human/dist/human.esm.js

    /*
      Human library
      homepage: <https://github.com/vladmandic/human>
      author: <https://github.com/vladmandic>'
    */
var Xy=Object.defineProperty;var hI=e=>Xy(e,"__esModule",{value:!0});var bm=e=>{if(typeof require!="undefined")return require(e);throw new Error('Dynamic require of "'+e+'" is not supported')};var Ky=(e,t)=>{hI(e);for(var n in t)Xy(e,n,{get:t[n],enumerable:!0})};var Zy=(e,t,n)=>{if(!t.has(e))throw TypeError("Cannot "+n)};var hn=(e,t,n)=>(Zy(e,t,"read from private field"),n?n.call(e):t.get(e)),ts=(e,t,n)=>{if(t.has(e))throw TypeError("Cannot add the same private member more than once");t instanceof WeakSet?t.add(e):t.set(e,n)},Ss=(e,t,n,s)=>(Zy(e,t,"write to private field"),s?s.call(e,n):t.set(e,n),n);function ft(e,t){let n=e.endsWith("/")?"":"/",r=t.startsWith(".")||t.startsWith("/")||t.startsWith("http:")||t.startsWith("https:")||t.startsWith("file:")?`${t}`:`${e}${n}${t}`;if(!r.toLocaleLowerCase().includes(".json"))throw new Error(`Human: ModelPath Error: ${r} Expecting JSON file`);return r}function ue(...e){let t=new Date,n=`${t.getHours().toString().padStart(2,"0")}:${t.getMinutes().toString().padStart(2,"0")}:${t.getSeconds().toString().padStart(2,"0")}.${t.getMilliseconds().toString().padStart(3,"0")}`;e&&console.log(n,"Human:",...e)}var Ke=()=>typeof performance!="undefined"?performance.now():parseInt((Number(process.hrtime.bigint())/1e3/1e3).toString());function pn(...e){let t=n=>n&&typeof n=="object";return e.reduce((n,s)=>(Object.keys(s||{}).forEach(r=>{let a=n[r],o=s[r];Array.isArray(a)&&Array.isArray(o)?n[r]=a.concat(...o):t(a)&&t(o)?n[r]=pn(a,o):n[r]=o}),n),{})}var Yy={backend:"webgl",modelBasePath:"../models/",wasmPath:"../node_modules/@tensorflow/tfjs-backend-wasm/dist/",debug:!0,async:!0,warmup:"full",cacheSensitivity:.75,skipFrame:!1,filter:{enabled:!0,width:0,height:0,flip:!1,return:!0,brightness:0,contrast:0,sharpness:0,blur:0,saturation:0,hue:0,negative:!1,sepia:!1,vintage:!1,kodachrome:!1,technicolor:!1,polaroid:!1,pixelate:0},gesture:{enabled:!0},face:{enabled:!0,detector:{modelPath:"blazeface.json",rotation:!0,maxDetected:15,skipFrames:15,minConfidence:.2,iouThreshold:.1,return:!1},mesh:{enabled:!0,modelPath:"facemesh.json"},iris:{enabled:!0,modelPath:"iris.json"},description:{enabled:!0,modelPath:"faceres.json",skipFrames:11,minConfidence:.1},emotion:{enabled:!0,minConfidence:.1,skipFrames:17,modelPath:"emotion.json"}},body:{enabled:!0,modelPath:"movenet-lightning.json",maxDetected:1,minConfidence:.2,skipFrames:1},hand:{enabled:!0,rotation:!0,skipFrames:18,minConfidence:.1,iouThreshold:.1,maxDetected:2,landmarks:!0,detector:{modelPath:"handdetect.json"},skeleton:{modelPath:"handskeleton.json"}},object:{enabled:!1,modelPath:"mb3-centernet.json",minConfidence:.2,iouThreshold:.4,maxDetected:10,skipFrames:19},segmentation:{enabled:!1,modelPath:"selfie.json"}};function Jy(){let e="",t="";if(typeof navigator!="undefined"){let n=navigator.userAgent.match(/\(([^()]+)\)/g);if(n&&n[0]){let s=n[0].match(/\(([^()]+)\)/g);e=s&&s[0]?s[0].replace(/\(|\)/g,""):"",t=navigator.userAgent.replace(n[0],""),e[1]&&(t=t.replace(n[1],"")),t=t.replace(/  /g," ")}}else typeof process!="undefined"&&(e=`${process.platform} ${process.arch}`,t=`NodeJS ${process.version}`);return{platform:e,agent:t}}var Yc={};Ky(Yc,{Abs:()=>di,Acos:()=>hi,Acosh:()=>pi,AdadeltaOptimizer:()=>ap,AdagradOptimizer:()=>op,AdamOptimizer:()=>ip,AdamaxOptimizer:()=>lp,Add:()=>Dr,AddN:()=>ga,All:()=>fi,Any:()=>mi,ArgMax:()=>ya,ArgMin:()=>Cu,Asin:()=>Ai,Asinh:()=>gi,Atan:()=>yi,Atan2:()=>bi,Atanh:()=>xi,AvgPool:()=>xa,AvgPool3D:()=>Tu,AvgPool3DGrad:()=>Ld,AvgPoolGrad:()=>zd,BackendWasm:()=>bk,BatchMatMul:()=>ba,BatchToSpaceND:()=>vi,Bincount:()=>Bd,BroadcastTo:()=>A5,Callback:()=>bv,CallbackList:()=>h3,Cast:()=>va,Ceil:()=>wa,ClipByValue:()=>Or,Complex:()=>Wd,ComplexAbs:()=>Nu,Concat:()=>wi,Conv2D:()=>ka,Conv2DBackpropFilter:()=>Vd,Conv2DBackpropInput:()=>Ia,Conv3D:()=>Eu,Conv3DBackpropFilterV2:()=>Ud,Conv3DBackpropInputV2:()=>Hd,Cos:()=>Sa,Cosh:()=>Ca,CropAndResize:()=>ki,Cumsum:()=>Ta,CustomCallback:()=>f3,DataStorage:()=>Fd,DenseBincount:()=>Gd,DepthToSpace:()=>Ii,DepthwiseConv2dNative:()=>Na,DepthwiseConv2dNativeBackpropFilter:()=>jd,DepthwiseConv2dNativeBackpropInput:()=>qd,Diag:()=>Xd,Dilation2D:()=>Ru,Dilation2DBackpropFilter:()=>Zd,Dilation2DBackpropInput:()=>Kd,ENV:()=>ns,EarlyStopping:()=>wv,Einsum:()=>Yd,Elu:()=>Si,EluGrad:()=>Jd,Environment:()=>f5,Equal:()=>Ti,Erf:()=>Ci,Exp:()=>Ra,ExpandDims:()=>Ni,Expm1:()=>Ei,FFT:()=>Qd,Fill:()=>_u,FlipLeftRight:()=>Ri,Floor:()=>_a,FloorDiv:()=>$a,FromPixels:()=>bh,FusedBatchNorm:()=>Fa,FusedConv2D:()=>fo,FusedDepthwiseConv2D:()=>mo,GPGPUContext:()=>uf,GatherNd:()=>$i,GatherV2:()=>_i,GraphModel:()=>e7,Greater:()=>Fi,GreaterEqual:()=>Da,History:()=>p3,IFFT:()=>eh,Identity:()=>Oa,Imag:()=>th,InputSpec:()=>Pt,IsFinite:()=>Di,IsInf:()=>Oi,IsNan:()=>Pi,KernelBackend:()=>ku,LRN:()=>Du,LRNGrad:()=>sh,LayerVariable:()=>i3,LayersModel:()=>wr,LeakyRelu:()=>Pa,Less:()=>Mi,LessEqual:()=>zi,LinSpace:()=>nh,Log:()=>Ma,Log1p:()=>Li,LogSoftmax:()=>g5,LogicalAnd:()=>Bi,LogicalNot:()=>$u,LogicalOr:()=>Fu,MathBackendCPU:()=>Kp,MathBackendWebGL:()=>Jl,Max:()=>za,MaxPool:()=>Ba,MaxPool3D:()=>Ou,MaxPool3DGrad:()=>ah,MaxPoolGrad:()=>rh,MaxPoolWithArgmax:()=>oh,Maximum:()=>La,Mean:()=>Wa,Min:()=>Va,Minimum:()=>Ua,MirrorPad:()=>Ha,Mod:()=>Wi,MomentumOptimizer:()=>up,Multinomial:()=>ih,Multiply:()=>Ga,Neg:()=>Vi,NonMaxSuppressionV3:()=>Hi,NonMaxSuppressionV4:()=>Gi,NonMaxSuppressionV5:()=>ji,NotEqual:()=>Ui,OP_SCOPE_SUFFIX:()=>$5,OneHot:()=>ja,OnesLike:()=>qi,Optimizer:()=>xr,Pack:()=>Xi,PadV2:()=>qa,Pool:()=>AS,Pow:()=>Xa,Prelu:()=>Ka,Prod:()=>Ki,RMSPropOptimizer:()=>cp,RNN:()=>sr,Range:()=>Pu,Rank:()=>_m,Real:()=>lh,RealDiv:()=>Ea,Reciprocal:()=>Zi,Reduction:()=>yn,Relu:()=>Za,Relu6:()=>Ja,Reshape:()=>Yi,ResizeBilinear:()=>Ya,ResizeBilinearGrad:()=>ch,ResizeNearestNeighbor:()=>Mu,ResizeNearestNeighborGrad:()=>uh,Reverse:()=>Qa,RotateWithOffset:()=>hl,Round:()=>eo,Rsqrt:()=>to,SGDOptimizer:()=>cc,ScatterNd:()=>Ji,Select:()=>Qi,Selu:()=>el,Sequential:()=>Ml,Sigmoid:()=>so,Sign:()=>sl,Sin:()=>no,Sinh:()=>nl,Slice:()=>tl,Softmax:()=>oo,Softplus:()=>rl,SpaceToBatchND:()=>al,SparseFillEmptyRows:()=>dh,SparseReshape:()=>hh,SparseSegmentMean:()=>ph,SparseSegmentSum:()=>fh,SparseToDense:()=>mh,SplitV:()=>ol,Sqrt:()=>ro,Square:()=>zu,SquaredDifference:()=>io,Step:()=>Mr,StridedSlice:()=>il,StringNGrams:()=>Ah,StringSplit:()=>gh,StringToHashBucketFast:()=>yh,Sub:()=>lo,Sum:()=>ao,SymbolicTensor:()=>Ps,Tan:()=>uo,Tanh:()=>co,Tensor:()=>Ue,TensorBuffer:()=>Bt,Tile:()=>Pr,TopK:()=>ll,Transform:()=>ul,Transpose:()=>ho,Unique:()=>xh,Unpack:()=>cl,UnsortedSegmentSum:()=>Lu,Variable:()=>qu,ZerosLike:()=>dl,_FusedMatMul:()=>po,abs:()=>Wt,acos:()=>Ax,acosh:()=>gx,add:()=>ae,addN:()=>$h,all:()=>oA,any:()=>Fh,argMax:()=>Xs,argMin:()=>yx,asin:()=>xx,asinh:()=>bx,atan:()=>vx,atan2:()=>wx,atanh:()=>kx,avgPool:()=>Oh,avgPool3d:()=>uA,backend:()=>mx,backend_util:()=>$,basicLSTMCell:()=>tT,batchNorm:()=>kl,batchNorm2d:()=>Tx,batchNorm3d:()=>Nx,batchNorm4d:()=>Ex,batchToSpaceND:()=>Ph,bincount:()=>cA,booleanMaskAsync:()=>pR,broadcastTo:()=>ec,browser:()=>rs,buffer:()=>Be,callbacks:()=>Ez,cast:()=>ce,ceil:()=>Rx,clipByValue:()=>Wn,clone:()=>Ns,complex:()=>Lr,concat:()=>ht,concat1d:()=>_x,concat2d:()=>Il,concat3d:()=>$x,concat4d:()=>Fx,constraints:()=>Bb,conv1d:()=>dA,conv2d:()=>Hr,conv2dTranspose:()=>pA,conv3d:()=>fA,conv3dTranspose:()=>Ox,copyRegisteredKernels:()=>xS,cos:()=>Mh,cosh:()=>mA,cosineWindow:()=>BA,cumsum:()=>AA,customGrad:()=>Zs,data:()=>t7,denseBincount:()=>Px,deprecationWarn:()=>sA,depthToSpace:()=>Mx,depthwiseConv2d:()=>tc,deregisterOp:()=>_z,device_util:()=>Ku,diag:()=>RT,dilation2d:()=>zx,disableDeprecationWarnings:()=>fC,dispose:()=>K,disposeVariables:()=>mC,div:()=>de,divNoNan:()=>Lx,dot:()=>zT,dropout:()=>mb,einsum:()=>Bx,elu:()=>nc,enableDebugMode:()=>pC,enableProdMode:()=>hC,enclosingPowerOfTwo:()=>Ab,engine:()=>Ar,env:()=>ee,equal:()=>as,erf:()=>Wx,exp:()=>os,expandDims:()=>Ft,expm1:()=>Vx,eye:()=>gA,fft:()=>Yh,fill:()=>Sl,findBackend:()=>rA,findBackendFactory:()=>wC,floor:()=>sc,floorDiv:()=>aA,forceHalfFloat:()=>N6,fused:()=>qr,gather:()=>Cl,gatherND:()=>fb,gather_util:()=>Zm,getBackend:()=>bC,getGradient:()=>Tm,getKernel:()=>vh,getKernelsForBackend:()=>fl,gpgpu_util:()=>Jw,grad:()=>uN,grads:()=>cN,greater:()=>Vn,greaterEqual:()=>Io,ifft:()=>ic,imag:()=>zh,image:()=>_e,inTopKAsync:()=>IR,initializers:()=>qb,input:()=>M3,io:()=>Tn,irfft:()=>DA,isFinite:()=>QT,isInf:()=>tN,isNaN:()=>Ux,keep:()=>Kt,kernel_impls:()=>Js,layers:()=>r3,leakyRelu:()=>Lh,less:()=>yA,lessEqual:()=>So,linalg:()=>Cb,linspace:()=>Hx,loadGraphModel:()=>pt,loadLayersModel:()=>LP,localResponseNormalization:()=>Gx,log:()=>is,log1p:()=>Bh,logSigmoid:()=>AN,logSoftmax:()=>xA,logSumExp:()=>Zx,logicalAnd:()=>Rs,logicalNot:()=>Vh,logicalOr:()=>wA,logicalXor:()=>NN,losses:()=>o$,matMul:()=>We,math:()=>Z5,max:()=>ls,maxPool:()=>Uh,maxPool3d:()=>kA,maxPoolWithArgmax:()=>Yx,maximum:()=>gr,mean:()=>Et,memory:()=>_h,meshgrid:()=>DN,metrics:()=>gv,min:()=>Hh,minimum:()=>rc,mirrorPad:()=>Jx,mod:()=>Qx,model:()=>MP,models:()=>yv,moments:()=>Gh,movingAverage:()=>AR,mul:()=>z,multiRNNCell:()=>VN,multinomial:()=>eb,neg:()=>St,nextFrame:()=>dp,norm:()=>zA,notEqual:()=>Nl,oneHot:()=>Ju,ones:()=>Un,onesLike:()=>us,op:()=>V,outerProduct:()=>qN,pad:()=>Gr,pad1d:()=>ZN,pad2d:()=>JN,pad3d:()=>eE,pad4d:()=>nE,pool:()=>iE,pow:()=>jr,prelu:()=>qh,print:()=>H5,prod:()=>IA,profile:()=>AC,rand:()=>hE,randomGamma:()=>AE,randomNormal:()=>tb,randomUniform:()=>El,range:()=>Rl,ready:()=>xC,real:()=>ac,reciprocal:()=>nb,registerBackend:()=>bl,registerCallbackConstructor:()=>BP,registerGradient:()=>y5,registerKernel:()=>Ao,registerOp:()=>Rz,regularizers:()=>xv,relu:()=>Ys,relu6:()=>TA,removeBackend:()=>vC,reshape:()=>U,reverse:()=>cs,reverse1d:()=>SE,reverse2d:()=>TE,reverse3d:()=>EE,reverse4d:()=>_E,rfft:()=>Jh,round:()=>NA,rsqrt:()=>EA,scalar:()=>Ie,scatterND:()=>pb,scatter_util:()=>Ym,selu:()=>RA,separableConv2d:()=>sb,sequential:()=>zP,serialization:()=>oe,setBackend:()=>yC,setPlatform:()=>kC,setWasmPath:()=>rie,setWasmPaths:()=>aie,setWebGLContext:()=>tf,setdiff1dAsync:()=>rb,shared:()=>Z2,sigmoid:()=>Bn,sign:()=>ab,signal:()=>a$,sin:()=>_A,sinh:()=>$A,slice:()=>Re,slice1d:()=>Xh,slice2d:()=>FA,slice3d:()=>Kh,slice4d:()=>oc,slice_util:()=>An,softmax:()=>Zh,softplus:()=>Tl,spaceToBatchND:()=>jh,sparse:()=>uc,sparseToDense:()=>LA,spectral:()=>r$,split:()=>nn,sqrt:()=>ln,square:()=>lt,squaredDifference:()=>OA,squeeze:()=>ot,stack:()=>Nn,step:()=>lc,stridedSlice:()=>ob,string:()=>rp,sub:()=>Ae,sum:()=>ve,sumOutType:()=>Ch,tan:()=>ib,tanh:()=>wl,tensor:()=>on,tensor1d:()=>Ot,tensor2d:()=>_s,tensor3d:()=>Eh,tensor4d:()=>sR,tensor5d:()=>rR,tensor6d:()=>aR,tensor_util:()=>Cs,test_util:()=>hx,tidy:()=>H,tile:()=>Es,time:()=>gC,topk:()=>lb,train:()=>No,transpose:()=>je,truncatedNormal:()=>Qh,unique:()=>PA,unregisterGradient:()=>yS,unregisterKernel:()=>gS,unsortedSegmentSum:()=>ub,unstack:()=>ds,upcastType:()=>bs,util:()=>I,valueAndGrad:()=>dN,valueAndGrads:()=>hN,variable:()=>cb,variableGrads:()=>jx,version:()=>lie,version_converter:()=>DL,version_core:()=>dC,version_cpu:()=>mW,version_layers:()=>vg,version_wasm:()=>oie,version_webgl:()=>LX,webgl:()=>BX,webgl_util:()=>Sw,where:()=>gn,whereAsync:()=>MA,zeros:()=>Dt,zerosLike:()=>qe});var pI=Object.create,$d=Object.defineProperty,fI=Object.getOwnPropertyDescriptor,mI=Object.getOwnPropertyNames,AI=Object.getPrototypeOf,gI=Object.prototype.hasOwnProperty,Qy=e=>$d(e,"__esModule",{value:!0}),li=e=>{if(typeof bm!="undefined")return bm(e);throw new Error('Dynamic require of "'+e+'" is not supported')},xt=(e,t)=>function(){return t||(0,e[Object.keys(e)[0]])((t={exports:{}}).exports,t),t.exports},Pe=(e,t)=>{Qy(e);for(var n in t)$d(e,n,{get:t[n],enumerable:!0})},yI=(e,t,n)=>{if(t&&typeof t=="object"||typeof t=="function")for(let s of mI(t))!gI.call(e,s)&&s!=="default"&&$d(e,s,{get:()=>t[s],enumerable:!(n=fI(t,s))||n.enumerable});return e},fa=e=>yI(Qy($d(e!=null?pI(AI(e)):{},"default",e&&e.__esModule&&"default"in e?{get:()=>e.default,enumerable:!0}:{value:e,enumerable:!0})),e),xI=xt({"node_modules/.pnpm/long@4.0.0/node_modules/long/src/long.js"(e,t){t.exports=s;var n=null;try{n=new WebAssembly.Instance(new WebAssembly.Module(new Uint8Array([0,97,115,109,1,0,0,0,1,13,2,96,0,1,127,96,4,127,127,127,127,1,127,3,7,6,0,1,1,1,1,1,6,6,1,127,1,65,0,11,7,50,6,3,109,117,108,0,1,5,100,105,118,95,115,0,2,5,100,105,118,95,117,0,3,5,114,101,109,95,115,0,4,5,114,101,109,95,117,0,5,8,103,101,116,95,104,105,103,104,0,0,10,191,1,6,4,0,35,0,11,36,1,1,126,32,0,173,32,1,173,66,32,134,132,32,2,173,32,3,173,66,32,134,132,126,34,4,66,32,135,167,36,0,32,4,167,11,36,1,1,126,32,0,173,32,1,173,66,32,134,132,32,2,173,32,3,173,66,32,134,132,127,34,4,66,32,135,167,36,0,32,4,167,11,36,1,1,126,32,0,173,32,1,173,66,32,134,132,32,2,173,32,3,173,66,32,134,132,128,34,4,66,32,135,167,36,0,32,4,167,11,36,1,1,126,32,0,173,32,1,173,66,32,134,132,32,2,173,32,3,173,66,32,134,132,129,34,4,66,32,135,167,36,0,32,4,167,11,36,1,1,126,32,0,173,32,1,173,66,32,134,132,32,2,173,32,3,173,66,32,134,132,130,34,4,66,32,135,167,36,0,32,4,167,11])),{}).exports}catch(_){}function s(_,N,O){this.low=_|0,this.high=N|0,this.unsigned=!!O}s.prototype.__isLong__,Object.defineProperty(s.prototype,"__isLong__",{value:!0});function r(_){return(_&&_.__isLong__)===!0}s.isLong=r;var a={},o={};function i(_,N){var O,W,j;return N?(_>>>=0,(j=0<=_&&_<256)&&(W=o[_],W)?W:(O=u(_,(_|0)<0?-1:0,!0),j&&(o[_]=O),O)):(_|=0,(j=-128<=_&&_<128)&&(W=a[_],W)?W:(O=u(_,_<0?-1:0,!1),j&&(a[_]=O),O))}s.fromInt=i;function l(_,N){if(isNaN(_))return N?b:x;if(N){if(_<0)return b;if(_>=A)return E}else{if(_<=-g)return M;if(_+1>=g)return C}return _<0?l(-_,N).neg():u(_%m|0,_/m|0,N)}s.fromNumber=l;function u(_,N,O){return new s(_,N,O)}s.fromBits=u;var c=Math.pow;function d(_,N,O){if(_.length===0)throw Error("empty string");if(_==="NaN"||_==="Infinity"||_==="+Infinity"||_==="-Infinity")return x;if(typeof N=="number"?(O=N,N=!1):N=!!N,O=O||10,O<2||36<O)throw RangeError("radix");var W;if((W=_.indexOf("-"))>0)throw Error("interior hyphen");if(W===0)return d(_.substring(1),N,O).neg();for(var j=l(c(O,8)),q=x,X=0;X<_.length;X+=8){var Q=Math.min(8,_.length-X),ne=parseInt(_.substring(X,X+Q),O);if(Q<8){var te=l(c(O,Q));q=q.mul(te).add(l(ne))}else q=q.mul(j),q=q.add(l(ne))}return q.unsigned=N,q}s.fromString=d;function h(_,N){return typeof _=="number"?l(_,N):typeof _=="string"?d(_,N):u(_.low,_.high,typeof N=="boolean"?N:_.unsigned)}s.fromValue=h;var p=1<<16,f=1<<24,m=p*p,A=m*m,g=A/2,y=i(f),x=i(0);s.ZERO=x;var b=i(0,!0);s.UZERO=b;var v=i(1);s.ONE=v;var k=i(1,!0);s.UONE=k;var w=i(-1);s.NEG_ONE=w;var C=u(4294967295|0,2147483647|0,!1);s.MAX_VALUE=C;var E=u(4294967295|0,4294967295|0,!0);s.MAX_UNSIGNED_VALUE=E;var M=u(0,2147483648|0,!1);s.MIN_VALUE=M;var R=s.prototype;R.toInt=function(){return this.unsigned?this.low>>>0:this.low},R.toNumber=function(){return this.unsigned?(this.high>>>0)*m+(this.low>>>0):this.high*m+(this.low>>>0)},R.toString=function(N){if(N=N||10,N<2||36<N)throw RangeError("radix");if(this.isZero())return"0";if(this.isNegative())if(this.eq(M)){var O=l(N),W=this.div(O),j=W.mul(O).sub(this);return W.toString(N)+j.toInt().toString(N)}else return"-"+this.neg().toString(N);for(var q=l(c(N,6),this.unsigned),X=this,Q="";;){var ne=X.div(q),te=X.sub(ne.mul(q)).toInt()>>>0,se=te.toString(N);if(X=ne,X.isZero())return se+Q;for(;se.length<6;)se="0"+se;Q=""+se+Q}},R.getHighBits=function(){return this.high},R.getHighBitsUnsigned=function(){return this.high>>>0},R.getLowBits=function(){return this.low},R.getLowBitsUnsigned=function(){return this.low>>>0},R.getNumBitsAbs=function(){if(this.isNegative())return this.eq(M)?64:this.neg().getNumBitsAbs();for(var N=this.high!=0?this.high:this.low,O=31;O>0&&(N&1<<O)==0;O--);return this.high!=0?O+33:O+1},R.isZero=function(){return this.high===0&&this.low===0},R.eqz=R.isZero,R.isNegative=function(){return!this.unsigned&&this.high<0},R.isPositive=function(){return this.unsigned||this.high>=0},R.isOdd=function(){return(this.low&1)==1},R.isEven=function(){return(this.low&1)==0},R.equals=function(N){return r(N)||(N=h(N)),this.unsigned!==N.unsigned&&this.high>>>31==1&&N.high>>>31==1?!1:this.high===N.high&&this.low===N.low},R.eq=R.equals,R.notEquals=function(N){return!this.eq(N)},R.neq=R.notEquals,R.ne=R.notEquals,R.lessThan=function(N){return this.comp(N)<0},R.lt=R.lessThan,R.lessThanOrEqual=function(N){return this.comp(N)<=0},R.lte=R.lessThanOrEqual,R.le=R.lessThanOrEqual,R.greaterThan=function(N){return this.comp(N)>0},R.gt=R.greaterThan,R.greaterThanOrEqual=function(N){return this.comp(N)>=0},R.gte=R.greaterThanOrEqual,R.ge=R.greaterThanOrEqual,R.compare=function(N){if(r(N)||(N=h(N)),this.eq(N))return 0;var O=this.isNegative(),W=N.isNegative();return O&&!W?-1:!O&&W?1:this.unsigned?N.high>>>0>this.high>>>0||N.high===this.high&&N.low>>>0>this.low>>>0?-1:1:this.sub(N).isNegative()?-1:1},R.comp=R.compare,R.negate=function(){return!this.unsigned&&this.eq(M)?M:this.not().add(v)},R.neg=R.negate,R.add=function(N){r(N)||(N=h(N));var O=this.high>>>16,W=this.high&65535,j=this.low>>>16,q=this.low&65535,X=N.high>>>16,Q=N.high&65535,ne=N.low>>>16,te=N.low&65535,se=0,J=0,ie=0,le=0;return le+=q+te,ie+=le>>>16,le&=65535,ie+=j+ne,J+=ie>>>16,ie&=65535,J+=W+Q,se+=J>>>16,J&=65535,se+=O+X,se&=65535,u(ie<<16|le,se<<16|J,this.unsigned)},R.subtract=function(N){return r(N)||(N=h(N)),this.add(N.neg())},R.sub=R.subtract,R.multiply=function(N){if(this.isZero())return x;if(r(N)||(N=h(N)),n){var O=n.mul(this.low,this.high,N.low,N.high);return u(O,n.get_high(),this.unsigned)}if(N.isZero())return x;if(this.eq(M))return N.isOdd()?M:x;if(N.eq(M))return this.isOdd()?M:x;if(this.isNegative())return N.isNegative()?this.neg().mul(N.neg()):this.neg().mul(N).neg();if(N.isNegative())return this.mul(N.neg()).neg();if(this.lt(y)&&N.lt(y))return l(this.toNumber()*N.toNumber(),this.unsigned);var W=this.high>>>16,j=this.high&65535,q=this.low>>>16,X=this.low&65535,Q=N.high>>>16,ne=N.high&65535,te=N.low>>>16,se=N.low&65535,J=0,ie=0,le=0,he=0;return he+=X*se,le+=he>>>16,he&=65535,le+=q*se,ie+=le>>>16,le&=65535,le+=X*te,ie+=le>>>16,le&=65535,ie+=j*se,J+=ie>>>16,ie&=65535,ie+=q*te,J+=ie>>>16,ie&=65535,ie+=X*ne,J+=ie>>>16,ie&=65535,J+=W*se+j*te+q*ne+X*Q,J&=65535,u(le<<16|he,J<<16|ie,this.unsigned)},R.mul=R.multiply,R.divide=function(N){if(r(N)||(N=h(N)),N.isZero())throw Error("division by zero");if(n){if(!this.unsigned&&this.high===-2147483648&&N.low===-1&&N.high===-1)return this;var O=(this.unsigned?n.div_u:n.div_s)(this.low,this.high,N.low,N.high);return u(O,n.get_high(),this.unsigned)}if(this.isZero())return this.unsigned?b:x;var W,j,q;if(this.unsigned){if(N.unsigned||(N=N.toUnsigned()),N.gt(this))return b;if(N.gt(this.shru(1)))return k;q=b}else{if(this.eq(M)){if(N.eq(v)||N.eq(w))return M;if(N.eq(M))return v;var X=this.shr(1);return W=X.div(N).shl(1),W.eq(x)?N.isNegative()?v:w:(j=this.sub(N.mul(W)),q=W.add(j.div(N)),q)}else if(N.eq(M))return this.unsigned?b:x;if(this.isNegative())return N.isNegative()?this.neg().div(N.neg()):this.neg().div(N).neg();if(N.isNegative())return this.div(N.neg()).neg();q=x}for(j=this;j.gte(N);){W=Math.max(1,Math.floor(j.toNumber()/N.toNumber()));for(var Q=Math.ceil(Math.log(W)/Math.LN2),ne=Q<=48?1:c(2,Q-48),te=l(W),se=te.mul(N);se.isNegative()||se.gt(j);)W-=ne,te=l(W,this.unsigned),se=te.mul(N);te.isZero()&&(te=v),q=q.add(te),j=j.sub(se)}return q},R.div=R.divide,R.modulo=function(N){if(r(N)||(N=h(N)),n){var O=(this.unsigned?n.rem_u:n.rem_s)(this.low,this.high,N.low,N.high);return u(O,n.get_high(),this.unsigned)}return this.sub(this.div(N).mul(N))},R.mod=R.modulo,R.rem=R.modulo,R.not=function(){return u(~this.low,~this.high,this.unsigned)},R.and=function(N){return r(N)||(N=h(N)),u(this.low&N.low,this.high&N.high,this.unsigned)},R.or=function(N){return r(N)||(N=h(N)),u(this.low|N.low,this.high|N.high,this.unsigned)},R.xor=function(N){return r(N)||(N=h(N)),u(this.low^N.low,this.high^N.high,this.unsigned)},R.shiftLeft=function(N){return r(N)&&(N=N.toInt()),(N&=63)===0?this:N<32?u(this.low<<N,this.high<<N|this.low>>>32-N,this.unsigned):u(0,this.low<<N-32,this.unsigned)},R.shl=R.shiftLeft,R.shiftRight=function(N){return r(N)&&(N=N.toInt()),(N&=63)===0?this:N<32?u(this.low>>>N|this.high<<32-N,this.high>>N,this.unsigned):u(this.high>>N-32,this.high>=0?0:-1,this.unsigned)},R.shr=R.shiftRight,R.shiftRightUnsigned=function(N){if(r(N)&&(N=N.toInt()),N&=63,N===0)return this;var O=this.high;if(N<32){var W=this.low;return u(W>>>N|O<<32-N,O>>>N,this.unsigned)}else return N===32?u(O,0,this.unsigned):u(O>>>N-32,0,this.unsigned)},R.shru=R.shiftRightUnsigned,R.shr_u=R.shiftRightUnsigned,R.toSigned=function(){return this.unsigned?u(this.low,this.high,!1):this},R.toUnsigned=function(){return this.unsigned?this:u(this.low,this.high,!0)},R.toBytes=function(N){return N?this.toBytesLE():this.toBytesBE()},R.toBytesLE=function(){var N=this.high,O=this.low;return[O&255,O>>>8&255,O>>>16&255,O>>>24,N&255,N>>>8&255,N>>>16&255,N>>>24]},R.toBytesBE=function(){var N=this.high,O=this.low;return[N>>>24,N>>>16&255,N>>>8&255,N&255,O>>>24,O>>>16&255,O>>>8&255,O&255]},s.fromBytes=function(N,O,W){return W?s.fromBytesLE(N,O):s.fromBytesBE(N,O)},s.fromBytesLE=function(N,O){return new s(N[0]|N[1]<<8|N[2]<<16|N[3]<<24,N[4]|N[5]<<8|N[6]<<16|N[7]<<24,O)},s.fromBytesBE=function(N,O){return new s(N[4]<<24|N[5]<<16|N[6]<<8|N[7],N[0]<<24|N[1]<<16|N[2]<<8|N[3],O)}}}),bI=xt({"(disabled):node_modules/.pnpm/node-fetch@2.6.1/node_modules/node-fetch/browser.js"(){}}),vI=xt({"node_modules/.pnpm/seedrandom@2.4.3/node_modules/seedrandom/lib/alea.js"(e,t){(function(n,s,r){function a(u){var c=this,d=l();c.next=function(){var h=2091639*c.s0+c.c*23283064365386963e-26;return c.s0=c.s1,c.s1=c.s2,c.s2=h-(c.c=h|0)},c.c=1,c.s0=d(" "),c.s1=d(" "),c.s2=d(" "),c.s0-=d(u),c.s0<0&&(c.s0+=1),c.s1-=d(u),c.s1<0&&(c.s1+=1),c.s2-=d(u),c.s2<0&&(c.s2+=1),d=null}function o(u,c){return c.c=u.c,c.s0=u.s0,c.s1=u.s1,c.s2=u.s2,c}function i(u,c){var d=new a(u),h=c&&c.state,p=d.next;return p.int32=function(){return d.next()*4294967296|0},p.double=function(){return p()+(p()*2097152|0)*11102230246251565e-32},p.quick=p,h&&(typeof h=="object"&&o(h,d),p.state=function(){return o(d,{})}),p}function l(){var u=4022871197,c=function(d){d=d.toString();for(var h=0;h<d.length;h++){u+=d.charCodeAt(h);var p=.02519603282416938*u;u=p>>>0,p-=u,p*=u,u=p>>>0,p-=u,u+=p*4294967296}return(u>>>0)*23283064365386963e-26};return c}s&&s.exports?s.exports=i:r&&r.amd?r(function(){return i}):this.alea=i})(e,typeof t=="object"&&t,typeof define=="function"&&define)}}),wI=xt({"node_modules/.pnpm/seedrandom@2.4.3/node_modules/seedrandom/lib/xor128.js"(e,t){(function(n,s,r){function a(l){var u=this,c="";u.x=0,u.y=0,u.z=0,u.w=0,u.next=function(){var h=u.x^u.x<<11;return u.x=u.y,u.y=u.z,u.z=u.w,u.w^=u.w>>>19^h^h>>>8},l===(l|0)?u.x=l:c+=l;for(var d=0;d<c.length+64;d++)u.x^=c.charCodeAt(d)|0,u.next()}function o(l,u){return u.x=l.x,u.y=l.y,u.z=l.z,u.w=l.w,u}function i(l,u){var c=new a(l),d=u&&u.state,h=function(){return(c.next()>>>0)/4294967296};return h.double=function(){do var p=c.next()>>>11,f=(c.next()>>>0)/4294967296,m=(p+f)/(1<<21);while(m===0);return m},h.int32=c.next,h.quick=h,d&&(typeof d=="object"&&o(d,c),h.state=function(){return o(c,{})}),h}s&&s.exports?s.exports=i:r&&r.amd?r(function(){return i}):this.xor128=i})(e,typeof t=="object"&&t,typeof define=="function"&&define)}}),kI=xt({"node_modules/.pnpm/seedrandom@2.4.3/node_modules/seedrandom/lib/xorwow.js"(e,t){(function(n,s,r){function a(l){var u=this,c="";u.next=function(){var h=u.x^u.x>>>2;return u.x=u.y,u.y=u.z,u.z=u.w,u.w=u.v,(u.d=u.d+362437|0)+(u.v=u.v^u.v<<4^(h^h<<1))|0},u.x=0,u.y=0,u.z=0,u.w=0,u.v=0,l===(l|0)?u.x=l:c+=l;for(var d=0;d<c.length+64;d++)u.x^=c.charCodeAt(d)|0,d==c.length&&(u.d=u.x<<10^u.x>>>4),u.next()}function o(l,u){return u.x=l.x,u.y=l.y,u.z=l.z,u.w=l.w,u.v=l.v,u.d=l.d,u}function i(l,u){var c=new a(l),d=u&&u.state,h=function(){return(c.next()>>>0)/4294967296};return h.double=function(){do var p=c.next()>>>11,f=(c.next()>>>0)/4294967296,m=(p+f)/(1<<21);while(m===0);return m},h.int32=c.next,h.quick=h,d&&(typeof d=="object"&&o(d,c),h.state=function(){return o(c,{})}),h}s&&s.exports?s.exports=i:r&&r.amd?r(function(){return i}):this.xorwow=i})(e,typeof t=="object"&&t,typeof define=="function"&&define)}}),II=xt({"node_modules/.pnpm/seedrandom@2.4.3/node_modules/seedrandom/lib/xorshift7.js"(e,t){(function(n,s,r){function a(l){var u=this;u.next=function(){var d=u.x,h=u.i,p,f,m;return p=d[h],p^=p>>>7,f=p^p<<24,p=d[h+1&7],f^=p^p>>>10,p=d[h+3&7],f^=p^p>>>3,p=d[h+4&7],f^=p^p<<7,p=d[h+7&7],p=p^p<<13,f^=p^p<<9,d[h]=f,u.i=h+1&7,f};function c(d,h){var p,f,m=[];if(h===(h|0))f=m[0]=h;else for(h=""+h,p=0;p<h.length;++p)m[p&7]=m[p&7]<<15^h.charCodeAt(p)+m[p+1&7]<<13;for(;m.length<8;)m.push(0);for(p=0;p<8&&m[p]===0;++p);for(p==8?f=m[7]=-1:f=m[p],d.x=m,d.i=0,p=256;p>0;--p)d.next()}c(u,l)}function o(l,u){return u.x=l.x.slice(),u.i=l.i,u}function i(l,u){l==null&&(l=+new Date);var c=new a(l),d=u&&u.state,h=function(){return(c.next()>>>0)/4294967296};return h.double=function(){do var p=c.next()>>>11,f=(c.next()>>>0)/4294967296,m=(p+f)/(1<<21);while(m===0);return m},h.int32=c.next,h.quick=h,d&&(d.x&&o(d,c),h.state=function(){return o(c,{})}),h}s&&s.exports?s.exports=i:r&&r.amd?r(function(){return i}):this.xorshift7=i})(e,typeof t=="object"&&t,typeof define=="function"&&define)}}),SI=xt({"node_modules/.pnpm/seedrandom@2.4.3/node_modules/seedrandom/lib/xor4096.js"(e,t){(function(n,s,r){function a(l){var u=this;u.next=function(){var d=u.w,h=u.X,p=u.i,f,m;return u.w=d=d+1640531527|0,m=h[p+34&127],f=h[p=p+1&127],m^=m<<13,f^=f<<17,m^=m>>>15,f^=f>>>12,m=h[p]=m^f,u.i=p,m+(d^d>>>16)|0};function c(d,h){var p,f,m,A,g,y=[],x=128;for(h===(h|0)?(f=h,h=null):(h=h+"\0",f=0,x=Math.max(x,h.length)),m=0,A=-32;A<x;++A)h&&(f^=h.charCodeAt((A+32)%h.length)),A===0&&(g=f),f^=f<<10,f^=f>>>15,f^=f<<4,f^=f>>>13,A>=0&&(g=g+1640531527|0,p=y[A&127]^=f+g,m=p==0?m+1:0);for(m>=128&&(y[(h&&h.length||0)&127]=-1),m=127,A=4*128;A>0;--A)f=y[m+34&127],p=y[m=m+1&127],f^=f<<13,p^=p<<17,f^=f>>>15,p^=p>>>12,y[m]=f^p;d.w=g,d.X=y,d.i=m}c(u,l)}function o(l,u){return u.i=l.i,u.w=l.w,u.X=l.X.slice(),u}function i(l,u){l==null&&(l=+new Date);var c=new a(l),d=u&&u.state,h=function(){return(c.next()>>>0)/4294967296};return h.double=function(){do var p=c.next()>>>11,f=(c.next()>>>0)/4294967296,m=(p+f)/(1<<21);while(m===0);return m},h.int32=c.next,h.quick=h,d&&(d.X&&o(d,c),h.state=function(){return o(c,{})}),h}s&&s.exports?s.exports=i:r&&r.amd?r(function(){return i}):this.xor4096=i})(e,typeof t=="object"&&t,typeof define=="function"&&define)}}),CI=xt({"node_modules/.pnpm/seedrandom@2.4.3/node_modules/seedrandom/lib/tychei.js"(e,t){(function(n,s,r){function a(l){var u=this,c="";u.next=function(){var h=u.b,p=u.c,f=u.d,m=u.a;return h=h<<25^h>>>7^p,p=p-f|0,f=f<<24^f>>>8^m,m=m-h|0,u.b=h=h<<20^h>>>12^p,u.c=p=p-f|0,u.d=f<<16^p>>>16^m,u.a=m-h|0},u.a=0,u.b=0,u.c=2654435769|0,u.d=1367130551,l===Math.floor(l)?(u.a=l/4294967296|0,u.b=l|0):c+=l;for(var d=0;d<c.length+20;d++)u.b^=c.charCodeAt(d)|0,u.next()}function o(l,u){return u.a=l.a,u.b=l.b,u.c=l.c,u.d=l.d,u}function i(l,u){var c=new a(l),d=u&&u.state,h=function(){return(c.next()>>>0)/4294967296};return h.double=function(){do var p=c.next()>>>11,f=(c.next()>>>0)/4294967296,m=(p+f)/(1<<21);while(m===0);return m},h.int32=c.next,h.quick=h,d&&(typeof d=="object"&&o(d,c),h.state=function(){return o(c,{})}),h}s&&s.exports?s.exports=i:r&&r.amd?r(function(){return i}):this.tychei=i})(e,typeof t=="object"&&t,typeof define=="function"&&define)}}),e5=xt({"(disabled):crypto"(){}}),TI=xt({"node_modules/.pnpm/seedrandom@2.4.3/node_modules/seedrandom/seedrandom.js"(e,t){(function(n,s){var r=this,a=256,o=6,i=52,l="random",u=s.pow(a,o),c=s.pow(2,i),d=c*2,h=a-1,p;function f(v,k,w){var C=[];k=k==!0?{entropy:!0}:k||{};var E=y(g(k.entropy?[v,b(n)]:v==null?x():v,3),C),M=new m(C),R=function(){for(var _=M.g(o),N=u,O=0;_<c;)_=(_+O)*a,N*=a,O=M.g(1);for(;_>=d;)_/=2,N/=2,O>>>=1;return(_+O)/N};return R.int32=function(){return M.g(4)|0},R.quick=function(){return M.g(4)/4294967296},R.double=R,y(b(M.S),n),(k.pass||w||function(_,N,O,W){return W&&(W.S&&A(W,M),_.state=function(){return A(M,{})}),O?(s[l]=_,N):_})(R,E,"global"in k?k.global:this==s,k.state)}s["seed"+l]=f;function m(v){var k,w=v.length,C=this,E=0,M=C.i=C.j=0,R=C.S=[];for(w||(v=[w++]);E<a;)R[E]=E++;for(E=0;E<a;E++)R[E]=R[M=h&M+v[E%w]+(k=R[E])],R[M]=k;(C.g=function(_){for(var N,O=0,W=C.i,j=C.j,q=C.S;_--;)N=q[W=h&W+1],O=O*a+q[h&(q[W]=q[j=h&j+N])+(q[j]=N)];return C.i=W,C.j=j,O})(a)}function A(v,k){return k.i=v.i,k.j=v.j,k.S=v.S.slice(),k}function g(v,k){var w=[],C=typeof v,E;if(k&&C=="object")for(E in v)try{w.push(g(v[E],k-1))}catch(M){}return w.length?w:C=="string"?v:v+"\0"}function y(v,k){for(var w=v+"",C,E=0;E<w.length;)k[h&E]=h&(C^=k[h&E]*19)+w.charCodeAt(E++);return b(k)}function x(){try{var v;return p&&(v=p.randomBytes)?v=v(a):(v=new Uint8Array(a),(r.crypto||r.msCrypto).getRandomValues(v)),b(v)}catch(C){var k=r.navigator,w=k&&k.plugins;return[+new Date,r,w,r.screen,b(n)]}}function b(v){return String.fromCharCode.apply(0,v)}if(y(s.random(),n),typeof t=="object"&&t.exports){t.exports=f;try{p=e5()}catch(v){}}else typeof define=="function"&&define.amd&&define(function(){return f})})([],Math)}}),t5=xt({"node_modules/.pnpm/seedrandom@2.4.3/node_modules/seedrandom/index.js"(e,t){var n=vI(),s=wI(),r=kI(),a=II(),o=SI(),i=CI(),l=TI();l.alea=n,l.xor128=s,l.xorwow=r,l.xorshift7=a,l.xor4096=o,l.tychei=i,t.exports=l}}),NI=xt({"node_modules/.pnpm/seedrandom@3.0.5/node_modules/seedrandom/lib/alea.js"(e,t){(function(n,s,r){function a(u){var c=this,d=l();c.next=function(){var h=2091639*c.s0+c.c*23283064365386963e-26;return c.s0=c.s1,c.s1=c.s2,c.s2=h-(c.c=h|0)},c.c=1,c.s0=d(" "),c.s1=d(" "),c.s2=d(" "),c.s0-=d(u),c.s0<0&&(c.s0+=1),c.s1-=d(u),c.s1<0&&(c.s1+=1),c.s2-=d(u),c.s2<0&&(c.s2+=1),d=null}function o(u,c){return c.c=u.c,c.s0=u.s0,c.s1=u.s1,c.s2=u.s2,c}function i(u,c){var d=new a(u),h=c&&c.state,p=d.next;return p.int32=function(){return d.next()*4294967296|0},p.double=function(){return p()+(p()*2097152|0)*11102230246251565e-32},p.quick=p,h&&(typeof h=="object"&&o(h,d),p.state=function(){return o(d,{})}),p}function l(){var u=4022871197,c=function(d){d=String(d);for(var h=0;h<d.length;h++){u+=d.charCodeAt(h);var p=.02519603282416938*u;u=p>>>0,p-=u,p*=u,u=p>>>0,p-=u,u+=p*4294967296}return(u>>>0)*23283064365386963e-26};return c}s&&s.exports?s.exports=i:r&&r.amd?r(function(){return i}):this.alea=i})(e,typeof t=="object"&&t,typeof define=="function"&&define)}}),EI=xt({"node_modules/.pnpm/seedrandom@3.0.5/node_modules/seedrandom/lib/xor128.js"(e,t){(function(n,s,r){function a(l){var u=this,c="";u.x=0,u.y=0,u.z=0,u.w=0,u.next=function(){var h=u.x^u.x<<11;return u.x=u.y,u.y=u.z,u.z=u.w,u.w^=u.w>>>19^h^h>>>8},l===(l|0)?u.x=l:c+=l;for(var d=0;d<c.length+64;d++)u.x^=c.charCodeAt(d)|0,u.next()}function o(l,u){return u.x=l.x,u.y=l.y,u.z=l.z,u.w=l.w,u}function i(l,u){var c=new a(l),d=u&&u.state,h=function(){return(c.next()>>>0)/4294967296};return h.double=function(){do var p=c.next()>>>11,f=(c.next()>>>0)/4294967296,m=(p+f)/(1<<21);while(m===0);return m},h.int32=c.next,h.quick=h,d&&(typeof d=="object"&&o(d,c),h.state=function(){return o(c,{})}),h}s&&s.exports?s.exports=i:r&&r.amd?r(function(){return i}):this.xor128=i})(e,typeof t=="object"&&t,typeof define=="function"&&define)}}),RI=xt({"node_modules/.pnpm/seedrandom@3.0.5/node_modules/seedrandom/lib/xorwow.js"(e,t){(function(n,s,r){function a(l){var u=this,c="";u.next=function(){var h=u.x^u.x>>>2;return u.x=u.y,u.y=u.z,u.z=u.w,u.w=u.v,(u.d=u.d+362437|0)+(u.v=u.v^u.v<<4^(h^h<<1))|0},u.x=0,u.y=0,u.z=0,u.w=0,u.v=0,l===(l|0)?u.x=l:c+=l;for(var d=0;d<c.length+64;d++)u.x^=c.charCodeAt(d)|0,d==c.length&&(u.d=u.x<<10^u.x>>>4),u.next()}function o(l,u){return u.x=l.x,u.y=l.y,u.z=l.z,u.w=l.w,u.v=l.v,u.d=l.d,u}function i(l,u){var c=new a(l),d=u&&u.state,h=function(){return(c.next()>>>0)/4294967296};return h.double=function(){do var p=c.next()>>>11,f=(c.next()>>>0)/4294967296,m=(p+f)/(1<<21);while(m===0);return m},h.int32=c.next,h.quick=h,d&&(typeof d=="object"&&o(d,c),h.state=function(){return o(c,{})}),h}s&&s.exports?s.exports=i:r&&r.amd?r(function(){return i}):this.xorwow=i})(e,typeof t=="object"&&t,typeof define=="function"&&define)}}),_I=xt({"node_modules/.pnpm/seedrandom@3.0.5/node_modules/seedrandom/lib/xorshift7.js"(e,t){(function(n,s,r){function a(l){var u=this;u.next=function(){var d=u.x,h=u.i,p,f,m;return p=d[h],p^=p>>>7,f=p^p<<24,p=d[h+1&7],f^=p^p>>>10,p=d[h+3&7],f^=p^p>>>3,p=d[h+4&7],f^=p^p<<7,p=d[h+7&7],p=p^p<<13,f^=p^p<<9,d[h]=f,u.i=h+1&7,f};function c(d,h){var p,f,m=[];if(h===(h|0))f=m[0]=h;else for(h=""+h,p=0;p<h.length;++p)m[p&7]=m[p&7]<<15^h.charCodeAt(p)+m[p+1&7]<<13;for(;m.length<8;)m.push(0);for(p=0;p<8&&m[p]===0;++p);for(p==8?f=m[7]=-1:f=m[p],d.x=m,d.i=0,p=256;p>0;--p)d.next()}c(u,l)}function o(l,u){return u.x=l.x.slice(),u.i=l.i,u}function i(l,u){l==null&&(l=+new Date);var c=new a(l),d=u&&u.state,h=function(){return(c.next()>>>0)/4294967296};return h.double=function(){do var p=c.next()>>>11,f=(c.next()>>>0)/4294967296,m=(p+f)/(1<<21);while(m===0);return m},h.int32=c.next,h.quick=h,d&&(d.x&&o(d,c),h.state=function(){return o(c,{})}),h}s&&s.exports?s.exports=i:r&&r.amd?r(function(){return i}):this.xorshift7=i})(e,typeof t=="object"&&t,typeof define=="function"&&define)}}),$I=xt({"node_modules/.pnpm/seedrandom@3.0.5/node_modules/seedrandom/lib/xor4096.js"(e,t){(function(n,s,r){function a(l){var u=this;u.next=function(){var d=u.w,h=u.X,p=u.i,f,m;return u.w=d=d+1640531527|0,m=h[p+34&127],f=h[p=p+1&127],m^=m<<13,f^=f<<17,m^=m>>>15,f^=f>>>12,m=h[p]=m^f,u.i=p,m+(d^d>>>16)|0};function c(d,h){var p,f,m,A,g,y=[],x=128;for(h===(h|0)?(f=h,h=null):(h=h+"\0",f=0,x=Math.max(x,h.length)),m=0,A=-32;A<x;++A)h&&(f^=h.charCodeAt((A+32)%h.length)),A===0&&(g=f),f^=f<<10,f^=f>>>15,f^=f<<4,f^=f>>>13,A>=0&&(g=g+1640531527|0,p=y[A&127]^=f+g,m=p==0?m+1:0);for(m>=128&&(y[(h&&h.length||0)&127]=-1),m=127,A=4*128;A>0;--A)f=y[m+34&127],p=y[m=m+1&127],f^=f<<13,p^=p<<17,f^=f>>>15,p^=p>>>12,y[m]=f^p;d.w=g,d.X=y,d.i=m}c(u,l)}function o(l,u){return u.i=l.i,u.w=l.w,u.X=l.X.slice(),u}function i(l,u){l==null&&(l=+new Date);var c=new a(l),d=u&&u.state,h=function(){return(c.next()>>>0)/4294967296};return h.double=function(){do var p=c.next()>>>11,f=(c.next()>>>0)/4294967296,m=(p+f)/(1<<21);while(m===0);return m},h.int32=c.next,h.quick=h,d&&(d.X&&o(d,c),h.state=function(){return o(c,{})}),h}s&&s.exports?s.exports=i:r&&r.amd?r(function(){return i}):this.xor4096=i})(e,typeof t=="object"&&t,typeof define=="function"&&define)}}),FI=xt({"node_modules/.pnpm/seedrandom@3.0.5/node_modules/seedrandom/lib/tychei.js"(e,t){(function(n,s,r){function a(l){var u=this,c="";u.next=function(){var h=u.b,p=u.c,f=u.d,m=u.a;return h=h<<25^h>>>7^p,p=p-f|0,f=f<<24^f>>>8^m,m=m-h|0,u.b=h=h<<20^h>>>12^p,u.c=p=p-f|0,u.d=f<<16^p>>>16^m,u.a=m-h|0},u.a=0,u.b=0,u.c=2654435769|0,u.d=1367130551,l===Math.floor(l)?(u.a=l/4294967296|0,u.b=l|0):c+=l;for(var d=0;d<c.length+20;d++)u.b^=c.charCodeAt(d)|0,u.next()}function o(l,u){return u.a=l.a,u.b=l.b,u.c=l.c,u.d=l.d,u}function i(l,u){var c=new a(l),d=u&&u.state,h=function(){return(c.next()>>>0)/4294967296};return h.double=function(){do var p=c.next()>>>11,f=(c.next()>>>0)/4294967296,m=(p+f)/(1<<21);while(m===0);return m},h.int32=c.next,h.quick=h,d&&(typeof d=="object"&&o(d,c),h.state=function(){return o(c,{})}),h}s&&s.exports?s.exports=i:r&&r.amd?r(function(){return i}):this.tychei=i})(e,typeof t=="object"&&t,typeof define=="function"&&define)}}),DI=xt({"node_modules/.pnpm/seedrandom@3.0.5/node_modules/seedrandom/seedrandom.js"(e,t){(function(n,s,r){var a=256,o=6,i=52,l="random",u=r.pow(a,o),c=r.pow(2,i),d=c*2,h=a-1,p;function f(v,k,w){var C=[];k=k==!0?{entropy:!0}:k||{};var E=y(g(k.entropy?[v,b(s)]:v==null?x():v,3),C),M=new m(C),R=function(){for(var _=M.g(o),N=u,O=0;_<c;)_=(_+O)*a,N*=a,O=M.g(1);for(;_>=d;)_/=2,N/=2,O>>>=1;return(_+O)/N};return R.int32=function(){return M.g(4)|0},R.quick=function(){return M.g(4)/4294967296},R.double=R,y(b(M.S),s),(k.pass||w||function(_,N,O,W){return W&&(W.S&&A(W,M),_.state=function(){return A(M,{})}),O?(r[l]=_,N):_})(R,E,"global"in k?k.global:this==r,k.state)}function m(v){var k,w=v.length,C=this,E=0,M=C.i=C.j=0,R=C.S=[];for(w||(v=[w++]);E<a;)R[E]=E++;for(E=0;E<a;E++)R[E]=R[M=h&M+v[E%w]+(k=R[E])],R[M]=k;(C.g=function(_){for(var N,O=0,W=C.i,j=C.j,q=C.S;_--;)N=q[W=h&W+1],O=O*a+q[h&(q[W]=q[j=h&j+N])+(q[j]=N)];return C.i=W,C.j=j,O})(a)}function A(v,k){return k.i=v.i,k.j=v.j,k.S=v.S.slice(),k}function g(v,k){var w=[],C=typeof v,E;if(k&&C=="object")for(E in v)try{w.push(g(v[E],k-1))}catch(M){}return w.length?w:C=="string"?v:v+"\0"}function y(v,k){for(var w=v+"",C,E=0;E<w.length;)k[h&E]=h&(C^=k[h&E]*19)+w.charCodeAt(E++);return b(k)}function x(){try{var v;return p&&(v=p.randomBytes)?v=v(a):(v=new Uint8Array(a),(n.crypto||n.msCrypto).getRandomValues(v)),b(v)}catch(C){var k=n.navigator,w=k&&k.plugins;return[+new Date,n,w,n.screen,b(s)]}}function b(v){return String.fromCharCode.apply(0,v)}if(y(r.random(),s),typeof t=="object"&&t.exports){t.exports=f;try{p=e5()}catch(v){}}else typeof define=="function"&&define.amd?define(function(){return f}):r["seed"+l]=f})(typeof self!="undefined"?self:e,[],Math)}}),n5=xt({"node_modules/.pnpm/seedrandom@3.0.5/node_modules/seedrandom/index.js"(e,t){var n=NI(),s=EI(),r=RI(),a=_I(),o=$I(),i=FI(),l=DI();l.alea=n,l.xor128=s,l.xorwow=r,l.xorshift7=a,l.xor4096=o,l.tychei=i,t.exports=l}}),OI=xt({"(disabled):node_modules/.pnpm/string_decoder@1.1.1/node_modules/string_decoder/lib/string_decoder.js"(){}}),wu=xt({"(disabled):path"(){}}),PI=xt({"(disabled):worker_threads"(){}}),MI=xt({"(disabled):perf_hooks"(){}}),zI=xt({"node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.8.0_@tensorflow+tfjs-core@3.8.0/node_modules/@tensorflow/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm-threaded-simd.js"(e,t){var n=function(){var s=typeof document!="undefined"&&document.currentScript?document.currentScript.src:void 0;return typeof __filename!="undefined"&&(s=s||__filename),function(r){r=r||{};function a(){return J.buffer!=Ve&&en(J.buffer),In}function o(){return J.buffer!=Ve&&en(J.buffer),kt}function i(){return J.buffer!=Ve&&en(J.buffer),gs}function l(){return J.buffer!=Ve&&en(J.buffer),cn}function u(){return J.buffer!=Ve&&en(J.buffer),Jn}var c=typeof r!="undefined"?r:{},d,h;c.ready=new Promise(function(T,F){d=T,h=F});var p={},f;for(f in c)c.hasOwnProperty(f)&&(p[f]=c[f]);var m=[],A="./this.program",g=function(T,F){throw F},y=!1,x=!1,b=!1,v=!1;y=typeof window=="object",x=typeof importScripts=="function",b=typeof process=="object"&&typeof process.versions=="object"&&typeof process.versions.node=="string",v=!y&&!b&&!x;var k=c.ENVIRONMENT_IS_PTHREAD||!1;k&&(Ve=c.buffer);var w="";function C(T){return c.locateFile?c.locateFile(T,w):w+T}var E,M,R,_,N,O;if(b){x?w=wu().dirname(w)+"/":w=__dirname+"/",E=function(F,B){return N||(N=li("fs")),O||(O=wu()),F=O.normalize(F),N.readFileSync(F,B?null:"utf8")},R=function(F){var B=E(F,!0);return B.buffer||(B=new Uint8Array(B)),ge(B.buffer),B},process.argv.length>1&&(A=process.argv[1].replace(/\\/g,"/")),m=process.argv.slice(2),process.on("uncaughtException",function(T){if(!(T instanceof vu))throw T}),process.on("unhandledRejection",dr),g=function(T){process.exit(T)},c.inspect=function(){return"[Emscripten Module object]"};var W;try{W=PI()}catch(T){throw console.error('The "worker_threads" module is not supported in this node.js build - perhaps a newer version is needed?'),T}global.Worker=W.Worker}else v?(typeof read!="undefined"&&(E=function(F){return read(F)}),R=function(F){var B;return typeof readbuffer=="function"?new Uint8Array(readbuffer(F)):(B=read(F,"binary"),ge(typeof B=="object"),B)},typeof scriptArgs!="undefined"?m=scriptArgs:typeof arguments!="undefined"&&(m=arguments),typeof quit=="function"&&(g=function(T){quit(T)}),typeof print!="undefined"&&(typeof console=="undefined"&&(console={}),console.log=print,console.warn=console.error=typeof printErr!="undefined"?printErr:print)):(y||x)&&(x?w=self.location.href:typeof document!="undefined"&&document.currentScript&&(w=document.currentScript.src),typeof s!="undefined"&&s&&(w=s),w.indexOf("blob:")!==0?w=w.substr(0,w.lastIndexOf("/")+1):w="",b?(E=function(F,B){return N||(N=li("fs")),O||(O=wu()),F=O.normalize(F),N.readFileSync(F,B?null:"utf8")},R=function(F){var B=E(F,!0);return B.buffer||(B=new Uint8Array(B)),ge(B.buffer),B}):(E=function(T){var F=new XMLHttpRequest;return F.open("GET",T,!1),F.send(null),F.responseText},x&&(R=function(T){var F=new XMLHttpRequest;return F.open("GET",T,!1),F.responseType="arraybuffer",F.send(null),new Uint8Array(F.response)}),M=function(T,F,B){var Z=new XMLHttpRequest;Z.open("GET",T,!0),Z.responseType="arraybuffer",Z.onload=function(){if(Z.status==200||Z.status==0&&Z.response){F(Z.response);return}B()},Z.onerror=B,Z.send(null)}),_=function(T){document.title=T});b&&typeof performance=="undefined"&&(global.performance=MI().performance);var j=c.print||console.log.bind(console),q=c.printErr||console.warn.bind(console);for(f in p)p.hasOwnProperty(f)&&(c[f]=p[f]);p=null,c.arguments&&(m=c.arguments),c.thisProgram&&(A=c.thisProgram),c.quit&&(g=c.quit);var X=Atomics.load,Q=Atomics.store,ne=Atomics.compareExchange,te;c.wasmBinary&&(te=c.wasmBinary);var se=c.noExitRuntime||!0;typeof WebAssembly!="object"&&dr("no native wasm support detected");var J,ie,le=!1,he;function ge(T,F){T||dr("Assertion failed: "+F)}function Ce(T){var F=c["_"+T];return ge(F,"Cannot call unknown function "+T+", make sure it is exported"),F}function Te(T,F,B,Z,me){var pe={string:function(dn){var ii=0;if(dn!=null&&dn!==0){var qy=(dn.length<<2)+1;ii=ri(qy),et(dn,ii,qy)}return ii},array:function(dn){var ii=ri(dn.length);return Je(dn,ii),ii}};function fe(dn){return F==="string"?De(dn):F==="boolean"?Boolean(dn):dn}var we=Ce(T),nt=[],jt=0;if(Z)for(var Lt=0;Lt<Z.length;Lt++){var _r=pe[B[Lt]];_r?(jt===0&&(jt=bu()),nt[Lt]=_r(Z[Lt])):nt[Lt]=Z[Lt]}var oi=we.apply(null,nt);return oi=fe(oi),jt!==0&&si(jt),oi}function $e(T,F,B,Z){B=B||[];var me=B.every(function(fe){return fe==="number"}),pe=F!=="string";return pe&&me&&!Z?Ce(T):function(){return Te(T,F,B,arguments,Z)}}function Me(T,F,B){for(var Z=F+B,me="";!(F>=Z);){var pe=T[F++];if(!pe)return me;if(!(pe&128)){me+=String.fromCharCode(pe);continue}var fe=T[F++]&63;if((pe&224)==192){me+=String.fromCharCode((pe&31)<<6|fe);continue}var we=T[F++]&63;if((pe&240)==224?pe=(pe&15)<<12|fe<<6|we:pe=(pe&7)<<18|fe<<12|we<<6|T[F++]&63,pe<65536)me+=String.fromCharCode(pe);else{var nt=pe-65536;me+=String.fromCharCode(55296|nt>>10,56320|nt&1023)}}return me}function De(T,F){return T?Me(o(),T,F):""}function it(T,F,B,Z){if(!(Z>0))return 0;for(var me=B,pe=B+Z-1,fe=0;fe<T.length;++fe){var we=T.charCodeAt(fe);if(we>=55296&&we<=57343){var nt=T.charCodeAt(++fe);we=65536+((we&1023)<<10)|nt&1023}if(we<=127){if(B>=pe)break;F[B++]=we}else if(we<=2047){if(B+1>=pe)break;F[B++]=192|we>>6,F[B++]=128|we&63}else if(we<=65535){if(B+2>=pe)break;F[B++]=224|we>>12,F[B++]=128|we>>6&63,F[B++]=128|we&63}else{if(B+3>=pe)break;F[B++]=240|we>>18,F[B++]=128|we>>12&63,F[B++]=128|we>>6&63,F[B++]=128|we&63}}return F[B]=0,B-me}function et(T,F,B){return it(T,o(),F,B)}function tt(T){for(var F=0,B=0;B<T.length;++B){var Z=T.charCodeAt(B);Z>=55296&&Z<=57343&&(Z=65536+((Z&1023)<<10)|T.charCodeAt(++B)&1023),Z<=127?++F:Z<=2047?F+=2:Z<=65535?F+=3:F+=4}return F}function Je(T,F){a().set(T,F)}function at(T,F){return T%F>0&&(T+=F-T%F),T}var Ve,In,kt,Mn,Qt,gs,cn,Yn,Jn;function en(T){Ve=T,c.HEAP8=In=new Int8Array(T),c.HEAP16=Mn=new Int16Array(T),c.HEAP32=gs=new Int32Array(T),c.HEAPU8=kt=new Uint8Array(T),c.HEAPU16=Qt=new Uint16Array(T),c.HEAPU32=cn=new Uint32Array(T),c.HEAPF32=Yn=new Float32Array(T),c.HEAPF64=Jn=new Float64Array(T)}var Qn=c.INITIAL_MEMORY||16777216;if(k)J=c.wasmMemory,Ve=c.buffer;else if(c.wasmMemory)J=c.wasmMemory;else if(J=new WebAssembly.Memory({initial:Qn/65536,maximum:2147483648/65536,shared:!0}),!(J.buffer instanceof SharedArrayBuffer))throw q("requested a shared WebAssembly.Memory but the returned buffer is not a SharedArrayBuffer, indicating that while the browser has SharedArrayBuffer it does not have WebAssembly threads support - you may need to set a flag"),b&&console.log("(on node you may need: --experimental-wasm-threads --experimental-wasm-bulk-memory and also use a recent version)"),Error("bad memory");J&&(Ve=J.buffer),Qn=Ve.byteLength,en(Ve);var es,zn=[],Hs=[],ur=[],Cr=[],Yo=[],Gs=!1,cd=!1;k||Hs.push({func:function(){Sd()}});function Kf(){if(!k){if(c.preRun)for(typeof c.preRun=="function"&&(c.preRun=[c.preRun]);c.preRun.length;)hd(c.preRun.shift());Qo(zn)}}function du(){Gs=!0,!k&&Qo(Hs)}function Zf(){k||Qo(ur)}function dd(){k||(cd=!0)}function Sn(){if(!k){if(c.postRun)for(typeof c.postRun=="function"&&(c.postRun=[c.postRun]);c.postRun.length;)Yf(c.postRun.shift());Qo(Yo)}}function hd(T){zn.unshift(T)}function Yf(T){Yo.unshift(T)}var cr=0,Tr=null,da=null;function Jf(T){ge(!k,"addRunDependency cannot be used in a pthread worker"),cr++,c.monitorRunDependencies&&c.monitorRunDependencies(cr)}function Qf(T){if(cr--,c.monitorRunDependencies&&c.monitorRunDependencies(cr),cr==0&&(Tr!==null&&(clearInterval(Tr),Tr=null),da)){var F=da;da=null,F()}}c.preloadedImages={},c.preloadedAudios={};function dr(T){c.onAbort&&c.onAbort(T),k&&console.error("Pthread aborting at "+new Error().stack),T+="",q(T),le=!0,he=1,T="abort("+T+"). Build with -s ASSERTIONS=1 for more info.";var F=new WebAssembly.RuntimeError(T);throw h(F),F}function pd(T,F){return String.prototype.startsWith?T.startsWith(F):T.indexOf(F)===0}var Jo="data:application/octet-stream;base64,";function fd(T){return pd(T,Jo)}var e0="file://";function md(T){return pd(T,e0)}var Cn="tfjs-backend-wasm-threaded-simd.wasm";fd(Cn)||(Cn=C(Cn));function Ad(T){try{if(T==Cn&&te)return new Uint8Array(te);if(R)return R(T);throw"both async and sync fetching of the wasm failed"}catch(F){dr(F)}}function t0(){if(!te&&(y||x)){if(typeof fetch=="function"&&!md(Cn))return fetch(Cn,{credentials:"same-origin"}).then(function(T){if(!T.ok)throw"failed to load wasm binary file at '"+Cn+"'";return T.arrayBuffer()}).catch(function(){return Ad(Cn)});if(M)return new Promise(function(T,F){M(Cn,function(B){T(new Uint8Array(B))},F)})}return Promise.resolve().then(function(){return Ad(Cn)})}function n0(){var T={a:j0};function F(fe,we){var nt=fe.exports;if(c.asm=nt,es=c.asm.F,ie=we,!k){var jt=Se.unusedWorkers.length;Se.unusedWorkers.forEach(function(Lt){Se.loadWasmModuleToWorker(Lt,function(){--jt||Qf("wasm-instantiate")})})}}k||Jf("wasm-instantiate");function B(fe){F(fe.instance,fe.module)}function Z(fe){return t0().then(function(we){return WebAssembly.instantiate(we,T)}).then(fe,function(we){q("failed to asynchronously prepare wasm: "+we),dr(we)})}function me(){return!te&&typeof WebAssembly.instantiateStreaming=="function"&&!fd(Cn)&&!md(Cn)&&typeof fetch=="function"?fetch(Cn,{credentials:"same-origin"}).then(function(fe){var we=WebAssembly.instantiateStreaming(fe,T);return we.then(B,function(nt){return q("wasm streaming compile failed: "+nt),q("falling back to ArrayBuffer instantiation"),Z(B)})}):Z(B)}if(c.instantiateWasm)try{var pe=c.instantiateWasm(T,F);return pe}catch(fe){return q("Module.instantiateWasm callback failed with error: "+fe),!1}return me().catch(h),{}}var s0={9832:function(){throw"Canceled!"},9850:function(T,F){setTimeout(function(){Wy(T,F)},0)}};function gd(){Se.initRuntime()}function Qo(T){for(;T.length>0;){var F=T.shift();if(typeof F=="function"){F(c);continue}var B=F.func;typeof B=="number"?F.arg===void 0?es.get(B)():es.get(B)(F.arg):B(F.arg===void 0?null:F.arg)}}function hu(T,F){if(T<=0||T>a().length||T&!0||F<0)return-28;if(F==0)return 0;F>=2147483647&&(F=1/0);var B=Atomics.load(i(),ai>>2),Z=0;if(B==T){var me=Atomics.compareExchange(i(),ai>>2,B,0);if(me==B&&(--F,Z=1,F<=0))return 1}var pe=Atomics.notify(i(),T>>2,F);if(pe>=0)return pe+Z;throw"Atomics.notify returned an unexpected value "+pe}c._emscripten_futex_wake=hu;function r0(T){if(k)throw"Internal Error! killThread() can only ever be called from main application thread!";if(!T)throw"Internal Error! Null pthread_ptr in killThread!";i()[T+12>>2]=0;var F=Se.pthreads[T];F.worker.terminate(),Se.freeThreadData(F),Se.runningWorkers.splice(Se.runningWorkers.indexOf(F.worker),1),F.worker.pthread=void 0}function a0(T){if(k)throw"Internal Error! cancelThread() can only ever be called from main application thread!";if(!T)throw"Internal Error! Null pthread_ptr in cancelThread!";var F=Se.pthreads[T];F.worker.postMessage({cmd:"cancel"})}function o0(T){if(k)throw"Internal Error! cleanupThread() can only ever be called from main application thread!";if(!T)throw"Internal Error! Null pthread_ptr in cleanupThread!";var F=Se.pthreads[T];if(F){i()[T+12>>2]=0;var B=F.worker;Se.returnWorkerToPool(B)}}var Se={unusedWorkers:[],runningWorkers:[],initMainThreadBlock:function(){for(var T=Math.min(4,Math.max(1,(navigator.hardwareConcurrency||1)/2)),F=0;F<T;++F)Se.allocateUnusedWorker()},initRuntime:function(){for(var T=pa(228),F=0;F<228/4;++F)l()[T/4+F]=0;i()[T+12>>2]=T;var B=T+152;i()[B>>2]=B;for(var Z=pa(512),F=0;F<128;++F)l()[Z/4+F]=0;Atomics.store(l(),T+100>>2,Z),Atomics.store(l(),T+40>>2,T),ym(T,!x,1),By(T)},initWorker:function(){},pthreads:{},threadExitHandlers:[],setThreadStatus:function(){},runExitHandlers:function(){for(;Se.threadExitHandlers.length>0;)Se.threadExitHandlers.pop()();k&&ni()&&Ly()},runExitHandlersAndDeinitThread:function(T,F){Atomics.store(l(),T+56>>2,1),Atomics.store(l(),T+60>>2,0),Se.runExitHandlers(),Atomics.store(l(),T+4>>2,F),Atomics.store(l(),T+0>>2,1),hu(T+0,2147483647),ym(0,0,0)},threadExit:function(T){var F=ni();F&&(Se.runExitHandlersAndDeinitThread(F,T),k&&postMessage({cmd:"exit"}))},threadCancel:function(){Se.runExitHandlersAndDeinitThread(ni(),-1),postMessage({cmd:"cancelDone"})},terminateAllThreads:function(){for(var T in Se.pthreads){var F=Se.pthreads[T];F&&F.worker&&Se.returnWorkerToPool(F.worker)}Se.pthreads={};for(var B=0;B<Se.unusedWorkers.length;++B){var Z=Se.unusedWorkers[B];Z.terminate()}Se.unusedWorkers=[];for(var B=0;B<Se.runningWorkers.length;++B){var Z=Se.runningWorkers[B],F=Z.pthread;Se.freeThreadData(F),Z.terminate()}Se.runningWorkers=[]},freeThreadData:function(T){if(!!T){if(T.threadInfoStruct){var F=i()[T.threadInfoStruct+100>>2];i()[T.threadInfoStruct+100>>2]=0,xu(F),xu(T.threadInfoStruct)}T.threadInfoStruct=0,T.allocatedOwnStack&&T.stackBase&&xu(T.stackBase),T.stackBase=0,T.worker&&(T.worker.pthread=null)}},returnWorkerToPool:function(T){Se.runWithoutMainThreadQueuedCalls(function(){delete Se.pthreads[T.pthread.threadInfoStruct],Se.unusedWorkers.push(T),Se.runningWorkers.splice(Se.runningWorkers.indexOf(T),1),Se.freeThreadData(T.pthread),T.pthread=void 0})},runWithoutMainThreadQueuedCalls:function(T){i()[jy>>2]=0;try{T()}finally{i()[jy>>2]=1}},receiveObjectTransfer:function(T){},loadWasmModuleToWorker:function(T,F){T.onmessage=function(B){var Z=B.data,me=Z.cmd;if(T.pthread&&(Se.currentProxiedOperationCallerThread=T.pthread.threadInfoStruct),Z.targetThread&&Z.targetThread!=ni()){var pe=Se.pthreads[Z.targetThread];pe?pe.worker.postMessage(B.data,Z.transferList):console.error('Internal error! Worker sent a message "'+me+'" to target pthread '+Z.targetThread+", but that thread no longer exists!"),Se.currentProxiedOperationCallerThread=void 0;return}if(me==="processQueuedMainThreadWork")Am();else if(me==="spawnThread")kd(B.data);else if(me==="cleanupThread")o0(Z.thread);else if(me==="killThread")r0(Z.thread);else if(me==="cancelThread")a0(Z.thread);else if(me==="loaded")T.loaded=!0,F&&F(T),T.runPthread&&(T.runPthread(),delete T.runPthread);else if(me==="print")j("Thread "+Z.threadId+": "+Z.text);else if(me==="printErr")q("Thread "+Z.threadId+": "+Z.text);else if(me==="alert")alert("Thread "+Z.threadId+": "+Z.text);else if(me==="exit"){var fe=T.pthread&&Atomics.load(l(),T.pthread.threadInfoStruct+64>>2);fe&&Se.returnWorkerToPool(T)}else if(me==="exitProcess")try{dI(Z.returnCode)}catch(we){if(we instanceof vu)return;throw we}else me==="cancelDone"?Se.returnWorkerToPool(T):me==="objectTransfer"?Se.receiveObjectTransfer(B.data):B.data.target==="setimmediate"?T.postMessage(B.data):q("worker sent an unknown command "+me);Se.currentProxiedOperationCallerThread=void 0},T.onerror=function(B){q("pthread sent an error! "+B.filename+":"+B.lineno+": "+B.message)},b&&(T.on("message",function(B){T.onmessage({data:B})}),T.on("error",function(B){T.onerror(B)}),T.on("exit",function(B){})),T.postMessage({cmd:"load",urlOrBlob:c.mainScriptUrlOrBlob||s,wasmMemory:J,wasmModule:ie})},allocateUnusedWorker:function(){var T=C("tfjs-backend-wasm-threaded-simd.worker.js");Se.unusedWorkers.push(new Worker(T))},getNewWorker:function(){return Se.unusedWorkers.length==0&&(Se.allocateUnusedWorker(),Se.loadWasmModuleToWorker(Se.unusedWorkers[0])),Se.unusedWorkers.length>0?Se.unusedWorkers.pop():null},busySpinWait:function(T){for(var F=performance.now()+T;performance.now()<F;);}};function i0(T,F){Hy(T,F),si(T)}c.establishStackSpace=i0;function l0(){return se}c.getNoExitRuntime=l0;function u0(T,F){return es.get(T)(F)}c.invokeEntryPoint=u0;function c0(T,F,B,Z){dr("Assertion failed: "+De(T)+", at: "+[F?De(F):"unknown filename",B,Z?De(Z):"unknown function"])}function d0(T,F){var B=_main(T,F)}var ha;b?ha=function(){var T=process.hrtime();return T[0]*1e3+T[1]/1e6}:k?ha=function(){return performance.now()-c.__performance_now_clock_drift}:typeof dateNow!="undefined"?ha=dateNow:ha=function(){return performance.now()};function h0(T){return i()[My()>>2]=T,T}function p0(T,F){if(k)return Nr(1,1,T,F)}function f0(T,F){if(T==F)postMessage({cmd:"processQueuedMainThreadWork"});else if(k)postMessage({targetThread:T,cmd:"processThreadQueue"});else{var B=Se.pthreads[T],Z=B&&B.worker;if(!Z)return;Z.postMessage({cmd:"processThreadQueue"})}return 1}function m0(){dr()}function A0(T,F,B){var Z=v0(F,B);return s0[T].apply(null,Z)}function g0(T,F){}function y0(T,F,B){if(T<=0||T>a().length||T&!0)return-28;if(y){if(Atomics.load(i(),T>>2)!=F)return-6;for(var me=performance.now(),pe=me+B,fe=Atomics.exchange(i(),ai>>2,T);;){if(me=performance.now(),me>pe)return fe=Atomics.exchange(i(),ai>>2,0),-73;if(fe=Atomics.exchange(i(),ai>>2,0),fe==0)break;if(Am(),Atomics.load(i(),T>>2)!=F)return-6;fe=Atomics.exchange(i(),ai>>2,T)}return 0}else{var Z=Atomics.wait(i(),T>>2,F,B);if(Z==="timed-out")return-73;if(Z==="not-equal")return-6;if(Z==="ok")return 0;throw"Atomics.wait returned an unexpected value "+Z}}function x0(T,F,B){o().copyWithin(T,F,F+B)}function b0(){return b?li("os").cpus().length:navigator.hardwareConcurrency}function Nr(T,F){for(var B=arguments.length-2,Z=bu(),me=B,pe=ri(me*8),fe=pe>>3,we=0;we<B;we++){var nt=arguments[2+we];u()[fe+we]=nt}var jt=Uy(T,me,pe,F);return si(Z),jt}var pu=[],fu=[];function v0(T,F){fu.length=0;var B;for(F>>=2;B=o()[T++];){var Z=B<105;Z&&F&1&&F++,fu.push(Z?u()[F++>>1]:i()[F]),++F}return fu}function w0(T,F,B){pu.length=F;for(var Z=B>>3,me=0;me<F;me++)pu[me]=u()[Z+me];var pe=T<0,fe=pe?s0[-T-1]:G0[T];return fe.apply(null,pu)}function k0(){return o().length}function I0(T){try{return J.grow(T-Ve.byteLength+65535>>>16),en(J.buffer),1}catch(F){}}function S0(T){var F=k0();if(T<=F)return!1;var B=2147483648;if(T>B)return!1;for(var Z=1;Z<=4;Z*=2){var me=F*(1+.2/Z);me=Math.min(me,T+100663296);var pe=Math.min(B,at(Math.max(T,me),65536)),fe=I0(pe);if(fe)return!0}return!1}var Le={inEventHandler:0,removeAllEventListeners:function(){for(var T=Le.eventHandlers.length-1;T>=0;--T)Le._removeHandler(T);Le.eventHandlers=[],Le.deferredCalls=[]},registerRemoveEventListeners:function(){Le.removeEventListenersRegistered||(Cr.push(Le.removeAllEventListeners),Le.removeEventListenersRegistered=!0)},deferredCalls:[],deferCall:function(T,F,B){function Z(fe,we){if(fe.length!=we.length)return!1;for(var nt in fe)if(fe[nt]!=we[nt])return!1;return!0}for(var me in Le.deferredCalls){var pe=Le.deferredCalls[me];if(pe.targetFunction==T&&Z(pe.argsList,B))return}Le.deferredCalls.push({targetFunction:T,precedence:F,argsList:B}),Le.deferredCalls.sort(function(fe,we){return fe.precedence<we.precedence})},removeDeferredCalls:function(T){for(var F=0;F<Le.deferredCalls.length;++F)Le.deferredCalls[F].targetFunction==T&&(Le.deferredCalls.splice(F,1),--F)},canPerformEventHandlerRequests:function(){return Le.inEventHandler&&Le.currentEventHandler.allowsDeferredCalls},runDeferredCalls:function(){if(!!Le.canPerformEventHandlerRequests())for(var T=0;T<Le.deferredCalls.length;++T){var F=Le.deferredCalls[T];Le.deferredCalls.splice(T,1),--T,F.targetFunction.apply(null,F.argsList)}},eventHandlers:[],removeAllHandlersOnTarget:function(T,F){for(var B=0;B<Le.eventHandlers.length;++B)Le.eventHandlers[B].target==T&&(!F||F==Le.eventHandlers[B].eventTypeString)&&Le._removeHandler(B--)},_removeHandler:function(T){var F=Le.eventHandlers[T];F.target.removeEventListener(F.eventTypeString,F.eventListenerFunc,F.useCapture),Le.eventHandlers.splice(T,1)},registerOrRemoveHandler:function(T){var F=function(me){++Le.inEventHandler,Le.currentEventHandler=T,Le.runDeferredCalls(),T.handlerFunc(me),Le.runDeferredCalls(),--Le.inEventHandler};if(T.callbackfunc)T.eventListenerFunc=F,T.target.addEventListener(T.eventTypeString,F,T.useCapture),Le.eventHandlers.push(T),Le.registerRemoveEventListeners();else for(var B=0;B<Le.eventHandlers.length;++B)Le.eventHandlers[B].target==T.target&&Le.eventHandlers[B].eventTypeString==T.eventTypeString&&Le._removeHandler(B--)},queueEventHandlerOnThread_iiii:function(T,F,B,Z,me){var pe=bu(),fe=ri(12);i()[fe>>2]=B,i()[fe+4>>2]=Z,i()[fe+8>>2]=me,gm(0,T,637534208,F,Z,fe),si(pe)},getTargetThreadForEventCallback:function(T){switch(T){case 1:return 0;case 2:return Se.currentProxiedOperationCallerThread;default:return T}},getNodeNameForTarget:function(T){return T?T==window?"#window":T==screen?"#screen":T&&T.nodeName?T.nodeName:"":""},fullscreenEnabled:function(){return document.fullscreenEnabled||document.webkitFullscreenEnabled}};function C0(T){var F=tt(T)+1,B=pa(F);return et(T,B,F),B}function T0(T,F,B,Z){var me=bu(),pe=ri(12),fe=0;F&&(fe=C0(F)),i()[pe>>2]=fe,i()[pe+4>>2]=B,i()[pe+8>>2]=Z,gm(0,T,657457152,0,fe,pe),si(me)}function N0(T,F,B,Z){F=F?De(F):"",T0(T,F,B,Z)}function E0(T){return T>2?De(T):T}var R0=[0,typeof document!="undefined"?document:0,typeof window!="undefined"?window:0];function _0(T){T=E0(T);var F=R0[T]||(typeof document!="undefined"?document.querySelector(T):void 0);return F}function mu(T){return _0(T)}function yd(T,F,B){var Z=mu(T);if(!Z)return-4;if(Z.canvasSharedPtr&&(i()[Z.canvasSharedPtr>>2]=F,i()[Z.canvasSharedPtr+4>>2]=B),Z.offscreenCanvas||!Z.controlTransferredOffscreen){Z.offscreenCanvas&&(Z=Z.offscreenCanvas);var me=!1;if(Z.GLctxObject&&Z.GLctxObject.GLctx){var pe=Z.GLctxObject.GLctx.getParameter(2978);me=pe[0]===0&&pe[1]===0&&pe[2]===Z.width&&pe[3]===Z.height}Z.width=F,Z.height=B,me&&Z.GLctxObject.GLctx.viewport(0,0,F,B)}else if(Z.canvasSharedPtr){var fe=i()[Z.canvasSharedPtr+8>>2];return N0(fe,T,F,B),1}else return-4;return 0}function xd(T,F,B){return k?Nr(2,1,T,F,B):yd(T,F,B)}function $0(T,F,B){var Z=mu(T);return Z?yd(T,F,B):xd(T,F,B)}function F0(T){}function D0(T,F){}function O0(T){var F=T.getExtension("ANGLE_instanced_arrays");if(F)return T.vertexAttribDivisor=function(B,Z){F.vertexAttribDivisorANGLE(B,Z)},T.drawArraysInstanced=function(B,Z,me,pe){F.drawArraysInstancedANGLE(B,Z,me,pe)},T.drawElementsInstanced=function(B,Z,me,pe,fe){F.drawElementsInstancedANGLE(B,Z,me,pe,fe)},1}function P0(T){var F=T.getExtension("OES_vertex_array_object");if(F)return T.createVertexArray=function(){return F.createVertexArrayOES()},T.deleteVertexArray=function(B){F.deleteVertexArrayOES(B)},T.bindVertexArray=function(B){F.bindVertexArrayOES(B)},T.isVertexArray=function(B){return F.isVertexArrayOES(B)},1}function M0(T){var F=T.getExtension("WEBGL_draw_buffers");if(F)return T.drawBuffers=function(B,Z){F.drawBuffersWEBGL(B,Z)},1}function z0(T){return!!(T.multiDrawWebgl=T.getExtension("WEBGL_multi_draw"))}var Qe={counter:1,buffers:[],programs:[],framebuffers:[],renderbuffers:[],textures:[],uniforms:[],shaders:[],vaos:[],contexts:{},offscreenCanvases:{},timerQueriesEXT:[],programInfos:{},stringCache:{},unpackAlignment:4,recordError:function(F){Qe.lastError||(Qe.lastError=F)},getNewId:function(T){for(var F=Qe.counter++,B=T.length;B<F;B++)T[B]=null;return F},getSource:function(T,F,B,Z){for(var me="",pe=0;pe<F;++pe){var fe=Z?i()[Z+pe*4>>2]:-1;me+=De(i()[B+pe*4>>2],fe<0?void 0:fe)}return me},createContext:function(T,F){var B=T.getContext("webgl",F);if(!B)return 0;var Z=Qe.registerContext(B,F);return Z},registerContext:function(T,F){var B=pa(8);i()[B+4>>2]=ni();var Z={handle:B,attributes:F,version:F.majorVersion,GLctx:T};return T.canvas&&(T.canvas.GLctxObject=Z),Qe.contexts[B]=Z,(typeof F.enableExtensionsByDefault=="undefined"||F.enableExtensionsByDefault)&&Qe.initExtensions(Z),B},makeContextCurrent:function(T){return Qe.currentContext=Qe.contexts[T],c.ctx=Er=Qe.currentContext&&Qe.currentContext.GLctx,!(T&&!Er)},getContext:function(T){return Qe.contexts[T]},deleteContext:function(T){Qe.currentContext===Qe.contexts[T]&&(Qe.currentContext=null),typeof Le=="object"&&Le.removeAllHandlersOnTarget(Qe.contexts[T].GLctx.canvas),Qe.contexts[T]&&Qe.contexts[T].GLctx.canvas&&(Qe.contexts[T].GLctx.canvas.GLctxObject=void 0),xu(Qe.contexts[T].handle),Qe.contexts[T]=null},initExtensions:function(T){if(T||(T=Qe.currentContext),!T.initExtensionsDone){T.initExtensionsDone=!0;var F=T.GLctx;O0(F),P0(F),M0(F),F.disjointTimerQueryExt=F.getExtension("EXT_disjoint_timer_query"),z0(F);var B=F.getSupportedExtensions()||[];B.forEach(function(Z){Z.indexOf("lose_context")<0&&Z.indexOf("debug")<0&&F.getExtension(Z)})}},populateUniformTable:function(T){for(var F=Qe.programs[T],B=Qe.programInfos[T]={uniforms:{},maxUniformLength:0,maxAttributeLength:-1,maxUniformBlockNameLength:-1},Z=B.uniforms,me=Er.getProgramParameter(F,35718),pe=0;pe<me;++pe){var fe=Er.getActiveUniform(F,pe),we=fe.name;B.maxUniformLength=Math.max(B.maxUniformLength,we.length+1),we.slice(-1)=="]"&&(we=we.slice(0,we.lastIndexOf("[")));var nt=Er.getUniformLocation(F,we);if(nt){var jt=Qe.getNewId(Qe.uniforms);Z[we]=[fe.size,jt],Qe.uniforms[jt]=nt;for(var Lt=1;Lt<fe.size;++Lt){var _r=we+"["+Lt+"]";nt=Er.getUniformLocation(F,_r),jt=Qe.getNewId(Qe.uniforms),Qe.uniforms[jt]=nt}}}}},L0=["default","low-power","high-performance"];function B0(T,F){var B=F>>2,Z=i()[B+(24>>2)],me={alpha:!!i()[B+(0>>2)],depth:!!i()[B+(4>>2)],stencil:!!i()[B+(8>>2)],antialias:!!i()[B+(12>>2)],premultipliedAlpha:!!i()[B+(16>>2)],preserveDrawingBuffer:!!i()[B+(20>>2)],powerPreference:L0[Z],failIfMajorPerformanceCaveat:!!i()[B+(28>>2)],majorVersion:i()[B+(32>>2)],minorVersion:i()[B+(36>>2)],enableExtensionsByDefault:i()[B+(40>>2)],explicitSwapControl:i()[B+(44>>2)],proxyContextToMainThread:i()[B+(48>>2)],renderViaOffscreenBackBuffer:i()[B+(52>>2)]},pe=mu(T);if(!pe||me.explicitSwapControl)return 0;var fe=Qe.createContext(pe,me);return fe}function W0(T,F){return B0(T,F)}var ei={mappings:{},buffers:[null,[],[]],printChar:function(T,F){var B=ei.buffers[T];F===0||F===10?((T===1?j:q)(Me(B,0)),B.length=0):B.push(F)},varargs:void 0,get:function(){ei.varargs+=4;var T=i()[ei.varargs-4>>2];return T},getStr:function(T){var F=De(T);return F},get64:function(T,F){return T}};function bd(T){return k?Nr(3,1,T):0}function vd(T,F,B,Z,me){if(k)return Nr(4,1,T,F,B,Z,me)}function wd(T,F,B,Z){if(k)return Nr(5,1,T,F,B,Z);for(var me=0,pe=0;pe<B;pe++){for(var fe=i()[F+pe*8>>2],we=i()[F+(pe*8+4)>>2],nt=0;nt<we;nt++)ei.printChar(T,o()[fe+nt]);me+=we}return i()[Z>>2]=me,0}function V0(T){var F=Se.threadExitHandlers.pop();T&&F()}function U0(T,F){Se.threadExitHandlers.push(function(){es.get(T)(F)})}function kd(T){if(k)throw"Internal Error! spawnThread() can only ever be called from main application thread!";var F=Se.getNewWorker();if(F.pthread!==void 0)throw"Internal error!";if(!T.pthread_ptr)throw"Internal error, no pthread ptr!";Se.runningWorkers.push(F);for(var B=pa(128*4),Z=0;Z<128;++Z)i()[B+Z*4>>2]=0;var me=T.stackBase+T.stackSize,pe=Se.pthreads[T.pthread_ptr]={worker:F,stackBase:T.stackBase,stackSize:T.stackSize,allocatedOwnStack:T.allocatedOwnStack,threadInfoStruct:T.pthread_ptr},fe=pe.threadInfoStruct>>2;Atomics.store(l(),fe+(64>>2),T.detached),Atomics.store(l(),fe+(100>>2),B),Atomics.store(l(),fe+(40>>2),pe.threadInfoStruct),Atomics.store(l(),fe+(80>>2),T.stackSize),Atomics.store(l(),fe+(76>>2),me),Atomics.store(l(),fe+(104>>2),T.stackSize),Atomics.store(l(),fe+(104+8>>2),me),Atomics.store(l(),fe+(104+12>>2),T.detached);var we=zy(),nt=we+40;Atomics.store(l(),fe+(172>>2),nt),F.pthread=pe;var jt={cmd:"run",start_routine:T.startRoutine,arg:T.arg,threadInfoStruct:T.pthread_ptr,stackBase:T.stackBase,stackSize:T.stackSize};F.runPthread=function(){jt.time=performance.now(),F.postMessage(jt,T.transferList)},F.loaded&&(F.runPthread(),delete F.runPthread)}function H0(T,F,B,Z){if(typeof SharedArrayBuffer=="undefined")return q("Current environment does not support SharedArrayBuffer, pthreads are not available!"),6;if(!T)return q("pthread_create called with a null thread pointer!"),28;var me=[],pe=0;if(k&&(me.length===0||pe))return Vy(687865856,T,F,B,Z);if(pe)return pe;var fe=0,we=0,nt=0;F&&F!=-1?(fe=i()[F>>2],fe+=81920,we=i()[F+8>>2],nt=i()[F+12>>2]!==0):fe=2097152;var jt=we==0;jt?we=Gy(16,fe):(we-=fe,ge(we>0));for(var Lt=pa(228),_r=0;_r<228>>2;++_r)l()[(Lt>>2)+_r]=0;i()[T>>2]=Lt,i()[Lt+12>>2]=Lt;var oi=Lt+152;i()[oi>>2]=oi;var dn={stackBase:we,stackSize:fe,allocatedOwnStack:jt,detached:nt,startRoutine:B,pthread_ptr:Lt,arg:Z,transferList:me};return k?(dn.cmd="spawnThread",postMessage(dn,me)):kd(dn),0}function Id(T){if(k)return Nr(6,1,T);switch(T){case 30:return 16384;case 85:var F=2147483648;return F/16384;case 132:case 133:case 12:case 137:case 138:case 15:case 235:case 16:case 17:case 18:case 19:case 20:case 149:case 13:case 10:case 236:case 153:case 9:case 21:case 22:case 159:case 154:case 14:case 77:case 78:case 139:case 82:case 68:case 67:case 164:case 11:case 29:case 47:case 48:case 95:case 52:case 51:case 46:return 200809;case 27:case 246:case 127:case 128:case 23:case 24:case 160:case 161:case 181:case 182:case 242:case 183:case 184:case 243:case 244:case 245:case 165:case 178:case 179:case 49:case 50:case 168:case 169:case 175:case 170:case 171:case 172:case 97:case 76:case 32:case 173:case 35:case 80:case 81:case 79:return-1;case 176:case 177:case 7:case 155:case 8:case 157:case 125:case 126:case 92:case 93:case 129:case 130:case 131:case 94:case 91:return 1;case 74:case 60:case 69:case 70:case 4:return 1024;case 31:case 42:case 72:return 32;case 87:case 26:case 33:return 2147483647;case 34:case 1:return 47839;case 38:case 36:return 99;case 43:case 37:return 2048;case 0:return 2097152;case 3:return 65536;case 28:return 32768;case 44:return 32767;case 75:return 16384;case 39:return 1e3;case 89:return 700;case 71:return 256;case 40:return 255;case 2:return 100;case 180:return 64;case 25:return 20;case 5:return 16;case 6:return 6;case 73:return 4;case 84:return typeof navigator=="object"&&navigator.hardwareConcurrency||1}return h0(28),-1}k||Se.initMainThreadBlock();var Er,G0=[null,p0,xd,bd,vd,wd,Id],j0={e:c0,r:d0,x:f0,b:m0,y:A0,j:g0,c:y0,d:hu,f:ha,p:x0,z:b0,u:w0,q:S0,v:$0,i:F0,t:D0,w:W0,m:bd,n:vd,g:wd,o:gd,a:J||c.wasmMemory,k:V0,l:U0,h:H0,s:Id},Py=n0(),Sd=c.___wasm_call_ctors=function(){return(Sd=c.___wasm_call_ctors=c.asm.A).apply(null,arguments)},q0=c._init=function(){return(q0=c._init=c.asm.B).apply(null,arguments)},X0=c._register_tensor=function(){return(X0=c._register_tensor=c.asm.C).apply(null,arguments)},K0=c._dispose_data=function(){return(K0=c._dispose_data=c.asm.D).apply(null,arguments)},Z0=c._dispose=function(){return(Z0=c._dispose=c.asm.E).apply(null,arguments)},Y0=c._Abs=function(){return(Y0=c._Abs=c.asm.G).apply(null,arguments)},J0=c._Add=function(){return(J0=c._Add=c.asm.H).apply(null,arguments)},Q0=c._AddN=function(){return(Q0=c._AddN=c.asm.I).apply(null,arguments)},em=c._All=function(){return(em=c._All=c.asm.J).apply(null,arguments)},tm=c._Any=function(){return(tm=c._Any=c.asm.K).apply(null,arguments)},nm=c._ArgMax=function(){return(nm=c._ArgMax=c.asm.L).apply(null,arguments)},sm=c._AvgPool=function(){return(sm=c._AvgPool=c.asm.M).apply(null,arguments)},rm=c._BatchMatMul=function(){return(rm=c._BatchMatMul=c.asm.N).apply(null,arguments)},am=c._Ceil=function(){return(am=c._Ceil=c.asm.O).apply(null,arguments)},om=c._ClipByValue=function(){return(om=c._ClipByValue=c.asm.P).apply(null,arguments)},im=c._Conv2D=function(){return(im=c._Conv2D=c.asm.Q).apply(null,arguments)},lm=c._Conv2DBackpropInput=function(){return(lm=c._Conv2DBackpropInput=c.asm.R).apply(null,arguments)},um=c._Cos=function(){return(um=c._Cos=c.asm.S).apply(null,arguments)},cm=c._Cosh=function(){return(cm=c._Cosh=c.asm.T).apply(null,arguments)},dm=c._CropAndResize=function(){return(dm=c._CropAndResize=c.asm.U).apply(null,arguments)},hm=c._Cumsum=function(){return(hm=c._Cumsum=c.asm.V).apply(null,arguments)},pm=c._DepthToSpace=function(){return(pm=c._DepthToSpace=c.asm.W).apply(null,arguments)},fm=c._DepthwiseConv2dNative=function(){return(fm=c._DepthwiseConv2dNative=c.asm.X).apply(null,arguments)},Cd=c._Equal=function(){return(Cd=c._Equal=c.asm.Y).apply(null,arguments)},Td=c._Exp=function(){return(Td=c._Exp=c.asm.Z).apply(null,arguments)},Nd=c._FlipLeftRight=function(){return(Nd=c._FlipLeftRight=c.asm._).apply(null,arguments)},Au=c._Floor=function(){return(Au=c._Floor=c.asm.$).apply(null,arguments)},ti=c._FloorDiv=function(){return(ti=c._FloorDiv=c.asm.aa).apply(null,arguments)},mm=c._FusedBatchNorm=function(){return(mm=c._FusedBatchNorm=c.asm.ba).apply(null,arguments)},gu=c._FusedConv2D=function(){return(gu=c._FusedConv2D=c.asm.ca).apply(null,arguments)},Y=c._FusedDepthwiseConv2D=function(){return(Y=c._FusedDepthwiseConv2D=c.asm.da).apply(null,arguments)},re=c._Gather=function(){return(re=c._Gather=c.asm.ea).apply(null,arguments)},xe=c._GatherNd=function(){return(xe=c._GatherNd=c.asm.fa).apply(null,arguments)},Ye=c._Greater=function(){return(Ye=c._Greater=c.asm.ga).apply(null,arguments)},Tt=c._GreaterEqual=function(){return(Tt=c._GreaterEqual=c.asm.ha).apply(null,arguments)},yt=c._LeakyRelu=function(){return(yt=c._LeakyRelu=c.asm.ia).apply(null,arguments)},He=c._Less=function(){return(He=c._Less=c.asm.ja).apply(null,arguments)},Ge=c._LessEqual=function(){return(Ge=c._LessEqual=c.asm.ka).apply(null,arguments)},tn=c._Log=function(){return(tn=c._Log=c.asm.la).apply(null,arguments)},hr=c._LogicalAnd=function(){return(hr=c._LogicalAnd=c.asm.ma).apply(null,arguments)},pr=c._Max=function(){return(pr=c._Max=c.asm.na).apply(null,arguments)},Ed=c._MaxPool=function(){return(Ed=c._MaxPool=c.asm.oa).apply(null,arguments)},yu=c._Maximum=function(){return(yu=c._Maximum=c.asm.pa).apply(null,arguments)},Ln=c._Mean=function(){return(Ln=c._Mean=c.asm.qa).apply(null,arguments)},Rr=c._Min=function(){return(Rr=c._Min=c.asm.ra).apply(null,arguments)},Rd=c._Minimum=function(){return(Rd=c._Minimum=c.asm.sa).apply(null,arguments)},I8=c._MirrorPad=function(){return(I8=c._MirrorPad=c.asm.ta).apply(null,arguments)},S8=c._Multiply=function(){return(S8=c._Multiply=c.asm.ua).apply(null,arguments)},C8=c._Neg=function(){return(C8=c._Neg=c.asm.va).apply(null,arguments)},T8=c._NonMaxSuppressionV3=function(){return(T8=c._NonMaxSuppressionV3=c.asm.wa).apply(null,arguments)},N8=c._NonMaxSuppressionV4=function(){return(N8=c._NonMaxSuppressionV4=c.asm.xa).apply(null,arguments)},E8=c._NonMaxSuppressionV5=function(){return(E8=c._NonMaxSuppressionV5=c.asm.ya).apply(null,arguments)},R8=c._NotEqual=function(){return(R8=c._NotEqual=c.asm.za).apply(null,arguments)},_8=c._OneHot=function(){return(_8=c._OneHot=c.asm.Aa).apply(null,arguments)},$8=c._PadV2=function(){return($8=c._PadV2=c.asm.Ba).apply(null,arguments)},F8=c._Pow=function(){return(F8=c._Pow=c.asm.Ca).apply(null,arguments)},D8=c._Prelu=function(){return(D8=c._Prelu=c.asm.Da).apply(null,arguments)},O8=c._Prod=function(){return(O8=c._Prod=c.asm.Ea).apply(null,arguments)},P8=c._RealDiv=function(){return(P8=c._RealDiv=c.asm.Fa).apply(null,arguments)},M8=c._Relu=function(){return(M8=c._Relu=c.asm.Ga).apply(null,arguments)},z8=c._Relu6=function(){return(z8=c._Relu6=c.asm.Ha).apply(null,arguments)},L8=c._ResizeBilinear=function(){return(L8=c._ResizeBilinear=c.asm.Ia).apply(null,arguments)},B8=c._Reverse=function(){return(B8=c._Reverse=c.asm.Ja).apply(null,arguments)},W8=c._RotateWithOffset=function(){return(W8=c._RotateWithOffset=c.asm.Ka).apply(null,arguments)},V8=c._Round=function(){return(V8=c._Round=c.asm.La).apply(null,arguments)},U8=c._Rsqrt=function(){return(U8=c._Rsqrt=c.asm.Ma).apply(null,arguments)},H8=c._ScatterNd=function(){return(H8=c._ScatterNd=c.asm.Na).apply(null,arguments)},G8=c._SelectV2=function(){return(G8=c._SelectV2=c.asm.Oa).apply(null,arguments)},j8=c._Sigmoid=function(){return(j8=c._Sigmoid=c.asm.Pa).apply(null,arguments)},q8=c._Sin=function(){return(q8=c._Sin=c.asm.Qa).apply(null,arguments)},X8=c._Softmax=function(){return(X8=c._Softmax=c.asm.Ra).apply(null,arguments)},K8=c._Sqrt=function(){return(K8=c._Sqrt=c.asm.Sa).apply(null,arguments)},Z8=c._Square=function(){return(Z8=c._Square=c.asm.Ta).apply(null,arguments)},Y8=c._SquaredDifference=function(){return(Y8=c._SquaredDifference=c.asm.Ua).apply(null,arguments)},J8=c._Step=function(){return(J8=c._Step=c.asm.Va).apply(null,arguments)},Q8=c._StridedSlice=function(){return(Q8=c._StridedSlice=c.asm.Wa).apply(null,arguments)},eI=c._Sub=function(){return(eI=c._Sub=c.asm.Xa).apply(null,arguments)},tI=c._Sum=function(){return(tI=c._Sum=c.asm.Ya).apply(null,arguments)},nI=c._Tan=function(){return(nI=c._Tan=c.asm.Za).apply(null,arguments)},sI=c._Tanh=function(){return(sI=c._Tanh=c.asm._a).apply(null,arguments)},rI=c._Tile=function(){return(rI=c._Tile=c.asm.$a).apply(null,arguments)},aI=c._TopK=function(){return(aI=c._TopK=c.asm.ab).apply(null,arguments)},oI=c._Transform=function(){return(oI=c._Transform=c.asm.bb).apply(null,arguments)},iI=c._Transpose=function(){return(iI=c._Transpose=c.asm.cb).apply(null,arguments)},lI=c.__FusedMatMul=function(){return(lI=c.__FusedMatMul=c.asm.db).apply(null,arguments)},pa=c._malloc=function(){return(pa=c._malloc=c.asm.eb).apply(null,arguments)},xu=c._free=function(){return(xu=c._free=c.asm.fb).apply(null,arguments)},My=c.___errno_location=function(){return(My=c.___errno_location=c.asm.gb).apply(null,arguments)},zy=c._emscripten_get_global_libc=function(){return(zy=c._emscripten_get_global_libc=c.asm.hb).apply(null,arguments)},ni=c._pthread_self=function(){return(ni=c._pthread_self=c.asm.ib).apply(null,arguments)},Ly=c.___pthread_tsd_run_dtors=function(){return(Ly=c.___pthread_tsd_run_dtors=c.asm.jb).apply(null,arguments)},Am=c._emscripten_main_thread_process_queued_calls=function(){return(Am=c._emscripten_main_thread_process_queued_calls=c.asm.kb).apply(null,arguments)},uI=c._emscripten_current_thread_process_queued_calls=function(){return(uI=c._emscripten_current_thread_process_queued_calls=c.asm.lb).apply(null,arguments)},By=c._emscripten_register_main_browser_thread_id=function(){return(By=c._emscripten_register_main_browser_thread_id=c.asm.mb).apply(null,arguments)},Wy=c.__emscripten_do_dispatch_to_thread=function(){return(Wy=c.__emscripten_do_dispatch_to_thread=c.asm.nb).apply(null,arguments)},Vy=c._emscripten_sync_run_in_main_thread_4=function(){return(Vy=c._emscripten_sync_run_in_main_thread_4=c.asm.ob).apply(null,arguments)},Uy=c._emscripten_run_in_main_runtime_thread_js=function(){return(Uy=c._emscripten_run_in_main_runtime_thread_js=c.asm.pb).apply(null,arguments)},gm=c.__emscripten_call_on_thread=function(){return(gm=c.__emscripten_call_on_thread=c.asm.qb).apply(null,arguments)},cI=c._emscripten_tls_init=function(){return(cI=c._emscripten_tls_init=c.asm.rb).apply(null,arguments)},ym=c.__emscripten_thread_init=function(){return(ym=c.__emscripten_thread_init=c.asm.sb).apply(null,arguments)},bu=c.stackSave=function(){return(bu=c.stackSave=c.asm.tb).apply(null,arguments)},si=c.stackRestore=function(){return(si=c.stackRestore=c.asm.ub).apply(null,arguments)},ri=c.stackAlloc=function(){return(ri=c.stackAlloc=c.asm.vb).apply(null,arguments)},Hy=c._emscripten_stack_set_limits=function(){return(Hy=c._emscripten_stack_set_limits=c.asm.wb).apply(null,arguments)},Gy=c._memalign=function(){return(Gy=c._memalign=c.asm.xb).apply(null,arguments)},jy=c.__emscripten_allow_main_runtime_queued_calls=9824,ai=c.__emscripten_main_thread_futex=11448;c.cwrap=$e,c.PThread=Se,c.PThread=Se,c.wasmMemory=J,c.ExitStatus=vu;var _d;function vu(T){this.name="ExitStatus",this.message="Program terminated with exit("+T+")",this.status=T}da=function T(){_d||xm(),_d||(da=T)};function xm(T){if(T=T||m,cr>0)return;if(k){d(c),du(),postMessage({cmd:"loaded"});return}if(Kf(),cr>0)return;function F(){_d||(_d=!0,c.calledRun=!0,!le&&(du(),Zf(),d(c),c.onRuntimeInitialized&&c.onRuntimeInitialized(),Sn()))}c.setStatus?(c.setStatus("Running..."),setTimeout(function(){setTimeout(function(){c.setStatus("")},1),F()},1)):F()}c.run=xm;function dI(T,F){if(!(F&&se&&T===0)){if(!F&&k)throw postMessage({cmd:"exitProcess",returnCode:T}),new vu(T);se||(Se.terminateAllThreads(),he=T,dd(),c.onExit&&c.onExit(T),le=!0),g(T,new vu(T))}}if(c.preInit)for(typeof c.preInit=="function"&&(c.preInit=[c.preInit]);c.preInit.length>0;)c.preInit.pop()();return k&&(se=!1,Se.initWorker()),xm(),r.ready}}();typeof e=="object"&&typeof t=="object"?t.exports=n:typeof define=="function"&&define.amd?define([],function(){return n}):typeof e=="object"&&(e.WasmBackendModuleThreadedSimd=n)}}),LI=xt({"node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.8.0_@tensorflow+tfjs-core@3.8.0/node_modules/@tensorflow/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm.js"(e,t){var n=function(){var s=typeof document!="undefined"&&document.currentScript?document.currentScript.src:void 0;return typeof __filename!="undefined"&&(s=s||__filename),function(r){r=r||{};var a=typeof r!="undefined"?r:{},o,i;a.ready=new Promise(function(Y,re){o=Y,i=re});var l={},u;for(u in a)a.hasOwnProperty(u)&&(l[u]=a[u]);var c=[],d="./this.program",h=function(Y,re){throw re},p=!1,f=!1,m=!1,A=!1;p=typeof window=="object",f=typeof importScripts=="function",m=typeof process=="object"&&typeof process.versions=="object"&&typeof process.versions.node=="string",A=!p&&!m&&!f;var g="";function y(Y){return a.locateFile?a.locateFile(Y,g):g+Y}var x,b,v,k,w,C;m?(f?g=wu().dirname(g)+"/":g=__dirname+"/",x=function(re,xe){return w||(w=li("fs")),C||(C=wu()),re=C.normalize(re),w.readFileSync(re,xe?null:"utf8")},v=function(re){var xe=x(re,!0);return xe.buffer||(xe=new Uint8Array(xe)),j(xe.buffer),xe},process.argv.length>1&&(d=process.argv[1].replace(/\\/g,"/")),c=process.argv.slice(2),process.on("uncaughtException",function(Y){if(!(Y instanceof mm))throw Y}),process.on("unhandledRejection",Gs),h=function(Y){process.exit(Y)},a.inspect=function(){return"[Emscripten Module object]"}):A?(typeof read!="undefined"&&(x=function(re){return read(re)}),v=function(re){var xe;return typeof readbuffer=="function"?new Uint8Array(readbuffer(re)):(xe=read(re,"binary"),j(typeof xe=="object"),xe)},typeof scriptArgs!="undefined"?c=scriptArgs:typeof arguments!="undefined"&&(c=arguments),typeof quit=="function"&&(h=function(Y){quit(Y)}),typeof print!="undefined"&&(typeof console=="undefined"&&(console={}),console.log=print,console.warn=console.error=typeof printErr!="undefined"?printErr:print)):(p||f)&&(f?g=self.location.href:typeof document!="undefined"&&document.currentScript&&(g=document.currentScript.src),s&&(g=s),g.indexOf("blob:")!==0?g=g.substr(0,g.lastIndexOf("/")+1):g="",x=function(Y){var re=new XMLHttpRequest;return re.open("GET",Y,!1),re.send(null),re.responseText},f&&(v=function(Y){var re=new XMLHttpRequest;return re.open("GET",Y,!1),re.responseType="arraybuffer",re.send(null),new Uint8Array(re.response)}),b=function(Y,re,xe){var Ye=new XMLHttpRequest;Ye.open("GET",Y,!0),Ye.responseType="arraybuffer",Ye.onload=function(){if(Ye.status==200||Ye.status==0&&Ye.response){re(Ye.response);return}xe()},Ye.onerror=xe,Ye.send(null)},k=function(Y){document.title=Y});var E=a.print||console.log.bind(console),M=a.printErr||console.warn.bind(console);for(u in l)l.hasOwnProperty(u)&&(a[u]=l[u]);l=null,a.arguments&&(c=a.arguments),a.thisProgram&&(d=a.thisProgram),a.quit&&(h=a.quit);var R;a.wasmBinary&&(R=a.wasmBinary);var _=a.noExitRuntime||!0;typeof WebAssembly!="object"&&Gs("no native wasm support detected");var N,O=!1,W;function j(Y,re){Y||Gs("Assertion failed: "+re)}function q(Y){var re=a["_"+Y];return j(re,"Cannot call unknown function "+Y+", make sure it is exported"),re}function X(Y,re,xe,Ye,Tt){var yt={string:function(Ln){var Rr=0;if(Ln!=null&&Ln!==0){var Rd=(Ln.length<<2)+1;Rr=Au(Rd),ie(Ln,Rr,Rd)}return Rr},array:function(Ln){var Rr=Au(Ln.length);return le(Ln,Rr),Rr}};function He(Ln){return re==="string"?se(Ln):re==="boolean"?Boolean(Ln):Ln}var Ge=q(Y),tn=[],hr=0;if(Ye)for(var pr=0;pr<Ye.length;pr++){var Ed=yt[xe[pr]];Ed?(hr===0&&(hr=Td()),tn[pr]=Ed(Ye[pr])):tn[pr]=Ye[pr]}var yu=Ge.apply(null,tn);return yu=He(yu),hr!==0&&Nd(hr),yu}function Q(Y,re,xe,Ye){xe=xe||[];var Tt=xe.every(function(He){return He==="number"}),yt=re!=="string";return yt&&Tt&&!Ye?q(Y):function(){return X(Y,re,xe,arguments,Ye)}}var ne=typeof TextDecoder!="undefined"?new TextDecoder("utf8"):void 0;function te(Y,re,xe){for(var Ye=re+xe,Tt=re;Y[Tt]&&!(Tt>=Ye);)++Tt;if(Tt-re>16&&Y.subarray&&ne)return ne.decode(Y.subarray(re,Tt));for(var yt="";re<Tt;){var He=Y[re++];if(!(He&128)){yt+=String.fromCharCode(He);continue}var Ge=Y[re++]&63;if((He&224)==192){yt+=String.fromCharCode((He&31)<<6|Ge);continue}var tn=Y[re++]&63;if((He&240)==224?He=(He&15)<<12|Ge<<6|tn:He=(He&7)<<18|Ge<<12|tn<<6|Y[re++]&63,He<65536)yt+=String.fromCharCode(He);else{var hr=He-65536;yt+=String.fromCharCode(55296|hr>>10,56320|hr&1023)}}return yt}function se(Y,re){return Y?te(Te,Y,re):""}function J(Y,re,xe,Ye){if(!(Ye>0))return 0;for(var Tt=xe,yt=xe+Ye-1,He=0;He<Y.length;++He){var Ge=Y.charCodeAt(He);if(Ge>=55296&&Ge<=57343){var tn=Y.charCodeAt(++He);Ge=65536+((Ge&1023)<<10)|tn&1023}if(Ge<=127){if(xe>=yt)break;re[xe++]=Ge}else if(Ge<=2047){if(xe+1>=yt)break;re[xe++]=192|Ge>>6,re[xe++]=128|Ge&63}else if(Ge<=65535){if(xe+2>=yt)break;re[xe++]=224|Ge>>12,re[xe++]=128|Ge>>6&63,re[xe++]=128|Ge&63}else{if(xe+3>=yt)break;re[xe++]=240|Ge>>18,re[xe++]=128|Ge>>12&63,re[xe++]=128|Ge>>6&63,re[xe++]=128|Ge&63}}return re[xe]=0,xe-Tt}function ie(Y,re,xe){return J(Y,Te,re,xe)}function le(Y,re){Ce.set(Y,re)}function he(Y,re){return Y%re>0&&(Y+=re-Y%re),Y}var ge,Ce,Te,$e,Me,De,it,et,tt;function Je(Y){ge=Y,a.HEAP8=Ce=new Int8Array(Y),a.HEAP16=$e=new Int16Array(Y),a.HEAP32=De=new Int32Array(Y),a.HEAPU8=Te=new Uint8Array(Y),a.HEAPU16=Me=new Uint16Array(Y),a.HEAPU32=it=new Uint32Array(Y),a.HEAPF32=et=new Float32Array(Y),a.HEAPF64=tt=new Float64Array(Y)}var at=a.INITIAL_MEMORY||16777216,Ve,In=[],kt=[],Mn=[],Qt=[],gs=!1;kt.push({func:function(){gd()}});function cn(){if(a.preRun)for(typeof a.preRun=="function"&&(a.preRun=[a.preRun]);a.preRun.length;)Qn(a.preRun.shift());Tr(In)}function Yn(){gs=!0,Tr(kt)}function Jn(){Tr(Mn)}function en(){if(a.postRun)for(typeof a.postRun=="function"&&(a.postRun=[a.postRun]);a.postRun.length;)es(a.postRun.shift());Tr(Qt)}function Qn(Y){In.unshift(Y)}function es(Y){Qt.unshift(Y)}var zn=0,Hs=null,ur=null;function Cr(Y){zn++,a.monitorRunDependencies&&a.monitorRunDependencies(zn)}function Yo(Y){if(zn--,a.monitorRunDependencies&&a.monitorRunDependencies(zn),zn==0&&(Hs!==null&&(clearInterval(Hs),Hs=null),ur)){var re=ur;ur=null,re()}}a.preloadedImages={},a.preloadedAudios={};function Gs(Y){a.onAbort&&a.onAbort(Y),Y+="",M(Y),O=!0,W=1,Y="abort("+Y+"). Build with -s ASSERTIONS=1 for more info.";var re=new WebAssembly.RuntimeError(Y);throw i(re),re}function cd(Y,re){return String.prototype.startsWith?Y.startsWith(re):Y.indexOf(re)===0}var Kf="data:application/octet-stream;base64,";function du(Y){return cd(Y,Kf)}var Zf="file://";function dd(Y){return cd(Y,Zf)}var Sn="tfjs-backend-wasm.wasm";du(Sn)||(Sn=y(Sn));function hd(Y){try{if(Y==Sn&&R)return new Uint8Array(R);if(v)return v(Y);throw"both async and sync fetching of the wasm failed"}catch(re){Gs(re)}}function Yf(){if(!R&&(p||f)){if(typeof fetch=="function"&&!dd(Sn))return fetch(Sn,{credentials:"same-origin"}).then(function(Y){if(!Y.ok)throw"failed to load wasm binary file at '"+Sn+"'";return Y.arrayBuffer()}).catch(function(){return hd(Sn)});if(b)return new Promise(function(Y,re){b(Sn,function(xe){Y(new Uint8Array(xe))},re)})}return Promise.resolve().then(function(){return hd(Sn)})}function cr(){var Y={a:n0};function re(He,Ge){var tn=He.exports;a.asm=tn,N=a.asm.i,Je(N.buffer),Ve=a.asm.o,Yo("wasm-instantiate")}Cr("wasm-instantiate");function xe(He){re(He.instance)}function Ye(He){return Yf().then(function(Ge){return WebAssembly.instantiate(Ge,Y)}).then(He,function(Ge){M("failed to asynchronously prepare wasm: "+Ge),Gs(Ge)})}function Tt(){return!R&&typeof WebAssembly.instantiateStreaming=="function"&&!du(Sn)&&!dd(Sn)&&typeof fetch=="function"?fetch(Sn,{credentials:"same-origin"}).then(function(He){var Ge=WebAssembly.instantiateStreaming(He,Y);return Ge.then(xe,function(tn){return M("wasm streaming compile failed: "+tn),M("falling back to ArrayBuffer instantiation"),Ye(xe)})}):Ye(xe)}if(a.instantiateWasm)try{var yt=a.instantiateWasm(Y,re);return yt}catch(He){return M("Module.instantiateWasm callback failed with error: "+He),!1}return Tt().catch(i),{}}function Tr(Y){for(;Y.length>0;){var re=Y.shift();if(typeof re=="function"){re(a);continue}var xe=re.func;typeof xe=="number"?re.arg===void 0?Ve.get(xe)():Ve.get(xe)(re.arg):xe(re.arg===void 0?null:re.arg)}}function da(){Gs()}function Jf(Y,re,xe){Te.copyWithin(Y,re,re+xe)}function Qf(){return Te.length}function dr(Y){try{return N.grow(Y-ge.byteLength+65535>>>16),Je(N.buffer),1}catch(re){}}function pd(Y){var re=Qf(),xe=2147483648;if(Y>xe)return!1;for(var Ye=1;Ye<=4;Ye*=2){var Tt=re*(1+.2/Ye);Tt=Math.min(Tt,Y+100663296);var yt=Math.min(xe,he(Math.max(Y,Tt),65536)),He=dr(yt);if(He)return!0}return!1}var Jo={mappings:{},buffers:[null,[],[]],printChar:function(Y,re){var xe=Jo.buffers[Y];re===0||re===10?