlanes.length){const t=d.uuid,e=i.uuid;let n=c[t];void 0===n&&(n={},c[t]=n);let r=n[e];void 0===r&&(r=d.clone(),n[e]=r),d=r}return d.visible=i.visible,d.wireframe=i.wireframe,d.side=3===h?null!==i.shadowSide?i.shadowSide:i.side:null!==i.shadowSide?i.shadowSide:u[i.side],d.clipShadows=i.clipShadows,d.clippingPlanes=i.clippingPlanes,d.clipIntersection=i.clipIntersection,d.wireframeLinewidth=i.wireframeLinewidth,d.linewidth=i.linewidth,!0===r.isPointLight&&!0===d.isMeshDistanceMaterial&&(d.referencePosition.setFromMatrixPosition(r.matrixWorld),d.nearDistance=s,d.farDistance=a),d}function M(n,r,s,a,o){if(!1===n.visible)return;if(n.layers.test(r.layers)&&(n.isMesh||n.isLine||n.isPoints)&&(n.castShadow||n.receiveShadow&&3===o)&&(!n.frustumCulled||i.intersectsObject(n))){n.modelViewMatrix.multiplyMatrices(s.matrixWorldInverse,n.matrixWorld);const i=e.update(n),r=n.material;if(Array.isArray(r)){const e=i.groups;for(let l=0,c=e.length;l<c;l++){const c=e[l],h=r[c.materialIndex];if(h&&h.visible){const e=_(n,0,h,a,s.near,s.far,o);t.renderBufferDirect(s,null,i,e,n,c)}}}else if(r.visible){const e=_(n,0,r,a,s.near,s.far,o);t.renderBufferDirect(s,null,i,e,n,null)}}const l=n.children;for(let t=0,e=l.length;t<e;t++)M(l[t],r,s,a,o)}this.enabled=!1,this.autoUpdate=!0,this.needsUpdate=!1,this.type=1,this.render=function(e,n,o){if(!1===y.enabled)return;if(!1===y.autoUpdate&&!1===y.needsUpdate)return;if(0===e.length)return;const l=t.getRenderTarget(),c=t.getActiveCubeFace(),u=t.getActiveMipmapLevel(),d=t.state;d.setBlending(0),d.buffers.color.setClear(1,1,1,1),d.buffers.depth.setTest(!0),d.setScissorTest(!1);for(let l=0,c=e.length;l<c;l++){const c=e[l],u=c.shadow;if(void 0===u){console.warn(\\\\\\\"THREE.WebGLShadowMap:\\\\\\\",c,\\\\\\\"has no shadow.\\\\\\\");continue}if(!1===u.autoUpdate&&!1===u.needsUpdate)continue;r.copy(u.mapSize);const m=u.getFrameExtents();if(r.multiply(m),s.copy(u.mapSize),(r.x>h||r.y>h)&&(r.x>h&&(s.x=Math.floor(h/m.x),r.x=s.x*m.x,u.mapSize.x=s.x),r.y>h&&(s.y=Math.floor(h/m.y),r.y=s.y*m.y,u.mapSize.y=s.y)),null===u.map&&!u.isPointLightShadow&&3===this.type){const t={minFilter:g,magFilter:g,format:E};u.map=new Tt(r.x,r.y,t),u.map.texture.name=c.name+\\\\\\\".shadowMap\\\\\\\",u.mapPass=new Tt(r.x,r.y,t),u.camera.updateProjectionMatrix()}if(null===u.map){const t={minFilter:p,magFilter:p,format:E};u.map=new Tt(r.x,r.y,t),u.map.texture.name=c.name+\\\\\\\".shadowMap\\\\\\\",u.camera.updateProjectionMatrix()}t.setRenderTarget(u.map),t.clear();const f=u.getViewportCount();for(let t=0;t<f;t++){const e=u.getViewport(t);a.set(s.x*e.x,s.y*e.y,s.x*e.z,s.y*e.w),d.viewport(a),u.updateMatrices(c,t),i=u.getFrustum(),M(n,o,u.camera,c,this.type)}u.isPointLightShadow||3!==this.type||x(u,o),u.needsUpdate=!1}y.needsUpdate=!1,t.setRenderTarget(l,c,u)}}function Gs(t,e,i){const r=i.isWebGL2;const s=new function(){let e=!1;const n=new St;let i=null;const r=new St(0,0,0,0);return{setMask:function(n){i===n||e||(t.colorMask(n,n,n,n),i=n)},setLocked:function(t){e=t},setClear:function(e,i,s,a,o){!0===o&&(e*=a,i*=a,s*=a),n.set(e,i,s,a),!1===r.equals(n)&&(t.clearColor(e,i,s,a),r.copy(n))},reset:function(){e=!1,i=null,r.set(-1,0,0,0)}}},a=new function(){let e=!1,n=null,i=null,r=null;return{setTest:function(t){t?O(2929):U(2929)},setMask:function(i){n===i||e||(t.depthMask(i),n=i)},setFunc:function(e){if(i!==e){if(e)switch(e){case 0:t.depthFunc(512);break;case 1:t.depthFunc(519);break;case 2:t.depthFunc(513);break;case 3:t.depthFunc(515);break;case 4:t.depthFunc(514);break;case 5:t.depthFunc(518);break;case 6:t.depthFunc(516);break;case 7:t.depthFunc(517);break;default:t.depthFunc(515)}else t.depthFunc(515);i=e}},setLocked:function(t){e=t},setClear:function(e){r!==e&&(t.clearDepth(e),r=e)},reset:function(){e=!1,n=null,i=null,r=null}}},o=new function(){let e=!1,n=null,i=null,r=null,s=null,a=null,o=null,l=null,c=null;return{setTest:function(t){e||(t?O(2960):U(2960))},setMask:function(i){n===i||e||(t.stencilMask(i),n=i)},setFunc:function(e,n,a){i===e&&r===n&&s===a||(t.stencilFunc(e,n,a),i=e,r=n,s=a)},setOp:function(e,n,i){a===e&&o===n&&l===i||(t.stencilOp(e,n,i),a=e,o=n,l=i)},setLocked:function(t){e=t},setClear:function(e){c!==e&&(t.clearStencil(e),c=e)},reset:function(){e=!1,n=null,i=null,r=null,s=null,a=null,o=null,l=null,c=null}}};let l={},c=null,h={},u=null,d=!1,p=null,m=null,f=null,g=null,v=null,y=null,x=null,_=!1,M=null,b=null,w=null,S=null,T=null;const E=t.getParameter(35661);let A=!1,L=0;const R=t.getParameter(7938);-1!==R.indexOf(\\\\\\\"WebGL\\\\\\\")?(L=parseFloat(/^WebGL (\\\\d)/.exec(R)[1]),A=L>=1):-1!==R.indexOf(\\\\\\\"OpenGL ES\\\\\\\")&&(L=parseFloat(/^OpenGL ES (\\\\d)/.exec(R)[1]),A=L>=2);let C=null,P={};const I=t.getParameter(3088),D=t.getParameter(2978),N=(new St).fromArray(I),B=(new St).fromArray(D);function z(e,n,i){const r=new Uint8Array(4),s=t.createTexture();t.bindTexture(e,s),t.texParameteri(e,10241,9728),t.texParameteri(e,10240,9728);for(let e=0;e<i;e++)t.texImage2D(n+e,0,6408,1,1,0,6408,5121,r);return s}const F={};function O(e){!0!==l[e]&&(t.enable(e),l[e]=!0)}function U(e){!1!==l[e]&&(t.disable(e),l[e]=!1)}F[3553]=z(3553,3553,1),F[34067]=z(34067,34069,6),s.setClear(0,0,0,1),a.setClear(1),o.setClear(0),O(2929),a.setFunc(3),V(!1),W(1),O(2884),k(0);const H={[n]:32774,101:32778,102:32779};if(r)H[103]=32775,H[104]=32776;else{const t=e.get(\\\\\\\"EXT_blend_minmax\\\\\\\");null!==t&&(H[103]=t.MIN_EXT,H[104]=t.MAX_EXT)}const G={200:0,201:1,202:768,204:770,210:776,208:774,206:772,203:769,205:771,209:775,207:773};function k(e,i,r,s,a,o,l,c){if(0!==e){if(!1===d&&(O(3042),d=!0),5===e)a=a||i,o=o||r,l=l||s,i===m&&a===v||(t.blendEquationSeparate(H[i],H[a]),m=i,v=a),r===f&&s===g&&o===y&&l===x||(t.blendFuncSeparate(G[r],G[s],G[o],G[l]),f=r,g=s,y=o,x=l),p=e,_=null;else 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if(y.length>0&&h){for(let t=0,e=y.length;t<e;t++)m=y[t],n.texImage2D(3553,t,v,d,f,m);i.generateMipmaps=!1,e.__maxMipLevel=y.length-1}else n.texImage2D(3553,0,v,d,f,c),e.__maxMipLevel=0;z(i,h)&&F(a,i,c.width,c.height),e.__version=i.version,i.onUpdate&&i.onUpdate(i)}function Z(e,r,a,o,l){const c=s.convert(a.format),h=s.convert(a.type),u=O(a.internalFormat,c,h);32879===l||35866===l?n.texImage3D(l,0,u,r.width,r.height,r.depth,0,c,h,null):n.texImage2D(l,0,u,r.width,r.height,0,c,h,null),n.bindFramebuffer(36160,e),t.framebufferTexture2D(36160,o,l,i.get(a).__webglTexture,0),n.bindFramebuffer(36160,null)}function Q(e,n,i){if(t.bindRenderbuffer(36161,e),n.depthBuffer&&!n.stencilBuffer){let r=33189;if(i){const e=n.depthTexture;e&&e.isDepthTexture&&(e.type===b?r=36012:e.type===M&&(r=33190));const i=$(n);t.renderbufferStorageMultisample(36161,i,r,n.width,n.height)}else t.renderbufferStorage(36161,r,n.width,n.height);t.framebufferRenderbuffer(36160,36096,36161,e)}else 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tt=!1,et=!1;this.allocateTextureUnit=function(){const t=k;return t>=l&&console.warn(\\\\\\\"THREE.WebGLTextures: Trying to use \\\\\\\"+t+\\\\\\\" texture units while this GPU supports only \\\\\\\"+l),k+=1,t},this.resetTextureUnits=function(){k=0},this.setTexture2D=V,this.setTexture2DArray=function(t,e){const r=i.get(t);t.version>0&&r.__version!==t.version?J(r,t,e):(n.activeTexture(33984+e),n.bindTexture(35866,r.__webglTexture))},this.setTexture3D=function(t,e){const r=i.get(t);t.version>0&&r.__version!==t.version?J(r,t,e):(n.activeTexture(33984+e),n.bindTexture(32879,r.__webglTexture))},this.setTextureCube=W,this.setupRenderTarget=function(e){const l=e.texture,c=i.get(e),h=i.get(l);e.addEventListener(\\\\\\\"dispose\\\\\\\",G),!0!==e.isWebGLMultipleRenderTargets&&(h.__webglTexture=t.createTexture(),h.__version=l.version,a.memory.textures++);const u=!0===e.isWebGLCubeRenderTarget,d=!0===e.isWebGLMultipleRenderTargets,p=!0===e.isWebGLMultisampleRenderTarget,m=l.isDataTexture3D||l.isDataTexture2DArray,f=B(e)||o;if(!o||l.format!==T||l.type!==b&&l.type!==w||(l.format=E,console.warn(\\\\\\\"THREE.WebGLRenderer: Rendering to textures with RGB format is not supported. 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Use their .texture property instead.\\\\\\\"),et=!0),t=t.texture),W(t,e)}}function Vs(t,e,n){const i=n.isWebGL2;return{convert:function(t){let n;if(t===x)return 5121;if(1017===t)return 32819;if(1018===t)return 32820;if(1019===t)return 33635;if(1010===t)return 5120;if(1011===t)return 5122;if(t===_)return 5123;if(1013===t)return 5124;if(t===M)return 5125;if(t===b)return 5126;if(t===w)return i?5131:(n=e.get(\\\\\\\"OES_texture_half_float\\\\\\\"),null!==n?n.HALF_FLOAT_OES:null);if(1021===t)return 6406;if(t===T)return 6407;if(t===E)return 6408;if(1024===t)return 6409;if(1025===t)return 6410;if(t===A)return 6402;if(t===L)return 34041;if(1028===t)return 6403;if(1029===t)return 36244;if(1030===t)return 33319;if(1031===t)return 33320;if(1032===t)return 36248;if(1033===t)return 36249;if(t===R||t===C||t===P||t===I){if(n=e.get(\\\\\\\"WEBGL_compressed_texture_s3tc\\\\\\\"),null===n)return null;if(t===R)return n.COMPRESSED_RGB_S3TC_DXT1_EXT;if(t===C)return n.COMPRESSED_RGBA_S3TC_DXT1_EXT;if(t===P)return n.COMPRESSED_RGBA_S3TC_DXT3_EXT;if(t===I)return n.COMPRESSED_RGBA_S3TC_DXT5_EXT}if(t===D||t===N||t===B||t===z){if(n=e.get(\\\\\\\"WEBGL_compressed_texture_pvrtc\\\\\\\"),null===n)return null;if(t===D)return n.COMPRESSED_RGB_PVRTC_4BPPV1_IMG;if(t===N)return n.COMPRESSED_RGB_PVRTC_2BPPV1_IMG;if(t===B)return n.COMPRESSED_RGBA_PVRTC_4BPPV1_IMG;if(t===z)return n.COMPRESSED_RGBA_PVRTC_2BPPV1_IMG}if(36196===t)return n=e.get(\\\\\\\"WEBGL_compressed_texture_etc1\\\\\\\"),null!==n?n.COMPRESSED_RGB_ETC1_WEBGL:null;if((t===F||t===O)&&(n=e.get(\\\\\\\"WEBGL_compressed_texture_etc\\\\\\\"),null!==n)){if(t===F)return n.COMPRESSED_RGB8_ETC2;if(t===O)return n.COMPRESSED_RGBA8_ETC2_EAC}return 37808===t||37809===t||37810===t||37811===t||37812===t||37813===t||37814===t||37815===t||37816===t||37817===t||37818===t||37819===t||37820===t||37821===t||37840===t||37841===t||37842===t||37843===t||37844===t||37845===t||37846===t||37847===t||37848===t||37849===t||37850===t||37851===t||37852===t||37853===t?(n=e.get(\\\\\\\"WEBGL_compressed_texture_astc\\\\\\\"),null!==n?t:null):36492===t?(n=e.get(\\\\\\\"EXT_texture_compression_bptc\\\\\\\"),null!==n?t:null):t===S?i?34042:(n=e.get(\\\\\\\"WEBGL_depth_texture\\\\\\\"),null!==n?n.UNSIGNED_INT_24_8_WEBGL:null):void 0}}}class Ws extends Jn{constructor(t=[]){super(),this.cameras=t}}Ws.prototype.isArrayCamera=!0;class js extends Pe{constructor(){super(),this.type=\\\\\\\"Group\\\\\\\"}}js.prototype.isGroup=!0;const qs={type:\\\\\\\"move\\\\\\\"};class Xs{constructor(){this._targetRay=null,this._grip=null,this._hand=null}getHandSpace(){return null===this._hand&&(this._hand=new js,this._hand.matrixAutoUpdate=!1,this._hand.visible=!1,this._hand.joints={},this._hand.inputState={pinching:!1}),this._hand}getTargetRaySpace(){return null===this._targetRay&&(this._targetRay=new js,this._targetRay.matrixAutoUpdate=!1,this._targetRay.visible=!1,this._targetRay.hasLinearVelocity=!1,this._targetRay.linearVelocity=new Rt,this._targetRay.hasAngularVelocity=!1,this._targetRay.angularVelocity=new Rt),this._targetRay}getGripSpace(){return null===this._grip&&(this._grip=new js,this._grip.matrixAutoUpdate=!1,this._grip.visible=!1,this._grip.hasLinearVelocity=!1,this._grip.linearVelocity=new Rt,this._grip.hasAngularVelocity=!1,this._grip.angularVelocity=new Rt),this._grip}dispatchEvent(t){return null!==this._targetRay&&this._targetRay.dispatchEvent(t),null!==this._grip&&this._grip.dispatchEvent(t),null!==this._hand&&this._hand.dispatchEvent(t),this}disconnect(t){return this.dispatchEvent({type:\\\\\\\"disconnected\\\\\\\",data:t}),null!==this._targetRay&&(this._targetRay.visible=!1),null!==this._grip&&(this._grip.visible=!1),null!==this._hand&&(this._hand.visible=!1),this}update(t,e,n){let i=null,r=null,s=null;const a=this._targetRay,o=this._grip,l=this._hand;if(t&&\\\\\\\"visible-blurred\\\\\\\"!==e.session.visibilityState)if(null!==a&&(i=e.getPose(t.targetRaySpace,n),null!==i&&(a.matrix.fromArray(i.transform.matrix),a.matrix.decompose(a.position,a.rotation,a.scale),i.linearVelocity?(a.hasLinearVelocity=!0,a.linearVelocity.copy(i.linearVelocity)):a.hasLinearVelocity=!1,i.angularVelocity?(a.hasAngularVelocity=!0,a.angularVelocity.copy(i.angularVelocity)):a.hasAngularVelocity=!1,this.dispatchEvent(qs))),l&&t.hand){s=!0;for(const i of t.hand.values()){const t=e.getJointPose(i,n);if(void 0===l.joints[i.jointName]){const t=new js;t.matrixAutoUpdate=!1,t.visible=!1,l.joints[i.jointName]=t,l.add(t)}const r=l.joints[i.jointName];null!==t&&(r.matrix.fromArray(t.transform.matrix),r.matrix.decompose(r.position,r.rotation,r.scale),r.jointRadius=t.radius),r.visible=null!==t}const i=l.joints[\\\\\\\"index-finger-tip\\\\\\\"],r=l.joints[\\\\\\\"thumb-tip\\\\\\\"],a=i.position.distanceTo(r.position),o=.02,c=.005;l.inputState.pinching&&a>o+c?(l.inputState.pinching=!1,this.dispatchEvent({type:\\\\\\\"pinchend\\\\\\\",handedness:t.handedness,target:this})):!l.inputState.pinching&&a<=o-c&&(l.inputState.pinching=!0,this.dispatchEvent({type:\\\\\\\"pinchstart\\\\\\\",handedness:t.handedness,target:this}))}else null!==o&&t.gripSpace&&(r=e.getPose(t.gripSpace,n),null!==r&&(o.matrix.fromArray(r.transform.matrix),o.matrix.decompose(o.position,o.rotation,o.scale),r.linearVelocity?(o.hasLinearVelocity=!0,o.linearVelocity.copy(r.linearVelocity)):o.hasLinearVelocity=!1,r.angularVelocity?(o.hasAngularVelocity=!0,o.angularVelocity.copy(r.angularVelocity)):o.hasAngularVelocity=!1));return null!==a&&(a.visible=null!==i),null!==o&&(o.visible=null!==r),null!==l&&(l.visible=null!==s),this}}class Ys extends rt{constructor(t,e){super();const n=this,i=t.state;let r=null,s=1,a=null,o=\\\\\\\"local-floor\\\\\\\",l=null,c=null,h=null,u=null,d=null;const p=[],m=new Map,f=new Jn;f.layers.enable(1),f.viewport=new St;const g=new Jn;g.layers.enable(2),g.viewport=new St;const v=[f,g],y=new Ws;y.layers.enable(1),y.layers.enable(2);let x=null,_=null;function M(t){const e=m.get(t.inputSource);e&&e.dispatchEvent({type:t.type,data:t.inputSource})}function b(){m.forEach((function(t,e){t.disconnect(e)})),m.clear(),x=null,_=null,i.bindXRFramebuffer(null),t.setRenderTarget(t.getRenderTarget()),L.stop(),n.isPresenting=!1,n.dispatchEvent({type:\\\\\\\"sessionend\\\\\\\"})}function w(t){const e=r.inputSources;for(let t=0;t<p.length;t++)m.set(e[t],p[t]);for(let e=0;e<t.removed.length;e++){const n=t.removed[e],i=m.get(n);i&&(i.dispatchEvent({type:\\\\\\\"disconnected\\\\\\\",data:n}),m.delete(n))}for(let e=0;e<t.added.length;e++){const n=t.added[e],i=m.get(n);i&&i.dispatchEvent({type:\\\\\\\"connected\\\\\\\",data:n})}}this.cameraAutoUpdate=!0,this.enabled=!1,this.isPresenting=!1,this.getController=function(t){let e=p[t];return void 0===e&&(e=new Xs,p[t]=e),e.getTargetRaySpace()},this.getControllerGrip=function(t){let e=p[t];return void 0===e&&(e=new Xs,p[t]=e),e.getGripSpace()},this.getHand=function(t){let e=p[t];return void 0===e&&(e=new Xs,p[t]=e),e.getHandSpace()},this.setFramebufferScaleFactor=function(t){s=t,!0===n.isPresenting&&console.warn(\\\\\\\"THREE.WebXRManager: Cannot change framebuffer scale while presenting.\\\\\\\")},this.setReferenceSpaceType=function(t){o=t,!0===n.isPresenting&&console.warn(\\\\\\\"THREE.WebXRManager: Cannot change reference space type while presenting.\\\\\\\")},this.getReferenceSpace=function(){return a},this.getSession=function(){return r},this.setSession=async function(t){if(r=t,null!==r){r.addEventListener(\\\\\\\"select\\\\\\\",M),r.addEventListener(\\\\\\\"selectstart\\\\\\\",M),r.addEventListener(\\\\\\\"selectend\\\\\\\",M),r.addEventListener(\\\\\\\"squeeze\\\\\\\",M),r.addEventListener(\\\\\\\"squeezestart\\\\\\\",M),r.addEventListener(\\\\\\\"squeezeend\\\\\\\",M),r.addEventListener(\\\\\\\"end\\\\\\\",b),r.addEventListener(\\\\\\\"inputsourceschange\\\\\\\",w);const t=e.getContextAttributes();if(!0!==t.xrCompatible&&await e.makeXRCompatible(),void 0===r.renderState.layers){const n={antialias:t.antialias,alpha:t.alpha,depth:t.depth,stencil:t.stencil,framebufferScaleFactor:s};d=new XRWebGLLayer(r,e,n),r.updateRenderState({baseLayer:d})}else{let n=0;if(t.antialias){const n={antialias:!0,alpha:t.alpha,depth:t.depth,stencil:t.stencil,framebufferScaleFactor:s};d=new XRWebGLLayer(r,e,n),r.updateRenderState({layers:[d]})}else{t.depth&&(n=t.stencil?34041:6402);const i={colorFormat:t.alpha?6408:6407,depthFormat:n,scaleFactor:s};c=new XRWebGLBinding(r,e),u=c.createProjectionLayer(i),h=e.createFramebuffer(),r.updateRenderState({layers:[u]})}}a=await r.requestReferenceSpace(o),L.setContext(r),L.start(),n.isPresenting=!0,n.dispatchEvent({type:\\\\\\\"sessionstart\\\\\\\"})}};const S=new Rt,T=new Rt;function E(t,e){null===e?t.matrixWorld.copy(t.matrix):t.matrixWorld.multiplyMatrices(e.matrixWorld,t.matrix),t.matrixWorldInverse.copy(t.matrixWorld).invert()}this.updateCamera=function(t){if(null===r)return;y.near=g.near=f.near=t.near,y.far=g.far=f.far=t.far,x===y.near&&_===y.far||(r.updateRenderState({depthNear:y.near,depthFar:y.far}),x=y.near,_=y.far);const e=t.parent,n=y.cameras;E(y,e);for(let t=0;t<n.length;t++)E(n[t],e);y.matrixWorld.decompose(y.position,y.quaternion,y.scale),t.position.copy(y.position),t.quaternion.copy(y.quaternion),t.scale.copy(y.scale),t.matrix.copy(y.matrix),t.matrixWorld.copy(y.matrixWorld);const i=t.children;for(let t=0,e=i.length;t<e;t++)i[t].updateMatrixWorld(!0);2===n.length?function(t,e,n){S.setFromMatrixPosition(e.matrixWorld),T.setFromMatrixPosition(n.matrixWorld);const i=S.distanceTo(T),r=e.projectionMatrix.elements,s=n.projectionMatrix.elements,a=r[14]/(r[10]-1),o=r[14]/(r[10]+1),l=(r[9]+1)/r[5],c=(r[9]-1)/r[5],h=(r[8]-1)/r[0],u=(s[8]+1)/s[0],d=a*h,p=a*u,m=i/(-h+u),f=m*-h;e.matrixWorld.decompose(t.position,t.quaternion,t.scale),t.translateX(f),t.translateZ(m),t.matrixWorld.compose(t.position,t.quaternion,t.scale),t.matrixWorldInverse.copy(t.matrixWorld).invert();const g=a+m,v=o+m,y=d-f,x=p+(i-f),_=l*o/v*g,M=c*o/v*g;t.projectionMatrix.makePerspective(y,x,_,M,g,v)}(y,f,g):y.projectionMatrix.copy(f.projectionMatrix)},this.getCamera=function(){return y},this.getFoveation=function(){return null!==u?u.fixedFoveation:null!==d?d.fixedFoveation:void 0},this.setFoveation=function(t){null!==u&&(u.fixedFoveation=t),null!==d&&void 0!==d.fixedFoveation&&(d.fixedFoveation=t)};let A=null;const L=new oi;L.setAnimationLoop((function(t,n){if(l=n.getViewerPose(a),null!==l){const t=l.views;null!==d&&i.bindXRFramebuffer(d.framebuffer);let n=!1;t.length!==y.cameras.length&&(y.cameras.length=0,n=!0);for(let r=0;r<t.length;r++){const s=t[r];let a=null;if(null!==d)a=d.getViewport(s);else{const t=c.getViewSubImage(u,s);i.bindXRFramebuffer(h),void 0!==t.depthStencilTexture&&e.framebufferTexture2D(36160,36096,3553,t.depthStencilTexture,0),e.framebufferTexture2D(36160,36064,3553,t.colorTexture,0),a=t.viewport}const o=v[r];o.matrix.fromArray(s.transform.matrix),o.projectionMatrix.fromArray(s.projectionMatrix),o.viewport.set(a.x,a.y,a.width,a.height),0===r&&y.matrix.copy(o.matrix),!0===n&&y.cameras.push(o)}}const s=r.inputSources;for(let t=0;t<p.length;t++){const e=p[t],i=s[t];e.update(i,n,a)}A&&A(t,n)})),this.setAnimationLoop=function(t){A=t},this.dispose=function(){}}}function Js(t){function e(e,n){e.opacity.value=n.opacity,n.color&&e.diffuse.value.copy(n.color),n.emissive&&e.emissive.value.copy(n.emissive).multiplyScalar(n.emissiveIntensity),n.map&&(e.map.value=n.map),n.alphaMap&&(e.alphaMap.value=n.alphaMap),n.specularMap&&(e.specularMap.value=n.specularMap);const i=t.get(n).envMap;if(i){e.envMap.value=i,e.flipEnvMap.value=i.isCubeTexture&&!1===i.isRenderTargetTexture?-1:1,e.reflectivity.value=n.reflectivity,e.refractionRatio.value=n.refractionRatio;const r=t.get(i).__maxMipLevel;void 0!==r&&(e.maxMipLevel.value=r)}let r,s;n.lightMap&&(e.lightMap.value=n.lightMap,e.lightMapIntensity.value=n.lightMapIntensity),n.aoMap&&(e.aoMap.value=n.aoMap,e.aoMapIntensity.value=n.aoMapIntensity),n.map?r=n.map:n.specularMap?r=n.specularMap:n.displacementMap?r=n.displacementMap:n.normalMap?r=n.normalMap:n.bumpMap?r=n.bumpMap:n.roughnessMap?r=n.roughnessMap:n.metalnessMap?r=n.metalnessMap:n.alphaMap?r=n.alphaMap:n.emissiveMap?r=n.emissiveMap:n.clearcoatMap?r=n.clearcoatMap:n.clearcoatNormalMap?r=n.clearcoatNormalMap:n.clearcoatRoughnessMap?r=n.clearcoatRoughnessMap:n.specularIntensityMap?r=n.specularIntensityMap:n.specularTintMap&&(r=n.specularTintMap),void 0!==r&&(r.isWebGLRenderTarget&&(r=r.texture),!0===r.matrixAutoUpdate&&r.updateMatrix(),e.uvTransform.value.copy(r.matrix)),n.aoMap?s=n.aoMap:n.lightMap&&(s=n.lightMap),void 0!==s&&(s.isWebGLRenderTarget&&(s=s.texture),!0===s.matrixAutoUpdate&&s.updateMatrix(),e.uv2Transform.value.copy(s.matrix))}function n(e,n){e.roughness.value=n.roughness,e.metalness.value=n.metalness,n.roughnessMap&&(e.roughnessMap.value=n.roughnessMap),n.metalnessMap&&(e.metalnessMap.value=n.metalnessMap),n.emissiveMap&&(e.emissiveMap.value=n.emissiveMap),n.bumpMap&&(e.bumpMap.value=n.bumpMap,e.bumpScale.value=n.bumpScale,1===n.side&&(e.bumpScale.value*=-1)),n.normalMap&&(e.normalMap.value=n.normalMap,e.normalScale.value.copy(n.normalScale),1===n.side&&e.normalScale.value.negate()),n.displacementMap&&(e.displacementMap.value=n.displacementMap,e.displacementScale.value=n.displacementScale,e.displacementBias.value=n.displacementBias);t.get(n).envMap&&(e.envMapIntensity.value=n.envMapIntensity)}return{refreshFogUniforms:function(t,e){t.fogColor.value.copy(e.color),e.isFog?(t.fogNear.value=e.near,t.fogFar.value=e.far):e.isFogExp2&&(t.fogDensity.value=e.density)},refreshMaterialUniforms:function(t,i,r,s,a){i.isMeshBasicMaterial?e(t,i):i.isMeshLambertMaterial?(e(t,i),function(t,e){e.emissiveMap&&(t.emissiveMap.value=e.emissiveMap)}(t,i)):i.isMeshToonMaterial?(e(t,i),function(t,e){e.gradientMap&&(t.gradientMap.value=e.gradientMap);e.emissiveMap&&(t.emissiveMap.value=e.emissiveMap);e.bumpMap&&(t.bumpMap.value=e.bumpMap,t.bumpScale.value=e.bumpScale,1===e.side&&(t.bumpScale.value*=-1));e.normalMap&&(t.normalMap.value=e.normalMap,t.normalScale.value.copy(e.normalScale),1===e.side&&t.normalScale.value.negate());e.displacementMap&&(t.displacementMap.value=e.displacementMap,t.displacementScale.value=e.displacementScale,t.displacementBias.value=e.displacementBias)}(t,i)):i.isMeshPhongMaterial?(e(t,i),function(t,e){t.specular.value.copy(e.specular),t.shininess.value=Math.max(e.shininess,1e-4),e.emissiveMap&&(t.emissiveMap.value=e.emissiveMap);e.bumpMap&&(t.bumpMap.value=e.bumpMap,t.bumpScale.value=e.bumpScale,1===e.side&&(t.bumpScale.value*=-1));e.normalMap&&(t.normalMap.value=e.normalMap,t.normalScale.value.copy(e.normalScale),1===e.side&&t.normalScale.value.negate());e.displacementMap&&(t.displacementMap.value=e.displacementMap,t.displacementScale.value=e.displacementScale,t.displacementBias.value=e.displacementBias)}(t,i)):i.isMeshStandardMaterial?(e(t,i),i.isMeshPhysicalMaterial?function(t,e,i){n(t,e),t.reflectivity.value=e.reflectivity,t.clearcoat.value=e.clearcoat,t.clearcoatRoughness.value=e.clearcoatRoughness,e.sheen&&t.sheen.value.copy(e.sheen);e.clearcoatMap&&(t.clearcoatMap.value=e.clearcoatMap);e.clearcoatRoughnessMap&&(t.clearcoatRoughnessMap.value=e.clearcoatRoughnessMap);e.clearcoatNormalMap&&(t.clearcoatNormalScale.value.copy(e.clearcoatNormalScale),t.clearcoatNormalMap.value=e.clearcoatNormalMap,1===e.side&&t.clearcoatNormalScale.value.negate());t.transmission.value=e.transmission,e.transmissionMap&&(t.transmissionMap.value=e.transmissionMap);e.transmission>0&&(t.transmissionSamplerMap.value=i.texture,t.transmissionSamplerSize.value.set(i.width,i.height));t.thickness.value=e.thickness,e.thicknessMap&&(t.thicknessMap.value=e.thicknessMap);t.attenuationDistance.value=e.attenuationDistance,t.attenuationTint.value.copy(e.attenuationTint),t.specularIntensity.value=e.specularIntensity,t.specularTint.value.copy(e.specularTint),e.specularIntensityMap&&(t.specularIntensityMap.value=e.specularIntensityMap);e.specularTintMap&&(t.specularTintMap.value=e.specularTintMap)}(t,i,a):n(t,i)):i.isMeshMatcapMaterial?(e(t,i),function(t,e){e.matcap&&(t.matcap.value=e.matcap);e.bumpMap&&(t.bumpMap.value=e.bumpMap,t.bumpScale.value=e.bumpScale,1===e.side&&(t.bumpScale.value*=-1));e.normalMap&&(t.normalMap.value=e.normalMap,t.normalScale.value.copy(e.normalScale),1===e.side&&t.normalScale.value.negate());e.displacementMap&&(t.displacementMap.value=e.displacementMap,t.displacementScale.value=e.displacementScale,t.displacementBias.value=e.displacementBias)}(t,i)):i.isMeshDepthMaterial?(e(t,i),function(t,e){e.displacementMap&&(t.displacementMap.value=e.displacementMap,t.displacementScale.value=e.displacementScale,t.displacementBias.value=e.displacementBias)}(t,i)):i.isMeshDistanceMaterial?(e(t,i),function(t,e){e.displacementMap&&(t.displacementMap.value=e.displacementMap,t.displacementScale.value=e.displacementScale,t.displacementBias.value=e.displacementBias);t.referencePosition.value.copy(e.referencePosition),t.nearDistance.value=e.nearDistance,t.farDistance.value=e.farDistance}(t,i)):i.isMeshNormalMaterial?(e(t,i),function(t,e){e.bumpMap&&(t.bumpMap.value=e.bumpMap,t.bumpScale.value=e.bumpScale,1===e.side&&(t.bumpScale.value*=-1));e.normalMap&&(t.normalMap.value=e.normalMap,t.normalScale.value.copy(e.normalScale),1===e.side&&t.normalScale.value.negate());e.displacementMap&&(t.displacementMap.value=e.displacementMap,t.displacementScale.value=e.displacementScale,t.displacementBias.value=e.displacementBias)}(t,i)):i.isLineBasicMaterial?(function(t,e){t.diffuse.value.copy(e.color),t.opacity.value=e.opacity}(t,i),i.isLineDashedMaterial&&function(t,e){t.dashSize.value=e.dashSize,t.totalSize.value=e.dashSize+e.gapSize,t.scale.value=e.scale}(t,i)):i.isPointsMaterial?function(t,e,n,i){t.diffuse.value.copy(e.color),t.opacity.value=e.opacity,t.size.value=e.size*n,t.scale.value=.5*i,e.map&&(t.map.value=e.map);e.alphaMap&&(t.alphaMap.value=e.alphaMap);let r;e.map?r=e.map:e.alphaMap&&(r=e.alphaMap);void 0!==r&&(!0===r.matrixAutoUpdate&&r.updateMatrix(),t.uvTransform.value.copy(r.matrix))}(t,i,r,s):i.isSpriteMaterial?function(t,e){t.diffuse.value.copy(e.color),t.opacity.value=e.opacity,t.rotation.value=e.rotation,e.map&&(t.map.value=e.map);e.alphaMap&&(t.alphaMap.value=e.alphaMap);let n;e.map?n=e.map:e.alphaMap&&(n=e.alphaMap);void 0!==n&&(!0===n.matrixAutoUpdate&&n.updateMatrix(),t.uvTransform.value.copy(n.matrix))}(t,i):i.isShadowMaterial?(t.color.value.copy(i.color),t.opacity.value=i.opacity):i.isShaderMaterial&&(i.uniformsNeedUpdate=!1)}}}function Zs(t={}){const e=void 0!==t.canvas?t.canvas:function(){const t=document.createElementNS(\\\\\\\"http://www.w3.org/1999/xhtml\\\\\\\",\\\\\\\"canvas\\\\\\\");return t.style.display=\\\\\\\"block\\\\\\\",t}(),n=void 0!==t.context?t.context:null,i=void 0!==t.alpha&&t.alpha,r=void 0===t.depth||t.depth,s=void 0===t.stencil||t.stencil,a=void 0!==t.antialias&&t.antialias,o=void 0===t.premultipliedAlpha||t.premultipliedAlpha,l=void 0!==t.preserveDrawingBuffer&&t.preserveDrawingBuffer,c=void 0!==t.powerPreference?t.powerPreference:\\\\\\\"default\\\\\\\",h=void 0!==t.failIfMajorPerformanceCaveat&&t.failIfMajorPerformanceCaveat;let d=null,m=null;const f=[],g=[];this.domElement=e,this.debug={checkShaderErrors:!0},this.autoClear=!0,this.autoClearColor=!0,this.autoClearDepth=!0,this.autoClearStencil=!0,this.sortObjects=!0,this.clippingPlanes=[],this.localClippingEnabled=!1,this.gammaFactor=2,this.outputEncoding=X,this.physicallyCorrectLights=!1,this.toneMapping=0,this.toneMappingExposure=1;const v=this;let _=!1,M=0,S=0,T=null,A=-1,L=null;const R=new St,C=new St;let P=null,I=e.width,D=e.height,N=1,B=null,z=null;const F=new St(0,0,I,D),O=new St(0,0,I,D);let U=!1;const H=[],G=new ai;let k=!1,V=!1,W=null;const j=new ae,q=new Rt,Y={background:null,fog:null,environment:null,overrideMaterial:null,isScene:!0};function J(){return null===T?N:1}let 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zt(t,e,n,i,r,s){if(t.onBeforeRender(v,e,n,i,r,s),t.modelViewMatrix.multiplyMatrices(n.matrixWorldInverse,t.matrixWorld),t.normalMatrix.getNormalMatrix(t.modelViewMatrix),t.isImmediateRenderObject){const i=Ut(n,e,r,t);K.setMaterial(r),yt.reset(),function(t,e){t.render((function(t){v.renderBufferImmediate(t,e)}))}(t,i)}else!0===r.transparent&&2===r.side?(r.side=1,r.needsUpdate=!0,v.renderBufferDirect(n,e,i,r,t,s),r.side=0,r.needsUpdate=!0,v.renderBufferDirect(n,e,i,r,t,s),r.side=2):v.renderBufferDirect(n,e,i,r,t,s);t.onAfterRender(v,e,n,i,r,s)}function Ft(t,e,n){!0!==e.isScene&&(e=Y);const i=tt.get(t),r=m.state.lights,s=m.state.shadowsArray,a=r.state.version,o=ot.getParameters(t,r.state,s,e,n),l=ot.getProgramCacheKey(o);let c=i.programs;i.environment=t.isMeshStandardMaterial?e.environment:null,i.fog=e.fog,i.envMap=(t.isMeshStandardMaterial?it:nt).get(t.envMap||i.environment),void 0===c&&(t.addEventListener(\\\\\\\"dispose\\\\\\\",Lt),c=new Map,i.programs=c);let h=c.get(l);if(void 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t.isMeshLambertMaterial||t.isMeshToonMaterial||t.isMeshPhongMaterial||t.isMeshStandardMaterial||t.isShadowMaterial||t.isShaderMaterial&&!0===t.lights}(t),i.lightsStateVersion=a,i.needsLights&&(u.ambientLightColor.value=r.state.ambient,u.lightProbe.value=r.state.probe,u.directionalLights.value=r.state.directional,u.directionalLightShadows.value=r.state.directionalShadow,u.spotLights.value=r.state.spot,u.spotLightShadows.value=r.state.spotShadow,u.rectAreaLights.value=r.state.rectArea,u.ltc_1.value=r.state.rectAreaLTC1,u.ltc_2.value=r.state.rectAreaLTC2,u.pointLights.value=r.state.point,u.pointLightShadows.value=r.state.pointShadow,u.hemisphereLights.value=r.state.hemi,u.directionalShadowMap.value=r.state.directionalShadowMap,u.directionalShadowMatrix.value=r.state.directionalShadowMatrix,u.spotShadowMap.value=r.state.spotShadowMap,u.spotShadowMatrix.value=r.state.spotShadowMatrix,u.pointShadowMap.value=r.state.pointShadowMap,u.pointShadowMatrix.value=r.state.pointShadowMatrix);const d=h.getUniforms(),p=ss.seqWithValue(d.seq,u);return i.currentProgram=h,i.uniformsList=p,h}function Ot(t,e){const n=tt.get(t);n.outputEncoding=e.outputEncoding,n.instancing=e.instancing,n.skinning=e.skinning,n.morphTargets=e.morphTargets,n.morphNormals=e.morphNormals,n.numClippingPlanes=e.numClippingPlanes,n.numIntersection=e.numClipIntersection,n.vertexAlphas=e.vertexAlphas,n.vertexTangents=e.vertexTangents}function Ut(t,e,n,i){!0!==e.isScene&&(e=Y),et.resetTextureUnits();const r=e.fog,s=n.isMeshStandardMaterial?e.environment:null,a=null===T?v.outputEncoding:T.texture.encoding,o=(n.isMeshStandardMaterial?it:nt).get(n.envMap||s),l=!0===n.vertexColors&&!!i.geometry&&!!i.geometry.attributes.color&&4===i.geometry.attributes.color.itemSize,c=!!i.geometry&&!!i.geometry.attributes.tangent,h=!!i.geometry&&!!i.geometry.morphAttributes.position,u=!!i.geometry&&!!i.geometry.morphAttributes.normal,d=tt.get(n),p=m.state.lights;if(!0===k&&(!0===V||t!==L)){const 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M=g.getUniforms(),b=d.uniforms;if(K.useProgram(g.program)&&(y=!0,x=!0,_=!0),n.id!==A&&(A=n.id,x=!0),y||L!==t){if(M.setValue(xt,\\\\\\\"projectionMatrix\\\\\\\",t.projectionMatrix),Q.logarithmicDepthBuffer&&M.setValue(xt,\\\\\\\"logDepthBufFC\\\\\\\",2/(Math.log(t.far+1)/Math.LN2)),L!==t&&(L=t,x=!0,_=!0),n.isShaderMaterial||n.isMeshPhongMaterial||n.isMeshToonMaterial||n.isMeshStandardMaterial||n.envMap){const e=M.map.cameraPosition;void 0!==e&&e.setValue(xt,q.setFromMatrixPosition(t.matrixWorld))}(n.isMeshPhongMaterial||n.isMeshToonMaterial||n.isMeshLambertMaterial||n.isMeshBasicMaterial||n.isMeshStandardMaterial||n.isShaderMaterial)&&M.setValue(xt,\\\\\\\"isOrthographic\\\\\\\",!0===t.isOrthographicCamera),(n.isMeshPhongMaterial||n.isMeshToonMaterial||n.isMeshLambertMaterial||n.isMeshBasicMaterial||n.isMeshStandardMaterial||n.isShaderMaterial||n.isShadowMaterial||i.isSkinnedMesh)&&M.setValue(xt,\\\\\\\"viewMatrix\\\\\\\",t.matrixWorldInverse)}if(i.isSkinnedMesh){M.setOptional(xt,i,\\\\\\\"bindMatrix\\\\\\\"),M.setOptional(xt,i,\\\\\\\"bindMatrixInverse\\\\\\\");const t=i.skeleton;t&&(Q.floatVertexTextures?(null===t.boneTexture&&t.computeBoneTexture(),M.setValue(xt,\\\\\\\"boneTexture\\\\\\\",t.boneTexture,et),M.setValue(xt,\\\\\\\"boneTextureSize\\\\\\\",t.boneTextureSize)):M.setOptional(xt,t,\\\\\\\"boneMatrices\\\\\\\"))}var 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s=v.toneMapping;v.toneMapping=0,Bt(t,n,i),v.toneMapping=s,et.updateMultisampleRenderTarget(W),et.updateRenderTargetMipmap(W),v.setRenderTarget(r),Bt(e,n,i)}(i,r,t,e),s.length>0&&Bt(s,t,e),null!==T&&(et.updateMultisampleRenderTarget(T),et.updateRenderTargetMipmap(T)),!0===t.isScene&&t.onAfterRender(v,t,e),K.buffers.depth.setTest(!0),K.buffers.depth.setMask(!0),K.buffers.color.setMask(!0),K.setPolygonOffset(!1),yt.resetDefaultState(),A=-1,L=null,g.pop(),m=g.length>0?g[g.length-1]:null,f.pop(),d=f.length>0?f[f.length-1]:null},this.getActiveCubeFace=function(){return M},this.getActiveMipmapLevel=function(){return S},this.getRenderTarget=function(){return T},this.setRenderTarget=function(t,e=0,n=0){T=t,M=e,S=n,t&&void 0===tt.get(t).__webglFramebuffer&&et.setupRenderTarget(t);let i=null,r=!1,s=!1;if(t){const n=t.texture;(n.isDataTexture3D||n.isDataTexture2DArray)&&(s=!0);const a=tt.get(t).__webglFramebuffer;t.isWebGLCubeRenderTarget?(i=a[e],r=!0):i=t.isWebGLMultisampleRenderTarget?tt.get(t).__webglMultisampledFramebuffer:a,R.copy(t.viewport),C.copy(t.scissor),P=t.scissorTest}else R.copy(F).multiplyScalar(N).floor(),C.copy(O).multiplyScalar(N).floor(),P=U;if(K.bindFramebuffer(36160,i)&&Q.drawBuffers){let e=!1;if(t)if(t.isWebGLMultipleRenderTargets){const n=t.texture;if(H.length!==n.length||36064!==H[0]){for(let t=0,e=n.length;t<e;t++)H[t]=36064+t;H.length=n.length,e=!0}}else 1===H.length&&36064===H[0]||(H[0]=36064,H.length=1,e=!0);else 1===H.length&&1029===H[0]||(H[0]=1029,H.length=1,e=!0);e&&(Q.isWebGL2?xt.drawBuffers(H):Z.get(\\\\\\\"WEBGL_draw_buffers\\\\\\\").drawBuffersWEBGL(H))}if(K.viewport(R),K.scissor(C),K.setScissorTest(P),r){const i=tt.get(t.texture);xt.framebufferTexture2D(36160,36064,34069+e,i.__webglTexture,n)}else if(s){const i=tt.get(t.texture),r=e||0;xt.framebufferTextureLayer(36160,36064,i.__webglTexture,n||0,r)}},this.readRenderTargetPixels=function(t,e,n,i,r,s,a){if(!t||!t.isWebGLRenderTarget)return void console.error(\\\\\\\"THREE.WebGLRenderer.readRenderTargetPixels: renderTarget is not THREE.WebGLRenderTarget.\\\\\\\");let o=tt.get(t).__webglFramebuffer;if(t.isWebGLCubeRenderTarget&&void 0!==a&&(o=o[a]),o){K.bindFramebuffer(36160,o);try{const a=t.texture,o=a.format,l=a.type;if(o!==E&&vt.convert(o)!==xt.getParameter(35739))return void console.error(\\\\\\\"THREE.WebGLRenderer.readRenderTargetPixels: renderTarget is not in RGBA or implementation defined format.\\\\\\\");const c=l===w&&(Z.has(\\\\\\\"EXT_color_buffer_half_float\\\\\\\")||Q.isWebGL2&&Z.has(\\\\\\\"EXT_color_buffer_float\\\\\\\"));if(!(l===x||vt.convert(l)===xt.getParameter(35738)||l===b&&(Q.isWebGL2||Z.has(\\\\\\\"OES_texture_float\\\\\\\")||Z.has(\\\\\\\"WEBGL_color_buffer_float\\\\\\\"))||c))return void console.error(\\\\\\\"THREE.WebGLRenderer.readRenderTargetPixels: renderTarget is not in UnsignedByteType or implementation defined type.\\\\\\\");36053===xt.checkFramebufferStatus(36160)?e>=0&&e<=t.width-i&&n>=0&&n<=t.height-r&&xt.readPixels(e,n,i,r,vt.convert(o),vt.convert(l),s):console.error(\\\\\\\"THREE.WebGLRenderer.readRenderTargetPixels: readPixels from renderTarget failed. Framebuffer not complete.\\\\\\\")}finally{const t=null!==T?tt.get(T).__webglFramebuffer:null;K.bindFramebuffer(36160,t)}}},this.copyFramebufferToTexture=function(t,e,n=0){const i=Math.pow(2,-n),r=Math.floor(e.image.width*i),s=Math.floor(e.image.height*i);let a=vt.convert(e.format);Q.isWebGL2&&(6407===a&&(a=32849),6408===a&&(a=32856)),et.setTexture2D(e,0),xt.copyTexImage2D(3553,n,a,t.x,t.y,r,s,0),K.unbindTexture()},this.copyTextureToTexture=function(t,e,n,i=0){const r=e.image.width,s=e.image.height,a=vt.convert(n.format),o=vt.convert(n.type);et.setTexture2D(n,0),xt.pixelStorei(37440,n.flipY),xt.pixelStorei(37441,n.premultiplyAlpha),xt.pixelStorei(3317,n.unpackAlignment),e.isDataTexture?xt.texSubImage2D(3553,i,t.x,t.y,r,s,a,o,e.image.data):e.isCompressedTexture?xt.compressedTexSubImage2D(3553,i,t.x,t.y,e.mipmaps[0].width,e.mipmaps[0].height,a,e.mipmaps[0].data):xt.texSubImage2D(3553,i,t.x,t.y,a,o,e.image),0===i&&n.generateMipmaps&&xt.generateMipmap(3553),K.unbindTexture()},this.copyTextureToTexture3D=function(t,e,n,i,r=0){if(v.isWebGL1Renderer)return void console.warn(\\\\\\\"THREE.WebGLRenderer.copyTextureToTexture3D: can only be used with WebGL2.\\\\\\\");const s=t.max.x-t.min.x+1,a=t.max.y-t.min.y+1,o=t.max.z-t.min.z+1,l=vt.convert(i.format),c=vt.convert(i.type);let h;if(i.isDataTexture3D)et.setTexture3D(i,0),h=32879;else{if(!i.isDataTexture2DArray)return void console.warn(\\\\\\\"THREE.WebGLRenderer.copyTextureToTexture3D: only supports THREE.DataTexture3D and THREE.DataTexture2DArray.\\\\\\\");et.setTexture2DArray(i,0),h=35866}xt.pixelStorei(37440,i.flipY),xt.pixelStorei(37441,i.premultiplyAlpha),xt.pixelStorei(3317,i.unpackAlignment);const u=xt.getParameter(3314),d=xt.getParameter(32878),p=xt.getParameter(3316),m=xt.getParameter(3315),f=xt.getParameter(32877),g=n.isCompressedTexture?n.mipmaps[0]:n.image;xt.pixelStorei(3314,g.width),xt.pixelStorei(32878,g.height),xt.pixelStorei(3316,t.min.x),xt.pixelStorei(3315,t.min.y),xt.pixelStorei(32877,t.min.z),n.isDataTexture||n.isDataTexture3D?xt.texSubImage3D(h,r,e.x,e.y,e.z,s,a,o,l,c,g.data):n.isCompressedTexture?(console.warn(\\\\\\\"THREE.WebGLRenderer.copyTextureToTexture3D: untested support for compressed srcTexture.\\\\\\\"),xt.compressedTexSubImage3D(h,r,e.x,e.y,e.z,s,a,o,l,g.data)):xt.texSubImage3D(h,r,e.x,e.y,e.z,s,a,o,l,c,g),xt.pixelStorei(3314,u),xt.pixelStorei(32878,d),xt.pixelStorei(3316,p),xt.pixelStorei(3315,m),xt.pixelStorei(32877,f),0===r&&i.generateMipmaps&&xt.generateMipmap(h),K.unbindTexture()},this.initTexture=function(t){et.setTexture2D(t,0),K.unbindTexture()},this.resetState=function(){M=0,S=0,T=null,K.reset(),yt.reset()},\\\\\\\"undefined\\\\\\\"!=typeof __THREE_DEVTOOLS__&&__THREE_DEVTOOLS__.dispatchEvent(new CustomEvent(\\\\\\\"observe\\\\\\\",{detail:this}))}class Qs extends Zs{}Qs.prototype.isWebGL1Renderer=!0;class Ks{constructor(t,e=25e-5){this.name=\\\\\\\"\\\\\\\",this.color=new Qe(t),this.density=e}clone(){return new Ks(this.color,this.density)}toJSON(){return{type:\\\\\\\"FogExp2\\\\\\\",color:this.color.getHex(),density:this.density}}}Ks.prototype.isFogExp2=!0;class $s{constructor(t,e=1,n=1e3){this.name=\\\\\\\"\\\\\\\",this.color=new Qe(t),this.near=e,this.far=n}clone(){return new $s(this.color,this.near,this.far)}toJSON(){return{type:\\\\\\\"Fog\\\\\\\",color:this.color.getHex(),near:this.near,far:this.far}}}$s.prototype.isFog=!0;class ta extends Pe{constructor(){super(),this.type=\\\\\\\"Scene\\\\\\\",this.background=null,this.environment=null,this.fog=null,this.overrideMaterial=null,this.autoUpdate=!0,\\\\\\\"undefined\\\\\\\"!=typeof __THREE_DEVTOOLS__&&__THREE_DEVTOOLS__.dispatchEvent(new CustomEvent(\\\\\\\"observe\\\\\\\",{detail:this}))}copy(t,e){return super.copy(t,e),null!==t.background&&(this.background=t.background.clone()),null!==t.environment&&(this.environment=t.environment.clone()),null!==t.fog&&(this.fog=t.fog.clone()),null!==t.overrideMaterial&&(this.overrideMaterial=t.overrideMaterial.clone()),this.autoUpdate=t.autoUpdate,this.matrixAutoUpdate=t.matrixAutoUpdate,this}toJSON(t){const e=super.toJSON(t);return null!==this.fog&&(e.object.fog=this.fog.toJSON()),e}}ta.prototype.isScene=!0;class ea{constructor(t,e){this.array=t,this.stride=e,this.count=void 0!==t?t.length/e:0,this.usage=et,this.updateRange={offset:0,count:-1},this.version=0,this.uuid=ct()}onUploadCallback(){}set needsUpdate(t){!0===t&&this.version++}setUsage(t){return this.usage=t,this}copy(t){return this.array=new t.array.constructor(t.array),this.count=t.count,this.stride=t.stride,this.usage=t.usage,this}copyAt(t,e,n){t*=this.stride,n*=e.stride;for(let i=0,r=this.stride;i<r;i++)this.array[t+i]=e.array[n+i];return this}set(t,e=0){return this.array.set(t,e),this}clone(t){void 0===t.arrayBuffers&&(t.arrayBuffers={}),void 0===this.array.buffer._uuid&&(this.array.buffer._uuid=ct()),void 0===t.arrayBuffers[this.array.buffer._uuid]&&(t.arrayBuffers[this.array.buffer._uuid]=this.array.slice(0).buffer);const e=new this.array.constructor(t.arrayBuffers[this.array.buffer._uuid]),n=new this.constructor(e,this.stride);return n.setUsage(this.usage),n}onUpload(t){return this.onUploadCallback=t,this}toJSON(t){return void 0===t.arrayBuffers&&(t.arrayBuffers={}),void 0===this.array.buffer._uuid&&(this.array.buffer._uuid=ct()),void 0===t.arrayBuffers[this.array.buffer._uuid]&&(t.arrayBuffers[this.array.buffer._uuid]=Array.prototype.slice.call(new Uint32Array(this.array.buffer))),{uuid:this.uuid,buffer:this.array.buffer._uuid,type:this.array.constructor.name,stride:this.stride}}}ea.prototype.isInterleavedBuffer=!0;const na=new Rt;class ia{constructor(t,e,n,i=!1){this.name=\\\\\\\"\\\\\\\",this.data=t,this.itemSize=e,this.offset=n,this.normalized=!0===i}get count(){return this.data.count}get array(){return this.data.array}set needsUpdate(t){this.data.needsUpdate=t}applyMatrix4(t){for(let e=0,n=this.data.count;e<n;e++)na.x=this.getX(e),na.y=this.getY(e),na.z=this.getZ(e),na.applyMatrix4(t),this.setXYZ(e,na.x,na.y,na.z);return this}applyNormalMatrix(t){for(let e=0,n=this.count;e<n;e++)na.x=this.getX(e),na.y=this.getY(e),na.z=this.getZ(e),na.applyNormalMatrix(t),this.setXYZ(e,na.x,na.y,na.z);return this}transformDirection(t){for(let e=0,n=this.count;e<n;e++)na.x=this.getX(e),na.y=this.getY(e),na.z=this.getZ(e),na.transformDirection(t),this.setXYZ(e,na.x,na.y,na.z);return this}setX(t,e){return this.data.array[t*this.data.stride+this.offset]=e,this}setY(t,e){return this.data.array[t*this.data.stride+this.offset+1]=e,this}setZ(t,e){return this.data.array[t*this.data.stride+this.offset+2]=e,this}setW(t,e){return this.data.array[t*this.data.stride+this.offset+3]=e,this}getX(t){return this.data.array[t*this.data.stride+this.offset]}getY(t){return this.data.array[t*this.data.stride+this.offset+1]}getZ(t){return this.data.array[t*this.data.stride+this.offset+2]}getW(t){return this.data.array[t*this.data.stride+this.offset+3]}setXY(t,e,n){return t=t*this.data.stride+this.offset,this.data.array[t+0]=e,this.data.array[t+1]=n,this}setXYZ(t,e,n,i){return t=t*this.data.stride+this.offset,this.data.array[t+0]=e,this.data.array[t+1]=n,this.data.array[t+2]=i,this}setXYZW(t,e,n,i,r){return t=t*this.data.stride+this.offset,this.data.array[t+0]=e,this.data.array[t+1]=n,this.data.array[t+2]=i,this.data.array[t+3]=r,this}clone(t){if(void 0===t){console.log(\\\\\\\"THREE.InterleavedBufferAttribute.clone(): Cloning an interlaved buffer attribute will deinterleave buffer data.\\\\\\\");const t=[];for(let e=0;e<this.count;e++){const n=e*this.data.stride+this.offset;for(let e=0;e<this.itemSize;e++)t.push(this.data.array[n+e])}return new en(new this.array.constructor(t),this.itemSize,this.normalized)}return void 0===t.interleavedBuffers&&(t.interleavedBuffers={}),void 0===t.interleavedBuffers[this.data.uuid]&&(t.interleavedBuffers[this.data.uuid]=this.data.clone(t)),new ia(t.interleavedBuffers[this.data.uuid],this.itemSize,this.offset,this.normalized)}toJSON(t){if(void 0===t){console.log(\\\\\\\"THREE.InterleavedBufferAttribute.toJSON(): Serializing an interlaved buffer attribute will deinterleave buffer data.\\\\\\\");const t=[];for(let e=0;e<this.count;e++){const n=e*this.data.stride+this.offset;for(let e=0;e<this.itemSize;e++)t.push(this.data.array[n+e])}return{itemSize:this.itemSize,type:this.array.constructor.name,array:t,normalized:this.normalized}}return void 0===t.interleavedBuffers&&(t.interleavedBuffers={}),void 0===t.interleavedBuffers[this.data.uuid]&&(t.interleavedBuffers[this.data.uuid]=this.data.toJSON(t)),{isInterleavedBufferAttribute:!0,itemSize:this.itemSize,data:this.data.uuid,offset:this.offset,normalized:this.normalized}}}ia.prototype.isInterleavedBufferAttribute=!0;class ra extends We{constructor(t){super(),this.type=\\\\\\\"SpriteMaterial\\\\\\\",this.color=new Qe(16777215),this.map=null,this.alphaMap=null,this.rotation=0,this.sizeAttenuation=!0,this.transparent=!0,this.setValues(t)}copy(t){return super.copy(t),this.color.copy(t.color),this.map=t.map,this.alphaMap=t.alphaMap,this.rotation=t.rotation,this.sizeAttenuation=t.sizeAttenuation,this}}let sa;ra.prototype.isSpriteMaterial=!0;const aa=new Rt,oa=new Rt,la=new Rt,ca=new vt,ha=new vt,ua=new ae,da=new Rt,pa=new Rt,ma=new Rt,fa=new vt,ga=new vt,va=new vt;class ya extends Pe{constructor(t){if(super(),this.type=\\\\\\\"Sprite\\\\\\\",void 0===sa){sa=new wn;const t=new Float32Array([-.5,-.5,0,0,0,.5,-.5,0,1,0,.5,.5,0,1,1,-.5,.5,0,0,1]),e=new ea(t,5);sa.setIndex([0,1,2,0,2,3]),sa.setAttribute(\\\\\\\"position\\\\\\\",new ia(e,3,0,!1)),sa.setAttribute(\\\\\\\"uv\\\\\\\",new ia(e,2,3,!1))}this.geometry=sa,this.material=void 0!==t?t:new ra,this.center=new vt(.5,.5)}raycast(t,e){null===t.camera&&console.error('THREE.Sprite: \\\\\\\"Raycaster.camera\\\\\\\" needs to be set in order to raycast against sprites.'),oa.setFromMatrixScale(this.matrixWorld),ua.copy(t.camera.matrixWorld),this.modelViewMatrix.multiplyMatrices(t.camera.matrixWorldInverse,this.matrixWorld),la.setFromMatrixPosition(this.modelViewMatrix),t.camera.isPerspectiveCamera&&!1===this.material.sizeAttenuation&&oa.multiplyScalar(-la.z);const n=this.material.rotation;let i,r;0!==n&&(r=Math.cos(n),i=Math.sin(n));const s=this.center;xa(da.set(-.5,-.5,0),la,s,oa,i,r),xa(pa.set(.5,-.5,0),la,s,oa,i,r),xa(ma.set(.5,.5,0),la,s,oa,i,r),fa.set(0,0),ga.set(1,0),va.set(1,1);let a=t.ray.intersectTriangle(da,pa,ma,!1,aa);if(null===a&&(xa(pa.set(-.5,.5,0),la,s,oa,i,r),ga.set(0,1),a=t.ray.intersectTriangle(da,ma,pa,!1,aa),null===a))return;const o=t.ray.origin.distanceTo(aa);o<t.near||o>t.far||e.push({distance:o,point:aa.clone(),uv:ke.getUV(aa,da,pa,ma,fa,ga,va,new vt),face:null,object:this})}copy(t){return super.copy(t),void 0!==t.center&&this.center.copy(t.center),this.material=t.material,this}}function xa(t,e,n,i,r,s){ca.subVectors(t,n).addScalar(.5).multiply(i),void 0!==r?(ha.x=s*ca.x-r*ca.y,ha.y=r*ca.x+s*ca.y):ha.copy(ca),t.copy(e),t.x+=ha.x,t.y+=ha.y,t.applyMatrix4(ua)}ya.prototype.isSprite=!0;const _a=new Rt,Ma=new Rt;class ba extends Pe{constructor(){super(),this._currentLevel=0,this.type=\\\\\\\"LOD\\\\\\\",Object.defineProperties(this,{levels:{enumerable:!0,value:[]},isLOD:{value:!0}}),this.autoUpdate=!0}copy(t){super.copy(t,!1);const e=t.levels;for(let t=0,n=e.length;t<n;t++){const n=e[t];this.addLevel(n.object.clone(),n.distance)}return this.autoUpdate=t.autoUpdate,this}addLevel(t,e=0){e=Math.abs(e);const n=this.levels;let i;for(i=0;i<n.length&&!(e<n[i].distance);i++);return n.splice(i,0,{distance:e,object:t}),this.add(t),this}getCurrentLevel(){return this._currentLevel}getObjectForDistance(t){const e=this.levels;if(e.length>0){let n,i;for(n=1,i=e.length;n<i&&!(t<e[n].distance);n++);return e[n-1].object}return null}raycast(t,e){if(this.levels.length>0){_a.setFromMatrixPosition(this.matrixWorld);const n=t.ray.origin.distanceTo(_a);this.getObjectForDistance(n).raycast(t,e)}}update(t){const e=this.levels;if(e.length>1){_a.setFromMatrixPosition(t.matrixWorld),Ma.setFromMatrixPosition(this.matrixWorld);const n=_a.distanceTo(Ma)/t.zoom;let i,r;for(e[0].object.visible=!0,i=1,r=e.length;i<r&&n>=e[i].distance;i++)e[i-1].object.visible=!1,e[i].object.visible=!0;for(this._currentLevel=i-1;i<r;i++)e[i].object.visible=!1}}toJSON(t){const e=super.toJSON(t);!1===this.autoUpdate&&(e.object.autoUpdate=!1),e.object.levels=[];const n=this.levels;for(let t=0,i=n.length;t<i;t++){const i=n[t];e.object.levels.push({object:i.object.uuid,distance:i.distance})}return e}}const wa=new Rt,Sa=new St,Ta=new St,Ea=new Rt,Aa=new ae;class La extends Gn{constructor(t,e){super(t,e),this.type=\\\\\\\"SkinnedMesh\\\\\\\",this.bindMode=\\\\\\\"attached\\\\\\\",this.bindMatrix=new ae,this.bindMatrixInverse=new ae}copy(t){return super.copy(t),this.bindMode=t.bindMode,this.bindMatrix.copy(t.bindMatrix),this.bindMatrixInverse.copy(t.bindMatrixInverse),this.skeleton=t.skeleton,this}bind(t,e){this.skeleton=t,void 0===e&&(this.updateMatrixWorld(!0),this.skeleton.calculateInverses(),e=this.matrixWorld),this.bindMatrix.copy(e),this.bindMatrixInverse.copy(e).invert()}pose(){this.skeleton.pose()}normalizeSkinWeights(){const t=new St,e=this.geometry.attributes.skinWeight;for(let n=0,i=e.count;n<i;n++){t.x=e.getX(n),t.y=e.getY(n),t.z=e.getZ(n),t.w=e.getW(n);const i=1/t.manhattanLength();i!==1/0?t.multiplyScalar(i):t.set(1,0,0,0),e.setXYZW(n,t.x,t.y,t.z,t.w)}}updateMatrixWorld(t){super.updateMatrixWorld(t),\\\\\\\"attached\\\\\\\"===this.bindMode?this.bindMatrixInverse.copy(this.matrixWorld).invert():\\\\\\\"detached\\\\\\\"===this.bindMode?this.bindMatrixInverse.copy(this.bindMatrix).invert():console.warn(\\\\\\\"THREE.SkinnedMesh: Unrecognized bindMode: \\\\\\\"+this.bindMode)}boneTransform(t,e){const n=this.skeleton,i=this.geometry;Sa.fromBufferAttribute(i.attributes.skinIndex,t),Ta.fromBufferAttribute(i.attributes.skinWeight,t),wa.fromBufferAttribute(i.attributes.position,t).applyMatrix4(this.bindMatrix),e.set(0,0,0);for(let t=0;t<4;t++){const i=Ta.getComponent(t);if(0!==i){const r=Sa.getComponent(t);Aa.multiplyMatrices(n.bones[r].matrixWorld,n.boneInverses[r]),e.addScaledVector(Ea.copy(wa).applyMatrix4(Aa),i)}}return e.applyMatrix4(this.bindMatrixInverse)}}La.prototype.isSkinnedMesh=!0;class Ra extends Pe{constructor(){super(),this.type=\\\\\\\"Bone\\\\\\\"}}Ra.prototype.isBone=!0;class Ca extends bt{constructor(t=null,e=1,n=1,i,r,s,a,o,l=1003,c=1003,h,u){super(null,s,a,o,l,c,i,r,h,u),this.image={data:t,width:e,height:n},this.magFilter=l,this.minFilter=c,this.generateMipmaps=!1,this.flipY=!1,this.unpackAlignment=1,this.needsUpdate=!0}}Ca.prototype.isDataTexture=!0;const Pa=new ae,Ia=new ae;class Da{constructor(t=[],e=[]){this.uuid=ct(),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<e;t++)this.boneInverses.push(new ae)}}calculateInverses(){this.boneInverses.length=0;for(let t=0,e=this.bones.length;t<e;t++){const e=new ae;this.bones[t]&&e.copy(this.bones[t].matrixWorld).invert(),this.boneInverses.push(e)}}pose(){for(let t=0,e=this.bones.length;t<e;t++){const e=this.bones[t];e&&e.matrixWorld.copy(this.boneInverses[t]).invert()}for(let t=0,e=this.bones.length;t<e;t++){const e=this.bones[t];e&&(e.parent&&e.parent.isBone?(e.matrix.copy(e.parent.matrixWorld).invert(),e.matrix.multiply(e.matrixWorld)):e.matrix.copy(e.matrixWorld),e.matrix.decompose(e.position,e.quaternion,e.scale))}}update(){const t=this.bones,e=this.boneInverses,n=this.boneMatrices,i=this.boneTexture;for(let i=0,r=t.length;i<r;i++){const r=t[i]?t[i].matrixWorld:Ia;Pa.multiplyMatrices(r,e[i]),Pa.toArray(n,16*i)}null!==i&&(i.needsUpdate=!0)}clone(){return new Da(this.bones,this.boneInverses)}computeBoneTexture(){let t=Math.sqrt(4*this.bones.length);t=mt(t),t=Math.max(t,4);const e=new Float32Array(t*t*4);e.set(this.boneMatrices);const n=new Ca(e,t,t,E,b);return this.boneMatrices=e,this.boneTexture=n,this.boneTextureSize=t,this}getBoneByName(t){for(let e=0,n=this.bones.length;e<n;e++){const n=this.bones[e];if(n.name===t)return n}}dispose(){null!==this.boneTexture&&(this.boneTexture.dispose(),this.boneTexture=null)}fromJSON(t,e){this.uuid=t.uuid;for(let n=0,i=t.bones.length;n<i;n++){const i=t.bones[n];let r=e[i];void 0===r&&(console.warn(\\\\\\\"THREE.Skeleton: No bone found with UUID:\\\\\\\",i),r=new Ra),this.bones.push(r),this.boneInverses.push((new ae).fromArray(t.boneInverses[n]))}return this.init(),this}toJSON(){const t={metadata:{version:4.5,type:\\\\\\\"Skeleton\\\\\\\",generator:\\\\\\\"Skeleton.toJSON\\\\\\\"},bones:[],boneInverses:[]};t.uuid=this.uuid;const e=this.bones,n=this.boneInverses;for(let i=0,r=e.length;i<r;i++){const r=e[i];t.bones.push(r.uuid);const s=n[i];t.boneInverses.push(s.toArray())}return t}}const Na=new ae,Ba=new ae,za=[],Fa=new Gn;class Oa extends Gn{constructor(t,e,n){super(t,e),this.instanceMatrix=new en(new Float32Array(16*n),16),this.instanceColor=null,this.count=n,this.frustumCulled=!1}copy(t){return super.copy(t),this.instanceMatrix.copy(t.instanceMatrix),null!==t.instanceColor&&(this.instanceColor=t.instanceColor.clone()),this.count=t.count,this}getColorAt(t,e){e.fromArray(this.instanceColor.array,3*t)}getMatrixAt(t,e){e.fromArray(this.instanceMatrix.array,16*t)}raycast(t,e){const n=this.matrixWorld,i=this.count;if(Fa.geometry=this.geometry,Fa.material=this.material,void 0!==Fa.material)for(let r=0;r<i;r++){this.getMatrixAt(r,Na),Ba.multiplyMatrices(n,Na),Fa.matrixWorld=Ba,Fa.raycast(t,za);for(let t=0,n=za.length;t<n;t++){const n=za[t];n.instanceId=r,n.object=this,e.push(n)}za.length=0}}setColorAt(t,e){null===this.instanceColor&&(this.instanceColor=new en(new Float32Array(3*this.instanceMatrix.count),3)),e.toArray(this.instanceColor.array,3*t)}setMatrixAt(t,e){e.toArray(this.instanceMatrix.array,16*t)}updateMorphTargets(){}dispose(){this.dispatchEvent({type:\\\\\\\"dispose\\\\\\\"})}}Oa.prototype.isInstancedMesh=!0;class Ua extends We{constructor(t){super(),this.type=\\\\\\\"LineBasicMaterial\\\\\\\",this.color=new Qe(16777215),this.linewidth=1,this.linecap=\\\\\\\"round\\\\\\\",this.linejoin=\\\\\\\"round\\\\\\\",this.setValues(t)}copy(t){return super.copy(t),this.color.copy(t.color),this.linewidth=t.linewidth,this.linecap=t.linecap,this.linejoin=t.linejoin,this}}Ua.prototype.isLineBasicMaterial=!0;const Ha=new Rt,Ga=new Rt,ka=new ae,Va=new se,Wa=new Qt;class ja extends Pe{constructor(t=new wn,e=new Ua){super(),this.type=\\\\\\\"Line\\\\\\\",this.geometry=t,this.material=e,this.updateMorphTargets()}copy(t){return super.copy(t),this.material=t.material,this.geometry=t.geometry,this}computeLineDistances(){const t=this.geometry;if(t.isBufferGeometry)if(null===t.index){const e=t.attributes.position,n=[0];for(let t=1,i=e.count;t<i;t++)Ha.fromBufferAttribute(e,t-1),Ga.fromBufferAttribute(e,t),n[t]=n[t-1],n[t]+=Ha.distanceTo(Ga);t.setAttribute(\\\\\\\"lineDistance\\\\\\\",new un(n,1))}else console.warn(\\\\\\\"THREE.Line.computeLineDistances(): Computation only possible with non-indexed BufferGeometry.\\\\\\\");else t.isGeometry&&console.error(\\\\\\\"THREE.Line.computeLineDistances() no longer supports THREE.Geometry. Use THREE.BufferGeometry instead.\\\\\\\");return this}raycast(t,e){const n=this.geometry,i=this.matrixWorld,r=t.params.Line.threshold,s=n.drawRange;if(null===n.boundingSphere&&n.computeBoundingSphere(),Wa.copy(n.boundingSphere),Wa.applyMatrix4(i),Wa.radius+=r,!1===t.ray.intersectsSphere(Wa))return;ka.copy(i).invert(),Va.copy(t.ray).applyMatrix4(ka);const a=r/((this.scale.x+this.scale.y+this.scale.z)/3),o=a*a,l=new Rt,c=new Rt,h=new Rt,u=new Rt,d=this.isLineSegments?2:1;if(n.isBufferGeometry){const i=n.index,r=n.attributes.position;if(null!==i){for(let n=Math.max(0,s.start),a=Math.min(i.count,s.start+s.count)-1;n<a;n+=d){const s=i.getX(n),a=i.getX(n+1);l.fromBufferAttribute(r,s),c.fromBufferAttribute(r,a);if(Va.distanceSqToSegment(l,c,u,h)>o)continue;u.applyMatrix4(this.matrixWorld);const d=t.ray.origin.distanceTo(u);d<t.near||d>t.far||e.push({distance:d,point:h.clone().applyMatrix4(this.matrixWorld),index:n,face:null,faceIndex:null,object:this})}}else{for(let n=Math.max(0,s.start),i=Math.min(r.count,s.start+s.count)-1;n<i;n+=d){l.fromBufferAttribute(r,n),c.fromBufferAttribute(r,n+1);if(Va.distanceSqToSegment(l,c,u,h)>o)continue;u.applyMatrix4(this.matrixWorld);const i=t.ray.origin.distanceTo(u);i<t.near||i>t.far||e.push({distance:i,point:h.clone().applyMatrix4(this.matrixWorld),index:n,face:null,faceIndex:null,object:this})}}}else n.isGeometry&&console.error(\\\\\\\"THREE.Line.raycast() no longer supports THREE.Geometry. Use THREE.BufferGeometry instead.\\\\\\\")}updateMorphTargets(){const t=this.geometry;if(t.isBufferGeometry){const e=t.morphAttributes,n=Object.keys(e);if(n.length>0){const t=e[n[0]];if(void 0!==t){this.morphTargetInfluences=[],this.morphTargetDictionary={};for(let e=0,n=t.length;e<n;e++){const n=t[e].name||String(e);this.morphTargetInfluences.push(0),this.morphTargetDictionary[n]=e}}}}else{const e=t.morphTargets;void 0!==e&&e.length>0&&console.error(\\\\\\\"THREE.Line.updateMorphTargets() does not support THREE.Geometry. Use THREE.BufferGeometry instead.\\\\\\\")}}}ja.prototype.isLine=!0;const qa=new Rt,Xa=new Rt;class Ya extends ja{constructor(t,e){super(t,e),this.type=\\\\\\\"LineSegments\\\\\\\"}computeLineDistances(){const t=this.geometry;if(t.isBufferGeometry)if(null===t.index){const e=t.attributes.position,n=[];for(let t=0,i=e.count;t<i;t+=2)qa.fromBufferAttribute(e,t),Xa.fromBufferAttribute(e,t+1),n[t]=0===t?0:n[t-1],n[t+1]=n[t]+qa.distanceTo(Xa);t.setAttribute(\\\\\\\"lineDistance\\\\\\\",new un(n,1))}else console.warn(\\\\\\\"THREE.LineSegments.computeLineDistances(): Computation only possible with non-indexed BufferGeometry.\\\\\\\");else t.isGeometry&&console.error(\\\\\\\"THREE.LineSegments.computeLineDistances() no longer supports THREE.Geometry. Use THREE.BufferGeometry instead.\\\\\\\");return this}}Ya.prototype.isLineSegments=!0;class Ja extends ja{constructor(t,e){super(t,e),this.type=\\\\\\\"LineLoop\\\\\\\"}}Ja.prototype.isLineLoop=!0;class Za extends We{constructor(t){super(),this.type=\\\\\\\"PointsMaterial\\\\\\\",this.color=new Qe(16777215),this.map=null,this.alphaMap=null,this.size=1,this.sizeAttenuation=!0,this.setValues(t)}copy(t){return super.copy(t),this.color.copy(t.color),this.map=t.map,this.alphaMap=t.alphaMap,this.size=t.size,this.sizeAttenuation=t.sizeAttenuation,this}}Za.prototype.isPointsMaterial=!0;const Qa=new ae,Ka=new se,$a=new Qt,to=new Rt;class eo extends Pe{constructor(t=new wn,e=new Za){super(),this.type=\\\\\\\"Points\\\\\\\",this.geometry=t,this.material=e,this.updateMorphTargets()}copy(t){return super.copy(t),this.material=t.material,this.geometry=t.geometry,this}raycast(t,e){const n=this.geometry,i=this.matrixWorld,r=t.params.Points.threshold,s=n.drawRange;if(null===n.boundingSphere&&n.computeBoundingSphere(),$a.copy(n.boundingSphere),$a.applyMatrix4(i),$a.radius+=r,!1===t.ray.intersectsSphere($a))return;Qa.copy(i).invert(),Ka.copy(t.ray).applyMatrix4(Qa);const a=r/((this.scale.x+this.scale.y+this.scale.z)/3),o=a*a;if(n.isBufferGeometry){const r=n.index,a=n.attributes.position;if(null!==r){for(let n=Math.max(0,s.start),l=Math.min(r.count,s.start+s.count);n<l;n++){const s=r.getX(n);to.fromBufferAttribute(a,s),no(to,s,o,i,t,e,this)}}else{for(let n=Math.max(0,s.start),r=Math.min(a.count,s.start+s.count);n<r;n++)to.fromBufferAttribute(a,n),no(to,n,o,i,t,e,this)}}else console.error(\\\\\\\"THREE.Points.raycast() no longer supports THREE.Geometry. Use THREE.BufferGeometry instead.\\\\\\\")}updateMorphTargets(){const t=this.geometry;if(t.isBufferGeometry){const e=t.morphAttributes,n=Object.keys(e);if(n.length>0){const t=e[n[0]];if(void 0!==t){this.morphTargetInfluences=[],this.morphTargetDictionary={};for(let e=0,n=t.length;e<n;e++){const n=t[e].name||String(e);this.morphTargetInfluences.push(0),this.morphTargetDictionary[n]=e}}}}else{const e=t.morphTargets;void 0!==e&&e.length>0&&console.error(\\\\\\\"THREE.Points.updateMorphTargets() does not support THREE.Geometry. Use THREE.BufferGeometry instead.\\\\\\\")}}}function no(t,e,n,i,r,s,a){const o=Ka.distanceSqToPoint(t);if(o<n){const n=new Rt;Ka.closestPointToPoint(t,n),n.applyMatrix4(i);const l=r.ray.origin.distanceTo(n);if(l<r.near||l>r.far)return;s.push({distance:l,distanceToRay:Math.sqrt(o),point:n,index:e,face:null,object:a})}}eo.prototype.isPoints=!0;class io extends bt{constructor(t,e,n,i,r,s,a,o,l){super(t,e,n,i,r,s,a,o,l),this.format=void 0!==a?a:T,this.minFilter=void 0!==s?s:g,this.magFilter=void 0!==r?r:g,this.generateMipmaps=!1;const c=this;\\\\\\\"requestVideoFrameCallback\\\\\\\"in t&&t.requestVideoFrameCallback((function e(){c.needsUpdate=!0,t.requestVideoFrameCallback(e)}))}clone(){return new this.constructor(this.image).copy(this)}update(){const t=this.image;!1===\\\\\\\"requestVideoFrameCallback\\\\\\\"in t&&t.readyState>=t.HAVE_CURRENT_DATA&&(this.needsUpdate=!0)}}io.prototype.isVideoTexture=!0;class ro extends bt{constructor(t,e,n,i,r,s,a,o,l,c,h,u){super(null,s,a,o,l,c,i,r,h,u),this.image={width:e,height:n},this.mipmaps=t,this.flipY=!1,this.generateMipmaps=!1}}ro.prototype.isCompressedTexture=!0;class so extends bt{constructor(t,e,n,i,r,s,a,o,l){super(t,e,n,i,r,s,a,o,l),this.needsUpdate=!0}}so.prototype.isCanvasTexture=!0;class ao extends bt{constructor(t,e,n,i,r,s,a,o,l,c){if((c=void 0!==c?c:A)!==A&&c!==L)throw new Error(\\\\\\\"DepthTexture format must be either THREE.DepthFormat or THREE.DepthStencilFormat\\\\\\\");void 0===n&&c===A&&(n=_),void 0===n&&c===L&&(n=S),super(null,i,r,s,a,o,c,n,l),this.image={width:t,height:e},this.magFilter=void 0!==a?a:p,this.minFilter=void 0!==o?o:p,this.flipY=!1,this.generateMipmaps=!1}}ao.prototype.isDepthTexture=!0;class oo extends wn{constructor(t=1,e=8,n=0,i=2*Math.PI){super(),this.type=\\\\\\\"CircleGeometry\\\\\\\",this.parameters={radius:t,segments:e,thetaStart:n,thetaLength:i},e=Math.max(3,e);const r=[],s=[],a=[],o=[],l=new Rt,c=new vt;s.push(0,0,0),a.push(0,0,1),o.push(.5,.5);for(let r=0,h=3;r<=e;r++,h+=3){const u=n+r/e*i;l.x=t*Math.cos(u),l.y=t*Math.sin(u),s.push(l.x,l.y,l.z),a.push(0,0,1),c.x=(s[h]/t+1)/2,c.y=(s[h+1]/t+1)/2,o.push(c.x,c.y)}for(let t=1;t<=e;t++)r.push(t,t+1,0);this.setIndex(r),this.setAttribute(\\\\\\\"position\\\\\\\",new un(s,3)),this.setAttribute(\\\\\\\"normal\\\\\\\",new un(a,3)),this.setAttribute(\\\\\\\"uv\\\\\\\",new un(o,2))}static fromJSON(t){return new oo(t.radius,t.segments,t.thetaStart,t.thetaLength)}}class lo extends wn{constructor(t=1,e=1,n=1,i=8,r=1,s=!1,a=0,o=2*Math.PI){super(),this.type=\\\\\\\"CylinderGeometry\\\\\\\",this.parameters={radiusTop:t,radiusBottom:e,height:n,radialSegments:i,heightSegments:r,openEnded:s,thetaStart:a,thetaLength:o};const l=this;i=Math.floor(i),r=Math.floor(r);const c=[],h=[],u=[],d=[];let p=0;const m=[],f=n/2;let g=0;function v(n){const r=p,s=new vt,m=new Rt;let v=0;const y=!0===n?t:e,x=!0===n?1:-1;for(let t=1;t<=i;t++)h.push(0,f*x,0),u.push(0,x,0),d.push(.5,.5),p++;const _=p;for(let t=0;t<=i;t++){const e=t/i*o+a,n=Math.cos(e),r=Math.sin(e);m.x=y*r,m.y=f*x,m.z=y*n,h.push(m.x,m.y,m.z),u.push(0,x,0),s.x=.5*n+.5,s.y=.5*r*x+.5,d.push(s.x,s.y),p++}for(let t=0;t<i;t++){const e=r+t,i=_+t;!0===n?c.push(i,i+1,e):c.push(i+1,i,e),v+=3}l.addGroup(g,v,!0===n?1:2),g+=v}!function(){const s=new Rt,v=new Rt;let y=0;const x=(e-t)/n;for(let l=0;l<=r;l++){const c=[],g=l/r,y=g*(e-t)+t;for(let t=0;t<=i;t++){const e=t/i,r=e*o+a,l=Math.sin(r),m=Math.cos(r);v.x=y*l,v.y=-g*n+f,v.z=y*m,h.push(v.x,v.y,v.z),s.set(l,x,m).normalize(),u.push(s.x,s.y,s.z),d.push(e,1-g),c.push(p++)}m.push(c)}for(let t=0;t<i;t++)for(let e=0;e<r;e++){const n=m[e][t],i=m[e+1][t],r=m[e+1][t+1],s=m[e][t+1];c.push(n,i,s),c.push(i,r,s),y+=6}l.addGroup(g,y,0),g+=y}(),!1===s&&(t>0&&v(!0),e>0&&v(!1)),this.setIndex(c),this.setAttribute(\\\\\\\"position\\\\\\\",new un(h,3)),this.setAttribute(\\\\\\\"normal\\\\\\\",new un(u,3)),this.setAttribute(\\\\\\\"uv\\\\\\\",new un(d,2))}static fromJSON(t){return new lo(t.radiusTop,t.radiusBottom,t.height,t.radialSegments,t.heightSegments,t.openEnded,t.thetaStart,t.thetaLength)}}class co extends lo{constructor(t=1,e=1,n=8,i=1,r=!1,s=0,a=2*Math.PI){super(0,t,e,n,i,r,s,a),this.type=\\\\\\\"ConeGeometry\\\\\\\",this.parameters={radius:t,height:e,radialSegments:n,heightSegments:i,openEnded:r,thetaStart:s,thetaLength:a}}static fromJSON(t){return new co(t.radius,t.height,t.radialSegments,t.heightSegments,t.openEnded,t.thetaStart,t.thetaLength)}}class ho extends wn{constructor(t,e,n=1,i=0){super(),this.type=\\\\\\\"PolyhedronGeometry\\\\\\\",this.parameters={vertices:t,indices:e,radius:n,detail:i};const r=[],s=[];function a(t,e,n,i){const r=i+1,s=[];for(let i=0;i<=r;i++){s[i]=[];const a=t.clone().lerp(n,i/r),o=e.clone().lerp(n,i/r),l=r-i;for(let t=0;t<=l;t++)s[i][t]=0===t&&i===r?a:a.clone().lerp(o,t/l)}for(let t=0;t<r;t++)for(let e=0;e<2*(r-t)-1;e++){const n=Math.floor(e/2);e%2==0?(o(s[t][n+1]),o(s[t+1][n]),o(s[t][n])):(o(s[t][n+1]),o(s[t+1][n+1]),o(s[t+1][n]))}}function o(t){r.push(t.x,t.y,t.z)}function l(e,n){const i=3*e;n.x=t[i+0],n.y=t[i+1],n.z=t[i+2]}function c(t,e,n,i){i<0&&1===t.x&&(s[e]=t.x-1),0===n.x&&0===n.z&&(s[e]=i/2/Math.PI+.5)}function h(t){return Math.atan2(t.z,-t.x)}!function(t){const n=new Rt,i=new Rt,r=new Rt;for(let s=0;s<e.length;s+=3)l(e[s+0],n),l(e[s+1],i),l(e[s+2],r),a(n,i,r,t)}(i),function(t){const e=new Rt;for(let n=0;n<r.length;n+=3)e.x=r[n+0],e.y=r[n+1],e.z=r[n+2],e.normalize().multiplyScalar(t),r[n+0]=e.x,r[n+1]=e.y,r[n+2]=e.z}(n),function(){const t=new Rt;for(let n=0;n<r.length;n+=3){t.x=r[n+0],t.y=r[n+1],t.z=r[n+2];const i=h(t)/2/Math.PI+.5,a=(e=t,Math.atan2(-e.y,Math.sqrt(e.x*e.x+e.z*e.z))/Math.PI+.5);s.push(i,1-a)}var e;(function(){const t=new Rt,e=new Rt,n=new Rt,i=new Rt,a=new vt,o=new vt,l=new vt;for(let u=0,d=0;u<r.length;u+=9,d+=6){t.set(r[u+0],r[u+1],r[u+2]),e.set(r[u+3],r[u+4],r[u+5]),n.set(r[u+6],r[u+7],r[u+8]),a.set(s[d+0],s[d+1]),o.set(s[d+2],s[d+3]),l.set(s[d+4],s[d+5]),i.copy(t).add(e).add(n).divideScalar(3);const p=h(i);c(a,d+0,t,p),c(o,d+2,e,p),c(l,d+4,n,p)}})(),function(){for(let t=0;t<s.length;t+=6){const e=s[t+0],n=s[t+2],i=s[t+4],r=Math.max(e,n,i),a=Math.min(e,n,i);r>.9&&a<.1&&(e<.2&&(s[t+0]+=1),n<.2&&(s[t+2]+=1),i<.2&&(s[t+4]+=1))}}()}(),this.setAttribute(\\\\\\\"position\\\\\\\",new un(r,3)),this.setAttribute(\\\\\\\"normal\\\\\\\",new un(r.slice(),3)),this.setAttribute(\\\\\\\"uv\\\\\\\",new un(s,2)),0===i?this.computeVertexNormals():this.normalizeNormals()}static fromJSON(t){return new ho(t.vertices,t.indices,t.radius,t.details)}}class uo extends ho{constructor(t=1,e=0){const n=(1+Math.sqrt(5))/2,i=1/n;super([-1,-1,-1,-1,-1,1,-1,1,-1,-1,1,1,1,-1,-1,1,-1,1,1,1,-1,1,1,1,0,-i,-n,0,-i,n,0,i,-n,0,i,n,-i,-n,0,-i,n,0,i,-n,0,i,n,0,-n,0,-i,n,0,-i,-n,0,i,n,0,i],[3,11,7,3,7,15,3,15,13,7,19,17,7,17,6,7,6,15,17,4,8,17,8,10,17,10,6,8,0,16,8,16,2,8,2,10,0,12,1,0,1,18,0,18,16,6,10,2,6,2,13,6,13,15,2,16,18,2,18,3,2,3,13,18,1,9,18,9,11,18,11,3,4,14,12,4,12,0,4,0,8,11,9,5,11,5,19,11,19,7,19,5,14,19,14,4,19,4,17,1,12,14,1,14,5,1,5,9],t,e),this.type=\\\\\\\"DodecahedronGeometry\\\\\\\",this.parameters={radius:t,detail:e}}static fromJSON(t){return new uo(t.radius,t.detail)}}const po=new Rt,mo=new Rt,fo=new Rt,go=new ke;class vo extends wn{constructor(t,e){if(super(),this.type=\\\\\\\"EdgesGeometry\\\\\\\",this.parameters={thresholdAngle:e},e=void 0!==e?e:1,!0===t.isGeometry)return void console.error(\\\\\\\"THREE.EdgesGeometry no longer supports THREE.Geometry. Use THREE.BufferGeometry instead.\\\\\\\");const n=Math.cos(ot*e),i=t.getIndex(),r=t.getAttribute(\\\\\\\"position\\\\\\\"),s=i?i.count:r.count,a=[0,0,0],o=[\\\\\\\"a\\\\\\\",\\\\\\\"b\\\\\\\",\\\\\\\"c\\\\\\\"],l=new Array(3),c={},h=[];for(let t=0;t<s;t+=3){i?(a[0]=i.getX(t),a[1]=i.getX(t+1),a[2]=i.getX(t+2)):(a[0]=t,a[1]=t+1,a[2]=t+2);const{a:e,b:s,c:u}=go;if(e.fromBufferAttribute(r,a[0]),s.fromBufferAttribute(r,a[1]),u.fromBufferAttribute(r,a[2]),go.getNormal(fo),l[0]=`${e.x},${e.y},${e.z}`,l[1]=`${s.x},${s.y},${s.z}`,l[2]=`${u.x},${u.y},${u.z}`,l[0]!==l[1]&&l[1]!==l[2]&&l[2]!==l[0])for(let t=0;t<3;t++){const e=(t+1)%3,i=l[t],r=l[e],s=go[o[t]],u=go[o[e]],d=`${i}_${r}`,p=`${r}_${i}`;p in c&&c[p]?(fo.dot(c[p].normal)<=n&&(h.push(s.x,s.y,s.z),h.push(u.x,u.y,u.z)),c[p]=null):d in c||(c[d]={index0:a[t],index1:a[e],normal:fo.clone()})}}for(const t in c)if(c[t]){const{index0:e,index1:n}=c[t];po.fromBufferAttribute(r,e),mo.fromBufferAttribute(r,n),h.push(po.x,po.y,po.z),h.push(mo.x,mo.y,mo.z)}this.setAttribute(\\\\\\\"position\\\\\\\",new un(h,3))}}class yo{constructor(){this.type=\\\\\\\"Curve\\\\\\\",this.arcLengthDivisions=200}getPoint(){return console.warn(\\\\\\\"THREE.Curve: .getPoint() not implemented.\\\\\\\"),null}getPointAt(t,e){const n=this.getUtoTmapping(t);return this.getPoint(n,e)}getPoints(t=5){const e=[];for(let n=0;n<=t;n++)e.push(this.getPoint(n/t));return e}getSpacedPoints(t=5){const e=[];for(let n=0;n<=t;n++)e.push(this.getPointAt(n/t));return e}getLength(){const t=this.getLengths();return t[t.length-1]}getLengths(t=this.arcLengthDivisions){if(this.cacheArcLengths&&this.cacheArcLengths.length===t+1&&!this.needsUpdate)return this.cacheArcLengths;this.needsUpdate=!1;const e=[];let n,i=this.getPoint(0),r=0;e.push(0);for(let s=1;s<=t;s++)n=this.getPoint(s/t),r+=n.distanceTo(i),e.push(r),i=n;return this.cacheArcLengths=e,e}updateArcLengths(){this.needsUpdate=!0,this.getLengths()}getUtoTmapping(t,e){const n=this.getLengths();let i=0;const r=n.length;let s;s=e||t*n[r-1];let a,o=0,l=r-1;for(;o<=l;)if(i=Math.floor(o+(l-o)/2),a=n[i]-s,a<0)o=i+1;else{if(!(a>0)){l=i;break}l=i-1}if(i=l,n[i]===s)return i/(r-1);const c=n[i];return(i+(s-c)/(n[i+1]-c))/(r-1)}getTangent(t,e){const n=1e-4;let i=t-n,r=t+n;i<0&&(i=0),r>1&&(r=1);const s=this.getPoint(i),a=this.getPoint(r),o=e||(s.isVector2?new vt:new Rt);return o.copy(a).sub(s).normalize(),o}getTangentAt(t,e){const n=this.getUtoTmapping(t);return this.getTangent(n,e)}computeFrenetFrames(t,e){const n=new Rt,i=[],r=[],s=[],a=new Rt,o=new ae;for(let e=0;e<=t;e++){const n=e/t;i[e]=this.getTangentAt(n,new Rt),i[e].normalize()}r[0]=new Rt,s[0]=new Rt;let l=Number.MAX_VALUE;const c=Math.abs(i[0].x),h=Math.abs(i[0].y),u=Math.abs(i[0].z);c<=l&&(l=c,n.set(1,0,0)),h<=l&&(l=h,n.set(0,1,0)),u<=l&&n.set(0,0,1),a.crossVectors(i[0],n).normalize(),r[0].crossVectors(i[0],a),s[0].crossVectors(i[0],r[0]);for(let e=1;e<=t;e++){if(r[e]=r[e-1].clone(),s[e]=s[e-1].clone(),a.crossVectors(i[e-1],i[e]),a.length()>Number.EPSILON){a.normalize();const t=Math.acos(ht(i[e-1].dot(i[e]),-1,1));r[e].applyMatrix4(o.makeRotationAxis(a,t))}s[e].crossVectors(i[e],r[e])}if(!0===e){let e=Math.acos(ht(r[0].dot(r[t]),-1,1));e/=t,i[0].dot(a.crossVectors(r[0],r[t]))>0&&(e=-e);for(let n=1;n<=t;n++)r[n].applyMatrix4(o.makeRotationAxis(i[n],e*n)),s[n].crossVectors(i[n],r[n])}return{tangents:i,normals:r,binormals:s}}clone(){return(new this.constructor).copy(this)}copy(t){return this.arcLengthDivisions=t.arcLengthDivisions,this}toJSON(){const t={metadata:{version:4.5,type:\\\\\\\"Curve\\\\\\\",generator:\\\\\\\"Curve.toJSON\\\\\\\"}};return t.arcLengthDivisions=this.arcLengthDivisions,t.type=this.type,t}fromJSON(t){return this.arcLengthDivisions=t.arcLengthDivisions,this}}class xo extends yo{constructor(t=0,e=0,n=1,i=1,r=0,s=2*Math.PI,a=!1,o=0){super(),this.type=\\\\\\\"EllipseCurve\\\\\\\",this.aX=t,this.aY=e,this.xRadius=n,this.yRadius=i,this.aStartAngle=r,this.aEndAngle=s,this.aClockwise=a,this.aRotation=o}getPoint(t,e){const n=e||new vt,i=2*Math.PI;let r=this.aEndAngle-this.aStartAngle;const s=Math.abs(r)<Number.EPSILON;for(;r<0;)r+=i;for(;r>i;)r-=i;r<Number.EPSILON&&(r=s?0:i),!0!==this.aClockwise||s||(r===i?r=-i:r-=i);const a=this.aStartAngle+t*r;let o=this.aX+this.xRadius*Math.cos(a),l=this.aY+this.yRadius*Math.sin(a);if(0!==this.aRotation){const t=Math.cos(this.aRotation),e=Math.sin(this.aRotation),n=o-this.aX,i=l-this.aY;o=n*t-i*e+this.aX,l=n*e+i*t+this.aY}return n.set(o,l)}copy(t){return super.copy(t),this.aX=t.aX,this.aY=t.aY,this.xRadius=t.xRadius,this.yRadius=t.yRadius,this.aStartAngle=t.aStartAngle,this.aEndAngle=t.aEndAngle,this.aClockwise=t.aClockwise,this.aRotation=t.aRotation,this}toJSON(){const t=super.toJSON();return t.aX=this.aX,t.aY=this.aY,t.xRadius=this.xRadius,t.yRadius=this.yRadius,t.aStartAngle=this.aStartAngle,t.aEndAngle=this.aEndAngle,t.aClockwise=this.aClockwise,t.aRotation=this.aRotation,t}fromJSON(t){return super.fromJSON(t),this.aX=t.aX,this.aY=t.aY,this.xRadius=t.xRadius,this.yRadius=t.yRadius,this.aStartAngle=t.aStartAngle,this.aEndAngle=t.aEndAngle,this.aClockwise=t.aClockwise,this.aRotation=t.aRotation,this}}xo.prototype.isEllipseCurve=!0;class _o extends xo{constructor(t,e,n,i,r,s){super(t,e,n,n,i,r,s),this.type=\\\\\\\"ArcCurve\\\\\\\"}}function Mo(){let t=0,e=0,n=0,i=0;function r(r,s,a,o){t=r,e=a,n=-3*r+3*s-2*a-o,i=2*r-2*s+a+o}return{initCatmullRom:function(t,e,n,i,s){r(e,n,s*(n-t),s*(i-e))},initNonuniformCatmullRom:function(t,e,n,i,s,a,o){let l=(e-t)/s-(n-t)/(s+a)+(n-e)/a,c=(n-e)/a-(i-e)/(a+o)+(i-n)/o;l*=a,c*=a,r(e,n,l,c)},calc:function(r){const s=r*r;return t+e*r+n*s+i*(s*r)}}}_o.prototype.isArcCurve=!0;const bo=new Rt,wo=new Mo,So=new Mo,To=new Mo;class Eo extends yo{constructor(t=[],e=!1,n=\\\\\\\"centripetal\\\\\\\",i=.5){super(),this.type=\\\\\\\"CatmullRomCurve3\\\\\\\",this.points=t,this.closed=e,this.curveType=n,this.tension=i}getPoint(t,e=new Rt){const n=e,i=this.points,r=i.length,s=(r-(this.closed?0:1))*t;let a,o,l=Math.floor(s),c=s-l;this.closed?l+=l>0?0:(Math.floor(Math.abs(l)/r)+1)*r:0===c&&l===r-1&&(l=r-2,c=1),this.closed||l>0?a=i[(l-1)%r]:(bo.subVectors(i[0],i[1]).add(i[0]),a=bo);const h=i[l%r],u=i[(l+1)%r];if(this.closed||l+2<r?o=i[(l+2)%r]:(bo.subVectors(i[r-1],i[r-2]).add(i[r-1]),o=bo),\\\\\\\"centripetal\\\\\\\"===this.curveType||\\\\\\\"chordal\\\\\\\"===this.curveType){const t=\\\\\\\"chordal\\\\\\\"===this.curveType?.5:.25;let e=Math.pow(a.distanceToSquared(h),t),n=Math.pow(h.distanceToSquared(u),t),i=Math.pow(u.distanceToSquared(o),t);n<1e-4&&(n=1),e<1e-4&&(e=n),i<1e-4&&(i=n),wo.initNonuniformCatmullRom(a.x,h.x,u.x,o.x,e,n,i),So.initNonuniformCatmullRom(a.y,h.y,u.y,o.y,e,n,i),To.initNonuniformCatmullRom(a.z,h.z,u.z,o.z,e,n,i)}else\\\\\\\"catmullrom\\\\\\\"===this.curveType&&(wo.initCatmullRom(a.x,h.x,u.x,o.x,this.tension),So.initCatmullRom(a.y,h.y,u.y,o.y,this.tension),To.initCatmullRom(a.z,h.z,u.z,o.z,this.tension));return n.set(wo.calc(c),So.calc(c),To.calc(c)),n}copy(t){super.copy(t),this.points=[];for(let e=0,n=t.points.length;e<n;e++){const n=t.points[e];this.points.push(n.clone())}return this.closed=t.closed,this.curveType=t.curveType,this.tension=t.tension,this}toJSON(){const t=super.toJSON();t.points=[];for(let e=0,n=this.points.length;e<n;e++){const n=this.points[e];t.points.push(n.toArray())}return t.closed=this.closed,t.curveType=this.curveType,t.tension=this.tension,t}fromJSON(t){super.fromJSON(t),this.points=[];for(let e=0,n=t.points.length;e<n;e++){const n=t.points[e];this.points.push((new Rt).fromArray(n))}return this.closed=t.closed,this.curveType=t.curveType,this.tension=t.tension,this}}function Ao(t,e,n,i,r){const s=.5*(i-e),a=.5*(r-n),o=t*t;return(2*n-2*i+s+a)*(t*o)+(-3*n+3*i-2*s-a)*o+s*t+n}function Lo(t,e,n,i){return function(t,e){const n=1-t;return n*n*e}(t,e)+function(t,e){return 2*(1-t)*t*e}(t,n)+function(t,e){return t*t*e}(t,i)}function Ro(t,e,n,i,r){return function(t,e){const n=1-t;return n*n*n*e}(t,e)+function(t,e){const n=1-t;return 3*n*n*t*e}(t,n)+function(t,e){return 3*(1-t)*t*t*e}(t,i)+function(t,e){return t*t*t*e}(t,r)}Eo.prototype.isCatmullRomCurve3=!0;class Co extends yo{constructor(t=new vt,e=new vt,n=new vt,i=new vt){super(),this.type=\\\\\\\"CubicBezierCurve\\\\\\\",this.v0=t,this.v1=e,this.v2=n,this.v3=i}getPoint(t,e=new vt){const n=e,i=this.v0,r=this.v1,s=this.v2,a=this.v3;return n.set(Ro(t,i.x,r.x,s.x,a.x),Ro(t,i.y,r.y,s.y,a.y)),n}copy(t){return super.copy(t),this.v0.copy(t.v0),this.v1.copy(t.v1),this.v2.copy(t.v2),this.v3.copy(t.v3),this}toJSON(){const t=super.toJSON();return t.v0=this.v0.toArray(),t.v1=this.v1.toArray(),t.v2=this.v2.toArray(),t.v3=this.v3.toArray(),t}fromJSON(t){return super.fromJSON(t),this.v0.fromArray(t.v0),this.v1.fromArray(t.v1),this.v2.fromArray(t.v2),this.v3.fromArray(t.v3),this}}Co.prototype.isCubicBezierCurve=!0;class Po extends yo{constructor(t=new Rt,e=new Rt,n=new Rt,i=new Rt){super(),this.type=\\\\\\\"CubicBezierCurve3\\\\\\\",this.v0=t,this.v1=e,this.v2=n,this.v3=i}getPoint(t,e=new Rt){const n=e,i=this.v0,r=this.v1,s=this.v2,a=this.v3;return n.set(Ro(t,i.x,r.x,s.x,a.x),Ro(t,i.y,r.y,s.y,a.y),Ro(t,i.z,r.z,s.z,a.z)),n}copy(t){return super.copy(t),this.v0.copy(t.v0),this.v1.copy(t.v1),this.v2.copy(t.v2),this.v3.copy(t.v3),this}toJSON(){const t=super.toJSON();return t.v0=this.v0.toArray(),t.v1=this.v1.toArray(),t.v2=this.v2.toArray(),t.v3=this.v3.toArray(),t}fromJSON(t){return super.fromJSON(t),this.v0.fromArray(t.v0),this.v1.fromArray(t.v1),this.v2.fromArray(t.v2),this.v3.fromArray(t.v3),this}}Po.prototype.isCubicBezierCurve3=!0;class Io extends yo{constructor(t=new vt,e=new vt){super(),this.type=\\\\\\\"LineCurve\\\\\\\",this.v1=t,this.v2=e}getPoint(t,e=new vt){const n=e;return 1===t?n.copy(this.v2):(n.copy(this.v2).sub(this.v1),n.multiplyScalar(t).add(this.v1)),n}getPointAt(t,e){return this.getPoint(t,e)}getTangent(t,e){const n=e||new vt;return n.copy(this.v2).sub(this.v1).normalize(),n}copy(t){return super.copy(t),this.v1.copy(t.v1),this.v2.copy(t.v2),this}toJSON(){const t=super.toJSON();return t.v1=this.v1.toArray(),t.v2=this.v2.toArray(),t}fromJSON(t){return super.fromJSON(t),this.v1.fromArray(t.v1),this.v2.fromArray(t.v2),this}}Io.prototype.isLineCurve=!0;class Do extends yo{constructor(t=new Rt,e=new Rt){super(),this.type=\\\\\\\"LineCurve3\\\\\\\",this.isLineCurve3=!0,this.v1=t,this.v2=e}getPoint(t,e=new Rt){const n=e;return 1===t?n.copy(this.v2):(n.copy(this.v2).sub(this.v1),n.multiplyScalar(t).add(this.v1)),n}getPointAt(t,e){return this.getPoint(t,e)}copy(t){return super.copy(t),this.v1.copy(t.v1),this.v2.copy(t.v2),this}toJSON(){const t=super.toJSON();return t.v1=this.v1.toArray(),t.v2=this.v2.toArray(),t}fromJSON(t){return super.fromJSON(t),this.v1.fromArray(t.v1),this.v2.fromArray(t.v2),this}}class No extends yo{constructor(t=new vt,e=new vt,n=new vt){super(),this.type=\\\\\\\"QuadraticBezierCurve\\\\\\\",this.v0=t,this.v1=e,this.v2=n}getPoint(t,e=new vt){const n=e,i=this.v0,r=this.v1,s=this.v2;return n.set(Lo(t,i.x,r.x,s.x),Lo(t,i.y,r.y,s.y)),n}copy(t){return super.copy(t),this.v0.copy(t.v0),this.v1.copy(t.v1),this.v2.copy(t.v2),this}toJSON(){const t=super.toJSON();return t.v0=this.v0.toArray(),t.v1=this.v1.toArray(),t.v2=this.v2.toArray(),t}fromJSON(t){return super.fromJSON(t),this.v0.fromArray(t.v0),this.v1.fromArray(t.v1),this.v2.fromArray(t.v2),this}}No.prototype.isQuadraticBezierCurve=!0;class Bo extends yo{constructor(t=new Rt,e=new Rt,n=new Rt){super(),this.type=\\\\\\\"QuadraticBezierCurve3\\\\\\\",this.v0=t,this.v1=e,this.v2=n}getPoint(t,e=new Rt){const n=e,i=this.v0,r=this.v1,s=this.v2;return n.set(Lo(t,i.x,r.x,s.x),Lo(t,i.y,r.y,s.y),Lo(t,i.z,r.z,s.z)),n}copy(t){return super.copy(t),this.v0.copy(t.v0),this.v1.copy(t.v1),this.v2.copy(t.v2),this}toJSON(){const t=super.toJSON();return t.v0=this.v0.toArray(),t.v1=this.v1.toArray(),t.v2=this.v2.toArray(),t}fromJSON(t){return super.fromJSON(t),this.v0.fromArray(t.v0),this.v1.fromArray(t.v1),this.v2.fromArray(t.v2),this}}Bo.prototype.isQuadraticBezierCurve3=!0;class zo extends yo{constructor(t=[]){super(),this.type=\\\\\\\"SplineCurve\\\\\\\",this.points=t}getPoint(t,e=new vt){const n=e,i=this.points,r=(i.length-1)*t,s=Math.floor(r),a=r-s,o=i[0===s?s:s-1],l=i[s],c=i[s>i.length-2?i.length-1:s+1],h=i[s>i.length-3?i.length-1:s+2];return n.set(Ao(a,o.x,l.x,c.x,h.x),Ao(a,o.y,l.y,c.y,h.y)),n}copy(t){super.copy(t),this.points=[];for(let e=0,n=t.points.length;e<n;e++){const n=t.points[e];this.points.push(n.clone())}return this}toJSON(){const t=super.toJSON();t.points=[];for(let e=0,n=this.points.length;e<n;e++){const n=this.points[e];t.points.push(n.toArray())}return t}fromJSON(t){super.fromJSON(t),this.points=[];for(let e=0,n=t.points.length;e<n;e++){const n=t.points[e];this.points.push((new vt).fromArray(n))}return this}}zo.prototype.isSplineCurve=!0;var 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e=c?A(w[t],N[t],B):w[t];M?(x.copy(v.normals[0]).multiplyScalar(e.x),y.copy(v.binormals[0]).multiplyScalar(e.y),_.copy(g[0]).add(x).add(y),F(_.x,_.y,_.z)):F(e.x,e.y,0)}for(let t=1;t<=o;t++)for(let e=0;e<L;e++){const n=c?A(w[e],N[e],B):w[e];M?(x.copy(v.normals[t]).multiplyScalar(n.x),y.copy(v.binormals[t]).multiplyScalar(n.y),_.copy(g[t]).add(x).add(y),F(_.x,_.y,_.z)):F(n.x,n.y,l/o*t)}for(let t=p-1;t>=0;t--){const e=t/p,n=h*Math.cos(e*Math.PI/2),i=u*Math.sin(e*Math.PI/2)+d;for(let t=0,e=E.length;t<e;t++){const e=A(E[t],P[t],i);F(e.x,e.y,l+n)}for(let t=0,e=S.length;t<e;t++){const e=S[t];D=I[t];for(let t=0,r=e.length;t<r;t++){const r=A(e[t],D[t],i);M?F(r.x,r.y+g[o-1].y,g[o-1].x+n):F(r.x,r.y,l+n)}}}function z(t,e){let n=t.length;for(;--n>=0;){const i=n;let r=n-1;r<0&&(r=t.length-1);for(let t=0,n=o+2*p;t<n;t++){const n=L*t,s=L*(t+1);U(e+i+n,e+r+n,e+r+s,e+i+s)}}}function F(t,e,n){s.push(t),s.push(e),s.push(n)}function O(t,e,r){H(t),H(e),H(r);const s=i.length/3,a=f.generateTopUV(n,i,s-3,s-2,s-1);G(a[0]),G(a[1]),G(a[2])}function U(t,e,r,s){H(t),H(e),H(s),H(e),H(r),H(s);const a=i.length/3,o=f.generateSideWallUV(n,i,a-6,a-3,a-2,a-1);G(o[0]),G(o[1]),G(o[3]),G(o[1]),G(o[2]),G(o[3])}function H(t){i.push(s[3*t+0]),i.push(s[3*t+1]),i.push(s[3*t+2])}function G(t){r.push(t.x),r.push(t.y)}!function(){const t=i.length/3;if(c){let t=0,e=L*t;for(let t=0;t<R;t++){const n=T[t];O(n[2]+e,n[1]+e,n[0]+e)}t=o+2*p,e=L*t;for(let t=0;t<R;t++){const n=T[t];O(n[0]+e,n[1]+e,n[2]+e)}}else{for(let t=0;t<R;t++){const e=T[t];O(e[2],e[1],e[0])}for(let t=0;t<R;t++){const e=T[t];O(e[0]+L*o,e[1]+L*o,e[2]+L*o)}}n.addGroup(t,i.length/3-t,0)}(),function(){const t=i.length/3;let e=0;z(E,e),e+=E.length;for(let t=0,n=S.length;t<n;t++){const n=S[t];z(n,e),e+=n.length}n.addGroup(t,i.length/3-t,1)}()}this.setAttribute(\\\\\\\"position\\\\\\\",new un(i,3)),this.setAttribute(\\\\\\\"uv\\\\\\\",new un(r,2)),this.computeVertexNormals()}toJSON(){const t=super.toJSON();return function(t,e,n){if(n.shapes=[],Array.isArray(t))for(let e=0,i=t.length;e<i;e++){const i=t[e];n.shapes.push(i.uuid)}else n.shapes.push(t.uuid);void 0!==e.extrudePath&&(n.options.extrudePath=e.extrudePath.toJSON());return n}(this.parameters.shapes,this.parameters.options,t)}static fromJSON(t,e){const n=[];for(let i=0,r=t.shapes.length;i<r;i++){const r=e[t.shapes[i]];n.push(r)}const i=t.options.extrudePath;return void 0!==i&&(t.options.extrudePath=(new Fo[i.type]).fromJSON(i)),new dl(n,t.options)}}const pl={generateTopUV:function(t,e,n,i,r){const s=e[3*n],a=e[3*n+1],o=e[3*i],l=e[3*i+1],c=e[3*r],h=e[3*r+1];return[new vt(s,a),new vt(o,l),new vt(c,h)]},generateSideWallUV:function(t,e,n,i,r,s){const a=e[3*n],o=e[3*n+1],l=e[3*n+2],c=e[3*i],h=e[3*i+1],u=e[3*i+2],d=e[3*r],p=e[3*r+1],m=e[3*r+2],f=e[3*s],g=e[3*s+1],v=e[3*s+2];return Math.abs(o-h)<Math.abs(a-c)?[new vt(a,1-l),new vt(c,1-u),new vt(d,1-m),new vt(f,1-v)]:[new vt(o,1-l),new vt(h,1-u),new vt(p,1-m),new vt(g,1-v)]}};class ml extends ho{constructor(t=1,e=0){const n=(1+Math.sqrt(5))/2;super([-1,n,0,1,n,0,-1,-n,0,1,-n,0,0,-1,n,0,1,n,0,-1,-n,0,1,-n,n,0,-1,n,0,1,-n,0,-1,-n,0,1],[0,11,5,0,5,1,0,1,7,0,7,10,0,10,11,1,5,9,5,11,4,11,10,2,10,7,6,7,1,8,3,9,4,3,4,2,3,2,6,3,6,8,3,8,9,4,9,5,2,4,11,6,2,10,8,6,7,9,8,1],t,e),this.type=\\\\\\\"IcosahedronGeometry\\\\\\\",this.parameters={radius:t,detail:e}}static fromJSON(t){return new ml(t.radius,t.detail)}}class fl extends wn{constructor(t,e=12,n=0,i=2*Math.PI){super(),this.type=\\\\\\\"LatheGeometry\\\\\\\",this.parameters={points:t,segments:e,phiStart:n,phiLength:i},e=Math.floor(e),i=ht(i,0,2*Math.PI);const r=[],s=[],a=[],o=1/e,l=new Rt,c=new vt;for(let r=0;r<=e;r++){const h=n+r*o*i,u=Math.sin(h),d=Math.cos(h);for(let n=0;n<=t.length-1;n++)l.x=t[n].x*u,l.y=t[n].y,l.z=t[n].x*d,s.push(l.x,l.y,l.z),c.x=r/e,c.y=n/(t.length-1),a.push(c.x,c.y)}for(let n=0;n<e;n++)for(let e=0;e<t.length-1;e++){const i=e+n*t.length,s=i,a=i+t.length,o=i+t.length+1,l=i+1;r.push(s,a,l),r.push(a,o,l)}if(this.setIndex(r),this.setAttribute(\\\\\\\"position\\\\\\\",new un(s,3)),this.setAttribute(\\\\\\\"uv\\\\\\\",new un(a,2)),this.computeVertexNormals(),i===2*Math.PI){const n=this.attributes.normal.array,i=new Rt,r=new Rt,s=new Rt,a=e*t.length*3;for(let e=0,o=0;e<t.length;e++,o+=3)i.x=n[o+0],i.y=n[o+1],i.z=n[o+2],r.x=n[a+o+0],r.y=n[a+o+1],r.z=n[a+o+2],s.addVectors(i,r).normalize(),n[o+0]=n[a+o+0]=s.x,n[o+1]=n[a+o+1]=s.y,n[o+2]=n[a+o+2]=s.z}}static fromJSON(t){return new fl(t.points,t.segments,t.phiStart,t.phiLength)}}class gl extends ho{constructor(t=1,e=0){super([1,0,0,-1,0,0,0,1,0,0,-1,0,0,0,1,0,0,-1],[0,2,4,0,4,3,0,3,5,0,5,2,1,2,5,1,5,3,1,3,4,1,4,2],t,e),this.type=\\\\\\\"OctahedronGeometry\\\\\\\",this.parameters={radius:t,detail:e}}static fromJSON(t){return new gl(t.radius,t.detail)}}class vl extends wn{constructor(t,e,n){super(),this.type=\\\\\\\"ParametricGeometry\\\\\\\",this.parameters={func:t,slices:e,stacks:n};const i=[],r=[],s=[],a=[],o=1e-5,l=new Rt,c=new Rt,h=new Rt,u=new Rt,d=new Rt;t.length<3&&console.error(\\\\\\\"THREE.ParametricGeometry: Function must now modify a Vector3 as third parameter.\\\\\\\");const p=e+1;for(let i=0;i<=n;i++){const p=i/n;for(let n=0;n<=e;n++){const i=n/e;t(i,p,c),r.push(c.x,c.y,c.z),i-o>=0?(t(i-o,p,h),u.subVectors(c,h)):(t(i+o,p,h),u.subVectors(h,c)),p-o>=0?(t(i,p-o,h),d.subVectors(c,h)):(t(i,p+o,h),d.subVectors(h,c)),l.crossVectors(u,d).normalize(),s.push(l.x,l.y,l.z),a.push(i,p)}}for(let t=0;t<n;t++)for(let n=0;n<e;n++){const e=t*p+n,r=t*p+n+1,s=(t+1)*p+n+1,a=(t+1)*p+n;i.push(e,r,a),i.push(r,s,a)}this.setIndex(i),this.setAttribute(\\\\\\\"position\\\\\\\",new un(r,3)),this.setAttribute(\\\\\\\"normal\\\\\\\",new un(s,3)),this.setAttribute(\\\\\\\"uv\\\\\\\",new un(a,2))}}class yl extends wn{constructor(t=.5,e=1,n=8,i=1,r=0,s=2*Math.PI){super(),this.type=\\\\\\\"RingGeometry\\\\\\\",this.parameters={innerRadius:t,outerRadius:e,thetaSegments:n,phiSegments:i,thetaStart:r,thetaLength:s},n=Math.max(3,n);const a=[],o=[],l=[],c=[];let h=t;const u=(e-t)/(i=Math.max(1,i)),d=new Rt,p=new vt;for(let t=0;t<=i;t++){for(let t=0;t<=n;t++){const i=r+t/n*s;d.x=h*Math.cos(i),d.y=h*Math.sin(i),o.push(d.x,d.y,d.z),l.push(0,0,1),p.x=(d.x/e+1)/2,p.y=(d.y/e+1)/2,c.push(p.x,p.y)}h+=u}for(let t=0;t<i;t++){const e=t*(n+1);for(let t=0;t<n;t++){const i=t+e,r=i,s=i+n+1,o=i+n+2,l=i+1;a.push(r,s,l),a.push(s,o,l)}}this.setIndex(a),this.setAttribute(\\\\\\\"position\\\\\\\",new un(o,3)),this.setAttribute(\\\\\\\"normal\\\\\\\",new un(l,3)),this.setAttribute(\\\\\\\"uv\\\\\\\",new un(c,2))}static fromJSON(t){return new yl(t.innerRadius,t.outerRadius,t.thetaSegments,t.phiSegments,t.thetaStart,t.thetaLength)}}class xl extends wn{constructor(t,e=12){super(),this.type=\\\\\\\"ShapeGeometry\\\\\\\",this.parameters={shapes:t,curveSegments:e};const n=[],i=[],r=[],s=[];let a=0,o=0;if(!1===Array.isArray(t))l(t);else for(let e=0;e<t.length;e++)l(t[e]),this.addGroup(a,o,e),a+=o,o=0;function l(t){const a=i.length/3,l=t.extractPoints(e);let c=l.shape;const h=l.holes;!1===cl.isClockWise(c)&&(c=c.reverse());for(let t=0,e=h.length;t<e;t++){const e=h[t];!0===cl.isClockWise(e)&&(h[t]=e.reverse())}const u=cl.triangulateShape(c,h);for(let t=0,e=h.length;t<e;t++){const e=h[t];c=c.concat(e)}for(let t=0,e=c.length;t<e;t++){const e=c[t];i.push(e.x,e.y,0),r.push(0,0,1),s.push(e.x,e.y)}for(let t=0,e=u.length;t<e;t++){const e=u[t],i=e[0]+a,r=e[1]+a,s=e[2]+a;n.push(i,r,s),o+=3}}this.setIndex(n),this.setAttribute(\\\\\\\"position\\\\\\\",new un(i,3)),this.setAttribute(\\\\\\\"normal\\\\\\\",new un(r,3)),this.setAttribute(\\\\\\\"uv\\\\\\\",new un(s,2))}toJSON(){const t=super.toJSON();return function(t,e){if(e.shapes=[],Array.isArray(t))for(let n=0,i=t.length;n<i;n++){const i=t[n];e.shapes.push(i.uuid)}else e.shapes.push(t.uuid);return e}(this.parameters.shapes,t)}static fromJSON(t,e){const n=[];for(let i=0,r=t.shapes.length;i<r;i++){const r=e[t.shapes[i]];n.push(r)}return new xl(n,t.curveSegments)}}class _l extends wn{constructor(t=1,e=32,n=16,i=0,r=2*Math.PI,s=0,a=Math.PI){super(),this.type=\\\\\\\"SphereGeometry\\\\\\\",this.parameters={radius:t,widthSegments:e,heightSegments:n,phiStart:i,phiLength:r,thetaStart:s,thetaLength:a},e=Math.max(3,Math.floor(e)),n=Math.max(2,Math.floor(n));const o=Math.min(s+a,Math.PI);let l=0;const c=[],h=new Rt,u=new Rt,d=[],p=[],m=[],f=[];for(let d=0;d<=n;d++){const g=[],v=d/n;let y=0;0==d&&0==s?y=.5/e:d==n&&o==Math.PI&&(y=-.5/e);for(let n=0;n<=e;n++){const o=n/e;h.x=-t*Math.cos(i+o*r)*Math.sin(s+v*a),h.y=t*Math.cos(s+v*a),h.z=t*Math.sin(i+o*r)*Math.sin(s+v*a),p.push(h.x,h.y,h.z),u.copy(h).normalize(),m.push(u.x,u.y,u.z),f.push(o+y,1-v),g.push(l++)}c.push(g)}for(let t=0;t<n;t++)for(let i=0;i<e;i++){const e=c[t][i+1],r=c[t][i],a=c[t+1][i],l=c[t+1][i+1];(0!==t||s>0)&&d.push(e,r,l),(t!==n-1||o<Math.PI)&&d.push(r,a,l)}this.setIndex(d),this.setAttribute(\\\\\\\"position\\\\\\\",new un(p,3)),this.setAttribute(\\\\\\\"normal\\\\\\\",new un(m,3)),this.setAttribute(\\\\\\\"uv\\\\\\\",new un(f,2))}static fromJSON(t){return new _l(t.radius,t.widthSegments,t.heightSegments,t.phiStart,t.phiLength,t.thetaStart,t.thetaLength)}}class Ml extends ho{constructor(t=1,e=0){super([1,1,1,-1,-1,1,-1,1,-1,1,-1,-1],[2,1,0,0,3,2,1,3,0,2,3,1],t,e),this.type=\\\\\\\"TetrahedronGeometry\\\\\\\",this.parameters={radius:t,detail:e}}static fromJSON(t){return new Ml(t.radius,t.detail)}}class bl extends dl{constructor(t,e={}){const n=e.font;if(!n||!n.isFont)return console.error(\\\\\\\"THREE.TextGeometry: font parameter is not an instance of THREE.Font.\\\\\\\"),new wn;const i=n.generateShapes(t,e.size);e.depth=void 0!==e.height?e.height:50,void 0===e.bevelThickness&&(e.bevelThickness=10),void 0===e.bevelSize&&(e.bevelSize=8),void 0===e.bevelEnabled&&(e.bevelEnabled=!1),super(i,e),this.type=\\\\\\\"TextGeometry\\\\\\\"}}class wl extends wn{constructor(t=1,e=.4,n=8,i=6,r=2*Math.PI){super(),this.type=\\\\\\\"TorusGeometry\\\\\\\",this.parameters={radius:t,tube:e,radialSegments:n,tubularSegments:i,arc:r},n=Math.floor(n),i=Math.floor(i);const s=[],a=[],o=[],l=[],c=new Rt,h=new Rt,u=new Rt;for(let s=0;s<=n;s++)for(let d=0;d<=i;d++){const p=d/i*r,m=s/n*Math.PI*2;h.x=(t+e*Math.cos(m))*Math.cos(p),h.y=(t+e*Math.cos(m))*Math.sin(p),h.z=e*Math.sin(m),a.push(h.x,h.y,h.z),c.x=t*Math.cos(p),c.y=t*Math.sin(p),u.subVectors(h,c).normalize(),o.push(u.x,u.y,u.z),l.push(d/i),l.push(s/n)}for(let t=1;t<=n;t++)for(let e=1;e<=i;e++){const n=(i+1)*t+e-1,r=(i+1)*(t-1)+e-1,a=(i+1)*(t-1)+e,o=(i+1)*t+e;s.push(n,r,o),s.push(r,a,o)}this.setIndex(s),this.setAttribute(\\\\\\\"position\\\\\\\",new un(a,3)),this.setAttribute(\\\\\\\"normal\\\\\\\",new un(o,3)),this.setAttribute(\\\\\\\"uv\\\\\\\",new un(l,2))}static fromJSON(t){return new wl(t.radius,t.tube,t.radialSegments,t.tubularSegments,t.arc)}}class Sl extends wn{constructor(t=1,e=.4,n=64,i=8,r=2,s=3){super(),this.type=\\\\\\\"TorusKnotGeometry\\\\\\\",this.parameters={radius:t,tube:e,tubularSegments:n,radialSegments:i,p:r,q:s},n=Math.floor(n),i=Math.floor(i);const a=[],o=[],l=[],c=[],h=new Rt,u=new Rt,d=new Rt,p=new Rt,m=new Rt,f=new Rt,g=new Rt;for(let a=0;a<=n;++a){const y=a/n*r*Math.PI*2;v(y,r,s,t,d),v(y+.01,r,s,t,p),f.subVectors(p,d),g.addVectors(p,d),m.crossVectors(f,g),g.crossVectors(m,f),m.normalize(),g.normalize();for(let t=0;t<=i;++t){const r=t/i*Math.PI*2,s=-e*Math.cos(r),p=e*Math.sin(r);h.x=d.x+(s*g.x+p*m.x),h.y=d.y+(s*g.y+p*m.y),h.z=d.z+(s*g.z+p*m.z),o.push(h.x,h.y,h.z),u.subVectors(h,d).normalize(),l.push(u.x,u.y,u.z),c.push(a/n),c.push(t/i)}}for(let t=1;t<=n;t++)for(let e=1;e<=i;e++){const n=(i+1)*(t-1)+(e-1),r=(i+1)*t+(e-1),s=(i+1)*t+e,o=(i+1)*(t-1)+e;a.push(n,r,o),a.push(r,s,o)}function v(t,e,n,i,r){const s=Math.cos(t),a=Math.sin(t),o=n/e*t,l=Math.cos(o);r.x=i*(2+l)*.5*s,r.y=i*(2+l)*a*.5,r.z=i*Math.sin(o)*.5}this.setIndex(a),this.setAttribute(\\\\\\\"position\\\\\\\",new un(o,3)),this.setAttribute(\\\\\\\"normal\\\\\\\",new un(l,3)),this.setAttribute(\\\\\\\"uv\\\\\\\",new un(c,2))}static fromJSON(t){return new Sl(t.radius,t.tube,t.tubularSegments,t.radialSegments,t.p,t.q)}}class Tl extends wn{constructor(t,e=64,n=1,i=8,r=!1){super(),this.type=\\\\\\\"TubeGeometry\\\\\\\",this.parameters={path:t,tubularSegments:e,radius:n,radialSegments:i,closed:r};const s=t.computeFrenetFrames(e,r);this.tangents=s.tangents,this.normals=s.normals,this.binormals=s.binormals;const a=new Rt,o=new Rt,l=new vt;let c=new Rt;const h=[],u=[],d=[],p=[];function m(r){c=t.getPointAt(r/e,c);const l=s.normals[r],d=s.binormals[r];for(let t=0;t<=i;t++){const e=t/i*Math.PI*2,r=Math.sin(e),s=-Math.cos(e);o.x=s*l.x+r*d.x,o.y=s*l.y+r*d.y,o.z=s*l.z+r*d.z,o.normalize(),u.push(o.x,o.y,o.z),a.x=c.x+n*o.x,a.y=c.y+n*o.y,a.z=c.z+n*o.z,h.push(a.x,a.y,a.z)}}!function(){for(let t=0;t<e;t++)m(t);m(!1===r?e:0),function(){for(let t=0;t<=e;t++)for(let n=0;n<=i;n++)l.x=t/e,l.y=n/i,d.push(l.x,l.y)}(),function(){for(let t=1;t<=e;t++)for(let e=1;e<=i;e++){const n=(i+1)*(t-1)+(e-1),r=(i+1)*t+(e-1),s=(i+1)*t+e,a=(i+1)*(t-1)+e;p.push(n,r,a),p.push(r,s,a)}}()}(),this.setIndex(p),this.setAttribute(\\\\\\\"position\\\\\\\",new un(h,3)),this.setAttribute(\\\\\\\"normal\\\\\\\",new un(u,3)),this.setAttribute(\\\\\\\"uv\\\\\\\",new un(d,2))}toJSON(){const t=super.toJSON();return t.path=this.parameters.path.toJSON(),t}static fromJSON(t){return new Tl((new Fo[t.path.type]).fromJSON(t.path),t.tubularSegments,t.radius,t.radialSegments,t.closed)}}class El extends wn{constructor(t){if(super(),this.type=\\\\\\\"WireframeGeometry\\\\\\\",!0===t.isGeometry)return void console.error(\\\\\\\"THREE.WireframeGeometry no longer supports THREE.Geometry. Use THREE.BufferGeometry instead.\\\\\\\");const e=[],n=new Set,i=new Rt,r=new Rt;if(null!==t.index){const s=t.attributes.position,a=t.index;let o=t.groups;0===o.length&&(o=[{start:0,count:a.count,materialIndex:0}]);for(let t=0,l=o.length;t<l;++t){const l=o[t],c=l.start;for(let t=c,o=c+l.count;t<o;t+=3)for(let o=0;o<3;o++){const l=a.getX(t+o),c=a.getX(t+(o+1)%3);i.fromBufferAttribute(s,l),r.fromBufferAttribute(s,c),!0===Al(i,r,n)&&(e.push(i.x,i.y,i.z),e.push(r.x,r.y,r.z))}}}else{const s=t.attributes.position;for(let t=0,a=s.count/3;t<a;t++)for(let a=0;a<3;a++){const o=3*t+a,l=3*t+(a+1)%3;i.fromBufferAttribute(s,o),r.fromBufferAttribute(s,l),!0===Al(i,r,n)&&(e.push(i.x,i.y,i.z),e.push(r.x,r.y,r.z))}}this.setAttribute(\\\\\\\"position\\\\\\\",new un(e,3))}}function Al(t,e,n){const i=`${t.x},${t.y},${t.z}-${e.x},${e.y},${e.z}`,r=`${e.x},${e.y},${e.z}-${t.x},${t.y},${t.z}`;return!0!==n.has(i)&&!0!==n.has(r)&&(n.add(i,r),!0)}var Ll=Object.freeze({__proto__:null,BoxGeometry:Vn,BoxBufferGeometry:Vn,CircleGeometry:oo,CircleBufferGeometry:oo,ConeGeometry:co,ConeBufferGeometry:co,CylinderGeometry:lo,CylinderBufferGeometry:lo,DodecahedronGeometry:uo,DodecahedronBufferGeometry:uo,EdgesGeometry:vo,ExtrudeGeometry:dl,ExtrudeBufferGeometry:dl,IcosahedronGeometry:ml,IcosahedronBufferGeometry:ml,LatheGeometry:fl,LatheBufferGeometry:fl,OctahedronGeometry:gl,OctahedronBufferGeometry:gl,ParametricGeometry:vl,ParametricBufferGeometry:vl,PlaneGeometry:ci,PlaneBufferGeometry:ci,PolyhedronGeometry:ho,PolyhedronBufferGeometry:ho,RingGeometry:yl,RingBufferGeometry:yl,ShapeGeometry:xl,ShapeBufferGeometry:xl,SphereGeometry:_l,SphereBufferGeometry:_l,TetrahedronGeometry:Ml,TetrahedronBufferGeometry:Ml,TextGeometry:bl,TextBufferGeometry:bl,TorusGeometry:wl,TorusBufferGeometry:wl,TorusKnotGeometry:Sl,TorusKnotBufferGeometry:Sl,TubeGeometry:Tl,TubeBufferGeometry:Tl,WireframeGeometry:El});class Rl extends We{constructor(t){super(),this.type=\\\\\\\"ShadowMaterial\\\\\\\",this.color=new Qe(0),this.transparent=!0,this.setValues(t)}copy(t){return super.copy(t),this.color.copy(t.color),this}}Rl.prototype.isShadowMaterial=!0;class Cl extends We{constructor(t){super(),this.defines={STANDARD:\\\\\\\"\\\\\\\"},this.type=\\\\\\\"MeshStandardMaterial\\\\\\\",this.color=new Qe(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 Qe(0),this.emissiveIntensity=1,this.emissiveMap=null,this.bumpMap=null,this.bumpScale=1,this.normalMap=null,this.normalMapType=0,this.normalScale=new vt(1,1),this.displacementMap=null,this.displacementScale=1,this.displacementBias=0,this.roughnessMap=null,this.metalnessMap=null,this.alphaMap=null,this.envMap=null,this.envMapIntensity=1,this.refractionRatio=.98,this.wireframe=!1,this.wireframeLinewidth=1,this.wireframeLinecap=\\\\\\\"round\\\\\\\",this.wireframeLinejoin=\\\\\\\"round\\\\\\\",this.flatShading=!1,this.setValues(t)}copy(t){return super.copy(t),this.defines={STANDARD:\\\\\\\"\\\\\\\"},this.color.copy(t.color),this.roughness=t.roughness,this.metalness=t.metalness,this.map=t.map,this.lightMap=t.lightMap,this.lightMapIntensity=t.lightMapIntensity,this.aoMap=t.aoMap,this.aoMapIntensity=t.aoMapIntensity,this.emissive.copy(t.emissive),this.emissiveMap=t.emissiveMap,this.emissiveIntensity=t.emissiveIntensity,this.bumpMap=t.bumpMap,this.bumpScale=t.bumpScale,this.normalMap=t.normalMap,this.normalMapType=t.normalMapType,this.normalScale.copy(t.normalScale),this.displacementMap=t.displacementMap,this.displacementScale=t.displacementScale,this.displacementBias=t.displacementBias,this.roughnessMap=t.roughnessMap,this.metalnessMap=t.metalnessMap,this.alphaMap=t.alphaMap,this.envMap=t.envMap,this.envMapIntensity=t.envMapIntensity,this.refractionRatio=t.refractionRatio,this.wireframe=t.wireframe,this.wireframeLinewidth=t.wireframeLinewidth,this.wireframeLinecap=t.wireframeLinecap,this.wireframeLinejoin=t.wireframeLinejoin,this.flatShading=t.flatShading,this}}Cl.prototype.isMeshStandardMaterial=!0;class Pl extends Cl{constructor(t){super(),this.defines={STANDARD:\\\\\\\"\\\\\\\",PHYSICAL:\\\\\\\"\\\\\\\"},this.type=\\\\\\\"MeshPhysicalMaterial\\\\\\\",this.clearcoat=0,this.clearcoatMap=null,this.clearcoatRoughness=0,this.clearcoatRoughnessMap=null,this.clearcoatNormalScale=new vt(1,1),this.clearcoatNormalMap=null,this.reflectivity=.5,Object.defineProperty(this,\\\\\\\"ior\\\\\\\",{get:function(){return(1+.4*this.reflectivity)/(1-.4*this.reflectivity)},set:function(t){this.reflectivity=ht(2.5*(t-1)/(t+1),0,1)}}),this.sheen=null,this.transmission=0,this.transmissionMap=null,this.thickness=.01,this.thicknessMap=null,this.attenuationDistance=0,this.attenuationTint=new Qe(1,1,1),this.specularIntensity=1,this.specularIntensityMap=null,this.specularTint=new Qe(1,1,1),this.specularTintMap=null,this.setValues(t)}copy(t){return super.copy(t),this.defines={STANDARD:\\\\\\\"\\\\\\\",PHYSICAL:\\\\\\\"\\\\\\\"},this.clearcoat=t.clearcoat,this.clearcoatMap=t.clearcoatMap,this.clearcoatRoughness=t.clearcoatRoughness,this.clearcoatRoughnessMap=t.clearcoatRoughnessMap,this.clearcoatNormalMap=t.clearcoatNormalMap,this.clearcoatNormalScale.copy(t.clearcoatNormalScale),this.reflectivity=t.reflectivity,t.sheen?this.sheen=(this.sheen||new Qe).copy(t.sheen):this.sheen=null,this.transmission=t.transmission,this.transmissionMap=t.transmissionMap,this.thickness=t.thickness,this.thicknessMap=t.thicknessMap,this.attenuationDistance=t.attenuationDistance,this.attenuationTint.copy(t.attenuationTint),this.specularIntensity=t.specularIntensity,this.specularIntensityMap=t.specularIntensityMap,this.specularTint.copy(t.specularTint),this.specularTintMap=t.specularTintMap,this}}Pl.prototype.isMeshPhysicalMaterial=!0;class Il extends We{constructor(t){super(),this.type=\\\\\\\"MeshPhongMaterial\\\\\\\",this.color=new Qe(16777215),this.specular=new Qe(1118481),this.shininess=30,this.map=null,this.lightMap=null,this.lightMapIntensity=1,this.aoMap=null,this.aoMapIntensity=1,this.emissive=new Qe(0),this.emissiveIntensity=1,this.emissiveMap=null,this.bumpMap=null,this.bumpScale=1,this.normalMap=null,this.normalMapType=0,this.normalScale=new vt(1,1),this.displacementMap=null,this.displacementScale=1,this.displacementBias=0,this.specularMap=null,this.alphaMap=null,this.envMap=null,this.combine=0,this.reflectivity=1,this.refractionRatio=.98,this.wireframe=!1,this.wireframeLinewidth=1,this.wireframeLinecap=\\\\\\\"round\\\\\\\",this.wireframeLinejoin=\\\\\\\"round\\\\\\\",this.flatShading=!1,this.setValues(t)}copy(t){return super.copy(t),this.color.copy(t.color),this.specular.copy(t.specular),this.shininess=t.shininess,this.map=t.map,this.lightMap=t.lightMap,this.lightMapIntensity=t.lightMapIntensity,this.aoMap=t.aoMap,this.aoMapIntensity=t.aoMapIntensity,this.emissive.copy(t.emissive),this.emissiveMap=t.emissiveMap,this.emissiveIntensity=t.emissiveIntensity,this.bumpMap=t.bumpMap,this.bumpScale=t.bumpScale,this.normalMap=t.normalMap,this.normalMapType=t.normalMapType,this.normalScale.copy(t.normalScale),this.displacementMap=t.displacementMap,this.displacementScale=t.displacementScale,this.displacementBias=t.displacementBias,this.specularMap=t.specularMap,this.alphaMap=t.alphaMap,this.envMap=t.envMap,this.combine=t.combine,this.reflectivity=t.reflectivity,this.refractionRatio=t.refractionRatio,this.wireframe=t.wireframe,this.wireframeLinewidth=t.wireframeLinewidth,this.wireframeLinecap=t.wireframeLinecap,this.wireframeLinejoin=t.wireframeLinejoin,this.flatShading=t.flatShading,this}}Il.prototype.isMeshPhongMaterial=!0;class Dl extends We{constructor(t){super(),this.defines={TOON:\\\\\\\"\\\\\\\"},this.type=\\\\\\\"MeshToonMaterial\\\\\\\",this.color=new Qe(16777215),this.map=null,this.gradientMap=null,this.lightMap=null,this.lightMapIntensity=1,this.aoMap=null,this.aoMapIntensity=1,this.emissive=new Qe(0),this.emissiveIntensity=1,this.emissiveMap=null,this.bumpMap=null,this.bumpScale=1,this.normalMap=null,this.normalMapType=0,this.normalScale=new vt(1,1),this.displacementMap=null,this.displacementScale=1,this.displacementBias=0,this.alphaMap=null,this.wireframe=!1,this.wireframeLinewidth=1,this.wireframeLinecap=\\\\\\\"round\\\\\\\",this.wireframeLinejoin=\\\\\\\"round\\\\\\\",this.setValues(t)}copy(t){return super.copy(t),this.color.copy(t.color),this.map=t.map,this.gradientMap=t.gradientMap,this.lightMap=t.lightMap,this.lightMapIntensity=t.lightMapIntensity,this.aoMap=t.aoMap,this.aoMapIntensity=t.aoMapIntensity,this.emissive.copy(t.emissive),this.emissiveMap=t.emissiveMap,this.emissiveIntensity=t.emissiveIntensity,this.bumpMap=t.bumpMap,this.bumpScale=t.bumpScale,this.normalMap=t.normalMap,this.normalMapType=t.normalMapType,this.normalScale.copy(t.normalScale),this.displacementMap=t.displacementMap,this.displacementScale=t.displacementScale,this.displacementBias=t.displacementBias,this.alphaMap=t.alphaMap,this.wireframe=t.wireframe,this.wireframeLinewidth=t.wireframeLinewidth,this.wireframeLinecap=t.wireframeLinecap,this.wireframeLinejoin=t.wireframeLinejoin,this}}Dl.prototype.isMeshToonMaterial=!0;class Nl extends We{constructor(t){super(),this.type=\\\\\\\"MeshNormalMaterial\\\\\\\",this.bumpMap=null,this.bumpScale=1,this.normalMap=null,this.normalMapType=0,this.normalScale=new vt(1,1),this.displacementMap=null,this.displacementScale=1,this.displacementBias=0,this.wireframe=!1,this.wireframeLinewidth=1,this.fog=!1,this.flatShading=!1,this.setValues(t)}copy(t){return super.copy(t),this.bumpMap=t.bumpMap,this.bumpScale=t.bumpScale,this.normalMap=t.normalMap,this.normalMapType=t.normalMapType,this.normalScale.copy(t.normalScale),this.displacementMap=t.displacementMap,this.displacementScale=t.displacementScale,this.displacementBias=t.displacementBias,this.wireframe=t.wireframe,this.wireframeLinewidth=t.wireframeLinewidth,this.flatShading=t.flatShading,this}}Nl.prototype.isMeshNormalMaterial=!0;class Bl extends We{constructor(t){super(),this.type=\\\\\\\"MeshLambertMaterial\\\\\\\",this.color=new Qe(16777215),this.map=null,this.lightMap=null,this.lightMapIntensity=1,this.aoMap=null,this.aoMapIntensity=1,this.emissive=new Qe(0),this.emissiveIntensity=1,this.emissiveMap=null,this.specularMap=null,this.alphaMap=null,this.envMap=null,this.combine=0,this.reflectivity=1,this.refractionRatio=.98,this.wireframe=!1,this.wireframeLinewidth=1,this.wireframeLinecap=\\\\\\\"round\\\\\\\",this.wireframeLinejoin=\\\\\\\"round\\\\\\\",this.setValues(t)}copy(t){return super.copy(t),this.color.copy(t.color),this.map=t.map,this.lightMap=t.lightMap,this.lightMapIntensity=t.lightMapIntensity,this.aoMap=t.aoMap,this.aoMapIntensity=t.aoMapIntensity,this.emissive.copy(t.emissive),this.emissiveMap=t.emissiveMap,this.emissiveIntensity=t.emissiveIntensity,this.specularMap=t.specularMap,this.alphaMap=t.alphaMap,this.envMap=t.envMap,this.combine=t.combine,this.reflectivity=t.reflectivity,this.refractionRatio=t.refractionRatio,this.wireframe=t.wireframe,this.wireframeLinewidth=t.wireframeLinewidth,this.wireframeLinecap=t.wireframeLinecap,this.wireframeLinejoin=t.wireframeLinejoin,this}}Bl.prototype.isMeshLambertMaterial=!0;class zl extends We{constructor(t){super(),this.defines={MATCAP:\\\\\\\"\\\\\\\"},this.type=\\\\\\\"MeshMatcapMaterial\\\\\\\",this.color=new Qe(16777215),this.matcap=null,this.map=null,this.bumpMap=null,this.bumpScale=1,this.normalMap=null,this.normalMapType=0,this.normalScale=new vt(1,1),this.displacementMap=null,this.displacementScale=1,this.displacementBias=0,this.alphaMap=null,this.flatShading=!1,this.setValues(t)}copy(t){return super.copy(t),this.defines={MATCAP:\\\\\\\"\\\\\\\"},this.color.copy(t.color),this.matcap=t.matcap,this.map=t.map,this.bumpMap=t.bumpMap,this.bumpScale=t.bumpScale,this.normalMap=t.normalMap,this.normalMapType=t.normalMapType,this.normalScale.copy(t.normalScale),this.displacementMap=t.displacementMap,this.displacementScale=t.displacementScale,this.displacementBias=t.displacementBias,this.alphaMap=t.alphaMap,this.flatShading=t.flatShading,this}}zl.prototype.isMeshMatcapMaterial=!0;class Fl extends Ua{constructor(t){super(),this.type=\\\\\\\"LineDashedMaterial\\\\\\\",this.scale=1,this.dashSize=3,this.gapSize=1,this.setValues(t)}copy(t){return super.copy(t),this.scale=t.scale,this.dashSize=t.dashSize,this.gapSize=t.gapSize,this}}Fl.prototype.isLineDashedMaterial=!0;var Ol=Object.freeze({__proto__:null,ShadowMaterial:Rl,SpriteMaterial:ra,RawShaderMaterial:_i,ShaderMaterial:Xn,PointsMaterial:Za,MeshPhysicalMaterial:Pl,MeshStandardMaterial:Cl,MeshPhongMaterial:Il,MeshToonMaterial:Dl,MeshNormalMaterial:Nl,MeshLambertMaterial:Bl,MeshDepthMaterial:Os,MeshDistanceMaterial:Us,MeshBasicMaterial:Ke,MeshMatcapMaterial:zl,LineDashedMaterial:Fl,LineBasicMaterial:Ua,Material:We});const Ul={arraySlice:function(t,e,n){return Ul.isTypedArray(t)?new t.constructor(t.subarray(e,void 0!