(()=>{var Th=Uint8Array.fromBase64||(()=>{for(var i=new Uint8Array(128),e=0;e<64;e++)i[e<26?e+65:e<52?e+71:e<62?e-4:e*4-205]=e;return t=>{for(var n=t.length,s=new Uint8Array((n-(t[n-1]=="=")-(t[n-2]=="="))*3/4|0),r=0,o=0;r<n;){var a=i[t.charCodeAt(r++)],c=i[t.charCodeAt(r++)],h=i[t.charCodeAt(r++)],l=i[t.charCodeAt(r++)];s[o++]=a<<2|c>>4,s[o++]=c<<4|h>>2,s[o++]=h<<6|l}return s}})();var hp=0,Uh=1,lp=2;var i0=1,pp=2,In=3,On=0,Bt=1,dn=2,oi=0,ts=1,so=2,Ih=3,Rh=4,fp=5,yi=100,dp=101,up=102,gp=103,_p=104,mp=200,xp=201,bp=202,Pp=203,Aa=204,va=205,wp=206,Op=207,yp=208,Ap=209,vp=210,Hp=211,Sp=212,Ep=213,Wp=214,Ha=0,Sa=1,Ea=2,rs=3,Wa=4,Ma=5,Ca=6,Ga=7,s0=0,Mp=1,Cp=2,ai=0,Gp=1,Tp=2,Up=3,Xc=4,Ip=5,Rp=6,Xp=7,Xh="attached",Bp="detached",r0=300,os=301,as=302,Ta=303,Ua=304,Go=306,Fn=1e3,Xn=1001,tr=1002,$t=1003,Bc=1004;var Ki=1005;var kt=1006,Ys=1007;var wn=1008;var Dn=1009,o0=1010,a0=1011,nr=1012,$c=1013,vi=1014,gn=1015,fr=1016,Fc=1017,Dc=1018,cs=1020,c0=35902,h0=1021,l0=1022,sn=1023,p0=1024,f0=1025,ns=1026,hs=1027,Nc=1028,Vc=1029,d0=1030,Lc=1031;var zc=1033,Kr=33776,eo=33777,to=33778,no=33779,Ia=35840,Ra=35841,Xa=35842,Ba=35843,$a=36196,Fa=37492,Da=37496,Na=37808,Va=37809,La=37810,za=37811,ka=37812,qa=37813,Ja=37814,ja=37815,Qa=37816,Ya=37817,Za=37818,Ka=37819,ec=37820,tc=37821,io=36492,nc=36494,ic=36495,u0=36283,sc=36284,rc=36285,oc=36286;var ls=2300,ps=2301,zo=2302,Bh=2400,$h=2401,Fh=2402,$p=2500;var g0=0,To=1,dr=2,Fp=3200,Dp=3201;var _0=0,Np=1,Pn="",_t="srgb",Ft="srgb-linear",Uo="linear",ct="srgb";var Ui=7680;var Dh=519,Vp=512,Lp=513,zp=514,m0=515,kp=516,qp=517,Jp=518,jp=519,ac=35044;var Nh="300 es",Bn=2e3,ro=2001,ci=class{addEventListener(e,t){this._listeners===void 0&&(this._listeners={});let n=this._listeners;n[e]===void 0&&(n[e]=[]),n[e].indexOf(t)===-1&&n[e].push(t)}hasEventListener(e,t){if(this._listeners===void 0)return!1;let n=this._listeners;return n[e]!==void 0&&n[e].indexOf(t)!==-1}removeEventListener(e,t){if(this._listeners===void 0)return;let s=this._listeners[e];if(s!==void 0){let r=s.indexOf(t);r!==-1&&s.splice(r,1)}}dispatchEvent(e){if(this._listeners===void 0)return;let n=this._listeners[e.type];if(n!==void 0){e.target=this;let s=n.slice(0);for(let r=0,o=s.length;r<o;r++)s[r].call(this,e);e.target=null}}},Ut=["00","01","02","03","04","05","06","07","08","09","0a","0b","0c","0d","0e","0f","10","11","12","13","14","15","16","17","18","19","1a","1b","1c","1d","1e","1f","20","21","22","23","24","25","26","27","28","29","2a","2b","2c","2d","2e","2f","30","31","32","33","34","35","36","37","38","39","3a","3b","3c","3d","3e","3f","40","41","42","43","44","45","46","47","48","49","4a","4b","4c","4d","4e","4f","50","51","52","53","54","55","56","57","58","59","5a","5b","5c","5d","5e","5f","60","61","62","63","64","65","66","67","68","69","6a","6b","6c","6d","6e","6f","70","71","72","73","74","75","76","77","78","79","7a","7b","7c","7d","7e","7f","80","81","82","83","84","85","86","87","88","89","8a","8b","8c","8d","8e","8f","90","91","92","93","94","95","96","97","98","99","9a","9b","9c","9d","9e","9f","a0","a1","a2","a3","a4","a5","a6","a7","a8","a9","aa","ab","ac","ad","ae","af","b0","b1","b2","b3","b4","b5","b6","b7","b8","b9","ba","bb","bc","bd","be","bf","c0","c1","c2","c3","c4","c5","c6","c7","c8","c9","ca","cb","cc","cd","ce","cf","d0","d1","d2","d3","d4","d5","d6","d7","d8","d9","da","db","dc","dd","de","df","e0","e1","e2","e3","e4","e5","e6","e7","e8","e9","ea","eb","ec","ed","ee","ef","f0","f1","f2","f3","f4","f5","f6","f7","f8","f9","fa","fb","fc","fd","fe","ff"],Vh=1234567,Zs=Math.PI/180,fs=180/Math.PI;function _n(){let i=Math.random()*4294967295|0,e=Math.random()*4294967295|0,t=Math.random()*4294967295|0,n=Math.random()*4294967295|0;return(Ut[i&255]+Ut[i>>8&255]+Ut[i>>16&255]+Ut[i>>24&255]+"-"+Ut[e&255]+Ut[e>>8&255]+"-"+Ut[e>>16&15|64]+Ut[e>>24&255]+"-"+Ut[t&63|128]+Ut[t>>8&255]+"-"+Ut[t>>16&255]+Ut[t>>24&255]+Ut[n&255]+Ut[n>>8&255]+Ut[n>>16&255]+Ut[n>>24&255]).toLowerCase()}function Rt(i,e,t){return Math.max(e,Math.min(t,i))}function kc(i,e){return(i%e+e)%e}function Qp(i,e,t,n,s){return n+(i-e)*(s-n)/(t-e)}function Yp(i,e,t){return i!==e?(t-i)/(e-i):0}function Ks(i,e,t){return(1-t)*i+t*e}function Zp(i,e,t,n){return Ks(i,e,1-Math.exp(-t*n))}function Kp(i,e=1){return e-Math.abs(kc(i,e*2)-e)}function ef(i,e,t){return i<=e?0:i>=t?1:(i=(i-e)/(t-e),i*i*(3-2*i))}function tf(i,e,t){return i<=e?0:i>=t?1:(i=(i-e)/(t-e),i*i*i*(i*(i*6-15)+10))}function nf(i,e){return i+Math.floor(Math.random()*(e-i+1))}function sf(i,e){return i+Math.random()*(e-i)}function rf(i){return i*(.5-Math.random())}function of(i){i!==void 0&&(Vh=i);let e=Vh+=1831565813;return e=Math.imul(e^e>>>15,e|1),e^=e+Math.imul(e^e>>>7,e|61),((e^e>>>14)>>>0)/4294967296}function af(i){return i*Zs}function cf(i){return i*fs}function hf(i){return(i&i-1)===0&&i!==0}function lf(i){return Math.pow(2,Math.ceil(Math.log(i)/Math.LN2))}function pf(i){return Math.pow(2,Math.floor(Math.log(i)/Math.LN2))}function ff(i,e,t,n,s){let r=Math.cos,o=Math.sin,a=r(t/2),c=o(t/2),h=r((e+n)/2),l=o((e+n)/2),p=r((e-n)/2),f=o((e-n)/2),u=r((n-e)/2),_=o((n-e)/2);switch(s){case"XYX":i.set(a*l,c*p,c*f,a*h);break;case"YZY":i.set(c*f,a*l,c*p,a*h);break;case"ZXZ":i.set(c*p,c*f,a*l,a*h);break;case"XZX":i.set(a*l,c*_,c*u,a*h);break;case"YXY":i.set(c*u,a*l,c*_,a*h);break;case"ZYZ":i.set(c*_,c*u,a*l,a*h);break;default:console.warn("THREE.MathUtils: .setQuaternionFromProperEuler() encountered an unknown order: "+s)}}function un(i,e){switch(e.constructor){case Float32Array:return i;case Uint32Array:return i/4294967295;case Uint16Array:return i/65535;case Uint8Array:return i/255;case Int32Array:return Math.max(i/2147483647,-1);case Int16Array:return Math.max(i/32767,-1);case Int8Array:return Math.max(i/127,-1);default:throw new Error("Invalid component type.")}}function rt(i,e){switch(e.constructor){case Float32Array:return i;case Uint32Array:return Math.round(i*4294967295);case Uint16Array:return Math.round(i*65535);case Uint8Array:return Math.round(i*255);case Int32Array:return Math.round(i*2147483647);case Int16Array:return Math.round(i*32767);case Int8Array:return Math.round(i*127);default:throw new Error("Invalid component type.")}}var Ci={DEG2RAD:Zs,RAD2DEG:fs,generateUUID:_n,clamp:Rt,euclideanModulo:kc,mapLinear:Qp,inverseLerp:Yp,lerp:Ks,damp:Zp,pingpong:Kp,smoothstep:ef,smootherstep:tf,randInt:nf,randFloat:sf,randFloatSpread:rf,seededRandom:of,degToRad:af,radToDeg:cf,isPowerOfTwo:hf,ceilPowerOfTwo:lf,floorPowerOfTwo:pf,setQuaternionFromProperEuler:ff,normalize:rt,denormalize:un},Re=class i{constructor(e=0,t=0){i.prototype.isVector2=!0,this.x=e,this.y=t}get width(){return this.x}set width(e){this.x=e}get height(){return this.y}set height(e){this.y=e}set(e,t){return this.x=e,this.y=t,this}setScalar(e){return this.x=e,this.y=e,this}setX(e){return this.x=e,this}setY(e){return this.y=e,this}setComponent(e,t){switch(e){case 0:this.x=t;break;case 1:this.y=t;break;default:throw new Error("index is out of range: "+e)}return this}getComponent(e){switch(e){case 0:return this.x;case 1:return this.y;default:throw new Error("index is out of range: "+e)}}clone(){return new this.constructor(this.x,this.y)}copy(e){return this.x=e.x,this.y=e.y,this}add(e){return this.x+=e.x,this.y+=e.y,this}addScalar(e){return this.x+=e,this.y+=e,this}addVectors(e,t){return this.x=e.x+t.x,this.y=e.y+t.y,this}addScaledVector(e,t){return this.x+=e.x*t,this.y+=e.y*t,this}sub(e){return this.x-=e.x,this.y-=e.y,this}subScalar(e){return this.x-=e,this.y-=e,this}subVectors(e,t){return this.x=e.x-t.x,this.y=e.y-t.y,this}multiply(e){return this.x*=e.x,this.y*=e.y,this}multiplyScalar(e){return this.x*=e,this.y*=e,this}divide(e){return this.x/=e.x,this.y/=e.y,this}divideScalar(e){return this.multiplyScalar(1/e)}applyMatrix3(e){let t=this.x,n=this.y,s=e.elements;return this.x=s[0]*t+s[3]*n+s[6],this.y=s[1]*t+s[4]*n+s[7],this}min(e){return this.x=Math.min(this.x,e.x),this.y=Math.min(this.y,e.y),this}max(e){return this.x=Math.max(this.x,e.x),this.y=Math.max(this.y,e.y),this}clamp(e,t){return this.x=Math.max(e.x,Math.min(t.x,this.x)),this.y=Math.max(e.y,Math.min(t.y,this.y)),this}clampScalar(e,t){return this.x=Math.max(e,Math.min(t,this.x)),this.y=Math.max(e,Math.min(t,this.y)),this}clampLength(e,t){let n=this.length();return this.divideScalar(n||1).multiplyScalar(Math.max(e,Math.min(t,n)))}floor(){return this.x=Math.floor(this.x),this.y=Math.floor(this.y),this}ceil(){return this.x=Math.ceil(this.x),this.y=Math.ceil(this.y),this}round(){return this.x=Math.round(this.x),this.y=Math.round(this.y),this}roundToZero(){return this.x=Math.trunc(this.x),this.y=Math.trunc(this.y),this}negate(){return this.x=-this.x,this.y=-this.y,this}dot(e){return this.x*e.x+this.y*e.y}cross(e){return this.x*e.y-this.y*e.x}lengthSq(){return this.x*this.x+this.y*this.y}length(){return Math.sqrt(this.x*this.x+this.y*this.y)}manhattanLength(){return Math.abs(this.x)+Math.abs(this.y)}normalize(){return this.divideScalar(this.length()||1)}angle(){return Math.atan2(-this.y,-this.x)+Math.PI}angleTo(e){let t=Math.sqrt(this.lengthSq()*e.lengthSq());if(t===0)return Math.PI/2;let n=this.dot(e)/t;return Math.acos(Rt(n,-1,1))}distanceTo(e){return Math.sqrt(this.distanceToSquared(e))}distanceToSquared(e){let t=this.x-e.x,n=this.y-e.y;return t*t+n*n}manhattanDistanceTo(e){return Math.abs(this.x-e.x)+Math.abs(this.y-e.y)}setLength(e){return this.normalize().multiplyScalar(e)}lerp(e,t){return this.x+=(e.x-this.x)*t,this.y+=(e.y-this.y)*t,this}lerpVectors(e,t,n){return this.x=e.x+(t.x-e.x)*n,this.y=e.y+(t.y-e.y)*n,this}equals(e){return e.x===this.x&&e.y===this.y}fromArray(e,t=0){return this.x=e[t],this.y=e[t+1],this}toArray(e=[],t=0){return e[t]=this.x,e[t+1]=this.y,e}fromBufferAttribute(e,t){return this.x=e.getX(t),this.y=e.getY(t),this}rotateAround(e,t){let n=Math.cos(t),s=Math.sin(t),r=this.x-e.x,o=this.y-e.y;return this.x=r*n-o*s+e.x,this.y=r*s+o*n+e.y,this}random(){return this.x=Math.random(),this.y=Math.random(),this}*[Symbol.iterator](){yield this.x,yield this.y}},ze=class i{constructor(e,t,n,s,r,o,a,c,h){i.prototype.isMatrix3=!0,this.elements=[1,0,0,0,1,0,0,0,1],e!==void 0&&this.set(e,t,n,s,r,o,a,c,h)}set(e,t,n,s,r,o,a,c,h){let l=this.elements;return l[0]=e,l[1]=s,l[2]=a,l[3]=t,l[4]=r,l[5]=c,l[6]=n,l[7]=o,l[8]=h,this}identity(){return this.set(1,0,0,0,1,0,0,0,1),this}copy(e){let t=this.elements,n=e.elements;return t[0]=n[0],t[1]=n[1],t[2]=n[2],t[3]=n[3],t[4]=n[4],t[5]=n[5],t[6]=n[6],t[7]=n[7],t[8]=n[8],this}extractBasis(e,t,n){return e.setFromMatrix3Column(this,0),t.setFromMatrix3Column(this,1),n.setFromMatrix3Column(this,2),this}setFromMatrix4(e){let t=e.elements;return this.set(t[0],t[4],t[8],t[1],t[5],t[9],t[2],t[6],t[10]),this}multiply(e){return this.multiplyMatrices(this,e)}premultiply(e){return this.multiplyMatrices(e,this)}multiplyMatrices(e,t){let n=e.elements,s=t.elements,r=this.elements,o=n[0],a=n[3],c=n[6],h=n[1],l=n[4],p=n[7],f=n[2],u=n[5],_=n[8],m=s[0],g=s[3],d=s[6],A=s[1],O=s[4],P=s[7],G=s[2],H=s[5],S=s[8];return r[0]=o*m+a*A+c*G,r[3]=o*g+a*O+c*H,r[6]=o*d+a*P+c*S,r[1]=h*m+l*A+p*G,r[4]=h*g+l*O+p*H,r[7]=h*d+l*P+p*S,r[2]=f*m+u*A+_*G,r[5]=f*g+u*O+_*H,r[8]=f*d+u*P+_*S,this}multiplyScalar(e){let t=this.elements;return t[0]*=e,t[3]*=e,t[6]*=e,t[1]*=e,t[4]*=e,t[7]*=e,t[2]*=e,t[5]*=e,t[8]*=e,this}determinant(){let e=this.elements,t=e[0],n=e[1],s=e[2],r=e[3],o=e[4],a=e[5],c=e[6],h=e[7],l=e[8];return t*o*l-t*a*h-n*r*l+n*a*c+s*r*h-s*o*c}invert(){let e=this.elements,t=e[0],n=e[1],s=e[2],r=e[3],o=e[4],a=e[5],c=e[6],h=e[7],l=e[8],p=l*o-a*h,f=a*c-l*r,u=h*r-o*c,_=t*p+n*f+s*u;if(_===0)return this.set(0,0,0,0,0,0,0,0,0);let m=1/_;return e[0]=p*m,e[1]=(s*h-l*n)*m,e[2]=(a*n-s*o)*m,e[3]=f*m,e[4]=(l*t-s*c)*m,e[5]=(s*r-a*t)*m,e[6]=u*m,e[7]=(n*c-h*t)*m,e[8]=(o*t-n*r)*m,this}transpose(){let e,t=this.elements;return e=t[1],t[1]=t[3],t[3]=e,e=t[2],t[2]=t[6],t[6]=e,e=t[5],t[5]=t[7],t[7]=e,this}getNormalMatrix(e){return this.setFromMatrix4(e).invert().transpose()}transposeIntoArray(e){let t=this.elements;return e[0]=t[0],e[1]=t[3],e[2]=t[6],e[3]=t[1],e[4]=t[4],e[5]=t[7],e[6]=t[2],e[7]=t[5],e[8]=t[8],this}setUvTransform(e,t,n,s,r,o,a){let c=Math.cos(r),h=Math.sin(r);return this.set(n*c,n*h,-n*(c*o+h*a)+o+e,-s*h,s*c,-s*(-h*o+c*a)+a+t,0,0,1),this}scale(e,t){return this.premultiply(ko.makeScale(e,t)),this}rotate(e){return this.premultiply(ko.makeRotation(-e)),this}translate(e,t){return this.premultiply(ko.makeTranslation(e,t)),this}makeTranslation(e,t){return e.isVector2?this.set(1,0,e.x,0,1,e.y,0,0,1):this.set(1,0,e,0,1,t,0,0,1),this}makeRotation(e){let t=Math.cos(e),n=Math.sin(e);return this.set(t,-n,0,n,t,0,0,0,1),this}makeScale(e,t){return this.set(e,0,0,0,t,0,0,0,1),this}equals(e){let t=this.elements,n=e.elements;for(let s=0;s<9;s++)if(t[s]!==n[s])return!1;return!0}fromArray(e,t=0){for(let n=0;n<9;n++)this.elements[n]=e[n+t];return this}toArray(e=[],t=0){let n=this.elements;return e[t]=n[0],e[t+1]=n[1],e[t+2]=n[2],e[t+3]=n[3],e[t+4]=n[4],e[t+5]=n[5],e[t+6]=n[6],e[t+7]=n[7],e[t+8]=n[8],e}clone(){return new this.constructor().fromArray(this.elements)}},ko=new ze;function x0(i){for(let e=i.length-1;e>=0;--e)if(i[e]>=65535)return!0;return!1}function ir(i){return document.createElementNS("http://www.w3.org/1999/xhtml",i)}function df(){let i=ir("canvas");return i.style.display="block",i}var Lh={};function js(i){i in Lh||(Lh[i]=!0,console.warn(i))}function uf(i,e,t){return new Promise(function(n,s){function r(){switch(i.clientWaitSync(e,i.SYNC_FLUSH_COMMANDS_BIT,0)){case i.WAIT_FAILED:s();break;case i.TIMEOUT_EXPIRED:setTimeout(r,t);break;default:n()}}setTimeout(r,t)})}function gf(i){let e=i.elements;e[2]=.5*e[2]+.5*e[3],e[6]=.5*e[6]+.5*e[7],e[10]=.5*e[10]+.5*e[11],e[14]=.5*e[14]+.5*e[15]}function _f(i){let e=i.elements;e[11]===-1?(e[10]=-e[10]-1,e[14]=-e[14]):(e[10]=-e[10],e[14]=-e[14]+1)}var Ze={enabled:!0,workingColorSpace:Ft,spaces:{},convert:function(i,e,t){return this.enabled===!1||e===t||!e||!t||(this.spaces[e].transfer===ct&&(i.r=$n(i.r),i.g=$n(i.g),i.b=$n(i.b)),this.spaces[e].primaries!==this.spaces[t].primaries&&(i.applyMatrix3(this.spaces[e].toXYZ),i.applyMatrix3(this.spaces[t].fromXYZ)),this.spaces[t].transfer===ct&&(i.r=is(i.r),i.g=is(i.g),i.b=is(i.b))),i},fromWorkingColorSpace:function(i,e){return this.convert(i,this.workingColorSpace,e)},toWorkingColorSpace:function(i,e){return this.convert(i,e,this.workingColorSpace)},getPrimaries:function(i){return this.spaces[i].primaries},getTransfer:function(i){return i===Pn?Uo:this.spaces[i].transfer},getLuminanceCoefficients:function(i,e=this.workingColorSpace){return i.fromArray(this.spaces[e].luminanceCoefficients)},define:function(i){Object.assign(this.spaces,i)},_getMatrix:function(i,e,t){return i.copy(this.spaces[e].toXYZ).multiply(this.spaces[t].fromXYZ)},_getDrawingBufferColorSpace:function(i){return this.spaces[i].outputColorSpaceConfig.drawingBufferColorSpace},_getUnpackColorSpace:function(i=this.workingColorSpace){return this.spaces[i].workingColorSpaceConfig.unpackColorSpace}};function $n(i){return i<.04045?i*.0773993808:Math.pow(i*.9478672986+.0521327014,2.4)}function is(i){return i<.0031308?i*12.92:1.055*Math.pow(i,.41666)-.055}var zh=[.64,.33,.3,.6,.15,.06],kh=[.2126,.7152,.0722],qh=[.3127,.329],Jh=new ze().set(.4123908,.3575843,.1804808,.212639,.7151687,.0721923,.0193308,.1191948,.9505322),jh=new ze().set(3.2409699,-1.5373832,-.4986108,-.9692436,1.8759675,.0415551,.0556301,-.203977,1.0569715);Ze.define({[Ft]:{primaries:zh,whitePoint:qh,transfer:Uo,toXYZ:Jh,fromXYZ:jh,luminanceCoefficients:kh,workingColorSpaceConfig:{unpackColorSpace:_t},outputColorSpaceConfig:{drawingBufferColorSpace:_t}},[_t]:{primaries:zh,whitePoint:qh,transfer:ct,toXYZ:Jh,fromXYZ:jh,luminanceCoefficients:kh,outputColorSpaceConfig:{drawingBufferColorSpace:_t}}});var Ii,cc=class{static getDataURL(e){if(/^data:/i.test(e.src)||typeof HTMLCanvasElement>"u")return e.src;let t;if(e instanceof HTMLCanvasElement)t=e;else{Ii===void 0&&(Ii=ir("canvas")),Ii.width=e.width,Ii.height=e.height;let n=Ii.getContext("2d");e instanceof ImageData?n.putImageData(e,0,0):n.drawImage(e,0,0,e.width,e.height),t=Ii}return t.width>2048||t.height>2048?(console.warn("THREE.ImageUtils.getDataURL: Image converted to jpg for performance reasons",e),t.toDataURL("image/jpeg",.6)):t.toDataURL("image/png")}static sRGBToLinear(e){if(typeof HTMLImageElement<"u"&&e instanceof HTMLImageElement||typeof HTMLCanvasElement<"u"&&e instanceof HTMLCanvasElement||typeof ImageBitmap<"u"&&e instanceof ImageBitmap){let t=ir("canvas");t.width=e.width,t.height=e.height;let n=t.getContext("2d");n.drawImage(e,0,0,e.width,e.height);let s=n.getImageData(0,0,e.width,e.height),r=s.data;for(let o=0;o<r.length;o++)r[o]=$n(r[o]/255)*255;return n.putImageData(s,0,0),t}else if(e.data){let t=e.data.slice(0);for(let n=0;n<t.length;n++)t instanceof Uint8Array||t instanceof Uint8ClampedArray?t[n]=Math.floor($n(t[n]/255)*255):t[n]=$n(t[n]);return{data:t,width:e.width,height:e.height}}else return console.warn("THREE.ImageUtils.sRGBToLinear(): Unsupported image type. No color space conversion applied."),e}},mf=0,oo=class{constructor(e=null){this.isSource=!0,Object.defineProperty(this,"id",{value:mf++}),this.uuid=_n(),this.data=e,this.dataReady=!0,this.version=0}set needsUpdate(e){e===!0&&this.version++}toJSON(e){let t=e===void 0||typeof e=="string";if(!t&&e.images[this.uuid]!==void 0)return e.images[this.uuid];let n={uuid:this.uuid,url:""},s=this.data;if(s!==null){let r;if(Array.isArray(s)){r=[];for(let o=0,a=s.length;o<a;o++)s[o].isDataTexture?r.push(qo(s[o].image)):r.push(qo(s[o]))}else r=qo(s);n.url=r}return t||(e.images[this.uuid]=n),n}};function qo(i){return typeof HTMLImageElement<"u"&&i instanceof HTMLImageElement||typeof HTMLCanvasElement<"u"&&i instanceof HTMLCanvasElement||typeof ImageBitmap<"u"&&i instanceof ImageBitmap?cc.getDataURL(i):i.data?{data:Array.from(i.data),width:i.width,height:i.height,type:i.data.constructor.name}:(console.warn("THREE.Texture: Unable to serialize Texture."),{})}var xf=0,Ct=class i extends ci{constructor(e=i.DEFAULT_IMAGE,t=i.DEFAULT_MAPPING,n=Xn,s=Xn,r=kt,o=wn,a=sn,c=Dn,h=i.DEFAULT_ANISOTROPY,l=Pn){super(),this.isTexture=!0,Object.defineProperty(this,"id",{value:xf++}),this.uuid=_n(),this.name="",this.source=new oo(e),this.mipmaps=[],this.mapping=t,this.channel=0,this.wrapS=n,this.wrapT=s,this.magFilter=r,this.minFilter=o,this.anisotropy=h,this.format=a,this.internalFormat=null,this.type=c,this.offset=new Re(0,0),this.repeat=new Re(1,1),this.center=new Re(0,0),this.rotation=0,this.matrixAutoUpdate=!0,this.matrix=new ze,this.generateMipmaps=!0,this.premultiplyAlpha=!1,this.flipY=!0,this.unpackAlignment=4,this.colorSpace=l,this.userData={},this.version=0,this.onUpdate=null,this.isRenderTargetTexture=!1,this.pmremVersion=0}get image(){return this.source.data}set image(e=null){this.source.data=e}updateMatrix(){this.matrix.setUvTransform(this.offset.x,this.offset.y,this.repeat.x,this.repeat.y,this.rotation,this.center.x,this.center.y)}clone(){return new this.constructor().copy(this)}copy(e){return this.name=e.name,this.source=e.source,this.mipmaps=e.mipmaps.slice(0),this.mapping=e.mapping,this.channel=e.channel,this.wrapS=e.wrapS,this.wrapT=e.wrapT,this.magFilter=e.magFilter,this.minFilter=e.minFilter,this.anisotropy=e.anisotropy,this.format=e.format,this.internalFormat=e.internalFormat,this.type=e.type,this.offset.copy(e.offset),this.repeat.copy(e.repeat),this.center.copy(e.center),this.rotation=e.rotation,this.matrixAutoUpdate=e.matrixAutoUpdate,this.matrix.copy(e.matrix),this.generateMipmaps=e.generateMipmaps,this.premultiplyAlpha=e.premultiplyAlpha,this.flipY=e.flipY,this.unpackAlignment=e.unpackAlignment,this.colorSpace=e.colorSpace,this.userData=JSON.parse(JSON.stringify(e.userData)),this.needsUpdate=!0,this}toJSON(e){let t=e===void 0||typeof e=="string";if(!t&&e.textures[this.uuid]!==void 0)return e.textures[this.uuid];let n={metadata:{version:4.6,type:"Texture",generator:"Texture.toJSON"},uuid:this.uuid,name:this.name,image:this.source.toJSON(e).uuid,mapping:this.mapping,channel:this.channel,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,internalFormat:this.internalFormat,type:this.type,colorSpace:this.colorSpace,minFilter:this.minFilter,magFilter:this.magFilter,anisotropy:this.anisotropy,flipY:this.flipY,generateMipmaps:this.generateMipmaps,premultiplyAlpha:this.premultiplyAlpha,unpackAlignment:this.unpackAlignment};return Object.keys(this.userData).length>0&&(n.userData=this.userData),t||(e.textures[this.uuid]=n),n}dispose(){this.dispatchEvent({type:"dispose"})}transformUv(e){if(this.mapping!==r0)return e;if(e.applyMatrix3(this.matrix),e.x<0||e.x>1)switch(this.wrapS){case Fn:e.x=e.x-Math.floor(e.x);break;case Xn:e.x=e.x<0?0:1;break;case tr:Math.abs(Math.floor(e.x)%2)===1?e.x=Math.ceil(e.x)-e.x:e.x=e.x-Math.floor(e.x);break}if(e.y<0||e.y>1)switch(this.wrapT){case Fn:e.y=e.y-Math.floor(e.y);break;case Xn:e.y=e.y<0?0:1;break;case tr:Math.abs(Math.floor(e.y)%2)===1?e.y=Math.ceil(e.y)-e.y:e.y=e.y-Math.floor(e.y);break}return this.flipY&&(e.y=1-e.y),e}set needsUpdate(e){e===!0&&(this.version++,this.source.needsUpdate=!0)}set needsPMREMUpdate(e){e===!0&&this.pmremVersion++}};Ct.DEFAULT_IMAGE=null;Ct.DEFAULT_MAPPING=r0;Ct.DEFAULT_ANISOTROPY=1;var nt=class i{constructor(e=0,t=0,n=0,s=1){i.prototype.isVector4=!0,this.x=e,this.y=t,this.z=n,this.w=s}get width(){return this.z}set width(e){this.z=e}get height(){return this.w}set height(e){this.w=e}set(e,t,n,s){return this.x=e,this.y=t,this.z=n,this.w=s,this}setScalar(e){return this.x=e,this.y=e,this.z=e,this.w=e,this}setX(e){return this.x=e,this}setY(e){return this.y=e,this}setZ(e){return this.z=e,this}setW(e){return this.w=e,this}setComponent(e,t){switch(e){case 0:this.x=t;break;case 1:this.y=t;break;case 2:this.z=t;break;case 3:this.w=t;break;default:throw new Error("index is out of range: "+e)}return this}getComponent(e){switch(e){case 0:return this.x;case 1:return this.y;case 2:return this.z;case 3:return this.w;default:throw new Error("index is out of range: "+e)}}clone(){return new this.constructor(this.x,this.y,this.z,this.w)}copy(e){return this.x=e.x,this.y=e.y,this.z=e.z,this.w=e.w!==void 0?e.w:1,this}add(e){return this.x+=e.x,this.y+=e.y,this.z+=e.z,this.w+=e.w,this}addScalar(e){return this.x+=e,this.y+=e,this.z+=e,this.w+=e,this}addVectors(e,t){return this.x=e.x+t.x,this.y=e.y+t.y,this.z=e.z+t.z,this.w=e.w+t.w,this}addScaledVector(e,t){return this.x+=e.x*t,this.y+=e.y*t,this.z+=e.z*t,this.w+=e.w*t,this}sub(e){return this.x-=e.x,this.y-=e.y,this.z-=e.z,this.w-=e.w,this}subScalar(e){return this.x-=e,this.y-=e,this.z-=e,this.w-=e,this}subVectors(e,t){return this.x=e.x-t.x,this.y=e.y-t.y,this.z=e.z-t.z,this.w=e.w-t.w,this}multiply(e){return this.x*=e.x,this.y*=e.y,this.z*=e.z,this.w*=e.w,this}multiplyScalar(e){return this.x*=e,this.y*=e,this.z*=e,this.w*=e,this}applyMatrix4(e){let t=this.x,n=this.y,s=this.z,r=this.w,o=e.elements;return this.x=o[0]*t+o[4]*n+o[8]*s+o[12]*r,this.y=o[1]*t+o[5]*n+o[9]*s+o[13]*r,this.z=o[2]*t+o[6]*n+o[10]*s+o[14]*r,this.w=o[3]*t+o[7]*n+o[11]*s+o[15]*r,this}divide(e){return this.x/=e.x,this.y/=e.y,this.z/=e.z,this.w/=e.w,this}divideScalar(e){return this.multiplyScalar(1/e)}setAxisAngleFromQuaternion(e){this.w=2*Math.acos(e.w);let t=Math.sqrt(1-e.w*e.w);return t<1e-4?(this.x=1,this.y=0,this.z=0):(this.x=e.x/t,this.y=e.y/t,this.z=e.z/t),this}setAxisAngleFromRotationMatrix(e){let t,n,s,r,c=e.elements,h=c[0],l=c[4],p=c[8],f=c[1],u=c[5],_=c[9],m=c[2],g=c[6],d=c[10];if(Math.abs(l-f)<.01&&Math.abs(p-m)<.01&&Math.abs(_-g)<.01){if(Math.abs(l+f)<.1&&Math.abs(p+m)<.1&&Math.abs(_+g)<.1&&Math.abs(h+u+d-3)<.1)return this.set(1,0,0,0),this;t=Math.PI;let O=(h+1)/2,P=(u+1)/2,G=(d+1)/2,H=(l+f)/4,S=(p+m)/4,M=(_+g)/4;return O>P&&O>G?O<.01?(n=0,s=.707106781,r=.707106781):(n=Math.sqrt(O),s=H/n,r=S/n):P>G?P<.01?(n=.707106781,s=0,r=.707106781):(s=Math.sqrt(P),n=H/s,r=M/s):G<.01?(n=.707106781,s=.707106781,r=0):(r=Math.sqrt(G),n=S/r,s=M/r),this.set(n,s,r,t),this}let A=Math.sqrt((g-_)*(g-_)+(p-m)*(p-m)+(f-l)*(f-l));return Math.abs(A)<.001&&(A=1),this.x=(g-_)/A,this.y=(p-m)/A,this.z=(f-l)/A,this.w=Math.acos((h+u+d-1)/2),this}setFromMatrixPosition(e){let t=e.elements;return this.x=t[12],this.y=t[13],this.z=t[14],this.w=t[15],this}min(e){return this.x=Math.min(this.x,e.x),this.y=Math.min(this.y,e.y),this.z=Math.min(this.z,e.z),this.w=Math.min(this.w,e.w),this}max(e){return this.x=Math.max(this.x,e.x),this.y=Math.max(this.y,e.y),this.z=Math.max(this.z,e.z),this.w=Math.max(this.w,e.w),this}clamp(e,t){return this.x=Math.max(e.x,Math.min(t.x,this.x)),this.y=Math.max(e.y,Math.min(t.y,this.y)),this.z=Math.max(e.z,Math.min(t.z,this.z)),this.w=Math.max(e.w,Math.min(t.w,this.w)),this}clampScalar(e,t){return this.x=Math.max(e,Math.min(t,this.x)),this.y=Math.max(e,Math.min(t,this.y)),this.z=Math.max(e,Math.min(t,this.z)),this.w=Math.max(e,Math.min(t,this.w)),this}clampLength(e,t){let n=this.length();return this.divideScalar(n||1).multiplyScalar(Math.max(e,Math.min(t,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=Math.trunc(this.x),this.y=Math.trunc(this.y),this.z=Math.trunc(this.z),this.w=Math.trunc(this.w),this}negate(){return this.x=-this.x,this.y=-this.y,this.z=-this.z,this.w=-this.w,this}dot(e){return this.x*e.x+this.y*e.y+this.z*e.z+this.w*e.w}lengthSq(){return this.x*this.x+this.y*this.y+this.z*this.z+this.w*this.w}length(){return Math.sqrt(this.x*this.x+this.y*this.y+this.z*this.z+this.w*this.w)}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(e){return this.normalize().multiplyScalar(e)}lerp(e,t){return this.x+=(e.x-this.x)*t,this.y+=(e.y-this.y)*t,this.z+=(e.z-this.z)*t,this.w+=(e.w-this.w)*t,this}lerpVectors(e,t,n){return this.x=e.x+(t.x-e.x)*n,this.y=e.y+(t.y-e.y)*n,this.z=e.z+(t.z-e.z)*n,this.w=e.w+(t.w-e.w)*n,this}equals(e){return e.x===this.x&&e.y===this.y&&e.z===this.z&&e.w===this.w}fromArray(e,t=0){return this.x=e[t],this.y=e[t+1],this.z=e[t+2],this.w=e[t+3],this}toArray(e=[],t=0){return e[t]=this.x,e[t+1]=this.y,e[t+2]=this.z,e[t+3]=this.w,e}fromBufferAttribute(e,t){return this.x=e.getX(t),this.y=e.getY(t),this.z=e.getZ(t),this.w=e.getW(t),this}random(){return this.x=Math.random(),this.y=Math.random(),this.z=Math.random(),this.w=Math.random(),this}*[Symbol.iterator](){yield this.x,yield this.y,yield this.z,yield this.w}},hc=class extends ci{constructor(e=1,t=1,n={}){super(),this.isRenderTarget=!0,this.width=e,this.height=t,this.depth=1,this.scissor=new nt(0,0,e,t),this.scissorTest=!1,this.viewport=new nt(0,0,e,t);let s={width:e,height:t,depth:1};n=Object.assign({generateMipmaps:!1,internalFormat:null,minFilter:kt,depthBuffer:!0,stencilBuffer:!1,resolveDepthBuffer:!0,resolveStencilBuffer:!0,depthTexture:null,samples:0,count:1},n);let r=new Ct(s,n.mapping,n.wrapS,n.wrapT,n.magFilter,n.minFilter,n.format,n.type,n.anisotropy,n.colorSpace);r.flipY=!1,r.generateMipmaps=n.generateMipmaps,r.internalFormat=n.internalFormat,this.textures=[];let o=n.count;for(let a=0;a<o;a++)this.textures[a]=r.clone(),this.textures[a].isRenderTargetTexture=!0;this.depthBuffer=n.depthBuffer,this.stencilBuffer=n.stencilBuffer,this.resolveDepthBuffer=n.resolveDepthBuffer,this.resolveStencilBuffer=n.resolveStencilBuffer,this.depthTexture=n.depthTexture,this.samples=n.samples}get texture(){return this.textures[0]}set texture(e){this.textures[0]=e}setSize(e,t,n=1){if(this.width!==e||this.height!==t||this.depth!==n){this.width=e,this.height=t,this.depth=n;for(let s=0,r=this.textures.length;s<r;s++)this.textures[s].image.width=e,this.textures[s].image.height=t,this.textures[s].image.depth=n;this.dispose()}this.viewport.set(0,0,e,t),this.scissor.set(0,0,e,t)}clone(){return new this.constructor().copy(this)}copy(e){this.width=e.width,this.height=e.height,this.depth=e.depth,this.scissor.copy(e.scissor),this.scissorTest=e.scissorTest,this.viewport.copy(e.viewport),this.textures.length=0;for(let n=0,s=e.textures.length;n<s;n++)this.textures[n]=e.textures[n].clone(),this.textures[n].isRenderTargetTexture=!0;let t=Object.assign({},e.texture.image);return this.texture.source=new oo(t),this.depthBuffer=e.depthBuffer,this.stencilBuffer=e.stencilBuffer,this.resolveDepthBuffer=e.resolveDepthBuffer,this.resolveStencilBuffer=e.resolveStencilBuffer,e.depthTexture!==null&&(this.depthTexture=e.depthTexture.clone()),this.samples=e.samples,this}dispose(){this.dispatchEvent({type:"dispose"})}},Nn=class extends hc{constructor(e=1,t=1,n={}){super(e,t,n),this.isWebGLRenderTarget=!0}},ao=class extends Ct{constructor(e=null,t=1,n=1,s=1){super(null),this.isDataArrayTexture=!0,this.image={data:e,width:t,height:n,depth:s},this.magFilter=$t,this.minFilter=$t,this.wrapR=Xn,this.generateMipmaps=!1,this.flipY=!1,this.unpackAlignment=1,this.layerUpdates=new Set}addLayerUpdate(e){this.layerUpdates.add(e)}clearLayerUpdates(){this.layerUpdates.clear()}};var lc=class extends Ct{constructor(e=null,t=1,n=1,s=1){super(null),this.isData3DTexture=!0,this.image={data:e,width:t,height:n,depth:s},this.magFilter=$t,this.minFilter=$t,this.wrapR=Xn,this.generateMipmaps=!1,this.flipY=!1,this.unpackAlignment=1}};var Yt=class{constructor(e=0,t=0,n=0,s=1){this.isQuaternion=!0,this._x=e,this._y=t,this._z=n,this._w=s}static slerpFlat(e,t,n,s,r,o,a){let c=n[s+0],h=n[s+1],l=n[s+2],p=n[s+3],f=r[o+0],u=r[o+1],_=r[o+2],m=r[o+3];if(a===0){e[t+0]=c,e[t+1]=h,e[t+2]=l,e[t+3]=p;return}if(a===1){e[t+0]=f,e[t+1]=u,e[t+2]=_,e[t+3]=m;return}if(p!==m||c!==f||h!==u||l!==_){let g=1-a,d=c*f+h*u+l*_+p*m,A=d>=0?1:-1,O=1-d*d;if(O>Number.EPSILON){let G=Math.sqrt(O),H=Math.atan2(G,d*A);g=Math.sin(g*H)/G,a=Math.sin(a*H)/G}let P=a*A;if(c=c*g+f*P,h=h*g+u*P,l=l*g+_*P,p=p*g+m*P,g===1-a){let G=1/Math.sqrt(c*c+h*h+l*l+p*p);c*=G,h*=G,l*=G,p*=G}}e[t]=c,e[t+1]=h,e[t+2]=l,e[t+3]=p}static multiplyQuaternionsFlat(e,t,n,s,r,o){let a=n[s],c=n[s+1],h=n[s+2],l=n[s+3],p=r[o],f=r[o+1],u=r[o+2],_=r[o+3];return e[t]=a*_+l*p+c*u-h*f,e[t+1]=c*_+l*f+h*p-a*u,e[t+2]=h*_+l*u+a*f-c*p,e[t+3]=l*_-a*p-c*f-h*u,e}get x(){return this._x}set x(e){this._x=e,this._onChangeCallback()}get y(){return this._y}set y(e){this._y=e,this._onChangeCallback()}get z(){return this._z}set z(e){this._z=e,this._onChangeCallback()}get w(){return this._w}set w(e){this._w=e,this._onChangeCallback()}set(e,t,n,s){return this._x=e,this._y=t,this._z=n,this._w=s,this._onChangeCallback(),this}clone(){return new this.constructor(this._x,this._y,this._z,this._w)}copy(e){return this._x=e.x,this._y=e.y,this._z=e.z,this._w=e.w,this._onChangeCallback(),this}setFromEuler(e,t=!0){let n=e._x,s=e._y,r=e._z,o=e._order,a=Math.cos,c=Math.sin,h=a(n/2),l=a(s/2),p=a(r/2),f=c(n/2),u=c(s/2),_=c(r/2);switch(o){case"XYZ":this._x=f*l*p+h*u*_,this._y=h*u*p-f*l*_,this._z=h*l*_+f*u*p,this._w=h*l*p-f*u*_;break;case"YXZ":this._x=f*l*p+h*u*_,this._y=h*u*p-f*l*_,this._z=h*l*_-f*u*p,this._w=h*l*p+f*u*_;break;case"ZXY":this._x=f*l*p-h*u*_,this._y=h*u*p+f*l*_,this._z=h*l*_+f*u*p,this._w=h*l*p-f*u*_;break;case"ZYX":this._x=f*l*p-h*u*_,this._y=h*u*p+f*l*_,this._z=h*l*_-f*u*p,this._w=h*l*p+f*u*_;break;case"YZX":this._x=f*l*p+h*u*_,this._y=h*u*p+f*l*_,this._z=h*l*_-f*u*p,this._w=h*l*p-f*u*_;break;case"XZY":this._x=f*l*p-h*u*_,this._y=h*u*p-f*l*_,this._z=h*l*_+f*u*p,this._w=h*l*p+f*u*_;break;default:console.warn("THREE.Quaternion: .setFromEuler() encountered an unknown order: "+o)}return t===!0&&this._onChangeCallback(),this}setFromAxisAngle(e,t){let n=t/2,s=Math.sin(n);return this._x=e.x*s,this._y=e.y*s,this._z=e.z*s,this._w=Math.cos(n),this._onChangeCallback(),this}setFromRotationMatrix(e){let t=e.elements,n=t[0],s=t[4],r=t[8],o=t[1],a=t[5],c=t[9],h=t[2],l=t[6],p=t[10],f=n+a+p;if(f>0){let u=.5/Math.sqrt(f+1);this._w=.25/u,this._x=(l-c)*u,this._y=(r-h)*u,this._z=(o-s)*u}else if(n>a&&n>p){let u=2*Math.sqrt(1+n-a-p);this._w=(l-c)/u,this._x=.25*u,this._y=(s+o)/u,this._z=(r+h)/u}else if(a>p){let u=2*Math.sqrt(1+a-n-p);this._w=(r-h)/u,this._x=(s+o)/u,this._y=.25*u,this._z=(c+l)/u}else{let u=2*Math.sqrt(1+p-n-a);this._w=(o-s)/u,this._x=(r+h)/u,this._y=(c+l)/u,this._z=.25*u}return this._onChangeCallback(),this}setFromUnitVectors(e,t){let n=e.dot(t)+1;return n<Number.EPSILON?(n=0,Math.abs(e.x)>Math.abs(e.z)?(this._x=-e.y,this._y=e.x,this._z=0,this._w=n):(this._x=0,this._y=-e.z,this._z=e.y,this._w=n)):(this._x=e.y*t.z-e.z*t.y,this._y=e.z*t.x-e.x*t.z,this._z=e.x*t.y-e.y*t.x,this._w=n),this.normalize()}angleTo(e){return 2*Math.acos(Math.abs(Rt(this.dot(e),-1,1)))}rotateTowards(e,t){let n=this.angleTo(e);if(n===0)return this;let s=Math.min(1,t/n);return this.slerp(e,s),this}identity(){return this.set(0,0,0,1)}invert(){return this.conjugate()}conjugate(){return this._x*=-1,this._y*=-1,this._z*=-1,this._onChangeCallback(),this}dot(e){return this._x*e._x+this._y*e._y+this._z*e._z+this._w*e._w}lengthSq(){return this._x*this._x+this._y*this._y+this._z*this._z+this._w*this._w}length(){return Math.sqrt(this._x*this._x+this._y*this._y+this._z*this._z+this._w*this._w)}normalize(){let e=this.length();return e===0?(this._x=0,this._y=0,this._z=0,this._w=1):(e=1/e,this._x=this._x*e,this._y=this._y*e,this._z=this._z*e,this._w=this._w*e),this._onChangeCallback(),this}multiply(e){return this.multiplyQuaternions(this,e)}premultiply(e){return this.multiplyQuaternions(e,this)}multiplyQuaternions(e,t){let n=e._x,s=e._y,r=e._z,o=e._w,a=t._x,c=t._y,h=t._z,l=t._w;return this._x=n*l+o*a+s*h-r*c,this._y=s*l+o*c+r*a-n*h,this._z=r*l+o*h+n*c-s*a,this._w=o*l-n*a-s*c-r*h,this._onChangeCallback(),this}slerp(e,t){if(t===0)return this;if(t===1)return this.copy(e);let n=this._x,s=this._y,r=this._z,o=this._w,a=o*e._w+n*e._x+s*e._y+r*e._z;if(a<0?(this._w=-e._w,this._x=-e._x,this._y=-e._y,this._z=-e._z,a=-a):this.copy(e),a>=1)return this._w=o,this._x=n,this._y=s,this._z=r,this;let c=1-a*a;if(c<=Number.EPSILON){let u=1-t;return this._w=u*o+t*this._w,this._x=u*n+t*this._x,this._y=u*s+t*this._y,this._z=u*r+t*this._z,this.normalize(),this}let h=Math.sqrt(c),l=Math.atan2(h,a),p=Math.sin((1-t)*l)/h,f=Math.sin(t*l)/h;return this._w=o*p+this._w*f,this._x=n*p+this._x*f,this._y=s*p+this._y*f,this._z=r*p+this._z*f,this._onChangeCallback(),this}slerpQuaternions(e,t,n){return this.copy(e).slerp(t,n)}random(){let e=2*Math.PI*Math.random(),t=2*Math.PI*Math.random(),n=Math.random(),s=Math.sqrt(1-n),r=Math.sqrt(n);return this.set(s*Math.sin(e),s*Math.cos(e),r*Math.sin(t),r*Math.cos(t))}equals(e){return e._x===this._x&&e._y===this._y&&e._z===this._z&&e._w===this._w}fromArray(e,t=0){return this._x=e[t],this._y=e[t+1],this._z=e[t+2],this._w=e[t+3],this._onChangeCallback(),this}toArray(e=[],t=0){return e[t]=this._x,e[t+1]=this._y,e[t+2]=this._z,e[t+3]=this._w,e}fromBufferAttribute(e,t){return this._x=e.getX(t),this._y=e.getY(t),this._z=e.getZ(t),this._w=e.getW(t),this._onChangeCallback(),this}toJSON(){return this.toArray()}_onChange(e){return this._onChangeCallback=e,this}_onChangeCallback(){}*[Symbol.iterator](){yield this._x,yield this._y,yield this._z,yield this._w}},C=class i{constructor(e=0,t=0,n=0){i.prototype.isVector3=!0,this.x=e,this.y=t,this.z=n}set(e,t,n){return n===void 0&&(n=this.z),this.x=e,this.y=t,this.z=n,this}setScalar(e){return this.x=e,this.y=e,this.z=e,this}setX(e){return this.x=e,this}setY(e){return this.y=e,this}setZ(e){return this.z=e,this}setComponent(e,t){switch(e){case 0:this.x=t;break;case 1:this.y=t;break;case 2:this.z=t;break;default:throw new Error("index is out of range: "+e)}return this}getComponent(e){switch(e){case 0:return this.x;case 1:return this.y;case 2:return this.z;default:throw new Error("index is out of range: "+e)}}clone(){return new this.constructor(this.x,this.y,this.z)}copy(e){return this.x=e.x,this.y=e.y,this.z=e.z,this}add(e){return this.x+=e.x,this.y+=e.y,this.z+=e.z,this}addScalar(e){return this.x+=e,this.y+=e,this.z+=e,this}addVectors(e,t){return this.x=e.x+t.x,this.y=e.y+t.y,this.z=e.z+t.z,this}addScaledVector(e,t){return this.x+=e.x*t,this.y+=e.y*t,this.z+=e.z*t,this}sub(e){return this.x-=e.x,this.y-=e.y,this.z-=e.z,this}subScalar(e){return this.x-=e,this.y-=e,this.z-=e,this}subVectors(e,t){return this.x=e.x-t.x,this.y=e.y-t.y,this.z=e.z-t.z,this}multiply(e){return this.x*=e.x,this.y*=e.y,this.z*=e.z,this}multiplyScalar(e){return this.x*=e,this.y*=e,this.z*=e,this}multiplyVectors(e,t){return this.x=e.x*t.x,this.y=e.y*t.y,this.z=e.z*t.z,this}applyEuler(e){return this.applyQuaternion(Qh.setFromEuler(e))}applyAxisAngle(e,t){return this.applyQuaternion(Qh.setFromAxisAngle(e,t))}applyMatrix3(e){let t=this.x,n=this.y,s=this.z,r=e.elements;return this.x=r[0]*t+r[3]*n+r[6]*s,this.y=r[1]*t+r[4]*n+r[7]*s,this.z=r[2]*t+r[5]*n+r[8]*s,this}applyNormalMatrix(e){return this.applyMatrix3(e).normalize()}applyMatrix4(e){let t=this.x,n=this.y,s=this.z,r=e.elements,o=1/(r[3]*t+r[7]*n+r[11]*s+r[15]);return this.x=(r[0]*t+r[4]*n+r[8]*s+r[12])*o,this.y=(r[1]*t+r[5]*n+r[9]*s+r[13])*o,this.z=(r[2]*t+r[6]*n+r[10]*s+r[14])*o,this}applyQuaternion(e){let t=this.x,n=this.y,s=this.z,r=e.x,o=e.y,a=e.z,c=e.w,h=2*(o*s-a*n),l=2*(a*t-r*s),p=2*(r*n-o*t);return this.x=t+c*h+o*p-a*l,this.y=n+c*l+a*h-r*p,this.z=s+c*p+r*l-o*h,this}project(e){return this.applyMatrix4(e.matrixWorldInverse).applyMatrix4(e.projectionMatrix)}unproject(e){return this.applyMatrix4(e.projectionMatrixInverse).applyMatrix4(e.matrixWorld)}transformDirection(e){let t=this.x,n=this.y,s=this.z,r=e.elements;return this.x=r[0]*t+r[4]*n+r[8]*s,this.y=r[1]*t+r[5]*n+r[9]*s,this.z=r[2]*t+r[6]*n+r[10]*s,this.normalize()}divide(e){return this.x/=e.x,this.y/=e.y,this.z/=e.z,this}divideScalar(e){return this.multiplyScalar(1/e)}min(e){return this.x=Math.min(this.x,e.x),this.y=Math.min(this.y,e.y),this.z=Math.min(this.z,e.z),this}max(e){return this.x=Math.max(this.x,e.x),this.y=Math.max(this.y,e.y),this.z=Math.max(this.z,e.z),this}clamp(e,t){return this.x=Math.max(e.x,Math.min(t.x,this.x)),this.y=Math.max(e.y,Math.min(t.y,this.y)),this.z=Math.max(e.z,Math.min(t.z,this.z)),this}clampScalar(e,t){return this.x=Math.max(e,Math.min(t,this.x)),this.y=Math.max(e,Math.min(t,this.y)),this.z=Math.max(e,Math.min(t,this.z)),this}clampLength(e,t){let n=this.length();return this.divideScalar(n||1).multiplyScalar(Math.max(e,Math.min(t,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=Math.trunc(this.x),this.y=Math.trunc(this.y),this.z=Math.trunc(this.z),this}negate(){return this.x=-this.x,this.y=-this.y,this.z=-this.z,this}dot(e){return this.x*e.x+this.y*e.y+this.z*e.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(e){return this.normalize().multiplyScalar(e)}lerp(e,t){return this.x+=(e.x-this.x)*t,this.y+=(e.y-this.y)*t,this.z+=(e.z-this.z)*t,this}lerpVectors(e,t,n){return this.x=e.x+(t.x-e.x)*n,this.y=e.y+(t.y-e.y)*n,this.z=e.z+(t.z-e.z)*n,this}cross(e){return this.crossVectors(this,e)}crossVectors(e,t){let n=e.x,s=e.y,r=e.z,o=t.x,a=t.y,c=t.z;return this.x=s*c-r*a,this.y=r*o-n*c,this.z=n*a-s*o,this}projectOnVector(e){let t=e.lengthSq();if(t===0)return this.set(0,0,0);let n=e.dot(this)/t;return this.copy(e).multiplyScalar(n)}projectOnPlane(e){return Jo.copy(this).projectOnVector(e),this.sub(Jo)}reflect(e){return this.sub(Jo.copy(e).multiplyScalar(2*this.dot(e)))}angleTo(e){let t=Math.sqrt(this.lengthSq()*e.lengthSq());if(t===0)return Math.PI/2;let n=this.dot(e)/t;return Math.acos(Rt(n,-1,1))}distanceTo(e){return Math.sqrt(this.distanceToSquared(e))}distanceToSquared(e){let t=this.x-e.x,n=this.y-e.y,s=this.z-e.z;return t*t+n*n+s*s}manhattanDistanceTo(e){return Math.abs(this.x-e.x)+Math.abs(this.y-e.y)+Math.abs(this.z-e.z)}setFromSpherical(e){return this.setFromSphericalCoords(e.radius,e.phi,e.theta)}setFromSphericalCoords(e,t,n){let s=Math.sin(t)*e;return this.x=s*Math.sin(n),this.y=Math.cos(t)*e,this.z=s*Math.cos(n),this}setFromCylindrical(e){return this.setFromCylindricalCoords(e.radius,e.theta,e.y)}setFromCylindricalCoords(e,t,n){return this.x=e*Math.sin(t),this.y=n,this.z=e*Math.cos(t),this}setFromMatrixPosition(e){let t=e.elements;return this.x=t[12],this.y=t[13],this.z=t[14],this}setFromMatrixScale(e){let t=this.setFromMatrixColumn(e,0).length(),n=this.setFromMatrixColumn(e,1).length(),s=this.setFromMatrixColumn(e,2).length();return this.x=t,this.y=n,this.z=s,this}setFromMatrixColumn(e,t){return this.fromArray(e.elements,t*4)}setFromMatrix3Column(e,t){return this.fromArray(e.elements,t*3)}setFromEuler(e){return this.x=e._x,this.y=e._y,this.z=e._z,this}setFromColor(e){return this.x=e.r,this.y=e.g,this.z=e.b,this}equals(e){return e.x===this.x&&e.y===this.y&&e.z===this.z}fromArray(e,t=0){return this.x=e[t],this.y=e[t+1],this.z=e[t+2],this}toArray(e=[],t=0){return e[t]=this.x,e[t+1]=this.y,e[t+2]=this.z,e}fromBufferAttribute(e,t){return this.x=e.getX(t),this.y=e.getY(t),this.z=e.getZ(t),this}random(){return this.x=Math.random(),this.y=Math.random(),this.z=Math.random(),this}randomDirection(){let e=Math.random()*Math.PI*2,t=Math.random()*2-1,n=Math.sqrt(1-t*t);return this.x=n*Math.cos(e),this.y=t,this.z=n*Math.sin(e),this}*[Symbol.iterator](){yield this.x,yield this.y,yield this.z}},Jo=new C,Qh=new Yt,rn=class{constructor(e=new C(1/0,1/0,1/0),t=new C(-1/0,-1/0,-1/0)){this.isBox3=!0,this.min=e,this.max=t}set(e,t){return this.min.copy(e),this.max.copy(t),this}setFromArray(e){this.makeEmpty();for(let t=0,n=e.length;t<n;t+=3)this.expandByPoint(ln.fromArray(e,t));return this}setFromBufferAttribute(e){this.makeEmpty();for(let t=0,n=e.count;t<n;t++)this.expandByPoint(ln.fromBufferAttribute(e,t));return this}setFromPoints(e){this.makeEmpty();for(let t=0,n=e.length;t<n;t++)this.expandByPoint(e[t]);return this}setFromCenterAndSize(e,t){let n=ln.copy(t).multiplyScalar(.5);return this.min.copy(e).sub(n),this.max.copy(e).add(n),this}setFromObject(e,t=!1){return this.makeEmpty(),this.expandByObject(e,t)}clone(){return new this.constructor().copy(this)}copy(e){return this.min.copy(e.min),this.max.copy(e.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(e){return this.isEmpty()?e.set(0,0,0):e.addVectors(this.min,this.max).multiplyScalar(.5)}getSize(e){return this.isEmpty()?e.set(0,0,0):e.subVectors(this.max,this.min)}expandByPoint(e){return this.min.min(e),this.max.max(e),this}expandByVector(e){return this.min.sub(e),this.max.add(e),this}expandByScalar(e){return this.min.addScalar(-e),this.max.addScalar(e),this}expandByObject(e,t=!1){e.updateWorldMatrix(!1,!1);let n=e.geometry;if(n!==void 0){let r=n.getAttribute("position");if(t===!0&&r!==void 0&&e.isInstancedMesh!==!0)for(let o=0,a=r.count;o<a;o++)e.isMesh===!0?e.getVertexPosition(o,ln):ln.fromBufferAttribute(r,o),ln.applyMatrix4(e.matrixWorld),this.expandByPoint(ln);else e.boundingBox!==void 0?(e.boundingBox===null&&e.computeBoundingBox(),yr.copy(e.boundingBox)):(n.boundingBox===null&&n.computeBoundingBox(),yr.copy(n.boundingBox)),yr.applyMatrix4(e.matrixWorld),this.union(yr)}let s=e.children;for(let r=0,o=s.length;r<o;r++)this.expandByObject(s[r],t);return this}containsPoint(e){return e.x>=this.min.x&&e.x<=this.max.x&&e.y>=this.min.y&&e.y<=this.max.y&&e.z>=this.min.z&&e.z<=this.max.z}containsBox(e){return this.min.x<=e.min.x&&e.max.x<=this.max.x&&this.min.y<=e.min.y&&e.max.y<=this.max.y&&this.min.z<=e.min.z&&e.max.z<=this.max.z}getParameter(e,t){return t.set((e.x-this.min.x)/(this.max.x-this.min.x),(e.y-this.min.y)/(this.max.y-this.min.y),(e.z-this.min.z)/(this.max.z-this.min.z))}intersectsBox(e){return e.max.x>=this.min.x&&e.min.x<=this.max.x&&e.max.y>=this.min.y&&e.min.y<=this.max.y&&e.max.z>=this.min.z&&e.min.z<=this.max.z}intersectsSphere(e){return this.clampPoint(e.center,ln),ln.distanceToSquared(e.center)<=e.radius*e.radius}intersectsPlane(e){let t,n;return e.normal.x>0?(t=e.normal.x*this.min.x,n=e.normal.x*this.max.x):(t=e.normal.x*this.max.x,n=e.normal.x*this.min.x),e.normal.y>0?(t+=e.normal.y*this.min.y,n+=e.normal.y*this.max.y):(t+=e.normal.y*this.max.y,n+=e.normal.y*this.min.y),e.normal.z>0?(t+=e.normal.z*this.min.z,n+=e.normal.z*this.max.z):(t+=e.normal.z*this.max.z,n+=e.normal.z*this.min.z),t<=-e.constant&&n>=-e.constant}intersectsTriangle(e){if(this.isEmpty())return!1;this.getCenter(Bs),Ar.subVectors(this.max,Bs),Ri.subVectors(e.a,Bs),Xi.subVectors(e.b,Bs),Bi.subVectors(e.c,Bs),Zn.subVectors(Xi,Ri),Kn.subVectors(Bi,Xi),_i.subVectors(Ri,Bi);let t=[0,-Zn.z,Zn.y,0,-Kn.z,Kn.y,0,-_i.z,_i.y,Zn.z,0,-Zn.x,Kn.z,0,-Kn.x,_i.z,0,-_i.x,-Zn.y,Zn.x,0,-Kn.y,Kn.x,0,-_i.y,_i.x,0];return!jo(t,Ri,Xi,Bi,Ar)||(t=[1,0,0,0,1,0,0,0,1],!jo(t,Ri,Xi,Bi,Ar))?!1:(vr.crossVectors(Zn,Kn),t=[vr.x,vr.y,vr.z],jo(t,Ri,Xi,Bi,Ar))}clampPoint(e,t){return t.copy(e).clamp(this.min,this.max)}distanceToPoint(e){return this.clampPoint(e,ln).distanceTo(e)}getBoundingSphere(e){return this.isEmpty()?e.makeEmpty():(this.getCenter(e.center),e.radius=this.getSize(ln).length()*.5),e}intersect(e){return this.min.max(e.min),this.max.min(e.max),this.isEmpty()&&this.makeEmpty(),this}union(e){return this.min.min(e.min),this.max.max(e.max),this}applyMatrix4(e){return this.isEmpty()?this:(Wn[0].set(this.min.x,this.min.y,this.min.z).applyMatrix4(e),Wn[1].set(this.min.x,this.min.y,this.max.z).applyMatrix4(e),Wn[2].set(this.min.x,this.max.y,this.min.z).applyMatrix4(e),Wn[3].set(this.min.x,this.max.y,this.max.z).applyMatrix4(e),Wn[4].set(this.max.x,this.min.y,this.min.z).applyMatrix4(e),Wn[5].set(this.max.x,this.min.y,this.max.z).applyMatrix4(e),Wn[6].set(this.max.x,this.max.y,this.min.z).applyMatrix4(e),Wn[7].set(this.max.x,this.max.y,this.max.z).applyMatrix4(e),this.setFromPoints(Wn),this)}translate(e){return this.min.add(e),this.max.add(e),this}equals(e){return e.min.equals(this.min)&&e.max.equals(this.max)}},Wn=[new C,new C,new C,new C,new C,new C,new C,new C],ln=new C,yr=new rn,Ri=new C,Xi=new C,Bi=new C,Zn=new C,Kn=new C,_i=new C,Bs=new C,Ar=new C,vr=new C,mi=new C;function jo(i,e,t,n,s){for(let r=0,o=i.length-3;r<=o;r+=3){mi.fromArray(i,r);let a=s.x*Math.abs(mi.x)+s.y*Math.abs(mi.y)+s.z*Math.abs(mi.z),c=e.dot(mi),h=t.dot(mi),l=n.dot(mi);if(Math.max(-Math.max(c,h,l),Math.min(c,h,l))>a)return!1}return!0}var bf=new rn,$s=new C,Qo=new C,Zt=class{constructor(e=new C,t=-1){this.isSphere=!0,this.center=e,this.radius=t}set(e,t){return this.center.copy(e),this.radius=t,this}setFromPoints(e,t){let n=this.center;t!==void 0?n.copy(t):bf.setFromPoints(e).getCenter(n);let s=0;for(let r=0,o=e.length;r<o;r++)s=Math.max(s,n.distanceToSquared(e[r]));return this.radius=Math.sqrt(s),this}copy(e){return this.center.copy(e.center),this.radius=e.radius,this}isEmpty(){return this.radius<0}makeEmpty(){return this.center.set(0,0,0),this.radius=-1,this}containsPoint(e){return e.distanceToSquared(this.center)<=this.radius*this.radius}distanceToPoint(e){return e.distanceTo(this.center)-this.radius}intersectsSphere(e){let t=this.radius+e.radius;return e.center.distanceToSquared(this.center)<=t*t}intersectsBox(e){return e.intersectsSphere(this)}intersectsPlane(e){return Math.abs(e.distanceToPoint(this.center))<=this.radius}clampPoint(e,t){let n=this.center.distanceToSquared(e);return t.copy(e),n>this.radius*this.radius&&(t.sub(this.center).normalize(),t.multiplyScalar(this.radius).add(this.center)),t}getBoundingBox(e){return this.isEmpty()?(e.makeEmpty(),e):(e.set(this.center,this.center),e.expandByScalar(this.radius),e)}applyMatrix4(e){return this.center.applyMatrix4(e),this.radius=this.radius*e.getMaxScaleOnAxis(),this}translate(e){return this.center.add(e),this}expandByPoint(e){if(this.isEmpty())return this.center.copy(e),this.radius=0,this;$s.subVectors(e,this.center);let t=$s.lengthSq();if(t>this.radius*this.radius){let n=Math.sqrt(t),s=(n-this.radius)*.5;this.center.addScaledVector($s,s/n),this.radius+=s}return this}union(e){return e.isEmpty()?this:this.isEmpty()?(this.copy(e),this):(this.center.equals(e.center)===!0?this.radius=Math.max(this.radius,e.radius):(Qo.subVectors(e.center,this.center).setLength(e.radius),this.expandByPoint($s.copy(e.center).add(Qo)),this.expandByPoint($s.copy(e.center).sub(Qo))),this)}equals(e){return e.center.equals(this.center)&&e.radius===this.radius}clone(){return new this.constructor().copy(this)}},Mn=new C,Yo=new C,Hr=new C,ei=new C,Zo=new C,Sr=new C,Ko=new C,Hi=class{constructor(e=new C,t=new C(0,0,-1)){this.origin=e,this.direction=t}set(e,t){return this.origin.copy(e),this.direction.copy(t),this}copy(e){return this.origin.copy(e.origin),this.direction.copy(e.direction),this}at(e,t){return t.copy(this.origin).addScaledVector(this.direction,e)}lookAt(e){return this.direction.copy(e).sub(this.origin).normalize(),this}recast(e){return this.origin.copy(this.at(e,Mn)),this}closestPointToPoint(e,t){t.subVectors(e,this.origin);let n=t.dot(this.direction);return n<0?t.copy(this.origin):t.copy(this.origin).addScaledVector(this.direction,n)}distanceToPoint(e){return Math.sqrt(this.distanceSqToPoint(e))}distanceSqToPoint(e){let t=Mn.subVectors(e,this.origin).dot(this.direction);return t<0?this.origin.distanceToSquared(e):(Mn.copy(this.origin).addScaledVector(this.direction,t),Mn.distanceToSquared(e))}distanceSqToSegment(e,t,n,s){Yo.copy(e).add(t).multiplyScalar(.5),Hr.copy(t).sub(e).normalize(),ei.copy(this.origin).sub(Yo);let r=e.distanceTo(t)*.5,o=-this.direction.dot(Hr),a=ei.dot(this.direction),c=-ei.dot(Hr),h=ei.lengthSq(),l=Math.abs(1-o*o),p,f,u,_;if(l>0)if(p=o*c-a,f=o*a-c,_=r*l,p>=0)if(f>=-_)if(f<=_){let m=1/l;p*=m,f*=m,u=p*(p+o*f+2*a)+f*(o*p+f+2*c)+h}else f=r,p=Math.max(0,-(o*f+a)),u=-p*p+f*(f+2*c)+h;else f=-r,p=Math.max(0,-(o*f+a)),u=-p*p+f*(f+2*c)+h;else f<=-_?(p=Math.max(0,-(-o*r+a)),f=p>0?-r:Math.min(Math.max(-r,-c),r),u=-p*p+f*(f+2*c)+h):f<=_?(p=0,f=Math.min(Math.max(-r,-c),r),u=f*(f+2*c)+h):(p=Math.max(0,-(o*r+a)),f=p>0?r:Math.min(Math.max(-r,-c),r),u=-p*p+f*(f+2*c)+h);else f=o>0?-r:r,p=Math.max(0,-(o*f+a)),u=-p*p+f*(f+2*c)+h;return n&&n.copy(this.origin).addScaledVector(this.direction,p),s&&s.copy(Yo).addScaledVector(Hr,f),u}intersectSphere(e,t){Mn.subVectors(e.center,this.origin);let n=Mn.dot(this.direction),s=Mn.dot(Mn)-n*n,r=e.radius*e.radius;if(s>r)return null;let o=Math.sqrt(r-s),a=n-o,c=n+o;return c<0?null:a<0?this.at(c,t):this.at(a,t)}intersectsSphere(e){return this.distanceSqToPoint(e.center)<=e.radius*e.radius}distanceToPlane(e){let t=e.normal.dot(this.direction);if(t===0)return e.distanceToPoint(this.origin)===0?0:null;let n=-(this.origin.dot(e.normal)+e.constant)/t;return n>=0?n:null}intersectPlane(e,t){let n=this.distanceToPlane(e);return n===null?null:this.at(n,t)}intersectsPlane(e){let t=e.distanceToPoint(this.origin);return t===0||e.normal.dot(this.direction)*t<0}intersectBox(e,t){let n,s,r,o,a,c,h=1/this.direction.x,l=1/this.direction.y,p=1/this.direction.z,f=this.origin;return h>=0?(n=(e.min.x-f.x)*h,s=(e.max.x-f.x)*h):(n=(e.max.x-f.x)*h,s=(e.min.x-f.x)*h),l>=0?(r=(e.min.y-f.y)*l,o=(e.max.y-f.y)*l):(r=(e.max.y-f.y)*l,o=(e.min.y-f.y)*l),n>o||r>s||((r>n||isNaN(n))&&(n=r),(o<s||isNaN(s))&&(s=o),p>=0?(a=(e.min.z-f.z)*p,c=(e.max.z-f.z)*p):(a=(e.max.z-f.z)*p,c=(e.min.z-f.z)*p),n>c||a>s)||((a>n||n!==n)&&(n=a),(c<s||s!==s)&&(s=c),s<0)?null:this.at(n>=0?n:s,t)}intersectsBox(e){return this.intersectBox(e,Mn)!==null}intersectTriangle(e,t,n,s,r){Zo.subVectors(t,e),Sr.subVectors(n,e),Ko.crossVectors(Zo,Sr);let o=this.direction.dot(Ko),a;if(o>0){if(s)return null;a=1}else if(o<0)a=-1,o=-o;else return null;ei.subVectors(this.origin,e);let c=a*this.direction.dot(Sr.crossVectors(ei,Sr));if(c<0)return null;let h=a*this.direction.dot(Zo.cross(ei));if(h<0||c+h>o)return null;let l=-a*ei.dot(Ko);return l<0?null:this.at(l/o,r)}applyMatrix4(e){return this.origin.applyMatrix4(e),this.direction.transformDirection(e),this}equals(e){return e.origin.equals(this.origin)&&e.direction.equals(this.direction)}clone(){return new this.constructor().copy(this)}},Fe=class i{constructor(e,t,n,s,r,o,a,c,h,l,p,f,u,_,m,g){i.prototype.isMatrix4=!0,this.elements=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1],e!==void 0&&this.set(e,t,n,s,r,o,a,c,h,l,p,f,u,_,m,g)}set(e,t,n,s,r,o,a,c,h,l,p,f,u,_,m,g){let d=this.elements;return d[0]=e,d[4]=t,d[8]=n,d[12]=s,d[1]=r,d[5]=o,d[9]=a,d[13]=c,d[2]=h,d[6]=l,d[10]=p,d[14]=f,d[3]=u,d[7]=_,d[11]=m,d[15]=g,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 i().fromArray(this.elements)}copy(e){let t=this.elements,n=e.elements;return t[0]=n[0],t[1]=n[1],t[2]=n[2],t[3]=n[3],t[4]=n[4],t[5]=n[5],t[6]=n[6],t[7]=n[7],t[8]=n[8],t[9]=n[9],t[10]=n[10],t[11]=n[11],t[12]=n[12],t[13]=n[13],t[14]=n[14],t[15]=n[15],this}copyPosition(e){let t=this.elements,n=e.elements;return t[12]=n[12],t[13]=n[13],t[14]=n[14],this}setFromMatrix3(e){let t=e.elements;return this.set(t[0],t[3],t[6],0,t[1],t[4],t[7],0,t[2],t[5],t[8],0,0,0,0,1),this}extractBasis(e,t,n){return e.setFromMatrixColumn(this,0),t.setFromMatrixColumn(this,1),n.setFromMatrixColumn(this,2),this}makeBasis(e,t,n){return this.set(e.x,t.x,n.x,0,e.y,t.y,n.y,0,e.z,t.z,n.z,0,0,0,0,1),this}extractRotation(e){let t=this.elements,n=e.elements,s=1/$i.setFromMatrixColumn(e,0).length(),r=1/$i.setFromMatrixColumn(e,1).length(),o=1/$i.setFromMatrixColumn(e,2).length();return t[0]=n[0]*s,t[1]=n[1]*s,t[2]=n[2]*s,t[3]=0,t[4]=n[4]*r,t[5]=n[5]*r,t[6]=n[6]*r,t[7]=0,t[8]=n[8]*o,t[9]=n[9]*o,t[10]=n[10]*o,t[11]=0,t[12]=0,t[13]=0,t[14]=0,t[15]=1,this}makeRotationFromEuler(e){let t=this.elements,n=e.x,s=e.y,r=e.z,o=Math.cos(n),a=Math.sin(n),c=Math.cos(s),h=Math.sin(s),l=Math.cos(r),p=Math.sin(r);if(e.order==="XYZ"){let f=o*l,u=o*p,_=a*l,m=a*p;t[0]=c*l,t[4]=-c*p,t[8]=h,t[1]=u+_*h,t[5]=f-m*h,t[9]=-a*c,t[2]=m-f*h,t[6]=_+u*h,t[10]=o*c}else if(e.order==="YXZ"){let f=c*l,u=c*p,_=h*l,m=h*p;t[0]=f+m*a,t[4]=_*a-u,t[8]=o*h,t[1]=o*p,t[5]=o*l,t[9]=-a,t[2]=u*a-_,t[6]=m+f*a,t[10]=o*c}else if(e.order==="ZXY"){let f=c*l,u=c*p,_=h*l,m=h*p;t[0]=f-m*a,t[4]=-o*p,t[8]=_+u*a,t[1]=u+_*a,t[5]=o*l,t[9]=m-f*a,t[2]=-o*h,t[6]=a,t[10]=o*c}else if(e.order==="ZYX"){let f=o*l,u=o*p,_=a*l,m=a*p;t[0]=c*l,t[4]=_*h-u,t[8]=f*h+m,t[1]=c*p,t[5]=m*h+f,t[9]=u*h-_,t[2]=-h,t[6]=a*c,t[10]=o*c}else if(e.order==="YZX"){let f=o*c,u=o*h,_=a*c,m=a*h;t[0]=c*l,t[4]=m-f*p,t[8]=_*p+u,t[1]=p,t[5]=o*l,t[9]=-a*l,t[2]=-h*l,t[6]=u*p+_,t[10]=f-m*p}else if(e.order==="XZY"){let f=o*c,u=o*h,_=a*c,m=a*h;t[0]=c*l,t[4]=-p,t[8]=h*l,t[1]=f*p+m,t[5]=o*l,t[9]=u*p-_,t[2]=_*p-u,t[6]=a*l,t[10]=m*p+f}return t[3]=0,t[7]=0,t[11]=0,t[12]=0,t[13]=0,t[14]=0,t[15]=1,this}makeRotationFromQuaternion(e){return this.compose(Pf,e,wf)}lookAt(e,t,n){let s=this.elements;return jt.subVectors(e,t),jt.lengthSq()===0&&(jt.z=1),jt.normalize(),ti.crossVectors(n,jt),ti.lengthSq()===0&&(Math.abs(n.z)===1?jt.x+=1e-4:jt.z+=1e-4,jt.normalize(),ti.crossVectors(n,jt)),ti.normalize(),Er.crossVectors(jt,ti),s[0]=ti.x,s[4]=Er.x,s[8]=jt.x,s[1]=ti.y,s[5]=Er.y,s[9]=jt.y,s[2]=ti.z,s[6]=Er.z,s[10]=jt.z,this}multiply(e){return this.multiplyMatrices(this,e)}premultiply(e){return this.multiplyMatrices(e,this)}multiplyMatrices(e,t){let n=e.elements,s=t.elements,r=this.elements,o=n[0],a=n[4],c=n[8],h=n[12],l=n[1],p=n[5],f=n[9],u=n[13],_=n[2],m=n[6],g=n[10],d=n[14],A=n[3],O=n[7],P=n[11],G=n[15],H=s[0],S=s[4],M=s[8],y=s[12],w=s[1],W=s[5],$=s[9],F=s[13],k=s[2],Q=s[6],V=s[10],te=s[14],z=s[3],pe=s[7],me=s[11],He=s[15];return r[0]=o*H+a*w+c*k+h*z,r[4]=o*S+a*W+c*Q+h*pe,r[8]=o*M+a*$+c*V+h*me,r[12]=o*y+a*F+c*te+h*He,r[1]=l*H+p*w+f*k+u*z,r[5]=l*S+p*W+f*Q+u*pe,r[9]=l*M+p*$+f*V+u*me,r[13]=l*y+p*F+f*te+u*He,r[2]=_*H+m*w+g*k+d*z,r[6]=_*S+m*W+g*Q+d*pe,r[10]=_*M+m*$+g*V+d*me,r[14]=_*y+m*F+g*te+d*He,r[3]=A*H+O*w+P*k+G*z,r[7]=A*S+O*W+P*Q+G*pe,r[11]=A*M+O*$+P*V+G*me,r[15]=A*y+O*F+P*te+G*He,this}multiplyScalar(e){let t=this.elements;return t[0]*=e,t[4]*=e,t[8]*=e,t[12]*=e,t[1]*=e,t[5]*=e,t[9]*=e,t[13]*=e,t[2]*=e,t[6]*=e,t[10]*=e,t[14]*=e,t[3]*=e,t[7]*=e,t[11]*=e,t[15]*=e,this}determinant(){let e=this.elements,t=e[0],n=e[4],s=e[8],r=e[12],o=e[1],a=e[5],c=e[9],h=e[13],l=e[2],p=e[6],f=e[10],u=e[14],_=e[3],m=e[7],g=e[11],d=e[15];return _*(+r*c*p-s*h*p-r*a*f+n*h*f+s*a*u-n*c*u)+m*(+t*c*u-t*h*f+r*o*f-s*o*u+s*h*l-r*c*l)+g*(+t*h*p-t*a*u-r*o*p+n*o*u+r*a*l-n*h*l)+d*(-s*a*l-t*c*p+t*a*f+s*o*p-n*o*f+n*c*l)}transpose(){let e=this.elements,t;return t=e[1],e[1]=e[4],e[4]=t,t=e[2],e[2]=e[8],e[8]=t,t=e[6],e[6]=e[9],e[9]=t,t=e[3],e[3]=e[12],e[12]=t,t=e[7],e[7]=e[13],e[13]=t,t=e[11],e[11]=e[14],e[14]=t,this}setPosition(e,t,n){let s=this.elements;return e.isVector3?(s[12]=e.x,s[13]=e.y,s[14]=e.z):(s[12]=e,s[13]=t,s[14]=n),this}invert(){let e=this.elements,t=e[0],n=e[1],s=e[2],r=e[3],o=e[4],a=e[5],c=e[6],h=e[7],l=e[8],p=e[9],f=e[10],u=e[11],_=e[12],m=e[13],g=e[14],d=e[15],A=p*g*h-m*f*h+m*c*u-a*g*u-p*c*d+a*f*d,O=_*f*h-l*g*h-_*c*u+o*g*u+l*c*d-o*f*d,P=l*m*h-_*p*h+_*a*u-o*m*u-l*a*d+o*p*d,G=_*p*c-l*m*c-_*a*f+o*m*f+l*a*g-o*p*g,H=t*A+n*O+s*P+r*G;if(H===0)return this.set(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0);let S=1/H;return e[0]=A*S,e[1]=(m*f*r-p*g*r-m*s*u+n*g*u+p*s*d-n*f*d)*S,e[2]=(a*g*r-m*c*r+m*s*h-n*g*h-a*s*d+n*c*d)*S,e[3]=(p*c*r-a*f*r-p*s*h+n*f*h+a*s*u-n*c*u)*S,e[4]=O*S,e[5]=(l*g*r-_*f*r+_*s*u-t*g*u-l*s*d+t*f*d)*S,e[6]=(_*c*r-o*g*r-_*s*h+t*g*h+o*s*d-t*c*d)*S,e[7]=(o*f*r-l*c*r+l*s*h-t*f*h-o*s*u+t*c*u)*S,e[8]=P*S,e[9]=(_*p*r-l*m*r-_*n*u+t*m*u+l*n*d-t*p*d)*S,e[10]=(o*m*r-_*a*r+_*n*h-t*m*h-o*n*d+t*a*d)*S,e[11]=(l*a*r-o*p*r-l*n*h+t*p*h+o*n*u-t*a*u)*S,e[12]=G*S,e[13]=(l*m*s-_*p*s+_*n*f-t*m*f-l*n*g+t*p*g)*S,e[14]=(_*a*s-o*m*s-_*n*c+t*m*c+o*n*g-t*a*g)*S,e[15]=(o*p*s-l*a*s+l*n*c-t*p*c-o*n*f+t*a*f)*S,this}scale(e){let t=this.elements,n=e.x,s=e.y,r=e.z;return t[0]*=n,t[4]*=s,t[8]*=r,t[1]*=n,t[5]*=s,t[9]*=r,t[2]*=n,t[6]*=s,t[10]*=r,t[3]*=n,t[7]*=s,t[11]*=r,this}getMaxScaleOnAxis(){let e=this.elements,t=e[0]*e[0]+e[1]*e[1]+e[2]*e[2],n=e[4]*e[4]+e[5]*e[5]+e[6]*e[6],s=e[8]*e[8]+e[9]*e[9]+e[10]*e[10];return Math.sqrt(Math.max(t,n,s))}makeTranslation(e,t,n){return e.isVector3?this.set(1,0,0,e.x,0,1,0,e.y,0,0,1,e.z,0,0,0,1):this.set(1,0,0,e,0,1,0,t,0,0,1,n,0,0,0,1),this}makeRotationX(e){let t=Math.cos(e),n=Math.sin(e);return this.set(1,0,0,0,0,t,-n,0,0,n,t,0,0,0,0,1),this}makeRotationY(e){let t=Math.cos(e),n=Math.sin(e);return this.set(t,0,n,0,0,1,0,0,-n,0,t,0,0,0,0,1),this}makeRotationZ(e){let t=Math.cos(e),n=Math.sin(e);return this.set(t,-n,0,0,n,t,0,0,0,0,1,0,0,0,0,1),this}makeRotationAxis(e,t){let n=Math.cos(t),s=Math.sin(t),r=1-n,o=e.x,a=e.y,c=e.z,h=r*o,l=r*a;return this.set(h*o+n,h*a-s*c,h*c+s*a,0,h*a+s*c,l*a+n,l*c-s*o,0,h*c-s*a,l*c+s*o,r*c*c+n,0,0,0,0,1),this}makeScale(e,t,n){return this.set(e,0,0,0,0,t,0,0,0,0,n,0,0,0,0,1),this}makeShear(e,t,n,s,r,o){return this.set(1,n,r,0,e,1,o,0,t,s,1,0,0,0,0,1),this}compose(e,t,n){let s=this.elements,r=t._x,o=t._y,a=t._z,c=t._w,h=r+r,l=o+o,p=a+a,f=r*h,u=r*l,_=r*p,m=o*l,g=o*p,d=a*p,A=c*h,O=c*l,P=c*p,G=n.x,H=n.y,S=n.z;return s[0]=(1-(m+d))*G,s[1]=(u+P)*G,s[2]=(_-O)*G,s[3]=0,s[4]=(u-P)*H,s[5]=(1-(f+d))*H,s[6]=(g+A)*H,s[7]=0,s[8]=(_+O)*S,s[9]=(g-A)*S,s[10]=(1-(f+m))*S,s[11]=0,s[12]=e.x,s[13]=e.y,s[14]=e.z,s[15]=1,this}decompose(e,t,n){let s=this.elements,r=$i.set(s[0],s[1],s[2]).length(),o=$i.set(s[4],s[5],s[6]).length(),a=$i.set(s[8],s[9],s[10]).length();this.determinant()<0&&(r=-r),e.x=s[12],e.y=s[13],e.z=s[14],pn.copy(this);let h=1/r,l=1/o,p=1/a;return pn.elements[0]*=h,pn.elements[1]*=h,pn.elements[2]*=h,pn.elements[4]*=l,pn.elements[5]*=l,pn.elements[6]*=l,pn.elements[8]*=p,pn.elements[9]*=p,pn.elements[10]*=p,t.setFromRotationMatrix(pn),n.x=r,n.y=o,n.z=a,this}makePerspective(e,t,n,s,r,o,a=Bn){let c=this.elements,h=2*r/(t-e),l=2*r/(n-s),p=(t+e)/(t-e),f=(n+s)/(n-s),u,_;if(a===Bn)u=-(o+r)/(o-r),_=-2*o*r/(o-r);else if(a===ro)u=-o/(o-r),_=-o*r/(o-r);else throw new Error("THREE.Matrix4.makePerspective(): Invalid coordinate system: "+a);return c[0]=h,c[4]=0,c[8]=p,c[12]=0,c[1]=0,c[5]=l,c[9]=f,c[13]=0,c[2]=0,c[6]=0,c[10]=u,c[14]=_,c[3]=0,c[7]=0,c[11]=-1,c[15]=0,this}makeOrthographic(e,t,n,s,r,o,a=Bn){let c=this.elements,h=1/(t-e),l=1/(n-s),p=1/(o-r),f=(t+e)*h,u=(n+s)*l,_,m;if(a===Bn)_=(o+r)*p,m=-2*p;else if(a===ro)_=r*p,m=-1*p;else throw new Error("THREE.Matrix4.makeOrthographic(): Invalid coordinate system: "+a);return c[0]=2*h,c[4]=0,c[8]=0,c[12]=-f,c[1]=0,c[5]=2*l,c[9]=0,c[13]=-u,c[2]=0,c[6]=0,c[10]=m,c[14]=-_,c[3]=0,c[7]=0,c[11]=0,c[15]=1,this}equals(e){let t=this.elements,n=e.elements;for(let s=0;s<16;s++)if(t[s]!==n[s])return!1;return!0}fromArray(e,t=0){for(let n=0;n<16;n++)this.elements[n]=e[n+t];return this}toArray(e=[],t=0){let n=this.elements;return e[t]=n[0],e[t+1]=n[1],e[t+2]=n[2],e[t+3]=n[3],e[t+4]=n[4],e[t+5]=n[5],e[t+6]=n[6],e[t+7]=n[7],e[t+8]=n[8],e[t+9]=n[9],e[t+10]=n[10],e[t+11]=n[11],e[t+12]=n[12],e[t+13]=n[13],e[t+14]=n[14],e[t+15]=n[15],e}},$i=new C,pn=new Fe,Pf=new C(0,0,0),wf=new C(1,1,1),ti=new C,Er=new C,jt=new C,Yh=new Fe,Zh=new Yt,yn=class i{constructor(e=0,t=0,n=0,s=i.DEFAULT_ORDER){this.isEuler=!0,this._x=e,this._y=t,this._z=n,this._order=s}get x(){return this._x}set x(e){this._x=e,this._onChangeCallback()}get y(){return this._y}set y(e){this._y=e,this._onChangeCallback()}get z(){return this._z}set z(e){this._z=e,this._onChangeCallback()}get order(){return this._order}set order(e){this._order=e,this._onChangeCallback()}set(e,t,n,s=this._order){return this._x=e,this._y=t,this._z=n,this._order=s,this._onChangeCallback(),this}clone(){return new this.constructor(this._x,this._y,this._z,this._order)}copy(e){return this._x=e._x,this._y=e._y,this._z=e._z,this._order=e._order,this._onChangeCallback(),this}setFromRotationMatrix(e,t=this._order,n=!0){let s=e.elements,r=s[0],o=s[4],a=s[8],c=s[1],h=s[5],l=s[9],p=s[2],f=s[6],u=s[10];switch(t){case"XYZ":this._y=Math.asin(Rt(a,-1,1)),Math.abs(a)<.9999999?(this._x=Math.atan2(-l,u),this._z=Math.atan2(-o,r)):(this._x=Math.atan2(f,h),this._z=0);break;case"YXZ":this._x=Math.asin(-Rt(l,-1,1)),Math.abs(l)<.9999999?(this._y=Math.atan2(a,u),this._z=Math.atan2(c,h)):(this._y=Math.atan2(-p,r),this._z=0);break;case"ZXY":this._x=Math.asin(Rt(f,-1,1)),Math.abs(f)<.9999999?(this._y=Math.atan2(-p,u),this._z=Math.atan2(-o,h)):(this._y=0,this._z=Math.atan2(c,r));break;case"ZYX":this._y=Math.asin(-Rt(p,-1,1)),Math.abs(p)<.9999999?(this._x=Math.atan2(f,u),this._z=Math.atan2(c,r)):(this._x=0,this._z=Math.atan2(-o,h));break;case"YZX":this._z=Math.asin(Rt(c,-1,1)),Math.abs(c)<.9999999?(this._x=Math.atan2(-l,h),this._y=Math.atan2(-p,r)):(this._x=0,this._y=Math.atan2(a,u));break;case"XZY":this._z=Math.asin(-Rt(o,-1,1)),Math.abs(o)<.9999999?(this._x=Math.atan2(f,h),this._y=Math.atan2(a,r)):(this._x=Math.atan2(-l,u),this._y=0);break;default:console.warn("THREE.Euler: .setFromRotationMatrix() encountered an unknown order: "+t)}return this._order=t,n===!0&&this._onChangeCallback(),this}setFromQuaternion(e,t,n){return Yh.makeRotationFromQuaternion(e),this.setFromRotationMatrix(Yh,t,n)}setFromVector3(e,t=this._order){return this.set(e.x,e.y,e.z,t)}reorder(e){return Zh.setFromEuler(this),this.setFromQuaternion(Zh,e)}equals(e){return e._x===this._x&&e._y===this._y&&e._z===this._z&&e._order===this._order}fromArray(e){return this._x=e[0],this._y=e[1],this._z=e[2],e[3]!==void 0&&(this._order=e[3]),this._onChangeCallback(),this}toArray(e=[],t=0){return e[t]=this._x,e[t+1]=this._y,e[t+2]=this._z,e[t+3]=this._order,e}_onChange(e){return this._onChangeCallback=e,this}_onChangeCallback(){}*[Symbol.iterator](){yield this._x,yield this._y,yield this._z,yield this._order}};yn.DEFAULT_ORDER="XYZ";var sr=class{constructor(){this.mask=1}set(e){this.mask=(1<<e|0)>>>0}enable(e){this.mask|=1<<e|0}enableAll(){this.mask=-1}toggle(e){this.mask^=1<<e|0}disable(e){this.mask&=~(1<<e|0)}disableAll(){this.mask=0}test(e){return(this.mask&e.mask)!==0}isEnabled(e){return(this.mask&(1<<e|0))!==0}},Of=0,Kh=new C,Fi=new Yt,Cn=new Fe,Wr=new C,Fs=new C,yf=new C,Af=new Yt,el=new C(1,0,0),tl=new C(0,1,0),nl=new C(0,0,1),il={type:"added"},vf={type:"removed"},Di={type:"childadded",child:null},ea={type:"childremoved",child:null},mt=class i extends ci{constructor(){super(),this.isObject3D=!0,Object.defineProperty(this,"id",{value:Of++}),this.uuid=_n(),this.name="",this.type="Object3D",this.parent=null,this.children=[],this.up=i.DEFAULT_UP.clone();let e=new C,t=new yn,n=new Yt,s=new C(1,1,1);function r(){n.setFromEuler(t,!1)}function o(){t.setFromQuaternion(n,void 0,!1)}t._onChange(r),n._onChange(o),Object.defineProperties(this,{position:{configurable:!0,enumerable:!0,value:e},rotation:{configurable:!0,enumerable:!0,value:t},quaternion:{configurable:!0,enumerable:!0,value:n},scale:{configurable:!0,enumerable:!0,value:s},modelViewMatrix:{value:new Fe},normalMatrix:{value:new ze}}),this.matrix=new Fe,this.matrixWorld=new Fe,this.matrixAutoUpdate=i.DEFAULT_MATRIX_AUTO_UPDATE,this.matrixWorldAutoUpdate=i.DEFAULT_MATRIX_WORLD_AUTO_UPDATE,this.matrixWorldNeedsUpdate=!1,this.layers=new sr,this.visible=!0,this.castShadow=!1,this.receiveShadow=!1,this.frustumCulled=!0,this.renderOrder=0,this.animations=[],this.userData={}}onBeforeShadow(){}onAfterShadow(){}onBeforeRender(){}onAfterRender(){}applyMatrix4(e){this.matrixAutoUpdate&&this.updateMatrix(),this.matrix.premultiply(e),this.matrix.decompose(this.position,this.quaternion,this.scale)}applyQuaternion(e){return this.quaternion.premultiply(e),this}setRotationFromAxisAngle(e,t){this.quaternion.setFromAxisAngle(e,t)}setRotationFromEuler(e){this.quaternion.setFromEuler(e,!0)}setRotationFromMatrix(e){this.quaternion.setFromRotationMatrix(e)}setRotationFromQuaternion(e){this.quaternion.copy(e)}rotateOnAxis(e,t){return Fi.setFromAxisAngle(e,t),this.quaternion.multiply(Fi),this}rotateOnWorldAxis(e,t){return Fi.setFromAxisAngle(e,t),this.quaternion.premultiply(Fi),this}rotateX(e){return this.rotateOnAxis(el,e)}rotateY(e){return this.rotateOnAxis(tl,e)}rotateZ(e){return this.rotateOnAxis(nl,e)}translateOnAxis(e,t){return Kh.copy(e).applyQuaternion(this.quaternion),this.position.add(Kh.multiplyScalar(t)),this}translateX(e){return this.translateOnAxis(el,e)}translateY(e){return this.translateOnAxis(tl,e)}translateZ(e){return this.translateOnAxis(nl,e)}localToWorld(e){return this.updateWorldMatrix(!0,!1),e.applyMatrix4(this.matrixWorld)}worldToLocal(e){return this.updateWorldMatrix(!0,!1),e.applyMatrix4(Cn.copy(this.matrixWorld).invert())}lookAt(e,t,n){e.isVector3?Wr.copy(e):Wr.set(e,t,n);let s=this.parent;this.updateWorldMatrix(!0,!1),Fs.setFromMatrixPosition(this.matrixWorld),this.isCamera||this.isLight?Cn.lookAt(Fs,Wr,this.up):Cn.lookAt(Wr,Fs,this.up),this.quaternion.setFromRotationMatrix(Cn),s&&(Cn.extractRotation(s.matrixWorld),Fi.setFromRotationMatrix(Cn),this.quaternion.premultiply(Fi.invert()))}add(e){if(arguments.length>1){for(let t=0;t<arguments.length;t++)this.add(arguments[t]);return this}return e===this?(console.error("THREE.Object3D.add: object can't be added as a child of itself.",e),this):(e&&e.isObject3D?(e.removeFromParent(),e.parent=this,this.children.push(e),e.dispatchEvent(il),Di.child=e,this.dispatchEvent(Di),Di.child=null):console.error("THREE.Object3D.add: object not an instance of THREE.Object3D.",e),this)}remove(e){if(arguments.length>1){for(let n=0;n<arguments.length;n++)this.remove(arguments[n]);return this}let t=this.children.indexOf(e);return t!==-1&&(e.parent=null,this.children.splice(t,1),e.dispatchEvent(vf),ea.child=e,this.dispatchEvent(ea),ea.child=null),this}removeFromParent(){let e=this.parent;return e!==null&&e.remove(this),this}clear(){return this.remove(...this.children)}attach(e){return this.updateWorldMatrix(!0,!1),Cn.copy(this.matrixWorld).invert(),e.parent!==null&&(e.parent.updateWorldMatrix(!0,!1),Cn.multiply(e.parent.matrixWorld)),e.applyMatrix4(Cn),e.removeFromParent(),e.parent=this,this.children.push(e),e.updateWorldMatrix(!1,!0),e.dispatchEvent(il),Di.child=e,this.dispatchEvent(Di),Di.child=null,this}getObjectById(e){return this.getObjectByProperty("id",e)}getObjectByName(e){return this.getObjectByProperty("name",e)}getObjectByProperty(e,t){if(this[e]===t)return this;for(let n=0,s=this.children.length;n<s;n++){let o=this.children[n].getObjectByProperty(e,t);if(o!==void 0)return o}}getObjectsByProperty(e,t,n=[]){this[e]===t&&n.push(this);let s=this.children;for(let r=0,o=s.length;r<o;r++)s[r].getObjectsByProperty(e,t,n);return n}getWorldPosition(e){return this.updateWorldMatrix(!0,!1),e.setFromMatrixPosition(this.matrixWorld)}getWorldQuaternion(e){return this.updateWorldMatrix(!0,!1),this.matrixWorld.decompose(Fs,e,yf),e}getWorldScale(e){return this.updateWorldMatrix(!0,!1),this.matrixWorld.decompose(Fs,Af,e),e}getWorldDirection(e){this.updateWorldMatrix(!0,!1);let t=this.matrixWorld.elements;return e.set(t[8],t[9],t[10]).normalize()}raycast(){}traverse(e){e(this);let t=this.children;for(let n=0,s=t.length;n<s;n++)t[n].traverse(e)}traverseVisible(e){if(this.visible===!1)return;e(this);let t=this.children;for(let n=0,s=t.length;n<s;n++)t[n].traverseVisible(e)}traverseAncestors(e){let t=this.parent;t!==null&&(e(t),t.traverseAncestors(e))}updateMatrix(){this.matrix.compose(this.position,this.quaternion,this.scale),this.matrixWorldNeedsUpdate=!0}updateMatrixWorld(e){this.matrixAutoUpdate&&this.updateMatrix(),(this.matrixWorldNeedsUpdate||e)&&(this.matrixWorldAutoUpdate===!0&&(this.parent===null?this.matrixWorld.copy(this.matrix):this.matrixWorld.multiplyMatrices(this.parent.matrixWorld,this.matrix)),this.matrixWorldNeedsUpdate=!1,e=!0);let t=this.children;for(let n=0,s=t.length;n<s;n++)t[n].updateMatrixWorld(e)}updateWorldMatrix(e,t){let n=this.parent;if(e===!0&&n!==null&&n.updateWorldMatrix(!0,!1),this.matrixAutoUpdate&&this.updateMatrix(),this.matrixWorldAutoUpdate===!0&&(this.parent===null?this.matrixWorld.copy(this.matrix):this.matrixWorld.multiplyMatrices(this.parent.matrixWorld,this.matrix)),t===!0){let s=this.children;for(let r=0,o=s.length;r<o;r++)s[r].updateWorldMatrix(!1,!0)}}toJSON(e){let t=e===void 0||typeof e=="string",n={};t&&(e={geometries:{},materials:{},textures:{},images:{},shapes:{},skeletons:{},animations:{},nodes:{}},n.metadata={version:4.6,type:"Object",generator:"Object3D.toJSON"});let s={};s.uuid=this.uuid,s.type=this.type,this.name!==""&&(s.name=this.name),this.castShadow===!0&&(s.castShadow=!0),this.receiveShadow===!0&&(s.receiveShadow=!0),this.visible===!1&&(s.visible=!1),this.frustumCulled===!1&&(s.frustumCulled=!1),this.renderOrder!==0&&(s.renderOrder=this.renderOrder),Object.keys(this.userData).length>0&&(s.userData=this.userData),s.layers=this.layers.mask,s.matrix=this.matrix.toArray(),s.up=this.up.toArray(),this.matrixAutoUpdate===!1&&(s.matrixAutoUpdate=!1),this.isInstancedMesh&&(s.type="InstancedMesh",s.count=this.count,s.instanceMatrix=this.instanceMatrix.toJSON(),this.instanceColor!==null&&(s.instanceColor=this.instanceColor.toJSON())),this.isBatchedMesh&&(s.type="BatchedMesh",s.perObjectFrustumCulled=this.perObjectFrustumCulled,s.sortObjects=this.sortObjects,s.drawRanges=this._drawRanges,s.reservedRanges=this._reservedRanges,s.visibility=this._visibility,s.active=this._active,s.bounds=this._bounds.map(a=>({boxInitialized:a.boxInitialized,boxMin:a.box.min.toArray(),boxMax:a.box.max.toArray(),sphereInitialized:a.sphereInitialized,sphereRadius:a.sphere.radius,sphereCenter:a.sphere.center.toArray()})),s.maxInstanceCount=this._maxInstanceCount,s.maxVertexCount=this._maxVertexCount,s.maxIndexCount=this._maxIndexCount,s.geometryInitialized=this._geometryInitialized,s.geometryCount=this._geometryCount,s.matricesTexture=this._matricesTexture.toJSON(e),this._colorsTexture!==null&&(s.colorsTexture=this._colorsTexture.toJSON(e)),this.boundingSphere!==null&&(s.boundingSphere={center:s.boundingSphere.center.toArray(),radius:s.boundingSphere.radius}),this.boundingBox!==null&&(s.boundingBox={min:s.boundingBox.min.toArray(),max:s.boundingBox.max.toArray()}));function r(a,c){return a[c.uuid]===void 0&&(a[c.uuid]=c.toJSON(e)),c.uuid}if(this.isScene)this.background&&(this.background.isColor?s.background=this.background.toJSON():this.background.isTexture&&(s.background=this.background.toJSON(e).uuid)),this.environment&&this.environment.isTexture&&this.environment.isRenderTargetTexture!==!0&&(s.environment=this.environment.toJSON(e).uuid);else if(this.isMesh||this.isLine||this.isPoints){s.geometry=r(e.geometries,this.geometry);let a=this.geometry.parameters;if(a!==void 0&&a.shapes!==void 0){let c=a.shapes;if(Array.isArray(c))for(let h=0,l=c.length;h<l;h++){let p=c[h];r(e.shapes,p)}else r(e.shapes,c)}}if(this.isSkinnedMesh&&(s.bindMode=this.bindMode,s.bindMatrix=this.bindMatrix.toArray(),this.skeleton!==void 0&&(r(e.skeletons,this.skeleton),s.skeleton=this.skeleton.uuid)),this.material!==void 0)if(Array.isArray(this.material)){let a=[];for(let c=0,h=this.material.length;c<h;c++)a.push(r(e.materials,this.material[c]));s.material=a}else s.material=r(e.materials,this.material);if(this.children.length>0){s.children=[];for(let a=0;a<this.children.length;a++)s.children.push(this.children[a].toJSON(e).object)}if(this.animations.length>0){s.animations=[];for(let a=0;a<this.animations.length;a++){let c=this.animations[a];s.animations.push(r(e.animations,c))}}if(t){let a=o(e.geometries),c=o(e.materials),h=o(e.textures),l=o(e.images),p=o(e.shapes),f=o(e.skeletons),u=o(e.animations),_=o(e.nodes);a.length>0&&(n.geometries=a),c.length>0&&(n.materials=c),h.length>0&&(n.textures=h),l.length>0&&(n.images=l),p.length>0&&(n.shapes=p),f.length>0&&(n.skeletons=f),u.length>0&&(n.animations=u),_.length>0&&(n.nodes=_)}return n.object=s,n;function o(a){let c=[];for(let h in a){let l=a[h];delete l.metadata,c.push(l)}return c}}clone(e){return new this.constructor().copy(this,e)}copy(e,t=!0){if(this.name=e.name,this.up.copy(e.up),this.position.copy(e.position),this.rotation.order=e.rotation.order,this.quaternion.copy(e.quaternion),this.scale.copy(e.scale),this.matrix.copy(e.matrix),this.matrixWorld.copy(e.matrixWorld),this.matrixAutoUpdate=e.matrixAutoUpdate,this.matrixWorldAutoUpdate=e.matrixWorldAutoUpdate,this.matrixWorldNeedsUpdate=e.matrixWorldNeedsUpdate,this.layers.mask=e.layers.mask,this.visible=e.visible,this.castShadow=e.castShadow,this.receiveShadow=e.receiveShadow,this.frustumCulled=e.frustumCulled,this.renderOrder=e.renderOrder,this.animations=e.animations.slice(),this.userData=JSON.parse(JSON.stringify(e.userData)),t===!0)for(let n=0;n<e.children.length;n++){let s=e.children[n];this.add(s.clone())}return this}};mt.DEFAULT_UP=new C(0,1,0);mt.DEFAULT_MATRIX_AUTO_UPDATE=!0;mt.DEFAULT_MATRIX_WORLD_AUTO_UPDATE=!0;var fn=new C,Gn=new C,ta=new C,Tn=new C,Ni=new C,Vi=new C,sl=new C,na=new C,ia=new C,sa=new C,ra=new nt,oa=new nt,aa=new nt,si=class i{constructor(e=new C,t=new C,n=new C){this.a=e,this.b=t,this.c=n}static getNormal(e,t,n,s){s.subVectors(n,t),fn.subVectors(e,t),s.cross(fn);let r=s.lengthSq();return r>0?s.multiplyScalar(1/Math.sqrt(r)):s.set(0,0,0)}static getBarycoord(e,t,n,s,r){fn.subVectors(s,t),Gn.subVectors(n,t),ta.subVectors(e,t);let o=fn.dot(fn),a=fn.dot(Gn),c=fn.dot(ta),h=Gn.dot(Gn),l=Gn.dot(ta),p=o*h-a*a;if(p===0)return r.set(0,0,0),null;let f=1/p,u=(h*c-a*l)*f,_=(o*l-a*c)*f;return r.set(1-u-_,_,u)}static containsPoint(e,t,n,s){return this.getBarycoord(e,t,n,s,Tn)===null?!1:Tn.x>=0&&Tn.y>=0&&Tn.x+Tn.y<=1}static getInterpolation(e,t,n,s,r,o,a,c){return this.getBarycoord(e,t,n,s,Tn)===null?(c.x=0,c.y=0,"z"in c&&(c.z=0),"w"in c&&(c.w=0),null):(c.setScalar(0),c.addScaledVector(r,Tn.x),c.addScaledVector(o,Tn.y),c.addScaledVector(a,Tn.z),c)}static getInterpolatedAttribute(e,t,n,s,r,o){return ra.setScalar(0),oa.setScalar(0),aa.setScalar(0),ra.fromBufferAttribute(e,t),oa.fromBufferAttribute(e,n),aa.fromBufferAttribute(e,s),o.setScalar(0),o.addScaledVector(ra,r.x),o.addScaledVector(oa,r.y),o.addScaledVector(aa,r.z),o}static isFrontFacing(e,t,n,s){return fn.subVectors(n,t),Gn.subVectors(e,t),fn.cross(Gn).dot(s)<0}set(e,t,n){return this.a.copy(e),this.b.copy(t),this.c.copy(n),this}setFromPointsAndIndices(e,t,n,s){return this.a.copy(e[t]),this.b.copy(e[n]),this.c.copy(e[s]),this}setFromAttributeAndIndices(e,t,n,s){return this.a.fromBufferAttribute(e,t),this.b.fromBufferAttribute(e,n),this.c.fromBufferAttribute(e,s),this}clone(){return new this.constructor().copy(this)}copy(e){return this.a.copy(e.a),this.b.copy(e.b),this.c.copy(e.c),this}getArea(){return fn.subVectors(this.c,this.b),Gn.subVectors(this.a,this.b),fn.cross(Gn).length()*.5}getMidpoint(e){return e.addVectors(this.a,this.b).add(this.c).multiplyScalar(1/3)}getNormal(e){return i.getNormal(this.a,this.b,this.c,e)}getPlane(e){return e.setFromCoplanarPoints(this.a,this.b,this.c)}getBarycoord(e,t){return i.getBarycoord(e,this.a,this.b,this.c,t)}getInterpolation(e,t,n,s,r){return i.getInterpolation(e,this.a,this.b,this.c,t,n,s,r)}containsPoint(e){return i.containsPoint(e,this.a,this.b,this.c)}isFrontFacing(e){return i.isFrontFacing(this.a,this.b,this.c,e)}intersectsBox(e){return e.intersectsTriangle(this)}closestPointToPoint(e,t){let n=this.a,s=this.b,r=this.c,o,a;Ni.subVectors(s,n),Vi.subVectors(r,n),na.subVectors(e,n);let c=Ni.dot(na),h=Vi.dot(na);if(c<=0&&h<=0)return t.copy(n);ia.subVectors(e,s);let l=Ni.dot(ia),p=Vi.dot(ia);if(l>=0&&p<=l)return t.copy(s);let f=c*p-l*h;if(f<=0&&c>=0&&l<=0)return o=c/(c-l),t.copy(n).addScaledVector(Ni,o);sa.subVectors(e,r);let u=Ni.dot(sa),_=Vi.dot(sa);if(_>=0&&u<=_)return t.copy(r);let m=u*h-c*_;if(m<=0&&h>=0&&_<=0)return a=h/(h-_),t.copy(n).addScaledVector(Vi,a);let g=l*_-u*p;if(g<=0&&p-l>=0&&u-_>=0)return sl.subVectors(r,s),a=(p-l)/(p-l+(u-_)),t.copy(s).addScaledVector(sl,a);let d=1/(g+m+f);return o=m*d,a=f*d,t.copy(n).addScaledVector(Ni,o).addScaledVector(Vi,a)}equals(e){return e.a.equals(this.a)&&e.b.equals(this.b)&&e.c.equals(this.c)}},b0={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},ni={h:0,s:0,l:0},Mr={h:0,s:0,l:0};function ca(i,e,t){return t<0&&(t+=1),t>1&&(t-=1),t<1/6?i+(e-i)*6*t:t<1/2?e:t<2/3?i+(e-i)*6*(2/3-t):i}var Ge=class{constructor(e,t,n){return this.isColor=!0,this.r=1,this.g=1,this.b=1,this.set(e,t,n)}set(e,t,n){if(t===void 0&&n===void 0){let s=e;s&&s.isColor?this.copy(s):typeof s=="number"?this.setHex(s):typeof s=="string"&&this.setStyle(s)}else this.setRGB(e,t,n);return this}setScalar(e){return this.r=e,this.g=e,this.b=e,this}setHex(e,t=_t){return e=Math.floor(e),this.r=(e>>16&255)/255,this.g=(e>>8&255)/255,this.b=(e&255)/255,Ze.toWorkingColorSpace(this,t),this}setRGB(e,t,n,s=Ze.workingColorSpace){return this.r=e,this.g=t,this.b=n,Ze.toWorkingColorSpace(this,s),this}setHSL(e,t,n,s=Ze.workingColorSpace){if(e=kc(e,1),t=Rt(t,0,1),n=Rt(n,0,1),t===0)this.r=this.g=this.b=n;else{let r=n<=.5?n*(1+t):n+t-n*t,o=2*n-r;this.r=ca(o,r,e+1/3),this.g=ca(o,r,e),this.b=ca(o,r,e-1/3)}return Ze.toWorkingColorSpace(this,s),this}setStyle(e,t=_t){function n(r){r!==void 0&&parseFloat(r)<1&&console.warn("THREE.Color: Alpha component of "+e+" will be ignored.")}let s;if(s=/^(\w+)\(([^\)]*)\)/.exec(e)){let r,o=s[1],a=s[2];switch(o){case"rgb":case"rgba":if(r=/^\s*(\d+)\s*,\s*(\d+)\s*,\s*(\d+)\s*(?:,\s*(\d*\.?\d+)\s*)?$/.exec(a))return n(r[4]),this.setRGB(Math.min(255,parseInt(r[1],10))/255,Math.min(255,parseInt(r[2],10))/255,Math.min(255,parseInt(r[3],10))/255,t);if(r=/^\s*(\d+)\%\s*,\s*(\d+)\%\s*,\s*(\d+)\%\s*(?:,\s*(\d*\.?\d+)\s*)?$/.exec(a))return n(r[4]),this.setRGB(Math.min(100,parseInt(r[1],10))/100,Math.min(100,parseInt(r[2],10))/100,Math.min(100,parseInt(r[3],10))/100,t);break;case"hsl":case"hsla":if(r=/^\s*(\d*\.?\d+)\s*,\s*(\d*\.?\d+)\%\s*,\s*(\d*\.?\d+)\%\s*(?:,\s*(\d*\.?\d+)\s*)?$/.exec(a))return n(r[4]),this.setHSL(parseFloat(r[1])/360,parseFloat(r[2])/100,parseFloat(r[3])/100,t);break;default:console.warn("THREE.Color: Unknown color model "+e)}}else if(s=/^\#([A-Fa-f\d]+)$/.exec(e)){let r=s[1],o=r.length;if(o===3)return this.setRGB(parseInt(r.charAt(0),16)/15,parseInt(r.charAt(1),16)/15,parseInt(r.charAt(2),16)/15,t);if(o===6)return this.setHex(parseInt(r,16),t);console.warn("THREE.Color: Invalid hex color "+e)}else if(e&&e.length>0)return this.setColorName(e,t);return this}setColorName(e,t=_t){let n=b0[e.toLowerCase()];return n!==void 0?this.setHex(n,t):console.warn("THREE.Color: Unknown color "+e),this}clone(){return new this.constructor(this.r,this.g,this.b)}copy(e){return this.r=e.r,this.g=e.g,this.b=e.b,this}copySRGBToLinear(e){return this.r=$n(e.r),this.g=$n(e.g),this.b=$n(e.b),this}copyLinearToSRGB(e){return this.r=is(e.r),this.g=is(e.g),this.b=is(e.b),this}convertSRGBToLinear(){return this.copySRGBToLinear(this),this}convertLinearToSRGB(){return this.copyLinearToSRGB(this),this}getHex(e=_t){return Ze.fromWorkingColorSpace(It.copy(this),e),Math.round(Rt(It.r*255,0,255))*65536+Math.round(Rt(It.g*255,0,255))*256+Math.round(Rt(It.b*255,0,255))}getHexString(e=_t){return("000000"+this.getHex(e).toString(16)).slice(-6)}getHSL(e,t=Ze.workingColorSpace){Ze.fromWorkingColorSpace(It.copy(this),t);let n=It.r,s=It.g,r=It.b,o=Math.max(n,s,r),a=Math.min(n,s,r),c,h,l=(a+o)/2;if(a===o)c=0,h=0;else{let p=o-a;switch(h=l<=.5?p/(o+a):p/(2-o-a),o){case n:c=(s-r)/p+(s<r?6:0);break;case s:c=(r-n)/p+2;break;case r:c=(n-s)/p+4;break}c/=6}return e.h=c,e.s=h,e.l=l,e}getRGB(e,t=Ze.workingColorSpace){return Ze.fromWorkingColorSpace(It.copy(this),t),e.r=It.r,e.g=It.g,e.b=It.b,e}getStyle(e=_t){Ze.fromWorkingColorSpace(It.copy(this),e);let t=It.r,n=It.g,s=It.b;return e!==_t?`color(${e} ${t.toFixed(3)} ${n.toFixed(3)} ${s.toFixed(3)})`:`rgb(${Math.round(t*255)},${Math.round(n*255)},${Math.round(s*255)})`}offsetHSL(e,t,n){return this.getHSL(ni),this.setHSL(ni.h+e,ni.s+t,ni.l+n)}add(e){return this.r+=e.r,this.g+=e.g,this.b+=e.b,this}addColors(e,t){return this.r=e.r+t.r,this.g=e.g+t.g,this.b=e.b+t.b,this}addScalar(e){return this.r+=e,this.g+=e,this.b+=e,this}sub(e){return this.r=Math.max(0,this.r-e.r),this.g=Math.max(0,this.g-e.g),this.b=Math.max(0,this.b-e.b),this}multiply(e){return this.r*=e.r,this.g*=e.g,this.b*=e.b,this}multiplyScalar(e){return this.r*=e,this.g*=e,this.b*=e,this}lerp(e,t){return this.r+=(e.r-this.r)*t,this.g+=(e.g-this.g)*t,this.b+=(e.b-this.b)*t,this}lerpColors(e,t,n){return this.r=e.r+(t.r-e.r)*n,this.g=e.g+(t.g-e.g)*n,this.b=e.b+(t.b-e.b)*n,this}lerpHSL(e,t){this.getHSL(ni),e.getHSL(Mr);let n=Ks(ni.h,Mr.h,t),s=Ks(ni.s,Mr.s,t),r=Ks(ni.l,Mr.l,t);return this.setHSL(n,s,r),this}setFromVector3(e){return this.r=e.x,this.g=e.y,this.b=e.z,this}applyMatrix3(e){let t=this.r,n=this.g,s=this.b,r=e.elements;return this.r=r[0]*t+r[3]*n+r[6]*s,this.g=r[1]*t+r[4]*n+r[7]*s,this.b=r[2]*t+r[5]*n+r[8]*s,this}equals(e){return e.r===this.r&&e.g===this.g&&e.b===this.b}fromArray(e,t=0){return this.r=e[t],this.g=e[t+1],this.b=e[t+2],this}toArray(e=[],t=0){return e[t]=this.r,e[t+1]=this.g,e[t+2]=this.b,e}fromBufferAttribute(e,t){return this.r=e.getX(t),this.g=e.getY(t),this.b=e.getZ(t),this}toJSON(){return this.getHex()}*[Symbol.iterator](){yield this.r,yield this.g,yield this.b}},It=new Ge;Ge.NAMES=b0;var Hf=0,qt=class extends ci{static get type(){return"Material"}get type(){return this.constructor.type}set type(e){}constructor(){super(),this.isMaterial=!0,Object.defineProperty(this,"id",{value:Hf++}),this.uuid=_n(),this.name="",this.blending=ts,this.side=On,this.vertexColors=!1,this.opacity=1,this.transparent=!1,this.alphaHash=!1,this.blendSrc=Aa,this.blendDst=va,this.blendEquation=yi,this.blendSrcAlpha=null,this.blendDstAlpha=null,this.blendEquationAlpha=null,this.blendColor=new Ge(0,0,0),this.blendAlpha=0,this.depthFunc=rs,this.depthTest=!0,this.depthWrite=!0,this.stencilWriteMask=255,this.stencilFunc=Dh,this.stencilRef=0,this.stencilFuncMask=255,this.stencilFail=Ui,this.stencilZFail=Ui,this.stencilZPass=Ui,this.stencilWrite=!1,this.clippingPlanes=null,this.clipIntersection=!1,this.clipShadows=!1,this.shadowSide=null,this.colorWrite=!0,this.precision=null,this.polygonOffset=!1,this.polygonOffsetFactor=0,this.polygonOffsetUnits=0,this.dithering=!1,this.alphaToCoverage=!1,this.premultipliedAlpha=!1,this.forceSinglePass=!1,this.visible=!0,this.toneMapped=!0,this.userData={},this.version=0,this._alphaTest=0}get alphaTest(){return this._alphaTest}set alphaTest(e){this._alphaTest>0!=e>0&&this.version++,this._alphaTest=e}onBeforeRender(){}onBeforeCompile(){}customProgramCacheKey(){return this.onBeforeCompile.toString()}setValues(e){if(e!==void 0)for(let t in e){let n=e[t];if(n===void 0){console.warn(`THREE.Material: parameter '${t}' has value of undefined.`);continue}let s=this[t];if(s===void 0){console.warn(`THREE.Material: '${t}' is not a property of THREE.${this.type}.`);continue}s&&s.isColor?s.set(n):s&&s.isVector3&&n&&n.isVector3?s.copy(n):this[t]=n}}toJSON(e){let t=e===void 0||typeof e=="string";t&&(e={textures:{},images:{}});let n={metadata:{version:4.6,type:"Material",generator:"Material.toJSON"}};n.uuid=this.uuid,n.type=this.type,this.name!==""&&(n.name=this.name),this.color&&this.color.isColor&&(n.color=this.color.getHex()),this.roughness!==void 0&&(n.roughness=this.roughness),this.metalness!==void 0&&(n.metalness=this.metalness),this.sheen!==void 0&&(n.sheen=this.sheen),this.sheenColor&&this.sheenColor.isColor&&(n.sheenColor=this.sheenColor.getHex()),this.sheenRoughness!==void 0&&(n.sheenRoughness=this.sheenRoughness),this.emissive&&this.emissive.isColor&&(n.emissive=this.emissive.getHex()),this.emissiveIntensity!==void 0&&this.emissiveIntensity!==1&&(n.emissiveIntensity=this.emissiveIntensity),this.specular&&this.specular.isColor&&(n.specular=this.specular.getHex()),this.specularIntensity!==void 0&&(n.specularIntensity=this.specularIntensity),this.specularColor&&this.specularColor.isColor&&(n.specularColor=this.specularColor.getHex()),this.shininess!==void 0&&(n.shininess=this.shininess),this.clearcoat!==void 0&&(n.clearcoat=this.clearcoat),this.clearcoatRoughness!==void 0&&(n.clearcoatRoughness=this.clearcoatRoughness),this.clearcoatMap&&this.clearcoatMap.isTexture&&(n.clearcoatMap=this.clearcoatMap.toJSON(e).uuid),this.clearcoatRoughnessMap&&this.clearcoatRoughnessMap.isTexture&&(n.clearcoatRoughnessMap=this.clearcoatRoughnessMap.toJSON(e).uuid),this.clearcoatNormalMap&&this.clearcoatNormalMap.isTexture&&(n.clearcoatNormalMap=this.clearcoatNormalMap.toJSON(e).uuid,n.clearcoatNormalScale=this.clearcoatNormalScale.toArray()),this.dispersion!==void 0&&(n.dispersion=this.dispersion),this.iridescence!==void 0&&(n.iridescence=this.iridescence),this.iridescenceIOR!==void 0&&(n.iridescenceIOR=this.iridescenceIOR),this.iridescenceThicknessRange!==void 0&&(n.iridescenceThicknessRange=this.iridescenceThicknessRange),this.iridescenceMap&&this.iridescenceMap.isTexture&&(n.iridescenceMap=this.iridescenceMap.toJSON(e).uuid),this.iridescenceThicknessMap&&this.iridescenceThicknessMap.isTexture&&(n.iridescenceThicknessMap=this.iridescenceThicknessMap.toJSON(e).uuid),this.anisotropy!==void 0&&(n.anisotropy=this.anisotropy),this.anisotropyRotation!==void 0&&(n.anisotropyRotation=this.anisotropyRotation),this.anisotropyMap&&this.anisotropyMap.isTexture&&(n.anisotropyMap=this.anisotropyMap.toJSON(e).uuid),this.map&&this.map.isTexture&&(n.map=this.map.toJSON(e).uuid),this.matcap&&this.matcap.isTexture&&(n.matcap=this.matcap.toJSON(e).uuid),this.alphaMap&&this.alphaMap.isTexture&&(n.alphaMap=this.alphaMap.toJSON(e).uuid),this.lightMap&&this.lightMap.isTexture&&(n.lightMap=this.lightMap.toJSON(e).uuid,n.lightMapIntensity=this.lightMapIntensity),this.aoMap&&this.aoMap.isTexture&&(n.aoMap=this.aoMap.toJSON(e).uuid,n.aoMapIntensity=this.aoMapIntensity),this.bumpMap&&this.bumpMap.isTexture&&(n.bumpMap=this.bumpMap.toJSON(e).uuid,n.bumpScale=this.bumpScale),this.normalMap&&this.normalMap.isTexture&&(n.normalMap=this.normalMap.toJSON(e).uuid,n.normalMapType=this.normalMapType,n.normalScale=this.normalScale.toArray()),this.displacementMap&&this.displacementMap.isTexture&&(n.displacementMap=this.displacementMap.toJSON(e).uuid,n.displacementScale=this.displacementScale,n.displacementBias=this.displacementBias),this.roughnessMap&&this.roughnessMap.isTexture&&(n.roughnessMap=this.roughnessMap.toJSON(e).uuid),this.metalnessMap&&this.metalnessMap.isTexture&&(n.metalnessMap=this.metalnessMap.toJSON(e).uuid),this.emissiveMap&&this.emissiveMap.isTexture&&(n.emissiveMap=this.emissiveMap.toJSON(e).uuid),this.specularMap&&this.specularMap.isTexture&&(n.specularMap=this.specularMap.toJSON(e).uuid),this.specularIntensityMap&&this.specularIntensityMap.isTexture&&(n.specularIntensityMap=this.specularIntensityMap.toJSON(e).uuid),this.specularColorMap&&this.specularColorMap.isTexture&&(n.specularColorMap=this.specularColorMap.toJSON(e).uuid),this.envMap&&this.envMap.isTexture&&(n.envMap=this.envMap.toJSON(e).uuid,this.combine!==void 0&&(n.combine=this.combine)),this.envMapRotation!==void 0&&(n.envMapRotation=this.envMapRotation.toArray()),this.envMapIntensity!==void 0&&(n.envMapIntensity=this.envMapIntensity),this.reflectivity!==void 0&&(n.reflectivity=this.reflectivity),this.refractionRatio!==void 0&&(n.refractionRatio=this.refractionRatio),this.gradientMap&&this.gradientMap.isTexture&&(n.gradientMap=this.gradientMap.toJSON(e).uuid),this.transmission!==void 0&&(n.transmission=this.transmission),this.transmissionMap&&this.transmissionMap.isTexture&&(n.transmissionMap=this.transmissionMap.toJSON(e).uuid),this.thickness!==void 0&&(n.thickness=this.thickness),this.thicknessMap&&this.thicknessMap.isTexture&&(n.thicknessMap=this.thicknessMap.toJSON(e).uuid),this.attenuationDistance!==void 0&&this.attenuationDistance!==1/0&&(n.attenuationDistance=this.attenuationDistance),this.attenuationColor!==void 0&&(n.attenuationColor=this.attenuationColor.getHex()),this.size!==void 0&&(n.size=this.size),this.shadowSide!==null&&(n.shadowSide=this.shadowSide),this.sizeAttenuation!==void 0&&(n.sizeAttenuation=this.sizeAttenuation),this.blending!==ts&&(n.blending=this.blending),this.side!==On&&(n.side=this.side),this.vertexColors===!0&&(n.vertexColors=!0),this.opacity<1&&(n.opacity=this.opacity),this.transparent===!0&&(n.transparent=!0),this.blendSrc!==Aa&&(n.blendSrc=this.blendSrc),this.blendDst!==va&&(n.blendDst=this.blendDst),this.blendEquation!==yi&&(n.blendEquation=this.blendEquation),this.blendSrcAlpha!==null&&(n.blendSrcAlpha=this.blendSrcAlpha),this.blendDstAlpha!==null&&(n.blendDstAlpha=this.blendDstAlpha),this.blendEquationAlpha!==null&&(n.blendEquationAlpha=this.blendEquationAlpha),this.blendColor&&this.blendColor.isColor&&(n.blendColor=this.blendColor.getHex()),this.blendAlpha!==0&&(n.blendAlpha=this.blendAlpha),this.depthFunc!==rs&&(n.depthFunc=this.depthFunc),this.depthTest===!1&&(n.depthTest=this.depthTest),this.depthWrite===!1&&(n.depthWrite=this.depthWrite),this.colorWrite===!1&&(n.colorWrite=this.colorWrite),this.stencilWriteMask!==255&&(n.stencilWriteMask=this.stencilWriteMask),this.stencilFunc!==Dh&&(n.stencilFunc=this.stencilFunc),this.stencilRef!==0&&(n.stencilRef=this.stencilRef),this.stencilFuncMask!==255&&(n.stencilFuncMask=this.stencilFuncMask),this.stencilFail!==Ui&&(n.stencilFail=this.stencilFail),this.stencilZFail!==Ui&&(n.stencilZFail=this.stencilZFail),this.stencilZPass!==Ui&&(n.stencilZPass=this.stencilZPass),this.stencilWrite===!0&&(n.stencilWrite=this.stencilWrite),this.rotation!==void 0&&this.rotation!==0&&(n.rotation=this.rotation),this.polygonOffset===!0&&(n.polygonOffset=!0),this.polygonOffsetFactor!==0&&(n.polygonOffsetFactor=this.polygonOffsetFactor),this.polygonOffsetUnits!==0&&(n.polygonOffsetUnits=this.polygonOffsetUnits),this.linewidth!==void 0&&this.linewidth!==1&&(n.linewidth=this.linewidth),this.dashSize!==void 0&&(n.dashSize=this.dashSize),this.gapSize!==void 0&&(n.gapSize=this.gapSize),this.scale!==void 0&&(n.scale=this.scale),this.dithering===!0&&(n.dithering=!0),this.alphaTest>0&&(n.alphaTest=this.alphaTest),this.alphaHash===!0&&(n.alphaHash=!0),this.alphaToCoverage===!0&&(n.alphaToCoverage=!0),this.premultipliedAlpha===!0&&(n.premultipliedAlpha=!0),this.forceSinglePass===!0&&(n.forceSinglePass=!0),this.wireframe===!0&&(n.wireframe=!0),this.wireframeLinewidth>1&&(n.wireframeLinewidth=this.wireframeLinewidth),this.wireframeLinecap!=="round"&&(n.wireframeLinecap=this.wireframeLinecap),this.wireframeLinejoin!=="round"&&(n.wireframeLinejoin=this.wireframeLinejoin),this.flatShading===!0&&(n.flatShading=!0),this.visible===!1&&(n.visible=!1),this.toneMapped===!1&&(n.toneMapped=!1),this.fog===!1&&(n.fog=!1),Object.keys(this.userData).length>0&&(n.userData=this.userData);function s(r){let o=[];for(let a in r){let c=r[a];delete c.metadata,o.push(c)}return o}if(t){let r=s(e.textures),o=s(e.images);r.length>0&&(n.textures=r),o.length>0&&(n.images=o)}return n}clone(){return new this.constructor().copy(this)}copy(e){this.name=e.name,this.blending=e.blending,this.side=e.side,this.vertexColors=e.vertexColors,this.opacity=e.opacity,this.transparent=e.transparent,this.blendSrc=e.blendSrc,this.blendDst=e.blendDst,this.blendEquation=e.blendEquation,this.blendSrcAlpha=e.blendSrcAlpha,this.blendDstAlpha=e.blendDstAlpha,this.blendEquationAlpha=e.blendEquationAlpha,this.blendColor.copy(e.blendColor),this.blendAlpha=e.blendAlpha,this.depthFunc=e.depthFunc,this.depthTest=e.depthTest,this.depthWrite=e.depthWrite,this.stencilWriteMask=e.stencilWriteMask,this.stencilFunc=e.stencilFunc,this.stencilRef=e.stencilRef,this.stencilFuncMask=e.stencilFuncMask,this.stencilFail=e.stencilFail,this.stencilZFail=e.stencilZFail,this.stencilZPass=e.stencilZPass,this.stencilWrite=e.stencilWrite;let t=e.clippingPlanes,n=null;if(t!==null){let s=t.length;n=new Array(s);for(let r=0;r!==s;++r)n[r]=t[r].clone()}return this.clippingPlanes=n,this.clipIntersection=e.clipIntersection,this.clipShadows=e.clipShadows,this.shadowSide=e.shadowSide,this.colorWrite=e.colorWrite,this.precision=e.precision,this.polygonOffset=e.polygonOffset,this.polygonOffsetFactor=e.polygonOffsetFactor,this.polygonOffsetUnits=e.polygonOffsetUnits,this.dithering=e.dithering,this.alphaTest=e.alphaTest,this.alphaHash=e.alphaHash,this.alphaToCoverage=e.alphaToCoverage,this.premultipliedAlpha=e.premultipliedAlpha,this.forceSinglePass=e.forceSinglePass,this.visible=e.visible,this.toneMapped=e.toneMapped,this.userData=JSON.parse(JSON.stringify(e.userData)),this}dispose(){this.dispatchEvent({type:"dispose"})}set needsUpdate(e){e===!0&&this.version++}onBuild(){console.warn("Material: onBuild() has been removed.")}},yt=class extends qt{static get type(){return"MeshBasicMaterial"}constructor(e){super(),this.isMeshBasicMaterial=!0,this.color=new Ge(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.envMapRotation=new yn,this.combine=s0,this.reflectivity=1,this.refractionRatio=.98,this.wireframe=!1,this.wireframeLinewidth=1,this.wireframeLinecap="round",this.wireframeLinejoin="round",this.fog=!0,this.setValues(e)}copy(e){return super.copy(e),this.color.copy(e.color),this.map=e.map,this.lightMap=e.lightMap,this.lightMapIntensity=e.lightMapIntensity,this.aoMap=e.aoMap,this.aoMapIntensity=e.aoMapIntensity,this.specularMap=e.specularMap,this.alphaMap=e.alphaMap,this.envMap=e.envMap,this.envMapRotation.copy(e.envMapRotation),this.combine=e.combine,this.reflectivity=e.reflectivity,this.refractionRatio=e.refractionRatio,this.wireframe=e.wireframe,this.wireframeLinewidth=e.wireframeLinewidth,this.wireframeLinecap=e.wireframeLinecap,this.wireframeLinejoin=e.wireframeLinejoin,this.fog=e.fog,this}};var vt=new C,Cr=new Re,wt=class{constructor(e,t,n=!1){if(Array.isArray(e))throw new TypeError("THREE.BufferAttribute: array should be a Typed Array.");this.isBufferAttribute=!0,this.name="",this.array=e,this.itemSize=t,this.count=e!==void 0?e.length/t:0,this.normalized=n,this.usage=ac,this.updateRanges=[],this.gpuType=gn,this.version=0}onUploadCallback(){}set needsUpdate(e){e===!0&&this.version++}setUsage(e){return this.usage=e,this}addUpdateRange(e,t){this.updateRanges.push({start:e,count:t})}clearUpdateRanges(){this.updateRanges.length=0}copy(e){return this.name=e.name,this.array=new e.array.constructor(e.array),this.itemSize=e.itemSize,this.count=e.count,this.normalized=e.normalized,this.usage=e.usage,this.gpuType=e.gpuType,this}copyAt(e,t,n){e*=this.itemSize,n*=t.itemSize;for(let s=0,r=this.itemSize;s<r;s++)this.array[e+s]=t.array[n+s];return this}copyArray(e){return this.array.set(e),this}applyMatrix3(e){if(this.itemSize===2)for(let t=0,n=this.count;t<n;t++)Cr.fromBufferAttribute(this,t),Cr.applyMatrix3(e),this.setXY(t,Cr.x,Cr.y);else if(this.itemSize===3)for(let t=0,n=this.count;t<n;t++)vt.fromBufferAttribute(this,t),vt.applyMatrix3(e),this.setXYZ(t,vt.x,vt.y,vt.z);return this}applyMatrix4(e){for(let t=0,n=this.count;t<n;t++)vt.fromBufferAttribute(this,t),vt.applyMatrix4(e),this.setXYZ(t,vt.x,vt.y,vt.z);return this}applyNormalMatrix(e){for(let t=0,n=this.count;t<n;t++)vt.fromBufferAttribute(this,t),vt.applyNormalMatrix(e),this.setXYZ(t,vt.x,vt.y,vt.z);return this}transformDirection(e){for(let t=0,n=this.count;t<n;t++)vt.fromBufferAttribute(this,t),vt.transformDirection(e),this.setXYZ(t,vt.x,vt.y,vt.z);return this}set(e,t=0){return this.array.set(e,t),this}getComponent(e,t){let n=this.array[e*this.itemSize+t];return this.normalized&&(n=un(n,this.array)),n}setComponent(e,t,n){return this.normalized&&(n=rt(n,this.array)),this.array[e*this.itemSize+t]=n,this}getX(e){let t=this.array[e*this.itemSize];return this.normalized&&(t=un(t,this.array)),t}setX(e,t){return this.normalized&&(t=rt(t,this.array)),this.array[e*this.itemSize]=t,this}getY(e){let t=this.array[e*this.itemSize+1];return this.normalized&&(t=un(t,this.array)),t}setY(e,t){return this.normalized&&(t=rt(t,this.array)),this.array[e*this.itemSize+1]=t,this}getZ(e){let t=this.array[e*this.itemSize+2];return this.normalized&&(t=un(t,this.array)),t}setZ(e,t){return this.normalized&&(t=rt(t,this.array)),this.array[e*this.itemSize+2]=t,this}getW(e){let t=this.array[e*this.itemSize+3];return this.normalized&&(t=un(t,this.array)),t}setW(e,t){return this.normalized&&(t=rt(t,this.array)),this.array[e*this.itemSize+3]=t,this}setXY(e,t,n){return e*=this.itemSize,this.normalized&&(t=rt(t,this.array),n=rt(n,this.array)),this.array[e+0]=t,this.array[e+1]=n,this}setXYZ(e,t,n,s){return e*=this.itemSize,this.normalized&&(t=rt(t,this.array),n=rt(n,this.array),s=rt(s,this.array)),this.array[e+0]=t,this.array[e+1]=n,this.array[e+2]=s,this}setXYZW(e,t,n,s,r){return e*=this.itemSize,this.normalized&&(t=rt(t,this.array),n=rt(n,this.array),s=rt(s,this.array),r=rt(r,this.array)),this.array[e+0]=t,this.array[e+1]=n,this.array[e+2]=s,this.array[e+3]=r,this}onUpload(e){return this.onUploadCallback=e,this}clone(){return new this.constructor(this.array,this.itemSize).copy(this)}toJSON(){let e={itemSize:this.itemSize,type:this.array.constructor.name,array:Array.from(this.array),normalized:this.normalized};return this.name!==""&&(e.name=this.name),this.usage!==ac&&(e.usage=this.usage),e}};var co=class extends wt{constructor(e,t,n){super(new Uint16Array(e),t,n)}};var ho=class extends wt{constructor(e,t,n){super(new Uint32Array(e),t,n)}};var Ot=class extends wt{constructor(e,t,n){super(new Float32Array(e),t,n)}},Sf=0,nn=new Fe,ha=new mt,Li=new C,Qt=new rn,Ds=new rn,Mt=new C,Gt=class i extends ci{constructor(){super(),this.isBufferGeometry=!0,Object.defineProperty(this,"id",{value:Sf++}),this.uuid=_n(),this.name="",this.type="BufferGeometry",this.index=null,this.indirect=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(e){return Array.isArray(e)?this.index=new(x0(e)?ho:co)(e,1):this.index=e,this}setIndirect(e){return this.indirect=e,this}getIndirect(){return this.indirect}getAttribute(e){return this.attributes[e]}setAttribute(e,t){return this.attributes[e]=t,this}deleteAttribute(e){return delete this.attributes[e],this}hasAttribute(e){return this.attributes[e]!==void 0}addGroup(e,t,n=0){this.groups.push({start:e,count:t,materialIndex:n})}clearGroups(){this.groups=[]}setDrawRange(e,t){this.drawRange.start=e,this.drawRange.count=t}applyMatrix4(e){let t=this.attributes.position;t!==void 0&&(t.applyMatrix4(e),t.needsUpdate=!0);let n=this.attributes.normal;if(n!==void 0){let r=new ze().getNormalMatrix(e);n.applyNormalMatrix(r),n.needsUpdate=!0}let s=this.attributes.tangent;return s!==void 0&&(s.transformDirection(e),s.needsUpdate=!0),this.boundingBox!==null&&this.computeBoundingBox(),this.boundingSphere!==null&&this.computeBoundingSphere(),this}applyQuaternion(e){return nn.makeRotationFromQuaternion(e),this.applyMatrix4(nn),this}rotateX(e){return nn.makeRotationX(e),this.applyMatrix4(nn),this}rotateY(e){return nn.makeRotationY(e),this.applyMatrix4(nn),this}rotateZ(e){return nn.makeRotationZ(e),this.applyMatrix4(nn),this}translate(e,t,n){return nn.makeTranslation(e,t,n),this.applyMatrix4(nn),this}scale(e,t,n){return nn.makeScale(e,t,n),this.applyMatrix4(nn),this}lookAt(e){return ha.lookAt(e),ha.updateMatrix(),this.applyMatrix4(ha.matrix),this}center(){return this.computeBoundingBox(),this.boundingBox.getCenter(Li).negate(),this.translate(Li.x,Li.y,Li.z),this}setFromPoints(e){let t=this.getAttribute("position");if(t===void 0){let n=[];for(let s=0,r=e.length;s<r;s++){let o=e[s];n.push(o.x,o.y,o.z||0)}this.setAttribute("position",new Ot(n,3))}else{for(let n=0,s=t.count;n<s;n++){let r=e[n];t.setXYZ(n,r.x,r.y,r.z||0)}e.length>t.count&&console.warn("THREE.BufferGeometry: Buffer size too small for points data. Use .dispose() and create a new geometry."),t.needsUpdate=!0}return this}computeBoundingBox(){this.boundingBox===null&&(this.boundingBox=new rn);let e=this.attributes.position,t=this.morphAttributes.position;if(e&&e.isGLBufferAttribute){console.error("THREE.BufferGeometry.computeBoundingBox(): GLBufferAttribute requires a manual bounding box.",this),this.boundingBox.set(new C(-1/0,-1/0,-1/0),new C(1/0,1/0,1/0));return}if(e!==void 0){if(this.boundingBox.setFromBufferAttribute(e),t)for(let n=0,s=t.length;n<s;n++){let r=t[n];Qt.setFromBufferAttribute(r),this.morphTargetsRelative?(Mt.addVectors(this.boundingBox.min,Qt.min),this.boundingBox.expandByPoint(Mt),Mt.addVectors(this.boundingBox.max,Qt.max),this.boundingBox.expandByPoint(Mt)):(this.boundingBox.expandByPoint(Qt.min),this.boundingBox.expandByPoint(Qt.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(){this.boundingSphere===null&&(this.boundingSphere=new Zt);let e=this.attributes.position,t=this.morphAttributes.position;if(e&&e.isGLBufferAttribute){console.error("THREE.BufferGeometry.computeBoundingSphere(): GLBufferAttribute requires a manual bounding sphere.",this),this.boundingSphere.set(new C,1/0);return}if(e){let n=this.boundingSphere.center;if(Qt.setFromBufferAttribute(e),t)for(let r=0,o=t.length;r<o;r++){let a=t[r];Ds.setFromBufferAttribute(a),this.morphTargetsRelative?(Mt.addVectors(Qt.min,Ds.min),Qt.expandByPoint(Mt),Mt.addVectors(Qt.max,Ds.max),Qt.expandByPoint(Mt)):(Qt.expandByPoint(Ds.min),Qt.expandByPoint(Ds.max))}Qt.getCenter(n);let s=0;for(let r=0,o=e.count;r<o;r++)Mt.fromBufferAttribute(e,r),s=Math.max(s,n.distanceToSquared(Mt));if(t)for(let r=0,o=t.length;r<o;r++){let a=t[r],c=this.morphTargetsRelative;for(let h=0,l=a.count;h<l;h++)Mt.fromBufferAttribute(a,h),c&&(Li.fromBufferAttribute(e,h),Mt.add(Li)),s=Math.max(s,n.distanceToSquared(Mt))}this.boundingSphere.radius=Math.sqrt(s),isNaN(this.boundingSphere.radius)&&console.error('THREE.BufferGeometry.computeBoundingSphere(): Computed radius is NaN. The "position" attribute is likely to have NaN values.',this)}}computeTangents(){let e=this.index,t=this.attributes;if(e===null||t.position===void 0||t.normal===void 0||t.uv===void 0){console.error("THREE.BufferGeometry: .computeTangents() failed. Missing required attributes (index, position, normal or uv)");return}let n=t.position,s=t.normal,r=t.uv;this.hasAttribute("tangent")===!1&&this.setAttribute("tangent",new wt(new Float32Array(4*n.count),4));let o=this.getAttribute("tangent"),a=[],c=[];for(let M=0;M<n.count;M++)a[M]=new C,c[M]=new C;let h=new C,l=new C,p=new C,f=new Re,u=new Re,_=new Re,m=new C,g=new C;function d(M,y,w){h.fromBufferAttribute(n,M),l.fromBufferAttribute(n,y),p.fromBufferAttribute(n,w),f.fromBufferAttribute(r,M),u.fromBufferAttribute(r,y),_.fromBufferAttribute(r,w),l.sub(h),p.sub(h),u.sub(f),_.sub(f);let W=1/(u.x*_.y-_.x*u.y);isFinite(W)&&(m.copy(l).multiplyScalar(_.y).addScaledVector(p,-u.y).multiplyScalar(W),g.copy(p).multiplyScalar(u.x).addScaledVector(l,-_.x).multiplyScalar(W),a[M].add(m),a[y].add(m),a[w].add(m),c[M].add(g),c[y].add(g),c[w].add(g))}let A=this.groups;A.length===0&&(A=[{start:0,count:e.count}]);for(let M=0,y=A.length;M<y;++M){let w=A[M],W=w.start,$=w.count;for(let F=W,k=W+$;F<k;F+=3)d(e.getX(F+0),e.getX(F+1),e.getX(F+2))}let O=new C,P=new C,G=new C,H=new C;function S(M){G.fromBufferAttribute(s,M),H.copy(G);let y=a[M];O.copy(y),O.sub(G.multiplyScalar(G.dot(y))).normalize(),P.crossVectors(H,y);let W=P.dot(c[M])<0?-1:1;o.setXYZW(M,O.x,O.y,O.z,W)}for(let M=0,y=A.length;M<y;++M){let w=A[M],W=w.start,$=w.count;for(let F=W,k=W+$;F<k;F+=3)S(e.getX(F+0)),S(e.getX(F+1)),S(e.getX(F+2))}}computeVertexNormals(){let e=this.index,t=this.getAttribute("position");if(t!==void 0){let n=this.getAttribute("normal");if(n===void 0)n=new wt(new Float32Array(t.count*3),3),this.setAttribute("normal",n);else for(let f=0,u=n.count;f<u;f++)n.setXYZ(f,0,0,0);let s=new C,r=new C,o=new C,a=new C,c=new C,h=new C,l=new C,p=new C;if(e)for(let f=0,u=e.count;f<u;f+=3){let _=e.getX(f+0),m=e.getX(f+1),g=e.getX(f+2);s.fromBufferAttribute(t,_),r.fromBufferAttribute(t,m),o.fromBufferAttribute(t,g),l.subVectors(o,r),p.subVectors(s,r),l.cross(p),a.fromBufferAttribute(n,_),c.fromBufferAttribute(n,m),h.fromBufferAttribute(n,g),a.add(l),c.add(l),h.add(l),n.setXYZ(_,a.x,a.y,a.z),n.setXYZ(m,c.x,c.y,c.z),n.setXYZ(g,h.x,h.y,h.z)}else for(let f=0,u=t.count;f<u;f+=3)s.fromBufferAttribute(t,f+0),r.fromBufferAttribute(t,f+1),o.fromBufferAttribute(t,f+2),l.subVectors(o,r),p.subVectors(s,r),l.cross(p),n.setXYZ(f+0,l.x,l.y,l.z),n.setXYZ(f+1,l.x,l.y,l.z),n.setXYZ(f+2,l.x,l.y,l.z);this.normalizeNormals(),n.needsUpdate=!0}}normalizeNormals(){let e=this.attributes.normal;for(let t=0,n=e.count;t<n;t++)Mt.fromBufferAttribute(e,t),Mt.normalize(),e.setXYZ(t,Mt.x,Mt.y,Mt.z)}toNonIndexed(){function e(a,c){let h=a.array,l=a.itemSize,p=a.normalized,f=new h.constructor(c.length*l),u=0,_=0;for(let m=0,g=c.length;m<g;m++){a.isInterleavedBufferAttribute?u=c[m]*a.data.stride+a.offset:u=c[m]*l;for(let d=0;d<l;d++)f[_++]=h[u++]}return new wt(f,l,p)}if(this.index===null)return console.warn("THREE.BufferGeometry.toNonIndexed(): BufferGeometry is already non-indexed."),this;let t=new i,n=this.index.array,s=this.attributes;for(let a in s){let c=s[a],h=e(c,n);t.setAttribute(a,h)}let r=this.morphAttributes;for(let a in r){let c=[],h=r[a];for(let l=0,p=h.length;l<p;l++){let f=h[l],u=e(f,n);c.push(u)}t.morphAttributes[a]=c}t.morphTargetsRelative=this.morphTargetsRelative;let o=this.groups;for(let a=0,c=o.length;a<c;a++){let h=o[a];t.addGroup(h.start,h.count,h.materialIndex)}return t}toJSON(){let e={metadata:{version:4.6,type:"BufferGeometry",generator:"BufferGeometry.toJSON"}};if(e.uuid=this.uuid,e.type=this.type,this.name!==""&&(e.name=this.name),Object.keys(this.userData).length>0&&(e.userData=this.userData),this.parameters!==void 0){let c=this.parameters;for(let h in c)c[h]!==void 0&&(e[h]=c[h]);return e}e.data={attributes:{}};let t=this.index;t!==null&&(e.data.index={type:t.array.constructor.name,array:Array.prototype.slice.call(t.array)});let n=this.attributes;for(let c in n){let h=n[c];e.data.attributes[c]=h.toJSON(e.data)}let s={},r=!1;for(let c in this.morphAttributes){let h=this.morphAttributes[c],l=[];for(let p=0,f=h.length;p<f;p++){let u=h[p];l.push(u.toJSON(e.data))}l.length>0&&(s[c]=l,r=!0)}r&&(e.data.morphAttributes=s,e.data.morphTargetsRelative=this.morphTargetsRelative);let o=this.groups;o.length>0&&(e.data.groups=JSON.parse(JSON.stringify(o)));let a=this.boundingSphere;return a!==null&&(e.data.boundingSphere={center:a.center.toArray(),radius:a.radius}),e}clone(){return new this.constructor().copy(this)}copy(e){this.index=null,this.attributes={},this.morphAttributes={},this.groups=[],this.boundingBox=null,this.boundingSphere=null;let t={};this.name=e.name;let n=e.index;n!==null&&this.setIndex(n.clone(t));let s=e.attributes;for(let h in s){let l=s[h];this.setAttribute(h,l.clone(t))}let r=e.morphAttributes;for(let h in r){let l=[],p=r[h];for(let f=0,u=p.length;f<u;f++)l.push(p[f].clone(t));this.morphAttributes[h]=l}this.morphTargetsRelative=e.morphTargetsRelative;let o=e.groups;for(let h=0,l=o.length;h<l;h++){let p=o[h];this.addGroup(p.start,p.count,p.materialIndex)}let a=e.boundingBox;a!==null&&(this.boundingBox=a.clone());let c=e.boundingSphere;return c!==null&&(this.boundingSphere=c.clone()),this.drawRange.start=e.drawRange.start,this.drawRange.count=e.drawRange.count,this.userData=e.userData,this}dispose(){this.dispatchEvent({type:"dispose"})}},rl=new Fe,xi=new Hi,Gr=new Zt,ol=new C,Tr=new C,Ur=new C,Ir=new C,la=new C,Rr=new C,al=new C,Xr=new C,De=class extends mt{constructor(e=new Gt,t=new yt){super(),this.isMesh=!0,this.type="Mesh",this.geometry=e,this.material=t,this.updateMorphTargets()}copy(e,t){return super.copy(e,t),e.morphTargetInfluences!==void 0&&(this.morphTargetInfluences=e.morphTargetInfluences.slice()),e.morphTargetDictionary!==void 0&&(this.morphTargetDictionary=Object.assign({},e.morphTargetDictionary)),this.material=Array.isArray(e.material)?e.material.slice():e.material,this.geometry=e.geometry,this}updateMorphTargets(){let t=this.geometry.morphAttributes,n=Object.keys(t);if(n.length>0){let s=t[n[0]];if(s!==void 0){this.morphTargetInfluences=[],this.morphTargetDictionary={};for(let r=0,o=s.length;r<o;r++){let a=s[r].name||String(r);this.morphTargetInfluences.push(0),this.morphTargetDictionary[a]=r}}}}getVertexPosition(e,t){let n=this.geometry,s=n.attributes.position,r=n.morphAttributes.position,o=n.morphTargetsRelative;t.fromBufferAttribute(s,e);let a=this.morphTargetInfluences;if(r&&a){Rr.set(0,0,0);for(let c=0,h=r.length;c<h;c++){let l=a[c],p=r[c];l!==0&&(la.fromBufferAttribute(p,e),o?Rr.addScaledVector(la,l):Rr.addScaledVector(la.sub(t),l))}t.add(Rr)}return t}raycast(e,t){let n=this.geometry,s=this.material,r=this.matrixWorld;s!==void 0&&(n.boundingSphere===null&&n.computeBoundingSphere(),Gr.copy(n.boundingSphere),Gr.applyMatrix4(r),xi.copy(e.ray).recast(e.near),!(Gr.containsPoint(xi.origin)===!1&&(xi.intersectSphere(Gr,ol)===null||xi.origin.distanceToSquared(ol)>(e.far-e.near)**2))&&(rl.copy(r).invert(),xi.copy(e.ray).applyMatrix4(rl),!(n.boundingBox!==null&&xi.intersectsBox(n.boundingBox)===!1)&&this._computeIntersections(e,t,xi)))}_computeIntersections(e,t,n){let s,r=this.geometry,o=this.material,a=r.index,c=r.attributes.position,h=r.attributes.uv,l=r.attributes.uv1,p=r.attributes.normal,f=r.groups,u=r.drawRange;if(a!==null)if(Array.isArray(o))for(let _=0,m=f.length;_<m;_++){let g=f[_],d=o[g.materialIndex],A=Math.max(g.start,u.start),O=Math.min(a.count,Math.min(g.start+g.count,u.start+u.count));for(let P=A,G=O;P<G;P+=3){let H=a.getX(P),S=a.getX(P+1),M=a.getX(P+2);s=Br(this,d,e,n,h,l,p,H,S,M),s&&(s.faceIndex=Math.floor(P/3),s.face.materialIndex=g.materialIndex,t.push(s))}}else{let _=Math.max(0,u.start),m=Math.min(a.count,u.start+u.count);for(let g=_,d=m;g<d;g+=3){let A=a.getX(g),O=a.getX(g+1),P=a.getX(g+2);s=Br(this,o,e,n,h,l,p,A,O,P),s&&(s.faceIndex=Math.floor(g/3),t.push(s))}}else if(c!==void 0)if(Array.isArray(o))for(let _=0,m=f.length;_<m;_++){let g=f[_],d=o[g.materialIndex],A=Math.max(g.start,u.start),O=Math.min(c.count,Math.min(g.start+g.count,u.start+u.count));for(let P=A,G=O;P<G;P+=3){let H=P,S=P+1,M=P+2;s=Br(this,d,e,n,h,l,p,H,S,M),s&&(s.faceIndex=Math.floor(P/3),s.face.materialIndex=g.materialIndex,t.push(s))}}else{let _=Math.max(0,u.start),m=Math.min(c.count,u.start+u.count);for(let g=_,d=m;g<d;g+=3){let A=g,O=g+1,P=g+2;s=Br(this,o,e,n,h,l,p,A,O,P),s&&(s.faceIndex=Math.floor(g/3),t.push(s))}}}};function Ef(i,e,t,n,s,r,o,a){let c;if(e.side===Bt?c=n.intersectTriangle(o,r,s,!0,a):c=n.intersectTriangle(s,r,o,e.side===On,a),c===null)return null;Xr.copy(a),Xr.applyMatrix4(i.matrixWorld);let h=t.ray.origin.distanceTo(Xr);return h<t.near||h>t.far?null:{distance:h,point:Xr.clone(),object:i}}function Br(i,e,t,n,s,r,o,a,c,h){i.getVertexPosition(a,Tr),i.getVertexPosition(c,Ur),i.getVertexPosition(h,Ir);let l=Ef(i,e,t,n,Tr,Ur,Ir,al);if(l){let p=new C;si.getBarycoord(al,Tr,Ur,Ir,p),s&&(l.uv=si.getInterpolatedAttribute(s,a,c,h,p,new Re)),r&&(l.uv1=si.getInterpolatedAttribute(r,a,c,h,p,new Re)),o&&(l.normal=si.getInterpolatedAttribute(o,a,c,h,p,new C),l.normal.dot(n.direction)>0&&l.normal.multiplyScalar(-1));let f={a,b:c,c:h,normal:new C,materialIndex:0};si.getNormal(Tr,Ur,Ir,f.normal),l.face=f,l.barycoord=p}return l}var An=class i extends Gt{constructor(e=1,t=1,n=1,s=1,r=1,o=1){super(),this.type="BoxGeometry",this.parameters={width:e,height:t,depth:n,widthSegments:s,heightSegments:r,depthSegments:o};let a=this;s=Math.floor(s),r=Math.floor(r),o=Math.floor(o);let c=[],h=[],l=[],p=[],f=0,u=0;_("z","y","x",-1,-1,n,t,e,o,r,0),_("z","y","x",1,-1,n,t,-e,o,r,1),_("x","z","y",1,1,e,n,t,s,o,2),_("x","z","y",1,-1,e,n,-t,s,o,3),_("x","y","z",1,-1,e,t,n,s,r,4),_("x","y","z",-1,-1,e,t,-n,s,r,5),this.setIndex(c),this.setAttribute("position",new Ot(h,3)),this.setAttribute("normal",new Ot(l,3)),this.setAttribute("uv",new Ot(p,2));function _(m,g,d,A,O,P,G,H,S,M,y){let w=P/S,W=G/M,$=P/2,F=G/2,k=H/2,Q=S+1,V=M+1,te=0,z=0,pe=new C;for(let me=0;me<V;me++){let He=me*W-F;for(let Ne=0;Ne<Q;Ne++){let tt=Ne*w-$;pe[m]=tt*A,pe[g]=He*O,pe[d]=k,h.push(pe.x,pe.y,pe.z),pe[m]=0,pe[g]=0,pe[d]=H>0?1:-1,l.push(pe.x,pe.y,pe.z),p.push(Ne/S),p.push(1-me/M),te+=1}}for(let me=0;me<M;me++)for(let He=0;He<S;He++){let Ne=f+He+Q*me,tt=f+He+Q*(me+1),J=f+(He+1)+Q*(me+1),ie=f+(He+1)+Q*me;c.push(Ne,tt,ie),c.push(tt,J,ie),z+=6}a.addGroup(u,z,y),u+=z,f+=te}}copy(e){return super.copy(e),this.parameters=Object.assign({},e.parameters),this}static fromJSON(e){return new i(e.width,e.height,e.depth,e.widthSegments,e.heightSegments,e.depthSegments)}};function ds(i){let e={};for(let t in i){e[t]={};for(let n in i[t]){let s=i[t][n];s&&(s.isColor||s.isMatrix3||s.isMatrix4||s.isVector2||s.isVector3||s.isVector4||s.isTexture||s.isQuaternion)?s.isRenderTargetTexture?(console.warn("UniformsUtils: Textures of render targets cannot be cloned via cloneUniforms() or mergeUniforms()."),e[t][n]=null):e[t][n]=s.clone():Array.isArray(s)?e[t][n]=s.slice():e[t][n]=s}}return e}function Vt(i){let e={};for(let t=0;t<i.length;t++){let n=ds(i[t]);for(let s in n)e[s]=n[s]}return e}function Wf(i){let e=[];for(let t=0;t<i.length;t++)e.push(i[t].clone());return e}function P0(i){let e=i.getRenderTarget();return e===null?i.outputColorSpace:e.isXRRenderTarget===!0?e.texture.colorSpace:Ze.workingColorSpace}var Mf={clone:ds,merge:Vt},Cf=`void main() {
	gl_Position = projectionMatrix * modelViewMatrix * vec4( position, 1.0 );
}`,Gf=`void main() {
	gl_FragColor = vec4( 1.0, 0.0, 0.0, 1.0 );
}`,vn=class extends qt{static get type(){return"ShaderMaterial"}constructor(e){super(),this.isShaderMaterial=!0,this.defines={},this.uniforms={},this.uniformsGroups=[],this.vertexShader=Cf,this.fragmentShader=Gf,this.linewidth=1,this.wireframe=!1,this.wireframeLinewidth=1,this.fog=!1,this.lights=!1,this.clipping=!1,this.forceSinglePass=!0,this.extensions={clipCullDistance:!1,multiDraw:!1},this.defaultAttributeValues={color:[1,1,1],uv:[0,0],uv1:[0,0]},this.index0AttributeName=void 0,this.uniformsNeedUpdate=!1,this.glslVersion=null,e!==void 0&&this.setValues(e)}copy(e){return super.copy(e),this.fragmentShader=e.fragmentShader,this.vertexShader=e.vertexShader,this.uniforms=ds(e.uniforms),this.uniformsGroups=Wf(e.uniformsGroups),this.defines=Object.assign({},e.defines),this.wireframe=e.wireframe,this.wireframeLinewidth=e.wireframeLinewidth,this.fog=e.fog,this.lights=e.lights,this.clipping=e.clipping,this.extensions=Object.assign({},e.extensions),this.glslVersion=e.glslVersion,this}toJSON(e){let t=super.toJSON(e);t.glslVersion=this.glslVersion,t.uniforms={};for(let s in this.uniforms){let o=this.uniforms[s].value;o&&o.isTexture?t.uniforms[s]={type:"t",value:o.toJSON(e).uuid}:o&&o.isColor?t.uniforms[s]={type:"c",value:o.getHex()}:o&&o.isVector2?t.uniforms[s]={type:"v2",value:o.toArray()}:o&&o.isVector3?t.uniforms[s]={type:"v3",value:o.toArray()}:o&&o.isVector4?t.uniforms[s]={type:"v4",value:o.toArray()}:o&&o.isMatrix3?t.uniforms[s]={type:"m3",value:o.toArray()}:o&&o.isMatrix4?t.uniforms[s]={type:"m4",value:o.toArray()}:t.uniforms[s]={value:o}}Object.keys(this.defines).length>0&&(t.defines=this.defines),t.vertexShader=this.vertexShader,t.fragmentShader=this.fragmentShader,t.lights=this.lights,t.clipping=this.clipping;let n={};for(let s in this.extensions)this.extensions[s]===!0&&(n[s]=!0);return Object.keys(n).length>0&&(t.extensions=n),t}},lo=class extends mt{constructor(){super(),this.isCamera=!0,this.type="Camera",this.matrixWorldInverse=new Fe,this.projectionMatrix=new Fe,this.projectionMatrixInverse=new Fe,this.coordinateSystem=Bn}copy(e,t){return super.copy(e,t),this.matrixWorldInverse.copy(e.matrixWorldInverse),this.projectionMatrix.copy(e.projectionMatrix),this.projectionMatrixInverse.copy(e.projectionMatrixInverse),this.coordinateSystem=e.coordinateSystem,this}getWorldDirection(e){return super.getWorldDirection(e).negate()}updateMatrixWorld(e){super.updateMatrixWorld(e),this.matrixWorldInverse.copy(this.matrixWorld).invert()}updateWorldMatrix(e,t){super.updateWorldMatrix(e,t),this.matrixWorldInverse.copy(this.matrixWorld).invert()}clone(){return new this.constructor().copy(this)}},ii=new C,cl=new Re,hl=new Re,St=class extends lo{constructor(e=50,t=1,n=.1,s=2e3){super(),this.isPerspectiveCamera=!0,this.type="PerspectiveCamera",this.fov=e,this.zoom=1,this.near=n,this.far=s,this.focus=10,this.aspect=t,this.view=null,this.filmGauge=35,this.filmOffset=0,this.updateProjectionMatrix()}copy(e,t){return super.copy(e,t),this.fov=e.fov,this.zoom=e.zoom,this.near=e.near,this.far=e.far,this.focus=e.focus,this.aspect=e.aspect,this.view=e.view===null?null:Object.assign({},e.view),this.filmGauge=e.filmGauge,this.filmOffset=e.filmOffset,this}setFocalLength(e){let t=.5*this.getFilmHeight()/e;this.fov=fs*2*Math.atan(t),this.updateProjectionMatrix()}getFocalLength(){let e=Math.tan(Zs*.5*this.fov);return .5*this.getFilmHeight()/e}getEffectiveFOV(){return fs*2*Math.atan(Math.tan(Zs*.5*this.fov)/this.zoom)}getFilmWidth(){return this.filmGauge*Math.min(this.aspect,1)}getFilmHeight(){return this.filmGauge/Math.max(this.aspect,1)}getViewBounds(e,t,n){ii.set(-1,-1,.5).applyMatrix4(this.projectionMatrixInverse),t.set(ii.x,ii.y).multiplyScalar(-e/ii.z),ii.set(1,1,.5).applyMatrix4(this.projectionMatrixInverse),n.set(ii.x,ii.y).multiplyScalar(-e/ii.z)}getViewSize(e,t){return this.getViewBounds(e,cl,hl),t.subVectors(hl,cl)}setViewOffset(e,t,n,s,r,o){this.aspect=e/t,this.view===null&&(this.view={enabled:!0,fullWidth:1,fullHeight:1,offsetX:0,offsetY:0,width:1,height:1}),this.view.enabled=!0,this.view.fullWidth=e,this.view.fullHeight=t,this.view.offsetX=n,this.view.offsetY=s,this.view.width=r,this.view.height=o,this.updateProjectionMatrix()}clearViewOffset(){this.view!==null&&(this.view.enabled=!1),this.updateProjectionMatrix()}updateProjectionMatrix(){let e=this.near,t=e*Math.tan(Zs*.5*this.fov)/this.zoom,n=2*t,s=this.aspect*n,r=-.5*s,o=this.view;if(this.view!==null&&this.view.enabled){let c=o.fullWidth,h=o.fullHeight;r+=o.offsetX*s/c,t-=o.offsetY*n/h,s*=o.width/c,n*=o.height/h}let a=this.filmOffset;a!==0&&(r+=e*a/this.getFilmWidth()),this.projectionMatrix.makePerspective(r,r+s,t,t-n,e,this.far,this.coordinateSystem),this.projectionMatrixInverse.copy(this.projectionMatrix).invert()}toJSON(e){let t=super.toJSON(e);return t.object.fov=this.fov,t.object.zoom=this.zoom,t.object.near=this.near,t.object.far=this.far,t.object.focus=this.focus,t.object.aspect=this.aspect,this.view!==null&&(t.object.view=Object.assign({},this.view)),t.object.filmGauge=this.filmGauge,t.object.filmOffset=this.filmOffset,t}},zi=-90,ki=1,pc=class extends mt{constructor(e,t,n){super(),this.type="CubeCamera",this.renderTarget=n,this.coordinateSystem=null,this.activeMipmapLevel=0;let s=new St(zi,ki,e,t);s.layers=this.layers,this.add(s);let r=new St(zi,ki,e,t);r.layers=this.layers,this.add(r);let o=new St(zi,ki,e,t);o.layers=this.layers,this.add(o);let a=new St(zi,ki,e,t);a.layers=this.layers,this.add(a);let c=new St(zi,ki,e,t);c.layers=this.layers,this.add(c);let h=new St(zi,ki,e,t);h.layers=this.layers,this.add(h)}updateCoordinateSystem(){let e=this.coordinateSystem,t=this.children.concat(),[n,s,r,o,a,c]=t;for(let h of t)this.remove(h);if(e===Bn)n.up.set(0,1,0),n.lookAt(1,0,0),s.up.set(0,1,0),s.lookAt(-1,0,0),r.up.set(0,0,-1),r.lookAt(0,1,0),o.up.set(0,0,1),o.lookAt(0,-1,0),a.up.set(0,1,0),a.lookAt(0,0,1),c.up.set(0,1,0),c.lookAt(0,0,-1);else if(e===ro)n.up.set(0,-1,0),n.lookAt(-1,0,0),s.up.set(0,-1,0),s.lookAt(1,0,0),r.up.set(0,0,1),r.lookAt(0,1,0),o.up.set(0,0,-1),o.lookAt(0,-1,0),a.up.set(0,-1,0),a.lookAt(0,0,1),c.up.set(0,-1,0),c.lookAt(0,0,-1);else throw new Error("THREE.CubeCamera.updateCoordinateSystem(): Invalid coordinate system: "+e);for(let h of t)this.add(h),h.updateMatrixWorld()}update(e,t){this.parent===null&&this.updateMatrixWorld();let{renderTarget:n,activeMipmapLevel:s}=this;this.coordinateSystem!==e.coordinateSystem&&(this.coordinateSystem=e.coordinateSystem,this.updateCoordinateSystem());let[r,o,a,c,h,l]=this.children,p=e.getRenderTarget(),f=e.getActiveCubeFace(),u=e.getActiveMipmapLevel(),_=e.xr.enabled;e.xr.enabled=!1;let m=n.texture.generateMipmaps;n.texture.generateMipmaps=!1,e.setRenderTarget(n,0,s),e.render(t,r),e.setRenderTarget(n,1,s),e.render(t,o),e.setRenderTarget(n,2,s),e.render(t,a),e.setRenderTarget(n,3,s),e.render(t,c),e.setRenderTarget(n,4,s),e.render(t,h),n.texture.generateMipmaps=m,e.setRenderTarget(n,5,s),e.render(t,l),e.setRenderTarget(p,f,u),e.xr.enabled=_,n.texture.needsPMREMUpdate=!0}},po=class extends Ct{constructor(e,t,n,s,r,o,a,c,h,l){e=e!==void 0?e:[],t=t!==void 0?t:os,super(e,t,n,s,r,o,a,c,h,l),this.isCubeTexture=!0,this.flipY=!1}get images(){return this.image}set images(e){this.image=e}},fc=class extends Nn{constructor(e=1,t={}){super(e,e,t),this.isWebGLCubeRenderTarget=!0;let n={width:e,height:e,depth:1},s=[n,n,n,n,n,n];this.texture=new po(s,t.mapping,t.wrapS,t.wrapT,t.magFilter,t.minFilter,t.format,t.type,t.anisotropy,t.colorSpace),this.texture.isRenderTargetTexture=!0,this.texture.generateMipmaps=t.generateMipmaps!==void 0?t.generateMipmaps:!1,this.texture.minFilter=t.minFilter!==void 0?t.minFilter:kt}fromEquirectangularTexture(e,t){this.texture.type=t.type,this.texture.colorSpace=t.colorSpace,this.texture.generateMipmaps=t.generateMipmaps,this.texture.minFilter=t.minFilter,this.texture.magFilter=t.magFilter;let n={uniforms:{tEquirect:{value:null}},vertexShader:`