((Y===1?E:M)(te(xe,0)),xe.length=0):xe.push(re)},varargs:void 0,get:function(){Jo.varargs+=4;var Y=De[Jo.varargs-4>>2];return Y},getStr:function(Y){var re=se(Y);return re},get64:function(Y,re){return Y}};function fd(Y){return 0}function e0(Y,re,xe,Ye,Tt){}function md(Y,re,xe,Ye){for(var Tt=0,yt=0;yt<xe;yt++){for(var He=De[re+yt*8>>2],Ge=De[re+(yt*8+4)>>2],tn=0;tn<Ge;tn++)Jo.printChar(Y,Te[He+tn]);Tt+=Ge}return De[Ye>>2]=Tt,0}function Cn(){return 6}function Ad(Y){return De[Cd()>>2]=Y,Y}function t0(Y){switch(Y){case 30:return 16384;case 85:var re=2147483648;return re/16384;case 132:case 133:case 12:case 137:case 138:case 15:case 235:case 16:case 17:case 18:case 19:case 20:case 149:case 13:case 10:case 236:case 153:case 9:case 21:case 22:case 159:case 154:case 14:case 77:case 78:case 139:case 82:case 68:case 67:case 164:case 11:case 29:case 47:case 48:case 95:case 52:case 51:case 46:return 200809;case 27:case 246:case 127:case 128:case 23:case 24:case 160:case 161:case 181:case 182:case 242:case 183:case 184:case 243:case 244:case 245:case 165:case 178:case 179:case 49:case 50:case 168:case 169:case 175:case 170:case 171:case 172:case 97:case 76:case 32:case 173:case 35:case 80:case 81:case 79:return-1;case 176:case 177:case 7:case 155:case 8:case 157:case 125:case 126:case 92:case 93:case 129:case 130:case 131:case 94:case 91:return 1;case 74:case 60:case 69:case 70:case 4:return 1024;case 31:case 42:case 72:return 32;case 87:case 26:case 33:return 2147483647;case 34:case 1:return 47839;case 38:case 36:return 99;case 43:case 37:return 2048;case 0:return 2097152;case 3:return 65536;case 28:return 32768;case 44:return 32767;case 75:return 16384;case 39:return 1e3;case 89:return 700;case 71:return 256;case 40:return 255;case 2:return 100;case 180:return 64;case 25:return 20;case 5:return 16;case 6:return 6;case 73:return 4;case 84:return typeof navigator=="object"&&navigator.hardwareConcurrency||1}return Ad(28),-1}var n0={a:da,d:Jf,e:pd,f:fd,c:e0,b:md,g:Cn,h:t0},s0=cr(),gd=a.___wasm_call_ctors=function(){return(gd=a.___wasm_call_ctors=a.asm.j).apply(null,arguments)},Qo=a._init=function(){return(Qo=a._init=a.asm.k).apply(null,arguments)},hu=a._register_tensor=function(){return(hu=a._register_tensor=a.asm.l).apply(null,arguments)},r0=a._dispose_data=function(){return(r0=a._dispose_data=a.asm.m).apply(null,arguments)},a0=a._dispose=function(){return(a0=a._dispose=a.asm.n).apply(null,arguments)},o0=a._Abs=function(){return(o0=a._Abs=a.asm.p).apply(null,arguments)},Se=a._Add=function(){return(Se=a._Add=a.asm.q).apply(null,arguments)},i0=a._AddN=function(){return(i0=a._AddN=a.asm.r).apply(null,arguments)},l0=a._All=function(){return(l0=a._All=a.asm.s).apply(null,arguments)},u0=a._Any=function(){return(u0=a._Any=a.asm.t).apply(null,arguments)},c0=a._ArgMax=function(){return(c0=a._ArgMax=a.asm.u).apply(null,arguments)},d0=a._AvgPool=function(){return(d0=a._AvgPool=a.asm.v).apply(null,arguments)},ha=a._BatchMatMul=function(){return(ha=a._BatchMatMul=a.asm.w).apply(null,arguments)},h0=a._Ceil=function(){return(h0=a._Ceil=a.asm.x).apply(null,arguments)},p0=a._ClipByValue=function(){return(p0=a._ClipByValue=a.asm.y).apply(null,arguments)},f0=a._Conv2D=function(){return(f0=a._Conv2D=a.asm.z).apply(null,arguments)},m0=a._Conv2DBackpropInput=function(){return(m0=a._Conv2DBackpropInput=a.asm.A).apply(null,arguments)},A0=a._Cos=function(){return(A0=a._Cos=a.asm.B).apply(null,arguments)},g0=a._Cosh=function(){return(g0=a._Cosh=a.asm.C).apply(null,arguments)},y0=a._CropAndResize=function(){return(y0=a._CropAndResize=a.asm.D).apply(null,arguments)},x0=a._Cumsum=function(){return(x0=a._Cumsum=a.asm.E).apply(null,arguments)},b0=a._DepthToSpace=function(){return(b0=a._DepthToSpace=a.asm.F).apply(null,arguments)},Nr=a._DepthwiseConv2dNative=function(){return(Nr=a._DepthwiseConv2dNative=a.asm.G).apply(null,arguments)},pu=a._Equal=function(){return(pu=a._Equal=a.asm.H).apply(null,arguments)},fu=a._Exp=function(){return(fu=a._Exp=a.asm.I).apply(null,arguments)},v0=a._FlipLeftRight=function(){return(v0=a._FlipLeftRight=a.asm.J).apply(null,arguments)},w0=a._Floor=function(){return(w0=a._Floor=a.asm.K).apply(null,arguments)},k0=a._FloorDiv=function(){return(k0=a._FloorDiv=a.asm.L).apply(null,arguments)},I0=a._FusedBatchNorm=function(){return(I0=a._FusedBatchNorm=a.asm.M).apply(null,arguments)},S0=a._FusedConv2D=function(){return(S0=a._FusedConv2D=a.asm.N).apply(null,arguments)},Le=a._FusedDepthwiseConv2D=function(){return(Le=a._FusedDepthwiseConv2D=a.asm.O).apply(null,arguments)},C0=a._Gather=function(){return(C0=a._Gather=a.asm.P).apply(null,arguments)},T0=a._GatherNd=function(){return(T0=a._GatherNd=a.asm.Q).apply(null,arguments)},N0=a._Greater=function(){return(N0=a._Greater=a.asm.R).apply(null,arguments)},E0=a._GreaterEqual=function(){return(E0=a._GreaterEqual=a.asm.S).apply(null,arguments)},R0=a._LeakyRelu=function(){return(R0=a._LeakyRelu=a.asm.T).apply(null,arguments)},_0=a._Less=function(){return(_0=a._Less=a.asm.U).apply(null,arguments)},mu=a._LessEqual=function(){return(mu=a._LessEqual=a.asm.V).apply(null,arguments)},yd=a._Log=function(){return(yd=a._Log=a.asm.W).apply(null,arguments)},xd=a._LogicalAnd=function(){return(xd=a._LogicalAnd=a.asm.X).apply(null,arguments)},$0=a._Max=function(){return($0=a._Max=a.asm.Y).apply(null,arguments)},F0=a._MaxPool=function(){return(F0=a._MaxPool=a.asm.Z).apply(null,arguments)},D0=a._Maximum=function(){return(D0=a._Maximum=a.asm._).apply(null,arguments)},O0=a._Mean=function(){return(O0=a._Mean=a.asm.$).apply(null,arguments)},P0=a._Min=function(){return(P0=a._Min=a.asm.aa).apply(null,arguments)},M0=a._Minimum=function(){return(M0=a._Minimum=a.asm.ba).apply(null,arguments)},z0=a._MirrorPad=function(){return(z0=a._MirrorPad=a.asm.ca).apply(null,arguments)},Qe=a._Multiply=function(){return(Qe=a._Multiply=a.asm.da).apply(null,arguments)},L0=a._Neg=function(){return(L0=a._Neg=a.asm.ea).apply(null,arguments)},B0=a._NonMaxSuppressionV3=function(){return(B0=a._NonMaxSuppressionV3=a.asm.fa).apply(null,arguments)},W0=a._NonMaxSuppressionV4=function(){return(W0=a._NonMaxSuppressionV4=a.asm.ga).apply(null,arguments)},ei=a._NonMaxSuppressionV5=function(){return(ei=a._NonMaxSuppressionV5=a.asm.ha).apply(null,arguments)},bd=a._NotEqual=function(){return(bd=a._NotEqual=a.asm.ia).apply(null,arguments)},vd=a._OneHot=function(){return(vd=a._OneHot=a.asm.ja).apply(null,arguments)},wd=a._PadV2=function(){return(wd=a._PadV2=a.asm.ka).apply(null,arguments)},V0=a._Pow=function(){return(V0=a._Pow=a.asm.la).apply(null,arguments)},U0=a._Prelu=function(){return(U0=a._Prelu=a.asm.ma).apply(null,arguments)},kd=a._Prod=function(){return(kd=a._Prod=a.asm.na).apply(null,arguments)},H0=a._RealDiv=function(){return(H0=a._RealDiv=a.asm.oa).apply(null,arguments)},Id=a._Relu=function(){return(Id=a._Relu=a.asm.pa).apply(null,arguments)},Er=a._Relu6=function(){return(Er=a._Relu6=a.asm.qa).apply(null,arguments)},G0=a._ResizeBilinear=function(){return(G0=a._ResizeBilinear=a.asm.ra).apply(null,arguments)},j0=a._Reverse=function(){return(j0=a._Reverse=a.asm.sa).apply(null,arguments)},Py=a._RotateWithOffset=function(){return(Py=a._RotateWithOffset=a.asm.ta).apply(null,arguments)},Sd=a._Round=function(){return(Sd=a._Round=a.asm.ua).apply(null,arguments)},q0=a._Rsqrt=function(){return(q0=a._Rsqrt=a.asm.va).apply(null,arguments)},X0=a._ScatterNd=function(){return(X0=a._ScatterNd=a.asm.wa).apply(null,arguments)},K0=a._SelectV2=function(){return(K0=a._SelectV2=a.asm.xa).apply(null,arguments)},Z0=a._Sigmoid=function(){return(Z0=a._Sigmoid=a.asm.ya).apply(null,arguments)},Y0=a._Sin=function(){return(Y0=a._Sin=a.asm.za).apply(null,arguments)},J0=a._Softmax=function(){return(J0=a._Softmax=a.asm.Aa).apply(null,arguments)},Q0=a._Sqrt=function(){return(Q0=a._Sqrt=a.asm.Ba).apply(null,arguments)},em=a._Square=function(){return(em=a._Square=a.asm.Ca).apply(null,arguments)},tm=a._SquaredDifference=function(){return(tm=a._SquaredDifference=a.asm.Da).apply(null,arguments)},nm=a._Step=function(){return(nm=a._Step=a.asm.Ea).apply(null,arguments)},sm=a._StridedSlice=function(){return(sm=a._StridedSlice=a.asm.Fa).apply(null,arguments)},rm=a._Sub=function(){return(rm=a._Sub=a.asm.Ga).apply(null,arguments)},am=a._Sum=function(){return(am=a._Sum=a.asm.Ha).apply(null,arguments)},om=a._Tan=function(){return(om=a._Tan=a.asm.Ia).apply(null,arguments)},im=a._Tanh=function(){return(im=a._Tanh=a.asm.Ja).apply(null,arguments)},lm=a._Tile=function(){return(lm=a._Tile=a.asm.Ka).apply(null,arguments)},um=a._TopK=function(){return(um=a._TopK=a.asm.La).apply(null,arguments)},cm=a._Transform=function(){return(cm=a._Transform=a.asm.Ma).apply(null,arguments)},dm=a._Transpose=function(){return(dm=a._Transpose=a.asm.Na).apply(null,arguments)},hm=a.__FusedMatMul=function(){return(hm=a.__FusedMatMul=a.asm.Oa).apply(null,arguments)},pm=a._malloc=function(){return(pm=a._malloc=a.asm.Pa).apply(null,arguments)},fm=a._free=function(){return(fm=a._free=a.asm.Qa).apply(null,arguments)},Cd=a.___errno_location=function(){return(Cd=a.___errno_location=a.asm.Ra).apply(null,arguments)},Td=a.stackSave=function(){return(Td=a.stackSave=a.asm.Sa).apply(null,arguments)},Nd=a.stackRestore=function(){return(Nd=a.stackRestore=a.asm.Ta).apply(null,arguments)},Au=a.stackAlloc=function(){return(Au=a.stackAlloc=a.asm.Ua).apply(null,arguments)};a.cwrap=Q;var ti;function mm(Y){this.name="ExitStatus",this.message="Program terminated with exit("+Y+")",this.status=Y}ur=function Y(){ti||gu(),ti||(ur=Y)};function gu(Y){if(Y=Y||c,zn>0||(cn(),zn>0))return;function re(){ti||(ti=!0,a.calledRun=!0,!O&&(Yn(),Jn(),o(a),a.onRuntimeInitialized&&a.onRuntimeInitialized(),en()))}a.setStatus?(a.setStatus("Running..."),setTimeout(function(){setTimeout(function(){a.setStatus("")},1),re()},1)):re()}if(a.run=gu,a.preInit)for(typeof a.preInit=="function"&&(a.preInit=[a.preInit]);a.preInit.length>0;)a.preInit.pop()();return gu(),r.ready}}();typeof e=="object"&&typeof t=="object"?t.exports=n:typeof define=="function"&&define.amd?define([],function(){return n}):typeof e=="object"&&(e.WasmBackendModule=n)}}),BI="3.8.0",WI="3.8.0",VI="3.8.0",UI="3.8.0",HI="3.8.0",GI="3.8.0",jI="3.8.0",qI="3.8.0",XI=1e-7,KI=1e-4,Fd=class{constructor(e,t){this.backend=e,this.dataMover=t,this.data=new WeakMap,this.dataIdsCount=0}get(e){return this.data.has(e)||this.dataMover.moveData(this.backend,e),this.data.get(e)}set(e,t){this.dataIdsCount++,this.data.set(e,t)}has(e){return this.data.has(e)}delete(e){return this.dataIdsCount--,this.data.delete(e)}numDataIds(){return this.dataIdsCount}},ku=class{refCount(e){return ys("refCount")}incRef(e){return ys("incRef")}timerAvailable(){return!0}time(e){return ys("time")}read(e){return ys("read")}readSync(e){return ys("readSync")}numDataIds(){return ys("numDataIds")}disposeData(e,t){return ys("disposeData")}write(e,t,n){return ys("write")}move(e,t,n,s,r){return ys("move")}memory(){return ys("memory")}floatPrecision(){return ys("floatPrecision")}epsilon(){return this.floatPrecision()===32?XI:KI}dispose(){return ys("dispose")}};function ys(e){throw new Error(`'${e}' not yet implemented or not found in the registry. This kernel may not be supported by the tfjs backend you have chosen`)}function s5(e){let t=e.length,n=0;for(;t>0;)n=Math.random()*t|0,t--,Dd(e,t,n)}function ZI(e,t){if(e.length!==t.length)throw new Error(`Array sizes must match to be shuffled together First array length was ${e.length}Second array length was ${t.length}`);let n=e.length,s=0;for(;n>0;)s=Math.random()*n|0,n--,Dd(e,n,s),Dd(t,n,s)}function Iu(e,t,n){return Math.max(e,Math.min(t,n))}function YI(e){return e%2==0?e:e+1}function Dd(e,t,n){let s=e[t];e[t]=e[n],e[n]=s}function JI(e){let t=0;for(let n=0;n<e.length;n++)t+=e[n];return t}function QI(e,t){let n=Math.random();return t*n+(1-n)*e}function eS(e,t){let n=0;for(let s=0;s<e.length;s++){let r=Number(e[s])-Number(t[s]);n+=r*r}return n}function P(e,t){if(!e)throw new Error(typeof t=="string"?t:t())}function fn(e,t,n=""){P(fr(e,t),()=>n+` Shapes ${e} and ${t} must match`)}function ma(e){P(e!=null,()=>"The input to the tensor constructor must be a non-null value.")}function Aa(e,t=[],n=!1){if(t==null&&(t=[]),Array.isArray(e)||an(e)&&!n)for(let s=0;s<e.length;++s)Aa(e[s],t,n);else t.push(e);return t}function _t(e){if(e.length===0)return 1;let t=e[0];for(let n=1;n<e.length;n++)t*=e[n];return t}function tS(e){return e.length===0}function fr(e,t){if(e===t)return!0;if(e==null||t==null||e.length!==t.length)return!1;for(let n=0;n<e.length;n++)if(e[n]!==t[n])return!1;return!0}function qt(e){return e%1==0}function nS(e){if(Math.tanh!=null)return Math.tanh(e);if(e===1/0)return 1;if(e===-1/0)return-1;{let t=Math.exp(2*e);return(t-1)/(t+1)}}function sS(e){let t=Math.ceil(Math.sqrt(e));return[t,Math.ceil(e/t)]}function rS(e){let t=new Uint32Array(e);for(let n=0;n<e;++n)t[n]=n;return s5(t),t}function Su(e,t){return t<=e.length?e:e+" ".repeat(t-e.length)}function aS(e,t=s=>0,n){return new Promise((s,r)=>{let a=0,o=()=>{if(e()){s();return}a++;let i=t(a);if(n!=null&&a>=n){r();return}setTimeout(o,i)};o()})}function oS(e,t){let n=1,s=-1;for(let a=0;a<e.length;++a)if(e[a]>=0)n*=e[a];else if(e[a]===-1){if(s!==-1)throw Error(`Shapes can only have 1 implicit size. Found -1 at dim ${s} and dim ${a}`);s=a}else if(e[a]<0)throw Error(`Shapes can not be < 0. Found ${e[a]} at dim ${a}`);if(s===-1){if(t>0&&t!==n)throw Error(`Size(${t}) must match the product of shape ${e}`);return e}if(n===0)throw Error(`Cannot infer the missing size in [${e}] when there are 0 elements`);if(t%n!=0)throw Error(`The implicit shape can't be a fractional number. Got ${t} / ${n}`);let r=e.slice();return r[s]=t/n,r}function xs(e,t){let n=t.length;return e=e==null?t.map((s,r)=>r):[].concat(e),P(e.every(s=>s>=-n&&s<n),()=>`All values in axis param must be in range [-${n}, ${n}) but got axis ${e}`),P(e.every(s=>qt(s)),()=>`All values in axis param must be integers but got axis ${e}`),e.map(s=>s<0?n+s:s)}function r5(e,t){let n=[],s=[],r=t!=null&&Array.isArray(t)&&t.length===0,a=t==null||r?null:xs(t,e).sort(),o=0;for(let i=0;i<e.length;++i){if(a!=null){if(a[o]===i&&e[i]!==1)throw new Error(`Can't squeeze axis ${i} since its dim '${e[i]}' is not 1`);(a[o]==null||a[o]>i)&&e[i]===1&&(n.push(e[i]),s.push(i)),a[o]<=i&&o++}e[i]!==1&&(n.push(e[i]),s.push(i))}return{newShape:n,keptDims:s}}function a5(e,t){let n=null;if(e==null||e==="float32")n=new Float32Array(t);else if(e==="int32")n=new Int32Array(t);else if(e==="bool")n=new Uint8Array(t);else throw new Error(`Unknown data type ${e}`);return n}function o5(e,t){let n=null;if(e==null||e==="float32")n=new Float32Array(t);else if(e==="int32")n=new Int32Array(t);else if(e==="bool")n=new Uint8Array(t);else if(e==="string")n=new Array(t);else throw new Error(`Unknown data type ${e}`);return n}function i5(e,t){for(let n=0;n<e.length;n++){let s=e[n];if(isNaN(s)||!isFinite(s))throw Error(`A tensor of type ${t} being uploaded contains ${s}.`)}}function l5(e){return e==="bool"||e==="complex64"||e==="float32"||e==="int32"||e==="string"}function iS(e,t){return!(t==="complex64"||t==="float32"&&e!=="complex64"||t==="int32"&&e!=="float32"&&e!=="complex64"||t==="bool"&&e==="bool")}function an(e){return e instanceof Float32Array||e instanceof Int32Array||e instanceof Uint8Array}function vm(e){if(e==="float32"||e==="int32")return 4;if(e==="complex64")return 8;if(e==="bool")return 1;throw new Error(`Unknown dtype ${e}`)}function u5(e){if(e==null)return 0;let t=0;return e.forEach(n=>t+=n.length),t}function $r(e){return typeof e=="string"||e instanceof String}function c5(e){return typeof e=="boolean"}function d5(e){return typeof e=="number"}function Od(e){return Array.isArray(e)?Od(e[0]):e instanceof Float32Array?"float32":e instanceof Int32Array||e instanceof Uint8Array?"int32":d5(e)?"float32":$r(e)?"string":c5(e)?"bool":"float32"}function Fr(e){return!!(e&&e.constructor&&e.call&&e.apply)}function Pd(e,t){for(let n=t;n<e;++n)if(e%n==0)return n;return e}function ui(e){let t=e.length;if(t<2)return[];let n=new Array(t-1);n[t-2]=e[t-1];for(let s=t-3;s>=0;--s)n[s]=n[s+1]*e[s+1];return n}function h5(e,t,n,s=!1){let r=new Array;if(t.length===1){let a=t[0]*(s?2:1);for(let o=0;o<a;o++)r[o]=n[e+o]}else{let a=t[0],o=t.slice(1),i=o.reduce((l,u)=>l*u)*(s?2:1);for(let l=0;l<a;l++)r[l]=h5(e+l*i,o,n,s)}return r}function ci(e,t,n=!1){if(e.length===0)return t[0];let s=e.reduce((r,a)=>r*a)*(n?2:1);if(s===0)return[];if(s!==t.length)throw new Error(`[${e}] does not match the input size ${t.length}${n?" for a complex tensor":""}.`);return h5(0,e,t,n)}function wm(e,t){let n=Md(e,t);for(let s=0;s<n.length;s++)n[s]=1;return n}function Md(e,t){if(t==null||t==="float32"||t==="complex64")return new Float32Array(e);if(t==="int32")return new Int32Array(e);if(t==="bool")return new Uint8Array(e);throw new Error(`Unknown data type ${t}`)}function lS(e,t){let n=e.reduce((s,r)=>s*r,1);if(t==null||t==="float32")return ci(e,new Float32Array(n));if(t==="int32")return ci(e,new Int32Array(n));if(t==="bool")return ci(e,new Uint8Array(n));throw new Error(`Unknown data type ${t}`)}function km(e){e.forEach(t=>{P(Number.isInteger(t)&&t>=0,()=>`Tensor must have a shape comprised of positive integers but got shape [${e}].`)})}function uS(e,t,n){if(t===0)return 0;if(t===1)return e[0];let s=e[e.length-1];for(let r=0;r<e.length-1;++r)s+=n[r]*e[r];return s}function cS(e,t,n){if(t===0)return[];if(t===1)return[e];let s=new Array(t);for(let r=0;r<s.length-1;++r)s[r]=Math.floor(e/n[r]),e-=s[r]*n[r];return s[s.length-1]=e,s}function Im(e){return e&&e.then&&typeof e.then=="function"}var p5="tfjsflags",f5=class{constructor(e){this.global=e,this.flags={},this.flagRegistry={},this.urlFlags={},this.getQueryParams=dS,this.populateURLFlags()}setPlatform(e,t){this.platform!=null&&console.warn(`Platform ${this.platformName} has already been set. Overwriting the platform with ${t}.`),this.platformName=e,this.platform=t}registerFlag(e,t,n){if(this.flagRegistry[e]={evaluationFn:t,setHook:n},this.urlFlags[e]!=null){let s=this.urlFlags[e];console.warn(`Setting feature override from URL ${e}: ${s}.`),this.set(e,s)}}async getAsync(e){return e in this.flags?this.flags[e]:(this.flags[e]=await this.evaluateFlag(e),this.flags[e])}get(e){if(e in this.flags)return this.flags[e];let t=this.evaluateFlag(e);if(Im(t))throw new Error(`Flag ${e} cannot be synchronously evaluated. Please use getAsync() instead.`);return this.flags[e]=t,this.flags[e]}getNumber(e){return this.get(e)}getBool(e){return this.get(e)}getFlags(){return this.flags}get features(){return this.flags}set(e,t){if(this.flagRegistry[e]==null)throw new Error(`Cannot set flag ${e} as it has not been registered.`);this.flags[e]=t,this.flagRegistry[e].setHook!=null&&this.flagRegistry[e].setHook(t)}evaluateFlag(e){if(this.flagRegistry[e]==null)throw new Error(`Cannot evaluate flag '${e}': no evaluation function found.`);return this.flagRegistry[e].evaluationFn()}setFlags(e){this.flags=Object.assign({},e)}reset(){this.flags={},this.urlFlags={},this.populateURLFlags()}populateURLFlags(){if(typeof this.global=="undefined"||typeof this.global.location=="undefined"||typeof this.global.location.search=="undefined")return;let e=this.getQueryParams(this.global.location.search);p5 in e&&e[p5].split(",").forEach(n=>{let[s,r]=n.split(":");this.urlFlags[s]=pS(s,r)})}};function dS(e){let t={};return e.replace(/[?&]([^=?&]+)(?:=([^&]*))?/g,(n,...s)=>(hS(t,s[0],s[1]),s.join("="))),t}function hS(e,t,n){e[decodeURIComponent(t)]=decodeURIComponent(n||"")}function pS(e,t){if(t=t.toLowerCase(),t==="true"||t==="false")return t==="true";if(`${+t}`===t)return+t;throw new Error(`Could not parse value flag value ${t} for flag ${e}.`)}function ee(){return ns}var ns=null;function fS(e){ns=e}var Sm;function m5(){if(Sm==null){let e;if(typeof window!="undefined")e=window;else if(typeof global!="undefined")e=global;else if(typeof process!="undefined")e=process;else if(typeof self!="undefined")e=self;else throw new Error("Could not find a global object");Sm=e}return Sm}function mS(){let e=m5();return e._tfGlobals==null&&(e._tfGlobals=new Map),e._tfGlobals}function Cm(e,t){let n=mS();if(n.has(e))return n.get(e);{let s=t();return n.set(e,s),n.get(e)}}var di="Abs",hi="Acos",pi="Acosh",Dr="Add",ga="AddN",fi="All",mi="Any",ya="ArgMax",Cu="ArgMin",Ai="Asin",gi="Asinh",yi="Atan",xi="Atanh",bi="Atan2",xa="AvgPool",zd="AvgPoolGrad",Tu="AvgPool3D",Ld="AvgPool3DGrad",ba="BatchMatMul",vi="BatchToSpaceND",Bd="Bincount",A5="BroadcastTo",va="Cast",wa="Ceil",Or="ClipByValue",Wd="Complex",Nu="ComplexAbs",wi="Concat",ka="Conv2D",Vd="Conv2DBackpropFilter",Ia="Conv2DBackpropInput",Eu="Conv3D",Ud="Conv3DBackpropFilterV2",Hd="Conv3DBackpropInputV2",Sa="Cos",Ca="Cosh",Ta="Cumsum",ki="CropAndResize",Gd="DenseBincount",Ii="DepthToSpace",Na="DepthwiseConv2dNative",jd="DepthwiseConv2dNativeBackpropFilter",qd="DepthwiseConv2dNativeBackpropInput",Xd="Diag",Ru="Dilation2D",Kd="Dilation2DBackpropInput",Zd="Dilation2DBackpropFilter",Ea="RealDiv",Yd="Einsum",Si="Elu",Jd="EluGrad",Ci="Erf",Ti="Equal",Ra="Exp",Ni="ExpandDims",Ei="Expm1",Qd="FFT",_u="Fill",Ri="FlipLeftRight",_a="Floor",$a="FloorDiv",Fa="FusedBatchNorm",_i="GatherV2",$i="GatherNd",Fi="Greater",Da="GreaterEqual",Oa="Identity",eh="IFFT",th="Imag",Di="IsFinite",Oi="IsInf",Pi="IsNan",Pa="LeakyRelu",Mi="Less",zi="LessEqual",nh="LinSpace",Ma="Log",Li="Log1p",Bi="LogicalAnd",$u="LogicalNot",Fu="LogicalOr",g5="LogSoftmax",Du="LRN",sh="LRNGrad",za="Max",La="Maximum",Ba="MaxPool",rh="MaxPoolGrad",Ou="MaxPool3D",ah="MaxPool3DGrad",oh="MaxPoolWithArgmax",Wa="Mean",Va="Min",Ua="Minimum",Ha="MirrorPad",Wi="Mod",ih="Multinomial",Ga="Multiply",Vi="Neg",Ui="NotEqual",Hi="NonMaxSuppressionV3",Gi="NonMaxSuppressionV4",ji="NonMaxSuppressionV5",qi="OnesLike",ja="OneHot",Xi="Pack",qa="PadV2",AS="Pool",Xa="Pow",Ka="Prelu",Ki="Prod",Pu="Range",lh="Real",Zi="Reciprocal",Za="Relu",Yi="Reshape",Mu="ResizeNearestNeighbor",uh="ResizeNearestNeighborGrad",Ya="ResizeBilinear",ch="ResizeBilinearGrad",Ja="Relu6",Qa="Reverse",eo="Round",to="Rsqrt",Ji="ScatterNd",Qi="Select",el="Selu",tl="Slice",no="Sin",nl="Sinh",sl="Sign",so="Sigmoid",rl="Softplus",ro="Sqrt",ao="Sum",al="SpaceToBatchND",ol="SplitV",oo="Softmax",dh="SparseFillEmptyRows",hh="SparseReshape",ph="SparseSegmentMean",fh="SparseSegmentSum",mh="SparseToDense",io="SquaredDifference",zu="Square",il="StridedSlice",Ah="StringNGrams",gh="StringSplit",yh="StringToHashBucketFast",lo="Sub",uo="Tan",co="Tanh",Pr="Tile",ll="TopK",ul="Transform",ho="Transpose",xh="Unique",cl="Unpack",Lu="UnsortedSegmentSum",dl="ZerosLike",Mr="Step",bh="FromPixels",hl="RotateWithOffset",po="_FusedMatMul",fo="FusedConv2D",mo="FusedDepthwiseConv2D",pl=Cm("kernelRegistry",()=>new Map),Bu=Cm("gradRegistry",()=>new Map);function vh(e,t){let n=Nm(e,t);return pl.get(n)}function Tm(e){return Bu.get(e)}function fl(e){let t=pl.entries(),n=[];for(;;){let{done:s,value:r}=t.next();if(s)break;let[a,o]=r,[i]=a.split("_");i===e&&n.push(o)}return n}function Ao(e){let{kernelName:t,backendName:n}=e,s=Nm(t,n);pl.has(s)&&console.warn(`The kernel '${t}' for backend '${n}' is already registered`),pl.set(s,e)}function y5(e){let{kernelName:t}=e;Bu.has(t)&&ee().getBool("DEBUG")&&console.warn(`Overriding the gradient for '${t}'`),Bu.set(t,e)}function gS(e,t){let n=Nm(e,t);if(!pl.has(n))throw new Error(`The kernel '${e}' for backend '${t}' is not registered`);pl.delete(n)}function yS(e){if(!Bu.has(e))throw new Error(`The gradient '${e}' for backend is not registered`);Bu.delete(e)}function xS(e,t){fl(e).forEach(s=>{let r=Object.assign({},s,{backendName:t});Ao(r)})}function Nm(e,t){return`${t}_${e}`}var I={};Pe(I,{arraysEqual:()=>fr,assert:()=>P,assertNonNegativeIntegerDimensions:()=>km,assertNonNull:()=>ma,assertShapesMatch:()=>fn,bytesFromStringArray:()=>u5,bytesPerElement:()=>vm,checkConversionForErrors:()=>i5,clamp:()=>Iu,computeStrides:()=>ui,createScalarValue:()=>SS,createShuffledIndices:()=>rS,decodeString:()=>Ih,distSquared:()=>eS,encodeString:()=>Uu,fetch:()=>TS,fingerPrint64:()=>IS,flatten:()=>Aa,getArrayFromDType:()=>o5,getTypedArrayFromDType:()=>a5,hasEncodingLoss:()=>iS,hexToLong:()=>Wu,indexToLoc:()=>cS,inferDtype:()=>Od,inferFromImplicitShape:()=>oS,isBoolean:()=>c5,isFunction:()=>Fr,isInt:()=>qt,isNumber:()=>d5,isPromise:()=>Im,isScalarShape:()=>tS,isString:()=>$r,isTypedArray:()=>an,isValidDtype:()=>l5,locToIndex:()=>uS,makeOnesTypedArray:()=>wm,makeZerosNestedTypedArray:()=>lS,makeZerosTypedArray:()=>Md,nearestDivisor:()=>Pd,nearestLargerEven:()=>YI,now:()=>Vu,parseAxisParam:()=>xs,randUniform:()=>QI,repeatedTry:()=>aS,rightPad:()=>Su,shuffle:()=>s5,shuffleCombo:()=>ZI,sizeFromShape:()=>_t,sizeToSquarishShape:()=>sS,squeezeShape:()=>r5,sum:()=>JI,swap:()=>Dd,tanh:()=>nS,toNestedArray:()=>ci,toTypedArray:()=>kh});var x5=fa(xI()),go=x5.default||x5;function Wu(e){return go.fromString(e,!0,16)}var b5=Wu("c3a5c85c97cb3127"),yo=Wu("b492b66fbe98f273"),mn=Wu("9ae16a3b2f90404f");function Em(e){return e.xor(e.shru(47))}function v5(e,t,n){let s=e.slice(t,t+n);return go.fromBytes(Array.from(s),!0,!0)}function dt(e,t){return v5(e,t,8)}function w5(e,t){return v5(e,t,4)}function Xt(e,t){return t===0?e:e.shru(t).or(e.shl(64-t))}function zr(e,t,n=Wu("9ddfea08eb382d69")){let s=e.xor(t).mul(n);s=s.xor(s.shru(47));let r=t.xor(s).mul(n);return r=r.xor(r.shru(47)),r=r.mul(n),r}function bS(e,t,n,s,r,a){r=r.add(e),a=Xt(a.add(r).add(s),21);let o=r;return r=r.add(t),r=r.add(n),a=a.add(Xt(r,44)),[r.add(s),a.add(o)]}function wh(e,t,n,s){return bS(dt(e,t),dt(e,t+8),dt(e,t+16),dt(e,t+24),n,s)}function vS(e,t=e.length){if(t>=8){let n=mn.add(t*2),s=dt(e,0).add(mn),r=dt(e,t-8),a=Xt(r,37).mul(n).add(s),o=Xt(s,25).add(r).mul(n);return zr(a,o,n)}if(t>=4){let n=mn.add(t*2),s=w5(e,0);return zr(s.shl(3).add(t),w5(e,t-4),n)}if(t>0){let n=e[0],s=e[t>>1],r=e[t-1],a=n+(s<<8),o=t+(r<<2);return Em(mn.mul(a).xor(b5.mul(o))).mul(mn)}return mn}function wS(e,t=e.length){let n=mn.add(t*2),s=dt(e,0).mul(yo),r=dt(e,8),a=dt(e,t-8).mul(n),o=dt(e,t-16).mul(mn);return zr(Xt(s.add(r),43).add(Xt(a,30)).add(o),s.add(Xt(r.add(mn),18)).add(a),n)}function kS(e,t=e.length){let n=mn.add(t*2),s=dt(e,0).mul(mn),r=dt(e,8),a=dt(e,t-8).mul(n),o=dt(e,t-16).mul(mn),i=Xt(s.add(r),43).add(Xt(a,30)).add(o),l=zr(i,s.add(Xt(r.add(mn),18)).add(a),n),u=dt(e,16).mul(n),c=dt(e,24),d=i.add(dt(e,t-32)).mul(n),h=l.add(dt(e,t-24)).mul(n);return zr(Xt(u.add(c),43).add(Xt(d,30)).add(h),u.add(Xt(c.add(s),18)).add(d),n)}function IS(e,t=e.length){let n=go.fromNumber(81,!0);if(t<=32)return t<=16?vS(e,t):wS(e,t);if(t<=64)return kS(e,t);let s=n,r=n.mul(yo).add(113),a=Em(r.mul(mn).add(113)).mul(mn),o=[go.UZERO,go.UZERO],i=[go.UZERO,go.UZERO];s=s.mul(mn).add(dt(e,0));let l=0,u=(t-1>>6)*64,c=u+(t-1&63)-63;do s=Xt(s.add(r).add(o[0]).add(dt(e,l+8)),37).mul(yo),r=Xt(r.add(o[1]).add(dt(e,l+48)),42).mul(yo),s=s.xor(i[1]),r=r.add(o[0]).add(dt(e,l+40)),a=Xt(a.add(i[0]),33).mul(yo),o=wh(e,l,o[1].mul(yo),s.add(i[0])),i=wh(e,l+32,a.add(i[1]),r.add(dt(e,l+16))),[a,s]=[s,a],l+=64;while(l!==u);let d=yo.add(a.and(255).shl(1));return l=c,i[0]=i[0].add(t-1&63),o[0]=o[0].add(i[0]),i[0]=i[0].add(o[0]),s=Xt(s.add(r).add(o[0]).add(dt(e,l+8)),37).mul(d),r=Xt(r.add(o[1]).add(dt(e,l+48)),42).mul(d),s=s.xor(i[1].mul(9)),r=r.add(o[0].mul(9).add(dt(e,l+40))),a=Xt(a.add(i[0]),33).mul(d),o=wh(e,l,o[1].mul(d),s.add(i[0])),i=wh(e,l+32,a.add(i[1]),r.add(dt(e,l+16))),[a,s]=[s,a],zr(zr(o[0],i[0],d).add(Em(r).mul(b5)).add(a),zr(o[1],i[1],d).add(s),d)}function SS(e,t){return t==="string"?Uu(e):kh([e],t)}function CS(e,t){return e instanceof Float32Array&&t==="float32"||e instanceof Int32Array&&t==="int32"||e instanceof Uint8Array&&t==="bool"}function kh(e,t){if(t==="string")throw new Error("Cannot convert a string[] to a TypedArray");if(Array.isArray(e)&&(e=Aa(e)),ee().getBool("DEBUG")&&i5(e,t),CS(e,t))return e;if(t==null||t==="float32"||t==="complex64")return new Float32Array(e);if(t==="int32")return new Int32Array(e);if(t==="bool"){let n=new Uint8Array(e.length);for(let s=0;s<n.length;++s)Math.round(e[s])!==0&&(n[s]=1);return n}else throw new Error(`Unknown data type ${t}`)}function Vu(){return ee().platform.now()}function TS(e,t){return ee().platform.fetch(e,t)}function Uu(e,t="utf-8"){return t=t||"utf-8",ee().platform.encode(e,t)}function Ih(e,t="utf-8"){return t=t||"utf-8",ee().platform.decode(e,t)}var NS=class{constructor(e,t){this.backendTimer=e,this.logger=t,t==null&&(this.logger=new RS)}profileKernel(e,t,n){let s,r=()=>{s=n()},a,o=Vu();if(this.backendTimer.timerAvailable())a=this.backendTimer.time(r);else{r();for(let l of s)l.dataSync();a=Promise.resolve({kernelMs:Vu()-o})}if(ee().getBool("CHECK_COMPUTATION_FOR_ERRORS"))for(let l=0;l<s.length;l++){let u=s[l];u.data().then(c=>{ES(c,u.dtype,e)})}return{kernelName:e,outputs:s,inputs:t,timeMs:a.then(l=>l.kernelMs),extraInfo:a.then(l=>l.getExtraProfileInfo!=null?l.getExtraProfileInfo():"")}}logKernelProfile(e){let{kernelName:t,outputs:n,timeMs:s,inputs:r,extraInfo:a}=e;n.forEach(o=>{Promise.all([o.data(),s,a]).then(i=>{this.logger.logKernelProfile(t,o,i[0],i[1],r,i[2])})})}};function ES(e,t,n){if(t!=="float32")return!1;for(let s=0;s<e.length;s++){let r=e[s];if(isNaN(r)||!isFinite(r))return console.warn(`Found ${r} in the result of '${n}'`),!0}return!1}var RS=class{logKernelProfile(e,t,n,s,r,a){let o=typeof s=="number"?Su(`${s}ms`,9):s.error,i=Su(e,25),l=t.rank,u=t.size,c=Su(t.shape.toString(),14),d="";for(let h in r){let p=r[h];if(p!=null){let f=p.shape||t.shape,m=f.length;d+=`${h}: ${m}D ${m>0?f:""} `}}console.log(`%c${i}	%c${o}	%c${l}D ${c}	%c${u}	%c${d}	%c${a}`,"font-weight:bold","color:red","color:blue","color: orange","color: green","color: steelblue")}};function _S(e,t,n){let s={},r={};for(let l=0;l<t.length;l++)s[t[l].id]=!0;for(let l=0;l<e.length;l++){let u=e[l],c=u.inputs;for(let d in c){let h=c[d],p=!1;for(let f=0;f<t.length;f++)if(s[h.id]){u.outputs.forEach(m=>s[m.id]=!0),p=!0,r[u.id]=!0;break}if(p)break}}let a={};a[n.id]=!0;let o={};for(let l=e.length-1;l>=0;l--){let u=e[l],c=u.inputs;for(let d=0;d<u.outputs.length;d++)if(a[u.outputs[d].id]){for(let h in c)a[c[h].id]=!0,o[u.id]=!0;break}}let i=[];for(let l=0;l<e.length;l++){let u=e[l];if(r[u.id]&&o[u.id]){let c={};for(let h in u.inputs){let p=u.inputs[h];s[p.id]&&(c[h]=p)}let d=Object.assign({},u);d.inputs=c,d.outputs=u.outputs,i.push(d)}}return i}function $S(e,t,n,s){for(let r=t.length-1;r>=0;r--){let a=t[r],o=[];if(a.outputs.forEach(l=>{let u=e[l.id];u!=null?o.push(u):o.push(null)}),a.gradient==null)throw new Error(`Cannot compute gradient: gradient function not found for ${a.kernelName}.`);let i=a.gradient(o);for(let l in a.inputs){if(!(l in i))throw new Error(`Cannot backprop through input ${l}. Available gradients found: ${Object.keys(i)}.`);let u=n(()=>i[l]());if(u.dtype!=="float32")throw new Error(`Error in gradient for op ${a.kernelName}. The gradient of input ${l} must have 'float32' dtype, but has '${u.dtype}'`);let c=a.inputs[l];if(!fr(u.shape,c.shape))throw new Error(`Error in gradient for op ${a.kernelName}. The gradient of input '${l}' has shape '${u.shape}', which does not match the shape of the input '${c.shape}'`);if(e[c.id]==null)e[c.id]=u;else{let d=e[c.id];e[c.id]=s(d,u),d.dispose()}}}}var k5=20,Hu=3,Rm=7;function FS(e,t,n,s){let r=ui(t),a=DS(e,t,n,r),o=t.length,i=Sh(e,t,n,r,a),l=["Tensor"];return s&&(l.push(`  dtype: ${n}`),l.push(`  rank: ${o}`),l.push(`  shape: [${t}]`),l.push("  values:")),l.push(i.map(u=>"    "+u).join(`
`)),l.join(`
`)}function DS(e,t,n,s){let r=_t(t),a=s[s.length-1],o=new Array(a).fill(0),i=t.length,l=n==="complex64"?ju(e):e;if(i>1)for(let u=0;u<r/a;u++){let c=u*a;for(let d=0;d<a;d++)o[d]=Math.max(o[d],Gu(l[c+d],0,n).length)}return o}function Gu(e,t,n){let s;return Array.isArray(e)?s=`${parseFloat(e[0].toFixed(Rm))} + ${parseFloat(e[1].toFixed(Rm))}j`:$r(e)?s=`'${e}'`:n==="bool"?s=I5(e):s=parseFloat(e.toFixed(Rm)).toString(),Su(s,t)}function I5(e){return e===0?"false":"true"}function Sh(e,t,n,s,r,a=!0){let o=n==="complex64"?2:1,i=t[0],l=t.length;if(l===0){if(n==="complex64"){let m=ju(e);return[Gu(m[0],0,n)]}return n==="bool"?[I5(e[0])]:[e[0].toString()]}if(l===1){if(i>k5){let A=Hu*o,g=Array.from(e.slice(0,A)),y=Array.from(e.slice((i-Hu)*o,i*o));return n==="complex64"&&(g=ju(g),y=ju(y)),["["+g.map((x,b)=>Gu(x,r[b],n)).join(", ")+", ..., "+y.map((x,b)=>Gu(x,r[i-Hu+b],n)).join(", ")+"]"]}let m=n==="complex64"?ju(e):Array.from(e);return["["+m.map((A,g)=>Gu(A,r[g],n)).join(", ")+"]"]}let u=t.slice(1),c=s.slice(1),d=s[0]*o,h=[];if(i>k5){for(let m=0;m<Hu;m++){let A=m*d,g=A+d;h.push(...Sh(e.slice(A,g),u,n,c,r,!1))}h.push("...");for(let m=i-Hu;m<i;m++){let A=m*d,g=A+d;h.push(...Sh(e.slice(A,g),u,n,c,r,m===i-1))}}else for(let m=0;m<i;m++){let A=m*d,g=A+d;h.push(...Sh(e.slice(A,g),u,n,c,r,m===i-1))}let p=l===2?",":"";h[0]="["+h[0]+p;for(let m=1;m<h.length-1;m++)h[m]=" "+h[m]+p;let f=`,
`;for(let m=2;m<l;m++)f+=`
`;return h[h.length-1]=" "+h[h.length-1]+"]"+(a?"":f),h}function ju(e){let t=[];for(let n=0;n<e.length;n+=2)t.push([e[n],e[n+1]]);return t}var Bt=class{constructor(e,t,n){if(this.dtype=t,this.shape=e.slice(),this.size=_t(e),n!=null){let s=n.length;P(s===this.size,()=>`Length of values '${s}' does not match the size inferred by the shape '${this.size}'.`)}if(t==="complex64")throw new Error("complex64 dtype TensorBuffers are not supported. Please create a TensorBuffer for the real and imaginary parts separately and call tf.complex(real, imag).");this.values=n||o5(t,this.size),this.strides=ui(e)}set(e,...t){t.length===0&&(t=[0]),P(t.length===this.rank,()=>`The number of provided coordinates (${t.length}) must match the rank (${this.rank})`);let n=this.locToIndex(t);this.values[n]=e}get(...e){e.length===0&&(e=[0]);let t=0;for(let s of e){if(s<0||s>=this.shape[t]){let r=`Requested out of range element at ${e}.   Buffer shape=${this.shape}`;throw new Error(r)}t++}let n=e[e.length-1];for(let s=0;s<e.length-1;++s)n+=this.strides[s]*e[s];return this.values[n]}locToIndex(e){if(this.rank===0)return 0;if(this.rank===1)return e[0];let t=e[e.length-1];for(let n=0;n<e.length-1;++n)t+=this.strides[n]*e[n];return t}indexToLoc(e){if(this.rank===0)return[];if(this.rank===1)return[e];let t=new Array(this.shape.length);for(let n=0;n<t.length-1;++n)t[n]=Math.floor(e/this.strides[n]),e-=t[n]*this.strides[n];return t[t.length-1]=e,t}get rank(){return this.shape.length}toTensor(){return js().makeTensor(this.values,this.shape,this.dtype)}},js=null,ml=null,OS=null;function PS(e){js=e}function MS(e){ml=e}function zS(e){OS=e}var Ue=class{constructor(e,t,n,s){this.kept=!1,this.isDisposedInternal=!1,this.shape=e.slice(),this.dtype=t||"float32",this.size=_t(e),this.strides=ui(e),this.dataId=n,this.id=s,this.rankType=this.rank<5?this.rank.toString():"higher"}get rank(){return this.shape.length}async buffer(){let e=await this.data();return ml.buffer(this.shape,this.dtype,e)}bufferSync(){return ml.buffer(this.shape,this.dtype,this.dataSync())}async array(){let e=await this.data();return ci(this.shape,e,this.dtype==="complex64")}arraySync(){return ci(this.shape,this.dataSync(),this.dtype==="complex64")}async data(){this.throwIfDisposed();let e=js().read(this.dataId);if(this.dtype==="string"){let t=await e;try{return t.map(n=>Ih(n))}catch(n){throw new Error("Failed to decode the string bytes into utf-8. To get the original bytes, call tensor.bytes().")}}return e}dataSync(){this.throwIfDisposed();let e=js().readSync(this.dataId);if(this.dtype==="string")try{return e.map(t=>Ih(t))}catch(t){throw new Error("Failed to decode the string bytes into utf-8. To get the original bytes, call tensor.bytes().")}return e}async bytes(){this.throwIfDisposed();let e=await js().read(this.dataId);return this.dtype==="string"?e:new Uint8Array(e.buffer)}dispose(){this.isDisposed||(js().disposeTensor(this),this.isDisposedInternal=!0)}get isDisposed(){return this.isDisposedInternal}throwIfDisposed(){if(this.isDisposed)throw new Error("Tensor is disposed.")}print(e=!1){return ml.print(this,e)}clone(){return this.throwIfDisposed(),ml.clone(this)}toString(e=!1){let t=this.dataSync();return FS(t,this.shape,this.dtype,e)}cast(e){return this.throwIfDisposed(),ml.cast(this,e)}variable(e=!0,t,n){return this.throwIfDisposed(),js().makeVariable(this,e,t,n)}};Object.defineProperty(Ue,Symbol.hasInstance,{value:e=>!!e&&e.data!=null&&e.dataSync!=null&&e.throwIfDisposed!=null});function LS(){return Cm("Tensor",()=>Ue)}LS();var qu=class extends Ue{constructor(e,t,n,s){super(e.shape,e.dtype,e.dataId,s);this.trainable=t,this.name=n}assign(e){if(e.dtype!==this.dtype)throw new Error(`dtype of the new value (${e.dtype}) and previous value (${this.dtype}) must match`);if(!fr(e.shape,this.shape))throw new Error(`shape of the new value (${e.shape}) and previous value (${this.shape}) must match`);js().disposeTensor(this),this.dataId=e.dataId,js().incRef(this,null)}dispose(){js().disposeVariable(this),this.isDisposedInternal=!0}};Object.defineProperty(qu,Symbol.hasInstance,{value:e=>e instanceof Ue&&e.assign!=null&&e.assign instanceof Function});var Cs={};Pe(Cs,{assertTypesMatch:()=>S5,getTensorsInContainer:()=>Pm,isTensorInList:()=>WS,makeTypesMatch:()=>It});var _m;(function(e){e.R0="R0",e.R1="R1",e.R2="R2",e.R3="R3",e.R4="R4",e.R5="R5",e.R6="R6"})(_m||(_m={}));var $m;(function(e){e.float32="float32",e.int32="int32",e.bool="int32",e.complex64="complex64"})($m||($m={}));var Fm;(function(e){e.float32="float32",e.int32="int32",e.bool="bool",e.complex64="complex64"})(Fm||(Fm={}));var Dm;(function(e){e.float32="float32",e.int32="float32",e.bool="float32",e.complex64="complex64"})(Dm||(Dm={}));var Om;(function(e){e.float32="complex64",e.int32="complex64",e.bool="complex64",e.complex64="complex64"})(Om||(Om={}));var BS={float32:Dm,int32:$m,bool:Fm,complex64:Om};function bs(e,t){if(e==="string"||t==="string"){if(e==="string"&&t==="string")return"string";throw new Error(`Can not upcast ${e} with ${t}`)}return BS[e][t]}function Ch(e){return bs(e,"int32")}function It(e,t){if(e.dtype===t.dtype)return[e,t];let n=bs(e.dtype,t.dtype);return[e.cast(n),t.cast(n)]}function S5(e,t){P(e.dtype===t.dtype,()=>`The dtypes of the first(${e.dtype}) and second(${t.dtype}) input must match`)}function WS(e,t){return t.some(n=>n.id===e.id)}function Pm(e){let t=[],n=new Set;return C5(e,t,n),t}function C5(e,t,n){if(e==null)return;if(e instanceof Ue){t.push(e);return}if(!VS(e))return;let s=e;for(let r in s){let a=s[r];n.has(a)||(n.add(a),C5(a,t,n))}}function VS(e){return Array.isArray(e)||typeof e=="object"}function Mm(e){return e.kernelName!=null}var T5=class{constructor(){this.registeredVariables={},this.nextTapeNodeId=0,this.numBytes=0,this.numTensors=0,this.numStringTensors=0,this.numDataBuffers=0,this.gradientDepth=0,this.kernelDepth=0,this.scopeStack=[],this.numDataMovesStack=[],this.nextScopeId=0,this.tensorInfo=new WeakMap,this.profiling=!1,this.activeProfile={newBytes:0,newTensors:0,peakBytes:0,kernels:[],result:null,get kernelNames(){return Array.from(new Set(this.kernels.map(e=>e.name)))}}}dispose(){for(let e in this.registeredVariables)this.registeredVariables[e].dispose()}},Xu=class{constructor(e){this.ENV=e,this.registry={},this.registryFactory={},this.pendingBackendInitId=0,this.state=new T5}async ready(){if(this.pendingBackendInit!=null)return this.pendingBackendInit.then(()=>{});if(this.backendInstance!=null)return;let e=this.getSortedBackends();for(let t=0;t<e.length;t++){let n=e[t];if(await this.initializeBackend(n).success){await this.setBackend(n);return}}throw new Error("Could not initialize any backends, all backend initializations failed.")}get backend(){if(this.pendingBackendInit!=null)throw new Error(`Backend '${this.backendName}' has not yet been initialized. Make sure to await tf.ready() or await tf.setBackend() before calling other methods`);if(this.backendInstance==null){let{name:e,asyncInit:t}=this.initializeBackendsAndReturnBest();if(t)throw new Error(`The highest priority backend '${e}' has not yet been initialized. Make sure to await tf.ready() or await tf.setBackend() before calling other methods`);this.setBackend(e)}return this.backendInstance}backendNames(){return Object.keys(this.registryFactory)}findBackend(e){if(!(e in this.registry))if(e in this.registryFactory){let{asyncInit:t}=this.initializeBackend(e);if(t)return null}else return null;return this.registry[e]}findBackendFactory(e){return e in this.registryFactory?this.registryFactory[e].factory:null}registerBackend(e,t,n=1){return e in this.registryFactory?(console.warn(`${e} backend was already registered. Reusing existing backend factory.`),!1):(this.registryFactory[e]={factory:t,priority:n},!0)}async setBackend(e){if(this.registryFactory[e]==null)throw new Error(`Backend name '${e}' not found in registry`);if(this.backendName=e,this.registry[e]==null){this.backendInstance=null;let{success:t,asyncInit:n}=this.initializeBackend(e);if(!(n?await t:t))return!1}return this.backendInstance=this.registry[e],this.setupRegisteredKernels(),this.profiler=new NS(this.backendInstance),!0}setupRegisteredKernels(){fl(this.backendName).forEach(t=>{t.setupFunc!=null&&t.setupFunc(this.backendInstance)})}disposeRegisteredKernels(e){fl(e).forEach(n=>{n.disposeFunc!=null&&n.disposeFunc(this.registry[e])})}initializeBackend(e){let t=this.registryFactory[e];if(t==null)throw new Error(`Cannot initialize backend ${e}, no registration found.`);try{let n=t.factory();if(n&&!(n instanceof ku)&&typeof n.then=="function"){let s=++this.pendingBackendInitId,r=n.then(a=>s<this.pendingBackendInitId?!1:(this.registry[e]=a,this.pendingBackendInit=null,!0)).catch(a=>(s<this.pendingBackendInitId||(this.pendingBackendInit=null,console.warn(`Initialization of backend ${e} failed`),console.warn(a.stack||a.message)),!1));return this.pendingBackendInit=r,{success:r,asyncInit:!0}}else return this.registry[e]=n,{success:!0,asyncInit:!1}}catch(n){return console.warn(`Initialization of backend ${e} failed`),console.warn(n.stack||n.message),{success:!1,asyncInit:!1}}}removeBackend(e){if(!(e in this.registryFactory))throw new Error(`${e} backend not found in registry`);this.backendName===e&&this.pendingBackendInit!=null&&this.pendingBackendInitId++,e in this.registry&&(this.disposeRegisteredKernels(e),this.registry[e].dispose(),delete this.registry[e]),delete this.registryFactory[e],this.backendName===e&&(this.pendingBackendInit=null,this.backendName=null,this.backendInstance=null)}getSortedBackends(){if(Object.keys(this.registryFactory).length===0)throw new Error("No backend found in registry.");return Object.keys(this.registryFactory).sort((e,t)=>this.registryFactory[t].priority-this.registryFactory[e].priority)}initializeBackendsAndReturnBest(){let e=this.getSortedBackends();for(let t=0;t<e.length;t++){let n=e[t],{success:s,asyncInit:r}=this.initializeBackend(n);if(r||s)return{name:n,asyncInit:r}}throw new Error("Could not initialize any backends, all backend initializations failed.")}moveData(e,t){let n=this.state.tensorInfo.get(t),s=n.backend,r=this.readSync(t),a=s.refCount(t);s.disposeData(t,!0),n.backend=e,e.move(t,r,n.shape,n.dtype,a),this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack[this.state.numDataMovesStack.length-1]++}tidy(e,t){let n=null;if(t==null){if(typeof e!="function")throw new Error("Please provide a function to tidy()");t=e}else{if(typeof e!="string"&&!(e instanceof String))throw new Error("When calling with two arguments, the first argument to tidy() must be a string");if(typeof t!="function")throw new Error("When calling with two arguments, the 2nd argument to tidy() must be a function");n=e}let s;return this.scopedRun(()=>this.startScope(n),()=>this.endScope(s),()=>(s=t(),s instanceof Promise&&console.error("Cannot return a Promise inside of tidy."),s))}scopedRun(e,t,n){e();try{let s=n();return t(),s}catch(s){throw t(),s}}nextTensorId(){return Xu.nextTensorId++}nextVariableId(){return Xu.nextVariableId++}clone(e){let t=L.runKernel(Oa,{x:e}),n={x:e},s=a=>({x:()=>{let o="float32",i={x:a},l={dtype:o};return L.runKernel(va,i,l)}}),r=[];return this.addTapeNode(this.state.activeScope.name,n,[t],s,r,{}),t}runKernel(e,t,n){if(this.backendName==null&&this.backend,!(vh(e,this.backendName)!=null))throw new Error(`Kernel '${e}' not registered for backend '${this.backendName}'`);return this.runKernelFunc({kernelName:e,inputs:t,attrs:n})}shouldCheckForMemLeaks(){return this.ENV.getBool("IS_TEST")}checkKernelForMemLeak(e,t,n){let s=this.backend.numDataIds(),r=0;n.forEach(i=>{r+=i.dtype==="complex64"?3:1});let a=this.state.numDataMovesStack[this.state.numDataMovesStack.length-1],o=s-t-r-a;if(o>0)throw new Error(`Backend '${this.backendName}' has an internal memory leak (${o} data ids) after running '${e}'`)}runKernelFunc(e){let t,n=[],s=this.isTapeOn(),r=this.state.numBytes,a=this.state.numTensors;this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack.push(0);let o;this.backendName==null&&this.backend;let i,l=Mm(e)?e.kernelName:this.state.activeScope!=null?this.state.activeScope.name:"";if(Mm(e)){let{kernelName:p,inputs:f,attrs:m}=e;this.backendName==null&&this.backend;let A=vh(p,this.backendName);P(A!=null,()=>`Cannot find registered kernel '${p}' for backend '${this.backendName}'`),o=()=>{let g=this.backend.numDataIds();i=A.kernelFunc({inputs:f,attrs:m,backend:this.backend});let y=Array.isArray(i)?i:[i];this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(p,g,y);let x=y.map(b=>{if(b.rank!=null)return b;let{dataId:v,shape:k,dtype:w}=b;return this.makeTensorFromDataId(v,k,w)});if(s){let b=this.getTensorsForGradient(p,f,x);n=this.saveTensorsForBackwardMode(b)}return x}}else{let{forwardFunc:p}=e,f=m=>{!s||(n=m.map(A=>this.keep(this.clone(A))))};o=()=>{let m=this.backend.numDataIds();i=this.tidy(()=>p(this.backend,f));let A=Array.isArray(i)?i:[i];return this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(l,m,A),A}}let{inputs:u,attrs:c}=e,d=Mm(e)?null:e.backwardsFunc,h;return this.scopedRun(()=>this.state.kernelDepth++,()=>this.state.kernelDepth--,()=>{!this.ENV.getBool("DEBUG")&&!this.state.profiling?t=o():(h=this.profiler.profileKernel(l,u,()=>o()),this.ENV.getBool("DEBUG")&&this.profiler.logKernelProfile(h),t=h.outputs)}),s&&this.addTapeNode(l,u,t,d,n,c),this.state.profiling&&this.state.activeProfile.kernels.push({name:l,bytesAdded:this.state.numBytes-r,totalBytesSnapshot:this.state.numBytes,tensorsAdded:this.state.numTensors-a,totalTensorsSnapshot:this.state.numTensors,inputShapes:Object.keys(u).map(p=>u[p]!=null?u[p].shape:null),outputShapes:t.map(p=>p.shape),kernelTimeMs:h.timeMs,extraInfo:h.extraInfo}),Array.isArray(i)?t:t[0]}saveTensorsForBackwardMode(e){return e.map(n=>this.keep(this.clone(n)))}getTensorsForGradient(e,t,n){let s=Tm(e);if(s!=null){let r=s.inputsToSave||[],a=s.outputsToSave||[],o;s.saveAllInputs?(P(Array.isArray(t),()=>"saveAllInputs is true, expected inputs to be an array."),o=Object.keys(t).map(l=>t[l])):o=r.map(l=>t[l]);let i=n.filter((l,u)=>a[u]);return o.concat(i)}return[]}makeTensor(e,t,n,s){if(e==null)throw new Error("Values passed to engine.makeTensor() are null");n=n||"float32",s=s||this.backend;let r=e;n==="string"&&$r(e[0])&&(r=e.map(i=>Uu(i)));let a=s.write(r,t,n),o=new Ue(t,n,a,this.nextTensorId());if(this.trackTensor(o,s),n==="string"){let i=this.state.tensorInfo.get(a),l=u5(r);this.state.numBytes+=l-i.bytes,i.bytes=l}return o}makeTensorFromDataId(e,t,n,s){n=n||"float32";let r=new Ue(t,n,e,this.nextTensorId());return this.trackTensor(r,s),r}makeVariable(e,t=!0,n,s){n=n||this.nextVariableId().toString(),s!=null&&s!==e.dtype&&(e=e.cast(s));let r=new qu(e,t,n,this.nextTensorId());if(this.state.registeredVariables[r.name]!=null)throw new Error(`Variable with name ${r.name} was already registered`);return this.state.registeredVariables[r.name]=r,this.incRef(r,this.backend),r}trackTensor(e,t){this.state.numTensors++,e.dtype==="string"&&this.state.numStringTensors++;let n=0;e.dtype!=="complex64"&&e.dtype!=="string"&&(n=e.size*vm(e.dtype)),this.state.numBytes+=n,this.state.tensorInfo.has(e.dataId)||(this.state.numDataBuffers++,this.state.tensorInfo.set(e.dataId,{backend:t||this.backend,dtype:e.dtype,shape:e.shape,bytes:n})),e instanceof qu||this.track(e)}incRef(e,t){this.trackTensor(e,t),this.backend.incRef(e.dataId)}removeDataId(e,t){this.state.tensorInfo.has(e)&&this.state.tensorInfo.get(e).backend===t&&(this.state.tensorInfo.delete(e),this.state.numDataBuffers--)}disposeTensor(e){if(!this.state.tensorInfo.has(e.dataId))return;let t=this.state.tensorInfo.get(e.dataId);if(this.state.numTensors--,e.dtype==="string"&&(this.state.numStringTensors--,this.state.numBytes-=t.bytes),e.dtype!=="complex64"&&e.dtype!=="string"){let n=e.size*vm(e.dtype);this.state.numBytes-=n}t.backend.disposeData(e.dataId)&&this.removeDataId(e.dataId,t.backend)}disposeVariables(){for(let e in this.state.registeredVariables){let t=this.state.registeredVariables[e];this.disposeVariable(t)}}disposeVariable(e){this.disposeTensor(e),this.state.registeredVariables[e.name]!=null&&delete this.state.registeredVariables[e.name]}memory(){let e=this.backend.memory();return e.numTensors=this.state.numTensors,e.numDataBuffers=this.state.numDataBuffers,e.numBytes=this.state.numBytes,this.state.numStringTensors>0&&(e.unreliable=!0,e.reasons==null&&(e.reasons=[]),e.reasons.push("Memory usage by string tensors is approximate (2 bytes per character)")),e}async profile(e){this.state.profiling=!0;let t=this.state.numBytes,n=this.state.numTensors;this.state.activeProfile.kernels=[],this.state.activeProfile.result=await e(),this.state.profiling=!1,this.state.activeProfile.peakBytes=Math.max(...this.state.activeProfile.kernels.map(s=>s.totalBytesSnapshot)),this.state.activeProfile.newBytes=this.state.numBytes-t,this.state.activeProfile.newTensors=this.state.numTensors-n;for(let s of this.state.activeProfile.kernels)s.kernelTimeMs=await s.kernelTimeMs,s.extraInfo=await s.extraInfo;return this.state.activeProfile}isTapeOn(){return this.state.gradientDepth>0&&this.state.kernelDepth===0}addTapeNode(e,t,n,s,r,a){let o={id:this.state.nextTapeNodeId++,kernelName:e,inputs:t,outputs:n,saved:r},i=Tm(e);i!=null&&(s=i.gradFunc),s!=null&&(o.gradient=l=>(l=l.map((u,c)=>{if(u==null){let d=n[c],h=Md(d.size,d.dtype);return this.makeTensor(h,d.shape,d.dtype)}return u}),s(l.length>1?l:l[0],r,a))),this.state.activeTape.push(o)}keep(e){return e.kept=!0,e}startTape(){this.state.gradientDepth===0&&(this.state.activeTape=[]),this.state.gradientDepth++}endTape(){this.state.gradientDepth--}startScope(e){let t={track:[],name:"unnamed scope",id:this.state.nextScopeId++};e&&(t.name=e),this.state.scopeStack.push(t),this.state.activeScope=t}endScope(e){let t=Pm(e),n=new Set(t.map(r=>r.id));for(let r=0;r<this.state.activeScope.track.length;r++){let a=this.state.activeScope.track[r];!a.kept&&!n.has(a.id)&&a.dispose()}let s=this.state.scopeStack.pop();this.state.activeScope=this.state.scopeStack.length===0?null:this.state.scopeStack[this.state.scopeStack.length-1],t.forEach(r=>{!r.kept&&r.scopeId===s.id&&this.track(r)})}gradients(e,t,n,s=!1){if(P(t.length>0,()=>"gradients() received an empty list of xs."),n!=null&&n.dtype!=="float32")throw new Error(`dy must have 'float32' dtype, but has '${n.dtype}'`);let r=this.scopedRun(()=>this.startTape(),()=>this.endTape(),()=>this.tidy("forward",e));P(r instanceof Ue,()=>"The result y returned by f() must be a tensor.");let a=_S(this.state.activeTape,t,r);if(!s&&a.length===0&&t.length>0)throw new Error("Cannot compute gradient of y=f(x) with respect to x. Make sure that the f you passed encloses all operations that lead from x to y.");return this.tidy("backward",()=>{let o={};o[r.id]=n==null?US(r.shape):n,$S(o,a,l=>this.tidy(l),HS);let i=t.map(l=>o[l.id]);return this.state.gradientDepth===0&&(this.state.activeTape.forEach(l=>{for(let u of l.saved)u.dispose()}),this.state.activeTape=null),{value:r,grads:i}})}customGrad(e){return P(Fr(e),()=>"The f passed in customGrad(f) must be a function."),(...t)=>{P(t.every(o=>o instanceof Ue),()=>"The args passed in customGrad(f)(x1, x2,...) must all be tensors");let n,s={};t.forEach((o,i)=>{s[i]=o});let r=(o,i)=>(n=e(...t,i),P(n.value instanceof Ue,()=>"The function f passed in customGrad(f) must return an object where `obj.value` is a tensor"),P(Fr(n.gradFunc),()=>"The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function."),n.value),a=(o,i)=>{let l=n.gradFunc(o,i),u=Array.isArray(l)?l:[l];P(u.length===t.length,()=>"The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function that returns the same number of tensors as inputs passed to f(...)."),P(u.every(d=>d instanceof Ue),()=>"The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function that returns a list of only tensors.");let c={};return u.forEach((d,h)=>{c[h]=()=>d}),c};return this.runKernelFunc({forwardFunc:r,backwardsFunc:a,inputs:s})}}readSync(e){return this.state.tensorInfo.get(e).backend.readSync(e)}read(e){return this.state.tensorInfo.get(e).backend.read(e)}async time(e){let t=Vu(),n=await this.backend.time(e);return n.wallMs=Vu()-t,n}track(e){return this.state.activeScope!=null&&(e.scopeId=this.state.activeScope.id,this.state.activeScope.track.push(e)),e}get registeredVariables(){return this.state.registeredVariables}reset(){this.pendingBackendInitId++,this.state.dispose(),this.ENV.reset(),this.state=new T5;for(let e in this.registry)this.disposeRegisteredKernels(e),this.registry[e].dispose(),delete this.registry[e];this.backendName=null,this.backendInstance=null,this.pendingBackendInit=null}};Xu.nextTensorId=0;Xu.nextVariableId=0;function US(e){let t=wm(_t(e),"float32");return L.makeTensor(t,e,"float32")}function N5(){let e=m5();if(e._tfengine==null){let t=new f5(e);e._tfengine=new Xu(t)}return fS(e._tfengine.ENV),PS(()=>e._tfengine),e._tfengine}var L=N5();function HS(e,t){let n={a:e,b:t};return L.runKernel(Dr,n)}var Ku={};Pe(Ku,{isBrowser:()=>E5,isMobile:()=>jS});function GS(){return typeof navigator!="undefined"&&navigator!=null}function jS(e){if(e||GS()){if(e||(e=navigator),e.product==="ReactNative")return!0;let t=e.userAgent||e.vendor||window.opera;return/(android|bb\d+|meego).+mobile|avantgo|bada\/|blackberry|blazer|compal|elaine|fennec|hiptop|iemobile|ip(hone|od)|iris|kindle|lge |maemo|midp|mmp|mobile.+firefox|netfront|opera m(ob|in)i|palm( os)?|phone|p(ixi|re)\/|plucker|pocket|psp|series(4|6)0|symbian|treo|up\.(browser|link)|vodafone|wap|windows ce|xda|xiino/i.test(t)||/1207|6310|6590|3gso|4thp|50[1-6]i|770s|802s|a wa|abac|ac(er|oo|s\-)|ai(ko|rn)|al(av|ca|co)|amoi|an(ex|ny|yw)|aptu|ar(ch|go)|as(te|us)|attw|au(di|\-m|r |s )|avan|be(ck|ll|nq)|bi(lb|rd)|bl(ac|az)|br(e|v)w|bumb|bw\-(n|u)|c55\/|capi|ccwa|cdm\-|cell|chtm|cldc|cmd\-|co(mp|nd)|craw|da(it|ll|ng)|dbte|dc\-s|devi|dica|dmob|do(c|p)o|ds(12|\-d)|el(49|ai)|em(l2|ul)|er(ic|k0)|esl8|ez([4-7]0|os|wa|ze)|fetc|fly(\-|_)|g1 u|g560|gene|gf\-5|g\-mo|go(\.w|od)|gr(ad|un)|haie|hcit|hd\-(m|p|t)|hei\-|hi(pt|ta)|hp( i|ip)|hs\-c|ht(c(\-| |_|a|g|p|s|t)|tp)|hu(aw|tc)|i\-(20|go|ma)|i230|iac( |\-|\/)|ibro|idea|ig01|ikom|im1k|inno|ipaq|iris|ja(t|v)a|jbro|jemu|jigs|kddi|keji|kgt( |\/)|klon|kpt |kwc\-|kyo(c|k)|le(no|xi)|lg( g|\/(k|l|u)|50|54|\-[a-w])|libw|lynx|m1\-w|m3ga|m50\/|ma(te|ui|xo)|mc(01|21|ca)|m\-cr|me(rc|ri)|mi(o8|oa|ts)|mmef|mo(01|02|bi|de|do|t(\-| |o|v)|zz)|mt(50|p1|v )|mwbp|mywa|n10[0-2]|n20[2-3]|n30(0|2)|n50(0|2|5)|n7(0(0|1)|10)|ne((c|m)\-|on|tf|wf|wg|wt)|nok(6|i)|nzph|o2im|op(ti|wv)|oran|owg1|p800|pan(a|d|t)|pdxg|pg(13|\-([1-8]|c))|phil|pire|pl(ay|uc)|pn\-2|po(ck|rt|se)|prox|psio|pt\-g|qa\-a|qc(07|12|21|32|60|\-[2-7]|i\-)|qtek|r380|r600|raks|rim9|ro(ve|zo)|s55\/|sa(ge|ma|mm|ms|ny|va)|sc(01|h\-|oo|p\-)|sdk\/|se(c(\-|0|1)|47|mc|nd|ri)|sgh\-|shar|sie(\-|m)|sk\-0|sl(45|id)|sm(al|ar|b3|it|t5)|so(ft|ny)|sp(01|h\-|v\-|v )|sy(01|mb)|t2(18|50)|t6(00|10|18)|ta(gt|lk)|tcl\-|tdg\-|tel(i|m)|tim\-|t\-mo|to(pl|sh)|ts(70|m\-|m3|m5)|tx\-9|up(\.b|g1|si)|utst|v400|v750|veri|vi(rg|te)|vk(40|5[0-3]|\-v)|vm40|voda|vulc|vx(52|53|60|61|70|80|81|83|85|98)|w3c(\-| )|webc|whit|wi(g |nc|nw)|wmlb|wonu|x700|yas\-|your|zeto|zte\-/i.test(t.substr(0,4))}return!1}function E5(){return typeof window!="undefined"&&window.document!=null||typeof WorkerGlobalScope!="undefined"}var Ts=ee();Ts.registerFlag("DEBUG",()=>!1,e=>{e&&console.warn("Debugging mode is ON. The output of every math call will be downloaded to CPU and checked for NaNs. This significantly impacts performance.")});Ts.registerFlag("IS_BROWSER",()=>E5());Ts.registerFlag("IS_NODE",()=>typeof process!="undefined"&&typeof process.versions!="undefined"&&typeof process.versions.node!="undefined");Ts.registerFlag("IS_CHROME",()=>typeof navigator!="undefined"&&navigator!=null&&navigator.userAgent!=null&&/Chrome/.test(navigator.userAgent)&&/Google Inc/.test(navigator.vendor));Ts.registerFlag("PROD",()=>!1);Ts.registerFlag("TENSORLIKE_CHECK_SHAPE_CONSISTENCY",()=>Ts.getBool("DEBUG"));Ts.registerFlag("DEPRECATION_WARNINGS_ENABLED",()=>!0);Ts.registerFlag("IS_TEST",()=>!1);Ts.registerFlag("CHECK_COMPUTATION_FOR_ERRORS",()=>!0);Ts.registerFlag("WRAP_TO_IMAGEBITMAP",()=>!1);function qs(e,t){let n=e;if(an(e))return t==="string"?[]:[e.length];if(!Array.isArray(e))return[];let s=[];for(;Array.isArray(n)||an(n)&&t!=="string";)s.push(n.length),n=n[0];return Array.isArray(e)&&ee().getBool("TENSORLIKE_CHECK_SHAPE_CONSISTENCY")&&R5(e,s,[]),s}function R5(e,t,n){if(n=n||[],!Array.isArray(e)&&!an(e)){P(t.length===0,()=>`Element arr[${n.join("][")}] is a primitive, but should be an array/TypedArray of ${t[0]} elements`);return}P(t.length>0,()=>`Element arr[${n.join("][")}] should be a primitive, but is an array of ${e.length} elements`),P(e.length===t[0],()=>`Element arr[${n.join("][")}] should have ${t[0]} elements, but has ${e.length} elements`);let s=t.slice(1);for(let r=0;r<e.length;++r)R5(e[r],s,n.concat(r))}function _5(e,t,n,s){if(e!=="string_or_numeric"){if(e==null)throw new Error("Expected dtype cannot be null.");if(e!=="numeric"&&e!==t||e==="numeric"&&t==="string")throw new Error(`Argument '${n}' passed to '${s}' must be ${e} tensor, but got ${t} tensor`)}}function D(e,t,n,s="numeric"){if(e instanceof Ue)return _5(s,e.dtype,t,n),e;let r=Od(e);if(r!=="string"&&["bool","int32","float32"].indexOf(s)>=0&&(r=s),_5(s,r,t,n),e==null||!an(e)&&!Array.isArray(e)&&typeof e!="number"&&typeof e!="boolean"&&typeof e!="string"){let l=e==null?"null":e.constructor.name;throw new Error(`Argument '${t}' passed to '${n}' must be a Tensor or TensorLike, but got '${l}'`)}let a=qs(e,r);!an(e)&&!Array.isArray(e)&&(e=[e]);let i=r!=="string"?kh(e,r):Aa(e,[],!0);return L.makeTensor(i,a,r)}function Zu(e,t,n,s="numeric"){if(!Array.isArray(e))throw new Error(`Argument ${t} passed to ${n} must be a \`Tensor[]\` or \`TensorLike[]\``);return e.map((a,o)=>D(a,`${t}[${o}]`,n,s))}var $5="__op";function V(e){let t=Object.keys(e);if(t.length!==1)throw new Error(`Please provide an object with a single key (operation name) mapping to a function. Got an object with ${t.length} keys.`);let n=t[0],s=e[n];n.endsWith("_")&&(n=n.substring(0,n.length-1)),n=n+$5;let r=(...a)=>{L.startScope(n);try{let o=s(...a);return Im(o)&&console.error("Cannot return a Promise inside of tidy."),L.endScope(o),o}catch(o){throw L.endScope(null),o}};return Object.defineProperty(r,"name",{value:n,configurable:!0}),r}function qS(e,t){let n=D(e,"real","complex"),s=D(t,"imag","complex");fn(n.shape,s.shape,`real and imag shapes, ${n.shape} and ${s.shape}, must match in call to tf.complex().`);let r={real:n,imag:s};return L.runKernel(Wd,r)}var Lr=V({complex_:qS});function Br(e,t,n,s){if(s==null&&(s=Od(e)),s==="complex64")throw new Error("Cannot construct a complex64 tensor directly. Please use tf.complex(real, imag).");if(!an(e)&&!Array.isArray(e)&&typeof e!="number"&&typeof e!="boolean"&&typeof e!="string")throw new Error("values passed to tensor(values) must be a number/boolean/string or an array of numbers/booleans/strings, or a TypedArray");if(t!=null){km(t);let r=_t(t),a=_t(n);P(r===a,()=>`Based on the provided shape, [${t}], the tensor should have ${r} values but has ${a}`);for(let o=0;o<n.length;++o){let i=n[o],l=o===n.length-1?i!==_t(t.slice(o)):!0;P(n[o]===t[o]||!l,()=>`Error creating a new Tensor. Inferred shape (${n}) does not match the provided shape (${t}). `)}}return!an(e)&&!Array.isArray(e)&&(e=[e]),t=t||n,e=s!=="string"?kh(e,s):Aa(e,[],!0),L.makeTensor(e,t,s)}function on(e,t,n){let s=qs(e,n);return Br(e,t,s,n)}var zm={float32:4,float16:2,int32:4,uint16:2,uint8:1,bool:1,complex64:8},Th=4;async function XS(e,t){let n=[],s=[],r=Array.isArray(e)?e.map(o=>o.name):Object.keys(e);for(let o=0;o<r.length;++o){let i=r[o],l=Array.isArray(e)?e[o].tensor:e[i];if(l.dtype!=="float32"&&l.dtype!=="int32"&&l.dtype!=="bool"&&l.dtype!=="string"&&l.dtype!=="complex64")throw new Error(`Unsupported dtype in weight '${i}': ${l.dtype}`);let u={name:i,shape:l.shape,dtype:l.dtype};if(l.dtype==="string"){let c=new Promise(async d=>{let h=await l.bytes(),p=h.reduce((A,g)=>A+g.length,0)+Th*h.length,f=new Uint8Array(p),m=0;for(let A=0;A<h.length;A++){let g=h[A],y=new Uint8Array(new Uint32Array([g.length]).buffer);f.set(y,m),m+=Th,f.set(g,m),m+=g.length}d(f)});s.push(c)}else s.push(l.data());t!=null&&(u.group=t),n.push(u)}let a=await Promise.all(s);return{data:KS(a),specs:n}}function F5(e,t){let n={},s,r=0;for(let a of t){let o=a.name,i=a.dtype,l=a.shape,u=_t(l),c;if("quantization"in a){let d=a.quantization;if(d.dtype==="uint8"||d.dtype==="uint16"){if(!("min"in d&&"scale"in d))throw new Error(`Weight ${a.name} with quantization ${d.dtype} doesn't have corresponding metadata min and scale.`)}else if(d.dtype==="float16"){if(i!=="float32")throw new Error(`Weight ${a.name} is quantized with ${d.dtype} which only supports weights of type float32 not ${i}.`)}else throw new Error(`Weight ${a.name} has unknown quantization dtype ${d.dtype}. Supported quantization dtypes are: 'uint8', 'uint16', and 'float16'.`);let h=zm[d.dtype],p=e.slice(r,r+u*h),f=d.dtype==="uint8"?new Uint8Array(p):new Uint16Array(p);if(i==="float32")if(d.dtype==="uint8"||d.dtype==="uint16"){c=new Float32Array(f.length);for(let m=0;m<f.length;m++){let A=f[m];c[m]=A*d.scale+d.min}}else if(d.dtype==="float16")s===void 0&&(s=t9()),c=s(f);else throw new Error(`Unsupported quantization type ${d.dtype} for weight type float32.`);else if(i==="int32"){if(d.dtype!=="uint8"&&d.dtype!=="uint16")throw new Error(`Unsupported quantization type ${d.dtype} for weight type int32.`);c=new Int32Array(f.length);for(let m=0;m<f.length;m++){let A=f[m];c[m]=Math.round(A*d.scale+d.min)}}else throw new Error(`Unsupported dtype in weight '${o}': ${i}`);r+=u*h}else if(i==="string"){let d=_t(a.shape);c=[];for(let h=0;h<d;h++){let p=new Uint32Array(e.slice(r,r+Th))[0];r+=Th;let f=new Uint8Array(e.slice(r,r+p));c.push(f),r+=p}}else{let d=zm[i],h=e.slice(r,r+u*d);if(i==="float32")c=new Float32Array(h);else if(i==="int32")c=new Int32Array(h);else if(i==="bool")c=new Uint8Array(h);else if(i==="complex64"){c=new Float32Array(h);let p=new Float32Array(c.length/2),f=new Float32Array(c.length/2);for(let g=0;g<p.length;g++)p[g]=c[g*2],f[g]=c[g*2+1];let m=on(p,l,"float32"),A=on(f,l,"float32");n[o]=Lr(m,A),m.dispose(),A.dispose()}else throw new Error(`Unsupported dtype in weight '${o}': ${i}`);r+=u*d}i!=="complex64"&&(n[o]=on(c,l,i))}return n}function KS(e){if(e===null)throw new Error(`Invalid input value: ${JSON.stringify(e)}`);let t=0,n=[];e.forEach(a=>{if(t+=a.byteLength,n.push(a.byteLength===a.buffer.byteLength?a:new a.constructor(a)),!(a instanceof Float32Array||a instanceof Int32Array||a instanceof Uint8Array))throw new Error(`Unsupported TypedArray subtype: ${a.constructor.name}`)});let s=new Uint8Array(t),r=0;return n.forEach(a=>{s.set(new Uint8Array(a.buffer),r),r+=a.byteLength}),s.buffer}var Lm=typeof Buffer!="undefined"&&(typeof Blob=="undefined"||typeof atob=="undefined"||typeof btoa=="undefined");function D5(e){return Lm?Buffer.byteLength(e):new Blob([e]).size}function ZS(e){if(Lm)return Buffer.from(e).toString("base64");let t=new Uint8Array(e),n="";for(let s=0,r=t.length;s<r;s++)n+=String.fromCharCode(t[s]);return btoa(n)}function YS(e){if(Lm){let s=Buffer.from(e,"base64");return s.buffer.slice(s.byteOffset,s.byteOffset+s.byteLength)}let t=atob(e),n=new Uint8Array(t.length);for(let s=0;s<t.length;++s)n.set([t.charCodeAt(s)],s);return n.buffer}function Bm(e){if(e.length===1)return e[0];let t=0;e.forEach(r=>{t+=r.byteLength});let n=new Uint8Array(t),s=0;return e.forEach(r=>{n.set(new Uint8Array(r),s),s+=r.byteLength}),n.buffer}function O5(e){let t="/";for(e=e.trim();e.endsWith(t);)e=e.slice(0,e.length-1);let n=e.split(t);return n[n.length-1]}function P5(e,t){let n={modelTopology:e.modelTopology,format:e.format,generatedBy:e.generatedBy,convertedBy:e.convertedBy,weightsManifest:t};return e.signature!=null&&(n.signature=e.signature),e.userDefinedMetadata!=null&&(n.userDefinedMetadata=e.userDefinedMetadata),e.modelInitializer!=null&&(n.modelInitializer=e.modelInitializer),e.trainingConfig!=null&&(n.trainingConfig=e.trainingConfig),n}async function Wm(e,t){let n={modelTopology:e.modelTopology,format:e.format,generatedBy:e.generatedBy,convertedBy:e.convertedBy};if(e.trainingConfig!=null&&(n.trainingConfig=e.trainingConfig),e.weightsManifest!=null){let[s,r]=await t(e.weightsManifest);n.weightSpecs=s,n.weightData=r}return e.signature!=null&&(n.signature=e.signature),e.userDefinedMetadata!=null&&(n.userDefinedMetadata=e.userDefinedMetadata),e.modelInitializer!=null&&(n.modelInitializer=e.modelInitializer),n}function Yu(e){if(e.modelTopology instanceof ArrayBuffer)throw new Error("Expected JSON model topology, received ArrayBuffer.");return{dateSaved:new Date,modelTopologyType:"JSON",modelTopologyBytes:e.modelTopology==null?0:D5(JSON.stringify(e.modelTopology)),weightSpecsBytes:e.weightSpecs==null?0:D5(JSON.stringify(e.weightSpecs)),weightDataBytes:e.weightData==null?0:e.weightData.byteLength}}function JS(){let e=n=>{let s=n<<13,r=0;for(;(s&8388608)==0;)r-=8388608,s<<=1;return s&=~8388608,r+=947912704,s|r},t=new Uint32Array(2048);t[0]=0;for(let n=1;n<1024;n++)t[n]=e(n);for(let n=1024;n<2048;n++)t[n]=939524096+(n-1024<<13);return t}function QS(){let e=new Uint32Array(64);e[0]=0,e[31]=1199570944,e[32]=2147483648,e[63]=3347054592;for(let t=1;t<31;t++)e[t]=t<<23;for(let t=33;t<63;t++)e[t]=2147483648+(t-32<<23);return e}function e9(){let e=new Uint32Array(64);for(let t=0;t<64;t++)e[t]=1024;return e[0]=e[32]=0,e}function t9(){let e=JS(),t=QS(),n=e9();return s=>{let r=new ArrayBuffer(4*s.length),a=new Uint32Array(r);for(let o=0;o<s.length;o++){let i=s[o],l=e[n[i>>10]+(i&1023)]+t[i>>10];a[o]=l}return new Float32Array(r)}}var Nt=class{constructor(){this.saveRouters=[],this.loadRouters=[]}static getInstance(){return Nt.instance==null&&(Nt.instance=new Nt),Nt.instance}static registerSaveRouter(e){Nt.getInstance().saveRouters.push(e)}static registerLoadRouter(e){Nt.getInstance().loadRouters.push(e)}static getSaveHandlers(e){return Nt.getHandlers(e,"save")}static getLoadHandlers(e,t){return Nt.getHandlers(e,"load",t)}static getHandlers(e,t,n){let s=[];return(t==="load"?Nt.getInstance().loadRouters:Nt.getInstance().saveRouters).forEach(a=>{let o=a(e,n);o!==null&&s.push(o)}),s}},n9=e=>Nt.registerSaveRouter(e),s9=e=>Nt.registerLoadRouter(e),r9=e=>Nt.getSaveHandlers(e),a9=(e,t)=>Nt.getLoadHandlers(e,t),Vm="tensorflowjs",Um=1,xo="models_store",Wr="model_info_store";function M5(){if(!ee().getBool("IS_BROWSER"))throw new Error("Failed to obtain IndexedDB factory because the current environmentis not a web browser.");let e=typeof window=="undefined"?self:window,t=e.indexedDB||e.mozIndexedDB||e.webkitIndexedDB||e.msIndexedDB||e.shimIndexedDB;if(t==null)throw new Error("The current browser does not appear to support IndexedDB.");return t}function Hm(e){let t=e.result;t.createObjectStore(xo,{keyPath:"modelPath"}),t.createObjectStore(Wr,{keyPath:"modelPath"})}var bo=class{constructor(e){if(this.indexedDB=M5(),e==null||!e)throw new Error("For IndexedDB, modelPath must not be null, undefined or empty.");this.modelPath=e}async save(e){if(e.modelTopology instanceof ArrayBuffer)throw new Error("BrowserLocalStorage.save() does not support saving model topology in binary formats yet.");return this.databaseAction(this.modelPath,e)}async load(){return this.databaseAction(this.modelPath)}databaseAction(e,t){return new Promise((n,s)=>{let r=this.indexedDB.open(Vm,Um);r.onupgradeneeded=()=>Hm(r),r.onsuccess=()=>{let a=r.result;if(t==null){let o=a.transaction(xo,"readonly"),l=o.objectStore(xo).get(this.modelPath);l.onsuccess=()=>{if(l.result==null)return a.close(),s(new Error(`Cannot find model with path '${this.modelPath}' in IndexedDB.`));n(l.result.modelArtifacts)},l.onerror=u=>(a.close(),s(l.error)),o.oncomplete=()=>a.close()}else{let o=Yu(t),i=a.transaction(Wr,"readwrite"),l=i.objectStore(Wr),u=l.put({modelPath:this.modelPath,modelArtifactsInfo:o}),c;u.onsuccess=()=>{c=a.transaction(xo,"readwrite");let h=c.objectStore(xo).put({modelPath:this.modelPath,modelArtifacts:t,modelArtifactsInfo:o});h.onsuccess=()=>n({modelArtifactsInfo:o}),h.onerror=p=>{l=i.objectStore(Wr);let f=l.delete(this.modelPath);f.onsuccess=()=>(a.close(),s(h.error)),f.onerror=m=>(a.close(),s(h.error))}},u.onerror=d=>(a.close(),s(u.error)),i.oncomplete=()=>{c==null?a.close():c.oncomplete=()=>a.close()}}},r.onerror=a=>s(r.error)})}};bo.URL_SCHEME="indexeddb://";var z5=e=>ee().getBool("IS_BROWSER")&&!Array.isArray(e)&&e.startsWith(bo.URL_SCHEME)?o9(e.slice(bo.URL_SCHEME.length)):null;Nt.registerSaveRouter(z5);Nt.registerLoadRouter(z5);function o9(e){return new bo(e)}function i9(e){return e.startsWith(bo.URL_SCHEME)?e.slice(bo.URL_SCHEME.length):e}var l9=class{constructor(){this.indexedDB=M5()}async listModels(){return new Promise((e,t)=>{let n=this.indexedDB.open(Vm,Um);n.onupgradeneeded=()=>Hm(n),n.onsuccess=()=>{let s=n.result,r=s.transaction(Wr,"readonly"),o=r.objectStore(Wr).getAll();o.onsuccess=()=>{let i={};for(let l of o.result)i[l.modelPath]=l.modelArtifactsInfo;e(i)},o.onerror=i=>(s.close(),t(o.error)),r.oncomplete=()=>s.close()},n.onerror=s=>t(n.error)})}async removeModel(e){return e=i9(e),new Promise((t,n)=>{let s=this.indexedDB.open(Vm,Um);s.onupgradeneeded=()=>Hm(s),s.onsuccess=()=>{let r=s.result,a=r.transaction(Wr,"readwrite"),o=a.objectStore(Wr),i=o.get(e),l;i.onsuccess=()=>{if(i.result==null)return r.close(),n(new Error(`Cannot find model with path '${e}' in IndexedDB.`));{let u=o.delete(e),c=()=>{l=r.transaction(xo,"readwrite");let h=l.objectStore(xo).delete(e);h.onsuccess=()=>t(i.result.modelArtifactsInfo),h.onerror=p=>n(i.error)};u.onsuccess=c,u.onerror=d=>(c(),r.close(),n(i.error))}},i.onerror=u=>(r.close(),n(i.error)),a.oncomplete=()=>{l==null?r.close():l.oncomplete=()=>r.close()}},s.onerror=r=>n(s.error)})}},mr="/",Al="tensorflowjs_models",L5="info",u9="model_topology",c9="weight_specs",d9="weight_data",h9="model_metadata";function B5(e){return{info:[Al,e,L5].join(mr),topology:[Al,e,u9].join(mr),weightSpecs:[Al,e,c9].join(mr),weightData:[Al,e,d9].join(mr),modelMetadata:[Al,e,h9].join(mr)}}function W5(e){for(let t of Object.values(e))window.localStorage.removeItem(t)}function p9(e){let t=e.split(mr);if(t.length<3)throw new Error(`Invalid key format: ${e}`);return t.slice(1,t.length-1).join(mr)}function f9(e){return e.startsWith(vo.URL_SCHEME)?e.slice(vo.URL_SCHEME.length):e}var vo=class{constructor(e){if(!ee().getBool("IS_BROWSER")||typeof window=="undefined"||typeof window.localStorage=="undefined")throw new Error("The current environment does not support local storage.");if(this.LS=window.localStorage,e==null||!e)throw new Error("For local storage, modelPath must not be null, undefined or empty.");this.modelPath=e,this.keys=B5(this.modelPath)}async save(e){if(e.modelTopology instanceof ArrayBuffer)throw new Error("BrowserLocalStorage.save() does not support saving model topology in binary formats yet.");{let t=JSON.stringify(e.modelTopology),n=JSON.stringify(e.weightSpecs),s=Yu(e);try{this.LS.setItem(this.keys.info,JSON.stringify(s)),this.LS.setItem(this.keys.topology,t),this.LS.setItem(this.keys.weightSpecs,n),this.LS.setItem(this.keys.weightData,ZS(e.weightData));let r={format:e.format,generatedBy:e.generatedBy,convertedBy:e.convertedBy,signature:e.signature!=null?e.signature:void 0,userDefinedMetadata:e.userDefinedMetadata!=null?e.userDefinedMetadata:void 0,modelInitializer:e.modelInitializer!=null?e.modelInitializer:void 0,trainingConfig:e.trainingConfig!=null?e.trainingConfig:void 0};return this.LS.setItem(this.keys.modelMetadata,JSON.stringify(r)),{modelArtifactsInfo:s}}catch(r){throw W5(this.keys),new Error(`Failed to save model '${this.modelPath}' to local storage: size quota being exceeded is a possible cause of this failure: modelTopologyBytes=${s.modelTopologyBytes}, weightSpecsBytes=${s.weightSpecsBytes}, weightDataBytes=${s.weightDataBytes}.`)}}}async load(){let e=JSON.parse(this.LS.getItem(this.keys.info));if(e==null)throw new Error(`In local storage, there is no model with name '${this.modelPath}'`);if(e.modelTopologyType!=="JSON")throw new Error("BrowserLocalStorage does not support loading non-JSON model topology yet.");let t={},n=JSON.parse(this.LS.getItem(this.keys.topology));if(n==null)throw new Error(`In local storage, the topology of model '${this.modelPath}' is missing.`);t.modelTopology=n;let s=JSON.parse(this.LS.getItem(this.keys.weightSpecs));if(s==null)throw new Error(`In local storage, the weight specs of model '${this.modelPath}' are missing.`);t.weightSpecs=s;let r=this.LS.getItem(this.keys.modelMetadata);if(r!=null){let o=JSON.parse(r);t.format=o.format,t.generatedBy=o.generatedBy,t.convertedBy=o.convertedBy,o.signature!=null&&(t.signature=o.signature),o.userDefinedMetadata!=null&&(t.userDefinedMetadata=o.userDefinedMetadata),o.modelInitializer!=null&&(t.modelInitializer=o.modelInitializer),o.trainingConfig!=null&&(t.trainingConfig=o.trainingConfig)}let a=this.LS.getItem(this.keys.weightData);if(a==null)throw new Error(`In local storage, the binary weight values of model '${this.modelPath}' are missing.`);return t.weightData=YS(a),t}};vo.URL_SCHEME="localstorage://";var V5=e=>ee().getBool("IS_BROWSER")&&!Array.isArray(e)&&e.startsWith(vo.URL_SCHEME)?m9(e.slice(vo.URL_SCHEME.length)):null;Nt.registerSaveRouter(V5);Nt.registerLoadRouter(V5);function m9(e){return new vo(e)}var A9=class{constructor(){P(ee().getBool("IS_BROWSER"),()=>"Current environment is not a web browser"),P(typeof window=="undefined"||typeof window.localStorage!="undefined",()=>"Current browser does not appear to support localStorage"),this.LS=window.localStorage}async listModels(){let e={},t=Al+mr,n=mr+L5;for(let s=0;s<this.LS.length;++s){let r=this.LS.key(s);if(r.startsWith(t)&&r.endsWith(n)){let a=p9(r);e[a]=JSON.parse(this.LS.getItem(r))}}return e}async removeModel(e){e=f9(e);let t=B5(e);if(this.LS.getItem(t.info)==null)throw new Error(`Cannot find model at path '${e}'`);let n=JSON.parse(this.LS.getItem(t.info));return W5(t),n}},gl="://",ss=class{constructor(){this.managers={}}static getInstance(){return ss.instance==null&&(ss.instance=new ss),ss.instance}static registerManager(e,t){P(e!=null,()=>"scheme must not be undefined or null."),e.endsWith(gl)&&(e=e.slice(0,e.indexOf(gl))),P(e.length>0,()=>"scheme must not be an empty string.");let n=ss.getInstance();P(n.managers[e]==null,()=>`A model store manager is already registered for scheme '${e}'.`),n.managers[e]=t}static getManager(e){let t=this.getInstance().managers[e];if(t==null)throw new Error(`Cannot find model manager for scheme '${e}'`);return t}static getSchemes(){return Object.keys(this.getInstance().managers)}};function Nh(e){if(e.indexOf(gl)===-1)throw new Error(`The url string provided does not contain a scheme. Supported schemes are: ${ss.getSchemes().join(",")}`);return{scheme:e.split(gl)[0],path:e.split(gl)[1]}}async function U5(e,t,n=!1){P(e!==t,()=>`Old path and new path are the same: '${e}'`);let s=Nt.getLoadHandlers(e);P(s.length>0,()=>`Copying failed because no load handler is found for source URL ${e}.`),P(s.length<2,()=>`Copying failed because more than one (${s.length}) load handlers for source URL ${e}.`);let r=s[0],a=Nt.getSaveHandlers(t);P(a.length>0,()=>`Copying failed because no save handler is found for destination URL ${t}.`),P(a.length<2,()=>`Copying failed because more than one (${s.length}) save handlers for destination URL ${t}.`);let o=a[0],i=Nh(e).scheme,l=Nh(e).path,u=i===Nh(e).scheme,c=await r.load();n&&u&&await ss.getManager(i).removeModel(l);let d=await o.save(c);return n&&!u&&await ss.getManager(i).removeModel(l),d.modelArtifactsInfo}async function g9(){let e=ss.getSchemes(),t={};for(let n of e){let s=await ss.getManager(n).listModels();for(let r in s){let a=n+gl+r;t[a]=s[r]}}return t}async function y9(e){let t=Nh(e);return ss.getManager(t.scheme).removeModel(t.path)}async function x9(e,t){return U5(e,t,!1)}async function b9(e,t){return U5(e,t,!0)}var v9=class{fetch(e,t){return fetch(e,t)}now(){return performance.now()}encode(e,t){if(t!=="utf-8"&&t!=="utf8")throw new Error(`Browser's encoder only supports utf-8, but got ${t}`);return this.textEncoder==null&&(this.textEncoder=new TextEncoder),this.textEncoder.encode(e)}decode(e,t){return new TextDecoder(t).decode(e)}};if(ee().get("IS_BROWSER")){ee().setPlatform("browser",new v9);try{ss.registerManager(vo.URL_SCHEME,new A9)}catch(e){}try{ss.registerManager(bo.URL_SCHEME,new l9)}catch(e){}}var w9={importFetch:()=>bI()},Gm,k9=class{constructor(){this.util=li("util"),this.textEncoder=new this.util.TextEncoder}fetch(e,t){return ee().global.fetch!=null?ee().global.fetch(e,t):(Gm==null&&(Gm=w9.importFetch()),Gm(e,t))}now(){let e=process.hrtime();return e[0]*1e3+e[1]/1e6}encode(e,t){if(t!=="utf-8"&&t!=="utf8")throw new Error(`Node built-in encoder only supports utf-8, but got ${t}`);return this.textEncoder.encode(e)}decode(e,t){return e.length===0?"":new this.util.TextDecoder(t).decode(e)}};ee().get("IS_NODE")&&ee().setPlatform("node",new k9);function Be(e,t="float32",n){return t=t||"float32",km(e),new Bt(e,t,n)}function I9(e,t){let n=D(e,"x","cast");if(!l5(t))throw new Error(`Failed to cast to unknown dtype ${t}`);if(t==="string"&&n.dtype!=="string"||t!=="string"&&n.dtype==="string")throw new Error("Only strings can be casted to strings");let s={x:n},r={dtype:t};return L.runKernel(va,s,r)}var ce=V({cast_:I9});function S9(e){let n={x:D(e,"x","clone","string_or_numeric")};return L.runKernel(Oa,n)}var Ns=V({clone_:S9});function H5(e,t=!1){console.log(e.toString(t))}N5();var C9={buffer:Be,cast:ce,clone:Ns,print:H5};MS(C9);var Tn={};Pe(Tn,{browserFiles:()=>F9,browserHTTPRequest:()=>z9,concatenateArrayBuffers:()=>Bm,copyModel:()=>x9,decodeWeights:()=>F5,encodeWeights:()=>XS,fromMemory:()=>B9,getLoadHandlers:()=>a9,getModelArtifactsForJSON:()=>Wm,getModelArtifactsInfoForJSON:()=>Yu,getSaveHandlers:()=>r9,http:()=>Xm,isHTTPScheme:()=>qm,listModels:()=>g9,loadWeights:()=>D9,moveModel:()=>b9,registerLoadRouter:()=>s9,registerSaveRouter:()=>n9,removeModel:()=>y9,weightsLoaderFactory:()=>X5,withSaveHandler:()=>W9});var T9="model",N9=".json",E9=".weights.bin";function G5(e){return new Promise(t=>setTimeout(t)).then(e)}var yl=class{constructor(e){if(!ee().getBool("IS_BROWSER"))throw new Error("browserDownloads() cannot proceed because the current environment is not a browser.");e.startsWith(yl.URL_SCHEME)&&(e=e.slice(yl.URL_SCHEME.length)),(e==null||e.length===0)&&(e=T9),this.modelJsonFileName=e+N9,this.weightDataFileName=e+E9}async save(e){if(typeof document=="undefined")throw new Error("Browser downloads are not supported in this environment since `document` is not present");let t=window.URL.createObjectURL(new Blob([e.weightData],{type:"application/octet-stream"}));if(e.modelTopology instanceof ArrayBuffer)throw new Error("BrowserDownloads.save() does not support saving model topology in binary formats yet.");{let n=[{paths:["./"+this.weightDataFileName],weights:e.weightSpecs}],s=P5(e,n),r=window.URL.createObjectURL(new Blob([JSON.stringify(s)],{type:"application/json"})),a=this.modelJsonAnchor==null?document.createElement("a"):this.modelJsonAnchor;if(a.download=this.modelJsonFileName,a.href=r,await G5(()=>a.dispatchEvent(new MouseEvent("click"))),e.weightData!=null){let o=this.weightDataAnchor==null?document.createElement("a"):this.weightDataAnchor;o.download=this.weightDataFileName,o.href=t,await G5(()=>o.dispatchEvent(new MouseEvent("click")))}return{modelArtifactsInfo:Yu(e)}}}};yl.URL_SCHEME="downloads://";var R9=class{constructor(e){if(e==null||e.length<1)throw new Error(`When calling browserFiles, at least 1 file is required, but received ${e}`);this.jsonFile=e[0],this.weightsFiles=e.slice(1)}async load(){return new Promise((e,t)=>{let n=new FileReader;n.onload=s=>{let r=JSON.parse(s.target.result),a=r.modelTopology;if(a==null){t(new Error(`modelTopology field is missing from file ${this.jsonFile.name}`));return}if(r.weightsManifest==null){t(new Error(`weightManifest field is missing from file ${this.jsonFile.name}`));return}if(this.weightsFiles.length===0){e({modelTopology:a});return}let i=Wm(r,l=>this.loadWeights(l));e(i)},n.onerror=s=>t(`Failed to read model topology and weights manifest JSON from file '${this.jsonFile.name}'. BrowserFiles supports loading Keras-style tf.Model artifacts only.`),n.readAsText(this.jsonFile)})}loadWeights(e){let t=[],n=[];for(let a of e)t.push(...a.weights),n.push(...a.paths);let s=this.checkManifestAndWeightFiles(e),r=n.map(a=>this.loadWeightsFile(a,s[a]));return Promise.all(r).then(a=>[t,Bm(a)])}loadWeightsFile(e,t){return new Promise((n,s)=>{let r=new FileReader;r.onload=a=>{let o=a.target.result;n(o)},r.onerror=a=>s(`Failed to weights data from file of path '${e}'.`),r.readAsArrayBuffer(t)})}checkManifestAndWeightFiles(e){let t=[],n=this.weightsFiles.map(r=>O5(r.name)),s={};for(let r of e)r.paths.forEach(a=>{let o=O5(a);if(t.indexOf(o)!==-1)throw new Error(`Duplicate file basename found in weights manifest: '${o}'`);if(t.push(o),n.indexOf(o)===-1)throw new Error(`Weight file with basename '${o}' is not provided.`);s[a]=this.weightsFiles[n.indexOf(o)]});if(t.length!==this.weightsFiles.length)throw new Error(`Mismatch in the number of files in weights manifest (${t.length}) and the number of weight files provided (${this.weightsFiles.length}).`);return s}},_9=e=>ee().getBool("IS_BROWSER")&&!Array.isArray(e)&&e.startsWith(yl.URL_SCHEME)?$9(e.slice(yl.URL_SCHEME.length)):null;Nt.registerSaveRouter(_9);function $9(e="model"){return new yl(e)}function F9(e){return new R9(e)}function j5(e,t,n,s){o(e),n=n==null?0:n,s=s==null?1:s,i(n,s);let r=0,a=l=>(l.then(u=>{let c=n+ ++r/e.length*(s-n);return t(c),u}),l);function o(l){P(l!=null&&Array.isArray(l)&&l.length>0,()=>"promises must be a none empty array")}function i(l,u){P(l>=0&&l<=1,()=>`Progress fraction must be in range [0, 1], but got startFraction ${l}`),P(u>=0&&u<=1,()=>`Progress fraction must be in range [0, 1], but got endFraction ${u}`),P(u>=l,()=>`startFraction must be no more than endFraction, but got startFraction ${l} and endFraction ${u}`)}return Promise.all(e.map(a))}async function q5(e,t){t==null&&(t={});let n=t.fetchFunc==null?ee().platform.fetch:t.fetchFunc,s=e.map(d=>n(d,t.requestInit,{isBinary:!0})),r=0,a=.5,i=(t.onProgress==null?await Promise.all(s):await j5(s,t.onProgress,r,a)).map(d=>d.arrayBuffer()),l=.5,u=1;return t.onProgress==null?await Promise.all(i):await j5(i,t.onProgress,l,u)}async function D9(e,t="",n,s){return X5(o=>q5(o,{requestInit:s}))(e,t,n)}function X5(e){return async(t,n="",s)=>{let r=t.map(()=>!1),a={},o=s!=null?s.map(()=>!1):[],i=[];if(t.forEach((p,f)=>{let m=0;p.weights.forEach(A=>{let g="quantization"in A?A.quantization.dtype:A.dtype,y=zm[g]*_t(A.shape),x=()=>{r[f]=!0,a[f]==null&&(a[f]=[]),a[f].push({manifestEntry:A,groupOffset:m,sizeBytes:y})};s!=null?s.forEach((b,v)=>{b===A.name&&(x(),o[v]=!0)}):x(),i.push(A.name),m+=y})}),!o.every(p=>p)){let p=s.filter((f,m)=>!o[m]);throw new Error(`Could not find weights in manifest with names: ${p.join(", ")}. 