==n?n:t.length)):t.slice(e,n)},convertArray:function(t,e,n){return!t||!n&&t.constructor===e?t:\\\\\\\"number\\\\\\\"==typeof e.BYTES_PER_ELEMENT?new e(t):Array.prototype.slice.call(t)},isTypedArray:function(t){return ArrayBuffer.isView(t)&&!(t instanceof DataView)},getKeyframeOrder:function(t){const e=t.length,n=new Array(e);for(let t=0;t!==e;++t)n[t]=t;return n.sort((function(e,n){return t[e]-t[n]})),n},sortedArray:function(t,e,n){const i=t.length,r=new t.constructor(i);for(let s=0,a=0;a!==i;++s){const i=n[s]*e;for(let n=0;n!==e;++n)r[a++]=t[i+n]}return r},flattenJSON:function(t,e,n,i){let r=1,s=t[0];for(;void 0!==s&&void 0===s[i];)s=t[r++];if(void 0===s)return;let a=s[i];if(void 0!==a)if(Array.isArray(a))do{a=s[i],void 0!==a&&(e.push(s.time),n.push.apply(n,a)),s=t[r++]}while(void 0!==s);else if(void 0!==a.toArray)do{a=s[i],void 0!==a&&(e.push(s.time),a.toArray(n,n.length)),s=t[r++]}while(void 0!==s);else do{a=s[i],void 0!==a&&(e.push(s.time),n.push(a)),s=t[r++]}while(void 0!==s)},subclip:function(t,e,n,i,r=30){const s=t.clone();s.name=e;const a=[];for(let t=0;t<s.tracks.length;++t){const e=s.tracks[t],o=e.getValueSize(),l=[],c=[];for(let t=0;t<e.times.length;++t){const s=e.times[t]*r;if(!(s<n||s>=i)){l.push(e.times[t]);for(let n=0;n<o;++n)c.push(e.values[t*o+n])}}0!==l.length&&(e.times=Ul.convertArray(l,e.times.constructor),e.values=Ul.convertArray(c,e.values.constructor),a.push(e))}s.tracks=a;let o=1/0;for(let t=0;t<s.tracks.length;++t)o>s.tracks[t].times[0]&&(o=s.tracks[t].times[0]);for(let t=0;t<s.tracks.length;++t)s.tracks[t].shift(-1*o);return s.resetDuration(),s},makeClipAdditive:function(t,e=0,n=t,i=30){i<=0&&(i=30);const r=n.tracks.length,s=e/i;for(let e=0;e<r;++e){const i=n.tracks[e],r=i.ValueTypeName;if(\\\\\\\"bool\\\\\\\"===r||\\\\\\\"string\\\\\\\"===r)continue;const a=t.tracks.find((function(t){return t.name===i.name&&t.ValueTypeName===r}));if(void 0===a)continue;let o=0;const l=i.getValueSize();i.createInterpolant.isInterpolantFactoryMethodGLTFCubicSpline&&(o=l/3);let c=0;const h=a.getValueSize();a.createInterpolant.isInterpolantFactoryMethodGLTFCubicSpline&&(c=h/3);const u=i.times.length-1;let d;if(s<=i.times[0]){const t=o,e=l-o;d=Ul.arraySlice(i.values,t,e)}else if(s>=i.times[u]){const t=u*l+o,e=t+l-o;d=Ul.arraySlice(i.values,t,e)}else{const t=i.createInterpolant(),e=o,n=l-o;t.evaluate(s),d=Ul.arraySlice(t.resultBuffer,e,n)}if(\\\\\\\"quaternion\\\\\\\"===r){(new Lt).fromArray(d).normalize().conjugate().toArray(d)}const p=a.times.length;for(let t=0;t<p;++t){const e=t*h+c;if(\\\\\\\"quaternion\\\\\\\"===r)Lt.multiplyQuaternionsFlat(a.values,e,d,0,a.values,e);else{const t=h-2*c;for(let n=0;n<t;++n)a.values[e+n]-=d[n]}}}return t.blendMode=q,t}};class Hl{constructor(t,e,n,i){this.parameterPositions=t,this._cachedIndex=0,this.resultBuffer=void 0!==i?i:new e.constructor(n),this.sampleValues=e,this.valueSize=n,this.settings=null,this.DefaultSettings_={}}evaluate(t){const e=this.parameterPositions;let n=this._cachedIndex,i=e[n],r=e[n-1];t:{e:{let s;n:{i:if(!(t<i)){for(let s=n+2;;){if(void 0===i){if(t<r)break i;return n=e.length,this._cachedIndex=n,this.afterEnd_(n-1,t,r)}if(n===s)break;if(r=i,i=e[++n],t<i)break e}s=e.length;break n}if(t>=r)break t;{const a=e[1];t<a&&(n=2,r=a);for(let s=n-2;;){if(void 0===r)return this._cachedIndex=0,this.beforeStart_(0,t,i);if(n===s)break;if(i=r,r=e[--n-1],t>=r)break e}s=n,n=0}}for(;n<s;){const i=n+s>>>1;t<e[i]?s=i:n=i+1}if(i=e[n],r=e[n-1],void 0===r)return this._cachedIndex=0,this.beforeStart_(0,t,i);if(void 0===i)return n=e.length,this._cachedIndex=n,this.afterEnd_(n-1,r,t)}this._cachedIndex=n,this.intervalChanged_(n,r,i)}return this.interpolate_(n,r,t,i)}getSettings_(){return this.settings||this.DefaultSettings_}copySampleValue_(t){const e=this.resultBuffer,n=this.sampleValues,i=this.valueSize,r=t*i;for(let t=0;t!==i;++t)e[t]=n[r+t];return e}interpolate_(){throw new Error(\\\\\\\"call to abstract method\\\\\\\")}intervalChanged_(){}}Hl.prototype.beforeStart_=Hl.prototype.copySampleValue_,Hl.prototype.afterEnd_=Hl.prototype.copySampleValue_;class Gl extends Hl{constructor(t,e,n,i){super(t,e,n,i),this._weightPrev=-0,this._offsetPrev=-0,this._weightNext=-0,this._offsetNext=-0,this.DefaultSettings_={endingStart:k,endingEnd:k}}intervalChanged_(t,e,n){const i=this.parameterPositions;let r=t-2,s=t+1,a=i[r],o=i[s];if(void 0===a)switch(this.getSettings_().endingStart){case V:r=t,a=2*e-n;break;case W:r=i.length-2,a=e+i[r]-i[r+1];break;default:r=t,a=n}if(void 0===o)switch(this.getSettings_().endingEnd){case V:s=t,o=2*n-e;break;case W:s=1,o=n+i[1]-i[0];break;default:s=t-1,o=e}const l=.5*(n-e),c=this.valueSize;this._weightPrev=l/(e-a),this._weightNext=l/(o-n),this._offsetPrev=r*c,this._offsetNext=s*c}interpolate_(t,e,n,i){const r=this.resultBuffer,s=this.sampleValues,a=this.valueSize,o=t*a,l=o-a,c=this._offsetPrev,h=this._offsetNext,u=this._weightPrev,d=this._weightNext,p=(n-e)/(i-e),m=p*p,f=m*p,g=-u*f+2*u*m-u*p,v=(1+u)*f+(-1.5-2*u)*m+(-.5+u)*p+1,y=(-1-d)*f+(1.5+d)*m+.5*p,x=d*f-d*m;for(let t=0;t!==a;++t)r[t]=g*s[c+t]+v*s[l+t]+y*s[o+t]+x*s[h+t];return r}}class kl extends Hl{constructor(t,e,n,i){super(t,e,n,i)}interpolate_(t,e,n,i){const r=this.resultBuffer,s=this.sampleValues,a=this.valueSize,o=t*a,l=o-a,c=(n-e)/(i-e),h=1-c;for(let t=0;t!==a;++t)r[t]=s[l+t]*h+s[o+t]*c;return r}}class Vl extends Hl{constructor(t,e,n,i){super(t,e,n,i)}interpolate_(t){return this.copySampleValue_(t-1)}}class Wl{constructor(t,e,n,i){if(void 0===t)throw new Error(\\\\\\\"THREE.KeyframeTrack: track name is undefined\\\\\\\");if(void 0===e||0===e.length)throw new Error(\\\\\\\"THREE.KeyframeTrack: no keyframes in track named \\\\\\\"+t);this.name=t,this.times=Ul.convertArray(e,this.TimeBufferType),this.values=Ul.convertArray(n,this.ValueBufferType),this.setInterpolation(i||this.DefaultInterpolation)}static toJSON(t){const e=t.constructor;let n;if(e.toJSON!==this.toJSON)n=e.toJSON(t);else{n={name:t.name,times:Ul.convertArray(t.times,Array),values:Ul.convertArray(t.values,Array)};const e=t.getInterpolation();e!==t.DefaultInterpolation&&(n.interpolation=e)}return n.type=t.ValueTypeName,n}InterpolantFactoryMethodDiscrete(t){return new Vl(this.times,this.values,this.getValueSize(),t)}InterpolantFactoryMethodLinear(t){return new kl(this.times,this.values,this.getValueSize(),t)}InterpolantFactoryMethodSmooth(t){return new Gl(this.times,this.values,this.getValueSize(),t)}setInterpolation(t){let e;switch(t){case U:e=this.InterpolantFactoryMethodDiscrete;break;case H:e=this.InterpolantFactoryMethodLinear;break;case G:e=this.InterpolantFactoryMethodSmooth}if(void 0===e){const e=\\\\\\\"unsupported interpolation for \\\\\\\"+this.ValueTypeName+\\\\\\\" keyframe track named \\\\\\\"+this.name;if(void 0===this.createInterpolant){if(t===this.DefaultInterpolation)throw new Error(e);this.setInterpolation(this.DefaultInterpolation)}return console.warn(\\\\\\\"THREE.KeyframeTrack:\\\\\\\",e),this}return this.createInterpolant=e,this}getInterpolation(){switch(this.createInterpolant){case this.InterpolantFactoryMethodDiscrete:return U;case this.InterpolantFactoryMethodLinear:return H;case this.InterpolantFactoryMethodSmooth:return G}}getValueSize(){return this.values.length/this.times.length}shift(t){if(0!==t){const e=this.times;for(let n=0,i=e.length;n!==i;++n)e[n]+=t}return this}scale(t){if(1!==t){const e=this.times;for(let n=0,i=e.length;n!==i;++n)e[n]*=t}return this}trim(t,e){const n=this.times,i=n.length;let r=0,s=i-1;for(;r!==i&&n[r]<t;)++r;for(;-1!==s&&n[s]>e;)--s;if(++s,0!==r||s!==i){r>=s&&(s=Math.max(s,1),r=s-1);const t=this.getValueSize();this.times=Ul.arraySlice(n,r,s),this.values=Ul.arraySlice(this.values,r*t,s*t)}return this}validate(){let t=!0;const e=this.getValueSize();e-Math.floor(e)!=0&&(console.error(\\\\\\\"THREE.KeyframeTrack: Invalid value size in track.\\\\\\\",this),t=!1);const n=this.times,i=this.values,r=n.length;0===r&&(console.error(\\\\\\\"THREE.KeyframeTrack: Track is empty.\\\\\\\",this),t=!1);let s=null;for(let e=0;e!==r;e++){const i=n[e];if(\\\\\\\"number\\\\\\\"==typeof i&&isNaN(i)){console.error(\\\\\\\"THREE.KeyframeTrack: Time is not a valid number.\\\\\\\",this,e,i),t=!1;break}if(null!==s&&s>i){console.error(\\\\\\\"THREE.KeyframeTrack: Out of order keys.\\\\\\\",this,e,i,s),t=!1;break}s=i}if(void 0!==i&&Ul.isTypedArray(i))for(let e=0,n=i.length;e!==n;++e){const n=i[e];if(isNaN(n)){console.error(\\\\\\\"THREE.KeyframeTrack: Value is not a valid number.\\\\\\\",this,e,n),t=!1;break}}return t}optimize(){const t=Ul.arraySlice(this.times),e=Ul.arraySlice(this.values),n=this.getValueSize(),i=this.getInterpolation()===G,r=t.length-1;let s=1;for(let a=1;a<r;++a){let r=!1;const o=t[a];if(o!==t[a+1]&&(1!==a||o!==t[0]))if(i)r=!0;else{const t=a*n,i=t-n,s=t+n;for(let a=0;a!==n;++a){const n=e[t+a];if(n!==e[i+a]||n!==e[s+a]){r=!0;break}}}if(r){if(a!==s){t[s]=t[a];const i=a*n,r=s*n;for(let t=0;t!==n;++t)e[r+t]=e[i+t]}++s}}if(r>0){t[s]=t[r];for(let t=r*n,i=s*n,a=0;a!==n;++a)e[i+a]=e[t+a];++s}return s!==t.length?(this.times=Ul.arraySlice(t,0,s),this.values=Ul.arraySlice(e,0,s*n)):(this.times=t,this.values=e),this}clone(){const t=Ul.arraySlice(this.times,0),e=Ul.arraySlice(this.values,0),n=new(0,this.constructor)(this.name,t,e);return n.createInterpolant=this.createInterpolant,n}}Wl.prototype.TimeBufferType=Float32Array,Wl.prototype.ValueBufferType=Float32Array,Wl.prototype.DefaultInterpolation=H;class jl extends Wl{}jl.prototype.ValueTypeName=\\\\\\\"bool\\\\\\\",jl.prototype.ValueBufferType=Array,jl.prototype.DefaultInterpolation=U,jl.prototype.InterpolantFactoryMethodLinear=void 0,jl.prototype.InterpolantFactoryMethodSmooth=void 0;class ql extends Wl{}ql.prototype.ValueTypeName=\\\\\\\"color\\\\\\\";class Xl extends Wl{}Xl.prototype.ValueTypeName=\\\\\\\"number\\\\\\\";class Yl extends Hl{constructor(t,e,n,i){super(t,e,n,i)}interpolate_(t,e,n,i){const r=this.resultBuffer,s=this.sampleValues,a=this.valueSize,o=(n-e)/(i-e);let l=t*a;for(let t=l+a;l!==t;l+=4)Lt.slerpFlat(r,0,s,l-a,s,l,o);return r}}class Jl extends Wl{InterpolantFactoryMethodLinear(t){return new Yl(this.times,this.values,this.getValueSize(),t)}}Jl.prototype.ValueTypeName=\\\\\\\"quaternion\\\\\\\",Jl.prototype.DefaultInterpolation=H,Jl.prototype.InterpolantFactoryMethodSmooth=void 0;class Zl extends Wl{}Zl.prototype.ValueTypeName=\\\\\\\"string\\\\\\\",Zl.prototype.ValueBufferType=Array,Zl.prototype.DefaultInterpolation=U,Zl.prototype.InterpolantFactoryMethodLinear=void 0,Zl.prototype.InterpolantFactoryMethodSmooth=void 0;class Ql extends Wl{}Ql.prototype.ValueTypeName=\\\\\\\"vector\\\\\\\";class Kl{constructor(t,e=-1,n,i=2500){this.name=t,this.tracks=n,this.duration=e,this.blendMode=i,this.uuid=ct(),this.duration<0&&this.resetDuration()}static parse(t){const e=[],n=t.tracks,i=1/(t.fps||1);for(let t=0,r=n.length;t!==r;++t)e.push($l(n[t]).scale(i));const r=new this(t.name,t.duration,e,t.blendMode);return r.uuid=t.uuid,r}static toJSON(t){const e=[],n=t.tracks,i={name:t.name,duration:t.duration,tracks:e,uuid:t.uuid,blendMode:t.blendMode};for(let t=0,i=n.length;t!==i;++t)e.push(Wl.toJSON(n[t]));return i}static CreateFromMorphTargetSequence(t,e,n,i){const r=e.length,s=[];for(let t=0;t<r;t++){let a=[],o=[];a.push((t+r-1)%r,t,(t+1)%r),o.push(0,1,0);const l=Ul.getKeyframeOrder(a);a=Ul.sortedArray(a,1,l),o=Ul.sortedArray(o,1,l),i||0!==a[0]||(a.push(r),o.push(o[0])),s.push(new Xl(\\\\\\\".morphTargetInfluences[\\\\\\\"+e[t].name+\\\\\\\"]\\\\\\\",a,o).scale(1/n))}return new this(t,-1,s)}static findByName(t,e){let n=t;if(!Array.isArray(t)){const e=t;n=e.geometry&&e.geometry.animations||e.animations}for(let t=0;t<n.length;t++)if(n[t].name===e)return n[t];return null}static CreateClipsFromMorphTargetSequences(t,e,n){const i={},r=/^([\\\\w-]*?)([\\\\d]+)$/;for(let e=0,n=t.length;e<n;e++){const n=t[e],s=n.name.match(r);if(s&&s.length>1){const t=s[1];let e=i[t];e||(i[t]=e=[]),e.push(n)}}const s=[];for(const t in i)s.push(this.CreateFromMorphTargetSequence(t,i[t],e,n));return s}static parseAnimation(t,e){if(!t)return console.error(\\\\\\\"THREE.AnimationClip: No animation in JSONLoader data.\\\\\\\"),null;const n=function(t,e,n,i,r){if(0!==n.length){const s=[],a=[];Ul.flattenJSON(n,s,a,i),0!==s.length&&r.push(new t(e,s,a))}},i=[],r=t.name||\\\\\\\"default\\\\\\\",s=t.fps||30,a=t.blendMode;let o=t.length||-1;const l=t.hierarchy||[];for(let t=0;t<l.length;t++){const r=l[t].keys;if(r&&0!==r.length)if(r[0].morphTargets){const t={};let e;for(e=0;e<r.length;e++)if(r[e].morphTargets)for(let n=0;n<r[e].morphTargets.length;n++)t[r[e].morphTargets[n]]=-1;for(const n in t){const t=[],s=[];for(let i=0;i!==r[e].morphTargets.length;++i){const i=r[e];t.push(i.time),s.push(i.morphTarget===n?1:0)}i.push(new Xl(\\\\\\\".morphTargetInfluence[\\\\\\\"+n+\\\\\\\"]\\\\\\\",t,s))}o=t.length*(s||1)}else{const s=\\\\\\\".bones[\\\\\\\"+e[t].name+\\\\\\\"]\\\\\\\";n(Ql,s+\\\\\\\".position\\\\\\\",r,\\\\\\\"pos\\\\\\\",i),n(Jl,s+\\\\\\\".quaternion\\\\\\\",r,\\\\\\\"rot\\\\\\\",i),n(Ql,s+\\\\\\\".scale\\\\\\\",r,\\\\\\\"scl\\\\\\\",i)}}if(0===i.length)return null;return new this(r,o,i,a)}resetDuration(){let t=0;for(let e=0,n=this.tracks.length;e!==n;++e){const n=this.tracks[e];t=Math.max(t,n.times[n.times.length-1])}return this.duration=t,this}trim(){for(let t=0;t<this.tracks.length;t++)this.tracks[t].trim(0,this.duration);return this}validate(){let t=!0;for(let e=0;e<this.tracks.length;e++)t=t&&this.tracks[e].validate();return t}optimize(){for(let t=0;t<this.tracks.length;t++)this.tracks[t].optimize();return this}clone(){const t=[];for(let e=0;e<this.tracks.length;e++)t.push(this.tracks[e].clone());return new this.constructor(this.name,this.duration,t,this.blendMode)}toJSON(){return this.constructor.toJSON(this)}}function $l(t){if(void 0===t.type)throw new Error(\\\\\\\"THREE.KeyframeTrack: track type undefined, can not parse\\\\\\\");const e=function(t){switch(t.toLowerCase()){case\\\\\\\"scalar\\\\\\\":case\\\\\\\"double\\\\\\\":case\\\\\\\"float\\\\\\\":case\\\\\\\"number\\\\\\\":case\\\\\\\"integer\\\\\\\":return Xl;case\\\\\\\"vector\\\\\\\":case\\\\\\\"vector2\\\\\\\":case\\\\\\\"vector3\\\\\\\":case\\\\\\\"vector4\\\\\\\":return Ql;case\\\\\\\"color\\\\\\\":return ql;case\\\\\\\"quaternion\\\\\\\":return Jl;case\\\\\\\"bool\\\\\\\":case\\\\\\\"boolean\\\\\\\":return jl;case\\\\\\\"string\\\\\\\":return Zl}throw new Error(\\\\\\\"THREE.KeyframeTrack: Unsupported typeName: \\\\\\\"+t)}(t.type);if(void 0===t.times){const e=[],n=[];Ul.flattenJSON(t.keys,e,n,\\\\\\\"value\\\\\\\"),t.times=e,t.values=n}return void 0!==e.parse?e.parse(t):new e(t.name,t.times,t.values,t.interpolation)}const tc={enabled:!1,files:{},add:function(t,e){!1!==this.enabled&&(this.files[t]=e)},get:function(t){if(!1!==this.enabled)return this.files[t]},remove:function(t){delete this.files[t]},clear:function(){this.files={}}};class ec{constructor(t,e,n){const i=this;let r,s=!1,a=0,o=0;const l=[];this.onStart=void 0,this.onLoad=t,this.onProgress=e,this.onError=n,this.itemStart=function(t){o++,!1===s&&void 0!==i.onStart&&i.onStart(t,a,o),s=!0},this.itemEnd=function(t){a++,void 0!==i.onProgress&&i.onProgress(t,a,o),a===o&&(s=!1,void 0!==i.onLoad&&i.onLoad())},this.itemError=function(t){void 0!==i.onError&&i.onError(t)},this.resolveURL=function(t){return r?r(t):t},this.setURLModifier=function(t){return r=t,this},this.addHandler=function(t,e){return l.push(t,e),this},this.removeHandler=function(t){const e=l.indexOf(t);return-1!==e&&l.splice(e,2),this},this.getHandler=function(t){for(let e=0,n=l.length;e<n;e+=2){const n=l[e],i=l[e+1];if(n.global&&(n.lastIndex=0),n.test(t))return i}return null}}}const nc=new ec;class ic{constructor(t){this.manager=void 0!==t?t:nc,this.crossOrigin=\\\\\\\"anonymous\\\\\\\",this.withCredentials=!1,this.path=\\\\\\\"\\\\\\\",this.resourcePath=\\\\\\\"\\\\\\\",this.requestHeader={}}load(){}loadAsync(t,e){const n=this;return new Promise((function(i,r){n.load(t,i,e,r)}))}parse(){}setCrossOrigin(t){return this.crossOrigin=t,this}setWithCredentials(t){return this.withCredentials=t,this}setPath(t){return this.path=t,this}setResourcePath(t){return this.resourcePath=t,this}setRequestHeader(t){return this.requestHeader=t,this}}const rc={};class sc extends ic{constructor(t){super(t)}load(t,e,n,i){void 0===t&&(t=\\\\\\\"\\\\\\\"),void 0!==this.path&&(t=this.path+t),t=this.manager.resolveURL(t);const r=this,s=tc.get(t);if(void 0!==s)return r.manager.itemStart(t),setTimeout((function(){e&&e(s),r.manager.itemEnd(t)}),0),s;if(void 0!==rc[t])return void rc[t].push({onLoad:e,onProgress:n,onError:i});const a=t.match(/^data:(.*?)(;base64)?,(.*)$/);let o;if(a){const n=a[1],s=!!a[2];let o=a[3];o=decodeURIComponent(o),s&&(o=atob(o));try{let i;const s=(this.responseType||\\\\\\\"\\\\\\\").toLowerCase();switch(s){case\\\\\\\"arraybuffer\\\\\\\":case\\\\\\\"blob\\\\\\\":const t=new Uint8Array(o.length);for(let e=0;e<o.length;e++)t[e]=o.charCodeAt(e);i=\\\\\\\"blob\\\\\\\"===s?new Blob([t.buffer],{type:n}):t.buffer;break;case\\\\\\\"document\\\\\\\":const e=new DOMParser;i=e.parseFromString(o,n);break;case\\\\\\\"json\\\\\\\":i=JSON.parse(o);break;default:i=o}setTimeout((function(){e&&e(i),r.manager.itemEnd(t)}),0)}catch(e){setTimeout((function(){i&&i(e),r.manager.itemError(t),r.manager.itemEnd(t)}),0)}}else{rc[t]=[],rc[t].push({onLoad:e,onProgress:n,onError:i}),o=new XMLHttpRequest,o.open(\\\\\\\"GET\\\\\\\",t,!0),o.addEventListener(\\\\\\\"load\\\\\\\",(function(e){const n=this.response,i=rc[t];if(delete rc[t],200===this.status||0===this.status){0===this.status&&console.warn(\\\\\\\"THREE.FileLoader: HTTP Status 0 received.\\\\\\\"),tc.add(t,n);for(let t=0,e=i.length;t<e;t++){const e=i[t];e.onLoad&&e.onLoad(n)}r.manager.itemEnd(t)}else{for(let t=0,n=i.length;t<n;t++){const n=i[t];n.onError&&n.onError(e)}r.manager.itemError(t),r.manager.itemEnd(t)}}),!1),o.addEventListener(\\\\\\\"progress\\\\\\\",(function(e){const n=rc[t];for(let t=0,i=n.length;t<i;t++){const i=n[t];i.onProgress&&i.onProgress(e)}}),!1),o.addEventListener(\\\\\\\"error\\\\\\\",(function(e){const n=rc[t];delete rc[t];for(let t=0,i=n.length;t<i;t++){const i=n[t];i.onError&&i.onError(e)}r.manager.itemError(t),r.manager.itemEnd(t)}),!1),o.addEventListener(\\\\\\\"abort\\\\\\\",(function(e){const n=rc[t];delete rc[t];for(let t=0,i=n.length;t<i;t++){const i=n[t];i.onError&&i.onError(e)}r.manager.itemError(t),r.manager.itemEnd(t)}),!1),void 0!==this.responseType&&(o.responseType=this.responseType),void 0!==this.withCredentials&&(o.withCredentials=this.withCredentials),o.overrideMimeType&&o.overrideMimeType(void 0!==this.mimeType?this.mimeType:\\\\\\\"text/plain\\\\\\\");for(const t in this.requestHeader)o.setRequestHeader(t,this.requestHeader[t]);o.send(null)}return r.manager.itemStart(t),o}setResponseType(t){return this.responseType=t,this}setMimeType(t){return this.mimeType=t,this}}class ac extends ic{constructor(t){super(t)}load(t,e,n,i){void 0!==this.path&&(t=this.path+t),t=this.manager.resolveURL(t);const r=this,s=tc.get(t);if(void 0!==s)return r.manager.itemStart(t),setTimeout((function(){e&&e(s),r.manager.itemEnd(t)}),0),s;const a=document.createElementNS(\\\\\\\"http://www.w3.org/1999/xhtml\\\\\\\",\\\\\\\"img\\\\\\\");function o(){a.removeEventListener(\\\\\\\"load\\\\\\\",o,!1),a.removeEventListener(\\\\\\\"error\\\\\\\",l,!1),tc.add(t,this),e&&e(this),r.manager.itemEnd(t)}function l(e){a.removeEventListener(\\\\\\\"load\\\\\\\",o,!1),a.removeEventListener(\\\\\\\"error\\\\\\\",l,!1),i&&i(e),r.manager.itemError(t),r.manager.itemEnd(t)}return a.addEventListener(\\\\\\\"load\\\\\\\",o,!1),a.addEventListener(\\\\\\\"error\\\\\\\",l,!1),\\\\\\\"data:\\\\\\\"!==t.substr(0,5)&&void 0!==this.crossOrigin&&(a.crossOrigin=this.crossOrigin),r.manager.itemStart(t),a.src=t,a}}class oc extends ic{constructor(t){super(t)}load(t,e,n,i){const r=new Kn,s=new ac(this.manager);s.setCrossOrigin(this.crossOrigin),s.setPath(this.path);let a=0;function o(n){s.load(t[n],(function(t){r.images[n]=t,a++,6===a&&(r.needsUpdate=!0,e&&e(r))}),void 0,i)}for(let e=0;e<t.length;++e)o(e);return r}}class lc extends ic{constructor(t){super(t)}load(t,e,n,i){const r=this,s=new Ca,a=new sc(this.manager);return a.setResponseType(\\\\\\\"arraybuffer\\\\\\\"),a.setRequestHeader(this.requestHeader),a.setPath(this.path),a.setWithCredentials(r.withCredentials),a.load(t,(function(t){const n=r.parse(t);n&&(void 0!==n.image?s.image=n.image:void 0!==n.data&&(s.image.width=n.width,s.image.height=n.height,s.image.data=n.data),s.wrapS=void 0!==n.wrapS?n.wrapS:u,s.wrapT=void 0!==n.wrapT?n.wrapT:u,s.magFilter=void 0!==n.magFilter?n.magFilter:g,s.minFilter=void 0!==n.minFilter?n.minFilter:g,s.anisotropy=void 0!==n.anisotropy?n.anisotropy:1,void 0!==n.encoding&&(s.encoding=n.encoding),void 0!==n.flipY&&(s.flipY=n.flipY),void 0!==n.format&&(s.format=n.format),void 0!==n.type&&(s.type=n.type),void 0!==n.mipmaps&&(s.mipmaps=n.mipmaps,s.minFilter=y),1===n.mipmapCount&&(s.minFilter=g),void 0!==n.generateMipmaps&&(s.generateMipmaps=n.generateMipmaps),s.needsUpdate=!0,e&&e(s,n))}),n,i),s}}class cc extends ic{constructor(t){super(t)}load(t,e,n,i){const r=new bt,s=new ac(this.manager);return s.setCrossOrigin(this.crossOrigin),s.setPath(this.path),s.load(t,(function(n){r.image=n;const i=t.search(/\\\\.jpe?g($|\\\\?)/i)>0||0===t.search(/^data\\\\:image\\\\/jpeg/);r.format=i?T:E,r.needsUpdate=!0,void 0!==e&&e(r)}),n,i),r}}class hc extends yo{constructor(){super(),this.type=\\\\\\\"CurvePath\\\\\\\",this.curves=[],this.autoClose=!1}add(t){this.curves.push(t)}closePath(){const t=this.curves[0].getPoint(0),e=this.curves[this.curves.length-1].getPoint(1);t.equals(e)||this.curves.push(new Io(e,t))}getPoint(t){const e=t*this.getLength(),n=this.getCurveLengths();let i=0;for(;i<n.length;){if(n[i]>=e){const t=n[i]-e,r=this.curves[i],s=r.getLength(),a=0===s?0:1-t/s;return r.getPointAt(a)}i++}return null}getLength(){const t=this.getCurveLengths();return t[t.length-1]}updateArcLengths(){this.needsUpdate=!0,this.cacheLengths=null,this.getCurveLengths()}getCurveLengths(){if(this.cacheLengths&&this.cacheLengths.length===this.curves.length)return this.cacheLengths;const t=[];let e=0;for(let n=0,i=this.curves.length;n<i;n++)e+=this.curves[n].getLength(),t.push(e);return this.cacheLengths=t,t}getSpacedPoints(t=40){const e=[];for(let n=0;n<=t;n++)e.push(this.getPoint(n/t));return this.autoClose&&e.push(e[0]),e}getPoints(t=12){const e=[];let n;for(let i=0,r=this.curves;i<r.length;i++){const s=r[i],a=s&&s.isEllipseCurve?2*t:s&&(s.isLineCurve||s.isLineCurve3)?1:s&&s.isSplineCurve?t*s.points.length:t,o=s.getPoints(a);for(let t=0;t<o.length;t++){const i=o[t];n&&n.equals(i)||(e.push(i),n=i)}}return this.autoClose&&e.length>1&&!e[e.length-1].equals(e[0])&&e.push(e[0]),e}copy(t){super.copy(t),this.curves=[];for(let e=0,n=t.curves.length;e<n;e++){const n=t.curves[e];this.curves.push(n.clone())}return this.autoClose=t.autoClose,this}toJSON(){const t=super.toJSON();t.autoClose=this.autoClose,t.curves=[];for(let e=0,n=this.curves.length;e<n;e++){const n=this.curves[e];t.curves.push(n.toJSON())}return t}fromJSON(t){super.fromJSON(t),this.autoClose=t.autoClose,this.curves=[];for(let e=0,n=t.curves.length;e<n;e++){const n=t.curves[e];this.curves.push((new Fo[n.type]).fromJSON(n))}return this}}class uc extends hc{constructor(t){super(),this.type=\\\\\\\"Path\\\\\\\",this.currentPoint=new vt,t&&this.setFromPoints(t)}setFromPoints(t){this.moveTo(t[0].x,t[0].y);for(let e=1,n=t.length;e<n;e++)this.lineTo(t[e].x,t[e].y);return this}moveTo(t,e){return this.currentPoint.set(t,e),this}lineTo(t,e){const n=new Io(this.currentPoint.clone(),new vt(t,e));return this.curves.push(n),this.currentPoint.set(t,e),this}quadraticCurveTo(t,e,n,i){const r=new No(this.currentPoint.clone(),new vt(t,e),new vt(n,i));return this.curves.push(r),this.currentPoint.set(n,i),this}bezierCurveTo(t,e,n,i,r,s){const a=new Co(this.currentPoint.clone(),new vt(t,e),new vt(n,i),new vt(r,s));return this.curves.push(a),this.currentPoint.set(r,s),this}splineThru(t){const e=[this.currentPoint.clone()].concat(t),n=new zo(e);return this.curves.push(n),this.currentPoint.copy(t[t.length-1]),this}arc(t,e,n,i,r,s){const a=this.currentPoint.x,o=this.currentPoint.y;return this.absarc(t+a,e+o,n,i,r,s),this}absarc(t,e,n,i,r,s){return this.absellipse(t,e,n,n,i,r,s),this}ellipse(t,e,n,i,r,s,a,o){const l=this.currentPoint.x,c=this.currentPoint.y;return this.absellipse(t+l,e+c,n,i,r,s,a,o),this}absellipse(t,e,n,i,r,s,a,o){const l=new xo(t,e,n,i,r,s,a,o);if(this.curves.length>0){const t=l.getPoint(0);t.equals(this.currentPoint)||this.lineTo(t.x,t.y)}this.curves.push(l);const c=l.getPoint(1);return this.currentPoint.copy(c),this}copy(t){return super.copy(t),this.currentPoint.copy(t.currentPoint),this}toJSON(){const t=super.toJSON();return t.currentPoint=this.currentPoint.toArray(),t}fromJSON(t){return super.fromJSON(t),this.currentPoint.fromArray(t.currentPoint),this}}class dc extends uc{constructor(t){super(t),this.uuid=ct(),this.type=\\\\\\\"Shape\\\\\\\",this.holes=[]}getPointsHoles(t){const e=[];for(let n=0,i=this.holes.length;n<i;n++)e[n]=this.holes[n].getPoints(t);return e}extractPoints(t){return{shape:this.getPoints(t),holes:this.getPointsHoles(t)}}copy(t){super.copy(t),this.holes=[];for(let e=0,n=t.holes.length;e<n;e++){const n=t.holes[e];this.holes.push(n.clone())}return this}toJSON(){const t=super.toJSON();t.uuid=this.uuid,t.holes=[];for(let e=0,n=this.holes.length;e<n;e++){const n=this.holes[e];t.holes.push(n.toJSON())}return t}fromJSON(t){super.fromJSON(t),this.uuid=t.uuid,this.holes=[];for(let e=0,n=t.holes.length;e<n;e++){const n=t.holes[e];this.holes.push((new uc).fromJSON(n))}return this}}class pc extends Pe{constructor(t,e=1){super(),this.type=\\\\\\\"Light\\\\\\\",this.color=new Qe(t),this.intensity=e}dispose(){}copy(t){return super.copy(t),this.color.copy(t.color),this.intensity=t.intensity,this}toJSON(t){const e=super.toJSON(t);return e.object.color=this.color.getHex(),e.object.intensity=this.intensity,void 0!==this.groundColor&&(e.object.groundColor=this.groundColor.getHex()),void 0!==this.distance&&(e.object.distance=this.distance),void 0!==this.angle&&(e.object.angle=this.angle),void 0!==this.decay&&(e.object.decay=this.decay),void 0!==this.penumbra&&(e.object.penumbra=this.penumbra),void 0!==this.shadow&&(e.object.shadow=this.shadow.toJSON()),e}}pc.prototype.isLight=!0;class mc extends pc{constructor(t,e,n){super(t,n),this.type=\\\\\\\"HemisphereLight\\\\\\\",this.position.copy(Pe.DefaultUp),this.updateMatrix(),this.groundColor=new Qe(e)}copy(t){return pc.prototype.copy.call(this,t),this.groundColor.copy(t.groundColor),this}}mc.prototype.isHemisphereLight=!0;const fc=new ae,gc=new Rt,vc=new Rt;class yc{constructor(t){this.camera=t,this.bias=0,this.normalBias=0,this.radius=1,this.mapSize=new vt(512,512),this.map=null,this.mapPass=null,this.matrix=new ae,this.autoUpdate=!0,this.needsUpdate=!1,this._frustum=new ai,this._frameExtents=new vt(1,1),this._viewportCount=1,this._viewports=[new St(0,0,1,1)]}getViewportCount(){return this._viewportCount}getFrustum(){return this._frustum}updateMatrices(t){const e=this.camera,n=this.matrix;gc.setFromMatrixPosition(t.matrixWorld),e.position.copy(gc),vc.setFromMatrixPosition(t.target.matrixWorld),e.lookAt(vc),e.updateMatrixWorld(),fc.multiplyMatrices(e.projectionMatrix,e.matrixWorldInverse),this._frustum.setFromProjectionMatrix(fc),n.set(.5,0,0,.5,0,.5,0,.5,0,0,.5,.5,0,0,0,1),n.multiply(e.projectionMatrix),n.multiply(e.matrixWorldInverse)}getViewport(t){return this._viewports[t]}getFrameExtents(){return this._frameExtents}dispose(){this.map&&this.map.dispose(),this.mapPass&&this.mapPass.dispose()}copy(t){return this.camera=t.camera.clone(),this.bias=t.bias,this.radius=t.radius,this.mapSize.copy(t.mapSize),this}clone(){return(new this.constructor).copy(this)}toJSON(){const t={};return 0!==this.bias&&(t.bias=this.bias),0!==this.normalBias&&(t.normalBias=this.normalBias),1!==this.radius&&(t.radius=this.radius),512===this.mapSize.x&&512===this.mapSize.y||(t.mapSize=this.mapSize.toArray()),t.camera=this.camera.toJSON(!1).object,delete t.camera.matrix,t}}class xc extends yc{constructor(){super(new Jn(50,1,.5,500)),this.focus=1}updateMatrices(t){const e=this.camera,n=2*lt*t.angle*this.focus,i=this.mapSize.width/this.mapSize.height,r=t.distance||e.far;n===e.fov&&i===e.aspect&&r===e.far||(e.fov=n,e.aspect=i,e.far=r,e.updateProjectionMatrix()),super.updateMatrices(t)}copy(t){return super.copy(t),this.focus=t.focus,this}}xc.prototype.isSpotLightShadow=!0;class _c extends pc{constructor(t,e,n=0,i=Math.PI/3,r=0,s=1){super(t,e),this.type=\\\\\\\"SpotLight\\\\\\\",this.position.copy(Pe.DefaultUp),this.updateMatrix(),this.target=new Pe,this.distance=n,this.angle=i,this.penumbra=r,this.decay=s,this.shadow=new xc}get power(){return this.intensity*Math.PI}set power(t){this.intensity=t/Math.PI}dispose(){this.shadow.dispose()}copy(t){return super.copy(t),this.distance=t.distance,this.angle=t.angle,this.penumbra=t.penumbra,this.decay=t.decay,this.target=t.target.clone(),this.shadow=t.shadow.clone(),this}}_c.prototype.isSpotLight=!0;const Mc=new ae,bc=new Rt,wc=new Rt;class Sc extends yc{constructor(){super(new Jn(90,1,.5,500)),this._frameExtents=new vt(4,2),this._viewportCount=6,this._viewports=[new St(2,1,1,1),new St(0,1,1,1),new St(3,1,1,1),new St(1,1,1,1),new St(3,0,1,1),new St(1,0,1,1)],this._cubeDirections=[new Rt(1,0,0),new Rt(-1,0,0),new Rt(0,0,1),new Rt(0,0,-1),new Rt(0,1,0),new Rt(0,-1,0)],this._cubeUps=[new Rt(0,1,0),new Rt(0,1,0),new Rt(0,1,0),new Rt(0,1,0),new Rt(0,0,1),new Rt(0,0,-1)]}updateMatrices(t,e=0){const n=this.camera,i=this.matrix,r=t.distance||n.far;r!==n.far&&(n.far=r,n.updateProjectionMatrix()),bc.setFromMatrixPosition(t.matrixWorld),n.position.copy(bc),wc.copy(n.position),wc.add(this._cubeDirections[e]),n.up.copy(this._cubeUps[e]),n.lookAt(wc),n.updateMatrixWorld(),i.makeTranslation(-bc.x,-bc.y,-bc.z),Mc.multiplyMatrices(n.projectionMatrix,n.matrixWorldInverse),this._frustum.setFromProjectionMatrix(Mc)}}Sc.prototype.isPointLightShadow=!0;class Tc extends pc{constructor(t,e,n=0,i=1){super(t,e),this.type=\\\\\\\"PointLight\\\\\\\",this.distance=n,this.decay=i,this.shadow=new Sc}get power(){return 4*this.intensity*Math.PI}set power(t){this.intensity=t/(4*Math.PI)}dispose(){this.shadow.dispose()}copy(t){return super.copy(t),this.distance=t.distance,this.decay=t.decay,this.shadow=t.shadow.clone(),this}}Tc.prototype.isPointLight=!0;class Ec extends yc{constructor(){super(new xi(-5,5,5,-5,.5,500))}}Ec.prototype.isDirectionalLightShadow=!0;class Ac extends pc{constructor(t,e){super(t,e),this.type=\\\\\\\"DirectionalLight\\\\\\\",this.position.copy(Pe.DefaultUp),this.updateMatrix(),this.target=new Pe,this.shadow=new Ec}dispose(){this.shadow.dispose()}copy(t){return super.copy(t),this.target=t.target.clone(),this.shadow=t.shadow.clone(),this}}Ac.prototype.isDirectionalLight=!0;class Lc extends pc{constructor(t,e){super(t,e),this.type=\\\\\\\"AmbientLight\\\\\\\"}}Lc.prototype.isAmbientLight=!0;class Rc extends pc{constructor(t,e,n=10,i=10){super(t,e),this.type=\\\\\\\"RectAreaLight\\\\\\\",this.width=n,this.height=i}copy(t){return super.copy(t),this.width=t.width,this.height=t.height,this}toJSON(t){const e=super.toJSON(t);return e.object.width=this.width,e.object.height=this.height,e}}Rc.prototype.isRectAreaLight=!0;class Cc{constructor(){this.coefficients=[];for(let t=0;t<9;t++)this.coefficients.push(new Rt)}set(t){for(let e=0;e<9;e++)this.coefficients[e].copy(t[e]);return this}zero(){for(let t=0;t<9;t++)this.coefficients[t].set(0,0,0);return this}getAt(t,e){const n=t.x,i=t.y,r=t.z,s=this.coefficients;return e.copy(s[0]).multiplyScalar(.282095),e.addScaledVector(s[1],.488603*i),e.addScaledVector(s[2],.488603*r),e.addScaledVector(s[3],.488603*n),e.addScaledVector(s[4],n*i*1.092548),e.addScaledVector(s[5],i*r*1.092548),e.addScaledVector(s[6],.315392*(3*r*r-1)),e.addScaledVector(s[7],n*r*1.092548),e.addScaledVector(s[8],.546274*(n*n-i*i)),e}getIrradianceAt(t,e){const n=t.x,i=t.y,r=t.z,s=this.coefficients;return e.copy(s[0]).multiplyScalar(.886227),e.addScaledVector(s[1],1.023328*i),e.addScaledVector(s[2],1.023328*r),e.addScaledVector(s[3],1.023328*n),e.addScaledVector(s[4],.858086*n*i),e.addScaledVector(s[5],.858086*i*r),e.addScaledVector(s[6],.743125*r*r-.247708),e.addScaledVector(s[7],.858086*n*r),e.addScaledVector(s[8],.429043*(n*n-i*i)),e}add(t){for(let e=0;e<9;e++)this.coefficients[e].add(t.coefficients[e]);return this}addScaledSH(t,e){for(let n=0;n<9;n++)this.coefficients[n].addScaledVector(t.coefficients[n],e);return this}scale(t){for(let e=0;e<9;e++)this.coefficients[e].multiplyScalar(t);return this}lerp(t,e){for(let n=0;n<9;n++)this.coefficients[n].lerp(t.coefficients[n],e);return this}equals(t){for(let e=0;e<9;e++)if(!this.coefficients[e].equals(t.coefficients[e]))return!1;return!0}copy(t){return this.set(t.coefficients)}clone(){return(new this.constructor).copy(this)}fromArray(t,e=0){const n=this.coefficients;for(let i=0;i<9;i++)n[i].fromArray(t,e+3*i);return this}toArray(t=[],e=0){const n=this.coefficients;for(let i=0;i<9;i++)n[i].toArray(t,e+3*i);return t}static getBasisAt(t,e){const n=t.x,i=t.y,r=t.z;e[0]=.282095,e[1]=.488603*i,e[2]=.488603*r,e[3]=.488603*n,e[4]=1.092548*n*i,e[5]=1.092548*i*r,e[6]=.315392*(3*r*r-1),e[7]=1.092548*n*r,e[8]=.546274*(n*n-i*i)}}Cc.prototype.isSphericalHarmonics3=!0;class Pc extends pc{constructor(t=new Cc,e=1){super(void 0,e),this.sh=t}copy(t){return super.copy(t),this.sh.copy(t.sh),this}fromJSON(t){return this.intensity=t.intensity,this.sh.fromArray(t.sh),this}toJSON(t){const e=super.toJSON(t);return e.object.sh=this.sh.toArray(),e}}Pc.prototype.isLightProbe=!0;class Ic extends ic{constructor(t){super(t),this.textures={}}load(t,e,n,i){const r=this,s=new sc(r.manager);s.setPath(r.path),s.setRequestHeader(r.requestHeader),s.setWithCredentials(r.withCredentials),s.load(t,(function(n){try{e(r.parse(JSON.parse(n)))}catch(e){i?i(e):console.error(e),r.manager.itemError(t)}}),n,i)}parse(t){const e=this.textures;function n(t){return void 0===e[t]&&console.warn(\\\\\\\"THREE.MaterialLoader: Undefined texture\\\\\\\",t),e[t]}const i=new Ol[t.type];if(void 0!==t.uuid&&(i.uuid=t.uuid),void 0!==t.name&&(i.name=t.name),void 0!==t.color&&void 0!==i.color&&i.color.setHex(t.color),void 0!==t.roughness&&(i.roughness=t.roughness),void 0!==t.metalness&&(i.metalness=t.metalness),void 0!==t.sheen&&(i.sheen=(new Qe).setHex(t.sheen)),void 0!==t.emissive&&void 0!==i.emissive&&i.emissive.setHex(t.emissive),void 0!==t.specular&&void 0!==i.specular&&i.specular.setHex(t.specular),void 0!==t.specularIntensity&&(i.specularIntensity=t.specularIntensity),void 0!==t.specularTint&&void 0!==i.specularTint&&i.specularTint.setHex(t.specularTint),void 0!==t.shininess&&(i.shininess=t.shininess),void 0!==t.clearcoat&&(i.clearcoat=t.clearcoat),void 0!==t.clearcoatRoughness&&(i.clearcoatRoughness=t.clearcoatRoughness),void 0!==t.transmission&&(i.transmission=t.transmission),void 0!==t.thickness&&(i.thickness=t.thickness),void 0!==t.attenuationDistance&&(i.attenuationDistance=t.attenuationDistance),void 0!==t.attenuationTint&&void 0!==i.attenuationTint&&i.attenuationTint.setHex(t.attenuationTint),void 0!==t.fog&&(i.fog=t.fog),void 0!==t.flatShading&&(i.flatShading=t.flatShading),void 0!==t.blending&&(i.blending=t.blending),void 0!==t.combine&&(i.combine=t.combine),void 0!==t.side&&(i.side=t.side),void 0!==t.shadowSide&&(i.shadowSide=t.shadowSide),void 0!==t.opacity&&(i.opacity=t.opacity),void 0!==t.transparent&&(i.transparent=t.transparent),void 0!==t.alphaTest&&(i.alphaTest=t.alphaTest),void 0!==t.depthTest&&(i.depthTest=t.depthTest),void 0!==t.depthWrite&&(i.depthWrite=t.depthWrite),void 0!==t.colorWrite&&(i.colorWrite=t.colorWrite),void 0!==t.stencilWrite&&(i.stencilWrite=t.stencilWrite),void 0!==t.stencilWriteMask&&(i.stencilWriteMask=t.stencilWriteMask),void 0!==t.stencilFunc&&(i.stencilFunc=t.stencilFunc),void 0!==t.stencilRef&&(i.stencilRef=t.stencilRef),void 0!==t.stencilFuncMask&&(i.stencilFuncMask=t.stencilFuncMask),void 0!==t.stencilFail&&(i.stencilFail=t.stencilFail),void 0!==t.stencilZFail&&(i.stencilZFail=t.stencilZFail),void 0!==t.stencilZPass&&(i.stencilZPass=t.stencilZPass),void 0!==t.wireframe&&(i.wireframe=t.wireframe),void 0!==t.wireframeLinewidth&&(i.wireframeLinewidth=t.wireframeLinewidth),void 0!==t.wireframeLinecap&&(i.wireframeLinecap=t.wireframeLinecap),void 0!==t.wireframeLinejoin&&(i.wireframeLinejoin=t.wireframeLinejoin),void 0!==t.rotation&&(i.rotation=t.rotation),1!==t.linewidth&&(i.linewidth=t.linewidth),void 0!==t.dashSize&&(i.dashSize=t.dashSize),void 0!==t.gapSize&&(i.gapSize=t.gapSize),void 0!==t.scale&&(i.scale=t.scale),void 0!==t.polygonOffset&&(i.polygonOffset=t.polygonOffset),void 0!==t.polygonOffsetFactor&&(i.polygonOffsetFactor=t.polygonOffsetFactor),void 0!==t.polygonOffsetUnits&&(i.polygonOffsetUnits=t.polygonOffsetUnits),void 0!==t.dithering&&(i.dithering=t.dithering),void 0!==t.alphaToCoverage&&(i.alphaToCoverage=t.alphaToCoverage),void 0!==t.premultipliedAlpha&&(i.premultipliedAlpha=t.premultipliedAlpha),void 0!==t.visible&&(i.visible=t.visible),void 0!==t.toneMapped&&(i.toneMapped=t.toneMapped),void 0!==t.userData&&(i.userData=t.userData),void 0!==t.vertexColors&&(\\\\\\\"number\\\\\\\"==typeof t.vertexColors?i.vertexColors=t.vertexColors>0:i.vertexColors=t.vertexColors),void 0!==t.uniforms)for(const e in t.uniforms){const r=t.uniforms[e];switch(i.uniforms[e]={},r.type){case\\\\\\\"t\\\\\\\":i.uniforms[e].value=n(r.value);break;case\\\\\\\"c\\\\\\\":i.uniforms[e].value=(new Qe).setHex(r.value);break;case\\\\\\\"v2\\\\\\\":i.uniforms[e].value=(new vt).fromArray(r.value);break;case\\\\\\\"v3\\\\\\\":i.uniforms[e].value=(new Rt).fromArray(r.value);break;case\\\\\\\"v4\\\\\\\":i.uniforms[e].value=(new St).fromArray(r.value);break;case\\\\\\\"m3\\\\\\\":i.uniforms[e].value=(new yt).fromArray(r.value);break;case\\\\\\\"m4\\\\\\\":i.uniforms[e].value=(new ae).fromArray(r.value);break;default:i.uniforms[e].value=r.value}}if(void 0!==t.defines&&(i.defines=t.defines),void 0!==t.vertexShader&&(i.vertexShader=t.vertexShader),void 0!==t.fragmentShader&&(i.fragmentShader=t.fragmentShader),void 0!==t.extensions)for(const e in t.extensions)i.extensions[e]=t.extensions[e];if(void 0!==t.shading&&(i.flatShading=1===t.shading),void 0!==t.size&&(i.size=t.size),void 0!==t.sizeAttenuation&&(i.sizeAttenuation=t.sizeAttenuation),void 0!==t.map&&(i.map=n(t.map)),void 0!==t.matcap&&(i.matcap=n(t.matcap)),void 0!==t.alphaMap&&(i.alphaMap=n(t.alphaMap)),void 0!==t.bumpMap&&(i.bumpMap=n(t.bumpMap)),void 0!==t.bumpScale&&(i.bumpScale=t.bumpScale),void 0!==t.normalMap&&(i.normalMap=n(t.normalMap)),void 0!==t.normalMapType&&(i.normalMapType=t.normalMapType),void 0!==t.normalScale){let e=t.normalScale;!1===Array.isArray(e)&&(e=[e,e]),i.normalScale=(new vt).fromArray(e)}return void 0!==t.displacementMap&&(i.displacementMap=n(t.displacementMap)),void 0!==t.displacementScale&&(i.displacementScale=t.displacementScale),void 0!==t.displacementBias&&(i.displacementBias=t.displacementBias),void 0!==t.roughnessMap&&(i.roughnessMap=n(t.roughnessMap)),void 0!==t.metalnessMap&&(i.metalnessMap=n(t.metalnessMap)),void 0!==t.emissiveMap&&(i.emissiveMap=n(t.emissiveMap)),void 0!==t.emissiveIntensity&&(i.emissiveIntensity=t.emissiveIntensity),void 0!==t.specularMap&&(i.specularMap=n(t.specularMap)),void 0!==t.specularIntensityMap&&(i.specularIntensityMap=n(t.specularIntensityMap)),void 0!==t.specularTintMap&&(i.specularTintMap=n(t.specularTintMap)),void 0!==t.envMap&&(i.envMap=n(t.envMap)),void 0!==t.envMapIntensity&&(i.envMapIntensity=t.envMapIntensity),void 0!==t.reflectivity&&(i.reflectivity=t.reflectivity),void 0!==t.refractionRatio&&(i.refractionRatio=t.refractionRatio),void 0!==t.lightMap&&(i.lightMap=n(t.lightMap)),void 0!==t.lightMapIntensity&&(i.lightMapIntensity=t.lightMapIntensity),void 0!==t.aoMap&&(i.aoMap=n(t.aoMap)),void 0!==t.aoMapIntensity&&(i.aoMapIntensity=t.aoMapIntensity),void 0!==t.gradientMap&&(i.gradientMap=n(t.gradientMap)),void 0!==t.clearcoatMap&&(i.clearcoatMap=n(t.clearcoatMap)),void 0!==t.clearcoatRoughnessMap&&(i.clearcoatRoughnessMap=n(t.clearcoatRoughnessMap)),void 0!==t.clearcoatNormalMap&&(i.clearcoatNormalMap=n(t.clearcoatNormalMap)),void 0!==t.clearcoatNormalScale&&(i.clearcoatNormalScale=(new vt).fromArray(t.clearcoatNormalScale)),void 0!==t.transmissionMap&&(i.transmissionMap=n(t.transmissionMap)),void 0!==t.thicknessMap&&(i.thicknessMap=n(t.thicknessMap)),i}setTextures(t){return this.textures=t,this}}class Dc{static decodeText(t){if(\\\\\\\"undefined\\\\\\\"!=typeof TextDecoder)return(new TextDecoder).decode(t);let e=\\\\\\\"\\\\\\\";for(let n=0,i=t.length;n<i;n++)e+=String.fromCharCode(t[n]);try{return decodeURIComponent(escape(e))}catch(t){return e}}static extractUrlBase(t){const e=t.lastIndexOf(\\\\\\\"/\\\\\\\");return-1===e?\\\\\\\"./\\\\\\\":t.substr(0,e+1)}}class Nc extends wn{constructor(){super(),this.type=\\\\\\\"InstancedBufferGeometry\\\\\\\",this.instanceCount=1/0}copy(t){return super.copy(t),this.instanceCount=t.instanceCount,this}clone(){return(new this.constructor).copy(this)}toJSON(){const t=super.toJSON(this);return t.instanceCount=this.instanceCount,t.isInstancedBufferGeometry=!0,t}}Nc.prototype.isInstancedBufferGeometry=!0;class Bc extends en{constructor(t,e,n,i=1){\\\\\\\"number\\\\\\\"==typeof n&&(i=n,n=!1,console.error(\\\\\\\"THREE.InstancedBufferAttribute: The constructor now expects normalized as the third argument.\\\\\\\")),super(t,e,n),this.meshPerAttribute=i}copy(t){return super.copy(t),this.meshPerAttribute=t.meshPerAttribute,this}toJSON(){const t=super.toJSON();return t.meshPerAttribute=this.meshPerAttribute,t.isInstancedBufferAttribute=!0,t}}Bc.prototype.isInstancedBufferAttribute=!0;class zc extends ic{constructor(t){super(t)}load(t,e,n,i){const r=this,s=new sc(r.manager);s.setPath(r.path),s.setRequestHeader(r.requestHeader),s.setWithCredentials(r.withCredentials),s.load(t,(function(n){try{e(r.parse(JSON.parse(n)))}catch(e){i?i(e):console.error(e),r.manager.itemError(t)}}),n,i)}parse(t){const e={},n={};function i(t,i){if(void 0!==e[i])return e[i];const r=t.interleavedBuffers[i],s=function(t,e){if(void 0!==n[e])return n[e];const i=t.arrayBuffers[e],r=new Uint32Array(i).buffer;return n[e]=r,r}(t,r.buffer),a=fn(r.type,s),o=new ea(a,r.stride);return o.uuid=r.uuid,e[i]=o,o}const r=t.isInstancedBufferGeometry?new Nc:new wn,s=t.data.index;if(void 0!==s){const t=fn(s.type,s.array);r.setIndex(new en(t,1))}const a=t.data.attributes;for(const e in a){const n=a[e];let s;if(n.isInterleavedBufferAttribute){const e=i(t.data,n.data);s=new ia(e,n.itemSize,n.offset,n.normalized)}else{const t=fn(n.type,n.array);s=new(n.isInstancedBufferAttribute?Bc:en)(t,n.itemSize,n.normalized)}void 0!==n.name&&(s.name=n.name),void 0!==n.usage&&s.setUsage(n.usage),void 0!==n.updateRange&&(s.updateRange.offset=n.updateRange.offset,s.updateRange.count=n.updateRange.count),r.setAttribute(e,s)}const o=t.data.morphAttributes;if(o)for(const e in o){const n=o[e],s=[];for(let e=0,r=n.length;e<r;e++){const r=n[e];let a;if(r.isInterleavedBufferAttribute){const e=i(t.data,r.data);a=new ia(e,r.itemSize,r.offset,r.normalized)}else{const t=fn(r.type,r.array);a=new en(t,r.itemSize,r.normalized)}void 0!==r.name&&(a.name=r.name),s.push(a)}r.morphAttributes[e]=s}t.data.morphTargetsRelative&&(r.morphTargetsRelative=!0);const l=t.data.groups||t.data.drawcalls||t.data.offsets;if(void 0!==l)for(let t=0,e=l.length;t!==e;++t){const e=l[t];r.addGroup(e.start,e.count,e.materialIndex)}const c=t.data.boundingSphere;if(void 0!==c){const t=new Rt;void 0!==c.center&&t.fromArray(c.center),r.boundingSphere=new Qt(t,c.radius)}return t.name&&(r.name=t.name),t.userData&&(r.userData=t.userData),r}}const Fc={UVMapping:i,CubeReflectionMapping:r,CubeRefractionMapping:s,EquirectangularReflectionMapping:a,EquirectangularRefractionMapping:o,CubeUVReflectionMapping:l,CubeUVRefractionMapping:c},Oc={RepeatWrapping:h,ClampToEdgeWrapping:u,MirroredRepeatWrapping:d},Uc={NearestFilter:p,NearestMipmapNearestFilter:m,NearestMipmapLinearFilter:f,LinearFilter:g,LinearMipmapNearestFilter:v,LinearMipmapLinearFilter:y};class Hc extends ic{constructor(t){super(t),\\\\\\\"undefined\\\\\\\"==typeof createImageBitmap&&console.warn(\\\\\\\"THREE.ImageBitmapLoader: createImageBitmap() not supported.\\\\\\\"),\\\\\\\"undefined\\\\\\\"==typeof fetch&&console.warn(\\\\\\\"THREE.ImageBitmapLoader: fetch() not supported.\\\\\\\"),this.options={premultiplyAlpha:\\\\\\\"none\\\\\\\"}}setOptions(t){return this.options=t,this}load(t,e,n,i){void 0===t&&(t=\\\\\\\"\\\\\\\"),void 0!==this.path&&(t=this.path+t),t=this.manager.resolveURL(t);const r=this,s=tc.get(t);if(void 0!==s)return r.manager.itemStart(t),setTimeout((function(){e&&e(s),r.manager.itemEnd(t)}),0),s;const a={};a.credentials=\\\\\\\"anonymous\\\\\\\"===this.crossOrigin?\\\\\\\"same-origin\\\\\\\":\\\\\\\"include\\\\\\\",a.headers=this.requestHeader,fetch(t,a).then((function(t){return t.blob()})).then((function(t){return createImageBitmap(t,Object.assign(r.options,{colorSpaceConversion:\\\\\\\"none\\\\\\\"}))})).then((function(n){tc.add(t,n),e&&e(n),r.manager.itemEnd(t)})).catch((function(e){i&&i(e),r.manager.itemError(t),r.manager.itemEnd(t)})),r.manager.itemStart(t)}}Hc.prototype.isImageBitmapLoader=!0;class Gc{constructor(){this.type=\\\\\\\"ShapePath\\\\\\\",this.color=new Qe,this.subPaths=[],this.currentPath=null}moveTo(t,e){return this.currentPath=new uc,this.subPaths.push(this.currentPath),this.currentPath.moveTo(t,e),this}lineTo(t,e){return this.currentPath.lineTo(t,e),this}quadraticCurveTo(t,e,n,i){return this.currentPath.quadraticCurveTo(t,e,n,i),this}bezierCurveTo(t,e,n,i,r,s){return this.currentPath.bezierCurveTo(t,e,n,i,r,s),this}splineThru(t){return this.currentPath.splineThru(t),this}toShapes(t,e){function n(t){const e=[];for(let n=0,i=t.length;n<i;n++){const i=t[n],r=new dc;r.curves=i.curves,e.push(r)}return e}function i(t,e){const n=e.length;let i=!1;for(let r=n-1,s=0;s<n;r=s++){let n=e[r],a=e[s],o=a.x-n.x,l=a.y-n.y;if(Math.abs(l)>Number.EPSILON){if(l<0&&(n=e[s],o=-o,a=e[r],l=-l),t.y<n.y||t.y>a.y)continue;if(t.y===n.y){if(t.x===n.x)return!0}else{const e=l*(t.x-n.x)-o*(t.y-n.y);if(0===e)return!0;if(e<0)continue;i=!i}}else{if(t.y!==n.y)continue;if(a.x<=t.x&&t.x<=n.x||n.x<=t.x&&t.x<=a.x)return!0}}return i}const r=cl.isClockWise,s=this.subPaths;if(0===s.length)return[];if(!0===e)return n(s);let a,o,l;const c=[];if(1===s.length)return o=s[0],l=new dc,l.curves=o.curves,c.push(l),c;let h=!r(s[0].getPoints());h=t?!h:h;const u=[],d=[];let p,m,f=[],g=0;d[g]=void 0,f[g]=[];for(let e=0,n=s.length;e<n;e++)o=s[e],p=o.getPoints(),a=r(p),a=t?!a:a,a?(!h&&d[g]&&g++,d[g]={s:new dc,p:p},d[g].s.curves=o.curves,h&&g++,f[g]=[]):f[g].push({h:o,p:p[0]});if(!d[0])return n(s);if(d.length>1){let t=!1;const e=[];for(let t=0,e=d.length;t<e;t++)u[t]=[];for(let n=0,r=d.length;n<r;n++){const r=f[n];for(let s=0;s<r.length;s++){const a=r[s];let o=!0;for(let r=0;r<d.length;r++)i(a.p,d[r].p)&&(n!==r&&e.push({froms:n,tos:r,hole:s}),o?(o=!1,u[r].push(a)):t=!0);o&&u[n].push(a)}}e.length>0&&(t||(f=u))}for(let t=0,e=d.length;t<e;t++){l=d[t].s,c.push(l),m=f[t];for(let t=0,e=m.length;t<e;t++)l.holes.push(m[t].h)}return c}}class kc{constructor(t){this.type=\\\\\\\"Font\\\\\\\",this.data=t}generateShapes(t,e=100){const n=[],i=function(t,e,n){const i=Array.from(t),r=e/n.resolution,s=(n.boundingBox.yMax-n.boundingBox.yMin+n.underlineThickness)*r,a=[];let o=0,l=0;for(let t=0;t<i.length;t++){const e=i[t];if(\\\\\\\"\\\\n\\\\\\\"===e)o=0,l-=s;else{const t=Vc(e,r,o,l,n);o+=t.offsetX,a.push(t.path)}}return a}(t,e,this.data);for(let t=0,e=i.length;t<e;t++)Array.prototype.push.apply(n,i[t].toShapes());return n}}function Vc(t,e,n,i,r){const s=r.glyphs[t]||r.glyphs[\\\\\\\"?\\\\\\\"];if(!s)return void console.error('THREE.Font: character \\\\\\\"'+t+'\\\\\\\" does not exists in font family '+r.familyName+\\\\\\\".\\\\\\\");const a=new Gc;let o,l,c,h,u,d,p,m;if(s.o){const t=s._cachedOutline||(s._cachedOutline=s.o.split(\\\\\\\" \\\\\\\"));for(let r=0,s=t.length;r<s;){switch(t[r++]){case\\\\\\\"m\\\\\\\":o=t[r++]*e+n,l=t[r++]*e+i,a.moveTo(o,l);break;case\\\\\\\"l\\\\\\\":o=t[r++]*e+n,l=t[r++]*e+i,a.lineTo(o,l);break;case\\\\\\\"q\\\\\\\":c=t[r++]*e+n,h=t[r++]*e+i,u=t[r++]*e+n,d=t[r++]*e+i,a.quadraticCurveTo(u,d,c,h);break;case\\\\\\\"b\\\\\\\":c=t[r++]*e+n,h=t[r++]*e+i,u=t[r++]*e+n,d=t[r++]*e+i,p=t[r++]*e+n,m=t[r++]*e+i,a.bezierCurveTo(u,d,p,m,c,h)}}}return{offsetX:s.ha*e,path:a}}kc.prototype.isFont=!0;let Wc;const jc={getContext:function(){return void 0===Wc&&(Wc=new(window.AudioContext||window.webkitAudioContext)),Wc},setContext:function(t){Wc=t}};class qc extends ic{constructor(t){super(t)}load(t,e,n,i){const r=this,s=new sc(this.manager);s.setResponseType(\\\\\\\"arraybuffer\\\\\\\"),s.setPath(this.path),s.setRequestHeader(this.requestHeader),s.setWithCredentials(this.withCredentials),s.load(t,(function(n){try{const t=n.slice(0);jc.getContext().decodeAudioData(t,(function(t){e(t)}))}catch(e){i?i(e):console.error(e),r.manager.itemError(t)}}),n,i)}}class Xc extends Pc{constructor(t,e,n=1){super(void 0,n);const i=(new Qe).set(t),r=(new Qe).set(e),s=new Rt(i.r,i.g,i.b),a=new Rt(r.r,r.g,r.b),o=Math.sqrt(Math.PI),l=o*Math.sqrt(.75);this.sh.coefficients[0].copy(s).add(a).multiplyScalar(o),this.sh.coefficients[1].copy(s).sub(a).multiplyScalar(l)}}Xc.prototype.isHemisphereLightProbe=!0;class Yc extends Pc{constructor(t,e=1){super(void 0,e);const n=(new Qe).set(t);this.sh.coefficients[0].set(n.r,n.g,n.b).multiplyScalar(2*Math.sqrt(Math.PI))}}Yc.prototype.isAmbientLightProbe=!0;const Jc=new ae,Zc=new ae;class Qc{constructor(t=!0){this.autoStart=t,this.startTime=0,this.oldTime=0,this.elapsedTime=0,this.running=!1}start(){this.startTime=Kc(),this.oldTime=this.startTime,this.elapsedTime=0,this.running=!0}stop(){this.getElapsedTime(),this.running=!1,this.autoStart=!1}getElapsedTime(){return this.getDelta(),this.elapsedTime}getDelta(){let t=0;if(this.autoStart&&!this.running)return this.start(),0;if(this.running){const e=Kc();t=(e-this.oldTime)/1e3,this.oldTime=e,this.elapsedTime+=t}return t}}function Kc(){return(\\\\\\\"undefined\\\\\\\"==typeof performance?Date:performance).now()}const $c=new Rt,th=new Lt,eh=new Rt,nh=new Rt;class ih extends Pe{constructor(t){super(),this.type=\\\\\\\"Audio\\\\\\\",this.listener=t,this.context=t.context,this.gain=this.context.createGain(),this.gain.connect(t.getInput()),this.autoplay=!1,this.buffer=null,this.detune=0,this.loop=!1,this.loopStart=0,this.loopEnd=0,this.offset=0,this.duration=void 0,this.playbackRate=1,this.isPlaying=!1,this.hasPlaybackControl=!0,this.source=null,this.sourceType=\\\\\\\"empty\\\\\\\",this._startedAt=0,this._progress=0,this._connected=!1,this.filters=[]}getOutput(){return this.gain}setNodeSource(t){return this.hasPlaybackControl=!1,this.sourceType=\\\\\\\"audioNode\\\\\\\",this.source=t,this.connect(),this}setMediaElementSource(t){return this.hasPlaybackControl=!1,this.sourceType=\\\\\\\"mediaNode\\\\\\\",this.source=this.context.createMediaElementSource(t),this.connect(),this}setMediaStreamSource(t){return this.hasPlaybackControl=!1,this.sourceType=\\\\\\\"mediaStreamNode\\\\\\\",this.source=this.context.createMediaStreamSource(t),this.connect(),this}setBuffer(t){return this.buffer=t,this.sourceType=\\\\\\\"buffer\\\\\\\",this.autoplay&&this.play(),this}play(t=0){if(!0===this.isPlaying)return void console.warn(\\\\\\\"THREE.Audio: Audio is already playing.\\\\\\\");if(!1===this.hasPlaybackControl)return void console.warn(\\\\\\\"THREE.Audio: this Audio has no playback control.\\\\\\\");this._startedAt=this.context.currentTime+t;const e=this.context.createBufferSource();return e.buffer=this.buffer,e.loop=this.loop,e.loopStart=this.loopStart,e.loopEnd=this.loopEnd,e.onended=this.onEnded.bind(this),e.start(this._startedAt,this._progress+this.offset,this.duration),this.isPlaying=!0,this.source=e,this.setDetune(this.detune),this.setPlaybackRate(this.playbackRate),this.connect()}pause(){if(!1!==this.hasPlaybackControl)return!0===this.isPlaying&&(this._progress+=Math.max(this.context.currentTime-this._startedAt,0)*this.playbackRate,!0===this.loop&&(this._progress=this._progress%(this.duration||this.buffer.duration)),this.source.stop(),this.source.onended=null,this.isPlaying=!1),this;console.warn(\\\\\\\"THREE.Audio: this Audio has no playback control.\\\\\\\")}stop(){if(!1!==this.hasPlaybackControl)return this._progress=0,this.source.stop(),this.source.onended=null,this.isPlaying=!1,this;console.warn(\\\\\\\"THREE.Audio: this Audio has no playback control.\\\\\\\")}connect(){if(this.filters.length>0){this.source.connect(this.filters[0]);for(let t=1,e=this.filters.length;t<e;t++)this.filters[t-1].connect(this.filters[t]);this.filters[this.filters.length-1].connect(this.getOutput())}else this.source.connect(this.getOutput());return this._connected=!0,this}disconnect(){if(this.filters.length>0){this.source.disconnect(this.filters[0]);for(let t=1,e=this.filters.length;t<e;t++)this.filters[t-1].disconnect(this.filters[t]);this.filters[this.filters.length-1].disconnect(this.getOutput())}else this.source.disconnect(this.getOutput());return this._connected=!1,this}getFilters(){return this.filters}setFilters(t){return t||(t=[]),!0===this._connected?(this.disconnect(),this.filters=t.slice(),this.connect()):this.filters=t.slice(),this}setDetune(t){if(this.detune=t,void 0!==this.source.detune)return!0===this.isPlaying&&this.source.detune.setTargetAtTime(this.detune,this.context.currentTime,.01),this}getDetune(){return this.detune}getFilter(){return this.getFilters()[0]}setFilter(t){return this.setFilters(t?[t]:[])}setPlaybackRate(t){if(!1!==this.hasPlaybackControl)return this.playbackRate=t,!0===this.isPlaying&&this.source.playbackRate.setTargetAtTime(this.playbackRate,this.context.currentTime,.01),this;console.warn(\\\\\\\"THREE.Audio: this Audio has no playback control.\\\\\\\")}getPlaybackRate(){return this.playbackRate}onEnded(){this.isPlaying=!1}getLoop(){return!1===this.hasPlaybackControl?(console.warn(\\\\\\\"THREE.Audio: this Audio has no playback control.\\\\\\\"),!1):this.loop}setLoop(t){if(!1!==this.hasPlaybackControl)return this.loop=t,!0===this.isPlaying&&(this.source.loop=this.loop),this;console.warn(\\\\\\\"THREE.Audio: this Audio has no playback control.\\\\\\\")}setLoopStart(t){return this.loopStart=t,this}setLoopEnd(t){return this.loopEnd=t,this}getVolume(){return this.gain.gain.value}setVolume(t){return this.gain.gain.setTargetAtTime(t,this.context.currentTime,.01),this}}const rh=new Rt,sh=new Lt,ah=new Rt,oh=new Rt;class lh{constructor(t,e=2048){this.analyser=t.context.createAnalyser(),this.analyser.fftSize=e,this.data=new Uint8Array(this.analyser.frequencyBinCount),t.getOutput().connect(this.analyser)}getFrequencyData(){return this.analyser.getByteFrequencyData(this.data),this.data}getAverageFrequency(){let t=0;const e=this.getFrequencyData();for(let n=0;n<e.length;n++)t+=e[n];return t/e.length}}class ch{constructor(t,e,n){let i,r,s;switch(this.binding=t,this.valueSize=n,e){case\\\\\\\"quaternion\\\\\\\":i=this._slerp,r=this._slerpAdditive,s=this._setAdditiveIdentityQuaternion,this.buffer=new Float64Array(6*n),this._workIndex=5;break;case\\\\\\\"string\\\\\\\":case\\\\\\\"bool\\\\\\\":i=this._select,r=this._select,s=this._setAdditiveIdentityOther,this.buffer=new Array(5*n);break;default:i=this._lerp,r=this._lerpAdditive,s=this._setAdditiveIdentityNumeric,this.buffer=new Float64Array(5*n)}this._mixBufferRegion=i,this._mixBufferRegionAdditive=r,this._setIdentity=s,this._origIndex=3,this._addIndex=4,this.cumulativeWeight=0,this.cumulativeWeightAdditive=0,this.useCount=0,this.referenceCount=0}accumulate(t,e){const n=this.buffer,i=this.valueSize,r=t*i+i;let s=this.cumulativeWeight;if(0===s){for(let t=0;t!==i;++t)n[r+t]=n[t];s=e}else{s+=e;const t=e/s;this._mixBufferRegion(n,r,0,t,i)}this.cumulativeWeight=s}accumulateAdditive(t){const e=this.buffer,n=this.valueSize,i=n*this._addIndex;0===this.cumulativeWeightAdditive&&this._setIdentity(),this._mixBufferRegionAdditive(e,i,0,t,n),this.cumulativeWeightAdditive+=t}apply(t){const e=this.valueSize,n=this.buffer,i=t*e+e,r=this.cumulativeWeight,s=this.cumulativeWeightAdditive,a=this.binding;if(this.cumulativeWeight=0,this.cumulativeWeightAdditive=0,r<1){const t=e*this._origIndex;this._mixBufferRegion(n,i,t,1-r,e)}s>0&&this._mixBufferRegionAdditive(n,i,this._addIndex*e,1,e);for(let t=e,r=e+e;t!==r;++t)if(n[t]!==n[t+e]){a.setValue(n,i);break}}saveOriginalState(){const t=this.binding,e=this.buffer,n=this.valueSize,i=n*this._origIndex;t.getValue(e,i);for(let t=n,r=i;t!==r;++t)e[t]=e[i+t%n];this._setIdentity(),this.cumulativeWeight=0,this.cumulativeWeightAdditive=0}restoreOriginalState(){const t=3*this.valueSize;this.binding.setValue(this.buffer,t)}_setAdditiveIdentityNumeric(){const t=this._addIndex*this.valueSize,e=t+this.valueSize;for(let n=t;n<e;n++)this.buffer[n]=0}_setAdditiveIdentityQuaternion(){this._setAdditiveIdentityNumeric(),this.buffer[this._addIndex*this.valueSize+3]=1}_setAdditiveIdentityOther(){const t=this._origIndex*this.valueSize,e=this._addIndex*this.valueSize;for(let n=0;n<this.valueSize;n++)this.buffer[e+n]=this.buffer[t+n]}_select(t,e,n,i,r){if(i>=.5)for(let i=0;i!==r;++i)t[e+i]=t[n+i]}_slerp(t,e,n,i){Lt.slerpFlat(t,e,t,e,t,n,i)}_slerpAdditive(t,e,n,i,r){const s=this._workIndex*r;Lt.multiplyQuaternionsFlat(t,s,t,e,t,n),Lt.slerpFlat(t,e,t,e,t,s,i)}_lerp(t,e,n,i,r){const s=1-i;for(let a=0;a!==r;++a){const r=e+a;t[r]=t[r]*s+t[n+a]*i}}_lerpAdditive(t,e,n,i,r){for(let s=0;s!==r;++s){const r=e+s;t[r]=t[r]+t[n+s]*i}}}const hh=\\\\\\\"\\\\\\\\[\\\\\\\\]\\\\\\\\.:\\\\\\\\/\\\\\\\",uh=new RegExp(\\\\\\\"[\\\\\\\\[\\\\\\\\]\\\\\\\\.:\\\\\\\\/]\\\\\\\",\\\\\\\"g\\\\\\\"),dh=\\\\\\\"[^\\\\\\\\[\\\\\\\\]\\\\\\\\.:\\\\\\\\/]\\\\\\\",ph=\\\\\\\"[^\\\\\\\"+hh.replace(\\\\\\\"\\\\\\\\.\\\\\\\",\\\\\\\"\\\\\\\")+\\\\\\\"]\\\\\\\",mh=/((?:WC+[\\\\/:])*)/.source.replace(\\\\\\\"WC\\\\\\\",dh),fh=/(WCOD+)?/.source.replace(\\\\\\\"WCOD\\\\\\\",ph),gh=/(?:\\\\.(WC+)(?:\\\\[(.+)\\\\])?)?/.source.replace(\\\\\\\"WC\\\\\\\",dh),vh=/\\\\.(WC+)(?:\\\\[(.+)\\\\])?/.source.replace(\\\\\\\"WC\\\\\\\",dh),yh=new RegExp(\\\\\\\"^\\\\\\\"+mh+fh+gh+vh+\\\\\\\"$\\\\\\\"),xh=[\\\\\\\"material\\\\\\\",\\\\\\\"materials\\\\\\\",\\\\\\\"bones\\\\\\\"];class _h{constructor(t,e,n){this.path=e,this.parsedPath=n||_h.parseTrackName(e),this.node=_h.findNode(t,this.parsedPath.nodeName)||t,this.rootNode=t,this.getValue=this._getValue_unbound,this.setValue=this._setValue_unbound}static create(t,e,n){return t&&t.isAnimationObjectGroup?new _h.Composite(t,e,n):new _h(t,e,n)}static sanitizeNodeName(t){return t.replace(/\\\\s/g,\\\\\\\"_\\\\\\\").replace(uh,\\\\\\\"\\\\\\\")}static parseTrackName(t){const e=yh.exec(t);if(!e)throw new Error(\\\\\\\"PropertyBinding: Cannot parse trackName: \\\\\\\"+t);const n={nodeName:e[2],objectName:e[3],objectIndex:e[4],propertyName:e[5],propertyIndex:e[6]},i=n.nodeName&&n.nodeName.lastIndexOf(\\\\\\\".\\\\\\\");if(void 0!==i&&-1!==i){const t=n.nodeName.substring(i+1);-1!==xh.indexOf(t)&&(n.nodeName=n.nodeName.substring(0,i),n.objectName=t)}if(null===n.propertyName||0===n.propertyName.length)throw new Error(\\\\\\\"PropertyBinding: can not parse propertyName from trackName: \\\\\\\"+t);return n}static findNode(t,e){if(!e||\\\\\\\"\\\\\\\"===e||\\\\\\\".\\\\\\\"===e||-1===e||e===t.name||e===t.uuid)return t;if(t.skeleton){const n=t.skeleton.getBoneByName(e);if(void 0!==n)return n}if(t.children){const n=function(t){for(let i=0;i<t.length;i++){const r=t[i];if(r.name===e||r.uuid===e)return r;const s=n(r.children);if(s)return s}return null},i=n(t.children);if(i)return i}return null}_getValue_unavailable(){}_setValue_unavailable(){}_getValue_direct(t,e){t[e]=this.node[this.propertyName]}_getValue_array(t,e){const n=this.resolvedProperty;for(let i=0,r=n.length;i!==r;++i)t[e++]=n[i]}_getValue_arrayElement(t,e){t[e]=this.resolvedProperty[this.propertyIndex]}_getValue_toArray(t,e){this.resolvedProperty.toArray(t,e)}_setValue_direct(t,e){this.targetObject[this.propertyName]=t[e]}_setValue_direct_setNeedsUpdate(t,e){this.targetObject[this.propertyName]=t[e],this.targetObject.needsUpdate=!0}_setValue_direct_setMatrixWorldNeedsUpdate(t,e){this.targetObject[this.propertyName]=t[e],this.targetObject.matrixWorldNeedsUpdate=!0}_setValue_array(t,e){const n=this.resolvedProperty;for(let i=0,r=n.length;i!==r;++i)n[i]=t[e++]}_setValue_array_setNeedsUpdate(t,e){const n=this.resolvedProperty;for(let i=0,r=n.length;i!==r;++i)n[i]=t[e++];this.targetObject.needsUpdate=!0}_setValue_array_setMatrixWorldNeedsUpdate(t,e){const n=this.resolvedProperty;for(let i=0,r=n.length;i!==r;++i)n[i]=t[e++];this.targetObject.matrixWorldNeedsUpdate=!0}_setValue_arrayElement(t,e){this.resolvedProperty[this.propertyIndex]=t[e]}_setValue_arrayElement_setNeedsUpdate(t,e){this.resolvedProperty[this.propertyIndex]=t[e],this.targetObject.needsUpdate=!0}_setValue_arrayElement_setMatrixWorldNeedsUpdate(t,e){this.resolvedProperty[this.propertyIndex]=t[e],this.targetObject.matrixWorldNeedsUpdate=!0}_setValue_fromArray(t,e){this.resolvedProperty.fromArray(t,e)}_setValue_fromArray_setNeedsUpdate(t,e){this.resolvedProperty.fromArray(t,e),this.targetObject.needsUpdate=!0}_setValue_fromArray_setMatrixWorldNeedsUpdate(t,e){this.resolvedProperty.fromArray(t,e),this.targetObject.matrixWorldNeedsUpdate=!0}_getValue_unbound(t,e){this.bind(),this.getValue(t,e)}_setValue_unbound(t,e){this.bind(),this.setValue(t,e)}bind(){let t=this.node;const e=this.parsedPath,n=e.objectName,i=e.propertyName;let r=e.propertyIndex;if(t||(t=_h.findNode(this.rootNode,e.nodeName)||this.rootNode,this.node=t),this.getValue=this._getValue_unavailable,this.setValue=this._setValue_unavailable,!t)return void console.error(\\\\\\\"THREE.PropertyBinding: Trying to update node for track: \\\\\\\"+this.path+\\\\\\\" but it wasn't found.\\\\\\\");if(n){let i=e.objectIndex;switch(n){case\\\\\\\"materials\\\\\\\":if(!t.material)return void console.error(\\\\\\\"THREE.PropertyBinding: Can not bind to material as node does not have a material.\\\\\\\",this);if(!t.material.materials)return void console.error(\\\\\\\"THREE.PropertyBinding: Can not bind to material.materials as node.material does not have a materials array.\\\\\\\",this);t=t.material.materials;break;case\\\\\\\"bones\\\\\\\":if(!t.skeleton)return void console.error(\\\\\\\"THREE.PropertyBinding: Can not bind to bones as node does not have a skeleton.\\\\\\\",this);t=t.skeleton.bones;for(let e=0;e<t.length;e++)if(t[e].name===i){i=e;break}break;default:if(void 0===t[n])return void console.error(\\\\\\\"THREE.PropertyBinding: Can not bind to objectName of node undefined.\\\\\\\",this);t=t[n]}if(void 0!==i){if(void 0===t[i])return void console.error(\\\\\\\"THREE.PropertyBinding: Trying to bind to objectIndex of objectName, but is undefined.\\\\\\\",this,t);t=t[i]}}const s=t[i];if(void 0===s){const n=e.nodeName;return void console.error(\\\\\\\"THREE.PropertyBinding: Trying to update property for track: \\\\\\\"+n+\\\\\\\".\\\\\\\"+i+\\\\\\\" but it wasn't found.\\\\\\\",t)}let a=this.Versioning.None;this.targetObject=t,void 0!==t.needsUpdate?a=this.Versioning.NeedsUpdate:void 0!==t.matrixWorldNeedsUpdate&&(a=this.Versioning.MatrixWorldNeedsUpdate);let o=this.BindingType.Direct;if(void 0!==r){if(\\\\\\\"morphTargetInfluences\\\\\\\"===i){if(!t.geometry)return void console.error(\\\\\\\"THREE.PropertyBinding: Can not bind to morphTargetInfluences because node does not have a geometry.\\\\\\\",this);if(!t.geometry.isBufferGeometry)return void console.error(\\\\\\\"THREE.PropertyBinding: Can not bind to morphTargetInfluences on THREE.Geometry. Use THREE.BufferGeometry instead.\\\\\\\",this);if(!t.geometry.morphAttributes)return void console.error(\\\\\\\"THREE.PropertyBinding: Can not bind to morphTargetInfluences because node does not have a geometry.morphAttributes.\\\\\\\",this);void 0!==t.morphTargetDictionary[r]&&(r=t.morphTargetDictionary[r])}o=this.BindingType.ArrayElement,this.resolvedProperty=s,this.propertyIndex=r}else void 0!==s.fromArray&&void 0!==s.toArray?(o=this.BindingType.HasFromToArray,this.resolvedProperty=s):Array.isArray(s)?(o=this.BindingType.EntireArray,this.resolvedProperty=s):this.propertyName=i;this.getValue=this.GetterByBindingType[o],this.setValue=this.SetterByBindingTypeAndVersioning[o][a]}unbind(){this.node=null,this.getValue=this._getValue_unbound,this.setValue=this._setValue_unbound}}_h.Composite=class{constructor(t,e,n){const i=n||_h.parseTrackName(e);this._targetGroup=t,this._bindings=t.subscribe_(e,i)}getValue(t,e){this.bind();const n=this._targetGroup.nCachedObjects_,i=this._bindings[n];void 0!==i&&i.getValue(t,e)}setValue(t,e){const n=this._bindings;for(let i=this._targetGroup.nCachedObjects_,r=n.length;i!==r;++i)n[i].setValue(t,e)}bind(){const t=this._bindings;for(let e=this._targetGroup.nCachedObjects_,n=t.length;e!==n;++e)t[e].bind()}unbind(){const t=this._bindings;for(let e=this._targetGroup.nCachedObjects_,n=t.length;e!==n;++e)t[e].unbind()}},_h.prototype.BindingType={Direct:0,EntireArray:1,ArrayElement:2,HasFromToArray:3},_h.prototype.Versioning={None:0,NeedsUpdate:1,MatrixWorldNeedsUpdate:2},_h.prototype.GetterByBindingType=[_h.prototype._getValue_direct,_h.prototype._getValue_array,_h.prototype._getValue_arrayElement,_h.prototype._getValue_toArray],_h.prototype.SetterByBindingTypeAndVersioning=[[_h.prototype._setValue_direct,_h.prototype._setValue_direct_setNeedsUpdate,_h.prototype._setValue_direct_setMatrixWorldNeedsUpdate],[_h.prototype._setValue_array,_h.prototype._setValue_array_setNeedsUpdate,_h.prototype._setValue_array_setMatrixWorldNeedsUpdate],[_h.prototype._setValue_arrayElement,_h.prototype._setValue_arrayElement_setNeedsUpdate,_h.prototype._setValue_arrayElement_setMatrixWorldNeedsUpdate],[_h.prototype._setValue_fromArray,_h.prototype._setValue_fromArray_setNeedsUpdate,_h.prototype._setValue_fromArray_setMatrixWorldNeedsUpdate]];class Mh{constructor(){this.uuid=ct(),this._objects=Array.prototype.slice.call(arguments),this.nCachedObjects_=0;const t={};this._indicesByUUID=t;for(let e=0,n=arguments.length;e!==n;++e)t[arguments[e].uuid]=e;this._paths=[],this._parsedPaths=[],this._bindings=[],this._bindingsIndicesByPath={};const e=this;this.stats={objects:{get total(){return e._objects.length},get inUse(){return this.total-e.nCachedObjects_}},get bindingsPerObject(){return e._bindings.length}}}add(){const t=this._objects,e=this._indicesByUUID,n=this._paths,i=this._parsedPaths,r=this._bindings,s=r.length;let a,o=t.length,l=this.nCachedObjects_;for(let c=0,h=arguments.length;c!==h;++c){const h=arguments[c],u=h.uuid;let d=e[u];if(void 0===d){d=o++,e[u]=d,t.push(h);for(let t=0,e=s;t!==e;++t)r[t].push(new _h(h,n[t],i[t]))}else if(d<l){a=t[d];const o=--l,c=t[o];e[c.uuid]=d,t[d]=c,e[u]=o,t[o]=h;for(let t=0,e=s;t!==e;++t){const e=r[t],s=e[o];let a=e[d];e[d]=s,void 0===a&&(a=new _h(h,n[t],i[t])),e[o]=a}}else t[d]!==a&&console.error(\\\\\\\"THREE.AnimationObjectGroup: Different objects with the same UUID detected. Clean the caches or recreate your infrastructure when reloading scenes.\\\\\\\")}this.nCachedObjects_=l}remove(){const t=this._objects,e=this._indicesByUUID,n=this._bindings,i=n.length;let r=this.nCachedObjects_;for(let s=0,a=arguments.length;s!==a;++s){const a=arguments[s],o=a.uuid,l=e[o];if(void 0!==l&&l>=r){const s=r++,c=t[s];e[c.uuid]=l,t[l]=c,e[o]=s,t[s]=a;for(let t=0,e=i;t!==e;++t){const e=n[t],i=e[s],r=e[l];e[l]=i,e[s]=r}}}this.nCachedObjects_=r}uncache(){const t=this._objects,e=this._indicesByUUID,n=this._bindings,i=n.length;let r=this.nCachedObjects_,s=t.length;for(let a=0,o=arguments.length;a!==o;++a){const o=arguments[a].uuid,l=e[o];if(void 0!==l)if(delete e[o],l<r){const a=--r,o=t[a],c=--s,h=t[c];e[o.uuid]=l,t[l]=o,e[h.uuid]=a,t[a]=h,t.pop();for(let t=0,e=i;t!==e;++t){const e=n[t],i=e[a],r=e[c];e[l]=i,e[a]=r,e.pop()}}else{const r=--s,a=t[r];r>0&&(e[a.uuid]=l),t[l]=a,t.pop();for(let t=0,e=i;t!==e;++t){const e=n[t];e[l]=e[r],e.pop()}}}this.nCachedObjects_=r}subscribe_(t,e){const n=this._bindingsIndicesByPath;let i=n[t];const r=this._bindings;if(void 0!==i)return r[i];const s=this._paths,a=this._parsedPaths,o=this._objects,l=o.length,c=this.nCachedObjects_,h=new Array(l);i=r.length,n[t]=i,s.push(t),a.push(e),r.push(h);for(let n=c,i=o.length;n!==i;++n){const i=o[n];h[n]=new _h(i,t,e)}return h}unsubscribe_(t){const e=this._bindingsIndicesByPath,n=e[t];if(void 0!==n){const i=this._paths,r=this._parsedPaths,s=this._bindings,a=s.length-1,o=s[a];e[t[a]]=n,s[n]=o,s.pop(),r[n]=r[a],r.pop(),i[n]=i[a],i.pop()}}}Mh.prototype.isAnimationObjectGroup=!0;class bh{constructor(t,e,n=null,i=e.blendMode){this._mixer=t,this._clip=e,this._localRoot=n,this.blendMode=i;const r=e.tracks,s=r.length,a=new Array(s),o={endingStart:k,endingEnd:k};for(let t=0;t!==s;++t){const e=r[t].createInterpolant(null);a[t]=e,e.settings=o}this._interpolantSettings=o,this._interpolants=a,this._propertyBindings=new Array(s),this._cacheIndex=null,this._byClipCacheIndex=null,this._timeScaleInterpolant=null,this._weightInterpolant=null,this.loop=2201,this._loopCount=-1,this._startTime=null,this.time=0,this.timeScale=1,this._effectiveTimeScale=1,this.weight=1,this._effectiveWeight=1,this.repetitions=1/0,this.paused=!1,this.enabled=!0,this.clampWhenFinished=!1,this.zeroSlopeAtStart=!0,this.zeroSlopeAtEnd=!0}play(){return this._mixer._activateAction(this),this}stop(){return this._mixer._deactivateAction(this),this.reset()}reset(){return this.paused=!1,this.enabled=!0,this.time=0,this._loopCount=-1,this._startTime=null,this.stopFading().stopWarping()}isRunning(){return this.enabled&&!this.paused&&0!==this.timeScale&&null===this._startTime&&this._mixer._isActiveAction(this)}isScheduled(){return this._mixer._isActiveAction(this)}startAt(t){return this._startTime=t,this}setLoop(t,e){return this.loop=t,this.repetitions=e,this}setEffectiveWeight(t){return this.weight=t,this._effectiveWeight=this.enabled?t:0,this.stopFading()}getEffectiveWeight(){return this._effectiveWeight}fadeIn(t){return this._scheduleFading(t,0,1)}fadeOut(t){return this._scheduleFading(t,1,0)}crossFadeFrom(t,e,n){if(t.fadeOut(e),this.fadeIn(e),n){const n=this._clip.duration,i=t._clip.duration,r=i/n,s=n/i;t.warp(1,r,e),this.warp(s,1,e)}return this}crossFadeTo(t,e,n){return t.crossFadeFrom(this,e,n)}stopFading(){const t=this._weightInterpolant;return null!==t&&(this._weightInterpolant=null,this._mixer._takeBackControlInterpolant(t)),this}setEffectiveTimeScale(t){return this.timeScale=t,this._effectiveTimeScale=this.paused?0:t,this.stopWarping()}getEffectiveTimeScale(){return this._effectiveTimeScale}setDuration(t){return this.timeScale=this._clip.duration/t,this.stopWarping()}syncWith(t){return this.time=t.time,this.timeScale=t.timeScale,this.stopWarping()}halt(t){return this.warp(this._effectiveTimeScale,0,t)}warp(t,e,n){const i=this._mixer,r=i.time,s=this.timeScale;let a=this._timeScaleInterpolant;null===a&&(a=i._lendControlInterpolant(),this._timeScaleInterpolant=a);const o=a.parameterPositions,l=a.sampleValues;return o[0]=r,o[1]=r+n,l[0]=t/s,l[1]=e/s,this}stopWarping(){const t=this._timeScaleInterpolant;return null!==t&&(this._timeScaleInterpolant=null,this._mixer._takeBackControlInterpolant(t)),this}getMixer(){return this._mixer}getClip(){return this._clip}getRoot(){return this._localRoot||this._mixer._root}_update(t,e,n,i){if(!this.enabled)return void this._updateWeight(t);const r=this._startTime;if(null!==r){const i=(t-r)*n;if(i<0||0===n)return;this._startTime=null,e=n*i}e*=this._updateTimeScale(t);const s=this._updateTime(e),a=this._updateWeight(t);if(a>0){const t=this._interpolants,e=this._propertyBindings;switch(this.blendMode){case q:for(let n=0,i=t.length;n!==i;++n)t[n].evaluate(s),e[n].accumulateAdditive(a);break;case j:default:for(let n=0,r=t.length;n!==r;++n)t[n].evaluate(s),e[n].accumulate(i,a)}}}_updateWeight(t){let e=0;if(this.enabled){e=this.weight;const n=this._weightInterpolant;if(null!==n){const i=n.evaluate(t)[0];e*=i,t>n.parameterPositions[1]&&(this.stopFading(),0===i&&(this.enabled=!1))}}return this._effectiveWeight=e,e}_updateTimeScale(t){let e=0;if(!this.paused){e=this.timeScale;const n=this._timeScaleInterpolant;if(null!==n){e*=n.evaluate(t)[0],t>n.parameterPositions[1]&&(this.stopWarping(),0===e?this.paused=!0:this.timeScale=e)}}return this._effectiveTimeScale=e,e}_updateTime(t){const e=this._clip.duration,n=this.loop;let i=this.time+t,r=this._loopCount;const s=2202===n;if(0===t)return-1===r?i:s&&1==(1&r)?e-i:i;if(2200===n){-1===r&&(this._loopCount=0,this._setEndings(!0,!0,!1));t:{if(i>=e)i=e;else{if(!(i<0)){this.time=i;break t}i=0}this.clampWhenFinished?this.paused=!0:this.enabled=!1,this.time=i,this._mixer.dispatchEvent({type:\\\\\\\"finished\\\\\\\",action:this,direction:t<0?-1:1})}}else{if(-1===r&&(t>=0?(r=0,this._setEndings(!0,0===this.repetitions,s)):this._setEndings(0===this.repetitions,!0,s)),i>=e||i<0){const n=Math.floor(i/e);i-=e*n,r+=Math.abs(n);const a=this.repetitions-r;if(a<=0)this.clampWhenFinished?this.paused=!0:this.enabled=!1,i=t>0?e:0,this.time=i,this._mixer.dispatchEvent({type:\\\\\\\"finished\\\\\\\",action:this,direction:t>0?1:-1});else{if(1===a){const e=t<0;this._setEndings(e,!e,s)}else this._setEndings(!1,!1,s);this._loopCount=r,this.time=i,this._mixer.dispatchEvent({type:\\\\\\\"loop\\\\\\\",action:this,loopDelta:n})}}else this.time=i;if(s&&1==(1&r))return e-i}return i}_setEndings(t,e,n){const i=this._interpolantSettings;n?(i.endingStart=V,i.endingEnd=V):(i.endingStart=t?this.zeroSlopeAtStart?V:k:W,i.endingEnd=e?this.zeroSlopeAtEnd?V:k:W)}_scheduleFading(t,e,n){const i=this._mixer,r=i.time;let s=this._weightInterpolant;null===s&&(s=i._lendControlInterpolant(),this._weightInterpolant=s);const a=s.parameterPositions,o=s.sampleValues;return a[0]=r,o[0]=e,a[1]=r+t,o[1]=n,this}}class wh extends rt{constructor(t){super(),this._root=t,this._initMemoryManager(),this._accuIndex=0,this.time=0,this.timeScale=1}_bindAction(t,e){const n=t._localRoot||this._root,i=t._clip.tracks,r=i.length,s=t._propertyBindings,a=t._interpolants,o=n.uuid,l=this._bindingsByRootAndName;let c=l[o];void 0===c&&(c={},l[o]=c);for(let t=0;t!==r;++t){const r=i[t],l=r.name;let h=c[l];if(void 0!==h)s[t]=h;else{if(h=s[t],void 0!==h){null===h._cacheIndex&&(++h.referenceCount,this._addInactiveBinding(h,o,l));continue}const i=e&&e._propertyBindings[t].binding.parsedPath;h=new ch(_h.create(n,l,i),r.ValueTypeName,r.getValueSize()),++h.referenceCount,this._addInactiveBinding(h,o,l),s[t]=h}a[t].resultBuffer=h.buffer}}_activateAction(t){if(!this._isActiveAction(t)){if(null===t._cacheIndex){const e=(t._localRoot||this._root).uuid,n=t._clip.uuid,i=this._actionsByClip[n];this._bindAction(t,i&&i.knownActions[0]),this._addInactiveAction(t,n,e)}const e=t._propertyBindings;for(let t=0,n=e.length;t!==n;++t){const n=e[t];0==n.useCount++&&(this._lendBinding(n),n.saveOriginalState())}this._lendAction(t)}}_deactivateAction(t){if(this._isActiveAction(t)){const e=t._propertyBindings;for(let t=0,n=e.length;t!==n;++t){const n=e[t];0==--n.useCount&&(n.restoreOriginalState(),this._takeBackBinding(n))}this._takeBackAction(t)}}_initMemoryManager(){this._actions=[],this._nActiveActions=0,this._actionsByClip={},this._bindings=[],this._nActiveBindings=0,this._bindingsByRootAndName={},this._controlInterpolants=[],this._nActiveControlInterpolants=0;const t=this;this.stats={actions:{get total(){return t._actions.length},get inUse(){return t._nActiveActions}},bindings:{get total(){return t._bindings.length},get inUse(){return t._nActiveBindings}},controlInterpolants:{get total(){return t._controlInterpolants.length},get inUse(){return t._nActiveControlInterpolants}}}}_isActiveAction(t){const e=t._cacheIndex;return null!==e&&e<this._nActiveActions}_addInactiveAction(t,e,n){const i=this._actions,r=this._actionsByClip;let s=r[e];if(void 0===s)s={knownActions:[t],actionByRoot:{}},t._byClipCacheIndex=0,r[e]=s;else{const e=s.knownActions;t._byClipCacheIndex=e.length,e.push(t)}t._cacheIndex=i.length,i.push(t),s.actionByRoot[n]=t}_removeInactiveAction(t){const e=this._actions,n=e[e.length-1],i=t._cacheIndex;n._cacheIndex=i,e[i]=n,e.pop(),t._cacheIndex=null;const r=t._clip.uuid,s=this._actionsByClip,a=s[r],o=a.knownActions,l=o[o.length-1],c=t._byClipCacheIndex;l._byClipCacheIndex=c,o[c]=l,o.pop(),t._byClipCacheIndex=null;delete a.actionByRoot[(t._localRoot||this._root).uuid],0===o.length&&delete s[r],this._removeInactiveBindingsForAction(t)}_removeInactiveBindingsForAction(t){const e=t._propertyBindings;for(let t=0,n=e.length;t!==n;++t){const n=e[t];0==--n.referenceCount&&this._removeInactiveBinding(n)}}_lendAction(t){const e=this._actions,n=t._cacheIndex,i=this._nActiveActions++,r=e[i];t._cacheIndex=i,e[i]=t,r._cacheIndex=n,e[n]=r}_takeBackAction(t){const e=this._actions,n=t._cacheIndex,i=--this._nActiveActions,r=e[i];t._cacheIndex=i,e[i]=t,r._cacheIndex=n,e[n]=r}_addInactiveBinding(t,e,n){const i=this._bindingsByRootAndName,r=this._bindings;let s=i[e];void 0===s&&(s={},i[e]=s),s[n]=t,t._cacheIndex=r.length,r.push(t)}_removeInactiveBinding(t){const e=this._bindings,n=t.binding,i=n.rootNode.uuid,r=n.path,s=this._bindingsByRootAndName,a=s[i],o=e[e.length-1],l=t._cacheIndex;o._cacheIndex=l,e[l]=o,e.pop(),delete a[r],0===Object.keys(a).length&&delete s[i]}_lendBinding(t){const e=this._bindings,n=t._cacheIndex,i=this._nActiveBindings++,r=e[i];t._cacheIndex=i,e[i]=t,r._cacheIndex=n,e[n]=r}_takeBackBinding(t){const e=this._bindings,n=t._cacheIndex,i=--this._nActiveBindings,r=e[i];t._cacheIndex=i,e[i]=t,r._cacheIndex=n,e[n]=r}_lendControlInterpolant(){const t=this._controlInterpolants,e=this._nActiveControlInterpolants++;let n=t[e];return void 0===n&&(n=new kl(new Float32Array(2),new Float32Array(2),1,this._controlInterpolantsResultBuffer),n.__cacheIndex=e,t[e]=n),n}_takeBackControlInterpolant(t){const e=this._controlInterpolants,n=t.__cacheIndex,i=--this._nActiveControlInterpolants,r=e[i];t.__cacheIndex=i,e[i]=t,r.__cacheIndex=n,e[n]=r}clipAction(t,e,n){const i=e||this._root,r=i.uuid;let s=\\\\\\\"string\\\\\\\"==typeof t?Kl.findByName(i,t):t;const a=null!==s?s.uuid:t,o=this._actionsByClip[a];let l=null;if(void 0===n&&(n=null!==s?s.blendMode:j),void 0!==o){const t=o.actionByRoot[r];if(void 0!==t&&t.blendMode===n)return t;l=o.knownActions[0],null===s&&(s=l._clip)}if(null===s)return null;const c=new bh(this,s,e,n);return this._bindAction(c,l),this._addInactiveAction(c,a,r),c}existingAction(t,e){const n=e||this._root,i=n.uuid,r=\\\\\\\"string\\\\\\\"==typeof t?Kl.findByName(n,t):t,s=r?r.uuid:t,a=this._actionsByClip[s];return void 0!==a&&a.actionByRoot[i]||null}stopAllAction(){const t=this._actions;for(let e=this._nActiveActions-1;e>=0;--e)t[e].stop();return this}update(t){t*=this.timeScale;const e=this._actions,n=this._nActiveActions,i=this.time+=t,r=Math.sign(t),s=this._accuIndex^=1;for(let a=0;a!==n;++a){e[a]._update(i,t,r,s)}const a=this._bindings,o=this._nActiveBindings;for(let t=0;t!==o;++t)a[t].apply(s);return this}setTime(t){this.time=0;for(let t=0;t<this._actions.length;t++)this._actions[t].time=0;return this.update(t)}getRoot(){return this._root}uncacheClip(t){const e=this._actions,n=t.uuid,i=this._actionsByClip,r=i[n];if(void 0!==r){const t=r.knownActions;for(let n=0,i=t.length;n!==i;++n){const i=t[n];this._deactivateAction(i);const r=i._cacheIndex,s=e[e.length-1];i._cacheIndex=null,i._byClipCacheIndex=null,s._cacheIndex=r,e[r]=s,e.pop(),this._removeInactiveBindingsForAction(i)}delete i[n]}}uncacheRoot(t){const e=t.uuid,n=this._actionsByClip;for(const t in n){const i=n[t].actionByRoot[e];void 0!==i&&(this._deactivateAction(i),this._removeInactiveAction(i))}const i=this._bindingsByRootAndName[e];if(void 0!==i)for(const t in i){const e=i[t];e.restoreOriginalState(),this._removeInactiveBinding(e)}}uncacheAction(t,e){const n=this.existingAction(t,e);null!==n&&(this._deactivateAction(n),this._removeInactiveAction(n))}}wh.prototype._controlInterpolantsResultBuffer=new Float32Array(1);class Sh{constructor(t){\\\\\\\"string\\\\\\\"==typeof t&&(console.warn(\\\\\\\"THREE.Uniform: Type parameter is no longer needed.\\\\\\\"),t=arguments[1]),this.value=t}clone(){return new Sh(void 0===this.value.clone?this.value:this.value.clone())}}class Th extends ea{constructor(t,e,n=1){super(t,e),this.meshPerAttribute=n}copy(t){return super.copy(t),this.meshPerAttribute=t.meshPerAttribute,this}clone(t){const e=super.clone(t);return e.meshPerAttribute=this.meshPerAttribute,e}toJSON(t){const e=super.toJSON(t);return e.isInstancedInterleavedBuffer=!0,e.meshPerAttribute=this.meshPerAttribute,e}}Th.prototype.isInstancedInterleavedBuffer=!0;class Eh{constructor(t,e,n,i,r){this.buffer=t,this.type=e,this.itemSize=n,this.elementSize=i,this.count=r,this.version=0}set needsUpdate(t){!0===t&&this.version++}setBuffer(t){return this.buffer=t,this}setType(t,e){return this.type=t,this.elementSize=e,this}setItemSize(t){return this.itemSize=t,this}setCount(t){return this.count=t,this}}Eh.prototype.isGLBufferAttribute=!0;function Ah(t,e){return t.distance-e.distance}function Lh(t,e,n,i){if(t.layers.test(e.layers)&&t.raycast(e,n),!0===i){const i=t.children;for(let t=0,r=i.length;t<r;t++)Lh(i[t],e,n,!0)}}const Rh=new vt;class Ch{constructor(t=new vt(1/0,1/0),e=new vt(-1/0,-1/0)){this.min=t,this.max=e}set(t,e){return this.min.copy(t),this.max.copy(e),this}setFromPoints(t){this.makeEmpty();for(let e=0,n=t.length;e<n;e++)this.expandByPoint(t[e]);return this}setFromCenterAndSize(t,e){const n=Rh.copy(e).multiplyScalar(.5);return this.min.copy(t).sub(n),this.max.copy(t).add(n),this}clone(){return(new this.constructor).copy(this)}copy(t){return this.min.copy(t.min),this.max.copy(t.max),this}makeEmpty(){return this.min.x=this.min.y=1/0,this.max.x=this.max.y=-1/0,this}isEmpty(){return this.max.x<this.min.x||this.max.y<this.min.y}getCenter(t){return this.isEmpty()?t.set(0,0):t.addVectors(this.min,this.max).multiplyScalar(.5)}getSize(t){return this.isEmpty()?t.set(0,0):t.subVectors(this.max,this.min)}expandByPoint(t){return this.min.min(t),this.max.max(t),this}expandByVector(t){return this.min.sub(t),this.max.add(t),this}expandByScalar(t){return this.min.addScalar(-t),this.max.addScalar(t),this}containsPoint(t){return!(t.x<this.min.x||t.x>this.max.x||t.y<this.min.y||t.y>this.max.y)}containsBox(t){return this.min.x<=t.min.x&&t.max.x<=this.max.x&&this.min.y<=t.min.y&&t.max.y<=this.max.y}getParameter(t,e){return e.set((t.x-this.min.x)/(this.max.x-this.min.x),(t.y-this.min.y)/(this.max.y-this.min.y))}intersectsBox(t){return!(t.max.x<this.min.x||t.min.x>this.max.x||t.max.y<this.min.y||t.min.y>this.max.y)}clampPoint(t,e){return e.copy(t).clamp(this.min,this.max)}distanceToPoint(t){return Rh.copy(t).clamp(this.min,this.max).sub(t).length()}intersect(t){return this.min.max(t.min),this.max.min(t.max),this}union(t){return this.min.min(t.min),this.max.max(t.max),this}translate(t){return this.min.add(t),this.max.add(t),this}equals(t){return t.min.equals(this.min)&&t.max.equals(this.max)}}Ch.prototype.isBox2=!0;const Ph=new Rt,Ih=new Rt;class Dh{constructor(t=new Rt,e=new Rt){this.start=t,this.end=e}set(t,e){return this.start.copy(t),this.end.copy(e),this}copy(t){return this.start.copy(t.start),this.end.copy(t.end),this}getCenter(t){return t.addVectors(this.start,this.end).multiplyScalar(.5)}delta(t){return t.subVectors(this.end,this.start)}distanceSq(){return this.start.distanceToSquared(this.end)}distance(){return this.start.distanceTo(this.end)}at(t,e){return this.delta(e).multiplyScalar(t).add(this.start)}closestPointToPointParameter(t,e){Ph.subVectors(t,this.start),Ih.subVectors(this.end,this.start);const n=Ih.dot(Ih);let i=Ih.dot(Ph)/n;return e&&(i=ht(i,0,1)),i}closestPointToPoint(t,e,n){const i=this.closestPointToPointParameter(t,e);return this.delta(n).multiplyScalar(i).add(this.start)}applyMatrix4(t){return this.start.applyMatrix4(t),this.end.applyMatrix4(t),this}equals(t){return t.start.equals(this.start)&&t.end.equals(this.end)}clone(){return(new this.constructor).copy(this)}}class Nh extends Pe{constructor(t){super(),this.material=t,this.render=function(){},this.hasPositions=!1,this.hasNormals=!1,this.hasColors=!1,this.hasUvs=!1,this.positionArray=null,this.normalArray=null,this.colorArray=null,this.uvArray=null,this.count=0}}Nh.prototype.isImmediateRenderObject=!0;const Bh=new Rt;const zh=new Rt,Fh=new ae,Oh=new ae;class Uh extends Ya{constructor(t){const e=Hh(t),n=new wn,i=[],r=[],s=new Qe(0,0,1),a=new Qe(0,1,0);for(let t=0;t<e.length;t++){const n=e[t];n.parent&&n.parent.isBone&&(i.push(0,0,0),i.push(0,0,0),r.push(s.r,s.g,s.b),r.push(a.r,a.g,a.b))}n.setAttribute(\\\\\\\"position\\\\\\\",new un(i,3)),n.setAttribute(\\\\\\\"color\\\\\\\",new un(r,3));super(n,new Ua({vertexColors:!0,depthTest:!1,depthWrite:!1,toneMapped:!1,transparent:!0})),this.type=\\\\\\\"SkeletonHelper\\\\\\\",this.isSkeletonHelper=!0,this.root=t,this.bones=e,this.matrix=t.matrixWorld,this.matrixAutoUpdate=!1}updateMatrixWorld(t){const e=this.bones,n=this.geometry,i=n.getAttribute(\\\\\\\"position\\\\\\\");Oh.copy(this.root.matrixWorld).invert();for(let t=0,n=0;t<e.length;t++){const r=e[t];r.parent&&r.parent.isBone&&(Fh.multiplyMatrices(Oh,r.matrixWorld),zh.setFromMatrixPosition(Fh),i.setXYZ(n,zh.x,zh.y,zh.z),Fh.multiplyMatrices(Oh,r.parent.matrixWorld),zh.setFromMatrixPosition(Fh),i.setXYZ(n+1,zh.x,zh.y,zh.z),n+=2)}n.getAttribute(\\\\\\\"position\\\\\\\").needsUpdate=!0,super.updateMatrixWorld(t)}}function Hh(t){const e=[];t&&t.isBone&&e.push(t);for(let n=0;n<t.children.length;n++)e.push.apply(e,Hh(t.children[n]));return e}const Gh=new Rt,kh=new Qe,Vh=new Qe;class Wh extends Ya{constructor(t=10,e=10,n=4473924,i=8947848){n=new Qe(n),i=new Qe(i);const r=e/2,s=t/e,a=t/2,o=[],l=[];for(let t=0,c=0,h=-a;t<=e;t++,h+=s){o.push(-a,0,h,a,0,h),o.push(h,0,-a,h,0,a);const e=t===r?n:i;e.toArray(l,c),c+=3,e.toArray(l,c),c+=3,e.toArray(l,c),c+=3,e.toArray(l,c),c+=3}const c=new wn;c.setAttribute(\\\\\\\"position\\\\\\\",new un(o,3)),c.setAttribute(\\\\\\\"color\\\\\\\",new un(l,3));super(c,new Ua({vertexColors:!0,toneMapped:!1})),this.type=\\\\\\\"GridHelper\\\\\\\"}}const jh=new Rt,qh=new Rt,Xh=new Rt;const Yh=new Rt,Jh=new Yn;function Zh(t,e,n,i,r,s,a){Yh.set(r,s,a).unproject(i);const o=e[t];if(void 0!==o){const t=n.getAttribute(\\\\\\\"position\\\\\\\");for(let e=0,n=o.length;e<n;e++)t.setXYZ(o[e],Yh.x,Yh.y,Yh.z)}}const Qh=new It;class Kh extends Ya{constructor(t,e=16776960){const n=new Uint16Array([0,1,1,2,2,3,3,0,4,5,5,6,6,7,7,4,0,4,1,5,2,6,3,7]),i=new Float32Array(24),r=new wn;r.setIndex(new en(n,1)),r.setAttribute(\\\\\\\"position\\\\\\\",new en(i,3)),super(r,new Ua({color:e,toneMapped:!1})),this.object=t,this.type=\\\\\\\"BoxHelper\\\\\\\",this.matrixAutoUpdate=!1,this.update()}update(t){if(void 0!==t&&console.warn(\\\\\\\"THREE.BoxHelper: .update() has no longer arguments.\\\\\\\"),void 0!==this.object&&Qh.setFromObject(this.object),Qh.isEmpty())return;const e=Qh.min,n=Qh.max,i=this.geometry.attributes.position,r=i.array;r[0]=n.x,r[1]=n.y,r[2]=n.z,r[3]=e.x,r[4]=n.y,r[5]=n.z,r[6]=e.x,r[7]=e.y,r[8]=n.z,r[9]=n.x,r[10]=e.y,r[11]=n.z,r[12]=n.x,r[13]=n.y,r[14]=e.z,r[15]=e.x,r[16]=n.y,r[17]=e.z,r[18]=e.x,r[19]=e.y,r[20]=e.z,r[21]=n.x,r[22]=e.y,r[23]=e.z,i.needsUpdate=!0,this.geometry.computeBoundingSphere()}setFromObject(t){return this.object=t,this.update(),this}copy(t){return Ya.prototype.copy.call(this,t),this.object=t.object,this}}const $h=new Rt;let tu,eu;class nu extends Ya{constructor(t=1){const e=[0,0,0,t,0,0,0,0,0,0,t,0,0,0,0,0,0,t],n=new wn;n.setAttribute(\\\\\\\"position\\\\\\\",new un(e,3)),n.setAttribute(\\\\\\\"color\\\\\\\",new un([1,0,0,1,.6,0,0,1,0,.6,1,0,0,0,1,0,.6,1],3));super(n,new Ua({vertexColors:!0,toneMapped:!1})),this.type=\\\\\\\"AxesHelper\\\\\\\"}setColors(t,e,n){const i=new Qe,r=this.geometry.attributes.color.array;return i.set(t),i.toArray(r,0),i.toArray(r,3),i.set(e),i.toArray(r,6),i.toArray(r,9),i.set(n),i.toArray(r,12),i.toArray(r,15),this.geometry.attributes.color.needsUpdate=!0,this}dispose(){this.geometry.dispose(),this.material.dispose()}}const iu=new Float32Array(1),ru=new Int32Array(iu.buffer);yo.create=function(t,e){return console.log(\\\\\\\"THREE.Curve.create() has been deprecated\\\\\\\"),t.prototype=Object.create(yo.prototype),t.prototype.constructor=t,t.prototype.getPoint=e,t},uc.prototype.fromPoints=function(t){return console.warn(\\\\\\\"THREE.Path: .fromPoints() has been renamed to .setFromPoints().\\\\\\\"),this.setFromPoints(t)},Wh.prototype.setColors=function(){console.error(\\\\\\\"THREE.GridHelper: setColors() has been deprecated, pass them in the constructor instead.\\\\\\\")},Uh.prototype.update=function(){console.error(\\\\\\\"THREE.SkeletonHelper: update() no longer needs to be called.\\\\\\\")},ic.prototype.extractUrlBase=function(t){return console.warn(\\\\\\\"THREE.Loader: .extractUrlBase() has been deprecated. Use THREE.LoaderUtils.extractUrlBase() instead.\\\\\\\"),Dc.extractUrlBase(t)},ic.Handlers={add:function(){console.error(\\\\\\\"THREE.Loader: Handlers.add() has been removed. Use LoadingManager.addHandler() instead.\\\\\\\")},get:function(){console.error(\\\\\\\"THREE.Loader: Handlers.get() has been removed. Use LoadingManager.getHandler() instead.\\\\\\\")}},Ch.prototype.center=function(t){return console.warn(\\\\\\\"THREE.Box2: .center() has been renamed to .getCenter().\\\\\\\"),this.getCenter(t)},Ch.prototype.empty=function(){return console.warn(\\\\\\\"THREE.Box2: .empty() has been renamed to .isEmpty().\\\\\\\"),this.isEmpty()},Ch.prototype.isIntersectionBox=function(t){return console.warn(\\\\\\\"THREE.Box2: .isIntersectionBox() has been renamed to .intersectsBox().\\\\\\\"),this.intersectsBox(t)},Ch.prototype.size=function(t){return console.warn(\\\\\\\"THREE.Box2: .size() has been renamed to .getSize().\\\\\\\"),this.getSize(t)},It.prototype.center=function(t){return console.warn(\\\\\\\"THREE.Box3: .center() has been renamed to .getCenter().\\\\\\\"),this.getCenter(t)},It.prototype.empty=function(){return console.warn(\\\\\\\"THREE.Box3: .empty() has been renamed to .isEmpty().\\\\\\\"),this.isEmpty()},It.prototype.isIntersectionBox=function(t){return console.warn(\\\\\\\"THREE.Box3: .isIntersectionBox() has been renamed to .intersectsBox().\\\\\\\"),this.intersectsBox(t)},It.prototype.isIntersectionSphere=function(t){return console.warn(\\\\\\\"THREE.Box3: .isIntersectionSphere() has been renamed to .intersectsSphere().\\\\\\\"),this.intersectsSphere(t)},It.prototype.size=function(t){return console.warn(\\\\\\\"THREE.Box3: .size() has been renamed to .getSize().\\\\\\\"),this.getSize(t)},Qt.prototype.empty=function(){return console.warn(\\\\\\\"THREE.Sphere: .empty() has been renamed to .isEmpty().\\\\\\\"),this.isEmpty()},ai.prototype.setFromMatrix=function(t){return console.warn(\\\\\\\"THREE.Frustum: .setFromMatrix() has been renamed to .setFromProjectionMatrix().\\\\\\\"),this.setFromProjectionMatrix(t)},Dh.prototype.center=function(t){return console.warn(\\\\\\\"THREE.Line3: .center() has been renamed to .getCenter().\\\\\\\"),this.getCenter(t)},yt.prototype.flattenToArrayOffset=function(t,e){return console.warn(\\\\\\\"THREE.Matrix3: .flattenToArrayOffset() has been deprecated. Use .toArray() instead.\\\\\\\"),this.toArray(t,e)},yt.prototype.multiplyVector3=function(t){return console.warn(\\\\\\\"THREE.Matrix3: .multiplyVector3() has been removed. Use vector.applyMatrix3( matrix ) instead.\\\\\\\"),t.applyMatrix3(this)},yt.prototype.multiplyVector3Array=function(){console.error(\\\\\\\"THREE.Matrix3: .multiplyVector3Array() has been removed.\\\\\\\")},yt.prototype.applyToBufferAttribute=function(t){return console.warn(\\\\\\\"THREE.Matrix3: .applyToBufferAttribute() has been removed. Use attribute.applyMatrix3( matrix ) instead.\\\\\\\"),t.applyMatrix3(this)},yt.prototype.applyToVector3Array=function(){console.error(\\\\\\\"THREE.Matrix3: .applyToVector3Array() has been removed.\\\\\\\")},yt.prototype.getInverse=function(t){return console.warn(\\\\\\\"THREE.Matrix3: .getInverse() has been removed. Use matrixInv.copy( matrix ).invert(); instead.\\\\\\\"),this.copy(t).invert()},ae.prototype.extractPosition=function(t){return console.warn(\\\\\\\"THREE.Matrix4: .extractPosition() has been renamed to .copyPosition().\\\\\\\"),this.copyPosition(t)},ae.prototype.flattenToArrayOffset=function(t,e){return console.warn(\\\\\\\"THREE.Matrix4: .flattenToArrayOffset() has been deprecated. Use .toArray() instead.\\\\\\\"),this.toArray(t,e)},ae.prototype.getPosition=function(){return console.warn(\\\\\\\"THREE.Matrix4: .getPosition() has been removed. Use Vector3.setFromMatrixPosition( matrix ) instead.\\\\\\\"),(new Rt).setFromMatrixColumn(this,3)},ae.prototype.setRotationFromQuaternion=function(t){return console.warn(\\\\\\\"THREE.Matrix4: .setRotationFromQuaternion() has been renamed to .makeRotationFromQuaternion().\\\\\\\"),this.makeRotationFromQuaternion(t)},ae.prototype.multiplyToArray=function(){console.warn(\\\\\\\"THREE.Matrix4: .multiplyToArray() has been removed.\\\\\\\")},ae.prototype.multiplyVector3=function(t){return console.warn(\\\\\\\"THREE.Matrix4: .multiplyVector3() has been removed. Use vector.applyMatrix4( matrix ) instead.\\\\\\\"),t.applyMatrix4(this)},ae.prototype.multiplyVector4=function(t){return console.warn(\\\\\\\"THREE.Matrix4: .multiplyVector4() has been removed. Use vector.applyMatrix4( matrix ) instead.\\\\\\\"),t.applyMatrix4(this)},ae.prototype.multiplyVector3Array=function(){console.error(\\\\\\\"THREE.Matrix4: .multiplyVector3Array() has been removed.\\\\\\\")},ae.prototype.rotateAxis=function(t){console.warn(\\\\\\\"THREE.Matrix4: .rotateAxis() has been removed. Use Vector3.transformDirection( matrix ) instead.\\\\\\\"),t.transformDirection(this)},ae.prototype.crossVector=function(t){return console.warn(\\\\\\\"THREE.Matrix4: .crossVector() has been removed. Use vector.applyMatrix4( matrix ) instead.\\\\\\\"),t.applyMatrix4(this)},ae.prototype.translate=function(){console.error(\\\\\\\"THREE.Matrix4: .translate() has been removed.\\\\\\\")},ae.prototype.rotateX=function(){console.error(\\\\\\\"THREE.Matrix4: .rotateX() has been removed.\\\\\\\")},ae.prototype.rotateY=function(){console.error(\\\\\\\"THREE.Matrix4: .rotateY() has been removed.\\\\\\\")},ae.prototype.rotateZ=function(){console.error(\\\\\\\"THREE.Matrix4: .rotateZ() has been removed.\\\\\\\")},ae.prototype.rotateByAxis=function(){console.error(\\\\\\\"THREE.Matrix4: .rotateByAxis() has been removed.\\\\\\\")},ae.prototype.applyToBufferAttribute=function(t){return console.warn(\\\\\\\"THREE.Matrix4: .applyToBufferAttribute() has been removed. Use attribute.applyMatrix4( matrix ) instead.\\\\\\\"),t.applyMatrix4(this)},ae.prototype.applyToVector3Array=function(){console.error(\\\\\\\"THREE.Matrix4: .applyToVector3Array() has been removed.\\\\\\\")},ae.prototype.makeFrustum=function(t,e,n,i,r,s){return console.warn(\\\\\\\"THREE.Matrix4: .makeFrustum() has been removed. Use .makePerspective( left, right, top, bottom, near, far ) instead.\\\\\\\"),this.makePerspective(t,e,i,n,r,s)},ae.prototype.getInverse=function(t){return console.warn(\\\\\\\"THREE.Matrix4: .getInverse() has been removed. Use matrixInv.copy( matrix ).invert(); instead.\\\\\\\"),this.copy(t).invert()},ii.prototype.isIntersectionLine=function(t){return console.warn(\\\\\\\"THREE.Plane: .isIntersectionLine() has been renamed to .intersectsLine().\\\\\\\"),this.intersectsLine(t)},Lt.prototype.multiplyVector3=function(t){return console.warn(\\\\\\\"THREE.Quaternion: .multiplyVector3() has been removed. Use is now vector.applyQuaternion( quaternion ) instead.\\\\\\\"),t.applyQuaternion(this)},Lt.prototype.inverse=function(){return console.warn(\\\\\\\"THREE.Quaternion: .inverse() has been renamed to invert().\\\\\\\"),this.invert()},se.prototype.isIntersectionBox=function(t){return console.warn(\\\\\\\"THREE.Ray: .isIntersectionBox() has been renamed to .intersectsBox().\\\\\\\"),this.intersectsBox(t)},se.prototype.isIntersectionPlane=function(t){return console.warn(\\\\\\\"THREE.Ray: .isIntersectionPlane() has been renamed to .intersectsPlane().\\\\\\\"),this.intersectsPlane(t)},se.prototype.isIntersectionSphere=function(t){return console.warn(\\\\\\\"THREE.Ray: .isIntersectionSphere() has been renamed to .intersectsSphere().\\\\\\\"),this.intersectsSphere(t)},ke.prototype.area=function(){return console.warn(\\\\\\\"THREE.Triangle: .area() has been renamed to .getArea().\\\\\\\"),this.getArea()},ke.prototype.barycoordFromPoint=function(t,e){return console.warn(\\\\\\\"THREE.Triangle: .barycoordFromPoint() has been renamed to .getBarycoord().\\\\\\\"),this.getBarycoord(t,e)},ke.prototype.midpoint=function(t){return console.warn(\\\\\\\"THREE.Triangle: .midpoint() has been renamed to .getMidpoint().\\\\\\\"),this.getMidpoint(t)},ke.prototypenormal=function(t){return console.warn(\\\\\\\"THREE.Triangle: .normal() has been renamed to .getNormal().\\\\\\\"),this.getNormal(t)},ke.prototype.plane=function(t){return console.warn(\\\\\\\"THREE.Triangle: .plane() has been renamed to .getPlane().\\\\\\\"),this.getPlane(t)},ke.barycoordFromPoint=function(t,e,n,i,r){return console.warn(\\\\\\\"THREE.Triangle: .barycoordFromPoint() has been renamed to .getBarycoord().\\\\\\\"),ke.getBarycoord(t,e,n,i,r)},ke.normal=function(t,e,n,i){return console.warn(\\\\\\\"THREE.Triangle: .normal() has been renamed to .getNormal().\\\\\\\"),ke.getNormal(t,e,n,i)},dc.prototype.extractAllPoints=function(t){return console.warn(\\\\\\\"THREE.Shape: .extractAllPoints() has been removed. Use .extractPoints() instead.\\\\\\\"),this.extractPoints(t)},dc.prototype.extrude=function(t){return console.warn(\\\\\\\"THREE.Shape: .extrude() has been removed. Use ExtrudeGeometry() instead.\\\\\\\"),new dl(this,t)},dc.prototype.makeGeometry=function(t){return console.warn(\\\\\\\"THREE.Shape: .makeGeometry() has been removed. Use ShapeGeometry() instead.\\\\\\\"),new xl(this,t)},vt.prototype.fromAttribute=function(t,e,n){return console.warn(\\\\\\\"THREE.Vector2: .fromAttribute() has been renamed to .fromBufferAttribute().\\\\\\\"),this.fromBufferAttribute(t,e,n)},vt.prototype.distanceToManhattan=function(t){return console.warn(\\\\\\\"THREE.Vector2: .distanceToManhattan() has been renamed to .manhattanDistanceTo().\\\\\\\"),this.manhattanDistanceTo(t)},vt.prototype.lengthManhattan=function(){return console.warn(\\\\\\\"THREE.Vector2: .lengthManhattan() has been renamed to .manhattanLength().\\\\\\\"),this.manhattanLength()},Rt.prototype.setEulerFromRotationMatrix=function(){console.error(\\\\\\\"THREE.Vector3: .setEulerFromRotationMatrix() has been removed. Use Euler.setFromRotationMatrix() instead.\\\\\\\")},Rt.prototype.setEulerFromQuaternion=function(){console.error(\\\\\\\"THREE.Vector3: .setEulerFromQuaternion() has been removed. Use Euler.setFromQuaternion() instead.\\\\\\\")},Rt.prototype.getPositionFromMatrix=function(t){return console.warn(\\\\\\\"THREE.Vector3: .getPositionFromMatrix() has been renamed to .setFromMatrixPosition().\\\\\\\"),this.setFromMatrixPosition(t)},Rt.prototype.getScaleFromMatrix=function(t){return console.warn(\\\\\\\"THREE.Vector3: .getScaleFromMatrix() has been renamed to .setFromMatrixScale().\\\\\\\"),this.setFromMatrixScale(t)},Rt.prototype.getColumnFromMatrix=function(t,e){return console.warn(\\\\\\\"THREE.Vector3: .getColumnFromMatrix() has been renamed to .setFromMatrixColumn().\\\\\\\"),this.setFromMatrixColumn(e,t)},Rt.prototype.applyProjection=function(t){return console.warn(\\\\\\\"THREE.Vector3: .applyProjection() has been removed. Use .applyMatrix4( m ) instead.\\\\\\\"),this.applyMatrix4(t)},Rt.prototype.fromAttribute=function(t,e,n){return console.warn(\\\\\\\"THREE.Vector3: .fromAttribute() has been renamed to .fromBufferAttribute().\\\\\\\"),this.fromBufferAttribute(t,e,n)},Rt.prototype.distanceToManhattan=function(t){return console.warn(\\\\\\\"THREE.Vector3: .distanceToManhattan() has been renamed to .manhattanDistanceTo().\\\\\\\"),this.manhattanDistanceTo(t)},Rt.prototype.lengthManhattan=function(){return console.warn(\\\\\\\"THREE.Vector3: .lengthManhattan() has been renamed to .manhattanLength().\\\\\\\"),this.manhattanLength()},St.prototype.fromAttribute=function(t,e,n){return console.warn(\\\\\\\"THREE.Vector4: .fromAttribute() has been renamed to .fromBufferAttribute().\\\\\\\"),this.fromBufferAttribute(t,e,n)},St.prototype.lengthManhattan=function(){return console.warn(\\\\\\\"THREE.Vector4: .lengthManhattan() has been renamed to .manhattanLength().\\\\\\\"),this.manhattanLength()},Pe.prototype.getChildByName=function(t){return console.warn(\\\\\\\"THREE.Object3D: .getChildByName() has been renamed to .getObjectByName().\\\\\\\"),this.getObjectByName(t)},Pe.prototype.renderDepth=function(){console.warn(\\\\\\\"THREE.Object3D: .renderDepth has been removed. Use .renderOrder, instead.\\\\\\\")},Pe.prototype.translate=function(t,e){return console.warn(\\\\\\\"THREE.Object3D: .translate() has been removed. Use .translateOnAxis( axis, distance ) instead.\\\\\\\"),this.translateOnAxis(e,t)},Pe.prototype.getWorldRotation=function(){console.error(\\\\\\\"THREE.Object3D: .getWorldRotation() has been removed. Use THREE.Object3D.getWorldQuaternion( target ) instead.\\\\\\\")},Pe.prototype.applyMatrix=function(t){return console.warn(\\\\\\\"THREE.Object3D: .applyMatrix() has been renamed to .applyMatrix4().\\\\\\\"),this.applyMatrix4(t)},Object.defineProperties(Pe.prototype,{eulerOrder:{get:function(){return console.warn(\\\\\\\"THREE.Object3D: .eulerOrder is now .rotation.order.