				varying vec3 vWorldDirection;

				vec3 transformDirection( in vec3 dir, in mat4 matrix ) {

					return normalize( ( matrix * vec4( dir, 0.0 ) ).xyz );

				}

				void main() {

					vWorldDirection = transformDirection( position, modelMatrix );

					#include <begin_vertex>
					#include <project_vertex>

				}
			`,fragmentShader:`

				uniform sampler2D tEquirect;

				varying vec3 vWorldDirection;

				#include <common>

				void main() {

					vec3 direction = normalize( vWorldDirection );

					vec2 sampleUV = equirectUv( direction );

					gl_FragColor = texture2D( tEquirect, sampleUV );

				}
			`},s=new An(5,5,5),r=new vn({name:"CubemapFromEquirect",uniforms:ds(n.uniforms),vertexShader:n.vertexShader,fragmentShader:n.fragmentShader,side:Bt,blending:oi});r.uniforms.tEquirect.value=t;let o=new De(s,r),a=t.minFilter;return t.minFilter===wn&&(t.minFilter=kt),new pc(1,10,this).update(e,o),t.minFilter=a,o.geometry.dispose(),o.material.dispose(),this}clear(e,t,n,s){let r=e.getRenderTarget();for(let o=0;o<6;o++)e.setRenderTarget(this,o),e.clear(t,n,s);e.setRenderTarget(r)}},pa=new C,Tf=new C,Uf=new ze,Rn=class{constructor(e=new C(1,0,0),t=0){this.isPlane=!0,this.normal=e,this.constant=t}set(e,t){return this.normal.copy(e),this.constant=t,this}setComponents(e,t,n,s){return this.normal.set(e,t,n),this.constant=s,this}setFromNormalAndCoplanarPoint(e,t){return this.normal.copy(e),this.constant=-t.dot(this.normal),this}setFromCoplanarPoints(e,t,n){let s=pa.subVectors(n,t).cross(Tf.subVectors(e,t)).normalize();return this.setFromNormalAndCoplanarPoint(s,e),this}copy(e){return this.normal.copy(e.normal),this.constant=e.constant,this}normalize(){let e=1/this.normal.length();return this.normal.multiplyScalar(e),this.constant*=e,this}negate(){return this.constant*=-1,this.normal.negate(),this}distanceToPoint(e){return this.normal.dot(e)+this.constant}distanceToSphere(e){return this.distanceToPoint(e.center)-e.radius}projectPoint(e,t){return t.copy(e).addScaledVector(this.normal,-this.distanceToPoint(e))}intersectLine(e,t){let n=e.delta(pa),s=this.normal.dot(n);if(s===0)return this.distanceToPoint(e.start)===0?t.copy(e.start):null;let r=-(e.start.dot(this.normal)+this.constant)/s;return r<0||r>1?null:t.copy(e.start).addScaledVector(n,r)}intersectsLine(e){let t=this.distanceToPoint(e.start),n=this.distanceToPoint(e.end);return t<0&&n>0||n<0&&t>0}intersectsBox(e){return e.intersectsPlane(this)}intersectsSphere(e){return e.intersectsPlane(this)}coplanarPoint(e){return e.copy(this.normal).multiplyScalar(-this.constant)}applyMatrix4(e,t){let n=t||Uf.getNormalMatrix(e),s=this.coplanarPoint(pa).applyMatrix4(e),r=this.normal.applyMatrix3(n).normalize();return this.constant=-s.dot(r),this}translate(e){return this.constant-=e.dot(this.normal),this}equals(e){return e.normal.equals(this.normal)&&e.constant===this.constant}clone(){return new this.constructor().copy(this)}},bi=new Zt,$r=new C,rr=class{constructor(e=new Rn,t=new Rn,n=new Rn,s=new Rn,r=new Rn,o=new Rn){this.planes=[e,t,n,s,r,o]}set(e,t,n,s,r,o){let a=this.planes;return a[0].copy(e),a[1].copy(t),a[2].copy(n),a[3].copy(s),a[4].copy(r),a[5].copy(o),this}copy(e){let t=this.planes;for(let n=0;n<6;n++)t[n].copy(e.planes[n]);return this}setFromProjectionMatrix(e,t=Bn){let n=this.planes,s=e.elements,r=s[0],o=s[1],a=s[2],c=s[3],h=s[4],l=s[5],p=s[6],f=s[7],u=s[8],_=s[9],m=s[10],g=s[11],d=s[12],A=s[13],O=s[14],P=s[15];if(n[0].setComponents(c-r,f-h,g-u,P-d).normalize(),n[1].setComponents(c+r,f+h,g+u,P+d).normalize(),n[2].setComponents(c+o,f+l,g+_,P+A).normalize(),n[3].setComponents(c-o,f-l,g-_,P-A).normalize(),n[4].setComponents(c-a,f-p,g-m,P-O).normalize(),t===Bn)n[5].setComponents(c+a,f+p,g+m,P+O).normalize();else if(t===ro)n[5].setComponents(a,p,m,O).normalize();else throw new Error("THREE.Frustum.setFromProjectionMatrix(): Invalid coordinate system: "+t);return this}intersectsObject(e){if(e.boundingSphere!==void 0)e.boundingSphere===null&&e.computeBoundingSphere(),bi.copy(e.boundingSphere).applyMatrix4(e.matrixWorld);else{let t=e.geometry;t.boundingSphere===null&&t.computeBoundingSphere(),bi.copy(t.boundingSphere).applyMatrix4(e.matrixWorld)}return this.intersectsSphere(bi)}intersectsSprite(e){return bi.center.set(0,0,0),bi.radius=.7071067811865476,bi.applyMatrix4(e.matrixWorld),this.intersectsSphere(bi)}intersectsSphere(e){let t=this.planes,n=e.center,s=-e.radius;for(let r=0;r<6;r++)if(t[r].distanceToPoint(n)<s)return!1;return!0}intersectsBox(e){let t=this.planes;for(let n=0;n<6;n++){let s=t[n];if($r.x=s.normal.x>0?e.max.x:e.min.x,$r.y=s.normal.y>0?e.max.y:e.min.y,$r.z=s.normal.z>0?e.max.z:e.min.z,s.distanceToPoint($r)<0)return!1}return!0}containsPoint(e){let t=this.planes;for(let n=0;n<6;n++)if(t[n].distanceToPoint(e)<0)return!1;return!0}clone(){return new this.constructor().copy(this)}};function w0(){let i=null,e=!1,t=null,n=null;function s(r,o){t(r,o),n=i.requestAnimationFrame(s)}return{start:function(){e!==!0&&t!==null&&(n=i.requestAnimationFrame(s),e=!0)},stop:function(){i.cancelAnimationFrame(n),e=!1},setAnimationLoop:function(r){t=r},setContext:function(r){i=r}}}function If(i){let e=new WeakMap;function t(a,c){let h=a.array,l=a.usage,p=h.byteLength,f=i.createBuffer();i.bindBuffer(c,f),i.bufferData(c,h,l),a.onUploadCallback();let u;if(h instanceof Float32Array)u=i.FLOAT;else if(h instanceof Uint16Array)a.isFloat16BufferAttribute?u=i.HALF_FLOAT:u=i.UNSIGNED_SHORT;else if(h instanceof Int16Array)u=i.SHORT;else if(h instanceof Uint32Array)u=i.UNSIGNED_INT;else if(h instanceof Int32Array)u=i.INT;else if(h instanceof Int8Array)u=i.BYTE;else if(h instanceof Uint8Array)u=i.UNSIGNED_BYTE;else if(h instanceof Uint8ClampedArray)u=i.UNSIGNED_BYTE;else throw new Error("THREE.WebGLAttributes: Unsupported buffer data format: "+h);return{buffer:f,type:u,bytesPerElement:h.BYTES_PER_ELEMENT,version:a.version,size:p}}function n(a,c,h){let l=c.array,p=c.updateRanges;if(i.bindBuffer(h,a),p.length===0)i.bufferSubData(h,0,l);else{p.sort((u,_)=>u.start-_.start);let f=0;for(let u=1;u<p.length;u++){let _=p[f],m=p[u];m.start<=_.start+_.count+1?_.count=Math.max(_.count,m.start+m.count-_.start):(++f,p[f]=m)}p.length=f+1;for(let u=0,_=p.length;u<_;u++){let m=p[u];i.bufferSubData(h,m.start*l.BYTES_PER_ELEMENT,l,m.start,m.count)}c.clearUpdateRanges()}c.onUploadCallback()}function s(a){return a.isInterleavedBufferAttribute&&(a=a.data),e.get(a)}function r(a){a.isInterleavedBufferAttribute&&(a=a.data);let c=e.get(a);c&&(i.deleteBuffer(c.buffer),e.delete(a))}function o(a,c){if(a.isInterleavedBufferAttribute&&(a=a.data),a.isGLBufferAttribute){let l=e.get(a);(!l||l.version<a.version)&&e.set(a,{buffer:a.buffer,type:a.type,bytesPerElement:a.elementSize,version:a.version});return}let h=e.get(a);if(h===void 0)e.set(a,t(a,c));else if(h.version<a.version){if(h.size!==a.array.byteLength)throw new Error("THREE.WebGLAttributes: The size of the buffer attribute's array buffer does not match the original size. Resizing buffer attributes is not supported.");n(h.buffer,a,c),h.version=a.version}}return{get:s,remove:r,update:o}}var us=class i extends Gt{constructor(e=1,t=1,n=1,s=1){super(),this.type="PlaneGeometry",this.parameters={width:e,height:t,widthSegments:n,heightSegments:s};let r=e/2,o=t/2,a=Math.floor(n),c=Math.floor(s),h=a+1,l=c+1,p=e/a,f=t/c,u=[],_=[],m=[],g=[];for(let d=0;d<l;d++){let A=d*f-o;for(let O=0;O<h;O++){let P=O*p-r;_.push(P,-A,0),m.push(0,0,1),g.push(O/a),g.push(1-d/c)}}for(let d=0;d<c;d++)for(let A=0;A<a;A++){let O=A+h*d,P=A+h*(d+1),G=A+1+h*(d+1),H=A+1+h*d;u.push(O,P,H),u.push(P,G,H)}this.setIndex(u),this.setAttribute("position",new Ot(_,3)),this.setAttribute("normal",new Ot(m,3)),this.setAttribute("uv",new Ot(g,2))}copy(e){return super.copy(e),this.parameters=Object.assign({},e.parameters),this}static fromJSON(e){return new i(e.width,e.height,e.widthSegments,e.heightSegments)}},Rf=`#ifdef USE_ALPHAHASH
	if ( diffuseColor.a < getAlphaHashThreshold( vPosition ) ) discard;
#endif`,Xf=`#ifdef USE_ALPHAHASH
	const float ALPHA_HASH_SCALE = 0.05;
	float hash2D( vec2 value ) {
		return fract( 1.0e4 * sin( 17.0 * value.x + 0.1 * value.y ) * ( 0.1 + abs( sin( 13.0 * value.y + value.x ) ) ) );
	}
	float hash3D( vec3 value ) {
		return hash2D( vec2( hash2D( value.xy ), value.z ) );
	}
	float getAlphaHashThreshold( vec3 position ) {
		float maxDeriv = max(
			length( dFdx( position.xyz ) ),
			length( dFdy( position.xyz ) )
		);
		float pixScale = 1.0 / ( ALPHA_HASH_SCALE * maxDeriv );
		vec2 pixScales = vec2(
			exp2( floor( log2( pixScale ) ) ),
			exp2( ceil( log2( pixScale ) ) )
		);
		vec2 alpha = vec2(
			hash3D( floor( pixScales.x * position.xyz ) ),
			hash3D( floor( pixScales.y * position.xyz ) )
		);
		float lerpFactor = fract( log2( pixScale ) );
		float x = ( 1.0 - lerpFactor ) * alpha.x + lerpFactor * alpha.y;
		float a = min( lerpFactor, 1.0 - lerpFactor );
		vec3 cases = vec3(
			x * x / ( 2.0 * a * ( 1.0 - a ) ),
			( x - 0.5 * a ) / ( 1.0 - a ),
			1.0 - ( ( 1.0 - x ) * ( 1.0 - x ) / ( 2.0 * a * ( 1.0 - a ) ) )
		);
		float threshold = ( x < ( 1.0 - a ) )
			? ( ( x < a ) ? cases.x : cases.y )
			: cases.z;
		return clamp( threshold , 1.0e-6, 1.0 );
	}
#endif`,Bf=`#ifdef USE_ALPHAMAP
	diffuseColor.a *= texture2D( alphaMap, vAlphaMapUv ).g;
#endif`,$f=`#ifdef USE_ALPHAMAP
	uniform sampler2D alphaMap;
#endif`,Ff=`#ifdef USE_ALPHATEST
	#ifdef ALPHA_TO_COVERAGE
	diffuseColor.a = smoothstep( alphaTest, alphaTest + fwidth( diffuseColor.a ), diffuseColor.a );
	if ( diffuseColor.a == 0.0 ) discard;
	#else
	if ( diffuseColor.a < alphaTest ) discard;
	#endif
#endif`,Df=`#ifdef USE_ALPHATEST
	uniform float alphaTest;
#endif`,Nf=`#ifdef USE_AOMAP
	float ambientOcclusion = ( texture2D( aoMap, vAoMapUv ).r - 1.0 ) * aoMapIntensity + 1.0;
	reflectedLight.indirectDiffuse *= ambientOcclusion;
	#if defined( USE_CLEARCOAT ) 
		clearcoatSpecularIndirect *= ambientOcclusion;
	#endif
	#if defined( USE_SHEEN ) 
		sheenSpecularIndirect *= ambientOcclusion;
	#endif
	#if defined( USE_ENVMAP ) && defined( STANDARD )
		float dotNV = saturate( dot( geometryNormal, geometryViewDir ) );
		reflectedLight.indirectSpecular *= computeSpecularOcclusion( dotNV, ambientOcclusion, material.roughness );
	#endif
#endif`,Vf=`#ifdef USE_AOMAP
	uniform sampler2D aoMap;
	uniform float aoMapIntensity;
#endif`,Lf=`#ifdef USE_BATCHING
	#if ! defined( GL_ANGLE_multi_draw )
	#define gl_DrawID _gl_DrawID
	uniform int _gl_DrawID;
	#endif
	uniform highp sampler2D batchingTexture;
	uniform highp usampler2D batchingIdTexture;
	mat4 getBatchingMatrix( const in float i ) {
		int size = textureSize( batchingTexture, 0 ).x;
		int j = int( i ) * 4;
		int x = j % size;
		int y = j / size;
		vec4 v1 = texelFetch( batchingTexture, ivec2( x, y ), 0 );
		vec4 v2 = texelFetch( batchingTexture, ivec2( x + 1, y ), 0 );
		vec4 v3 = texelFetch( batchingTexture, ivec2( x + 2, y ), 0 );
		vec4 v4 = texelFetch( batchingTexture, ivec2( x + 3, y ), 0 );
		return mat4( v1, v2, v3, v4 );
	}
	float getIndirectIndex( const in int i ) {
		int size = textureSize( batchingIdTexture, 0 ).x;
		int x = i % size;
		int y = i / size;
		return float( texelFetch( batchingIdTexture, ivec2( x, y ), 0 ).r );
	}
#endif
#ifdef USE_BATCHING_COLOR
	uniform sampler2D batchingColorTexture;
	vec3 getBatchingColor( const in float i ) {
		int size = textureSize( batchingColorTexture, 0 ).x;
		int j = int( i );
		int x = j % size;
		int y = j / size;
		return texelFetch( batchingColorTexture, ivec2( x, y ), 0 ).rgb;
	}
#endif`,zf=`#ifdef USE_BATCHING
	mat4 batchingMatrix = getBatchingMatrix( getIndirectIndex( gl_DrawID ) );
#endif`,kf=`vec3 transformed = vec3( position );
#ifdef USE_ALPHAHASH
	vPosition = vec3( position );
#endif`,qf=`vec3 objectNormal = vec3( normal );
#ifdef USE_TANGENT
	vec3 objectTangent = vec3( tangent.xyz );
#endif`,Jf=`float G_BlinnPhong_Implicit( ) {
	return 0.25;
}
float D_BlinnPhong( const in float shininess, const in float dotNH ) {
	return RECIPROCAL_PI * ( shininess * 0.5 + 1.0 ) * pow( dotNH, shininess );
}
vec3 BRDF_BlinnPhong( const in vec3 lightDir, const in vec3 viewDir, const in vec3 normal, const in vec3 specularColor, const in float shininess ) {
	vec3 halfDir = normalize( lightDir + viewDir );
	float dotNH = saturate( dot( normal, halfDir ) );
	float dotVH = saturate( dot( viewDir, halfDir ) );
	vec3 F = F_Schlick( specularColor, 1.0, dotVH );
	float G = G_BlinnPhong_Implicit( );
	float D = D_BlinnPhong( shininess, dotNH );
	return F * ( G * D );
} // validated`,jf=`#ifdef USE_IRIDESCENCE
	const mat3 XYZ_TO_REC709 = mat3(
		 3.2404542, -0.9692660,  0.0556434,
		-1.5371385,  1.8760108, -0.2040259,
		-0.4985314,  0.0415560,  1.0572252
	);
	vec3 Fresnel0ToIor( vec3 fresnel0 ) {
		vec3 sqrtF0 = sqrt( fresnel0 );
		return ( vec3( 1.0 ) + sqrtF0 ) / ( vec3( 1.0 ) - sqrtF0 );
	}
	vec3 IorToFresnel0( vec3 transmittedIor, float incidentIor ) {
		return pow2( ( transmittedIor - vec3( incidentIor ) ) / ( transmittedIor + vec3( incidentIor ) ) );
	}
	float IorToFresnel0( float transmittedIor, float incidentIor ) {
		return pow2( ( transmittedIor - incidentIor ) / ( transmittedIor + incidentIor ));
	}
	vec3 evalSensitivity( float OPD, vec3 shift ) {
		float phase = 2.0 * PI * OPD * 1.0e-9;
		vec3 val = vec3( 5.4856e-13, 4.4201e-13, 5.2481e-13 );
		vec3 pos = vec3( 1.6810e+06, 1.7953e+06, 2.2084e+06 );
		vec3 var = vec3( 4.3278e+09, 9.3046e+09, 6.6121e+09 );
		vec3 xyz = val * sqrt( 2.0 * PI * var ) * cos( pos * phase + shift ) * exp( - pow2( phase ) * var );
		xyz.x += 9.7470e-14 * sqrt( 2.0 * PI * 4.5282e+09 ) * cos( 2.2399e+06 * phase + shift[ 0 ] ) * exp( - 4.5282e+09 * pow2( phase ) );
		xyz /= 1.0685e-7;
		vec3 rgb = XYZ_TO_REC709 * xyz;
		return rgb;
	}
	vec3 evalIridescence( float outsideIOR, float eta2, float cosTheta1, float thinFilmThickness, vec3 baseF0 ) {
		vec3 I;
		float iridescenceIOR = mix( outsideIOR, eta2, smoothstep( 0.0, 0.03, thinFilmThickness ) );
		float sinTheta2Sq = pow2( outsideIOR / iridescenceIOR ) * ( 1.0 - pow2( cosTheta1 ) );
		float cosTheta2Sq = 1.0 - sinTheta2Sq;
		if ( cosTheta2Sq < 0.0 ) {
			return vec3( 1.0 );
		}
		float cosTheta2 = sqrt( cosTheta2Sq );
		float R0 = IorToFresnel0( iridescenceIOR, outsideIOR );
		float R12 = F_Schlick( R0, 1.0, cosTheta1 );
		float T121 = 1.0 - R12;
		float phi12 = 0.0;
		if ( iridescenceIOR < outsideIOR ) phi12 = PI;
		float phi21 = PI - phi12;
		vec3 baseIOR = Fresnel0ToIor( clamp( baseF0, 0.0, 0.9999 ) );		vec3 R1 = IorToFresnel0( baseIOR, iridescenceIOR );
		vec3 R23 = F_Schlick( R1, 1.0, cosTheta2 );
		vec3 phi23 = vec3( 0.0 );
		if ( baseIOR[ 0 ] < iridescenceIOR ) phi23[ 0 ] = PI;
		if ( baseIOR[ 1 ] < iridescenceIOR ) phi23[ 1 ] = PI;
		if ( baseIOR[ 2 ] < iridescenceIOR ) phi23[ 2 ] = PI;
		float OPD = 2.0 * iridescenceIOR * thinFilmThickness * cosTheta2;
		vec3 phi = vec3( phi21 ) + phi23;
		vec3 R123 = clamp( R12 * R23, 1e-5, 0.9999 );
		vec3 r123 = sqrt( R123 );
		vec3 Rs = pow2( T121 ) * R23 / ( vec3( 1.0 ) - R123 );
		vec3 C0 = R12 + Rs;
		I = C0;
		vec3 Cm = Rs - T121;
		for ( int m = 1; m <= 2; ++ m ) {
			Cm *= r123;
			vec3 Sm = 2.0 * evalSensitivity( float( m ) * OPD, float( m ) * phi );
			I += Cm * Sm;
		}
		return max( I, vec3( 0.0 ) );
	}
#endif`,Qf=`#ifdef USE_BUMPMAP
	uniform sampler2D bumpMap;
	uniform float bumpScale;
	vec2 dHdxy_fwd() {
		vec2 dSTdx = dFdx( vBumpMapUv );
		vec2 dSTdy = dFdy( vBumpMapUv );
		float Hll = bumpScale * texture2D( bumpMap, vBumpMapUv ).x;
		float dBx = bumpScale * texture2D( bumpMap, vBumpMapUv + dSTdx ).x - Hll;
		float dBy = bumpScale * texture2D( bumpMap, vBumpMapUv + dSTdy ).x - Hll;
		return vec2( dBx, dBy );
	}
	vec3 perturbNormalArb( vec3 surf_pos, vec3 surf_norm, vec2 dHdxy, float faceDirection ) {
		vec3 vSigmaX = normalize( dFdx( surf_pos.xyz ) );
		vec3 vSigmaY = normalize( dFdy( surf_pos.xyz ) );
		vec3 vN = surf_norm;
		vec3 R1 = cross( vSigmaY, vN );
		vec3 R2 = cross( vN, vSigmaX );
		float fDet = dot( vSigmaX, R1 ) * faceDirection;
		vec3 vGrad = sign( fDet ) * ( dHdxy.x * R1 + dHdxy.y * R2 );
		return normalize( abs( fDet ) * surf_norm - vGrad );
	}
#endif`,Yf=`#if NUM_CLIPPING_PLANES > 0
	vec4 plane;
	#ifdef ALPHA_TO_COVERAGE
		float distanceToPlane, distanceGradient;
		float clipOpacity = 1.0;
		#pragma unroll_loop_start
		for ( int i = 0; i < UNION_CLIPPING_PLANES; i ++ ) {
			plane = clippingPlanes[ i ];
			distanceToPlane = - dot( vClipPosition, plane.xyz ) + plane.w;
			distanceGradient = fwidth( distanceToPlane ) / 2.0;
			clipOpacity *= smoothstep( - distanceGradient, distanceGradient, distanceToPlane );
			if ( clipOpacity == 0.0 ) discard;
		}
		#pragma unroll_loop_end
		#if UNION_CLIPPING_PLANES < NUM_CLIPPING_PLANES
			float unionClipOpacity = 1.0;
			#pragma unroll_loop_start
			for ( int i = UNION_CLIPPING_PLANES; i < NUM_CLIPPING_PLANES; i ++ ) {
				plane = clippingPlanes[ i ];
				distanceToPlane = - dot( vClipPosition, plane.xyz ) + plane.w;
				distanceGradient = fwidth( distanceToPlane ) / 2.0;
				unionClipOpacity *= 1.0 - smoothstep( - distanceGradient, distanceGradient, distanceToPlane );
			}
			#pragma unroll_loop_end
			clipOpacity *= 1.0 - unionClipOpacity;
		#endif
		diffuseColor.a *= clipOpacity;
		if ( diffuseColor.a == 0.0 ) discard;
	#else
		#pragma unroll_loop_start
		for ( int i = 0; i < UNION_CLIPPING_PLANES; i ++ ) {
			plane = clippingPlanes[ i ];
			if ( dot( vClipPosition, plane.xyz ) > plane.w ) discard;
		}
		#pragma unroll_loop_end
		#if UNION_CLIPPING_PLANES < NUM_CLIPPING_PLANES
			bool clipped = true;
			#pragma unroll_loop_start
			for ( int i = UNION_CLIPPING_PLANES; i < NUM_CLIPPING_PLANES; i ++ ) {
				plane = clippingPlanes[ i ];
				clipped = ( dot( vClipPosition, plane.xyz ) > plane.w ) && clipped;
			}
			#pragma unroll_loop_end
			if ( clipped ) discard;
		#endif
	#endif
#endif`,Zf=`#if NUM_CLIPPING_PLANES > 0
	varying vec3 vClipPosition;
	uniform vec4 clippingPlanes[ NUM_CLIPPING_PLANES ];
#endif`,Kf=`#if NUM_CLIPPING_PLANES > 0
	varying vec3 vClipPosition;
#endif`,ed=`#if NUM_CLIPPING_PLANES > 0
	vClipPosition = - mvPosition.xyz;
#endif`,td=`#if defined( USE_COLOR_ALPHA )
	diffuseColor *= vColor;
#elif defined( USE_COLOR )
	diffuseColor.rgb *= vColor;
#endif`,nd=`#if defined( USE_COLOR_ALPHA )
	varying vec4 vColor;
#elif defined( USE_COLOR )
	varying vec3 vColor;
#endif`,id=`#if defined( USE_COLOR_ALPHA )
	varying vec4 vColor;
#elif defined( USE_COLOR ) || defined( USE_INSTANCING_COLOR ) || defined( USE_BATCHING_COLOR )
	varying vec3 vColor;
#endif`,sd=`#if defined( USE_COLOR_ALPHA )
	vColor = vec4( 1.0 );
#elif defined( USE_COLOR ) || defined( USE_INSTANCING_COLOR ) || defined( USE_BATCHING_COLOR )
	vColor = vec3( 1.0 );
#endif
#ifdef USE_COLOR
	vColor *= color;
#endif
#ifdef USE_INSTANCING_COLOR
	vColor.xyz *= instanceColor.xyz;
#endif
#ifdef USE_BATCHING_COLOR
	vec3 batchingColor = getBatchingColor( getIndirectIndex( gl_DrawID ) );
	vColor.xyz *= batchingColor.xyz;
#endif`,rd=`#define PI 3.141592653589793
#define PI2 6.283185307179586
#define PI_HALF 1.5707963267948966
#define RECIPROCAL_PI 0.3183098861837907
#define RECIPROCAL_PI2 0.15915494309189535
#define EPSILON 1e-6
#ifndef saturate
#define saturate( a ) clamp( a, 0.0, 1.0 )
#endif
#define whiteComplement( a ) ( 1.0 - saturate( a ) )
float pow2( const in float x ) { return x*x; }
vec3 pow2( const in vec3 x ) { return x*x; }
float pow3( const in float x ) { return x*x*x; }
float pow4( const in float x ) { float x2 = x*x; return x2*x2; }
float max3( const in vec3 v ) { return max( max( v.x, v.y ), v.z ); }
float average( const in vec3 v ) { return dot( v, vec3( 0.3333333 ) ); }
highp float rand( const in vec2 uv ) {
	const highp float a = 12.9898, b = 78.233, c = 43758.5453;
	highp float dt = dot( uv.xy, vec2( a,b ) ), sn = mod( dt, PI );
	return fract( sin( sn ) * c );
}
#ifdef HIGH_PRECISION
	float precisionSafeLength( vec3 v ) { return length( v ); }
#else
	float precisionSafeLength( vec3 v ) {
		float maxComponent = max3( abs( v ) );
		return length( v / maxComponent ) * maxComponent;
	}
#endif
struct IncidentLight {
	vec3 color;
	vec3 direction;
	bool visible;
};
struct ReflectedLight {
	vec3 directDiffuse;
	vec3 directSpecular;
	vec3 indirectDiffuse;
	vec3 indirectSpecular;
};
#ifdef USE_ALPHAHASH
	varying vec3 vPosition;
#endif
vec3 transformDirection( in vec3 dir, in mat4 matrix ) {
	return normalize( ( matrix * vec4( dir, 0.0 ) ).xyz );
}
vec3 inverseTransformDirection( in vec3 dir, in mat4 matrix ) {
	return normalize( ( vec4( dir, 0.0 ) * matrix ).xyz );
}
mat3 transposeMat3( const in mat3 m ) {
	mat3 tmp;
	tmp[ 0 ] = vec3( m[ 0 ].x, m[ 1 ].x, m[ 2 ].x );
	tmp[ 1 ] = vec3( m[ 0 ].y, m[ 1 ].y, m[ 2 ].y );
	tmp[ 2 ] = vec3( m[ 0 ].z, m[ 1 ].z, m[ 2 ].z );
	return tmp;
}
bool isPerspectiveMatrix( mat4 m ) {
	return m[ 2 ][ 3 ] == - 1.0;
}
vec2 equirectUv( in vec3 dir ) {
	float u = atan( dir.z, dir.x ) * RECIPROCAL_PI2 + 0.5;
	float v = asin( clamp( dir.y, - 1.0, 1.0 ) ) * RECIPROCAL_PI + 0.5;
	return vec2( u, v );
}
vec3 BRDF_Lambert( const in vec3 diffuseColor ) {
	return RECIPROCAL_PI * diffuseColor;
}
vec3 F_Schlick( const in vec3 f0, const in float f90, const in float dotVH ) {
	float fresnel = exp2( ( - 5.55473 * dotVH - 6.98316 ) * dotVH );
	return f0 * ( 1.0 - fresnel ) + ( f90 * fresnel );
}
float F_Schlick( const in float f0, const in float f90, const in float dotVH ) {
	float fresnel = exp2( ( - 5.55473 * dotVH - 6.98316 ) * dotVH );
	return f0 * ( 1.0 - fresnel ) + ( f90 * fresnel );
} // validated`,od=`#ifdef ENVMAP_TYPE_CUBE_UV
	#define cubeUV_minMipLevel 4.0
	#define cubeUV_minTileSize 16.0
	float getFace( vec3 direction ) {
		vec3 absDirection = abs( direction );
		float face = - 1.0;
		if ( absDirection.x > absDirection.z ) {
			if ( absDirection.x > absDirection.y )
				face = direction.x > 0.0 ? 0.0 : 3.0;
			else
				face = direction.y > 0.0 ? 1.0 : 4.0;
		} else {
			if ( absDirection.z > absDirection.y )
				face = direction.z > 0.0 ? 2.0 : 5.0;
			else
				face = direction.y > 0.0 ? 1.0 : 4.0;
		}
		return face;
	}
	vec2 getUV( vec3 direction, float face ) {
		vec2 uv;
		if ( face == 0.0 ) {
			uv = vec2( direction.z, direction.y ) / abs( direction.x );
		} else if ( face == 1.0 ) {
			uv = vec2( - direction.x, - direction.z ) / abs( direction.y );
		} else if ( face == 2.0 ) {
			uv = vec2( - direction.x, direction.y ) / abs( direction.z );
		} else if ( face == 3.0 ) {
			uv = vec2( - direction.z, direction.y ) / abs( direction.x );
		} else if ( face == 4.0 ) {
			uv = vec2( - direction.x, direction.z ) / abs( direction.y );
		} else {
			uv = vec2( direction.x, direction.y ) / abs( direction.z );
		}
		return 0.5 * ( uv + 1.0 );
	}
	vec3 bilinearCubeUV( sampler2D envMap, vec3 direction, float mipInt ) {
		float face = getFace( direction );
		float filterInt = max( cubeUV_minMipLevel - mipInt, 0.0 );
		mipInt = max( mipInt, cubeUV_minMipLevel );
		float faceSize = exp2( mipInt );
		highp vec2 uv = getUV( direction, face ) * ( faceSize - 2.0 ) + 1.0;
		if ( face > 2.0 ) {
			uv.y += faceSize;
			face -= 3.0;
		}
		uv.x += face * faceSize;
		uv.x += filterInt * 3.0 * cubeUV_minTileSize;
		uv.y += 4.0 * ( exp2( CUBEUV_MAX_MIP ) - faceSize );
		uv.x *= CUBEUV_TEXEL_WIDTH;
		uv.y *= CUBEUV_TEXEL_HEIGHT;
		#ifdef texture2DGradEXT
			return texture2DGradEXT( envMap, uv, vec2( 0.0 ), vec2( 0.0 ) ).rgb;
		#else
			return texture2D( envMap, uv ).rgb;
		#endif
	}
	#define cubeUV_r0 1.0
	#define cubeUV_m0 - 2.0
	#define cubeUV_r1 0.8
	#define cubeUV_m1 - 1.0
	#define cubeUV_r4 0.4
	#define cubeUV_m4 2.0
	#define cubeUV_r5 0.305
	#define cubeUV_m5 3.0
	#define cubeUV_r6 0.21
	#define cubeUV_m6 4.0
	float roughnessToMip( float roughness ) {
		float mip = 0.0;
		if ( roughness >= cubeUV_r1 ) {
			mip = ( cubeUV_r0 - roughness ) * ( cubeUV_m1 - cubeUV_m0 ) / ( cubeUV_r0 - cubeUV_r1 ) + cubeUV_m0;
		} else if ( roughness >= cubeUV_r4 ) {
			mip = ( cubeUV_r1 - roughness ) * ( cubeUV_m4 - cubeUV_m1 ) / ( cubeUV_r1 - cubeUV_r4 ) + cubeUV_m1;
		} else if ( roughness >= cubeUV_r5 ) {
			mip = ( cubeUV_r4 - roughness ) * ( cubeUV_m5 - cubeUV_m4 ) / ( cubeUV_r4 - cubeUV_r5 ) + cubeUV_m4;
		} else if ( roughness >= cubeUV_r6 ) {
			mip = ( cubeUV_r5 - roughness ) * ( cubeUV_m6 - cubeUV_m5 ) / ( cubeUV_r5 - cubeUV_r6 ) + cubeUV_m5;
		} else {
			mip = - 2.0 * log2( 1.16 * roughness );		}
		return mip;
	}
	vec4 textureCubeUV( sampler2D envMap, vec3 sampleDir, float roughness ) {
		float mip = clamp( roughnessToMip( roughness ), cubeUV_m0, CUBEUV_MAX_MIP );
		float mipF = fract( mip );
		float mipInt = floor( mip );
		vec3 color0 = bilinearCubeUV( envMap, sampleDir, mipInt );
		if ( mipF == 0.0 ) {
			return vec4( color0, 1.0 );
		} else {
			vec3 color1 = bilinearCubeUV( envMap, sampleDir, mipInt + 1.0 );
			return vec4( mix( color0, color1, mipF ), 1.0 );
		}
	}
#endif`,ad=`vec3 transformedNormal = objectNormal;
#ifdef USE_TANGENT
	vec3 transformedTangent = objectTangent;
#endif
#ifdef USE_BATCHING
	mat3 bm = mat3( batchingMatrix );
	transformedNormal /= vec3( dot( bm[ 0 ], bm[ 0 ] ), dot( bm[ 1 ], bm[ 1 ] ), dot( bm[ 2 ], bm[ 2 ] ) );
	transformedNormal = bm * transformedNormal;
	#ifdef USE_TANGENT
		transformedTangent = bm * transformedTangent;
	#endif
#endif
#ifdef USE_INSTANCING
	mat3 im = mat3( instanceMatrix );
	transformedNormal /= vec3( dot( im[ 0 ], im[ 0 ] ), dot( im[ 1 ], im[ 1 ] ), dot( im[ 2 ], im[ 2 ] ) );
	transformedNormal = im * transformedNormal;
	#ifdef USE_TANGENT
		transformedTangent = im * transformedTangent;
	#endif
#endif
transformedNormal = normalMatrix * transformedNormal;
#ifdef FLIP_SIDED
	transformedNormal = - transformedNormal;
#endif
#ifdef USE_TANGENT
	transformedTangent = ( modelViewMatrix * vec4( transformedTangent, 0.0 ) ).xyz;
	#ifdef FLIP_SIDED
		transformedTangent = - transformedTangent;
	#endif
#endif`,cd=`#ifdef USE_DISPLACEMENTMAP
	uniform sampler2D displacementMap;
	uniform float displacementScale;
	uniform float displacementBias;
#endif`,hd=`#ifdef USE_DISPLACEMENTMAP
	transformed += normalize( objectNormal ) * ( texture2D( displacementMap, vDisplacementMapUv ).x * displacementScale + displacementBias );
#endif`,ld=`#ifdef USE_EMISSIVEMAP
	vec4 emissiveColor = texture2D( emissiveMap, vEmissiveMapUv );
	#ifdef DECODE_VIDEO_TEXTURE_EMISSIVE
		emissiveColor = sRGBTransferEOTF( emissiveColor );
	#endif
	totalEmissiveRadiance *= emissiveColor.rgb;
#endif`,pd=`#ifdef USE_EMISSIVEMAP
	uniform sampler2D emissiveMap;
#endif`,fd="gl_FragColor = linearToOutputTexel( gl_FragColor );",dd=`vec4 LinearTransferOETF( in vec4 value ) {
	return value;
}
vec4 sRGBTransferEOTF( in vec4 value ) {
	return 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 );
}
vec4 sRGBTransferOETF( in vec4 value ) {
	return 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 );
}`,ud=`#ifdef USE_ENVMAP
	#ifdef ENV_WORLDPOS
		vec3 cameraToFrag;
		if ( isOrthographic ) {
			cameraToFrag = normalize( vec3( - viewMatrix[ 0 ][ 2 ], - viewMatrix[ 1 ][ 2 ], - viewMatrix[ 2 ][ 2 ] ) );
		} else {
			cameraToFrag = normalize( vWorldPosition - cameraPosition );
		}
		vec3 worldNormal = inverseTransformDirection( normal, viewMatrix );
		#ifdef ENVMAP_MODE_REFLECTION
			vec3 reflectVec = reflect( cameraToFrag, worldNormal );
		#else
			vec3 reflectVec = refract( cameraToFrag, worldNormal, refractionRatio );
		#endif
	#else
		vec3 reflectVec = vReflect;
	#endif
	#ifdef ENVMAP_TYPE_CUBE
		vec4 envColor = textureCube( envMap, envMapRotation * vec3( flipEnvMap * reflectVec.x, reflectVec.yz ) );
	#else
		vec4 envColor = vec4( 0.0 );
	#endif
	#ifdef ENVMAP_BLENDING_MULTIPLY
		outgoingLight = mix( outgoingLight, outgoingLight * envColor.xyz, specularStrength * reflectivity );
	#elif defined( ENVMAP_BLENDING_MIX )
		outgoingLight = mix( outgoingLight, envColor.xyz, specularStrength * reflectivity );
	#elif defined( ENVMAP_BLENDING_ADD )
		outgoingLight += envColor.xyz * specularStrength * reflectivity;
	#endif
#endif`,gd=`#ifdef USE_ENVMAP
	uniform float envMapIntensity;
	uniform float flipEnvMap;
	uniform mat3 envMapRotation;
	#ifdef ENVMAP_TYPE_CUBE
		uniform samplerCube envMap;
	#else
		uniform sampler2D envMap;
	#endif
	