Manifest JSON has weights with names: ${i.join(", ")}.`)}let l=r.reduce((p,f,m)=>(f&&p.push(m),p),[]),u=[];l.forEach(p=>{t[p].paths.forEach(f=>{let m=n+(n.endsWith("/")?"":"/")+f;u.push(m)})});let c=await e(u),d={},h=0;return l.forEach(p=>{let f=t[p].paths.length,m=0;for(let b=0;b<f;b++)m+=c[h+b].byteLength;let A=new ArrayBuffer(m),g=new Uint8Array(A),y=0;for(let b=0;b<f;b++){let v=new Uint8Array(c[h+b]);g.set(v,y),y+=v.byteLength}a[p].forEach(b=>{let v=A.slice(b.groupOffset,b.groupOffset+b.sizeBytes),k=F5(v,[b.manifestEntry]);for(let w in k)d[w]=k[w]}),h+=f}),d}}var O9="application/octet-stream",P9="application/json",jm=class{constructor(e,t){if(this.DEFAULT_METHOD="POST",t==null&&(t={}),this.weightPathPrefix=t.weightPathPrefix,this.onProgress=t.onProgress,this.weightUrlConverter=t.weightUrlConverter,t.fetchFunc!=null?(P(typeof t.fetchFunc=="function",()=>"Must pass a function that matches the signature of `fetch` (see https://developer.mozilla.org/en-US/docs/Web/API/Fetch_API)"),this.fetch=t.fetchFunc):this.fetch=ee().platform.fetch,P(e!=null&&e.length>0,()=>"URL path for http must not be null, undefined or empty."),Array.isArray(e)&&P(e.length===2,()=>`URL paths for http must have a length of 2, (actual length is ${e.length}).`),this.path=e,t.requestInit!=null&&t.requestInit.body!=null)throw new Error("requestInit is expected to have no pre-existing body, but has one.");this.requestInit=t.requestInit||{}}async save(e){if(e.modelTopology instanceof ArrayBuffer)throw new Error("BrowserHTTPRequest.save() does not support saving model topology in binary formats yet.");let t=Object.assign({method:this.DEFAULT_METHOD},this.requestInit);t.body=new FormData;let n=[{paths:["./model.weights.bin"],weights:e.weightSpecs}],s=P5(e,n);t.body.append("model.json",new Blob([JSON.stringify(s)],{type:P9}),"model.json"),e.weightData!=null&&t.body.append("model.weights.bin",new Blob([e.weightData],{type:O9}),"model.weights.bin");let r=await this.fetch(this.path,t);if(r.ok)return{modelArtifactsInfo:Yu(e),responses:[r]};throw new Error(`BrowserHTTPRequest.save() failed due to HTTP response status ${r.status}.`)}async load(){let e=await this.fetch(this.path,this.requestInit);if(!e.ok)throw new Error(`Request to ${this.path} failed with status code ${e.status}. Please verify this URL points to the model JSON of the model to load.`);let t;try{t=await e.json()}catch(r){let a=`Failed to parse model JSON of response from ${this.path}.`;throw this.path.endsWith(".pb")?a+=" Your path contains a .pb file extension. Support for .pb models have been removed in TensorFlow.js 1.0 in favor of .json models. You can re-convert your Python TensorFlow model using the TensorFlow.js 1.0 conversion scripts or you can convert your.pb models with the 'pb2json'NPM script in the tensorflow/tfjs-converter repository.":a+=" Please make sure the server is serving valid JSON for this request.",new Error(a)}let n=t.modelTopology,s=t.weightsManifest;if(n==null&&s==null)throw new Error(`The JSON from HTTP path ${this.path} contains neither model topology or manifest for weights.`);return Wm(t,r=>this.loadWeights(r))}async loadWeights(e){let t=Array.isArray(this.path)?this.path[1]:this.path,[n,s]=M9(t),r=this.weightPathPrefix||n,a=[];for(let u of e)a.push(...u.weights);let o=[],i=[];for(let u of e)for(let c of u.paths)this.weightUrlConverter!=null?i.push(this.weightUrlConverter(c)):o.push(r+c+s);this.weightUrlConverter&&o.push(...await Promise.all(i));let l=await q5(o,{requestInit:this.requestInit,fetchFunc:this.fetch,onProgress:this.onProgress});return[a,Bm(l)]}};jm.URL_SCHEME_REGEX=/^https?:\/\//;function M9(e){let t=e.lastIndexOf("/"),n=e.lastIndexOf("?"),s=e.substring(0,t),r=n>t?e.substring(n):"";return[s+"/",r]}function qm(e){return e.match(jm.URL_SCHEME_REGEX)!=null}var K5=(e,t)=>{if(typeof fetch=="undefined"&&(t==null||t.fetchFunc==null))return null;{let n=!0;if(Array.isArray(e)?n=e.every(s=>qm(s)):n=qm(e),n)return Xm(e,t)}return null};Nt.registerSaveRouter(K5);Nt.registerLoadRouter(K5);function Xm(e,t){return new jm(e,t)}function z9(e,t){return Xm(e,t)}var Km=class{constructor(e){this.modelArtifacts=e}async load(){return this.modelArtifacts}},L9=class{constructor(e){this.saveHandler=e}async save(e){return this.saveHandler(e)}};function B9(e,t,n,s){return arguments.length===1?e.modelTopology!=null||e.weightSpecs!=null?new Km(e):(console.warn("Please call tf.io.fromMemory() with only one argument. The argument should be of type ModelArtifacts. The multi-argument signature of tf.io.fromMemory() has been deprecated and will be removed in a future release."),new Km({modelTopology:e})):(console.warn("Please call tf.io.fromMemory() with only one argument. The argument should be of type ModelArtifacts. The multi-argument signature of tf.io.fromMemory() has been deprecated and will be removed in a future release."),new Km({modelTopology:e,weightSpecs:t,weightData:n,trainingConfig:s}))}function W9(e){return new L9(e)}var Z5={};Pe(Z5,{confusionMatrix:()=>j9});function V9(e,t,n=!1,s=!1){let r=D(e,"a","matMul"),a=D(t,"b","matMul");[r,a]=It(r,a);let o={a:r,b:a},i={transposeA:n,transposeB:s};return L.runKernel(ba,o,i)}var We=V({matMul_:V9});function U9(e,t,n=1,s=0){if(t<2)throw new Error(`Error in oneHot: depth must be >=2, but it is ${t}`);let a={indices:D(e,"indices","oneHot","int32")},o={depth:t,onValue:n,offValue:s};return L.runKernel(ja,a,o)}var Ju=V({oneHot_:U9});function H9(e,t){let n=D(e,"x","transpose");if(t==null&&(t=n.shape.map((a,o)=>o).reverse()),P(n.rank===t.length,()=>`Error in transpose: rank of input ${n.rank} must match length of perm ${t}.`),t.forEach(a=>{P(a>=0&&a<n.rank,()=>`All entries in 'perm' must be between 0 and ${n.rank-1} but got ${t}`)}),n.rank<=1)return n.clone();let s={x:n},r={perm:t};return L.runKernel(ho,s,r)}var je=V({transpose_:H9});function G9(e,t,n){let s=D(e,"labels","confusionMatrix"),r=D(t,"predictions","confusionMatrix");P(n==null||n>0&&Number.isInteger(n),()=>`If provided, numClasses must be a positive integer, but got ${n}`),P(s.rank===1,()=>`Expected the rank of labels to be 1, but got ${s.rank}`),P(r.rank===1,()=>`Expected the rank of predictions to be 1, but got ${r.rank}`),P(s.shape[0]===r.shape[0],()=>`Mismatch in the number of examples: ${s.shape[0]} vs. ${r.shape[0]}. Labels and predictions should have the same number of elements.`),P(n>0&&Number.isInteger(n),()=>`numClasses is required to be a positive integer, but got ${n}`);let a=Ju(ce(s,"int32"),n),o=Ju(ce(r,"int32"),n),i=je(a),l=We(i,o);return ce(l,"int32")}var j9=V({confusionMatrix_:G9}),rs={};Pe(rs,{fromPixels:()=>Q9,fromPixelsAsync:()=>Y9,toPixels:()=>J9});function Eh(e,t,n){if(ma(e),t!=null&&t.length!==3)throw new Error("tensor3d() requires shape to have three numbers");let s=qs(e,n);if(s.length!==3&&s.length!==1)throw new Error("tensor3d() requires values to be number[][][] or flat/TypedArray");if(s.length===1&&t==null)throw new Error("tensor3d() requires shape to be provided when `values` are a flat array");return Br(e,t,s,n)}var xl;function Y5(e,t=3){if(t>4)throw new Error("Cannot construct Tensor with more than 4 channels from pixels.");if(e==null)throw new Error("pixels passed to tf.browser.fromPixels() can not be null");let n=!1,s=!1,r=!1,a=!1,o=!1,i=!1;if(e.data instanceof Uint8Array)n=!0;else if(typeof ImageData!="undefined"&&e instanceof ImageData)s=!0;else if(typeof HTMLVideoElement!="undefined"&&e instanceof HTMLVideoElement)r=!0;else if(typeof HTMLImageElement!="undefined"&&e instanceof HTMLImageElement)a=!0;else if(e.getContext!=null)o=!0;else if(typeof ImageBitmap!="undefined"&&e instanceof ImageBitmap)i=!0;else throw new Error(`pixels passed to tf.browser.fromPixels() must be either an HTMLVideoElement, HTMLImageElement, HTMLCanvasElement, ImageData in browser, or OffscreenCanvas, ImageData in webworker or {data: Uint32Array, width: number, height: number}, but was ${e.constructor.name}`);if(r){let f=2;if(r&&e.readyState<f)throw new Error("The video element has not loaded data yet. Please wait for `loadeddata` event on the <video> element.")}if(vh(bh,L.backendName)!=null){let f={pixels:e},m={numChannels:t};return L.runKernel(bh,f,m)}let[u,c]=r?[e.videoWidth,e.videoHeight]:[e.width,e.height],d;o?d=e.getContext("2d").getImageData(0,0,u,c).data:s||n?d=e.data:(a||r||i)&&(xl==null&&(xl=document.createElement("canvas").getContext("2d")),xl.canvas.width=u,xl.canvas.height=c,xl.drawImage(e,0,0,u,c),d=xl.getImageData(0,0,u,c).data);let h;if(t===4)h=new Int32Array(d);else{let f=u*c;h=new Int32Array(f*t);for(let m=0;m<f;m++)for(let A=0;A<t;++A)h[m*t+A]=d[m*4+A]}return Eh(h,[c,u,t],"int32")}function q9(e){return e!=null&&e.data instanceof Uint8Array}function X9(){return typeof window!="undefined"&&typeof ImageBitmap!="undefined"&&window.hasOwnProperty("createImageBitmap")}function K9(e){return e!=null&&e.width!==0&&e.height!==0}function Z9(e){return X9()&&!(e instanceof ImageBitmap)&&K9(e)&&!q9(e)}async function Y9(e,t=3){let n=null;if(ee().getBool("WRAP_TO_IMAGEBITMAP")&&Z9(e)){let s;try{s=await createImageBitmap(e,{premultiplyAlpha:"none"})}catch(r){s=null}s!=null&&s.width===e.width&&s.height===e.height?n=s:n=e}else n=e;return Y5(n,t)}async function J9(e,t){let n=D(e,"img","toPixels");if(!(e instanceof Ue)){let u=n;n=ce(u,"int32"),u.dispose()}if(n.rank!==2&&n.rank!==3)throw new Error(`toPixels only supports rank 2 or 3 tensors, got rank ${n.rank}.`);let[s,r]=n.shape.slice(0,2),a=n.rank===2?1:n.shape[2];if(a>4||a===2)throw new Error(`toPixels only supports depth of size 1, 3 or 4 but got ${a}`);if(n.dtype!=="float32"&&n.dtype!=="int32")throw new Error(`Unsupported type for toPixels: ${n.dtype}. Please use float32 or int32 tensors.`);let o=await n.data(),i=n.dtype==="float32"?255:1,l=new Uint8ClampedArray(r*s*4);for(let u=0;u<s*r;++u){let c=[0,0,0,255];for(let h=0;h<a;h++){let p=o[u*a+h];if(n.dtype==="float32"){if(p<0||p>1)throw new Error(`Tensor values for a float32 Tensor must be in the range [0 - 1] but encountered ${p}.`)}else if(n.dtype==="int32"&&(p<0||p>255))throw new Error(`Tensor values for a int32 Tensor must be in the range [0 - 255] but encountered ${p}.`);a===1?(c[0]=p*i,c[1]=p*i,c[2]=p*i):c[h]=p*i}let d=u*4;l[d+0]=Math.round(c[0]),l[d+1]=Math.round(c[1]),l[d+2]=Math.round(c[2]),l[d+3]=Math.round(c[3])}if(t!=null){t.width=r,t.height=s;let u=t.getContext("2d"),c=new ImageData(l,r,s);u.putImageData(c,0,0)}return n!==e&&n.dispose(),l}var Q9=V({fromPixels_:Y5}),Zm={};Pe(Zm,{prepareAndValidate:()=>J5});function J5(e,t){let n=e.shape.length,s=t.shape.length;if(n<1)throw new Error(`tf.gatherND() expects the input to be rank 1 or higher, but the rank was ${n}.`);if(s<1)throw new Error(`tf.gatherND() expects the indices to be rank 1 or higher, but the rank was ${s}.`);if(t.dtype!=="int32")throw new Error(`tf.gatherND() expects the indices to be int32 type, but the dtype was ${t.dtype}.`);if(t.shape[s-1]>n)throw new Error(`index innermost dimension length must be <= tensor rank; saw: ${t.shape[s-1]} vs. ${n}`);if(_t(e.shape)===0)throw new Error(`Requested more than 0 entries, but input is empty. Input shape: ${e.shape}.`);let r=t.shape,a=r[r.length-1],o=1;for(let d=0;d<r.length-1;++d)o*=r[d];let i=e.shape,l=r.slice();l.pop();let u=1;for(let d=a;d<n;++d)u*=i[d],l.push(i[d]);let c=[...ui(e.shape).map(d=>d/u),1].slice(0,a);return[l,o,u,c]}var Ym={};Pe(Ym,{calculateShapes:()=>Q5,validateInput:()=>Qm,validateUpdateShape:()=>Jm});function Jm(e,t,n){let s=t.rank>1?t.shape[t.rank-1]:1,r=t.rank>1?t.rank-1:1,a=`Must have updates.shape = indices.shape[:batchDim] + shape[sliceDim:], got updates.shape: ${n.shape}, indices.shape: ${t.shape}, shape: ${e}, sliceDim: ${s}, and batchDim: ${r}.`;if(n.rank<r)throw new Error(a+` update.rank < ${r}. `);if(e.length<s+(n.rank-r))throw new Error(a+` Output shape length < ${s+(n.rank-r)}`);if(n.rank!==r+e.length-s)throw new Error(a+` update.rank != ${r+e.length-s}`);for(let o=0;o<r;++o)if(n.shape[o]!==t.shape[o])throw new Error(a+` updates.shape[${o}] (${n.shape[o]}) != indices.shape[${o}] (${t.shape[o]}).`);for(let o=0;o<n.rank-r;++o)if(n.shape[o+r]!==e[o+s])throw new Error(a+` updates.shape[${o+r}] (${n.shape[o+r]}) != shape[${o+r}] (${e[o+r]})`)}function Qm(e,t,n){if(t.rank<1)throw new Error(`tf.scatterND() expects the indices to be rank 1 or higher, but the rank was ${t.rank}.`);if(e.rank<1)throw new Error(`tf.scatterND() expects the updates to be rank 1 or higher, but the rank was ${e.rank}.`);if(t.dtype!=="int32")throw new Error(`The dtype of 'indices' should be int32, but got dtype: ${t.dtype}`);if(n.length<1)throw new Error(`Output rank must be greater or equal to 1, but got shape: ${n}`);if(n.length===0){if(t.size===0)throw new Error(`Indices specified for empty output. indices shape: ${t.shape}`);if(e.size===0)throw new Error(`Updates specified for empty output. updates shape: ${e.shape}`)}Jm(n,t,e)}function Q5(e,t,n){let s=t.shape.length,r=s>1?t.shape[s-1]:1,a=n.length,o=1;for(let d=r;d<a;++d)o*=n[d];let i=r<1?1:r,l=_t(t.shape)/i,u=[...ui(n.slice(0,r)),1],c=_t(n);return{sliceRank:r,numUpdates:l,sliceSize:o,strides:u,outputSize:c}}var An={};Pe(An,{assertParamsValid:()=>eC,computeFlatOffset:()=>nC,computeOutShape:()=>ex,getNormalizedAxes:()=>rx,isSliceContinous:()=>tC,maskToAxes:()=>Rh,parseSliceParams:()=>cx,sliceInfo:()=>sC,startForAxis:()=>lx,startIndicesWithElidedDims:()=>ax,stopForAxis:()=>ux,stopIndicesWithElidedDims:()=>ox,stridesForAxis:()=>ix,stridesWithElidedDims:()=>tx});function eC(e,t,n){let s=e.shape.length;P(s===t.length,()=>`Error in slice${s}D: Length of begin ${t} must match the rank of the array (${s}).`),P(s===n.length,()=>`Error in slice${s}D: Length of size ${n} must match the rank of the array (${s}).`);for(let r=0;r<s;++r)P(t[r]+n[r]<=e.shape[r],()=>`Error in slice${s}D: begin[${r}] + size[${r}] (${t[r]+n[r]}) would overflow input.shape[${r}] (${e.shape[r]})`)}function Rh(e){let t=[],n=0;for(;e>0;)e&1&&t.push(n),e/=2,n++;return t}function ex(e,t,n){let s=[];for(let r=0;r<e.length;r++)s[r]=Math.ceil((t[r]-e[r])/n[r]);return s}function tx(e,t,n,s){let r=[...e];for(let a=r.length;a<s.length;a++)r.push(1);for(let a=0;a<n;a++)a===0?r[t]=1:(r.splice(t,0,1),r.pop());return r}function nx(e,t,n){return n<=e?n:n-(t-1)}function sx(e,t){let n=[];for(let s=0;s<e;s++)n.push(t+s);return n}function rx(e,t,n,s,r,a,o,i,l){let u=e.length,c=new Array(u),d=new Array(u),h=new Array(u);if(t.length&&n>0){let p=t[0],f=n+1;c=ax(o,p,f,s,e),d=ox(i,p,f,r,e),h=tx(a,p,f,e)}else for(let p=0;p<u;p++)c[p]=lx(o,s,a,e,p,l),d[p]=ux(i,r,a,e,p,l),h[p]=ix(a,p,l);return{begin:c,end:d,strides:h}}function ax(e,t,n,s,r){let a=[...r],o=sx(n,t);for(let i=0;i<a.length;i++)if(o.indexOf(i)>-1)a[i]=0;else{let l=nx(t,n,i),u=s[l];e&1<<l&&(u=0),a[i]=u}return a}function ox(e,t,n,s,r){let a=[...r],o=sx(n,t);for(let i=0;i<a.length;i++)if(o.indexOf(i)>-1)a[i]=Number.MAX_SAFE_INTEGER;else{let l=nx(t,n,i),u=s[l];e&1<<l&&(u=Number.MAX_SAFE_INTEGER),a[i]=u}for(let i=0;i<a.length;i++){let l=r[i];a[i]<0&&(a[i]+=l),a[i]=Iu(0,a[i],r[i])}return a}function ix(e,t,n){let s=e[t];return(n&1<<t||s==null)&&(s=1),s}function lx(e,t,n,s,r,a){let o=t[r],i=n[r]||1;(e&1<<r||a&1<<r||o==null)&&(i>0?o=Number.MIN_SAFE_INTEGER:o=Number.MAX_SAFE_INTEGER);let l=s[r];return o<0&&(o+=l),o=Iu(0,o,l-1),o}function ux(e,t,n,s,r,a){let o=t[r],i=n[r]||1;(e&1<<r||a&1<<r||o==null)&&(i>0?o=Number.MAX_SAFE_INTEGER:o=Number.MIN_SAFE_INTEGER);let l=s[r];return o<0&&(o+=l),i>0?o=Iu(0,o,l):o=Iu(-1,o,l-1),o}function tC(e,t,n){let s=n.length;for(let r=0;r<n.length;r++)if(n[r]>1){s=r;break}for(let r=s+1;r<n.length;r++)if(t[r]>0||n[r]!==e[r])return!1;return!0}function nC(e,t){let n=e.length>0?e[e.length-1]:1;for(let s=0;s<e.length-1;s++)n+=e[s]*t[s];return n}function cx(e,t,n){let s,r=e.shape.length;typeof t=="number"?s=[t,...new Array(r-1).fill(0)]:t.length<r?s=t.concat(new Array(r-t.length).fill(0)):s=t.slice(),s.forEach(o=>{P(o!==-1,()=>"slice() does not support negative begin indexing.")});let a;return n==null?a=new Array(r).fill(-1):typeof n=="number"?a=[n,...new Array(r-1).fill(-1)]:n.length<r?a=n.concat(new Array(r-n.length).fill(-1)):a=n,a=a.map((o,i)=>o>=0?o:(P(o===-1,()=>`Negative size values should be exactly -1 but got ${o} for the slice() size at index ${i}.`),e.shape[i]-s[i])),[s,a]}function sC(e,t,n,s,r,a,o,i,l){let u=t.slice(),c=n.slice(),d=s;s==null&&(d=new Array(u.length));let h=Rh(o);if(h.length>1)throw new Error("Multiple ellipses in slice is not allowed.");if(o!==0&&i!==0)throw new Error("Using both ellipsisMask and newAxisMask is not yet supported.");if(o!==0&&l!==0)throw new Error("Using both ellipsisMask and shrinkAxisMask is not yet supported.");let p=e.length-u.length,f=Rh(i),m=e.slice();f.forEach(w=>{u[w]=0,c[w]=1,m.splice(w,0,1)});let{begin:A,end:g,strides:y}=rx(m,h,p,u,c,d,r,a,o);u=A,c=g,d=y;let x=Rh(l);x.forEach(w=>{c[w]=u[w]+1,d[w]=1});let b=ex(u,c,d),v=b.filter((w,C)=>x.indexOf(C)===-1);return{nonStrided:d.every(w=>w===1),$begin:u,$end:c,$strides:d,size:b,newShape:m,outShape:v}}var oe={};Pe(oe,{Serializable:()=>dx,SerializationMap:()=>wo,registerClass:()=>Vr});var dx=class{getClassName(){return this.constructor.className}static fromConfig(e,t){return new e(t)}},wo=class{constructor(){this.classNameMap={}}static getMap(){return wo.instance==null&&(wo.instance=new wo),wo.instance}static register(e){wo.getMap().classNameMap[e.className]=[e,e.fromConfig]}};function Vr(e){P(e.className!=null,()=>"Class being registered does not have the static className property defined."),P(typeof e.className=="string",()=>"className is required to be a string, but got type "+typeof e.className),P(e.className.length>0,()=>"Class being registered has an empty-string as its className, which is disallowed."),wo.register(e)}var hx={};Pe(hx,{TEST_EPSILON_FLOAT16:()=>px,encodeStrings:()=>fx,expectArrayBuffersEqual:()=>cC,expectArraysClose:()=>aC,expectArraysEqual:()=>iC,expectNumbersClose:()=>lC,expectPromiseToFail:()=>oC,expectValuesInRange:()=>uC,testEpsilon:()=>eA});var rC=.001,px=.1;function aC(e,t,n){return n==null&&(n=eA()),tA(e,t,(s,r)=>nA(s,r,n))}function eA(){return L.backend.floatPrecision()===32?rC:px}function tA(e,t,n){let s=!0;if((an(e)||an(t))&&(s=!1),an(e)&&an(t)&&(s=!0),s){let o=e.constructor.name,i=t.constructor.name;if(o!==i)throw new Error(`Arrays are of different type. Actual: ${o}. Expected: ${i}`)}if(Array.isArray(e)&&Array.isArray(t)){let o=qs(e),i=qs(t);if(!fr(o,i))throw new Error(`Arrays have different shapes. Actual: [${o}]. Expected: [${i}]`)}let r=an(e)?e:Aa(e),a=an(t)?t:Aa(t);if(r.length!==a.length)throw new Error(`Arrays have different lengths actual: ${r.length} vs expected: ${a.length}.
Actual:   ${r}.
Expected: ${a}.`);for(let o=0;o<a.length;++o){let i=r[o],l=a[o];if(!n(i,l))throw new Error(`Arrays differ: actual[${o}] = ${i}, expected[${o}] = ${l}.
Actual:   ${r}.
Expected: ${a}.`)}}function oC(e,t){e().then(()=>t.fail(),()=>t())}function iC(e,t){let n=typeof t=="string"||typeof t=="number"||typeof t=="boolean"?[t]:t;return $r(e)||$r(e[0])||$r(t)||$r(t[0])?tA(e,n,(s,r)=>s==r):tA(e,t,(s,r)=>nA(s,r,0))}function lC(e,t,n){if(n==null&&(n=eA()),!nA(e,t,n))throw new Error(`Numbers differ: actual === ${e}, expected === ${t}`)}function nA(e,t,n){return!isFinite(e)&&!isFinite(t)?!0:!(isNaN(e)||isNaN(t)||Math.abs(e-t)>n)}function uC(e,t,n){for(let s=0;s<e.length;s++)if(e[s]<t||e[s]>n)throw new Error(`Value out of range:${e[s]} low: ${t}, high: ${n}`)}function cC(e,t){expect(new Float32Array(e)).toEqual(new Float32Array(t))}function fx(e){for(let t=0;t<e.length;t++){let n=e[t];Array.isArray(n)?fx(n):e[t]=Uu(n)}return e}var dC="3.8.0";function hC(){ee().set("PROD",!0)}function pC(){ee().set("DEBUG",!0)}function fC(){ee().set("DEPRECATION_WARNINGS_ENABLED",!1),console.warn("TensorFlow.js deprecation warnings have been disabled.")}function sA(e){ee().getBool("DEPRECATION_WARNINGS_ENABLED")&&console.warn(e+" You can disable deprecation warnings with tf.disableDeprecationWarnings().")}zS(sA);function mC(){L.disposeVariables()}function Ar(){return L}function _h(){return L.memory()}function AC(e){return L.profile(e)}function H(e,t){return L.tidy(e,t)}function K(e){Pm(e).forEach(n=>n.dispose())}function Kt(e){return L.keep(e)}function gC(e){return L.time(e)}function yC(e){return L.setBackend(e)}function xC(){return L.ready()}function bC(){return L.backendName}function vC(e){L.removeBackend(e)}function rA(e){return L.findBackend(e)}function wC(e){return L.findBackendFactory(e)}function bl(e,t,n=1){return L.registerBackend(e,t,n)}function mx(){return L.backend}function kC(e,t){ee().setPlatform(e,t)}function IC(e,t){let n=D(e,"a","add"),s=D(t,"b","add");[n,s]=It(n,s);let r={a:n,b:s};return L.runKernel(Dr,r)}var ae=V({add_:IC});function SC(e,t){let n=D(e,"a","floorDiv"),s=D(t,"b","floorDiv");[n,s]=It(n,s);let r={a:n,b:s};return L.runKernel($a,r)}var aA=V({floorDiv_:SC});function CC(e,t){let n=D(e,"a","div"),s=D(t,"b","div");if([n,s]=It(n,s),n.dtype==="int32"&&s.dtype==="int32")return aA(n,s);let r={a:n,b:s},a={};return L.runKernel(Ea,r,a)}var de=V({div_:CC});function TC(e,t){let n=D(e,"a","mul"),s=D(t,"b","mul");[n,s]=It(n,s);let r={a:n,b:s};return L.runKernel(Ga,r)}var z=V({mul_:TC});function NC(e){let t=D(e,"x","abs");if(t.dtype==="complex64"){let n={x:t};return L.runKernel(Nu,n)}else{let n={x:t};return L.runKernel(di,n)}}var Wt=V({abs_:NC});function EC(e){let n={x:D(e,"x","acos")};return L.runKernel(hi,n)}var Ax=V({acos_:EC});function RC(e){let n={x:D(e,"x","acosh")};return L.runKernel(pi,n)}var gx=V({acosh_:RC});function _C(e){P(Array.isArray(e),()=>"The argument passed to tf.addN() must be a list of tensors"),P(e.length>=1,()=>`Must pass at least one tensor to tf.addN(), but got ${e.length}`);let t=e.map((r,a)=>D(r,`tensors${a}`,"addN")),n=t[0];t.forEach(r=>{if(r.dtype!==n.dtype)throw new Error("All tensors passed to tf.addN() must have the same dtype")}),t.forEach(r=>{if(!fr(r.shape,n.shape))throw new Error("All tensors passed to tf.addN() must have the same shape")});let s=t;return L.runKernel(ga,s)}var $h=V({addN_:_C});function $C(e,t=null,n=!1){let r={x:D(e,"x","all","bool")},a={axis:t,keepDims:n};return L.runKernel(fi,r,a)}var oA=V({all_:$C});function FC(e,t=null,n=!1){let r={x:D(e,"x","any","bool")},a={axis:t,keepDims:n};return L.runKernel(mi,r,a)}var Fh=V({any_:FC});function DC(e,t=0){let s={x:D(e,"x","argMax")},r={axis:t};return L.runKernel(ya,s,r)}var Xs=V({argMax_:DC});function OC(e,t=0){let s={x:D(e,"x","argMin")},r={axis:t};return L.runKernel(Cu,s,r)}var yx=V({argMin_:OC});function PC(e){let n={x:D(e,"x","asin")};return L.runKernel(Ai,n)}var xx=V({asin_:PC});function MC(e){let n={x:D(e,"x","asinh")};return L.runKernel(gi,n)}var bx=V({asinh_:MC});function zC(e){let n={x:D(e,"x","atan")};return L.runKernel(yi,n)}var vx=V({atan_:zC});function LC(e,t){let n=D(e,"a","atan2"),s=D(t,"b","atan2");[n,s]=It(n,s);let r={a:n,b:s};return L.runKernel(bi,r)}var wx=V({atan2_:LC});function BC(e){let n={x:D(e,"x","atanh")};return L.runKernel(xi,n)}var kx=V({atanh_:BC});function WC(e,t,n,s,r="NHWC",a){let o=e[3],i=[...t,o],l=Cx(r);return Qu(e,i,n,a,s,null,null,l)}function Ix(e,t,n,s,r,a,o="channelsLast"){let[i,l]=Dh(t),u;if(o==="channelsLast")u=[i,l,e[3],e[3]];else if(o==="channelsFirst")u=[i,l,e[1],e[1]];else throw new Error(`Unknown dataFormat ${o}`);return Qu(e,u,n,s,r,a,!1,o)}function VC(e,t,n,s,r,a,o="NDHWC"){let[i,l,u]=lA(t),c,d;if(o==="NDHWC")d="channelsLast",c=[i,l,u,e[4],e[4]];else if(o==="NCDHW")d="channelsFirst",c=[i,l,u,e[1],e[1]];else throw new Error(`Unknown dataFormat ${o}`);return Sx(e,c,n,s,r,!1,d,a)}function Qu(e,t,n,s,r,a,o=!1,i="channelsLast"){let[l,u,c,d]=[-1,-1,-1,-1];if(i==="channelsLast")[l,u,c,d]=e;else if(i==="channelsFirst")[l,d,u,c]=e;else throw new Error(`Unknown dataFormat ${i}`);let[h,p,,f]=t,[m,A]=Dh(n),[g,y]=Dh(s),x=vl(h,g),b=vl(p,y),{padInfo:v,outHeight:k,outWidth:w}=GC(r,u,c,m,A,x,b,a,i),C=o?f*d:f,E;return i==="channelsFirst"?E=[l,C,k,w]:i==="channelsLast"&&(E=[l,k,w,C]),{batchSize:l,dataFormat:i,inHeight:u,inWidth:c,inChannels:d,outHeight:k,outWidth:w,outChannels:C,padInfo:v,strideHeight:m,strideWidth:A,filterHeight:h,filterWidth:p,effectiveFilterHeight:x,effectiveFilterWidth:b,dilationHeight:g,dilationWidth:y,inShape:e,outShape:E,filterShape:t}}function Sx(e,t,n,s,r,a=!1,o="channelsLast",i){let[l,u,c,d,h]=[-1,-1,-1,-1,-1];if(o==="channelsLast")[l,u,c,d,h]=e;else if(o==="channelsFirst")[l,h,u,c,d]=e;else throw new Error(`Unknown dataFormat ${o}`);let[p,f,m,,A]=t,[g,y,x]=lA(n),[b,v,k]=lA(s),w=vl(p,b),C=vl(f,v),E=vl(m,k),{padInfo:M,outDepth:R,outHeight:_,outWidth:N}=jC(r,u,c,d,g,y,x,w,C,E,i),O=a?A*h:A,W;return o==="channelsFirst"?W=[l,O,R,_,N]:o==="channelsLast"&&(W=[l,R,_,N,O]),{batchSize:l,dataFormat:o,inDepth:u,inHeight:c,inWidth:d,inChannels:h,outDepth:R,outHeight:_,outWidth:N,outChannels:O,padInfo:M,strideDepth:g,strideHeight:y,strideWidth:x,filterDepth:p,filterHeight:f,filterWidth:m,effectiveFilterDepth:w,effectiveFilterHeight:C,effectiveFilterWidth:E,dilationDepth:b,dilationHeight:v,dilationWidth:k,inShape:e,outShape:W,filterShape:t}}function UC(e,t,n,s,r){s==null&&(s=iA(e,t,n));let a=e[0],o=e[1],i=ko((a-t+2*s)/n+1,r),l=ko((o-t+2*s)/n+1,r);return[i,l]}function HC(e,t,n,s,r,a){r==null&&(r=iA(e,t,s));let o=e[0],i=e[1],l=e[2],u=ko((o-t+2*r)/s+1,a),c=ko((i-t+2*r)/s+1,a),d=ko((l-t+2*r)/s+1,a);return[u,c,d,n]}function iA(e,t,n,s=1){let r=vl(t,s);return Math.floor((e[0]*(n-1)-n+r)/2)}function Dh(e){return typeof e=="number"?[e,e,e]:e.length===2?[e[0],e[1],1]:e}function lA(e){return typeof e=="number"?[e,e,e]:e}function vl(e,t){return t<=1?e:e+(e-1)*(t-1)}function GC(e,t,n,s,r,a,o,i,l){let u,c,d;if(typeof e=="number"){u={top:e,bottom:e,left:e,right:e,type:e===0?"VALID":"NUMBER"};let p=UC([t,n],a,s,e,i);c=p[0],d=p[1]}else if(e==="same"){c=Math.ceil(t/s),d=Math.ceil(n/r);let h=Math.max(0,(c-1)*s+a-t),p=Math.max(0,(d-1)*r+o-n),f=Math.floor(h/2),m=h-f,A=Math.floor(p/2),g=p-A;u={top:f,bottom:m,left:A,right:g,type:"SAME"}}else if(e==="valid")u={top:0,bottom:0,left:0,right:0,type:"VALID"},c=Math.ceil((t-a+1)/s),d=Math.ceil((n-o+1)/r);else if(typeof e=="object"){let h=l==="channelsLast"?e[1][0]:e[2][0],p=l==="channelsLast"?e[1][1]:e[2][1],f=l==="channelsLast"?e[2][0]:e[3][0],m=l==="channelsLast"?e[2][1]:e[3][1];u={top:h,bottom:p,left:f,right:m,type:h===0&&p===0&&f===0&&m===0?"VALID":"EXPLICIT"},c=ko((t-a+h+p)/s+1,i),d=ko((n-o+f+m)/r+1,i)}else throw Error(`Unknown padding parameter: ${e}`);return{padInfo:u,outHeight:c,outWidth:d}}function jC(e,t,n,s,r,a,o,i,l,u,c){let d,h,p,f;if(typeof e=="number"){d={top:e,bottom:e,left:e,right:e,front:e,back:e,type:e===0?"VALID":"NUMBER"};let A=HC([t,n,s,1],i,1,r,e,c);h=A[0],p=A[1],f=A[2]}else if(e==="same"){h=Math.ceil(t/r),p=Math.ceil(n/a),f=Math.ceil(s/o);let m=(h-1)*r+i-t,A=(p-1)*a+l-n,g=(f-1)*o+u-s,y=Math.floor(m/2),x=m-y,b=Math.floor(A/2),v=A-b,k=Math.floor(g/2),w=g-k;d={top:b,bottom:v,left:k,right:w,front:y,back:x,type:"SAME"}}else if(e==="valid")d={top:0,bottom:0,left:0,right:0,front:0,back:0,type:"VALID"},h=Math.ceil((t-i+1)/r),p=Math.ceil((n-l+1)/a),f=Math.ceil((s-u+1)/o);else throw Error(`Unknown padding parameter: ${e}`);return{padInfo:d,outDepth:h,outHeight:p,outWidth:f}}function ko(e,t){if(!t)return Math.trunc(e);switch(t){case"round":return Math.round(e);case"ceil":return Math.ceil(e);case"floor":return Math.floor(e);default:throw new Error(`Unknown roundingMode ${t}`)}}function Ur(e){let[t,n,s]=Dh(e);return t===1&&n===1&&s===1}function Ks(e,t){return Ur(e)||Ur(t)}function Cx(e){if(e==="NHWC")return"channelsLast";if(e==="NCHW")return"channelsFirst";throw new Error(`Unknown dataFormat ${e}`)}function qC(e,t){let s={x:D(e,"x","reshape","string_or_numeric")},r={shape:t};return L.runKernel(Yi,s,r)}var U=V({reshape_:qC});function XC(e,t,n,s,r){let a=D(e,"x","avgPool","float32"),o=1;P(Ks(n,o),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${n} and dilations '${o}'`);let i=a,l=!1;a.rank===3&&(l=!0,i=U(a,[1,a.shape[0],a.shape[1],a.shape[2]])),P(i.rank===4,()=>`Error in avgPool: x must be rank 4 but got rank ${i.rank}.`),r!=null&&P(qt(s),()=>`Error in avgPool: pad must be an integer when using, dimRoundingMode ${r} but got pad ${s}.`);let u={x:i},c={filterSize:t,strides:n,pad:s,dimRoundingMode:r},d=L.runKernel(xa,u,c);return d=ce(d,a.dtype),l?U(d,[d.shape[1],d.shape[2],d.shape[3]]):d}var Oh=V({avgPool_:XC});function KC(e,t,n,s,r,a="NDHWC"){let o=D(e,"x","avgPool3d","float32"),i=o,l=!1;o.rank===4&&(l=!0,i=U(o,[1,o.shape[0],o.shape[1],o.shape[2],o.shape[3]])),P(i.rank===5,()=>`Error in avgPool3d: x must be rank 5 but got rank ${i.rank}.`),P(a==="NDHWC",()=>`Error in avgPool3d: Only NDHWC is currently supported, but got dataFormat of ${a}`),r!=null&&P(qt(s),()=>`Error in avgPool3d: pad must be an integer when using, dimRoundingMode ${r} but got pad ${s}.`);let u={x:i},c={filterSize:t,strides:n,pad:s,dimRoundingMode:r,dataFormat:a},d=L.runKernel(Tu,u,c);return d=ce(d,i.dtype),l?U(d,[d.shape[1],d.shape[2],d.shape[3],d.shape[4]]):d}var uA=V({avgPool3d_:KC});function ZC(e,t=0){P(e.length>=1,()=>"Pass at least one tensor to concat");let n=Zu(e,"tensors","concat","string_or_numeric");if(n[0].dtype==="complex64"&&n.forEach(a=>{if(a.dtype!=="complex64")throw new Error(`Cannot concatenate complex64 tensors with a tensor
          with dtype ${a.dtype}. `)}),n.length===1)return Ns(n[0]);let s=n,r={axis:t};return L.runKernel(wi,s,r)}var ht=V({concat_:ZC});function YC(e){let n={x:D(e,"x","sigmoid")};return L.runKernel(so,n)}var Bn=V({sigmoid_:YC});function JC(e,t,n){let s=D(e,"x","slice","string_or_numeric");if(s.rank===0)throw new Error("Slicing scalar is not possible");let r={x:s},a={begin:t,size:n};return L.runKernel(tl,r,a)}var Re=V({slice_:JC});function QC(e){let n={x:D(e,"x","tanh")};return L.runKernel(co,n)}var wl=V({tanh_:QC});function eT(e,t,n,s,r,a){let o=D(e,"forgetBias","basicLSTMCell"),i=D(t,"lstmKernel","basicLSTMCell"),l=D(n,"lstmBias","basicLSTMCell"),u=D(s,"data","basicLSTMCell"),c=D(r,"c","basicLSTMCell"),d=D(a,"h","basicLSTMCell"),h=ht([u,d],1),p=We(h,i),f=ae(p,l),m=f.shape[0],A=f.shape[1]/4,g=[m,A],y=Re(f,[0,0],g),x=Re(f,[0,A],g),b=Re(f,[0,A*2],g),v=Re(f,[0,A*3],g),k=ae(z(Bn(y),wl(x)),z(c,Bn(ae(o,b)))),w=z(wl(k),Bn(v));return[k,w]}var tT=V({basicLSTMCell_:eT});function nT(e,t,n){let s=D(e,"x","batchToSpaceND"),r=t.reduce((i,l)=>i*l);P(s.rank>=1+t.length,()=>`input rank is ${s.rank} but should be > than blockShape.length ${t.length}`),P(n.length===t.length,()=>`crops.length is ${n.length} but should be equal to blockShape.length  ${t.length}`),P(s.shape[0]%r==0,()=>`input tensor batch is ${s.shape[0]} but is not divisible by the product of the elements of blockShape ${t.join(" * ")} === ${r}`);let a={x:s},o={blockShape:t,crops:n};return L.runKernel(vi,a,o)}var Ph=V({batchToSpaceND_:nT});function sT(e){let t;return e.rank===0||e.rank===1?t=U(e,[1,1,1,e.size]):e.rank===2?t=U(e,[1,1,e.shape[0],e.shape[1]]):e.rank===3?t=U(e,[1,e.shape[0],e.shape[1],e.shape[2]]):t=e,t}function rT(e,t,n,s,r,a){a==null&&(a=.001);let o=D(e,"x","batchNorm"),i=D(t,"mean","batchNorm"),l=D(n,"variance","batchNorm"),u;r!=null&&(u=D(r,"scale","batchNorm"));let c;s!=null&&(c=D(s,"offset","batchNorm")),P(i.rank===l.rank,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),P(c==null||i.rank===c.rank,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),P(u==null||i.rank===u.rank,()=>"Batch normalization gradient requires mean and scale to have equal ranks.");let h={x:sT(o),scale:u,offset:c,mean:i,variance:l},p={varianceEpsilon:a},f=L.runKernel(Fa,h,p);return U(f,o.shape)}var kl=V({batchNorm_:rT});function aT(e,t,n,s,r,a){let o=D(e,"x","batchNorm"),i=D(t,"mean","batchNorm"),l=D(n,"variance","batchNorm"),u;r!=null&&(u=D(r,"scale","batchNorm"));let c;return s!=null&&(c=D(s,"offset","batchNorm")),P(o.rank===2,()=>`Error in batchNorm2D: x must be rank 2 but got rank ${o.rank}.`),P(i.rank===2||i.rank===1,()=>`Error in batchNorm2D: mean must be rank 2 or rank 1 but got rank ${i.rank}.`),P(l.rank===2||l.rank===1,()=>`Error in batchNorm2D: variance must be rank 2 or rank 1 but got rank ${l.rank}.`),u!=null&&P(u.rank===2||u.rank===1,()=>`Error in batchNorm2D: scale must be rank 2 or rank 1 but got rank ${u.rank}.`),c!=null&&P(c.rank===2||c.rank===1,()=>`Error in batchNorm2D: offset must be rank 2 or rank 1 but got rank ${c.rank}.`),kl(o,i,l,c,u,a)}var Tx=V({batchNorm2d_:aT});function oT(e,t,n,s,r,a){let o=D(e,"x","batchNorm"),i=D(t,"mean","batchNorm"),l=D(n,"variance","batchNorm"),u;r!=null&&(u=D(r,"scale","batchNorm"));let c;return s!=null&&(c=D(s,"offset","batchNorm")),P(o.rank===3,()=>`Error in batchNorm3D: x must be rank 3 but got rank ${o.rank}.`),P(i.rank===3||i.rank===1,()=>`Error in batchNorm3D: mean must be rank 3 or rank 1 but got rank ${i.rank}.`),P(l.rank===3||l.rank===1,()=>`Error in batchNorm3D: variance must be rank 3 or rank 1 but got rank ${l.rank}.`),u!=null&&P(u.rank===3||u.rank===1,()=>`Error in batchNorm3D: scale must be rank 3 or rank 1 but got rank ${u.rank}.`),c!=null&&P(c.rank===3||c.rank===1,()=>`Error in batchNorm3D: offset must be rank 3 or rank 1 but got rank ${c.rank}.`),kl(o,i,l,c,u,a)}var Nx=V({batchNorm3d_:oT});function iT(e,t,n,s,r,a){let o=D(e,"x","batchNorm"),i=D(t,"mean","batchNorm"),l=D(n,"variance","batchNorm"),u;r!=null&&(u=D(r,"scale","batchNorm"));let c;return s!=null&&(c=D(s,"offset","batchNorm")),P(o.rank===4,()=>`Error in batchNorm4D: x must be rank 4 but got rank ${o.rank}.`),P(i.rank===4||i.rank===1,()=>`Error in batchNorm4D: mean must be rank 4 or rank 1 but got rank ${i.rank}.`),P(l.rank===4||l.rank===1,()=>`Error in batchNorm4D: variance must be rank 4 or rank 1 but got rank ${l.rank}.`),u!=null&&P(u.rank===4||u.rank===1,()=>`Error in batchNorm4D: scale must be rank 4 or rank 1 but got rank ${u.rank}.`),c!=null&&P(c.rank===4||c.rank===1,()=>`Error in batchNorm4D: offset must be rank 4 or rank 1 but got rank ${c.rank}.`),kl(o,i,l,c,u,a)}var Ex=V({batchNorm4d_:iT});function lT(e,t,n){let s=D(e,"x","bincount"),r=D(t,"weights","bincount");P(s.dtype==="int32",()=>`Error in bincount: input dtype must be int32, but got ${s.dtype}`),P(n>=0,()=>`size must be non-negative, but got ${n}.`),P(r.size===s.size||r.size===0,()=>`Error in bincount: weights must have the same size as input or0-length, but got input shape: ${s.shape}, weights shape: ${r.shape}.`);let a={x:s,weights:r},o={size:n};return L.runKernel(Bd,a,o)}var cA=V({bincount_:lT});function uT(e,t){let n=D(e,"broadcastTo","x"),s=n.shape;if(t.some(u=>!(u>0)||u%1!=0))throw new Error(`broadcastTo(): Invalid broadcast shape [${t}].`);if(t.length<n.rank)throw new Error(`broadcastTo(): shape.length=${t.length} < input.rank=${n.rank}.`);if(t.length>n.rank){let u=n.shape.slice();for(;u.length<t.length;)u.unshift(1);n=U(n,u)}let r=n.shape,a=Array.from(t);for(let u=t.length-1;u>=0;u--)if(r[u]===t[u])a[u]=1;else if(n.shape[u]!==1)throw new Error(`broadcastTo(): [${s}] cannot be broadcast to [${t}].`);if(a.map((u,c)=>u>1?c:-1).filter(u=>u>=0).length===0)return Ns(n);let i={x:n},l={reps:a};return L.runKernel(Pr,i,l)}var ec=V({broadcastTo_:uT});function cT(e){let n={x:D(e,"x","ceil")};return L.runKernel(wa,n)}var Rx=V({ceil_:cT});function dT(e,t,n){let s=D(e,"x","clipByValue");P(t<=n,()=>`Error in clip: min (${t}) must be less than or equal to max (${n}).`);let r={x:s},a={clipValueMin:t,clipValueMax:n};return L.runKernel(Or,r,a)}var Wn=V({clipByValue_:dT});function hT(e){return ht(e,0)}var _x=V({concat1d_:hT});function pT(e,t){return ht(e,t)}var Il=V({concat2d_:pT});function fT(e,t){return ht(e,t)}var $x=V({concat3d_:fT});function mT(e,t){return ht(e,t)}var Fx=V({concat4d_:mT});function AT(e,t,n,s,r="NHWC",a=[1,1],o){let i=D(e,"x","conv2d"),l=D(t,"filter","conv2d"),u=i,c=!1;i.rank===3&&(c=!0,u=U(i,[1,i.shape[0],i.shape[1],i.shape[2]])),P(u.rank===4,()=>`Error in conv2d: input must be rank 4, but got rank ${u.rank}.`),P(l.rank===4,()=>`Error in conv2d: filter must be rank 4, but got rank ${l.rank}.`),o!=null&&P(qt(s),()=>`Error in conv2d: pad must be an integer when using, dimRoundingMode ${o} but got pad ${s}.`);let d=r==="NHWC"?u.shape[3]:u.shape[1];P(d===l.shape[2],()=>`Error in conv2d: depth of input (${d}) must match input depth for filter ${l.shape[2]}.`),P(Ks(n,a),()=>`Error in conv2D: Either strides or dilations must be 1. Got strides ${n} and dilations '${a}'`);let h={x:u,filter:l},p={strides:n,pad:s,dataFormat:r,dilations:a,dimRoundingMode:o},f=L.runKernel(ka,h,p);return c?U(f,[f.shape[1],f.shape[2],f.shape[3]]):f}var Hr=V({conv2d_:AT});function gT(e,t,n,s,r="NWC",a=1,o){let i=D(e,"x","conv1d"),l=D(t,"filter","conv1d"),u=i,c=!1;i.rank===2&&(c=!0,u=U(i,[1,i.shape[0],i.shape[1]])),P(u.rank===3,()=>`Error in conv1d: input must be rank 3, but got rank ${u.rank}.`),P(l.rank===3,()=>`Error in conv1d: filter must be rank 3, but got rank ${l.rank}.`),o!=null&&P(qt(s),()=>`Error in conv1d: pad must be an integer when using, dimRoundingMode ${o} but got pad ${s}.`),P(u.shape[2]===l.shape[1],()=>`Error in conv1d: depth of input (${u.shape[2]}) must match input depth for filter ${l.shape[1]}.`),P(Ks(n,a),()=>`Error in conv1D: Either stride or dilation must be 1. Got stride ${n} and dilation '${a}'`),P(r==="NWC",()=>`Error in conv1d: got dataFormat of ${r} but only NWC is currently supported.`);let d=U(l,[1,l.shape[0],l.shape[1],l.shape[2]]),h=U(u,[u.shape[0],1,u.shape[1],u.shape[2]]),A=Hr(h,d,[1,n],s,"NHWC",[1,a],o);return c?U(A,[A.shape[2],A.shape[3]]):U(A,[A.shape[0],A.shape[2],A.shape[3]])}var dA=V({conv1d_:gT});function yT(e,t,n,s,r,a="NHWC",o){P(e.length===t.rank,()=>`Length of inShape (${e.length}) and rank of dy (${t.rank}) must match`);let i=e,l=t,u=!1;t.rank===3&&(u=!0,l=U(t,[1,t.shape[0],t.shape[1],t.shape[2]]),i=[1,e[0],e[1],e[2]]),P(i.length===4,()=>`Error in conv2dDerInput: inShape must be length 4, but got length ${i.length}.`),P(l.rank===4,()=>`Error in conv2dDerInput: dy must be rank 4, but got rank ${l.rank}`),P(n.rank===4,()=>`Error in conv2dDerInput: filter must be rank 4, but got rank ${n.rank}`);let c=a==="NHWC"?i[3]:i[1],d=a==="NHWC"?l.shape[3]:l.shape[1];P(c===n.shape[2],()=>`Error in conv2dDerInput: depth of input (${c}) must match input depth for filter ${n.shape[2]}.`),P(d===n.shape[3],()=>`Error in conv2dDerInput: depth of output (${d}) must match output depth for filter ${n.shape[3]}.`),o!=null&&P(qt(r),()=>`Error in conv2dDerInput: pad must be an integer when using, dimRoundingMode ${o} but got pad ${r}.`);let h={dy:l,filter:n},p={strides:s,pad:r,dataFormat:a,dimRoundingMode:o,inputShape:i},f=L.runKernel(Ia,h,p);return u?U(f,[f.shape[1],f.shape[2],f.shape[3]]):f}var hA=V({conv2DBackpropInput_:yT});function xT(e,t,n,s,r,a){let o=D(e,"x","conv2dTranspose"),i=D(t,"filter","conv2dTranspose");return hA(n,o,i,s,r,"NHWC",a)}var pA=V({conv2dTranspose_:xT});function bT(e,t,n,s,r="NDHWC",a=[1,1,1]){let o=D(e,"x","conv3d"),i=D(t,"filter","conv3d"),l=o,u=!1;o.rank===4&&(u=!0,l=U(o,[1,o.shape[0],o.shape[1],o.shape[2],o.shape[3]])),P(l.rank===5,()=>`Error in conv3d: input must be rank 5, but got rank ${l.rank}.`),P(i.rank===5,()=>`Error in conv3d: filter must be rank 5, but got rank ${i.rank}.`),P(l.shape[4]===i.shape[3],()=>`Error in conv3d: depth of input (${l.shape[4]}) must match input depth for filter ${i.shape[3]}.`),P(Ks(n,a),()=>`Error in conv3D: Either strides or dilations must be 1. Got strides ${n} and dilations '${a}'`),P(r==="NDHWC",()=>`Error in conv3d: got dataFormat of ${r} but only NDHWC is currently supported.`);let c={x:l,filter:i},d={strides:n,pad:s,dataFormat:r,dilations:a},h=L.runKernel(Eu,c,d);return u?U(h,[h.shape[1],h.shape[2],h.shape[3],h.shape[4]]):h}var fA=V({conv3d_:bT});function vT(e,t,n,s,r){P(e.length===t.rank,()=>`Length of inShape (${e.length}) and rank of dy (${t.rank}) must match`);let a=e,o=t,i=!1;t.rank===4&&(i=!0,o=U(t,[1,t.shape[0],t.shape[1],t.shape[2],t.shape[3]]),a=[1,e[0],e[1],e[2],e[3]]);let l=a[4],u=o.shape[4];P(a.length===5,()=>`Error in conv3dDerInput: inShape must be length 5, but got length ${a.length}.`),P(o.rank===5,()=>`Error in conv3dDerInput: dy must be rank 5, but got rank ${o.rank}`),P(n.rank===5,()=>`Error in conv3dDerInput: filter must be rank 5, but got rank ${n.rank}`),P(l===n.shape[3],()=>`Error in conv3dDerInput: depth of input (${l}) must match input depth for filter ${n.shape[3]}.`),P(u===n.shape[4],()=>`Error in conv3dDerInput: depth of output (${u}) must match output depth for filter ${n.shape[4]}.`);let c={dy:o,filter:n},d={pad:r,strides:s,inputShape:a},h=L.runKernel(Hd,c,d);return i?U(h,[h.shape[1],h.shape[2],h.shape[3],h.shape[4]]):h}var Dx=V({conv3DBackpropInput_:vT});function wT(e,t,n,s,r){let a=D(e,"x","conv3dTranspose"),o=D(t,"filter","conv3dTranspose");return Dx(n,a,o,s,r)}var Ox=V({conv3dTranspose_:wT});function kT(e){let n={x:D(e,"x","cos")};return L.runKernel(Sa,n)}var Mh=V({cos_:kT});function IT(e){let n={x:D(e,"x","cosh")};return L.runKernel(Ca,n)}var mA=V({cosh_:IT});function ST(e,t=0,n=!1,s=!1){let a={x:D(e,"x","cumsum")},o={axis:t,exclusive:n,reverse:s};return L.runKernel(Ta,a,o)}var AA=V({cumsum_:ST});function CT(e,t,n,s=!1){let r=D(e,"x","denseBincount"),a=D(t,"weights","denseBincount");P(r.dtype==="int32",()=>`Error in denseBincount: input dtype must be int32, but got ${r.dtype}`),P(r.rank<=2,()=>`Error in denseBincount: input must be at most rank 2, but got rank ${r.rank}.`),P(n>=0,()=>`size must be non-negative, but got ${n}.`),P(a.size===r.size||a.size===0,()=>`Error in denseBincount: weights must have the same shape as x or 0-length, but got x shape: ${r.shape}, weights shape: ${a.shape}.`);let o={x:r,weights:a},i={size:n,binaryOutput:s};return L.runKernel(Gd,o,i)}var Px=V({denseBincount_:CT});function TT(e,t,n="NHWC"){let s=D(e,"x","depthToSpace"),r=n==="NHWC"?s.shape[1]:s.shape[2],a=n==="NHWC"?s.shape[2]:s.shape[3],o=n==="NHWC"?s.shape[3]:s.shape[1];P(r*t>=0,()=>`Negative dimension size caused by overflow when multiplying
    ${r} and ${t}  for depthToSpace with input shape
    ${s.shape}`),P(a*t>=0,()=>`Negative dimension size caused by overflow when multiplying
    ${a} and ${t} for depthToSpace with input shape
        ${s.shape}`),P(o%(t*t)==0,()=>`Dimension size must be evenly divisible by ${t*t} but is ${o} for depthToSpace with input shape ${s.shape}`);let i={x:s},l={blockSize:t,dataFormat:n};return L.runKernel(Ii,i,l)}var Mx=V({depthToSpace_:TT});function NT(e,t,n,s,r="NHWC",a=[1,1],o){let i=D(e,"x","depthwiseConv2d"),l=D(t,"filter","depthwiseConv2d"),u=i,c=!1;i.rank===3&&(c=!0,u=U(i,[1,i.shape[0],i.shape[1],i.shape[2]])),P(u.rank===4,()=>`Error in depthwiseConv2d: input must be rank 4, but got rank ${u.rank}.`),P(l.rank===4,()=>`Error in depthwiseConv2d: filter must be rank 4, but got rank ${l.rank}.`),P(u.shape[3]===l.shape[2],()=>`Error in depthwiseConv2d: number of input channels (${u.shape[3]}) must match the inChannels dimension in filter ${l.shape[2]}.`),o!=null&&P(qt(s),()=>`Error in depthwiseConv2d: pad must be an integer when using, dimRoundingMode ${o} but got pad ${s}.`);let d={x:u,filter:l},h={strides:n,pad:s,dataFormat:r,dilations:a,dimRoundingMode:o},p=L.runKernel(Na,d,h);return c?U(p,[p.shape[1],p.shape[2],p.shape[3]]):p}var tc=V({depthwiseConv2d_:NT});function ET(e){let n={x:D(e,"x","diag")};return L.runKernel(Xd,n)}var RT=V({diag_:ET});function _T(e,t,n,s,r=[1,1],a="NHWC"){let o=D(e,"x","dilation2d"),i=D(t,"filter","dilation2d");P(o.rank===3||o.rank===4,()=>`Error in dilation2d: input must be rank 3 or 4, but got rank ${o.rank}.`),P(i.rank===3,()=>`Error in dilation2d: filter must be rank 3, but got rank ${i.rank}.`),P(a==="NHWC",()=>`Error in dilation2d: Only NHWC is currently supported, but got dataFormat of ${a}`);let l=o,u=!1;o.rank===3&&(l=U(o,[1,o.shape[0],o.shape[1],o.shape[2]]),u=!0);let c={x:l,filter:i},d={strides:n,pad:s,dilations:r},h=L.runKernel(Ru,c,d);return u?U(h,[h.shape[1],h.shape[2],h.shape[3]]):h}var zx=V({dilation2d_:_T});function $T(e,t){let n=e.length,s=[];for(let r=0;r<n;r++){let a=n-1-r,o=e[a]||1;(t[t.length-1-r]||1)>1&&o===1&&s.unshift(a)}return s}function Vt(e,t){let n=[];for(let s=0;s<t.length;s++){let r=e[e.length-s-1],a=t.length-s-1,o=t[a];(r==null||r===1&&o>1)&&n.unshift(a)}return n}function mt(e,t){let n=[],s=Math.max(e.length,t.length);for(let r=0;r<s;r++){let a=e[e.length-r-1];a==null&&(a=1);let o=t[t.length-r-1];if(o==null&&(o=1),a===1)n.unshift(o);else if(o===1)n.unshift(a);else if(a!==o){let i=`Operands could not be broadcast together with shapes ${e} and ${t}.`;throw Error(i)}else n.unshift(a)}return n}function FT(e,t){let n=D(e,"a","equal","string_or_numeric"),s=D(t,"b","equal","string_or_numeric");[n,s]=It(n,s),mt(n.shape,s.shape);let r={a:n,b:s};return L.runKernel(Ti,r)}var as=V({equal_:FT});function DT(e,t,n){let s=D(t,"a","where"),r=D(n,"b","where"),a=D(e,"condition","where","bool"),o=mt(mt(a.shape,s.shape),r.shape),i=ec(a,o),l=ec(s,o),u=ec(r,o),c={condition:i,t:l,e:u};return L.runKernel(Qi,c)}var gn=V({where_:DT});function OT(e){let n={x:D(e,"x","zerosLike")};return L.runKernel(dl,n)}var qe=V({zerosLike_:OT});function PT(e,t){let n=D(e,"a","div"),s=D(t,"b","div");[n,s]=It(n,s);let r=de(n,s),a=qe(r),o=as(s,a);return gn(o,a,r)}var Lx=V({divNoNan_:PT});function MT(e,t){let n=D(e,"t1","dot"),s=D(t,"t2","dot");P((n.rank===1||n.rank===2)&&(s.rank===1||s.rank===2),()=>`Error in dot: inputs must all be rank 1 or 2, but got ranks ${n.rank} and ${s.rank}.`);let r=n.rank===1?n.size:n.shape[1],a=s.rank===1?s.size:s.shape[0];if(P(r===a,()=>`Error in dot: inner dimensions of inputs must match, but got ${r} and ${a}.`),n.rank===1&&s.rank===1){let o=U(n,[1,-1]),i=U(s,[-1,1]),l=We(o,i);return U(l,[])}else if(n.rank===1&&s.rank===2){let o=U(n,[1,-1]),i=U(s,[s.shape[0],s.shape[1]]),l=We(o,i);return U(l,[l.size])}else if(n.rank===2&&s.rank===1){let o=U(s,[-1,1]),i=We(n,o);return U(i,[i.size])}else{let o=U(s,[s.shape[0],s.shape[1]]);return We(n,o)}}var zT=V({dot_:MT});function LT(e,...t){let n=t.map((r,a)=>D(r,`tensors${a}`,"einsum")),s={equation:e};return L.runKernel(Yd,n,s)}var Bx=V({einsum_:LT});function BT(e){let n={x:D(e,"x","elu")};return L.runKernel(Si,n)}var nc=V({elu_:BT});function WT(e){let t=D(e,"x","erf");P(t.dtype==="int32"||t.dtype==="float32",()=>"Input dtype must be `int32` or `float32`."),t.dtype==="int32"&&(t=ce(t,"float32"));let n={x:t};return L.runKernel(Ci,n)}var Wx=V({erf_:WT});function VT(e){let n={x:D(e,"x","exp")};return L.runKernel(Ra,n)}var os=V({exp_:VT});function UT(e,t=0){let n=D(e,"x","expandDims","string_or_numeric");P(t<=n.rank,()=>"Axis must be <= rank of the tensor");let s={input:n},r={dim:t};return L.runKernel(Ni,s,r)}var Ft=V({expandDims_:UT});function HT(e){let n={x:D(e,"x","expm1")};return L.runKernel(Ei,n)}var Vx=V({expm1_:HT});function GT(e,t){let n=D(e,"x","tile","string_or_numeric");P(n.rank===t.length,()=>`Error in transpose: rank of input ${n.rank} must match length of reps ${t}.`);let s={x:n},r={reps:t};return L.runKernel(Pr,s,r)}var Es=V({tile_:GT});function jT(e,t,n,s="float32"){t==null&&(t=e);let r=Be([e,t],s),a=e<=t?e:t;for(let i=0;i<a;++i)r.set(1,i,i);let o=U(r.toTensor(),[e,t]);if(n==null)return o;if(n.length===1)return Es(Ft(o,0),[n[0],1,1]);if(n.length===2)return Es(Ft(Ft(o,0),0),[n[0],n[1],1,1]);if(n.length===3)return Es(Ft(Ft(Ft(o,0),0),0),[n[0],n[1],n[2],1,1]);throw new Error(`eye() currently supports only 1D and 2D batchShapes, but received ${n.length}D.`)}var gA=V({eye_:jT});function Sl(e,t,n){let s={shape:e,value:t,dtype:n};return L.runKernel(_u,{},s)}function qT(e){let n={x:D(e,"x","floor")};return L.runKernel(_a,n)}var sc=V({floor_:qT});function XT(e,t,n=0,s=0){let r=D(e,"x","gather"),a=D(t,"indices","gather","int32"),o={x:r,indices:a},i={axis:n,batchDims:s};return L.runKernel(_i,o,i)}var Cl=V({gather_:XT});function KT(e,t){let n=D(e,"a","greater","string_or_numeric"),s=D(t,"b","greater","string_or_numeric");[n,s]=It(n,s),mt(n.shape,s.shape);let r={a:n,b:s};return L.runKernel(Fi,r)}var Vn=V({greater_:KT});function ZT(e,t){let n=D(e,"a","greaterEqual","string_or_numeric"),s=D(t,"b","greaterEqual","string_or_numeric");[n,s]=It(n,s),mt(n.shape,s.shape);let r={a:n,b:s};return L.runKernel(Da,r)}var Io=V({greaterEqual_:ZT});function YT(e){let n={input:D(e,"input","imag")};return L.runKernel(th,n)}var zh=V({imag_:YT});function JT(e){let n={x:D(e,"x","isFinite")};return L.runKernel(Di,n)}var QT=V({isFinite_:JT});function eN(e){let n={x:D(e,"x","isInf")};return L.runKernel(Oi,n)}var tN=V({isInf_:eN});function nN(e){let n={x:D(e,"x","isNaN")};return L.runKernel(Pi,n)}var Ux=V({isNaN_:nN});function sN(e,t=.2){let s={x:D(e,"x","leakyRelu")},r={alpha:t};return L.runKernel(Pa,s,r)}var Lh=V({leakyRelu_:sN});function rN(e,t){let n=D(e,"a","less","string_or_numeric"),s=D(t,"b","less","string_or_numeric");[n,s]=It(n,s),mt(n.shape,s.shape);let r={a:n,b:s};return L.runKernel(Mi,r)}var yA=V({less_:rN});function aN(e,t){let n=D(e,"a","lessEqual","string_or_numeric"),s=D(t,"b","lessEqual","string_or_numeric");[n,s]=It(n,s),mt(n.shape,s.shape);let r={a:n,b:s};return L.runKernel(zi,r)}var So=V({lessEqual_:aN});function Hx(e,t,n){if(n<=0)throw new Error("The number of values should be positive.");let s={start:e,stop:t,num:n};return L.runKernel(nh,{},s)}function oN(e,t=5,n=1,s=1,r=.5){let a=D(e,"x","localResponseNormalization");P(a.rank===4||a.rank===3,()=>`Error in localResponseNormalization: x must be rank 3 or 4 but got
               rank ${a.rank}.`),P(qt(t),()=>`Error in localResponseNormalization: depthRadius must be an integer but got depthRadius ${t}.`);let o=a,i=!1;a.rank===3&&(i=!0,o=U(a,[1,a.shape[0],a.shape[1],a.shape[2]]));let l={x:o},u={depthRadius:t,bias:n,alpha:s,beta:r},c=L.runKernel(Du,l,u);return i?U(c,[c.shape[1],c.shape[2],c.shape[3]]):c}var Gx=V({localResponseNormalization_:oN});function iN(e){let n={x:D(e,"x","log")};return L.runKernel(Ma,n)}var is=V({log_:iN});function lN(e){let n={x:D(e,"x","log1p")};return L.runKernel(Li,n)}var Bh=V({log1p_:lN});function uN(e){return P(Fr(e),()=>"The f passed in grad(f) must be a function"),(t,n)=>{let s=D(t,"x","tf.grad","string_or_numeric"),r=n!=null?D(n,"dy","tf.grad"):null;return L.tidy(()=>{let{value:a,grads:o}=L.gradients(()=>e(s),[s],r);return r!=null&&fn(a.shape,r.shape,"The shape of dy passed in grad(f)(x, dy) must match the shape returned by f(x)"),Wh(o),o[0]})}}function cN(e){return P(Fr(e),()=>"The f passed in grads(f) must be a function"),(t,n)=>{P(Array.isArray(t),()=>"The args passed in grads(f)(args) must be an array of `Tensor`s or `TensorLike`s");let s=Zu(t,"args","tf.grads","string_or_numeric"),r=n!=null?D(n,"dy","tf.grads"):null;return L.tidy(()=>{let{value:a,grads:o}=L.gradients(()=>e(...s),s,r);return r!=null&&fn(a.shape,r.shape,"The shape of dy passed in grads(f)([x1,...], dy) must match the shape returned by f([x1,...])"),Wh(o),o})}}function dN(e){return P(Fr(e),()=>"The f passed in valueAndGrad(f) must be a function"),(t,n)=>{P(t instanceof Ue,()=>"The x passed in valueAndGrad(f)(x) must be a tensor"),P(n==null||n instanceof Ue,()=>"The dy passed in valueAndGrad(f)(x, dy) must be a tensor");let{grads:s,value:r}=L.gradients(()=>e(t),[t],n);return Wh(s),{grad:s[0],value:r}}}function hN(e){return P(Fr(e),()=>"The f passed in valueAndGrads(f) must be a function"),(t,n)=>{P(Array.isArray(t)&&t.every(r=>r instanceof Ue),()=>"The args passed in valueAndGrads(f)(args) must be array of tensors"),P(n==null||n instanceof Ue,()=>"The dy passed in valueAndGrads(f)(args, dy) must be a tensor");let s=L.gradients(()=>e(...t),t,n);return n!=null&&fn(s.value.shape,n.shape,"The shape of dy passed in valueAndGrads(f)([x1,...], dy) must match the shape returned by f([x1,...])"),Wh(s.grads),s}}function jx(e,t){P(Fr(e),()=>"The f passed in variableGrads(f) must be a function"),P(t==null||Array.isArray(t)&&t.every(u=>u instanceof qu),()=>"The varList passed in variableGrads(f, varList) must be an array of variables");let n=t!=null;if(!n){t=[];for(let u in L.registeredVariables)t.push(L.registeredVariables[u])}let s=n?t.filter(u=>!u.trainable):null,r=t.length;t=t.filter(u=>u.trainable),P(t.length>0,()=>`variableGrads() expects at least one of the input variables to be trainable, but none of the ${r} variables is trainable.`);let a=!0,{value:o,grads:i}=L.gradients(e,t,null,a);P(i.some(u=>u!=null),()=>"Cannot find a connection between any variable and the result of the loss function y=f(x). Please make sure the operations that use variables are inside the function f passed to minimize()."),P(o.rank===0,()=>`The f passed in variableGrads(f) must return a scalar, but it returned a rank-${o.rank} tensor`);let l={};return t.forEach((u,c)=>{i[c]!=null&&(l[u.name]=i[c])}),s!=null&&s.forEach(u=>l[u.name]=null),{value:o,grads:l}}function Zs(e){return L.customGrad(e)}function Wh(e){if(e.filter(n=>n==null).length>0)throw new Error(`Cannot compute gradient of y=f(x) with respect to x. Make sure that
    the f you passed encloses all operations that lead from x to y.`)}function pN(e){let n={x:D(e,"x","neg")};return L.runKernel(Vi,n)}var St=V({neg_:pN});function fN(e){let n={x:D(e,"x","softplus")};return L.runKernel(rl,n)}var Tl=V({softplus_:fN});function mN(e){let t=D(e,"x","logSigmoid");return Zs(s=>({value:St(Tl(St(s))),gradFunc:o=>z(o,Bn(St(s)))}))(t)}var AN=V({logSigmoid_:mN});function gN(e,t=null,n=!1){let r={x:D(e,"x","max")},a={reductionIndices:t,keepDims:n};return L.runKernel(za,r,a)}var ls=V({max_:gN});function yN(e,t){let n=D(e,"a","sub"),s=D(t,"b","sub");[n,s]=It(n,s);let r={a:n,b:s};return L.runKernel(lo,r)}var Ae=V({sub_:yN});function xN(e,t=null,n=!1){let s=D(e,"x","sum");s.dtype==="bool"&&(s=ce(s,"int32"));let r={x:s},a={axis:t,keepDims:n};return L.runKernel(ao,r,a)}var ve=V({sum_:xN});function bN(e,t=-1){let n=D(e,"logits","logSoftmax");if(t===-1&&(t=n.rank-1),t!==n.rank-1)throw Error(`Log Softmax along a non-last dimension is not yet supported. Logits was rank ${n.rank} and axis was ${t}`);return Zs((r,a)=>{let o=!0,i=ls(r,t,!0),l=Ae(r,i),u=Ae(ce(l,"float32"),is(ve(os(l),t,o)));return a([u]),{value:u,gradFunc:(d,h)=>{let[p]=h,f=!0,m=os(p);return Ae(d,z(ve(d,t,f),m))}}})(n)}var xA=V({logSoftmax_:bN});function bA(e,t){for(let n=0;n<e.length;++n)if(e[e.length-n-1]!==t-1-n)return!1;return!0}function qx(e,t,n){let s=e.length+t.length,r=[],a=0,o=0;for(let i=0;i<s;i++)n.indexOf(i)===-1?r.push(e[a++]):r.push(t[o++]);return r}function Xx(e,t){let n=[],s=e.length;for(let a=0;a<s;a++)t.indexOf(a)===-1&&n.push(e[a]);let r=t.map(a=>e[a]);return[n,r]}function Co(e,t){let n=t.map(s=>1);return qx(e,n,t)}function vN(e,t,n){P(bA(t,n),()=>`${e} supports only inner-most axes for now. Got axes ${t} and rank-${n} input.`)}function Kx(e,t){if(bA(e,t))return null;let n=[];for(let s=0;s<t;++s)e.indexOf(s)===-1&&n.push(s);return e.forEach(s=>n.push(s)),n}function vA(e){return e.map((t,n)=>[n,t]).sort((t,n)=>t[1]-n[1]).map(t=>t[0])}function wN(e,t){let n=[];for(let s=t-e;s<t;++s)n.push(s);return n}function kN(e,t=null,n=!1){let s=D(e,"x","logSumExp"),r=xs(t,s.shape),a=ls(s,r,!0),o=Ae(s,a),i=os(o),l=ve(i,r),u=is(l),c=ae(U(a,u.shape),u);if(n){let d=Co(c.shape,r);return U(c,d)}return c}var Zx=V({logSumExp_:kN});function IN(e,t){let n=D(e,"a","logicalAnd","bool"),s=D(t,"b","logicalAnd","bool");mt(n.shape,s.shape);let r={a:n,b:s};return L.runKernel(Bi,r)}var Rs=V({logicalAnd_:IN});function SN(e){let n={x:D(e,"x","logicalNot","bool")};return L.runKernel($u,n)}var Vh=V({logicalNot_:SN});function CN(e,t){let n=D(e,"a","logicalOr","bool"),s=D(t,"b","logicalOr","bool");mt(n.shape,s.shape);let r={a:n,b:s};return L.runKernel(Fu,r)}var wA=V({logicalOr_:CN});function TN(e,t){let n=D(e,"a","logicalXor","bool"),s=D(t,"b","logicalXor","bool");return mt(n.shape,s.shape),Rs(wA(e,t),Vh(Rs(e,t)))}var NN=V({logicalXor_:TN});function EN(e,t,n,s,r){let a=D(e,"x","maxPool"),o=1,i=a,l=!1;a.rank===3&&(l=!0,i=U(a,[1,a.shape[0],a.shape[1],a.shape[2]])),P(i.rank===4,()=>`Error in maxPool: input must be rank 4 but got rank ${i.rank}.`),P(Ks(n,o),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${n} and dilations '${o}'`),r!=null&&P(qt(s),()=>`Error in maxPool: pad must be an integer when using, dimRoundingMode ${r} but got pad ${s}.`);let u={x:i},c={filterSize:t,strides:n,pad:s,dimRoundingMode:r},d=L.runKernel(Ba,u,c);return l?U(d,[d.shape[1],d.shape[2],d.shape[3]]):d}var Uh=V({maxPool_:EN});function RN(e,t=[1,1,1],n,s,r,a="NDHWC"){let o=D(e,"x","maxPool3d"),i=o,l=!1;o.rank===4&&(l=!0,i=U(o,[1,o.shape[0],o.shape[1],o.shape[2],o.shape[3]])),P(i.rank===5,()=>`Error in maxPool3d: x must be rank 5 but got rank ${i.rank}.`),P(a==="NDHWC",()=>`Error in maxPool3d: Only NDHWC is currently supported, but got dataFormat of ${a}`),r!=null&&P(qt(s),()=>`Error in maxPool3d: pad must be an integer when using, dimRoundingMode ${r} but got pad ${s}.`);let u={x:i},c={filterSize:t,strides:n,pad:s,dimRoundingMode:r,dataFormat:a},d=L.runKernel(Ou,u,c);return l?U(d,[d.shape[1],d.shape[2],d.shape[3],d.shape[4]]):d}var kA=V({maxPool3d_:RN});function _N(e,t,n,s,r=!1){let o={x:D(e,"x","maxPoolWithArgmax")},i={filterSize:t,strides:n,pad:s,includeBatchInIndex:r},l=L.runKernel(oh,o,i);return{result:l[0],indexes:l[1]}}var Yx=V({maxPoolWithArgmax_:_N});function $N(e,t){let n=D(e,"a","maximum"),s=D(t,"b","maximum");[n,s]=It(n,s),n.dtype==="bool"&&(n=ce(n,"int32"),s=ce(s,"int32")),mt(n.shape,s.shape);let r={a:n,b:s};return L.runKernel(La,r)}var gr=V({maximum_:$N});function FN(e,t=null,n=!1){let r={x:D(e,"x","mean")},a={axis:t,keepDims:n};return L.runKernel(Wa,r,a)}var Et=V({mean_:FN});function Dt(e,t="float32"){if(t==="complex64"){let s=Dt(e,"float32"),r=Dt(e,"float32");return Lr(s,r)}let n=Md(_t(e),t);return L.makeTensor(n,e,t)}function Un(e,t="float32"){if(t==="complex64"){let s=Un(e,"float32"),r=Dt(e,"float32");return Lr(s,r)}let n=wm(_t(e),t);return L.makeTensor(n,e,t)}function DN(e,t,{indexing:n="xy"}={}){if(n!=="xy"&&n!=="ij")throw new TypeError(`${n} is not a valid third argument to meshgrid`);if(e===void 0)return[];let s=D(e,"x","meshgrid",e instanceof Ue?e.dtype:"float32");if(t===void 0)return[s];let r=D(t,"y","meshgrid",t instanceof Ue?t.dtype:"float32"),a=_t(s.shape),o=_t(r.shape);return n==="xy"?(s=U(s,[1,-1]),r=U(r,[-1,1]),[We(Un([o,1],s.dtype),s),We(r,Un([1,a],r.dtype))]):(s=U(s,[-1,1]),r=U(r,[1,-1]),[We(s,Un([1,o],s.dtype)),We(Un([a,1],r.dtype),r)])}function ON(e,t=null,n=!1){let r={x:D(e,"x","min")},a={axis:t,keepDims:n};return L.runKernel(Va,r,a)}var Hh=V({min_:ON});function PN(e,t){let n=D(e,"a","minimum"),s=D(t,"b","minimum");[n,s]=It(n,s),n.dtype==="bool"&&(n=ce(n,"int32"),s=ce(s,"int32")),mt(n.shape,s.shape);let r={a:n,b:s};return L.runKernel(Ua,r)}var rc=V({minimum_:PN});function MN(e,t,n){P(n==="reflect"||n==="symmetric",()=>`Invalid mode. Mode must be either reflect or symmetric. Got ${n}.`);let s=D(e,"x","mirrorPad");if(s.rank===0)throw new Error("mirrorPad(scalar) is not defined. Pass non-scalar to mirrorPad");P(t.length===s.rank,()=>`Padding doesn't match input. Must be ${s.rank}. Got ${t.length}.`);let r=n==="reflect"?1:0;for(let i=0;i<s.rank;i++)P(t[i].length===2,()=>"Invalid number of paddings. Must be length of 2 each."),P(t[i][0]>=0&&t[i][0]<=s.shape[i]-r&&t[i][1]>=0&&t[i][1]<=s.shape[i]-r,()=>`Padding in dimension ${i} cannot be greater than or equal to ${s.shape[i]-r} or less than 0 for input of shape ${s.shape}`);let a={paddings:t,mode:n},o={x:s};return L.runKernel(Ha,o,a)}var Jx=V({mirrorPad_:MN});function zN(e,t){let n=D(e,"a","mod"),s=D(t,"b","mod");[n,s]=It(n,s);let r={a:n,b:s};return L.runKernel(Wi,r)}var Qx=V({mod_:zN});function LN(e){let t=D(e,"x","square"),n={};return L.runKernel("Square",{x:t},n)}var lt=V({square_:LN});function BN(e,t=null,n=!1){e=D(e,"x","moments");let s=xs(t,e.shape),r=Et(e,s,n),a=r.shape;n||(a=Co(r.shape,s));let o=lt(Ae(ce(e,"float32"),U(r,a))),i=Et(o,s,n);return{mean:r,variance:i}}var Gh=V({moments_:BN});function WN(e,t,n,s){let r=D(t,"data","multiRNNCell"),a=Zu(n,"c","multiRNNCell"),o=Zu(s,"h","multiRNNCell"),i=r,l=[];for(let d=0;d<e.length;d++){let h=e[d](i,a[d],o[d]);l.push(h[0]),l.push(h[1]),i=h[1]}let u=[],c=[];for(let d=0;d<l.length;d+=2)u.push(l[d]),c.push(l[d+1]);return[u,c]}var VN=V({multiRNNCell_:WN});function UN(e,t,n,s=!1){let r=D(e,"logits","multinomial"),a=r.size,o=r.rank;if(a<2)throw new Error(`Error in multinomial: you need at least 2 outcomes, but got ${a}.`);if(o>2)throw new Error(`Rank of probabilities must be 1 or 2, but is ${o}`);n=n||Math.random();let l={logits:o===1?U(r,[1,-1]):r},u={numSamples:t,seed:n,normalized:s},c=L.runKernel(ih,l,u);return o===1?U(c,[c.size]):c}var eb=V({multinomial_:UN});function HN(e,t){let n=D(e,"a","notEqual","string_or_numeric"),s=D(t,"b","notEqual","string_or_numeric");[n,s]=It(n,s),mt(n.shape,s.shape);let r={a:n,b:s};return L.runKernel(Ui,r)}var Nl=V({notEqual_:HN});function GN(e){let n={x:D(e,"x","onesLike")};return L.runKernel(qi,n)}var us=V({onesLike_:GN});function jN(e,t){let n=D(e,"v1","outerProduct"),s=D(t,"v2","outerProduct");P(n.rank===1&&s.rank===1,()=>`Error in outerProduct: inputs must be rank 1, but got ranks ${n.rank} and ${s.rank}.`);let r=U(n,[-1,1]),a=U(s,[1,-1]);return We(r,a)}var qN=V({outerProduct_:jN});function XN(e,t,n=0){let s=D(e,"x","pad");if(s.rank===0)throw new Error("pad(scalar) is not defined. Pass non-scalar to pad");let r={paddings:t,constantValue:n},a={x:s};return L.runKernel(qa,a,r)}var Gr=V({pad_:XN});function KN(e,t,n=0){return P(t.length===2,()=>"Invalid number of paddings. Must be length of 2."),Gr(e,[t],n)}var ZN=V({pad1d_:KN});function YN(e,t,n=0){return P(t.length===2&&t[0].length===2&&t[1].length===2,()=>"Invalid number of paddings. Must be length of 2 each."),Gr(e,t,n)}var JN=V({pad2d_:YN});function QN(e,t,n=0){return P(t.length===3&&t[0].length===2&&t[1].length===2&&t[2].length===2,()=>"Invalid number of paddings. Must be length of 2 each."),Gr(e,t,n)}var eE=V({pad3d_:QN});function tE(e,t,n=0){return P(t.length===4&&t[0].length===2&&t[1].length===2&&t[2].length===2&&t[3].length===2,()=>"Invalid number of paddings. Must be length of 2 each."),Gr(e,t,n)}var nE=V({pad4d_:tE});function sE(e,t,n){let s=D(e,"x","spaceToBatchND");P(s.rank>=1+t.length,()=>`input rank ${s.rank} should be > than [blockShape] ${t.length}`),P(n.length===t.length,()=>`paddings.shape[0] ${n.length} must be equal to [blockShape] ${t.length}`),P(s.shape.reduce((o,i,l)=>l>0&&l<=t.length?o&&(i+n[l-1][0]+n[l-1][1])%t[l-1]==0:o,!0),()=>`input spatial dimensions ${s.shape.slice(1)} with paddings ${n.toString()} must be divisible by blockShapes ${t.toString()}`);let r={x:s},a={blockShape:t,paddings:n};return L.runKernel(al,r,a)}var jh=V({spaceToBatchND_:sE});function rE(e,t,n,s,r,a){r==null&&(r=[1,1]),a==null&&(a=1),s===0&&(s="valid");let o=D(e,"x","maxPool"),i=o,l=!1;o.rank===3&&(l=!0,i=U(o,[1,o.shape[0],o.shape[1],o.shape[2]])),P(Ks(a,r),()=>`Error in pool: Either strides or dilations must be 1. Got strides ${a} and dilations '${r}'`);let u=Ix(i.shape,t,a,r,s),c=[u.dilationHeight,u.dilationWidth],d;s==="same"?d=oE([u.filterHeight,u.filterWidth],c):d=[[0,0],[0,0]];let h=c[0]===1&&c[1]===1,[p,f]=aE([u.inHeight,u.inWidth],c,d),m=h?s:"valid",A=h?i:jh(i,c,p),y=(n==="avg"?()=>Oh(A,t,a,m):()=>Uh(A,t,a,m))(),x=h?y:Ph(y,c,f);return l?U(x,[x.shape[1],x.shape[2],x.shape[3]]):x}function aE(e,t,n){let s=n.map(c=>c[0]),r=n.map(c=>c[1]),a=e.concat(s,r),o=t.map((c,d)=>(c-a[d]%c)%c),i=r.map((c,d)=>c+o[d]),l=t.map((c,d)=>[s[d],i[d]]),u=t.map((c,d)=>[0,o[d]]);return[l,u]}function oE(e,t){let s=e.map((o,i)=>o+(o-1)*(t[i]-1)).map(o=>o-1),r=s.map(o=>Math.floor(o/2)),a=s.map((o,i)=>o-r[i]);return s.map((o,i)=>[r[i],a[i]])}var iE=V({pool_:rE});function lE(e,t){let n=D(e,"base","pow"),s=D(t,"exp","pow");[n,s]=It(n,s);let r={a:n,b:s};return L.runKernel(Xa,r)}var jr=V({pow_:lE});function uE(e,t){let n=D(e,"x","prelu"),s=D(t,"alpha","prelu"),r={x:n,alpha:s};return L.runKernel(Ka,r)}var qh=V({prelu_:uE});function cE(e,t=null,n=!1){let s=D(e,"x","prod");s.dtype==="bool"&&(s=ce(s,"int32"));let r={x:s},a={axis:t,keepDims:n};return L.runKernel(Ki,r,a)}var IA=V({prod_:cE});function dE(e,t,n){let s=_t(e),r=null;if(n==null||n==="float32")r=new Float32Array(s);else if(n==="int32")r=new Int32Array(s);else if(n==="bool")r=new Uint8Array(s);else throw new Error(`Unknown data type ${n}`);for(let a=0;a<s;a++)r[a]=t();return L.makeTensor(r,e,n)}var hE=V({rand_:dE}),SA=fa(t5()),CA=class{constructor(e,t,n,s,r){this.mean=e,this.stdDev=t,this.dtype=n,this.nextVal=NaN,this.truncated=s,this.truncated&&(this.upper=this.mean+this.stdDev*2,this.lower=this.mean-this.stdDev*2);let a=r||Math.random();this.random=SA.alea(a.toString())}nextValue(){if(!isNaN(this.nextVal)){let s=this.nextVal;return this.nextVal=NaN,s}let e,t,n=!1;for(;!n;){let s,r,a;do s=2*this.random()-1,r=2*this.random()-1,a=s*s+r*r;while(a>=1||a===0);let o=Math.sqrt(-2*Math.log(a)/a);e=this.mean+this.stdDev*s*o,t=this.mean+this.stdDev*r*o,(!this.truncated||this.isValidTruncated(e))&&(n=!0)}return(!this.truncated||this.isValidTruncated(t))&&(this.nextVal=this.convertValue(t)),this.convertValue(e)}convertValue(e){return this.dtype==null||this.dtype==="float32"?e:Math.round(e)}isValidTruncated(e){return e<=this.upper&&e>=this.lower}},pE=class{constructor(e,t,n,s){this.alpha=e,this.beta=1/t,this.dtype=n;let r=s||Math.random();this.randu=SA.alea(r.toString()),this.randn=new CA(0,1,n,!1,this.randu()),e<1?this.d=e+2/3:this.d=e-1/3,this.c=1/Math.sqrt(9*this.d)}nextValue(){let e,t,n,s,r,a;for(;;){do s=this.randn.nextValue(),a=1+this.c*s;while(a<=0);if(a*=a*a,e=s*s,t=1-.331*e*e,n=.5*e+this.d*(1-a+Math.log(a)),r=this.randu(),r<t||Math.log(r)<n)break}return a=1/this.beta*this.d*a,this.alpha<1&&(a*=Math.pow(this.randu(),1/this.alpha)),this.convertValue(a)}convertValue(e){return this.dtype==="float32"?e:Math.round(e)}},fE=class{constructor(e=0,t=1,n,s){if(this.canReturnFloat=()=>this.dtype==null||this.dtype==="float32",this.min=e,this.range=t-e,this.dtype=n,s==null&&(s=Math.random()),typeof s=="number"&&(s=s.toString()),!this.canReturnFloat()&&this.range<=1)throw new Error(`The difference between ${e} - ${t} <= 1 and dtype is not float`);this.random=SA.alea(s)}convertValue(e){return this.canReturnFloat()?e:Math.round(e)}nextValue(){return this.convertValue(this.min+this.range*this.random())}};function mE(e,t,n=1,s="float32",r){if(n==null&&(n=1),s==null&&(s="float32"),s!=="float32"&&s!=="int32")throw new Error(`Unsupported data type ${s}`);let a=new pE(t,n,s,r),o=Be(e,s);for(let i=0;i<o.values.length;i++)o.values[i]=a.nextValue();return o.toTensor()}var AE=V({randomGamma_:mE});function gE(e,t=0,n=1,s,r){if(s!=null&&s==="bool")throw new Error(`Unsupported data type ${s}`);let a=new CA(t,n,s,!1,r),o=Be(e,s);for(let i=0;i<o.values.length;i++)o.values[i]=a.nextValue();return o.toTensor()}var tb=V({randomNormal_:gE});function yE(e,t=0,n=1,s="float32",r){let a=Be(e,s),o=new fE(t,n,null,r);for(let i=0;i<a.values.length;i++)a.values[i]=o.nextValue();return a.toTensor()}var El=V({randomUniform_:yE});function Rl(e,t,n=1,s="float32"){if(n===0)throw new Error("Cannot have a step of zero");let r={start:e,stop:t,step:n,dtype:s};return L.runKernel(Pu,{},r)}function xE(e){let n={input:D(e,"input","real")};return L.runKernel(lh,n)}var ac=V({real_:xE});function bE(e){let n={x:D(e,"x","reciprocal")};return L.runKernel(Zi,n)}var nb=V({reciprocal_:bE});function vE(e){let n={x:D(e,"x","relu")};return L.runKernel(Za,n)}var Ys=V({relu_:vE});function wE(e){let n={x:D(e,"x","relu6")};return L.runKernel(Ja,n)}var TA=V({relu6_:wE});function kE(e,t){let s={x:D(e,"x","reverse")},r={dims:t};return L.runKernel(Qa,s,r)}var cs=V({reverse_:kE});function IE(e){let t=D(e,"x","reverse");return P(t.rank===1,()=>`Error in reverse1D: x must be rank 1 but got rank ${t.rank}.`),cs(t,0)}var SE=V({reverse1d_:IE});function CE(e,t){let n=D(e,"x","reverse");return P(n.rank===2,()=>`Error in reverse2D: x must be rank 2 but got rank ${n.rank}.`),cs(n,t)}var TE=V({reverse2d_:CE});function NE(e,t){let n=D(e,"x","reverse");return P(n.rank===3,()=>`Error in reverse3D: x must be rank 3 but got rank ${n.rank}.`),cs(n,t)}var EE=V({reverse3d_:NE});function RE(e,t){let n=D(e,"x","reverse");return P(n.rank===4,()=>`Error in reverse4D: x must be rank 4 but got rank ${n.rank}.`),cs(n,t)}var _E=V({reverse4d_:RE});function $E(e){let n={x:D(e,"x","round")};return L.runKernel(eo,n)}var NA=V({round_:$E});function FE(e){let n={x:D(e,"x","rsqrt")};return L.runKernel(to,n)}var EA=V({rsqrt_:FE});function Ie(e,t){if((an(e)&&t!=="string"||Array.isArray(e))&&t!=="complex64")throw new Error("Error creating a new Scalar: value must be a primitive (number|boolean|string)");if(t==="string"&&an(e)&&!(e instanceof Uint8Array))throw new Error("When making a scalar from encoded string, the value must be `Uint8Array`.");return Br(e,[],[],t)}function DE(e){let n={x:D(e,"x","selu")};return L.runKernel(el,n)}var RA=V({selu_:DE});function OE(e,t,n,s,r,a=[1,1],o="NHWC"){let i=D(e,"x","separableConv2d"),l=D(t,"depthwiseFilter","separableConv2d"),u=D(n,"pointwiseFilter","separableConv2d"),c=i,d=!1;if(i.rank===3&&(d=!0,c=U(i,[1,i.shape[0],i.shape[1],i.shape[2]])),o==="NCHW")throw new Error("separableConv2d currently does not support dataFormat NCHW; only NHWC is supported");P(c.rank===4,()=>`Error in separableConv2d: input must be rank 4, but got rank ${c.rank}.`),P(l.rank===4,()=>`Error in separableConv2d: depthwise filter must be rank 4, but got rank ${l.rank}.`),P(u.rank===4,()=>`Error in separableConv2d: pointwise filter must be rank 4, but got rank ${l.rank}.`),P(u.shape[0]===1,()=>`Error in separableConv2d: the first dimension of pointwise filter  must be 1, but got ${u.shape[0]}.`),P(u.shape[1]===1,()=>`Error in separableConv2d: the second dimension of pointwise filter must be 1, but got ${u.shape[1]}.`);let h=l.shape[2],p=l.shape[3];P(u.shape[2]===h*p,()=>`Error in separableConv2d: the third dimension of pointwise filter must be ${h*p}, but got ${u.shape[2]}.`);let f=tc(c,l,s,r,o,a),A=Hr(f,u,1,"valid",o);return d?U(A,[A.shape[1],A.shape[2],A.shape[3]]):A}var sb=V({separableConv2d_:OE});async function PE(e,t){let n=D(e,"x","setdiff1d"),s=D(t,"y","setdiff1d");P(n.dtype===s.dtype,()=>`x and y should have the same dtype, but got x (${n.dtype}) and y (${s.dtype}).`),P(n.rank===1,()=>`x should be 1D tensor, but got x (${n.shape}).`),P(s.rank===1,()=>`y should be 1D tensor, but got y (${s.shape}).`);let r=await n.data(),a=await s.data(),o=new Set(a),i=0;for(let c=0;c<r.length;c++)o.has(r[c])||i++;let l=new Bt([i],n.dtype),u=new Bt([i],"int32");for(let c=0,d=0;c<r.length;c++)o.has(r[c])||(l.values[d]=r[c],u.values[d]=c,d++);return[l.toTensor(),u.toTensor()]}var rb=PE;function ME(e){let n={x:D(e,"x","sign")};return L.runKernel(sl,n)}var ab=V({sign_:ME});function zE(e){let n={x:D(e,"x","sin")};return L.runKernel(no,n)}var _A=V({sin_:zE});function LE(e){let n={x:D(e,"x","sinh")};return L.runKernel(nl,n)}var $A=V({sinh_:LE});function BE(e,t,n){let s=D(e,"x","slice1d");return P(s.rank===1,()=>`slice1d expects a rank-1 tensor, but got a rank-${s.rank} tensor`),Re(s,[t],[n])}var Xh=V({slice1d_:BE});function WE(e,t,n){let s=D(e,"x","slice2d");return P(s.rank===2,()=>`slice2d expects a rank-2 tensor, but got a rank-${s.rank} tensor`),Re(s,t,n)}var FA=V({slice2d_:WE});function VE(e,t,n){let s=D(e,"x","slice3d");return P(s.rank===3,()=>`slice3d expects a rank-3 tensor, but got a rank-${s.rank} tensor`),Re(s,t,n)}var Kh=V({slice3d_:VE});function UE(e,t,n){let s=D(e,"x","slice4d");return P(s.rank===4,()=>`slice4d expects a rank-4 tensor, but got a rank-${s.rank} tensor`),Re(s,t,n)}var oc=V({slice4d_:UE});function HE(e,t=-1){let n=D(e,"logits","softmax","float32");if(t===-1&&(t=n.rank-1),t!==n.rank-1)throw Error(`Softmax along a non-last dimension is not yet supported. Logits was rank ${n.rank} and dim was ${t}`);let s={logits:n},r={dim:t};return L.runKernel(oo,s,r)}var Zh=V({softmax_:HE});function GE(e){P(e.dtype==="complex64",()=>`The dtype for tf.spectral.fft() must be complex64 but got ${e.dtype}.`);let t={input:e};return L.runKernel(Qd,t)}var Yh=V({fft_:GE});function jE(e){P(e.dtype==="complex64",()=>`The dtype for tf.spectral.ifft() must be complex64 but got ${e.dtype}.`);let t={input:e};return L.runKernel(eh,t)}var ic=V({ifft_:jE});function qE(e){let t=e.shape[e.shape.length-1],n=e.size/t,s;if(t<=2){let r=U(e,[n,t]);s=ic(r)}else{let r=[n,2*(t-1)],a=U(ac(e),[n,t]),o=U(zh(e),[n,t]),i=cs(Re(a,[0,1],[n,t-2]),1),l=z(cs(Re(o,[0,1],[n,t-2]),1),Ie(-1)),u=ht([a,i],1),c=ht([o,l],1),d=U(Lr(u,c),[r[0],r[1]]);s=ic(d)}if(s=ac(s),e.rank===3&&e.shape[0]!==0){let r=s,a=e.shape[0];s=U(s,[a,s.shape[0]/a,s.shape[1]]),r.dispose()}return s}var DA=V({irfft_:qE});function XE(e,t,n=0){let r={x:D(e,"x","split")},a={numOrSizeSplits:t,axis:n};return L.runKernel(ol,r,a)}var nn=V({split_:XE});function KE(e,t){P(e.dtype==="float32",()=>`The dtype for rfft() must be real value but got ${e.dtype}`);let n=e.shape[e.shape.length-1],s=e.size/n,r;if(t!=null&&t<n){let f=e.shape.map(A=>0),m=e.shape.map(A=>A);m[e.shape.length-1]=t,r=Re(e,f,m),n=t}else if(t!=null&&t>n){let f=e.shape.map(m=>m);f[e.shape.length-1]=t-n,r=ht([e,Dt(f)],e.shape.length-1),n=t}else r=e;let a=qe(r),o=U(Lr(r,a),[s,n]),i=Yh(o),l=Math.floor(n/2)+1,u=ac(i),c=zh(i),d=nn(u,[l,n-l],u.shape.length-1),h=nn(c,[l,n-l],c.shape.length-1),p=r.shape.slice();return p[r.shape.length-1]=l,U(Lr(d[0],h[0]),p)}var Jh=V({rfft_:KE});function ZE(e){let n={x:D(e,"x","sqrt")};return L.runKernel(ro,n)}var ln=V({sqrt_:ZE});function YE(e,t){let n=D(e,"a","squaredDifference"),s=D(t,"b","squaredDifference");[n,s]=It(n,s),mt(n.shape,s.shape);let r={a:n,b:s},a={};return L.runKernel(io,r,a)}var OA=V({squaredDifference_:YE});function JE(e,t){let n=D(e,"x","squeeze");return U(n,r5(n.shape,t).newShape)}var ot=V({squeeze_:JE});function QE(e,t=0){let n=Zu(e,"tensors","stack","string_or_numeric");P(n.length>=1,()=>"Pass at least one tensor to tf.stack"),n.length>0&&P(t<=n[0].rank,()=>"Axis must be <= rank of the tensor");let s=n,r={axis:t};return L.runKernel(Xi,s,r)}var Nn=V({stack_:QE});function eR(e,t=0){let s={x:D(e,"x","step")},r={alpha:t};return L.runKernel(Mr,s,r)}var lc=V({step_:eR});function tR(e,t,n,s,r=0,a=0,o=0,i=0,l=0){let c={x:D(e,"x","stridedSlice","string_or_numeric")},d={begin:t,end:n,strides:s,beginMask:r,endMask:a,ellipsisMask:o,newAxisMask:i,shrinkAxisMask:l};return L.runKernel(il,c,d)}var ob=V({stridedSlice_:tR});function nR(e){let n={x:D(e,"x","tan")};return L.runKernel(uo,n)}var ib=V({tan_:nR});function Ot(e,t){ma(e);let n=qs(e,t);if(n.length!==1)throw new Error("tensor1d() requires values to be a flat/TypedArray");return Br(e,null,n,t)}function _s(e,t,n){if(ma(e),t!=null&&t.length!==2)throw new Error("tensor2d() requires shape to have two numbers");let s=qs(e,n);if(s.length!==2&&s.length!==1)throw new Error("tensor2d() requires values to be number[][] or flat/TypedArray");if(s.length===1&&t==null)throw new Error("tensor2d() requires shape to be provided when `values` are a flat/TypedArray");return Br(e,t,s,n)}function sR(e,t,n){if(ma(e),t!=null&&t.length!==4)throw new Error("tensor4d() requires shape to have four numbers");let s=qs(e,n);if(s.length!==4&&s.length!==1)throw new Error("tensor4d() requires values to be number[][][][] or flat/TypedArray");if(s.length===1&&t==null)throw new Error("tensor4d() requires shape to be provided when `values` are a flat array");return Br(e,t,s,n)}function rR(e,t,n){if(ma(e),t!=null&&t.length!==5)throw new Error("tensor5d() requires shape to have five numbers");let s=qs(e,n);if(s.length!==5&&s.length!==1)throw new Error("tensor5d() requires values to be number[][][][][] or flat/TypedArray");if(s.length===1&&t==null)throw new Error("tensor5d() requires shape to be provided when `values` are a flat array");return Br(e,t,s,n)}function aR(e,t,n){if(ma(e),t!=null&&t.length!==6)throw new Error("tensor6d() requires shape to have six numbers");let s=qs(e,n);if(s.length!==6&&s.length!==1)throw new Error("tensor6d() requires values to be number[][][][][][] or flat/TypedArray");if(s.length===1&&t==null)throw new Error("tensor6d() requires shape to be provided when `values` are a flat array");return t=t||s,Br(e,t,s,n)}function oR(e,t=1,n=!0){let s=D(e,"x","topk");if(s.rank===0)throw new Error("topk() expects the input to be of rank 1 or higher");let r=s.shape[s.shape.length-1];if(t<0)throw new Error(`'k' passed to topk() must be >= 0 but got ${t}`);if(t>r)throw new Error(`'k' passed to topk() must be <= the last dimension (${r}) but got ${t}`);let a={x:s},o={k:t,sorted:n},[i,l]=L.runKernel(ll,a,o);return{values:i,indices:l}}var lb=V({topk_:oR});function iR(e,t=0,n=1,s,r){if(s!=null&&s==="bool")throw new Error("Unsupported data type $ { dtype }");let a=new CA(t,n,s,!0,r),o=Be(e,s);for(let i=0;i<o.values.length;i++)o.values[i]=a.nextValue();return o.toTensor()}var Qh=V({truncatedNormal_:iR});function lR(e,t=0){let n=D(e,"x","unique","string_or_numeric");P(n.rank>0,()=>"The input tensor must be at least 1D");let s={x:n},r={axis:t},[a,o]=L.runKernel(xh,s,r);return{values:a,indices:o}}var PA=V({unique_:lR});function uR(e,t,n){let s=D(e,"x","unsortedSegmentSum"),r=D(t,"segmentIds","unsortedSegmentSum","int32");P(qt(n),()=>"numSegments must be of dtype int");let a={x:s,segmentIds:r},o={numSegments:n};return L.runKernel(Lu,a,o)}var ub=V({unsortedSegmentSum_:uR});function cR(e,t=0){let n=D(e,"x","unstack","string_or_numeric");P(t>=-n.shape.length&&t<n.shape.length,()=>`Axis = ${t} is not in [-${n.shape.length}, ${n.shape.length})`);let s={value:n},r={axis:t};return L.runKernel(cl,s,r)}var ds=V({unstack_:cR});function cb(e,t=!0,n,s){return L.makeVariable(e,t,n,s)}function db(e,t){let n=[];for(let a=0;a<t.length;a++)t[a]&&n.push(a);let s=Be(e,"int32"),r=Be([n.length,e.length],"int32");for(let a=0;a<n.length;a++){let o=s.indexToLoc(n[a]),i=a*e.length;r.values.set(o,i)}return r.toTensor()}async function dR(e){let t=D(e,"condition","whereAsync","bool"),n=await t.data(),s=db(t.shape,n);return e!==t&&t.dispose(),s}var MA=dR;async function hR(e,t,n){let s=D(e,"tensor","boolMask"),r=D(t,"mask","boolMask","bool"),a=n==null?0:n,o=r.rank,i=s.shape;P(o>0,()=>"mask cannot be scalar"),fn(i.slice(a,a+o),r.shape,"mask's shape must match the first K dimensions of tensor's shape,");let l=1;for(let m=a;m<a+o;m++)l*=i[m];let u=i.slice(0,a).concat([l],i.slice(a+o)),c=U(s,u),d=U(r,[-1]),h=await MA(d),p=ot(h,[1]),f=Cl(c,p,a);return e!==s&&s.dispose(),t!==r&&r.dispose(),p.dispose(),c.dispose(),d.dispose(),h.dispose(),f}var pR=hR;function fR(e,t="euclidean",n=null,s=!1){e=D(e,"x","norm");let r=hb(e,t,n),a=r.shape;if(s){let o=xs(n,e.shape);a=Co(r.shape,o)}return U(r,a)}function hb(e,t,n=null){if(e.rank===0)return Wt(e);if(e.rank!