\\\\\\\"),this.rotation.order},set:function(t){console.warn(\\\\\\\"THREE.Object3D: .eulerOrder is now .rotation.order.\\\\\\\"),this.rotation.order=t}},useQuaternion:{get:function(){console.warn(\\\\\\\"THREE.Object3D: .useQuaternion has been removed. The library now uses quaternions by default.\\\\\\\")},set:function(){console.warn(\\\\\\\"THREE.Object3D: .useQuaternion has been removed. The library now uses quaternions by default.\\\\\\\")}}}),Gn.prototype.setDrawMode=function(){console.error(\\\\\\\"THREE.Mesh: .setDrawMode() has been removed. The renderer now always assumes THREE.TrianglesDrawMode. Transform your geometry via BufferGeometryUtils.toTrianglesDrawMode() if necessary.\\\\\\\")},Object.defineProperties(Gn.prototype,{drawMode:{get:function(){return console.error(\\\\\\\"THREE.Mesh: .drawMode has been removed. The renderer now always assumes THREE.TrianglesDrawMode.\\\\\\\"),0},set:function(){console.error(\\\\\\\"THREE.Mesh: .drawMode has been removed. The renderer now always assumes THREE.TrianglesDrawMode. Transform your geometry via BufferGeometryUtils.toTrianglesDrawMode() if necessary.\\\\\\\")}}}),La.prototype.initBones=function(){console.error(\\\\\\\"THREE.SkinnedMesh: initBones() has been removed.\\\\\\\")},Jn.prototype.setLens=function(t,e){console.warn(\\\\\\\"THREE.PerspectiveCamera.setLens is deprecated. Use .setFocalLength and .filmGauge for a photographic setup.\\\\\\\"),void 0!==e&&(this.filmGauge=e),this.setFocalLength(t)},Object.defineProperties(pc.prototype,{onlyShadow:{set:function(){console.warn(\\\\\\\"THREE.Light: .onlyShadow has been removed.\\\\\\\")}},shadowCameraFov:{set:function(t){console.warn(\\\\\\\"THREE.Light: .shadowCameraFov is now .shadow.camera.fov.\\\\\\\"),this.shadow.camera.fov=t}},shadowCameraLeft:{set:function(t){console.warn(\\\\\\\"THREE.Light: .shadowCameraLeft is now .shadow.camera.left.\\\\\\\"),this.shadow.camera.left=t}},shadowCameraRight:{set:function(t){console.warn(\\\\\\\"THREE.Light: .shadowCameraRight is now .shadow.camera.right.\\\\\\\"),this.shadow.camera.right=t}},shadowCameraTop:{set:function(t){console.warn(\\\\\\\"THREE.Light: .shadowCameraTop is now .shadow.camera.top.\\\\\\\"),this.shadow.camera.top=t}},shadowCameraBottom:{set:function(t){console.warn(\\\\\\\"THREE.Light: .shadowCameraBottom is now .shadow.camera.bottom.\\\\\\\"),this.shadow.camera.bottom=t}},shadowCameraNear:{set:function(t){console.warn(\\\\\\\"THREE.Light: .shadowCameraNear is now .shadow.camera.near.\\\\\\\"),this.shadow.camera.near=t}},shadowCameraFar:{set:function(t){console.warn(\\\\\\\"THREE.Light: .shadowCameraFar is now .shadow.camera.far.\\\\\\\"),this.shadow.camera.far=t}},shadowCameraVisible:{set:function(){console.warn(\\\\\\\"THREE.Light: .shadowCameraVisible has been removed. Use new THREE.CameraHelper( light.shadow.camera ) instead.\\\\\\\")}},shadowBias:{set:function(t){console.warn(\\\\\\\"THREE.Light: .shadowBias is now .shadow.bias.\\\\\\\"),this.shadow.bias=t}},shadowDarkness:{set:function(){console.warn(\\\\\\\"THREE.Light: .shadowDarkness has been removed.\\\\\\\")}},shadowMapWidth:{set:function(t){console.warn(\\\\\\\"THREE.Light: .shadowMapWidth is now .shadow.mapSize.width.\\\\\\\"),this.shadow.mapSize.width=t}},shadowMapHeight:{set:function(t){console.warn(\\\\\\\"THREE.Light: .shadowMapHeight is now .shadow.mapSize.height.\\\\\\\"),this.shadow.mapSize.height=t}}}),Object.defineProperties(en.prototype,{length:{get:function(){return console.warn(\\\\\\\"THREE.BufferAttribute: .length has been deprecated. Use .count instead.\\\\\\\"),this.array.length}},dynamic:{get:function(){return console.warn(\\\\\\\"THREE.BufferAttribute: .dynamic has been deprecated. Use .usage instead.\\\\\\\"),this.usage===nt},set:function(){console.warn(\\\\\\\"THREE.BufferAttribute: .dynamic has been deprecated. Use .usage instead.\\\\\\\"),this.setUsage(nt)}}}),en.prototype.setDynamic=function(t){return console.warn(\\\\\\\"THREE.BufferAttribute: .setDynamic() has been deprecated. Use .setUsage() instead.\\\\\\\"),this.setUsage(!0===t?nt:et),this},en.prototype.copyIndicesArray=function(){console.error(\\\\\\\"THREE.BufferAttribute: .copyIndicesArray() has been removed.\\\\\\\")},en.prototype.setArray=function(){console.error(\\\\\\\"THREE.BufferAttribute: .setArray has been removed. Use BufferGeometry .setAttribute to replace/resize attribute buffers\\\\\\\")},wn.prototype.addIndex=function(t){console.warn(\\\\\\\"THREE.BufferGeometry: .addIndex() has been renamed to .setIndex().\\\\\\\"),this.setIndex(t)},wn.prototype.addAttribute=function(t,e){return console.warn(\\\\\\\"THREE.BufferGeometry: .addAttribute() has been renamed to .setAttribute().\\\\\\\"),e&&e.isBufferAttribute||e&&e.isInterleavedBufferAttribute?\\\\\\\"index\\\\\\\"===t?(console.warn(\\\\\\\"THREE.BufferGeometry.addAttribute: Use .setIndex() for index attribute.\\\\\\\"),this.setIndex(e),this):this.setAttribute(t,e):(console.warn(\\\\\\\"THREE.BufferGeometry: .addAttribute() now expects ( name, attribute ).\\\\\\\"),this.setAttribute(t,new en(arguments[1],arguments[2])))},wn.prototype.addDrawCall=function(t,e,n){void 0!==n&&console.warn(\\\\\\\"THREE.BufferGeometry: .addDrawCall() no longer supports indexOffset.\\\\\\\"),console.warn(\\\\\\\"THREE.BufferGeometry: .addDrawCall() is now .addGroup().\\\\\\\"),this.addGroup(t,e)},wn.prototype.clearDrawCalls=function(){console.warn(\\\\\\\"THREE.BufferGeometry: .clearDrawCalls() is now .clearGroups().\\\\\\\"),this.clearGroups()},wn.prototype.computeOffsets=function(){console.warn(\\\\\\\"THREE.BufferGeometry: .computeOffsets() has been removed.\\\\\\\")},wn.prototype.removeAttribute=function(t){return console.warn(\\\\\\\"THREE.BufferGeometry: .removeAttribute() has been renamed to .deleteAttribute().\\\\\\\"),this.deleteAttribute(t)},wn.prototype.applyMatrix=function(t){return console.warn(\\\\\\\"THREE.BufferGeometry: .applyMatrix() has been renamed to .applyMatrix4().\\\\\\\"),this.applyMatrix4(t)},Object.defineProperties(wn.prototype,{drawcalls:{get:function(){return console.error(\\\\\\\"THREE.BufferGeometry: .drawcalls has been renamed to .groups.\\\\\\\"),this.groups}},offsets:{get:function(){return console.warn(\\\\\\\"THREE.BufferGeometry: .offsets has been renamed to .groups.\\\\\\\"),this.groups}}}),ea.prototype.setDynamic=function(t){return console.warn(\\\\\\\"THREE.InterleavedBuffer: .setDynamic() has been deprecated. Use .setUsage() instead.\\\\\\\"),this.setUsage(!0===t?nt:et),this},ea.prototype.setArray=function(){console.error(\\\\\\\"THREE.InterleavedBuffer: .setArray has been removed. Use BufferGeometry .setAttribute to replace/resize attribute buffers\\\\\\\")},dl.prototype.getArrays=function(){console.error(\\\\\\\"THREE.ExtrudeGeometry: .getArrays() has been removed.\\\\\\\")},dl.prototype.addShapeList=function(){console.error(\\\\\\\"THREE.ExtrudeGeometry: .addShapeList() has been removed.\\\\\\\")},dl.prototype.addShape=function(){console.error(\\\\\\\"THREE.ExtrudeGeometry: .addShape() has been removed.\\\\\\\")},ta.prototype.dispose=function(){console.error(\\\\\\\"THREE.Scene: .dispose() has been removed.\\\\\\\")},Sh.prototype.onUpdate=function(){return console.warn(\\\\\\\"THREE.Uniform: .onUpdate() has been removed. Use object.onBeforeRender() instead.\\\\\\\"),this},Object.defineProperties(We.prototype,{wrapAround:{get:function(){console.warn(\\\\\\\"THREE.Material: .wrapAround has been removed.\\\\\\\")},set:function(){console.warn(\\\\\\\"THREE.Material: .wrapAround has been removed.\\\\\\\")}},overdraw:{get:function(){console.warn(\\\\\\\"THREE.Material: .overdraw has been removed.\\\\\\\")},set:function(){console.warn(\\\\\\\"THREE.Material: .overdraw has been removed.\\\\\\\")}},wrapRGB:{get:function(){return console.warn(\\\\\\\"THREE.Material: .wrapRGB has been removed.\\\\\\\"),new Qe}},shading:{get:function(){console.error(\\\\\\\"THREE.\\\\\\\"+this.type+\\\\\\\": .shading has been removed. Use the boolean .flatShading instead.\\\\\\\")},set:function(t){console.warn(\\\\\\\"THREE.\\\\\\\"+this.type+\\\\\\\": .shading has been removed. Use the boolean .flatShading instead.\\\\\\\"),this.flatShading=1===t}},stencilMask:{get:function(){return console.warn(\\\\\\\"THREE.\\\\\\\"+this.type+\\\\\\\": .stencilMask has been removed. Use .stencilFuncMask instead.\\\\\\\"),this.stencilFuncMask},set:function(t){console.warn(\\\\\\\"THREE.\\\\\\\"+this.type+\\\\\\\": .stencilMask has been removed. Use .stencilFuncMask instead.\\\\\\\"),this.stencilFuncMask=t}},vertexTangents:{get:function(){console.warn(\\\\\\\"THREE.\\\\\\\"+this.type+\\\\\\\": .vertexTangents has been removed.\\\\\\\")},set:function(){console.warn(\\\\\\\"THREE.\\\\\\\"+this.type+\\\\\\\": .vertexTangents has been removed.\\\\\\\")}}}),Object.defineProperties(Xn.prototype,{derivatives:{get:function(){return console.warn(\\\\\\\"THREE.ShaderMaterial: .derivatives has been moved to .extensions.derivatives.\\\\\\\"),this.extensions.derivatives},set:function(t){console.warn(\\\\\\\"THREE. ShaderMaterial: .derivatives has been moved to .extensions.derivatives.\\\\\\\"),this.extensions.derivatives=t}}}),Zs.prototype.clearTarget=function(t,e,n,i){console.warn(\\\\\\\"THREE.WebGLRenderer: .clearTarget() has been deprecated. Use .setRenderTarget() and .clear() instead.\\\\\\\"),this.setRenderTarget(t),this.clear(e,n,i)},Zs.prototype.animate=function(t){console.warn(\\\\\\\"THREE.WebGLRenderer: .animate() is now .setAnimationLoop().\\\\\\\"),this.setAnimationLoop(t)},Zs.prototype.getCurrentRenderTarget=function(){return console.warn(\\\\\\\"THREE.WebGLRenderer: .getCurrentRenderTarget() is now .getRenderTarget().\\\\\\\"),this.getRenderTarget()},Zs.prototype.getMaxAnisotropy=function(){return console.warn(\\\\\\\"THREE.WebGLRenderer: .getMaxAnisotropy() is now .capabilities.getMaxAnisotropy().\\\\\\\"),this.capabilities.getMaxAnisotropy()},Zs.prototype.getPrecision=function(){return console.warn(\\\\\\\"THREE.WebGLRenderer: .getPrecision() is now .capabilities.precision.\\\\\\\"),this.capabilities.precision},Zs.prototype.resetGLState=function(){return console.warn(\\\\\\\"THREE.WebGLRenderer: .resetGLState() is now .state.reset().\\\\\\\"),this.state.reset()},Zs.prototype.supportsFloatTextures=function(){return console.warn(\\\\\\\"THREE.WebGLRenderer: .supportsFloatTextures() is now .extensions.get( 'OES_texture_float' ).\\\\\\\"),this.extensions.get(\\\\\\\"OES_texture_float\\\\\\\")},Zs.prototype.supportsHalfFloatTextures=function(){return console.warn(\\\\\\\"THREE.WebGLRenderer: .supportsHalfFloatTextures() is now .extensions.get( 'OES_texture_half_float' ).\\\\\\\"),this.extensions.get(\\\\\\\"OES_texture_half_float\\\\\\\")},Zs.prototype.supportsStandardDerivatives=function(){return console.warn(\\\\\\\"THREE.WebGLRenderer: .supportsStandardDerivatives() is now .extensions.get( 'OES_standard_derivatives' ).\\\\\\\"),this.extensions.get(\\\\\\\"OES_standard_derivatives\\\\\\\")},Zs.prototype.supportsCompressedTextureS3TC=function(){return console.warn(\\\\\\\"THREE.WebGLRenderer: .supportsCompressedTextureS3TC() is now .extensions.get( 'WEBGL_compressed_texture_s3tc' ).\\\\\\\"),this.extensions.get(\\\\\\\"WEBGL_compressed_texture_s3tc\\\\\\\")},Zs.prototype.supportsCompressedTexturePVRTC=function(){return console.warn(\\\\\\\"THREE.WebGLRenderer: .supportsCompressedTexturePVRTC() is now .extensions.get( 'WEBGL_compressed_texture_pvrtc' ).\\\\\\\"),this.extensions.get(\\\\\\\"WEBGL_compressed_texture_pvrtc\\\\\\\")},Zs.prototype.supportsBlendMinMax=function(){return console.warn(\\\\\\\"THREE.WebGLRenderer: .supportsBlendMinMax() is now .extensions.get( 'EXT_blend_minmax' ).\\\\\\\"),this.extensions.get(\\\\\\\"EXT_blend_minmax\\\\\\\")},Zs.prototype.supportsVertexTextures=function(){return console.warn(\\\\\\\"THREE.WebGLRenderer: .supportsVertexTextures() is now .capabilities.vertexTextures.\\\\\\\"),this.capabilities.vertexTextures},Zs.prototype.supportsInstancedArrays=function(){return console.warn(\\\\\\\"THREE.WebGLRenderer: .supportsInstancedArrays() is now .extensions.get( 'ANGLE_instanced_arrays' ).\\\\\\\"),this.extensions.get(\\\\\\\"ANGLE_instanced_arrays\\\\\\\")},Zs.prototype.enableScissorTest=function(t){console.warn(\\\\\\\"THREE.WebGLRenderer: .enableScissorTest() is now .setScissorTest().\\\\\\\"),this.setScissorTest(t)},Zs.prototype.initMaterial=function(){console.warn(\\\\\\\"THREE.WebGLRenderer: .initMaterial() has been removed.\\\\\\\")},Zs.prototype.addPrePlugin=function(){console.warn(\\\\\\\"THREE.WebGLRenderer: .addPrePlugin() has been removed.\\\\\\\")},Zs.prototype.addPostPlugin=function(){console.warn(\\\\\\\"THREE.WebGLRenderer: .addPostPlugin() has been removed.\\\\\\\")},Zs.prototype.updateShadowMap=function(){console.warn(\\\\\\\"THREE.WebGLRenderer: .updateShadowMap() has been removed.\\\\\\\")},Zs.prototype.setFaceCulling=function(){console.warn(\\\\\\\"THREE.WebGLRenderer: .setFaceCulling() has been removed.\\\\\\\")},Zs.prototype.allocTextureUnit=function(){console.warn(\\\\\\\"THREE.WebGLRenderer: .allocTextureUnit() has been removed.\\\\\\\")},Zs.prototype.setTexture=function(){console.warn(\\\\\\\"THREE.WebGLRenderer: .setTexture() has been removed.\\\\\\\")},Zs.prototype.setTexture2D=function(){console.warn(\\\\\\\"THREE.WebGLRenderer: .setTexture2D() has been removed.\\\\\\\")},Zs.prototype.setTextureCube=function(){console.warn(\\\\\\\"THREE.WebGLRenderer: .setTextureCube() has been removed.\\\\\\\")},Zs.prototype.getActiveMipMapLevel=function(){return console.warn(\\\\\\\"THREE.WebGLRenderer: .getActiveMipMapLevel() is now .getActiveMipmapLevel().\\\\\\\"),this.getActiveMipmapLevel()},Object.defineProperties(Zs.prototype,{shadowMapEnabled:{get:function(){return this.shadowMap.enabled},set:function(t){console.warn(\\\\\\\"THREE.WebGLRenderer: .shadowMapEnabled is now .shadowMap.enabled.\\\\\\\"),this.shadowMap.enabled=t}},shadowMapType:{get:function(){return this.shadowMap.type},set:function(t){console.warn(\\\\\\\"THREE.WebGLRenderer: .shadowMapType is now .shadowMap.type.\\\\\\\"),this.shadowMap.type=t}},shadowMapCullFace:{get:function(){console.warn(\\\\\\\"THREE.WebGLRenderer: .shadowMapCullFace has been removed. Set Material.shadowSide instead.\\\\\\\")},set:function(){console.warn(\\\\\\\"THREE.WebGLRenderer: .shadowMapCullFace has been removed. Set Material.shadowSide instead.\\\\\\\")}},context:{get:function(){return console.warn(\\\\\\\"THREE.WebGLRenderer: .context has been removed. Use .getContext() instead.\\\\\\\"),this.getContext()}},vr:{get:function(){return console.warn(\\\\\\\"THREE.WebGLRenderer: .vr has been renamed to .xr\\\\\\\"),this.xr}},gammaInput:{get:function(){return console.warn(\\\\\\\"THREE.WebGLRenderer: .gammaInput has been removed. Set the encoding for textures via Texture.encoding instead.\\\\\\\"),!1},set:function(){console.warn(\\\\\\\"THREE.WebGLRenderer: .gammaInput has been removed. Set the encoding for textures via Texture.encoding instead.\\\\\\\")}},gammaOutput:{get:function(){return console.warn(\\\\\\\"THREE.WebGLRenderer: .gammaOutput has been removed. Set WebGLRenderer.outputEncoding instead.\\\\\\\"),!1},set:function(t){console.warn(\\\\\\\"THREE.WebGLRenderer: .gammaOutput has been removed. Set WebGLRenderer.outputEncoding instead.\\\\\\\"),this.outputEncoding=!0===t?Y:X}},toneMappingWhitePoint:{get:function(){return console.warn(\\\\\\\"THREE.WebGLRenderer: .toneMappingWhitePoint has been removed.\\\\\\\"),1},set:function(){console.warn(\\\\\\\"THREE.WebGLRenderer: .toneMappingWhitePoint has been removed.\\\\\\\")}}}),Object.defineProperties(Hs.prototype,{cullFace:{get:function(){console.warn(\\\\\\\"THREE.WebGLRenderer: .shadowMap.cullFace has been removed. Set Material.shadowSide instead.\\\\\\\")},set:function(){console.warn(\\\\\\\"THREE.WebGLRenderer: .shadowMap.cullFace has been removed. Set Material.shadowSide instead.\\\\\\\")}},renderReverseSided:{get:function(){console.warn(\\\\\\\"THREE.WebGLRenderer: .shadowMap.renderReverseSided has been removed. Set Material.shadowSide instead.\\\\\\\")},set:function(){console.warn(\\\\\\\"THREE.WebGLRenderer: .shadowMap.renderReverseSided has been removed. Set Material.shadowSide instead.\\\\\\\")}},renderSingleSided:{get:function(){console.warn(\\\\\\\"THREE.WebGLRenderer: .shadowMap.renderSingleSided has been removed. Set Material.shadowSide instead.\\\\\\\")},set:function(){console.warn(\\\\\\\"THREE.WebGLRenderer: .shadowMap.renderSingleSided has been removed. Set Material.shadowSide instead.\\\\\\\")}}}),Object.defineProperties(Tt.prototype,{wrapS:{get:function(){return console.warn(\\\\\\\"THREE.WebGLRenderTarget: .wrapS is now .texture.wrapS.\\\\\\\"),this.texture.wrapS},set:function(t){console.warn(\\\\\\\"THREE.WebGLRenderTarget: .wrapS is now .texture.wrapS.\\\\\\\"),this.texture.wrapS=t}},wrapT:{get:function(){return console.warn(\\\\\\\"THREE.WebGLRenderTarget: .wrapT is now .texture.wrapT.\\\\\\\"),this.texture.wrapT},set:function(t){console.warn(\\\\\\\"THREE.WebGLRenderTarget: .wrapT is now .texture.wrapT.\\\\\\\"),this.texture.wrapT=t}},magFilter:{get:function(){return console.warn(\\\\\\\"THREE.WebGLRenderTarget: .magFilter is now .texture.magFilter.\\\\\\\"),this.texture.magFilter},set:function(t){console.warn(\\\\\\\"THREE.WebGLRenderTarget: .magFilter is now .texture.magFilter.\\\\\\\"),this.texture.magFilter=t}},minFilter:{get:function(){return console.warn(\\\\\\\"THREE.WebGLRenderTarget: .minFilter is now .texture.minFilter.\\\\\\\"),this.texture.minFilter},set:function(t){console.warn(\\\\\\\"THREE.WebGLRenderTarget: .minFilter is now .texture.minFilter.\\\\\\\"),this.texture.minFilter=t}},anisotropy:{get:function(){return console.warn(\\\\\\\"THREE.WebGLRenderTarget: .anisotropy is now .texture.anisotropy.\\\\\\\"),this.texture.anisotropy},set:function(t){console.warn(\\\\\\\"THREE.WebGLRenderTarget: .anisotropy is now .texture.anisotropy.\\\\\\\"),this.texture.anisotropy=t}},offset:{get:function(){return console.warn(\\\\\\\"THREE.WebGLRenderTarget: .offset is now .texture.offset.\\\\\\\"),this.texture.offset},set:function(t){console.warn(\\\\\\\"THREE.WebGLRenderTarget: .offset is now .texture.offset.\\\\\\\"),this.texture.offset=t}},repeat:{get:function(){return console.warn(\\\\\\\"THREE.WebGLRenderTarget: .repeat is now .texture.repeat.\\\\\\\"),this.texture.repeat},set:function(t){console.warn(\\\\\\\"THREE.WebGLRenderTarget: .repeat is now .texture.repeat.\\\\\\\"),this.texture.repeat=t}},format:{get:function(){return console.warn(\\\\\\\"THREE.WebGLRenderTarget: .format is now .texture.format.\\\\\\\"),this.texture.format},set:function(t){console.warn(\\\\\\\"THREE.WebGLRenderTarget: .format is now .texture.format.\\\\\\\"),this.texture.format=t}},type:{get:function(){return console.warn(\\\\\\\"THREE.WebGLRenderTarget: .type is now .texture.type.\\\\\\\"),this.texture.type},set:function(t){console.warn(\\\\\\\"THREE.WebGLRenderTarget: .type is now .texture.type.\\\\\\\"),this.texture.type=t}},generateMipmaps:{get:function(){return console.warn(\\\\\\\"THREE.WebGLRenderTarget: .generateMipmaps is now .texture.generateMipmaps.\\\\\\\"),this.texture.generateMipmaps},set:function(t){console.warn(\\\\\\\"THREE.WebGLRenderTarget: .generateMipmaps is now .texture.generateMipmaps.\\\\\\\"),this.texture.generateMipmaps=t}}}),ih.prototype.load=function(t){console.warn(\\\\\\\"THREE.Audio: .load has been deprecated. Use THREE.AudioLoader instead.\\\\\\\");const e=this;return(new qc).load(t,(function(t){e.setBuffer(t)})),this},lh.prototype.getData=function(){return console.warn(\\\\\\\"THREE.AudioAnalyser: .getData() is now .getFrequencyData().\\\\\\\"),this.getFrequencyData()},Qn.prototype.updateCubeMap=function(t,e){return console.warn(\\\\\\\"THREE.CubeCamera: .updateCubeMap() is now .update().\\\\\\\"),this.update(t,e)},Qn.prototype.clear=function(t,e,n,i){return console.warn(\\\\\\\"THREE.CubeCamera: .clear() is now .renderTarget.clear().\\\\\\\"),this.renderTarget.clear(t,e,n,i)},_t.crossOrigin=void 0,_t.loadTexture=function(t,e,n,i){console.warn(\\\\\\\"THREE.ImageUtils.loadTexture has been deprecated. Use THREE.TextureLoader() instead.\\\\\\\");const r=new cc;r.setCrossOrigin(this.crossOrigin);const s=r.load(t,n,void 0,i);return e&&(s.mapping=e),s},_t.loadTextureCube=function(t,e,n,i){console.warn(\\\\\\\"THREE.ImageUtils.loadTextureCube has been deprecated. Use THREE.CubeTextureLoader() instead.\\\\\\\");const r=new oc;r.setCrossOrigin(this.crossOrigin);const s=r.load(t,n,void 0,i);return e&&(s.mapping=e),s},_t.loadCompressedTexture=function(){console.error(\\\\\\\"THREE.ImageUtils.loadCompressedTexture has been removed. Use THREE.DDSLoader instead.\\\\\\\")},_t.loadCompressedTextureCube=function(){console.error(\\\\\\\"THREE.ImageUtils.loadCompressedTextureCube has been removed. Use THREE.DDSLoader instead.\\\\\\\")};const su={createMultiMaterialObject:function(){console.error(\\\\\\\"THREE.SceneUtils has been moved to /examples/jsm/utils/SceneUtils.js\\\\\\\")},detach:function(){console.error(\\\\\\\"THREE.SceneUtils has been moved to /examples/jsm/utils/SceneUtils.js\\\\\\\")},attach:function(){console.error(\\\\\\\"THREE.SceneUtils has been moved to /examples/jsm/utils/SceneUtils.js\\\\\\\")}};\\\\\\\"undefined\\\\\\\"!=typeof __THREE_DEVTOOLS__&&__THREE_DEVTOOLS__.dispatchEvent(new CustomEvent(\\\\\\\"register\\\\\\\",{detail:{revision:e}})),\\\\\\\"undefined\\\\\\\"!=typeof window&&(window.__THREE__?console.warn(\\\\\\\"WARNING: Multiple instances of Three.js being imported.\\\\\\\"):window.__THREE__=e),t.ACESFilmicToneMapping=4,t.AddEquation=n,t.AddOperation=2,t.AdditiveAnimationBlendMode=q,t.AdditiveBlending=2,t.AlphaFormat=1021,t.AlwaysDepth=1,t.AlwaysStencilFunc=519,t.AmbientLight=Lc,t.AmbientLightProbe=Yc,t.AnimationClip=Kl,t.AnimationLoader=class extends ic{constructor(t){super(t)}load(t,e,n,i){const r=this,s=new sc(this.manager);s.setPath(this.path),s.setRequestHeader(this.requestHeader),s.setWithCredentials(this.withCredentials),s.load(t,(function(n){try{e(r.parse(JSON.parse(n)))}catch(e){i?i(e):console.error(e),r.manager.itemError(t)}}),n,i)}parse(t){const e=[];for(let n=0;n<t.length;n++){const i=Kl.parse(t[n]);e.push(i)}return e}},t.AnimationMixer=wh,t.AnimationObjectGroup=Mh,t.AnimationUtils=Ul,t.ArcCurve=_o,t.ArrayCamera=Ws,t.ArrowHelper=class extends Pe{constructor(t=new Rt(0,0,1),e=new Rt(0,0,0),n=1,i=16776960,r=.2*n,s=.2*r){super(),this.type=\\\\\\\"ArrowHelper\\\\\\\",void 0===tu&&(tu=new wn,tu.setAttribute(\\\\\\\"position\\\\\\\",new un([0,0,0,0,1,0],3)),eu=new lo(0,.5,1,5,1),eu.translate(0,-.5,0)),this.position.copy(e),this.line=new ja(tu,new Ua({color:i,toneMapped:!1})),this.line.matrixAutoUpdate=!1,this.add(this.line),this.cone=new Gn(eu,new Ke({color:i,toneMapped:!1})),this.cone.matrixAutoUpdate=!1,this.add(this.cone),this.setDirection(t),this.setLength(n,r,s)}setDirection(t){if(t.y>.99999)this.quaternion.set(0,0,0,1);else if(t.y<-.99999)this.quaternion.set(1,0,0,0);else{$h.set(t.z,0,-t.x).normalize();const e=Math.acos(t.y);this.quaternion.setFromAxisAngle($h,e)}}setLength(t,e=.2*t,n=.2*e){this.line.scale.set(1,Math.max(1e-4,t-e),1),this.line.updateMatrix(),this.cone.scale.set(n,e,n),this.cone.position.y=t,this.cone.updateMatrix()}setColor(t){this.line.material.color.set(t),this.cone.material.color.set(t)}copy(t){return super.copy(t,!1),this.line.copy(t.line),this.cone.copy(t.cone),this}},t.Audio=ih,t.AudioAnalyser=lh,t.AudioContext=jc,t.AudioListener=class extends Pe{constructor(){super(),this.type=\\\\\\\"AudioListener\\\\\\\",this.context=jc.getContext(),this.gain=this.context.createGain(),this.gain.connect(this.context.destination),this.filter=null,this.timeDelta=0,this._clock=new Qc}getInput(){return this.gain}removeFilter(){return null!==this.filter&&(this.gain.disconnect(this.filter),this.filter.disconnect(this.context.destination),this.gain.connect(this.context.destination),this.filter=null),this}getFilter(){return this.filter}setFilter(t){return null!==this.filter?(this.gain.disconnect(this.filter),this.filter.disconnect(this.context.destination)):this.gain.disconnect(this.context.destination),this.filter=t,this.gain.connect(this.filter),this.filter.connect(this.context.destination),this}getMasterVolume(){return this.gain.gain.value}setMasterVolume(t){return this.gain.gain.setTargetAtTime(t,this.context.currentTime,.01),this}updateMatrixWorld(t){super.updateMatrixWorld(t);const e=this.context.listener,n=this.up;if(this.timeDelta=this._clock.getDelta(),this.matrixWorld.decompose($c,th,eh),nh.set(0,0,-1).applyQuaternion(th),e.positionX){const t=this.context.currentTime+this.timeDelta;e.positionX.linearRampToValueAtTime($c.x,t),e.positionY.linearRampToValueAtTime($c.y,t),e.positionZ.linearRampToValueAtTime($c.z,t),e.forwardX.linearRampToValueAtTime(nh.x,t),e.forwardY.linearRampToValueAtTime(nh.y,t),e.forwardZ.linearRampToValueAtTime(nh.z,t),e.upX.linearRampToValueAtTime(n.x,t),e.upY.linearRampToValueAtTime(n.y,t),e.upZ.linearRampToValueAtTime(n.z,t)}else e.setPosition($c.x,$c.y,$c.z),e.setOrientation(nh.x,nh.y,nh.z,n.x,n.y,n.z)}},t.AudioLoader=qc,t.AxesHelper=nu,t.AxisHelper=function(t){return console.warn(\\\\\\\"THREE.AxisHelper has been renamed to THREE.AxesHelper.\\\\\\\"),new nu(t)},t.BackSide=1,t.BasicDepthPacking=3200,t.BasicShadowMap=0,t.BinaryTextureLoader=function(t){return console.warn(\\\\\\\"THREE.BinaryTextureLoader has been renamed to THREE.DataTextureLoader.\\\\\\\"),new lc(t)},t.Bone=Ra,t.BooleanKeyframeTrack=jl,t.BoundingBoxHelper=function(t,e){return console.warn(\\\\\\\"THREE.BoundingBoxHelper has been deprecated. Creating a THREE.BoxHelper instead.\\\\\\\"),new Kh(t,e)},t.Box2=Ch,t.Box3=It,t.Box3Helper=class extends Ya{constructor(t,e=16776960){const n=new Uint16Array([0,1,1,2,2,3,3,0,4,5,5,6,6,7,7,4,0,4,1,5,2,6,3,7]),i=new wn;i.setIndex(new en(n,1)),i.setAttribute(\\\\\\\"position\\\\\\\",new un([1,1,1,-1,1,1,-1,-1,1,1,-1,1,1,1,-1,-1,1,-1,-1,-1,-1,1,-1,-1],3)),super(i,new Ua({color:e,toneMapped:!1})),this.box=t,this.type=\\\\\\\"Box3Helper\\\\\\\",this.geometry.computeBoundingSphere()}updateMatrixWorld(t){const e=this.box;e.isEmpty()||(e.getCenter(this.position),e.getSize(this.scale),this.scale.multiplyScalar(.5),super.updateMatrixWorld(t))}},t.BoxBufferGeometry=Vn,t.BoxGeometry=Vn,t.BoxHelper=Kh,t.BufferAttribute=en,t.BufferGeometry=wn,t.BufferGeometryLoader=zc,t.ByteType=1010,t.Cache=tc,t.Camera=Yn,t.CameraHelper=class extends Ya{constructor(t){const e=new wn,n=new Ua({color:16777215,vertexColors:!0,toneMapped:!1}),i=[],r=[],s={},a=new Qe(16755200),o=new Qe(16711680),l=new Qe(43775),c=new Qe(16777215),h=new Qe(3355443);function u(t,e,n){d(t,n),d(e,n)}function d(t,e){i.push(0,0,0),r.push(e.r,e.g,e.b),void 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un(i,3)),e.setAttribute(\\\\\\\"color\\\\\\\",new un(r,3)),super(e,n),this.type=\\\\\\\"CameraHelper\\\\\\\",this.camera=t,this.camera.updateProjectionMatrix&&this.camera.updateProjectionMatrix(),this.matrix=t.matrixWorld,this.matrixAutoUpdate=!1,this.pointMap=s,this.update()}update(){const t=this.geometry,e=this.pointMap;Jh.projectionMatrixInverse.copy(this.camera.projectionMatrixInverse),Zh(\\\\\\\"c\\\\\\\",e,t,Jh,0,0,-1),Zh(\\\\\\\"t\\\\\\\",e,t,Jh,0,0,1),Zh(\\\\\\\"n1\\\\\\\",e,t,Jh,-1,-1,-1),Zh(\\\\\\\"n2\\\\\\\",e,t,Jh,1,-1,-1),Zh(\\\\\\\"n3\\\\\\\",e,t,Jh,-1,1,-1),Zh(\\\\\\\"n4\\\\\\\",e,t,Jh,1,1,-1),Zh(\\\\\\\"f1\\\\\\\",e,t,Jh,-1,-1,1),Zh(\\\\\\\"f2\\\\\\\",e,t,Jh,1,-1,1),Zh(\\\\\\\"f3\\\\\\\",e,t,Jh,-1,1,1),Zh(\\\\\\\"f4\\\\\\\",e,t,Jh,1,1,1),Zh(\\\\\\\"u1\\\\\\\",e,t,Jh,.7,1.1,-1),Zh(\\\\\\\"u2\\\\\\\",e,t,Jh,-.7,1.1,-1),Zh(\\\\\\\"u3\\\\\\\",e,t,Jh,0,2,-1),Zh(\\\\\\\"cf1\\\\\\\",e,t,Jh,-1,0,1),Zh(\\\\\\\"cf2\\\\\\\",e,t,Jh,1,0,1),Zh(\\\\\\\"cf3\\\\\\\",e,t,Jh,0,-1,1),Zh(\\\\\\\"cf4\\\\\\\",e,t,Jh,0,1,1),Zh(\\\\\\\"cn1\\\\\\\",e,t,Jh,-1,0,-1),Zh(\\\\\\\"cn2\\\\\\\",e,t,Jh,1,0,-1),Zh(\\\\\\\"cn3\\\\\\\",e,t,Jh,0,-1,-1),Zh(\\\\\\\"cn4\\\\\\\",e,t,Jh,0,1,-1),t.getAttribute(\\\\\\\"position\\\\\\\").needsUpdate=!0}dispose(){this.geometry.dispose(),this.material.dispose()}},t.CanvasRenderer=function(){console.error(\\\\\\\"THREE.CanvasRenderer has been removed\\\\\\\")},t.CanvasTexture=so,t.CatmullRomCurve3=Eo,t.CineonToneMapping=3,t.CircleBufferGeometry=oo,t.CircleGeometry=oo,t.ClampToEdgeWrapping=u,t.Clock=Qc,t.Color=Qe,t.ColorKeyframeTrack=ql,t.CompressedTexture=ro,t.CompressedTextureLoader=class extends ic{constructor(t){super(t)}load(t,e,n,i){const r=this,s=[],a=new ro,o=new sc(this.manager);o.setPath(this.path),o.setResponseType(\\\\\\\"arraybuffer\\\\\\\"),o.setRequestHeader(this.requestHeader),o.setWithCredentials(r.withCredentials);let l=0;function c(c){o.load(t[c],(function(t){const n=r.parse(t,!0);s[c]={width:n.width,height:n.height,format:n.format,mipmaps:n.mipmaps},l+=1,6===l&&(1===n.mipmapCount&&(a.minFilter=g),a.image=s,a.format=n.format,a.needsUpdate=!0,e&&e(a))}),n,i)}if(Array.isArray(t))for(let e=0,n=t.length;e<n;++e)c(e);else o.load(t,(function(t){const n=r.parse(t,!0);if(n.isCubemap){const t=n.mipmaps.length/n.mipmapCount;for(let e=0;e<t;e++){s[e]={mipmaps:[]};for(let t=0;t<n.mipmapCount;t++)s[e].mipmaps.push(n.mipmaps[e*n.mipmapCount+t]),s[e].format=n.format,s[e].width=n.width,s[e].height=n.height}a.image=s}else a.image.width=n.width,a.image.height=n.height,a.mipmaps=n.mipmaps;1===n.mipmapCount&&(a.minFilter=g),a.format=n.format,a.needsUpdate=!0,e&&e(a)}),n,i);return a}},t.ConeBufferGeometry=co,t.ConeGeometry=co,t.CubeCamera=Qn,t.CubeReflectionMapping=r,t.CubeRefractionMapping=s,t.CubeTexture=Kn,t.CubeTextureLoader=oc,t.CubeUVReflectionMapping=l,t.CubeUVRefractionMapping=c,t.CubicBezierCurve=Co,t.CubicBezierCurve3=Po,t.CubicInterpolant=Gl,t.CullFaceBack=1,t.CullFaceFront=2,t.CullFaceFrontBack=3,t.CullFaceNone=0,t.Curve=yo,t.CurvePath=hc,t.CustomBlending=5,t.CustomToneMapping=5,t.CylinderBufferGeometry=lo,t.CylinderGeometry=lo,t.Cylindrical=class{constructor(t=1,e=0,n=0){return this.radius=t,this.theta=e,this.y=n,this}set(t,e,n){return this.radius=t,this.theta=e,this.y=n,this}copy(t){return this.radius=t.radius,this.theta=t.theta,this.y=t.y,this}setFromVector3(t){return this.setFromCartesianCoords(t.x,t.y,t.z)}setFromCartesianCoords(t,e,n){return this.radius=Math.sqrt(t*t+n*n),this.theta=Math.atan2(t,n),this.y=e,this}clone(){return(new this.constructor).copy(this)}},t.DataTexture=Ca,t.DataTexture2DArray=nr,t.DataTexture3D=ir,t.DataTextureLoader=lc,t.DataUtils=class{static toHalfFloat(t){iu[0]=t;const e=ru[0];let n=e>>16&32768,i=e>>12&2047;const r=e>>23&255;return r<103?n:r>142?(n|=31744,n|=(255==r?0:1)&&8388607&e,n):r<113?(i|=2048,n|=(i>>114-r)+(i>>113-r&1),n):(n|=r-112<<10|i>>1,n+=1&i,n)}},t.DecrementStencilOp=7683,t.DecrementWrapStencilOp=34056,t.DefaultLoadingManager=nc,t.DepthFormat=A,t.DepthStencilFormat=L,t.DepthTexture=ao,t.DirectionalLight=Ac,t.DirectionalLightHelper=class extends Pe{constructor(t,e,n){super(),this.light=t,this.light.updateMatrixWorld(),this.matrix=t.matrixWorld,this.matrixAutoUpdate=!1,this.color=n,void 0===e&&(e=1);let i=new wn;i.setAttribute(\\\\\\\"position\\\\\\\",new un([-e,e,0,e,e,0,e,-e,0,-e,-e,0,-e,e,0],3));const r=new Ua({fog:!1,toneMapped:!1});this.lightPlane=new ja(i,r),this.add(this.lightPlane),i=new wn,i.setAttribute(\\\\\\\"position\\\\\\\",new un([0,0,0,0,0,1],3)),this.targetLine=new ja(i,r),this.add(this.targetLine),this.update()}dispose(){this.lightPlane.geometry.dispose(),this.lightPlane.material.dispose(),this.targetLine.geometry.dispose(),this.targetLine.material.dispose()}update(){jh.setFromMatrixPosition(this.light.matrixWorld),qh.setFromMatrixPosition(this.light.target.matrixWorld),Xh.subVectors(qh,jh),this.lightPlane.lookAt(qh),void 0!==this.color?(this.lightPlane.material.color.set(this.color),this.targetLine.material.color.set(this.color)):(this.lightPlane.material.color.copy(this.light.color),this.targetLine.material.color.copy(this.light.color)),this.targetLine.lookAt(qh),this.targetLine.scale.z=Xh.length()}},t.DiscreteInterpolant=Vl,t.DodecahedronBufferGeometry=uo,t.DodecahedronGeometry=uo,t.DoubleSide=2,t.DstAlphaFactor=206,t.DstColorFactor=208,t.DynamicBufferAttribute=function(t,e){return console.warn(\\\\\\\"THREE.DynamicBufferAttribute has been removed. Use new THREE.BufferAttribute().setUsage( THREE.DynamicDrawUsage ) instead.\\\\\\\"),new en(t,e).setUsage(nt)},t.DynamicCopyUsage=35050,t.DynamicDrawUsage=nt,t.DynamicReadUsage=35049,t.EdgesGeometry=vo,t.EdgesHelper=function(t,e){return console.warn(\\\\\\\"THREE.EdgesHelper has been removed. Use THREE.EdgesGeometry instead.\\\\\\\"),new Ya(new vo(t.geometry),new Ua({color:void 0!==e?e:16777215}))},t.EllipseCurve=xo,t.EqualDepth=4,t.EqualStencilFunc=514,t.EquirectangularReflectionMapping=a,t.EquirectangularRefractionMapping=o,t.Euler=ge,t.EventDispatcher=rt,t.ExtrudeBufferGeometry=dl,t.ExtrudeGeometry=dl,t.FaceColors=1,t.FileLoader=sc,t.FlatShading=1,t.Float16BufferAttribute=hn,t.Float32Attribute=function(t,e){return console.warn(\\\\\\\"THREE.Float32Attribute has been removed. Use new THREE.Float32BufferAttribute() instead.\\\\\\\"),new un(t,e)},t.Float32BufferAttribute=un,t.Float64Attribute=function(t,e){return console.warn(\\\\\\\"THREE.Float64Attribute has been removed. Use new THREE.Float64BufferAttribute() instead.\\\\\\\"),new dn(t,e)},t.Float64BufferAttribute=dn,t.FloatType=b,t.Fog=$s,t.FogExp2=Ks,t.Font=kc,t.FontLoader=class extends ic{constructor(t){super(t)}load(t,e,n,i){const r=this,s=new sc(this.manager);s.setPath(this.path),s.setRequestHeader(this.requestHeader),s.setWithCredentials(r.withCredentials),s.load(t,(function(t){let n;try{n=JSON.parse(t)}catch(e){console.warn(\\\\\\\"THREE.FontLoader: typeface.js support is being deprecated. Use typeface.json instead.\\\\\\\"),n=JSON.parse(t.substring(65,t.length-2))}const i=r.parse(n);e&&e(i)}),n,i)}parse(t){return new kc(t)}},t.FrontSide=0,t.Frustum=ai,t.GLBufferAttribute=Eh,t.GLSL1=\\\\\\\"100\\\\\\\",t.GLSL3=it,t.GammaEncoding=J,t.GreaterDepth=6,t.GreaterEqualDepth=5,t.GreaterEqualStencilFunc=518,t.GreaterStencilFunc=516,t.GridHelper=Wh,t.Group=js,t.HalfFloatType=w,t.HemisphereLight=mc,t.HemisphereLightHelper=class extends Pe{constructor(t,e,n){super(),this.light=t,this.light.updateMatrixWorld(),this.matrix=t.matrixWorld,this.matrixAutoUpdate=!1,this.color=n;const i=new gl(e);i.rotateY(.5*Math.PI),this.material=new Ke({wireframe:!0,fog:!1,toneMapped:!1}),void 0===this.color&&(this.material.vertexColors=!0);const r=i.getAttribute(\\\\\\\"position\\\\\\\"),s=new Float32Array(3*r.count);i.setAttribute(\\\\\\\"color\\\\\\\",new en(s,3)),this.add(new Gn(i,this.material)),this.update()}dispose(){this.children[0].geometry.dispose(),this.children[0].material.dispose()}update(){const t=this.children[0];if(void 0!==this.color)this.material.color.set(this.color);else{const e=t.geometry.getAttribute(\\\\\\\"color\\\\\\\");kh.copy(this.light.color),Vh.copy(this.light.groundColor);for(let t=0,n=e.count;t<n;t++){const i=t<n/2?kh:Vh;e.setXYZ(t,i.r,i.g,i.b)}e.needsUpdate=!0}t.lookAt(Gh.setFromMatrixPosition(this.light.matrixWorld).negate())}},t.HemisphereLightProbe=Xc,t.IcosahedronBufferGeometry=ml,t.IcosahedronGeometry=ml,t.ImageBitmapLoader=Hc,t.ImageLoader=ac,t.ImageUtils=_t,t.ImmediateRenderObject=Nh,t.IncrementStencilOp=7682,t.IncrementWrapStencilOp=34055,t.InstancedBufferAttribute=Bc,t.InstancedBufferGeometry=Nc,t.InstancedInterleavedBuffer=Th,t.InstancedMesh=Oa,t.Int16Attribute=function(t,e){return console.warn(\\\\\\\"THREE.Int16Attribute has been removed. Use new THREE.Int16BufferAttribute() instead.\\\\\\\"),new an(t,e)},t.Int16BufferAttribute=an,t.Int32Attribute=function(t,e){return console.warn(\\\\\\\"THREE.Int32Attribute has been removed. Use new THREE.Int32BufferAttribute() instead.\\\\\\\"),new ln(t,e)},t.Int32BufferAttribute=ln,t.Int8Attribute=function(t,e){return console.warn(\\\\\\\"THREE.Int8Attribute has been removed. Use new THREE.Int8BufferAttribute() instead.\\\\\\\"),new nn(t,e)},t.Int8BufferAttribute=nn,t.IntType=1013,t.InterleavedBuffer=ea,t.InterleavedBufferAttribute=ia,t.Interpolant=Hl,t.InterpolateDiscrete=U,t.InterpolateLinear=H,t.InterpolateSmooth=G,t.InvertStencilOp=5386,t.JSONLoader=function(){console.error(\\\\\\\"THREE.JSONLoader has been removed.\\\\\\\")},t.KeepStencilOp=tt,t.KeyframeTrack=Wl,t.LOD=ba,t.LatheBufferGeometry=fl,t.LatheGeometry=fl,t.Layers=ve,t.LensFlare=function(){console.error(\\\\\\\"THREE.LensFlare has been moved to /examples/jsm/objects/Lensflare.js\\\\\\\")},t.LessDepth=2,t.LessEqualDepth=3,t.LessEqualStencilFunc=515,t.LessStencilFunc=513,t.Light=pc,t.LightProbe=Pc,t.Line=ja,t.Line3=Dh,t.LineBasicMaterial=Ua,t.LineCurve=Io,t.LineCurve3=Do,t.LineDashedMaterial=Fl,t.LineLoop=Ja,t.LinePieces=1,t.LineSegments=Ya,t.LineStrip=0,t.LinearEncoding=X,t.LinearFilter=g,t.LinearInterpolant=kl,t.LinearMipMapLinearFilter=1008,t.LinearMipMapNearestFilter=1007,t.LinearMipmapLinearFilter=y,t.LinearMipmapNearestFilter=v,t.LinearToneMapping=1,t.Loader=ic,t.LoaderUtils=Dc,t.LoadingManager=ec,t.LogLuvEncoding=3003,t.LoopOnce=2200,t.LoopPingPong=2202,t.LoopRepeat=2201,t.LuminanceAlphaFormat=1025,t.LuminanceFormat=1024,t.MOUSE={LEFT:0,MIDDLE:1,RIGHT:2,ROTATE:0,DOLLY:1,PAN:2},t.Material=We,t.MaterialLoader=Ic,t.Math=gt,t.MathUtils=gt,t.Matrix3=yt,t.Matrix4=ae,t.MaxEquation=104,t.Mesh=Gn,t.MeshBasicMaterial=Ke,t.MeshDepthMaterial=Os,t.MeshDistanceMaterial=Us,t.MeshFaceMaterial=function(t){return console.warn(\\\\\\\"THREE.MeshFaceMaterial has been removed. Use an Array instead.\\\\\\\"),t},t.MeshLambertMaterial=Bl,t.MeshMatcapMaterial=zl,t.MeshNormalMaterial=Nl,t.MeshPhongMaterial=Il,t.MeshPhysicalMaterial=Pl,t.MeshStandardMaterial=Cl,t.MeshToonMaterial=Dl,t.MinEquation=103,t.MirroredRepeatWrapping=d,t.MixOperation=1,t.MultiMaterial=function(t=[]){return console.warn(\\\\\\\"THREE.MultiMaterial has been removed. Use an Array instead.\\\\\\\"),t.isMultiMaterial=!0,t.materials=t,t.clone=function(){return t.slice()},t},t.MultiplyBlending=4,t.MultiplyOperation=0,t.NearestFilter=p,t.NearestMipMapLinearFilter=1005,t.NearestMipMapNearestFilter=1004,t.NearestMipmapLinearFilter=f,t.NearestMipmapNearestFilter=m,t.NeverDepth=0,t.NeverStencilFunc=512,t.NoBlending=0,t.NoColors=0,t.NoToneMapping=0,t.NormalAnimationBlendMode=j,t.NormalBlending=1,t.NotEqualDepth=7,t.NotEqualStencilFunc=517,t.NumberKeyframeTrack=Xl,t.Object3D=Pe,t.ObjectLoader=class extends ic{constructor(t){super(t)}load(t,e,n,i){const r=this,s=\\\\\\\"\\\\\\\"===this.path?Dc.extractUrlBase(t):this.path;this.resourcePath=this.resourcePath||s;const a=new sc(this.manager);a.setPath(this.path),a.setRequestHeader(this.requestHeader),a.setWithCredentials(this.withCredentials),a.load(t,(function(n){let s=null;try{s=JSON.parse(n)}catch(e){return void 0!==i&&i(e),void console.error(\\\\\\\"THREE:ObjectLoader: Can't parse \\\\\\\"+t+\\\\\\\".\\\\\\\",e.message)}const a=s.metadata;void 0!==a&&void 0!==a.type&&\\\\\\\"geometry\\\\\\\"!==a.type.toLowerCase()?r.parse(s,e):console.error(\\\\\\\"THREE.ObjectLoader: Can't load \\\\\\\"+t)}),n,i)}async loadAsync(t,e){const n=\\\\\\\"\\\\\\\"===this.path?Dc.extractUrlBase(t):this.path;this.resourcePath=this.resourcePath||n;const i=new sc(this.manager);i.setPath(this.path),i.setRequestHeader(this.requestHeader),i.setWithCredentials(this.withCredentials);const r=await i.loadAsync(t,e),s=JSON.parse(r),a=s.metadata;if(void 0===a||void 0===a.type||\\\\\\\"geometry\\\\\\\"===a.type.toLowerCase())throw new Error(\\\\\\\"THREE.ObjectLoader: Can't load \\\\\\\"+t);return await this.parseAsync(s)}parse(t,e){const n=this.parseAnimations(t.animations),i=this.parseShapes(t.shapes),r=this.parseGeometries(t.geometries,i),s=this.parseImages(t.images,(function(){void 0!==e&&e(l)})),a=this.parseTextures(t.textures,s),o=this.parseMaterials(t.materials,a),l=this.parseObject(t.object,r,o,a,n),c=this.parseSkeletons(t.skeletons,l);if(this.bindSkeletons(l,c),void 0!==e){let t=!1;for(const e in s)if(s[e]instanceof HTMLImageElement){t=!0;break}!1===t&&e(l)}return l}async parseAsync(t){const e=this.parseAnimations(t.animations),n=this.parseShapes(t.shapes),i=this.parseGeometries(t.geometries,n),r=await this.parseImagesAsync(t.images),s=this.parseTextures(t.textures,r),a=this.parseMaterials(t.materials,s),o=this.parseObject(t.object,i,a,s,e),l=this.parseSkeletons(t.skeletons,o);return this.bindSkeletons(o,l),o}parseShapes(t){const e={};if(void 0!==t)for(let n=0,i=t.length;n<i;n++){const i=(new dc).fromJSON(t[n]);e[i.uuid]=i}return e}parseSkeletons(t,e){const n={},i={};if(e.traverse((function(t){t.isBone&&(i[t.uuid]=t)})),void 0!==t)for(let e=0,r=t.length;e<r;e++){const r=(new Da).fromJSON(t[e],i);n[r.uuid]=r}return n}parseGeometries(t,e){const n={};if(void 0!==t){const i=new zc;for(let r=0,s=t.length;r<s;r++){let s;const a=t[r];switch(a.type){case\\\\\\\"BufferGeometry\\\\\\\":case\\\\\\\"InstancedBufferGeometry\\\\\\\":s=i.parse(a);break;case\\\\\\\"Geometry\\\\\\\":console.error(\\\\\\\"THREE.ObjectLoader: The legacy Geometry type is no longer supported.\\\\\\\");break;default:a.type in Ll?s=Ll[a.type].fromJSON(a,e):console.warn(`THREE.ObjectLoader: Unsupported geometry type \\\\\\\"${a.type}\\\\\\\"`)}s.uuid=a.uuid,void 0!==a.name&&(s.name=a.name),!0===s.isBufferGeometry&&void 0!==a.userData&&(s.userData=a.userData),n[a.uuid]=s}}return n}parseMaterials(t,e){const n={},i={};if(void 0!==t){const r=new Ic;r.setTextures(e);for(let e=0,s=t.length;e<s;e++){const s=t[e];if(\\\\\\\"MultiMaterial\\\\\\\"===s.type){const t=[];for(let e=0;e<s.materials.length;e++){const i=s.materials[e];void 0===n[i.uuid]&&(n[i.uuid]=r.parse(i)),t.push(n[i.uuid])}i[s.uuid]=t}else void 0===n[s.uuid]&&(n[s.uuid]=r.parse(s)),i[s.uuid]=n[s.uuid]}}return i}parseAnimations(t){const e={};if(void 0!==t)for(let n=0;n<t.length;n++){const i=t[n],r=Kl.parse(i);e[r.uuid]=r}return e}parseImages(t,e){const n=this,i={};let r;function s(t){if(\\\\\\\"string\\\\\\\"==typeof t){const e=t;return function(t){return n.manager.itemStart(t),r.load(t,(function(){n.manager.itemEnd(t)}),void 0,(function(){n.manager.itemError(t),n.manager.itemEnd(t)}))}(/^(\\\\/\\\\/)|([a-z]+:(\\\\/\\\\/)?)/i.test(e)?e:n.resourcePath+e)}return t.data?{data:fn(t.type,t.data),width:t.width,height:t.height}:null}if(void 0!==t&&t.length>0){const n=new ec(e);r=new ac(n),r.setCrossOrigin(this.crossOrigin);for(let e=0,n=t.length;e<n;e++){const n=t[e],r=n.url;if(Array.isArray(r)){i[n.uuid]=[];for(let t=0,e=r.length;t<e;t++){const e=s(r[t]);null!==e&&(e instanceof HTMLImageElement?i[n.uuid].push(e):i[n.uuid].push(new Ca(e.data,e.width,e.height)))}}else{const t=s(n.url);null!==t&&(i[n.uuid]=t)}}}return i}async parseImagesAsync(t){const e=this,n={};let i;async function r(t){if(\\\\\\\"string\\\\\\\"==typeof t){const n=t,r=/^(\\\\/\\\\/)|([a-z]+:(\\\\/\\\\/)?)/i.test(n)?n:e.resourcePath+n;return await i.loadAsync(r)}return t.data?{data:fn(t.type,t.data),width:t.width,height:t.height}:null}if(void 0!==t&&t.length>0){i=new ac(this.manager),i.setCrossOrigin(this.crossOrigin);for(let e=0,i=t.length;e<i;e++){const i=t[e],s=i.url;if(Array.isArray(s)){n[i.uuid]=[];for(let t=0,e=s.length;t<e;t++){const e=s[t],a=await r(e);null!==a&&(a instanceof HTMLImageElement?n[i.uuid].push(a):n[i.uuid].push(new Ca(a.data,a.width,a.height)))}}else{const t=await r(i.url);null!==t&&(n[i.uuid]=t)}}}return n}parseTextures(t,e){function n(t,e){return\\\\\\\"number\\\\\\\"==typeof t?t:(console.warn(\\\\\\\"THREE.ObjectLoader.parseTexture: Constant should be in numeric form.\\\\\\\",t),e[t])}const i={};if(void 0!==t)for(let r=0,s=t.length;r<s;r++){const s=t[r];let a;void 0===s.image&&console.warn('THREE.ObjectLoader: No \\\\\\\"image\\\\\\\" specified for',s.uuid),void 0===e[s.image]&&console.warn(\\\\\\\"THREE.ObjectLoader: Undefined image\\\\\\\",s.image);const o=e[s.image];Array.isArray(o)?(a=new Kn(o),6===o.length&&(a.needsUpdate=!0)):(a=o&&o.data?new Ca(o.data,o.width,o.height):new bt(o),o&&(a.needsUpdate=!0)),a.uuid=s.uuid,void 0!==s.name&&(a.name=s.name),void 0!==s.mapping&&(a.mapping=n(s.mapping,Fc)),void 0!==s.offset&&a.offset.fromArray(s.offset),void 0!==s.repeat&&a.repeat.fromArray(s.repeat),void 0!==s.center&&a.center.fromArray(s.center),void 0!==s.rotation&&(a.rotation=s.rotation),void 0!==s.wrap&&(a.wrapS=n(s.wrap[0],Oc),a.wrapT=n(s.wrap[1],Oc)),void 0!==s.format&&(a.format=s.format),void 0!==s.type&&(a.type=s.type),void 0!==s.encoding&&(a.encoding=s.encoding),void 0!==s.minFilter&&(a.minFilter=n(s.minFilter,Uc)),void 0!==s.magFilter&&(a.magFilter=n(s.magFilter,Uc)),void 0!==s.anisotropy&&(a.anisotropy=s.anisotropy),void 0!==s.flipY&&(a.flipY=s.flipY),void 0!==s.premultiplyAlpha&&(a.premultiplyAlpha=s.premultiplyAlpha),void 0!==s.unpackAlignment&&(a.unpackAlignment=s.unpackAlignment),i[s.uuid]=a}return i}parseObject(t,e,n,i,r){let s,a,o;function l(t){return void 0===e[t]&&console.warn(\\\\\\\"THREE.ObjectLoader: Undefined geometry\\\\\\\",t),e[t]}function c(t){if(void 0!==t){if(Array.isArray(t)){const e=[];for(let i=0,r=t.length;i<r;i++){const r=t[i];void 0===n[r]&&console.warn(\\\\\\\"THREE.ObjectLoader: Undefined material\\\\\\\",r),e.push(n[r])}return e}return void 0===n[t]&&console.warn(\\\\\\\"THREE.ObjectLoader: Undefined material\\\\\\\",t),n[t]}}function h(t){return void 0===i[t]&&console.warn(\\\\\\\"THREE.ObjectLoader: Undefined texture\\\\\\\",t),i[t]}switch(t.type){case\\\\\\\"Scene\\\\\\\":s=new ta,void 0!==t.background&&(Number.isInteger(t.background)?s.background=new Qe(t.background):s.background=h(t.background)),void 0!==t.environment&&(s.environment=h(t.environment)),void 0!==t.fog&&(\\\\\\\"Fog\\\\\\\"===t.fog.type?s.fog=new $s(t.fog.color,t.fog.near,t.fog.far):\\\\\\\"FogExp2\\\\\\\"===t.fog.type&&(s.fog=new Ks(t.fog.color,t.fog.density)));break;case\\\\\\\"PerspectiveCamera\\\\\\\":s=new Jn(t.fov,t.aspect,t.near,t.far),void 0!==t.focus&&(s.focus=t.focus),void 0!==t.zoom&&(s.zoom=t.zoom),void 0!==t.filmGauge&&(s.filmGauge=t.filmGauge),void 0!==t.filmOffset&&(s.filmOffset=t.filmOffset),void 0!==t.view&&(s.view=Object.assign({},t.view));break;case\\\\\\\"OrthographicCamera\\\\\\\":s=new xi(t.left,t.right,t.top,t.bottom,t.near,t.far),void 0!==t.zoom&&(s.zoom=t.zoom),void 0!==t.view&&(s.view=Object.assign({},t.view));break;case\\\\\\\"AmbientLight\\\\\\\":s=new Lc(t.color,t.intensity);break;case\\\\\\\"DirectionalLight\\\\\\\":s=new Ac(t.color,t.intensity);break;case\\\\\\\"PointLight\\\\\\\":s=new Tc(t.color,t.intensity,t.distance,t.decay);break;case\\\\\\\"RectAreaLight\\\\\\\":s=new Rc(t.color,t.intensity,t.width,t.height);break;case\\\\\\\"SpotLight\\\\\\\":s=new _c(t.color,t.intensity,t.distance,t.angle,t.penumbra,t.decay);break;case\\\\\\\"HemisphereLight\\\\\\\":s=new mc(t.color,t.groundColor,t.intensity);break;case\\\\\\\"LightProbe\\\\\\\":s=(new Pc).fromJSON(t);break;case\\\\\\\"SkinnedMesh\\\\\\\":a=l(t.geometry),o=c(t.material),s=new La(a,o),void 0!==t.bindMode&&(s.bindMode=t.bindMode),void 0!==t.bindMatrix&&s.bindMatrix.fromArray(t.bindMatrix),void 0!==t.skeleton&&(s.skeleton=t.skeleton);break;case\\\\\\\"Mesh\\\\\\\":a=l(t.geometry),o=c(t.material),s=new Gn(a,o);break;case\\\\\\\"InstancedMesh\\\\\\\":a=l(t.geometry),o=c(t.material);const e=t.count,n=t.instanceMatrix,i=t.instanceColor;s=new Oa(a,o,e),s.instanceMatrix=new en(new Float32Array(n.array),16),void 0!==i&&(s.instanceColor=new en(new Float32Array(i.array),i.itemSize));break;case\\\\\\\"LOD\\\\\\\":s=new ba;break;case\\\\\\\"Line\\\\\\\":s=new ja(l(t.geometry),c(t.material));break;case\\\\\\\"LineLoop\\\\\\\":s=new Ja(l(t.geometry),c(t.material));break;case\\\\\\\"LineSegments\\\\\\\":s=new Ya(l(t.geometry),c(t.material));break;case\\\\\\\"PointCloud\\\\\\\":case\\\\\\\"Points\\\\\\\":s=new eo(l(t.geometry),c(t.material));break;case\\\\\\\"Sprite\\\\\\\":s=new ya(c(t.material));break;case\\\\\\\"Group\\\\\\\":s=new js;break;case\\\\\\\"Bone\\\\\\\":s=new Ra;break;default:s=new Pe}if(s.uuid=t.uuid,void 0!==t.name&&(s.name=t.name),void 0!==t.matrix?(s.matrix.fromArray(t.matrix),void 0!==t.matrixAutoUpdate&&(s.matrixAutoUpdate=t.matrixAutoUpdate),s.matrixAutoUpdate&&s.matrix.decompose(s.position,s.quaternion,s.scale)):(void 0!==t.position&&s.position.fromArray(t.position),void 0!==t.rotation&&s.rotation.fromArray(t.rotation),void 0!==t.quaternion&&s.quaternion.fromArray(t.quaternion),void 0!==t.scale&&s.scale.fromArray(t.scale)),void 0!==t.castShadow&&(s.castShadow=t.castShadow),void 0!==t.receiveShadow&&(s.receiveShadow=t.receiveShadow),t.shadow&&(void 0!==t.shadow.bias&&(s.shadow.bias=t.shadow.bias),void 0!==t.shadow.normalBias&&(s.shadow.normalBias=t.shadow.normalBias),void 0!==t.shadow.radius&&(s.shadow.radius=t.shadow.radius),void 0!==t.shadow.mapSize&&s.shadow.mapSize.fromArray(t.shadow.mapSize),void 0!==t.shadow.camera&&(s.shadow.camera=this.parseObject(t.shadow.camera))),void 0!==t.visible&&(s.visible=t.visible),void 0!==t.frustumCulled&&(s.frustumCulled=t.frustumCulled),void 0!==t.renderOrder&&(s.renderOrder=t.renderOrder),void 0!==t.userData&&(s.userData=t.userData),void 0!==t.layers&&(s.layers.mask=t.layers),void 0!==t.children){const a=t.children;for(let t=0;t<a.length;t++)s.add(this.parseObject(a[t],e,n,i,r))}if(void 0!==t.animations){const e=t.animations;for(let t=0;t<e.length;t++){const n=e[t];s.animations.push(r[n])}}if(\\\\\\\"LOD\\\\\\\"===t.type){void 0!==t.autoUpdate&&(s.autoUpdate=t.autoUpdate);const e=t.levels;for(let t=0;t<e.length;t++){const n=e[t],i=s.getObjectByProperty(\\\\\\\"uuid\\\\\\\",n.object);void 0!==i&&s.addLevel(i,n.distance)}}return s}bindSkeletons(t,e){0!==Object.keys(e).length&&t.traverse((function(t){if(!0===t.isSkinnedMesh&&void 0!==t.skeleton){const n=e[t.skeleton];void 0===n?console.warn(\\\\\\\"THREE.ObjectLoader: No skeleton found with UUID:\\\\\\\",t.skeleton):t.bind(n,t.bindMatrix)}}))}setTexturePath(t){return console.warn(\\\\\\\"THREE.ObjectLoader: .setTexturePath() has been renamed to .setResourcePath().\\\\\\\"),this.setResourcePath(t)}},t.ObjectSpaceNormalMap=1,t.OctahedronBufferGeometry=gl,t.OctahedronGeometry=gl,t.OneFactor=201,t.OneMinusDstAlphaFactor=207,t.OneMinusDstColorFactor=209,t.OneMinusSrcAlphaFactor=205,t.OneMinusSrcColorFactor=203,t.OrthographicCamera=xi,t.PCFShadowMap=1,t.PCFSoftShadowMap=2,t.PMREMGenerator=Oi,t.ParametricBufferGeometry=vl,t.ParametricGeometry=vl,t.Particle=function(t){return console.warn(\\\\\\\"THREE.Particle has been renamed to THREE.Sprite.\\\\\\\"),new ya(t)},t.ParticleBasicMaterial=function(t){return console.warn(\\\\\\\"THREE.ParticleBasicMaterial has been renamed to THREE.PointsMaterial.\\\\\\\"),new Za(t)},t.ParticleSystem=function(t,e){return console.warn(\\\\\\\"THREE.ParticleSystem has been renamed to THREE.Points.\\\\\\\"),new eo(t,e)},t.ParticleSystemMaterial=function(t){return console.warn(\\\\\\\"THREE.ParticleSystemMaterial has been renamed to THREE.PointsMaterial.\\\\\\\"),new Za(t)},t.Path=uc,t.PerspectiveCamera=Jn,t.Plane=ii,t.PlaneBufferGeometry=ci,t.PlaneGeometry=ci,t.PlaneHelper=class extends ja{constructor(t,e=1,n=16776960){const i=n,r=new wn;r.setAttribute(\\\\\\\"position\\\\\\\",new un([1,-1,1,-1,1,1,-1,-1,1,1,1,1,-1,1,1,-1,-1,1,1,-1,1,1,1,1,0,0,1,0,0,0],3)),r.computeBoundingSphere(),super(r,new Ua({color:i,toneMapped:!1})),this.type=\\\\\\\"PlaneHelper\\\\\\\",this.plane=t,this.size=e;const s=new wn;s.setAttribute(\\\\\\\"position\\\\\\\",new un([1,1,1,-1,1,1,-1,-1,1,1,1,1,-1,-1,1,1,-1,1],3)),s.computeBoundingSphere(),this.add(new Gn(s,new Ke({color:i,opacity:.2,transparent:!0,depthWrite:!1,toneMapped:!1})))}updateMatrixWorld(t){let e=-this.plane.constant;Math.abs(e)<1e-8&&(e=1e-8),this.scale.set(.5*this.size,.5*this.size,e),this.children[0].material.side=e<0?1:0,this.lookAt(this.plane.normal),super.updateMatrixWorld(t)}},t.PointCloud=function(t,e){return console.warn(\\\\\\\"THREE.PointCloud has been renamed to THREE.Points.\\\\\\\"),new eo(t,e)},t.PointCloudMaterial=function(t){return console.warn(\\\\\\\"THREE.PointCloudMaterial has been renamed to THREE.PointsMaterial.\\\\\\\"),new Za(t)},t.PointLight=Tc,t.PointLightHelper=class extends Gn{constructor(t,e,n){super(new _l(e,4,2),new Ke({wireframe:!0,fog:!1,toneMapped:!1})),this.light=t,this.light.updateMatrixWorld(),this.color=n,this.type=\\\\\\\"PointLightHelper\\\\\\\",this.matrix=this.light.matrixWorld,this.matrixAutoUpdate=!1,this.update()}dispose(){this.geometry.dispose(),this.material.dispose()}update(){void 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O}},{\\\\\\\"./resize_filter_info\\\\\\\":7}],7:[function(r,i,n){\\\\\\\"use strict\\\\\\\";i.exports=[{win:.5,filter:function(l){return l>=-.5&&l<.5?1:0}},{win:1,filter:function(l){if(l<=-1||l>=1)return 0;if(l>-11920929e-14&&l<11920929e-14)return 1;var a=l*Math.PI;return Math.sin(a)/a*(.54+.46*Math.cos(a/1))}},{win:2,filter:function(l){if(l<=-2||l>=2)return 0;if(l>-11920929e-14&&l<11920929e-14)return 1;var a=l*Math.PI;return Math.sin(a)/a*Math.sin(a/2)/(a/2)}},{win:3,filter:function(l){if(l<=-3||l>=3)return 0;if(l>-11920929e-14&&l<11920929e-14)return 1;var a=l*Math.PI;return Math.sin(a)/a*Math.sin(a/3)/(a/3)}}]},{}],8:[function(r,i,n){\\\\\\\"use strict\\\\\\\";var o=r(\\\\\\\"./resize_filter_gen\\\\\\\");function l(d,h,p){for(var f=3,m=h*p*4|0;f<m;)d[f]=255,f=f+4|0}function a(d){return new Uint8Array(d.buffer,0,d.byteLength)}var c=!0;try{c=new Uint32Array(new Uint8Array([1,0,0,0]).buffer)[0]===1}catch(d){}function u(d,h,p){if(c){h.set(a(d),p);return}for(var 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y=Math.ceil(Math.log(Math.min(f,m))/Math.log(g));if(y<=1)return[[u,d]];for(var x=[],v=0;v<y;v++){var b=Math.round(Math.pow(Math.pow(a,y-v-1)*Math.pow(u,v+1),1/y)),w=Math.round(Math.pow(Math.pow(c,y-v-1)*Math.pow(d,v+1),1/y));x.push([b,w])}return x}},{}],15:[function(r,i,n){\\\\\\\"use strict\\\\\\\";var o=1e-5;function l(c){var u=Math.round(c);return Math.abs(c-u)<o?u:Math.floor(c)}function a(c){var u=Math.round(c);return Math.abs(c-u)<o?u:Math.ceil(c)}i.exports=function(u){var d=u.toWidth/u.width,h=u.toHeight/u.height,p=l(u.srcTileSize*d)-2*u.destTileBorder,f=l(u.srcTileSize*h)-2*u.destTileBorder;if(p<1||f<1)throw new Error(\\\\\\\"Internal error in pica: target tile width/height is too small.\\\\\\\");var m,g,y,x,v,b,w=[],N;for(x=0;x<u.toHeight;x+=f)for(y=0;y<u.toWidth;y+=p)m=y-u.destTileBorder,m<0&&(m=0),v=y+p+u.destTileBorder-m,m+v>=u.toWidth&&(v=u.toWidth-m),g=x-u.destTileBorder,g<0&&(g=0),b=x+f+u.destTileBorder-g,g+b>=u.toHeight&&(b=u.toHeight-g),N={toX:m,toY:g,toWidth:v,toHeight:b,toInnerX:y,toInnerY:x,toInnerWidth:p,toInnerHeight:f,offsetX:m/d-l(m/d),offsetY:g/h-l(g/h),scaleX:d,scaleY:h,x:l(m/d),y:l(g/h),width:a(v/d),height:a(b/h)},w.push(N);return w}},{}],16:[function(r,i,n){\\\\\\\"use strict\\\\\\\";function o(l){return Object.prototype.toString.call(l)}i.exports.isCanvas=function(a){var c=o(a);return c===\\\\\\\"[object HTMLCanvasElement]\\\\\\\"||c===\\\\\\\"[object OffscreenCanvas]\\\\\\\"||c===\\\\\\\"[object Canvas]\\\\\\\"},i.exports.isImage=function(a){return o(a)===\\\\\\\"[object HTMLImageElement]\\\\\\\"},i.exports.isImageBitmap=function(a){return o(a)===\\\\\\\"[object ImageBitmap]\\\\\\\"},i.exports.limiter=function(a){var c=0,u=[];function 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);\\\\\\\",\\\\\\\"}\\\\\\\"].join(`\\n`)),sRGBToLinear:new se([\\\\\\\"vec4 sRGBToLinear( in vec4 value ) {\\\\\\\",\\\\\\\"\\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.w );\\\\\\\",\\\\\\\"}\\\\\\\"].join(`\\n`)),LinearTosRGB:new se([\\\\\\\"vec4 LinearTosRGB( in vec4 value ) {\\\\\\\",\\\\\\\"\\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.w );\\\\\\\",\\\\\\\"}\\\\\\\"].join(`\\n`)),RGBEToLinear:new se([\\\\\\\"vec4 RGBEToLinear( in vec4 value ) {\\\\\\\",\\\\\\\"\\treturn vec4( value.rgb * exp2( value.a * 255.0 - 128.0 ), 1.0 );\\\\\\\",\\\\\\\"}\\\\\\\"].join(`\\n`)),LinearToRGBE:new se([\\\\\\\"vec4 LinearToRGBE( in vec4 value ) {\\\\\\\",\\\\\\\"\\tfloat maxComponent = max( max( value.r, value.g ), value.b );\\\\\\\",\\\\\\\"\\tfloat fExp = clamp( ceil( log2( maxComponent ) ), -128.0, 127.0 );\\\\\\\",\\\\\\\"\\treturn vec4( value.rgb / exp2( fExp ), ( fExp + 128.0 ) / 255.0 );\\\\\\\",\\\\\\\"}\\\\\\\"].join(`\\n`)),RGBMToLinear:new se([\\\\\\\"vec3 RGBMToLinear( in vec4 value, in float maxRange ) {\\\\\\\",\\\\\\\"\\treturn vec4( value.xyz * value.w * maxRange, 1.0 );\\\\\\\",\\\\\\\"}\\\\\\\"].join(`\\n`)),LinearToRGBM:new se([\\\\\\\"vec3 LinearToRGBM( in vec4 value, in float maxRange ) {\\\\\\\",\\\\\\\"\\tfloat maxRGB = max( value.x, max( value.g, value.b ) );\\\\\\\",\\\\\\\"\\tfloat M      = clamp( maxRGB / maxRange, 0.0, 1.0 );\\\\\\\",\\\\\\\"\\tM            = ceil( M * 255.0 ) / 255.0;\\\\\\\",\\\\\\\"\\treturn vec4( value.rgb / ( M * maxRange ), M );\\\\\\\",\\\\\\\"}\\\\\\\"].join(`\\n`)),RGBDToLinear:new se([\\\\\\\"vec3 RGBDToLinear( in vec4 value, in float maxRange ) {\\\\\\\",\\\\\\\"\\treturn vec4( value.rgb * ( ( maxRange / 255.0 ) / value.a ), 1.0 );\\\\\\\",\\\\\\\"}\\\\\\\"].join(`\\n`)),LinearToRGBD:new se([\\\\\\\"vec3 LinearToRGBD( in vec4 value, in float maxRange ) {\\\\\\\",\\\\\\\"\\tfloat maxRGB = max( value.x, max( value.g, value.b ) );\\\\\\\",\\\\\\\"\\tfloat D      = max( maxRange / maxRGB, 1.0 );\\\\\\\",\\\\\\\"\\tD            = clamp( floor( D ) / 255.0, 0.0, 1.0 );\\\\\\\",\\\\\\\"\\treturn vec4( value.rgb * ( D * ( 255.0 / maxRange ) ), D );\\\\\\\",\\\\\\\"}\\\\\\\"].join(`\\n`)),cLogLuvM:new Ee(\\\\\\\"const mat3 cLogLuvM = mat3( 0.2209, 0.3390, 0.4184, 0.1138, 0.6780, 0.7319, 0.0102, 0.1130, 0.2969 );\\\\\\\"),LinearToLogLuv:new se([\\\\\\\"vec4 LinearToLogLuv( in vec4 value ) {\\\\\\\",\\\\\\\"\\tvec3 Xp_Y_XYZp = cLogLuvM * value.rgb;\\\\\\\",\\\\\\\"\\tXp_Y_XYZp = max(Xp_Y_XYZp, vec3(1e-6, 1e-6, 1e-6));\\\\\\\",\\\\\\\"\\tvec4 vResult;\\\\\\\",\\\\\\\"\\tvResult.xy = Xp_Y_XYZp.xy / Xp_Y_XYZp.z;\\\\\\\",\\\\\\\"\\tfloat Le = 2.0 * log2(Xp_Y_XYZp.y) + 127.0;\\\\\\\",\\\\\\\"\\tvResult.w = fract(Le);\\\\\\\",\\\\\\\"\\tvResult.z = (Le - (floor(vResult.w*255.0))/255.0)/255.0;\\\\\\\",\\\\\\\"\\treturn vResult;\\\\\\\",\\\\\\\"}\\\\\\\"].join(`\\n`),[new Ee(\\\\\\\"const mat3 cLogLuvM = mat3( 0.2209, 0.3390, 0.4184, 0.1138, 0.6780, 0.7319, 0.0102, 0.1130, 0.2969 );\\\\\\\")]),cLogLuvInverseM:new Ee(\\\\\\\"const mat3 cLogLuvInverseM = mat3( 6.0014, -2.7008, -1.7996, -1.3320, 3.1029, -5.7721, 0.3008, -1.0882, 5.6268 );\\\\\\\"),LogLuvToLinear:new se([\\\\\\\"vec4 LogLuvToLinear( in vec4 value ) {\\\\\\\",\\\\\\\"\\tfloat Le = value.z * 255.0 + value.w;\\\\\\\",\\\\\\\"\\tvec3 Xp_Y_XYZp;\\\\\\\",\\\\\\\"\\tXp_Y_XYZp.y = exp2((Le - 127.0) / 2.0);\\\\\\\",\\\\\\\"\\tXp_Y_XYZp.z = Xp_Y_XYZp.y / value.y;\\\\\\\",\\\\\\\"\\tXp_Y_XYZp.x = value.x * Xp_Y_XYZp.z;\\\\\\\",\\\\\\\"\\tvec3 vRGB = cLogLuvInverseM * Xp_Y_XYZp.rgb;\\\\\\\",\\\\\\\"\\treturn vec4( max(vRGB, 0.0), 1.0 );\\\\\\\",\\\\\\\"}\\\\\\\"].join(`\\n`),[new Ee(\\\\\\\"const mat3 cLogLuvInverseM = mat3( 6.0014, -2.7008, -1.7996, -1.3320, 3.1029, -5.7721, 0.3008, -1.0882, 5.6268 );\\\\\\\")])},qe.LINEAR_TO_LINEAR=\\\\\\\"LinearToLinear\\\\\\\",qe.GAMMA_TO_LINEAR=\\\\\\\"GammaToLinear\\\\\\\",qe.LINEAR_TO_GAMMA=\\\\\\\"LinearToGamma\\\\\\\",qe.SRGB_TO_LINEAR=\\\\\\\"sRGBToLinear\\\\\\\",qe.LINEAR_TO_SRGB=\\\\\\\"LinearTosRGB\\\\\\\",qe.RGBE_TO_LINEAR=\\\\\\\"RGBEToLinear\\\\\\\",qe.LINEAR_TO_RGBE=\\\\\\\"LinearToRGBE\\\\\\\",qe.RGBM_TO_LINEAR=\\\\\\\"RGBMToLinear\\\\\\\",qe.LINEAR_TO_RGBM=\\\\\\\"LinearToRGBM\\\\\\\",qe.RGBD_TO_LINEAR=\\\\\\\"RGBDToLinear\\\\\\\",qe.LINEAR_TO_RGBD=\\\\\\\"LinearToRGBD\\\\\\\",qe.LINEAR_TO_LOG_LUV=\\\\\\\"LinearToLogLuv\\\\\\\",qe.LOG_LUV_TO_LINEAR=\\\\\\\"LogLuvToLinear\\\\\\\";var Ju=F(z()),nr=class extends Ju.Texture{toJSON(e){let t=super.toJSON(e),r=e===void 0||typeof e==\\\\\\\"string\\\\\\\";if(this.image!==void 0&&!r){let i=this.image;if(Array.isArray(i)){e.images[i.uuid].url=[];for(let n=0;n<i.length;n++)e.images[i.uuid].url[n]=Qu(i[n])}else e.images[i.uuid].url=Qu(i)}return t}};function Qu(s){return typeof HTMLImageElement!=\\\\\\\"undefined\\\\\\\"&&s instanceof HTMLImageElement||typeof HTMLCanvasElement!=\\\\\\\"undefined\\\\\\\"&&s instanceof HTMLCanvasElement||typeof ImageBitmap!=\\\\\\\"undefined\\\\\\\"&&s instanceof ImageBitmap?Hg(s):(console.warn(\\\\\\\"THREE.Texture: Unable to serialize Texture.\\\\\\\"),\\\\\\\"\\\\\\\")}var ms;function Hg(s){if(/^data:/i.test(s.src))return s.src;let e;if(s instanceof HTMLCanvasElement)e=s;else{ms===void 0&&(ms=document.createElement(\\\\\\\"canvas\\\\\\\")),ms.width=s.width,ms.height=s.height;let r=ms.getContext(\\\\\\\"2d\\\\\\\");s instanceof ImageData?r.putImageData(s,0,0):r.drawImage(s,0,0,s.width,s.height),e=ms}let t=s.src.startsWith(\\\\\\\"blob:\\\\\\\")?s.fileName:s.src;return/\\\\.jpe?g$/i.test(t)?e.toDataURL(\\\\\\\"image/jpeg\\\\\\\",.6):e.toDataURL(\\\\\\\"image/png\\\\\\\")}var _t=class extends Ke{constructor(e=new nr,t,r,i){super(\\\\\\\"v4\\\\\\\",{shared:!0});this.nodeType=\\\\\\\"Texture\\\\\\\";this.value=e,this.uv=t??new zi,this.bias=r,this.project=i!==void 0?i:!1}getTexture(e,t){return super.generate(e,t,this.value.uuid,\\\\\\\"t\\\\\\\")}generate(e,t){if(t===\\\\\\\"sampler2D\\\\\\\")return this.getTexture(e,t);let r=this.getTexture(e,t),i=this.uv.build(e,this.project?\\\\\\\"v4\\\\\\\":\\\\\\\"v2\\\\\\\"),n=this.bias?this.bias.build(e,\\\\\\\"f\\\\\\\"):void 0;n===void 0&&e.context.bias&&(n=e.context.bias.setTexture(this).build(e,\\\\\\\"f\\\\\\\"));let o,l;this.project?o=\\\\\\\"texture2DProj\\\\\\\":o=n?\\\\\\\"tex2DBias\\\\\\\":\\\\\\\"tex2D\\\\\\\",n?l=o+\\\\\\\"( \\\\\\\"+r+\\\\\\\", \\\\\\\"+i+\\\\\\\", \\\\\\\"+n+\\\\\\\" )\\\\\\\":l=o+\\\\\\\"( \\\\\\\"+r+\\\\\\\", \\\\\\\"+i+\\\\\\\" )\\\\\\\";let a={include:e.isShader(\\\\\\\"vertex\\\\\\\"),ignoreCache:!0},c=this.getType(e);return e.addContext(a),this.colorSpace=this.colorSpace??new qe(new Ve(\\\\\\\"\\\\\\\",c)),this.colorSpace.fromDecoding(e.getTextureEncodingFromMap(this.value)),this.colorSpace.input.parse(l),l=this.colorSpace.build(e,c),e.removeContext(),e.format(l,c,t)}copy(e){return super.copy(e),e.value.isRenderTargetTexture?this.value=e.value:this.value.copy(e.value),this.uv.copy(e.uv),e.bias?this.bias?this.bias.copy(e.bias):this.bias=e.bias.clone():this.bias=void 0,e.colorSpace?this.colorSpace?this.colorSpace.copy(e.colorSpace):this.colorSpace=e.colorSpace.clone():this.colorSpace=void 0,this.project=e.project,e.value.isRenderTargetTexture||(this.value.updateMatrix(),this.value.needsUpdate=!0),this}toJSON(e){let t=this.getJSONNode(e);return t||(t=this.createJSONNode(e),this.value&&(t.value=this.value.toJSON(e).uuid),t.uv=this.uv.toJSON(e).uuid,t.project=this.project,this.bias&&(t.bias=this.bias.toJSON(e).uuid)),t.nodeType=this.nodeType,t}fromJSON(e,t){return super.fromJSON(e,t),this.project=e.project,e.value&&(this.value=t.getTexture(e.value),this.value.updateMatrix()),e.uv&&(this.uv=t.getNode(e.uv)),e.bias&&(this.bias=t.getNode(e.bias)),this}};var Yu=F(z());var ii=class extends Ke{constructor(e){super(\\\\\\\"m3\\\\\\\");this.nodeType=\\\\\\\"Matrix3\\\\\\\";this.value=e??new Yu.Matrix3}generateReadonly(e,t,r,i,n,o){return e.format(\\\\\\\"mat3(\\\\\\\"+this.value.elements.join(\\\\\\\", \\\\\\\")+\\\\\\\")\\\\\\\",i,t)}copy(e){return super.copy(e),this.elements=e.elements,this}toJSON(e){let t=this.getJSONNode(e);return t||(t=this.createJSONNode(e),t.elements=this.value.elements.concat()),t.nodeType=this.nodeType,t}fromJSON(e,t){return super.fromJSON(e,t),e.elements&&(this.elements=e.elements),this}get elements(){return this.value.elements}set elements(e){this.value.fromArray(e)}};var gs;(function(i){i[i.UV=0]=\\\\\\\"UV\\\\\\\",i[i.PLANAR=1]=\\\\\\\"PLANAR\\\\\\\",i[i.SPHERICAL=2]=\\\\\\\"SPHERICAL\\\\\\\",i[i.CYLINDRICAL=3]=\\\\\\\"CYLINDRICAL\\\\\\\"})(gs||(gs={}));var wn=class extends ue{constructor(e=new _t,t,r,i,n,o,l,a){super(\\\\\\\"v3\\\\\\\");this.nodeType=\\\\\\\"CustomTexture\\\\\\\";this.firstTime=!0,this.texture=e,this.textureSize=t,this.crop=r,this.projection=i,this.axis=n,this.size=o,this.mat=new ii(this.texture.value.matrix),this.alpha=l,this.mode=a,this.calpha=`g${this.uuid.toString().replace(/-/g,\\\\\\\"\\\\\\\")}_calpha`}generate(e,t){e.require(\\\\\\\"position\\\\\\\"),e.require(\\\\\\\"uv\\\\\\\"),e.requires.uv=[!0],e.extensions.shaderTextureLOD=!0,e.extensions.derivatives=!0;let r;switch(this.projection.value){case 3:r=e.include(wn.Nodes.cylindrical);break;case 2:r=e.include(wn.Nodes.spherical);break;case 1:let n=new se(`\\n\\t\\tvec3 g${this.uuid.toString().replace(/-/g,\\\\\\\"\\\\\\\")}_planarTexture(sampler2D tex, vec2 textureSize, float crop, mat3 mat, vec2 size, float alpha, int mode, out float calpha) {\\n\\n\\t\\t\\t\\tvec2 uvs = ( mat * vec3( (g${this.uuid.toString().replace(/-/g,\\\\\\\"\\\\\\\")}_vCustomUv * 2. - 1.) / (size * .5), 1. ) / 2. + 0.5 ).xy;\\n\\n\\t\\t\\t\\tvec4 tmp = texture2D( tex, uvs );\\n\\n\\t\\t\\t\\tvec3 col = tmp.rgb;\\n\\t\\t\\t\\tfloat lalpha = alpha * tmp.a;\\n\\t\\t\\t\\tif ( crop > 0.5 ) {\\n\\t\\t\\t\\t\\tif ( uvs.x < 0.0 || uvs.x > 1.0 || uvs.y < 0.0 || uvs.y > 1.0 )  {\\n\\t\\t\\t\\t\\t\\tlalpha = 0.0;\\n\\t\\t\\t\\t\\t}\\n\\t\\t\\t\\t}\\n\\t\\t\\t\\tcalpha =  lalpha / clamp( lalpha + accumAlpha, 0.00001, 1.0 );\\n\\t\\t\\t\\taccumAlpha += ( 1.0 - accumAlpha ) * lalpha;\\n\\t\\t\\t\\treturn col;\\n\\t\\t\\t}`);r=e.include(n);break;default:r=e.include(wn.Nodes.uv);break}if(this.projection.value===1&&this.firstTime){let n=`g${this.uuid.toString().replace(/-/g,\\\\\\\"\\\\\\\")}`;e.addVertexParsCode(`varying vec2 ${n}_vCustomUv;`),e.addFragmentParsCode(`varying vec2 ${n}_vCustomUv;`),e.addVertexFinalCode(`\\n                        vec3 ${n}_posN = transformed;\\n${this.axis.value===0?`\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t   float ${n}_u = (1. + (${n}_posN.z)) / 2.;\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t   float ${n}_v = (1. + (${n}_posN.y)) / 2.;\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t   `:\\\\\\\"\\\\\\\"}\\n\\n${this.axis.value===1?`\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t   float ${n}_u = (1. + (${n}_posN.x)) / 2.;\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t   float ${n}_v = (1. - (${n}_posN.z)) / 2.;\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t   `:\\\\\\\"\\\\\\\"}\\n\\n${this.axis.value===2?`\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t   float ${n}_u = (1. + (${n}_posN.x)) / 2.;\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t   float ${n}_v = (1. + (${n}_posN.y)) / 2.;\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t   `:\\\\\\\"\\\\\\\"}\\n\\n                        ${n}_vCustomUv = vec2(${n}_u, ${n}_v);\\n                    `)}e.addFragmentVariable(this.calpha,\\\\\\\"float\\\\\\\");let i=[];return i.push(this.texture.getTexture(e,\\\\\\\"t\\\\\\\")),i.push(this.textureSize.build(e,\\\\\\\"v2\\\\\\\")),i.push(this.crop.build(e,\\\\\\\"f\\\\\\\")),i.push(this.mat.build(e,\\\\\\\"mat3\\\\\\\")),i.push(this.size.build(e,\\\\\\\"v2\\\\\\\")),i.push(this.alpha.build(e,\\\\\\\"f\\\\\\\")),i.push(this.mode.build(e,\\\\\\\"i\\\\\\\")),i.push(this.calpha),this.firstTime=!this.firstTime,e.format(r+\\\\\\\"(\\\\\\\"+i.join(\\\\\\\",\\\\\\\")+\\\\\\\")\\\\\\\",this.getType(e),t)}toJSON(e){let t=this.getJSONNode(e);return t||(t=this.createJSONNode(e),t.texture=this.texture.toJSON(e).uuid,t.textureSize=this.textureSize.toJSON(e).uuid,t.crop=this.crop.toJSON(e).uuid,t.projection=this.projection.toJSON(e).uuid,t.axis=this.axis.toJSON(e).uuid,t.size=this.size.toJSON(e).uuid,t.alpha=this.alpha.toJSON(e).uuid,t.mode=this.mode.toJSON(e).uuid,t.calpha=this.calpha),t.nodeType=this.nodeType,t}fromJSON(e,t){return super.fromJSON(e,t),e.texture&&(this.texture=t.getNode(e.texture),this.texture.value.updateMatrix(),this.mat=new ii(this.texture.value.matrix)),e.textureSize&&(this.textureSize=t.getNode(e.textureSize)),e.crop&&(this.crop=t.getNode(e.crop)),e.projection&&(this.projection=t.getNode(e.projection)),e.axis&&(this.axis=t.getNode(e.axis)),e.size&&(this.size=t.getNode(e.size)),e.alpha&&(this.alpha=t.getNode(e.alpha)),e.mode&&(this.mode=t.getNode(e.mode)),e.calpha&&(this.calpha=e.calpha),this}copy(e){return super.copy(e),this.texture.copy(e.texture),this.textureSize=e.textureSize.clone(),this.crop=e.crop.clone(),this.projection=e.projection.clone(),this.axis=e.axis.clone(),this.size=e.size.clone(),this.alpha=e.alpha.clone(),this.mode=e.mode.clone(),this}},ys=wn;ys.Nodes=function(){let e=new se(`\\nvec3 cylindricalTexture(sampler2D tex, vec2 textureSize, float crop, mat3 mat, vec2 size, float alpha, int mode, out float calpha) {\\n                vec3 posN = normalize(position);\\n                float u = 0.5 + atan(posN.z, posN.x) / (2.*3.1415);\\n                float scaledHeight = position.y / (size.y * 0.5);\\n                float v =  (scaledHeight / 2.) + .5;\\n\\n                vec2 calculatedUv = vec2(u,v);\\n\\t\\t\\t\\tvec2 uvs = ( mat * vec3( calculatedUv * 2. - 1., 1. ) / 2. + 0.5 ).xy;\\n\\n                vec2 df = fwidth(uvs);\\n               \\tif(df.x > 0.5) df.x = 0.;\\n\\n\\t\\t\\t\\t#ifdef GL_EXT_shader_texture_lod\\n                vec4 tmp = texture2DLodEXT(tex, uvs, log2(max(df.x, df.y)*min(textureSize.x, textureSize.y)));\\n\\t\\t\\t\\t#else\\n                vec4 tmp = textureLod(tex, uvs, log2(max(df.x, df.y)*min(textureSize.x, textureSize.y)));\\n\\t\\t\\t\\t#endif\\n\\n\\t\\t\\t\\tvec3 col = tmp.rgb;\\n\\t\\t\\t\\tfloat lalpha = alpha * tmp.a;\\n\\t\\t\\t\\tif ( crop > 0.5 ) {\\n\\t\\t\\t\\t\\tif ( uvs.x < 0.0 || uvs.x > 1.0 || uvs.y < 0.0 || uvs.y > 1.0 )  {\\n\\t\\t\\t\\t\\t\\tlalpha = 0.0;\\n\\t\\t\\t\\t\\t}\\n\\t\\t\\t\\t}\\n\\t\\t\\t\\tcalpha =  lalpha / clamp( lalpha + accumAlpha, 0.00001, 1.0 );\\n\\t\\t\\t\\taccumAlpha += ( 1.0 - accumAlpha ) * lalpha;\\n\\t\\t\\t\\treturn col;\\n\\t\\t\\t}\\n`),t=new se(`\\nvec3 sphericalTexture(sampler2D tex, vec2 textureSize, float crop, mat3 mat, vec2 size, float alpha, int mode, out float calpha) {\\n                vec3 posN = normalize(vPosition);\\n                float u = 0.5 + atan(posN.z, posN.x) / (2.*3.1415);\\n                float v = 0.5 + asin(posN.y) / 3.1415;\\n\\n                vec2 calculatedUv = vec2(u,v);\\n\\t\\t\\t\\tvec2 uvs = ( mat * vec3( calculatedUv * 2. - 1., 1. ) / 2. + 0.5 ).xy;\\n\\n                vec2 df = fwidth(uvs);\\n               \\tif(df.x > 0.5) df.x = 0.;\\n\\t\\t\\t\\t#ifdef GL_EXT_shader_texture_lod\\n                vec4 tmp = texture2DLodEXT(tex, uvs, log2(max(df.x, df.y)*min(textureSize.x, textureSize.y)));\\n\\t\\t\\t\\t#else\\n                vec4 tmp = textureLod(tex, uvs, log2(max(df.x, df.y)*min(textureSize.x, textureSize.y)));\\n\\t\\t\\t\\t#endif\\n\\n\\t\\t\\t\\tvec3 col = tmp.rgb;\\n\\t\\t\\t\\tfloat lalpha = alpha * tmp.a;\\n\\t\\t\\t\\tif ( crop > 0.5 ) {\\n\\t\\t\\t\\t\\tif ( uvs.x < 0.0 || uvs.x > 1.0 || uvs.y < 0.0 || uvs.y > 1.0 )  {\\n\\t\\t\\t\\t\\t\\tlalpha = 0.0;\\n\\t\\t\\t\\t\\t}\\n\\t\\t\\t\\t}\\n\\t\\t\\t\\tcalpha =  lalpha / clamp( lalpha + accumAlpha, 0.00001, 1.0 );\\n\\t\\t\\t\\taccumAlpha += ( 1.0 - accumAlpha ) * lalpha;\\n\\t\\t\\t\\treturn col;\\n\\t\\t\\t}\\n`),r=new se(`vec3 uvTexture(sampler2D tex, vec2 textureSize, float crop, mat3 mat, vec2 size, float alpha, int mode, out float calpha) {\\n\\n\\t\\t\\t\\tvec2 uvs = ( mat * vec3( vUv * 2. - 1., 1. ) / 2. + 0.5 ).xy;\\n\\t\\t\\t\\tvec4 tmp = texture2D( tex, uvs );\\n\\n\\t\\t\\t\\tvec3 col = tmp.rgb;\\n\\n\\t\\t\\t\\tfloat lalpha = alpha * tmp.a;\\n\\t\\t\\t\\tif ( crop > 0.5 ) {\\n\\t\\t\\t\\t\\tif ( uvs.x < 0.0 || uvs.x > 1.0 || uvs.y < 0.0 || uvs.y > 1.0 )  {\\n\\t\\t\\t\\t\\t\\tlalpha = 0.0;\\n\\t\\t\\t\\t\\t}\\n\\t\\t\\t\\t}\\n\\t\\t\\t\\tcalpha =  lalpha / clamp( lalpha + accumAlpha, 0.00001, 1.0 );\\n\\t\\t\\t\\taccumAlpha += ( 1.0 - accumAlpha ) * lalpha;\\n\\t\\t\\t\\treturn col;\\n\\t\\t\\t}`);return{cylindrical:e,spherical:t,uv:r}}();var Ml=class extends ue{constructor(e,t,r,i,n,o,l){super(\\\\\\\"v3\\\\\\\");this.nodeType=\\\\\\\"Fresnel\\\\\\\";this.color=e,this.bias=t,this.scale=r,this.intensity=i,this.factor=n,this.alpha=o,this.mode=l,this.calpha=`g${this.uuid.toString().replace(/-/g,\\\\\\\"\\\\\\\")}_calpha`}generate(e,t){if(e.require(\\\\\\\"vWorldViewDir\\\\\\\"),e.require(\\\\\\\"vWorldNormal\\\\\\\"),e.isShader(\\\\\\\"fragment\\\\\\\")){e.addFragmentVariable(this.calpha,\\\\\\\"float\\\\\\\");let r=e.include(Ml.Nodes.fresnel),i=[];return i.push(this.color.build(e,\\\\\\\"c\\\\\\\")),i.push(this.bias.build(e,\\\\\\\"f\\\\\\\")),i.push(this.scale.build(e,\\\\\\\"f\\\\\\\")),i.push(this.intensity.build(e,\\\\\\\"f\\\\\\\")),i.push(this.factor.build(e,\\\\\\\"f\\\\\\\")),i.push(this.alpha.build(e,\\\\\\\"f\\\\\\\")),i.push(this.mode.build(e,\\\\\\\"i\\\\\\\")),i.push(this.calpha),e.format(r+\\\\\\\"(\\\\\\\"+i.join(\\\\\\\",\\\\\\\")+\\\\\\\")\\\\\\\",this.getType(e),t)}else return console.warn(\\\\\\\"FresnelNode is not compatible with \\\\\\\"+e.shader+\\\\\\\" shader.\\\\\\\"),e.format(\\\\\\\"vec3( 0.0 )\\\\\\\",this.getType(e),t)}toJSON(e){let t=this.getJSONNode(e);return t||(t=this.createJSONNode(e),t.color=this.color.toJSON(e).uuid,t.bias=this.bias.toJSON(e).uuid,t.scale=this.scale.toJSON(e).uuid,t.intensity=this.intensity.toJSON(e).uuid,t.factor=this.factor.toJSON(e).uuid,t.alpha=this.alpha.toJSON(e).uuid,t.mode=this.mode.toJSON(e).uuid,t.calpha=this.calpha),t.nodeType=this.nodeType,t}fromJSON(e,t){return super.fromJSON(e,t),e.color&&(this.color=t.getNode(e.color)),e.bias&&(this.bias=t.getNode(e.bias)),e.scale&&(this.scale=t.getNode(e.scale)),e.intensity&&(this.intensity=t.getNode(e.intensity)),e.factor&&(this.factor=t.getNode(e.factor)),e.alpha&&(this.alpha=t.getNode(e.alpha)),e.mode&&(this.mode=t.getNode(e.mode)),e.calpha&&(this.calpha=e.calpha),this}copy(e){return super.copy(e),this.color=e.color.clone(),this.bias=e.bias.clone(),this.scale=e.scale.clone(),this.intensity=e.intensity.clone(),this.factor=e.factor.clone(),this.alpha=e.alpha.clone(),this.mode=e.mode.clone(),this.calpha=e.calpha,this}},vs=Ml;vs.Nodes=function(){return{fresnel:new se(`vec3 fresnel(vec3 color, float bias, float scale, float intensity, float factor, float alpha, int mode, out float calpha) {\\n\\t\\t\\t\\tfloat fresnel = bias + scale * pow( abs( factor + dot( normalize( vWorldViewDir ), normalize( vWorldNormal ) ) ), intensity );\\n\\n\\t\\t\\t\\tfloat lalpha = clamp( fresnel, 0.0, 1.0 ) * alpha;\\n\\t\\t\\t\\tcalpha = lalpha / clamp(lalpha + accumAlpha, 0.001, 1.0);\\n\\t\\t\\t\\taccumAlpha += (1.0 - accumAlpha) * lalpha;\\n\\t\\t\\t\\treturn color;\\n\\t\\t\\t}`)}}();var xs;(function(n){n.SIMPLEX=\\\\\\\"simplex3d\\\\\\\",n.SIMPLEX_FRACTAL=\\\\\\\"simplex3dFractal\\\\\\\",n.ASHIMA=\\\\\\\"simplexAshima\\\\\\\",n.FBM=\\\\\\\"fbm\\\\\\\",n.PERLIN=\\\\\\\"perlin\\\\\\\"})(xs||(xs={}));var Jt=function(){let s=5,e=new se(`vec3 random3(vec3 c) {\\n\\t\\t\\tfloat j = 4096.0*sin(dot(c,vec3(17.0, 59.4, 15.0)));\\n\\t\\t\\tvec3 r;\\n\\t\\t\\tr.z = fract(512.0*j);\\n\\t\\t\\tj *= .125;\\n\\t\\t\\tr.x = fract(512.0*j);\\n\\t\\t\\tj *= .125;\\n\\t\\t\\tr.y = fract(512.0*j);\\n\\t\\t\\treturn r-0.5;\\n\\t\\t}`),t=new se(`float simplex3d(vec3 p) {\\n\\t\\t\\t vec3 s = floor(p + dot(p, vec3(F3)));\\n\\t\\t\\t vec3 x = p - s + dot(s, vec3(G3));\\n\\t\\t\\t \\n\\t\\t\\t vec3 e = step(vec3(0.0), x - x.yzx);\\n\\t\\t\\t vec3 i1 = e*(1.0 - e.zxy);\\n\\t\\t\\t vec3 i2 = 1.0 - e.zxy*(1.0 - e);\\n\\t\\t\\t\\t\\n\\t\\t\\t vec3 x1 = x - i1 + G3;\\n\\t\\t\\t vec3 x2 = x - i2 + 2.0*G3;\\n\\t\\t\\t vec3 x3 = x - 1.0 + 3.0*G3;\\n\\t\\t\\t \\n\\t\\t\\t vec4 w, d;\\n\\t\\t\\t \\n\\t\\t\\t w.x = dot(x, x);\\n\\t\\t\\t w.y = dot(x1, x1);\\n\\t\\t\\t w.z = dot(x2, x2);\\n\\t\\t\\t w.w = dot(x3, x3);\\n\\t\\t\\t \\n\\t\\t\\t w = max(0.6 - w, 0.0);\\n\\t\\t\\t \\n\\t\\t\\t d.x = dot(random3(s), x);\\n\\t\\t\\t d.y = dot(random3(s + i1), x1);\\n\\t\\t\\t d.z = dot(random3(s + i2), x2);\\n\\t\\t\\t d.w = dot(random3(s + 1.0), x3);\\n\\t\\t\\t \\n\\t\\t\\t w *= w;\\n\\t\\t\\t w *= w;\\n\\t\\t\\t d *= w;\\n\\t\\t\\t \\n\\t\\t\\t return dot(d, vec4(52.0));\\n\\t\\t}`,[e]);t.keywords.F3=new Ee(\\\\\\\"float F3 0.3333333\\\\\\\"),t.keywords.G3=new Ee(\\\\\\\"float G3 0.1666667\\\\\\\");let r=new se(`float simplex3dFractal(vec3 m) {\\n\\t\\t\\tmat3 rot1 = mat3(-0.37, 0.36, 0.85,-0.14,-0.93, 0.34,0.92, 0.01,0.4);\\n\\t\\t\\tmat3 rot2 = mat3(-0.55,-0.39, 0.74, 0.33,-0.91,-0.24,0.77, 0.12,0.63);\\n\\t\\t\\tmat3 rot3 = mat3(-0.71, 0.52,-0.47,-0.08,-0.72,-0.68,-0.7,-0.45,0.56);\\n\\t\\t\\treturn 0.5333333 * simplex3d(m * rot1)\\n\\t\\t\\t\\t + 0.2666667 * simplex3d(2.0 * m * rot2)\\n\\t\\t\\t\\t + 0.1333333 * simplex3d(4.0 * m * rot3)\\n\\t\\t\\t\\t + 0.0666667 * simplex3d(8.0 * m);\\n\\t\\t}`,[t]),i=new se(\\\\\\\"vec4 permute(vec4 x){return mod(((x*34.0)+1.0)*x, 289.0);}\\\\\\\"),n=new se(\\\\\\\"vec4 taylorInvSqrt(vec4 r){return 1.79284291400159 - 0.85373472095314 * r;}\\\\\\\"),o=new se(`float simplexAshima(vec3 v) {\\n\\t\\t  const vec2  C = vec2(1.0/6.0, 1.0/3.0) ;\\n\\t\\t  const vec4  D = vec4(0.0, 0.5, 1.0, 2.0);\\n\\t\\t  vec3 i  = floor(v + dot(v, C.yyy) );\\n\\t\\t  vec3 x0 =   v - i + dot(i, C.xxx) ;\\n\\t\\t  vec3 g = step(x0.yzx, x0.xyz);\\n\\t\\t  vec3 l = 1.0 - g;\\n\\t\\t  vec3 i1 = min( g.xyz, l.zxy );\\n\\t\\t  vec3 i2 = max( g.xyz, l.zxy );\\n\\t\\t  vec3 x1 = x0 - i1 + 1.0 * C.xxx;\\n\\t\\t  vec3 x2 = x0 - i2 + 2.0 * C.xxx;\\n\\t\\t  vec3 x3 = x0 - 1. + 3.0 * C.xxx;\\n\\t\\t  i = mod(i, 289.0 ); \\n\\t\\t  vec4 p = permute( permute( permute( \\n\\t\\t\\t\\t\\t i.z + vec4(0.0, i1.z, i2.z, 1.0 ))\\n\\t\\t\\t\\t   + i.y + vec4(0.0, i1.y, i2.y, 1.0 )) \\n\\t\\t\\t\\t   + i.x + vec4(0.0, i1.x, i2.x, 1.0 ));\\n\\t\\t  float n_ = 1.0/7.0; // N=7\\n\\t\\t  vec3  ns = n_ * D.wyz - D.xzx;\\n\\t\\t  vec4 j = p - 49.0 * floor(p * ns.z *ns.z);  //  mod(p,N*N)\\n\\t\\t  vec4 x_ = floor(j * ns.z);\\n\\t\\t  vec4 y_ = floor(j - 7.0 * x_ );    // mod(j,N)\\n\\t\\t  vec4 x = x_ *ns.x + ns.yyyy;\\n\\t\\t  vec4 y = y_ *ns.x + ns.yyyy;\\n\\t\\t  vec4 h = 1.0 - abs(x) - abs(y);\\n\\t\\t  vec4 b0 = vec4( x.xy, y.xy );\\n\\t\\t  vec4 b1 = vec4( x.zw, y.zw );\\n\\t\\t  vec4 s0 = floor(b0)*2.0 + 1.0;\\n\\t\\t  vec4 s1 = floor(b1)*2.0 + 1.0;\\n\\t\\t  vec4 sh = -step(h, vec4(0.0));\\n\\t\\t  vec4 a0 = b0.xzyw + s0.xzyw*sh.xxyy ;\\n\\t\\t  vec4 a1 = b1.xzyw + s1.xzyw*sh.zzww ;\\n\\t\\t  vec3 p0 = vec3(a0.xy,h.x);\\n\\t\\t  vec3 p1 = vec3(a0.zw,h.y);\\n\\t\\t  vec3 p2 = vec3(a1.xy,h.z);\\n\\t\\t  vec3 p3 = vec3(a1.zw,h.w);\\n\\t\\t  vec4 norm = taylorInvSqrt(vec4(dot(p0,p0), dot(p1,p1), dot(p2, p2), dot(p3,p3)));\\n\\t\\t  p0 *= norm.x;\\n\\t\\t  p1 *= norm.y;\\n\\t\\t  p2 *= norm.z;\\n\\t\\t  p3 *= norm.w;\\n\\t\\t  vec4 m = max(0.6 - vec4(dot(x0,x0), dot(x1,x1), dot(x2,x2), dot(x3,x3)), 0.0);\\n\\t\\t  m = m * m;\\n\\t\\t  return 42.0 * dot( m*m, vec4( dot(p0,x0), dot(p1,x1), \\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\tdot(p2,x2), dot(p3,x3) ) );\\n\\t\\t}`,[i,n]),l=new se(\\\\\\\"vec4 mod289(vec4 x){return x - floor(x * (1.0 / 289.0)) * 289.0;}\\\\\\\"),a=new se(\\\\\\\"vec4 perm(vec4 x){return mod289(((x * 34.0) + 1.0) * x);}\\\\\\\",[l]),c=new se(`float noise(vec3 p){\\n\\t\\t\\tvec3 a = floor(p);\\n\\t\\t\\tvec3 d = p - a;\\n\\t\\t\\td = d * d * (3.0 - 2.0 * d);\\n\\t\\t\\tvec4 b = a.xxyy + vec4(0.0, 1.0, 0.0, 1.0);\\n\\t\\t\\tvec4 k1 = perm(b.xyxy);\\n\\t\\t\\tvec4 k2 = perm(k1.xyxy + b.zzww);\\n\\t\\t\\tvec4 c = k2 + a.zzzz;\\n\\t\\t\\tvec4 k3 = perm(c);\\n\\t\\t\\tvec4 k4 = perm(c + 1.0);\\n\\t\\t\\tvec4 o1 = fract(k3 * (1.0 / 41.0));\\n\\t\\t\\tvec4 o2 = fract(k4 * (1.0 / 41.0));\\n\\t\\t\\tvec4 o3 = o2 * d.z + o1 * (1.0 - d.z);\\n\\t\\t\\tvec2 o4 = o3.yw * d.x + o3.xz * (1.0 - d.x);\\n\\t\\t\\treturn o4.y * d.y + o4.x * (1.0 - d.y);\\n\\t\\t}`,[a]),u=new se(`float fbm(vec3 x) {\\n\\t\\t\\tfloat v = 0.0;\\n\\t\\t\\tfloat a = 0.5;\\n\\t\\t\\tvec3 shift = vec3(100);\\n\\t\\t\\tfor (int i = 0; i < NUM_OCTAVES; ++i) {\\n\\t\\t\\t\\tv += a * noise(x);\\n\\t\\t\\t\\tx = x * 2.0 + shift;\\n\\t\\t\\t\\ta *= 0.5;\\n\\t\\t\\t}\\n\\t\\t\\treturn v;\\n\\t\\t}`,[c]);u.keywords.NUM_OCTAVES=new Ee(`int NUM_OCTAVES ${s}`);let d=new se(\\\\\\\"vec3 fade(vec3 t) {return t*t*t*(t*(t*6.0-15.0)+10.0);}\\\\\\\"),h=new se(`float perlin(vec3 P){\\n\\t\\t  vec3 Pi0 = floor(P);\\n\\t\\t  vec3 Pi1 = Pi0 + vec3(1.0);\\n\\t\\t  Pi0 = mod(Pi0, 289.0);\\n\\t\\t  Pi1 = mod(Pi1, 289.0);\\n\\t\\t  vec3 Pf0 = fract(P);\\n\\t\\t  vec3 Pf1 = Pf0 - vec3(1.0);\\n\\t\\t  vec4 ix = vec4(Pi0.x, Pi1.x, Pi0.x, Pi1.x);\\n\\t\\t  vec4 iy = vec4(Pi0.yy, Pi1.yy);\\n\\t\\t  vec4 iz0 = Pi0.zzzz;\\n\\t\\t  vec4 iz1 = Pi1.zzzz;\\n\\t\\t  vec4 ixy = permute(permute(ix) + iy);\\n\\t\\t  vec4 ixy0 = permute(ixy + iz0);\\n\\t\\t  vec4 ixy1 = permute(ixy + iz1);\\n\\t\\t  vec4 gx0 = ixy0 / 7.0;\\n\\t\\t  vec4 gy0 = fract(floor(gx0) / 7.0) - 0.5;\\n\\t\\t  gx0 = fract(gx0);\\n\\t\\t  vec4 gz0 = vec4(0.5) - abs(gx0) - abs(gy0);\\n\\t\\t  vec4 sz0 = step(gz0, vec4(0.0));\\n\\t\\t  gx0 -= sz0 * (step(0.0, gx0) - 0.5);\\n\\t\\t  gy0 -= sz0 * (step(0.0, gy0) - 0.5);\\n\\t\\t  vec4 gx1 = ixy1 / 7.0;\\n\\t\\t  vec4 gy1 = fract(floor(gx1) / 7.0) - 0.5;\\n\\t\\t  gx1 = fract(gx1);\\n\\t\\t  vec4 gz1 = vec4(0.5) - abs(gx1) - abs(gy1);\\n\\t\\t  vec4 sz1 = step(gz1, vec4(0.0));\\n\\t\\t  gx1 -= sz1 * (step(0.0, gx1) - 0.5);\\n\\t\\t  gy1 -= sz1 * (step(0.0, gy1) - 0.5);\\n\\t\\t  vec3 g000 = vec3(gx0.x,gy0.x,gz0.x);\\n\\t\\t  vec3 g100 = vec3(gx0.y,gy0.y,gz0.y);\\n\\t\\t  vec3 g010 = vec3(gx0.z,gy0.z,gz0.z);\\n\\t\\t  vec3 g110 = vec3(gx0.w,gy0.w,gz0.w);\\n\\t\\t  vec3 g001 = vec3(gx1.x,gy1.x,gz1.x);\\n\\t\\t  vec3 g101 = vec3(gx1.y,gy1.y,gz1.y);\\n\\t\\t  vec3 g011 = vec3(gx1.z,gy1.z,gz1.z);\\n\\t\\t  vec3 g111 = vec3(gx1.w,gy1.w,gz1.w);\\n\\t\\t  vec4 norm0 = taylorInvSqrt(vec4(dot(g000, g000), dot(g010, g010), dot(g100, g100), dot(g110, g110)));\\n\\t\\t  g000 *= norm0.x;\\n\\t\\t  g010 *= norm0.y;\\n\\t\\t  g100 *= norm0.z;\\n\\t\\t  g110 *= norm0.w;\\n\\t\\t  vec4 norm1 = taylorInvSqrt(vec4(dot(g001, g001), dot(g011, g011), dot(g101, g101), dot(g111, g111)));\\n\\t\\t  g001 *= norm1.x;\\n\\t\\t  g011 *= norm1.y;\\n\\t\\t  g101 *= norm1.z;\\n\\t\\t  g111 *= norm1.w;\\n\\t\\t  float n000 = dot(g000, Pf0);\\n\\t\\t  float n100 = dot(g100, vec3(Pf1.x, Pf0.yz));\\n\\t\\t  float n010 = dot(g010, vec3(Pf0.x, Pf1.y, Pf0.z));\\n\\t\\t  float n110 = dot(g110, vec3(Pf1.xy, Pf0.z));\\n\\t\\t  float n001 = dot(g001, vec3(Pf0.xy, Pf1.z));\\n\\t\\t  float n101 = dot(g101, vec3(Pf1.x, Pf0.y, Pf1.z));\\n\\t\\t  float n011 = dot(g011, vec3(Pf0.x, Pf1.yz));\\n\\t\\t  float n111 = dot(g111, Pf1);\\n\\t\\t  vec3 fade_xyz = fade(Pf0);\\n\\t\\t  vec4 n_z = mix(vec4(n000, n100, n010, n110), vec4(n001, n101, n011, n111), fade_xyz.z);\\n\\t\\t  vec2 n_yz = mix(n_z.xy, n_z.zw, fade_xyz.y);\\n\\t\\t  float n_xyz = mix(n_yz.x, n_yz.y, fade_xyz.x); \\n\\t\\t  return 2.2 * n_xyz;\\n\\t\\t}`,[i,n,d]);return{simplex:t,simplexFractal:r,simplexAshima:o,fbm:u,perlin:h}}();var El=class extends ue{constructor(e,t,r,i,n,o,l){super(\\\\\\\"v3\\\\\\\");this.nodeType=\\\\\\\"Rainbow\\\\\\\";this.filmThickness=e,this.movement=t,this.wavelengths=r,this.noiseStrength=i,this.noiseScale=n,this.offset=o,this.alpha=l,this.calpha=`g${this.uuid.toString().replace(/-/g,\\\\\\\"\\\\\\\")}_calpha`}generate(e,t){if(e.isShader(\\\\\\\"fragment\\\\\\\")){e.require(\\\\\\\"normal\\\\\\\"),e.require(\\\\\\\"uv\\\\\\\"),e.requires.uv=[!0],e.addFragmentVariable(this.calpha,\\\\\\\"float\\\\\\\");let r=e.include(El.Nodes.rainbow),i=[];return i.push(this.filmThickness.build(e,\\\\\\\"f\\\\\\\")),i.push(this.movement.build(e,\\\\\\\"f\\\\\\\")),i.push(this.wavelengths.build(e,\\\\\\\"v3\\\\\\\")),i.push(this.noiseStrength.build(e,\\\\\\\"f\\\\\\\")),i.push(this.noiseScale.build(e,\\\\\\\"f\\\\\\\")),i.push(this.offset.build(e,\\\\\\\"v3\\\\\\\")),i.push(this.alpha.build(e,\\\\\\\"f\\\\\\\")),i.push(this.calpha),e.format(r+\\\\\\\"(\\\\\\\"+i.join(\\\\\\\",\\\\\\\")+\\\\\\\")\\\\\\\",this.getType(e),t)}else return console.warn(\\\\\\\"RainbowNode is not compatible with \\\\\\\"+e.shader+\\\\\\\" shader.\\\\\\\"),e.format(\\\\\\\"vec3( 0.0 )\\\\\\\",this.getType(e),t)}toJSON(e){let t=this.getJSONNode(e);return t||(t=this.createJSONNode(e),t.filmThickness=this.filmThickness.toJSON(e).uuid,t.movement=this.movement.toJSON(e).uuid,t.wavelengths=this.wavelengths.toJSON(e).uuid,t.noiseStrength=this.noiseStrength.toJSON(e).uuid,t.noiseScale=this.noiseScale.toJSON(e).uuid,t.offset=this.offset.toJSON(e).uuid,t.alpha=this.alpha.toJSON(e).uuid,t.calpha=this.calpha),t.nodeType=this.nodeType,t}fromJSON(e,t){return super.fromJSON(e,t),e.filmThickness&&(this.filmThickness=t.getNode(e.filmThickness)),e.movement&&(this.movement=t.getNode(e.movement)),e.wavelengths&&(this.wavelengths=t.getNode(e.wavelengths)),e.noiseStrength&&(this.noiseStrength=t.getNode(e.noiseStrength)),e.noiseScale&&(this.noiseScale=t.getNode(e.noiseScale)),e.offset&&(this.offset=t.getNode(e.offset)),e.alpha&&(this.alpha=t.getNode(e.alpha)),e.calpha&&(this.calpha=e.calpha),this}copy(e){return super.copy(e),this.filmThickness=e.filmThickness.clone(),this.movement=e.movement.clone(),this.wavelengths=e.wavelengths.clone(),this.noiseStrength=e.noiseStrength.clone(),this.noiseScale=e.noiseScale.clone(),this.offset=e.offset.clone(),this.alpha=e.alpha.clone(),this.calpha=e.calpha,this}},bs=El;bs.Nodes=function(){let e=new se(`vec3 attenuation(vec3 wavelengths, float filmThickness, float movement, float noiseStrength, float noiseScale, vec3 offset) {\\n                 vec3 viewDir = normalize(-(cameraPosition + offset));\\n                 vec3 st = position / noiseScale;\\n\\t\\t\\t\\t vec3 q = vec3(simplex3d(st),\\n\\t\\t\\t\\t\\t\\t\\t  simplex3d(st + vec3(1.0)),\\n\\t\\t\\t\\t\\t\\t\\t  simplex3d(st + vec3(1.0)));\\n\\n\\t\\t\\t\\t vec3 r = vec3(simplex3d(st + vec3(1.4, 1.3, 1.0) * q + vec3(1.7, 9.2, 1.0)),\\n\\t\\t\\t\\t\\t\\t\\t  simplex3d(st + vec3(2.0, 1.2, 1.0) * q + vec3(8.3, 2.8, 1.0)),\\n\\t\\t\\t\\t\\t\\t\\t  simplex3d(st * q));\\n\\n                 float noise = simplex3d(st + r);\\n\\n                 return .5 + .5 * cos((((filmThickness + (noise * noiseStrength)) / (vec3(wavelengths.r * 1.0, wavelengths.g * 0.8, wavelengths.b * 0.6) + 1.0)) * dot(normalize(vObjectNormal), viewDir)) + movement);\\n             }`,[Jt.simplex]);return{rainbow:new se(`vec3 rainbow(float filmThickness, float movement, vec3 wavelengths, float noiseStrength, float noiseScale, vec3 offset, float alpha, out float calpha) {\\n                 vec3 res = clamp(attenuation(wavelengths, filmThickness, movement, noiseStrength, noiseScale, offset), 0.0, 2.0);\\n\\n                 float rainbowContribution = clamp(res.r + res.g + res.b, 0.0, 1.0);\\n                 float lalpha = alpha * rainbowContribution;\\n                 calpha =  lalpha / clamp( lalpha + accumAlpha, 0.00001, 1.0 );\\n                 accumAlpha += ( 1.0 - accumAlpha ) * lalpha;\\n\\n                 return res;\\n             }`,[e])}}();var Il=class extends ue{constructor(e,t,r,i,n,o,l,a){super(\\\\\\\"v3\\\\\\\");this.nodeType=\\\\\\\"Transmission\\\\\\\";this.thickness=e,this.ior=t,this.roughness=r,this.transmissionSamplerSize=i,this.transmissionSamplerMap=n,this.transmissionDepthMap=o,this.aspectRatio=l,this.alpha=a,this.calpha=`g${this.uuid.toString().replace(/-/g,\\\\\\\"\\\\\\\")}_calpha`}generate(e,t){if(e.extensions.shaderTextureLOD=!0,e.extensions.derivatives=!0,e.isShader(\\\\\\\"fragment\\\\\\\")){e.define(\\\\\\\"NUM_SAMPLES\\\\\\\",30),e.require(\\\\\\\"worldPosition\\\\\\\"),e.requires.worldNormal=!0,e.requires.modelMatrix=!0,e.requires.projectionMatrix=!0,e.addFragmentVariable(this.calpha,\\\\\\\"float\\\\\\\");let r=e.include(Il.Nodes.transmission),i=[];return i.push(this.thickness.build(e,\\\\\\\"f\\\\\\\")),i.push(this.ior.build(e,\\\\\\\"f\\\\\\\")),i.push(this.roughness.build(e,\\\\\\\"f\\\\\\\")),i.push(this.transmissionSamplerSize.build(e,\\\\\\\"v2\\\\\\\")),i.push(this.transmissionSamplerMap.getTexture(e,\\\\\\\"t\\\\\\\")),i.push(this.transmissionDepthMap.getTexture(e,\\\\\\\"t\\\\\\\")),i.push(this.aspectRatio.build(e,\\\\\\\"v2\\\\\\\")),i.push(\\\\\\\"normal\\\\\\\"),i.push(this.alpha.build(e,\\\\\\\"f\\\\\\\")),i.push(this.calpha),e.format(r+\\\\\\\"(\\\\\\\"+i.join(\\\\\\\",\\\\\\\")+\\\\\\\")\\\\\\\",this.getType(e),t)}else return console.warn(\\\\\\\"TransmissionNode is not compatible with \\\\\\\"+e.shader+\\\\\\\" shader.\\\\\\\"),e.format(\\\\\\\"vec3( 0.0 )\\\\\\\",this.getType(e),t)}toJSON(e){let t=this.getJSONNode(e);return t||(t=this.createJSONNode(e),t.thickness=this.thickness.toJSON(e).uuid,t.ior=this.ior.toJSON(e).uuid,t.roughness=this.roughness.toJSON(e).uuid,t.transmissionSamplerMap=this.transmissionSamplerMap.toJSON(e).uuid,t.transmissionDepthMap=this.transmissionDepthMap.toJSON(e).uuid,t.alpha=this.alpha.toJSON(e).uuid,t.calpha=this.calpha),t.nodeType=this.nodeType,t}fromJSON(e,t){return super.fromJSON(e,t),e.thickness&&(this.thickness=t.getNode(e.thickness)),e.ior&&(this.ior=t.getNode(e.ior)),e.roughness&&(this.roughness=t.getNode(e.roughness)),e.transmissionSamplerMap&&(this.transmissionSamplerMap=t.getNode(e.transmissionSamplerMap)),e.transmissionDepthMap&&(this.transmissionDepthMap=t.getNode(e.transmissionDepthMap)),e.alpha&&(this.alpha=t.getNode(e.alpha)),e.calpha&&(this.calpha=e.calpha),this}copy(e){return super.copy(this),this.thickness=e.thickness.clone(),this.ior=e.ior.clone(),this.roughness=e.roughness.clone(),this.transmissionSamplerSize=e.transmissionSamplerSize.clone(),this.transmissionSamplerMap=e.transmissionSamplerMap,this.transmissionDepthMap=e.transmissionDepthMap,this.alpha=e.alpha.clone(),this.calpha=e.calpha,this}},As=Il;As.Nodes=function(){let e=new se(`\\n            float gaussian(vec2 i) {\\n                const float sigma = float(NUM_SAMPLES) * .25;\\n                return exp( -.5* dot(i/=sigma,i) ) / ( 6.28 * sigma*sigma );\\n            }`),t=new se(`\\n            vec4 blur(sampler2D sp, vec2 U, vec2 scale, float lod, sampler2D dm, vec2 unrefractedU, vec2 aspectRatio) {\\n                // Slightly modified version of this:\\n                // https://www.shadertoy.com/view/ltScRG\\n\\n                const int LOD = 2;\\n                const int sLOD = 4; // tile size = 2^LOD\\n\\n                vec4 O = vec4(0);\\n                const int s = NUM_SAMPLES/sLOD;\\n                for ( int i = 0; i < s*s; i++ ) {\\n                    int modulo = (i)-((i)/(s))*(s);\\n                    vec2 d = vec2(float(modulo), float(i/s))*float(sLOD) - float(NUM_SAMPLES)/2.;\\n                    vec2 uv = U + (scale * aspectRatio) * d;\\n                    // What is the depth of the opaque object we're trying to sample\\n                    float opaqueDepth = texture2D(dm, uv).r;\\n                    if (opaqueDepth < gl_FragCoord.z) {\\n                        uv = unrefractedU + ((scale * min(lod / 2., 1.)) * aspectRatio) * d;\\n                        lod = lod > 4.0 ? lod : lod / 2.0;\\n                    }\\n\\t\\t            #ifdef TEXTURE_LOD_EXT\\n                    O += gaussian(d) * texture2DLodEXT( sp, uv, lod);\\n                    #else\\n                    O += gaussian(d) * textureLod( sp, uv, lod);\\n                    #endif\\n                }\\n                return O / O.a;\\n            }`,[e]),r=new se(`\\n            vec3 getVolumeTransmissionRay( vec3 n, vec3 v, float thickness, float ior, mat4 modelMatrix ) {\\n\\t\\t        // Direction of refracted light.\\n\\t\\t        vec3 refractionVector = refract( -v,  n, 1.0 / ior );\\n\\t\\t        // Compute rotation-independant scaling of the model matrix.\\n\\t\\t        vec3 modelScale;\\n\\t\\t        modelScale.x = length( vec3( modelMatrix[ 0 ].xyz ) );\\n\\t\\t        modelScale.y = length( vec3( modelMatrix[ 1 ].xyz ) );\\n\\t\\t        modelScale.z = length( vec3( modelMatrix[ 2 ].xyz ) );\\n\\t\\t        // The thickness is specified in local space.\\n\\t\\t        return normalize( refractionVector ) * thickness * modelScale;\\n\\t        }`),i=new se(`\\nfloat applyIorToRoughness( float roughness, float ior ) {\\n\\t\\t// Scale roughness with IOR so that an IOR of 1.0 results in no microfacet refraction and\\n\\t\\t// an IOR of 1.5 results in the default amount of microfacet refraction.\\n\\t\\treturn roughness * clamp( ior * 2.0 - 2.0, 0.0, 1.0 );\\n\\t} `),n=new se(`\\nvec4 getTransmissionSample( vec2 fragCoord, float roughness, float ior, vec2 transmissionSamplerSize, sampler2D transmissionSamplerMap, sampler2D transmissionDepthMap, vec2 unrefractedCoords, vec2 aspectRatio) {\\n\\t\\tfloat framebufferLod = log2( transmissionSamplerSize.x ) * applyIorToRoughness( roughness, ior );\\n        float lod = applyIorToRoughness(roughness, ior);\\n\\n        return blur(transmissionSamplerMap, fragCoord, vec2(lod / (transmissionSamplerSize.x / 2.)), min(framebufferLod / 5.5, 8.5), transmissionDepthMap, unrefractedCoords, aspectRatio);\\n\\t}`,[i,t]),o=new se(`\\nvec4 getIBLVolumeRefraction( vec3 n, vec3 v, float roughness, vec3 position, mat4 modelMatrix, mat4 viewMatrix, mat4 projMatrix, float ior, float thickness, vec2 transmissionSamplerSize, sampler2D transmissionSamplerMap, sampler2D transmissionDepthMap, vec2 aspectRatio ) {\\n        vec3 transmissionRay = getVolumeTransmissionRay( n, v, thickness, ior, modelMatrix );\\n        vec3 refractedRayExit = position + transmissionRay;\\n\\n        // Project refracted vector on the framebuffer, while mapping to normalized device coordinates.\\n        vec4 ndcPos = projMatrix * viewMatrix *  vec4( refractedRayExit, 1.0 );\\n        vec2 refractionCoords = ndcPos.xy / ndcPos.w;\\n        refractionCoords += 1.0;\\n        refractionCoords /= 2.0;\\n\\n        vec4 ndcPosUnrefracted = projMatrix * viewMatrix * vec4(position, 1.0 );\\n        vec2 unrefractedCoords = ndcPosUnrefracted.xy / ndcPosUnrefracted.w;\\n        unrefractedCoords += 1.0;\\n        unrefractedCoords /= 2.0;\\n\\n        // Sample framebuffer to get pixel the refracted ray hits.\\n        vec4 transmittedLight = getTransmissionSample( refractionCoords, roughness, ior, transmissionSamplerSize, transmissionSamplerMap, transmissionDepthMap, unrefractedCoords, aspectRatio );\\n        // Get the specular component.\\n        return vec4( ( 1.0 ) * transmittedLight.rgb, transmittedLight.a );\\n    }`,[n,r]);return{transmission:new se(`\\n            vec3 transmission(float thickness, float ior, float roughness, vec2 transmissionSamplerSize, sampler2D transmissionSamplerMap, sampler2D transmissionDepthMap, vec2 aspectRatio, vec3 normal, float alpha, out float calpha) {\\n                vec3 v = vec3(0.);\\n                if (isOrthographic) {\\n                    v = normalize( vec3( - viewMatrix[ 0 ][ 2 ], - viewMatrix[ 1 ][ 2 ], - viewMatrix[ 2 ][ 2 ] ) );\\n                } else {\\n                    v = normalize(vWPosition - cameraPosition);\\n                }\\n                vec4 transmission = getIBLVolumeRefraction(vWNormal, -v, roughness,  vWPosition, modelMatrix, viewMatrix, projectionMatrix, ior, thickness, transmissionSamplerSize, transmissionSamplerMap, transmissionDepthMap, aspectRatio );\\n                float lalpha = alpha;\\n\\n                 calpha =  lalpha / clamp( lalpha + accumAlpha, 0.00001, 1.0 );\\n                 accumAlpha += ( 1.0 - accumAlpha ) * lalpha;\\n                 return transmission.rgb;\\n            }`,[o])}}();var Ll=class extends ue{constructor(e,t){super(\\\\\\\"v3\\\\\\\");this.nodeType=\\\\\\\"CustomNormal\\\\\\\";this.cnormal=e,this.alpha=t}generate(e,t){if(e.isShader(\\\\\\\"fragment\\\\\\\")){let r=e.include(Ll.Nodes.customNormal),i=[];return i.push(this.cnormal.build(e,\\\\\\\"v3\\\\\\\")),i.push(\\\\\\\"normal\\\\\\\"),i.push(this.alpha.build(e,\\\\\\\"f\\\\\\\")),e.format(r+\\\\\\\"(\\\\\\\"+i.join(\\\\\\\",\\\\\\\")+\\\\\\\")\\\\\\\",this.getType(e),t)}else return console.warn(\\\\\\\"CustomNormalNode is not compatible with \\\\\\\"+e.shader+\\\\\\\" shader.\\\\\\\"),e.format(\\\\\\\"vec3( 0.0 )\\\\\\\",this.getType(e),t)}toJSON(e){let t=this.getJSONNode(e);return t||(t=this.createJSONNode(e),t.cnormal=this.cnormal.toJSON(e).uuid,t.alpha=this.alpha.toJSON(e).uuid),t.nodeType=this.nodeType,t}fromJSON(e,t){return super.fromJSON(e,t),e.cnormal&&(this.cnormal=t.getNode(e.cnormal)),e.alpha&&(this.alpha=t.getNode(e.alpha)),this}copy(e){return super.copy(e),this.cnormal.copy(e.cnormal),this.alpha.copy(e.alpha),this}},ws=Ll;ws.Nodes=function(){return{customNormal:new se(`vec3 customNormal(vec3 cnormal, vec3 norm, float alpha) {\\n\\t\\t\\t\\tvec3 normal = packNormalToRGB( norm ).rgb;\\n\\t\\t\\t\\tnormal *= step( vec3(0.5), cnormal );\\n\\n\\t\\t\\t\\taccumAlpha += ( 1.0 - accumAlpha ) * alpha;\\n\\n\\t\\t\\t\\treturn normal;\\n\\t\\t\\t}`)}}();var Dl=class extends ue{constructor(e,t,r,i,n,o,l,a,c){super(\\\\\\\"v3\\\\\\\");this.nodeType=\\\\\\\"Gradient\\\\\\\";this.glType=e,this.num=t,this.smooth=r,this.colors=i,this.steps=n,this.offset=o,this.morph=l,this.angle=a,this.alpha=c,this.calpha=`g${this.uuid.toString().replace(/-/g,\\\\\\\"\\\\\\\")}_calpha`}generate(e,t){if(e.isShader(\\\\\\\"fragment\\\\\\\")){e.define(\\\\\\\"GRAD_MAX\\\\\\\",10),e.require(\\\\\\\"uv\\\\\\\"),e.requires.uv=[!0],e.addFragmentVariable(this.calpha,\\\\\\\"float\\\\\\\");let r=e.include(Dl.Nodes.gradient),i=[];return i.push(this.glType.build(e,\\\\\\\"i\\\\\\\")),i.push(this.num.build(e,\\\\\\\"i\\\\\\\")),i.push(this.smooth.build(e,\\\\\\\"b\\\\\\\")),i.push(this.colors.build(e,\\\\\\\"v4[]\\\\\\\")),i.push(this.steps.build(e,\\\\\\\"f[]\\\\\\\")),i.push(this.offset.build(e,\\\\\\\"v2\\\\\\\")),i.push(this.morph.build(e,\\\\\\\"v2\\\\\\\")),i.push(this.angle.build(e,\\\\\\\"f\\\\\\\")),i.push(this.alpha.build(e,\\\\\\\"f\\\\\\\")),i.push(this.calpha),e.format(r+\\\\\\\"(\\\\\\\"+i.join(\\\\\\\",\\\\\\\")+\\\\\\\")\\\\\\\",this.getType(e),t)}else return console.warn(\\\\\\\"GradientNode is not compatible with \\\\\\\"+e.shader+\\\\\\\" shader.\\\\\\\"),e.format(\\\\\\\"vec3( 0.0 )\\\\\\\",this.getType(e),t)}toJSON(e){let t=this.getJSONNode(e);return t||(t=this.createJSONNode(e),t.glType=this.glType.toJSON(e).uuid,t.num=this.num.toJSON(e).uuid,t.smooth=this.smooth.toJSON(e).uuid,t.colors=this.colors.toJSON(e).uuid,t.steps=this.steps.toJSON(e).uuid,t.offset=this.offset.toJSON(e).uuid,t.morph=this.morph.toJSON(e).uuid,t.angle=this.angle.toJSON(e).uuid,t.alpha=this.alpha.toJSON(e).uuid,t.calpha=this.calpha),t.nodeType=this.nodeType,t}copy(e){return super.copy(e),this.glType=e.glType.clone(),this.num=e.num.clone(),this.smooth=e.smooth.clone(),this.colors=e.colors.clone(),this.steps=e.steps.clone(),this.offset=e.offset.clone(),this.morph=e.morph.clone(),this.angle=e.angle.clone(),this.alpha=e.alpha.clone(),this.calpha=e.calpha,this}fromJSON(e,t){return super.fromJSON(e,t),e.calpha&&(this.calpha=e.calpha),e.glType&&(this.glType=t.getNode(e.glType)),e.num&&(this.num=t.getNode(e.num)),e.smooth&&(this.smooth=t.getNode(e.smooth)),e.colors&&(this.colors=t.getNode(e.colors)),e.steps&&(this.steps=t.getNode(e.steps)),e.offset&&(this.offset=t.getNode(e.offset)),e.morph&&(this.morph=t.getNode(e.morph)),e.angle&&(this.angle=t.getNode(e.angle)),e.alpha&&(this.alpha=t.getNode(e.alpha)),this}},Ss=Dl;Ss.Nodes=function(){return{gradient:new se(`vec3 gradient(int glType, int num, bool smoothed, vec4 colors[GRAD_MAX], float steps[GRAD_MAX], vec2 offset, vec2 morph, float angle, float alpha, out float calpha) {\\n\\t\\t\\t\\tvec4 color = colors[0];\\n\\t\\t\\t\\tvec2 m = morph / vUv.xy;\\n\\t\\t\\t\\tvec2 rot = vec2( 0.5 + m.x, m.y );\\n\\t\\t\\t\\tvec2 dt = vec2(\\n\\t\\t\\t\\t\\tcos( angle ) * rot.x - sin( angle ) * rot.y,\\n\\t\\t\\t\\t\\tsin( angle ) * rot.x + cos( angle ) * rot.y\\n\\t\\t\\t\\t);\\n\\t\\t\\t\\tvec2 pt = ( vUv - 0.5 + offset ) / 2.0 + dt / 2.0;\\n\\t\\t\\t\\tfloat t = dot( pt, dt ) / dot( dt, dt );\\n\\t\\t\\t\\tif ( glType == 1 ) {\\n\\t\\t\\t\\t\\tt = distance (\\n\\t\\t\\t\\t\\t\\t( vUv + morph ) * 3.0,\\n\\t\\t\\t\\t\\t\\t( vUv + offset ) + 1.0\\n\\t\\t\\t\\t\\t) + angle;\\n\\t\\t\\t\\t} else if ( glType == 2 ) {\\n\\t\\t\\t\\t\\tfloat polar = atan(\\n\\t\\t\\t\\t\\t\\tvUv.x + morph.x - 0.5 + offset.x,\\n\\t\\t\\t\\t\\t\\tvUv.y + morph.y - 0.5 + offset.y\\n\\t\\t\\t\\t\\t) * -1.0;\\n\\t\\t\\t\\t\\tt = fract( ( angle / PI / -2.0 ) + 0.5 * ( polar / PI ) );\\n\\t\\t\\t\\t}\\n\\n\\t\\t\\t\\tfloat p;\\n\\t\\t\\t\\tif (smoothed) {\\n\\t\\t\\t\\t\\tfor ( int i = 1; i < GRAD_MAX; i++ ) {\\n\\t\\t\\t\\t\\t\\tp = clamp( ( t - steps[i-1] ) / ( steps[i] - steps[i-1] ), 0.0, 1.0 );\\n\\t\\t\\t\\t\\t\\tcolor = mix(color, colors[i], smoothstep(0.0, 1.0, p));\\n\\t\\t\\t\\t\\t}\\n\\n\\t\\t\\t\\t} else {\\n\\t\\t\\t\\t\\tfor ( int i = 1; i < GRAD_MAX; i++ ) {\\n\\t\\t\\t\\t\\t\\tp = clamp( ( t - steps[i-1] ) / ( steps[i] - steps[i-1] ), 0.0, 1.0 );\\n\\t\\t\\t\\t\\t\\tcolor = mix(color, colors[i], p);\\n\\t\\t\\t\\t\\t}\\n\\t\\t\\t\\t}\\n\\n\\t\\t\\t\\tfloat lalpha = alpha * color.a;\\n\\t\\t\\t\\tcalpha =  lalpha / clamp( lalpha + accumAlpha, 0.00001, 1.0 );\\n\\t\\t\\t\\taccumAlpha += ( 1.0 - accumAlpha ) * lalpha;\\n\\n\\t\\t\\t\\treturn color.xyz;\\n\\t\\t\\t}`)}}();var ji;(function(t){t.NOISE=\\\\\\\"noise\\\\\\\",t.MAP=\\\\\\\"map\\\\\\\"})(ji||(ji={}));var Bl=class extends ue{constructor(e=new Me(0),t,r,i,n,o){super(\\\\\\\"v3\\\\\\\");this.nodeType=\\\\\\\"VertexDisplacement\\\\\\\";this.displacementTypeIndex=e,this.intensity=t,this.movementOrTexture=r,Object.values(ji)[this.displacementTypeIndex.value]===ji.MAP&&(this.mat=new ii(this.movementOrTexture.value.matrix)),this.cropOrOffset=i,this.scale=n,this.noiseFunctionIndex=o}generate(e,t){if(e.isShader(\\\\\\\"vertex\\\\\\\")){e.define(\\\\\\\"USE_LAYER_DISPLACE\\\\\\\");let r,i=[];switch(i.push(\\\\\\\"displaced_position\\\\\\\"),i.push(\\\\\\\"displaced_normal\\\\\\\"),Object.values(ji)[this.displacementTypeIndex.value]){case ji.MAP:{r=e.include(Bl.Nodes.map),i.push(this.movementOrTexture.getTexture(e,\\\\\\\"t\\\\\\\")),i.push(\\\\\\\"uv\\\\\\\"),i.push(this.cropOrOffset.build(e,\\\\\\\"f\\\\\\\")),this.mat&&i.push(this.mat.build(e,\\\\\\\"mat3\\\\\\\"));break}case ji.NOISE:{let o=Object.values(xs)[this.noiseFunctionIndex.value],l=new se(`vec3 orthogonal(vec3 v) {\\n\\t\\t\\t\\t\\t\\t\\treturn normalize(abs(v.x) > abs(v.z) ? vec3(-v.y, v.x, 0.0) : vec3(0.0, -v.z, v.y));\\n\\t\\t\\t\\t\\t\\t}`),a=new se(`vec3 distorted(vec3 p, vec3 n, float scale, float intensity, vec3 offset, float neighbour_offset, float movement) {\\n\\t\\t\\t\\t\\t\\t\\treturn p + n * ${o}((p + offset) * scale * 0.001 + neighbour_offset + (movement * 0.1)) * intensity;\\n\\t\\t\\t\\t\\t\\t}`,[Jt.simplex,Jt.simplexFractal,Jt.simplexAshima,Jt.fbm,Jt.perlin]),c=new se(`vec3 vertexDisplacementNoise(vec3 position, vec3 normal, float scale, vec3 offset, float movement, float intensity, out vec3 displaced_normal) {\\n\\t\\t\\t\\t\\t\\t\\tvec3 displaced_position = distorted(position, normal, scale, intensity, offset, neighbor_offset, movement);\\n\\t\\t\\t\\t\\t\\t\\tvec3 tangent1 = orthogonal(normal);\\n\\t\\t\\t\\t\\t\\t\\tvec3 tangent2 = normalize(cross(normal, tangent1));\\n\\n                            // TODO(Max): The distance to the neighbors was originally scaled by 0.1.\\n                            // This caused some small oval/circular visual artifacts in the lighting.\\n                            // For now, simply using neighbors further away betters the problem,\\n                            // but we should figure out the underlying cause when we have some time.\\n                            // Maybe its related to how we calculate the tangent and bitangent?