#endif`,_d=`#ifdef USE_ENVMAP
	uniform float reflectivity;
	#if defined( USE_BUMPMAP ) || defined( USE_NORMALMAP ) || defined( PHONG ) || defined( LAMBERT )
		#define ENV_WORLDPOS
	#endif
	#ifdef ENV_WORLDPOS
		varying vec3 vWorldPosition;
		uniform float refractionRatio;
	#else
		varying vec3 vReflect;
	#endif
#endif`,md=`#ifdef USE_ENVMAP
	#if defined( USE_BUMPMAP ) || defined( USE_NORMALMAP ) || defined( PHONG ) || defined( LAMBERT )
		#define ENV_WORLDPOS
	#endif
	#ifdef ENV_WORLDPOS
		
		varying vec3 vWorldPosition;
	#else
		varying vec3 vReflect;
		uniform float refractionRatio;
	#endif
#endif`,xd=`#ifdef USE_ENVMAP
	#ifdef ENV_WORLDPOS
		vWorldPosition = worldPosition.xyz;
	#else
		vec3 cameraToVertex;
		if ( isOrthographic ) {
			cameraToVertex = normalize( vec3( - viewMatrix[ 0 ][ 2 ], - viewMatrix[ 1 ][ 2 ], - viewMatrix[ 2 ][ 2 ] ) );
		} else {
			cameraToVertex = normalize( worldPosition.xyz - cameraPosition );
		}
		vec3 worldNormal = inverseTransformDirection( transformedNormal, viewMatrix );
		#ifdef ENVMAP_MODE_REFLECTION
			vReflect = reflect( cameraToVertex, worldNormal );
		#else
			vReflect = refract( cameraToVertex, worldNormal, refractionRatio );
		#endif
	#endif
#endif`,bd=`#ifdef USE_FOG
	vFogDepth = - mvPosition.z;
#endif`,Pd=`#ifdef USE_FOG
	varying float vFogDepth;
#endif`,wd=`#ifdef USE_FOG
	#ifdef FOG_EXP2
		float fogFactor = 1.0 - exp( - fogDensity * fogDensity * vFogDepth * vFogDepth );
	#else
		float fogFactor = smoothstep( fogNear, fogFar, vFogDepth );
	#endif
	gl_FragColor.rgb = mix( gl_FragColor.rgb, fogColor, fogFactor );
#endif`,Od=`#ifdef USE_FOG
	uniform vec3 fogColor;
	varying float vFogDepth;
	#ifdef FOG_EXP2
		uniform float fogDensity;
	#else
		uniform float fogNear;
		uniform float fogFar;
	#endif
#endif`,yd=`#ifdef USE_GRADIENTMAP
	uniform sampler2D gradientMap;
#endif
vec3 getGradientIrradiance( vec3 normal, vec3 lightDirection ) {
	float dotNL = dot( normal, lightDirection );
	vec2 coord = vec2( dotNL * 0.5 + 0.5, 0.0 );
	#ifdef USE_GRADIENTMAP
		return vec3( texture2D( gradientMap, coord ).r );
	#else
		vec2 fw = fwidth( coord ) * 0.5;
		return mix( vec3( 0.7 ), vec3( 1.0 ), smoothstep( 0.7 - fw.x, 0.7 + fw.x, coord.x ) );
	#endif
}`,Ad=`#ifdef USE_LIGHTMAP
	uniform sampler2D lightMap;
	uniform float lightMapIntensity;
#endif`,vd=`LambertMaterial material;
material.diffuseColor = diffuseColor.rgb;
material.specularStrength = specularStrength;`,Hd=`varying vec3 vViewPosition;
struct LambertMaterial {
	vec3 diffuseColor;
	float specularStrength;
};
void RE_Direct_Lambert( const in IncidentLight directLight, const in vec3 geometryPosition, const in vec3 geometryNormal, const in vec3 geometryViewDir, const in vec3 geometryClearcoatNormal, const in LambertMaterial material, inout ReflectedLight reflectedLight ) {
	float dotNL = saturate( dot( geometryNormal, directLight.direction ) );
	vec3 irradiance = dotNL * directLight.color;
	reflectedLight.directDiffuse += irradiance * BRDF_Lambert( material.diffuseColor );
}
void RE_IndirectDiffuse_Lambert( const in vec3 irradiance, const in vec3 geometryPosition, const in vec3 geometryNormal, const in vec3 geometryViewDir, const in vec3 geometryClearcoatNormal, const in LambertMaterial material, inout ReflectedLight reflectedLight ) {
	reflectedLight.indirectDiffuse += irradiance * BRDF_Lambert( material.diffuseColor );
}
#define RE_Direct				RE_Direct_Lambert
#define RE_IndirectDiffuse		RE_IndirectDiffuse_Lambert`,Sd=`uniform bool receiveShadow;
uniform vec3 ambientLightColor;
#if defined( USE_LIGHT_PROBES )
	uniform vec3 lightProbe[ 9 ];
#endif
vec3 shGetIrradianceAt( in vec3 normal, in vec3 shCoefficients[ 9 ] ) {
	float x = normal.x, y = normal.y, z = normal.z;
	vec3 result = shCoefficients[ 0 ] * 0.886227;
	result += shCoefficients[ 1 ] * 2.0 * 0.511664 * y;
	result += shCoefficients[ 2 ] * 2.0 * 0.511664 * z;
	result += shCoefficients[ 3 ] * 2.0 * 0.511664 * x;
	result += shCoefficients[ 4 ] * 2.0 * 0.429043 * x * y;
	result += shCoefficients[ 5 ] * 2.0 * 0.429043 * y * z;
	result += shCoefficients[ 6 ] * ( 0.743125 * z * z - 0.247708 );
	result += shCoefficients[ 7 ] * 2.0 * 0.429043 * x * z;
	result += shCoefficients[ 8 ] * 0.429043 * ( x * x - y * y );
	return result;
}
vec3 getLightProbeIrradiance( const in vec3 lightProbe[ 9 ], const in vec3 normal ) {
	vec3 worldNormal = inverseTransformDirection( normal, viewMatrix );
	vec3 irradiance = shGetIrradianceAt( worldNormal, lightProbe );
	return irradiance;
}
vec3 getAmbientLightIrradiance( const in vec3 ambientLightColor ) {
	vec3 irradiance = ambientLightColor;
	return irradiance;
}
float getDistanceAttenuation( const in float lightDistance, const in float cutoffDistance, const in float decayExponent ) {
	float distanceFalloff = 1.0 / max( pow( lightDistance, decayExponent ), 0.01 );
	if ( cutoffDistance > 0.0 ) {
		distanceFalloff *= pow2( saturate( 1.0 - pow4( lightDistance / cutoffDistance ) ) );
	}
	return distanceFalloff;
}
float getSpotAttenuation( const in float coneCosine, const in float penumbraCosine, const in float angleCosine ) {
	return smoothstep( coneCosine, penumbraCosine, angleCosine );
}
#if NUM_DIR_LIGHTS > 0
	struct DirectionalLight {
		vec3 direction;
		vec3 color;
	};
	uniform DirectionalLight directionalLights[ NUM_DIR_LIGHTS ];
	void getDirectionalLightInfo( const in DirectionalLight directionalLight, out IncidentLight light ) {
		light.color = directionalLight.color;
		light.direction = directionalLight.direction;
		light.visible = true;
	}
#endif
#if NUM_POINT_LIGHTS > 0
	struct PointLight {
		vec3 position;
		vec3 color;
		float distance;
		float decay;
	};
	uniform PointLight pointLights[ NUM_POINT_LIGHTS ];
	void getPointLightInfo( const in PointLight pointLight, const in vec3 geometryPosition, out IncidentLight light ) {
		vec3 lVector = pointLight.position - geometryPosition;
		light.direction = normalize( lVector );
		float lightDistance = length( lVector );
		light.color = pointLight.color;
		light.color *= getDistanceAttenuation( lightDistance, pointLight.distance, pointLight.decay );
		light.visible = ( light.color != vec3( 0.0 ) );
	}
#endif
#if NUM_SPOT_LIGHTS > 0
	struct SpotLight {
		vec3 position;
		vec3 direction;
		vec3 color;
		float distance;
		float decay;
		float coneCos;
		float penumbraCos;
	};
	uniform SpotLight spotLights[ NUM_SPOT_LIGHTS ];
	void getSpotLightInfo( const in SpotLight spotLight, const in vec3 geometryPosition, out IncidentLight light ) {
		vec3 lVector = spotLight.position - geometryPosition;
		light.direction = normalize( lVector );
		float angleCos = dot( light.direction, spotLight.direction );
		float spotAttenuation = getSpotAttenuation( spotLight.coneCos, spotLight.penumbraCos, angleCos );
		if ( spotAttenuation > 0.0 ) {
			float lightDistance = length( lVector );
			light.color = spotLight.color * spotAttenuation;
			light.color *= getDistanceAttenuation( lightDistance, spotLight.distance, spotLight.decay );
			light.visible = ( light.color != vec3( 0.0 ) );
		} else {
			light.color = vec3( 0.0 );
			light.visible = false;
		}
	}
#endif
#if NUM_RECT_AREA_LIGHTS > 0
	struct RectAreaLight {
		vec3 color;
		vec3 position;
		vec3 halfWidth;
		vec3 halfHeight;
	};
	uniform sampler2D ltc_1;	uniform sampler2D ltc_2;
	uniform RectAreaLight rectAreaLights[ NUM_RECT_AREA_LIGHTS ];
#endif
#if NUM_HEMI_LIGHTS > 0
	struct HemisphereLight {
		vec3 direction;
		vec3 skyColor;
		vec3 groundColor;
	};
	uniform HemisphereLight hemisphereLights[ NUM_HEMI_LIGHTS ];
	vec3 getHemisphereLightIrradiance( const in HemisphereLight hemiLight, const in vec3 normal ) {
		float dotNL = dot( normal, hemiLight.direction );
		float hemiDiffuseWeight = 0.5 * dotNL + 0.5;
		vec3 irradiance = mix( hemiLight.groundColor, hemiLight.skyColor, hemiDiffuseWeight );
		return irradiance;
	}
#endif`,Ed=`#ifdef USE_ENVMAP
	vec3 getIBLIrradiance( const in vec3 normal ) {
		#ifdef ENVMAP_TYPE_CUBE_UV
			vec3 worldNormal = inverseTransformDirection( normal, viewMatrix );
			vec4 envMapColor = textureCubeUV( envMap, envMapRotation * worldNormal, 1.0 );
			return PI * envMapColor.rgb * envMapIntensity;
		#else
			return vec3( 0.0 );
		#endif
	}
	vec3 getIBLRadiance( const in vec3 viewDir, const in vec3 normal, const in float roughness ) {
		#ifdef ENVMAP_TYPE_CUBE_UV
			vec3 reflectVec = reflect( - viewDir, normal );
			reflectVec = normalize( mix( reflectVec, normal, roughness * roughness) );
			reflectVec = inverseTransformDirection( reflectVec, viewMatrix );
			vec4 envMapColor = textureCubeUV( envMap, envMapRotation * reflectVec, roughness );
			return envMapColor.rgb * envMapIntensity;
		#else
			return vec3( 0.0 );
		#endif
	}
	#ifdef USE_ANISOTROPY
		vec3 getIBLAnisotropyRadiance( const in vec3 viewDir, const in vec3 normal, const in float roughness, const in vec3 bitangent, const in float anisotropy ) {
			#ifdef ENVMAP_TYPE_CUBE_UV
				vec3 bentNormal = cross( bitangent, viewDir );
				bentNormal = normalize( cross( bentNormal, bitangent ) );
				bentNormal = normalize( mix( bentNormal, normal, pow2( pow2( 1.0 - anisotropy * ( 1.0 - roughness ) ) ) ) );
				return getIBLRadiance( viewDir, bentNormal, roughness );
			#else
				return vec3( 0.0 );
			#endif
		}
	#endif
#endif`,Wd=`ToonMaterial material;
material.diffuseColor = diffuseColor.rgb;`,Md=`varying vec3 vViewPosition;
struct ToonMaterial {
	vec3 diffuseColor;
};
void RE_Direct_Toon( const in IncidentLight directLight, const in vec3 geometryPosition, const in vec3 geometryNormal, const in vec3 geometryViewDir, const in vec3 geometryClearcoatNormal, const in ToonMaterial material, inout ReflectedLight reflectedLight ) {
	vec3 irradiance = getGradientIrradiance( geometryNormal, directLight.direction ) * directLight.color;
	reflectedLight.directDiffuse += irradiance * BRDF_Lambert( material.diffuseColor );
}
void RE_IndirectDiffuse_Toon( const in vec3 irradiance, const in vec3 geometryPosition, const in vec3 geometryNormal, const in vec3 geometryViewDir, const in vec3 geometryClearcoatNormal, const in ToonMaterial material, inout ReflectedLight reflectedLight ) {
	reflectedLight.indirectDiffuse += irradiance * BRDF_Lambert( material.diffuseColor );
}
#define RE_Direct				RE_Direct_Toon
#define RE_IndirectDiffuse		RE_IndirectDiffuse_Toon`,Cd=`BlinnPhongMaterial material;
material.diffuseColor = diffuseColor.rgb;
material.specularColor = specular;
material.specularShininess = shininess;
material.specularStrength = specularStrength;`,Gd=`varying vec3 vViewPosition;
struct BlinnPhongMaterial {
	vec3 diffuseColor;
	vec3 specularColor;
	float specularShininess;
	float specularStrength;
};
void RE_Direct_BlinnPhong( const in IncidentLight directLight, const in vec3 geometryPosition, const in vec3 geometryNormal, const in vec3 geometryViewDir, const in vec3 geometryClearcoatNormal, const in BlinnPhongMaterial material, inout ReflectedLight reflectedLight ) {
	float dotNL = saturate( dot( geometryNormal, directLight.direction ) );
	vec3 irradiance = dotNL * directLight.color;
	reflectedLight.directDiffuse += irradiance * BRDF_Lambert( material.diffuseColor );
	reflectedLight.directSpecular += irradiance * BRDF_BlinnPhong( directLight.direction, geometryViewDir, geometryNormal, material.specularColor, material.specularShininess ) * material.specularStrength;
}
void RE_IndirectDiffuse_BlinnPhong( const in vec3 irradiance, const in vec3 geometryPosition, const in vec3 geometryNormal, const in vec3 geometryViewDir, const in vec3 geometryClearcoatNormal, const in BlinnPhongMaterial material, inout ReflectedLight reflectedLight ) {
	reflectedLight.indirectDiffuse += irradiance * BRDF_Lambert( material.diffuseColor );
}
#define RE_Direct				RE_Direct_BlinnPhong
#define RE_IndirectDiffuse		RE_IndirectDiffuse_BlinnPhong`,Td=`PhysicalMaterial material;
material.diffuseColor = diffuseColor.rgb * ( 1.0 - metalnessFactor );
vec3 dxy = max( abs( dFdx( nonPerturbedNormal ) ), abs( dFdy( nonPerturbedNormal ) ) );
float geometryRoughness = max( max( dxy.x, dxy.y ), dxy.z );
material.roughness = max( roughnessFactor, 0.0525 );material.roughness += geometryRoughness;
material.roughness = min( material.roughness, 1.0 );
#ifdef IOR
	material.ior = ior;
	#ifdef USE_SPECULAR
		float specularIntensityFactor = specularIntensity;
		vec3 specularColorFactor = specularColor;
		#ifdef USE_SPECULAR_COLORMAP
			specularColorFactor *= texture2D( specularColorMap, vSpecularColorMapUv ).rgb;
		#endif
		#ifdef USE_SPECULAR_INTENSITYMAP
			specularIntensityFactor *= texture2D( specularIntensityMap, vSpecularIntensityMapUv ).a;
		#endif
		material.specularF90 = mix( specularIntensityFactor, 1.0, metalnessFactor );
	#else
		float specularIntensityFactor = 1.0;
		vec3 specularColorFactor = vec3( 1.0 );
		material.specularF90 = 1.0;
	#endif
	material.specularColor = mix( min( pow2( ( material.ior - 1.0 ) / ( material.ior + 1.0 ) ) * specularColorFactor, vec3( 1.0 ) ) * specularIntensityFactor, diffuseColor.rgb, metalnessFactor );
#else
	material.specularColor = mix( vec3( 0.04 ), diffuseColor.rgb, metalnessFactor );
	material.specularF90 = 1.0;
#endif
#ifdef USE_CLEARCOAT
	material.clearcoat = clearcoat;
	material.clearcoatRoughness = clearcoatRoughness;
	material.clearcoatF0 = vec3( 0.04 );
	material.clearcoatF90 = 1.0;
	#ifdef USE_CLEARCOATMAP
		material.clearcoat *= texture2D( clearcoatMap, vClearcoatMapUv ).x;
	#endif
	#ifdef USE_CLEARCOAT_ROUGHNESSMAP
		material.clearcoatRoughness *= texture2D( clearcoatRoughnessMap, vClearcoatRoughnessMapUv ).y;
	#endif
	material.clearcoat = saturate( material.clearcoat );	material.clearcoatRoughness = max( material.clearcoatRoughness, 0.0525 );
	material.clearcoatRoughness += geometryRoughness;
	material.clearcoatRoughness = min( material.clearcoatRoughness, 1.0 );
#endif
#ifdef USE_DISPERSION
	material.dispersion = dispersion;
#endif
#ifdef USE_IRIDESCENCE
	material.iridescence = iridescence;
	material.iridescenceIOR = iridescenceIOR;
	#ifdef USE_IRIDESCENCEMAP
		material.iridescence *= texture2D( iridescenceMap, vIridescenceMapUv ).r;
	#endif
	#ifdef USE_IRIDESCENCE_THICKNESSMAP
		material.iridescenceThickness = (iridescenceThicknessMaximum - iridescenceThicknessMinimum) * texture2D( iridescenceThicknessMap, vIridescenceThicknessMapUv ).g + iridescenceThicknessMinimum;
	#else
		material.iridescenceThickness = iridescenceThicknessMaximum;
	#endif
#endif
#ifdef USE_SHEEN
	material.sheenColor = sheenColor;
	#ifdef USE_SHEEN_COLORMAP
		material.sheenColor *= texture2D( sheenColorMap, vSheenColorMapUv ).rgb;
	#endif
	material.sheenRoughness = clamp( sheenRoughness, 0.07, 1.0 );
	#ifdef USE_SHEEN_ROUGHNESSMAP
		material.sheenRoughness *= texture2D( sheenRoughnessMap, vSheenRoughnessMapUv ).a;
	#endif
#endif
#ifdef USE_ANISOTROPY
	#ifdef USE_ANISOTROPYMAP
		mat2 anisotropyMat = mat2( anisotropyVector.x, anisotropyVector.y, - anisotropyVector.y, anisotropyVector.x );
		vec3 anisotropyPolar = texture2D( anisotropyMap, vAnisotropyMapUv ).rgb;
		vec2 anisotropyV = anisotropyMat * normalize( 2.0 * anisotropyPolar.rg - vec2( 1.0 ) ) * anisotropyPolar.b;
	#else
		vec2 anisotropyV = anisotropyVector;
	#endif
	material.anisotropy = length( anisotropyV );
	if( material.anisotropy == 0.0 ) {
		anisotropyV = vec2( 1.0, 0.0 );
	} else {
		anisotropyV /= material.anisotropy;
		material.anisotropy = saturate( material.anisotropy );
	}
	material.alphaT = mix( pow2( material.roughness ), 1.0, pow2( material.anisotropy ) );
	material.anisotropyT = tbn[ 0 ] * anisotropyV.x + tbn[ 1 ] * anisotropyV.y;
	material.anisotropyB = tbn[ 1 ] * anisotropyV.x - tbn[ 0 ] * anisotropyV.y;
#endif`,Ud=`struct PhysicalMaterial {
	vec3 diffuseColor;
	float roughness;
	vec3 specularColor;
	float specularF90;
	float dispersion;
	#ifdef USE_CLEARCOAT
		float clearcoat;
		float clearcoatRoughness;
		vec3 clearcoatF0;
		float clearcoatF90;
	#endif
	#ifdef USE_IRIDESCENCE
		float iridescence;
		float iridescenceIOR;
		float iridescenceThickness;
		vec3 iridescenceFresnel;
		vec3 iridescenceF0;
	#endif
	#ifdef USE_SHEEN
		vec3 sheenColor;
		float sheenRoughness;
	#endif
	#ifdef IOR
		float ior;
	#endif
	#ifdef USE_TRANSMISSION
		float transmission;
		float transmissionAlpha;
		float thickness;
		float attenuationDistance;
		vec3 attenuationColor;
	#endif
	#ifdef USE_ANISOTROPY
		float anisotropy;
		float alphaT;
		vec3 anisotropyT;
		vec3 anisotropyB;
	#endif
};
vec3 clearcoatSpecularDirect = vec3( 0.0 );
vec3 clearcoatSpecularIndirect = vec3( 0.0 );
vec3 sheenSpecularDirect = vec3( 0.0 );
vec3 sheenSpecularIndirect = vec3(0.0 );
vec3 Schlick_to_F0( const in vec3 f, const in float f90, const in float dotVH ) {
    float x = clamp( 1.0 - dotVH, 0.0, 1.0 );
    float x2 = x * x;
    float x5 = clamp( x * x2 * x2, 0.0, 0.9999 );
    return ( f - vec3( f90 ) * x5 ) / ( 1.0 - x5 );
}
float V_GGX_SmithCorrelated( const in float alpha, const in float dotNL, const in float dotNV ) {
	float a2 = pow2( alpha );
	float gv = dotNL * sqrt( a2 + ( 1.0 - a2 ) * pow2( dotNV ) );
	float gl = dotNV * sqrt( a2 + ( 1.0 - a2 ) * pow2( dotNL ) );
	return 0.5 / max( gv + gl, EPSILON );
}
float D_GGX( const in float alpha, const in float dotNH ) {
	float a2 = pow2( alpha );
	float denom = pow2( dotNH ) * ( a2 - 1.0 ) + 1.0;
	return RECIPROCAL_PI * a2 / pow2( denom );
}
#ifdef USE_ANISOTROPY
	float V_GGX_SmithCorrelated_Anisotropic( const in float alphaT, const in float alphaB, const in float dotTV, const in float dotBV, const in float dotTL, const in float dotBL, const in float dotNV, const in float dotNL ) {
		float gv = dotNL * length( vec3( alphaT * dotTV, alphaB * dotBV, dotNV ) );
		float gl = dotNV * length( vec3( alphaT * dotTL, alphaB * dotBL, dotNL ) );
		float v = 0.5 / ( gv + gl );
		return saturate(v);
	}
	float D_GGX_Anisotropic( const in float alphaT, const in float alphaB, const in float dotNH, const in float dotTH, const in float dotBH ) {
		float a2 = alphaT * alphaB;
		highp vec3 v = vec3( alphaB * dotTH, alphaT * dotBH, a2 * dotNH );
		highp float v2 = dot( v, v );
		float w2 = a2 / v2;
		return RECIPROCAL_PI * a2 * pow2 ( w2 );
	}
#endif
#ifdef USE_CLEARCOAT
	vec3 BRDF_GGX_Clearcoat( const in vec3 lightDir, const in vec3 viewDir, const in vec3 normal, const in PhysicalMaterial material) {
		vec3 f0 = material.clearcoatF0;
		float f90 = material.clearcoatF90;
		float roughness = material.clearcoatRoughness;
		float alpha = pow2( roughness );
		vec3 halfDir = normalize( lightDir + viewDir );
		float dotNL = saturate( dot( normal, lightDir ) );
		float dotNV = saturate( dot( normal, viewDir ) );
		float dotNH = saturate( dot( normal, halfDir ) );
		float dotVH = saturate( dot( viewDir, halfDir ) );
		vec3 F = F_Schlick( f0, f90, dotVH );
		float V = V_GGX_SmithCorrelated( alpha, dotNL, dotNV );
		float D = D_GGX( alpha, dotNH );
		return F * ( V * D );
	}
#endif
vec3 BRDF_GGX( const in vec3 lightDir, const in vec3 viewDir, const in vec3 normal, const in PhysicalMaterial material ) {
	vec3 f0 = material.specularColor;
	float f90 = material.specularF90;
	float roughness = material.roughness;
	float alpha = pow2( roughness );
	vec3 halfDir = normalize( lightDir + viewDir );
	float dotNL = saturate( dot( normal, lightDir ) );
	float dotNV = saturate( dot( normal, viewDir ) );
	float dotNH = saturate( dot( normal, halfDir ) );
	float dotVH = saturate( dot( viewDir, halfDir ) );
	vec3 F = F_Schlick( f0, f90, dotVH );
	#ifdef USE_IRIDESCENCE
		F = mix( F, material.iridescenceFresnel, material.iridescence );
	#endif
	#ifdef USE_ANISOTROPY
		float dotTL = dot( material.anisotropyT, lightDir );
		float dotTV = dot( material.anisotropyT, viewDir );
		float dotTH = dot( material.anisotropyT, halfDir );
		float dotBL = dot( material.anisotropyB, lightDir );
		float dotBV = dot( material.anisotropyB, viewDir );
		float dotBH = dot( material.anisotropyB, halfDir );
		float V = V_GGX_SmithCorrelated_Anisotropic( material.alphaT, alpha, dotTV, dotBV, dotTL, dotBL, dotNV, dotNL );
		float D = D_GGX_Anisotropic( material.alphaT, alpha, dotNH, dotTH, dotBH );
	#else
		float V = V_GGX_SmithCorrelated( alpha, dotNL, dotNV );
		float D = D_GGX( alpha, dotNH );
	#endif
	return F * ( V * D );
}
vec2 LTC_Uv( const in vec3 N, const in vec3 V, const in float roughness ) {
	const float LUT_SIZE = 64.0;
	const float LUT_SCALE = ( LUT_SIZE - 1.0 ) / LUT_SIZE;
	const float LUT_BIAS = 0.5 / LUT_SIZE;
	float dotNV = saturate( dot( N, V ) );
	vec2 uv = vec2( roughness, sqrt( 1.0 - dotNV ) );
	uv = uv * LUT_SCALE + LUT_BIAS;
	return uv;
}
float LTC_ClippedSphereFormFactor( const in vec3 f ) {
	float l = length( f );
	return max( ( l * l + f.z ) / ( l + 1.0 ), 0.0 );
}
vec3 LTC_EdgeVectorFormFactor( const in vec3 v1, const in vec3 v2 ) {
	float x = dot( v1, v2 );
	float y = abs( x );
	float a = 0.8543985 + ( 0.4965155 + 0.0145206 * y ) * y;
	float b = 3.4175940 + ( 4.1616724 + y ) * y;
	float v = a / b;
	float theta_sintheta = ( x > 0.0 ) ? v : 0.5 * inversesqrt( max( 1.0 - x * x, 1e-7 ) ) - v;
	return cross( v1, v2 ) * theta_sintheta;
}
vec3 LTC_Evaluate( const in vec3 N, const in vec3 V, const in vec3 P, const in mat3 mInv, const in vec3 rectCoords[ 4 ] ) {
	vec3 v1 = rectCoords[ 1 ] - rectCoords[ 0 ];
	vec3 v2 = rectCoords[ 3 ] - rectCoords[ 0 ];
	vec3 lightNormal = cross( v1, v2 );
	if( dot( lightNormal, P - rectCoords[ 0 ] ) < 0.0 ) return vec3( 0.0 );
	vec3 T1, T2;
	T1 = normalize( V - N * dot( V, N ) );
	T2 = - cross( N, T1 );
	mat3 mat = mInv * transposeMat3( mat3( T1, T2, N ) );
	vec3 coords[ 4 ];
	coords[ 0 ] = mat * ( rectCoords[ 0 ] - P );
	coords[ 1 ] = mat * ( rectCoords[ 1 ] - P );
	coords[ 2 ] = mat * ( rectCoords[ 2 ] - P );
	coords[ 3 ] = mat * ( rectCoords[ 3 ] - P );
	coords[ 0 ] = normalize( coords[ 0 ] );
	coords[ 1 ] = normalize( coords[ 1 ] );
	coords[ 2 ] = normalize( coords[ 2 ] );
	coords[ 3 ] = normalize( coords[ 3 ] );
	vec3 vectorFormFactor = vec3( 0.0 );
	vectorFormFactor += LTC_EdgeVectorFormFactor( coords[ 0 ], coords[ 1 ] );
	vectorFormFactor += LTC_EdgeVectorFormFactor( coords[ 1 ], coords[ 2 ] );
	vectorFormFactor += LTC_EdgeVectorFormFactor( coords[ 2 ], coords[ 3 ] );
	vectorFormFactor += LTC_EdgeVectorFormFactor( coords[ 3 ], coords[ 0 ] );
	float result = LTC_ClippedSphereFormFactor( vectorFormFactor );
	return vec3( result );
}
#if defined( USE_SHEEN )
float D_Charlie( float roughness, float dotNH ) {
	float alpha = pow2( roughness );
	float invAlpha = 1.0 / alpha;
	float cos2h = dotNH * dotNH;
	float sin2h = max( 1.0 - cos2h, 0.0078125 );
	return ( 2.0 + invAlpha ) * pow( sin2h, invAlpha * 0.5 ) / ( 2.0 * PI );
}
float V_Neubelt( float dotNV, float dotNL ) {
	return saturate( 1.0 / ( 4.0 * ( dotNL + dotNV - dotNL * dotNV ) ) );
}
vec3 BRDF_Sheen( const in vec3 lightDir, const in vec3 viewDir, const in vec3 normal, vec3 sheenColor, const in float sheenRoughness ) {
	vec3 halfDir = normalize( lightDir + viewDir );
	float dotNL = saturate( dot( normal, lightDir ) );
	float dotNV = saturate( dot( normal, viewDir ) );
	float dotNH = saturate( dot( normal, halfDir ) );
	float D = D_Charlie( sheenRoughness, dotNH );
	float V = V_Neubelt( dotNV, dotNL );
	return sheenColor * ( D * V );
}
#endif
float IBLSheenBRDF( const in vec3 normal, const in vec3 viewDir, const in float roughness ) {
	float dotNV = saturate( dot( normal, viewDir ) );
	float r2 = roughness * roughness;
	float a = roughness < 0.25 ? -339.2 * r2 + 161.4 * roughness - 25.9 : -8.48 * r2 + 14.3 * roughness - 9.95;
	float b = roughness < 0.25 ? 44.0 * r2 - 23.7 * roughness + 3.26 : 1.97 * r2 - 3.27 * roughness + 0.72;
	float DG = exp( a * dotNV + b ) + ( roughness < 0.25 ? 0.0 : 0.1 * ( roughness - 0.25 ) );
	return saturate( DG * RECIPROCAL_PI );
}
vec2 DFGApprox( const in vec3 normal, const in vec3 viewDir, const in float roughness ) {
	float dotNV = saturate( dot( normal, viewDir ) );
	const vec4 c0 = vec4( - 1, - 0.0275, - 0.572, 0.022 );
	const vec4 c1 = vec4( 1, 0.0425, 1.04, - 0.04 );
	vec4 r = roughness * c0 + c1;
	float a004 = min( r.x * r.x, exp2( - 9.28 * dotNV ) ) * r.x + r.y;
	vec2 fab = vec2( - 1.04, 1.04 ) * a004 + r.zw;
	return fab;
}
vec3 EnvironmentBRDF( const in vec3 normal, const in vec3 viewDir, const in vec3 specularColor, const in float specularF90, const in float roughness ) {
	vec2 fab = DFGApprox( normal, viewDir, roughness );
	return specularColor * fab.x + specularF90 * fab.y;
}
#ifdef USE_IRIDESCENCE
void computeMultiscatteringIridescence( const in vec3 normal, const in vec3 viewDir, const in vec3 specularColor, const in float specularF90, const in float iridescence, const in vec3 iridescenceF0, const in float roughness, inout vec3 singleScatter, inout vec3 multiScatter ) {
#else
void computeMultiscattering( const in vec3 normal, const in vec3 viewDir, const in vec3 specularColor, const in float specularF90, const in float roughness, inout vec3 singleScatter, inout vec3 multiScatter ) {
#endif
	vec2 fab = DFGApprox( normal, viewDir, roughness );
	#ifdef USE_IRIDESCENCE
		vec3 Fr = mix( specularColor, iridescenceF0, iridescence );
	#else
		vec3 Fr = specularColor;
	#endif
	vec3 FssEss = Fr * fab.x + specularF90 * fab.y;
	float Ess = fab.x + fab.y;
	float Ems = 1.0 - Ess;
	vec3 Favg = Fr + ( 1.0 - Fr ) * 0.047619;	vec3 Fms = FssEss * Favg / ( 1.0 - Ems * Favg );
	singleScatter += FssEss;
	multiScatter += Fms * Ems;
}
#if NUM_RECT_AREA_LIGHTS > 0
	void RE_Direct_RectArea_Physical( const in RectAreaLight rectAreaLight, const in vec3 geometryPosition, const in vec3 geometryNormal, const in vec3 geometryViewDir, const in vec3 geometryClearcoatNormal, const in PhysicalMaterial material, inout ReflectedLight reflectedLight ) {
		vec3 normal = geometryNormal;
		vec3 viewDir = geometryViewDir;
		vec3 position = geometryPosition;
		vec3 lightPos = rectAreaLight.position;
		vec3 halfWidth = rectAreaLight.halfWidth;
		vec3 halfHeight = rectAreaLight.halfHeight;
		vec3 lightColor = rectAreaLight.color;
		float roughness = material.roughness;
		vec3 rectCoords[ 4 ];
		rectCoords[ 0 ] = lightPos + halfWidth - halfHeight;		rectCoords[ 1 ] = lightPos - halfWidth - halfHeight;
		rectCoords[ 2 ] = lightPos - halfWidth + halfHeight;
		rectCoords[ 3 ] = lightPos + halfWidth + halfHeight;
		vec2 uv = LTC_Uv( normal, viewDir, roughness );
		vec4 t1 = texture2D( ltc_1, uv );
		vec4 t2 = texture2D( ltc_2, uv );
		mat3 mInv = mat3(
			vec3( t1.x, 0, t1.y ),
			vec3(    0, 1,    0 ),
			vec3( t1.z, 0, t1.w )
		);
		vec3 fresnel = ( material.specularColor * t2.x + ( vec3( 1.0 ) - material.specularColor ) * t2.y );
		reflectedLight.directSpecular += lightColor * fresnel * LTC_Evaluate( normal, viewDir, position, mInv, rectCoords );
		reflectedLight.directDiffuse += lightColor * material.diffuseColor * LTC_Evaluate( normal, viewDir, position, mat3( 1.0 ), rectCoords );
	}
#endif
void RE_Direct_Physical( const in IncidentLight directLight, const in vec3 geometryPosition, const in vec3 geometryNormal, const in vec3 geometryViewDir, const in vec3 geometryClearcoatNormal, const in PhysicalMaterial material, inout ReflectedLight reflectedLight ) {
	float dotNL = saturate( dot( geometryNormal, directLight.direction ) );
	vec3 irradiance = dotNL * directLight.color;
	#ifdef USE_CLEARCOAT
		float dotNLcc = saturate( dot( geometryClearcoatNormal, directLight.direction ) );
		vec3 ccIrradiance = dotNLcc * directLight.color;
		clearcoatSpecularDirect += ccIrradiance * BRDF_GGX_Clearcoat( directLight.direction, geometryViewDir, geometryClearcoatNormal, material );
	#endif
	#ifdef USE_SHEEN
		sheenSpecularDirect += irradiance * BRDF_Sheen( directLight.direction, geometryViewDir, geometryNormal, material.sheenColor, material.sheenRoughness );
	#endif
	reflectedLight.directSpecular += irradiance * BRDF_GGX( directLight.direction, geometryViewDir, geometryNormal, material );
	reflectedLight.directDiffuse += irradiance * BRDF_Lambert( material.diffuseColor );
}
void RE_IndirectDiffuse_Physical( const in vec3 irradiance, const in vec3 geometryPosition, const in vec3 geometryNormal, const in vec3 geometryViewDir, const in vec3 geometryClearcoatNormal, const in PhysicalMaterial material, inout ReflectedLight reflectedLight ) {
	reflectedLight.indirectDiffuse += irradiance * BRDF_Lambert( material.diffuseColor );
}
void RE_IndirectSpecular_Physical( const in vec3 radiance, const in vec3 irradiance, const in vec3 clearcoatRadiance, const in vec3 geometryPosition, const in vec3 geometryNormal, const in vec3 geometryViewDir, const in vec3 geometryClearcoatNormal, const in PhysicalMaterial material, inout ReflectedLight reflectedLight) {
	#ifdef USE_CLEARCOAT
		clearcoatSpecularIndirect += clearcoatRadiance * EnvironmentBRDF( geometryClearcoatNormal, geometryViewDir, material.clearcoatF0, material.clearcoatF90, material.clearcoatRoughness );
	#endif
	#ifdef USE_SHEEN
		sheenSpecularIndirect += irradiance * material.sheenColor * IBLSheenBRDF( geometryNormal, geometryViewDir, material.sheenRoughness );
	#endif
	vec3 singleScattering = vec3( 0.0 );
	vec3 multiScattering = vec3( 0.0 );
	vec3 cosineWeightedIrradiance = irradiance * RECIPROCAL_PI;
	#ifdef USE_IRIDESCENCE
		computeMultiscatteringIridescence( geometryNormal, geometryViewDir, material.specularColor, material.specularF90, material.iridescence, material.iridescenceFresnel, material.roughness, singleScattering, multiScattering );
	#else
		computeMultiscattering( geometryNormal, geometryViewDir, material.specularColor, material.specularF90, material.roughness, singleScattering, multiScattering );
	#endif
	vec3 totalScattering = singleScattering + multiScattering;
	vec3 diffuse = material.diffuseColor * ( 1.0 - max( max( totalScattering.r, totalScattering.g ), totalScattering.b ) );
	reflectedLight.indirectSpecular += radiance * singleScattering;
	reflectedLight.indirectSpecular += multiScattering * cosineWeightedIrradiance;
	reflectedLight.indirectDiffuse += diffuse * cosineWeightedIrradiance;
}
#define RE_Direct				RE_Direct_Physical
#define RE_Direct_RectArea		RE_Direct_RectArea_Physical
#define RE_IndirectDiffuse		RE_IndirectDiffuse_Physical
#define RE_IndirectSpecular		RE_IndirectSpecular_Physical
float computeSpecularOcclusion( const in float dotNV, const in float ambientOcclusion, const in float roughness ) {
	return saturate( pow( dotNV + ambientOcclusion, exp2( - 16.0 * roughness - 1.0 ) ) - 1.0 + ambientOcclusion );
}`,Id=`
vec3 geometryPosition = - vViewPosition;
vec3 geometryNormal = normal;
vec3 geometryViewDir = ( isOrthographic ) ? vec3( 0, 0, 1 ) : normalize( vViewPosition );
vec3 geometryClearcoatNormal = vec3( 0.0 );
#ifdef USE_CLEARCOAT
	geometryClearcoatNormal = clearcoatNormal;
#endif
#ifdef USE_IRIDESCENCE
	float dotNVi = saturate( dot( normal, geometryViewDir ) );
	if ( material.iridescenceThickness == 0.0 ) {
		material.iridescence = 0.0;
	} else {
		material.iridescence = saturate( material.iridescence );
	}
	if ( material.iridescence > 0.0 ) {
		material.iridescenceFresnel = evalIridescence( 1.0, material.iridescenceIOR, dotNVi, material.iridescenceThickness, material.specularColor );
		material.iridescenceF0 = Schlick_to_F0( material.iridescenceFresnel, 1.0, dotNVi );
	}
#endif
IncidentLight directLight;
#if ( NUM_POINT_LIGHTS > 0 ) && defined( RE_Direct )
	PointLight pointLight;
	#if defined( USE_SHADOWMAP ) && NUM_POINT_LIGHT_SHADOWS > 0
	PointLightShadow pointLightShadow;
	#endif
	#pragma unroll_loop_start
	for ( int i = 0; i < NUM_POINT_LIGHTS; i ++ ) {
		pointLight = pointLights[ i ];
		getPointLightInfo( pointLight, geometryPosition, directLight );
		#if defined( USE_SHADOWMAP ) && ( UNROLLED_LOOP_INDEX < NUM_POINT_LIGHT_SHADOWS )
		pointLightShadow = pointLightShadows[ i ];
		directLight.color *= ( directLight.visible && receiveShadow ) ? getPointShadow( pointShadowMap[ i ], pointLightShadow.shadowMapSize, pointLightShadow.shadowIntensity, pointLightShadow.shadowBias, pointLightShadow.shadowRadius, vPointShadowCoord[ i ], pointLightShadow.shadowCameraNear, pointLightShadow.shadowCameraFar ) : 1.0;
		#endif
		RE_Direct( directLight, geometryPosition, geometryNormal, geometryViewDir, geometryClearcoatNormal, material, reflectedLight );
	}
	#pragma unroll_loop_end
#endif
#if ( NUM_SPOT_LIGHTS > 0 ) && defined( RE_Direct )
	SpotLight spotLight;
	vec4 spotColor;
	vec3 spotLightCoord;
	bool inSpotLightMap;
	#if defined( USE_SHADOWMAP ) && NUM_SPOT_LIGHT_SHADOWS > 0
	SpotLightShadow spotLightShadow;
	#endif
	#pragma unroll_loop_start
	for ( int i = 0; i < NUM_SPOT_LIGHTS; i ++ ) {
		spotLight = spotLights[ i ];
		getSpotLightInfo( spotLight, geometryPosition, directLight );
		#if ( UNROLLED_LOOP_INDEX < NUM_SPOT_LIGHT_SHADOWS_WITH_MAPS )
		#define SPOT_LIGHT_MAP_INDEX UNROLLED_LOOP_INDEX
		#elif ( UNROLLED_LOOP_INDEX < NUM_SPOT_LIGHT_SHADOWS )
		#define SPOT_LIGHT_MAP_INDEX NUM_SPOT_LIGHT_MAPS
		#else
		#define SPOT_LIGHT_MAP_INDEX ( UNROLLED_LOOP_INDEX - NUM_SPOT_LIGHT_SHADOWS + NUM_SPOT_LIGHT_SHADOWS_WITH_MAPS )
		#endif
		#if ( SPOT_LIGHT_MAP_INDEX < NUM_SPOT_LIGHT_MAPS )
			spotLightCoord = vSpotLightCoord[ i ].xyz / vSpotLightCoord[ i ].w;
			inSpotLightMap = all( lessThan( abs( spotLightCoord * 2. - 1. ), vec3( 1.0 ) ) );
			spotColor = texture2D( spotLightMap[ SPOT_LIGHT_MAP_INDEX ], spotLightCoord.xy );
			directLight.color = inSpotLightMap ? directLight.color * spotColor.rgb : directLight.color;
		#endif
		#undef SPOT_LIGHT_MAP_INDEX
		#if defined( USE_SHADOWMAP ) && ( UNROLLED_LOOP_INDEX < NUM_SPOT_LIGHT_SHADOWS )
		spotLightShadow = spotLightShadows[ i ];
		directLight.color *= ( directLight.visible && receiveShadow ) ? getShadow( spotShadowMap[ i ], spotLightShadow.shadowMapSize, spotLightShadow.shadowIntensity, spotLightShadow.shadowBias, spotLightShadow.shadowRadius, vSpotLightCoord[ i ] ) : 1.0;
		#endif
		RE_Direct( directLight, geometryPosition, geometryNormal, geometryViewDir, geometryClearcoatNormal, material, reflectedLight );
	}
	#pragma unroll_loop_end
#endif
#if ( NUM_DIR_LIGHTS > 0 ) && defined( RE_Direct )
	DirectionalLight directionalLight;
	#if defined( USE_SHADOWMAP ) && NUM_DIR_LIGHT_SHADOWS > 0
	DirectionalLightShadow directionalLightShadow;
	#endif
	#pragma unroll_loop_start
	for ( int i = 0; i < NUM_DIR_LIGHTS; i ++ ) {
		directionalLight = directionalLights[ i ];
		getDirectionalLightInfo( directionalLight, directLight );
		#if defined( USE_SHADOWMAP ) && ( UNROLLED_LOOP_INDEX < NUM_DIR_LIGHT_SHADOWS )
		directionalLightShadow = directionalLightShadows[ i ];
		directLight.color *= ( directLight.visible && receiveShadow ) ? getShadow( directionalShadowMap[ i ], directionalLightShadow.shadowMapSize, directionalLightShadow.shadowIntensity, directionalLightShadow.shadowBias, directionalLightShadow.shadowRadius, vDirectionalShadowCoord[ i ] ) : 1.0;
		#endif
		RE_Direct( directLight, geometryPosition, geometryNormal, geometryViewDir, geometryClearcoatNormal, material, reflectedLight );
	}
	#pragma unroll_loop_end
#endif
#if ( NUM_RECT_AREA_LIGHTS > 0 ) && defined( RE_Direct_RectArea )
	RectAreaLight rectAreaLight;
	#pragma unroll_loop_start
	for ( int i = 0; i < NUM_RECT_AREA_LIGHTS; i ++ ) {
		rectAreaLight = rectAreaLights[ i ];
		RE_Direct_RectArea( rectAreaLight, geometryPosition, geometryNormal, geometryViewDir, geometryClearcoatNormal, material, reflectedLight );
	}
	#pragma unroll_loop_end
#endif
#if defined( RE_IndirectDiffuse )
	vec3 iblIrradiance = vec3( 0.0 );
	vec3 irradiance = getAmbientLightIrradiance( ambientLightColor );
	#if defined( USE_LIGHT_PROBES )
		irradiance += getLightProbeIrradiance( lightProbe, geometryNormal );
	#endif
	#if ( NUM_HEMI_LIGHTS > 0 )
		#pragma unroll_loop_start
		for ( int i = 0; i < NUM_HEMI_LIGHTS; i ++ ) {
			irradiance += getHemisphereLightIrradiance( hemisphereLights[ i ], geometryNormal );
		}
		#pragma unroll_loop_end
	#endif
#endif
#if defined( RE_IndirectSpecular )
	vec3 radiance = vec3( 0.0 );
	vec3 clearcoatRadiance = vec3( 0.0 );
#endif`,Rd=`#if defined( RE_IndirectDiffuse )
	#ifdef USE_LIGHTMAP
		vec4 lightMapTexel = texture2D( lightMap, vLightMapUv );
		vec3 lightMapIrradiance = lightMapTexel.rgb * lightMapIntensity;
		irradiance += lightMapIrradiance;
	#endif
	#if defined( USE_ENVMAP ) && defined( STANDARD ) && defined( ENVMAP_TYPE_CUBE_UV )
		iblIrradiance += getIBLIrradiance( geometryNormal );
	#endif
#endif
#if defined( USE_ENVMAP ) && defined( RE_IndirectSpecular )
	#ifdef USE_ANISOTROPY
		radiance += getIBLAnisotropyRadiance( geometryViewDir, geometryNormal, material.roughness, material.anisotropyB, material.anisotropy );
	#else
		radiance += getIBLRadiance( geometryViewDir, geometryNormal, material.roughness );
	#endif
	#ifdef USE_CLEARCOAT
		clearcoatRadiance += getIBLRadiance( geometryViewDir, geometryClearcoatNormal, material.clearcoatRoughness );
	#endif
#endif`,Xd=`#if defined( RE_IndirectDiffuse )
	RE_IndirectDiffuse( irradiance, geometryPosition, geometryNormal, geometryViewDir, geometryClearcoatNormal, material, reflectedLight );
#endif
#if defined( RE_IndirectSpecular )
	RE_IndirectSpecular( radiance, iblIrradiance, clearcoatRadiance, geometryPosition, geometryNormal, geometryViewDir, geometryClearcoatNormal, material, reflectedLight );
#endif`,Bd=`#if defined( USE_LOGDEPTHBUF )
	gl_FragDepth = vIsPerspective == 0.0 ? gl_FragCoord.z : log2( vFragDepth ) * logDepthBufFC * 0.5;
#endif`,$d=`#if defined( USE_LOGDEPTHBUF )
	uniform float logDepthBufFC;
	varying float vFragDepth;
	varying float vIsPerspective;
#endif`,Fd=`#ifdef USE_LOGDEPTHBUF
	varying float vFragDepth;
	varying float vIsPerspective;
#endif`,Dd=`#ifdef USE_LOGDEPTHBUF
	vFragDepth = 1.0 + gl_Position.w;
	vIsPerspective = float( isPerspectiveMatrix( projectionMatrix ) );
#endif`,Nd=`#ifdef USE_MAP
	vec4 sampledDiffuseColor = texture2D( map, vMapUv );
	#ifdef DECODE_VIDEO_TEXTURE
		sampledDiffuseColor = sRGBTransferEOTF( sampledDiffuseColor );
	#endif
	diffuseColor *= sampledDiffuseColor;
#endif`,Vd=`#ifdef USE_MAP
	uniform sampler2D map;
#endif`,Ld=`#if defined( USE_MAP ) || defined( USE_ALPHAMAP )
	#if defined( USE_POINTS_UV )
		vec2 uv = vUv;
	#else
		vec2 uv = ( uvTransform * vec3( gl_PointCoord.x, 1.0 - gl_PointCoord.y, 1 ) ).xy;
	#endif
#endif
#ifdef USE_MAP
	diffuseColor *= texture2D( map, uv );
#endif
#ifdef USE_ALPHAMAP
	diffuseColor.a *= texture2D( alphaMap, uv ).g;
#endif`,zd=`#if defined( USE_POINTS_UV )
	varying vec2 vUv;
#else
	#if defined( USE_MAP ) || defined( USE_ALPHAMAP )
		uniform mat3 uvTransform;
	#endif
#endif
#ifdef USE_MAP
	uniform sampler2D map;
#endif
#ifdef USE_ALPHAMAP
	uniform sampler2D alphaMap;
#endif`,kd=`float metalnessFactor = metalness;
#ifdef USE_METALNESSMAP
	vec4 texelMetalness = texture2D( metalnessMap, vMetalnessMapUv );
	metalnessFactor *= texelMetalness.b;
#endif`,qd=`#ifdef USE_METALNESSMAP
	uniform sampler2D metalnessMap;
#endif`,Jd=`#ifdef USE_INSTANCING_MORPH
	float morphTargetInfluences[ MORPHTARGETS_COUNT ];
	float morphTargetBaseInfluence = texelFetch( morphTexture, ivec2( 0, gl_InstanceID ), 0 ).r;
	for ( int i = 0; i < MORPHTARGETS_COUNT; i ++ ) {
		morphTargetInfluences[i] =  texelFetch( morphTexture, ivec2( i + 1, gl_InstanceID ), 0 ).r;
	}
#endif`,jd=`#if defined( USE_MORPHCOLORS )
	vColor *= morphTargetBaseInfluence;
	for ( int i = 0; i < MORPHTARGETS_COUNT; i ++ ) {
		#if defined( USE_COLOR_ALPHA )
			if ( morphTargetInfluences[ i ] != 0.0 ) vColor += getMorph( gl_VertexID, i, 2 ) * morphTargetInfluences[ i ];
		#elif defined( USE_COLOR )
			if ( morphTargetInfluences[ i ] != 0.0 ) vColor += getMorph( gl_VertexID, i, 2 ).rgb * morphTargetInfluences[ i ];
		#endif
	}
#endif`,Qd=`#ifdef USE_MORPHNORMALS
	objectNormal *= morphTargetBaseInfluence;
	for ( int i = 0; i < MORPHTARGETS_COUNT; i ++ ) {
		if ( morphTargetInfluences[ i ] != 0.0 ) objectNormal += getMorph( gl_VertexID, i, 1 ).xyz * morphTargetInfluences[ i ];
	}
#endif`,Yd=`#ifdef USE_MORPHTARGETS
	#ifndef USE_INSTANCING_MORPH
		uniform float morphTargetBaseInfluence;
		uniform float morphTargetInfluences[ MORPHTARGETS_COUNT ];
	#endif
	uniform sampler2DArray morphTargetsTexture;
	uniform ivec2 morphTargetsTextureSize;
	vec4 getMorph( const in int vertexIndex, const in int morphTargetIndex, const in int offset ) {
		int texelIndex = vertexIndex * MORPHTARGETS_TEXTURE_STRIDE + offset;
		int y = texelIndex / morphTargetsTextureSize.x;
		int x = texelIndex - y * morphTargetsTextureSize.x;
		ivec3 morphUV = ivec3( x, y, morphTargetIndex );
		return texelFetch( morphTargetsTexture, morphUV, 0 );
	}
#endif`,Zd=`#ifdef USE_MORPHTARGETS
	transformed *= morphTargetBaseInfluence;
	for ( int i = 0; i < MORPHTARGETS_COUNT; i ++ ) {
		if ( morphTargetInfluences[ i ] != 0.0 ) transformed += getMorph( gl_VertexID, i, 0 ).xyz * morphTargetInfluences[ i ];
	}
#endif`,Kd=`float faceDirection = gl_FrontFacing ? 1.0 : - 1.0;
#ifdef FLAT_SHADED
	vec3 fdx = dFdx( vViewPosition );
	vec3 fdy = dFdy( vViewPosition );
	vec3 normal = normalize( cross( fdx, fdy ) );
#else
	vec3 normal = normalize( vNormal );
	#ifdef DOUBLE_SIDED
		normal *= faceDirection;
	#endif
#endif
#if defined( USE_NORMALMAP_TANGENTSPACE ) || defined( USE_CLEARCOAT_NORMALMAP ) || defined( USE_ANISOTROPY )
	#ifdef USE_TANGENT
		mat3 tbn = mat3( normalize( vTangent ), normalize( vBitangent ), normal );
	#else
		mat3 tbn = getTangentFrame( - vViewPosition, normal,
		#if defined( USE_NORMALMAP )
			vNormalMapUv
		#elif defined( USE_CLEARCOAT_NORMALMAP )
			vClearcoatNormalMapUv
		#else
			vUv
		#endif
		);
	#endif
	#if defined( DOUBLE_SIDED ) && ! defined( FLAT_SHADED )
		tbn[0] *= faceDirection;
		tbn[1] *= faceDirection;
	#endif
#endif
#ifdef USE_CLEARCOAT_NORMALMAP
	#ifdef USE_TANGENT
		mat3 tbn2 = mat3( normalize( vTangent ), normalize( vBitangent ), normal );
	#else
		mat3 tbn2 = getTangentFrame( - vViewPosition, normal, vClearcoatNormalMapUv );
	#endif
	#if defined( DOUBLE_SIDED ) && ! defined( FLAT_SHADED )
		tbn2[0] *= faceDirection;
		tbn2[1] *= faceDirection;
	#endif
#endif
vec3 nonPerturbedNormal = normal;`,eu=`#ifdef USE_NORMALMAP_OBJECTSPACE
	normal = texture2D( normalMap, vNormalMapUv ).xyz * 2.0 - 1.0;
	#ifdef FLIP_SIDED
		normal = - normal;
	#endif
	#ifdef DOUBLE_SIDED
		normal = normal * faceDirection;
	#endif
	normal = normalize( normalMatrix * normal );
#elif defined( USE_NORMALMAP_TANGENTSPACE )
	vec3 mapN = texture2D( normalMap, vNormalMapUv ).xyz * 2.0 - 1.0;
	mapN.xy *= normalScale;
	normal = normalize( tbn * mapN );
#elif defined( USE_BUMPMAP )
	normal = perturbNormalArb( - vViewPosition, normal, dHdxy_fwd(), faceDirection );
#endif`,tu=`#ifndef FLAT_SHADED
	varying vec3 vNormal;
	#ifdef USE_TANGENT
		varying vec3 vTangent;
		varying vec3 vBitangent;
	#endif
#endif`,nu=`#ifndef FLAT_SHADED
	varying vec3 vNormal;
	#ifdef USE_TANGENT
		varying vec3 vTangent;
		varying vec3 vBitangent;
	#endif
#endif`,iu=`#ifndef FLAT_SHADED
	vNormal = normalize( transformedNormal );
	#ifdef USE_TANGENT
		vTangent = normalize( transformedTangent );
		vBitangent = normalize( cross( vNormal, vTangent ) * tangent.w );
	#endif
#endif`,su=`#ifdef USE_NORMALMAP
	uniform sampler2D normalMap;
	uniform vec2 normalScale;
#endif
#ifdef USE_NORMALMAP_OBJECTSPACE
	uniform mat3 normalMatrix;
#endif
#if ! defined ( USE_TANGENT ) && ( defined ( USE_NORMALMAP_TANGENTSPACE ) || defined ( USE_CLEARCOAT_NORMALMAP ) || defined( USE_ANISOTROPY ) )
	mat3 getTangentFrame( vec3 eye_pos, vec3 surf_norm, vec2 uv ) {
		vec3 q0 = dFdx( eye_pos.xyz );
		vec3 q1 = dFdy( eye_pos.xyz );
		vec2 st0 = dFdx( uv.st );
		vec2 st1 = dFdy( uv.st );
		vec3 N = surf_norm;
		vec3 q1perp = cross( q1, N );
		vec3 q0perp = cross( N, q0 );
		vec3 T = q1perp * st0.x + q0perp * st1.x;
		vec3 B = q1perp * st0.y + q0perp * st1.y;
		float det = max( dot( T, T ), dot( B, B ) );
		float scale = ( det == 0.0 ) ? 0.0 : inversesqrt( det );
		return mat3( T * scale, B * scale, N );
	}
#endif`,ru=`#ifdef USE_CLEARCOAT
	vec3 clearcoatNormal = nonPerturbedNormal;
#endif`,ou=`#ifdef USE_CLEARCOAT_NORMALMAP
	vec3 clearcoatMapN = texture2D( clearcoatNormalMap, vClearcoatNormalMapUv ).xyz * 2.0 - 1.0;
	clearcoatMapN.xy *= clearcoatNormalScale;
	clearcoatNormal = normalize( tbn2 * clearcoatMapN );
#endif`,au=`#ifdef USE_CLEARCOATMAP
	uniform sampler2D clearcoatMap;
#endif
#ifdef USE_CLEARCOAT_NORMALMAP
	uniform sampler2D clearcoatNormalMap;
	uniform vec2 clearcoatNormalScale;
#endif
#ifdef USE_CLEARCOAT_ROUGHNESSMAP
	uniform sampler2D clearcoatRoughnessMap;
#endif`,cu=`#ifdef USE_IRIDESCENCEMAP
	uniform sampler2D iridescenceMap;
#endif
#ifdef USE_IRIDESCENCE_THICKNESSMAP
	uniform sampler2D iridescenceThicknessMap;
#endif`,hu=`#ifdef OPAQUE
diffuseColor.a = 1.0;
#endif
#ifdef USE_TRANSMISSION
diffuseColor.a *= material.transmissionAlpha;
#endif
gl_FragColor = vec4( outgoingLight, diffuseColor.a );`,lu=`vec3 packNormalToRGB( const in vec3 normal ) {
	return normalize( normal ) * 0.5 + 0.5;
}
vec3 unpackRGBToNormal( const in vec3 rgb ) {
	return 2.0 * rgb.xyz - 1.0;
}
const float PackUpscale = 256. / 255.;const float UnpackDownscale = 255. / 256.;const float ShiftRight8 = 1. / 256.;
const float Inv255 = 1. / 255.;
const vec4 PackFactors = vec4( 1.0, 256.0, 256.0 * 256.0, 256.0 * 256.0 * 256.0 );
const vec2 UnpackFactors2 = vec2( UnpackDownscale, 1.0 / PackFactors.g );
const vec3 UnpackFactors3 = vec3( UnpackDownscale / PackFactors.rg, 1.0 / PackFactors.b );
const vec4 UnpackFactors4 = vec4( UnpackDownscale / PackFactors.rgb, 1.0 / PackFactors.a );
vec4 packDepthToRGBA( const in float v ) {
	if( v <= 0.0 )
		return vec4( 0., 0., 0., 0. );
	if( v >= 1.0 )
		return vec4( 1., 1., 1., 1. );
	float vuf;
	float af = modf( v * PackFactors.a, vuf );
	float bf = modf( vuf * ShiftRight8, vuf );
	float gf = modf( vuf * ShiftRight8, vuf );
	return vec4( vuf * Inv255, gf * PackUpscale, bf * PackUpscale, af );
}
vec3 packDepthToRGB( const in float v ) {
	if( v <= 0.0 )
		return vec3( 0., 0., 0. );
	if( v >= 1.0 )
		return vec3( 1., 1., 1. );
	float vuf;
	float bf = modf( v * PackFactors.b, vuf );
	float gf = modf( vuf * ShiftRight8, vuf );
	return vec3( vuf * Inv255, gf * PackUpscale, bf );
}
vec2 packDepthToRG( const in float v ) {
	if( v <= 0.0 )
		return vec2( 0., 0. );
	if( v >= 1.0 )
		return vec2( 1., 1. );
	float vuf;
	float gf = modf( v * 256., vuf );
	return vec2( vuf * Inv255, gf );
}
float unpackRGBAToDepth( const in vec4 v ) {
	return dot( v, UnpackFactors4 );
}
float unpackRGBToDepth( const in vec3 v ) {
	return dot( v, UnpackFactors3 );
}
float unpackRGToDepth( const in vec2 v ) {
	return v.r * UnpackFactors2.r + v.g * UnpackFactors2.g;
}
vec4 pack2HalfToRGBA( const in vec2 v ) {
	vec4 r = vec4( v.x, fract( v.x * 255.0 ), v.y, fract( v.y * 255.0 ) );
	return vec4( r.x - r.y / 255.0, r.y, r.z - r.w / 255.0, r.w );
}
vec2 unpackRGBATo2Half( const in vec4 v ) {
	return vec2( v.x + ( v.y / 255.0 ), v.z + ( v.w / 255.0 ) );
}
float viewZToOrthographicDepth( const in float viewZ, const in float near, const in float far ) {
	return ( viewZ + near ) / ( near - far );
}
float orthographicDepthToViewZ( const in float depth, const in float near, const in float far ) {
	return depth * ( near - far ) - near;
}
float viewZToPerspectiveDepth( const in float viewZ, const in float near, const in float far ) {
	return ( ( near + viewZ ) * far ) / ( ( far - near ) * viewZ );
}
float perspectiveDepthToViewZ( const in float depth, const in float near, const in float far ) {
	return ( near * far ) / ( ( far - near ) * depth - far );
}`,pu=`#ifdef PREMULTIPLIED_ALPHA
	gl_FragColor.rgb *= gl_FragColor.a;
#endif`,fu=`vec4 mvPosition = vec4( transformed, 1.0 );
#ifdef USE_BATCHING
	mvPosition = batchingMatrix * mvPosition;
#endif
#ifdef USE_INSTANCING
	mvPosition = instanceMatrix * mvPosition;
#endif
mvPosition = modelViewMatrix * mvPosition;
gl_Position = projectionMatrix * mvPosition;`,du=`#ifdef DITHERING
	gl_FragColor.rgb = dithering( gl_FragColor.rgb );
#endif`,uu=`#ifdef DITHERING
	vec3 dithering( vec3 color ) {
		float grid_position = rand( gl_FragCoord.xy );
		vec3 dither_shift_RGB = vec3( 0.25 / 255.0, -0.25 / 255.0, 0.25 / 255.0 );
		dither_shift_RGB = mix( 2.0 * dither_shift_RGB, -2.0 * dither_shift_RGB, grid_position );
		return color + dither_shift_RGB;
	}
#endif`,gu=`float roughnessFactor = roughness;
#ifdef USE_ROUGHNESSMAP
	vec4 texelRoughness = texture2D( roughnessMap, vRoughnessMapUv );
	roughnessFactor *= texelRoughness.g;
#endif`,_u=`#ifdef USE_ROUGHNESSMAP
	uniform sampler2D roughnessMap;
#endif`,mu=`#if NUM_SPOT_LIGHT_COORDS > 0
	varying vec4 vSpotLightCoord[ NUM_SPOT_LIGHT_COORDS ];
#endif
#if NUM_SPOT_LIGHT_MAPS > 0
	uniform sampler2D spotLightMap[ NUM_SPOT_LIGHT_MAPS ];
#endif
#ifdef USE_SHADOWMAP
	#if NUM_DIR_LIGHT_SHADOWS > 0
		uniform sampler2D directionalShadowMap[ NUM_DIR_LIGHT_SHADOWS ];
		varying vec4 vDirectionalShadowCoord[ NUM_DIR_LIGHT_SHADOWS ];
		struct DirectionalLightShadow {
			float shadowIntensity;
			float shadowBias;
			float shadowNormalBias;
			float shadowRadius;
			vec2 shadowMapSize;
		};
		uniform DirectionalLightShadow directionalLightShadows[ NUM_DIR_LIGHT_SHADOWS ];
	#endif
	#if NUM_SPOT_LIGHT_SHADOWS > 0
		uniform sampler2D spotShadowMap[ NUM_SPOT_LIGHT_SHADOWS ];
		struct SpotLightShadow {
			float shadowIntensity;
			float shadowBias;
			float shadowNormalBias;
			float shadowRadius;
			vec2 shadowMapSize;
		};
		uniform SpotLightShadow spotLightShadows[ NUM_SPOT_LIGHT_SHADOWS ];
	#endif
	#if NUM_POINT_LIGHT_SHADOWS > 0
		uniform sampler2D pointShadowMap[ NUM_POINT_LIGHT_SHADOWS ];
		varying vec4 vPointShadowCoord[ NUM_POINT_LIGHT_SHADOWS ];
		struct PointLightShadow {
			float shadowIntensity;
			float shadowBias;
			float shadowNormalBias;
			float shadowRadius;
			vec2 shadowMapSize;
			float shadowCameraNear;
			float shadowCameraFar;
		};
		uniform PointLightShadow pointLightShadows[ NUM_POINT_LIGHT_SHADOWS ];
	#endif
	float texture2DCompare( sampler2D depths, vec2 uv, float compare ) {
		return step( compare, unpackRGBAToDepth( texture2D( depths, uv ) ) );
	}
	vec2 texture2DDistribution( sampler2D shadow, vec2 uv ) {
		return unpackRGBATo2Half( texture2D( shadow, uv ) );
	}
	float VSMShadow (sampler2D shadow, vec2 uv, float compare ){
		float occlusion = 1.0;
		vec2 distribution = texture2DDistribution( shadow, uv );
		float hard_shadow = step( compare , distribution.x );
		if (hard_shadow != 1.0 ) {
			float distance = compare - distribution.x ;
			float variance = max( 0.00000, distribution.y * distribution.y );
			float softness_probability = variance / (variance + distance * distance );			softness_probability = clamp( ( softness_probability - 0.3 ) / ( 0.95 - 0.3 ), 0.0, 1.0 );			occlusion = clamp( max( hard_shadow, softness_probability ), 0.0, 1.0 );
		}
		return occlusion;
	}
	float getShadow( sampler2D shadowMap, vec2 shadowMapSize, float shadowIntensity, float shadowBias, float shadowRadius, vec4 shadowCoord ) {
		float shadow = 1.0;
		shadowCoord.xyz /= shadowCoord.w;
		shadowCoord.z += shadowBias;
		bool inFrustum = shadowCoord.x >= 0.0 && shadowCoord.x <= 1.0 && shadowCoord.y >= 0.0 && shadowCoord.y <= 1.0;
		bool frustumTest = inFrustum && shadowCoord.z <= 1.0;
		if ( frustumTest ) {
		#if defined( SHADOWMAP_TYPE_PCF )
			vec2 texelSize = vec2( 1.0 ) / shadowMapSize;
			float dx0 = - texelSize.x * shadowRadius;
			float dy0 = - texelSize.y * shadowRadius;
			float dx1 = + texelSize.x * shadowRadius;
			float dy1 = + texelSize.y * shadowRadius;
			float dx2 = dx0 / 2.0;
			float dy2 = dy0 / 2.0;
			float dx3 = dx1 / 2.0;
			float dy3 = dy1 / 2.0;
			shadow = (
				texture2DCompare( shadowMap, shadowCoord.xy + vec2( dx0, dy0 ), shadowCoord.z ) +
				texture2DCompare( shadowMap, shadowCoord.xy + vec2( 0.0, dy0 ), shadowCoord.z ) +
				texture2DCompare( shadowMap, shadowCoord.xy + vec2( dx1, dy0 ), shadowCoord.z ) +
				texture2DCompare( shadowMap, shadowCoord.xy + vec2( dx2, dy2 ), shadowCoord.z ) +
				texture2DCompare( shadowMap, shadowCoord.xy + vec2( 0.0, dy2 ), shadowCoord.z ) +
				texture2DCompare( shadowMap, shadowCoord.xy + vec2( dx3, dy2 ), shadowCoord.z ) +
				texture2DCompare( shadowMap, shadowCoord.xy + vec2( dx0, 0.0 ), shadowCoord.z ) +
				texture2DCompare( shadowMap, shadowCoord.xy + vec2( dx2, 0.0 ), shadowCoord.z ) +
				texture2DCompare( shadowMap, shadowCoord.xy, shadowCoord.z ) +
				texture2DCompare( shadowMap, shadowCoord.xy + vec2( dx3, 0.0 ), shadowCoord.z ) +
				texture2DCompare( shadowMap, shadowCoord.xy + vec2( dx1, 0.0 ), shadowCoord.z ) +
				texture2DCompare( shadowMap, shadowCoord.xy + vec2( dx2, dy3 ), shadowCoord.z ) +
				texture2DCompare( shadowMap, shadowCoord.xy + vec2( 0.0, dy3 ), shadowCoord.z ) +
				texture2DCompare( shadowMap, shadowCoord.xy + vec2( dx3, dy3 ), shadowCoord.z ) +
				texture2DCompare( shadowMap, shadowCoord.xy + vec2( dx0, dy1 ), shadowCoord.z ) +
				texture2DCompare( shadowMap, shadowCoord.xy + vec2( 0.0, dy1 ), shadowCoord.z ) +
				texture2DCompare( shadowMap, shadowCoord.xy + vec2( dx1, dy1 ), shadowCoord.z )
			) * ( 1.0 / 17.0 );
		#elif defined( SHADOWMAP_TYPE_PCF_SOFT )
			vec2 texelSize = vec2( 1.0 ) / shadowMapSize;
			float dx = texelSize.x;
			float dy = texelSize.y;
			vec2 uv = shadowCoord.xy;
			vec2 f = fract( uv * shadowMapSize + 0.5 );
			uv -= f * texelSize;
			shadow = (
				texture2DCompare( shadowMap, uv, shadowCoord.z ) +
				texture2DCompare( shadowMap, uv + vec2( dx, 0.0 ), shadowCoord.z ) +
				texture2DCompare( shadowMap, uv + vec2( 0.0, dy ), shadowCoord.z ) +
				texture2DCompare( shadowMap, uv + texelSize, shadowCoord.z ) +
				mix( texture2DCompare( shadowMap, uv + vec2( -dx, 0.0 ), shadowCoord.z ),
					 texture2DCompare( shadowMap, uv + vec2( 2.0 * dx, 0.0 ), shadowCoord.z ),
					 f.x ) +
				mix( texture2DCompare( shadowMap, uv + vec2( -dx, dy ), shadowCoord.z ),
					 texture2DCompare( shadowMap, uv + vec2( 2.0 * dx, dy ), shadowCoord.z ),
					 f.x ) +
				mix( texture2DCompare( shadowMap, uv + vec2( 0.0, -dy ), shadowCoord.z ),
					 texture2DCompare( shadowMap, uv + vec2( 0.0, 2.0 * dy ), shadowCoord.z ),
					 f.y ) +
				mix( texture2DCompare( shadowMap, uv + vec2( dx, -dy ), shadowCoord.z ),
					 texture2DCompare( shadowMap, uv + vec2( dx, 2.0 * dy ), shadowCoord.z ),
					 f.y ) +
				mix( mix( texture2DCompare( shadowMap, uv + vec2( -dx, -dy ), shadowCoord.z ),
						  texture2DCompare( shadowMap, uv + vec2( 2.0 * dx, -dy ), shadowCoord.z ),
						  f.x ),
					 mix( texture2DCompare( shadowMap, uv + vec2( -dx, 2.0 * dy ), shadowCoord.z ),
						  texture2DCompare( shadowMap, uv + vec2( 2.0 * dx, 2.0 * dy ), shadowCoord.z ),
						  f.x ),
					 f.y )
			) * ( 1.0 / 9.0 );
		#elif defined( SHADOWMAP_TYPE_VSM )
			shadow = VSMShadow( shadowMap, shadowCoord.xy, shadowCoord.z );
		#else
			shadow = texture2DCompare( shadowMap, shadowCoord.xy, shadowCoord.z );
		#endif
		}
		return mix( 1.0, shadow, shadowIntensity );
	}
	vec2 cubeToUV( vec3 v, float texelSizeY ) {
		vec3 absV = abs( v );
		float scaleToCube = 1.0 / max( absV.x, max( absV.y, absV.z ) );
		absV *= scaleToCube;
		v *= scaleToCube * ( 1.0 - 2.0 * texelSizeY );
		vec2 planar = v.xy;
		float almostATexel = 1.5 * texelSizeY;
		float almostOne = 1.0 - almostATexel;
		if ( absV.z >= almostOne ) {
			if ( v.z > 0.0 )
				planar.x = 4.0 - v.x;
		} else if ( absV.x >= almostOne ) {
			float signX = sign( v.x );
			planar.x = v.z * signX + 2.0 * signX;
		} else if ( absV.y >= almostOne ) {
			float signY = sign( v.y );
			planar.x = v.x + 2.0 * signY + 2.0;
			planar.y = v.z * signY - 2.0;
		}
		return vec2( 0.125, 0.25 ) * planar + vec2( 0.375, 0.75 );
	}
	float getPointShadow( sampler2D shadowMap, vec2 shadowMapSize, float shadowIntensity, float shadowBias, float shadowRadius, vec4 shadowCoord, float shadowCameraNear, float shadowCameraFar ) {
		float shadow = 1.0;
		vec3 lightToPosition = shadowCoord.xyz;
		
		float lightToPositionLength = length( lightToPosition );
		if ( lightToPositionLength - shadowCameraFar <= 0.0 && lightToPositionLength - shadowCameraNear >= 0.0 ) {
			float dp = ( lightToPositionLength - shadowCameraNear ) / ( shadowCameraFar - shadowCameraNear );			dp += shadowBias;
			vec3 bd3D = normalize( lightToPosition );
			vec2 texelSize = vec2( 1.0 ) / ( shadowMapSize * vec2( 4.0, 2.0 ) );
			#if defined( SHADOWMAP_TYPE_PCF ) || defined( SHADOWMAP_TYPE_PCF_SOFT ) || defined( SHADOWMAP_TYPE_VSM )
				vec2 offset = vec2( - 1, 1 ) * shadowRadius * texelSize.y;
				shadow = (
					texture2DCompare( shadowMap, cubeToUV( bd3D + offset.xyy, texelSize.y ), dp ) +
					texture2DCompare( shadowMap, cubeToUV( bd3D + offset.yyy, texelSize.y ), dp ) +
					texture2DCompare( shadowMap, cubeToUV( bd3D + offset.xyx, texelSize.y ), dp ) +
					texture2DCompare( shadowMap, cubeToUV( bd3D + offset.yyx, texelSize.y ), dp ) +
					texture2DCompare( shadowMap, cubeToUV( bd3D, texelSize.y ), dp ) +
					texture2DCompare( shadowMap, cubeToUV( bd3D + offset.xxy, texelSize.y ), dp ) +
					texture2DCompare( shadowMap, cubeToUV( bd3D + offset.yxy, texelSize.y ), dp ) +
					texture2DCompare( shadowMap, cubeToUV( bd3D + offset.xxx, texelSize.y ), dp ) +
					texture2DCompare( shadowMap, cubeToUV( bd3D + offset.yxx, texelSize.y ), dp )
				) * ( 1.0 / 9.0 );
			#else
				shadow = texture2DCompare( shadowMap, cubeToUV( bd3D, texelSize.y ), dp );
			#endif
		}
		return mix( 1.0, shadow, shadowIntensity );
	}
#endif`,xu=`#if NUM_SPOT_LIGHT_COORDS > 0
	uniform mat4 spotLightMatrix[ NUM_SPOT_LIGHT_COORDS ];
	varying vec4 vSpotLightCoord[ NUM_SPOT_LIGHT_COORDS ];
#endif
#ifdef USE_SHADOWMAP
	#if NUM_DIR_LIGHT_SHADOWS > 0
		uniform mat4 directionalShadowMatrix[ NUM_DIR_LIGHT_SHADOWS ];
		varying vec4 vDirectionalShadowCoord[ NUM_DIR_LIGHT_SHADOWS ];
		struct DirectionalLightShadow {
			float shadowIntensity;
			float shadowBias;
			float shadowNormalBias;
			float shadowRadius;
			vec2 shadowMapSize;
		};
		uniform DirectionalLightShadow directionalLightShadows[ NUM_DIR_LIGHT_SHADOWS ];
	#endif
	#if NUM_SPOT_LIGHT_SHADOWS > 0
		struct SpotLightShadow {
			float shadowIntensity;
			float shadowBias;
			float shadowNormalBias;
			float shadowRadius;
			vec2 shadowMapSize;
		};
		uniform SpotLightShadow spotLightShadows[ NUM_SPOT_LIGHT_SHADOWS ];
	#endif
	#if NUM_POINT_LIGHT_SHADOWS > 0
		uniform mat4 pointShadowMatrix[ NUM_POINT_LIGHT_SHADOWS ];
		varying vec4 vPointShadowCoord[ NUM_POINT_LIGHT_SHADOWS ];
		struct PointLightShadow {
			float shadowIntensity;
			float shadowBias;
			float shadowNormalBias;
			float shadowRadius;
			vec2 shadowMapSize;
			float shadowCameraNear;
			float shadowCameraFar;
		};
		uniform PointLightShadow pointLightShadows[ NUM_POINT_LIGHT_SHADOWS ];
	#endif
#endif`,bu=`#if ( defined( USE_SHADOWMAP ) && ( NUM_DIR_LIGHT_SHADOWS > 0 || NUM_POINT_LIGHT_SHADOWS > 0 ) ) || ( NUM_SPOT_LIGHT_COORDS > 0 )
	vec3 shadowWorldNormal = inverseTransformDirection( transformedNormal, viewMatrix );
	vec4 shadowWorldPosition;
#endif
#if defined( USE_SHADOWMAP )
	#if NUM_DIR_LIGHT_SHADOWS > 0
		#pragma unroll_loop_start
		for ( int i = 0; i < NUM_DIR_LIGHT_SHADOWS; i ++ ) {
			shadowWorldPosition = worldPosition + vec4( shadowWorldNormal * directionalLightShadows[ i ].shadowNormalBias, 0 );
			vDirectionalShadowCoord[ i ] = directionalShadowMatrix[ i ] * shadowWorldPosition;
		}
		#pragma unroll_loop_end
	#endif
	#if NUM_POINT_LIGHT_SHADOWS > 0
		#pragma unroll_loop_start
		for ( int i = 0; i < NUM_POINT_LIGHT_SHADOWS; i ++ ) {
			shadowWorldPosition = worldPosition + vec4( shadowWorldNormal * pointLightShadows[ i ].shadowNormalBias, 0 );
			vPointShadowCoord[ i ] = pointShadowMatrix[ i ] * shadowWorldPosition;
		}
		#pragma unroll_loop_end
	#endif
#endif
#if NUM_SPOT_LIGHT_COORDS > 0
	#pragma unroll_loop_start
	for ( int i = 0; i < NUM_SPOT_LIGHT_COORDS; i ++ ) {
		shadowWorldPosition = worldPosition;
		#if ( defined( USE_SHADOWMAP ) && UNROLLED_LOOP_INDEX < NUM_SPOT_LIGHT_SHADOWS )
			shadowWorldPosition.xyz += shadowWorldNormal * spotLightShadows[ i ].shadowNormalBias;
		#endif
		vSpotLightCoord[ i ] = spotLightMatrix[ i ] * shadowWorldPosition;
	}
	#pragma unroll_loop_end
#endif`,Pu=`float getShadowMask() {
	float shadow = 1.0;
	#ifdef USE_SHADOWMAP
	#if NUM_DIR_LIGHT_SHADOWS > 0
	DirectionalLightShadow directionalLight;
	#pragma unroll_loop_start
	for ( int i = 0; i < NUM_DIR_LIGHT_SHADOWS; i ++ ) {
		directionalLight = directionalLightShadows[ i ];
		shadow *= receiveShadow ? getShadow( directionalShadowMap[ i ], directionalLight.shadowMapSize, directionalLight.shadowIntensity, directionalLight.shadowBias, directionalLight.shadowRadius, vDirectionalShadowCoord[ i ] ) : 1.0;
	}
	#pragma unroll_loop_end
	#endif
	#if NUM_SPOT_LIGHT_SHADOWS > 0
	SpotLightShadow spotLight;
	#pragma unroll_loop_start
	for ( int i = 0; i < NUM_SPOT_LIGHT_SHADOWS; i ++ ) {
		spotLight = spotLightShadows[ i ];
		shadow *= receiveShadow ? getShadow( spotShadowMap[ i ], spotLight.shadowMapSize, spotLight.shadowIntensity, spotLight.shadowBias, spotLight.shadowRadius, vSpotLightCoord[ i ] ) : 1.0;
	}
	#pragma unroll_loop_end
	#endif
	#if NUM_POINT_LIGHT_SHADOWS > 0
	PointLightShadow pointLight;
	#pragma unroll_loop_start
	for ( int i = 0; i < NUM_POINT_LIGHT_SHADOWS; i ++ ) {
		pointLight = pointLightShadows[ i ];
		shadow *= receiveShadow ? getPointShadow( pointShadowMap[ i ], pointLight.shadowMapSize, pointLight.shadowIntensity, pointLight.shadowBias, pointLight.shadowRadius, vPointShadowCoord[ i ], pointLight.shadowCameraNear, pointLight.shadowCameraFar ) : 1.0;
	}
	#pragma unroll_loop_end
	#endif
	#endif
	return shadow;
}`,wu=`#ifdef USE_SKINNING
	mat4 boneMatX = getBoneMatrix( skinIndex.x );
	mat4 boneMatY = getBoneMatrix( skinIndex.y );
	mat4 boneMatZ = getBoneMatrix( skinIndex.z );
	mat4 boneMatW = getBoneMatrix( skinIndex.w );
#endif`,Ou=`#ifdef USE_SKINNING
	uniform mat4 bindMatrix;
	uniform mat4 bindMatrixInverse;
	uniform highp sampler2D boneTexture;
	mat4 getBoneMatrix( const in float i ) {
		int size = textureSize( boneTexture, 0 ).x;
		int j = int( i ) * 4;
		int x = j % size;
		int y = j / size;
		vec4 v1 = texelFetch( boneTexture, ivec2( x, y ), 0 );
		vec4 v2 = texelFetch( boneTexture, ivec2( x + 1, y ), 0 );
		vec4 v3 = texelFetch( boneTexture, ivec2( x + 2, y ), 0 );
		vec4 v4 = texelFetch( boneTexture, ivec2( x + 3, y ), 0 );
		return mat4( v1, v2, v3, v4 );
	}
#endif`,yu=`#ifdef USE_SKINNING
	vec4 skinVertex = bindMatrix * vec4( transformed, 1.0 );
	vec4 skinned = vec4( 0.0 );
	skinned += boneMatX * skinVertex * skinWeight.x;
	skinned += boneMatY * skinVertex * skinWeight.y;
	skinned += boneMatZ * skinVertex * skinWeight.z;
	skinned += boneMatW * skinVertex * skinWeight.w;
	transformed = ( bindMatrixInverse * skinned ).xyz;
#endif`,Au=`#ifdef USE_SKINNING
	mat4 skinMatrix = mat4( 0.0 );
	skinMatrix += skinWeight.x * boneMatX;
	skinMatrix += skinWeight.y * boneMatY;
	skinMatrix += skinWeight.z * boneMatZ;
	skinMatrix += skinWeight.w * boneMatW;
	skinMatrix = bindMatrixInverse * skinMatrix * bindMatrix;
	objectNormal = vec4( skinMatrix * vec4( objectNormal, 0.0 ) ).xyz;
	#ifdef USE_TANGENT
		objectTangent = vec4( skinMatrix * vec4( objectTangent, 0.0 ) ).xyz;
	#endif
#endif`,vu=`float specularStrength;
#ifdef USE_SPECULARMAP
	vec4 texelSpecular = texture2D( specularMap, vSpecularMapUv );
	specularStrength = texelSpecular.r;
#else
	specularStrength = 1.0;
#endif`,Hu=`#ifdef USE_SPECULARMAP
	uniform sampler2D specularMap;
#endif`,Su=`#if defined( TONE_MAPPING )
	gl_FragColor.rgb = toneMapping( gl_FragColor.rgb );
#endif`,Eu=`#ifndef saturate
#define saturate( a ) clamp( a, 0.0, 1.0 )
#endif
uniform float toneMappingExposure;
vec3 LinearToneMapping( vec3 color ) {
	return saturate( toneMappingExposure * color );
}
vec3 ReinhardToneMapping( vec3 color ) {
	color *= toneMappingExposure;
	return saturate( color / ( vec3( 1.0 ) + color ) );
}
vec3 CineonToneMapping( vec3 color ) {
	color *= toneMappingExposure;
	color = max( vec3( 0.0 ), color - 0.004 );
	return pow( ( color * ( 6.2 * color + 0.5 ) ) / ( color * ( 6.2 * color + 1.7 ) + 0.06 ), vec3( 2.2 ) );
}
vec3 RRTAndODTFit( vec3 v ) {
	vec3 a = v * ( v + 0.0245786 ) - 0.000090537;
	vec3 b = v * ( 0.983729 * v + 0.4329510 ) + 0.238081;
	return a / b;
}
vec3 ACESFilmicToneMapping( vec3 color ) {
	const mat3 ACESInputMat = mat3(
		vec3( 0.59719, 0.07600, 0.02840 ),		vec3( 0.35458, 0.90834, 0.13383 ),
		vec3( 0.04823, 0.01566, 0.83777 )
	);
	const mat3 ACESOutputMat = mat3(
		vec3(  1.60475, -0.10208, -0.00327 ),		vec3( -0.53108,  1.10813, -0.07276 ),
		vec3( -0.07367, -0.00605,  1.07602 )
	);
	color *= toneMappingExposure / 0.6;
	color = ACESInputMat * color;
	color = RRTAndODTFit( color );
	color = ACESOutputMat * color;
	return saturate( color );
}
const mat3 LINEAR_REC2020_TO_LINEAR_SRGB = mat3(
	vec3( 1.6605, - 0.1246, - 0.0182 ),
	vec3( - 0.5876, 1.1329, - 0.1006 ),
	vec3( - 0.0728, - 0.0083, 1.1187 )
);
const mat3 LINEAR_SRGB_TO_LINEAR_REC2020 = mat3(
	vec3( 0.6274, 0.0691, 0.0164 ),
	vec3( 0.3293, 0.9195, 0.0880 ),
	vec3( 0.0433, 0.0113, 0.8956 )
);
vec3 agxDefaultContrastApprox( vec3 x ) {
	vec3 x2 = x * x;
	vec3 x4 = x2 * x2;
	return + 15.5 * x4 * x2
		- 40.14 * x4 * x
		+ 31.96 * x4
		- 6.868 * x2 * x
		+ 0.4298 * x2
		+ 0.1191 * x
		- 0.00232;
}
vec3 AgXToneMapping( vec3 color ) {
	const mat3 AgXInsetMatrix = mat3(
		vec3( 0.856627153315983, 0.137318972929847, 0.11189821299995 ),
		vec3( 0.0951212405381588, 0.761241990602591, 0.0767994186031903 ),
		vec3( 0.0482516061458583, 0.101439036467562, 0.811302368396859 )
	);
	const mat3 AgXOutsetMatrix = mat3(
		vec3( 1.1271005818144368, - 0.1413297634984383, - 0.14132976349843826 ),
		vec3( - 0.11060664309660323, 1.157823702216272, - 0.11060664309660294 ),
		vec3( - 0.016493938717834573, - 0.016493938717834257, 1.2519364065950405 )
	);
	const float AgxMinEv = - 12.47393;	const float AgxMaxEv = 4.026069;
	color *= toneMappingExposure;
	color = LINEAR_SRGB_TO_LINEAR_REC2020 * color;
	color = AgXInsetMatrix * color;
	color = max( color, 1e-10 );	color = log2( color );
	color = ( color - AgxMinEv ) / ( AgxMaxEv - AgxMinEv );
	color = clamp( color, 0.0, 1.0 );
	color = agxDefaultContrastApprox( color );
	color = AgXOutsetMatrix * color;
	color = pow( max( vec3( 0.0 ), color ), vec3( 2.2 ) );
	color = LINEAR_REC2020_TO_LINEAR_SRGB * color;
	color = clamp( color, 0.0, 1.0 );
	return color;
}
vec3 NeutralToneMapping( vec3 color ) {
	const float StartCompression = 0.8 - 0.04;
	const float Desaturation = 0.15;
	color *= toneMappingExposure;
	float x = min( color.r, min( color.g, color.b ) );
	float offset = x < 0.08 ? x - 6.25 * x * x : 0.04;
	color -= offset;
	float peak = max( color.r, max( color.g, color.b ) );
	if ( peak < StartCompression ) return color;
	float d = 1. - StartCompression;
	float newPeak = 1. - d * d / ( peak + d - StartCompression );
	color *= newPeak / peak;
	float g = 1. - 1. / ( Desaturation * ( peak - newPeak ) + 1. );
	return mix( color, vec3( newPeak ), g );
}
vec3 CustomToneMapping( vec3 color ) { return color; }`,Wu=`#ifdef USE_TRANSMISSION
	material.transmission = transmission;
	material.transmissionAlpha = 1.0;
	material.thickness = thickness;
	material.attenuationDistance = attenuationDistance;
	material.attenuationColor = attenuationColor;
	#ifdef USE_TRANSMISSIONMAP
		material.transmission *= texture2D( transmissionMap, vTransmissionMapUv ).r;
	#endif
	#ifdef USE_THICKNESSMAP
		material.thickness *= texture2D( thicknessMap, vThicknessMapUv ).g;
	#endif
	vec3 pos = vWorldPosition;
	vec3 v = normalize( cameraPosition - pos );
	vec3 n = inverseTransformDirection( normal, viewMatrix );
	vec4 transmitted = getIBLVolumeRefraction(
		n, v, material.roughness, material.diffuseColor, material.specularColor, material.specularF90,
		pos, modelMatrix, viewMatrix, projectionMatrix, material.dispersion, material.ior, material.thickness,
		material.attenuationColor, material.attenuationDistance );
	material.transmissionAlpha = mix( material.transmissionAlpha, transmitted.a, material.transmission );
	totalDiffuse = mix( totalDiffuse, transmitted.rgb, material.transmission );
#endif`,Mu=`#ifdef USE_TRANSMISSION
	uniform float transmission;
	uniform float thickness;
	uniform float attenuationDistance;
	uniform vec3 attenuationColor;
	#ifdef USE_TRANSMISSIONMAP
		uniform sampler2D transmissionMap;
	#endif
	#ifdef USE_THICKNESSMAP
		uniform sampler2D thicknessMap;
	#endif
	uniform vec2 transmissionSamplerSize;
	uniform sampler2D transmissionSamplerMap;
	uniform mat4 modelMatrix;
	uniform mat4 projectionMatrix;
	varying vec3 vWorldPosition;
	float w0( float a ) {
		return ( 1.0 / 6.0 ) * ( a * ( a * ( - a + 3.0 ) - 3.0 ) + 1.0 );
	}
	float w1( float a ) {
		return ( 1.0 / 6.0 ) * ( a *  a * ( 3.0 * a - 6.0 ) + 4.0 );
	}
	float w2( float a ){
		return ( 1.0 / 6.0 ) * ( a * ( a * ( - 3.0 * a + 3.0 ) + 3.0 ) + 1.0 );
	}
	float w3( float a ) {
		return ( 1.0 / 6.0 ) * ( a * a * a );
	}
	float g0( float a ) {
		return w0( a ) + w1( a );
	}
	float g1( float a ) {
		return w2( a ) + w3( a );
	}
	float h0( float a ) {
		return - 1.0 + w1( a ) / ( w0( a ) + w1( a ) );
	}
	float h1( float a ) {
		return 1.0 + w3( a ) / ( w2( a ) + w3( a ) );
	}
	vec4 bicubic( sampler2D tex, vec2 uv, vec4 texelSize, float lod ) {
		uv = uv * texelSize.zw + 0.5;
		vec2 iuv = floor( uv );
		vec2 fuv = fract( uv );
		float g0x = g0( fuv.x );
		float g1x = g1( fuv.x );
		float h0x = h0( fuv.x );
		float h1x = h1( fuv.x );
		float h0y = h0( fuv.y );
		float h1y = h1( fuv.y );
		vec2 p0 = ( vec2( iuv.x + h0x, iuv.y + h0y ) - 0.5 ) * texelSize.xy;
		vec2 p1 = ( vec2( iuv.x + h1x, iuv.y + h0y ) - 0.5 ) * texelSize.xy;
		vec2 p2 = ( vec2( iuv.x + h0x, iuv.y + h1y ) - 0.5 ) * texelSize.xy;
		vec2 p3 = ( vec2( iuv.x + h1x, iuv.y + h1y ) - 0.5 ) * texelSize.xy;
		return g0( fuv.y ) * ( g0x * textureLod( tex, p0, lod ) + g1x * textureLod( tex, p1, lod ) ) +
			g1( fuv.y ) * ( g0x * textureLod( tex, p2, lod ) + g1x * textureLod( tex, p3, lod ) );
	}
	vec4 textureBicubic( sampler2D sampler, vec2 uv, float lod ) {
		vec2 fLodSize = vec2( textureSize( sampler, int( lod ) ) );
		vec2 cLodSize = vec2( textureSize( sampler, int( lod + 1.0 ) ) );
		vec2 fLodSizeInv = 1.0 / fLodSize;
		vec2 cLodSizeInv = 1.0 / cLodSize;
		vec4 fSample = bicubic( sampler, uv, vec4( fLodSizeInv, fLodSize ), floor( lod ) );
		vec4 cSample = bicubic( sampler, uv, vec4( cLodSizeInv, cLodSize ), ceil( lod ) );
		return mix( fSample, cSample, fract( lod ) );
	}
	vec3 getVolumeTransmissionRay( const in vec3 n, const in vec3 v, const in float thickness, const in float ior, const in mat4 modelMatrix ) {
		vec3 refractionVector = refract( - v, normalize( n ), 1.0 / ior );
		vec3 modelScale;
		modelScale.x = length( vec3( modelMatrix[ 0 ].xyz ) );
		modelScale.y = length( vec3( modelMatrix[ 1 ].xyz ) );
		modelScale.z = length( vec3( modelMatrix[ 2 ].xyz ) );
		return normalize( refractionVector ) * thickness * modelScale;
	}
	float applyIorToRoughness( const in float roughness, const in float ior ) {
		return roughness * clamp( ior * 2.0 - 2.0, 0.0, 1.0 );
	}
	vec4 getTransmissionSample( const in vec2 fragCoord, const in float roughness, const in float ior ) {
		float lod = log2( transmissionSamplerSize.x ) * applyIorToRoughness( roughness, ior );
		return textureBicubic( transmissionSamplerMap, fragCoord.xy, lod );
	}
	vec3 volumeAttenuation( const in float transmissionDistance, const in vec3 attenuationColor, const in float attenuationDistance ) {
		if ( isinf( attenuationDistance ) ) {
			return vec3( 1.0 );
		} else {
			vec3 attenuationCoefficient = -log( attenuationColor ) / attenuationDistance;
			vec3 transmittance = exp( - attenuationCoefficient * transmissionDistance );			return transmittance;
		}
	}
	vec4 getIBLVolumeRefraction( const in vec3 n, const in vec3 v, const in float roughness, const in vec3 diffuseColor,
		const in vec3 specularColor, const in float specularF90, const in vec3 position, const in mat4 modelMatrix,
		const in mat4 viewMatrix, const in mat4 projMatrix, const in float dispersion, const in float ior, const in float thickness,
		const in vec3 attenuationColor, const in float attenuationDistance ) {
		vec4 transmittedLight;
		vec3 transmittance;
		#ifdef USE_DISPERSION
			float halfSpread = ( ior - 1.0 ) * 0.025 * dispersion;
			vec3 iors = vec3( ior - halfSpread, ior, ior + halfSpread );
			for ( int i = 0; i < 3; i ++ ) {
				vec3 transmissionRay = getVolumeTransmissionRay( n, v, thickness, iors[ i ], modelMatrix );
				vec3 refractedRayExit = position + transmissionRay;
		
				vec4 ndcPos = projMatrix * viewMatrix * vec4( refractedRayExit, 1.0 );
				vec2 refractionCoords = ndcPos.xy / ndcPos.w;
				refractionCoords += 1.0;
				refractionCoords /= 2.0;
		
				vec4 transmissionSample = getTransmissionSample( refractionCoords, roughness, iors[ i ] );
				transmittedLight[ i ] = transmissionSample[ i ];
				transmittedLight.a += transmissionSample.a;
				transmittance[ i ] = diffuseColor[ i ] * volumeAttenuation( length( transmissionRay ), attenuationColor, attenuationDistance )[ i ];
			}
			transmittedLight.a /= 3.0;
		
		#else
		
			vec3 transmissionRay = getVolumeTransmissionRay( n, v, thickness, ior, modelMatrix );
			vec3 refractedRayExit = position + transmissionRay;
			vec4 ndcPos = projMatrix * viewMatrix * vec4( refractedRayExit, 1.0 );
			vec2 refractionCoords = ndcPos.xy / ndcPos.w;
			refractionCoords += 1.0;
			refractionCoords /= 2.0;
			transmittedLight = getTransmissionSample( refractionCoords, roughness, ior );
			transmittance = diffuseColor * volumeAttenuation( length( transmissionRay ), attenuationColor, attenuationDistance );
		