==1&&n===null)return hb(U(e,[-1]),t,n);if(e.rank===1||typeof n=="number"||Array.isArray(n)&&n.length===1){if(t===1)return ve(Wt(e),n);if(t===1/0)return ls(Wt(e),n);if(t===-1/0)return Hh(Wt(e),n);if(t==="euclidean"||t===2)return ln(ve(jr(Wt(e),Ie(2,"int32")),n));throw new Error(`Error in norm: invalid ord value: ${t}`)}if(Array.isArray(n)&&n.length===2){if(t===1)return ls(ve(Wt(e),n[0]),n[1]-1);if(t===1/0)return ls(ve(Wt(e),n[1]),n[0]);if(t===-1/0)return Hh(ve(Wt(e),n[1]),n[0]);if(t==="fro"||t==="euclidean")return ln(ve(lt(e),n));throw new Error(`Error in norm: invalid ord value: ${t}`)}throw new Error(`Error in norm: invalid axis: ${n}`)}var zA=V({norm_:fR});function mR(e,t,n,s,r=!0){let a=D(e,"v","movingAverage"),o=D(t,"x","movingAverage"),i=D(n,"decay","movingAverage");S5(a,o),P(fr(a.shape,o.shape),()=>"Shape mismatch in v and x");let l=Ie(1),u=Ae(l,i),c=z(Ae(o,a),u);if(r){P(s!=null,()=>"When using zeroDebias: true, step is required.");let d=D(s,"step","movingAverage");c=de(c,Ae(l,jr(i,d)))}return ae(a,c)}var AR=V({movingAverage_:mR});function gR(e,t,n){let s=D(e,"indices","scatterND","int32"),r=D(t,"updates","scatterND");Qm(r,s,n);let a={indices:s,updates:r},o={shape:n};return L.runKernel(Ji,a,o)}var pb=V({scatterND_:gR});function yR(e,t,n,s){if(e.dtype!=="int32")throw new Error(`tf.sparseToDense() expects the indices to be int32 type, but the dtype was ${e.dtype}.`);if(e.rank>2)throw new Error(`sparseIndices should be a scalar, vector, or matrix, but got shape ${e.shape}.`);let r=e.rank>0?e.shape[0]:1,a=e.rank>1?e.shape[1]:1;if(n.length!==a)throw new Error(`outputShape has incorrect number of elements:, ${n.length}, should be: ${a}.`);let o=t.size;if(!(t.rank===0||t.rank===1&&o===r))throw new Error(`sparseValues has incorrect shape ${t.shape}, should be [] or [${r}]`);if(t.dtype!==s.dtype)throw new Error("sparseValues.dtype must match defaultValues.dtype")}function xR(e,t,n,s=0){let r=D(e,"sparseIndices","sparseToDense","int32"),a=D(t,"sparseValues","sparseToDense"),o=D(s,"defaultValue","sparseToDense",a.dtype);yR(r,a,n,o);let i={sparseIndices:r,sparseValues:a,defaultValue:o},l={outputShape:n};return L.runKernel(mh,i,l)}var LA=V({sparseToDense_:xR});function bR(e,t){let n=D(t,"indices","gatherND","int32"),r={params:D(e,"x","gatherND","string_or_numeric"),indices:n};return L.runKernel($i,r)}var fb=V({gatherND_:bR});function vR(e,t){if(t==null)return e.shape.slice();if(fr(e.shape,t))return t;if(e.shape.length===t.length){let n=[];for(let s=0;s<e.shape.length;s++)t[s]==null&&e.shape[s]!=null?n.push(e.shape[s]):n.push(t[s]);return n}return t}function wR(e,t,n,s){let r=D(e,"x","dropout");if(P(r.dtype==="float32",()=>`x has to be a floating point tensor since it's going to be scaled, but got a ${r.dtype} tensor instead.`),P(t>=0&&t<1,()=>`rate must be a float in the range [0, 1), but got ${t}.`),t===0)return e instanceof Ue?r.clone():r;let a=vR(r,n),o=1-t,i=de(sc(ae(El(a,0,1,"float32",s),o)),o);return z(r,i)}var mb=V({dropout_:wR});function Ab(e){return Math.floor(Math.pow(2,Math.ceil(Math.log(e)/Math.log(2))))}function BA(e,t,n){let s=1-e%2,r=new Float32Array(e);for(let a=0;a<e;++a){let o=2*Math.PI*a/(e+s-1);r[a]=t-n*Math.cos(o)}return Ot(r,"float32")}async function kR(e,t,n=1){let s=D(e,"predictions","inTopK"),r=D(t,"targets","inTopK");P(s.rank>1,()=>`inTopK() expects the predictions to be of rank 2 or higher, but got ${s.rank}`),P(s.rank-1===r.rank,()=>`predictions rank should be 1 larger than targets rank, but got predictions rank ${s.rank} and targets rank ${r.rank}`),fn(s.shape.slice(0,s.shape.length-1),r.shape,"predictions's shape should be align with the targets' shape, except the last dimension.");let a=s.shape[s.shape.length-1];P(n>0&&n<=a,()=>`'k' passed to inTopK() must be > 0 && <= the predictions last dimension (${a}), but got ${n}`);let o=await s.data(),i=await r.data(),[l,u]=[o.length/a,a],c=a5("bool",l);for(let d=0;d<l;d++){let h=d*u,p=o.subarray(h,h+u),f=[];for(let m=0;m<p.length;m++)f.push({value:p[m],index:m});f.sort((m,A)=>A.value-m.value),c[d]=0;for(let m=0;m<n;m++)if(f[m].index===i[d]){c[d]=1;break}}return e!==s&&s.dispose(),t!==r&&r.dispose(),on(c,r.shape,"bool")}var IR=kR,qr={};Pe(qr,{conv2d:()=>TR,depthwiseConv2d:()=>_R,matMul:()=>FR});function SR(e,t,n,s,r,a="NHWC",o){let i=e;e.rank===3&&(i=U(e,[1,e.shape[0],e.shape[1],e.shape[2]]));let l=t;l.rank===3&&(l=U(t,[1,t.shape[0],t.shape[1],t.shape[2]])),P(i.rank===4,()=>`Error in conv2dDerFilter: input must be rank 4, but got shape ${i.shape}.`),P(l.rank===4,()=>`Error in conv2dDerFilter: dy must be rank 4, but got shape ${l.shape}.`),P(n.length===4,()=>`Error in conv2dDerFilter: filterShape must be length 4, but got ${n}.`);let u=a==="NHWC"?i.shape[3]:i.shape[1],c=a==="NHWC"?l.shape[3]:l.shape[1];P(u===n[2],()=>`Error in conv2dDerFilter: depth of input ${u}) must match input depth in filter (${n[2]}.`),P(c===n[3],()=>`Error in conv2dDerFilter: depth of dy (${c}) must match output depth for filter (${n[3]}).`),o!=null&&P(qt(r),()=>`Error in conv2dDerFilter: pad must be an integer when using, dimRoundingMode ${o} but got pad ${r}.`);let d={x:i,dy:l},h={strides:s,pad:r,dataFormat:a,dimRoundingMode:o,filterShape:n};return L.runKernel(Vd,d,h)}var WA=V({conv2DBackpropFilter_:SR});function ep(e,t,n){if(n==null||n==="linear")return e;if(n==="relu")return z(e,lc(t));throw new Error(`Cannot compute gradient for fused activation ${n}.`)}function tp(e,t){let n=t,s=Vt(e.shape,t.shape);return s.length>0&&(n=ve(n,s)),U(n,e.shape)}function np(e,t,n,s){if(t==="linear")return e;if(t==="relu")return Ys(e);if(t==="elu")return nc(e);if(t==="relu6")return TA(e);if(t==="prelu")return qh(e,n);if(t==="leakyrelu")return Lh(e,s);if(t==="sigmoid")return Bn(e);throw new Error(`Unknown fused activation ${t}.`)}var sp=(e,t)=>!(e>0)||t==="linear";function CR({x:e,filter:t,strides:n,pad:s,dataFormat:r="NHWC",dilations:a=[1,1],dimRoundingMode:o,bias:i,activation:l="linear",preluActivationWeights:u,leakyreluAlpha:c}){if(l=l||"linear",sp(L.state.gradientDepth,l)===!1){let v=Hr(e,t,n,s,r,a,o);return i!=null&&(v=ae(v,i)),np(v,l,u,c)}let d=D(e,"x","conv2d"),h=D(t,"filter","conv2d"),p=d,f=!1;d.rank===3&&(f=!0,p=U(d,[1,d.shape[0],d.shape[1],d.shape[2]])),P(p.rank===4,()=>`Error in fused conv2d: input must be rank 4, but got rank ${p.rank}.`),P(h.rank===4,()=>`Error in fused conv2d: filter must be rank 4, but got rank ${h.rank}.`),o!=null&&P(qt(s),()=>`Error in fused conv2d: pad must be an integer when using, dimRoundingMode ${o} but got pad ${s}.`),P(p.shape[3]===h.shape[2],()=>`Error in conv2d: depth of input (${p.shape[3]}) must match input depth for filter ${h.shape[2]}.`),P(Ks(n,a),()=>`Error in conv2D: Either strides or dilations must be 1. Got strides ${n} and dilations '${a}'`),P(r==="NHWC",()=>`Error in conv2d: got dataFormat of ${r} but only NHWC is currently supported.`);let m=Qu(p.shape,h.shape,n,a,s,o),A;i!=null&&(A=D(i,"bias","fused conv2d"),[A]=It(A,d),mt(m.outShape,A.shape));let g;u!=null&&(g=D(u,"prelu weights","fused conv2d"));let y=(v,k)=>{let[w,C,E,M]=k,R=ep(v,E,l);P(Ur(a),()=>`Error in gradient of fused conv2D: dilation rates greater than 1 are not yet supported in gradients. Got dilations '${a}'`);let _=hA(C.shape,R,w,n,s),N=WA(C,R,w.shape,n,s),O=[_,N];if(M!=null){let W=tp(M,R);O.push(W)}return O},x={x:p,filter:h,bias:A,preluActivationWeights:g},b={strides:n,pad:s,dataFormat:r,dilations:a,dimRoundingMode:o,activation:l,leakyreluAlpha:c};return i==null?Zs((k,w,C)=>{let E=L.runKernel(fo,x,b);return C([w,k,E]),f&&(E=U(E,[E.shape[1],E.shape[2],E.shape[3]])),{value:E,gradFunc:y}})(p,h):Zs((k,w,C,E)=>{let M=L.runKernel(fo,x,b);return E([w,k,M,C]),f&&(M=U(M,[M.shape[1],M.shape[2],M.shape[3]])),{value:M,gradFunc:y}})(p,h,A)}var TR=V({fusedConv2d_:CR});function NR(e,t,n,s,r,a=[1,1],o){let i=e;e.rank===3&&(i=U(e,[1,e.shape[0],e.shape[1],e.shape[2]]));let l=t;l.rank===3&&(l=U(t,[1,t.shape[0],t.shape[1],t.shape[2]]));let u={x:i,dy:l},c={strides:s,pad:r,dimRoundingMode:o,dilations:a,filterShape:n};return L.runKernel(jd,u,c)}var gb=V({depthwiseConv2dNativeBackpropFilter_:NR});function ER(e,t,n,s,r,a=[1,1],o){let i=t,l=!1;t.rank===3&&(l=!0,i=U(t,[1,t.shape[0],t.shape[1],t.shape[2]]));let u={dy:i,filter:n},c={strides:s,pad:r,dimRoundingMode:o,dilations:a,inputShape:e},d=L.runKernel(qd,u,c);return l?U(d,[d.shape[1],d.shape[2],d.shape[3]]):d}var yb=V({depthwiseConv2dNativeBackpropInput_:ER});function RR({x:e,filter:t,strides:n,pad:s,dataFormat:r="NHWC",dilations:a=[1,1],dimRoundingMode:o,bias:i,activation:l="linear",preluActivationWeights:u,leakyreluAlpha:c}){if(sp(L.state.gradientDepth,l)===!1){let v=tc(e,t,n,s,r,a,o);return i!=null&&(v=ae(v,i)),np(v,l,u,c)}let d=D(e,"x","depthwiseConv2d"),h=D(t,"filter","depthwiseConv2d"),p=d,f=!1;d.rank===3&&(f=!0,p=U(d,[1,d.shape[0],d.shape[1],d.shape[2]])),P(p.rank===4,()=>`Error in fused depthwiseConv2d: input must be rank 4, but got rank ${p.rank}.`),P(h.rank===4,()=>`Error in fused depthwiseConv2d: filter must be rank 4, but got rank ${h.rank}.`),P(p.shape[3]===h.shape[2],()=>`Error in fused depthwiseConv2d: number of input channels (${p.shape[3]}) must match the inChannels dimension in filter ${h.shape[2]}.`),a==null&&(a=[1,1]),P(Ks(n,a),()=>`Error in fused depthwiseConv2d: Either strides or dilations must be 1. Got strides ${n} and dilations '${a}'`),o!=null&&P(qt(s),()=>`Error in fused depthwiseConv2d: pad must be an integer when using dimRoundingMode ${o} but got pad ${s}.`);let m=Qu(p.shape,h.shape,n,a,s,o,!0),A;i!=null&&(A=D(i,"bias","fused conv2d"),[A]=It(A,d),mt(m.outShape,A.shape));let g;u!=null&&(g=D(u,"prelu weights","fused depthwiseConv2d"));let y=(v,k)=>{P(Ur(a),()=>`Error in gradient of fused depthwiseConv2d: dilation rates greater than 1 are not yet supported. Got dilations '${a}'`);let[w,C,E,M]=k,R=ep(v,E,l),_=yb(C.shape,R,w,n,s,a,o),N=gb(C,R,w.shape,n,s,a,o);if(M!=null){let O=tp(A,R);return[_,N,O]}return[_,N]},x={x:p,filter:h,bias:A,preluActivationWeights:g},b={strides:n,pad:s,dataFormat:r,dilations:a,dimRoundingMode:o,activation:l,leakyreluAlpha:c};return i==null?Zs((k,w,C)=>{let E=L.runKernel(mo,x,b);return C([w,k,E]),f&&(E=U(E,[E.shape[1],E.shape[2],E.shape[3]])),{value:E,gradFunc:y}})(p,h):Zs((k,w,C,E)=>{let M=L.runKernel(mo,x,b);return E([w,k,M,C]),f&&(M=U(M,[M.shape[1],M.shape[2],M.shape[3]])),{value:M,gradFunc:y}})(p,h,A)}var _R=V({fusedDepthwiseConv2d_:RR});function $R({a:e,b:t,transposeA:n=!1,transposeB:s=!1,bias:r,activation:a="linear",preluActivationWeights:o,leakyreluAlpha:i}){if(sp(L.state.gradientDepth,a)===!1){let M=We(e,t,n,s);return r!=null&&(M=ae(M,r)),np(M,a,o,i)}let l=D(e,"a","fused matMul"),u=D(t,"b","fused matMul");[l,u]=It(l,u);let c=n?l.shape[l.rank-2]:l.shape[l.rank-1],d=s?u.shape[u.rank-1]:u.shape[u.rank-2],h=n?l.shape[l.rank-1]:l.shape[l.rank-2],p=s?u.shape[u.rank-2]:u.shape[u.rank-1],f=l.shape.slice(0,-2),m=u.shape.slice(0,-2),A=_t(f),g=_t(m);P(l.rank>=2&&u.rank>=2&&l.rank===u.rank,()=>`Error in fused matMul: inputs must have the same rank of at least 2, got ranks ${l.rank} and ${u.rank}.`),P(fr(f,m),()=>`Error in fused matMul: outer dimensions (${f}) and (${m}) of Tensors with shapes ${l.shape} and ${u.shape} must match.`),P(c===d,()=>`Error in fused matMul: inner shapes (${c}) and (${d}) of Tensors with shapes ${l.shape} and ${u.shape} and transposeA=${n} and transposeB=${s} must match.`);let y=l.shape.slice(0,-2).concat([h,p]),x=n?U(l,[A,c,h]):U(l,[A,h,c]),b=s?U(u,[g,p,d]):U(u,[g,d,p]),v;r!=null&&(v=D(r,"bias","fused matMul"),[v]=It(v,l),mt(y,v.shape));let k;o!=null&&(k=D(o,"prelu weights","fused matMul"));let w=(M,R)=>{let[_,N,O,W]=R,j=ep(U(M,O.shape),O,a),q,X;if(!n&&!s?(q=We(j,N,!1,!0),X=We(_,j,!0,!1)):!n&&s?(q=We(j,N,!1,!1),X=We(j,_,!0,!1)):n&&!s?(q=We(N,j,!1,!0),X=We(_,j,!1,!1)):(q=We(N,j,!0,!0),X=We(j,_,!0,!0)),r!=null){let Q=tp(W,j);return[q,X,Q]}else return[q,X]},C={a:x,b,bias:v,preluActivationWeights:k},E={transposeA:n,transposeB:s,activation:a,leakyreluAlpha:i};return r==null?Zs((R,_,N)=>{let O=L.runKernel(po,C,E);return N([R,_,O]),{value:U(O,y),gradFunc:w}})(x,b):Zs((R,_,N,O)=>{let W=L.runKernel(po,C,E);return O([R,_,W,N]),{value:U(W,y),gradFunc:w}})(x,b,v)}var FR=V({fusedMatMul_:$R});function DR(e){return BA(e,.54,.46)}var OR=V({hammingWindow_:DR});function PR(e){return BA(e,.5,.5)}var xb=V({hannWindow_:PR});function MR(e,t,n,s=!1,r=0){let a=0,o=[];for(;a+t<=e.size;)o.push(Re(e,a,t)),a+=n;if(s)for(;a<e.size;){let i=a+t-e.size,l=ht([Re(e,a,t-i),Sl([i],r)]);o.push(l),a+=n}return o.length===0?_s([],[0,t]):U(ht(o),[o.length,t])}var bb=V({frame_:MR});function zR(e,t,n,s,r=xb){s==null&&(s=Ab(t));let a=bb(e,t,n),o=z(a,r(t));return Jh(o,s)}var LR=V({stft_:zR});function BR(e,t,n,s,r="bilinear",a=0){let o=D(e,"image","cropAndResize"),i=D(t,"boxes","cropAndResize","float32"),l=D(n,"boxInd","cropAndResize","int32"),u=i.shape[0];P(o.rank===4,()=>`Error in cropAndResize: image must be rank 4,but got rank ${o.rank}.`),P(i.rank===2&&i.shape[1]===4,()=>`Error in cropAndResize: boxes must be have size [${u},4] but had shape ${i.shape}.`),P(l.rank===1&&l.shape[0]===u,()=>`Error in cropAndResize: boxInd must be have size [${u}] but had shape ${i.shape}.`),P(s.length===2,()=>`Error in cropAndResize: cropSize must be of length 2, but got length ${s.length}.`),P(s[0]>=1&&s[1]>=1,()=>`cropSize must be atleast [1,1], but was ${s}`),P(r==="bilinear"||r==="nearest",()=>`method must be bilinear or nearest, but was ${r}`);let c={image:o,boxes:i,boxInd:l},d={method:r,extrapolationValue:a,cropSize:s};return L.runKernel(ki,c,d)}var WR=V({cropAndResize_:BR});function VR(e){let t=D(e,"image","flipLeftRight","float32");P(t.rank===4,()=>`Error in flipLeftRight: image must be rank 4,but got rank ${t.rank}.`);let n={image:t};return L.runKernel(Ri,n,{})}var UR=V({flipLeftRight_:VR});function HR(e,t,n=0,s=.5){let r=D(e,"image","rotateWithOffset","float32");P(r.rank===4,()=>`Error in rotateWithOffset: image must be rank 4,but got rank ${r.rank}.`);let a={image:r},o={radians:t,fillValue:n,center:s};return L.runKernel(hl,a,o)}var GR=V({rotateWithOffset_:HR});function _l(e,t,n,s,r,a){s==null&&(s=.5),r==null&&(r=Number.NEGATIVE_INFINITY),a==null&&(a=0);let o=e.shape[0];return n=Math.min(n,o),P(0<=s&&s<=1,()=>`iouThreshold must be in [0, 1], but was '${s}'`),P(e.rank===2,()=>`boxes must be a 2D tensor, but was of rank '${e.rank}'`),P(e.shape[1]===4,()=>`boxes must have 4 columns, but 2nd dimension was ${e.shape[1]}`),P(t.rank===1,()=>"scores must be a 1D tensor"),P(t.shape[0]===o,()=>`scores has incompatible shape with boxes. Expected ${o}, but was ${t.shape[0]}`),P(0<=a&&a<=1,()=>`softNmsSigma must be in [0, 1], but was '${a}'`),{maxOutputSize:n,iouThreshold:s,scoreThreshold:r,softNmsSigma:a}}function jR(e,t,n,s=.5,r=Number.NEGATIVE_INFINITY){let a=D(e,"boxes","nonMaxSuppression"),o=D(t,"scores","nonMaxSuppression"),i=_l(a,o,n,s,r);n=i.maxOutputSize,s=i.iouThreshold,r=i.scoreThreshold;let l={maxOutputSize:n,iouThreshold:s,scoreThreshold:r};return L.runKernel(Hi,{boxes:a,scores:o},l)}var qR=V({nonMaxSuppression_:jR});function XR(e,t,n){let s=KR(e,t,n),r=s<0?-(s+1):s;e.splice(r,0,t)}function KR(e,t,n){return YR(e,t,n||ZR)}function ZR(e,t){return e>t?1:e<t?-1:0}function YR(e,t,n){let s=0,r=e.length,a=0,o=!1;for(;s<r;){a=s+(r-s>>>1);let i=n(t,e[a]);i>0?s=a+1:(r=a,o=!i)}return o?s:-s-1}function vb(e,t,n,s,r){return VA(e,t,n,s,r,0)}function wb(e,t,n,s,r,a){return VA(e,t,n,s,r,0,!1,a,!0)}function kb(e,t,n,s,r,a){return VA(e,t,n,s,r,a,!0)}function VA(e,t,n,s,r,a,o=!1,i=!1,l=!1){let u=[];for(let A=0;A<t.length;A++)t[A]>r&&u.push({score:t[A],boxIndex:A,suppressBeginIndex:0});u.sort(Ib);let c=a>0?-.5/a:0,d=[],h=[];for(;d.length<n&&u.length>0;){let A=u.pop(),{score:g,boxIndex:y,suppressBeginIndex:x}=A;if(g<r)break;let b=!1;for(let v=d.length-1;v>=x;--v){let k=JR(e,y,d[v]);if(k>=s){b=!0;break}if(A.score=A.score*QR(s,c,k),A.score<=r)break}A.suppressBeginIndex=d.length,b||(A.score===g?(d.push(y),h.push(A.score)):A.score>r&&XR(u,A,Ib))}let p=d.length,f=n-p;i&&f>0&&(d.push(...new Array(f).fill(0)),h.push(...new Array(f).fill(0)));let m={selectedIndices:d};return o&&(m.selectedScores=h),l&&(m.validOutputs=p),m}function JR(e,t,n){let s=e.subarray(t*4,t*4+4),r=e.subarray(n*4,n*4+4),a=Math.min(s[0],s[2]),o=Math.min(s[1],s[3]),i=Math.max(s[0],s[2]),l=Math.max(s[1],s[3]),u=Math.min(r[0],r[2]),c=Math.min(r[1],r[3]),d=Math.max(r[0],r[2]),h=Math.max(r[1],r[3]),p=(i-a)*(l-o),f=(d-u)*(h-c);if(p<=0||f<=0)return 0;let m=Math.max(a,u),A=Math.max(o,c),g=Math.min(i,d),y=Math.min(l,h),x=Math.max(g-m,0)*Math.max(y-A,0);return x/(p+f-x)}function QR(e,t,n){let s=Math.exp(t*n*n);return n<=e?s:0}function Ib(e,t){return e.score-t.score||e.score===t.score&&t.boxIndex-e.boxIndex}async function e_(e,t,n,s=.5,r=Number.NEGATIVE_INFINITY){let a=D(e,"boxes","nonMaxSuppressionAsync"),o=D(t,"scores","nonMaxSuppressionAsync"),i=_l(a,o,n,s,r);n=i.maxOutputSize,s=i.iouThreshold,r=i.scoreThreshold;let l=await Promise.all([a.data(),o.data()]),u=l[0],c=l[1],{selectedIndices:d}=vb(u,c,n,s,r);return a!==e&&a.dispose(),o!==t&&o.dispose(),Ot(d,"int32")}var t_=e_;function n_(e,t,n,s=.5,r=Number.NEGATIVE_INFINITY,a=0){let o=D(e,"boxes","nonMaxSuppression"),i=D(t,"scores","nonMaxSuppression"),l=_l(o,i,n,s,r,a);n=l.maxOutputSize,s=l.iouThreshold,r=l.scoreThreshold,a=l.softNmsSigma;let u={boxes:o,scores:i},c={maxOutputSize:n,iouThreshold:s,scoreThreshold:r,softNmsSigma:a},d=L.runKernel(ji,u,c);return{selectedIndices:d[0],selectedScores:d[1]}}var s_=V({nonMaxSuppressionWithScore_:n_});async function r_(e,t,n,s=.5,r=Number.NEGATIVE_INFINITY,a=0){let o=D(e,"boxes","nonMaxSuppressionAsync"),i=D(t,"scores","nonMaxSuppressionAsync"),l=_l(o,i,n,s,r,a);n=l.maxOutputSize,s=l.iouThreshold,r=l.scoreThreshold,a=l.softNmsSigma;let u=await Promise.all([o.data(),i.data()]),c=u[0],d=u[1],{selectedIndices:h,selectedScores:p}=kb(c,d,n,s,r,a);return o!==e&&o.dispose(),i!==t&&i.dispose(),{selectedIndices:Ot(h,"int32"),selectedScores:Ot(p)}}var a_=r_;function o_(e,t,n,s=.5,r=Number.NEGATIVE_INFINITY,a=!1){let o=D(e,"boxes","nonMaxSuppression"),i=D(t,"scores","nonMaxSuppression"),l=_l(o,i,n,s,r,null),u=l.maxOutputSize,c=l.iouThreshold,d=l.scoreThreshold,h={boxes:o,scores:i},p={maxOutputSize:u,iouThreshold:c,scoreThreshold:d,padToMaxOutputSize:a},f=L.runKernel(Gi,h,p);return{selectedIndices:f[0],validOutputs:f[1]}}var i_=V({nonMaxSuppressionPadded_:o_});async function l_(e,t,n,s=.5,r=Number.NEGATIVE_INFINITY,a=!1){let o=D(e,"boxes","nonMaxSuppressionAsync"),i=D(t,"scores","nonMaxSuppressionAsync"),l=_l(o,i,n,s,r,null),u=l.maxOutputSize,c=l.iouThreshold,d=l.scoreThreshold,[h,p]=await Promise.all([o.data(),i.data()]),{selectedIndices:f,validOutputs:m}=wb(h,p,u,c,d,a);return o!==e&&o.dispose(),i!==t&&i.dispose(),{selectedIndices:Ot(f,"int32"),validOutputs:Ie(m,"int32")}}var u_=l_;function c_(e,t,n=!1,s=!1){let r=D(e,"images","resizeBilinear");P(r.rank===3||r.rank===4,()=>`Error in resizeBilinear: x must be rank 3 or 4, but got rank ${r.rank}.`),P(t.length===2,()=>`Error in resizeBilinear: new shape must 2D, but got shape ${t}.`),P(s===!1||n===!1,()=>"Error in resizeBilinear: If halfPixelCenters is true, alignCorners must be false.");let a=r,o=!1;r.rank===3&&(o=!0,a=U(r,[1,r.shape[0],r.shape[1],r.shape[2]]));let[]=t,i={images:a},l={alignCorners:n,halfPixelCenters:s,size:t},u=L.runKernel(Ya,i,l);return o?U(u,[u.shape[1],u.shape[2],u.shape[3]]):u}var d_=V({resizeBilinear_:c_});function h_(e,t,n=!1,s=!1){let r=D(e,"images","resizeNearestNeighbor");P(r.rank===3||r.rank===4,()=>`Error in resizeNearestNeighbor: x must be rank 3 or 4, but got rank ${r.rank}.`),P(t.length===2,()=>`Error in resizeNearestNeighbor: new shape must 2D, but got shape ${t}.`),P(r.dtype==="float32"||r.dtype==="int32",()=>"`images` must have `int32` or `float32` as dtype"),P(s===!1||n===!1,()=>"Error in resizeNearestNeighbor: If halfPixelCenters is true, alignCorners must be false.");let a=r,o=!1;r.rank===3&&(o=!0,a=U(r,[1,r.shape[0],r.shape[1],r.shape[2]]));let[]=t,i={images:a},l={alignCorners:n,halfPixelCenters:s,size:t},u=L.runKernel(Mu,i,l);return o?U(u,[u.shape[1],u.shape[2],u.shape[3]]):u}var p_=V({resizeNearestNeighbor_:h_});function f_(e,t="binary",n=!1,s=.5){let r=D(e,"image","threshold"),a=.2989,o=.587,i=.114,l=r.shape[0]*r.shape[1],u=z(Ot([s]),255),c,d,h,p;if(P(r.rank===3,()=>`Error in threshold: image must be rank 3,but got rank ${r.rank}.`),P(r.shape[2]===3||r.shape[2]===1,()=>`Error in threshold: image color channel must be equal to 3 or 1but got ${r.shape[2]}.`),P(r.dtype==="int32"||r.dtype==="float32",()=>`Error in dtype: image dtype must be int32 or float32,but got dtype ${r.dtype}.`),P(t==="otsu"||t==="binary",()=>`Method must be binary or otsu, but was ${t}`),r.shape[2]===3){[c,d,h]=nn(r,[1,1,1],-1);let A=z(c,a),g=z(d,o),y=z(h,i);p=ae(ae(A,g),y)}else p=e;if(t==="otsu"){let A=cA(ce(NA(p),"int32"),on([]),256);u=m_(A,l)}let f=n?So(p,u):Vn(p,u);return ce(z(f,255),"int32")}function m_(e,t){let n=Ot([-1]),s=Ot([0]),r=Ot([0]),a,o,i,l,u,c;for(let d=0;d<e.size-1;d++){a=Re(e,0,d+1),o=Re(e,d+1),u=de(ve(a),t),c=de(ve(o),t);let h=ve(z(a,Rl(0,a.size)));i=de(h,ve(a));let p=Sl(o.shape,a.size),f=ae(Rl(0,o.size),p),m=z(o,f);l=de(ve(m),ve(o));let A=Ae(i,l),g=Ae(i,l),y=z(u,c);r=z(z(y,A),g);let x=Vn(r,s);s=gn(x,r,s),n=gn(x,Ot([d]),n)}return n}var A_=V({threshold_:f_});function g_(e,t,n="nearest",s="constant",r=0,a){let o=D(e,"image","transform","float32"),i=D(t,"transforms","transform","float32");P(o.rank===4,()=>`Error in transform: image must be rank 4,but got rank ${o.rank}.`),P(i.rank===2&&(i.shape[0]===o.shape[0]||i.shape[0]===1)&&i.shape[1]===8,()=>"Error in transform: Input transform should be batch x 8 or 1 x 8"),P(a==null||a.length===2,()=>`Error in transform: outputShape must be [height, width] or null, but got ${a}.`);let l={image:o,transforms:i},u={interpolation:n,fillMode:s,fillValue:r,outputShape:a};return L.runKernel(ul,l,u)}var y_=V({transform_:g_});function x_(e,t,n){P(t%1==0,()=>`bandPart(): numLower must be an integer, got ${t}.`),P(n%1==0,()=>`bandPart(): numUpper must be an integer, got ${n}.`);let s=D(e,"a","bandPart");P(s.rank>=2,()=>`bandPart(): Rank must be at least 2, got ${s.rank}.`);let r=s.shape,[a,o]=s.shape.slice(-2);if(!(t<=a))throw new Error(`bandPart(): numLower (${t}) must not be greater than the number of rows (${a}).`);if(!(n<=o))throw new Error(`bandPart(): numUpper (${n}) must not be greater than the number of columns (${o}).`);t<0&&(t=a),n<0&&(n=o);let i=U(Rl(0,a,1,"int32"),[-1,1]),l=Rl(0,o,1,"int32"),u=Ae(i,l),c=Rs(So(u,Ie(+t,"int32")),Io(u,Ie(-n,"int32"))),d=Dt([a,o],s.dtype);return U(Nn(ds(U(s,[-1,a,o])).map(h=>gn(c,h,d))),r)}var b_=V({bandPart_:x_});function v_(e){let t;if(Array.isArray(e)){t=!1,P(e!=null&&e.length>0,()=>"Gram-Schmidt process: input must not be null, undefined, or empty");let r=e[0].shape[0];for(let a=1;a<e.length;++a)P(e[a].shape[0]===r,()=>`Gram-Schmidt: Non-unique lengths found in the input vectors: (${e[a].shape[0]} vs. ${r})`)}else t=!0,e=nn(e,e.shape[0],0).map(r=>ot(r,[0]));P(e.length<=e[0].shape[0],()=>`Gram-Schmidt: Number of vectors (${e.length}) exceeds number of dimensions (${e[0].shape[0]}).`);let n=[],s=e;for(let r=0;r<e.length;++r)n.push(L.tidy(()=>{let a=s[r];if(r>0)for(let o=0;o<r;++o){let i=z(ve(z(n[o],a)),n[o]);a=Ae(a,i)}return de(a,zA(a,"euclidean"))}));return t?Nn(n,0):n}var w_=V({gramSchmidt_:v_});function k_(e,t=!1){if(P(e.rank>=2,()=>`qr() requires input tensor to have a rank >= 2, but got rank ${e.rank}`),e.rank===2)return Sb(e,t);{let n=e.shape.slice(0,e.shape.length-2).reduce((l,u)=>l*u),s=ds(U(e,[n,e.shape[e.shape.length-2],e.shape[e.shape.length-1]]),0),r=[],a=[];s.forEach(l=>{let[u,c]=Sb(l,t);r.push(u),a.push(c)});let o=U(Nn(r,0),e.shape),i=U(Nn(a,0),e.shape);return[o,i]}}function Sb(e,t=!1){return L.tidy(()=>{P(e.shape.length===2,()=>`qr2d() requires a 2D Tensor, but got a ${e.shape.length}D Tensor.`);let n=e.shape[0],s=e.shape[1],r=gA(n),a=Ns(e),o=_s([[1]],[1,1]),i=Ns(o),l=n>=s?s:n;for(let u=0;u<l;++u){let c=a,d=i,h=r;[i,a,r]=L.tidy(()=>{let p=Re(a,[u,u],[n-u,1]),f=zA(p),m=Re(a,[u,u],[1,1]),A=gn(Vn(m,0),_s([[-1]]),_s([[1]])),g=Ae(m,z(A,f)),y=de(p,g);y.shape[0]===1?i=Ns(o):i=ht([o,Re(y,[1,0],[y.shape[0]-1,y.shape[1]])],0);let x=St(de(We(A,g),f)),b=Re(a,[u,0],[n-u,s]),v=z(x,i),k=je(i);if(u===0)a=Ae(b,We(v,We(k,b)));else{let E=Ae(b,We(v,We(k,b)));a=ht([Re(a,[0,0],[u,s]),E],0)}let w=je(v),C=Re(r,[0,u],[n,r.shape[1]-u]);if(u===0)r=Ae(C,We(We(C,i),w));else{let E=Ae(C,We(We(C,i),w));r=ht([Re(r,[0,0],[n,u]),E],1)}return[i,a,r]}),K([c,d,h])}return!t&&n>s&&(r=Re(r,[0,0],[n,s]),a=Re(a,[0,0],[s,s])),[r,a]})}var I_=V({qr_:k_}),yn;(function(e){e[e.NONE=0]="NONE",e[e.MEAN=1]="MEAN",e[e.SUM=2]="SUM",e[e.SUM_BY_NONZERO_WEIGHTS=3]="SUM_BY_NONZERO_WEIGHTS"})(yn||(yn={}));function S_(e,t,n=yn.SUM_BY_NONZERO_WEIGHTS){let s=D(e,"losses","computeWeightedLoss"),r=null;t!=null&&(r=D(t,"weights","computeWeightedLoss"));let a=r==null?s:z(s,r);if(n===yn.NONE)return a;if(n===yn.SUM)return ve(a);if(n===yn.MEAN){if(r==null)return Et(a);{let o=s.size/r.size,i=de(ve(a),ve(r));return o>1?de(i,Ie(o)):i}}if(n===yn.SUM_BY_NONZERO_WEIGHTS){if(r==null)return de(ve(a),Ie(s.size));{let o=z(r,Un(s.shape)),i=ce(ve(Nl(o,Ie(0))),"float32");return de(ve(a),i)}}throw Error(`Unknown reduction: ${n}`)}var yr=V({computeWeightedLoss_:S_});function C_(e,t,n,s=yn.SUM_BY_NONZERO_WEIGHTS){let r=D(e,"labels","absoluteDifference"),a=D(t,"predictions","absoluteDifference"),o=null;n!=null&&(o=D(n,"weights","absoluteDifference")),fn(r.shape,a.shape,"Error in absoluteDifference: ");let i=Wt(Ae(r,a));return yr(i,o,s)}var T_=V({absoluteDifference_:C_});function N_(e,t,n,s,r=yn.SUM_BY_NONZERO_WEIGHTS){let a=D(e,"labels","cosineDistance"),o=D(t,"predictions","cosineDistance"),i=null;s!=null&&(i=D(s,"weights","cosineDistance")),fn(a.shape,o.shape,"Error in cosineDistance: ");let l=Ie(1),u=Ae(l,ve(z(a,o),n,!0));return yr(u,i,r)}var E_=V({cosineDistance_:N_});function R_(e,t,n,s=yn.SUM_BY_NONZERO_WEIGHTS){let r=D(e,"labels","hingeLoss"),a=D(t,"predictions","hingeLoss"),o=null;n!=null&&(o=D(n,"weights","hingeLoss")),fn(r.shape,a.shape,"Error in hingeLoss: ");let i=Ie(1);r=Ae(z(Ie(2),r),i);let l=Ys(Ae(i,z(r,a)));return yr(l,o,s)}var __=V({hingeLoss_:R_});function $_(e,t,n,s=1,r=yn.SUM_BY_NONZERO_WEIGHTS){let a=D(e,"labels","huberLoss"),o=D(t,"predictions","huberLoss"),i=null;n!=null&&(i=D(n,"weights","huberLoss")),fn(a.shape,o.shape,"Error in huberLoss: ");let l=Ie(s),u=Wt(Ae(o,a)),c=rc(u,l),d=Ae(u,c),h=ae(z(Ie(.5),lt(c)),z(l,d));return yr(h,i,r)}var F_=V({huberLoss_:$_});function D_(e,t,n,s=1e-7,r=yn.SUM_BY_NONZERO_WEIGHTS){let a=D(e,"labels","logLoss"),o=D(t,"predictions","logLoss"),i=null;n!=null&&(i=D(n,"weights","logLoss")),fn(a.shape,o.shape,"Error in logLoss: ");let l=Ie(1),u=Ie(s),c=St(z(a,is(ae(o,u)))),d=z(Ae(l,a),is(ae(Ae(l,o),u))),h=Ae(c,d);return yr(h,i,r)}var O_=V({logLoss_:D_});function P_(e,t,n,s=yn.SUM_BY_NONZERO_WEIGHTS){let r=D(e,"labels","meanSquaredError"),a=D(t,"predictions","meanSquaredError"),o=null;n!=null&&(o=D(n,"weights","meanSquaredError")),fn(r.shape,a.shape,"Error in meanSquaredError: ");let i=OA(r,a);return yr(i,o,s)}var M_=V({meanSquaredError_:P_});function z_(e,t){let n=D(e,"labels","sigmoidCrossEntropyWithLogits"),s=D(t,"logits","sigmoidCrossEntropyWithLogits");fn(n.shape,s.shape,"Error in sigmoidCrossEntropyWithLogits: ");let r=Ys(s),a=z(s,n),o=Bh(os(St(Wt(s))));return ae(Ae(r,a),o)}function L_(e,t,n,s=0,r=yn.SUM_BY_NONZERO_WEIGHTS){let a=D(e,"multiClassLabels","sigmoidCrossEntropy"),o=D(t,"logits","sigmoidCrossEntropy"),i=null;if(n!=null&&(i=D(n,"weights","sigmoidCrossEntropy")),fn(a.shape,o.shape,"Error in sigmoidCrossEntropy: "),s>0){let u=Ie(s),c=Ie(1),d=Ie(.5);a=ae(z(a,Ae(c,u)),z(d,u))}let l=z_(a,o);return yr(l,i,r)}var B_=V({sigmoidCrossEntropy_:L_});function W_(e,t,n=-1){if(n===-1&&(n=t.rank-1),n!==t.rank-1)throw Error(`Softmax cross entropy along a non-last dimension is not yet supported. Labels / logits was rank ${t.rank} and dim was ${n}`);return Zs((r,a,o)=>{let l=Zx(a,[n],!0),u=Ae(ce(a,"float32"),l);o([r,u]);let c=St(z(u,r));return{value:ve(c,[n]),gradFunc:(p,f)=>{let[m,A]=f,g=Co(p.shape,[n]);return[z(U(p,g),Ae(ce(m,"float32"),os(A))),z(U(p,g),Ae(os(A),ce(m,"float32")))]}}})(e,t)}function V_(e,t,n,s=0,r=yn.SUM_BY_NONZERO_WEIGHTS){let a=D(e,"onehotLabels","softmaxCrossEntropy"),o=D(t,"logits","softmaxCrossEntropy"),i=null;if(n!=null&&(i=D(n,"weights","softmaxCrossEntropy")),fn(a.shape,o.shape,"Error in softmaxCrossEntropy: "),s>0){let u=Ie(s),c=Ie(1),d=Ie(a.shape[1]);a=ae(z(a,Ae(c,u)),de(u,d))}let l=W_(a,o);return yr(l,i,r)}var U_=V({softmaxCrossEntropy_:V_});function H_(e,t,n,s){let r=D(e,"indices","sparseFillEmptyRows"),a=D(t,"values","sparseFillEmptyRows"),o=D(n,"denseShape","sparseFillEmptyRows"),i=D(s,"defaultValue","sparseFillEmptyRows",a.dtype);if(r.rank!==2)throw new Error(`Indices should be Tensor2D but received shape
        ${r.shape}`);if(a.rank!==1)throw new Error(`Values should be Tensor1D but received shape ${a.shape}`);if(o.rank!==1)throw new Error(`Dense shape should be Tensor1D but received shape ${o.shape}`);if(i.rank!==0)throw new Error(`Default value should be a scalar but received shape ${i.shape}`);let l={indices:r,values:a,denseShape:o,defaultValue:i},u=L.runKernel(dh,l);return{outputIndices:u[0],outputValues:u[1],emptyRowIndicator:u[2],reverseIndexMap:u[3]}}var G_=V({sparseFillEmptyRows_:H_});function j_(e,t,n){let s=D(e,"inputIndices","sparseReshape"),r=D(t,"inputShape","sparseReshape"),a=D(n,"newShape","sparseReshape");if(s.rank!==2)throw new Error(`Input indices should be Tensor2D but received shape
        ${s.shape}`);if(r.rank!==1)throw new Error(`Input shape should be Tensor1D but received shape ${r.shape}`);if(a.rank!==1)throw new Error(`New shape should be Tensor1D but received shape ${a.shape}`);let o={inputIndices:s,inputShape:r,newShape:a},i=L.runKernel(hh,o);return{outputIndices:i[0],outputShape:i[1]}}var q_=V({sparseReshape_:j_});function X_(e,t,n){let s=D(e,"data","sparseSegmentMean"),r=D(t,"indices","sparseSegmentMean"),a=D(n,"segmentIds","sparseSegmentMean");if(s.rank<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(r.rank!==1)throw new Error(`Indices should be Tensor1D but received shape
          ${r.shape}`);if(a.rank!==1)throw new Error(`Segment ids should be Tensor1D but received shape
          ${a.shape}`);let o={data:s,indices:r,segmentIds:a};return L.runKernel(ph,o)}var K_=V({sparseSegmentMean_:X_});function Z_(e,t,n){let s=D(e,"data","sparseSegmentSum"),r=D(t,"indices","sparseSegmentSum"),a=D(n,"segmentIds","sparseSegmentSum");if(s.rank<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(r.rank!==1)throw new Error(`Indices should be Tensor1D but received shape
         ${r.shape}`);if(a.rank!==1)throw new Error(`Segment ids should be Tensor1D but received shape
         ${a.shape}`);let o={data:s,indices:r,segmentIds:a};return L.runKernel(fh,o)}var Y_=V({sparseSegmentSum_:Z_});function J_(e,t,n,s,r,a,o,i){let l=D(e,"data","stringNGrams","string");if(l.dtype!=="string")throw new Error("Data must be of datatype string");if(l.shape.length!==1)throw new Error(`Data must be a vector, saw: ${l.shape}`);let u=D(t,"dataSplits","stringNGrams");if(u.dtype!=="int32")throw new Error("Data splits must be of datatype int32");let c={separator:n,nGramWidths:s,leftPad:r,rightPad:a,padWidth:o,preserveShortSequences:i},d={data:l,dataSplits:u},h=L.runKernel(Ah,d,c);return{nGrams:h[0],nGramsSplits:h[1]}}var Q_=V({stringNGrams_:J_});function e$(e,t,n=!0){let s=D(e,"input","stringSplit","string"),r=D(t,"delimiter","stringSplit","string");if(s.rank!==1)throw new Error(`Input should be Tensor1D but received shape ${s.shape}`);if(r.rank!==0)throw new Error(`Delimiter should be a scalar but received shape ${r.shape}`);let a={skipEmpty:n},o={input:s,delimiter:r},i=L.runKernel(gh,o,a);return{indices:i[0],values:i[1],shape:i[2]}}var t$=V({stringSplit_:e$});function n$(e,t){let n=D(e,"input","stringToHashBucketFast","string"),s={numBuckets:t};if(t<=0)throw new Error("Number of buckets must be at least 1");let r={input:n};return L.runKernel(yh,r,s)}var s$=V({stringToHashBucketFast_:n$}),r$={fft:Yh,ifft:ic,rfft:Jh,irfft:DA},a$={hammingWindow:OR,hannWindow:xb,frame:bb,stft:LR},_e={flipLeftRight:UR,resizeNearestNeighbor:p_,resizeBilinear:d_,rotateWithOffset:GR,cropAndResize:WR,nonMaxSuppression:qR,nonMaxSuppressionAsync:t_,nonMaxSuppressionWithScore:s_,nonMaxSuppressionWithScoreAsync:a_,nonMaxSuppressionPadded:i_,nonMaxSuppressionPaddedAsync:u_,threshold:A_,transform:y_},Cb={bandPart:b_,gramSchmidt:w_,qr:I_},o$={absoluteDifference:T_,computeWeightedLoss:yr,cosineDistance:E_,hingeLoss:__,huberLoss:F_,logLoss:O_,meanSquaredError:M_,sigmoidCrossEntropy:B_,softmaxCrossEntropy:U_},uc={sparseFillEmptyRows:G_,sparseReshape:q_,sparseSegmentMean:K_,sparseSegmentSum:Y_},rp={stringNGrams:Q_,stringSplit:t$,stringToHashBucketFast:s$},xr=class extends dx{minimize(e,t=!1,n){let{value:s,grads:r}=this.computeGradients(e,n);if(n!=null){let a=n.map(o=>({name:o.name,tensor:r[o.name]}));this.applyGradients(a)}else this.applyGradients(r);return K(r),t?s:(s.dispose(),null)}get iterations(){return this.iterations_==null&&(this.iterations_=0),this.iterations_}incrementIterations(){this.iterations_=this.iterations+1}computeGradients(e,t){return jx(e,t)}dispose(){this.iterations_!=null&&K(this.iterations_)}async saveIterations(){return this.iterations_==null&&(this.iterations_=0),{name:"iter",tensor:Ie(this.iterations_,"int32")}}async getWeights(){throw new Error("getWeights() is not implemented for this optimizer yet.")}async setWeights(e){throw new Error(`setWeights() is not implemented for this optimizer class ${this.getClassName()}`)}async extractIterations(e){return this.iterations_=(await e[0].tensor.data())[0],e.slice(1)}};Object.defineProperty(xr,Symbol.hasInstance,{value:e=>e.minimize!=null&&e.computeGradients!=null&&e.applyGradients!=null});var ap=class extends xr{constructor(e,t,n=null){super();this.learningRate=e,this.rho=t,this.epsilon=n,this.accumulatedGrads=[],this.accumulatedUpdates=[],n==null&&(this.epsilon=L.backend.epsilon())}applyGradients(e){(Array.isArray(e)?e.map(n=>n.name):Object.keys(e)).forEach((n,s)=>{let r=L.registeredVariables[n],a=!1;this.accumulatedGrads[s]==null&&(this.accumulatedGrads[s]={originalName:`${n}/accum_grad`,variable:H(()=>qe(r).variable(a))}),this.accumulatedUpdates[s]==null&&(this.accumulatedUpdates[s]={originalName:`${n}/accum_var`,variable:H(()=>qe(r).variable(a))});let o=Array.isArray(e)?e[s].tensor:e[n];if(o==null)return;let i=this.accumulatedGrads[s].variable,l=this.accumulatedUpdates[s].variable;H(()=>{let u=ae(z(i,this.rho),z(lt(o),1-this.rho)),c=z(de(ln(ae(l,this.epsilon)),ln(ae(i,this.epsilon))),o),d=ae(z(l,this.rho),z(lt(c),1-this.rho));i.assign(u),l.assign(d);let h=ae(z(c,-this.learningRate),r);r.assign(h)})}),this.incrementIterations()}dispose(){this.accumulatedUpdates!=null&&(K(this.accumulatedGrads.map(e=>e.variable)),K(this.accumulatedUpdates.map(e=>e.variable)))}async getWeights(){let e=[...this.accumulatedGrads,...this.accumulatedUpdates];return[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=e.length/2,n=!1;this.accumulatedGrads=e.slice(0,t).map(s=>({originalName:s.name,variable:s.tensor.variable(n)})),this.accumulatedUpdates=e.slice(t,t*2).map(s=>({originalName:s.name,variable:s.tensor.variable(n)}))}getConfig(){return{learningRate:this.learningRate,rho:this.rho,epsilon:this.epsilon}}static fromConfig(e,t){return new e(t.learningRate,t.rho,t.epsilon)}};ap.className="Adadelta";Vr(ap);var op=class extends xr{constructor(e,t=.1){super();this.learningRate=e,this.initialAccumulatorValue=t,this.accumulatedGrads=[]}applyGradients(e){(Array.isArray(e)?e.map(n=>n.name):Object.keys(e)).forEach((n,s)=>{let r=L.registeredVariables[n];if(this.accumulatedGrads[s]==null){let i=!1;this.accumulatedGrads[s]={originalName:`${n}/accumulator`,variable:H(()=>Sl(r.shape,this.initialAccumulatorValue).variable(i))}}let a=Array.isArray(e)?e[s].tensor:e[n];if(a==null)return;let o=this.accumulatedGrads[s].variable;H(()=>{let i=ae(o,lt(a));o.assign(i);let l=ae(z(de(a,ln(ae(i,L.backend.epsilon()))),-this.learningRate),r);r.assign(l)})}),this.incrementIterations()}dispose(){this.accumulatedGrads!=null&&K(this.accumulatedGrads.map(e=>e.variable))}async getWeights(){return[await this.saveIterations()].concat(this.accumulatedGrads.map(e=>({name:e.originalName,tensor:e.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=!1;this.accumulatedGrads=e.map(n=>({originalName:n.name,variable:n.tensor.variable(t)}))}getConfig(){return{learningRate:this.learningRate,initialAccumulatorValue:this.initialAccumulatorValue}}static fromConfig(e,t){return new e(t.learningRate,t.initialAccumulatorValue)}};op.className="Adagrad";Vr(op);var ip=class extends xr{constructor(e,t,n,s=null){super();this.learningRate=e,this.beta1=t,this.beta2=n,this.epsilon=s,this.accumulatedFirstMoment=[],this.accumulatedSecondMoment=[],H(()=>{this.accBeta1=Ie(t).variable(),this.accBeta2=Ie(n).variable()}),s==null&&(this.epsilon=L.backend.epsilon())}applyGradients(e){let t=Array.isArray(e)?e.map(n=>n.name):Object.keys(e);H(()=>{let n=Ae(1,this.accBeta1),s=Ae(1,this.accBeta2);t.forEach((r,a)=>{let o=L.registeredVariables[r],i=!1;this.accumulatedFirstMoment[a]==null&&(this.accumulatedFirstMoment[a]={originalName:`${r}/m`,variable:H(()=>qe(o).variable(i))}),this.accumulatedSecondMoment[a]==null&&(this.accumulatedSecondMoment[a]={originalName:`${r}/v`,variable:H(()=>qe(o).variable(i))});let l=Array.isArray(e)?e[a].tensor:e[r];if(l==null)return;let u=this.accumulatedFirstMoment[a].variable,c=this.accumulatedSecondMoment[a].variable,d=ae(z(u,this.beta1),z(l,1-this.beta1)),h=ae(z(c,this.beta2),z(lt(l),1-this.beta2)),p=de(d,n),f=de(h,s);u.assign(d),c.assign(h);let m=ae(z(de(p,ae(ln(f),this.epsilon)),-this.learningRate),o);o.assign(m)}),this.accBeta1.assign(z(this.accBeta1,this.beta1)),this.accBeta2.assign(z(this.accBeta2,this.beta2))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.accBeta2.dispose(),this.accumulatedFirstMoment!=null&&K(this.accumulatedFirstMoment.map(e=>e.variable)),this.accumulatedSecondMoment!=null&&K(this.accumulatedSecondMoment.map(e=>e.variable))}async getWeights(){let e=[...this.accumulatedFirstMoment,...this.accumulatedSecondMoment];return[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e),H(()=>{this.accBeta1.assign(jr(this.beta1,this.iterations_+1)),this.accBeta2.assign(jr(this.beta2,this.iterations_+1))});let t=e.length/2,n=!1;this.accumulatedFirstMoment=e.slice(0,t).map(s=>({originalName:s.name,variable:s.tensor.variable(n)})),this.accumulatedSecondMoment=e.slice(t,t*2).map(s=>({originalName:s.name,variable:s.tensor.variable(n)}))}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon}}static fromConfig(e,t){return new e(t.learningRate,t.beta1,t.beta2,t.epsilon)}};ip.className="Adam";Vr(ip);var lp=class extends xr{constructor(e,t,n,s=null,r=0){super();this.learningRate=e,this.beta1=t,this.beta2=n,this.epsilon=s,this.decay=r,this.accumulatedFirstMoment=[],this.accumulatedWeightedInfNorm=[],H(()=>{this.iteration=Ie(0).variable(),this.accBeta1=Ie(t).variable()}),s==null&&(this.epsilon=L.backend.epsilon())}applyGradients(e){let t=Array.isArray(e)?e.map(n=>n.name):Object.keys(e);H(()=>{let n=Ae(1,this.accBeta1),s=de(-this.learningRate,ae(z(this.iteration,this.decay),1));t.forEach((r,a)=>{let o=L.registeredVariables[r],i=!1;this.accumulatedFirstMoment[a]==null&&(this.accumulatedFirstMoment[a]={originalName:`${r}/m`,variable:qe(o).variable(i)}),this.accumulatedWeightedInfNorm[a]==null&&(this.accumulatedWeightedInfNorm[a]={originalName:`${r}/v`,variable:qe(o).variable(i)});let l=Array.isArray(e)?e[a].tensor:e[r];if(l==null)return;let u=this.accumulatedFirstMoment[a].variable,c=this.accumulatedWeightedInfNorm[a].variable,d=ae(z(u,this.beta1),z(l,1-this.beta1)),h=z(c,this.beta2),p=Wt(l),f=gr(h,p);u.assign(d),c.assign(f);let m=ae(z(de(s,n),de(d,ae(f,this.epsilon))),o);o.assign(m)}),this.iteration.assign(ae(this.iteration,1)),this.accBeta1.assign(z(this.accBeta1,this.beta1))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.iteration.dispose(),this.accumulatedFirstMoment!=null&&K(this.accumulatedFirstMoment.map(e=>e.variable)),this.accumulatedWeightedInfNorm!=null&&K(this.accumulatedWeightedInfNorm.map(e=>e.variable))}async getWeights(){throw new Error("getWeights() is not implemented for Adamax yet.")}async setWeights(e){throw new Error("setWeights() is not implemented for Adamax yet.")}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon,decay:this.decay}}static fromConfig(e,t){return new e(t.learningRate,t.beta1,t.beta2,t.epsilon,t.decay)}};lp.className="Adamax";Vr(lp);var cc=class extends xr{constructor(e){super();this.learningRate=e,this.setLearningRate(e)}applyGradients(e){(Array.isArray(e)?e.map(n=>n.name):Object.keys(e)).forEach((n,s)=>{let r=Array.isArray(e)?e[s].tensor:e[n];if(r==null)return;let a=L.registeredVariables[n];H(()=>{let o=ae(z(this.c,r),a);a.assign(o)})}),this.incrementIterations()}setLearningRate(e){this.learningRate=e,this.c!=null&&this.c.dispose(),this.c=Kt(Ie(-e))}dispose(){this.c.dispose()}async getWeights(){return[await this.saveIterations()]}async setWeights(e){if(e=await this.extractIterations(e),e.length!==0)throw new Error("SGD optimizer does not have settable weights.")}getConfig(){return{learningRate:this.learningRate}}static fromConfig(e,t){return new e(t.learningRate)}};cc.className="SGD";Vr(cc);var up=class extends cc{constructor(e,t,n=!1){super(e);this.learningRate=e,this.momentum=t,this.useNesterov=n,this.accumulations=[],this.m=Ie(this.momentum)}applyGradients(e){(Array.isArray(e)?e.map(n=>n.name):Object.keys(e)).forEach((n,s)=>{let r=L.registeredVariables[n];if(this.accumulations[s]==null){let i=!1;this.accumulations[s]={originalName:`${n}/momentum`,variable:H(()=>qe(r).variable(i))}}let a=this.accumulations[s].variable,o=Array.isArray(e)?e[s].tensor:e[n];o!=null&&H(()=>{let i,l=ae(z(this.m,a),o);this.useNesterov?i=ae(z(this.c,ae(o,z(l,this.m))),r):i=ae(z(this.c,l),r),a.assign(l),r.assign(i)})}),this.incrementIterations()}dispose(){this.m.dispose(),this.accumulations!=null&&K(this.accumulations.map(e=>e.variable))}setMomentum(e){this.momentum=e}async getWeights(){return[await this.saveIterations()].concat(this.accumulations.map(e=>({name:e.originalName,tensor:e.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=!1;this.accumulations=e.map(n=>({originalName:n.name,variable:n.tensor.variable(t)}))}getConfig(){return{learningRate:this.learningRate,momentum:this.momentum,useNesterov:this.useNesterov}}static fromConfig(e,t){return new e(t.learningRate,t.momentum,t.useNesterov)}};up.className="Momentum";Vr(up);var cp=class extends xr{constructor(e,t=.9,n=0,s=null,r=!1){super();if(this.learningRate=e,this.decay=t,this.momentum=n,this.epsilon=s,this.accumulatedMeanSquares=[],this.accumulatedMoments=[],this.accumulatedMeanGrads=[],this.centered=r,s==null&&(this.epsilon=L.backend.epsilon()),e==null)throw new Error("learningRate for RMSPropOptimizer must be defined.")}applyGradients(e){(Array.isArray(e)?e.map(n=>n.name):Object.keys(e)).forEach((n,s)=>{let r=L.registeredVariables[n],a=!1;this.accumulatedMeanSquares[s]==null&&(this.accumulatedMeanSquares[s]={originalName:`${n}/rms`,variable:H(()=>qe(r).variable(a))}),this.accumulatedMoments[s]==null&&(this.accumulatedMoments[s]={originalName:`${n}/momentum`,variable:H(()=>qe(r).variable(a))}),this.accumulatedMeanGrads[s]==null&&this.centered&&(this.accumulatedMeanGrads[s]={originalName:`${n}/mg`,variable:H(()=>qe(r).variable(a))});let o=Array.isArray(e)?e[s].tensor:e[n];if(o==null)return;let i=this.accumulatedMeanSquares[s].variable,l=this.accumulatedMoments[s].variable;H(()=>{let u=ae(z(i,this.decay),z(lt(o),1-this.decay));if(this.centered){let c=this.accumulatedMeanGrads[s].variable,d=ae(z(c,this.decay),z(o,1-this.decay)),h=de(z(o,this.learningRate),ln(Ae(u,ae(lt(d),this.epsilon)))),p=ae(z(l,this.momentum),h);i.assign(u),c.assign(d),l.assign(p);let f=Ae(r,p);r.assign(f)}else{let c=ae(z(i,this.decay),z(lt(o),1-this.decay)),d=ae(z(l,this.momentum),de(z(o,this.learningRate),ln(ae(c,this.epsilon))));i.assign(c),l.assign(d);let h=Ae(r,d);r.assign(h)}})}),this.incrementIterations()}dispose(){this.accumulatedMeanSquares!=null&&K(this.accumulatedMeanSquares.map(e=>e.variable)),this.accumulatedMeanGrads!=null&&this.centered&&K(this.accumulatedMeanGrads.map(e=>e.variable)),this.accumulatedMoments!=null&&K(this.accumulatedMoments.map(e=>e.variable))}async getWeights(){let e=[...this.accumulatedMeanSquares,...this.accumulatedMoments];return this.centered&&e.push(...this.accumulatedMeanGrads),[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=this.centered?e.length/3:e.length/2,n=!1;this.accumulatedMeanSquares=e.slice(0,t).map(s=>({originalName:s.name,variable:s.tensor.variable(n)})),this.accumulatedMoments=e.slice(t,t*2).map(s=>({originalName:s.name,variable:s.tensor.variable(n)})),this.centered&&(this.accumulatedMeanGrads=e.slice(t*2,t*3).map(s=>({originalName:s.name,variable:s.tensor.variable(n)})))}getConfig(){return{learningRate:this.learningRate,decay:this.decay,momentum:this.momentum,epsilon:this.epsilon,centered:this.centered}}static fromConfig(e,t){return new e(t.learningRate,t.decay,t.momentum,t.epsilon,t.centered)}};cp.className="RMSProp";Vr(cp);var To=class{static sgd(e){return new cc(e)}static momentum(e,t,n=!1){return new up(e,t,n)}static rmsprop(e,t=.9,n=0,s=null,r=!1){return new cp(e,t,n,s,r)}static adam(e=.001,t=.9,n=.999,s=null){return new ip(e,t,n,s)}static adadelta(e=.001,t=.95,n=null){return new ap(e,t,n)}static adamax(e=.002,t=.9,n=.999,s=null,r=0){return new lp(e,t,n,s,r)}static adagrad(e,t=.1){return new op(e,t)}},No={sgd:To.sgd,momentum:To.momentum,adadelta:To.adadelta,adagrad:To.adagrad,rmsprop:To.rmsprop,adamax:To.adamax,adam:To.adam},i$=(()=>typeof requestAnimationFrame!="undefined"?requestAnimationFrame:typeof setImmediate!="undefined"?setImmediate:e=>e())();function dp(){return new Promise(e=>i$(()=>e()))}var $={};Pe($,{ERF_A1:()=>y$,ERF_A2:()=>x$,ERF_A3:()=>b$,ERF_A4:()=>v$,ERF_A5:()=>w$,ERF_P:()=>g$,PARALLELIZE_THRESHOLD:()=>UA,SELU_SCALE:()=>Nb,SELU_SCALEALPHA:()=>Tb,applyActivation:()=>np,assertAndGetBroadcastShape:()=>mt,assertAxesAreInnerMostDims:()=>vN,assertParamsConsistent:()=>l$,assignToTypedArray:()=>R$,axesAreInnerMostDims:()=>bA,calculateShapes:()=>Q5,checkEinsumDimSizes:()=>P$,combineLocations:()=>qx,complexWithEvenIndex:()=>T$,complexWithOddIndex:()=>N$,computeConv2DInfo:()=>Qu,computeConv3DInfo:()=>Sx,computeDefaultPad:()=>iA,computeDilation2DInfo:()=>WC,computeOptimalWindowSize:()=>c$,computeOutAndReduceShapes:()=>Xx,computeOutShape:()=>u$,computePool2DInfo:()=>Ix,computePool3DInfo:()=>VC,convertConv2DDataFormat:()=>Cx,decodeEinsumEquation:()=>D$,eitherStridesOrDilationsAreOne:()=>Ks,expandShapeToKeepDim:()=>Co,exponent:()=>$$,exponents:()=>_$,fromStringArrayToUint8:()=>G$,fromUint8ToStringArray:()=>H$,getAxesPermutation:()=>Kx,getBroadcastDims:()=>$T,getComplexWithIndex:()=>E$,getEinsumComputePath:()=>M$,getEinsumPermutation:()=>O$,getFusedBiasGradient:()=>tp,getFusedDyActivation:()=>ep,getImageCenter:()=>d$,getInnerMostAxes:()=>wN,getPermuted:()=>p$,getReductionAxes:()=>Vt,getReshaped:()=>h$,getReshapedPermuted:()=>f$,getSliceBeginCoords:()=>m$,getSliceSize:()=>A$,getUndoAxesPermutation:()=>vA,isIdentityPermutation:()=>z$,log:()=>I$,mergeRealAndImagArrays:()=>S$,prepareAndValidate:()=>J5,prepareSplitSize:()=>B$,segment_util:()=>_b,shouldFuse:()=>sp,slice_util:()=>An,splitRealAndImagArrays:()=>C$,tupleValuesAreOne:()=>Ur,upcastType:()=>bs,validateInput:()=>Qm,validateUpdateShape:()=>Jm,warn:()=>k$});function l$(e,t){let n=e[0].length;e.forEach((r,a)=>{P(r.length===n,()=>`Error in concat${n}D: rank of tensors[${a}] must be the same as the rank of the rest (${n})`)}),P(t>=0&&t<n,()=>`Error in concat${n}D: axis must be between 0 and ${n-1}.`);let s=e[0];e.forEach((r,a)=>{for(let o=0;o<n;o++)P(o===t||r[o]===s[o],()=>`Error in concat${n}D: Shape of tensors[${a}] (${r}) does not match the shape of the rest (${s}) along the non-concatenated axis ${a}.`)})}function u$(e,t){let n=e[0].slice();for(let s=1;s<e.length;s++)n[t]+=e[s][t];return n}var UA=30;function c$(e){return e<=UA?e:Pd(e,Math.floor(Math.sqrt(e)))}function d$(e,t,n){let s=n*(typeof e=="number"?e:e[0]),r=t*(typeof e=="number"?e:e[1]);return[s,r]}function h$(e,t,n,s=!0){let r=[];if(s)r=r.concat(t.slice(0)),r.push(e[0]/n),r=r.concat(e.slice(1));else{r=r.concat(e[0]);let a=t.length;for(let o=0;o<a;++o)r=r.concat([e[o+1]/t[o],t[o]]);r=r.concat(e.slice(a+1))}return r}function p$(e,t,n=!0){let s=[];if(n){s.push(t);for(let r=t+1;r<e;++r)r<=2*t?(s.push(r),s.push(r-(t+1))):s.push(r)}else{let r=[],a=[];for(let o=1;o<e;++o)o>=t*2+1||o%2==1?a.push(o):r.push(o);s.push(...r),s.push(0),s.push(...a)}return s}function f$(e,t,n,s=!0){let r=[];s?r.push(e[0]/n):r.push(e[0]*n);for(let a=1;a<e.length;++a)a<=t.length?s?r.push(t[a-1]*e[a]):r.push(e[a]/t[a-1]):r.push(e[a]);return r}function m$(e,t){let n=[0];for(let s=0;s<t;++s)n.push(e[s][0]);return n}function A$(e,t,n){let s=e.slice(0,1);for(let r=0;r<n;++r)s.push(e[r+1]-t[r][0]-t[r][1]);return s}var Tb=1.7580993408473768,Nb=1.0507009873554805,g$=.3275911,y$=.254829592,x$=-.284496736,b$=1.421413741,v$=-1.453152027,w$=1.061405429;function k$(...e){ee().getBool("IS_TEST")||console.warn(...e)}function I$(...e){ee().getBool("IS_TEST")||console.log(...e)}function S$(e,t){if(e.length!==t.length)throw new Error(`Cannot merge real and imag arrays of different lengths. real:${e.length}, imag: ${t.length}.`);let n=new Float32Array(e.length*2);for(let s=0;s<n.length;s+=2)n[s]=e[s/2],n[s+1]=t[s/2];return n}function C$(e){let t=new Float32Array(e.length/2),n=new Float32Array(e.length/2);for(let s=0;s<e.length;s+=2)t[s/2]=e[s],n[s/2]=e[s+1];return{real:t,imag:n}}function T$(e){let t=Math.ceil(e.length/4),n=new Float32Array(t),s=new Float32Array(t);for(let r=0;r<e.length;r+=4)n[Math.floor(r/4)]=e[r],s[Math.floor(r/4)]=e[r+1];return{real:n,imag:s}}function N$(e){let t=Math.floor(e.length/4),n=new Float32Array(t),s=new Float32Array(t);for(let r=2;r<e.length;r+=4)n[Math.floor(r/4)]=e[r],s[Math.floor(r/4)]=e[r+1];return{real:n,imag:s}}function E$(e,t){let n=e[t*2],s=e[t*2+1];return{real:n,imag:s}}function R$(e,t,n,s){e[s*2]=t,e[s*2+1]=n}function _$(e,t){let n=new Float32Array(e/2),s=new Float32Array(e/2);for(let r=0;r<Math.ceil(e/2);r++){let a=(t?2:-2)*Math.PI*(r/e);n[r]=Math.cos(a),s[r]=Math.sin(a)}return{real:n,imag:s}}function $$(e,t,n){let s=(n?2:-2)*Math.PI*(e/t),r=Math.cos(s),a=Math.sin(s);return{real:r,imag:a}}var HA="->",F$=/->/g,Eb=",",Rb="...";function D$(e,t){e=e.replace(/\s/g,"");let n=(e.length-e.replace(F$,"").length)/HA.length;if(n<1)throw new Error("Equations without an arrow are not supported.");if(n>1)throw new Error(`Equation must contain exactly one arrow ("${HA}").`);let[s,r]=e.split(HA);P(s.indexOf(Rb)===-1,()=>`The ellipsis notation ("${Rb}") is not supported yet.`);let a=s.split(Eb),o=a.length;if(t!==o)throw new Error(`Expected ${o} input tensors, received ${t}`);if(o>2)throw new Error("Support for more than 2 input tensors is not implemented yet.");let i=[];for(let h=0;h<r.length;++h){let p=r[h];if(!a.some(f=>f.indexOf(p)!==-1))throw new Error(`Output subscripts contain the label ${p} not present in the input subscripts.`);i.indexOf(p)===-1&&i.push(p)}for(let h=0;h<s.length;++h){let p=s[h];i.indexOf(p)===-1&&p!==Eb&&i.push(p)}let l=new Array(a.length);for(let h=0;h<o;++h){if(new Set(a[h].split("")).size!==a[h].length)throw new Error(`Found duplicate axes in input component ${a[h]}. Support for duplicate axes in input is not implemented yet.`);l[h]=[];for(let p=0;p<a[h].length;++p)l[h].push(i.indexOf(a[h][p]))}let u=i.length,c=r.length,d=[];for(let h=c;h<u;++h)d.push(h);return{allDims:i,summedDims:d,idDims:l}}function O$(e,t){let n=new Array(e);n.fill(-1);for(let r=0;r<t.length;++r)n[t[r]]=r;let s=[];for(let r=0;r<e;++r)n[r]===-1&&s.push(r);return n=n.filter(r=>r!==-1),{permutationIndices:n,expandDims:s}}function P$(e,t,n){let s=new Array(e);for(let r=0;r<n.length;++r){let a=n[r].shape;for(let o=0;o<t[r].length;++o)s[t[r][o]]===void 0?s[t[r][o]]=a[o]:P(s[t[r][o]]===a[o],()=>`Expected dimension ${s[t[r][o]]} at axis ${o} of input shaped ${JSON.stringify(a)}, but got dimension ${a[o]}`)}}function M$(e,t){let n=e,s=[],r=0;e.length===0&&n.push(-1),r=e.length+1;for(let o=0;o<r;++o)s.push([]);let a=[];for(let o=0;o<n.length;++o){let i=n[o],l=L$(t,i);for(let u of l)a.indexOf(u)===-1&&(s[o].push(u),a.push(u))}return{path:n,steps:s}}function z$(e){return e.every((t,n)=>t===n)}function L$(e,t){let n=[];for(let s=0;s<e.length;++s)(e[s].length===0||e[s].indexOf(t)!==-1||t===-1)&&n.push(s);return n}function B$(e,t,n=0){let s=[];if(typeof t=="number")P(e.shape[n]%t==0,()=>"Number of splits must evenly divide the axis."),s=new Array(t).fill(e.shape[n]/t);else{let r=t.reduce((o,i)=>(i===-1&&(o+=1),o),0);P(r<=1,()=>"There should be only one negative value in split array.");let a=t.indexOf(-1);if(a!==-1){let o=t.reduce((i,l)=>l>0?i+l:i);t[a]=e.shape[n]-o}P(e.shape[n]===t.reduce((o,i)=>o+i),()=>"The sum of sizes must match the size of the axis dimension."),s=t}return s}var _b={};Pe(_b,{collectGatherOpShapeInfo:()=>U$,computeOutShape:()=>V$,segOpComputeOptimalWindowSize:()=>W$});function W$(e,t){let n=!1,s;for(e<=UA?(s=e,n=!0):s=Pd(e,Math.floor(Math.sqrt(e)));!n;)s>t||s===e?n=!0:s=Pd(e,s+1);return s}function V$(e,t,n){let s=[],r=e.length;for(let a=0;a<r;a++)a!==t?s.push(e[a]):s.push(n);return s}function U$(e,t,n,s){let r=t.shape.length,a=e.shape.length;if(s!==0&&(s<-r||s>r))throw new Error(`Expect batchDims in the range of [-${r}, ${r}], but got ${s}`);if(s<0&&(s+=r),s>a)throw new Error(`batchDims (${s}) must be less than rank(x) (
    ${a}).`);if(n<s)throw new Error(`batchDims (${s}) must be less than or equal to axis (${n}).`);for(let d=0;d<s;++d)if(e.shape[d]!==t.shape[d])throw new Error(`x.shape[${d}]: ${e.shape[d]} should be equal to indices.shape[${d}]: ${t.shape[d]}.`);let o=e.shape[n],i=[],l=1,u=1,c=1;for(let d=0;d<s;++d)i.push(e.shape[d]),l*=e.shape[d];for(let d=s;d<n;d++)i.push(e.shape[d]),u*=e.shape[d];for(let d=s;d<r;d++)i.push(t.shape[d]);for(let d=n+1;d<a;d++)i.push(e.shape[d]),c*=e.shape[d];return{batchSize:l,sliceSize:c,outerSize:u,dimSize:o,outputShape:i}}function H$(e){try{return e.map(t=>Ih(t))}catch(t){throw new Error(`Failed to decode encoded string bytes into utf-8, error: ${t}`)}}function G$(e){return e.map(t=>Uu(t))}var Js={};Pe(Js,{nonMaxSuppressionV3Impl:()=>vb,nonMaxSuppressionV4Impl:()=>wb,nonMaxSuppressionV5Impl:()=>kb,whereImpl:()=>db});var $b={kernelName:di,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>z(e,lc(ce(n,"float32"),-1))}}},j$={kernelName:hi,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>{let s=lt(ce(n,"float32")),r=ln(Ae(Ie(1),s));return St(de(e,r))}}}},q$={kernelName:pi,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>{let s=ln(Ae(lt(ce(n,"float32")),1));return de(e,s)}}}},X$={kernelName:Dr,inputsToSave:["a","b"],gradFunc:(e,t)=>{let[n,s]=t,r=mt(n.shape,s.shape);return{a:()=>{let i=e,l=Vt(n.shape,r);return l.length>0&&(i=ve(i,l)),U(i,n.shape)},b:()=>{let i=e,l=Vt(s.shape,r);return l.length>0&&(i=ve(i,l)),U(i,s.shape)}}}},K$={kernelName:ga,saveAllInputs:!0,gradFunc:(e,t)=>{let n={};return t.forEach((s,r)=>{n[r]=()=>e.clone()}),n}},Z$={kernelName:ya,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>qe(n)}}},Y$={kernelName:Cu,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>qe(n)}}},J$={kernelName:Ai,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>de(e,ln(Ae(Ie(1),lt(ce(n,"float32")))))}}},Q$={kernelName:gi,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>{let s=ln(ae(Ie(1),lt(ce(n,"float32"))));return de(e,s)}}}},eF={kernelName:bi,inputsToSave:["a","b"],gradFunc:(e,t)=>{let[n,s]=t,r=mt(n.shape,s.shape);return{a:()=>{let i=ae(lt(n),lt(s)),l=z(e,de(s,i)),u=Vt(n.shape,r);return u.length>0&&(l=ve(l,u)),U(l,n.shape)},b:()=>{let i=ae(lt(n),lt(s)),l=St(z(e,de(n,i))),u=Vt(s.shape,r);return u.length>0&&(l=ve(l,u)),U(l,s.shape)}}}},tF={kernelName:yi,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>de(e,ae(lt(ce(n,"float32")),1))}}},nF={kernelName:xi,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>de(e,Ae(Ie(1),lt(ce(n,"float32"))))}}};function sF(e,t,n,s,r,a){let o=D(e,"dy","avgPool3dGrad"),i=D(t,"input","avgPool3dGrad"),l=o,u=i,c=!1;i.rank===4&&(c=!0,l=U(o,[1,o.shape[0],o.shape[1],o.shape[2],o.shape[3]]),u=U(i,[1,i.shape[0],i.shape[1],i.shape[2],i.shape[3]])),P(l.rank===5,()=>`Error in avgPool3dGrad: dy must be rank 5 but got rank ${l.rank}.`),P(u.rank===5,()=>`Error in avgPool3dGrad: input must be rank 5 but got rank ${u.rank}.`),a!=null&&P(qt(r),()=>`Error in avgPool3dGrad: pad must be an integer when using, dimRoundingMode ${a} but got pad ${r}.`);let d={dy:l,input:u},h={filterSize:n,strides:s,pad:r,dimRoundingMode:a},p=L.runKernel(Ld,d,h);return c?U(p,[p.shape[1],p.shape[2],p.shape[3],p.shape[4]]):p}var rF=V({avgPool3dGrad_:sF}),aF={kernelName:Tu,inputsToSave:["x"],gradFunc:(e,t,n)=>{let[s]=t,{filterSize:r,strides:a,pad:o,dimRoundingMode:i}=n;return{x:()=>rF(e,s,r,a,o,i)}}};function oF(e,t,n,s,r){let a=D(e,"dy","avgPoolGrad"),o=D(t,"input","avgPoolGrad");P(o.rank===a.rank,()=>`Rank of input (${o.rank}) does not match rank of dy (${a.rank})`);let i=o,l=a,u=!1;o.rank===3&&(u=!0,i=U(o,[1,o.shape[0],o.shape[1],o.shape[2]]),l=U(a,[1,a.shape[0],a.shape[1],a.shape[2]])),P(l.rank===4,()=>`Error in avgPoolGrad: dy must be rank 4 but got rank ${l.rank}.`),P(i.rank===4,()=>`Error in avgPoolGrad: input must be rank 4 but got rank ${i.rank}.`);let c={dy:l,input:i},d={filterSize:n,strides:s,pad:r},h=L.runKernel(zd,c,d);return u?U(h,[h.shape[1],h.shape[2],h.shape[3]]):h}var iF=V({avgPoolGrad_:oF}),lF={kernelName:xa,inputsToSave:["x"],gradFunc:(e,t,n)=>{let[s]=t,{filterSize:r,strides:a,pad:o}=n;return{x:()=>iF(e,s,r,a,o)}}},uF={kernelName:ba,inputsToSave:["a","b"],gradFunc:(e,t,n)=>{let[s,r]=t,{transposeA:a,transposeB:o}=n;return!a&&!o?{a:()=>We(e,r,!1,!0),b:()=>We(s,e,!0,!1)}:!a&&o?{a:()=>We(e,r,!1,!1),b:()=>We(e,s,!0,!1)}:a&&!o?{a:()=>We(r,e,!1,!0),b:()=>We(s,e,!1,!1)}:{a:()=>We(r,e,!0,!0),b:()=>We(e,s,!0,!0)}}},cF={kernelName:vi,gradFunc:(e,t,n)=>{let{blockShape:s,crops:r}=n;return{x:()=>jh(e,s,r)}}},dF={kernelName:A5,gradFunc:(e,t,n)=>{let s=n,r=s.inputShape,a=s.shape,o=Array.from(a);for(let l=r.length-1;l>=0;l--)if(r[l]===a[l])o[l]=1;else if(r[l]!==1)throw new Error(`broadcastTo(): [${r}] cannot be broadcast to [${a}].`);let i=[];for(let l=0;l<o.length;l++)o[l]>1&&i.push(l);return{x:()=>ve(e,i,!0)}}},hF={kernelName:va,gradFunc:e=>({x:()=>e.clone()})},pF={kernelName:wa,gradFunc:e=>({x:()=>qe(e)})},fF={kernelName:Or,inputsToSave:["x"],gradFunc:(e,t,n)=>{let[s]=t,{clipValueMin:r,clipValueMax:a}=n;return{x:()=>gn(Rs(Io(s,r),So(s,a)),e,qe(e))}}},mF={kernelName:Nu,inputsToSave:["x"],gradFunc:$b.gradFunc},AF={kernelName:wi,saveAllInputs:!0,gradFunc:(e,t,n)=>{let s=t.map(l=>l.shape),{axis:r}=n,a=xs(r,t[0].shape)[0],o=s.map(l=>l[a]);return nn(e,o,a).map(l=>()=>l)}},gF={kernelName:ka,inputsToSave:["x","filter"],gradFunc:(e,t,n)=>{let[s,r]=t,{dilations:a,strides:o,pad:i,dataFormat:l}=n;return P(Ur(a),()=>`Error in gradient of conv2D: dilation rates greater than 1 are not yet supported in gradients. Got dilations '${a}'`),{x:()=>hA(s.shape,e,r,o,i,l),filter:()=>WA(s,e,r.shape,o,i,l)}}},yF={kernelName:Ia,inputsToSave:["dy","filter"],gradFunc:(e,t,n)=>{let[s,r]=t,{strides:a,pad:o,dataFormat:i,dimRoundingMode:l}=n;return{dy:()=>Hr(e,r,a,o,i,1,l),filter:()=>WA(e,s,r.shape,a,o,i,l)}}};function xF(e,t,n,s,r){let a=e;e.rank===4&&(a=U(e,[1,e.shape[0],e.shape[1],e.shape[2],e.shape[3]]));let o=t;o.rank===4&&(o=U(t,[1,t.shape[0],t.shape[1],t.shape[2],t.shape[3]])),P(a.rank===5,()=>`Error in conv3dDerFilter: input must be rank 5, but got shape ${a.shape}.`),P(o.rank===5,()=>`Error in conv3dDerFilter: dy must be rank 5, but got shape ${o.shape}.`),P(n.length===5,()=>`Error in conv3dDerFilter: filterShape must be length 5, but got ${n}.`),P(a.shape[4]===n[3],()=>`Error in conv3dDerFilter: depth of input ${a.shape[4]}) must match input depth in filter (${n[3]}.`),P(o.shape[4]===n[4],()=>`Error in conv3dDerFilter: depth of dy (${o.shape[4]}) must match output depth for filter (${n[4]}).`);let i={x:a,dy:o},l={strides:s,pad:r,filterShape:n};return L.runKernel(Ud,i,l)}var bF=V({conv3DBackpropFilter_:xF}),vF={kernelName:Eu,inputsToSave:["x","filter"],gradFunc:(e,t,n)=>{let{dilations:s,strides:r,pad:a}=n;P(Ur(s),()=>`Error in gradient of conv3D: dilation rates greater than 1 are not yet supported in gradients. Got dilations '${s}'`);let[o,i]=t;return{x:()=>Dx(o.shape,e,i,r,a),filter:()=>bF(o,e,i.shape,r,a)}}},wF={kernelName:Sa,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>z(St(_A(ce(n,"float32"))),e)}}},kF={kernelName:Ca,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>z($A(ce(n,"float32")),e)}}},IF={kernelName:Ta,inputsToSave:["x"],gradFunc:(e,t,n)=>{let[s]=t,{axis:r,exclusive:a,reverse:o}=n;return{x:()=>{let i=Kx([r],s.rank),l=AA(e,r,a,!o);return i!=null&&(l=je(l,i)),l}}}},SF={kernelName:Na,inputsToSave:["x","filter"],gradFunc:(e,t,n)=>{let{dilations:s,strides:r,pad:a,dimRoundingMode:o}=n,i=s==null?[1,1]:s;P(Ur(i),()=>`Error in gradient of depthwiseConv2dNative: dilation rates greater than 1 are not yet supported. Got dilations '${i}'`);let[l,u]=t;return P(l.rank===4,()=>`Error in gradient of depthwiseConv2dNative: input must be rank 4, but got rank ${l.rank}.`),P(u.rank===4,()=>`Error in gradient of depthwiseConv2dNative: filter must be rank 4, but got rank ${u.rank}.`),P(l.shape[3]===u.shape[2],()=>`Error in gradient of depthwiseConv2d: number of input channels (${l.shape[3]}) must match the inChannels dimension in filter ${u.shape[2]}.`),P(Ks(r,i),()=>`Error in gradient of depthwiseConv2d: Either strides or dilations must be  1. Got strides ${r} and dilations '${i}'.`),o!=null&&P(qt(a),()=>`Error in depthwiseConv2d: pad must be an integer when using, dimRoundingMode ${o} but got pad ${a}.`),{x:()=>yb(l.shape,e,u,r,a,s,o),filter:()=>gb(l,e,u.shape,r,a,s,o)}}},CF={kernelName:Ru,inputsToSave:["x","filter"],gradFunc:(e,t,n)=>{let[s,r]=t,a={x:s,filter:r,dy:e},o={x:s,filter:r,dy:e};return{x:()=>L.runKernel(Kd,a,n),filter:()=>L.runKernel(Zd,o,n)}}},TF={kernelName:Si,outputsToSave:[!0],gradFunc:(e,t)=>{let[n]=t,s={dy:e,y:n};return{x:()=>L.runKernel(Jd,s)}}},NF={kernelName:Ci,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t,s=z(os(St(lt(n))),2/Math.sqrt(Math.PI));return{x:()=>z(e,s)}}},EF={kernelName:Ra,outputsToSave:[!0],gradFunc:(e,t)=>{let[n]=t;return{x:()=>z(e,n)}}},RF={kernelName:Ni,inputsToSave:["input"],gradFunc:(e,t)=>{let[n]=t;return{input:()=>U(e,n.shape)}}},_F={kernelName:Ei,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>z(e,os(n))}}},$F={kernelName:_a,gradFunc:e=>({x:()=>qe(e)})},FF={kernelName:$a,inputsToSave:["a","b"],gradFunc:(e,t)=>{let[n,s]=t,r=mt(n.shape,s.shape);return{a:()=>{let i=de(e,ce(s,"float32")),l=Vt(n.shape,r);return l.length>0?U(ve(i,l),n.shape):i},b:()=>{let i=z(e,ce(n,"float32")),l=Vt(s.shape,r);l.length>0&&(i=U(ve(i,l),s.shape));let u=lt(s);return St(de(i,ce(u,"float32")))}}}},DF={kernelName:Fa,inputsToSave:["x","mean","variance","scale"],gradFunc:(e,t,n)=>{let{varianceEpsilon:s}=n,[r,a,o,i]=t,l=i==null?Ie(1):i,u=Vt(a.shape,r.shape),c=[];if(a.rank===1){for(let b=0;b<r.shape.length-1;++b)c.push(r.shape[b]);c.push(1)}let d=Ae(r,a),h=z(e,l),p=EA(ae(o,Ie(s))),f=z(z(z(p,p),p),Ie(-.5));return{x:()=>a.rank===1?U(z(z(e,Es(U(p,[1,1,1,a.shape[0]]),c)),l),r.shape):U(z(z(e,p),l),r.shape),mean:()=>{let b=z(z(p,Ie(-1)),h);return a.rank===1&&(b=ve(b,u)),U(b,a.shape)},variance:()=>{let b=z(z(f,d),h);return a.rank===1&&(b=ve(b,u)),U(b,a.shape)},scale:()=>{let b=z(d,p),v=z(e,b);return a.rank===1&&(v=ve(v,u)),U(v,a.shape)},offset:()=>{let b=e;return a.rank===1&&(b=ve(b,u)),U(b,a.shape)}}}},OF={kernelName:_i,inputsToSave:["x","indices"],gradFunc:(e,t,n)=>{let[s,r]=t,{axis:a}=n,o=xs(a,s.shape)[0];return{x:()=>{let l=s.shape,u=r.size,c=l.slice(0,o),d=c.length,h=l.slice(a,l.length).slice(1),p=h.length,f=Fb(0,d),m=Fb(d+1,d+1+p),A=Db([c,[u],h]),g=U(e,A),y=U(r,[u]),x=Db([[d],f,m]),b=je(g,x),v=ub(b,y,s.shape[o]),k=vA(x);return v=je(v,k),v},indices:()=>r}}};function Fb(e,t){let n=[];for(let s=e;s<t;++s)n.push(s);return n}function Db(e){let t=[];for(let n=0;n<e.length;++n)for(let s=0;s<e[n].length;++s)t.push(e[n][s]);return t}var PF={kernelName:Da,inputsToSave:["a","b"],gradFunc:(e,t)=>{let[n,s]=t;return{a:()=>qe(n),b:()=>qe(s)}}},MF={kernelName:Oa,gradFunc:e=>({x:()=>ce(e,"float32")})},zF={kernelName:Di,gradFunc:e=>({x:()=>qe(e)})},LF={kernelName:Oi,gradFunc:e=>({x:()=>qe(e)})},BF={kernelName:Pi,gradFunc:e=>({x:()=>qe(e)})},WF={kernelName:Pa,inputsToSave:["x"],gradFunc:(e,t,n)=>{let[s]=t,{alpha:r}=n,a=Vn(s,0);return{x:()=>gn(a,e,z(e,r))}}},VF={kernelName:Li,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>de(e,ae(n,1))}}},UF={kernelName:Ma,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>de(e,ce(n,"float32"))}}},HF={kernelName:g5,inputsToSave:[],outputsToSave:[!0],gradFunc:(e,t,n)=>{let[s]=t,{axis:r}=n;return{logits:()=>{let a=!0,o=os(s);return Ae(e,z(ve(e,r,a),o))}}}};function GF(e,t,n,s=5,r=1,a=1,o=.5){let i={x:e,y:t,dy:n},l={depthRadius:s,bias:r,alpha:a,beta:o};return L.runKernel(sh,i,l)}var jF=V({localResponseNormalizationBackprop_:GF}),qF={kernelName:Du,inputsToSave:["x"],outputsToSave:[!0],gradFunc:(e,t,n)=>{let[s,r]=t,{depthRadius:a,bias:o,alpha:i,beta:l}=n;return{x:()=>jF(s,r,e,a,o,i,l)}}};function Ob(e,t,n,s){return t.rank<n.rank&&(t=U(t,Co(t.shape,s))),e.rank<n.rank&&(e=U(e,Co(e.shape,s))),{x:()=>z(e,ce(as(n,t),e.dtype))}}var Pb={kernelName:za,inputsToSave:["x"],outputsToSave:[!0],gradFunc:(e,t,n)=>{let s=n,{reductionIndices:r}=s,a=t[0],o=t[1],i=xs(r,a.shape),l=Ob(e,o,a,i);return{x:()=>l.x()}}},XF={kernelName:La,inputsToSave:["a","b"],gradFunc:(e,t)=>{let[n,s]=t;return{a:()=>z(e,ce(Io(n,s),"float32")),b:()=>z(e,ce(yA(n,s),"float32"))}}};function KF(e,t,n,s,r,a,o){let i=D(e,"dy","maxPool3dGrad"),l=D(t,"input","maxPool3dGrad"),u=D(n,"output","maxPool3dGrad"),c=i,d=l,h=u,p=!1;l.rank===4&&(p=!0,c=U(i,[1,i.shape[0],i.shape[1],i.shape[2],i.shape[3]]),d=U(l,[1,l.shape[0],l.shape[1],l.shape[2],l.shape[3]]),h=U(u,[1,u.shape[0],u.shape[1],u.shape[2],u.shape[3]])),P(c.rank===5,()=>`Error in maxPool3dGrad: dy must be rank 5 but got rank ${c.rank}.`),P(d.rank===5,()=>`Error in maxPool3dGrad: input must be rank 5 but got rank ${d.rank}.`),P(h.rank===5,()=>`Error in maxPool3dGrad: output must be rank 5 but got rank ${h.rank}.`),o!=null&&P(qt(a),()=>`Error in maxPool3dGrad: pad must be an integer when using, dimRoundingMode ${o} but got pad ${a}.`);let f={dy:c,input:d,output:h},m={filterSize:s,strides:r,pad:a,dimRoundingMode:o},A=L.runKernel(ah,f,m);return p?U(A,[A.shape[1],A.shape[2],A.shape[3],A.shape[4]]):A}var ZF=V({maxPool3dGrad_:KF}),YF={kernelName:Ou,inputsToSave:["x"],outputsToSave:[!0],gradFunc:(e,t,n)=>{let[s,r]=t,{filterSize:a,strides:o,pad:i,dimRoundingMode:l}=n;return{x:()=>ZF(e,s,r,a,o,i,l)}}};function JF(e,t,n,s,r,a,o){let i=D(e,"dy","maxPoolGrad"),l=D(t,"input","maxPoolGrad"),u=D(n,"output","maxPoolGrad");P(l.rank===i.rank,()=>`Rank of input (${l.rank}) does not match rank of dy (${i.rank})`),P(i.rank===4,()=>`Error in maxPoolGrad: dy must be rank 4 but got rank ${i.rank}.`),P(l.rank===4,()=>`Error in maxPoolGrad: input must be rank 4 but got rank ${l.rank}.`),o!=null&&P(qt(a),()=>`Error in maxPoolGrad: pad must be an integer when using, dimRoundingMode ${o} but got pad ${a}.`);let c={dy:i,input:l,output:u},d={filterSize:s,strides:r,pad:a,dimRoundingMode:o};return L.runKernel(rh,c,d)}var QF=V({maxPoolGrad_:JF}),eD={kernelName:Ba,inputsToSave:["x"],outputsToSave:[!0],gradFunc:(e,t,n)=>{let[s,r]=t,{filterSize:a,strides:o,pad:i}=n;return{x:()=>QF(e,s,r,a,o,i)}}},tD={kernelName:Wa,inputsToSave:["x"],gradFunc:(e,t,n)=>{let[s]=t,{axis:r}=n,a=xs(r,s.shape),i=Xx(s.shape,a)[1],l=_t(i);return{x:()=>{let c=s.shape.slice();a.forEach(p=>{c[p]=1});let d=U(e,c);return de(z(d,Un(s.shape,"float32")),l)}}}},nD={kernelName:Va,inputsToSave:["x"],outputsToSave:[!0],gradFunc:(e,t,n)=>{let s=n,{axis:r}=s,[a,o]=t,i=xs(r,a.shape),l=Ob(e,o,a,i);return{x:()=>l.x()}}},sD={kernelName:Ua,inputsToSave:["a","b"],gradFunc:(e,t)=>{let[n,s]=t;return{a:()=>z(e,ce(So(n,s),"float32")),b:()=>z(e,ce(Vn(n,s),"float32"))}}},rD={kernelName:Ha,inputsToSave:["x"],gradFunc:(e,t,n)=>{let s=t[0],{paddings:r}=n,a=r.map(o=>o[0]);return{x:()=>Re(e,a,s.shape)}}},aD={kernelName:Wi,inputsToSave:["a","b"],gradFunc:(e,t)=>{let[n,s]=t,r=mt(n.shape,s.shape);return{a:()=>{let i=Vt(n.shape,r);return i.length>0?U(ve(e,i),n.shape):e},b:()=>{let i=z(e,St(sc(de(n,s)))),l=Vt(s.shape,r);return l.length>0?U(ve(i,l),s.shape):i}}}},oD={kernelName:Ga,inputsToSave:["a","b"],gradFunc:(e,t)=>{let[n,s]=t,r=mt(n.shape,s.shape);return{a:()=>{let i=z(e,ce(s,"float32")),l=Vt(n.shape,r);return l.length>0?U(ve(i,l),n.shape):i},b:()=>{let i=z(e,ce(n,"float32")),l=Vt(s.shape,r);return l.length>0?U(ve(i,l),s.shape):i}}}},iD={kernelName:Vi,gradFunc:e=>({x:()=>St(e)})},lD={kernelName:ja,inputsToSave:["indices"],gradFunc:(e,t)=>{let n=t[0];return{indices:()=>Dt(n.shape,"float32")}}},uD={kernelName:qi,gradFunc:e=>({x:()=>qe(e)})},cD={kernelName:Xi,saveAllInputs:!0,gradFunc:(e,t,n)=>{let{axis:s}=n;return ds(e,s).map(a=>()=>a)}},Mb={kernelName:qa,inputsToSave:["x"],gradFunc:(e,t,n)=>{let s=t[0],{paddings:r}=n,a=r.map(o=>o[0]);return{x:()=>Re(e,a,s.shape)}}},dD={kernelName:Xa,inputsToSave:["a","b"],outputsToSave:[!0],gradFunc:(e,t)=>{let[n,s,r]=t,a=n,o=s,i=mt(a.shape,o.shape);return{a:()=>{let c=ce(o,"float32"),d=z(e,z(c,jr(a,Ae(c,Ie(1))))),h=Vt(a.shape,i);return h.length>0&&(d=ve(d,h)),U(d,a.shape)},b:()=>{let c=Vn(a,0),d=gn(c,is(a),qe(a)),h=z(e,z(r,d)),p=Vt(o.shape,i);return p.length>0&&(h=ve(h,p)),U(h,o.shape)}}}},hD={kernelName:Ka,inputsToSave:["x","alpha"],gradFunc:(e,t)=>{let[n,s]=t,r=Vn(n,0);return{x:()=>gn(r,e,z(e,s)),alpha:()=>{let a=gn(r,qe(e),z(e,n)),o=Vt(s.shape,e.shape);return o.length>0&&(a=ve(a,o)),U(a,s.shape)}}}},pD={kernelName:Ea,inputsToSave:["a","b"],gradFunc:(e,t)=>{let[n,s]=t,r=mt(n.shape,s.shape);return{a:()=>{let i=de(e,ce(s,"float32")),l=Vt(n.shape,r);return l.length>0?U(ve(i,l),n.shape):i},b:()=>{let i=z(e,ce(n,"float32")),l=Vt(s.shape,r);l.length>0&&(i=U(ve(i,l),s.shape));let u=lt(s);return St(de(i,ce(u,"float32")))}}}},fD={kernelName:Zi,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>de(e,St(lt(n)))}}},mD={kernelName:Ja,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t,s=z(So(n,6),lc(n));return{x:()=>z(e,ce(s,"float32"))}}},AD={kernelName:Za,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>z(e,ce(lc(n),"float32"))}}},gD={kernelName:Yi,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>U(e,n.shape)}}},yD={kernelName:Ya,inputsToSave:["images"],gradFunc:(e,t,n)=>{let[s]=t,r={dy:e,images:s};return{images:()=>L.runKernel(ch,r,n)}}},xD={kernelName:Mu,inputsToSave:["images"],gradFunc:(e,t,n)=>{let[s]=t,r={dy:e,images:s};return{images:()=>L.runKernel(uh,r,n)}}},bD={kernelName:Qa,gradFunc:(e,t,n)=>{let{dims:s}=n,r=xs(s,e.shape);return{x:()=>cs(e,r)}}},vD={kernelName:eo,gradFunc:e=>({x:()=>qe(e)})},wD={kernelName:to,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>St(de(e,z(jr(n,1.5),2)))}}},kD={kernelName:Qi,inputsToSave:["condition"],gradFunc:(e,t)=>{let[n]=t;return{condition:()=>ce(qe(n),"float32"),t:()=>z(e,ce(n,e.dtype)),e:()=>z(e,ce(Vh(n),e.dtype))}}},ID={kernelName:el,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>{let s=Vn(n,Ie(0)),r=Ie(Tb),a=Ie(Nb),o=z(e,a),i=z(z(e,r),os(ce(n,"float32")));return gn(s,o,i)}}}},SD={kernelName:so,outputsToSave:[!0],gradFunc:(e,t)=>{let[n]=t;return{x:()=>z(e,z(n,Ae(Ie(1),n)))}}},CD={kernelName:sl,gradFunc:e=>({x:()=>qe(e)})},TD={kernelName:no,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>z(Mh(ce(n,"float32")),e)}}},ND={kernelName:nl,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>z(mA(ce(n,"float32")),e)}}},ED={kernelName:tl,inputsToSave:["x"],gradFunc:(e,t,n)=>{let[s]=t,{begin:r,size:a}=n,o=s.shape,[i,l]=cx(s,r,a),u=[];for(let c=0;c<e.rank;c++)u.push([i[c],o[c]-i[c]-l[c]]);return{x:()=>Gr(e,u)}}},RD={kernelName:oo,outputsToSave:[!0],gradFunc:(e,t,n)=>{let[s]=t,{dim:r}=n,a=!0,o=z(e,s);return{logits:()=>Ae(o,z(ve(o,[r],a),s))}}},_D={kernelName:rl,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>z(e,Bn(n))}}},zb={kernelName:al,gradFunc:(e,t,n)=>{let{blockShape:s,paddings:r}=n;return{x:()=>Ph(e,s,r)}}},Lb={kernelName:ol,gradFunc:(e,t,n)=>{let{axis:s}=n;return{x:()=>ht(e,s)}}},$D={kernelName:ro,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>de(e,z(ln(ce(n,"float32")),2))}}},FD={kernelName:zu,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>z(e,z(ce(n,"float32"),2))}}},DD={kernelName:io,inputsToSave:["a","b"],gradFunc:(e,t)=>{let[n,s]=t,r=Ie(2);return{a:()=>z(e,z(r,Ae(n,s))),b:()=>z(e,z(r,Ae(s,n)))}}},OD={kernelName:Mr,gradFunc:e=>({x:()=>qe(e)})},PD={kernelName:lo,inputsToSave:["a","b"],gradFunc:(e,t)=>{let[n,s]=t,r=mt(n.shape,s.shape);return{a:()=>{let i=e,l=Vt(n.shape,r);return l.length>0&&(i=ve(i,l)),U(i,n.shape)},b:()=>{let i=e,l=Vt(s.shape,r);return l.length>0&&(i=ve(i,l)),U(St(i),s.shape)}}}},MD={kernelName:ao,inputsToSave:["x"],gradFunc:(e,t,n)=>{let[s]=t,r=s.shape.slice(),{axis:a}=n;xs(a,s.shape).forEach(u=>{r[u]=1});let i=U(e,r),l=z(i,Un(s.shape,"float32"));return{x:()=>l}}},zD={kernelName:uo,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>de(e,lt(Mh(n)))}}},LD={kernelName:co,outputsToSave:[!0],gradFunc:(e,t)=>{let[n]=t;return{x:()=>z(Ae(Ie(1),lt(n)),e)}}},BD={kernelName:Pr,inputsToSave:["x"],gradFunc:(e,t,n)=>{let[s]=t,{reps:r}=n;return{x:()=>{let o=qe(s);if(s.rank===1)for(let i=0;i<r[0];++i)o=ae(o,Re(e,[i*s.shape[0]],[s.shape[0]]));else if(s.rank===2)for(let i=0;i<r[0];++i)for(let l=0;l<r[1];++l)o=ae(o,Re(e,[i*s.shape[0],l*s.shape[1]],[s.shape[0],s.shape[1]]));else if(s.rank===3)for(let i=0;i<r[0];++i)for(let l=0;l<r[1];++l)for(let u=0;u<r[2];++u)o=ae(o,Re(e,[i*s.shape[0],l*s.shape[1],u*s.shape[2]],[s.shape[0],s.shape[1],s.shape[2]]));else if(s.rank===4)for(let i=0;i<r[0];++i)for(let l=0;l<r[1];++l)for(let u=0;u<r[2];++u)for(let c=0;c<r[3];++c)o=ae(o,Re(e,[i*s.shape[0],l*s.shape[1],u*s.shape[2],c*s.shape[3]],[s.shape[0],s.shape[1],s.shape[2],s.shape[3]]));else throw new Error(`Gradient for tile operation is not implemented for rank-${s.rank} tensors yet.`);return o}}}},WD={kernelName:ho,gradFunc:(e,t,n)=>{let s=n,{perm:r}=s,a=vA(r);return{x:()=>je(e,a)}}},VD={kernelName:cl,gradFunc:(e,t,n)=>{let s=n,{axis:r}=s;return{value:()=>Nn(e,r)}}},UD={kernelName:Lu,inputsToSave:["segmentIds"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>HD(e,n)}}};function HD(e,t){let n=gr(t,qe(t)),s=Cl(e,n),r=Io(t,Ie(0,"int32")),a=s.rank-r.rank;for(let i=0;i<a;++i)r=Ft(r,i+1);r=Rs(r,Un(s.shape,"bool"));let o=qe(s);return gn(r,s,o)}var GD={kernelName:dl,gradFunc:e=>({x:()=>qe(e)})},jD=[$b,j$,q$,X$,K$,Z$,Y$,J$,Q$,eF,tF,nF,aF,lF,uF,cF,dF,hF,pF,fF,mF,AF,yF,gF,vF,wF,kF,IF,SF,CF,pD,TF,NF,EF,RF,_F,FF,$F,DF,OF,PF,MF,zF,LF,BF,WF,VF,UF,HF,qF,Pb,Pb,XF,YF,eD,tD,nD,sD,rD,aD,oD,iD,lD,uD,cD,Mb,Mb,dD,hD,fD,mD,AD,gD,yD,xD,bD,vD,wD,kD,ID,SD,CD,TD,ND,ED,RD,_D,zb,zb,Lb,Lb,$D,DD,FD,OD,PD,MD,zD,LD,BD,WD,VD,UD,GD];for(let e of jD)y5(e);var Bb={};Pe(Bb,{maxNorm:()=>ZD,minMaxNorm:()=>QD,nonNeg:()=>JD,unitNorm:()=>YD});var GA;function Ut(){return GA==null&&(GA=mx().epsilon()),GA}function $s(){return"channelsLast"}var br=class extends Error{constructor(e){super(e);Object.setPrototypeOf(this,br.prototype)}},Fs=class extends Error{constructor(e){super(e);Object.setPrototypeOf(this,Fs.prototype)}},G=class extends Error{constructor(e){super(e);Object.setPrototypeOf(this,G.prototype)}},Oe=class extends Error{constructor(e){super(e);Object.setPrototypeOf(this,Oe.prototype)}},Wb=class extends Error{constructor(e){super(e);Object.setPrototypeOf(this,Wb.prototype)}};function Eo(e,t){if(Array.isArray(e)){let n=[];for(let s=0;s<t;s++)n=n.concat(e);return n}else{let n=new Array(t);return n.fill(e),n}}function Qs(e,t){if(!e)throw new Wb(t)}function Vb(e,t){let n=0;for(let s of e)s===t&&n++;return n}function En(e){return e.length===1?e[0]:e}function At(e){return Array.isArray(e)?e:[e]}function vr(e){let n=e.replace(/(.)([A-Z][a-z0-9]+)/g,"$1_$2").replace(/([a-z])([A-Z])/g,"$1_$2").toLowerCase();return n[0]!=="_"?n:"private"+n}function Ro(e){return e.length<=1||e.indexOf("_")===-1?e:e.replace(/[_]+(\w|$)/g,(t,n)=>n.toUpperCase())}var vs={};function jA(e){if(e==null)return null;let t={};return t.className=e.getClassName(),t.config=e.getConfig(),t}function qA(e){if(!(e==null||typeof e!="object"))if(Array.isArray(e))e.forEach(t=>qA(t));else{let t=Object.keys(e);for(let n of t){let s=e[n];s!=null&&typeof s=="object"&&(!Array.isArray(s)&&s.type==="ndarray"&&typeof s.value=="number"?e[n]=s.value:qA(s))}}}function dc(e,t={},n={},s="object",r=!1){if(typeof e=="string"){let a=e,o;if(a in n)o=n[a];else if(a in vs)o=vs[a];else if(o=t[a],o==null)throw new G(`Unknown ${s}: ${e}. This may be due to one of the following reasons:
1. The ${s} is defined in Python, in which case it needs to be ported to TensorFlow.js or your JavaScript code.
2. The custom ${s} is defined in JavaScript, but is not registered properly with tf.serialization.registerClass().`);return o}else{let a=e;if(a.className==null||a.config==null)throw new G(`${s}: Improper config format: ${JSON.stringify(a)}.
'className' and 'config' must set.`);let o=a.className,i,l;if(o in n?[i,l]=n[o]:o in vs?[i,l]=vs.className:o in t&&([i,l]=t[o]),i==null)throw new G(`Unknown ${s}: ${o}. This may be due to one of the following reasons:
1. The ${s} is defined in Python, in which case it needs to be ported to TensorFlow.js or your JavaScript code.