\\n\\t\\t\\t\\t\\t\\t\\tvec3 nearby1 = position + tangent1;\\n\\t\\t\\t\\t\\t\\t\\tvec3 nearby2 = position + tangent2;\\n\\t\\t\\t\\t\\t\\t\\tvec3 distorted1 = distorted(nearby1, normal, scale, intensity, offset, neighbor_offset, movement);\\n\\t\\t\\t\\t\\t\\t\\tvec3 distorted2 = distorted(nearby2, normal, scale, intensity, offset, neighbor_offset, movement);\\n\\t\\t\\t\\t\\t\\t\\tdisplaced_normal = normalize(cross(distorted1 - displaced_position, distorted2 - displaced_position));\\n\\t\\t\\t\\t\\t\\t\\treturn displaced_position;\\n\\t\\t\\t\\t\\t\\t}`,[a,l]);r=e.include(c),i.push(this.scale.build(e,\\\\\\\"f\\\\\\\")),i.push(this.cropOrOffset.build(e,\\\\\\\"v3\\\\\\\")),i.push(this.movementOrTexture.build(e,\\\\\\\"f\\\\\\\"));break}}return i.push(this.intensity.build(e,\\\\\\\"f\\\\\\\")),i.push(\\\\\\\"displaced_normal\\\\\\\"),e.format(r+\\\\\\\"(\\\\\\\"+i.join(\\\\\\\",\\\\\\\")+\\\\\\\")\\\\\\\",this.getType(e),t)}else return console.warn(\\\\\\\"VertexDisplacementNode is not compatible with \\\\\\\"+e.shader+\\\\\\\" shader.\\\\\\\"),e.format(\\\\\\\"vec3( 0.0 )\\\\\\\",this.getType(e),t)}toJSON(e){let t=this.getJSONNode(e);return t||(t=this.createJSONNode(e),t.intensity=this.intensity.toJSON(e).uuid,t.scale=this.scale?.toJSON(e).uuid,t.movementOrTexture=this.movementOrTexture.toJSON(e).uuid,t.cropOrOffset=this.cropOrOffset.toJSON(e).uuid),t.displacementTypeIndex=this.displacementTypeIndex,t.noiseFunctionIndex=this.noiseFunctionIndex,t.nodeType=this.nodeType,t}fromJSON(e,t){return super.fromJSON(e,t),this.displacementTypeIndex=e.displacementTypeIndex,this.noiseFunctionIndex=e.noiseFunctionIndex,e.cropOrOffset&&(this.cropOrOffset=t.getNode(e.cropOrOffset)),e.intensity&&(this.intensity=t.getNode(e.intensity)),e.movementOrTexture&&(this.displacementTypeIndex.value===1?(this.movementOrTexture=t.getNode(e.movementOrTexture),this.movementOrTexture.value.updateMatrix(),this.mat=new ii(this.movementOrTexture.value.matrix)):this.displacementTypeIndex.value===0&&(this.movementOrTexture=t.getNode(e.movementOrTexture))),e.scale&&(this.scale=t.getNode(e.scale)),this}copy(e){return super.copy(e),this.noiseFunctionIndex=e.noiseFunctionIndex?.clone(),this.scale=e.scale?.clone(),this.cropOrOffset=e.cropOrOffset.clone(),this.intensity=e.intensity.clone(),this.movementOrTexture=e.movementOrTexture.clone(),this}},ki=Bl;ki.Nodes=function(){let e=new se(`vec3 orthogonal(vec3 v) {\\n\\t\\t\\t\\treturn normalize(abs(v.x) > abs(v.z) ? vec3(-v.y, v.x, 0.0) : vec3(0.0, -v.z, v.y));\\n\\t\\t\\t}`),t=new se(`float displacementMapTexture(sampler2D tex, float crop, vec2 uv, mat3 mat, vec2 offset) {\\n\\t\\t\\t\\tvec2 uvs = (mat * vec3(uv * 2.0 - 1.0, 1.0) / 2.0 + 0.5).xy + offset;\\n\\t\\t\\t\\tvec4 tmp = texture2D(tex, uvs);\\n\\t\\t\\t\\tvec3 col = tmp.rgb;\\n\\t\\t\\t\\tif (crop > 0.5) {\\n\\t\\t\\t\\t\\tif ( uvs.x < 0.0 || uvs.x > 1.0 || uvs.y < 0.0 || uvs.y > 1.0 )  {\\n\\t\\t\\t\\t\\t\\treturn 0.0;\\n\\t\\t\\t\\t\\t}\\n\\t\\t\\t\\t}\\n\\t\\t\\t\\treturn col.r;\\n\\t\\t\\t}`);return{map:new se(`vec3 vertexDisplacementMap(vec3 position, vec3 normal, sampler2D tex, vec2 uv, float crop, mat3 mat, float intensity, out vec3 displaced_normal) {\\n\\t\\t\\t\\tvec3 displaced_position = position + normal * displacementMapTexture(tex, crop, uv, mat, vec2(0.0)) * intensity;\\n\\t\\t\\t\\tvec3 tangent1 = normalize(orthogonal(normal));\\n\\t\\t\\t\\tvec3 tangent2 = normalize(cross(normal, tangent1));\\n\\t\\t\\t\\tvec3 nearby1 = position + tangent1 * 0.1;\\n\\t\\t\\t\\tvec3 nearby2 = position + tangent2 * 0.1;\\n\\t\\t\\t\\tvec3 distorted1 = nearby1 + normal * displacementMapTexture(tex, crop, uv, mat, vec2(neighbor_offset)) * intensity;\\n\\t\\t\\t\\tvec3 distorted2 = nearby2 + normal * displacementMapTexture(tex, crop, uv, mat, vec2(neighbor_offset)) * intensity;\\n\\t\\t\\t\\tdisplaced_normal = normalize(cross(distorted1 - displaced_position, distorted2 - displaced_position));\\n\\t\\t\\t\\treturn displaced_position;\\n\\t\\t\\t}`,[e,t])}}();var Ns=class extends ue{constructor(e=new oe,t=new oe,r=new oe,i=new oe,n=new oe,o=new oe,l=new oe,a=new oe,c=new oe,u=new oe,d=new oe,h=new oe,p=new oe,f=new oe,m=new oe,g=new oe){super(\\\\\\\"v3\\\\\\\");this.nodeType=\\\\\\\"Noise\\\\\\\";this.scale=e,this.size=t,this.move=r,this.fA=i,this.fB=n,this.distortion=o,this.colorA=l,this.colorB=a,this.colorC=c,this.colorD=u,this.intA=d,this.intB=h,this.intC=p,this.intD=f,this.alpha=m,this.noiseFunctionIndex=g}generate(e,t,r,i,n){e.require(\\\\\\\"uv\\\\\\\"),e.requires.uv=[!0];let o=Object.values(xs)[this.noiseFunctionIndex.value],l=new se(`vec3 ${o}customNoise(float scale, vec3 size, float move, vec2 fA, vec2 fB, vec2 distortion, vec3 colorA, vec3 colorB, vec3 colorC, vec3 colorD, float intA, float intB, float intC, float intD, float alpha) {\\n                vec3 st = position / size;\\n\\t\\t\\t\\tst /= scale;\\n\\t\\t\\t\\tvec3 q = vec3(${o}(st),\\n\\t\\t\\t\\t\\t\\t\\t  ${o}(st + vec3(1.0)),\\n\\t\\t\\t\\t\\t\\t\\t  ${o}(st + vec3(1.0)));\\n\\t\\t\\t\\tvec3 r = vec3(${o}(st + vec3(distortion, 1.0) * q + vec3(fA, 1.0) + move),\\n\\t\\t\\t\\t\\t\\t\\t  ${o}(st + vec3(distortion, 1.0) * q + vec3(fB, 1.0) + move), \\n\\t\\t\\t\\t\\t\\t\\t  ${o}(st * q));\\n\\t\\t\\t\\tfloat f = ${o}(st + r);\\n\\t\\t\\t\\tvec3 color;\\n\\t\\t\\t\\tcolor = mix(colorA * intA, colorB * intB, clamp((f * f) * 4.0, 0.0, 1.0));\\n\\t\\t\\t\\tcolor = mix(color, colorC * intC, clamp(length(q), 0.0, 1.0));\\n\\t\\t\\t\\tcolor = mix(color, colorD * intD, clamp(length(r.x), 0.0, 1.0));\\n\\t\\t\\t\\taccumAlpha += (1.0 - accumAlpha) * alpha;\\n\\t\\t\\t\\treturn clamp(color, 0.0, 1.0);\\n\\t\\t\\t}`,[Jt.simplex,Jt.simplexFractal,Jt.simplexAshima,Jt.fbm,Jt.perlin]),a=e.include(l),c=[];return c.push(this.scale.build(e,\\\\\\\"f\\\\\\\")),c.push(this.size.build(e,\\\\\\\"v3\\\\\\\")),c.push(this.move.build(e,\\\\\\\"f\\\\\\\")),c.push(this.fA.build(e,\\\\\\\"v2\\\\\\\")),c.push(this.fB.build(e,\\\\\\\"v2\\\\\\\")),c.push(this.distortion.build(e,\\\\\\\"v2\\\\\\\")),c.push(this.colorA.build(e,\\\\\\\"c\\\\\\\")),c.push(this.colorB.build(e,\\\\\\\"c\\\\\\\")),c.push(this.colorC.build(e,\\\\\\\"c\\\\\\\")),c.push(this.colorD.build(e,\\\\\\\"c\\\\\\\")),c.push(this.intA.build(e,\\\\\\\"f\\\\\\\")),c.push(this.intB.build(e,\\\\\\\"f\\\\\\\")),c.push(this.intC.build(e,\\\\\\\"f\\\\\\\")),c.push(this.intD.build(e,\\\\\\\"f\\\\\\\")),c.push(this.alpha.build(e,\\\\\\\"f\\\\\\\")),e.format(a+\\\\\\\"(\\\\\\\"+c.join(\\\\\\\",\\\\\\\")+\\\\\\\")\\\\\\\",this.getType(e),t)}toJSON(e){let t=this.getJSONNode(e);return t||(t=this.createJSONNode(e),t.scale=this.scale.toJSON(e).uuid,t.size=this.size.toJSON(e).uuid,t.move=this.move.toJSON(e).uuid,t.fA=this.fA.toJSON(e).uuid,t.fB=this.fB.toJSON(e).uuid,t.distortion=this.distortion.toJSON(e).uuid,t.colorA=this.colorA.toJSON(e).uuid,t.colorB=this.colorB.toJSON(e).uuid,t.colorC=this.colorC.toJSON(e).uuid,t.colorD=this.colorD.toJSON(e).uuid,t.intA=this.intA.toJSON(e).uuid,t.intB=this.intB.toJSON(e).uuid,t.intC=this.intC.toJSON(e).uuid,t.intD=this.intD.toJSON(e).uuid,t.alpha=this.alpha.toJSON(e).uuid),t.noiseFunction=this.noiseFunctionIndex,t.nodeType=this.nodeType,t}fromJSON(e,t){return super.fromJSON(e,t),e.scale&&(this.scale=t.getNode(e.scale)),e.size&&(this.size=t.getNode(e.size)),e.move&&(this.move=t.getNode(e.move)),e.fA&&(this.fA=t.getNode(e.fA)),e.fB&&(this.fB=t.getNode(e.fB)),e.distortion&&(this.distortion=t.getNode(e.distortion)),e.colorA&&(this.colorA=t.getNode(e.colorA)),e.colorB&&(this.colorB=t.getNode(e.colorB)),e.colorC&&(this.colorC=t.getNode(e.colorC)),e.colorD&&(this.colorD=t.getNode(e.colorD)),e.intA&&(this.intA=t.getNode(e.intA)),e.intB&&(this.intB=t.getNode(e.intB)),e.intC&&(this.intC=t.getNode(e.intC)),e.intD&&(this.intD=t.getNode(e.intD)),e.alpha&&(this.alpha=t.getNode(e.alpha)),e.noiseFunctionIndex&&(this.noiseFunctionIndex=t.getNode(e.noiseFunctionIndex)),this}copy(e){return super.copy(e),this.scale.copy(e.scale),this.size.copy(e.size),this.move.copy(e.move),this.fA.copy(e.fA),this.fB.copy(e.fB),this.distortion.copy(e.distortion),this.colorA.copy(e.colorA),this.colorB.copy(e.colorB),this.colorC.copy(e.colorC),this.colorD.copy(e.colorD),this.intA.copy(e.intA),this.intB.copy(e.intB),this.intC.copy(e.intC),this.intD.copy(e.intD),this.alpha.copy(e.alpha),this.noiseFunctionIndex.copy(e.noiseFunctionIndex),this}};Ns.numOctaves=5;var Ts;(function(i){i.ADD=\\\\\\\"+\\\\\\\",i.SUB=\\\\\\\"-\\\\\\\",i.MUL=\\\\\\\"*\\\\\\\",i.DIV=\\\\\\\"/\\\\\\\"})(Ts||(Ts={}));var _l=class extends ue{constructor(e=new oe,t=new oe,r=_l.ADD){super();this.nodeType=\\\\\\\"Operator\\\\\\\";this.type=e.type,this.a=e,this.b=t,this.op=r}getType(e){let t=this.a.getType(e),r=this.b.getType(e);return e.isTypeMatrix(t)?\\\\\\\"v4\\\\\\\":e.getTypeLength(r)>e.getTypeLength(t)?r:t}generate(e,t){let r=this.getType(e);this.type=r;let i=this.a.build(e,r),n=this.b.build(e,r);return e.format(\\\\\\\"( \\\\\\\"+i+\\\\\\\" \\\\\\\"+this.op+\\\\\\\" \\\\\\\"+n+\\\\\\\" )\\\\\\\",r,t)}copy(e){return super.copy(e),this.a.copy(e.a),this.b.copy(e.b),this.op=e.op,this}toJSON(e){let t=this.getJSONNode(e);return t||(t=this.createJSONNode(e),t.a=this.a.toJSON(e).uuid,t.b=this.b.toJSON(e).uuid,t.op=this.op),t.nodeType=this.nodeType,t}fromJSON(e,t){return super.fromJSON(e,t),e.a&&(this.a=t.getNode(e.a)),e.b&&(this.b=t.getNode(e.b)),e.op&&(this.op=e.op),this}},zt=_l;zt.ADD=Ts.ADD,zt.SUB=Ts.SUB,zt.MUL=Ts.MUL,zt.DIV=Ts.DIV;var Cs=class extends ue{constructor(e=new oe,t=new oe,r=new oe,i=new oe){super(\\\\\\\"v3\\\\\\\");this.nodeType=\\\\\\\"Blend\\\\\\\";this.a=e,this.b=t,this.alpha=r,this.mode=i}generate(e,t){if(e.isShader(\\\\\\\"fragment\\\\\\\")){let r=[];return r.push(this.a.build(e,\\\\\\\"c\\\\\\\")),r.push(this.b.build(e,\\\\\\\"c\\\\\\\")),r.push(this.alpha.build(e,\\\\\\\"f\\\\\\\")),r.push(this.mode.build(e,\\\\\\\"i\\\\\\\")),e.format(\\\\\\\"spe_blend(\\\\\\\"+r.join(\\\\\\\",\\\\\\\")+\\\\\\\")\\\\\\\",this.getType(e),t)}else return console.warn(\\\\\\\"BlendNode is not compatible with \\\\\\\"+e.shader+\\\\\\\" shader.\\\\\\\"),e.format(\\\\\\\"vec3( 0.0 )\\\\\\\",this.getType(e),t)}copy(e){return super.copy(e),this.a.copy(e.a),this.b.copy(e.b),this.alpha.copy(e.alpha),this.mode.copy(e.mode),this}toJSON(e){let t=this.getJSONNode(e);return t||(t=this.createJSONNode(e),t.a=this.a.toJSON(e).uuid,t.b=this.b.toJSON(e).uuid,t.alpha=this.alpha.toJSON(e).uuid,t.mode=this.mode.toJSON(e).uuid),t.nodeType=this.nodeType,t}fromJSON(e,t){return super.fromJSON(e,t),e.a&&(this.a=t.getNode(e.a)),e.b&&(this.b=t.getNode(e.b)),e.alpha&&(this.alpha=t.getNode(e.alpha)),e.mode&&(this.mode=t.getNode(e.mode)),this}};var Ol=class extends ue{constructor(e,t,r,i,n,o,l,a){super(\\\\\\\"v3\\\\\\\");this.nodeType=\\\\\\\"Depth\\\\\\\";this.near=e,this.far=t,this.isVector=r,this.isWorldSpace=i,this.origin=n,this.colorA=o,this.colorB=l,this.alpha=a}generate(e,t){if(e.isShader(\\\\\\\"fragment\\\\\\\")){e.require(\\\\\\\"worldPosition\\\\\\\");let r=e.include(Ol.Nodes.sdepth),i=[];return i.push(this.near.build(e,\\\\\\\"f\\\\\\\")),i.push(this.far.build(e,\\\\\\\"f\\\\\\\")),i.push(this.isVector.build(e,\\\\\\\"f\\\\\\\")),i.push(this.isWorldSpace.build(e,\\\\\\\"f\\\\\\\")),i.push(this.origin.build(e,\\\\\\\"v3\\\\\\\")),i.push(this.colorA.build(e,\\\\\\\"v3\\\\\\\")),i.push(this.colorB.build(e,\\\\\\\"v3\\\\\\\")),i.push(this.alpha.build(e,\\\\\\\"f\\\\\\\")),e.format(r+\\\\\\\"(\\\\\\\"+i.join(\\\\\\\",\\\\\\\")+\\\\\\\")\\\\\\\",this.getType(e),t)}else return console.warn(\\\\\\\"DepthNode is not compatible with \\\\\\\"+e.shader+\\\\\\\" shader.\\\\\\\"),e.format(\\\\\\\"vec3( 0.0 )\\\\\\\",this.getType(e),t)}toJSON(e){let t=this.getJSONNode(e);return t||(t=this.createJSONNode(e),t.near=this.near.toJSON(e).uuid,t.far=this.far.toJSON(e).uuid,t.isVector=this.isVector.toJSON(e).uuid,t.isWorldSpace=this.isWorldSpace.toJSON(e).uuid,t.origin=this.origin.toJSON(e).uuid,t.colorA=this.colorA.toJSON(e).uuid,t.colorB=this.colorB.toJSON(e).uuid,t.alpha=this.alpha.toJSON(e).uuid),t.nodeType=this.nodeType,t}fromJSON(e,t){return super.fromJSON(e,t),e.near&&(this.near=t.getNode(e.near)),e.far&&(this.far=t.getNode(e.far)),e.isVector&&(this.isVector=t.getNode(e.isVector)),e.isWorldSpace&&(this.isWorldSpace=t.getNode(e.isWorldSpace)),e.origin&&(this.origin=t.getNode(e.origin)),e.colorA&&(this.colorA=t.getNode(e.colorA)),e.colorB&&(this.colorB=t.getNode(e.colorB)),e.alpha&&(this.alpha=t.getNode(e.alpha)),this}copy(e){return super.copy(e),this.near=e.near.clone(),this.far=e.far.clone(),this.isVector=e.isVector.clone(),this.isWorldSpace=e.isWorldSpace.clone(),this.origin=e.origin.clone(),this.colorA=e.colorA.clone(),this.colorB=e.colorB.clone(),this.alpha=e.alpha.clone(),this}},Ms=Ol;Ms.Nodes=function(){return{sdepth:new se(`vec3 sdepth(float near, float far, float isVector, float isWorldSpace, vec3 origin, vec3 colorA, vec3 colorB, float alpha) {\\n               float dist;\\n               if (isVector > 0.5) {\\n                   dist = (isWorldSpace > 0.5) ? length(vWPosition - origin) : length(position - origin);\\n               } else {\\n                   dist = length (vWPosition - cameraPosition);\\n               }\\n\\t\\t\\t   float dep = ( dist - near ) / ( far - near );\\n\\t\\t\\t   vec3 depth =  mix( colorB, colorA, 1.0 - clamp( dep, 0., 1. ) );\\n\\n\\t\\t\\t   accumAlpha += ( 1.0 - accumAlpha ) * alpha;\\n\\n\\t\\t\\t   return depth;\\n\\t\\t\\t}`)}}();var Pl=class extends ue{constructor(e,t,r,i){super(\\\\\\\"v3\\\\\\\");this.nodeType=\\\\\\\"Matcap\\\\\\\";this.color=e,this.texture=t,this.alpha=r,this.mode=i,this.calpha=`g${this.uuid.toString().replace(/-/g,\\\\\\\"\\\\\\\")}_calpha`}generate(e,t){if(e.isShader(\\\\\\\"fragment\\\\\\\")){e.addFragmentVariable(this.calpha,\\\\\\\"float\\\\\\\");let r=e.include(Pl.Nodes.matcap);e.require(\\\\\\\"normal\\\\\\\"),e.requires.normal=!0;let i=[];return i.push(this.color.build(e,\\\\\\\"v3\\\\\\\")),i.push(this.texture.getTexture(e,\\\\\\\"t\\\\\\\")),i.push(\\\\\\\"normal\\\\\\\"),i.push(this.alpha.build(e,\\\\\\\"f\\\\\\\")),i.push(this.mode.build(e,\\\\\\\"i\\\\\\\")),i.push(this.calpha),e.format(r+\\\\\\\"(\\\\\\\"+i.join(\\\\\\\",\\\\\\\")+\\\\\\\")\\\\\\\",this.getType(e),t)}else return console.warn(\\\\\\\"MatcapNode is not compatible with \\\\\\\"+e.shader+\\\\\\\" shader.\\\\\\\"),e.format(\\\\\\\"vec3( 0.0 )\\\\\\\",this.getType(e),t)}copy(e){return super.copy(e),this.texture=e.texture.clone(),this.color=e.color.clone(),this.alpha=e.alpha.clone(),this.mode=e.mode.clone(),this.calpha=e.calpha,this}toJSON(e){let t=this.getJSONNode(e);return t||(t=this.createJSONNode(e),t.texture=this.texture.toJSON(e).uuid,t.color=this.color.toJSON(e).uuid,t.alpha=this.alpha.toJSON(e).uuid,t.mode=this.mode.toJSON(e).uuid,t.calpha=this.calpha),t.nodeType=this.nodeType,t}fromJSON(e,t){return super.fromJSON(e,t),e.calpha&&(this.calpha=e.calpha),e.alpha&&(this.alpha=t.getNode(e.alpha)),e.texture&&(this.texture=t.getNode(e.texture)),e.color&&(this.color=t.getNode(e.color)),e.mode&&(this.mode=t.getNode(e.mode)),this}},Es=Pl;Es.Nodes=function(){return{matcap:new se(`vec3 matcap(vec3 color, sampler2D matcapTex, vec3 normal, float alpha, int mode, out float calpha) {\\n                vec3 viewDir = normalize( vViewPosition );\\n                vec3 x = normalize( vec3( viewDir.z, 0.0, - viewDir.x ) );\\n                vec3 y = cross( viewDir, x );\\n                vec2 uv = vec2( dot( x, normal ), dot( y, normal ) ) * 0.495 + 0.5; // 0.495 to remove artifacts caused by undersized matcap disks\\n                vec4 matcapColor = texture2D( matcapTex, uv );\\n                // matcapColor = matcapTexelToLinear( matcapColor );\\n                matcapColor.rgb *= color;\\n                \\n                calpha =  alpha / clamp( alpha + accumAlpha, 0.00001, 1.0 );\\n\\t\\t\\t\\taccumAlpha += ( 1.0 - accumAlpha ) * alpha;\\n                \\n                return matcapColor.rgb;\\n            }\\n            `)}}();var Ut=class{constructor(e){this.id=2,this.layerCount=2,this.uuid=Oe.MathUtils.generateUUID(),this.needsUpdate=!1,this._material=e,this._layerNodes=[];let t=this._createLayer({id:0,type:Be.COLOR});this._material.color=t.color,this._material.alpha===void 0&&(this._material.alpha=new te(1));let r=new te(1),i=new Me(0);\\\\\\\"shadingAlpha\\\\\\\"in this._material&&\\\\\\\"shadingBlend\\\\\\\"in this._material&&(this._material.shadingAlpha=r,this._material.shadingBlend=i),this._layerNodes.push({id:0,type:$e.COLOR,color:t.color,alpha:t.alpha,mode:t.mode}),this._layerNodes.push({id:1,type:$e.LIGHTING,alpha:r,mode:i}),this.head=t.layer,this.head.next=new it(1,{type:Be.LIGHTING,alpha:r,mode:i}),this.attachLightNodes()}get material(){return this._material}set material(e){this._material=e;let t,r,i=this.head;for(;i!==void 0;){if(i.type===Be.LIGHTING){t=i.uniforms[`f${i.id}_alpha`],r=i.uniforms[`f${i.id}_mode`];break}i=i.next}\\\\\\\"shadingAlpha\\\\\\\"in this._material&&\\\\\\\"shadingBlend\\\\\\\"in this._material&&(this._material.shadingAlpha=t,this._material.shadingBlend=r),this.attachLightNodes(),this.blendColors(),this.blendAfterColors(),this.blendPositions()}getLayersOfType(e){let t=[],r=this.head;for(;r;)r.type===e&&t.push(r),r=r.next;return t}addLayer(e){if(e.id=e.id??++this.id,this.layerCount++,e.type===Be.LIGHTING){let i=this.createLightLayer(e);return this.uuid=Oe.MathUtils.generateUUID(),this.blendColors(),this.blendAfterColors(),this.blendPositions(),i}let t=this._createLayer(e),r=t.layer;if(this.head===void 0)this.head=r;else{let i=this.head;for(;i.next!=null;)i=i.next;i.next=r}return t.color&&this._layerNodes.push({id:r.id,type:$e.COLOR,color:t.color,alpha:t.alpha,mode:t.mode}),t.position&&this._layerNodes.push({id:r.id,type:$e.POSITION,position:t.position}),this.uuid=Oe.MathUtils.generateUUID(),this.blendColors(),this.blendAfterColors(),this.blendPositions(),r}addLayerBeforeAt(e,t){let r=this.head;e.id=e.id??++this.id,this.layerCount++;let i=this._createLayer(e),n=i.layer;n.next=t;let o=0;if(r===t)this.head=n,i.color&&this._layerNodes.splice(0,0,{id:n.id,type:$e.COLOR,color:i.color,alpha:i.alpha,mode:i.mode}),i.position&&this._layerNodes.splice(0,0,{id:n.id,type:$e.POSITION,position:i.position});else{for(o=1;r?.next!==t;)r=r?.next,o++;r.next=n,i.color&&this._layerNodes.splice(o,0,{id:n.id,type:$e.COLOR,color:i.color,alpha:i.alpha,mode:i.mode}),i.position&&this._layerNodes.splice(o,0,{id:n.id,type:$e.POSITION,position:i.position})}return this.uuid=Oe.MathUtils.generateUUID(),this.blendColors(),this.blendAfterColors(),this.blendPositions(),n}addLayerAt(e,t){t.id=t.id??++this.id,this.layerCount++;let r=this._createLayer(t),i=r.layer;if(r.color&&this._layerNodes.splice(e,0,{id:i.id,type:$e.COLOR,color:r.color,alpha:r.alpha,mode:r.mode}),r.position&&this._layerNodes.splice(e,0,{id:i.id,type:$e.POSITION,position:r.position}),e==0)i.next=this.head,this.head=i;else{let n=this.head,o=this.head.next;for(let l=0;l<e-1;l++)n=o,o=o.next;i.next=o,n.next=i}return this.uuid=Oe.MathUtils.generateUUID(),this.blendColors(),this.blendAfterColors(),this.blendPositions(),i}removeLayer(e){let t=this.head,r,i=0;if(t?.id==e)this.head=t.next;else for(i=1,r=t,t=t?.next;t!=null;){if(t.id==e){r.next=t.next;break}i++,r=t,t=t.next}return this.cleanupChangedLayer(t),this.blendColors(),this.blendAfterColors(),this.blendPositions(),this.uuid=Oe.MathUtils.generateUUID(),this.layerCount--,i}changeLayer(e,t){let r,i=this.head,n;if(i?.id==e){let o=this._createLayer(le({id:e},t));n=o.layer,n.next=i.next,this.head=n,o.color&&(this._layerNodes[0]={id:n.id,type:$e.COLOR,color:o.color,alpha:o.alpha,mode:o.mode}),o.position&&(this._layerNodes[0]={id:n.id,type:$e.POSITION,position:o.position}),n.uniforms[`f${e}_mode`].value=i.uniforms[`f${e}_mode`].value,n.uniforms[`f${e}_alpha`].value=i.uniforms[`f${e}_alpha`].value}else{r=i,i=i.next;let o=1;for(;i!=null;){if(i.id==e){let l=this._createLayer(le({id:e},t));n=l.layer,r.next=n,n.next=i.next,l.color&&(this._layerNodes[o]={id:n.id,type:$e.COLOR,color:l.color,alpha:l.alpha,mode:l.mode}),l.position&&(this._layerNodes[o]={id:n.id,type:$e.POSITION,position:l.position}),n.uniforms[`f${e}_mode`].value=i.uniforms[`f${e}_mode`].value,n.uniforms[`f${e}_alpha`].value=i.uniforms[`f${e}_alpha`].value;break}r=i,i=i.next,o++}}return this.uuid=Oe.MathUtils.generateUUID(),this.blendColors(),this.blendAfterColors(),this.blendPositions(),n}getLayer(e){let t=this.head;for(;t!=null&&t.id!=e;)t=t.next;return t}getLayers(){let e=[],t=this.head;for(;t!=null;)e.push(t),t=t.next;return e}getLayerPosition(e){let t=this.head,r=0;for(;t!==void 0&&t.id!=e;)r++,t=t.next;return r}getDefines(){}getBeforeProgram(){}getLightingProgram(){}getAfterProgram(){}getVarPrograms(){}getUniforms(){}moveLayer(e,t){let r,i=this.head,n;if(e==0)r=this.head,this.head=r.next;else{for(let l=0;l<e;l++)n=i,i=i.next;n.next=i.next,r=i}if(i=this.head,n=void 0,t==0)r.next=this.head,this.head=r;else{for(let l=0;l<t-1;l++)i=i.next;r.next=i?.next,i.next=r}let o=this._layerNodes.splice(e,1)[0];this._layerNodes.splice(t,0,o),this.uuid=Oe.MathUtils.generateUUID(),this.blendColors(),this.blendAfterColors()}updateLayerUniform(){this.uuid=Oe.MathUtils.generateUUID(),this.blendColors(),this.blendAfterColors()}copy(e){this.needsUpdate=!1,this.layerCount=e.layerCount,this._layerNodes=[],this.layerCount=0,this.head=void 0,this.rebuildLayerNodes(this.head,e.head);let t=e.head,r=this.head;for(;t.next!=null;)this.rebuildLayerNodes(r,t.next),r=r.next,t=t.next;return this.id=e.id,this.uuid=e.uuid,this.blendColors(),this.blendAfterColors(),this.blendPositions(),this}createLightLayer(e){let t=new te(e.alpha),r=new Me(e.mode);this._material.shadingAlpha=t,this._material.shadingBlend=r,this._layerNodes.push({id:e.id,type:$e.LIGHTING,alpha:t,mode:r});let i=new it(e.id,{type:Be.LIGHTING,alpha:t,mode:r});if(this.head===void 0)this.head=i;else{let n=this.head;for(;n.next!=null;)n=n.next;n.next=i}return this.attachLightNodes(),i}rebuildLayerNodes(e,t,r=!1){if(t.type===Be.LIGHTING){let i=r?t.uniforms[`f${t.id}_alpha`]:new te(t.uniforms[`f${t.id}_alpha`].value),n=r?t.uniforms[`f${t.id}_mode`]:new Me(t.uniforms[`f${t.id}_mode`].value);this._material.shadingAlpha=i,this._material.shadingBlend=n,this._layerNodes.push({id:t.id,type:$e.LIGHTING,alpha:i,mode:n}),this.head===void 0?this.head=new it(t.id,{type:Be.LIGHTING,alpha:i,mode:n}):e&&(e.next=new it(t.id,{type:Be.LIGHTING,alpha:i,mode:n})),this.attachLightNodes(t)}else{let i={type:t.type,id:t.id};for(let n in t.uniforms){let o=t.getName(n);if(!o)continue;let l=`f${t.id}_${o}`;if(Array.isArray(t.uniforms[l].value))i[o]=t.uniforms[l].value.map(a=>a.clone&&!r?a.clone():a);else{let a=t.uniforms[l].value;if(o===\\\\\\\"transmissionDepthMap\\\\\\\"){i[o]=a;continue}a!=null&&(i[o]=a.clone&&!r&&!a.isRenderTargetTexture?a.clone():a)}}this.addLayer(i)}}attachLightNodes(e){let t={},r=this.getLightLayer();switch(this._material.userData.category){case\\\\\\\"Lambert\\\\\\\":t.emissive=new _e(e?.getValue(\\\\\\\"emissive\\\\\\\")?.clone()??0);break;case\\\\\\\"Phong\\\\\\\":t.shininess=new te(e?.getValue(\\\\\\\"shininess\\\\\\\")??30),t.specular=new _e(e?.getValue(\\\\\\\"specular\\\\\\\")?.clone()??1118481);break;case\\\\\\\"Toon\\\\\\\":t.shininess=new te(e?.getValue(\\\\\\\"shininess\\\\\\\")??30),t.specular=new _e(e?.getValue(\\\\\\\"specular\\\\\\\")?.clone()??1118481);break;case\\\\\\\"Physical\\\\\\\":t.roughness=new te(e?.getValue(\\\\\\\"roughness\\\\\\\")??.3),t.metalness=new te(e?.getValue(\\\\\\\"metalness\\\\\\\")??0),t.reflectivity=new te(e?.getValue(\\\\\\\"reflectivity\\\\\\\")??.5);break;default:break}Object.keys(t).forEach(i=>{this._material[i]=t[i],r.uniforms[`f${r.id}_${i}`]=t[i]})}clone(e){return new Ut(e).copy(this)}toJSON(e){return{id:this.id,uuid:this.uuid,head:this.head.toJSON(e)}}fromJSON(e,t,r){let i=new it(e.head.id,{type:e.head.type}).fromJSON(e.head,t),n=e.head.next,o=i;for(;n!=null;)o.next=new it(n.id,{type:n.type}).fromJSON(n,t),n=n.next,o=o.next;this._layerNodes=[],this.head=void 0,this.rebuildLayerNodes(this.head,i,!0);let l=i;for(o=this.head;l.next!=null;)this.rebuildLayerNodes(o,l.next,!0),o=o.next,l=l.next;return this._material=r,this.id=e.id,this.uuid=e.uuid,this.blendColors(),this.blendAfterColors(),this.blendPositions(),this}getLightLayer(){let e=this.head;for(;e!==void 0&&e.type!==\\\\\\\"light\\\\\\\";)e=e.next??e;return e}dispose(){let e=this.head;for(this._layerNodes=[],this.layerCount=0;e!==void 0;)e.hasOwnProperty(\\\\\\\"dispose\\\\\\\")===!0&&e.dispose(),e=e.next;this.head=void 0}_createLayer(e){let t=e.type;switch(t){case Be.COLOR:{let r=new _e(e.color??5855577),i=new te(e.alpha??1),n=new Ve(\\\\\\\"alpha / clamp(alpha + accumAlpha, 0.00001, 1.0 )\\\\\\\",\\\\\\\"f\\\\\\\");n.keywords.alpha=i;let o=new Me(e.mode??0);return r.alpha=i,{layer:new it(e.id,{type:t,color:r,alpha:i,calpha:n,mode:o}),color:r,alpha:n,mode:o}}case Be.TEXTURE:{let r=e.texture??new nr,i=r.matrix;e.mat&&i.copy(e.mat),r.needsUpdate=!0;let n=new te(e.crop??0),o=new Me(e.projection??0),l=new Me(e.axis??0),a=new Ct(e.size??new Oe.Vector2(100,100)),c=new te(e.alpha??1),u=new Me(e.mode??0),d=new _t(r),h=new Bt(e.textureSize??new Oe.Vector3(r.image?r.image.width:0,r.image?r.image.height:0)),p=new ys(d,h,n,o,l,a,c,u),f=new Ve(p.calpha,\\\\\\\"f\\\\\\\");return{layer:new it(e.id,{type:t,texture:d,textureSize:h,crop:n,projection:o,axis:l,size:a,mat:p.mat,alpha:c,calpha:f,mode:u}),color:p,alpha:f,mode:u}}case Be.MATCAP:{let r=new _e(e.color??16777215),i=e.texture??new nr;i.needsUpdate=!0;let n=new te(e.alpha??1),o=new _t(i),l=new Me(e.mode??0),a=new Es(r,o,n,l),c=new Ve(a.calpha,\\\\\\\"f\\\\\\\");return{layer:new it(e.id,{type:t,texture:o,color:r,alpha:n,calpha:c,mode:l}),color:a,alpha:c,mode:l}}case Be.FRESNEL:{let r=new _e(e.color??16777215),i=new te(e.bias??.1),n=new te(e.scale??1),o=new te(e.intensity??2),l=new te(e.factor??1),a=new te(e.alpha??1),c=new Me(e.mode??0),u=new vs(r,i,n,o,l,a,c),d=new Ve(u.calpha,\\\\\\\"f\\\\\\\");return{layer:new it(e.id,{type:t,color:r,bias:i,scale:n,intensity:o,factor:l,alpha:a,calpha:d,mode:c}),color:u,alpha:d,mode:c}}case Be.RAINBOW:{let r=new te(e.filmThickness??30),i=new te(e.movement??0),n=new Bt(e.wavelengths??new Oe.Vector3(0,0,0)),o=new te(e.noiseStrength??0),l=new te(e.noiseScale??1),a=new Bt(e.offset??new Oe.Vector3(0,0,0)),c=new te(e.alpha??1),u=new bs(r,i,n,o,l,a,c),d=new Ve(u.calpha,\\\\\\\"f\\\\\\\"),h=new Me(e.mode??0);return{layer:new it(e.id,{type:t,filmThickness:r,movement:i,wavelengths:n,noiseStrength:o,noiseScale:l,offset:a,alpha:c,calpha:d,mode:h}),color:u,alpha:d,mode:h}}case Be.TRANSMISSION:{let r=new te(e.thickness??10),i=new te(e.ior??1.5),n=new te(e.roughness??.5),o=new Ct(e.transmissionSamplerSize??new Oe.Vector2(2048,2048)),l=e.transmissionSamplerMap??new nr,a=e.transmissionDepthMap??new nr,c=new _t(l),u=new _t(a),d=window.innerWidth,h=window.innerHeight,p=d>=h?new Ct(h/d,1):new Ct(1,d/h),f=new te(e.alpha??1),m=new As(r,i,n,o,c,u,p,f),g=new Ve(m.calpha,\\\\\\\"f\\\\\\\"),y=new Me(e.mode??0);return{layer:new it(e.id,{type:t,thickness:r,ior:i,roughness:n,transmissionSamplerSize:o,transmissionSamplerMap:c,transmissionDepthMap:u,aspectRatio:p,alpha:f,calpha:g,mode:y}),color:m,alpha:g,mode:y}}case Be.DEPTH:{let r=new te(e.near??50),i=new te(e.far??200),n=new te(e.isVector??1),o=new te(e.isWorldSpace??0),l=new Bt(e.origin??new Oe.Vector3),a=new _e(e.colorA??new Oe.Color),c=new _e(e.colorB??new Oe.Color(0,0,0)),u=new te(e.alpha??1),d=new Me(e.mode??0),h=new Ve(\\\\\\\"alpha / clamp(alpha + accumAlpha, 0.001, 1.0 )\\\\\\\",\\\\\\\"f\\\\\\\");h.keywords.alpha=u;let p=new Ms(r,i,n,o,l,a,c,u);return{layer:new it(e.id,{type:t,near:r,far:i,isVector:n,isWorldSpace:o,origin:l,colorA:a,colorB:c,alpha:u,calpha:h,mode:d}),color:p,alpha:h,mode:d}}case 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Ie;(function(T){T.RAD=\\\\\\\"radians\\\\\\\",T.DEG=\\\\\\\"degrees\\\\\\\",T.EXP=\\\\\\\"exp\\\\\\\",T.EXP2=\\\\\\\"exp2\\\\\\\",T.LOG=\\\\\\\"log\\\\\\\",T.LOG2=\\\\\\\"log2\\\\\\\",T.SQRT=\\\\\\\"sqrt\\\\\\\",T.INV_SQRT=\\\\\\\"inversesqrt\\\\\\\",T.FLOOR=\\\\\\\"floor\\\\\\\",T.CEIL=\\\\\\\"ceil\\\\\\\",T.NORMALIZE=\\\\\\\"normalize\\\\\\\",T.FRACT=\\\\\\\"fract\\\\\\\",T.SATURATE=\\\\\\\"saturate\\\\\\\",T.SIN=\\\\\\\"sin\\\\\\\",T.COS=\\\\\\\"cos\\\\\\\",T.TAN=\\\\\\\"tan\\\\\\\",T.ASIN=\\\\\\\"asin\\\\\\\",T.ACOS=\\\\\\\"acos\\\\\\\",T.ARCTAN=\\\\\\\"atan\\\\\\\",T.ABS=\\\\\\\"abs\\\\\\\",T.SIGN=\\\\\\\"sign\\\\\\\",T.LENGTH=\\\\\\\"length\\\\\\\",T.NEGATE=\\\\\\\"negate\\\\\\\",T.INVERT=\\\\\\\"invert\\\\\\\",T.MIN=\\\\\\\"min\\\\\\\",T.MAX=\\\\\\\"max\\\\\\\",T.MOD=\\\\\\\"mod\\\\\\\",T.STEP=\\\\\\\"step\\\\\\\",T.REFLECT=\\\\\\\"reflect\\\\\\\",T.DISTANCE=\\\\\\\"distance\\\\\\\",T.DOT=\\\\\\\"dot\\\\\\\",T.CROSS=\\\\\\\"cross\\\\\\\",T.POW=\\\\\\\"pow\\\\\\\",T.MIX=\\\\\\\"mix\\\\\\\",T.CLAMP=\\\\\\\"clamp\\\\\\\",T.REFRACT=\\\\\\\"refract\\\\\\\",T.SMOOTHSTEP=\\\\\\\"smoothstep\\\\\\\",T.FACEFORWARD=\\\\\\\"faceforward\\\\\\\"})(Ie||(Ie={}));var He=class extends ue{constructor(e=new oe,t=He.ABS,r,i){super();this.nodeType=\\\\\\\"Math\\\\\\\";this.a=e,typeof t!=\\\\\\\"string\\\\\\\"?this.b=t:i=t,typeof r!=\\\\\\\"string\\\\\\\"?this.c=r:i=r,this.method=i,this.hashProperties=[\\\\\\\"method\\\\\\\"]}getNumInputs(e){switch(this.method){case He.MIX:case He.CLAMP:case He.REFRACT:case He.SMOOTHSTEP:case He.FACEFORWARD:return 3;case He.MIN:case He.MAX:case He.MOD:case He.STEP:case He.REFLECT:case He.DISTANCE:case He.DOT:case He.CROSS:case He.POW:return 2;default:return 1}}getInputType(e){let t=e.getTypeLength(this.a.getType(e)),r=this.b?e.getTypeLength(this.b.getType(e)):0,i=this.c?e.getTypeLength(this.c.getType(e)):0;return t>r&&t>i?this.a.getType(e):r>i?this.b.getType(e):this.c.getType(e)}getType(e){switch(this.method){case He.LENGTH:case He.DISTANCE:case He.DOT:return\\\\\\\"f\\\\\\\";case He.CROSS:return\\\\\\\"v3\\\\\\\"}return this.getInputType(e)}generate(e,t){let r,i,n,o=this.a?e.getTypeLength(this.a.getType(e)):0,l=this.b?e.getTypeLength(this.b.getType(e)):0,a=this.c?e.getTypeLength(this.c.getType(e)):0,c=this.getInputType(e),u=this.getType(e);switch(this.type=u,this.method){case He.NEGATE:return e.format(\\\\\\\"( -\\\\\\\"+this.a.build(e,c)+\\\\\\\" )\\\\\\\",c,t);case He.INVERT:return e.format(\\\\\\\"( 1.0 - \\\\\\\"+this.a.build(e,c)+\\\\\\\" )\\\\\\\",c,t);case He.CROSS:r=this.a.build(e,\\\\\\\"v3\\\\\\\"),i=this.b.build(e,\\\\\\\"v3\\\\\\\");break;case He.STEP:r=this.a.build(e,o===1?\\\\\\\"f\\\\\\\":c),i=this.b.build(e,c);break;case He.MIN:case He.MAX:case He.MOD:r=this.a.build(e,c),i=this.b.build(e,l===1?\\\\\\\"f\\\\\\\":c);break;case He.REFRACT:r=this.a.build(e,c),i=this.b.build(e,c),n=this.c.build(e,\\\\\\\"f\\\\\\\");break;case He.MIX:r=this.a.build(e,c),i=this.b.build(e,c),n=this.c.build(e,a===1?\\\\\\\"f\\\\\\\":c);break;default:r=this.a.build(e,c),this.b&&(i=this.b.build(e,c)),this.c&&(n=this.c.build(e,c));break}let d=[];d.push(r),i&&d.push(i),n&&d.push(n);let h=this.getNumInputs(e);if(d.length!==h)throw Error(`Arguments not match used in \\\\\\\"${this.method}\\\\\\\". Require ${h}, currently ${d.length}.`);return e.format(this.method+\\\\\\\"( \\\\\\\"+d.join(\\\\\\\", \\\\\\\")+\\\\\\\" )\\\\\\\",u,t)}copy(e){return super.copy(e),this.a.copy(e.a),this.b=e.b instanceof oe?e.b.clone():e.b,this.c=e.c instanceof oe?e.c.clone():e.c,this.method=e.method,this}toJSON(e){let t=this.getJSONNode(e);return t||(t=this.createJSONNode(e),t.a=this.a.toJSON(e).uuid,this.b&&(t.b=this.b.toJSON(e).uuid),this.c&&(t.c=this.c.toJSON(e).uuid),t.method=this.method),t.nodeType=this.nodeType,t}fromJSON(e,t){return super.fromJSON(e,t),e.a&&(this.a=t.getNode(e.a)),e.b&&(this.b=t.getNode(e.b)),e.c&&(this.c=t.getNode(e.c)),e.method&&(this.method=e.method),this}},ve=He;ve.RAD=Ie.RAD,ve.DEG=Ie.DEG,ve.EXP=Ie.EXP,ve.EXP2=Ie.EXP2,ve.LOG=Ie.LOG,ve.LOG2=Ie.LOG2,ve.SQRT=Ie.SQRT,ve.INV_SQRT=Ie.INV_SQRT,ve.FLOOR=Ie.FLOOR,ve.CEIL=Ie.CEIL,ve.NORMALIZE=Ie.NORMALIZE,ve.FRACT=Ie.FRACT,ve.SATURATE=Ie.SATURATE,ve.SIN=Ie.SIN,ve.COS=Ie.COS,ve.TAN=Ie.TAN,ve.ASIN=Ie.ASIN,ve.ACOS=Ie.ACOS,ve.ARCTAN=Ie.ARCTAN,ve.ABS=Ie.ABS,ve.SIGN=Ie.SIGN,ve.LENGTH=Ie.LENGTH,ve.NEGATE=Ie.NEGATE,ve.INVERT=Ie.INVERT,ve.MIN=Ie.MIN,ve.MAX=Ie.MAX,ve.MOD=Ie.MOD,ve.STEP=Ie.STEP,ve.REFLECT=Ie.REFLECT,ve.DISTANCE=Ie.DISTANCE,ve.DOT=Ie.DOT,ve.CROSS=Ie.CROSS,ve.POW=Ie.POW,ve.MIX=Ie.MIX,ve.CLAMP=Ie.CLAMP,ve.REFRACT=Ie.REFRACT,ve.SMOOTHSTEP=Ie.SMOOTHSTEP,ve.FACEFORWARD=Ie.FACEFORWARD;var Is=class extends ue{constructor(e=new oe,t=new oe,r=new oe){super(\\\\\\\"v4\\\\\\\");this.nodeType=\\\\\\\"TextureCubeUV\\\\\\\";this.value=e,this.uv=t,this.bias=r}bilinearCubeUV(e,t,r,i){let n=new zo(Is.Nodes.bilinearCubeUV,[t,r,i]);this.colorSpaceTL=this.colorSpaceTL??new qe(new Ve(\\\\\\\"\\\\\\\",\\\\\\\"v4\\\\\\\")),this.colorSpaceTL.fromDecoding(e.getTextureEncodingFromMap(this.value.value)),this.colorSpaceTL.input.parse(n.build(e)+\\\\\\\".tl\\\\\\\"),this.colorSpaceTR=this.colorSpaceTR??new qe(new Ve(\\\\\\\"\\\\\\\",\\\\\\\"v4\\\\\\\")),this.colorSpaceTR.fromDecoding(e.getTextureEncodingFromMap(this.value.value)),this.colorSpaceTR.input.parse(n.build(e)+\\\\\\\".tr\\\\\\\"),this.colorSpaceBL=this.colorSpaceBL??new qe(new Ve(\\\\\\\"\\\\\\\",\\\\\\\"v4\\\\\\\")),this.colorSpaceBL.fromDecoding(e.getTextureEncodingFromMap(this.value.value)),this.colorSpaceBL.input.parse(n.build(e)+\\\\\\\".bl\\\\\\\"),this.colorSpaceBR=this.colorSpaceBR??new qe(new Ve(\\\\\\\"\\\\\\\",\\\\\\\"v4\\\\\\\")),this.colorSpaceBR.fromDecoding(e.getTextureEncodingFromMap(this.value.value)),this.colorSpaceBR.input.parse(n.build(e)+\\\\\\\".br\\\\\\\");let o={include:e.isShader(\\\\\\\"vertex\\\\\\\"),ignoreCache:!0};e.addContext(o),this.colorSpaceTLExp=new Ve(this.colorSpaceTL.build(e,\\\\\\\"v4\\\\\\\"),\\\\\\\"v4\\\\\\\"),this.colorSpaceTRExp=new Ve(this.colorSpaceTR.build(e,\\\\\\\"v4\\\\\\\"),\\\\\\\"v4\\\\\\\"),this.colorSpaceBLExp=new Ve(this.colorSpaceBL.build(e,\\\\\\\"v4\\\\\\\"),\\\\\\\"v4\\\\\\\"),this.colorSpaceBRExp=new Ve(this.colorSpaceBR.build(e,\\\\\\\"v4\\\\\\\"),\\\\\\\"v4\\\\\\\"),e.removeContext();let l=new Ve(\\\\\\\"mix( mix( cubeUV_TL, cubeUV_TR, cubeUV.f.x ), mix( cubeUV_BL, cubeUV_BR, cubeUV.f.x ), cubeUV.f.y )\\\\\\\",\\\\\\\"v4\\\\\\\");return l.keywords.cubeUV_TL=this.colorSpaceTLExp,l.keywords.cubeUV_TR=this.colorSpaceTRExp,l.keywords.cubeUV_BL=this.colorSpaceBLExp,l.keywords.cubeUV_BR=this.colorSpaceBRExp,l.keywords.cubeUV=n,l}generate(e,t){if(e.isShader(\\\\\\\"fragment\\\\\\\")){let r=this.uv,i=this.bias||e.context.roughness,n=new zo(Is.Nodes.roughnessToMip,[i]),o=new ve(n,Is.Nodes.m0,Is.Nodes.cubeUV_maxMipLevel,ve.CLAMP),l=new ve(o,ve.FLOOR),a=new ve(o,ve.FRACT),c=this.bilinearCubeUV(e,this.value,r,l),u=this.bilinearCubeUV(e,this.value,r,new zt(l,new te(1).setReadonly(!0),zt.ADD)),d=new ve(c,u,a,ve.MIX);return e.format(d.build(e),\\\\\\\"v4\\\\\\\",t)}else return console.warn(\\\\\\\"TextureCubeUVNode is not compatible with \\\\\\\"+e.shader+\\\\\\\" shader.\\\\\\\"),e.format(\\\\\\\"vec4( 0.0 )\\\\\\\",this.getType(e),t)}copy(e){return super.copy(e),this.uv.copy(e.uv),this.bias.copy(e.bias),this.value.copy(e.value),e.colorSpaceTL?this.colorSpaceTL?this.colorSpaceTL.copy(e.colorSpaceTL):this.colorSpaceTL=e.colorSpaceTL.clone():this.colorSpaceTL=void 0,e.colorSpaceTR?this.colorSpaceTR?this.colorSpaceTR.copy(e.colorSpaceTR):this.colorSpaceTR=e.colorSpaceTR.clone():this.colorSpaceTR=void 0,e.colorSpaceBL?this.colorSpaceBL?this.colorSpaceBL.copy(e.colorSpaceBL):this.colorSpaceBL=e.colorSpaceBL.clone():this.colorSpaceBL=void 0,e.colorSpaceBR?this.colorSpaceBR?this.colorSpaceBR.copy(e.colorSpaceBR):this.colorSpaceBR=e.colorSpaceBR.clone():this.colorSpaceBR=void 0,e.colorSpaceTLExp?this.colorSpaceTLExp?this.colorSpaceTLExp.copy(e.colorSpaceTLExp):this.colorSpaceTLExp=e.colorSpaceTLExp.clone():this.colorSpaceTLExp=void 0,e.colorSpaceTRExp?this.colorSpaceTRExp?this.colorSpaceTRExp.copy(e.colorSpaceTRExp):this.colorSpaceTRExp=e.colorSpaceTRExp.clone():this.colorSpaceTRExp=void 0,e.colorSpaceBLExp?this.colorSpaceBLExp?this.colorSpaceBLExp.copy(e.colorSpaceBLExp):this.colorSpaceBLExp=e.colorSpaceBLExp.clone():this.colorSpaceBLExp=void 0,e.colorSpaceBRExp?this.colorSpaceBRExp?this.colorSpaceBRExp.copy(e.colorSpaceBRExp):this.colorSpaceBRExp=e.colorSpaceBRExp.clone():this.colorSpaceBRExp=void 0,this}toJSON(e){let t=this.getJSONNode(e);return t||(t=this.createJSONNode(e),t.value=this.value.toJSON(e).uuid,t.uv=this.uv.toJSON(e).uuid,t.bias=this.bias.toJSON(e).uuid,this.colorSpaceTL&&this.colorSpaceTL.toJSON(e).uuid,this.colorSpaceTR&&this.colorSpaceTR.toJSON(e).uuid,this.colorSpaceBL&&this.colorSpaceBL.toJSON(e).uuid,this.colorSpaceBR&&this.colorSpaceBR.toJSON(e).uuid,this.colorSpaceTLExp&&this.colorSpaceTLExp.toJSON(e).uuid,this.colorSpaceTRExp&&this.colorSpaceTRExp.toJSON(e).uuid,this.colorSpaceBLExp&&this.colorSpaceBLExp.toJSON(e).uuid,this.colorSpaceBRExp&&this.colorSpaceBRExp.toJSON(e).uuid),t.nodeType=this.nodeType,t}fromJSON(e,t){return super.fromJSON(e,t),e.value&&(this.value=t.getNode(e.value)),e.uv&&(this.uv=t.getNode(e.uv)),e.bias&&(this.bias=t.getNode(e.bias)),e.colorSpaceTL&&(this.colorSpaceTL=t.getNode(e.colorSpaceTL)),e.colorSpaceTR&&(this.colorSpaceTR=t.getNode(e.colorSpaceTR)),e.colorSpaceBL&&(this.colorSpaceBL=t.getNode(e.colorSpaceBL)),e.colorSpaceBR&&(this.colorSpaceBR=t.getNode(e.colorSpaceBR)),e.colorSpaceTLExp&&(this.colorSpaceTLExp=t.getNode(e.colorSpaceTLExp)),e.colorSpaceTRExp&&(this.colorSpaceTRExp=t.getNode(e.colorSpaceTRExp)),e.colorSpaceBLExp&&(this.colorSpaceBLExp=t.getNode(e.colorSpaceBLExp)),e.colorSpaceBRExp&&(this.colorSpaceBRExp=t.getNode(e.colorSpaceBRExp)),this}},Hi=Is;Hi.Nodes=function(){let e=new Vi(`struct TextureCubeUVData {\\n\\t\\t\\tvec4 tl;\\n\\t\\t\\tvec4 tr;\\n\\t\\t\\tvec4 br;\\n\\t\\t\\tvec4 bl;\\n\\t\\t\\tvec2 f;\\n\\t\\t}`),t=new Ee(\\\\\\\"float cubeUV_maxMipLevel 8.0\\\\\\\",!0),r=new Ee(\\\\\\\"float cubeUV_minMipLevel 4.0\\\\\\\",!0),i=new Ee(\\\\\\\"float cubeUV_maxTileSize 256.0\\\\\\\",!0),n=new Ee(\\\\\\\"float cubeUV_minTileSize 16.0\\\\\\\",!0),o=new se(`float getFace(vec3 direction) {\\n\\t\\t\\t\\tvec3 absDirection = abs(direction);\\n\\t\\t\\t\\tfloat face = -1.0;\\n\\t\\t\\t\\tif (absDirection.x > absDirection.z) {\\n\\t\\t\\t\\t\\tif (absDirection.x > absDirection.y)\\n\\t\\t\\t\\t\\t\\tface = direction.x > 0.0 ? 0.0 : 3.0;\\n\\t\\t\\t\\t\\telse\\n\\t\\t\\t\\t\\t\\tface = direction.y > 0.0 ? 1.0 : 4.0;\\n\\t\\t\\t\\t} else {\\n\\t\\t\\t\\t\\tif (absDirection.z > absDirection.y)\\n\\t\\t\\t\\t\\t\\tface = direction.z > 0.0 ? 2.0 : 5.0;\\n\\t\\t\\t\\t\\telse\\n\\t\\t\\t\\t\\t\\tface = direction.y > 0.0 ? 1.0 : 4.0;\\n\\t\\t\\t\\t}\\n\\t\\t\\t\\treturn face;\\n\\t\\t}`);o.useKeywords=!1;let l=new se(`vec2 getUV(vec3 direction, float face) {\\n\\t\\t\\t\\tvec2 uv;\\n\\t\\t\\t\\tif (face == 0.0) {\\n\\t\\t\\t\\t\\tuv = vec2(direction.z, direction.y) / abs(direction.x); // pos x\\n\\t\\t\\t\\t} else if (face == 1.0) {\\n\\t\\t\\t\\t\\tuv = vec2(-direction.x, -direction.z) / abs(direction.y); // pos y\\n\\t\\t\\t\\t} else if (face == 2.0) {\\n\\t\\t\\t\\t\\tuv = vec2(-direction.x, direction.y) / abs(direction.z); // pos z\\n\\t\\t\\t\\t} else if (face == 3.0) {\\n\\t\\t\\t\\t\\tuv = vec2(-direction.z, direction.y) / abs(direction.x); // neg x\\n\\t\\t\\t\\t} else if (face == 4.0) {\\n\\t\\t\\t\\t\\tuv = vec2(-direction.x, direction.z) / abs(direction.y); // neg y\\n\\t\\t\\t\\t} else {\\n\\t\\t\\t\\t\\tuv = vec2(direction.x, direction.y) / abs(direction.z); // neg z\\n\\t\\t\\t\\t}\\n\\t\\t\\t\\treturn 0.5 * (uv + 1.0);\\n\\t\\t}`);l.useKeywords=!1;let a=new se(`TextureCubeUVData bilinearCubeUV(sampler2D envMap, vec3 direction, float mipInt) {\\n\\t\\t\\tfloat face = getFace(direction);\\n\\t\\t\\tfloat filterInt = max(cubeUV_minMipLevel - mipInt, 0.0);\\n\\t\\t\\tmipInt = max(mipInt, cubeUV_minMipLevel);\\n\\t\\t\\tfloat faceSize = exp2(mipInt);\\n\\t\\t\\tfloat texelSize = 1.0 / (3.0 * cubeUV_maxTileSize);\\n\\t\\t\\tvec2 uv = getUV(direction, face) * (faceSize - 1.0);\\n\\t\\t\\tvec2 f = fract(uv);\\n\\t\\t\\tuv += 0.5 - f;\\n\\t\\t\\tif (face > 2.0) {\\n\\t\\t\\t\\tuv.y += faceSize;\\n\\t\\t\\t\\tface -= 3.0;\\n\\t\\t\\t}\\n\\t\\t\\tuv.x += face * faceSize;\\n\\t\\t\\tif(mipInt < cubeUV_maxMipLevel){\\n\\t\\t\\t\\tuv.y += 2.0 * cubeUV_maxTileSize;\\n\\t\\t\\t}\\n\\t\\t\\tuv.y += filterInt * 2.0 * cubeUV_minTileSize;\\n\\t\\t\\tuv.x += 3.0 * max(0.0, cubeUV_maxTileSize - 2.0 * faceSize);\\n\\t\\t\\tuv *= texelSize;\\n\\t\\t\\tvec4 tl = texture2D(envMap, uv);\\n\\t\\t\\tuv.x += texelSize;\\n\\t\\t\\tvec4 tr = texture2D(envMap, uv);\\n\\t\\t\\tuv.y += texelSize;\\n\\t\\t\\tvec4 br = texture2D(envMap, uv);\\n\\t\\t\\tuv.x -= texelSize;\\n\\t\\t\\tvec4 bl = texture2D(envMap, uv);\\n\\t\\t\\treturn TextureCubeUVData( tl, tr, br, bl, f );\\n\\t\\t}`,[e,o,l,t,r,i,n]);a.useKeywords=!1;let c=new Ee(\\\\\\\"float r0 1.0\\\\\\\",!0),u=new Ee(\\\\\\\"float v0 0.339\\\\\\\",!0),d=new Ee(\\\\\\\"float m0 -2.0\\\\\\\",!0),h=new Ee(\\\\\\\"float r1 0.8\\\\\\\",!0),p=new Ee(\\\\\\\"float v1 0.276\\\\\\\",!0),f=new Ee(\\\\\\\"float m1 -1.0\\\\\\\",!0),m=new Ee(\\\\\\\"float r4 0.4\\\\\\\",!0),g=new Ee(\\\\\\\"float v4 0.046\\\\\\\",!0),y=new Ee(\\\\\\\"float m4 2.0\\\\\\\",!0),x=new Ee(\\\\\\\"float r5 0.305\\\\\\\",!0),v=new Ee(\\\\\\\"float v5 0.016\\\\\\\",!0),b=new Ee(\\\\\\\"float m5 3.0\\\\\\\",!0),w=new Ee(\\\\\\\"float r6 0.21\\\\\\\",!0),N=new Ee(\\\\\\\"float v6 0.0038\\\\\\\",!0),S=new Ee(\\\\\\\"float m6 4.0\\\\\\\",!0),A=[c,u,d,h,p,f,m,g,y,x,v,b,w,N,S],I=new se(`float roughnessToMip(float roughness) {\\n\\t\\t\\tfloat mip = 0.0;\\n\\t\\t\\tif (roughness >= r1) {\\n\\t\\t\\t\\tmip = (r0 - roughness) * (m1 - m0) / (r0 - r1) + m0;\\n\\t\\t\\t} else if (roughness >= r4) {\\n\\t\\t\\t\\tmip = (r1 - roughness) * (m4 - m1) / (r1 - r4) + m1;\\n\\t\\t\\t} else if (roughness >= r5) {\\n\\t\\t\\t\\tmip = (r4 - roughness) * (m5 - m4) / (r4 - r5) + m4;\\n\\t\\t\\t} else if (roughness >= r6) {\\n\\t\\t\\t\\tmip = (r5 - roughness) * (m6 - m5) / (r5 - r6) + m5;\\n\\t\\t\\t} else {\\n\\t\\t\\t\\tmip = -2.0 * log2(1.16 * roughness);// 1.16 = 1.79^0.25\\n\\t\\t\\t}\\n\\t\\t\\treturn mip;\\n\\t\\t}`,A);return{bilinearCubeUV:a,roughnessToMip:I,m0:d,cubeUV_maxMipLevel:t}}();var Wi=class extends ue{constructor(e){super(\\\\\\\"v3\\\\\\\");this.nodeType=\\\\\\\"Normal\\\\\\\";this.scope=e??Wi.VIEW}getShared(){return this.scope===Wi.WORLD}build(e,t,r,i){let n=e.context[this.scope+\\\\\\\"Normal\\\\\\\"];return n?n.build(e,t,r,i):super.build(e,t,r)}generate(e,t,r,i,n){let o;switch(this.scope){case Wi.VIEW:e.isShader(\\\\\\\"vertex\\\\\\\")?o=\\\\\\\"transformedNormal\\\\\\\":o=\\\\\\\"geometryNormal\\\\\\\";break;case Wi.LOCAL:e.isShader(\\\\\\\"vertex\\\\\\\")?o=\\\\\\\"objectNormal\\\\\\\":(e.requires.normal=!0,o=\\\\\\\"vObjectNormal\\\\\\\");break;case Wi.WORLD:e.isShader(\\\\\\\"vertex\\\\\\\")?o=\\\\\\\"inverseTransformDirection( transformedNormal, viewMatrix ).xyz\\\\\\\":(e.requires.worldNormal=!0,o=\\\\\\\"vWNormal\\\\\\\");break}return e.format(o,this.getType(e),t)}copy(e){return super.copy(e),this.scope=e.scope,this}toJSON(e){let t=this.getJSONNode(e);return t||(t=this.createJSONNode(e),t.scope=this.scope),t.nodeType=this.nodeType,t}fromJSON(e,t){return super.fromJSON(e,t),e.scope&&(this.scope=e.scope),this}},mt=Wi;mt.LOCAL=\\\\\\\"local\\\\\\\",mt.WORLD=\\\\\\\"world\\\\\\\",mt.VIEW=\\\\\\\"view\\\\\\\",mt.NORMAL=\\\\\\\"normal\\\\\\\";pt.addKeyword(\\\\\\\"viewNormal\\\\\\\",function(){return new mt(mt.VIEW)});pt.addKeyword(\\\\\\\"localNormal\\\\\\\",function(){return new mt(mt.NORMAL)});pt.addKeyword(\\\\\\\"worldNormal\\\\\\\",function(){return new mt(mt.WORLD)});var zr=class extends ue{constructor(e){super(\\\\\\\"v3\\\\\\\");this.nodeType=\\\\\\\"Position\\\\\\\";this.scope=e??zr.LOCAL}getType(){switch(this.scope){case zr.PROJECTION:return\\\\\\\"v4\\\\\\\"}return this.type}getShader(){switch(this.scope){case zr.LOCAL:case zr.WORLD:return!1}return!0}generate(e,t,r,i,n){let o;switch(this.scope){case zr.LOCAL:e.isShader(\\\\\\\"vertex\\\\\\\")?o=\\\\\\\"transformed\\\\\\\":(e.requires.position=!0,o=\\\\\\\"vPosition\\\\\\\");break;case zr.WORLD:if(e.isShader(\\\\\\\"vertex\\\\\\\"))return\\\\\\\"( modelMatrix * vec4( transformed, 1.0 ) ).xyz\\\\\\\";e.requires.worldPosition=!0,o=\\\\\\\"vWPosition\\\\\\\";break;case zr.VIEW:o=e.isShader(\\\\\\\"vertex\\\\\\\")?\\\\\\\"-mvPosition.xyz\\\\\\\":\\\\\\\"vViewPosition\\\\\\\";break;case zr.PROJECTION:o=e.isShader(\\\\\\\"vertex\\\\\\\")?\\\\\\\"( projectionMatrix * modelViewMatrix * vec4( position, 1.0 ) )\\\\\\\":\\\\\\\"vec4( 0.0 )\\\\\\\";break}return e.format(o,this.getType(),t)}copy(e){return super.copy(e),this.scope=e.scope,this}toJSON(e){let t=this.getJSONNode(e);return t||(t=this.createJSONNode(e),t.scope=this.scope),t.nodeType=this.nodeType,t}fromJSON(e,t){return super.fromJSON(e,t),e.scope&&(this.scope=e.scope),this}},wt=zr;wt.LOCAL=\\\\\\\"local\\\\\\\",wt.WORLD=\\\\\\\"world\\\\\\\",wt.VIEW=\\\\\\\"view\\\\\\\",wt.PROJECTION=\\\\\\\"projection\\\\\\\";pt.addKeyword(\\\\\\\"position\\\\\\\",function(){return new wt});pt.addKeyword(\\\\\\\"worldPosition\\\\\\\",function(){return new wt(wt.WORLD)});pt.addKeyword(\\\\\\\"viewPosition\\\\\\\",function(){return new wt(wt.VIEW)});var Nr=class extends ue{constructor(e){super(\\\\\\\"v3\\\\\\\");this.nodeType=\\\\\\\"Reflect\\\\\\\";this.scope=e??Nr.CUBE}getUnique(e){return!e.context.viewNormal}getType(){switch(this.scope){case Nr.SPHERE:return\\\\\\\"v2\\\\\\\"}return this.type}generate(e,t){let r=this.getUnique(e);if(e.isShader(\\\\\\\"fragment\\\\\\\")){let i;switch(this.scope){case Nr.VECTOR:{let n=new mt(mt.VIEW),o=e.context.roughness,l=n.build(e,\\\\\\\"v3\\\\\\\"),a=new wt(wt.VIEW).build(e,\\\\\\\"v3\\\\\\\"),c=o?o.build(e,\\\\\\\"f\\\\\\\"):void 0,u=`reflect( -normalize( ${a} ), ${l} )`;c&&(u=`normalize( mix( ${u}, ${l}, ${c} * ${c} ) )`);let d=`inverseTransformDirection( ${u}, viewMatrix )`;r?(e.addNodeCode(`vec3 reflectVec = ${d};`),i=\\\\\\\"reflectVec\\\\\\\"):i=d;break}case Nr.CUBE:{let n=new Nr(Nr.VECTOR).build(e,\\\\\\\"v3\\\\\\\"),o=\\\\\\\"vec3( -\\\\\\\"+n+\\\\\\\".x, \\\\\\\"+n+\\\\\\\".yz )\\\\\\\";r?(e.addNodeCode(`vec3 reflectCubeVec = ${o};`),i=\\\\\\\"reflectCubeVec\\\\\\\"):i=o;break}case Nr.SPHERE:{let n=new Nr(Nr.VECTOR).build(e,\\\\\\\"v3\\\\\\\"),o=\\\\\\\"normalize( ( viewMatrix * vec4( \\\\\\\"+n+\\\\\\\", 0.0 ) ).xyz + vec3( 0.0, 0.0, 1.0 ) ).xy * 0.5 + 0.5\\\\\\\";r?(e.addNodeCode(`vec2 reflectSphereVec = ${o};`),i=\\\\\\\"reflectSphereVec\\\\\\\"):i=o;break}}return e.format(i,this.getType(),t)}else return console.warn(\\\\\\\"ReflectNode is not compatible with \\\\\\\"+e.shader+\\\\\\\" shader.\\\\\\\"),e.format(\\\\\\\"vec3( 0.0 )\\\\\\\",this.type,t)}copy(e){return super.copy(e),this.scope=e.scope,this}toJSON(e){let t=this.getJSONNode(e);return t||(t=this.createJSONNode(e),t.scope=this.scope),t.nodeType=this.nodeType,t}fromJSON(e,t){return super.fromJSON(e,t),e.scope&&(this.scope=e.scope),this}},Sr=Nr;Sr.CUBE=\\\\\\\"cube\\\\\\\",Sr.SPHERE=\\\\\\\"sphere\\\\\\\",Sr.VECTOR=\\\\\\\"vector\\\\\\\";var Nn=class extends ue{constructor(e=new _t,t,r){super(\\\\\\\"v4\\\\\\\");this.nodeType=\\\\\\\"TextureCube\\\\\\\";this.value=e,this.radianceNode=new Hi(this.value,t??new Sr(Sr.VECTOR),r),this.irradianceNode=new Hi(this.value,new mt(mt.WORLD),new te(1).setReadonly(!0))}generate(e,t){return e.isShader(\\\\\\\"fragment\\\\\\\")?(e.require(\\\\\\\"irradiance\\\\\\\"),e.context.bias&&e.context.bias.setTexture(this.value),(e.slot===\\\\\\\"irradiance\\\\\\\"?this.irradianceNode:this.radianceNode).build(e,t)):(console.warn(\\\\\\\"TextureCubeNode is not compatible with \\\\\\\"+e.shader+\\\\\\\" shader.\\\\\\\"),e.format(\\\\\\\"vec4( 0.0 )\\\\\\\",this.getType(e),t))}copy(e){return super.copy(e),this.value.copy(e.value),this.radianceNode.copy(e.radianceNode),this.irradianceNode.copy(e.irradianceNode),this}toJSON(e){let t=this.getJSONNode(e);return t||(t=this.createJSONNode(e),t.value=this.value.toJSON(e).uuid,this.radianceNode&&(t.radianceNode=this.radianceNode.toJSON(e).uuid),this.irradianceNode&&(t.irradianceNode=this.irradianceNode.toJSON(e).uuid)),t.nodeType=this.nodeType,t}fromJSON(e,t){return super.fromJSON(e,t),e.value&&(this.value=t.getNode(e.value)),e.radianceNode&&(this.radianceNode=t.getNode(e.radianceNode)),e.irradianceNode&&(this.irradianceNode=t.getNode(e.irradianceNode)),this}};var Ku=F(z());var Tn=class extends Ke{constructor(e=new Ku.CubeTexture,t,r){super(\\\\\\\"v4\\\\\\\",{shared:!0});this.nodeType=\\\\\\\"CubeTexture\\\\\\\";this.value=e,this.uv=t??new Sr,this.bias=r}getTexture(e,t){return super.generate(e,t,this.value.uuid,\\\\\\\"tc\\\\\\\")}generate(e,t){if(t===\\\\\\\"samplerCube\\\\\\\")return this.getTexture(e,t);let r=this.getTexture(e,t),i=this.uv?.build(e,\\\\\\\"v3\\\\\\\"),n=this.bias?this.bias.build(e,\\\\\\\"f\\\\\\\"):void 0;n===void 0&&e.context.bias&&(n=e.context.bias.setTexture(this).build(e,\\\\\\\"f\\\\\\\"));let o;n?o=\\\\\\\"texCubeBias( \\\\\\\"+r+\\\\\\\", \\\\\\\"+i+\\\\\\\", \\\\\\\"+n+\\\\\\\" )\\\\\\\":o=\\\\\\\"texCube( \\\\\\\"+r+\\\\\\\", \\\\\\\"+i+\\\\\\\" )\\\\\\\";let l={include:e.isShader(\\\\\\\"vertex\\\\\\\"),ignoreCache:!0},a=this.getType(e);return e.addContext(l),this.colorSpace=this.colorSpace??new qe(new Ve(\\\\\\\"\\\\\\\",a)),this.colorSpace.fromDecoding(e.getTextureEncodingFromMap(this.value)),this.colorSpace.input.parse(o),o=this.colorSpace.build(e,a),e.removeContext(),e.format(o,a,t)}copy(e){return super.copy(e),this.value.copy(e.value),e.uv?this.uv?this.uv.copy(e.uv):this.uv=e.uv.clone():this.uv=void 0,e.bias?this.bias?this.bias.copy(e.bias):this.bias=e.bias.clone():this.bias=void 0,this}toJSON(e){let t=this.getJSONNode(e);return t||(t=this.createJSONNode(e),this.value&&(t.value=this.value.uuid),t.uv=this.uv?.toJSON(e).uuid,this.bias&&(t.bias=this.bias.toJSON(e).uuid)),t.nodeType=this.nodeType,t}fromJSON(e,t){return super.fromJSON(e,t),e.value&&(this.value=t.getTexture(e.value)),e.uv&&(this.uv=t.getNode(e.uv)),e.bias&&(this.bias=t.getNode(e.bias)),this}};var Xu=[\\\\\\\"x\\\\\\\",\\\\\\\"y\\\\\\\",\\\\\\\"z\\\\\\\",\\\\\\\"w\\\\\\\"],Qg=[\\\\\\\"float\\\\\\\",\\\\\\\"vec2\\\\\\\",\\\\\\\"vec3\\\\\\\",\\\\\\\"vec4\\\\\\\"],Yg={float:\\\\\\\"f\\\\\\\",vec2:\\\\\\\"v2\\\\\\\",vec3:\\\\\\\"v3\\\\\\\",vec4:\\\\\\\"v4\\\\\\\",mat4:\\\\\\\"v4\\\\\\\",int:\\\\\\\"i\\\\\\\",bool:\\\\\\\"b\\\\\\\",\\\\\\\"float[]\\\\\\\":\\\\\\\"f[]\\\\\\\",\\\\\\\"vec4[]\\\\\\\":\\\\\\\"v4[]\\\\\\\"},Kg={t:\\\\\\\"sampler2D\\\\\\\",tc:\\\\\\\"samplerCube\\\\\\\",b:\\\\\\\"bool\\\\\\\",i:\\\\\\\"int\\\\\\\",f:\\\\\\\"float\\\\\\\",c:\\\\\\\"vec3\\\\\\\",v2:\\\\\\\"vec2\\\\\\\",v3:\\\\\\\"vec3\\\\\\\",v4:\\\\\\\"vec4\\\\\\\",m3:\\\\\\\"mat3\\\\\\\",m4:\\\\\\\"mat4\\\\\\\",\\\\\\\"f[]\\\\\\\":\\\\\\\"float[]\\\\\\\",\\\\\\\"v4[]\\\\\\\":\\\\\\\"vec4[]\\\\\\\"},Fl=class{constructor(){this.includes={consts:{},functions:{},structs:{}};this.cache=\\\\\\\"\\\\\\\";this.slot=\\\\\\\"\\\\\\\";this.shader=\\\\\\\"\\\\\\\";this.context={};this.getIncludesCode=function(){function e(t,r){return t.deps.length-r.deps.length}return function(r,i){let n=this.getIncludes(r,i);if(!n)return\\\\\\\"\\\\\\\";let o=\\\\\\\"\\\\\\\";n=n.sort(e);for(let l=0;l<n.length;l++)n[l].src&&(o+=n[l].src+`\\n`);return o}}();this.slots=[],this.caches=[],this.contexts=[],this.keywords={},this.nodeData={},this.fragmentVariables={},this.requires={uv:[],color:[],lights:!1,fog:!1,transparent:!1,irradiance:!1,position:!1,worldPosition:!1,normal:!1,worldNormal:!1,vWorldViewDir:!1,vWorldNormal:!1,modelMatrix:!1,viewMatrix:!1,projectionMatrix:!1},this.includes={consts:[],functions:[],structs:[]},this.attributes={},this.prefixCode=[\\\\\\\"#ifdef TEXTURE_LOD_EXT\\\\\\\",\\\\\\\"\\t#define texCube(a, b) textureCube(a, b)\\\\\\\",\\\\\\\"\\t#define texCubeBias(a, b, c) textureCubeLodEXT(a, b, c)\\\\\\\",\\\\\\\"\\t#define tex2D(a, b) texture2D(a, b)\\\\\\\",\\\\\\\"\\t#define tex2DBias(a, b, c) texture2DLodEXT(a, b, c)\\\\\\\",\\\\\\\"#else\\\\\\\",\\\\\\\"\\t#define texCube(a, b) textureCube(a, b)\\\\\\\",\\\\\\\"\\t#define texCubeBias(a, b, c) textureCube(a, b, c)\\\\\\\",\\\\\\\"\\t#define tex2D(a, b) texture2D(a, b)\\\\\\\",\\\\\\\"\\t#define tex2DBias(a, b, c) texture2D(a, b, c)\\\\\\\",\\\\\\\"#endif\\\\\\\",`\\n\\t\\t\\t// NOTE: Include Spline's blending modes. This could be part of BlendNode\\n\\t\\t\\t#define SPE_BLENDING_NORMAL 0\\n\\t\\t\\t#define SPE_BLENDING_MULTIPLY 1\\n\\t\\t\\t#define SPE_BLENDING_SCREEN 2\\n\\t\\t\\t#define SPE_BLENDING_OVERLAY 3\\n\\n\\t\\t\\tvec3 spe_normalBlend( vec3 a, vec3 b, float alpha ) {\\n\\t\\t\\t\\treturn mix( a, b, alpha );\\n\\t\\t\\t}\\n\\n\\t\\t\\tvec3 spe_multiplyBlend( vec3 a, vec3 b, float alpha ) {\\n\\t\\t\\t\\treturn mix( a, a * b, alpha );\\n\\t\\t\\t}\\n\\n\\t\\t\\tvec3 spe_screenBlend( vec3 a, vec3 b, float alpha ) {\\n\\t\\t\\t\\tvec3 tmp = 1.0 - ( 1.0 - a ) * ( 1.0 - b );\\n\\t\\t\\t\\treturn mix( a, tmp, alpha );\\n\\t\\t\\t}\\n\\n\\t\\t\\tvec3 spe_overlayBlend( vec3 a, vec3 b, float alpha ) {\\n\\t\\t\\t\\tvec3 tmp = mix( 1. - 2. * (1. - a) * (1. - b), 2. * a * b, step( a, vec3(.5) ) );\\n\\t\\t\\t\\treturn clamp( mix( a, tmp, alpha ), 0.0, 1.0 );\\n\\t\\t\\t}\\n\\n\\t\\t\\tvec3 spe_blend( vec3 a, vec3 b, float alpha, int mode ) {\\n\\t\\t\\t\\tif ( mode == SPE_BLENDING_NORMAL ) return spe_normalBlend( a, b, alpha );\\n\\t\\t\\t\\telse if ( mode == SPE_BLENDING_MULTIPLY ) return spe_multiplyBlend( a, b, alpha );\\n\\t\\t\\t\\telse if ( mode == SPE_BLENDING_SCREEN ) return spe_screenBlend( a, b, alpha );\\n\\t\\t\\t\\telse if ( mode == SPE_BLENDING_OVERLAY ) return spe_overlayBlend( a, b, alpha );\\n\\t\\t\\t\\treturn vec3( 1.0 );\\n\\t\\t\\t}\\n\\t\\t\\t`,\\\\\\\"#include <packing>\\\\\\\",\\\\\\\"#include <common>\\\\\\\"].join(`\\n`),this.parsCode={vertex:[\\\\\\\"float neighbor_offset = 0.0001;\\\\\\\",\\\\\\\"\\\\\\\"].join(`\\n`),fragment:[\\\\\\\"float accumAlpha = 0.0;\\\\\\\",`void accumulateAlpha(float alpha) {\\n\\t\\t\\t\\t\\taccumAlpha += (1.0 - accumAlpha) * alpha;\\n\\t\\t\\t\\t}`,\\\\\\\"\\\\\\\"].join(`\\n`)},this.code={vertex:\\\\\\\"\\\\\\\",fragment:\\\\\\\"\\\\\\\"},this.nodeCode={vertex:\\\\\\\"\\\\\\\",fragment:\\\\\\\"\\\\\\\"},this.resultCode={vertex:\\\\\\\"\\\\\\\",fragment:\\\\\\\"\\\\\\\"},this.finalCode={vertex:\\\\\\\"\\\\\\\",fragment:\\\\\\\"\\\\\\\"},this.inputs={uniforms:{list:[],vertex:[],fragment:[]},arrayUniforms:{list:[],vertex:[],fragment:[]},vars:{varying:[],vertex:[],fragment:[]}},this.defines={},this.uniforms={},this.extensions={derivatives:!1,fragDepth:!1,drawBuffers:!1,shaderTextureLOD:!1},this.updaters=[],this.nodes=[],this.analyzing=!1}build(e,t){this.buildShader(\\\\\\\"vertex\\\\\\\",e),this.buildShader(\\\\\\\"fragment\\\\\\\",t);for(let r=0;r<this.requires.uv.length;r++)if(this.requires.uv[r]){let i=r>0?r+1:\\\\\\\"\\\\\\\";this.addVaryCode(\\\\\\\"varying vec2 vUv\\\\\\\"+i+\\\\\\\";\\\\\\\"),r>0&&this.addVertexParsCode(\\\\\\\"attribute vec2 uv\\\\\\\"+i+\\\\\\\";\\\\\\\"),this.addVertexFinalCode(\\\\\\\"vUv\\\\\\\"+i+\\\\\\\" = uv\\\\\\\"+i+\\\\\\\";\\\\\\\")}return this.requires.color[0]&&(this.addVaryCode(\\\\\\\"varying vec4 vColor;\\\\\\\"),this.addVertexParsCode(\\\\\\\"attribute vec4 color;\\\\\\\"),this.addVertexFinalCode(\\\\\\\"vColor = color;\\\\\\\")),this.requires.color[1]&&(this.addVaryCode(\\\\\\\"varying vec4 vColor2;\\\\\\\"),this.addVertexParsCode(\\\\\\\"attribute vec4 color2;\\\\\\\"),this.addVertexFinalCode(\\\\\\\"vColor2 = color2;\\\\\\\")),this.requires.position&&(this.addVaryCode(\\\\\\\"varying vec3 vPosition;\\\\\\\"),this.addVertexFinalCode(\\\\\\\"vPosition = transformed;\\\\\\\")),this.requires.worldPosition&&(this.addVaryCode(\\\\\\\"varying vec3 vWPosition;\\\\\\\"),this.addVertexFinalCode(\\\\\\\"vWPosition = ( modelMatrix * vec4( transformed, 1.0 ) ).xyz;\\\\\\\")),this.requires.normal&&(this.addVaryCode(\\\\\\\"varying vec3 vObjectNormal;\\\\\\\"),this.addVertexFinalCode(\\\\\\\"vObjectNormal = normal;\\\\\\\")),this.requires.modelMatrix&&this.addFragmentParsCode(\\\\\\\"uniform mat4 modelMatrix;\\\\\\\"),this.requires.viewMatrix&&this.addFragmentParsCode(\\\\\\\"uniform mat4 viewMatrix;\\\\\\\"),this.requires.projectionMatrix&&this.addFragmentParsCode(\\\\\\\"uniform mat4 projectionMatrix;\\\\\\\"),this.requires.worldNormal&&(this.addVaryCode(\\\\\\\"varying vec3 vWNormal;\\\\\\\"),this.addVertexFinalCode(\\\\\\\"vWNormal = inverseTransformDirection( transformedNormal, viewMatrix ).xyz;\\\\\\\")),this.requires.vWorldViewDir&&(this.addVaryCode(\\\\\\\"varying vec3 vWorldViewDir;\\\\\\\"),this.addVertexFinalCode(\\\\\\\"vWorldViewDir = isPerspectiveMatrix( projectionMatrix ) ?  ( (modelMatrix * vec4(position, 1.0)).xyz - cameraPosition ) : vec3( -viewMatrix[0][2], -viewMatrix[1][2], -viewMatrix[2][2] );\\\\\\\")),this.requires.vWorldNormal&&(this.addVaryCode(\\\\\\\"varying vec3 vWorldNormal;\\\\\\\"),this.addVertexFinalCode(\\\\\\\"vWorldNormal = normalize( mat3( modelMatrix[0].xyz, modelMatrix[1].xyz, modelMatrix[2].xyz ) * normal );\\\\\\\")),this}buildShader(e,t){this.resultCode[e]=t.build(this.setShader(e),\\\\\\\"v4\\\\\\\")}setMaterial(e,t){return this.material=e,this.renderer=t,this.requires.lights=e.lights,this.requires.fog=e.fog,this.mergeDefines(e.defines),this}addFlow(e,t,r){return this.addSlot(e).addCache(t).addContext(r)}removeFlow(){return this.removeSlot().removeCache().removeContext()}addCache(e){return this.cache=e??\\\\\\\"\\\\\\\",this.caches.push(this.cache),this}removeCache(){return this.caches.pop(),this.cache=this.caches[this.caches.length-1]||\\\\\\\"\\\\\\\",this}addContext(e){return this.context=Object.assign({},this.context,e),this.context.extra=this.context.extra||{},this.contexts.push(this.context),this}removeContext(){return this.contexts.pop(),this.context=this.contexts[this.contexts.length-1]||{},this}addSlot(e){return this.slot=e||\\\\\\\"\\\\\\\",this.slots.push(this.slot),this}removeSlot(){return this.slots.pop(),this.slot=this.slots[this.slots.length-1]||\\\\\\\"\\\\\\\",this}addFragmentVariable(e,t){this.fragmentVariables[e]===void 0&&(this.addFragmentCode(`${t} ${e};`),this.fragmentVariables[e]=\\\\\\\"\\\\\\\")}addVertexCode(e){this.addCode(e,\\\\\\\"vertex\\\\\\\")}addFragmentCode(e){this.addCode(e,\\\\\\\"fragment\\\\\\\")}addCode(e,t){this.code[t??this.shader]+=e+`\\n`}addVertexNodeCode(e){this.addNodeCode(e,\\\\\\\"vertex\\\\\\\")}addFragmentNodeCode(e){this.addNodeCode(e,\\\\\\\"fragment\\\\\\\")}addNodeCode(e,t){this.nodeCode[t??this.shader]+=e+`\\n`}clearNodeCode(e){e=e??this.shader;let t=this.nodeCode[e];return this.nodeCode[e]=\\\\\\\"\\\\\\\",t}clearVertexNodeCode(){return this.clearNodeCode(\\\\\\\"vertex\\\\\\\")}clearFragmentNodeCode(){return this.clearNodeCode(\\\\\\\"fragment\\\\\\\")}addVertexFinalCode(e){this.addFinalCode(e,\\\\\\\"vertex\\\\\\\")}addFragmentFinalCode(e){this.addFinalCode(e,\\\\\\\"fragment\\\\\\\")}addFinalCode(e,t){this.finalCode[t??this.shader]+=e+`\\n`}addVertexParsCode(e){this.addParsCode(e,\\\\\\\"vertex\\\\\\\")}addFragmentParsCode(e){this.addParsCode(e,\\\\\\\"fragment\\\\\\\")}addParsCode(e,t){this.parsCode[t??this.shader]+=e+`\\n`}addVaryCode(e){this.addVertexParsCode(e),this.addFragmentParsCode(e)}isCache(e){return this.caches.indexOf(e)!==-1}isSlot(e){return this.slots.indexOf(e)!==-1}define(e,t){this.defines[e]=t===void 0?1:t}require(e){this.requires[e]=!0}isDefined(e){return this.defines[e]!==void 0}getVar(e,t,r,i=\\\\\\\"varying\\\\\\\",n=\\\\\\\"V\\\\\\\",o=\\\\\\\"\\\\\\\"){let l=this.getVars(i),a=l[e];if(!a){let c=l.length;a={name:r||\\\\\\\"node\\\\\\\"+n+c+(o?\\\\\\\"_\\\\\\\"+o:\\\\\\\"\\\\\\\"),type:t},l.push(a),l[e]=a}return a}getTempVar(e,t,r,i){return this.getVar(e,t,r,this.shader,\\\\\\\"T\\\\\\\",i)}getAttribute(e,t){if(!this.attributes[e]){let r=this.getVar(e,t);this.addVertexParsCode(\\\\\\\"attribute \\\\\\\"+t+\\\\\\\" \\\\\\\"+e+\\\\\\\";\\\\\\\"),this.addVertexFinalCode(r.name+\\\\\\\" = \\\\\\\"+e+\\\\\\\";\\\\\\\"),this.attributes[e]={varying:r,name:e,type:t}}return this.attributes[e]}getCode(e){return[this.prefixCode,this.parsCode[e],this.getVarListCode(this.getVars(\\\\\\\"varying\\\\\\\"),\\\\\\\"varying\\\\\\\"),this.getVarListCode(this.inputs.uniforms[e],\\\\\\\"uniform\\\\\\\"),this.getVarListCode(this.inputs.arrayUniforms[e],\\\\\\\"uniform\\\\\\\"),this.getIncludesCode(\\\\\\\"consts\\\\\\\",e),this.getIncludesCode(\\\\\\\"structs\\\\\\\",e),this.getIncludesCode(\\\\\\\"functions\\\\\\\",e),\\\\\\\"void main() {\\\\\\\",this.getVarListCode(this.getVars(e)),this.code[e],this.resultCode[e],this.finalCode[e],\\\\\\\"}\\\\\\\"].join(`\\n`)}getVarListCode(e,t){t=t??\\\\\\\"\\\\\\\";let r=\\\\\\\"\\\\\\\";for(let i=0,n=e.length;i<n;++i){let o=e[i],l=o.type,a=o.name,c=o.size,u=this.getFormatByType(l);if(u===void 0)throw new Error(\\\\\\\"Node pars \\\\\\\"+u+\\\\\\\" not found.\\\\\\\");u.includes(\\\\\\\"[]\\\\\\\")?r+=t+\\\\\\\" \\\\\\\"+u.substring(0,u.length-2)+\\\\\\\" \\\\\\\"+a+`[${c}];\\n`:r+=t+\\\\\\\" \\\\\\\"+u+\\\\\\\" \\\\\\\"+a+`;\\n`}return r}getVars(e){return this.inputs.vars[e??this.shader]}getNodeData(e){let t=e instanceof oe?e.uuid:e;return this.nodeData[t]=this.nodeData[t]||{}}createUniform(e,t,r,i,n,o){if(t.includes(\\\\\\\"[]\\\\\\\")){let l=this.inputs.arrayUniforms,a=l.list.length,c=new Go({type:t,size:r.size,name:i||\\\\\\\"nodeUA\\\\\\\"+a+(o?\\\\\\\"_\\\\\\\"+o:\\\\\\\"\\\\\\\"),node:r,needsUpdate:n});return l.list.push(c),l[e].push(c),l[e][c.name]=c,this.uniforms[c.name]=c,c}else{let l=this.inputs.uniforms,a=l.list.length,c=new Go({type:t,name:i||\\\\\\\"nodeU\\\\\\\"+a+(o?\\\\\\\"_\\\\\\\"+o:\\\\\\\"\\\\\\\"),node:r,needsUpdate:n});return l.list.push(c),l[e].push(c),l[e][c.name]=c,this.uniforms[c.name]=c,c}}createVertexUniform(e,t,r,i,n){return this.createUniform(\\\\\\\"vertex\\\\\\\",e,t,r,i,n)}createFragmentUniform(e,t,r,i,n){return this.createUniform(\\\\\\\"fragment\\\\\\\",e,t,r,i,n)}include(e,t,r){let i;if(e=typeof e==\\\\\\\"string\\\\\\\"?pt.get(e):e,this.context.include===!1)return e.name;e instanceof se?i=this.includes.functions:e instanceof Ee?i=this.includes.consts:e instanceof Vi&&(i=this.includes.structs);let n=i[this.shader]=i[this.shader]||[];if(e){let o=n[e.name];if(o||(o=n[e.name]={node:e,deps:[]},n.push(o),o.src=e.build(this,\\\\\\\"source\\\\\\\")),e instanceof se&&t&&n[t.name]&&n[t.name].deps.indexOf(e)==-1&&(n[t.name].deps.push(e),e.includes?.length)){let l=0;do this.include(e.includes[l++],t);while(l<e.includes.length)}return r&&(o.src=r),e.name}else throw new Error(\\\\\\\"Include not found.\\\\\\\")}colorToVectorProperties(e){return e.replace(\\\\\\\"r\\\\\\\",\\\\\\\"x\\\\\\\").replace(\\\\\\\"g\\\\\\\",\\\\\\\"y\\\\\\\").replace(\\\\\\\"b\\\\\\\",\\\\\\\"z\\\\\\\").replace(\\\\\\\"a\\\\\\\",\\\\\\\"w\\\\\\\")}colorToVector(e){return e.replace(/c/g,\\\\\\\"v3\\\\\\\")}getIncludes(e,t){return this.includes[e][t||this.shader]}getConstructorFromLength(e){return Qg[e-1]}isTypeMatrix(e){return/^m/.test(e)}getTypeLength(e){return e===\\\\\\\"f\\\\\\\"?1:parseInt(this.colorToVector(e).substr(1))}getTypeFromLength(e){return e===1?\\\\\\\"f\\\\\\\":\\\\\\\"v\\\\\\\"+e}findNode(...e){for(let t=0;t<arguments.length;t++){let r=e[t];if(r?.isNode)return r}}resolve(...e){for(let t=0;t<arguments.length;t++){let r=e[t];if(r!==void 0){if(r.isNode)return r;if(r.isTexture)switch(r.mapping){case or.CubeReflectionMapping:case or.CubeRefractionMapping:return new Tn(r);case or.CubeUVReflectionMapping:case or.CubeUVRefractionMapping:return new Nn(new _t(r));default:return new _t(r)}else{if(r.isVector2)return new Ct(r);if(r.isVector3)return new Bt(r);if(r.isVector4)return new Sn(r)}}}}format(e,t,r){switch(this.colorToVector(r+\\\\\\\" <- \\\\\\\"+t)){case\\\\\\\"f <- v2\\\\\\\":return e+\\\\\\\".x\\\\\\\";case\\\\\\\"f <- v3\\\\\\\":return e+\\\\\\\".x\\\\\\\";case\\\\\\\"f <- v4\\\\\\\":return e+\\\\\\\".x\\\\\\\";case\\\\\\\"f <- i\\\\\\\":case\\\\\\\"f <- b\\\\\\\":return\\\\\\\"float( \\\\\\\"+e+\\\\\\\" )\\\\\\\";case\\\\\\\"v2 <- f\\\\\\\":return\\\\\\\"vec2( \\\\\\\"+e+\\\\\\\" )\\\\\\\";case\\\\\\\"v2 <- v3\\\\\\\":return e+\\\\\\\".xy\\\\\\\";case\\\\\\\"v2 <- v4\\\\\\\":return e+\\\\\\\".xy\\\\\\\";case\\\\\\\"v2 <- i\\\\\\\":case\\\\\\\"v2 <- b\\\\\\\":return\\\\\\\"vec2( float( \\\\\\\"+e+\\\\\\\" ) )\\\\\\\";case\\\\\\\"v3 <- f\\\\\\\":return\\\\\\\"vec3( \\\\\\\"+e+\\\\\\\" )\\\\\\\";case\\\\\\\"v3 <- v2\\\\\\\":return\\\\\\\"vec3( \\\\\\\"+e+\\\\\\\", 0.0 )\\\\\\\";case\\\\\\\"v3 <- v4\\\\\\\":return e+\\\\\\\".xyz\\\\\\\";case\\\\\\\"v3 <- i\\\\\\\":case\\\\\\\"v3 <- b\\\\\\\":return\\\\\\\"vec2( float( \\\\\\\"+e+\\\\\\\" ) )\\\\\\\";case\\\\\\\"v4 <- f\\\\\\\":return\\\\\\\"vec4( \\\\\\\"+e+\\\\\\\" )\\\\\\\";case\\\\\\\"v4 <- v2\\\\\\\":return\\\\\\\"vec4( \\\\\\\"+e+\\\\\\\", 0.0, 1.0 )\\\\\\\";case\\\\\\\"v4 <- v3\\\\\\\":return\\\\\\\"vec4( \\\\\\\"+e+\\\\\\\", 1.0 )\\\\\\\";case\\\\\\\"v4 <- i\\\\\\\":case\\\\\\\"v4 <- b\\\\\\\":return\\\\\\\"vec4( float( \\\\\\\"+e+\\\\\\\" ) )\\\\\\\";case\\\\\\\"i <- f\\\\\\\":case\\\\\\\"i <- b\\\\\\\":return\\\\\\\"int( \\\\\\\"+e+\\\\\\\" )\\\\\\\";case\\\\\\\"i <- v2\\\\\\\":return\\\\\\\"int( \\\\\\\"+e+\\\\\\\".x )\\\\\\\";case\\\\\\\"i <- v3\\\\\\\":return\\\\\\\"int( \\\\\\\"+e+\\\\\\\".x )\\\\\\\";case\\\\\\\"i <- v4\\\\\\\":return\\\\\\\"int( \\\\\\\"+e+\\\\\\\".x )\\\\\\\";case\\\\\\\"b <- f\\\\\\\":return\\\\\\\"( \\\\\\\"+e+\\\\\\\" != 0.0 )\\\\\\\";case\\\\\\\"b <- v2\\\\\\\":return\\\\\\\"( \\\\\\\"+e+\\\\\\\" != vec2( 0.0 ) )\\\\\\\";case\\\\\\\"b <- v3\\\\\\\":return\\\\\\\"( \\\\\\\"+e+\\\\\\\" != vec3( 0.0 ) )\\\\\\\";case\\\\\\\"b <- v4\\\\\\\":return\\\\\\\"( \\\\\\\"+e+\\\\\\\" != vec4( 0.0 ) )\\\\\\\";case\\\\\\\"b <- i\\\\\\\":return\\\\\\\"( \\\\\\\"+e+\\\\\\\" != 0 )\\\\\\\"}return e}getTypeByFormat(e){return Yg[e]||e}getFormatByType(e){return Kg[e]||e}getUUID(e,t){return t=t!==void 0?t:!0,t&&this.cache&&(e=this.cache+\\\\\\\"-\\\\\\\"+e),e}getElementByIndex(e){return Xu[e]}getIndexByElement(e){return Xu.indexOf(e)}isShader(e){return this.shader===e}setShader(e){return this.shader=e,this}mergeDefines(e){for(let t in e)this.defines[t]=e[t];return this.defines}mergeUniform(e){for(let t in e)this.uniforms[t]=e[t];return this.uniforms}getTextureEncodingFromMap(e){let t;return e?e.isTexture&&(t=e.encoding):t=or.LinearEncoding,t===or.LinearEncoding&&this.context.gamma&&(t=or.GammaEncoding),t}};var Ls=class extends oe{constructor(e=new oe){super(\\\\\\\"v4\\\\\\\");this.nodeType=\\\\\\\"Raw\\\\\\\";this.value=e}generate(e){let t=this.value.analyzeAndFlow(e,this.type),r=t.code+`\\n`;return e.isShader(\\\\\\\"vertex\\\\\\\")?r+=\\\\\\\"gl_Position = \\\\\\\"+t.result+\\\\\\\";\\\\\\\":r+=\\\\\\\"gl_FragColor = \\\\\\\"+t.result+\\\\\\\";\\\\\\\",r}copy(e){return super.copy(e),this.value.copy(e.value),this}toJSON(e){let t=this.getJSONNode(e);return t||(t=this.createJSONNode(e),t.value=this.value.toJSON(e).uuid),t.nodeType=this.nodeType,t}fromJSON(e,t){return super.fromJSON(e,t),e.value&&(this.value=t.getNode(e.value)),this}};var hr=class extends Ds.ShaderMaterial{constructor(e,t,r){super(r);this.isNodeMaterial=!0;this.type=\\\\\\\"NodeMaterial\\\\\\\";this.wireframeLinecap=\\\\\\\"\\\\\\\";this.wireframeLinejoin=\\\\\\\"\\\\\\\";this.uniformsBackup={};this.userData={type:\\\\\\\"\\\\\\\",category:\\\\\\\"\\\\\\\",nodeType:\\\\\\\"\\\\\\\"};this.fog=!0,this.vertex=e??new Ls(new wt(wt.PROJECTION)),this.fragment=t??new Ls(new _e(5855577)),this.updaters=[],this.isDetached=!0,this.dithering=!0,this.onBeforeCompile=this._onBeforeCompile}getDefines(){return this.defines}getUniforms(){return this.uniforms}getVertexShader(){return this.vertexShader}getFragmentShader(){return this.fragmentShader}_onBeforeCompile(e,t){this.build({renderer:t}),e.defines=this.defines,e.uniforms=this.uniforms,e.vertexShader=this.vertexShader,e.fragmentShader=this.fragmentShader,e.extensionDerivatives=this.extensions.derivatives===!0,e.extensionFragDepth=this.extensions.fragDepth===!0,e.extensionDrawBuffers=this.extensions.drawBuffers===!0,e.extensionShaderTextureLOD=this.extensions.shaderTextureLOD===!0}_getLayerStack(e){let t=new Ut(this);return e&&(async()=>{for(;e.image===void 0;)await new Promise(r=>requestAnimationFrame(r));t.addLayerAt(1,{type:Be.TEXTURE,texture:e}),this.dispose()})(),t}clampUniformsForPreview(e,t){let r=(i,n,o)=>Math.min(Math.max(i,n),o);if(this.userData.layers){for(let i of this.userData.layers.getLayers())if(i.type==Be.DISPLACE){this.uniformsBackup[`f${i.id}_intensity`]=i.uniforms[`f${i.id}_intensity`].value;let n=r(i.uniforms[`f${i.id}_intensity`].value,e,t);i.uniforms[`f${i.id}_intensity`].value=n}}}restoreClampedUniforms(){if(this.userData.layers)for(let e of this.userData.layers.getLayers())e.type==Be.DISPLACE&&(e.uniforms[`f${e.id}_intensity`].value=this.uniformsBackup[`f${e.id}_intensity`])}customProgramCacheKey(){return this.getHash()}updateFrame(e){for(let t=0;t<this.updaters.length;++t)e.updateNode(this.updaters[t])}build(e){e=e??{};let t=e.builder??new Fl;return t.setMaterial(this,e.renderer),t.build(this.vertex,this.fragment),this.vertexShader=t.getCode(\\\\\\\"vertex\\\\\\\"),this.fragmentShader=t.getCode(\\\\\\\"fragment\\\\\\\"),this.defines=t.defines,this.uniforms=t.uniforms,this.extensions=t.extensions,this.updaters=t.updaters,this.fog=t.requires.fog,this.lights=t.requires.lights,this.transparent=t.requires.transparent||this.blending>Ds.NormalBlending,this}getHash(){let e=\\\\\\\"{\\\\\\\";return e+='\\\\\\\"vertex\\\\\\\":'+this.vertex.getHash()+\\\\\\\",\\\\\\\",e+='\\\\\\\"fragment\\\\\\\":'+this.fragment.getHash(),e+=\\\\\\\"}\\\\\\\",e}copy(e){let t=this.uuid;for(let r in e)this[r]=e[r];return this.uuid=t,e.userData!==void 0&&(this.userData=JSON.parse(JSON.stringify(e.userData))),this}toJSON(e){let t=this.userData.layers;this.userData.layers=void 0;let r=super.toJSON(e);return r.type=\\\\\\\"ShaderMaterial\\\\\\\",r.userData={type:this.userData.type,category:this.userData.category,nodeType:this.type,layers:t.toJSON(e)},r.vertex=this.vertex.toJSON(e).uuid,r.fragment=this.fragment.toJSON(e).uuid,delete r.vertexShader,delete r.fragmentShader,delete r.color,delete r.shininess,delete r.specular,delete r.roughness,delete r.metalness,delete r.uniforms,e&&!e.materials[this.uuid]&&(e.materials[this.uuid]=r),this.userData.layers=t,r}fromJSON(e,t){this.defines=e.defines??{},this.depthFunc=e.depthFunc,this.depthWrite=e.depthWrite,this.side=e.side!==void 0?e.side:Ds.FrontSide,this.transparent=e.transparent,this.wireframeLinecap=e.wireframeLinecap,this.wireframeLinejoin=e.wireframeLinejoin,this.flatShading=e.flatShading,this.wireframe=e.wireframe,this.userData.layers.fromJSON(e.userData.layers,t,this)}};Object.defineProperties(hr.prototype,{properties:{get:function(){return this.fragment.properties}},needsUpdate:{set:function(s){s===!0&&this.version++,this.needsCompile=s},get:function(){return this.needsCompile}}});var Uo=F(z()),Cn=class extends oe{constructor(){super(\\\\\\\"basic\\\\\\\");this.nodeType=\\\\\\\"Basic\\\\\\\";this.color=new _e(5855577)}generate(e){let t;if(e.isShader(\\\\\\\"vertex\\\\\\\")){let r=this.position?this.position.analyzeAndFlow(e,\\\\\\\"v3\\\\\\\",{cache:\\\\\\\"position\\\\\\\"}):void 0;e.mergeUniform(Uo.UniformsUtils.merge([Uo.UniformsLib.fog])),e.addParsCode([\\\\\\\"varying vec3 vViewPosition;\\\\\\\",\\\\\\\"#ifndef FLAT_SHADED\\\\\\\",\\\\\\\" varying vec3 vNormal;\\\\\\\",\\\\\\\"#endif\\\\\\\",\\\\\\\"#include <fog_pars_vertex>\\\\\\\"].join(`\\n`));let i=[\\\\\\\"#include <beginnormal_vertex>\\\\\\\",`\\n\\t\\t\\t\\t#if !defined( USE_LAYER_DISPLACE )\\n\\t\\t\\t\\t\\t#include <defaultnormal_vertex>\\n\\t\\t\\t\\t#endif\\n\\n\\t\\t\\t\\tvec3 displaced_position = position;\\n\\t\\t\\t\\tvec3 displaced_normal = normal;\\n\\n\\t\\t\\t\\t#if defined( USE_LAYER_DISPLACE )\\n\\t\\t\\t\\t\\tvec3 transformed;\\n\\t\\t\\t\\t\\tvec3 transformedNormal;\\n\\t\\t\\t\\t#endif\\n\\t\\t\\t\\t`,\\\\\\\"#ifndef FLAT_SHADED\\\\\\\",\\\\\\\" vNormal = normalize( transformedNormal );\\\\\\\",\\\\\\\"#endif\\\\\\\",`\\n\\t\\t\\t\\t#if !defined( USE_LAYER_DISPLACE )\\n\\t\\t\\t\\t\\t#include <begin_vertex>\\n\\t\\t\\t\\t#endif /* !USE_LAYER_DISPLACE */\\n\\t\\t\\t\\t`];r&&i.push(r.code,r.result?\\\\\\\"displaced_position = \\\\\\\"+r.result+\\\\\\\";\\\\\\\":\\\\\\\"\\\\\\\"),i.push(\\\\\\\"transformed = displaced_position;\\\\\\\",\\\\\\\"transformedNormal = normalMatrix * displaced_normal;\\\\\\\",\\\\\\\"#ifndef FLAT_SHADED\\\\\\\",\\\\\\\"\\tvNormal = transformedNormal;\\\\\\\",\\\\\\\"#endif\\\\\\\"),i.push(\\\\\\\"#include <project_vertex>\\\\\\\",\\\\\\\"#include <fog_vertex>\\\\\\\",\\\\\\\"#include <clipping_planes_vertex>\\\\\\\",\\\\\\\"\\tvViewPosition = - mvPosition.xyz;\\\\\\\",\\\\\\\"#include <worldpos_vertex>\\\\\\\"),t=i.join(`\\n`)}else{this.color===void 0&&(this.color=new _e(5855577)),this.color.analyze(e,{slot:\\\\\\\"color\\\\\\\"}),this.alpha&&this.alpha.analyze(e),this.afterColor&&this.afterColor.analyze(e,{slot:\\\\\\\"afterColor\\\\\\\"});let r=this.color.flow(e,\\\\\\\"c\\\\\\\",{slot:\\\\\\\"color\\\\\\\"}),i=this.alpha?this.alpha.flow(e,\\\\\\\"f\\\\\\\"):void 0,n=this.afterColor?this.afterColor.flow(e,\\\\\\\"c\\\\\\\",{slot:\\\\\\\"afterColor\\\\\\\"}):void 0;e.requires.transparent=i!==void 0,e.addParsCode([\\\\\\\"#include <fog_pars_fragment>\\\\\\\",\\\\\\\"#include <dithering_pars_fragment>\\\\\\\",\\\\\\\"varying vec3 vViewPosition;\\\\\\\",\\\\\\\"#ifndef FLAT_SHADED\\\\\\\",\\\\\\\" varying vec3 vNormal;\\\\\\\",\\\\\\\"#endif\\\\\\\"].join(`\\n`));let o=[\\\\\\\"#include <normal_fragment_begin>\\\\\\\",r.code];i&&o.push(i.code,\\\\\\\"#ifdef ALPHATEST\\\\\\\",\\\\\\\" if ( \\\\\\\"+i.result+\\\\\\\" <= ALPHATEST ) discard;\\\\\\\",\\\\\\\"#endif\\\\\\\"),n?o.push(n.code,`vec3 outgoingLight = ${r.result};`,`vec3 finalColor = spe_blend(outgoingLight, ${n.result}, 1.0, SPE_BLENDING_NORMAL);`):o.push(`vec3 finalColor = ${r.result};`),i?o.push(`gl_FragColor = vec4( finalColor, accumAlpha * ${i.result} );`):o.push(\\\\\\\"gl_FragColor = vec4(\\\\\\\"+r.result+\\\\\\\", 1.0 );\\\\\\\"),o.push(\\\\\\\"#include <fog_fragment>\\\\\\\",\\\\\\\"#include <dithering_fragment>\\\\\\\"),t=o.join(`\\n`)}return t}copy(e){return super.copy(e),e.color&&(this.color=e.color.clone()),e.position&&(this.position=e.position.clone()),e.alpha&&(this.alpha=e.alpha.clone()),e.afterColor&&(this.afterColor=e.afterColor.clone()),e.shadingAlpha&&(this.shadingAlpha=e.shadingAlpha.clone()),e.shadingBlend&&(this.shadingBlend=e.shadingBlend.clone()),this}toJSON(e){let t=this.getJSONNode(e);return t||(t=this.createJSONNode(e),this.color&&(t.color=this.color.toJSON(e).uuid),this.position&&(t.position=this.position.toJSON(e).uuid),this.alpha&&(t.alpha=this.alpha.toJSON(e).uuid),this.afterColor&&(t.afterColor=this.afterColor.toJSON(e).uuid),this.shadingAlpha&&(t.shadingAlpha=this.shadingAlpha.toJSON(e).uuid),this.shadingBlend&&(t.shadingBlend=this.shadingBlend.toJSON(e).uuid)),t.nodeType=this.nodeType,t}fromJSON(e,t){return super.fromJSON(e,t),e.position&&(this.position=t.getNode(e.position)),e.color&&(this.color=t.getNode(e.color)),e.alpha&&(this.alpha=t.getNode(e.alpha)),e.afterColor&&(this.afterColor=t.getNode(e.afterColor)),e.shadingAlpha&&(this.shadingAlpha=t.getNode(e.shadingAlpha)),e.shadingBlend&&(this.shadingBlend=t.getNode(e.shadingBlend)),this}};var Gl=class extends hr{get color(){return this.fragment.color}set color(e){this.fragment.color=e}get afterColor(){return this.fragment.afterColor}set afterColor(e){this.fragment.afterColor=e}get alpha(){return this.fragment.alpha}set alpha(e){this.fragment.alpha=e}get shadingAlpha(){return this.fragment.shadingAlpha}set shadingAlpha(e){this.fragment.shadingAlpha=e}get shadingBlend(){return this.fragment.shadingBlend}set shadingBlend(e){this.fragment.shadingBlend=e}get position(){return this.fragment.position}set position(e){this.fragment.position=e}constructor(e=new Cn,t){super(e,e,t);this.type=\\\\\\\"BasicNodeMaterial\\\\\\\",this.fragment=e}};var Qt=class extends Gl{constructor(e,t,r){super(t,e);this.userData.type=\\\\\\\"BasicMaterial\\\\\\\",this.userData.category=\\\\\\\"Basic\\\\\\\",this.userData.layers=r??this._getLayerStack(e?.map)}get layersList(){return this.userData.layers}set layersList(e){this.userData.layers=e}equals(e){return this.userData.type===e.userData.type&&this.userData.layers.uuid==e.userData.layers.uuid}copy(e){if(e.userData.layers!==void 0&&e.userData.layers instanceof Ut){let t=e.userData.layers,r=e.fragment;super.copy(e);let i=r.clone();this.fragment=i,this.vertex=i;let n=t.clone(this);this.userData.layers=n}else super.copy(e);return this}static fromJSON(e,t,r){let i=r.getNode(t.vertex),n=new Qt(void 0,i);return n.fromJSON(t,r),n}static fromMaterial(e){let t=new Qt(e.map?{map:e.map}:{}),r=t.fragment;return r.color.value.copy(e.color),r.alpha.value=e.opacity,t}dispose(){super.dispose()}};var Mn=F(z());var En=class extends oe{constructor(){super(\\\\\\\"phong\\\\\\\");this.nodeType=\\\\\\\"Phong\\\\\\\";this.color=new _e(5855577),this.specular=new _e(1118481),this.shininess=new te(30),this.shadingAlpha=new te(1),this.shadingBlend=new Me(0)}build(e){let t;if(e.define(\\\\\\\"PHONG\\\\\\\"),e.requires.lights=!0,e.isShader(\\\\\\\"vertex\\\\\\\")){let r=this.position?this.position.analyzeAndFlow(e,\\\\\\\"v3\\\\\\\",{cache:\\\\\\\"position\\\\\\\"}):void 0;e.mergeUniform(Mn.UniformsUtils.merge([Mn.UniformsLib.fog,Mn.UniformsLib.lights])),e.addParsCode([\\\\\\\"varying vec3 vViewPosition;\\\\\\\",\\\\\\\"#ifndef FLAT_SHADED\\\\\\\",\\\\\\\"\\tvarying vec3 vNormal;\\\\\\\",\\\\\\\"#endif\\\\\\\",\\\\\\\"#include <fog_pars_vertex>\\\\\\\",\\\\\\\"#include <shadowmap_pars_vertex>\\\\\\\",\\\\\\\"#include <clipping_planes_pars_vertex>\\\\\\\"].join(`\\n`));let i=[\\\\\\\"#include <beginnormal_vertex>\\\\\\\",`\\n\\t\\t\\t\\t#ifndef USE_LAYER_DISPLACE\\n\\t\\t\\t\\t\\t#include <defaultnormal_vertex>\\n\\t\\t\\t\\t#endif\\n\\n\\t\\t\\t\\tvec3 displaced_position = position;\\n\\t\\t\\t\\tvec3 displaced_normal = normal;\\n\\n\\t\\t\\t\\t#ifdef USE_LAYER_DISPLACE\\n\\t\\t\\t\\t\\tvec3 transformed;\\n\\t\\t\\t\\t\\tvec3 transformedNormal;\\n\\t\\t\\t\\t#endif\\n\\t\\t\\t\\t`,\\\\\\\"#ifndef FLAT_SHADED\\\\\\\",\\\\\\\"\\tvNormal = normalize( transformedNormal );\\\\\\\",\\\\\\\"#endif\\\\\\\",`\\n\\t\\t\\t\\t#ifndef USE_LAYER_DISPLACE\\n\\t\\t\\t\\t\\t#include <begin_vertex>\\n\\t\\t\\t\\t#endif\\n\\t\\t\\t\\t`];r&&i.push(r.code,r.result?\\\\\\\"displaced_position = \\\\\\\"+r.result+\\\\\\\";\\\\\\\":\\\\\\\"\\\\\\\"),i.push(\\\\\\\"transformed = displaced_position;\\\\\\\",\\\\\\\"transformedNormal = normalMatrix * displaced_normal;\\\\\\\",\\\\\\\"#ifndef FLAT_SHADED\\\\\\\",\\\\\\\"    vNormal = transformedNormal;\\\\\\\",\\\\\\\"#endif\\\\\\\"),i.push(\\\\\\\"\\t#include <project_vertex>\\\\\\\",\\\\\\\"\\t#include <clipping_planes_vertex>\\\\\\\",\\\\\\\"\\tvViewPosition = - mvPosition.xyz;\\\\\\\",\\\\\\\"\\t#include <worldpos_vertex>\\\\\\\",\\\\\\\"\\t#include <shadowmap_vertex>\\\\\\\",\\\\\\\"\\t#include <fog_vertex>\\\\\\\"),t=i.join(`\\n`)}else{this.color===void 0&&(this.color=new _e(5855577)),this.color.analyze(e,{slot:\\\\\\\"color\\\\\\\"}),this.specular.analyze(e),this.shininess.analyze(e),this.shadingAlpha.analyze(e),this.shadingBlend.analyze(e),this.afterColor&&this.afterColor.analyze(e,{slot:\\\\\\\"afterColor\\\\\\\"}),this.alpha&&this.alpha.analyze(e);let r=this.color.flow(e,\\\\\\\"c\\\\\\\",{slot:\\\\\\\"color\\\\\\\"}),i=this.specular.flow(e,\\\\\\\"c\\\\\\\"),n=this.shininess.flow(e,\\\\\\\"f\\\\\\\"),o=this.shadingAlpha.flow(e,\\\\\\\"f\\\\\\\"),l=this.shadingBlend.flow(e,\\\\\\\"i\\\\\\\"),a=this.afterColor?this.afterColor.flow(e,\\\\\\\"c\\\\\\\",{slot:\\\\\\\"afterColor\\\\\\\"}):void 0,c=this.alpha?this.alpha.flow(e,\\\\\\\"f\\\\\\\"):void 0;e.requires.transparent=c!==void 0,e.addParsCode([\\\\\\\"#include <fog_pars_fragment>\\\\\\\",\\\\\\\"#include <bsdfs>\\\\\\\",\\\\\\\"#include <lights_pars_begin>\\\\\\\",\\\\\\\"#include <lights_phong_pars_fragment>\\\\\\\",\\\\\\\"#include <shadowmap_pars_fragment>\\\\\\\",\\\\\\\"#include <dithering_pars_fragment>\\\\\\\"].join(`\\n`));let u=[\\\\\\\"#include <normal_fragment_begin>\\\\\\\",`\\n\\t\\t\\t\\t// NOTE: gl_FrontFacing alternative using face normal estimation.\\n\\t\\t\\t\\tvec3 viewdx = dFdx(vViewPosition);\\n\\t\\t\\t\\tvec3 viewdy = dFdy(vViewPosition);\\n\\t\\t\\t\\tvec3 faceNormal = normalize(cross(viewdx,viewdy));\\n\\t\\t\\t\\tif (dot(normal, faceNormal) < 0.0) {\\n\\t\\t\\t\\t\\tnormal *= -1.0;\\n\\t\\t\\t\\t}\\n\\t\\t\\t\\t`,\\\\\\\"\\tBlinnPhongMaterial material;\\\\\\\"];u.push(r.code,\\\\\\\"\\tvec3 diffuseColor = \\\\\\\"+r.result+\\\\\\\";\\\\\\\",\\\\\\\"\\tReflectedLight reflectedLight = ReflectedLight( vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ) );\\\\\\\",i.code,\\\\\\\"\\tvec3 specular = \\\\\\\"+i.result+\\\\\\\";\\\\\\\",n.code,\\\\\\\"\\tfloat shininess = max( 0.0001, \\\\\\\"+n.result+\\\\\\\" );\\\\\\\",\\\\\\\"\\tfloat specularStrength = 1.0;\\\\\\\"),c&&u.push(c.code,\\\\\\\"#ifdef ALPHATEST\\\\\\\",\\\\\\\"if ( \\\\\\\"+c.result+\\\\\\\" <= ALPHATEST ) discard;\\\\\\\",\\\\\\\"#endif\\\\\\\"),u.push(\\\\\\\"material.diffuseColor = diffuseColor;\\\\\\\"),u.push(\\\\\\\"material.specularColor = specular;\\\\\\\",\\\\\\\"material.specularShininess = shininess;\\\\\\\",\\\\\\\"material.specularStrength = specularStrength;\\\\\\\",\\\\\\\"#include <lights_fragment_begin>\\\\\\\",\\\\\\\"#include <lights_fragment_end>\\\\\\\"),u.push(\\\\\\\"vec3 outgoingLight = reflectedLight.directDiffuse + reflectedLight.indirectDiffuse + reflectedLight.directSpecular;\\\\\\\"),u.push(`\\n\\t\\t\\t\\tif (outgoingLight != diffuseColor) {\\n\\t\\t\\t\\t\\tfloat lightAccu = clamp( length( reflectedLight.directSpecular + reflectedLight.indirectSpecular ), 0.0, 1.0 );\\n\\t\\t\\t\\t\\taccumAlpha += ( 1.0 - accumAlpha ) * ${o.result} * lightAccu;\\n\\t\\t\\t\\t\\toutgoingLight = spe_blend( diffuseColor, outgoingLight, ${o.result}, ${l.result} );\\n\\t\\t\\t\\t}\\n\\t\\t\\t\\t`),a&&u.push(a.code,`outgoingLight = spe_blend(outgoingLight, ${a.result}, 1.0, SPE_BLENDING_NORMAL);`),c?u.push(`gl_FragColor = vec4( outgoingLight, accumAlpha * ${c.result} );`):u.push(\\\\\\\"gl_FragColor = vec4( outgoingLight, 1.0 );\\\\\\\"),u.push(\\\\\\\"#include <encodings_fragment>\\\\\\\",\\\\\\\"#include <fog_fragment>\\\\\\\",\\\\\\\"#include <dithering_fragment>\\\\\\\"),t=u.join(`\\n`)}return t}copy(e){return super.copy(e),e.color&&(this.color=e.color.clone()),this.specular=e.specular.clone(),this.shininess=e.shininess.clone(),e.position&&(this.position=e.position.clone()),e.afterColor&&(this.afterColor=e.afterColor.clone()),e.alpha&&(this.alpha=e.alpha.clone()),e.shadingAlpha&&(this.shadingAlpha=e.shadingAlpha.clone()),e.shadingBlend&&(this.shadingBlend=e.shadingBlend.clone()),this}toJSON(e){let t=this.getJSONNode(e);return t||(t=this.createJSONNode(e),this.position&&(t.position=this.position.toJSON(e).uuid),this.color&&(t.color=this.color.toJSON(e).uuid),t.specular=this.specular.toJSON(e).uuid,t.shininess=this.shininess.toJSON(e).uuid,t.shadingAlpha=this.shadingAlpha.toJSON(e).uuid,t.shadingBlend=this.shadingBlend.toJSON(e).uuid,this.afterColor&&(t.afterColor=this.afterColor.toJSON(e).uuid),this.alpha&&(t.alpha=this.alpha.toJSON(e).uuid)),t.nodeType=this.nodeType,t}fromJSON(e,t){return super.fromJSON(e,t),e.position&&(this.position=t.getNode(e.position)),e.color&&(this.color=t.getNode(e.color)),e.specular&&(this.specular=t.getNode(e.specular)),e.shininess&&(this.shininess=t.getNode(e.shininess)),e.alpha&&(this.alpha=t.getNode(e.alpha)),e.shadingAlpha&&(this.shadingAlpha=t.getNode(e.shadingAlpha)),e.shadingBlend&&(this.shadingBlend=t.getNode(e.shadingBlend)),e.afterColor&&(this.afterColor=t.getNode(e.afterColor)),this}};var zl=class extends hr{get color(){return this.fragment.color}set color(e){this.fragment.color=e}get afterColor(){return this.fragment.afterColor}set afterColor(e){this.fragment.afterColor=e}get alpha(){return this.fragment.alpha}set alpha(e){this.fragment.alpha=e}get shadingAlpha(){return this.fragment.shadingAlpha}set shadingAlpha(e){this.fragment.shadingAlpha=e}get shadingBlend(){return this.fragment.shadingBlend}set shadingBlend(e){this.fragment.shadingBlend=e}get position(){return this.fragment.position}set position(e){this.fragment.position=e}get specular(){return this.fragment.specular}set specular(e){this.fragment.specular=e}get shininess(){return this.fragment.shininess}set shininess(e){this.fragment.shininess=e}constructor(e=new En,t){super(e,e,t);this.type=\\\\\\\"PhongNodeMaterial\\\\\\\",this.fragment=e}};var fr=class extends zl{constructor(e,t,r){super(t,e);this.userData.type=\\\\\\\"PhongMaterial\\\\\\\",this.userData.category=\\\\\\\"Phong\\\\\\\",this.userData.layers=r??this._getLayerStack(e?.map)}get layersList(){return this.userData.layers}set layersList(e){this.userData.layers=e}equals(e){return this.userData.type===e.userData.type&&this.userData.layers.uuid==e.userData.layers.uuid}copy(e){if(e.userData.layers!==void 0&&e.userData.layers instanceof Ut){let t=e.userData.layers,r=e.fragment;super.copy(e);let i=r.clone();this.fragment=i,this.vertex=i;let n=t.clone(this);this.userData.layers=n,i.shadingAlpha.value=r.shadingAlpha.value,i.shadingBlend.value=r.shadingBlend.value}else super.copy(e);return this}static fromJSON(e,t,r){let i=r.getNode(t.vertex),n=new fr(void 0,i);return n.fromJSON(t,r),n}static fromMaterial(e){let t=new fr(e.map?{map:e.map}:{}),r=t.fragment;return r.color.value.copy(e.color),r.alpha.value=e.opacity,t}dispose(){super.dispose()}};var In=F(z());var Ln=class extends oe{constructor(){super(\\\\\\\"lambert\\\\\\\");this.nodeType=\\\\\\\"Lambert\\\\\\\";this.color=new _e(5855577),this.emissive=new _e(0),this.emissiveIntensity=new te(1),this.shadingAlpha=new te(1),this.shadingBlend=new Me(0)}build(e){let t;if(e.define(\\\\\\\"LAMBERT\\\\\\\"),e.requires.lights=!0,e.isShader(\\\\\\\"vertex\\\\\\\")){let r=this.position?this.position.analyzeAndFlow(e,\\\\\\\"v3\\\\\\\",{cache:\\\\\\\"position\\\\\\\"}):void 0;e.mergeUniform(In.UniformsUtils.merge([In.UniformsLib.fog,In.UniformsLib.lights])),e.addParsCode([\\\\\\\"varying vec3 vViewPosition;\\\\\\\",\\\\\\\"varying vec3 vLightFront;\\\\\\\",\\\\\\\"varying vec3 vIndirectFront;\\\\\\\",\\\\\\\"#ifndef DOUBLE_SIDED\\\\\\\",\\\\\\\"   #define DOUBLE_SIDED\\\\\\\",\\\\\\\"#endif\\\\\\\",\\\\\\\"#ifndef FLAT_SHADED\\\\\\\",\\\\\\\"\\tvarying vec3 vNormal;\\\\\\\",\\\\\\\"#endif\\\\\\\",\\\\\\\"#ifdef DOUBLE_SIDED\\\\\\\",\\\\\\\"\\tvarying vec3 vLightBack;\\\\\\\",\\\\\\\"\\tvarying vec3 vIndirectBack;\\\\\\\",\\\\\\\"#endif\\\\\\\",\\\\\\\"#include <bsdfs>\\\\\\\",\\\\\\\"#include <lights_pars_begin>\\\\\\\",\\\\\\\"#include <color_pars_vertex>\\\\\\\",\\\\\\\"#include <fog_pars_vertex>\\\\\\\",\\\\\\\"#include <shadowmap_pars_vertex>\\\\\\\",\\\\\\\"#include <clipping_planes_pars_vertex>\\\\\\\"].join(`\\n`));let i=[\\\\\\\"#include <beginnormal_vertex>\\\\\\\",`\\n\\t\\t\\t\\t#ifndef USE_LAYER_DISPLACE\\n\\t\\t\\t\\t\\t#include <defaultnormal_vertex>\\n\\t\\t\\t\\t#endif\\n\\n\\t\\t\\t\\tvec3 displaced_position = position;\\n\\t\\t\\t\\tvec3 displaced_normal = normal;\\n\\n\\t\\t\\t\\t#ifdef USE_LAYER_DISPLACE\\n\\t\\t\\t\\t\\tvec3 transformed;\\n\\t\\t\\t\\t\\tvec3 transformedNormal;\\n\\t\\t\\t\\t#endif\\n\\t\\t\\t\\t`,\\\\\\\"#ifndef FLAT_SHADED\\\\\\\",\\\\\\\"\\tvNormal = normalize( transformedNormal );\\\\\\\",\\\\\\\"#endif\\\\\\\",`\\n\\t\\t\\t\\t#ifndef USE_LAYER_DISPLACE\\n\\t\\t\\t\\t\\t#include <begin_vertex>\\n\\t\\t\\t\\t#endif\\n\\t\\t\\t\\t`];r&&i.push(r.code,r.result?\\\\\\\"displaced_position = \\\\\\\"+r.result+\\\\\\\";\\\\\\\":\\\\\\\"\\\\\\\"),i.push(\\\\\\\"transformed = displaced_position;\\\\\\\",\\\\\\\"transformedNormal = normalMatrix * displaced_normal;\\\\\\\",\\\\\\\"#ifndef FLAT_SHADED\\\\\\\",\\\\\\\"    vNormal = transformedNormal;\\\\\\\",\\\\\\\"#endif\\\\\\\"),i.push(\\\\\\\"\\t#include <project_vertex>\\\\\\\",\\\\\\\"\\t#include <clipping_planes_vertex>\\\\\\\",\\\\\\\"\\tvViewPosition = - mvPosition.xyz;\\\\\\\",\\\\\\\"\\t#include <worldpos_vertex>\\\\\\\",`\\n\\t\\t\\t\\t\\tvec3 diffuse = vec3( 1.0 );\\n\\t\\t\\t\\t\\tGeometricContext geometry;\\n\\t\\t\\t\\t\\tgeometry.position = mvPosition.xyz;\\n\\t\\t\\t\\t\\tgeometry.normal = normalize( transformedNormal );\\n\\t\\t\\t\\t\\tgeometry.viewDir = ( isOrthographic ) ? vec3( 0, 0, 1 ) : normalize( -mvPosition.xyz );\\n\\t\\t\\t\\t\\tGeometricContext backGeometry;\\n\\t\\t\\t\\t\\tbackGeometry.position = geometry.position;\\n\\t\\t\\t\\t\\tbackGeometry.normal = -geometry.normal;\\n\\t\\t\\t\\t\\tbackGeometry.viewDir = geometry.viewDir;\\n\\t\\t\\t\\t\\tvLightFront = vec3( 0.0 );\\n\\t\\t\\t\\t\\tvIndirectFront = vec3( 0.0 );\\n\\t\\t\\t\\t\\t#ifdef DOUBLE_SIDED\\n\\t\\t\\t\\t\\t\\tvLightBack = vec3( 0.0 );\\n\\t\\t\\t\\t\\t\\tvIndirectBack = vec3( 0.0 );\\n\\t\\t\\t\\t\\t#endif\\n\\t\\t\\t\\t\\tIncidentLight directLight;\\n\\t\\t\\t\\t\\tfloat dotNL;\\n\\t\\t\\t\\t\\tvec3 directLightColor_Diffuse;\\n\\t\\t\\t\\t\\t#if NUM_POINT_LIGHTS > 0\\n\\t\\t\\t\\t\\t\\t#pragma unroll_loop_start\\n\\t\\t\\t\\t\\t\\tfor ( int i = 0; i < NUM_POINT_LIGHTS; i ++ ) {\\n\\t\\t\\t\\t\\t\\t\\tgetPointDirectLightIrradiance( pointLights[ i ], geometry, directLight );\\n\\t\\t\\t\\t\\t\\t\\tdotNL = dot( geometry.normal, directLight.direction );\\n\\t\\t\\t\\t\\t\\t\\tdirectLightColor_Diffuse = PI * directLight.color;\\n\\t\\t\\t\\t\\t\\t\\tvLightFront += saturate( dotNL ) * directLightColor_Diffuse;\\n\\t\\t\\t\\t\\t\\t\\t#ifdef DOUBLE_SIDED\\n\\t\\t\\t\\t\\t\\t\\t\\tvLightBack += saturate( -dotNL ) * directLightColor_Diffuse;\\n\\t\\t\\t\\t\\t\\t\\t#endif\\n\\t\\t\\t\\t\\t\\t}\\n\\t\\t\\t\\t\\t\\t#pragma unroll_loop_end\\n\\t\\t\\t\\t\\t#endif\\n\\t\\t\\t\\t\\t#if NUM_SPOT_LIGHTS > 0\\n\\t\\t\\t\\t\\t\\t#pragma unroll_loop_start\\n\\t\\t\\t\\t\\t\\tfor ( int i = 0; i < NUM_SPOT_LIGHTS; i ++ ) {\\n\\t\\t\\t\\t\\t\\t\\tgetSpotDirectLightIrradiance( spotLights[ i ], geometry, directLight );\\n\\t\\t\\t\\t\\t\\t\\tdotNL = dot( geometry.normal, directLight.direction );\\n\\t\\t\\t\\t\\t\\t\\tdirectLightColor_Diffuse = PI * directLight.color;\\n\\t\\t\\t\\t\\t\\t\\tvLightFront += saturate( dotNL ) * directLightColor_Diffuse;\\n\\t\\t\\t\\t\\t\\t\\t#ifdef DOUBLE_SIDED\\n\\t\\t\\t\\t\\t\\t\\t\\tvLightBack += saturate( -dotNL ) * directLightColor_Diffuse;\\n\\t\\t\\t\\t\\t\\t\\t#endif\\n\\t\\t\\t\\t\\t\\t}\\n\\t\\t\\t\\t\\t\\t#pragma unroll_loop_end\\n\\t\\t\\t\\t\\t#endif\\n\\t\\t\\t\\t\\t#if NUM_DIR_LIGHTS > 0\\n\\t\\t\\t\\t\\t\\t#pragma unroll_loop_start\\n\\t\\t\\t\\t\\t\\tfor ( int i = 0; i < NUM_DIR_LIGHTS; i ++ ) {\\n\\t\\t\\t\\t\\t\\t\\tgetDirectionalDirectLightIrradiance( directionalLights[ i ], geometry, directLight );\\n\\t\\t\\t\\t\\t\\t\\tdotNL = dot( geometry.normal, directLight.direction );\\n\\t\\t\\t\\t\\t\\t\\tdirectLightColor_Diffuse = PI * directLight.color;\\n\\t\\t\\t\\t\\t\\t\\tvLightFront += saturate( dotNL ) * directLightColor_Diffuse;\\n\\t\\t\\t\\t\\t\\t\\t#ifdef DOUBLE_SIDED\\n\\t\\t\\t\\t\\t\\t\\t\\tvLightBack += saturate( -dotNL ) * directLightColor_Diffuse;\\n\\t\\t\\t\\t\\t\\t\\t#endif\\n\\t\\t\\t\\t\\t\\t}\\n\\t\\t\\t\\t\\t\\t#pragma unroll_loop_end\\n\\t\\t\\t\\t\\t#endif\\n\\t\\t\\t\\t\\t#if NUM_HEMI_LIGHTS > 0\\n\\t\\t\\t\\t\\t\\t#pragma unroll_loop_start\\n\\t\\t\\t\\t\\t\\tfor ( int i = 0; i < NUM_HEMI_LIGHTS; i ++ ) {\\n\\t\\t\\t\\t\\t\\t\\tvIndirectFront += getHemisphereLightIrradiance( hemisphereLights[ i ], geometry );\\n\\t\\t\\t\\t\\t\\t\\t#ifdef DOUBLE_SIDED\\n\\t\\t\\t\\t\\t\\t\\t\\tvIndirectBack += getHemisphereLightIrradiance( hemisphereLights[ i ], backGeometry );\\n\\t\\t\\t\\t\\t\\t\\t#endif\\n\\t\\t\\t\\t\\t\\t}\\n\\t\\t\\t\\t\\t\\t#pragma unroll_loop_end\\n\\t\\t\\t\\t\\t#endif\\n\\t\\t\\t\\t`,\\\\\\\"\\t#include <shadowmap_vertex>\\\\\\\",\\\\\\\"\\t#include <fog_vertex>\\\\\\\"),t=i.join(`\\n`)}else{this.color===void 0&&(this.color=new _e(5855577)),this.color.analyze(e,{slot:\\\\\\\"color\\\\\\\"}),this.shadingAlpha.analyze(e),this.shadingBlend.analyze(e),this.afterColor&&this.afterColor.analyze(e,{slot:\\\\\\\"afterColor\\\\\\\"}),this.alpha&&this.alpha.analyze(e);let r=this.color.flow(e,\\\\\\\"c\\\\\\\",{slot:\\\\\\\"color\\\\\\\"}),i=this.emissive.flow(e,\\\\\\\"c\\\\\\\",{slot:\\\\\\\"emissive\\\\\\\"}),n=this.emissiveIntensity.flow(e,\\\\\\\"f\\\\\\\",{slot:\\\\\\\"emissive\\\\\\\"}),o=this.shadingAlpha.flow(e,\\\\\\\"f\\\\\\\"),l=this.shadingBlend.flow(e,\\\\\\\"i\\\\\\\"),a=this.afterColor?this.afterColor.flow(e,\\\\\\\"c\\\\\\\",{slot:\\\\\\\"afterColor\\\\\\\"}):void 0,c=this.alpha?this.alpha.flow(e,\\\\\\\"f\\\\\\\"):void 0;e.requires.transparent=c!==void 0,e.addParsCode([\\\\\\\"varying vec3 vViewPosition;\\\\\\\",\\\\\\\"varying vec3 vLightFront;\\\\\\\",\\\\\\\"varying vec3 vIndirectFront;\\\\\\\",\\\\\\\"#ifndef DOUBLE_SIDED\\\\\\\",\\\\\\\"   #define DOUBLE_SIDED\\\\\\\",\\\\\\\"#endif\\\\\\\",\\\\\\\"#ifndef FLAT_SHADED\\\\\\\",\\\\\\\"\\tvarying vec3 vNormal;\\\\\\\",\\\\\\\"#endif\\\\\\\",\\\\\\\"#ifdef DOUBLE_SIDED\\\\\\\",\\\\\\\"\\tvarying vec3 vLightBack;\\\\\\\",\\\\\\\"\\tvarying vec3 vIndirectBack;\\\\\\\",\\\\\\\"#endif\\\\\\\",\\\\\\\"#include <bsdfs>\\\\\\\",\\\\\\\"#include <lights_pars_begin>\\\\\\\",\\\\\\\"#include <fog_pars_fragment>\\\\\\\",\\\\\\\"#include <shadowmap_pars_fragment>\\\\\\\",\\\\\\\"#include <shadowmask_pars_fragment>\\\\\\\",\\\\\\\"#include <clipping_planes_pars_fragment>\\\\\\\",\\\\\\\"#include <dithering_pars_fragment>\\\\\\\"].join(`\\n`));let u=[\\\\\\\"#include <normal_fragment_begin>\\\\\\\",`\\n\\t\\t\\t\\t// NOTE: gl_FrontFacing alternative using face normal estimation.\\n\\t\\t\\t\\tvec3 viewdx = dFdx(vViewPosition);\\n\\t\\t\\t\\tvec3 viewdy = dFdy(vViewPosition);\\n\\t\\t\\t\\tvec3 faceNormal = normalize(cross(viewdx, viewdy));\\n\\t\\t\\t\\tbool isFrontFacing = (dot(normal, faceNormal) >= 0.0);\\n\\t\\t\\t\\t`,\\\\\\\"#include <clipping_planes_fragment>\\\\\\\"];u.push(r.code,\\\\\\\"vec3 diffuseColor = \\\\\\\"+r.result+\\\\\\\";\\\\\\\",\\\\\\\"ReflectedLight reflectedLight = ReflectedLight( vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ) );\\\\\\\"),c&&u.push(c.code,\\\\\\\"#ifdef ALPHATEST\\\\\\\",\\\\\\\"if ( \\\\\\\"+c.result+\\\\\\\" <= ALPHATEST ) discard;\\\\\\\",\\\\\\\"#endif\\\\\\\"),u.push(\\\\\\\"#ifdef DOUBLE_SIDED\\\\\\\",\\\\\\\"\\treflectedLight.indirectDiffuse += (isFrontFacing) ? vIndirectFront : vIndirectBack;\\\\\\\",\\\\\\\"#else\\\\\\\",\\\\\\\"\\treflectedLight.indirectDiffuse += vIndirectFront;\\\\\\\",\\\\\\\"#endif\\\\\\\",\\\\\\\"#include <lightmap_fragment>\\\\\\\",\\\\\\\"reflectedLight.indirectDiffuse *= BRDF_Diffuse_Lambert( diffuseColor.rgb );\\\\\\\",\\\\\\\"#ifdef DOUBLE_SIDED\\\\\\\",\\\\\\\"\\treflectedLight.directDiffuse = (isFrontFacing) ? vLightFront : vLightBack;\\\\\\\",\\\\\\\"#else\\\\\\\",\\\\\\\"\\treflectedLight.directDiffuse = vLightFront;\\\\\\\",\\\\\\\"#endif\\\\\\\",\\\\\\\"reflectedLight.directDiffuse *= BRDF_Diffuse_Lambert( diffuseColor.rgb ) * getShadowMask();\\\\\\\"),i&&u.push(i.code,\\\\\\\"reflectedLight.directDiffuse += \\\\\\\"+i.result+\\\\\\\" * \\\\\\\"+n.result+\\\\\\\";\\\\\\\"),u.push(\\\\\\\"vec3 outgoingLight = reflectedLight.directDiffuse + reflectedLight.indirectDiffuse;\\\\\\\"),u.push(`\\n\\t\\t\\t\\tif (outgoingLight != diffuseColor) {\\n\\t\\t\\t\\t\\tfloat lightAccu = clamp( length( reflectedLight.directSpecular + reflectedLight.indirectSpecular ), 0.0, 1.0 );\\n\\t\\t\\t\\t\\taccumAlpha += ( 1.0 - accumAlpha ) * ${o.result} * lightAccu;\\n\\t\\t\\t\\t\\toutgoingLight = spe_blend( diffuseColor, outgoingLight, ${o.result}, ${l.result} );\\n\\t\\t\\t\\t}\\n\\t\\t\\t\\t`),a&&u.push(a.code,`outgoingLight = spe_blend(outgoingLight, ${a.result}, 1.0, SPE_BLENDING_NORMAL);`),c?u.push(`gl_FragColor = vec4( outgoingLight, accumAlpha * ${c.result} );`):u.push(\\\\\\\"gl_FragColor = vec4( outgoingLight, 1.0 );\\\\\\\"),u.push(\\\\\\\"#include <encodings_fragment>\\\\\\\",\\\\\\\"#include <fog_fragment>\\\\\\\",\\\\\\\"#include <dithering_fragment>\\\\\\\"),t=u.join(`\\n`)}return t}copy(e){return super.copy(e),this.emissiveIntensity=e.emissiveIntensity.clone(),e.color&&(this.color=e.color.clone()),e.position&&(this.position=e.position.clone()),e.afterColor&&(this.afterColor=e.afterColor.clone()),e.alpha&&(this.alpha=e.alpha.clone()),e.shadingAlpha&&(this.shadingAlpha=e.shadingAlpha.clone()),e.shadingBlend&&(this.shadingBlend=e.shadingBlend.clone()),e.emissive&&(this.emissive=e.emissive.clone()),this}toJSON(e){let t=this.getJSONNode(e);return t||(t=this.createJSONNode(e),this.position&&(t.position=this.position.toJSON(e).uuid),this.color&&(t.color=this.color.toJSON(e).uuid),t.emissiveIntensity=this.emissiveIntensity.toJSON(e).uuid,t.shadingAlpha=this.shadingAlpha.toJSON(e).uuid,t.shadingBlend=this.shadingBlend.toJSON(e).uuid,this.afterColor&&(t.afterColor=this.afterColor.toJSON(e).uuid),this.alpha&&(t.alpha=this.alpha.toJSON(e).uuid),this.emissive&&(t.emissive=this.emissive.toJSON(e).uuid)),t.nodeType=this.nodeType,t}fromJSON(e,t){return super.fromJSON(e,t),e.position&&(this.position=t.getNode(e.position)),e.color&&(this.color=t.getNode(e.color)),e.shadingAlpha&&(this.shadingAlpha=t.getNode(e.shadingAlpha)),e.shadingBlend&&(this.shadingBlend=t.getNode(e.shadingBlend)),e.afterColor&&(this.afterColor=t.getNode(e.afterColor)),e.alpha&&(this.alpha=t.getNode(e.alpha)),e.emissiveIntensity&&(this.emissiveIntensity=t.getNode(e.emissiveIntensity)),this}};var Ul=class extends hr{get color(){return this.fragment.color}set color(e){this.fragment.color=e}get afterColor(){return this.fragment.afterColor}set afterColor(e){this.fragment.afterColor=e}get alpha(){return this.fragment.alpha}set alpha(e){this.fragment.alpha=e}get shadingAlpha(){return this.fragment.shadingAlpha}set shadingAlpha(e){this.fragment.shadingAlpha=e}get shadingBlend(){return this.fragment.shadingBlend}set shadingBlend(e){this.fragment.shadingBlend=e}get position(){return this.fragment.position}set position(e){this.fragment.position=e}get emissive(){return this.fragment.emissive}set emissive(e){this.fragment.emissive=e}get emissiveIntensity(){return this.fragment.emissiveIntensity}set emissiveIntensity(e){this.fragment.emissiveIntensity=e}constructor(e=new Ln,t){super(e,e,t);this.type=\\\\\\\"LambertNodeMaterial\\\\\\\",this.fragment=e}};var Dn=class extends Ul{constructor(e,t,r){super(t,e);this.userData.type=\\\\\\\"LambertMaterial\\\\\\\",this.userData.category=\\\\\\\"Lambert\\\\\\\",this.userData.layers=r??this._getLayerStack(e?.map)}get layersList(){return this.userData.layers}set layersList(e){this.userData.layers=e}equals(e){return this.userData.type===e.userData.type&&this.userData.layers.uuid==e.userData.layers.uuid}copy(e){if(e.userData.layers!==void 0&&e.userData.layers instanceof Ut){let t=e.userData.layers,r=e.fragment;super.copy(e);let i=r.clone();this.fragment=i,this.vertex=i;let n=t.clone(this);this.userData.layers=n,i.shadingAlpha.value=r.shadingAlpha.value,i.shadingBlend.value=r.shadingBlend.value}else super.copy(e);return this}static fromJSON(e,t,r){let i=r.getNode(t.vertex),n=new Dn(void 0,i);return n.fromJSON(t,r),n}dispose(){super.dispose()}};var Bn=F(z());var _n=class extends oe{constructor(){super(\\\\\\\"toon\\\\\\\");this.nodeType=\\\\\\\"Toon\\\\\\\";this.color=new _e(5855577),this.specular=new _e(1118481),this.shininess=new te(30),this.shadingAlpha=new te(1),this.shadingBlend=new Me(0)}build(e){let t;if(e.define(\\\\\\\"TOON\\\\\\\"),e.requires.lights=!0,e.isShader(\\\\\\\"vertex\\\\\\\")){let r=this.position?this.position.analyzeAndFlow(e,\\\\\\\"v3\\\\\\\",{cache:\\\\\\\"position\\\\\\\"}):void 0;e.mergeUniform(Bn.UniformsUtils.merge([Bn.UniformsLib.fog,Bn.UniformsLib.lights])),e.addParsCode([\\\\\\\"varying vec3 vViewPosition;\\\\\\\",\\\\\\\"#ifndef FLAT_SHADED\\\\\\\",\\\\\\\"\\tvarying vec3 vNormal;\\\\\\\",\\\\\\\"#endif\\\\\\\",\\\\\\\"#include <fog_pars_vertex>\\\\\\\",\\\\\\\"#include <shadowmap_pars_vertex>\\\\\\\",\\\\\\\"#include <clipping_planes_pars_vertex>\\\\\\\"].join(`\\n`));let i=[\\\\\\\"#include <beginnormal_vertex>\\\\\\\",`\\n\\t\\t\\t\\t#ifndef USE_LAYER_DISPLACE\\n\\t\\t\\t\\t\\t#include <defaultnormal_vertex>\\n\\t\\t\\t\\t#endif\\n\\n\\t\\t\\t\\tvec3 displaced_position = position;\\n\\t\\t\\t\\tvec3 displaced_normal = normal;\\n\\n\\t\\t\\t\\t#ifdef USE_LAYER_DISPLACE\\n\\t\\t\\t\\t\\tvec3 transformed;\\n\\t\\t\\t\\t\\tvec3 transformedNormal;\\n\\t\\t\\t\\t#endif\\n\\t\\t\\t\\t`,\\\\\\\"#ifndef FLAT_SHADED\\\\\\\",\\\\\\\"\\tvNormal = normalize( transformedNormal );\\\\\\\",\\\\\\\"#endif\\\\\\\",`\\n\\t\\t\\t\\t#ifndef USE_LAYER_DISPLACE\\n\\t\\t\\t\\t\\t#include <begin_vertex>\\n\\t\\t\\t\\t#endif\\n\\t\\t\\t\\t`];r&&i.push(r.code,r.result?\\\\\\\"displaced_position = \\\\\\\"+r.result+\\\\\\\";\\\\\\\":\\\\\\\"\\\\\\\"),i.push(\\\\\\\"transformed = displaced_position;\\\\\\\",\\\\\\\"transformedNormal = normalMatrix * displaced_normal;\\\\\\\",\\\\\\\"#ifndef FLAT_SHADED\\\\\\\",\\\\\\\"    vNormal = transformedNormal;\\\\\\\",\\\\\\\"#endif\\\\\\\"),i.push(\\\\\\\"\\t#include <project_vertex>\\\\\\\",\\\\\\\"\\t#include <fog_vertex>\\\\\\\",\\\\\\\"\\t#include <clipping_planes_vertex>\\\\\\\",\\\\\\\"\\tvViewPosition = - mvPosition.xyz;\\\\\\\",\\\\\\\"\\t#include <worldpos_vertex>\\\\\\\",\\\\\\\"\\t#include <shadowmap_vertex>\\\\\\\",\\\\\\\"\\t#include <fog_vertex>\\\\\\\"),t=i.join(`\\n`)}else{this.color===void 0&&(this.color=new _e(5855577)),this.color.analyze(e,{slot:\\\\\\\"color\\\\\\\"}),this.specular.analyze(e),this.shininess.analyze(e),this.shadingAlpha.analyze(e),this.shadingBlend.analyze(e),this.afterColor&&this.afterColor.analyze(e,{slot:\\\\\\\"afterColor\\\\\\\"}),this.alpha&&this.alpha.analyze(e);let r=this.color.flow(e,\\\\\\\"c\\\\\\\",{slot:\\\\\\\"color\\\\\\\"}),i=this.specular.flow(e,\\\\\\\"c\\\\\\\"),n=this.shininess.flow(e,\\\\\\\"f\\\\\\\"),o=this.shadingAlpha.flow(e,\\\\\\\"f\\\\\\\"),l=this.shadingBlend.flow(e,\\\\\\\"i\\\\\\\"),a=this.afterColor?this.afterColor.flow(e,\\\\\\\"c\\\\\\\",{slot:\\\\\\\"afterColor\\\\\\\"}):void 0,c=this.alpha?this.alpha.flow(e,\\\\\\\"f\\\\\\\"):void 0;e.requires.transparent=c!==void 0,e.addParsCode([\\\\\\\"#include <gradientmap_pars_fragment>\\\\\\\",\\\\\\\"#include <fog_pars_fragment>\\\\\\\",\\\\\\\"#include <bsdfs>\\\\\\\",\\\\\\\"#include <lights_pars_begin>\\\\\\\",\\\\\\\"#include <dithering_pars_fragment>\\\\\\\",`\\n\\t\\t\\t\\t\\tvarying vec3 vViewPosition;\\n\\t\\t\\t\\t\\t#ifndef FLAT_SHADED\\n\\t\\t\\t\\t\\t\\tvarying vec3 vNormal;\\n\\t\\t\\t\\t\\t#endif\\n\\t\\t\\t\\t\\tstruct ToonMaterial {\\n\\t\\t\\t\\t\\t\\tvec3\\tdiffuseColor;\\n\\t\\t\\t\\t\\t\\tvec3\\tspecularColor;\\n\\t\\t\\t\\t\\t\\tfloat\\tspecularShininess;\\n\\t\\t\\t\\t\\t\\tfloat\\tspecularStrength;\\n\\t\\t\\t\\t\\t};\\n\\t\\t\\t\\t\\tvoid RE_Direct_Toon( const in IncidentLight directLight, const in GeometricContext geometry, const in ToonMaterial material, inout ReflectedLight reflectedLight ) {\\n\\t\\t\\t\\t\\t\\tvec3 irradiance = getGradientIrradiance( geometry.normal, directLight.direction ) * directLight.color;\\n\\t\\t\\t\\t\\t\\t#ifndef PHYSICALLY_CORRECT_LIGHTS\\n\\t\\t\\t\\t\\t\\t\\tirradiance *= PI;\\n\\t\\t\\t\\t\\t\\t#endif\\n\\t\\t\\t\\t\\t\\treflectedLight.directDiffuse += irradiance * BRDF_Diffuse_Lambert( material.diffuseColor );\\n\\t\\t\\t\\t\\t\\treflectedLight.directSpecular += irradiance * BRDF_Specular_BlinnPhong( directLight, geometry, material.specularColor, material.specularShininess ) * material.specularStrength;\\n\\t\\t\\t\\t\\t}\\n\\t\\t\\t\\t\\tvoid RE_IndirectDiffuse_Toon( const in vec3 irradiance, const in GeometricContext geometry, const in ToonMaterial material, inout ReflectedLight reflectedLight ) {\\n\\t\\t\\t\\t\\t\\treflectedLight.indirectDiffuse += irradiance * BRDF_Diffuse_Lambert( material.diffuseColor );\\n\\t\\t\\t\\t\\t}\\n\\t\\t\\t\\t\\t#define RE_Direct\\t\\t\\t\\tRE_Direct_Toon\\n\\t\\t\\t\\t\\t#define RE_IndirectDiffuse\\t\\tRE_IndirectDiffuse_Toon\\n\\t\\t\\t\\t\\t#define Material_LightProbeLOD( material )\\t(0)\\n\\t\\t\\t\\t\\t`,\\\\\\\"#include <shadowmap_pars_fragment>\\\\\\\",\\\\\\\"#include <bumpmap_pars_fragment>\\\\\\\",\\\\\\\"#include <normalmap_pars_fragment>\\\\\\\"].join(`\\n`));let u=[\\\\\\\"#include <normal_fragment_begin>\\\\\\\",`\\n\\t\\t\\t\\t// NOTE: gl_FrontFacing alternative using face normal estimation.