		#endif
		vec3 attenuatedColor = transmittance * transmittedLight.rgb;
		vec3 F = EnvironmentBRDF( n, v, specularColor, specularF90, roughness );
		float transmittanceFactor = ( transmittance.r + transmittance.g + transmittance.b ) / 3.0;
		return vec4( ( 1.0 - F ) * attenuatedColor, 1.0 - ( 1.0 - transmittedLight.a ) * transmittanceFactor );
	}
#endif`,Cu=`#if defined( USE_UV ) || defined( USE_ANISOTROPY )
	varying vec2 vUv;
#endif
#ifdef USE_MAP
	varying vec2 vMapUv;
#endif
#ifdef USE_ALPHAMAP
	varying vec2 vAlphaMapUv;
#endif
#ifdef USE_LIGHTMAP
	varying vec2 vLightMapUv;
#endif
#ifdef USE_AOMAP
	varying vec2 vAoMapUv;
#endif
#ifdef USE_BUMPMAP
	varying vec2 vBumpMapUv;
#endif
#ifdef USE_NORMALMAP
	varying vec2 vNormalMapUv;
#endif
#ifdef USE_EMISSIVEMAP
	varying vec2 vEmissiveMapUv;
#endif
#ifdef USE_METALNESSMAP
	varying vec2 vMetalnessMapUv;
#endif
#ifdef USE_ROUGHNESSMAP
	varying vec2 vRoughnessMapUv;
#endif
#ifdef USE_ANISOTROPYMAP
	varying vec2 vAnisotropyMapUv;
#endif
#ifdef USE_CLEARCOATMAP
	varying vec2 vClearcoatMapUv;
#endif
#ifdef USE_CLEARCOAT_NORMALMAP
	varying vec2 vClearcoatNormalMapUv;
#endif
#ifdef USE_CLEARCOAT_ROUGHNESSMAP
	varying vec2 vClearcoatRoughnessMapUv;
#endif
#ifdef USE_IRIDESCENCEMAP
	varying vec2 vIridescenceMapUv;
#endif
#ifdef USE_IRIDESCENCE_THICKNESSMAP
	varying vec2 vIridescenceThicknessMapUv;
#endif
#ifdef USE_SHEEN_COLORMAP
	varying vec2 vSheenColorMapUv;
#endif
#ifdef USE_SHEEN_ROUGHNESSMAP
	varying vec2 vSheenRoughnessMapUv;
#endif
#ifdef USE_SPECULARMAP
	varying vec2 vSpecularMapUv;
#endif
#ifdef USE_SPECULAR_COLORMAP
	varying vec2 vSpecularColorMapUv;
#endif
#ifdef USE_SPECULAR_INTENSITYMAP
	varying vec2 vSpecularIntensityMapUv;
#endif
#ifdef USE_TRANSMISSIONMAP
	uniform mat3 transmissionMapTransform;
	varying vec2 vTransmissionMapUv;
#endif
#ifdef USE_THICKNESSMAP
	uniform mat3 thicknessMapTransform;
	varying vec2 vThicknessMapUv;
#endif`,Gu=`#if defined( USE_UV ) || defined( USE_ANISOTROPY )
	varying vec2 vUv;
#endif
#ifdef USE_MAP
	uniform mat3 mapTransform;
	varying vec2 vMapUv;
#endif
#ifdef USE_ALPHAMAP
	uniform mat3 alphaMapTransform;
	varying vec2 vAlphaMapUv;
#endif
#ifdef USE_LIGHTMAP
	uniform mat3 lightMapTransform;
	varying vec2 vLightMapUv;
#endif
#ifdef USE_AOMAP
	uniform mat3 aoMapTransform;
	varying vec2 vAoMapUv;
#endif
#ifdef USE_BUMPMAP
	uniform mat3 bumpMapTransform;
	varying vec2 vBumpMapUv;
#endif
#ifdef USE_NORMALMAP
	uniform mat3 normalMapTransform;
	varying vec2 vNormalMapUv;
#endif
#ifdef USE_DISPLACEMENTMAP
	uniform mat3 displacementMapTransform;
	varying vec2 vDisplacementMapUv;
#endif
#ifdef USE_EMISSIVEMAP
	uniform mat3 emissiveMapTransform;
	varying vec2 vEmissiveMapUv;
#endif
#ifdef USE_METALNESSMAP
	uniform mat3 metalnessMapTransform;
	varying vec2 vMetalnessMapUv;
#endif
#ifdef USE_ROUGHNESSMAP
	uniform mat3 roughnessMapTransform;
	varying vec2 vRoughnessMapUv;
#endif
#ifdef USE_ANISOTROPYMAP
	uniform mat3 anisotropyMapTransform;
	varying vec2 vAnisotropyMapUv;
#endif
#ifdef USE_CLEARCOATMAP
	uniform mat3 clearcoatMapTransform;
	varying vec2 vClearcoatMapUv;
#endif
#ifdef USE_CLEARCOAT_NORMALMAP
	uniform mat3 clearcoatNormalMapTransform;
	varying vec2 vClearcoatNormalMapUv;
#endif
#ifdef USE_CLEARCOAT_ROUGHNESSMAP
	uniform mat3 clearcoatRoughnessMapTransform;
	varying vec2 vClearcoatRoughnessMapUv;
#endif
#ifdef USE_SHEEN_COLORMAP
	uniform mat3 sheenColorMapTransform;
	varying vec2 vSheenColorMapUv;
#endif
#ifdef USE_SHEEN_ROUGHNESSMAP
	uniform mat3 sheenRoughnessMapTransform;
	varying vec2 vSheenRoughnessMapUv;
#endif
#ifdef USE_IRIDESCENCEMAP
	uniform mat3 iridescenceMapTransform;
	varying vec2 vIridescenceMapUv;
#endif
#ifdef USE_IRIDESCENCE_THICKNESSMAP
	uniform mat3 iridescenceThicknessMapTransform;
	varying vec2 vIridescenceThicknessMapUv;
#endif
#ifdef USE_SPECULARMAP
	uniform mat3 specularMapTransform;
	varying vec2 vSpecularMapUv;
#endif
#ifdef USE_SPECULAR_COLORMAP
	uniform mat3 specularColorMapTransform;
	varying vec2 vSpecularColorMapUv;
#endif
#ifdef USE_SPECULAR_INTENSITYMAP
	uniform mat3 specularIntensityMapTransform;
	varying vec2 vSpecularIntensityMapUv;
#endif
#ifdef USE_TRANSMISSIONMAP
	uniform mat3 transmissionMapTransform;
	varying vec2 vTransmissionMapUv;
#endif
#ifdef USE_THICKNESSMAP
	uniform mat3 thicknessMapTransform;
	varying vec2 vThicknessMapUv;
#endif`,Tu=`#if defined( USE_UV ) || defined( USE_ANISOTROPY )
	vUv = vec3( uv, 1 ).xy;
#endif
#ifdef USE_MAP
	vMapUv = ( mapTransform * vec3( MAP_UV, 1 ) ).xy;
#endif
#ifdef USE_ALPHAMAP
	vAlphaMapUv = ( alphaMapTransform * vec3( ALPHAMAP_UV, 1 ) ).xy;
#endif
#ifdef USE_LIGHTMAP
	vLightMapUv = ( lightMapTransform * vec3( LIGHTMAP_UV, 1 ) ).xy;
#endif
#ifdef USE_AOMAP
	vAoMapUv = ( aoMapTransform * vec3( AOMAP_UV, 1 ) ).xy;
#endif
#ifdef USE_BUMPMAP
	vBumpMapUv = ( bumpMapTransform * vec3( BUMPMAP_UV, 1 ) ).xy;
#endif
#ifdef USE_NORMALMAP
	vNormalMapUv = ( normalMapTransform * vec3( NORMALMAP_UV, 1 ) ).xy;
#endif
#ifdef USE_DISPLACEMENTMAP
	vDisplacementMapUv = ( displacementMapTransform * vec3( DISPLACEMENTMAP_UV, 1 ) ).xy;
#endif
#ifdef USE_EMISSIVEMAP
	vEmissiveMapUv = ( emissiveMapTransform * vec3( EMISSIVEMAP_UV, 1 ) ).xy;
#endif
#ifdef USE_METALNESSMAP
	vMetalnessMapUv = ( metalnessMapTransform * vec3( METALNESSMAP_UV, 1 ) ).xy;
#endif
#ifdef USE_ROUGHNESSMAP
	vRoughnessMapUv = ( roughnessMapTransform * vec3( ROUGHNESSMAP_UV, 1 ) ).xy;
#endif
#ifdef USE_ANISOTROPYMAP
	vAnisotropyMapUv = ( anisotropyMapTransform * vec3( ANISOTROPYMAP_UV, 1 ) ).xy;
#endif
#ifdef USE_CLEARCOATMAP
	vClearcoatMapUv = ( clearcoatMapTransform * vec3( CLEARCOATMAP_UV, 1 ) ).xy;
#endif
#ifdef USE_CLEARCOAT_NORMALMAP
	vClearcoatNormalMapUv = ( clearcoatNormalMapTransform * vec3( CLEARCOAT_NORMALMAP_UV, 1 ) ).xy;
#endif
#ifdef USE_CLEARCOAT_ROUGHNESSMAP
	vClearcoatRoughnessMapUv = ( clearcoatRoughnessMapTransform * vec3( CLEARCOAT_ROUGHNESSMAP_UV, 1 ) ).xy;
#endif
#ifdef USE_IRIDESCENCEMAP
	vIridescenceMapUv = ( iridescenceMapTransform * vec3( IRIDESCENCEMAP_UV, 1 ) ).xy;
#endif
#ifdef USE_IRIDESCENCE_THICKNESSMAP
	vIridescenceThicknessMapUv = ( iridescenceThicknessMapTransform * vec3( IRIDESCENCE_THICKNESSMAP_UV, 1 ) ).xy;
#endif
#ifdef USE_SHEEN_COLORMAP
	vSheenColorMapUv = ( sheenColorMapTransform * vec3( SHEEN_COLORMAP_UV, 1 ) ).xy;
#endif
#ifdef USE_SHEEN_ROUGHNESSMAP
	vSheenRoughnessMapUv = ( sheenRoughnessMapTransform * vec3( SHEEN_ROUGHNESSMAP_UV, 1 ) ).xy;
#endif
#ifdef USE_SPECULARMAP
	vSpecularMapUv = ( specularMapTransform * vec3( SPECULARMAP_UV, 1 ) ).xy;
#endif
#ifdef USE_SPECULAR_COLORMAP
	vSpecularColorMapUv = ( specularColorMapTransform * vec3( SPECULAR_COLORMAP_UV, 1 ) ).xy;
#endif
#ifdef USE_SPECULAR_INTENSITYMAP
	vSpecularIntensityMapUv = ( specularIntensityMapTransform * vec3( SPECULAR_INTENSITYMAP_UV, 1 ) ).xy;
#endif
#ifdef USE_TRANSMISSIONMAP
	vTransmissionMapUv = ( transmissionMapTransform * vec3( TRANSMISSIONMAP_UV, 1 ) ).xy;
#endif
#ifdef USE_THICKNESSMAP
	vThicknessMapUv = ( thicknessMapTransform * vec3( THICKNESSMAP_UV, 1 ) ).xy;
#endif`,Uu=`#if defined( USE_ENVMAP ) || defined( DISTANCE ) || defined ( USE_SHADOWMAP ) || defined ( USE_TRANSMISSION ) || NUM_SPOT_LIGHT_COORDS > 0
	vec4 worldPosition = vec4( transformed, 1.0 );
	#ifdef USE_BATCHING
		worldPosition = batchingMatrix * worldPosition;
	#endif
	#ifdef USE_INSTANCING
		worldPosition = instanceMatrix * worldPosition;
	#endif
	worldPosition = modelMatrix * worldPosition;
#endif`,Iu=`varying vec2 vUv;
uniform mat3 uvTransform;
void main() {
	vUv = ( uvTransform * vec3( uv, 1 ) ).xy;
	gl_Position = vec4( position.xy, 1.0, 1.0 );
}`,Ru=`uniform sampler2D t2D;
uniform float backgroundIntensity;
varying vec2 vUv;
void main() {
	vec4 texColor = texture2D( t2D, vUv );
	#ifdef DECODE_VIDEO_TEXTURE
		texColor = vec4( mix( pow( texColor.rgb * 0.9478672986 + vec3( 0.0521327014 ), vec3( 2.4 ) ), texColor.rgb * 0.0773993808, vec3( lessThanEqual( texColor.rgb, vec3( 0.04045 ) ) ) ), texColor.w );
	#endif
	texColor.rgb *= backgroundIntensity;
	gl_FragColor = texColor;
	#include <tonemapping_fragment>
	#include <colorspace_fragment>
}`,Xu=`varying vec3 vWorldDirection;
#include <common>
void main() {
	vWorldDirection = transformDirection( position, modelMatrix );
	#include <begin_vertex>
	#include <project_vertex>
	gl_Position.z = gl_Position.w;
}`,Bu=`#ifdef ENVMAP_TYPE_CUBE
	uniform samplerCube envMap;
#elif defined( ENVMAP_TYPE_CUBE_UV )
	uniform sampler2D envMap;
#endif
uniform float flipEnvMap;
uniform float backgroundBlurriness;
uniform float backgroundIntensity;
uniform mat3 backgroundRotation;
varying vec3 vWorldDirection;
#include <cube_uv_reflection_fragment>
void main() {
	#ifdef ENVMAP_TYPE_CUBE
		vec4 texColor = textureCube( envMap, backgroundRotation * vec3( flipEnvMap * vWorldDirection.x, vWorldDirection.yz ) );
	#elif defined( ENVMAP_TYPE_CUBE_UV )
		vec4 texColor = textureCubeUV( envMap, backgroundRotation * vWorldDirection, backgroundBlurriness );
	#else
		vec4 texColor = vec4( 0.0, 0.0, 0.0, 1.0 );
	#endif
	texColor.rgb *= backgroundIntensity;
	gl_FragColor = texColor;
	#include <tonemapping_fragment>
	#include <colorspace_fragment>
}`,$u=`varying vec3 vWorldDirection;
#include <common>
void main() {
	vWorldDirection = transformDirection( position, modelMatrix );
	#include <begin_vertex>
	#include <project_vertex>
	gl_Position.z = gl_Position.w;
}`,Fu=`uniform samplerCube tCube;
uniform float tFlip;
uniform float opacity;
varying vec3 vWorldDirection;
void main() {
	vec4 texColor = textureCube( tCube, vec3( tFlip * vWorldDirection.x, vWorldDirection.yz ) );
	gl_FragColor = texColor;
	gl_FragColor.a *= opacity;
	#include <tonemapping_fragment>
	#include <colorspace_fragment>
}`,Du=`#include <common>
#include <batching_pars_vertex>
#include <uv_pars_vertex>
#include <displacementmap_pars_vertex>
#include <morphtarget_pars_vertex>
#include <skinning_pars_vertex>
#include <logdepthbuf_pars_vertex>
#include <clipping_planes_pars_vertex>
varying vec2 vHighPrecisionZW;
void main() {
	#include <uv_vertex>
	#include <batching_vertex>
	#include <skinbase_vertex>
	#include <morphinstance_vertex>
	#ifdef USE_DISPLACEMENTMAP
		#include <beginnormal_vertex>
		#include <morphnormal_vertex>
		#include <skinnormal_vertex>
	#endif
	#include <begin_vertex>
	#include <morphtarget_vertex>
	#include <skinning_vertex>
	#include <displacementmap_vertex>
	#include <project_vertex>
	#include <logdepthbuf_vertex>
	#include <clipping_planes_vertex>
	vHighPrecisionZW = gl_Position.zw;
}`,Nu=`#if DEPTH_PACKING == 3200
	uniform float opacity;
#endif
#include <common>
#include <packing>
#include <uv_pars_fragment>
#include <map_pars_fragment>
#include <alphamap_pars_fragment>
#include <alphatest_pars_fragment>
#include <alphahash_pars_fragment>
#include <logdepthbuf_pars_fragment>
#include <clipping_planes_pars_fragment>
varying vec2 vHighPrecisionZW;
void main() {
	vec4 diffuseColor = vec4( 1.0 );
	#include <clipping_planes_fragment>
	#if DEPTH_PACKING == 3200
		diffuseColor.a = opacity;
	#endif
	#include <map_fragment>
	#include <alphamap_fragment>
	#include <alphatest_fragment>
	#include <alphahash_fragment>
	#include <logdepthbuf_fragment>
	float fragCoordZ = 0.5 * vHighPrecisionZW[0] / vHighPrecisionZW[1] + 0.5;
	#if DEPTH_PACKING == 3200
		gl_FragColor = vec4( vec3( 1.0 - fragCoordZ ), opacity );
	#elif DEPTH_PACKING == 3201
		gl_FragColor = packDepthToRGBA( fragCoordZ );
	#elif DEPTH_PACKING == 3202
		gl_FragColor = vec4( packDepthToRGB( fragCoordZ ), 1.0 );
	#elif DEPTH_PACKING == 3203
		gl_FragColor = vec4( packDepthToRG( fragCoordZ ), 0.0, 1.0 );
	#endif
}`,Vu=`#define DISTANCE
varying vec3 vWorldPosition;
#include <common>
#include <batching_pars_vertex>
#include <uv_pars_vertex>
#include <displacementmap_pars_vertex>
#include <morphtarget_pars_vertex>
#include <skinning_pars_vertex>
#include <clipping_planes_pars_vertex>
void main() {
	#include <uv_vertex>
	#include <batching_vertex>
	#include <skinbase_vertex>
	#include <morphinstance_vertex>
	#ifdef USE_DISPLACEMENTMAP
		#include <beginnormal_vertex>
		#include <morphnormal_vertex>
		#include <skinnormal_vertex>
	#endif
	#include <begin_vertex>
	#include <morphtarget_vertex>
	#include <skinning_vertex>
	#include <displacementmap_vertex>
	#include <project_vertex>
	#include <worldpos_vertex>
	#include <clipping_planes_vertex>
	vWorldPosition = worldPosition.xyz;
}`,Lu=`#define DISTANCE
uniform vec3 referencePosition;
uniform float nearDistance;
uniform float farDistance;
varying vec3 vWorldPosition;
#include <common>
#include <packing>
#include <uv_pars_fragment>
#include <map_pars_fragment>
#include <alphamap_pars_fragment>
#include <alphatest_pars_fragment>
#include <alphahash_pars_fragment>
#include <clipping_planes_pars_fragment>
void main () {
	vec4 diffuseColor = vec4( 1.0 );
	#include <clipping_planes_fragment>
	#include <map_fragment>
	#include <alphamap_fragment>
	#include <alphatest_fragment>
	#include <alphahash_fragment>
	float dist = length( vWorldPosition - referencePosition );
	dist = ( dist - nearDistance ) / ( farDistance - nearDistance );
	dist = saturate( dist );
	gl_FragColor = packDepthToRGBA( dist );
}`,zu=`varying vec3 vWorldDirection;
#include <common>
void main() {
	vWorldDirection = transformDirection( position, modelMatrix );
	#include <begin_vertex>
	#include <project_vertex>
}`,ku=`uniform sampler2D tEquirect;
varying vec3 vWorldDirection;
#include <common>
void main() {
	vec3 direction = normalize( vWorldDirection );
	vec2 sampleUV = equirectUv( direction );
	gl_FragColor = texture2D( tEquirect, sampleUV );
	#include <tonemapping_fragment>
	#include <colorspace_fragment>
}`,qu=`uniform float scale;
attribute float lineDistance;
varying float vLineDistance;
#include <common>
#include <uv_pars_vertex>
#include <color_pars_vertex>
#include <fog_pars_vertex>
#include <morphtarget_pars_vertex>
#include <logdepthbuf_pars_vertex>
#include <clipping_planes_pars_vertex>
void main() {
	vLineDistance = scale * lineDistance;
	#include <uv_vertex>
	#include <color_vertex>
	#include <morphinstance_vertex>
	#include <morphcolor_vertex>
	#include <begin_vertex>
	#include <morphtarget_vertex>
	#include <project_vertex>
	#include <logdepthbuf_vertex>
	#include <clipping_planes_vertex>
	#include <fog_vertex>
}`,Ju=`uniform vec3 diffuse;
uniform float opacity;
uniform float dashSize;
uniform float totalSize;
varying float vLineDistance;
#include <common>
#include <color_pars_fragment>
#include <uv_pars_fragment>
#include <map_pars_fragment>
#include <fog_pars_fragment>
#include <logdepthbuf_pars_fragment>
#include <clipping_planes_pars_fragment>
void main() {
	vec4 diffuseColor = vec4( diffuse, opacity );
	#include <clipping_planes_fragment>
	if ( mod( vLineDistance, totalSize ) > dashSize ) {
		discard;
	}
	vec3 outgoingLight = vec3( 0.0 );
	#include <logdepthbuf_fragment>
	#include <map_fragment>
	#include <color_fragment>
	outgoingLight = diffuseColor.rgb;
	#include <opaque_fragment>
	#include <tonemapping_fragment>
	#include <colorspace_fragment>
	#include <fog_fragment>
	#include <premultiplied_alpha_fragment>
}`,ju=`#include <common>
#include <batching_pars_vertex>
#include <uv_pars_vertex>
#include <envmap_pars_vertex>
#include <color_pars_vertex>
#include <fog_pars_vertex>
#include <morphtarget_pars_vertex>
#include <skinning_pars_vertex>
#include <logdepthbuf_pars_vertex>
#include <clipping_planes_pars_vertex>
void main() {
	#include <uv_vertex>
	#include <color_vertex>
	#include <morphinstance_vertex>
	#include <morphcolor_vertex>
	#include <batching_vertex>
	#if defined ( USE_ENVMAP ) || defined ( USE_SKINNING )
		#include <beginnormal_vertex>
		#include <morphnormal_vertex>
		#include <skinbase_vertex>
		#include <skinnormal_vertex>
		#include <defaultnormal_vertex>
	#endif
	#include <begin_vertex>
	#include <morphtarget_vertex>
	#include <skinning_vertex>
	#include <project_vertex>
	#include <logdepthbuf_vertex>
	#include <clipping_planes_vertex>
	#include <worldpos_vertex>
	#include <envmap_vertex>
	#include <fog_vertex>
}`,Qu=`uniform vec3 diffuse;
uniform float opacity;
#ifndef FLAT_SHADED
	varying vec3 vNormal;
#endif
#include <common>
#include <dithering_pars_fragment>
#include <color_pars_fragment>
#include <uv_pars_fragment>
#include <map_pars_fragment>
#include <alphamap_pars_fragment>
#include <alphatest_pars_fragment>
#include <alphahash_pars_fragment>
#include <aomap_pars_fragment>
#include <lightmap_pars_fragment>
#include <envmap_common_pars_fragment>
#include <envmap_pars_fragment>
#include <fog_pars_fragment>
#include <specularmap_pars_fragment>
#include <logdepthbuf_pars_fragment>
#include <clipping_planes_pars_fragment>
void main() {
	vec4 diffuseColor = vec4( diffuse, opacity );
	#include <clipping_planes_fragment>
	#include <logdepthbuf_fragment>
	#include <map_fragment>
	#include <color_fragment>
	#include <alphamap_fragment>
	#include <alphatest_fragment>
	#include <alphahash_fragment>
	#include <specularmap_fragment>
	ReflectedLight reflectedLight = ReflectedLight( vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ) );
	#ifdef USE_LIGHTMAP
		vec4 lightMapTexel = texture2D( lightMap, vLightMapUv );
		reflectedLight.indirectDiffuse += lightMapTexel.rgb * lightMapIntensity * RECIPROCAL_PI;
	#else
		reflectedLight.indirectDiffuse += vec3( 1.0 );
	#endif
	#include <aomap_fragment>
	reflectedLight.indirectDiffuse *= diffuseColor.rgb;
	vec3 outgoingLight = reflectedLight.indirectDiffuse;
	#include <envmap_fragment>
	#include <opaque_fragment>
	#include <tonemapping_fragment>
	#include <colorspace_fragment>
	#include <fog_fragment>
	#include <premultiplied_alpha_fragment>
	#include <dithering_fragment>
}`,Yu=`#define LAMBERT
varying vec3 vViewPosition;
#include <common>
#include <batching_pars_vertex>
#include <uv_pars_vertex>
#include <displacementmap_pars_vertex>
#include <envmap_pars_vertex>
#include <color_pars_vertex>
#include <fog_pars_vertex>
#include <normal_pars_vertex>
#include <morphtarget_pars_vertex>
#include <skinning_pars_vertex>
#include <shadowmap_pars_vertex>
#include <logdepthbuf_pars_vertex>
#include <clipping_planes_pars_vertex>
void main() {
	#include <uv_vertex>
	#include <color_vertex>
	#include <morphinstance_vertex>
	#include <morphcolor_vertex>
	#include <batching_vertex>
	#include <beginnormal_vertex>
	#include <morphnormal_vertex>
	#include <skinbase_vertex>
	#include <skinnormal_vertex>
	#include <defaultnormal_vertex>
	#include <normal_vertex>
	#include <begin_vertex>
	#include <morphtarget_vertex>
	#include <skinning_vertex>
	#include <displacementmap_vertex>
	#include <project_vertex>
	#include <logdepthbuf_vertex>
	#include <clipping_planes_vertex>
	vViewPosition = - mvPosition.xyz;
	#include <worldpos_vertex>
	#include <envmap_vertex>
	#include <shadowmap_vertex>
	#include <fog_vertex>
}`,Zu=`#define LAMBERT
uniform vec3 diffuse;
uniform vec3 emissive;
uniform float opacity;
#include <common>
#include <packing>
#include <dithering_pars_fragment>
#include <color_pars_fragment>
#include <uv_pars_fragment>
#include <map_pars_fragment>
#include <alphamap_pars_fragment>
#include <alphatest_pars_fragment>
#include <alphahash_pars_fragment>
#include <aomap_pars_fragment>
#include <lightmap_pars_fragment>
#include <emissivemap_pars_fragment>
#include <envmap_common_pars_fragment>
#include <envmap_pars_fragment>
#include <fog_pars_fragment>
#include <bsdfs>
#include <lights_pars_begin>
#include <normal_pars_fragment>
#include <lights_lambert_pars_fragment>
#include <shadowmap_pars_fragment>
#include <bumpmap_pars_fragment>
#include <normalmap_pars_fragment>
#include <specularmap_pars_fragment>
#include <logdepthbuf_pars_fragment>
#include <clipping_planes_pars_fragment>
void main() {
	vec4 diffuseColor = vec4( diffuse, opacity );
	#include <clipping_planes_fragment>
	ReflectedLight reflectedLight = ReflectedLight( vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ) );
	vec3 totalEmissiveRadiance = emissive;
	#include <logdepthbuf_fragment>
	#include <map_fragment>
	#include <color_fragment>
	#include <alphamap_fragment>
	#include <alphatest_fragment>
	#include <alphahash_fragment>
	#include <specularmap_fragment>
	#include <normal_fragment_begin>
	#include <normal_fragment_maps>
	#include <emissivemap_fragment>
	#include <lights_lambert_fragment>
	#include <lights_fragment_begin>
	#include <lights_fragment_maps>
	#include <lights_fragment_end>
	#include <aomap_fragment>
	vec3 outgoingLight = reflectedLight.directDiffuse + reflectedLight.indirectDiffuse + totalEmissiveRadiance;
	#include <envmap_fragment>
	#include <opaque_fragment>
	#include <tonemapping_fragment>
	#include <colorspace_fragment>
	#include <fog_fragment>
	#include <premultiplied_alpha_fragment>
	#include <dithering_fragment>
}`,Ku=`#define MATCAP
varying vec3 vViewPosition;
#include <common>
#include <batching_pars_vertex>
#include <uv_pars_vertex>
#include <color_pars_vertex>
#include <displacementmap_pars_vertex>
#include <fog_pars_vertex>
#include <normal_pars_vertex>
#include <morphtarget_pars_vertex>
#include <skinning_pars_vertex>
#include <logdepthbuf_pars_vertex>
#include <clipping_planes_pars_vertex>
void main() {
	#include <uv_vertex>
	#include <color_vertex>
	#include <morphinstance_vertex>
	#include <morphcolor_vertex>
	#include <batching_vertex>
	#include <beginnormal_vertex>
	#include <morphnormal_vertex>
	#include <skinbase_vertex>
	#include <skinnormal_vertex>
	#include <defaultnormal_vertex>
	#include <normal_vertex>
	#include <begin_vertex>
	#include <morphtarget_vertex>
	#include <skinning_vertex>
	#include <displacementmap_vertex>
	#include <project_vertex>
	#include <logdepthbuf_vertex>
	#include <clipping_planes_vertex>
	#include <fog_vertex>
	vViewPosition = - mvPosition.xyz;
}`,eg=`#define MATCAP
uniform vec3 diffuse;
uniform float opacity;
uniform sampler2D matcap;
varying vec3 vViewPosition;
#include <common>
#include <dithering_pars_fragment>
#include <color_pars_fragment>
#include <uv_pars_fragment>
#include <map_pars_fragment>
#include <alphamap_pars_fragment>
#include <alphatest_pars_fragment>
#include <alphahash_pars_fragment>
#include <fog_pars_fragment>
#include <normal_pars_fragment>
#include <bumpmap_pars_fragment>
#include <normalmap_pars_fragment>
#include <logdepthbuf_pars_fragment>
#include <clipping_planes_pars_fragment>
void main() {
	vec4 diffuseColor = vec4( diffuse, opacity );
	#include <clipping_planes_fragment>
	#include <logdepthbuf_fragment>
	#include <map_fragment>
	#include <color_fragment>
	#include <alphamap_fragment>
	#include <alphatest_fragment>
	#include <alphahash_fragment>
	#include <normal_fragment_begin>
	#include <normal_fragment_maps>
	vec3 viewDir = normalize( vViewPosition );
	vec3 x = normalize( vec3( viewDir.z, 0.0, - viewDir.x ) );
	vec3 y = cross( viewDir, x );
	vec2 uv = vec2( dot( x, normal ), dot( y, normal ) ) * 0.495 + 0.5;
	#ifdef USE_MATCAP
		vec4 matcapColor = texture2D( matcap, uv );
	#else
		vec4 matcapColor = vec4( vec3( mix( 0.2, 0.8, uv.y ) ), 1.0 );
	#endif
	vec3 outgoingLight = diffuseColor.rgb * matcapColor.rgb;
	#include <opaque_fragment>
	#include <tonemapping_fragment>
	#include <colorspace_fragment>
	#include <fog_fragment>
	#include <premultiplied_alpha_fragment>
	#include <dithering_fragment>
}`,tg=`#define NORMAL
#if defined( FLAT_SHADED ) || defined( USE_BUMPMAP ) || defined( USE_NORMALMAP_TANGENTSPACE )
	varying vec3 vViewPosition;
#endif
#include <common>
#include <batching_pars_vertex>
#include <uv_pars_vertex>
#include <displacementmap_pars_vertex>
#include <normal_pars_vertex>
#include <morphtarget_pars_vertex>
#include <skinning_pars_vertex>
#include <logdepthbuf_pars_vertex>
#include <clipping_planes_pars_vertex>
void main() {
	#include <uv_vertex>
	#include <batching_vertex>
	#include <beginnormal_vertex>
	#include <morphinstance_vertex>
	#include <morphnormal_vertex>
	#include <skinbase_vertex>
	#include <skinnormal_vertex>
	#include <defaultnormal_vertex>
	#include <normal_vertex>
	#include <begin_vertex>
	#include <morphtarget_vertex>
	#include <skinning_vertex>
	#include <displacementmap_vertex>
	#include <project_vertex>
	#include <logdepthbuf_vertex>
	#include <clipping_planes_vertex>
#if defined( FLAT_SHADED ) || defined( USE_BUMPMAP ) || defined( USE_NORMALMAP_TANGENTSPACE )
	vViewPosition = - mvPosition.xyz;
#endif
}`,ng=`#define NORMAL
uniform float opacity;
#if defined( FLAT_SHADED ) || defined( USE_BUMPMAP ) || defined( USE_NORMALMAP_TANGENTSPACE )
	varying vec3 vViewPosition;
#endif
#include <packing>
#include <uv_pars_fragment>
#include <normal_pars_fragment>
#include <bumpmap_pars_fragment>
#include <normalmap_pars_fragment>
#include <logdepthbuf_pars_fragment>
#include <clipping_planes_pars_fragment>
void main() {
	vec4 diffuseColor = vec4( 0.0, 0.0, 0.0, opacity );
	#include <clipping_planes_fragment>
	#include <logdepthbuf_fragment>
	#include <normal_fragment_begin>
	#include <normal_fragment_maps>
	gl_FragColor = vec4( packNormalToRGB( normal ), diffuseColor.a );
	#ifdef OPAQUE
		gl_FragColor.a = 1.0;
	#endif
}`,ig=`#define PHONG
varying vec3 vViewPosition;
#include <common>
#include <batching_pars_vertex>
#include <uv_pars_vertex>
#include <displacementmap_pars_vertex>
#include <envmap_pars_vertex>
#include <color_pars_vertex>
#include <fog_pars_vertex>
#include <normal_pars_vertex>
#include <morphtarget_pars_vertex>
#include <skinning_pars_vertex>
#include <shadowmap_pars_vertex>
#include <logdepthbuf_pars_vertex>
#include <clipping_planes_pars_vertex>
void main() {
	#include <uv_vertex>
	#include <color_vertex>
	#include <morphcolor_vertex>
	#include <batching_vertex>
	#include <beginnormal_vertex>
	#include <morphinstance_vertex>
	#include <morphnormal_vertex>
	#include <skinbase_vertex>
	#include <skinnormal_vertex>
	#include <defaultnormal_vertex>
	#include <normal_vertex>
	#include <begin_vertex>
	#include <morphtarget_vertex>
	#include <skinning_vertex>
	#include <displacementmap_vertex>
	#include <project_vertex>
	#include <logdepthbuf_vertex>
	#include <clipping_planes_vertex>
	vViewPosition = - mvPosition.xyz;
	#include <worldpos_vertex>
	#include <envmap_vertex>
	#include <shadowmap_vertex>
	#include <fog_vertex>
}`,sg=`#define PHONG
uniform vec3 diffuse;
uniform vec3 emissive;
uniform vec3 specular;
uniform float shininess;
uniform float opacity;
#include <common>
#include <packing>
#include <dithering_pars_fragment>
#include <color_pars_fragment>
#include <uv_pars_fragment>
#include <map_pars_fragment>
#include <alphamap_pars_fragment>
#include <alphatest_pars_fragment>
#include <alphahash_pars_fragment>
#include <aomap_pars_fragment>
#include <lightmap_pars_fragment>
#include <emissivemap_pars_fragment>
#include <envmap_common_pars_fragment>
#include <envmap_pars_fragment>
#include <fog_pars_fragment>
#include <bsdfs>
#include <lights_pars_begin>
#include <normal_pars_fragment>
#include <lights_phong_pars_fragment>
#include <shadowmap_pars_fragment>
#include <bumpmap_pars_fragment>
#include <normalmap_pars_fragment>
#include <specularmap_pars_fragment>
#include <logdepthbuf_pars_fragment>
#include <clipping_planes_pars_fragment>
void main() {
	vec4 diffuseColor = vec4( diffuse, opacity );
	#include <clipping_planes_fragment>
	ReflectedLight reflectedLight = ReflectedLight( vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ) );
	vec3 totalEmissiveRadiance = emissive;
	#include <logdepthbuf_fragment>
	#include <map_fragment>
	#include <color_fragment>
	#include <alphamap_fragment>
	#include <alphatest_fragment>
	#include <alphahash_fragment>
	#include <specularmap_fragment>
	#include <normal_fragment_begin>
	#include <normal_fragment_maps>
	#include <emissivemap_fragment>
	#include <lights_phong_fragment>
	#include <lights_fragment_begin>
	#include <lights_fragment_maps>
	#include <lights_fragment_end>
	#include <aomap_fragment>
	vec3 outgoingLight = reflectedLight.directDiffuse + reflectedLight.indirectDiffuse + reflectedLight.directSpecular + reflectedLight.indirectSpecular + totalEmissiveRadiance;
	#include <envmap_fragment>
	#include <opaque_fragment>
	#include <tonemapping_fragment>
	#include <colorspace_fragment>
	#include <fog_fragment>
	#include <premultiplied_alpha_fragment>
	#include <dithering_fragment>
}`,rg=`#define STANDARD
varying vec3 vViewPosition;
#ifdef USE_TRANSMISSION
	varying vec3 vWorldPosition;
#endif
#include <common>
#include <batching_pars_vertex>
#include <uv_pars_vertex>
#include <displacementmap_pars_vertex>
#include <color_pars_vertex>
#include <fog_pars_vertex>
#include <normal_pars_vertex>
#include <morphtarget_pars_vertex>
#include <skinning_pars_vertex>
#include <shadowmap_pars_vertex>
#include <logdepthbuf_pars_vertex>
#include <clipping_planes_pars_vertex>
void main() {
	#include <uv_vertex>
	#include <color_vertex>
	#include <morphinstance_vertex>
	#include <morphcolor_vertex>
	#include <batching_vertex>
	#include <beginnormal_vertex>
	#include <morphnormal_vertex>
	#include <skinbase_vertex>
	#include <skinnormal_vertex>
	#include <defaultnormal_vertex>
	#include <normal_vertex>
	#include <begin_vertex>
	#include <morphtarget_vertex>
	#include <skinning_vertex>
	#include <displacementmap_vertex>
	#include <project_vertex>
	#include <logdepthbuf_vertex>
	#include <clipping_planes_vertex>
	vViewPosition = - mvPosition.xyz;
	#include <worldpos_vertex>
	#include <shadowmap_vertex>
	#include <fog_vertex>
#ifdef USE_TRANSMISSION
	vWorldPosition = worldPosition.xyz;
#endif
}`,og=`#define STANDARD
#ifdef PHYSICAL
	#define IOR
	#define USE_SPECULAR
#endif
uniform vec3 diffuse;
uniform vec3 emissive;
uniform float roughness;
uniform float metalness;
uniform float opacity;
#ifdef IOR
	uniform float ior;
#endif
#ifdef USE_SPECULAR
	uniform float specularIntensity;
	uniform vec3 specularColor;
	#ifdef USE_SPECULAR_COLORMAP
		uniform sampler2D specularColorMap;
	#endif
	#ifdef USE_SPECULAR_INTENSITYMAP
		uniform sampler2D specularIntensityMap;
	#endif
#endif
#ifdef USE_CLEARCOAT
	uniform float clearcoat;
	uniform float clearcoatRoughness;
#endif
#ifdef USE_DISPERSION
	uniform float dispersion;
#endif
#ifdef USE_IRIDESCENCE
	uniform float iridescence;
	uniform float iridescenceIOR;
	uniform float iridescenceThicknessMinimum;
	uniform float iridescenceThicknessMaximum;
#endif
#ifdef USE_SHEEN
	uniform vec3 sheenColor;
	uniform float sheenRoughness;
	#ifdef USE_SHEEN_COLORMAP
		uniform sampler2D sheenColorMap;
	#endif
	#ifdef USE_SHEEN_ROUGHNESSMAP
		uniform sampler2D sheenRoughnessMap;
	#endif
#endif
#ifdef USE_ANISOTROPY
	uniform vec2 anisotropyVector;
	#ifdef USE_ANISOTROPYMAP
		uniform sampler2D anisotropyMap;
	#endif
#endif
varying vec3 vViewPosition;
#include <common>
#include <packing>
#include <dithering_pars_fragment>
#include <color_pars_fragment>
#include <uv_pars_fragment>
#include <map_pars_fragment>
#include <alphamap_pars_fragment>
#include <alphatest_pars_fragment>
#include <alphahash_pars_fragment>
#include <aomap_pars_fragment>
#include <lightmap_pars_fragment>
#include <emissivemap_pars_fragment>
#include <iridescence_fragment>
#include <cube_uv_reflection_fragment>
#include <envmap_common_pars_fragment>
#include <envmap_physical_pars_fragment>
#include <fog_pars_fragment>
#include <lights_pars_begin>
#include <normal_pars_fragment>
#include <lights_physical_pars_fragment>
#include <transmission_pars_fragment>
#include <shadowmap_pars_fragment>
#include <bumpmap_pars_fragment>
#include <normalmap_pars_fragment>
#include <clearcoat_pars_fragment>
#include <iridescence_pars_fragment>
#include <roughnessmap_pars_fragment>
#include <metalnessmap_pars_fragment>
#include <logdepthbuf_pars_fragment>
#include <clipping_planes_pars_fragment>
void main() {
	vec4 diffuseColor = vec4( diffuse, opacity );
	#include <clipping_planes_fragment>
	ReflectedLight reflectedLight = ReflectedLight( vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ) );
	vec3 totalEmissiveRadiance = emissive;
	#include <logdepthbuf_fragment>
	#include <map_fragment>
	#include <color_fragment>
	#include <alphamap_fragment>
	#include <alphatest_fragment>
	#include <alphahash_fragment>
	#include <roughnessmap_fragment>
	#include <metalnessmap_fragment>
	#include <normal_fragment_begin>
	#include <normal_fragment_maps>
	#include <clearcoat_normal_fragment_begin>
	#include <clearcoat_normal_fragment_maps>
	#include <emissivemap_fragment>
	#include <lights_physical_fragment>
	#include <lights_fragment_begin>
	#include <lights_fragment_maps>
	#include <lights_fragment_end>
	#include <aomap_fragment>
	vec3 totalDiffuse = reflectedLight.directDiffuse + reflectedLight.indirectDiffuse;
	vec3 totalSpecular = reflectedLight.directSpecular + reflectedLight.indirectSpecular;
	#include <transmission_fragment>
	vec3 outgoingLight = totalDiffuse + totalSpecular + totalEmissiveRadiance;
	#ifdef USE_SHEEN
		float sheenEnergyComp = 1.0 - 0.157 * max3( material.sheenColor );
		outgoingLight = outgoingLight * sheenEnergyComp + sheenSpecularDirect + sheenSpecularIndirect;
	#endif
	#ifdef USE_CLEARCOAT
		float dotNVcc = saturate( dot( geometryClearcoatNormal, geometryViewDir ) );
		vec3 Fcc = F_Schlick( material.clearcoatF0, material.clearcoatF90, dotNVcc );
		outgoingLight = outgoingLight * ( 1.0 - material.clearcoat * Fcc ) + ( clearcoatSpecularDirect + clearcoatSpecularIndirect ) * material.clearcoat;
	#endif
	#include <opaque_fragment>
	#include <tonemapping_fragment>
	#include <colorspace_fragment>
	#include <fog_fragment>
	#include <premultiplied_alpha_fragment>
	#include <dithering_fragment>
}`,ag=`#define TOON
varying vec3 vViewPosition;
#include <common>
#include <batching_pars_vertex>
#include <uv_pars_vertex>
#include <displacementmap_pars_vertex>
#include <color_pars_vertex>
#include <fog_pars_vertex>
#include <normal_pars_vertex>
#include <morphtarget_pars_vertex>
#include <skinning_pars_vertex>
#include <shadowmap_pars_vertex>
#include <logdepthbuf_pars_vertex>
#include <clipping_planes_pars_vertex>
void main() {
	#include <uv_vertex>
	#include <color_vertex>
	#include <morphinstance_vertex>
	#include <morphcolor_vertex>
	#include <batching_vertex>
	#include <beginnormal_vertex>
	#include <morphnormal_vertex>
	#include <skinbase_vertex>
	#include <skinnormal_vertex>
	#include <defaultnormal_vertex>
	#include <normal_vertex>
	#include <begin_vertex>
	#include <morphtarget_vertex>
	#include <skinning_vertex>
	#include <displacementmap_vertex>
	#include <project_vertex>
	#include <logdepthbuf_vertex>
	#include <clipping_planes_vertex>
	vViewPosition = - mvPosition.xyz;
	#include <worldpos_vertex>
	#include <shadowmap_vertex>
	#include <fog_vertex>
}`,cg=`#define TOON
uniform vec3 diffuse;
uniform vec3 emissive;
uniform float opacity;
#include <common>
#include <packing>
#include <dithering_pars_fragment>
#include <color_pars_fragment>
#include <uv_pars_fragment>
#include <map_pars_fragment>
#include <alphamap_pars_fragment>
#include <alphatest_pars_fragment>
#include <alphahash_pars_fragment>
#include <aomap_pars_fragment>
#include <lightmap_pars_fragment>
#include <emissivemap_pars_fragment>
#include <gradientmap_pars_fragment>
#include <fog_pars_fragment>
#include <bsdfs>
#include <lights_pars_begin>
#include <normal_pars_fragment>
#include <lights_toon_pars_fragment>
#include <shadowmap_pars_fragment>
#include <bumpmap_pars_fragment>
#include <normalmap_pars_fragment>
#include <logdepthbuf_pars_fragment>
#include <clipping_planes_pars_fragment>
void main() {
	vec4 diffuseColor = vec4( diffuse, opacity );
	#include <clipping_planes_fragment>
	ReflectedLight reflectedLight = ReflectedLight( vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ) );
	vec3 totalEmissiveRadiance = emissive;
	#include <logdepthbuf_fragment>
	#include <map_fragment>
	#include <color_fragment>
	#include <alphamap_fragment>
	#include <alphatest_fragment>
	#include <alphahash_fragment>
	#include <normal_fragment_begin>
	#include <normal_fragment_maps>
	#include <emissivemap_fragment>
	#include <lights_toon_fragment>
	#include <lights_fragment_begin>
	#include <lights_fragment_maps>
	#include <lights_fragment_end>
	#include <aomap_fragment>
	vec3 outgoingLight = reflectedLight.directDiffuse + reflectedLight.indirectDiffuse + totalEmissiveRadiance;
	#include <opaque_fragment>
	#include <tonemapping_fragment>
	#include <colorspace_fragment>
	#include <fog_fragment>
	#include <premultiplied_alpha_fragment>
	#include <dithering_fragment>
}`,hg=`uniform float size;
uniform float scale;
#include <common>
#include <color_pars_vertex>
#include <fog_pars_vertex>
#include <morphtarget_pars_vertex>
#include <logdepthbuf_pars_vertex>
#include <clipping_planes_pars_vertex>
#ifdef USE_POINTS_UV
	varying vec2 vUv;
	uniform mat3 uvTransform;
#endif
void main() {
	#ifdef USE_POINTS_UV
		vUv = ( uvTransform * vec3( uv, 1 ) ).xy;
	#endif
	#include <color_vertex>
	#include <morphinstance_vertex>
	#include <morphcolor_vertex>
	#include <begin_vertex>
	#include <morphtarget_vertex>
	#include <project_vertex>
	gl_PointSize = size;
	#ifdef USE_SIZEATTENUATION
		bool isPerspective = isPerspectiveMatrix( projectionMatrix );
		if ( isPerspective ) gl_PointSize *= ( scale / - mvPosition.z );
	#endif
	#include <logdepthbuf_vertex>
	#include <clipping_planes_vertex>
	#include <worldpos_vertex>
	#include <fog_vertex>
}`,lg=`uniform vec3 diffuse;
uniform float opacity;
#include <common>
#include <color_pars_fragment>
#include <map_particle_pars_fragment>
#include <alphatest_pars_fragment>
#include <alphahash_pars_fragment>
#include <fog_pars_fragment>
#include <logdepthbuf_pars_fragment>
#include <clipping_planes_pars_fragment>
void main() {
	vec4 diffuseColor = vec4( diffuse, opacity );
	#include <clipping_planes_fragment>
	vec3 outgoingLight = vec3( 0.0 );
	#include <logdepthbuf_fragment>
	#include <map_particle_fragment>
	#include <color_fragment>
	#include <alphatest_fragment>
	#include <alphahash_fragment>
	outgoingLight = diffuseColor.rgb;
	#include <opaque_fragment>
	#include <tonemapping_fragment>
	#include <colorspace_fragment>
	#include <fog_fragment>
	#include <premultiplied_alpha_fragment>
}`,pg=`#include <common>
#include <batching_pars_vertex>
#include <fog_pars_vertex>
#include <morphtarget_pars_vertex>
#include <skinning_pars_vertex>
#include <logdepthbuf_pars_vertex>
#include <shadowmap_pars_vertex>
void main() {
	#include <batching_vertex>
	#include <beginnormal_vertex>
	#include <morphinstance_vertex>
	#include <morphnormal_vertex>
	#include <skinbase_vertex>
	#include <skinnormal_vertex>
	#include <defaultnormal_vertex>
	#include <begin_vertex>
	#include <morphtarget_vertex>
	#include <skinning_vertex>
	#include <project_vertex>
	#include <logdepthbuf_vertex>
	#include <worldpos_vertex>
	#include <shadowmap_vertex>
	#include <fog_vertex>
}`,fg=`uniform vec3 color;
uniform float opacity;
#include <common>
#include <packing>
#include <fog_pars_fragment>
#include <bsdfs>
#include <lights_pars_begin>
#include <logdepthbuf_pars_fragment>
#include <shadowmap_pars_fragment>
#include <shadowmask_pars_fragment>
void main() {
	#include <logdepthbuf_fragment>
	gl_FragColor = vec4( color, opacity * ( 1.0 - getShadowMask() ) );
	#include <tonemapping_fragment>
	#include <colorspace_fragment>
	#include <fog_fragment>
}`,dg=`uniform float rotation;
uniform vec2 center;
#include <common>
#include <uv_pars_vertex>
#include <fog_pars_vertex>
#include <logdepthbuf_pars_vertex>
#include <clipping_planes_pars_vertex>
void main() {
	#include <uv_vertex>
	vec4 mvPosition = modelViewMatrix[ 3 ];
	vec2 scale = vec2( length( modelMatrix[ 0 ].xyz ), length( modelMatrix[ 1 ].xyz ) );
	#ifndef USE_SIZEATTENUATION
		bool isPerspective = isPerspectiveMatrix( projectionMatrix );
		if ( isPerspective ) scale *= - mvPosition.z;
	#endif
	vec2 alignedPosition = ( position.xy - ( center - vec2( 0.5 ) ) ) * scale;
	vec2 rotatedPosition;
	rotatedPosition.x = cos( rotation ) * alignedPosition.x - sin( rotation ) * alignedPosition.y;
	rotatedPosition.y = sin( rotation ) * alignedPosition.x + cos( rotation ) * alignedPosition.y;
	mvPosition.xy += rotatedPosition;
	gl_Position = projectionMatrix * mvPosition;
	#include <logdepthbuf_vertex>
	#include <clipping_planes_vertex>
	#include <fog_vertex>
}`,ug=`uniform vec3 diffuse;
uniform float opacity;
#include <common>
#include <uv_pars_fragment>
#include <map_pars_fragment>
#include <alphamap_pars_fragment>
#include <alphatest_pars_fragment>
#include <alphahash_pars_fragment>
#include <fog_pars_fragment>
#include <logdepthbuf_pars_fragment>
#include <clipping_planes_pars_fragment>
void main() {
	vec4 diffuseColor = vec4( diffuse, opacity );
	#include <clipping_planes_fragment>
	vec3 outgoingLight = vec3( 0.0 );
	#include <logdepthbuf_fragment>
	#include <map_fragment>
	#include <alphamap_fragment>
	#include <alphatest_fragment>
	#include <alphahash_fragment>
	outgoingLight = diffuseColor.rgb;
	#include <opaque_fragment>
	#include <tonemapping_fragment>
	#include <colorspace_fragment>
	#include <fog_fragment>
}`,qe={alphahash_fragment:Rf,alphahash_pars_fragment:Xf,alphamap_fragment:Bf,alphamap_pars_fragment:$f,alphatest_fragment:Ff,alphatest_pars_fragment:Df,aomap_fragment:Nf,aomap_pars_fragment:Vf,batching_pars_vertex:Lf,batching_vertex:zf,begin_vertex:kf,beginnormal_vertex:qf,bsdfs:Jf,iridescence_fragment:jf,bumpmap_pars_fragment:Qf,clipping_planes_fragment:Yf,clipping_planes_pars_fragment:Zf,clipping_planes_pars_vertex:Kf,clipping_planes_vertex:ed,color_fragment:td,color_pars_fragment:nd,color_pars_vertex:id,color_vertex:sd,common:rd,cube_uv_reflection_fragment:od,defaultnormal_vertex:ad,displacementmap_pars_vertex:cd,displacementmap_vertex:hd,emissivemap_fragment:ld,emissivemap_pars_fragment:pd,colorspace_fragment:fd,colorspace_pars_fragment:dd,envmap_fragment:ud,envmap_common_pars_fragment:gd,envmap_pars_fragment:_d,envmap_pars_vertex:md,envmap_physical_pars_fragment:Ed,envmap_vertex:xd,fog_vertex:bd,fog_pars_vertex:Pd,fog_fragment:wd,fog_pars_fragment:Od,gradientmap_pars_fragment:yd,lightmap_pars_fragment:Ad,lights_lambert_fragment:vd,lights_lambert_pars_fragment:Hd,lights_pars_begin:Sd,lights_toon_fragment:Wd,lights_toon_pars_fragment:Md,lights_phong_fragment:Cd,lights_phong_pars_fragment:Gd,lights_physical_fragment:Td,lights_physical_pars_fragment:Ud,lights_fragment_begin:Id,lights_fragment_maps:Rd,lights_fragment_end:Xd,logdepthbuf_fragment:Bd,logdepthbuf_pars_fragment:$d,logdepthbuf_pars_vertex:Fd,logdepthbuf_vertex:Dd,map_fragment:Nd,map_pars_fragment:Vd,map_particle_fragment:Ld,map_particle_pars_fragment:zd,metalnessmap_fragment:kd,metalnessmap_pars_fragment:qd,morphinstance_vertex:Jd,morphcolor_vertex:jd,morphnormal_vertex:Qd,morphtarget_pars_vertex:Yd,morphtarget_vertex:Zd,normal_fragment_begin:Kd,normal_fragment_maps:eu,normal_pars_fragment:tu,normal_pars_vertex:nu,normal_vertex:iu,normalmap_pars_fragment:su,clearcoat_normal_fragment_begin:ru,clearcoat_normal_fragment_maps:ou,clearcoat_pars_fragment:au,iridescence_pars_fragment:cu,opaque_fragment:hu,packing:lu,premultiplied_alpha_fragment:pu,project_vertex:fu,dithering_fragment:du,dithering_pars_fragment:uu,roughnessmap_fragment:gu,roughnessmap_pars_fragment:_u,shadowmap_pars_fragment:mu,shadowmap_pars_vertex:xu,shadowmap_vertex:bu,shadowmask_pars_fragment:Pu,skinbase_vertex:wu,skinning_pars_vertex:Ou,skinning_vertex:yu,skinnormal_vertex:Au,specularmap_fragment:vu,specularmap_pars_fragment:Hu,tonemapping_fragment:Su,tonemapping_pars_fragment:Eu,transmission_fragment:Wu,transmission_pars_fragment:Mu,uv_pars_fragment:Cu,uv_pars_vertex:Gu,uv_vertex:Tu,worldpos_vertex:Uu,background_vert:Iu,background_frag:Ru,backgroundCube_vert:Xu,backgroundCube_frag:Bu,cube_vert:$u,cube_frag:Fu,depth_vert:Du,depth_frag:Nu,distanceRGBA_vert:Vu,distanceRGBA_frag:Lu,equirect_vert:zu,equirect_frag:ku,linedashed_vert:qu,linedashed_frag:Ju,meshbasic_vert:ju,meshbasic_frag:Qu,meshlambert_vert:Yu,meshlambert_frag:Zu,meshmatcap_vert:Ku,meshmatcap_frag:eg,meshnormal_vert:tg,meshnormal_frag:ng,meshphong_vert:ig,meshphong_frag:sg,meshphysical_vert:rg,meshphysical_frag:og,meshtoon_vert:ag,meshtoon_frag:cg,points_vert:hg,points_frag:lg,shadow_vert:pg,shadow_frag:fg,sprite_vert:dg,sprite_frag:ug},he={common:{diffuse:{value:new Ge(16777215)},opacity:{value:1},map:{value:null},mapTransform:{value:new ze},alphaMap:{value:null},alphaMapTransform:{value:new ze},alphaTest:{value:0}},specularmap:{specularMap:{value:null},specularMapTransform:{value:new ze}},envmap:{envMap:{value:null},envMapRotation:{value:new ze},flipEnvMap:{value:-1},reflectivity:{value:1},ior:{value:1.5},refractionRatio:{value:.98}},aomap:{aoMap:{value:null},aoMapIntensity:{value:1},aoMapTransform:{value:new ze}},lightmap:{lightMap:{value:null},lightMapIntensity:{value:1},lightMapTransform:{value:new ze}},bumpmap:{bumpMap:{value:null},bumpMapTransform:{value:new ze},bumpScale:{value:1}},normalmap:{normalMap:{value:null},normalMapTransform:{value:new ze},normalScale:{value:new Re(1,1)}},displacementmap:{displacementMap:{value:null},displacementMapTransform:{value:new ze},displacementScale:{value:1},displacementBias:{value:0}},emissivemap:{emissiveMap:{value:null},emissiveMapTransform:{value:new ze}},metalnessmap:{metalnessMap:{value:null},metalnessMapTransform:{value:new ze}},roughnessmap:{roughnessMap:{value:null},roughnessMapTransform:{value:new ze}},gradientmap:{gradientMap:{value:null}},fog:{fogDensity:{value:25e-5},fogNear:{value:1},fogFar:{value:2e3},fogColor:{value:new Ge(16777215)}},lights:{ambientLightColor:{value:[]},lightProbe:{value:[]},directionalLights:{value:[],properties:{direction:{},color:{}}},directionalLightShadows:{value:[],properties:{shadowIntensity:1,shadowBias:{},shadowNormalBias:{},shadowRadius:{},shadowMapSize:{}}},directionalShadowMap:{value:[]},directionalShadowMatrix:{value:[]},spotLights:{value:[],properties:{color:{},position:{},direction:{},distance:{},coneCos:{},penumbraCos:{},decay:{}}},spotLightShadows:{value:[],properties:{shadowIntensity:1,shadowBias:{},shadowNormalBias:{},shadowRadius:{},shadowMapSize:{}}},spotLightMap:{value:[]},spotShadowMap:{value:[]},spotLightMatrix:{value:[]},pointLights:{value:[],properties:{color:{},position:{},decay:{},distance:{}}},pointLightShadows:{value:[],properties:{shadowIntensity:1,shadowBias:{},shadowNormalBias:{},shadowRadius:{},shadowMapSize:{},shadowCameraNear:{},shadowCameraFar:{}}},pointShadowMap:{value:[]},pointShadowMatrix:{value:[]},hemisphereLights:{value:[],properties:{direction:{},skyColor:{},groundColor:{}}},rectAreaLights:{value:[],properties:{color:{},position:{},width:{},height:{}}},ltc_1:{value:null},ltc_2:{value:null}},points:{diffuse:{value:new Ge(16777215)},opacity:{value:1},size:{value:1},scale:{value:1},map:{value:null},alphaMap:{value:null},alphaMapTransform:{value:new ze},alphaTest:{value:0},uvTransform:{value:new ze}},sprite:{diffuse:{value:new Ge(16777215)},opacity:{value:1},center:{value:new Re(.5,.5)},rotation:{value:0},map:{value:null},mapTransform:{value:new ze},alphaMap:{value:null},alphaMapTransform:{value:new ze},alphaTest:{value:0}}},bn={basic:{uniforms:Vt([he.common,he.specularmap,he.envmap,he.aomap,he.lightmap,he.fog]),vertexShader:qe.meshbasic_vert,fragmentShader:qe.meshbasic_frag},lambert:{uniforms:Vt([he.common,he.specularmap,he.envmap,he.aomap,he.lightmap,he.emissivemap,he.bumpmap,he.normalmap,he.displacementmap,he.fog,he.lights,{emissive:{value:new Ge(0)}}]),vertexShader:qe.meshlambert_vert,fragmentShader:qe.meshlambert_frag},phong:{uniforms:Vt([he.common,he.specularmap,he.envmap,he.aomap,he.lightmap,he.emissivemap,he.bumpmap,he.normalmap,he.displacementmap,he.fog,he.lights,{emissive:{value:new Ge(0)},specular:{value:new Ge(1118481)},shininess:{value:30}}]),vertexShader:qe.meshphong_vert,fragmentShader:qe.meshphong_frag},standard:{uniforms:Vt([he.common,he.envmap,he.aomap,he.lightmap,he.emissivemap,he.bumpmap,he.normalmap,he.displacementmap,he.roughnessmap,he.metalnessmap,he.fog,he.lights,{emissive:{value:new Ge(0)},roughness:{value:1},metalness:{value:0},envMapIntensity:{value:1}}]),vertexShader:qe.meshphysical_vert,fragmentShader:qe.meshphysical_frag},toon:{uniforms:Vt([he.common,he.aomap,he.lightmap,he.emissivemap,he.bumpmap,he.normalmap,he.displacementmap,he.gradientmap,he.fog,he.lights,{emissive:{value:new Ge(0)}}]),vertexShader:qe.meshtoon_vert,fragmentShader:qe.meshtoon_frag},matcap:{uniforms:Vt([he.common,he.bumpmap,he.normalmap,he.displacementmap,he.fog,{matcap:{value:null}}]),vertexShader:qe.meshmatcap_vert,fragmentShader:qe.meshmatcap_frag},points:{uniforms:Vt([he.points,he.fog]),vertexShader:qe.points_vert,fragmentShader:qe.points_frag},dashed:{uniforms:Vt([he.common,he.fog,{scale:{value:1},dashSize:{value:1},totalSize:{value:2}}]),vertexShader:qe.linedashed_vert,fragmentShader:qe.linedashed_frag},depth:{uniforms:Vt([he.common,he.displacementmap]),vertexShader:qe.depth_vert,fragmentShader:qe.depth_frag},normal:{uniforms:Vt([he.common,he.bumpmap,he.normalmap,he.displacementmap,{opacity:{value:1}}]),vertexShader:qe.meshnormal_vert,fragmentShader:qe.meshnormal_frag},sprite:{uniforms:Vt([he.sprite,he.fog]),vertexShader:qe.sprite_vert,fragmentShader:qe.sprite_frag},background:{uniforms:{uvTransform:{value:new ze},t2D:{value:null},backgroundIntensity:{value:1}},vertexShader:qe.background_vert,fragmentShader:qe.background_frag},backgroundCube:{uniforms:{envMap:{value:null},flipEnvMap:{value:-1},backgroundBlurriness:{value:0},backgroundIntensity:{value:1},backgroundRotation:{value:new ze}},vertexShader:qe.backgroundCube_vert,fragmentShader:qe.backgroundCube_frag},cube:{uniforms:{tCube:{value:null},tFlip:{value:-1},opacity:{value:1}},vertexShader:qe.cube_vert,fragmentShader:qe.cube_frag},equirect:{uniforms:{tEquirect:{value:null}},vertexShader:qe.equirect_vert,fragmentShader:qe.equirect_frag},distanceRGBA:{uniforms:Vt([he.common,he.displacementmap,{referencePosition:{value:new C},nearDistance:{value:1},farDistance:{value:1e3}}]),vertexShader:qe.distanceRGBA_vert,fragmentShader:qe.distanceRGBA_frag},shadow:{uniforms:Vt([he.lights,he.fog,{color:{value:new Ge(0)},opacity:{value:1}}]),vertexShader:qe.shadow_vert,fragmentShader:qe.shadow_frag}};bn.physical={uniforms:Vt([bn.standard.uniforms,{clearcoat:{value:0},clearcoatMap:{value:null},clearcoatMapTransform:{value:new ze},clearcoatNormalMap:{value:null},clearcoatNormalMapTransform:{value:new ze},clearcoatNormalScale:{value:new Re(1,1)},clearcoatRoughness:{value:0},clearcoatRoughnessMap:{value:null},clearcoatRoughnessMapTransform:{value:new ze},dispersion:{value:0},iridescence:{value:0},iridescenceMap:{value:null},iridescenceMapTransform:{value:new ze},iridescenceIOR:{value:1.3},iridescenceThicknessMinimum:{value:100},iridescenceThicknessMaximum:{value:400},iridescenceThicknessMap:{value:null},iridescenceThicknessMapTransform:{value:new ze},sheen:{value:0},sheenColor:{value:new Ge(0)},sheenColorMap:{value:null},sheenColorMapTransform:{value:new ze},sheenRoughness:{value:1},sheenRoughnessMap:{value:null},sheenRoughnessMapTransform:{value:new ze},transmission:{value:0},transmissionMap:{value:null},transmissionMapTransform:{value:new ze},transmissionSamplerSize:{value:new Re},transmissionSamplerMap:{value:null},thickness:{value:0},thicknessMap:{value:null},thicknessMapTransform:{value:new ze},attenuationDistance:{value:0},attenuationColor:{value:new Ge(0)},specularColor:{value:new Ge(1,1,1)},specularColorMap:{value:null},specularColorMapTransform:{value:new ze},specularIntensity:{value:1},specularIntensityMap:{value:null},specularIntensityMapTransform:{value:new ze},anisotropyVector:{value:new Re},anisotropyMap:{value:null},anisotropyMapTransform:{value:new ze}}]),vertexShader:qe.meshphysical_vert,fragmentShader:qe.meshphysical_frag};var Fr={r:0,b:0,g:0},Pi=new yn,gg=new Fe;function _g(i,e,t,n,s,r,o){let a=new Ge(0),c=r===!0?0:1,h,l,p=null,f=0,u=null;function _(A){let O=A.isScene===!0?A.background:null;return O&&O.isTexture&&(O=(A.backgroundBlurriness>0?t:e).get(O)),O}function m(A){let O=!1,P=_(A);P===null?d(a,c):P&&P.isColor&&(d(P,1),O=!0);let G=i.xr.getEnvironmentBlendMode();G==="additive"?n.buffers.color.setClear(0,0,0,1,o):G==="alpha-blend"&&n.buffers.color.setClear(0,0,0,0,o),(i.autoClear||O)&&(n.buffers.depth.setTest(!0),n.buffers.depth.setMask(!0),n.buffers.color.setMask(!0),i.clear(i.autoClearColor,i.autoClearDepth,i.autoClearStencil))}function g(A,O){let P=_(O);P&&(P.isCubeTexture||P.mapping===Go)?(l===void 0&&(l=new De(new An(1,1,1),new vn({name:"BackgroundCubeMaterial",uniforms:ds(bn.backgroundCube.uniforms),vertexShader:bn.backgroundCube.vertexShader,fragmentShader:bn.backgroundCube.fragmentShader,side:Bt,depthTest:!1,depthWrite:!1,fog:!1})),l.geometry.deleteAttribute("normal"),l.geometry.deleteAttribute("uv"),l.onBeforeRender=function(G,H,S){this.matrixWorld.copyPosition(S.matrixWorld)},Object.defineProperty(l.material,"envMap",{get:function(){return this.uniforms.envMap.value}}),s.update(l)),Pi.copy(O.backgroundRotation),Pi.x*=-1,Pi.y*=-1,Pi.z*=-1,P.isCubeTexture&&P.isRenderTargetTexture===!1&&(Pi.y*=-1,Pi.z*=-1),l.material.uniforms.envMap.value=P,l.material.uniforms.flipEnvMap.value=P.isCubeTexture&&P.isRenderTargetTexture===!1?-1:1,l.material.uniforms.backgroundBlurriness.value=O.backgroundBlurriness,l.material.uniforms.backgroundIntensity.value=O.backgroundIntensity,l.material.uniforms.backgroundRotation.value.setFromMatrix4(gg.makeRotationFromEuler(Pi)),l.material.toneMapped=Ze.getTransfer(P.colorSpace)!==ct,(p!==P||f!==P.version||u!==i.toneMapping)&&(l.material.needsUpdate=!0,p=P,f=P.version,u=i.toneMapping),l.layers.enableAll(),A.unshift(l,l.geometry,l.material,0,0,null)):P&&P.isTexture&&(h===void 0&&(h=new De(new us(2,2),new vn({name:"BackgroundMaterial",uniforms:ds(bn.background.uniforms),vertexShader:bn.background.vertexShader,fragmentShader:bn.background.fragmentShader,side:On,depthTest:!1,depthWrite:!1,fog:!1})),h.geometry.deleteAttribute("normal"),Object.defineProperty(h.material,"map",{get:function(){return this.uniforms.t2D.value}}),s.update(h)),h.material.uniforms.t2D.value=P,h.material.uniforms.backgroundIntensity.value=O.backgroundIntensity,h.material.toneMapped=Ze.getTransfer(P.colorSpace)!==ct,P.matrixAutoUpdate===!0&&P.updateMatrix(),h.material.uniforms.uvTransform.value.copy(P.matrix),(p!==P||f!==P.version||u!==i.toneMapping)&&(h.material.needsUpdate=!0,p=P,f=P.version,u=i.toneMapping),h.layers.enableAll(),A.unshift(h,h.geometry,h.material,0,0,null))}function d(A,O){A.getRGB(Fr,P0(i)),n.buffers.color.setClear(Fr.r,Fr.g,Fr.b,O,o)}return{getClearColor:function(){return a},setClearColor:function(A,O=1){a.set(A),c=O,d(a,c)},getClearAlpha:function(){return c},setClearAlpha:function(A){c=A,d(a,c)},render:m,addToRenderList:g}}function mg(i,e){let t=i.getParameter(i.MAX_VERTEX_ATTRIBS),n={},s=f(null),r=s,o=!1;function a(w,W,$,F,k){let Q=!1,V=p(F,$,W);r!==V&&(r=V,h(r.object)),Q=u(w,F,$,k),Q&&_(w,F,$,k),k!==null&&e.update(k,i.ELEMENT_ARRAY_BUFFER),(Q||o)&&(o=!1,P(w,W,$,F),k!==null&&i.bindBuffer(i.ELEMENT_ARRAY_BUFFER,e.get(k).buffer))}function c(){return i.createVertexArray()}function h(w){return i.bindVertexArray(w)}function l(w){return i.deleteVertexArray(w)}function p(w,W,$){let F=$.wireframe===!0,k=n[w.id];k===void 0&&(k={},n[w.id]=k);let Q=k[W.id];Q===void 0&&(Q={},k[W.id]=Q);let V=Q[F];return V===void 0&&(V=f(c()),Q[F]=V),V}function f(w){let W=[],$=[],F=[];for(let k=0;k<t;k++)W[k]=0,$[k]=0,F[k]=0;return{geometry:null,program:null,wireframe:!1,newAttributes:W,enabledAttributes:$,attributeDivisors:F,object:w,attributes:{},index:null}}function u(w,W,$,F){let k=r.attributes,Q=W.attributes,V=0,te=$.getAttributes();for(let z in te)if(te[z].location>=0){let me=k[z],He=Q[z];if(He===void 0&&(z==="instanceMatrix"&&w.instanceMatrix&&(He=w.instanceMatrix),z==="instanceColor"&&w.instanceColor&&(He=w.instanceColor)),me===void 0||me.attribute!==He||He&&me.data!==He.data)return!0;V++}return r.attributesNum!==V||r.index!==F}function _(w,W,$,F){let k={},Q=W.attributes,V=0,te=$.getAttributes();for(let z in te)if(te[z].location>=0){let me=Q[z];me===void 0&&(z==="instanceMatrix"&&w.instanceMatrix&&(me=w.instanceMatrix),z==="instanceColor"&&w.instanceColor&&(me=w.instanceColor));let He={};He.attribute=me,me&&me.data&&(He.data=me.data),k[z]=He,V++}r.attributes=k,r.attributesNum=V,r.index=F}function m(){let w=r.newAttributes;for(let W=0,$=w.length;W<$;W++)w[W]=0}function g(w){d(w,0)}function d(w,W){let $=r.newAttributes,F=r.enabledAttributes,k=r.attributeDivisors;$[w]=1,F[w]===0&&(i.enableVertexAttribArray(w),F[w]=1),k[w]!==W&&(i.vertexAttribDivisor(w,W),k[w]=W)}function A(){let w=r.newAttributes,W=r.enabledAttributes;for(let $=0,F=W.length;$<F;$++)W[$]!==w[$]&&(i.disableVertexAttribArray($),W[$]=0)}function O(w,W,$,F,k,Q,V){V===!0?i.vertexAttribIPointer(w,W,$,k,Q):i.vertexAttribPointer(w,W,$,F,k,Q)}function P(w,W,$,F){m();let k=F.attributes,Q=$.getAttributes(),V=W.defaultAttributeValues;for(let te in Q){let z=Q[te];if(z.location>=0){let pe=k[te];if(pe===void 0&&(te==="instanceMatrix"&&w.instanceMatrix&&(pe=w.instanceMatrix),te==="instanceColor"&&w.instanceColor&&(pe=w.instanceColor)),pe!==void 0){let me=pe.normalized,He=pe.itemSize,Ne=e.get(pe);if(Ne===void 0)continue;let tt=Ne.buffer,J=Ne.type,ie=Ne.bytesPerElement,xe=J===i.INT||J===i.UNSIGNED_INT||pe.gpuType===$c;if(pe.isInterleavedBufferAttribute){let oe=pe.data,be=oe.stride,Te=pe.offset;if(oe.isInstancedInterleavedBuffer){for(let ke=0;ke<z.locationSize;ke++)d(z.location+ke,oe.meshPerAttribute);w.isInstancedMesh!==!0&&F._maxInstanceCount===void 0&&(F._maxInstanceCount=oe.meshPerAttribute*oe.count)}else for(let ke=0;ke<z.locationSize;ke++)g(z.location+ke);i.bindBuffer(i.ARRAY_BUFFER,tt);for(let ke=0;ke<z.locationSize;ke++)O(z.location+ke,He/z.locationSize,J,me,be*ie,(Te+He/z.locationSize*ke)*ie,xe)}else{if(pe.isInstancedBufferAttribute){for(let oe=0;oe<z.locationSize;oe++)d(z.location+oe,pe.meshPerAttribute);w.isInstancedMesh!==!0&&F._maxInstanceCount===void 0&&(F._maxInstanceCount=pe.meshPerAttribute*pe.count)}else for(let oe=0;oe<z.locationSize;oe++)g(z.location+oe);i.bindBuffer(i.ARRAY_BUFFER,tt);for(let oe=0;oe<z.locationSize;oe++)O(z.location+oe,He/z.locationSize,J,me,He*ie,He/z.locationSize*oe*ie,xe)}}else if(V!==void 0){let me=V[te];if(me!==void 0)switch(me.length){case 2:i.vertexAttrib2fv(z.location,me);break;case 3:i.vertexAttrib3fv(z.location,me);break;case 4:i.vertexAttrib4fv(z.location,me);break;default:i.vertexAttrib1fv(z.location,me)}}}}A()}function G(){M();for(let w in n){let W=n[w];for(let $ in W){let F=W[$];for(let k in F)l(F[k].object),delete F[k];delete W[$]}delete n[w]}}function H(w){if(n[w.id]===void 0)return;let W=n[w.id];for(let $ in W){let F=W[$];for(let k in F)l(F[k].object),delete F[k];delete W[$]}delete n[w.id]}function S(w){for(let W in n){let $=n[W];if($[w.id]===void 0)continue;let F=$[w.id];for(let k in F)l(F[k].object),delete F[k];delete $[w.id]}}function M(){y(),o=!0,r!==s&&(r=s,h(r.object))}function y(){s.geometry=null,s.program=null,s.wireframe=!1}return{setup:a,reset:M,resetDefaultState:y,dispose:G,releaseStatesOfGeometry:H,releaseStatesOfProgram:S,initAttributes:m,enableAttribute:g,disableUnusedAttributes:A}}function xg(i,e,t){let n;function s(h){n=h}function r(h,l){i.drawArrays(n,h,l),t.update(l,n,1)}function o(h,l,p){p!==0&&(i.drawArraysInstanced(n,h,l,p),t.update(l,n,p))}function a(h,l,p){if(p===0)return;e.get("WEBGL_multi_draw").multiDrawArraysWEBGL(n,h,0,l,0,p);let u=0;for(let _=0;_<p;_++)u+=l[_];t.update(u,n,1)}function c(h,l,p,f){if(p===0)return;let u=e.get("WEBGL_multi_draw");if(u===null)for(let _=0;_<h.length;_++)o(h[_],l[_],f[_]);else{u.multiDrawArraysInstancedWEBGL(n,h,0,l,0,f,0,p);let _=0;for(let m=0;m<p;m++)_+=l[m]*f[m];t.update(_,n,1)}}this.setMode=s,this.render=r,this.renderInstances=o,this.renderMultiDraw=a,this.renderMultiDrawInstances=c}function bg(i,e,t,n){let s;function r(){if(s!==void 0)return s;if(e.has("EXT_texture_filter_anisotropic")===!0){let S=e.get("EXT_texture_filter_anisotropic");s=i.getParameter(S.MAX_TEXTURE_MAX_ANISOTROPY_EXT)}else s=0;return s}function o(S){return!(S!==sn&&n.convert(S)!==i.getParameter(i.IMPLEMENTATION_COLOR_READ_FORMAT))}function a(S){let M=S===fr&&(e.has("EXT_color_buffer_half_float")||e.has("EXT_color_buffer_float"));return!(S!==Dn&&n.convert(S)!==i.getParameter(i.IMPLEMENTATION_COLOR_READ_TYPE)&&S!==gn&&!M)}function c(S){if(S==="highp"){if(i.getShaderPrecisionFormat(i.VERTEX_SHADER,i.HIGH_FLOAT).precision>0&&i.getShaderPrecisionFormat(i.FRAGMENT_SHADER,i.HIGH_FLOAT).precision>0)return"highp";S="mediump"}return S==="mediump"&&i.getShaderPrecisionFormat(i.VERTEX_SHADER,i.MEDIUM_FLOAT).precision>0&&i.getShaderPrecisionFormat(i.FRAGMENT_SHADER,i.MEDIUM_FLOAT).precision>0?"mediump":"lowp"}let h=t.precision!==void 0?t.precision:"highp",l=c(h);l!==h&&(console.warn("THREE.WebGLRenderer:",h,"not supported, using",l,"instead."),h=l);let p=t.logarithmicDepthBuffer===!0,f=t.reverseDepthBuffer===!0&&e.has("EXT_clip_control"),u=i.getParameter(i.MAX_TEXTURE_IMAGE_UNITS),_=i.getParameter(i.MAX_VERTEX_TEXTURE_IMAGE_UNITS),m=i.getParameter(i.MAX_TEXTURE_SIZE),g=i.getParameter(i.MAX_CUBE_MAP_TEXTURE_SIZE),d=i.getParameter(i.MAX_VERTEX_ATTRIBS),A=i.getParameter(i.MAX_VERTEX_UNIFORM_VECTORS),O=i.getParameter(i.MAX_VARYING_VECTORS),P=i.getParameter(i.MAX_FRAGMENT_UNIFORM_VECTORS),G=_>0,H=i.getParameter(i.MAX_SAMPLES);return{isWebGL2:!0,getMaxAnisotropy:r,getMaxPrecision:c,textureFormatReadable:o,textureTypeReadable:a,precision:h,logarithmicDepthBuffer:p,reverseDepthBuffer:f,maxTextures:u,maxVertexTextures:_,maxTextureSize:m,maxCubemapSize:g,maxAttributes:d,maxVertexUniforms:A,maxVaryings:O,maxFragmentUniforms:P,vertexTextures:G,maxSamples:H}}function Pg(i){let e=this,t=null,n=0,s=!1,r=!1,o=new Rn,a=new ze,c={value:null,needsUpdate:!1};this.uniform=c,this.numPlanes=0,this.numIntersection=0,this.init=function(p,f){let u=p.length!==0||f||n!==0||s;return s=f,n=p.length,u},this.beginShadows=function(){r=!0,l(null)},this.endShadows=function(){r=!1},this.setGlobalState=function(p,f){t=l(p,f,0)},this.setState=function(p,f,u){let _=p.clippingPlanes,m=p.clipIntersection,g=p.clipShadows,d=i.get(p);if(!s||_===null||_.length===0||r&&!g)r?l(null):h();else{let A=r?0:n,O=A*4,P=d.clippingState||null;c.value=P,P=l(_,f,O,u);for(let G=0;G!==O;++G)P[G]=t[G];d.clippingState=P,this.numIntersection=m?this.numPlanes:0,this.numPlanes+=A}};function h(){c.value!==t&&(c.value=t,c.needsUpdate=n>0),e.numPlanes=n,e.numIntersection=0}function l(p,f,u,_){let m=p!==null?p.length:0,g=null;if(m!==0){if(g=c.value,_!==!0||g===null){let d=u+m*4,A=f.matrixWorldInverse;a.getNormalMatrix(A),(g===null||g.length<d)&&(g=new Float32Array(d));for(let O=0,P=u;O!==m;++O,P+=4)o.copy(p[O]).applyMatrix4(A,a),o.normal.toArray(g,P),g[P+3]=o.constant}c.value=g,c.needsUpdate=!0}return e.numPlanes=m,e.numIntersection=0,g}}function wg(i){let e=new WeakMap;function t(o,a){return a===Ta?o.mapping=os:a===Ua&&(o.mapping=as),o}function n(o){if(o&&o.isTexture){let a=o.mapping;if(a===Ta||a===Ua)if(e.has(o)){let c=e.get(o).texture;return t(c,o.mapping)}else{let c=o.image;if(c&&c.height>0){let h=new fc(c.height);return h.fromEquirectangularTexture(i,o),e.set(o,h),o.addEventListener("dispose",s),t(h.texture,o.mapping)}else return null}}return o}function s(o){let a=o.target;a.removeEventListener("dispose",s);let c=e.get(a);c!==void 0&&(e.delete(a),c.dispose())}function r(){e=new WeakMap}return{get:n,dispose:r}}var gs=class extends lo{constructor(e=-1,t=1,n=1,s=-1,r=.1,o=2e3){super(),this.isOrthographicCamera=!0,this.type="OrthographicCamera",this.zoom=1,this.view=null,this.left=e,this.right=t,this.top=n,this.bottom=s,this.near=r,this.far=o,this.updateProjectionMatrix()}copy(e,t){return super.copy(e,t),this.left=e.left,this.right=e.right,this.top=e.top,this.bottom=e.bottom,this.near=e.near,this.far=e.far,this.zoom=e.zoom,this.view=e.view===null?null:Object.assign({},e.view),this}setViewOffset(e,t,n,s,r,o){this.view===null&&(this.view={enabled:!0,fullWidth:1,fullHeight:1,offsetX:0,offsetY:0,width:1,height:1}),this.view.enabled=!0,this.view.fullWidth=e,this.view.fullHeight=t,this.view.offsetX=n,this.view.offsetY=s,this.view.width=r,this.view.height=o,this.updateProjectionMatrix()}clearViewOffset(){this.view!==null&&(this.view.enabled=!1),this.updateProjectionMatrix()}updateProjectionMatrix(){let e=(this.right-this.left)/(2*this.zoom),t=(this.top-this.bottom)/(2*this.zoom),n=(this.right+this.left)/2,s=(this.top+this.bottom)/2,r=n-e,o=n+e,a=s+t,c=s-t;if(this.view!==null&&this.view.enabled){let h=(this.right-this.left)/this.view.fullWidth/this.zoom,l=(this.top-this.bottom)/this.view.fullHeight/this.zoom;r+=h*this.view.offsetX,o=r+h*this.view.width,a-=l*this.view.offsetY,c=a-l*this.view.height}this.projectionMatrix.makeOrthographic(r,o,a,c,this.near,this.far,this.coordinateSystem),this.projectionMatrixInverse.copy(this.projectionMatrix).invert()}toJSON(e){let t=super.toJSON(e);return t.object.zoom=this.zoom,t.object.left=this.left,t.object.right=this.right,t.object.top=this.top,t.object.bottom=this.bottom,t.object.near=this.near,t.object.far=this.far,this.view!==null&&(t.object.view=Object.assign({},this.view)),t}},es=4,ll=[.125,.215,.35,.446,.526,.582],Ai=20,fa=new gs,pl=new Ge,da=null,ua=0,ga=0,_a=!1,Oi=(1+Math.sqrt(5))/2,qi=1/Oi,fl=[new C(-Oi,qi,0),new C(Oi,qi,0),new C(-qi,0,Oi),new C(qi,0,Oi),new C(0,Oi,-qi),new C(0,Oi,qi),new C(-1,1,-1),new C(1,1,-1),new C(-1,1,1),new C(1,1,1)],_s=class{constructor(e){this._renderer=e,this._pingPongRenderTarget=null,this._lodMax=0,this._cubeSize=0,this._lodPlanes=[],this._sizeLods=[],this._sigmas=[],this._blurMaterial=null,this._cubemapMaterial=null,this._equirectMaterial=null,this._compileMaterial(this._blurMaterial)}fromScene(e,t=0,n=.1,s=100){da=this._renderer.getRenderTarget(),ua=this._renderer.getActiveCubeFace(),ga=this._renderer.getActiveMipmapLevel(),_a=this._renderer.xr.enabled,this._renderer.xr.enabled=!1,this._setSize(256);let r=this._allocateTargets();return r.depthBuffer=!0,this._sceneToCubeUV(e,n,s,r),t>0&&this._blur(r,0,0,t),this._applyPMREM(r),this._cleanup(r),r}fromEquirectangular(e,t=null){return this._fromTexture(e,t)}fromCubemap(e,t=null){return this._fromTexture(e,t)}compileCubemapShader(){this._cubemapMaterial===null&&(this._cubemapMaterial=gl(),this._compileMaterial(this._cubemapMaterial))}compileEquirectangularShader(){this._equirectMaterial===null&&(this._equirectMaterial=ul(),this._compileMaterial(this._equirectMaterial))}dispose(){this._dispose(),this._cubemapMaterial!==null&&this._cubemapMaterial.dispose(),this._equirectMaterial!==null&&this._equirectMaterial.dispose()}_setSize(e){this._lodMax=Math.floor(Math.log2(e)),this._cubeSize=Math.pow(2,this._lodMax)}_dispose(){this._blurMaterial!==null&&this._blurMaterial.dispose(),this._pingPongRenderTarget!==null&&this._pingPongRenderTarget.dispose();for(let e=0;e<this._lodPlanes.length;e++)this._lodPlanes[e].dispose()}_cleanup(e){this._renderer.setRenderTarget(da,ua,ga),this._renderer.xr.enabled=_a,e.scissorTest=!1,Dr(e,0,0,e.width,e.height)}_fromTexture(e,t){e.mapping===os||e.mapping===as?this._setSize(e.image.length===0?16:e.image[0].width||e.image[0].image.width):this._setSize(e.image.width/4),da=this._renderer.getRenderTarget(),ua=this._renderer.getActiveCubeFace(),ga=this._renderer.getActiveMipmapLevel(),_a=this._renderer.xr.enabled,this._renderer.xr.enabled=!1;let n=t||this._allocateTargets();return this._textureToCubeUV(e,n),this._applyPMREM(n),this._cleanup(n),n}_allocateTargets(){let e=3*Math.max(this._cubeSize,112),t=4*this._cubeSize,n={magFilter:kt,minFilter:kt,generateMipmaps:!1,type:fr,format:sn,colorSpace:Ft,depthBuffer:!1},s=dl(e,t,n);if(this._pingPongRenderTarget===null||this._pingPongRenderTarget.width!==e||this._pingPongRenderTarget.height!==t){this._pingPongRenderTarget!==null&&this._dispose(),this._pingPongRenderTarget=dl(e,t,n);let{_lodMax:r}=this;({sizeLods:this._sizeLods,lodPlanes:this._lodPlanes,sigmas:this._sigmas}=Og(r)),this._blurMaterial=yg(r,e,t)}return s}_compileMaterial(e){let t=new De(this._lodPlanes[0],e);this._renderer.compile(t,fa)}_sceneToCubeUV(e,t,n,s){let a=new St(90,1,t,n),c=[1,-1,1,1,1,1],h=[1,1,1,-1,-1,-1],l=this._renderer,p=l.autoClear,f=l.toneMapping;l.getClearColor(pl),l.toneMapping=ai,l.autoClear=!1;let u=new yt({name:"PMREM.Background",side:Bt,depthWrite:!1,depthTest:!1}),_=new De(new An,u),m=!1,g=e.background;g?g.isColor&&(u.color.copy(g),e.background=null,m=!0):(u.color.copy(pl),m=!0);for(let d=0;d<6;d++){let A=d%3;A===0?(a.up.set(0,c[d],0),a.lookAt(h[d],0,0)):A===1?(a.up.set(0,0,c[d]),a.lookAt(0,h[d],0)):(a.up.set(0,c[d],0),a.lookAt(0,0,h[d]));let O=this._cubeSize;Dr(s,A*O,d>2?O:0,O,O),l.setRenderTarget(s),m&&l.render(_,a),l.render(e,a)}_.geometry.dispose(),_.material.dispose(),l.toneMapping=f,l.autoClear=p,e.background=g}_textureToCubeUV(e,t){let n=this._renderer,s=e.mapping===os||e.mapping===as;s?(this._cubemapMaterial===null&&(this._cubemapMaterial=gl()),this._cubemapMaterial.uniforms.flipEnvMap.value=e.isRenderTargetTexture===!1?-1:1):this._equirectMaterial===null&&(this._equirectMaterial=ul());let r=s?this._cubemapMaterial:this._equirectMaterial,o=new De(this._lodPlanes[0],r),a=r.uniforms;a.envMap.value=e;let c=this._cubeSize;Dr(t,0,0,3*c,2*c),n.setRenderTarget(t),n.render(o,fa)}_applyPMREM(e){let t=this._renderer,n=t.autoClear;t.autoClear=!1;let s=this._lodPlanes.length;for(let r=1;r<s;r++){let o=Math.sqrt(this._sigmas[r]*this._sigmas[r]-this._sigmas[r-1]*this._sigmas[r-1]),a=fl[(s-r-1)%fl.length];this._blur(e,r-1,r,o,a)}t.autoClear=n}_blur(e,t,n,s,r){let o=this._pingPongRenderTarget;this._halfBlur(e,o,t,n,s,"latitudinal",r),this._halfBlur(o,e,n,n,s,"longitudinal",r)}_halfBlur(e,t,n,s,r,o,a){let c=this._renderer,h=this._blurMaterial;o!=="latitudinal"&&o!=="longitudinal"&&console.error("blur direction must be either latitudinal or longitudinal!");let l=3,p=new De(this._lodPlanes[s],h),f=h.uniforms,u=this._sizeLods[n]-1,_=isFinite(r)?Math.PI/(2*u):2*Math.PI/(2*Ai-1),m=r/_,g=isFinite(r)?1+Math.floor(l*m):Ai;g>Ai&&console.warn(`sigmaRadians, ${r}, is too large and will clip, as it requested ${g} samples when the maximum is set to ${Ai}`);let d=[],A=0;for(let S=0;S<Ai;++S){let M=S/m,y=Math.exp(-M*M/2);d.push(y),S===0?A+=y:S<g&&(A+=2*y)}for(let S=0;S<d.length;S++)d[S]=d[S]/A;f.envMap.value=e.texture,f.samples.value=g,f.weights.value=d,f.latitudinal.value=o==="latitudinal",a&&(f.poleAxis.value=a);let{_lodMax:O}=this;f.dTheta.value=_,f.mipInt.value=O-n;let P=this._sizeLods[s],G=3*P*(s>O-es?s-O+es:0),H=4*(this._cubeSize-P);Dr(t,G,H,3*P,2*P),c.setRenderTarget(t),c.render(p,fa)}};function Og(i){let e=[],t=[],n=[],s=i,r=i-es+1+ll.length;for(let o=0;o<r;o++){let a=Math.pow(2,s);t.push(a);let c=1/a;o>i-es?c=ll[o-i+es-1]:o===0&&(c=0),n.push(c);let h=1/(a-2),l=-h,p=1+h,f=[l,l,p,l,p,p,l,l,p,p,l,p],u=6,_=6,m=3,g=2,d=1,A=new Float32Array(m*_*u),O=new Float32Array(g*_*u),P=new Float32Array(d*_*u);for(let H=0;H<u;H++){let S=H%3*2/3-1,M=H>2?0:-1,y=[S,M,0,S+2/3,M,0,S+2/3,M+1,0,S,M,0,S+2/3,M+1,0,S,M+1,0];A.set(y,m*_*H),O.set(f,g*_*H);let w=[H,H,H,H,H,H];P.set(w,d*_*H)}let G=new Gt;G.setAttribute("position",new wt(A,m)),G.setAttribute("uv",new wt(O,g)),G.setAttribute("faceIndex",new wt(P,d)),e.push(G),s>es&&s--}return{lodPlanes:e,sizeLods:t,sigmas:n}}function dl(i,e,t){let n=new Nn(i,e,t);return n.texture.mapping=Go,n.texture.name="PMREM.cubeUv",n.scissorTest=!0,n}function Dr(i,e,t,n,s){i.viewport.set(e,t,n,s),i.scissor.set(e,t,n,s)}function yg(i,e,t){let n=new Float32Array(Ai),s=new C(0,1,0);return new vn({name:"SphericalGaussianBlur",defines:{n:Ai,CUBEUV_TEXEL_WIDTH:1/e,CUBEUV_TEXEL_HEIGHT:1/t,CUBEUV_MAX_MIP:`${i}.0`},uniforms:{envMap:{value:null},samples:{value:1},weights:{value:n},latitudinal:{value:!1},dTheta:{value:0},mipInt:{value:0},poleAxis:{value:s}},vertexShader:qc(),fragmentShader:`

			precision mediump float;
			precision mediump int;

			varying vec3 vOutputDirection;

			uniform sampler2D envMap;
			uniform int samples;
			uniform float weights[ n ];
			uniform bool latitudinal;
			uniform float dTheta;
			uniform float mipInt;
			uniform vec3 poleAxis;

			#define ENVMAP_TYPE_CUBE_UV
			#include <cube_uv_reflection_fragment>

			vec3 getSample( float theta, vec3 axis ) {

				float cosTheta = cos( theta );
				// Rodrigues' axis-angle rotation
				vec3 sampleDirection = vOutputDirection * cosTheta
					+ cross( axis, vOutputDirection ) * sin( theta )
					+ axis * dot( axis, vOutputDirection ) * ( 1.0 - cosTheta );

				return bilinearCubeUV( envMap, sampleDirection, mipInt );

			}

			void main() {

				vec3 axis = latitudinal ? poleAxis : cross( poleAxis, vOutputDirection );

				if ( all( equal( axis, vec3( 0.0 ) ) ) ) {

					axis = vec3( vOutputDirection.z, 0.0, - vOutputDirection.x );

				}

				axis = normalize( axis );

				gl_FragColor = vec4( 0.0, 0.0, 0.0, 1.0 );
				gl_FragColor.rgb += weights[ 0 ] * getSample( 0.0, axis );

				for ( int i = 1; i < n; i++ ) {

					if ( i >= samples ) {

						break;

					}

					float theta = dTheta * float( i );
					gl_FragColor.rgb += weights[ i ] * getSample( -1.0 * theta, axis );
					gl_FragColor.rgb += weights[ i ] * getSample( theta, axis );

				}

			}
		`,blending:oi,depthTest:!1,depthWrite:!1})}function ul(){return new vn({name:"EquirectangularToCubeUV",uniforms:{envMap:{value:null}},vertexShader:qc(),fragmentShader:`

			precision mediump float;
			precision mediump int;

			varying vec3 vOutputDirection;

			uniform sampler2D envMap;

			#include <common>

			void main() {

				vec3 outputDirection = normalize( vOutputDirection );
				vec2 uv = equirectUv( outputDirection );

				gl_FragColor = vec4( texture2D ( envMap, uv ).rgb, 1.0 );

			}
		`,blending:oi,depthTest:!1,depthWrite:!1})}function gl(){return new vn({name:"CubemapToCubeUV",uniforms:{envMap:{value:null},flipEnvMap:{value:-1}},vertexShader:qc(),fragmentShader:`

			precision mediump float;
			precision mediump int;

			uniform float flipEnvMap;

			varying vec3 vOutputDirection;

			uniform samplerCube envMap;

			void main() {

				gl_FragColor = textureCube( envMap, vec3( flipEnvMap * vOutputDirection.x, vOutputDirection.yz ) );

			}
		`,blending:oi,depthTest:!1,depthWrite:!1})}function qc(){return`

		precision mediump float;
		precision mediump int;

		attribute float faceIndex;

		varying vec3 vOutputDirection;

		// RH coordinate system; PMREM face-indexing convention
		vec3 getDirection( vec2 uv, float face ) {

			uv = 2.0 * uv - 1.0;

			vec3 direction = vec3( uv, 1.0 );

			if ( face == 0.0 ) {

				direction = direction.zyx; // ( 1, v, u ) pos x

			} else if ( face == 1.0 ) {

				direction = direction.xzy;
				direction.xz *= -1.0; // ( -u, 1, -v ) pos y

			} else if ( face == 2.0 ) {

				direction.x *= -1.0; // ( -u, v, 1 ) pos z

			} else if ( face == 3.0 ) {

				direction = direction.zyx;
				direction.xz *= -1.0; // ( -1, v, -u ) neg x

			} else if ( face == 4.0 ) {

				direction = direction.xzy;
				direction.xy *= -1.0; // ( -u, -1, v ) neg y

			} else if ( face == 5.0 ) {

				direction.z *= -1.0; // ( u, v, -1 ) neg z

			}

			return direction;

		}

		void main() {

			vOutputDirection = getDirection( uv, faceIndex );
			gl_Position = vec4( position, 1.0 );

		}
	`}function Ag(i){let e=new WeakMap,t=null;function n(a){if(a&&a.isTexture){let c=a.mapping,h=c===Ta||c===Ua,l=c===os||c===as;if(h||l){let p=e.get(a),f=p!==void 0?p.texture.pmremVersion:0;if(a.isRenderTargetTexture&&a.pmremVersion!==f)return t===null&&(t=new _s(i)),p=h?t.fromEquirectangular(a,p):t.fromCubemap(a,p),p.texture.pmremVersion=a.pmremVersion,e.set(a,p),p.texture;if(p!==void 0)return p.texture;{let u=a.image;return h&&u&&u.height>0||l&&u&&s(u)?(t===null&&(t=new _s(i)),p=h?t.fromEquirectangular(a):t.fromCubemap(a),p.texture.pmremVersion=a.pmremVersion,e.set(a,p),a.addEventListener("dispose",r),p.texture):null}}}return a}function s(a){let c=0,h=6;for(let l=0;l<h;l++)a[l]!==void 0&&c++;return c===h}function r(a){let c=a.target;c.removeEventListener("dispose",r);let h=e.get(c);h!==void 0&&(e.delete(c),h.dispose())}function o(){e=new WeakMap,t!==null&&(t.dispose(),t=null)}return{get:n,dispose:o}}function vg(i){let e={};function t(n){if(e[n]!==void 0)return e[n];let s;switch(n){case"WEBGL_depth_texture":s=i.getExtension("WEBGL_depth_texture")||i.getExtension("MOZ_WEBGL_depth_texture")||i.getExtension("WEBKIT_WEBGL_depth_texture");break;case"EXT_texture_filter_anisotropic":s=i.getExtension("EXT_texture_filter_anisotropic")||i.getExtension("MOZ_EXT_texture_filter_anisotropic")||i.getExtension("WEBKIT_EXT_texture_filter_anisotropic");break;case"WEBGL_compressed_texture_s3tc":s=i.getExtension("WEBGL_compressed_texture_s3tc")||i.getExtension("MOZ_WEBGL_compressed_texture_s3tc")||i.getExtension("WEBKIT_WEBGL_compressed_texture_s3tc");break;case"WEBGL_compressed_texture_pvrtc":s=i.getExtension("WEBGL_compressed_texture_pvrtc")||i.getExtension("WEBKIT_WEBGL_compressed_texture_pvrtc");break;default:s=i.getExtension(n)}return e[n]=s,s}return{has:function(n){return t(n)!==null},init:function(){t("EXT_color_buffer_float"),t("WEBGL_clip_cull_distance"),t("OES_texture_float_linear"),t("EXT_color_buffer_half_float"),t("WEBGL_multisampled_render_to_texture"),t("WEBGL_render_shared_exponent")},get:function(n){let s=t(n);return s===null&&js("THREE.WebGLRenderer: "+n+" extension not supported."),s}}}function Hg(i,e,t,n){let s={},r=new WeakMap;function o(p){let f=p.target;f.index!==null&&e.remove(f.index);for(let _ in f.attributes)e.remove(f.attributes[_]);for(let _ in f.morphAttributes){let m=f.morphAttributes[_];for(let g=0,d=m.length;g<d;g++)e.remove(m[g])}f.removeEventListener("dispose",o),delete s[f.id];let u=r.get(f);u&&(e.remove(u),r.delete(f)),n.releaseStatesOfGeometry(f),f.isInstancedBufferGeometry===!0&&delete f._maxInstanceCount,t.memory.geometries--}function a(p,f){return s[f.id]===!0||(f.addEventListener("dispose",o),s[f.id]=!0,t.memory.geometries++),f}function c(p){let f=p.attributes;for(let _ in f)e.update(f[_],i.ARRAY_BUFFER);let u=p.morphAttributes;for(let _ in u){let m=u[_];for(let g=0,d=m.length;g<d;g++)e.update(m[g],i.ARRAY_BUFFER)}}function h(p){let f=[],u=p.index,_=p.attributes.position,m=0;if(u!==null){let A=u.array;m=u.version;for(let O=0,P=A.length;O<P;O+=3){let G=A[O+0],H=A[O+1],S=A[O+2];f.push(G,H,H,S,S,G)}}else if(_!==void 0){let A=_.array;m=_.version;for(let O=0,P=A.length/3-1;O<P;O+=3){let G=O+0,H=O+1,S=O+2;f.push(G,H,H,S,S,G)}}else return;let g=new(x0(f)?ho:co)(f,1);g.version=m;let d=r.get(p);d&&e.remove(d),r.set(p,g)}function l(p){let f=r.get(p);if(f){let u=p.index;u!==null&&f.version<u.version&&h(p)}else h(p);return r.get(p)}return{get:a,update:c,getWireframeAttribute:l}}function Sg(i,e,t){let n;function s(f){n=f}let r,o;function a(f){r=f.type,o=f.bytesPerElement}function c(f,u){i.drawElements(n,u,r,f*o),t.update(u,n,1)}function h(f,u,_){_!==0&&(i.drawElementsInstanced(n,u,r,f*o,_),t.update(u,n,_))}function l(f,u,_){if(_===0)return;e.get("WEBGL_multi_draw").multiDrawElementsWEBGL(n,u,0,r,f,0,_);let g=0;for(let d=0;d<_;d++)g+=u[d];t.update(g,n,1)}function p(f,u,_,m){if(_===0)return;let g=e.get("WEBGL_multi_draw");if(g===null)for(let d=0;d<f.length;d++)h(f[d]/o,u[d],m[d]);else{g.multiDrawElementsInstancedWEBGL(n,u,0,r,f,0,m,0,_);let d=0;for(let A=0;A<_;A++)d+=u[A]*m[A];t.update(d,n,1)}}this.setMode=s,this.setIndex=a,this.render=c,this.renderInstances=h,this.renderMultiDraw=l,this.renderMultiDrawInstances=p}function Eg(i){let e={geometries:0,textures:0},t={frame:0,calls:0,triangles:0,points:0,lines:0};function n(r,o,a){switch(t.calls++,o){case i.TRIANGLES:t.triangles+=a*(r/3);break;case i.LINES:t.lines+=a*(r/2);break;case i.LINE_STRIP:t.lines+=a*(r-1);break;case i.LINE_LOOP:t.lines+=a*r;break;case i.POINTS:t.points+=a*r;break;default:console.error("THREE.WebGLInfo: Unknown draw mode:",o);break}}function s(){t.calls=0,t.triangles=0,t.points=0,t.lines=0}return{memory:e,render:t,programs:null,autoReset:!0,reset:s,update:n}}function Wg(i,e,t){let n=new WeakMap,s=new nt;function r(o,a,c){let h=o.morphTargetInfluences,l=a.morphAttributes.position||a.morphAttributes.normal||a.morphAttributes.color,p=l!==void 0?l.length:0,f=n.get(a);if(f===void 0||f.count!==p){let y=function(){S.dispose(),n.delete(a),a.removeEventListener("dispose",y)};f!==void 0&&f.texture.dispose();let u=a.morphAttributes.position!==void 0,_=a.morphAttributes.normal!==void 0,m=a.morphAttributes.color!==void 0,g=a.morphAttributes.position||[],d=a.morphAttributes.normal||[],A=a.morphAttributes.color||[],O=0;u===!0&&(O=1),_===!0&&(O=2),m===!0&&(O=3);let P=a.attributes.position.count*O,G=1;P>e.maxTextureSize&&(G=Math.ceil(P/e.maxTextureSize),P=e.maxTextureSize);let H=new Float32Array(P*G*4*p),S=new ao(H,P,G,p);S.type=gn,S.needsUpdate=!0;let M=O*4;for(let w=0;w<p;w++){let W=g[w],$=d[w],F=A[w],k=P*G*4*w;for(let Q=0;Q<W.count;Q++){let V=Q*M;u===!0&&(s.fromBufferAttribute(W,Q),H[k+V+0]=s.x,H[k+V+1]=s.y,H[k+V+2]=s.z,H[k+V+3]=0),_===!0&&(s.fromBufferAttribute($,Q),H[k+V+4]=s.x,H[k+V+5]=s.y,H[k+V+6]=s.z,H[k+V+7]=0),m===!0&&(s.fromBufferAttribute(F,Q),H[k+V+8]=s.x,H[k+V+9]=s.y,H[k+V+10]=s.z,H[k+V+11]=F.itemSize===4?s.w:1)}}f={count:p,texture:S,size:new Re(P,G)},n.set(a,f),a.addEventListener("dispose",y)}if(o.isInstancedMesh===!0&&o.morphTexture!==null)c.getUniforms().setValue(i,"morphTexture",o.morphTexture,t);else{let u=0;for(let m=0;m<h.length;m++)u+=h[m];let _=a.morphTargetsRelative?1:1-u;c.getUniforms().setValue(i,"morphTargetBaseInfluence",_),c.getUniforms().setValue(i,"morphTargetInfluences",h)}c.getUniforms().setValue(i,"morphTargetsTexture",f.texture,t),c.getUniforms().setValue(i,"morphTargetsTextureSize",f.size)}return{update:r}}function Mg(i,e,t,n){let s=new WeakMap;function r(c){let h=n.render.frame,l=c.geometry,p=e.get(c,l);if(s.get(p)!==h&&(e.update(p),s.set(p,h)),c.isInstancedMesh&&(c.hasEventListener("dispose",a)===!1&&c.addEventListener("dispose",a),s.get(c)!==h&&(t.update(c.instanceMatrix,i.ARRAY_BUFFER),c.instanceColor!==null&&t.update(c.instanceColor,i.ARRAY_BUFFER),s.set(c,h))),c.isSkinnedMesh){let f=c.skeleton;s.get(f)!==h&&(f.update(),s.set(f,h))}return p}function o(){s=new WeakMap}function a(c){let h=c.target;h.removeEventListener("dispose",a),t.remove(h.instanceMatrix),h.instanceColor!==null&&t.remove(h.instanceColor)}return{update:r,dispose:o}}var fo=class extends Ct{constructor(e,t,n,s,r,o,a,c,h,l=ns){if(l!==ns&&l!==hs)throw new Error("DepthTexture format must be either THREE.DepthFormat or THREE.DepthStencilFormat");n===void 0&&l===ns&&(n=vi),n===void 0&&l===hs&&(n=cs),super(null,s,r,o,a,c,l,n,h),this.isDepthTexture=!0,this.image={width:e,height:t},this.magFilter=a!==void 0?a:$t,this.minFilter=c!==void 0?c:$t,this.flipY=!1,this.generateMipmaps=!1,this.compareFunction=null}copy(e){return super.copy(e),this.compareFunction=e.compareFunction,this}toJSON(e){let t=super.toJSON(e);return this.compareFunction!==null&&(t.compareFunction=this.compareFunction),t}},O0=new Ct,_l=new fo(1,1),y0=new ao,A0=new lc,v0=new po,ml=[],xl=[],bl=new Float32Array(16),Pl=new Float32Array(9),wl=new Float32Array(4);function Hs(i,e,t){let n=i[0];if(n<=0||n>0)return i;let s=e*t,r=ml[s];if(r===void 0&&(r=new Float32Array(s),ml[s]=r),e!==0){n.toArray(r,0);for(let o=1,a=0;o!==e;++o)a+=t,i[o].toArray(r,a)}return r}function Et(i,e){if(i.length!==e.length)return!1;for(let t=0,n=i.length;t<n;t++)if(i[t]!==e[t])return!1;return!0}function Wt(i,e){for(let t=0,n=e.length;t<n;t++)i[t]=e[t]}function Io(i,e){let t=xl[e];t===void 0&&(t=new Int32Array(e),xl[e]=t);for(let n=0;n!==e;++n)t[n]=i.allocateTextureUnit();return t}function Cg(i,e){let t=this.cache;t[0]!==e&&(i.uniform1f(this.addr,e),t[0]=e)}function Gg(i,e){let t=this.cache;if(e.x!==void 0)(t[0]!==e.x||t[1]!==e.y)&&(i.uniform2f(this.addr,e.x,e.y),t[0]=e.x,t[1]=e.y);else{if(Et(t,e))return;i.uniform2fv(this.addr,e),Wt(t,e)}}function Tg(i,e){let t=this.cache;if(e.x!==void 0)(t[0]!==e.x||t[1]!==e.y||t[2]!==e.z)&&(i.uniform3f(this.addr,e.x,e.y,e.z),t[0]=e.x,t[1]=e.y,t[2]=e.z);else if(e.r!==void 0)(t[0]!==e.r||t[1]!==e.g||t[2]!==e.b)&&(i.uniform3f(this.addr,e.r,e.g,e.b),t[0]=e.r,t[1]=e.g,t[2]=e.b);else{if(Et(t,e))return;i.uniform3fv(this.addr,e),Wt(t,e)}}function Ug(i,e){let t=this.cache;if(e.x!==void 0)(t[0]!==e.x||t[1]!==e.y||t[2]!==e.z||t[3]!==e.w)&&(i.uniform4f(this.addr,e.x,e.y,e.z,e.w),t[0]=e.x,t[1]=e.y,t[2]=e.z,t[3]=e.w);else{if(Et(t,e))return;i.uniform4fv(this.addr,e),Wt(t,e)}}function Ig(i,e){let t=this.cache,n=e.elements;if(n===void 0){if(Et(t,e))return;i.uniformMatrix2fv(this.addr,!1,e),Wt(t,e)}else{if(Et(t,n))return;wl.set(n),i.uniformMatrix2fv(this.addr,!1,wl),Wt(t,n)}}function Rg(i,e){let t=this.cache,n=e.elements;if(n===void 0){if(Et(t,e))return;i.uniformMatrix3fv(this.addr,!1,e),Wt(t,e)}else{if(Et(t,n))return;Pl.set(n),i.uniformMatrix3fv(this.addr,!1,Pl),Wt(t,n)}}function Xg(i,e){let t=this.cache,n=e.elements;if(n===void 0){if(Et(t,e))return;i.uniformMatrix4fv(this.addr,!1,e),Wt(t,e)}else{if(Et(t,n))return;bl.set(n),i.uniformMatrix4fv(this.addr,!1,bl),Wt(t,n)}}function Bg(i,e){let t=this.cache;t[0]!==e&&(i.uniform1i(this.addr,e),t[0]=e)}function $g(i,e){let t=this.cache;if(e.x!==void 0)(t[0]!==e.x||t[1]!==e.y)&&(i.uniform2i(this.addr,e.x,e.y),t[0]=e.x,t[1]=e.y);else{if(Et(t,e))return;i.uniform2iv(this.addr,e),Wt(t,e)}}function Fg(i,e){let t=this.cache;if(e.x!==void 0)(t[0]!==e.x||t[1]!==e.y||t[2]!==e.z)&&(i.uniform3i(this.addr,e.x,e.y,e.z),t[0]=e.x,t[1]=e.y,t[2]=e.z);else{if(Et(t,e))return;i.uniform3iv(this.addr,e),Wt(t,e)}}function Dg(i,e){let t=this.cache;if(e.x!==void 0)(t[0]!==e.x||t[1]!==e.y||t[2]!==e.z||t[3]!==e.w)&&(i.uniform4i(this.addr,e.x,e.y,e.z,e.w),t[0]=e.x,t[1]=e.y,t[2]=e.z,t[3]=e.w);else{if(Et(t,e))return;i.uniform4iv(this.addr,e),Wt(t,e)}}function Ng(i,e){let t=this.cache;t[0]!==e&&(i.uniform1ui(this.addr,e),t[0]=e)}function Vg(i,e){let t=this.cache;if(e.x!==void 0)(t[0]!==e.x||t[1]!==e.y)&&(i.uniform2ui(this.addr,e.x,e.y),t[0]=e.x,t[1]=e.y);else{if(Et(t,e))return;i.uniform2uiv(this.addr,e),Wt(t,e)}}function Lg(i,e){let t=this.cache;if(e.x!==void 0)(t[0]!==e.x||t[1]!==e.y||t[2]!==e.z)&&(i.uniform3ui(this.addr,e.x,e.y,e.z),t[0]=e.x,t[1]=e.y,t[2]=e.z);else{if(Et(t,e))return;i.uniform3uiv(this.addr,e),Wt(t,e)}}function zg(i,e){let t=this.cache;if(e.x!==void 0)(t[0]!==e.x||t[1]!==e.y||t[2]!==e.z||t[3]!==e.w)&&(i.uniform4ui(this.addr,e.x,e.y,e.z,e.w),t[0]=e.x,t[1]=e.y,t[2]=e.z,t[3]=e.w);else{if(Et(t,e))return;i.uniform4uiv(this.addr,e),Wt(t,e)}}function kg(i,e,t){let n=this.cache,s=t.allocateTextureUnit();n[0]!==s&&(i.uniform1i(this.addr,s),n[0]=s);let r;this.type===i.SAMPLER_2D_SHADOW?(_l.compareFunction=m0,r=_l):r=O0,t.setTexture2D(e||r,s)}function qg(i,e,t){let n=this.cache,s=t.allocateTextureUnit();n[0]!==s&&(i.uniform1i(this.addr,s),n[0]=s),t.setTexture3D(e||A0,s)}function Jg(i,e,t){let n=this.cache,s=t.allocateTextureUnit();n[0]!==s&&(i.uniform1i(this.addr,s),n[0]=s),t.setTextureCube(e||v0,s)}function jg(i,e,t){let n=this.cache,s=t.allocateTextureUnit();n[0]!==s&&(i.uniform1i(this.addr,s),n[0]=s),t.setTexture2DArray(e||y0,s)}function Qg(i){switch(i){case 5126:return Cg;case 35664:return Gg;case 35665:return Tg;case 35666:return Ug;case 35674:return Ig;case 35675:return Rg;case 35676:return Xg;case 5124:case 35670:return Bg;case 35667:case 35671:return $g;case 35668:case 35672:return Fg;case 35669:case 35673:return Dg;case 5125:return Ng;case 36294:return Vg;case 36295:return Lg;case 36296:return zg;case 35678:case 36198:case 36298:case 36306:case 35682:return kg;case 35679:case 36299:case 36307:return qg;case 35680:case 36300:case 36308:case 36293:return Jg;case 36289:case 36303:case 36311:case 36292:return jg}}function Yg(i,e){i.uniform1fv(this.addr,e)}function Zg(i,e){let t=Hs(e,this.size,2);i.uniform2fv(this.addr,t)}function Kg(i,e){let t=Hs(e,this.size,3);i.uniform3fv(this.addr,t)}function e_(i,e){let t=Hs(e,this.size,4);i.uniform4fv(this.addr,t)}function t_(i,e){let t=Hs(e,this.size,4);i.uniformMatrix2fv(this.addr,!1,t)}function n_(i,e){let t=Hs(e,this.size,9);i.uniformMatrix3fv(this.addr,!1,t)}function i_(i,e){let t=Hs(e,this.size,16);i.uniformMatrix4fv(this.addr,!1,t)}function s_(i,e){i.uniform1iv(this.addr,e)}function r_(i,e){i.uniform2iv(this.addr,e)}function o_(i,e){i.uniform3iv(this.addr,e)}function a_(i,e){i.uniform4iv(this.addr,e)}function c_(i,e){i.uniform1uiv(this.addr,e)}function h_(i,e){i.uniform2uiv(this.addr,e)}function l_(i,e){i.uniform3uiv(this.addr,e)}function p_(i,e){i.uniform4uiv(this.addr,e)}function f_(i,e,t){let n=this.cache,s=e.length,r=Io(t,s);Et(n,r)||(i.uniform1iv(this.addr,r),Wt(n,r));for(let o=0;o!==s;++o)t.setTexture2D(e[o]||O0,r[o])}function d_(i,e,t){let n=this.cache,s=e.length,r=Io(t,s);Et(n,r)||(i.uniform1iv(this.addr,r),Wt(n,r));for(let o=0;o!==s;++o)t.setTexture3D(e[o]||A0,r[o])}function u_(i,e,t){let n=this.cache,s=e.length,r=Io(t,s);Et(n,r)||(i.uniform1iv(this.addr,r),Wt(n,r));for(let o=0;o!==s;++o)t.setTextureCube(e[o]||v0,r[o])}function g_(i,e,t){let n=this.cache,s=e.length,r=Io(t,s);Et(n,r)||(i.uniform1iv(this.addr,r),Wt(n,r));for(let o=0;o!==s;++o)t.setTexture2DArray(e[o]||y0,r[o])}function __(i){switch(i){case 5126:return Yg;case 35664:return Zg;case 35665:return Kg;case 35666:return e_;case 35674:return t_;case 35675:return n_;case 35676:return i_;case 5124:case 35670:return s_;case 35667:case 35671:return r_;case 35668:case 35672:return o_;case 35669:case 35673:return a_;case 5125:return c_;case 36294:return h_;case 36295:return l_;case 36296:return p_;case 35678:case 36198:case 36298:case 36306:case 35682:return f_;case 35679:case 36299:case 36307:return d_;case 35680:case 36300:case 36308:case 36293:return u_;case 36289:case 36303:case 36311:case 36292:return g_}}var dc=class{constructor(e,t,n){this.id=e,this.addr=n,this.cache=[],this.type=t.type,this.setValue=Qg(t.type)}},uc=class{constructor(e,t,n){this.id=e,this.addr=n,this.cache=[],this.type=t.type,this.size=t.size,this.setValue=__(t.type)}},gc=class{constructor(e){this.id=e,this.seq=[],this.map={}}setValue(e,t,n){let s=this.seq;for(let r=0,o=s.length;r!==o;++r){let a=s[r];a.setValue(e,t[a.id],n)}}},ma=/(\w+)(\])?(\[|\.)?/g;function Ol(i,e){i.seq.push(e),i.map[e.id]=e}function m_(i,e,t){let n=i.name,s=n.length;for(ma.lastIndex=0;;){let r=ma.exec(n),o=ma.lastIndex,a=r[1],c=r[2]==="]",h=r[3];if(c&&(a=a|0),h===void 0||h==="["&&o+2===s){Ol(t,h===void 0?new dc(a,i,e):new uc(a,i,e));break}else{let p=t.map[a];p===void 0&&(p=new gc(a),Ol(t,p)),t=p}}}var ss=class{constructor(e,t){this.seq=[],this.map={};let n=e.getProgramParameter(t,e.ACTIVE_UNIFORMS);for(let s=0;s<n;++s){let r=e.getActiveUniform(t,s),o=e.getUniformLocation(t,r.name);m_(r,o,this)}}setValue(e,t,n,s){let r=this.map[t];r!==void 0&&r.setValue(e,n,s)}setOptional(e,t,n){let s=t[n];s!==void 0&&this.setValue(e,n,s)}static upload(e,t,n,s){for(let r=0,o=t.length;r!==o;++r){let a=t[r],c=n[a.id];c.needsUpdate!==!1&&a.setValue(e,c.value,s)}}static seqWithValue(e,t){let n=[];for(let s=0,r=e.length;s!==r;++s){let o=e[s];o.id in t&&n.push(o)}return n}};function yl(i,e,t){let n=i.createShader(e);return i.shaderSource(n,t),i.compileShader(n),n}var x_=37297,b_=0;function P_(i,e){let t=i.split(`
`),n=[],s=Math.max(e-6,0),r=Math.min(e+6,t.length);for(let o=s;o<r;o++){let a=o+1;n.push(`${a===e?">":" "} ${a}: ${t[o]}`)}return n.join(`
`)}var Al=new ze;function w_(i){Ze._getMatrix(Al,Ze.workingColorSpace,i);let e=`mat3( ${Al.elements.map(t=>t.toFixed(4))} )`;switch(Ze.getTransfer(i)){case Uo:return[e,"LinearTransferOETF"];case ct:return[e,"sRGBTransferOETF"];default:return console.warn("THREE.WebGLProgram: Unsupported color space: ",i),[e,"LinearTransferOETF"]}}function vl(i,e,t){let n=i.getShaderParameter(e,i.COMPILE_STATUS),s=i.getShaderInfoLog(e).trim();if(n&&s==="")return"";let r=/ERROR: 0:(\d+)/.exec(s);if(r){let o=parseInt(r[1]);return t.toUpperCase()+`