2. The custom ${s} is defined in JavaScript, but is not registered properly with tf.serialization.registerClass().`);if(l!=null){let u={};for(let p of Object.keys(vs))u[p]=vs[p];for(let p of Object.keys(n))u[p]=n[p];let c=a.config;c.customObjects=u;let d=Object.assign({},vs);for(let p of Object.keys(n))vs[p]=n[p];qA(a.config);let h=l(i,a.config,n,r);return vs=Object.assign({},d),h}else{let u=Object.assign({},vs);for(let d of Object.keys(n))vs[d]=n[d];let c=new i(a.config);return vs=Object.assign({},u),c}}}function qD(e,t){return e<t?-1:e>t?1:0}function hp(e,t){return-1*qD(e,t)}function Xr(e){if(e==null)return e;let t=[];for(let n of e)t.indexOf(n)===-1&&t.push(n);return t}function XD(e){if(e==null)throw new G(`Invalid value in obj: ${JSON.stringify(e)}`);for(let t in e)if(e.hasOwnProperty(t))return!1;return!0}function _o(e,t,n){if(n!=null&&e.indexOf(n)<0)throw new G(`${n} is not a valid ${t}.  Valid values are ${e} or null/undefined.`)}function XA(e,t,n=0,s=1/0){return Qs(n>=0),Qs(s>=n),Array.isArray(e)&&e.length>=n&&e.length<=s&&e.every(r=>typeof r===t)}function Zt(e,t){Array.isArray(e)?(I.assert(e.length>0,()=>`${t} is unexpectedly an empty array.`),e.forEach((n,s)=>Zt(n,`element ${s+1} of ${t}`))):I.assert(Number.isInteger(e)&&e>0,()=>`Expected ${t} to be a positive integer, but got ${Ub(e)}.`)}function Ub(e){return e===null?"null":Array.isArray(e)?"["+e.map(t=>Ub(t)).join(",")+"]":typeof e=="string"?`"${e}"`:`${e}`}function KD(e,t){let n=I.now(),s;return(...a)=>{let o=I.now();return o-n<t||(n=o,s=e(...a)),s}}function Hb(e){return e==="relu"?"relu":e==="linear"?"linear":e==="elu"?"elu":null}function KA(e,t){return H(()=>ln(ve(z(e,e),t,!0)))}var hc=class extends oe.Serializable{getConfig(){return{}}},ZA=class extends hc{constructor(e){super();this.defaultMaxValue=2,this.defaultAxis=0,this.maxValue=e.maxValue!=null?e.maxValue:this.defaultMaxValue,this.axis=e.axis!=null?e.axis:this.defaultAxis}apply(e){return H(()=>{let t=KA(e,this.axis),n=Wn(t,0,this.maxValue);return z(e,de(n,ae(Ut(),t)))})}getConfig(){return{maxValue:this.maxValue,axis:this.axis}}};ZA.className="MaxNorm";oe.registerClass(ZA);var YA=class extends hc{constructor(e){super();this.defaultAxis=0,this.axis=e.axis!=null?e.axis:this.defaultAxis}apply(e){return H(()=>de(e,ae(Ut(),KA(e,this.axis))))}getConfig(){return{axis:this.axis}}};YA.className="UnitNorm";oe.registerClass(YA);var JA=class extends hc{apply(e){return Ys(e)}};JA.className="NonNeg";oe.registerClass(JA);var QA=class extends hc{constructor(e){super();this.defaultMinValue=0,this.defaultMaxValue=1,this.defaultRate=1,this.defaultAxis=0,this.minValue=e.minValue!=null?e.minValue:this.defaultMinValue,this.maxValue=e.maxValue!=null?e.maxValue:this.defaultMaxValue,this.rate=e.rate!=null?e.rate:this.defaultRate,this.axis=e.axis!=null?e.axis:this.defaultAxis}apply(e){return H(()=>{let t=KA(e,this.axis),n=ae(z(this.rate,Wn(t,this.minValue,this.maxValue)),z(1-this.rate,t));return z(e,de(n,ae(Ut(),t)))})}getConfig(){return{minValue:this.minValue,maxValue:this.maxValue,rate:this.rate,axis:this.axis}}};QA.className="MinMaxNorm";oe.registerClass(QA);var Gb={maxNorm:"MaxNorm",minMaxNorm:"MinMaxNorm",nonNeg:"NonNeg",unitNorm:"UnitNorm"};function Ht(e){return jA(e)}function jb(e,t={}){return dc(e,oe.SerializationMap.getMap().classNameMap,t,"constraint")}function Gt(e){if(e==null)return null;if(typeof e=="string"){let n={className:e in Gb?Gb[e]:e,config:{}};return jb(n)}else return e instanceof hc?e:jb(e)}function ZD(e){return new ZA(e)}function YD(e){return new YA(e)}function JD(){return new JA}function QD(e){return new QA(e)}var qb={};Pe(qb,{constant:()=>wO,glorotNormal:()=>EO,glorotUniform:()=>NO,heNormal:()=>RO,heUniform:()=>_O,identity:()=>CO,leCunNormal:()=>$O,leCunUniform:()=>FO,ones:()=>vO,orthogonal:()=>DO,randomNormal:()=>IO,randomUniform:()=>kO,truncatedNormal:()=>SO,varianceScaling:()=>TO,zeros:()=>bO});var eO=["channelsFirst","channelsLast"],tO=["nearest","bilinear"],nO=["valid","same","causal"],sO=["max","avg"],rO=["sum","mul","concat","ave"],$l=new Map;function $t(e){_o(eO,"DataFormat",e)}function aO(e){_o(tO,"InterpolationFormat",e)}function hs(e){_o(nO,"PaddingMode",e)}function Xb(e){_o(sO,"PoolMode",e)}var pc=[],Kb="/";function $o(e,t){pc.push(e);try{let n=t();return pc.pop(),n}catch(n){throw pc.pop(),n}}function oO(){return pc.length===0?"":pc.join(Kb)+Kb}function Zb(e){if(!Jb(e))throw new Error("Not a valid tensor name: '"+e+"'");return oO()+e}function Yb(e){if(!Jb(e))throw new Error("Not a valid tensor name: '"+e+"'");$l.has(e)||$l.set(e,0);let t=$l.get(e);if($l.set(e,$l.get(e)+1),t>0){let n=`${e}_${t}`;return $l.set(n,1),n}else return e}var iO=new RegExp(/^[A-Za-z0-9][-A-Za-z0-9\._\/]*$/);function Jb(e){return!!e.match(iO)}function lO(e){return e===parseInt(e.toString(),10)}function Kr(e,t,n){t==null&&(t=0),n==null&&(n=e.length);let s=1;for(let r=t;r<n;++r)s*=e[r];return s}function Fl(e){if(e.length===0)return Number.NaN;let t=Number.POSITIVE_INFINITY;for(let n=0;n<e.length;n++){let s=e[n];s<t&&(t=s)}return t}function Zr(e){if(e.length===0)return Number.NaN;let t=Number.NEGATIVE_INFINITY;for(let n=0;n<e.length;n++){let s=e[n];s>t&&(t=s)}return t}function Ds(e,t){if(t<e)throw new G(`end (${t}) < begin (${e}) is forbidden.`);let n=[];for(let s=e;s<t;++s)n.push(s);return n}function pp(e,t){return ce(e,t)}function fc(e,t=-1){let n=e.shape.slice();return t<0&&(t=n.length+t+1),n.splice(t,0,1),U(e,n)}function uO(e,t){return H(()=>{if(e.shape.length!==2)throw new G(`repeat() expects a rank-2 tensor, but received a rank-${e.shape.length} tensor.`);let n=fc(e,1);return ng(n,[1,t,1])})}function cO(e){let t=[Kr(e.shape)];return U(e,t)}function dO(e){if(e.rank<=1)throw new G(`batchFlatten requires a minimum rank of 2. Got rank: ${e.rank}.`);let t=[e.shape[0],Kr(e.shape,1)];return U(e,t)}function Fo(e,t,n){return H(()=>{switch(e.rank){case 1:return Xh(e,t,n);case 2:return FA(e,[t,0],[n,e.shape[1]]);case 3:return Kh(e,[t,0,0],[n,e.shape[1],e.shape[2]]);case 4:return oc(e,[t,0,0,0],[n,e.shape[1],e.shape[2],e.shape[3]]);case 5:return Re(e,[t,0,0,0,0],[n,e.shape[1],e.shape[2],e.shape[3],e.shape[4]]);case 6:return Re(e,[t,0,0,0,0,0],[n,e.shape[1],e.shape[2],e.shape[3],e.shape[4],e.shape[5]]);default:throw new G(`sliceAlongFirstAxis() received an unsupported tensor rank: ${e.rank}`)}})}function eg(e,t,n){return H(()=>{switch(e.rank){case 1:return Xh(e,t,n);case 2:return FA(e,[0,t],[e.shape[0],n]);case 3:return Kh(e,[0,0,t],[e.shape[0],e.shape[1],n]);case 4:return oc(e,[0,0,0,t],[e.shape[0],e.shape[1],e.shape[2],n]);default:throw new G(`sliceAlongLastAxis() received an unsupported tensor rank: ${e.rank}`)}})}function fp(e,t,n,s){return H(()=>{switch(e.rank){case 1:return Xh(e,t,n);case 2:switch(s){case 1:return Fo(e,t,n);case 2:return eg(e,t,n);default:throw new G(`The axis is not within the rank of the tensor ${s}`)}case 3:switch(s){case 1:return Fo(e,t,n);case 2:return Kh(e,[0,t,0],[e.shape[0],n,e.shape[2]]);case 3:return eg(e,t,n);default:throw new G(`The axis is not within the rank of the tensor ${s}`)}case 4:switch(s){case 1:return Fo(e,t,n);case 2:return oc(e,[0,t,0,0],[e.shape[0],n,e.shape[2],e.shape[3]]);case 3:return oc(e,[0,0,t,0],[e.shape[0],e.shape[1],n,e.shape[3]]);case 4:return eg(e,t,n);default:throw new G(`The axis is not within the rank of the tensor ${s}`)}default:throw new G(`sliceAlongLastAxis() received an unsupported tensor rank: ${e.rank}`)}})}function tg(e,t=-1){let n;return t<0&&(n=e[0].rank,n!==0?t=n:t=0),t===e[0].rank&&(t=-1),ht(e,t)}function Qb(e,t){switch(e.rank){case 1:return _x([e,t]);case 2:return Il([e,t],0);case 3:return $x([e,t],0);case 4:return Fx([e,t],0);default:throw new G(`concatAlongFirstAxis() received an unsupported tensor rank: ${e.rank}`)}}function ng(e,t){if(Array.isArray(t)||(t=[t]),e.rank!==t.length)throw new G(`The length of input n (${t.length}) does not match the number of dimensions in input x (${e.rank})`);return Es(e,t)}function mp(e,t=0,n=1,s,r){return tb(e,t,n,s,r)}function er(e,t,n,s){if(e.rank<2||t.rank<2)throw new Oe(`dot requires both inputs to be rank >= 2 but got x shape = ${e.shape} and y shape = ${t.shape}`);if(t.rank>=3){let r=e.shape.slice(-1)[0],a=t.shape.slice(-2)[0];if(r!==a)throw new Oe(`If rank y >= 3, then the second last dim of y must equal the last dim of x but got x shape = ${e.shape} and  y shape = ${t.shape}`)}if(e.rank===2&&t.rank===2){let r=!1,a=!1;return qr.matMul({a:e,b:t,transposeA:r,transposeB:a,bias:s?sg(e.rank,s,$s()):null,activation:n})}else{let r=e.shape.slice(),a=r.pop();e=U(e,[-1,a]);let o=t.shape.slice(),i=o.pop(),l=o.pop(),u=[...o,i],c=Array.from({length:t.rank},(f,m)=>m===0?t.rank-2:m<=t.rank-2?m-1:m);t=U(je(t,c),[l,-1]);let d=[...r,...u],h=!1,p=!1;return U(qr.matMul({a:e,b:t,transposeA:h,transposeB:p,bias:s?sg(e.rank,s,$s()):null,activation:n}),d)}}function e3(e,t,n){return H(()=>(Array.isArray(t)?t=Ot(t,"int32"):t=ce(t,"int32"),Cl(e,t,n)))}function mc(e){return z(e,e)}function sg(e,t,n){let s=t.shape;if(t.rank!==1&&t.rank!==e)throw new G(`Unexpected bias dimensions: ${t.rank}; expected it to be 1 or ${e}`);if(e===5){if(n==="channelsFirst")return s.length===1?U(t,[1,s[0],1,1,1]):U(t,[1,s[3],s[0],s[1],s[2]]);if(n==="channelsLast")return s.length===1?U(t,[1,1,1,1,s[0]]):U(t,[1].concat(s))}else if(e===4){if(n==="channelsFirst")return s.length===1?U(t,[1,s[0],1,1]):U(t,[1,s[2],s[0],s[1]]);if(n==="channelsLast")return s.length===1?U(t,[1,1,1,s[0]]):U(t,[1].concat(s))}else if(e===3){if(n==="channelsFirst")return s.length===1?U(t,[1,s[0],1]):U(t,[1,s[1],s[0]]);if(n==="channelsLast")return s.length===1?U(t,[1,1,s[0]]):U(t,[1].concat(s))}else if(e<3)return t;throw new G(`Unsupported input rank by biasAdd: ${t.rank}`)}function Os(e,t,n){return H(()=>(n==null&&(n=$s()),$t(n),ae(e,sg(e.rank,t,n))))}function hO(e,t=1){if(t!==1)throw new Oe(`Support for alpha values other than 1 (${t}) is not implemented yet.`);return nc(e)}function pO(e){return H(()=>de(e,ae(Wt(e),1)))}function t3(e,t,n,s){return H(()=>mb(e,t,n,s))}function fO(e){return H(()=>{let t=ae(.5,z(.2,e));return Wn(t,0,1)})}function Ac(e,t,n=!1){return n?e():t()}var mO=["fanIn","fanOut","fanAvg"],AO=["normal","uniform","truncatedNormal"];function gO(e){_o(mO,"FanMode",e)}function yO(e){_o(AO,"Distribution",e)}var ws=class extends oe.Serializable{fromConfigUsesCustomObjects(){return!1}getConfig(){return{}}},rg=class extends ws{apply(e,t){return Dt(e,t)}};rg.className="Zeros";oe.registerClass(rg);var Ap=class extends ws{apply(e,t){return Un(e,t)}};Ap.className="Ones";oe.registerClass(Ap);var ag=class extends ws{constructor(e){super();if(typeof e!="object")throw new G(`Expected argument of type ConstantConfig but got ${e}`);if(e.value===void 0)throw new G(`config must have value set but got ${e}`);this.value=e.value}apply(e,t){return H(()=>z(Ie(this.value),Un(e,t)))}getConfig(){return{value:this.value}}};ag.className="Constant";oe.registerClass(ag);var og=class extends ws{constructor(e){super();this.DEFAULT_MINVAL=-.05,this.DEFAULT_MAXVAL=.05,this.minval=e.minval||this.DEFAULT_MINVAL,this.maxval=e.maxval||this.DEFAULT_MAXVAL,this.seed=e.seed}apply(e,t){return El(e,this.minval,this.maxval,t)}getConfig(){return{minval:this.minval,maxval:this.maxval,seed:this.seed}}};og.className="RandomUniform";oe.registerClass(og);var ig=class extends ws{constructor(e){super();this.DEFAULT_MEAN=0,this.DEFAULT_STDDEV=.05,this.mean=e.mean||this.DEFAULT_MEAN,this.stddev=e.stddev||this.DEFAULT_STDDEV,this.seed=e.seed}apply(e,t){if(t=t||"float32",t!=="float32"&&t!=="int32")throw new Oe(`randomNormal does not support dType ${t}.`);return mp(e,this.mean,this.stddev,t,this.seed)}getConfig(){return{mean:this.mean,stddev:this.stddev,seed:this.seed}}};ig.className="RandomNormal";oe.registerClass(ig);var lg=class extends ws{constructor(e){super();this.DEFAULT_MEAN=0,this.DEFAULT_STDDEV=.05,this.mean=e.mean||this.DEFAULT_MEAN,this.stddev=e.stddev||this.DEFAULT_STDDEV,this.seed=e.seed}apply(e,t){if(t=t||"float32",t!=="float32"&&t!=="int32")throw new Oe(`truncatedNormal does not support dType ${t}.`);return Qh(e,this.mean,this.stddev,t,this.seed)}getConfig(){return{mean:this.mean,stddev:this.stddev,seed:this.seed}}};lg.className="TruncatedNormal";oe.registerClass(lg);var ug=class extends ws{constructor(e){super();this.gain=e.gain!=null?e.gain:1}apply(e,t){return H(()=>{if(e.length!==2||e[0]!==e[1])throw new G("Identity matrix initializer can only be used for 2D square matrices.");return z(this.gain,gA(e[0]))})}getConfig(){return{gain:this.gain}}};ug.className="Identity";oe.registerClass(ug);function xO(e,t="channelsLast"){let n,s;if($t(t),e.length===2)n=e[0],s=e[1];else if([3,4,5].indexOf(e.length)!==-1){if(t==="channelsFirst"){let r=Kr(e,2);n=e[1]*r,s=e[0]*r}else if(t==="channelsLast"){let r=Kr(e,0,e.length-2);n=e[e.length-2]*r,s=e[e.length-1]*r}}else{let r=Kr(e);n=Math.sqrt(r),s=Math.sqrt(r)}return[n,s]}var Rn=class extends ws{constructor(e){super();if(e.scale<0)throw new G(`scale must be a positive float. Got: ${e.scale}`);this.scale=e.scale==null?1:e.scale,this.mode=e.mode==null?"fanIn":e.mode,gO(this.mode),this.distribution=e.distribution==null?"normal":e.distribution,yO(this.distribution),this.seed=e.seed}apply(e,t){let n=xO(e),s=n[0],r=n[1],a=this.scale;if(this.mode==="fanIn"?a/=Math.max(1,s):this.mode==="fanOut"?a/=Math.max(1,r):a/=Math.max(1,(s+r)/2),this.distribution==="normal"){let o=Math.sqrt(a);if(t=t||"float32",t!=="float32"&&t!=="int32")throw new Oe(`${this.getClassName()} does not support dType ${t}.`);return Qh(e,0,o,t,this.seed)}else{let o=Math.sqrt(3*a);return El(e,-o,o,t)}}getConfig(){return{scale:this.scale,mode:this.mode,distribution:this.distribution,seed:this.seed}}};Rn.className="VarianceScaling";oe.registerClass(Rn);var gp=class extends Rn{constructor(e){super({scale:1,mode:"fanAvg",distribution:"uniform",seed:e==null?null:e.seed})}getClassName(){return Rn.className}};gp.className="GlorotUniform";oe.registerClass(gp);var yp=class extends Rn{constructor(e){super({scale:1,mode:"fanAvg",distribution:"normal",seed:e==null?null:e.seed})}getClassName(){return Rn.className}};yp.className="GlorotNormal";oe.registerClass(yp);var xp=class extends Rn{constructor(e){super({scale:2,mode:"fanIn",distribution:"normal",seed:e==null?null:e.seed})}getClassName(){return Rn.className}};xp.className="HeNormal";oe.registerClass(xp);var bp=class extends Rn{constructor(e){super({scale:2,mode:"fanIn",distribution:"uniform",seed:e==null?null:e.seed})}getClassName(){return Rn.className}};bp.className="HeUniform";oe.registerClass(bp);var vp=class extends Rn{constructor(e){super({scale:1,mode:"fanIn",distribution:"normal",seed:e==null?null:e.seed})}getClassName(){return Rn.className}};vp.className="LeCunNormal";oe.registerClass(vp);var wp=class extends Rn{constructor(e){super({scale:1,mode:"fanIn",distribution:"uniform",seed:e==null?null:e.seed})}getClassName(){return Rn.className}};wp.className="LeCunNormal";oe.registerClass(wp);var cg=class extends ws{constructor(e){super();if(this.DEFAULT_GAIN=1,this.gain=e.gain==null?this.DEFAULT_GAIN:e.gain,this.seed=e.seed,this.seed!=null)throw new Oe("Random seed is not implemented for Orthogonal Initializer yet.")}apply(e,t){return H(()=>{if(e.length<2)throw new Oe("Shape must be at least 2D.");e[0]*e[1]>2e3&&console.warn(`Orthogonal initializer is being called on a matrix with more than 2000 (${e[0]*e[1]}) elements: Slowness may result.`);let n=e[0]>e[1]?[e[1],e[0]]:e,s=mp(n,0,1,"float32"),r=Cb.gramSchmidt(s);return e[0]>e[1]&&(r=je(r)),z(this.gain,r)})}getConfig(){return{gain:this.gain,seed:this.seed}}};cg.className="Orthogonal";oe.registerClass(cg);var n3={constant:"Constant",glorotNormal:"GlorotNormal",glorotUniform:"GlorotUniform",heNormal:"HeNormal",heUniform:"HeUniform",identity:"Identity",leCunNormal:"LeCunNormal",leCunUniform:"LeCunUniform",ones:"Ones",orthogonal:"Orthogonal",randomNormal:"RandomNormal",randomUniform:"RandomUniform",truncatedNormal:"TruncatedNormal",varianceScaling:"VarianceScaling",zeros:"Zeros"};function s3(e,t={}){return dc(e,oe.SerializationMap.getMap().classNameMap,t,"initializer")}function Ct(e){return jA(e)}function bt(e){if(typeof e=="string"){let t=e in n3?n3[e]:e;if(t==="GlorotNormal")return new yp;if(t==="GlorotUniform")return new gp;if(t==="HeNormal")return new xp;if(t==="HeUniform")return new bp;if(t==="LeCunNormal")return new vp;if(t==="LeCunUniform")return new wp;{let n={};return n.className=t,n.config={},s3(n)}}else return e instanceof ws?e:s3(e)}function bO(){return new rg}function vO(){return new Ap}function wO(e){return new ag(e)}function kO(e){return new og(e)}function IO(e){return new ig(e)}function SO(e){return new lg(e)}function CO(e){return new ug(e)}function TO(e){return new Rn(e)}function NO(e){return new gp(e)}function EO(e){return new yp(e)}function RO(e){return new xp(e)}function _O(e){return new bp(e)}function $O(e){return new vp(e)}function FO(e){return new wp(e)}function DO(e){return new cg(e)}var r3={};Pe(r3,{Layer:()=>Xe,RNN:()=>sr,RNNCell:()=>Sc,activation:()=>AM,add:()=>SM,alphaDropout:()=>lz,average:()=>CM,averagePooling1d:()=>N2,averagePooling2d:()=>E2,averagePooling3d:()=>R2,avgPool1d:()=>OM,avgPool2d:()=>MM,avgPool3d:()=>LM,avgPooling1d:()=>PM,avgPooling2d:()=>zM,avgPooling3d:()=>BM,batchNormalization:()=>$M,bidirectional:()=>ez,concatenate:()=>TM,conv1d:()=>iM,conv2d:()=>lM,conv2dTranspose:()=>uM,conv3d:()=>cM,conv3dTranspose:()=>dM,convLstm2d:()=>ZM,convLstm2dCell:()=>YM,cropping2D:()=>pM,dense:()=>gM,depthwiseConv2d:()=>mM,dot:()=>_M,dropout:()=>yM,elu:()=>tM,embedding:()=>IM,flatten:()=>bM,gaussianDropout:()=>iz,gaussianNoise:()=>oz,globalAveragePooling1d:()=>WM,globalAveragePooling2d:()=>VM,globalMaxPool1d:()=>nz,globalMaxPool2d:()=>sz,globalMaxPooling1d:()=>pv,globalMaxPooling2d:()=>fv,gru:()=>HM,gruCell:()=>GM,input:()=>M3,inputLayer:()=>eM,layerNormalization:()=>FM,leakyReLU:()=>sM,lstm:()=>jM,lstmCell:()=>qM,masking:()=>uz,maxPool1d:()=>rz,maxPool2d:()=>az,maxPooling1d:()=>mv,maxPooling2d:()=>Av,maxPooling3d:()=>UM,maximum:()=>NM,minimum:()=>EM,multiply:()=>RM,permute:()=>kM,prelu:()=>rM,reLU:()=>nM,repeatVector:()=>vM,reshape:()=>wM,rnn:()=>JM,separableConv2d:()=>hM,simpleRNN:()=>XM,simpleRNNCell:()=>KM,softmax:()=>aM,spatialDropout1d:()=>xM,stackedRNNCells:()=>QM,thresholdedReLU:()=>oM,timeDistributed:()=>tz,upSampling2d:()=>fM,zeroPadding2d:()=>DM});var OO=0;function a3(){return OO++}var kp={};function Ip(e=""){return e in kp||(kp[e]=0),kp[e]+=1,e+kp[e].toString()}function dg(e){return Array.isArray(e)&&Array.isArray(e[0])}function Sp(e){return e.length===0?[]:Array.isArray(e[0])?e:[e]}function ze(e){let t;if(Array.isArray(e)){if(e.length!==1)throw new G(`Expected Tensor length to be 1; got ${e.length}`);t=e[0]}else t=e;return t}function st(e){if(Array.isArray(e)&&Array.isArray(e[0])){if(e.length===1)return e=e,e[0];throw new G(`Expected exactly 1 Shape; got ${e.length}`)}else return e}function Cp(e){let t=0;for(let n of e)n.shape.length===0?t+=1:t+=n.shape.reduce((s,r)=>s*r);return t}var o3="Variable",i3=class{constructor(e,t="float32",n=o3,s=!0,r=null){this.dtype=t==null?"float32":t,this.shape=e.shape,this.id=a3(),n=n==null?o3:n,this.originalName=Zb(n),this.name=Yb(this.originalName),this.trainable_=s,this.constraint=r,this.val=cb(e,this.trainable_,this.name,this.dtype)}read(){return this.assertNotDisposed(),this.val}write(e){return this.assertNotDisposed(),PO(this.val,e),this.val.id!==e.id&&(this.val.assign(e),this.constraint!=null&&this.val.assign(this.constraint.apply(this.val))),this}dispose(){this.assertNotDisposed(),this.val.dispose()}assertNotDisposed(){if(this.val.isDisposed)throw new Error(`LayersVariable ${this.name} is already disposed.`)}get trainable(){return this.trainable_}set trainable(e){this.trainable_=e,this.val.trainable=e}};function PO(e,t){if(e.shape.toString()!==t.shape.toString())throw new Error("Shape mismatch: "+JSON.stringify(e.shape)+" vs. "+JSON.stringify(t.shape))}function hg(e){return e.map(t=>t.read())}function pg(e){e.forEach(t=>{t[0].write(t[1])})}var Pt=class{constructor(e){this.dtype=e.dtype,this.shape=e.shape,e.shape!=null?this.ndim=e.shape.length:this.ndim=e.ndim,this.maxNDim=e.maxNDim,this.minNDim=e.minNDim,this.axes=e.axes||{}}},Ps=class{constructor(e,t,n,s,r,a,o){this.dtype=e,this.shape=t,this.sourceLayer=n,this.inputs=s,this.callArgs=r,this.outputTensorIndex=o,this.id=a3(),a!=null&&(this.originalName=Zb(a),this.name=Yb(this.originalName)),this.rank=t.length}},MO=0,Tp=class{constructor(e,t){this.callArgs=t,this.id=MO++,this.outboundLayer=e.outboundLayer,this.inboundLayers=e.inboundLayers,this.nodeIndices=e.nodeIndices,this.tensorIndices=e.tensorIndices,this.inputTensors=e.inputTensors,this.outputTensors=e.outputTensors,this.inputMasks=e.inputMasks,this.outputMasks=e.outputMasks,this.inputShapes=e.inputShapes,this.outputShapes=e.outputShapes;for(let n of e.inboundLayers)n!=null&&n.outboundNodes.push(this);e.outboundLayer.inboundNodes.push(this)}getConfig(){let e=[];for(let t of this.inboundLayers)t!=null?e.push(t.name):e.push(null);return{outboundLayer:this.outboundLayer?this.outboundLayer.name:null,inboundLayers:e,nodeIndices:this.nodeIndices,tensorIndices:this.tensorIndices}}},zO=0,Xe=class extends oe.Serializable{constructor(e={}){super();this._callHook=null,this._addedWeightNames=[],this._stateful=!1,this.id=zO++,this.activityRegularizer=null,this.inputSpec=null,this.supportsMasking=!1,this._trainableWeights=[],this._nonTrainableWeights=[],this._losses=[],this._updates=[],this._built=!1,this.inboundNodes=[],this.outboundNodes=[];let t=e.name;if(!t){let n=this.getClassName();t=vr(n)+"_"+Ip(n)}if(this.name=t,this.trainable_=e.trainable==null?!0:e.trainable,e.inputShape!=null||e.batchInputShape!=null){let n;if(e.batchInputShape!=null)n=e.batchInputShape;else if(e.inputShape!=null){let r=null;e.batchSize!=null&&(r=e.batchSize),n=[r].concat(e.inputShape)}this.batchInputShape=n;let s=e.dtype;s==null&&(s=e.inputDType),s==null&&(s="float32"),this.dtype=s}e.weights!=null?this.initialWeights=e.weights:this.initialWeights=null,this._refCount=null,this.fastWeightInitDuringBuild=!1}static nodeKey(e,t){return e.name+"_ib-"+t.toString()}getNodeAtIndex(e,t){if(this.inboundNodes.length===0)throw new Fs(`The layer has never been called and thus has no defined ${t}.`);if(this.inboundNodes.length<=e)throw new G(`Asked to get ${t} at node ${e}, but the layer has only ${this.inboundNodes.length} inbound nodes.`);return this.inboundNodes[e]}getInputAt(e){return En(this.getNodeAtIndex(e,"input").inputTensors)}getOutputAt(e){return En(this.getNodeAtIndex(e,"output").outputTensors)}get input(){if(this.inboundNodes.length>1)throw new br(`Layer ${this.name} has multiple inbound nodes, hence the notion of "layer input" is ill-defined. Use \`getInputAt(nodeIndex)\` instead.`);if(this.inboundNodes.length===0)throw new br(`Layer ${this.name} is not connected, no input to return.`);return En(this.getNodeAtIndex(0,"input").inputTensors)}get output(){if(this.inboundNodes.length===0)throw new br(`Layer ${this.name} has no inbound nodes.`);if(this.inboundNodes.length>1)throw new br(`Layer ${this.name} has multiple inbound nodes, hence the notion of "layer output" is ill-defined. Use \`getOutputAt(nodeIndex)\` instead.`);return En(this.getNodeAtIndex(0,"output").outputTensors)}get losses(){return this._losses}calculateLosses(){return this.losses.map(e=>e())}get updates(){return this._updates}get built(){return this._built}set built(e){this._built=e}get trainable(){return this.trainable_}set trainable(e){this._trainableWeights.forEach(t=>t.trainable=e),this.trainable_=e}get trainableWeights(){return this.trainable_?this._trainableWeights.filter(e=>e.trainable):[]}set trainableWeights(e){this._trainableWeights=e}get nonTrainableWeights(){return this.trainable?this._trainableWeights.filter(e=>!e.trainable).concat(this._nonTrainableWeights):this._trainableWeights.concat(this._nonTrainableWeights)}set nonTrainableWeights(e){this._nonTrainableWeights=e}get weights(){return this.trainableWeights.concat(this.nonTrainableWeights)}get stateful(){return this._stateful}resetStates(){if(!this.stateful)throw new Error("Cannot call the resetStates() method of a non-stateful Layer object.")}assertInputCompatibility(e){if(e=At(e),this.inputSpec==null||this.inputSpec.length===0)return;let t=At(this.inputSpec);if(e.length!==t.length)throw new G(`Layer ${this.name} expects ${t.length} inputs, but it received ${e.length} input tensors. Input received: ${e}`);for(let n=0;n<e.length;n++){let s=e[n],r=t[n];if(r==null)continue;let a=s.rank;if(r.ndim!=null&&a!==r.ndim)throw new G(`Input ${n} is incompatible with layer ${this.name}: expected ndim=${r.ndim}, found ndim=${a}`);if(r.maxNDim!=null&&a>r.maxNDim)throw new G(`Input ${n} is incompatible with layer ${this.name}: expected max_ndim=${r.maxNDim}, found ndim=${a}`);if(r.minNDim!=null&&a<r.minNDim)throw new G(`Input ${n} is incompatible with layer ${this.name}: expected min_ndim=${r.minNDim}, found ndim=${a}.`);if(r.dtype!=null&&s.dtype!==r.dtype)throw new G(`Input ${n} is incompatible with layer ${this.name} : expected dtype=${r.dtype}, found dtype=${s.dtype}.`);if(r.axes){let o=s.shape;for(let i in r.axes){let l=Number(i),u=r.axes[i],c=l>=0?o[l]:o[o.length+l];if(u!=null&&[u,null].indexOf(c)===-1)throw new G(`Input ${n} is incompatible with layer ${this.name}: expected axis ${l} of input shape to have value ${u} but got shape ${o}.`)}}if(r.shape!=null)for(let o=0;o<r.shape.length;++o){let i=r.shape[o],l=s.shape[o];if(i!=null&&l!=null&&i!==l)throw new G(`Input ${n} is incompatible with layer ${this.name}: expected shape=${r.shape}, found shape=${s.shape}.`)}}}call(e,t){return e}invokeCallHook(e,t){this._callHook!=null&&this._callHook(e,t)}setCallHook(e){this._callHook=e}clearCallHook(){this._callHook=null}apply(e,t){t=t||{},this.assertNotDisposed();let n=At(e),s=!0;for(let a of n)if(!(a instanceof Ps)){s=!1;break}let r=!0;for(let a of n)if(a instanceof Ps){r=!1;break}if(s===r)throw new G("Arguments to apply() must be all SymbolicTensors or all Tensors");return $o(this.name,()=>{if(!this.built){this.assertInputCompatibility(e);let a=[];for(let o of At(e))a.push(o.shape);this.build(En(a)),this.built=!0,this.initialWeights&&this.setWeights(this.initialWeights),this._refCount===null&&r&&(this._refCount=1)}if(this.assertInputCompatibility(e),r){let a=this.call(e,t),o=At(a),i=[];for(let l of o)n.indexOf(l)!==-1&&(l=l.clone()),i.push(l);if(a=En(i),this.activityRegularizer!=null)throw new Oe("Layer invocation in the presence of activity regularizer(s) is not supported yet.");return a}else{let a=LO(e),o=this.computeOutputShape(a),i,l=BO(e);if(this.warnOnIncompatibleInputShape(Array.isArray(e)?a[0]:a),o!=null&&o.length>0&&Array.isArray(o[0])?i=o.map((u,c)=>new Ps(l,u,this,At(e),t,this.name,c)):i=new Ps(l,o,this,At(e),t,this.name),this.addInboundNode(e,i,null,null,a,o,t),this._refCount++,this.activityRegularizer!=null)throw new Oe("Layer invocation in the presence of activity regularizer(s) is not supported yet.");return i}})}warnOnIncompatibleInputShape(e){if(this.batchInputShape!=null)if(e.length!==this.batchInputShape.length)console.warn(`The rank of the input tensor provided (shape: ${JSON.stringify(e)}) does not match that of the batchInputShape (${JSON.stringify(this.batchInputShape)}) of the layer ${this.name}`);else{let t=!1;this.batchInputShape.forEach((n,s)=>{n!=null&&e[s]!=null&&e[s]!==n&&(t=!0)}),t&&console.warn(`The shape of the input tensor (${JSON.stringify(e)}) does not match the expectation of layer ${this.name}: ${JSON.stringify(this.batchInputShape)}`)}}get outputShape(){if(this.inboundNodes==null||this.inboundNodes.length===0)throw new br(`The layer ${this.name} has never been called and thus has no defined output shape.`);let e=[];for(let t of this.inboundNodes){let n=JSON.stringify(t.outputShapes);e.indexOf(n)===-1&&e.push(n)}if(e.length===1){let t=this.inboundNodes[0].outputShapes;return Array.isArray(t)&&Array.isArray(t[0])&&t.length===1?t[0]:t}else throw new br(`The layer ${this.name} has multiple inbound nodes with different output shapes. Hence the notion of "output shape" is ill-defined for the layer.`)}countParams(){if(!this.built)throw new Fs(`You tried to call countParams() on ${this.name}, but the layer is not built yet. Build it first by calling build(batchInputShape).`);return Cp(this.weights)}build(e){this.built=!0}getWeights(e=!1){return hg(e?this.trainableWeights:this.weights)}setWeights(e){H(()=>{let t=this.weights;if(t.length!==e.length)throw new G(`You called setWeights(weights) on layer "${this.name}" with a weight list of length ${e.length}, but the layer was expecting ${t.length} weights. Provided weights: ${e}...`);if(t.length===0)return;let n=[],s=hg(t);for(let r=0;r<s.length;++r){let a=s[r],o=t[r],i=e[r];if(!I.arraysEqual(a.shape,i.shape))throw new G(`Layer weight shape ${a.shape} not compatible with provided weight shape ${i.shape}`);n.push([o,i])}pg(n)})}addWeight(e,t,n,s,r,a,o){if(this._addedWeightNames.indexOf(e)!==-1)throw new G(`Duplicate weight name ${e} for layer ${this.name}`);this._addedWeightNames.push(e),n==null&&(n="float32"),this.fastWeightInitDuringBuild&&(s=bt("zeros"));let i=s.apply(t,n),l=new i3(i,n,e,a,o);return i.dispose(),r!=null&&this.addLoss(()=>r.apply(l.read())),a==null&&(a=!0),a?this._trainableWeights.push(l):this._nonTrainableWeights.push(l),l}setFastWeightInitDuringBuild(e){this.fastWeightInitDuringBuild=e}addLoss(e){e==null||Array.isArray(e)&&e.length===0||(e=At(e),this._losses!==void 0&&this._losses!==null&&this.losses.push(...e))}computeOutputShape(e){return e}computeMask(e,t){if(!this.supportsMasking){if(t!=null)if(Array.isArray(t))t.forEach(n=>{if(n!=null)throw new TypeError(`Layer ${this.name} does not support masking, but was passed an inputMask.`)});else throw new TypeError(`Layer ${this.name} does not support masking, but was passed an inputMask.`);return null}return t}addInboundNode(e,t,n,s,r,a,o=null){let i=At(e);t=At(t),n=At(n),s=At(s),r=Sp(r),a=Sp(a);let l=[],u=[],c=[];for(let d of i)l.push(d.sourceLayer),u.push(d.nodeIndex),c.push(d.tensorIndex);new Tp({outboundLayer:this,inboundLayers:l,nodeIndices:u,tensorIndices:c,inputTensors:i,outputTensors:t,inputMasks:n,outputMasks:s,inputShapes:r,outputShapes:a},o);for(let d=0;d<t.length;d++)t[d].sourceLayer=this,t[d].nodeIndex=this.inboundNodes.length-1,t[d].tensorIndex=d}getConfig(){let e={name:this.name,trainable:this.trainable};return this.batchInputShape!=null&&(e.batchInputShape=this.batchInputShape),this.dtype!=null&&(e.dtype=this.dtype),e}disposeWeights(){return this.weights.forEach(e=>e.dispose()),this.weights.length}assertNotDisposed(){if(this._refCount===0)throw new Error(`Layer '${this.name}' is already disposed.`)}dispose(){if(!this.built)throw new Error(`Cannot dispose Layer ${this.name} because it has not been built yet.`);if(this._refCount===null)throw new Error(`Cannot dispose Layer ${this.name} because it has not been used yet.`);this.assertNotDisposed();let e=0;return--this._refCount==0&&(e=this.disposeWeights()),{refCountAfterDispose:this._refCount,numDisposedVariables:e}}};function LO(e){e=At(e);let t=[];for(let n of e)t.push(n.shape);return En(t)}function BO(e){return"float32"}function l3(e,t,n){if((t==null||n!=null&&n>0)&&(t=e.sourceLayer,n=e.nodeIndex),t.inboundNodes.length===0)return[e];{let s=t.inboundNodes[n];if(s.inboundLayers.length===0)return s.inputTensors;{let r=[];for(let a=0;a<s.inboundLayers.length;a++){let o=s.inputTensors[a],i=s.inboundLayers[a],l=s.nodeIndices[a],u=l3(o,i,l);for(let c of u)r.indexOf(c)===-1&&r.push(c)}return r}}}var Dl=class extends Xe{constructor(e){super({dtype:e.dtype,name:e.name!=null?e.name:Ip("input").toString()});if(e.batchSize==null&&(e.batchSize=null),e.sparse==null&&(e.sparse=!1),this.trainable=!1,this.built=!0,this.sparse=e.sparse,e.inputShape!=null&&e.batchInputShape!=null)throw new G("Only provide the inputShape OR batchInputShape argument to inputLayer, not both at the same time.");let t=e.batchInputShape;if(t==null){if(e.inputShape==null)throw new G("An InputLayer should be passed either a `batchInputShape` or an `inputShape`.");t=[e.batchSize].concat(e.inputShape)}else if(e.batchSize!=null)throw new G("Cannot specify batchSize if batchInputShape is specified when creating an InputLayer.");let n=e.dtype||"float32";this.batchInputShape=t,this.dtype=n,this.inputSpec=[{shape:t}];let s=new Ps(this.dtype,this.batchInputShape,this,[],{},this.name);s.nodeIndex=0,s.tensorIndex=0,new Tp({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:[s],outputTensors:[s],inputMasks:[null],outputMasks:[null],inputShapes:[t],outputShapes:[t]})}apply(e,t){throw new G(`Cannot pass any input to an InputLayer's apply() method. InputLayer name: ${this.name}`)}dispose(){return{refCountAfterDispose:this._refCount,numDisposedVariables:0}}getConfig(){return{batchInputShape:this.batchInputShape,dtype:this.dtype,sparse:this.sparse,name:this.name}}};Dl.className="InputLayer";oe.registerClass(Dl);function u3(e){if(e.batchShape==null&&e.shape==null)throw new Error("Please provide to Input either a `shape` or a `batchShape` argument. Note that `shape` does not include the batch dimension.");if(e.batchShape!=null&&e.shape!=null)throw new G("Please provide either a `shape` or `batchShape` argument to Input, but not both.");let t=e.batchShape;e.shape!=null&&t==null&&(t=[null].concat(e.shape));let n=e.dtype;return n==null&&(n="float32"),new Dl({batchInputShape:t,name:e.name,dtype:n,sparse:e.sparse}).inboundNodes[0].outputTensors[0]}async function Yr(e){if(e==null)return;let t=[],n=[],s=[];for(let r in e){let a=e[r];if(typeof a!="number"){let o=a;t.push(o.data()),n.push(r),s.push(o)}}if(t.length>0){let r=await Promise.all(t);for(let a=0;a<r.length;++a)e[n[a]]=r[a][0];K(s)}}function c3(e){if(e!=null)for(let t in e){let n=e[t];typeof n!="number"&&n.dispose()}}var d3;(function(e){e[e.SILENT=0]="SILENT",e[e.VERBOSE=1]="VERBOSE"})(d3||(d3={}));var WO=125,Ol=class{constructor(){this.validationData=null}setParams(e){this.params=e}async onEpochBegin(e,t){}async onEpochEnd(e,t){}async onBatchBegin(e,t){}async onBatchEnd(e,t){}async onTrainBegin(e){}async onTrainEnd(e){}setModel(e){}},h3=class{constructor(e,t=10){e==null&&(e=[]),this.callbacks=e,this.queueLength=t}append(e){this.callbacks.push(e)}setParams(e){for(let t of this.callbacks)t.setParams(e)}setModel(e){for(let t of this.callbacks)t.setModel(e)}async onEpochBegin(e,t){t==null&&(t={});for(let n of this.callbacks)await n.onEpochBegin(e,t)}async onEpochEnd(e,t){t==null&&(t={});for(let n of this.callbacks)await n.onEpochEnd(e,t)}async onBatchBegin(e,t){t==null&&(t={});for(let n of this.callbacks)await n.onBatchBegin(e,t)}async onBatchEnd(e,t){t==null&&(t={});for(let n of this.callbacks)await n.onBatchEnd(e,t)}async onTrainBegin(e){e==null&&(e={});for(let t of this.callbacks)await t.onTrainBegin(e)}async onTrainEnd(e){e==null&&(e={});for(let t of this.callbacks)await t.onTrainEnd(e)}},VO=class extends Ol{constructor(){super()}async onEpochBegin(e){this.seen=0,this.totals={}}async onBatchEnd(e,t){t==null&&(t={});let n=t.size==null?0:t.size;this.seen+=n;for(let s in t){let r=t[s];if(typeof r=="number")this.totals.hasOwnProperty(s)||(this.totals[s]=0),this.totals[s]=this.totals[s]+r*n;else{let a;s in this.totals?a=this.totals[s]:this.totals[s]=0;let o=H(()=>ae(this.totals[s],z(r,n)));this.totals[s]=o,a!=null&&a.dispose()}}}async onEpochEnd(e,t){if(t!=null)for(let n of this.params.metrics)this.totals[n]!=null&&(typeof this.totals[n]=="number"?t[n]=this.totals[n]/this.seen:H(()=>{let s=z(de(1,this.seen),this.totals[n]);t[n]=s,this.totals[n].dispose(),Kt(t[n])}))}},p3=class extends Ol{async onTrainBegin(e){this.epoch=[],this.history={}}async onEpochEnd(e,t){t==null&&(t={}),this.epoch.push(e);for(let n in t)this.history[n]==null&&(this.history[n]=[]),this.history[n].push(t[n])}async syncData(){let e=[],t=[],n=[];for(let r in this.history){let a=this.history[r];for(let o=0;o<a.length;++o)if(typeof a[o]!="number"){let i=a[o];e.push(i.data()),t.push(r),n.push(o)}}let s=await Promise.all(e);for(let r=0;r<s.length;++r)this.history[t[r]][n[r]].dispose(),this.history[t[r]][n[r]]=s[r][0]}},f3=class extends Ol{constructor(e,t){super();if(this.currentEpoch=0,this.yieldEvery=t||"auto",this.yieldEvery==="auto"&&(this.yieldEvery=WO),this.yieldEvery==="never"&&e.onYield!=null)throw new Error("yieldEvery is `never` but you provided an `onYield` callback. Either change `yieldEvery` or remove the callback");I.isNumber(this.yieldEvery)&&(this.maybeWait=KD(this.maybeWait.bind(this),this.yieldEvery)),this.trainBegin=e.onTrainBegin,this.trainEnd=e.onTrainEnd,this.epochBegin=e.onEpochBegin,this.epochEnd=e.onEpochEnd,this.batchBegin=e.onBatchBegin,this.batchEnd=e.onBatchEnd,this.yield=e.onYield}async maybeWait(e,t,n){let s=[];this.yield!=null&&(await Yr(n),s.push(this.yield(e,t,n))),s.push(dp()),await Promise.all(s)}async onEpochBegin(e,t){this.currentEpoch=e,this.epochBegin!=null&&(await Yr(t),await this.epochBegin(e,t))}async onEpochEnd(e,t){let n=[];this.epochEnd!=null&&(await Yr(t),n.push(this.epochEnd(e,t))),this.yieldEvery==="epoch"&&n.push(dp()),await Promise.all(n)}async onBatchBegin(e,t){this.batchBegin!=null&&(await Yr(t),await this.batchBegin(e,t))}async onBatchEnd(e,t){let n=[];this.batchEnd!=null&&(await Yr(t),n.push(this.batchEnd(e,t))),this.yieldEvery==="batch"?n.push(dp()):I.isNumber(this.yieldEvery)&&n.push(this.maybeWait(this.currentEpoch,e,t)),await Promise.all(n)}async onTrainBegin(e){this.trainBegin!=null&&(await Yr(e),await this.trainBegin(e))}async onTrainEnd(e){this.trainEnd!=null&&(await Yr(e),await this.trainEnd(e))}};function m3(e,t){return e==null&&(e={}),e instanceof Ol?[e]:Array.isArray(e)&&e[0]instanceof Ol?e:At(e).map(s=>new f3(s,t))}var ks=class{constructor(){}static registerCallbackConstructor(e,t){I.assert(e>=0&&Number.isInteger(e),()=>`Verbosity level is expected to be an integer >= 0, but got ${e}`),ks.checkForDuplicate(t),ks.constructors[e]==null&&(ks.constructors[e]=[]),ks.constructors[e].push(t)}static checkForDuplicate(e){for(let t in ks.constructors)ks.constructors[+t].forEach(s=>{if(s===e)throw new G("Duplicate callback constructor.")})}static clear(){ks.constructors={}}static createCallbacks(e){let t=[];for(let n in ks.constructors){let s=+n;e>=s&&t.push(...ks.constructors[s])}return t.map(n=>new n)}};ks.constructors={};function A3(e,t,n,s,r,a,o,i,l){let u=new p3,c=[new VO,...ks.createCallbacks(t)];e!=null&&c.push(...e),c.push(u);let d=new h3(c);return d.setParams({epochs:n,initialEpoch:s,samples:r,steps:a,batchSize:o,verbose:t,doValidation:i,metrics:l}),{callbackList:d,history:u}}function Ms(e,t={},n=!1){return dc(e,oe.SerializationMap.getMap().classNameMap,t,"layer",n)}function Np(e,t){return H(()=>{e.dtype!=="float32"&&(e=ce(e,"float32"));let n=ve(mc(e),t,!0),s=Sl(n.shape,Ut()),r=ln(gr(n,s));return de(e,r)})}function Do(e,t){return H(()=>Et(mc(Ae(t,e)),-1))}function Ep(e,t){return H(()=>Et(Wt(Ae(t,e)),-1))}function Pl(e,t){return H(()=>{let n=Ae(e,t),s=Wn(Wt(e),Ut(),Number.MAX_VALUE),r=Wt(de(n,s));return z(100,Et(r,-1))})}function UO(e,t){return H(()=>{let n=Wn(t,Ut(),Number.MAX_VALUE),s=is(ae(1,n)),r=Wn(e,Ut(),Number.MAX_VALUE),a=is(ae(1,r));return Et(mc(Ae(s,a)),-1)})}function HO(e,t){return H(()=>{let n=gr(0,Ae(1,z(e,t)));return Et(mc(n),-1)})}function GO(e,t){return H(()=>{let n=gr(0,Ae(1,z(e,t)));return Et(n,-1)})}function jO(e,t){return H(()=>{let n=ve(z(e,t),-1),s=ls(z(Ae(1,e),t),-1);return gr(0,ae(1,Ae(s,n)))})}function qO(e,t){return H(()=>{let n=Math.log(2),s=Ae(t,e),r=Ae(ae(s,Tl(z(-2,s))),n);return Et(r,-1)})}function gc(e,t,n=!1){return H(()=>{if(n)t=Zh(t);else{let s=ve(t,t.shape.length-1,!0);t=de(t,s)}return t=Wn(t,Ut(),1-Ut()),St(ve(z(ce(e,"float32"),is(t)),t.shape.length-1))})}function Rp(e,t,n=!1){return H(()=>{let s=ce(sc(cO(e)),"int32");t=Wn(t,Ut(),1-Ut());let r=t.shape,a=U(Ju(s,r[r.length-1]),r);return gc(a,t,n)})}function XO(e,t){if(!I.arraysEqual(e.shape,t.shape))throw new G(`logits and labels must have the same shape, but got shapes ${JSON.stringify(e.shape)} and ${JSON.stringify(t.shape)}`);return H(()=>{let n=Ys(t),s=St(Wt(t));return ae(Ae(n,z(t,e)),Bh(os(s)))})}function _p(e,t){return H(()=>{let n;return n=Wn(t,Ut(),1-Ut()),n=is(de(n,Ae(1,n))),Et(XO(e,n),-1)})}function KO(e,t){return H(()=>{let n=Wn(e,Ut(),1),s=Wn(t,Ut(),1);return ve(z(e,is(de(n,s))),-1)})}function ZO(e,t){return H(()=>{let n=is(ae(Ut(),t));return Et(Ae(t,z(e,n)),-1)})}function fg(e,t){return H(()=>{let n=Np(e,-1),s=Np(t,-1),r=z(n,s);return St(ve(r,-1))})}var $p={meanSquaredError:Do,meanAbsoluteError:Ep,meanAbsolutePercentageError:Pl,meanSquaredLogarithmicError:UO,squaredHinge:HO,hinge:GO,categoricalHinge:jO,logcosh:qO,categoricalCrossentropy:gc,sparseCategoricalCrossentropy:Rp,binaryCrossentropy:_p,kullbackLeiblerDivergence:KO,poisson:ZO,cosineProximity:fg};function mg(e){if(typeof e=="string"){if(e in $p)return $p[e];let t=`Unknown loss ${e}`;throw e.toLowerCase().includes("softmaxcrossentropy")&&(t=`Unknown loss ${e}. Use "categoricalCrossentropy" as the string name for tf.losses.softmaxCrossEntropy`),new G(t)}else return e}function Ag(e,t){return H(()=>{let n=z(.5,us(t)),s=pp(Vn(t,n),e.dtype);return Et(as(e,s),-1)})}function gg(e,t){return H(()=>pp(as(Xs(e,-1),Xs(t,-1)),"float32"))}function g3(e,t){return H(()=>ce(ve(Rs(as(e,1),as(t,1))),"float32"))}function YO(e,t){return H(()=>ce(ve(Rs(as(e,1),as(t,0))),"float32"))}function JO(e,t){return H(()=>ce(ve(Rs(as(e,0),as(t,1))),"float32"))}function y3(e,t){return H(()=>{let n=g3(e,t),s=JO(e,t),r=ae(n,s);return ce(gn(Vn(r,0),de(n,r),0),"float32")})}function QO(e,t){return H(()=>{let n=g3(e,t),s=YO(e,t),r=ae(n,s);return ce(gn(Vn(r,0),de(n,r),0),"float32")})}function x3(e,t){return _p(e,t)}function b3(e,t){return e.rank===t.rank&&(e=ot(e,[e.rank-1])),t=Xs(t,-1),t.dtype!==e.dtype&&(t=ce(t,e.dtype)),ce(as(e,t),"float32")}var eP=Do,tP=Do,nP=Ep,sP=Ep,rP=Pl,aP=Pl,yg=gc,oP=fg,v3=Rp,Fp={binaryAccuracy:Ag,categoricalAccuracy:gg,precision:y3,categoricalCrossentropy:yg,sparseCategoricalCrossentropy:v3,mse:eP,MSE:tP,mae:nP,MAE:sP,mape:rP,MAPE:aP,cosine:oP};function iP(e){if(typeof e=="string"&&e in Fp)return Fp[e];if(typeof e!="string"&&e!=null)return e;throw new G(`Unknown metric ${e}`)}function Dp(e){if(Qs(e!==null,`Unknown LossOrMetricFn ${e}`),typeof e=="string")return e;{let t;for(let n of Object.keys($p))if($p[n]===e){t=n;break}if(t!==void 0)return t;for(let n of Object.keys(Fp))if(Fp[n]===e){t=n;break}return t!==void 0?t:e.name}}function lP(e){let t={Adagrad:()=>No.adagrad(.01),Adadelta:()=>No.adadelta(1,.95,Ut()),Adam:()=>No.adam(.001,.9,.999,Ut()),Adamax:()=>No.adamax(.002,.9,.999,Ut(),0),RMSProp:()=>No.rmsprop(.001,.9,0,Ut()),SGD:()=>No.sgd(.01)};if(t.adagrad=t.Adagrad,t.adadelta=t.Adadelta,t.adam=t.Adam,t.adamax=t.Adamax,t.rmsprop=t.RMSProp,t.sgd=t.SGD,e in t)return t[e]();throw new G(`Unknown Optimizer ${e}`)}var w3=1*1024*1024;function k3(e,t,n=!1){if(e==null||typeof e!="object"||Object.getPrototypeOf(e)!==Object.prototype||!xg(e))throw new Error("User-defined metadata is expected to be a JSON object, but is not.");if(n){let s=JSON.stringify(e);s.length>w3&&console.warn(`User-defined metadata of model "${t}" is too large in size (length=${s.length} when serialized). It is not recommended to store such large objects in user-defined metadata. Please make sure its serialized length is <= ${w3}.`)}}function xg(e){if(e===null)return!0;if(typeof e=="object")if(Object.getPrototypeOf(e)===Object.prototype){let t=Object.keys(e);for(let n of t)if(typeof n!="string"||!xg(e[n]))return!1;return!0}else if(Array.isArray(e)){for(let t of e)if(!xg(t))return!1;return!0}else return!1;else{let t=typeof e;return t==="string"||t==="number"||t==="boolean"}}function uP(e,t,n,s=console.log){let r=dP(e),a=["Layer (type)","Output shape","Param #"];r?(t=t||65,n=n||[.45,.85,1]):(t=t||98,n=n||[.33,.55,.67,1]),n[n.length-1]<=1&&(n=n.map(c=>Math.floor(t*c)));let o;if(!r){a.push("Receives inputs"),o=[];for(let c in e.nodesByDepth)o.push(...e.nodesByDepth[c])}s("_".repeat(t)),Op(a,n,s),s("=".repeat(t));let i=e.layers;for(let c=0;c<i.length;++c)r?hP(i[c],n,s):pP(i[c],n,o,s),s((c===i.length-1?"=":"_").repeat(t));e.checkTrainableWeightsConsistency();let l=cP(e),u=Cp(e.nonTrainableWeights);s(`Total params: ${l+u}`),s(`Trainable params: ${l}`),s(`Non-trainable params: ${u}`),s("_".repeat(t))}function cP(e){let t;return e.collectedTrainableWeights!=null?t=Cp(e.collectedTrainableWeights):t=Cp(e.trainableWeights),t}function dP(e){let t=!0,n=[],s=[];for(let r in e.nodesByDepth)n.push(e.nodesByDepth[r]);for(let r of n){if(r.length>1||r.length===1&&r[0].inboundLayers.length>1){t=!1;break}s.push(...r)}if(t)for(let r of e.layers){let a=!1;for(let o of r.inboundNodes)if(s.indexOf(o)!==-1)if(a){t=!1;break}else a=!0;if(!t)break}return t}function Op(e,t,n=console.log){let s="";for(let r=0;r<e.length;++r)r>0&&(s=s.slice(0,s.length-1)+" "),s+=e[r],s=s.slice(0,t[r]),s+=" ".repeat(t[r]-s.length);n(s)}function hP(e,t,n){let s;try{s=JSON.stringify(e.outputShape)}catch(i){s="multiple"}let r=e.name,a=e.getClassName(),o=[`${r} (${a})`,s,e.countParams().toString()];Op(o,t,n)}function pP(e,t,n,s){let r;try{r=JSON.stringify(e.outputShape)}catch(c){r="multiple"}let a=[];for(let c of e.inboundNodes)if(!(n!=null&&n.length>0&&n.indexOf(c)===-1))for(let d=0;d<c.inboundLayers.length;++d){let h=c.inboundLayers[d].name,p=c.nodeIndices[d],f=c.tensorIndices[d];a.push(`${h}[${p}][${f}]`)}let o=e.name,i=e.getClassName(),l=a.length===0?"":a[0],u=[`${o} (${i})`,r,e.countParams().toString(),l];Op(u,t,s);for(let c=1;c<a.length;++c)Op(["","","",a[c]],t,s)}function I3(e,t,n){return(e==="inboundNodes"||e==="outputLayers"||e==="inputLayers")&&t===0&&typeof n=="string"}function yc(e,t){if(e===null)return null;if(typeof e=="string")return Ro(e);if(typeof e=="number"||typeof e=="boolean")return e;if(e instanceof Array){let n=[],s=e.length;for(let r=0;r<s;++r){let a=e[r];I3(t,r,a)?n.push(a):n.push(yc(a,t))}return n}else{let n={};for(let s of Object.keys(e)){let r=e[s];if(s==="name"&&typeof r=="string")n[s]=r;else{let a=Ro(s);n[a]=yc(r,a)}}return n}}function bg(e,t){if(e==null)return null;if(typeof e=="string")return vr(e);if(typeof e=="number"||typeof e=="boolean")return e;if(e instanceof Array){let n=[],s=e.length;for(let r=0;r<s;++r){let a=e[r];I3(t,r,a)?n.push(a):n.push(bg(a,t))}return n}else{let n={};for(let s of Object.keys(e)){let r=e[s],a=vr(s);(s==="name"||s==="className")&&typeof r=="string"?n[a]=r:n[a]=bg(r,s)}return n}}var vg="3.8.0";function fP(e,t){if(e.dtype==null||e.dtype===t.dtype)return t;try{return ce(t,e.dtype)}catch(n){throw new G(`The dtype of the feed (${t.dtype}) can not be cast to the dtype of the key '${e.name}' (${e.dtype}).`)}}var Oo=class{constructor(e){if(this.id2Value={},this.id2Mask={},this.name2Id={},e instanceof Oo)for(let t in e.id2Value)this.id2Value[t]=e.id2Value[t],t in e.id2Mask&&(this.id2Mask[t]=e.id2Mask[t]);else{if(e==null)return;for(let t of e)this.add(t.key,t.value)}}add(e,t,n){if(this.id2Value[e.id]==null)this.id2Value[e.id]=fP(e,t),this.name2Id[e.name]=e.id,n!=null&&(this.id2Mask[e.id]=n);else throw new G(`Duplicate key: name=${e.name}, id=${e.id}`);return this}addFeed(e){this.add(e.key,e.value)}hasKey(e){return this.id2Value[e.id]!=null}names(){return Object.keys(this.name2Id)}getValue(e){if(e instanceof Ps){if(this.id2Value[e.id]==null)throw new G(`Nonexistent key: ${e.name}`);return this.id2Value[e.id]}else{let t=this.name2Id[e];if(t==null)throw new G(`Feed dict has no SymbolicTensor name: ${e}`);return this.id2Value[t]}}getMask(e){if(e instanceof Ps){if(this.id2Value[e.id]==null)throw new G(`Nonexistent key: ${e.name}`);return this.id2Mask[e.id]}else{let t=this.name2Id[e];if(t==null)throw new G(`Feed dict has no SymbolicTensor name: ${e}`);return this.id2Mask[t]}}disposeMasks(){this.id2Mask!=null&&K(this.id2Mask)}},wg={},S3={};function xc(e,t,n,s){let r=n==null?!1:n.training,a=Array.isArray(e),o=a?e:[e],i=o.map(f=>f.name),l=[],u=t.names();for(let f of i)u.indexOf(f)!==-1?l.push(t.getValue(f)):l.push(null);s!=null&&(s.maxNumTensors=-1/0,s.minNumTensors=1/0);let c=i.join(",")+"|"+t.names().join(","),d,h;if(wg[c]==null){let f=mP(o,t);d=f.sorted,h=f.recipientCounts,wg[c]=d,S3[c]=h}d=wg[c],h={},r||Object.assign(h,S3[c]);let p=new Oo(t);for(let f=0;f<d.length;++f){if(s!=null){let E=_h().numTensors;E>s.maxNumTensors&&(s.maxNumTensors=E),E<s.minNumTensors&&(s.minNumTensors=E)}let m=d[f],A=m.sourceLayer;if(A instanceof Dl)continue;let g=[],y=[],x=[],b=!1;for(let E of m.inputs){let M=p.getValue(E),R=p.getMask(E);g.push(M),y.push(R),R!=null&&(b=!0),r||(h[E.name]--,h[E.name]===0&&!t.hasKey(E)&&i.indexOf(E.name)===-1&&!M.isDisposed&&E.sourceLayer.stateful!==!0&&x.push(M))}b&&(n=n||{},n.mask=y[0]);let v=At(A.apply(g,n)),k=null;A.supportsMasking&&(k=A.computeMask(g,y));let w=gP(m),C=Array.isArray(w)?w:[w];for(let E=0;E<C.length;++E){p.hasKey(C[E])||p.add(C[E],v[E],Array.isArray(k)?k[0]:k);let M=i.indexOf(C[E].name);M!==-1&&(l[M]=v[E])}r||K(x)}return p.disposeMasks(),a?l:l[0]}function mP(e,t){I.assert(e!=null&&e.length>0,()=>"Expected at least one fetch, got none");let n=[],s={};if(e.length===1){let r=C3(e[0],t);n=r.sorted,s=r.recipientMap}else{let r=new Set;for(let a of e){let{sorted:o,recipientMap:i}=C3(a,t);for(let l of o)r.has(l.name)||(n.push(l),r.add(l.name));for(let l in i)s[l]==null&&(s[l]=new Set),i[l].forEach(u=>s[l].add(u))}}return{sorted:n,recipientCounts:AP(s)}}function AP(e){let t={};for(let n in e)t[n]=e[n].size;return t}function C3(e,t){let n=new Set,s=[],r={};for(let i of t.names())n.add(i);let a=[],o=[];for(a.push(e);a.length>0;){let i=a[a.length-1];if(n.has(i.name)){a.pop();continue}let l=o[o.length-1]===a.length-1;if(i.inputs.length===0||l)a.pop(),s.push(i),n.add(i.name),l&&o.pop();else{o.push(a.length-1);for(let u of i.inputs)r[u.name]==null&&(r[u.name]=new Set),r[u.name].add(i.name),!n.has(u.name)&&a.push(u)}}return{sorted:s,recipientMap:r}}function gP(e){let t;if(e.sourceLayer.inboundNodes.length===1)t=e.sourceLayer.output;else{let n=null;for(let s=0;s<e.sourceLayer.inboundNodes.length;++s)for(let r of e.sourceLayer.inboundNodes[s].outputTensors)if(r.id===e.id){n=s;break}t=e.sourceLayer.getOutputAt(n)}return t}var tr=class extends Xe{constructor(e){super({});if(this.containerNodes=new Set,this.name=e.name,this.name==null){let g=this.getClassName().toLowerCase();this.name=Ip(g)}if(this.supportsMasking=!1,this.trainable_=!0,Array.isArray(e.inputs)?this.inputs=e.inputs.slice():this.inputs=[e.inputs],Array.isArray(e.outputs)?this.outputs=e.outputs.slice():this.outputs=[e.outputs],Xr(this.inputs).length!==this.inputs.length)throw new G(`The list of inputs passed to the model is redundant. All inputs should only appear once. Found: ${this.inputs.map(g=>g.name)}`);Xr(this.outputs).length!==this.outputs.length&&console.warn(`The list of outputs passed to the model is redundant. All outputs should only appear once. Found: ${this.outputs.map(g=>g.name)}`),this.inputLayers=[],this.inputLayersNodeIndices=[],this.inputLayersTensorIndices=[],this.outputLayers=[],this.outputLayersNodeIndices=[],this.outputLayersTensorIndices=[],this.layers=[],this.internalContainerRefs=[];for(let g of this.outputs){let y=g.sourceLayer,x=g.nodeIndex,b=g.tensorIndex;this.outputLayers.push(y),this.outputLayersNodeIndices.push(x),this.outputLayersTensorIndices.push(b)}for(let g of this.inputs){let y=g.sourceLayer,x=g.nodeIndex,b=g.tensorIndex;Qs(x===0,"input layer has >1 nodes"),Qs(b===0,"input layer has >1 tensors"),this.inputLayers.push(y),this.inputLayersNodeIndices.push(x),this.inputLayersTensorIndices.push(b)}this.inputNames=[],this.outputNames=[],this.feedInputShapes=[],this.feedInputNames=[],this.feedOutputNames=[];for(let g=0;g<this.inputLayers.length;g++){let y=this.inputLayers[g];if(!(y instanceof Dl))throw new TypeError(`Input layers to a LayersModel must be InputLayer objects. Received inputs: ${e.inputs}. Input ${g} (0-based) originates from layer type ${y.getClassName()}.`);this.inputNames.push(y.name),this.feedInputShapes.push(y.batchInputShape),this.feedInputNames.push(y.name)}for(let g of this.outputLayers)this.outputNames.push(g.name);this.internalInputShapes=this.inputs.map(g=>g.shape),this.internalOutputShapes=this.outputs.map(g=>g.shape);let t={},n={},s={},r={},a={},o=[],i=(g,y,x,b,v,k)=>{(b==null||v==null||k==null)&&(b=g.sourceLayer,v=g.nodeIndex,k=g.tensorIndex);let w=b.inboundNodes[v];if(x.indexOf(w)!==-1)throw new Fs(`The tensor ${g.name} at layer "${b.name}" is part of a cycle.`);if(y.indexOf(w)!==-1)return;this.containerNodes.add(tr.nodeKey(b,v)),b.id in a||(a[b.id]=Object.keys(a).length),x.indexOf(w)===-1&&x.push(w);let C=w.inboundLayers.length;for(let E=0;E<C;E++){let M=w.inputTensors[E],R=w.inboundLayers[E],_=w.nodeIndices[E],N=w.tensorIndices[E];i(M,y,x,R,_,N)}for(y.push(w);x.indexOf(w)>=0;)x.splice(x.indexOf(w),1);o.push(w)},l=[],u=[];for(let g of this.outputs)i(g,l,u);let c=o.slice().reverse();for(let g of c){n[g.id]=g,g.id in t||(t[g.id]=0);let y=t[g.id],x=s[g.outboundLayer.id]==null?0:s[g.outboundLayer.id];y=Math.max(y,x),s[g.outboundLayer.id]=y,r[g.outboundLayer.id]=g.outboundLayer,t[g.id]=y;for(let b=0;b<g.inboundLayers.length;b++){let v=g.inboundLayers[b],k=g.nodeIndices[b],w=v.inboundNodes[k],C=t[w.id]==null?0:t[w.id];t[w.id]=Math.max(y+1,C),n[w.id]=w}}let d={};for(let g in t){let y=t[g];y in d||(d[y]=[]),d[y].push(n[g])}let h={};for(let g in s){let y=s[g];y in h||(h[y]=[]),h[y].push(r[g])}let p=Object.keys(h).map(g=>parseInt(g,10)).sort(hp);this.layers=[];for(let g of p){let y=h[g];y.sort((x,b)=>{let v=a[x.id],k=a[b.id];return v<k?-1:v>k?1:0});for(let x of y)x instanceof tr&&this.internalContainerRefs.push(x),this.layers.push(x)}this.layersByDepth=h,p=Object.keys(d).map(g=>parseInt(g,10)).sort(hp);let f=this.inputs.slice(),m=[];for(let g of p)for(let y of d[g]){let x=y.outboundLayer;if(x!=null){for(let b of y.inputTensors)if(f.indexOf(b)===-1)throw new Fs(`Graph disconnected: cannot obtain value for tensor ${b} at layer "${x.name}". The following previous layers were accessed without issue: ${m}`);for(let b of y.outputTensors)f.push(b);m.push(x.name)}}this.nodesByDepth=d;let A=this.layers.map(g=>g.name);for(let g of A){let y=A.filter(x=>x===g).length;if(y!==1)throw new Fs(`The name "${g}" is used ${y} times in the model. All layer names should be unique. Layer names: `+JSON.stringify(A))}this.outboundNodes=[],this.inboundNodes=[],new Tp({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:this.inputs,outputTensors:this.outputs,inputMasks:this.inputs.map(g=>null),outputMasks:this.outputs.map(g=>null),inputShapes:this.inputs.map(g=>g.shape),outputShapes:this.outputs.map(g=>g.shape)}),this.built=!0,this._refCount=1}assertNotDisposed(){if(this._refCount===0)throw new Error(`Container '${this.name}' is already disposed.`)}dispose(){this.assertNotDisposed();let e={refCountAfterDispose:null,numDisposedVariables:0};if(--this._refCount==0){for(let t of this.layers)e.numDisposedVariables+=t.dispose().numDisposedVariables;for(let t of this.internalContainerRefs)e.numDisposedVariables+=t.dispose().numDisposedVariables}return e.refCountAfterDispose=this._refCount,e}get trainable(){return this.trainable_}set trainable(e){this.layers.forEach(t=>{t._trainableWeights.forEach(n=>n.trainable=e)}),this.trainable_=e}get trainableWeights(){if(this._trainableWeights.length>0)throw new G("Container instance unexpectedly contains _trainableWeights.The trainable weights of a Container are a union of the trainable weights of its consituent Layers. Its own _trainableWeights must remain an empty Array.");if(!this.trainable)return[];let e=[];for(let t of this.layers)e=e.concat(t.trainableWeights);return e}get nonTrainableWeights(){let e=[];for(let t of this.layers)e.push(...t.nonTrainableWeights);if(!this.trainable){let t=[];for(let n of this.layers)t.push(...n.trainableWeights);return t.concat(e)}return e}get weights(){return this.trainableWeights.concat(this.nonTrainableWeights)}loadWeights(e,t=!0){let n={},s=0;for(let a of this.layers)for(let o of a.weights){if(n[o.originalName]!=null)throw new G(`Duplicate weight name: ${o.originalName}`);n[o.originalName]=o,s++}let r=[];for(let a in e){let o=a;if(n[a]==null){let i=a.split("/");o=i.slice(0,-2).concat([i[i.length-1]]).join("/")}if(n[o]!=null)r.push([n[o],e[a]]);else if(t)throw new G(`Provided weight data has no target variable: ${a}`);delete n[o]}if(t){let a=[];for(let o in n)a.push(o);if(a.length>0)throw new G(`${a.length} of ${s} weights are not set: ${a}`)}pg(r)}updatedConfig(){let e=this.getConfig(),t={};return t.className=this.getClassName(),t.config=e,t.kerasVersion=`tfjs-layers ${vg}`,t.backend="TensorFlow.js",t}toJSON(e,t=!0){let n=bg(this.updatedConfig());return t?JSON.stringify(n):n}call(e,t){return H(()=>{e=At(e);let n=new Oo;for(let s=0;s<this.inputs.length;++s)n.add(this.inputs[s],e[s]);return xc(this.outputs,n,t)})}computeMask(e,t){return H(()=>{e=At(e);let n;return t==null?n=Eo(null,e.length):n=At(t),this.runInternalGraph(e,n)[1]})}computeOutputShape(e){let t=Sp(e);if(t.length!==this.inputLayers.length)throw new G(`Invalid inputShape argument ${e}: model has ${this.inputLayers.length} tensor inputs.`);let n={};for(let o=0;o<t.length;o++){let i=this.inputLayers[o],l=t[o],u=i.name+"_0_0";n[u]=l}let s=Object.keys(this.nodesByDepth).map(o=>parseInt(o,10)).sort(hp);if(s.length>1)for(let o of s){let i=this.nodesByDepth[o];for(let l of i){let u=l.outboundLayer;if(this.inputLayers.map(f=>f.id).indexOf(u.id)!==-1)continue;let c=[];for(let f=0;f<l.inboundLayers.length;f++){let m=l.inboundLayers[f],A=l.nodeIndices[f],g=l.tensorIndices[f],y=`${m.name}_${A}_${g}`,x=n[y];c.push(x)}let d=u.computeOutputShape(En(c)),h=Sp(d),p=u.inboundNodes.indexOf(l);for(let f=0;f<h.length;f++){let m=`${u.name}_${p}_${f}`;n[m]=h[f]}}}let r=[],a=[];for(let o=0;o<this.outputLayers.length;o++){let i=this.outputLayers[o],l=this.outputLayersNodeIndices[o],u=this.outputLayersTensorIndices[o],c=`${i.name}_${l}_${u}`;a.push(c)}for(let o=0;o<a.length;o++){let i=a[o];Qs(i in n),r.push(n[i])}return En(r)}runInternalGraph(e,t){t==null&&(t=Eo(null,e.length));let n={};for(let i=0;i<this.inputs.length;++i){let l=this.inputs[i],u=e[i],c=t[i];n[l.id]=[u,c]}let s=Object.keys(this.nodesByDepth).map(i=>parseInt(i,10)).sort(hp);for(let i of s){let l=this.nodesByDepth[i];for(let u of l){let c=u.outboundLayer,d=u.inputTensors,h=u.outputTensors,p=new Array;for(let f of d)f.id in n&&p.push(n[f.id]);if(p.length===d.length){let f={},m,A,g,y;if(u.callArgs!=null&&(f=u.callArgs),p.length===1){let[x,b]=p[0];f.mask==null&&(f.mask=b),g=At(c.call(x,f)),y=At(c.computeMask(x,b)),m=[x],A=[b]}else m=p.map(x=>x[0]),A=p.map(x=>x[1]),f.mask==null&&(f.mask=A),g=At(c.call(m,f)),y=At(c.computeMask(m,A));if(c.activityRegularizer)throw new Oe("LayersModel invocation with concrete Tensor value(s) in the presence of activity regularizer(s) is not supported yet.");for(let x=0;x<h.length;++x){let b=h[x],v=g[x],k=y[x];n[b.id]=[v,k]}}}}let r=[],a=[],o=[];for(let i of this.outputs){Qs(i.id in n,`Could not compute output ${i.name} : ${i.id}`);let[l,u]=n[i.id];o.push(l.shape),r.push(l),a.push(u)}return[r,a,o]}buildNodeConversionMap(e){let t={},n;for(let s of this.layers){n=s instanceof tr?1:0;for(let r=0;r<s.inboundNodes.length;r++){let a=tr.nodeKey(s,r);this.containerNodes.has(a)&&(t[a]=n,n+=1)}}return t}getLayer(e,t){if(t!=null){if(this.layers.length<=t)throw new G(`Was asked to retrieve layer at index ${t}, but model only has ${this.layers.length} layer(s).`);return this.layers[t]}else if(e==null)throw new G("Provide either a layer name or layer index");for(let n of this.layers)if(n.name===e)return n;throw new G(`No such layer: ${e}`)}calculateLosses(){return H(()=>{let e=[];for(let t of this.layers)for(let n=0;n<t.inboundNodes.length;++n){let s=tr.nodeKey(t,n);this.containerNodes.has(s)&&e.push(...t.calculateLosses())}return e})}getConfig(){let e={name:this.name},t=this.buildNodeConversionMap(this.layers),n=[];for(let a of this.layers){let o=a.getClassName(),i=a.getConfig(),l=[];for(let c=0;c<a.inboundNodes.length;c++){let d=a.inboundNodes[c],h=tr.nodeKey(a,c),p={};if(this.containerNodes.has(h)){if(d.callArgs)try{JSON.stringify(d.callArgs),p=d.callArgs}catch(f){console.warn(`Layer ${a.name} was passed non-serializable keyword arguments: ${d.callArgs}. They will not be included in the serialized model (and thus will be missing at deserialization time).`),p={}}if(d.inboundLayers.length>0){let f=[];for(let m=0;m<d.inboundLayers.length;m++){let A=d.inboundLayers[m],g=d.nodeIndices[m],y=d.tensorIndices[m],x=tr.nodeKey(A,g),b=t[x];b==null&&(b=0),f.push([A.name,b,y,p])}l.push(f)}}}let u={};u.name=a.name,u.className=o,u.config=i,u.inboundNodes=l,n.push(u)}e.layers=n;let s=[];for(let a=0;a<this.inputLayers.length;a++){let o=this.inputLayers[a],i=this.inputLayersNodeIndices[a],l=tr.nodeKey(o,i);if(!this.containerNodes.has(l))continue;let u=t[l];u==null&&(u=0);let c=this.inputLayersTensorIndices[a];s.push([o.name,u,c])}e.inputLayers=s;let r=[];for(let a=0;a<this.outputLayers.length;a++){let o=this.outputLayers[a],i=this.outputLayersNodeIndices[a],l=tr.nodeKey(o,i);if(!this.containerNodes.has(l))continue;let u=t[l];u==null&&(u=0);let c=this.outputLayersTensorIndices[a];r.push([o.name,u,c])}return e.outputLayers=r,e}static fromConfig(e,t,n={},s=!1){let r={},a={};function o(m,A){m.name in a?a[m.name].push(A):a[m.name]=[A]}function i(m,A){let g=[],y;for(let x of A){let b=x[0],v=x[1],k=x[2];if(y=x[3]==null?{}:x[3],!(b in r)){o(m,A);return}let w=r[b];if(w.inboundNodes.length<=v){o(m,A);return}let C=w.inboundNodes[v];g.push(C.outputTensors[k])}g.length>0&&m.apply(En(g),y)}function l(m){let A=m.name,g=Ms(m,t.customObjects!=null?t.customObjects:{});g.setFastWeightInitDuringBuild(s),r[A]=g,m.inboundNodes.forEach(x=>{if(!(x instanceof Array))throw new G(`Corrupted configuration, expected array for nodeData: ${x}`);o(g,x)})}let u=t.name,c=t.layers;for(let m of c)l(m);for(;!XD(a);)for(let m of c){let A=r[m.name];if(A.name in a){let g=a[A.name];delete a[A.name];for(let y of g)i(A,y)}}let d=[],h=[],p=t.inputLayers;for(let m of p){let A=m[0],g=m[1],y=m[2];Qs(A in r);let b=r[A].inboundNodes[g].outputTensors;d.push(b[y])}let f=t.outputLayers;for(let m of f){let A=m[0],g=m[1],y=m[2];Qs(A in r);let b=r[A].inboundNodes[g].outputTensors;h.push(b[y])}return new e({inputs:d,outputs:h,name:u})}get stateful(){if(this._stateful)throw new G("Container instance unexpectedly has _stateful = true. The statefulness of a Container is determined by the Layers it contains. Its _stateful property must remain the default false.");for(let e of this.layers)if(e.stateful)return!0;return!1}resetStates(){H(()=>{this.layers.forEach(e=>{e.stateful&&e.resetStates()})})}};function yP(e,t,n){let s=t.length;if(e==null||Array.isArray(e)&&e.length===0)return t.map(r=>null);if(s===1)return Array.isArray(e)&&e.length===1?e:typeof e=="object"&&t[0]in e?[e[t[0]]]:[e];if(Array.isArray(e)){if(e.length!==s)throw new Error(`Provided ${n} is an array of ${e.length} element(s), but the model has ${s} outputs. Make sure a set of weights is provided for each model output.`);return e}else if(typeof e=="object"&&Object.keys(e).length>0&&typeof e[Object.keys(e)[0]]=="object"){let r=[];return t.forEach(a=>{a in e?r.push(e[a]):r.push(null)}),r}else throw new Error(`The model has multiple (${s}) outputs, so ${n} must be either an array with ${s} elements or an object with ${t} keys. Provided ${n} not understood: ${JSON.stringify(e)}`)}function T3(e,t){return yP(e,t,"classWeight")}async function N3(e,t,n,s){if(t!=null||s!=null)throw new Error("Support sampleWeight is not implemented yet");if(n!=null){let r=H(()=>{if(e.shape.length===1)return Ns(e);if(e.shape.length===2){if(e.shape[1]>1)return Xs(e,1);if(e.shape[1]===1)return U(e,[e.shape[0]]);throw new Error(`Encountered unexpected last-dimension size (${e.shape[1]}) during handling of class weights. The size is expected to be >= 1.`)}else throw new Error(`Unexpected rank of target (y) tensor (${e.rank}) during handling of class weights. The rank is expected to be 1 or 2.`)}),a=Array.from(await r.data());K(r);let o=[];return a.forEach(i=>{if(n[i]==null)throw new Error(`classWeight must contain all classes in the training data. The class ${i} exists in the data but not in classWeight`);o.push(n[i])}),Ot(o,"float32")}else return null}function xP(e,t){return z(e,t)}var bP=32;function E3(e,t){let n,s,r=t;n=r.xs,s=r.ys,I.assert(n!=null&&s!=null,()=>`A Dataset iterator for fitDataset() is expected to generate objects of the form \`{xs: xVal, ys: yVal}\`, where the two values may be \`tf.Tensor\`, an array of Tensors, or a map of string to Tensor.  The provided Dataset instead generates ${t}`);let a=R3("input",e.inputNames,n),o=R3("output",e.outputNames,s),i=a[0].shape[0];I.assert(a.length===e.inputs.length,()=>`LayersModel has ${e.inputs.length} inputs, but the dataset provides ${a.length} inputs.  (Expected input keys: ${JSON.stringify(e.inputNames)})`),I.assert(o.length===e.outputs.length,()=>`LayersModel has ${e.outputs.length} outputs, but the dataset provides ${o.length} outputs.  (Expected output keys: ${JSON.stringify(e.outputNames)})`);for(let l=0;l<a.length;l++)I.assert(a[l].shape[0]===i,()=>`Batch size mismatch: input ${e.inputNames[l]} has ${a[l].shape[0]}; expected  ${i} based on input ${e.inputNames[0]}.`);for(let l=0;l<o.length;l++)I.assert(o[l].shape[0]===i,()=>`Batch size mismatch: output ${e.outputNames[l]} has ${o[l].shape[0]}; expected  ${i} based on input ${e.inputNames[0]}.`);return{xs:a,ys:o}}function R3(e,t,n){if(n instanceof Ue)return[n];if(Array.isArray(n))return I.assert(n.length===t.length,()=>`Received an array of ${n.length} Tensors, but expected ${t.length} to match the ${e} keys ${t}.`),n;{let s=[];for(let r of t){if(n[r]==null)throw new G(`The feature data generated by the dataset lacks the required ${e} key '${r}'.`);s.push(n[r])}return s}}function vP(e){if(e.length===3)throw new Oe("Validation with sample weights is not implemented yet.");return{xs:e[0],ys:e[1]}}async function wP(e,t,n){let s=n.batchesPerEpoch!=null;if(I.assert(e.optimizer!=null,()=>"You must compile a model before training/testing. Use LayersModel.compile(modelCompileConfig)."),I.assert(n!=null,()=>"For fitDataset(), the 2nd argument (config) is required, but it is not provided in this call."),I.assert(n.epochs!=null&&n.epochs>0&&Number.isInteger(n.epochs),()=>`For fitDataset(), config.epochs is expected to be a positive integer, but got ${n.epochs}`),I.assert(!s||n.batchesPerEpoch>0&&Number.isInteger(n.batchesPerEpoch),()=>`For fitDataset(), config.batchesPerEpoch is expected to be a positive integer if specified, but got ${n.batchesPerEpoch}`),I.assert(n.validationSplit==null,()=>"`validationSplit` is not supported by `fitDataset()`. Use validationData instead."),e.isTraining)throw new Error("Cannot start training because another fit() call is ongoing.");e.isTraining=!0;try{let r=n.validationData!=null,a,o;if(r)if(_3(n.validationData))I.assert(n.validationBatches==null||n.validationBatches>0&&Number.isInteger(n.validationBatches),()=>`For fitDataset() with dataset-based validation, config.validationBatches is expected not to be provided, or to be a positive integer, but got ${n.validationBatches}`);else{let A=vP(n.validationData);a=A.xs,o=A.ys}let i=e.makeTrainFunction(),l=e.getDedupedMetricsNames(),u;r?u=l.slice().concat(l.map(A=>"val_"+A)):u=l.slice();let c=m3(n.callbacks,n.yieldEvery),d=n.verbose==null?1:n.verbose,{callbackList:h,history:p}=A3(c,d,n.epochs,null,null,kP(t,n),null,r,u);h.setModel(e),e.history=p,await h.onTrainBegin(),e.stopTraining_=!1;let f=n.initialEpoch==null?0:n.initialEpoch,m=await t.iterator();for(;f<n.epochs;){let A={};await h.onEpochBegin(f);let g=0,y=0;for(s||(m=await t.iterator());s?g<n.batchesPerEpoch:!0;){let x=await m.next();if(s&&x.done){console.warn(`You provided \`batchesPerEpoch\` as ${n.batchesPerEpoch}, but your dataset iterator ran out of data after ${g} batches; interrupting training. Make sure that your dataset can generate at least \`batchesPerEpoch * epochs\` batches (in this case, ${n.batchesPerEpoch*n.epochs} batches). You may need to use the repeat() function when building your dataset.`);break}if(x.value!=null){let{xs:b,ys:v}=E3(e,x.value),k={};k.batch=y,k.size=b[0].shape[0],await h.onBatchBegin(y,k);let w=[];if(n.classWeight!=null){let M=T3(n.classWeight,e.outputNames);for(let R=0;R<M.length;++R)w.push(await N3(v[R],null,M[R]))}let C=b.concat(v).concat(w),E=i(C);K(C);for(let M=0;M<l.length;++M){let R=l[M],_=E[M];k[R]=_,Kt(_)}await h.onBatchEnd(y,k),c3(k),y++,g++}if(s?g>=n.batchesPerEpoch:x.done){if(r){let b;_3(n.validationData)?b=At(await e.evaluateDataset(n.validationData,{batches:n.validationBatches})):b=At(e.evaluate(a,o,{batchSize:n.validationBatchSize==null?bP:n.validationBatchSize,verbose:0}));for(let v=0;v<e.metricsNames.length;++v)A[`val_${e.metricsNames[v]}`]=b[v]}break}if(e.stopTraining_)break}if(await h.onEpochEnd(f,A),f++,e.stopTraining_)break}return await h.onTrainEnd(),await e.history.syncData(),e.history}finally{e.isTraining=!1}}function kP(e,t){let n=null;return t.batchesPerEpoch!=null?n=t.batchesPerEpoch:Number.isFinite(e.size)&&(n=e.size),n}function _3(e){return typeof e.iterator=="function"}function IP(e){return typeof e.next=="function"}async function SP(e,t,n){n=n||{};let s=n.batches!=null,r=e.testFunction,a=[];if(n.verbose>0)throw new Oe("Verbose mode is not implemented yet.");I.assert(!s||n.batches>0&&Number.isInteger(n.batches),()=>`Test loop expects \`batches\` to be a positive integer, but received ${JSON.stringify(n.batches)}`);let o=IP(t)?t:await t.iterator(),i=0,l=0;for(;s?l<n.batches:!0;){let u=await o.next();if(a=H(()=>{if(u.value){let{xs:c,ys:d}=E3(e,u.value),h=c.concat(d),p=H(()=>r(h));if(K(h),l===0)for(let m=0;m<p.length;++m)a.push(Ie(0));let f=h[0].shape[0];for(let m=0;m<p.length;++m){let A=p[m],g=a[m];a[m]=H(()=>ae(a[m],z(f,A))),l>0&&K(g)}K(p),i+=f,++l}return a}),u.done){s&&console.warn(`Your dataset iterator ran out of data during evaluateDataset(). Interrupting evalution. Make sure that your dataset can generate at least \`batches\` batches (in this case, ${n.batches} batches). You may need to use the repeat() function when building your dataset.`);break}}for(let u=0;u<a.length;++u){let c=a[u];a[u]=de(a[u],i),K(c)}return En(a)}function kg(e){I.assert(e>0&&Number.isInteger(e),()=>`batchSize is required to be a positive integer, but got ${e}`)}function bc(e,t,n){return e==null?[null]:Array.isArray(e)?e.map(s=>Fo(s,t,n-t)):Fo(e,t,n-t)}function Ig(e,t){return H(()=>e==null?null:Array.isArray(e)?e.map(n=>Ig(n,t)):e3(e,t.dtype==="int32"?t:ce(t,"int32")))}function Sg(e,t){let n=[],s=0,r=null;for(;s<e;)r=s+t,r>=e&&(r=e),n.push([s,r]),s=r;return n}async function CP(e,t,n,s,r,a,o,i,l,u,c,d,h,p,f){r==null&&(r=32),a==null&&(a=1),c==null&&(c=!0),h==null&&(h=0);let m=!1;if(l!=null&&u!=null&&(m=!0),f!=null&&(m=!0,p==null))throw new G("Can only use `validationSteps` when doing step-wise training, i.e., `stepsPerEpoch` must be set.");let A=e.checkNumSamples(n,r,p,"steps_per_epoch"),g;A!=null&&(g=Ds(0,A)),o==null&&(o=1);let{callbackList:y,history:x}=A3(i,o,a,h,A,p,r,m,d);y.setModel(e),e.history=x,await y.onTrainBegin(),e.stopTraining_=!1;for(let b=h;b<a;++b){await y.onEpochBegin(b);let v={};if(p!=null)throw new Oe("stepsPerEpoch mode is not implemented yet.");{if(c==="batch")throw new Oe("batch shuffling is not implemneted yet");c&&I.shuffle(g);let k=Ot(g),w=Sg(A,r);for(let C=0;C<w.length;++C){let E={};if(await y.onBatchBegin(C,E),H(()=>{let M=w[C][0],R=w[C][1],_=Fo(k,M,R-M);E.batch=C,E.size=R-M;let N=Ig(n,_),O=t(N);for(let W=0;W<s.length;++W){let j=s[W],q=O[W];E[j]=q,Kt(q)}if(C===w.length-1&&m){let W=e.testLoop(l,u,r);for(let j=0;j<s.length;++j){let q=s[j],X=W[j];Kt(X),v["val_"+q]=X}}}),await y.onBatchEnd(C,E),c3(E),e.stopTraining_)break}k.dispose()}if(await y.onEpochEnd(b,v),e.stopTraining_)break}return await y.onTrainEnd(),await e.history.syncData(),e.history}async function TP(e,t,n,s={}){if(e.isTraining)throw new Error("Cannot start training because another fit() call is ongoing.");e.isTraining=!0;let r,a,o,i,l,u,c;try{let d=s.batchSize==null?32:s.batchSize;kg(d);let h=!1,p=await e.standardizeUserData(t,n,s.sampleWeight,s.classWeight,h,d);r=p[0],a=p[1],c=p[2];let f=!1,m;if(s.validationData!=null&&s.validationData.length>0){if(f=!0,s.validationData.length===2)o=s.validationData[0],i=s.validationData[1];else throw s.validationData.length===3?new Oe("validationData including sample weights is not supported yet."):new G(`When passing validation data, it must contain 2 (valX, valY) or 3 (valX, valY, valSampleWeight) items; ${s.validationData} is invalid.`);let w=!0,C=await e.standardizeUserData(o,i,null,null,w,d);l=C[0],u=C[1],m=l.concat(u)}else if(s.validationSplit!=null&&s.validationSplit>0&&s.validationSplit<1){f=!0;let w=Math.floor(r[0].shape[0]*(1-s.validationSplit)),C=r[0].shape[0];l=bc(r,w,C),r=bc(r,0,w),u=bc(a,w,C),a=bc(a,0,w),m=l.concat(u)}else s.validationSteps!=null&&(f=!0);let A=r.concat(a).concat(c);e.checkTrainableWeightsConsistency();let g=e.makeTrainFunction(),y=e.getDedupedMetricsNames(),x,b;f?(e.makeTestFunction(),x=e.testFunction,b=y.slice().concat(y.map(w=>"val_"+w))):(x=null,m=[],b=y.slice());let v=m3(s.callbacks,s.yieldEvery);return await CP(e,g,A,y,d,s.epochs,s.verbose,v,x,m,s.shuffle,b,s.initialEpoch,null,null)}finally{e.isTraining=!1,Po(r,t),Po(a,n),Po(l,o),Po(u,i),c!=null&&K(c)}}function $3(e){let t=[];e instanceof Ue&&(e=[e]);for(let n=0;n<e.length;++n){let s=e[n];if(s.rank===1)t.push(fc(s,1));else{if(s.rank===0)throw new Error("Expected tensor to be at least 1D, but received a 0D tensor (scalar).");t.push(s)}}return t}function Po(e,t){if(e==null)return;let n=[];if(t instanceof Ue)n.push(t.id);else if(Array.isArray(t))t.forEach(r=>n.push(r.id));else if(t!=null)for(let r in t){let a=t[r];n.push(a.id)}let s=[];if(e instanceof Ue)n.indexOf(e.id)===-1&&s.push(e);else if(Array.isArray(e))e.forEach(r=>{n.indexOf(r.id)===-1&&s.push(r)});else if(e!=null)for(let r in e){let a=e[r];n.indexOf(a.id)===-1&&s.push(a)}s.forEach(r=>{r.isDisposed||r.dispose()})}function NP(e){return e instanceof Ue}function Cg(e){return Array.isArray(e)}function F3(e){return!NP(e)&&!Cg(e)}function D3(e,t,n,s=!0,r=""){if(t==null||t.length===0){if(e!=null){let o=!1;if(Cg(e)&&e.length>0)o=!0;else if(F3(e)){for(let i in e)if(e.hasOwnProperty(i)){o=!0;break}}else o=!0;if(o)throw new G(`Error when checking model ${r} expected no data, but got ${e}`)}return[]}if(e==null)return t.map(o=>null);let a;if(F3(e)){e=e,a=[];for(let o of t){if(e[o]==null)throw new G(`No data provided for "${o}". Need data for each key in: ${t}`);a.push(e[o])}}else if(Cg(e)){if(e=e,e.length!==t.length)throw new G(`Error when checking model ${r}: the Array of Tensors that you are passing to your model is not the size the model expected. Expected to see ${t.length} Tensor(s), but instead got the following list of Tensor(s): ${e}`);a=e}else{if(e=e,t.length>1)throw new G(`The model ${r} expects ${t.length} Tensor(s), but only received one Tensor. Found: Tensor with shape ${e.shape}`);a=[e]}if(a=$3(a),n!=null)for(let o=0;o<t.length;++o){if(n[o]==null)continue;let i=a[o];if(i.shape.length!==n[o].length)throw new G(`Error when checking ${r}: expected ${t[o]} to have ${n[o].length} dimension(s). but got array with shape ${i.shape}`);for(let l=0;l<n[o].length;++l){if(l===0&&!s)continue;let u=i.shape[l],c=n[o][l];if(c!=null&&c>=0&&u!==c)throw new G(`Error when checking ${r}: expected ${t[o]} to have shape [${n[o]}], but got array with shape [${i.shape}].`)}}return a}function EP(e,t,n){let s=Xr(e.map(a=>a.shape[0]));s.sort();let r=Xr(t.map(a=>a.shape[0]));if(r.sort(),s.length>1)throw new G(`All input Tensors (x) should have the same number of samples. Got array shapes: ${JSON.stringify(e.map(a=>a.shape))}`);if(r.length>1)throw new G(`All target Tensors (y) should have the same number of samples. Got array shapes: ${JSON.stringify(t.map(a=>a.shape))}`);if(s.length>0&&r.length>0&&!I.arraysEqual(s,r))throw new G(`Input Tensors should have the same number of samples as target Tensors. Found ${s[0]} input sample(s) and ${r[0]} target sample(s).`)}function RP(e,t,n){let s=[Do,_p,gc];for(let r=0;r<e.length;++r){let a=e[r],o=t[r],i=n[r];if(o!=null){if(o===gc&&a.shape[a.shape.length-1]===1)throw new G(`You are passing a target array of shape ${a.shape} while using a loss 'categorical_crossentropy'. 'categorical_crossentropy'expects targets to be binary matrices (1s and 0s) of shape [samples, classes].`);if(s.indexOf(o)!==-1){let l=a.shape.slice(1),u=i.slice(1);for(let c=0;c<l.length;++c){let d=l[c],h=u[c];if(h!=null&&d!==h)throw new G(`A target Tensor with shape ${a.shape} was passed for an output of shape ${i}, while using a loss function that expects targets to have the same shape as the output.`)}}}}}function O3(e,t,n,s=!0,r=""){let a;if(Array.isArray(e)){if(e.length!==t.length)throw new G(`Error when checking model ${r}: the Array of Tensors that you are passing to your model is not the size the the model expected. Expected to see ${t.length} Tensor(s), but instead got ${e.length} Tensors(s).`);a=e}else{if(t.length>1)throw new G(`The model expects ${t.length} ${r} Tensors, but only received one Tensor. Found: array with shape ${JSON.stringify(e.shape)}.`);a=[e]}if(n!=null)for(let o=0;o<t.length;++o){if(n[o]==null)continue;let i=a[o];if(i.shape.length!==n[o].length)throw new G(`Error when checking ${r}: expected ${t[o]} to have ${n[o].length} dimension(s), but got array with shape ${JSON.stringify(i.shape)}`);for(let l=0;l<n[o].length;++l){if(l===0&&!s)continue;let u=i.shape[l],c=n[o][l];if(c!=null&&c!==u)throw new G(`Error when checking ${r}: expected ${t[o]} to have shape ${JSON.stringify(n[o])} but got array with shape ${JSON.stringify(i.shape)}.`)}}}function _P(e,t){if(e==null||Array.isArray(e)&&e.length===0)return t.map(s=>[]);let n;if(typeof e=="string"||typeof e=="function")n=[e];else if(Array.isArray(e)||typeof e=="object")n=e;else throw new TypeError(`Type of metrics argument not understood. Expected an string,function, Array, or Object, found: ${e}`);if(Array.isArray(n))return t.map(s=>n);{let s=[];for(let r of t){let a=n.hasOwnProperty(r)?n[r]:[];Array.isArray(a)||(a=[a]),s.push(a)}return s}}var $P="layers-model",wr=class extends tr{constructor(e){super(e);this.isTraining=!1}summary(e,t,n=console.log){if(!this.built)throw new G("This model has never been called, thus its weights have not been created yet. So no summary can be displayed. Build the model first (e.g., by calling it on some test data).");uP(this,e,t,n)}compile(e){if(e.loss==null&&(e.loss=[]),this.loss=e.loss,typeof e.optimizer=="string")this.optimizer_=lP(e.optimizer),this.isOptimizerOwned=!0;else{if(!(e.optimizer instanceof xr))throw new G("User-defined optimizer must be an instance of tf.Optimizer.");this.optimizer_=e.optimizer,this.isOptimizerOwned=!1}let t=[];if(!Array.isArray(e.loss)&&typeof e.loss!="string"&&typeof e.loss!="function"){e.loss=e.loss;for(let a in e.loss)if(this.outputNames.indexOf(a)===-1)throw new G(`Unknown entry in loss dictionary: "${a}". Only expected the following keys: ${this.outputNames}`);for(let a of this.outputNames)e.loss[a]==null&&console.warn(`Output "${a}" is missing from loss dictionary. We assume this was done on purpose, and we will not be expecting data to be passed to ${a} during training`),t.push(mg(e.loss[a]))}else if(Array.isArray(e.loss)){if(e.loss.length!