\\n\\t\\t\\t\\tvec3 viewdx = dFdx(vViewPosition);\\n\\t\\t\\t\\tvec3 viewdy = dFdy(vViewPosition);\\n\\t\\t\\t\\tvec3 faceNormal = normalize(cross(viewdx,viewdy));\\n\\t\\t\\t\\tif (dot(normal, faceNormal) < 0.0) {\\n\\t\\t\\t\\t\\tnormal *= -1.0;\\n\\t\\t\\t\\t}\\n\\t\\t\\t\\t`,\\\\\\\"\\tToonMaterial material;\\\\\\\"];u.push(r.code,\\\\\\\"\\tvec3 diffuseColor = \\\\\\\"+r.result+\\\\\\\";\\\\\\\",\\\\\\\"\\tReflectedLight reflectedLight = ReflectedLight( vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ) );\\\\\\\",i.code,\\\\\\\"\\tvec3 specular = \\\\\\\"+i.result+\\\\\\\";\\\\\\\",n.code,\\\\\\\"\\tfloat shininess = max( 0.0001, \\\\\\\"+n.result+\\\\\\\" );\\\\\\\",\\\\\\\"\\tfloat specularStrength = 1.0;\\\\\\\"),c&&u.push(c.code,\\\\\\\"#ifdef ALPHATEST\\\\\\\",\\\\\\\"if ( \\\\\\\"+c.result+\\\\\\\" <= ALPHATEST ) discard;\\\\\\\",\\\\\\\"#endif\\\\\\\"),u.push(\\\\\\\"material.diffuseColor = diffuseColor;\\\\\\\"),u.push(\\\\\\\"material.specularColor = specular;\\\\\\\",\\\\\\\"material.specularShininess = shininess;\\\\\\\",\\\\\\\"material.specularStrength = specularStrength;\\\\\\\",\\\\\\\"#include <lights_fragment_begin>\\\\\\\",\\\\\\\"#include <lights_fragment_end>\\\\\\\"),u.push(\\\\\\\"vec3 outgoingLight = reflectedLight.directDiffuse + reflectedLight.indirectDiffuse + reflectedLight.directSpecular;\\\\\\\"),u.push(`\\n\\t\\t\\t\\tif (outgoingLight != diffuseColor) {\\n\\t\\t\\t\\t\\tfloat lightAccu = clamp( length( reflectedLight.directSpecular + reflectedLight.indirectSpecular ), 0.0, 1.0 );\\n\\t\\t\\t\\t\\taccumAlpha += ( 1.0 - accumAlpha ) * ${o.result} * lightAccu;\\n\\t\\t\\t\\t\\toutgoingLight = spe_blend( diffuseColor, outgoingLight, ${o.result}, ${l.result} );\\n\\t\\t\\t\\t}\\n\\t\\t\\t\\t`),a&&u.push(a.code,`outgoingLight = spe_blend(outgoingLight, ${a.result}, 1.0, SPE_BLENDING_NORMAL);`),c?u.push(`gl_FragColor = vec4( outgoingLight, accumAlpha * ${c.result} );`):u.push(\\\\\\\"gl_FragColor = vec4( outgoingLight, 1.0 );\\\\\\\"),u.push(\\\\\\\"#include <encodings_fragment>\\\\\\\",\\\\\\\"#include <fog_fragment>\\\\\\\",\\\\\\\"#include <dithering_fragment>\\\\\\\"),t=u.join(`\\n`)}return t}copy(e){return super.copy(e),e.color&&(this.color=e.color.clone()),this.specular=e.specular.clone(),this.shininess=e.shininess.clone(),e.position&&(this.position=e.position.clone()),e.afterColor&&(this.afterColor=e.afterColor.clone()),e.alpha&&(this.alpha=e.alpha.clone()),e.shadingAlpha&&(this.shadingAlpha=e.shadingAlpha.clone()),e.shadingBlend&&(this.shadingBlend=e.shadingBlend.clone()),this}toJSON(e){let t=this.getJSONNode(e);return t||(t=this.createJSONNode(e),this.position&&(t.position=this.position.toJSON(e).uuid),this.color&&(t.color=this.color.toJSON(e).uuid),t.specular=this.specular.toJSON(e).uuid,t.shininess=this.shininess.toJSON(e).uuid,this.alpha&&(t.alpha=this.alpha.toJSON(e).uuid),this.shadingAlpha&&(t.shadingAlpha=this.shadingAlpha.toJSON(e).uuid),this.shadingBlend&&(t.shadingBlend=this.shadingBlend.toJSON(e).uuid),this.afterColor&&(t.afterColor=this.afterColor.toJSON(e).uuid)),t.nodeType=this.nodeType,t}fromJSON(e,t){return super.fromJSON(e,t),e.position&&(this.position=t.getNode(e.position)),e.color&&(this.color=t.getNode(e.color)),e.specular&&(this.specular=t.getNode(e.specular)),e.shininess&&(this.shininess=t.getNode(e.shininess)),e.alpha&&(this.alpha=t.getNode(e.alpha)),e.shadingAlpha&&(this.shadingAlpha=t.getNode(e.shadingAlpha)),e.shadingBlend&&(this.shadingBlend=t.getNode(e.shadingBlend)),e.afterColor&&(this.afterColor=t.getNode(e.afterColor)),this}};var jl=class extends hr{get color(){return this.fragment.color}set color(e){this.fragment.color=e}get afterColor(){return this.fragment.afterColor}set afterColor(e){this.fragment.afterColor=e}get alpha(){return this.fragment.alpha}set alpha(e){this.fragment.alpha=e}get shadingAlpha(){return this.fragment.shadingAlpha}set shadingAlpha(e){this.fragment.shadingAlpha=e}get shadingBlend(){return this.fragment.shadingBlend}set shadingBlend(e){this.fragment.shadingBlend=e}get position(){return this.fragment.position}set position(e){this.fragment.position=e}get specular(){return this.fragment.specular}set specular(e){this.fragment.specular=e}get shininess(){return this.fragment.shininess}set shininess(e){this.fragment.shininess=e}constructor(e=new _n,t){super(e,e,t);this.type=\\\\\\\"ToonNodeMaterial\\\\\\\",this.fragment=e}};var On=class extends jl{constructor(e,t,r){super(t,e);this.userData.type=\\\\\\\"ToonMaterial\\\\\\\",this.userData.category=\\\\\\\"Toon\\\\\\\",this.userData.layers=r??this._getLayerStack(e?.map)}get layersList(){return this.userData.layers}set layersList(e){this.userData.layers=e}equals(e){return this.userData.type===e.userData.type&&this.userData.layers.uuid==e.userData.layers.uuid}copy(e){if(e.userData.layers!==void 0&&e.userData.layers instanceof Ut){let t=e.userData.layers,r=e.fragment;super.copy(e);let i=r.clone();this.fragment=i,this.vertex=i;let n=t.clone(this);this.userData.layers=n,i.shadingAlpha.value=r.shadingAlpha.value,i.shadingBlend.value=r.shadingBlend.value}else super.copy(e);return this}static fromJSON(e,t,r){let i=r.getNode(t.vertex),n=new On(void 0,i);return n.fromJSON(t,r),n}dispose(){super.dispose()}};var Bs=F(z());var Pn=class extends oe{constructor(){super(\\\\\\\"standard\\\\\\\");this.nodeType=\\\\\\\"Standard\\\\\\\";this.color=new _e(5855577),this.roughness=new te(.3),this.metalness=new te(0),this.reflectivity=new te(.5),this.shadingAlpha=new te(1),this.shadingBlend=new Me(0)}build(e){let t;if(e.define(\\\\\\\"STANDARD\\\\\\\"),e.requires.lights=!0,e.extensions.derivatives=!0,e.extensions.shaderTextureLOD=!0,e.isShader(\\\\\\\"vertex\\\\\\\")){let r=this.position?this.position.analyzeAndFlow(e,\\\\\\\"v3\\\\\\\",{cache:\\\\\\\"position\\\\\\\"}):void 0;e.mergeUniform(Bs.UniformsUtils.merge([Bs.UniformsLib.fog,Bs.UniformsLib.lights])),Bs.UniformsLib.LTC_1&&(e.uniforms.ltc_1={value:void 0},e.uniforms.ltc_2={value:void 0}),e.addParsCode([\\\\\\\"varying vec3 vViewPosition;\\\\\\\",\\\\\\\"#ifndef FLAT_SHADED\\\\\\\",\\\\\\\"\\tvarying vec3 vNormal;\\\\\\\",\\\\\\\"#endif\\\\\\\",\\\\\\\"#include <fog_pars_vertex>\\\\\\\",\\\\\\\"#include <shadowmap_pars_vertex>\\\\\\\",\\\\\\\"#include <clipping_planes_pars_vertex>\\\\\\\"].join(`\\n`));let i=[\\\\\\\"#include <beginnormal_vertex>\\\\\\\",`\\n\\t\\t\\t\\t#if !defined( USE_LAYER_DISPLACE )\\n\\t\\t\\t\\t\\t#include <defaultnormal_vertex>\\n\\t\\t\\t\\t#endif\\n\\n\\t\\t\\t\\tvec3 displaced_position = position;\\n\\t\\t\\t\\tvec3 displaced_normal = normal;\\n\\n\\t\\t\\t\\t#if defined( USE_LAYER_DISPLACE )\\n\\t\\t\\t\\t\\tvec3 transformed;\\n\\t\\t\\t\\t\\tvec3 transformedNormal;\\n\\t\\t\\t\\t#endif\\n\\t\\t\\t\\t`,\\\\\\\"#ifndef FLAT_SHADED\\\\\\\",\\\\\\\"\\tvNormal = normalize( transformedNormal );\\\\\\\",\\\\\\\"#endif\\\\\\\",`\\n\\t\\t\\t\\t#if !defined( USE_LAYER_DISPLACE )\\n\\t\\t\\t\\t\\t#include <begin_vertex>\\n\\t\\t\\t\\t#endif /* !USE_LAYER_DISPLACE */\\n\\t\\t\\t\\t`];r&&i.push(r.code,r.result?\\\\\\\"displaced_position = \\\\\\\"+r.result+\\\\\\\";\\\\\\\":\\\\\\\"\\\\\\\"),i.push(\\\\\\\"transformed = displaced_position;\\\\\\\",\\\\\\\"transformedNormal = normalMatrix * displaced_normal;\\\\\\\",\\\\\\\"#ifndef FLAT_SHADED\\\\\\\",\\\\\\\"    vNormal = transformedNormal;\\\\\\\",\\\\\\\"#endif\\\\\\\"),i.push(\\\\\\\"#include <project_vertex>\\\\\\\",\\\\\\\"#include <fog_vertex>\\\\\\\",\\\\\\\"#include <clipping_planes_vertex>\\\\\\\",\\\\\\\"\\tvViewPosition = - mvPosition.xyz;\\\\\\\",\\\\\\\"#include <worldpos_vertex>\\\\\\\",\\\\\\\"#include <shadowmap_vertex>\\\\\\\"),t=i.join(`\\n`)}else{let r={gamma:!0};this.color===void 0&&(this.color=new _e(5855577)),this.color.analyze(e,{slot:\\\\\\\"color\\\\\\\",context:r}),this.roughness.analyze(e),this.metalness.analyze(e),this.shadingAlpha.analyze(e),this.shadingBlend.analyze(e),this.afterColor&&this.afterColor.analyze(e,{slot:\\\\\\\"afterColor\\\\\\\"}),this.alpha&&this.alpha.analyze(e),this.reflectivity&&this.reflectivity.analyze(e);let i=this.color.flow(e,\\\\\\\"c\\\\\\\",{slot:\\\\\\\"color\\\\\\\",context:r}),n=this.roughness.flow(e,\\\\\\\"f\\\\\\\"),o=this.metalness.flow(e,\\\\\\\"f\\\\\\\"),l=this.shadingAlpha.flow(e,\\\\\\\"f\\\\\\\"),a=this.shadingBlend.flow(e,\\\\\\\"i\\\\\\\"),c=this.afterColor?this.afterColor.flow(e,\\\\\\\"c\\\\\\\",{slot:\\\\\\\"afterColor\\\\\\\"}):void 0,u=this.alpha?this.alpha.flow(e,\\\\\\\"f\\\\\\\"):void 0,d=this.reflectivity?this.reflectivity.flow(e,\\\\\\\"f\\\\\\\"):void 0;e.requires.transparent=u!==void 0,e.addParsCode([\\\\\\\"varying vec3 vViewPosition;\\\\\\\",\\\\\\\"#ifndef FLAT_SHADED\\\\\\\",\\\\\\\"\\tvarying vec3 vNormal;\\\\\\\",\\\\\\\"#endif\\\\\\\",\\\\\\\"#include <dithering_pars_fragment>\\\\\\\",\\\\\\\"#include <fog_pars_fragment>\\\\\\\",\\\\\\\"#include <bsdfs>\\\\\\\",\\\\\\\"#include <lights_pars_begin>\\\\\\\",\\\\\\\"#include <lights_physical_pars_fragment>\\\\\\\",\\\\\\\"#include <shadowmap_pars_fragment>\\\\\\\"].join(`\\n`));let h=[\\\\\\\"#include <clipping_planes_fragment>\\\\\\\",\\\\\\\"\\t#include <normal_fragment_begin>\\\\\\\",`\\n\\t\\t\\t\\t// NOTE: gl_FrontFacing alternative using face normal estimation.\\n\\t\\t\\t\\tvec3 viewdx = dFdx(vViewPosition);\\n\\t\\t\\t\\tvec3 viewdy = dFdy(vViewPosition);\\n\\t\\t\\t\\tvec3 faceNormal = normalize(cross(viewdx,viewdy));\\n\\t\\t\\t\\tif (dot(normal, faceNormal) < 0.0) {\\n\\t\\t\\t\\t\\tnormal *= -1.0;\\n\\t\\t\\t\\t}\\n\\t\\t\\t\\t`,\\\\\\\"\\tPhysicalMaterial material;\\\\\\\",\\\\\\\"\\tmaterial.diffuseColor = vec3( 1.0 );\\\\\\\"];h.push(i.code,\\\\\\\"\\tvec3 diffuseColor = \\\\\\\"+i.result+\\\\\\\";\\\\\\\",\\\\\\\"\\tReflectedLight reflectedLight = ReflectedLight( vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ) );\\\\\\\",n.code,\\\\\\\"\\tfloat roughnessFactor = \\\\\\\"+n.result+\\\\\\\";\\\\\\\",o.code,\\\\\\\"\\tfloat metalnessFactor = \\\\\\\"+o.result+\\\\\\\";\\\\\\\"),u&&h.push(u.code,\\\\\\\"#ifdef ALPHATEST\\\\\\\",\\\\\\\"\\tif ( \\\\\\\"+u.result+\\\\\\\" <= ALPHATEST ) discard;\\\\\\\",\\\\\\\"#endif\\\\\\\"),h.push(\\\\\\\"vec3 dxy = max( abs( dFdx( geometryNormal ) ), abs( dFdy( geometryNormal ) ) );\\\\\\\",\\\\\\\"float geometryRoughness = max( max( dxy.x, dxy.y ), dxy.z );\\\\\\\"),h.push(\\\\\\\"material.diffuseColor = diffuseColor * ( 1.0 - metalnessFactor );\\\\\\\",\\\\\\\"material.specularRoughness = max( roughnessFactor, 0.0525 );\\\\\\\",\\\\\\\"material.specularRoughness += geometryRoughness;\\\\\\\",\\\\\\\"material.specularRoughness = min( material.specularRoughness, 1.0 );\\\\\\\",\\\\\\\"material.specularRoughness = clamp( roughnessFactor, 0.04, 1.0 );\\\\\\\"),d?h.push(d.code,\\\\\\\"material.specularColor = mix( vec3( MAXIMUM_SPECULAR_COEFFICIENT * pow2( \\\\\\\"+d.result+\\\\\\\" ) ), diffuseColor, metalnessFactor );\\\\\\\"):h.push(\\\\\\\"material.specularColor = mix( vec3( DEFAULT_SPECULAR_COEFFICIENT ), diffuseColor, metalnessFactor );\\\\\\\"),h.push(\\\\\\\"#include <lights_fragment_begin>\\\\\\\"),h.push(\\\\\\\"#include <lights_fragment_end>\\\\\\\"),h.push(\\\\\\\"vec3 outgoingLight = reflectedLight.directDiffuse + reflectedLight.indirectDiffuse + reflectedLight.directSpecular + reflectedLight.indirectSpecular;\\\\\\\"),h.push(`\\n\\t\\t\\t\\tif (outgoingLight != diffuseColor) {\\n\\t\\t\\t\\t\\tfloat lightAccu = clamp( length( reflectedLight.directSpecular + reflectedLight.indirectSpecular ), 0.0, 1.0 );\\n\\t\\t\\t\\t\\taccumAlpha += ( 1.0 - accumAlpha ) * ${l.result} * lightAccu;\\n\\t\\t\\t\\t\\toutgoingLight = spe_blend( diffuseColor, outgoingLight, ${l.result}, ${a.result} );\\n\\t\\t\\t\\t}\\n\\t\\t\\t\\t`),c&&h.push(c.code,`outgoingLight = spe_blend(outgoingLight, ${c.result}, 1.0, SPE_BLENDING_NORMAL);`),u?h.push(`gl_FragColor = vec4( outgoingLight, accumAlpha * ${u.result} );`):h.push(\\\\\\\"gl_FragColor = vec4( outgoingLight, 1.0 );\\\\\\\"),h.push(\\\\\\\"#include <encodings_fragment>\\\\\\\",\\\\\\\"#include <fog_fragment>\\\\\\\",\\\\\\\"#include <dithering_fragment>\\\\\\\"),t=h.join(`\\n`)}return t}copy(e){return super.copy(e),e.color&&(this.color=e.color.clone()),this.roughness=e.roughness.clone(),this.metalness=e.metalness.clone(),e.position&&(this.position=e.position.clone()),e.afterColor&&(this.afterColor=e.afterColor.clone()),e.alpha&&(this.alpha=e.alpha.clone()),e.reflectivity&&(this.reflectivity=e.reflectivity.clone()),e.shadingAlpha&&(this.shadingAlpha=e.shadingAlpha.clone()),e.shadingBlend&&(this.shadingBlend=e.shadingBlend.clone()),this}toJSON(e){let t=this.getJSONNode(e);return t||(t=this.createJSONNode(e),this.position&&(t.position=this.position.toJSON(e).uuid),this.color&&(t.color=this.color.toJSON(e).uuid),t.roughness=this.roughness.toJSON(e).uuid,t.metalness=this.metalness.toJSON(e).uuid,this.alpha&&(t.alpha=this.alpha.toJSON(e).uuid),this.reflectivity&&(t.reflectivity=this.reflectivity.toJSON(e).uuid),this.shadingAlpha&&(t.shadingAlpha=this.shadingAlpha.toJSON(e).uuid),this.shadingBlend&&(t.shadingBlend=this.shadingBlend.toJSON(e).uuid),this.afterColor&&(t.afterColor=this.afterColor.toJSON(e).uuid)),t.nodeType=this.nodeType,t}fromJSON(e,t){return super.fromJSON(e,t),e.position&&(this.position=t.getNode(e.position)),e.color&&(this.color=t.getNode(e.color)),e.roughness&&(this.roughness=t.getNode(e.roughness)),e.metalness&&(this.metalness=t.getNode(e.metalness)),e.alpha&&(this.alpha=t.getNode(e.alpha)),e.reflectivity&&(this.reflectivity=t.getNode(e.reflectivity)),e.shadingAlpha&&(this.shadingAlpha=t.getNode(e.shadingAlpha)),e.shadingBlend&&(this.shadingBlend=t.getNode(e.shadingBlend)),e.afterColor&&(this.afterColor=t.getNode(e.afterColor)),this}};var kl=class extends hr{get color(){return this.fragment.color}set color(e){this.fragment.color=e}get afterColor(){return this.fragment.afterColor}set afterColor(e){this.fragment.afterColor=e}get alpha(){return this.fragment.alpha}set alpha(e){this.fragment.alpha=e}get shadingAlpha(){return this.fragment.shadingAlpha}set shadingAlpha(e){this.fragment.shadingAlpha=e}get shadingBlend(){return this.fragment.shadingBlend}set shadingBlend(e){this.fragment.shadingBlend=e}get position(){return this.fragment.position}set position(e){this.fragment.position=e}get roughness(){return this.fragment.roughness}set roughness(e){this.fragment.roughness=e}get metalness(){return this.fragment.metalness}set metalness(e){this.fragment.metalness=e}get reflectivity(){return this.fragment.reflectivity}set reflectivity(e){this.fragment.reflectivity=e}constructor(e=new Pn,t){super(e,e,t);this.type=\\\\\\\"StandardNodeMaterial\\\\\\\",this.fragment=e}};var Rn=class extends kl{constructor(e,t,r){super(t,e);this.userData.type=\\\\\\\"PhysicalMaterial\\\\\\\",this.userData.category=\\\\\\\"Physical\\\\\\\",this.userData.layers=r??this._getLayerStack(e?.map)}get layersList(){return this.userData.layers}set layersList(e){this.userData.layers=e}equals(e){return this.userData.type===e.userData.type&&this.userData.layers.uuid==e.userData.layers.uuid}copy(e){if(e.userData.layers!==void 0&&e.userData.layers instanceof Ut){let t=e.userData.layers,r=e.fragment;super.copy(e);let i=r.clone();this.fragment=i,this.vertex=i;let n=t.clone(this);this.userData.layers=n,i.shadingAlpha.value=r.shadingAlpha.value,i.shadingBlend.value=r.shadingBlend.value}else super.copy(e);return this}static fromJSON(e,t,r){let i=r.getNode(t.vertex),n=new Rn(void 0,i);return n.fromJSON(t,r),n}dispose(){super.dispose()}};var qu={BasicMaterial:Qt,PhongMaterial:fr,LambertMaterial:Dn,ToonMaterial:On,PhysicalMaterial:Rn};var Lh=F(z());var _s=F(z());var et=F(z()),Zu=class{static create(s){return this.build(this.normalizeInputs(s))}static normalizeInputs(s,e){let t=Object.assign({},e?.parameters??{width:100,radialSegments:64,heightSegments:1,openEnded:!1,thetaStart:0,thetaLength:360,cornerRadius:0,cornerSegments:8,hollow:0},s.parameters),r=t.width/2,i=t.radiusTop??r,n=t.radiusBottom??r;return i===n?(i=r,n=r):i>n?(i=r,n=n*r/i):(i=i*r/n,n=r),{parameters:Object.assign(t,{width:Math.abs(t.width),height:Math.abs(t.height??t.width),depth:Math.abs(t.depth??t.width),radiusTop:i,radiusBottom:n})}}static build(s){let{width:e,depth:t,height:r,radialSegments:i,heightSegments:n,openEnded:o,thetaStart:l,thetaLength:a,radiusTop:c,radiusBottom:u,cornerRadius:d,cornerSegments:h,hollow:p}=s.parameters,f;return d||p?f=new jo(c,u,r,i,n,o,l,a*Math.PI/180,d,d,h,p):f=new et.CylinderBufferGeometry(c,u,r,i,n,o,l,a*Math.PI/180),f.scale(1,1,t/e),Object.assign(f,{userData:Ce(le({},s),{type:\\\\\\\"CylinderGeometry\\\\\\\"})})}};function gi(s,e,t){t.x=s.x*e.x,t.y=s.y,t.z=s.x*e.y}function $u(s){return new et.Vector2(s.y,-s.x)}var jo=class extends et.BufferGeometry{constructor(e,t,r,i,n,o,l,a,c,u,d,h,p=!1){super();this.type=\\\\\\\"RoundedCylinderBufferGeometry\\\\\\\",e=e!==void 0?e:1,t=t!==void 0?t:1,r=r||1,i=Math.floor(i)||8,n=Math.floor(n)||1,o=o!==void 0?o:!1,l=l!==void 0?l:0,a=a!==void 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q=P[H],K=_[v-H],Y=P[H+1],Q=_[v-(H+1)];[q,K,Y,Y,K,Q].forEach(V=>{l.push(V[0].x,V[0].y,V[0].z),c.push(V[1].x,V[1].y,V[1].z)})}L.push(P[0][0],_[v][0],P[v][0],_[0][0])}}for(;L.length;){let B,D,O,T;[B,D]=L.splice(0,2);let P=[B];for(;B!=D;)P.push(D),O=L.indexOf(D),T=O%2,D=L.splice(O-T,2)[1-T];b.subVectors(P[0],P[1]).cross(f.subVectors(P[0],P[2])).normalize();let _=b.dot(P[0])<0;_&&b.negate();for(let H=1;H<=P.length-2;H++)[P[H+ +_],P[H+1-+_],P[0]].forEach(q=>{l.push(q.x,q.y,q.z),c.push(b.x,b.y,b.z)})}}function d(){let h=new ot.Vector3;for(let S=0;S<l.length;S+=3){h.x=l[S+0],h.y=l[S+1],h.z=l[S+2];let A=w(h)/2/Math.PI+.5,I=N(h)/Math.PI+.5;a.push(A,1-I)}let p=new ot.Vector3,f=new ot.Vector3,m=new ot.Vector3,g=new ot.Vector3,y=new ot.Vector2,x=new ot.Vector2,v=new ot.Vector2,b=(S,A,I,U)=>{U<0&&S.x===1&&(a[A]=S.x-1),I.x===0&&I.z===0&&(a[A]=U/2/Math.PI+.5)};for(let S=0,A=0;S<l.length;S+=9,A+=6){p.set(l[S+0],l[S+1],l[S+2]),f.set(l[S+3],l[S+4],l[S+5]),m.set(l[S+6],l[S+7],l[S+8]),y.set(a[A+0],a[A+1]),x.set(a[A+2],a[A+3]),v.set(a[A+4],a[A+5]),g.copy(p).add(f).add(m).divideScalar(3);let I=w(g);b(y,A+0,p,I),b(x,A+2,f,I),b(v,A+4,m,I)}for(let S=0;S<a.length;S+=6){let A=a[S+0],I=a[S+2],U=a[S+4],E=Math.max(A,I,U),j=Math.min(A,I,U);E>.9&&j<.1&&(A<.2&&(a[S+0]+=1),I<.2&&(a[S+2]+=1),U<.2&&(a[S+4]+=1))}function w(S){return Math.atan2(S.z,-S.x)}function N(S){return Math.atan2(-S.y,Math.sqrt(S.x*S.x+S.z*S.z))}}}static fromJSON(e){return new Ji(e.vertices,e.indices,e.radius,e.corner,e.cornerSides)}};var id=F(z()),sd=class{static create(s){return this.build(this.normalizeInputs(s))}static normalizeInputs(s,e){let t=Object.assign({},e?.parameters??{width:100,detail:0,corner:0,cornerSides:4},s.parameters);return{parameters:Object.assign(t,{width:Math.abs(t.width),height:Math.abs(t.height??t.width),depth:Math.abs(t.depth??t.width)})}}static build(s){let{width:e,height:t,depth:r,detail:i,corner:n,cornerSides:o}=s.parameters,l=i===0&&n!==0?new ko(e*.5,n,o):new id.DodecahedronBufferGeometry(e*.5,i);return l.scale(1,t/e,r/e),Object.assign(l,{userData:Ce(le({},s),{type:\\\\\\\"DodecahedronGeometry\\\\\\\"})})}},ko=class extends Ji{constructor(e=1,t=.2,r=4){let i=(1+Math.sqrt(5))/2,n=1/i,o=[-1,-1,-1,-1,-1,1,-1,1,-1,-1,1,1,1,-1,-1,1,-1,1,1,1,-1,1,1,1,0,-n,-i,0,-n,i,0,n,-i,0,n,i,-n,-i,0,-n,i,0,n,-i,0,n,i,0,-i,0,-n,i,0,-n,-i,0,n,i,0,n],l=[3,11,7,3,7,15,3,15,13,7,19,17,7,17,6,7,6,15,17,4,8,17,8,10,17,10,6,8,0,16,8,16,2,8,2,10,0,12,1,0,1,18,0,18,16,6,10,2,6,2,13,6,13,15,2,16,18,2,18,3,2,3,13,18,1,9,18,9,11,18,11,3,4,14,12,4,12,0,4,0,8,11,9,5,11,5,19,11,19,7,19,5,14,19,14,4,19,4,17,1,12,14,1,14,5,1,5,9],a=\\\\\\\"DodecahedronGeometry\\\\\\\";super(o,l,a,e,t,r);this.type=a}static fromJSON(e){return new ko(e.radius,e.corner,e.cornerSides)}};var Ge=F(z());var Pe=F(z()),Fn=1e-12,Gn=class{constructor(e){this.position=new Pe.Vector2;this.startPosition=new Pe.Vector2;this.uuid=Pe.MathUtils.generateUUID();this.position=e.clone()}start(){this.reset()}reset(){this.startPosition.copy(this.position)}applyOffset(e){this.position.copy(this.startPosition).add(e)}copy(e){return this.position.copy(e.position),this.startPosition.copy(e.startPosition),this}clone(){return new Gn(this.position).copy(this)}toJSON(){return[this.position.x,this.position.y]}},zn=class extends Gn{constructor(e){super(e.position);this.parent=e}copy(e){return super.copy(e),this}clone(){return new zn(this.parent).copy(this)}},yi=class extends Gn{constructor(e,t){super(t);this.controls=[];this.roundness=0;this.areControlsDirectionsMirrored=!0;this.uuid=e,this.controls.push(new zn(this),new zn(this))}getOppositeControl(e){let t=this.controls.indexOf(e);return t===0?this.controls[1]:t===1?this.controls[0]:null}applyOffsetToControls(e,t=1){for(let r=0,i=this.controls.length;r<i;r++){let n=this.controls[r];this.position.distanceTo(n.position)<=t?n.position.copy(this.position):n.applyOffset(e)}}controlsMoved(){return!(this.position.equals(this.controls[0].position)&&this.position.equals(this.controls[1].position))}copy(e){return super.copy(e),this.controls[0].copy(e.controls[0]),this.controls[1].copy(e.controls[1]),this.roundness=e.roundness,this.uuid=e.uuid,this}clone(){return new yi(this.uuid,this.position).copy(this)}toJSON(){return super.toJSON().concat(this.controls[0].toJSON(),this.controls[1].toJSON(),[this.roundness])}},Hl=s=>s,Un=new Pe.Vector2,Wl=new Pe.Vector2,Xg=new Pe.Vector2,qg=new Pe.Vector2,Zg=new Pe.Vector2,$g=new Pe.Vector2,nd=new Pe.Vector3,od=new Pe.Vector3;function ad(s){let e=new Pe.Vector2;e.addVectors(s.v0,Un.subVectors(s.v1,s.v0).multiplyScalar(2/3));let t=new Pe.Vector2;return t.addVectors(s.v2,Wl.subVectors(s.v1,s.v2).multiplyScalar(2/3)),new Pe.CubicBezierCurve(s.v0,e,t,s.v2)}function jn(s,e,t=Number.EPSILON){return Math.abs(s-e)<t}function ey(s,e,t=Number.EPSILON){return s.distanceTo(e)<t}function ty(s,e,t=Number.EPSILON){return s.distanceTo(e)<t}function Jl(s,e,t){let r=Math.sqrt(Math.pow(e.x-s.x,2)+Math.pow(e.y-s.y,2)),i=Math.sqrt(Math.pow(e.x-t.x,2)+Math.pow(e.y-t.y,2)),n=Math.sqrt(Math.pow(t.x-s.x,2)+Math.pow(t.y-s.y,2));return Math.acos((i*i+r*r-n*n)/(2*i*r))}function ld(s,e,t,r,i){let n=Math.sqrt(Math.pow(e.x-s.x,2)+Math.pow(e.y-s.y,2)),o=(s.y+e.y)/2,l=(s.x+e.x)/2,a=Math.sqrt(Math.pow(t,2)-Math.pow(n/2,2))*(s.y-e.y)/n,c=Math.sqrt(Math.pow(t,2)-Math.pow(n/2,2))*(e.x-s.x)/n;return r.set(l+a,o+c),i.set(l-a,o-c),[r,i]}function cd(s,e,t){let r=s.distanceTo(t),i=e.distanceTo(t);return r<i?e:s}function ud(s,e,t,r,i,n){let o=e.x-s.x,l=e.y-s.y,a=t.x-s.x,c=t.y-s.y,u=Math.sqrt((o+a)*(o+a)+(l+c)*(l+c)),d;return Jl(e,s,t)>Math.PI&&(u*=-1),jn(c,l)?d=(l+c)*(r/u-.5)*8/3/(o-a):d=(o+a)*(r/u-.5)*8/3/(c-l),i.set(e.x-d*l,e.y+d*o),n.set(t.x+d*c,t.y-d*a),[i,n]}function Ql(s,e){return s.position.equals(s.controls[1].position)&&e.position.equals(e.controls[0].position)}function dd(s,e,t,r,i=.5){let n=Un.subVectors(e,s).multiplyScalar(i).add(s),o=Wl.subVectors(t,e).multiplyScalar(i).add(e),l=Xg.subVectors(r,t).multiplyScalar(i).add(t),a=n,c=qg.subVectors(o,n).multiplyScalar(i).add(n),u=Zg.subVectors(l,o).multiplyScalar(i).add(o),d=l,h=$g.subVectors(u,c).multiplyScalar(i).add(c);return[s.x,s.y,a.x,a.y,c.x,c.y,h.x,h.y,u.x,u.y,d.x,d.y,r.x,r.y]}function hd(s,e,t=12,r=!0){let i=od.set(0,0,0),n,o=0,l=[];for(let a=0;a<e.length;a++){let c=Hl(e[a]),u=Un,d=vi(c,t);l.push(d);for(let h=0;h<=d;h++)if(c instanceof Pe.CubicBezierCurve||c instanceof Pe.QuadraticBezierCurve||c instanceof Pe.LineCurve){if(c.getPoint(h/d,u),i.set(u.x,u.y,0),n!==void 0&&ty(n,i))continue;n===void 0&&(n=nd),n.copy(i),s.setXYZ(o,i.x,i.y,i.z),o++}}return r&&o>1&&!(s.getX(o-1)===s.getX(0)&&s.getY(o-1)===s.getY(0)&&s.getZ(o-1)===s.getZ(0))&&(s.setXYZ(o,s.getX(0),s.getY(0),s.getZ(0)),o++),s}function fd(s,e,t,r=12,i=!0){let n=od.set(0,0,0),o=0,l=[];for(let a=0;a<e.length;a++){if(t[a]===!1)continue;let c,u=Hl(e[a]),d=Un,h=vi(u,r);l.push(h);for(let p=0;p<=h;p++)if(u instanceof Pe.CubicBezierCurve||u instanceof Pe.QuadraticBezierCurve||u instanceof Pe.LineCurve){if(u.getPoint(p/h,d),n.set(d.x,d.y,0),c?.equals(n))continue;c===void 0?c=nd:(s.setXYZ(o,c.x,c.y,c.z),o++,s.setXYZ(o,n.x,n.y,n.z),o++),c.copy(n)}}return i&&o>1&&!(s.getX(o-1)===s.getX(0)&&s.getY(o-1)===s.getY(0)&&s.getZ(o-1)===s.getZ(0))&&(s.setXYZ(o,s.getX(0),s.getY(0),s.getZ(0)),o++),l}function Yl(s,e=12,t=!1){let r=[];for(let i=0,n=s.length;i<n;i++){let o=s[i],l=0;if(t&&o.roundedCurveCorner!==void 0){let a=vi(o.roundedCurveCorner,e)*.5;i>0&&(r[i-1]+=a),l+=a}o.curveAfter!==void 0&&(l+=vi(o.curveAfter,e)),r.push(l)}return s.length>0&&t&&s[0].roundedCurveCorner!==void 0&&(r[s.length-1]+=vi(s[0].roundedCurveCorner,e)*.5),r}function vi(s,e=12){return s&&s instanceof Pe.EllipseCurve?e*2:s&&(s instanceof Pe.LineCurve||s instanceof Pe.LineCurve3)?1:s&&s instanceof Pe.SplineCurve?e*s.points.length:e}function pd(s,e,t=12,r=!0){let i,n=0;for(let o=0;o<e.length;o++){let l=Hl(e[o]),a=vi(l,t),c=Un;for(let u=0;u<=a;u++)if(l instanceof Pe.CubicBezierCurve||l instanceof Pe.QuadraticBezierCurve||l instanceof Pe.LineCurve){if(l.getPoint(u/a,c),i!==void 0&&ey(i,c,Fn))continue;i===void 0&&(i=Wl),i.copy(c),s.push(c.x,c.y),n++}}return jn(s[0],s[s.length-2],Fn)&&jn(s[1],s[s.length-1],Fn)&&(s.pop(),s.pop()),r&&n>1&&!(jn(s[n-1],s[1],Fn)&&jn(s[n-2],s[0],Fn))&&(s.push(s[0],s[1]),n++),s}var Kl=new Ge.Vector2,ry=new Ge.Vector2,iy=new Ge.Vector2,sy=new Ge.Vector2,ny=new Ge.Vector2,oy=new Ge.Vector2,Xe=class extends Ge.Shape{constructor(e=100,t=100){super();this.points=[];this.shapeHoles=[];this.eventDispatcher=new Ge.EventDispatcher;this.plane=new Ge.Plane(new Ge.Vector3(0,0,-1));this.subdivision=0;this.controlSnapDistance=4;this.pointIDs=0;this.isMesh2D=!1;this._roundness=0;this.isClosed=!1;this.useCubicForRoundedCorners=!0;this.uuid=Ge.MathUtils.generateUUID();this.needsUpdate=!1;this.roundedCurves=[];this._width=e,this._height=t}get width(){return this._width}get height(){return this._height}get roundness(){return this._roundness}set roundness(e){if(this._roundness!==e){this._roundness=e;for(let t=0,r=this.points.length;t<r;t++)this.points[t].roundness=e;this.needsUpdate=!0}}getBezierPoint(e){if(e<=this.points.length-1)return this.points[e];if(this.shapeHoles.length>0)for(let t=0,r=this.shapeHoles.length;t<r;t++){let i=this.shapeHoles[t],n=e-this.points.length;if(n<=i.points.length-1)return i.points[n]}throw new Error(\\\\\\\"This shape does not have a point for this index: \\\\\\\"+e)}getBezierPointIndex(e){let t=this.points.indexOf(e);if(t>=0)return t;if(t=this.points.length,this.shapeHoles.length>0)for(let r=0,i=this.shapeHoles.length;r<i;r++){let n=this.shapeHoles[r],o=n.points.indexOf(e);if(o>=0)return t+o;t+=n.points.length}return-1}getAllPoints(){let e=[].concat(...this.shapeHoles.map(t=>t.points));return[...this.points,...e]}applySize(e,t){e===0&&(e=.001),t===0&&(t=.001),this.applyScale(e/this.width,t/this.height),this._width=e,this._height=t}applyScale(e,t){let r=Kl.set(e,t);for(let i=0,n=this.points.length;i<n;i++){let o=this.points[i];o.position.multiply(r),o.controls[0].position.multiply(r),o.controls[1].position.multiply(r)}for(let i=0,n=this.shapeHoles.length;i<n;i++)this.shapeHoles[i].applyScale(e,t);this._update(!1)}createPoint(e,t=0,r=Ge.MathUtils.generateUUID()){let i;e instanceof Ge.Vector2?i=e:i=new Ge.Vector2(e,t);let n=new yi(r,i);return n.roundness=this.roundness,n}addPoint(e){this.points.push(e),this.needsUpdate=!0}addPointAt(e,t){this.points.splice(t,0,e),this.needsUpdate=!0}getPointByUuid(e){for(let t=0,r=this.points.length;t<r;t++){let i=this.points[t];if(i.uuid===e)return i}return null}getFirstPoint(){return this.points[0]}getLastPoint(){return this.points[this.points.length-1]}removePoint(e){let t=this.points.indexOf(e);t>=0&&this.points.splice(t,1),this.needsUpdate=!0}update(e=!0){for(let t=0,r=this.shapeHoles.length;t<r;t++)this.shapeHoles[t].update(!1);this._update(e)}extractShapePointsToBuffer(e,t=12,r=!1){this.subdivision=t,this.curveDivisions===void 0&&this.computeCurveDivisions(t);let i=r?this.roundedCurveDivisions:this.curveDivisions;return hd(e,r?this.roundedCurves:this.curves,t,this.autoClose),i.reduce((n,o)=>n+o,0)+1}computeCurveDivisions(e=12){return this.curveDivisions=Yl(this.points,e,!1),this.roundedCurveDivisions=Yl(this.points,e,!0),this.curveDivisions}extractFilteredShapePointsToBuffer(e,t,r=12){return fd(e,this.curves,t,r,this.autoClose).reduce((n,o)=>n+o,0)*2}extractShapePointsToFlatArray(e,t=12){return this.subdivision=t,this.curveDivisions===void 0&&this.computeCurveDivisions(t),pd(e,this.roundedCurves,t,this.autoClose)}getCurveIndexFromVertexId(e,t=!1){let r=0;this.curveDivisions===void 0&&this.computeCurveDivisions(this.subdivision);let i=t?this.roundedCurveDivisions:this.curveDivisions,n=0;t&&this.points[0].roundedCurveCorner!==void 0&&(n=vi(this.points[0].roundedCurveCorner,this.subdivision)*.5);let o=e-n;o<0&&(o+=i.reduce((l,a)=>l+a,0));for(let l=0,a=i.length;l<a;l++){let c=i[l];if(o<r+c)return[l,(o-r+1)/c];r+=c}return[0,1]}getCurveT(e,t,r){let i=this.points[e],n=this.points[e>=this.points.length-1?0:e+1],o=this.curveDivisions,l=o[e];if(Ql(i,n)){let u=i.position.distanceTo(n.position);return i.position.distanceTo(Kl.set(r.x,r.y))/u}let a=0;for(let u=0;u<e;u++)a+=o[u];return(t-a)/l}dispose(){this.eventDispatcher=null}_applyCurveForPoint(e,t){Ql(t,e)?this.lineTo(e.position.x,e.position.y):this.bezierCurveTo(t.controls[1].position.x,t.controls[1].position.y,e.controls[0].position.x,e.controls[0].position.y,e.position.x,e.position.y);let r=this.curves[this.curves.length-1];e.curveBefore=r,t.curveAfter=r;let i=r.clone();e.roundedCurveBefore=i,t.roundedCurveAfter=i,e.roundedCurveCorner=void 0,this.roundedCurves.push(i)}_update(e=!0){if(this.curves=[],this.roundedCurves=[],!this.points.length)return;for(let r=0,i=this.points.length;r<i;r++){let n=this.points[r];if(r===0)this.moveTo(n.position.x,n.position.y);else{let o=this.points[r-1];this._applyCurveForPoint(n,o)}}let t=this.getLastPoint();if(t?.curveAfter&&(t.curveAfter=void 0),this.isClosed){let r=this.points[0],i=this.points[this.points.length-1];this._applyCurveForPoint(r,i)}if(this.points.length>2){let r=0;for(let i=0,n=this.points.length;i<n;i++){let o=this.points[i],l=o.roundness;if(!o.controlsMoved()&&l>0){let a=o.curveBefore,c=o.curveAfter;if(a===void 0||c===void 0)continue;let u=o.roundedCurveBefore,d=o.roundedCurveAfter,h=a.getLength(),p=c.getLength(),f=Math.min(l,h*.499),m=Math.min(l,p*.499),g=Math.min(f,m),y=1-g/h,x=g/p,v=a.getPointAt(y,Kl),b=c.getPointAt(x,ry);this._subSplitCurve(a,u,y,v,void 0),this._subSplitCurve(c,d,x,void 0,b);let w;if(this.useCubicForRoundedCorners){let N=Jl(v,o.position,b)/2,S=Math.tan(N)*v.distanceTo(o.position),[A,I]=ld(v,b,S,iy,sy),U=cd(A,I,o.position),[E,j]=ud(U,v,b,S,ny,oy);w=new Ge.CubicBezierCurve(v.clone(),E.clone(),j.clone(),b.clone())}else w=new Ge.QuadraticBezierCurve(v.clone(),o.position.clone(),b.clone());o.roundedCurveCorner=w,this.roundedCurves.splice(i+r,0,w),r++}}}e&&this.eventDispatcher?.dispatchEvent({type:\\\\\\\"update\\\\\\\"})}_subSplitCurve(e,t,r,i,n){if(e instanceof Ge.LineCurve)i!==void 0&&t.v2.copy(i),n!==void 0&&t.v1.copy(n);else{let o=e,l=t,a=o.getUtoTmapping(r,0),c=dd(o.v0,o.v1,o.v2,o.v3,a);return i!==void 0&&(l.v0.set(c[0],c[1]),l.v1.set(c[2],c[3]),l.v2.set(c[4],c[5]),l.v3.set(c[6],c[7])),n!==void 0&&(l.v0.set(c[6],c[7]),l.v1.set(c[8],c[9]),l.v2.set(c[10],c[11]),l.v3.set(c[12],c[13])),l}return t}clone(){let e=new Xe(this._width,this._height);return e.points=this.points.map(t=>t.clone()),e.isClosed=this.isClosed,e.roundness=this.roundness,e.isMesh2D=this.isMesh2D,e.shapeHoles=this.shapeHoles.map(t=>t.clone()),e}toJSON(){return{points:this.points.reduce((e,t)=>e.concat(t.toJSON()),[]),shapeHoles:this.shapeHoles.map(e=>e.toJSON()),isClosed:this.isClosed,roundness:this.roundness}}fromJSON(e){this.points=[],this.pointIDs=0;let t=e.points.length/7;for(let r=0;r<t;r++){let i=r*7,n=e.points[i+0],o=e.points[i+1],l=e.points[i+2],a=e.points[i+3],c=e.points[i+4],u=e.points[i+5],d=e.points[i+6],h=new yi(Ge.MathUtils.generateUUID(),new Ge.Vector2(n,o));h.controls[0].position.set(l,a),h.controls[1].position.set(c,u),h.roundness=d,this.points.push(h)}return this.shapeHoles=e.shapeHoles?.length?e.shapeHoles.map(r=>{let i=new Xe;return i.fromJSON(r),i}):[],this.isClosed=e.isClosed,this._roundness=e.roundness,this._update(),this}fromShape(e){let t=(i,n)=>{n instanceof Ge.CubicBezierCurve&&n.v3.equals(i.position)&&i.controls[0].position.copy(n.v2)},r=i=>{let n=[],o,l;for(o=0,l=i.length;o<l;o++)i[o]instanceof Ge.QuadraticBezierCurve&&(i[o]=ad(i[o]));for(o=0,l=i.length;o<l;o++){let u=i[o],d=o>0?i[o-1]:null,h;u instanceof Ge.CubicBezierCurve?(h=this.createPoint(u.v0),h.controls[1].position.copy(u.v1)):u instanceof Ge.LineCurve&&(h=this.createPoint(u.v1)),h!==void 0&&(d!==null&&t(h,d),n.push(h))}let a=i[i.length-1],c=!1;return a instanceof Ge.CubicBezierCurve?a.v3.equals(n[0].position)&&(n[0].controls[0].position.copy(a.v2),c=!0):a instanceof Ge.LineCurve&&a.v2.equals(n[0].position)&&(c=!0),this.isClosed=c,n};return 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pe(s,e){if(!s)throw e||\\\\\\\"Assertion Failed!\\\\\\\"}var de=function(){function s(){}return s.vertEq=function(e,t){return e.s===t.s&&e.t===t.t},s.vertLeq=function(e,t){return e.s<t.s||e.s===t.s&&e.t<=t.t},s.transLeq=function(e,t){return e.t<t.t||e.t===t.t&&e.s<=t.s},s.edgeGoesLeft=function(e){return s.vertLeq(e.Dst,e.Org)},s.edgeGoesRight=function(e){return s.vertLeq(e.Org,e.Dst)},s.vertL1dist=function(e,t){return Math.abs(e.s-t.s)+Math.abs(e.t-t.t)},s.edgeEval=function(e,t,r){pe(s.vertLeq(e,t)&&s.vertLeq(t,r));var i=t.s-e.s,n=r.s-t.s;return i+n>0?i<n?t.t-e.t+(e.t-r.t)*(i/(i+n)):t.t-r.t+(r.t-e.t)*(n/(i+n)):0},s.edgeSign=function(e,t,r){pe(s.vertLeq(e,t)&&s.vertLeq(t,r));var i=t.s-e.s,n=r.s-t.s;return i+n>0?(t.t-r.t)*i+(t.t-e.t)*n:0},s.transEval=function(e,t,r){pe(s.transLeq(e,t)&&s.transLeq(t,r));var i=t.t-e.t,n=r.t-t.t;return i+n>0?i<n?t.s-e.s+(e.s-r.s)*(i/(i+n)):t.s-r.s+(r.s-e.s)*(n/(i+n)):0},s.transSign=function(e,t,r){pe(s.transLeq(e,t)&&s.transLeq(t,r));var i=t.t-e.t,n=r.t-t.t;return i+n>0?(t.s-r.s)*i+(t.s-e.s)*n:0},s.vertCCW=function(e,t,r){return e.s*(t.t-r.t)+t.s*(r.t-e.t)+r.s*(e.t-t.t)>=0},s.interpolate=function(e,t,r,i){return e=e<0?0:e,r=r<0?0:r,e<=r?r===0?(t+i)/2:t+(i-t)*(e/(e+r)):i+(t-i)*(r/(e+r))},s.intersect=function(e,t,r,i,n){var o,l,a;s.vertLeq(e,t)||(a=e,e=t,t=a),s.vertLeq(r,i)||(a=r,r=i,i=a),s.vertLeq(e,r)||(a=e,e=r,r=a,a=t,t=i,i=a),s.vertLeq(r,t)?s.vertLeq(t,i)?(o=s.edgeEval(e,r,t),l=s.edgeEval(r,t,i),o+l<0&&(o=-o,l=-l),n.s=s.interpolate(o,r.s,l,t.s)):(o=s.edgeSign(e,r,t),l=-s.edgeSign(e,i,t),o+l<0&&(o=-o,l=-l),n.s=s.interpolate(o,r.s,l,i.s)):n.s=(r.s+t.s)/2,s.transLeq(e,t)||(a=e,e=t,t=a),s.transLeq(r,i)||(a=r,r=i,i=a),s.transLeq(e,r)||(a=e,e=r,r=a,a=t,t=i,i=a),s.transLeq(r,t)?s.transLeq(t,i)?(o=s.transEval(e,r,t),l=s.transEval(r,t,i),o+l<0&&(o=-o,l=-l),n.t=s.interpolate(o,r.t,l,t.t)):(o=s.transSign(e,r,t),l=-s.transSign(e,i,t),o+l<0&&(o=-o,l=-l),n.t=s.interpolate(o,r.t,l,i.t)):n.t=(r.t+t.t)/2},s}(),kn=function(){function s(){this.next=null,this.prev=null,this.anEdge=null,this.trail=null,this.n=0,this.marked=!1,this.inside=!1}return s}(),Vo=function(){function s(e){this.side=e,this.next=null,this.Org=null,this.Sym=null,this.Onext=null,this.Lnext=null,this.Lface=null,this.activeRegion=null,this.winding=0}return Object.defineProperty(s.prototype,\\\\\\\"Rface\\\\\\\",{get:function(){return this.Sym.Lface},set:function(e){this.Sym.Lface=e},enumerable:!0,configurable:!0}),Object.defineProperty(s.prototype,\\\\\\\"Dst\\\\\\\",{get:function(){return this.Sym.Org},set:function(e){this.Sym.Org=e},enumerable:!0,configurable:!0}),Object.defineProperty(s.prototype,\\\\\\\"Oprev\\\\\\\",{get:function(){return this.Sym.Lnext},set:function(e){this.Sym.Lnext=e},enumerable:!0,configurable:!0}),Object.defineProperty(s.prototype,\\\\\\\"Lprev\\\\\\\",{get:function(){return this.Onext.Sym},set:function(e){this.Onext.Sym=e},enumerable:!0,configurable:!0}),Object.defineProperty(s.prototype,\\\\\\\"Dprev\\\\\\\",{get:function(){return this.Lnext.Sym},set:function(e){this.Lnext.Sym=e},enumerable:!0,configurable:!0}),Object.defineProperty(s.prototype,\\\\\\\"Rprev\\\\\\\",{get:function(){return this.Sym.Onext},set:function(e){this.Sym.Onext=e},enumerable:!0,configurable:!0}),Object.defineProperty(s.prototype,\\\\\\\"Dnext\\\\\\\",{get:function(){return this.Sym.Onext.Sym},set:function(e){this.Sym.Onext.Sym=e},enumerable:!0,configurable:!0}),Object.defineProperty(s.prototype,\\\\\\\"Rnext\\\\\\\",{get:function(){return this.Sym.Lnext.Sym},set:function(e){this.Sym.Lnext.Sym=e},enumerable:!0,configurable:!0}),s}(),Os=function(){function s(){this.next=null,this.prev=null,this.anEdge=null,this.coords=[0,0,0],this.s=0,this.t=0,this.pqHandle=0,this.n=0,this.idx=0}return s}(),yd=function(){function s(){var e=new Os,t=new kn,r=new Vo(0),i=new Vo(1);e.next=e.prev=e,e.anEdge=null,t.next=t.prev=t,r.next=r,r.Sym=i,i.next=i,i.Sym=r,this.vHead=e,this.fHead=t,this.eHead=r,this.eHeadSym=i}return s.prototype.makeEdge_=function(e){var t=new Vo(0),r=new Vo(1);e.Sym.side<e.side&&(e=e.Sym);var i=e.Sym.next;return r.next=i,i.Sym.next=t,t.next=e,e.Sym.next=r,t.Sym=r,t.Onext=t,t.Lnext=r,t.Org=null,t.Lface=null,t.winding=0,t.activeRegion=null,r.Sym=t,r.Onext=r,r.Lnext=t,r.Org=null,r.Lface=null,r.winding=0,r.activeRegion=null,t},s.prototype.splice_=function(e,t){var r=e.Onext,i=t.Onext;r.Sym.Lnext=t,i.Sym.Lnext=e,e.Onext=i,t.Onext=r},s.prototype.makeVertex_=function(e,t,r){var i=e;pe(i,\\\\\\\"Vertex can't be null!\\\\\\\");var n=r.prev;i.prev=n,n.next=i,i.next=r,r.prev=i,i.anEdge=t;var o=t;do o.Org=i,o=o.Onext;while(o!==t)},s.prototype.makeFace_=function(e,t,r){var i=e;pe(i,\\\\\\\"Face can't be null\\\\\\\");var n=r.prev;i.prev=n,n.next=i,i.next=r,r.prev=i,i.anEdge=t,i.trail=null,i.marked=!1,i.inside=r.inside;var o=t;do o.Lface=i,o=o.Lnext;while(o!==t)},s.prototype.killEdge_=function(e){e.Sym.side<e.side&&(e=e.Sym);var t=e.next,r=e.Sym.next;t.Sym.next=r,r.Sym.next=t},s.prototype.killVertex_=function(e,t){var r=e.anEdge,i=r;do i.Org=t,i=i.Onext;while(i!==r);var n=e.prev,o=e.next;o.prev=n,n.next=o},s.prototype.killFace_=function(e,t){var r=e.anEdge,i=r;do i.Lface=t,i=i.Lnext;while(i!==r);var n=e.prev,o=e.next;o.prev=n,n.next=o},s.prototype.makeEdge=function(){var e=new Os,t=new Os,r=new kn,i=this.makeEdge_(this.eHead);return this.makeVertex_(e,i,this.vHead),this.makeVertex_(t,i.Sym,this.vHead),this.makeFace_(r,i,this.fHead),i},s.prototype.splice=function(e,t){var r=!1,i=!1;if(e!==t){if(t.Org!==e.Org&&(i=!0,this.killVertex_(t.Org,e.Org)),t.Lface!==e.Lface&&(r=!0,this.killFace_(t.Lface,e.Lface)),this.splice_(t,e),!i){var n=new Os;this.makeVertex_(n,t,e.Org),e.Org.anEdge=e}if(!r){var o=new kn;this.makeFace_(o,t,e.Lface),e.Lface.anEdge=e}}},s.prototype.delete=function(e){var t=e.Sym,r=!1;if(e.Lface!==e.Rface&&(r=!0,this.killFace_(e.Lface,e.Rface)),e.Onext===e)this.killVertex_(e.Org,null);else if(e.Rface.anEdge=e.Oprev,e.Org.anEdge=e.Onext,this.splice_(e,e.Oprev),!r){var i=new kn;this.makeFace_(i,e,e.Lface)}t.Onext===t?(this.killVertex_(t.Org,null),this.killFace_(t.Lface,null)):(e.Lface.anEdge=t.Oprev,t.Org.anEdge=t.Onext,this.splice_(t,t.Oprev)),this.killEdge_(e)},s.prototype.addEdgeVertex=function(e){var t=this.makeEdge_(e),r=t.Sym;this.splice_(t,e.Lnext),t.Org=e.Dst;var i=new Os;return this.makeVertex_(i,r,t.Org),t.Lface=r.Lface=e.Lface,t},s.prototype.splitEdge=function(e){var t=this.addEdgeVertex(e),r=t.Sym;return this.splice_(e.Sym,e.Sym.Oprev),this.splice_(e.Sym,r),e.Dst=r.Org,r.Dst.anEdge=r.Sym,r.Rface=e.Rface,r.winding=e.winding,r.Sym.winding=e.Sym.winding,r.idx=e.idx,r.Sym.idx=e.Sym.idx,r},s.prototype.connect=function(e,t){var r=!1,i=this.makeEdge_(e),n=i.Sym;if(t.Lface!==e.Lface&&(r=!0,this.killFace_(t.Lface,e.Lface)),this.splice_(i,e.Lnext),this.splice_(n,t),i.Org=e.Dst,n.Org=t.Org,i.Lface=n.Lface=e.Lface,e.Lface.anEdge=n,!r){var o=new kn;this.makeFace_(o,i,e.Lface)}return i},s.prototype.zapFace=function(e){var t=e.anEdge,r,i,n,o,l;i=t.Lnext;do r=i,i=r.Lnext,r.Lface=null,r.Rface===null&&(r.Onext===r?this.killVertex_(r.Org,null):(r.Org.anEdge=r.Onext,this.splice_(r,r.Oprev)),n=r.Sym,n.Onext===n?this.killVertex_(n.Org,null):(n.Org.anEdge=n.Onext,this.splice_(n,n.Oprev)),this.killEdge_(r));while(r!=t);o=e.prev,l=e.next,l.prev=o,o.next=l},s.prototype.countFaceVerts_=function(e){var t=e.anEdge,r=0;do r++,t=t.Lnext;while(t!==e.anEdge);return r},s.prototype.mergeConvexFaces=function(e){var t,r,i,n,o,l,a;for(t=this.fHead.next;t!==this.fHead;t=t.next)if(!!t.inside)for(r=t.anEdge,o=r.Org;i=r.Lnext,n=r.Sym,n&&n.Lface&&n.Lface.inside&&(l=this.countFaceVerts_(t),a=this.countFaceVerts_(n.Lface),l+a-2<=e&&de.vertCCW(r.Lprev.Org,r.Org,n.Lnext.Lnext.Org)&&de.vertCCW(n.Lprev.Org,n.Org,r.Lnext.Lnext.Org)&&(i=n.Lnext,this.delete(n),r=null,n=null)),!(r&&r.Lnext.Org===o);)r=i;return!0},s.prototype.check=function(){var e=this.fHead,t=this.vHead,r=this.eHead,i,n,o,l,a,c;for(n=e,n=e;(i=n.next)!==e;n=i){pe(i.prev===n),a=i.anEdge;do pe(a.Sym!==a),pe(a.Sym.Sym===a),pe(a.Lnext.Onext.Sym===a),pe(a.Onext.Sym.Lnext===a),pe(a.Lface===i),a=a.Lnext;while(a!==i.anEdge)}for(pe(i.prev===n&&i.anEdge===null),l=t,l=t;(o=l.next)!==t;l=o){pe(o.prev===l),a=o.anEdge;do pe(a.Sym!==a),pe(a.Sym.Sym===a),pe(a.Lnext.Onext.Sym===a),pe(a.Onext.Sym.Lnext===a),pe(a.Org===o),a=a.Onext;while(a!==o.anEdge)}for(pe(o.prev===l&&o.anEdge===null),c=r,c=r;(a=c.next)!==r;c=a)pe(a.Sym.next===c.Sym),pe(a.Sym!==a),pe(a.Sym.Sym===a),pe(a.Org!==null),pe(a.Dst!==null),pe(a.Lnext.Onext.Sym===a),pe(a.Onext.Sym.Lnext===a);pe(a.Sym.next===c.Sym&&a.Sym===this.eHeadSym&&a.Sym.Sym===a&&a.Org===null&&a.Dst===null&&a.Lface===null&&a.Rface===null)},s}(),vd=function(){function s(){this.handle=null}return s}(),xd=function(){function s(){this.key=null,this.node=0}return s}(),cy=function(){function s(e,t){this.leq=t,this.max=0,this.nodes=[],this.handles=[],this.initialized=!1,this.freeList=0,this.size=0,this.max=e,this.nodes=[],this.handles=[];for(var r=0;r<e+1;r++)this.nodes[r]=new vd,this.handles[r]=new xd;this.initialized=!1,this.nodes[1].handle=1,this.handles[1].key=null}return s.prototype.floatDown_=function(e){var t=this.nodes,r=this.handles,i,n,o;for(i=t[e].handle;;){if(o=e<<1,o<this.size&&this.leq(r[t[o+1].handle].key,r[t[o].handle].key)&&++o,pe(o<=this.max),n=t[o].handle,o>this.size||this.leq(r[i].key,r[n].key)){t[e].handle=i,r[i].node=e;break}t[e].handle=n,r[n].node=e,e=o}},s.prototype.floatUp_=function(e){var t=this.nodes,r=this.handles,i,n,o;for(i=t[e].handle;;){if(o=e>>1,n=t[o].handle,o===0||this.leq(r[n].key,r[i].key)){t[e].handle=i,r[i].node=e;break}t[e].handle=n,r[n].node=e,e=o}},s.prototype.init=function(){for(var e=this.size;e>=1;--e)this.floatDown_(e);this.initialized=!0},s.prototype.min=function(){return this.handles[this.nodes[1].handle].key},s.prototype.insert=function(e){var t,r;if(t=++this.size,t*2>this.max){this.max*=2;var i,n;for(n=this.nodes.length,this.nodes.length=this.max+1,i=n;i<this.nodes.length;i++)this.nodes[i]=new vd;for(n=this.handles.length,this.handles.length=this.max+1,i=n;i<this.handles.length;i++)this.handles[i]=new xd}return this.freeList===0?r=t:(r=this.freeList,this.freeList=this.handles[r].node),this.nodes[t].handle=r,this.handles[r].node=t,this.handles[r].key=e,this.initialized&&this.floatUp_(t),r},s.prototype.extractMin=function(){var e=this.nodes,t=this.handles,r=e[1].handle,i=t[r].key;return this.size>0&&(e[1].handle=e[this.size].handle,t[e[1].handle].node=1,t[r].key=null,t[r].node=this.freeList,this.freeList=r,--this.size,this.size>0&&this.floatDown_(1)),i},s.prototype.delete=function(e){var t=this.nodes,r=this.handles,i;pe(e>=1&&e<=this.max&&r[e].key!==null),i=r[e].node,t[i].handle=t[this.size].handle,r[t[i].handle].node=i,--this.size,i<=this.size&&(i<=1||this.leq(r[t[i>>1].handle].key,r[t[i].handle].key)?this.floatDown_(i):this.floatUp_(i)),r[e].key=null,r[e].node=this.freeList,this.freeList=e},s}(),Zl=function(){function s(){this.eUp=null,this.nodeUp=null,this.windingNumber=0,this.inside=!1,this.sentinel=!1,this.dirty=!1,this.fixUpperEdge=!1}return s}(),bd=function(){function s(){this.key=null,this.next=null,this.prev=null}return s}(),uy=function(){function s(e,t){this.frame=e,this.leq=t,this.head=new bd,this.head.next=this.head,this.head.prev=this.head}return s.prototype.min=function(){return this.head.next},s.prototype.max=function(){return this.head.prev},s.prototype.insert=function(e){return this.insertBefore(this.head,e)},s.prototype.search=function(e){var t=this.head;do t=t.next;while(t.key!==null&&!this.leq(this.frame,e,t.key));return t},s.prototype.insertBefore=function(e,t){do e=e.prev;while(e.key!==null&&!this.leq(this.frame,e.key,t));var r=new bd;return r.key=t,r.next=e.next,e.next.prev=r,r.prev=e,e.next=r,r},s.prototype.delete=function(e){e.next.prev=e.prev,e.prev.next=e.next},s}(),dy=function(){function s(){}return s.regionBelow=function(e){return e.nodeUp.prev.key},s.regionAbove=function(e){return e.nodeUp.next.key},s.debugEvent=function(e){},s.addWinding=function(e,t){e.winding+=t.winding,e.Sym.winding+=t.Sym.winding},s.edgeLeq=function(e,t,r){var i=e.event,n=t.eUp,o=r.eUp;if(n.Dst===i)return o.Dst===i?de.vertLeq(n.Org,o.Org)?de.edgeSign(o.Dst,n.Org,o.Org)<=0:de.edgeSign(n.Dst,o.Org,n.Org)>=0:de.edgeSign(o.Dst,i,o.Org)<=0;if(o.Dst===i)return de.edgeSign(n.Dst,i,n.Org)>=0;var l=de.edgeEval(n.Dst,i,n.Org),a=de.edgeEval(o.Dst,i,o.Org);return l>=a},s.deleteRegion=function(e,t){t.fixUpperEdge&&pe(t.eUp.winding===0),t.eUp.activeRegion=null,e.dict.delete(t.nodeUp)},s.fixUpperEdge=function(e,t,r){pe(t.fixUpperEdge),e.mesh.delete(t.eUp),t.fixUpperEdge=!1,t.eUp=r,r.activeRegion=t},s.topLeftRegion=function(e,t){var r=t.eUp.Org,i;do t=s.regionAbove(t);while(t.eUp.Org===r);if(t.fixUpperEdge){if(i=e.mesh.connect(s.regionBelow(t).eUp.Sym,t.eUp.Lnext),i===null)return null;s.fixUpperEdge(e,t,i),t=s.regionAbove(t)}return t},s.topRightRegion=function(e){var t=e.eUp.Dst;do e=s.regionAbove(e);while(e.eUp.Dst===t);return e},s.addRegionBelow=function(e,t,r){var i=new Zl;return i.eUp=r,i.nodeUp=e.dict.insertBefore(t.nodeUp,i),i.fixUpperEdge=!1,i.sentinel=!1,i.dirty=!1,r.activeRegion=i,i},s.isWindingInside=function(e,t){switch(e.windingRule){case st.ODD:return(t&1)!=0;case st.NONZERO:return t!==0;case st.POSITIVE:return t>0;case st.NEGATIVE:return t<0;case st.ABS_GEQ_TWO:return t>=2||t<=-2}throw new Error(\\\\\\\"Invalid winding rulle\\\\\\\")},s.computeWinding=function(e,t){t.windingNumber=s.regionAbove(t).windingNumber+t.eUp.winding,t.inside=s.isWindingInside(e,t.windingNumber)},s.finishRegion=function(e,t){var r=t.eUp,i=r.Lface;i.inside=t.inside,i.anEdge=r,s.deleteRegion(e,t)},s.finishLeftRegions=function(e,t,r){for(var i,n=null,o=t,l=t.eUp;o!==r;){if(o.fixUpperEdge=!1,n=s.regionBelow(o),i=n.eUp,i.Org!=l.Org){if(!n.fixUpperEdge){s.finishRegion(e,o);break}i=e.mesh.connect(l.Lprev,i.Sym),s.fixUpperEdge(e,n,i)}l.Onext!==i&&(e.mesh.splice(i.Oprev,i),e.mesh.splice(l,i)),s.finishRegion(e,o),l=n.eUp,o=n}return l},s.addRightEdges=function(e,t,r,i,n,o){var l,a,c,u,d=!0;c=r;do pe(de.vertLeq(c.Org,c.Dst)),s.addRegionBelow(e,t,c.Sym),c=c.Onext;while(c!==i);for(n===null&&(n=s.regionBelow(t).eUp.Rprev),a=t,u=n;l=s.regionBelow(a),c=l.eUp.Sym,c.Org===u.Org;)c.Onext!==u&&(e.mesh.splice(c.Oprev,c),e.mesh.splice(u.Oprev,c)),l.windingNumber=a.windingNumber-c.winding,l.inside=s.isWindingInside(e,l.windingNumber),a.dirty=!0,!d&&s.checkForRightSplice(e,a)&&(s.addWinding(c,u),s.deleteRegion(e,a),e.mesh.delete(u)),d=!1,a=l,u=c;a.dirty=!0,pe(a.windingNumber-c.winding===l.windingNumber),o&&s.walkDirtyRegions(e,a)},s.spliceMergeVertices=function(e,t,r){e.mesh.splice(t,r)},s.vertexWeights=function(e,t,r){var i=de.vertL1dist(t,e),n=de.vertL1dist(r,e),o=.5*n/(i+n),l=.5*i/(i+n);e.coords[0]+=o*t.coords[0]+l*r.coords[0],e.coords[1]+=o*t.coords[1]+l*r.coords[1],e.coords[2]+=o*t.coords[2]+l*r.coords[2]},s.getIntersectData=function(e,t,r,i,n,o){t.coords[0]=t.coords[1]=t.coords[2]=0,t.idx=-1,s.vertexWeights(t,r,i),s.vertexWeights(t,n,o)},s.checkForRightSplice=function(e,t){var r=s.regionBelow(t),i=t.eUp,n=r.eUp;if(de.vertLeq(i.Org,n.Org)){if(de.edgeSign(n.Dst,i.Org,n.Org)>0)return!1;de.vertEq(i.Org,n.Org)?i.Org!==n.Org&&(e.pq.delete(i.Org.pqHandle),s.spliceMergeVertices(e,n.Oprev,i)):(e.mesh.splitEdge(n.Sym),e.mesh.splice(i,n.Oprev),t.dirty=r.dirty=!0)}else{if(de.edgeSign(i.Dst,n.Org,i.Org)<0)return!1;s.regionAbove(t).dirty=t.dirty=!0,e.mesh.splitEdge(i.Sym),e.mesh.splice(n.Oprev,i)}return!0},s.checkForLeftSplice=function(e,t){var r=s.regionBelow(t),i=t.eUp,n=r.eUp,o;if(pe(!de.vertEq(i.Dst,n.Dst)),de.vertLeq(i.Dst,n.Dst)){if(de.edgeSign(i.Dst,n.Dst,i.Org)<0)return!1;s.regionAbove(t).dirty=t.dirty=!0,o=e.mesh.splitEdge(i),e.mesh.splice(n.Sym,o),o.Lface.inside=t.inside}else{if(de.edgeSign(n.Dst,i.Dst,n.Org)>0)return!1;t.dirty=r.dirty=!0,o=e.mesh.splitEdge(n),e.mesh.splice(i.Lnext,n.Sym),o.Rface.inside=t.inside}return!0},s.checkForIntersect=function(e,t){var r=s.regionBelow(t),i=t.eUp,n=r.eUp,o=i.Org,l=n.Org,a=i.Dst,c=n.Dst,u,d,h=new Os,p,f;if(pe(!de.vertEq(c,a)),pe(de.edgeSign(a,e.event,o)<=0),pe(de.edgeSign(c,e.event,l)>=0),pe(o!==e.event&&l!==e.event),pe(!t.fixUpperEdge&&!r.fixUpperEdge),o===l||(u=Math.min(o.t,a.t),d=Math.max(l.t,c.t),u>d))return!1;if(de.vertLeq(o,l)){if(de.edgeSign(c,o,l)>0)return!1}else if(de.edgeSign(a,l,o)<0)return!1;return s.debugEvent(e),de.intersect(a,o,c,l,h),pe(Math.min(o.t,a.t)<=h.t),pe(h.t<=Math.max(l.t,c.t)),pe(Math.min(c.s,a.s)<=h.s),pe(h.s<=Math.max(l.s,o.s)),de.vertLeq(h,e.event)&&(h.s=e.event.s,h.t=e.event.t),p=de.vertLeq(o,l)?o:l,de.vertLeq(p,h)&&(h.s=p.s,h.t=p.t),de.vertEq(h,o)||de.vertEq(h,l)?(s.checkForRightSplice(e,t),!1):!de.vertEq(a,e.event)&&de.edgeSign(a,e.event,h)>=0||!de.vertEq(c,e.event)&&de.edgeSign(c,e.event,h)<=0?c===e.event?(e.mesh.splitEdge(i.Sym),e.mesh.splice(n.Sym,i),t=s.topLeftRegion(e,t),i=s.regionBelow(t).eUp,s.finishLeftRegions(e,s.regionBelow(t),r),s.addRightEdges(e,t,i.Oprev,i,i,!0),!0):a===e.event?(e.mesh.splitEdge(n.Sym),e.mesh.splice(i.Lnext,n.Oprev),r=t,t=s.topRightRegion(t),f=s.regionBelow(t).eUp.Rprev,r.eUp=n.Oprev,n=s.finishLeftRegions(e,r,null),s.addRightEdges(e,t,n.Onext,i.Rprev,f,!0),!0):(de.edgeSign(a,e.event,h)>=0&&(s.regionAbove(t).dirty=t.dirty=!0,e.mesh.splitEdge(i.Sym),i.Org.s=e.event.s,i.Org.t=e.event.t),de.edgeSign(c,e.event,h)<=0&&(t.dirty=r.dirty=!0,e.mesh.splitEdge(n.Sym),n.Org.s=e.event.s,n.Org.t=e.event.t),!1):(e.mesh.splitEdge(i.Sym),e.mesh.splitEdge(n.Sym),e.mesh.splice(n.Oprev,i),i.Org.s=h.s,i.Org.t=h.t,i.Org.pqHandle=e.pq.insert(i.Org),s.getIntersectData(e,i.Org,o,a,l,c),s.regionAbove(t).dirty=t.dirty=r.dirty=!0,!1)},s.walkDirtyRegions=function(e,t){for(var r=s.regionBelow(t),i,n;;){for(;r.dirty;)t=r,r=s.regionBelow(r);if(!t.dirty&&(r=t,t=s.regionAbove(t),t===null||!t.dirty))return;if(t.dirty=!1,i=t.eUp,n=r.eUp,i.Dst!==n.Dst&&s.checkForLeftSplice(e,t)&&(r.fixUpperEdge?(s.deleteRegion(e,r),e.mesh.delete(n),r=s.regionBelow(t),n=r.eUp):t.fixUpperEdge&&(s.deleteRegion(e,t),e.mesh.delete(i),t=s.regionAbove(r),i=t.eUp)),i.Org!==n.Org)if(i.Dst!==n.Dst&&!t.fixUpperEdge&&!r.fixUpperEdge&&(i.Dst===e.event||n.Dst===e.event)){if(s.checkForIntersect(e,t))return}else s.checkForRightSplice(e,t);i.Org===n.Org&&i.Dst===n.Dst&&(s.addWinding(n,i),s.deleteRegion(e,t),e.mesh.delete(i),t=s.regionAbove(r))}},s.connectRightVertex=function(e,t,r){var i,n=r.Onext,o=s.regionBelow(t),l=t.eUp,a=o.eUp,c=!1;if(l.Dst!==a.Dst&&s.checkForIntersect(e,t),de.vertEq(l.Org,e.event)&&(e.mesh.splice(n.Oprev,l),t=s.topLeftRegion(e,t),n=s.regionBelow(t).eUp,s.finishLeftRegions(e,s.regionBelow(t),o),c=!0),de.vertEq(a.Org,e.event)&&(e.mesh.splice(r,a.Oprev),r=s.finishLeftRegions(e,o,null),c=!0),c){s.addRightEdges(e,t,r.Onext,n,n,!0);return}de.vertLeq(a.Org,l.Org)?i=a.Oprev:i=l,i=e.mesh.connect(r.Lprev,i),s.addRightEdges(e,t,i,i.Onext,i.Onext,!1),i.Sym.activeRegion.fixUpperEdge=!0,s.walkDirtyRegions(e,t)},s.connectLeftDegenerate=function(e,t,r){var i,n,o,l,a;if(i=t.eUp,de.vertEq(i.Org,r)){pe(!1),s.spliceMergeVertices(e,i,r.anEdge);return}if(!de.vertEq(i.Dst,r)){e.mesh.splitEdge(i.Sym),t.fixUpperEdge&&(e.mesh.delete(i.Onext),t.fixUpperEdge=!1),e.mesh.splice(r.anEdge,i),s.sweepEvent(e,r);return}pe(!1),t=s.topRightRegion(t),a=s.regionBelow(t),o=a.eUp.Sym,n=l=o.Onext,a.fixUpperEdge&&(pe(n!==o),s.deleteRegion(e,a),e.mesh.delete(o),o=n.Oprev),e.mesh.splice(r.anEdge,o),de.edgeGoesLeft(n)||(n=null),s.addRightEdges(e,t,o.Onext,l,n,!0)},s.connectLeftVertex=function(e,t){var r,i,n,o,l,a,c=new Zl;if(c.eUp=t.anEdge.Sym,r=e.dict.search(c).key,i=s.regionBelow(r),!!i){if(o=r.eUp,l=i.eUp,de.edgeSign(o.Dst,t,o.Org)===0){s.connectLeftDegenerate(e,r,t);return}if(n=de.vertLeq(l.Dst,o.Dst)?r:i,r.inside||n.fixUpperEdge){if(n===r)a=e.mesh.connect(t.anEdge.Sym,o.Lnext);else{var u=e.mesh.connect(l.Dnext,t.anEdge);a=u.Sym}n.fixUpperEdge?s.fixUpperEdge(e,n,a):s.computeWinding(e,s.addRegionBelow(e,r,a)),s.sweepEvent(e,t)}else s.addRightEdges(e,r,t.anEdge,t.anEdge,null,!0)}},s.sweepEvent=function(e,t){e.event=t,s.debugEvent(e);for(var r=t.anEdge;r.activeRegion===null;)if(r=r.Onext,r===t.anEdge){s.connectLeftVertex(e,t);return}var i=s.topLeftRegion(e,r.activeRegion);pe(i!==null);var n=s.regionBelow(i),o=n.eUp,l=s.finishLeftRegions(e,n,null);l.Onext===o?s.connectRightVertex(e,i,l):s.addRightEdges(e,i,l.Onext,o,o,!0)},s.addSentinel=function(e,t,r,i){var n=new Zl,o=e.mesh.makeEdge();o.Org.s=r,o.Org.t=i,o.Dst.s=t,o.Dst.t=i,e.event=o.Dst,n.eUp=o,n.windingNumber=0,n.inside=!1,n.fixUpperEdge=!1,n.sentinel=!0,n.dirty=!1,n.nodeUp=e.dict.insert(n)},s.initEdgeDict=function(e){e.dict=new uy(e,s.edgeLeq);var t=e.bmax[0]-e.bmin[0],r=e.bmax[1]-e.bmin[1],i=e.bmin[0]-t,n=e.bmax[0]+t,o=e.bmin[1]-r,l=e.bmax[1]+r;s.addSentinel(e,i,n,o),s.addSentinel(e,i,n,l)},s.doneEdgeDict=function(e){for(var t,r=0;(t=e.dict.min().key)!==null;)t.sentinel||(pe(t.fixUpperEdge),pe(++r==1)),pe(t.windingNumber===0),s.deleteRegion(e,t)},s.removeDegenerateEdges=function(e){var t,r,i,n=e.mesh.eHead;for(t=n.next;t!==n;t=r)r=t.next,i=t.Lnext,de.vertEq(t.Org,t.Dst)&&t.Lnext.Lnext!==t&&(s.spliceMergeVertices(e,i,t),e.mesh.delete(t),t=i,i=t.Lnext),i.Lnext===t&&(i!==t&&((i===r||i===r.Sym)&&(r=r.next),e.mesh.delete(i)),(t===r||t===r.Sym)&&(r=r.next),e.mesh.delete(t))},s.initPriorityQ=function(e){var t,r,i,n=0;for(i=e.mesh.vHead,r=i.next;r!==i;r=r.next)n++;for(n+=8,t=e.pq=new cy(n,de.vertLeq),i=e.mesh.vHead,r=i.next;r!==i;r=r.next)r.pqHandle=t.insert(r);return r!==i?!1:(t.init(),!0)},s.donePriorityQ=function(e){e.pq=null},s.removeDegenerateFaces=function(e,t){var r,i,n;for(r=t.fHead.next;r!==t.fHead;r=i)i=r.next,n=r.anEdge,pe(n.Lnext!==n),n.Lnext.Lnext===n&&(s.addWinding(n.Onext,n),e.mesh.delete(n));return!0},s.computeInterior=function(e,t){t===void 0&&(t=!0);var r,i;if(s.removeDegenerateEdges(e),!s.initPriorityQ(e))return!1;for(s.initEdgeDict(e);(r=e.pq.extractMin())!==null;){for(;i=e.pq.min(),!(i===null||!de.vertEq(i,r));)i=e.pq.extractMin(),s.spliceMergeVertices(e,r.anEdge,i.anEdge);s.sweepEvent(e,r)}return e.event=e.dict.min().key.eUp.Org,s.debugEvent(e),s.doneEdgeDict(e),s.donePriorityQ(e),s.removeDegenerateFaces(e,e.mesh)?(t&&e.mesh.check(),!0):!1},s}(),hy=function(){function s(){this.mesh=new yd,this.normal=[0,0,0],this.sUnit=[0,0,0],this.tUnit=[0,0,0],this.bmin=[0,0],this.bmax=[0,0],this.windingRule=st.ODD,this.dict=null,this.pq=null,this.event=null,this.vertexIndexCounter=0,this.vertices=[],this.vertexIndices=[],this.vertexCount=0,this.elements=[],this.elementCount=0}return s.prototype.dot_=function(e,t){return e[0]*t[0]+e[1]*t[1]+e[2]*t[2]},s.prototype.normalize_=function(e){var t=e[0]*e[0]+e[1]*e[1]+e[2]*e[2];if(!t)throw\\\\\\\"Zero-size vector!\\\\\\\";t=Math.sqrt(t),e[0]/=t,e[1]/=t,e[2]/=t},s.prototype.longAxis_=function(e){var t=0;return Math.abs(e[1])>Math.abs(e[0])&&(t=1),Math.abs(e[2])>Math.abs(e[t])&&(t=2),t},s.prototype.computeNormal_=function(e){var t,r,i,n,o,l,a=[0,0,0],c=[0,0,0],u=[0,0,0],d=[0,0,0],h=[0,0,0],p=[null,null,null],f=[null,null,null],m=this.mesh.vHead;t=m.next;for(var g=0;g<3;++g)n=t.coords[g],c[g]=n,f[g]=t,a[g]=n,p[g]=t;for(t=m.next;t!==m;t=t.next)for(var y=0;y<3;++y)n=t.coords[y],n<c[y]&&(c[y]=n,f[y]=t),n>a[y]&&(a[y]=n,p[y]=t);var x=0;if(a[1]-c[1]>a[0]-c[0]&&(x=1),a[2]-c[2]>a[x]-c[x]&&(x=2),c[x]>=a[x]){e[0]=0,e[1]=0,e[2]=1;return}for(l=0,r=f[x],i=p[x],u[0]=r.coords[0]-i.coords[0],u[1]=r.coords[1]-i.coords[1],u[2]=r.coords[2]-i.coords[2],t=m.next;t!==m;t=t.next)d[0]=t.coords[0]-i.coords[0],d[1]=t.coords[1]-i.coords[1],d[2]=t.coords[2]-i.coords[2],h[0]=u[1]*d[2]-u[2]*d[1],h[1]=u[2]*d[0]-u[0]*d[2],h[2]=u[0]*d[1]-u[1]*d[0],o=h[0]*h[0]+h[1]*h[1]+h[2]*h[2],o>l&&(l=o,e[0]=h[0],e[1]=h[1],e[2]=h[2]);l<=0&&(e[0]=e[1]=e[2]=0,e[this.longAxis_(u)]=1)},s.prototype.checkOrientation_=function(){for(var e=this.mesh.fHead,t,r=this.mesh.vHead,i,n=0,o=e.next;o!==e;o=o.next)if(i=o.anEdge,!(i.winding<=0))do n+=(i.Org.s-i.Dst.s)*(i.Org.t+i.Dst.t),i=i.Lnext;while(i!==o.anEdge);if(n<0){for(t=r.next;t!==r;t=t.next)t.t=-t.t;this.tUnit[0]=-this.tUnit[0],this.tUnit[1]=-this.tUnit[1],this.tUnit[2]=-this.tUnit[2]}},s.prototype.projectPolygon_=function(){var e=this.mesh.vHead,t=[0,0,0],r,i,n=!1;t[0]=this.normal[0],t[1]=this.normal[1],t[2]=this.normal[2],!t[0]&&!t[1]&&!t[2]&&(this.computeNormal_(t),n=!0),r=this.sUnit,i=this.tUnit;var o=this.longAxis_(t);r[o]=0,r[(o+1)%3]=1,r[(o+2)%3]=0,i[o]=0,i[(o+1)%3]=0,i[(o+2)%3]=t[o]>0?1:-1;for(var l=e.next;l!==e;l=l.next)l.s=this.dot_(l.coords,r),l.t=this.dot_(l.coords,i);n&&this.checkOrientation_();for(var a=!0,c=e.next;c!==e;c=c.next)a?(this.bmin[0]=this.bmax[0]=c.s,this.bmin[1]=this.bmax[1]=c.t,a=!1):(c.s<this.bmin[0]&&(this.bmin[0]=c.s),c.s>this.bmax[0]&&(this.bmax[0]=c.s),c.t<this.bmin[1]&&(this.bmin[1]=c.t),c.t>this.bmax[1]&&(this.bmax[1]=c.t))},s.prototype.addWinding_=function(e,t){e.winding+=t.winding,e.Sym.winding+=t.Sym.winding},s.prototype.tessellateMonoRegion_=function(e,t){var r,i;if(r=t.anEdge,!(r.Lnext!==r&&r.Lnext.Lnext!==r))throw\\\\\\\"Mono region invalid\\\\\\\";for(;de.vertLeq(r.Dst,r.Org);r=r.Lprev);for(;de.vertLeq(r.Org,r.Dst);r=r.Lnext);i=r.Lprev;for(var n=void 0;r.Lnext!==i;)if(de.vertLeq(r.Dst,i.Org)){for(;i.Lnext!==r&&(de.edgeGoesLeft(i.Lnext)||de.edgeSign(i.Org,i.Dst,i.Lnext.Dst)<=0);)n=e.connect(i.Lnext,i),i=n.Sym;i=i.Lprev}else{for(;i.Lnext!==r&&(de.edgeGoesRight(r.Lprev)||de.edgeSign(r.Dst,r.Org,r.Lprev.Org)>=0);)n=e.connect(r,r.Lprev),r=n.Sym;r=r.Lnext}if(i.Lnext===r)throw\\\\\\\"Mono region invalid\\\\\\\";for(;i.Lnext.Lnext!==r;)n=e.connect(i.Lnext,i),i=n.Sym;return!0},s.prototype.tessellateInterior_=function(e){for(var t,r=e.fHead.next;r!==e.fHead;r=t)if(t=r.next,r.inside&&!this.tessellateMonoRegion_(e,r))return!1;return!0},s.prototype.discardExterior_=function(e){for(var t,r=e.fHead.next;r!==e.fHead;r=t)t=r.next,r.inside||e.zapFace(r)},s.prototype.setWindingNumber_=function(e,t,r){for(var i,n=e.eHead.next;n!==e.eHead;n=i)i=n.next,n.Rface.inside!==n.Lface.inside?n.winding=n.Lface.inside?t:-t:r?e.delete(n):n.winding=0},s.prototype.getNeighbourFace_=function(e){return!e.Rface||!e.Rface.inside?-1:e.Rface.n},s.prototype.outputPolymesh_=function(e,t,r,i){var n,o=0,l=0,a;r>3&&e.mergeConvexFaces(r);for(var c=e.vHead.next;c!==e.vHead;c=c.next)c.n=-1;for(var u=e.fHead.next;u!==e.fHead;u=u.next)if(u.n=-1,!!u.inside){n=u.anEdge,a=0;do{var c=n.Org;c.n===-1&&(c.n=l,l++),a++,n=n.Lnext}while(n!==u.anEdge);if(a>r)throw\\\\\\\"Face vertex greater that support polygon\\\\\\\";u.n=o,++o}this.elementCount=o,t===gt.CONNECTED_POLYGONS&&(o*=2),this.elements=[],this.elements.length=o*r,this.vertexCount=l,this.vertices=[],this.vertices.length=l*i,this.vertexIndices=[],this.vertexIndices.length=l;for(var c=e.vHead.next;c!==e.vHead;c=c.next)if(c.n!==-1){var d=c.n*i;this.vertices[d+0]=c.coords[0],this.vertices[d+1]=c.coords[1],i>2&&(this.vertices[d+2]=c.coords[2]),this.vertexIndices[c.n]=c.idx}for(var h=0,u=e.fHead.next;u!==e.fHead;u=u.next)if(!!u.inside){n=u.anEdge,a=0;do{var c=n.Org;this.elements[h++]=c.n,a++,n=n.Lnext}while(n!==u.anEdge);for(var p=a;p<r;++p)this.elements[h++]=-1;if(t===gt.CONNECTED_POLYGONS){n=u.anEdge;do this.elements[h++]=this.getNeighbourFace_(n),n=n.Lnext;while(n!==u.anEdge);for(var f=a;f<r;++f)this.elements[h++]=-1}}},s.prototype.outputContours_=function(e,t){var r,i,n=0,o=0;this.vertexCount=0,this.elementCount=0;for(var l=e.fHead.next;l!==e.fHead;l=l.next)if(!!l.inside){i=r=l.anEdge;do this.vertexCount++,r=r.Lnext;while(r!==i);this.elementCount++}this.elements=[],this.elements.length=this.elementCount*2,this.vertices=[],this.vertices.length=this.vertexCount*t,this.vertexIndices=[],this.vertexIndices.length=this.vertexCount;var a=0,c=0,u=0;n=0;for(var l=e.fHead.next;l!==e.fHead;l=l.next)if(!!l.inside){o=0,i=r=l.anEdge;do this.vertices[a++]=r.Org.coords[0],this.vertices[a++]=r.Org.coords[1],t>2&&(this.vertices[a++]=r.Org.coords[2]),this.vertexIndices[c++]=this.vertexIdCallback?this.vertexIdCallback(r):r.Org.idx,o++,r=r.Lnext;while(r!==i);this.elements[u++]=n,this.elements[u++]=o,n+=o}},s.prototype.addContour=function(e,t){this.mesh===null&&(this.mesh=new yd),e<2&&(e=2),e>3&&(e=3);for(var r=null,i=0;i<t.length;i+=e)r===null?(r=this.mesh.makeEdge(),this.mesh.splice(r,r.Sym)):(this.mesh.splitEdge(r),r=r.Lnext),r.Org.coords[0]=t[i+0],r.Org.coords[1]=t[i+1],e>2?r.Org.coords[2]=t[i+2]:r.Org.coords[2]=0,r.Org.idx=this.vertexIndexCounter++,this.edgeCreateCallback&&this.edgeCreateCallback(r),r.winding=1,r.Sym.winding=-1},s.prototype.tesselate=function(e,t,r,i,n,o){if(e===void 0&&(e=st.ODD),t===void 0&&(t=gt.POLYGONS),o===void 0&&(o=!0),this.vertices=[],this.elements=[],this.vertexIndices=[],this.vertexIndexCounter=0,n&&(this.normal[0]=n[0],this.normal[1]=n[1],this.normal[2]=n[2]),this.windingRule=e,i<2&&(i=2),i>3&&(i=3),!this.mesh)return!1;this.projectPolygon_(),dy.computeInterior(this,o);var l=this.mesh;return t===gt.BOUNDARY_CONTOURS?this.setWindingNumber_(l,1,!0):this.tessellateInterior_(l),o&&l.check(),t===gt.BOUNDARY_CONTOURS?this.outputContours_(l,i):this.outputPolymesh_(l,t,r,i),!0},s}();function Qi(s){var e=s.windingRule,t=e===void 0?st.ODD:e,r=s.elementType,i=r===void 0?gt.POLYGONS:r,n=s.polySize,o=n===void 0?3:n,l=s.vertexSize,a=l===void 0?2:l,c=s.normal,u=c===void 0?[0,0,1]:c,d=s.contours,h=d===void 0?[]:d,p=s.strict,f=p===void 0?!0:p,m=s.debug,g=m===void 0?!1:m;if(!h&&f)throw new Error(\\\\\\\"Contours can't be empty\\\\\\\");if(!!h){var y=new hy;s.edgeCreateCallback&&(y.edgeCreateCallback=s.edgeCreateCallback),s.vertexIdCallback&&(y.vertexIdCallback=s.vertexIdCallback);for(var x=0;x<h.length;x++)y.addContour(a||2,h[x]);return y.tesselate(t,i,o,a,u,f),{vertices:y.vertices,vertexIndices:y.vertexIndices,vertexCount:y.vertexCount,elements:y.elements,elementCount:y.elementCount,mesh:g?y.mesh:void 0}}}var tE=st.ODD,rE=st.NONZERO,iE=st.POSITIVE,sE=st.NEGATIVE,nE=st.ABS_GEQ_TWO,oE=gt.POLYGONS,aE=gt.CONNECTED_POLYGONS,lE=gt.BOUNDARY_CONTOURS;var Vn=class extends Yi.BufferGeometry{constructor(e,t=12,r=100,i={}){super();this.type=\\\\\\\"ShapeGeometry\\\\\\\";this.windingRule=st.ODD;this.elementType=gt.POLYGONS;this.polySize=3;this.vertexSize=2;this.strict=!0;this._drawCount=0,this._shape=e,this._curveSegments=t,this._maxCount=r,this._maxDrawCount=r*3,this._triangulationOptions=Object.assign({windingRule:st.ODD,elementType:gt.POLYGONS,polySize:3,vertexSize:2,strict:!0},i),this._positionAttribute=new Yi.BufferAttribute(new Float32Array(r*3),3),this._normalAttribute=new Yi.BufferAttribute(new Float32Array(r*3),3),this._uvAttribute=new Yi.BufferAttribute(new Float32Array(r*2),2),this._indexAttribute=new Yi.BufferAttribute(new Uint32Array(r*3),1),this.setAttribute(\\\\\\\"position\\\\\\\",this._positionAttribute),this.setAttribute(\\\\\\\"normal\\\\\\\",this._normalAttribute),this.setAttribute(\\\\\\\"uv\\\\\\\",this._uvAttribute),this.setIndex(this._indexAttribute),this.updateFromShape()}copy(e){return this._drawCount=e.drawCount,this._maxDrawCount=e._maxDrawCount,this._maxCount=e.maxCount,super.copy(e)}get curveSegments(){return this._curveSegments}set curveSegments(e){this._curveSegments=e,this.updateFromShape()}get drawCount(){return this._drawCount}get maxDrawCount(){return this._maxDrawCount}get maxCount(){return this._maxCount}updateFromShape(){let e=this._shape.extractShapePointsToFlatArray([],this._curveSegments),t=this._shape.shapeHoles.map(a=>a.extractShapePointsToFlatArray([],this._curveSegments)),r,i=!0,n=!0,o,l;for(let a=0,c=e.length/2;a<c;a++){let u=a*2,d=e[u+0],h=e[u+1];if(o!==void 0&&d!==o&&(i=!1),l!==void 0&&h!==l&&(n=!1),o=d,l=h,!i&&!n)break}if(!i&&!n&&(r=Qi({contours:[e,...t],windingRule:this._triangulationOptions.windingRule,elementType:this._triangulationOptions.elementType,polySize:this._triangulationOptions.polySize,vertexSize:this._triangulationOptions.vertexSize,strict:this._triangulationOptions.strict})),this._positionAttribute.array.fill(0),this._normalAttribute.array.fill(0),this._uvAttribute.array.fill(0),this._indexAttribute.array.fill(0),this._drawCount=0,r){let a=1/0,c=-1/0,u=1/0,d=-1/0;for(let f=0,m=r.vertexCount;f<m;f++){let g=f*2,y=r.vertices[g+0],x=r.vertices[g+1];y<a&&(a=y),y>c&&(c=y),x<u&&(u=x),x>d&&(d=x)}let h=c-a,p=d-u;for(let f=0,m=r.vertexCount;f<m;f++){let g=f*2,y=r.vertices[g+0],x=r.vertices[g+1],v=(y-a)/h,b=(x-u)/p;this._positionAttribute.setXYZ(f,y,x,0),this._normalAttribute.setXYZ(f,0,0,1),this._uvAttribute.setXY(f,v,b)}for(let f=0,m=r.elementCount;f<m;f++){let g=f*3,y=r.elements[g+0],x=r.elements[g+1],v=r.elements[g+2];this._indexAttribute.setX(g+0,y),this._indexAttribute.setX(g+1,x),this._indexAttribute.setX(g+2,v),this._drawCount+=3}}return this._positionAttribute.needsUpdate=!0,this._normalAttribute.needsUpdate=!0,this._uvAttribute.needsUpdate=!0,this._indexAttribute.needsUpdate=!0,this.setDrawRange(0,this._drawCount),this._drawCount>this._maxDrawCount}};var Ki=F(z());var Wo=class{constructor(e=256,t=!1){this.capacity=e,this.size=0,this.debug=t,this.debug&&console.log(`allocating with cap ${e}`);let r=e*Wo.eSize;this.buffer=new ArrayBuffer(r);let i=Float32Array.BYTES_PER_ELEMENT,n=0;this.positions=new Float32Array(this.buffer,n*i,3*e),n+=3*e,this.normals=new Float32Array(this.buffer,n*i,3*e),n+=3*e,this.uvs=new Float32Array(this.buffer,n*i,2*e)}realloc(e,t=!1){if(e<this.size)throw Error(\\\\\\\"cannot shrink buffer\\\\\\\");if(e<=this.capacity&&!t)return;this.debug&&console.log(`resizing from ${this.capacity} \\\\u2192 ${e}`);let r=e*Wo.eSize,i=new ArrayBuffer(r),n=Float32Array.BYTES_PER_ELEMENT,o=0,l=new Float32Array(i,o*n,3*e);o+=3*e;let a=new Float32Array(i,o*n,3*e);o+=3*e;let c=new Float32Array(i,o*n,2*e);l.set(this.positions.slice(0,this.size*3)),a.set(this.normals.slice(0,this.size*3)),c.set(this.uvs.slice(0,this.size*2)),this.buffer=i,this.positions=l,this.normals=a,this.uvs=c,this.capacity=e}get(e=1){let t=this.size+e;if(t>this.capacity){let i=this.capacity;for(;t>i;)i*=2;this.realloc(i)}let r=this.size;return this.size=t,r}reserve(e){let t=this.size+e;t>this.capacity&&this.realloc(t)}shrink(){this.debug&&console.log(`shrinking ${this.capacity} \\\\u2192 ${this.size}`),this.realloc(this.size,!0)}},Ho=Wo;Ho.eSize=(3+3+2)*Float32Array.BYTES_PER_ELEMENT;var $l=(s,e)=>([t,r])=>(r<t&&(r+=e),(s>=t?s:s+e)<=r),ec=class extends Ki.BufferGeometry{constructor(e,t,r=0,i=12,n=3){super();this.type=\\\\\\\"ShapeGeometry\\\\\\\";this.vertexCache={};this._shape=e,this._depth=t,r<=0?(this._bevelSize=0,this._bevelSegments=0):(this._bevelSize=Math.min(r,t/2-1e-12),this._bevelSegments=Math.floor(n));let o=this._shape.extractShapePointsToFlatArray([],i),l=this._shape.shapeHoles.map(N=>{let S=N.extractShapePointsToFlatArray([],i),A=[];for(let I=S.length-1;I>=1;I-=2){let U=S[I-1],E=S[I-0];A.push(U,E)}return A}),a=Qi({windingRule:st.ODD,elementType:gt.BOUNDARY_CONTOURS,vertexSize:2,strict:!0,contours:[o]}),c=Qi({windingRule:st.ODD,elementType:gt.BOUNDARY_CONTOURS,vertexSize:2,strict:!0,contours:[...l]});if(!a)throw new Error(\\\\\\\"error generating geometry\\\\\\\");let u=a.elementCount;if(c){a.elementCount+=c.elementCount;for(let N=0;N<c.elements.length;N++){let S=c.elements[N],A=N%2==0?a.vertexCount:0;a.elements.push(S+A)}for(let N=0;N<c.vertexIndices.length;N++){let S=c.vertexIndices[N],A=a.vertexCount;a.vertexIndices.push(S+A)}for(let N=0;N<c.vertices.length;N++){let S=c.vertices[N];a.vertices.push(S)}}let d=1/0,h=-1/0,p=1/0,f=-1/0;for(let N=0,S=a.vertexCount;N<S;N++){let A=N*2,I=a.vertices[A+0],U=a.vertices[A+1];I<d&&(d=I),I>h&&(h=I),U<p&&(p=U),U>f&&(f=U)}this._minX=d,this._minY=p,this._width=h-d,this._height=f-p;let m=a.vertexCount*2*(2+this._bevelSegments);this._buffer=new Ho(m);let g=[],y=[];for(let N=a.elementCount-1,S=a.elementCount;N>=0;N--){let A=N>=u,I=N*2,U=a.elements[I+0],E=a.elements[I+1],j=U+E,R={start:U,count:E,normals:[],continuous:[],concave:[]},J=U,W=j-1,M=U+1,k=this._shape.roundedCurves.length;do{let O=J-U,T=a.vertices[W*2+0],P=a.vertices[W*2+1],_=a.vertices[J*2+0],H=a.vertices[J*2+1],q=a.vertices[M*2+0],K=a.vertices[M*2+1],Y=_-T,Q=H-P,V=Math.sqrt(Y*Y+Q*Q);Y/=V,Q/=V;let re=_-q,ee=H-K,Z=Math.sqrt(re*re+ee*ee);re/=Z,ee/=Z,R.normals[O*2+0]=-ee,R.normals[O*2+1]=re,R.concave[O]=Y*ee-Q*re>0;let X=a.vertexIndices[J];if(Array.isArray(X))R.continuous[O]=!1;else{let[ie,$]=this._shape.getCurveIndexFromVertexId(X-1,!0);if($>0&&$<1)R.continuous[O]=!0;else{let ne=$===1?ie+1:ie-1;ne=(ne+k)%k;let fe=$===1?0:1,ce=this._shape.roundedCurves[ie].getTangent($),be=this._shape.roundedCurves[ne].getTangent(fe);R.continuous[O]=ce.dot(be)>.95}}A&&(R.normals[O*2+0]*=-1,R.normals[O*2+1]*=-1),[W,J,M]=[J,M,M+1],M>=j&&(M-=E)}while(M!==U+1);let L=[];L.push({bevelI:0,angle:0,size:0,boundary:{vertices:a.vertices.slice(U*2,j*2),vertexCount:E,vertexIndices:new Array(E).fill(!0).map((O,T)=>[T,T]),elements:[0,E],elementCount:1,mesh:null},reverseMap:[],insetPoints:a.vertices.slice(U*2,j*2)});for(let O=1;O<=this._bevelSegments;O++){let T=O/this._bevelSegments*Math.PI/2,P=(1-Math.cos(T))*this._bevelSize,_=[],H=[],q=[],K=[],Y=0;for(let V=0;V<E;V++){let re=V*2,ee=(V-1+E)%E*2,Z=a.vertices[R.start*2+re+0],X=a.vertices[R.start*2+re+1],ie=-R.normals[ee+0]*P,$=-R.normals[ee+1]*P,ne=-R.normals[re+0]*P,fe=-R.normals[re+1]*P;if(R.concave[V]||!R.concave[V]&&A){let ce=Math.atan2($,ie),be=Math.atan2(fe,ne);be>ce&&(be-=Math.PI*2);let ge=be-ce;if(R.continuous[V]||A){let ye=ce+ge/2,De=Math.cos(ye)*P,Te=Math.sin(ye)*P;_[2*Y+0]=Z+De*(A?-1:1),_[2*Y+1]=X+Te*(A?-1:1),K[Y]=V,Y++}else{let ye=Math.max(1,Math.floor(i/4*Math.abs(ge)/Math.PI));for(let De=0;De<=ye;De++){let Te=ce+ge*(De/ye),Lt=Math.cos(Te)*P,Dt=Math.sin(Te)*P;_[2*Y+0]=Z+Lt,_[2*Y+1]=X+Dt,K[Y]=V,Y++}}}else _[2*Y+0]=Z+ie,_[2*Y+1]=X+$,K[Y]=V,H[V]=Y,Y++,_[2*Y+0]=Z,_[2*Y+1]=X,K[Y]=V,Y++,_[2*Y+0]=Z+ne,_[2*Y+1]=X+fe,K[Y]=V,q[V]=Y,Y++}let Q=Qi({windingRule:st.POSITIVE,elementType:gt.BOUNDARY_CONTOURS,vertexSize:2,strict:!0,contours:[_],edgeCreateCallback:V=>{let ee=V.Org.idx,Z=K[ee],X=K[(ee+1)%K.length];V.idx=[Z,X],V.Sym.idx=[X,Z]},vertexIdCallback:V=>{let re=V.Lprev.idx;return[re?re[1]:0,V.idx[0]]}});if(!Q)throw console.log(\\\\\\\"Error\\\\\\\"),new Error(`error generating bevel geometry for ${O}'th loop`);if(!Q.vertexCount)break;for(let V=0;V<Q.vertexIndices.length;V++){let[re,ee]=Q.vertexIndices[V];if(re===ee)continue;let Z=ee;ee<re&&(Z+=E);for(let X=re;X<Z;X++){let ie=X%E,$=(X+1)%E;if(!R.continuous[ie]||!R.continuous[$]){Q.vertexIndices[V]=[re,ie],Q.vertexIndices.splice(V+1,0,[$,ee]),Q.vertices.splice((V+1)*2,0,Q.vertices[V*2],Q.vertices[V*2+1]);break}}}L.push({bevelI:O,angle:T,size:P,boundary:Q,reverseMap:K,insetPoints:_})}let B=(O,T,P)=>{let _=0,H=O.boundary.vertexIndices.length;for(;_<H&&P(O.boundary.vertexIndices[T]);)T=(T+1)%H,_++;return _},D=g.length;for(let O=1;O<L.length;O++){let T=L[O-1],P=L[O],_=T.boundary.vertexIndices.length,H=P.boundary.vertexIndices.length;if(!_||!H)break;let q=R.concave.length,K=0,Y=$l(K,E);for(;!T.boundary.vertexIndices.filter(Y).length||!P.boundary.vertexIndices.filter(Y).length;)K++,Y=$l(K,E);let Q=T.boundary.vertexIndices.findIndex(Y),V=P.boundary.vertexIndices.findIndex(Y);do Q=(Q+1)%_;while(Y(T.boundary.vertexIndices[Q]));do V=(V+1)%H;while(Y(P.boundary.vertexIndices[V]));K=(K+1)%E;let re=K,ee=this.buildBevelVert(R,T,(Q-1+_)%_),Z=this.buildBevelVert(R,P,(V-1+H)%H),X=ee,ie=Z,$,ne,fe=!1;do{Y=$l(K,E);let ce=B(T,Q,Y),be=B(P,V,Y),ge=fe;if(fe=!1,ce&&!be){for(let ye=0;ye<ce;ye++)$=this.buildBevelVert(R,T,(Q+ye)%_,ye/(ce-1)),g.push(X.topN,$.topP,ie.topN),g.push($.bottomP,X.bottomN,ie.bottomN),X=$;fe=!0}else if(!ce&&be)for(let ye=0;ye<be;ye++)ne=this.buildBevelVert(R,P,(V+ye)%H,ye/(be-1)),g.push(ie.topN,X.topP,ne.topP),g.push(X.bottomP,ie.bottomN,ne.bottomP),ie=ne;else if(ce&&be)if($=this.buildBevelVert(R,T,Q,0),ne=this.buildBevelVert(R,P,V,0),ge?(g.push(X.topN,ne.topP,ie.topN),g.push(X.topN,$.topP,ne.topP),g.push(ne.bottomP,X.bottomN,ie.bottomN),g.push($.bottomP,X.bottomN,ne.bottomP)):(g.push(X.topN,$.topP,ie.topN),g.push(ie.topN,$.topP,ne.topP),g.push($.bottomP,X.bottomN,ie.bottomN),g.push($.bottomP,ie.bottomN,ne.bottomP)),X=$,ie=ne,ce===be)for(let ye=1;ye<ce;ye++)$=this.buildBevelVert(R,T,(Q+ye)%_,ye/(ce-1)),ne=this.buildBevelVert(R,P,(V+ye)%H,ye/(be-1)),g.push(X.topN,$.topP,ie.topN),g.push(ie.topN,$.topP,ne.topP),g.push($.bottomP,X.bottomN,ie.bottomN),g.push($.bottomP,ie.bottomN,ne.bottomP),X=$,ie=ne;else if(ce>be){let ye=ce/be,De=0;for(let Te=1;Te<ce;Te++)$=this.buildBevelVert(R,T,(Q+Te)%_,Te/(ce-1)),g.push(X.topN,$.topP,ie.topN),g.push($.bottomP,X.bottomN,ie.bottomN),X=$,Te>(De+1)*ye&&(De++,ne=this.buildBevelVert(R,P,(V+De)%H,De/(be-1)),g.push(ie.topN,$.topP,ne.topP),g.push($.bottomP,ie.bottomN,ne.bottomP),ie=ne)}else{let ye=be/ce,De=0;for(let Te=1;Te<be;Te++)ne=this.buildBevelVert(R,P,(V+Te)%H,Te/(be-1)),g.push(ie.topN,$.topP,ne.topP),g.push($.bottomP,ie.bottomN,ne.bottomP),ie=ne,Te>(De+1)*ye&&(De++,$=this.buildBevelVert(R,T,(Q+De)%_,De/(ce-1)),g.push(X.topN,$.topP,ie.topN),g.push($.bottomP,X.bottomN,ie.bottomN),X=$)}Q=(Q+ce)%_,V=(V+be)%H,K=(K+1)%q}while(K!==re)}{let O=L[0];for(let T=0,P=O.boundary.vertexCount;T<P;T++){let _=this.buildBevelVert(R,O,T),H=this.buildBevelVert(R,O,(T+1)%P);g.push(H.topP,_.topN,_.bottomN),g.push(H.topP,_.bottomN,H.bottomP)}}if(A){let O=[];for(let T=g.length-1;T>=D+2;T-=3){let P=g[T-2],_=g[T-1],H=g[T-0];O.push(H,_,P)}g.splice(D,g.length-D,...O)}if(A){let O=[];for(let T=L[L.length-1].boundary.vertices.length-1;T>=1;T-=2){let P=L[L.length-1].boundary.vertices[T-1],_=L[L.length-1].boundary.vertices[T-0];O.push(P,_)}y.push(O)}if(!A){let O=L[L.length-1],T=Qi({windingRule:L.length>1?st.POSITIVE:st.ODD,elementType:gt.POLYGONS,vertexSize:2,strict:!0,contours:[O.insetPoints,...y]});if(!T)throw new Error(\\\\\\\"Error generating geometry for surface\\\\\\\");for(let P=0;P<T.elementCount*3;P+=3){let _=this.buildSurfaceVert(T,T.elements[P+0]),H=this.buildSurfaceVert(T,T.elements[P+1]),q=this.buildSurfaceVert(T,T.elements[P+2]);g.push(_.top,H.top,q.top),g.push(q.bottom,H.bottom,_.bottom)}}this.vertexCache={}}this._buffer.shrink();let x=new Ki.BufferAttribute(Uint32Array.from(g),1),v=new Ki.BufferAttribute(this._buffer.positions,3),b=new Ki.BufferAttribute(this._buffer.normals,3),w=new Ki.BufferAttribute(this._buffer.uvs,2);v.needsUpdate=!0,b.needsUpdate=!0,w.needsUpdate=!0,x.needsUpdate=!0,this.setAttribute(\\\\\\\"position\\\\\\\",v),this.setAttribute(\\\\\\\"normal\\\\\\\",b),this.setAttribute(\\\\\\\"uv\\\\\\\",w),this.setIndex(x)}buildSurfaceVert(e,t){let r=t.toString();if(r in this.vertexCache)return this.vertexCache[r];let i=e.vertices[t*2+0],n=e.vertices[t*2+1],o=(i-this._minX)/this._width,l=(n-this._minY)/this._height,a=this._buffer.get(2),c=a*3,u=a*2,d={top:a+0,bottom:a+1};return this._buffer.positions[c+0]=i,this._buffer.positions[c+1]=n,this._buffer.positions[c+2]=this._depth,this._buffer.normals[c+0]=0,this._buffer.normals[c+1]=0,this._buffer.normals[c+2]=1,this._buffer.uvs[u+0]=o,this._buffer.uvs[u+1]=l,this._buffer.positions[c+3]=i,this._buffer.positions[c+4]=n,this._buffer.positions[c+5]=0,this._buffer.normals[c+3]=0,this._buffer.normals[c+4]=0,this._buffer.normals[c+5]=-1,this._buffer.uvs[u+2]=o,this._buffer.uvs[u+3]=l,this.vertexCache[r]=d,d}buildBevelVert(e,t,r,i=1){let n=`${t.bevelI}:${r}`;if(n in this.vertexCache)return this.vertexCache[n];let[o,l]=t.boundary.vertexIndices[r],a,c,u,d;o!==l?(c=o,a=l,d=!1,u=e.continuous[c]&&e.continuous[a]):(a=o,c=(a-1+e.count)%e.count,d=e.concave[a]&&t.bevelI>0,u=e.continuous[a]||d);let h=Math.cos(t.angle),p=Math.sin(t.angle),f=r*2,m=a*2,g=c*2,y=t.boundary.vertices[f+0],x=t.boundary.vertices[f+1],v=(1-p)*this._bevelSize,b=(y-this._minX)/this._width,w=(x-this._minY)/this._height,N=e.normals[m+0],S=e.normals[m+1],A=e.normals[g+0],I=e.normals[g+1];if(d){let J=A-N,W=I-S;N=N+J*(1-i),S=S+W*(1-i);let M=Math.sqrt(N*N+S*S);N/=M,S/=M}let U=this._buffer.get(u?2:4),E=U*3,j=U*2,R={i:r,fi:a,topP:U+0,topN:U+0,bottomP:U+1,bottomN:U+1};return this._buffer.positions[E+0]=y,this._buffer.positions[E+1]=x,this._buffer.positions[E+2]=this._depth-v,this._buffer.normals[E+0]=N*h,this._buffer.normals[E+1]=S*h,this._buffer.normals[E+2]=p,this._buffer.uvs[j+0]=b,this._buffer.uvs[j+1]=w,this._buffer.positions[E+3]=y,this._buffer.positions[E+4]=x,this._buffer.positions[E+5]=v,this._buffer.normals[E+3]=N*h,this._buffer.normals[E+4]=S*h,this._buffer.normals[E+5]=-p,this._buffer.uvs[j+2]=w,this._buffer.uvs[j+3]=b,u||(U+=2,E+=6,j+=4,R.topP=U+0,R.bottomP=U+1,this._buffer.positions[E+0]=y,this._buffer.positions[E+1]=x,this._buffer.positions[E+2]=this._depth-v,this._buffer.normals[E+0]=A*h,this._buffer.normals[E+1]=I*h,this._buffer.normals[E+2]=p,this._buffer.uvs[j+0]=b,this._buffer.uvs[j+1]=w,this._buffer.positions[E+3]=y,this._buffer.positions[E+4]=x,this._buffer.positions[E+5]=v,this._buffer.normals[E+3]=A*h,this._buffer.normals[E+4]=I*h,this._buffer.normals[E+5]=-p,this._buffer.uvs[j+2]=w,this._buffer.uvs[j+3]=b),this.vertexCache[n]=R,R}};var Mt=class{static create(s){return this.build(this.normalizeInputs(s))}static normalizeInputs(s,e){let t=Object.assign({},e?.parameters??{width:100,subdivisions:40,roundness:0,extrudeDepth:0,surfaceMaxCount:100,extrudeBevelSize:0,extrudeBevelSegments:3},s.parameters),r=Math.abs(t.width),i=Math.abs(t.height??t.width),n=Math.abs(t.depth!==void 0&&t.depth===0&&t.extrudeDepth>0?t.extrudeDepth:t.depth??0),o=s.shape??e?.shape,l=o?.roundness??t.roundness;o!==void 0&&(o instanceof Xe?(o.width!==r||o.height!==i)&&o.applySize(r,i):o=new Xe(r,i).fromJSON(o),s.parameters?.roundness!==void 0&&s.parameters?.roundness>0&&o.update(!1));let a=o??new Xe(r,i);return{parameters:Object.assign(t,{width:r,height:i,depth:n,extrudeDepth:n,roundness:l}),shape:a}}static build(s){let{extrudeDepth:e,extrudeBevelSize:t,extrudeBevelSegments:r,subdivisions:i,roundness:n,surfaceMaxCount:o}=s.parameters;s.shape.roundness=n;let l;return e<=0?l=new Vn(s.shape,i,o):l=new ec(s.shape,e,t,i,r),Object.assign(l,{userData:Ce(le({},s),{type:\\\\\\\"VectorGeometry\\\\\\\"})})}};var Hn=F(z()),Ad=Math.PI*2,wd=class{static create(s){return this.build(this.normalizeInputs(s))}static normalizeInputs(s,e){let t=Object.assign({},e?.parameters??{width:100,depth:0,spikes:128,angle:360,innerRadius:0,extrudeDepth:0,extrudeBevelSize:0,extrudeBevelSegments:1,surfaceMaxCount:1e3},s.parameters);return{shape:s.shape&&s.shape instanceof Xe?s.shape:new Xe,parameters:Object.assign(t,{width:Math.abs(t.width),height:Math.abs(t.height??t.width),depth:Math.abs(t.depth!==void 0&&t.depth===0&&t.extrudeDepth>0?t.extrudeDepth:t.depth??0)})}}static build(s){let{width:e,height:t,spikes:r,angle:i,innerRadius:n,depth:o,extrudeBevelSize:l,extrudeBevelSegments:a,surfaceMaxCount:c}=s.parameters,u=s.shape,d=e*.5,h=t*.5,p=fy(u,d,h,i*Math.PI/180,r,n);u.isClosed=!0,u.update();let f=Mt.create({shape:u,parameters:{subdivisions:p,surfaceMaxCount:c,depth:o,extrudeBevelSize:l,extrudeBevelSegments:a}});return Object.assign(f,{userData:Ce(le({},s),{type:\\\\\\\"EllipseGeometry\\\\\\\"})})}};function fy(s,e,t,r,i,n){if(r>=Ad)return i>30||i%4==0?(my(s,e,t,n),Math.round(i/4)):Sd(s,r,i,e,t,n);let o={x:0,y:t},l=r+Math.PI*.5,a={x:Math.cos(l)*e,y:Math.sin(l)*t},c=gd({px:o.x,py:o.y,cx:a.x,cy:a.y,rx:e,ry:t,largeArcFlag:r>Math.PI,sweepFlag:!0});return i>30||i%c.length==0?py(s,o.x,o.y,c,i,e,t,n):Sd(s,r,i,e,t,n)}function py(s,e,t,r,i,n,o,l){let a=Math.round(i/r.length);s.addPoint(Ps(e,t));for(let c=0,u=r.length;c<u;c++){let d=r[c],h=s.points[c],p=Ps(d.x,d.y);h.controls[1].position.set(d.x1,d.y1),p.controls[0].position.set(d.x2,d.y2),s.addPoint(p)}return l>0?Nd(s,n,o,l):s.addPoint(Ps(0,0)),a}function Sd(s,e,t,r,i,n){let o=-e/t;for(let l=0;l<=t;l++){let a=o*l,c=Math.sin(a)*r,u=Math.cos(a)*i;s.addPoint(Ps(c,u))}return e<Ad?n>0?Nd(s,r,i,n):s.addPoint(Ps(0,0)):(s.removePoint(s.points[s.points.length-1]),n>0&&Td(s,r,i,n)),1}function my(s,e,t,r=0,i=0,n=0){let o=.5522847498,l=e*o,a=t*o;s.addPoint(Jo(i-e,n,i-e,n-a,i-e,n+a)),s.addPoint(Jo(i,n+t,i-l,n+t,i+l,n+t)),s.addPoint(Jo(i+e,n,i+e,n+a,i+e,n-a)),s.addPoint(Jo(i,n-t,i+l,n-t,i-l,n-t)),r>0&&Td(s,e,t,r)}function Ps(s,e){return new yi(Hn.MathUtils.generateUUID(),new Hn.Vector2(s,e))}function Jo(s,e,t,r,i,n){let o=Ps(s,e);return o.controls[0].position.set(t,r),o.controls[1].position.set(i,n),o}function Nd(s,e,t,r){Cd(s,e,t,r).forEach(n=>s.addPoint(n))}function Td(s,e,t,r){let i=Cd(s,e,t,r),n=new Xe;i.forEach(o=>n.addPoint(o)),n.isClosed=!0,s.shapeHoles.push(n)}function Cd(s,e,t,r){let i=r*e/100,n=i*(Math.abs(t)/Math.abs(e)),o=new Hn.Vector2(i/e,n/t),l=s.points.map(a=>a.clone()).reverse();return l.forEach(a=>{a.position.multiply(o);let c=a.controls[0].position.clone().multiply(o),u=a.controls[1].position.clone().multiply(o);a.controls[0].position.copy(u),a.controls[1].position.copy(c)}),l}var St=F(z()),Md=class{static create(s){return this.build(this.normalizeInputs(s))}static normalizeInputs(s,e){let t=Object.assign({},e?.parameters??{width:100,revolutions:2,segments:40,pathRadius:10,pathType:0,pathSegments:30,cornerRadius:30,cornerSegments:4},s.parameters),r=Math.abs(t.width),i=Math.abs(t.height??r),n=Math.abs(t.depth??r),o=Math.abs(Math.min(r,n))/2;return{parameters:Object.assign(t,{width:r,height:i,depth:n,radius:o,segments:Math.round(t.segments),pathSegments:Math.round(t.pathSegments),cornerSegments:Math.round(t.cornerSegments)})}}static build(s){let{width:e,height:t,depth:r,radius:i,revolutions:n,segments:o,pathRadius:l,pathType:a,pathSegments:c,cornerRadius:u,cornerSegments:d}=s.parameters,h=new Qo(!1,e,t,r,i,n,o,l,a,c,u,d);return Object.assign(h,{userData:Ce(le({},s),{type:\\\\\\\"HelixGeometry\\\\\\\"})})}},Qo=class extends St.BufferGeometry{constructor(e,t,r,i,n,o,l,a,c,u,d,h){super();let p=[],f=[],m=[],g=[],y=[],x=[],v=[],b=new St.Vector3,w=new St.Vector3,N=new St.Vector3,S=new St.Vector3,A,I,U,E,j,R,J,W,M=new St.Vector3,k=new St.Vector3,L=new St.Vector3,B=new St.Vector3,D=new St.Vector3,O=new St.Vector3,T=new St.Vector3,P=n-a,_=r-2*a+.001,H=_/o,q=Math.ceil(l*o),K=_/q,Y=-_/2,Q=2*Math.PI/u,V=Math.PI/2/h,re=Math.min((1-d/100)*a,a-.1),ee=a-re,Z=e&&o==1;Z&&(h=0);function X(ge,ye){let De=Math.PI/2;R=ye*K,W=2*Math.PI*(R%H)/H+De,R+=Y,J=Math.sin(W)*P,j=Math.cos(W)*P,e?ge.set(j,J,R):ge.set(j,R,J)}X(N,-1),X(S,0),M.copy(N);let ie=N.distanceTo(S),$=ie*q+2*ee,ne=0,fe=$-ee;for(let ge=0;ge<=q;ge++){X(b,ge),T.subVectors(b,M).normalize(),M.copy(b),D.copy(b).setComponent(+e+1,0).normalize(),O.crossVectors(T,D).normalize();let ye=ge===0?3*Math.PI/2:V,De=ge===0?ne:fe;for(let Te=0,Lt=0;Te<=u;Te++,Lt=Te*Q){if(k.addVectors(N.copy(D).multiplyScalar(a*Math.cos(Lt)),S.copy(O).multiplyScalar(a*Math.sin(Lt))),B.copy(k).normalize(),ge===0||ge===q){S.copy(B).multiplyScalar(re),w.addVectors(b,S);for(let Dt=0,wr=ye;Dt<h;Dt++,wr=Dt*V+ye){L.addVectors(N.copy(T).multiplyScalar(ee*Math.sin(wr)),S.copy(B).multiplyScalar(ee*Math.cos(wr))),S.addVectors(w,L),L.normalize(),y.push(S.x,S.y,S.z),x.push(L.x,L.y,L.z);let Gt=+(ge==0)+Math.sin(wr);v.push((De+ee*Gt)/$,Te/u,0)}}S.addVectors(b,k),f.push(S.x,S.y,S.z),m.push(B.x,B.y,B.z),g.push((ee+ge*ie)/$,Te/u)}}let ce=y.length/2;for(let ge=0;ge<h;ge++)for(let ye=0;ye<=u;ye++){let De=ce+(ye*h+ge)*3,Te=((u-ye)*h+h-ge-1)*3;f.push(...y.slice(De,De+3)),f.unshift(...y.slice(Te,Te+3)),m.push(...x.slice(De,De+3)),m.unshift(...x.slice(Te,Te+3)),g.push(...v.slice(De,De+2)),g.unshift(...v.slice(Te,Te+2))}let be=u+1;for(let ge=0;ge<q+2*h;ge++){let ye=Z&&ge==q-1;for(let De=0;De<be-1;De++)A=ge*be+De,I=A+1,U=ye?De:A+be,E=ye?De+1:I+be,p.push(A,I,U,I,E,U)}if(A=f.length/3-u-1,!Z)for(let ge=0;ge<u-2;ge++)p.push(A,A+ge+1,A+ge+2),p.push(0,ge+2,ge+1);this.setIndex(p),this.setAttribute(\\\\\\\"position\\\\\\\",new St.Float32BufferAttribute(f,3)),this.setAttribute(\\\\\\\"normal\\\\\\\",new St.Float32BufferAttribute(m,3)),this.setAttribute(\\\\\\\"uv\\\\\\\",new St.Float32BufferAttribute(g,2))}};var Ed=F(z());var Id=class{static create(s){return this.build(this.normalizeInputs(s))}static normalizeInputs(s,e){let t=Object.assign({},e?.parameters??{width:100,detail:0,corner:0,cornerSides:4},s.parameters);return{parameters:Object.assign(t,{width:Math.abs(t.width),height:Math.abs(t.height??t.width),depth:Math.abs(t.depth??t.width)})}}static build(s){let{width:e,height:t,depth:r,detail:i,corner:n,cornerSides:o}=s.parameters,l=i===0&&n!==0?new Yo(e*.5,n,o):new Ed.IcosahedronBufferGeometry(e*.5,i);return l.scale(1,t/e,r/e),Object.assign(l,{userData:Ce(le({},s),{type:\\\\\\\"IcosahedronGeometry\\\\\\\"})})}},Yo=class extends Ji{constructor(e=1,t=.2,r=4){let i=(1+Math.sqrt(5))/2,n=[-1,i,0,1,i,0,-1,-i,0,1,-i,0,0,-1,i,0,1,i,0,-1,-i,0,1,-i,i,0,-1,i,0,1,-i,0,-1,-i,0,1],o=[0,11,5,0,5,1,0,1,7,0,7,10,0,10,11,1,5,9,5,11,4,11,10,2,10,7,6,7,1,8,3,9,4,3,4,2,3,2,6,3,6,8,3,8,9,4,9,5,2,4,11,6,2,10,8,6,7,9,8,1],l=\\\\\\\"IcosahedronGeometry\\\\\\\";super(n,o,l,e,t,r);this.type=l}static fromJSON(e){return new Yo(e.radius,e.corner,e.cornerSides)}};var Ko=F(z()),Ld=class{static create(s){return this.build(this.normalizeInputs(s))}static normalizeInputs(s,e){(s.parameters?.points??[]).forEach(r=>{Array.isArray(r)&&(r.x=r[0],r.y=r[1])});let t=Object.assign({},e?.parameters??{width:100,segments:64,verticalSegments:64,points:[{x:0,y:-50,id:0},{x:50,y:-50,id:1},{x:50,y:50,id:2},{x:0,y:50,id:3}]},s.parameters);return{parameters:Object.assign(t,{width:Math.abs(t.width),height:Math.abs(t.height??t.width),depth:Math.abs(t.depth??t.width)})}}static build(s){let{points:e,segments:t,verticalSegments:r}=s.parameters,i=new Ko.Shape;i.moveTo(e[0].x,e[0].y),i.bezierCurveTo(e[1].x,e[1].y,e[2].x,e[2].y,e[3].x,e[3].y);let n=new Ko.LatheBufferGeometry(i.extractPoints(r).shape,t);return n.rotateZ(Math.PI),Object.assign(n,{userData:Ce(le({},s),{type:\\\\\\\"LatheGeometry\\\\\\\"})})}};var jr=F(z());var si=F(z());var ae=F(z()),Ur=new ae.Matrix4,tc=new ae.Object3D,Xo=new ae.Vector3,Xi=class extends ae.EventDispatcher{constructor(){super();this.uuid=ae.MathUtils.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}applyMatrix4(e){let t=new ae.Matrix3().getNormalMatrix(e);for(let r=0,i=this.vertices.length;r<i;r++)this.vertices[r].applyMatrix4(e);for(let r=0,i=this.faces.length;r<i;r++){let n=this.faces[r];n.normal.applyMatrix3(t).normalize();for(let o=0,l=n.vertexNormals.length;o<l;o++)n.vertexNormals[o].applyMatrix3(t).normalize()}return this.boundingBox!==null&&this.computeBoundingBox(),this.boundingSphere!==null&&this.computeBoundingSphere(),this.verticesNeedUpdate=!0,this.normalsNeedUpdate=!0,this}rotateX(e){return Ur.makeRotationX(e),this.applyMatrix4(Ur),this}rotateY(e){return Ur.makeRotationY(e),this.applyMatrix4(Ur),this}rotateZ(e){return Ur.makeRotationZ(e),this.applyMatrix4(Ur),this}translate(e,t,r){return Ur.makeTranslation(e,t,r),this.applyMatrix4(Ur),this}scale(e,t,r){return Ur.makeScale(e,t,r),this.applyMatrix4(Ur),this}lookAt(e){return tc.lookAt(e),tc.updateMatrix(),this.applyMatrix4(tc.matrix),this}fromBufferGeometry(e){let t=this,r=e.index!==null?e.index:void 0,i=e.attributes;if(i.position===void 0)return console.error(\\\\\\\"THREE.Geometry.fromBufferGeometry(): Position attribute required for conversion.\\\\\\\"),this;let n=i.position,o=i.normal,l=i.color,a=i.uv,c=i.uv2;c!==void 0&&(this.faceVertexUvs[1]=[]);for(let h=0;h<n.count;h++)t.vertices.push(new ae.Vector3().fromBufferAttribute(n,h)),l!==void 0&&t.colors.push(new ae.Color().fromBufferAttribute(l,h));function u(h,p,f,m){let g=l===void 0?[]:[t.colors[h].clone(),t.colors[p].clone(),t.colors[f].clone()],y=o===void 0?[]:[new ae.Vector3().fromBufferAttribute(o,h),new ae.Vector3().fromBufferAttribute(o,p),new ae.Vector3().fromBufferAttribute(o,f)],x=new Wn(h,p,f,y,g,m);t.faces.push(x),a!==void 0&&t.faceVertexUvs[0].push([new ae.Vector2().fromBufferAttribute(a,h),new ae.Vector2().fromBufferAttribute(a,p),new ae.Vector2().fromBufferAttribute(a,f)]),c!==void 0&&t.faceVertexUvs[1].push([new ae.Vector2().fromBufferAttribute(c,h),new ae.Vector2().fromBufferAttribute(c,p),new ae.Vector2().fromBufferAttribute(c,f)])}let d=e.groups;if(d.length>0)for(let h=0;h<d.length;h++){let p=d[h],f=p.start,m=p.count;for(let g=f,y=f+m;g<y;g+=3)r!==void 0?u(r.getX(g),r.getX(g+1),r.getX(g+2),p.materialIndex):u(g,g+1,g+2,p.materialIndex)}else if(r!==void 0)for(let h=0;h<r.count;h+=3)u(r.getX(h),r.getX(h+1),r.getX(h+2));else for(let h=0;h<n.count;h+=3)u(h,h+1,h+2);return this.computeFaceNormals(),e.boundingBox!==null&&(this.boundingBox=e.boundingBox.clone()),e.boundingSphere!==null&&(this.boundingSphere=e.boundingSphere.clone()),this}center(){return this.computeBoundingBox(),this.boundingBox.getCenter(Xo).negate(),this.translate(Xo.x,Xo.y,Xo.z),this}normalize(){this.computeBoundingSphere();let e=this.boundingSphere.center,t=this.boundingSphere.radius,r=t===0?1:1/t,i=new ae.Matrix4;return i.set(r,0,0,-r*e.x,0,r,0,-r*e.y,0,0,r,-r*e.z,0,0,0,1),this.applyMatrix4(i),this}computeFaceNormals(){let e=new ae.Vector3,t=new ae.Vector3;for(let r=0,i=this.faces.length;r<i;r++){let n=this.faces[r],o=this.vertices[n.a],l=this.vertices[n.b],a=this.vertices[n.c];e.subVectors(a,l),t.subVectors(o,l),e.cross(t),e.normalize(),n.normal.copy(e)}}computeVertexNormals(e=!0){let t=new Array(this.vertices.length);for(let r=0,i=this.vertices.length;r<i;r++)t[r]=new ae.Vector3;if(e){let r=new ae.Vector3,i=new ae.Vector3;for(let n=0,o=this.faces.length;n<o;n++){let l=this.faces[n],a=this.vertices[l.a],c=this.vertices[l.b],u=this.vertices[l.c];r.subVectors(u,c),i.subVectors(a,c),r.cross(i),t[l.a].add(r),t[l.b].add(r),t[l.c].add(r)}}else{this.computeFaceNormals();for(let r=0,i=this.faces.length;r<i;r++){let n=this.faces[r];t[n.a].add(n.normal),t[n.b].add(n.normal),t[n.c].add(n.normal)}}for(let r=0,i=this.vertices.length;r<i;r++)t[r].normalize();for(let r=0,i=this.faces.length;r<i;r++){let n=this.faces[r],o=n.vertexNormals;o.length===3?(o[0].copy(t[n.a]),o[1].copy(t[n.b]),o[2].copy(t[n.c])):(o[0]=t[n.a].clone(),o[1]=t[n.b].clone(),o[2]=t[n.c].clone())}this.faces.length>0&&(this.normalsNeedUpdate=!0)}computeFlatVertexNormals(){this.computeFaceNormals();for(let e=0,t=this.faces.length;e<t;e++){let r=this.faces[e],i=r.vertexNormals;i.length===3?(i[0].copy(r.normal),i[1].copy(r.normal),i[2].copy(r.normal)):(i[0]=r.normal.clone(),i[1]=r.normal.clone(),i[2]=r.normal.clone())}this.faces.length>0&&(this.normalsNeedUpdate=!0)}computeMorphNormals(){for(let t=0,r=this.faces.length;t<r;t++){let i=this.faces[t];i.__originalFaceNormal?i.__originalFaceNormal.copy(i.normal):i.__originalFaceNormal=i.normal.clone(),i.__originalVertexNormals||(i.__originalVertexNormals=[]);for(let n=0,o=i.vertexNormals.length;n<o;n++)i.__originalVertexNormals[n]?i.__originalVertexNormals[n].copy(i.vertexNormals[n]):i.__originalVertexNormals[n]=i.vertexNormals[n].clone()}let e=new Xi;e.faces=this.faces;for(let t=0,r=this.morphTargets.length;t<r;t++){if(!this.morphNormals[t]){this.morphNormals[t]={},this.morphNormals[t].faceNormals=[],this.morphNormals[t].vertexNormals=[];let n=this.morphNormals[t].faceNormals,o=this.morphNormals[t].vertexNormals;for(let l=0,a=this.faces.length;l<a;l++){let c=new ae.Vector3,u={a:new ae.Vector3,b:new ae.Vector3,c:new ae.Vector3};n.push(c),o.push(u)}}let i=this.morphNormals[t];e.vertices=this.morphTargets[t].vertices,e.computeFaceNormals(),e.computeVertexNormals();for(let n=0,o=this.faces.length;n<o;n++){let l=this.faces[n],a=i.faceNormals[n],c=i.vertexNormals[n];a.copy(l.normal),c.a.copy(l.vertexNormals[0]),c.b.copy(l.vertexNormals[1]),c.c.copy(l.vertexNormals[2])}}for(let t=0,r=this.faces.length;t<r;t++){let i=this.faces[t];i.normal=i.__originalFaceNormal,i.vertexNormals=i.__originalVertexNormals}}computeBoundingBox(){this.boundingBox===null&&(this.boundingBox=new ae.Box3),this.boundingBox.setFromPoints(this.vertices)}computeBoundingSphere(){this.boundingSphere===null&&(this.boundingSphere=new ae.Sphere),this.boundingSphere.setFromPoints(this.vertices)}merge(e,t,r=0){if(!(e&&e.isGeometry)){console.error(\\\\\\\"THREE.Geometry.merge(): geometry not an instance of THREE.Geometry.\\\\\\\",e);return}let i,n=this.vertices.length,o=this.vertices,l=e.vertices,a=this.faces,c=e.faces,u=this.colors,d=e.colors;t!==void 0&&(i=new ae.Matrix3().getNormalMatrix(t));for(let h=0,p=l.length;h<p;h++){let m=l[h].clone();t!==void 0&&m.applyMatrix4(t),o.push(m)}for(let h=0,p=d.length;h<p;h++)u.push(d[h].clone());for(let h=0,p=c.length;h<p;h++){let f=c[h],m,g,y=f.vertexNormals,x=f.vertexColors,v=new Wn(f.a+n,f.b+n,f.c+n);v.normal.copy(f.normal),i!==void 0&&v.normal.applyMatrix3(i).normalize();for(let b=0,w=y.length;b<w;b++)m=y[b].clone(),i!==void 0&&m.applyMatrix3(i).normalize(),v.vertexNormals.push(m);v.color.copy(f.color);for(let b=0,w=x.length;b<w;b++)g=x[b],v.vertexColors.push(g.clone());v.materialIndex=f.materialIndex+r,a.push(v)}for(let h=0,p=e.faceVertexUvs.length;h<p;h++){let f=e.faceVertexUvs[h];this.faceVertexUvs[h]===void 0&&(this.faceVertexUvs[h]=[]);for(let m=0,g=f.length;m<g;m++){let y=f[m],x=[];for(let v=0,b=y.length;v<b;v++)x.push(y[v].clone());this.faceVertexUvs[h].push(x)}}}mergeMesh(e){if(!(e&&e.isMesh)){console.error(\\\\\\\"THREE.Geometry.mergeMesh(): mesh not an instance of THREE.Mesh.\\\\\\\",e);return}e.matrixAutoUpdate&&e.updateMatrix(),this.merge(e.geometry,e.matrix)}mergeVertices(e=4){let t={},r=[],i=[],n=Math.pow(10,e);for(let a=0,c=this.vertices.length;a<c;a++){let u=this.vertices[a],d=Math.round(u.x*n)+\\\\\\\"_\\\\\\\"+Math.round(u.y*n)+\\\\\\\"_\\\\\\\"+Math.round(u.z*n);t[d]===void 0?(t[d]=a,r.push(this.vertices[a]),i[a]=r.length-1):i[a]=i[t[d]]}let o=[];for(let a=0,c=this.faces.length;a<c;a++){let u=this.faces[a];u.a=i[u.a],u.b=i[u.b],u.c=i[u.c];let d=[u.a,u.b,u.c];for(let h=0;h<3;h++)if(d[h]===d[(h+1)%3]){o.push(a);break}}for(let a=o.length-1;a>=0;a--){let c=o[a];this.faces.splice(c,1);for(let u=0,d=this.faceVertexUvs.length;u<d;u++)this.faceVertexUvs[u].splice(c,1)}let l=this.vertices.length-r.length;return this.vertices=r,l}setFromPoints(e){this.vertices=[];for(let t=0,r=e.length;t<r;t++){let i=e[t];this.vertices.push(new ae.Vector3(i.x,i.y,i.z||0))}return this}sortFacesByMaterialIndex(){let e=this.faces,t=e.length;for(let a=0;a<t;a++)e[a]._id=a;function r(a,c){return a.materialIndex-c.materialIndex}e.sort(r);let i=this.faceVertexUvs[0],n=this.faceVertexUvs[1],o,l;i&&i.length===t&&(o=[]),n&&n.length===t&&(l=[]);for(let a=0;a<t;a++){let c=e[a]._id;o&&o.push(i[c]),l&&l.push(n[c])}o&&(this.faceVertexUvs[0]=o),l&&(this.faceVertexUvs[1]=l)}toJSON(){let e={metadata:{version:4.5,type:\\\\\\\"Geometry\\\\\\\",generator:\\\\\\\"Geometry.toJSON\\\\\\\"}};if(e.uuid=this.uuid,e.type=this.type,this.name!==\\\\\\\"\\\\\\\"&&(e.name=this.name),this.parameters!==void 0){let f=this.parameters;for(let m in f)f[m]!==void 0&&(e[m]=f[m]);return e}let t=[];for(let f=0;f<this.vertices.length;f++){let m=this.vertices[f];t.push(m.x,m.y,m.z)}let r=[],i=[],n={},o=[],l={},a=[],c={};for(let f=0;f<this.faces.length;f++){let m=this.faces[f],g=!0,y=!1,x=this.faceVertexUvs[0][f]!==void 0,v=m.normal.length()>0,b=m.vertexNormals.length>0,w=m.color.r!==1||m.color.g!==1||m.color.b!==1,N=m.vertexColors.length>0,S=0;if(S=u(S,0,0),S=u(S,1,g),S=u(S,2,y),S=u(S,3,x),S=u(S,4,v),S=u(S,5,b),S=u(S,6,w),S=u(S,7,N),r.push(S),r.push(m.a,m.b,m.c),r.push(m.materialIndex),x){let A=this.faceVertexUvs[0][f];r.push(p(A[0]),p(A[1]),p(A[2]))}if(v&&r.push(d(m.normal)),b){let A=m.vertexNormals;r.push(d(A[0]),d(A[1]),d(A[2]))}if(w&&r.push(h(m.color)),N){let A=m.vertexColors;r.push(h(A[0]),h(A[1]),h(A[2]))}}function u(f,m,g){return g?f|1<<m:f&~(1<<m)}function d(f){let m=f.x.toString()+f.y.toString()+f.z.toString();return n[m]!==void 0||(n[m]=i.length/3,i.push(f.x,f.y,f.z)),n[m]}function h(f){let m=f.r.toString()+f.g.toString()+f.b.toString();return l[m]!==void 0||(l[m]=o.length,o.push(f.getHex())),l[m]}function p(f){let m=f.x.toString()+f.y.toString();return c[m]!==void 0||(c[m]=a.length/2,a.push(f.x,f.y)),c[m]}return e.data={},e.data.vertices=t,e.data.normals=i,o.length>0&&(e.data.colors=o),a.length>0&&(e.data.uvs=[a]),e.data.faces=r,e}clone(){return new Xi().copy(this)}copy(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;let t=e.vertices;for(let h=0,p=t.length;h<p;h++)this.vertices.push(t[h].clone());let r=e.colors;for(let h=0,p=r.length;h<p;h++)this.colors.push(r[h].clone());let i=e.faces;for(let h=0,p=i.length;h<p;h++)this.faces.push(i[h].clone());for(let h=0,p=e.faceVertexUvs.length;h<p;h++){let f=e.faceVertexUvs[h];this.faceVertexUvs[h]===void 0&&(this.faceVertexUvs[h]=[]);for(let m=0,g=f.length;m<g;m++){let y=f[m],x=[];for(let v=0,b=y.length;v<b;v++){let w=y[v];x.push(w.clone())}this.faceVertexUvs[h].push(x)}}let n=e.morphTargets;for(let h=0,p=n.length;h<p;h++){let f={};if(f.name=n[h].name,n[h].vertices!==void 0){f.vertices=[];for(let m=0,g=n[h].vertices.length;m<g;m++)f.vertices.push(n[h].vertices[m].clone())}if(n[h].normals!==void 0){f.normals=[];for(let m=0,g=n[h].normals.length;m<g;m++)f.normals.push(n[h].normals[m].clone())}this.morphTargets.push(f)}let o=e.morphNormals;for(let h=0,p=o.length;h<p;h++){let f={};if(o[h].vertexNormals!==void 0){f.vertexNormals=[];for(let m=0,g=o[h].vertexNormals.length;m<g;m++){let y=o[h].vertexNormals[m],x={};x.a=y.a.clone(),x.b=y.b.clone(),x.c=y.c.clone(),f.vertexNormals.push(x)}}if(o[h].faceNormals!==void 0){f.faceNormals=[];for(let m=0,g=o[h].faceNormals.length;m<g;m++)f.faceNormals.push(o[h].faceNormals[m].clone())}this.morphNormals.push(f)}let l=e.skinWeights;for(let h=0,p=l.length;h<p;h++)this.skinWeights.push(l[h].clone());let a=e.skinIndices;for(let h=0,p=a.length;h<p;h++)this.skinIndices.push(a[h].clone());let c=e.lineDistances;for(let h=0,p=c.length;h<p;h++)this.lineDistances.push(c[h]);let u=e.boundingBox;u!==null&&(this.boundingBox=u.clone());let d=e.boundingSphere;return d!==null&&(this.boundingSphere=d.clone()),this.elementsNeedUpdate=e.elementsNeedUpdate,this.verticesNeedUpdate=e.verticesNeedUpdate,this.uvsNeedUpdate=e.uvsNeedUpdate,this.normalsNeedUpdate=e.normalsNeedUpdate,this.colorsNeedUpdate=e.colorsNeedUpdate,this.lineDistancesNeedUpdate=e.lineDistancesNeedUpdate,this.groupsNeedUpdate=e.groupsNeedUpdate,this}toBufferGeometry(){let e=new Dd().fromGeometry(this),t=new ae.BufferGeometry,r=new Float32Array(e.vertices.length*3);if(t.setAttribute(\\\\\\\"position\\\\\\\",new ae.BufferAttribute(r,3).copyVector3sArray(e.vertices)),e.normals.length>0){let i=new Float32Array(e.normals.length*3);t.setAttribute(\\\\\\\"normal\\\\\\\",new ae.BufferAttribute(i,3).copyVector3sArray(e.normals))}if(e.colors.length>0){let i=new Float32Array(e.colors.length*3);t.setAttribute(\\\\\\\"color\\\\\\\",new ae.BufferAttribute(i,3).copyColorsArray(e.colors))}if(e.uvs.length>0){let i=new Float32Array(e.uvs.length*2);t.setAttribute(\\\\\\\"uv\\\\\\\",new ae.BufferAttribute(i,2).copyVector2sArray(e.uvs))}if(e.uvs2.length>0){let i=new Float32Array(e.uvs2.length*2);t.setAttribute(\\\\\\\"uv2\\\\\\\",new ae.BufferAttribute(i,2).copyVector2sArray(e.uvs2))}t.groups=e.groups;for(let i in e.morphTargets){let n=[],o=e.morphTargets[i];for(let l=0,a=o.length;l<a;l++){let c=o[l],u=new ae.Float32BufferAttribute(c.data.length*3,3);u.name=c.name,n.push(u.copyVector3sArray(c.data))}t.morphAttributes[i]=n}if(e.skinIndices.length>0){let i=new ae.Float32BufferAttribute(e.skinIndices.length*4,4);t.setAttribute(\\\\\\\"skinIndex\\\\\\\",i.copyVector4sArray(e.skinIndices))}if(e.skinWeights.length>0){let i=new ae.Float32BufferAttribute(e.skinWeights.length*4,4);t.setAttribute(\\\\\\\"skinWeight\\\\\\\",i.copyVector4sArray(e.skinWeights))}return e.boundingSphere!==null&&(t.boundingSphere=e.boundingSphere.clone()),e.boundingBox!==null&&(t.boundingBox=e.boundingBox.clone()),t}computeTangents(){console.error(\\\\\\\"THREE.Geometry: .computeTangents() has been removed.\\\\\\\")}computeLineDistances(){console.error(\\\\\\\"THREE.Geometry: .computeLineDistances() has been removed. Use THREE.Line.computeLineDistances() instead.\\\\\\\")}applyMatrix(e){return console.warn(\\\\\\\"THREE.Geometry: .applyMatrix() has been renamed to .applyMatrix4().\\\\\\\"),this.applyMatrix4(e)}dispose(){this.dispatchEvent({type:\\\\\\\"dispose\\\\\\\"})}static createBufferGeometryFromObject(e){let t=new ae.BufferGeometry,r=e.geometry;if(e.isPoints||e.isLine){let i=new ae.Float32BufferAttribute(r.vertices.length*3,3),n=new ae.Float32BufferAttribute(r.colors.length*3,3);if(t.setAttribute(\\\\\\\"position\\\\\\\",i.copyVector3sArray(r.vertices)),t.setAttribute(\\\\\\\"color\\\\\\\",n.copyColorsArray(r.colors)),r.lineDistances&&r.lineDistances.length===r.vertices.length){let o=new ae.Float32BufferAttribute(r.lineDistances.length,1);t.setAttribute(\\\\\\\"lineDistance\\\\\\\",o.copyArray(r.lineDistances))}r.boundingSphere!==null&&(t.boundingSphere=r.boundingSphere.clone()),r.boundingBox!==null&&(t.boundingBox=r.boundingBox.clone())}else e.isMesh&&(t=r.toBufferGeometry());return t}};Xi.prototype.isGeometry=!0;var Dd=class{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){let t=[],r,i,n,o=e.faces;for(i=0;i<o.length;i++){let l=o[i];l.materialIndex!==n&&(n=l.materialIndex,r!==void 0&&(r.count=i*3-r.start,t.push(r)),r={start:i*3,materialIndex:n})}r!==void 0&&(r.count=i*3-r.start,t.push(r)),this.groups=t}fromGeometry(e){let t=e.faces,r=e.vertices,i=e.faceVertexUvs,n=i[0]&&i[0].length>0,o=i[1]&&i[1].length>0,l=e.morphTargets,a=l.length,c;if(a>0){c=[];for(let y=0;y<a;y++)c[y]={name:l[y].name,data:[]};this.morphTargets.position=c}let u=e.morphNormals,d=u.length,h;if(d>0){h=[];for(let y=0;y<d;y++)h[y]={name:u[y].name,data:[]};this.morphTargets.normal=h}let p=e.skinIndices,f=e.skinWeights,m=p.length===r.length,g=f.length===r.length;r.length>0&&t.length===0&&console.error(\\\\\\\"THREE.DirectGeometry: Faceless geometries are not supported.\\\\\\\");for(let y=0;y<t.length;y++){let x=t[y];this.vertices.push(r[x.a],r[x.b],r[x.c]);let v=x.vertexNormals;if(v.length===3)this.normals.push(v[0],v[1],v[2]);else{let w=x.normal;this.normals.push(w,w,w)}let b=x.vertexColors;if(b.length===3)this.colors.push(b[0],b[1],b[2]);else{let w=x.color;this.colors.push(w,w,w)}if(n===!0){let w=i[0][y];w!==void 0?this.uvs.push(w[0],w[1],w[2]):(console.warn(\\\\\\\"THREE.DirectGeometry.fromGeometry(): Undefined vertexUv \\\\\\\",y),this.uvs.push(new ae.Vector2,new ae.Vector2,new ae.Vector2))}if(o===!0){let w=i[1][y];w!