`+s+`

`+P_(i.getShaderSource(e),o)}else return s}function O_(i,e){let t=w_(e);return[`vec4 ${i}( vec4 value ) {`,`	return ${t[1]}( vec4( value.rgb * ${t[0]}, value.a ) );`,"}"].join(`
`)}function y_(i,e){let t;switch(e){case Gp:t="Linear";break;case Tp:t="Reinhard";break;case Up:t="Cineon";break;case Xc:t="ACESFilmic";break;case Rp:t="AgX";break;case Xp:t="Neutral";break;case Ip:t="Custom";break;default:console.warn("THREE.WebGLProgram: Unsupported toneMapping:",e),t="Linear"}return"vec3 "+i+"( vec3 color ) { return "+t+"ToneMapping( color ); }"}var Nr=new C;function A_(){Ze.getLuminanceCoefficients(Nr);let i=Nr.x.toFixed(4),e=Nr.y.toFixed(4),t=Nr.z.toFixed(4);return["float luminance( const in vec3 rgb ) {",`	const vec3 weights = vec3( ${i}, ${e}, ${t} );`,"	return dot( weights, rgb );","}"].join(`
`)}function v_(i){return[i.extensionClipCullDistance?"#extension GL_ANGLE_clip_cull_distance : require":"",i.extensionMultiDraw?"#extension GL_ANGLE_multi_draw : require":""].filter(Qs).join(`
`)}function H_(i){let e=[];for(let t in i){let n=i[t];n!==!1&&e.push("#define "+t+" "+n)}return e.join(`
`)}function S_(i,e){let t={},n=i.getProgramParameter(e,i.ACTIVE_ATTRIBUTES);for(let s=0;s<n;s++){let r=i.getActiveAttrib(e,s),o=r.name,a=1;r.type===i.FLOAT_MAT2&&(a=2),r.type===i.FLOAT_MAT3&&(a=3),r.type===i.FLOAT_MAT4&&(a=4),t[o]={type:r.type,location:i.getAttribLocation(e,o),locationSize:a}}return t}function Qs(i){return i!==""}function Hl(i,e){let t=e.numSpotLightShadows+e.numSpotLightMaps-e.numSpotLightShadowsWithMaps;return i.replace(/NUM_DIR_LIGHTS/g,e.numDirLights).replace(/NUM_SPOT_LIGHTS/g,e.numSpotLights).replace(/NUM_SPOT_LIGHT_MAPS/g,e.numSpotLightMaps).replace(/NUM_SPOT_LIGHT_COORDS/g,t).replace(/NUM_RECT_AREA_LIGHTS/g,e.numRectAreaLights).replace(/NUM_POINT_LIGHTS/g,e.numPointLights).replace(/NUM_HEMI_LIGHTS/g,e.numHemiLights).replace(/NUM_DIR_LIGHT_SHADOWS/g,e.numDirLightShadows).replace(/NUM_SPOT_LIGHT_SHADOWS_WITH_MAPS/g,e.numSpotLightShadowsWithMaps).replace(/NUM_SPOT_LIGHT_SHADOWS/g,e.numSpotLightShadows).replace(/NUM_POINT_LIGHT_SHADOWS/g,e.numPointLightShadows)}function Sl(i,e){return i.replace(/NUM_CLIPPING_PLANES/g,e.numClippingPlanes).replace(/UNION_CLIPPING_PLANES/g,e.numClippingPlanes-e.numClipIntersection)}var E_=/^[ \t]*#include +<([\w\d./]+)>/gm;function _c(i){return i.replace(E_,M_)}var W_=new Map;function M_(i,e){let t=qe[e];if(t===void 0){let n=W_.get(e);if(n!==void 0)t=qe[n],console.warn('THREE.WebGLRenderer: Shader chunk "%s" has been deprecated. Use "%s" instead.',e,n);else throw new Error("Can not resolve #include <"+e+">")}return _c(t)}var C_=/#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 El(i){return i.replace(C_,G_)}function G_(i,e,t,n){let s="";for(let r=parseInt(e);r<parseInt(t);r++)s+=n.replace(/\[\s*i\s*\]/g,"[ "+r+" ]").replace(/UNROLLED_LOOP_INDEX/g,r);return s}function Wl(i){let e=`precision ${i.precision} float;
	precision ${i.precision} int;
	precision ${i.precision} sampler2D;
	precision ${i.precision} samplerCube;
	precision ${i.precision} sampler3D;
	precision ${i.precision} sampler2DArray;
	precision ${i.precision} sampler2DShadow;
	precision ${i.precision} samplerCubeShadow;
	precision ${i.precision} sampler2DArrayShadow;
	precision ${i.precision} isampler2D;
	precision ${i.precision} isampler3D;
	precision ${i.precision} isamplerCube;
	precision ${i.precision} isampler2DArray;
	precision ${i.precision} usampler2D;
	precision ${i.precision} usampler3D;
	precision ${i.precision} usamplerCube;
	precision ${i.precision} usampler2DArray;
	`;return i.precision==="highp"?e+=`
#define HIGH_PRECISION`:i.precision==="mediump"?e+=`
#define MEDIUM_PRECISION`:i.precision==="lowp"&&(e+=`
#define LOW_PRECISION`),e}function T_(i){let e="SHADOWMAP_TYPE_BASIC";return i.shadowMapType===i0?e="SHADOWMAP_TYPE_PCF":i.shadowMapType===pp?e="SHADOWMAP_TYPE_PCF_SOFT":i.shadowMapType===In&&(e="SHADOWMAP_TYPE_VSM"),e}function U_(i){let e="ENVMAP_TYPE_CUBE";if(i.envMap)switch(i.envMapMode){case os:case as:e="ENVMAP_TYPE_CUBE";break;case Go:e="ENVMAP_TYPE_CUBE_UV";break}return e}function I_(i){let e="ENVMAP_MODE_REFLECTION";return i.envMap&&i.envMapMode===as&&(e="ENVMAP_MODE_REFRACTION"),e}function R_(i){let e="ENVMAP_BLENDING_NONE";if(i.envMap)switch(i.combine){case s0:e="ENVMAP_BLENDING_MULTIPLY";break;case Mp:e="ENVMAP_BLENDING_MIX";break;case Cp:e="ENVMAP_BLENDING_ADD";break}return e}function X_(i){let e=i.envMapCubeUVHeight;if(e===null)return null;let t=Math.log2(e)-2,n=1/e;return{texelWidth:1/(3*Math.max(Math.pow(2,t),112)),texelHeight:n,maxMip:t}}function B_(i,e,t,n){let s=i.getContext(),r=t.defines,o=t.vertexShader,a=t.fragmentShader,c=T_(t),h=U_(t),l=I_(t),p=R_(t),f=X_(t),u=v_(t),_=H_(r),m=s.createProgram(),g,d,A=t.glslVersion?"#version "+t.glslVersion+`
`:"";t.isRawShaderMaterial?(g=["#define SHADER_TYPE "+t.shaderType,"#define SHADER_NAME "+t.shaderName,_].filter(Qs).join(`
`),g.length>0&&(g+=`
`),d=["#define SHADER_TYPE "+t.shaderType,"#define SHADER_NAME "+t.shaderName,_].filter(Qs).join(`
`),d.length>0&&(d+=`
`)):(g=[Wl(t),"#define SHADER_TYPE "+t.shaderType,"#define SHADER_NAME "+t.shaderName,_,t.extensionClipCullDistance?"#define USE_CLIP_DISTANCE":"",t.batching?"#define USE_BATCHING":"",t.batchingColor?"#define USE_BATCHING_COLOR":"",t.instancing?"#define USE_INSTANCING":"",t.instancingColor?"#define USE_INSTANCING_COLOR":"",t.instancingMorph?"#define USE_INSTANCING_MORPH":"",t.useFog&&t.fog?"#define USE_FOG":"",t.useFog&&t.fogExp2?"#define FOG_EXP2":"",t.map?"#define USE_MAP":"",t.envMap?"#define USE_ENVMAP":"",t.envMap?"#define "+l:"",t.lightMap?"#define USE_LIGHTMAP":"",t.aoMap?"#define USE_AOMAP":"",t.bumpMap?"#define USE_BUMPMAP":"",t.normalMap?"#define USE_NORMALMAP":"",t.normalMapObjectSpace?"#define USE_NORMALMAP_OBJECTSPACE":"",t.normalMapTangentSpace?"#define USE_NORMALMAP_TANGENTSPACE":"",t.displacementMap?"#define USE_DISPLACEMENTMAP":"",t.emissiveMap?"#define USE_EMISSIVEMAP":"",t.anisotropy?"#define USE_ANISOTROPY":"",t.anisotropyMap?"#define USE_ANISOTROPYMAP":"",t.clearcoatMap?"#define USE_CLEARCOATMAP":"",t.clearcoatRoughnessMap?"#define USE_CLEARCOAT_ROUGHNESSMAP":"",t.clearcoatNormalMap?"#define USE_CLEARCOAT_NORMALMAP":"",t.iridescenceMap?"#define USE_IRIDESCENCEMAP":"",t.iridescenceThicknessMap?"#define USE_IRIDESCENCE_THICKNESSMAP":"",t.specularMap?"#define USE_SPECULARMAP":"",t.specularColorMap?"#define USE_SPECULAR_COLORMAP":"",t.specularIntensityMap?"#define USE_SPECULAR_INTENSITYMAP":"",t.roughnessMap?"#define USE_ROUGHNESSMAP":"",t.metalnessMap?"#define USE_METALNESSMAP":"",t.alphaMap?"#define USE_ALPHAMAP":"",t.alphaHash?"#define USE_ALPHAHASH":"",t.transmission?"#define USE_TRANSMISSION":"",t.transmissionMap?"#define USE_TRANSMISSIONMAP":"",t.thicknessMap?"#define USE_THICKNESSMAP":"",t.sheenColorMap?"#define USE_SHEEN_COLORMAP":"",t.sheenRoughnessMap?"#define USE_SHEEN_ROUGHNESSMAP":"",t.mapUv?"#define MAP_UV "+t.mapUv:"",t.alphaMapUv?"#define ALPHAMAP_UV "+t.alphaMapUv:"",t.lightMapUv?"#define LIGHTMAP_UV "+t.lightMapUv:"",t.aoMapUv?"#define AOMAP_UV "+t.aoMapUv:"",t.emissiveMapUv?"#define EMISSIVEMAP_UV "+t.emissiveMapUv:"",t.bumpMapUv?"#define BUMPMAP_UV "+t.bumpMapUv:"",t.normalMapUv?"#define NORMALMAP_UV "+t.normalMapUv:"",t.displacementMapUv?"#define DISPLACEMENTMAP_UV "+t.displacementMapUv:"",t.metalnessMapUv?"#define METALNESSMAP_UV "+t.metalnessMapUv:"",t.roughnessMapUv?"#define ROUGHNESSMAP_UV "+t.roughnessMapUv:"",t.anisotropyMapUv?"#define ANISOTROPYMAP_UV "+t.anisotropyMapUv:"",t.clearcoatMapUv?"#define CLEARCOATMAP_UV "+t.clearcoatMapUv:"",t.clearcoatNormalMapUv?"#define CLEARCOAT_NORMALMAP_UV "+t.clearcoatNormalMapUv:"",t.clearcoatRoughnessMapUv?"#define CLEARCOAT_ROUGHNESSMAP_UV "+t.clearcoatRoughnessMapUv:"",t.iridescenceMapUv?"#define IRIDESCENCEMAP_UV "+t.iridescenceMapUv:"",t.iridescenceThicknessMapUv?"#define IRIDESCENCE_THICKNESSMAP_UV "+t.iridescenceThicknessMapUv:"",t.sheenColorMapUv?"#define SHEEN_COLORMAP_UV "+t.sheenColorMapUv:"",t.sheenRoughnessMapUv?"#define SHEEN_ROUGHNESSMAP_UV "+t.sheenRoughnessMapUv:"",t.specularMapUv?"#define SPECULARMAP_UV "+t.specularMapUv:"",t.specularColorMapUv?"#define SPECULAR_COLORMAP_UV "+t.specularColorMapUv:"",t.specularIntensityMapUv?"#define SPECULAR_INTENSITYMAP_UV "+t.specularIntensityMapUv:"",t.transmissionMapUv?"#define TRANSMISSIONMAP_UV "+t.transmissionMapUv:"",t.thicknessMapUv?"#define THICKNESSMAP_UV "+t.thicknessMapUv:"",t.vertexTangents&&t.flatShading===!1?"#define USE_TANGENT":"",t.vertexColors?"#define USE_COLOR":"",t.vertexAlphas?"#define USE_COLOR_ALPHA":"",t.vertexUv1s?"#define USE_UV1":"",t.vertexUv2s?"#define USE_UV2":"",t.vertexUv3s?"#define USE_UV3":"",t.pointsUvs?"#define USE_POINTS_UV":"",t.flatShading?"#define FLAT_SHADED":"",t.skinning?"#define USE_SKINNING":"",t.morphTargets?"#define USE_MORPHTARGETS":"",t.morphNormals&&t.flatShading===!1?"#define USE_MORPHNORMALS":"",t.morphColors?"#define USE_MORPHCOLORS":"",t.morphTargetsCount>0?"#define MORPHTARGETS_TEXTURE_STRIDE "+t.morphTextureStride:"",t.morphTargetsCount>0?"#define MORPHTARGETS_COUNT "+t.morphTargetsCount:"",t.doubleSided?"#define DOUBLE_SIDED":"",t.flipSided?"#define FLIP_SIDED":"",t.shadowMapEnabled?"#define USE_SHADOWMAP":"",t.shadowMapEnabled?"#define "+c:"",t.sizeAttenuation?"#define USE_SIZEATTENUATION":"",t.numLightProbes>0?"#define USE_LIGHT_PROBES":"",t.logarithmicDepthBuffer?"#define USE_LOGDEPTHBUF":"",t.reverseDepthBuffer?"#define USE_REVERSEDEPTHBUF":"","uniform mat4 modelMatrix;","uniform mat4 modelViewMatrix;","uniform mat4 projectionMatrix;","uniform mat4 viewMatrix;","uniform mat3 normalMatrix;","uniform vec3 cameraPosition;","uniform bool isOrthographic;","#ifdef USE_INSTANCING","	attribute mat4 instanceMatrix;","#endif","#ifdef USE_INSTANCING_COLOR","	attribute vec3 instanceColor;","#endif","#ifdef USE_INSTANCING_MORPH","	uniform sampler2D morphTexture;","#endif","attribute vec3 position;","attribute vec3 normal;","attribute vec2 uv;","#ifdef USE_UV1","	attribute vec2 uv1;","#endif","#ifdef USE_UV2","	attribute vec2 uv2;","#endif","#ifdef USE_UV3","	attribute vec2 uv3;","#endif","#ifdef USE_TANGENT","	attribute vec4 tangent;","#endif","#if defined( USE_COLOR_ALPHA )","	attribute vec4 color;","#elif defined( USE_COLOR )","	attribute vec3 color;","#endif","#ifdef USE_SKINNING","	attribute vec4 skinIndex;","	attribute vec4 skinWeight;","#endif",`
`].filter(Qs).join(`
`),d=[Wl(t),"#define SHADER_TYPE "+t.shaderType,"#define SHADER_NAME "+t.shaderName,_,t.useFog&&t.fog?"#define USE_FOG":"",t.useFog&&t.fogExp2?"#define FOG_EXP2":"",t.alphaToCoverage?"#define ALPHA_TO_COVERAGE":"",t.map?"#define USE_MAP":"",t.matcap?"#define USE_MATCAP":"",t.envMap?"#define USE_ENVMAP":"",t.envMap?"#define "+h:"",t.envMap?"#define "+l:"",t.envMap?"#define "+p:"",f?"#define CUBEUV_TEXEL_WIDTH "+f.texelWidth:"",f?"#define CUBEUV_TEXEL_HEIGHT "+f.texelHeight:"",f?"#define CUBEUV_MAX_MIP "+f.maxMip+".0":"",t.lightMap?"#define USE_LIGHTMAP":"",t.aoMap?"#define USE_AOMAP":"",t.bumpMap?"#define USE_BUMPMAP":"",t.normalMap?"#define USE_NORMALMAP":"",t.normalMapObjectSpace?"#define USE_NORMALMAP_OBJECTSPACE":"",t.normalMapTangentSpace?"#define USE_NORMALMAP_TANGENTSPACE":"",t.emissiveMap?"#define USE_EMISSIVEMAP":"",t.anisotropy?"#define USE_ANISOTROPY":"",t.anisotropyMap?"#define USE_ANISOTROPYMAP":"",t.clearcoat?"#define USE_CLEARCOAT":"",t.clearcoatMap?"#define USE_CLEARCOATMAP":"",t.clearcoatRoughnessMap?"#define USE_CLEARCOAT_ROUGHNESSMAP":"",t.clearcoatNormalMap?"#define USE_CLEARCOAT_NORMALMAP":"",t.dispersion?"#define USE_DISPERSION":"",t.iridescence?"#define USE_IRIDESCENCE":"",t.iridescenceMap?"#define USE_IRIDESCENCEMAP":"",t.iridescenceThicknessMap?"#define USE_IRIDESCENCE_THICKNESSMAP":"",t.specularMap?"#define USE_SPECULARMAP":"",t.specularColorMap?"#define USE_SPECULAR_COLORMAP":"",t.specularIntensityMap?"#define USE_SPECULAR_INTENSITYMAP":"",t.roughnessMap?"#define USE_ROUGHNESSMAP":"",t.metalnessMap?"#define USE_METALNESSMAP":"",t.alphaMap?"#define USE_ALPHAMAP":"",t.alphaTest?"#define USE_ALPHATEST":"",t.alphaHash?"#define USE_ALPHAHASH":"",t.sheen?"#define USE_SHEEN":"",t.sheenColorMap?"#define USE_SHEEN_COLORMAP":"",t.sheenRoughnessMap?"#define USE_SHEEN_ROUGHNESSMAP":"",t.transmission?"#define USE_TRANSMISSION":"",t.transmissionMap?"#define USE_TRANSMISSIONMAP":"",t.thicknessMap?"#define USE_THICKNESSMAP":"",t.vertexTangents&&t.flatShading===!1?"#define USE_TANGENT":"",t.vertexColors||t.instancingColor||t.batchingColor?"#define USE_COLOR":"",t.vertexAlphas?"#define USE_COLOR_ALPHA":"",t.vertexUv1s?"#define USE_UV1":"",t.vertexUv2s?"#define USE_UV2":"",t.vertexUv3s?"#define USE_UV3":"",t.pointsUvs?"#define USE_POINTS_UV":"",t.gradientMap?"#define USE_GRADIENTMAP":"",t.flatShading?"#define FLAT_SHADED":"",t.doubleSided?"#define DOUBLE_SIDED":"",t.flipSided?"#define FLIP_SIDED":"",t.shadowMapEnabled?"#define USE_SHADOWMAP":"",t.shadowMapEnabled?"#define "+c:"",t.premultipliedAlpha?"#define PREMULTIPLIED_ALPHA":"",t.numLightProbes>0?"#define USE_LIGHT_PROBES":"",t.decodeVideoTexture?"#define DECODE_VIDEO_TEXTURE":"",t.decodeVideoTextureEmissive?"#define DECODE_VIDEO_TEXTURE_EMISSIVE":"",t.logarithmicDepthBuffer?"#define USE_LOGDEPTHBUF":"",t.reverseDepthBuffer?"#define USE_REVERSEDEPTHBUF":"","uniform mat4 viewMatrix;","uniform vec3 cameraPosition;","uniform bool isOrthographic;",t.toneMapping!==ai?"#define TONE_MAPPING":"",t.toneMapping!==ai?qe.tonemapping_pars_fragment:"",t.toneMapping!==ai?y_("toneMapping",t.toneMapping):"",t.dithering?"#define DITHERING":"",t.opaque?"#define OPAQUE":"",qe.colorspace_pars_fragment,O_("linearToOutputTexel",t.outputColorSpace),A_(),t.useDepthPacking?"#define DEPTH_PACKING "+t.depthPacking:"",`
`].filter(Qs).join(`
`)),o=_c(o),o=Hl(o,t),o=Sl(o,t),a=_c(a),a=Hl(a,t),a=Sl(a,t),o=El(o),a=El(a),t.isRawShaderMaterial!==!0&&(A=`#version 300 es
`,g=[u,"#define attribute in","#define varying out","#define texture2D texture"].join(`
`)+`
`+g,d=["#define varying in",t.glslVersion===Nh?"":"layout(location = 0) out highp vec4 pc_fragColor;",t.glslVersion===Nh?"":"#define gl_FragColor pc_fragColor","#define gl_FragDepthEXT gl_FragDepth","#define texture2D texture","#define textureCube texture","#define texture2DProj textureProj","#define texture2DLodEXT textureLod","#define texture2DProjLodEXT textureProjLod","#define textureCubeLodEXT textureLod","#define texture2DGradEXT textureGrad","#define texture2DProjGradEXT textureProjGrad","#define textureCubeGradEXT textureGrad"].join(`
`)+`
`+d);let O=A+g+o,P=A+d+a,G=yl(s,s.VERTEX_SHADER,O),H=yl(s,s.FRAGMENT_SHADER,P);s.attachShader(m,G),s.attachShader(m,H),t.index0AttributeName!==void 0?s.bindAttribLocation(m,0,t.index0AttributeName):t.morphTargets===!0&&s.bindAttribLocation(m,0,"position"),s.linkProgram(m);function S(W){if(i.debug.checkShaderErrors){let $=s.getProgramInfoLog(m).trim(),F=s.getShaderInfoLog(G).trim(),k=s.getShaderInfoLog(H).trim(),Q=!0,V=!0;if(s.getProgramParameter(m,s.LINK_STATUS)===!1)if(Q=!1,typeof i.debug.onShaderError=="function")i.debug.onShaderError(s,m,G,H);else{let te=vl(s,G,"vertex"),z=vl(s,H,"fragment");console.error("THREE.WebGLProgram: Shader Error "+s.getError()+" - VALIDATE_STATUS "+s.getProgramParameter(m,s.VALIDATE_STATUS)+`

Material Name: `+W.name+`
Material Type: `+W.type+`

Program Info Log: `+$+`
`+te+`
`+z)}else $!==""?console.warn("THREE.WebGLProgram: Program Info Log:",$):(F===""||k==="")&&(V=!1);V&&(W.diagnostics={runnable:Q,programLog:$,vertexShader:{log:F,prefix:g},fragmentShader:{log:k,prefix:d}})}s.deleteShader(G),s.deleteShader(H),M=new ss(s,m),y=S_(s,m)}let M;this.getUniforms=function(){return M===void 0&&S(this),M};let y;this.getAttributes=function(){return y===void 0&&S(this),y};let w=t.rendererExtensionParallelShaderCompile===!1;return this.isReady=function(){return w===!1&&(w=s.getProgramParameter(m,x_)),w},this.destroy=function(){n.releaseStatesOfProgram(this),s.deleteProgram(m),this.program=void 0},this.type=t.shaderType,this.name=t.shaderName,this.id=b_++,this.cacheKey=e,this.usedTimes=1,this.program=m,this.vertexShader=G,this.fragmentShader=H,this}var $_=0,mc=class{constructor(){this.shaderCache=new Map,this.materialCache=new Map}update(e){let t=e.vertexShader,n=e.fragmentShader,s=this._getShaderStage(t),r=this._getShaderStage(n),o=this._getShaderCacheForMaterial(e);return o.has(s)===!1&&(o.add(s),s.usedTimes++),o.has(r)===!1&&(o.add(r),r.usedTimes++),this}remove(e){let t=this.materialCache.get(e);for(let n of t)n.usedTimes--,n.usedTimes===0&&this.shaderCache.delete(n.code);return this.materialCache.delete(e),this}getVertexShaderID(e){return this._getShaderStage(e.vertexShader).id}getFragmentShaderID(e){return this._getShaderStage(e.fragmentShader).id}dispose(){this.shaderCache.clear(),this.materialCache.clear()}_getShaderCacheForMaterial(e){let t=this.materialCache,n=t.get(e);return n===void 0&&(n=new Set,t.set(e,n)),n}_getShaderStage(e){let t=this.shaderCache,n=t.get(e);return n===void 0&&(n=new xc(e),t.set(e,n)),n}},xc=class{constructor(e){this.id=$_++,this.code=e,this.usedTimes=0}};function F_(i,e,t,n,s,r,o){let a=new sr,c=new mc,h=new Set,l=[],p=s.logarithmicDepthBuffer,f=s.vertexTextures,u=s.precision,_={MeshDepthMaterial:"depth",MeshDistanceMaterial:"distanceRGBA",MeshNormalMaterial:"normal",MeshBasicMaterial:"basic",MeshLambertMaterial:"lambert",MeshPhongMaterial:"phong",MeshToonMaterial:"toon",MeshStandardMaterial:"physical",MeshPhysicalMaterial:"physical",MeshMatcapMaterial:"matcap",LineBasicMaterial:"basic",LineDashedMaterial:"dashed",PointsMaterial:"points",ShadowMaterial:"shadow",SpriteMaterial:"sprite"};function m(y){return h.add(y),y===0?"uv":`uv${y}`}function g(y,w,W,$,F){let k=$.fog,Q=F.geometry,V=y.isMeshStandardMaterial?$.environment:null,te=(y.isMeshStandardMaterial?t:e).get(y.envMap||V),z=te&&te.mapping===Go?te.image.height:null,pe=_[y.type];y.precision!==null&&(u=s.getMaxPrecision(y.precision),u!==y.precision&&console.warn("THREE.WebGLProgram.getParameters:",y.precision,"not supported, using",u,"instead."));let me=Q.morphAttributes.position||Q.morphAttributes.normal||Q.morphAttributes.color,He=me!==void 0?me.length:0,Ne=0;Q.morphAttributes.position!==void 0&&(Ne=1),Q.morphAttributes.normal!==void 0&&(Ne=2),Q.morphAttributes.color!==void 0&&(Ne=3);let tt,J,ie,xe;if(pe){let Ke=bn[pe];tt=Ke.vertexShader,J=Ke.fragmentShader}else tt=y.vertexShader,J=y.fragmentShader,c.update(y),ie=c.getVertexShaderID(y),xe=c.getFragmentShaderID(y);let oe=i.getRenderTarget(),be=i.state.buffers.depth.getReversed(),Te=F.isInstancedMesh===!0,ke=F.isBatchedMesh===!0,lt=!!y.map,Se=!!y.matcap,ot=!!te,I=!!y.aoMap,Dt=!!y.lightMap,je=!!y.bumpMap,Ye=!!y.normalMap,Ee=!!y.displacementMap,ht=!!y.emissiveMap,we=!!y.metalnessMap,v=!!y.roughnessMap,b=y.anisotropy>0,R=y.clearcoat>0,j=y.dispersion>0,Z=y.iridescence>0,q=y.sheen>0,Pe=y.transmission>0,se=b&&!!y.anisotropyMap,de=R&&!!y.clearcoatMap,We=R&&!!y.clearcoatNormalMap,ne=R&&!!y.clearcoatRoughnessMap,ge=Z&&!!y.iridescenceMap,Me=Z&&!!y.iridescenceThicknessMap,Ue=q&&!!y.sheenColorMap,_e=q&&!!y.sheenRoughnessMap,Je=!!y.specularMap,Xe=!!y.specularColorMap,at=!!y.specularIntensityMap,U=Pe&&!!y.transmissionMap,le=Pe&&!!y.thicknessMap,L=!!y.gradientMap,Y=!!y.alphaMap,ae=y.alphaTest>0,re=!!y.alphaHash,Ie=!!y.extensions,pt=ai;y.toneMapped&&(oe===null||oe.isXRRenderTarget===!0)&&(pt=i.toneMapping);let Ht={shaderID:pe,shaderType:y.type,shaderName:y.name,vertexShader:tt,fragmentShader:J,defines:y.defines,customVertexShaderID:ie,customFragmentShaderID:xe,isRawShaderMaterial:y.isRawShaderMaterial===!0,glslVersion:y.glslVersion,precision:u,batching:ke,batchingColor:ke&&F._colorsTexture!==null,instancing:Te,instancingColor:Te&&F.instanceColor!==null,instancingMorph:Te&&F.morphTexture!==null,supportsVertexTextures:f,outputColorSpace:oe===null?i.outputColorSpace:oe.isXRRenderTarget===!0?oe.texture.colorSpace:Ft,alphaToCoverage:!!y.alphaToCoverage,map:lt,matcap:Se,envMap:ot,envMapMode:ot&&te.mapping,envMapCubeUVHeight:z,aoMap:I,lightMap:Dt,bumpMap:je,normalMap:Ye,displacementMap:f&&Ee,emissiveMap:ht,normalMapObjectSpace:Ye&&y.normalMapType===Np,normalMapTangentSpace:Ye&&y.normalMapType===_0,metalnessMap:we,roughnessMap:v,anisotropy:b,anisotropyMap:se,clearcoat:R,clearcoatMap:de,clearcoatNormalMap:We,clearcoatRoughnessMap:ne,dispersion:j,iridescence:Z,iridescenceMap:ge,iridescenceThicknessMap:Me,sheen:q,sheenColorMap:Ue,sheenRoughnessMap:_e,specularMap:Je,specularColorMap:Xe,specularIntensityMap:at,transmission:Pe,transmissionMap:U,thicknessMap:le,gradientMap:L,opaque:y.transparent===!1&&y.blending===ts&&y.alphaToCoverage===!1,alphaMap:Y,alphaTest:ae,alphaHash:re,combine:y.combine,mapUv:lt&&m(y.map.channel),aoMapUv:I&&m(y.aoMap.channel),lightMapUv:Dt&&m(y.lightMap.channel),bumpMapUv:je&&m(y.bumpMap.channel),normalMapUv:Ye&&m(y.normalMap.channel),displacementMapUv:Ee&&m(y.displacementMap.channel),emissiveMapUv:ht&&m(y.emissiveMap.channel),metalnessMapUv:we&&m(y.metalnessMap.channel),roughnessMapUv:v&&m(y.roughnessMap.channel),anisotropyMapUv:se&&m(y.anisotropyMap.channel),clearcoatMapUv:de&&m(y.clearcoatMap.channel),clearcoatNormalMapUv:We&&m(y.clearcoatNormalMap.channel),clearcoatRoughnessMapUv:ne&&m(y.clearcoatRoughnessMap.channel),iridescenceMapUv:ge&&m(y.iridescenceMap.channel),iridescenceThicknessMapUv:Me&&m(y.iridescenceThicknessMap.channel),sheenColorMapUv:Ue&&m(y.sheenColorMap.channel),sheenRoughnessMapUv:_e&&m(y.sheenRoughnessMap.channel),specularMapUv:Je&&m(y.specularMap.channel),specularColorMapUv:Xe&&m(y.specularColorMap.channel),specularIntensityMapUv:at&&m(y.specularIntensityMap.channel),transmissionMapUv:U&&m(y.transmissionMap.channel),thicknessMapUv:le&&m(y.thicknessMap.channel),alphaMapUv:Y&&m(y.alphaMap.channel),vertexTangents:!!Q.attributes.tangent&&(Ye||b),vertexColors:y.vertexColors,vertexAlphas:y.vertexColors===!0&&!!Q.attributes.color&&Q.attributes.color.itemSize===4,pointsUvs:F.isPoints===!0&&!!Q.attributes.uv&&(lt||Y),fog:!!k,useFog:y.fog===!0,fogExp2:!!k&&k.isFogExp2,flatShading:y.flatShading===!0,sizeAttenuation:y.sizeAttenuation===!0,logarithmicDepthBuffer:p,reverseDepthBuffer:be,skinning:F.isSkinnedMesh===!0,morphTargets:Q.morphAttributes.position!==void 0,morphNormals:Q.morphAttributes.normal!==void 0,morphColors:Q.morphAttributes.color!==void 0,morphTargetsCount:He,morphTextureStride:Ne,numDirLights:w.directional.length,numPointLights:w.point.length,numSpotLights:w.spot.length,numSpotLightMaps:w.spotLightMap.length,numRectAreaLights:w.rectArea.length,numHemiLights:w.hemi.length,numDirLightShadows:w.directionalShadowMap.length,numPointLightShadows:w.pointShadowMap.length,numSpotLightShadows:w.spotShadowMap.length,numSpotLightShadowsWithMaps:w.numSpotLightShadowsWithMaps,numLightProbes:w.numLightProbes,numClippingPlanes:o.numPlanes,numClipIntersection:o.numIntersection,dithering:y.dithering,shadowMapEnabled:i.shadowMap.enabled&&W.length>0,shadowMapType:i.shadowMap.type,toneMapping:pt,decodeVideoTexture:lt&&y.map.isVideoTexture===!0&&Ze.getTransfer(y.map.colorSpace)===ct,decodeVideoTextureEmissive:ht&&y.emissiveMap.isVideoTexture===!0&&Ze.getTransfer(y.emissiveMap.colorSpace)===ct,premultipliedAlpha:y.premultipliedAlpha,doubleSided:y.side===dn,flipSided:y.side===Bt,useDepthPacking:y.depthPacking>=0,depthPacking:y.depthPacking||0,index0AttributeName:y.index0AttributeName,extensionClipCullDistance:Ie&&y.extensions.clipCullDistance===!0&&n.has("WEBGL_clip_cull_distance"),extensionMultiDraw:(Ie&&y.extensions.multiDraw===!0||ke)&&n.has("WEBGL_multi_draw"),rendererExtensionParallelShaderCompile:n.has("KHR_parallel_shader_compile"),customProgramCacheKey:y.customProgramCacheKey()};return Ht.vertexUv1s=h.has(1),Ht.vertexUv2s=h.has(2),Ht.vertexUv3s=h.has(3),h.clear(),Ht}function d(y){let w=[];if(y.shaderID?w.push(y.shaderID):(w.push(y.customVertexShaderID),w.push(y.customFragmentShaderID)),y.defines!==void 0)for(let W in y.defines)w.push(W),w.push(y.defines[W]);return y.isRawShaderMaterial===!1&&(A(w,y),O(w,y),w.push(i.outputColorSpace)),w.push(y.customProgramCacheKey),w.join()}function A(y,w){y.push(w.precision),y.push(w.outputColorSpace),y.push(w.envMapMode),y.push(w.envMapCubeUVHeight),y.push(w.mapUv),y.push(w.alphaMapUv),y.push(w.lightMapUv),y.push(w.aoMapUv),y.push(w.bumpMapUv),y.push(w.normalMapUv),y.push(w.displacementMapUv),y.push(w.emissiveMapUv),y.push(w.metalnessMapUv),y.push(w.roughnessMapUv),y.push(w.anisotropyMapUv),y.push(w.clearcoatMapUv),y.push(w.clearcoatNormalMapUv),y.push(w.clearcoatRoughnessMapUv),y.push(w.iridescenceMapUv),y.push(w.iridescenceThicknessMapUv),y.push(w.sheenColorMapUv),y.push(w.sheenRoughnessMapUv),y.push(w.specularMapUv),y.push(w.specularColorMapUv),y.push(w.specularIntensityMapUv),y.push(w.transmissionMapUv),y.push(w.thicknessMapUv),y.push(w.combine),y.push(w.fogExp2),y.push(w.sizeAttenuation),y.push(w.morphTargetsCount),y.push(w.morphAttributeCount),y.push(w.numDirLights),y.push(w.numPointLights),y.push(w.numSpotLights),y.push(w.numSpotLightMaps),y.push(w.numHemiLights),y.push(w.numRectAreaLights),y.push(w.numDirLightShadows),y.push(w.numPointLightShadows),y.push(w.numSpotLightShadows),y.push(w.numSpotLightShadowsWithMaps),y.push(w.numLightProbes),y.push(w.shadowMapType),y.push(w.toneMapping),y.push(w.numClippingPlanes),y.push(w.numClipIntersection),y.push(w.depthPacking)}function O(y,w){a.disableAll(),w.supportsVertexTextures&&a.enable(0),w.instancing&&a.enable(1),w.instancingColor&&a.enable(2),w.instancingMorph&&a.enable(3),w.matcap&&a.enable(4),w.envMap&&a.enable(5),w.normalMapObjectSpace&&a.enable(6),w.normalMapTangentSpace&&a.enable(7),w.clearcoat&&a.enable(8),w.iridescence&&a.enable(9),w.alphaTest&&a.enable(10),w.vertexColors&&a.enable(11),w.vertexAlphas&&a.enable(12),w.vertexUv1s&&a.enable(13),w.vertexUv2s&&a.enable(14),w.vertexUv3s&&a.enable(15),w.vertexTangents&&a.enable(16),w.anisotropy&&a.enable(17),w.alphaHash&&a.enable(18),w.batching&&a.enable(19),w.dispersion&&a.enable(20),w.batchingColor&&a.enable(21),y.push(a.mask),a.disableAll(),w.fog&&a.enable(0),w.useFog&&a.enable(1),w.flatShading&&a.enable(2),w.logarithmicDepthBuffer&&a.enable(3),w.reverseDepthBuffer&&a.enable(4),w.skinning&&a.enable(5),w.morphTargets&&a.enable(6),w.morphNormals&&a.enable(7),w.morphColors&&a.enable(8),w.premultipliedAlpha&&a.enable(9),w.shadowMapEnabled&&a.enable(10),w.doubleSided&&a.enable(11),w.flipSided&&a.enable(12),w.useDepthPacking&&a.enable(13),w.dithering&&a.enable(14),w.transmission&&a.enable(15),w.sheen&&a.enable(16),w.opaque&&a.enable(17),w.pointsUvs&&a.enable(18),w.decodeVideoTexture&&a.enable(19),w.decodeVideoTextureEmissive&&a.enable(20),w.alphaToCoverage&&a.enable(21),y.push(a.mask)}function P(y){let w=_[y.type],W;if(w){let $=bn[w];W=Mf.clone($.uniforms)}else W=y.uniforms;return W}function G(y,w){let W;for(let $=0,F=l.length;$<F;$++){let k=l[$];if(k.cacheKey===w){W=k,++W.usedTimes;break}}return W===void 0&&(W=new B_(i,w,y,r),l.push(W)),W}function H(y){if(--y.usedTimes===0){let w=l.indexOf(y);l[w]=l[l.length-1],l.pop(),y.destroy()}}function S(y){c.remove(y)}function M(){c.dispose()}return{getParameters:g,getProgramCacheKey:d,getUniforms:P,acquireProgram:G,releaseProgram:H,releaseShaderCache:S,programs:l,dispose:M}}function D_(){let i=new WeakMap;function e(o){return i.has(o)}function t(o){let a=i.get(o);return a===void 0&&(a={},i.set(o,a)),a}function n(o){i.delete(o)}function s(o,a,c){i.get(o)[a]=c}function r(){i=new WeakMap}return{has:e,get:t,remove:n,update:s,dispose:r}}function N_(i,e){return i.groupOrder!==e.groupOrder?i.groupOrder-e.groupOrder:i.renderOrder!==e.renderOrder?i.renderOrder-e.renderOrder:i.material.id!==e.material.id?i.material.id-e.material.id:i.z!==e.z?i.z-e.z:i.id-e.id}function Ml(i,e){return i.groupOrder!==e.groupOrder?i.groupOrder-e.groupOrder:i.renderOrder!==e.renderOrder?i.renderOrder-e.renderOrder:i.z!==e.z?e.z-i.z:i.id-e.id}function Cl(){let i=[],e=0,t=[],n=[],s=[];function r(){e=0,t.length=0,n.length=0,s.length=0}function o(p,f,u,_,m,g){let d=i[e];return d===void 0?(d={id:p.id,object:p,geometry:f,material:u,groupOrder:_,renderOrder:p.renderOrder,z:m,group:g},i[e]=d):(d.id=p.id,d.object=p,d.geometry=f,d.material=u,d.groupOrder=_,d.renderOrder=p.renderOrder,d.z=m,d.group=g),e++,d}function a(p,f,u,_,m,g){let d=o(p,f,u,_,m,g);u.transmission>0?n.push(d):u.transparent===!0?s.push(d):t.push(d)}function c(p,f,u,_,m,g){let d=o(p,f,u,_,m,g);u.transmission>0?n.unshift(d):u.transparent===!0?s.unshift(d):t.unshift(d)}function h(p,f){t.length>1&&t.sort(p||N_),n.length>1&&n.sort(f||Ml),s.length>1&&s.sort(f||Ml)}function l(){for(let p=e,f=i.length;p<f;p++){let u=i[p];if(u.id===null)break;u.id=null,u.object=null,u.geometry=null,u.material=null,u.group=null}}return{opaque:t,transmissive:n,transparent:s,init:r,push:a,unshift:c,finish:l,sort:h}}function V_(){let i=new WeakMap;function e(n,s){let r=i.get(n),o;return r===void 0?(o=new Cl,i.set(n,[o])):s>=r.length?(o=new Cl,r.push(o)):o=r[s],o}function t(){i=new WeakMap}return{get:e,dispose:t}}function L_(){let i={};return{get:function(e){if(i[e.id]!==void 0)return i[e.id];let t;switch(e.type){case"DirectionalLight":t={direction:new C,color:new Ge};break;case"SpotLight":t={position:new C,direction:new C,color:new Ge,distance:0,coneCos:0,penumbraCos:0,decay:0};break;case"PointLight":t={position:new C,color:new Ge,distance:0,decay:0};break;case"HemisphereLight":t={direction:new C,skyColor:new Ge,groundColor:new Ge};break;case"RectAreaLight":t={color:new Ge,position:new C,halfWidth:new C,halfHeight:new C};break}return i[e.id]=t,t}}}function z_(){let i={};return{get:function(e){if(i[e.id]!==void 0)return i[e.id];let t;switch(e.type){case"DirectionalLight":t={shadowIntensity:1,shadowBias:0,shadowNormalBias:0,shadowRadius:1,shadowMapSize:new Re};break;case"SpotLight":t={shadowIntensity:1,shadowBias:0,shadowNormalBias:0,shadowRadius:1,shadowMapSize:new Re};break;case"PointLight":t={shadowIntensity:1,shadowBias:0,shadowNormalBias:0,shadowRadius:1,shadowMapSize:new Re,shadowCameraNear:1,shadowCameraFar:1e3};break}return i[e.id]=t,t}}}var k_=0;function q_(i,e){return(e.castShadow?2:0)-(i.castShadow?2:0)+(e.map?1:0)-(i.map?1:0)}function J_(i){let e=new L_,t=z_(),n={version:0,hash:{directionalLength:-1,pointLength:-1,spotLength:-1,rectAreaLength:-1,hemiLength:-1,numDirectionalShadows:-1,numPointShadows:-1,numSpotShadows:-1,numSpotMaps:-1,numLightProbes:-1},ambient:[0,0,0],probe:[],directional:[],directionalShadow:[],directionalShadowMap:[],directionalShadowMatrix:[],spot:[],spotLightMap:[],spotShadow:[],spotShadowMap:[],spotLightMatrix:[],rectArea:[],rectAreaLTC1:null,rectAreaLTC2:null,point:[],pointShadow:[],pointShadowMap:[],pointShadowMatrix:[],hemi:[],numSpotLightShadowsWithMaps:0,numLightProbes:0};for(let h=0;h<9;h++)n.probe.push(new C);let s=new C,r=new Fe,o=new Fe;function a(h){let l=0,p=0,f=0;for(let y=0;y<9;y++)n.probe[y].set(0,0,0);let u=0,_=0,m=0,g=0,d=0,A=0,O=0,P=0,G=0,H=0,S=0;h.sort(q_);for(let y=0,w=h.length;y<w;y++){let W=h[y],$=W.color,F=W.intensity,k=W.distance,Q=W.shadow&&W.shadow.map?W.shadow.map.texture:null;if(W.isAmbientLight)l+=$.r*F,p+=$.g*F,f+=$.b*F;else if(W.isLightProbe){for(let V=0;V<9;V++)n.probe[V].addScaledVector(W.sh.coefficients[V],F);S++}else if(W.isDirectionalLight){let V=e.get(W);if(V.color.copy(W.color).multiplyScalar(W.intensity),W.castShadow){let te=W.shadow,z=t.get(W);z.shadowIntensity=te.intensity,z.shadowBias=te.bias,z.shadowNormalBias=te.normalBias,z.shadowRadius=te.radius,z.shadowMapSize=te.mapSize,n.directionalShadow[u]=z,n.directionalShadowMap[u]=Q,n.directionalShadowMatrix[u]=W.shadow.matrix,A++}n.directional[u]=V,u++}else if(W.isSpotLight){let V=e.get(W);V.position.setFromMatrixPosition(W.matrixWorld),V.color.copy($).multiplyScalar(F),V.distance=k,V.coneCos=Math.cos(W.angle),V.penumbraCos=Math.cos(W.angle*(1-W.penumbra)),V.decay=W.decay,n.spot[m]=V;let te=W.shadow;if(W.map&&(n.spotLightMap[G]=W.map,G++,te.updateMatrices(W),W.castShadow&&H++),n.spotLightMatrix[m]=te.matrix,W.castShadow){let z=t.get(W);z.shadowIntensity=te.intensity,z.shadowBias=te.bias,z.shadowNormalBias=te.normalBias,z.shadowRadius=te.radius,z.shadowMapSize=te.mapSize,n.spotShadow[m]=z,n.spotShadowMap[m]=Q,P++}m++}else if(W.isRectAreaLight){let V=e.get(W);V.color.copy($).multiplyScalar(F),V.halfWidth.set(W.width*.5,0,0),V.halfHeight.set(0,W.height*.5,0),n.rectArea[g]=V,g++}else if(W.isPointLight){let V=e.get(W);if(V.color.copy(W.color).multiplyScalar(W.intensity),V.distance=W.distance,V.decay=W.decay,W.castShadow){let te=W.shadow,z=t.get(W);z.shadowIntensity=te.intensity,z.shadowBias=te.bias,z.shadowNormalBias=te.normalBias,z.shadowRadius=te.radius,z.shadowMapSize=te.mapSize,z.shadowCameraNear=te.camera.near,z.shadowCameraFar=te.camera.far,n.pointShadow[_]=z,n.pointShadowMap[_]=Q,n.pointShadowMatrix[_]=W.shadow.matrix,O++}n.point[_]=V,_++}else if(W.isHemisphereLight){let V=e.get(W);V.skyColor.copy(W.color).multiplyScalar(F),V.groundColor.copy(W.groundColor).multiplyScalar(F),n.hemi[d]=V,d++}}g>0&&(i.has("OES_texture_float_linear")===!0?(n.rectAreaLTC1=he.LTC_FLOAT_1,n.rectAreaLTC2=he.LTC_FLOAT_2):(n.rectAreaLTC1=he.LTC_HALF_1,n.rectAreaLTC2=he.LTC_HALF_2)),n.ambient[0]=l,n.ambient[1]=p,n.ambient[2]=f;let M=n.hash;(M.directionalLength!==u||M.pointLength!==_||M.spotLength!==m||M.rectAreaLength!==g||M.hemiLength!==d||M.numDirectionalShadows!==A||M.numPointShadows!==O||M.numSpotShadows!==P||M.numSpotMaps!==G||M.numLightProbes!==S)&&(n.directional.length=u,n.spot.length=m,n.rectArea.length=g,n.point.length=_,n.hemi.length=d,n.directionalShadow.length=A,n.directionalShadowMap.length=A,n.pointShadow.length=O,n.pointShadowMap.length=O,n.spotShadow.length=P,n.spotShadowMap.length=P,n.directionalShadowMatrix.length=A,n.pointShadowMatrix.length=O,n.spotLightMatrix.length=P+G-H,n.spotLightMap.length=G,n.numSpotLightShadowsWithMaps=H,n.numLightProbes=S,M.directionalLength=u,M.pointLength=_,M.spotLength=m,M.rectAreaLength=g,M.hemiLength=d,M.numDirectionalShadows=A,M.numPointShadows=O,M.numSpotShadows=P,M.numSpotMaps=G,M.numLightProbes=S,n.version=k_++)}function c(h,l){let p=0,f=0,u=0,_=0,m=0,g=l.matrixWorldInverse;for(let d=0,A=h.length;d<A;d++){let O=h[d];if(O.isDirectionalLight){let P=n.directional[p];P.direction.setFromMatrixPosition(O.matrixWorld),s.setFromMatrixPosition(O.target.matrixWorld),P.direction.sub(s),P.direction.transformDirection(g),p++}else if(O.isSpotLight){let P=n.spot[u];P.position.setFromMatrixPosition(O.matrixWorld),P.position.applyMatrix4(g),P.direction.setFromMatrixPosition(O.matrixWorld),s.setFromMatrixPosition(O.target.matrixWorld),P.direction.sub(s),P.direction.transformDirection(g),u++}else if(O.isRectAreaLight){let P=n.rectArea[_];P.position.setFromMatrixPosition(O.matrixWorld),P.position.applyMatrix4(g),o.identity(),r.copy(O.matrixWorld),r.premultiply(g),o.extractRotation(r),P.halfWidth.set(O.width*.5,0,0),P.halfHeight.set(0,O.height*.5,0),P.halfWidth.applyMatrix4(o),P.halfHeight.applyMatrix4(o),_++}else if(O.isPointLight){let P=n.point[f];P.position.setFromMatrixPosition(O.matrixWorld),P.position.applyMatrix4(g),f++}else if(O.isHemisphereLight){let P=n.hemi[m];P.direction.setFromMatrixPosition(O.matrixWorld),P.direction.transformDirection(g),m++}}}return{setup:a,setupView:c,state:n}}function Gl(i){let e=new J_(i),t=[],n=[];function s(l){h.camera=l,t.length=0,n.length=0}function r(l){t.push(l)}function o(l){n.push(l)}function a(){e.setup(t)}function c(l){e.setupView(t,l)}let h={lightsArray:t,shadowsArray:n,camera:null,lights:e,transmissionRenderTarget:{}};return{init:s,state:h,setupLights:a,setupLightsView:c,pushLight:r,pushShadow:o}}function j_(i){let e=new WeakMap;function t(s,r=0){let o=e.get(s),a;return o===void 0?(a=new Gl(i),e.set(s,[a])):r>=o.length?(a=new Gl(i),o.push(a)):a=o[r],a}function n(){e=new WeakMap}return{get:t,dispose:n}}var bc=class extends qt{static get type(){return"MeshDepthMaterial"}constructor(e){super(),this.isMeshDepthMaterial=!0,this.depthPacking=Fp,this.map=null,this.alphaMap=null,this.displacementMap=null,this.displacementScale=1,this.displacementBias=0,this.wireframe=!1,this.wireframeLinewidth=1,this.setValues(e)}copy(e){return super.copy(e),this.depthPacking=e.depthPacking,this.map=e.map,this.alphaMap=e.alphaMap,this.displacementMap=e.displacementMap,this.displacementScale=e.displacementScale,this.displacementBias=e.displacementBias,this.wireframe=e.wireframe,this.wireframeLinewidth=e.wireframeLinewidth,this}},Pc=class extends qt{static get type(){return"MeshDistanceMaterial"}constructor(e){super(),this.isMeshDistanceMaterial=!0,this.map=null,this.alphaMap=null,this.displacementMap=null,this.displacementScale=1,this.displacementBias=0,this.setValues(e)}copy(e){return super.copy(e),this.map=e.map,this.alphaMap=e.alphaMap,this.displacementMap=e.displacementMap,this.displacementScale=e.displacementScale,this.displacementBias=e.displacementBias,this}},Q_=`void main() {
	gl_Position = vec4( position, 1.0 );
}`,Y_=`uniform sampler2D shadow_pass;
uniform vec2 resolution;
uniform float radius;
#include <packing>
void main() {
	const float samples = float( VSM_SAMPLES );
	float mean = 0.0;
	float squared_mean = 0.0;
	float uvStride = samples <= 1.0 ? 0.0 : 2.0 / ( samples - 1.0 );
	float uvStart = samples <= 1.0 ? 0.0 : - 1.0;
	for ( float i = 0.0; i < samples; i ++ ) {
		float uvOffset = uvStart + i * uvStride;
		#ifdef HORIZONTAL_PASS
			vec2 distribution = unpackRGBATo2Half( texture2D( shadow_pass, ( gl_FragCoord.xy + vec2( uvOffset, 0.0 ) * radius ) / resolution ) );
			mean += distribution.x;
			squared_mean += distribution.y * distribution.y + distribution.x * distribution.x;
		#else
			float depth = unpackRGBAToDepth( texture2D( shadow_pass, ( gl_FragCoord.xy + vec2( 0.0, uvOffset ) * radius ) / resolution ) );
			mean += depth;
			squared_mean += depth * depth;
		#endif
	}
	mean = mean / samples;
	squared_mean = squared_mean / samples;
	float std_dev = sqrt( squared_mean - mean * mean );
	gl_FragColor = pack2HalfToRGBA( vec2( mean, std_dev ) );
}`;function Z_(i,e,t){let n=new rr,s=new Re,r=new Re,o=new nt,a=new bc({depthPacking:Dp}),c=new Pc,h={},l=t.maxTextureSize,p={[On]:Bt,[Bt]:On,[dn]:dn},f=new vn({defines:{VSM_SAMPLES:8},uniforms:{shadow_pass:{value:null},resolution:{value:new Re},radius:{value:4}},vertexShader:Q_,fragmentShader:Y_}),u=f.clone();u.defines.HORIZONTAL_PASS=1;let _=new Gt;_.setAttribute("position",new wt(new Float32Array([-1,-1,.5,3,-1,.5,-1,3,.5]),3));let m=new De(_,f),g=this;this.enabled=!1,this.autoUpdate=!0,this.needsUpdate=!1,this.type=i0;let d=this.type;this.render=function(H,S,M){if(g.enabled===!1||g.autoUpdate===!1&&g.needsUpdate===!1||H.length===0)return;let y=i.getRenderTarget(),w=i.getActiveCubeFace(),W=i.getActiveMipmapLevel(),$=i.state;$.setBlending(oi),$.buffers.color.setClear(1,1,1,1),$.buffers.depth.setTest(!0),$.setScissorTest(!1);let F=d!==In&&this.type===In,k=d===In&&this.type!==In;for(let Q=0,V=H.length;Q<V;Q++){let te=H[Q],z=te.shadow;if(z===void 0){console.warn("THREE.WebGLShadowMap:",te,"has no shadow.");continue}if(z.autoUpdate===!1&&z.needsUpdate===!1)continue;s.copy(z.mapSize);let pe=z.getFrameExtents();if(s.multiply(pe),r.copy(z.mapSize),(s.x>l||s.y>l)&&(s.x>l&&(r.x=Math.floor(l/pe.x),s.x=r.x*pe.x,z.mapSize.x=r.x),s.y>l&&(r.y=Math.floor(l/pe.y),s.y=r.y*pe.y,z.mapSize.y=r.y)),z.map===null||F===!0||k===!0){let He=this.type!==In?{minFilter:$t,magFilter:$t}:{};z.map!==null&&z.map.dispose(),z.map=new Nn(s.x,s.y,He),z.map.texture.name=te.name+".shadowMap",z.camera.updateProjectionMatrix()}i.setRenderTarget(z.map),i.clear();let me=z.getViewportCount();for(let He=0;He<me;He++){let Ne=z.getViewport(He);o.set(r.x*Ne.x,r.y*Ne.y,r.x*Ne.z,r.y*Ne.w),$.viewport(o),z.updateMatrices(te,He),n=z.getFrustum(),P(S,M,z.camera,te,this.type)}z.isPointLightShadow!==!0&&this.type===In&&A(z,M),z.needsUpdate=!1}d=this.type,g.needsUpdate=!1,i.setRenderTarget(y,w,W)};function A(H,S){let M=e.update(m);f.defines.VSM_SAMPLES!==H.blurSamples&&(f.defines.VSM_SAMPLES=H.blurSamples,u.defines.VSM_SAMPLES=H.blurSamples,f.needsUpdate=!0,u.needsUpdate=!0),H.mapPass===null&&(H.mapPass=new Nn(s.x,s.y)),f.uniforms.shadow_pass.value=H.map.texture,f.uniforms.resolution.value=H.mapSize,f.uniforms.radius.value=H.radius,i.setRenderTarget(H.mapPass),i.clear(),i.renderBufferDirect(S,null,M,f,m,null),u.uniforms.shadow_pass.value=H.mapPass.texture,u.uniforms.resolution.value=H.mapSize,u.uniforms.radius.value=H.radius,i.setRenderTarget(H.map),i.clear(),i.renderBufferDirect(S,null,M,u,m,null)}function O(H,S,M,y){let w=null,W=M.isPointLight===!0?H.customDistanceMaterial:H.customDepthMaterial;if(W!==void 0)w=W;else if(w=M.isPointLight===!0?c:a,i.localClippingEnabled&&S.clipShadows===!0&&Array.isArray(S.clippingPlanes)&&S.clippingPlanes.length!==0||S.displacementMap&&S.displacementScale!==0||S.alphaMap&&S.alphaTest>0||S.map&&S.alphaTest>0){let $=w.uuid,F=S.uuid,k=h[$];k===void 0&&(k={},h[$]=k);let Q=k[F];Q===void 0&&(Q=w.clone(),k[F]=Q,S.addEventListener("dispose",G)),w=Q}if(w.visible=S.visible,w.wireframe=S.wireframe,y===In?w.side=S.shadowSide!==null?S.shadowSide:S.side:w.side=S.shadowSide!==null?S.shadowSide:p[S.side],w.alphaMap=S.alphaMap,w.alphaTest=S.alphaTest,w.map=S.map,w.clipShadows=S.clipShadows,w.clippingPlanes=S.clippingPlanes,w.clipIntersection=S.clipIntersection,w.displacementMap=S.displacementMap,w.displacementScale=S.displacementScale,w.displacementBias=S.displacementBias,w.wireframeLinewidth=S.wireframeLinewidth,w.linewidth=S.linewidth,M.isPointLight===!0&&w.isMeshDistanceMaterial===!0){let $=i.properties.get(w);$.light=M}return w}function P(H,S,M,y,w){if(H.visible===!1)return;if(H.layers.test(S.layers)&&(H.isMesh||H.isLine||H.isPoints)&&(H.castShadow||H.receiveShadow&&w===In)&&(!H.frustumCulled||n.intersectsObject(H))){H.modelViewMatrix.multiplyMatrices(M.matrixWorldInverse,H.matrixWorld);let F=e.update(H),k=H.material;if(Array.isArray(k)){let Q=F.groups;for(let V=0,te=Q.length;V<te;V++){let z=Q[V],pe=k[z.materialIndex];if(pe&&pe.visible){let me=O(H,pe,y,w);H.onBeforeShadow(i,H,S,M,F,me,z),i.renderBufferDirect(M,null,F,me,H,z),H.onAfterShadow(i,H,S,M,F,me,z)}}}else if(k.visible){let Q=O(H,k,y,w);H.onBeforeShadow(i,H,S,M,F,Q,null),i.renderBufferDirect(M,null,F,Q,H,null),H.onAfterShadow(i,H,S,M,F,Q,null)}}let $=H.children;for(let F=0,k=$.length;F<k;F++)P($[F],S,M,y,w)}function G(H){H.target.removeEventListener("dispose",G);for(let M in h){let y=h[M],w=H.target.uuid;w in y&&(y[w].dispose(),delete y[w])}}}var K_={[Ha]:Sa,[Ea]:Ca,[Wa]:Ga,[rs]:Ma,[Sa]:Ha,[Ca]:Ea,[Ga]:Wa,[Ma]:rs};function em(i,e){function t(){let U=!1,le=new nt,L=null,Y=new nt(0,0,0,0);return{setMask:function(ae){L!==ae&&!U&&(i.colorMask(ae,ae,ae,ae),L=ae)},setLocked:function(ae){U=ae},setClear:function(ae,re,Ie,pt,Ht){Ht===!0&&(ae*=pt,re*=pt,Ie*=pt),le.set(ae,re,Ie,pt),Y.equals(le)===!1&&(i.clearColor(ae,re,Ie,pt),Y.copy(le))},reset:function(){U=!1,L=null,Y.set(-1,0,0,0)}}}function n(){let U=!1,le=!1,L=null,Y=null,ae=null;return{setReversed:function(re){if(le!==re){let Ie=e.get("EXT_clip_control");le?Ie.clipControlEXT(Ie.LOWER_LEFT_EXT,Ie.ZERO_TO_ONE_EXT):Ie.clipControlEXT(Ie.LOWER_LEFT_EXT,Ie.NEGATIVE_ONE_TO_ONE_EXT);let pt=ae;ae=null,this.setClear(pt)}le=re},getReversed:function(){return le},setTest:function(re){re?oe(i.DEPTH_TEST):be(i.DEPTH_TEST)},setMask:function(re){L!==re&&!U&&(i.depthMask(re),L=re)},setFunc:function(re){if(le&&(re=K_[re]),Y!==re){switch(re){case Ha:i.depthFunc(i.NEVER);break;case Sa:i.depthFunc(i.ALWAYS);break;case Ea:i.depthFunc(i.LESS);break;case rs:i.depthFunc(i.LEQUAL);break;case Wa:i.depthFunc(i.EQUAL);break;case Ma:i.depthFunc(i.GEQUAL);break;case Ca:i.depthFunc(i.GREATER);break;case Ga:i.depthFunc(i.NOTEQUAL);break;default:i.depthFunc(i.LEQUAL)}Y=re}},setLocked:function(re){U=re},setClear:function(re){ae!==re&&(le&&(re=1-re),i.clearDepth(re),ae=re)},reset:function(){U=!1,L=null,Y=null,ae=null,le=!1}}}function s(){let U=!1,le=null,L=null,Y=null,ae=null,re=null,Ie=null,pt=null,Ht=null;return{setTest:function(Ke){U||(Ke?oe(i.STENCIL_TEST):be(i.STENCIL_TEST))},setMask:function(Ke){le!==Ke&&!U&&(i.stencilMask(Ke),le=Ke)},setFunc:function(Ke,Lt,Pt){(L!==Ke||Y!==Lt||ae!==Pt)&&(i.stencilFunc(Ke,Lt,Pt),L=Ke,Y=Lt,ae=Pt)},setOp:function(Ke,Lt,Pt){(re!==Ke||Ie!==Lt||pt!==Pt)&&(i.stencilOp(Ke,Lt,Pt),re=Ke,Ie=Lt,pt=Pt)},setLocked:function(Ke){U=Ke},setClear:function(Ke){Ht!==Ke&&(i.clearStencil(Ke),Ht=Ke)},reset:function(){U=!1,le=null,L=null,Y=null,ae=null,re=null,Ie=null,pt=null,Ht=null}}}let r=new t,o=new n,a=new s,c=new WeakMap,h=new WeakMap,l={},p={},f=new WeakMap,u=[],_=null,m=!1,g=null,d=null,A=null,O=null,P=null,G=null,H=null,S=new Ge(0,0,0),M=0,y=!1,w=null,W=null,$=null,F=null,k=null,Q=i.getParameter(i.MAX_COMBINED_TEXTURE_IMAGE_UNITS),V=!1,te=0,z=i.getParameter(i.VERSION);z.indexOf("WebGL")!==-1?(te=parseFloat(/^WebGL (\d)/.exec(z)[1]),V=te>=1):z.indexOf("OpenGL ES")!==-1&&(te=parseFloat(/^OpenGL ES (\d)/.exec(z)[1]),V=te>=2);let pe=null,me={},He=i.getParameter(i.SCISSOR_BOX),Ne=i.getParameter(i.VIEWPORT),tt=new nt().fromArray(He),J=new nt().fromArray(Ne);function ie(U,le,L,Y){let ae=new Uint8Array(4),re=i.createTexture();i.bindTexture(U,re),i.texParameteri(U,i.TEXTURE_MIN_FILTER,i.NEAREST),i.texParameteri(U,i.TEXTURE_MAG_FILTER,i.NEAREST);for(let Ie=0;Ie<L;Ie++)U===i.TEXTURE_3D||U===i.TEXTURE_2D_ARRAY?i.texImage3D(le,0,i.RGBA,1,1,Y,0,i.RGBA,i.UNSIGNED_BYTE,ae):i.texImage2D(le+Ie,0,i.RGBA,1,1,0,i.RGBA,i.UNSIGNED_BYTE,ae);return re}let xe={};xe[i.TEXTURE_2D]=ie(i.TEXTURE_2D,i.TEXTURE_2D,1),xe[i.TEXTURE_CUBE_MAP]=ie(i.TEXTURE_CUBE_MAP,i.TEXTURE_CUBE_MAP_POSITIVE_X,6),xe[i.TEXTURE_2D_ARRAY]=ie(i.TEXTURE_2D_ARRAY,i.TEXTURE_2D_ARRAY,1,1),xe[i.TEXTURE_3D]=ie(i.TEXTURE_3D,i.TEXTURE_3D,1,1),r.setClear(0,0,0,1),o.setClear(1),a.setClear(0),oe(i.DEPTH_TEST),o.setFunc(rs),je(!1),Ye(Uh),oe(i.CULL_FACE),I(oi);function oe(U){l[U]!==!0&&(i.enable(U),l[U]=!0)}function be(U){l[U]!==!1&&(i.disable(U),l[U]=!1)}function Te(U,le){return p[U]!==le?(i.bindFramebuffer(U,le),p[U]=le,U===i.DRAW_FRAMEBUFFER&&(p[i.FRAMEBUFFER]=le),U===i.FRAMEBUFFER&&(p[i.DRAW_FRAMEBUFFER]=le),!0):!1}function ke(U,le){let L=u,Y=!1;if(U){L=f.get(le),L===void 0&&(L=[],f.set(le,L));let ae=U.textures;if(L.length!==ae.length||L[0]!==i.COLOR_ATTACHMENT0){for(let re=0,Ie=ae.length;re<Ie;re++)L[re]=i.COLOR_ATTACHMENT0+re;L.length=ae.length,Y=!0}}else L[0]!==i.BACK&&(L[0]=i.BACK,Y=!0);Y&&i.drawBuffers(L)}function lt(U){return _!==U?(i.useProgram(U),_=U,!0):!1}let Se={[yi]:i.FUNC_ADD,[dp]:i.FUNC_SUBTRACT,[up]:i.FUNC_REVERSE_SUBTRACT};Se[gp]=i.MIN,Se[_p]=i.MAX;let ot={[mp]:i.ZERO,[xp]:i.ONE,[bp]:i.SRC_COLOR,[Aa]:i.SRC_ALPHA,[vp]:i.SRC_ALPHA_SATURATE,[yp]:i.DST_COLOR,[wp]:i.DST_ALPHA,[Pp]:i.ONE_MINUS_SRC_COLOR,[va]:i.ONE_MINUS_SRC_ALPHA,[Ap]:i.ONE_MINUS_DST_COLOR,[Op]:i.ONE_MINUS_DST_ALPHA,[Hp]:i.CONSTANT_COLOR,[Sp]:i.ONE_MINUS_CONSTANT_COLOR,[Ep]:i.CONSTANT_ALPHA,[Wp]:i.ONE_MINUS_CONSTANT_ALPHA};function I(U,le,L,Y,ae,re,Ie,pt,Ht,Ke){if(U===oi){m===!0&&(be(i.BLEND),m=!1);return}if(m===!1&&(oe(i.BLEND),m=!0),U!==fp){if(U!==g||Ke!==y){if((d!==yi||P!==yi)&&(i.blendEquation(i.FUNC_ADD),d=yi,P=yi),Ke)switch(U){case ts:i.blendFuncSeparate(i.ONE,i.ONE_MINUS_SRC_ALPHA,i.ONE,i.ONE_MINUS_SRC_ALPHA);break;case so:i.blendFunc(i.ONE,i.ONE);break;case Ih:i.blendFuncSeparate(i.ZERO,i.ONE_MINUS_SRC_COLOR,i.ZERO,i.ONE);break;case Rh:i.blendFuncSeparate(i.ZERO,i.SRC_COLOR,i.ZERO,i.SRC_ALPHA);break;default:console.error("THREE.WebGLState: Invalid blending: ",U);break}else switch(U){case ts:i.blendFuncSeparate(i.SRC_ALPHA,i.ONE_MINUS_SRC_ALPHA,i.ONE,i.ONE_MINUS_SRC_ALPHA);break;case so:i.blendFunc(i.SRC_ALPHA,i.ONE);break;case Ih:i.blendFuncSeparate(i.ZERO,i.ONE_MINUS_SRC_COLOR,i.ZERO,i.ONE);break;case Rh:i.blendFunc(i.ZERO,i.SRC_COLOR);break;default:console.error("THREE.WebGLState: Invalid blending: ",U);break}A=null,O=null,G=null,H=null,S.set(0,0,0),M=0,g=U,y=Ke}return}ae=ae||le,re=re||L,Ie=Ie||Y,(le!==d||ae!==P)&&(i.blendEquationSeparate(Se[le],Se[ae]),d=le,P=ae),(L!==A||Y!==O||re!==G||Ie!==H)&&(i.blendFuncSeparate(ot[L],ot[Y],ot[re],ot[Ie]),A=L,O=Y,G=re,H=Ie),(pt.equals(S)===!1||Ht!==M)&&(i.blendColor(pt.r,pt.g,pt.b,Ht),S.copy(pt),M=Ht),g=U,y=!1}function Dt(U,le){U.side===dn?be(i.CULL_FACE):oe(i.CULL_FACE);let L=U.side===Bt;le&&(L=!L),je(L),U.blending===ts&&U.transparent===!1?I(oi):I(U.blending,U.blendEquation,U.blendSrc,U.blendDst,U.blendEquationAlpha,U.blendSrcAlpha,U.blendDstAlpha,U.blendColor,U.blendAlpha,U.premultipliedAlpha),o.setFunc(U.depthFunc),o.setTest(U.depthTest),o.setMask(U.depthWrite),r.setMask(U.colorWrite);let Y=U.stencilWrite;a.setTest(Y),Y&&(a.setMask(U.stencilWriteMask),a.setFunc(U.stencilFunc,U.stencilRef,U.stencilFuncMask),a.setOp(U.stencilFail,U.stencilZFail,U.stencilZPass)),ht(U.polygonOffset,U.polygonOffsetFactor,U.polygonOffsetUnits),U.alphaToCoverage===!0?oe(i.SAMPLE_ALPHA_TO_COVERAGE):be(i.SAMPLE_ALPHA_TO_COVERAGE)}function je(U){w!==U&&(U?i.frontFace(i.CW):i.frontFace(i.CCW),w=U)}function Ye(U){U!==hp?(oe(i.CULL_FACE),U!==W&&(U===Uh?i.cullFace(i.BACK):U===lp?i.cullFace(i.FRONT):i.cullFace(i.FRONT_AND_BACK))):be(i.CULL_FACE),W=U}function Ee(U){U!==$&&(V&&i.lineWidth(U),$=U)}function ht(U,le,L){U?(oe(i.POLYGON_OFFSET_FILL),(F!==le||k!==L)&&(i.polygonOffset(le,L),F=le,k=L)):be(i.POLYGON_OFFSET_FILL)}function we(U){U?oe(i.SCISSOR_TEST):be(i.SCISSOR_TEST)}function v(U){U===void 0&&(U=i.TEXTURE0+Q-1),pe!==U&&(i.activeTexture(U),pe=U)}function b(U,le,L){L===void 0&&(pe===null?L=i.TEXTURE0+Q-1:L=pe);let Y=me[L];Y===void 0&&(Y={type:void 0,texture:void 0},me[L]=Y),(Y.type!==U||Y.texture!==le)&&(pe!==L&&(i.activeTexture(L),pe=L),i.bindTexture(U,le||xe[U]),Y.type=U,Y.texture=le)}function R(){let U=me[pe];U!==void 0&&U.type!==void 0&&(i.bindTexture(U.type,null),U.type=void 0,U.texture=void 0)}function j(){try{i.compressedTexImage2D.apply(i,arguments)}catch(U){console.error("THREE.WebGLState:",U)}}function Z(){try{i.compressedTexImage3D.apply(i,arguments)}catch(U){console.error("THREE.WebGLState:",U)}}function q(){try{i.texSubImage2D.apply(i,arguments)}catch(U){console.error("THREE.WebGLState:",U)}}function Pe(){try{i.texSubImage3D.apply(i,arguments)}catch(U){console.error("THREE.WebGLState:",U)}}function se(){try{i.compressedTexSubImage2D.apply(i,arguments)}catch(U){console.error("THREE.WebGLState:",U)}}function de(){try{i.compressedTexSubImage3D.apply(i,arguments)}catch(U){console.error("THREE.WebGLState:",U)}}function We(){try{i.texStorage2D.apply(i,arguments)}catch(U){console.error("THREE.WebGLState:",U)}}function ne(){try{i.texStorage3D.apply(i,arguments)}catch(U){console.error("THREE.WebGLState:",U)}}function ge(){try{i.texImage2D.apply(i,arguments)}catch(U){console.error("THREE.WebGLState:",U)}}function Me(){try{i.texImage3D.apply(i,arguments)}catch(U){console.error("THREE.WebGLState:",U)}}function Ue(U){tt.equals(U)===!1&&(i.scissor(U.x,U.y,U.z,U.w),tt.copy(U))}function _e(U){J.equals(U)===!1&&(i.viewport(U.x,U.y,U.z,U.w),J.copy(U))}function Je(U,le){let L=h.get(le);L===void 0&&(L=new WeakMap,h.set(le,L));let Y=L.get(U);Y===void 0&&(Y=i.getUniformBlockIndex(le,U.name),L.set(U,Y))}function Xe(U,le){let Y=h.get(le).get(U);c.get(le)!==Y&&(i.uniformBlockBinding(le,Y,U.__bindingPointIndex),c.set(le,Y))}function at(){i.disable(i.BLEND),i.disable(i.CULL_FACE),i.disable(i.DEPTH_TEST),i.disable(i.POLYGON_OFFSET_FILL),i.disable(i.SCISSOR_TEST),i.disable(i.STENCIL_TEST),i.disable(i.SAMPLE_ALPHA_TO_COVERAGE),i.blendEquation(i.FUNC_ADD),i.blendFunc(i.ONE,i.ZERO),i.blendFuncSeparate(i.ONE,i.ZERO,i.ONE,i.ZERO),i.blendColor(0,0,0,0),i.colorMask(!0,!0,!0,!0),i.clearColor(0,0,0,0),i.depthMask(!0),i.depthFunc(i.LESS),o.setReversed(!1),i.clearDepth(1),i.stencilMask(4294967295),i.stencilFunc(i.ALWAYS,0,4294967295),i.stencilOp(i.KEEP,i.KEEP,i.KEEP),i.clearStencil(0),i.cullFace(i.BACK),i.frontFace(i.CCW),i.polygonOffset(0,0),i.activeTexture(i.TEXTURE0),i.bindFramebuffer(i.FRAMEBUFFER,null),i.bindFramebuffer(i.DRAW_FRAMEBUFFER,null),i.bindFramebuffer(i.READ_FRAMEBUFFER,null),i.useProgram(null),i.lineWidth(1),i.scissor(0,0,i.canvas.width,i.canvas.height),i.viewport(0,0,i.canvas.width,i.canvas.height),l={},pe=null,me={},p={},f=new WeakMap,u=[],_=null,m=!1,g=null,d=null,A=null,O=null,P=null,G=null,H=null,S=new Ge(0,0,0),M=0,y=!1,w=null,W=null,$=null,F=null,k=null,tt.set(0,0,i.canvas.width,i.canvas.height),J.set(0,0,i.canvas.width,i.canvas.height),r.reset(),o.reset(),a.reset()}return{buffers:{color:r,depth:o,stencil:a},enable:oe,disable:be,bindFramebuffer:Te,drawBuffers:ke,useProgram:lt,setBlending:I,setMaterial:Dt,setFlipSided:je,setCullFace:Ye,setLineWidth:Ee,setPolygonOffset:ht,setScissorTest:we,activeTexture:v,bindTexture:b,unbindTexture:R,compressedTexImage2D:j,compressedTexImage3D:Z,texImage2D:ge,texImage3D:Me,updateUBOMapping:Je,uniformBlockBinding:Xe,texStorage2D:We,texStorage3D:ne,texSubImage2D:q,texSubImage3D:Pe,compressedTexSubImage2D:se,compressedTexSubImage3D:de,scissor:Ue,viewport:_e,reset:at}}function Tl(i,e,t,n){let s=tm(n);switch(t){case h0:return i*e;case p0:return i*e;case f0:return i*e*2;case Nc:return i*e/s.components*s.byteLength;case Vc:return i*e/s.components*s.byteLength;case d0:return i*e*2/s.components*s.byteLength;case Lc:return i*e*2/s.components*s.byteLength;case l0:return i*e*3/s.components*s.byteLength;case sn:return i*e*4/s.components*s.byteLength;case zc:return i*e*4/s.components*s.byteLength;case Kr:case eo:return Math.floor((i+3)/4)*Math.floor((e+3)/4)*8;case to:case no:return Math.floor((i+3)/4)*Math.floor((e+3)/4)*16;case Ra:case Ba:return Math.max(i,16)*Math.max(e,8)/4;case Ia:case Xa:return Math.max(i,8)*Math.max(e,8)/2;case $a:case Fa:return Math.floor((i+3)/4)*Math.floor((e+3)/4)*8;case Da:return Math.floor((i+3)/4)*Math.floor((e+3)/4)*16;case Na:return Math.floor((i+3)/4)*Math.floor((e+3)/4)*16;case Va:return Math.floor((i+4)/5)*Math.floor((e+3)/4)*16;case La:return Math.floor((i+4)/5)*Math.floor((e+4)/5)*16;case za:return Math.floor((i+5)/6)*Math.floor((e+4)/5)*16;case ka:return Math.floor((i+5)/6)*Math.floor((e+5)/6)*16;case qa:return Math.floor((i+7)/8)*Math.floor((e+4)/5)*16;case Ja:return Math.floor((i+7)/8)*Math.floor((e+5)/6)*16;case ja:return Math.floor((i+7)/8)*Math.floor((e+7)/8)*16;case Qa:return Math.floor((i+9)/10)*Math.floor((e+4)/5)*16;case Ya:return Math.floor((i+9)/10)*Math.floor((e+5)/6)*16;case Za:return Math.floor((i+9)/10)*Math.floor((e+7)/8)*16;case Ka:return Math.floor((i+9)/10)*Math.floor((e+9)/10)*16;case ec:return Math.floor((i+11)/12)*Math.floor((e+9)/10)*16;case tc:return Math.floor((i+11)/12)*Math.floor((e+11)/12)*16;case io:case nc:case ic:return Math.ceil(i/4)*Math.ceil(e/4)*16;case u0:case sc:return Math.ceil(i/4)*Math.ceil(e/4)*8;case rc:case oc:return Math.ceil(i/4)*Math.ceil(e/4)*16}throw new Error(`Unable to determine texture byte length for ${t} format.`)}function tm(i){switch(i){case Dn:case o0:return{byteLength:1,components:1};case nr:case a0:case fr:return{byteLength:2,components:1};case Fc:case Dc:return{byteLength:2,components:4};case vi:case $c:case gn:return{byteLength:4,components:1};case c0:return{byteLength:4,components:3}}throw new Error(`Unknown texture type ${i}.`)}function nm(i,e,t,n,s,r,o){let a=e.has("WEBGL_multisampled_render_to_texture")?e.get("WEBGL_multisampled_render_to_texture"):null,c=typeof navigator>"u"?!1:/OculusBrowser/g.test(navigator.userAgent),h=new Re,l=new WeakMap,p,f=new WeakMap,u=!1;try{u=typeof OffscreenCanvas<"u"&&new OffscreenCanvas(1,1).getContext("2d")!==null}catch{}function _(v,b){return u?new OffscreenCanvas(v,b):ir("canvas")}function m(v,b,R){let j=1,Z=we(v);if((Z.width>R||Z.height>R)&&(j=R/Math.max(Z.width,Z.height)),j<1)if(typeof HTMLImageElement<"u"&&v instanceof HTMLImageElement||typeof HTMLCanvasElement<"u"&&v instanceof HTMLCanvasElement||typeof ImageBitmap<"u"&&v instanceof ImageBitmap||typeof VideoFrame<"u"&&v instanceof VideoFrame){let q=Math.floor(j*Z.width),Pe=Math.floor(j*Z.height);p===void 0&&(p=_(q,Pe));let se=b?_(q,Pe):p;return se.width=q,se.height=Pe,se.getContext("2d").drawImage(v,0,0,q,Pe),console.warn("THREE.WebGLRenderer: Texture has been resized from ("+Z.width+"x"+Z.height+") to ("+q+"x"+Pe+")."),se}else return"data"in v&&console.warn("THREE.WebGLRenderer: Image in DataTexture is too big ("+Z.width+"x"+Z.height+")."),v;return v}function g(v){return v.generateMipmaps}function d(v){i.generateMipmap(v)}function A(v){return v.isWebGLCubeRenderTarget?i.TEXTURE_CUBE_MAP:v.isWebGL3DRenderTarget?i.TEXTURE_3D:v.isWebGLArrayRenderTarget||v.isCompressedArrayTexture?i.TEXTURE_2D_ARRAY:i.TEXTURE_2D}function O(v,b,R,j,Z=!1){if(v!==null){if(i[v]!==void 0)return i[v];console.warn("THREE.WebGLRenderer: Attempt to use non-existing WebGL internal format '"+v+"'")}let q=b;if(b===i.RED&&(R===i.FLOAT&&(q=i.R32F),R===i.HALF_FLOAT&&(q=i.R16F),R===i.UNSIGNED_BYTE&&(q=i.R8)),b===i.RED_INTEGER&&(R===i.UNSIGNED_BYTE&&(q=i.R8UI),R===i.UNSIGNED_SHORT&&(q=i.R16UI),R===i.UNSIGNED_INT&&(q=i.R32UI),R===i.BYTE&&(q=i.R8I),R===i.SHORT&&(q=i.R16I),R===i.INT&&(q=i.R32I)),b===i.RG&&(R===i.FLOAT&&(q=i.RG32F),R===i.HALF_FLOAT&&(q=i.RG16F),R===i.UNSIGNED_BYTE&&(q=i.RG8)),b===i.RG_INTEGER&&(R===i.UNSIGNED_BYTE&&(q=i.RG8UI),R===i.UNSIGNED_SHORT&&(q=i.RG16UI),R===i.UNSIGNED_INT&&(q=i.RG32UI),R===i.BYTE&&(q=i.RG8I),R===i.SHORT&&(q=i.RG16I),R===i.INT&&(q=i.RG32I)),b===i.RGB_INTEGER&&(R===i.UNSIGNED_BYTE&&(q=i.RGB8UI),R===i.UNSIGNED_SHORT&&(q=i.RGB16UI),R===i.UNSIGNED_INT&&(q=i.RGB32UI),R===i.BYTE&&(q=i.RGB8I),R===i.SHORT&&(q=i.RGB16I),R===i.INT&&(q=i.RGB32I)),b===i.RGBA_INTEGER&&(R===i.UNSIGNED_BYTE&&(q=i.RGBA8UI),R===i.UNSIGNED_SHORT&&(q=i.RGBA16UI),R===i.UNSIGNED_INT&&(q=i.RGBA32UI),R===i.BYTE&&(q=i.RGBA8I),R===i.SHORT&&(q=i.RGBA16I),R===i.INT&&(q=i.RGBA32I)),b===i.RGB&&R===i.UNSIGNED_INT_5_9_9_9_REV&&(q=i.RGB9_E5),b===i.RGBA){let Pe=Z?Uo:Ze.getTransfer(j);R===i.FLOAT&&(q=i.RGBA32F),R===i.HALF_FLOAT&&(q=i.RGBA16F),R===i.UNSIGNED_BYTE&&(q=Pe===ct?i.SRGB8_ALPHA8:i.RGBA8),R===i.UNSIGNED_SHORT_4_4_4_4&&(q=i.RGBA4),R===i.UNSIGNED_SHORT_5_5_5_1&&(q=i.RGB5_A1)}return(q===i.R16F||q===i.R32F||q===i.RG16F||q===i.RG32F||q===i.RGBA16F||q===i.RGBA32F)&&e.get("EXT_color_buffer_float"),q}function P(v,b){let R;return v?b===null||b===vi||b===cs?R=i.DEPTH24_STENCIL8:b===gn?R=i.DEPTH32F_STENCIL8:b===nr&&(R=i.DEPTH24_STENCIL8,console.warn("DepthTexture: 16 bit depth attachment is not supported with stencil. Using 24-bit attachment.")):b===null||b===vi||b===cs?R=i.DEPTH_COMPONENT24:b===gn?R=i.DEPTH_COMPONENT32F:b===nr&&(R=i.DEPTH_COMPONENT16),R}function G(v,b){return g(v)===!0||v.isFramebufferTexture&&v.minFilter!==$t&&v.minFilter!==kt?Math.log2(Math.max(b.width,b.height))+1:v.mipmaps!==void 0&&v.mipmaps.length>0?v.mipmaps.length:v.isCompressedTexture&&Array.isArray(v.image)?b.mipmaps.length:1}function H(v){let b=v.target;b.removeEventListener("dispose",H),M(b),b.isVideoTexture&&l.delete(b)}function S(v){let b=v.target;b.removeEventListener("dispose",S),w(b)}function M(v){let b=n.get(v);if(b.__webglInit===void 0)return;let R=v.source,j=f.get(R);if(j){let Z=j[b.__cacheKey];Z.usedTimes--,Z.usedTimes===0&&y(v),Object.keys(j).length===0&&f.delete(R)}n.remove(v)}function y(v){let b=n.get(v);i.deleteTexture(b.__webglTexture);let R=v.source,j=f.get(R);delete j[b.__cacheKey],o.memory.textures--}function w(v){let b=n.get(v);if(v.depthTexture&&(v.depthTexture.dispose(),n.remove(v.depthTexture)),v.isWebGLCubeRenderTarget)for(let j=0;j<6;j++){if(Array.isArray(b.__webglFramebuffer[j]))for(let Z=0;Z<b.__webglFramebuffer[j].length;Z++)i.deleteFramebuffer(b.__webglFramebuffer[j][Z]);else i.deleteFramebuffer(b.__webglFramebuffer[j]);b.__webglDepthbuffer&&i.deleteRenderbuffer(b.__webglDepthbuffer[j])}else{if(Array.isArray(b.__webglFramebuffer))for(let j=0;j<b.__webglFramebuffer.length;j++)i.deleteFramebuffer(b.__webglFramebuffer[j]);else i.deleteFramebuffer(b.__webglFramebuffer);if(b.__webglDepthbuffer&&i.deleteRenderbuffer(b.__webglDepthbuffer),b.__webglMultisampledFramebuffer&&i.deleteFramebuffer(b.__webglMultisampledFramebuffer),b.__webglColorRenderbuffer)for(let j=0;j<b.__webglColorRenderbuffer.length;j++)b.__webglColorRenderbuffer[j]&&i.deleteRenderbuffer(b.__webglColorRenderbuffer[j]);b.__webglDepthRenderbuffer&&i.deleteRenderbuffer(b.__webglDepthRenderbuffer)}let R=v.textures;for(let j=0,Z=R.length;j<Z;j++){let q=n.get(R[j]);q.__webglTexture&&(i.deleteTexture(q.__webglTexture),o.memory.textures--),n.remove(R[j])}n.remove(v)}let W=0;function $(){W=0}function F(){let v=W;return v>=s.maxTextures&&console.warn("THREE.WebGLTextures: Trying to use "+v+" texture units while this GPU supports only "+s.maxTextures),W+=1,v}function k(v){let b=[];return b.push(v.wrapS),b.push(v.wrapT),b.push(v.wrapR||0),b.push(v.magFilter),b.push(v.minFilter),b.push(v.anisotropy),b.push(v.internalFormat),b.push(v.format),b.push(v.type),b.push(v.generateMipmaps),b.push(v.premultiplyAlpha),b.push(v.flipY),b.push(v.unpackAlignment),b.push(v.colorSpace),b.join()}function Q(v,b){let R=n.get(v);if(v.isVideoTexture&&Ee(v),v.isRenderTargetTexture===!1&&v.version>0&&R.__version!==v.version){let j=v.image;if(j===null)console.warn("THREE.WebGLRenderer: Texture marked for update but no image data found.");else if(j.complete===!1)console.warn("THREE.WebGLRenderer: Texture marked for update but image is incomplete");else{J(R,v,b);return}}t.bindTexture(i.TEXTURE_2D,R.__webglTexture,i.TEXTURE0+b)}function V(v,b){let R=n.get(v);if(v.version>0&&R.__version!==v.version){J(R,v,b);return}t.bindTexture(i.TEXTURE_2D_ARRAY,R.__webglTexture,i.TEXTURE0+b)}function te(v,b){let R=n.get(v);if(v.version>0&&R.__version!==v.version){J(R,v,b);return}t.bindTexture(i.TEXTURE_3D,R.__webglTexture,i.TEXTURE0+b)}function z(v,b){let R=n.get(v);if(v.version>0&&R.__version!==v.version){ie(R,v,b);return}t.bindTexture(i.TEXTURE_CUBE_MAP,R.__webglTexture,i.TEXTURE0+b)}let pe={[Fn]:i.REPEAT,[Xn]:i.CLAMP_TO_EDGE,[tr]:i.MIRRORED_REPEAT},me={[$t]:i.NEAREST,[Bc]:i.NEAREST_MIPMAP_NEAREST,[Ki]:i.NEAREST_MIPMAP_LINEAR,[kt]:i.LINEAR,[Ys]:i.LINEAR_MIPMAP_NEAREST,[wn]:i.LINEAR_MIPMAP_LINEAR},He={[Vp]:i.NEVER,[jp]:i.ALWAYS,[Lp]:i.LESS,[m0]:i.LEQUAL,[zp]:i.EQUAL,[Jp]:i.GEQUAL,[kp]:i.GREATER,[qp]:i.NOTEQUAL};function Ne(v,b){if(b.type===gn&&e.has("OES_texture_float_linear")===!1&&(b.magFilter===kt||b.magFilter===Ys||b.magFilter===Ki||b.magFilter===wn||b.minFilter===kt||b.minFilter===Ys||b.minFilter===Ki||b.minFilter===wn)&&console.warn("THREE.WebGLRenderer: Unable to use linear filtering with floating point textures. OES_texture_float_linear not supported on this device."),i.texParameteri(v,i.TEXTURE_WRAP_S,pe[b.wrapS]),i.texParameteri(v,i.TEXTURE_WRAP_T,pe[b.wrapT]),(v===i.TEXTURE_3D||v===i.TEXTURE_2D_ARRAY)&&i.texParameteri(v,i.TEXTURE_WRAP_R,pe[b.wrapR]),i.texParameteri(v,i.TEXTURE_MAG_FILTER,me[b.magFilter]),i.texParameteri(v,i.TEXTURE_MIN_FILTER,me[b.minFilter]),b.compareFunction&&(i.texParameteri(v,i.TEXTURE_COMPARE_MODE,i.COMPARE_REF_TO_TEXTURE),i.texParameteri(v,i.TEXTURE_COMPARE_FUNC,He[b.compareFunction])),e.has("EXT_texture_filter_anisotropic")===!0){if(b.magFilter===$t||b.minFilter!==Ki&&b.minFilter!==wn||b.type===gn&&e.has("OES_texture_float_linear")===!1)return;if(b.anisotropy>1||n.get(b).__currentAnisotropy){let R=e.get("EXT_texture_filter_anisotropic");i.texParameterf(v,R.TEXTURE_MAX_ANISOTROPY_EXT,Math.min(b.anisotropy,s.getMaxAnisotropy())),n.get(b).__currentAnisotropy=b.anisotropy}}}function tt(v,b){let R=!1;v.__webglInit===void 0&&(v.__webglInit=!0,b.addEventListener("dispose",H));let j=b.source,Z=f.get(j);Z===void 0&&(Z={},f.set(j,Z));let q=k(b);if(q!==v.__cacheKey){Z[q]===void 0&&(Z[q]={texture:i.createTexture(),usedTimes:0},o.memory.textures++,R=!0),Z[q].usedTimes++;let Pe=Z[v.__cacheKey];Pe!==void 0&&(Z[v.__cacheKey].usedTimes--,Pe.usedTimes===0&&y(b)),v.__cacheKey=q,v.__webglTexture=Z[q].texture}return R}function J(v,b,R){let j=i.TEXTURE_2D;(b.isDataArrayTexture||b.isCompressedArrayTexture)&&(j=i.TEXTURE_2D_ARRAY),b.isData3DTexture&&(j=i.TEXTURE_3D);let Z=tt(v,b),q=b.source;t.bindTexture(j,v.__webglTexture,i.TEXTURE0+R);let Pe=n.get(q);if(q.version!==Pe.__version||Z===!0){t.activeTexture(i.TEXTURE0+R);let se=Ze.getPrimaries(Ze.workingColorSpace),de=b.colorSpace===Pn?null:Ze.getPrimaries(b.colorSpace),We=b.colorSpace===Pn||se===de?i.NONE:i.BROWSER_DEFAULT_WEBGL;i.pixelStorei(i.UNPACK_FLIP_Y_WEBGL,b.flipY),i.pixelStorei(i.UNPACK_PREMULTIPLY_ALPHA_WEBGL,b.premultiplyAlpha),i.pixelStorei(i.UNPACK_ALIGNMENT,b.unpackAlignment),i.pixelStorei(i.UNPACK_COLORSPACE_CONVERSION_WEBGL,We);let ne=m(b.image,!1,s.maxTextureSize);ne=ht(b,ne);let ge=r.convert(b.format,b.colorSpace),Me=r.convert(b.type),Ue=O(b.internalFormat,ge,Me,b.colorSpace,b.isVideoTexture);Ne(j,b);let _e,Je=b.mipmaps,Xe=b.isVideoTexture!==!0,at=Pe.__version===void 0||Z===!0,U=q.dataReady,le=G(b,ne);if(b.isDepthTexture)Ue=P(b.format===hs,b.type),at&&(Xe?t.texStorage2D(i.TEXTURE_2D,1,Ue,ne.width,ne.height):t.texImage2D(i.TEXTURE_2D,0,Ue,ne.width,ne.height,0,ge,Me,null));else if(b.isDataTexture)if(Je.length>0){Xe&&at&&t.texStorage2D(i.TEXTURE_2D,le,Ue,Je[0].width,Je[0].height);for(let L=0,Y=Je.length;L<Y;L++)_e=Je[L],Xe?U&&t.texSubImage2D(i.TEXTURE_2D,L,0,0,_e.width,_e.height,ge,Me,_e.data):t.texImage2D(i.TEXTURE_2D,L,Ue,_e.width,_e.height,0,ge,Me,_e.data);b.generateMipmaps=!1}else Xe?(at&&t.texStorage2D(i.TEXTURE_2D,le,Ue,ne.width,ne.height),U&&t.texSubImage2D(i.TEXTURE_2D,0,0,0,ne.width,ne.height,ge,Me,ne.data)):t.texImage2D(i.TEXTURE_2D,0,Ue,ne.width,ne.height,0,ge,Me,ne.data);else if(b.isCompressedTexture)if(b.isCompressedArrayTexture){Xe&&at&&t.texStorage3D(i.TEXTURE_2D_ARRAY,le,Ue,Je[0].width,Je[0].height,ne.depth);for(let L=0,Y=Je.length;L<Y;L++)if(_e=Je[L],b.format!==sn)if(ge!==null)if(Xe){if(U)if(b.layerUpdates.size>0){let ae=Tl(_e.width,_e.height,b.format,b.type);for(let re of b.layerUpdates){let Ie=_e.data.subarray(re*ae/_e.data.BYTES_PER_ELEMENT,(re+1)*ae/_e.data.BYTES_PER_ELEMENT);t.compressedTexSubImage3D(i.TEXTURE_2D_ARRAY,L,0,0,re,_e.width,_e.height,1,ge,Ie)}b.clearLayerUpdates()}else t.compressedTexSubImage3D(i.TEXTURE_2D_ARRAY,L,0,0,0,_e.width,_e.height,ne.depth,ge,_e.data)}else t.compressedTexImage3D(i.TEXTURE_2D_ARRAY,L,Ue,_e.width,_e.height,ne.depth,0,_e.data,0,0);else console.warn("THREE.WebGLRenderer: Attempt to load unsupported compressed texture format in .uploadTexture()");else Xe?U&&t.texSubImage3D(i.TEXTURE_2D_ARRAY,L,0,0,0,_e.width,_e.height,ne.depth,ge,Me,_e.data):t.texImage3D(i.TEXTURE_2D_ARRAY,L,Ue,_e.width,_e.height,ne.depth,0,ge,Me,_e.data)}else{Xe&&at&&t.texStorage2D(i.TEXTURE_2D,le,Ue,Je[0].width,Je[0].height);for(let L=0,Y=Je.length;L<Y;L++)_e=Je[L],b.format!==sn?ge!==null?Xe?U&&t.compressedTexSubImage2D(i.TEXTURE_2D,L,0,0,_e.width,_e.height,ge,_e.data):t.compressedTexImage2D(i.TEXTURE_2D,L,Ue,_e.width,_e.height,0,_e.data):console.warn("THREE.WebGLRenderer: Attempt to load unsupported compressed texture format in .uploadTexture()"):Xe?U&&t.texSubImage2D(i.TEXTURE_2D,L,0,0,_e.width,_e.height,ge,Me,_e.data):t.texImage2D(i.TEXTURE_2D,L,Ue,_e.width,_e.height,0,ge,Me,_e.data)}else if(b.isDataArrayTexture)if(Xe){if(at&&t.texStorage3D(i.TEXTURE_2D_ARRAY,le,Ue,ne.width,ne.height,ne.depth),U)if(b.layerUpdates.size>0){let L=Tl(ne.width,ne.height,b.format,b.type);for(let Y of b.layerUpdates){let ae=ne.data.subarray(Y*L/ne.data.BYTES_PER_ELEMENT,(Y+1)*L/ne.data.BYTES_PER_ELEMENT);t.texSubImage3D(i.TEXTURE_2D_ARRAY,0,0,0,Y,ne.width,ne.height,1,ge,Me,ae)}b.clearLayerUpdates()}else t.texSubImage3D(i.TEXTURE_2D_ARRAY,0,0,0,0,ne.width,ne.height,ne.depth,ge,Me,ne.data)}else t.texImage3D(i.TEXTURE_2D_ARRAY,0,Ue,ne.width,ne.height,ne.depth,0,ge,Me,ne.data);else if(b.isData3DTexture)Xe?(at&&t.texStorage3D(i.TEXTURE_3D,le,Ue,ne.width,ne.height,ne.depth),U&&t.texSubImage3D(i.TEXTURE_3D,0,0,0,0,ne.width,ne.height,ne.depth,ge,Me,ne.data)):t.texImage3D(i.TEXTURE_3D,0,Ue,ne.width,ne.height,ne.depth,0,ge,Me,ne.data);else if(b.isFramebufferTexture){if(at)if(Xe)t.texStorage2D(i.TEXTURE_2D,le,Ue,ne.width,ne.height);else{let L=ne.width,Y=ne.height;for(let ae=0;ae<le;ae++)t.texImage2D(i.TEXTURE_2D,ae,Ue,L,Y,0,ge,Me,null),L>>=1,Y>>=1}}else if(Je.length>0){if(Xe&&at){let L=we(Je[0]);t.texStorage2D(i.TEXTURE_2D,le,Ue,L.width,L.height)}for(let L=0,Y=Je.length;L<Y;L++)_e=Je[L],Xe?U&&t.texSubImage2D(i.TEXTURE_2D,L,0,0,ge,Me,_e):t.texImage2D(i.TEXTURE_2D,L,Ue,ge,Me,_e);b.generateMipmaps=!1}else if(Xe){if(at){let L=we(ne);t.texStorage2D(i.TEXTURE_2D,le,Ue,L.width,L.height)}U&&t.texSubImage2D(i.TEXTURE_2D,0,0,0,ge,Me,ne)}else t.texImage2D(i.TEXTURE_2D,0,Ue,ge,Me,ne);g(b)&&d(j),Pe.__version=q.version,b.onUpdate&&b.onUpdate(b)}v.__version=b.version}function ie(v,b,R){if(b.image.length!==6)return;let j=tt(v,b),Z=b.source;t.bindTexture(i.TEXTURE_CUBE_MAP,v.__webglTexture,i.TEXTURE0+R);let q=n.get(Z);if(Z.version!==q.__version||j===!0){t.activeTexture(i.TEXTURE0+R);let Pe=Ze.getPrimaries(Ze.workingColorSpace),se=b.colorSpace===Pn?null:Ze.getPrimaries(b.colorSpace),de=b.colorSpace===Pn||Pe===se?i.NONE:i.BROWSER_DEFAULT_WEBGL;i.pixelStorei(i.UNPACK_FLIP_Y_WEBGL,b.flipY),i.pixelStorei(i.UNPACK_PREMULTIPLY_ALPHA_WEBGL,b.premultiplyAlpha),i.pixelStorei(i.UNPACK_ALIGNMENT,b.unpackAlignment),i.pixelStorei(i.UNPACK_COLORSPACE_CONVERSION_WEBGL,de);let We=b.isCompressedTexture||b.image[0].isCompressedTexture,ne=b.image[0]&&b.image[0].isDataTexture,ge=[];for(let Y=0;Y<6;Y++)!We&&!ne?ge[Y]=m(b.image[Y],!0,s.maxCubemapSize):ge[Y]=ne?b.image[Y].image:b.image[Y],ge[Y]=ht(b,ge[Y]);let Me=ge[0],Ue=r.convert(b.format,b.colorSpace),_e=r.convert(b.type),Je=O(b.internalFormat,Ue,_e,b.colorSpace),Xe=b.isVideoTexture!==!0,at=q.__version===void 0||j===!0,U=Z.dataReady,le=G(b,Me);Ne(i.TEXTURE_CUBE_MAP,b);let L;if(We){Xe&&at&&t.texStorage2D(i.TEXTURE_CUBE_MAP,le,Je,Me.width,Me.height);for(let Y=0;Y<6;Y++){L=ge[Y].mipmaps;for(let ae=0;ae<L.length;ae++){let re=L[ae];b.format!==sn?Ue!==null?Xe?U&&t.compressedTexSubImage2D(i.TEXTURE_CUBE_MAP_POSITIVE_X+Y,ae,0,0,re.width,re.height,Ue,re.data):t.compressedTexImage2D(i.TEXTURE_CUBE_MAP_POSITIVE_X+Y,ae,Je,re.width,re.height,0,re.data):console.warn("THREE.WebGLRenderer: Attempt to load unsupported compressed texture format in .setTextureCube()"):Xe?U&&t.texSubImage2D(i.TEXTURE_CUBE_MAP_POSITIVE_X+Y,ae,0,0,re.width,re.height,Ue,_e,re.data):t.texImage2D(i.TEXTURE_CUBE_MAP_POSITIVE_X+Y,ae,Je,re.width,re.height,0,Ue,_e,re.data)}}}else{if(L=b.mipmaps,Xe&&at){L.length>0&&le++;let Y=we(ge[0]);t.texStorage2D(i.TEXTURE_CUBE_MAP,le,Je,Y.width,Y.height)}for(let Y=0;Y<6;Y++)if(ne){Xe?U&&t.texSubImage2D(i.TEXTURE_CUBE_MAP_POSITIVE_X+Y,0,0,0,ge[Y].width,ge[Y].height,Ue,_e,ge[Y].data):t.texImage2D(i.TEXTURE_CUBE_MAP_POSITIVE_X+Y,0,Je,ge[Y].width,ge[Y].height,0,Ue,_e,ge[Y].data);for(let ae=0;ae<L.length;ae++){let Ie=L[ae].image[Y].image;Xe?U&&t.texSubImage2D(i.TEXTURE_CUBE_MAP_POSITIVE_X+Y,ae+1,0,0,Ie.width,Ie.height,Ue,_e,Ie.data):t.texImage2D(i.TEXTURE_CUBE_MAP_POSITIVE_X+Y,ae+1,Je,Ie.width,Ie.height,0,Ue,_e,Ie.data)}}else{Xe?U&&t.texSubImage2D(i.TEXTURE_CUBE_MAP_POSITIVE_X+Y,0,0,0,Ue,_e,ge[Y]):t.texImage2D(i.TEXTURE_CUBE_MAP_POSITIVE_X+Y,0,Je,Ue,_e,ge[Y]);for(let ae=0;ae<L.length;ae++){let re=L[ae];Xe?U&&t.texSubImage2D(i.TEXTURE_CUBE_MAP_POSITIVE_X+Y,ae+1,0,0,Ue,_e,re.image[Y]):t.texImage2D(i.TEXTURE_CUBE_MAP_POSITIVE_X+Y,ae+1,Je,Ue,_e,re.image[Y])}}}g(b)&&d(i.TEXTURE_CUBE_MAP),q.__version=Z.version,b.onUpdate&&b.onUpdate(b)}v.__version=b.version}function xe(v,b,R,j,Z,q){let Pe=r.convert(R.format,R.colorSpace),se=r.convert(R.type),de=O(R.internalFormat,Pe,se,R.colorSpace),We=n.get(b),ne=n.get(R);if(ne.__renderTarget=b,!We.__hasExternalTextures){let ge=Math.max(1,b.width>>q),Me=Math.max(1,b.height>>q);Z===i.TEXTURE_3D||Z===i.TEXTURE_2D_ARRAY?t.texImage3D(Z,q,de,ge,Me,b.depth,0,Pe,se,null):t.texImage2D(Z,q,de,ge,Me,0,Pe,se,null)}t.bindFramebuffer(i.FRAMEBUFFER,v),Ye(b)?a.framebufferTexture2DMultisampleEXT(i.FRAMEBUFFER,j,Z,ne.__webglTexture,0,je(b)):(Z===i.TEXTURE_2D||Z>=i.TEXTURE_CUBE_MAP_POSITIVE_X&&Z<=i.TEXTURE_CUBE_MAP_NEGATIVE_Z)&&i.framebufferTexture2D(i.FRAMEBUFFER,j,Z,ne.__webglTexture,q),t.bindFramebuffer(i.FRAMEBUFFER,null)}function oe(v,b,R){if(i.bindRenderbuffer(i.RENDERBUFFER,v),b.depthBuffer){let j=b.depthTexture,Z=j&&j.isDepthTexture?j.type:null,q=P(b.stencilBuffer,Z),Pe=b.stencilBuffer?i.DEPTH_STENCIL_ATTACHMENT:i.DEPTH_ATTACHMENT,se=je(b);Ye(b)?a.renderbufferStorageMultisampleEXT(i.RENDERBUFFER,se,q,b.width,b.height):R?i.renderbufferStorageMultisample(i.RENDERBUFFER,se,q,b.width,b.height):i.renderbufferStorage(i.RENDERBUFFER,q,b.width,b.height),i.framebufferRenderbuffer(i.FRAMEBUFFER,Pe,i.RENDERBUFFER,v)}else{let j=b.textures;for(let Z=0;Z<j.length;Z++){let q=j[Z],Pe=r.convert(q.format,q.colorSpace),se=r.convert(q.type),de=O(q.internalFormat,Pe,se,q.colorSpace),We=je(b);R&&Ye(b)===!1?i.renderbufferStorageMultisample(i.RENDERBUFFER,We,de,b.width,b.height):Ye(b)?a.renderbufferStorageMultisampleEXT(i.RENDERBUFFER,We,de,b.width,b.height):i.renderbufferStorage(i.RENDERBUFFER,de,b.width,b.height)}}i.bindRenderbuffer(i.RENDERBUFFER,null)}function be(v,b){if(b&&b.isWebGLCubeRenderTarget)throw new Error("Depth Texture with cube render targets is not supported");if(t.bindFramebuffer(i.FRAMEBUFFER,v),!(b.depthTexture&&b.depthTexture.isDepthTexture))throw new Error("renderTarget.depthTexture must be an instance of THREE.DepthTexture");let j=n.get(b.depthTexture);j.__renderTarget=b,(!j.__webglTexture||b.depthTexture.image.width!==b.width||b.depthTexture.image.height!==b.height)&&(b.depthTexture.image.width=b.width,b.depthTexture.image.height=b.height,b.depthTexture.needsUpdate=!0),Q(b.depthTexture,0);let Z=j.__webglTexture,q=je(b);if(b.depthTexture.format===ns)Ye(b)?a.framebufferTexture2DMultisampleEXT(i.FRAMEBUFFER,i.DEPTH_ATTACHMENT,i.TEXTURE_2D,Z,0,q):i.framebufferTexture2D(i.FRAMEBUFFER,i.DEPTH_ATTACHMENT,i.TEXTURE_2D,Z,0);else if(b.depthTexture.format===hs)Ye(b)?a.framebufferTexture2DMultisampleEXT(i.FRAMEBUFFER,i.DEPTH_STENCIL_ATTACHMENT,i.TEXTURE_2D,Z,0,q):i.framebufferTexture2D(i.FRAMEBUFFER,i.DEPTH_STENCIL_ATTACHMENT,i.TEXTURE_2D,Z,0);else throw new Error("Unknown depthTexture format")}function Te(v){let b=n.get(v),R=v.isWebGLCubeRenderTarget===!0;if(b.__boundDepthTexture!==v.depthTexture){let j=v.depthTexture;if(b.__depthDisposeCallback&&b.__depthDisposeCallback(),j){let Z=()=>{delete b.__boundDepthTexture,delete b.__depthDisposeCallback,j.removeEventListener("dispose",Z)};j.addEventListener("dispose",Z),b.__depthDisposeCallback=Z}b.__boundDepthTexture=j}if(v.depthTexture&&!b.__autoAllocateDepthBuffer){if(R)throw new Error("target.depthTexture not supported in Cube render targets");be(b.__webglFramebuffer,v)}else if(R){b.__webglDepthbuffer=[];for(let j=0;j<6;j++)if(t.bindFramebuffer(i.FRAMEBUFFER,b.__webglFramebuffer[j]),b.__webglDepthbuffer[j]===void 0)b.__webglDepthbuffer[j]=i.createRenderbuffer(),oe(b.__webglDepthbuffer[j],v,!1);else{let Z=v.stencilBuffer?i.DEPTH_STENCIL_ATTACHMENT:i.DEPTH_ATTACHMENT,q=b.__webglDepthbuffer[j];i.bindRenderbuffer(i.RENDERBUFFER,q),i.framebufferRenderbuffer(i.FRAMEBUFFER,Z,i.RENDERBUFFER,q)}}else if(t.bindFramebuffer(i.FRAMEBUFFER,b.__webglFramebuffer),b.__webglDepthbuffer===void 0)b.__webglDepthbuffer=i.createRenderbuffer(),oe(b.__webglDepthbuffer,v,!1);else{let j=v.stencilBuffer?i.DEPTH_STENCIL_ATTACHMENT:i.DEPTH_ATTACHMENT,Z=b.__webglDepthbuffer;i.bindRenderbuffer(i.RENDERBUFFER,Z),i.framebufferRenderbuffer(i.FRAMEBUFFER,j,i.RENDERBUFFER,Z)}t.bindFramebuffer(i.FRAMEBUFFER,null)}function ke(v,b,R){let j=n.get(v);b!==void 0&&xe(j.__webglFramebuffer,v,v.texture,i.COLOR_ATTACHMENT0,i.TEXTURE_2D,0),R!==void 0&&Te(v)}function lt(v){let b=v.texture,R=n.get(v),j=n.get(b);v.addEventListener("dispose",S);let Z=v.textures,q=v.isWebGLCubeRenderTarget===!0,Pe=Z.length>1;if(Pe||(j.__webglTexture===void 0&&(j.__webglTexture=i.createTexture()),j.__version=b.version,o.memory.textures++),q){R.__webglFramebuffer=[];for(let se=0;se<6;se++)if(b.mipmaps&&b.mipmaps.length>0){R.__webglFramebuffer[se]=[];for(let de=0;de<b.mipmaps.length;de++)R.__webglFramebuffer[se][de]=i.createFramebuffer()}else R.__webglFramebuffer[se]=i.createFramebuffer()}else{if(b.mipmaps&&b.mipmaps.length>0){R.__webglFramebuffer=[];for(let se=0;se<b.mipmaps.length;se++)R.__webglFramebuffer[se]=i.createFramebuffer()}else R.__webglFramebuffer=i.createFramebuffer();if(Pe)for(let se=0,de=Z.length;se<de;se++){let We=n.get(Z[se]);We.__webglTexture===void 0&&(We.__webglTexture=i.createTexture(),o.memory.textures++)}if(v.samples>0&&Ye(v)===!1){R.__webglMultisampledFramebuffer=i.createFramebuffer(),R.__webglColorRenderbuffer=[],t.bindFramebuffer(i.FRAMEBUFFER,R.__webglMultisampledFramebuffer);for(let se=0;se<Z.length;se++){let de=Z[se];R.__webglColorRenderbuffer[se]=i.createRenderbuffer(),i.bindRenderbuffer(i.RENDERBUFFER,R.__webglColorRenderbuffer[se]);let We=r.convert(de.format,de.colorSpace),ne=r.convert(de.type),ge=O(de.internalFormat,We,ne,de.colorSpace,v.isXRRenderTarget===!0),Me=je(v);i.renderbufferStorageMultisample(i.RENDERBUFFER,Me,ge,v.width,v.height),i.framebufferRenderbuffer(i.FRAMEBUFFER,i.COLOR_ATTACHMENT0+se,i.RENDERBUFFER,R.__webglColorRenderbuffer[se])}i.bindRenderbuffer(i.RENDERBUFFER,null),v.depthBuffer&&(R.__webglDepthRenderbuffer=i.createRenderbuffer(),oe(R.__webglDepthRenderbuffer,v,!0)),t.bindFramebuffer(i.FRAMEBUFFER,null)}}if(q){t.bindTexture(i.TEXTURE_CUBE_MAP,j.__webglTexture),Ne(i.TEXTURE_CUBE_MAP,b);for(let se=0;se<6;se++)if(b.mipmaps&&b.mipmaps.length>0)for(let de=0;de<b.mipmaps.length;de++)xe(R.__webglFramebuffer[se][de],v,b,i.COLOR_ATTACHMENT0,i.TEXTURE_CUBE_MAP_POSITIVE_X+se,de);else xe(R.__webglFramebuffer[se],v,b,i.COLOR_ATTACHMENT0,i.TEXTURE_CUBE_MAP_POSITIVE_X+se,0);g(b)&&d(i.TEXTURE_CUBE_MAP),t.unbindTexture()}else if(Pe){for(let se=0,de=Z.length;se<de;se++){let We=Z[se],ne=n.get(We);t.bindTexture(i.TEXTURE_2D,ne.__webglTexture),Ne(i.TEXTURE_2D,We),xe(R.__webglFramebuffer,v,We,i.COLOR_ATTACHMENT0+se,i.TEXTURE_2D,0),g(We)&&d(i.TEXTURE_2D)}t.unbindTexture()}else{let se=i.TEXTURE_2D;if((v.isWebGL3DRenderTarget||v.isWebGLArrayRenderTarget)&&(se=v.isWebGL3DRenderTarget?i.TEXTURE_3D:i.TEXTURE_2D_ARRAY),t.bindTexture(se,j.__webglTexture),Ne(se,b),b.mipmaps&&b.mipmaps.length>0)for(let de=0;de<b.mipmaps.length;de++)xe(R.__webglFramebuffer[de],v,b,i.COLOR_ATTACHMENT0,se,de);else xe(R.__webglFramebuffer,v,b,i.COLOR_ATTACHMENT0,se,0);g(b)&&d(se),t.unbindTexture()}v.depthBuffer&&Te(v)}function Se(v){let b=v.textures;for(let R=0,j=b.length;R<j;R++){let Z=b[R];if(g(Z)){let q=A(v),Pe=n.get(Z).__webglTexture;t.bindTexture(q,Pe),d(q),t.unbindTexture()}}}let ot=[],I=[];function Dt(v){if(v.samples>0){if(Ye(v)===!1){let b=v.textures,R=v.width,j=v.height,Z=i.COLOR_BUFFER_BIT,q=v.stencilBuffer?i.DEPTH_STENCIL_ATTACHMENT:i.DEPTH_ATTACHMENT,Pe=n.get(v),se=b.length>1;if(se)for(let de=0;de<b.length;de++)t.bindFramebuffer(i.FRAMEBUFFER,Pe.__webglMultisampledFramebuffer),i.framebufferRenderbuffer(i.FRAMEBUFFER,i.COLOR_ATTACHMENT0+de,i.RENDERBUFFER,null),t.bindFramebuffer(i.FRAMEBUFFER,Pe.__webglFramebuffer),i.framebufferTexture2D(i.DRAW_FRAMEBUFFER,i.COLOR_ATTACHMENT0+de,i.TEXTURE_2D,null,0);t.bindFramebuffer(i.READ_FRAMEBUFFER,Pe.__webglMultisampledFramebuffer),t.bindFramebuffer(i.DRAW_FRAMEBUFFER,Pe.__webglFramebuffer);for(let de=0;de<b.length;de++){if(v.resolveDepthBuffer&&(v.depthBuffer&&(Z|=i.DEPTH_BUFFER_BIT),v.stencilBuffer&&v.resolveStencilBuffer&&(Z|=i.STENCIL_BUFFER_BIT)),se){i.framebufferRenderbuffer(i.READ_FRAMEBUFFER,i.COLOR_ATTACHMENT0,i.RENDERBUFFER,Pe.__webglColorRenderbuffer[de]);let We=n.get(b[de]).__webglTexture;i.framebufferTexture2D(i.DRAW_FRAMEBUFFER,i.COLOR_ATTACHMENT0,i.TEXTURE_2D,We,0)}i.blitFramebuffer(0,0,R,j,0,0,R,j,Z,i.NEAREST),c===!0&&(ot.length=0,I.length=0,ot.push(i.COLOR_ATTACHMENT0+de),v.depthBuffer&&v.resolveDepthBuffer===!1&&(ot.push(q),I.push(q),i.invalidateFramebuffer(i.DRAW_FRAMEBUFFER,I)),i.invalidateFramebuffer(i.READ_FRAMEBUFFER,ot))}if(t.bindFramebuffer(i.READ_FRAMEBUFFER,null),t.bindFramebuffer(i.DRAW_FRAMEBUFFER,null),se)for(let de=0;de<b.length;de++){t.bindFramebuffer(i.FRAMEBUFFER,Pe.__webglMultisampledFramebuffer),i.framebufferRenderbuffer(i.FRAMEBUFFER,i.COLOR_ATTACHMENT0+de,i.RENDERBUFFER,Pe.__webglColorRenderbuffer[de]);let We=n.get(b[de]).__webglTexture;t.bindFramebuffer(i.FRAMEBUFFER,Pe.__webglFramebuffer),i.framebufferTexture2D(i.DRAW_FRAMEBUFFER,i.COLOR_ATTACHMENT0+de,i.TEXTURE_2D,We,0)}t.bindFramebuffer(i.DRAW_FRAMEBUFFER,Pe.__webglMultisampledFramebuffer)}else if(v.depthBuffer&&v.resolveDepthBuffer===!1&&c){let b=v.stencilBuffer?i.DEPTH_STENCIL_ATTACHMENT:i.DEPTH_ATTACHMENT;i.invalidateFramebuffer(i.DRAW_FRAMEBUFFER,[b])}}}function je(v){return Math.min(s.maxSamples,v.samples)}function Ye(v){let b=n.get(v);return v.samples>0&&e.has("WEBGL_multisampled_render_to_texture")===!0&&b.__useRenderToTexture!==!1}function Ee(v){let b=o.render.frame;l.get(v)!==b&&(l.set(v,b),v.update())}function ht(v,b){let R=v.colorSpace,j=v.format,Z=v.type;return v.isCompressedTexture===!0||v.isVideoTexture===!0||R!==Ft&&R!==Pn&&(Ze.getTransfer(R)===ct?(j!==sn||Z!==Dn)&&console.warn("THREE.WebGLTextures: sRGB encoded textures have to use RGBAFormat and UnsignedByteType."):console.error("THREE.WebGLTextures: Unsupported texture color space:",R)),b}function we(v){return typeof HTMLImageElement<"u"&&v instanceof HTMLImageElement?(h.width=v.naturalWidth||v.width,h.height=v.naturalHeight||v.height):typeof VideoFrame<"u"&&v instanceof VideoFrame?(h.width=v.displayWidth,h.height=v.displayHeight):(h.width=v.width,h.height=v.height),h}this.allocateTextureUnit=F,this.resetTextureUnits=$,this.setTexture2D=Q,this.setTexture2DArray=V,this.setTexture3D=te,this.setTextureCube=z,this.rebindTextures=ke,this.setupRenderTarget=lt,this.updateRenderTargetMipmap=Se,this.updateMultisampleRenderTarget=Dt,this.setupDepthRenderbuffer=Te,this.setupFrameBufferTexture=xe,this.useMultisampledRTT=Ye}function im(i,e){function t(n,s=Pn){let r,o=Ze.getTransfer(s);if(n===Dn)return i.UNSIGNED_BYTE;if(n===Fc)return i.UNSIGNED_SHORT_4_4_4_4;if(n===Dc)return i.UNSIGNED_SHORT_5_5_5_1;if(n===c0)return i.UNSIGNED_INT_5_9_9_9_REV;if(n===o0)return i.BYTE;if(n===a0)return i.SHORT;if(n===nr)return i.UNSIGNED_SHORT;if(n===$c)return i.INT;if(n===vi)return i.UNSIGNED_INT;if(n===gn)return i.FLOAT;if(n===fr)return i.HALF_FLOAT;if(n===h0)return i.ALPHA;if(n===l0)return i.RGB;if(n===sn)return i.RGBA;if(n===p0)return i.LUMINANCE;if(n===f0)return i.LUMINANCE_ALPHA;if(n===ns)return i.DEPTH_COMPONENT;if(n===hs)return i.DEPTH_STENCIL;if(n===Nc)return i.RED;if(n===Vc)return i.RED_INTEGER;if(n===d0)return i.RG;if(n===Lc)return i.RG_INTEGER;if(n===zc)return i.RGBA_INTEGER;if(n===Kr||n===eo||n===to||n===no)if(o===ct)if(r=e.get("WEBGL_compressed_texture_s3tc_srgb"),r!==null){if(n===Kr)return r.COMPRESSED_SRGB_S3TC_DXT1_EXT;if(n===eo)return r.COMPRESSED_SRGB_ALPHA_S3TC_DXT1_EXT;if(n===to)return r.COMPRESSED_SRGB_ALPHA_S3TC_DXT3_EXT;if(n===no)return r.COMPRESSED_SRGB_ALPHA_S3TC_DXT5_EXT}else return null;else if(r=e.get("WEBGL_compressed_texture_s3tc"),r!==null){if(n===Kr)return r.COMPRESSED_RGB_S3TC_DXT1_EXT;if(n===eo)return r.COMPRESSED_RGBA_S3TC_DXT1_EXT;if(n===to)return r.COMPRESSED_RGBA_S3TC_DXT3_EXT;if(n===no)return r.COMPRESSED_RGBA_S3TC_DXT5_EXT}else return null;if(n===Ia||n===Ra||n===Xa||n===Ba)if(r=e.get("WEBGL_compressed_texture_pvrtc"),r!==null){if(n===Ia)return r.COMPRESSED_RGB_PVRTC_4BPPV1_IMG;if(n===Ra)return r.COMPRESSED_RGB_PVRTC_2BPPV1_IMG;if(n===Xa)return r.COMPRESSED_RGBA_PVRTC_4BPPV1_IMG;if(n===Ba)return r.COMPRESSED_RGBA_PVRTC_2BPPV1_IMG}else return null;if(n===$a||n===Fa||n===Da)if(r=e.get("WEBGL_compressed_texture_etc"),r!==null){if(n===$a||n===Fa)return o===ct?r.COMPRESSED_SRGB8_ETC2:r.COMPRESSED_RGB8_ETC2;if(n===Da)return o===ct?r.COMPRESSED_SRGB8_ALPHA8_ETC2_EAC:r.COMPRESSED_RGBA8_ETC2_EAC}else return null;if(n===Na||n===Va||n===La||n===za||n===ka||n===qa||n===Ja||n===ja||n===Qa||n===Ya||n===Za||n===Ka||n===ec||n===tc)if(r=e.get("WEBGL_compressed_texture_astc"),r!==null){if(n===Na)return o===ct?r.COMPRESSED_SRGB8_ALPHA8_ASTC_4x4_KHR:r.COMPRESSED_RGBA_ASTC_4x4_KHR;if(n===Va)return o===ct?r.COMPRESSED_SRGB8_ALPHA8_ASTC_5x4_KHR:r.COMPRESSED_RGBA_ASTC_5x4_KHR;if(n===La)return o===ct?r.COMPRESSED_SRGB8_ALPHA8_ASTC_5x5_KHR:r.COMPRESSED_RGBA_ASTC_5x5_KHR;if(n===za)return o===ct?r.COMPRESSED_SRGB8_ALPHA8_ASTC_6x5_KHR:r.COMPRESSED_RGBA_ASTC_6x5_KHR;if(n===ka)return o===ct?r.COMPRESSED_SRGB8_ALPHA8_ASTC_6x6_KHR:r.COMPRESSED_RGBA_ASTC_6x6_KHR;if(n===qa)return o===ct?r.COMPRESSED_SRGB8_ALPHA8_ASTC_8x5_KHR:r.COMPRESSED_RGBA_ASTC_8x5_KHR;if(n===Ja)return o===ct?r.COMPRESSED_SRGB8_ALPHA8_ASTC_8x6_KHR:r.COMPRESSED_RGBA_ASTC_8x6_KHR;if(n===ja)return o===ct?r.COMPRESSED_SRGB8_ALPHA8_ASTC_8x8_KHR:r.COMPRESSED_RGBA_ASTC_8x8_KHR;if(n===Qa)return o===ct?r.COMPRESSED_SRGB8_ALPHA8_ASTC_10x5_KHR:r.COMPRESSED_RGBA_ASTC_10x5_KHR;if(n===Ya)return o===ct?r.COMPRESSED_SRGB8_ALPHA8_ASTC_10x6_KHR:r.COMPRESSED_RGBA_ASTC_10x6_KHR;if(n===Za)return o===ct?r.COMPRESSED_SRGB8_ALPHA8_ASTC_10x8_KHR:r.COMPRESSED_RGBA_ASTC_10x8_KHR;if(n===Ka)return o===ct?r.COMPRESSED_SRGB8_ALPHA8_ASTC_10x10_KHR:r.COMPRESSED_RGBA_ASTC_10x10_KHR;if(n===ec)return o===ct?r.COMPRESSED_SRGB8_ALPHA8_ASTC_12x10_KHR:r.COMPRESSED_RGBA_ASTC_12x10_KHR;if(n===tc)return o===ct?r.COMPRESSED_SRGB8_ALPHA8_ASTC_12x12_KHR:r.COMPRESSED_RGBA_ASTC_12x12_KHR}else return null;if(n===io||n===nc||n===ic)if(r=e.get("EXT_texture_compression_bptc"),r!==null){if(n===io)return o===ct?r.COMPRESSED_SRGB_ALPHA_BPTC_UNORM_EXT:r.COMPRESSED_RGBA_BPTC_UNORM_EXT;if(n===nc)return r.COMPRESSED_RGB_BPTC_SIGNED_FLOAT_EXT;if(n===ic)return r.COMPRESSED_RGB_BPTC_UNSIGNED_FLOAT_EXT}else return null;if(n===u0||n===sc||n===rc||n===oc)if(r=e.get("EXT_texture_compression_rgtc"),r!==null){if(n===io)return r.COMPRESSED_RED_RGTC1_EXT;if(n===sc)return r.COMPRESSED_SIGNED_RED_RGTC1_EXT;if(n===rc)return r.COMPRESSED_RED_GREEN_RGTC2_EXT;if(n===oc)return r.COMPRESSED_SIGNED_RED_GREEN_RGTC2_EXT}else return null;return n===cs?i.UNSIGNED_INT_24_8:i[n]!==void 0?i[n]:null}return{convert:t}}var wc=class extends St{constructor(e=[]){super(),this.isArrayCamera=!0,this.cameras=e}},Xt=class extends mt{constructor(){super(),this.isGroup=!0,this.type="Group"}},sm={type:"move"},er=class{constructor(){this._targetRay=null,this._grip=null,this._hand=null}getHandSpace(){return this._hand===null&&(this._hand=new Xt,this._hand.matrixAutoUpdate=!1,this._hand.visible=!1,this._hand.joints={},this._hand.inputState={pinching:!1}),this._hand}getTargetRaySpace(){return this._targetRay===null&&(this._targetRay=new Xt,this._targetRay.matrixAutoUpdate=!1,this._targetRay.visible=!1,this._targetRay.hasLinearVelocity=!1,this._targetRay.linearVelocity=new C,this._targetRay.hasAngularVelocity=!1,this._targetRay.angularVelocity=new C),this._targetRay}getGripSpace(){return this._grip===null&&(this._grip=new Xt,this._grip.matrixAutoUpdate=!1,this._grip.visible=!1,this._grip.hasLinearVelocity=!1,this._grip.linearVelocity=new C,this._grip.hasAngularVelocity=!1,this._grip.angularVelocity=new C),this._grip}dispatchEvent(e){return this._targetRay!==null&&this._targetRay.dispatchEvent(e),this._grip!==null&&this._grip.dispatchEvent(e),this._hand!==null&&this._hand.dispatchEvent(e),this}connect(e){if(e&&e.hand){let t=this._hand;if(t)for(let n of e.hand.values())this._getHandJoint(t,n)}return this.dispatchEvent({type:"connected",data:e}),this}disconnect(e){return this.dispatchEvent({type:"disconnected",data:e}),this._targetRay!==null&&(this._targetRay.visible=!1),this._grip!==null&&(this._grip.visible=!1),this._hand!==null&&(this._hand.visible=!1),this}update(e,t,n){let s=null,r=null,o=null,a=this._targetRay,c=this._grip,h=this._hand;if(e&&t.session.visibilityState!=="visible-blurred"){if(h&&e.hand){o=!0;for(let m of e.hand.values()){let g=t.getJointPose(m,n),d=this._getHandJoint(h,m);g!==null&&(d.matrix.fromArray(g.transform.matrix),d.matrix.decompose(d.position,d.rotation,d.scale),d.matrixWorldNeedsUpdate=!0,d.jointRadius=g.radius),d.visible=g!==null}let l=h.joints["index-finger-tip"],p=h.joints["thumb-tip"],f=l.position.distanceTo(p.position),u=.02,_=.005;h.inputState.pinching&&f>u+_?(h.inputState.pinching=!1,this.dispatchEvent({type:"pinchend",handedness:e.handedness,target:this})):!h.inputState.pinching&&f<=u-_&&(h.inputState.pinching=!0,this.dispatchEvent({type:"pinchstart",handedness:e.handedness,target:this}))}else c!==null&&e.gripSpace&&(r=t.getPose(e.gripSpace,n),r!==null&&(c.matrix.fromArray(r.transform.matrix),c.matrix.decompose(c.position,c.rotation,c.scale),c.matrixWorldNeedsUpdate=!0,r.linearVelocity?(c.hasLinearVelocity=!0,c.linearVelocity.copy(r.linearVelocity)):c.hasLinearVelocity=!1,r.angularVelocity?(c.hasAngularVelocity=!0,c.angularVelocity.copy(r.angularVelocity)):c.hasAngularVelocity=!1));a!==null&&(s=t.getPose(e.targetRaySpace,n),s===null&&r!==null&&(s=r),s!==null&&(a.matrix.fromArray(s.transform.matrix),a.matrix.decompose(a.position,a.rotation,a.scale),a.matrixWorldNeedsUpdate=!0,s.linearVelocity?(a.hasLinearVelocity=!0,a.linearVelocity.copy(s.linearVelocity)):a.hasLinearVelocity=!1,s.angularVelocity?(a.hasAngularVelocity=!0,a.angularVelocity.copy(s.angularVelocity)):a.hasAngularVelocity=!1,this.dispatchEvent(sm)))}return a!==null&&(a.visible=s!==null),c!==null&&(c.visible=r!==null),h!==null&&(h.visible=o!==null),this}_getHandJoint(e,t){if(e.joints[t.jointName]===void 0){let n=new Xt;n.matrixAutoUpdate=!1,n.visible=!1,e.joints[t.jointName]=n,e.add(n)}return e.joints[t.jointName]}},rm=`
void main() {

	gl_Position = vec4( position, 1.0 );