==this.outputs.length)throw new G(`When passing an Array as loss, it should have one entry per model output. The model has ${this.outputs.length} output(s), but you passed loss=${e.loss}.`);t=e.loss.map(o=>mg(o))}else{let a=mg(e.loss);this.outputs.forEach(o=>{t.push(a)})}this.lossFunctions=t,this.feedOutputNames=[],this.feedOutputShapes=[],this.feedLossFns=[];for(let a=0;a<this.outputs.length;++a){let o=this.internalOutputShapes[a],i=this.outputNames[a];this.feedOutputNames.push(i),this.feedOutputShapes.push(o),this.feedLossFns.push(this.lossFunctions[a])}let n=[];this.metrics=e.metrics,this.metricsNames=["loss"],this.metricsTensors=[],$o("loss",()=>{for(let a=0;a<this.outputs.length;++a){if(n.indexOf(a)!==-1)continue;let o=this.lossFunctions[a];this.outputs.length>1&&(this.metricsTensors.push([o,a]),this.metricsNames.push(this.outputNames[a]+"_loss"))}});let s=_P(e.metrics,this.outputNames),r=(a,o,i)=>{this.outputNames.length>1&&(o=this.outputNames[a]+"_"+o),this.metricsNames.push(o),this.metricsTensors.push([i,a])};$o("metric",()=>{for(let a=0;a<this.outputs.length;++a){if(n.indexOf(a)!==-1)continue;let o=s[a];(l=>{let u="",c,d,h;for(let p of l){if(typeof p=="string"&&["accuracy","acc","crossentropy","ce"].indexOf(p)!==-1){let m=this.internalOutputShapes[a];m[m.length-1]===1||this.lossFunctions[a]===_p?["accuracy","acc"].indexOf(p)!==-1?d=Ag:["crossentropy","ce"].indexOf(p)!==-1&&(d=x3):this.lossFunctions[a]===Rp?["accuracy","acc"].indexOf(p)!==-1?d=b3:["crossentropy","ce"].indexOf(p)!==-1&&(d=v3):["accuracy","acc"].indexOf(p)!==-1?d=gg:["crossentropy","ce"].indexOf(p)!==-1&&(d=yg);let A;["accuracy","acc"].indexOf(p)!==-1?A="acc":["crossentropy","ce"].indexOf(p)!==-1&&(A="ce"),h=d,c=u+A}else h=iP(p),c=u+Dp(p);let f;$o(c,()=>{f=h}),r(a,c,f)}})(o)}}),this.collectedTrainableWeights=this.trainableWeights}checkTrainableWeightsConsistency(){this.collectedTrainableWeights!=null&&this.trainableWeights.length!==this.collectedTrainableWeights.length&&console.warn("Discrepancy between trainableweights and collected trainable weights. Did you set `model.trainable` without calling `model.compile()` afterwards?")}evaluate(e,t,n={}){let s=n.batchSize==null?32:n.batchSize;kg(s);let r=!0,a=this.standardizeUserDataXY(e,t,r,s);try{let o=a[0].concat(a[1]);this.makeTestFunction();let i=this.testFunction,l=this.testLoop(i,o,s,n.verbose,n.steps);return En(l)}finally{Po(a[0],e),Po(a[1],t)}}async evaluateDataset(e,t){return this.makeTestFunction(),SP(this,e,t)}checkNumSamples(e,t,n,s="steps"){let r;if(n!=null){if(r=null,t!=null)throw new G(`If ${s} is set, batchSize must be null or undefined.Got batchSize = ${t}`)}else if(e!=null)Array.isArray(e)?r=e[0].shape[0]:r=e.shape[0];else throw new G(`Either the input data should have a defined shape, or ${s} shoud be specified.`);return r}execute(e,t){if(Array.isArray(t)&&t.length===0)throw new G("`outputs` is an empty Array, which is not allowed.");let n=Array.isArray(t),s=n?t:[t],r=this.retrieveSymbolicTensors(s),a=new Oo;if(e instanceof Ue&&(e=[e]),Array.isArray(e)){if(e.length!==this.inputs.length)throw new G(`The number of inputs provided (${e.length}) does not match the number of inputs of this model (${this.inputs.length}).`);for(let i=0;i<this.inputs.length;++i)a.add(this.inputs[i],e[i])}else for(let i of this.inputs){let l=e[i.name];if(l==null)throw new G(`No value is provided for the model's input ${i.name}`);a.add(i,l)}let o=xc(r,a);return n?o:o[0]}retrieveSymbolicTensors(e){let t=Eo(null,e.length),n=e.length;for(let s of this.layers){let r=Array.isArray(s.output)?s.output:[s.output],a=r.map(o=>o.name);for(let o=0;o<e.length;++o){let i=a.indexOf(e[o]);if(i!==-1&&(t[o]=r[i],n--),n===0)break}if(n===0)break}if(n>0){let s=[];throw t.forEach((r,a)=>{r==null&&s.push(e[a])}),new G(`Cannot find SymbolicTensors for output name(s): ${JSON.stringify(s)}`)}return t}predictLoop(e,t=32,n=!1){return H(()=>{let s=this.checkNumSamples(e);if(n)throw new Oe("Verbose predictLoop() is not implemented yet.");let r=Sg(s,t),a=this.outputs.map(o=>[]);for(let o=0;o<r.length;++o)H(()=>{let l=r[o][0],u=r[o][1],c=bc(e,l,u),d=[];if(Array.isArray(c))for(let p=0;p<c.length;++p)d.push({key:this.inputs[p],value:c[p]});else d.push({key:this.inputs[0],value:c});let h=new Oo(d);return xc(this.outputs,h)}).forEach((l,u)=>a[u].push(l));return En(a.map(o=>ht(o,0)))})}predict(e,t={}){let n=$3(e);O3(n,this.inputNames,this.feedInputShapes,!1);try{let s=t.batchSize==null?32:t.batchSize;return kg(s),this.predictLoop(n,s)}finally{Po(n,e)}}predictOnBatch(e){O3(e,this.inputNames,this.feedInputShapes,!0);let t=(Array.isArray(e)?e[0]:e).shape[0];return this.predictLoop(e,t)}standardizeUserDataXY(e,t,n=!0,s){if(this.optimizer_==null)throw new Fs("You must compile a model before training/testing. Use LayersModel.compile(modelCompileArgs).");let r=[];for(let a=0;a<this.feedOutputShapes.length;++a){let o=this.feedOutputShapes[a];this.feedLossFns[a]===Rp?r.push(o.slice(0,o.length-1).concat([1])):r.push(o)}if(e=D3(e,this.feedInputNames,this.feedInputShapes,!1,"input"),t=D3(t,this.feedOutputNames,r,!1,"target"),EP(e,t,null),RP(t,this.feedLossFns,this.feedOutputShapes),this.stateful&&s!=null&&s>0&&e[0].shape[0]%s!=0)throw new G(`In a stateful network, you should only pass inputs with a number of samples that is divisible by the batch size ${s}. Found: ${e[0].shape[0]} sample(s).`);return[e,t]}async standardizeUserData(e,t,n,s,r=!0,a){let[o,i]=this.standardizeUserDataXY(e,t,r,a);if(n!=null)throw new Error("sample weight is not supported yet.");let l=null;if(s!=null){let u=T3(s,this.outputNames);l=[];for(let c=0;c<u.length;++c)l.push(await N3(i[c],null,u[c]))}return[o,i,l]}testLoop(e,t,n,s=0,r){return H(()=>{let a=this.checkNumSamples(t,n,r,"steps"),o=[];if(s>0)throw new Oe("Verbose mode is not implemented yet.");if(r!=null)throw new Oe("steps mode in testLoop() is not implemented yet");{let i=Sg(a,n),l=Ot(Ds(0,a));for(let u=0;u<i.length;++u){let c=i[u][0],d=i[u][1],h=Fo(l,c,d-c),p=Ig(t,h),f=e(p);if(u===0)for(let m=0;m<f.length;++m)o.push(Ie(0));for(let m=0;m<f.length;++m){let A=f[m];o[m]=ae(o[m],z(d-c,A))}}for(let u=0;u<o.length;++u)o[u]=de(o[u],a)}return o})}getDedupedMetricsNames(){let e=this.metricsNames,t=[];for(let n=0;n<e.length;++n){let s=e[n],r=s;Vb(e,s)>1&&(r+=`_${Vb(e.slice(0,n),s)}`),t.push(r)}return t}makeTrainFunction(){return e=>{let t=[],n=e.slice(0,this.inputs.length),s=e.slice(this.inputs.length,this.inputs.length+this.outputs.length),r=e.slice(this.inputs.length+this.outputs.length,this.inputs.length+this.outputs.length*2),a=[],o=()=>{let c=[];for(let f=0;f<this.inputs.length;++f)c.push({key:this.inputs[f],value:n[f]});let d=new Oo(c),h=xc(this.outputs,d,{training:!0}),p;for(let f=0;f<this.lossFunctions.length;++f){let A=this.lossFunctions[f](s[f],h[f]);r[f]!=null&&(A=xP(A,r[f]));let g=Et(A);t.push(g),f===0?p=A:p=ae(p,A)}for(let f=0;f<this.metricsTensors.length;++f){let m;if(this.outputs.length>1&&f<this.outputs.length)m=t[f];else{let A=this.metricsTensors[f][0],g=this.metricsTensors[f][1];m=Et(A(s[g],h[g]))}Kt(m),a.push(m)}return p=Et(p),this.calculateLosses().forEach(f=>{p=ae(p,f)}),p},i=this.collectedTrainableWeights.map(c=>c.read()),l=!0;return[this.optimizer_.minimize(o,l,i)].concat(a)}}makeTestFunction(){this.testFunction=e=>H(()=>{let t=[],n,s=e.slice(0,this.inputs.length),r=e.slice(this.inputs.length,this.inputs.length+this.outputs.length),a=[];for(let l=0;l<this.inputs.length;++l)a.push({key:this.inputs[l],value:s[l]});let o=new Oo(a),i=xc(this.outputs,o);for(let l=0;l<this.lossFunctions.length;++l){let u=this.lossFunctions[l],c=Et(u(r[l],i[l]));l===0?n=c:n=ae(n,c),t.push(n)}for(let l=0;l<this.metricsTensors.length;++l){let u=this.metricsTensors[l][0],c=this.metricsTensors[l][1],d=Et(u(r[c],i[c]));t.push(d)}return t})}async fit(e,t,n={}){return TP(this,e,t,n)}async fitDataset(e,t){return wP(this,e,t)}async trainOnBatch(e,t){let n=await this.standardizeUserData(e,t),s=n[0],r=n[1],o=this.makeTrainFunction()(s.concat(r)),i=[];for(let l of o){let u=await l.data();i.push(u[0])}return K(o),En(i)}getNamedWeights(e){let t=[],n=e!=null&&e.trainableOnly,s=n?this.trainableWeights:this.weights,r=this.getWeights(n);for(let a=0;a<s.length;++a)n&&!s[a].trainable||t.push({name:s[a].originalName,tensor:r[a]});return t}set stopTraining(e){this.stopTraining_=e}get stopTraining(){return this.stopTraining_}get optimizer(){return this.optimizer_}set optimizer(e){this.optimizer_!==e&&(this.optimizer_=e,this.isOptimizerOwned=!1)}dispose(){let e=super.dispose();if(e.refCountAfterDispose===0&&this.optimizer!=null&&this.isOptimizerOwned){let t=_h().numTensors;this.optimizer_.dispose(),e.numDisposedVariables+=t-_h().numTensors}return e}getLossIdentifiers(){let e;if(typeof this.loss=="string")e=vr(this.loss);else if(Array.isArray(this.loss)){for(let t of this.loss)if(typeof t!="string")throw new Error("Serialization of non-string loss is not supported.");e=this.loss.map(t=>vr(t))}else{let t=Object.keys(this.loss);e={};let n=this.loss;for(let s of t)if(typeof n[s]=="string")e[s]=vr(n[s]);else throw new Error("Serialization of non-string loss is not supported.")}return e}getMetricIdentifiers(){if(typeof this.metrics=="string"||typeof this.metrics=="function")return[vr(Dp(this.metrics))];if(Array.isArray(this.metrics))return this.metrics.map(e=>vr(Dp(e)));{let e={};for(let t in this.metrics)e[t]=vr(Dp(this.metrics[t]));return e}}getTrainingConfig(){return{loss:this.getLossIdentifiers(),metrics:this.getMetricIdentifiers(),optimizer_config:{class_name:this.optimizer.getClassName(),config:this.optimizer.getConfig()}}}loadTrainingConfig(e){if(e.weighted_metrics!=null)throw new Error("Loading weight_metrics is not supported yet.");if(e.loss_weights!=null)throw new Error("Loading loss_weights is not supported yet.");if(e.sample_weight_mode!=null)throw new Error("Loading sample_weight_mode is not supported yet.");let t=yc(e.optimizer_config),n=Ms(t),s;if(typeof e.loss=="string")s=Ro(e.loss);else if(Array.isArray(e.loss))s=e.loss.map(a=>Ro(a));else if(e.loss!=null){s={};for(let a in e.loss)s[a]=Ro(e.loss[a])}let r;if(Array.isArray(e.metrics))r=e.metrics.map(a=>Ro(a));else if(e.metrics!=null){r={};for(let a in e.metrics)r[a]=Ro(e.metrics[a])}this.compile({loss:s,metrics:r,optimizer:n})}async save(e,t){if(typeof e=="string"){let l=Tn.getSaveHandlers(e);if(l.length===0)throw new G(`Cannot find any save handlers for URL '${e}'`);if(l.length>1)throw new G(`Found more than one (${l.length}) save handlers for URL '${e}'`);e=l[0]}if(e.save==null)throw new G("LayersModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");let n=await Tn.encodeWeights(this.getNamedWeights(t)),s=!1,r=null,o={modelTopology:this.toJSON(r,s),format:$P,generatedBy:`TensorFlow.js tfjs-layers v${vg}`,convertedBy:null};if((t==null?!1:t.includeOptimizer)&&this.optimizer!=null){o.trainingConfig=this.getTrainingConfig();let l="optimizer",{data:u,specs:c}=await Tn.encodeWeights(await this.optimizer.getWeights(),l);n.specs.push(...c),n.data=Tn.concatenateArrayBuffers([n.data,u])}if(this.userDefinedMetadata!=null){let l=!0;k3(this.userDefinedMetadata,this.name,l),o.userDefinedMetadata=this.userDefinedMetadata}return o.weightData=n.data,o.weightSpecs=n.specs,e.save(o)}setUserDefinedMetadata(e){k3(e,this.name),this.userDefinedMetadata=e}getUserDefinedMetadata(){return this.userDefinedMetadata}};wr.className="Model";oe.registerClass(wr);var P3=class extends wr{};P3.className="Functional";oe.registerClass(P3);async function FP(e,t){"modelTopology"in e||(e={modelTopology:e}),e=e;let n=e.modelTopology;n.model_config!=null&&(n=n.model_config);let s=yc(n),r=Ms(s,t);if(e.weightsManifest!=null){let a=await Tn.loadWeights(e.weightsManifest,e.pathPrefix,r.weights.map(i=>i.originalName)),o={};for(let i of r.weights)o[i.originalName]=a[i.originalName];r.loadWeights(o),K(a)}return r}async function DP(e,t){if(t==null&&(t={}),typeof e=="string"){let n=Tn.getLoadHandlers(e,t);if(n.length===0)n.push(Tn.browserHTTPRequest(e,t));else if(n.length>1)throw new G(`Found more than one (${n.length}) load handlers for URL '${e}'`);e=n[0]}return OP(e,void 0,t)}async function OP(e,t,n){if(n==null&&(n={}),e.load==null)throw new G("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");let s=await e.load(),r=s.modelTopology;r.model_config!=null&&(r=r.model_config);let a=n.strict==null?!0:n.strict,o=s.weightData!=null&&s.weightSpecs!=null&&a,i=Ms(yc(r),t,o),l=s.trainingConfig;if(l!=null&&i.loadTrainingConfig(l),s.userDefinedMetadata!=null&&i.setUserDefinedMetadata(s.userDefinedMetadata),s.weightData!=null){if(s.weightSpecs==null)throw new G("LayersModel artifacts contains weight data, but not weight specs. Therefore loading of weights cannot proceed.");let{modelWeights:u,optimizerWeights:c}=PP(s.weightData,s.weightSpecs);i.loadWeights(u,a),i.optimizer!=null&&c.length>0&&await i.optimizer.setWeights(c),K(u),K(c.map(d=>d.tensor))}return i}function PP(e,t){let n=Tn.decodeWeights(e,t),s={},r=[];return t.forEach(a=>{a.group==="optimizer"?r.push({name:a.name,tensor:n[a.name]}):s[a.name]=n[a.name]}),{modelWeights:s,optimizerWeights:r}}var Ml=class extends wr{constructor(e){super({inputs:[],outputs:[]});if(e=e||{},this.trainable=!0,this.built=!1,this.name=e.name!=null?e.name:Ip("sequential_"),e.layers!=null)for(let t of e.layers)this.add(t)}checkShape(e){if(e.inboundNodes[0].outputTensors[0].shape.some(n=>n<0))throw new G(`Negative dimension size caused by adding layer ${e.name} with input shape [${e.inboundNodes[0].inputTensors[0].shape}]`)}add(e){let t=e instanceof Ml||e instanceof wr,n;if(t){if(n=e,n.outputs.length!==1)throw new G("All layers in a Sequential model should have a single output tensor. For multi-output layers, use the functional API.");if(n.inputs.length!==1)throw new G("All layers in a Sequential model should have a single input tensor. For multi-input layers, use the functional API.")}if(this.outputs.length===0){if(e.inboundNodes.length===0){if(e.batchInputShape==null)throw new G("The first layer in a Sequential model must get an `inputShape` or `batchInputShape` argument.");let s=u3({batchShape:e.batchInputShape,dtype:e.dtype,name:e.name+"_input"});e.apply(s)}if(t)this.outputs=n.outputs,this.inputs=n.inputs;else{if(e.inboundNodes.length!==1)throw new G(`A layer added to a Sequential model must not already be connected somewhere else. LayersModel received layer ${e.name} which has ${e.inboundNodes.length} pre-existing inbound connections.`);if(e.inboundNodes[0].outputTensors.length!==1)throw new G("All layers in a Sequential model should have a single output tensor. For multi-output layers, use the functional API.");this.checkShape(e),this.outputs=[e.inboundNodes[0].outputTensors[0]],this.inputs=l3(this.outputs[0])}this.inboundNodes=[],new Tp({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:this.inputs,outputTensors:this.outputs,inputMasks:Eo(null,this.inputs.length),outputMasks:[null],inputShapes:this.inputs.map(s=>s.shape),outputShapes:this.outputs[0].shape})}else{let s=e.apply(this.outputs[0]);if(Array.isArray(s))throw new TypeError("All layers in a Sequential model should have a single output tensor. For multi-output layers, use the functional API.");this.checkShape(e),this.outputs=[s],this.inboundNodes[0].outputTensors=this.outputs,this.inboundNodes[0].outputShapes=[this.outputs[0].shape]}this.layers.push(e),this.built=!1}pop(){if(this.layers.length===0)throw new TypeError("There are no layers in the model.");if(this.layers.pop(),this.layers.length===0)this.outputs=[],this.inboundNodes=[],this.outboundNodes=[];else{let e=this.layers.length-1;this.layers[e].outboundNodes=[],this.outputs=[this.layers[e].output],this.inboundNodes[0].outputTensors=this.outputs,this.inboundNodes[0].outputShapes=[this.outputs[0].shape]}}call(e,t){return this.model==null&&this.build(),this.model.call(e,t)}build(e){if(st(e),this.inputs.length===0||this.outputs.length===0)throw new TypeError("Sequential model cannot be built: model is empty. Add some layers first.");this.model=new wr({inputs:this.inputs,outputs:this.outputs[0],name:this.name+"_model"}),this.model.trainable=this.trainable,this.supportsMasking=this.model.supportsMasking,this.inputLayers=this.model.inputLayers,this.inputLayersNodeIndices=this.model.inputLayersNodeIndices,this.inputLayersTensorIndices=this.model.inputLayersTensorIndices,this.outputLayers=this.model.outputLayers,this.outputLayersNodeIndices=this.model.outputLayersNodeIndices,this.outputLayersTensorIndices=this.model.outputLayersTensorIndices,this.nodesByDepth=this.model.nodesByDepth,this.containerNodes=this.model.containerNodes,this.outputNames=this.model.outputNames,this.inputNames=this.model.inputNames,this.built=!0}countParams(){return this.built||this.build(),super.countParams()}summary(e,t,n=console.log){this.built||this.build(),super.summary(e,t,n)}setWeights(e){this.model==null&&this.build(),this.model.setWeights(e)}evaluate(e,t,n={}){if(!this.built)throw new Fs("The model needs to be compiled before being used.");return this.model.evaluate(e,t,n)}async evaluateDataset(e,t){if(!this.built)throw new Fs("The model needs to be compiled before being used.");return this.model.evaluateDataset(e,t)}predict(e,t={}){return this.model==null&&this.build(),this.model.predict(e,t)}predictOnBatch(e){return this.model==null&&this.build(),this.model.predictOnBatch(e)}compile(e){this.build(),this.model.compile(e),this.optimizer_=this.model.optimizer,this.isOptimizerOwned=this.model.isOptimizerOwned,this.loss=this.model.loss,this.metrics=this.model.metrics,this.metricsTensors=this.model.metricsTensors,this.metricsNames=this.model.metricsNames}get optimizer(){return this.model==null?void 0:this.model.optimizer}set optimizer(e){this.model.optimizer=e}async fit(e,t,n={}){if(!this.built)throw new Fs("The model needs to be compiled before being used.");return this.model.fit(e,t,n)}async fitDataset(e,t){if(!this.built)throw new Fs("The model needs to be compiled before being used.");return this.model.fitDataset(e,t)}async trainOnBatch(e,t){return this.model.trainOnBatch(e,t)}static fromConfig(e,t,n={},s=!1){let r,a={};if(t instanceof Array){if(t[0].className==null||t[0].className==="Merge")throw new G("Legacy serialization format not supported yet.");r=t}else I.assert(t.layers!=null,()=>"When the config data for a Sequential model is not an Array, it must be an Object that contains the 'layers' field."),r=t.layers,delete t.layers,a=t;let o=new e(a);if(!(o instanceof Ml))throw new Oe(`Sequential.fromConfig called on non-Sequential input: ${o}`);for(let i of r){let u=Ms(i,void 0,s);s&&u.setFastWeightInitDuringBuild(!0),o.add(u)}return o}set stopTraining(e){if(this.model==null)throw new G("Cannot set the stopTraining property of a sequential model before it is compiled.");this.model.stopTraining=e}get stopTraining(){if(this.model==null)throw new G("Cannot get the stopTraining property of a sequential model before it is compiled.");return this.model.stopTraining}getConfig(){let e=[];for(let t of this.layers){let n={};n.className=t.getClassName(),n.config=t.getConfig(),e.push(n)}return{name:this.name,layers:e}}};Ml.className="Sequential";oe.registerClass(Ml);function MP(e){return new wr(e)}function zP(e){return new Ml(e)}function LP(e,t){return t==null&&(t={}),DP(e,t)}function M3(e){return u3(e)}function BP(e,t){ks.registerCallbackConstructor(e,t)}var _n=class extends oe.Serializable{getConfig(){return{}}},z3=class extends _n{apply(e,t=1){return hO(e,t)}};z3.className="elu";oe.registerClass(z3);var L3=class extends _n{apply(e){return RA(e)}};L3.className="selu";oe.registerClass(L3);var B3=class extends _n{apply(e){return Ys(e)}};B3.className="relu";oe.registerClass(B3);var W3=class extends _n{apply(e){return H(()=>rc(6,Ys(e)))}};W3.className="relu6";oe.registerClass(W3);var V3=class extends _n{apply(e){return e}};V3.className="linear";oe.registerClass(V3);var U3=class extends _n{apply(e){return Bn(e)}};U3.className="sigmoid";oe.registerClass(U3);var H3=class extends _n{apply(e){return fO(e)}};H3.className="hardSigmoid";oe.registerClass(H3);var G3=class extends _n{apply(e){return Tl(e)}};G3.className="softplus";oe.registerClass(G3);var j3=class extends _n{apply(e){return pO(e)}};j3.className="softsign";oe.registerClass(j3);var q3=class extends _n{apply(e){return wl(e)}};q3.className="tanh";oe.registerClass(q3);var Tg=class extends _n{apply(e,t=-1){return Zh(e,t)}};Tg.className="softmax";oe.registerClass(Tg);var X3=class extends _n{apply(e,t=-1){return xA(e,t)}};X3.className="logSoftmax";oe.registerClass(X3);var K3=class extends _n{apply(e,t=1){return H(()=>z(Bn(z(e,t)),e))}};K3.className="swish";oe.registerClass(K3);var Z3=class extends _n{apply(e){return H(()=>z(e,wl(Tl(e))))}};Z3.className="mish";oe.registerClass(Z3);function Jr(e){return e.getClassName()}function Ng(e,t={}){return dc(e,oe.SerializationMap.getMap().classNameMap,t,"activation")}function Qr(e){if(e==null){let t={};return t.className="linear",t.config={},Ng(t)}if(typeof e=="string"){let t={};return t.className=e,t.config={},Ng(t)}else return e instanceof _n?e:Ng(e)}function Eg(e){if(e!=null&&typeof e!="object")throw new Error(`Argument to L1L2 regularizer's constructor is expected to be an object, but received: ${e}`)}var Y3=class extends oe.Serializable{},vc=class extends Y3{constructor(e){super();Eg(e),this.l1=e==null||e.l1==null?.01:e.l1,this.l2=e==null||e.l2==null?.01:e.l2,this.hasL1=this.l1!==0,this.hasL2=this.l2!==0}apply(e){return H(()=>{let t=Dt([1]);return this.hasL1&&(t=ae(t,ve(z(this.l1,Wt(e))))),this.hasL2&&(t=ae(t,ve(z(this.l2,mc(e))))),U(t,[])})}getConfig(){return{l1:this.l1,l2:this.l2}}static fromConfig(e,t){return new e({l1:t.l1,l2:t.l2})}};vc.className="L1L2";oe.registerClass(vc);function WP(e){return Eg(e),new vc({l1:e!=null?e.l1:null,l2:0})}function VP(e){return Eg(e),new vc({l2:e!=null?e.l2:null,l1:0})}var J3={l1l2:"L1L2"};function ut(e){return jA(e)}function Q3(e,t={}){return dc(e,oe.SerializationMap.getMap().classNameMap,t,"regularizer")}function vt(e){if(e==null)return null;if(typeof e=="string"){let n={className:e in J3?J3[e]:e,config:{}};return Q3(n)}else return e instanceof Y3?e:Q3(e)}var Rg=class extends Xe{constructor(e){super(e==null?{}:e);this.supportsMasking=!0,e!=null&&(this.maxValue=e.maxValue)}call(e,t){e=ze(e);let n=Ys(e);return this.maxValue!=null&&(n=Wn(n,0,this.maxValue)),n}computeOutputShape(e){return e}getConfig(){let e={maxValue:this.maxValue},t=super.getConfig();return Object.assign(e,t),e}};Rg.className="ReLU";oe.registerClass(Rg);var _g=class extends Xe{constructor(e){super(e==null?{}:e);this.DEFAULT_ALPHA=.3,e==null&&(e={}),this.alpha=e.alpha==null?this.DEFAULT_ALPHA:e.alpha}call(e,t){let n=ze(e);return Lh(n,this.alpha)}computeOutputShape(e){return e}getConfig(){let e={alpha:this.alpha},t=super.getConfig();return Object.assign(e,t),e}};_g.className="LeakyReLU";oe.registerClass(_g);var $g=class extends Xe{constructor(e){super(e==null?{}:e);if(this.DEFAULT_ALPHA_INITIALIZER="zeros",e==null&&(e={}),this.supportsMasking=!0,this.alphaInitializer=bt(e.alphaInitializer||this.DEFAULT_ALPHA_INITIALIZER),this.alphaRegularizer=vt(e.alphaRegularizer),this.alphaConstraint=Gt(e.alphaConstraint),e.sharedAxes==null)this.sharedAxes=null;else if(Array.isArray(e.sharedAxes))this.sharedAxes=e.sharedAxes;else if(typeof e.sharedAxes=="number")this.sharedAxes=[e.sharedAxes];else throw new G(`Expected sharedAxes to be a number or an array of numbers, but got ${e.sharedAxes}`)}build(e){e=st(e);let t=e.slice(1);if(this.sharedAxes!=null)for(let s of this.sharedAxes)t[s-1]=1;this.alpha=this.addWeight("alpha",t,"float32",this.alphaInitializer,this.alphaRegularizer,!0,this.alphaConstraint);let n={};if(this.sharedAxes!=null)for(let s=1;s<e.length;++s)n[s]=e[s];this.inputSpec=[new Pt({ndim:e.length,axes:n})],this.built=!0}call(e,t){return e=ze(e),qh(e,this.alpha.read())}getConfig(){let e={alphaInitializer:Ct(this.alphaInitializer),alphaRegularizer:ut(this.alphaRegularizer),alphaConstraint:Ht(this.alphaConstraint),sharedAxes:this.sharedAxes},t=super.getConfig();return Object.assign(e,t),e}};$g.className="PReLU";oe.registerClass($g);var Fg=class extends Xe{constructor(e){super(e==null?{}:e);if(this.DEFAULT_ALPHA=1,e==null&&(e={}),e.alpha!=null&&e.alpha!==this.DEFAULT_ALPHA)throw new Oe(`Non-default alpha value (${e.alpha}) is not supported by the ELU layer yet.`);this.alpha=e.alpha==null?this.DEFAULT_ALPHA:e.alpha}call(e,t){let n=ze(e);return nc(n)}computeOutputShape(e){return e}getConfig(){let e={alpha:this.alpha},t=super.getConfig();return Object.assign(e,t),e}};Fg.className="ELU";oe.registerClass(Fg);var Dg=class extends Xe{constructor(e){super(e==null?{}:e);this.DEFAULT_THETA=1,e==null&&(e={}),this.theta=e.theta==null?this.DEFAULT_THETA:e.theta}call(e,t){let n=ze(e);return z(n,ce(Vn(n,this.theta),"float32"))}computeOutputShape(e){return e}getConfig(){let e={theta:this.theta},t=super.getConfig();return Object.assign(e,t),e}};Dg.className="ThresholdedReLU";oe.registerClass(Dg);var Og=class extends Xe{constructor(e){super(e==null?{}:e);this.DEFAULT_AXIS=1,e==null&&(e={}),this.softmax=new Tg().apply,this.axis=e.axis==null?this.DEFAULT_AXIS:e.axis}call(e,t){let n=ze(e);return this.softmax(n,this.axis)}computeOutputShape(e){return e}getConfig(){let e={axis:this.axis},t=super.getConfig();return Object.assign(e,t),e}};Og.className="Softmax";oe.registerClass(Og);function zl(e,t,n){if(typeof e=="number")return Eo(e,t);if(e.length!==t)throw new G(`The ${n} argument must be an integer or tuple of ${t} integers. Received: ${e.length} elements.`);for(let s=0;s<t;++s){let r=e[s];if(!lO(r))throw new G(`The ${n} argument must be an integer or tuple of ${t} integers. Received: ${JSON.stringify(e)} including a non-integer number ${r}`)}return e}function zs(e,t,n,s,r=1){if(e==null)return e;let a=t+(t-1)*(r-1),o;return n==="same"?o=e:o=e-a+1,Math.floor((o+s-1)/s)}function nr(e,t,n,s){if(e==null)return null;if(s==="valid")e=e*t+Zr([n-t,0]);else if(s==="same")e=e*t;else throw new G(`Unsupport padding mode: ${s}.`);return e}function Pg(e,t){return H(()=>($t(t),t==="channelsFirst"?je(e,[0,2,3,1]):e))}function ev(e,t){return H(()=>($t(t),t==="channelsFirst"?je(e,[0,2,3,4,1]):e))}function UP(e,t,n,s=1,r="valid",a,o=1){return H(()=>{if(a==null&&(a=$s()),$t(a),e.shape.length!==3)throw new G(`The input of a conv1dWithBias operation should be 3, but is ${e.shape.length} instead.`);if(t.shape.length!==3)throw new G(`The kernel for a conv1dWithBias operation should be 3, but is ${t.shape.length} instead`);if(n!=null&&n.shape.length!==1)throw new G(`The bias for a conv1dWithBias operation should be 1, but is ${t.shape.length} instead`);if(a==="channelsFirst"&&(e=je(e,[0,2,1])),r==="causal")throw new Oe("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");let i=dA(e,t,s,r==="same"?"same":"valid","NWC",o);return n!=null&&(i=Os(i,n)),i})}function tv(e,t,n,s=[1,1],r="valid",a,o,i=null){return H(()=>{if(a==null&&(a=$s()),$t(a),e.rank!==3&&e.rank!==4)throw new G(`conv2dWithBiasActivation expects input to be of rank 3 or 4, but received ${e.rank}.`);if(t.rank!==3&&t.rank!==4)throw new G(`conv2dWithBiasActivation expects kernel to be of rank 3 or 4, but received ${e.rank}.`);let l=Pg(e,a);if(r==="causal")throw new Oe("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");return l=qr.conv2d({x:l,filter:t,strides:s,pad:r==="same"?"same":"valid",dilations:o,dataFormat:"NHWC",bias:n,activation:i}),a==="channelsFirst"&&(l=je(l,[0,3,1,2])),l})}function HP(e,t,n,s=[1,1,1],r="valid",a,o){return H(()=>{if(a==null&&(a=$s()),$t(a),e.rank!==4&&e.rank!==5)throw new G(`conv3dWithBias expects input to be of rank 4 or 5, but received ${e.rank}.`);if(t.rank!==4&&t.rank!==5)throw new G(`conv3dWithBias expects kernel to be of rank 4 or 5, but received ${e.rank}.`);let i=ev(e,a);if(r==="causal")throw new Oe("The support for CAUSAL padding mode in conv3dWithBias is not implemented yet.");return i=fA(i,t,s,r==="same"?"same":"valid","NDHWC",o),n!=null&&(i=Os(i,n)),a==="channelsFirst"&&(i=je(i,[0,4,1,2,3])),i})}var Mg=class extends Xe{constructor(e,t){super(t);if(this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",Mg.verifyArgs(t),this.rank=e,Zt(this.rank,"rank"),this.rank!==1&&this.rank!==2&&this.rank!==3)throw new Oe(`Convolution layer for rank other than 1, 2, or 3 (${this.rank}) is not implemented yet.`);if(this.kernelSize=zl(t.kernelSize,e,"kernelSize"),this.strides=zl(t.strides==null?1:t.strides,e,"strides"),this.padding=t.padding==null?"valid":t.padding,hs(this.padding),this.dataFormat=t.dataFormat==null?"channelsLast":t.dataFormat,$t(this.dataFormat),this.activation=Qr(t.activation),this.useBias=t.useBias==null?!0:t.useBias,this.biasInitializer=bt(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.biasConstraint=Gt(t.biasConstraint),this.biasRegularizer=vt(t.biasRegularizer),this.activityRegularizer=vt(t.activityRegularizer),this.dilationRate=zl(t.dilationRate==null?1:t.dilationRate,e,"dilationRate"),this.rank===1&&Array.isArray(this.dilationRate)&&this.dilationRate.length!==1)throw new G(`dilationRate must be a number or an array of a single number for 1D convolution, but received ${JSON.stringify(this.dilationRate)}`);if(this.rank===2){if(typeof this.dilationRate=="number")this.dilationRate=[this.dilationRate,this.dilationRate];else if(this.dilationRate.length!==2)throw new G(`dilationRate must be a number or array of two numbers for 2D convolution, but received ${JSON.stringify(this.dilationRate)}`)}else if(this.rank===3){if(typeof this.dilationRate=="number")this.dilationRate=[this.dilationRate,this.dilationRate,this.dilationRate];else if(this.dilationRate.length!==3)throw new G(`dilationRate must be a number or array of three numbers for 3D convolution, but received ${JSON.stringify(this.dilationRate)}`)}}static verifyArgs(e){if(Qs("kernelSize"in e,"required key 'kernelSize' not in config"),typeof e.kernelSize!="number"&&!XA(e.kernelSize,"number",1,3))throw new G(`BaseConv expects config.kernelSize to be number or number[] with length 1, 2, or 3, but received ${JSON.stringify(e.kernelSize)}.`)}getConfig(){let e={kernelSize:this.kernelSize,strides:this.strides,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,activation:Jr(this.activation),useBias:this.useBias,biasInitializer:Ct(this.biasInitializer),biasRegularizer:ut(this.biasRegularizer),activityRegularizer:ut(this.activityRegularizer),biasConstraint:Ht(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}},wc=class extends Mg{constructor(e,t){super(e,t);this.kernel=null,wc.verifyArgs(t),this.filters=t.filters,Zt(this.filters,"filters"),this.kernelInitializer=bt(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.kernelConstraint=Gt(t.kernelConstraint),this.kernelRegularizer=vt(t.kernelRegularizer)}build(e){e=st(e);let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new G(`The channel dimension of the input should be defined. Found ${e[t]}`);let n=e[t],s=this.kernelSize.concat([n,this.filters]);this.kernel=this.addWeight("kernel",s,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[{ndim:this.rank+2,axes:{[t]:n}}],this.built=!0}call(e,t){return H(()=>{e=ze(e);let n,s=this.bias==null?null:this.bias.read(),r=Hb(this.activation.getClassName());if(r!=null&&this.rank===2)n=tv(e,this.kernel.read(),s,this.strides,this.padding,this.dataFormat,this.dilationRate,r);else{if(this.rank===1)n=UP(e,this.kernel.read(),s,this.strides[0],this.padding,this.dataFormat,this.dilationRate[0]);else if(this.rank===2)n=tv(e,this.kernel.read(),s,this.strides,this.padding,this.dataFormat,this.dilationRate);else if(this.rank===3)n=HP(e,this.kernel.read(),s,this.strides,this.padding,this.dataFormat,this.dilationRate);else throw new Oe("convolutions greater than 3D are not implemented yet.");this.activation!=null&&(n=this.activation.apply(n))}return n})}computeOutputShape(e){e=st(e);let t=[],n=this.dataFormat==="channelsLast"?e.slice(1,e.length-1):e.slice(2);for(let r=0;r<n.length;++r){let a=zs(n[r],this.kernelSize[r],this.padding,this.strides[r],typeof this.dilationRate=="number"?this.dilationRate:this.dilationRate[r]);t.push(a)}let s=[e[0]];return this.dataFormat==="channelsLast"?(s=s.concat(t),s.push(this.filters)):(s.push(this.filters),s=s.concat(t)),s}getConfig(){let e={filters:this.filters,kernelInitializer:Ct(this.kernelInitializer),kernelRegularizer:ut(this.kernelRegularizer),kernelConstraint:Ht(this.kernelConstraint)},t=super.getConfig();return Object.assign(e,t),e}static verifyArgs(e){if(!("filters"in e)||typeof e.filters!="number"||e.filters<1)throw new G(`Convolution layer expected config.filters to be a 'number' > 0 but got ${JSON.stringify(e.filters)}`)}},kc=class extends wc{constructor(e){super(2,e);kc.verifyArgs(e)}getConfig(){let e=super.getConfig();return delete e.rank,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!XA(e.kernelSize,"number",1,2))throw new G(`Conv2D expects config.kernelSize to be number or number[] with length 1 or 2, but received ${JSON.stringify(e.kernelSize)}.`)}};kc.className="Conv2D";oe.registerClass(kc);var Ic=class extends wc{constructor(e){super(3,e);Ic.verifyArgs(e)}getConfig(){let e=super.getConfig();return delete e.rank,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!(Array.isArray(e.kernelSize)&&(e.kernelSize.length===1||e.kernelSize.length===3)))throw new G(`Conv3D expects config.kernelSize to be number or [number, number, number], but received ${JSON.stringify(e.kernelSize)}.`)}};Ic.className="Conv3D";oe.registerClass(Ic);var zg=class extends kc{constructor(e){super(e);if(this.inputSpec=[new Pt({ndim:4})],this.padding!=="same"&&this.padding!=="valid")throw new G(`Conv2DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(e){if(e=st(e),e.length!==4)throw new G("Input should have rank 4; Received input shape: "+JSON.stringify(e));let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new G("The channel dimension of the inputs should be defined. Found `None`.");let n=e[t],s=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight("kernel",s,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new Pt({ndim:4,axes:{[t]:n}})],this.built=!0}call(e,t){return H(()=>{let n=ze(e);if(n.shape.length!==4)throw new G(`Conv2DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${n.shape.length}`);let s=n.shape,r=s[0],a,o;this.dataFormat==="channelsFirst"?(a=2,o=3):(a=1,o=2);let i=s[a],l=s[o],u=this.kernelSize[0],c=this.kernelSize[1],d=this.strides[0],h=this.strides[1],p=nr(i,d,u,this.padding),f=nr(l,h,c,this.padding),m=[r,p,f,this.filters];this.dataFormat!=="channelsLast"&&(n=je(n,[0,2,3,1]));let A=pA(n,this.kernel.read(),m,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(A=je(A,[0,3,1,2])),this.bias!=null&&(A=Os(A,this.bias.read(),this.dataFormat)),this.activation!=null&&(A=this.activation.apply(A)),A})}computeOutputShape(e){e=st(e);let t=e.slice(),n,s,r;this.dataFormat==="channelsFirst"?(n=1,s=2,r=3):(n=3,s=1,r=2);let a=this.kernelSize[0],o=this.kernelSize[1],i=this.strides[0],l=this.strides[1];return t[n]=this.filters,t[s]=nr(t[s],i,a,this.padding),t[r]=nr(t[r],l,o,this.padding),t}getConfig(){let e=super.getConfig();return delete e.dilationRate,e}};zg.className="Conv2DTranspose";oe.registerClass(zg);var Lg=class extends Ic{constructor(e){super(e);if(this.inputSpec=[new Pt({ndim:5})],this.padding!=="same"&&this.padding!=="valid")throw new G(`Conv3DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(e){if(e=st(e),e.length!==5)throw new G("Input should have rank 5; Received input shape: "+JSON.stringify(e));let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new G("The channel dimension of the inputs should be defined. Found `None`.");let n=e[t],s=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight("kernel",s,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new Pt({ndim:5,axes:{[t]:n}})],this.built=!0}call(e,t){return H(()=>{let n=ze(e);if(n.shape.length!==5)throw new G(`Conv3DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${n.shape.length}`);let s=n.shape,r=s[0],a,o,i;this.dataFormat==="channelsFirst"?(i=2,a=3,o=4):(i=1,a=2,o=3);let l=s[i],u=s[a],c=s[o],d=this.kernelSize[0],h=this.kernelSize[1],p=this.kernelSize[2],f=this.strides[0],m=this.strides[1],A=this.strides[2],g=nr(l,f,d,this.padding),y=nr(u,m,h,this.padding),x=nr(c,A,p,this.padding),b=[r,g,y,x,this.filters];this.dataFormat!=="channelsLast"&&(n=je(n,[0,2,3,4,1]));let v=Ox(n,this.kernel.read(),b,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(v=je(v,[0,4,1,2,3])),this.bias!==null&&(v=Os(v,this.bias.read(),this.dataFormat)),this.activation!==null&&(v=this.activation.apply(v)),v})}computeOutputShape(e){e=st(e);let t=e.slice(),n,s,r,a;this.dataFormat==="channelsFirst"?(n=1,s=2,r=3,a=4):(n=4,s=1,r=2,a=3);let o=this.kernelSize[0],i=this.kernelSize[1],l=this.kernelSize[2],u=this.strides[0],c=this.strides[1],d=this.strides[2];return t[n]=this.filters,t[s]=nr(t[s],u,o,this.padding),t[r]=nr(t[r],c,i,this.padding),t[a]=nr(t[a],d,l,this.padding),t}getConfig(){let e=super.getConfig();return delete e.dilationRate,e}};Lg.className="Conv3DTranspose";oe.registerClass(Lg);var nv=class extends wc{constructor(e,t){super(e,t);if(this.DEFAULT_DEPTHWISE_INITIALIZER="glorotUniform",this.DEFAULT_POINTWISE_INITIALIZER="glorotUniform",this.depthwiseKernel=null,this.pointwiseKernel=null,t.filters==null)throw new G("The `filters` configuration field is required by SeparableConv, but is unspecified.");if(t.kernelInitializer!=null||t.kernelRegularizer!=null||t.kernelConstraint!=null)throw new G("Fields kernelInitializer, kernelRegularizer and kernelConstraint are invalid for SeparableConv2D. Use depthwiseInitializer, depthwiseRegularizer, depthwiseConstraint, pointwiseInitializer, pointwiseRegularizer and pointwiseConstraint instead.");if(t.padding!=null&&t.padding!=="same"&&t.padding!=="valid")throw new G(`SeparableConv${this.rank}D supports only padding modes: 'same' and 'valid', but received ${JSON.stringify(t.padding)}`);this.depthMultiplier=t.depthMultiplier==null?1:t.depthMultiplier,this.depthwiseInitializer=bt(t.depthwiseInitializer||this.DEFAULT_DEPTHWISE_INITIALIZER),this.depthwiseRegularizer=vt(t.depthwiseRegularizer),this.depthwiseConstraint=Gt(t.depthwiseConstraint),this.pointwiseInitializer=bt(t.depthwiseInitializer||this.DEFAULT_POINTWISE_INITIALIZER),this.pointwiseRegularizer=vt(t.pointwiseRegularizer),this.pointwiseConstraint=Gt(t.pointwiseConstraint)}build(e){if(e=st(e),e.length<this.rank+2)throw new G(`Inputs to SeparableConv${this.rank}D should have rank ${this.rank+2}, but received input shape: ${JSON.stringify(e)}`);let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null||e[t]<0)throw new G(`The channel dimension of the inputs should be defined, but found ${JSON.stringify(e[t])}`);let n=e[t],s=this.kernelSize.concat([n,this.depthMultiplier]),r=[];for(let o=0;o<this.rank;++o)r.push(1);r.push(n*this.depthMultiplier,this.filters);let a=!0;this.depthwiseKernel=this.addWeight("depthwise_kernel",s,"float32",this.depthwiseInitializer,this.depthwiseRegularizer,a,this.depthwiseConstraint),this.pointwiseKernel=this.addWeight("pointwise_kernel",r,"float32",this.pointwiseInitializer,this.pointwiseRegularizer,a,this.pointwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,a,this.biasConstraint):this.bias=null,this.inputSpec=[new Pt({ndim:this.rank+2,axes:{[t]:n}})],this.built=!0}call(e,t){return H(()=>{e=ze(e);let n;if(this.rank===1)throw new Oe("1D separable convolution is not implemented yet.");return this.rank===2&&(this.dataFormat==="channelsFirst"&&(e=je(e,[0,2,3,1])),n=sb(e,this.depthwiseKernel.read(),this.pointwiseKernel.read(),this.strides,this.padding,this.dilationRate,"NHWC")),this.useBias&&(n=Os(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),this.dataFormat==="channelsFirst"&&(n=je(n,[0,3,1,2])),n})}getConfig(){let e=super.getConfig();return delete e.rank,delete e.kernelInitializer,delete e.kernelRegularizer,delete e.kernelConstraint,e.depthwiseInitializer=Ct(this.depthwiseInitializer),e.pointwiseInitializer=Ct(this.pointwiseInitializer),e.depthwiseRegularizer=ut(this.depthwiseRegularizer),e.pointwiseRegularizer=ut(this.pointwiseRegularizer),e.depthwiseConstraint=Ht(this.depthwiseConstraint),e.pointwiseConstraint=Ht(this.pointwiseConstraint),e}};nv.className="SeparableConv";var Bg=class extends nv{constructor(e){super(2,e)}};Bg.className="SeparableConv2D";oe.registerClass(Bg);var Pp=class extends wc{constructor(e){super(1,e);Pp.verifyArgs(e),this.inputSpec=[{ndim:3}]}getConfig(){let e=super.getConfig();return delete e.rank,delete e.dataFormat,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!XA(e.kernelSize,"number",1,1))throw new G(`Conv1D expects config.kernelSize to be number or number[] with length 1, but received ${JSON.stringify(e.kernelSize)}.`)}};Pp.className="Conv1D";oe.registerClass(Pp);var Wg=class extends Xe{constructor(e){super(e);typeof e.cropping=="number"?this.cropping=[[e.cropping,e.cropping],[e.cropping,e.cropping]]:typeof e.cropping[0]=="number"?this.cropping=[[e.cropping[0],e.cropping[0]],[e.cropping[1],e.cropping[1]]]:this.cropping=e.cropping,this.dataFormat=e.dataFormat===void 0?"channelsLast":e.dataFormat,this.inputSpec=[{ndim:4}]}computeOutputShape(e){return this.dataFormat==="channelsFirst"?[e[0],e[1],e[2]-this.cropping[0][0]-this.cropping[0][1],e[3]-this.cropping[1][0]-this.cropping[1][1]]:[e[0],e[1]-this.cropping[0][0]-this.cropping[0][1],e[2]-this.cropping[1][0]-this.cropping[1][1],e[3]]}call(e,t){return H(()=>{if(e=ze(e),this.dataFormat==="channelsLast"){let n=fp(e,this.cropping[0][0],e.shape[1]-this.cropping[0][0]-this.cropping[0][1],2);return fp(n,this.cropping[1][0],e.shape[2]-this.cropping[1][1]-this.cropping[1][0],3)}else{let n=fp(e,this.cropping[0][0],e.shape[2]-this.cropping[0][0]-this.cropping[0][1],3);return fp(n,this.cropping[1][0],e.shape[3]-this.cropping[1][1]-this.cropping[1][0],4)}})}getConfig(){let e={cropping:this.cropping,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};Wg.className="Cropping2D";oe.registerClass(Wg);var Vg=class extends Xe{constructor(e){super(e);this.DEFAULT_SIZE=[2,2],this.inputSpec=[{ndim:4}],this.size=e.size==null?this.DEFAULT_SIZE:e.size,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,$t(this.dataFormat),this.interpolation=e.interpolation==null?"nearest":e.interpolation,aO(this.interpolation)}computeOutputShape(e){if(this.dataFormat==="channelsFirst"){let t=e[2]==null?null:this.size[0]*e[2],n=e[3]==null?null:this.size[1]*e[3];return[e[0],e[1],t,n]}else{let t=e[1]==null?null:this.size[0]*e[1],n=e[2]==null?null:this.size[1]*e[2];return[e[0],t,n,e[3]]}}call(e,t){return H(()=>{let n=ze(e),s=n.shape;if(this.dataFormat==="channelsFirst"){n=je(n,[0,2,3,1]);let r=this.size[0]*s[2],a=this.size[1]*s[3],o=this.interpolation==="nearest"?_e.resizeNearestNeighbor(n,[r,a]):_e.resizeBilinear(n,[r,a]);return je(o,[0,3,1,2])}else{let r=this.size[0]*s[1],a=this.size[1]*s[2];return this.interpolation==="nearest"?_e.resizeNearestNeighbor(n,[r,a]):_e.resizeBilinear(n,[r,a])}})}getConfig(){let e={size:this.size,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};Vg.className="UpSampling2D";oe.registerClass(Vg);function GP(e,t,n=[1,1],s="valid",r,a){return H(()=>{r==null&&(r=$s()),$t(r);let o=Pg(e,r);if(e.rank!==4)throw new G(`Input for depthwiseConv2d is required to be 4-D, but is instead ${e.rank}-D`);if(t.rank!==4)throw new G(`depthwiseKernel is required to be 4-D, but is instead ${t.rank}-D`);return o=tc(o,t,n,s==="same"?"same":"valid","NHWC",a),r==="channelsFirst"&&(o=je(o,[0,3,1,2])),o})}var Ug=class extends Mg{constructor(e){super(2,e);this.depthwiseKernel=null,this.depthMultiplier=e.depthMultiplier==null?1:e.depthMultiplier,this.depthwiseInitializer=bt(e.depthwiseInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.depthwiseConstraint=Gt(e.depthwiseConstraint),this.depthwiseRegularizer=vt(e.depthwiseRegularizer)}build(e){if(e=st(e),e.length<4)throw new G(`Inputs to DepthwiseConv2D should have rank 4. Received input shape: ${JSON.stringify(e)}.`);let t=this.dataFormat==="channelsFirst"?1:3;if(e[t]==null||e[t]<0)throw new G(`The channel dimension of the inputs to DepthwiseConv2D should be defined, but is not (${e[t]}).`);let n=e[t],s=[this.kernelSize[0],this.kernelSize[1],n,this.depthMultiplier];this.depthwiseKernel=this.addWeight("depthwise_kernel",s,null,this.depthwiseInitializer,this.depthwiseRegularizer,!0,this.depthwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[n*this.depthMultiplier],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return H(()=>{e=ze(e);let n=GP(e,this.depthwiseKernel.read(),this.strides,this.padding,this.dataFormat,null);return this.useBias&&(n=Os(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),n})}computeOutputShape(e){e=st(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2],s=this.dataFormat==="channelsFirst"?e[1]*this.depthMultiplier:e[3]*this.depthMultiplier,r=zs(t,this.kernelSize[0],this.padding,this.strides[0]),a=zs(n,this.kernelSize[1],this.padding,this.strides[1]);return this.dataFormat==="channelsFirst"?[e[0],s,r,a]:[e[0],r,a,s]}getConfig(){let e=super.getConfig();return e.depthMultiplier=this.depthMultiplier,e.depthwiseInitializer=Ct(this.depthwiseInitializer),e.depthwiseRegularizer=ut(this.depthwiseRegularizer),e.depthwiseConstraint=Ht(this.depthwiseRegularizer),e}};Ug.className="DepthwiseConv2D";oe.registerClass(Ug);function sv(e,t,n,s){if(Array.isArray(e)){if(t!=null||n!=null)throw new G("When inputs is an array, neither initialState or constants should be provided");s!=null&&(n=e.slice(e.length-s,e.length),e=e.slice(0,e.length-s)),e.length>1&&(t=e.slice(1,e.length)),e=e[0]}function r(a){return a==null||Array.isArray(a)?a:[a]}return t=r(t),n=r(n),{inputs:e,initialState:t,constants:n}}function rv(e,t,n,s=!1,r,a,o=!1,i=!1){return H(()=>{let l=t.shape.length;if(l<3)throw new G(`Input should be at least 3D, but is ${l}D.`);let u=[1,0].concat(Ds(2,l));if(t=je(t,u),a!=null)throw new Oe("The rnn() functoin of the deeplearn.js backend does not support constants yet.");o&&console.warn("Backend rnn(): the unroll = true option is not applicable to the imperative deeplearn.js backend."),r!=null&&(r=ce(ce(r,"bool"),"float32"),r.rank===l-1&&(r=Ft(r,-1)),r=je(r,u)),s&&(t=cs(t,0),r!=null&&(r=cs(r,0)));let c=[],d,h=n,p=t.shape[0],f=ds(t),m;r!=null&&(m=ds(r));for(let g=0;g<p;++g){let y=f[g],x=H(()=>e(y,h));if(r==null)d=x[0],h=x[1];else{let b=H(()=>{let v=m[g],k=Ae(us(v),v),w=ae(z(x[0],v),z(h[0],k)),C=h.map((E,M)=>ae(z(x[1][M],v),z(E,k)));return{output:w,newStates:C}});d=b.output,h=b.newStates}i&&c.push(d)}let A;return i&&(A=Nn(c,1)),[d,A,h]})}var sr=class extends Xe{constructor(e){super(e);let t;if(e.cell==null)throw new G("cell property is missing for the constructor of RNN.");if(Array.isArray(e.cell)?t=new Lp({cells:e.cell}):t=e.cell,t.stateSize==null)throw new G("The RNN cell should have an attribute `stateSize` (tuple of integers, one integer per RNN state).");this.cell=t,this.returnSequences=e.returnSequences==null?!1:e.returnSequences,this.returnState=e.returnState==null?!1:e.returnState,this.goBackwards=e.goBackwards==null?!1:e.goBackwards,this._stateful=e.stateful==null?!1:e.stateful,this.unroll=e.unroll==null?!1:e.unroll,this.supportsMasking=!0,this.inputSpec=[new Pt({ndim:3})],this.stateSpec=null,this.states_=null,this.numConstants=null,this.keptStates=[]}getStates(){if(this.states_==null){let e=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;return Ds(0,e).map(t=>null)}else return this.states_}setStates(e){this.states_=e}computeOutputShape(e){dg(e)&&(e=e[0]),e=e;let t=this.cell.stateSize;Array.isArray(t)||(t=[t]);let n=t[0],s;if(this.returnSequences?s=[e[0],e[1],n]:s=[e[0],n],this.returnState){let r=[];for(let a of t)r.push([e[0],a]);return[s].concat(r)}else return s}computeMask(e,t){return H(()=>{Array.isArray(t)&&(t=t[0]);let n=this.returnSequences?t:null;if(this.returnState){let s=this.states.map(r=>null);return[n].concat(s)}else return n})}get states(){if(this.states_==null){let e=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1,t=[];for(let n=0;n<e;++n)t.push(null);return t}else return this.states_}set states(e){this.states_=e}build(e){let t=null;if(this.numConstants!=null)throw new Oe("Constants support is not implemented in RNN yet.");dg(e)&&(e=e[0]),e=e;let n=this.stateful?e[0]:null,s=e.slice(2);this.inputSpec[0]=new Pt({shape:[n,null,...s]});let r=[e[0]].concat(e.slice(2));if(t!=null)throw new Oe("Constants support is not implemented in RNN yet.");this.cell.build(r);let a;if(Array.isArray(this.cell.stateSize)?a=this.cell.stateSize:a=[this.cell.stateSize],this.stateSpec!=null){if(!I.arraysEqual(this.stateSpec.map(o=>o.shape[o.shape.length-1]),a))throw new G(`An initialState was passed that is not compatible with cell.stateSize. Received stateSpec=${this.stateSpec}; However cell.stateSize is ${this.cell.stateSize}`)}else this.stateSpec=a.map(o=>new Pt({shape:[null,o]}));this.stateful&&this.resetStates()}resetStates(e,t=!1){H(()=>{if(!this.stateful)throw new br("Cannot call resetStates() on an RNN Layer that is not stateful.");let n=this.inputSpec[0].shape[0];if(n==null)throw new G("If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.");if(this.states_==null)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(s=>Dt([n,s])):this.states_=[Dt([n,this.cell.stateSize])];else if(e==null)K(this.states_),this.keptStates!=null&&(K(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(s=>Dt([n,s])):this.states_[0]=Dt([n,this.cell.stateSize]);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new G(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${e.length} state value(s). Input received: ${e}`);t===!0?this.keptStates.push(this.states_.slice()):K(this.states_);for(let s=0;s<this.states_.length;++s){let r=e[s],a=Array.isArray(this.cell.stateSize)?this.cell.stateSize[s]:this.cell.stateSize,o=[n,a];if(!I.arraysEqual(r.shape,o))throw new G(`State ${s} is incompatible with layer ${this.name}: expected shape=${o}, received shape=${r.shape}`);this.states_[s]=r}}this.states_=this.states_.map(s=>Kt(s.clone()))})}apply(e,t){let n=t==null?null:t.initialState,s=t==null?null:t.constants;t==null&&(t={});let r=sv(e,n,s,this.numConstants);e=r.inputs,n=r.initialState,s=r.constants;let a=[],o=[];if(n!=null){t.initialState=n,a=a.concat(n),this.stateSpec=[];for(let l of n)this.stateSpec.push(new Pt({shape:l.shape}));o=o.concat(this.stateSpec)}if(s!=null&&(t.constants=s,a=a.concat(s),this.numConstants=s.length),a[0]instanceof Ps){let l=[e].concat(a),u=this.inputSpec.concat(o),c=this.inputSpec;this.inputSpec=u;let d=super.apply(l,t);return this.inputSpec=c,d}else return super.apply(e,t)}call(e,t){return H(()=>{let n=t==null?null:t.mask,s=t==null?null:t.training,r=t==null?null:t.initialState;e=ze(e),r==null&&(this.stateful?r=this.states_:r=this.getInitialState(e));let a=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;if(r.length!==a)throw new G(`RNN Layer has ${a} state(s) but was passed ${r.length} initial state(s).`);this.unroll&&console.warn("Ignoring unroll = true for RNN layer, due to imperative backend.");let o={training:s},l=rv((p,f)=>{let m=this.cell.call([p].concat(f),o);return[m[0],m.slice(1)]},e,r,this.goBackwards,n,null,this.unroll,this.returnSequences),u=l[0],c=l[1],d=l[2];this.stateful&&this.resetStates(d,s);let h=this.returnSequences?c:u;return this.returnState?[h].concat(d):h})}getInitialState(e){return H(()=>{let t=Dt(e.shape);return t=ve(t,[1,2]),t=fc(t),Array.isArray(this.cell.stateSize)?this.cell.stateSize.map(n=>n>1?ng(t,[1,n]):t):this.cell.stateSize>1?[ng(t,[1,this.cell.stateSize])]:[t]})}get trainableWeights(){return this.trainable?this.cell.trainableWeights:[]}get nonTrainableWeights(){return this.trainable?this.cell.nonTrainableWeights:this.cell.weights}setFastWeightInitDuringBuild(e){super.setFastWeightInitDuringBuild(e),this.cell!=null&&this.cell.setFastWeightInitDuringBuild(e)}getConfig(){let e=super.getConfig(),t={returnSequences:this.returnSequences,returnState:this.returnState,goBackwards:this.goBackwards,stateful:this.stateful,unroll:this.unroll};this.numConstants!=null&&(t.numConstants=this.numConstants);let n=this.cell.getConfig();return this.getClassName()===sr.className&&(t.cell={className:this.cell.getClassName(),config:n}),Object.assign({},n,e,t)}static fromConfig(e,t,n={}){let s=t.cell,r=Ms(s,n);return new e(Object.assign(t,{cell:r}))}};sr.className="RNN";oe.registerClass(sr);var Sc=class extends Xe{},Mp=class extends Sc{constructor(e){super(e);this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",this.units=e.units,Zt(this.units,"units"),this.activation=Qr(e.activation==null?this.DEFAULT_ACTIVATION:e.activation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=bt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=bt(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=bt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=vt(e.kernelRegularizer),this.recurrentRegularizer=vt(e.recurrentRegularizer),this.biasRegularizer=vt(e.biasRegularizer),this.kernelConstraint=Gt(e.kernelConstraint),this.recurrentConstraint=Gt(e.recurrentConstraint),this.biasConstraint=Gt(e.biasConstraint),this.dropout=Fl([1,Zr([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Fl([1,Zr([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=st(e),this.kernel=this.addWeight("kernel",[e[e.length-1],this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight("bias",[this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return H(()=>{if(e=e,e.length!==2)throw new G(`SimpleRNNCell expects 2 input Tensors, got ${e.length}.`);let n=e[1];e=e[0];let s=t.training==null?!1:t.training;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=ea({ones:()=>us(e),rate:this.dropout,training:s})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=ea({ones:()=>us(n),rate:this.recurrentDropout,training:s}));let r,a=this.dropoutMask,o=this.recurrentDropoutMask;a!=null?r=er(z(e,a),this.kernel.read()):r=er(e,this.kernel.read()),this.bias!=null&&(r=Os(r,this.bias.read())),o!=null&&(n=z(n,o));let i=ae(r,er(n,this.recurrentKernel.read()));return this.activation!=null&&(i=this.activation.apply(i)),[i,i]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:Jr(this.activation),useBias:this.useBias,kernelInitializer:Ct(this.kernelInitializer),recurrentInitializer:Ct(this.recurrentInitializer),biasInitializer:Ct(this.biasInitializer),kernelRegularizer:ut(this.kernelRegularizer),recurrentRegularizer:ut(this.recurrentRegularizer),biasRegularizer:ut(this.biasRegularizer),activityRegularizer:ut(this.activityRegularizer),kernelConstraint:Ht(this.kernelConstraint),recurrentConstraint:Ht(this.recurrentConstraint),biasConstraint:Ht(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout};return Object.assign({},e,t)}};Mp.className="SimpleRNNCell";oe.registerClass(Mp);var Hg=class extends sr{constructor(e){e.cell=new Mp(e);super(e)}call(e,t){return H(()=>{this.cell.dropoutMask!=null&&(K(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(K(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,s=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:s,initialState:r})})}static fromConfig(e,t){return new e(t)}};Hg.className="SimpleRNN";oe.registerClass(Hg);var zp=class extends Sc{constructor(e){super(e);if(this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_RECURRENT_ACTIVATION="hardSigmoid",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",e.resetAfter)throw new G("GRUCell does not support reset_after parameter set to true.");this.units=e.units,Zt(this.units,"units"),this.activation=Qr(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=Qr(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=bt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=bt(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=bt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=vt(e.kernelRegularizer),this.recurrentRegularizer=vt(e.recurrentRegularizer),this.biasRegularizer=vt(e.biasRegularizer),this.kernelConstraint=Gt(e.kernelConstraint),this.recurrentConstraint=Gt(e.recurrentConstraint),this.biasConstraint=Gt(e.biasConstraint),this.dropout=Fl([1,Zr([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Fl([1,Zr([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.implementation=e.implementation,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=st(e);let t=e[e.length-1];this.kernel=this.addWeight("kernel",[t,this.units*3],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units*3],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight("bias",[this.units*3],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return H(()=>{if(e=e,e.length!==2)throw new G(`GRUCell expects 2 input Tensors (inputs, h, c), got ${e.length}.`);let n=t.training==null?!1:t.training,s=e[1];e=e[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=ea({ones:()=>us(e),rate:this.dropout,training:n,count:3})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=ea({ones:()=>us(s),rate:this.recurrentDropout,training:n,count:3}));let r=this.dropoutMask,a=this.recurrentDropoutMask,o,i,l;0<this.dropout&&this.dropout<1&&(e=z(e,r[0]));let u=er(e,this.kernel.read());this.useBias&&(u=Os(u,this.bias.read())),0<this.recurrentDropout&&this.recurrentDropout<1&&(s=z(s,a[0]));let c=this.recurrentKernel.read(),[d,h]=nn(c,[2*this.units,this.units],c.rank-1),p=er(s,d),[f,m,A]=nn(u,3,u.rank-1),[g,y]=nn(p,2,p.rank-1);o=this.recurrentActivation.apply(ae(f,g)),i=this.recurrentActivation.apply(ae(m,y));let x=er(z(i,s),h);l=this.activation.apply(ae(A,x));let b=ae(z(o,s),z(ae(1,St(o)),l));return[b,b]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:Jr(this.activation),recurrentActivation:Jr(this.recurrentActivation),useBias:this.useBias,kernelInitializer:Ct(this.kernelInitializer),recurrentInitializer:Ct(this.recurrentInitializer),biasInitializer:Ct(this.biasInitializer),kernelRegularizer:ut(this.kernelRegularizer),recurrentRegularizer:ut(this.recurrentRegularizer),biasRegularizer:ut(this.biasRegularizer),activityRegularizer:ut(this.activityRegularizer),kernelConstraint:Ht(this.kernelConstraint),recurrentConstraint:Ht(this.recurrentConstraint),biasConstraint:Ht(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation,resetAfter:!1};return Object.assign({},e,t)}};zp.className="GRUCell";oe.registerClass(zp);var Gg=class extends sr{constructor(e){e.implementation===0&&console.warn("`implementation=0` has been deprecated, and now defaults to `implementation=1`. Please update your layer call."),e.cell=new zp(e);super(e)}call(e,t){return H(()=>{this.cell.dropoutMask!=null&&(K(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(K(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,s=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:s,initialState:r})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};Gg.className="GRU";oe.registerClass(Gg);var Cc=class extends Sc{constructor(e){super(e);this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_RECURRENT_ACTIVATION="hardSigmoid",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",this.units=e.units,Zt(this.units,"units"),this.activation=Qr(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=Qr(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=bt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=bt(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=bt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.unitForgetBias=e.unitForgetBias,this.kernelRegularizer=vt(e.kernelRegularizer),this.recurrentRegularizer=vt(e.recurrentRegularizer),this.biasRegularizer=vt(e.biasRegularizer),this.kernelConstraint=Gt(e.kernelConstraint),this.recurrentConstraint=Gt(e.recurrentConstraint),this.biasConstraint=Gt(e.biasConstraint),this.dropout=Fl([1,Zr([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Fl([1,Zr([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.implementation=e.implementation,this.stateSize=[this.units,this.units],this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){var t;e=st(e);let n=e[e.length-1];this.kernel=this.addWeight("kernel",[n,this.units*4],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units*4],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint);let s;if(this.useBias){if(this.unitForgetBias){let r=this.biasInitializer,a=this.units;s=new(t=class extends ws{apply(i,l){let u=r.apply([a]),c=new Ap().apply([a]),d=r.apply([a*2]);return Qb(Qb(u,c),d)}},t.className="CustomInit",t)}else s=this.biasInitializer;this.bias=this.addWeight("bias",[this.units*4],null,s,this.biasRegularizer,!0,this.biasConstraint)}else this.bias=null;this.built=!0}call(e,t){return H(()=>{let n=t.training==null?!1:t.training;if(e=e,e.length!==3)throw new G(`LSTMCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let s=e[1],r=e[2];e=e[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=ea({ones:()=>us(e),rate:this.dropout,training:n,count:4})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=ea({ones:()=>us(s),rate:this.recurrentDropout,training:n,count:4}));let a=this.dropoutMask,o=this.recurrentDropoutMask,i,l,u,c;0<this.dropout&&this.dropout<1&&(e=z(e,a[0]));let d=er(e,this.kernel.read());0<this.recurrentDropout&&this.recurrentDropout<1&&(s=z(s,o[0])),d=ae(d,er(s,this.recurrentKernel.read())),this.useBias&&(d=Os(d,this.bias.read()));let[h,p,f,m]=nn(d,4,d.rank-1);i=this.recurrentActivation.apply(h),l=this.recurrentActivation.apply(p),u=ae(z(l,r),z(i,this.activation.apply(f))),c=this.recurrentActivation.apply(m);let A=z(c,this.activation.apply(u));return[A,A,u]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:Jr(this.activation),recurrentActivation:Jr(this.recurrentActivation),useBias:this.useBias,kernelInitializer:Ct(this.kernelInitializer),recurrentInitializer:Ct(this.recurrentInitializer),biasInitializer:Ct(this.biasInitializer),unitForgetBias:this.unitForgetBias,kernelRegularizer:ut(this.kernelRegularizer),recurrentRegularizer:ut(this.recurrentRegularizer),biasRegularizer:ut(this.biasRegularizer),activityRegularizer:ut(this.activityRegularizer),kernelConstraint:Ht(this.kernelConstraint),recurrentConstraint:Ht(this.recurrentConstraint),biasConstraint:Ht(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation};return Object.assign({},e,t)}};Cc.className="LSTMCell";oe.registerClass(Cc);var jg=class extends sr{constructor(e){e.implementation===0&&console.warn("`implementation=0` has been deprecated, and now defaults to `implementation=1`. Please update your layer call."),e.cell=new Cc(e);super(e)}call(e,t){return H(()=>{this.cell.dropoutMask!=null&&(K(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(K(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,s=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:s,initialState:r})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};jg.className="LSTM";oe.registerClass(jg);var Lp=class extends Sc{constructor(e){super(e);this.cells=e.cells}get stateSize(){let e=[];for(let t of this.cells.slice().reverse())Array.isArray(t.stateSize)?e.push(...t.stateSize):e.push(t.stateSize);return e}call(e,t){return H(()=>{e=e;let n=e.slice(1),s=[];for(let o of this.cells.slice().reverse())Array.isArray(o.stateSize)?s.push(n.splice(0,o.stateSize.length)):s.push(n.splice(0,1));s.reverse();let r=[],a;for(let o=0;o<this.cells.length;++o){let i=this.cells[o];n=s[o],o===0?a=[e[0]].concat(n):a=[a[0]].concat(n),a=i.call(a,t),r.push(a.slice(1))}n=[];for(let o of r.slice().reverse())n.push(...o);return[a[0]].concat(n)})}build(e){dg(e)&&(e=e[0]),e=e;let t;this.cells.forEach((n,s)=>{$o(`RNNCell_${s}`,()=>{n.build(e),Array.isArray(n.stateSize)?t=n.stateSize[0]:t=n.stateSize,e=[e[0],t]})}),this.built=!0}getConfig(){let e=super.getConfig(),t=r=>({className:r.getClassName(),config:r.getConfig()}),s={cells:this.cells.map(t)};return Object.assign({},e,s)}static fromConfig(e,t,n={}){let s=[];for(let r of t.cells)s.push(Ms(r,n));return new e({cells:s})}get trainableWeights(){if(!this.trainable)return[];let e=[];for(let t of this.cells)e.push(...t.trainableWeights);return e}get nonTrainableWeights(){let e=[];for(let t of this.cells)e.push(...t.nonTrainableWeights);if(!this.trainable){let t=[];for(let n of this.cells)t.push(...n.trainableWeights);return t.concat(e)}return e}getWeights(){let e=[];for(let t of this.cells)e.push(...t.weights);return hg(e)}setWeights(e){let t=[];for(let n of this.cells){let s=n.weights.length,r=e.splice(s);for(let a=0;a<n.weights.length;++a)t.push([n.weights[a],r[a]])}pg(t)}};Lp.className="StackedRNNCells";oe.registerClass(Lp);function ea(e){let{ones:t,rate:n,training:s=!1,count:r=1}=e,a=()=>t3(t(),n),o=()=>Ac(a,t,s);return!r||r<=1?Kt(o().clone()):Array(r).fill(void 0).map(o).map(l=>Kt(l.clone()))}var jP=function(e,t){var n={};for(var s in e)Object.prototype.hasOwnProperty.call(e,s)&&t.indexOf(s)<0&&(n[s]=e[s]);if(e!=null&&typeof Object.getOwnPropertySymbols=="function")for(var r=0,s=Object.getOwnPropertySymbols(e);r<s.length;r++)t.indexOf(s[r])<0&&Object.prototype.propertyIsEnumerable.call(e,s[r])&&(n[s[r]]=e[s[r]]);return n},av=class extends sr{constructor(e){if(e.unroll)throw new Oe("Unrolling is not possible with convolutional RNNs.");if(Array.isArray(e.cell))throw new Oe("It is not possible at the moment to stack convolutional cells.");super(e);this.inputSpec=[new Pt({ndim:5})]}call(e,t){return H(()=>{if(this.cell.dropoutMask!=null&&(K(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(K(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null),t&&t.constants)throw new G("ConvRNN2D cell does not support constants");let n=t==null?null:t.mask,s=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:s,initialState:r})})}computeOutputShape(e){let t=this.computeSingleOutputShape(e);return this.returnSequences||(t=[t[0],...t.slice(2)]),this.returnState&&(t=[t,...Array(2).fill([e[0],...t.slice(-3)])]),t}getInitialState(e){return H(()=>{let{stateSize:t}=this.cell,n=e.shape,s=this.computeSingleOutputShape(n),r=[s[0],...s.slice(2)],a=Dt(r);return Array.isArray(t)?Array(t.length).fill(a):[a]})}resetStates(e,t=!1){H(()=>{if(!this.stateful)throw new br("Cannot call resetStates() on an RNN Layer that is not stateful.");let n=this.inputSpec[0].shape,s=this.computeSingleOutputShape(n),r=[s[0],...s.slice(2)];if(n[0]==null)throw new G("If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.");if(this.getStates()==null)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>Dt(r)):this.states_=[Dt(r)];else if(e==null)K(this.states_),this.keptStates!=null&&(K(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>Dt(r)):this.states_[0]=Dt(r);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new G(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${e.length} state value(s). Input received: ${e}`);t?this.keptStates.push(this.states_.slice()):K(this.states_);for(let o=0;o<this.states_.length;++o){let i=e[o],l=r;if(!I.arraysEqual(i.shape,l))throw new G(`State ${o} is incompatible with layer ${this.name}: expected shape=${l}, received shape=${i.shape}`);this.states_[o]=i}}this.states_=this.states_.map(o=>Kt(o.clone()))})}computeSingleOutputShape(e){let{dataFormat:t,filters:n,kernelSize:s,padding:r,strides:a,dilationRate:o}=this.cell,i=t==="channelsFirst",l=e[i?3:2],u=e[i?4:3],c=zs(l,s[0],r,a[0],o[0]),d=zs(u,s[1],r,a[1],o[1]);return[...e.slice(0,2),...i?[n,c,d]:[c,d,n]]}};av.className="ConvRNN2D";var Bp=class extends Cc{constructor(e){let{filters:t,kernelSize:n,strides:s,padding:r,dataFormat:a,dilationRate:o}=e;super(Object.assign({},e,{units:t}));this.filters=t,Zt(this.filters,"filters"),this.kernelSize=zl(n,2,"kernelSize"),this.kernelSize.forEach(i=>Zt(i,"kernelSize")),this.strides=zl(s||1,2,"strides"),this.strides.forEach(i=>Zt(i,"strides")),this.padding=r||"valid",hs(this.padding),this.dataFormat=a||"channelsLast",$t(this.dataFormat),this.dilationRate=zl(o||1,2,"dilationRate"),this.dilationRate.forEach(i=>Zt(i,"dilationRate"))}build(e){var t;e=st(e);let n=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[n]==null)throw new G(`The channel dimension of the input should be defined. Found ${e[n]}`);let s=e[n],r=4,a=this.kernelSize.concat([s,this.filters*r]);this.kernel=this.addWeight("kernel",a,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint);let o=this.kernelSize.concat([this.filters,this.filters*r]);if(this.recurrentKernel=this.addWeight("recurrent_kernel",o,null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias){let i;if(this.unitForgetBias){let l=this.biasInitializer,u=this.filters;i=new(t=class extends ws{apply(d,h){let p=l.apply([u]),f=Un([u]),m=l.apply([u*2]);return tg([p,f,m])}},t.className="CustomInit",t)}else i=this.biasInitializer;this.bias=this.addWeight("bias",[this.filters*r],null,i,this.biasRegularizer,!0,this.biasConstraint)}this.built=!0}call(e,t){return H(()=>{if(e.length!==3)throw new G(`ConvLSTM2DCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let n=t.training||!1,s=e[0],r=e[1],a=e[2],o=4;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=ea({ones:()=>us(s),rate:this.dropout,training:n,count:o}));let i=this.dropoutMask,l=(Q,ne,te)=>!ne||!ne[te]?Q:z(ne[te],Q),u=l(s,i,0),c=l(s,i,1),d=l(s,i,2),h=l(s,i,3);0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=ea({ones:()=>us(r),rate:this.recurrentDropout,training:n,count:o}));let p=this.recurrentDropoutMask,f=l(r,p,0),m=l(r,p,1),A=l(r,p,2),g=l(r,p,3),y=3,[x,b,v,k]=nn(this.kernel.read(),o,y),[w,C,E,M]=this.useBias?nn(this.bias.read(),o):[null,null,null,null];u=this.inputConv(u,x,w,this.padding),c=this.inputConv(c,b,C,this.padding),d=this.inputConv(d,v,E,this.padding),h=this.inputConv(h,k,M,this.padding);let[R,_,N,O]=nn(this.recurrentKernel.read(),o,y);f=this.recurrentConv(f,R),m=this.recurrentConv(m,_),A=this.recurrentConv(A,N),g=this.recurrentConv(g,O);let W=this.recurrentActivation.apply(ae(u,f)),j=this.recurrentActivation.apply(ae(c,m)),q=ae(z(j,a),z(W,this.activation.apply(ae(d,A)))),X=z(this.recurrentActivation.apply(ae(h,g)),this.activation.apply(q));return[X,X,q]})}getConfig(){let e=super.getConfig(),{units:t}=e,n=jP(e,["units"]),s={filters:this.filters,kernelSize:this.kernelSize,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,strides:this.strides};return Object.assign({},n,s)}inputConv(e,t,n,s){let r=Hr(e,t,this.strides,s||"valid",this.dataFormat==="channelsFirst"?"NCHW":"NHWC",this.dilationRate);return n?Os(r,n,this.dataFormat):r}recurrentConv(e,t){return Hr(e,t,1,"same",this.dataFormat==="channelsFirst"?"NCHW":"NHWC")}};Bp.className="ConvLSTM2DCell";oe.registerClass(Bp);var qg=class extends av{constructor(e){let t=new Bp(e);super(Object.assign({},e,{cell:t}))}static fromConfig(e,t){return new e(t)}};qg.className="ConvLSTM2D";oe.registerClass(qg);var Wp=class extends Xe{constructor(e){super(e);this.rate=Math.max(Math.min(e.rate,1),0),this.noiseShape=e.noiseShape,this.seed=e.seed,this.supportsMasking=!0}getNoiseShape(e){if(this.noiseShape==null)return this.noiseShape;let t=e.shape,n=[];for(let s=0;s<this.noiseShape.length;++s)n.push(this.noiseShape[s]==null?t[s]:this.noiseShape[s]);return n}call(e,t){return H(()=>{this.invokeCallHook(e,t);let n=ze(e);if(0<this.rate&&this.rate<1){let s=t.training==null?!1:t.training,r=this.getNoiseShape(n);return Ac(()=>t3(n,this.rate,r,this.seed),()=>n,s)}return e})}getConfig(){let e={rate:this.rate,noiseShape:this.noiseShape,seed:this.seed},t=super.getConfig();return Object.assign(e,t),e}dispose(){return super.dispose()}};Wp.className="Dropout";oe.registerClass(Wp);var Xg=class extends Wp{constructor(e){super(e);this.inputSpec=[{ndim:3}]}getNoiseShape(e){let t=e.shape;return[t[0],1,t[2]]}};Xg.className="SpatialDropout1D";oe.registerClass(Xg);var Kg=class extends Xe{constructor(e){super(e);if(this.activation=null,this.useBias=!0,this.kernel=null,this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",e.batchInputShape==null&&e.inputShape==null&&e.inputDim!=null){let t=null;e.batchSize!=null&&(t=e.batchSize),this.batchInputShape=[t,e.inputDim]}this.units=e.units,Zt(this.units,"units"),this.activation=Qr(e.activation),e.useBias!=null&&(this.useBias=e.useBias),this.kernelInitializer=bt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.biasInitializer=bt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelConstraint=Gt(e.kernelConstraint),this.biasConstraint=Gt(e.biasConstraint),this.kernelRegularizer=vt(e.kernelRegularizer),this.biasRegularizer=vt(e.biasRegularizer),this.activityRegularizer=vt(e.activityRegularizer),this.supportsMasking=!0,this.inputSpec=[{minNDim:2}]}build(e){e=st(e);let t=e[e.length-1];this.kernel==null&&(this.kernel=this.addWeight("kernel",[t,this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint))),this.inputSpec=[{minNDim:2,axes:{[-1]:t}}],this.built=!0}computeOutputShape(e){e=st(e);let t=e.slice();return t[t.length-1]=this.units,t}call(e,t){return H(()=>{this.invokeCallHook(e,t);let n=ze(e),s=Hb(this.activation.getClassName()),r;return s!=null?r=er(n,this.kernel.read(),s,this.bias?this.bias.read():null):(r=er(n,this.kernel.read()),this.bias!=null&&(r=Os(r,this.bias.read())),this.activation!=null&&(r=this.activation.apply(r))),r})}getConfig(){let e={units:this.units,activation:Jr(this.activation),useBias:this.useBias,kernelInitializer:Ct(this.kernelInitializer),biasInitializer:Ct(this.biasInitializer),kernelRegularizer:ut(this.kernelRegularizer),biasRegularizer:ut(this.biasRegularizer),activityRegularizer:ut(this.activityRegularizer),kernelConstraint:Ht(this.kernelConstraint),biasConstraint:Ht(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}};Kg.className="Dense";oe.registerClass(Kg);var Zg=class extends Xe{constructor(e){e=e||{};super(e);this.inputSpec=[{minNDim:3}],this.dataFormat=e.dataFormat}computeOutputShape(e){e=st(e);for(let t of e.slice(1))if(t==null)throw new G(`The shape of the input to "Flatten" is not fully defined (got ${e.slice(1)}). Make sure to pass a complete "input_shape" or "batch_input_shape" argument to the first layer in your model.`);return[e[0],Kr(e,1)]}call(e,t){return H(()=>{this.invokeCallHook(e,t);let n=ze(e);if(this.dataFormat==="channelsFirst"&&n.rank>1){let s=[0];for(let r=2;r<n.rank;++r)s.push(r);s.push(1),n=je(n,s)}return dO(n)})}getConfig(){let e={};this.dataFormat!=null&&(e.dataFormat=this.dataFormat);let t=super.getConfig();return Object.assign(e,t),e}};Zg.className="Flatten";oe.registerClass(Zg);var Yg=class extends Xe{constructor(e){super(e);this.supportsMasking=!0,this.activation=Qr(e.activation)}call(e,t){return H(()=>{this.invokeCallHook(e,t);let n=ze(e);return this.activation.apply(n)})}getConfig(){let e={activation:Jr(this.activation)},t=super.getConfig();return Object.assign(e,t),e}};Yg.className="Activation";oe.registerClass(Yg);var Jg=class extends Xe{constructor(e){super(e);this.n=e.n,this.inputSpec=[{ndim:2}]}computeOutputShape(e){return[e[0],this.n,e[1]]}call(e,t){return H(()=>(e=ze(e),uO(e,this.n)))}getConfig(){let e={n:this.n},t=super.getConfig();return Object.assign(e,t),e}};Jg.className="RepeatVector";oe.registerClass(Jg);var Qg=class extends Xe{constructor(e){super(e);this.targetShape=e.targetShape;for(let t=0;t<this.targetShape.length;++t)this.isUnknown(this.targetShape[t])&&(this.targetShape[t]=null)}isUnknown(e){return e<0||e==null}fixUnknownDimension(e,t){let n="Total size of new array must be unchanged.",s=t.slice(),r=1,a=null;for(let i=0;i<s.length;++i){let l=s[i];if(this.isUnknown(l))if(a===null)a=i;else throw new G("Can only specifiy one unknown dimension.");else r*=l}let o=Kr(e);if(a!==null){if(r===0||o%r!=0)throw new G(n);s[a]=o/r}else if(o!==r)throw new G(n);return s}computeOutputShape(e){let t=!1;for(let n=0;n<e.length;++n)if(this.isUnknown(e[n])){t=!0;break}return t?e.slice(0,1).concat(this.targetShape):e.slice(0,1).concat(this.fixUnknownDimension(e.slice(1),this.targetShape))}call(e,t){return H(()=>{this.invokeCallHook(e,t);let n=ze(e),s=n.shape,r=s.slice(0,1).concat(this.fixUnknownDimension(s.slice(1),this.targetShape));return U(n,r)})}getConfig(){let e={targetShape:this.targetShape},t=super.getConfig();return Object.assign(e,t),e}};Qg.className="Reshape";oe.registerClass(Qg);var e2=class extends Xe{constructor(e){super(e);if(e.dims==null)throw new Error("Required configuration field `dims` is missing during Permute constructor call.");if(!Array.isArray(e.dims))throw new Error(`Permute constructor requires \`dims\` to be an Array, but received ${e.dims} instead.`);let t=Ds(1,e.dims.length+1);if(!I.arraysEqual(e.dims.slice().sort(),t))throw new Error("Invalid permutation `dims`: "+JSON.stringify(e.dims)+" `dims` must contain consecutive integers starting from 1.");this.dims=e.dims,this.dimsIncludingBatch=[0].concat(this.dims),this.inputSpec=[new Pt({ndim:this.dims.length+1})]}computeOutputShape(e){e=st(e);let t=e.slice();return this.dims.forEach((n,s)=>{t[s+1]=e[n]}),t}call(e,t){return je(ze(e),this.dimsIncludingBatch)}getConfig(){let e={dims:this.dims},t=super.getConfig();return Object.assign(e,t),e}};e2.className="Permute";oe.registerClass(e2);var t2=class extends Xe{constructor(e){super(e==null?{}:e);this.supportsMasking=!0,e!=null?this.maskValue=e.maskValue==null?0:e.maskValue:this.maskValue=0}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={maskValue:this.maskValue};return Object.assign(t,e),t}computeMask(e,t){let n=ze(e),s=-1;return Fh(Nl(n,this.maskValue),s)}call(e,t){return H(()=>{this.invokeCallHook(e,t);let n=ze(e),s=-1,r=!0,a=Fh(Nl(n,this.maskValue),s,r);return z(n,ce(a,n.dtype))})}};t2.className="Masking";oe.registerClass(t2);var n2=class extends Xe{constructor(e){super(e);if(this.embeddings=null,this.DEFAULT_EMBEDDINGS_INITIALIZER="randomUniform",e.batchInputShape==null&&e.inputShape==null){let t=null;e.batchSize!=null&&(t=e.batchSize),e.inputLength==null?this.batchInputShape=[t,null]:this.batchInputShape=[t].concat(At(e.inputLength))}this.inputDim=e.inputDim,Zt(this.inputDim,"inputDim"),this.outputDim=e.outputDim,Zt(this.outputDim,"outputDim"),this.embeddingsInitializer=bt(e.embeddingsInitializer||this.DEFAULT_EMBEDDINGS_INITIALIZER),this.embeddingsRegularizer=vt(e.embeddingsRegularizer),this.activityRegularizer=vt(e.activityRegularizer),this.embeddingsConstraint=Gt(e.embeddingsConstraint),this.maskZero=e.maskZero,this.supportsMasking=e.maskZero,this.inputLength=e.inputLength}build(e){this.embeddings=this.addWeight("embeddings",[this.inputDim,this.outputDim],this.dtype,this.embeddingsInitializer,this.embeddingsRegularizer,!0,this.embeddingsConstraint),this.built=!0}warnOnIncompatibleInputShape(e){}computeMask(e,t){return H(()=>this.maskZero?(e=ze(e),Nl(e,qe(e))):null)}computeOutputShape(e){if(e=st(e),this.inputLength==null)return[...e,this.outputDim];let t=At(this.inputLength);if(t.length!==e.length-1)throw new G(`"inputLength" is ${this.inputLength}, but received input shape has shape ${e}`);{let n=0;for(let s=0;s<t.length;++s){let r=t[s],a=e[s+1];if(r!