==void 0?this.uvs2.push(w[0],w[1],w[2]):(console.warn(\\\\\\\"THREE.DirectGeometry.fromGeometry(): Undefined vertexUv2 \\\\\\\",y),this.uvs2.push(new ae.Vector2,new ae.Vector2,new ae.Vector2))}for(let w=0;w<a;w++){let N=l[w].vertices;c[w].data.push(N[x.a],N[x.b],N[x.c])}for(let w=0;w<d;w++){let N=u[w].vertexNormals[y];h[w].data.push(N.a,N.b,N.c)}m&&this.skinIndices.push(p[x.a],p[x.b],p[x.c]),g&&this.skinWeights.push(f[x.a],f[x.b],f[x.c])}return this.computeGroups(e),this.verticesNeedUpdate=e.verticesNeedUpdate,this.normalsNeedUpdate=e.normalsNeedUpdate,this.colorsNeedUpdate=e.colorsNeedUpdate,this.uvsNeedUpdate=e.uvsNeedUpdate,this.groupsNeedUpdate=e.groupsNeedUpdate,e.boundingSphere!==null&&(this.boundingSphere=e.boundingSphere.clone()),e.boundingBox!==null&&(this.boundingBox=e.boundingBox.clone()),this}},Wn=class{constructor(e,t,r,i,n,o=0){this.a=e,this.b=t,this.c=r,this.normal=i&&i.isVector3?i:new ae.Vector3,this.vertexNormals=Array.isArray(i)?i:[],this.color=n&&n.isColor?n:new ae.Color,this.vertexColors=Array.isArray(n)?n:[],this.materialIndex=o}clone(){return new this.constructor().copy(this)}copy(e){this.a=e.a,this.b=e.b,this.c=e.c,this.normal.copy(e.normal),this.color.copy(e.color),this.materialIndex=e.materialIndex;for(let t=0,r=e.vertexNormals.length;t<r;t++)this.vertexNormals[t]=e.vertexNormals[t].clone();for(let t=0,r=e.vertexColors.length;t<r;t++)this.vertexColors[t]=e.vertexColors[t].clone();return this}};var gy=[\\\\\\\"a\\\\\\\",\\\\\\\"b\\\\\\\",\\\\\\\"c\\\\\\\"];function yy(s,e){switch(e){case\\\\\\\"c\\\\\\\":return s.c;case\\\\\\\"b\\\\\\\":return s.b;case\\\\\\\"a\\\\\\\":default:return s.a}}function rc(s,e,t){let r=Math.min(s,e),i=Math.max(s,e),n=r+\\\\\\\"_\\\\\\\"+i;return t.get(n)}function ic(s,e,t,r,i,n){let o=Math.min(s,e),l=Math.max(s,e),a=o+\\\\\\\"_\\\\\\\"+l,c;if(r.has(a))c=r.get(a);else{let u=t[o],d=t[l];c={a:u,b:d,newEdge:null,faces:[]},r.set(a,c)}c.faces.push(i),n[s].edges.push(c),n[e].edges.push(c)}function vy(s,e,t,r){let i,n,o;for(i=0,n=s.length;i<n;i++)t[i]={edges:[]};for(i=0,n=e.length;i<n;i++)o=e[i],ic(o.a,o.b,s,r,o,t),ic(o.b,o.c,s,r,o,t),ic(o.c,o.a,s,r,o,t)}function qo(s,e,t,r,i){s.push(new Wn(e,t,r,void 0,void 0,i))}function Rs(s,e){return Math.abs(e-s)/2+Math.min(s,e)}function Zo(s,e,t,r){s.push([e.clone(),t.clone(),r.clone()])}var sc=class{constructor(e=1){this.subdivisions=e}modify(e){e instanceof si.BufferGeometry?e=new Xi().fromBufferGeometry(e):e=e.clone(),e.mergeVertices();let t=this.subdivisions;for(;t-- >0;)this._smooth(e);return e.computeFaceNormals(),e.computeVertexNormals(),e}_smooth(e){let t=new si.Vector3,r,i,n,o,l,a=e.vertices,c=e.faces,u=e.faceVertexUvs[0],d=u!==void 0&&u.length>0,h=[],p=new Map;vy(a,c,h,p);let f=[],m,g,y,x,v,b,w;for(let Y of Array.from(p.keys())){for(g=p.get(Y),y=new si.Vector3,v=3/8,b=1/8,w=g.faces.length,w!=2&&(v=.5,b=0,w!=1),y.addVectors(g.a,g.b).multiplyScalar(v),t.set(0,0,0),o=0;o<w;o++){for(x=g.faces[o],l=0;l<3&&(m=a[yy(x,gy[l])],!(m!==g.a&&m!==g.b));l++);m&&t.add(m)}t.multiplyScalar(b),y.add(t),g.newEdge=f.length,f.push(y)}let N,S,A,I,U,E,j,R=[];for(i=0,n=a.length;i<n;i++){for(E=a[i],U=h[i].edges,r=U.length,r==3?N=3/16:r>3&&(N=3/(8*r)),S=1-r*Number(N),A=N,r<=2&&(r==2?(S=3/4,A=1/8):r==1||r==0),j=E.clone().multiplyScalar(S),t.set(0,0,0),o=0;o<r;o++)I=U[o],m=I.a!==E?I.a:I.b,t.add(m);t.multiplyScalar(Number(A)),j.add(t),R.push(j)}let J=R.concat(f),W=R.length,M,k,L,B=[],D=[],O,T,P,_,H=new si.Vector2,q=new si.Vector2,K=new si.Vector2;for(i=0,n=c.length;i<n;i++)x=c[i],M=Number(rc(x.a,x.b,p).newEdge)+W,k=Number(rc(x.b,x.c,p).newEdge)+W,L=Number(rc(x.c,x.a,p).newEdge)+W,qo(B,M,k,L,x.materialIndex),qo(B,x.a,M,L,x.materialIndex),qo(B,x.b,k,M,x.materialIndex),qo(B,x.c,L,k,x.materialIndex),d&&(O=u[i],T=O[0],P=O[1],_=O[2],H.set(Rs(T.x,P.x),Rs(T.y,P.y)),q.set(Rs(P.x,_.x),Rs(P.y,_.y)),K.set(Rs(T.x,_.x),Rs(T.y,_.y)),Zo(D,H,q,K),Zo(D,T,H,K),Zo(D,P,q,H),Zo(D,_,K,q));e.vertices=J,e.faces=B,d&&(e.faceVertexUvs[0]=D)}};var yt=new jr.Vector3,qi=class{static create(s){return this.build(this.normalizeInputs(s))}static normalizeInputs(s,e){let t=s.geometry??e?.geometry??new jr.BufferGeometry().copy(new jr.BoxBufferGeometry(100,100,100)),r;e===void 0?(t.computeBoundingBox(),t.boundingBox.getSize(yt),r={width:yt.x,height:yt.y,depth:yt.z,subdivisions:0}):r=e.parameters;let i=le(le({},r),s.parameters);return{parameters:{width:Math.abs(i.width),height:Math.abs(i.height),depth:Math.abs(i.depth),subdivisions:Math.abs(i.subdivisions)},geometry:t}}static build(s){let{width:e,height:t,depth:r,subdivisions:i}=s.parameters,n=s.geometry??new jr.BufferGeometry().copy(new jr.BoxBufferGeometry(100,100,100)),o=n.userData.parameters;o===void 0?(n.computeBoundingBox(),n.boundingBox.getSize(yt)):yt.set(o.width,o.height,o.depth),(e!==yt.x||t!==yt.y||r!==yt.z)&&n.scale(yt.x===0?1:e/yt.x,yt.y===0?1:t/yt.y,yt.z===0?1:r/yt.z);let l=n.originalGeometry;return i>0?(l===void 0||o?.subdivisions>i)&&(l===void 0&&(l=n),n=new sc(i).modify(l).toBufferGeometry()):(l!==void 0&&(n=l),l=void 0),l!==void 0&&Object.assign(n,{originalGeometry:l}),delete s.geometry,Object.assign(n,{userData:Ce(le({},s),{type:\\\\\\\"NonParametricGeometry\\\\\\\"})})}static loadFromUrl(s,e,t){new jr.BufferGeometryLoader(t).load(s,i=>{let n=this.normalizeInputs({geometry:i});i.boundingBox.getSize(yt);let o=100/yt.x;Object.assign(n.parameters,{width:100,height:yt.y*o,depth:yt.z*o}),e(this.build(n))})}};var Bd=class{static create(s){return this.build(this.normalizeInputs(s))}static normalizeInputs(s,e){let t=Object.assign({},e?.parameters??{width:100,depth:0,spikes:5,cornerRadius:0,extrudeDepth:0,extrudeBevelSize:0,extrudeBevelSegments:3},s.parameters);return{shape:s.shape&&s.shape instanceof Xe?s.shape:new Xe,parameters:Object.assign(t,{surfaceMaxCount:t.surfaceMaxCount??t.cornerRadius>0?1e3:100,width:Math.abs(t.width),height:Math.abs(t.height??t.width),depth:Math.abs(t.depth!==void 0&&t.depth===0&&t.extrudeDepth>0?t.extrudeDepth:t.depth??0)})}}static build(s){let{width:e,height:t,spikes:r,cornerRadius:i,depth:n,extrudeBevelSize:o,extrudeBevelSegments:l,surfaceMaxCount:a}=s.parameters,c=s.shape,u=e*.5,d=t*.5,h=0,p=0,f=2*Math.PI/r;for(let g=0;g<r;g++){let y=f*g,x=h+Math.sin(y)*u,v=p+Math.cos(y)*d;c.addPoint(c.createPoint(x,v))}c.isClosed=!0;for(let g=0,y=c.points.length;g<y;g++)c.points[g].roundness=i;c.roundness=i,c.update();let m=Mt.create({shape:c,parameters:{surfaceMaxCount:a,roundness:i,depth:n,extrudeBevelSize:o,extrudeBevelSegments:l}});return Object.assign(m,{userData:Ce(le({},s),{type:\\\\\\\"PolygonGeometry\\\\\\\"})})}};var Re=F(z()),_d=class{static create(s){return this.build(this.normalizeInputs(s))}static normalizeInputs(s,e){let t=Object.assign({},e?.parameters??{width:100,radialSegments:4,heightSegments:1,cornerRadius:0,cornerSegments:8,openEnded:!1},s.parameters);return{parameters:Object.assign(t,{width:Math.abs(t.width),height:Math.abs(t.height??t.width),depth:Math.abs(t.depth??t.width)})}}static build(s){let{width:e,height:t,depth:r,radialSegments:i,heightSegments:n,openEnded:o,cornerRadius:l,cornerSegments:a}=s.parameters,c=new Od(e*.5,t,i,n,o,l,a);return c.scale(1,1,r/e),Object.assign(c,{userData:Ce(le({},s),{type:\\\\\\\"PyramidGeometry\\\\\\\"})})}};function Jn(s,e,t){t.x=s.x*e.x,t.y=s.y,t.z=s.x*e.y}function nc(s,e,t,r,i,n){let o=e.clone().sub(s),l=t.clone().sub(s),a=o.angleTo(l);if(o.normalize(),l.normalize(),r===i){let c=o.add(l).normalize();n.copy(s).addScaledVector(c,r/Math.sin(a/2))}else{let c=o.angleTo(l);n.copy(s),n.addScaledVector(o,i/Math.sin(c)),n.addScaledVector(l,r/Math.sin(c))}}function xy(s,e,t){let r=s.clone().sub(e),i=t.clone().sub(e);return r.projectOnVector(i),r.add(e)}var Od=class extends Re.BufferGeometry{constructor(e=.5,t=1,r=4,i=1,n=!1,o=0,l=4){super();r=Math.floor(Math.max(3,r)),i=Math.floor(i),l=Math.floor(l);let a=[],c=[],u=[],d=[],h=0,p=t/2,f=Math.PI/r,m=e*Math.cos(Math.PI/r),g=2*Math.PI/r,y=(r-2)*Math.PI/r,x=Math.PI-y,v=new Re.Vector3(0,-p,0),b=new Re.Vector3(0,p,0),w=new Re.Vector2(e,-p),N=new Re.Vector2(m,-p),S=new Re.Vector2(0,b.y).sub(N),A=new Re.Vector2(0,b.y).sub(w),I=new Re.Vector2(S.y,-S.x).normalize(),U=new Re.Vector2(A.y,-A.x).normalize(),j=e*Math.cos(Math.PI/r)*Math.tan((Math.PI-S.angle())/2)-1e-8;o=Math.min(o,j);let R;{let D=new Re.Vector3(I.x,I.y,0),O=new Re.Vector3(Math.cos(g)*D.x,D.y,Math.sin(g)*D.x);R=D.angleTo(O)}let J=o/Math.tan((Math.PI-S.angle())/2),W=o/Math.tan((Math.PI-R)/2),M=new Re.Vector3;if(!n){c.push(v.x,v.y,v.z),u.push(0,-1,0),d.push(0,0);let D=h++,O=[],T=w.clone(),P=J/Math.cos(Math.PI/r);T.x-=P;for(let _=0;_<r;_++){let H=_/r*Math.PI*2+f,q=new Re.Vector2(Math.sin(H),Math.cos(H));Jn(T,q,M),c.push(M.x,M.y,M.z),u.push(0,-1,0),d.push(0,0),O.push(h++)}for(let _=0;_<O.length;_++)a.push(O[_],D,O[(_+1)%O.length])}let k=[];{let D=new Re.Vector3,O=new Re.Vector3,T=new Re.Vector3,P=new Re.Vector3,_=new Re.Vector3,H=new Re.Vector3;for(let q=0;q<r;q++){let K=q/r*Math.PI*2+f,Y=(q+.5)/r*Math.PI*2+f,Q=(q+1)/r*Math.PI*2+f,V=new Re.Vector2(Math.sin(K),Math.cos(K)),re=new Re.Vector2(Math.sin(Y),Math.cos(Y)),ee=new Re.Vector2(Math.sin(Q),Math.cos(Q));Jn(w,V,O),Jn(w,ee,T),Jn(I,re,D),nc(b,O,T,W,W,P),c.push(P.x,P.y,P.z),nc(O,b,T,W,J,_),c.push(_.x,_.y,_.z),nc(T,O,b,J,W,H),c.push(H.x,H.y,H.z),u.push(D.x,D.y,D.z),u.push(D.x,D.y,D.z),u.push(D.x,D.y,D.z),d.push(0,0),d.push(0,0),d.push(0,0);let Z=h++,X=h++,ie=h++;if(a.push(Z,X,ie),o>0){{let fe=O.clone().add(T).multiplyScalar(.5),ce=b.clone().sub(fe).normalize(),ge=v.clone().sub(fe).normalize().add(ce).normalize().multiplyScalar(-1),ye=H.clone().sub(_);L(fe,ye,ge,S.angle())}let $,ne;{let fe=new Re.Vector3;Jn(U,ee,fe);let ce=H.clone().add(P).multiplyScalar(.5);ce=xy(ce,T,b);let be=H.clone().sub(P);[$,ne]=L(ce,be,fe,R,P.y)}{let fe=$,ce=fe.clone().setY(0).normalize(),be=new Re.Vector3(0,-1,0),ge=ce.clone().cross(be);B(fe,ce,be,ge)}k.concat(ne);{let fe=S.angle(),ce=Math.PI-fe,be=b.clone();be.y-=o/Math.sin(fe-Math.PI/2);let ge=new Re.Vector3,ye=[];for(let Te=0;Te<l;Te++){let Lt=[],Dt=Math.PI/2-ce*Te/l,wr=Math.cos(Dt),Gt=Math.sin(Dt),mi=Y;for(let Gi=0;Gi<=Te;Gi++){let pl=Math.cos(mi),ml=Math.sin(mi);D.x=wr*ml,D.y=Gt,D.z=wr*pl,ge.copy(be).addScaledVector(D,o),c.push(ge.x,ge.y,ge.z),u.push(D.x,D.y,D.z),d.push(0,0),Lt.push(h++),mi+=Math.PI*2/Te/r}ye.push(Lt)}ne.reverse(),ye.push(ne);let De=ye.length-1;for(let Te=0;Te<De;Te++){let Lt=ye[Te],Dt=ye[Te+1],wr=Lt.length-1;a.push(Dt[1],Lt[0],Dt[0]);for(let Gt=1;Gt<=wr;Gt++)a.push(Lt[Gt],Lt[Gt-1],Dt[Gt]),a.push(Dt[Gt+1],Lt[Gt],Dt[Gt])}}}}}this.setIndex(a),this.setAttribute(\\\\\\\"position\\\\\\\",new Re.Float32BufferAttribute(c,3)),this.setAttribute(\\\\\\\"normal\\\\\\\",new Re.Float32BufferAttribute(u,3)),this.setAttribute(\\\\\\\"uv\\\\\\\",new Re.Float32BufferAttribute(d,2));function L(D,O,T,P,_){let H=-P/2,q=(Math.PI-P)/2,K=O.clone().normalize().cross(T);D.addScaledVector(T,-o/Math.sin(q));let Y=new Re.Vector3,Q=new Re.Vector3,V=1,re=h,ee=[];for(let Z=0;Z<=l;Z++){let X=H+Z/l*P;Q.set(0,0,0),Q.addScaledVector(K,Math.sin(X)),Q.addScaledVector(T,Math.cos(X));for(let ie=0;ie<=V;ie++){let $=ie/V-.5;if(Y.copy(D),Y.addScaledVector(O,$),Y.addScaledVector(Q,o),_!=null){let ne=Math.max(0,Y.y-_);Y.addScaledVector(O,-ne/O.y)}c.push(Y.x,Y.y,Y.z),u.push(Q.x,Q.y,Q.z),d.push(0,0),ie===0&&ee.push(h),h++}}for(let Z=0;Z<l;Z++)for(let X=0;X<V;X++){let ie=re+X+(V+1)*Z,$=ie+(V+1),ne=$+1,fe=ie+1;a.push(ie,$,fe),a.push($,ne,fe)}return[D.clone().addScaledVector(O,.5),ee]}function B(D,O,T,P){let _=Math.PI/2,H=A.angle()-_,q=[],K=new Re.Vector3,Y=new Re.Vector3;for(let V=0;V<=l;V++){let re=[],ee=V/l;for(let Z=0;Z<=V;Z++){let ie=((V?Z/V:0)-.5)*x,$=Math.cos(ie),ne=Math.sin(ie),fe=Math.atan(Math.tan(H)*$),ce=(_+fe)*ee,be=Math.cos(ce),ge=Math.sin(ce);K.set(0,0,0),K.addScaledVector(O,ge*$),K.addScaledVector(T,be),K.addScaledVector(P,ge*ne),Y.copy(D).addScaledVector(K,o),c.push(Y.x,Y.y,Y.z),u.push(K.x,K.y,K.z),d.push(0,0),re.push(h++)}q.push(re)}let Q=q.length-1;for(let V=0;V<Q;V++){let re=q[V],ee=q[V+1],Z=re.length-1;a.push(re[0],ee[1],ee[0]);for(let X=1;X<=Z;X++)a.push(re[X-1],re[X],ee[X]),a.push(re[X],ee[X+1],ee[X])}}}};var $o=class{static create(s){return this.build(this.normalizeInputs(s))}static normalizeInputs(s,e){let t=Object.assign({},e?.parameters??{width:100,depth:0,cornerRadius:[0,0,0,0],cornerType:1,extrudeDepth:0,extrudeBevelSize:0,extrudeBevelSegments:1},s.parameters),r=Object.assign(e?.ui??{enabledIndieCorners:!1},s.ui),i=t.cornerRadius.reduce((o,l)=>o+l,0);return{shape:s.shape&&s.shape instanceof Xe?s.shape:new Xe,parameters:Object.assign(t,{surfaceMaxCount:t.surfaceMaxCount??i>0?1e3:100,width:Math.abs(t.width),height:Math.abs(t.height??t.width),depth:Math.abs(t.depth!==void 0&&t.depth===0&&t.extrudeDepth>0?t.extrudeDepth:t.depth??0)}),ui:r}}static build(s){let e=s.shape,{width:t,height:r,cornerRadius:i,cornerType:n,depth:o,extrudeBevelSize:l,extrudeBevelSegments:a,surfaceMaxCount:c}=s.parameters,u={x:t*.5,y:r*.5},d={x:-u.x,y:-u.y},h={x:u.x,y:u.y};function p(w,N,S){return N>t&&S>r?Math.min(w*t/N,w*r/S):N>t?w*t/N:S>r?w*r/S:w}let f=[];f[0]=i[0]===0?0:p(i[0],i[0]+i[3],i[0]+i[1]),f[1]=i[1]===0?0:p(i[1],i[1]+i[2],i[1]+i[0]),f[2]=i[2]===0?0:p(i[2],i[2]+i[1],i[2]+i[3]),f[3]=i[3]===0?0:p(i[3],i[3]+i[0],i[3]+i[2]);let m=d.x,g=h.x,y=h.y,x=d.y;e.addPoint(e.createPoint(m,y)),e.addPoint(e.createPoint(g,y)),e.addPoint(e.createPoint(g,x)),e.addPoint(e.createPoint(m,x)),e.isClosed=!0;let v=!0;for(let w=0,N=e.points.length;w<N;w++)e.points[w].roundness=f[w],w>0&&f[w]!==f[w-1]&&(v=!1);v&&(e.roundness=f[0]),e.useCubicForRoundedCorners=n!==1,e.update();let b=Mt.create({shape:e,parameters:{surfaceMaxCount:c,depth:o,extrudeBevelSize:l,extrudeBevelSegments:a}});return Object.assign(b,{userData:Ce(le({},s),{type:\\\\\\\"RectangleGeometry\\\\\\\"})})}};var Pd=F(z()),Rd=class{static create(s){return this.build(this.normalizeInputs(s))}static normalizeInputs(s,e){let t=Object.assign({},e?.parameters??{width:100,widthSegments:64,heightSegments:64,phiStart:0,phiLength:2*Math.PI,thetaStart:0,thetaLength:Math.PI},s.parameters);return{parameters:Object.assign(t,{width:Math.abs(t.width),height:Math.abs(t.height??t.width),depth:Math.abs(t.depth??t.width)})}}static build(s){let{width:e=100,height:t=e,depth:r=e,widthSegments:i=64,heightSegments:n=64,phiStart:o,phiLength:l,thetaStart:a,thetaLength:c}=s.parameters,u=new Pd.SphereBufferGeometry(.5*e,i,n,o,l,a,c);return u.scale(1,t/e,r/e),Object.assign(u,{userData:Ce(le({},s),{type:\\\\\\\"SphereGeometry\\\\\\\"})})}};var Fd=class{static create(s){return this.build(this.normalizeInputs(s))}static normalizeInputs(s,e){let t=Object.assign({},e?.parameters??{width:100,depth:0,innerRadiusPercent:38.19,spikes:5,cornerRadius:0,angle:360,extrudeDepth:0,extrudeBevelSize:0,extrudeBevelSegments:1},s.parameters);return{shape:s.shape&&s.shape instanceof Xe?s.shape:new Xe,parameters:Object.assign(t,{surfaceMaxCount:t.surfaceMaxCount??t.cornerRadius>0?1e3:100,width:Math.abs(t.width),height:Math.abs(t.height??t.width),depth:Math.abs(t.depth!==void 0&&t.depth===0&&t.extrudeDepth>0?t.extrudeDepth:t.depth??0)})}}static build(s){let{width:e,height:t,innerRadiusPercent:r,spikes:i,cornerRadius:n,angle:o,depth:l,extrudeBevelSize:a,extrudeBevelSegments:c,surfaceMaxCount:u}=s.parameters,d=s.shape,h=e*.5,p=t*.5,f=0,m=0,g=o*Math.PI/360/i,y=Math.PI/2*3*-1,x=h*r/100,v=p*r/100;if(i==3&&r==50){g=2*Math.PI/i;for(let w=0;w<i;w++){let N=g*w,S=f+Math.sin(N)*h,A=m+Math.cos(N)*p;d.addPoint(d.createPoint(S,A))}}else for(let w=0;w<i;w++){let N=f+Math.cos(y)*h,S=m+Math.sin(y)*p;d.addPoint(d.createPoint(N,S)),y+=g,N=f+Math.cos(y)*x,S=m+Math.sin(y)*v,w<=i,d.addPoint(d.createPoint(N,S)),y+=g}d.isClosed=!0;for(let w=0,N=d.points.length;w<N;w++)d.points[w].roundness=n;d.roundness=n,d.update();let b=Mt.create({shape:d,parameters:{surfaceMaxCount:u,roundness:n,depth:l,extrudeBevelSize:a,extrudeBevelSegments:c}});return Object.assign(b,{userData:Ce(le({},s),{type:\\\\\\\"StarGeometry\\\\\\\"})})}};var Gd=F(z()),zd=class{static create(s){return this.build(this.normalizeInputs(s))}static normalizeInputs(s,e){let t=Object.assign({},e?.parameters??{width:100,depth:0},s.parameters);return{parameters:Object.assign(t,{width:Math.abs(t.width),height:Math.abs(t.height??t.width),depth:Math.abs(t.depth??0)})}}static build(s){let{width:e,height:t}=s.parameters,r=new Gd.PlaneBufferGeometry(e,t);return Object.assign(r,{userData:Ce(le({},s),{type:\\\\\\\"TextFrameGeometry\\\\\\\"})})}};var Ud=class{static create(s){return this.build(this.normalizeInputs(s))}static normalizeInputs(s,e){let t=Object.assign({},e?.parameters??{width:100,radialSegments:64,tubularSegments:64,arc:Math.PI*2,cornerRadius:30,cornerSegments:8},s.parameters),r=Math.abs(t.width),i=Math.abs(t.height??t.width),n=Math.abs(t.depth??t.width*.25);return{parameters:Object.assign(t,{width:r,height:i,depth:n})}}static build(s){let{width:e,height:t,depth:r,radialSegments:i,tubularSegments:n,arc:o,cornerRadius:l,cornerSegments:a}=s.parameters,c=by(e,t,r,e*.5,o,n,0,0,i,l,a);return c.scale(1,t/e,1),Object.assign(c,{userData:Ce(le({},s),{type:\\\\\\\"TorusGeometry\\\\\\\"})})}};function by(s,e,t,r,i,n,o,l,a,c,u){return[e,t]=[t,e],o=e/2,i/=2*Math.PI,i==1&&(c=0),new Qo(!0,s,e,t,r,i,n,o,l,a,c,u)}var jd=F(z()),kd=class{static create(s){return this.build(this.normalizeInputs(s))}static normalizeInputs(s,e){let t=Object.assign({},e?.parameters??{width:100,depth:1,tube:50,tubularSegments:64,radialSegments:32,p:2,q:3},s.parameters);return{parameters:Object.assign(t,{width:Math.abs(t.width),height:Math.abs(t.height??t.width),depth:Math.abs(t.depth??1)})}}static build(s){let{width:e,tube:t,tubularSegments:r,radialSegments:i,p:n,q:o}=s.parameters,l=e*.5-t,a=new jd.TorusKnotBufferGeometry(l,t,r,i,n,o);return Object.assign(a,{userData:Ce(le({},s),{type:\\\\\\\"TorusKnotGeometry\\\\\\\"})})}};var Vd=class{static create(s){return this.build(this.normalizeInputs(s))}static normalizeInputs(s,e){let t=Object.assign({},e?.parameters??{width:100,depth:0,spikes:5,cornerRadius:0,extrudeDepth:0,extrudeBevelSize:0,extrudeBevelSegments:1,isRect:!1},s.parameters);return{shape:s.shape&&s.shape instanceof Xe?s.shape:new Xe,parameters:Object.assign(t,{surfaceMaxCount:t.surfaceMaxCount??t.cornerRadius>0?1e3:100,width:Math.abs(t.width),height:Math.abs(t.height??t.width*(t.isRect?1:Math.sqrt(3)/2)),depth:Math.abs(t.depth!==void 0&&t.depth===0&&t.extrudeDepth>0?t.extrudeDepth:t.depth??0)})}}static build(s){let{width:e=100,height:t,cornerRadius:r,depth:i,extrudeBevelSize:n,extrudeBevelSegments:o,isRect:l,surfaceMaxCount:a}=s.parameters,c=s.shape,u=e*.5,d=t*.5;l?(c.addPoint(c.createPoint(-u,d)),c.addPoint(c.createPoint(u,-d)),c.addPoint(c.createPoint(-u,-d))):(c.addPoint(c.createPoint(0,d)),c.addPoint(c.createPoint(u,-d)),c.addPoint(c.createPoint(-u,-d))),c.isClosed=!0;for(let p=0,f=c.points.length;p<f;p++)c.points[p].roundness=r;c.roundness=r,c.update();let h=Mt.create({shape:c,parameters:{surfaceMaxCount:a,roundness:r,depth:i,extrudeBevelSize:n,extrudeBevelSegments:o}});return Object.assign(h,{userData:Ce(le({},s),{type:\\\\\\\"TriangleGeometry\\\\\\\"})})}};var nt=F(z());var We=F(z()),Fs=class{static computeTangents(e){e.computeTangents(),console.warn(\\\\\\\"THREE.BufferGeometryUtils: .computeTangents() has been removed. Use BufferGeometry.computeTangents() instead.\\\\\\\")}static mergeBufferGeometries(e,t=!1){let r=e[0].index!==null,i=new Set(Object.keys(e[0].attributes)),n=new Set(Object.keys(e[0].morphAttributes)),o={},l={},a=e[0].morphTargetsRelative,c=new We.BufferGeometry,u=0;for(let d=0;d<e.length;++d){let h=e[d],p=0;if(r!==(h.index!==null))return console.error(\\\\\\\"THREE.BufferGeometryUtils: .mergeBufferGeometries() failed with geometry at index \\\\\\\"+d+\\\\\\\". All geometries must have compatible attributes; make sure index attribute exists among all geometries, or in none of them.\\\\\\\"),null;for(let f in h.attributes){if(!i.has(f))return console.error(\\\\\\\"THREE.BufferGeometryUtils: .mergeBufferGeometries() failed with geometry at index \\\\\\\"+d+'. All geometries must have compatible attributes; make sure \\\\\\\"'+f+'\\\\\\\" attribute exists among all geometries, or in none of them.'),null;o[f]===void 0&&(o[f]=[]),o[f].push(h.attributes[f]),p++}if(p!==i.size)return console.error(\\\\\\\"THREE.BufferGeometryUtils: .mergeBufferGeometries() failed with geometry at index \\\\\\\"+d+\\\\\\\". Make sure all geometries have the same number of attributes.\\\\\\\"),null;if(a!==h.morphTargetsRelative)return console.error(\\\\\\\"THREE.BufferGeometryUtils: .mergeBufferGeometries() failed with geometry at index \\\\\\\"+d+\\\\\\\". .morphTargetsRelative must be consistent throughout all geometries.\\\\\\\"),null;for(let f in h.morphAttributes){if(!n.has(f))return console.error(\\\\\\\"THREE.BufferGeometryUtils: .mergeBufferGeometries() failed with geometry at index \\\\\\\"+d+\\\\\\\".  .morphAttributes must be consistent throughout all geometries.\\\\\\\"),null;l[f]===void 0&&(l[f]=[]),l[f].push(h.morphAttributes[f])}if(c.userData.mergedUserData=c.userData.mergedUserData||[],c.userData.mergedUserData.push(h.userData),t){let f;if(r)f=h.index.count;else if(h.attributes.position!==void 0)f=h.attributes.position.count;else return console.error(\\\\\\\"THREE.BufferGeometryUtils: .mergeBufferGeometries() failed with geometry at index \\\\\\\"+d+\\\\\\\". The geometry must have either an index or a position attribute\\\\\\\"),null;c.addGroup(u,f,d),u+=f}}if(r){let d=0,h=[];for(let p=0;p<e.length;++p){let f=e[p].index;for(let m=0;m<f.count;++m)h.push(f.getX(m)+d);d+=e[p].attributes.position.count}c.setIndex(h)}for(let d in o){let h=this.mergeBufferAttributes(o[d]);if(!h)return console.error(\\\\\\\"THREE.BufferGeometryUtils: .mergeBufferGeometries() failed while trying to merge the \\\\\\\"+d+\\\\\\\" attribute.\\\\\\\"),null;c.setAttribute(d,h)}for(let d in l){let h=l[d][0].length;if(h===0)break;c.morphAttributes=c.morphAttributes||{},c.morphAttributes[d]=[];for(let p=0;p<h;++p){let f=[];for(let g=0;g<l[d].length;++g)f.push(l[d][g][p]);let m=this.mergeBufferAttributes(f);if(!m)return console.error(\\\\\\\"THREE.BufferGeometryUtils: .mergeBufferGeometries() failed while trying to merge the \\\\\\\"+d+\\\\\\\" morphAttribute.\\\\\\\"),null;c.morphAttributes[d].push(m)}}return c}static mergeBufferAttributes(e){let t,r,i,n=0;for(let a=0;a<e.length;++a){let c=e[a];if(c.isInterleavedBufferAttribute)return console.error(\\\\\\\"THREE.BufferGeometryUtils: .mergeBufferAttributes() failed. InterleavedBufferAttributes are not supported.\\\\\\\"),null;if(t===void 0&&(t=c.array.constructor),t!==c.array.constructor)return console.error(\\\\\\\"THREE.BufferGeometryUtils: .mergeBufferAttributes() failed. BufferAttribute.array must be of consistent array types across matching attributes.\\\\\\\"),null;if(r===void 0&&(r=c.itemSize),r!==c.itemSize)return console.error(\\\\\\\"THREE.BufferGeometryUtils: .mergeBufferAttributes() failed. BufferAttribute.itemSize must be consistent across matching attributes.\\\\\\\"),null;if(i===void 0&&(i=c.normalized),i!==c.normalized)return console.error(\\\\\\\"THREE.BufferGeometryUtils: .mergeBufferAttributes() failed. BufferAttribute.normalized must be consistent across matching attributes.\\\\\\\"),null;n+=c.array.length}let o=new t(n),l=0;for(let a=0;a<e.length;++a)o.set(e[a].array,l),l+=e[a].array.length;return new We.BufferAttribute(o,r,i)}static interleaveAttributes(e){let t,r=0,i=0;for(let u=0,d=e.length;u<d;++u){let h=e[u];if(t===void 0&&(t=h.array.constructor),t!==h.array.constructor)return console.error(\\\\\\\"AttributeBuffers of different types cannot be interleaved\\\\\\\"),null;r+=h.array.length,i+=h.itemSize}let n=new We.InterleavedBuffer(new t(r),i),o=0,l=[],a=[\\\\\\\"getX\\\\\\\",\\\\\\\"getY\\\\\\\",\\\\\\\"getZ\\\\\\\",\\\\\\\"getW\\\\\\\"],c=[\\\\\\\"setX\\\\\\\",\\\\\\\"setY\\\\\\\",\\\\\\\"setZ\\\\\\\",\\\\\\\"setW\\\\\\\"];for(let u=0,d=e.length;u<d;u++){let h=e[u],p=h.itemSize,f=h.count,m=new We.InterleavedBufferAttribute(n,p,o,h.normalized);l.push(m),o+=p;for(let g=0;g<f;g++)for(let y=0;y<p;y++)m[c[y]](g,h[a[y]](g))}return l}static estimateBytesUsed(e){let t=0;for(let i in e.attributes){let n=e.getAttribute(i);t+=n.count*n.itemSize*n.array.BYTES_PER_ELEMENT}let r=e.getIndex();return t+=r?r.count*r.itemSize*r.array.BYTES_PER_ELEMENT:0,t}static mergeVertices(e,t=1e-4){t=Math.max(t,Number.EPSILON);let r={},i=e.getIndex(),n=e.getAttribute(\\\\\\\"position\\\\\\\"),o=i?i.count:n.count,l=0,a=Object.keys(e.attributes),c={},u={},d=[],h=[\\\\\\\"getX\\\\\\\",\\\\\\\"getY\\\\\\\",\\\\\\\"getZ\\\\\\\",\\\\\\\"getW\\\\\\\"];for(let g=0,y=a.length;g<y;g++){let x=a[g];c[x]=[];let v=e.morphAttributes[x];v&&(u[x]=new Array(v.length).fill().map(()=>[]))}let p=Math.log10(1/t),f=Math.pow(10,p);for(let g=0;g<o;g++){let y=i?i.getX(g):g,x=\\\\\\\"\\\\\\\";for(let v=0,b=a.length;v<b;v++){let w=a[v],N=e.getAttribute(w),S=N.itemSize;for(let A=0;A<S;A++)x+=`${~~(N[h[A]](y)*f)},`}if(x in r)d.push(r[x]);else{for(let v=0,b=a.length;v<b;v++){let w=a[v],N=e.getAttribute(w),S=e.morphAttributes[w],A=N.itemSize,I=c[w],U=u[w];for(let E=0;E<A;E++){let j=h[E];if(I.push(N[j](y)),S)for(let R=0,J=S.length;R<J;R++)U[R].push(S[R][j](y))}}r[x]=l,d.push(l),l++}}let m=e.clone();for(let g=0,y=a.length;g<y;g++){let x=a[g],v=e.getAttribute(x),b=new v.array.constructor(c[x]),w=new We.BufferAttribute(b,v.itemSize,v.normalized);if(m.setAttribute(x,w),x in u)for(let N=0;N<u[x].length;N++){let S=e.morphAttributes[x][N],A=new S.array.constructor(u[x][N]),I=new We.BufferAttribute(A,S.itemSize,S.normalized);m.morphAttributes[x][N]=I}}return m.setIndex(d),m}static toTrianglesDrawMode(e,t){if(t===We.TrianglesDrawMode)return console.warn(\\\\\\\"THREE.BufferGeometryUtils.toTrianglesDrawMode(): Geometry already defined as triangles.\\\\\\\"),e;if(t===We.TriangleFanDrawMode||t===We.TriangleStripDrawMode){let r=e.getIndex();if(r===null){let l=[],a=e.getAttribute(\\\\\\\"position\\\\\\\");if(a!==void 0){for(let c=0;c<a.count;c++)l.push(c);e.setIndex(l),r=e.getIndex()}else return console.error(\\\\\\\"THREE.BufferGeometryUtils.toTrianglesDrawMode(): Undefined position attribute. Processing not possible.\\\\\\\"),e}let i=r.count-2,n=[];if(t===We.TriangleFanDrawMode)for(let l=1;l<=i;l++)n.push(r.getX(0)),n.push(r.getX(l)),n.push(r.getX(l+1));else for(let l=0;l<i;l++)l%2==0?(n.push(r.getX(l)),n.push(r.getX(l+1)),n.push(r.getX(l+2))):(n.push(r.getX(l+2)),n.push(r.getX(l+1)),n.push(r.getX(l)));n.length/3!==i&&console.error(\\\\\\\"THREE.BufferGeometryUtils.toTrianglesDrawMode(): Unable to generate correct amount of triangles.\\\\\\\");let o=e.clone();return o.setIndex(n),o.clearGroups(),o}else return console.error(\\\\\\\"THREE.BufferGeometryUtils.toTrianglesDrawMode(): Unknown draw mode:\\\\\\\",t),e}static computeMorphedAttributes(e){if(e.geometry.isBufferGeometry!==!0)return console.error(\\\\\\\"THREE.BufferGeometryUtils: Geometry is not of type BufferGeometry.\\\\\\\"),null;let t=new We.Vector3,r=new We.Vector3,i=new We.Vector3,n=new We.Vector3,o=new We.Vector3,l=new We.Vector3,a=new We.Vector3,c=new We.Vector3,u=new We.Vector3;function d(O,T,P,_,H,q,K,Y,Q){t.fromBufferAttribute(P,q),r.fromBufferAttribute(P,K),i.fromBufferAttribute(P,Y);let V=O.morphTargetInfluences;if(T.morphTargets&&_&&V){a.set(0,0,0),c.set(0,0,0),u.set(0,0,0);for(let re=0,ee=_.length;re<ee;re++){let Z=V[re],X=_[re];Z!==0&&(n.fromBufferAttribute(X,q),o.fromBufferAttribute(X,K),l.fromBufferAttribute(X,Y),H?(a.addScaledVector(n,Z),c.addScaledVector(o,Z),u.addScaledVector(l,Z)):(a.addScaledVector(n.sub(t),Z),c.addScaledVector(o.sub(r),Z),u.addScaledVector(l.sub(i),Z)))}t.add(a),r.add(c),i.add(u)}O.isSkinnedMesh&&(O.boneTransform(q,t),O.boneTransform(K,r),O.boneTransform(Y,i)),Q[q*3+0]=t.x,Q[q*3+1]=t.y,Q[q*3+2]=t.z,Q[K*3+0]=r.x,Q[K*3+1]=r.y,Q[K*3+2]=r.z,Q[Y*3+0]=i.x,Q[Y*3+1]=i.y,Q[Y*3+2]=i.z}let h=e.geometry,p=e.material,f,m,g,y=h.index,x=h.attributes.position,v=h.morphAttributes.position,b=h.morphTargetsRelative,w=h.attributes.normal,N=h.morphAttributes.position,S=h.groups,A=h.drawRange,I,U,E,j,R,J,W,M,k=new Float32Array(x.count*x.itemSize),L=new Float32Array(w.count*w.itemSize);if(y!==null)if(Array.isArray(p))for(I=0,E=S.length;I<E;I++)for(R=S[I],J=p[R.materialIndex],W=Math.max(R.start,A.start),M=Math.min(R.start+R.count,A.start+A.count),U=W,j=M;U<j;U+=3)f=y.getX(U),m=y.getX(U+1),g=y.getX(U+2),d(e,J,x,v,b,f,m,g,k),d(e,J,w,N,b,f,m,g,L);else for(W=Math.max(0,A.start),M=Math.min(y.count,A.start+A.count),I=W,E=M;I<E;I+=3)f=y.getX(I),m=y.getX(I+1),g=y.getX(I+2),d(e,p,x,v,b,f,m,g,k),d(e,p,w,N,b,f,m,g,L);else if(x!==void 0)if(Array.isArray(p))for(I=0,E=S.length;I<E;I++)for(R=S[I],J=p[R.materialIndex],W=Math.max(R.start,A.start),M=Math.min(R.start+R.count,A.start+A.count),U=W,j=M;U<j;U+=3)f=U,m=U+1,g=U+2,d(e,J,x,v,b,f,m,g,k),d(e,J,w,N,b,f,m,g,L);else for(W=Math.max(0,A.start),M=Math.min(x.count,A.start+A.count),I=W,E=M;I<E;I+=3)f=I,m=I+1,g=I+2,d(e,p,x,v,b,f,m,g,k),d(e,p,w,N,b,f,m,g,L);let B=new We.Float32BufferAttribute(k,3),D=new We.Float32BufferAttribute(L,3);return{positionAttribute:x,normalAttribute:w,morphedPositionAttribute:B,morphedNormalAttribute:D}}};var Ay,Hd=new Promise(s=>{Ay=s});var Le;Hd.then(s=>{Le=s});var Qn=new nt.Vector3,kr=new nt.Vector3,Yn=new nt.Matrix4;function Wd(s,...e){let t=0;for(let n of e)t+=n.length;let r=new s(t),i=0;for(let n of e)r.set(n,i),i+=n.length;return r}var Gs,jt=(Gs=class{static create(s){return this.build(this.normalizeInputs(s))}static normalizeInputs(s,e){let t=s.geometry??e?.geometry,{width:r,height:i,depth:n}=t.userData.parameters,o=le(le({},e?.parameters??{width:r,height:i,depth:n,subdivisions:1}),s.parameters);return{parameters:Ce(le({},o),{width:Math.abs(o.width),height:Math.abs(o.height),depth:Math.abs(o.depth)}),geometry:t,subdivPointer:e?.subdivPointer,smoothShading:e?.smoothShading}}static build(s){let e=s.geometry,t=s.geometry.originalGeometry,{width:r,height:i,depth:n,subdivisions:o}=s.parameters,l=e.userData.parameters;Qn.set(l.width,l.height,l.depth),(r!==Qn.x||i!==Qn.y||n!==Qn.z)&&(kr.set(r,i,n).divide(Qn),Yn.makeScale(isNaN(kr.x)||kr.x===0?1:kr.x,isNaN(kr.y)||kr.y===0?1:kr.y,isNaN(kr.z)||kr.z===0?1:kr.z),e.applyMatrix4(Yn),t?(t.applyMatrix4(Yn),t.getAttribute(\\\\\\\"positionWASM\\\\\\\").applyMatrix4(Yn)):e.getAttribute(\\\\\\\"positionWASM\\\\\\\").applyMatrix4(Yn));let a,c;return t!==void 0?(c=e,a=t):a=e,s.subdivPointer===void 0&&(this.allocate(a),a?.dispose(),a=this.buildLevel(!0,s.smoothShading),a.computeBoundingSphere(),s.subdivPointer=this.activeSubdivPtr,this.getTopologicalMesh(a)),o>0?((o!==l?.subdivisions||t===void 0)&&(s.subdivPointer!==this.activeSubdivPtr&&(this.activeSubdivPtr=s.subdivPointer),c?.dispose(),Le.set_destination_refinement_level(s.subdivPointer,o),c=this.buildLevel(!1,s.smoothShading),c.boundingSphere=a.boundingSphere),e=c,t=a,delete t.userData.parameters):(e=a,c?.dispose(),t=void 0),Object.assign(e,{originalGeometry:t,subdivPointer:this.activeSubdivPtr,userData:{parameters:s.parameters,type:\\\\\\\"SubdivGeometry\\\\\\\"}})}static allocate(s){let e,t,r,i=[],n=[];if(s.userData.type!==\\\\\\\"SubdivGeometry\\\\\\\"){let v=Fs.mergeVertices(s,this.mergeVerticesToleranceExceptions[s.userData.type]??1.1);e=v.attributes.position.array;let b=v.getIndex().array,w=b.length;switch(s.userData.type){case\\\\\\\"TorusGeometry\\\\\\\":case\\\\\\\"TorusKnotGeometry\\\\\\\":case\\\\\\\"CubeGeometry\\\\\\\":t=new Uint32Array(w/3*2),r=new Uint8Array(w/6).fill(4);for(let L=0,B=0;L<w;L+=6)t[B++]=b[L],t[B++]=b[L+1],t[B++]=b[L+4],t[B++]=b[L+5];break;case\\\\\\\"HelixGeometry\\\\\\\":let{pathSegments:N,segments:S,revolutions:A}=s.userData.parameters,I=N*S*A,U=2*(N-2);t=new Uint32Array(I*4+U*3),r=new Uint8Array(I+U).fill(3);let E=0,j=0;for(let L=0;E<I*6;E+=6,L++)t[j++]=b[E],t[j++]=b[E+1],t[j++]=b[E+4],t[j++]=b[E+5],r[L]=4;for(;j<t.length;)t[j++]=b[E++],t[j++]=b[E++],t[j++]=b[E++];break;case\\\\\\\"SphereGeometry\\\\\\\":case\\\\\\\"CylinderGeometry\\\\\\\":case\\\\\\\"ConeGeometry\\\\\\\":let R,J;s.userData.type===\\\\\\\"SphereGeometry\\\\\\\"?(J=s.parameters.heightSegments,R=s.parameters.widthSegments):s.userData.type===\\\\\\\"CylinderGeometry\\\\\\\"?(J=s.parameters.heightSegments+2,R=s.parameters.radialSegments):(J=s.parameters.heightSegments+1,R=s.parameters.radialSegments),t=new Uint32Array(2*R*3+(J-2)*R*4),r=new Uint8Array(2*R+(J-2)*R);let W=0,M=0,k=0;if(s.userData.type===\\\\\\\"SphereGeometry\\\\\\\"){for(;M<3*R;)t[M++]=b[W++],t[M++]=b[W++],t[M++]=b[W++],r[k++]=3;for(;M<3*R+4*(J-2)*R;W+=6)t[M++]=b[W],t[M++]=b[W+1],t[M++]=b[W+4],t[M++]=b[W+5],r[k++]=4}else for(;M<4*(J-2)*R;W+=6)t[M++]=b[W],t[M++]=b[W+1],t[M++]=b[W+4],t[M++]=b[W+5],r[k++]=4;for(;M<t.length;)t[M++]=b[W++],t[M++]=b[W++],t[M++]=b[W++],r[k++]=3;break;default:t=b,r=new Uint8Array(w/3).fill(3);break}}else if(s.getAttribute(\\\\\\\"positionWASM\\\\\\\")!==void 0)e=s.getAttribute(\\\\\\\"positionWASM\\\\\\\").array,t=s.getAttribute(\\\\\\\"indexWASM\\\\\\\").array,r=s.getAttribute(\\\\\\\"verticesPerFaceWASM\\\\\\\").array;else{let v=s.getAttribute(\\\\\\\"faceMap\\\\\\\");s.deleteAttribute(\\\\\\\"faceMap\\\\\\\");let b=Fs.mergeVertices(s,2);s.setAttribute(\\\\\\\"faceMap\\\\\\\",v),e=b.attributes.position.array;let w=[],N=v.array;r=new Uint8Array(N[N.length-1]+1).fill(3);let S=b.getIndex().array;for(let A=0,I=N.length;A<I;)N[A]===N[A+1]?(r[N[A]]++,w.push(...new Set(S.slice(3*A,3*A+6))),A+=2):(w.push(...S.slice(3*A,3*A+3)),A++);t=new Uint32Array(w)}let o=e.length,l=t.length,a=r.length,c=e.length+i.length+n.length,u=t.length+r.length,d=c*Float32Array.BYTES_PER_ELEMENT+u*Uint32Array.BYTES_PER_ELEMENT,h=c*Float32Array.BYTES_PER_ELEMENT,p=u*Uint32Array.BYTES_PER_ELEMENT,f=Le._malloc(d),m=new Float32Array(Le.HEAPF32.buffer,f,c),g=new Uint32Array(Le.HEAPU32.buffer,f+h,u),y=Wd(Float32Array,e,i,n),x=Wd(Uint32Array,t,r);m.set(y,0),g.set(x,0),this.activeSubdivPtr=Le.alloc_subdivision_surface(f,o,f+h,l,f+h+t.length*Uint32Array.BYTES_PER_ELEMENT,a),Le._free(f)}static buildLevel(s,e,t){let r=Le.get_mesh_data(this.activeSubdivPtr,s?Le.Level.CONTROL:Le.Level.REFINED,e??!s),i=8,n=Le.HEAPU32.subarray(r>>2,(r>>2)+i),o=n.subarray(4,4+4),l=0,a=Le.HEAPU32[n[l]>>2],c=Le.HEAPF32.subarray(a>>2,(a>>2)+o[l]);l++;let u=Le.HEAPU32[n[l]>>2],d=Le.HEAPF32.subarray(u>>2,(u>>2)+o[l]);l++;let h=Le.HEAPU32[n[l]>>2],p=Le.HEAPU32.subarray(h>>2,(h>>2)+o[l]);l++;let f=Le.HEAPU32[n[l]>>2],m=Le.HEAPU32.subarray(f>>2,(f>>2)+o[l]);if(l++,t===void 0){let g=new nt.BufferGeometry;if(g.setIndex(new nt.Uint32BufferAttribute(m,1)),g.setAttribute(\\\\\\\"position\\\\\\\",new nt.Float32BufferAttribute(c,3)),g.setAttribute(\\\\\\\"normal\\\\\\\",new nt.Float32BufferAttribute(d,3)),s){g.setAttribute(\\\\\\\"faceMap\\\\\\\",new nt.Uint32BufferAttribute(p,1));let y=new Float32Array(d.length/3*4).fill(0);g.setAttribute(\\\\\\\"color\\\\\\\",new nt.BufferAttribute(y,4))}return Le.free_mesh_data(r),g.userData.type=\\\\\\\"SubdivGeometry\\\\\\\",g}t.getAttribute(\\\\\\\"position\\\\\\\").copyArray(c),t.getAttribute(\\\\\\\"normal\\\\\\\").copyArray(d),t.attributes.position.needsUpdate=!0,t.attributes.normal.needsUpdate=!0,Le.free_mesh_data(r)}static buildControlCageWireframe(s,e){let t=Le.get_wireframe_data_for_base_level(this.activeSubdivPtr),r=4,i=Le.HEAPU32.subarray(t>>2,(t>>2)+r),n=i.subarray(2,2+2),o=0,l=Le.HEAPU32[i[o]>>2],a=Le.HEAPF32.subarray(l>>2,(l>>2)+n[o]);o++;let c=Le.HEAPU32[i[o]>>2],u=Le.HEAPU32.subarray(c>>2,(c>>2)+n[o]);if(s===void 0){let d=new nt.BufferGeometry;d.setAttribute(\\\\\\\"position\\\\\\\",new nt.Float32BufferAttribute(a,3));let h=new Float32Array(a.length);for(let p=0,f=a.length;p<f;)h[p++]=e.r,h[p++]=e.g,h[p++]=e.b;return d.setAttribute(\\\\\\\"color\\\\\\\",new nt.BufferAttribute(h,3)),d.setIndex(new nt.Uint32BufferAttribute(u,1)),Le.free_wireframe_data_for_base_level(t),d}s.getAttribute(\\\\\\\"position\\\\\\\").copyArray(a),s.attributes.position.needsUpdate=!0,Le.free_wireframe_data_for_base_level(t)}static clone(s,e,t){this.activeSubdivPtr=Le.copy_subdivision_surface(e),Le.set_destination_refinement_level(this.activeSubdivPtr,s.subdivisions);let r=this.buildLevel(!0,t);jt.getTopologicalMesh(r);let i,n,o;return s.subdivisions>0?(o=this.buildLevel(!1,t),i=o,n=r,delete n.userData.parameters):(i=r,n=void 0),Object.assign(i,{originalGeometry:n,userData:{parameters:s,type:\\\\\\\"SubdivGeometry\\\\\\\"}})}static getTopologicalMesh(s){let e=Le.get_topological_data(this.activeSubdivPtr),t=6,r=Le.HEAPU32.subarray(e>>2,(e>>2)+t),i=r.subarray(3,3+3),n=0,o=Le.HEAPU32[r[n]>>2],l=Le.HEAPF32.subarray(o>>2,(o>>2)+i[n]);n++;let a=Le.HEAPU32[r[n]>>2],c=Le.HEAPU32.subarray(a>>2,(a>>2)+i[n]);n++;let u=Le.HEAPU32[r[n]>>2],d=Le.HEAPU32.subarray(u>>2,(u>>2)+i[n]);s.setAttribute(\\\\\\\"positionWASM\\\\\\\",new nt.Float32BufferAttribute(l,3)),s.setAttribute(\\\\\\\"indexWASM\\\\\\\",new nt.Uint32BufferAttribute(c,1)),s.setAttribute(\\\\\\\"verticesPerFaceWASM\\\\\\\",new nt.Uint8BufferAttribute(d,1)),Le.free_topological_data(e)}},Gs.checkpoints=[],Gs.checkpointCurrentIndex=-1,Gs.maxCheckpoints=30,Gs.mergeVerticesToleranceExceptions={DodecahedronGeometry:2},Gs);var Kn={};Du(Kn,{addBarycentricAttribute:()=>Ny,fixUvs:()=>Cy,loadFromUrl:()=>Ty,resizeGeometry:()=>Sy,roundShapePolygon:()=>wy});var xi=F(z()),Jd=function(s,e){let t=e.x-s.x,r=e.y-s.y,i=Math.sqrt(t*t+r*r),n=t/i,o=r/i,l=Math.atan2(o,n);return{x:t,y:r,len:i,nx:n,ny:o,ang:l}},wy=(s,e,t)=>{let r,i,n,o,l,a,c,u,d,h,p,f,m,g,y=e.length;for(o=e[y-2],s.curves=[],r=1;r<y-1;r++){l=e[r%y],a=e[(r+1)%y];let x=Jd(l,o),v=Jd(l,a);c=x.nx*v.ny-x.ny*v.nx,u=x.nx*v.nx-x.ny*-v.ny,p=Math.asin(c),d=1,h=!1,u<0?p<0?p=Math.PI+p:(p=Math.PI-p,d=-1,h=!0):p>0&&(d=-1,h=!0),f=p/2,g=Math.abs(Math.cos(f)*t/Math.sin(f)),g>Math.min(x.len/2,v.len/2)?(g=Math.min(x.len/2,v.len/2),m=Math.abs(g*Math.sin(f)/Math.cos(f))):m=t,i=l.x+v.nx*g,n=l.y+v.ny*g,i+=-v.ny*m*d,n+=v.nx*m*d,s.absarc(i,n,m,x.ang+Math.PI/2*d,v.ang-Math.PI/2*d,h),o=l,l=a}s.closePath()},Sy=(s,{width:e,height:t,depth:r})=>{e=Math.abs(e),t=Math.abs(t),r=Math.abs(r);let i=s.userData.parameters,n,o,l;e===0?(e=i.width,n=1):n=e/i.width,t===0?(t=i.height,o=1):o=t/i.height,r===0?(r=i.depth,l=1):l=r/i.depth,s.scale(n,o,l),i.width=e,i.height=t,i.depth=r},Ny=(s,e)=>{let t=[new xi.Vector3(1,0,0),new xi.Vector3(0,1,0),new xi.Vector3(0,0,1)],r=s.attributes.position,i=new Float32Array(r.count*3);for(let n=0,o=r.count;n<o;n++)t[n%3].toArray(i,n*3);s.setAttribute(e,new xi.Float32BufferAttribute(i,3))},Ty=s=>new Promise(e=>{new xi.BufferGeometryLoader().load(s,r=>e(r))}),Cy=(s,e,t)=>{let r=s.getAttribute(\\\\\\\"uv\\\\\\\");if(r)for(let i=0;i<r.count;i++){let n=r.getX(i),o=r.getY(i);r.setXY(i,(n+e/2)/e,1-(o-t/2)/t*-1)}};var Xn={ConeGeometry:ed,CubeGeometry:td,CylinderGeometry:Zu,DodecahedronGeometry:sd,EllipseGeometry:wd,HelixGeometry:Md,IcosahedronGeometry:Id,LatheGeometry:Ld,NonParametricGeometry:qi,PolygonGeometry:Bd,PyramidGeometry:_d,RectangleGeometry:$o,SphereGeometry:Rd,StarGeometry:Fd,TextFrameGeometry:zd,TorusGeometry:Ud,TorusKnotGeometry:kd,TriangleGeometry:Vd,VectorGeometry:Mt,SubdivGeometry:jt},qn=s=>Xn[s.type].create(s);var da=F(z());var Ae=F(z());var ea=s=>\\\\\\\"isEntity\\\\\\\"in s,Kt=s=>\\\\\\\"isAbstractMesh\\\\\\\"in s;var ta=s=>\\\\\\\"objectHelper\\\\\\\"in s;var zs=new Ae.Box3,Et=new Ae.Vector3,he=new Ae.Vector3,vt=new Ae.Matrix4;function My(s,e,t=0,r=e.count){let i=1/0,n=1/0,o=1/0,l=-1/0,a=-1/0,c=-1/0;for(let u=t;u<r;u++){let d=e.getX(u),h=e.getY(u),p=e.getZ(u);d<i&&(i=d),h<n&&(n=h),p<o&&(o=p),d>l&&(l=d),h>a&&(a=h),p>c&&(c=p)}return s.min.set(i,n,o),s.max.set(l,a,c),s}var Qd=(s,e,t,r)=>{if(Kt(s)){let i=s.geometry.userData.parameters,n=s.geometry.getAttribute(\\\\\\\"position\\\\\\\");s.geometry.userData.type===\\\\\\\"SubdivGeometry\\\\\\\"?Et.copy((s.geometry.originalGeometry??s.geometry).boundingSphere.center):(My(zs,n,s.geometry.drawRange.start,s.geometry.drawRange.count<1/0?s.geometry.drawRange.count:n.count),zs.getCenter(Et)),s.forceComputeSize?zs.getSize(he).multiplyScalar(.5):he.set(i.width,i.height,i.depth??0).multiplyScalar(.5)}else if(ta(s)&&r===!0){let i=s.geometryHelper.getAttribute(\\\\\\\"position\\\\\\\");zs.setFromArray(i.array),zs.getCenter(Et),zs.getSize(he).multiplyScalar(.5)}else Et.setScalar(0),he.setScalar(0);vt.copy(e).multiply(s.matrixWorld),he.x===0&&he.y===0&&he.z===0?t.push(new Ae.Vector3(Et.x,Et.y,Et.z).applyMatrix4(vt)):t.push(new Ae.Vector3(-he.x,he.y,he.z).add(Et).applyMatrix4(vt),new Ae.Vector3(-he.x,-he.y,he.z).add(Et).applyMatrix4(vt),new Ae.Vector3(he.x,-he.y,he.z).add(Et).applyMatrix4(vt),new Ae.Vector3(he.x,he.y,he.z).add(Et).applyMatrix4(vt),new Ae.Vector3(-he.x,he.y,-he.z).add(Et).applyMatrix4(vt),new Ae.Vector3(-he.x,-he.y,-he.z).add(Et).applyMatrix4(vt),new Ae.Vector3(he.x,-he.y,-he.z).add(Et).applyMatrix4(vt),new Ae.Vector3(he.x,he.y,-he.z).add(Et).applyMatrix4(vt))},ra=class extends Ae.Box3{constructor(e,t){super(e,t);this.transform=new Ae.Matrix4;this.vertices=[];this.faces=[];this.edges=[];this.centerEdges=[]}setFromObjectSize(e,t=!1){e.updateWorldMatrix(!1,!1),this.makeEmpty(),this.transform=e.matrixWorld;let r=new Ae.Matrix4().copy(e.matrixWorld).invert();return this.expandByObjectSize(e,r,t)}expandByObjectSize(e,t,r=!1){let i=[];return r===!0?e.traverseEntity(n=>Qd(n,t,i,\\\\\\\"enableHelper\\\\\\\"in e&&e.enableHelper)):Qd(e,t,i,\\\\\\\"enableHelper\\\\\\\"in e&&e.enableHelper),this.setFromPoints(i)}getCenter(e){return e=super.getCenter(e),e.applyMatrix4(this.transform),e}getPositionToCenter(e){return e=super.getCenter(e),e}computeVertices(){this.getSize(he).multiplyScalar(.5),this.getCenter(Et),vt.copy(this.transform).setPosition(Et),this.vertices=[new Ae.Vector3(-he.x,he.y,he.z).applyMatrix4(vt),new Ae.Vector3(-he.x,-he.y,he.z).applyMatrix4(vt),new Ae.Vector3(he.x,-he.y,he.z).applyMatrix4(vt),new Ae.Vector3(he.x,he.y,he.z).applyMatrix4(vt),new Ae.Vector3(-he.x,he.y,-he.z).applyMatrix4(vt),new Ae.Vector3(-he.x,-he.y,-he.z).applyMatrix4(vt),new Ae.Vector3(he.x,-he.y,-he.z).applyMatrix4(vt),new Ae.Vector3(he.x,he.y,-he.z).applyMatrix4(vt)]}computeEdges(){this.vertices.length>0&&this.computeVertices(),this.edges=[new Ae.Line3(this.vertices[0],this.vertices[3]),new Ae.Line3(this.vertices[1],this.vertices[2]),new Ae.Line3(this.vertices[5],this.vertices[6]),new Ae.Line3(this.vertices[4],this.vertices[7]),new Ae.Line3(this.vertices[0],this.vertices[1]),new Ae.Line3(this.vertices[3],this.vertices[2]),new Ae.Line3(this.vertices[7],this.vertices[6]),new Ae.Line3(this.vertices[4],this.vertices[5]),new Ae.Line3(this.vertices[0],this.vertices[4]),new Ae.Line3(this.vertices[1],this.vertices[5]),new Ae.Line3(this.vertices[2],this.vertices[6]),new Ae.Line3(this.vertices[3],this.vertices[7])],this.centerEdges=this.edges.map(e=>e.getCenter(new Ae.Vector3))}computeFaces(){this.vertices.length>0&&this.computeVertices(),this.faces=[new Ae.Vector3().copy(this.vertices[0]).sub(this.vertices[2]).multiplyScalar(.5).add(this.vertices[2]),new Ae.Vector3().copy(this.vertices[7]).sub(this.vertices[5]).multiplyScalar(.5).add(this.vertices[5]),new Ae.Vector3().copy(this.vertices[4]).sub(this.vertices[1]).multiplyScalar(.5).add(this.vertices[1]),new 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m;e.itemSize>4?m=e.array[p*e.itemSize+f]:f===0?m=e.getX(p):f===1?m=e.getY(p):f===2?m=e.getZ(p):f===3&&(m=e.getW(p)),t===Qe.FLOAT?c.setFloat32(u,m,!0):t===Qe.UNSIGNED_INT?c.setUint32(u,m,!0):t===Qe.UNSIGNED_SHORT?c.setUint16(u,m,!0):t===Qe.UNSIGNED_BYTE&&c.setUint8(u,m),u+=l}let d={buffer:this.processBuffer(c.buffer),byteOffset:this.byteOffset,byteLength:a};return n!==void 0&&(d.target=n),n===Qe.ARRAY_BUFFER&&(d.byteStride=e.itemSize*l),this.byteOffset+=a,o.bufferViews.push(d),{id:o.bufferViews.length-1,byteLength:0}}processBufferViewImage(e){let t=this,r=t.json;return r.bufferViews||(r.bufferViews=[]),new Promise(function(i){let n=new window.FileReader;n.readAsArrayBuffer(e),n.onloadend=function(){let o=Ac(n.result),l={buffer:t.processBuffer(o),byteOffset:t.byteOffset,byteLength:o.byteLength};t.byteOffset+=o.byteLength,i(r.bufferViews.push(l)-1)}})}processAccessor(e,t,r,i){let n=this.options,o=this.json,l={1:\\\\\\\"SCALAR\\\\\\\",2:\\\\\\\"VEC2\\\\\\\",3:\\\\\\\"VEC3\\\\\\\",4:\\\\\\\"VEC4\\\\\\\",16:\\\\\\\"MAT4\\\\\\\"},a;if(e.array.constructor===Float32Array)a=Qe.FLOAT;else if(e.array.constructor===Uint32Array)a=Qe.UNSIGNED_INT;else if(e.array.constructor===Uint16Array)a=Qe.UNSIGNED_SHORT;else if(e.array.constructor===Uint8Array)a=Qe.UNSIGNED_BYTE;else throw new Error(\\\\\\\"THREE.GLTFExporter: Unsupported bufferAttribute component type.\\\\\\\");if(r===void 0&&(r=0),i===void 0&&(i=e.count),n.truncateDrawRange&&t!==void 0&&t.index===null){let d=r+i,h=t.drawRange.count===1/0?e.count:t.drawRange.start+t.drawRange.count;r=Math.max(r,t.drawRange.start),i=Math.min(d,h)-r,i<0&&(i=0)}if(i===0)return null;let c=vv(e,r,i),u={componentType:a,count:i,max:c.max,min:c.min,type:l[e.itemSize]};if(!this.options.draco){let d;t!==void 0&&(d=e===t.index?Qe.ELEMENT_ARRAY_BUFFER:Qe.ARRAY_BUFFER);let h=this.processBufferView(e,a,r,i,d);u.bufferView=h.id,u.byteOffset=h.byteOffset}return e.normalized===!0&&(u.normalized=!0),o.accessors||(o.accessors=[]),o.accessors.push(u)-1}processImage(e,t,r){let i=this,n=i.cache,o=i.json,l=i.options,a=i.pending;n.images.has(e)||n.images.set(e,{});let c=n.images.get(e),u=t===xe.RGBAFormat?\\\\\\\"image/png\\\\\\\":\\\\\\\"image/jpeg\\\\\\\",d=u+\\\\\\\":flipY/\\\\\\\"+r.toString();if(c[d]!==void 0)return c[d];o.images||(o.images=[]);let h={mimeType:u};if(l.embedImages){let f=Ah=Ah||document.createElement(\\\\\\\"canvas\\\\\\\");f.width=Math.min(e.width,l.maxTextureSize),f.height=Math.min(e.height,l.maxTextureSize);let m=f.getContext(\\\\\\\"2d\\\\\\\");if(r===!0&&(m.translate(0,f.height),m.scale(1,-1)),typeof HTMLImageElement!=\\\\\\\"undefined\\\\\\\"&&e instanceof HTMLImageElement||typeof HTMLCanvasElement!=\\\\\\\"undefined\\\\\\\"&&e instanceof HTMLCanvasElement||typeof OffscreenCanvas!=\\\\\\\"undefined\\\\\\\"&&e instanceof OffscreenCanvas||typeof ImageBitmap!=\\\\\\\"undefined\\\\\\\"&&e instanceof ImageBitmap)m.drawImage(e,0,0,f.width,f.height);else{t!==xe.RGBAFormat&&t!==xe.RGBFormat&&console.error(\\\\\\\"GLTFExporter: Only RGB and RGBA formats are supported.\\\\\\\"),(e.width>l.maxTextureSize||e.height>l.maxTextureSize)&&console.warn(\\\\\\\"GLTFExporter: Image size is bigger than maxTextureSize\\\\\\\",e);let g=new Uint8ClampedArray(e.height*e.width*4);if(t===xe.RGBAFormat)for(let y=0;y<g.length;y+=4)g[y+0]=e.data[y+0],g[y+1]=e.data[y+1],g[y+2]=e.data[y+2],g[y+3]=e.data[y+3];else for(let y=0,x=0;y<g.length;y+=4,x+=3)g[y+0]=e.data[x+0],g[y+1]=e.data[x+1],g[y+2]=e.data[x+2],g[y+3]=255;m.putImageData(new ImageData(g,e.width,e.height),0,0)}l.binary===!0?a.push(new Promise(function(g){f.toBlob(function(y){i.processBufferViewImage(y).then(function(x){h.bufferView=x,g()})},u)})):h.uri=f.toDataURL(u)}else h.uri=e.src;let p=o.images.push(h)-1;return c[d]=p,p}processSampler(e){let t=this.json;t.samplers||(t.samplers=[]);let r={magFilter:ar[e.magFilter],minFilter:ar[e.minFilter],wrapS:ar[e.wrapS],wrapT:ar[e.wrapT]};return t.samplers.push(r)-1}processTexture(e){let t=this.cache,r=this.json;if(t.textures.has(e))return t.textures.get(e);r.textures||(r.textures=[]);let i={sampler:this.processSampler(e),source:this.processImage(e.image,e.format,e.flipY)};e.name&&(i.name=e.name),this._invokeAll(function(o){o.writeTexture&&o.writeTexture(e,i)});let n=r.textures.push(i)-1;return t.textures.set(e,n),n}processMaterial(e){let t=this.cache,r=this.json;if(t.materials.has(e))return t.materials.get(e);if(e.isShaderMaterial)return console.warn(\\\\\\\"GLTFExporter: THREE.ShaderMaterial not supported.\\\\\\\"),null;r.materials||(r.materials=[]);let i={pbrMetallicRoughness:{}};e.isMeshStandardMaterial!==!0&&e.isMeshBasicMaterial!==!0&&console.warn(\\\\\\\"GLTFExporter: Use MeshStandardMaterial or MeshBasicMaterial for best results.\\\\\\\");let n=e.color.toArray().concat([e.opacity]);if(to(n,[1,1,1,1])||(i.pbrMetallicRoughness.baseColorFactor=n),e.isMeshStandardMaterial?(i.pbrMetallicRoughness.metallicFactor=e.metalness,i.pbrMetallicRoughness.roughnessFactor=e.roughness):(i.pbrMetallicRoughness.metallicFactor=.5,i.pbrMetallicRoughness.roughnessFactor=.5),e.metalnessMap||e.roughnessMap)if(e.metalnessMap===e.roughnessMap){let l={index:this.processTexture(e.metalnessMap)};this.applyTextureTransform(l,e.metalnessMap),i.pbrMetallicRoughness.metallicRoughnessTexture=l}else console.warn(\\\\\\\"THREE.GLTFExporter: Ignoring metalnessMap and roughnessMap because they are not the same Texture.\\\\\\\");if(e.map){let l={index:this.processTexture(e.map)};this.applyTextureTransform(l,e.map),i.pbrMetallicRoughness.baseColorTexture=l}if(e.emissive){let l=e.emissive.clone().multiplyScalar(e.emissiveIntensity),a=Math.max(l.r,l.g,l.b);if(a>1&&(l.multiplyScalar(1/a),console.warn(\\\\\\\"THREE.GLTFExporter: Some emissive components exceed 1; emissive has been limited\\\\\\\")),a>0&&(i.emissiveFactor=l.toArray()),e.emissiveMap){let c={index:this.processTexture(e.emissiveMap)};this.applyTextureTransform(c,e.emissiveMap),i.emissiveTexture=c}}if(e.normalMap){let l={index:this.processTexture(e.normalMap)};e.normalScale&&e.normalScale.x!==-1&&(e.normalScale.x!==e.normalScale.y&&console.warn(\\\\\\\"THREE.GLTFExporter: Normal scale components are different, ignoring Y and exporting X.\\\\\\\"),l.scale=e.normalScale.x),this.applyTextureTransform(l,e.normalMap),i.normalTexture=l}if(e.aoMap){let l={index:this.processTexture(e.aoMap),texCoord:1};e.aoMapIntensity!==1&&(l.strength=e.aoMapIntensity),this.applyTextureTransform(l,e.aoMap),i.occlusionTexture=l}e.transparent?i.alphaMode=\\\\\\\"BLEND\\\\\\\":e.alphaTest>0&&(i.alphaMode=\\\\\\\"MASK\\\\\\\",i.alphaCutoff=e.alphaTest),e.side===xe.DoubleSide&&(i.doubleSided=!0),e.name!==\\\\\\\"\\\\\\\"&&(i.name=e.name),this.serializeUserData(e,i),this._invokeAll(function(l){l.writeMaterial&&l.writeMaterial(e,i)});let o=r.materials.push(i)-1;return t.materials.set(e,o),o}async processMesh(e){let t=this.cache,r=this.json,i=[e.geometry.uuid];if(Array.isArray(e.material))for(let v=0,b=e.material.length;v<b;v++)i.push(e.material[v].uuid);else i.push(e.material.uuid);let n=i.join(\\\\\\\":\\\\\\\");if(t.meshes.has(n))return t.meshes.get(n);let o=e.geometry,l;if(e.isLineSegments?l=Qe.LINES:e.isLineLoop?l=Qe.LINE_LOOP:e.isLine?l=Qe.LINE_STRIP:e.isPoints?l=Qe.POINTS:l=e.material.wireframe?Qe.LINES:Qe.TRIANGLES,o.isBufferGeometry!==!0)throw new Error(\\\\\\\"THREE.GLTFExporter: Geometry is not of type THREE.BufferGeometry.\\\\\\\");let a={},c={},u=[],d=[],h={uv:\\\\\\\"TEXCOORD_0\\\\\\\",uv2:\\\\\\\"TEXCOORD_1\\\\\\\",color:\\\\\\\"COLOR_0\\\\\\\",skinWeight:\\\\\\\"WEIGHTS_0\\\\\\\",skinIndex:\\\\\\\"JOINTS_0\\\\\\\"},p=o.getAttribute(\\\\\\\"normal\\\\\\\");p!==void 0&&!this.isNormalizedNormalAttribute(p)&&(console.warn(\\\\\\\"THREE.GLTFExporter: Creating normalized normal attribute from the non-normalized one.\\\\\\\"),o.setAttribute(\\\\\\\"normal\\\\\\\",this.createNormalizedNormalAttribute(p)));let f=null;for(let v in o.attributes){if(v.substr(0,5)===\\\\\\\"morph\\\\\\\")continue;let b=o.attributes[v];if(v=h[v]||v.toUpperCase(),/^(POSITION|NORMAL|TANGENT|TEXCOORD_\\\\d+|COLOR_\\\\d+|JOINTS_\\\\d+|WEIGHTS_\\\\d+)$/.test(v)||(v=\\\\\\\"_\\\\\\\"+v),t.attributes.has(this.getUID(b))){c[v]=t.attributes.get(this.getUID(b));continue}f=null;let N=b.array;v===\\\\\\\"JOINTS_0\\\\\\\"&&!(N instanceof Uint16Array)&&!(N instanceof Uint8Array)&&(console.warn('GLTFExporter: Attribute \\\\\\\"skinIndex\\\\\\\" converted to type UNSIGNED_SHORT.'),f=new xe.BufferAttribute(new Uint16Array(N),b.itemSize,b.normalized));let S=this.processAccessor(f||b,o);S!==null&&(c[v]=S,t.attributes.set(this.getUID(b),S))}if(p!==void 0&&o.setAttribute(\\\\\\\"normal\\\\\\\",p),Object.keys(c).length===0)return null;if(e.morphTargetInfluences!==void 0&&e.morphTargetInfluences.length>0){let v=[],b=[],w={};if(e.morphTargetDictionary!==void 0)for(let N in e.morphTargetDictionary)w[e.morphTargetDictionary[N]]=N;for(let N=0;N<e.morphTargetInfluences.length;++N){let S={},A=!1;for(let I in o.morphAttributes){if(I!==\\\\\\\"position\\\\\\\"&&I!==\\\\\\\"normal\\\\\\\"){A||(console.warn(\\\\\\\"GLTFExporter: Only POSITION and NORMAL morph are supported.\\\\\\\"),A=!0);continue}let U=o.morphAttributes[I][N],E=I.toUpperCase(),j=o.attributes[I];if(t.attributes.has(this.getUID(U))){S[E]=t.attributes.get(this.getUID(U));continue}let R=U.clone();if(!o.morphTargetsRelative)for(let J=0,W=U.count;J<W;J++)R.setXYZ(J,U.getX(J)-j.getX(J),U.getY(J)-j.getY(J),U.getZ(J)-j.getZ(J));S[E]=this.processAccessor(R,o),t.attributes.set(this.getUID(j),S[E])}d.push(S),v.push(e.morphTargetInfluences[N]),e.morphTargetDictionary!==void 0&&b.push(w[N])}a.weights=v,b.length>0&&(a.extras={},a.extras.targetNames=b)}let m=Array.isArray(e.material);if(m&&o.groups.length===0)return null;let g=m?e.material:[e.material],y=m?o.groups:[{materialIndex:0,start:void 0,count:void 0}];for(let v=0,b=y.length;v<b;v++){let w={mode:l,attributes:c};if(this.serializeUserData(o,w),d.length>0&&(w.targets=d),o.index!==null){let S=this.getUID(o.index);(y[v].start!==void 0||y[v].count!==void 0)&&(S+=\\\\\\\":\\\\\\\"+y[v].start+\\\\\\\":\\\\\\\"+y[v].count),t.attributes.has(S)?w.indices=t.attributes.get(S):(w.indices=this.processAccessor(o.index,o,y[v].start,y[v].count),t.attributes.set(S,w.indices)),w.indices===null&&delete w.indices}let N=this.processMaterial(g[y[v].materialIndex]);N!==null&&(w.material=N),u.push(w)}if(a.primitives=u,this.options.draco){let v=await this.dracoExporter.parse(e);this.processBuffer(v.buffer);let b={buffer:0,byteOffset:this.byteOffset,byteLength:v.buffer.length};this.byteOffset+=b.byteLength,r.bufferViews||(r.bufferViews=[]),r.bufferViews.push(b),u.forEach(w=>{w.extensions||(w.extensions={}),w.extensions.KHR_draco_mesh_compression={bufferView:r.bufferViews.length-1,attributes:v.attributeIDs}}),r.extensionsRequired||(r.extensionsRequired=[]),r.extensionsRequired.includes(\\\\\\\"KHR_draco_mesh_compression\\\\\\\")||r.extensionsRequired.push(\\\\\\\"KHR_draco_mesh_compression\\\\\\\"),this.extensionsUsed.KHR_draco_mesh_compression=!0}r.meshes||(r.meshes=[]),this._invokeAll(function(v){v.writeMesh&&v.writeMesh(e,a)});let x=r.meshes.push(a)-1;return t.meshes.set(n,x),x}processCamera(e){let t=this.json;t.cameras||(t.cameras=[]);let r=e.isOrthographicCamera,i={type:r?\\\\\\\"orthographic\\\\\\\":\\\\\\\"perspective\\\\\\\"};return r?i.orthographic={xmag:e.right*2,ymag:e.top*2,zfar:e.far<=0?.001:e.far,znear:e.near<0?0:e.near}:i.perspective={aspectRatio:e.aspect,yfov:xe.MathUtils.degToRad(e.fov),zfar:e.far<=0?.001:e.far,znear:e.near<0?0:e.near},e.name!==\\\\\\\"\\\\\\\"&&(i.name=e.type),t.cameras.push(i)-1}processAnimation(e,t){let r=this.json,i=this.nodeMap;r.animations||(r.animations=[]),e=eo.Utils.mergeMorphTargetTracks(e.clone(),t);let n=e.tracks,o=[],l=[];for(let a=0;a<n.length;++a){let c=n[a],u=xe.PropertyBinding.parseTrackName(c.name),d=xe.PropertyBinding.findNode(t,u.nodeName),h=yh[u.propertyName];if(u.objectName===\\\\\\\"bones\\\\\\\"&&(d.isSkinnedMesh===!0?d=d.skeleton.getBoneByName(u.objectIndex):d=void 0),!d||!h)return console.warn('THREE.GLTFExporter: Could not export animation track \\\\\\\"%s\\\\\\\".',c.name),null;let p=1,f=c.values.length/c.times.length;h===yh.morphTargetInfluences&&(f/=d.morphTargetInfluences.length);let m;c.createInterpolant.isInterpolantFactoryMethodGLTFCubicSpline===!0?(m=\\\\\\\"CUBICSPLINE\\\\\\\",f/=3):c.getInterpolation()===xe.InterpolateDiscrete?m=\\\\\\\"STEP\\\\\\\":m=\\\\\\\"LINEAR\\\\\\\",l.push({input:this.processAccessor(new xe.BufferAttribute(c.times,p)),output:this.processAccessor(new xe.BufferAttribute(c.values,f)),interpolation:m}),o.push({sampler:l.length-1,target:{node:i.get(d),path:h}})}return r.animations.push({name:e.name||\\\\\\\"clip_\\\\\\\"+r.animations.length,samplers:l,channels:o}),r.animations.length-1}processSkin(e){let t=this.json,r=this.nodeMap,i=t.nodes[r.get(e)],n=e.skeleton;if(n===void 0)return null;let o=e.skeleton.bones[0];if(o===void 0)return null;let l=[],a=new Float32Array(n.bones.length*16),c=new xe.Matrix4;for(let d=0;d<n.bones.length;++d)l.push(r.get(n.bones[d])),c.copy(n.boneInverses[d]),c.multiply(e.bindMatrix).toArray(a,d*16);return t.skins===void 0&&(t.skins=[]),t.skins.push({inverseBindMatrices:this.processAccessor(new xe.BufferAttribute(a,16)),joints:l,skeleton:r.get(o)}),i.skin=t.skins.length-1}async processNode(e){let t=this.json,r=this.options,i=this.nodeMap;t.nodes||(t.nodes=[]);let n={};if(r.trs){let l=e.quaternion.toArray(),a=e.position.toArray(),c=e.scale.toArray();to(l,[0,0,0,1])||(n.rotation=l),to(a,[0,0,0])||(n.translation=a),to(c,[1,1,1])||(n.scale=c)}else e.matrixAutoUpdate&&e.updateMatrix(),yv(e.matrix)===!1&&(n.matrix=e.matrix.elements);if(e.name!==\\\\\\\"\\\\\\\"&&(n.name=String(e.name)),this.serializeUserData(e,n),e.isMesh||e.isLine||e.isPoints){let l=await this.processMesh(e);l!==null&&(n.mesh=l)}else e.isCamera&&(n.camera=this.processCamera(e));if(e.isSkinnedMesh&&this.skins.push(e),e.children.length>0){let l=[];for(let a=0,c=e.children.length;a<c;a++){let u=e.children[a];if(u.visible||r.onlyVisible===!1){let d=await this.processNode(u);d!==null&&l.push(d)}}l.length>0&&(n.children=l)}this._invokeAll(function(l){l.writeNode&&l.writeNode(e,n)});let o=t.nodes.push(n)-1;return i.set(e,o),o}async processScene(e){let t=this.json,r=this.options;t.scenes||(t.scenes=[],t.scene=0);let i={};e.name!==\\\\\\\"\\\\\\\"&&(i.name=e.name),t.scenes.push(i);let n=[];for(let o=0,l=e.children.length;o<l;o++){let a=e.children[o];if(a.visible||r.onlyVisible===!1){let c=await this.processNode(a);c!==null&&n.push(c)}}n.length>0&&(i.nodes=n),this.serializeUserData(e,i)}async processObjects(e){let t=new xe.Scene;t.name=\\\\\\\"AuxScene\\\\\\\";for(let r=0;r<e.length;r++)t.children.push(e[r]);await this.processScene(t)}async processInput(e){let t=this.options;e=e instanceof Array?e:[e],this._invokeAll(function(i){i.beforeParse&&i.beforeParse(e)});let r=[];for(let i=0;i<e.length;i++)e[i]instanceof xe.Scene?await this.processScene(e[i]):r.push(e[i]);r.length>0&&await this.processObjects(r);for(let i=0;i<this.skins.length;++i)this.processSkin(this.skins[i]);for(let i=0;i<t.animations.length;++i)this.processAnimation(t.animations[i],e[0]);this._invokeAll(function(i){i.afterParse&&i.afterParse(e)})}_invokeAll(e){for(let t=0,r=this.plugins.length;t<r;t++)e(this.plugins[t])}},Sh=class{constructor(e){this.writer=e,this.name=\\\\\\\"KHR_lights_punctual\\\\\\\"}writeNode(e,t){if(!e.isLight)return;if(!e.isDirectionalLight&&!e.isPointLight&&!e.isSpotLight){console.warn(\\\\\\\"THREE.GLTFExporter: Only directional, point, and spot lights are supported.\\\\\\\",e);return}let r=this.writer,i=r.json,n=r.extensionsUsed,o={};e.name&&(o.name=e.name),o.color=e.color.toArray(),o.intensity=e.intensity,e.isDirectionalLight?o.type=\\\\\\\"directional\\\\\\\":e.isPointLight?(o.type=\\\\\\\"point\\\\\\\",e.distance>0&&(o.range=e.distance)):e.isSpotLight&&(o.type=\\\\\\\"spot\\\\\\\",e.distance>0&&(o.range=e.distance),o.spot={},o.spot.innerConeAngle=(e.penumbra-1)*e.angle*-1,o.spot.outerConeAngle=e.angle),e.decay!==void 0&&e.decay!==2&&console.warn(\\\\\\\"THREE.GLTFExporter: Light decay may be lost. glTF is physically-based, and expects light.decay=2.\\\\\\\"),e.target&&(e.target.parent!==e||e.target.position.x!==0||e.target.position.y!==0||e.target.position.z!==-1)&&console.warn(\\\\\\\"THREE.GLTFExporter: Light direction may be lost. For best results, make light.target a child of the light with position 0,0,-1.\\\\\\\"),n[this.name]||(i.extensions=i.extensions||{},i.extensions[this.name]={lights:[]},n[this.name]=!0);let l=i.extensions[this.name].lights;l.push(o),t.extensions=t.extensions||{},t.extensions[this.name]={light:l.length-1}}},Nh=class{constructor(e){this.writer=e,this.name=\\\\\\\"KHR_materials_unlit\\\\\\\"}writeMaterial(e,t){if(!e.isMeshBasicMaterial)return;let i=this.writer.extensionsUsed;t.extensions=t.extensions||{},t.extensions[this.name]={},i[this.name]=!0,t.pbrMetallicRoughness.metallicFactor=0,t.pbrMetallicRoughness.roughnessFactor=.9}},Th=class{constructor(e){this.writer=e,this.name=\\\\\\\"KHR_materials_pbrSpecularGlossiness\\\\\\\"}writeMaterial(e,t){if(!e.isGLTFSpecularGlossinessMaterial)return;let r=this.writer,i=r.extensionsUsed,n={};t.pbrMetallicRoughness.baseColorFactor&&(n.diffuseFactor=t.pbrMetallicRoughness.baseColorFactor);let o=[1,1,1];if(e.specular.toArray(o,0),n.specularFactor=o,n.glossinessFactor=e.glossiness,t.pbrMetallicRoughness.baseColorTexture&&(n.diffuseTexture=t.pbrMetallicRoughness.baseColorTexture),e.specularMap){let l={index:r.processTexture(e.specularMap)};r.applyTextureTransform(l,e.specularMap),n.specularGlossinessTexture=l}t.extensions=t.extensions||{},t.extensions[this.name]=n,i[this.name]=!0}};eo.Utils={insertKeyframe:function(s,e){let t=.001,r=s.getValueSize(),i=new s.TimeBufferType(s.times.length+1),n=new s.ValueBufferType(s.values.length+r),o=s.createInterpolant(new s.ValueBufferType(r)),l;if(s.times.length===0){i[0]=e;for(let a=0;a<r;a++)n[a]=0;l=0}else if(e<s.times[0]){if(Math.abs(s.times[0]-e)<t)return 0;i[0]=e,i.set(s.times,1),n.set(o.evaluate(e),0),n.set(s.values,r),l=0}else if(e>s.times[s.times.length-1]){if(Math.abs(s.times[s.times.length-1]-e)<t)return s.times.length-1;i[i.length-1]=e,i.set(s.times,0),n.set(s.values,0),n.set(o.evaluate(e),s.values.length),l=i.length-1}else for(let a=0;a<s.times.length;a++){if(Math.abs(s.times[a]-e)<t)return a;if(s.times[a]<e&&s.times[a+1]>e){i.set(s.times.slice(0,a+1),0),i[a+1]=e,i.set(s.times.slice(a+1),a+2),n.set(s.values.slice(0,(a+1)*r),0),n.set(o.evaluate(e),(a+1)*r),n.set(s.values.slice((a+1)*r),(a+2)*r),l=a+1;break}}return s.times=i,s.values=n,l},mergeMorphTargetTracks:function(s,e){let t=[],r={},i=s.tracks;for(let n=0;n<i.length;++n){let o=i[n],l=xe.PropertyBinding.parseTrackName(o.name),a=xe.PropertyBinding.findNode(e,l.nodeName);if(l.propertyName!==\\\\\\\"morphTargetInfluences\\\\\\\"||l.propertyIndex===void 0){t.push(o);continue}if(o.createInterpolant!==o.InterpolantFactoryMethodDiscrete&&o.createInterpolant!==o.InterpolantFactoryMethodLinear){if(o.createInterpolant.isInterpolantFactoryMethodGLTFCubicSpline)throw new Error(\\\\\\\"THREE.GLTFExporter: Cannot merge tracks with glTF CUBICSPLINE interpolation.\\\\\\\");console.warn(\\\\\\\"THREE.GLTFExporter: Morph target interpolation mode not yet supported. Using LINEAR instead.\\\\\\\"),o=o.clone(),o.setInterpolation(xe.InterpolateLinear)}let c=a.morphTargetInfluences.length,u=a.morphTargetDictionary[l.propertyIndex];if(u===void 0)throw new Error(\\\\\\\"THREE.GLTFExporter: Morph target name not found: \\\\\\\"+l.propertyIndex);let d;if(r[a.uuid]===void 0){d=o.clone();let p=new d.ValueBufferType(c*d.times.length);for(let f=0;f<d.times.length;f++)p[f*c+u]=d.values[f];d.name=(l.nodeName||\\\\\\\"\\\\\\\")+\\\\\\\".morphTargetInfluences\\\\\\\",d.values=p,r[a.uuid]=d,t.push(d);continue}let h=o.createInterpolant(new o.ValueBufferType(1));d=r[a.uuid];for(let p=0;p<d.times.length;p++)d.values[p*c+u]=h.evaluate(d.times[p]);for(let p=0;p<o.times.length;p++){let f=this.insertKeyframe(d,o.times[p]);d.values[f*c+u]=o.values[p]}}return s.tracks=t,s}};var la={isPublish:!1,gltfQueue:[]};function ro(s,e,t){return s[e.uuid]===void 0&&(s[e.uuid]=e.toJSON(t)),e.uuid}var wc=new eo,Ch=new kt;Ch.setEncoderPath(\\\\\\\"/_libraries/\\\\\\\");wc.setDRACOExporter(Ch);function Mh(s,e,t){if(s[e.uuid]===void 0)if(e.userData.type===\\\\\\\"NonParametricGeometry\\\\\\\"||e.userData.type===\\\\\\\"SubdivGeometry\\\\\\\"){if(e.userData.type===\\\\\\\"SubdivGeometry\\\\\\\")if(la.isPublish)e=qi.create({geometry:e.clone()}),e.userData.parameters.subdivisions===0&&(delete e.attributes.color,delete e.attributes.faceMap,delete e.attributes.positionWASM,delete e.attributes.indexWASM,delete e.attributes.verticesPerFaceWASM);else{let i=e.originalGeometry;if(i){let n=e.userData;e=i.clone(),e.userData=n}else e=e.clone();delete e.attributes.color,delete e.attributes.faceMap,delete e.attributes.position,delete e.attributes.normal,e.index=null}let r=e.originalGeometry;if(r!==void 0){let i=e.userData;e=r,e.userData=i}if(!la.isPublish)s[e.uuid]=e.toJSON();else{let i={type:\\\\\\\"NonParametricGeometry\\\\\\\",uuid:e.uuid,userData:{}};e.name!==\\\\\\\"\\\\\\\"&&(i.name=e.name),Object.keys(e.userData).length>0&&(i.userData=le({},e.userData));let n=new Ai.Group;if(Array.isArray(t))mh(e).forEach(l=>{let a=new Ai.Mesh(l,new Ai.MeshStandardMaterial);n.add(a)});else{let o=new Ai.Mesh(e,new Ai.MeshStandardMaterial);n.add(o)}la.gltfQueue.push(new Promise(async o=>{await Promise.all(la.gltfQueue),wc.parse(n,l=>{i.gltf=l,o()},l=>{wc.parse(n,a=>{i.gltf=a,o()},()=>{},{draco:!1})},{draco:!0})})),s[e.uuid]=i}}else{let r=e.userData;if(r.type===\\\\\\\"VectorGeometry\\\\\\\"){let i=e.userData.shape;r=Object.assign({},r,{shape:i.toJSON()})}s[e.uuid]={uuid:e.uuid,userData:r}}return e.uuid}function Vs(s){let e=[];for(let t in s){let r=s[t];delete r.metadata,e.push(r)}return e}function Eh(s){let e=[];for(let t in s)e.push(s[t]);return e}var Si=class{constructor(e){this.object=e;this.uuid=xt.MathUtils.generateUUID();this.states=[];this.events=[];this._interpolateMaterialBetweenValues=(e,t,r,i=t.getNames())=>{for(let n=0,o=i.length;n<o;++n){let l=i[n],a=`f${t.id}_${l}`,c=`f${r.id}_${l}`,u=t.uniforms[a],d=r.uniforms[c];if(!(u.value===void 0||d===void 0)){if(l===\\\\\\\"mode\\\\\\\"){u.value!==d.value&&(u.value=d.value);continue}if(typeof u.value==\\\\\\\"boolean\\\\\\\"){u.value!==d.value&&(u.value=d.value);continue}if(typeof u.value==\\\\\\\"number\\\\\\\"){u.value!==d.value&&this._animation?.add({targets:u,value:Number(d.value)},0);continue}if(\\\\\\\"isVector2\\\\\\\"in u.value){u.value.equals(d.value)===!1&&this._animation?.add({targets:u,x:d.value.x,y:d.value.y},0);continue}if(\\\\\\\"isVector3\\\\\\\"in u.value){u.value.equals(d.value)===!1&&this._animation?.add({targets:u,x:d.value.x,y:d.value.y,z:d.value.z},0);continue}if(\\\\\\\"isColor\\\\\\\"in u.value){u.value.equals(d.value)===!1&&this._animation?.add({targets:u.value,r:d.value.r,g:d.value.g,b:d.value.b},0);continue}}}if(t.backLayer){let o=`f${t.backLayer.id}_alpha`,l=t.backLayer.uniforms[o];this._animation?.add({targets:l,value:0},0)}};this._interpolateMaterialBetweenLayers=(e,t,r)=>{let i=e.layersList;if(t.backLayer===void 0){let c=r.getValues();c.alpha=0,t.backLayer=i.addLayerBeforeAt(c,t),t.backLayer.isBack=!0,e.dispose()}let n=`f${t.id}_alpha`,o=`f${t.backLayer?.id}_alpha`,l=t.uniforms[n],a=t.backLayer?.uniforms[o];this._animation?.add({targets:l,value:0},0).add({targets:a,value:r.uniforms[`f${r.id}_alpha`].value},0)};this._interpolateMaterialTexture=(e,t,r)=>{let i=`f${t.id}_texture`,n=t.uniforms[i].value,o=r.uniforms[`f${r.id}_texture`].value,l=t.uniforms[`f${t.id}_crop`],a=r.uniforms[`f${r.id}_crop`];l&&a&&l.value!==a.value&&(l.value=Number(a.value)),(n.wrapS!==o.wrapS||n.wrapT!==o.wrapT)&&(n.wrapS=o.wrapS,n.wrapT=o.wrapT,n.needsUpdate=!0);let c=n.matrix,u=o.matrix;if(c.equals(u)===!1){let h={repeatX:n.repeat.x,repeatY:n.repeat.y,offsetX:n.offset.x,offsetY:n.offset.y};this._animation?.add({targets:h,repeatX:o.repeat.x,repeatY:o.repeat.y,offsetX:o.offset.x,offsetY:o.offset.y,update:()=>{n.repeat.set(h.repeatX,h.repeatY),n.offset.set(h.offsetX,h.offsetY),n.updateMatrix(),n.needsUpdate=!0}},0)}let d=[\\\\\\\"alpha\\\\\\\",\\\\\\\"mode\\\\\\\"];t.type===\\\\\\\"displace_map\\\\\\\"&&d.push(\\\\\\\"intensity\\\\\\\"),this._interpolateMaterialBetweenValues(e,t,r,d)};this._interpolateMaterialGradient=(e,t,r)=>{let i=t.uniforms[`f${t.id}_num`],n=r.uniforms[`f${r.id}_num`],o=`f${t.id}_steps`,l=`f${r.id}_steps`,a=t.uniforms[o],c=r.uniforms[l],u=`f${t.id}_colors`,d=`f${r.id}_colors`,h=t.uniforms[u],p=r.uniforms[d];if(i.value<=n.value){let f=h.value[i.value-1];for(let m=Number(i.value);m<n.value;++m)a.value[m]=1,h.value[m].copy(f);i.value!==n.value&&(i.value=Number(n.value));for(let m=0;m<n.value;++m){let g=h.value[m],y=p.value[m];if(a.value[m]!==c.value[m]||!g.equals(y)){let x={value:a.value[m]};this._animation?.add({targets:[x,g],value:c.value[m],x:y.x,y:y.y,z:y.z,w:y.w,update:()=>{a.value[m]=x.value}},0)}}}else{let f=[];for(let g=0;g<n.value;++g){let y=h.value[g],x=p.value[g];if(a.value[g]!==c.value[g]||!y.equals(x)){let v={value:a.value[g]};f.push(new Promise(b=>{this._animation?.add({targets:[v,y],value:c.value[g],x:x.x,y:x.y,z:x.z,w:x.w,update:()=>{a.value[g]=v.value},complete:()=>{b(g)}},0)}))}}let m=p.value[n.value-1];for(let g=Number(n.value);g<i.value;++g){let y=h.value[g];(a.value[g]!==c.value[g]||!y.equals(m))&&f.push(new Promise(x=>{let v={value:a.value[g]};this._animation?.add({targets:[v,y],value:1,x:m.x,y:m.y,z:m.z,w:m.w,update:()=>{a.value[g]=v.value},complete:()=>{x(g)}},0)}))}Promise.all(f).then(()=>{i.value=Number(n.value)},()=>{})}this._interpolateMaterialBetweenValues(e,t,r,[\\\\\\\"alpha\\\\\\\",\\\\\\\"mode\\\\\\\",\\\\\\\"gl_type\\\\\\\",\\\\\\\"smooth\\\\\\\",\\\\\\\"offset\\\\\\\",\\\\\\\"morph\\\\\\\",\\\\\\\"angle\\\\\\\"])};this.statesManager=new bc(this)}static get isAllAnimationsEnded(){let e;for(let t in Si.isAnimating){if(Si.isAnimating[t])return!1;e=!0}return e}computeCache(){return this.cache={mouseDown:this.events.find(e=>e.type===we.MOUSE_DOWN),mouseUp:this.events.find(e=>e.type===we.MOUSE_UP),mouseHover:this.events.find(e=>e.type===we.MOUSE_HOVER),start:this.events.find(e=>e.type===we.START),lookAt:this.events.find(e=>e.type===we.LOOK_AT),follow:this.events.find(e=>e.type===we.FOLLOW),keyDown:this.events.filter(e=>e.type===we.KEY_DOWN),keyUp:this.events.filter(e=>e.type===we.KEY_UP)},this.cache}newState(e){let t=new bi;return t.name=e,t.update(this.object),this.selectedState=this.states.length,this.states.push(t),t}addState(e,t){this.states.splice(t,0,e)}removeState(e){this.selectedState===e&&(this.selectedState=void 0),this.states.splice(e,1)}selectState(e){e!==void 0&&this.states[e].execute(this.object),this.selectedState=e}hasEventType(e,t){return e===we.KEY_DOWN||e===we.KEY_UP?t===void 0?!1:this.events.some(r=>r.type===e&&r.key===t):this.events.some(r=>r.type===e)}newEvent(){let e=[we.MOUSE_DOWN,we.MOUSE_UP,we.MOUSE_HOVER,we.KEY_DOWN].find(r=>this.hasEventType(r)===!1),t=new js(e);return this.events.push(t),t}addEvent(e,t){this.events.splice(t,0,e);let r=this.events[t].targets;for(let i=0,n=r.length;i<n;++i)r[i].object!==void 0&&Zi.add(r[i])}removeEvent(e){let t=this.events[e].targets;for(let r=0,i=t.length;r<i;++r)t[r].object!==void 0&&Zi.delete(t[r]);this.events.splice(e,1)}copy(e){this.selectedState=e.selectedState;for(let t=0,r=e.states.length;t<r;++t)this.addState(e.states[t].clone(),t);for(let t=0,r=e.events.length;t<r;++t){this.addEvent(e.events[t].clone(),t);let i=e.events[t].targets;for(let n=0,o=i.length;n<o;++n){let l=i[n].clone();i[n].object===e.object?(l.object=this.object,l.state=this.states[e.states.indexOf(i[n].state)]):(l.object=i[n].object,l.state=i[n].state),this.events[t].addTarget(l,n)}}return this}clone(e){return new Si(e).copy(this)}toJSON(e){e===void 0&&(e={nodes:{},geometries:{},materials:{},textures:{},images:{},interactionStates:{}});let t={uuid:this.uuid};if(this.selectedState!==void 0&&(t.selectedState=this.selectedState),this.states.length>0){t.states=[];for(let r=0,i=this.states.length;r<i;++r)t.states.push(ro(e.interactionStates,this.states[r],e))}if(this.events.length>0){t.events=[];for(let r=0,i=this.events.length;r<i;++r)t.events.push(this.events[r].toJSON())}return t}fromJSON(e,t,r){if(this.uuid=e.uuid,this.selectedState=e.selectedState,this.states=[],this.events=[],e.states!==void 0)for(let i=0,n=e.states.length;i<n;++i){let o=r[e.states[i]];this.states.push(o),o.camera?.targetOffset===-1&&(o.camera.targetOffset=this.object.getDistanceToTarget())}if(e.events!==void 0)for(let i=0,n=e.events.length;i<n;++i)this.events.push(new js().fromJSON(e.events[i],t,r));return this}start(){this._animation!==void 0&&(this._animation.pause(),this._animation=void 0,this._removeBackLayer()),this.states.length>1?(this.states[0].execute(this.object),this._currentState=this.states[0],this._prevState=void 0):this._state0!==void 0?this._state0.execute(this.object):this.events.some(e=>e.type===we.LOOK_AT||e.type===we.FOLLOW)&&(this._state0=new bi,this._state0.update(this.object))}end(){this._animation!==void 0&&(this._animation.pause(),this._animation=void 0,this._removeBackLayer()),this.states.length>1?this.states[this.selectedState??0].execute(this.object):this._state0!==void 0&&(this._state0.execute(this.object),this._state0=void 0),this._currentState=void 0,this._prevState=void 0}lookAt(e){this.object.lookAt(e),this._animation?.finished.then(()=>{this.lookAt(e)})}follow(e){this.object.position.copy(e),this.object.parent!==null&&this.object.position.applyMatrix4(new xt.Matrix4().copy(this.object.parent.matrixWorld).invert()),this.object.position.applyMatrix4(new xt.Matrix4().copy(this.object.hiddenMatrix).invert())}play(e){this.object.visible&&e.state!==void 0&&this.animate(e.state,e)}reverse(e){this.object.visible&&this._prevState!==void 0&&this.animate(this._prevState,e)}_removeBackLayer(){if(\\\\\\\"material\\\\\\\"in this.object){let e=this.object.material;if(e instanceof Array)for(let t=0,r=e.length;t<r;++t){let i=e[t].layersList,n=i.head;for(;n;)if(n.backLayer){delete n.backLayer;let o=n.next;i.removeLayer(n.id),n=o}else n.isBack&&(n.isBack=!1),n=n.next}else{let t=e.layersList,r=t.head;for(;r;)for(;r;)if(r.backLayer){delete r.backLayer;let i=r.next;t.removeLayer(r.id),r=i}else r.isBack&&(r.isBack=!1),r=r.next}}}getTimingFunction(e,t,r){switch(e){case Tr.LINEAR:return\\\\\\\"cubicBezier( 0, 0, 1, 1 )\\\\\\\";case Tr.EASE:return\\\\\\\"cubicBezier( .25, .1, .25, 1 )\\\\\\\";case Tr.EASE_IN:return\\\\\\\"cubicBezier( .42, 0, 1, 1 )\\\\\\\";case Tr.EASE_OUT:return\\\\\\\"cubicBezier( 0, 0, .58, 1 )\\\\\\\";case Tr.EASE_IN_OUT:return\\\\\\\"cubicBezier( .42, 0, .58, 1 )\\\\\\\";case Tr.CUBIC:return`cubicBezier( ${t[0]}, ${t[1]}, ${t[2]}, ${t[3]} )`;case Tr.SPRING:return`spring( ${r.mass}, ${r.stiffness}, ${r.damping}, ${r.velocity} )`}}animate(e,t){if(Si.isAnimating[e.uuid]=!0,this._currentState===e){if(this._animation?.reversed){let r=this._animation?.currentTime??0;this._animation?.reset(),this._animation?.seek(r),this._animation?.play()}}else this._prevState=this._currentState,this._currentState=e,this._animation!==void 0&&(Si.isAnimating[this._prevState.uuid]=!1,this._animation.pause()),this._removeBackLayer(),t.cycle===!0&&t.repeat===!0?this._animation=$n.timeline({duration:t.duration,delay:t.delay/2,endDelay:t.delay/2,direction:\\\\\\\"alternate\\\\\\\",loop:!0,rewind:t.rewind,easing:this.getTimingFunction(t.easing,t.cubicControls,t.springParameters)}):t.cycle===!0&&t.repeat===!1?this._animation=$n.timeline({duration:t.duration,delay:t.delay,endDelay:t.delay/2,direction:\\\\\\\"alternate\\\\\\\",rewind:t.rewind,easing:this.getTimingFunction(t.easing,t.cubicControls,t.springParameters)}):t.cycle===!1&&t.repeat===!0?this._animation=$n.timeline({duration:t.duration,delay:t.delay,direction:\\\\\\\"normal\\\\\\\",loop:!0,easing:this.getTimingFunction(t.easing,t.cubicControls,t.springParameters)}):this._animation=$n.timeline({duration:t.duration,delay:t.delay,direction:\\\\\\\"normal\\\\\\\",easing:this.getTimingFunction(t.easing,t.cubicControls,t.springParameters)}),this._animation.finished.then(()=>{this._removeBackLayer(),this._animation=void 0,Si.isAnimating[e.uuid]=!1}),this.animateMatrix(e),this.animateGeometry(e),this.animateMaterial(e),this.animateCamera(e)}animateMatrix(e){if(this.object.position.equals(e.position)===!1){let n=this.object.position.clone(),o={t:0};this._animation?.add({targets:o,t:1,update:()=>{this.object.position.lerpVectors(n,e.position,o.t)}},0)}if(this.object.scale.equals(e.scale)===!1){let n=this.object.scale.clone(),o={t:0};this._animation?.add({targets:o,t:1,update:()=>{this.object.scale.lerpVectors(n,e.scale,o.t)}},0)}if(this.object.rotation.equals(e.rotation)===!1){let n=this.object.quaternion.clone(),o={t:0},l={x:this.object.rotation.x-e.rotation.x,y:this.object.rotation.y-e.rotation.y,z:this.object.rotation.z-e.rotation.z},a=e.rotation.clone(),c=.01*xt.MathUtils.DEG2RAD,u=359.99*xt.MathUtils.DEG2RAD;l.x===Math.PI*2?a.x===0?a.x=c:a.x===Math.PI*2&&(a.x=u):l.x===-Math.PI*2&&(a.x===0?a.x=-c:a.x===Math.PI*2&&(a.x=-u)),l.y===Math.PI*2?a.y===0?a.y=c:a.y===Math.PI*2&&(a.y=u):l.y===-Math.PI*2&&(a.y===0?a.y=-c:a.y===Math.PI*2&&(a.y=-u)),l.z===Math.PI*2?a.z===0?a.z=c:a.z===Math.PI*2&&(a.z=u):l.z===-Math.PI*2&&(a.z===0?a.z=-c:a.z===Math.PI*2&&(a.z=-u));let d=new xt.Quaternion().setFromEuler(a);this._animation?.add({targets:o,t:1,update:()=>{oc(n,d,this.object.quaternion,o.t)}},0)}let t=new xt.Vector3,r=new xt.Quaternion,i=new xt.Vector3;if(e.hiddenMatrix.decompose(t,r,i),\\\\\\\"hiddenMatrix\\\\\\\"in this.object){let n=new xt.Vector3,o=new xt.Quaternion,l=new xt.Vector3;if(this.object.hiddenMatrix.decompose(n,o,l),this.object.hiddenMatrix.equals(e.hiddenMatrix)===!1){let a=new xt.Vector3,c=new xt.Quaternion,u=new xt.Vector3,d={t:0},h=this.object.hiddenMatrix;this._animation?.add({targets:d,t:1,update:()=>{c.slerpQuaternions(o,r,d.t),a.lerpVectors(n,t,d.t),u.lerpVectors(l,i,d.t),h.compose(a,c,u)}},0)}}}animateGeometry(e){if(\\\\\\\"geometry\\\\\\\"in this.object&&e.geometry!==void 0){let r=this.object.geometry.userData.parameters,i=e.geometry;if(i.width!==r.width||i.height!==r.height||i.depth!==r.depth){let n=this.object;this._animation?.add({targets:r,width:i.width,height:i.height,depth:i.depth,update:()=>{n.updateGeometry({parameters:{width:r.width,height:r.height,depth:r.depth}})}},0)}}}animateMaterial(e){if(\\\\\\\"material\\\\\\\"in this.object)if(this.object.material instanceof Array)for(let t=0,r=this.object.material.length;t<r;++t)this._interpolateMaterial(this.object.material[t],e.material[t]);else{if(this.object.material.userData.isAsset)return;this._interpolateMaterial(this.object.material,e.material)}}_interpolateMaterial(e,t){let r=e.layersList.head,i=0;for(;r;){if(r.isBack===!0){r=r.next;continue}let n=t.layersList[i++];if(Po(r)){let o=r.uniforms[`f${r.id}_texture`].value,l=n.uniforms[`f${n.id}_texture`].value;o.image===l.image?this._interpolateMaterialTexture(e,r,n):this._interpolateMaterialBetweenLayers(e,r,n)}else r.type===\\\\\\\"gradient\\\\\\\"?r.uniforms[`f${r.id}_gl_type`].value===n.uniforms[`f${n.id}_gl_type`].value?this._interpolateMaterialGradient(e,r,n):this._interpolateMaterialBetweenLayers(e,r,n):this._interpolateMaterialBetweenValues(e,r,n);r=r.next}}animateCamera(e){if(this.object.objectType===\\\\\\\"CombinedCamera\\\\\\\"&&e.camera!==void 0){let t=this.object,r=t.cameraType===\\\\\\\"OrthographicCamera\\\\\\\"?e.camera.zoomOrtho:e.camera.zoomPersp;t.zoom!==r&&this._animation?.add({targets:t,zoom:r,update:()=>{t.updateProjectionMatrix()}},0),t.targetOffset!==e.camera.targetOffset&&this._animation?.add({targets:t,targetOffset:e.camera.targetOffset},0)}}},wi=Si;wi.isAnimating={};var ca=F(z());var ua=s=>class extends s{hasEntityChild(){return this.children.some(t=>ea(t))}attach(t,r){this.updateWorldMatrix(!0,!1);let i=new ca.Matrix4().copy(this.matrixWorld).invert();return t.parent!==null&&(t.parent.updateWorldMatrix(!0,!1),i.multiply(t.parent.matrixWorld)),ea(t)?t.hiddenMatrix.premultiply(i):t.applyMatrix4(i),t.updateWorldMatrix(!1,!1),this.add(t),r!==void 0&&(this.children.pop(),this.children.splice(r,0,t)),this}copy(t,r=!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)),r===!0)for(let i=0;i<t.children.length;i++){let n=t.children[i];this.add(n.clone())}return this}toJSON(t){let r=t===void 0,i={object:{uuid:\\\\\\\"\\\\\\\",objectType:\\\\\\\"\\\\\\\"}};t===void 0&&(t={geometries:{},materials:{},textures:{},images:{},interactionStates:{},nodes:{}},i.metadata={version:1.5,type:\\\\\\\"Object\\\\\\\",generator:\\\\\\\"Object3D.toJSON\\\\\\\"});let n={uuid:this.uuid,objectType:this.type};if(this.name!==\\\\\\\"\\\\\\\"&&(n.name=this.name),n.matrix=this.matrix.toArray(),this.castShadow===!0&&(n.castShadow=!0),this.receiveShadow===!0&&(n.receiveShadow=!0),this.visible===!1&&(n.visible=!1),this.frustumCulled===!1&&(n.frustumCulled=!1),this.renderOrder!==0&&(n.renderOrder=this.renderOrder),n.layers=this.layers.mask,JSON.stringify(this.userData)!==\\\\\\\"{}\\\\\\\"&&(n.userData=this.userData),this.children.length>0){n.children=[];for(let o of this.children)(ea(o)||o instanceof ca.Light)&&n.children.push(o.toJSON(t).object)}if(r){let o=Vs(t.geometries),l=Vs(t.materials),a=Vs(t.textures),c=Vs(t.images),u=Vs(t.interactionStates),d=Eh(t.nodes);o.length>0&&(i.geometries=o),l.length>0&&(i.materials=l),a.length>0&&(i.textures=a),c.length>0&&(i.images=c),u.length>0&&(i.interactionStates=u),d.length>0&&(i.nodes=d)}return i.object=n,i}fromJSON(t){return this.uuid=t.uuid,t.name!==void 0&&(this.name=t.name),t.matrix!==void 0?(this.matrix.fromArray(t.matrix),t.matrixAutoUpdate!==void 0&&(this.matrixAutoUpdate=t.matrixAutoUpdate),this.matrixAutoUpdate&&this.matrix.decompose(this.position,this.quaternion,this.scale)):(t.position!==void 0&&this.position.fromArray(t.position),t.rotation!==void 0&&this.rotation.fromArray(t.rotation),t.quaternion!==void 0&&this.quaternion.fromArray(t.quaternion),t.scale!==void 0&&this.scale.fromArray(t.scale)),this.castShadow=t.castShadow!==void 0,this.receiveShadow=t.receiveShadow!==void 0,t.visible!==void 0&&(this.visible=t.visible),t.frustumCulled!==void 0&&(this.frustumCulled=t.frustumCulled),t.renderOrder!==void 0&&(this.renderOrder=t.renderOrder),t.layers!==void 0&&(this.layers.mask=t.layers),t.userData!==void 0&&(this.userData=t.userData),this}};var Vt=s=>\\\\\\\"isEntity\\\\\\\"in s,Ih=s=>\\\\\\\"isAbstractMesh\\\\\\\"in s,Sc=s=>\\\\\\\"objectHelper\\\\\\\"in s,pr=s=>class extends ua(s){constructor(){super(...arguments);this.objectType=\\\\\\\"\\\\\\\";this.isEntity=!0;this.raycastLock=!1;this.scaleLock=!1;this.hiddenMatrix=new da.Matrix4;this.interaction=new wi(this);this.forceComputeSize=!1}set visibility(t){this.visible=t;for(let r of this.children)Vt(r)&&r.traverseEntity(i=>{Sc(i)&&i.visible&&(i.objectHelper.visible=t)})}get visibility(){return this.visible}get interactionCache(){return this.interaction.cache===void 0&&this.interaction.computeCache(),this.interaction.cache}traverseEntity(t){t(this);for(let r of this.children)Vt(r)&&r.traverseEntity(t)}computeSingleBoundingBox(){this.singleBoundingBox||(this.singleBoundingBox=new ra),this.singleBoundingBox.setFromObjectSize(this,!1),this.singleBoundingBox.computeVertices(),this.singleBoundingBox.computeEdges(),this.singleBoundingBox.computeFaces()}computeComplexBoundingBox(t){this.complexBoundingBox||(this.complexBoundingBox=new ra),this.complexBoundingBox.setFromObjectSize(this,t),this.complexBoundingBox.computeVertices(),this.complexBoundingBox.computeEdges(),this.complexBoundingBox.computeFaces()}updateMatrixWorld(t){this.matrixAutoUpdate&&this.updateMatrix(),(this.matrixWorldNeedsUpdate||t)&&(this.parent===null?this.matrixWorld.multiplyMatrices(this.hiddenMatrix,this.matrix):(this.matrixWorld.multiplyMatrices(this.parent.matrixWorld,this.hiddenMatrix),this.matrixWorld.multiplyMatrices(this.matrixWorld,this.matrix)),this.matrixWorldNeedsUpdate=!1,t=!0);for(let r of this.children)r.updateMatrixWorld(t)}updateWorldMatrix(t,r){let i=this.parent;if(t&&i!==null&&i.updateWorldMatrix(!0,!1),this.matrixAutoUpdate&&this.updateMatrix(),this.parent===null?this.matrixWorld.multiplyMatrices(this.hiddenMatrix,this.matrix):(this.matrixWorld.multiplyMatrices(this.parent.matrixWorld,this.hiddenMatrix),this.matrixWorld.multiplyMatrices(this.matrixWorld,this.matrix)),r)for(let n of this.children)n.updateWorldMatrix(!1,!0)}shallowClone(t){return new this.constructor().shallowCopy(this,t)}shallowCopy(t,r=!0){if(super.copy(t,!1),this.raycastLock=t.raycastLock,this.scaleLock=t.scaleLock,this.hiddenMatrix.copy(t.hiddenMatrix),t.interaction!==void 0&&this.interaction.copy(t.interaction),r===!0)for(let i of t.children)Vt(i)&&this.add(i.shallowClone());return this}clone(t){return new this.constructor().copy(this,t)}copy(t,r=!0){if(super.copy(t,!1),this.raycastLock=t.raycastLock,this.scaleLock=t.scaleLock,this.hiddenMatrix.copy(t.hiddenMatrix),t.interaction!==void 0&&this.interaction.copy(t.interaction),r===!0)for(let i of t.children)Vt(i)&&this.add(i.clone());return this}keepChildrenMatrixWorld(){let t=new da.Matrix4,r=this.matrixWorld.clone();this.updateWorldMatrix(!1,!1),t.copy(this.matrixWorld).invert(),t.multiply(r);for(let i of this.children)Vt(i)&&i.hiddenMatrix.premultiply(t)}toJSON(t){let r=super.toJSON(t),i=r.object;return this.raycastLock===!0&&(i.raycastLock=!0),this.scaleLock===!0&&(i.scaleLock=!0),i.hiddenMatrix=this.hiddenMatrix.toArray(),this.interaction!==void 0&&(i.interaction=this.interaction.toJSON(t)),r}fromJSON(t){return super.fromJSON(t),t.raycastLock!==void 0&&(this.raycastLock=t.raycastLock),t.scaleLock!==void 0&&(this.scaleLock=t.scaleLock),this.hiddenMatrix.fromArray(t.hiddenMatrix),this}fromObject3D(t){let r=t.children;return t.children=[],Object.assign(t,{raycastLock:!1,scaleLock:!1,hiddenMatrix:new da.Matrix4}),this.copy(t),t.children=r,this}};var Ye=F(z());var ha=class extends Ye.Object3D{constructor(e,t={}){super();this.object=e,e.computeComplexBoundingBox(!0);let r=e.complexBoundingBox.getSize(new Ye.Vector3),i=.1;this.parameters={type:t.type!==void 0?t.type:\\\\\\\"radial\\\\\\\",hideBase:t.hideBase!==void 0?t.hideBase:!1,count:t.count!==void 0?t.count:3,ra_radius:t.ra_radius!==void 0?t.ra_radius:Math.max(r.x,r.y)*2,ra_startAngle:t.ra_startAngle!==void 0?t.ra_startAngle:0,ra_endAngle:t.ra_endAngle!==void 0?t.ra_endAngle:360,ra_align:t.ra_align!==void 0?t.ra_align:!1,ra_axis:t.ra_axis!==void 0?t.ra_axis:\\\\\\\"y\\\\\\\",ra_scale:t.ra_scale!==void 0?t.ra_scale.clone():new Ye.Vector3,ra_rotation:t.ra_rotation!==void 0?t.ra_rotation.clone():new Ye.Euler,ra_position:t.ra_position!==void 0?t.ra_position.clone():new Ye.Vector3,li_scale:t.li_scale!==void 0?t.li_scale.clone():new Ye.Vector3,li_rotation:t.li_rotation!==void 0?t.li_rotation.clone():new Ye.Euler,li_position:t.li_position!==void 0?t.li_position.clone():new Ye.Vector3(r.x+r.x*i,0,0).round(),gr_count:t.gr_count!==void 0?t.gr_count.clone():new Ye.Vector3(2,2,2),gr_size:t.gr_size!==void 0?t.gr_size.clone():new Ye.Vector3(r.x+r.x*i,r.y+r.y*i,r.z+r.z*i).round(),gr_fromCenter:t.gr_fromCenter!==void 0?t.gr_fromCenter:!0},this.update(),this.setHideBase(this.parameters.hideBase)}refreshMaterial(){if(\\\\\\\"material\\\\\\\"in this.object)for(let e of this.children)e.material=this.object.material}setHideBase(e){if(\\\\\\\"material\\\\\\\"in this.object){if(Array.isArray(this.object.material)){if(this.children.length>0){for(let t of this.object.material)t.visible=!0;if(e){let t=this.object.material.map(r=>r.clone());for(let r of this.children)r.material=t}else for(let t of this.children)t.material=this.object.material}for(let t of this.object.material)t.visible=!e}else{if(this.children.length>0)if(this.object.material.visible=!0,e){let t=this.object.material.clone();for(let r of this.children)r.material=t}else for(let t of this.children)t.material=this.object.material;this.object.material.visible=!e}this.parameters.hideBase=e}}update(){switch(this._updateCount(),this.parameters.type){case\\\\\\\"radial\\\\\\\":this._updateRadial(this.parameters);break;case\\\\\\\"linear\\\\\\\":this._updateLinear(this.parameters);break;case\\\\\\\"grid\\\\\\\":this._updateGrid(this.parameters)}}_updateCount(){let e=this.parameters.type===\\\\\\\"grid\\\\\\\"?this.parameters.gr_count.x*this.parameters.gr_count.y*this.parameters.gr_count.z:this.parameters.count;if(this.children.length!==e)if(this.children.length<e)for(let t=0,r=e-this.children.length;t<r;++t){let i=this.object.shallowClone(!1);i.visible=!0,this.add(i),this.parameters.hideBase&&this.setHideBase(!0)}else for(let t=0,r=this.children.length-e;t<r;++t)this.remove(this.children[0])}_updateRadial(e){let t=e.ra_startAngle*Ye.MathUtils.DEG2RAD,r=e.ra_endAngle*Ye.MathUtils.DEG2RAD,i=t-r,n=new Ye.Euler(e.ra_rotation.x*Ye.MathUtils.DEG2RAD,e.ra_rotation.y*Ye.MathUtils.DEG2RAD,e.ra_rotation.z*Ye.MathUtils.DEG2RAD),o;switch(e.ra_axis){case\\\\\\\"x\\\\\\\":o=new Ye.Vector3(1,0,0);break;case\\\\\\\"y\\\\\\\":o=new Ye.Vector3(0,1,0);break;case\\\\\\\"z\\\\\\\":o=new Ye.Vector3(0,0,1);break}for(let[l,a]of this.children.entries()){a.hiddenMatrix.identity(),a.scale.x=e.ra_scale.x+1,a.scale.y=e.ra_scale.y+1,a.scale.z=e.ra_scale.z+1,a.position.setScalar(0);let c=i/e.count*l-t;switch(e.ra_axis){case\\\\\\\"x\\\\\\\":a.rotation.set(0,c,0);break;case\\\\\\\"y\\\\\\\":a.rotation.set(0,0,c);break;case\\\\\\\"z\\\\\\\":a.rotation.set(c,0,0);break}a.translateOnAxis(o,e.ra_radius),a.position.add(e.ra_position),e.ra_align===!0?(a.rotation.x+=n.x,a.rotation.y+=n.y,a.rotation.z+=n.z):a.rotation.copy(n)}}_updateLinear(e){if(e.type!==\\\\\\\"linear\\\\\\\")throw new Error;let t=new Ye.Euler(e.li_rotation.x*Ye.MathUtils.DEG2RAD,e.li_rotation.y*Ye.MathUtils.DEG2RAD,e.li_rotation.z*Ye.MathUtils.DEG2RAD);for(let[r,i]of this.children.entries())i.hiddenMatrix.identity(),i.scale.x=e.li_scale.x*r+1,i.scale.y=e.li_scale.y*r+1,i.scale.z=e.li_scale.z*r+1,i.rotation.x=t.x*r,i.rotation.y=t.y*r,i.rotation.z=t.z*r,i.position.x=e.li_position.x*r,i.position.y=e.li_position.y*r,i.position.z=e.li_position.z*r}_updateGrid(e){let t=0;if(e.gr_fromCenter===!0){let r={x:e.gr_count.x%2==0?2:1,y:e.gr_count.y%2==0?2:1,z:e.gr_count.z%2==0?2:1},i=new Ye.Vector3(e.gr_size.x*(e.gr_count.x-r.x)*.5,e.gr_size.y*(e.gr_count.y-r.y)*.5,e.gr_size.z*(e.gr_count.z-r.z)*.5);for(let n=0;n<e.gr_count.x;n++)for(let o=0;o<e.gr_count.y;o++)for(let l=0;l<e.gr_count.z;l++){let a=this.children[t++];a.hiddenMatrix.identity(),a.scale.setScalar(1),a.rotation.set(0,0,0),a.position.x=e.gr_size.x*n-i.x,a.position.y=e.gr_size.y*o-i.y,a.position.z=e.gr_size.z*l-i.z}}else for(let r=0;r<e.gr_count.x;r++)for(let i=0;i<e.gr_count.y;i++)for(let n=0;n<e.gr_count.z;n++){let o=this.children[t++];o.hiddenMatrix.identity(),o.scale.setScalar(1),o.rotation.set(0,0,0),o.position.x=e.gr_size.x*r,o.position.y=-e.gr_size.y*i,o.position.z=-e.gr_size.z*n}}toJSON(){return{type:\\\\\\\"Cloner\\\\\\\",parameters:{type:this.parameters.type,hideBase:this.parameters.hideBase,count:this.parameters.count,ra_radius:this.parameters.ra_radius,ra_startAngle:this.parameters.ra_startAngle,ra_endAngle:this.parameters.ra_endAngle,ra_align:this.parameters.ra_align,ra_axis:this.parameters.ra_axis,ra_scale:this.parameters.ra_scale.toArray(),ra_rotation:this.parameters.ra_rotation.toArray(),ra_position:this.parameters.ra_position.toArray(),li_scale:this.parameters.li_scale.toArray(),li_rotation:this.parameters.li_rotation.toArray(),li_position:this.parameters.li_position.toArray(),gr_count:this.parameters.gr_count.toArray(),gr_size:this.parameters.gr_size.toArray(),gr_fromCenter:this.parameters.gr_fromCenter}}}fromJSON(e){let t=e.parameters;return this.parameters.type=t.type,this.parameters.hideBase=t.hideBase,this.parameters.count=t.count,this.parameters.ra_radius=t.ra_radius,this.parameters.ra_startAngle=t.ra_startAngle,this.parameters.ra_endAngle=t.ra_endAngle,this.parameters.ra_align=t.ra_align,this.parameters.ra_axis=t.ra_axis,this.parameters.ra_scale.fromArray(t.ra_scale),this.parameters.ra_rotation.fromArray(t.ra_rotation),this.parameters.ra_position.fromArray(t.ra_position),this.parameters.li_scale.fromArray(t.li_scale),this.parameters.li_rotation.fromArray(t.li_rotation),this.parameters.li_position.fromArray(t.li_position),this.parameters.gr_count.fromArray(t.gr_count),this.parameters.gr_size.fromArray(t.gr_size),this.parameters.gr_fromCenter=t.gr_fromCenter,this.update(),this.setHideBase(this.parameters.hideBase),this}};var Ot=class extends pr(Lh.Mesh){constructor(e,t){super(e,t);this.isAbstractMesh=!0;Array.isArray(t)&&(this.selectedMaterial=0)}get cloner(){return this._cloner}set cloner(e){this._cloner&&this.remove(this._cloner),e&&this.add(e),this._cloner=e}getSelectedMaterial(e){return Array.isArray(this.material)?(this.selectedMaterial===void 0&&(this.selectedMaterial=e??0),this.material[e??this.selectedMaterial]):this.material}setSelectedMaterial(e,t){Array.isArray(this.material)?(this.selectedMaterial===void 0&&(this.selectedMaterial=t??0),t=t??this.selectedMaterial,this.material[t].dispose(),this.material[t]=e):(this.material.dispose(),this.material=e)}updateGeometry(e){let t=this.geometry,r=Xn[t.userData.type],i=this.objectType===\\\\\\\"NonParametric\\\\\\\"?Object.assign({},t.userData,{geometry:t}):t.userData,n=r.build(r.normalizeInputs(e,i)),o=t.uuid;this.geometry.dispose(),this.geometry=n,this.geometry.uuid=o,this.geometry.computeBoundingSphere()}resizeGeometry(e,t,r){Kn.resizeGeometry(this.geometry,{width:e,height:t,depth:r})}shallowClone(e){return new this.constructor(this.geometry,this.material).shallowCopy(this,e)}clone(e){let t=this.objectType===\\\\\\\"NonParametric\\\\\\\"?Object.assign({},this.geometry.userData,{geometry:this.geometry.clone()}):this.geometry.userData,r=qn(t),i=Array.isArray(this.material)?this.material.map(n=>n.clone()):this.material.clone();return new this.constructor(r,i).copy(this,e)}copy(e,t=!0){return super.copy(e,t),e.cloner&&(this.cloner=new ha(e,e.cloner.parameters),this.add(this.cloner)),this}toJSON(e){let t=super.toJSON(e),r=t.object;if(this.cloner!==void 0&&(r.cloner=this.cloner.toJSON()),r.geometry=Mh(e.geometries,this.geometry,this.material),Array.isArray(this.material)){let i=[];for(let n=0,o=this.material.length;n<o;n++)i.push(ro(e.materials,this.material[n],e));r.material=i}else r.material=ro(e.materials,this.material,e);return t}fromJSON(e){return super.fromJSON(e),e.selectedMaterial!==void 0&&(this.selectedMaterial=e.selectedMaterial),e.cloner!==void 0&&(this.cloner=new ha(this).fromJSON(e.cloner)),this}};var tt=F(z());var Dh=F(z()),Wr=s=>{var e;return e=class extends s{constructor(){super(...arguments);this.isObjectHelper=!0}},e.geometryHelper=new Dh.BoxBufferGeometry(30,30,30),e};var Cr=F(z());var mr=F(z()),Nc=new mr.Ray,Tc=new mr.Sphere,Bh=new mr.Matrix4,Jr=(s,e,t,r)=>{let i=e,n=s.matrixWorld;if(i.boundingSphere===null&&i.computeBoundingSphere(),Tc.copy(i.boundingSphere),Tc.applyMatrix4(n),t.ray.intersectsSphere(Tc)===!1||(Bh.copy(n).invert(),Nc.copy(t.ray).applyMatrix4(Bh),i.boundingBox!==null&&Nc.intersectsBox(i.boundingBox)===!1))return;let o,l,a,c,u=i.index,d=i.attributes.position,h=i.drawRange,p,f,m=Math.max(0,h.start),g=Math.min(u.count,h.start+h.count);for(p=m,f=g;p<f;p+=3)if(l=u.getX(p),a=u.getX(p+1),c=u.getX(p+2),o=y(s,t,Nc,d,l,a,c),o){o.faceIndex=Math.floor(p/3),r.push(o);return}function y(x,v,b,w,N,S,A){let I=new mr.Vector3,U=new mr.Vector3,E=new mr.Vector3,j=new mr.Vector3,R=new mr.Vector3;if(I.fromBufferAttribute(w,N),U.fromBufferAttribute(w,S),E.fromBufferAttribute(w,A),b.intersectTriangle(I,U,E,!1,j)===null)return null;R.copy(j),R.applyMatrix4(x.matrixWorld);let W=v.ray.origin.distanceTo(R);return W<v.near||W>v.far?null:{faceIndex:1,distance:W,point:R.clone(),object:x}}};var io=class extends Wr(Cr.CameraHelper){constructor(e){super(e);this.object=e;let t=new Cr.SphereGeometry(15,4,2),r=new Cr.MeshBasicMaterial({wireframe:!0,fog:!1,toneMapped:!1}),i=new Cr.Mesh(t,r);this.add(i);let n=new Cr.SphereGeometry(15,4,2),o=new Cr.MeshBasicMaterial({wireframe:!0,fog:!1,toneMapped:!1,depthTest:!1,depthWrite:!1}),l=new Cr.Mesh(n,o);this.add(l),this.targetHelper=l,this.object=e,this.name=`CombinedCameraHelper: ${e.uuid}`}updateMatrixWorld(e){super.updateMatrixWorld(e),this.updateTarget()}updateTarget(){let e=this.object.getTarget();this.updateWorldMatrix(!0,!1),this.worldToLocal(e),this.targetHelper.position.copy(e),this.targetHelper.updateWorldMatrix(!1,!0)}raycast(e,t){Jr(this.object,io.geometryHelper,e,t)}};var _h=F(z());var so=class extends Wr(_h.DirectionalLightHelper){constructor(e,t=15,r=10066329){super(e,t,r);this.object=e;this.name=`DirectionalLightHelper: ${e.uuid}`}raycast(e,t){Jr(this.object,so.geometryHelper,e,t)}};var Oh=F(z());var no=class extends Wr(Oh.AxesHelper){constructor(e,t=15){super(t);this.object=e;this.object.updateMatrixWorld(),this.name=`EmptyObjectHelper: ${e.uuid}`,this.matrix=e.matrixWorld,this.matrixAutoUpdate=!1}raycast(e,t){Jr(this.object,no.geometryHelper,e,t)}update(){}};var Ph=F(z());var oo=class extends Wr(Ph.PointLightHelper){constructor(e,t=15,r=6710886){super(e,t,r);this.object=e;this.name=`PointLightHelper: ${e.uuid}`}raycast(e,t){Jr(this.object,oo.geometryHelper,e,t)}};var fa=F(z());var ma=class extends Wr(fa.SpotLightHelper){constructor(e,t=6710886){super(e,t);this.object=e;this.name=`SpotLightHelper: ${e.uuid}`}raycast(e,t){Jr(this.object,ma.geometryHelper,e,t)}update(){if(this.object!==void 0){let e=ma._vector,t=this.object.distance?this.object.distance:1e3,r=t*Math.tan(this.object.angle);this.cone.scale.set(r,r,t),e.setFromMatrixPosition(this.object.target.matrixWorld),this.cone.lookAt(e);let i=this.color!==void 0?this.color:this.light.color;if(this.cone.material instanceof Array)for(let n=0,o=this.cone.material.length;n<o;n++)this.cone.material[n].color.set(i);else this.cone.material.color.set(i)}}},pa=ma;pa._vector=new fa.Vector3;var Qr=F(z());var xv=new Qr.Vector3(0,0,1),Rh=new Qr.Vector3,Fh=new Qr.Vector3,Gh=new Qr.Matrix3,Ni=class extends Ot{constructor(e=Mt.create({}),t=new Qt({side:Qr.DoubleSide})){super(e,t);this.recursiveSelection=!1;this.objectType=\\\\\\\"VectorObject\\\\\\\";this.eventDispatcher=new Qr.EventDispatcher;this._onShapeUpdate=()=>{if(this.geometry instanceof Vn){if(this.geometry.updateFromShape()){let e=this.geometry.drawCount,t=this.geometry.userData;this.updateGeometry(Object.assign(this.geometry.userData,{parameters:Object.assign(t.parameters,{surfaceMaxCount:e+1e3})}))}}else this.updateGeometry({});this.computeSingleBoundingBox(),this.geometry.computeBoundingSphere(),this.geometry.computeBoundingBox()};this.castShadow=!0,this.receiveShadow=!0,this.forceComputeSize=!0,this.shape=e.userData.shape,this.shape.eventDispatcher?.addEventListener(\\\\\\\"update\\\\\\\",this._onShapeUpdate)}toJSON(e){let t=super.toJSON(e);return t.object.objectType=\\\\\\\"VectorObject\\\\\\\",t}setHelperVisibility(){}updateGeometry(e){if(super.updateGeometry(e),\\\\\\\"userData\\\\\\\"in this.geometry){let t=this.geometry.userData.parameters;this.eventDispatcher.dispatchEvent({type:\\\\\\\"geometryUpdate\\\\\\\",parameters:t})}}setShape(e){this.shape&&this.shape.eventDispatcher?.removeEventListener(\\\\\\\"update\\\\\\\",this._onShapeUpdate),this.shape=e,this.shape.eventDispatcher?.addEventListener(\\\\\\\"update\\\\\\\",this._onShapeUpdate)}updateWorldMatrix(e,t){super.updateWorldMatrix(e,t),Gh.getNormalMatrix(this.matrixWorld),Rh.copy(xv).applyMatrix3(Gh).normalize(),Fh.setFromMatrixPosition(this.matrixWorld),this.shape.plane.setFromNormalAndCoplanarPoint(Rh,Fh)}clone(e){let t=this.shape.clone(),r=this.material.clone(),i=this.geometry.userData,n=Mt.create(Object.assign({},i,{shape:t})),o=new Ni(n,r).copy(this,e);return o.shape=t,t.update(),o}raycast(e,t){Ot.prototype.raycast.call(this,e,t)}};var Yr=(s,e)=>class extends s{constructor(){super(...arguments);this.objectHelper=new e(this);this.enableHelper=!1}set visibility(r){this.visible=r,this.setHelperVisibility(r),this.setHelperChildrenVisibility(r)}get visibility(){return this.visible}get geometryHelper(){return e.geometryHelper}setHelperVisibility(r){this.objectHelper.visible=r}setHelperChildrenVisibility(r){for(let i of this.children)Vt(i)&&i.traverseEntity(n=>{Sc(n)&&n.visible&&(n.objectHelper.visible=r)})}raycast(r,i){this.objectHelper.raycast(r,i)}copy(r,i=!0){return super.copy(r,i),r.enableHelper!==void 0&&(this.enableHelper=r.enableHelper),r.objectHelper!==void 0&&(this.objectHelper.visible=r.objectHelper.visible),this}toJSON(r){let i=super.toJSON(r),n=i.object;return n.enableHelper=this.enableHelper,i}fromJSON(r){return super.fromJSON(r),r.enableHelper!==void 0&&(this.enableHelper=!0),this}};var Hs=new tt.Vector3,Cc=new tt.Vector3,Mc=class extends Yr(pr(tt.Camera),io){constructor(e=100,t=100,r=45,i=50,n=5e4){super();this.objectType=\\\\\\\"CombinedCamera\\\\\\\";this._cameraType=\\\\\\\"OrthographicCamera\\\\\\\";this.targetOffset=Mc.DefaultTargetOffset;this.isUpVectorFlipped=!1;this.angleOffsetFromUp=0;this.width=window.innerWidth,this.height=window.innerHeight,this.orthoCamera=new tt.OrthographicCamera(e*-.5,e*.5,t*.5,t*-.5,i,n),this.perspCamera=new tt.PerspectiveCamera(r,e/t,i,n),this.left=this.orthoCamera.left,this.right=this.orthoCamera.right,this.top=this.orthoCamera.top,this.bottom=this.orthoCamera.bottom,this.far=this.orthoCamera.far,this.view=this.orthoCamera.view,this.aspect=this.perspCamera.aspect,this.fov=this.perspCamera.fov,this.focus=this.perspCamera.focus,this.filmGauge=this.perspCamera.filmGauge,this.filmOffset=this.perspCamera.filmOffset,this.toOrthographic(!0)}setDefault(){this.name=\\\\\\\"Default Camera\\\\\\\",this.isEntity=!1,this.enableHelper=!1,this.objectHelper.visible=!1}get isPerspectiveCamera(){return this.cameraType===\\\\\\\"PerspectiveCamera\\\\\\\"}get isOrthographicCamera(){return!this.isPerspectiveCamera}get cameraType(){return this._cameraType}set cameraType(e){e===\\\\\\\"PerspectiveCamera\\\\\\\"?this.toPerspective():e===\\\\\\\"OrthographicCamera\\\\\\\"&&this.toOrthographic()}get near(){return this._cameraType===\\\\\\\"PerspectiveCamera\\\\\\\"?this.perspCamera.near:this.orthoCamera.near}set near(e){this._cameraType===\\\\\\\"PerspectiveCamera\\\\\\\"?this.perspCamera.near=e:this.orthoCamera.near=e}get zoom(){return this._cameraType===\\\\\\\"PerspectiveCamera\\\\\\\"?this.perspCamera.zoom:this.orthoCamera.zoom}set zoom(e){e>=0&&(this._cameraType===\\\\\\\"PerspectiveCamera\\\\\\\"?this.perspCamera.zoom=e:this.orthoCamera.zoom=e)}lookAt(e){super.lookAt(e),this.getWorldPosition(Hs),this.targetOffset=Hs.distanceTo(e)}getTarget(e=new tt.Vector3){return this.getWorldDirection(Cc),this.getWorldPosition(Hs),Cc.multiplyScalar(this.targetOffset),e.copy(Hs).add(Cc),e}getDistanceToTarget(){let e=this.getTarget();return this.getWorldPosition(Hs),Hs.distanceTo(e)}updateUp(){let e=this.getWorldQuaternion(new tt.Quaternion),t=new tt.Vector3(0,0,1).applyQuaternion(e),r=new tt.Vector3().copy(tt.Object3D.DefaultUp).applyQuaternion(e),i=new tt.Vector3().copy(tt.Object3D.DefaultUp).projectOnPlane(t),n=new tt.Vector3().crossVectors(i,r).dot(t)>=0?1:-1;this.angleOffsetFromUp=i.angleTo(r)*n}getViewFrontToObject(e){let t=e.getWorldPosition(new tt.Vector3),i=e.getWorldDirection(new tt.Vector3).multiplyScalar(this.targetOffset);return{position:t.clone().add(i),target:t}}getViewToObject(e){let t=e.getWorldPosition(new tt.Vector3),i=this.getWorldDirection(new tt.Vector3).multiplyScalar(this.targetOffset);return{position:t.clone().sub(i),target:t}}setViewplaneSize(e,t){this.left=-e*.5,this.right=e*.5,this.top=t*.5,this.bottom=-t*.5,this.aspect=e/t,this.updateProjectionMatrix()}toOrthographic(e){this.orthoCamera.left=this.left,this.orthoCamera.right=this.right,this.orthoCamera.top=this.top,this.orthoCamera.bottom=this.bottom,this.orthoCamera.view=this.view,this.orthoCamera.far=this.far,this.orthoCamera.updateProjectionMatrix(),this.projectionMatrix=this.orthoCamera.projectionMatrix,this.projectionMatrixInverse=this.orthoCamera.projectionMatrixInverse,this._cameraType=\\\\\\\"OrthographicCamera\\\\\\\",(this.enableHelper===!0||e===!0)&&this.objectHelper.update()}toPerspective(e){this.perspCamera.aspect=this.aspect,this.perspCamera.fov=this.fov,this.perspCamera.view=this.view,this.perspCamera.far=this.far,this.perspCamera.updateProjectionMatrix(),this.projectionMatrix=this.perspCamera.projectionMatrix,this.projectionMatrixInverse=this.perspCamera.projectionMatrixInverse,this._cameraType=\\\\\\\"PerspectiveCamera\\\\\\\",(this.enableHelper===!0||e===!0)&&this.objectHelper.update()}setFocalLength(e){this.perspCamera.setFocalLength(e),this.toPerspective()}getFocalLength(){return this.perspCamera.getFocalLength()}getEffectiveFOV(){return this.perspCamera.getEffectiveFOV()}getFilmWidth(){return this.perspCamera.getFilmWidth()}getFilmHeight(){return this.perspCamera.getFilmHeight()}setViewOffset(e,t,r,i,n,o){this._cameraType===\\\\\\\"PerspectiveCamera\\\\\\\"?this.perspCamera.setViewOffset(e,t,r,i,n,o):this.orthoCamera.setViewOffset(e,t,r,i,n,o)}clearViewOffset(){this._cameraType===\\\\\\\"PerspectiveCamera\\\\\\\"?