}`,om=`
uniform sampler2DArray depthColor;
uniform float depthWidth;
uniform float depthHeight;

void main() {

	vec2 coord = vec2( gl_FragCoord.x / depthWidth, gl_FragCoord.y / depthHeight );

	if ( coord.x >= 1.0 ) {

		gl_FragDepth = texture( depthColor, vec3( coord.x - 1.0, coord.y, 1 ) ).r;

	} else {

		gl_FragDepth = texture( depthColor, vec3( coord.x, coord.y, 0 ) ).r;

	}

}`,Oc=class{constructor(){this.texture=null,this.mesh=null,this.depthNear=0,this.depthFar=0}init(e,t,n){if(this.texture===null){let s=new Ct,r=e.properties.get(s);r.__webglTexture=t.texture,(t.depthNear!=n.depthNear||t.depthFar!=n.depthFar)&&(this.depthNear=t.depthNear,this.depthFar=t.depthFar),this.texture=s}}getMesh(e){if(this.texture!==null&&this.mesh===null){let t=e.cameras[0].viewport,n=new vn({vertexShader:rm,fragmentShader:om,uniforms:{depthColor:{value:this.texture},depthWidth:{value:t.z},depthHeight:{value:t.w}}});this.mesh=new De(new us(20,20),n)}return this.mesh}reset(){this.texture=null,this.mesh=null}getDepthTexture(){return this.texture}},yc=class extends ci{constructor(e,t){super();let n=this,s=null,r=1,o=null,a="local-floor",c=1,h=null,l=null,p=null,f=null,u=null,_=null,m=new Oc,g=t.getContextAttributes(),d=null,A=null,O=[],P=[],G=new Re,H=null,S=new St;S.viewport=new nt;let M=new St;M.viewport=new nt;let y=[S,M],w=new wc,W=null,$=null;this.cameraAutoUpdate=!0,this.enabled=!1,this.isPresenting=!1,this.getController=function(J){let ie=O[J];return ie===void 0&&(ie=new er,O[J]=ie),ie.getTargetRaySpace()},this.getControllerGrip=function(J){let ie=O[J];return ie===void 0&&(ie=new er,O[J]=ie),ie.getGripSpace()},this.getHand=function(J){let ie=O[J];return ie===void 0&&(ie=new er,O[J]=ie),ie.getHandSpace()};function F(J){let ie=P.indexOf(J.inputSource);if(ie===-1)return;let xe=O[ie];xe!==void 0&&(xe.update(J.inputSource,J.frame,h||o),xe.dispatchEvent({type:J.type,data:J.inputSource}))}function k(){s.removeEventListener("select",F),s.removeEventListener("selectstart",F),s.removeEventListener("selectend",F),s.removeEventListener("squeeze",F),s.removeEventListener("squeezestart",F),s.removeEventListener("squeezeend",F),s.removeEventListener("end",k),s.removeEventListener("inputsourceschange",Q);for(let J=0;J<O.length;J++){let ie=P[J];ie!==null&&(P[J]=null,O[J].disconnect(ie))}W=null,$=null,m.reset(),e.setRenderTarget(d),u=null,f=null,p=null,s=null,A=null,tt.stop(),n.isPresenting=!1,e.setPixelRatio(H),e.setSize(G.width,G.height,!1),n.dispatchEvent({type:"sessionend"})}this.setFramebufferScaleFactor=function(J){r=J,n.isPresenting===!0&&console.warn("THREE.WebXRManager: Cannot change framebuffer scale while presenting.")},this.setReferenceSpaceType=function(J){a=J,n.isPresenting===!0&&console.warn("THREE.WebXRManager: Cannot change reference space type while presenting.")},this.getReferenceSpace=function(){return h||o},this.setReferenceSpace=function(J){h=J},this.getBaseLayer=function(){return f!==null?f:u},this.getBinding=function(){return p},this.getFrame=function(){return _},this.getSession=function(){return s},this.setSession=async function(J){if(s=J,s!==null){if(d=e.getRenderTarget(),s.addEventListener("select",F),s.addEventListener("selectstart",F),s.addEventListener("selectend",F),s.addEventListener("squeeze",F),s.addEventListener("squeezestart",F),s.addEventListener("squeezeend",F),s.addEventListener("end",k),s.addEventListener("inputsourceschange",Q),g.xrCompatible!==!0&&await t.makeXRCompatible(),H=e.getPixelRatio(),e.getSize(G),s.renderState.layers===void 0){let ie={antialias:g.antialias,alpha:!0,depth:g.depth,stencil:g.stencil,framebufferScaleFactor:r};u=new XRWebGLLayer(s,t,ie),s.updateRenderState({baseLayer:u}),e.setPixelRatio(1),e.setSize(u.framebufferWidth,u.framebufferHeight,!1),A=new Nn(u.framebufferWidth,u.framebufferHeight,{format:sn,type:Dn,colorSpace:e.outputColorSpace,stencilBuffer:g.stencil})}else{let ie=null,xe=null,oe=null;g.depth&&(oe=g.stencil?t.DEPTH24_STENCIL8:t.DEPTH_COMPONENT24,ie=g.stencil?hs:ns,xe=g.stencil?cs:vi);let be={colorFormat:t.RGBA8,depthFormat:oe,scaleFactor:r};p=new XRWebGLBinding(s,t),f=p.createProjectionLayer(be),s.updateRenderState({layers:[f]}),e.setPixelRatio(1),e.setSize(f.textureWidth,f.textureHeight,!1),A=new Nn(f.textureWidth,f.textureHeight,{format:sn,type:Dn,depthTexture:new fo(f.textureWidth,f.textureHeight,xe,void 0,void 0,void 0,void 0,void 0,void 0,ie),stencilBuffer:g.stencil,colorSpace:e.outputColorSpace,samples:g.antialias?4:0,resolveDepthBuffer:f.ignoreDepthValues===!1})}A.isXRRenderTarget=!0,this.setFoveation(c),h=null,o=await s.requestReferenceSpace(a),tt.setContext(s),tt.start(),n.isPresenting=!0,n.dispatchEvent({type:"sessionstart"})}},this.getEnvironmentBlendMode=function(){if(s!==null)return s.environmentBlendMode},this.getDepthTexture=function(){return m.getDepthTexture()};function Q(J){for(let ie=0;ie<J.removed.length;ie++){let xe=J.removed[ie],oe=P.indexOf(xe);oe>=0&&(P[oe]=null,O[oe].disconnect(xe))}for(let ie=0;ie<J.added.length;ie++){let xe=J.added[ie],oe=P.indexOf(xe);if(oe===-1){for(let Te=0;Te<O.length;Te++)if(Te>=P.length){P.push(xe),oe=Te;break}else if(P[Te]===null){P[Te]=xe,oe=Te;break}if(oe===-1)break}let be=O[oe];be&&be.connect(xe)}}let V=new C,te=new C;function z(J,ie,xe){V.setFromMatrixPosition(ie.matrixWorld),te.setFromMatrixPosition(xe.matrixWorld);let oe=V.distanceTo(te),be=ie.projectionMatrix.elements,Te=xe.projectionMatrix.elements,ke=be[14]/(be[10]-1),lt=be[14]/(be[10]+1),Se=(be[9]+1)/be[5],ot=(be[9]-1)/be[5],I=(be[8]-1)/be[0],Dt=(Te[8]+1)/Te[0],je=ke*I,Ye=ke*Dt,Ee=oe/(-I+Dt),ht=Ee*-I;if(ie.matrixWorld.decompose(J.position,J.quaternion,J.scale),J.translateX(ht),J.translateZ(Ee),J.matrixWorld.compose(J.position,J.quaternion,J.scale),J.matrixWorldInverse.copy(J.matrixWorld).invert(),be[10]===-1)J.projectionMatrix.copy(ie.projectionMatrix),J.projectionMatrixInverse.copy(ie.projectionMatrixInverse);else{let we=ke+Ee,v=lt+Ee,b=je-ht,R=Ye+(oe-ht),j=Se*lt/v*we,Z=ot*lt/v*we;J.projectionMatrix.makePerspective(b,R,j,Z,we,v),J.projectionMatrixInverse.copy(J.projectionMatrix).invert()}}function pe(J,ie){ie===null?J.matrixWorld.copy(J.matrix):J.matrixWorld.multiplyMatrices(ie.matrixWorld,J.matrix),J.matrixWorldInverse.copy(J.matrixWorld).invert()}this.updateCamera=function(J){if(s===null)return;let ie=J.near,xe=J.far;m.texture!==null&&(m.depthNear>0&&(ie=m.depthNear),m.depthFar>0&&(xe=m.depthFar)),w.near=M.near=S.near=ie,w.far=M.far=S.far=xe,(W!==w.near||$!==w.far)&&(s.updateRenderState({depthNear:w.near,depthFar:w.far}),W=w.near,$=w.far),S.layers.mask=J.layers.mask|2,M.layers.mask=J.layers.mask|4,w.layers.mask=S.layers.mask|M.layers.mask;let oe=J.parent,be=w.cameras;pe(w,oe);for(let Te=0;Te<be.length;Te++)pe(be[Te],oe);be.length===2?z(w,S,M):w.projectionMatrix.copy(S.projectionMatrix),me(J,w,oe)};function me(J,ie,xe){xe===null?J.matrix.copy(ie.matrixWorld):(J.matrix.copy(xe.matrixWorld),J.matrix.invert(),J.matrix.multiply(ie.matrixWorld)),J.matrix.decompose(J.position,J.quaternion,J.scale),J.updateMatrixWorld(!0),J.projectionMatrix.copy(ie.projectionMatrix),J.projectionMatrixInverse.copy(ie.projectionMatrixInverse),J.isPerspectiveCamera&&(J.fov=fs*2*Math.atan(1/J.projectionMatrix.elements[5]),J.zoom=1)}this.getCamera=function(){return w},this.getFoveation=function(){if(!(f===null&&u===null))return c},this.setFoveation=function(J){c=J,f!==null&&(f.fixedFoveation=J),u!==null&&u.fixedFoveation!==void 0&&(u.fixedFoveation=J)},this.hasDepthSensing=function(){return m.texture!==null},this.getDepthSensingMesh=function(){return m.getMesh(w)};let He=null;function Ne(J,ie){if(l=ie.getViewerPose(h||o),_=ie,l!==null){let xe=l.views;u!==null&&(e.setRenderTargetFramebuffer(A,u.framebuffer),e.setRenderTarget(A));let oe=!1;xe.length!==w.cameras.length&&(w.cameras.length=0,oe=!0);for(let Te=0;Te<xe.length;Te++){let ke=xe[Te],lt=null;if(u!==null)lt=u.getViewport(ke);else{let ot=p.getViewSubImage(f,ke);lt=ot.viewport,Te===0&&(e.setRenderTargetTextures(A,ot.colorTexture,f.ignoreDepthValues?void 0:ot.depthStencilTexture),e.setRenderTarget(A))}let Se=y[Te];Se===void 0&&(Se=new St,Se.layers.enable(Te),Se.viewport=new nt,y[Te]=Se),Se.matrix.fromArray(ke.transform.matrix),Se.matrix.decompose(Se.position,Se.quaternion,Se.scale),Se.projectionMatrix.fromArray(ke.projectionMatrix),Se.projectionMatrixInverse.copy(Se.projectionMatrix).invert(),Se.viewport.set(lt.x,lt.y,lt.width,lt.height),Te===0&&(w.matrix.copy(Se.matrix),w.matrix.decompose(w.position,w.quaternion,w.scale)),oe===!0&&w.cameras.push(Se)}let be=s.enabledFeatures;if(be&&be.includes("depth-sensing")){let Te=p.getDepthInformation(xe[0]);Te&&Te.isValid&&Te.texture&&m.init(e,Te,s.renderState)}}for(let xe=0;xe<O.length;xe++){let oe=P[xe],be=O[xe];oe!==null&&be!==void 0&&be.update(oe,ie,h||o)}He&&He(J,ie),ie.detectedPlanes&&n.dispatchEvent({type:"planesdetected",data:ie}),_=null}let tt=new w0;tt.setAnimationLoop(Ne),this.setAnimationLoop=function(J){He=J},this.dispose=function(){}}},wi=new yn,am=new Fe;function cm(i,e){function t(g,d){g.matrixAutoUpdate===!0&&g.updateMatrix(),d.value.copy(g.matrix)}function n(g,d){d.color.getRGB(g.fogColor.value,P0(i)),d.isFog?(g.fogNear.value=d.near,g.fogFar.value=d.far):d.isFogExp2&&(g.fogDensity.value=d.density)}function s(g,d,A,O,P){d.isMeshBasicMaterial||d.isMeshLambertMaterial?r(g,d):d.isMeshToonMaterial?(r(g,d),p(g,d)):d.isMeshPhongMaterial?(r(g,d),l(g,d)):d.isMeshStandardMaterial?(r(g,d),f(g,d),d.isMeshPhysicalMaterial&&u(g,d,P)):d.isMeshMatcapMaterial?(r(g,d),_(g,d)):d.isMeshDepthMaterial?r(g,d):d.isMeshDistanceMaterial?(r(g,d),m(g,d)):d.isMeshNormalMaterial?r(g,d):d.isLineBasicMaterial?(o(g,d),d.isLineDashedMaterial&&a(g,d)):d.isPointsMaterial?c(g,d,A,O):d.isSpriteMaterial?h(g,d):d.isShadowMaterial?(g.color.value.copy(d.color),g.opacity.value=d.opacity):d.isShaderMaterial&&(d.uniformsNeedUpdate=!1)}function r(g,d){g.opacity.value=d.opacity,d.color&&g.diffuse.value.copy(d.color),d.emissive&&g.emissive.value.copy(d.emissive).multiplyScalar(d.emissiveIntensity),d.map&&(g.map.value=d.map,t(d.map,g.mapTransform)),d.alphaMap&&(g.alphaMap.value=d.alphaMap,t(d.alphaMap,g.alphaMapTransform)),d.bumpMap&&(g.bumpMap.value=d.bumpMap,t(d.bumpMap,g.bumpMapTransform),g.bumpScale.value=d.bumpScale,d.side===Bt&&(g.bumpScale.value*=-1)),d.normalMap&&(g.normalMap.value=d.normalMap,t(d.normalMap,g.normalMapTransform),g.normalScale.value.copy(d.normalScale),d.side===Bt&&g.normalScale.value.negate()),d.displacementMap&&(g.displacementMap.value=d.displacementMap,t(d.displacementMap,g.displacementMapTransform),g.displacementScale.value=d.displacementScale,g.displacementBias.value=d.displacementBias),d.emissiveMap&&(g.emissiveMap.value=d.emissiveMap,t(d.emissiveMap,g.emissiveMapTransform)),d.specularMap&&(g.specularMap.value=d.specularMap,t(d.specularMap,g.specularMapTransform)),d.alphaTest>0&&(g.alphaTest.value=d.alphaTest);let A=e.get(d),O=A.envMap,P=A.envMapRotation;O&&(g.envMap.value=O,wi.copy(P),wi.x*=-1,wi.y*=-1,wi.z*=-1,O.isCubeTexture&&O.isRenderTargetTexture===!1&&(wi.y*=-1,wi.z*=-1),g.envMapRotation.value.setFromMatrix4(am.makeRotationFromEuler(wi)),g.flipEnvMap.value=O.isCubeTexture&&O.isRenderTargetTexture===!1?-1:1,g.reflectivity.value=d.reflectivity,g.ior.value=d.ior,g.refractionRatio.value=d.refractionRatio),d.lightMap&&(g.lightMap.value=d.lightMap,g.lightMapIntensity.value=d.lightMapIntensity,t(d.lightMap,g.lightMapTransform)),d.aoMap&&(g.aoMap.value=d.aoMap,g.aoMapIntensity.value=d.aoMapIntensity,t(d.aoMap,g.aoMapTransform))}function o(g,d){g.diffuse.value.copy(d.color),g.opacity.value=d.opacity,d.map&&(g.map.value=d.map,t(d.map,g.mapTransform))}function a(g,d){g.dashSize.value=d.dashSize,g.totalSize.value=d.dashSize+d.gapSize,g.scale.value=d.scale}function c(g,d,A,O){g.diffuse.value.copy(d.color),g.opacity.value=d.opacity,g.size.value=d.size*A,g.scale.value=O*.5,d.map&&(g.map.value=d.map,t(d.map,g.uvTransform)),d.alphaMap&&(g.alphaMap.value=d.alphaMap,t(d.alphaMap,g.alphaMapTransform)),d.alphaTest>0&&(g.alphaTest.value=d.alphaTest)}function h(g,d){g.diffuse.value.copy(d.color),g.opacity.value=d.opacity,g.rotation.value=d.rotation,d.map&&(g.map.value=d.map,t(d.map,g.mapTransform)),d.alphaMap&&(g.alphaMap.value=d.alphaMap,t(d.alphaMap,g.alphaMapTransform)),d.alphaTest>0&&(g.alphaTest.value=d.alphaTest)}function l(g,d){g.specular.value.copy(d.specular),g.shininess.value=Math.max(d.shininess,1e-4)}function p(g,d){d.gradientMap&&(g.gradientMap.value=d.gradientMap)}function f(g,d){g.metalness.value=d.metalness,d.metalnessMap&&(g.metalnessMap.value=d.metalnessMap,t(d.metalnessMap,g.metalnessMapTransform)),g.roughness.value=d.roughness,d.roughnessMap&&(g.roughnessMap.value=d.roughnessMap,t(d.roughnessMap,g.roughnessMapTransform)),d.envMap&&(g.envMapIntensity.value=d.envMapIntensity)}function u(g,d,A){g.ior.value=d.ior,d.sheen>0&&(g.sheenColor.value.copy(d.sheenColor).multiplyScalar(d.sheen),g.sheenRoughness.value=d.sheenRoughness,d.sheenColorMap&&(g.sheenColorMap.value=d.sheenColorMap,t(d.sheenColorMap,g.sheenColorMapTransform)),d.sheenRoughnessMap&&(g.sheenRoughnessMap.value=d.sheenRoughnessMap,t(d.sheenRoughnessMap,g.sheenRoughnessMapTransform))),d.clearcoat>0&&(g.clearcoat.value=d.clearcoat,g.clearcoatRoughness.value=d.clearcoatRoughness,d.clearcoatMap&&(g.clearcoatMap.value=d.clearcoatMap,t(d.clearcoatMap,g.clearcoatMapTransform)),d.clearcoatRoughnessMap&&(g.clearcoatRoughnessMap.value=d.clearcoatRoughnessMap,t(d.clearcoatRoughnessMap,g.clearcoatRoughnessMapTransform)),d.clearcoatNormalMap&&(g.clearcoatNormalMap.value=d.clearcoatNormalMap,t(d.clearcoatNormalMap,g.clearcoatNormalMapTransform),g.clearcoatNormalScale.value.copy(d.clearcoatNormalScale),d.side===Bt&&g.clearcoatNormalScale.value.negate())),d.dispersion>0&&(g.dispersion.value=d.dispersion),d.iridescence>0&&(g.iridescence.value=d.iridescence,g.iridescenceIOR.value=d.iridescenceIOR,g.iridescenceThicknessMinimum.value=d.iridescenceThicknessRange[0],g.iridescenceThicknessMaximum.value=d.iridescenceThicknessRange[1],d.iridescenceMap&&(g.iridescenceMap.value=d.iridescenceMap,t(d.iridescenceMap,g.iridescenceMapTransform)),d.iridescenceThicknessMap&&(g.iridescenceThicknessMap.value=d.iridescenceThicknessMap,t(d.iridescenceThicknessMap,g.iridescenceThicknessMapTransform))),d.transmission>0&&(g.transmission.value=d.transmission,g.transmissionSamplerMap.value=A.texture,g.transmissionSamplerSize.value.set(A.width,A.height),d.transmissionMap&&(g.transmissionMap.value=d.transmissionMap,t(d.transmissionMap,g.transmissionMapTransform)),g.thickness.value=d.thickness,d.thicknessMap&&(g.thicknessMap.value=d.thicknessMap,t(d.thicknessMap,g.thicknessMapTransform)),g.attenuationDistance.value=d.attenuationDistance,g.attenuationColor.value.copy(d.attenuationColor)),d.anisotropy>0&&(g.anisotropyVector.value.set(d.anisotropy*Math.cos(d.anisotropyRotation),d.anisotropy*Math.sin(d.anisotropyRotation)),d.anisotropyMap&&(g.anisotropyMap.value=d.anisotropyMap,t(d.anisotropyMap,g.anisotropyMapTransform))),g.specularIntensity.value=d.specularIntensity,g.specularColor.value.copy(d.specularColor),d.specularColorMap&&(g.specularColorMap.value=d.specularColorMap,t(d.specularColorMap,g.specularColorMapTransform)),d.specularIntensityMap&&(g.specularIntensityMap.value=d.specularIntensityMap,t(d.specularIntensityMap,g.specularIntensityMapTransform))}function _(g,d){d.matcap&&(g.matcap.value=d.matcap)}function m(g,d){let A=e.get(d).light;g.referencePosition.value.setFromMatrixPosition(A.matrixWorld),g.nearDistance.value=A.shadow.camera.near,g.farDistance.value=A.shadow.camera.far}return{refreshFogUniforms:n,refreshMaterialUniforms:s}}function hm(i,e,t,n){let s={},r={},o=[],a=i.getParameter(i.MAX_UNIFORM_BUFFER_BINDINGS);function c(A,O){let P=O.program;n.uniformBlockBinding(A,P)}function h(A,O){let P=s[A.id];P===void 0&&(_(A),P=l(A),s[A.id]=P,A.addEventListener("dispose",g));let G=O.program;n.updateUBOMapping(A,G);let H=e.render.frame;r[A.id]!==H&&(f(A),r[A.id]=H)}function l(A){let O=p();A.__bindingPointIndex=O;let P=i.createBuffer(),G=A.__size,H=A.usage;return i.bindBuffer(i.UNIFORM_BUFFER,P),i.bufferData(i.UNIFORM_BUFFER,G,H),i.bindBuffer(i.UNIFORM_BUFFER,null),i.bindBufferBase(i.UNIFORM_BUFFER,O,P),P}function p(){for(let A=0;A<a;A++)if(o.indexOf(A)===-1)return o.push(A),A;return console.error("THREE.WebGLRenderer: Maximum number of simultaneously usable uniforms groups reached."),0}function f(A){let O=s[A.id],P=A.uniforms,G=A.__cache;i.bindBuffer(i.UNIFORM_BUFFER,O);for(let H=0,S=P.length;H<S;H++){let M=Array.isArray(P[H])?P[H]:[P[H]];for(let y=0,w=M.length;y<w;y++){let W=M[y];if(u(W,H,y,G)===!0){let $=W.__offset,F=Array.isArray(W.value)?W.value:[W.value],k=0;for(let Q=0;Q<F.length;Q++){let V=F[Q],te=m(V);typeof V=="number"||typeof V=="boolean"?(W.__data[0]=V,i.bufferSubData(i.UNIFORM_BUFFER,$+k,W.__data)):V.isMatrix3?(W.__data[0]=V.elements[0],W.__data[1]=V.elements[1],W.__data[2]=V.elements[2],W.__data[3]=0,W.__data[4]=V.elements[3],W.__data[5]=V.elements[4],W.__data[6]=V.elements[5],W.__data[7]=0,W.__data[8]=V.elements[6],W.__data[9]=V.elements[7],W.__data[10]=V.elements[8],W.__data[11]=0):(V.toArray(W.__data,k),k+=te.storage/Float32Array.BYTES_PER_ELEMENT)}i.bufferSubData(i.UNIFORM_BUFFER,$,W.__data)}}}i.bindBuffer(i.UNIFORM_BUFFER,null)}function u(A,O,P,G){let H=A.value,S=O+"_"+P;if(G[S]===void 0)return typeof H=="number"||typeof H=="boolean"?G[S]=H:G[S]=H.clone(),!0;{let M=G[S];if(typeof H=="number"||typeof H=="boolean"){if(M!==H)return G[S]=H,!0}else if(M.equals(H)===!1)return M.copy(H),!0}return!1}function _(A){let O=A.uniforms,P=0,G=16;for(let S=0,M=O.length;S<M;S++){let y=Array.isArray(O[S])?O[S]:[O[S]];for(let w=0,W=y.length;w<W;w++){let $=y[w],F=Array.isArray($.value)?$.value:[$.value];for(let k=0,Q=F.length;k<Q;k++){let V=F[k],te=m(V),z=P%G,pe=z%te.boundary,me=z+pe;P+=pe,me!==0&&G-me<te.storage&&(P+=G-me),$.__data=new Float32Array(te.storage/Float32Array.BYTES_PER_ELEMENT),$.__offset=P,P+=te.storage}}}let H=P%G;return H>0&&(P+=G-H),A.__size=P,A.__cache={},this}function m(A){let O={boundary:0,storage:0};return typeof A=="number"||typeof A=="boolean"?(O.boundary=4,O.storage=4):A.isVector2?(O.boundary=8,O.storage=8):A.isVector3||A.isColor?(O.boundary=16,O.storage=12):A.isVector4?(O.boundary=16,O.storage=16):A.isMatrix3?(O.boundary=48,O.storage=48):A.isMatrix4?(O.boundary=64,O.storage=64):A.isTexture?console.warn("THREE.WebGLRenderer: Texture samplers can not be part of an uniforms group."):console.warn("THREE.WebGLRenderer: Unsupported uniform value type.",A),O}function g(A){let O=A.target;O.removeEventListener("dispose",g);let P=o.indexOf(O.__bindingPointIndex);o.splice(P,1),i.deleteBuffer(s[O.id]),delete s[O.id],delete r[O.id]}function d(){for(let A in s)i.deleteBuffer(s[A]);o=[],s={},r={}}return{bind:c,update:h,dispose:d}}var uo=class{constructor(e={}){let{canvas:t=df(),context:n=null,depth:s=!0,stencil:r=!1,alpha:o=!1,antialias:a=!1,premultipliedAlpha:c=!0,preserveDrawingBuffer:h=!1,powerPreference:l="default",failIfMajorPerformanceCaveat:p=!1,reverseDepthBuffer:f=!1}=e;this.isWebGLRenderer=!0;let u;if(n!==null){if(typeof WebGLRenderingContext<"u"&&n instanceof WebGLRenderingContext)throw new Error("THREE.WebGLRenderer: WebGL 1 is not supported since r163.");u=n.getContextAttributes().alpha}else u=o;let _=new Uint32Array(4),m=new Int32Array(4),g=null,d=null,A=[],O=[];this.domElement=t,this.debug={checkShaderErrors:!0,onShaderError:null},this.autoClear=!0,this.autoClearColor=!0,this.autoClearDepth=!0,this.autoClearStencil=!0,this.sortObjects=!0,this.clippingPlanes=[],this.localClippingEnabled=!1,this._outputColorSpace=_t,this.toneMapping=ai,this.toneMappingExposure=1;let P=this,G=!1,H=0,S=0,M=null,y=-1,w=null,W=new nt,$=new nt,F=null,k=new Ge(0),Q=0,V=t.width,te=t.height,z=1,pe=null,me=null,He=new nt(0,0,V,te),Ne=new nt(0,0,V,te),tt=!1,J=new rr,ie=!1,xe=!1,oe=new Fe,be=new Fe,Te=new C,ke=new nt,lt={background:null,fog:null,environment:null,overrideMaterial:null,isScene:!0},Se=!1;function ot(){return M===null?z:1}let I=n;function Dt(x,E){return t.getContext(x,E)}try{let x={alpha:!0,depth:s,stencil:r,antialias:a,premultipliedAlpha:c,preserveDrawingBuffer:h,powerPreference:l,failIfMajorPerformanceCaveat:p};if("setAttribute"in t&&t.setAttribute("data-engine","three.js r170"),t.addEventListener("webglcontextlost",Y,!1),t.addEventListener("webglcontextrestored",ae,!1),t.addEventListener("webglcontextcreationerror",re,!1),I===null){let E="webgl2";if(I=Dt(E,x),I===null)throw Dt(E)?new Error("Error creating WebGL context with your selected attributes."):new Error("Error creating WebGL context.")}}catch(x){throw console.error("THREE.WebGLRenderer: "+x.message),x}let je,Ye,Ee,ht,we,v,b,R,j,Z,q,Pe,se,de,We,ne,ge,Me,Ue,_e,Je,Xe,at,U;function le(){je=new vg(I),je.init(),Xe=new im(I,je),Ye=new bg(I,je,e,Xe),Ee=new em(I,je),Ye.reverseDepthBuffer&&f&&Ee.buffers.depth.setReversed(!0),ht=new Eg(I),we=new D_,v=new nm(I,je,Ee,we,Ye,Xe,ht),b=new wg(P),R=new Ag(P),j=new If(I),at=new mg(I,j),Z=new Hg(I,j,ht,at),q=new Mg(I,Z,j,ht),Ue=new Wg(I,Ye,v),ne=new Pg(we),Pe=new F_(P,b,R,je,Ye,at,ne),se=new cm(P,we),de=new V_,We=new j_(je),Me=new _g(P,b,R,Ee,q,u,c),ge=new Z_(P,q,Ye),U=new hm(I,ht,Ye,Ee),_e=new xg(I,je,ht),Je=new Sg(I,je,ht),ht.programs=Pe.programs,P.capabilities=Ye,P.extensions=je,P.properties=we,P.renderLists=de,P.shadowMap=ge,P.state=Ee,P.info=ht}le();let L=new yc(P,I);this.xr=L,this.getContext=function(){return I},this.getContextAttributes=function(){return I.getContextAttributes()},this.forceContextLoss=function(){let x=je.get("WEBGL_lose_context");x&&x.loseContext()},this.forceContextRestore=function(){let x=je.get("WEBGL_lose_context");x&&x.restoreContext()},this.getPixelRatio=function(){return z},this.setPixelRatio=function(x){x!==void 0&&(z=x,this.setSize(V,te,!1))},this.getSize=function(x){return x.set(V,te)},this.setSize=function(x,E,X=!0){if(L.isPresenting){console.warn("THREE.WebGLRenderer: Can't change size while VR device is presenting.");return}V=x,te=E,t.width=Math.floor(x*z),t.height=Math.floor(E*z),X===!0&&(t.style.width=x+"px",t.style.height=E+"px"),this.setViewport(0,0,x,E)},this.getDrawingBufferSize=function(x){return x.set(V*z,te*z).floor()},this.setDrawingBufferSize=function(x,E,X){V=x,te=E,z=X,t.width=Math.floor(x*X),t.height=Math.floor(E*X),this.setViewport(0,0,x,E)},this.getCurrentViewport=function(x){return x.copy(W)},this.getViewport=function(x){return x.copy(He)},this.setViewport=function(x,E,X,B){x.isVector4?He.set(x.x,x.y,x.z,x.w):He.set(x,E,X,B),Ee.viewport(W.copy(He).multiplyScalar(z).round())},this.getScissor=function(x){return x.copy(Ne)},this.setScissor=function(x,E,X,B){x.isVector4?Ne.set(x.x,x.y,x.z,x.w):Ne.set(x,E,X,B),Ee.scissor($.copy(Ne).multiplyScalar(z).round())},this.getScissorTest=function(){return tt},this.setScissorTest=function(x){Ee.setScissorTest(tt=x)},this.setOpaqueSort=function(x){pe=x},this.setTransparentSort=function(x){me=x},this.getClearColor=function(x){return x.copy(Me.getClearColor())},this.setClearColor=function(){Me.setClearColor.apply(Me,arguments)},this.getClearAlpha=function(){return Me.getClearAlpha()},this.setClearAlpha=function(){Me.setClearAlpha.apply(Me,arguments)},this.clear=function(x=!0,E=!0,X=!0){let B=0;if(x){let T=!1;if(M!==null){let K=M.texture.format;T=K===zc||K===Lc||K===Vc}if(T){let K=M.texture.type,ce=K===Dn||K===vi||K===nr||K===cs||K===Fc||K===Dc,Oe=Me.getClearColor(),ye=Me.getClearAlpha(),Be=Oe.r,Le=Oe.g,Ae=Oe.b;ce?(_[0]=Be,_[1]=Le,_[2]=Ae,_[3]=ye,I.clearBufferuiv(I.COLOR,0,_)):(m[0]=Be,m[1]=Le,m[2]=Ae,m[3]=ye,I.clearBufferiv(I.COLOR,0,m))}else B|=I.COLOR_BUFFER_BIT}E&&(B|=I.DEPTH_BUFFER_BIT),X&&(B|=I.STENCIL_BUFFER_BIT,this.state.buffers.stencil.setMask(4294967295)),I.clear(B)},this.clearColor=function(){this.clear(!0,!1,!1)},this.clearDepth=function(){this.clear(!1,!0,!1)},this.clearStencil=function(){this.clear(!1,!1,!0)},this.dispose=function(){t.removeEventListener("webglcontextlost",Y,!1),t.removeEventListener("webglcontextrestored",ae,!1),t.removeEventListener("webglcontextcreationerror",re,!1),de.dispose(),We.dispose(),we.dispose(),b.dispose(),R.dispose(),q.dispose(),at.dispose(),U.dispose(),Pe.dispose(),L.dispose(),L.removeEventListener("sessionstart",Cs),L.removeEventListener("sessionend",Gs),mn.stop()};function Y(x){x.preventDefault(),console.log("THREE.WebGLRenderer: Context Lost."),G=!0}function ae(){console.log("THREE.WebGLRenderer: Context Restored."),G=!1;let x=ht.autoReset,E=ge.enabled,X=ge.autoUpdate,B=ge.needsUpdate,T=ge.type;le(),ht.autoReset=x,ge.enabled=E,ge.autoUpdate=X,ge.needsUpdate=B,ge.type=T}function re(x){console.error("THREE.WebGLRenderer: A WebGL context could not be created. Reason: ",x.statusMessage)}function Ie(x){let E=x.target;E.removeEventListener("dispose",Ie),pt(E)}function pt(x){Ht(x),we.remove(x)}function Ht(x){let E=we.get(x).programs;E!==void 0&&(E.forEach(function(X){Pe.releaseProgram(X)}),x.isShaderMaterial&&Pe.releaseShaderCache(x))}this.renderBufferDirect=function(x,E,X,B,T,K){E===null&&(E=lt);let ce=T.isMesh&&T.matrixWorld.determinant()<0,Oe=Ce(x,E,X,B,T);Ee.setMaterial(B,ce);let ye=X.index,Be=1;if(B.wireframe===!0){if(ye=Z.getWireframeAttribute(X),ye===void 0)return;Be=2}let Le=X.drawRange,Ae=X.attributes.position,et=Le.start*Be,ft=(Le.start+Le.count)*Be;K!==null&&(et=Math.max(et,K.start*Be),ft=Math.min(ft,(K.start+K.count)*Be)),ye!==null?(et=Math.max(et,0),ft=Math.min(ft,ye.count)):Ae!=null&&(et=Math.max(et,0),ft=Math.min(ft,Ae.count));let ut=ft-et;if(ut<0||ut===1/0)return;at.setup(T,B,Oe,X,ye);let zt,it=_e;if(ye!==null&&(zt=j.get(ye),it=Je,it.setIndex(zt)),T.isMesh)B.wireframe===!0?(Ee.setLineWidth(B.wireframeLinewidth*ot()),it.setMode(I.LINES)):it.setMode(I.TRIANGLES);else if(T.isLine){let ve=B.linewidth;ve===void 0&&(ve=1),Ee.setLineWidth(ve*ot()),T.isLineSegments?it.setMode(I.LINES):T.isLineLoop?it.setMode(I.LINE_LOOP):it.setMode(I.LINE_STRIP)}else T.isPoints?it.setMode(I.POINTS):T.isSprite&&it.setMode(I.TRIANGLES);if(T.isBatchedMesh)if(T._multiDrawInstances!==null)it.renderMultiDrawInstances(T._multiDrawStarts,T._multiDrawCounts,T._multiDrawCount,T._multiDrawInstances);else if(je.get("WEBGL_multi_draw"))it.renderMultiDraw(T._multiDrawStarts,T._multiDrawCounts,T._multiDrawCount);else{let ve=T._multiDrawStarts,En=T._multiDrawCounts,st=T._multiDrawCount,hn=ye?j.get(ye).bytesPerElement:1,Ti=we.get(B).currentProgram.getUniforms();for(let Jt=0;Jt<st;Jt++)Ti.setValue(I,"_gl_DrawID",Jt),it.render(ve[Jt]/hn,En[Jt])}else if(T.isInstancedMesh)it.renderInstances(et,ut,T.count);else if(X.isInstancedBufferGeometry){let ve=X._maxInstanceCount!==void 0?X._maxInstanceCount:1/0,En=Math.min(X.instanceCount,ve);it.renderInstances(et,ut,En)}else it.render(et,ut)};function Ke(x,E,X){x.transparent===!0&&x.side===dn&&x.forceSinglePass===!1?(x.side=Bt,x.needsUpdate=!0,ee(x,E,X),x.side=On,x.needsUpdate=!0,ee(x,E,X),x.side=dn):ee(x,E,X)}this.compile=function(x,E,X=null){X===null&&(X=x),d=We.get(X),d.init(E),O.push(d),X.traverseVisible(function(T){T.isLight&&T.layers.test(E.layers)&&(d.pushLight(T),T.castShadow&&d.pushShadow(T))}),x!==X&&x.traverseVisible(function(T){T.isLight&&T.layers.test(E.layers)&&(d.pushLight(T),T.castShadow&&d.pushShadow(T))}),d.setupLights();let B=new Set;return x.traverse(function(T){if(!(T.isMesh||T.isPoints||T.isLine||T.isSprite))return;let K=T.material;if(K)if(Array.isArray(K))for(let ce=0;ce<K.length;ce++){let Oe=K[ce];Ke(Oe,X,T),B.add(Oe)}else Ke(K,X,T),B.add(K)}),O.pop(),d=null,B},this.compileAsync=function(x,E,X=null){let B=this.compile(x,E,X);return new Promise(T=>{function K(){if(B.forEach(function(ce){we.get(ce).currentProgram.isReady()&&B.delete(ce)}),B.size===0){T(x);return}setTimeout(K,10)}je.get("KHR_parallel_shader_compile")!==null?K():setTimeout(K,10)})};let Lt=null;function Pt(x){Lt&&Lt(x)}function Cs(){mn.stop()}function Gs(){mn.start()}let mn=new w0;mn.setAnimationLoop(Pt),typeof self<"u"&&mn.setContext(self),this.setAnimationLoop=function(x){Lt=x,L.setAnimationLoop(x),x===null?mn.stop():mn.start()},L.addEventListener("sessionstart",Cs),L.addEventListener("sessionend",Gs),this.render=function(x,E){if(E!==void 0&&E.isCamera!==!0){console.error("THREE.WebGLRenderer.render: camera is not an instance of THREE.Camera.");return}if(G===!0)return;if(x.matrixWorldAutoUpdate===!0&&x.updateMatrixWorld(),E.parent===null&&E.matrixWorldAutoUpdate===!0&&E.updateMatrixWorld(),L.enabled===!0&&L.isPresenting===!0&&(L.cameraAutoUpdate===!0&&L.updateCamera(E),E=L.getCamera()),x.isScene===!0&&x.onBeforeRender(P,x,E,M),d=We.get(x,O.length),d.init(E),O.push(d),be.multiplyMatrices(E.projectionMatrix,E.matrixWorldInverse),J.setFromProjectionMatrix(be),xe=this.localClippingEnabled,ie=ne.init(this.clippingPlanes,xe),g=de.get(x,A.length),g.init(),A.push(g),L.enabled===!0&&L.isPresenting===!0){let K=P.xr.getDepthSensingMesh();K!==null&&Ts(K,E,-1/0,P.sortObjects)}Ts(x,E,0,P.sortObjects),g.finish(),P.sortObjects===!0&&g.sort(pe,me),Se=L.enabled===!1||L.isPresenting===!1||L.hasDepthSensing()===!1,Se&&Me.addToRenderList(g,x),this.info.render.frame++,ie===!0&&ne.beginShadows();let X=d.state.shadowsArray;ge.render(X,x,E),ie===!0&&ne.endShadows(),this.info.autoReset===!0&&this.info.reset();let B=g.opaque,T=g.transmissive;if(d.setupLights(),E.isArrayCamera){let K=E.cameras;if(T.length>0)for(let ce=0,Oe=K.length;ce<Oe;ce++){let ye=K[ce];Is(B,T,x,ye)}Se&&Me.render(x);for(let ce=0,Oe=K.length;ce<Oe;ce++){let ye=K[ce];Us(g,x,ye,ye.viewport)}}else T.length>0&&Is(B,T,x,E),Se&&Me.render(x),Us(g,x,E);M!==null&&(v.updateMultisampleRenderTarget(M),v.updateRenderTargetMipmap(M)),x.isScene===!0&&x.onAfterRender(P,x,E),at.resetDefaultState(),y=-1,w=null,O.pop(),O.length>0?(d=O[O.length-1],ie===!0&&ne.setGlobalState(P.clippingPlanes,d.state.camera)):d=null,A.pop(),A.length>0?g=A[A.length-1]:g=null};function Ts(x,E,X,B){if(x.visible===!1)return;if(x.layers.test(E.layers)){if(x.isGroup)X=x.renderOrder;else if(x.isLOD)x.autoUpdate===!0&&x.update(E);else if(x.isLight)d.pushLight(x),x.castShadow&&d.pushShadow(x);else if(x.isSprite){if(!x.frustumCulled||J.intersectsSprite(x)){B&&ke.setFromMatrixPosition(x.matrixWorld).applyMatrix4(be);let ce=q.update(x),Oe=x.material;Oe.visible&&g.push(x,ce,Oe,X,ke.z,null)}}else if((x.isMesh||x.isLine||x.isPoints)&&(!x.frustumCulled||J.intersectsObject(x))){let ce=q.update(x),Oe=x.material;if(B&&(x.boundingSphere!==void 0?(x.boundingSphere===null&&x.computeBoundingSphere(),ke.copy(x.boundingSphere.center)):(ce.boundingSphere===null&&ce.computeBoundingSphere(),ke.copy(ce.boundingSphere.center)),ke.applyMatrix4(x.matrixWorld).applyMatrix4(be)),Array.isArray(Oe)){let ye=ce.groups;for(let Be=0,Le=ye.length;Be<Le;Be++){let Ae=ye[Be],et=Oe[Ae.materialIndex];et&&et.visible&&g.push(x,ce,et,X,ke.z,Ae)}}else Oe.visible&&g.push(x,ce,Oe,X,ke.z,null)}}let K=x.children;for(let ce=0,Oe=K.length;ce<Oe;ce++)Ts(K[ce],E,X,B)}function Us(x,E,X,B){let T=x.opaque,K=x.transmissive,ce=x.transparent;d.setupLightsView(X),ie===!0&&ne.setGlobalState(P.clippingPlanes,X),B&&Ee.viewport(W.copy(B)),T.length>0&&N(T,E,X),K.length>0&&N(K,E,X),ce.length>0&&N(ce,E,X),Ee.buffers.depth.setTest(!0),Ee.buffers.depth.setMask(!0),Ee.buffers.color.setMask(!0),Ee.setPolygonOffset(!1)}function Is(x,E,X,B){if((X.isScene===!0?X.overrideMaterial:null)!==null)return;d.state.transmissionRenderTarget[B.id]===void 0&&(d.state.transmissionRenderTarget[B.id]=new Nn(1,1,{generateMipmaps:!0,type:je.has("EXT_color_buffer_half_float")||je.has("EXT_color_buffer_float")?fr:Dn,minFilter:wn,samples:4,stencilBuffer:r,resolveDepthBuffer:!1,resolveStencilBuffer:!1,colorSpace:Ze.workingColorSpace}));let K=d.state.transmissionRenderTarget[B.id],ce=B.viewport||W;K.setSize(ce.z,ce.w);let Oe=P.getRenderTarget();P.setRenderTarget(K),P.getClearColor(k),Q=P.getClearAlpha(),Q<1&&P.setClearColor(16777215,.5),P.clear(),Se&&Me.render(X);let ye=P.toneMapping;P.toneMapping=ai;let Be=B.viewport;if(B.viewport!==void 0&&(B.viewport=void 0),d.setupLightsView(B),ie===!0&&ne.setGlobalState(P.clippingPlanes,B),N(x,X,B),v.updateMultisampleRenderTarget(K),v.updateRenderTargetMipmap(K),je.has("WEBGL_multisampled_render_to_texture")===!1){let Le=!1;for(let Ae=0,et=E.length;Ae<et;Ae++){let ft=E[Ae],ut=ft.object,zt=ft.geometry,it=ft.material,ve=ft.group;if(it.side===dn&&ut.layers.test(B.layers)){let En=it.side;it.side=Bt,it.needsUpdate=!0,D(ut,X,B,zt,it,ve),it.side=En,it.needsUpdate=!0,Le=!0}}Le===!0&&(v.updateMultisampleRenderTarget(K),v.updateRenderTargetMipmap(K))}P.setRenderTarget(Oe),P.setClearColor(k,Q),Be!==void 0&&(B.viewport=Be),P.toneMapping=ye}function N(x,E,X){let B=E.isScene===!0?E.overrideMaterial:null;for(let T=0,K=x.length;T<K;T++){let ce=x[T],Oe=ce.object,ye=ce.geometry,Be=B===null?ce.material:B,Le=ce.group;Oe.layers.test(X.layers)&&D(Oe,E,X,ye,Be,Le)}}function D(x,E,X,B,T,K){x.onBeforeRender(P,E,X,B,T,K),x.modelViewMatrix.multiplyMatrices(X.matrixWorldInverse,x.matrixWorld),x.normalMatrix.getNormalMatrix(x.modelViewMatrix),T.onBeforeRender(P,E,X,B,x,K),T.transparent===!0&&T.side===dn&&T.forceSinglePass===!1?(T.side=Bt,T.needsUpdate=!0,P.renderBufferDirect(X,E,B,T,x,K),T.side=On,T.needsUpdate=!0,P.renderBufferDirect(X,E,B,T,x,K),T.side=dn):P.renderBufferDirect(X,E,B,T,x,K),x.onAfterRender(P,E,X,B,T,K)}function ee(x,E,X){E.isScene!==!0&&(E=lt);let B=we.get(x),T=d.state.lights,K=d.state.shadowsArray,ce=T.state.version,Oe=Pe.getParameters(x,T.state,K,E,X),ye=Pe.getProgramCacheKey(Oe),Be=B.programs;B.environment=x.isMeshStandardMaterial?E.environment:null,B.fog=E.fog,B.envMap=(x.isMeshStandardMaterial?R:b).get(x.envMap||B.environment),B.envMapRotation=B.environment!==null&&x.envMap===null?E.environmentRotation:x.envMapRotation,Be===void 0&&(x.addEventListener("dispose",Ie),Be=new Map,B.programs=Be);let Le=Be.get(ye);if(Le!==void 0){if(B.currentProgram===Le&&B.lightsStateVersion===ce)return fe(x,Oe),Le}else Oe.uniforms=Pe.getUniforms(x),x.onBeforeCompile(Oe,P),Le=Pe.acquireProgram(Oe,ye),Be.set(ye,Le),B.uniforms=Oe.uniforms;let Ae=B.uniforms;return(!x.isShaderMaterial&&!x.isRawShaderMaterial||x.clipping===!0)&&(Ae.clippingPlanes=ne.uniform),fe(x,Oe),B.needsLights=Ve(x),B.lightsStateVersion=ce,B.needsLights&&(Ae.ambientLightColor.value=T.state.ambient,Ae.lightProbe.value=T.state.probe,Ae.directionalLights.value=T.state.directional,Ae.directionalLightShadows.value=T.state.directionalShadow,Ae.spotLights.value=T.state.spot,Ae.spotLightShadows.value=T.state.spotShadow,Ae.rectAreaLights.value=T.state.rectArea,Ae.ltc_1.value=T.state.rectAreaLTC1,Ae.ltc_2.value=T.state.rectAreaLTC2,Ae.pointLights.value=T.state.point,Ae.pointLightShadows.value=T.state.pointShadow,Ae.hemisphereLights.value=T.state.hemi,Ae.directionalShadowMap.value=T.state.directionalShadowMap,Ae.directionalShadowMatrix.value=T.state.directionalShadowMatrix,Ae.spotShadowMap.value=T.state.spotShadowMap,Ae.spotLightMatrix.value=T.state.spotLightMatrix,Ae.spotLightMap.value=T.state.spotLightMap,Ae.pointShadowMap.value=T.state.pointShadowMap,Ae.pointShadowMatrix.value=T.state.pointShadowMatrix),B.currentProgram=Le,B.uniformsList=null,Le}function ue(x){if(x.uniformsList===null){let E=x.currentProgram.getUniforms();x.uniformsList=ss.seqWithValue(E.seq,x.uniforms)}return x.uniformsList}function fe(x,E){let X=we.get(x);X.outputColorSpace=E.outputColorSpace,X.batching=E.batching,X.batchingColor=E.batchingColor,X.instancing=E.instancing,X.instancingColor=E.instancingColor,X.instancingMorph=E.instancingMorph,X.skinning=E.skinning,X.morphTargets=E.morphTargets,X.morphNormals=E.morphNormals,X.morphColors=E.morphColors,X.morphTargetsCount=E.morphTargetsCount,X.numClippingPlanes=E.numClippingPlanes,X.numIntersection=E.numClipIntersection,X.vertexAlphas=E.vertexAlphas,X.vertexTangents=E.vertexTangents,X.toneMapping=E.toneMapping}function Ce(x,E,X,B,T){E.isScene!==!0&&(E=lt),v.resetTextureUnits();let K=E.fog,ce=B.isMeshStandardMaterial?E.environment:null,Oe=M===null?P.outputColorSpace:M.isXRRenderTarget===!0?M.texture.colorSpace:Ft,ye=(B.isMeshStandardMaterial?R:b).get(B.envMap||ce),Be=B.vertexColors===!0&&!!X.attributes.color&&X.attributes.color.itemSize===4,Le=!!X.attributes.tangent&&(!!B.normalMap||B.anisotropy>0),Ae=!!X.morphAttributes.position,et=!!X.morphAttributes.normal,ft=!!X.morphAttributes.color,ut=ai;B.toneMapped&&(M===null||M.isXRRenderTarget===!0)&&(ut=P.toneMapping);let zt=X.morphAttributes.position||X.morphAttributes.normal||X.morphAttributes.color,it=zt!==void 0?zt.length:0,ve=we.get(B),En=d.state.lights;if(ie===!0&&(xe===!0||x!==w)){let tn=x===w&&B.id===y;ne.setState(B,x,tn)}let st=!1;B.version===ve.__version?(ve.needsLights&&ve.lightsStateVersion!==En.state.version||ve.outputColorSpace!==Oe||T.isBatchedMesh&&ve.batching===!1||!T.isBatchedMesh&&ve.batching===!0||T.isBatchedMesh&&ve.batchingColor===!0&&T.colorTexture===null||T.isBatchedMesh&&ve.batchingColor===!1&&T.colorTexture!==null||T.isInstancedMesh&&ve.instancing===!1||!T.isInstancedMesh&&ve.instancing===!0||T.isSkinnedMesh&&ve.skinning===!1||!T.isSkinnedMesh&&ve.skinning===!0||T.isInstancedMesh&&ve.instancingColor===!0&&T.instanceColor===null||T.isInstancedMesh&&ve.instancingColor===!1&&T.instanceColor!==null||T.isInstancedMesh&&ve.instancingMorph===!0&&T.morphTexture===null||T.isInstancedMesh&&ve.instancingMorph===!1&&T.morphTexture!==null||ve.envMap!==ye||B.fog===!0&&ve.fog!==K||ve.numClippingPlanes!==void 0&&(ve.numClippingPlanes!==ne.numPlanes||ve.numIntersection!==ne.numIntersection)||ve.vertexAlphas!==Be||ve.vertexTangents!==Le||ve.morphTargets!==Ae||ve.morphNormals!==et||ve.morphColors!==ft||ve.toneMapping!==ut||ve.morphTargetsCount!==it)&&(st=!0):(st=!0,ve.__version=B.version);let hn=ve.currentProgram;st===!0&&(hn=ee(B,E,T));let Ti=!1,Jt=!1,Rs=!1,gt=hn.getUniforms(),xn=ve.uniforms;if(Ee.useProgram(hn.program)&&(Ti=!0,Jt=!0,Rs=!0),B.id!==y&&(y=B.id,Jt=!0),Ti||w!==x){Ee.buffers.depth.getReversed()?(oe.copy(x.projectionMatrix),gf(oe),_f(oe),gt.setValue(I,"projectionMatrix",oe)):gt.setValue(I,"projectionMatrix",x.projectionMatrix),gt.setValue(I,"viewMatrix",x.matrixWorldInverse);let Qn=gt.map.cameraPosition;Qn!==void 0&&Qn.setValue(I,Te.setFromMatrixPosition(x.matrixWorld)),Ye.logarithmicDepthBuffer&&gt.setValue(I,"logDepthBufFC",2/(Math.log(x.far+1)/Math.LN2)),(B.isMeshPhongMaterial||B.isMeshToonMaterial||B.isMeshLambertMaterial||B.isMeshBasicMaterial||B.isMeshStandardMaterial||B.isShaderMaterial)&&gt.setValue(I,"isOrthographic",x.isOrthographicCamera===!0),w!==x&&(w=x,Jt=!0,Rs=!0)}if(T.isSkinnedMesh){gt.setOptional(I,T,"bindMatrix"),gt.setOptional(I,T,"bindMatrixInverse");let tn=T.skeleton;tn&&(tn.boneTexture===null&&tn.computeBoneTexture(),gt.setValue(I,"boneTexture",tn.boneTexture,v))}T.isBatchedMesh&&(gt.setOptional(I,T,"batchingTexture"),gt.setValue(I,"batchingTexture",T._matricesTexture,v),gt.setOptional(I,T,"batchingIdTexture"),gt.setValue(I,"batchingIdTexture",T._indirectTexture,v),gt.setOptional(I,T,"batchingColorTexture"),T._colorsTexture!==null&&gt.setValue(I,"batchingColorTexture",T._colorsTexture,v));let Xs=X.morphAttributes;if((Xs.position!==void 0||Xs.normal!==void 0||Xs.color!==void 0)&&Ue.update(T,X,hn),(Jt||ve.receiveShadow!==T.receiveShadow)&&(ve.receiveShadow=T.receiveShadow,gt.setValue(I,"receiveShadow",T.receiveShadow)),B.isMeshGouraudMaterial&&B.envMap!==null&&(xn.envMap.value=ye,xn.flipEnvMap.value=ye.isCubeTexture&&ye.isRenderTargetTexture===!1?-1:1),B.isMeshStandardMaterial&&B.envMap===null&&E.environment!==null&&(xn.envMapIntensity.value=E.environmentIntensity),Jt&&(gt.setValue(I,"toneMappingExposure",P.toneMappingExposure),ve.needsLights&&$e(xn,Rs),K&&B.fog===!0&&se.refreshFogUniforms(xn,K),se.refreshMaterialUniforms(xn,B,z,te,d.state.transmissionRenderTarget[x.id]),ss.upload(I,ue(ve),xn,v)),B.isShaderMaterial&&B.uniformsNeedUpdate===!0&&(ss.upload(I,ue(ve),xn,v),B.uniformsNeedUpdate=!1),B.isSpriteMaterial&&gt.setValue(I,"center",T.center),gt.setValue(I,"modelViewMatrix",T.modelViewMatrix),gt.setValue(I,"normalMatrix",T.normalMatrix),gt.setValue(I,"modelMatrix",T.matrixWorld),B.isShaderMaterial||B.isRawShaderMaterial){let tn=B.uniformsGroups;for(let Qn=0,Yn=tn.length;Qn<Yn;Qn++){let Gh=tn[Qn];U.update(Gh,hn),U.bind(Gh,hn)}}return hn}function $e(x,E){x.ambientLightColor.needsUpdate=E,x.lightProbe.needsUpdate=E,x.directionalLights.needsUpdate=E,x.directionalLightShadows.needsUpdate=E,x.pointLights.needsUpdate=E,x.pointLightShadows.needsUpdate=E,x.spotLights.needsUpdate=E,x.spotLightShadows.needsUpdate=E,x.rectAreaLights.needsUpdate=E,x.hemisphereLights.needsUpdate=E}function Ve(x){return x.isMeshLambertMaterial||x.isMeshToonMaterial||x.isMeshPhongMaterial||x.isMeshStandardMaterial||x.isShadowMaterial||x.isShaderMaterial&&x.lights===!0}this.getActiveCubeFace=function(){return H},this.getActiveMipmapLevel=function(){return S},this.getRenderTarget=function(){return M},this.setRenderTargetTextures=function(x,E,X){we.get(x.texture).__webglTexture=E,we.get(x.depthTexture).__webglTexture=X;let B=we.get(x);B.__hasExternalTextures=!0,B.__autoAllocateDepthBuffer=X===void 0,B.__autoAllocateDepthBuffer||je.has("WEBGL_multisampled_render_to_texture")===!0&&(console.warn("THREE.WebGLRenderer: Render-to-texture extension was disabled because an external texture was provided"),B.__useRenderToTexture=!1)},this.setRenderTargetFramebuffer=function(x,E){let X=we.get(x);X.__webglFramebuffer=E,X.__useDefaultFramebuffer=E===void 0},this.setRenderTarget=function(x,E=0,X=0){M=x,H=E,S=X;let B=!0,T=null,K=!1,ce=!1;if(x){let ye=we.get(x);if(ye.__useDefaultFramebuffer!==void 0)Ee.bindFramebuffer(I.FRAMEBUFFER,null),B=!1;else if(ye.__webglFramebuffer===void 0)v.setupRenderTarget(x);else if(ye.__hasExternalTextures)v.rebindTextures(x,we.get(x.texture).__webglTexture,we.get(x.depthTexture).__webglTexture);else if(x.depthBuffer){let Ae=x.depthTexture;if(ye.__boundDepthTexture!==Ae){if(Ae!==null&&we.has(Ae)&&(x.width!==Ae.image.width||x.height!==Ae.image.height))throw new Error("WebGLRenderTarget: Attached DepthTexture is initialized to the incorrect size.");v.setupDepthRenderbuffer(x)}}let Be=x.texture;(Be.isData3DTexture||Be.isDataArrayTexture||Be.isCompressedArrayTexture)&&(ce=!0);let Le=we.get(x).__webglFramebuffer;x.isWebGLCubeRenderTarget?(Array.isArray(Le[E])?T=Le[E][X]:T=Le[E],K=!0):x.samples>0&&v.useMultisampledRTT(x)===!1?T=we.get(x).__webglMultisampledFramebuffer:Array.isArray(Le)?T=Le[X]:T=Le,W.copy(x.viewport),$.copy(x.scissor),F=x.scissorTest}else W.copy(He).multiplyScalar(z).floor(),$.copy(Ne).multiplyScalar(z).floor(),F=tt;if(Ee.bindFramebuffer(I.FRAMEBUFFER,T)&&B&&Ee.drawBuffers(x,T),Ee.viewport(W),Ee.scissor($),Ee.setScissorTest(F),K){let ye=we.get(x.texture);I.framebufferTexture2D(I.FRAMEBUFFER,I.COLOR_ATTACHMENT0,I.TEXTURE_CUBE_MAP_POSITIVE_X+E,ye.__webglTexture,X)}else if(ce){let ye=we.get(x.texture),Be=E||0;I.framebufferTextureLayer(I.FRAMEBUFFER,I.COLOR_ATTACHMENT0,ye.__webglTexture,X||0,Be)}y=-1},this.readRenderTargetPixels=function(x,E,X,B,T,K,ce){if(!(x&&x.isWebGLRenderTarget)){console.error("THREE.WebGLRenderer.readRenderTargetPixels: renderTarget is not THREE.WebGLRenderTarget.");return}let Oe=we.get(x).__webglFramebuffer;if(x.isWebGLCubeRenderTarget&&ce!==void 0&&(Oe=Oe[ce]),Oe){Ee.bindFramebuffer(I.FRAMEBUFFER,Oe);try{let ye=x.texture,Be=ye.format,Le=ye.type;if(!Ye.textureFormatReadable(Be)){console.error("THREE.WebGLRenderer.readRenderTargetPixels: renderTarget is not in RGBA or implementation defined format.");return}if(!Ye.textureTypeReadable(Le)){console.error("THREE.WebGLRenderer.readRenderTargetPixels: renderTarget is not in UnsignedByteType or implementation defined type.");return}E>=0&&E<=x.width-B&&X>=0&&X<=x.height-T&&I.readPixels(E,X,B,T,Xe.convert(Be),Xe.convert(Le),K)}finally{let ye=M!==null?we.get(M).__webglFramebuffer:null;Ee.bindFramebuffer(I.FRAMEBUFFER,ye)}}},this.readRenderTargetPixelsAsync=async function(x,E,X,B,T,K,ce){if(!(x&&x.isWebGLRenderTarget))throw new Error("THREE.WebGLRenderer.readRenderTargetPixels: renderTarget is not THREE.WebGLRenderTarget.");let Oe=we.get(x).__webglFramebuffer;if(x.isWebGLCubeRenderTarget&&ce!==void 0&&(Oe=Oe[ce]),Oe){let ye=x.texture,Be=ye.format,Le=ye.type;if(!Ye.textureFormatReadable(Be))throw new Error("THREE.WebGLRenderer.readRenderTargetPixelsAsync: renderTarget is not in RGBA or implementation defined format.");if(!Ye.textureTypeReadable(Le))throw new Error("THREE.WebGLRenderer.readRenderTargetPixelsAsync: renderTarget is not in UnsignedByteType or implementation defined type.");if(E>=0&&E<=x.width-B&&X>=0&&X<=x.height-T){Ee.bindFramebuffer(I.FRAMEBUFFER,Oe);let Ae=I.createBuffer();I.bindBuffer(I.PIXEL_PACK_BUFFER,Ae),I.bufferData(I.PIXEL_PACK_BUFFER,K.byteLength,I.STREAM_READ),I.readPixels(E,X,B,T,Xe.convert(Be),Xe.convert(Le),0);let et=M!==null?we.get(M).__webglFramebuffer:null;Ee.bindFramebuffer(I.FRAMEBUFFER,et);let ft=I.fenceSync(I.SYNC_GPU_COMMANDS_COMPLETE,0);return I.flush(),await uf(I,ft,4),I.bindBuffer(I.PIXEL_PACK_BUFFER,Ae),I.getBufferSubData(I.PIXEL_PACK_BUFFER,0,K),I.deleteBuffer(Ae),I.deleteSync(ft),K}else throw new Error("THREE.WebGLRenderer.readRenderTargetPixelsAsync: requested read bounds are out of range.")}},this.copyFramebufferToTexture=function(x,E=null,X=0){x.isTexture!==!0&&(js("WebGLRenderer: copyFramebufferToTexture function signature has changed."),E=arguments[0]||null,x=arguments[1]);let B=Math.pow(2,-X),T=Math.floor(x.image.width*B),K=Math.floor(x.image.height*B),ce=E!==null?E.x:0,Oe=E!==null?E.y:0;v.setTexture2D(x,0),I.copyTexSubImage2D(I.TEXTURE_2D,X,0,0,ce,Oe,T,K),Ee.unbindTexture()},this.copyTextureToTexture=function(x,E,X=null,B=null,T=0){x.isTexture!==!0&&(js("WebGLRenderer: copyTextureToTexture function signature has changed."),B=arguments[0]||null,x=arguments[1],E=arguments[2],T=arguments[3]||0,X=null);let K,ce,Oe,ye,Be,Le,Ae,et,ft,ut=x.isCompressedTexture?x.mipmaps[T]:x.image;X!==null?(K=X.max.x-X.min.x,ce=X.max.y-X.min.y,Oe=X.isBox3?X.max.z-X.min.z:1,ye=X.min.x,Be=X.min.y,Le=X.isBox3?X.min.z:0):(K=ut.width,ce=ut.height,Oe=ut.depth||1,ye=0,Be=0,Le=0),B!==null?(Ae=B.x,et=B.y,ft=B.z):(Ae=0,et=0,ft=0);let zt=Xe.convert(E.format),it=Xe.convert(E.type),ve;E.isData3DTexture?(v.setTexture3D(E,0),ve=I.TEXTURE_3D):E.isDataArrayTexture||E.isCompressedArrayTexture?(v.setTexture2DArray(E,0),ve=I.TEXTURE_2D_ARRAY):(v.setTexture2D(E,0),ve=I.TEXTURE_2D),I.pixelStorei(I.UNPACK_FLIP_Y_WEBGL,E.flipY),I.pixelStorei(I.UNPACK_PREMULTIPLY_ALPHA_WEBGL,E.premultiplyAlpha),I.pixelStorei(I.UNPACK_ALIGNMENT,E.unpackAlignment);let En=I.getParameter(I.UNPACK_ROW_LENGTH),st=I.getParameter(I.UNPACK_IMAGE_HEIGHT),hn=I.getParameter(I.UNPACK_SKIP_PIXELS),Ti=I.getParameter(I.UNPACK_SKIP_ROWS),Jt=I.getParameter(I.UNPACK_SKIP_IMAGES);I.pixelStorei(I.UNPACK_ROW_LENGTH,ut.width),I.pixelStorei(I.UNPACK_IMAGE_HEIGHT,ut.height),I.pixelStorei(I.UNPACK_SKIP_PIXELS,ye),I.pixelStorei(I.UNPACK_SKIP_ROWS,Be),I.pixelStorei(I.UNPACK_SKIP_IMAGES,Le);let Rs=x.isDataArrayTexture||x.isData3DTexture,gt=E.isDataArrayTexture||E.isData3DTexture;if(x.isRenderTargetTexture||x.isDepthTexture){let xn=we.get(x),Xs=we.get(E),tn=we.get(xn.__renderTarget),Qn=we.get(Xs.__renderTarget);Ee.bindFramebuffer(I.READ_FRAMEBUFFER,tn.__webglFramebuffer),Ee.bindFramebuffer(I.DRAW_FRAMEBUFFER,Qn.__webglFramebuffer);for(let Yn=0;Yn<Oe;Yn++)Rs&&I.framebufferTextureLayer(I.READ_FRAMEBUFFER,I.COLOR_ATTACHMENT0,we.get(x).__webglTexture,T,Le+Yn),x.isDepthTexture?(gt&&I.framebufferTextureLayer(I.DRAW_FRAMEBUFFER,I.COLOR_ATTACHMENT0,we.get(E).__webglTexture,T,ft+Yn),I.blitFramebuffer(ye,Be,K,ce,Ae,et,K,ce,I.DEPTH_BUFFER_BIT,I.NEAREST)):gt?I.copyTexSubImage3D(ve,T,Ae,et,ft+Yn,ye,Be,K,ce):I.copyTexSubImage2D(ve,T,Ae,et,ft+Yn,ye,Be,K,ce);Ee.bindFramebuffer(I.READ_FRAMEBUFFER,null),Ee.bindFramebuffer(I.DRAW_FRAMEBUFFER,null)}else gt?x.isDataTexture||x.isData3DTexture?I.texSubImage3D(ve,T,Ae,et,ft,K,ce,Oe,zt,it,ut.data):E.isCompressedArrayTexture?I.compressedTexSubImage3D(ve,T,Ae,et,ft,K,ce,Oe,zt,ut.data):I.texSubImage3D(ve,T,Ae,et,ft,K,ce,Oe,zt,it,ut):x.isDataTexture?I.texSubImage2D(I.TEXTURE_2D,T,Ae,et,K,ce,zt,it,ut.data):x.isCompressedTexture?I.compressedTexSubImage2D(I.TEXTURE_2D,T,Ae,et,ut.width,ut.height,zt,ut.data):I.texSubImage2D(I.TEXTURE_2D,T,Ae,et,K,ce,zt,it,ut);I.pixelStorei(I.UNPACK_ROW_LENGTH,En),I.pixelStorei(I.UNPACK_IMAGE_HEIGHT,st),I.pixelStorei(I.UNPACK_SKIP_PIXELS,hn),I.pixelStorei(I.UNPACK_SKIP_ROWS,Ti),I.pixelStorei(I.UNPACK_SKIP_IMAGES,Jt),T===0&&E.generateMipmaps&&I.generateMipmap(ve),Ee.unbindTexture()},this.copyTextureToTexture3D=function(x,E,X=null,B=null,T=0){return x.isTexture!==!0&&(js("WebGLRenderer: copyTextureToTexture3D function signature has changed."),X=arguments[0]||null,B=arguments[1]||null,x=arguments[2],E=arguments[3],T=arguments[4]||0),js('WebGLRenderer: copyTextureToTexture3D function has been deprecated. Use "copyTextureToTexture" instead.'),this.copyTextureToTexture(x,E,X,B,T)},this.initRenderTarget=function(x){we.get(x).__webglFramebuffer===void 0&&v.setupRenderTarget(x)},this.initTexture=function(x){x.isCubeTexture?v.setTextureCube(x,0):x.isData3DTexture?v.setTexture3D(x,0):x.isDataArrayTexture||x.isCompressedArrayTexture?v.setTexture2DArray(x,0):v.setTexture2D(x,0),Ee.unbindTexture()},this.resetState=function(){H=0,S=0,M=null,Ee.reset(),at.reset()},typeof __THREE_DEVTOOLS__<"u"&&__THREE_DEVTOOLS__.dispatchEvent(new CustomEvent("observe",{detail:this}))}get coordinateSystem(){return Bn}get outputColorSpace(){return this._outputColorSpace}set outputColorSpace(e){this._outputColorSpace=e;let t=this.getContext();t.drawingBufferColorspace=Ze._getDrawingBufferColorSpace(e),t.unpackColorSpace=Ze._getUnpackColorSpace()}};var go=class i{constructor(e,t=1,n=1e3){this.isFog=!0,this.name="",this.color=new Ge(e),this.near=t,this.far=n}clone(){return new i(this.color,this.near,this.far)}toJSON(){return{type:"Fog",name:this.name,color:this.color.getHex(),near:this.near,far:this.far}}},ms=class extends mt{constructor(){super(),this.isScene=!0,this.type="Scene",this.background=null,this.environment=null,this.fog=null,this.backgroundBlurriness=0,this.backgroundIntensity=1,this.backgroundRotation=new yn,this.environmentIntensity=1,this.environmentRotation=new yn,this.overrideMaterial=null,typeof __THREE_DEVTOOLS__<"u"&&__THREE_DEVTOOLS__.dispatchEvent(new CustomEvent("observe",{detail:this}))}copy(e,t){return super.copy(e,t),e.background!==null&&(this.background=e.background.clone()),e.environment!==null&&(this.environment=e.environment.clone()),e.fog!==null&&(this.fog=e.fog.clone()),this.backgroundBlurriness=e.backgroundBlurriness,this.backgroundIntensity=e.backgroundIntensity,this.backgroundRotation.copy(e.backgroundRotation),this.environmentIntensity=e.environmentIntensity,this.environmentRotation.copy(e.environmentRotation),e.overrideMaterial!==null&&(this.overrideMaterial=e.overrideMaterial.clone()),this.matrixAutoUpdate=e.matrixAutoUpdate,this}toJSON(e){let t=super.toJSON(e);return this.fog!==null&&(t.object.fog=this.fog.toJSON()),this.backgroundBlurriness>0&&(t.object.backgroundBlurriness=this.backgroundBlurriness),this.backgroundIntensity!==1&&(t.object.backgroundIntensity=this.backgroundIntensity),t.object.backgroundRotation=this.backgroundRotation.toArray(),this.environmentIntensity!==1&&(t.object.environmentIntensity=this.environmentIntensity),t.object.environmentRotation=this.environmentRotation.toArray(),t}},xs=class{constructor(e,t){this.isInterleavedBuffer=!0,this.array=e,this.stride=t,this.count=e!==void 0?e.length/t:0,this.usage=ac,this.updateRanges=[],this.version=0,this.uuid=_n()}onUploadCallback(){}set needsUpdate(e){e===!0&&this.version++}setUsage(e){return this.usage=e,this}addUpdateRange(e,t){this.updateRanges.push({start:e,count:t})}clearUpdateRanges(){this.updateRanges.length=0}copy(e){return this.array=new e.array.constructor(e.array),this.count=e.count,this.stride=e.stride,this.usage=e.usage,this}copyAt(e,t,n){e*=this.stride,n*=t.stride;for(let s=0,r=this.stride;s<r;s++)this.array[e+s]=t.array[n+s];return this}set(e,t=0){return this.array.set(e,t),this}clone(e){e.arrayBuffers===void 0&&(e.arrayBuffers={}),this.array.buffer._uuid===void 0&&(this.array.buffer._uuid=_n()),e.arrayBuffers[this.array.buffer._uuid]===void 0&&(e.arrayBuffers[this.array.buffer._uuid]=this.array.slice(0).buffer);let t=new this.array.constructor(e.arrayBuffers[this.array.buffer._uuid]),n=new this.constructor(t,this.stride);return n.setUsage(this.usage),n}onUpload(e){return this.onUploadCallback=e,this}toJSON(e){return e.arrayBuffers===void 0&&(e.arrayBuffers={}),this.array.buffer._uuid===void 0&&(this.array.buffer._uuid=_n()),e.arrayBuffers[this.array.buffer._uuid]===void 0&&(e.arrayBuffers[this.array.buffer._uuid]=Array.from(new Uint32Array(this.array.buffer))),{uuid:this.uuid,buffer:this.array.buffer._uuid,type:this.array.constructor.name,stride:this.stride}}},Nt=new C,Si=class i{constructor(e,t,n,s=!1){this.isInterleavedBufferAttribute=!0,this.name="",this.data=e,this.itemSize=t,this.offset=n,this.normalized=s}get count(){return this.data.count}get array(){return this.data.array}set needsUpdate(e){this.data.needsUpdate=e}applyMatrix4(e){for(let t=0,n=this.data.count;t<n;t++)Nt.fromBufferAttribute(this,t),Nt.applyMatrix4(e),this.setXYZ(t,Nt.x,Nt.y,Nt.z);return this}applyNormalMatrix(e){for(let t=0,n=this.count;t<n;t++)Nt.fromBufferAttribute(this,t),Nt.applyNormalMatrix(e),this.setXYZ(t,Nt.x,Nt.y,Nt.z);return this}transformDirection(e){for(let t=0,n=this.count;t<n;t++)Nt.fromBufferAttribute(this,t),Nt.transformDirection(e),this.setXYZ(t,Nt.x,Nt.y,Nt.z);return this}getComponent(e,t){let n=this.array[e*this.data.stride+this.offset+t];return this.normalized&&(n=un(n,this.array)),n}setComponent(e,t,n){return this.normalized&&(n=rt(n,this.array)),this.data.array[e*this.data.stride+this.offset+t]=n,this}setX(e,t){return this.normalized&&(t=rt(t,this.array)),this.data.array[e*this.data.stride+this.offset]=t,this}setY(e,t){return this.normalized&&(t=rt(t,this.array)),this.data.array[e*this.data.stride+this.offset+1]=t,this}setZ(e,t){return this.normalized&&(t=rt(t,this.array)),this.data.array[e*this.data.stride+this.offset+2]=t,this}setW(e,t){return this.normalized&&(t=rt(t,this.array)),this.data.array[e*this.data.stride+this.offset+3]=t,this}getX(e){let t=this.data.array[e*this.data.stride+this.offset];return this.normalized&&(t=un(t,this.array)),t}getY(e){let t=this.data.array[e*this.data.stride+this.offset+1];return this.normalized&&(t=un(t,this.array)),t}getZ(e){let t=this.data.array[e*this.data.stride+this.offset+2];return this.normalized&&(t=un(t,this.array)),t}getW(e){let t=this.data.array[e*this.data.stride+this.offset+3];return this.normalized&&(t=un(t,this.array)),t}setXY(e,t,n){return e=e*this.data.stride+this.offset,this.normalized&&(t=rt(t,this.array),n=rt(n,this.array)),this.data.array[e+0]=t,this.data.array[e+1]=n,this}setXYZ(e,t,n,s){return e=e*this.data.stride+this.offset,this.normalized&&(t=rt(t,this.array),n=rt(n,this.array),s=rt(s,this.array)),this.data.array[e+0]=t,this.data.array[e+1]=n,this.data.array[e+2]=s,this}setXYZW(e,t,n,s,r){return e=e*this.data.stride+this.offset,this.normalized&&(t=rt(t,this.array),n=rt(n,this.array),s=rt(s,this.array),r=rt(r,this.array)),this.data.array[e+0]=t,this.data.array[e+1]=n,this.data.array[e+2]=s,this.data.array[e+3]=r,this}clone(e){if(e===void 0){console.log("THREE.InterleavedBufferAttribute.clone(): Cloning an interleaved buffer attribute will de-interleave buffer data.");let t=[];for(let n=0;n<this.count;n++){let s=n*this.data.stride+this.offset;for(let r=0;r<this.itemSize;r++)t.push(this.data.array[s+r])}return new wt(new this.array.constructor(t),this.itemSize,this.normalized)}else return e.interleavedBuffers===void 0&&(e.interleavedBuffers={}),e.interleavedBuffers[this.data.uuid]===void 0&&(e.interleavedBuffers[this.data.uuid]=this.data.clone(e)),new i(e.interleavedBuffers[this.data.uuid],this.itemSize,this.offset,this.normalized)}toJSON(e){if(e===void 0){console.log("THREE.InterleavedBufferAttribute.toJSON(): Serializing an interleaved buffer attribute will de-interleave buffer data.");let t=[];for(let n=0;n<this.count;n++){let s=n*this.data.stride+this.offset;for(let r=0;r<this.itemSize;r++)t.push(this.data.array[s+r])}return{itemSize:this.itemSize,type:this.array.constructor.name,array:t,normalized:this.normalized}}else return e.interleavedBuffers===void 0&&(e.interleavedBuffers={}),e.interleavedBuffers[this.data.uuid]===void 0&&(e.interleavedBuffers[this.data.uuid]=this.data.toJSON(e)),{isInterleavedBufferAttribute:!0,itemSize:this.itemSize,data:this.data.uuid,offset:this.offset,normalized:this.normalized}}},or=class extends qt{static get type(){return"SpriteMaterial"}constructor(e){super(),this.isSpriteMaterial=!0,this.color=new Ge(16777215),this.map=null,this.alphaMap=null,this.rotation=0,this.sizeAttenuation=!0,this.transparent=!0,this.fog=!0,this.setValues(e)}copy(e){return super.copy(e),this.color.copy(e.color),this.map=e.map,this.alphaMap=e.alphaMap,this.rotation=e.rotation,this.sizeAttenuation=e.sizeAttenuation,this.fog=e.fog,this}},Ji,Ns=new C,ji=new C,Qi=new C,Yi=new Re,Vs=new Re,H0=new Fe,Vr=new C,Ls=new C,Lr=new C,Ul=new Re,xa=new Re,Il=new Re,_o=class extends mt{constructor(e=new or){if(super(),this.isSprite=!0,this.type="Sprite",Ji===void 0){Ji=new Gt;let t=new Float32Array([-.5,-.5,0,0,0,.5,-.5,0,1,0,.5,.5,0,1,1,-.5,.5,0,0,1]),n=new xs(t,5);Ji.setIndex([0,1,2,0,2,3]),Ji.setAttribute("position",new Si(n,3,0,!1)),Ji.setAttribute("uv",new Si(n,2,3,!1))}this.geometry=Ji,this.material=e,this.center=new Re(.5,.5)}raycast(e,t){e.camera===null&&console.error('THREE.Sprite: "Raycaster.camera" needs to be set in order to raycast against sprites.'),ji.setFromMatrixScale(this.matrixWorld),H0.copy(e.camera.matrixWorld),this.modelViewMatrix.multiplyMatrices(e.camera.matrixWorldInverse,this.matrixWorld),Qi.setFromMatrixPosition(this.modelViewMatrix),e.camera.isPerspectiveCamera&&this.material.sizeAttenuation===!1&&ji.multiplyScalar(-Qi.z);let n=this.material.rotation,s,r;n!==0&&(r=Math.cos(n),s=Math.sin(n));let o=this.center;zr(Vr.set(-.5,-.5,0),Qi,o,ji,s,r),zr(Ls.set(.5,-.5,0),Qi,o,ji,s,r),zr(Lr.set(.5,.5,0),Qi,o,ji,s,r),Ul.set(0,0),xa.set(1,0),Il.set(1,1);let a=e.ray.intersectTriangle(Vr,Ls,Lr,!1,Ns);if(a===null&&(zr(Ls.set(-.5,.5,0),Qi,o,ji,s,r),xa.set(0,1),a=e.ray.intersectTriangle(Vr,Lr,Ls,!1,Ns),a===null))return;let c=e.ray.origin.distanceTo(Ns);c<e.near||c>e.far||t.push({distance:c,point:Ns.clone(),uv:si.getInterpolation(Ns,Vr,Ls,Lr,Ul,xa,Il,new Re),face:null,object:this})}copy(e,t){return super.copy(e,t),e.center!==void 0&&this.center.copy(e.center),this.material=e.material,this}};function zr(i,e,t,n,s,r){Yi.subVectors(i,t).addScalar(.5).multiply(n),s!==void 0?(Vs.x=r*Yi.x-s*Yi.y,Vs.y=s*Yi.x+r*Yi.y):Vs.copy(Yi),i.copy(e),i.x+=Vs.x,i.y+=Vs.y,i.applyMatrix4(H0)}var Rl=new C,Xl=new nt,Bl=new nt,lm=new C,$l=new Fe,kr=new C,ba=new Zt,Fl=new Fe,Pa=new Hi,mo=class extends De{constructor(e,t){super(e,t),this.isSkinnedMesh=!0,this.type="SkinnedMesh",this.bindMode=Xh,this.bindMatrix=new Fe,this.bindMatrixInverse=new Fe,this.boundingBox=null,this.boundingSphere=null}computeBoundingBox(){let e=this.geometry;this.boundingBox===null&&(this.boundingBox=new rn),this.boundingBox.makeEmpty();let t=e.getAttribute("position");for(let n=0;n<t.count;n++)this.getVertexPosition(n,kr),this.boundingBox.expandByPoint(kr)}computeBoundingSphere(){let e=this.geometry;this.boundingSphere===null&&(this.boundingSphere=new Zt),this.boundingSphere.makeEmpty();let t=e.getAttribute("position");for(let n=0;n<t.count;n++)this.getVertexPosition(n,kr),this.boundingSphere.expandByPoint(kr)}copy(e,t){return super.copy(e,t),this.bindMode=e.bindMode,this.bindMatrix.copy(e.bindMatrix),this.bindMatrixInverse.copy(e.bindMatrixInverse),this.skeleton=e.skeleton,e.boundingBox!==null&&(this.boundingBox=e.boundingBox.clone()),e.boundingSphere!==null&&(this.boundingSphere=e.boundingSphere.clone()),this}raycast(e,t){let n=this.material,s=this.matrixWorld;n!==void 0&&(this.boundingSphere===null&&this.computeBoundingSphere(),ba.copy(this.boundingSphere),ba.applyMatrix4(s),e.ray.intersectsSphere(ba)!==!1&&(Fl.copy(s).invert(),Pa.copy(e.ray).applyMatrix4(Fl),!(this.boundingBox!==null&&Pa.intersectsBox(this.boundingBox)===!1)&&this._computeIntersections(e,t,Pa)))}getVertexPosition(e,t){return super.getVertexPosition(e,t),this.applyBoneTransform(e,t),t}bind(e,t){this.skeleton=e,t===void 0&&(this.updateMatrixWorld(!0),this.skeleton.calculateInverses(),t=this.matrixWorld),this.bindMatrix.copy(t),this.bindMatrixInverse.copy(t).invert()}pose(){this.skeleton.pose()}normalizeSkinWeights(){let e=new nt,t=this.geometry.attributes.skinWeight;for(let n=0,s=t.count;n<s;n++){e.fromBufferAttribute(t,n);let r=1/e.manhattanLength();r!==1/0?e.multiplyScalar(r):e.set(1,0,0,0),t.setXYZW(n,e.x,e.y,e.z,e.w)}}updateMatrixWorld(e){super.updateMatrixWorld(e),this.bindMode===Xh?this.bindMatrixInverse.copy(this.matrixWorld).invert():this.bindMode===Bp?this.bindMatrixInverse.copy(this.bindMatrix).invert():console.warn("THREE.SkinnedMesh: Unrecognized bindMode: "+this.bindMode)}applyBoneTransform(e,t){let n=this.skeleton,s=this.geometry;Xl.fromBufferAttribute(s.attributes.skinIndex,e),Bl.fromBufferAttribute(s.attributes.skinWeight,e),Rl.copy(t).applyMatrix4(this.bindMatrix),t.set(0,0,0);for(let r=0;r<4;r++){let o=Bl.getComponent(r);if(o!==0){let a=Xl.getComponent(r);$l.multiplyMatrices(n.bones[a].matrixWorld,n.boneInverses[a]),t.addScaledVector(lm.copy(Rl).applyMatrix4($l),o)}}return t.applyMatrix4(this.bindMatrixInverse)}},ar=class extends mt{constructor(){super(),this.isBone=!0,this.type="Bone"}},xo=class extends Ct{constructor(e=null,t=1,n=1,s,r,o,a,c,h=$t,l=$t,p,f){super(null,o,a,c,h,l,s,r,p,f),this.isDataTexture=!0,this.image={data:e,width:t,height:n},this.generateMipmaps=!1,this.flipY=!1,this.unpackAlignment=1}},Dl=new Fe,pm=new Fe,bo=class i{constructor(e=[],t=[]){this.uuid=_n(),this.bones=e.slice(0),this.boneInverses=t,this.boneMatrices=null,this.boneTexture=null,this.init()}init(){let e=this.bones,t=this.boneInverses;if(this.boneMatrices=new Float32Array(e.length*16),t.length===0)this.calculateInverses();else if(e.length!==t.length){console.warn("THREE.Skeleton: Number of inverse bone matrices does not match amount of bones."),this.boneInverses=[];for(let n=0,s=this.bones.length;n<s;n++)this.boneInverses.push(new Fe)}}calculateInverses(){this.boneInverses.length=0;for(let e=0,t=this.bones.length;e<t;e++){let n=new Fe;this.bones[e]&&n.copy(this.bones[e].matrixWorld).invert(),this.boneInverses.push(n)}}pose(){for(let e=0,t=this.bones.length;e<t;e++){let n=this.bones[e];n&&n.matrixWorld.copy(this.boneInverses[e]).invert()}for(let e=0,t=this.bones.length;e<t;e++){let n=this.bones[e];n&&(n.parent&&n.parent.isBone?(n.matrix.copy(n.parent.matrixWorld).invert(),n.matrix.multiply(n.matrixWorld)):n.matrix.copy(n.matrixWorld),n.matrix.decompose(n.position,n.quaternion,n.scale))}}update(){let e=this.bones,t=this.boneInverses,n=this.boneMatrices,s=this.boneTexture;for(let r=0,o=e.length;r<o;r++){let a=e[r]?e[r].matrixWorld:pm;Dl.multiplyMatrices(a,t[r]),Dl.toArray(n,r*16)}s!==null&&(s.needsUpdate=!0)}clone(){return new i(this.bones,this.boneInverses)}computeBoneTexture(){let e=Math.sqrt(this.bones.length*4);e=Math.ceil(e/4)*4,e=Math.max(e,4);let t=new Float32Array(e*e*4);t.set(this.boneMatrices);let n=new xo(t,e,e,sn,gn);return n.needsUpdate=!0,this.boneMatrices=t,this.boneTexture=n,this}getBoneByName(e){for(let t=0,n=this.bones.length;t<n;t++){let s=this.bones[t];if(s.name===e)return s}}dispose(){this.boneTexture!==null&&(this.boneTexture.dispose(),this.boneTexture=null)}fromJSON(e,t){this.uuid=e.uuid;for(let n=0,s=e.bones.length;n<s;n++){let r=e.bones[n],o=t[r];o===void 0&&(console.warn("THREE.Skeleton: No bone found with UUID:",r),o=new ar),this.bones.push(o),this.boneInverses.push(new Fe().fromArray(e.boneInverses[n]))}return this.init(),this}toJSON(){let e={metadata:{version:4.6,type:"Skeleton",generator:"Skeleton.toJSON"},bones:[],boneInverses:[]};e.uuid=this.uuid;let t=this.bones,n=this.boneInverses;for(let s=0,r=t.length;s<r;s++){let o=t[s];e.bones.push(o.uuid);let a=n[s];e.boneInverses.push(a.toArray())}return e}},Ei=class extends wt{constructor(e,t,n,s=1){super(e,t,n),this.isInstancedBufferAttribute=!0,this.meshPerAttribute=s}copy(e){return super.copy(e),this.meshPerAttribute=e.meshPerAttribute,this}toJSON(){let e=super.toJSON();return e.meshPerAttribute=this.meshPerAttribute,e.isInstancedBufferAttribute=!0,e}},Zi=new Fe,Nl=new Fe,qr=[],Vl=new rn,fm=new Fe,zs=new De,ks=new Zt,Po=class extends De{constructor(e,t,n){super(e,t),this.isInstancedMesh=!0,this.instanceMatrix=new Ei(new Float32Array(n*16),16),this.instanceColor=null,this.morphTexture=null,this.count=n,this.boundingBox=null,this.boundingSphere=null;for(let s=0;s<n;s++)this.setMatrixAt(s,fm)}computeBoundingBox(){let e=this.geometry,t=this.count;this.boundingBox===null&&(this.boundingBox=new rn),e.boundingBox===null&&e.computeBoundingBox(),this.boundingBox.makeEmpty();for(let n=0;n<t;n++)this.getMatrixAt(n,Zi),Vl.copy(e.boundingBox).applyMatrix4(Zi),this.boundingBox.union(Vl)}computeBoundingSphere(){let e=this.geometry,t=this.count;this.boundingSphere===null&&(this.boundingSphere=new Zt),e.boundingSphere===null&&e.computeBoundingSphere(),this.boundingSphere.makeEmpty();for(let n=0;n<t;n++)this.getMatrixAt(n,Zi),ks.copy(e.boundingSphere).applyMatrix4(Zi),this.boundingSphere.union(ks)}copy(e,t){return super.copy(e,t),this.instanceMatrix.copy(e.instanceMatrix),e.morphTexture!==null&&(this.morphTexture=e.morphTexture.clone()),e.instanceColor!==null&&(this.instanceColor=e.instanceColor.clone()),this.count=e.count,e.boundingBox!==null&&(this.boundingBox=e.boundingBox.clone()),e.boundingSphere!==null&&(this.boundingSphere=e.boundingSphere.clone()),this}getColorAt(e,t){t.fromArray(this.instanceColor.array,e*3)}getMatrixAt(e,t){t.fromArray(this.instanceMatrix.array,e*16)}getMorphAt(e,t){let n=t.morphTargetInfluences,s=this.morphTexture.source.data.data,r=n.length+1,o=e*r+1;for(let a=0;a<n.length;a++)n[a]=s[o+a]}raycast(e,t){let n=this.matrixWorld,s=this.count;if(zs.geometry=this.geometry,zs.material=this.material,zs.material!==void 0&&(this.boundingSphere===null&&this.computeBoundingSphere(),ks.copy(this.boundingSphere),ks.applyMatrix4(n),e.ray.intersectsSphere(ks)!==!1))for(let r=0;r<s;r++){this.getMatrixAt(r,Zi),Nl.multiplyMatrices(n,Zi),zs.matrixWorld=Nl,zs.raycast(e,qr);for(let o=0,a=qr.length;o<a;o++){let c=qr[o];c.instanceId=r,c.object=this,t.push(c)}qr.length=0}}setColorAt(e,t){this.instanceColor===null&&(this.instanceColor=new Ei(new Float32Array(this.instanceMatrix.count*3).fill(1),3)),t.toArray(this.instanceColor.array,e*3)}setMatrixAt(e,t){t.toArray(this.instanceMatrix.array,e*16)}setMorphAt(e,t){let n=t.morphTargetInfluences,s=n.length+1;this.morphTexture===null&&(this.morphTexture=new xo(new Float32Array(s*this.count),s,this.count,Nc,gn));let r=this.morphTexture.source.data.data,o=0;for(let h=0;h<n.length;h++)o+=n[h];let a=this.geometry.morphTargetsRelative?1:1-o,c=s*e;r[c]=a,r.set(n,c+1)}updateMorphTargets(){}dispose(){return this.dispatchEvent({type:"dispose"}),this.morphTexture!==null&&(this.morphTexture.dispose(),this.morphTexture=null),this}};var cr=class extends qt{static get type(){return"LineBasicMaterial"}constructor(e){super(),this.isLineBasicMaterial=!0,this.color=new Ge(16777215),this.map=null,this.linewidth=1,this.linecap="round",this.linejoin="round",this.fog=!0,this.setValues(e)}copy(e){return super.copy(e),this.color.copy(e.color),this.map=e.map,this.linewidth=e.linewidth,this.linecap=e.linecap,this.linejoin=e.linejoin,this.fog=e.fog,this}},wo=new C,Oo=new C,Ll=new Fe,qs=new Hi,Jr=new Zt,wa=new C,zl=new C,bs=class extends mt{constructor(e=new Gt,t=new cr){super(),this.isLine=!0,this.type="Line",this.geometry=e,this.material=t,this.updateMorphTargets()}copy(e,t){return super.copy(e,t),this.material=Array.isArray(e.material)?e.material.slice():e.material,this.geometry=e.geometry,this}computeLineDistances(){let e=this.geometry;if(e.index===null){let t=e.attributes.position,n=[0];for(let s=1,r=t.count;s<r;s++)wo.fromBufferAttribute(t,s-1),Oo.fromBufferAttribute(t,s),n[s]=n[s-1],n[s]+=wo.distanceTo(Oo);e.setAttribute("lineDistance",new Ot(n,1))}else console.warn("THREE.Line.computeLineDistances(): Computation only possible with non-indexed BufferGeometry.");return this}raycast(e,t){let n=this.geometry,s=this.matrixWorld,r=e.params.Line.threshold,o=n.drawRange;if(n.boundingSphere===null&&n.computeBoundingSphere(),Jr.copy(n.boundingSphere),Jr.applyMatrix4(s),Jr.radius+=r,e.ray.intersectsSphere(Jr)===!1)return;Ll.copy(s).invert(),qs.copy(e.ray).applyMatrix4(Ll);let a=r/((this.scale.x+this.scale.y+this.scale.z)/3),c=a*a,h=this.isLineSegments?2:1,l=n.index,f=n.attributes.position;if(l!==null){let u=Math.max(0,o.start),_=Math.min(l.count,o.start+o.count);for(let m=u,g=_-1;m<g;m+=h){let d=l.getX(m),A=l.getX(m+1),O=jr(this,e,qs,c,d,A);O&&t.push(O)}if(this.isLineLoop){let m=l.getX(_-1),g=l.getX(u),d=jr(this,e,qs,c,m,g);d&&t.push(d)}}else{let u=Math.max(0,o.start),_=Math.min(f.count,o.start+o.count);for(let m=u,g=_-1;m<g;m+=h){let d=jr(this,e,qs,c,m,m+1);d&&t.push(d)}if(this.isLineLoop){let m=jr(this,e,qs,c,_-1,u);m&&t.push(m)}}}updateMorphTargets(){let t=this.geometry.morphAttributes,n=Object.keys(t);if(n.length>0){let s=t[n[0]];if(s!==void 0){this.morphTargetInfluences=[],this.morphTargetDictionary={};for(let r=0,o=s.length;r<o;r++){let a=s[r].name||String(r);this.morphTargetInfluences.push(0),this.morphTargetDictionary[a]=r}}}}};function jr(i,e,t,n,s,r){let o=i.geometry.attributes.position;if(wo.fromBufferAttribute(o,s),Oo.fromBufferAttribute(o,r),t.distanceSqToSegment(wo,Oo,wa,zl)>n)return;wa.applyMatrix4(i.matrixWorld);let c=e.ray.origin.distanceTo(wa);if(!(c<e.near||c>e.far))return{distance:c,point:zl.clone().applyMatrix4(i.matrixWorld),index:s,face:null,faceIndex:null,barycoord:null,object:i}}var kl=new C,ql=new C,yo=class extends bs{constructor(e,t){super(e,t),this.isLineSegments=!0,this.type="LineSegments"}computeLineDistances(){let e=this.geometry;if(e.index===null){let t=e.attributes.position,n=[];for(let s=0,r=t.count;s<r;s+=2)kl.fromBufferAttribute(t,s),ql.fromBufferAttribute(t,s+1),n[s]=s===0?0:n[s-1],n[s+1]=n[s]+kl.distanceTo(ql);e.setAttribute("lineDistance",new Ot(n,1))}else console.warn("THREE.LineSegments.computeLineDistances(): Computation only possible with non-indexed BufferGeometry.");return this}},Ao=class extends bs{constructor(e,t){super(e,t),this.isLineLoop=!0,this.type="LineLoop"}},Wi=class extends qt{static get type(){return"PointsMaterial"}constructor(e){super(),this.isPointsMaterial=!0,this.color=new Ge(16777215),this.map=null,this.alphaMap=null,this.size=1,this.sizeAttenuation=!0,this.fog=!0,this.setValues(e)}copy(e){return super.copy(e),this.color.copy(e.color),this.map=e.map,this.alphaMap=e.alphaMap,this.size=e.size,this.sizeAttenuation=e.sizeAttenuation,this.fog=e.fog,this}},Jl=new Fe,Ac=new Hi,Qr=new Zt,Yr=new C,Ps=class extends mt{constructor(e=new Gt,t=new Wi){super(),this.isPoints=!0,this.type="Points",this.geometry=e,this.material=t,this.updateMorphTargets()}copy(e,t){return super.copy(e,t),this.material=Array.isArray(e.material)?e.material.slice():e.material,this.geometry=e.geometry,this}raycast(e,t){let n=this.geometry,s=this.matrixWorld,r=e.params.Points.threshold,o=n.drawRange;if(n.boundingSphere===null&&n.computeBoundingSphere(),Qr.copy(n.boundingSphere),Qr.applyMatrix4(s),Qr.radius+=r,e.ray.intersectsSphere(Qr)===!1)return;Jl.copy(s).invert(),Ac.copy(e.ray).applyMatrix4(Jl);let a=r/((this.scale.x+this.scale.y+this.scale.z)/3),c=a*a,h=n.index,p=n.attributes.position;if(h!==null){let f=Math.max(0,o.start),u=Math.min(h.count,o.start+o.count);for(let _=f,m=u;_<m;_++){let g=h.getX(_);Yr.fromBufferAttribute(p,g),jl(Yr,g,c,s,e,t,this)}}else{let f=Math.max(0,o.start),u=Math.min(p.count,o.start+o.count);for(let _=f,m=u;_<m;_++)Yr.fromBufferAttribute(p,_),jl(Yr,_,c,s,e,t,this)}}updateMorphTargets(){let t=this.geometry.morphAttributes,n=Object.keys(t);if(n.length>0){let s=t[n[0]];if(s!==void 0){this.morphTargetInfluences=[],this.morphTargetDictionary={};for(let r=0,o=s.length;r<o;r++){let a=s[r].name||String(r);this.morphTargetInfluences.push(0),this.morphTargetDictionary[a]=r}}}}};function jl(i,e,t,n,s,r,o){let a=Ac.distanceSqToPoint(i);if(a<t){let c=new C;Ac.closestPointToPoint(i,c),c.applyMatrix4(n);let h=s.ray.origin.distanceTo(c);if(h<s.near||h>s.far)return;r.push({distance:h,distanceToRay:Math.sqrt(a),point:c,index:e,face:null,faceIndex:null,barycoord:null,object:o})}}var Mi=class extends Ct{constructor(e,t,n,s,r,o,a,c,h){super(e,t,n,s,r,o,a,c,h),this.isCanvasTexture=!0,this.needsUpdate=!0}};var ws=class i extends Gt{constructor(e=1,t=32,n=0,s=Math.PI*2){super(),this.type="CircleGeometry",this.parameters={radius:e,segments:t,thetaStart:n,thetaLength:s},t=Math.max(3,t);let r=[],o=[],a=[],c=[],h=new C,l=new Re;o.push(0,0,0),a.push(0,0,1),c.push(.5,.5);for(let p=0,f=3;p<=t;p++,f+=3){let u=n+p/t*s;h.x=e*Math.cos(u),h.y=e*Math.sin(u),o.push(h.x,h.y,h.z),a.push(0,0,1),l.x=(o[f]/e+1)/2,l.y=(o[f+1]/e+1)/2,c.push(l.x,l.y)}for(let p=1;p<=t;p++)r.push(p,p+1,0);this.setIndex(r),this.setAttribute("position",new Ot(o,3)),this.setAttribute("normal",new Ot(a,3)),this.setAttribute("uv",new Ot(c,2))}copy(e){return super.copy(e),this.parameters=Object.assign({},e.parameters),this}static fromJSON(e){return new i(e.radius,e.segments,e.thetaStart,e.thetaLength)}},hr=class i extends Gt{constructor(e=1,t=1,n=1,s=32,r=1,o=!1,a=0,c=Math.PI*2){super(),this.type="CylinderGeometry",this.parameters={radiusTop:e,radiusBottom:t,height:n,radialSegments:s,heightSegments:r,openEnded:o,thetaStart:a,thetaLength:c};let h=this;s=Math.floor(s),r=Math.floor(r);let l=[],p=[],f=[],u=[],_=0,m=[],g=n/2,d=0;A(),o===!1&&(e>0&&O(!0),t>0&&O(!1)),this.setIndex(l),this.setAttribute("position",new Ot(p,3)),this.setAttribute("normal",new Ot(f,3)),this.setAttribute("uv",new Ot(u,2));function A(){let P=new C,G=new C,H=0,S=(t-e)/n;for(let M=0;M<=r;M++){let y=[],w=M/r,W=w*(t-e)+e;for(let $=0;$<=s;$++){let F=$/s,k=F*c+a,Q=Math.sin(k),V=Math.cos(k);G.x=W*Q,G.y=-w*n+g,G.z=W*V,p.push(G.x,G.y,G.z),P.set(Q,S,V).normalize(),f.push(P.x,P.y,P.z),u.push(F,1-w),y.push(_++)}m.push(y)}for(let M=0;M<s;M++)for(let y=0;y<r;y++){let w=m[y][M],W=m[y+1][M],$=m[y+1][M+1],F=m[y][M+1];(e>0||y!