=null&&a!=null&&r!==a)throw new G(`"inputLength" is ${this.inputLength}, but received input shape has shape ${e}`);r==null&&(t[n]=a),n++}}return[e[0],...t,this.outputDim]}call(e,t){return H(()=>{this.invokeCallHook(e,t);let n=ze(e);n.dtype!=="int32"&&(n=pp(n,"int32"));let s=e3(this.embeddings.read(),U(n,[n.size]));return U(s,st(this.computeOutputShape(n.shape)))})}getConfig(){let e={inputDim:this.inputDim,outputDim:this.outputDim,embeddingsInitializer:Ct(this.embeddingsInitializer),embeddingsRegularizer:ut(this.embeddingsRegularizer),activityRegularizer:ut(this.activityRegularizer),embeddingsConstraint:Ht(this.embeddingsConstraint),maskZero:this.maskZero,inputLength:this.inputLength},t=super.getConfig();return Object.assign(e,t),e}};n2.className="Embedding";oe.registerClass(n2);var Mo=class extends Xe{constructor(e){super(e||{});this.supportsMasking=!0}mergeFunction(e){throw new Oe}computeElementwiseOpOutputShape(e,t){if(e==null||t==null)return null;if(e.length<t.length)return this.computeElementwiseOpOutputShape(t,e);if(t.length===0)return e;let n=e.slice(0,e.length-t.length);for(let s=0;s<t.length;++s){let r=e[e.length-t.length+s],a=t[s];if(r==null||a==null||r<0||a<0)n.push(null);else if(r===1)n.push(a);else if(a===1)n.push(r);else{if(r!==a)throw new G("Operands could not be broadcast together with shapes "+JSON.stringify(e)+" "+JSON.stringify(t));n.push(r)}}return n}build(e){if(Array.isArray(e)&&!Array.isArray(e[0])&&(e=[st(e)]),e=e,e.length<2)throw new G(`A merge layer should be called on an Array of at least 2 inputs. Got ${e.length} input(s).`);let t=[];for(let r of e)r!=null&&r[0]!==null&&t.push(r[0]);if(t=Xr(t),t.length>1)throw new G(`Can not merge tensors with different batch sizes. Got tensors with shapes: ${JSON.stringify(e)}.`);let n=e[0]==null?null:e[0].slice(1);for(let r=1;r<e.length;++r){let a=e[r]==null?null:e[r].slice(1);n=this.computeElementwiseOpOutputShape(n,a)}let s=e.map(r=>r.length);e.indexOf(null)===-1&&Xr(s).length===1?this.reshapeRequired=!1:this.reshapeRequired=!0}call(e,t){return H(()=>{if(e=e,this.reshapeRequired){let n=[],s=e.map(r=>r.rank);if(s.indexOf(null)===-1){let r=Zr(s);for(let a of e){let o=a.rank;for(let i=0;i<r-o;++i)a=fc(a,1);n.push(a)}return this.mergeFunction(n)}else{let r=!1;for(let i of e){let l=i.rank;if(l==null){let u=i.shape,c=u[0],d=u.slice(1).concat([c]),h=U(i,[c].concat(Kr(u.slice(1))));h=je(h,[1,0]),h=U(h,d),n.push(h),r=!0}else if(l>1){let u=Ds(1,l).concat([0]);n.push(je(i,u)),r=!0}else n.push(i)}let a=this.mergeFunction(n),o=a.rank;if(r){if(o==null){let i=a.shape,l=i.length,u=i[l-1],c=[u].concat(i.slice(0,i.length-1));a=U(je(U(a,[-1,u]),[1,0]),c)}else if(o>1){let i=[o-1].concat(Ds(0,o-1));a=je(a,i)}}return a}}else return this.mergeFunction(e)})}computeOutputShape(e){e=e;let t;e[0]==null?t=null:t=e[0].slice(1);for(let s=1;s<e.length;++s){let r=e[s]==null?null:e[s].slice(1);t=this.computeElementwiseOpOutputShape(t,r)}let n=[];for(let s of e)s!=null&&s[0]!==null&&n.push(s[0]);return n=Xr(n),n.length===1?t=n.concat(t):t=[null].concat(t),t}computeMask(e,t){return H(()=>{if(t==null)return null;if(!Array.isArray(t))throw new G("`mask` should be an Array");if(!Array.isArray(e))throw new G("`inputs` should be an Array");if(t.length!==e.length)throw new G(`The Array 'inputs' and 'mask' are expected to have the same length, but have different lengths (${e.length} vs ${t.length})`);if(t.every(s=>s==null))return null;t=t.map(s=>s==null?s:Ft(s,0));let n=t[0];for(let s=1;s<t.length-1;++s)n=Rs(n,t[s]);return n})}},s2=class extends Mo{constructor(e){super(e)}mergeFunction(e){return H(()=>{let t=e[0].clone();for(let n=1;n<e.length;++n)t=ae(t,e[n]);return t})}};s2.className="Add";oe.registerClass(s2);var r2=class extends Mo{constructor(e){super(e)}mergeFunction(e){return H(()=>{let t=e[0].clone();for(let n=1;n<e.length;++n)t=z(t,e[n]);return t})}};r2.className="Multiply";oe.registerClass(r2);var a2=class extends Mo{constructor(e){super(e)}mergeFunction(e){return H(()=>{let t=e[0].clone();for(let n=1;n<e.length;++n)t=ae(t,e[n]);return z(1/e.length,t)})}};a2.className="Average";oe.registerClass(a2);var o2=class extends Mo{constructor(e){super(e)}mergeFunction(e){return H(()=>{let t=e[0];for(let n=1;n<e.length;++n)t=gr(t,e[n]);return t})}};o2.className="Maximum";oe.registerClass(o2);var i2=class extends Mo{constructor(e){super(e)}mergeFunction(e){return H(()=>{let t=e[0];for(let n=1;n<e.length;++n)t=rc(t,e[n]);return t})}};i2.className="Minimum";oe.registerClass(i2);var l2=class extends Mo{constructor(e){super(e);this.DEFAULT_AXIS=-1,e==null&&(e={}),this.axis=e.axis==null?this.DEFAULT_AXIS:e.axis,this.supportsMasking=!0,this.reshapeRequired=!1}build(e){if(!(Array.isArray(e)&&Array.isArray(e[0]))||e.length===1)throw new G("A `Concatenate` layer should be called on a list of at least 2 inputs");e=e;let t=!0;for(let s of e)if(s!=null){t=!1;break}if(t)return;let n=[];for(let s=0;s<e.length;++s){let r=e[s].slice();r.splice(this.axis,1);let a=!1;for(let o of n)if(I.arraysEqual(o,r)){a=!0;break}a||n.push(r)}if(n.length>1)throw new G("A `Concatenate` layer requires inputs with matching shapes except for the concat axis. Got input shapes: "+JSON.stringify(e))}mergeFunction(e){return H(()=>tg(e,this.axis))}computeOutputShape(e){if(!(Array.isArray(e)&&Array.isArray(e[0])))throw new G("A `Concatenate` layer should be called on a list of inputs.");let t=e,n=t[0].slice(),s=this.axis<0?n.length+this.axis:this.axis;for(let r of t.slice(1)){if(n[s]==null||r[s]==null){n[s]=null;break}n[s]+=r[s]}return n}computeMask(e,t){if(t==null)return null;if(!Array.isArray(t))throw new G("`mask` should be an array for Concatenate");if(!Array.isArray(e))throw new G("`inputs` should be an array for Concatenate");if(t.length!==e.length)throw new G(`Mismatch in the length of mask (${t.length}) and the legnth of inputs (${e.length})`);return H(()=>{let n=!0;if(t.forEach(a=>{if(a!=null){n=!1;return}}),n)return null;let s=[];for(let a=0;a<e.length;++a)t[a]==null?s.push(ce(us(e[a]),"bool")):t[a].rank<e[a].rank?s.push(Ft(t[a],-1)):s.push(t[a]);let r=ht(s,this.axis);return oA(r,-1,!1)})}getConfig(){let e={axis:this.axis},t=super.getConfig();return Object.assign(e,t),e}};l2.className="Concatenate";oe.registerClass(l2);function Tc(e,t){for(;e<0;)e+=t;return e}function qP(e,t,n){if(e.shape.length>3||t.shape.length>3)throw new Oe("batchDot is not implemented for tensors of 4D or higher rank yet");if(I.assert(e.shape.length>=2,()=>`batchDot requires the rank of x to be >= 2, but got ${e.shape.length}`),I.assert(e.shape.length>=2,()=>`batchDot requires the rank of y to be >= 2, but got ${t.shape.length}`),typeof n=="number"&&(n=[n,n]),e.dtype==="complex64"||t.dtype==="complex64")throw new Oe("batchDot is not implemented for complex64-type Tensors yet.");let s=e.shape.length,r=t.shape.length;n==null&&(n=[s-1,r-2]);let a=n;return H(()=>{let o;if(s>r){o=s-r;let l=[];for(let u=0;u<o;++u)l.push(1);t=U(t,t.shape.concat(l))}else if(r>s){o=r-s;let l=[];for(let u=0;u<o;++u)l.push(1);e=U(e,e.shape.concat(l))}else o=0;let i;if(e.shape.length===2&&t.shape.length===2)a[0]===a[1]?i=ve(z(e,t),a[0]):i=ve(z(je(e,[1,0]),t),a[1]);else{let l=a[0]!==e.shape.length-1,u=a[1]===t.shape.length-1;i=We(e,t,l,u)}if(o>0){let l;s>r?l=s+r-3:l=s-1;let u=[];for(let c=l;c<l+o;++c)u.push(c);i=ot(i,u)}return i.shape.length===1&&(i=Ft(i,1)),i})}var u2=class extends Mo{constructor(e){super(e);this.axes=e.axes,this.normalize=e.normalize==null?!1:e.normalize,this.supportsMasking=!0,this.reshapeRequired=!1}build(e){I.assert(Array.isArray(e)&&e.length===2&&Array.isArray(e[0])&&Array.isArray(e[1]),()=>"A `Dot` layer should be called on a list of exactly 2 inputs.");let t=e[0],n=e[1];if(t.length>3||n.length>3)throw new Oe("Dot layer does not support tensors of 4D or higher rank yet.");let s=this.interpretAxes(t,n);if(t[s[0]]!==n[s[1]])throw new G(`Dimension incompatibility: ${t[s[0]]} !== ${n[s[1]]}`)}mergeFunction(e){if(e.length!==2)throw new G(`A \`Dot\` layer must be called on exactly 2 inputs, but received ${e.length} input(s).`);let t=e[0],n=e[1],s;return Array.isArray(this.axes)?s=this.axes.map((r,a)=>Tc(r,e[a].shape.length)):s=[Tc(this.axes,t.shape.length),Tc(this.axes,n.shape.length)],this.normalize&&(t=Np(t,s[0]),n=Np(n,s[1])),qP(t,n,s)}interpretAxes(e,t){let n;return Array.isArray(this.axes)?n=this.axes:n=[Tc(this.axes,e.length),Tc(this.axes,t.length)],n}computeOutputShape(e){I.assert(Array.isArray(e)&&e.length===2&&Array.isArray(e[0])&&Array.isArray(e[1]),()=>"A `Dot` layer should be called on a list of exactly 2 inputs.");let t=e[0].slice(),n=e[1].slice();if(t.length>3||n.length>3)throw new Oe("Dot layer does not support tensors of 4D or higher rank yet.");let s=this.interpretAxes(t,n);t.splice(s[0],1),n.splice(s[1],1),n.splice(0,1);let r=t.concat(n);return r.length===1&&r.push(1),r}computeMask(e,t){return null}getConfig(){let e={axes:this.axes,normalize:this.normalize},t=super.getConfig();return Object.assign(e,t),e}};u2.className="Dot";oe.registerClass(u2);var c2=class extends Xe{constructor(e){super(e);this.supportsMasking=!0,this.stddev=e.stddev}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={stddev:this.stddev};return Object.assign(t,e),t}call(e,t){return H(()=>{this.invokeCallHook(e,t);let n=ze(e);return Ac(()=>ae(mp(n.shape,0,this.stddev),n),()=>n,t.training||!1)})}};c2.className="GaussianNoise";oe.registerClass(c2);var d2=class extends Xe{constructor(e){super(e);this.supportsMasking=!0,this.rate=e.rate}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={rate:this.rate};return Object.assign(t,e),t}call(e,t){return H(()=>{this.invokeCallHook(e,t);let n=ze(e);return this.rate>0&&this.rate<1?Ac(()=>{let r=Math.sqrt(this.rate/(1-this.rate));return z(n,mp(n.shape,1,r))},()=>n,t.training||!1):n})}};d2.className="GaussianDropout";oe.registerClass(d2);var h2=class extends Xe{constructor(e){super(e);this.supportsMasking=!0,this.rate=e.rate,this.noiseShape=e.noiseShape}_getNoiseShape(e){return this.noiseShape||ze(e).shape}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={rate:this.rate};return Object.assign(t,e),t}call(e,t){return H(()=>{if(this.rate<1&&this.rate>0){let n=this._getNoiseShape(e);return Ac(()=>{let r=ze(e),a=1.6732632423543772,o=1.0507009873554805,i=-a*o,l=Io(El(n),this.rate);l=pp(l,"float32");let u=((1-this.rate)*(1+this.rate*i**2))**-.5,c=-u*i*this.rate,d=ae(z(r,l),z(ae(l,-1),i));return ae(z(d,u),c)},()=>ze(e),t.training||!1)}return e})}};h2.className="AlphaDropout";oe.registerClass(h2);function Nc(e,t,n,s,r,a=.001){let o;if(e.rank===2)o=Tx(e,t,n,s,r,a);else if(e.rank===3)o=Nx(e,t,n,s,r,a);else if(e.rank===4)o=Ex(e,t,n,s,r,a);else throw new Oe(`batchNormalization is not implemented for array of rank ${e.rank} yet`);return o}function XP(e,t,n,s,r=.001){return H(()=>{let a=Gh(e,s),o=a.mean,i=a.variance;return[Nc(e,o,i,n,t,r),o,i]})}function KP(e,t,n,s,r=.001){return H(()=>{let a=Gh(e,s),o=a.mean,i=a.variance,l=[];for(let f of Ds(0,e.rank))s.indexOf(f)!==-1?l.push(1):l.push(e.shape[f]);let u=U(o,l),c=U(i,l),d=t==null?null:U(t,l),h=n==null?null:U(n,l);return[Nc(e,u,c,h,d,r),o,i]})}function ZP(e,t,n,s,r=.001){return I.arraysEqual(s.slice().sort(),Ds(0,e.rank-1))?XP(e,t,n,s,r):KP(e,t,n,s,r)}var p2=class extends Xe{constructor(e){e==null&&(e={});super(e);this.supportsMasking=!0,this.axis=e.axis==null?-1:e.axis,this.momentum=e.momentum==null?.99:e.momentum,this.epsilon=e.epsilon==null?.001:e.epsilon,this.center=e.center==null?!0:e.center,this.scale=e.scale==null?!0:e.scale,this.betaInitializer=bt(e.betaInitializer||"zeros"),this.gammaInitializer=bt(e.gammaInitializer||"ones"),this.movingMeanInitializer=bt(e.movingMeanInitializer||"zeros"),this.movingVarianceInitializer=bt(e.movingVarianceInitializer||"ones"),this.betaConstraint=Gt(e.betaConstraint),this.gammaConstraint=Gt(e.gammaConstraint),this.betaRegularizer=vt(e.betaRegularizer),this.gammaRegularizer=vt(e.gammaRegularizer)}build(e){e=st(e);let t=this.axis>=0?this.axis:this.axis+e.length,n=e[t];if(n==null)throw new G(`Axis ${t} of input tensor should have a defined dimension but the layer received an input with shape ${JSON.stringify(e)}.`);this.inputSpec=[new Pt({ndim:e.length,axes:{[t]:n}})];let s=[n];this.scale&&(this.gamma=this.addWeight("gamma",s,null,this.gammaInitializer,this.gammaRegularizer,!0,this.gammaConstraint)),this.center&&(this.beta=this.addWeight("beta",s,null,this.betaInitializer,this.betaRegularizer,!0,this.betaConstraint)),this.movingMean=this.addWeight("moving_mean",s,null,this.movingMeanInitializer,null,!1),this.movingVariance=this.addWeight("moving_variance",s,null,this.movingVarianceInitializer,null,!1),this.built=!0}call(e,t){return H(()=>{let n=t.training==null?!1:t.training,s=ze(e),r=s.shape,a=r.length,o=Ds(0,a),i=this.axis>=0?this.axis:this.axis+a;o.splice(i,1);let l=Eo(1,a);l[i]=r[i];let u=o.slice();u.sort();let c=!I.arraysEqual(u,Ds(0,a).slice(0,a-1)),d=()=>{if(c){let g=U(this.movingMean.read(),l),y=U(this.movingVariance.read(),l),x=this.center?U(this.beta.read(),l):null,b=this.scale?U(this.gamma.read(),l):null;return Nc(s,g,y,x,b,this.epsilon)}else return Nc(s,this.movingMean.read(),this.movingVariance.read(),this.beta==null?null:this.beta.read(),this.gamma==null?null:this.gamma.read(),this.epsilon)};if(!n)return d();let[h,p,f]=ZP(s,this.gamma.read(),this.beta.read(),o,this.epsilon),m=(g,y,x)=>{H(()=>{let b=1-x,v=g.read(),k=z(Ae(v,y),b);g.write(Ae(v,k))})};return(()=>{m(this.movingMean,p,this.momentum),m(this.movingVariance,f,this.momentum)})(),h})}getConfig(){let e={axis:this.axis,momentum:this.momentum,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:Ct(this.betaInitializer),gammaInitializer:Ct(this.gammaInitializer),movingMeanInitializer:Ct(this.movingMeanInitializer),movingVarianceInitializer:Ct(this.movingVarianceInitializer),betaRegularizer:ut(this.betaRegularizer),gammaRegularizer:ut(this.gammaRegularizer),betaConstraint:Ht(this.betaConstraint),gammaConstraint:Ht(this.gammaConstraint)},t=super.getConfig();return Object.assign(e,t),e}};p2.className="BatchNormalization";oe.registerClass(p2);var f2=class extends Xe{constructor(e){e==null&&(e={});super(e);if(this.axis=e.axis==null?-1:e.axis,typeof this.axis=="number"){if(!Number.isInteger(this.axis))throw new Error(`Expected axis to be an integer, but received ${this.axis}`)}else if(Array.isArray(this.axis)){for(let t of this.axis)if(!Number.isInteger(t))throw new Error(`Expected axis to be an array of integers, but received ${JSON.stringify(this.axis)}`)}else throw new Error(`Expected axis to be an integer or an array of integers, but received ${JSON.stringify(this.axis)}`);this.epsilon=e.epsilon==null?.001:e.epsilon,this.center=e.center==null?!0:e.center,this.scale=e.scale==null?!0:e.scale,this.betaInitializer=bt(e.betaInitializer||"zeros"),this.gammaInitializer=bt(e.gammaInitializer||"ones"),this.betaRegularizer=vt(e.betaRegularizer),this.gammaRegularizer=vt(e.gammaRegularizer),this.supportsMasking=!0}build(e){e=st(e);let t=e.length;typeof this.axis=="number"&&(this.axis=[this.axis]);for(let r=0;r<this.axis.length;++r)this.axis[r]<0&&(this.axis[r]+=t);for(let r of this.axis)if(r<0||r>=t)throw new Error(`Invalid axis: ${r}`);if(this.axis.length!==Xr(this.axis).length)throw new Error(`Found duplicate axes in: ${this.axis}`);let n=this.axis.map(r=>e[r]),s=!0;this.scale?this.gamma=this.addWeight("gamma",n,"float32",this.gammaInitializer,this.gammaRegularizer,s):this.gamma=null,this.center?this.beta=this.addWeight("beta",n,"float32",this.betaInitializer,this.betaRegularizer,s):this.beta=null,this.built=!0}call(e,t){let n=ze(e),s=n.shape,r=s.length;return H(()=>{let a=!0,{mean:o,variance:i}=Gh(n,this.axis,a),l=Eo(1,r);for(let f of this.axis)l[f]=s[f];let u=f=>f!=null&&f.shape.length!==r&&this.axis!==[r-1]?U(f,l):f,c=u(this.gamma.read()),d=u(this.beta.read()),h=[],p=[];for(let f=0;f<r;++f)this.axis.indexOf(f)!==-1?(h.push(s[f]),p.push(1)):(h.push(1),p.push(s[f]));return o=Es(o,h),i=Es(i,h),c=Es(c,p),d=Es(d,p),Nc(n,o,i,d,c,this.epsilon)})}getConfig(){let e={axis:this.axis,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:Ct(this.betaInitializer),gammaInitializer:Ct(this.gammaInitializer),betaRegularizer:ut(this.betaRegularizer),gammaRegularizer:ut(this.gammaRegularizer)},t=super.getConfig();return Object.assign(e,t),e}};f2.className="LayerNormalization";oe.registerClass(f2);function YP(e,t,n){return H(()=>{if(e.rank!==4)throw new G(`temporalPadding expects input tensor to be 4-D, but received a ${e.rank}-D tensor.`);if(t==null&&(t=[[1,1],[1,1]]),t.length!==2||t[0].length!==2||t[1].length!==2)throw new G("spatial2dPadding expects `padding` to be an Array of two Arrays, each of which is an Array of two integers.");if(n==null&&(n=$s()),n!=="channelsLast"&&n!=="channelsFirst")throw new G(`Unknown data format: ${n}. Supported data formats are 'channelsLast' and 'channelsFirst.`);let s;return n==="channelsFirst"?s=[[0,0],[0,0],t[0],t[1]]:s=[[0,0],t[0],t[1],[0,0]],Gr(e,s)})}var m2=class extends Xe{constructor(e){e==null&&(e={});super(e);if(this.dataFormat=e.dataFormat==null?$s():e.dataFormat,e.padding==null)this.padding=[[1,1],[1,1]];else if(typeof e.padding=="number")this.padding=[[e.padding,e.padding],[e.padding,e.padding]];else{if(e.padding=e.padding,e.padding.length!==2)throw new G(`ZeroPadding2D expects padding to be a length-2 array, but received a length-${e.padding.length} array.`);let t,n;if(typeof e.padding[0]=="number")t=[e.padding[0],e.padding[0]],n=[e.padding[1],e.padding[1]];else{if(e.padding=e.padding,e.padding[0].length!==2)throw new G(`ZeroPadding2D expects height padding to be a length-2 array, but received a length-${e.padding[0].length} array.`);if(t=e.padding[0],e.padding[1].length!==2)throw new G(`ZeroPadding2D expects width padding to be a length-2 array, but received a length-${e.padding[1].length} array.`);n=e.padding[1]}this.padding=[t,n]}this.inputSpec=[new Pt({ndim:4})]}computeOutputShape(e){e=st(e);let t,n;return this.dataFormat==="channelsFirst"?(e[2]!=null&&e[2]>=0?t=e[2]+this.padding[0][0]+this.padding[0][1]:t=null,e[3]!=null&&e[3]>=0?n=e[3]+this.padding[1][0]+this.padding[1][1]:n=null,[e[0],e[1],t,n]):(e[1]!=null&&e[1]>=0?t=e[1]+this.padding[0][0]+this.padding[0][1]:t=null,e[2]!=null&&e[2]>=0?n=e[2]+this.padding[1][0]+this.padding[1][1]:n=null,[e[0],t,n,e[3]])}call(e,t){return H(()=>YP(ze(e),this.padding,this.dataFormat))}getConfig(){let e={padding:this.padding,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};m2.className="ZeroPadding2D";oe.registerClass(m2);function Vp(e,t,n,s,r,a){return H(()=>{$t(r),Xb(a),hs(s),n==null&&(n=[1,1]),s==null&&(s="valid"),r==null&&(r=$s()),a==null&&(a="max"),e=Pg(e,r);let o,i=s==="same"?"same":"valid";return a==="max"?o=Uh(e,t,n,i):o=Oh(e,t,n,i),r==="channelsFirst"&&(o=je(o,[0,3,1,2])),o})}function ov(e,t,n,s,r,a){return H(()=>{$t(r),Xb(a),hs(s),n==null&&(n=[1,1,1]),s==null&&(s="valid"),r==null&&(r=$s()),a==null&&(a="max"),e=ev(e,r);let o,i=s==="same"?"same":"valid";return a==="max"?o=kA(e,t,n,i):o=uA(e,t,n,i),r==="channelsFirst"&&(o=je(o,[0,4,1,2,3])),o})}var iv=class extends Xe{constructor(e){e.poolSize==null&&(e.poolSize=2);super(e);if(typeof e.poolSize=="number")this.poolSize=[e.poolSize];else if(Array.isArray(e.poolSize)&&e.poolSize.length===1&&typeof e.poolSize[0]=="number")this.poolSize=e.poolSize;else throw new G(`poolSize for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.poolSize)}`);if(Zt(this.poolSize,"poolSize"),e.strides==null)this.strides=this.poolSize;else if(typeof e.strides=="number")this.strides=[e.strides];else if(Array.isArray(e.strides)&&e.strides.length===1&&typeof e.strides[0]=="number")this.strides=e.strides;else throw new G(`strides for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.strides)}`);Zt(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,hs(this.padding),this.inputSpec=[new Pt({ndim:3})]}computeOutputShape(e){e=st(e);let t=zs(e[1],this.poolSize[0],this.padding,this.strides[0]);return[e[0],t,e[2]]}call(e,t){return H(()=>{this.invokeCallHook(e,t),e=fc(ze(e),2);let n=this.poolingFunction(ze(e),[this.poolSize[0],1],[this.strides[0],1],this.padding,"channelsLast");return ot(n,[2])})}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides},t=super.getConfig();return Object.assign(e,t),e}},A2=class extends iv{constructor(e){super(e)}poolingFunction(e,t,n,s,r){return $t(r),hs(s),Vp(e,t,n,s,r,"max")}};A2.className="MaxPooling1D";oe.registerClass(A2);var g2=class extends iv{constructor(e){super(e)}poolingFunction(e,t,n,s,r){return $t(r),hs(s),Vp(e,t,n,s,r,"avg")}};g2.className="AveragePooling1D";oe.registerClass(g2);var lv=class extends Xe{constructor(e){e.poolSize==null&&(e.poolSize=[2,2]);super(e);if(this.poolSize=Array.isArray(e.poolSize)?e.poolSize:[e.poolSize,e.poolSize],e.strides==null)this.strides=this.poolSize;else if(Array.isArray(e.strides)){if(e.strides.length!==2)throw new G(`If the strides property of a 2D pooling layer is an Array, it is expected to have a length of 2, but received length ${e.strides.length}.`);this.strides=e.strides}else this.strides=[e.strides,e.strides];Zt(this.poolSize,"poolSize"),Zt(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,$t(this.dataFormat),hs(this.padding),this.inputSpec=[new Pt({ndim:4})]}computeOutputShape(e){e=st(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2];return t=zs(t,this.poolSize[0],this.padding,this.strides[0]),n=zs(n,this.poolSize[1],this.padding,this.strides[1]),this.dataFormat==="channelsFirst"?[e[0],e[1],t,n]:[e[0],t,n,e[3]]}call(e,t){return H(()=>(this.invokeCallHook(e,t),this.poolingFunction(ze(e),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},y2=class extends lv{constructor(e){super(e)}poolingFunction(e,t,n,s,r){return $t(r),hs(s),Vp(e,t,n,s,r,"max")}};y2.className="MaxPooling2D";oe.registerClass(y2);var x2=class extends lv{constructor(e){super(e)}poolingFunction(e,t,n,s,r){return $t(r),hs(s),Vp(e,t,n,s,r,"avg")}};x2.className="AveragePooling2D";oe.registerClass(x2);var uv=class extends Xe{constructor(e){e.poolSize==null&&(e.poolSize=[2,2,2]);super(e);if(this.poolSize=Array.isArray(e.poolSize)?e.poolSize:[e.poolSize,e.poolSize,e.poolSize],e.strides==null)this.strides=this.poolSize;else if(Array.isArray(e.strides)){if(e.strides.length!==3)throw new G(`If the strides property of a 3D pooling layer is an Array, it is expected to have a length of 3, but received length ${e.strides.length}.`);this.strides=e.strides}else this.strides=[e.strides,e.strides,e.strides];Zt(this.poolSize,"poolSize"),Zt(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,$t(this.dataFormat),hs(this.padding),this.inputSpec=[new Pt({ndim:5})]}computeOutputShape(e){e=st(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2],s=this.dataFormat==="channelsFirst"?e[4]:e[3];return t=zs(t,this.poolSize[0],this.padding,this.strides[0]),n=zs(n,this.poolSize[1],this.padding,this.strides[1]),s=zs(s,this.poolSize[2],this.padding,this.strides[2]),this.dataFormat==="channelsFirst"?[e[0],e[1],t,n,s]:[e[0],t,n,s,e[4]]}call(e,t){return H(()=>(this.invokeCallHook(e,t),this.poolingFunction(ze(e),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},b2=class extends uv{constructor(e){super(e)}poolingFunction(e,t,n,s,r){return $t(r),hs(s),ov(e,t,n,s,r,"max")}};b2.className="MaxPooling3D";oe.registerClass(b2);var v2=class extends uv{constructor(e){super(e)}poolingFunction(e,t,n,s,r){return $t(r),hs(s),ov(e,t,n,s,r,"avg")}};v2.className="AveragePooling3D";oe.registerClass(v2);var cv=class extends Xe{constructor(e){super(e);this.inputSpec=[new Pt({ndim:3})]}computeOutputShape(e){return[e[0],e[2]]}call(e,t){throw new Oe}},w2=class extends cv{constructor(e){super(e||{})}call(e,t){return H(()=>{let n=ze(e);return Et(n,1)})}};w2.className="GlobalAveragePooling1D";oe.registerClass(w2);var k2=class extends cv{constructor(e){super(e||{})}call(e,t){return H(()=>{let n=ze(e);return ls(n,1)})}};k2.className="GlobalMaxPooling1D";oe.registerClass(k2);var dv=class extends Xe{constructor(e){super(e);this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,$t(this.dataFormat),this.inputSpec=[new Pt({ndim:4})]}computeOutputShape(e){return e=e,this.dataFormat==="channelsLast"?[e[0],e[3]]:[e[0],e[1]]}call(e,t){throw new Oe}getConfig(){let e={dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},I2=class extends dv{call(e,t){return H(()=>{let n=ze(e);return this.dataFormat==="channelsLast"?Et(n,[1,2]):Et(n,[2,3])})}};I2.className="GlobalAveragePooling2D";oe.registerClass(I2);var S2=class extends dv{call(e,t){return H(()=>{let n=ze(e);return this.dataFormat==="channelsLast"?ls(n,[1,2]):ls(n,[2,3])})}};S2.className="GlobalMaxPooling2D";oe.registerClass(S2);var hv=class extends Xe{constructor(e){super(e);this.layer=e.layer}build(e){this.built=!0}get trainable(){return this.layer!=null?this.layer.trainable:!1}set trainable(e){this.layer!=null&&(this.layer.trainable=e)}get trainableWeights(){return this.layer.trainableWeights}get nonTrainableWeights(){return this.layer.nonTrainableWeights}get updates(){return this.layer._updates}get losses(){return this.layer.losses}getWeights(){return this.layer.getWeights()}setWeights(e){this.layer.setWeights(e)}getConfig(){let e={layer:{className:this.layer.getClassName(),config:this.layer.getConfig()}},t=super.getConfig();return Object.assign(e,t),e}setFastWeightInitDuringBuild(e){super.setFastWeightInitDuringBuild(e),this.layer!=null&&this.layer.setFastWeightInitDuringBuild(e)}static fromConfig(e,t,n={}){let s=t.layer,r=Ms(s,n);delete t.layer;let a={layer:r};return Object.assign(a,t),new e(a)}},C2=class extends hv{constructor(e){super(e);this.supportsMasking=!0}build(e){if(e=st(e),e.length<3)throw new G(`TimeDistributed layer expects an input shape >= 3D, but received input shape ${JSON.stringify(e)}`);this.inputSpec=[{shape:e}];let t=[e[0]].concat(e.slice(2));this.layer.built||(this.layer.build(t),this.layer.built=!0),super.build(e)}computeOutputShape(e){e=st(e);let t=[e[0]].concat(e.slice(2)),n=this.layer.computeOutputShape(t),s=e[1];return[n[0],s].concat(n.slice(1))}call(e,t){return H(()=>(e=ze(e),rv((a,o)=>[ze(this.layer.call(a,t)),[]],e,[],!1,null,null,!1,!0)[1]))}};C2.className="TimeDistributed";oe.registerClass(C2);function JP(e){_o(rO,"BidirectionalMergeMode",e)}var QP="concat",T2=class extends hv{constructor(e){super(e);let t=e.layer.getConfig(),n={};n.className=e.layer.getClassName(),n.config=t,this.forwardLayer=Ms(n),t.goBackwards=t.goBackwards!==!0;let s={};if(s.className=e.layer.getClassName(),s.config=t,this.backwardLayer=Ms(s),this.forwardLayer.name="forward_"+this.forwardLayer.name,this.backwardLayer.name="backward_"+this.backwardLayer.name,this.mergeMode=e.mergeMode===void 0?QP:e.mergeMode,JP(this.mergeMode),e.weights)throw new Oe("weights support is not implemented for Bidirectional layer yet.");this._stateful=e.layer.stateful,this.returnSequences=e.layer.returnSequences,this.returnState=e.layer.returnState,this.supportsMasking=!0,this._trainable=!0,this.inputSpec=e.layer.inputSpec,this.numConstants=null}get trainable(){return this._trainable}set trainable(e){this._trainable=e,this.forwardLayer!=null&&(this.forwardLayer.trainable=e),this.backwardLayer!=null&&(this.backwardLayer.trainable=e)}getWeights(){return this.forwardLayer.getWeights().concat(this.backwardLayer.getWeights())}setWeights(e){let t=e.length,n=Math.floor(t/2);this.forwardLayer.setWeights(e.slice(0,n)),this.backwardLayer.setWeights(e.slice(n))}computeOutputShape(e){let t=this.forwardLayer.computeOutputShape(e);Array.isArray(t)&&Array.isArray(t[0])||(t=[t]),t=t;let n,s,r;return this.returnState&&(r=t.slice(1)),n=t[0],n=n,this.mergeMode==="concat"?(n[n.length-1]*=2,s=[n]):this.mergeMode==null?s=[n,n.slice()]:s=[n],this.returnState?this.mergeMode==null?s.concat(r).concat(r.slice()):[n].concat(r).concat(r.slice()):En(s)}apply(e,t){let n=t==null?null:t.initialState,s=t==null?null:t.constants;t==null&&(t={});let r=sv(e,n,s,this.numConstants);if(e=r.inputs,n=r.initialState,s=r.constants,Array.isArray(e)&&(n=e.slice(1),e=e[0]),(n==null||n.length===0)&&s==null)return super.apply(e,t);let a=[],o=[];if(n!=null){let l=n.length;if(l%2>0)throw new G("When passing `initialState` to a Bidrectional RNN, the state should be an Array containing the states of the underlying RNNs.");t.initialState=n,a.push(...n);let u=n.map(c=>new Pt({shape:c.shape}));this.forwardLayer.stateSpec=u.slice(0,l/2),this.backwardLayer.stateSpec=u.slice(l/2),o.push(...u)}if(s!=null)throw new Oe("Support for constants in Bidirectional layers is not implemented yet.");let i=a[0]instanceof Ps;for(let l of a)if(l instanceof Ps!==i)throw new G("The initial state of a Bidirectional layer cannot be specified as a mix of symbolic and non-symbolic tensors");if(i){let l=[e].concat(a),u=this.inputSpec.concat(o),c=this.inputSpec;this.inputSpec=u;let d=super.apply(l,t);return this.inputSpec=c,d}else return super.apply(e,t)}call(e,t){return H(()=>{let n=t.initialState,s,r;if(n==null)s=this.forwardLayer.call(e,t),r=this.backwardLayer.call(e,t);else{let i=n.slice(0,n.length/2),l=n.slice(n.length/2);s=this.forwardLayer.call(e,Object.assign(t,{initialState:i})),r=this.backwardLayer.call(e,Object.assign(t,{initialState:l}))}let a;this.returnState&&(Array.isArray(s)&&(a=s.slice(1).concat(r.slice(1))),s=s[0],r=r[0]),this.returnSequences&&(r=cs(r,1));let o;return this.mergeMode==="concat"?o=tg([s,r]):this.mergeMode==="sum"?o=ae(s,r):this.mergeMode==="ave"?o=z(.5,ae(s,r)):this.mergeMode==="mul"?o=z(s,r):this.mergeMode==null&&(o=[s,r]),this.returnState?this.mergeMode==null?o.concat(a):[o].concat(a):o})}resetStates(e){this.forwardLayer.resetStates(),this.backwardLayer.resetStates()}build(e){$o(this.forwardLayer.name,()=>{this.forwardLayer.build(e)}),$o(this.backwardLayer.name,()=>{this.backwardLayer.build(e)}),this.built=!0}computeMask(e,t){Array.isArray(t)&&(t=t[0]);let n;if(this.returnSequences?this.mergeMode==null?n=[t,t]:n=t:this.mergeMode==null?n=[null,null]:n=null,this.returnState){let r=this.forwardLayer.states.map(a=>null);return Array.isArray(n)?n.concat(r).concat(r):[n].concat(r).concat(r)}else return n}get trainableWeights(){return this.forwardLayer.trainableWeights.concat(this.backwardLayer.trainableWeights)}get nonTrainableWeights(){return this.forwardLayer.nonTrainableWeights.concat(this.backwardLayer.nonTrainableWeights)}setFastWeightInitDuringBuild(e){super.setFastWeightInitDuringBuild(e),this.forwardLayer!=null&&this.forwardLayer.setFastWeightInitDuringBuild(e),this.backwardLayer!=null&&this.backwardLayer.setFastWeightInitDuringBuild(e)}getConfig(){let e={mergeMode:this.mergeMode},t=super.getConfig();return Object.assign(e,t),e}static fromConfig(e,t){let n=Ms(t.layer);if(delete t.layer,t.numConstants!=null)throw new Oe("Deserialization of a Bidirectional layer with numConstants present is not supported yet.");let s=t;return s.layer=n,new e(s)}};T2.className="Bidirectional";oe.registerClass(T2);function eM(e){return new Dl(e)}function tM(e){return new Fg(e)}function nM(e){return new Rg(e)}function sM(e){return new _g(e)}function rM(e){return new $g(e)}function aM(e){return new Og(e)}function oM(e){return new Dg(e)}function iM(e){return new Pp(e)}function lM(e){return new kc(e)}function uM(e){return new zg(e)}function cM(e){return new Ic(e)}function dM(e){return new Lg(e)}function hM(e){return new Bg(e)}function pM(e){return new Wg(e)}function fM(e){return new Vg(e)}function mM(e){return new Ug(e)}function AM(e){return new Yg(e)}function gM(e){return new Kg(e)}function yM(e){return new Wp(e)}function xM(e){return new Xg(e)}function bM(e){return new Zg(e)}function vM(e){return new Jg(e)}function wM(e){return new Qg(e)}function kM(e){return new e2(e)}function IM(e){return new n2(e)}function SM(e){return new s2(e)}function CM(e){return new a2(e)}function TM(e){return new l2(e)}function NM(e){return new o2(e)}function EM(e){return new i2(e)}function RM(e){return new r2(e)}function _M(e){return new u2(e)}function $M(e){return new p2(e)}function FM(e){return new f2(e)}function DM(e){return new m2(e)}function N2(e){return new g2(e)}function OM(e){return N2(e)}function PM(e){return N2(e)}function E2(e){return new x2(e)}function MM(e){return E2(e)}function zM(e){return E2(e)}function R2(e){return new v2(e)}function LM(e){return R2(e)}function BM(e){return R2(e)}function WM(e){return new w2(e)}function VM(e){return new I2(e)}function pv(e){return new k2(e)}function fv(e){return new S2(e)}function mv(e){return new A2(e)}function Av(e){return new y2(e)}function UM(e){return new b2(e)}function HM(e){return new Gg(e)}function GM(e){return new zp(e)}function jM(e){return new jg(e)}function qM(e){return new Cc(e)}function XM(e){return new Hg(e)}function KM(e){return new Mp(e)}function ZM(e){return new qg(e)}function YM(e){return new Bp(e)}function JM(e){return new sr(e)}function QM(e){return new Lp(e)}function ez(e){return new T2(e)}function tz(e){return new C2(e)}var nz=pv,sz=fv,rz=mv,az=Av;function oz(e){return new c2(e)}function iz(e){return new d2(e)}function lz(e){return new h2(e)}function uz(e){return new t2(e)}var gv={};Pe(gv,{MAPE:()=>bz,MSE:()=>kz,binaryAccuracy:()=>cz,binaryCrossentropy:()=>dz,categoricalAccuracy:()=>pz,categoricalCrossentropy:()=>fz,cosineProximity:()=>gz,mape:()=>vz,meanAbsoluteError:()=>yz,meanAbsolutePercentageError:()=>xz,meanSquaredError:()=>wz,mse:()=>Iz,precision:()=>mz,recall:()=>Az,sparseCategoricalAccuracy:()=>hz});function cz(e,t){return Ag(e,t)}function dz(e,t){return x3(e,t)}function hz(e,t){return b3(e,t)}function pz(e,t){return gg(e,t)}function fz(e,t){return yg(e,t)}function mz(e,t){return y3(e,t)}function Az(e,t){return QO(e,t)}function gz(e,t){return fg(e,t)}function yz(e,t){return Ep(e,t)}function xz(e,t){return Pl(e,t)}function bz(e,t){return Pl(e,t)}function vz(e,t){return Pl(e,t)}function wz(e,t){return Do(e,t)}function kz(e,t){return Do(e,t)}function Iz(e,t){return Do(e,t)}var yv={};Pe(yv,{modelFromJSON:()=>FP});var xv={};Pe(xv,{l1:()=>Cz,l1l2:()=>Sz,l2:()=>Tz});function Sz(e){return new vc(e)}function Cz(e){return WP(e)}function Tz(e){return VP(e)}var bv=class extends Ol{constructor(){super(...arguments);this.model=null}setModel(e){if(!(e instanceof wr))throw new Error("model must be a LayersModel, not some other Container");this.model=e}};function Up(e,t){return e<t}function vv(e,t){return e>t}var wv=class extends bv{constructor(e){super();if(e==null&&(e={}),e.restoreBestWeights)throw new Oe("restoreBestWeights = True is not implemented in EarlyStopping yet.");this.monitor=e.monitor||"val_loss",this.minDelta=Math.abs(e.minDelta||0),this.patience=e.patience||0,this.verbose=e.verbose||0,this.mode=e.mode||"auto",this.baseline=e.baseline,["auto","min","max"].indexOf(this.mode)===-1&&(console.warn(`EarlyStopping mode '${this.mode}' is invalid. Falling back to mode 'auto'.`),this.mode="auto"),this.mode==="min"?this.monitorFunc=Up:this.mode==="max"?this.monitorFunc=vv:this.monitor.indexOf("acc")!==-1?this.monitorFunc=vv:this.monitorFunc=Up,this.monitorFunc===Up&&(this.minDelta*=-1)}async onTrainBegin(e){this.wait=0,this.stoppedEpoch=0,this.baseline!=null?this.best=this.baseline:this.best=this.monitorFunc===Up?1/0:-1/0}async onEpochEnd(e,t){await Yr(t);let n=this.getMonitorValue(t);n!=null&&(this.monitorFunc(n-this.minDelta,this.best)?(this.best=n,this.wait=0):(this.wait++,this.wait>=this.patience&&(this.stoppedEpoch=e,this.model.stopTraining=!0)))}async onTrainEnd(e){this.stoppedEpoch>0&&this.verbose&&console.log(`Epoch ${this.stoppedEpoch}: early stopping.`)}getMonitorValue(e){e==null&&(e={});let t=e[this.monitor];return t==null&&console.warn(`Metric for EarlyStopping ${this.monitor} is not available. Available metrics are: ${Object.keys(e)}`),t}};function Nz(e){return new wv(e)}var Ez={earlyStopping:Nz},Ls;(function(e){e[e.DT_INVALID=0]="DT_INVALID",e[e.DT_FLOAT=1]="DT_FLOAT",e[e.DT_DOUBLE=2]="DT_DOUBLE",e[e.DT_INT32=3]="DT_INT32",e[e.DT_UINT8=4]="DT_UINT8",e[e.DT_INT16=5]="DT_INT16",e[e.DT_INT8=6]="DT_INT8",e[e.DT_STRING=7]="DT_STRING",e[e.DT_COMPLEX64=8]="DT_COMPLEX64",e[e.DT_INT64=9]="DT_INT64",e[e.DT_BOOL=10]="DT_BOOL",e[e.DT_QINT8=11]="DT_QINT8",e[e.DT_QUINT8=12]="DT_QUINT8",e[e.DT_QINT32=13]="DT_QINT32",e[e.DT_BFLOAT16=14]="DT_BFLOAT16",e[e.DT_FLOAT_REF=101]="DT_FLOAT_REF",e[e.DT_DOUBLE_REF=102]="DT_DOUBLE_REF",e[e.DT_INT32_REF=103]="DT_INT32_REF",e[e.DT_UINT8_REF=104]="DT_UINT8_REF",e[e.DT_INT16_REF=105]="DT_INT16_REF",e[e.DT_INT8_REF=106]="DT_INT8_REF",e[e.DT_STRING_REF=107]="DT_STRING_REF",e[e.DT_COMPLEX64_REF=108]="DT_COMPLEX64_REF",e[e.DT_INT64_REF=109]="DT_INT64_REF",e[e.DT_BOOL_REF=110]="DT_BOOL_REF",e[e.DT_QINT8_REF=111]="DT_QINT8_REF",e[e.DT_QUINT8_REF=112]="DT_QUINT8_REF",e[e.DT_QINT32_REF=113]="DT_QINT32_REF",e[e.DT_BFLOAT16_REF=114]="DT_BFLOAT16_REF"})(Ls||(Ls={}));var kv;(function(e){let t;(function(n){n[n.LEGACY=0]="LEGACY",n[n.V1=1]="V1",n[n.V2=2]="V2"})(t=e.CheckpointFormatVersion||(e.CheckpointFormatVersion={}))})(kv||(kv={}));var _2={};function Rz(e,t){let n={tfOpName:e,category:"custom",inputs:[],attrs:[],customExecutor:t};_2[e]=n}function Iv(e){return _2[e]}function _z(e){delete _2[e]}function S(e,t,n,s,r){let a=t.inputParams[e];if(a&&a.inputIndexStart!==void 0){let i=a.inputIndexStart,l=a.inputIndexEnd===0?void 0:a.inputIndexEnd===void 0?i+1:a.inputIndexEnd;if(a.type==="tensor")return xn(t.inputNames[a.inputIndexStart],n,s,r);if(a.type==="tensors")return t.inputNames.slice(i,l).map(h=>xn(h,n,s,r));let u=xn(t.inputNames.slice(i)[0],n,s,r),c=u.dataSync();return a.type==="number"?c[0]:I.toNestedArray(u.shape,c)}let o=t.attrParams[e];return o&&o.value}function xn(e,t,n,s){let[r,a]=Hn(e);if(s!=null){let i=s.getHashTableHandleByName(r);if(i!=null)return i}let o=n.currentContextIds.find(i=>!!t[Hp(r,i)]);return o!==void 0?t[Hp(r,o)][a]:void 0}function $z(e,t,n){return t[Hp(e,n.currentContextId)]}function kr(e,t){let[n,s,r]=Hn(e);return[Hp(n,t&&t.currentContextId),s,r]}function Hp(e,t){return t?`${e}-${t}`:e}function Hn(e){let t=e.split(":");if(t.length===1)return[e,0,void 0];let n=t[0],s=t.length===3?t[1]:void 0,r=Number(t[t.length-1]);return[n,r,s]}function Gp(e,t,n){let s=S("pad",e,t,n);if(s==="explicit"){s=S("explicitPaddings",e,t,n);let r=[[0,0],[0,0],[0,0],[0,0]];for(let a=0;a<4;a++)r[a][0]=s[a*2],r[a][1]=s[a*2+1];return r}return s}function Ir(e){return e.kept?e:Ns(e)}var Sv={};Pe(Sv,{json:()=>Fz});var Fz=[{tfOpName:"Add",category:"arithmetic",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"AddV2",category:"arithmetic",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"AddN",category:"arithmetic",inputs:[{start:0,end:0,name:"tensors",type:"tensors"}]},{tfOpName:"BiasAdd",category:"arithmetic",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0},{tfName:"data_format",name:"dataFormat",type:"string",notSupported:!0}]},{tfOpName:"Sub",category:"arithmetic",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"RealDiv",category:"arithmetic",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Div",category:"arithmetic",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"DivNoNan",category:"arithmetic",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"FloorDiv",category:"arithmetic",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Mul",category:"arithmetic",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Maximum",category:"arithmetic",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Minimum",category:"arithmetic",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Pow",category:"arithmetic",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"SquaredDifference",category:"arithmetic",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Mod",category:"arithmetic",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"FloorMod",category:"arithmetic",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]}],Cv={};Pe(Cv,{json:()=>Dz});var Dz=[{tfOpName:"Abs",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Acos",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Asin",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Atan",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Atan2",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"y",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Ceil",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"ClipByValue",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"clipValueMin",type:"number"},{start:2,name:"clipValueMax",type:"number"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Complex",category:"basic_math",inputs:[{start:0,name:"real",type:"tensor"},{start:1,name:"imag",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"ComplexAbs",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Cos",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Cosh",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Elu",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Exp",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Floor",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Log",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Imag",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0},{tfName:"Tout",name:"outputType",type:"dtype",notSupported:!0}]},{tfOpName:"Neg",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Real",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0},{tfName:"Tout",name:"outputType",type:"dtype",notSupported:!0}]},{tfOpName:"Prelu",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"alpha",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Relu",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Relu6",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Selu",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Sigmoid",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Sin",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Sinh",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Sqrt",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Rsqrt",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Square",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Tan",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Tanh",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Sign",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Round",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Expm1",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Log1p",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Reciprocal",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Softplus",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Asinh",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Acosh",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Atanh",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Erf",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Prod",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axes",type:"number[]"}],attrs:[{tfName:"keep_dims",name:"keepDims",type:"bool",notSupported:!0},{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"LeakyRelu",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"alpha",name:"alpha",type:"number",defaultValue:.2},{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"IsNan",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]}],Tv={};Pe(Tv,{json:()=>Oz});var Oz=[{tfOpName:"EmptyTensorList",category:"control",inputs:[{start:0,name:"elementShape",type:"shape"},{start:1,name:"maxNumElements",type:"number"}],attrs:[{tfName:"element_dtype",name:"elementDType",type:"dtype"}]},{tfOpName:"LoopCond",category:"control",inputs:[{start:0,name:"pred",type:"tensor"}]},{tfOpName:"Switch",category:"control",inputs:[{start:0,name:"data",type:"tensor"},{start:1,name:"pred",type:"tensor"}]},{tfOpName:"Merge",category:"control",inputs:[{start:0,end:0,name:"tensors",type:"tensors"}]},{tfOpName:"Enter",category:"control",inputs:[{start:0,name:"tensor",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0},{tfName:"frame_name",name:"frameName",type:"string"},{tfName:"is_constant",name:"isConstant",type:"bool"}]},{tfOpName:"Exit",category:"control",inputs:[{start:0,name:"tensor",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"NextIteration",category:"control",inputs:[{start:0,name:"tensor",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"TensorArrayV3",category:"control",inputs:[{start:0,name:"size",type:"number"}],attrs:[{tfName:"dtype",name:"dtype",type:"dtype"},{tfName:"element_shape",name:"elementShape",type:"shape"},{tfName:"dynamic_size",name:"dynamicSize",type:"bool"},{tfName:"clear_after_read",name:"clearAfterRead",type:"bool"},{tfName:"identical_element_shapes",name:"identicalElementShapes",type:"bool"},{tfName:"tensor_array_name",name:"name",type:"string"}]},{tfOpName:"TensorArrayWriteV3",category:"control",inputs:[{start:0,name:"tensorArrayId",type:"tensor"},{start:1,name:"index",type:"number"},{start:2,name:"tensor",type:"tensor"},{start:3,name:"flowIn",type:"number"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"TensorArrayReadV3",category:"control",inputs:[{start:0,name:"tensorArrayId",type:"tensor"},{start:1,name:"index",type:"number"},{start:2,name:"flowIn",type:"number"}],attrs:[{tfName:"dtype",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"TensorArrayGatherV3",category:"control",inputs:[{start:0,name:"tensorArrayId",type:"tensor"},{start:1,name:"indices",type:"number[]"},{start:2,name:"flowIn",type:"number"}],attrs:[{tfName:"dtype",name:"dtype",type:"dtype"},{tfName:"element_shape",name:"elementShape",type:"shape"}]},{tfOpName:"TensorArrayScatterV3",category:"control",inputs:[{start:0,name:"tensorArrayId",type:"tensor"},{start:1,name:"indices",type:"number[]"},{start:2,name:"tensor",type:"tensor"},{start:3,name:"flowIn",type:"number"}],attrs:[{tfName:"T",name:"dtype",type:"dtype"}]},{tfOpName:"TensorArrayConcatV3",category:"control",inputs:[{start:0,name:"tensorArrayId",type:"tensor"},{start:1,name:"flowIn",type:"number"}],attrs:[{tfName:"dtype",name:"dtype",type:"dtype"},{tfName:"element_shape_except0",name:"elementShapeExcept0",type:"shape",notSupported:!0}]},{tfOpName:"TensorArraySplitV3",category:"control",inputs:[{start:0,name:"tensorArrayId",type:"tensor"},{start:1,name:"tensor",type:"tensor"},{start:2,name:"lengths",type:"number[]"},{start:3,name:"flowIn",type:"number"}],attrs:[{tfName:"T",name:"dtype",type:"dtype"}]},{tfOpName:"TensorArraySizeV3",category:"control",inputs:[{start:0,name:"tensorArrayId",type:"tensor"},{start:1,name:"flowIn",type:"number"}]},{tfOpName:"TensorArrayCloseV3",category:"control",inputs:[{start:0,name:"tensorArrayId",type:"tensor"}]},{tfOpName:"StatelessIf",category:"control",inputs:[{start:0,name:"cond",type:"tensor"},{start:1,end:0,name:"args",type:"tensors"}],attrs:[{tfName:"then_branch",name:"thenBranch",type:"func"},{tfName:"else_branch",name:"elseBranch",type:"func"}]},{tfOpName:"If",category:"control",inputs:[{start:0,name:"cond",type:"tensor"},{start:1,end:0,name:"args",type:"tensors"}],attrs:[{tfName:"then_branch",name:"thenBranch",type:"func"},{tfName:"else_branch",name:"elseBranch",type:"func"}]},{tfOpName:"StatelessWhile",category:"control",inputs:[{start:0,end:0,name:"args",type:"tensors"}],attrs:[{tfName:"cond",name:"cond",type:"func"},{tfName:"body",name:"body",type:"func"}]},{tfOpName:"While",category:"control",inputs:[{start:0,end:0,name:"args",type:"tensors"}],attrs:[{tfName:"cond",name:"cond",type:"func"},{tfName:"body",name:"body",type:"func"}]},{tfOpName:"TensorListScatter",category:"control",inputs:[{start:0,name:"tensor",type:"tensor"},{start:1,name:"indices",type:"number[]"},{start:2,name:"elementShape",type:"shape"}],attrs:[{tfName:"element_dtype",name:"elementDType",type:"dtype"}]},{tfOpName:"TensorListScatterV2",category:"control",inputs:[{start:0,name:"tensor",type:"tensor"},{start:1,name:"indices",type:"number[]"},{start:2,name:"elementShape",type:"shape"},{start:3,name:"numElements",type:"number"}],attrs:[{tfName:"element_dtype",name:"elementDType",type:"dtype"}]},{tfOpName:"TensorListGather",category:"control",inputs:[{start:0,name:"tensorListId",type:"tensor"},{start:1,name:"indices",type:"number[]"},{start:2,name:"elementShape",type:"shape"}],attrs:[{tfName:"element_dtype",name:"elementDType",type:"dtype"}]},{tfOpName:"TensorListGetItem",category:"control",inputs:[{start:0,name:"tensorListId",type:"tensor"},{start:1,name:"index",type:"number"},{start:2,name:"elementShape",type:"shape"}],attrs:[{tfName:"element_dtype",name:"elementDType",type:"dtype"}]},{tfOpName:"TensorListSetItem",category:"control",inputs:[{start:0,name:"tensorListId",type:"tensor"},{start:1,name:"index",type:"number"},{start:2,name:"tensor",type:"tensor"}],attrs:[{tfName:"element_dtype",name:"elementDType",type:"dtype"}]},{tfOpName:"TensorListReserve",category:"control",inputs:[{start:0,name:"elementShape",type:"shape"},{start:1,name:"numElements",type:"number"}],attrs:[{tfName:"element_dtype",name:"elementDType",type:"dtype"}]},{tfOpName:"TensorListFromTensor",category:"control",inputs:[{start:0,name:"tensor",type:"tensor"},{start:1,name:"elementShape",type:"shape"}],attrs:[{tfName:"element_dtype",name:"elementDType",type:"dtype"}]},{tfOpName:"TensorListStack",category:"control",inputs:[{start:0,name:"tensorListId",type:"tensor"},{start:1,name:"elementShape",type:"shape"}],attrs:[{tfName:"element_dtype",name:"elementDType",type:"dtype"},{tfName:"num_elements",name:"numElements",type:"dtype"}]},{tfOpName:"TensorListSplit",category:"control",inputs:[{start:0,name:"tensor",type:"tensor"},{start:1,name:"elementShape",type:"shape"},{start:2,name:"lengths",type:"number[]"}],attrs:[{tfName:"element_dtype",name:"elementDType",type:"dtype"}]},{tfOpName:"TensorListConcat",category:"control",inputs:[{start:0,name:"tensorListId",type:"tensor"}],attrs:[{tfName:"element_shape",name:"elementShape",type:"shape"},{tfName:"element_dtype",name:"elementDType",type:"dtype"}]},{tfOpName:"TensorListPopBack",category:"control",inputs:[{start:0,name:"tensorListId",type:"tensor"},{start:1,name:"elementShape",type:"shape"}],attrs:[{tfName:"element_dtype",name:"elementDType",type:"dtype"}]},{tfOpName:"TensorListPushBack",category:"control",inputs:[{start:0,name:"tensorListId",type:"tensor"},{start:1,name:"tensor",type:"tensor"}],attrs:[{tfName:"element_dtype",name:"elementDType",type:"dtype"}]}],Nv={};Pe(Nv,{json:()=>Pz});var Pz=[{tfOpName:"AvgPool",category:"convolution",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"strides",name:"strides",type:"number[]"},{tfName:"padding",name:"pad",type:"string"},{tfName:"data_format",name:"dataFormat",type:"string",notSupported:!0},{tfName:"ksize",name:"kernelSize",type:"number[]"},{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"MaxPool",category:"convolution",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"strides",name:"strides",type:"number[]"},{tfName:"padding",name:"pad",type:"string"},{tfName:"data_format",name:"dataFormat",type:"string",notSupported:!0},{tfName:"ksize",name:"kernelSize",type:"number[]"},{tfName:"explicit_paddings",name:"explicitPaddings",type:"number[]",defaultValue:[],notSupported:!0},{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"MaxPoolWithArgmax",category:"convolution",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"strides",name:"strides",type:"number[]"},{tfName:"padding",name:"pad",type:"string"},{tfName:"ksize",name:"kernelSize",type:"number[]"},{tfName:"include_batch_in_index",name:"includeBatchInIndex",type:"bool"},{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"AvgPool3D",category:"convolution",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"strides",name:"strides",type:"number[]"},{tfName:"padding",name:"pad",type:"string"},{tfName:"data_format",name:"dataFormat",type:"string",notSupported:!0},{tfName:"ksize",name:"kernelSize",type:"number[]"},{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"MaxPool3D",category:"convolution",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"strides",name:"strides",type:"number[]"},{tfName:"padding",name:"pad",type:"string"},{tfName:"data_format",name:"dataFormat",type:"string",notSupported:!0},{tfName:"ksize",name:"kernelSize",type:"number[]"},{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Conv1D",category:"convolution",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"filter",type:"tensor"}],attrs:[{tfName:"stride",name:"stride",type:"number"},{tfName:"padding",name:"pad",type:"string"},{tfName:"data_format",name:"dataFormat",type:"string",defaultValue:"NWC"},{tfName:"T",name:"dtype",type:"dtype",notSupported:!0},{tfName:"dilation",name:"dilation",type:"number",defaultValue:1}]},{tfOpName:"Conv2D",category:"convolution",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"filter",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0},{tfName:"strides",name:"strides",type:"number[]"},{tfName:"padding",name:"pad",type:"string"},{tfName:"useCudnnOnGpu",name:"useCudnnOnGpu",type:"bool"},{tfName:"data_format",name:"dataFormat",type:"string",defaultValue:"NHWC"},{tfName:"explicit_paddings",name:"explicitPaddings",type:"number[]",defaultValue:[]},{tfName:"dilations",name:"dilations",type:"number[]"}]},{tfOpName:"_FusedConv2D",category:"convolution",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"filter",type:"tensor"},{start:2,end:0,name:"args",type:"tensors"}],attrs:[{tfName:"num_args",name:"numArgs",type:"number"},{tfName:"T",name:"dtype",type:"dtype",notSupported:!0},{tfName:"strides",name:"strides",type:"number[]"},{tfName:"padding",name:"pad",type:"string"},{tfName:"explicit_paddings",name:"explicitPaddings",type:"number[]",defaultValue:[]},{tfName:"use_cudnn_on_gpu",name:"useCudnnOnGpu",type:"bool",defaultValue:!0},{tfName:"data_format",name:"dataFormat",type:"string",defaultValue:"NHWC"},{tfName:"dilations",name:"dilations",type:"number[]",defaultValue:[1,1,1,1]},{tfName:"fused_ops",name:"fusedOps",type:"string[]",defaultValue:[]},{tfName:"epsilon",name:"epsilon",type:"number",defaultValue:1e-4},{tfName:"leakyrelu_alpha",name:"leakyreluAlpha",type:"number"}]},{tfOpName:"Conv2DBackpropInput",category:"convolution",inputs:[{start:2,name:"x",type:"tensor"},{start:1,name:"filter",type:"tensor"},{start:0,name:"outputShape",type:"number[]"}],attrs:[{tfName:"strides",name:"strides",type:"number[]"},{tfName:"padding",name:"pad",type:"string"},{tfName:"data_format",name:"dataFormat",type:"string",notSupported:!0},{tfName:"explicit_paddings",name:"explicitPaddings",type:"number[]",defaultValue:[]},{tfName:"dilations",name:"dilations",type:"number[]",notSupported:!0}]},{tfOpName:"DepthwiseConv2d",category:"convolution",inputs:[{start:0,name:"input",type:"tensor"},{start:1,name:"filter",type:"tensor"}],attrs:[{tfName:"strides",name:"strides",type:"number[]"},{tfName:"padding",name:"pad",type:"string"},{tfName:"data_format",name:"dataFormat",type:"string",defaultValue:"NHWC"},{tfName:"explicit_paddings",name:"explicitPaddings",type:"number[]",defaultValue:[]},{tfName:"dilations",name:"dilations",type:"number[]"}]},{tfOpName:"DepthwiseConv2dNative",category:"convolution",inputs:[{start:0,name:"input",type:"tensor"},{start:1,name:"filter",type:"tensor"}],attrs:[{tfName:"strides",name:"strides",type:"number[]"},{tfName:"padding",name:"pad",type:"string"},{tfName:"data_format",name:"dataFormat",type:"string",defaultValue:"NHWC"},{tfName:"explicit_paddings",name:"explicitPaddings",type:"number[]",defaultValue:[]},{tfName:"dilations",name:"dilations",type:"number[]"}]},{tfOpName:"FusedDepthwiseConv2dNative",category:"convolution",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"filter",type:"tensor"},{start:2,end:0,name:"args",type:"tensors"}],attrs:[{tfName:"num_args",name:"numArgs",type:"number"},{tfName:"T",name:"dtype",type:"dtype",notSupported:!0},{tfName:"strides",name:"strides",type:"number[]"},{tfName:"padding",name:"pad",type:"string"},{tfName:"data_format",name:"dataFormat",type:"string",defaultValue:"NHWC"},{tfName:"dilations",name:"dilations",type:"number[]",defaultValue:[1,1,1,1]},{tfName:"fused_ops",name:"fusedOps",type:"string[]",defaultValue:[]},{tfName:"explicit_paddings",name:"explicitPaddings",type:"number[]",defaultValue:[]}]},{tfOpName:"Conv3D",category:"convolution",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"filter",type:"tensor"}],attrs:[{tfName:"strides",name:"strides",type:"number[]"},{tfName:"padding",name:"pad",type:"string"},{tfName:"data_format",name:"dataFormat",type:"string",defaultValue:"NHWC"},{tfName:"dilations",name:"dilations",type:"number[]"}]},{tfOpName:"Dilation2D",category:"convolution",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"filter",type:"tensor"}],attrs:[{tfName:"strides",name:"strides",type:"number[]"},{tfName:"rates",name:"dilations",type:"number[]"},{tfName:"padding",name:"pad",type:"string"}]}],Ev={};Pe(Ev,{json:()=>Mz});var Mz=[{tfOpName:"Fill",category:"creation",inputs:[{start:0,name:"shape",type:"number[]"},{start:1,name:"value",type:"number"}],attrs:[{tfName:"T",name:"dtype",type:"dtype"}]},{tfOpName:"LinSpace",category:"creation",inputs:[{start:0,name:"start",type:"number"},{start:1,name:"stop",type:"number"},{start:2,name:"num",type:"number"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"OneHot",category:"creation",inputs:[{start:0,name:"indices",type:"tensor"},{start:1,name:"depth",type:"number"},{start:2,name:"onValue",type:"number",defaultValue:1},{start:3,name:"offValue",type:"number",defaultValue:0}],attrs:[{tfName:"axis",name:"axis",type:"number",notSupported:!0},{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Ones",category:"creation",inputs:[{start:0,name:"shape",type:"number[]"}],attrs:[{tfName:"T",name:"dtype",type:"dtype"}]},{tfOpName:"OnesLike",category:"creation",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"dtype",name:"dtype",type:"dtype"}]},{tfOpName:"RandomUniform",category:"creation",inputs:[{start:0,name:"shape",type:"number[]"}],attrs:[{tfName:"minval",name:"minval",type:"number",defaultValue:0},{tfName:"maxval",name:"maxval",type:"number",defaultValue:1},{tfName:"dtype",name:"dtype",type:"dtype"},{tfName:"seed",name:"seed",type:"number",defaultValue:0},{tfName:"seed2",name:"seed2",type:"number",defaultValue:0,notSupported:!0},{tfName:"T",name:"T",type:"number",notSupported:!0}]},{tfOpName:"Range",category:"creation",inputs:[{start:0,name:"start",type:"number"},{start:1,name:"stop",type:"number"},{start:2,name:"step",type:"number",defaultValue:0}],attrs:[{tfName:"Tidx",name:"dtype",type:"dtype"}]},{tfOpName:"TruncatedNormal",category:"creation",inputs:[{start:0,name:"shape",type:"number[]"}],attrs:[{tfName:"means",name:"mean",type:"number",defaultValue:0},{tfName:"stddev",name:"stdDev",type:"number",defaultValue:1},{tfName:"seed",name:"seed",type:"number"},{tfName:"seed2",name:"seed2",type:"number",defaultValue:0,notSupported:!0},{tfName:"dtype",name:"dtype",type:"dtype"},{tfName:"T",name:"T",type:"number",notSupported:!0}]},{tfOpName:"Zeros",category:"creation",inputs:[{start:0,name:"shape",type:"number[]"}],attrs:[{tfName:"T",name:"dtype",type:"dtype"}]},{tfOpName:"ZerosLike",category:"creation",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype"}]},{tfOpName:"Multinomial",category:"creation",inputs:[{start:0,name:"logits",type:"tensor"},{start:1,name:"numSamples",type:"number"}],attrs:[{tfName:"seed",name:"seed",type:"number"},{tfName:"seed2",name:"seed2",type:"number"},{tfName:"T",name:"dtype",type:"dtype"},{tfName:"output_dtype",name:"output_dtype",type:"dtype"}]}],Rv={};Pe(Rv,{json:()=>zz});var zz=[{tfOpName:"NonMaxSuppressionV2",category:"dynamic",inputs:[{start:0,name:"boxes",type:"tensor"},{start:1,name:"scores",type:"tensor"},{start:2,name:"maxOutputSize",type:"number"},{start:3,name:"iouThreshold",type:"number"}]},{tfOpName:"NonMaxSuppressionV3",category:"dynamic",inputs:[{start:0,name:"boxes",type:"tensor"},{start:1,name:"scores",type:"tensor"},{start:2,name:"maxOutputSize",type:"number"},{start:3,name:"iouThreshold",type:"number"},{start:4,name:"scoreThreshold",type:"number"}]},{tfOpName:"NonMaxSuppressionV4",category:"dynamic",inputs:[{start:0,name:"boxes",type:"tensor"},{start:1,name:"scores",type:"tensor"},{start:2,name:"maxOutputSize",type:"number"},{start:3,name:"iouThreshold",type:"number"},{start:4,name:"scoreThreshold",type:"number"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0},{tfName:"T_threshold",name:"threshold",type:"dtype",notSupported:!0},{tfName:"pad_to_max_output_size",name:"padToMaxOutputSize",type:"bool"}]},{tfOpName:"NonMaxSuppressionV5",category:"dynamic",inputs:[{start:0,name:"boxes",type:"tensor"},{start:1,name:"scores",type:"tensor"},{start:2,name:"maxOutputSize",type:"number"},{start:3,name:"iouThreshold",type:"number"},{start:4,name:"scoreThreshold",type:"number"},{start:5,name:"softNmsSigma",type:"number"}]},{tfOpName:"Where",category:"dynamic",inputs:[{start:0,name:"condition",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"ListDiff",category:"dynamic",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"y",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]}],_v={};Pe(_v,{json:()=>Lz});var Lz=[{tfOpName:"TopKV2",category:"evaluation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"k",type:"number"}],attrs:[{tfName:"sorted",name:"sorted",type:"bool"}]},{tfOpName:"Unique",category:"evaluation",inputs:[{start:0,name:"x",type:"tensor"}]},{tfOpName:"UniqueV2",category:"evaluation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number"}]}],$v={};Pe($v,{json:()=>Bz});var Bz=[{tfOpName:"PlaceholderWithDefault",category:"graph",inputs:[{start:0,name:"default",type:"tensor"}],attrs:[{tfName:"shape",name:"shape",type:"shape"},{tfName:"dtype",name:"dtype",type:"dtype"}]},{tfOpName:"Placeholder",category:"graph",attrs:[{tfName:"shape",name:"shape",type:"shape"},{tfName:"dtype",name:"dtype",type:"dtype"}]},{tfOpName:"Const",category:"graph"},{tfOpName:"Identity",category:"graph",inputs:[{start:0,name:"x",type:"tensor"}]},{tfOpName:"IdentityN",category:"graph",inputs:[{start:0,end:0,name:"x",type:"tensors"}]},{tfOpName:"Snapshot",category:"graph",inputs:[{start:0,name:"x",type:"tensor"}]},{tfOpName:"Rank",category:"graph",inputs:[{start:0,name:"x",type:"tensor"}]},{tfOpName:"Size",category:"graph",inputs:[{start:0,name:"x",type:"tensor"}]},{tfOpName:"Shape",category:"graph",inputs:[{start:0,name:"x",type:"tensor"}]},{tfOpName:"ShapeN",category:"graph",inputs:[{start:0,end:0,name:"x",type:"tensors"}]},{tfOpName:"Print",category:"graph",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"data",type:"tensors"}],attrs:[{tfName:"message",name:"message",type:"string"},{tfName:"first_n",name:"firstN",type:"number",notSupported:!0},{tfName:"summarize",name:"summarize",type:"number",defaultValue:3}]},{tfOpName:"NoOp",category:"graph",inputs:[]},{tfOpName:"StopGradient",category:"graph",inputs:[{start:0,name:"x",type:"tensor"}]},{tfOpName:"FakeQuantWithMinMaxVars",category:"graph",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"min",name:"min",type:"number"},{tfName:"max",name:"max",type:"number"}]}],Fv={};Pe(Fv,{json:()=>Wz});var Wz=[{tfOpName:"HashTable",category:"hash_table",inputs:[],attrs:[{tfName:"shared_name",name:"sharedName",type:"string"},{tfName:"use_node_name_sharing",name:"useNodeNameSharing",type:"bool"},{tfName:"key_dtype",name:"keyDType",type:"dtype"},{tfName:"value_dtype",name:"valueDType",type:"dtype"}]},{tfOpName:"HashTableV2",category:"hash_table",inputs:[],attrs:[{tfName:"shared_name",name:"sharedName",type:"string"},{tfName:"use_node_name_sharing",name:"useNodeNameSharing",type:"bool"},{tfName:"key_dtype",name:"keyDType",type:"dtype"},{tfName:"value_dtype",name:"valueDType",type:"dtype"}]},{tfOpName:"LookupTableImport",category:"hash_table",inputs:[{start:0,name:"tableHandle",type:"tensor"},{start:1,name:"keys",type:"tensor"},{start:2,name:"values",type:"tensor"}],attrs:[{tfName:"Tin",name:"tIn",type:"dtype",notSupported:!0},{tfName:"Tout",name:"tOut",type:"dtype",notSupported:!0}]},{tfOpName:"LookupTableImportV2",category:"hash_table",inputs:[{start:0,name:"tableHandle",type:"tensor"},{start:1,name:"keys",type:"tensor"},{start:2,name:"values",type:"tensor"}],attrs:[{tfName:"Tin",name:"tIn",type:"dtype",notSupported:!0},{tfName:"Tout",name:"tOut",type:"dtype",notSupported:!0}]},{tfOpName:"LookupTableFind",category:"hash_table",inputs:[{start:0,name:"tableHandle",type:"tensor"},{start:1,name:"keys",type:"tensor"},{start:2,name:"defaultValue",type:"tensor"}],attrs:[{tfName:"Tin",name:"tIn",type:"dtype",notSupported:!0},{tfName:"Tout",name:"tOut",type:"dtype",notSupported:!0}]},{tfOpName:"LookupTableFindV2",category:"hash_table",inputs:[{start:0,name:"tableHandle",type:"tensor"},{start:1,name:"keys",type:"tensor"},{start:2,name:"defaultValue",type:"tensor"}],attrs:[{tfName:"Tin",name:"tIn",type:"dtype",notSupported:!0},{tfName:"Tout",name:"tOut",type:"dtype",notSupported:!0}]},{tfOpName:"LookupTableSize",category:"hash_table",inputs:[{start:0,name:"tableHandle",type:"tensor"}]},{tfOpName:"LookupTableSizeV2",category:"hash_table",inputs:[{start:0,name:"tableHandle",type:"tensor"}]}],Dv={};Pe(Dv,{json:()=>Vz});var Vz=[{tfOpName:"ResizeBilinear",category:"image",inputs:[{start:0,name:"images",type:"tensor"},{start:1,name:"size",type:"number[]"}],attrs:[{tfName:"align_corners",name:"alignCorners",type:"bool"},{tfName:"half_pixel_centers",name:"halfPixelCenters",type:"bool"},{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"ResizeNearestNeighbor",category:"image",inputs:[{start:0,name:"images",type:"tensor"},{start:1,name:"size",type:"number[]"}],attrs:[{tfName:"align_corners",name:"alignCorners",type:"bool"},{tfName:"half_pixel_centers",name:"halfPixelCenters",type:"bool"},{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"CropAndResize",category:"image",inputs:[{start:0,name:"image",type:"tensor"},{start:1,name:"boxes",type:"tensor"},{start:2,name:"boxInd",type:"tensor"},{start:3,name:"cropSize",type:"number[]"}],attrs:[{tfName:"method",name:"method",type:"string"},{tfName:"extrapolation_value",name:"extrapolationValue",type:"number"}]}],Ov={};Pe(Ov,{json:()=>Uz});var Uz=[{tfOpName:"Equal",category:"logical",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"NotEqual",category:"logical",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Greater",category:"logical",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"GreaterEqual",category:"logical",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Less",category:"logical",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"LessEqual",category:"logical",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"LogicalAnd",category:"logical",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"LogicalNot",category:"logical",inputs:[{start:0,name:"a",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"LogicalOr",category:"logical",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Select",category:"logical",inputs:[{start:0,name:"condition",type:"tensor"},{start:1,name:"a",type:"tensor"},{start:2,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"SelectV2",category:"logical",inputs:[{start:0,name:"condition",type:"tensor"},{start:1,name:"a",type:"tensor"},{start:2,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]}],Pv={};Pe(Pv,{json:()=>Hz});var Hz=[{tfOpName:"_FusedMatMul",category:"matrices",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"},{start:2,end:0,name:"args",type:"tensors"}],attrs:[{tfName:"num_args",name:"numArgs",type:"number"},{tfName:"fused_ops",name:"fusedOps",type:"string[]",defaultValue:[]},{tfName:"epsilon",name:"epsilon",type:"number",defaultValue:1e-4},{tfName:"transpose_a",name:"transposeA",type:"bool",defaultValue:!1},{tfName:"transpose_b",name:"transposeB",type:"bool",defaultValue:!1},{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"MatMul",category:"matrices",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"transpose_a",name:"transposeA",type:"bool",defaultValue:!1},{tfName:"transpose_b",name:"transposeB",type:"bool",defaultValue:!1},{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"BatchMatMul",category:"matrices",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"adj_x",name:"transposeA",type:"bool",defaultValue:!1},{tfName:"adj_y",name:"transposeB",type:"bool",defaultValue:!1},{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"BatchMatMulV2",category:"matrices",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"adj_x",name:"transposeA",type:"bool",defaultValue:!1},{tfName:"adj_y",name:"transposeB",type:"bool",defaultValue:!1},{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Transpose",category:"matrices",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"perm",type:"number[]"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Einsum",category:"matrices",inputs:[{start:0,end:0,name:"tensors",type:"tensors"}],attrs:[{tfName:"equation",name:"equation",type:"string"},{tfName:"N",name:"n",type:"number",defaultValue:2},{tfName:"T",name:"dtype",type:"dtype"}]}],Mv={};Pe(Mv,{json:()=>Gz});var Gz=[{tfOpName:"FusedBatchNorm",category:"normalization",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"scale",type:"tensor"},{start:2,name:"offset",type:"tensor"},{start:3,name:"mean",type:"tensor"},{start:4,name:"variance",type:"tensor"}],attrs:[{tfName:"epsilon",name:"epsilon",type:"number",defaultValue:.001},{tfName:"data_format",name:"dataFormat",type:"string",notSupported:!0}]},{tfOpName:"FusedBatchNormV2",category:"normalization",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"scale",type:"tensor"},{start:2,name:"offset",type:"tensor"},{start:3,name:"mean",type:"tensor"},{start:4,name:"variance",type:"tensor"}],attrs:[{tfName:"epsilon",name:"epsilon",type:"number",defaultValue:.001},{tfName:"data_format",name:"dataFormat",type:"string",notSupported:!0}]},{tfOpName:"FusedBatchNormV3",category:"normalization",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"scale",type:"tensor"},{start:2,name:"offset",type:"tensor"},{start:3,name:"mean",type:"tensor"},{start:4,name:"variance",type:"tensor"}],attrs:[{tfName:"epsilon",name:"epsilon",type:"number",defaultValue:.001},{tfName:"data_format",name:"dataFormat",type:"string",notSupported:!0}]},{tfOpName:"LRN",category:"normalization",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"depth_radius",name:"radius",type:"number",defaultValue:5},{tfName:"bias",name:"bias",type:"number",defaultValue:1},{tfName:"alpha",name:"alpha",type:"number",defaultValue:1},{tfName:"beta",name:"beta",type:"number",defaultValue:.5}]},{tfOpName:"Softmax",category:"normalization",inputs:[{start:0,name:"x",type:"tensor"}]},{tfOpName:"LogSoftmax",category:"normalization",inputs:[{start:0,name:"x",type:"tensor"}]},{tfOpName:"SparseToDense",category:"normalization",inputs:[{start:0,name:"sparseIndices",type:"tensor"},{start:1,name:"outputShape",type:"number[]"},{start:2,name:"sparseValues",type:"tensor"},{start:3,name:"defaultValue",type:"tensor"}],attrs:[{tfName:"validate_indices",name:"validateIndices",type:"bool",defaultValue:!0,notSupported:!0}]}],zv={};Pe(zv,{json:()=>jz});var jz=[{tfOpName:"Bincount",category:"reduction",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"size",type:"number"},{start:2,name:"weights",type:"tensor"}]},{tfOpName:"DenseBincount",category:"reduction",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"size",type:"number"},{start:2,name:"weights",type:"tensor"}],attrs:[{tfName:"binary_output",name:"binaryOutput",type:"bool"}]},{tfOpName:"Max",category:"reduction",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number[]"}],attrs:[{tfName:"keep_dims",name:"keepDims",type:"bool"}]},{tfOpName:"Mean",category:"reduction",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number[]"}],attrs:[{tfName:"keep_dims",name:"keepDims",type:"bool"}]},{tfOpName:"Min",category:"reduction",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number[]"}],attrs:[{tfName:"keep_dims",name:"keepDims",type:"bool"}]},{tfOpName:"Sum",category:"reduction",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number[]"}],attrs:[{tfName:"keep_dims",name:"keepDims",type:"bool"}]},{tfOpName:"All",category:"reduction",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number[]"}],attrs:[{tfName:"keep_dims",name:"keepDims",type:"bool"}]},{tfOpName:"Any",category:"reduction",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number[]"}],attrs:[{tfName:"keep_dims",name:"keepDims",type:"bool"}]},{tfOpName:"ArgMax",category:"reduction",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number"}]},{tfOpName:"ArgMin",category:"reduction",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number"}]},{tfOpName:"Prod",category:"reduction",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number[]"}],attrs:[{tfName:"keep_dims",name:"keepDims",type:"bool"}]},{tfOpName:"Cumsum",category:"reduction",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number"}],attrs:[{tfName:"exclusive",name:"exclusive",type:"bool"},{tfName:"reverse",name:"reverse",type:"bool"}]}],Lv={};Pe(Lv,{json:()=>qz});var qz=[{tfOpName:"ConcatV2",category:"slice_join",inputs:[{start:0,end:-1,name:"tensors",type:"tensors"},{start:-1,name:"axis",type:"number"}],attrs:[{tfName:"N",name:"n",type:"number",defaultValue:2}]},{tfOpName:"Concat",category:"slice_join",inputs:[{start:1,end:0,name:"tensors",type:"tensors"},{start:0,name:"axis",type:"number"}],attrs:[{tfName:"N",name:"n",type:"number",defaultValue:2}]},{tfOpName:"GatherV2",category:"slice_join",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"indices",type:"tensor"},{start:2,name:"axis",type:"number",defaultValue:0}],attrs:[{tfName:"batch_dims",name:"batchDims",type:"number",defaultValue:0}]},{tfOpName:"Gather",category:"slice_join",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"indices",type:"tensor"}],attrs:[{tfName:"validate_indices",name:"validateIndices",type:"bool",notSupported:!0}]},{tfOpName:"Reverse",category:"slice_join",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"dims",type:"bool[]"}]},{tfOpName:"ReverseV2",category:"slice_join",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number[]"}]},{tfOpName:"Slice",category:"slice_join",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"begin",type:"number[]"},{start:2,name:"size",type:"number[]"}]},{tfOpName:"StridedSlice",category:"slice_join",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"begin",type:"number[]"},{start:2,name:"end",type:"number[]"},{start:3,name:"strides",type:"number[]"}],attrs:[{tfName:"begin_mask",name:"beginMask",type:"number",defaultValue:0},{tfName:"end_mask",name:"endMask",type:"number",defaultValue:0},{tfName:"new_axis_mask",name:"newAxisMask",type:"number",defaultValue:0},{tfName:"ellipsis_mask",name:"ellipsisMask",type:"number",defaultValue:0},{tfName:"shrink_axis_mask",name:"shrinkAxisMask",type:"number",defaultValue:0}]},{tfOpName:"Pack",category:"slice_join",inputs:[{start:0,end:0,name:"tensors",type:"tensors"}],attrs:[{tfName:"axis",name:"axis",type:"number",defaultValue:0}]},{tfOpName:"Unpack",category:"slice_join",inputs:[{start:0,name:"tensor",type:"tensor"}],attrs:[{tfName:"axis",name:"axis",type:"number",defaultValue:0},{tfName:"num",name:"num",type:"number",defaultValue:0,notSupported:!0}]},{tfOpName:"Tile",category:"slice_join",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"reps",type:"number[]"}]},{tfOpName:"Split",category:"slice_join",inputs:[{start:0,name:"axis",type:"number",defaultValue:0},{start:1,name:"x",type:"tensor"}],attrs:[{tfName:"num_split",name:"numOrSizeSplits",type:"number",defaultValue:1}]},{tfOpName:"SplitV",category:"slice_join",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"numOrSizeSplits",type:"number[]"},{start:2,name:"axis",type:"number",defaultValue:0}]},{tfOpName:"ScatterNd",category:"slice_join",inputs:[{start:0,name:"indices",type:"tensor"},{start:1,name:"values",type:"tensor"},{start:2,name:"shape",type:"number[]"}]},{tfOpName:"GatherNd",category:"slice_join",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"indices",type:"tensor"}]},{tfOpName:"SparseToDense",category:"slice_join",inputs:[{start:0,name:"sparseIndices",type:"tensor"},{start:1,name:"outputShape",type:"number[]"},{start:2,name:"sparseValues",type:"tensor"},{start:3,name:"defaultValue",type:"tensor"}],attrs:[{tfName:"validate_indices",name:"validateIndices",type:"bool",defaultValue:!1,notSupported:!0}]}],Bv={};Pe(Bv,{json:()=>Xz});var Xz=[{tfOpName:"SparseFillEmptyRows",category:"sparse",inputs:[{start:0,name:"indices",type:"tensor"},{start:1,name:"values",type:"tensor"},{start:2,name:"denseShape",type:"tensor"},{start:3,name:"defaultValue",type:"tensor"}]},{tfOpName:"SparseReshape",category:"sparse",inputs:[{start:0,name:"inputIndices",type:"tensor"},{start:1,name:"inputShape",type:"tensor"},{start:2,name:"newShape",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"SparseSegmentMean",category:"sparse",inputs:[{start:0,name:"data",type:"tensor"},{start:1,name:"indices",type:"tensor"},{start:2,name:"segmentIds",type:"tensor"}]},{tfOpName:"SparseSegmentSum",category:"sparse",inputs:[{start:0,name:"data",type:"tensor"},{start:1,name:"indices",type:"tensor"},{start:2,name:"segmentIds",type:"tensor"}]}],Wv={};Pe(Wv,{json:()=>Kz});var Kz=[{tfOpName:"FFT",category:"spectral",inputs:[{start:0,name:"x",type:"tensor"}]},{tfOpName:"IFFT",category:"spectral",inputs:[{start:0,name:"x",type:"tensor"}]},{tfOpName:"RFFT",category:"spectral",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"fft_length",type:"number",notSupported:!0}]},{tfOpName:"IRFFT",category:"spectral",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"fft_length",type:"number",notSupported:!0}]}],Vv={};Pe(Vv,{json:()=>Zz});var Zz=[{tfOpName:"StringNGrams",category:"string",inputs:[{start:0,name:"data",type:"tensor"},{start:1,name:"dataSplits",type:"tensor"}],attrs:[{tfName:"separator",name:"separator",type:"string"},{tfName:"ngram_widths",name:"nGramWidths",type:"number[]"},{tfName:"left_pad",name:"leftPad",type:"string"},{tfName:"right_pad",name:"rightPad",type:"string"},{tfName:"pad_width",name:"padWidth",type:"number"},{tfName:"preserve_short_sequences",name:"preserveShortSequences",type:"bool"}],outputs:["ngrams","ngrams_splits"]},{tfOpName:"StringSplit",category:"string",inputs:[{start:0,name:"input",type:"tensor"},{start:1,name:"delimiter",type:"tensor"}],attrs:[{tfName:"skip_empty",name:"skipEmpty",type:"bool"}],outputs:["indices","values","shape"]},{tfOpName:"StringToHashBucketFast",category:"string",inputs:[{start:0,name:"input",type:"tensor"}],attrs:[{tfName:"num_buckets",name:"numBuckets",type:"number"}]}],Uv={};Pe(Uv,{json:()=>Yz});var Yz=[{tfOpName:"Cast",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"SrcT",name:"sdtype",type:"dtype",notSupported:!0},{tfName:"DstT",name:"dtype",type:"dtype"}]},{tfOpName:"ExpandDims",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number"}]},{tfOpName:"MirrorPad",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"padding",type:"number[]"}],attrs:[{tfName:"mode",name:"mode",type:"string"}]},{tfOpName:"Pad",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"padding",type:"number[]"}],attrs:[{tfName:"constant_value",name:"constantValue",type:"number",defaultValue:0}]},{tfOpName:"PadV2",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"padding",type:"number[]"},{start:2,name:"constantValue",type:"number",defaultValue:0}]},{tfOpName:"Reshape",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"shape",type:"number[]"}]},{tfOpName:"Squeeze",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"axis",tfDeprecatedName:"squeeze_dims",name:"axis",type:"number[]"}]},{tfOpName:"SpaceToBatchND",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"blockShape",type:"number[]"},{start:2,name:"paddings",type:"number[]"}]},{tfOpName:"BatchToSpaceND",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"blockShape",type:"number[]"},{start:2,name:"crops",type:"number[]"}]},{tfOpName:"DepthToSpace",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"block_size",name:"blockSize",type:"number"},{tfName:"data_format",name:"dataFormat",type:"string"}]},{tfOpName:"BroadcastTo",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"shape",type:"number[]"}],attrs:[]}],Hv=class{static get Instance(){return this._instance||(this._instance=new this)}constructor(){let e=[Sv,Cv,Tv,Nv,Ev,Rv,_v,$v,Fv,Dv,Ov,Pv,Mv,zv,Lv,Bv,Wv,Vv,Uv],t=[].concat(...e.map(n=>n.json));this.opMappers=t.reduce((n,s)=>(n[s.tfOpName]=s,n),{})}transformGraph(e,t={}){let n=e.node,s=[],r=[],a=[],o=n.reduce((f,m)=>(f[m.name]=this.mapNode(m),m.op.startsWith("Placeholder")?s.push(f[m.name]):m.op==="Const"?r.push(f[m.name]):(m.input==null||m.input.length===0)&&a.push(f[m.name]),f),{}),i=[],l=[],u={},c={};t!