(this.perspCamera.clearViewOffset(),this.toPerspective()):(this.orthoCamera.clearViewOffset(),this.toOrthographic())}updateProjectionMatrix(e){this._cameraType===\\\\\\\"PerspectiveCamera\\\\\\\"?this.toPerspective(e):this._cameraType===\\\\\\\"OrthographicCamera\\\\\\\"&&this.toOrthographic(e)}updateMatrixWorld(e){super.updateMatrixWorld(e),this.matrixWorldInverse.copy(this.matrixWorld).invert()}updateWorldMatrix(e,t){super.updateWorldMatrix(e,t),this.matrixWorldInverse.copy(this.matrixWorld).invert()}copy(e,t){return super.copy(e,t),this.orthoCamera.copy(e.orthoCamera),this.perspCamera.copy(e.perspCamera),this.left=e.left,this.right=e.right,this.top=e.top,this.bottom=e.bottom,this.far=e.far,this.view=e.view===null?null:Object.assign({},e.view),this._cameraType=e._cameraType,this.aspect=e.aspect,this.fov=e.fov,this.focus=e.focus,this.filmGauge=e.filmGauge,this.filmOffset=e.filmOffset,this.targetOffset=e.targetOffset,this.updateProjectionMatrix(),this}fromCameraRender(e){let t={near:this.orthoCamera.near,far:this.orthoCamera.far},r={near:this.perspCamera.near,far:this.perspCamera.far};return this.copy(e),this.name=\\\\\\\"\\\\\\\",this.isEntity=!0,this.enableHelper=!0,this.objectHelper.visible=!0,this.orthoCamera.near=t.near,this.orthoCamera.far=t.far,this.perspCamera.near=r.near,this.perspCamera.far=r.far,this.updateProjectionMatrix(),this}toJSON(e){let t=super.toJSON(e),r=t.object;return r.objectType=\\\\\\\"CombinedCamera\\\\\\\",r.cameraType=this.cameraType,r.targetOffset=this.targetOffset,r.isUpVectorFlipped=this.isUpVectorFlipped,r.angleOffsetFromUp=this.angleOffsetFromUp,r.left=this.left,r.right=this.right,r.top=this.top,r.bottom=this.bottom,this.view!==null&&(r.view=Object.assign({},this.view)),r.zoomOrtho=this.orthoCamera.zoom,r.nearOrtho=this.orthoCamera.near,r.far=this.far,r.aspect=this.aspect,r.fov=this.fov,r.focus=this.focus,r.filmGauge=this.filmGauge,r.filmOffset=this.filmOffset,r.zoomPersp=this.perspCamera.zoom,r.nearPersp=this.perspCamera.near,t}fromJSON(e){if(super.fromJSON(e),this.cameraType=e.cameraType,e.targetOffset!==void 0&&(this.targetOffset=e.targetOffset),e.orbitControlsTarget!==void 0){let t=this.getWorldPosition(new tt.Vector3),r=new tt.Vector3().fromArray(e.orbitControlsTarget);this.targetOffset=r.distanceTo(t)}else e.targetOffset!==void 0&&(this.targetOffset=e.targetOffset);return this.isUpVectorFlipped=e.isUpVectorFlipped??!1,this.angleOffsetFromUp=e.angleOffsetFromUp??0,e.left!==void 0&&(this.left=e.left),e.right!==void 0&&(this.right=e.right),e.top!==void 0&&(this.top=e.top),e.bottom!==void 0&&(this.bottom=e.bottom),e.view!==void 0&&(this.view=Object.assign({},e.view)),e.zoomOrtho!==void 0&&(this.orthoCamera.zoom=e.zoomOrtho),e.nearOrtho!==void 0&&(this.orthoCamera.near=e.nearOrtho),e.far!==void 0&&(this.far=e.far),e.aspect!==void 0&&(this.aspect=e.aspect),e.fov!==void 0&&(this.fov=e.fov),e.focus!==void 0&&(this.focus=e.focus),e.filmGauge!==void 0&&(this.filmGauge=e.filmGauge),e.filmOffset!==void 0&&(this.filmOffset=e.filmOffset),e.zoomPersp!==void 0&&(this.perspCamera.zoom=e.zoomPersp),e.nearPersp!==void 0&&(this.perspCamera.near=e.nearPersp),this.updateProjectionMatrix(),this}},Kr=Mc;Kr.DefaultTargetOffset=1e3;var zh=F(z());var gr=class extends Yr(pr(zh.Group),no){constructor(){super(...arguments);this.objectType=\\\\\\\"EmptyObject\\\\\\\"}toJSON(e){let t=super.toJSON(e);return t.object.objectType=\\\\\\\"EmptyObject\\\\\\\",t}};var $i=F(z());var ga=class extends Yr(pr($i.DirectionalLight),so){constructor(...e){super(...e);this.objectType=\\\\\\\"LightDirectional\\\\\\\";this._gizmos={};this.castShadow=!0,this.shadow.mapSize.width=1024,this.shadow.mapSize.height=1024;let r=this.shadow.camera;r.top=1250,r.bottom=-1250,r.right=1250,r.left=-1250,r.near=1,r.far=2500;let i=new $i.CameraHelper(this.shadow.camera);i.visible=!1,this._gizmos.shadowmap=i,this.update()}get gizmos(){return this._gizmos}showGizmos(){for(let e in this._gizmos){let t=this._gizmos[e];t instanceof $i.CameraHelper&&(t.visible=!0)}}hideGizmos(){for(let e in this._gizmos){let t=this._gizmos[e];t instanceof $i.CameraHelper&&(t.visible=!1)}}update(){this.shadow.camera.updateProjectionMatrix();for(let e in this._gizmos){let t=this._gizmos[e];t instanceof $i.CameraHelper&&t.update()}}updateMatrixWorld(e){super.updateMatrixWorld(e),this.enableHelper===!0&&this.objectHelper.visible===!0&&this.objectHelper.update()}copy(e,t=!0){return super.copy(e,t),this.color.copy(e.color),this.intensity=e.intensity,this.target=e.target.clone(),this.shadow=e.shadow.clone(),this}toJSON(e){let t=super.toJSON(e),r=t.object;return r.objectType=\\\\\\\"LightDirectional\\\\\\\",r.color=this.color.getHex(),r.intensity=this.intensity,r.shadow=this.shadow.toJSON(),t}fromJSON(e){super.fromJSON(e),this.color.set(e.color),this.intensity=e.intensity,this.shadow.normalBias=e.shadow.normalBias??0,this.shadow.radius=e.shadow.radius,this.shadow.mapSize.fromArray(e.shadow.mapSize);let t=this.shadow.camera,r=e.shadow.camera;return t.near=r.near,t.far=r.far,t.zoom=r.zoom,t.left=r.left,t.right=r.right,t.top=r.top,t.bottom=r.bottom,r.view!==void 0&&(t.view=Object.assign({},r.view)),this}};var Pt=F(z());var ya=class extends Yr(pr(Pt.PointLight),oo){constructor(...e){super(...e);this.objectType=\\\\\\\"LightPoint\\\\\\\";this._gizmos={};this.castShadow=!0,this.shadow.mapSize.width=1024,this.shadow.mapSize.height=1024;let r=this.shadow.camera;r.fov=90,r.aspect=1,r.near=100,r.far=2500;let i=new Pt.Vector3(-r.far+this.position.x,-r.far+this.position.y,-r.far+this.position.z),n=new Pt.Vector3(r.far+this.position.x,r.far+this.position.y,r.far+this.position.z),o=new Pt.Box3(i,n),l=new Pt.Box3Helper(o,new Pt.Color(16755200));l.visible=!1,this._gizmos.shadowmap=l,this.update()}get gizmos(){return this._gizmos}showGizmos(){for(let e in this._gizmos){let t=this._gizmos[e];t instanceof Pt.Box3Helper&&(t.visible=!0)}}hideGizmos(){for(let e in this._gizmos){let t=this._gizmos[e];t instanceof Pt.Box3Helper&&(t.visible=!1)}}update(){if(this.shadow&&(this.shadow.camera.updateProjectionMatrix(),this._gizmos))for(let e in this._gizmos){let t=this._gizmos[e];if(t instanceof Pt.Box3Helper){let r=this.shadow.camera,i=new Pt.Vector3(-r.far+this.position.x,-r.far+this.position.y,-r.far+this.position.z),n=new Pt.Vector3(r.far+this.position.x,r.far+this.position.y,r.far+this.position.z);t.box.set(i,n),t.updateMatrixWorld(!0)}}}updateMatrixWorld(e){super.updateMatrixWorld(e),this.enableHelper===!0&&this.objectHelper.visible===!0&&this.objectHelper.update()}copy(e,t=!0){return super.copy(e,t),this.color.copy(e.color),this.intensity=e.intensity,this.distance=e.distance,this.decay=e.decay,this.shadow=e.shadow.clone(),this}toJSON(e){let t=super.toJSON(e),r=t.object;return r.objectType=\\\\\\\"LightPoint\\\\\\\",r.color=this.color.getHex(),r.intensity=this.intensity,r.distance=this.distance,r.decay=this.decay,r.shadow=this.shadow.toJSON(),t}fromJSON(e){super.fromJSON(e),this.color.set(e.color),this.intensity=e.intensity,this.distance=e.distance,this.decay=e.decay,this.shadow.normalBias=e.shadow.normalBias??0,this.shadow.radius=e.shadow.radius,this.shadow.mapSize.fromArray(e.shadow.mapSize??[512,512]);let t=this.shadow.camera,r=e.shadow.camera;return t.near=r.near,t.far=r.far,t.zoom=r.zoom,t.fov=r.fov,t.focus=r.focus,t.aspect=r.aspect,t.filmGauge=r.filmGauge,t.filmOffset=r.filmOffset,r.view!==void 0&&(t.view=Object.assign({},r.view)),this}};var qt=F(z());var Uh=new qt.Vector3,jh=new qt.Vector3,kh=new qt.Quaternion,va=class extends Yr(pr(qt.SpotLight),pa){constructor(...e){super(...e);this.objectType=\\\\\\\"LightSpot\\\\\\\";this._gizmos={};this.castShadow=!0,this.shadow.mapSize.width=1024,this.shadow.mapSize.height=1024;let r=this.shadow.camera;r.fov=qt.MathUtils.RAD2DEG*2*this.angle,r.aspect=1,r.near=100,r.far=2500;let i=new qt.CameraHelper(this.shadow.camera);i.visible=!1,this._gizmos.shadowmap=i,this.update()}get gizmos(){return this._gizmos}showGizmos(){for(let e in this._gizmos){let t=this._gizmos[e];t instanceof qt.CameraHelper&&(t.visible=!0)}}hideGizmos(){for(let e in this._gizmos){let t=this._gizmos[e];t instanceof qt.CameraHelper&&(t.visible=!1)}}update(){this.shadow.camera.updateProjectionMatrix();for(let e in this._gizmos){let t=this._gizmos[e];t instanceof qt.CameraHelper&&t.update()}}updateMatrixWorld(e){super.updateMatrixWorld(e),jh.setFromMatrixPosition(this.matrixWorld),kh.setFromRotationMatrix(this.matrixWorld),Uh.copy(this.up).applyQuaternion(kh).negate().multiplyScalar(this.distance),this.target.position.copy(jh).add(Uh),this.target.updateMatrixWorld(),this.enableHelper===!0&&this.objectHelper.visible===!0&&this.objectHelper.update()}copy(e,t=!0){return super.copy(e,t),this.color.copy(e.color),this.intensity=e.intensity,this.distance=e.distance,this.angle=e.angle,this.penumbra=e.penumbra,this.decay=e.decay,this.target=e.target.clone(),this.shadow=e.shadow.clone(),this}toJSON(e){let t=super.toJSON(e),r=t.object;return r.objectType=\\\\\\\"LightSpot\\\\\\\",r.color=this.color.getHex(),r.intensity=this.intensity,r.distance=this.distance,r.angle=this.angle,r.decay=this.decay,r.penumbra=this.penumbra,r.shadow=this.shadow.toJSON(),t}fromJSON(e){super.fromJSON(e),this.color.set(e.color),this.intensity=e.intensity,this.distance=e.distance,this.angle=e.angle,this.decay=e.decay,this.penumbra=e.penumbra,this.shadow.normalBias=e.shadow.normalBias??0,this.shadow.radius=e.shadow.radius,this.shadow.mapSize.fromArray(e.shadow.mapSize);let t=this.shadow.camera,r=e.shadow.camera;return t.near=r.near,t.far=r.far,t.zoom=r.zoom,t.fov=r.fov,t.focus=r.focus,t.aspect=r.aspect,t.filmGauge=r.filmGauge,t.filmOffset=r.filmOffset,r.view!==void 0&&(t.view=Object.assign({},r.view)),this}};var Hh=F(z());var Vh=s=>s.tagName===\\\\\\\"VIDEO\\\\\\\",Ws=class{static resize(e,t,r){let i=e/t,n;if(!r.image)return;let 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Qt){super(e,t);this.objectType=\\\\\\\"Mesh2D\\\\\\\";this.castShadow=!0,this.receiveShadow=!0}updateGeometry(e){super.updateGeometry(e),this.material.userData.layers&&Ws.resizeTextureLayers(this.geometry.userData.parameters.width,this.geometry.userData.parameters.height,this.material)}resizeGeometry(e,t){super.resizeGeometry(e,t,0),this.material.userData.layers&&Ws.resizeTextureLayers(this.geometry.userData.parameters.width,this.geometry.userData.parameters.height,this.material)}toJSON(e){let t=super.toJSON(e);return t.object.objectType=\\\\\\\"Mesh2D\\\\\\\",t}clone(){let e=super.clone();return e.updateGeometry({}),e}static fromTexture(e){let t,r;if(e instanceof Hh.VideoTexture){let o=e.image;t=o.videoWidth*.5,r=o.videoHeight*.5}else{let o=e.image;t=o.width*.5,r=o.height*.5}let i=$o.create({parameters:{width:t,height:r}}),n=new Qt;return n.layersList.changeLayer(0,{type:Be.TEXTURE,texture:e}),n.layersList.moveLayer(0,1),n.dispose(),new Js(i,n)}};var Qs=class extends 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G.FileLoader(this.manager);a.setPath(this.path),a.setResponseType(\\\\\\\"arraybuffer\\\\\\\"),a.setRequestHeader(this.requestHeader),a.setWithCredentials(this.withCredentials),a.load(e,function(c){try{n.parse(c,o,function(u){t(u),n.manager.itemEnd(e)},l)}catch(u){l(u)}},r,l)}setDRACOLoader(e){return this.dracoLoader=e,this}setDDSLoader(){throw new Error('THREE.GLTFLoader: \\\\\\\"MSFT_texture_dds\\\\\\\" no longer supported. 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gf(a,{path:t||this.resourcePath||\\\\\\\"\\\\\\\",crossOrigin:this.crossOrigin,requestHeader:this.requestHeader,manager:this.manager,ktx2Loader:this.ktx2Loader,meshoptDecoder:this.meshoptDecoder});c.fileLoader.setRequestHeader(this.requestHeader);for(let u=0;u<this.pluginCallbacks.length;u++){let d=this.pluginCallbacks[u](c);l[d.name]=d,o[d.name]=!0}if(a.extensionsUsed)for(let u=0;u<a.extensionsUsed.length;++u){let d=a.extensionsUsed[u],h=a.extensionsRequired||[];switch(d){case ze.KHR_MATERIALS_UNLIT:o[d]=new Yh;break;case ze.KHR_MATERIALS_PBR_SPECULAR_GLOSSINESS:o[d]=new cf;break;case ze.KHR_DRACO_MESH_COMPRESSION:o[d]=new af(a,this.dracoLoader);break;case ze.KHR_TEXTURE_TRANSFORM:o[d]=new lf;break;case ze.KHR_MESH_QUANTIZATION:o[d]=new uf;break;default:h.indexOf(d)>=0&&l[d]===void 0&&console.warn('THREE.GLTFLoader: Unknown extension \\\\\\\"'+d+'\\\\\\\".')}}c.setExtensions(o),c.setPlugins(l),c.parse(r,i)}};function Av(){let s={};return{get:function(e){return s[e]},add:function(e,t){s[e]=t},remove:function(e){delete s[e]},removeAll:function(){s={}}}}var ze={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_IOR:\\\\\\\"KHR_materials_ior\\\\\\\",KHR_MATERIALS_PBR_SPECULAR_GLOSSINESS:\\\\\\\"KHR_materials_pbrSpecularGlossiness\\\\\\\",KHR_MATERIALS_SPECULAR:\\\\\\\"KHR_materials_specular\\\\\\\",KHR_MATERIALS_TRANSMISSION:\\\\\\\"KHR_materials_transmission\\\\\\\",KHR_MATERIALS_UNLIT:\\\\\\\"KHR_materials_unlit\\\\\\\",KHR_MATERIALS_VOLUME:\\\\\\\"KHR_materials_volume\\\\\\\",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\\\\\\\"},Qh=class{constructor(e){this.parser=e,this.name=ze.KHR_LIGHTS_PUNCTUAL,this.cache={refs:{},uses:{}}}_markDefs(){let e=this.parser,t=this.parser.json.nodes||[];for(let r=0,i=t.length;r<i;r++){let n=t[r];n.extensions&&n.extensions[this.name]&&n.extensions[this.name].light!==void 0&&e._addNodeRef(this.cache,n.extensions[this.name].light)}}_loadLight(e){let t=this.parser,r=\\\\\\\"light:\\\\\\\"+e,i=t.cache.get(r);if(i)return i;let n=t.json,a=((n.extensions&&n.extensions[this.name]||{}).lights||[])[e],c,u=new G.Color(16777215);a.color!==void 0&&u.fromArray(a.color);let d=a.range!==void 0?a.range:0;switch(a.type){case\\\\\\\"directional\\\\\\\":c=new G.DirectionalLight(u),c.target.position.set(0,0,-1),c.add(c.target);break;case\\\\\\\"point\\\\\\\":c=new G.PointLight(u),c.distance=d;break;case\\\\\\\"spot\\\\\\\":c=new G.SpotLight(u),c.distance=d,a.spot=a.spot||{},a.spot.innerConeAngle=a.spot.innerConeAngle!==void 0?a.spot.innerConeAngle:0,a.spot.outerConeAngle=a.spot.outerConeAngle!==void 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0&&i.push(r.assignTexture(e,\\\\\\\"map\\\\\\\",n.baseColorTexture))}return Promise.all(i)}},Kh=class{constructor(e){this.parser=e,this.name=ze.KHR_MATERIALS_CLEARCOAT}getMaterialType(e){let r=this.parser.json.materials[e];return!r.extensions||!r.extensions[this.name]?null:G.MeshPhysicalMaterial}extendMaterialParams(e,t){let r=this.parser,i=r.json.materials[e];if(!i.extensions||!i.extensions[this.name])return Promise.resolve();let n=[],o=i.extensions[this.name];if(o.clearcoatFactor!==void 0&&(t.clearcoat=o.clearcoatFactor),o.clearcoatTexture!==void 0&&n.push(r.assignTexture(t,\\\\\\\"clearcoatMap\\\\\\\",o.clearcoatTexture)),o.clearcoatRoughnessFactor!==void 0&&(t.clearcoatRoughness=o.clearcoatRoughnessFactor),o.clearcoatRoughnessTexture!==void 0&&n.push(r.assignTexture(t,\\\\\\\"clearcoatRoughnessMap\\\\\\\",o.clearcoatRoughnessTexture)),o.clearcoatNormalTexture!==void 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r.loadTexture(e)})}detectSupport(){return this.isSupported||(this.isSupported=new Promise(function(e){let t=new Image;t.src=\\\\\\\"data:image/webp;base64,UklGRiIAAABXRUJQVlA4IBYAAAAwAQCdASoBAAEADsD+JaQAA3AAAAAA\\\\\\\",t.onload=t.onerror=function(){e(t.height===1)}})),this.isSupported}},rf=class{constructor(e){this.name=ze.EXT_MESHOPT_COMPRESSION,this.parser=e}loadBufferView(e){let t=this.parser.json,r=t.bufferViews[e];if(r.extensions&&r.extensions[this.name]){let i=r.extensions[this.name],n=this.parser.getDependency(\\\\\\\"buffer\\\\\\\",i.buffer),o=this.parser.options.meshoptDecoder;if(!o||!o.supported){if(t.extensionsRequired&&t.extensionsRequired.indexOf(this.name)>=0)throw new Error(\\\\\\\"THREE.GLTFLoader: setMeshoptDecoder must be called before loading compressed files\\\\\\\");return null}return Promise.all([n,o.ready]).then(function(l){let a=i.byteOffset||0,c=i.byteLength||0,u=i.count,d=i.byteStride,h=new ArrayBuffer(u*d),p=new Uint8Array(l[0],a,c);return o.decodeGltfBuffer(new Uint8Array(h),u,d,p,i.mode,i.filter),h})}else return null}},sf=\\\\\\\"glTF\\\\\\\",ao=12,nf={JSON:1313821514,BIN:5130562},of=class{constructor(e){this.name=ze.KHR_BINARY_GLTF,this.content=null,this.body=null;let t=new DataView(e,0,ao);if(this.header={magic:G.LoaderUtils.decodeText(new Uint8Array(e.slice(0,4))),version:t.getUint32(4,!0),length:t.getUint32(8,!0)},this.header.magic!==sf)throw new Error(\\\\\\\"THREE.GLTFLoader: Unsupported glTF-Binary header.\\\\\\\");if(this.header.version<2)throw new Error(\\\\\\\"THREE.GLTFLoader: Legacy binary file detected.\\\\\\\");let r=this.header.length-ao,i=new DataView(e,ao),n=0;for(;n<r;){let o=i.getUint32(n,!0);n+=4;let l=i.getUint32(n,!0);if(n+=4,l===nf.JSON){let a=new Uint8Array(e,ao+n,o);this.content=G.LoaderUtils.decodeText(a)}else if(l===nf.BIN){let a=ao+n;this.body=e.slice(a,a+o)}n+=o}if(this.content===null)throw new Error(\\\\\\\"THREE.GLTFLoader: JSON content not found.\\\\\\\")}},af=class{constructor(e,t){if(!t)throw new Error(\\\\\\\"THREE.GLTFLoader: No DRACOLoader instance provided.\\\\\\\");this.name=ze.KHR_DRACO_MESH_COMPRESSION,this.json=e,this.dracoLoader=t,this.dracoLoader.preload()}decodePrimitive(e,t){let r=this.json,i=this.dracoLoader,n=e.extensions[this.name].bufferView,o=e.extensions[this.name].attributes,l={},a={},c={};for(let u in o){let d=Ic[u]||u.toLowerCase();l[d]=o[u]}for(let u in e.attributes){let d=Ic[u]||u.toLowerCase();if(o[u]!==void 0){let h=r.accessors[e.attributes[u]],p=lo[h.componentType];c[d]=p,a[d]=h.normalized===!0}}return t.getDependency(\\\\\\\"bufferView\\\\\\\",n).then(function(u){return new Promise(function(d){i.decodeDracoFile(u,function(h){for(let p in h.attributes){let f=h.attributes[p],m=a[p];m!==void 0&&(f.normalized=m)}d(h)},l,c)})})}},lf=class{constructor(){this.name=ze.KHR_TEXTURE_TRANSFORM}extendTexture(e,t){return t.texCoord!==void 0&&console.warn('THREE.GLTFLoader: Custom UV sets in \\\\\\\"'+this.name+'\\\\\\\" extension not yet supported.'),t.offset===void 0&&t.rotation===void 0&&t.scale===void 0||(e=e.clone(),t.offset!==void 0&&e.offset.fromArray(t.offset),t.rotation!==void 0&&(e.rotation=t.rotation),t.scale!==void 0&&e.repeat.fromArray(t.scale),e.needsUpdate=!0),e}},ba=class extends G.MeshStandardMaterial{constructor(e){super();this.isGLTFSpecularGlossinessMaterial=!0;let t=[\\\\\\\"#ifdef USE_SPECULARMAP\\\\\\\",\\\\\\\"\\tuniform sampler2D specularMap;\\\\\\\",\\\\\\\"#endif\\\\\\\"].join(`\\n`),r=[\\\\\\\"#ifdef USE_GLOSSINESSMAP\\\\\\\",\\\\\\\"\\tuniform sampler2D glossinessMap;\\\\\\\",\\\\\\\"#endif\\\\\\\"].join(`\\n`),i=[\\\\\\\"vec3 specularFactor = specular;\\\\\\\",\\\\\\\"#ifdef USE_SPECULARMAP\\\\\\\",\\\\\\\"\\tvec4 texelSpecular = texture2D( specularMap, vUv );\\\\\\\",\\\\\\\"\\ttexelSpecular = sRGBToLinear( texelSpecular );\\\\\\\",\\\\\\\"\\t// reads channel RGB, compatible with a glTF Specular-Glossiness (RGBA) texture\\\\\\\",\\\\\\\"\\tspecularFactor *= texelSpecular.rgb;\\\\\\\",\\\\\\\"#endif\\\\\\\"].join(`\\n`),n=[\\\\\\\"float glossinessFactor = glossiness;\\\\\\\",\\\\\\\"#ifdef USE_GLOSSINESSMAP\\\\\\\",\\\\\\\"\\tvec4 texelGlossiness = texture2D( glossinessMap, vUv );\\\\\\\",\\\\\\\"\\t// reads channel A, compatible with a glTF Specular-Glossiness (RGBA) texture\\\\\\\",\\\\\\\"\\tglossinessFactor *= texelGlossiness.a;\\\\\\\",\\\\\\\"#endif\\\\\\\"].join(`\\n`),o=[\\\\\\\"PhysicalMaterial material;\\\\\\\",\\\\\\\"material.diffuseColor = diffuseColor.rgb * ( 1. - max( specularFactor.r, max( specularFactor.g, specularFactor.b ) ) );\\\\\\\",\\\\\\\"vec3 dxy = max( abs( dFdx( geometryNormal ) ), abs( dFdy( geometryNormal ) ) );\\\\\\\",\\\\\\\"float geometryRoughness = max( max( dxy.x, dxy.y ), dxy.z );\\\\\\\",\\\\\\\"material.specularRoughness = max( 1.0 - glossinessFactor, 0.0525 ); // 0.0525 corresponds to the base mip of a 256 cubemap.\\\\\\\",\\\\\\\"material.specularRoughness += geometryRoughness;\\\\\\\",\\\\\\\"material.specularRoughness = min( material.specularRoughness, 1.0 );\\\\\\\",\\\\\\\"material.specularColor = specularFactor;\\\\\\\"].join(`\\n`),l={specular:{value:new G.Color().setHex(16777215)},glossiness:{value:1},specularMap:{value:null},glossinessMap:{value:null}};this._extraUniforms=l,this.onBeforeCompile=function(a){for(let c in l)a.uniforms[c]=l[c];a.fragmentShader=a.fragmentShader.replace(\\\\\\\"uniform float roughness;\\\\\\\",\\\\\\\"uniform vec3 specular;\\\\\\\").replace(\\\\\\\"uniform float metalness;\\\\\\\",\\\\\\\"uniform float glossiness;\\\\\\\").replace(\\\\\\\"#include <roughnessmap_pars_fragment>\\\\\\\",t).replace(\\\\\\\"#include <metalnessmap_pars_fragment>\\\\\\\",r).replace(\\\\\\\"#include <roughnessmap_fragment>\\\\\\\",i).replace(\\\\\\\"#include <metalnessmap_fragment>\\\\\\\",n).replace(\\\\\\\"#include <lights_physical_fragment>\\\\\\\",o)},Object.defineProperties(this,{specular:{get:function(){return l.specular.value},set:function(a){l.specular.value=a}},specularMap:{get:function(){return l.specularMap.value},set:function(a){l.specularMap.value=a,a?this.defines.USE_SPECULARMAP=\\\\\\\"\\\\\\\":delete this.defines.USE_SPECULARMAP}},glossiness:{get:function(){return l.glossiness.value},set:function(a){l.glossiness.value=a}},glossinessMap:{get:function(){return l.glossinessMap.value},set:function(a){l.glossinessMap.value=a,a?(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(e)}copy(e){return super.copy(e),this.specularMap=e.specularMap,this.specular.copy(e.specular),this.glossinessMap=e.glossinessMap,this.glossiness=e.glossiness,delete this.metalness,delete this.roughness,delete this.metalnessMap,delete this.roughnessMap,this}},cf=class{constructor(){this.name=ze.KHR_MATERIALS_PBR_SPECULAR_GLOSSINESS,this.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(){return ba}extendParams(e,t,r){let i=t.extensions[this.name];e.color=new G.Color(1,1,1),e.opacity=1;let n=[];if(Array.isArray(i.diffuseFactor)){let o=i.diffuseFactor;e.color.fromArray(o),e.opacity=o[3]}if(i.diffuseTexture!==void 0&&n.push(r.assignTexture(e,\\\\\\\"map\\\\\\\",i.diffuseTexture)),e.emissive=new G.Color(0,0,0),e.glossiness=i.glossinessFactor!==void 0?i.glossinessFactor:1,e.specular=new G.Color(1,1,1),Array.isArray(i.specularFactor)&&e.specular.fromArray(i.specularFactor),i.specularGlossinessTexture!==void 0){let o=i.specularGlossinessTexture;n.push(r.assignTexture(e,\\\\\\\"glossinessMap\\\\\\\",o)),n.push(r.assignTexture(e,\\\\\\\"specularMap\\\\\\\",o))}return Promise.all(n)}createMaterial(e){let t=new ba(e);return t.fog=!0,t.color=e.color,t.map=e.map===void 0?null:e.map,t.lightMap=null,t.lightMapIntensity=1,t.aoMap=e.aoMap===void 0?null:e.aoMap,t.aoMapIntensity=1,t.emissive=e.emissive,t.emissiveIntensity=1,t.emissiveMap=e.emissiveMap===void 0?null:e.emissiveMap,t.bumpMap=e.bumpMap===void 0?null:e.bumpMap,t.bumpScale=1,t.normalMap=e.normalMap===void 0?null:e.normalMap,t.normalMapType=G.TangentSpaceNormalMap,e.normalScale&&(t.normalScale=e.normalScale),t.displacementMap=null,t.displacementScale=1,t.displacementBias=0,t.specularMap=e.specularMap===void 0?null:e.specularMap,t.specular=e.specular,t.glossinessMap=e.glossinessMap===void 0?null:e.glossinessMap,t.glossiness=e.glossiness,t.alphaMap=null,t.envMap=e.envMap===void 0?null:e.envMap,t.envMapIntensity=1,t.refractionRatio=.98,t}},uf=class{constructor(){this.name=ze.KHR_MESH_QUANTIZATION}},es=class extends G.Interpolant{constructor(e,t,r,i){super(e,t,r,i)}copySampleValue_(e){let t=this.resultBuffer,r=this.sampleValues,i=this.valueSize,n=e*i*3+i;for(let o=0;o!==i;o++)t[o]=r[n+o];return t}};es.prototype.beforeStart_=es.prototype.copySampleValue_;es.prototype.afterEnd_=es.prototype.copySampleValue_;es.prototype.interpolate_=function(s,e,t,r){let i=this.resultBuffer,n=this.sampleValues,o=this.valueSize,l=o*2,a=o*3,c=r-e,u=(t-e)/c,d=u*u,h=d*u,p=s*a,f=p-a,m=-2*h+3*d,g=h-d,y=1-m,x=g-d+u;for(let v=0;v!==o;v++){let b=n[f+v+o],w=n[f+v+l]*c,N=n[p+v+o],S=n[p+v]*c;i[v]=y*b+x*w+m*N+g*S}return i};var ai={FLOAT:5126,FLOAT_MAT3:35675,FLOAT_MAT4:35676,FLOAT_VEC2:35664,FLOAT_VEC3:35665,FLOAT_VEC4:35666,LINEAR:9729,REPEAT:10497,SAMPLER_2D:35678,POINTS:0,LINES:1,LINE_LOOP:2,LINE_STRIP:3,TRIANGLES:4,TRIANGLE_STRIP:5,TRIANGLE_FAN:6,UNSIGNED_BYTE:5121,UNSIGNED_SHORT:5123},lo={5120:Int8Array,5121:Uint8Array,5122:Int16Array,5123:Uint16Array,5125:Uint32Array,5126:Float32Array},df={9728:G.NearestFilter,9729:G.LinearFilter,9984:G.NearestMipmapNearestFilter,9985:G.LinearMipmapNearestFilter,9986:G.NearestMipmapLinearFilter,9987:G.LinearMipmapLinearFilter},hf={33071:G.ClampToEdgeWrapping,33648:G.MirroredRepeatWrapping,10497:G.RepeatWrapping},ff={SCALAR:1,VEC2:2,VEC3:3,VEC4:4,MAT2:4,MAT3:9,MAT4:16},Ic={POSITION:\\\\\\\"position\\\\\\\",NORMAL:\\\\\\\"normal\\\\\\\",TANGENT:\\\\\\\"tangent\\\\\\\",TEXCOORD_0:\\\\\\\"uv\\\\\\\",TEXCOORD_1:\\\\\\\"uv2\\\\\\\",COLOR_0:\\\\\\\"color\\\\\\\",WEIGHTS_0:\\\\\\\"skinWeight\\\\\\\",JOINTS_0:\\\\\\\"skinIndex\\\\\\\"},Ci={scale:\\\\\\\"scale\\\\\\\",translation:\\\\\\\"position\\\\\\\",rotation:\\\\\\\"quaternion\\\\\\\",weights:\\\\\\\"morphTargetInfluences\\\\\\\"},wv={CUBICSPLINE:void 0,LINEAR:G.InterpolateLinear,STEP:G.InterpolateDiscrete},Lc={OPAQUE:\\\\\\\"OPAQUE\\\\\\\",MASK:\\\\\\\"MASK\\\\\\\",BLEND:\\\\\\\"BLEND\\\\\\\"};function pf(s,e){return typeof s!=\\\\\\\"string\\\\\\\"||s===\\\\\\\"\\\\\\\"?\\\\\\\"\\\\\\\":(/^https?:\\\\/\\\\//i.test(e)&&/^\\\\//.test(s)&&(e=e.replace(/(^https?:\\\\/\\\\/[^\\\\/]+).*/i,\\\\\\\"$1\\\\\\\")),/^(https?:)?\\\\/\\\\//i.test(s)||/^data:.*,.*$/i.test(s)||/^blob:.*$/i.test(s)?s:e+s)}function Sv(s){return s.DefaultMaterial===void 0&&(s.DefaultMaterial=new G.MeshStandardMaterial({color:16777215,emissive:0,metalness:1,roughness:1,transparent:!1,depthTest:!0,side:G.FrontSide})),s.DefaultMaterial}function co(s,e,t){for(let r in t.extensions)s[r]===void 0&&(e.userData.gltfExtensions=e.userData.gltfExtensions||{},e.userData.gltfExtensions[r]=t.extensions[r])}function ts(s,e){e.extras!==void 0&&(typeof e.extras==\\\\\\\"object\\\\\\\"?Object.assign(s.userData,e.extras):console.warn(\\\\\\\"THREE.GLTFLoader: Ignoring primitive type .extras, \\\\\\\"+e.extras))}function Nv(s,e,t){let r=!1,i=!1;for(let l=0,a=e.length;l<a;l++){let c=e[l];if(c.POSITION!==void 0&&(r=!0),c.NORMAL!==void 0&&(i=!0),r&&i)break}if(!r&&!i)return Promise.resolve(s);let n=[],o=[];for(let l=0,a=e.length;l<a;l++){let c=e[l];if(r){let u=c.POSITION!==void 0?t.getDependency(\\\\\\\"accessor\\\\\\\",c.POSITION):s.attributes.position;n.push(u)}if(i){let u=c.NORMAL!==void 0?t.getDependency(\\\\\\\"accessor\\\\\\\",c.NORMAL):s.attributes.normal;o.push(u)}}return Promise.all([Promise.all(n),Promise.all(o)]).then(function(l){let a=l[0],c=l[1];return r&&(s.morphAttributes.position=a),i&&(s.morphAttributes.normal=c),s.morphTargetsRelative=!0,s})}function Tv(s,e){if(s.updateMorphTargets(),e.weights!==void 0)for(let t=0,r=e.weights.length;t<r;t++)s.morphTargetInfluences[t]=e.weights[t];if(e.extras&&Array.isArray(e.extras.targetNames)){let t=e.extras.targetNames;if(s.morphTargetInfluences.length===t.length){s.morphTargetDictionary={};for(let r=0,i=t.length;r<i;r++)s.morphTargetDictionary[t[r]]=r}else console.warn(\\\\\\\"THREE.GLTFLoader: Invalid extras.targetNames length. Ignoring names.\\\\\\\")}}function Cv(s){let e=s.extensions&&s.extensions[ze.KHR_DRACO_MESH_COMPRESSION],t;return e?t=\\\\\\\"draco:\\\\\\\"+e.bufferView+\\\\\\\":\\\\\\\"+e.indices+\\\\\\\":\\\\\\\"+mf(e.attributes):t=s.indices+\\\\\\\":\\\\\\\"+mf(s.attributes)+\\\\\\\":\\\\\\\"+s.mode,t}function mf(s){let e=\\\\\\\"\\\\\\\",t=Object.keys(s).sort();for(let r=0,i=t.length;r<i;r++)e+=t[r]+\\\\\\\":\\\\\\\"+s[t[r]]+\\\\\\\";\\\\\\\";return e}function Dc(s){switch(s){case Int8Array:return 1/127;case Uint8Array:return 1/255;case Int16Array:return 1/32767;case Uint16Array:return 1/65535;default:throw new Error(\\\\\\\"THREE.GLTFLoader: Unsupported normalized accessor component type.\\\\\\\")}}var gf=class{constructor(e={},t={}){this.json=e,this.extensions={},this.plugins={},this.options=t,this.cache=new Av,this.associations=new Map,this.primitiveCache={},this.meshCache={refs:{},uses:{}},this.cameraCache={refs:{},uses:{}},this.lightCache={refs:{},uses:{}},this.textureCache={},this.nodeNamesUsed={},typeof createImageBitmap!=\\\\\\\"undefined\\\\\\\"&&/Firefox/.test(navigator.userAgent)===!1?this.textureLoader=new G.ImageBitmapLoader(this.options.manager):this.textureLoader=new G.TextureLoader(this.options.manager),this.textureLoader.setCrossOrigin(this.options.crossOrigin),this.textureLoader.setRequestHeader(this.options.requestHeader),this.fileLoader=new G.FileLoader(this.options.manager),this.fileLoader.setResponseType(\\\\\\\"arraybuffer\\\\\\\"),this.options.crossOrigin===\\\\\\\"use-credentials\\\\\\\"&&this.fileLoader.setWithCredentials(!0)}setExtensions(e){this.extensions=e}setPlugins(e){this.plugins=e}parse(e,t){let r=this,i=this.json,n=this.extensions;this.cache.removeAll(),this._invokeAll(function(o){return o._markDefs&&o._markDefs()}),Promise.all(this._invokeAll(function(o){return o.beforeRoot&&o.beforeRoot()})).then(function(){return Promise.all([r.getDependencies(\\\\\\\"scene\\\\\\\"),r.getDependencies(\\\\\\\"animation\\\\\\\"),r.getDependencies(\\\\\\\"camera\\\\\\\")])}).then(function(o){let l={scene:o[0][i.scene||0],scenes:o[0],animations:o[1],cameras:o[2],asset:i.asset,parser:r,userData:{}};co(n,l,i),ts(l,i),Promise.all(r._invokeAll(function(a){return a.afterRoot&&a.afterRoot(l)})).then(function(){e(l)})}).catch(t)}_markDefs(){let e=this.json.nodes||[],t=this.json.skins||[],r=this.json.meshes||[];for(let i=0,n=t.length;i<n;i++){let o=t[i].joints;for(let l=0,a=o.length;l<a;l++)e[o[l]].isBone=!0}for(let i=0,n=e.length;i<n;i++){let o=e[i];o.mesh!==void 0&&(this._addNodeRef(this.meshCache,o.mesh),o.skin!==void 0&&(r[o.mesh].isSkinnedMesh=!0)),o.camera!==void 0&&this._addNodeRef(this.cameraCache,o.camera)}}_addNodeRef(e,t){t!==void 0&&(e.refs[t]===void 0&&(e.refs[t]=e.uses[t]=0),e.refs[t]++)}_getNodeRef(e,t,r){if(e.refs[t]<=1)return r;let i=r.clone();return i.name+=\\\\\\\"_instance_\\\\\\\"+e.uses[t]++,i}_invokeOne(e){let t=Object.values(this.plugins);t.push(this);for(let r=0;r<t.length;r++){let i=e(t[r]);if(i)return i}return null}_invokeAll(e){let t=Object.values(this.plugins);t.unshift(this);let r=[];for(let i=0;i<t.length;i++){let n=e(t[i]);n&&r.push(n)}return r}getDependency(e,t){let r=e+\\\\\\\":\\\\\\\"+t,i=this.cache.get(r);if(!i){switch(e){case\\\\\\\"scene\\\\\\\":i=this.loadScene(t);break;case\\\\\\\"node\\\\\\\":i=this.loadNode(t);break;case\\\\\\\"mesh\\\\\\\":i=this._invokeOne(function(n){return n.loadMesh&&n.loadMesh(t)});break;case\\\\\\\"accessor\\\\\\\":i=this.loadAccessor(t);break;case\\\\\\\"bufferView\\\\\\\":i=this._invokeOne(function(n){return n.loadBufferView&&n.loadBufferView(t)});break;case\\\\\\\"buffer\\\\\\\":i=this.loadBuffer(t);break;case\\\\\\\"material\\\\\\\":i=this._invokeOne(function(n){return n.loadMaterial&&n.loadMaterial(t)});break;case\\\\\\\"texture\\\\\\\":i=this._invokeOne(function(n){return n.loadTexture&&n.loadTexture(t)});break;case\\\\\\\"skin\\\\\\\":i=this.loadSkin(t);break;case\\\\\\\"animation\\\\\\\":i=this.loadAnimation(t);break;case\\\\\\\"camera\\\\\\\":i=this.loadCamera(t);break;default:throw new Error(\\\\\\\"Unknown type: \\\\\\\"+e)}this.cache.add(r,i)}return i}getDependencies(e){let t=this.cache.get(e);if(!t){let r=this,i=this.json[e+(e===\\\\\\\"mesh\\\\\\\"?\\\\\\\"es\\\\\\\":\\\\\\\"s\\\\\\\")]||[];t=Promise.all(i.map(function(n,o){return r.getDependency(e,o)})),this.cache.add(e,t)}return t}loadBuffer(e){let t=this.json.buffers[e],r=this.fileLoader;if(t.type&&t.type!==\\\\\\\"arraybuffer\\\\\\\")throw new Error(\\\\\\\"THREE.GLTFLoader: \\\\\\\"+t.type+\\\\\\\" buffer type is not supported.\\\\\\\");if(t.uri===void 0&&e===0)return Promise.resolve(this.extensions[ze.KHR_BINARY_GLTF].body);let i=this.options;return new Promise(function(n,o){r.load(pf(t.uri,i.path),n,void 0,function(){o(new Error('THREE.GLTFLoader: Failed to load buffer \\\\\\\"'+t.uri+'\\\\\\\".'))})})}loadBufferView(e){let t=this.json.bufferViews[e];return this.getDependency(\\\\\\\"buffer\\\\\\\",t.buffer).then(function(r){let i=t.byteLength||0,n=t.byteOffset||0;return r.slice(n,n+i)})}loadAccessor(e){let t=this,r=this.json,i=this.json.accessors[e];if(i.bufferView===void 0&&i.sparse===void 0)return Promise.resolve(null);let n=[];return i.bufferView!==void 0?n.push(this.getDependency(\\\\\\\"bufferView\\\\\\\",i.bufferView)):n.push(null),i.sparse!==void 0&&(n.push(this.getDependency(\\\\\\\"bufferView\\\\\\\",i.sparse.indices.bufferView)),n.push(this.getDependency(\\\\\\\"bufferView\\\\\\\",i.sparse.values.bufferView))),Promise.all(n).then(function(o){let l=o[0],a=ff[i.type],c=lo[i.componentType],u=c.BYTES_PER_ELEMENT,d=u*a,h=i.byteOffset||0,p=i.bufferView!==void 0?r.bufferViews[i.bufferView].byteStride:void 0,f=i.normalized===!0,m,g;if(p&&p!==d){let y=Math.floor(h/p),x=\\\\\\\"InterleavedBuffer:\\\\\\\"+i.bufferView+\\\\\\\":\\\\\\\"+i.componentType+\\\\\\\":\\\\\\\"+y+\\\\\\\":\\\\\\\"+i.count,v=t.cache.get(x);v||(m=new c(l,y*p,i.count*p/u),v=new G.InterleavedBuffer(m,p/u),t.cache.add(x,v)),g=new G.InterleavedBufferAttribute(v,a,h%p/u,f)}else l===null?m=new c(i.count*a):m=new c(l,h,i.count*a),g=new G.BufferAttribute(m,a,f);if(i.sparse!==void 0){let y=ff.SCALAR,x=lo[i.sparse.indices.componentType],v=i.sparse.indices.byteOffset||0,b=i.sparse.values.byteOffset||0,w=new x(o[1],v,i.sparse.count*y),N=new c(o[2],b,i.sparse.count*a);l!==null&&(g=new G.BufferAttribute(g.array.slice(),g.itemSize,g.normalized));for(let S=0,A=w.length;S<A;S++){let I=w[S];if(g.setX(I,N[S*a]),a>=2&&g.setY(I,N[S*a+1]),a>=3&&g.setZ(I,N[S*a+2]),a>=4&&g.setW(I,N[S*a+3]),a>=5)throw new Error(\\\\\\\"THREE.GLTFLoader: Unsupported itemSize in sparse BufferAttribute.\\\\\\\")}}return g})}loadTexture(e){let t=this.json,r=this.options,i=t.textures[e],n=t.images[i.source],o=this.textureLoader;if(n.uri){let l=r.manager.getHandler(n.uri);l!==null&&(o=l)}return this.loadTextureImage(e,n,o)}loadTextureImage(e,t,r){let i=this,n=this.json,o=this.options,l=n.textures[e],a=(t.uri||t.bufferView)+\\\\\\\":\\\\\\\"+l.sampler;if(this.textureCache[a])return this.textureCache[a];let c=self.URL||self.webkitURL,u=t.uri||\\\\\\\"\\\\\\\",d=!1,h=!0,p=u.search(/\\\\.jpe?g($|\\\\?)/i)>0||u.search(/^data\\\\:image\\\\/jpeg/)===0;if((t.mimeType===\\\\\\\"image/jpeg\\\\\\\"||p)&&(h=!1),t.bufferView!==void 0)u=i.getDependency(\\\\\\\"bufferView\\\\\\\",t.bufferView).then(function(m){if(t.mimeType===\\\\\\\"image/png\\\\\\\"){let y=new DataView(m,25,1).getUint8(0,!1);h=y===6||y===4||y===3}d=!0;let g=new Blob([m],{type:t.mimeType});return u=c.createObjectURL(g),u});else if(t.uri===void 0)throw new Error(\\\\\\\"THREE.GLTFLoader: Image \\\\\\\"+e+\\\\\\\" is missing URI and bufferView\\\\\\\");let f=Promise.resolve(u).then(function(m){return new Promise(function(g,y){let x=g;r.isImageBitmapLoader===!0&&(x=function(v){let b=new G.Texture(v);b.needsUpdate=!0,g(b)}),r.load(pf(m,o.path),x,void 0,y)})}).then(function(m){d===!0&&c.revokeObjectURL(u),m.flipY=!1,l.name&&(m.name=l.name),h||(m.format=G.RGBFormat);let y=(n.samplers||{})[l.sampler]||{};return m.magFilter=df[y.magFilter]||G.LinearFilter,m.minFilter=df[y.minFilter]||G.LinearMipmapLinearFilter,m.wrapS=hf[y.wrapS]||G.RepeatWrapping,m.wrapT=hf[y.wrapT]||G.RepeatWrapping,i.associations.set(m,{type:\\\\\\\"textures\\\\\\\",index:e}),m}).catch(function(){return console.error(\\\\\\\"THREE.GLTFLoader: Couldn't load texture\\\\\\\",u),null});return this.textureCache[a]=f,f}assignTexture(e,t,r){let i=this;return this.getDependency(\\\\\\\"texture\\\\\\\",r.index).then(function(n){if(r.texCoord!==void 0&&r.texCoord!=0&&!(t===\\\\\\\"aoMap\\\\\\\"&&r.texCoord==1)&&console.warn(\\\\\\\"THREE.GLTFLoader: Custom UV set \\\\\\\"+r.texCoord+\\\\\\\" for texture \\\\\\\"+t+\\\\\\\" not yet supported.\\\\\\\"),i.extensions[ze.KHR_TEXTURE_TRANSFORM]){let o=r.extensions!==void 0?r.extensions[ze.KHR_TEXTURE_TRANSFORM]:void 0;if(o){let l=i.associations.get(n);n=i.extensions[ze.KHR_TEXTURE_TRANSFORM].extendTexture(n,o),i.associations.set(n,l)}}return e[t]=n,n})}assignFinalMaterial(e){let t=e.geometry,r=e.material,i=t.attributes.tangent!==void 0,n=t.attributes.color!==void 0,o=t.attributes.normal===void 0;if(e.isPoints){let l=\\\\\\\"PointsMaterial:\\\\\\\"+r.uuid,a=this.cache.get(l);a||(a=new G.PointsMaterial,G.Material.prototype.copy.call(a,r),a.color.copy(r.color),a.map=r.map,a.sizeAttenuation=!1,this.cache.add(l,a)),r=a}else if(e.isLine){let l=\\\\\\\"LineBasicMaterial:\\\\\\\"+r.uuid,a=this.cache.get(l);a||(a=new G.LineBasicMaterial,G.Material.prototype.copy.call(a,r),a.color.copy(r.color),this.cache.add(l,a)),r=a}if(i||n||o){let l=\\\\\\\"ClonedMaterial:\\\\\\\"+r.uuid+\\\\\\\":\\\\\\\";r.isGLTFSpecularGlossinessMaterial&&(l+=\\\\\\\"specular-glossiness:\\\\\\\"),i&&(l+=\\\\\\\"vertex-tangents:\\\\\\\"),n&&(l+=\\\\\\\"vertex-colors:\\\\\\\"),o&&(l+=\\\\\\\"flat-shading:\\\\\\\");let a=this.cache.get(l);a||(a=r.clone(),n&&(a.vertexColors=!0),o&&(a.flatShading=!0),i&&(a.normalScale&&(a.normalScale.y*=-1),a.clearcoatNormalScale&&(a.clearcoatNormalScale.y*=-1)),this.cache.add(l,a),this.associations.set(a,this.associations.get(r))),r=a}r.aoMap&&t.attributes.uv2===void 0&&t.attributes.uv!==void 0&&t.setAttribute(\\\\\\\"uv2\\\\\\\",t.attributes.uv),e.material=r}getMaterialType(){return G.MeshStandardMaterial}loadMaterial(e){let t=this,r=this.json,i=this.extensions,n=r.materials[e],o,l={},a=n.extensions||{},c=[];if(a[ze.KHR_MATERIALS_PBR_SPECULAR_GLOSSINESS]){let d=i[ze.KHR_MATERIALS_PBR_SPECULAR_GLOSSINESS];o=d.getMaterialType(),c.push(d.extendParams(l,n,t))}else if(a[ze.KHR_MATERIALS_UNLIT]){let d=i[ze.KHR_MATERIALS_UNLIT];o=d.getMaterialType(),c.push(d.extendParams(l,n,t))}else{let d=n.pbrMetallicRoughness||{};if(l.color=new G.Color(1,1,1),l.opacity=1,Array.isArray(d.baseColorFactor)){let h=d.baseColorFactor;l.color.fromArray(h),l.opacity=h[3]}d.baseColorTexture!==void 0&&c.push(t.assignTexture(l,\\\\\\\"map\\\\\\\",d.baseColorTexture)),l.metalness=d.metallicFactor!==void 0?d.metallicFactor:1,l.roughness=d.roughnessFactor!==void 0?d.roughnessFactor:1,d.metallicRoughnessTexture!==void 0&&(c.push(t.assignTexture(l,\\\\\\\"metalnessMap\\\\\\\",d.metallicRoughnessTexture)),c.push(t.assignTexture(l,\\\\\\\"roughnessMap\\\\\\\",d.metallicRoughnessTexture))),o=this._invokeOne(function(h){return h.getMaterialType&&h.getMaterialType(e)}),c.push(Promise.all(this._invokeAll(function(h){return h.extendMaterialParams&&h.extendMaterialParams(e,l)})))}n.doubleSided===!0&&(l.side=G.DoubleSide);let u=n.alphaMode||Lc.OPAQUE;return u===Lc.BLEND?(l.transparent=!0,l.depthWrite=!1):(l.transparent=!1,u===Lc.MASK&&(l.alphaTest=n.alphaCutoff!==void 0?n.alphaCutoff:.5)),n.normalTexture!==void 0&&o!==G.MeshBasicMaterial&&(c.push(t.assignTexture(l,\\\\\\\"normalMap\\\\\\\",n.normalTexture)),l.normalScale=new G.Vector2(1,-1),n.normalTexture.scale!==void 0&&l.normalScale.set(n.normalTexture.scale,-n.normalTexture.scale)),n.occlusionTexture!==void 0&&o!==G.MeshBasicMaterial&&(c.push(t.assignTexture(l,\\\\\\\"aoMap\\\\\\\",n.occlusionTexture)),n.occlusionTexture.strength!==void 0&&(l.aoMapIntensity=n.occlusionTexture.strength)),n.emissiveFactor!==void 0&&o!==G.MeshBasicMaterial&&(l.emissive=new G.Color().fromArray(n.emissiveFactor)),n.emissiveTexture!==void 0&&o!==G.MeshBasicMaterial&&c.push(t.assignTexture(l,\\\\\\\"emissiveMap\\\\\\\",n.emissiveTexture)),Promise.all(c).then(function(){let d;return o===ba?d=i[ze.KHR_MATERIALS_PBR_SPECULAR_GLOSSINESS].createMaterial(l):d=new o(l),n.name&&(d.name=n.name),d.map&&(d.map.encoding=G.sRGBEncoding),d.emissiveMap&&(d.emissiveMap.encoding=G.sRGBEncoding),ts(d,n),t.associations.set(d,{type:\\\\\\\"materials\\\\\\\",index:e}),n.extensions&&co(i,d,n),d})}createUniqueName(e){let t=G.PropertyBinding.sanitizeNodeName(e||\\\\\\\"\\\\\\\"),r=t;for(let i=1;this.nodeNamesUsed[r];++i)r=t+\\\\\\\"_\\\\\\\"+i;return this.nodeNamesUsed[r]=!0,r}loadGeometries(e){let t=this,r=this.extensions,i=this.primitiveCache;function n(l){return r[ze.KHR_DRACO_MESH_COMPRESSION].decodePrimitive(l,t).then(function(a){return vf(a,l,t)})}let o=[];for(let l=0,a=e.length;l<a;l++){let c=e[l],u=Cv(c),d=i[u];if(d)o.push(d.promise);else{let h;c.extensions&&c.extensions[ze.KHR_DRACO_MESH_COMPRESSION]?h=n(c):h=vf(new G.BufferGeometry,c,t),i[u]={primitive:c,promise:h},o.push(h)}}return Promise.all(o)}loadMesh(e){let t=this,r=this.json,i=this.extensions,n=r.meshes[e],o=n.primitives,l=[];for(let a=0,c=o.length;a<c;a++){let u=o[a].material===void 0?Sv(this.cache):this.getDependency(\\\\\\\"material\\\\\\\",o[a].material);l.push(u)}return l.push(t.loadGeometries(o)),Promise.all(l).then(function(a){let c=a.slice(0,a.length-1),u=a[a.length-1],d=[];for(let p=0,f=u.length;p<f;p++){let m=u[p],g=o[p],y,x=c[p];if(g.mode===ai.TRIANGLES||g.mode===ai.TRIANGLE_STRIP||g.mode===ai.TRIANGLE_FAN||g.mode===void 0)y=n.isSkinnedMesh===!0?new G.SkinnedMesh(m,x):new G.Mesh(m,x),y.isSkinnedMesh===!0&&!y.geometry.attributes.skinWeight.normalized&&y.normalizeSkinWeights(),g.mode===ai.TRIANGLE_STRIP?y.geometry=xf(y.geometry,G.TriangleStripDrawMode):g.mode===ai.TRIANGLE_FAN&&(y.geometry=xf(y.geometry,G.TriangleFanDrawMode));else if(g.mode===ai.LINES)y=new G.LineSegments(m,x);else if(g.mode===ai.LINE_STRIP)y=new G.Line(m,x);else if(g.mode===ai.LINE_LOOP)y=new G.LineLoop(m,x);else if(g.mode===ai.POINTS)y=new G.Points(m,x);else throw new Error(\\\\\\\"THREE.GLTFLoader: Primitive mode unsupported: \\\\\\\"+g.mode);Object.keys(y.geometry.morphAttributes).length>0&&Tv(y,n),y.name=t.createUniqueName(n.name||\\\\\\\"mesh_\\\\\\\"+e),ts(y,n),g.extensions&&co(i,y,g),t.assignFinalMaterial(y),d.push(y)}if(d.length===1)return d[0];let h=new G.Group;for(let p=0,f=d.length;p<f;p++)h.add(d[p]);return h})}loadCamera(e){let t,r=this.json.cameras[e],i=r[r.type];if(!i){console.warn(\\\\\\\"THREE.GLTFLoader: Missing camera parameters.\\\\\\\");return}return r.type===\\\\\\\"perspective\\\\\\\"?t=new G.PerspectiveCamera(G.MathUtils.radToDeg(i.yfov),i.aspectRatio||1,i.znear||1,i.zfar||2e6):r.type===\\\\\\\"orthographic\\\\\\\"&&(t=new G.OrthographicCamera(-i.xmag,i.xmag,i.ymag,-i.ymag,i.znear,i.zfar)),r.name&&(t.name=this.createUniqueName(r.name)),ts(t,r),Promise.resolve(t)}loadSkin(e){let t=this.json.skins[e],r={joints:t.joints};return t.inverseBindMatrices===void 0?Promise.resolve(r):this.getDependency(\\\\\\\"accessor\\\\\\\",t.inverseBindMatrices).then(function(i){return r.inverseBindMatrices=i,r})}loadAnimation(e){let r=this.json.animations[e],i=[],n=[],o=[],l=[],a=[];for(let c=0,u=r.channels.length;c<u;c++){let d=r.channels[c],h=r.samplers[d.sampler],p=d.target,f=p.node!==void 0?p.node:p.id,m=r.parameters!==void 0?r.parameters[h.input]:h.input,g=r.parameters!==void 0?r.parameters[h.output]:h.output;i.push(this.getDependency(\\\\\\\"node\\\\\\\",f)),n.push(this.getDependency(\\\\\\\"accessor\\\\\\\",m)),o.push(this.getDependency(\\\\\\\"accessor\\\\\\\",g)),l.push(h),a.push(p)}return Promise.all([Promise.all(i),Promise.all(n),Promise.all(o),Promise.all(l),Promise.all(a)]).then(function(c){let u=c[0],d=c[1],h=c[2],p=c[3],f=c[4],m=[];for(let y=0,x=u.length;y<x;y++){let v=u[y],b=d[y],w=h[y],N=p[y],S=f[y];if(v===void 0)continue;v.updateMatrix(),v.matrixAutoUpdate=!0;let A;switch(Ci[S.path]){case Ci.weights:A=G.NumberKeyframeTrack;break;case Ci.rotation:A=G.QuaternionKeyframeTrack;break;case Ci.position:case Ci.scale:default:A=G.VectorKeyframeTrack;break}let I=v.name?v.name:v.uuid,U=N.interpolation!==void 0?wv[N.interpolation]:G.InterpolateLinear,E=[];Ci[S.path]===Ci.weights?v.traverse(function(R){R.isMesh===!0&&R.morphTargetInfluences&&E.push(R.name?R.name:R.uuid)}):E.push(I);let j=w.array;if(w.normalized){let R=Dc(j.constructor),J=new Float32Array(j.length);for(let W=0,M=j.length;W<M;W++)J[W]=j[W]*R;j=J}for(let R=0,J=E.length;R<J;R++){let W=new A(E[R]+\\\\\\\".\\\\\\\"+Ci[S.path],b.array,j,U);N.interpolation===\\\\\\\"CUBICSPLINE\\\\\\\"&&(W.createInterpolant=function(k){return new es(this.times,this.values,this.getValueSize()/3,k)},W.createInterpolant.isInterpolantFactoryMethodGLTFCubicSpline=!0),m.push(W)}}let g=r.name?r.name:\\\\\\\"animation_\\\\\\\"+e;return new G.AnimationClip(g,void 0,m)})}createNodeMesh(e){let t=this.json,r=this,i=t.nodes[e];return i.mesh===void 0?null:r.getDependency(\\\\\\\"mesh\\\\\\\",i.mesh).then(function(n){let o=r._getNodeRef(r.meshCache,i.mesh,n);return i.weights!==void 0&&o.traverse(function(l){if(!!l.isMesh)for(let a=0,c=i.weights.length;a<c;a++)l.morphTargetInfluences[a]=i.weights[a]}),o})}loadNode(e){let t=this.json,r=this.extensions,i=this,n=t.nodes[e],o=n.name?i.createUniqueName(n.name):\\\\\\\"\\\\\\\";return function(){let l=[],a=i._invokeOne(function(c){return c.createNodeMesh&&c.createNodeMesh(e)});return a&&l.push(a),n.camera!==void 0&&l.push(i.getDependency(\\\\\\\"camera\\\\\\\",n.camera).then(function(c){return i._getNodeRef(i.cameraCache,n.camera,c)})),i._invokeAll(function(c){return c.createNodeAttachment&&c.createNodeAttachment(e)}).forEach(function(c){l.push(c)}),Promise.all(l)}().then(function(l){let a;if(n.isBone===!0?a=new G.Bone:l.length>1?a=new G.Group:l.length===1?a=l[0]:a=new G.Object3D,a!==l[0])for(let c=0,u=l.length;c<u;c++)a.add(l[c]);if(n.name&&(a.userData.name=n.name,a.name=o),ts(a,n),n.extensions&&co(r,a,n),n.matrix!==void 0){let c=new G.Matrix4;c.fromArray(n.matrix),a.applyMatrix4(c)}else n.translation!==void 0&&a.position.fromArray(n.translation),n.rotation!==void 0&&a.quaternion.fromArray(n.rotation),n.scale!==void 0&&a.scale.fromArray(n.scale);return i.associations.set(a,{type:\\\\\\\"nodes\\\\\\\",index:e}),a})}loadScene(e){let t=this.json,r=this.extensions,i=this.json.scenes[e],n=this,o=new G.Group;i.name&&(o.name=n.createUniqueName(i.name)),ts(o,i),i.extensions&&co(r,o,i);let l=i.nodes||[],a=[];for(let c=0,u=l.length;c<u;c++)a.push(yf(l[c],o,t,n));return Promise.all(a).then(function(){return o})}};function yf(s,e,t,r){let i=t.nodes[s];return r.getDependency(\\\\\\\"node\\\\\\\",s).then(function(n){if(i.skin===void 0)return n;let o;return r.getDependency(\\\\\\\"skin\\\\\\\",i.skin).then(function(l){o=l;let a=[];for(let c=0,u=o.joints.length;c<u;c++)a.push(r.getDependency(\\\\\\\"node\\\\\\\",o.joints[c]));return Promise.all(a)}).then(function(l){return n.traverse(function(a){if(!a.isMesh)return;let c=[],u=[];for(let d=0,h=l.length;d<h;d++){let p=l[d];if(p){c.push(p);let f=new G.Matrix4;o.inverseBindMatrices!==void 0&&f.fromArray(o.inverseBindMatrices.array,d*16),u.push(f)}else console.warn('THREE.GLTFLoader: Joint \\\\\\\"%s\\\\\\\" could not be found.',o.joints[d])}a.bind(new G.Skeleton(c,u),a.matrixWorld)}),n})}).then(function(n){e.add(n);let o=[];if(i.children){let l=i.children;for(let a=0,c=l.length;a<c;a++){let u=l[a];o.push(yf(u,n,t,r))}}return Promise.all(o)})}function Mv(s,e,t){let r=e.attributes,i=new G.Box3;if(r.POSITION!==void 0){let l=t.json.accessors[r.POSITION],a=l.min,c=l.max;if(a!==void 0&&c!==void 0){if(i.set(new G.Vector3(a[0],a[1],a[2]),new G.Vector3(c[0],c[1],c[2])),l.normalized){let u=Dc(lo[l.componentType]);i.min.multiplyScalar(u),i.max.multiplyScalar(u)}}else{console.warn(\\\\\\\"THREE.GLTFLoader: Missing min/max properties for accessor POSITION.\\\\\\\");return}}else return;let n=e.targets;if(n!==void 0){let l=new G.Vector3,a=new G.Vector3;for(let c=0,u=n.length;c<u;c++){let d=n[c];if(d.POSITION!==void 0){let h=t.json.accessors[d.POSITION],p=h.min,f=h.max;if(p!==void 0&&f!==void 0){if(a.setX(Math.max(Math.abs(p[0]),Math.abs(f[0]))),a.setY(Math.max(Math.abs(p[1]),Math.abs(f[1]))),a.setZ(Math.max(Math.abs(p[2]),Math.abs(f[2]))),h.normalized){let m=Dc(lo[h.componentType]);a.multiplyScalar(m)}l.max(a)}else console.warn(\\\\\\\"THREE.GLTFLoader: Missing min/max properties for accessor POSITION.\\\\\\\")}}i.expandByVector(l)}s.boundingBox=i;let o=new G.Sphere;i.getCenter(o.center),o.radius=i.min.distanceTo(i.max)/2,s.boundingSphere=o}function vf(s,e,t){let r=e.attributes,i=[];function n(o,l){return t.getDependency(\\\\\\\"accessor\\\\\\\",o).then(function(a){s.setAttribute(l,a)})}for(let o in r){let l=Ic[o]||o.toLowerCase();l in s.attributes||i.push(n(r[o],l))}if(e.indices!==void 0&&!s.index){let o=t.getDependency(\\\\\\\"accessor\\\\\\\",e.indices).then(function(l){s.setIndex(l)});i.push(o)}return ts(s,e),Mv(s,e,t),Promise.all(i).then(function(){return e.targets!==void 0?Nv(s,e.targets,t):s})}function xf(s,e){let t=s.getIndex();if(t===null){let o=[],l=s.getAttribute(\\\\\\\"position\\\\\\\");if(l!==void 0){for(let a=0;a<l.count;a++)o.push(a);s.setIndex(o),t=s.getIndex()}else return console.error(\\\\\\\"THREE.GLTFLoader.toTrianglesDrawMode(): Undefined position attribute. Processing not possible.\\\\\\\"),s}let r=t.count-2,i=[];if(e===G.TriangleFanDrawMode)for(let o=1;o<=r;o++)i.push(t.getX(0)),i.push(t.getX(o)),i.push(t.getX(o+1));else for(let o=0;o<r;o++)o%2==0?(i.push(t.getX(o)),i.push(t.getX(o+1)),i.push(t.getX(o+2))):(i.push(t.getX(o+2)),i.push(t.getX(o+1)),i.push(t.getX(o)));i.length/3!==r&&console.error(\\\\\\\"THREE.GLTFLoader.toTrianglesDrawMode(): Unable to generate correct amount of triangles.\\\\\\\");let n=s.clone();return n.setIndex(i),n}var Zt=F(z()),ct=class extends Zt.Mesh{constructor({char:e,originalChar:t,fontFamily:r,letterSpacing:i,fontSize:n,LOD:o=16},l=new Zt.MeshBasicMaterial({color:0,opacity:1,visible:!0,transparent:!0,side:Zt.DoubleSide})){let a=ct.loadChar(e,r,o);super(a.geometry,l);this.char=e,this.originalChar=t??e,this.fontFamily=r,this.letterSpacing=i,this.fontSize=n,this.LOD=o,this.resolution=a.resolution,this.glyphsHa=a.glyphsHa,this.localPosition=new Zt.Vector2,this.charSize=0,this.geometry.userData={type:\\\\\\\"CharacterGeometry\\\\\\\",parameters:{char:this.char,fontFamily:this.fontFamily,letterSpacing:this.letterSpacing,fontSize:this.fontSize,lod:this.LOD,resolution:this.resolution,charSize:this.charSize,localPosition:this.localPosition}},this.updateFontSize(this.fontSize)}static get FONTS_PATH(){return ct._fontPath}static set FONTS_PATH(e){ct._fontPath=e}updatePosition(e,t){this.localPosition.copy(e);let r=new Zt.Vector3(this.localPosition.x,-this.localPosition.y,0);this.position.copy(r).add(t)}updateFontSize(e){let t=e/this.resolution;this.fontSize=e,this.scale.set(this.fontSize,this.fontSize,1),this.charSize=this.glyphsHa*t*this.letterSpacing}updateFontFamily(e){if(this.fontFamily===e)return;this.fontFamily=e;let t=ct.loadChar(this.char,e,this.LOD);this.geometry=t.geometry,this.resolution=t.resolution,this.glyphsHa=t.glyphsHa,this.geometry.userData={type:\\\\\\\"CharacterGeometry\\\\\\\",parameters:{char:this.char,fontFamily:this.fontFamily,letterSpacing:this.letterSpacing,fontSize:this.fontSize,lod:this.LOD,resolution:this.resolution,charSize:this.charSize,localPosition:this.localPosition}},this.updateFontSize(this.fontSize)}updateChar(e){if(this.char===e)return;this.char=e;let t=ct.loadChar(e,this.fontFamily,this.LOD);this.geometry=t.geometry,this.resolution=t.resolution,this.glyphsHa=t.glyphsHa,this.geometry.userData={type:\\\\\\\"CharacterGeometry\\\\\\\",parameters:{char:this.char,fontFamily:this.fontFamily,letterSpacing:this.letterSpacing,fontSize:this.fontSize,lod:this.LOD,resolution:this.resolution,charSize:this.charSize,localPosition:this.localPosition}},this.updateFontSize(this.fontSize)}updateLetterSpacing(e){this.letterSpacing!==e&&(this.letterSpacing=e,this.updateFontSize(this.fontSize))}updateLOD(e){if(this.LOD===e)return;this.LOD=e;let t=ct.loadChar(this.char,this.fontFamily,this.LOD);this.geometry=t.geometry,this.resolution=t.resolution,this.glyphsHa=t.glyphsHa,this.geometry.userData={type:\\\\\\\"CharacterGeometry\\\\\\\",parameters:{char:this.char,fontFamily:this.fontFamily,letterSpacing:this.letterSpacing,fontSize:this.fontSize,lod:this.LOD,resolution:this.resolution,charSize:this.charSize,localPosition:this.localPosition}},this.updateFontSize(this.fontSize)}clone(){let e={char:this.char,originalChar:this.originalChar,fontFamily:this.fontFamily,letterSpacing:this.letterSpacing,fontSize:this.fontSize,LOD:this.LOD};return new ct(e).copy(this)}static loadFont(e){return new Promise(function(t,r){ct.fontCache[e]?t(ct.fontCache[e]):new Zt.FontLoader().load(ct.FONTS_PATH+e+\\\\\\\".json\\\\\\\",n=>{ct.fontCache[e]=n,t(n)},void 0,r)})}static loadChar(e,t,r){if(ct.charCache[e]){if(ct.charCache[e][r]&&ct.charCache[e][r].fontFamily===t)return ct.charCache[e][r]}else ct.charCache[e]={};let i=ct.fontCache[t],n=i.generateShapes(e,1);return ct.charCache[e][r]={geometry:new Zt.ShapeBufferGeometry(n,r),fontFamily:t,resolution:i.data.resolution,glyphsHa:i.data.glyphs[e].ha},ct.charCache[e][r]}},$t=ct;$t.charCache={},$t.fontCache={},$t._fontPath=\\\\\\\"/_assets/_fonts/\\\\\\\";var Xr=F(z());var Bc=F(z());var rs;(function(r){r[r.STANDARD=1]=\\\\\\\"STANDARD\\\\\\\",r[r.UPPERCASE=2]=\\\\\\\"UPPERCASE\\\\\\\",r[r.LOWERCASE=3]=\\\\\\\"LOWERCASE\\\\\\\"})(rs||(rs={}));var Mi;(function(r){r[r.TOP=1]=\\\\\\\"TOP\\\\\\\",r[r.MIDDLE=2]=\\\\\\\"MIDDLE\\\\\\\",r[r.BOTTOM=3]=\\\\\\\"BOTTOM\\\\\\\"})(Mi||(Mi={}));var er;(function(i){i[i.LEFT=1]=\\\\\\\"LEFT\\\\\\\",i[i.RIGHT=2]=\\\\\\\"RIGHT\\\\\\\",i[i.CENTER=3]=\\\\\\\"CENTER\\\\\\\",i[i.JUSTIFY=4]=\\\\\\\"JUSTIFY\\\\\\\"})(er||(er={}));var is;(function(t){t[t.TO_RIGHT=1]=\\\\\\\"TO_RIGHT\\\\\\\",t[t.TO_LEFT=-1]=\\\\\\\"TO_LEFT\\\\\\\"})(is||(is={}));var Aa=new Bc.Vector2,Ys=class{constructor(e,t,r){this.message=[];this.endLine=!0;this.yLinePos=e,this.lineHeight=t,this.maxCharSize=r,this.nextChar3DPos=new Bc.Vector2(0,this.yLinePos+this.maxCharSize*this.lineHeight),this.align=er.LEFT}addChar3D(e,t,r=this.message.length){this.message.splice(r,0,e),e.fontSize>this.maxCharSize?(this.maxCharSize=e.fontSize,this.nextChar3DPos.y=this.yLinePos+this.maxCharSize*this.lineHeight,this.fullUpdate(t)):(e.updatePosition(this.nextChar3DPos,t),this.nextChar3DPos.x+=e.charSize)}deleteChar3D(e=this.message.length-1){let t=this.message[e];if(t)return this.message.splice(e,1),this.nextChar3DPos.x-=t.charSize,t}isEndLine(e){this.endLine=e}fullUpdate(e,t=0){this.nextChar3DPos.x=0;for(let r=t,i=this.message.length;r<i;r+=1)this.message[r].updatePosition(this.nextChar3DPos,e),this.nextChar3DPos.x+=this.message[r].charSize}checkOverFlow(e){let t,r=this.message.length-1;if(r<=0)return!1;for(;r>=0;){if(this.message[r].char!==\\\\\\\" \\\\\\\"){t=this.message[r];break}r-=1}return!!(r>=0&&t&&t.localPosition.x+t.charSize>e)}containSpaceOverFlow(e=this.message.length-1){for(let t=e;t>=0;t-=1)if(this.message[t].char===\\\\\\\" \\\\\\\")return!0;return!1}containSpace(e=this.message.length-1){if(this.endLine)return!0;for(let t=e;t>=0;t-=1)if(this.message[t].char===\\\\\\\" \\\\\\\")return!0;return!1}popWord(e=this.message.length-1){let t=[],r=!0,i;for(i=e;i>=0;i-=1)if(this.message[i].char===\\\\\\\" \\\\\\\"){r=!1,t.length===0&&(i-=1,t.splice(0,0,this.message[i]));break}else t.splice(0,0,this.message[i]);return r?t=[]:this.message.splice(i+1,t.length),t}getWord(e=0,t=1){let r=[],i=e;for(i=e;;i+=t){if(!this.message[i]||this.message[i].char===\\\\\\\" \\\\\\\"){r.length===0&&this.message[i]&&(r.push(this.message[i]),this.message.splice(i,1));break}t>0?(r.push(this.message[i]),this.message.splice(i,1),i-=t):(r.splice(0,0,this.message[i]),this.message.splice(i,1))}return r}getWordAtIndex(e){let t=[];for(let r=e;r<this.message.length&&this.message[r].char!==\\\\\\\" \\\\\\\";r++)t.push(this.message[r]);for(let r=e-1;r>=0&&this.message[r].char!==\\\\\\\" \\\\\\\";r--)t.splice(0,0,this.message[r]);return t}wordSize(e=0,t=-1){let r=0,i=e;for(;i>=0&&i<this.message.length;){if(this.message[i].char===\\\\\\\" \\\\\\\"){r===0&&(r=this.message[i].charSize);break}r+=this.message[i].charSize,i+=t}return(i<0||i>=this.message.length)&&!this.endLine?this.message[e]?this.message[e].charSize:999999999:r===0?999999999:r}spaceLeft(e){return e-this.nextChar3DPos.x}popChar(e=this.message.length-1){return this.nextChar3DPos.x-=this.message[e].charSize,this.message.splice(e,1)}isEmpty(){return!this.message.length}updateNextCharPosY(){this.nextChar3DPos.y=this.yLinePos+this.maxCharSize*this.lineHeight}updateYLinePos(e){this.yLinePos=e,this.updateNextCharPosY()}updatelineHeight(e){this.lineHeight=e,this.updateNextCharPosY()}updateFontSize(e,t=0,r=this.message.length-1){for(let i=t;i<=r;i+=1)this.message[i].updateFontSize(e);this.maxCharSize=e,this.nextChar3DPos.y=this.yLinePos+this.maxCharSize*this.lineHeight}countSpaces(){let e=0;for(let t=0;t<this.message.length;t++)this.message[t].char===\\\\\\\" \\\\\\\"&&(e+=1);return e}alignText(e,t,r,i,n){switch(r){case er.LEFT:this.leftAlign(e,n);break;case er.CENTER:this.centerAlign(this.spaceLeft(t),e,n);break;case er.RIGHT:this.rightAlign(this.spaceLeft(t),e,n);break;case er.JUSTIFY:this.justifyAlign(this.spaceLeft(t),e,n);break}}offsetCharacters(e,t,r){Aa.set(t,r);let i=this.message.length;for(let n=0;n<i;n++)this.message[n].updatePosition(this.message[n].localPosition.add(Aa),e)}leftAlign(e,t){this.align=er.LEFT,this.offsetCharacters(e,0,t)}centerAlign(e,t,r){this.align=er.CENTER,this.offsetCharacters(t,e/2,r)}rightAlign(e,t,r){this.align=er.RIGHT,this.offsetCharacters(t,e,r)}justifyAlign(e,t,r){if(this.align=er.JUSTIFY,this.endLine){this.offsetCharacters(t,0,r);return}let i=this.countSpaces();if(i===0){this.offsetCharacters(t,0,r);return}let n=e/i,o=0;for(let l=0;l<this.message.length;l++)this.message[l].char===\\\\\\\" \\\\\\\"&&(o+=n),Aa.set(o,r),this.message[l].updatePosition(this.message[l].localPosition.add(Aa),t)}clone(){let e=new Ys(this.yLinePos,this.lineHeight,this.maxCharSize);e.nextChar3DPos=this.nextChar3DPos.clone(),e.align=this.align,e.endLine=this.endLine;for(let t=0;t<this.message.length;t++)e.message.push(this.message[t].clone());return e}};var Ei=class extends Ot{constructor(e,t=new Qt({transparent:!0,opacity:1,visible:!1,side:Xr.DoubleSide})){super(e,t);this.objectType=\\\\\\\"TextFrame\\\\\\\";this.material.visible=!1,this._geometryUserData=e.userData,this.userData.textFrame={hexColor:6974058,opacity:1,visible:!0,fontSize:16,lineHeight:1.5,letterSpacing:1,fontFamily:\\\\\\\"roboto_regular\\\\\\\",textTransform:rs.STANDARD,horizontalAlignment:1,verticalAlignment:Mi.TOP,LOD:16,maxLineSize:this._geometryUserData.parameters.width,textOrigin:new Xr.Vector3(this._geometryUserData.parameters.width*-.5,this._geometryUserData.parameters.height*.5,0),textLines:[]},this.createTextLine()}raycast(e,t){let r=[];if(super.raycast(e,r),r.length>0){t.push(r[0]);return}let i=[];for(let n=0,o=this.children.length;n<o;++n)if(this.children[n]instanceof $t&&(e.intersectObject(this.children[n],!1,i),i.length>0)){i[0].object=this,t.push(i[0]);return}}updateGeometry(e){let t=this.userData,r=this.geometry.userData,i=r.parameters.width,n=e.parameters?.width,o=e.parameters?.height,l=r.parameters.height,a=t.textFrame;super.updateGeometry(e),a.maxLineSize=r.parameters.width,a.textOrigin.set(r.parameters.width*-.5,r.parameters.height*.5,0),o!==void 0&&l!==o?(this.checkOverFlow(),this.checkCapacity()):n!==void 0&&(i<n?this.checkCapacity():i>n&&this.checkOverFlow())}checkOverFlow(e=0){let t=this.userData,r=t.textFrame.textOrigin,i=t.textFrame.textLines;for(let n=e;n<i.length;n++){i[n].updateYLinePos(this.getNewLinePosition(n)),i[n].fullUpdate(r);let o=[];for(;i[n].checkOverFlow(t.textFrame.maxLineSize);)i[n].containSpaceOverFlow()?o.splice(0,0,i[n].getWord(i[n].message.length-1,is.TO_LEFT)):o.splice(0,0,i[n].popChar());if(o.length>0){i[n+1]===void 0&&(i[n].isEndLine(!1),this.createTextLine());let l=0;for(let a=0;a<o.length;a+=1)for(let c=0;c<o[a].length;c+=1)i[n+1].addChar3D(o[a][c],r,l),l+=1;i[n+1].fullUpdate(r)}i[n].fullUpdate(r)}this.textFullUpdate(e)}checkCapacity(e=0){let t=this.userData,r=t.textFrame.textOrigin,i=t.textFrame.maxLineSize,n=t.textFrame.textLines;for(let o=e;o<n.length;o+=1)if(n[o].updateYLinePos(this.getNewLinePosition(o)),n[o].fullUpdate(r),!!n[o-1])for(;!n[o-1].endLine;){let l,a=n[o-1].spaceLeft(i);if(n[o].wordSize(0,is.TO_RIGHT)<=a){n[o].containSpace()?l=n[o].getWord(0,is.TO_RIGHT):l=n[o].popChar(0);for(let c=0;c<l.length;c+=1)l[c]&&n[o-1].addChar3D(l[c],r)}else{n[o].isEmpty()?(n[o].endLine&&n[o-1].isEndLine(!0),n.splice(o,1),o-=1):(n[o].updateYLinePos(this.getNewLinePosition(o)),n[o].fullUpdate(r));break}}this.textFullUpdate(e)}createTextLine(e=this.userData.textFrame.textLines.length){let r=this.userData.textFrame;r.textLines.splice(e,0,new Ys(this.getNewLinePosition(e),r.lineHeight,r.fontSize))}textFullUpdate(e=0){let r=this.userData.textFrame,i=r.textLines,n=this.getVerticalAlignmentOffSet();for(let o=e;o<i.length;o++)i[o].updateYLinePos(this.getNewLinePosition(o)),i[o].fullUpdate(r.textOrigin),i[o].alignText(r.textOrigin,r.maxLineSize,r.horizontalAlignment,r.verticalAlignment,n)}getVerticalAlignmentOffSet(){switch(this.userData.textFrame.verticalAlignment){case Mi.TOP:return 0;case Mi.MIDDLE:return this.getRemainingVerticalSpace()/2;case Mi.BOTTOM:return this.getRemainingVerticalSpace();default:return 0}}getRemainingVerticalSpace(){let t=this.userData.textFrame.textLines;return this.geometry.userData.parameters.height-this.getNewLinePosition(t.length)}getNewLinePosition(e){let r=this.userData.textFrame.textLines,i=0;for(let n=0;n<e;n+=1)i+=r[n].maxCharSize*r[n].lineHeight;return i}updateColor(e){let t=this.userData;t.textFrame.hexColor=e;let r=t.textFrame.textLines;for(let i=0;i<r.length;i++){let n=r[i].message;for(let o=0;o<n.length;o++){let l=n[o].material;l.color?.isColor&&l.color.setHex(e)}}}updateOpacity(e){let t=this.userData;t.textFrame.opacity=e;let r=t.textFrame.textLines;for(let i=0;i<r.length;i++){let n=r[i].message;for(let o=0;o<n.length;o++){let l=n[o].material;l.opacity=e}}}updateVisible(e){let t=this.userData;t.textFrame.visible=e;let r=t.textFrame.textLines;for(let i=0;i<r.length;i++){let n=r[i].message;for(let o=0;o<n.length;o++){let l=n[o].material;l.visible=e}}}async updateFontFamily(e){await $t.loadFont(e);let r=this.userData.textFrame,i=r.textLines;r.fontFamily=e;for(let n=0;n<i.length;n++){let o=i[n].message;for(let l=0;l<o.length;l++)o[l].updateFontFamily(e)}this.textFullUpdate(),this.checkOverFlow(),this.checkCapacity()}updateFontSize(e){let r=this.userData.textFrame,i=r.textLines,n=r.fontSize;r.fontSize=e;for(let o=0;o<i.length;o++)i[o].updateFontSize(e);this.textFullUpdate(),e>n?this.checkOverFlow():e<n&&this.checkCapacity()}async updateTextTransform(e){let r=this.userData.textFrame;await $t.loadFont(r.fontFamily);let i=r.textLines;switch(r.textTransform=e,e){case rs.UPPERCASE:for(let n=0;n<i.length;n++){let o=i[n].message;for(let l=0;l<o.length;l++)i[n].message[l].updateChar(o[l].char.toUpperCase())}break;case rs.LOWERCASE:for(let n=0;n<i.length;n++){let o=i[n].message;for(let l=0;l<o.length;l++)i[n].message[l].updateChar(o[l].char.toLowerCase())}break;default:for(let n=0;n<i.length;n++){let o=i[n].message;for(let l=0;l<o.length;l++)i[n].message[l].updateChar(o[l].originalChar)}}this.textFullUpdate(),this.checkOverFlow(),this.checkCapacity()}updateLetterSpacing(e){let t=this.userData,r=t.textFrame.textLines;t.textFrame.letterSpacing=e;for(let i=0;i<r.length;i++){let n=r[i].message;for(let o=0;o<n.length;o++)n[o].updateLetterSpacing(e)}this.textFullUpdate(),this.checkOverFlow(),this.checkCapacity()}updateLOD(e){let t=this.userData;t.textFrame.LOD=e;let r=t.textFrame.textLines;for(let i=0;i<r.length;i++);this.textFullUpdate(),this.checkOverFlow(),this.checkCapacity()}updateLineHeight(e){let t=this.userData,r=t.textFrame.textLines;t.textFrame.lineHeight=e;for(let i=0;i<r.length;i++)r[i].updatelineHeight(e);this.textFullUpdate()}updateVerticalAlignment(e){let t=this.userData;t.textFrame.verticalAlignment=e,this.textFullUpdate()}updateHorizontalAlignment(e){let t=this.userData;t.textFrame.horizontalAlignment=e,this.textFullUpdate()}copy(e,t){super.copy(e,t);let r=this.userData,i=e.userData,n=this.geometry.userData;r.textFrame.textOrigin=new Xr.Vector3(n.parameters.width*-.5,n.parameters.height*.5,0);let o=i.textFrame.textLines;r.textFrame.textLines=[];for(let l=0;l<o.length;l++){let a=o[l].clone(),c=a.message;r.textFrame.textLines.push(a);for(let u=0;u<c.length;u++)this.add(c[u])}return this.textFullUpdate(),this.updateColor(i.textFrame.hexColor),this}toJSON(e){let t=super.toJSON(e),r=t.object;r.objectType=\\\\\\\"TextFrame\\\\\\\";let n=this.userData.textFrame.textLines.map(o=>{let l=o.message.map(a=>({char:a.char,originalChar:a.originalChar,fontFamily:a.fontFamily,letterSpacing:a.letterSpacing,fontSize:a.fontSize,LOD:a.LOD}));return{align:o.align,endLine:o.endLine,lineHeight:o.lineHeight,maxCharSize:o.maxCharSize,yLinePos:o.yLinePos,message:l}});return r.userData.textFrame.textLinesData=n,t}async fromJSONasync(e){if(super.fromJSON(e),e.userData!==void 0){let t=e.userData.textFrame;await $t.loadFont(t.fontFamily),t.textOrigin=new Xr.Vector3(t.textOrigin.x,t.textOrigin.y,t.textOrigin.z);let r=new Xr.MeshBasicMaterial({color:t.hexColor,opacity:t.opacity,visible:t.visible,transparent:!0,side:Xr.DoubleSide});t.textLinesData&&(t.textLines=t.textLinesData.map((i,n)=>{let o=new Ys(Number(i.yLinePos),Number(i.lineHeight),Number(i.maxCharSize)),l=i.message.map((a,c)=>{if(a.char===void 0){let h=t.textLines[n].message[c];if(\\\\\\\"geometries\\\\\\\"in h){let p=h.geometries[0].userData.parameters;Object.assign(a,{LOD:p.lod,char:p.char,fontFamily:p.fontFamily,fontSize:p.fontSize,letterSpacing:p.letterSpacing,originalChar:p.char})}}let u={char:a.char,fontFamily:a.fontFamily,letterSpacing:Number(a.letterSpacing),fontSize:Number(a.fontSize),LOD:a.LOD},d=new $t(u,r.clone());return o.addChar3D(d,t.textOrigin),this.add(d),d});return o.message=l,o}),this.userData.textFrame=t),this.textFullUpdate()}return this}convertToVector(){let{fontFamily:e,hexColor:t}=this.userData.textFrame,r=new gr;r.name=\\\\\\\"Text Shape\\\\\\\";let i=$t.fontCache[e];for(let n of this.children)n instanceof $t&&i.generateShapes(n.char,1).forEach(o=>{let l=new Xe().fromShape(o);l.applyScale(n.scale.x,n.scale.y);let a=Mt.create({shape:l}),c=new Qt({side:Xr.DoubleSide});c.color?.value.setHex(t);let u=new Ni(a,c);u.name=n.char,u.position.copy(n.position),u.rotation.copy(n.rotation),r.attach(u)});return r}};Ei.VERTICAL_ALIGN=Mi,Ei.HORIZONTAL_ALIGN=er,Ei.TEXT_TRANSFORM=rs,Ei.TRAVEL_DIRECTION=is;var bt=F(z());var Je=F(z()),wa=F(z()),_c=F(z()),Rt=F(z()),yr=F(z()),Na=F(z()),tr=F(z()),ss=F(z()),Ca=F(z()),Ma=F(z()),Ea=F(z()),Mr=F(z()),Er=F(z()),Nt=F(z()),Oc=F(z()),Ir=F(z()),qr=F(z()),La=F(z()),lr=F(z()),Ks=F(z()),Ht=F(z()),$r=F(z()),Lr=F(z()),Pc=F(z()),Mf=F(z()),Dr=F(z()),vr=F(z()),Ba=F(z()),If=F(z()),Xs=F(z()),_a=F(z()),Oa=F(z()),Pa=F(z()),_r=F(z()),ho=F(z()),Df=F(z()),xr=F(z()),Ax=F(z()),jc=F(z()),Nx=F(z()),Fa=F(z()),Tt=F(z()),Gf=F(z()),Ex=F(z()),fo=F(z()),ns=F(z()),ir=F(z()),zf=F(z()),po=F(z()),ui=F(z()),cr=F(z()),os=F(z()),qs=F(z()),Vf=F(z()),Hf=F(z()),mo=F(z()),di=F(z()),Bx=F(z()),Ze=F(z()),ls=F(z()),go=F(z()),Zs=F(z()),kc=F(z()),Rx=F(z()),Ga=F(z()),yo=F(z()),za=F(z());var bf={RED:0,GREEN:1,BLUE:2,ALPHA:3};var uo=\\\\\\\"varying vec2 vUv;void main(){vUv=position.xy*0.5+0.5;gl_Position=vec4(position.xy,1.0,1.0);}\\\\\\\";var Ev=`#ifdef FRAMEBUFFER_PRECISION_HIGH\\nuniform mediump sampler2D inputBuffer;\\n#else\\nuniform lowp sampler2D inputBuffer;\\n#endif\\nuniform lowp sampler2D cocBuffer;uniform vec2 texelSize;uniform float scale;\\n#if PASS == 1\\nuniform vec4 kernel64[32];\\n#else\\nuniform vec4 kernel16[8];\\n#endif\\nvarying vec2 vUv;void main(){\\n#ifdef FOREGROUND\\nvec2 CoCNearFar=texture2D(cocBuffer,vUv).rg;float CoC=CoCNearFar.r*scale;\\n#else\\nfloat CoC=texture2D(cocBuffer,vUv).g*scale;\\n#endif\\nif(CoC==0.0){gl_FragColor=texture2D(inputBuffer,vUv);}else{\\n#ifdef FOREGROUND\\nvec2 step=texelSize*max(CoC,CoCNearFar.g*scale);\\n#else\\nvec2 step=texelSize*CoC;\\n#endif\\n#if PASS == 1\\nvec4 acc=vec4(0.0);for(int i=0;i<32;++i){vec4 kernel=kernel64[i];vec2 uv=step*kernel.xy+vUv;acc+=texture2D(inputBuffer,uv);uv=step*kernel.zw+vUv;acc+=texture2D(inputBuffer,uv);}gl_FragColor=acc/64.0;\\n#else\\nvec4 maxValue=texture2D(inputBuffer,vUv);for(int i=0;i<8;++i){vec4 kernel=kernel16[i];vec2 uv=step*kernel.xy+vUv;maxValue=max(texture2D(inputBuffer,uv),maxValue);uv=step*kernel.zw+vUv;maxValue=max(texture2D(inputBuffer,uv),maxValue);}gl_FragColor=maxValue;\\n#endif\\n}}`,Sa=class extends Rt.ShaderMaterial{constructor(s=!1,e=!1){super({type:\\\\\\\"BokehMaterial\\\\\\\",defines:{PASS:s?\\\\\\\"2\\\\\\\":\\\\\\\"1\\\\\\\"},uniforms:{kernel64:new Rt.Uniform(null),kernel16:new Rt.Uniform(null),inputBuffer:new Rt.Uniform(null),cocBuffer:new Rt.Uniform(null),texelSize:new Rt.Uniform(new Rt.Vector2),scale:new Rt.Uniform(1)},fragmentShader:Ev,vertexShader:uo,blending:Rt.NoBlending,depthWrite:!1,depthTest:!1});this.toneMapped=!1,e&&(this.defines.FOREGROUND=\\\\\\\"1\\\\\\\"),this.generateKernel()}generateKernel(){let s=2.39996323,e=new Float32Array(128),t=new Float32Array(32),r=0,i=0;for(let l=0;l<80;++l){let a=l*s,c=Math.sqrt(l)/Math.sqrt(80),u=c*Math.cos(a),d=c*Math.sin(a);l%5==0?(t[i++]=u,t[i++]=d):(e[r++]=u,e[r++]=d)}let n=[],o=[];for(let l=0;l<128;)n.push(new Rt.Vector4(e[l++],e[l++],e[l++],e[l++]));for(let l=0;l<32;)o.push(new Rt.Vector4(t[l++],t[l++],t[l++],t[l++]));this.uniforms.kernel64.value=n,this.uniforms.kernel16.value=o}setTexelSize(s,e){this.uniforms.texelSize.value.set(s,e)}},Iv=`#include <common>\\n#include <packing>\\n#ifdef GL_FRAGMENT_PRECISION_HIGH\\nuniform highp sampler2D depthBuffer;\\n#else\\nuniform mediump sampler2D depthBuffer;\\n#endif\\nuniform float focusDistance;uniform float focalLength;uniform float cameraNear;uniform float cameraFar;varying vec2 vUv;float readDepth(const in vec2 uv){\\n#if DEPTH_PACKING == 3201\\nreturn unpackRGBAToDepth(texture2D(depthBuffer,uv));\\n#else\\nreturn texture2D(depthBuffer,uv).r;\\n#endif\\n}void main(){float depth=readDepth(vUv);\\n#ifdef PERSPECTIVE_CAMERA\\nfloat viewZ=perspectiveDepthToViewZ(depth,cameraNear,cameraFar);float linearDepth=viewZToOrthographicDepth(viewZ,cameraNear,cameraFar);\\n#else\\nfloat linearDepth=depth;\\n#endif\\nfloat signedDistance=linearDepth-focusDistance;float magnitude=smoothstep(0.0,focalLength,abs(signedDistance));gl_FragColor.rg=vec2(step(signedDistance,0.0)*magnitude,step(0.0,signedDistance)*magnitude);}`,Lv=class extends yr.ShaderMaterial{constructor(s){super({type:\\\\\\\"CircleOfConfusionMaterial\\\\\\\",defines:{DEPTH_PACKING:\\\\\\\"0\\\\\\\"},uniforms:{depthBuffer:new yr.Uniform(null),focusDistance:new yr.Uniform(0),focalLength:new yr.Uniform(0),cameraNear:new yr.Uniform(.3),cameraFar:new yr.Uniform(1e3)},fragmentShader:Iv,vertexShader:uo,blending:yr.NoBlending,depthWrite:!1,depthTest:!1});this.toneMapped=!1,this.adoptCameraSettings(s)}get depthPacking(){return Number(this.defines.DEPTH_PACKING)}set depthPacking(s){this.defines.DEPTH_PACKING=s.toFixed(0),this.needsUpdate=!0}adoptCameraSettings(s=null){s!==null&&(this.uniforms.cameraNear.value=s.near,this.uniforms.cameraFar.value=s.far,s instanceof yr.PerspectiveCamera?this.defines.PERSPECTIVE_CAMERA=\\\\\\\"1\\\\\\\":delete this.defines.PERSPECTIVE_CAMERA,this.needsUpdate=!0)}},Dv=`varying vec2 vUv;varying vec2 vUv0;varying vec2 vUv1;\\n#if EDGE_DETECTION_MODE != 0\\nvarying vec2 vUv2;varying vec2 vUv3;varying vec2 vUv4;varying vec2 vUv5;\\n#endif\\n#if EDGE_DETECTION_MODE == 1\\n#include <common>\\n#endif\\n#if EDGE_DETECTION_MODE == 0 || PREDICATION_MODE == 1\\n#ifdef GL_FRAGMENT_PRECISION_HIGH\\nuniform highp sampler2D depthBuffer;\\n#else\\nuniform mediump sampler2D depthBuffer;\\n#endif\\nfloat readDepth(const in vec2 uv){\\n#if DEPTH_PACKING == 3201\\nreturn unpackRGBAToDepth(texture2D(depthBuffer,uv));\\n#else\\nreturn texture2D(depthBuffer,uv).r;\\n#endif\\n}vec3 gatherNeighbors(){float p=readDepth(vUv);float pLeft=readDepth(vUv0);float pTop=readDepth(vUv1);return vec3(p,pLeft,pTop);}\\n#elif PREDICATION_MODE == 2\\nuniform sampler2D predicationBuffer;vec3 gatherNeighbors(){float p=texture2D(predicationBuffer,vUv).r;float pLeft=texture2D(predicationBuffer,vUv0).r;float pTop=texture2D(predicationBuffer,vUv1).r;return vec3(p,pLeft,pTop);}\\n#endif\\n#if PREDICATION_MODE != 0\\nvec2 calculatePredicatedThreshold(){vec3 neighbours=gatherNeighbors();vec2 delta=abs(neighbours.xx-neighbours.yz);vec2 edges=step(PREDICATION_THRESHOLD,delta);return PREDICATION_SCALE*EDGE_THRESHOLD*(1.0-PREDICATION_STRENGTH*edges);}\\n#endif\\n#if EDGE_DETECTION_MODE != 0\\nuniform sampler2D inputBuffer;\\n#endif\\nvoid main(){\\n#if EDGE_DETECTION_MODE == 0\\nconst vec2 threshold=vec2(DEPTH_THRESHOLD);\\n#elif PREDICATION_MODE != 0\\nvec2 threshold=calculatePredicatedThreshold();\\n#else\\nconst vec2 threshold=vec2(EDGE_THRESHOLD);\\n#endif\\n#if EDGE_DETECTION_MODE == 0\\nvec3 neighbors=gatherNeighbors();vec2 delta=abs(neighbors.xx-vec2(neighbors.y,neighbors.z));vec2 edges=step(threshold,delta);if(dot(edges,vec2(1.0))==0.0){discard;}gl_FragColor=vec4(edges,0.0,1.0);\\n#elif EDGE_DETECTION_MODE == 1\\nfloat l=linearToRelativeLuminance(texture2D(inputBuffer,vUv).rgb);float lLeft=linearToRelativeLuminance(texture2D(inputBuffer,vUv0).rgb);float lTop=linearToRelativeLuminance(texture2D(inputBuffer,vUv1).rgb);vec4 delta;delta.xy=abs(l-vec2(lLeft,lTop));vec2 edges=step(threshold,delta.xy);if(dot(edges,vec2(1.0))==0.0){discard;}float lRight=linearToRelativeLuminance(texture2D(inputBuffer,vUv2).rgb);float lBottom=linearToRelativeLuminance(texture2D(inputBuffer,vUv3).rgb);delta.zw=abs(l-vec2(lRight,lBottom));vec2 maxDelta=max(delta.xy,delta.zw);float lLeftLeft=linearToRelativeLuminance(texture2D(inputBuffer,vUv4).rgb);float lTopTop=linearToRelativeLuminance(texture2D(inputBuffer,vUv5).rgb);delta.zw=abs(vec2(lLeft,lTop)-vec2(lLeftLeft,lTopTop));maxDelta=max(maxDelta.xy,delta.zw);float finalDelta=max(maxDelta.x,maxDelta.y);edges.xy*=step(finalDelta,LOCAL_CONTRAST_ADAPTATION_FACTOR*delta.xy);gl_FragColor=vec4(edges,0.0,1.0);\\n#elif EDGE_DETECTION_MODE == 2\\nvec4 delta;vec3 c=texture2D(inputBuffer,vUv).rgb;vec3 cLeft=texture2D(inputBuffer,vUv0).rgb;vec3 t=abs(c-cLeft);delta.x=max(max(t.r,t.g),t.b);vec3 cTop=texture2D(inputBuffer,vUv1).rgb;t=abs(c-cTop);delta.y=max(max(t.r,t.g),t.b);vec2 edges=step(threshold,delta.xy);if(dot(edges,vec2(1.0))==0.0){discard;}vec3 cRight=texture2D(inputBuffer,vUv2).rgb;t=abs(c-cRight);delta.z=max(max(t.r,t.g),t.b);vec3 cBottom=texture2D(inputBuffer,vUv3).rgb;t=abs(c-cBottom);delta.w=max(max(t.r,t.g),t.b);vec2 maxDelta=max(delta.xy,delta.zw);vec3 cLeftLeft=texture2D(inputBuffer,vUv4).rgb;t=abs(c-cLeftLeft);delta.z=max(max(t.r,t.g),t.b);vec3 cTopTop=texture2D(inputBuffer,vUv5).rgb;t=abs(c-cTopTop);delta.w=max(max(t.r,t.g),t.b);maxDelta=max(maxDelta.xy,delta.zw);float finalDelta=max(maxDelta.x,maxDelta.y);edges*=step(finalDelta,LOCAL_CONTRAST_ADAPTATION_FACTOR*delta.xy);gl_FragColor=vec4(edges,0.0,1.0);\\n#endif\\n}`,Bv=`uniform vec2 texelSize;varying vec2 vUv;varying vec2 vUv0;varying vec2 vUv1;\\n#if EDGE_DETECTION_MODE != 0\\nvarying vec2 vUv2;varying vec2 vUv3;varying vec2 vUv4;varying vec2 vUv5;\\n#endif\\nvoid main(){vUv=position.xy*0.5+0.5;vUv0=vUv+texelSize*vec2(-1.0,0.0);vUv1=vUv+texelSize*vec2(0.0,-1.0);\\n#if EDGE_DETECTION_MODE != 0\\nvUv2=vUv+texelSize*vec2(1.0,0.0);vUv3=vUv+texelSize*vec2(0.0,1.0);vUv4=vUv+texelSize*vec2(-2.0,0.0);vUv5=vUv+texelSize*vec2(0.0,-2.0);\\n#endif\\ngl_Position=vec4(position.xy,1.0,1.0);}`;var _v=`#include <common>\\n#include <dithering_pars_fragment>\\n#ifdef FRAMEBUFFER_PRECISION_HIGH\\nuniform mediump sampler2D inputBuffer;\\n#else\\nuniform lowp sampler2D inputBuffer;\\n#endif\\nvarying vec2 vUv0;varying vec2 vUv1;varying vec2 vUv2;varying vec2 vUv3;void main(){vec4 sum=texture2D(inputBuffer,vUv0);sum+=texture2D(inputBuffer,vUv1);sum+=texture2D(inputBuffer,vUv2);sum+=texture2D(inputBuffer,vUv3);gl_FragColor=sum*0.25;\\n#include <dithering_fragment>\\n}`,Ov=\\\\\\\"uniform vec2 texelSize;uniform vec2 halfTexelSize;uniform float kernel;uniform float scale;varying vec2 vUv0;varying vec2 vUv1;varying vec2 vUv2;varying vec2 vUv3;void main(){vec2 uv=position.xy*0.5+0.5;vec2 dUv=(texelSize*vec2(kernel)+halfTexelSize)*scale;vUv0=vec2(uv.x-dUv.x,uv.y+dUv.y);vUv1=vec2(uv.x+dUv.x,uv.y+dUv.y);vUv2=vec2(uv.x+dUv.x,uv.y-dUv.y);vUv3=vec2(uv.x-dUv.x,uv.y-dUv.y);gl_Position=vec4(position.xy,1.0,1.0);}\\\\\\\",Af=class extends tr.ShaderMaterial{constructor(s=new tr.Vector2){super({type:\\\\\\\"ConvolutionMaterial\\\\\\\",uniforms:{inputBuffer:new tr.Uniform(null),texelSize:new tr.Uniform(new tr.Vector2),halfTexelSize:new tr.Uniform(new tr.Vector2),kernel:new tr.Uniform(0),scale:new tr.Uniform(1)},fragmentShader:_v,vertexShader:Ov,blending:tr.NoBlending,depthWrite:!1,depthTest:!1});this.toneMapped=!1,this.setTexelSize(s.x,s.y),this.kernelSize=Ta.LARGE}getKernel(){return Pv[this.kernelSize]}setTexelSize(s,e){this.uniforms.texelSize.value.set(s,e),this.uniforms.halfTexelSize.value.set(s,e).multiplyScalar(.5)}},Pv=[new Float32Array([0,0]),new Float32Array([0,1,1]),new Float32Array([0,1,1,2]),new Float32Array([0,1,2,2,3]),new Float32Array([0,1,2,3,4,4,5]),new Float32Array([0,1,2,3,4,5,7,8,9,10])],Ta={VERY_SMALL:0,SMALL:1,MEDIUM:2,LARGE:3,VERY_LARGE:4,HUGE:5},Rv=`#ifdef FRAMEBUFFER_PRECISION_HIGH\\nuniform mediump sampler2D inputBuffer;\\n#else\\nuniform lowp sampler2D inputBuffer;\\n#endif\\nuniform float opacity;varying vec2 vUv;void main(){vec4 texel=texture2D(inputBuffer,vUv);gl_FragColor=opacity*texel;\\n#include <encodings_fragment>\\n}`,wf=class extends ss.ShaderMaterial{constructor(){super({type:\\\\\\\"CopyMaterial\\\\\\\",uniforms:{inputBuffer:new ss.Uniform(null),opacity:new ss.Uniform(1)},fragmentShader:Rv,vertexShader:uo,blending:ss.NoBlending,depthWrite:!1,depthTest:!1});this.toneMapped=!1}};var Fv=class extends Er.ShaderMaterial{constructor(s=new Er.Vector2,e=Ia.COLOR){super({type:\\\\\\\"EdgeDetectionMaterial\\\\\\\",defines:{LOCAL_CONTRAST_ADAPTATION_FACTOR:\\\\\\\"2.0\\\\\\\",EDGE_THRESHOLD:\\\\\\\"0.1\\\\\\\",DEPTH_THRESHOLD:\\\\\\\"0.01\\\\\\\",PREDICATION_MODE:\\\\\\\"0\\\\\\\",PREDICATION_THRESHOLD:\\\\\\\"0.01\\\\\\\",PREDICATION_SCALE:\\\\\\\"2.0\\\\\\\",PREDICATION_STRENGTH:\\\\\\\"1.0\\\\\\\",DEPTH_PACKING:\\\\\\\"0\\\\\\\"},uniforms:{inputBuffer:new Er.Uniform(null),depthBuffer:new Er.Uniform(null),predicationBuffer:new Er.Uniform(null),texelSize:new Er.Uniform(s)},fragmentShader:Dv,vertexShader:Bv,blending:Er.NoBlending,depthWrite:!1,depthTest:!1});this.toneMapped=!1,this.setEdgeDetectionMode(e)}get depthPacking(){return Number(this.defines.DEPTH_PACKING)}set depthPacking(s){this.defines.DEPTH_PACKING=s.toFixed(0),this.needsUpdate=!0}setEdgeDetectionMode(s){this.defines.EDGE_DETECTION_MODE=s.toFixed(0),this.needsUpdate=!0}setLocalContrastAdaptationFactor(s){this.defines.LOCAL_CONTRAST_ADAPTATION_FACTOR=s.toFixed(\\\\\\\"6\\\\\\\"),this.needsUpdate=!0}setEdgeDetectionThreshold(s){this.defines.EDGE_THRESHOLD=s.toFixed(\\\\\\\"6\\\\\\\"),this.defines.DEPTH_THRESHOLD=(s*.1).toFixed(\\\\\\\"6\\\\\\\"),this.needsUpdate=!0}setPredicationMode(s){this.defines.PREDICATION_MODE=s.toFixed(0),this.needsUpdate=!0}setPredicationBuffer(s){this.uniforms.predicationBuffer.value=s}setPredicationThreshold(s){this.defines.PREDICATION_THRESHOLD=s.toFixed(\\\\\\\"6\\\\\\\"),this.needsUpdate=!0}setPredicationScale(s){this.defines.PREDICATION_SCALE=s.toFixed(\\\\\\\"6\\\\\\\"),this.needsUpdate=!0}setPredicationStrength(s){this.defines.PREDICATION_STRENGTH=s.toFixed(\\\\\\\"6\\\\\\\"),this.needsUpdate=!0}},Ia={DEPTH:0,LUMA:1,COLOR:2},Sf={DISABLED:0,DEPTH:1,CUSTOM:2},Gv=`#include <common>\\n#include <packing>\\n#include <dithering_pars_fragment>\\n#ifdef FRAMEBUFFER_PRECISION_HIGH\\nuniform mediump sampler2D inputBuffer;\\n#else\\nuniform lowp sampler2D inputBuffer;\\n#endif\\n#ifdef GL_FRAGMENT_PRECISION_HIGH\\nuniform highp sampler2D depthBuffer;\\n#else\\nuniform mediump sampler2D depthBuffer;\\n#endif\\nuniform vec2 resolution;uniform vec2 texelSize;uniform float cameraNear;uniform float cameraFar;uniform float aspect;uniform float time;varying vec2 vUv;float readDepth(const in vec2 uv){\\n#if DEPTH_PACKING == 3201\\nreturn unpackRGBAToDepth(texture2D(depthBuffer,uv));\\n#else\\nreturn texture2D(depthBuffer,uv).r;\\n#endif\\n}float getViewZ(const in float depth){\\n#ifdef PERSPECTIVE_CAMERA\\nreturn perspectiveDepthToViewZ(depth,cameraNear,cameraFar);\\n#else\\nreturn orthographicDepthToViewZ(depth,cameraNear,cameraFar);\\n#endif\\n}FRAGMENT_HEADvoid main(){FRAGMENT_MAIN_UVvec4 color0=texture2D(inputBuffer,UV);vec4 color1=vec4(0.0);FRAGMENT_MAIN_IMAGEgl_FragColor=color0;\\n#ifdef ENCODE_OUTPUT\\n#include <encodings_fragment>\\n#endif\\n#include <dithering_fragment>\\n}`,zv=\\\\\\\"uniform vec2 resolution;uniform vec2 texelSize;uniform float cameraNear;uniform float cameraFar;uniform float aspect;uniform float time;varying vec2 vUv;VERTEX_HEADvoid main(){vUv=position.xy*0.5+0.5;VERTEX_MAIN_SUPPORTgl_Position=vec4(position.xy,1.0,1.0);}\\\\\\\",Uv=class extends Nt.ShaderMaterial{constructor(s=null,e=null,t=null,r,i=!1){super({type:\\\\\\\"EffectMaterial\\\\\\\",defines:{DEPTH_PACKING:\\\\\\\"0\\\\\\\",ENCODE_OUTPUT:\\\\\\\"1\\\\\\\"},uniforms:{inputBuffer:new Nt.Uniform(null),depthBuffer:new Nt.Uniform(null),resolution:new Nt.Uniform(new Nt.Vector2),texelSize:new Nt.Uniform(new Nt.Vector2),cameraNear:new Nt.Uniform(.3),cameraFar:new Nt.Uniform(1e3),aspect:new Nt.Uniform(1),time:new Nt.Uniform(0)},blending:Nt.NoBlending,depthWrite:!1,depthTest:!1,dithering:i});this.toneMapped=!1,s!==null&&this.setShaderParts(s),e!==null&&this.setDefines(e),t!==null&&this.setUniforms(t),this.adoptCameraSettings(r)}get depthPacking(){return Number(this.defines.DEPTH_PACKING)}set depthPacking(s){this.defines.DEPTH_PACKING=s.toFixed(0),this.needsUpdate=!0}setShaderParts(s){return this.fragmentShader=Gv.replace(Ue.FRAGMENT_HEAD,s.get(Ue.FRAGMENT_HEAD)).replace(Ue.FRAGMENT_MAIN_UV,s.get(Ue.FRAGMENT_MAIN_UV)).replace(Ue.FRAGMENT_MAIN_IMAGE,s.get(Ue.FRAGMENT_MAIN_IMAGE)),this.vertexShader=zv.replace(Ue.VERTEX_HEAD,s.get(Ue.VERTEX_HEAD)).replace(Ue.VERTEX_MAIN_SUPPORT,s.get(Ue.VERTEX_MAIN_SUPPORT)),this.needsUpdate=!0,this}setDefines(s){for(let e of s.entries())this.defines[e[0]]=e[1];return this.needsUpdate=!0,this}setUniforms(s){for(let e of s.entries())this.uniforms[e[0]]=e[1];return this}adoptCameraSettings(s=null){s!==null&&(this.uniforms.cameraNear.value=s.near,this.uniforms.cameraFar.value=s.far,s instanceof Nt.PerspectiveCamera?this.defines.PERSPECTIVE_CAMERA=\\\\\\\"1\\\\\\\":delete this.defines.PERSPECTIVE_CAMERA,this.needsUpdate=!0)}setSize(s,e){let t=Math.max(s,1),r=Math.max(e,1);this.uniforms.resolution.value.set(t,r),this.uniforms.texelSize.value.set(1/t,1/r),this.uniforms.aspect.value=t/r}},Ue={FRAGMENT_HEAD:\\\\\\\"FRAGMENT_HEAD\\\\\\\",FRAGMENT_MAIN_UV:\\\\\\\"FRAGMENT_MAIN_UV\\\\\\\",FRAGMENT_MAIN_IMAGE:\\\\\\\"FRAGMENT_MAIN_IMAGE\\\\\\\",VERTEX_HEAD:\\\\\\\"VERTEX_HEAD\\\\\\\",VERTEX_MAIN_SUPPORT:\\\\\\\"VERTEX_MAIN_SUPPORT\\\\\\\"};var jv=`#include <common>\\n#ifdef FRAMEBUFFER_PRECISION_HIGH\\nuniform mediump sampler2D inputBuffer;\\n#else\\nuniform lowp sampler2D inputBuffer;\\n#endif\\n#ifdef RANGE\\nuniform vec2 range;\\n#elif defined(THRESHOLD)\\nuniform float threshold;uniform float smoothing;\\n#endif\\nvarying vec2 vUv;void main(){vec4 texel=texture2D(inputBuffer,vUv);float l=linearToRelativeLuminance(texel.rgb);\\n#ifdef RANGE\\nfloat low=step(range.x,l);float high=step(l,range.y);l*=low*high;\\n#elif defined(THRESHOLD)\\nl=smoothstep(threshold,threshold+smoothing,l);\\n#endif\\n#ifdef COLOR\\ngl_FragColor=vec4(texel.rgb*l,l);\\n#else\\ngl_FragColor=vec4(l);\\n#endif\\n}`,kv=class extends Ir.ShaderMaterial{constructor(s=!1,e=null){let t=e!==null;super({type:\\\\\\\"LuminanceMaterial\\\\\\\",uniforms:{inputBuffer:new Ir.Uniform(null),threshold:new Ir.Uniform(0),smoothing:new Ir.Uniform(1),range:new Ir.Uniform(t?e:new Ir.Vector2)},fragmentShader:jv,vertexShader:uo,blending:Ir.NoBlending,depthWrite:!1,depthTest:!1});this.toneMapped=!1,this.colorOutput=s,this.useThreshold=!0,this.useRange=t}get threshold(){return this.uniforms.threshold.value}set threshold(s){this.uniforms.threshold.value=s}get smoothing(){return this.uniforms.smoothing.value}set smoothing(s){this.uniforms.smoothing.value=s}get useThreshold(){return this.defines.THRESHOLD!==void 0}set useThreshold(s){s?this.defines.THRESHOLD=\\\\\\\"1\\\\\\\":delete this.defines.THRESHOLD,this.needsUpdate=!0}get colorOutput(){return this.defines.COLOR!==void 0}set colorOutput(s){s?this.defines.COLOR=\\\\\\\"1\\\\\\\":delete this.defines.COLOR,this.needsUpdate=!0}setColorOutputEnabled(s){this.colorOutput=s}get useRange(){return this.defines.RANGE!==void 0}set useRange(s){s?this.defines.RANGE=\\\\\\\"1\\\\\\\":delete this.defines.RANGE,this.needsUpdate=!0}get luminanceRange(){return this.useRange}set luminanceRange(s){this.useRange=s}setLuminanceRangeEnabled(s){this.useRange=s}},Vv=`#ifdef FRAMEBUFFER_PRECISION_HIGH\\nuniform mediump sampler2D inputBuffer;\\n#else\\nuniform lowp sampler2D inputBuffer;\\n#endif\\n#ifdef MASK_PRECISION_HIGH\\nuniform mediump sampler2D maskTexture;\\n#else\\nuniform lowp sampler2D maskTexture;\\n#endif\\n#if MASK_FUNCTION != 0\\nuniform float strength;\\n#endif\\nvarying vec2 vUv;void main(){\\n#if COLOR_CHANNEL == 0\\nfloat mask=texture2D(maskTexture,vUv).r;\\n#elif COLOR_CHANNEL == 1\\nfloat mask=texture2D(maskTexture,vUv).g;\\n#elif COLOR_CHANNEL == 2\\nfloat mask=texture2D(maskTexture,vUv).b;\\n#else\\nfloat mask=texture2D(maskTexture,vUv).a;\\n#endif\\n#if MASK_FUNCTION == 0\\n#ifdef INVERTED\\nmask=step(mask,0.0);\\n#else\\nmask=1.0-step(mask,0.0);\\n#endif\\n#else\\nmask=clamp(mask*strength,0.0,1.0);\\n#ifdef INVERTED\\nmask=1.0-mask;\\n#endif\\n#endif\\n#if MASK_FUNCTION == 2\\ngl_FragColor=vec4(mask*texture2D(inputBuffer,vUv).rgb,mask);\\n#else\\ngl_FragColor=mask*texture2D(inputBuffer,vUv);\\n#endif\\n}`,Hv=class extends qr.ShaderMaterial{constructor(s=null){super({type:\\\\\\\"MaskMaterial\\\\\\\",uniforms:{maskTexture:new qr.Uniform(s),inputBuffer:new qr.Uniform(null),strength:new qr.Uniform(1)},fragmentShader:Vv,vertexShader:uo,blending:qr.NoBlending,depthWrite:!1,depthTest:!1});this.toneMapped=!1,this.colorChannel=bf.RED,this.maskFunction=Nf.DISCARD}set maskTexture(s){this.uniforms.maskTexture.value=s,delete this.defines.MASK_PRECISION_HIGH,s.type!==qr.UnsignedByteType&&(this.defines.MASK_PRECISION_HIGH=\\\\\\\"1\\\\\\\"),this.needsUpdate=!0}set colorChannel(s){this.defines.COLOR_CHANNEL=s.toFixed(0),this.needsUpdate=!0}set maskFunction(s){this.defines.MASK_FUNCTION=s.toFixed(0),this.needsUpdate=!0}get inverted(){return this.defines.INVERTED!==void 0}set inverted(s){this.inverted&&!s?delete this.defines.INVERTED:s&&(this.defines.INVERTED=\\\\\\\"1\\\\\\\"),this.needsUpdate=!0}get strength(){return this.uniforms.strength.value}set strength(s){this.uniforms.strength.value=s}},Nf={DISCARD:0,MULTIPLY:1,MULTIPLY_RGB_SET_ALPHA:2};var Wv=`#define sampleLevelZeroOffset(t, coord, offset) texture2D(t, coord + offset * texelSize)\\n#if __VERSION__ < 300\\n#define round(v) floor(v + 0.5)\\n#endif\\n#ifdef FRAMEBUFFER_PRECISION_HIGH\\nuniform mediump sampler2D inputBuffer;\\n#else\\nuniform lowp sampler2D inputBuffer;\\n#endif\\nuniform lowp sampler2D areaTexture;uniform lowp sampler2D searchTexture;uniform vec2 texelSize;uniform vec2 resolution;varying vec2 vUv;varying vec4 vOffset[3];varying vec2 vPixCoord;void movec(const in bvec2 c,inout vec2 variable,const in vec2 value){if(c.x){variable.x=value.x;}if(c.y){variable.y=value.y;}}void movec(const in bvec4 c,inout vec4 variable,const in vec4 value){movec(c.xy,variable.xy,value.xy);movec(c.zw,variable.zw,value.zw);}vec2 decodeDiagBilinearAccess(in vec2 e){e.r=e.r*abs(5.0*e.r-5.0*0.75);return round(e);}vec4 decodeDiagBilinearAccess(in vec4 e){e.rb=e.rb*abs(5.0*e.rb-5.0*0.75);return round(e);}vec2 searchDiag1(const in vec2 texCoord,const in vec2 dir,out vec2 e){vec4 coord=vec4(texCoord,-1.0,1.0);vec3 t=vec3(texelSize,1.0);for(int i=0;i<MAX_SEARCH_STEPS_INT;++i){if(!(coord.z<float(MAX_SEARCH_STEPS_DIAG_INT-1)&&coord.w>0.9)){break;}coord.xyz=t*vec3(dir,1.0)+coord.xyz;e=texture2D(inputBuffer,coord.xy).rg;coord.w=dot(e,vec2(0.5));}return coord.zw;}vec2 searchDiag2(const in vec2 texCoord,const in vec2 dir,out vec2 e){vec4 coord=vec4(texCoord,-1.0,1.0);coord.x+=0.25*texelSize.x;vec3 t=vec3(texelSize,1.0);for(int i=0;i<MAX_SEARCH_STEPS_INT;++i){if(!(coord.z<float(MAX_SEARCH_STEPS_DIAG_INT-1)&&coord.w>0.9)){break;}coord.xyz=t*vec3(dir,1.0)+coord.xyz;e=texture2D(inputBuffer,coord.xy).rg;e=decodeDiagBilinearAccess(e);coord.w=dot(e,vec2(0.5));}return coord.zw;}vec2 areaDiag(const in vec2 dist,const in vec2 e,const in float offset){vec2 texCoord=vec2(AREATEX_MAX_DISTANCE_DIAG,AREATEX_MAX_DISTANCE_DIAG)*e+dist;texCoord=AREATEX_PIXEL_SIZE*texCoord+0.5*AREATEX_PIXEL_SIZE;texCoord.x+=0.5;texCoord.y+=AREATEX_SUBTEX_SIZE*offset;return texture2D(areaTexture,texCoord).rg;}vec2 calculateDiagWeights(const in vec2 texCoord,const in vec2 e,const in vec4 subsampleIndices){vec2 weights=vec2(0.0);vec4 d;vec2 end;if(e.r>0.0){d.xz=searchDiag1(texCoord,vec2(-1.0,1.0),end);d.x+=float(end.y>0.9);}else{d.xz=vec2(0.0);}d.yw=searchDiag1(texCoord,vec2(1.0,-1.0),end);if(d.x+d.y>2.0){vec4 coords=vec4(-d.x+0.25,d.x,d.y,-d.y-0.25)*texelSize.xyxy+texCoord.xyxy;vec4 c;c.xy=sampleLevelZeroOffset(inputBuffer,coords.xy,vec2(-1,0)).rg;c.zw=sampleLevelZeroOffset(inputBuffer,coords.zw,vec2(1,0)).rg;c.yxwz=decodeDiagBilinearAccess(c.xyzw);vec2 cc=vec2(2.0)*c.xz+c.yw;movec(bvec2(step(0.9,d.zw)),cc,vec2(0.0));weights+=areaDiag(d.xy,cc,subsampleIndices.z);}d.xz=searchDiag2(texCoord,vec2(-1.0,-1.0),end);if(sampleLevelZeroOffset(inputBuffer,texCoord,vec2(1,0)).r>0.0){d.yw=searchDiag2(texCoord,vec2(1.0),end);d.y+=float(end.y>0.9);}else{d.yw=vec2(0.0);}if(d.x+d.y>2.0){vec4 coords=vec4(-d.x,-d.x,d.y,d.y)*texelSize.xyxy+texCoord.xyxy;vec4 c;c.x=sampleLevelZeroOffset(inputBuffer,coords.xy,vec2(-1,0)).g;c.y=sampleLevelZeroOffset(inputBuffer,coords.xy,vec2(0,-1)).r;c.zw=sampleLevelZeroOffset(inputBuffer,coords.zw,vec2(1,0)).gr;vec2 cc=vec2(2.0)*c.xz+c.yw;movec(bvec2(step(0.9,d.zw)),cc,vec2(0.0));weights+=areaDiag(d.xy,cc,subsampleIndices.w).gr;}return weights;}float searchLength(const in vec2 e,const in float offset){vec2 scale=SEARCHTEX_SIZE*vec2(0.5,-1.0);vec2 bias=SEARCHTEX_SIZE*vec2(offset,1.0);scale+=vec2(-1.0,1.0);bias+=vec2(0.5,-0.5);scale*=1.0/SEARCHTEX_PACKED_SIZE;bias*=1.0/SEARCHTEX_PACKED_SIZE;return texture2D(searchTexture,scale*e+bias).r;}float searchXLeft(in vec2 texCoord,const in float end){vec2 e=vec2(0.0,1.0);for(int i=0;i<MAX_SEARCH_STEPS_INT;++i){if(!(texCoord.x>end&&e.g>0.8281&&e.r==0.0)){break;}e=texture2D(inputBuffer,texCoord).rg;texCoord=vec2(-2.0,0.0)*texelSize+texCoord;}float offset=-(255.0/127.0)*searchLength(e,0.0)+3.25;return texelSize.x*offset+texCoord.x;}float searchXRight(vec2 texCoord,const in float end){vec2 e=vec2(0.0,1.0);for(int i=0;i<MAX_SEARCH_STEPS_INT;++i){if(!(texCoord.x<end&&e.g>0.8281&&e.r==0.0)){break;}e=texture2D(inputBuffer,texCoord).rg;texCoord=vec2(2.0,0.0)*texelSize.xy+texCoord;}float offset=-(255.0/127.0)*searchLength(e,0.5)+3.25;return-texelSize.x*offset+texCoord.x;}float searchYUp(vec2 texCoord,const in float end){vec2 e=vec2(1.0,0.0);for(int i=0;i<MAX_SEARCH_STEPS_INT;++i){if(!(texCoord.y>end&&e.r>0.8281&&e.g==0.0)){break;}e=texture2D(inputBuffer,texCoord).rg;texCoord=-vec2(0.0,2.0)*texelSize.xy+texCoord;}float offset=-(255.0/127.0)*searchLength(e.gr,0.0)+3.25;return texelSize.y*offset+texCoord.y;}float searchYDown(vec2 texCoord,const in float end){vec2 e=vec2(1.0,0.0);for(int i=0;i<MAX_SEARCH_STEPS_INT;i++){if(!(texCoord.y<end&&e.r>0.8281&&e.g==0.0)){break;}e=texture2D(inputBuffer,texCoord).rg;texCoord=vec2(0.0,2.0)*texelSize.xy+texCoord;}float offset=-(255.0/127.0)*searchLength(e.gr,0.5)+3.25;return-texelSize.y*offset+texCoord.y;}vec2 area(const in vec2 dist,const in float e1,const in float e2,const in float offset){vec2 texCoord=vec2(AREATEX_MAX_DISTANCE)*round(4.0*vec2(e1,e2))+dist;texCoord=AREATEX_PIXEL_SIZE*texCoord+0.5*AREATEX_PIXEL_SIZE;texCoord.y=AREATEX_SUBTEX_SIZE*offset+texCoord.y;return texture2D(areaTexture,texCoord).rg;}void detectHorizontalCornerPattern(inout vec2 weights,const in vec4 texCoord,const in vec2 d){\\n#if !defined(DISABLE_CORNER_DETECTION)\\nvec2 leftRight=step(d.xy,d.yx);vec2 rounding=(1.0-CORNER_ROUNDING_NORM)*leftRight;rounding/=leftRight.x+leftRight.y;vec2 factor=vec2(1.0);factor.x-=rounding.x*sampleLevelZeroOffset(inputBuffer,texCoord.xy,vec2(0,1)).r;factor.x-=rounding.y*sampleLevelZeroOffset(inputBuffer,texCoord.zw,vec2(1,1)).r;factor.y-=rounding.x*sampleLevelZeroOffset(inputBuffer,texCoord.xy,vec2(0,-2)).r;factor.y-=rounding.y*sampleLevelZeroOffset(inputBuffer,texCoord.zw,vec2(1,-2)).r;weights*=clamp(factor,0.0,1.0);\\n#endif\\n}void detectVerticalCornerPattern(inout vec2 weights,const in vec4 texCoord,const in vec2 d){\\n#if !defined(DISABLE_CORNER_DETECTION)\\nvec2 leftRight=step(d.xy,d.yx);vec2 rounding=(1.0-CORNER_ROUNDING_NORM)*leftRight;rounding/=leftRight.x+leftRight.y;vec2 factor=vec2(1.0);factor.x-=rounding.x*sampleLevelZeroOffset(inputBuffer,texCoord.xy,vec2(1,0)).g;factor.x-=rounding.y*sampleLevelZeroOffset(inputBuffer,texCoord.zw,vec2(1,1)).g;factor.y-=rounding.x*sampleLevelZeroOffset(inputBuffer,texCoord.xy,vec2(-2,0)).g;factor.y-=rounding.y*sampleLevelZeroOffset(inputBuffer,texCoord.zw,vec2(-2,1)).g;weights*=clamp(factor,0.0,1.0);\\n#endif\\n}void main(){vec4 weights=vec4(0.0);vec4 subsampleIndices=vec4(0.0);vec2 e=texture2D(inputBuffer,vUv).rg;if(e.g>0.0){\\n#if !defined(DISABLE_DIAG_DETECTION)\\nweights.rg=calculateDiagWeights(vUv,e,subsampleIndices);if(weights.r==-weights.g){\\n#endif\\nvec2 d;vec3 coords;coords.x=searchXLeft(vOffset[0].xy,vOffset[2].x);coords.y=vOffset[1].y;d.x=coords.x;float e1=texture2D(inputBuffer,coords.xy).r;coords.z=searchXRight(vOffset[0].zw,vOffset[2].y);d.y=coords.z;d=round(resolution.xx*d+-vPixCoord.xx);vec2 sqrtD=sqrt(abs(d));float e2=sampleLevelZeroOffset(inputBuffer,coords.zy,vec2(1,0)).r;weights.rg=area(sqrtD,e1,e2,subsampleIndices.y);coords.y=vUv.y;detectHorizontalCornerPattern(weights.rg,coords.xyzy,d);\\n#if !defined(DISABLE_DIAG_DETECTION)\\n}else{e.r=0.0;}\\n#endif\\n}if(e.r>0.0){vec2 d;vec3 coords;coords.y=searchYUp(vOffset[1].xy,vOffset[2].z);coords.x=vOffset[0].x;d.x=coords.y;float e1=texture2D(inputBuffer,coords.xy).g;coords.z=searchYDown(vOffset[1].zw,vOffset[2].w);d.y=coords.z;d=round(resolution.yy*d-vPixCoord.yy);vec2 sqrtD=sqrt(abs(d));float e2=sampleLevelZeroOffset(inputBuffer,coords.xz,vec2(0,1)).g;weights.ba=area(sqrtD,e1,e2,subsampleIndices.x);coords.x=vUv.x;detectVerticalCornerPattern(weights.ba,coords.xyxz,d);}gl_FragColor=weights;}`,Jv=\\\\\\\"uniform vec2 texelSize;uniform vec2 resolution;varying vec2 vUv;varying vec4 vOffset[3];varying vec2 vPixCoord;void main(){vUv=position.xy*0.5+0.5;vPixCoord=vUv*resolution;vOffset[0]=vUv.xyxy+texelSize.xyxy*vec4(-0.25,-0.125,1.25,-0.125);vOffset[1]=vUv.xyxy+texelSize.xyxy*vec4(-0.125,-0.25,-0.125,1.25);vOffset[2]=vec4(vOffset[0].xz,vOffset[1].yw)+vec4(-2.0,2.0,-2.0,2.0)*texelSize.xxyy*MAX_SEARCH_STEPS_FLOAT;gl_Position=vec4(position.xy,1.0,1.0);}\\\\\\\",Qv=class extends lr.ShaderMaterial{constructor(s=new lr.Vector2,e=new lr.Vector2){super({type:\\\\\\\"SMAAWeightsMaterial\\\\\\\",defines:{MAX_SEARCH_STEPS_INT:\\\\\\\"16\\\\\\\",MAX_SEARCH_STEPS_FLOAT:\\\\\\\"16.0\\\\\\\",MAX_SEARCH_STEPS_DIAG_INT:\\\\\\\"8\\\\\\\",MAX_SEARCH_STEPS_DIAG_FLOAT:\\\\\\\"8.0\\\\\\\",CORNER_ROUNDING:\\\\\\\"25\\\\\\\",CORNER_ROUNDING_NORM:\\\\\\\"0.25\\\\\\\",AREATEX_MAX_DISTANCE:\\\\\\\"16.0\\\\\\\",AREATEX_MAX_DISTANCE_DIAG:\\\\\\\"20.0\\\\\\\",AREATEX_PIXEL_SIZE:\\\\\\\"(1.0 / vec2(160.0, 560.0))\\\\\\\",AREATEX_SUBTEX_SIZE:\\\\\\\"(1.0 / 7.0)\\\\\\\",SEARCHTEX_SIZE:\\\\\\\"vec2(66.0, 33.0)\\\\\\\",SEARCHTEX_PACKED_SIZE:\\\\\\\"vec2(64.0, 16.0)\\\\\\\"},uniforms:{inputBuffer:new lr.Uniform(null),areaTexture:new lr.Uniform(null),searchTexture:new lr.Uniform(null),texelSize:new lr.Uniform(s),resolution:new lr.Uniform(e)},fragmentShader:Wv,vertexShader:Jv,blending:lr.NoBlending,depthWrite:!1,depthTest:!1});this.toneMapped=!1}setOrthogonalSearchSteps(s){let e=Math.min(Math.max(s,0),112);this.defines.MAX_SEARCH_STEPS_INT=e.toFixed(\\\\\\\"0\\\\\\\"),this.defines.MAX_SEARCH_STEPS_FLOAT=e.toFixed(\\\\\\\"1\\\\\\\"),this.needsUpdate=!0}setDiagonalSearchSteps(s){let e=Math.min(Math.max(s,0),20);this.defines.MAX_SEARCH_STEPS_DIAG_INT=e.toFixed(\\\\\\\"0\\\\\\\"),this.defines.MAX_SEARCH_STEPS_DIAG_FLOAT=e.toFixed(\\\\\\\"1\\\\\\\"),this.needsUpdate=!0}setCornerRounding(s){let e=Math.min(Math.max(s,0),100);this.defines.CORNER_ROUNDING=e.toFixed(\\\\\\\"4\\\\\\\"),this.defines.CORNER_ROUNDING_NORM=(e/100).toFixed(\\\\\\\"4\\\\\\\"),this.needsUpdate=!0}get diagonalDetection(){return this.defines.DISABLE_DIAG_DETECTION===void 0}set diagonalDetection(s){s?delete this.defines.DISABLE_DIAG_DETECTION:this.defines.DISABLE_DIAG_DETECTION=\\\\\\\"1\\\\\\\",this.needsUpdate=!0}get cornerRounding(){return this.defines.DISABLE_CORNER_DETECTION===void 0}set cornerRounding(s){s?delete this.defines.DISABLE_CORNER_DETECTION:this.defines.DISABLE_CORNER_DETECTION=\\\\\\\"1\\\\\\\",this.needsUpdate=!0}};var Yv=new Ht.Camera,Ii=null;function Kv(){if(Ii===null){let s=new Float32Array([-1,-1,0,3,-1,0,-1,3,0]),e=new Float32Array([0,0,2,0,0,2]);Ii=new Ht.BufferGeometry,Ii.setAttribute!==void 0?(Ii.setAttribute(\\\\\\\"position\\\\\\\",new Ht.BufferAttribute(s,3)),Ii.setAttribute(\\\\\\\"uv\\\\\\\",new Ht.BufferAttribute(e,2))):(Ii.addAttribute(\\\\\\\"position\\\\\\\",new Ht.BufferAttribute(s,3)),Ii.addAttribute(\\\\\\\"uv\\\\\\\",new Ht.BufferAttribute(e,2)))}return Ii}var Zr=class{constructor(s=\\\\\\\"Pass\\\\\\\",e=new Ht.Scene,t=Yv){this.name=s,this.scene=e,this.camera=t,this.screen=null,this.rtt=!0,this.needsSwap=!0,this.needsDepthTexture=!1,this.enabled=!0}get renderToScreen(){return!this.rtt}set renderToScreen(s){if(this.rtt===s){let e=this.getFullscreenMaterial();e!==null&&(e.needsUpdate=!0),this.rtt=!s}}getFullscreenMaterial(){return this.screen!==null?this.screen.material:null}setFullscreenMaterial(s){let e=this.screen;e!==null?e.material=s:(e=new Ht.Mesh(Kv(),s),e.frustumCulled=!1,this.scene===null&&(this.scene=new Ht.Scene),this.scene.add(e),this.screen=e)}getDepthTexture(){return null}setDepthTexture(s,e=0){}render(s,e,t,r,i){throw new Error(\\\\\\\"Render method not implemented!\\\\\\\")}setSize(s,e){}initialize(s,e,t){}dispose(){let s=this.getFullscreenMaterial();s!==null&&s.dispose();for(let e of Object.keys(this)){let t=this[e];if(t!==null&&typeof t.dispose==\\\\\\\"function\\\\\\\"){if(t instanceof Ht.Scene)continue;this[e].dispose()}}}},Tf=class extends Zr{constructor(s,e=!0){super(\\\\\\\"SavePass\\\\\\\");this.setFullscreenMaterial(new wf),this.needsSwap=!1,this.renderTarget=s,s===void 0&&(this.renderTarget=new $r.WebGLRenderTarget(1,1,{minFilter:$r.LinearFilter,magFilter:$r.LinearFilter,stencilBuffer:!1,depthBuffer:!1}),this.renderTarget.texture.name=\\\\\\\"SavePass.Target\\\\\\\"),this.resize=e}get texture(){return this.renderTarget.texture}render(s,e,t,r,i){this.getFullscreenMaterial().uniforms.inputBuffer.value=e.texture,s.setRenderTarget(this.renderToScreen?null:this.renderTarget),s.render(this.scene,this.camera)}setSize(s,e){if(this.resize){let t=Math.max(s,1),r=Math.max(e,1);this.renderTarget.setSize(t,r)}}initialize(s,e,t){if(!e&&t===$r.UnsignedByteType&&(this.renderTarget.texture.format=$r.RGBFormat),t!==void 0&&(this.renderTarget.texture.type=t,t!==$r.UnsignedByteType)){let r=this.getFullscreenMaterial();r.defines.FRAMEBUFFER_PRECISION_HIGH=\\\\\\\"1\\\\\\\"}}};var li=-1,rr=class{constructor(s,e=li,t=li,r=1){this.resizable=s,this.base=new Pc.Vector2(1,1),this.target=new Pc.Vector2(e,t),this.s=r}get scale(){return this.s}set scale(s){this.s=s,this.target.x=li,this.target.y=li,this.resizable.setSize(this.base.x,this.base.y)}get width(){let s=this.base,e=this.target,t;return e.x!==li?t=e.x:e.y!==li?t=Math.round(e.y*(s.x/s.y)):t=Math.round(s.x*this.s),t}set width(s){this.target.x=s,this.resizable.setSize(this.base.x,this.base.y)}get height(){let s=this.base,e=this.target,t;return e.y!==li?t=e.y:e.x!==li?t=Math.round(e.x/(s.x/s.y)):t=Math.round(s.y*this.s),t}set height(s){this.target.y=s,this.resizable.setSize(this.base.x,this.base.y)}static get AUTO_SIZE(){return li}},Cf=class extends Zr{constructor({resolutionScale:s=.5,width:e=rr.AUTO_SIZE,height:t=rr.AUTO_SIZE,kernelSize:r=Ta.LARGE}={}){super(\\\\\\\"BlurPass\\\\\\\");this.renderTargetA=new Lr.WebGLRenderTarget(1,1,{minFilter:Lr.LinearFilter,magFilter:Lr.LinearFilter,stencilBuffer:!1,depthBuffer:!1}),this.renderTargetA.texture.name=\\\\\\\"Blur.Target.A\\\\\\\",this.renderTargetB=this.renderTargetA.clone(),this.renderTargetB.texture.name=\\\\\\\"Blur.Target.B\\\\\\\",this.resolution=new rr(this,e,t,s),this.convolutionMaterial=new Af,this.ditheredConvolutionMaterial=new Af,this.ditheredConvolutionMaterial.dithering=!0,this.dithering=!1,this.kernelSize=r}get width(){return this.resolution.width}set width(s){this.resolution.width=s}get height(){return this.resolution.height}set height(s){this.resolution.height=s}get scale(){return this.convolutionMaterial.uniforms.scale.value}set scale(s){this.convolutionMaterial.uniforms.scale.value=s,this.ditheredConvolutionMaterial.uniforms.scale.value=s}get kernelSize(){return this.convolutionMaterial.kernelSize}set kernelSize(s){this.convolutionMaterial.kernelSize=s,this.ditheredConvolutionMaterial.kernelSize=s}getResolutionScale(){return this.resolution.scale}setResolutionScale(s){this.resolution.scale=s}render(s,e,t,r,i){let n=this.scene,o=this.camera,l=this.renderTargetA,a=this.renderTargetB,c=this.convolutionMaterial,u=c.uniforms,d=c.getKernel(),h=e,p,f,m;for(this.setFullscreenMaterial(c),f=0,m=d.length-1;f<m;++f)p=(f&1)==0?l:a,u.kernel.value=d[f],u.inputBuffer.value=h.texture,s.setRenderTarget(p),s.render(n,o),h=p;this.dithering&&(c=this.ditheredConvolutionMaterial,u=c.uniforms,this.setFullscreenMaterial(c)),u.kernel.value=d[f],u.inputBuffer.value=h.texture,s.setRenderTarget(this.renderToScreen?null:t),s.render(n,o)}setSize(s,e){let t=this.resolution;t.base.set(s,e);let r=t.width,i=t.height;this.renderTargetA.setSize(r,i),this.renderTargetB.setSize(r,i),this.convolutionMaterial.setTexelSize(1/r,1/i),this.ditheredConvolutionMaterial.setTexelSize(1/r,1/i)}initialize(s,e,t){if(!e&&t===Lr.UnsignedByteType&&(this.renderTargetA.texture.format=Lr.RGBFormat,this.renderTargetB.texture.format=Lr.RGBFormat),t!==void 0&&(this.renderTargetA.texture.type=t,this.renderTargetB.texture.type=t,t!==Lr.UnsignedByteType)){let r=this.convolutionMaterial,i=this.ditheredConvolutionMaterial;r.defines.FRAMEBUFFER_PRECISION_HIGH=\\\\\\\"1\\\\\\\",i.defines.FRAMEBUFFER_PRECISION_HIGH=\\\\\\\"1\\\\\\\"}}static get AUTO_SIZE(){return rr.AUTO_SIZE}},Xv=class extends Zr{constructor(){super(\\\\\\\"ClearMaskPass\\\\\\\",null,null);this.needsSwap=!1}render(s,e,t,r,i){let n=s.state.buffers.stencil;n.setLocked(!1),n.setTest(!1)}},Rc=new Mf.Color,Fc=class extends Zr{constructor(s=!0,e=!0,t=!1){super(\\\\\\\"ClearPass\\\\\\\",null,null);this.needsSwap=!1,this.color=s,this.depth=e,this.stencil=t,this.overrideClearColor=null,this.overrideClearAlpha=-1}render(s,e,t,r,i){let n=this.overrideClearColor,o=this.overrideClearAlpha,l=s.getClearAlpha(),a=n!==null,c=o>=0;a?