==0)&&(l.push(w,W,F),H+=3),(t>0||y!==r-1)&&(l.push(W,$,F),H+=3)}h.addGroup(d,H,0),d+=H}function O(P){let G=_,H=new Re,S=new C,M=0,y=P===!0?e:t,w=P===!0?1:-1;for(let $=1;$<=s;$++)p.push(0,g*w,0),f.push(0,w,0),u.push(.5,.5),_++;let W=_;for(let $=0;$<=s;$++){let k=$/s*c+a,Q=Math.cos(k),V=Math.sin(k);S.x=y*V,S.y=g*w,S.z=y*Q,p.push(S.x,S.y,S.z),f.push(0,w,0),H.x=Q*.5+.5,H.y=V*.5*w+.5,u.push(H.x,H.y),_++}for(let $=0;$<s;$++){let F=G+$,k=W+$;P===!0?l.push(k,k+1,F):l.push(k+1,k,F),M+=3}h.addGroup(d,M,P===!0?1:2),d+=M}}copy(e){return super.copy(e),this.parameters=Object.assign({},e.parameters),this}static fromJSON(e){return new i(e.radiusTop,e.radiusBottom,e.height,e.radialSegments,e.heightSegments,e.openEnded,e.thetaStart,e.thetaLength)}};var Os=class i extends Gt{constructor(e=1,t=.4,n=12,s=48,r=Math.PI*2){super(),this.type="TorusGeometry",this.parameters={radius:e,tube:t,radialSegments:n,tubularSegments:s,arc:r},n=Math.floor(n),s=Math.floor(s);let o=[],a=[],c=[],h=[],l=new C,p=new C,f=new C;for(let u=0;u<=n;u++)for(let _=0;_<=s;_++){let m=_/s*r,g=u/n*Math.PI*2;p.x=(e+t*Math.cos(g))*Math.cos(m),p.y=(e+t*Math.cos(g))*Math.sin(m),p.z=t*Math.sin(g),a.push(p.x,p.y,p.z),l.x=e*Math.cos(m),l.y=e*Math.sin(m),f.subVectors(p,l).normalize(),c.push(f.x,f.y,f.z),h.push(_/s),h.push(u/n)}for(let u=1;u<=n;u++)for(let _=1;_<=s;_++){let m=(s+1)*u+_-1,g=(s+1)*(u-1)+_-1,d=(s+1)*(u-1)+_,A=(s+1)*u+_;o.push(m,g,A),o.push(g,d,A)}this.setIndex(o),this.setAttribute("position",new Ot(a,3)),this.setAttribute("normal",new Ot(c,3)),this.setAttribute("uv",new Ot(h,2))}copy(e){return super.copy(e),this.parameters=Object.assign({},e.parameters),this}static fromJSON(e){return new i(e.radius,e.tube,e.radialSegments,e.tubularSegments,e.arc)}};var Tt=class extends qt{static get type(){return"MeshStandardMaterial"}constructor(e){super(),this.isMeshStandardMaterial=!0,this.defines={STANDARD:""},this.color=new Ge(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 Ge(0),this.emissiveIntensity=1,this.emissiveMap=null,this.bumpMap=null,this.bumpScale=1,this.normalMap=null,this.normalMapType=_0,this.normalScale=new Re(1,1),this.displacementMap=null,this.displacementScale=1,this.displacementBias=0,this.roughnessMap=null,this.metalnessMap=null,this.alphaMap=null,this.envMap=null,this.envMapRotation=new yn,this.envMapIntensity=1,this.wireframe=!1,this.wireframeLinewidth=1,this.wireframeLinecap="round",this.wireframeLinejoin="round",this.flatShading=!1,this.fog=!0,this.setValues(e)}copy(e){return super.copy(e),this.defines={STANDARD:""},this.color.copy(e.color),this.roughness=e.roughness,this.metalness=e.metalness,this.map=e.map,this.lightMap=e.lightMap,this.lightMapIntensity=e.lightMapIntensity,this.aoMap=e.aoMap,this.aoMapIntensity=e.aoMapIntensity,this.emissive.copy(e.emissive),this.emissiveMap=e.emissiveMap,this.emissiveIntensity=e.emissiveIntensity,this.bumpMap=e.bumpMap,this.bumpScale=e.bumpScale,this.normalMap=e.normalMap,this.normalMapType=e.normalMapType,this.normalScale.copy(e.normalScale),this.displacementMap=e.displacementMap,this.displacementScale=e.displacementScale,this.displacementBias=e.displacementBias,this.roughnessMap=e.roughnessMap,this.metalnessMap=e.metalnessMap,this.alphaMap=e.alphaMap,this.envMap=e.envMap,this.envMapRotation.copy(e.envMapRotation),this.envMapIntensity=e.envMapIntensity,this.wireframe=e.wireframe,this.wireframeLinewidth=e.wireframeLinewidth,this.wireframeLinecap=e.wireframeLinecap,this.wireframeLinejoin=e.wireframeLinejoin,this.flatShading=e.flatShading,this.fog=e.fog,this}},Kt=class extends Tt{static get type(){return"MeshPhysicalMaterial"}constructor(e){super(),this.isMeshPhysicalMaterial=!0,this.defines={STANDARD:"",PHYSICAL:""},this.anisotropyRotation=0,this.anisotropyMap=null,this.clearcoatMap=null,this.clearcoatRoughness=0,this.clearcoatRoughnessMap=null,this.clearcoatNormalScale=new Re(1,1),this.clearcoatNormalMap=null,this.ior=1.5,Object.defineProperty(this,"reflectivity",{get:function(){return Rt(2.5*(this.ior-1)/(this.ior+1),0,1)},set:function(t){this.ior=(1+.4*t)/(1-.4*t)}}),this.iridescenceMap=null,this.iridescenceIOR=1.3,this.iridescenceThicknessRange=[100,400],this.iridescenceThicknessMap=null,this.sheenColor=new Ge(0),this.sheenColorMap=null,this.sheenRoughness=1,this.sheenRoughnessMap=null,this.transmissionMap=null,this.thickness=0,this.thicknessMap=null,this.attenuationDistance=1/0,this.attenuationColor=new Ge(1,1,1),this.specularIntensity=1,this.specularIntensityMap=null,this.specularColor=new Ge(1,1,1),this.specularColorMap=null,this._anisotropy=0,this._clearcoat=0,this._dispersion=0,this._iridescence=0,this._sheen=0,this._transmission=0,this.setValues(e)}get anisotropy(){return this._anisotropy}set anisotropy(e){this._anisotropy>0!=e>0&&this.version++,this._anisotropy=e}get clearcoat(){return this._clearcoat}set clearcoat(e){this._clearcoat>0!=e>0&&this.version++,this._clearcoat=e}get iridescence(){return this._iridescence}set iridescence(e){this._iridescence>0!=e>0&&this.version++,this._iridescence=e}get dispersion(){return this._dispersion}set dispersion(e){this._dispersion>0!=e>0&&this.version++,this._dispersion=e}get sheen(){return this._sheen}set sheen(e){this._sheen>0!=e>0&&this.version++,this._sheen=e}get transmission(){return this._transmission}set transmission(e){this._transmission>0!=e>0&&this.version++,this._transmission=e}copy(e){return super.copy(e),this.defines={STANDARD:"",PHYSICAL:""},this.anisotropy=e.anisotropy,this.anisotropyRotation=e.anisotropyRotation,this.anisotropyMap=e.anisotropyMap,this.clearcoat=e.clearcoat,this.clearcoatMap=e.clearcoatMap,this.clearcoatRoughness=e.clearcoatRoughness,this.clearcoatRoughnessMap=e.clearcoatRoughnessMap,this.clearcoatNormalMap=e.clearcoatNormalMap,this.clearcoatNormalScale.copy(e.clearcoatNormalScale),this.dispersion=e.dispersion,this.ior=e.ior,this.iridescence=e.iridescence,this.iridescenceMap=e.iridescenceMap,this.iridescenceIOR=e.iridescenceIOR,this.iridescenceThicknessRange=[...e.iridescenceThicknessRange],this.iridescenceThicknessMap=e.iridescenceThicknessMap,this.sheen=e.sheen,this.sheenColor.copy(e.sheenColor),this.sheenColorMap=e.sheenColorMap,this.sheenRoughness=e.sheenRoughness,this.sheenRoughnessMap=e.sheenRoughnessMap,this.transmission=e.transmission,this.transmissionMap=e.transmissionMap,this.thickness=e.thickness,this.thicknessMap=e.thicknessMap,this.attenuationDistance=e.attenuationDistance,this.attenuationColor.copy(e.attenuationColor),this.specularIntensity=e.specularIntensity,this.specularIntensityMap=e.specularIntensityMap,this.specularColor.copy(e.specularColor),this.specularColorMap=e.specularColorMap,this}};function Zr(i,e,t){return!i||!t&&i.constructor===e?i:typeof e.BYTES_PER_ELEMENT=="number"?new e(i):Array.prototype.slice.call(i)}function dm(i){return ArrayBuffer.isView(i)&&!(i instanceof DataView)}function um(i){function e(s,r){return i[s]-i[r]}let t=i.length,n=new Array(t);for(let s=0;s!==t;++s)n[s]=s;return n.sort(e),n}function Ql(i,e,t){let n=i.length,s=new i.constructor(n);for(let r=0,o=0;o!==n;++r){let a=t[r]*e;for(let c=0;c!==e;++c)s[o++]=i[a+c]}return s}function S0(i,e,t,n){let s=1,r=i[0];for(;r!==void 0&&r[n]===void 0;)r=i[s++];if(r===void 0)return;let o=r[n];if(o!==void 0)if(Array.isArray(o))do o=r[n],o!==void 0&&(e.push(r.time),t.push.apply(t,o)),r=i[s++];while(r!==void 0);else if(o.toArray!==void 0)do o=r[n],o!==void 0&&(e.push(r.time),o.toArray(t,t.length)),r=i[s++];while(r!==void 0);else do o=r[n],o!==void 0&&(e.push(r.time),t.push(o)),r=i[s++];while(r!==void 0)}var hi=class{constructor(e,t,n,s){this.parameterPositions=e,this._cachedIndex=0,this.resultBuffer=s!==void 0?s:new t.constructor(n),this.sampleValues=t,this.valueSize=n,this.settings=null,this.DefaultSettings_={}}evaluate(e){let t=this.parameterPositions,n=this._cachedIndex,s=t[n],r=t[n-1];n:{e:{let o;t:{i:if(!(e<s)){for(let a=n+2;;){if(s===void 0){if(e<r)break i;return n=t.length,this._cachedIndex=n,this.copySampleValue_(n-1)}if(n===a)break;if(r=s,s=t[++n],e<s)break e}o=t.length;break t}if(!(e>=r)){let a=t[1];e<a&&(n=2,r=a);for(let c=n-2;;){if(r===void 0)return this._cachedIndex=0,this.copySampleValue_(0);if(n===c)break;if(s=r,r=t[--n-1],e>=r)break e}o=n,n=0;break t}break n}for(;n<o;){let a=n+o>>>1;e<t[a]?o=a:n=a+1}if(s=t[n],r=t[n-1],r===void 0)return this._cachedIndex=0,this.copySampleValue_(0);if(s===void 0)return n=t.length,this._cachedIndex=n,this.copySampleValue_(n-1)}this._cachedIndex=n,this.intervalChanged_(n,r,s)}return this.interpolate_(n,r,e,s)}getSettings_(){return this.settings||this.DefaultSettings_}copySampleValue_(e){let t=this.resultBuffer,n=this.sampleValues,s=this.valueSize,r=e*s;for(let o=0;o!==s;++o)t[o]=n[r+o];return t}interpolate_(){throw new Error("call to abstract method")}intervalChanged_(){}},vc=class extends hi{constructor(e,t,n,s){super(e,t,n,s),this._weightPrev=-0,this._offsetPrev=-0,this._weightNext=-0,this._offsetNext=-0,this.DefaultSettings_={endingStart:Bh,endingEnd:Bh}}intervalChanged_(e,t,n){let s=this.parameterPositions,r=e-2,o=e+1,a=s[r],c=s[o];if(a===void 0)switch(this.getSettings_().endingStart){case $h:r=e,a=2*t-n;break;case Fh:r=s.length-2,a=t+s[r]-s[r+1];break;default:r=e,a=n}if(c===void 0)switch(this.getSettings_().endingEnd){case $h:o=e,c=2*n-t;break;case Fh:o=1,c=n+s[1]-s[0];break;default:o=e-1,c=t}let h=(n-t)*.5,l=this.valueSize;this._weightPrev=h/(t-a),this._weightNext=h/(c-n),this._offsetPrev=r*l,this._offsetNext=o*l}interpolate_(e,t,n,s){let r=this.resultBuffer,o=this.sampleValues,a=this.valueSize,c=e*a,h=c-a,l=this._offsetPrev,p=this._offsetNext,f=this._weightPrev,u=this._weightNext,_=(n-t)/(s-t),m=_*_,g=m*_,d=-f*g+2*f*m-f*_,A=(1+f)*g+(-1.5-2*f)*m+(-.5+f)*_+1,O=(-1-u)*g+(1.5+u)*m+.5*_,P=u*g-u*m;for(let G=0;G!==a;++G)r[G]=d*o[l+G]+A*o[h+G]+O*o[c+G]+P*o[p+G];return r}},Hc=class extends hi{constructor(e,t,n,s){super(e,t,n,s)}interpolate_(e,t,n,s){let r=this.resultBuffer,o=this.sampleValues,a=this.valueSize,c=e*a,h=c-a,l=(n-t)/(s-t),p=1-l;for(let f=0;f!==a;++f)r[f]=o[h+f]*p+o[c+f]*l;return r}},Sc=class extends hi{constructor(e,t,n,s){super(e,t,n,s)}interpolate_(e){return this.copySampleValue_(e-1)}},on=class{constructor(e,t,n,s){if(e===void 0)throw new Error("THREE.KeyframeTrack: track name is undefined");if(t===void 0||t.length===0)throw new Error("THREE.KeyframeTrack: no keyframes in track named "+e);this.name=e,this.times=Zr(t,this.TimeBufferType),this.values=Zr(n,this.ValueBufferType),this.setInterpolation(s||this.DefaultInterpolation)}static toJSON(e){let t=e.constructor,n;if(t.toJSON!==this.toJSON)n=t.toJSON(e);else{n={name:e.name,times:Zr(e.times,Array),values:Zr(e.values,Array)};let s=e.getInterpolation();s!==e.DefaultInterpolation&&(n.interpolation=s)}return n.type=e.ValueTypeName,n}InterpolantFactoryMethodDiscrete(e){return new Sc(this.times,this.values,this.getValueSize(),e)}InterpolantFactoryMethodLinear(e){return new Hc(this.times,this.values,this.getValueSize(),e)}InterpolantFactoryMethodSmooth(e){return new vc(this.times,this.values,this.getValueSize(),e)}setInterpolation(e){let t;switch(e){case ls:t=this.InterpolantFactoryMethodDiscrete;break;case ps:t=this.InterpolantFactoryMethodLinear;break;case zo:t=this.InterpolantFactoryMethodSmooth;break}if(t===void 0){let n="unsupported interpolation for "+this.ValueTypeName+" keyframe track named "+this.name;if(this.createInterpolant===void 0)if(e!==this.DefaultInterpolation)this.setInterpolation(this.DefaultInterpolation);else throw new Error(n);return console.warn("THREE.KeyframeTrack:",n),this}return this.createInterpolant=t,this}getInterpolation(){switch(this.createInterpolant){case this.InterpolantFactoryMethodDiscrete:return ls;case this.InterpolantFactoryMethodLinear:return ps;case this.InterpolantFactoryMethodSmooth:return zo}}getValueSize(){return this.values.length/this.times.length}shift(e){if(e!==0){let t=this.times;for(let n=0,s=t.length;n!==s;++n)t[n]+=e}return this}scale(e){if(e!==1){let t=this.times;for(let n=0,s=t.length;n!==s;++n)t[n]*=e}return this}trim(e,t){let n=this.times,s=n.length,r=0,o=s-1;for(;r!==s&&n[r]<e;)++r;for(;o!==-1&&n[o]>t;)--o;if(++o,r!==0||o!==s){r>=o&&(o=Math.max(o,1),r=o-1);let a=this.getValueSize();this.times=n.slice(r,o),this.values=this.values.slice(r*a,o*a)}return this}validate(){let e=!0,t=this.getValueSize();t-Math.floor(t)!==0&&(console.error("THREE.KeyframeTrack: Invalid value size in track.",this),e=!1);let n=this.times,s=this.values,r=n.length;r===0&&(console.error("THREE.KeyframeTrack: Track is empty.",this),e=!1);let o=null;for(let a=0;a!==r;a++){let c=n[a];if(typeof c=="number"&&isNaN(c)){console.error("THREE.KeyframeTrack: Time is not a valid number.",this,a,c),e=!1;break}if(o!==null&&o>c){console.error("THREE.KeyframeTrack: Out of order keys.",this,a,c,o),e=!1;break}o=c}if(s!==void 0&&dm(s))for(let a=0,c=s.length;a!==c;++a){let h=s[a];if(isNaN(h)){console.error("THREE.KeyframeTrack: Value is not a valid number.",this,a,h),e=!1;break}}return e}optimize(){let e=this.times.slice(),t=this.values.slice(),n=this.getValueSize(),s=this.getInterpolation()===zo,r=e.length-1,o=1;for(let a=1;a<r;++a){let c=!1,h=e[a],l=e[a+1];if(h!==l&&(a!==1||h!==e[0]))if(s)c=!0;else{let p=a*n,f=p-n,u=p+n;for(let _=0;_!==n;++_){let m=t[p+_];if(m!==t[f+_]||m!==t[u+_]){c=!0;break}}}if(c){if(a!==o){e[o]=e[a];let p=a*n,f=o*n;for(let u=0;u!==n;++u)t[f+u]=t[p+u]}++o}}if(r>0){e[o]=e[r];for(let a=r*n,c=o*n,h=0;h!==n;++h)t[c+h]=t[a+h];++o}return o!==e.length?(this.times=e.slice(0,o),this.values=t.slice(0,o*n)):(this.times=e,this.values=t),this}clone(){let e=this.times.slice(),t=this.values.slice(),n=this.constructor,s=new n(this.name,e,t);return s.createInterpolant=this.createInterpolant,s}};on.prototype.TimeBufferType=Float32Array;on.prototype.ValueBufferType=Float32Array;on.prototype.DefaultInterpolation=ps;var li=class extends on{constructor(e,t,n){super(e,t,n)}};li.prototype.ValueTypeName="bool";li.prototype.ValueBufferType=Array;li.prototype.DefaultInterpolation=ls;li.prototype.InterpolantFactoryMethodLinear=void 0;li.prototype.InterpolantFactoryMethodSmooth=void 0;var vo=class extends on{};vo.prototype.ValueTypeName="color";var Vn=class extends on{};Vn.prototype.ValueTypeName="number";var Ec=class extends hi{constructor(e,t,n,s){super(e,t,n,s)}interpolate_(e,t,n,s){let r=this.resultBuffer,o=this.sampleValues,a=this.valueSize,c=(n-t)/(s-t),h=e*a;for(let l=h+a;h!==l;h+=4)Yt.slerpFlat(r,0,o,h-a,o,h,c);return r}},Ln=class extends on{InterpolantFactoryMethodLinear(e){return new Ec(this.times,this.values,this.getValueSize(),e)}};Ln.prototype.ValueTypeName="quaternion";Ln.prototype.InterpolantFactoryMethodSmooth=void 0;var pi=class extends on{constructor(e,t,n){super(e,t,n)}};pi.prototype.ValueTypeName="string";pi.prototype.ValueBufferType=Array;pi.prototype.DefaultInterpolation=ls;pi.prototype.InterpolantFactoryMethodLinear=void 0;pi.prototype.InterpolantFactoryMethodSmooth=void 0;var zn=class extends on{};zn.prototype.ValueTypeName="vector";var Ho=class{constructor(e="",t=-1,n=[],s=$p){this.name=e,this.tracks=n,this.duration=t,this.blendMode=s,this.uuid=_n(),this.duration<0&&this.resetDuration()}static parse(e){let t=[],n=e.tracks,s=1/(e.fps||1);for(let o=0,a=n.length;o!==a;++o)t.push(_m(n[o]).scale(s));let r=new this(e.name,e.duration,t,e.blendMode);return r.uuid=e.uuid,r}static toJSON(e){let t=[],n=e.tracks,s={name:e.name,duration:e.duration,tracks:t,uuid:e.uuid,blendMode:e.blendMode};for(let r=0,o=n.length;r!==o;++r)t.push(on.toJSON(n[r]));return s}static CreateFromMorphTargetSequence(e,t,n,s){let r=t.length,o=[];for(let a=0;a<r;a++){let c=[],h=[];c.push((a+r-1)%r,a,(a+1)%r),h.push(0,1,0);let l=um(c);c=Ql(c,1,l),h=Ql(h,1,l),!s&&c[0]===0&&(c.push(r),h.push(h[0])),o.push(new Vn(".morphTargetInfluences["+t[a].name+"]",c,h).scale(1/n))}return new this(e,-1,o)}static findByName(e,t){let n=e;if(!Array.isArray(e)){let s=e;n=s.geometry&&s.geometry.animations||s.animations}for(let s=0;s<n.length;s++)if(n[s].name===t)return n[s];return null}static CreateClipsFromMorphTargetSequences(e,t,n){let s={},r=/^([\w-]*?)([\d]+)$/;for(let a=0,c=e.length;a<c;a++){let h=e[a],l=h.name.match(r);if(l&&l.length>1){let p=l[1],f=s[p];f||(s[p]=f=[]),f.push(h)}}let o=[];for(let a in s)o.push(this.CreateFromMorphTargetSequence(a,s[a],t,n));return o}static parseAnimation(e,t){if(!e)return console.error("THREE.AnimationClip: No animation in JSONLoader data."),null;let n=function(p,f,u,_,m){if(u.length!==0){let g=[],d=[];S0(u,g,d,_),g.length!==0&&m.push(new p(f,g,d))}},s=[],r=e.name||"default",o=e.fps||30,a=e.blendMode,c=e.length||-1,h=e.hierarchy||[];for(let p=0;p<h.length;p++){let f=h[p].keys;if(!(!f||f.length===0))if(f[0].morphTargets){let u={},_;for(_=0;_<f.length;_++)if(f[_].morphTargets)for(let m=0;m<f[_].morphTargets.length;m++)u[f[_].morphTargets[m]]=-1;for(let m in u){let g=[],d=[];for(let A=0;A!==f[_].morphTargets.length;++A){let O=f[_];g.push(O.time),d.push(O.morphTarget===m?1:0)}s.push(new Vn(".morphTargetInfluence["+m+"]",g,d))}c=u.length*o}else{let u=".bones["+t[p].name+"]";n(zn,u+".position",f,"pos",s),n(Ln,u+".quaternion",f,"rot",s),n(zn,u+".scale",f,"scl",s)}}return s.length===0?null:new this(r,c,s,a)}resetDuration(){let e=this.tracks,t=0;for(let n=0,s=e.length;n!==s;++n){let r=this.tracks[n];t=Math.max(t,r.times[r.times.length-1])}return this.duration=t,this}trim(){for(let e=0;e<this.tracks.length;e++)this.tracks[e].trim(0,this.duration);return this}validate(){let e=!0;for(let t=0;t<this.tracks.length;t++)e=e&&this.tracks[t].validate();return e}optimize(){for(let e=0;e<this.tracks.length;e++)this.tracks[e].optimize();return this}clone(){let e=[];for(let t=0;t<this.tracks.length;t++)e.push(this.tracks[t].clone());return new this.constructor(this.name,this.duration,e,this.blendMode)}toJSON(){return this.constructor.toJSON(this)}};function gm(i){switch(i.toLowerCase()){case"scalar":case"double":case"float":case"number":case"integer":return Vn;case"vector":case"vector2":case"vector3":case"vector4":return zn;case"color":return vo;case"quaternion":return Ln;case"bool":case"boolean":return li;case"string":return pi}throw new Error("THREE.KeyframeTrack: Unsupported typeName: "+i)}function _m(i){if(i.type===void 0)throw new Error("THREE.KeyframeTrack: track type undefined, can not parse");let e=gm(i.type);if(i.times===void 0){let t=[],n=[];S0(i.keys,t,n,"value"),i.times=t,i.values=n}return e.parse!==void 0?e.parse(i):new e(i.name,i.times,i.values,i.interpolation)}var ri={enabled:!1,files:{},add:function(i,e){this.enabled!==!1&&(this.files[i]=e)},get:function(i){if(this.enabled!==!1)return this.files[i]},remove:function(i){delete this.files[i]},clear:function(){this.files={}}},Wc=class{constructor(e,t,n){let s=this,r=!1,o=0,a=0,c,h=[];this.onStart=void 0,this.onLoad=e,this.onProgress=t,this.onError=n,this.itemStart=function(l){a++,r===!1&&s.onStart!==void 0&&s.onStart(l,o,a),r=!0},this.itemEnd=function(l){o++,s.onProgress!==void 0&&s.onProgress(l,o,a),o===a&&(r=!1,s.onLoad!==void 0&&s.onLoad())},this.itemError=function(l){s.onError!==void 0&&s.onError(l)},this.resolveURL=function(l){return c?c(l):l},this.setURLModifier=function(l){return c=l,this},this.addHandler=function(l,p){return h.push(l,p),this},this.removeHandler=function(l){let p=h.indexOf(l);return p!==-1&&h.splice(p,2),this},this.getHandler=function(l){for(let p=0,f=h.length;p<f;p+=2){let u=h[p],_=h[p+1];if(u.global&&(u.lastIndex=0),u.test(l))return _}return null}}},mm=new Wc,kn=class{constructor(e){this.manager=e!==void 0?e:mm,this.crossOrigin="anonymous",this.withCredentials=!1,this.path="",this.resourcePath="",this.requestHeader={}}load(){}loadAsync(e,t){let n=this;return new Promise(function(s,r){n.load(e,s,t,r)})}parse(){}setCrossOrigin(e){return this.crossOrigin=e,this}setWithCredentials(e){return this.withCredentials=e,this}setPath(e){return this.path=e,this}setResourcePath(e){return this.resourcePath=e,this}setRequestHeader(e){return this.requestHeader=e,this}};kn.DEFAULT_MATERIAL_NAME="__DEFAULT";var Un={},Mc=class extends Error{constructor(e,t){super(e),this.response=t}},lr=class extends kn{constructor(e){super(e)}load(e,t,n,s){e===void 0&&(e=""),this.path!==void 0&&(e=this.path+e),e=this.manager.resolveURL(e);let r=ri.get(e);if(r!==void 0)return this.manager.itemStart(e),setTimeout(()=>{t&&t(r),this.manager.itemEnd(e)},0),r;if(Un[e]!==void 0){Un[e].push({onLoad:t,onProgress:n,onError:s});return}Un[e]=[],Un[e].push({onLoad:t,onProgress:n,onError:s});let o=new Request(e,{headers:new Headers(this.requestHeader),credentials:this.withCredentials?"include":"same-origin"}),a=this.mimeType,c=this.responseType;fetch(o).then(h=>{if(h.status===200||h.status===0){if(h.status===0&&console.warn("THREE.FileLoader: HTTP Status 0 received."),typeof ReadableStream>"u"||h.body===void 0||h.body.getReader===void 0)return h;let l=Un[e],p=h.body.getReader(),f=h.headers.get("X-File-Size")||h.headers.get("Content-Length"),u=f?parseInt(f):0,_=u!==0,m=0,g=new ReadableStream({start(d){A();function A(){p.read().then(({done:O,value:P})=>{if(O)d.close();else{m+=P.byteLength;let G=new ProgressEvent("progress",{lengthComputable:_,loaded:m,total:u});for(let H=0,S=l.length;H<S;H++){let M=l[H];M.onProgress&&M.onProgress(G)}d.enqueue(P),A()}},O=>{d.error(O)})}}});return new Response(g)}else throw new Mc(`fetch for "${h.url}" responded with ${h.status}: ${h.statusText}`,h)}).then(h=>{switch(c){case"arraybuffer":return h.arrayBuffer();case"blob":return h.blob();case"document":return h.text().then(l=>new DOMParser().parseFromString(l,a));case"json":return h.json();default:if(a===void 0)return h.text();{let p=/charset="?([^;"\s]*)"?/i.exec(a),f=p&&p[1]?p[1].toLowerCase():void 0,u=new TextDecoder(f);return h.arrayBuffer().then(_=>u.decode(_))}}}).then(h=>{ri.add(e,h);let l=Un[e];delete Un[e];for(let p=0,f=l.length;p<f;p++){let u=l[p];u.onLoad&&u.onLoad(h)}}).catch(h=>{let l=Un[e];if(l===void 0)throw this.manager.itemError(e),h;delete Un[e];for(let p=0,f=l.length;p<f;p++){let u=l[p];u.onError&&u.onError(h)}this.manager.itemError(e)}).finally(()=>{this.manager.itemEnd(e)}),this.manager.itemStart(e)}setResponseType(e){return this.responseType=e,this}setMimeType(e){return this.mimeType=e,this}};var Cc=class extends kn{constructor(e){super(e)}load(e,t,n,s){this.path!==void 0&&(e=this.path+e),e=this.manager.resolveURL(e);let r=this,o=ri.get(e);if(o!==void 0)return r.manager.itemStart(e),setTimeout(function(){t&&t(o),r.manager.itemEnd(e)},0),o;let a=ir("img");function c(){l(),ri.add(e,this),t&&t(this),r.manager.itemEnd(e)}function h(p){l(),s&&s(p),r.manager.itemError(e),r.manager.itemEnd(e)}function l(){a.removeEventListener("load",c,!1),a.removeEventListener("error",h,!1)}return a.addEventListener("load",c,!1),a.addEventListener("error",h,!1),e.slice(0,5)!=="data:"&&this.crossOrigin!==void 0&&(a.crossOrigin=this.crossOrigin),r.manager.itemStart(e),a.src=e,a}};var ys=class extends kn{constructor(e){super(e)}load(e,t,n,s){let r=new Ct,o=new Cc(this.manager);return o.setCrossOrigin(this.crossOrigin),o.setPath(this.path),o.load(e,function(a){r.image=a,r.needsUpdate=!0,t!==void 0&&t(r)},n,s),r}},As=class extends mt{constructor(e,t=1){super(),this.isLight=!0,this.type="Light",this.color=new Ge(e),this.intensity=t}dispose(){}copy(e,t){return super.copy(e,t),this.color.copy(e.color),this.intensity=e.intensity,this}toJSON(e){let t=super.toJSON(e);return t.object.color=this.color.getHex(),t.object.intensity=this.intensity,this.groundColor!==void 0&&(t.object.groundColor=this.groundColor.getHex()),this.distance!==void 0&&(t.object.distance=this.distance),this.angle!==void 0&&(t.object.angle=this.angle),this.decay!==void 0&&(t.object.decay=this.decay),this.penumbra!==void 0&&(t.object.penumbra=this.penumbra),this.shadow!==void 0&&(t.object.shadow=this.shadow.toJSON()),this.target!==void 0&&(t.object.target=this.target.uuid),t}},So=class extends As{constructor(e,t,n){super(e,n),this.isHemisphereLight=!0,this.type="HemisphereLight",this.position.copy(mt.DEFAULT_UP),this.updateMatrix(),this.groundColor=new Ge(t)}copy(e,t){return super.copy(e,t),this.groundColor.copy(e.groundColor),this}},Oa=new Fe,Yl=new C,Zl=new C,pr=class{constructor(e){this.camera=e,this.intensity=1,this.bias=0,this.normalBias=0,this.radius=1,this.blurSamples=8,this.mapSize=new Re(512,512),this.map=null,this.mapPass=null,this.matrix=new Fe,this.autoUpdate=!0,this.needsUpdate=!1,this._frustum=new rr,this._frameExtents=new Re(1,1),this._viewportCount=1,this._viewports=[new nt(0,0,1,1)]}getViewportCount(){return this._viewportCount}getFrustum(){return this._frustum}updateMatrices(e){let t=this.camera,n=this.matrix;Yl.setFromMatrixPosition(e.matrixWorld),t.position.copy(Yl),Zl.setFromMatrixPosition(e.target.matrixWorld),t.lookAt(Zl),t.updateMatrixWorld(),Oa.multiplyMatrices(t.projectionMatrix,t.matrixWorldInverse),this._frustum.setFromProjectionMatrix(Oa),n.set(.5,0,0,.5,0,.5,0,.5,0,0,.5,.5,0,0,0,1),n.multiply(Oa)}getViewport(e){return this._viewports[e]}getFrameExtents(){return this._frameExtents}dispose(){this.map&&this.map.dispose(),this.mapPass&&this.mapPass.dispose()}copy(e){return this.camera=e.camera.clone(),this.intensity=e.intensity,this.bias=e.bias,this.radius=e.radius,this.mapSize.copy(e.mapSize),this}clone(){return new this.constructor().copy(this)}toJSON(){let e={};return this.intensity!==1&&(e.intensity=this.intensity),this.bias!==0&&(e.bias=this.bias),this.normalBias!==0&&(e.normalBias=this.normalBias),this.radius!==1&&(e.radius=this.radius),(this.mapSize.x!==512||this.mapSize.y!==512)&&(e.mapSize=this.mapSize.toArray()),e.camera=this.camera.toJSON(!1).object,delete e.camera.matrix,e}},Gc=class extends pr{constructor(){super(new St(50,1,.5,500)),this.isSpotLightShadow=!0,this.focus=1}updateMatrices(e){let t=this.camera,n=fs*2*e.angle*this.focus,s=this.mapSize.width/this.mapSize.height,r=e.distance||t.far;(n!==t.fov||s!==t.aspect||r!==t.far)&&(t.fov=n,t.aspect=s,t.far=r,t.updateProjectionMatrix()),super.updateMatrices(e)}copy(e){return super.copy(e),this.focus=e.focus,this}},vs=class extends As{constructor(e,t,n=0,s=Math.PI/3,r=0,o=2){super(e,t),this.isSpotLight=!0,this.type="SpotLight",this.position.copy(mt.DEFAULT_UP),this.updateMatrix(),this.target=new mt,this.distance=n,this.angle=s,this.penumbra=r,this.decay=o,this.map=null,this.shadow=new Gc}get power(){return this.intensity*Math.PI}set power(e){this.intensity=e/Math.PI}dispose(){this.shadow.dispose()}copy(e,t){return super.copy(e,t),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}},Kl=new Fe,Js=new C,ya=new C,Tc=class extends pr{constructor(){super(new St(90,1,.5,500)),this.isPointLightShadow=!0,this._frameExtents=new Re(4,2),this._viewportCount=6,this._viewports=[new nt(2,1,1,1),new nt(0,1,1,1),new nt(3,1,1,1),new nt(1,1,1,1),new nt(3,0,1,1),new nt(1,0,1,1)],this._cubeDirections=[new C(1,0,0),new C(-1,0,0),new C(0,0,1),new C(0,0,-1),new C(0,1,0),new C(0,-1,0)],this._cubeUps=[new C(0,1,0),new C(0,1,0),new C(0,1,0),new C(0,1,0),new C(0,0,1),new C(0,0,-1)]}updateMatrices(e,t=0){let n=this.camera,s=this.matrix,r=e.distance||n.far;r!==n.far&&(n.far=r,n.updateProjectionMatrix()),Js.setFromMatrixPosition(e.matrixWorld),n.position.copy(Js),ya.copy(n.position),ya.add(this._cubeDirections[t]),n.up.copy(this._cubeUps[t]),n.lookAt(ya),n.updateMatrixWorld(),s.makeTranslation(-Js.x,-Js.y,-Js.z),Kl.multiplyMatrices(n.projectionMatrix,n.matrixWorldInverse),this._frustum.setFromProjectionMatrix(Kl)}},qn=class extends As{constructor(e,t,n=0,s=2){super(e,t),this.isPointLight=!0,this.type="PointLight",this.distance=n,this.decay=s,this.shadow=new Tc}get power(){return this.intensity*4*Math.PI}set power(e){this.intensity=e/(4*Math.PI)}dispose(){this.shadow.dispose()}copy(e,t){return super.copy(e,t),this.distance=e.distance,this.decay=e.decay,this.shadow=e.shadow.clone(),this}},Uc=class extends pr{constructor(){super(new gs(-5,5,5,-5,.5,500)),this.isDirectionalLightShadow=!0}},Eo=class extends As{constructor(e,t){super(e,t),this.isDirectionalLight=!0,this.type="DirectionalLight",this.position.copy(mt.DEFAULT_UP),this.updateMatrix(),this.target=new mt,this.shadow=new Uc}dispose(){this.shadow.dispose()}copy(e){return super.copy(e),this.target=e.target.clone(),this.shadow=e.shadow.clone(),this}};var fi=class{static decodeText(e){if(console.warn("THREE.LoaderUtils: decodeText() has been deprecated with r165 and will be removed with r175. Use TextDecoder instead."),typeof TextDecoder<"u")return new TextDecoder().decode(e);let t="";for(let n=0,s=e.length;n<s;n++)t+=String.fromCharCode(e[n]);try{return decodeURIComponent(escape(t))}catch{return t}}static extractUrlBase(e){let t=e.lastIndexOf("/");return t===-1?"./":e.slice(0,t+1)}static resolveURL(e,t){return typeof e!="string"||e===""?"":(/^https?:\/\//i.test(t)&&/^\//.test(e)&&(t=t.replace(/(^https?:\/\/[^\/]+).*/i,"$1")),/^(https?:)?\/\//i.test(e)||/^data:.*,.*$/i.test(e)||/^blob:.*$/i.test(e)?e:t+e)}};var Wo=class extends kn{constructor(e){super(e),this.isImageBitmapLoader=!0,typeof createImageBitmap>"u"&&console.warn("THREE.ImageBitmapLoader: createImageBitmap() not supported."),typeof fetch>"u"&&console.warn("THREE.ImageBitmapLoader: fetch() not supported."),this.options={premultiplyAlpha:"none"}}setOptions(e){return this.options=e,this}load(e,t,n,s){e===void 0&&(e=""),this.path!==void 0&&(e=this.path+e),e=this.manager.resolveURL(e);let r=this,o=ri.get(e);if(o!==void 0){if(r.manager.itemStart(e),o.then){o.then(h=>{t&&t(h),r.manager.itemEnd(e)}).catch(h=>{s&&s(h)});return}return setTimeout(function(){t&&t(o),r.manager.itemEnd(e)},0),o}let a={};a.credentials=this.crossOrigin==="anonymous"?"same-origin":"include",a.headers=this.requestHeader;let c=fetch(e,a).then(function(h){return h.blob()}).then(function(h){return createImageBitmap(h,Object.assign(r.options,{colorSpaceConversion:"none"}))}).then(function(h){return ri.add(e,h),t&&t(h),r.manager.itemEnd(e),h}).catch(function(h){s&&s(h),ri.remove(e),r.manager.itemError(e),r.manager.itemEnd(e)});ri.add(e,c),r.manager.itemStart(e)}};var Mo=class{constructor(e=!0){this.autoStart=e,this.startTime=0,this.oldTime=0,this.elapsedTime=0,this.running=!1}start(){this.startTime=e0(),this.oldTime=this.startTime,this.elapsedTime=0,this.running=!0}stop(){this.getElapsedTime(),this.running=!1,this.autoStart=!1}getElapsedTime(){return this.getDelta(),this.elapsedTime}getDelta(){let e=0;if(this.autoStart&&!this.running)return this.start(),0;if(this.running){let t=e0();e=(t-this.oldTime)/1e3,this.oldTime=t,this.elapsedTime+=e}return e}};function e0(){return performance.now()}var Jc="\\[\\]\\.:\\/",xm=new RegExp("["+Jc+"]","g"),jc="[^"+Jc+"]",bm="[^"+Jc.replace("\\.","")+"]",Pm=/((?:WC+[\/:])*)/.source.replace("WC",jc),wm=/(WCOD+)?/.source.replace("WCOD",bm),Om=/(?:\.(WC+)(?:\[(.+)\])?)?/.source.replace("WC",jc),ym=/\.(WC+)(?:\[(.+)\])?/.source.replace("WC",jc),Am=new RegExp("^"+Pm+wm+Om+ym+"$"),vm=["material","materials","bones","map"],Ic=class{constructor(e,t,n){let s=n||dt.parseTrackName(t);this._targetGroup=e,this._bindings=e.subscribe_(t,s)}getValue(e,t){this.bind();let n=this._targetGroup.nCachedObjects_,s=this._bindings[n];s!==void 0&&s.getValue(e,t)}setValue(e,t){let n=this._bindings;for(let s=this._targetGroup.nCachedObjects_,r=n.length;s!==r;++s)n[s].setValue(e,t)}bind(){let e=this._bindings;for(let t=this._targetGroup.nCachedObjects_,n=e.length;t!==n;++t)e[t].bind()}unbind(){let e=this._bindings;for(let t=this._targetGroup.nCachedObjects_,n=e.length;t!==n;++t)e[t].unbind()}},dt=class i{constructor(e,t,n){this.path=t,this.parsedPath=n||i.parseTrackName(t),this.node=i.findNode(e,this.parsedPath.nodeName),this.rootNode=e,this.getValue=this._getValue_unbound,this.setValue=this._setValue_unbound}static create(e,t,n){return e&&e.isAnimationObjectGroup?new i.Composite(e,t,n):new i(e,t,n)}static sanitizeNodeName(e){return e.replace(/\s/g,"_").replace(xm,"")}static parseTrackName(e){let t=Am.exec(e);if(t===null)throw new Error("PropertyBinding: Cannot parse trackName: "+e);let n={nodeName:t[2],objectName:t[3],objectIndex:t[4],propertyName:t[5],propertyIndex:t[6]},s=n.nodeName&&n.nodeName.lastIndexOf(".");if(s!==void 0&&s!==-1){let r=n.nodeName.substring(s+1);vm.indexOf(r)!==-1&&(n.nodeName=n.nodeName.substring(0,s),n.objectName=r)}if(n.propertyName===null||n.propertyName.length===0)throw new Error("PropertyBinding: can not parse propertyName from trackName: "+e);return n}static findNode(e,t){if(t===void 0||t===""||t==="."||t===-1||t===e.name||t===e.uuid)return e;if(e.skeleton){let n=e.skeleton.getBoneByName(t);if(n!==void 0)return n}if(e.children){let n=function(r){for(let o=0;o<r.length;o++){let a=r[o];if(a.name===t||a.uuid===t)return a;let c=n(a.children);if(c)return c}return null},s=n(e.children);if(s)return s}return null}_getValue_unavailable(){}_setValue_unavailable(){}_getValue_direct(e,t){e[t]=this.targetObject[this.propertyName]}_getValue_array(e,t){let n=this.resolvedProperty;for(let s=0,r=n.length;s!==r;++s)e[t++]=n[s]}_getValue_arrayElement(e,t){e[t]=this.resolvedProperty[this.propertyIndex]}_getValue_toArray(e,t){this.resolvedProperty.toArray(e,t)}_setValue_direct(e,t){this.targetObject[this.propertyName]=e[t]}_setValue_direct_setNeedsUpdate(e,t){this.targetObject[this.propertyName]=e[t],this.targetObject.needsUpdate=!0}_setValue_direct_setMatrixWorldNeedsUpdate(e,t){this.targetObject[this.propertyName]=e[t],this.targetObject.matrixWorldNeedsUpdate=!0}_setValue_array(e,t){let n=this.resolvedProperty;for(let s=0,r=n.length;s!==r;++s)n[s]=e[t++]}_setValue_array_setNeedsUpdate(e,t){let n=this.resolvedProperty;for(let s=0,r=n.length;s!==r;++s)n[s]=e[t++];this.targetObject.needsUpdate=!0}_setValue_array_setMatrixWorldNeedsUpdate(e,t){let n=this.resolvedProperty;for(let s=0,r=n.length;s!==r;++s)n[s]=e[t++];this.targetObject.matrixWorldNeedsUpdate=!0}_setValue_arrayElement(e,t){this.resolvedProperty[this.propertyIndex]=e[t]}_setValue_arrayElement_setNeedsUpdate(e,t){this.resolvedProperty[this.propertyIndex]=e[t],this.targetObject.needsUpdate=!0}_setValue_arrayElement_setMatrixWorldNeedsUpdate(e,t){this.resolvedProperty[this.propertyIndex]=e[t],this.targetObject.matrixWorldNeedsUpdate=!0}_setValue_fromArray(e,t){this.resolvedProperty.fromArray(e,t)}_setValue_fromArray_setNeedsUpdate(e,t){this.resolvedProperty.fromArray(e,t),this.targetObject.needsUpdate=!0}_setValue_fromArray_setMatrixWorldNeedsUpdate(e,t){this.resolvedProperty.fromArray(e,t),this.targetObject.matrixWorldNeedsUpdate=!0}_getValue_unbound(e,t){this.bind(),this.getValue(e,t)}_setValue_unbound(e,t){this.bind(),this.setValue(e,t)}bind(){let e=this.node,t=this.parsedPath,n=t.objectName,s=t.propertyName,r=t.propertyIndex;if(e||(e=i.findNode(this.rootNode,t.nodeName),this.node=e),this.getValue=this._getValue_unavailable,this.setValue=this._setValue_unavailable,!e){console.warn("THREE.PropertyBinding: No target node found for track: "+this.path+".");return}if(n){let h=t.objectIndex;switch(n){case"materials":if(!e.material){console.error("THREE.PropertyBinding: Can not bind to material as node does not have a material.",this);return}if(!e.material.materials){console.error("THREE.PropertyBinding: Can not bind to material.materials as node.material does not have a materials array.",this);return}e=e.material.materials;break;case"bones":if(!e.skeleton){console.error("THREE.PropertyBinding: Can not bind to bones as node does not have a skeleton.",this);return}e=e.skeleton.bones;for(let l=0;l<e.length;l++)if(e[l].name===h){h=l;break}break;case"map":if("map"in e){e=e.map;break}if(!e.material){console.error("THREE.PropertyBinding: Can not bind to material as node does not have a material.",this);return}if(!e.material.map){console.error("THREE.PropertyBinding: Can not bind to material.map as node.material does not have a map.",this);return}e=e.material.map;break;default:if(e[n]===void 0){console.error("THREE.PropertyBinding: Can not bind to objectName of node undefined.",this);return}e=e[n]}if(h!==void 0){if(e[h]===void 0){console.error("THREE.PropertyBinding: Trying to bind to objectIndex of objectName, but is undefined.",this,e);return}e=e[h]}}let o=e[s];if(o===void 0){let h=t.nodeName;console.error("THREE.PropertyBinding: Trying to update property for track: "+h+"."+s+" but it wasn't found.",e);return}let a=this.Versioning.None;this.targetObject=e,e.needsUpdate!==void 0?a=this.Versioning.NeedsUpdate:e.matrixWorldNeedsUpdate!==void 0&&(a=this.Versioning.MatrixWorldNeedsUpdate);let c=this.BindingType.Direct;if(r!==void 0){if(s==="morphTargetInfluences"){if(!e.geometry){console.error("THREE.PropertyBinding: Can not bind to morphTargetInfluences because node does not have a geometry.",this);return}if(!e.geometry.morphAttributes){console.error("THREE.PropertyBinding: Can not bind to morphTargetInfluences because node does not have a geometry.morphAttributes.",this);return}e.morphTargetDictionary[r]!==void 0&&(r=e.morphTargetDictionary[r])}c=this.BindingType.ArrayElement,this.resolvedProperty=o,this.propertyIndex=r}else o.fromArray!==void 0&&o.toArray!==void 0?(c=this.BindingType.HasFromToArray,this.resolvedProperty=o):Array.isArray(o)?(c=this.BindingType.EntireArray,this.resolvedProperty=o):this.propertyName=s;this.getValue=this.GetterByBindingType[c],this.setValue=this.SetterByBindingTypeAndVersioning[c][a]}unbind(){this.node=null,this.getValue=this._getValue_unbound,this.setValue=this._setValue_unbound}};dt.Composite=Ic;dt.prototype.BindingType={Direct:0,EntireArray:1,ArrayElement:2,HasFromToArray:3};dt.prototype.Versioning={None:0,NeedsUpdate:1,MatrixWorldNeedsUpdate:2};dt.prototype.GetterByBindingType=[dt.prototype._getValue_direct,dt.prototype._getValue_array,dt.prototype._getValue_arrayElement,dt.prototype._getValue_toArray];dt.prototype.SetterByBindingTypeAndVersioning=[[dt.prototype._setValue_direct,dt.prototype._setValue_direct_setNeedsUpdate,dt.prototype._setValue_direct_setMatrixWorldNeedsUpdate],[dt.prototype._setValue_array,dt.prototype._setValue_array_setNeedsUpdate,dt.prototype._setValue_array_setMatrixWorldNeedsUpdate],[dt.prototype._setValue_arrayElement,dt.prototype._setValue_arrayElement_setNeedsUpdate,dt.prototype._setValue_arrayElement_setMatrixWorldNeedsUpdate],[dt.prototype._setValue_fromArray,dt.prototype._setValue_fromArray_setNeedsUpdate,dt.prototype._setValue_fromArray_setMatrixWorldNeedsUpdate]];var ex=new Float32Array(1);var t0=new Fe,Co=class{constructor(e,t,n=0,s=1/0){this.ray=new Hi(e,t),this.near=n,this.far=s,this.camera=null,this.layers=new sr,this.params={Mesh:{},Line:{threshold:1},LOD:{},Points:{threshold:1},Sprite:{}}}set(e,t){this.ray.set(e,t)}setFromCamera(e,t){t.isPerspectiveCamera?(this.ray.origin.setFromMatrixPosition(t.matrixWorld),this.ray.direction.set(e.x,e.y,.5).unproject(t).sub(this.ray.origin).normalize(),this.camera=t):t.isOrthographicCamera?(this.ray.origin.set(e.x,e.y,(t.near+t.far)/(t.near-t.far)).unproject(t),this.ray.direction.set(0,0,-1).transformDirection(t.matrixWorld),this.camera=t):console.error("THREE.Raycaster: Unsupported camera type: "+t.type)}setFromXRController(e){return t0.identity().extractRotation(e.matrixWorld),this.ray.origin.setFromMatrixPosition(e.matrixWorld),this.ray.direction.set(0,0,-1).applyMatrix4(t0),this}intersectObject(e,t=!0,n=[]){return Rc(e,this,n,t),n.sort(n0),n}intersectObjects(e,t=!0,n=[]){for(let s=0,r=e.length;s<r;s++)Rc(e[s],this,n,t);return n.sort(n0),n}};function n0(i,e){return i.distance-e.distance}function Rc(i,e,t,n){let s=!0;if(i.layers.test(e.layers)&&i.raycast(e,t)===!1&&(s=!1),s===!0&&n===!0){let r=i.children;for(let o=0,a=r.length;o<a;o++)Rc(r[o],e,t,!0)}}typeof __THREE_DEVTOOLS__<"u"&&__THREE_DEVTOOLS__.dispatchEvent(new CustomEvent("register",{detail:{revision:"170"}}));typeof window<"u"&&(window.__THREE__?console.warn("WARNING: Multiple instances of Three.js being imported."):window.__THREE__="170");function Yc(i,e){if(e===g0)return console.warn("THREE.BufferGeometryUtils.toTrianglesDrawMode(): Geometry already defined as triangles."),i;if(e===dr||e===To){let t=i.getIndex();if(t===null){let o=[],a=i.getAttribute("position");if(a!==void 0){for(let c=0;c<a.count;c++)o.push(c);i.setIndex(o),t=i.getIndex()}else return console.error("THREE.BufferGeometryUtils.toTrianglesDrawMode(): Undefined position attribute. Processing not possible."),i}let n=t.count-2,s=[];if(e===dr)for(let o=1;o<=n;o++)s.push(t.getX(0)),s.push(t.getX(o)),s.push(t.getX(o+1));else for(let o=0;o<n;o++)o%2===0?(s.push(t.getX(o)),s.push(t.getX(o+1)),s.push(t.getX(o+2))):(s.push(t.getX(o+2)),s.push(t.getX(o+1)),s.push(t.getX(o)));s.length/3!==n&&console.error("THREE.BufferGeometryUtils.toTrianglesDrawMode(): Unable to generate correct amount of triangles.");let r=i.clone();return r.setIndex(s),r.clearGroups(),r}else return console.error("THREE.BufferGeometryUtils.toTrianglesDrawMode(): Unknown draw mode:",e),i}var Ro=class extends kn{constructor(e){super(e),this.dracoLoader=null,this.ktx2Loader=null,this.meshoptDecoder=null,this.pluginCallbacks=[],this.register(function(t){return new sh(t)}),this.register(function(t){return new rh(t)}),this.register(function(t){return new uh(t)}),this.register(function(t){return new gh(t)}),this.register(function(t){return new _h(t)}),this.register(function(t){return new ah(t)}),this.register(function(t){return new ch(t)}),this.register(function(t){return new hh(t)}),this.register(function(t){return new lh(t)}),this.register(function(t){return new ih(t)}),this.register(function(t){return new ph(t)}),this.register(function(t){return new oh(t)}),this.register(function(t){return new dh(t)}),this.register(function(t){return new fh(t)}),this.register(function(t){return new th(t)}),this.register(function(t){return new mh(t)}),this.register(function(t){return new xh(t)})}load(e,t,n,s){let r=this,o;if(this.resourcePath!=="")o=this.resourcePath;else if(this.path!==""){let h=fi.extractUrlBase(e);o=fi.resolveURL(h,this.path)}else o=fi.extractUrlBase(e);this.manager.itemStart(e);let a=function(h){s?s(h):console.error(h),r.manager.itemError(e),r.manager.itemEnd(e)},c=new lr(this.manager);c.setPath(this.path),c.setResponseType("arraybuffer"),c.setRequestHeader(this.requestHeader),c.setWithCredentials(this.withCredentials),c.load(e,function(h){try{r.parse(h,o,function(l){t(l),r.manager.itemEnd(e)},a)}catch(l){a(l)}},n,a)}setDRACOLoader(e){return this.dracoLoader=e,this}setKTX2Loader(e){return this.ktx2Loader=e,this}setMeshoptDecoder(e){return this.meshoptDecoder=e,this}register(e){return this.pluginCallbacks.indexOf(e)===-1&&this.pluginCallbacks.push(e),this}unregister(e){return this.pluginCallbacks.indexOf(e)!==-1&&this.pluginCallbacks.splice(this.pluginCallbacks.indexOf(e),1),this}parse(e,t,n,s){let r,o={},a={},c=new TextDecoder;if(typeof e=="string")r=JSON.parse(e);else if(e instanceof ArrayBuffer)if(c.decode(new Uint8Array(e,0,4))===G0){try{o[Qe.KHR_BINARY_GLTF]=new bh(e)}catch(p){s&&s(p);return}r=JSON.parse(o[Qe.KHR_BINARY_GLTF].content)}else r=JSON.parse(c.decode(e));else r=e;if(r.asset===void 0||r.asset.version[0]<2){s&&s(new Error("THREE.GLTFLoader: Unsupported asset. glTF versions >=2.0 are supported."));return}let h=new Hh(r,{path:t||this.resourcePath||"",crossOrigin:this.crossOrigin,requestHeader:this.requestHeader,manager:this.manager,ktx2Loader:this.ktx2Loader,meshoptDecoder:this.meshoptDecoder});h.fileLoader.setRequestHeader(this.requestHeader);for(let l=0;l<this.pluginCallbacks.length;l++){let p=this.pluginCallbacks[l](h);p.name||console.error("THREE.GLTFLoader: Invalid plugin found: missing name"),a[p.name]=p,o[p.name]=!0}if(r.extensionsUsed)for(let l=0;l<r.extensionsUsed.length;++l){let p=r.extensionsUsed[l],f=r.extensionsRequired||[];switch(p){case Qe.KHR_MATERIALS_UNLIT:o[p]=new nh;break;case Qe.KHR_DRACO_MESH_COMPRESSION:o[p]=new Ph(r,this.dracoLoader);break;case Qe.KHR_TEXTURE_TRANSFORM:o[p]=new wh;break;case Qe.KHR_MESH_QUANTIZATION:o[p]=new Oh;break;default:f.indexOf(p)>=0&&a[p]===void 0&&console.warn('THREE.GLTFLoader: Unknown extension "'+p+'".')}}h.setExtensions(o),h.setPlugins(a),h.parse(n,s)}parseAsync(e,t){let n=this;return new Promise(function(s,r){n.parse(e,t,s,r)})}};function Hm(){let i={};return{get:function(e){return i[e]},add:function(e,t){i[e]=t},remove:function(e){delete i[e]},removeAll:function(){i={}}}}var Qe={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_DISPERSION:"KHR_materials_dispersion",KHR_MATERIALS_IOR:"KHR_materials_ior",KHR_MATERIALS_SHEEN:"KHR_materials_sheen",KHR_MATERIALS_SPECULAR:"KHR_materials_specular",KHR_MATERIALS_TRANSMISSION:"KHR_materials_transmission",KHR_MATERIALS_IRIDESCENCE:"KHR_materials_iridescence",KHR_MATERIALS_ANISOTROPY:"KHR_materials_anisotropy",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",KHR_MATERIALS_EMISSIVE_STRENGTH:"KHR_materials_emissive_strength",EXT_MATERIALS_BUMP:"EXT_materials_bump",EXT_TEXTURE_WEBP:"EXT_texture_webp",EXT_TEXTURE_AVIF:"EXT_texture_avif",EXT_MESHOPT_COMPRESSION:"EXT_meshopt_compression",EXT_MESH_GPU_INSTANCING:"EXT_mesh_gpu_instancing"},th=class{constructor(e){this.parser=e,this.name=Qe.KHR_LIGHTS_PUNCTUAL,this.cache={refs:{},uses:{}}}_markDefs(){let e=this.parser,t=this.parser.json.nodes||[];for(let n=0,s=t.length;n<s;n++){let r=t[n];r.extensions&&r.extensions[this.name]&&r.extensions[this.name].light!==void 0&&e._addNodeRef(this.cache,r.extensions[this.name].light)}}_loadLight(e){let t=this.parser,n="light:"+e,s=t.cache.get(n);if(s)return s;let r=t.json,c=((r.extensions&&r.extensions[this.name]||{}).lights||[])[e],h,l=new Ge(16777215);c.color!==void 0&&l.setRGB(c.color[0],c.color[1],c.color[2],Ft);let p=c.range!==void 0?c.range:0;switch(c.type){case"directional":h=new Eo(l),h.target.position.set(0,0,-1),h.add(h.target);break;case"point":h=new qn(l),h.distance=p;break;case"spot":h=new vs(l),h.distance=p,c.spot=c.spot||{},c.spot.innerConeAngle=c.spot.innerConeAngle!==void 0?c.spot.innerConeAngle:0,c.spot.outerConeAngle=c.spot.outerConeAngle!==void 0?c.spot.outerConeAngle:Math.PI/4,h.angle=c.spot.outerConeAngle,h.penumbra=1-c.spot.innerConeAngle/c.spot.outerConeAngle,h.target.position.set(0,0,-1),h.add(h.target);break;default:throw new Error("THREE.GLTFLoader: Unexpected light type: "+c.type)}return h.position.set(0,0,0),h.decay=2,Jn(h,c),c.intensity!==void 0&&(h.intensity=c.intensity),h.name=t.createUniqueName(c.name||"light_"+e),s=Promise.resolve(h),t.cache.add(n,s),s}getDependency(e,t){if(e==="light")return this._loadLight(t)}createNodeAttachment(e){let t=this,n=this.parser,r=n.json.nodes[e],a=(r.extensions&&r.extensions[this.name]||{}).light;return a===void 0?null:this._loadLight(a).then(function(c){return n._getNodeRef(t.cache,a,c)})}},nh=class{constructor(){this.name=Qe.KHR_MATERIALS_UNLIT}getMaterialType(){return yt}extendParams(e,t,n){let s=[];e.color=new Ge(1,1,1),e.opacity=1;let r=t.pbrMetallicRoughness;if(r){if(Array.isArray(r.baseColorFactor)){let o=r.baseColorFactor;e.color.setRGB(o[0],o[1],o[2],Ft),e.opacity=o[3]}r.baseColorTexture!==void 0&&s.push(n.assignTexture(e,"map",r.baseColorTexture,_t))}return Promise.all(s)}},ih=class{constructor(e){this.parser=e,this.name=Qe.KHR_MATERIALS_EMISSIVE_STRENGTH}extendMaterialParams(e,t){let s=this.parser.json.materials[e];if(!s.extensions||!s.extensions[this.name])return Promise.resolve();let r=s.extensions[this.name].emissiveStrength;return r!==void 0&&(t.emissiveIntensity=r),Promise.resolve()}},sh=class{constructor(e){this.parser=e,this.name=Qe.KHR_MATERIALS_CLEARCOAT}getMaterialType(e){let n=this.parser.json.materials[e];return!n.extensions||!n.extensions[this.name]?null:Kt}extendMaterialParams(e,t){let n=this.parser,s=n.json.materials[e];if(!s.extensions||!s.extensions[this.name])return Promise.resolve();let r=[],o=s.extensions[this.name];if(o.clearcoatFactor!==void 0&&(t.clearcoat=o.clearcoatFactor),o.clearcoatTexture!==void 0&&r.push(n.assignTexture(t,"clearcoatMap",o.clearcoatTexture)),o.clearcoatRoughnessFactor!==void 0&&(t.clearcoatRoughness=o.clearcoatRoughnessFactor),o.clearcoatRoughnessTexture!==void 0&&r.push(n.assignTexture(t,"clearcoatRoughnessMap",o.clearcoatRoughnessTexture)),o.clearcoatNormalTexture!==void 0&&(r.push(n.assignTexture(t,"clearcoatNormalMap",o.clearcoatNormalTexture)),o.clearcoatNormalTexture.scale!==void 0)){let a=o.clearcoatNormalTexture.scale;t.clearcoatNormalScale=new Re(a,a)}return Promise.all(r)}},rh=class{constructor(e){this.parser=e,this.name=Qe.KHR_MATERIALS_DISPERSION}getMaterialType(e){let n=this.parser.json.materials[e];return!n.extensions||!n.extensions[this.name]?null:Kt}extendMaterialParams(e,t){let s=this.parser.json.materials[e];if(!s.extensions||!s.extensions[this.name])return Promise.resolve();let r=s.extensions[this.name];return t.dispersion=r.dispersion!==void 0?r.dispersion:0,Promise.resolve()}},oh=class{constructor(e){this.parser=e,this.name=Qe.KHR_MATERIALS_IRIDESCENCE}getMaterialType(e){let n=this.parser.json.materials[e];return!n.extensions||!n.extensions[this.name]?null:Kt}extendMaterialParams(e,t){let n=this.parser,s=n.json.materials[e];if(!s.extensions||!s.extensions[this.name])return Promise.resolve();let r=[],o=s.extensions[this.name];return o.iridescenceFactor!==void 0&&(t.iridescence=o.iridescenceFactor),o.iridescenceTexture!==void 0&&r.push(n.assignTexture(t,"iridescenceMap",o.iridescenceTexture)),o.iridescenceIor!==void 0&&(t.iridescenceIOR=o.iridescenceIor),t.iridescenceThicknessRange===void 0&&(t.iridescenceThicknessRange=[100,400]),o.iridescenceThicknessMinimum!==void 0&&(t.iridescenceThicknessRange[0]=o.iridescenceThicknessMinimum),o.iridescenceThicknessMaximum!==void 0&&(t.iridescenceThicknessRange[1]=o.iridescenceThicknessMaximum),o.iridescenceThicknessTexture!==void 0&&r.push(n.assignTexture(t,"iridescenceThicknessMap",o.iridescenceThicknessTexture)),Promise.all(r)}},ah=class{constructor(e){this.parser=e,this.name=Qe.KHR_MATERIALS_SHEEN}getMaterialType(e){let n=this.parser.json.materials[e];return!n.extensions||!n.extensions[this.name]?null:Kt}extendMaterialParams(e,t){let n=this.parser,s=n.json.materials[e];if(!s.extensions||!s.extensions[this.name])return Promise.resolve();let r=[];t.sheenColor=new Ge(0,0,0),t.sheenRoughness=0,t.sheen=1;let o=s.extensions[this.name];if(o.sheenColorFactor!==void 0){let a=o.sheenColorFactor;t.sheenColor.setRGB(a[0],a[1],a[2],Ft)}return o.sheenRoughnessFactor!==void 0&&(t.sheenRoughness=o.sheenRoughnessFactor),o.sheenColorTexture!==void 0&&r.push(n.assignTexture(t,"sheenColorMap",o.sheenColorTexture,_t)),o.sheenRoughnessTexture!==void 0&&r.push(n.assignTexture(t,"sheenRoughnessMap",o.sheenRoughnessTexture)),Promise.all(r)}},ch=class{constructor(e){this.parser=e,this.name=Qe.KHR_MATERIALS_TRANSMISSION}getMaterialType(e){let n=this.parser.json.materials[e];return!n.extensions||!n.extensions[this.name]?null:Kt}extendMaterialParams(e,t){let n=this.parser,s=n.json.materials[e];if(!s.extensions||!s.extensions[this.name])return Promise.resolve();let r=[],o=s.extensions[this.name];return o.transmissionFactor!==void 0&&(t.transmission=o.transmissionFactor),o.transmissionTexture!==void 0&&r.push(n.assignTexture(t,"transmissionMap",o.transmissionTexture)),Promise.all(r)}},hh=class{constructor(e){this.parser=e,this.name=Qe.KHR_MATERIALS_VOLUME}getMaterialType(e){let n=this.parser.json.materials[e];return!n.extensions||!n.extensions[this.name]?null:Kt}extendMaterialParams(e,t){let n=this.parser,s=n.json.materials[e];if(!s.extensions||!s.extensions[this.name])return Promise.resolve();let r=[],o=s.extensions[this.name];t.thickness=o.thicknessFactor!==void 0?o.thicknessFactor:0,o.thicknessTexture!==void 0&&r.push(n.assignTexture(t,"thicknessMap",o.thicknessTexture)),t.attenuationDistance=o.attenuationDistance||1/0;let a=o.attenuationColor||[1,1,1];return t.attenuationColor=new Ge().setRGB(a[0],a[1],a[2],Ft),Promise.all(r)}},lh=class{constructor(e){this.parser=e,this.name=Qe.KHR_MATERIALS_IOR}getMaterialType(e){let n=this.parser.json.materials[e];return!n.extensions||!n.extensions[this.name]?null:Kt}extendMaterialParams(e,t){let s=this.parser.json.materials[e];if(!s.extensions||!s.extensions[this.name])return Promise.resolve();let r=s.extensions[this.name];return t.ior=r.ior!==void 0?r.ior:1.5,Promise.resolve()}},ph=class{constructor(e){this.parser=e,this.name=Qe.KHR_MATERIALS_SPECULAR}getMaterialType(e){let n=this.parser.json.materials[e];return!n.extensions||!n.extensions[this.name]?null:Kt}extendMaterialParams(e,t){let n=this.parser,s=n.json.materials[e];if(!s.extensions||!s.extensions[this.name])return Promise.resolve();let r=[],o=s.extensions[this.name];t.specularIntensity=o.specularFactor!==void 0?o.specularFactor:1,o.specularTexture!==void 0&&r.push(n.assignTexture(t,"specularIntensityMap",o.specularTexture));let a=o.specularColorFactor||[1,1,1];return t.specularColor=new Ge().setRGB(a[0],a[1],a[2],Ft),o.specularColorTexture!==void 0&&r.push(n.assignTexture(t,"specularColorMap",o.specularColorTexture,_t)),Promise.all(r)}},fh=class{constructor(e){this.parser=e,this.name=Qe.EXT_MATERIALS_BUMP}getMaterialType(e){let n=this.parser.json.materials[e];return!n.extensions||!n.extensions[this.name]?null:Kt}extendMaterialParams(e,t){let n=this.parser,s=n.json.materials[e];if(!s.extensions||!s.extensions[this.name])return Promise.resolve();let r=[],o=s.extensions[this.name];return t.bumpScale=o.bumpFactor!==void 0?o.bumpFactor:1,o.bumpTexture!==void 0&&r.push(n.assignTexture(t,"bumpMap",o.bumpTexture)),Promise.all(r)}},dh=class{constructor(e){this.parser=e,this.name=Qe.KHR_MATERIALS_ANISOTROPY}getMaterialType(e){let n=this.parser.json.materials[e];return!n.extensions||!n.extensions[this.name]?null:Kt}extendMaterialParams(e,t){let n=this.parser,s=n.json.materials[e];if(!s.extensions||!s.extensions[this.name])return Promise.resolve();let r=[],o=s.extensions[this.name];return o.anisotropyStrength!==void 0&&(t.anisotropy=o.anisotropyStrength),o.anisotropyRotation!==void 0&&(t.anisotropyRotation=o.anisotropyRotation),o.anisotropyTexture!==void 0&&r.push(n.assignTexture(t,"anisotropyMap",o.anisotropyTexture)),Promise.all(r)}},uh=class{constructor(e){this.parser=e,this.name=Qe.KHR_TEXTURE_BASISU}loadTexture(e){let t=this.parser,n=t.json,s=n.textures[e];if(!s.extensions||!s.extensions[this.name])return null;let r=s.extensions[this.name],o=t.options.ktx2Loader;if(!o){if(n.extensionsRequired&&n.extensionsRequired.indexOf(this.name)>=0)throw new Error("THREE.GLTFLoader: setKTX2Loader must be called before loading KTX2 textures");return null}return t.loadTextureImage(e,r.source,o)}},gh=class{constructor(e){this.parser=e,this.name=Qe.EXT_TEXTURE_WEBP,this.isSupported=null}loadTexture(e){let t=this.name,n=this.parser,s=n.json,r=s.textures[e];if(!r.extensions||!r.extensions[t])return null;let o=r.extensions[t],a=s.images[o.source],c=n.textureLoader;if(a.uri){let h=n.options.manager.getHandler(a.uri);h!==null&&(c=h)}return this.detectSupport().then(function(h){if(h)return n.loadTextureImage(e,o.source,c);if(s.extensionsRequired&&s.extensionsRequired.indexOf(t)>=0)throw new Error("THREE.GLTFLoader: WebP required by asset but unsupported.");return n.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}},_h=class{constructor(e){this.parser=e,this.name=Qe.EXT_TEXTURE_AVIF,this.isSupported=null}loadTexture(e){let t=this.name,n=this.parser,s=n.json,r=s.textures[e];if(!r.extensions||!r.extensions[t])return null;let o=r.extensions[t],a=s.images[o.source],c=n.textureLoader;if(a.uri){let h=n.options.manager.getHandler(a.uri);h!==null&&(c=h)}return this.detectSupport().then(function(h){if(h)return n.loadTextureImage(e,o.source,c);if(s.extensionsRequired&&s.extensionsRequired.indexOf(t)>=0)throw new Error("THREE.GLTFLoader: AVIF required by asset but unsupported.");return n.loadTexture(e)})}detectSupport(){return this.isSupported||(this.isSupported=new Promise(function(e){let t=new Image;t.src="data:image/avif;base64,AAAAIGZ0eXBhdmlmAAAAAGF2aWZtaWYxbWlhZk1BMUIAAADybWV0YQAAAAAAAAAoaGRscgAAAAAAAAAAcGljdAAAAAAAAAAAAAAAAGxpYmF2aWYAAAAADnBpdG0AAAAAAAEAAAAeaWxvYwAAAABEAAABAAEAAAABAAABGgAAABcAAAAoaWluZgAAAAAAAQAAABppbmZlAgAAAAABAABhdjAxQ29sb3IAAAAAamlwcnAAAABLaXBjbwAAABRpc3BlAAAAAAAAAAEAAAABAAAAEHBpeGkAAAAAAwgICAAAAAxhdjFDgQAMAAAAABNjb2xybmNseAACAAIABoAAAAAXaXBtYQAAAAAAAAABAAEEAQKDBAAAAB9tZGF0EgAKCBgABogQEDQgMgkQAAAAB8dSLfI=",t.onload=t.onerror=function(){e(t.height===1)}})),this.isSupported}},mh=class{constructor(e){this.name=Qe.EXT_MESHOPT_COMPRESSION,this.parser=e}loadBufferView(e){let t=this.parser.json,n=t.bufferViews[e];if(n.extensions&&n.extensions[this.name]){let s=n.extensions[this.name],r=this.parser.getDependency("buffer",s.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 r.then(function(a){let c=s.byteOffset||0,h=s.byteLength||0,l=s.count,p=s.byteStride,f=new Uint8Array(a,c,h);return o.decodeGltfBufferAsync?o.decodeGltfBufferAsync(l,p,f,s.mode,s.filter).then(function(u){return u.buffer}):o.ready.then(function(){let u=new ArrayBuffer(l*p);return o.decodeGltfBuffer(new Uint8Array(u),l,p,f,s.mode,s.filter),u})})}else return null}},xh=class{constructor(e){this.name=Qe.EXT_MESH_GPU_INSTANCING,this.parser=e}createNodeMesh(e){let t=this.parser.json,n=t.nodes[e];if(!n.extensions||!n.extensions[this.name]||n.mesh===void 0)return null;let s=t.meshes[n.mesh];for(let h of s.primitives)if(h.mode!==an.TRIANGLES&&h.mode!==an.TRIANGLE_STRIP&&h.mode!==an.TRIANGLE_FAN&&h.mode!==void 0)return null;let o=n.extensions[this.name].attributes,a=[],c={};for(let h in o)a.push(this.parser.getDependency("accessor",o[h]).then(l=>(c[h]=l,c[h])));return a.length<1?null:(a.push(this.parser.createNodeMesh(e)),Promise.all(a).then(h=>{let l=h.pop(),p=l.isGroup?l.children:[l],f=h[0].count,u=[];for(let _ of p){let m=new Fe,g=new C,d=new Yt,A=new C(1,1,1),O=new Po(_.geometry,_.material,f);for(let P=0;P<f;P++)c.TRANSLATION&&g.fromBufferAttribute(c.TRANSLATION,P),c.ROTATION&&d.fromBufferAttribute(c.ROTATION,P),c.SCALE&&A.fromBufferAttribute(c.SCALE,P),O.setMatrixAt(P,m.compose(g,d,A));for(let P in c)if(P==="_COLOR_0"){let G=c[P];O.instanceColor=new Ei(G.array,G.itemSize,G.normalized)}else P!=="TRANSLATION"&&P!=="ROTATION"&&P!=="SCALE"&&_.geometry.setAttribute(P,c[P]);mt.prototype.copy.call(O,_),this.parser.assignFinalMaterial(O),u.push(O)}return l.isGroup?(l.clear(),l.add(...u),l):u[0]}))}},G0="glTF",ur=12,E0={JSON:1313821514,BIN:5130562},bh=class{constructor(e){this.name=Qe.KHR_BINARY_GLTF,this.content=null,this.body=null;let t=new DataView(e,0,ur),n=new TextDecoder;if(this.header={magic:n.decode(new Uint8Array(e.slice(0,4))),version:t.getUint32(4,!0),length:t.getUint32(8,!0)},this.header.magic!==G0)throw new Error("THREE.GLTFLoader: Unsupported glTF-Binary header.");if(this.header.version<2)throw new Error("THREE.GLTFLoader: Legacy binary file detected.");let s=this.header.length-ur,r=new DataView(e,ur),o=0;for(;o<s;){let a=r.getUint32(o,!0);o+=4;let c=r.getUint32(o,!0);if(o+=4,c===E0.JSON){let h=new Uint8Array(e,ur+o,a);this.content=n.decode(h)}else if(c===E0.BIN){let h=ur+o;this.body=e.slice(h,h+a)}o+=a}if(this.content===null)throw new Error("THREE.GLTFLoader: JSON content not found.")}},Ph=class{constructor(e,t){if(!t)throw new Error("THREE.GLTFLoader: No DRACOLoader instance provided.");this.name=Qe.KHR_DRACO_MESH_COMPRESSION,this.json=e,this.dracoLoader=t,this.dracoLoader.preload()}decodePrimitive(e,t){let n=this.json,s=this.dracoLoader,r=e.extensions[this.name].bufferView,o=e.extensions[this.name].attributes,a={},c={},h={};for(let l in o){let p=Ah[l]||l.toLowerCase();a[p]=o[l]}for(let l in e.attributes){let p=Ah[l]||l.toLowerCase();if(o[l]!==void 0){let f=n.accessors[e.attributes[l]],u=Ss[f.componentType];h[p]=u.name,c[p]=f.normalized===!0}}return t.getDependency("bufferView",r).then(function(l){return new Promise(function(p,f){s.decodeDracoFile(l,function(u){for(let _ in u.attributes){let m=u.attributes[_],g=c[_];g!==void 0&&(m.normalized=g)}p(u)},a,h,Ft,f)})})}},wh=class{constructor(){this.name=Qe.KHR_TEXTURE_TRANSFORM}extendTexture(e,t){return(t.texCoord===void 0||t.texCoord===e.channel)&&t.offset===void 0&&t.rotation===void 0&&t.scale===void 0||(e=e.clone(),t.texCoord!==void 0&&(e.channel=t.texCoord),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}},Oh=class{constructor(){this.name=Qe.KHR_MESH_QUANTIZATION}},Xo=class extends hi{constructor(e,t,n,s){super(e,t,n,s)}copySampleValue_(e){let t=this.resultBuffer,n=this.sampleValues,s=this.valueSize,r=e*s*3+s;for(let o=0;o!==s;o++)t[o]=n[r+o];return t}interpolate_(e,t,n,s){let r=this.resultBuffer,o=this.sampleValues,a=this.valueSize,c=a*2,h=a*3,l=s-t,p=(n-t)/l,f=p*p,u=f*p,_=e*h,m=_-h,g=-2*u+3*f,d=u-f,A=1-g,O=d-f+p;for(let P=0;P!==a;P++){let G=o[m+P+a],H=o[m+P+c]*l,S=o[_+P+a],M=o[_+P]*l;r[P]=A*G+O*H+g*S+d*M}return r}},Sm=new Yt,yh=class extends Xo{interpolate_(e,t,n,s){let r=super.interpolate_(e,t,n,s);return Sm.fromArray(r).normalize().toArray(r),r}},an={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},Ss={5120:Int8Array,5121:Uint8Array,5122:Int16Array,5123:Uint16Array,5125:Uint32Array,5126:Float32Array},W0={9728:$t,9729:kt,9984:Bc,9985:Ys,9986:Ki,9987:wn},M0={33071:Xn,33648:tr,10497:Fn},Zc={SCALAR:1,VEC2:2,VEC3:3,VEC4:4,MAT2:4,MAT3:9,MAT4:16},Ah={POSITION:"position",NORMAL:"normal",TANGENT:"tangent",TEXCOORD_0:"uv",TEXCOORD_1:"uv1",TEXCOORD_2:"uv2",TEXCOORD_3:"uv3",COLOR_0:"color",WEIGHTS_0:"skinWeight",JOINTS_0:"skinIndex"},di={scale:"scale",translation:"position",rotation:"quaternion",weights:"morphTargetInfluences"},Em={CUBICSPLINE:void 0,LINEAR:ps,STEP:ls},Kc={OPAQUE:"OPAQUE",MASK:"MASK",BLEND:"BLEND"};function Wm(i){return i.DefaultMaterial===void 0&&(i.DefaultMaterial=new Tt({color:16777215,emissive:0,metalness:1,roughness:1,transparent:!1,depthTest:!0,side:On})),i.DefaultMaterial}function Gi(i,e,t){for(let n in t.extensions)i[n]===void 0&&(e.userData.gltfExtensions=e.userData.gltfExtensions||{},e.userData.gltfExtensions[n]=t.extensions[n])}function Jn(i,e){e.extras!==void 0&&(typeof e.extras=="object"?Object.assign(i.userData,e.extras):console.warn("THREE.GLTFLoader: Ignoring primitive type .extras, "+e.extras))}function Mm(i,e,t){let n=!1,s=!1,r=!1;for(let h=0,l=e.length;h<l;h++){let p=e[h];if(p.POSITION!==void 0&&(n=!0),p.NORMAL!==void 0&&(s=!0),p.COLOR_0!==void 0&&(r=!0),n&&s&&r)break}if(!n&&!s&&!r)return Promise.resolve(i);let o=[],a=[],c=[];for(let h=0,l=e.length;h<l;h++){let p=e[h];if(n){let f=p.POSITION!==void 0?t.getDependency("accessor",p.POSITION):i.attributes.position;o.push(f)}if(s){let f=p.NORMAL!==void 0?t.getDependency("accessor",p.NORMAL):i.attributes.normal;a.push(f)}if(r){let f=p.COLOR_0!==void 0?t.getDependency("accessor",p.COLOR_0):i.attributes.color;c.push(f)}}return Promise.all([Promise.all(o),Promise.all(a),Promise.all(c)]).then(function(h){let l=h[0],p=h[1],f=h[2];return n&&(i.morphAttributes.position=l),s&&(i.morphAttributes.normal=p),r&&(i.morphAttributes.color=f),i.morphTargetsRelative=!0,i})}function Cm(i,e){if(i.updateMorphTargets(),e.weights!==void 0)for(let t=0,n=e.weights.length;t<n;t++)i.morphTargetInfluences[t]=e.weights[t];if(e.extras&&Array.isArray(e.extras.targetNames)){let t=e.extras.targetNames;if(i.morphTargetInfluences.length===t.length){i.morphTargetDictionary={};for(let n=0,s=t.length;n<s;n++)i.morphTargetDictionary[t[n]]=n}else console.warn("THREE.GLTFLoader: Invalid extras.targetNames length. Ignoring names.")}}function Gm(i){let e,t=i.extensions&&i.extensions[Qe.KHR_DRACO_MESH_COMPRESSION];if(t?e="draco:"+t.bufferView+":"+t.indices+":"+eh(t.attributes):e=i.indices+":"+eh(i.attributes)+":"+i.mode,i.targets!==void 0)for(let n=0,s=i.targets.length;n<s;n++)e+=":"+eh(i.targets[n]);return e}function eh(i){let e="",t=Object.keys(i).sort();for(let n=0,s=t.length;n<s;n++)e+=t[n]+":"+i[t[n]]+";";return e}function vh(i){switch(i){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.")}}function Tm(i){return i.search(/\.jpe?g($|\?)/i)>0||i.search(/^data\:image\/jpeg/)===0?"image/jpeg":i.search(/\.webp($|\?)/i)>0||i.search(/^data\:image\/webp/)===0?"image/webp":i.search(/\.ktx2($|\?)/i)>0||i.search(/^data\:image\/ktx2/)===0?"image/ktx2":"image/png"}var Um=new Fe,Hh=class{constructor(e={},t={}){this.json=e,this.extensions={},this.plugins={},this.options=t,this.cache=new Hm,this.associations=new Map,this.primitiveCache={},this.nodeCache={},this.meshCache={refs:{},uses:{}},this.cameraCache={refs:{},uses:{}},this.lightCache={refs:{},uses:{}},this.sourceCache={},this.textureCache={},this.nodeNamesUsed={};let n=!1,s=-1,r=!1,o=-1;if(typeof navigator<"u"){let a=navigator.userAgent;n=/^((?!chrome|android).)*safari/i.test(a)===!0;let c=a.match(/Version\/(\d+)/);s=n&&c?parseInt(c[1],10):-1,r=a.indexOf("Firefox")>-1,o=r?a.match(/Firefox\/([0-9]+)\./)[1]:-1}typeof createImageBitmap>"u"||n&&s<17||r&&o<98?this.textureLoader=new ys(this.options.manager):this.textureLoader=new Wo(this.options.manager),this.textureLoader.setCrossOrigin(this.options.crossOrigin),this.textureLoader.setRequestHeader(this.options.requestHeader),this.fileLoader=new lr(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 n=this,s=this.json,r=this.extensions;this.cache.removeAll(),this.nodeCache={},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([n.getDependencies("scene"),n.getDependencies("animation"),n.getDependencies("camera")])}).then(function(o){let a={scene:o[0][s.scene||0],scenes:o[0],animations:o[1],cameras:o[2],asset:s.asset,parser:n,userData:{}};return Gi(r,a,s),Jn(a,s),Promise.all(n._invokeAll(function(c){return c.afterRoot&&c.afterRoot(a)})).then(function(){for(let c of a.scenes)c.updateMatrixWorld();e(a)})}).catch(t)}_markDefs(){let e=this.json.nodes||[],t=this.json.skins||[],n=this.json.meshes||[];for(let s=0,r=t.length;s<r;s++){let o=t[s].joints;for(let a=0,c=o.length;a<c;a++)e[o[a]].isBone=!0}for(let s=0,r=e.length;s<r;s++){let o=e[s];o.mesh!==void 0&&(this._addNodeRef(this.meshCache,o.mesh),o.skin!==void 0&&(n[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,n){if(e.refs[t]<=1)return n;let s=n.clone(),r=(o,a)=>{let c=this.associations.get(o);c!=null&&this.associations.set(a,c);for(let[h,l]of o.children.entries())r(l,a.children[h])};return r(n,s),s.name+="_instance_"+e.uses[t]++,s}_invokeOne(e){let t=Object.values(this.plugins);t.push(this);for(let n=0;n<t.length;n++){let s=e(t[n]);if(s)return s}return null}_invokeAll(e){let t=Object.values(this.plugins);t.unshift(this);let n=[];for(let s=0;s<t.length;s++){let r=e(t[s]);r&&n.push(r)}return n}getDependency(e,t){let n=e+":"+t,s=this.cache.get(n);if(!s){switch(e){case"scene":s=this.loadScene(t);break;case"node":s=this._invokeOne(function(r){return r.loadNode&&r.loadNode(t)});break;case"mesh":s=this._invokeOne(function(r){return r.loadMesh&&r.loadMesh(t)});break;case"accessor":s=this.loadAccessor(t);break;case"bufferView":s=this._invokeOne(function(r){return r.loadBufferView&&r.loadBufferView(t)});break;case"buffer":s=this.loadBuffer(t);break;case"material":s=this._invokeOne(function(r){return r.loadMaterial&&r.loadMaterial(t)});break;case"texture":s=this._invokeOne(function(r){return r.loadTexture&&r.loadTexture(t)});break;case"skin":s=this.loadSkin(t);break;case"animation":s=this._invokeOne(function(r){return r.loadAnimation&&r.loadAnimation(t)});break;case"camera":s=this.loadCamera(t);break;default:if(s=this._invokeOne(function(r){return r!=this&&r.getDependency&&r.getDependency(e,t)}),!s)throw new Error("Unknown type: "+e);break}this.cache.add(n,s)}return s}getDependencies(e){let t=this.cache.get(e);if(!t){let n=this,s=this.json[e+(e==="mesh"?"es":"s")]||[];t=Promise.all(s.map(function(r,o){return n.getDependency(e,o)})),this.cache.add(e,t)}return t}loadBuffer(e){let t=this.json.buffers[e],n=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[Qe.KHR_BINARY_GLTF].body);let s=this.options;return new Promise(function(r,o){n.load(fi.resolveURL(t.uri,s.path),r,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(n){let s=t.byteLength||0,r=t.byteOffset||0;return n.slice(r,r+s)})}loadAccessor(e){let t=this,n=this.json,s=this.json.accessors[e];if(s.bufferView===void 0&&s.sparse===void 0){let o=Zc[s.type],a=Ss[s.componentType],c=s.normalized===!0,h=new a(s.count*o);return Promise.resolve(new wt(h,o,c))}let r=[];return s.bufferView!==void 0?r.push(this.getDependency("bufferView",s.bufferView)):r.push(null),s.sparse!==void 0&&(r.push(this.getDependency("bufferView",s.sparse.indices.bufferView)),r.push(this.getDependency("bufferView",s.sparse.values.bufferView))),Promise.all(r).then(function(o){let a=o[0],c=Zc[s.type],h=Ss[s.componentType],l=h.BYTES_PER_ELEMENT,p=l*c,f=s.byteOffset||0,u=s.bufferView!==void 0?n.bufferViews[s.bufferView].byteStride:void 0,_=s.normalized===!0,m,g;if(u&&u!==p){let d=Math.floor(f/u),A="InterleavedBuffer:"+s.bufferView+":"+s.componentType+":"+d+":"+s.count,O=t.cache.get(A);O||(m=new h(a,d*u,s.count*u/l),O=new xs(m,u/l),t.cache.add(A,O)),g=new Si(O,c,f%u/l,_)}else a===null?m=new h(s.count*c):m=new h(a,f,s.count*c),g=new wt(m,c,_);if(s.sparse!==void 0){let d=Zc.SCALAR,A=Ss[s.sparse.indices.componentType],O=s.sparse.indices.byteOffset||0,P=s.sparse.values.byteOffset||0,G=new A(o[1],O,s.sparse.count*d),H=new h(o[2],P,s.sparse.count*c);a!==null&&(g=new wt(g.array.slice(),g.itemSize,g.normalized)),g.normalized=!1;for(let S=0,M=G.length;S<M;S++){let y=G[S];if(g.setX(y,H[S*c]),c>=2&&g.setY(y,H[S*c+1]),c>=3&&g.setZ(y,H[S*c+2]),c>=4&&g.setW(y,H[S*c+3]),c>=5)throw new Error("THREE.GLTFLoader: Unsupported itemSize in sparse BufferAttribute.")}g.normalized=_}return g})}loadTexture(e){let t=this.json,n=this.options,r=t.textures[e].source,o=t.images[r],a=this.textureLoader;if(o.uri){let c=n.manager.getHandler(o.uri);c!==null&&(a=c)}return this.loadTextureImage(e,r,a)}loadTextureImage(e,t,n){let s=this,r=this.json,o=r.textures[e],a=r.images[t],c=(a.uri||a.bufferView)+":"+o.sampler;if(this.textureCache[c])return this.textureCache[c];let h=this.loadImageSource(t,n).then(function(l){l.flipY=!1,l.name=o.name||a.name||"",l.name===""&&typeof a.uri=="string"&&a.uri.startsWith("data:image/")===!1&&(l.name=a.uri);let f=(r.samplers||{})[o.sampler]||{};return l.magFilter=W0[f.magFilter]||kt,l.minFilter=W0[f.minFilter]||wn,l.wrapS=M0[f.wrapS]||Fn,l.wrapT=M0[f.wrapT]||Fn,l.generateMipmaps=!l.isCompressedTexture&&l.minFilter!==$t&&l.minFilter!==kt,s.associations.set(l,{textures:e}),l}).catch(function(){return null});return this.textureCache[c]=h,h}loadImageSource(e,t){let n=this,s=this.json,r=this.options;if(this.sourceCache[e]!==void 0)return this.sourceCache[e].then(p=>p.clone());let o=s.images[e],a=self.URL||self.webkitURL,c=o.uri||"",h=!1;if(o.bufferView!==void 0)c=n.getDependency("bufferView",o.bufferView).then(function(p){h=!0;let f=new Blob([p],{type:o.mimeType});return c=a.createObjectURL(f),c});else if(o.uri===void 0)throw new Error("THREE.GLTFLoader: Image "+e+" is missing URI and bufferView");let l=Promise.resolve(c).then(function(p){return new Promise(function(f,u){let _=f;t.isImageBitmapLoader===!0&&(_=function(m){let g=new Ct(m);g.needsUpdate=!0,f(g)}),t.load(fi.resolveURL(p,r.path),_,void 0,u)})}).then(function(p){return h===!0&&a.revokeObjectURL(c),Jn(p,o),p.userData.mimeType=o.mimeType||Tm(o.uri),p}).catch(function(p){throw console.error("THREE.GLTFLoader: Couldn't load texture",c),p});return this.sourceCache[e]=l,l}assignTexture(e,t,n,s){let r=this;return this.getDependency("texture",n.index).then(function(o){if(!o)return null;if(n.texCoord!==void 0&&n.texCoord>0&&(o=o.clone(),o.channel=n.texCoord),r.extensions[Qe.KHR_TEXTURE_TRANSFORM]){let a=n.extensions!==void 0?n.extensions[Qe.KHR_TEXTURE_TRANSFORM]:void 0;if(a){let c=r.associations.get(o);o=r.extensions[Qe.KHR_TEXTURE_TRANSFORM].extendTexture(o,a),r.associations.set(o,c)}}return s!==void 0&&(o.colorSpace=s),e[t]=o,o})}assignFinalMaterial(e){let t=e.geometry,n=e.material,s=t.attributes.tangent===void 0,r=t.attributes.color!==void 0,o=t.attributes.normal===void 0;if(e.isPoints){let a="PointsMaterial:"+n.uuid,c=this.cache.get(a);c||(c=new Wi,qt.prototype.copy.call(c,n),c.color.copy(n.color),c.map=n.map,c.sizeAttenuation=!1,this.cache.add(a,c)),n=c}else if(e.isLine){let a="LineBasicMaterial:"+n.uuid,c=this.cache.get(a);c||(c=new cr,qt.prototype.copy.call(c,n),c.color.copy(n.color),c.map=n.map,this.cache.add(a,c)),n=c}if(s||r||o){let a="ClonedMaterial:"+n.uuid+":";s&&(a+="derivative-tangents:"),r&&(a+="vertex-colors:"),o&&(a+="flat-shading:");let c=this.cache.get(a);c||(c=n.clone(),r&&(c.vertexColors=!0),o&&(c.flatShading=!0),s&&(c.normalScale&&(c.normalScale.y*=-1),c.clearcoatNormalScale&&(c.clearcoatNormalScale.y*=-1)),this.cache.add(a,c),this.associations.set(c,this.associations.get(n))),n=c}e.material=n}getMaterialType(){return Tt}loadMaterial(e){let t=this,n=this.json,s=this.extensions,r=n.materials[e],o,a={},c=r.extensions||{},h=[];if(c[Qe.KHR_MATERIALS_UNLIT]){let p=s[Qe.KHR_MATERIALS_UNLIT];o=p.getMaterialType(),h.push(p.extendParams(a,r,t))}else{let p=r.pbrMetallicRoughness||{};if(a.color=new Ge(1,1,1),a.opacity=1,Array.isArray(p.baseColorFactor)){let f=p.baseColorFactor;a.color.setRGB(f[0],f[1],f[2],Ft),a.opacity=f[3]}p.baseColorTexture!==void 0&&h.push(t.assignTexture(a,"map",p.baseColorTexture,_t)),a.metalness=p.metallicFactor!==void 0?p.metallicFactor:1,a.roughness=p.roughnessFactor!==void 0?p.roughnessFactor:1,p.metallicRoughnessTexture!==void 0&&(h.push(t.assignTexture(a,"metalnessMap",p.metallicRoughnessTexture)),h.push(t.assignTexture(a,"roughnessMap",p.metallicRoughnessTexture))),o=this._invokeOne(function(f){return f.getMaterialType&&f.getMaterialType(e)}),h.push(Promise.all(this._invokeAll(function(f){return f.extendMaterialParams&&f.extendMaterialParams(e,a)})))}r.doubleSided===!0&&(a.side=dn);let l=r.alphaMode||Kc.OPAQUE;if(l===Kc.BLEND?(a.transparent=!0,a.depthWrite=!1):(a.transparent=!1,l===Kc.MASK&&(a.alphaTest=r.alphaCutoff!==void 0?r.alphaCutoff:.5)),r.normalTexture!==void 0&&o!==yt&&(h.push(t.assignTexture(a,"normalMap",r.normalTexture)),a.normalScale=new Re(1,1),r.normalTexture.scale!==void 0)){let p=r.normalTexture.scale;a.normalScale.set(p,p)}if(r.occlusionTexture!==void 0&&o!==yt&&(h.push(t.assignTexture(a,"aoMap",r.occlusionTexture)),r.occlusionTexture.strength!==void 0&&(a.aoMapIntensity=r.occlusionTexture.strength)),r.emissiveFactor!==void 0&&o!==yt){let p=r.emissiveFactor;a.emissive=new Ge().setRGB(p[0],p[1],p[2],Ft)}return r.emissiveTexture!==void 0&&o!