(Rc.copy(s.getClearColor(Rc)),s.setClearColor(n,c?o:l)):c&&s.setClearAlpha(o),s.setRenderTarget(this.renderToScreen?null:e),s.clear(this.color,this.depth,this.stencil),a?s.setClearColor(Rc,l):c&&s.setClearAlpha(l)}},Gc=!1,Ef=class{constructor(s=null){this.originalMaterials=new Map,this.material=null,this.materials=null,this.materialsBackSide=null,this.materialsDoubleSide=null,this.materialsFlatShaded=null,this.materialsFlatShadedBackSide=null,this.materialsFlatShadedDoubleSide=null,this.setMaterial(s),this.meshCount=0,this.replaceMaterial=e=>{if(e.isMesh){let t;if(e.material.flatShading)switch(e.material.side){case vr.DoubleSide:t=this.materialsFlatShadedDoubleSide;break;case vr.BackSide:t=this.materialsFlatShadedBackSide;break;default:t=this.materialsFlatShaded;break}else switch(e.material.side){case vr.DoubleSide:t=this.materialsDoubleSide;break;case vr.BackSide:t=this.materialsBackSide;break;default:t=this.materials;break}this.originalMaterials.set(e,e.material),e.isSkinnedMesh?e.material=t[2]:e.isInstancedMesh?e.material=t[1]:e.material=t[0],++this.meshCount}}}setMaterial(s){if(this.disposeMaterials(),this.material=s,s!==null){let e=this.materials=[s.clone(),s.clone(),s.clone()];for(let t of e)t.uniforms=Object.assign({},s.uniforms),t.side=vr.FrontSide;e[2].skinning=!0,this.materialsBackSide=e.map(t=>{let r=t.clone();return r.uniforms=Object.assign({},s.uniforms),r.side=vr.BackSide,r}),this.materialsDoubleSide=e.map(t=>{let r=t.clone();return r.uniforms=Object.assign({},s.uniforms),r.side=vr.DoubleSide,r}),this.materialsFlatShaded=e.map(t=>{let r=t.clone();return r.uniforms=Object.assign({},s.uniforms),r.flatShading=!0,r}),this.materialsFlatShadedBackSide=e.map(t=>{let r=t.clone();return r.uniforms=Object.assign({},s.uniforms),r.flatShading=!0,r.side=vr.BackSide,r}),this.materialsFlatShadedDoubleSide=e.map(t=>{let r=t.clone();return r.uniforms=Object.assign({},s.uniforms),r.flatShading=!0,r.side=vr.DoubleSide,r})}}render(s,e,t){let r=s.shadowMap.enabled;if(s.shadowMap.enabled=!1,Gc){let i=this.originalMaterials;this.meshCount=0,e.traverse(this.replaceMaterial),s.render(e,t);for(let n of i)n[0].material=n[1];this.meshCount!==i.size&&i.clear()}else{let i=e.overrideMaterial;e.overrideMaterial=this.material,s.render(e,t),e.overrideMaterial=i}s.shadowMap.enabled=r}disposeMaterials(){if(this.material!==null){let s=this.materials.concat(this.materialsBackSide).concat(this.materialsDoubleSide).concat(this.materialsFlatShaded).concat(this.materialsFlatShadedBackSide).concat(this.materialsFlatShadedDoubleSide);for(let e of s)e.dispose()}}dispose(){this.originalMaterials.clear(),this.disposeMaterials()}static get workaroundEnabled(){return Gc}static set workaroundEnabled(s){Gc=s}},Da=class extends Zr{constructor(s,e,t=null){super(\\\\\\\"RenderPass\\\\\\\",s,e);this.needsSwap=!1,this.clearPass=new Fc,this.overrideMaterialManager=t===null?null:new Ef(t)}get renderToScreen(){return super.renderToScreen}set renderToScreen(s){super.renderToScreen=s,this.clearPass.renderToScreen=s}get overrideMaterial(){let s=this.overrideMaterialManager;return s!==null?s.material:null}set overrideMaterial(s){let e=this.overrideMaterialManager;s!==null?e!==null?e.setMaterial(s):this.overrideMaterialManager=new Ef(s):e!==null&&(e.dispose(),this.overrideMaterialManager=null)}get clear(){return this.clearPass.enabled}set clear(s){this.clearPass.enabled=s}getClearPass(){return this.clearPass}render(s,e,t,r,i){let n=this.scene,o=this.camera,l=n.background,a=this.renderToScreen?null:e;this.clear&&(this.clearPass.overrideClearColor!==null&&(n.background=null),this.clearPass.render(s,e)),s.setRenderTarget(a),this.overrideMaterialManager!==null?this.overrideMaterialManager.render(s,n,o):s.render(n,o),n.background!==l&&(n.background=l)}},zc=class extends Zr{constructor(s,e,{resolutionScale:t=1,width:r=rr.AUTO_SIZE,height:i=rr.AUTO_SIZE,renderTarget:n}={}){super(\\\\\\\"DepthPass\\\\\\\");this.needsSwap=!1,this.renderPass=new Da(s,e,new Dr.MeshDepthMaterial({depthPacking:Dr.RGBADepthPacking}));let o=this.renderPass.getClearPass();o.overrideClearColor=new Dr.Color(16777215),o.overrideClearAlpha=1,this.renderTarget=n,this.renderTarget===void 0&&(this.renderTarget=new Dr.WebGLRenderTarget(1,1,{minFilter:Dr.NearestFilter,magFilter:Dr.NearestFilter,stencilBuffer:!1}),this.renderTarget.texture.name=\\\\\\\"DepthPass.Target\\\\\\\"),this.resolution=new rr(this,r,i,t)}get texture(){return this.renderTarget.texture}getResolutionScale(){return this.resolutionScale}setResolutionScale(s){this.resolutionScale=s,this.setSize(this.resolution.base.x,this.resolution.base.y)}render(s,e,t,r,i){let n=this.renderToScreen?null:this.renderTarget;this.renderPass.render(s,n)}setSize(s,e){let t=this.resolution;t.base.set(s,e),this.renderTarget.setSize(t.width,t.height)}};var eO=new Float32Array([255/256/256**3,255/256/256**2,255/256/256,255/256]);var Fe={SKIP:0,ADD:1,ALPHA:2,AVERAGE:3,COLOR_BURN:4,COLOR_DODGE:5,DARKEN:6,DIFFERENCE:7,EXCLUSION:8,LIGHTEN:9,MULTIPLY:10,DIVIDE:11,NEGATION:12,NORMAL:13,OVERLAY:14,REFLECT:15,SCREEN:16,SOFT_LIGHT:17,SUBTRACT:18},qv=\\\\\\\"vec4 blend(const in vec4 x,const in vec4 y,const in float opacity){return min(x+y,1.0)*opacity+x*(1.0-opacity);}\\\\\\\",Zv=\\\\\\\"vec3 blend(const in vec3 x,const in vec3 y,const in float opacity){return y*opacity+x*(1.0-opacity);}vec4 blend(const in vec4 x,const in vec4 y,const in float opacity){float a=min(y.a,opacity);return vec4(blend(x.rgb,y.rgb,a),max(x.a,a));}\\\\\\\",$v=\\\\\\\"vec4 blend(const in vec4 x,const in vec4 y,const in float opacity){return(x+y)*0.5*opacity+x*(1.0-opacity);}\\\\\\\",ex=\\\\\\\"float blend(const in float x,const in float y){return(y==0.0)? y : max(1.0-(1.0-x)/y,0.0);}vec4 blend(const in vec4 x,const in vec4 y,const in float opacity){vec4 z=vec4(blend(x.r,y.r),blend(x.g,y.g),blend(x.b,y.b),blend(x.a,y.a));return z*opacity+x*(1.0-opacity);}\\\\\\\",tx=\\\\\\\"float blend(const in float x,const in float y){return(y==1.0)? y : min(x/(1.0-y),1.0);}vec4 blend(const in vec4 x,const in vec4 y,const in float opacity){vec4 z=vec4(blend(x.r,y.r),blend(x.g,y.g),blend(x.b,y.b),blend(x.a,y.a));return z*opacity+x*(1.0-opacity);}\\\\\\\",rx=\\\\\\\"vec4 blend(const in vec4 x,const in vec4 y,const in float opacity){return min(x,y)*opacity+x*(1.0-opacity);}\\\\\\\",ix=\\\\\\\"vec4 blend(const in vec4 x,const in vec4 y,const in float opacity){return abs(x-y)*opacity+x*(1.0-opacity);}\\\\\\\",sx=\\\\\\\"vec4 blend(const in vec4 x,const in vec4 y,const in float opacity){return(x+y-2.0*x*y)*opacity+x*(1.0-opacity);}\\\\\\\",nx=\\\\\\\"vec4 blend(const in vec4 x,const in vec4 y,const in float opacity){return max(x,y)*opacity+x*(1.0-opacity);}\\\\\\\",ox=\\\\\\\"vec4 blend(const in vec4 x,const in vec4 y,const in float opacity){return x*y*opacity+x*(1.0-opacity);}\\\\\\\",ax=\\\\\\\"float blend(const in float x,const in float y){return(y>0.0)? min(x/y,1.0): 1.0;}vec4 blend(const in vec4 x,const in vec4 y,const in float opacity){vec4 z=vec4(blend(x.r,y.r),blend(x.g,y.g),blend(x.b,y.b),blend(x.a,y.a));return z*opacity+x*(1.0-opacity);}\\\\\\\",lx=\\\\\\\"vec4 blend(const in vec4 x,const in vec4 y,const in float opacity){return(1.0-abs(1.0-x-y))*opacity+x*(1.0-opacity);}\\\\\\\",cx=\\\\\\\"vec4 blend(const in vec4 x,const in vec4 y,const in float opacity){return y*opacity+x*(1.0-opacity);}\\\\\\\",ux=\\\\\\\"float blend(const in float x,const in float y){return(x<0.5)?(2.0*x*y):(1.0-2.0*(1.0-x)*(1.0-y));}vec4 blend(const in vec4 x,const in vec4 y,const in float opacity){vec4 z=vec4(blend(x.r,y.r),blend(x.g,y.g),blend(x.b,y.b),blend(x.a,y.a));return z*opacity+x*(1.0-opacity);}\\\\\\\",dx=\\\\\\\"float blend(const in float x,const in float y){return(y==1.0)? y : min(x*x/(1.0-y),1.0);}vec4 blend(const in vec4 x,const in vec4 y,const in float opacity){vec4 z=vec4(blend(x.r,y.r),blend(x.g,y.g),blend(x.b,y.b),blend(x.a,y.a));return z*opacity+x*(1.0-opacity);}\\\\\\\",hx=\\\\\\\"vec4 blend(const in vec4 x,const in vec4 y,const in float opacity){return(1.0-(1.0-x)*(1.0-y))*opacity+x*(1.0-opacity);}\\\\\\\",fx=\\\\\\\"float blend(const in float x,const in float y){return(y<0.5)?(2.0*x*y+x*x*(1.0-2.0*y)):(sqrt(x)*(2.0*y-1.0)+2.0*x*(1.0-y));}vec4 blend(const in vec4 x,const in vec4 y,const in float opacity){vec4 z=vec4(blend(x.r,y.r),blend(x.g,y.g),blend(x.b,y.b),blend(x.a,y.a));return z*opacity+x*(1.0-opacity);}\\\\\\\",px=\\\\\\\"vec4 blend(const in vec4 x,const in vec4 y,const in float opacity){return max(x+y-1.0,0.0)*opacity+x*(1.0-opacity);}\\\\\\\",mx=new Map([[Fe.SKIP,null],[Fe.ADD,qv],[Fe.ALPHA,Zv],[Fe.AVERAGE,$v],[Fe.COLOR_BURN,ex],[Fe.COLOR_DODGE,tx],[Fe.DARKEN,rx],[Fe.DIFFERENCE,ix],[Fe.EXCLUSION,sx],[Fe.LIGHTEN,nx],[Fe.MULTIPLY,ox],[Fe.DIVIDE,ax],[Fe.NEGATION,lx],[Fe.NORMAL,cx],[Fe.OVERLAY,ux],[Fe.REFLECT,dx],[Fe.SCREEN,hx],[Fe.SOFT_LIGHT,fx],[Fe.SUBTRACT,px]]),gx=class extends Oa.EventDispatcher{constructor(s,e=1){super();this.blendFunction=s,this.opacity=new Oa.Uniform(e)}getBlendFunction(){return this.blendFunction}setBlendFunction(s){this.blendFunction=s,this.dispatchEvent({type:\\\\\\\"change\\\\\\\"})}getShaderCode(){return mx.get(this.blendFunction)}},ei=class extends Pa.EventDispatcher{constructor(s,e,{attributes:t=Br.NONE,blendFunction:r=Fe.SCREEN,defines:i=new Map,uniforms:n=new Map,extensions:o=null,vertexShader:l=null}={}){super();this.name=s,this.attributes=t,this.fragmentShader=e,this.vertexShader=l,this.defines=i,this.uniforms=n,this.extensions=o,this.blendMode=new gx(r),this.blendMode.addEventListener(\\\\\\\"change\\\\\\\",a=>this.setChanged())}setChanged(){this.dispatchEvent({type:\\\\\\\"change\\\\\\\"})}getAttributes(){return this.attributes}setAttributes(s){this.attributes=s,this.setChanged()}getFragmentShader(){return this.fragmentShader}setFragmentShader(s){this.fragmentShader=s,this.setChanged()}getVertexShader(){return this.vertexShader}setVertexShader(s){this.vertexShader=s,this.setChanged()}setDepthTexture(s,e=0){}update(s,e,t){}setSize(s,e){}initialize(s,e,t){}dispose(){for(let s of Object.keys(this)){let e=this[s];if(e!==null&&typeof e.dispose==\\\\\\\"function\\\\\\\"){if(e instanceof Pa.Scene)continue;this[s].dispose()}}}},Br={NONE:0,DEPTH:1,CONVOLUTION:2};function Uc(s,e){let t=[],r;for(;(r=s.exec(e))!==null;)t.push(r[1]);return t}function Lf(s,e,t){let r,i;for(let n of e){r=\\\\\\\"$1\\\\\\\"+s+n.charAt(0).toUpperCase()+n.slice(1),i=new RegExp(\\\\\\\"([^\\\\\\\\.])(\\\\\\\\b\\\\\\\"+n+\\\\\\\"\\\\\\\\b)\\\\\\\",\\\\\\\"g\\\\\\\");for(let o of t.entries())o[1]!==null&&t.set(o[0],o[1].replace(i,r))}}function yx(s,e,t,r,i,n,o){let l=/(?:\\\\w+\\\\s+(\\\\w+)\\\\([\\\\w\\\\s,]*\\\\)\\\\s*{[^}]+})/g,a=/(?:varying\\\\s+\\\\w+\\\\s+(\\\\w*))/g,c=e.blendMode,u=new Map([[\\\\\\\"fragment\\\\\\\",e.getFragmentShader()],[\\\\\\\"vertex\\\\\\\",e.getVertexShader()]]),d=u.get(\\\\\\\"fragment\\\\\\\")!==void 0&&/mainImage/.test(u.get(\\\\\\\"fragment\\\\\\\")),h=u.get(\\\\\\\"fragment\\\\\\\")!==void 0&&/mainUv/.test(u.get(\\\\\\\"fragment\\\\\\\")),p=[],f=[],m=!1,g=!1;if(u.get(\\\\\\\"fragment\\\\\\\")===void 0)console.error(\\\\\\\"Missing fragment shader\\\\\\\",e);else if(h&&(o&Br.CONVOLUTION)!=0)console.error(\\\\\\\"Effects that transform UV coordinates are incompatible with convolution effects\\\\\\\",e);else if(!d&&!h)console.error(\\\\\\\"The fragment shader contains neither a mainImage nor a mainUv function\\\\\\\",e);else{if(h&&(t.set(Ue.FRAGMENT_MAIN_UV,t.get(Ue.FRAGMENT_MAIN_UV)+\\\\\\\"\\t\\\\\\\"+s+`MainUv(UV);\\n`),m=!0),u.get(\\\\\\\"vertex\\\\\\\")!==null&&/mainSupport/.test(u.get(\\\\\\\"vertex\\\\\\\"))){let y=\\\\\\\"\\t\\\\\\\"+s+\\\\\\\"MainSupport(\\\\\\\";/mainSupport *\\\\([\\\\w\\\\s]*?uv\\\\s*?\\\\)/.test(u.get(\\\\\\\"vertex\\\\\\\"))&&(y+=\\\\\\\"vUv\\\\\\\"),y+=`);\\n`,t.set(Ue.VERTEX_MAIN_SUPPORT,t.get(Ue.VERTEX_MAIN_SUPPORT)+y),p=p.concat(Uc(a,u.get(\\\\\\\"vertex\\\\\\\"))),f=f.concat(p).concat(Uc(l,u.get(\\\\\\\"vertex\\\\\\\")))}if(f=f.concat(Uc(l,u.get(\\\\\\\"fragment\\\\\\\"))).concat(Array.from(e.defines.keys()).map(y=>y.replace(/\\\\([\\\\w\\\\s,]*\\\\)/g,\\\\\\\"\\\\\\\"))).concat(Array.from(e.uniforms.keys())),e.uniforms.forEach((y,x)=>n.set(s+x.charAt(0).toUpperCase()+x.slice(1),y)),e.defines.forEach((y,x)=>i.set(s+x.charAt(0).toUpperCase()+x.slice(1),y)),Lf(s,f,i),Lf(s,f,u),r.set(c.blendFunction,c),d){let y=/MainImage *\\\\([\\\\w\\\\s,]*?depth[\\\\w\\\\s,]*?\\\\)/,x=s+\\\\\\\"MainImage(color0, UV, \\\\\\\";(o&Br.DEPTH)!=0&&y.test(u.get(\\\\\\\"fragment\\\\\\\"))&&(x+=\\\\\\\"depth, \\\\\\\",g=!0),x+=`color1);\\n\\t`;let v=s+\\\\\\\"BlendOpacity\\\\\\\";n.set(v,c.opacity),x+=\\\\\\\"color0 = blend\\\\\\\"+c.getBlendFunction()+\\\\\\\"(color0, color1, \\\\\\\"+v+`);\\n\\n\\t`,t.set(Ue.FRAGMENT_MAIN_IMAGE,t.get(Ue.FRAGMENT_MAIN_IMAGE)+x),t.set(Ue.FRAGMENT_HEAD,t.get(Ue.FRAGMENT_HEAD)+\\\\\\\"uniform float \\\\\\\"+v+`;\\n\\n`)}t.set(Ue.FRAGMENT_HEAD,t.get(Ue.FRAGMENT_HEAD)+u.get(\\\\\\\"fragment\\\\\\\")+`\\n`),u.get(\\\\\\\"vertex\\\\\\\")!==null&&t.set(Ue.VERTEX_HEAD,t.get(Ue.VERTEX_HEAD)+u.get(\\\\\\\"vertex\\\\\\\")+`\\n`)}return{varyings:p,transformedUv:m,readDepth:g}}var Ra=class extends Zr{constructor(s,...e){super(\\\\\\\"EffectPass\\\\\\\");this.setFullscreenMaterial(new Uv(null,null,null,s)),this.effects=e.sort((t,r)=>r.attributes-t.attributes),this.skipRendering=!1,this.uniforms=0,this.varyings=0,this.minTime=1,this.maxTime=Number.POSITIVE_INFINITY}get encodeOutput(){return this.getFullscreenMaterial().defines.ENCODE_OUTPUT!==void 0}set encodeOutput(s){if(this.encodeOutput!==s){let e=this.getFullscreenMaterial();e.needsUpdate=!0,s?e.defines.ENCODE_OUTPUT=\\\\\\\"1\\\\\\\":delete e.defines.ENCODE_OUTPUT}}get dithering(){return this.getFullscreenMaterial().dithering}set dithering(s){let e=this.getFullscreenMaterial();e.dithering!==s&&(e.dithering=s,e.needsUpdate=!0)}verifyResources(s){let e=s.capabilities,t=Math.min(e.maxFragmentUniforms,e.maxVertexUniforms);this.uniforms>t&&console.warn(\\\\\\\"The current rendering context doesn't support more than \\\\\\\"+t+\\\\\\\" uniforms, but \\\\\\\"+this.uniforms+\\\\\\\" were defined\\\\\\\"),t=e.maxVaryings,this.varyings>t&&console.warn(\\\\\\\"The current rendering context doesn't support more than \\\\\\\"+t+\\\\\\\" varyings, but \\\\\\\"+this.varyings+\\\\\\\" were defined\\\\\\\")}updateMaterial(){let s=/\\\\bblend\\\\b/g,e=new Map([[Ue.FRAGMENT_HEAD,\\\\\\\"\\\\\\\"],[Ue.FRAGMENT_MAIN_UV,\\\\\\\"\\\\\\\"],[Ue.FRAGMENT_MAIN_IMAGE,\\\\\\\"\\\\\\\"],[Ue.VERTEX_HEAD,\\\\\\\"\\\\\\\"],[Ue.VERTEX_MAIN_SUPPORT,\\\\\\\"\\\\\\\"]]),t=new Map,r=new Map,i=new Map,n=new Set,o=0,l=0,a=0,c=!1,u=!1,d;for(let p of this.effects)if(p.blendMode.getBlendFunction()===Fe.SKIP)a|=p.getAttributes()&Br.DEPTH;else if((a&Br.CONVOLUTION)!=0&&(p.getAttributes()&Br.CONVOLUTION)!=0)console.error(\\\\\\\"Convolution effects cannot be merged\\\\\\\",p);else if(a|=p.getAttributes(),d=yx(\\\\\\\"e\\\\\\\"+o++,p,e,t,r,i,a),l+=d.varyings.length,c=c||d.transformedUv,u=u||d.readDepth,p.extensions!==null)for(let f of p.extensions)n.add(f);for(let p of t.values())e.set(Ue.FRAGMENT_HEAD,e.get(Ue.FRAGMENT_HEAD)+p.getShaderCode().replace(s,\\\\\\\"blend\\\\\\\"+p.getBlendFunction())+`\\n`);(a&Br.DEPTH)!=0?(u&&e.set(Ue.FRAGMENT_MAIN_IMAGE,`float depth = readDepth(UV);\\n\\n\\t`+e.get(Ue.FRAGMENT_MAIN_IMAGE)),this.needsDepthTexture=this.getDepthTexture()===null):this.needsDepthTexture=!1,c?(e.set(Ue.FRAGMENT_MAIN_UV,`vec2 transformedUv = vUv;\\n`+e.get(Ue.FRAGMENT_MAIN_UV)),r.set(\\\\\\\"UV\\\\\\\",\\\\\\\"transformedUv\\\\\\\")):r.set(\\\\\\\"UV\\\\\\\",\\\\\\\"vUv\\\\\\\"),e.forEach((p,f,m)=>m.set(f,p.trim().replace(/^#/,`\\n#`))),this.uniforms=i.size,this.varyings=l,this.skipRendering=o===0,this.needsSwap=!this.skipRendering;let h=this.getFullscreenMaterial();if(h.setShaderParts(e).setDefines(r).setUniforms(i),h.extensions={},n.size>0)for(let p of n)h.extensions[p]=!0;this.needsUpdate=!1}recompile(s){this.updateMaterial(),s!==void 0&&this.verifyResources(s)}getDepthTexture(){return this.getFullscreenMaterial().uniforms.depthBuffer.value}setDepthTexture(s,e=_a.BasicDepthPacking){let t=this.getFullscreenMaterial();t.uniforms.depthBuffer.value=s,t.depthPacking=e,t.needsUpdate=!0;for(let r of this.effects)r.setDepthTexture(s,e)}render(s,e,t,r,i){let n=this.getFullscreenMaterial(),o=n.uniforms.time.value+r;this.needsUpdate&&this.recompile(s);for(let l of this.effects)l.update(s,e,r);(!this.skipRendering||this.renderToScreen)&&(n.uniforms.inputBuffer.value=e.texture,n.uniforms.time.value=o<=this.maxTime?o:this.minTime,s.setRenderTarget(this.renderToScreen?null:t),s.render(this.scene,this.camera))}setSize(s,e){this.getFullscreenMaterial().setSize(s,e);for(let t of this.effects)t.setSize(s,e)}initialize(s,e,t){for(let r of this.effects)r.initialize(s,e,t),r.addEventListener(\\\\\\\"change\\\\\\\",i=>this.handleEvent(i));if(this.updateMaterial(),this.verifyResources(s),t!==void 0&&t!==_a.UnsignedByteType){let r=this.getFullscreenMaterial();r.defines.FRAMEBUFFER_PRECISION_HIGH=\\\\\\\"1\\\\\\\"}}dispose(){super.dispose();for(let s of this.effects)s.dispose()}handleEvent(s){switch(s.type){case\\\\\\\"change\\\\\\\":this.needsUpdate=!0;break}}};var vx=class extends Zr{constructor({width:s=rr.AUTO_SIZE,height:e=rr.AUTO_SIZE,renderTarget:t,luminanceRange:r,colorOutput:i}={}){super(\\\\\\\"LuminancePass\\\\\\\");this.setFullscreenMaterial(new kv(i,r)),this.needsSwap=!1,this.renderTarget=t,this.renderTarget===void 0&&(this.renderTarget=new _r.WebGLRenderTarget(1,1,{minFilter:_r.LinearFilter,magFilter:_r.LinearFilter,format:i?_r.RGBAFormat:_r.LuminanceFormat,stencilBuffer:!1,depthBuffer:!1}),this.renderTarget.texture.name=\\\\\\\"LuminancePass.Target\\\\\\\",this.renderTarget.texture.generateMipmaps=!1),this.resolution=new rr(this,s,e)}get texture(){return this.renderTarget.texture}render(s,e,t,r,i){let n=this.getFullscreenMaterial();n.uniforms.inputBuffer.value=e.texture,s.setRenderTarget(this.renderToScreen?null:this.renderTarget),s.render(this.scene,this.camera)}setSize(s,e){let t=this.resolution;t.base.set(s,e),this.renderTarget.setSize(t.width,t.height)}initialize(s,e,t){if(t!==void 0&&t!==_r.UnsignedByteType){let r=this.getFullscreenMaterial();r.defines.FRAMEBUFFER_PRECISION_HIGH=\\\\\\\"1\\\\\\\"}}},xx=class extends Zr{constructor(s,e){super(\\\\\\\"MaskPass\\\\\\\",s,e);this.needsSwap=!1,this.clearPass=new Fc(!1,!1,!0),this.inverse=!1}get clear(){return this.clearPass.enabled}set clear(s){this.clearPass.enabled=s}render(s,e,t,r,i){let n=s.getContext(),o=s.state.buffers,l=this.scene,a=this.camera,c=this.clearPass,u=this.inverse?0:1,d=1-u;o.color.setMask(!1),o.depth.setMask(!1),o.color.setLocked(!0),o.depth.setLocked(!0),o.stencil.setTest(!0),o.stencil.setOp(n.REPLACE,n.REPLACE,n.REPLACE),o.stencil.setFunc(n.ALWAYS,u,4294967295),o.stencil.setClear(d),o.stencil.setLocked(!0),this.clear&&(this.renderToScreen?c.render(s,null):(c.render(s,e),c.render(s,t))),this.renderToScreen?(s.setRenderTarget(null),s.render(l,a)):(s.setRenderTarget(e),s.render(l,a),s.setRenderTarget(t),s.render(l,a)),o.color.setLocked(!1),o.depth.setLocked(!1),o.stencil.setLocked(!1),o.stencil.setFunc(n.EQUAL,1,4294967295),o.stencil.setOp(n.KEEP,n.KEEP,n.KEEP),o.stencil.setLocked(!0)}};var ci=class extends Zr{constructor(s,e=\\\\\\\"inputBuffer\\\\\\\"){super(\\\\\\\"ShaderPass\\\\\\\");this.setFullscreenMaterial(s),this.uniform=null,this.setInput(e)}setInput(s){let e=this.getFullscreenMaterial();if(this.uniform=null,e!==null){let t=e.uniforms;t!==void 0&&t[s]!==void 0&&(this.uniform=t[s])}}render(s,e,t,r,i){this.uniform!==null&&e!==null&&(this.uniform.value=e.texture),s.setRenderTarget(this.renderToScreen?null:t),s.render(this.scene,this.camera)}initialize(s,e,t){if(t!==void 0&&t!==Df.UnsignedByteType){let r=this.getFullscreenMaterial();r.defines.FRAMEBUFFER_PRECISION_HIGH=\\\\\\\"1\\\\\\\"}}},Bf=class{constructor(s=null,{depthBuffer:e=!0,stencilBuffer:t=!1,alpha:r=!1,multisampling:i=0,frameBufferType:n}={}){this.renderer=s,this.inputBuffer=null,this.outputBuffer=null,this.renderer!==null&&(this.renderer.autoClear=!1,this.inputBuffer=this.createBuffer(e,t,n,i),this.outputBuffer=this.inputBuffer.clone()),this.copyPass=new ci(new wf),this.alpha=r,this.depthTexture=null,this.passes=[],this.autoRenderToScreen=!0}get multisampling(){return this.inputBuffer instanceof Je.WebGLMultisampleRenderTarget?this.inputBuffer.samples:0}set multisampling(s){let e=this.inputBuffer,t=this.multisampling;t>0&&s>0?(this.inputBuffer.samples=s,this.outputBuffer.samples=s):t!==s&&(this.inputBuffer.dispose(),this.outputBuffer.dispose(),this.inputBuffer=this.createBuffer(e.depthBuffer,e.stencilBuffer,e.texture.type,s),this.inputBuffer.depthTexture=this.depthTexture,this.outputBuffer=this.inputBuffer.clone())}getRenderer(){return this.renderer}replaceRenderer(s,e=!0){let t=this.renderer;if(t!==null&&t!==s){let r=t.getSize(new Je.Vector2),i=s.getSize(new Je.Vector2),n=t.domElement.parentNode;this.renderer=s,this.renderer.autoClear=!1,r.equals(i)||this.setSize(),e&&n!==null&&(n.removeChild(t.domElement),n.appendChild(s.domElement))}return t}createDepthTexture(){let s=this.depthTexture=new Je.DepthTexture;return this.inputBuffer.depthTexture=s,this.inputBuffer.dispose(),this.inputBuffer.stencilBuffer?(s.format=Je.DepthStencilFormat,s.type=Je.UnsignedInt248Type):s.type=Je.UnsignedIntType,s}deleteDepthTexture(){if(this.depthTexture!==null){this.depthTexture.dispose(),this.depthTexture=null,this.inputBuffer.depthTexture=null,this.inputBuffer.dispose();for(let s of this.passes)s.setDepthTexture(null)}}createBuffer(s,e,t,r){let i=this.renderer,n=i.getContext(),o=i.getDrawingBufferSize(new Je.Vector2),a={format:!(this.alpha||n.getContextAttributes().alpha)&&t===Je.UnsignedByteType?Je.RGBFormat:Je.RGBAFormat,minFilter:Je.LinearFilter,magFilter:Je.LinearFilter,stencilBuffer:e,depthBuffer:s,type:t},c=r>0?new Je.WebGLMultisampleRenderTarget(o.width,o.height,a):new Je.WebGLRenderTarget(o.width,o.height,a);return r>0&&(c.samples=r),c.texture.name=\\\\\\\"EffectComposer.Buffer\\\\\\\",c.texture.generateMipmaps=!1,c}addPass(s,e){let t=this.passes,r=this.renderer,i=r.getDrawingBufferSize(new Je.Vector2),n=r.getContext().getContextAttributes().alpha,o=this.inputBuffer.texture.type;if(s.setSize(i.width,i.height),s.initialize(r,n,o),this.autoRenderToScreen&&(t.length>0&&(t[t.length-1].renderToScreen=!1),s.renderToScreen&&(this.autoRenderToScreen=!1)),e!==void 0?t.splice(e,0,s):t.push(s),this.autoRenderToScreen&&(t[t.length-1].renderToScreen=!0),s.needsDepthTexture||this.depthTexture!==null)if(this.depthTexture===null){let l=this.createDepthTexture();for(s of t)s.setDepthTexture(l)}else s.setDepthTexture(this.depthTexture)}removePass(s){let e=this.passes,t=e.indexOf(s);if(t!==-1&&e.splice(t,1).length>0){if(this.depthTexture!==null){let n=(l,a)=>l||a.needsDepthTexture;e.reduce(n,!1)||(s.getDepthTexture()===this.depthTexture&&s.setDepthTexture(null),this.deleteDepthTexture())}this.autoRenderToScreen&&t===e.length&&(s.renderToScreen=!1,e.length>0&&(e[e.length-1].renderToScreen=!0))}}removeAllPasses(){let s=this.passes;this.deleteDepthTexture(),s.length>0&&(this.autoRenderToScreen&&(s[s.length-1].renderToScreen=!1),this.passes=[])}render(s){let e=this.renderer,t=this.copyPass,r=this.inputBuffer,i=this.outputBuffer,n=!1,o,l,a;for(let c of this.passes)c.enabled&&(c.render(e,r,i,s,n),c.needsSwap&&(n&&(t.renderToScreen=c.renderToScreen,o=e.getContext(),l=e.state.buffers.stencil,l.setFunc(o.NOTEQUAL,1,4294967295),t.render(e,r,i,s,n),l.setFunc(o.EQUAL,1,4294967295)),a=r,r=i,i=a),c instanceof xx?n=!0:c instanceof Xv&&(n=!1))}setSize(s,e,t){let r=this.renderer;if(s===void 0||e===void 0){let n=r.getSize(new Je.Vector2);s=n.width,e=n.height}else r.setSize(s,e,t);let i=r.getDrawingBufferSize(new Je.Vector2);this.inputBuffer.setSize(i.width,i.height),this.outputBuffer.setSize(i.width,i.height);for(let n of this.passes)n.setSize(i.width,i.height)}reset(){this.dispose(),this.autoRenderToScreen=!0}dispose(){for(let s of this.passes)s.dispose();this.passes=[],this.inputBuffer!==null&&this.inputBuffer.dispose(),this.outputBuffer!==null&&this.outputBuffer.dispose(),this.deleteDepthTexture(),this.copyPass.dispose()}};var bx=`#ifdef FRAMEBUFFER_PRECISION_HIGH\\nuniform mediump sampler2D map;\\n#else\\nuniform lowp sampler2D map;\\n#endif\\nuniform float intensity;void mainImage(const in vec4 inputColor,const in vec2 uv,out vec4 outputColor){outputColor=clamp(texture2D(map,uv)*intensity,0.0,1.0);}`,_f=class extends ei{constructor({blendFunction:s=Fe.SCREEN,luminanceThreshold:e=.9,luminanceSmoothing:t=.025,resolutionScale:r=.5,intensity:i=1,width:n=rr.AUTO_SIZE,height:o=rr.AUTO_SIZE,kernelSize:l=Ta.LARGE}={}){super(\\\\\\\"BloomEffect\\\\\\\",bx,{blendFunction:s,uniforms:new Map([[\\\\\\\"map\\\\\\\",new xr.Uniform(null)],[\\\\\\\"intensity\\\\\\\",new xr.Uniform(i)]])});this.renderTarget=new xr.WebGLRenderTarget(1,1,{minFilter:xr.LinearFilter,magFilter:xr.LinearFilter,stencilBuffer:!1,depthBuffer:!1}),this.renderTarget.texture.name=\\\\\\\"Bloom.Target\\\\\\\",this.renderTarget.texture.generateMipmaps=!1,this.uniforms.get(\\\\\\\"map\\\\\\\").value=this.renderTarget.texture,this.blurPass=new Cf({resolutionScale:r,width:n,height:o,kernelSize:l}),this.blurPass.resolution.resizable=this,this.luminancePass=new vx({renderTarget:this.renderTarget,colorOutput:!0}),this.luminancePass.resolution=this.resolution,this.luminanceMaterial.threshold=e,this.luminanceMaterial.smoothing=t}get texture(){return this.renderTarget.texture}get luminanceMaterial(){return this.luminancePass.getFullscreenMaterial()}get resolution(){return this.blurPass.resolution}get width(){return this.resolution.width}set width(s){this.resolution.width=s}get height(){return this.resolution.height}set height(s){this.resolution.height=s}get dithering(){return this.blurPass.dithering}set dithering(s){this.blurPass.dithering=s}get kernelSize(){return this.blurPass.kernelSize}set kernelSize(s){this.blurPass.kernelSize=s}get distinction(){return console.warn(this.name,\\\\\\\"The distinction field has been removed, use luminanceMaterial.threshold and luminanceMaterial.smoothing instead.\\\\\\\"),1}set distinction(s){console.warn(this.name,\\\\\\\"The distinction field has been removed, use luminanceMaterial.threshold and luminanceMaterial.smoothing instead.\\\\\\\")}get intensity(){return this.uniforms.get(\\\\\\\"intensity\\\\\\\").value}set intensity(s){this.uniforms.get(\\\\\\\"intensity\\\\\\\").value=s}getResolutionScale(){return this.resolution.scale}setResolutionScale(s){this.resolution.scale=s}update(s,e,t){let r=this.renderTarget;this.luminancePass.enabled?(this.luminancePass.render(s,e,r),this.blurPass.render(s,r,r)):this.blurPass.render(s,e,r)}setSize(s,e){this.blurPass.setSize(s,e),this.renderTarget.setSize(this.resolution.width,this.resolution.height)}initialize(s,e,t){this.blurPass.initialize(s,e,t),!e&&t===xr.UnsignedByteType&&(this.renderTarget.texture.format=xr.RGBFormat),t!==void 0&&(this.renderTarget.texture.type=t)}};var wx=\\\\\\\"uniform float brightness;uniform float contrast;void mainImage(const in vec4 inputColor,const in vec2 uv,out vec4 outputColor){vec3 color=inputColor.rgb+vec3(brightness-0.5);if(contrast>0.0){color/=vec3(1.0-contrast);}else{color*=vec3(1.0+contrast);}outputColor=vec4(min(color+vec3(0.5),1.0),inputColor.a);}\\\\\\\",Of=class extends ei{constructor({blendFunction:s=Fe.NORMAL,brightness:e=0,contrast:t=0}={}){super(\\\\\\\"BrightnessContrastEffect\\\\\\\",wx,{blendFunction:s,uniforms:new Map([[\\\\\\\"brightness\\\\\\\",new jc.Uniform(e)],[\\\\\\\"contrast\\\\\\\",new jc.Uniform(t)]])})}},Sx=\\\\\\\"void mainImage(const in vec4 inputColor,const in vec2 uv,out vec4 outputColor){float sum=inputColor.r+inputColor.g+inputColor.b;outputColor=vec4(vec3(sum/3.0),inputColor.a);}\\\\\\\",Pf=class extends ei{constructor(s=Fe.NORMAL){super(\\\\\\\"ColorAverageEffect\\\\\\\",Sx,{blendFunction:s})}};var Tx=`varying vec2 vUvR;varying vec2 vUvB;void mainImage(const in vec4 inputColor,const in vec2 uv,out vec4 outputColor){vec4 color=inputColor;\\n#ifdef ALPHA\\nvec2 ra=texture2D(inputBuffer,vUvR).ra;vec2 ba=texture2D(inputBuffer,vUvB).ba;color.r=ra.x;color.b=ba.x;color.a=max(max(ra.y,ba.y),inputColor.a);\\n#else\\ncolor.r=texture2D(inputBuffer,vUvR).r;color.b=texture2D(inputBuffer,vUvB).b;\\n#endif\\noutputColor=color;}`,Cx=\\\\\\\"uniform vec2 offset;varying vec2 vUvR;varying vec2 vUvB;void mainSupport(const in vec2 uv){vUvR=uv+offset;vUvB=uv-offset;}\\\\\\\",Rf=class extends ei{constructor({blendFunction:s=Fe.NORMAL,offset:e=new Fa.Vector2(.001,5e-4)}={}){super(\\\\\\\"ChromaticAberrationEffect\\\\\\\",Tx,{vertexShader:Cx,blendFunction:s,attributes:Br.CONVOLUTION,uniforms:new Map([[\\\\\\\"offset\\\\\\\",new Fa.Uniform(e)]])})}get offset(){return this.uniforms.get(\\\\\\\"offset\\\\\\\").value}set offset(s){this.uniforms.get(\\\\\\\"offset\\\\\\\").value=s}initialize(s,e,t){e?this.defines.set(\\\\\\\"ALPHA\\\\\\\",\\\\\\\"1\\\\\\\"):this.defines.delete(\\\\\\\"ALPHA\\\\\\\")}};var Mx=`#ifdef FRAMEBUFFER_PRECISION_HIGH\\nuniform mediump sampler2D nearColorBuffer;uniform mediump sampler2D farColorBuffer;\\n#else\\nuniform lowp sampler2D nearColorBuffer;uniform lowp sampler2D farColorBuffer;\\n#endif\\nuniform lowp sampler2D nearCoCBuffer;uniform float scale;void mainImage(const in vec4 inputColor,const in vec2 uv,const in float depth,out vec4 outputColor){vec4 colorNear=texture2D(nearColorBuffer,uv);vec4 colorFar=texture2D(farColorBuffer,uv);float CoCNear=texture2D(nearCoCBuffer,uv).r;CoCNear=min(CoCNear*scale,1.0);vec4 result=inputColor*(1.0-colorFar.a)+colorFar;result=mix(result,colorNear,CoCNear);outputColor=result;}`,Ff=class extends ei{constructor(s,{blendFunction:e=Fe.NORMAL,focusDistance:t=0,focalLength:r=.1,bokehScale:i=1,width:n=rr.AUTO_SIZE,height:o=rr.AUTO_SIZE}={}){super(\\\\\\\"DepthOfFieldEffect\\\\\\\",Mx,{blendFunction:e,attributes:Br.DEPTH,uniforms:new Map([[\\\\\\\"nearColorBuffer\\\\\\\",new Tt.Uniform(null)],[\\\\\\\"farColorBuffer\\\\\\\",new Tt.Uniform(null)],[\\\\\\\"nearCoCBuffer\\\\\\\",new Tt.Uniform(null)],[\\\\\\\"scale\\\\\\\",new Tt.Uniform(1)]])});this.camera=s,this.renderTarget=new Tt.WebGLRenderTarget(1,1,{minFilter:Tt.LinearFilter,magFilter:Tt.LinearFilter,stencilBuffer:!1,depthBuffer:!1}),this.renderTarget.texture.name=\\\\\\\"DoF.Intermediate\\\\\\\",this.renderTarget.texture.generateMipmaps=!1,this.renderTargetMasked=this.renderTarget.clone(),this.renderTargetMasked.texture.name=\\\\\\\"DoF.Masked.Far\\\\\\\",this.renderTargetNear=this.renderTarget.clone(),this.renderTargetNear.texture.name=\\\\\\\"DoF.Bokeh.Near\\\\\\\",this.uniforms.get(\\\\\\\"nearColorBuffer\\\\\\\").value=this.renderTargetNear.texture,this.renderTargetFar=this.renderTarget.clone(),this.renderTargetFar.texture.name=\\\\\\\"DoF.Bokeh.Far\\\\\\\",this.uniforms.get(\\\\\\\"farColorBuffer\\\\\\\").value=this.renderTargetFar.texture,this.renderTargetCoC=this.renderTarget.clone(),this.renderTargetCoC.texture.format=Tt.RGBFormat,this.renderTargetCoC.texture.name=\\\\\\\"DoF.CoC\\\\\\\",this.renderTargetCoCBlurred=this.renderTargetCoC.clone(),this.renderTargetCoCBlurred.texture.name=\\\\\\\"DoF.CoC.Blurred\\\\\\\",this.uniforms.get(\\\\\\\"nearCoCBuffer\\\\\\\").value=this.renderTargetCoCBlurred.texture,this.cocPass=new ci(new Lv(s));let l=this.circleOfConfusionMaterial;l.uniforms.focusDistance.value=t,l.uniforms.focalLength.value=r,this.blurPass=new Cf({width:n,height:o,kernelSize:Ta.MEDIUM}),this.blurPass.resolution.resizable=this,this.maskPass=new ci(new Hv(this.renderTargetCoC.texture));let a=this.maskPass.getFullscreenMaterial();a.maskFunction=Nf.MULTIPLY,a.colorChannel=bf.GREEN,this.bokehNearBasePass=new ci(new Sa(!1,!0)),this.bokehNearFillPass=new ci(new Sa(!0,!0)),this.bokehFarBasePass=new ci(new Sa(!1,!1)),this.bokehFarFillPass=new ci(new Sa(!0,!1)),this.bokehScale=i,this.target=null}get circleOfConfusionMaterial(){return this.cocPass.getFullscreenMaterial()}get resolution(){return this.blurPass.resolution}get bokehScale(){return this.uniforms.get(\\\\\\\"scale\\\\\\\").value}set bokehScale(s){[this.bokehNearBasePass,this.bokehNearFillPass,this.bokehFarBasePass,this.bokehFarFillPass].map(t=>t.getFullscreenMaterial().uniforms.scale).forEach(t=>{t.value=s}),this.maskPass.getFullscreenMaterial().uniforms.strength.value=s,this.uniforms.get(\\\\\\\"scale\\\\\\\").value=s}calculateFocusDistance(s){let e=this.camera,t=e.far-e.near,r=e.position.distanceTo(s);return Math.min(Math.max(r/t,0),1)}setDepthTexture(s,e=Tt.BasicDepthPacking){let t=this.circleOfConfusionMaterial;t.uniforms.depthBuffer.value=s,t.depthPacking=e}update(s,e,t){let r=this.renderTarget,i=this.renderTargetCoC,n=this.renderTargetCoCBlurred,o=this.renderTargetMasked,l=this.bokehFarBasePass,a=this.bokehFarFillPass,c=l.getFullscreenMaterial().uniforms,u=a.getFullscreenMaterial().uniforms,d=this.bokehNearBasePass,h=this.bokehNearFillPass,p=d.getFullscreenMaterial().uniforms,f=h.getFullscreenMaterial().uniforms;if(this.target!==null){let m=this.calculateFocusDistance(this.target);this.circleOfConfusionMaterial.uniforms.focusDistance.value=m}this.cocPass.render(s,null,i),this.blurPass.render(s,i,n),this.maskPass.render(s,e,o),c.cocBuffer.value=u.cocBuffer.value=i.texture,l.render(s,o,r),a.render(s,r,this.renderTargetFar),p.cocBuffer.value=f.cocBuffer.value=n.texture,d.render(s,e,r),h.render(s,r,this.renderTargetNear)}setSize(s,e){let t=this.resolution,r=[this.cocPass,this.blurPass,this.maskPass,this.bokehNearBasePass,this.bokehNearFillPass,this.bokehFarBasePass,this.bokehFarFillPass];r.push(this.renderTargetCoC,this.renderTargetMasked),r.forEach(l=>l.setSize(s,e));let i=t.width,n=t.height;r=[this.renderTarget,this.renderTargetNear,this.renderTargetFar,this.renderTargetCoCBlurred],r.forEach(l=>l.setSize(i,n)),[this.bokehNearBasePass,this.bokehNearFillPass,this.bokehFarBasePass,this.bokehFarFillPass].forEach(l=>l.getFullscreenMaterial().setTexelSize(1/i,1/n))}initialize(s,e,t){[this.cocPass,this.maskPass,this.bokehNearBasePass,this.bokehNearFillPass,this.bokehFarBasePass,this.bokehFarFillPass].forEach(i=>i.initialize(s,e,t)),this.blurPass.initialize(s,e,Tt.UnsignedByteType),!e&&t===Tt.UnsignedByteType&&(this.renderTargetNear.texture.type=Tt.RGBFormat),t!==void 0&&(this.renderTarget.texture.type=t,this.renderTargetNear.texture.type=t,this.renderTargetFar.texture.type=t,this.renderTargetMasked.texture.type=t)}};var tO=new ir.Vector3,rO=new ir.Matrix4;var Ix=\\\\\\\"uniform vec3 hue;uniform float saturation;void mainImage(const in vec4 inputColor,const in vec2 uv,out vec4 outputColor){vec3 color=vec3(dot(inputColor.rgb,hue.xyz),dot(inputColor.rgb,hue.zxy),dot(inputColor.rgb,hue.yzx));float average=(color.r+color.g+color.b)/3.0;vec3 diff=average-color;if(saturation>0.0){color+=diff*(1.0-1.0/(1.001-saturation));}else{color+=diff*-saturation;}outputColor=vec4(min(color,1.0),inputColor.a);}\\\\\\\",Uf=class extends ei{constructor({blendFunction:s=Fe.NORMAL,hue:e=0,saturation:t=0}={}){super(\\\\\\\"HueSaturationEffect\\\\\\\",Ix,{blendFunction:s,uniforms:new Map([[\\\\\\\"hue\\\\\\\",new po.Uniform(new po.Vector3)],[\\\\\\\"saturation\\\\\\\",new po.Uniform(t)]])});this.setHue(e)}setHue(s){let e=Math.sin(s),t=Math.cos(s);this.uniforms.get(\\\\\\\"hue\\\\\\\").value.set(2*t,-Math.sqrt(3)*e-t,Math.sqrt(3)*e-t).addScalar(1).divideScalar(3)}};var iO=new cr.Color;var Lx=`void mainImage(const in vec4 inputColor,const in vec2 uv,out vec4 outputColor){vec3 noise=vec3(rand(uv*time));\\n#ifdef PREMULTIPLY\\noutputColor=vec4(min(inputColor.rgb*noise,vec3(1.0)),inputColor.a);\\n#else\\noutputColor=vec4(noise,inputColor.a);\\n#endif\\n}`,jf=class extends ei{constructor({blendFunction:s=Fe.SCREEN,premultiply:e=!1}={}){super(\\\\\\\"NoiseEffect\\\\\\\",Lx,{blendFunction:s});this.premultiply=e}get premultiply(){return this.defines.has(\\\\\\\"PREMULTIPLY\\\\\\\")}set premultiply(s){this.premultiply!==s&&(s?this.defines.set(\\\\\\\"PREMULTIPLY\\\\\\\",\\\\\\\"1\\\\\\\"):this.defines.delete(\\\\\\\"PREMULTIPLY\\\\\\\"),this.setChanged())}};var Dx=\\\\\\\"uniform bool active;uniform vec2 d;void mainUv(inout vec2 uv){if(active){uv=vec2(d.x*(floor(uv.x/d.x)+0.5),d.y*(floor(uv.y/d.y)+0.5));}}\\\\\\\",kf=class extends ei{constructor(s=30){super(\\\\\\\"PixelationEffect\\\\\\\",Dx,{uniforms:new Map([[\\\\\\\"active\\\\\\\",new qs.Uniform(!1)],[\\\\\\\"d\\\\\\\",new qs.Uniform(new qs.Vector2)]])});this.resolution=new qs.Vector2,this.granularity=s}getGranularity(){return this.granularity}setGranularity(s){s=Math.floor(s),s%2>0&&(s+=1);let e=this.uniforms;e.get(\\\\\\\"active\\\\\\\").value=s>0,e.get(\\\\\\\"d\\\\\\\").value.set(s,s).divide(this.resolution),this.granularity=s}setSize(s,e){this.resolution.set(s,e),this.setGranularity(this.granularity)}};var sO=Math.PI*.5,nO=new mo.Vector3,oO=new mo.Vector3;var 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\\\\\\\",_x=\\\\\\\"uniform sampler2D weightMap;varying vec2 vOffset0;varying vec2 vOffset1;void movec(const in bvec2 c,inout vec2 variable,const in vec2 value){if(c.x){variable.x=value.x;}if(c.y){variable.y=value.y;}}void movec(const in bvec4 c,inout vec4 variable,const in vec4 value){movec(c.xy,variable.xy,value.xy);movec(c.zw,variable.zw,value.zw);}void mainImage(const in vec4 inputColor,const in vec2 uv,out vec4 outputColor){vec4 a;a.x=texture2D(weightMap,vOffset0).a;a.y=texture2D(weightMap,vOffset1).g;a.wz=texture2D(weightMap,uv).rb;vec4 color=inputColor;if(dot(a,vec4(1.0))>=1e-5){bool h=max(a.x,a.z)>max(a.y,a.w);vec4 blendingOffset=vec4(0.0,a.y,0.0,a.w);vec2 blendingWeight=a.yw;movec(bvec4(h),blendingOffset,vec4(a.x,0.0,a.z,0.0));movec(bvec2(h),blendingWeight,a.xz);blendingWeight/=dot(blendingWeight,vec2(1.0));vec4 blendingCoord=blendingOffset*vec4(texelSize,-texelSize)+uv.xyxy;color=blendingWeight.x*texture2D(inputBuffer,blendingCoord.xy);color+=blendingWeight.y*texture2D(inputBuffer,blendingCoord.zw);}outputColor=color;}\\\\\\\",Ox=\\\\\\\"varying vec2 vOffset0;varying vec2 vOffset1;void mainSupport(const in vec2 uv){vOffset0=uv+texelSize*vec2(1.0,0.0);vOffset1=uv+texelSize*vec2(0.0,1.0);}\\\\\\\",Qf=class extends ei{constructor(s,e,t=as.HIGH,r=Ia.COLOR){super(\\\\\\\"SMAAEffect\\\\\\\",_x,{vertexShader:Ox,blendFunction:Fe.NORMAL,attributes:Br.CONVOLUTION|Br.DEPTH,uniforms:new Map([[\\\\\\\"weightMap\\\\\\\",new Ze.Uniform(null)]])});this.renderTargetEdges=new Ze.WebGLRenderTarget(1,1,{minFilter:Ze.LinearFilter,stencilBuffer:!1,depthBuffer:!1,format:Ze.RGBFormat}),this.renderTargetEdges.texture.name=\\\\\\\"SMAA.Edges\\\\\\\",this.renderTargetWeights=this.renderTargetEdges.clone(),this.renderTargetWeights.texture.name=\\\\\\\"SMAA.Weights\\\\\\\",this.renderTargetWeights.texture.format=Ze.RGBAFormat,this.uniforms.get(\\\\\\\"weightMap\\\\\\\").value=this.renderTargetWeights.texture,this.clearPass=new Fc(!0,!1,!1),this.clearPass.overrideClearColor=new Ze.Color(0),this.clearPass.overrideClearAlpha=1,this.edgeDetectionPass=new ci(new Fv(new Ze.Vector2,r)),this.weightsPass=new ci(new Qv);let i=new Ze.Texture(s);i.name=\\\\\\\"SMAA.Search\\\\\\\",i.magFilter=Ze.NearestFilter,i.minFilter=Ze.NearestFilter,i.format=Ze.RGBAFormat,i.generateMipmaps=!1,i.needsUpdate=!0,i.flipY=!0;let n=new Ze.Texture(e);n.name=\\\\\\\"SMAA.Area\\\\\\\",n.magFilter=Ze.LinearFilter,n.minFilter=Ze.LinearFilter,n.format=Ze.RGBAFormat,n.generateMipmaps=!1,n.needsUpdate=!0,n.flipY=!1;let o=this.weightsPass.getFullscreenMaterial();o.uniforms.searchTexture.value=i,o.uniforms.areaTexture.value=n,this.applyPreset(t)}get edgeDetectionMaterial(){return this.edgeDetectionPass.getFullscreenMaterial()}get colorEdgesMaterial(){return this.edgeDetectionMaterial}get weightsMaterial(){return this.weightsPass.getFullscreenMaterial()}setEdgeDetectionThreshold(s){this.edgeDetectionPass.getFullscreenMaterial().setEdgeDetectionThreshold(s)}setOrthogonalSearchSteps(s){this.weightsPass.getFullscreenMaterial().setOrthogonalSearchSteps(s)}applyPreset(s){let e=this.edgeDetectionMaterial,t=this.weightsMaterial;switch(s){case as.LOW:e.setEdgeDetectionThreshold(.15),t.setOrthogonalSearchSteps(4),t.diagonalDetection=!1,t.cornerRounding=!1;break;case as.MEDIUM:e.setEdgeDetectionThreshold(.1),t.setOrthogonalSearchSteps(8),t.diagonalDetection=!1,t.cornerRounding=!1;break;case as.HIGH:e.setEdgeDetectionThreshold(.1),t.setOrthogonalSearchSteps(16),t.setDiagonalSearchSteps(8),t.setCornerRounding(25),t.diagonalDetection=!0,t.cornerRounding=!0;break;case as.ULTRA:e.setEdgeDetectionThreshold(.05),t.setOrthogonalSearchSteps(32),t.setDiagonalSearchSteps(16),t.setCornerRounding(25),t.diagonalDetection=!0,t.cornerRounding=!0;break}}setDepthTexture(s,e=Ze.BasicDepthPacking){let t=this.edgeDetectionMaterial;t.uniforms.depthBuffer.value=s,t.depthPacking=e}update(s,e,t){this.clearPass.render(s,this.renderTargetEdges),this.edgeDetectionPass.render(s,e,this.renderTargetEdges),this.weightsPass.render(s,this.renderTargetEdges,this.renderTargetWeights)}setSize(s,e){let t=this.edgeDetectionPass.getFullscreenMaterial(),r=this.weightsPass.getFullscreenMaterial();this.renderTargetEdges.setSize(s,e),this.renderTargetWeights.setSize(s,e),r.uniforms.resolution.value.set(s,e),r.uniforms.texelSize.value.set(1/s,1/e),t.uniforms.texelSize.value.copy(r.uniforms.texelSize.value)}dispose(){let s=this.weightsPass.getFullscreenMaterial().uniforms;s.searchTexture.value.dispose(),s.areaTexture.value.dispose(),super.dispose()}static get searchImageDataURL(){return Wf}static get areaImageDataURL(){return Jf}},as={LOW:0,MEDIUM:1,HIGH:2,ULTRA:3};var Px=`uniform float offset;uniform float darkness;void mainImage(const in vec4 inputColor,const in vec2 uv,out vec4 outputColor){const vec2 center=vec2(0.5);vec3 color=inputColor.rgb;\\n#ifdef ESKIL\\nvec2 coord=(uv-center)*vec2(offset);color=mix(color,vec3(1.0-darkness),dot(coord,coord));\\n#else\\nfloat d=distance(uv,center);color*=smoothstep(0.8,offset*0.799,d*(darkness+offset));\\n#endif\\noutputColor=vec4(color,inputColor.a);}`,Yf=class extends ei{constructor(s={}){let e=Object.assign({blendFunction:Fe.NORMAL,eskil:!1,offset:.5,darkness:.5},s);super(\\\\\\\"VignetteEffect\\\\\\\",Px,{blendFunction:e.blendFunction,uniforms:new Map([[\\\\\\\"offset\\\\\\\",new kc.Uniform(e.offset)],[\\\\\\\"darkness\\\\\\\",new kc.Uniform(e.darkness)]])});this.eskil=e.eskil}get eskil(){return this.defines.has(\\\\\\\"ESKIL\\\\\\\")}set eskil(s){this.eskil!==s&&(s?this.defines.set(\\\\\\\"ESKIL\\\\\\\",\\\\\\\"1\\\\\\\"):this.defines.delete(\\\\\\\"ESKIL\\\\\\\"),this.setChanged())}},aO=[new Float32Array(3),new Float32Array(3)],lO=[new Float32Array(3),new Float32Array(3),new Float32Array(3),new Float32Array(3)],cO=[[new Float32Array([0,0,0]),new Float32Array([1,0,0]),new Float32Array([1,1,0]),new Float32Array([1,1,1])],[new Float32Array([0,0,0]),new Float32Array([1,0,0]),new Float32Array([1,0,1]),new Float32Array([1,1,1])],[new Float32Array([0,0,0]),new Float32Array([0,0,1]),new Float32Array([1,0,1]),new Float32Array([1,1,1])],[new Float32Array([0,0,0]),new Float32Array([0,1,0]),new Float32Array([1,1,0]),new Float32Array([1,1,1])],[new Float32Array([0,0,0]),new Float32Array([0,1,0]),new Float32Array([0,1,1]),new Float32Array([1,1,1])],[new Float32Array([0,0,0]),new Float32Array([0,0,1]),new Float32Array([0,1,1]),new Float32Array([1,1,1])]];var uO=[new Float32Array(2),new Float32Array(2)];var dO=new Float32Array([0,-.25,.25,-.125,.125,-.375,.375]),hO=[new Float32Array([0,0]),new Float32Array([.25,-.25]),new Float32Array([-.25,.25]),new Float32Array([.125,-.125]),new Float32Array([-.125,.125])],fO=[new Uint8Array([0,0]),new Uint8Array([3,0]),new Uint8Array([0,3]),new Uint8Array([3,3]),new Uint8Array([1,0]),new Uint8Array([4,0]),new Uint8Array([1,3]),new Uint8Array([4,3]),new Uint8Array([0,1]),new Uint8Array([3,1]),new Uint8Array([0,4]),new Uint8Array([3,4]),new Uint8Array([1,1]),new Uint8Array([4,1]),new Uint8Array([1,4]),new Uint8Array([4,4])],pO=[new Uint8Array([0,0]),new Uint8Array([1,0]),new Uint8Array([0,2]),new Uint8Array([1,2]),new Uint8Array([2,0]),new Uint8Array([3,0]),new Uint8Array([2,2]),new Uint8Array([3,2]),new Uint8Array([0,1]),new Uint8Array([1,1]),new Uint8Array([0,3]),new Uint8Array([1,3]),new Uint8Array([2,1]),new Uint8Array([3,1]),new Uint8Array([2,3]),new Uint8Array([3,3])];var mO=new Map([[Ft(0,0,0,0),new Float32Array([0,0,0,0])],[Ft(0,0,0,1),new Float32Array([0,0,0,1])],[Ft(0,0,1,0),new Float32Array([0,0,1,0])],[Ft(0,0,1,1),new Float32Array([0,0,1,1])],[Ft(0,1,0,0),new Float32Array([0,1,0,0])],[Ft(0,1,0,1),new Float32Array([0,1,0,1])],[Ft(0,1,1,0),new Float32Array([0,1,1,0])],[Ft(0,1,1,1),new Float32Array([0,1,1,1])],[Ft(1,0,0,0),new Float32Array([1,0,0,0])],[Ft(1,0,0,1),new Float32Array([1,0,0,1])],[Ft(1,0,1,0),new Float32Array([1,0,1,0])],[Ft(1,0,1,1),new Float32Array([1,0,1,1])],[Ft(1,1,0,0),new Float32Array([1,1,0,0])],[Ft(1,1,0,1),new Float32Array([1,1,0,1])],[Ft(1,1,1,0),new Float32Array([1,1,1,0])],[Ft(1,1,1,1),new Float32Array([1,1,1,1])]]);function Vc(s,e,t){return s+(e-s)*t}function Ft(s,e,t,r){let i=Vc(s,e,1-.25),n=Vc(t,r,1-.25);return Vc(i,n,1-.125)}var Kf=class extends za.Loader{load(s=()=>{},e=null){arguments.length===4?(s=arguments[1],e=arguments[3]):(arguments.length===3||typeof arguments[0]!=\\\\\\\"function\\\\\\\")&&(s=arguments[1],e=null);let t=this.manager,r=new za.LoadingManager;return new Promise((i,n)=>{let o=new Image,l=new Image;r.onError=a=>{t.itemError(a),e!==null?(e(`Failed to load ${a}`),i()):n(`Failed to load ${a}`)},r.onLoad=()=>{let a=[o,l];s(a),i(a)},o.addEventListener(\\\\\\\"error\\\\\\\",a=>{r.itemError(\\\\\\\"smaa-search\\\\\\\")}),l.addEventListener(\\\\\\\"error\\\\\\\",a=>{r.itemError(\\\\\\\"smaa-area\\\\\\\")}),o.addEventListener(\\\\\\\"load\\\\\\\",()=>{t.itemEnd(\\\\\\\"smaa-search\\\\\\\"),r.itemEnd(\\\\\\\"smaa-search\\\\\\\")}),l.addEventListener(\\\\\\\"load\\\\\\\",()=>{t.itemEnd(\\\\\\\"smaa-area\\\\\\\"),r.itemEnd(\\\\\\\"smaa-area\\\\\\\")}),t.itemStart(\\\\\\\"smaa-search\\\\\\\"),t.itemStart(\\\\\\\"smaa-area\\\\\\\"),r.itemStart(\\\\\\\"smaa-search\\\\\\\"),r.itemStart(\\\\\\\"smaa-area\\\\\\\"),o.src=Wf,l.src=Jf})}};var It=F(z());var ht=class{constructor(e,t){this.enabled=!1;this.effect=new e(t),Object.defineProperty(this,\\\\\\\"opacity\\\\\\\",{enumerable:!0,set(r){this.effect.blendMode.opacity.value=r},get(){return this.effect.blendMode.opacity.value}}),Object.defineProperty(this,\\\\\\\"blendFunction\\\\\\\",{enumerable:!0,set(r){this.effect.blendMode.setBlendFunction(Number(r))},get(){return this.effect.blendMode.blendFunction}}),this.blendFunction=Fe.NORMAL}toJSON(){let e=[\\\\\\\"constructor\\\\\\\",\\\\\\\"effect\\\\\\\",\\\\\\\"subscriptions\\\\\\\"],t=le({},this),r=Object.getPrototypeOf(this),i=Object.getOwnPropertyNames(r);for(let n of i){let o=Object.getOwnPropertyDescriptor(r,n);o&&typeof o.get==\\\\\\\"function\\\\\\\"&&(t[n]=this[n])}return e.forEach(n=>delete t[n]),t}};var vo=class extends ht{constructor(){super(_f);this.blendFunction=Fe.SCREEN}set intensity(e){this.effect.intensity=e}get intensity(){return this.effect.intensity}set luminanceThreshold(e){this.effect.luminanceMaterial.threshold=e}get luminanceThreshold(){return this.effect.luminanceMaterial.threshold}set luminanceSmoothing(e){this.effect.luminanceMaterial.smoothing=e}get luminanceSmoothing(){return this.effect.luminanceMaterial.smoothing}set blurScale(e){this.effect.blurPass.scale=e}get blurScale(){return this.effect.blurPass.scale}set kernelSize(e){this.effect.blurPass.kernelSize=e}get kernelSize(){return this.effect.blurPass.kernelSize}};var xo=class extends ht{constructor(){super(Of)}set contrast(e){this.effect.uniforms.get(\\\\\\\"contrast\\\\\\\").value=e}get contrast(){return this.effect.uniforms.get(\\\\\\\"contrast\\\\\\\").value}set brightness(e){this.effect.uniforms.get(\\\\\\\"brightness\\\\\\\").value=e}get brightness(){return this.effect.uniforms.get(\\\\\\\"brightness\\\\\\\").value}};var Xf=F(z());var bo=class extends ht{constructor(){super(Rf);this.effect.offset=new Xf.Vector2(.01,.01)}set offset(e){this.effect.offset.set(e[0]/1e3,e[1]/1e3)}get offset(){return[this.effect.offset.x*1e3,this.effect.offset.y*1e3]}};var Ao=class extends ht{constructor(){super(Pf)}};var wo=class extends ht{constructor(){super(Uf);this._hue=0}set hue(e){this._hue=e,this.effect.setHue(e)}get hue(){return this._hue}set saturation(e){this.effect.uniforms.get(\\\\\\\"saturation\\\\\\\").value=e}get saturation(){return this.effect.uniforms.get(\\\\\\\"saturation\\\\\\\").value}};var So=class extends ht{constructor(){super(jf);this.blendFunction=Fe.OVERLAY}};var No=class extends ht{constructor(){super(Yf)}get eskil(){return this.effect.eskil}set eskil(e){this.effect.eskil=e}get darkness(){return this.effect.uniforms.get(\\\\\\\"darkness\\\\\\\").value}set darkness(e){this.effect.uniforms.get(\\\\\\\"darkness\\\\\\\").value=e}get offset(){return this.effect.uniforms.get(\\\\\\\"offset\\\\\\\").value}set offset(e){this.effect.uniforms.get(\\\\\\\"offset\\\\\\\").value=e}};var To=class extends ht{constructor(e){super(Ff,e)}set focalLength(e){this.effect.circleOfConfusionMaterial.uniforms.focalLength.value=e}get focalLength(){return this.effect.circleOfConfusionMaterial.uniforms.focalLength.value}set focusDistance(e){this.effect.circleOfConfusionMaterial.uniforms.focusDistance.value=e}get focusDistance(){return this.effect.circleOfConfusionMaterial.uniforms.focusDistance.value}get bokehScale(){return this.effect.bokehScale}set bokehScale(e){this.effect.bokehScale=e}};var Co=class extends ht{constructor(){super(kf)}get granularity(){return this.effect.getGranularity()}set granularity(e){this.effect.setGranularity(e)}};function qf(s,e){return e&&e.enabled&&s.push(e.effect),s}var Mo=class{constructor(){this._scene=new It.Scene;this._camera=new Kr;this.effects=new Map;this.loadingManager=new It.LoadingManager;this.smaaImageLoader=new Kf(this.loadingManager);this.renderPass=new Da;this.depthPass=new zc;this._renderToScreen=!0;this.clock=new It.Clock,this.enabled=!1,this.debug=!1,this.effects.set(\\\\\\\"bloom\\\\\\\",new vo),this.effects.set(\\\\\\\"chromaticAberration\\\\\\\",new bo),this.effects.set(\\\\\\\"vignette\\\\\\\",new No),this.effects.set(\\\\\\\"noise\\\\\\\",new So),this.effects.set(\\\\\\\"colorAverage\\\\\\\",new Ao),this.effects.set(\\\\\\\"hueSaturation\\\\\\\",new wo),this.effects.set(\\\\\\\"brightnessContrast\\\\\\\",new xo),this.effects.set(\\\\\\\"depthOfField\\\\\\\",new To),this.effects.set(\\\\\\\"pixelation\\\\\\\",new Co)}get scene(){return this._scene}set scene(e){this._scene=e,this._initPasses()}get camera(){return this._camera}set camera(e){this._camera=e,this._initPasses()}_initSmaa(){let e=this.effectComposer.passes.length;this.smaaImageLoader.load(([t,r])=>{if(!this.effectComposer)return;let i=new Qf(t,r,as.ULTRA,Ia.COLOR);i.edgeDetectionMaterial.setPredicationMode(Sf.DEPTH),i.edgeDetectionMaterial.setEdgeDetectionThreshold(.05),i.edgeDetectionMaterial.setPredicationThreshold(.002),i.edgeDetectionMaterial.setPredicationScale(1),this.effectComposer.addPass(new Ra(this.camera,i),e)})}_initPasses(){if(!(!this.effectComposer||!this.renderer))if(this.effectComposer.removeAllPasses(),this.renderPass=new Da(this.scene,this.camera),this.enabled){this.depthPass=new zc(this.scene,this.camera),this.effectComposer.addPass(this.renderPass),this.effectComposer.addPass(this.depthPass),this._initSmaa();let e=[this.effects.get(\\\\\\\"chromaticAberration\\\\\\\"),this.effects.get(\\\\\\\"bloom\\\\\\\"),this.effects.get(\\\\\\\"colorAverage\\\\\\\"),this.effects.get(\\\\\\\"hueSaturation\\\\\\\"),this.effects.get(\\\\\\\"brightnessContrast\\\\\\\"),this.effects.get(\\\\\\\"vignette\\\\\\\"),this.effects.get(\\\\\\\"noise\\\\\\\")].reduce(qf,[]),t=[this.effects.get(\\\\\\\"pixelation\\\\\\\")].reduce(qf,[]);t.length>0&&this.effectComposer.addPass(new Ra(this.camera,...t)),this.effectComposer.addPass(new Ra(this.camera,...e))}else this.effectComposer.addPass(this.renderPass)}reinit(){!this.renderer||(this.effectComposer=new Bf(this.renderer),this._initPasses())}init({renderer:e,camera:t,scene:r}){this.renderer=e,this.scene=r,this.camera=t,this._initSavePass(),this.reinit()}_initSavePass(){if(this._savePass)return;let e=new It.Vector2;this.renderer.getDrawingBufferSize(e);let t=this.renderer.capabilities.isWebGL2?It.WebGLMultisampleRenderTarget:It.WebGLRenderTarget;this._rt=new t(e.x,e.y,{depthBuffer:!1,stencilBuffer:!1,wrapS:It.RepeatWrapping,wrapT:It.RepeatWrapping}),this._savePass=new Tf(this._rt,!1),this._savePass.renderToScreen=!1}get renderToScreen(){return this._renderToScreen}set renderToScreen(e){let t=this.effectComposer;!t||(e===!0?(this.renderer?.capabilities.isWebGL2&&(t.multisampling=0),t.removePass(this._savePass),t.passes[t.passes.length-1].renderToScreen=!0,t.autoRenderToScreen=!0):(this.renderer?.capabilities.isWebGL2&&(t.multisampling=4),t.autoRenderToScreen=!1,t.passes[t.passes.length-1].renderToScreen=!1,this._savePass.renderToScreen=!1,t.addPass(this._savePass)),this._renderToScreen=e)}get texture(){return this._rt&&this._rt.texture}get renderTarget(){return this._rt}fromJSON(e){e&&(Object.keys(e).forEach(t=>{let r=e[t],i=this.effects.get(t);Object.keys(r).forEach(n=>{i[n]=r[n]})}),this.enabled=e.enabled)}toJSON(){let e={};return this.effects.forEach((t,r)=>{e[r]=t.toJSON()},e),le({enabled:this.enabled},e)}render(){this.effectComposer?.render(this.clock.getDelta())}setScissor(e,t,r,i){if(!this.effectComposer||!this.renderer)return;e instanceof It.Vector4?(this.effectComposer.inputBuffer.scissor.set(e.x,e.y,e.z,e.w),this.effectComposer.outputBuffer.scissor.set(e.x,e.y,e.z,e.w)):(this.effectComposer.inputBuffer.scissor.set(e,t,r,i),this.effectComposer.outputBuffer.scissor.set(e,t,r,i));let n=this.renderer.getPixelRatio();this.effectComposer.inputBuffer.scissor.multiplyScalar(n),this.effectComposer.outputBuffer.scissor.multiplyScalar(n),this.renderer.setScissor(e,t,r,i)}setScissorTest(e){!this.effectComposer||!this.renderer||(this.effectComposer.inputBuffer.scissorTest=e,this.effectComposer.outputBuffer.scissorTest=e,this.renderer.setScissorTest(e))}resize(e,t){if(this.effectComposer?.setSize(e,t),this._rt){let r=this.renderer?.getPixelRatio()||window.devicePixelRatio;this._rt.setSize(e*r,t*r)}}};var Ua=F(z()),Zf;(function(e){function s(t){new Ua.Vector4(t.r/255,t.g/255,t.b/255,t.a)}e.getThreeVector4=s})(Zf||(Zf={}));var ja;(function(t){function s(r,i){r.setRGB(i.r/255,i.g/255,i.b/255)}t.setThreeColor=s;function e(r){return new Ua.Color(r.r/255,r.g/255,r.b/255).getHex()}t.getHex=e})(ja||(ja={}));var Li=class extends ua(bt.Scene){constructor(){super();this.objectType=\\\\\\\"Scene\\\\\\\";this.alpha=1;this.backupFog=new bt.Fog(16777215,.1,2e3);this.fogUseBGColor=!1;this.directionalLightState=!0;this.ambientLightState=!0;this.wireframeState=!1;this._color=new bt.Color(1644825);this.canvas={mode:1,preset:\\\\\\\"fullscreen\\\\\\\",size:{width:512,height:512},editorSize:{width:1,height:1},color:[0,0,0,.5]};this.name=\\\\\\\"Untitled\\\\\\\",this.postprocessing=new Mo}get color(){return this._color}set color(e){this.fogUseBGColor===!0&&this.backupFog.color.copy(e),this._color.copy(e)}get enableFog(){return this.fog!==null}set enableFog(e){this.fog=e===!0?this.backupFog:null}init(e,t){this.camera=new Kr(e,t),this.camera.setDefault(),this.camera.orthoCamera.near=-5e4,this.camera.perspCamera.near=50,this.camera.orthoCamera.far=1e5,this.camera.perspCamera.far=1e5,this.camera.updateProjectionMatrix(),this.camera.position.set(0,0,Kr.DefaultTargetOffset),this.camera.lookAt(new bt.Vector3(0,0,-1)),this.add(this.camera),this.canvas.size.width=e,this.canvas.size.height=t,this.ambientLight=new bt.HemisphereLight(13882323,8553090,.75),this.ambientLight.name=\\\\\\\"Default Ambient Light\\\\\\\",this.add(this.ambientLight),this.directionalLight=new bt.DirectionalLight(16777215,.75),this.directionalLight.name=\\\\\\\"Default Directional Light\\\\\\\",this.directionalLight.position.set(85e4,13e5,1e6),this.activeCamera=this.camera,this.activeCamera.add(this.directionalLight)}initFromObjectLoader(e){return this.traverse(t=>{t.uuid===e.ambientLight?this.ambientLight=t:t.uuid===e.directionalLight?this.directionalLight=t:(t.uuid===e.activeCamera&&(this.activeCamera=t),t.uuid===e.camera&&(this.camera=t,this.camera.setDefault()))}),this}raycast(e){let t=[],r=i=>{for(let n of i.children)Vt(n)&&!n.raycastLock&&n.visible&&((Kt(n)||ta(n)&&n.enableHelper&&n.objectHelper.parent)&&e.intersectObject(n,!1,t),r(n))};return r(this),t}traverseEntity(e){for(let t of this.children)Vt(t)&&this.camera!==t&&t.traverseEntity(e)}updateFog(e){this.enableFog=e.enabled,this.fogUseBGColor=e.useBackgroundColor,e.useBackgroundColor?this.backupFog.color.set(this.color):ja.setThreeColor(this.backupFog.color,e.color),this.backupFog.near=e.near,this.backupFog.far=e.far}toJSON(e){let t=super.toJSON(e),r=t.object;return r.objectType=\\\\\\\"Scene\\\\\\\",r.activeCamera=this.activeCamera?.uuid,r.camera=this.camera?.uuid,r.directionalLight=this.directionalLight?.uuid,r.ambientLight=this.ambientLight?.uuid,r.color=this.color.toArray(),r.alpha=this.alpha,this.background instanceof bt.Texture?r.background=this.background.toJSON(e):this.background instanceof bt.Color&&(r.background=this.background.toArray()),this.environment!==null&&(r.environment=this.environment.toJSON(e)),this.enableFog===!0&&(r.enableFog=!0,this.fogUseBGColor===!0&&(r.fogUseBGColor=!0),this.fog!==null&&(r.fog=this.fog.toJSON())),this.directionalLightState===!0&&(r.directionalLightState=!0),this.ambientLightState===!0&&(r.ambientLightState=!0),this.wireframeState===!0&&(r.wireframeState=!0),r.canvas={mode:this.canvas.mode,preset:this.canvas.preset,editorSize:{width:window.innerWidth,height:window.innerHeight},color:this.canvas.color},this.canvas.mode!==1&&(r.canvas.size={width:this.canvas.size.width,height:this.canvas.size.height}),r.postprocessing=this.postprocessing?.toJSON(),t}fromJSON(e){super.fromJSON(e),this.postprocessing=new Mo,this.color.fromArray(e.color),this.alpha=e.alpha,e.background!==void 0&&Array.isArray(e.background)&&(this.background=new bt.Color().fromArray(e.background)),e.fog!==void 0&&(e.fog.type===\\\\\\\"FogExp2\\\\\\\"?this.backupFog=new bt.FogExp2(e.fog.color,e.fog.density):this.backupFog=new bt.Fog(e.fog.color,e.fog.near,e.fog.far)),this.fogUseBGColor=e.fogUseBGColor??!1,this.enableFog=e.enableFog??!1,this.directionalLightState=e.directionalLightState??!1,this.ambientLightState=e.ambientLightState??!1,this.wireframeState=e.wireframeState??!1;let t=e.canvas;return this.canvas.mode=t.mode,this.canvas.preset=t.preset,t.size&&(this.canvas.size={width:t.size.width,height:t.size.height}),this.canvas.editorSize=t.editorSize?{width:t.editorSize.width,height:t.editorSize.height}:{width:window.innerWidth,height:window.innerHeight},t.color&&(this.canvas.color=[...t.color]),this.postprocessing.fromJSON(e.postprocessing),this}dispose(){this.traverseEntity(e=>{\\\\\\\"geometry\\\\\\\"in e&&e.geometry.dispose(),\\\\\\\"material\\\\\\\"in e&&(e.material instanceof Array?e.material.forEach(t=>t.dispose()):e.material.dispose())})}};var tp=F(z());var Hc=class extends ue{constructor(e=\\\\\\\"\\\\\\\",t){super(t);this.nodeType=\\\\\\\"Attribute\\\\\\\";this.name=e}getAttributeType(e){return typeof this.type==\\\\\\\"number\\\\\\\"?e.getConstructorFromLength(this.type):this.type}getType(e){let t=this.getAttributeType(e);return e.getTypeByFormat(t)}generate(e,t,r,i,n){i=this.getAttributeType(e);let o=e.getAttribute(this.name,i),l=e.isShader(\\\\\\\"vertex\\\\\\\")?this.name:o.varying.name;return e.format(l,this.getType(e),t)}toJSON(e){let t=super.toJSON(e);return t.nodeType=this.nodeType,t.name=this.name,t}fromJSON(e,t){return super.fromJSON(e,t),e.scope&&(this.scope=e.scope),e.name&&(this.name=e.name),this}};var Wc=class extends oe{constructor(e=\\\\\\\"\\\\\\\",t=0){super(e);this.nodeType=\\\\\\\"Varying\\\\\\\";this.value=t}getType(e){return e.getTypeByFormat(this.type)}generate(e,t,r,i,n){let o=e.getVar(this.uuid,this.type);return this.value&&e.isShader(\\\\\\\"vertex\\\\\\\")&&e.addNodeCode(o.name+\\\\\\\" = \\\\\\\"+this.value.build(e,this.getType(e))+\\\\\\\";\\\\\\\"),e.format(o.name,this.getType(e),t)}copy(e){return super.copy(e),this.value=e.value,this}toJSON(e){let t=this.getJSONNode(e);return t||(t=this.createJSONNode(e),this.value&&(t.value=this.value)),t.nodeType=this.nodeType,t}fromJSON(e,t){return super.fromJSON(e,t),e.value&&(this.value=e.value),this}};var $f=F(z());var Jc=class extends Ke{constructor(e){super(\\\\\\\"m4\\\\\\\");this.nodeType=\\\\\\\"Matrix4\\\\\\\";this.value=e??new $f.Matrix4}generateReadonly(e,t,r,i,n,o){return e.format(\\\\\\\"mat4(\\\\\\\"+this.value.elements.join(\\\\\\\", \\\\\\\")+\\\\\\\")\\\\\\\",i,t)}copy(e){return super.copy(e),this.elements=e.elements,this}toJSON(e){let t=this.getJSONNode(e);return t||(t=this.createJSONNode(e),t.elements=this.value.elements.concat()),t.nodeType=this.nodeType,t}fromJSON(e,t){return super.fromJSON(e,t),e.elements&&(this.elements=e.elements),this}get elements(){return this.value.elements}set elements(e){this.value.fromArray(e)}};var ti;(function(a){a.EQUAL=\\\\\\\"==\\\\\\\",a.NOT_EQUAL=\\\\\\\"!=\\\\\\\",a.GREATER=\\\\\\\">\\\\\\\",a.GREATER_EQUAL=\\\\\\\">=\\\\\\\",a.LESS=\\\\\\\"<\\\\\\\",a.LESS_EQUAL=\\\\\\\"<=\\\\\\\",a.AND=\\\\\\\"&&\\\\\\\",a.OR=\\\\\\\"||\\\\\\\"})(ti||(ti={}));var Qc=class extends ue{constructor(e=new oe,t=new oe,r=Qc.GREATER,i=new oe,n=new oe){super();this.nodeType=\\\\\\\"Cond\\\\\\\";this.a=e,this.b=t,this.op=r,this.ifNode=i,this.elseNode=n}getType(e){if(this.ifNode){let t=this.ifNode.getType(e),r=this.elseNode.getType(e);return e.getTypeLength(r)>e.getTypeLength(t)?r:t}return\\\\\\\"b\\\\\\\"}getCondType(e){return e.getTypeLength(this.b.getType(e))>e.getTypeLength(this.a.getType(e))?this.b.getType(e):this.a.getType(e)}generate(e,t){let r=this.getType(e),i=this.getCondType(e),n=this.a.build(e,i),o=this.b.build(e,i),l;if(this.type=r,this.ifNode){let a=this.ifNode.build(e,r),c=this.elseNode.build(e,r);l=\\\\\\\"( \\\\\\\"+[n,this.op,o,\\\\\\\"?\\\\\\\",a,\\\\\\\":\\\\\\\",c].join(\\\\\\\" \\\\\\\")+\\\\\\\" )\\\\\\\"}else l=\\\\\\\"( \\\\\\\"+n+\\\\\\\" \\\\\\\"+this.op+\\\\\\\" \\\\\\\"+o+\\\\\\\" )\\\\\\\";return e.format(l,this.getType(e),t)}copy(e){return super.copy(e),this.a.copy(e.a),this.b.copy(e.b),this.op=e.op,this.ifNode.copy(e.ifNode),this.elseNode.copy(e.elseNode),this}toJSON(e){let t=this.getJSONNode(e);return t||(t=this.createJSONNode(e),t.a=this.a.toJSON(e).uuid,t.b=this.b.toJSON(e).uuid,t.op=this.op,this.ifNode&&(t.ifNode=this.ifNode.toJSON(e).uuid),this.elseNode&&(t.elseNode=this.elseNode.toJSON(e).uuid)),t.nodeType=this.nodeType,t}fromJSON(e,t){return super.fromJSON(e,t),e.a&&(this.a=t.getNode(e.a)),e.b&&(this.b=t.getNode(e.b)),e.op&&(this.op=e.op),e.ifNode&&(this.ifNode=t.getNode(e.ifNode)),e.elseNode&&(this.elseNode=t.getNode(e.elseNode)),this}},Or=Qc;Or.EQUAL=ti.EQUAL,Or.NOT_EQUAL=ti.NOT_EQUAL,Or.GREATER=ti.GREATER,Or.GREATER_EQUAL=ti.GREATER_EQUAL,Or.LESS=ti.LESS,Or.LESS_EQUAL=ti.LESS_EQUAL,Or.AND=ti.AND,Or.OR=ti.OR;var $s=class extends te{constructor(e){super();this.nodeType=\\\\\\\"MaxMIPLevel\\\\\\\";this.texture=e,this.maxMIPLevel=0}toJSON(e){let t=this.getJSONNode(e);return t||(t=this.createJSONNode(e),this.texture&&(t.texture=this.texture.toJSON(e).uuid),t.maxMIPLevel=this.maxMIPLevel),t.nodeType=this.nodeType,t}fromJSON(e,t){return super.fromJSON(e,t),e.texture&&(this.texture=t.getNode(e.texture)),e.maxMIPLevel!==void 0&&(this.maxMIPLevel=e.maxMIPLevel),this}};Object.defineProperties($s.prototype,{value:{get:function(){if(this.maxMIPLevel===0){let 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super.copy(e),this.roughness=e.roughness,e.texture?this.texture?this.texture.copy(e.texture):this.texture=e.texture.clone():this.texture=void 0,e.maxMIPLevel?this.maxMIPLevel?this.maxMIPLevel.copy(e.maxMIPLevel):this.maxMIPLevel=e.maxMIPLevel.clone():this.maxMIPLevel=void 0,this}toJSON(e){let t=this.getJSONNode(e);return t||(t=this.createJSONNode(e),this.texture&&(t.texture=this.texture.toJSON(e).uuid),this.roughness&&(t.roughness=this.roughness.toJSON(e).uuid),this.maxMIPLevel&&(t.maxMIPLevel=this.maxMIPLevel.toJSON(e).uuid)),t.nodeType=this.nodeType,t}fromJSON(e,t){return super.fromJSON(e,t),e.roughness&&(this.roughness=t.getNode(e.roughness)),e.texture&&(this.texture=t.getNode(e.texture)),e.maxMIPLevel&&(this.maxMIPLevel=t.getNode(e.maxMIPLevel)),this}},ka=Yc;ka.Nodes=function(){return{getSpecularMIPLevel:new se([\\\\\\\"float getSpecularMIPLevel( const in float roughness, const in float maxMIPLevelScalar ) {\\\\\\\",\\\\\\\"\\tfloat sigma = PI * roughness * roughness / ( 1.0 + 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Va=class{constructor(){this._nodes={};this._dataNodes={};this._textures={};this.uuid=tp.MathUtils.generateUUID()}init(e,t){this._dataNodes=e,this._textures=t}clear(){this._nodes={},this._dataNodes={},this._textures={}}getNode(e){let t=this._nodes[e];return t||(t=this.createNode(e)),t}getNodeData(e){return this._dataNodes[e]}getTexture(e){let t=this._textures[e];return t||new nr}createNode(e){let t=this._dataNodes[e];if(!t)throw new Error(\\\\\\\"Failed to create Node from uuid: \\\\\\\"+e+\\\\\\\", its data does not seem to exist\\\\\\\");let r=ep[t.nodeType];if(!r)throw new Error(\\\\\\\"Failed to create Node from uuid: \\\\\\\"+e+\\\\\\\" and nodeType: \\\\\\\"+t.nodeType+\\\\\\\", constructor for this not is not registered\\\\\\\");let i=new r().fromJSON(t,this);return this._nodes[e]=i,i}};var Fi=F(z());var cs=F(z());function rp(s){Fx(s.geometries),Gx(s.interactionStates),ip(s.object),zx(s.materials)}function Fx(s){if(s!==void 0){for(let e of s)if(e.userData?.type!==void 0){let t=e.userData.parameters;\\\\\\\"extrudeDepth\\\\\\\"in t||Object.assign(t,{extrudeDepth:0}),\\\\\\\"extrudeBevelSize\\\\\\\"in t||Object.assign(t,{extrudeBevelSize:0}),\\\\\\\"extrudeBevelSegments\\\\\\\"in t||Object.assign(t,{extrudeBevelSegments:1})}}}function Gx(s){if(s!==void 0){for(let e of s)if(e.quaternion!==void 0){let t=new cs.Quaternion().fromArray(e.quaternion);Object.assign(e,{rotation:new cs.Euler().setFromQuaternion(t).toArray()})}}}function ip(s){if(s.userData?.type===void 0)if(s.type===\\\\\\\"OrthographicCamera\\\\\\\"||s.type===\\\\\\\"PerspectiveCamera\\\\\\\"){let e=s;Object.assign(s,{objectType:\\\\\\\"CombinedCamera\\\\\\\",cameraType:e.type,isDefaultCamera:!0,hiddenMatrix:new cs.Matrix4().toArray(),name:\\\\\\\"Default Camera\\\\\\\",far:1e5,zoomOrtho:e.zoom,zoomPersp:1,nearOrtho:-5e4,nearPersp:50}),delete s.type,e.typeCamera!==void 0&&(Object.assign(s,{cameraType:e.typeCamera}),delete s.typeCamera)}else s.type===\\\\\\\"Scene\\\\\\\"?(Object.assign(s,{objectType:s.type}),delete s.type):s.type===\\\\\\\"HemisphereLight\\\\\\\"?Object.assign(s,{isDefaultAmbientLight:!0}):s.type===\\\\\\\"DirectionalLight\\\\\\\"&&Object.assign(s,{isDefaultDirectionalLight:!0});if(s.userData!==void 0){let e=s.userData;e.type!==void 0&&(Object.assign(s,{objectType:e.type}),delete e.type),e.interaction!==void 0&&(Object.assign(s,{interaction:e.interaction}),delete e.interaction),e.isDefaultAmbientLight===!0?(Object.assign(s,{isDefaultAmbientLight:!0}),delete e.isDefaultAmbientLight):e.isDefaultDirectionalLight===!0?(Object.assign(s,{isDefaultDirectionalLight:!0}),delete e.isDefaultDirectionalLight):e.isDefaultCamera===!0&&(Object.assign(s,{isDefaultCamera:!0}),delete e.isDefaultCamera),Object.assign(s,{raycastLock:e.lock,scaleLock:e.scaleLock,hiddenMatrix:e.hiddenMatrix?.elements??new cs.Matrix4().toArray()}),delete e.lock,delete e.scaleLock,delete e.hiddenMatrix,delete e.isEntity}if(s.interaction?.events!==void 0){for(let e of s.interaction.events)if(e.targets!==void 0)for(let t of e.targets)t.springControls!==void 0&&(Object.assign(t,{springParameters:{mass:t.springControls[0],stiffness:t.springControls[1],damping:t.springControls[2],velocity:t.springControls[3]}}),delete t.springControls)}if(s.children!==void 0)for(let e of s.children)ip(e)}function zx(s){if(s!==void 0)for(let e of s){let r=e.userData.layers.head;for(;r;){for(let i in r.uniforms){let n=`f${r.id}_tex`,o=`f${r.id}_texture`;i===n&&(r.uniforms[o]=r.uniforms[n],delete r.uniforms[n])}r=r.next}}}function sp(s){np(s.object,s.object)}function np(s,e){if(s.type===\\\\\\\"HemisphereLight\\\\\\\"||s.isDefaultAmbientLight===!0?e.ambientLight=s.uuid:s.userData?.isDefaultDirectionalLight===!0||s.isDefaultDirectionalLight===!0?e.directionalLight=s.uuid:s.isDefaultCamera===!0&&(e.camera=s.uuid,e.activeCamera=s.uuid,Object.assign(s,{name:\\\\\\\"Default Camera\\\\\\\"})),s.children!==void 0)for(let t of s.children)np(t,e)}var op=F(z());function ap(s){Ux(s.object)}function Ux(s){if(s.objectType===\\\\\\\"Scene\\\\\\\"){let e=s.userData,t=e.rgb??{r:25,g:25,b:25},r=e.fogColor??{r:25,g:25,b:25};Object.assign(s,{color:[t.r/255,t.g/255,t.b/255],alpha:e.alpha,enableFog:e.fogEnable,fogUseBGColor:e.fogUseBGColor,fog:{color:new op.Color().setRGB(r.r/255,r.g/255,r.b/255).getHex(),near:e.fogNear,far:e.fogFar},directionalLightState:e.directionalLightState,ambientLightState:e.ambientLightState,shadowsState:e.shadowsState,wireframeState:e.wireframeState,canvas:{mode:e.canvasMode??1,size:{width:e.canvasCustomSize?.width??512,height:e.canvasCustomSize?.height??512}}}),delete e.rgb,delete e.alpha,delete e.fogEnable,delete e.fogUseBGColor,delete e.fogColor,delete e.fogNear,delete e.fogFar,delete e.directionalLightState,delete e.ambientLightState,delete e.shadowsState,delete e.wireframeState,delete e.canvasMode,delete e.canvasFullScreen,delete e.canvasCustomSize}}var hi=F(z());var sr=F(z());function lp(s){return{uuid:sr.MathUtils.generateUUID(),type:\\\\\\\"b\\\\\\\",value:s,nodeType:\\\\\\\"Bool\\\\\\\"}}function ur(s,e){let t=new sr.Color().set(s);return{uuid:sr.MathUtils.generateUUID(),type:\\\\\\\"c\\\\\\\",r:t.r,g:t.g,b:t.b,value:s,alpha:e?.uuid,nodeType:\\\\\\\"Color\\\\\\\"}}function cp(s,e){return{uuid:sr.MathUtils.generateUUID(),type:\\\\\\\"f[]\\\\\\\",size:s,value:e,nodeType:\\\\\\\"FloatArray\\\\\\\"}}function ke(s){return{uuid:sr.MathUtils.generateUUID(),type:\\\\\\\"f\\\\\\\",value:s,nodeType:\\\\\\\"Float\\\\\\\"}}function ut(s){return{uuid:sr.MathUtils.generateUUID(),type:\\\\\\\"i\\\\\\\",value:s,nodeType:\\\\\\\"Int\\\\\\\"}}function en(s){let{x:e,y:t}=s;return{uuid:sr.MathUtils.generateUUID(),type:\\\\\\\"v2\\\\\\\",x:e,y:t,nodeType:\\\\\\\"Vector2\\\\\\\"}}function Kc(s){let{x:e,y:t,z:r}=s;return{uuid:sr.MathUtils.generateUUID(),type:\\\\\\\"v3\\\\\\\",x:e,y:t,z:r,nodeType:\\\\\\\"Vector3\\\\\\\"}}function up(s,e){return{uuid:sr.MathUtils.generateUUID(),type:\\\\\\\"v4[]\\\\\\\",size:s,value:e,nodeType:\\\\\\\"Vector4Array\\\\\\\"}}function dp(s){return{uuid:sr.MathUtils.generateUUID(),type:\\\\\\\"m3\\\\\\\",elements:s.elements,nodeType:\\\\\\\"Matrix3\\\\\\\"}}function hp(s,e){return{uuid:sr.MathUtils.generateUUID(),type:\\\\\\\"v4\\\\\\\",value:s,uv:e.uuid,project:!1,nodeType:\\\\\\\"Texture\\\\\\\"}}function fp(s){return{uuid:sr.MathUtils.generateUUID(),type:\\\\\\\"v2\\\\\\\",index:s,nodeType:\\\\\\\"UV\\\\\\\"}}function Xc(s,e){let t={uuid:hi.MathUtils.generateUUID(),type:\\\\\\\"v3\\\\\\\",nodeType:\\\\\\\"Blend\\\\\\\",a:s?.uuid,b:e.uuid};return\\\\\\\"mode\\\\\\\"in e&&(t.mode=e.mode),t}function pp(s,e){let t=s.uniforms,r=s.id,i=ke(t[`f${r}_alpha`].value),n=ut(t[`f${r}_mode`].value);return[i,n].forEach(o=>e[o.uuid]=o),Object.assign(s,{uniforms:{[`f${r}_alpha`]:i.uuid,[`f${r}_mode`]:n.uuid}}),{uuid:hi.MathUtils.generateUUID(),type:\\\\\\\"v3\\\\\\\",nodeType:\\\\\\\"CustomTexture\\\\\\\",alpha:i.uuid,mode:n.uuid}}function qc(s,e){let t=s.uniforms,r=s.id,i=ut(t[`f${r}_mode`].value),n=ke(t[`f${r}_alpha`].value),o=ur(t[`f${r}_color`].value,n);return[o,n,i].forEach(l=>e[l.uuid]=l),Object.assign(s,{uniforms:{[`f${r}_color`]:o.uuid,[`f${r}_alpha`]:n.uuid,[`f${r}_mode`]:i.uuid}}),o}function mp(s,e){let t=s.uniforms,r=s.id,i=ut(t[`f${r}_mode`].value),n=ke(t[`f${r}_alpha`].value),o=Kc(t[`f${r}_cnormal`].value);return[o,n,i].forEach(l=>e[l.uuid]=l),Object.assign(s,{uniforms:{[`f${r}_cnormal`]:o.uuid,[`f${r}_alpha`]:n.uuid,[`f${r}_mode`]:i.uuid}}),{uuid:hi.MathUtils.generateUUID(),type:\\\\\\\"v3\\\\\\\",nodeType:\\\\\\\"CustomNormal\\\\\\\",cnormal:o.uuid,alpha:n.uuid,mode:i.uuid}}function gp(s,e){let t=s.uniforms,r=s.id,i=ut(t[`f${r}_mode`].value),n=ke(t[`f${r}_alpha`].value),o=ke(t[`f${r}_near`].value),l=ke(t[`f${r}_far`].value),a=ke(t[`f${r}_isVector`].value),c=Kc(t[`f${r}_origin`].value),u=ur(t[`f${r}_colorA`].value),d=ur(t[`f${r}_colorB`].value);return[o,l,a,c,u,d,n,i].forEach(h=>e[h.uuid]=h),Object.assign(s,{uniforms:{[`f${r}_near`]:o.uuid,[`f${r}_far`]:l.uuid,[`f${r}_isVector`]:a.uuid,[`f${r}_origin`]:c.uuid,[`f${r}_colorA`]:u.uuid,[`f${r}_colorB`]:d.uuid,[`f${r}_alpha`]:n.uuid,[`f${r}_mode`]:i.uuid}}),{uuid:hi.MathUtils.generateUUID(),type:\\\\\\\"v3\\\\\\\",nodeType:\\\\\\\"Depth\\\\\\\",near:o.uuid,far:l.uuid,isVector:a.uuid,origin:c.uuid,colorA:u.uuid,colorB:d.uuid,alpha:n.uuid,mode:i.uuid}}function yp(s,e){let t=s.uniforms,r=s.id,i=ut(t[`f${r}_mode`].value),n=ke(t[`f${r}_alpha`].value),o=ur(t[`f${r}_color`].value),l=ke(t[`f${r}_bias`].value),a=ke(t[`f${r}_scale`].value),c=ke(t[`f${r}_intensity`].value),u=ke(t[`f${r}_factor`].value);return[o,l,a,c,u,n,i].forEach(d=>e[d.uuid]=d),Object.assign(s,{uniforms:{[`f${r}_color`]:o.uuid,[`f${r}_bias`]:l.uuid,[`f${r}_scale`]:a.uuid,[`f${r}_intensity`]:c.uuid,[`f${r}_factor`]:u.uuid,[`f${r}_alpha`]:n.uuid,[`f${r}_mode`]:i.uuid}}),{uuid:hi.MathUtils.generateUUID(),type:\\\\\\\"v3\\\\\\\",nodeType:\\\\\\\"Fresnel\\\\\\\",color:o.uuid,bias:l.uuid,scale:a.uuid,intensity:c.uuid,factor:u.uuid,alpha:n.uuid,mode:i.uuid}}function vp(s,e){let t=s.uniforms,r=s.id,i=ut(t[`f${r}_mode`].value),n=ke(t[`f${r}_alpha`].value),o=ut(t[`f${r}_gl_type`].value),l=ut(t[`f${r}_num`].value),a=lp(t[`f${r}_smooth`].value),c=up(10,t[`f${r}_colors`].value),u=cp(10,t[`f${r}_steps`].value),d=en(t[`f${r}_offset`].value),h=en(t[`f${r}_morph`].value),p=ke(t[`f${r}_angle`].value);return[o,l,a,c,u,d,h,p,n,i].forEach(f=>e[f.uuid]=f),Object.assign(s,{uniforms:{[`f${r}_gl_type`]:o.uuid,[`f${r}_num`]:l.uuid,[`f${r}_smooth`]:a.uuid,[`f${r}_colors`]:c.uuid,[`f${r}_steps`]:u.uuid,[`f${r}_offset`]:d.uuid,[`f${r}_morph`]:h.uuid,[`f${r}_angle`]:p.uuid,[`f${r}_alpha`]:n.uuid,[`f${r}_mode`]:i.uuid}}),{uuid:hi.MathUtils.generateUUID(),type:\\\\\\\"v3\\\\\\\",nodeType:\\\\\\\"Gradient\\\\\\\",gl_type:o.uuid,num:l.uuid,smooth:a.uuid,colors:c.uuid,steps:u.uuid,offset:d.uuid,morph:h.uuid,angle:p.uuid,alpha:n.uuid,mode:i.uuid}}function xp(s,e){let t=s.uniforms,r=s.id,i=hi.MathUtils.generateUUID(),n=ut(t[`f${r}_mode`].value),o=ke(t[`f${r}_alpha`].value),l=ke(t[`f${r}_crop`].value),a=fp(0),c=hp(t[`f${r}_texture`].value,a),u=dp(t[`f${r}_mat`].value);return[c,a,u,l,o,n].forEach(d=>e[d.uuid]=d),Object.assign(s,{uniforms:{[`f${r}_texture`]:c.uuid,[`f${r}_mat`]:u.uuid,[`f${r}_crop`]:l.uuid,[`f${r}_alpha`]:o.uuid,[`f${r}_mode`]:n.uuid}}),{uuid:i,type:\\\\\\\"v3\\\\\\\",nodeType:\\\\\\\"CustomTexture\\\\\\\",texture:c.uuid,crop:l.uuid,alpha:o.uuid,mode:n.uuid}}function bp(s,e){let t=s.uniforms,r=s.id,i=ut(t[`f${r}_mode`].value),n=ke(t[`f${r}_alpha`].value),o=ke(t[`f${r}_scale`].value),l=ke(t[`f${r}_move`].value),a=en(t[`f${r}_fA`].value),c=en(t[`f${r}_fB`].value),u=en(t[`f${r}_distortion`].value),d=ur(t[`f${r}_colorA`].value),h=ur(t[`f${r}_colorB`].value),p=ur(t[`f${r}_colorC`].value),f=ur(t[`f${r}_colorD`].value),m=ut(t[`f${r}_intA`].value),g=ut(t[`f${r}_intB`].value),y=ut(t[`f${r}_intC`].value),x=ut(t[`f${r}_intD`].value);return[o,l,a,c,u,d,h,p,f,m,g,y,x,n,i].forEach(v=>e[v.uuid]=v),Object.assign(s,{uniforms:{[`f${r}_scale`]:o.uuid,[`f${r}_move`]:l.uuid,[`f${r}_fA`]:a.uuid,[`f${r}_fB`]:c.uuid,[`f${r}_distortion`]:u.uuid,[`f${r}_colorA`]:d.uuid,[`f${r}_colorB`]:h.uuid,[`f${r}_colorC`]:p.uuid,[`f${r}_colorD`]:f.uuid,[`f${r}_intA`]:m.uuid,[`f${r}_intB`]:g.uuid,[`f${r}_intC`]:y.uuid,[`f${r}_intD`]:x.uuid,[`f${r}_alpha`]:n.uuid,[`f${r}_mode`]:i.uuid}}),{uuid:hi.MathUtils.generateUUID(),type:\\\\\\\"v3\\\\\\\",nodeType:\\\\\\\"Noise\\\\\\\",scale:o.uuid,move:l.uuid,fA:a.uuid,fB:c.uuid,distortion:u.uuid,colorA:d.uuid,colorB:h.uuid,colorC:p.uuid,colorD:f.uuid,intA:m.uuid,intB:g.uuid,intC:y.uuid,intD:x.uuid,alpha:n.uuid,mode:i.uuid}}var tn=F(z());function Zc(s,e,t){let r=e.uniforms,i=e.id,n=ke(r[`f${i}_alpha`].value),o=ut(r[`f${i}_mode`].value);return[n,o].forEach(l=>t[l.uuid]=l),Object.assign(e,{uniforms:{[`f${i}_alpha`]:n.uuid,[`f${i}_mode`]:o.uuid}}),{uuid:tn.MathUtils.generateUUID(),type:\\\\\\\"basic\\\\\\\",nodeType:\\\\\\\"Basic\\\\\\\",shadingAlpha:n.uuid,shadingBlend:o.uuid}}function Ap(s,e,t){let r=e.uniforms,i=e.id,n=ur(s.specular??69905),o=ke(s.shininess??30),l=ke(r[`f${i}_alpha`].value),a=ut(r[`f${i}_mode`].value);return[n,o,l,a].forEach(c=>t[c.uuid]=c),Object.assign(e,{uniforms:{[`f${i}_alpha`]:l.uuid,[`f${i}_mode`]:a.uuid}}),{uuid:tn.MathUtils.generateUUID(),type:\\\\\\\"phong\\\\\\\",nodeType:\\\\\\\"Phong\\\\\\\",specular:n.uuid,shininess:o.uuid,shadingAlpha:l.uuid,shadingBlend:a.uuid}}function wp(s,e,t){let r=e.uniforms,i=e.id,n=ur(s.emissive??0),o=ke(s.emissiveIntensity??1),l=ke(r[`f${i}_alpha`].value),a=ut(r[`f${i}_mode`].value);return[o,n,l,a].forEach(c=>t[c.uuid]=c),Object.assign(e,{uniforms:{[`f${i}_alpha`]:l.uuid,[`f${i}_mode`]:a.uuid}}),{uuid:tn.MathUtils.generateUUID(),type:\\\\\\\"lambert\\\\\\\",nodeType:\\\\\\\"Lambert\\\\\\\",emissiveIntensity:o.uuid,emissive:n.uuid,shadingAlpha:l.uuid,shadingBlend:a.uuid}}function Sp(s,e,t){let r=e.uniforms,i=e.id,n=ur(s.specular??69905),o=ke(s.shininess??30),l=ke(r[`f${i}_alpha`].value),a=ut(r[`f${i}_mode`].value);return[n,o,l,a].forEach(c=>t[c.uuid]=c),Object.assign(e,{uniforms:{[`f${i}_alpha`]:l.uuid,[`f${i}_mode`]:a.uuid}}),{uuid:tn.MathUtils.generateUUID(),type:\\\\\\\"toon\\\\\\\",nodeType:\\\\\\\"Toon\\\\\\\",specular:n.uuid,shininess:o.uuid,shadingAlpha:l.uuid,shadingBlend:a.uuid}}function Np(s,e,t){let r=e.uniforms,i=e.id,n=ke(s.roughness??.3),o=ke(s.metalness??0),l=ke(s.reflectivity??.5),a=ke(r[`f${i}_alpha`].value),c=ut(r[`f${i}_mode`].value);return[n,o,l,a,c].forEach(u=>t[u.uuid]=u),Object.assign(e,{uniforms:{[`f${i}_alpha`]:a.uuid,[`f${i}_mode`]:c.uuid}}),{uuid:tn.MathUtils.generateUUID(),type:\\\\\\\"standard\\\\\\\",nodeType:\\\\\\\"Standard\\\\\\\",roughness:n.uuid,metalness:o.uuid,reflectivity:l.uuid,shadingAlpha:a.uuid,shadingBlend:c.uuid}}var Ha=F(z()),Tp=new Ha.Matrix4,jx=[\\\\\\\"RectangleGeometry\\\\\\\",\\\\\\\"TriangleGeometry\\\\\\\",\\\\\\\"PolygonGeometry\\\\\\\",\\\\\\\"StarGeometry\\\\\\\"];function Cp(s){let e={};Hx(s.materials,e),Vx(s.interactionStates,e),kx(s.geometries,s.object),Object.keys(e).length&&(s.nodes=Qx(e))}function Mp(s,e){let t=s.geometry;if(t&&t===e)return s;let r=s.children;if(r)for(let i=0,n=r.length;i<n;i++){let o=r[i],l=Mp(o,e);if(l!==void 0)return l}}function kx(s,e){if(s!==void 0){for(let t of s)if(t.userData&&jx.includes(t.userData?.type)){let r=t.userData.parameters,i=r.extrudeBevelSize;if(i>0){r.width+=i*2,r.height+=i*2,r.extrudeDepth+=i*2;let n=Mp(e,t.uuid);if(n!==void 0){let o=new Ha.Object3D;Tp.fromArray(n.matrix),o.applyMatrix4(Tp),o.position.z-=i,o.updateMatrix(),n.matrix=o.matrix.toArray()}}}}}function Vx(s,e){if(s!==void 0){for(let t of s)if(t.material)if(Array.isArray(t.material))for(let r of t.material)for(let i of r.layersList)Eo(i,e);else for(let r of t.material.layersList)Eo(r,e)}}function Hx(s,e){if(s!==void 0)for(let t of s){let{beforeLayers:r,lightingLayer:i,afterLayers:n}=Wx(t.userData.layers);if(!i){console.error(\\\\\\\"Material upgrade: could not find a ligthing layer\\\\\\\");continue}let o=Jx(t,i,e);if(e[o.uuid]=o,r.length>0){let l=Eo(r[0],e);e[l.uuid]=l;for(let a=1,c=r.length;a<c;++a){let u=r[a],d=Eo(u,e),h=Xc(l,d);e[h.uuid]=h,e[d.uuid]=d,l=h}o.color=l.uuid}if(n.length>0){let l;for(let a of n){let c=Eo(a,e),u=Xc(l,c);e[u.uuid]=u,e[c.uuid]=c,l=u}o.afterColor=l?.uuid}Object.assign(t,{type:\\\\\\\"ShaderMaterial\\\\\\\",wireframeLinecap:\\\\\\\"\\\\\\\",wireframeLinejoin:\\\\\\\"\\\\\\\",vertex:o?.uuid,fragment:o?.uuid}),delete t.color,delete t.emissive,delete t.specular,delete t.shininess,delete t.roughness,delete t.metalness}}function Wx(s){let e=[],t=[],r,i=s.head;for(;i;){if(i.type===\\\\\\\"light\\\\\\\"){r=i,i=i.next;break}e.push(i),i=i.next}for(;i;)t.unshift(i),i=i.next;return{beforeLayers:e,lightingLayer:r,afterLayers:t}}function Jx(s,e,t){let r,i;switch(s.userData.type){case\\\\\\\"BasicMaterial\\\\\\\":r=Zc(s,e,t),i=\\\\\\\"BasicNodeMaterial\\\\\\\";break;case\\\\\\\"PhongMaterial\\\\\\\":r=Ap(s,e,t),i=\\\\\\\"PhongNodeMaterial\\\\\\\";break;case\\\\\\\"LambertMaterial\\\\\\\":r=wp(s,e,t),i=\\\\\\\"LambertNodeMaterial\\\\\\\";break;case\\\\\\\"ToonMaterial\\\\\\\":r=Sp(s,e,t),i=\\\\\\\"ToonNodeMaterial\\\\\\\";break;case\\\\\\\"PhysicalMaterial\\\\\\\":r=Np(s,e,t),i=\\\\\\\"StandardNodeMaterial\\\\\\\";break;default:console.warn(`Material upgrade: could not find a properly material node to ${s.userData.type}`),r=Zc(s,e,t),i=\\\\\\\"BasicNodeMaterial\\\\\\\"}return Object.assign(s.userData,{nodeType:i}),r}function Eo(s,e){switch(s.type){case\\\\\\\"light\\\\\\\":return pp(s,e);case\\\\\\\"color\\\\\\\":return qc(s,e);case\\\\\\\"depth\\\\\\\":return gp(s,e);case\\\\\\\"fresnel\\\\\\\":return yp(s,e);case\\\\\\\"gradient\\\\\\\":return vp(s,e);case\\\\\\\"noise\\\\\\\":return bp(s,e);case\\\\\\\"normal\\\\\\\":return mp(s,e);case\\\\\\\"texture\\\\\\\":return xp(s,e);default:return console.warn(`Material upgrade: could not find a properly \\\\\\\"layer\\\\\\\" node to ${s.type}`),qc(s,e)}}function Qx(s){let e=[];for(let t in s)e.push(s[t]);return e}function $c(s){let e=s.metadata;return console.info(`File format version ${e?.version}`),e?.version===void 0?(console.warn(\\\\\\\"Unknow file version\\\\\\\"),!1):((e.version<=1.1||e.version===4.5)&&(rp(s),e.version=1.2,console.warn(\\\\\\\"File upgraded from v1.1 or inferior to v1.2\\\\\\\")),e.version===1.2&&(sp(s),e.version=1.3,console.warn(\\\\\\\"File upgraded from v1.2 to v1.3\\\\\\\")),e.version===1.3&&(ap(s),e.version=1.4,console.warn(\\\\\\\"File upgraded from v1.3 to v1.4\\\\\\\")),e.version===1.4&&(Cp(s),e.version=1.5,console.warn(\\\\\\\"File upgraded from v1.4 to v1.5\\\\\\\")),!0)}var Di=F(z());var Lp=F(Ip()),Dp=new Di.Matrix4,Yx=new Di.Matrix4,Kx=new Di.Matrix4,Xx=new Di.Matrix4,rn=class extends Ot{constructor(e=jt.create({}),t=new 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t=this.material.clone(),r=jt.clone(le({},this.geometry.userData.parameters),this.subdivPointer,this.smoothShading),i=this.geometry.originalGeometry??this.geometry,n=r.originalGeometry??r;n.boundingSphere=i.boundingSphere.clone();let o=new rn(r,t).copy(this,e);return o.subdivPointer=jt.activeSubdivPtr,o}raycast(e,t){let r=this.geometry.originalGeometry;if(r){let i=this.geometry;this.geometry=r,Ot.prototype.raycast.call(this,e,t),this.geometry=i}else Ot.prototype.raycast.call(this,e,t)}applySVDScaling(){let e=this.matrixWorld.elements,t=[[e[0],e[4],e[8]],[e[1],e[5],e[9]],[e[2],e[6],e[10]]],{u:r,v:i,q:n}=(0,Lp.SVD)(t);this.isNegativeDeterminant=this.matrixWorld.determinant()<0,this.isUniformScaling=n.every(d=>Math.abs(n[0]-d)<.01);let o,l,a=Dp.set(r[0][0],r[0][1],r[0][2],0,r[1][0],r[1][1],r[1][2],0,r[2][0],r[2][1],r[2][2],0,0,0,0,1),c=Yx.set(i[0][0],i[0][1],i[0][2],0,i[1][0],i[1][1],i[1][2],0,i[2][0],i[2][1],i[2][2],0,0,0,0,1),u=Kx.copy(c).transpose();if(o=Xx.makeScale(n[0],n[1],n[2]).multiply(u).premultiply(c),l=a.multiply(u),!this.isUniformScaling){this.geometry.applyMatrix4(o);let d=this.geometry.originalGeometry;d?(d.applyMatrix4(o),d.getAttribute(\\\\\\\"positionWASM\\\\\\\").applyMatrix4(o)):this.geometry.getAttribute(\\\\\\\"positionWASM\\\\\\\").applyMatrix4(o),this.resized=!0}return{scaleMatrix:o,matrixWorldRigid:l}}revertSVDScaling(e){let t=this.geometry.originalGeometry;t?jt.getTopologicalMesh(t):jt.getTopologicalMesh(this.geometry);let r=e.elements,i=[r[0],r[5],r[10]];if(!i.every(o=>Math.abs(i[0]-o)<.01)){let 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t=this._vertexData.bufferViews[e],r=this._buffers[t.buffer],i=t.byteLength||0,n=t.byteOffset||0;return r.slice(n,n+i)}_loadBuffers(e){for(let t of e){if(!t.uri)continue;let r=t.uri.slice(t.uri.indexOf(\\\\\\\"base64\\\\\\\")+7),i=atob(r),n=new Uint8Array(i.length);for(let o=0;o<i.length;o++)n[o]=i.charCodeAt(o);this._buffers.push(n)}}},qx={5120:Int8Array,5121:Uint8Array,5122:Int16Array,5123:Uint16Array,5125:Uint32Array,5126:Float32Array},Zx={SCALAR:1,VEC2:2,VEC3:3,VEC4:4,MAT2:4,MAT3:9,MAT4:16};var ri=F(z()),tu=new WeakMap,ru=class extends ri.Loader{constructor(e){super(e);this.decoderPath=\\\\\\\"\\\\\\\",this.decoderConfig={},this.decoderBinary=null,this.decoderPending=null,this.workerLimit=4,this.workerPool=[],this.workerNextTaskID=1,this.workerSourceURL=\\\\\\\"\\\\\\\",this.defaultAttributeIDs={position:\\\\\\\"POSITION\\\\\\\",normal:\\\\\\\"NORMAL\\\\\\\",color:\\\\\\\"COLOR\\\\\\\",uv:\\\\\\\"TEX_COORD\\\\\\\"},this.defaultAttributeTypes={position:\\\\\\\"Float32Array\\\\\\\",normal:\\\\\\\"Float32Array\\\\\\\",color:\\\\\\\"Float32Array\\\\\\\",uv:\\\\\\\"Float32Array\\\\\\\"}}setDecoderPath(e){return this.decoderPath=e,this}setDecoderConfig(e){return this.decoderConfig=e,this}setWorkerLimit(e){return this.workerLimit=e,this}load(e,t,r,i){let n=new ri.FileLoader(this.manager);n.setPath(this.path),n.setResponseType(\\\\\\\"arraybuffer\\\\\\\"),n.setRequestHeader(this.requestHeader),n.setWithCredentials(this.withCredentials),n.load(e,o=>{let 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0||c.type.toLowerCase()===\\\\\\\"geometry\\\\\\\"){console.error(\\\\\\\"ObjectLoader: Can't load \\\\\\\"+e);return}this.parse(a,t)},r,i)}parse(e,t){if($c(e)===!1)return console.error(\\\\\\\"File can not be parser\\\\\\\"),{object:new Li,materials:{}};let r={},i=this.parseGeometries(e.geometries),n=this.loader.parseImages(e.images,()=>{t!==void 0&&t(u)}),o=this.parseTextures(e.textures,n),l=new Va;l.init(mn(e.nodes??[]),mn(o));let a=mn(this.loader.parseMaterials(e.materials,o)),c=this.customParseMaterials(a,e.materials,l),u=this.parseObject(e.object,i,c,r);this.upgradeNoise(u);let d=this.parseInteractionStates(e.interactionStates,l);return this.parseInteractions(e.object,r,d),(e.images===void 0||e.images.length===0)&&t!==void 0&&t(u),{object:u,materials:c}}upgradeNoise(e){e.traverseEntity(t=>{if(t.type===\\\\\\\"Mesh\\\\\\\"){let r=t,i=r.material;if(!i.userData)return;let n=i.userData.layers?.head;for(;n;){if(n.type===Be.NOISE&&n.uniforms[`f${n.id}_size`].value.equals(new Fi.Vector3(-1,-1,-1))&&(r.geometry.boundingBox||r.geometry.computeBoundingBox(),r.geometry.boundingBox)){let o=n.uniforms[`f${n.id}_scale`].value,{x:l,y:a,z:c}=r.geometry.boundingBox.max,u=Math.max(l,Math.max(a,c))/(o*o);n.uniforms[`f${n.id}_size`].value.x=u,n.uniforms[`f${n.id}_size`].value.y=u,n.uniforms[`f${n.id}_size`].value.z=u}n=n.next}}})}parseGeometries(e){let t={};if(e!==void 0)for(let r=0,i=e.length;r<i;r++){let n=e[r],o;try{switch(n.userData?.type){case void 0:o=this.loader.parseGeometries([n])[n.uuid];break;case\\\\\\\"NonParametricGeometry\\\\\\\":n.vertexData?o=new eu().parse(n):o=this.loader.parseGeometries([n])[n.uuid],o.userData={};let l=Object.assign({},n.userData,{geometry:o});o=qi.create(l);break;case\\\\\\\"SubdivGeometry\\\\\\\":o=this.loader.parseGeometries([n])[n.uuid],o=jt.create({parameters:n.userData.parameters,geometry:o}),jt.activeSubdivPtr=void 0;break;default:o=qn(n.userData)}o.uuid=n.uuid,n.name!==void 0&&(o.name=n.name),t[n.uuid]=o}catch(l){console.warn(\\\\\\\"ObjectLoader > parseGeometries: error parsing geometry of type .\\\\\\\",n.userData?.type),console.error(l)}}return t}async parseAsync(e){if($c(e)===!1)return console.error(\\\\\\\"File can not be parser\\\\\\\"),new Li;let t=new Va,r={},i=await this.parseGeometriesAsync(e.geometries),n=this.loader.parseImages(e.images,()=>{}),o=this.parseTextures(e.textures,n);t.init(mn(e.nodes??[]),mn(o));let l=mn(this.loader.parseMaterials(e.materials,o)),a=this.customParseMaterials(l,e.materials,t),c=await this.parseObjectAsync(e.object,i,a,r),u=this.parseInteractionStates(e.interactionStates,t);return this.parseInteractions(e.object,r,u),c}async parseGeometriesAsync(e){let t={};if(e!==void 0)for(let r=0,i=e.length;r<i;r++){let n=e[r],o;try{switch(n.userData?.type){case void 0:o=this.loader.parseGeometries([n])[n.uuid];break;case\\\\\\\"NonParametricGeometry\\\\\\\":if(n.gltf){if(!this.gltfLoader){this.gltfLoader=new Ec;let u=new ru;u.setDecoderPath(\\\\\\\"https://www.gstatic.com/draco/versioned/decoders/1.4.2/\\\\\\\"),this.gltfLoader.setDRACOLoader(u)}let a=await new Promise(u=>{this.gltfLoader.parse(JSON.stringify(n.gltf),\\\\\\\"\\\\\\\",u)}),c=[];a.scene.traverse(u=>{let d=u;d.isMesh&&c.push(d.geometry)}),c.length===1?o=c[0]:(c=pu(c,u=>n.gltf.meshes.findIndex(h=>{let p=h.primitives[0].attributes.POSITION,f=n.gltf.accessors[p];return u.index!==null?f.count===u.index.count:f.count===u.attributes.position.count})),o=Fs.mergeBufferGeometries(c,!0))}else o=this.loader.parseGeometries([n])[n.uuid];o.userData={};let l=Object.assign({},n.userData,{geometry:o});o=qi.create(l);break;default:o=qn(n.userData)}o.uuid=n.uuid,n.name!==void 0&&(o.name=n.name),t[n.uuid]=o}catch(l){console.warn(\\\\\\\"ObjectLoader > parseGeometries: error parsing geometry of type .\\\\\\\",n.userData?.type),console.error(l)}}return t}parseTextures(e,t){let r=this.loader.parseTextures(e,t);return Object.keys(r).forEach(i=>{let n=new nr().copy(r[i]);n.uuid=i,r[i]=n}),r}customParseMaterials(e,t,r){let i={};if(t!==void 0)for(let n=0,o=t.length;n<o;n++){let l=t[n],a=e[l.uuid],c=a.userData;if(c.type!==void 0){let u=qu[c.type];i[l.uuid]=u.fromJSON(a,l,r)}}return i}parseObject(e,t,r,i){let n;if(e.geometry!==void 0){let o=Cg(t,e.geometry),l=Mg(r,e.material);if(o){switch(e.objectType){case\\\\\\\"Mesh2D\\\\\\\":n=new Js(o,l).fromJSON(e);break;case\\\\\\\"Mesh3D\\\\\\\":n=new Qs(o,l).fromJSON(e);break;case\\\\\\\"NonParametric\\\\\\\":n=new xa(o,l).fromJSON(e);break;case\\\\\\\"TextFrame\\\\\\\":n=new Ei(o,l),n.fromJSONasync(e).then(()=>{}).catch(()=>{});break;case\\\\\\\"VectorObject\\\\\\\":n=new Ni(o,l).fromJSON(e);break;case\\\\\\\"SubdivObject\\\\\\\":n=new rn(o,l).fromJSON(e),n.subdivPointer=o.subdivPointer;break;default:console.warn(\\\\\\\"ObjectLoader > parseObject: unsupported object type\\\\\\\",e.objectType);return}l||(n.name+=\\\\\\\" (Recovered)\\\\\\\")}else n=new gr().fromJSON(e),n.name+=\\\\\\\" (Recovered)\\\\\\\",console.warn(\\\\\\\"An empty object is created instead\\\\\\\")}else switch(e.objectType){case\\\\\\\"Scene\\\\\\\":n=new Li().fromJSON(e);break;case\\\\\\\"CombinedCamera\\\\\\\":n=new Kr().fromJSON(e);break;case\\\\\\\"LightDirectional\\\\\\\":n=new ga().fromJSON(e);break;case\\\\\\\"LightPoint\\\\\\\":n=new ya().fromJSON(e);break;case\\\\\\\"LightSpot\\\\\\\":n=new va().fromJSON(e);break;case\\\\\\\"EmptyObject\\\\\\\":n=new gr().fromJSON(e);break;default:if(e.type===\\\\\\\"HemisphereLight\\\\\\\"||e.type===\\\\\\\"DirectionalLight\\\\\\\")n=this.loader.parseObject(e,t,r);else{console.warn(\\\\\\\"ObjectLoader > parseObject: unsupported object type\\\\\\\",e.objectType);return}}if(Vt(n)&&(i[e.uuid]=n),e.children!==void 0)for(let o of e.children){let l=this.parseObject(o,t,r,i);l!==void 0&&n.add(l)}return n instanceof Li&&n.initFromObjectLoader(e),n}async parseObjectAsync(e,t,r,i){let n;if(e.geometry!==void 0){let o=Cg(t,e.geometry),l=Mg(r,e.material);if(o){switch(e.objectType){case\\\\\\\"Mesh2D\\\\\\\":n=new Js(o,l).fromJSON(e);break;case\\\\\\\"Mesh3D\\\\\\\":n=new Qs(o,l).fromJSON(e);break;case\\\\\\\"NonParametric\\\\\\\":n=new xa(o,l).fromJSON(e);break;case\\\\\\\"TextFrame\\\\\\\":n=new Ei(o,l);try{await n.fromJSONasync(e)}catch(a){console.log(a)}break;case\\\\\\\"VectorObject\\\\\\\":n=new Ni(o,l).fromJSON(e);break;case\\\\\\\"SubdivObject\\\\\\\":n=new rn(o,l).fromJSON(e),n.subdivPointer=o.subdivPointer;break;default:console.warn(\\\\\\\"ObjectLoader > parseObject: unsupported object type\\\\\\\",e.objectType);return}l||(n.name+=\\\\\\\" (Recovered)\\\\\\\")}else n=new gr().fromJSON(e),n.name+=\\\\\\\" (Recovered)\\\\\\\",console.warn(\\\\\\\"An empty object is created instead\\\\\\\")}else switch(e.objectType){case\\\\\\\"Scene\\\\\\\":n=new Li().fromJSON(e);break;case\\\\\\\"CombinedCamera\\\\\\\":n=new Kr().fromJSON(e);break;case\\\\\\\"LightDirectional\\\\\\\":n=new ga().fromJSON(e);break;case\\\\\\\"LightPoint\\\\\\\":n=new ya().fromJSON(e);break;case\\\\\\\"LightSpot\\\\\\\":n=new va().fromJSON(e);break;case\\\\\\\"EmptyObject\\\\\\\":n=new gr().fromJSON(e);break;default:if(e.type===\\\\\\\"HemisphereLight\\\\\\\"||e.type===\\\\\\\"DirectionalLight\\\\\\\")n=this.loader.parseObject(e,t,r);else{console.warn(\\\\\\\"ObjectLoader > parseObject: unsupported object type\\\\\\\",e.objectType);return}}if(Vt(n)&&(i[e.uuid]=n),e.children!==void 0)for(let o of e.children){let l=await this.parseObjectAsync(o,t,r,i);l!==void 0&&n.add(l)}return n instanceof Li&&n.initFromObjectLoader(e),n}parseInteractionStates(e,t){let r={};if(e!==void 0)for(let i=0,n=e.length;i<n;i++){let o=new bi().fromJSON(e[i],t);r[o.uuid]=o}return r}parseInteractions(e,t,r){let i=t[e.uuid];if(e.interaction!==void 0&&i!==void 0&&(i.interaction=new wi(i).fromJSON(e.interaction,t,r)),e.children!==void 0)for(let n of e.children)this.parseInteractions(n,t,r)}};var gn=F(z());var gu=class{constructor(e,t,r,i){this.isEnable=!1;this._enableEvent={};this._map={};this._prevAncestors=[];this._raycaster=new Al;this._intersection=new gn.Vector3;this._plane=new gn.Plane;this._normal=new gn.Vector3;this._position=new 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\\\\\\\"},{\\\\\\\"uuid\\\\\\\":\\\\\\\"63D55746-6DD2-4058-AE3D-7BC975719FDA\\\\\\\",\\\\\\\"url\\\\\\\":\\\\\\\"data:image/png;base64,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n) 'n' exceeds maximum supported size\\u0000N5draco18AttributeTransformE\\u0000\\u0000\\u0000\\u0000�@\\u0000\\u0000 \\u0005\\u0000\\u0000\\u0000\\u0000\\u0000\\u0000�\\u0005\\u0000\\u0000\\u0019\\u0000\\u0000\\u0000\\u001a\\u0000\\u0000\\u0000\\u001b\\u0000\\u0000\\u0000\\u001c\\u0000\\u0000\\u0000\\u001d\\u0000\\u0000\\u0000\\u001e\\u0000\\u0000\\u0000\\u001f\\u0000\\u0000\\u0000 \\u0000\\u0000\\u0000!\\u0000\\u0000\\u0000\\\\\\\"\\u0000\\u0000\\u0000#\\u0000\\u0000\\u0000$\\u0000\\u0000\\u0000N5draco17AttributesDecoderE\\u0000N5draco26AttributesDecoderInterfaceE\\u0000\\u0000\\u0000\\u0000�@\\u0000\\u0000�\\u0005\\u0000\\u0000�@\\u0000\\u0000�\\u0005\\u0000\\u0000�\\u0005\\u0000\\u0000allocator<T>::allocate(size_t n) 'n' exceeds maximum supported 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n) 'n' exceeds maximum supported size\\u0000Unsupported encoding method.\\u0000Unsupported geometry type.\\u0000Input is not a point cloud.\\u0000Input is not a mesh.\\u0000skip_attribute_transform\\u0000allocator<T>::allocate(size_t n) 'n' exceeds maximum supported size\\u0000\\u0000\\u0000\\u0000\\u0000\\u0000\\u0000L,\\u0000\\u0000\\u0011\\u0001\\u0000\\u0000\\u0012\\u0001\\u0000\\u0000\\u0013\\u0001\\u0000\\u0000\\u0014\\u0001\\u0000\\u0000\\\\\\\"\\u0000\\u0000\\u0000\\u0015\\u0001\\u0000\\u0000\\u0016\\u0001\\u0000\\u0000\\u0017\\u0001\\u0000\\u0000\\u0018\\u0001\\u0000\\u0000\\u0019\\u0001\\u0000\\u0000\\u001a\\u0001\\u0000\\u0000\\u001b\\u0001\\u0000\\u0000\\\\\\\"\\u0000\\u0000\\u0000N5draco11MeshDecoderE\\u0000\\u0000\\u0000�@\\u0000\\u00004,\\u0000\\u0000\\u00146\\u0000\\u0000\\u0000\\u0000\\u0000\\u0000�,\\u0000\\u0000\\u001c\\u0001\\u0000\\u0000\\u001d\\u0001\\u0000\\u0000\\u0013\\u0001\\u0000\\u0000\\u001e\\u0001\\u0000\\u0000\\u001f\\u0001\\u0000\\u0000\\u0015\\u0001\\u0000\\u0000\\u0016\\u0001\\u0000\\u0000\\u0017\\u0001\\u0000\\u0000 \\u0001\\u0000\\u0000!\\u0001\\u0000\\u0000\\\\\\\"\\u0001\\u0000\\u0000#\\u0001\\u0000\\u0000$\\u0001\\u0000\\u0000N5draco22MeshEdgebreakerDecoderE\\u0000\\u0000\\u0000\\u0000�@\\u0000\\u0000�,\\u0000\\u0000L,\\u0000\\u0000\\u0000\\u0000\\u0000\\u0000�-\\u0000\\u0000%\\u0001\\u0000\\u0000&\\u0001\\u0000\\u0000'\\u0001\\u0000\\u0000(\\u0001\\u0000\\u0000)\\u0001\\u0000\\u0000*\\u0001\\u0000\\u0000+\\u0001\\u0000\\u0000,\\u0001\\u0000\\u0000-\\u0001\\u0000\\u0000.\\u0001\\u0000\\u0000����\\u0000\\u0000\\u0000\\u0000@.\\u0000\\u0000/\\u0001\\u0000\\u00000\\u0001\\u0000\\u00001\\u0001\\u0000\\u00002\\u0001\\u0000\\u00003\\u0001\\u0000\\u00004\\u0001\\u0000\\u00005\\u0001\\u0000\\u00006\\u0001\\u0000\\u00007\\u0001\\u0000\\u00008\\u0001\\u0000\\u0000\\u0000\\u0000\\u0000\\u0000�.\\u0000\\u00009\\u0001\\u0000\\u0000:\\u0001\\u0000\\u0000;\\u0001\\u0000\\u0000<\\u0001\\u0000\\u0000=\\u0001\\u0000\\u0000>\\u0001\\u0000\\u0000?\\u0001\\u0000\\u0000@\\u0001\\u0000\\u0000A\\u0001\\u0000\\u0000B\\u0001\\u0000\\u0000N5draco26MeshEdgebreakerDecoderImplINS_31MeshEdgebreakerTraversalDecoderEEE\\u0000N5draco35MeshEdgebreakerDecoderImplInterfaceE\\u0000\\u0000\\u0000�@\\u0000\\u0000�-\\u0000\\u0000�@\\u0000\\u0000X-\\u0000\\u0000�-\\u0000\\u0000N5draco26MeshEdgebreakerDecoderImplINS_41MeshEdgebreakerTraversalPredictiveDecoderEEE\\u0000\\u0000\\u0000�@\\u0000\\u0000�-\\u0000\\u0000�-\\u0000\\u0000N5draco26MeshEdgebreakerDecoderImplINS_38MeshEdgebreakerTraversalValenceDecoderEEE\\u0000\\u0000�@\\u0000\\u0000L.\\u0000\\u0000�-\\u0000\\u0000\\u0000\\u0000\\u0000\\u0000�/\\u0000\\u0000C\\u0001\\u0000\\u0000D\\u0001\\u0000\\u0000E\\u0001\\u0000\\u0000N5draco19DepthFirstTraverserINS_24MeshAttributeCornerTableENS_36MeshAttributeIndicesEncodingObserverIS1_EEEE\\u0000N5draco13TraverserBaseINS_24MeshAttributeCornerTableENS_36MeshAttributeIndicesEncodingObserverIS1_EEEE\\u0000�@\\u0000\\u0000-/\\u0000\\u0000�@\\u0000\\u0000�.\\u0000\\u0000�/\\u0000\\u0000\\u0000\\u0000\\u0000\\u0000�/\\u0000\\u0000F\\u0001\\u0000\\u0000G\\u0001\\u0000\\u0000E\\u0001\\u0000\\u0000allocator<T>::allocate(size_t n) 'n' exceeds maximum supported size\\u0000\\u0000\\u0000\\u0000\\u0000\\u0001\\u0000\\u0000\\u0000\\u0003\\u0000\\u0000\\u0000\\u0005\\u0000\\u0000\\u0000\\u0007\\u0000\\u0000\\u0000\\u0000\\u0000\\u0000\\u0000�0\\u0000\\u0000H\\u0001\\u0000\\u0000I\\u0001\\u0000\\u0000J\\u0001\\u0000\\u0000K\\u0001\\u0000\\u0000N5draco22MeshTraversalSequencerINS_28MaxPredictionDegreeTraverserINS_11CornerTableENS_36MeshAttributeIndicesEncodingObserverIS2_EEEEEE\\u0000N5draco15PointsSequencerE\\u0000\\u0000\\u0000\\u0000�@\\u0000\\u0000�0\\u0000\\u0000�@\\u0000\\u0000,0\\u0000\\u0000�0\\u0000\\u0000����\\u0000\\u0000\\u0000\\u0000�1\\u0000\\u0000L\\u0001\\u0000\\u0000M\\u0001\\u0000\\u0000N\\u0001\\u0000\\u0000N5draco28MaxPredictionDegreeTraverserINS_11CornerTableENS_36MeshAttributeIndicesEncodingObserverIS1_EEEE\\u0000N5draco13TraverserBaseINS_11CornerTableENS_36MeshAttributeIndicesEncodingObserverIS1_EEEE\\u0000\\u0000�@\\u0000\\u0000e1\\u0000\\u0000�@\\u0000\\u0000�0\\u0000\\u0000�1\\u0000\\u0000\\u0000\\u0000\\u0000\\u0000�1\\u0000\\u0000O\\u0001\\u0000\\u0000P\\u0001\\u0000\\u0000N\\u0001\\u0000\\u0000\\u0000\\u0000\\u0000\\u0000�2\\u0000\\u0000Q\\u0001\\u0000\\u0000R\\u0001\\u0000\\u0000S\\u0001\\u0000\\u0000T\\u0001\\u0000\\u0000N5draco22MeshTraversalSequencerINS_19DepthFirstTraverserINS_11CornerTableENS_36MeshAttributeIndicesEncodingObserverIS2_EEEEEE\\u0000\\u0000\\u0000�@\\u0000\\u0000\\u00002\\u0000\\u0000�0\\u0000\\u0000\\u0000\\u0000\\u0000\\u0000\\u00003\\u0000\\u0000U\\u0001\\u0000\\u0000V\\u0001\\u0000\\u0000N\\u0001\\u0000\\u0000N5draco19DepthFirstTraverserINS_11CornerTableENS_36MeshAttributeIndicesEncodingObserverIS1_EEEE\\u0000�@\\u0000\\u0000�2\\u0000\\u0000�1\\u0000\\u0000\\u0000\\u0000\\u0000\\u0000�3\\u0000\\u0000W\\u0001\\u0000\\u0000X\\u0001\\u0000\\u0000Y\\u0001\\u0000\\u0000Z\\u0001\\u0000\\u0000N5draco22MeshTraversalSequencerINS_19DepthFirstTraverserINS_24MeshAttributeCornerTableENS_36MeshAttributeIndicesEncodingObserverIS2_EEEEEE\\u0000\\u0000�@\\u0000\\u0000$3\\u0000\\u0000�0\\u0000A��\\u0000\\u000b�\\u0002\\u001c4\\u0000\\u0000\\u0011\\u0001\\u0000\\u0000[\\u0001\\u0000\\u0000\\u0013\\u0001\\u0000\\u0000\\u0014\\u0001\\u0000\\u0000\\\\\\u0001\\u0000\\u0000\\u0015\\u0001\\u0000\\u0000\\u0016\\u0001\\u0000\\u0000\\u0017\\u0001\\u0000\\u0000\\u0018\\u0001\\u0000\\u0000\\u0019\\u0001\\u0000\\u0000\\u001a\\u0001\\u0000\\u0000\\u001b\\u0001\\u0000\\u0000]\\u0001\\u0000\\u0000N5draco21MeshSequentialDecoderE\\u0000�@\\u0000\\u0000�3\\u0000\\u0000L,\\u0000\\u0000allocator<T>::allocate(size_t n) 'n' exceeds maximum supported size\\u0000\\u0000\\u0000\\u0000\\u0000�4\\u0000\\u0000^\\u0001\\u0000\\u0000_\\u0001\\u0000\\u0000`\\u0001\\u0000\\u0000a\\u0001\\u0000\\u0000N5draco15LinearSequencerE\\u0000\\u0000\\u0000�@\\u0000\\u0000�4\\u0000\\u0000�0\\u0000\\u0000\\u0000\\u0000\\u0000\\u0000\\u00146\\u0000\\u0000\\u0011\\u0001\\u0000\\u0000b\\u0001\\u0000\\u0000c\\u0001\\u0000\\u0000\\u0014\\u0001\\u0000\\u0000\\\\\\\"\\u0000\\u0000\\u0000d\\u0001\\u0000\\u0000\\u0016\\u0001\\u0000\\u0000\\u0017\\u0001\\u0000\\u0000\\u0018\\u0001\\u0000A��\\u0000\\u000b�\\u0004Failed to parse Draco header.\\u0000DRACO\\u0000Not a Draco file.\\u0000Failed to decode metadata.\\u0000Using incompatible decoder for the input geometry.\\u0000Unknown major version.\\u0000Unknown minor version.\\u0000Failed to initialize the decoder.\\u0000Failed to decode geometry data.\\u0000Failed to decode point attributes.\\u0000N5draco17PointCloudDecoderE\\u0000\\u0000�@\\u0000\\u0000�5\\u0000\\u0000\\u0000\\u0000\\u0000\\u0000l6\\u0000\\u0000\\u0011\\u0001\\u0000\\u0000e\\u0001\\u0000\\u0000c\\u0001\\u0000\\u0000\\u0014\\u0001\\u0000\\u0000f\\u0001\\u0000\\u0000g\\u0001\\u0000\\u0000\\u0016\\u0001\\u0000\\u0000\\u0017\\u0001\\u0000\\u0000\\u0018\\u0001\\u0000\\u0000N5draco23PointCloudKdTreeDecoderE\\u0000\\u0000\\u0000�@\\u0000\\u0000H6\\u0000\\u0000\\u00146\\u0000\\u0000\\u0000\\u0000\\u0000\\u0000�6\\u0000\\u0000\\u0011\\u0001\\u0000\\u0000h\\u0001\\u0000\\u0000c\\u0001\\u0000\\u0000\\u0014\\u0001\\u0000\\u0000i\\u0001\\u0000\\u0000j\\u0001\\u0000\\u0000\\u0016\\u0001\\u0000\\u0000\\u0017\\u0001\\u0000\\u0000\\u0018\\u0001\\u0000\\u0000N5draco27PointCloudSequentialDecoderE\\u0000\\u0000\\u0000�@\\u0000\\u0000�6\\u0000\\u0000\\u00146\\u0000\\u0000\\u0001\\u0000\\u0000\\u0000\\u0001\\u0000\\u0000\\u0000\\u0002\\u0000\\u0000\\u0000\\u0002\\u0000\\u0000\\u0000\\u0004\\u0000\\u0000\\u0000\\u0004\\u0000\\u0000\\u0000\\b\\u0000\\u0000\\u0000\\b\\u0000\\u0000\\u0000\\u0004\\u0000\\u0000\\u0000\\b\\u0000\\u0000\\u0000\\u0001\\u0000A��\\u0000\\u000b�\\u0007�\\u0000\\u0000\\u0000�\\u0000\\u0000\\u0000�\\u0000\\u0000\\u0000�allocator<T>::allocate(size_t n) 'n' exceeds maximum supported size\\u0000(size_t idx, size_t size)<::>{ throw 'Array index ' + idx + ' out of bounds: [0,' + size + ')'; }\\u0000\\u0000\\u0000��������allocator<T>::allocate(size_t n) 'n' exceeds maximum supported size\\u0000\\u0000\\u0000\\u0000\\u0000<8\\u0000\\u0000k\\u0001\\u0000\\u0000l\\u0001\\u0000\\u0000m\\u0001\\u0000\\u0000n\\u0001\\u0000\\u0000N5draco4MeshE\\u0000\\u0000\\u0000�@\\u0000\\u0000,8\\u0000\\u0000�9\\u0000\\u0000allocator<T>::allocate(size_t n) 'n' exceeds maximum supported size\\u0000����allocator<T>::allocate(size_t n) 'n' exceeds maximum supported size\\u0000allocator<T>::allocate(size_t n) 'n' exceeds maximum supported size\\u0000allocator<T>::allocate(size_t n) 'n' exceeds maximum supported size\\u0000allocator<T>::allocate(size_t n) 'n' exceeds maximum supported size\\u0000\\u0000\\u0000\\u0000\\u0000�9\\u0000\\u0000o\\u0001\\u0000\\u0000p\\u0001\\u0000\\u0000q\\u0001\\u0000\\u0000r\\u0001\\u0000\\u0000N5draco10PointCloudE\\u0000\\u0000\\u0000\\u0000�@\\u0000\\u0000�9\\u0000\\u0000allocator<T>::allocate(size_t n) 'n' exceeds maximum supported size\\u0000FloatPointsTreeDecoder: compression level %i not supported.\\n\\u0000allocator<T>::allocate(size_t n) 'n' exceeds maximum supported size\\u0000-+   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