==yt&&h.push(t.assignTexture(a,"emissiveMap",r.emissiveTexture,_t)),Promise.all(h).then(function(){let p=new o(a);return r.name&&(p.name=r.name),Jn(p,r),t.associations.set(p,{materials:e}),r.extensions&&Gi(s,p,r),p})}createUniqueName(e){let t=dt.sanitizeNodeName(e||"");return t in this.nodeNamesUsed?t+"_"+ ++this.nodeNamesUsed[t]:(this.nodeNamesUsed[t]=0,t)}loadGeometries(e){let t=this,n=this.extensions,s=this.primitiveCache;function r(a){return n[Qe.KHR_DRACO_MESH_COMPRESSION].decodePrimitive(a,t).then(function(c){return C0(c,a,t)})}let o=[];for(let a=0,c=e.length;a<c;a++){let h=e[a],l=Gm(h),p=s[l];if(p)o.push(p.promise);else{let f;h.extensions&&h.extensions[Qe.KHR_DRACO_MESH_COMPRESSION]?f=r(h):f=C0(new Gt,h,t),s[l]={primitive:h,promise:f},o.push(f)}}return Promise.all(o)}loadMesh(e){let t=this,n=this.json,s=this.extensions,r=n.meshes[e],o=r.primitives,a=[];for(let c=0,h=o.length;c<h;c++){let l=o[c].material===void 0?Wm(this.cache):this.getDependency("material",o[c].material);a.push(l)}return a.push(t.loadGeometries(o)),Promise.all(a).then(function(c){let h=c.slice(0,c.length-1),l=c[c.length-1],p=[];for(let u=0,_=l.length;u<_;u++){let m=l[u],g=o[u],d,A=h[u];if(g.mode===an.TRIANGLES||g.mode===an.TRIANGLE_STRIP||g.mode===an.TRIANGLE_FAN||g.mode===void 0)d=r.isSkinnedMesh===!0?new mo(m,A):new De(m,A),d.isSkinnedMesh===!0&&d.normalizeSkinWeights(),g.mode===an.TRIANGLE_STRIP?d.geometry=Yc(d.geometry,To):g.mode===an.TRIANGLE_FAN&&(d.geometry=Yc(d.geometry,dr));else if(g.mode===an.LINES)d=new yo(m,A);else if(g.mode===an.LINE_STRIP)d=new bs(m,A);else if(g.mode===an.LINE_LOOP)d=new Ao(m,A);else if(g.mode===an.POINTS)d=new Ps(m,A);else throw new Error("THREE.GLTFLoader: Primitive mode unsupported: "+g.mode);Object.keys(d.geometry.morphAttributes).length>0&&Cm(d,r),d.name=t.createUniqueName(r.name||"mesh_"+e),Jn(d,r),g.extensions&&Gi(s,d,g),t.assignFinalMaterial(d),p.push(d)}for(let u=0,_=p.length;u<_;u++)t.associations.set(p[u],{meshes:e,primitives:u});if(p.length===1)return r.extensions&&Gi(s,p[0],r),p[0];let f=new Xt;r.extensions&&Gi(s,f,r),t.associations.set(f,{meshes:e});for(let u=0,_=p.length;u<_;u++)f.add(p[u]);return f})}loadCamera(e){let t,n=this.json.cameras[e],s=n[n.type];if(!s){console.warn("THREE.GLTFLoader: Missing camera parameters.");return}return n.type==="perspective"?t=new St(Ci.radToDeg(s.yfov),s.aspectRatio||1,s.znear||1,s.zfar||2e6):n.type==="orthographic"&&(t=new gs(-s.xmag,s.xmag,s.ymag,-s.ymag,s.znear,s.zfar)),n.name&&(t.name=this.createUniqueName(n.name)),Jn(t,n),Promise.resolve(t)}loadSkin(e){let t=this.json.skins[e],n=[];for(let s=0,r=t.joints.length;s<r;s++)n.push(this._loadNodeShallow(t.joints[s]));return t.inverseBindMatrices!==void 0?n.push(this.getDependency("accessor",t.inverseBindMatrices)):n.push(null),Promise.all(n).then(function(s){let r=s.pop(),o=s,a=[],c=[];for(let h=0,l=o.length;h<l;h++){let p=o[h];if(p){a.push(p);let f=new Fe;r!==null&&f.fromArray(r.array,h*16),c.push(f)}else console.warn('THREE.GLTFLoader: Joint "%s" could not be found.',t.joints[h])}return new bo(a,c)})}loadAnimation(e){let t=this.json,n=this,s=t.animations[e],r=s.name?s.name:"animation_"+e,o=[],a=[],c=[],h=[],l=[];for(let p=0,f=s.channels.length;p<f;p++){let u=s.channels[p],_=s.samplers[u.sampler],m=u.target,g=m.node,d=s.parameters!==void 0?s.parameters[_.input]:_.input,A=s.parameters!==void 0?s.parameters[_.output]:_.output;m.node!==void 0&&(o.push(this.getDependency("node",g)),a.push(this.getDependency("accessor",d)),c.push(this.getDependency("accessor",A)),h.push(_),l.push(m))}return Promise.all([Promise.all(o),Promise.all(a),Promise.all(c),Promise.all(h),Promise.all(l)]).then(function(p){let f=p[0],u=p[1],_=p[2],m=p[3],g=p[4],d=[];for(let A=0,O=f.length;A<O;A++){let P=f[A],G=u[A],H=_[A],S=m[A],M=g[A];if(P===void 0)continue;P.updateMatrix&&P.updateMatrix();let y=n._createAnimationTracks(P,G,H,S,M);if(y)for(let w=0;w<y.length;w++)d.push(y[w])}return new Ho(r,void 0,d)})}createNodeMesh(e){let t=this.json,n=this,s=t.nodes[e];return s.mesh===void 0?null:n.getDependency("mesh",s.mesh).then(function(r){let o=n._getNodeRef(n.meshCache,s.mesh,r);return s.weights!==void 0&&o.traverse(function(a){if(a.isMesh)for(let c=0,h=s.weights.length;c<h;c++)a.morphTargetInfluences[c]=s.weights[c]}),o})}loadNode(e){let t=this.json,n=this,s=t.nodes[e],r=n._loadNodeShallow(e),o=[],a=s.children||[];for(let h=0,l=a.length;h<l;h++)o.push(n.getDependency("node",a[h]));let c=s.skin===void 0?Promise.resolve(null):n.getDependency("skin",s.skin);return Promise.all([r,Promise.all(o),c]).then(function(h){let l=h[0],p=h[1],f=h[2];f!==null&&l.traverse(function(u){u.isSkinnedMesh&&u.bind(f,Um)});for(let u=0,_=p.length;u<_;u++)l.add(p[u]);return l})}_loadNodeShallow(e){let t=this.json,n=this.extensions,s=this;if(this.nodeCache[e]!==void 0)return this.nodeCache[e];let r=t.nodes[e],o=r.name?s.createUniqueName(r.name):"",a=[],c=s._invokeOne(function(h){return h.createNodeMesh&&h.createNodeMesh(e)});return c&&a.push(c),r.camera!==void 0&&a.push(s.getDependency("camera",r.camera).then(function(h){return s._getNodeRef(s.cameraCache,r.camera,h)})),s._invokeAll(function(h){return h.createNodeAttachment&&h.createNodeAttachment(e)}).forEach(function(h){a.push(h)}),this.nodeCache[e]=Promise.all(a).then(function(h){let l;if(r.isBone===!0?l=new ar:h.length>1?l=new Xt:h.length===1?l=h[0]:l=new mt,l!==h[0])for(let p=0,f=h.length;p<f;p++)l.add(h[p]);if(r.name&&(l.userData.name=r.name,l.name=o),Jn(l,r),r.extensions&&Gi(n,l,r),r.matrix!==void 0){let p=new Fe;p.fromArray(r.matrix),l.applyMatrix4(p)}else r.translation!==void 0&&l.position.fromArray(r.translation),r.rotation!==void 0&&l.quaternion.fromArray(r.rotation),r.scale!==void 0&&l.scale.fromArray(r.scale);return s.associations.has(l)||s.associations.set(l,{}),s.associations.get(l).nodes=e,l}),this.nodeCache[e]}loadScene(e){let t=this.extensions,n=this.json.scenes[e],s=this,r=new Xt;n.name&&(r.name=s.createUniqueName(n.name)),Jn(r,n),n.extensions&&Gi(t,r,n);let o=n.nodes||[],a=[];for(let c=0,h=o.length;c<h;c++)a.push(s.getDependency("node",o[c]));return Promise.all(a).then(function(c){for(let l=0,p=c.length;l<p;l++)r.add(c[l]);let h=l=>{let p=new Map;for(let[f,u]of s.associations)(f instanceof qt||f instanceof Ct)&&p.set(f,u);return l.traverse(f=>{let u=s.associations.get(f);u!=null&&p.set(f,u)}),p};return s.associations=h(r),r})}_createAnimationTracks(e,t,n,s,r){let o=[],a=e.name?e.name:e.uuid,c=[];di[r.path]===di.weights?e.traverse(function(f){f.morphTargetInfluences&&c.push(f.name?f.name:f.uuid)}):c.push(a);let h;switch(di[r.path]){case di.weights:h=Vn;break;case di.rotation:h=Ln;break;case di.position:case di.scale:h=zn;break;default:n.itemSize===1?h=Vn:h=zn;break}let l=s.interpolation!==void 0?Em[s.interpolation]:ps,p=this._getArrayFromAccessor(n);for(let f=0,u=c.length;f<u;f++){let _=new h(c[f]+"."+di[r.path],t.array,p,l);s.interpolation==="CUBICSPLINE"&&this._createCubicSplineTrackInterpolant(_),o.push(_)}return o}_getArrayFromAccessor(e){let t=e.array;if(e.normalized){let n=vh(t.constructor),s=new Float32Array(t.length);for(let r=0,o=t.length;r<o;r++)s[r]=t[r]*n;t=s}return t}_createCubicSplineTrackInterpolant(e){e.createInterpolant=function(n){let s=this instanceof Ln?yh:Xo;return new s(this.times,this.values,this.getValueSize()/3,n)},e.createInterpolant.isInterpolantFactoryMethodGLTFCubicSpline=!0}};function Im(i,e,t){let n=e.attributes,s=new rn;if(n.POSITION!==void 0){let a=t.json.accessors[n.POSITION],c=a.min,h=a.max;if(c!==void 0&&h!==void 0){if(s.set(new C(c[0],c[1],c[2]),new C(h[0],h[1],h[2])),a.normalized){let l=vh(Ss[a.componentType]);s.min.multiplyScalar(l),s.max.multiplyScalar(l)}}else{console.warn("THREE.GLTFLoader: Missing min/max properties for accessor POSITION.");return}}else return;let r=e.targets;if(r!==void 0){let a=new C,c=new C;for(let h=0,l=r.length;h<l;h++){let p=r[h];if(p.POSITION!==void 0){let f=t.json.accessors[p.POSITION],u=f.min,_=f.max;if(u!==void 0&&_!==void 0){if(c.setX(Math.max(Math.abs(u[0]),Math.abs(_[0]))),c.setY(Math.max(Math.abs(u[1]),Math.abs(_[1]))),c.setZ(Math.max(Math.abs(u[2]),Math.abs(_[2]))),f.normalized){let m=vh(Ss[f.componentType]);c.multiplyScalar(m)}a.max(c)}else console.warn("THREE.GLTFLoader: Missing min/max properties for accessor POSITION.")}}s.expandByVector(a)}i.boundingBox=s;let o=new Zt;s.getCenter(o.center),o.radius=s.min.distanceTo(s.max)/2,i.boundingSphere=o}function C0(i,e,t){let n=e.attributes,s=[];function r(o,a){return t.getDependency("accessor",o).then(function(c){i.setAttribute(a,c)})}for(let o in n){let a=Ah[o]||o.toLowerCase();a in i.attributes||s.push(r(n[o],a))}if(e.indices!==void 0&&!i.index){let o=t.getDependency("accessor",e.indices).then(function(a){i.setIndex(a)});s.push(o)}return Ze.workingColorSpace!==Ft&&"COLOR_0"in n&&console.warn(`THREE.GLTFLoader: Converting vertex colors from "srgb-linear" to "${Ze.workingColorSpace}" not supported.`),Jn(i,e),Im(i,e,t),Promise.all(s).then(function(){return e.targets!==void 0?Mm(i,e.targets,t):i})}var T0=(function(){"use strict";var i="b9H79Tebbbe8Fv9Gbb9Gvuuuuueu9Giuuub9Geueu9Giuuueuikqbeeedddillviebeoweuec:q;iekr;leDo9TW9T9VV95dbH9F9F939H79T9F9J9H229F9Jt9VV7bb8A9TW79O9V9Wt9F9KW9J9V9KW9wWVtW949c919M9MWVbeY9TW79O9V9Wt9F9KW9J9V9KW69U9KW949c919M9MWVbdE9TW79O9V9Wt9F9KW9J9V9KW69U9KW949tWG91W9U9JWbiL9TW79O9V9Wt9F9KW9J9V9KWS9P2tWV9p9JtblK9TW79O9V9Wt9F9KW9J9V9KWS9P2tWV9r919HtbvL9TW79O9V9Wt9F9KW9J9V9KWS9P2tWVT949Wbol79IV9Rbrq:P8Yqdbk;3sezu8Jjjjjbcj;eb9Rgv8Kjjjjbc9:hodnadcefal0mbcuhoaiRbbc:Ge9hmbavaialfgrad9Radz1jjjbhwcj;abad9UhoaicefhldnadTmbaoc;WFbGgocjdaocjd6EhDcbhqinaqae9pmeaDaeaq9RaqaDfae6Egkcsfgocl4cifcd4hxdndndndnaoc9WGgmTmbcbhPcehsawcjdfhzalhHinaraH9Rax6midnaraHaxfgl9RcK6mbczhoinawcj;cbfaogifgoc9WfhOdndndndndnaHaic9WfgAco4fRbbaAci4coG4ciGPlbedibkaO9cb83ibaOcwf9cb83ibxikaOalRblalRbbgAco4gCaCciSgCE86bbaocGfalclfaCfgORbbaAcl4ciGgCaCciSgCE86bbaocVfaOaCfgORbbaAcd4ciGgCaCciSgCE86bbaoc7faOaCfgORbbaAciGgAaAciSgAE86bbaoctfaOaAfgARbbalRbegOco4gCaCciSgCE86bbaoc91faAaCfgARbbaOcl4ciGgCaCciSgCE86bbaoc4faAaCfgARbbaOcd4ciGgCaCciSgCE86bbaoc93faAaCfgARbbaOciGgOaOciSgOE86bbaoc94faAaOfgARbbalRbdgOco4gCaCciSgCE86bbaoc95faAaCfgARbbaOcl4ciGgCaCciSgCE86bbaoc96faAaCfgARbbaOcd4ciGgCaCciSgCE86bbaoc97faAaCfgARbbaOciGgOaOciSgOE86bbaoc98faAaOfgORbbalRbiglco4gAaAciSgAE86bbaoc99faOaAfgORbbalcl4ciGgAaAciSgAE86bbaoc9: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:faOaAfgORbbalRbrglcl4gAaAcsSgAE86bbaocufaOaAfgoRbbalcsGglalcsSglE86bbaoalfhlxekaOal8Pbb83bbaOcwfalcwf8Pbb83bbalczfhlkdnaiam9pmbaiczfhoaral9RcL0mekkaiam6mialTmidnakTmbawaPfRbbhOcbhoazhiinaiawcj;cbfaofRbbgAce4cbaAceG9R7aOfgO86bbaiadfhiaocefgoak9hmbkkazcefhzaPcefgPad6hsalhHaPad9hmexvkkcbhlasceGmdxikalaxad2fhCdnakTmbcbhHcehsawcjdfhminaral9Rax6mialTmdalaxfhlawaHfRbbhOcbhoamhiinaiawcj;cbfaofRbbgAce4cbaAceG9R7aOfgO86bbaiadfhiaocefgoak9hmbkamcefhmaHcefgHad6hsaHad9hmbkaChlxikcbhocehsinaral9Rax6mdalTmealaxfhlaocefgoad6hsadao9hmbkaChlxdkcbhlasceGTmekc9:hoxikabaqad2fawcjdfakad2z1jjjb8Aawawcjdfakcufad2fadz1jjjb8Aakaqfhqalmbkc9:hoxekcbc99aral9Radcaadca0ESEhokavcj;ebf8Kjjjjbaok;yzeHu8Jjjjjbc;ae9Rgv8Kjjjjbc9:hodnaeci9UgrcHfal0mbcuhoaiRbbgwc;WeGc;Ge9hmbawcsGgDce0mbavc;abfcFecjez:jjjjb8AavcUf9cu83ibavc8Wf9cu83ibavcyf9cu83ibavcaf9cu83ibavcKf9cu83ibavczf9cu83ibav9cu83iwav9cu83ibaialfc9WfhqaicefgwarfhodnaeTmbcmcsaDceSEhkcbhxcbhmcbhDcbhicbhlindnaoaq9nmbc9:hoxikdndnawRbbgrc;Ve0mbavc;abfalarcl4cu7fcsGcitfgPydlhsaPydbhzdnarcsGgPak9pmbavaiarcu7fcsGcdtfydbaxaPEhraPThPdndnadcd9hmbabaDcetfgHaz87ebaHcdfas87ebaHclfar87ebxekabaDcdtfgHazBdbaHclfasBdbaHcwfarBdbkaxaPfhxavc;abfalcitfgHarBdbaHasBdlavaicdtfarBdbavc;abfalcefcsGglcitfgHazBdbaHarBdlaiaPfhialcefhlxdkdndnaPcsSmbamaPfaPc987fcefhmxekaocefhrao8SbbgPcFeGhHdndnaPcu9mmbarhoxekaocvfhoaHcFbGhHcrhPdninar8SbbgOcFbGaPtaHVhHaOcu9kmearcefhraPcrfgPc8J9hmbxdkkarcefhokaHce4cbaHceG9R7amfhmkdndnadcd9hmbabaDcetfgraz87ebarcdfas87ebarclfam87ebxekabaDcdtfgrazBdbarclfasBdbarcwfamBdbkavc;abfalcitfgramBdbarasBdlavaicdtfamBdbavc;abfalcefcsGglcitfgrazBdbaramBdlaicefhialcefhlxekdnarcpe0mbaxcefgOavaiaqarcsGfRbbgPcl49RcsGcdtfydbaPcz6gHEhravaiaP9RcsGcdtfydbaOaHfgsaPcsGgOEhPaOThOdndnadcd9hmbabaDcetfgzax87ebazcdfar87ebazclfaP87ebxekabaDcdtfgzaxBdbazclfarBdbazcwfaPBdbkavaicdtfaxBdbavc;abfalcitfgzarBdbazaxBdlavaicefgicsGcdtfarBdbavc;abfalcefcsGcitfgzaPBdbazarBdlavaiaHfcsGgicdtfaPBdbavc;abfalcdfcsGglcitfgraxBdbaraPBdlalcefhlaiaOfhiasaOfhxxekaxcbaoRbbgzEgAarc;: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;abfalcitfgrasBdbaraABdlavaicdtfaABdbavc;abfalcefcsGcitfgraOBdbarasBdlavaicefgicsGcdtfasBdbavc;abfalcdfcsGcitfgraABdbaraOBdlavaiazcz6aXcsSVfgicsGcdtfaOBdbaiaCTaCcsSVfhialcifhlkawcefhwalcsGhlaicsGhiaDcifgDae6mbkkcbc99aoaqSEhokavc;aef8Kjjjjbaok:llevu8Jjjjjbcz9Rhvc9:hodnaecvfal0mbcuhoaiRbbc;:eGc;qe9hmbav9cb83iwaicefhraialfc98fhwdnaeTmbdnadcdSmbcbhDindnaraw6mbc9:skarcefhoar8SbbglcFeGhidndnalcu9mmbaohrxekarcvfhraicFbGhicrhldninao8SbbgdcFbGaltaiVhiadcu9kmeaocefhoalcrfglc8J9hmbxdkkaocefhrkabaDcdtfaicd4cbaice4ceG9R7avcwfaiceGcdtVgoydbfglBdbaoalBdbaDcefgDae9hmbxdkkcbhDindnaraw6mbc9:skarcefhoar8SbbglcFeGhidndnalcu9mmbaohrxekarcvfhraicFbGhicrhldninao8SbbgdcFbGaltaiVhiadcu9kmeaocefhoalcrfglc8J9hmbxdkkaocefhrkabaDcetfaicd4cbaice4ceG9R7avcwfaiceGcdtVgoydbfgl87ebaoalBdbaDcefgDae9hmbkkcbc99arawSEhokaok:Lvoeue99dud99eud99dndnadcl9hmbaeTmeindndnabcdfgd8Sbb:Yab8Sbbgi:Ygl:l:tabcefgv8Sbbgo:Ygr:l:tgwJbb;:9cawawNJbbbbawawJbbbb9GgDEgq:mgkaqaicb9iEalMgwawNakaqaocb9iEarMgqaqNMM:r:vglNJbbbZJbbb:;aDEMgr:lJbbb9p9DTmbar:Ohixekcjjjj94hikadai86bbdndnaqalNJbbbZJbbb:;aqJbbbb9GEMgq:lJbbb9p9DTmbaq:Ohdxekcjjjj94hdkavad86bbdndnawalNJbbbZJbbb:;awJbbbb9GEMgw:lJbbb9p9DTmbaw:Ohdxekcjjjj94hdkabad86bbabclfhbaecufgembxdkkaeTmbindndnabclfgd8Ueb:Yab8Uebgi:Ygl:l:tabcdfgv8Uebgo:Ygr:l:tgwJb;:FSawawNJbbbbawawJbbbb9GgDEgq:mgkaqaicb9iEalMgwawNakaqaocb9iEarMgqaqNMM:r:vglNJbbbZJbbb:;aDEMgr:lJbbb9p9DTmbar:Ohixekcjjjj94hikadai87ebdndnaqalNJbbbZJbbb:;aqJbbbb9GEMgq:lJbbb9p9DTmbaq:Ohdxekcjjjj94hdkavad87ebdndnawalNJbbbZJbbb:;awJbbbb9GEMgw:lJbbb9p9DTmbaw:Ohdxekcjjjj94hdkabad87ebabcwfhbaecufgembkkk;siliui99iue99dnaeTmbcbhiabhlindndnJ;Zl81Zalcof8UebgvciV:Y:vgoal8Ueb:YNgrJb;:FSNJbbbZJbbb:;arJbbbb9GEMgw:lJbbb9p9DTmbaw:OhDxekcjjjj94hDkalclf8Uebhqalcdf8UebhkabavcefciGaiVcetfaD87ebdndnaoak:YNgwJb;:FSNJbbbZJbbb:;awJbbbb9GEMgx:lJbbb9p9DTmbax:Ohkxekcjjjj94hkkabavcdfciGaiVcetfak87ebdndnaoaq:YNgoJb;:FSNJbbbZJbbb:;aoJbbbb9GEMgx:lJbbb9p9DTmbax:Ohqxekcjjjj94hqkabavcufciGaiVcetfaq87ebdndnJbbjZararN:tawawN:taoaoN:tgrJbbbbarJbbbb9GE:rJb;:FSNJbbbZMgr:lJbbb9p9DTmbar:Ohqxekcjjjj94hqkabavciGaiVcetfaq87ebalcwfhlaiclfhiaecufgembkkk9mbdnadcd4ae2geTmbinababydbgdcwtcw91:Yadce91cjjj;8ifcjjj98G::NUdbabclfhbaecufgembkkk9teiucbcbydj1jjbgeabcifc98GfgbBdj1jjbdndnabZbcztgd9nmbcuhiabad9RcFFifcz4nbcuSmekaehikaik;LeeeudndnaeabVciGTmbabhixekdndnadcz9pmbabhixekabhiinaiaeydbBdbaiclfaeclfydbBdbaicwfaecwfydbBdbaicxfaecxfydbBdbaiczfhiaeczfheadc9Wfgdcs0mbkkadcl6mbinaiaeydbBdbaeclfheaiclfhiadc98fgdci0mbkkdnadTmbinaiaeRbb86bbaicefhiaecefheadcufgdmbkkabk;aeedudndnabciGTmbabhixekaecFeGc:b:c:ew2hldndnadcz9pmbabhixekabhiinaialBdbaicxfalBdbaicwfalBdbaiclfalBdbaiczfhiadc9Wfgdcs0mbkkadcl6mbinaialBdbaiclfhiadc98fgdci0mbkkdnadTmbinaiae86bbaicefhiadcufgdmbkkabkkkebcjwklz9Kbb",e="b9H79TebbbeKl9Gbb9Gvuuuuueu9Giuuub9Geueuikqbbebeedddilve9Weeeviebeoweuec:q;Aekr;leDo9TW9T9VV95dbH9F9F939H79T9F9J9H229F9Jt9VV7bb8A9TW79O9V9Wt9F9KW9J9V9KW9wWVtW949c919M9MWVbdY9TW79O9V9Wt9F9KW9J9V9KW69U9KW949c919M9MWVblE9TW79O9V9Wt9F9KW9J9V9KW69U9KW949tWG91W9U9JWbvL9TW79O9V9Wt9F9KW9J9V9KWS9P2tWV9p9JtboK9TW79O9V9Wt9F9KW9J9V9KWS9P2tWV9r919HtbrL9TW79O9V9Wt9F9KW9J9V9KWS9P2tWVT949Wbwl79IV9RbDq;t9tqlbzik9:evu8Jjjjjbcz9Rhbcbheincbhdcbhiinabcwfadfaicjuaead4ceGglE86bbaialfhiadcefgdcw9hmbkaec:q:yjjbfai86bbaecitc:q1jjbfab8Piw83ibaecefgecjd9hmbkk;h8JlHud97euo978Jjjjjbcj;kb9Rgv8Kjjjjbc9:hodnadcefal0mbcuhoaiRbbc:Ge9hmbavaialfgrad9Rad;8qbbcj;abad9UhoaicefhldnadTmbaoc;WFbGgocjdaocjd6EhwcbhDinaDae9pmeawaeaD9RaDawfae6Egqcsfgoc9WGgkci2hxakcethmaocl4cifcd4hPabaDad2fhscbhzdnincehHalhOcbhAdninaraO9RaP6miavcj;cbfaAak2fhCaOaPfhlcbhidnakc;ab6mbaral9Rc;Gb6mbcbhoinaCaofhidndndndndnaOaoco4fRbbgXciGPlbedibkaipxbbbbbbbbbbbbbbbbpklbxikaialpbblalpbbbgQclp:meaQpmbzeHdOiAlCvXoQrLgQcdp:meaQpmbzeHdOiAlCvXoQrLpxiiiiiiiiiiiiiiiip9ogLpxiiiiiiiiiiiiiiiip8JgQp5b9cjF;8;4;W;G;ab9:9cU1:NgKcitc:q1jjbfpbibaKc:q:yjjbfpbbbgYaYpmbbbbbbbbbbbbbbbbaQp5e9cjF;8;4;W;G;ab9:9cU1:NgKcitc:q1jjbfpbibp9UpmbedilvorzHOACXQLpPaLaQp9spklbalclfaYpQbfaKc:q:yjjbfRbbfhlxdkaialpbbwalpbbbgQclp:meaQpmbzeHdOiAlCvXoQrLpxssssssssssssssssp9ogLpxssssssssssssssssp8JgQp5b9cjF;8;4;W;G;ab9:9cU1:NgKcitc:q1jjbfpbibaKc:q:yjjbfpbbbgYaYpmbbbbbbbbbbbbbbbbaQp5e9cjF;8;4;W;G;ab9:9cU1:NgKcitc:q1jjbfpbibp9UpmbedilvorzHOACXQLpPaLaQp9spklbalcwfaYpQbfaKc:q:yjjbfRbbfhlxekaialpbbbpklbalczfhlkdndndndndnaXcd4ciGPlbedibkaipxbbbbbbbbbbbbbbbbpklzxikaialpbblalpbbbgQclp:meaQpmbzeHdOiAlCvXoQrLgQcdp:meaQpmbzeHdOiAlCvXoQrLpxiiiiiiiiiiiiiiiip9ogLpxiiiiiiiiiiiiiiiip8JgQp5b9cjF;8;4;W;G;ab9:9cU1:NgKcitc:q1jjbfpbibaKc:q:yjjbfpbbbgYaYpmbbbbbbbbbbbbbbbbaQp5e9cjF;8;4;W;G;ab9:9cU1:NgKcitc:q1jjbfpbibp9UpmbedilvorzHOACXQLpPaLaQp9spklzalclfaYpQbfaKc:q:yjjbfRbbfhlxdkaialpbbwalpbbbgQclp:meaQpmbzeHdOiAlCvXoQrLpxssssssssssssssssp9ogLpxssssssssssssssssp8JgQp5b9cjF;8;4;W;G;ab9:9cU1:NgKcitc:q1jjbfpbibaKc:q:yjjbfpbbbgYaYpmbbbbbbbbbbbbbbbbaQp5e9cjF;8;4;W;G;ab9:9cU1:NgKcitc:q1jjbfpbibp9UpmbedilvorzHOACXQLpPaLaQp9spklzalcwfaYpQbfaKc:q:yjjbfRbbfhlxekaialpbbbpklzalczfhlkdndndndndnaXcl4ciGPlbedibkaipxbbbbbbbbbbbbbbbbpklaxikaialpbblalpbbbgQclp:meaQpmbzeHdOiAlCvXoQrLgQcdp:meaQpmbzeHdOiAlCvXoQrLpxiiiiiiiiiiiiiiiip9ogLpxiiiiiiiiiiiiiiiip8JgQp5b9cjF;8;4;W;G;ab9:9cU1:NgKcitc:q1jjbfpbibaKc:q:yjjbfpbbbgYaYpmbbbbbbbbbbbbbbbbaQp5e9cjF;8;4;W;G;ab9:9cU1:NgKcitc:q1jjbfpbibp9UpmbedilvorzHOACXQLpPaLaQp9spklaalclfaYpQbfaKc:q:yjjbfRbbfhlxdkaialpbbwalpbbbgQclp:meaQpmbzeHdOiAlCvXoQrLpxssssssssssssssssp9ogLpxssssssssssssssssp8JgQp5b9cjF;8;4;W;G;ab9:9cU1:NgKcitc:q1jjbfpbibaKc:q:yjjbfpbbbgYaYpmbbbbbbbbbbbbbbbbaQp5e9cjF;8;4;W;G;ab9:9cU1:NgKcitc:q1jjbfpbibp9UpmbedilvorzHOACXQLpPaLaQp9spklaalcwfaYpQbfaKc:q:yjjbfRbbfhlxekaialpbbbpklaalczfhlkdndndndndnaXco4Plbedibkaipxbbbbbbbbbbbbbbbbpkl8WxikaialpbblalpbbbgQclp:meaQpmbzeHdOiAlCvXoQrLgQcdp:meaQpmbzeHdOiAlCvXoQrLpxiiiiiiiiiiiiiiiip9ogLpxiiiiiiiiiiiiiiiip8JgQp5b9cjF;8;4;W;G;ab9:9cU1:NgXcitc:q1jjbfpbibaXc:q:yjjbfpbbbgYaYpmbbbbbbbbbbbbbbbbaQp5e9cjF;8;4;W;G;ab9:9cU1:NgXcitc:q1jjbfpbibp9UpmbedilvorzHOACXQLpPaLaQp9spkl8WalclfaYpQbfaXc:q:yjjbfRbbfhlxdkaialpbbwalpbbbgQclp:meaQpmbzeHdOiAlCvXoQrLpxssssssssssssssssp9ogLpxssssssssssssssssp8JgQp5b9cjF;8;4;W;G;ab9:9cU1:NgXcitc:q1jjbfpbibaXc:q:yjjbfpbbbgYaYpmbbbbbbbbbbbbbbbbaQp5e9cjF;8;4;W;G;ab9:9cU1:NgXcitc:q1jjbfpbibp9UpmbedilvorzHOACXQLpPaLaQp9spkl8WalcwfaYpQbfaXc:q:yjjbfRbbfhlxekaialpbbbpkl8Walczfhlkaoc;abfhiaocjefak0meaihoaral9Rc;Fb0mbkkdndnaiak9pmbaici4hoinaral9RcK6mdaCaifhXdndndndndnaOaico4fRbbaocoG4ciGPlbedibkaXpxbbbbbbbbbbbbbbbbpklbxikaXalpbblalpbbbgQclp:meaQpmbzeHdOiAlCvXoQrLgQcdp:meaQpmbzeHdOiAlCvXoQrLpxiiiiiiiiiiiiiiiip9ogLpxiiiiiiiiiiiiiiiip8JgQp5b9cjF;8;4;W;G;ab9:9cU1:NgKcitc:q1jjbfpbibaKc:q:yjjbfpbbbgYaYpmbbbbbbbbbbbbbbbbaQp5e9cjF;8;4;W;G;ab9:9cU1:NgKcitc:q1jjbfpbibp9UpmbedilvorzHOACXQLpPaLaQp9spklbalclfaYpQbfaKc:q:yjjbfRbbfhlxdkaXalpbbwalpbbbgQclp:meaQpmbzeHdOiAlCvXoQrLpxssssssssssssssssp9ogLpxssssssssssssssssp8JgQp5b9cjF;8;4;W;G;ab9:9cU1:NgKcitc:q1jjbfpbibaKc:q:yjjbfpbbbgYaYpmbbbbbbbbbbbbbbbbaQp5e9cjF;8;4;W;G;ab9:9cU1:NgKcitc:q1jjbfpbibp9UpmbedilvorzHOACXQLpPaLaQp9spklbalcwfaYpQbfaKc:q:yjjbfRbbfhlxekaXalpbbbpklbalczfhlkaocdfhoaiczfgiak6mbkkalTmbaAci6hHalhOaAcefgohAaoclSmdxekkcbhlaHceGmdkdnakTmbavcjdfazfhiavazfpbdbhYcbhXinaiavcj;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;8qbbavavcjdfaqcufad2fad;8qbbaqaDfhDc9:hoalmexikkc9:hoxekcbc99aral9Radcaadca0ESEhokavcj;kbf8Kjjjjbaokwbz:bjjjbk;uzeHu8Jjjjjbc;ae9Rgv8Kjjjjbc9:hodnaeci9UgrcHfal0mbcuhoaiRbbgwc;WeGc;Ge9hmbawcsGgDce0mbavc;abfcFecje;8kbavcUf9cu83ibavc8Wf9cu83ibavcyf9cu83ibavcaf9cu83ibavcKf9cu83ibavczf9cu83ibav9cu83iwav9cu83ibaialfc9WfhqaicefgwarfhodnaeTmbcmcsaDceSEhkcbhxcbhmcbhDcbhicbhlindnaoaq9nmbc9:hoxikdndnawRbbgrc;Ve0mbavc;abfalarcl4cu7fcsGcitfgPydlhsaPydbhzdnarcsGgPak9pmbavaiarcu7fcsGcdtfydbaxaPEhraPThPdndnadcd9hmbabaDcetfgHaz87ebaHcdfas87ebaHclfar87ebxekabaDcdtfgHazBdbaHclfasBdbaHcwfarBdbkaxaPfhxavc;abfalcitfgHarBdbaHasBdlavaicdtfarBdbavc;abfalcefcsGglcitfgHazBdbaHarBdlaiaPfhialcefhlxdkdndnaPcsSmbamaPfaPc987fcefhmxekaocefhrao8SbbgPcFeGhHdndnaPcu9mmbarhoxekaocvfhoaHcFbGhHcrhPdninar8SbbgOcFbGaPtaHVhHaOcu9kmearcefhraPcrfgPc8J9hmbxdkkarcefhokaHce4cbaHceG9R7amfhmkdndnadcd9hmbabaDcetfgraz87ebarcdfas87ebarclfam87ebxekabaDcdtfgrazBdbarclfasBdbarcwfamBdbkavc;abfalcitfgramBdbarasBdlavaicdtfamBdbavc;abfalcefcsGglcitfgrazBdbaramBdlaicefhialcefhlxekdnarcpe0mbaxcefgOavaiaqarcsGfRbbgPcl49RcsGcdtfydbaPcz6gHEhravaiaP9RcsGcdtfydbaOaHfgsaPcsGgOEhPaOThOdndnadcd9hmbabaDcetfgzax87ebazcdfar87ebazclfaP87ebxekabaDcdtfgzaxBdbazclfarBdbazcwfaPBdbkavaicdtfaxBdbavc;abfalcitfgzarBdbazaxBdlavaicefgicsGcdtfarBdbavc;abfalcefcsGcitfgzaPBdbazarBdlavaiaHfcsGgicdtfaPBdbavc;abfalcdfcsGglcitfgraxBdbaraPBdlalcefhlaiaOfhiasaOfhxxekaxcbaoRbbgzEgAarc;: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;abfalcitfgrasBdbaraABdlavaicdtfaABdbavc;abfalcefcsGcitfgraOBdbarasBdlavaicefgicsGcdtfasBdbavc;abfalcdfcsGcitfgraABdbaraOBdlavaiazcz6aXcsSVfgicsGcdtfaOBdbaiaCTaCcsSVfhialcifhlkawcefhwalcsGhlaicsGhiaDcifgDae6mbkkcbc99aoaqSEhokavc;aef8Kjjjjbaok:llevu8Jjjjjbcz9Rhvc9:hodnaecvfal0mbcuhoaiRbbc;:eGc;qe9hmbav9cb83iwaicefhraialfc98fhwdnaeTmbdnadcdSmbcbhDindnaraw6mbc9:skarcefhoar8SbbglcFeGhidndnalcu9mmbaohrxekarcvfhraicFbGhicrhldninao8SbbgdcFbGaltaiVhiadcu9kmeaocefhoalcrfglc8J9hmbxdkkaocefhrkabaDcdtfaicd4cbaice4ceG9R7avcwfaiceGcdtVgoydbfglBdbaoalBdbaDcefgDae9hmbxdkkcbhDindnaraw6mbc9:skarcefhoar8SbbglcFeGhidndnalcu9mmbaohrxekarcvfhraicFbGhicrhldninao8SbbgdcFbGaltaiVhiadcu9kmeaocefhoalcrfglc8J9hmbxdkkaocefhrkabaDcetfaicd4cbaice4ceG9R7avcwfaiceGcdtVgoydbfgl87ebaoalBdbaDcefgDae9hmbkkcbc99arawSEhokaok:EPliuo97eue978Jjjjjbca9Rhidndnadcl9hmbdnaec98GglTmbcbhvabhdinadadpbbbgocKp:RecKp:Sep;6egraocwp:RecKp:Sep;6earp;Geaoczp:RecKp:Sep;6egwp;Gep;Kep;LegDpxbbbbbbbbbbbbbbbbp:2egqarpxbbbjbbbjbbbjbbbjgkp9op9rp;Kegrpxbb;:9cbb;:9cbb;:9cbb;:9cararp;MeaDaDp;Meawaqawakp9op9rp;Kegrarp;Mep;Kep;Kep;Jep;Negwp;Mepxbbn0bbn0bbn0bbn0gqp;KepxFbbbFbbbFbbbFbbbp9oaopxbbbFbbbFbbbFbbbFp9op9qarawp;Meaqp;Kecwp:RepxbFbbbFbbbFbbbFbbp9op9qaDawp;Meaqp;Keczp:RepxbbFbbbFbbbFbbbFbp9op9qpkbbadczfhdavclfgval6mbkkalae9pmeaiaeciGgvcdtgdVcbczad9R;8kbaiabalcdtfglad;8qbbdnavTmbaiaipblbgocKp:RecKp:Sep;6egraocwp:RecKp:Sep;6earp;Geaoczp:RecKp:Sep;6egwp;Gep;Kep;LegDpxbbbbbbbbbbbbbbbbp:2egqarpxbbbjbbbjbbbjbbbjgkp9op9rp;Kegrpxbb;:9cbb;:9cbb;:9cbb;:9cararp;MeaDaDp;Meawaqawakp9op9rp;Kegrarp;Mep;Kep;Kep;Jep;Negwp;Mepxbbn0bbn0bbn0bbn0gqp;KepxFbbbFbbbFbbbFbbbp9oaopxbbbFbbbFbbbFbbbFp9op9qarawp;Meaqp;Kecwp:RepxbFbbbFbbbFbbbFbbp9op9qaDawp;Meaqp;Keczp:RepxbbFbbbFbbbFbbbFbp9op9qpklbkalaiad;8qbbskdnaec98GgxTmbcbhvabhdinadczfglalpbbbgopxbbbbbbFFbbbbbbFFgkp9oadpbbbgDaopmlvorxmPsCXQL358E8FpxFubbFubbFubbFubbp9op;6eaDaopmbediwDqkzHOAKY8AEgoczp:Sep;6egrp;Geaoczp:Reczp:Sep;6egwp;Gep;Kep;Legopxb;:FSb;:FSb;:FSb;:FSawaopxbbbbbbbbbbbbbbbbp:2egqawpxbbbjbbbjbbbjbbbjgmp9op9rp;Kegwawp;Meaoaop;Mearaqaramp9op9rp;Kegoaop;Mep;Kep;Kep;Jep;Negrp;Mepxbbn0bbn0bbn0bbn0gqp;Keczp:Reawarp;Meaqp;KepxFFbbFFbbFFbbFFbbp9op9qgwaoarp;Meaqp;KepxFFbbFFbbFFbbFFbbp9ogopmwDKYqk8AExm35Ps8E8Fp9qpkbbadaDakp9oawaopmbezHdiOAlvCXorQLp9qpkbbadcafhdavclfgvax6mbkkaxae9pmbaiaeciGgvcitgdfcbcaad9R;8kbaiabaxcitfglad;8qbbdnavTmbaiaipblzgopxbbbbbbFFbbbbbbFFgkp9oaipblbgDaopmlvorxmPsCXQL358E8FpxFubbFubbFubbFubbp9op;6eaDaopmbediwDqkzHOAKY8AEgoczp:Sep;6egrp;Geaoczp:Reczp:Sep;6egwp;Gep;Kep;Legopxb;:FSb;:FSb;:FSb;:FSawaopxbbbbbbbbbbbbbbbbp:2egqawpxbbbjbbbjbbbjbbbjgmp9op9rp;Kegwawp;Meaoaop;Mearaqaramp9op9rp;Kegoaop;Mep;Kep;Kep;Jep;Negrp;Mepxbbn0bbn0bbn0bbn0gqp;Keczp:Reawarp;Meaqp;KepxFFbbFFbbFFbbFFbbp9op9qgwaoarp;Meaqp;KepxFFbbFFbbFFbbFFbbp9ogopmwDKYqk8AExm35Ps8E8Fp9qpklzaiaDakp9oawaopmbezHdiOAlvCXorQLp9qpklbkalaiad;8qbbkk;4wllue97euv978Jjjjjbc8W9Rhidnaec98GglTmbcbhvabhoinaiaopbbbgraoczfgwpbbbgDpmlvorxmPsCXQL358E8Fgqczp:Segkclp:RepklbaopxbbjZbbjZbbjZbbjZpx;Zl81Z;Zl81Z;Zl81Z;Zl81Zakpxibbbibbbibbbibbbp9qp;6ep;NegkaraDpmbediwDqkzHOAKY8AEgrczp:Reczp:Sep;6ep;MegDaDp;Meakarczp:Sep;6ep;Megxaxp;Meakaqczp:Reczp:Sep;6ep;Megqaqp;Mep;Kep;Kep;Lepxbbbbbbbbbbbbbbbbp:4ep;Jepxb;:FSb;:FSb;:FSb;:FSgkp;Mepxbbn0bbn0bbn0bbn0grp;KepxFFbbFFbbFFbbFFbbgmp9oaxakp;Mearp;Keczp:Rep9qgxaqakp;Mearp;Keczp:ReaDakp;Mearp;Keamp9op9qgkpmbezHdiOAlvCXorQLgrp5baipblbpEb:T:j83ibaocwfarp5eaipblbpEe:T:j83ibawaxakpmwDKYqk8AExm35Ps8E8Fgkp5baipblbpEd:T:j83ibaocKfakp5eaipblbpEi:T:j83ibaocafhoavclfgval6mbkkdnalae9pmbaiaeciGgvcitgofcbcaao9R;8kbaiabalcitfgwao;8qbbdnavTmbaiaipblbgraipblzgDpmlvorxmPsCXQL358E8Fgqczp:Segkclp:RepklaaipxbbjZbbjZbbjZbbjZpx;Zl81Z;Zl81Z;Zl81Z;Zl81Zakpxibbbibbbibbbibbbp9qp;6ep;NegkaraDpmbediwDqkzHOAKY8AEgrczp:Reczp:Sep;6ep;MegDaDp;Meakarczp:Sep;6ep;Megxaxp;Meakaqczp:Reczp:Sep;6ep;Megqaqp;Mep;Kep;Kep;Lepxbbbbbbbbbbbbbbbbp:4ep;Jepxb;:FSb;:FSb;:FSb;:FSgkp;Mepxbbn0bbn0bbn0bbn0grp;KepxFFbbFFbbFFbbFFbbgmp9oaxakp;Mearp;Keczp:Rep9qgxaqakp;Mearp;Keczp:ReaDakp;Mearp;Keamp9op9qgkpmbezHdiOAlvCXorQLgrp5baipblapEb:T:j83ibaiarp5eaipblapEe:T:j83iwaiaxakpmwDKYqk8AExm35Ps8E8Fgkp5baipblapEd:T:j83izaiakp5eaipblapEi:T:j83iKkawaiao;8qbbkk:Pddiue978Jjjjjbc;ab9Rhidnadcd4ae2glc98GgvTmbcbhdabheinaeaepbbbgocwp:Recwp:Sep;6eaocep:SepxbbjZbbjZbbjZbbjZp:UepxbbjFbbjFbbjFbbjFp9op;Mepkbbaeczfheadclfgdav6mbkkdnaval9pmbaialciGgdcdtgeVcbc;abae9R;8kbaiabavcdtfgvae;8qbbdnadTmbaiaipblbgocwp:Recwp:Sep;6eaocep:SepxbbjZbbjZbbjZbbjZp:UepxbbjFbbjFbbjFbbjFp9op;Mepklbkavaiae;8qbbkk9teiucbcbydj1jjbgeabcifc98GfgbBdj1jjbdndnabZbcztgd9nmbcuhiabad9RcFFifcz4nbcuSmekaehikaikkkebcjwklz9Tbb",t=new Uint8Array([0,97,115,109,1,0,0,0,1,4,1,96,0,0,3,3,2,0,0,5,3,1,0,1,12,1,0,10,22,2,12,0,65,0,65,0,65,0,252,10,0,0,11,7,0,65,0,253,15,26,11]),n=new Uint8Array([32,0,65,2,1,106,34,33,3,128,11,4,13,64,6,253,10,7,15,116,127,5,8,12,40,16,19,54,20,9,27,255,113,17,42,67,24,23,146,148,18,14,22,45,70,69,56,114,101,21,25,63,75,136,108,28,118,29,73,115]);if(typeof WebAssembly!="object")return{supported:!1};var s=WebAssembly.validate(t)?e:i,r,o=WebAssembly.instantiate(a(s),{}).then(function(d){r=d.instance,r.exports.__wasm_call_ctors()});function a(d){for(var A=new Uint8Array(d.length),O=0;O<d.length;++O){var P=d.charCodeAt(O);A[O]=P>96?P-97:P>64?P-39:P+4}for(var G=0,O=0;O<d.length;++O)A[G++]=A[O]<60?n[A[O]]:(A[O]-60)*64+A[++O];return A.buffer.slice(0,G)}function c(d,A,O,P,G,H){var S=r.exports.sbrk,M=O+3&-4,y=S(M*P),w=S(G.length),W=new Uint8Array(r.exports.memory.buffer);W.set(G,w);var $=d(y,O,P,w,G.length);if($==0&&H&&H(y,M,P),A.set(W.subarray(y,y+O*P)),S(y-S(0)),$!=0)throw new Error("Malformed buffer data: "+$)}var h={NONE:"",OCTAHEDRAL:"meshopt_decodeFilterOct",QUATERNION:"meshopt_decodeFilterQuat",EXPONENTIAL:"meshopt_decodeFilterExp"},l={ATTRIBUTES:"meshopt_decodeVertexBuffer",TRIANGLES:"meshopt_decodeIndexBuffer",INDICES:"meshopt_decodeIndexSequence"},p=[],f=0;function u(d){var A={object:new Worker(d),pending:0,requests:{}};return A.object.onmessage=function(O){var P=O.data;A.pending-=P.count,A.requests[P.id][P.action](P.value),delete A.requests[P.id]},A}function _(d){for(var A="var instance; var ready = WebAssembly.instantiate(new Uint8Array(["+new Uint8Array(a(s))+"]), {}).then(function(result) { instance = result.instance; instance.exports.__wasm_call_ctors(); });self.onmessage = workerProcess;"+c.toString()+g.toString(),O=new Blob([A],{type:"text/javascript"}),P=URL.createObjectURL(O),G=0;G<d;++G)p[G]=u(P);URL.revokeObjectURL(P)}function m(d,A,O,P,G){for(var H=p[0],S=1;S<p.length;++S)p[S].pending<H.pending&&(H=p[S]);return new Promise(function(M,y){var w=new Uint8Array(O),W=f++;H.pending+=d,H.requests[W]={resolve:M,reject:y},H.object.postMessage({id:W,count:d,size:A,source:w,mode:P,filter:G},[w.buffer])})}function g(d){o.then(function(){var A=d.data;try{var O=new Uint8Array(A.count*A.size);c(r.exports[A.mode],O,A.count,A.size,A.source,r.exports[A.filter]),self.postMessage({id:A.id,count:A.count,action:"resolve",value:O},[O.buffer])}catch(P){self.postMessage({id:A.id,count:A.count,action:"reject",value:P})}})}return{ready:o,supported:!0,useWorkers:function(d){_(d)},decodeVertexBuffer:function(d,A,O,P,G){c(r.exports.meshopt_decodeVertexBuffer,d,A,O,P,r.exports[h[G]])},decodeIndexBuffer:function(d,A,O,P){c(r.exports.meshopt_decodeIndexBuffer,d,A,O,P)},decodeIndexSequence:function(d,A,O,P){c(r.exports.meshopt_decodeIndexSequence,d,A,O,P)},decodeGltfBuffer:function(d,A,O,P,G,H){c(r.exports[l[G]],d,A,O,P,r.exports[h[H]])},decodeGltfBufferAsync:function(d,A,O,P,G){return p.length>0?m(d,A,O,l[P],h[G]):o.then(function(){var H=new Uint8Array(d*A);return c(r.exports[l[P]],H,d,A,O,r.exports[h[G]]),H})}}})();var gr=Th("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");var U0="data:image/jpeg;base64,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";var I0="data:image/jpeg;base64,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";var R0="data:image/jpeg;base64,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";var X0="data:image/jpeg;base64,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";var B0="data:image/jpeg;base64,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";var $0="data:image/jpeg;base64,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";var F0="data:image/jpeg;base64,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";var D0="data:image/jpeg;base64,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";var N0="data:image/jpeg;base64,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";var V0="data:image/jpeg;base64,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";var L0="data:image/jpeg;base64,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";var z0="data:image/jpeg;base64,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";var xt={site:"ieee.lums.edu.pk",tagline:"Advancing technology for humanity",about:{lead:"We are the IEEE student branch at LUMS: a community of engineers, builders and researchers who learn, compete and create together through workshops, talks and projects.",pillars:[["Learn","Hands-on workshops and technical sessions across computing, electronics and emerging tech."],["Connect","Meet industry professionals, alumni and IEEE members through talks and networking events."],["Build","Turn ideas into projects, hackathon entries and research with peers who share your drive."],["Lead","Grow organisational and leadership skills by running events and initiatives with our team."]],doLead:"A year-round calendar of activities for every kind of technologist.",does:[["Workshops","Practical, skills-first sessions led by students and experts."],["Speaker Sessions","Talks from researchers and engineers on where technology is heading."],["Competitions","Technical contests and hackathons to test and showcase your skills."],["Community","Socials and collaborations that make the chapter feel like home."]]},events:{upcoming:"Event details are coming soon. Follow @ieee_lums for announcements.",recent:[["3 November","EE Faculty vs. EE Students Cricket Match","Faculty and students faced off at the Old Hockey Ground at 5:30 pm. The professors took the win."],["Late October","New Member Induction","We welcomed this year\u2019s new members into the IEEE LUMS family, with Papa Johns pizza for everyone."],["September","Applications & Orientation","Applications opened on 18 September and closed on 24 September, followed by an orientation for incoming members."]]},team:{lead:"The team leading IEEE LUMS this year.",people:[["Hassan Jaleel","Patron","Associate Professor, Electrical Engineering, LUMS.","HJ","https://www.linkedin.com/in/hassan-jaleel-345b18248/","teamHassan"],["Abdullah Ali Qureshi","President","BSc Electrical Engineering student at LUMS.","AQ","","teamAbdullah"],["Mishal Sagar","Vice President","BS Electrical Engineering student at LUMS, class of 2027.","MS","https://www.linkedin.com/in/mishalsagar/","teamMishal"],["Shahmeer Ahsan","General Secretary","BS Electrical Engineering student at LUMS, class of 2027.","SA","https://www.linkedin.com/in/shahmeer-ahsan-66a6771a4/","teamShahmeer"],["Syeda Rabieea Saqib","Treasurer","BS Computer Science student at LUMS, class of 2028.","RS","https://www.linkedin.com/in/rabieea-saqib-44b188303/",""]]},gallery:[["banner","RoboDog showing off at the IEEE LUMS stall."],["top","RoboDog, standing by."],["front","Head-on with RoboDog."],["hall","Handstand practice between sessions."],["igTeam","The directorate, suited up for orientation."],["igOrientation","Engineering Beyond the Classroom \u2014 the orientation panel."],["igCandid","Behind the scenes, prepping for orientation."],["igInduction","New members, officially inducted."]],contact:[["Email","ieee@lums.edu.pk","mailto:ieee@lums.edu.pk"],["Instagram","@ieee_lums","https://www.instagram.com/ieee_lums/"],["LinkedIn","IEEE LUMS Student Chapter","https://www.linkedin.com/company/ieee-lums-student-chapter/"],["YouTube","@ieeelums5580","https://www.youtube.com/@ieeelums5580"]],dog:{name:"RoboDog",text:"The heart of IEEE LUMS, unofficially. RoboDog is a Unitree Go2 quadruped who crashes every chapter event, has strong opinions about standing ovations, and has never once RSVP\u2019d. Pick a trick and see what mood it\u2019s in."}},_r=[{id:"about",label:"About",title:"About IEEE LUMS"},{id:"events",label:"Notice Board",title:"Events"},{id:"gallery",label:"Gallery",title:"Gallery"},{id:"contact",label:"Comms",title:"Contact Us"},{id:"team",label:"Meet the Team",title:"Meet the Team"},{id:"dog",label:"RoboDog",title:"RoboDog, the IEEE mascot"}];var k0=`
.ih-root{position:fixed;inset:0;z-index:2147483000;background:#020814;color:#e6f4ff;font-family:'Inter Tight',system-ui,-apple-system,'Segoe UI',Roboto,sans-serif;font-size:17px;line-height:1.45;overflow:hidden;-webkit-tap-highlight-color:transparent}
.ih-root *{box-sizing:border-box}
.ih-root canvas{position:absolute;inset:0;width:100%;height:100%;display:block;touch-action:none;outline:none}
.ih-h{font-family:'Space Grotesk','Inter Tight',system-ui,sans-serif;letter-spacing:.08em;text-transform:uppercase}
.ih-btn{font:inherit;font-family:'Space Grotesk','Inter Tight',system-ui,sans-serif;letter-spacing:.1em;text-transform:uppercase;font-size:13px;color:#e6f4ff;background:rgba(0,98,155,.35);border:1px solid #00b5e2;border-radius:4px;padding:11px 16px;cursor:pointer;backdrop-filter:blur(6px);-webkit-backdrop-filter:blur(6px);transition:background .15s,box-shadow .15s}
.ih-btn:hover,.ih-btn:focus-visible{background:rgba(0,181,226,.35);box-shadow:0 0 18px rgba(0,181,226,.55);outline:none}
.ih-btn.ih-ghost{background:rgba(2,8,20,.55);border-color:rgba(0,181,226,.45)}
.ih-btn.ih-big{font-size:16px;padding:16px 34px;background:rgba(0,98,155,.6)}
.ih-top{position:absolute;top:0;left:0;right:0;z-index:5;display:flex;justify-content:space-between;align-items:center;gap:10px;padding:max(12px,env(safe-area-inset-top)) 14px 12px;pointer-events:none}
.ih-top>*{pointer-events:auto}
.ih-brand{font-family:'Space Grotesk',system-ui,sans-serif;font-weight:600;letter-spacing:.16em;font-size:14px;text-shadow:0 0 12px rgba(0,181,226,.8)}
.ih-brand span{color:#00b5e2}
.ih-gate{position:absolute;inset:0;display:flex;flex-direction:column;justify-content:space-between;align-items:center;text-align:center;padding:max(78px,calc(env(safe-area-inset-top) + 64px)) 20px max(36px,env(safe-area-inset-bottom));pointer-events:none;transition:opacity .6s}
.ih-gate:before{content:"";position:absolute;inset:0;pointer-events:none;background:linear-gradient(180deg,rgba(1,5,12,.82) 0,rgba(1,5,12,.55) 40%,rgba(1,5,12,.3) 62%,rgba(1,5,12,.8) 100%)}
.ih-gate>*{pointer-events:auto;position:relative}
.ih-gate small{display:block;font-family:'Space Grotesk',system-ui,sans-serif;letter-spacing:.28em;font-size:12px;color:#7fd8f5;margin-bottom:12px;font-weight:500}
.ih-gate h1{margin:0;font-family:'Space Grotesk',system-ui,sans-serif;font-weight:600;font-size:clamp(26px,6vw,54px);letter-spacing:.01em;text-shadow:0 2px 14px rgba(0,0,0,.9),0 0 18px rgba(0,181,226,.3)}
.ih-gate h1 b{color:#00b5e2;font-weight:600}
.ih-gate p{margin:10px 0 0;color:#d6e6f0;text-shadow:0 1px 8px rgba(0,0,0,.9);font-size:clamp(15px,2.4vw,19px)}
.ih-gate .ih-row{display:flex;flex-direction:column;gap:12px;align-items:center}
.ih-hide{opacity:0;pointer-events:none!important}
.ih-hide *{pointer-events:none!important}
.ih-nav{position:absolute;left:0;right:0;bottom:0;display:flex;flex-direction:column;align-items:center;gap:10px;padding:12px 12px max(16px,env(safe-area-inset-bottom));pointer-events:none;transition:opacity .4s}
.ih-nav>*{pointer-events:auto}
.ih-dots{display:flex;gap:6px;flex-wrap:wrap;justify-content:center;max-width:100%}
.ih-dots button{font:inherit;font-size:13px;letter-spacing:.06em;text-transform:uppercase;color:#a9cfe6;background:rgba(2,8,20,.6);border:1px solid rgba(0,181,226,.3);border-radius:3px;padding:5px 9px;cursor:pointer}
.ih-dots button[aria-current="true"]{color:#020814;background:#00b5e2;border-color:#00b5e2;font-weight:700}
.ih-ctl{display:flex;gap:10px;align-items:center}
.ih-ctl .ih-btn{min-width:52px;min-height:48px;font-size:16px}
.ih-ctl .ih-open{min-width:150px}
.ih-hint{font-size:13px;color:#7fa6bf;letter-spacing:.04em}
.ih-panel{position:absolute;top:0;right:0;bottom:0;z-index:10;width:min(470px,100%);background:linear-gradient(180deg,rgba(7,13,22,.96),rgba(2,5,11,.98));border-left:1px solid #0a86a8;box-shadow:-20px 0 60px rgba(0,0,0,.5);transform:translateX(102%);transition:transform .45s cubic-bezier(.2,.8,.2,1);display:flex;flex-direction:column;backdrop-filter:blur(10px);-webkit-backdrop-filter:blur(10px)}
.ih-panel.ih-on{transform:none}
.ih-panel header{display:flex;justify-content:space-between;align-items:center;gap:10px;padding:max(14px,env(safe-area-inset-top)) 16px 12px;border-bottom:1px solid rgba(0,181,226,.3)}
.ih-panel header h2{margin:0;font-family:'Space Grotesk',system-ui,sans-serif;font-size:17px;letter-spacing:.1em;text-transform:uppercase;color:#00b5e2}
.ih-body{overflow:auto;-webkit-overflow-scrolling:touch;padding:16px 16px max(28px,env(safe-area-inset-bottom));flex:1}
@media (max-width:760px){.ih-panel{top:auto;left:0;width:100%;height:62%;border-left:0;border-top:1px solid #00b5e2;transform:translateY(102%);border-radius:14px 14px 0 0}.ih-panel header{padding-top:14px}}
.ih-body h3{font-family:'Space Grotesk',system-ui,sans-serif;font-size:13px;letter-spacing:.14em;text-transform:uppercase;color:#7fd8f5;margin:22px 0 10px}
.ih-body h3:first-child{margin-top:0}
.ih-body p{margin:0 0 12px;color:#cfe6f5}
.ih-body a{color:#5fd4f7}
.ih-grid{display:grid;grid-template-columns:repeat(auto-fit,minmax(180px,1fr));gap:10px}
.ih-card{background:rgba(0,98,155,.16);border:1px solid rgba(0,181,226,.28);border-left:3px solid #00b5e2;border-radius:4px;padding:12px 14px}
.ih-card b{display:block;font-family:'Space Grotesk',system-ui,sans-serif;font-size:13px;letter-spacing:.08em;text-transform:uppercase;color:#fff;margin-bottom:4px}
.ih-card span{color:#b9d7ea;font-size:16px}
.ih-card em{font-style:normal;display:block;color:#ffa300;font-size:13px;letter-spacing:.1em;text-transform:uppercase;margin-bottom:2px}
.ih-tl{border-left:2px solid rgba(0,181,226,.5);margin-left:6px;padding-left:18px;display:grid;gap:14px}
.ih-tl .ih-card{position:relative;border-left-width:1px}
.ih-tl .ih-card:before{content:"";position:absolute;left:-26px;top:16px;width:12px;height:12px;border-radius:50%;background:#00b5e2;box-shadow:0 0 12px #00b5e2}
.ih-av{width:46px;height:46px;border-radius:50%;display:grid;place-items:center;background:#00629b;border:1px solid #00b5e2;font-family:'Space Grotesk',system-ui,sans-serif;font-weight:700;font-size:14px;margin-bottom:8px;overflow:hidden}
.ih-av img{width:100%;height:100%;object-fit:cover;display:block}
.ih-pics{display:grid;grid-template-columns:1fr 1fr;gap:10px}
.ih-pics figure{margin:0}
.ih-pics img{width:100%;height:auto;display:block;border:1px solid rgba(0,181,226,.4);border-radius:4px}
.ih-pics figcaption{font-size:14px;color:#a9cfe6;margin-top:4px}
.ih-tricks{display:flex;flex-wrap:wrap;gap:8px;margin-top:4px}
.ih-plain{position:absolute;inset:0;background:#020814;overflow:auto;-webkit-overflow-scrolling:touch;padding:70px 18px 60px;display:none}
.ih-plain.ih-on{display:block}
.ih-plain .ih-w{max-width:860px;margin:0 auto}
.ih-plain h1{font-family:'Space Grotesk',system-ui,sans-serif;font-size:clamp(24px,5vw,40px);margin:0 0 6px}
.ih-plain h2{font-family:'Space Grotesk',system-ui,sans-serif;font-size:18px;letter-spacing:.1em;text-transform:uppercase;color:#00b5e2;margin:40px 0 14px;border-bottom:1px solid rgba(0,181,226,.3);padding-bottom:8px}
.ih-load{position:absolute;left:50%;top:50%;transform:translate(-50%,-50%);font-family:'Space Grotesk',system-ui,sans-serif;letter-spacing:.3em;font-size:12px;color:#7fd8f5}
@media (prefers-reduced-motion:reduce){.ih-panel,.ih-gate,.ih-nav{transition:none}}
`,At=i=>String(i).replace(/[&<>"]/g,e=>({"&":"&amp;","<":"&lt;",">":"&gt;",'"':"&quot;"})[e]),Sh=(i,e,t)=>`<div class="ih-card">${t?`<em>${At(t)}</em>`:""}<b>${At(i)}</b><span>${At(e)}</span></div>`;function Eh(i,e){switch(i){case"about":return`<p>${At(xt.about.lead)}</p><div class="ih-grid">${xt.about.pillars.map(t=>Sh(t[0],t[1])).join("")}</div>
<h3>What we do</h3><p>${At(xt.about.doLead)}</p><div class="ih-grid">${xt.about.does.map(t=>Sh(t[0],t[1])).join("")}</div>`;case"events":return`<h3>Upcoming</h3><p>${At(xt.events.upcoming)}</p><h3>Recent</h3><div class="ih-tl">${xt.events.recent.map(t=>Sh(t[1],t[2],t[0])).join("")}</div>
<p style="margin-top:18px"><a href="https://www.instagram.com/ieee_lums/" target="_blank" rel="noopener">Follow us on Instagram</a></p>`;case"team":return`<p>${At(xt.team.lead)}</p><div class="ih-grid">${xt.team.people.map(t=>`<div class="ih-card"><div class="ih-av" aria-hidden="true">${t[5]&&e[t[5]]?`<img src="${e[t[5]]}" alt="">`:t[3]}</div><em>${At(t[1])}</em><b>${At(t[0])}</b><span>${At(t[2])}</span>${t[4]?`<br><a href="${t[4]}" target="_blank" rel="noopener">${t[4].includes("linkedin.com")?"LinkedIn profile":"IEEE Collabratec profile"}</a>`:""}</div>`).join("")}</div>`;case"gallery":return`<div class="ih-pics">${xt.gallery.map(t=>`<figure><img src="${e[t[0]]}" alt="${At(t[1])}" loading="lazy"><figcaption>${At(t[1])}</figcaption></figure>`).join("")}</div>`;case"contact":return`<p>Reach IEEE LUMS through any of these.</p><div class="ih-grid">${xt.contact.map(t=>`<div class="ih-card"><b>${At(t[0])}</b><span>${At(t[1])}</span><br><a href="${t[2]}" target="_blank" rel="noopener">Open ${At(t[0])}</a></div>`).join("")}</div>`;case"dog":return`<p>${At(xt.dog.text)}</p><div class="ih-tricks">${["Wave","Sit","Stretch","Dance","Stand"].map(t=>`<button class="ih-btn" data-trick="${t.toLowerCase()}">${t}</button>`).join("")}</div>`}return""}function q0(i,e){i.innerHTML=`
<div class="ih-load" role="status">Loading hub</div>
<div class="ih-gate">
  <div><small>Welcome to ${At(xt.site)}</small><h1><b>IEEE</b> LUMS Student Chapter</h1><p>${At(xt.tagline)}</p></div>
  <div class="ih-row"><button class="ih-btn ih-big" data-act="enter">Enter the hub</button><span class="ih-hint">Press Enter, or tap the button</span></div>
</div>
<div class="ih-nav ih-hide">
  <div class="ih-dots" role="navigation" aria-label="Stations">${_r.map((n,s)=>`<button data-st="${s}">${At(n.label)}</button>`).join("")}</div>
  <div class="ih-ctl">
    <button class="ih-btn" data-act="left" aria-label="Look left">&#9664;</button>
    <button class="ih-btn ih-open" data-act="open">Open</button>
    <button class="ih-btn" data-act="right" aria-label="Look right">&#9654;</button>
  </div>
  <div class="ih-hint">Drag to look around. Arrow keys work too.</div>
</div>
<aside class="ih-panel" aria-hidden="true"><header><h2></h2><button class="ih-btn ih-ghost" data-act="back">Back</button></header><div class="ih-body" tabindex="0"></div></aside>
<div class="ih-plain"><div class="ih-w"><h1>IEEE LUMS Student Chapter</h1><p>${At(xt.tagline)}, built by students at the Lahore University of Management Sciences.</p>
${_r.filter(n=>n.id!=="dog").map(n=>`<h2>${At(n.title)}</h2>${Eh(n.id,e)}`).join("")}</div></div>
<div class="ih-top"><div class="ih-brand">IEEE <span>LUMS</span></div><button class="ih-btn ih-ghost" data-act="plain">Plain view</button></div>`;let t=n=>i.querySelector(n);return{load:t(".ih-load"),gate:t(".ih-gate"),nav:t(".ih-nav"),panel:t(".ih-panel"),body:t(".ih-body"),ptitle:t(".ih-panel h2"),plain:t(".ih-plain"),plainBtn:t('[data-act="plain"]'),openBtn:t(".ih-open"),dots:[...i.querySelectorAll(".ih-dots button")]}}var en="'Space Grotesk','Inter Tight',system-ui,sans-serif",ui="'Inter Tight',system-ui,-apple-system,'Segoe UI',sans-serif",cn="#00b5e2",J0="#00629b",mr="#ffa300",br="#e6f4ff",Wh="#a9cfe6";function Hn(i,e){let t=document.createElement("canvas");return t.width=i,t.height=e,[t,t.getContext("2d")]}function Sn(i){let e=new Mi(i);return e.colorSpace=_t,e.anisotropy=8,e}function xr(i,e,t,n,s,r,o=9){let a=e.split(" "),c="",h=0;for(let l=0;l<a.length;l++){let p=c?c+" "+a[l]:a[l];if(i.measureText(p).width>s&&c){if(h++,h===o)return i.fillText(c.replace(/.{0,2}$/,"")+"\u2026",t,n),n+r;i.fillText(c,t,n),n+=r,c=a[l]}else c=p}return c&&(i.fillText(c,t,n),n+=r),n}function Pr(i,e,t,n,s){let r=i.createLinearGradient(0,0,0,t);r.addColorStop(0,"#06203c"),r.addColorStop(1,"#030d1c"),i.fillStyle=r,i.fillRect(0,0,e,t),i.strokeStyle="rgba(0,181,226,.08)",i.lineWidth=1;for(let o=0;o<e;o+=40)i.beginPath(),i.moveTo(o,0),i.lineTo(o,t),i.stroke();for(let o=0;o<t;o+=40)i.beginPath(),i.moveTo(0,o),i.lineTo(e,o),i.stroke();i.strokeStyle=cn,i.lineWidth=6,i.strokeRect(3,3,e-6,t-6),i.fillStyle=cn;for(let[o,a]of[[0,0],[e-70,0],[0,t-14],[e-70,t-14]])i.fillRect(o,a,70,14);n&&(i.fillStyle="rgba(0,98,155,.55)",i.fillRect(6,6,e-12,110),i.fillStyle=br,i.font=`800 58px ${en}`,i.textBaseline="middle",i.textAlign="left",i.fillText(n,50,64),s&&(i.font=`600 30px ${en}`,i.fillStyle=cn,i.textAlign="right",i.fillText(s,e-50,64),i.textAlign="left")),i.textBaseline="alphabetic"}function Bo(i,e,t,n="TAP OR PRESS \u2191 TO OPEN"){i.font=`600 26px ${en}`,i.fillStyle=mr,i.textAlign="center",i.fillText(n,e/2,t-34),i.textAlign="left"}function $o(i,e,t,n,s){i.fillStyle="rgba(0,98,155,.28)",i.fillRect(e,t,n,s),i.fillStyle=cn,i.fillRect(e,t,8,s),i.strokeStyle="rgba(0,181,226,.4)",i.lineWidth=2,i.strokeRect(e,t,n,s)}function j0(){let[i,e]=Hn(1600,920);return Pr(e,1600,920,"ABOUT IEEE LUMS","SECTOR 01"),e.fillStyle=br,e.font=`500 40px ${ui}`,xr(e,xt.about.lead,50,190,1500,50,3),xt.about.pillars.forEach((t,n)=>{let s=50+n%2*760,r=350+Math.floor(n/2)*230;$o(e,s,r,740,205),e.fillStyle="#fff",e.font=`700 42px ${en}`,e.fillText(t[0].toUpperCase(),s+34,r+62),e.fillStyle=Wh,e.font=`500 33px ${ui}`,xr(e,t[1],s+34,r+110,680,40,2)}),Bo(e,1600,920),Sn(i)}function Q0(){let[i,e]=Hn(1600,920);return Pr(e,1600,920,"NOTICE BOARD","EVENTS"),xt.events.recent.forEach((t,n)=>{let s=50+n*508,r=160,o=484,a=560;$o(e,s,r,o,a),e.fillStyle=mr,e.beginPath(),e.arc(s+o/2,r+8,14,0,7),e.fill(),e.fillStyle=mr,e.font=`700 30px ${en}`,e.fillText(t[0].toUpperCase(),s+32,r+78),e.fillStyle="#fff",e.font=`700 40px ${ui}`;let c=xr(e,t[1],s+32,r+136,o-60,46,3);e.fillStyle=Wh,e.font=`500 31px ${ui}`,xr(e,t[2],s+32,c+14,o-60,37,Math.floor((r+a-24-c)/37))}),e.fillStyle=cn,e.font=`700 30px ${en}`,e.fillText("UPCOMING",50,790),e.fillStyle=br,e.font=`500 34px ${ui}`,e.fillText("Event details are coming soon.  @ieee_lums",290,790),Bo(e,1600,920),Sn(i)}function Y0(i={}){let[e,t]=Hn(1600,920),n=Sn(e);Pr(t,1600,920,"MEET THE TEAM","");let s=xt.team.people.length,r=50,o=24,a=170,c=610,h=(1600-r*2)/s,l=h-o,p=Math.max(1,357/l);return xt.team.people.forEach((f,u)=>{let _=r+u*h;$o(t,_,a,l,c);let m=88/p,g=_+l/2,d=a+140,A=f[5],O=A&&i[A],P=()=>{t.fillStyle=J0,t.beginPath(),t.arc(g,d,m,0,7),t.fill(),t.strokeStyle=cn,t.lineWidth=5,t.stroke(),t.textAlign="center",t.fillStyle="#fff",t.font=`700 ${60/p}px ${en}`,t.fillText(f[3],g,d+22/p),t.textAlign="left"};if(O){P();let M=new Image;M.onload=()=>{t.save(),t.beginPath(),t.arc(g,d,m,0,7),t.closePath(),t.clip(),t.drawImage(M,g-m,d-m,m*2,m*2),t.restore(),t.strokeStyle=cn,t.lineWidth=5,t.beginPath(),t.arc(g,d,m,0,7),t.stroke(),n.needsUpdate=!0},M.src=O}else P();t.textAlign="center",t.fillStyle=mr,t.font=`700 ${27/p}px ${en}`,t.fillText(f[1].toUpperCase(),_+l/2+4,a+300),t.fillStyle="#fff",t.font=`700 ${42/p}px ${ui}`;let G=f[0].split(" "),H=G.length>2?G.slice(0,2).join(" "):G[0],S=G.length>2?G.slice(2).join(" "):G.slice(1).join(" ");t.fillText(H,_+l/2+4,a+370),t.fillText(S,_+l/2+4,a+418),t.fillStyle=Wh,t.font=`500 ${30/p}px ${ui}`,xr(t,f[2],_+l/2+4,a+480,l-50,36/p,3),t.textAlign="left"}),Bo(t,1600,920),n}function Z0(){let[i,e]=Hn(1040,900);return Pr(e,1040,900,"COMMS","CONTACT"),xt.contact.forEach((t,n)=>{let s=160+n*150;$o(e,40,s,960,130),e.fillStyle=cn,e.font=`700 28px ${en}`,e.fillText(t[0].toUpperCase(),76,s+50),e.fillStyle="#fff",e.font=`600 44px ${ui}`,e.fillText(t[1],76,s+102)}),Bo(e,1040,900),Sn(i)}function K0(){let[i,e]=Hn(1200,380);return Pr(e,1200,380,"",""),e.textAlign="center",e.fillStyle="#fff",e.font=`800 100px ${en}`,e.fillText("ROBODOG",600,170),e.fillStyle=cn,e.font=`600 36px ${en}`,e.fillText("HEART OF IEEE  \xB7  TAP THE DOG",600,270),Sn(i)}function Mh(i){let[e,t]=Hn(1024,160);return t.clearRect(0,0,1024,160),t.textAlign="center",t.textBaseline="middle",t.font=`800 84px ${en}`,t.fillStyle=br,t.shadowColor=cn,t.shadowBlur=24,t.fillText(i,512,84),Sn(e)}function ep(){let[t,n]=Hn(1024,1024),s=n.createLinearGradient(0,0,0,1024);s.addColorStop(0,"#0b2238"),s.addColorStop(1,"#040d18"),n.fillStyle=s,n.fillRect(0,0,1024,1024),n.strokeStyle="rgba(0,181,226,.5)",n.lineWidth=6,n.strokeRect(30,30,964,964),n.fillStyle=mr;for(let c=0;c<16;c++)n.save(),n.translate(c*70,914),n.transform(1,0,-.6,1,0,0),n.fillRect(0,0,32,46),n.restore();n.fillStyle=cn,n.fillRect(1024/2-6,0,12,1024),n.fillStyle="#020814",n.fillRect(1024/2-2,0,4,1024);let r=Sn(t),o=r.clone(),a=r.clone();return o.repeat.set(.5,1),o.offset.set(0,0),a.repeat.set(.5,1),a.offset.set(.5,0),o.needsUpdate=a.needsUpdate=!0,[o,a]}function tp(){let[e,t]=Hn(2048,2048);t.fillStyle="#06182e",t.fillRect(0,0,2048,2048),t.translate(2048/2,2048/2),t.strokeStyle="rgba(0,181,226,.16)",t.lineWidth=2;for(let o=0;o<36;o++)t.beginPath(),t.moveTo(0,0),t.lineTo(Math.cos(o*Math.PI/18)*2048,Math.sin(o*Math.PI/18)*2048),t.stroke();for(let o=80;o<2048/2;o+=80)t.beginPath(),t.arc(0,0,o,0,7),t.stroke();t.strokeStyle=cn,t.lineWidth=10,t.beginPath(),t.arc(0,0,330,0,7),t.stroke(),t.lineWidth=4,t.beginPath(),t.arc(0,0,230,0,7),t.stroke(),t.strokeStyle=J0,t.lineWidth=26,t.beginPath(),t.arc(0,0,760,0,7),t.stroke();let n="IEEE LUMS STUDENT CHAPTER  \xB7  ADVANCING TECHNOLOGY FOR HUMANITY  \xB7  ";t.font=`700 40px ${en}`,t.fillStyle=br,t.textAlign="center",t.textBaseline="middle";let s=280,r=n.length;for(let o=0;o<r;o++)t.save(),t.rotate(o/r*Math.PI*2),t.translate(0,-s),t.fillText(n[o],0,0),t.restore();return Sn(e)}function np(){let[i,e]=Hn(256,256),t=e.createRadialGradient(128,128,10,128,128,128);return t.addColorStop(0,"rgba(0,0,0,.75)"),t.addColorStop(1,"rgba(0,0,0,0)"),e.fillStyle=t,e.fillRect(0,0,256,256),Sn(i)}function ip(){let[i,e]=Hn(128,128),t=e.createRadialGradient(64,64,2,64,64,64);return t.addColorStop(0,"rgba(160,235,255,1)"),t.addColorStop(.3,"rgba(0,181,226,.5)"),t.addColorStop(1,"rgba(0,181,226,0)"),e.fillStyle=t,e.fillRect(0,0,128,128),Sn(i)}function Es(i,e){let t=document.createElement("canvas");return t.width=i,t.height=e,[t,t.getContext("2d")]}function Ch(i){let e=i;return()=>(e=e*16807%2147483647)/2147483647}function wr(i,e,t,n,s){let r=i.getImageData(0,0,e,t),o=r.data;for(let a=0;a<o.length;a+=4){let c=(s()-.5)*n;o[a]+=c,o[a+1]+=c,o[a+2]+=c}i.putImageData(r,0,0)}function Or(i,e){let t=new Mi(i);return t.wrapS=t.wrapT=Fn,t.colorSpace=e?_t:Pn,t.anisotropy=8,t}var Fo=null;function Qm(){if(Fo)return Fo;let i=1024,e=i/2,t=Ch(11),[n,s]=Es(i,i),[r,o]=Es(i,i),[a,c]=Es(i,i);s.fillStyle="#04080e",s.fillRect(0,0,i,i),o.fillStyle="#2c2c2c",o.fillRect(0,0,i,i),c.fillStyle="#c8c8c8",c.fillRect(0,0,i,i);for(let h=0;h<2;h++)for(let l=0;l<2;l++){let p=h*e,f=l*e,u=(t()-.5)*12;s.fillStyle=`rgb(${24+u},${37+u},${52+u})`,s.fillRect(p+7,f+7,e-14,e-14);for(let m=0;m<220;m++)s.fillStyle=`rgba(190,215,235,${t()*.04})`,s.fillRect(p+9+t()*(e-18),f+9,1+t()*2,e-18);let _=s.createLinearGradient(p,f,p,f+e);_.addColorStop(0,"rgba(0,0,0,.4)"),_.addColorStop(.14,"rgba(0,0,0,0)"),_.addColorStop(.86,"rgba(0,0,0,0)"),_.addColorStop(1,"rgba(0,0,0,.45)"),s.fillStyle=_,s.fillRect(p,f,e,e),o.fillStyle="#9c9c9c",o.fillRect(p+7,f+7,e-14,e-14),o.fillStyle="#b4b4b4",o.fillRect(p+22,f+22,e-44,e-44),c.fillStyle=`rgb(${105+u*2},${105+u*2},${105+u*2})`,c.fillRect(p+7,f+7,e-14,e-14);for(let m=0;m<40;m++)c.fillStyle=`rgba(255,255,255,${t()*.18})`,c.fillRect(p+10+t()*(e-20),f+10+t()*(e-20),20+t()*80,2+t()*8);for(let[m,g]of[[34,34],[e-34,34],[34,e-34],[e-34,e-34],[e/2,34],[e/2,e-34]])s.fillStyle="rgba(0,0,0,.55)",s.beginPath(),s.arc(p+m+2,f+g+3,7,0,7),s.fill(),s.fillStyle="#34465b",s.beginPath(),s.arc(p+m,f+g,6.5,0,7),s.fill(),s.fillStyle="rgba(170,200,230,.35)",s.beginPath(),s.arc(p+m-2,f+g-2,2.2,0,7),s.fill(),o.fillStyle="#ffffff",o.beginPath(),o.arc(p+m,f+g,6.5,0,7),o.fill()}return wr(s,i,i,9,t),wr(o,i,i,14,t),wr(c,i,i,22,t),Fo={c:n,b:r,ro:a},Fo}function Do(i,e,t={}){let{c:n,b:s,ro:r}=Qm(),o=Or(n,!0),a=Or(s,!1),c=Or(r,!1);for(let h of[o,a,c])h.repeat.set(i,e);return new Tt(Object.assign({map:o,bumpMap:a,bumpScale:1.6,roughnessMap:c,roughness:.95,metalness:.78,color:16777215},t))}function sp(){let e=Ch(5),[t,n]=Es(1024,1024),[s,r]=Es(1024,1024);n.fillStyle="#9a9a9a",n.fillRect(0,0,1024,1024),r.fillStyle="#6a6a6a",r.fillRect(0,0,1024,1024);let o=16,a=1024/o;for(let c=0;c<=o;c++)n.fillStyle="#1e1e1e",n.fillRect(c*a-3,0,6,1024),n.fillRect(0,c*a-3,1024,6),r.fillStyle="#d6d6d6",r.fillRect(c*a-4,0,8,1024),r.fillRect(0,c*a-4,1024,8);for(let c=0;c<o;c++)for(let h=0;h<o;h++){let l=70+e()*60;r.fillStyle=`rgba(${l},${l},${l},.55)`,r.fillRect(c*a+5,h*a+5,a-10,a-10)}return wr(n,1024,1024,12,e),wr(r,1024,1024,26,e),{bump:Or(t,!1),rough:Or(s,!1)}}function rp(){let e=Ch(99),[t,n]=Es(1024,1024),s=n.createRadialGradient(1024*.5,1024*.4,20,1024*.5,1024*.5,1024*.75);s.addColorStop(0,"#0b1830"),s.addColorStop(.6,"#050b17"),s.addColorStop(1,"#010308"),n.fillStyle=s,n.fillRect(0,0,1024,1024);for(let[f,u,_,m]of[[.3,.7,380,"rgba(70,40,120,.22)"],[.75,.3,330,"rgba(0,130,170,.2)"],[.5,.15,260,"rgba(160,90,50,.12)"]]){let g=n.createRadialGradient(f*1024,u*1024,0,f*1024,u*1024,_);g.addColorStop(0,m),g.addColorStop(1,"rgba(0,0,0,0)"),n.fillStyle=g,n.fillRect(0,0,1024,1024)}for(let f=0;f<900;f++){let u=e()*1024,_=e()*1024,m=e()**4,g=.4+m*1.9,d=e()<.3;n.fillStyle=d?`rgba(255,224,190,${.35+m})`:`rgba(200,225,255,${.35+m})`,n.beginPath(),n.arc(u,_,g,0,7),n.fill(),m>.55&&(n.fillStyle="rgba(190,225,255,.12)",n.beginPath(),n.arc(u,_,g*4.5,0,7),n.fill())}let r=1024*.66,o=1024*.64,a=250,c=n.createRadialGradient(r,o,a*.95,r,o,a*1.28);c.addColorStop(0,"rgba(0,181,226,.55)"),c.addColorStop(1,"rgba(0,181,226,0)"),n.fillStyle=c,n.beginPath(),n.arc(r,o,a*1.28,0,7),n.fill(),n.save(),n.beginPath(),n.arc(r,o,a,0,7),n.clip();let h=n.createRadialGradient(r-a*.45,o-a*.4,a*.1,r,o,a*1.05);h.addColorStop(0,"#6fa8c0"),h.addColorStop(.35,"#2b5a78"),h.addColorStop(.75,"#0b2236"),h.addColorStop(1,"#02060c"),n.fillStyle=h,n.fillRect(r-a,o-a,a*2,a*2);for(let f=0;f<26;f++)n.fillStyle=`rgba(${200+e()*40},${225+e()*20},255,${.025+e()*.05})`,n.fillRect(r-a,o-a+e()*a*2,a*2,4+e()*16);let l=n.createLinearGradient(r-a,o-a,r+a,o+a);l.addColorStop(.45,"rgba(0,0,0,0)"),l.addColorStop(1,"rgba(0,0,0,.85)"),n.fillStyle=l,n.fillRect(r-a,o-a,a*2,a*2),n.restore();let p=new Mi(t);return p.colorSpace=_t,p.anisotropy=8,p}var No=class extends ms{constructor(){super();let e=new An;e.deleteAttribute("uv");let t=new Tt({side:Bt}),n=new Tt,s=new qn(16777215,900,28,2);s.position.set(.418,16.199,.3),this.add(s);let r=new De(e,t);r.position.set(-.757,13.219,.717),r.scale.set(31.713,28.305,28.591),this.add(r);let o=new De(e,n);o.position.set(-10.906,2.009,1.846),o.rotation.set(0,-.195,0),o.scale.set(2.328,7.905,4.651),this.add(o);let a=new De(e,n);a.position.set(-5.607,-.754,-.758),a.rotation.set(0,.994,0),a.scale.set(1.97,1.534,3.955),this.add(a);let c=new De(e,n);c.position.set(6.167,.857,7.803),c.rotation.set(0,.561,0),c.scale.set(3.927,6.285,3.687),this.add(c);let h=new De(e,n);h.position.set(-2.017,.018,6.124),h.rotation.set(0,.333,0),h.scale.set(2.002,4.566,2.064),this.add(h);let l=new De(e,n);l.position.set(2.291,-.756,-2.621),l.rotation.set(0,-.286,0),l.scale.set(1.546,1.552,1.496),this.add(l);let p=new De(e,n);p.position.set(-2.193,-.369,-5.547),p.rotation.set(0,.516,0),p.scale.set(3.875,3.487,2.986),this.add(p);let f=new De(e,Ws(50));f.position.set(-16.116,14.37,8.208),f.scale.set(.1,2.428,2.739),this.add(f);let u=new De(e,Ws(50));u.position.set(-16.109,18.021,-8.207),u.scale.set(.1,2.425,2.751),this.add(u);let _=new De(e,Ws(17));_.position.set(14.904,12.198,-1.832),_.scale.set(.15,4.265,6.331),this.add(_);let m=new De(e,Ws(43));m.position.set(-.462,8.89,14.52),m.scale.set(4.38,5.441,.088),this.add(m);let g=new De(e,Ws(20));g.position.set(3.235,11.486,-12.541),g.scale.set(2.5,2,.1),this.add(g);let d=new De(e,Ws(100));d.position.set(0,20,0),d.scale.set(1,.1,1),this.add(d)}dispose(){let e=new Set;this.traverse(t=>{t.isMesh&&(e.add(t.geometry),e.add(t.material))});for(let t of e)t.dispose()}};function Ws(i){let e=new yt;return e.color.setScalar(i),e}var Lo={banner:U0,top:I0,front:R0,hall:X0,igTeam:B0,igOrientation:$0,igCandid:F0,igInduction:D0,teamHassan:N0,teamAbdullah:V0,teamShahmeer:L0,teamMishal:z0},gi=7,jn=2*gi*Math.tan(Math.PI/6)+.05,bt=4.6,op=1.62,Ms=Math.PI/3,ap=46562;var Vo=window.matchMedia&&matchMedia("(prefers-reduced-motion: reduce)").matches;function cp(){if(document.querySelector(".ih-root"))return;let i=document.createElement("style");i.textContent=k0,document.head.appendChild(i);let e=document.createElement("link");e.rel="stylesheet",e.href="https://fonts.googleapis.com/css2?family=Space+Grotesk:wght@400;500;600;700&family=Inter+Tight:wght@400;500;600;700&display=swap",document.head.appendChild(e);let t=document.createElement("div");t.className="ih-root",document.body.appendChild(t);let n=document.documentElement.style.overflow;document.documentElement.style.overflow="hidden";let s=q0(t,Lo),r=!1,o=h=>{r=h,s.plain.classList.toggle("ih-on",h),s.plainBtn.textContent=h?"3D view":"Plain view",h&&(s.plain.scrollTop=0)},a;try{a=new uo({antialias:!0,powerPreference:"high-performance"})}catch(h){console.error("[ieee-hub] WebGL unavailable",h),s.load.remove(),s.gate.remove(),s.nav.remove(),s.plainBtn.remove(),o(!0);return}Promise.race([Promise.all([document.fonts.load("700 40px 'Space Grotesk'"),document.fonts.load("500 40px 'Inter Tight'")]).catch(()=>0),new Promise(h=>setTimeout(h,2500))]).then(()=>{try{Zm(t,s,a,o,()=>r)}catch(h){console.error("[ieee-hub] failed to start",h),s.load.remove(),s.gate.remove(),s.nav.remove(),o(!0)}})}function Zm(i,e,t,n,s){t.setPixelRatio(Math.min(window.devicePixelRatio||1,2)),t.toneMapping=Xc,t.toneMappingExposure=.8;let r=t.domElement;r.setAttribute("aria-label","IEEE LUMS 3D hub. Use the buttons below or the Plain view button for a text version."),r.tabIndex=0,i.insertBefore(r,i.firstChild);let o=new ms;o.background=new Ge(133140),o.fog=new go(66832,9,24);try{let N=new _s(t);o.environment=N.fromScene(new No,.04).texture,o.environmentIntensity=.28,N.dispose()}catch(N){console.error("[ieee-hub] env map failed",N)}let a=new St(68,1,.1,80);a.rotation.order="YXZ";let c=Do(4,2.3),h=new Tt({color:463391,roughness:.7,metalness:.4}),l=new yt({color:34470}),p=new yt({color:1006218}),f=(N,D,ee,ue)=>new De(new An(N,D,ee),ue),u=(N,D,ee)=>new De(new us(N,D),ee),_=(N,D)=>new yt(Object.assign({map:N,toneMapped:!1},D));o.add(new So(8363440,657414,.5));let m=new qn(16767395,26,26,1.7);m.position.set(0,4.2,0),o.add(m);let g=new De(new ws(8.6,72),(()=>{let N=sp();return new Tt({map:tp(),bumpMap:N.bump,bumpScale:1.4,roughnessMap:N.rough,roughness:.9,metalness:.65})})());g.rotation.x=-Math.PI/2,o.add(g);let d=new De(new ws(8.6,6),h);d.rotation.x=Math.PI/2,d.position.y=bt,o.add(d);let A=new De(new Os(2.6,.06,8,64),l);A.rotation.x=Math.PI/2,A.position.y=bt-.25,o.add(A);let O=new De(new Os(5.2,.04,8,64),p);O.rotation.x=Math.PI/2,O.position.y=bt-.12,o.add(O);{let N=Do(3,1,{color:12109526}),D=new yt({map:rp(),toneMapped:!1}),ee=new De(new ws(2.45,48),D);ee.rotation.x=Math.PI/2,ee.position.y=bt-.03,o.add(ee);for(let fe=0;fe<3;fe++){let Ce=f(5.1,.07,.1,h);Ce.position.y=bt-.07,Ce.rotation.y=fe*Math.PI/3,o.add(Ce)}let ue=new yt({color:13281400});for(let fe=0;fe<6;fe++){let Ce=(30+60*fe)*Math.PI/180,$e=f(.3,.3,5.6,N);$e.position.set(Math.sin(Ce)*5.4,bt-.15,-Math.cos(Ce)*5.4),$e.rotation.y=Math.PI-Ce,o.add($e);let Ve=new Xt;Ve.rotation.y=-(60*fe)*Math.PI/180;let x=u(1.5,2.2,ue);x.rotation.x=Math.PI/2,x.position.set(0,bt-.04,-5.2),Ve.add(x);let E=f(1.7,.05,2.4,h);E.position.set(0,bt-.02,-5.2),Ve.add(E),o.add(Ve)}}let P=ip(),G=new _o(new or({map:P,blending:so,depthWrite:!1,transparent:!0,opacity:.3}));G.scale.set(7,7,1),G.position.y=bt-.3,o.add(G);let H=_r.map((N,D)=>({...N,i:D,angle:D*Ms,hits:[],frames:[],bw:5.2})),S=[],M=H.map(N=>{let D=new Xt;D.position.set(gi*Math.sin(N.angle),0,-gi*Math.cos(N.angle)),D.rotation.y=-N.angle,o.add(D);let ee=f(.5,bt,.5,h);ee.position.set(jn/2,bt/2,.05),D.add(ee);let ue=f(.06,bt,.06,l);ue.position.set(jn/2-.2,bt/2,.36),D.add(ue);let fe=f(jn-.6,.07,.07,p);fe.position.set(0,bt-.45,.14),D.add(fe);let Ce=f(jn-.6,.05,.05,l);Ce.position.set(0,.12,.14),D.add(Ce);let $e=f(jn-.5,.3,.2,h);$e.position.set(0,bt-.15,.13),D.add($e);let Ve=new De(new hr(.045,.045,jn-.8,12),h);Ve.rotation.z=Math.PI/2,Ve.position.set(0,3.93,.17),D.add(Ve);for(let x=-3;x<=3;x++){let E=f(.07,.14,.08,h);E.position.set(x*1.1,3.93,.16),D.add(E)}if(N.id!=="contact"){let x=f(jn-.5,.36,.12,h);x.position.set(0,.18,.15),D.add(x);for(let E of[-1,1]){let X=f(.14,bt-.6,.12,h);X.position.set(E*3.68,bt/2,.15),D.add(X)}}return D}),y=N=>{let D=f(jn,bt,.2,c);D.position.set(0,bt/2,0),N.add(D)},w=(N,D)=>{N.userData.station=D.i,S.push(N),D.hits.push(N)};function W(N,D,ee,ue,fe,Ce=0){let $e=M[N.i],Ve=new Tt({color:331806,roughness:.4,metalness:.7,emissive:ap,emissiveIntensity:.05}),x=f(ee+.24,ue+.24,.1,Ve);x.position.set(Ce,fe,.16),$e.add(x);let E=u(ee,ue,_(D));return E.position.set(Ce,fe,.22),$e.add(E),N.frames.push(Ve),w(E,N),w(x,N),E}let $=Object.fromEntries(H.map(N=>[N.id,N]));y(M[$.about.i]),W($.about,j0(),5.2,2.99,2.15),y(M[$.events.i]),W($.events,Q0(),5.2,2.99,2.15),y(M[$.team.i]),W($.team,Y0(Lo),5.2,2.99,2.15),y(M[$.gallery.i]);let F=[];{let N=new ys,D=xt.gallery.map(Ve=>Ve[0]),ee=Object.fromEntries(D.map(Ve=>{let x=N.load(Lo[Ve]);return x.colorSpace=_t,x.anisotropy=8,[Ve,x]})),ue=[[-2.45,1.9,1.425],[-.72,1.35,1.8],[.78,1.35,1.8],[2.45,1.9,1.425]];F=ue.map(([Ve,x,E],X)=>({mesh:W($.gallery,ee[D[X%D.length]],x,E,2,Ve),slot:X}));for(let[Ve,x,E]of ue){let X=u(x,E,new yt({color:133140,toneMapped:!1}));X.position.set(Ve,2,.215),M[$.gallery.i].add(X)}let fe=u(3.2,.5,_(Mh("GALLERY"),{transparent:!0}));fe.position.set(0,3.45,.2),M[$.gallery.i].add(fe),$.gallery.bw=6.9;let Ce=4.5,$e=Ve=>{let x=Ve.mesh.material.map&&Ve.mesh.material.map.image;if(!x||!x.width||!x.height)return;let[,E,X]=ue[Ve.slot],B=x.width/x.height,T=E/X,K=B>T?1:B/T,ce=B>T?T/B:1;(Ve.mesh.scale.x!==K||Ve.mesh.scale.y!==ce)&&Ve.mesh.scale.set(K,ce,1)};var k=Ve=>{let x=D.length;for(let E of F)$e(E);if(!(x<=ue.length))for(let E of F){let X=(Math.floor(Ve/Ce)+E.slot)%x,B=D[X];E.mesh.material.map!==ee[B]&&(E.mesh.material.map=ee[B],E.mesh.material.needsUpdate=!0,$e(E))}}}y(M[$.dog.i]);{let N=W($.dog,K0(),3,.95,3.15);$.dog.bw=3.6}let Q=M[$.contact.i],V=1.6,te=3.2;{let N=(jn-2*V)/2;for(let fe of[-1,1]){let Ce=f(N,bt,.2,c);Ce.position.set(fe*(V+N/2),bt/2,0),Q.add(Ce)}let D=f(2*V,bt-te,.2,c);D.position.set(0,te+(bt-te)/2,0),Q.add(D);for(let fe of[-1,1]){let Ce=f(.06,te,.06,l);Ce.position.set(fe*(V+.06),te/2,.12),Q.add(Ce);let $e=Ce.clone();$e.position.z=-.12,Q.add($e)}let ee=f(2*V+.2,.06,.06,l);ee.position.set(0,te+.06,.12),Q.add(ee);let ue=ee.clone();ue.position.z=-.12,Q.add(ue)}let[z,pe]=ep(),me=new Tt({color:1192271,roughness:.45,metalness:.65}),He=[1,-1].map((N,D)=>{let ee=new Xt;ee.add(f(V,te,.12,me));let ue=u(V,te,_(D===0?z:pe));ue.rotation.y=Math.PI,ue.position.z=-.065,ee.add(ue);let fe=f(.04,te,.13,l);return fe.position.x=-N*(V/2-.02),ee.add(fe),ee.position.set(N*V/2,te/2,-.02),ee.userData.sx=N,Q.add(ee),ee}),Ne=_(Z0(),{transparent:!0,opacity:0}),tt=u(2.9,2.51,Ne);tt.position.set(0,1.72,.2),Q.add(tt),w(tt,$.contact);{let N=new Tt({color:331806,emissive:ap,emissiveIntensity:.05});$.contact.frames.push(N),$.contact.bw=3.3}{let N=u(2.6,.4,_(Mh("EXIT / COMMS"),{transparent:!0}));N.position.set(0,te+.7,.12),Q.add(N)}{let ue=gi+.1,fe=u(4.4,10,new Tt({color:661030,roughness:.4,metalness:.6}));fe.rotation.x=-Math.PI/2,fe.position.set(0,.001,ue+10/2),o.add(fe);let Ce=u(4.4,10,h);Ce.rotation.x=Math.PI/2,Ce.position.set(0,3.7,ue+10/2),o.add(Ce);for(let Ve of[-1,1]){let x=u(10,3.7,Do(5,1.85));x.rotation.y=-Ve*Math.PI/2,x.position.set(Ve*4.4/2,3.7/2,ue+10/2),o.add(x);for(let E of[.25,3.7-.25]){let X=f(.05,.05,10,l);X.position.set(Ve*(4.4/2-.04),E,ue+10/2),o.add(X)}for(let E=0;E<4;E++){let X=f(.12,3.7,.3,h);X.position.set(Ve*(4.4/2-.06),3.7/2,ue+1.2+E*2.4),o.add(X)}}let $e=new qn(7321812,12,12,1.9);$e.position.set(0,3.2,ue+3),o.add($e)}{let D=new Float32Array(780);for(let fe=0;fe<260;fe++){let Ce=Math.random()*6.28,$e=1+Math.random()*5.5;D[fe*3]=Math.cos(Ce)*$e,D[fe*3+1]=Math.random()*bt,D[fe*3+2]=Math.sin(Ce)*$e}let ee=new Gt;ee.setAttribute("position",new wt(D,3));let ue=new Ps(ee,new Wi({color:8378613,size:.035,transparent:!0,opacity:.55,depthWrite:!1}));ue.name="dust",o.add(ue)}let J=4.5,ie=2,xe=.14,oe=new C(Math.sin($.dog.angle),0,-Math.cos($.dog.angle)),be=new Xt;be.position.copy(oe).multiplyScalar(J),be.position.y=xe,be.scale.setScalar(ie),o.add(be);let Te=new Xt;be.add(Te);{let N=new De(new hr(1.25,1.35,xe,48),h);N.position.copy(be.position).setY(xe/2),o.add(N);let D=new De(new Os(1.2,.035,8,64),l);D.rotation.x=Math.PI/2,D.position.copy(be.position).setY(xe+.01),o.add(D);let ee=u(2.1,1.5,new yt({map:np(),transparent:!0,depthWrite:!1}));ee.rotation.x=-Math.PI/2,ee.position.copy(be.position).setY(xe+.012),o.add(ee);let ue=new vs(16770752,14,9,.6,.6,1.4);ue.position.copy(be.position).setY(4.2),ue.target.position.copy(be.position),o.add(ue,ue.target);let fe=new De(new An(1.7,1.25,1.1),new yt({visible:!1}));fe.position.copy(be.position).setY(xe+.62),fe.rotation.y=0,o.add(fe),w(fe,$.dog)}let ke=Math.atan2(oe.z,-oe.x),lt=ke+.75;be.rotation.y=lt;let Se={ready:!1,J:{},base:{},feet:{},cur:{},trick:"stand",t0:0,model:null},ot=["FL","FR","RL","RR"],I={hip:new C(1,0,0),thigh:new C(0,1,0),calf:new C(0,1,0)},Dt=[0,.78,-1.55];for(let N of ot)Se.cur[N]=Dt.slice();let je=new Ro;je.setMeshoptDecoder(T0),je.parse(gr.buffer.slice(gr.byteOffset,gr.byteOffset+gr.byteLength),"",N=>{let D=N.scene;Se.model=D,Te.add(D),D.traverse(ee=>{ee.isMesh&&(ee.material.metalness=.3,ee.material.roughness=.5,ee.material.color.multiplyScalar(.9))});for(let ee of ot){for(let ue of["hip","thigh","calf"]){let fe=D.getObjectByName(`${ee}_${ue}`);if(!fe)throw new Error("joint missing "+ee+ue);Se.J[`${ee}_${ue}`]=fe,Se.base[`${ee}_${ue}`]=fe.quaternion.clone()}Se.feet[ee]=D.getObjectByName(`${ee}_foot`)}Se.ready=!0},N=>console.error("[ieee-hub] dog model failed to load",N));let Ye=new Yt,Ee=new C;function ht(N){let D={};for(let Ce of ot)D[Ce]=Dt.slice();let ee=N-Se.t0,ue=0,fe=Math.sin(N*1.6)*.025;for(let Ce of ot)D[Ce][1]+=fe,D[Ce][2]-=fe*2;switch(Se.trick){case"sit":D.RL=D.RR=[0,1.45,-2.65],D.FL=D.FR=[0,.5,-.95];break;case"stretch":D.FL=D.FR=[0,1.05,-1.95],D.RL=D.RR=[0,.45,-1];break;case"wave":D.RL=D.RR=[0,1.3,-2.5],D.FL=[0,.5,-.95],D.FR=[-.25,-.9+Math.sin(ee*9)*.28,-1.15+Math.sin(ee*9+1)*.3],ee>3.2&&we("sit");break;case"dance":{let Ce=Math.sin(ee*7),$e=Math.sin(ee*7+Math.PI);D.FL=[Ce*.12,.78+Ce*.3,-1.55-Ce*.6],D.RR=[-Ce*.12,.78+Ce*.3,-1.55-Ce*.6],D.FR=[$e*.12,.78+$e*.3,-1.55-$e*.6],D.RL=[-$e*.12,.78+$e*.3,-1.55-$e*.6],ue=Math.sin(ee*3.5)*.35,ee>5&&we("stand");break}}return[D,ue]}function we(N){Se.trick=N,Se.t0=b.elapsedTime}function v(N,D){if(!Se.ready)return;let[ee,ue]=ht(N),fe=1-Math.exp(-D*7);for(let B of ot){let T=Se.cur[B];for(let K=0;K<3;K++)T[K]+=(ee[B][K]-T[K])*fe;["hip","thigh","calf"].forEach((K,ce)=>{Se.J[`${B}_${K}`].quaternion.copy(Se.base[`${B}_${K}`]).multiply(Ye.setFromAxisAngle(I[K],T[ce]))})}let Ce=(We===$.dog.i?ke+.25:lt)+ue;be.rotation.y+=(Ce-be.rotation.y)*(1-Math.exp(-D*4)),be.updateMatrixWorld(!0);let $e={};for(let B of ot)Se.feet[B].getWorldPosition(Ee),Se.model.worldToLocal(Ee),$e[B]=Ee.clone();let Ve=(B,T)=>$e[B].y<$e[T].y?$e[B]:$e[T],x=Se.trick==="wave"?$e.FL:Ve("FL","FR"),E=Ve("RL","RR"),X=Math.atan2(E.y-x.y,x.x-E.x);Te.rotation.z=X,Te.position.y=-(x.x*Math.sin(X)+x.y*Math.cos(X))+.023}let b=new Mo,R="gate",j=0,Z=0,q=0,Pe=0,se=0,de=0,We=-1,ne=-1,ge=0,Me=0,Ue=0,_e=0,Je=0,Xe=gi+6.4,at=Vo?.01:4.2,U=()=>(Math.round(j/Ms)%6+6)%6,le=()=>2*Math.atan(Math.tan(Ci.degToRad(a.fov)/2)*a.aspect),L=N=>{let D=i.clientWidth,ee=2*Math.atan(Math.tan(le()/2)*(D>760?1-Math.min(470,D)/D:1));if(N.id==="dog")return Math.max(0,J-Math.max(2.6,1.3/Math.tan(ee/2)));let ue=(N.bw/2+.25)/Math.tan(ee/2);return Ci.clamp(gi-.2-ue,0,3.6)};function Y(N){let D=Math.round(j/Ms),ee=(D%6+6)%6,ue=N-ee;ue>3&&(ue-=6),ue<-3&&(ue+=6),Z=(D+ue)*Ms,Pe=0}function ae(N){if(R!=="hub")return;let D=H[N];Y(N),We=N,de=L(D),Pe=D.id==="dog"?-.26:0,e.ptitle.textContent=D.title,e.body.innerHTML=Eh(D.id,Lo),e.body.scrollTop=0,e.panel.classList.add("ih-on"),e.panel.setAttribute("aria-hidden","false"),e.nav.classList.add("ih-hide"),D.id==="dog"&&we("wave"),setTimeout(()=>e.panel.querySelector('[data-act="back"]').focus({preventScroll:!0}),50)}function re(){We<0||(H[We].id==="dog"&&we("stand"),We=-1,de=0,Pe=0,e.panel.classList.remove("ih-on"),e.panel.setAttribute("aria-hidden","true"),e.nav.classList.remove("ih-hide"),r.focus({preventScroll:!0}))}function Ie(N){R==="hub"&&(We>=0&&re(),Z=(Math.round(Z/Ms)+N)*Ms,Pe=0)}function pt(){R==="gate"&&(R="entering",Je=0,_e=1,e.gate.classList.add("ih-hide"))}i.addEventListener("click",N=>{let D=N.target.closest("button");if(!D)return;let ee=D.dataset.act;ee==="enter"?pt():ee==="left"?Ie(-1):ee==="right"?Ie(1):ee==="open"?ae(U()):ee==="back"?re():ee==="plain"?n(!s()):D.dataset.st?ae(+D.dataset.st):D.dataset.trick&&we(D.dataset.trick)}),window.addEventListener("keydown",N=>{if(s()||N.metaKey||N.ctrlKey||N.altKey)return;let D=N.key;if(R==="gate"){(D==="Enter"||D==="ArrowUp"||D===" ")&&!(N.target.closest&&N.target.closest("button"))&&(N.preventDefault(),pt());return}R==="hub"&&(D==="ArrowLeft"?(N.preventDefault(),Ie(-1)):D==="ArrowRight"?(N.preventDefault(),Ie(1)):D==="ArrowUp"||D==="Enter"&&N.target===r?(N.preventDefault(),We<0&&ae(U())):(D==="ArrowDown"||D==="Escape")&&We>=0&&(N.preventDefault(),re()))});let Ht=new Co,Ke=new Re,Lt=N=>{let D=r.getBoundingClientRect();Ke.set((N.clientX-D.left)/D.width*2-1,-((N.clientY-D.top)/D.height)*2+1),Ht.setFromCamera(Ke,a);let ee=Ht.intersectObjects(S,!1)[0];return ee?ee.object.userData.station:-1},Pt=null;r.addEventListener("pointerdown",N=>{if(R==="hub"){Pt={x:N.clientX,y:N.clientY,yaw:Z,pitch:Pe,moved:!1};try{r.setPointerCapture(N.pointerId)}catch{}}}),r.addEventListener("pointermove",N=>{if(R==="hub")if(Pt){let D=N.clientX-Pt.x,ee=N.clientY-Pt.y;if(Math.abs(D)+Math.abs(ee)>6&&(Pt.moved=!0),Pt.moved&&We<0){let ue=Ci.degToRad(a.fov)/r.clientHeight*1.3;Z=Pt.yaw-D*ue,Pe=Ci.clamp(Pt.pitch+ee*ue,-.5,.5)}}else N.pointerType==="mouse"&&(ne=Lt(N),r.style.cursor=ne>=0?"pointer":"grab")});let Cs=N=>{if(!Pt)return;let D=Pt;if(Pt=null,!D.moved&&N.type==="pointerup"){let ee=Lt(N);ee>=0?We===ee&&H[ee].id==="dog"?we(["wave","dance","stretch"][Math.floor(Math.random()*3)]):ae(ee):We>=0&&re()}};r.addEventListener("pointerup",Cs),r.addEventListener("pointercancel",Cs),r.addEventListener("pointerleave",()=>{ne=-1});function Gs(){let N=i.clientWidth||window.innerWidth,D=i.clientHeight||window.innerHeight;t.setSize(N,D,!1),a.aspect=N/D,a.fov=N<D?88:68,a.updateProjectionMatrix(),We>=0&&(de=L(H[We]))}window.addEventListener("resize",Gs),Gs(),e.load.remove();let mn=-1,Ts=N=>N*N*(3-2*N),Us=o.getObjectByName("dust");function Is(){if(requestAnimationFrame(Is),s()||document.hidden){b.getDelta();return}let N=Math.min(b.getDelta(),.05),D=b.elapsedTime;if(R==="entering"){Je+=N;let fe=Math.min(1,Math.max(0,(Je-.9)/(at-.9)));Xe=(gi+6.4)*(1-Ts(fe)),Xe<gi-2.2&&(_e=0),fe>=1&&(R="hub",e.nav.classList.remove("ih-hide"),r.style.cursor="grab",r.focus({preventScroll:!0}))}Ue+=(_e-Ue)*(1-Math.exp(-N*(Vo?60:3.2)));for(let fe of He)fe.position.x=fe.userData.sx*(V/2+Ue*(V-.05));Ne.opacity=R==="hub"?Math.min(1,Ne.opacity+N*1.5):0;let ee=1-Math.exp(-N*(Vo?60:6));if(j+=(Z-j)*ee,q+=(Pe-q)*ee,se+=(de-se)*(1-Math.exp(-N*(Vo?60:3.5))),R==="hub"){let fe=We>=0?H[We].angle:j;a.position.set(Math.sin(fe)*se,op+Math.sin(D*1.1)*.012,-Math.cos(fe)*se)}else a.position.set(0,op+(R==="entering"?Math.sin(Je*7)*.02*(Xe>.2?1:0):0),Xe);a.rotation.y=-j,a.rotation.x=q;{let fe=i.clientWidth,Ce=i.clientHeight,$e=fe>760,Ve=We>=0&&$e?Math.min(470,fe)/2:0,x=We>=0&&!$e?Ce*.27:0;ge+=(Ve-ge)*ee,Me+=(x-Me)*ee,Math.abs(ge)+Math.abs(Me)>.5?a.setViewOffset(fe,Ce,ge,Me,fe,Ce):a.clearViewOffset()}let ue=U();R==="hub"&&ue!==mn&&(mn=ue,e.dots.forEach((fe,Ce)=>fe.setAttribute("aria-current",Ce===ue?"true":"false")),e.openBtn.textContent="Open "+H[ue].label);for(let fe of H){let Ce=fe.i===We?.28:fe.i===ne?.2:R==="hub"&&fe.i===ue?.12+Math.sin(D*3)*.04:.03;for(let $e of fe.frames)$e.emissiveIntensity+=(Ce-$e.emissiveIntensity)*ee}A.rotation.z=D*.15,O.rotation.z=-D*.06,Us&&(Us.rotation.y=D*.02),k(D),v(D,N),t.render(o,a)}Is(),(location.hostname==="localhost"||location.hostname==="127.0.0.1"||location.protocol==="file:")&&(window.__ieeeHub={dbg:(N,D)=>{we(N);let ee=b.elapsedTime;for(let ue=0;ue<D;ue++)ee+=.03,v(ee,.03);return Se.t0=1e9,{phi:Te.rotation.z,y:Te.position.y,yaw:be.rotation.y,cur:Se.cur}},enter:pt,openStation:ae,back:re,turn:Ie,setTrick:we,state:()=>({mode:R,focus:We,yaw:j,walk:se,dog:Se.ready})})}document.readyState==="loading"?document.addEventListener("DOMContentLoaded",cp):cp();})();
/*! Bundled license information:

three/build/three.module.js:
  (**
   * @license
   * Copyright 2010-2024 Three.js Authors
   * SPDX-License-Identifier: MIT
   *)
*/