=null&&(u=this.mapSignatureEntries(t.inputs),c=this.mapSignatureEntries(t.outputs));let d=Object.keys(o);d.forEach(f=>{let m=o[f];m.inputNames.forEach((A,g)=>{let[y,,x]=kr(A),b=o[y];if(b.outputs!=null){let v=b.outputs.indexOf(x);if(v!==-1){let k=`${y}:${v}`;m.inputNames[g]=k}}m.inputs.push(b),b.children.push(m)})}),Object.keys(c).length===0?d.forEach(f=>{let m=o[f];m.children.length===0&&l.push(m)}):Object.keys(c).forEach(f=>{let[m]=kr(f),A=o[m];A!=null&&(A.signatureKey=c[f],l.push(A))}),Object.keys(u).length>0?Object.keys(u).forEach(f=>{let[m]=kr(f),A=o[m];A&&(A.signatureKey=u[f],i.push(A))}):i=s;let h={};e.library!=null&&e.library.function!=null&&(h=e.library.function.reduce((f,m)=>(f[m.signature.name]=this.mapFunction(m),f),{}));let p={nodes:o,inputs:i,outputs:l,weights:r,placeholders:s,signature:t,functions:h};return a.length>0&&(p.initNodes=a),p}mapSignatureEntries(e){return Object.keys(e||{}).reduce((t,n)=>(t[e[n].name]=n,t),{})}mapNode(e){let t=Iv(e.op)||this.opMappers[e.op]||{};e.attr==null&&(e.attr={});let n={name:e.name,op:e.op,category:t.category,inputNames:(e.input||[]).map(s=>s.startsWith("^")?s.substr(1):s),inputs:[],children:[],inputParams:{},attrParams:{},rawAttrs:e.attr,outputs:t.outputs};return t.inputs!=null&&(n.inputParams=t.inputs.reduce((s,r)=>(s[r.name]={type:r.type,inputIndexStart:r.start,inputIndexEnd:r.end},s),{})),t.attrs!=null&&(n.attrParams=t.attrs.reduce((s,r)=>{let a=r.type,o;switch(r.type){case"string":o=$2(e.attr,r.tfName,r.defaultValue),o===void 0&&!!r.tfDeprecatedName&&(o=$2(e.attr,r.tfDeprecatedName,r.defaultValue));break;case"string[]":o=B2(e.attr,r.tfName,r.defaultValue),o===void 0&&!!r.tfDeprecatedName&&(o=B2(e.attr,r.tfDeprecatedName,r.defaultValue));break;case"number":o=D2(e.attr,r.tfName,r.defaultValue||0),o===void 0&&!!r.tfDeprecatedName&&(o=D2(e.attr,r.tfDeprecatedName,r.defaultValue));break;case"number[]":o=L2(e.attr,r.tfName,r.defaultValue),o===void 0&&!!r.tfDeprecatedName&&(o=L2(e.attr,r.tfDeprecatedName,r.defaultValue));break;case"bool":o=F2(e.attr,r.tfName,r.defaultValue),o===void 0&&!!r.tfDeprecatedName&&(o=F2(e.attr,r.tfDeprecatedName,r.defaultValue));break;case"bool[]":o=V2(e.attr,r.tfName,r.defaultValue),o===void 0&&!!r.tfDeprecatedName&&(o=V2(e.attr,r.tfDeprecatedName,r.defaultValue));break;case"shape":o=z2(e.attr,r.tfName,r.defaultValue),o===void 0&&!!r.tfDeprecatedName&&(o=z2(e.attr,r.tfDeprecatedName,r.defaultValue));break;case"shape[]":o=W2(e.attr,r.tfName,r.defaultValue),o===void 0&&!!r.tfDeprecatedName&&(o=W2(e.attr,r.tfDeprecatedName,r.defaultValue));break;case"dtype":o=P2(e.attr,r.tfName,r.defaultValue),o===void 0&&!!r.tfDeprecatedName&&(o=P2(e.attr,r.tfDeprecatedName,r.defaultValue));break;case"dtype[]":o=M2(e.attr,r.tfName,r.defaultValue),o===void 0&&!!r.tfDeprecatedName&&(o=M2(e.attr,r.tfDeprecatedName,r.defaultValue));break;case"func":o=jv(e.attr,r.tfName,r.defaultValue),o===void 0&&!!r.tfDeprecatedName&&(o=jv(e.attr,r.tfDeprecatedName,r.defaultValue));break;case"tensor":case"tensors":break;default:throw new Error(`Unsupported param type: ${r.type} for op: ${e.op}`)}return s[r.name]={value:o,type:a},s},{})),n}mapFunction(e){let t=e.nodeDef,n=[],s=[],r={};t!=null&&(r=t.reduce((c,d)=>(c[d.name]=this.mapNode(d),d.op==="Const"&&s.push(c[d.name]),c),{}));let a=[],o=[];e.signature.inputArg.forEach(c=>{let[d]=kr(c.name),h={name:d,op:"Placeholder",inputs:[],inputNames:[],category:"graph",inputParams:{},attrParams:{dtype:{value:O2(c.type),type:"dtype"}},children:[]};h.signatureKey=c.name,a.push(h),r[d]=h}),Object.keys(r).forEach(c=>{let d=r[c];d.inputNames.forEach((h,p)=>{let[f,,m]=kr(h),A=r[f];if(A.outputs!=null){let g=A.outputs.indexOf(m);if(g!==-1){let y=`${f}:${g}`;d.inputNames[p]=y}}d.inputs.push(A),A.children.push(d)})});let l=e.ret;e.signature.outputArg.forEach(c=>{let[d,h]=kr(l[c.name]),p=r[d];p!=null&&(p.defaultOutput=h,o.push(p))});let u=this.mapArgsToSignature(e);return{nodes:r,inputs:a,outputs:o,weights:s,placeholders:n,signature:u}}mapArgsToSignature(e){return{methodName:e.signature.name,inputs:e.signature.inputArg.reduce((t,n)=>(t[n.name]=this.mapArgToTensorInfo(n),t),{}),outputs:e.signature.outputArg.reduce((t,n)=>(t[n.name]=this.mapArgToTensorInfo(n,e.ret),t),{})}}mapArgToTensorInfo(e,t){let n=e.name;return t!=null&&(n=t[n]),{name:n,dtype:e.type}}};function Jz(e){let t=ee().global;if(typeof t.atob!="undefined")return t.atob(e);if(typeof Buffer!="undefined")return new Buffer(e,"base64").toString();throw new Error("Unable to decode base64 in this environment. Missing built-in atob() or Buffer()")}function Gv(e,t){let n=Array.isArray(e)?String.fromCharCode.apply(null,e):Jz(e);return t?n:n.toLowerCase()}function $2(e,t,n,s=!1){let r=e[t];return r!=null?Gv(r.s,s):n}function F2(e,t,n){let s=e[t];return s?s.b:n}function D2(e,t,n){let s=e[t]||{},r=s.i!=null?s.i:s.f!=null?s.f:n;return typeof r=="number"?r:parseInt(r,10)}function O2(e){switch(typeof e=="string"&&(e=Ls[e]),e){case Ls.DT_FLOAT:return"float32";case Ls.DT_INT32:case Ls.DT_INT64:case Ls.DT_INT8:case Ls.DT_UINT8:return"int32";case Ls.DT_BOOL:return"bool";case Ls.DT_DOUBLE:return"float32";case Ls.DT_STRING:return"string";default:return null}}function jv(e,t,n){let s=e[t];return s&&s.func?s.func.name:n}function P2(e,t,n){let s=e[t];return s&&s.type?O2(s.type):n}function M2(e,t,n){let s=e[t];return s&&s.list&&s.list.type?s.list.type.map(r=>O2(r)):n}function qv(e){if(!e.unknownRank)return e.dim!=null?e.dim.map(t=>typeof t.size=="number"?t.size:parseInt(t.size,10)):[]}function z2(e,t,n){let s=e[t];return s&&s.shape?qv(s.shape):n}function L2(e,t,n){let s=e[t];return s?((s.list.f&&s.list.f.length?s.list.f:s.list.i)||[]).map(r=>typeof r=="number"?r:parseInt(r,10)):n}function B2(e,t,n,s=!1){let r=e[t];return r&&r.list&&r.list.s?r.list.s.map(a=>Gv(a,s)):n}function W2(e,t,n){let s=e[t];return s&&s.list&&s.list.shape?s.list.shape.map(r=>qv(r)):n}function V2(e,t,n){let s=e[t];return s&&s.list&&s.list.b?s.list.b:n}var Qz=class{constructor(e,t,n){this.node=e,this.tensorMap=t,this.context=n,this.inputs=[],this.attrs={},this.inputs=e.inputNames.map(s=>this.getInput(s)),e.rawAttrs!=null&&(this.attrs=Object.keys(e.rawAttrs).reduce((s,r)=>(s[r]=this.getAttr(r),s),{}))}getInput(e){return xn(e,this.tensorMap,this.context)}getAttr(e,t){let n=this.node.rawAttrs[e];if(n.tensor!=null)return xn(e,this.tensorMap,this.context);if(n.i!=null||n.f!=null)return D2(this.node.rawAttrs,e,t);if(n.s!=null)return $2(this.node.rawAttrs,e,t);if(n.b!=null)return F2(this.node.rawAttrs,e,t);if(n.shape!=null)return z2(this.node.rawAttrs,e,t);if(n.type!=null)return P2(this.node.rawAttrs,e,t);if(n.list!=null){if(n.list.i!=null||n.list.f!=null)return L2(this.node.rawAttrs,e,t);if(n.list.s!=null)return B2(this.node.rawAttrs,e,t);if(n.list.shape!=null)return W2(this.node.rawAttrs,e,t);if(n.list.b!=null)return V2(this.node.rawAttrs,e,t);if(n.list.type!=null)return M2(this.node.rawAttrs,e,t)}return t}},eL=(e,t,n)=>{switch(e.op){case"BiasAdd":case"AddV2":case"Add":return[ae(S("a",e,t,n),S("b",e,t,n))];case"AddN":return[$h(S("tensors",e,t,n))];case"FloorMod":case"Mod":return[Qx(S("a",e,t,n),S("b",e,t,n))];case"Mul":return[z(S("a",e,t,n),S("b",e,t,n))];case"RealDiv":case"Div":return[de(S("a",e,t,n),S("b",e,t,n))];case"DivNoNan":return[Lx(S("a",e,t,n),S("b",e,t,n))];case"FloorDiv":return[aA(S("a",e,t,n),S("b",e,t,n))];case"Sub":return[Ae(S("a",e,t,n),S("b",e,t,n))];case"Minimum":return[rc(S("a",e,t,n),S("b",e,t,n))];case"Maximum":return[gr(S("a",e,t,n),S("b",e,t,n))];case"Pow":return[jr(S("a",e,t,n),S("b",e,t,n))];case"SquaredDifference":return[OA(S("a",e,t,n),S("b",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},tL=(e,t,n)=>{switch(e.op){case"Abs":case"ComplexAbs":return[Wt(S("x",e,t,n))];case"Acos":return[Ax(S("x",e,t,n))];case"Acosh":return[gx(S("x",e,t,n))];case"Asin":return[xx(S("x",e,t,n))];case"Asinh":return[bx(S("x",e,t,n))];case"Atan":return[vx(S("x",e,t,n))];case"Atan2":return[wx(S("x",e,t,n),S("y",e,t,n))];case"Atanh":return[kx(S("x",e,t,n))];case"Ceil":return[Rx(S("x",e,t,n))];case"Complex":return[Lr(S("real",e,t,n),S("imag",e,t,n))];case"Cos":return[Mh(S("x",e,t,n))];case"Cosh":return[mA(S("x",e,t,n))];case"Elu":return[nc(S("x",e,t,n))];case"Erf":return[Wx(S("x",e,t,n))];case"Exp":return[os(S("x",e,t,n))];case"Expm1":return[Vx(S("x",e,t,n))];case"Floor":return[sc(S("x",e,t,n))];case"Log":return[is(S("x",e,t,n))];case"Log1p":return[Bh(S("x",e,t,n))];case"Imag":return[zh(S("x",e,t,n))];case"Neg":return[St(S("x",e,t,n))];case"Reciprocal":return[nb(S("x",e,t,n))];case"Real":return[ac(S("x",e,t,n))];case"Relu":return[Ys(S("x",e,t,n))];case"Round":return[NA(S("x",e,t,n))];case"Selu":return[RA(S("x",e,t,n))];case"Sigmoid":return[Bn(S("x",e,t,n))];case"Sin":return[_A(S("x",e,t,n))];case"Sign":return[ab(S("x",e,t,n))];case"Sinh":return[$A(S("x",e,t,n))];case"Softplus":return[Tl(S("x",e,t,n))];case"Sqrt":return[ln(S("x",e,t,n))];case"Square":return[lt(S("x",e,t,n))];case"Tanh":return[wl(S("x",e,t,n))];case"Tan":return[ib(S("x",e,t,n))];case"ClipByValue":return[Wn(S("x",e,t,n),S("clipValueMin",e,t,n),S("clipValueMax",e,t,n))];case"Relu6":return[TA(S("x",e,t,n))];case"Rsqrt":return[EA(xn(e.inputNames[0],t,n))];case"Prod":return[IA(S("x",e,t,n),S("axes",e,t,n))];case"LeakyRelu":return[Lh(S("x",e,t,n),S("alpha",e,t,n))];case"Prelu":return[qh(S("x",e,t,n),S("alpha",e,t,n))];case"IsNan":return[Ux(xn(e.inputNames[0],t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}};function Is(e,t,n=""){if(!(typeof e=="number"||typeof t=="number")){I.assert(e.length===t.length,()=>n+` Shapes ${e} and ${t} must match`);for(let s=0;s<e.length;s++){let r=e[s],a=t[s];I.assert(r<0||a<0||r===a,()=>n+` Shapes ${e} and ${t} must match`)}}}function Xv(e){return!(typeof e=="number"||e.some(t=>t<0))}function Ec(e,t,n){let s=U2(e,n),r=!Xv(s);if(r&&t.length===0)throw new Error(`Tried to calculate elements of an empty list with non-fully-defined elementShape: ${s}`);if(r&&t.forEach(a=>{s=U2(a.shape,s)}),!Xv(s))throw new Error(`Non-fully-defined elementShape: ${s}`);return s}function U2(e,t){if(typeof e=="number")return t;if(typeof t=="number")return e;if(e.length!==t.length)throw new Error(`Incompatible ranks during merge: ${e} vs. ${t}`);let n=[];for(let s=0;s<e.length;++s){let r=e[s],a=t[s];if(r>=0&&a>=0&&r!==a)throw new Error(`Incompatible shape during merge: ${e} vs. ${t}`);n[s]=r>=0?r:a}return n}var nL=class{constructor(e,t,n,s,r,a,o){this.name=e,this.dtype=t,this.maxSize=n,this.elementShape=s,this.identicalElementShapes=r,this.dynamicSize=a,this.clearAfterRead=o,this.tensors=[],this.closed_=!1,this.idTensor=Ie(0),Kt(this.idTensor)}get id(){return this.idTensor.id}get closed(){return this.closed_}clearAndClose(e){this.tensors.forEach(t=>{(e==null||!e.has(t.tensor.id))&&t.tensor.dispose()}),this.tensors=[],this.closed_=!0,this.idTensor.dispose()}size(){return this.tensors.length}read(e){if(this.closed_)throw new Error(`TensorArray ${this.name} has already been closed.`);if(e<0||e>=this.size())throw new Error(`Tried to read from index ${e}, but array size is: ${this.size()}`);let t=this.tensors[e];if(t.cleared)throw new Error(`TensorArray ${this.name}: Could not read index ${e} twice because it was cleared after a previous read (perhaps try setting clear_after_read = false?).`);return this.clearAfterRead&&(t.cleared=!0),t.read=!0,t.tensor}readMany(e){return e.map(t=>this.read(t))}write(e,t){if(this.closed_)throw new Error(`TensorArray ${this.name} has already been closed.`);if(e<0||!this.dynamicSize&&e>=this.maxSize)throw new Error(`Tried to write to index ${e}, but array is not resizeable and size is: ${this.maxSize}`);let n=this.tensors[e]||{};if(t.dtype!==this.dtype)throw new Error(`TensorArray ${this.name}: Could not write to TensorArray index ${e},
          because the value dtype is ${t.dtype}, but TensorArray dtype is ${this.dtype}.`);if(this.size()===0&&(this.elementShape==null||this.elementShape.length===0)&&(this.elementShape=t.shape),Is(this.elementShape,t.shape,`TensorArray ${this.name}: Could not write to TensorArray index ${e}.`),n.read)throw new Error(`TensorArray ${this.name}: Could not write to TensorArray index ${e}, because it has already been read.`);if(n.written)throw new Error(`TensorArray ${this.name}: Could not write to TensorArray index ${e}, because it has already been written.`);n.tensor=t,Kt(t),n.written=!0,this.tensors[e]=n}writeMany(e,t){if(e.length!==t.length)throw new Error(`TensorArray ${this.name}: could not write multiple tensors,because the index size: ${e.length} is not the same as tensors size: ${t.length}.`);e.forEach((n,s)=>this.write(n,t[s]))}gather(e,t){if(!!t&&t!==this.dtype)throw new Error(`TensorArray dtype is ${this.dtype} but gather requested dtype ${t}`);if(e)e=e.slice(0,this.size());else{e=[];for(let s=0;s<this.size();s++)e.push(s)}if(e.length===0)return on([],[0].concat(this.elementShape));let n=this.readMany(e);return Is(this.elementShape,n[0].shape,"TensorArray shape mismatch: "),Nn(n,0)}concat(e){if(!!e&&e!==this.dtype)throw new Error(`TensorArray dtype is ${this.dtype} but concat requested dtype ${e}`);if(this.size()===0)return on([],[0].concat(this.elementShape));let t=[];for(let s=0;s<this.size();s++)t.push(s);let n=this.readMany(t);return Is(this.elementShape,n[0].shape,`TensorArray shape mismatch: tensor array shape (${this.elementShape}) vs first tensor shape (${n[0].shape})`),ht(n,0)}scatter(e,t){if(t.dtype!==this.dtype)throw new Error(`TensorArray dtype is ${this.dtype} but tensor has dtype ${t.dtype}`);if(e.length!==t.shape[0])throw new Error(`Expected len(indices) == tensor.shape[0], but saw: ${e.length} vs. ${t.shape[0]}`);let n=Math.max(...e);if(!this.dynamicSize&&n>=this.maxSize)throw new Error(`Max index must be < array size (${n}  vs. ${this.maxSize})`);this.writeMany(e,ds(t,0))}split(e,t){if(t.dtype!==this.dtype)throw new Error(`TensorArray dtype is ${this.dtype} but tensor has dtype ${t.dtype}`);let n=0,s=e.map(i=>(n+=i,n));if(n!==t.shape[0])throw new Error(`Expected sum of lengths to be equal to
          tensor.shape[0], but sum of lengths is
        ${n}, and tensor's shape is: ${t.shape}`);if(!this.dynamicSize&&e.length!==this.maxSize)throw new Error(`TensorArray's size is not equal to the size of lengths (${this.maxSize} vs. ${e.length}), and the TensorArray is not marked as dynamically resizeable`);let r=n===0?0:t.size/n,a=[];H(()=>{t=U(t,[1,n,r]);for(let i=0;i<e.length;++i){let l=i===0?0:s[i-1],u=[0,l,0],c=[1,e[i],r];a[i]=U(Re(t,u,c),this.elementShape)}return a});let o=[];for(let i=0;i<e.length;i++)o[i]=i;this.writeMany(o,a)}},Rc=class{constructor(e,t,n,s=-1){this.tensors=e,this.elementShape=t,this.elementDtype=n,e!=null&&e.forEach(r=>{if(n!==r.dtype)throw new Error(`Invalid data types; op elements ${n}, but list elements ${r.dtype}`);Is(t,r.shape,"TensorList shape mismatch: "),Kt(r)}),this.idTensor=Ie(0),this.maxNumElements=s,Kt(this.idTensor)}get id(){return this.idTensor.id}copy(){return new Rc([...this.tensors],this.elementShape,this.elementDtype)}clearAndClose(e){this.tensors.forEach(t=>{(e==null||!e.has(t.id))&&t.dispose()}),this.tensors.length=0,this.idTensor.dispose()}size(){return this.tensors.length}stack(e,t,n=-1){if(t!==this.elementDtype)throw new Error(`Invalid data types; op elements ${t}, but list elements ${this.elementDtype}`);if(n!==-1&&this.tensors.length!==n)throw new Error(`Operation expected a list with ${n} elements but got a list with ${this.tensors.length} elements.`);Is(e,this.elementShape,"TensorList shape mismatch: ");let s=Ec(this.elementShape,this.tensors,e);return H(()=>{let r=this.tensors.map(a=>U(a,s));return Nn(r,0)})}popBack(e,t){if(t!==this.elementDtype)throw new Error(`Invalid data types; op elements ${t}, but list elements ${this.elementDtype}`);if(this.size()===0)throw new Error("Trying to pop from an empty list.");let n=Ec(this.elementShape,this.tensors,e),s=this.tensors.pop();return Is(s.shape,e,"TensorList shape mismatch: "),U(s,n)}pushBack(e){if(e.dtype!==this.elementDtype)throw new Error(`Invalid data types; op elements ${e.dtype}, but list elements ${this.elementDtype}`);if(Is(e.shape,this.elementShape,"TensorList shape mismatch: "),this.maxNumElements===this.size())throw new Error("Trying to push element into a full list.");Kt(e),this.tensors.push(e)}resize(e){if(e<0)throw new Error(`TensorListResize expects size to be non-negative. Got: ${e}`);if(this.maxNumElements!==-1&&e>this.maxNumElements)throw new Error(`TensorListResize input size ${e} is greater maxNumElement ${this.maxNumElements}.`);this.tensors.length=e}getItem(e,t,n){if(n!==this.elementDtype)throw new Error(`Invalid data types; op elements ${n}, but list elements ${this.elementDtype}`);if(e<0||e>this.tensors.length)throw new Error(`Trying to access element ${e} in a list with ${this.tensors.length} elements.`);if(this.tensors[e]==null)throw new Error(`element at index ${e} is null.`);Is(this.tensors[e].shape,t,"TensorList shape mismatch: ");let s=Ec(this.elementShape,this.tensors,t);return U(this.tensors[e],s)}setItem(e,t){if(t.dtype!==this.elementDtype)throw new Error(`Invalid data types; op elements ${t.dtype}, but list elements ${this.elementDtype}`);if(e<0||this.maxNumElements!==-1&&e>=this.maxNumElements)throw new Error(`Trying to set element ${e} in a list with max ${this.maxNumElements} elements.`);Is(this.elementShape,t.shape,"TensorList shape mismatch: "),Kt(t),this.tensors[e]=t}gather(e,t,n){if(t!==this.elementDtype)throw new Error(`Invalid data types; op elements ${t}, but list elements ${this.elementDtype}`);Is(this.elementShape,n,"TensorList shape mismatch: "),e=e.slice(0,this.size());let s=Ec(this.elementShape,this.tensors,n);return e.length===0?on([],[0].concat(s)):H(()=>{let r=e.map(a=>U(this.tensors[a],s));return Nn(r,0)})}concat(e,t){if(!!e&&e!==this.elementDtype)throw new Error(`TensorList dtype is ${this.elementDtype} but concat requested dtype ${e}`);Is(this.elementShape,t,"TensorList shape mismatch: ");let n=Ec(this.elementShape,this.tensors,t);return this.size()===0?on([],[0].concat(n)):H(()=>{let s=this.tensors.map(r=>U(r,n));return ht(s,0)})}};function sL(e,t,n){let s=e.dtype;if(e.shape.length<1)throw new Error(`Tensor must be at least a vector, but saw shape: ${e.shape}`);if(e.dtype!==n)throw new Error(`Invalid data types; op elements ${e.dtype}, but list elements ${n}`);let r=e.shape.slice(1);Is(r,t,"TensorList shape mismatch: ");let a=ds(e);return new Rc(a,t,s)}function rL(e,t,n){return new Rc([],e,t,n)}function aL(e,t,n,s){if(t.length!==e.shape[0])throw new Error(`Expected len(indices) == tensor.shape[0], but saw: ${t.length} vs. ${e.shape[0]}`);let r=Math.max(...t);if(s!=null&&s!==-1&&r>=s)throw new Error(`Max index must be < array size (${r}  vs. ${s})`);let a=new Rc([],n,e.dtype,s),o=ds(e,0);return t.forEach((i,l)=>{a.setItem(i,o[l])}),a}function oL(e,t,n){let s=0,r=t.map(c=>(s+=c,s));if(s!==e.shape[0])throw new Error(`Expected sum of lengths to be equal to
          tensor.shape[0], but sum of lengths is
        ${s}, and tensor's shape is: ${e.shape}`);let a=e.shape.slice(1),o=U2(a,n),i=s===0?0:e.size/s,l=H(()=>{let c=[];e=U(e,[1,s,i]);for(let d=0;d<t.length;++d){let h=d===0?0:r[d-1],p=[0,h,0],f=[1,t[d],i];c[d]=U(Re(e,p,f),o)}return e.dispose(),c}),u=new Rc([],n,e.dtype,t.length);for(let c=0;c<l.length;c++)u.setItem(c,l[c]);return u}var iL=async(e,t,n)=>{switch(e.op){case"If":case"StatelessIf":{let s=S("thenBranch",e,t,n),r=S("elseBranch",e,t,n),a=S("cond",e,t,n),o=S("args",e,t,n);return(await a.data())[0]?n.functionMap[s].executeFunctionAsync(o,n.tensorArrayMap,n.tensorListMap):n.functionMap[r].executeFunctionAsync(o,n.tensorArrayMap,n.tensorListMap)}case"While":case"StatelessWhile":{let s=S("body",e,t,n),r=S("cond",e,t,n),a=S("args",e,t,n),o=await n.functionMap[r].executeFunctionAsync(a,n.tensorArrayMap,n.tensorListMap),i=a.map(c=>c.id),l=await o[0].data();o.forEach(c=>{!c.kept&&i.indexOf(c.id)===-1&&c.dispose()});let u=a;for(;l[0];){let c=u;u=await n.functionMap[s].executeFunctionAsync(u,n.tensorArrayMap,n.tensorListMap);let d=u.map(p=>p.id);c.forEach(p=>{!p.kept&&i.indexOf(p.id)===-1&&d.indexOf(p.id)===-1&&p.dispose()});let h=await n.functionMap[r].executeFunctionAsync(u,n.tensorArrayMap,n.tensorListMap);l=await h[0].data(),h.forEach(p=>{!p.kept&&i.indexOf(p.id)===-1&&d.indexOf(p.id)===-1&&p.dispose()})}return u}case"LoopCond":{let s=S("pred",e,t,n);return[Ir(s)]}case"Switch":{let s=S("pred",e,t,n),r=S("data",e,t,n);return r.kept||(r=Ir(r)),(await s.data())[0]?[void 0,r]:[r,void 0]}case"Merge":{let s=e.inputNames.find(r=>xn(r,t,n)!==void 0);if(s){let r=xn(s,t,n);return[Ir(r)]}return}case"Enter":{let s=S("frameName",e,t,n),r=S("tensor",e,t,n);return n.enterFrame(s),[Ir(r)]}case"Exit":{let s=S("tensor",e,t,n);return n.exitFrame(),[Ir(s)]}case"NextIteration":{let s=S("tensor",e,t,n);return n.nextIteration(),[Ir(s)]}case"TensorArrayV3":{let s=S("size",e,t,n),r=S("dtype",e,t,n),a=S("elementShape",e,t,n),o=S("dynamicSize",e,t,n),i=S("clearAfterRead",e,t,n),l=S("identicalElementShapes",e,t,n),u=S("name",e,t,n),c=new nL(u,r,s,a,l,o,i);return n.addTensorArray(c),[c.idTensor,Ie(1)]}case"TensorArrayWriteV3":{let s=S("tensorArrayId",e,t,n),r=S("index",e,t,n),a=S("tensor",e,t,n),o=n.getTensorArray(s.id);return o.write(r,a),[o.idTensor]}case"TensorArrayReadV3":{let s=S("tensorArrayId",e,t,n),r=S("index",e,t,n);return[n.getTensorArray(s.id).read(r)]}case"TensorArrayGatherV3":{let s=S("tensorArrayId",e,t,n),r=S("indices",e,t,n),a=S("dtype",e,t,n);return[n.getTensorArray(s.id).gather(r,a)]}case"TensorArrayScatterV3":{let s=S("tensorArrayId",e,t,n),r=S("indices",e,t,n),a=S("tensor",e,t,n),o=n.getTensorArray(s.id);return o.scatter(r,a),[o.idTensor]}case"TensorArrayConcatV3":{let s=S("tensorArrayId",e,t,n),r=n.getTensorArray(s.id),a=S("dtype",e,t,n);return[r.concat(a)]}case"TensorArraySplitV3":{let s=S("tensorArrayId",e,t,n),r=S("tensor",e,t,n),a=S("lengths",e,t,n),o=n.getTensorArray(s.id);return o.split(a,r),[o.idTensor]}case"TensorArraySizeV3":{let s=S("tensorArrayId",e,t,n),r=n.getTensorArray(s.id);return[Ie(r.size(),"int32")]}case"TensorArrayCloseV3":{let s=S("tensorArrayId",e,t,n),r=n.getTensorArray(s.id);return r.clearAndClose(),[r.idTensor]}case"TensorListSetItem":{let s=S("tensorListId",e,t,n),r=S("index",e,t,n),a=S("tensor",e,t,n),o=n.getTensorList(s.id);return o.setItem(r,a),[o.idTensor]}case"TensorListGetItem":{let s=S("tensorListId",e,t,n),r=S("index",e,t,n),a=S("elementShape",e,t,n),o=S("elementDType",e,t,n);return[n.getTensorList(s.id).getItem(r,a,o)]}case"TensorListScatterV2":case"TensorListScatter":{let s=S("indices",e,t,n),r=S("tensor",e,t,n),a=S("elementShape",e,t,n),o=S("numElements",e,t,n),i=aL(r,s,a,o);return n.addTensorList(i),[i.idTensor]}case"TensorListReserve":case"EmptyTensorList":{let s=S("elementShape",e,t,n),r=S("elementDType",e,t,n),a;e.op==="TensorListReserve"?a="numElements":a="maxNumElements";let o=S(a,e,t,n),i=rL(s,r,o);return n.addTensorList(i),[i.idTensor]}case"TensorListGather":{let s=S("tensorListId",e,t,n),r=S("indices",e,t,n),a=S("elementShape",e,t,n),o=S("elementDType",e,t,n);return[n.getTensorList(s.id).gather(r,o,a)]}case"TensorListStack":{let s=S("tensorListId",e,t,n),r=S("elementShape",e,t,n),a=S("elementDType",e,t,n),o=S("numElements",e,t,n);return[n.getTensorList(s.id).stack(r,a,o)]}case"TensorListFromTensor":{let s=S("tensor",e,t,n),r=S("elementShape",e,t,n),a=S("elementDType",e,t,n),o=sL(s,r,a);return n.addTensorList(o),[o.idTensor]}case"TensorListConcat":{let s=S("tensorListId",e,t,n),r=n.getTensorList(s.id),a=S("dtype",e,t,n),o=S("elementShape",e,t,n);return[r.concat(a,o)]}case"TensorListPushBack":{let s=S("tensorListId",e,t,n),r=S("tensor",e,t,n),a=n.getTensorList(s.id);return a.pushBack(r),[a.idTensor]}case"TensorListPopBack":{let s=S("tensorListId",e,t,n),r=S("elementShape",e,t,n),a=S("elementDType",e,t,n);return[n.getTensorList(s.id).popBack(r,a)]}case"TensorListSplit":{let s=S("tensor",e,t,n),r=S("elementShape",e,t,n),a=S("lengths",e,t,n),o=oL(s,a,r);return n.addTensorList(o),[o.idTensor]}default:throw TypeError(`Node type ${e.op} is not implemented`)}};function Kv(e,t,n){let[s,r]=S("fusedOps",e,t,n),a=s==="biasadd",o=!a,i=r==="prelu",l=s==="fusedbatchnorm",u=S("numArgs",e,t,n);if(a){if(i&&u!==2)throw new Error("FusedConv2d and DepthwiseConv2d with BiasAdd and Prelu must have two extra arguments: bias and alpha.");if(!i&&a&&u!==1)throw new Error("FusedConv2d and DepthwiseConv2d with BiasAdd must have one extra argument: bias.")}if(l)throw new Error("FusedConv2d and DepthwiseConv2d with FusedBatchNorm is not supported");let c=S("strides",e,t,n),d=Gp(e,t,n),h=S("dataFormat",e,t,n).toUpperCase(),p=S("dilations",e,t,n),[f,m]=S("args",e,t,n);o&&(m=f,f=void 0);let A=S("leakyreluAlpha",e,t,n);return{stride:c,pad:d,dataFormat:h,dilations:p,biasArg:f,preluArg:m,activationFunc:r,leakyreluAlpha:A}}var lL=(e,t,n)=>{switch(e.op){case"Conv1D":{let s=S("stride",e,t,n),r=S("pad",e,t,n),a=S("dataFormat",e,t,n).toUpperCase(),o=S("dilation",e,t,n);return[dA(S("x",e,t,n),S("filter",e,t,n),s,r,a,o)]}case"Conv2D":{let s=S("strides",e,t,n),r=Gp(e,t,n),a=S("dataFormat",e,t,n).toUpperCase(),o=S("dilations",e,t,n);return[Hr(S("x",e,t,n),S("filter",e,t,n),[s[1],s[2]],r,a,[o[1],o[2]])]}case"_FusedConv2D":{let{stride:s,pad:r,dataFormat:a,dilations:o,biasArg:i,preluArg:l,activationFunc:u,leakyreluAlpha:c}=Kv(e,t,n);return[qr.conv2d({x:S("x",e,t,n),filter:S("filter",e,t,n),strides:[s[1],s[2]],pad:r,dataFormat:a,dilations:[o[1],o[2]],bias:i,activation:u,preluActivationWeights:l,leakyreluAlpha:c})]}case"FusedDepthwiseConv2dNative":{let{stride:s,pad:r,dataFormat:a,dilations:o,biasArg:i,preluArg:l,activationFunc:u,leakyreluAlpha:c}=Kv(e,t,n);return[qr.depthwiseConv2d({x:S("x",e,t,n),filter:S("filter",e,t,n),strides:[s[1],s[2]],pad:r,dataFormat:a,dilations:[o[1],o[2]],bias:i,activation:u,preluActivationWeights:l,leakyreluAlpha:c})]}case"Conv2DBackpropInput":case"Conv2dTranspose":{let s=S("outputShape",e,t,n),r=S("strides",e,t,n),a=Gp(e,t,n);return[pA(S("x",e,t,n),S("filter",e,t,n),s,[r[1],r[2]],a)]}case"DepthwiseConv2dNative":case"DepthwiseConv2d":{let s=S("strides",e,t,n),r=Gp(e,t,n),a=S("dilations",e,t,n),o=S("dataFormat",e,t,n).toUpperCase();return[tc(S("input",e,t,n),S("filter",e,t,n),[s[1],s[2]],r,o,[a[1],a[2]])]}case"Conv3D":{let s=S("strides",e,t,n),r=S("pad",e,t,n),a=S("dataFormat",e,t,n).toUpperCase(),o=S("dilations",e,t,n);return[fA(S("x",e,t,n),S("filter",e,t,n),[s[1],s[2],s[3]],r,a,[o[1],o[2],o[3]])]}case"AvgPool":{let s=S("strides",e,t,n),r=S("pad",e,t,n),a=S("kernelSize",e,t,n);return[Oh(S("x",e,t,n),[a[1],a[2]],[s[1],s[2]],r)]}case"MaxPool":{let s=S("strides",e,t,n),r=S("pad",e,t,n),a=S("kernelSize",e,t,n);return[Uh(S("x",e,t,n),[a[1],a[2]],[s[1],s[2]],r)]}case"MaxPoolWithArgmax":{let s=S("strides",e,t,n),r=S("pad",e,t,n),a=S("kernelSize",e,t,n),o=S("includeBatchInIndex",e,t,n),{result:i,indexes:l}=Yx(S("x",e,t,n),[a[1],a[2]],[s[1],s[2]],r,o);return[i,l]}case"AvgPool3D":{let s=S("strides",e,t,n),r=S("pad",e,t,n),a=S("kernelSize",e,t,n);return[uA(S("x",e,t,n),[a[1],a[2],a[3]],[s[1],s[2],s[3]],r)]}case"MaxPool3D":{let s=S("strides",e,t,n),r=S("pad",e,t,n),a=S("kernelSize",e,t,n);return[kA(S("x",e,t,n),[a[1],a[2],a[3]],[s[1],s[2],s[3]],r)]}case"Dilation2D":{let s=S("strides",e,t,n),r=S("pad",e,t,n),a=S("dilations",e,t,n),o=s[1],i=s[2],l=a[1],u=a[2];return[zx(S("x",e,t,n),S("filter",e,t,n),[o,i],r,[l,u],"NHWC")]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},uL=(e,t,n)=>{switch(e.op){case"Fill":{let s=S("shape",e,t,n),r=S("dtype",e,t,n),a=S("value",e,t,n);return[Sl(s,a,r)]}case"LinSpace":{let s=S("start",e,t,n),r=S("stop",e,t,n),a=S("num",e,t,n);return[Hx(s,r,a)]}case"Multinomial":{let s=S("logits",e,t,n),r=S("numSamples",e,t,n),a=S("seed",e,t,n);return[eb(s,r,a)]}case"OneHot":{let s=S("indices",e,t,n),r=S("depth",e,t,n),a=S("onValue",e,t,n),o=S("offValue",e,t,n);return[Ju(s,r,a,o)]}case"Ones":return[Un(S("shape",e,t,n),S("dtype",e,t,n))];case"OnesLike":return[us(S("x",e,t,n))];case"RandomUniform":return[El(S("shape",e,t,n),S("minval",e,t,n),S("maxval",e,t,n),S("dtype",e,t,n))];case"Range":{let s=S("start",e,t,n),r=S("stop",e,t,n),a=S("step",e,t,n);return[Rl(s,r,a,S("dtype",e,t,n))]}case"TruncatedNormal":{let s=S("shape",e,t,n),r=S("mean",e,t,n),a=S("stdDev",e,t,n),o=S("seed",e,t,n);return[Qh(s,r,a,S("dtype",e,t,n),o)]}case"Zeros":return[Dt(S("shape",e,t,n),S("dtype",e,t,n))];case"ZerosLike":return[qe(S("x",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}};function H2(e,t,n){let s=S("boxes",e,t,n),r=S("scores",e,t,n),a=S("maxOutputSize",e,t,n),o=S("iouThreshold",e,t,n),i=S("scoreThreshold",e,t,n),l=S("softNmsSigma",e,t,n);return{boxes:s,scores:r,maxOutputSize:a,iouThreshold:o,scoreThreshold:i,softNmsSigma:l}}var cL=async(e,t,n)=>{switch(e.op){case"NonMaxSuppressionV5":{let{boxes:s,scores:r,maxOutputSize:a,iouThreshold:o,scoreThreshold:i,softNmsSigma:l}=H2(e,t,n),u=await _e.nonMaxSuppressionWithScoreAsync(s,r,a,o,i,l);return[u.selectedIndices,u.selectedScores]}case"NonMaxSuppressionV4":{let{boxes:s,scores:r,maxOutputSize:a,iouThreshold:o,scoreThreshold:i}=H2(e,t,n),l=S("padToMaxOutputSize",e,t,n),u=await _e.nonMaxSuppressionPaddedAsync(s,r,a,o,i,l);return[u.selectedIndices,u.validOutputs]}case"NonMaxSuppressionV3":case"NonMaxSuppressionV2":{let{boxes:s,scores:r,maxOutputSize:a,iouThreshold:o,scoreThreshold:i}=H2(e,t,n);return[await _e.nonMaxSuppressionAsync(s,r,a,o,i)]}case"Where":{let s=ce(S("condition",e,t,n),"bool"),r=[await MA(s)];return s.dispose(),r}case"ListDiff":return rb(S("x",e,t,n),S("y",e,t,n));default:throw TypeError(`Node type ${e.op} is not implemented`)}},dL=(e,t,n)=>{switch(e.op){case"TopKV2":{let s=S("x",e,t,n),r=S("k",e,t,n),a=S("sorted",e,t,n),o=lb(s,r,a);return[o.values,o.indices]}case"Unique":{let s=S("x",e,t,n),r=PA(s);return[r.values,r.indices]}case"UniqueV2":{let s=S("x",e,t,n),r=S("axis",e,t,n),a=PA(s,r);return[a.values,a.indices]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},hL=(e,t,n)=>{switch(e.op){case"Const":return t[e.name];case"PlaceholderWithDefault":let s=S("default",e,t,n);return[xn(e.name,t,n)||s];case"Placeholder":return[xn(e.name,t,n)];case"Identity":case"StopGradient":case"FakeQuantWithMinMaxVars":{let u=S("x",e,t,n);return[Ir(u)]}case"IdentityN":return S("x",e,t,n).map(u=>Ir(u));case"Snapshot":let r=S("x",e,t,n);return[Ir(r)];case"Shape":return[Ot(S("x",e,t,n).shape,"int32")];case"ShapeN":return S("x",e,t,n).map(u=>Ot(u.shape));case"Size":return[Ie(S("x",e,t,n).size,"int32")];case"Rank":return[Ie(S("x",e,t,n).rank,"int32")];case"NoOp":return[Ie(1)];case"Print":let a=S("x",e,t,n),o=S("data",e,t,n),i=S("message",e,t,n),l=S("summarize",e,t,n);console.warn("The graph has a tf.print() operation,usually used for debugging, which slows down performance."),console.log(i);for(let u=0;u<o.length;u++)console.log(Array.prototype.slice.call(o[u].dataSync()).slice(0,l));return[a];default:throw TypeError(`Node type ${e.op} is not implemented`)}},pL=class{constructor(e,t){this.keyDType=e,this.valueDType=t,this.handle=Ie(0),this.tensorMap=new Map,Kt(this.handle)}get id(){return this.handle.id}clearAndClose(){this.tensorMap.forEach(e=>e.dispose()),this.tensorMap.clear(),this.handle.dispose()}size(){return this.tensorMap.size}tensorSize(){return Ie(this.size(),"int32")}async import(e,t){this.checkKeyAndValueTensor(e,t);let n=await e.data();return this.tensorMap.forEach(s=>s.dispose()),this.tensorMap.clear(),H(()=>{let s=ds(t),r=n.length,a=s.length;I.assert(r===a,()=>`The number of elements doesn't match, keys has ${r} elements, the values has ${a} elements.`);for(let o=0;o<r;o++){let i=n[o],l=s[o];Kt(l),this.tensorMap.set(i,l)}return this.handle})}async find(e,t){this.checkKeyAndValueTensor(e,t);let n=await e.data();return H(()=>{let s=[];for(let r=0;r<n.length;r++){let a=n[r],o=this.findWithDefault(a,t);s.push(o)}return Nn(s)})}findWithDefault(e,t){let n=this.tensorMap.get(e);return n!=null?n:t}checkKeyAndValueTensor(e,t){if(e.dtype!==this.keyDType)throw new Error(`Expect key dtype ${this.keyDType}, but got ${e.dtype}`);if(t.dtype!==this.valueDType)throw new Error(`Expect value dtype ${this.valueDType}, but got ${t.dtype}`)}},fL=async(e,t,n,s)=>{switch(e.op){case"HashTable":case"HashTableV2":{let r=S("keyDType",e,t,n),a=S("valueDType",e,t,n),o=new pL(r,a);return s.addHashTable(e.name,o),[o.handle]}case"LookupTableImport":case"LookupTableImportV2":{let r=S("tableHandle",e,t,n,s),a=S("keys",e,t,n),o=S("values",e,t,n);return[await s.getHashTableById(r.id).import(a,o)]}case"LookupTableFind":case"LookupTableFindV2":{let r=S("tableHandle",e,t,n,s),a=S("keys",e,t,n),o=S("defaultValue",e,t,n);return[await s.getHashTableById(r.id).find(a,o)]}case"LookupTableSize":case"LookupTableSizeV2":{let r=S("tableHandle",e,t,n,s);return[s.getHashTableById(r.id).tensorSize()]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},mL=(e,t,n)=>{switch(e.op){case"ResizeBilinear":{let s=S("images",e,t,n),r=S("size",e,t,n),a=S("alignCorners",e,t,n),o=S("halfPixelCenters",e,t,n);return[_e.resizeBilinear(s,[r[0],r[1]],a,o)]}case"ResizeNearestNeighbor":{let s=S("images",e,t,n),r=S("size",e,t,n),a=S("alignCorners",e,t,n),o=S("halfPixelCenters",e,t,n);return[_e.resizeNearestNeighbor(s,[r[0],r[1]],a,o)]}case"CropAndResize":{let s=S("image",e,t,n),r=S("boxes",e,t,n),a=S("boxInd",e,t,n),o=S("cropSize",e,t,n),i=S("method",e,t,n),l=S("extrapolationValue",e,t,n);return[_e.cropAndResize(s,r,a,o,i,l)]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},AL=(e,t,n)=>{switch(e.op){case"Equal":return[as(S("a",e,t,n),S("b",e,t,n))];case"NotEqual":return[Nl(S("a",e,t,n),S("b",e,t,n))];case"Greater":return[Vn(S("a",e,t,n),S("b",e,t,n))];case"GreaterEqual":return[Io(S("a",e,t,n),S("b",e,t,n))];case"Less":return[yA(S("a",e,t,n),S("b",e,t,n))];case"LessEqual":return[So(S("a",e,t,n),S("b",e,t,n))];case"LogicalAnd":return[Rs(S("a",e,t,n),S("b",e,t,n))];case"LogicalNot":return[Vh(S("a",e,t,n))];case"LogicalOr":return[wA(S("a",e,t,n),S("b",e,t,n))];case"Select":case"SelectV2":return[gn(S("condition",e,t,n),S("a",e,t,n),S("b",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},gL=(e,t,n)=>{switch(e.op){case"BatchMatMul":case"BatchMatMulV2":case"MatMul":return[We(S("a",e,t,n),S("b",e,t,n),S("transposeA",e,t,n),S("transposeB",e,t,n))];case"Einsum":return[Bx(S("equation",e,t,n),...S("tensors",e,t,n))];case"Transpose":return[je(S("x",e,t,n),S("perm",e,t,n))];case"_FusedMatMul":let[s,r]=S("fusedOps",e,t,n),a=s==="biasadd",o=r==="prelu",i=S("numArgs",e,t,n),l=S("leakyreluAlpha",e,t,n);if(a){if(o&&i!==2)throw new Error("Fused MatMul with BiasAdd and Prelu must have two extra arguments: bias and alpha.");if(!o&&i!==1)throw new Error("Fused MatMul with BiasAdd must have one extra argument: bias.")}let[u,c]=S("args",e,t,n);return[qr.matMul({a:S("a",e,t,n),b:S("b",e,t,n),transposeA:S("transposeA",e,t,n),transposeB:S("transposeB",e,t,n),bias:u,activation:r,preluActivationWeights:c,leakyreluAlpha:l})];default:throw TypeError(`Node type ${e.op} is not implemented`)}},yL=(e,t,n)=>{switch(e.op){case"FusedBatchNorm":case"FusedBatchNormV2":return[kl(S("x",e,t,n),S("mean",e,t,n),S("variance",e,t,n),S("offset",e,t,n),S("scale",e,t,n),S("epsilon",e,t,n))];case"FusedBatchNormV3":return[kl(S("x",e,t,n),S("mean",e,t,n),S("variance",e,t,n),S("offset",e,t,n),S("scale",e,t,n),S("epsilon",e,t,n))];case"LRN":return[Gx(S("x",e,t,n),S("radius",e,t,n),S("bias",e,t,n),S("alpha",e,t,n),S("beta",e,t,n))];case"Softmax":return[Zh(S("x",e,t,n))];case"LogSoftmax":return[xA(S("x",e,t,n))];case"SparseToDense":return[LA(S("sparseIndices",e,t,n),S("outputShape",e,t,n),S("sparseValues",e,t,n),S("defaultValue",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},xL=(e,t,n)=>{switch(e.op){case"Max":{let o=S("axis",e,t,n),i=S("keepDims",e,t,n);return[ls(S("x",e,t,n),o,i)]}case"Mean":{let o=S("axis",e,t,n),i=S("keepDims",e,t,n);return[Et(S("x",e,t,n),o,i)]}case"Min":{let o=S("axis",e,t,n),i=S("keepDims",e,t,n);return[Hh(S("x",e,t,n),o,i)]}case"Sum":{let o=S("axis",e,t,n),i=S("keepDims",e,t,n);return[ve(S("x",e,t,n),o,i)]}case"All":{let o=S("axis",e,t,n),i=S("keepDims",e,t,n);return[oA(S("x",e,t,n),o,i)]}case"Any":{let o=S("axis",e,t,n),i=S("keepDims",e,t,n);return[Fh(S("x",e,t,n),o,i)]}case"ArgMax":{let o=S("axis",e,t,n);return[Xs(S("x",e,t,n),o)]}case"ArgMin":{let o=S("axis",e,t,n);return[yx(S("x",e,t,n),o)]}case"Prod":{let o=S("axis",e,t,n),i=S("keepDims",e,t,n);return[IA(S("x",e,t,n),o,i)]}case"Cumsum":{let o=S("axis",e,t,n),i=S("exclusive",e,t,n),l=S("reverse",e,t,n);return[AA(S("x",e,t,n),o,i,l)]}case"Bincount":let s=S("x",e,t,n),r=S("weights",e,t,n),a=S("size",e,t,n);return[cA(s,r,a)];case"DenseBincount":{let o=S("x",e,t,n),i=S("weights",e,t,n),l=S("size",e,t,n),u=S("binaryOutput",e,t,n);return[Px(o,i,l,u)]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},bL=(e,t,n)=>{switch(e.op){case"ConcatV2":case"Concat":{let s=S("n",e,t,n),r=S("axis",e,t,n),a=S("tensors",e,t,n);return a=a.slice(0,s),[ht(a,r)]}case"Gather":{let s=S("x",e,t,n),r=S("indices",e,t,n);return[Cl(s,ce(r,"int32"),0)]}case"GatherV2":{let s=S("axis",e,t,n),r=S("batchDims",e,t,n),a=S("x",e,t,n),o=S("indices",e,t,n);return[Cl(a,ce(o,"int32"),s,r)]}case"Reverse":{let s=S("dims",e,t,n),r=[];for(let o=0;o<s.length;o++)s[o]&&r.push(o);let a=S("x",e,t,n);return[cs(a,r)]}case"ReverseV2":{let s=S("axis",e,t,n),r=S("x",e,t,n);return[cs(r,s)]}case"Slice":{let s=S("begin",e,t,n),r=S("size",e,t,n);return[Re(S("x",e,t,n),s,r)]}case"StridedSlice":{let s=S("begin",e,t,n),r=S("end",e,t,n),a=S("strides",e,t,n),o=S("beginMask",e,t,n),i=S("endMask",e,t,n),l=S("ellipsisMask",e,t,n),u=S("newAxisMask",e,t,n),c=S("shrinkAxisMask",e,t,n),d=S("x",e,t,n);return[ob(d,s,r,a,o,i,l,u,c)]}case"Pack":return H(()=>{let s=S("axis",e,t,n),r=S("tensors",e,t,n),a=r[0].shape,o=ot(r[0]).shape,i=r.map(l=>{let u=I.arraysEqual(l.shape,a);if(!u&&!I.arraysEqual(ot(l).shape,o))throw new Error("the input tensors shape does not match");return u?l:U(l,a)});return[Nn(i,s)]});case"Unpack":{let s=S("axis",e,t,n),r=S("tensor",e,t,n);return ds(r,s)}case"Tile":{let s=S("reps",e,t,n);return[Es(S("x",e,t,n),s)]}case"Split":case"SplitV":{let s=S("axis",e,t,n),r=S("numOrSizeSplits",e,t,n),a=S("x",e,t,n);return nn(a,r,s)}case"ScatterNd":{let s=S("indices",e,t,n),r=S("values",e,t,n),a=S("shape",e,t,n);return[pb(s,r,a)]}case"GatherNd":{let s=S("x",e,t,n),r=S("indices",e,t,n);return[fb(s,r)]}case"SparseToDense":{let s=S("sparseIndices",e,t,n),r=S("outputShape",e,t,n),a=S("sparseValues",e,t,n),o=S("defaultValue",e,t,n);return[LA(s,a,r,a.dtype===o.dtype?o:ce(o,a.dtype))]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},vL=(e,t,n)=>{switch(e.op){case"SparseFillEmptyRows":{let{outputIndices:s,outputValues:r,emptyRowIndicator:a,reverseIndexMap:o}=uc.sparseFillEmptyRows(S("indices",e,t,n),S("values",e,t,n),S("denseShape",e,t,n),S("defaultValue",e,t,n));return[s,r,a,o]}case"SparseReshape":{let{outputIndices:s,outputShape:r}=uc.sparseReshape(S("inputIndices",e,t,n),S("inputShape",e,t,n),S("newShape",e,t,n));return[s,r]}case"SparseSegmentMean":return[uc.sparseSegmentMean(S("data",e,t,n),S("indices",e,t,n),S("segmentIds",e,t,n))];case"SparseSegmentSum":return[uc.sparseSegmentSum(S("data",e,t,n),S("indices",e,t,n),S("segmentIds",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},wL=(e,t,n)=>{switch(e.op){case"FFT":return[Yh(S("x",e,t,n))];case"IFFT":return[ic(S("x",e,t,n))];case"RFFT":return[Jh(S("x",e,t,n))];case"IRFFT":return[DA(S("x",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},kL=(e,t,n)=>{switch(e.op){case"StringNGrams":{let{nGrams:s,nGramsSplits:r}=rp.stringNGrams(S("data",e,t,n),S("dataSplits",e,t,n),S("separator",e,t,n),S("nGramWidths",e,t,n),S("leftPad",e,t,n),S("rightPad",e,t,n),S("padWidth",e,t,n),S("preserveShortSequences",e,t,n));return[s,r]}case"StringSplit":{let{indices:s,values:r,shape:a}=rp.stringSplit(S("input",e,t,n),S("delimiter",e,t,n),S("skipEmpty",e,t,n));return[s,r,a]}case"StringToHashBucketFast":return[rp.stringToHashBucketFast(S("input",e,t,n),S("numBuckets",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},IL=(e,t,n)=>{switch(e.op){case"Cast":return[ce(S("x",e,t,n),S("dtype",e,t,n))];case"ExpandDims":{let s=S("axis",e,t,n);return[Ft(S("x",e,t,n),s)]}case"Squeeze":{let s=S("axis",e,t,n);return[ot(S("x",e,t,n),s)]}case"Reshape":return[U(S("x",e,t,n),S("shape",e,t,n))];case"MirrorPad":return[Jx(S("x",e,t,n),S("padding",e,t,n),S("mode",e,t,n))];case"PadV2":case"Pad":return[Gr(S("x",e,t,n),S("padding",e,t,n),S("constantValue",e,t,n))];case"SpaceToBatchND":{let s=S("blockShape",e,t,n),r=S("paddings",e,t,n);return[jh(S("x",e,t,n),s,r)]}case"BatchToSpaceND":{let s=S("blockShape",e,t,n),r=S("crops",e,t,n);return[Ph(S("x",e,t,n),s,r)]}case"DepthToSpace":{let s=S("blockSize",e,t,n),r=S("dataFormat",e,t,n).toUpperCase();return[Mx(S("x",e,t,n),s,r)]}case"BroadcastTo":return[ec(S("x",e,t,n),S("shape",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}};function Zv(e,t,n,s){let r=((a,o,i)=>{switch(a.category){case"arithmetic":return H(()=>eL(a,o,i));case"basic_math":return H(()=>tL(a,o,i));case"control":return iL(a,o,i);case"convolution":return H(()=>lL(a,o,i));case"creation":return H(()=>uL(a,o,i));case"dynamic":return cL(a,o,i);case"evaluation":return H(()=>dL(a,o,i));case"image":return H(()=>mL(a,o,i));case"graph":return H(()=>hL(a,o,i));case"logical":return H(()=>AL(a,o,i));case"matrices":return H(()=>gL(a,o,i));case"normalization":return H(()=>yL(a,o,i));case"reduction":return H(()=>xL(a,o,i));case"slice_join":return H(()=>bL(a,o,i));case"sparse":return H(()=>vL(a,o,i));case"spectral":return H(()=>wL(a,o,i));case"string":return H(()=>kL(a,o,i));case"transformation":return H(()=>IL(a,o,i));case"hash_table":return fL(a,o,i,s);case"custom":let l=Iv(a.op);if(l&&l.customExecutor)return l.customExecutor(new Qz(a,o,i));throw TypeError(`Custom op ${a.op} is not registered.`);default:throw TypeError(`Unknown op '${a.op}'. File an issue at https://github.com/tensorflow/tfjs/issues so we can add it, or register a custom execution with tf.registerOp()`)}})(e,t,n);return I.isPromise(r)?r.then(a=>[].concat(a)):[].concat(r)}var Yv=class{constructor(e={},t={},n={},s={}){this.weightMap=e,this.tensorArrayMap=t,this.tensorListMap=n,this.functionMap=s,this.rootContext={id:0,frameName:"",iterationId:0},this.contexts=[this.rootContext],this.lastId=0,this.generateCurrentContextIds()}newFrame(e,t){return{id:e,frameName:t,iterationId:0}}set currentContext(e){this.contexts!==e&&(this.contexts=e,this.generateCurrentContextIds())}get currentContext(){return this.contexts}get currentContextId(){return this._currentContextIds[0]}get currentContextIds(){return this._currentContextIds}generateCurrentContextIds(){let e=[];for(let t=0;t<this.contexts.length-1;t++){let n=this.contexts.slice(0,this.contexts.length-t);e.push(this.contextIdforContexts(n))}e.push(""),this._currentContextIds=e}contextIdforContexts(e){return e?e.map(t=>t.id===0&&t.iterationId===0?"":`${t.frameName}-${t.iterationId}`).join("/"):""}enterFrame(e){this.contexts&&(this.lastId++,this.contexts=this.contexts.slice(),this.contexts.push(this.newFrame(this.lastId,e)),this._currentContextIds.unshift(this.contextIdforContexts(this.contexts)))}exitFrame(){if(this.contexts&&this.contexts.length>1)this.contexts=this.contexts.slice(),this.contexts.splice(-1),this.currentContextIds.shift();else throw new Error("Cannot exit frame, the context is empty")}nextIteration(){if(this.contexts&&this.contexts.length>0){this.contexts=this.contexts.slice(),this.lastId++;let e=Object.assign({},this.contexts[this.contexts.length-1]);e.iterationId+=1,e.id=this.lastId,this.contexts.splice(-1,1,e),this._currentContextIds.splice(0,1,this.contextIdforContexts(this.contexts))}else throw new Error("Cannot increase frame iteration, the context is empty")}getWeight(e){return this.weightMap[e]}addTensorArray(e){this.tensorArrayMap[e.id]=e}getTensorArray(e){return this.tensorArrayMap[e]}addTensorList(e){this.tensorListMap[e.id]=e}getTensorList(e){return this.tensorListMap[e]}dispose(e){for(let t in this.tensorArrayMap)this.tensorArrayMap[t].clearAndClose(e);for(let t in this.tensorListMap)this.tensorListMap[t].clearAndClose(e)}};function Jv(e,t,n,s){let r=new Set,a=[],o=null,i=null,l=new Set,u=Object.keys(e).map(h=>Hn(h)[0]),c=[];s!=null&&(c=s.map(h=>Hn(h.name)[0]));let d=[...t];for(;d.length>0;){let h=d.pop();if((Qv(h)||EL(h)||RL(h))&&o==null&&(o=h,i=o.children.map(p=>p.name).filter(p=>r.has(p))),r.add(h.name),n[h.name]==null&&u.indexOf(h.name)===-1&&c.indexOf(h.name)===-1){if(h.inputs.length===0){a.push(h.name);continue}h.inputs.forEach(p=>{l.has(p.name)||(l.add(p.name),d.push(p))})}}return{inputs:e,outputs:t,usedNodes:r,missingInputs:a,dynamicNode:o,syncInputs:i}}function SL(e,t,n){let{usedNodes:s,inputs:r}=n,a=[],o=Object.keys(r).map(c=>Hn(c)[0]).map(c=>e.nodes[c]),i=e.initNodes;o.forEach(c=>{s.has(c.name)&&a.push(c)}),e.weights.forEach(c=>{s.has(c.name)&&a.push(c)}),i!=null&&i.forEach(c=>{s.has(c.name)&&a.push(c)});let l=new Set,u=[];for(;a.length>0;){let c=a.pop();l.add(c.name),t[c.name]||u.push(c),c.children.forEach(d=>{!l.has(d.name)&&s.has(d.name)&&d.inputs.every(h=>l.has(h.name))&&a.push(d)})}return u}var CL=["Switch","Merge","Enter","Exit","NextIteration","StatelessIf","StatelessWhile","if","While"],TL=["NonMaxSuppressionV2","NonMaxSuppressionV3","NonMaxSuppressionV5","Where"],NL=["HashTable","HashTableV2","LookupTableImport","LookupTableImportV2","LookupTableFind","LookupTableFindV2","LookupTableSize","LookupTableSizeV2"];function Qv(e){return CL.indexOf(e.op)>=0}function EL(e){return TL.indexOf(e.op)>=0}function RL(e){return NL.indexOf(e.op)>=0}var G2=class{constructor(e,t){this.graph=e,this.parent=t,this.compiledMap=new Map,this._weightMap={},this.SEPERATOR=",",this._functions={},this._functionExecutorMap={},this._outputs=e.outputs,this._inputs=e.inputs,this._initNodes=e.initNodes,this._signature=e.signature,this._functions=e.functions,e.functions!=null&&Object.keys(e.functions).forEach(n=>{this._functionExecutorMap[n]=new G2(e.functions[n],this)})}get weightIds(){return this.parent?this.parent.weightIds:this._weightIds}get functionExecutorMap(){return this.parent?this.parent.functionExecutorMap:this._functionExecutorMap}get weightMap(){return this.parent?this.parent.weightMap:this._weightMap}set weightMap(e){let t=Object.keys(e).map(n=>e[n].map(s=>s.id));this._weightIds=[].concat(...t),this._weightMap=e}set resourceManager(e){this._resourceManager=e}get inputs(){return this._inputs.map(e=>({name:e.name,shape:e.attrParams.shape?e.attrParams.shape.value:void 0,dtype:e.attrParams.dtype?e.attrParams.dtype.value:void 0}))}get outputs(){return this._outputs.map(e=>({name:e.name,shape:e.attrParams.shape?e.attrParams.shape.value:void 0,dtype:e.attrParams.dtype?e.attrParams.dtype.value:void 0}))}get inputNodes(){return this._inputs.map(e=>e.signatureKey||e.name)}get outputNodes(){return this._outputs.map(e=>{let t=e.signatureKey||e.name;return e.defaultOutput?`${t}:${e.defaultOutput}`:t})}get functions(){return Object.keys(this._functions).reduce((e,t)=>(e[t]=this._functions[t].signature,e),{})}getCompilationKey(e,t){let n=e.map(r=>r.name).sort(),s=t.map(r=>r.name).sort();return n.join(this.SEPERATOR)+"--"+s.join(this.SEPERATOR)}compile(e,t){let n=Jv(e,t,this.weightMap,this._initNodes),{missingInputs:s,dynamicNode:r,syncInputs:a}=n;if(r!=null)throw new Error(`This execution contains the node '${r.name}', which has the dynamic op '${r.op}'. Please use model.executeAsync() instead. Alternatively, to avoid the dynamic ops, specify the inputs [${a}]`);if(s.length>0){let o=t.map(l=>l.name),i=Object.keys(e);throw new Error(`Cannot compute the outputs [${o}] from the provided inputs [${i}]. Missing the following inputs: [${s}]`)}return SL(this.graph,this.weightMap,n)}execute(e,t){e=this.mapInputs(e);let n=Object.keys(e).sort();this.checkInputs(e),this.checkInputShapeAndType(e),t=this.mapOutputs(t),this.checkOutputs(t);let s=n.map(c=>this.graph.nodes[Hn(c)[0]]),r=t.map(c=>Hn(c)[0]),a=r.map(c=>this.graph.nodes[c]);a.length===0&&(a=this._outputs);let o=this.getCompilationKey(s,a),i=this.compiledMap.get(o);i==null&&(i=this.compile(e,a),this.compiledMap.set(o,i));let l={},u={};return H(()=>{let c=new Yv(this.weightMap,l,u,this.functionExecutorMap),d=Object.assign({},this.weightMap);Object.keys(e).forEach(f=>{let[m,A]=Hn(f),g=[];g[A]=e[f],d[m]=g});let h=this.getFrozenTensorIds(d),p={};for(let f=0;f<i.length;f++){let m=i[f];if(!d[m.name]){let A=Zv(m,d,c,this._resourceManager);if(I.isPromise(A))throw new Error(`The execution of the op '${m.op}' returned a promise. Please use model.executeAsync() instead.`);d[m.name]=A,this.checkTensorForDisposal(m.name,m,d,c,h,r,p)}}return this.parent==null&&c.dispose(h),t.map(f=>xn(f,d,c))})}getFrozenTensorIds(e){let t=[].concat.apply([],Object.keys(e).map(n=>e[n]).map(n=>n.map(s=>s.id)));return new Set(t)}checkTensorForDisposal(e,t,n,s,r,a,o){t.category==="control"||a.indexOf(e)!==-1||(n[e].forEach(i=>{i!=null&&(o[i.id]=(o[i.id]||0)+t.children.length)}),t.inputs.forEach(i=>{if(i.category!=="control"){let l=$z(i.name,n,s);l!=null&&l.forEach(u=>{if(u&&!u.kept&&!r.has(u.id)){let c=o[u.id];c===1?(u.dispose(),delete o[u.id]):c!=null&&o[u.id]--}})}}))}async executeAsync(e,t){return this._executeAsync(e,t)}async _executeAsync(e,t,n=!1,s={},r={}){n||(e=this.mapInputs(e),this.checkInputs(e),this.checkInputShapeAndType(e),t=this.mapOutputs(t),this.checkOutputs(t));let a=new Yv(this.weightMap,s,r,this.functionExecutorMap),o=await this.executeWithControlFlow(e,a,t,n),i=t.map(d=>xn(d,o,a)),l=i.map(d=>d.id),u=Object.keys(e).map(d=>e[d].id),c=new Set([...l,...u,...this.weightIds]);return Object.keys(o).forEach(d=>{o[d].forEach(p=>{p&&!p.kept&&!p.isDisposed&&!c.has(p.id)&&p.dispose()})}),this.parent==null&&a.dispose(c),i}async executeFunctionAsync(e,t,n){let s=e.reduce((r,a,o)=>(r[this.inputs[o].name]=a,r),{});return this._executeAsync(s,this.outputNodes,!0,t,n)}async executeWithControlFlow(e,t,n,s){let r=Object.keys(e),a=r.map(y=>this.graph.nodes[Hn(y)[0]]),o=n.map(y=>Hn(y)[0]),i=o.map(y=>this.graph.nodes[y]);i.length===0&&(i=this._outputs);let{usedNodes:l,missingInputs:u,dynamicNode:c,syncInputs:d}=Jv(e,i,this.weightMap,this._initNodes),h=[...a,...this.graph.weights,...this._initNodes||[]].map(y=>({node:y,contexts:t.currentContext})),p=Object.assign({},this.weightMap);Object.keys(e).forEach(y=>{let[x,b]=Hn(y),v=[];v[b]=e[y],p[x]=v});let f={},m=this.getFrozenTensorIds(p),A={};for(;h.length>0;){let y=this.processStack(a,h,t,p,A,m,o,f,l);await Promise.all(y)}c==null&&!s&&console.warn("This model execution did not contain any nodes with control flow or dynamic output shapes. You can use model.execute() instead.");let g=i.filter(y=>!Qv(y)&&!xn(y.name,p,t)).map(y=>y.name);if(g.length>0){let y="";throw c!=null&&(y=`Alternatively, to avoid the dynamic ops, use model.execute() and specify the inputs [${d}]`),new Error(`Cannot compute the outputs [${g}] from the provided inputs [${r}]. Consider providing the following inputs: [${u}]. ${y}`)}return p}processStack(e,t,n,s,r,a,o,i,l){let u=[];for(;t.length>0;){let c=t.pop();n.currentContext=c.contexts;let d="";if(c.node.op==="Enter"&&S("isConstant",c.node,s,n)&&([d]=kr(c.node.name,n)),s[c.node.name]==null){let h=Zv(c.node,s,n,this._resourceManager);d||([d]=kr(c.node.name,n));let p=n.currentContext;I.isPromise(h)?u.push(h.then(f=>(s[d]=f,n.currentContext=p,this.checkTensorForDisposal(d,c.node,s,n,a,o,i),this.processChildNodes(c.node,t,n,s,r,l),f))):(s[d]=h,this.checkTensorForDisposal(d,c.node,s,n,a,o,i),this.processChildNodes(c.node,t,n,s,r,l))}else this.processChildNodes(c.node,t,n,s,r,l)}return u}processChildNodes(e,t,n,s,r,a){e.children.forEach(o=>{let[i]=kr(o.name,n);r[i]||!a.has(o.name)||(o.op==="Merge"?o.inputNames.some(l=>!!xn(l,s,n))&&(r[i]=!0,t.push({contexts:n.currentContext,node:o})):o.inputNames.every(l=>!!xn(l,s,n))&&(r[i]=!0,t.push({contexts:n.currentContext,node:o})))})}dispose(){Object.keys(this.weightMap).forEach(e=>this.weightMap[e].forEach(t=>t.dispose()))}checkInputShapeAndType(e){Object.keys(e).forEach(t=>{let n=e[t],[s]=Hn(t),r=this.graph.nodes[s];if(r.attrParams.shape&&r.attrParams.shape.value){let a=r.attrParams.shape.value,o=a.length===n.shape.length&&n.shape.every((i,l)=>a[l]===-1||a[l]===i);I.assert(o,()=>`The shape of dict['${r.name}'] provided in model.execute(dict) must be [${a}], but was [${n.shape}]`)}r.attrParams.dtype&&r.attrParams.dtype.value&&I.assert(n.dtype===r.attrParams.dtype.value,()=>`The dtype of dict['${r.name}'] provided in model.execute(dict) must be ${r.attrParams.dtype.value}, but was ${n.dtype}`)})}mapInputs(e){let t={};for(let n in e)if(this._signature!=null&&this._signature.inputs!=null&&this._signature.inputs[n]!=null){let s=this._signature.inputs[n];t[s.name]=e[n]}else t[n]=e[n];return t}checkInputs(e){let t=Object.keys(e).filter(n=>{let[s]=Hn(n);return this.graph.nodes[s]==null});if(t.length>0)throw new Error(`The dict provided in model.execute(dict) has keys: [${t}] that are not part of graph`)}mapOutputs(e){return e.map(t=>this._signature!=null&&this._signature.outputs!=null&&this._signature.outputs[t]!=null?this._signature.outputs[t].name:t,{})}checkOutputs(e){e.forEach(t=>{let[n]=Hn(t);if(!this.graph.nodes[n])throw new Error(`The output '${t}' is not found in the graph`)})}},_L=class{constructor(e={},t={}){this.hashTableNameToHandle=e,this.hashTableMap=t}addHashTable(e,t){this.hashTableNameToHandle[e]=t.handle,this.hashTableMap[t.id]=t}getHashTableHandleByName(e){return this.hashTableNameToHandle[e]}getHashTableById(e){return this.hashTableMap[e]}dispose(){for(let e in this.hashTableMap)this.hashTableMap[e].clearAndClose(),delete this.hashTableMap[e];for(let e in this.hashTableNameToHandle)this.hashTableNameToHandle[e].dispose(),delete this.hashTableNameToHandle[e]}},$L="?tfjs-format=file",FL="model.json",e7=class{constructor(e,t={}){this.modelUrl=e,this.loadOptions=t,this.version="n/a",t==null&&(this.loadOptions={}),this.resourceManager=new _L}get modelVersion(){return this.version}get inputNodes(){return this.executor.inputNodes}get outputNodes(){return this.executor.outputNodes}get inputs(){return this.executor.inputs}get outputs(){return this.executor.outputs}get weights(){return this.executor.weightMap}get metadata(){return this.artifacts.userDefinedMetadata}get modelSignature(){return this.signature}findIOHandler(){let e=this.modelUrl;if(e.load!=null)this.handler=e;else if(this.loadOptions.requestInit!=null)this.handler=Tn.browserHTTPRequest(e,this.loadOptions);else{let t=Tn.getLoadHandlers(e,this.loadOptions);if(t.length===0)t.push(Tn.browserHTTPRequest(e,this.loadOptions));else if(t.length>1)throw new Error(`Found more than one (${t.length}) load handlers for URL '${[e]}'`);this.handler=t[0]}}async load(){if(this.findIOHandler(),this.handler.load==null)throw new Error("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");let e=await this.handler.load();return this.loadSync(e)}loadSync(e){this.artifacts=e;let t=this.artifacts.modelTopology,n;this.artifacts.userDefinedMetadata!=null&&this.artifacts.userDefinedMetadata.signature!=null?n=this.artifacts.userDefinedMetadata.signature:n=this.artifacts.signature,this.signature=n,this.version=`${t.versions.producer}.${t.versions.minConsumer}`;let s=Tn.decodeWeights(this.artifacts.weightData,this.artifacts.weightSpecs);if(this.executor=new G2(Hv.Instance.transformGraph(t,this.signature)),this.executor.weightMap=this.convertTensorMapToTensorsMap(s),this.executor.resourceManager=this.resourceManager,e.modelInitializer!=null&&e.modelInitializer.node!=null){let r=Hv.Instance.transformGraph(e.modelInitializer);this.initializer=new G2(r),this.initializer.weightMap=this.executor.weightMap,this.initializer.resourceManager=this.resourceManager,this.initializer.executeAsync({},[])}return!0}async save(e,t){if(typeof e=="string"){let n=Tn.getSaveHandlers(e);if(n.length===0)throw new Error(`Cannot find any save handlers for URL '${e}'`);if(n.length>1)throw new Error(`Found more than one (${n.length}) save handlers for URL '${e}'`);e=n[0]}if(e.save==null)throw new Error("GraphModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");return e.save(this.artifacts)}predict(e,t){return this.execute(e,this.outputNodes)}normalizeInputs(e){if(!(e instanceof Ue)&&!Array.isArray(e))return e;if(e=Array.isArray(e)?e:[e],e.length!==this.inputNodes.length)throw new Error(`Input tensor count mismatch,the graph model has ${this.inputNodes.length} placeholders, while there are ${e.length} input tensors.`);return this.inputNodes.reduce((t,n,s)=>(t[n]=e[s],t),{})}normalizeOutputs(e){return e=e||this.outputNodes,Array.isArray(e)?e:[e]}execute(e,t){e=this.normalizeInputs(e),t=this.normalizeOutputs(t);let n=this.executor.execute(e,t);return n.length>1?n:n[0]}async executeAsync(e,t){e=this.normalizeInputs(e),t=this.normalizeOutputs(t);let n=await this.executor.executeAsync(e,t);return n.length>1?n:n[0]}convertTensorMapToTensorsMap(e){return Object.keys(e).reduce((t,n)=>(t[n]=[e[n]],t),{})}dispose(){this.executor.dispose(),this.initializer&&this.initializer.dispose(),this.resourceManager.dispose()}};async function pt(e,t={}){if(e==null)throw new Error("modelUrl in loadGraphModel() cannot be null. Please provide a url or an IOHandler that loads the model");t==null&&(t={}),t.fromTFHub&&e.load==null&&(e.endsWith("/")||(e=e+"/"),e=`${e}${FL}${$L}`);let n=new e7(e,t);return await n.load(),n}var DL="3.8.0",t7={};Pe(t7,{CSVDataset:()=>p7,Dataset:()=>Bl,FileDataSource:()=>b7,TextLineDataset:()=>c7,URLDataSource:()=>v7,array:()=>sB,csv:()=>fB,func:()=>mB,generator:()=>AB,microphone:()=>yB,version_data:()=>xB,webcam:()=>gB,zip:()=>rB});var OL=fa(n5()),PL=fa(n5());function ML(e,t){return jp(e,t)}function jp(e,t,n=new Map,s=new Set){if(e==null)return null;if(s.has(e))throw new Error("Circular references are not supported.");if(n.has(e))return n.get(e);let r=t(e);if(r.recurse&&r.value!==null)throw new Error("A deep map function may not return both a value and recurse=true.");if(r.recurse)if(Ll(e)){let a=Array.isArray(e)?[]:{};s.add(e);for(let o in e){let i=e[o],l=jp(i,t,n,s);a[o]=l}return s.delete(e),a}else throw new Error(`Can't recurse into non-iterable type: ${e}`);else return n.set(e,r.value),r.value}function zL(e,t=s7){return n7(e,t)}function n7(e,t,n=new Set){let s=e[0];if(n.has(s))throw new Error("Circular references are not supported.");let r=t(e);if(r.recurse&&r.value!==null)throw new Error("A deep zip function may not return both a value and recurse=true.");if(r.recurse)if(Ll(s)){let a=Array.isArray(s)?[]:{};n.add(s);for(let o in s){let i=e.map(u=>u[o]),l=n7(i,t,n);a[o]=l}return n.delete(s),a}else throw new Error(`Can't recurse into non-iterable type: ${s}`);else return r.value}function s7(e){return e===null?null:Ll(e[0])?{value:null,recurse:!0}:{value:e,recurse:!1}}async function r7(e,t){let n=new Map;jp(e,t,n);for(let r of Array.from(n.keys())){let a=n.get(r);if(I.isPromise(a)){let o=await a;n.set(r,o)}}return jp(e,t,n)}function Ll(e){return e!=null&&!ArrayBuffer.isView(e)&&(Array.isArray(e)||typeof e=="object"&&!(e instanceof Ue))}function LL(e){return e==null||BL(e)||Array.isArray(e)||typeof e=="object"&&e instanceof Ue||I.isTypedArray(e)}function BL(e){return e===null||typeof e!="object"&&typeof e!="function"}function WL(e){return ML(e,VL)}function VL(e){return e instanceof Ue?{value:e.clone(),recurse:!1}:Ll(e)?{value:null,recurse:!0}:{value:e,recurse:!1}}var a7=class{constructor(e){if(this.capacity=e,this.begin=0,this.end=0,e==null)throw new RangeError("Can't create a ring buffer of unknown capacity.");if(e<1)throw new RangeError("Can't create ring buffer of capacity < 1.");this.data=new Array(e),this.doubledCapacity=2*e}wrap(e){for(;e<0;)e+=this.doubledCapacity;return e%this.doubledCapacity}get(e){if(e<0)throw new RangeError("Can't get item at a negative index.");return this.data[e%this.capacity]}set(e,t){if(e<0)throw new RangeError("Can't set item at a negative index.");this.data[e%this.capacity]=t}length(){let e=this.end-this.begin;return e<0&&(e=this.doubledCapacity+e),e}isFull(){return this.length()===this.capacity}isEmpty(){return this.length()===0}push(e){if(this.isFull())throw new RangeError("Ring buffer is full.");this.set(this.end,e),this.end=this.wrap(this.end+1)}pushAll(e){for(let t of e)this.push(t)}pop(){if(this.isEmpty())throw new RangeError("Ring buffer is empty.");this.end=this.wrap(this.end-1);let e=this.get(this.end);return this.set(this.end,void 0),e}unshift(e){if(this.isFull())throw new RangeError("Ring buffer is full.");this.begin=this.wrap(this.begin-1),this.set(this.begin,e)}shift(){if(this.isEmpty())throw new RangeError("Ring buffer is empty.");let e=this.get(this.begin);return this.set(this.begin,void 0),this.begin=this.wrap(this.begin+1),e}shuffleExcise(e){if(this.isEmpty())throw new RangeError("Ring buffer is empty.");let t=this.wrap(this.begin+e),n=this.get(t);return this.set(t,this.pop()),n}},j2=class extends a7{constructor(){super(j2.INITIAL_CAPACITY)}isFull(){return!1}push(e){super.isFull()&&this.expand(),super.push(e)}unshift(e){super.isFull()&&this.expand(),super.unshift(e)}expand(){let e=this.capacity*2,t=new Array(e),n=this.length();for(let s=0;s<n;s++)t[s]=this.get(this.wrap(this.begin+s));this.data=t,this.capacity=e,this.doubledCapacity=2*this.capacity,this.begin=0,this.end=n}};j2.INITIAL_CAPACITY=32;function o7(e){return new GL(e)}function q2(e){return new jL(e)}function UL(e,t){return new l7(e,t)}function HL(e,t=ta.FAIL){return new tB(e,t)}var Yt=class{async toArray(){let e=[],t=await this.next();for(;!t.done;)e.push(t.value),t=await this.next();return e}async toArrayForTest(){let e=this.prefetch(100),t=[],n=await e.next();for(;!n.done;)t.push(n.value),n=await e.next();return t}async resolveFully(){let e=await this.next();for(;!e.done;)e=await this.next()}async resolveWhile(e){let t=await this.next(),n=e(t.value);for(;!t.done&&n;)t=await this.next(),n=e(t.value)}handleErrors(e){return new QL(this,e)}filter(e){return new YL(this,e)}map(e){return new JL(this,e)}mapAsync(e){return new i7(this,e)}serialMapAsync(e){return new i7(this,e).serial()}flatmap(e){return new eB(this,e)}async forEachAsync(e){return this.map(e).resolveFully()}async serialForEach(e){return this.serialMapAsync(e).resolveWhile(t=>t===!0)}rowMajorBatch(e,t=!0){return new ZL(this,e,t)}columnMajorBatch(e,t=!0,n=s7){return this.rowMajorBatch(e,t).map(r=>zL(r,n))}concatenate(e,t){return new l7(o7([this,e]),t)}take(e){return e<0||e==null?this:new KL(this,e)}skip(e){return e<0||e==null?this:new XL(this,e)}prefetch(e){return new u7(this,e)}shuffle(e,t){return new nB(this,e,t)}serial(){return new qL(this)}},GL=class extends Yt{constructor(e){super();this.items=e,this.trav=0}summary(){return`Array of ${this.items.length} items`}async next(){if(this.trav>=this.items.length)return{value:null,done:!0};let e=this.items[this.trav];return this.trav++,{value:WL(e),done:!1}}},jL=class extends Yt{constructor(e){super();this.nextFn=e}summary(){return"Function call"}async next(){try{return this.nextFn()}catch(e){throw e.message=`Error thrown while iterating through a dataset: ${e.message}`,e}}},qL=class extends Yt{constructor(e){super();this.upstream=e,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Serial`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){return this.upstream.next()}},XL=class extends Yt{constructor(e,t){super();this.upstream=e,this.maxCount=t,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Skip`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.count++<this.maxCount;){let e=await this.upstream.next();if(e.done)return e;K(e.value)}return this.upstream.next()}},KL=class extends Yt{constructor(e,t){super();this.upstream=e,this.maxCount=t,this.count=0}summary(){return`${this.upstream.summary()} -> Take`}async next(){return this.count++>=this.maxCount?{value:null,done:!0}:this.upstream.next()}},ZL=class extends Yt{constructor(e,t,n=!0){super();this.upstream=e,this.batchSize=t,this.enableSmallLastBatch=n,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> RowMajorBatch`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){let e=[];for(;e.length<this.batchSize;){let t=await this.upstream.next();if(t.done)return this.enableSmallLastBatch&&e.length>0?{value:e,done:!1}:{value:null,done:!0};e.push(t.value)}return{value:e,done:!1}}},YL=class extends Yt{constructor(e,t){super();this.upstream=e,this.predicate=t,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Filter`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;){let e=await this.upstream.next();if(e.done||this.predicate(e.value))return e;K(e.value)}}},JL=class extends Yt{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> Map`}async next(){let e=await this.upstream.next();if(e.done)return{value:null,done:!0};let t=Cs.getTensorsInContainer(e.value),n=this.transform(e.value),s=Cs.getTensorsInContainer(n);for(let r of t)Cs.isTensorInList(r,s)||r.dispose();return{value:n,done:!1}}},QL=class extends Yt{constructor(e,t){super();this.upstream=e,this.handler=t,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> handleErrors`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;)try{return await this.upstream.next()}catch(e){if(!this.handler(e))return{value:null,done:!0}}}},i7=class extends Yt{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> AsyncMap`}async next(){let e=await this.upstream.next();if(e.done)return{value:null,done:!0};let t=Cs.getTensorsInContainer(e.value),n=await this.transform(e.value),s=Cs.getTensorsInContainer(n);for(let r of t)Cs.isTensorInList(r,s)||r.dispose();return{value:n,done:!1}}},X2=class extends Yt{constructor(){super();this.outputQueue=new j2,this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.outputQueue.length()===0;)if(!await this.pump())return{value:null,done:!0};return{value:this.outputQueue.shift(),done:!1}}},eB=class extends X2{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> Flatmap`}async pump(){let e=await this.upstream.next();if(e.done)return!1;let t=Cs.getTensorsInContainer(e.value),n=this.transform(e.value),s=Cs.getTensorsInContainer(n);this.outputQueue.pushAll(n);for(let r of t)Cs.isTensorInList(r,s)||r.dispose();return!0}},l7=class extends Yt{constructor(e,t){super();this.baseErrorHandler=t,this.lastRead=null,this.iterator=null,this.moreIterators=e}summary(){return"TODO: fill in upstream of chained summaries -> Chained"}async next(){return this.lastRead=this.readFromChain(this.lastRead),this.lastRead}async readFromChain(e){if(await e,this.iterator==null){let n=await this.moreIterators.next();if(n.done)return{value:null,done:!0};this.iterator=n.value,this.baseErrorHandler!=null&&(this.iterator=this.iterator.handleErrors(this.baseErrorHandler))}let t=await this.iterator.next();return t.done?(this.iterator=null,this.readFromChain(e)):t}},ta;(function(e){e[e.FAIL=0]="FAIL",e[e.SHORTEST=1]="SHORTEST",e[e.LONGEST=2]="LONGEST"})(ta||(ta={}));var tB=class extends Yt{constructor(e,t=ta.FAIL){super();this.iterators=e,this.mismatchMode=t,this.count=0,this.currentPromise=null}summary(){return"{TODO: fill in upstream of zip summaries} -> Zip"}async nextState(e){await e;let t=0,n=0;function s(a){return a instanceof Yt?{value:a.next().then(i=>(t++,i.done&&n++,i.value)),recurse:!1}:{value:null,recurse:!0}}let r=await r7(this.iterators,s);if(t===n)return{value:null,done:!0};if(n>0)switch(this.mismatchMode){case ta.FAIL:throw new Error(`Zipped streams should have the same length. Mismatched at element ${this.count}.`);case ta.SHORTEST:return{value:null,done:!0};case ta.LONGEST:default:}return this.count++,{value:r,done:!1}}async next(){return this.currentPromise=this.nextState(this.currentPromise),this.currentPromise}},u7=class extends Yt{constructor(e,t){super();this.upstream=e,this.bufferSize=t,this.buffer=new a7(t)}summary(){return`${this.upstream.summary()} -> Prefetch`}refill(){for(;!this.buffer.isFull();){let e=this.upstream.next();this.buffer.push(e)}}next(){return this.refill(),this.buffer.shift()}},nB=class extends u7{constructor(e,t,n){super(e,t);this.upstream=e,this.windowSize=t,this.upstreamExhausted=!1,this.random=PL.alea(n||I.now().toString()),this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}randomInt(e){return Math.floor(this.random()*e)}chooseIndex(){return this.randomInt(this.buffer.length())}async serialNext(){for(this.upstreamExhausted||this.refill();!this.buffer.isEmpty();){let e=this.chooseIndex(),t=await this.buffer.shuffleExcise(e);if(t.done)this.upstreamExhausted=!0;else return this.refill(),t}return{value:null,done:!0}}},Bl=class{constructor(){this.size=null}batch(e,t=!0){let n=this;I.assert(e>0,()=>`batchSize needs to be positive, but it is
      ${e}`);let s;return this.size===1/0||this.size==null?s=this.size:t?s=Math.ceil(this.size/e):s=Math.floor(this.size/e),Gn(async()=>(await n.iterator()).columnMajorBatch(e,t,aB),s)}concatenate(e){let t=this,n;return this.size===1/0||e.size===1/0?n=1/0:this.size!=null&&e.size!=null?n=this.size+e.size:n=null,Gn(async()=>(await t.iterator()).concatenate(await e.iterator()),n)}filter(e){let t=this,n;return this.size===1/0?n=1/0:n=null,Gn(async()=>(await t.iterator()).filter(s=>H(()=>e(s))),n)}async forEachAsync(e){return(await this.iterator()).forEachAsync(e)}map(e){let t=this;return Gn(async()=>(await t.iterator()).map(n=>H(()=>e(n))),this.size)}mapAsync(e){let t=this;return Gn(async()=>(await t.iterator()).mapAsync(e),this.size)}prefetch(e){if(e==null)throw new RangeError("`Dataset.prefetch()` requires bufferSize to be specified.");let t=this;return Gn(async()=>(await t.iterator()).prefetch(e),this.size)}repeat(e){let t=this,n;return this.size!=null&&e>0?n=this.size*e:e===0?n=0:this.size!=null&&(e===void 0||e<0)?n=1/0:n=null,Gn(async()=>{let s=q2(async()=>({value:await t.iterator(),done:!1}));return UL(s.take(e))},n)}skip(e){let t=this,n;return this.size!=null&&e>=0&&this.size>=e?n=this.size-e:this.size!=null&&(this.size<e||e===void 0||e<0)?n=0:n=null,Gn(async()=>(await t.iterator()).skip(e),n)}shuffle(e,t,n=!0){if(e==null||e<0)throw this.size==null?new RangeError("`Dataset.shuffle()` requires bufferSize to be specified."):new RangeError(`\`Dataset.shuffle()\` requires bufferSize to be specified.  If your data fits in main memory (for regular JS objects), and/or GPU memory (for \`tf.Tensor\`s), consider setting bufferSize to the dataset size (${this.size} elements)`);let s=this,r=OL.alea(t||I.now().toString());return Gn(async()=>{let a=r.int32();return n&&(a+=r.int32()),(await s.iterator()).shuffle(e,a.toString())},this.size)}take(e){let t=this,n;return this.size!=null&&this.size>e?n=e:this.size!=null&&this.size<=e?n=this.size:n=null,Gn(async()=>(await t.iterator()).take(e),n)}async toArray(){if(this.size===1/0)throw new Error("Can not convert infinite data stream to array.");return(await this.iterator()).toArray()}async toArrayForTest(){if(this.size===1/0)throw new Error("Can not convert infinite data stream to array.");return(await this.iterator()).toArrayForTest()}};Bl.MAX_BUFFER_SIZE=1e4;function Gn(e,t=null){return new class extends Bl{constructor(){super(...arguments);this.size=t}async iterator(){return e()}}}function sB(e){return Gn(async()=>o7(e),e.length)}function rB(e){if(!Ll(e))throw new Error("The argument to zip() must be an object or array.");let t;if(Array.isArray(e))for(let n=0;n<e.length;n++)t=t==null?e[n].size:Math.min(t,e[n].size);else if(e instanceof Object)for(let n in e)t=t==null?e[n].size:Math.min(t,e[n].size);return Gn(async()=>{let n=await r7(e,s=>{if(s instanceof Bl)return{value:s.iterator(),recurse:!1};if(Ll(s))return{value:null,recurse:!0};throw new Error("Leaves of the structure passed to zip() must be Datasets, not primitives.")});return HL(n,ta.SHORTEST)},t)}function aB(e){if(e===null)return null;let t=e[0];return LL(t)?{value:oB(e),recurse:!1}:{value:null,recurse:!0}}function oB(e){if(e.length===0)throw new Error("Can't make a batch of zero elements.");return e[0]instanceof Ue?Nn(e):on(e)}var c7=class extends Bl{constructor(e){super();this.input=e}async iterator(){return(await this.input.iterator()).decodeUTF8().split(`
`).map(s=>(s.endsWith("\r")&&(s=s.slice(0,-1)),s))}},qp='"',_c=Symbol("out"),d7=Symbol("field"),Xp=Symbol("quote"),K2=Symbol("quoteafterquote"),h7=Symbol("quoteinquote"),p7=class extends Bl{constructor(e,t){super();this.input=e,this.hasHeader=!0,this.fullColumnNames=null,this.columnNamesValidated=!1,this.columnConfigs=null,this.configuredColumnsOnly=!1,this.delimiter=",",this.delimWhitespace=!1,this.base=new c7(e),t||(t={}),this.hasHeader=t.hasHeader!==!1,this.fullColumnNames=t.columnNames,this.columnConfigs=t.columnConfigs,this.configuredColumnsOnly=t.configuredColumnsOnly,t.delimWhitespace?(I.assert(t.delimiter==null,()=>"Delimiter should not be provided when delimWhitespace is true."),this.delimWhitespace=!0,this.delimiter=" "):this.delimiter=t.delimiter?t.delimiter:","}async columnNames(){return this.columnNamesValidated||await this.setColumnNames(),this.configuredColumnsOnly?Object.keys(this.columnConfigs):this.fullColumnNames}async setColumnNames(){let e=await this.maybeReadHeaderLine();if(!this.fullColumnNames&&!e)throw new Error("Column names must be provided if there is no header line.");this.fullColumnNames&&e&&I.assert(e.length===this.fullColumnNames.length,()=>"The length of provided columnNames ("+this.fullColumnNames.length.toString()+") does not match the length of the header line read from file ("+e.length.toString()+")."),this.fullColumnNames||(this.fullColumnNames=e);let t=this.fullColumnNames.reduce((s,r)=>(s[r]=s[r]+1||1,s),{}),n=Object.keys(t).filter(s=>t[s]>1);if(I.assert(n.length===0,()=>"Duplicate column names found: "+n.toString()),this.columnConfigs){for(let s of Object.keys(this.columnConfigs))if(this.fullColumnNames.indexOf(s)===-1)throw new Error('The key "'+s+'" provided in columnConfigs does not match any of the column names ('+this.fullColumnNames.toString()+").")}this.columnNamesValidated=!0}async maybeReadHeaderLine(){if(this.hasHeader){let t=await(await this.base.iterator()).next();if(t.done)throw new Error("No data was found for CSV parsing.");let n=t.value;return this.parseRow(n,!1)}else return null}async iterator(){this.columnNamesValidated||await this.setColumnNames();let e=await this.base.iterator();return this.hasHeader&&(e=e.skip(1)),e.map(t=>this.makeDataElement(t))}makeDataElement(e){let t=this.parseRow(e),n={},s={};for(let r=0;r<this.fullColumnNames.length;r++){let a=this.fullColumnNames[r],o=this.columnConfigs?this.columnConfigs[a]:null;if(!(this.configuredColumnsOnly&&!o)){let i=t[r],l=null;if(i==="")if(o&&o.default!==void 0)l=o.default;else{if(o&&(o.required||o.isLabel))throw new Error(`Required column ${a} is empty in this line: ${e}`);l=void 0}else{let u=Number(i);if(isNaN(u))o&&o.dtype==="bool"?l=this.getBoolean(i):l=i;else if(!o||!o.dtype)l=u;else switch(o.dtype){case"float32":l=u;break;case"int32":l=Math.floor(u);break;case"bool":l=this.getBoolean(i);break;default:l=u}}o&&o.isLabel?s[a]=l:n[a]=l}}return Object.keys(s).length===0?n:{xs:n,ys:s}}getBoolean(e){return e==="1"||e.toLowerCase()==="true"?1:0}parseRow(e,t=!0){let n=[],s=0,r=e.length,a=_c;for(let o=0;o<r;o++)switch(a){case _c:switch(e.charAt(o)){case qp:s=o+1,a=Xp;break;case this.delimiter:if(s=o+1,this.delimiter===" "&&this.delimWhitespace)break;n.push(""),a=_c;break;default:a=d7,s=o;break}break;case d7:switch(e.charAt(o)){case this.delimiter:n.push(e.substring(s,o)),a=_c,s=o+1;break;default:}break;case Xp:switch(e.charAt(o)){case qp:a=K2;break;default:}break;case K2:switch(e.charAt(o)){case this.delimiter:n.push(e.substring(s,o-1)),a=_c,s=o+1;break;case qp:a=Xp;break;default:a=h7;break}break;case h7:switch(e.charAt(o)){case qp:a=Xp;break;default:}break;default:}if(a===K2?n.push(e.substring(s,r-1)):n.push(e.substring(s)),t&&n.length!==this.fullColumnNames.length)throw new Error(`Invalid row in csv file. Should have ${this.fullColumnNames.length} elements in a row, but got ${n}`);return n}},f7=class extends Yt{constructor(e){super();this.microphoneConfig=e,this.isClosed=!1,this.fftSize=e.fftSize||1024;let t=Math.log2(this.fftSize);if(this.fftSize<0||t<4||t>14||!Number.isInteger(t))throw new Error(`Invalid fftSize: it must be a power of 2 between 2 to 4 and 2 to 14, but got ${this.fftSize}`);if(this.numFrames=e.numFramesPerSpectrogram||43,this.sampleRateHz=e.sampleRateHz,this.columnTruncateLength=e.columnTruncateLength||this.fftSize,this.audioTrackConstraints=e.audioTrackConstraints,this.smoothingTimeConstant=e.smoothingTimeConstant||0,this.includeSpectrogram=e.includeSpectrogram!==!1,this.includeWaveform=e.includeWaveform===!0,!this.includeSpectrogram&&!this.includeWaveform)throw new Error("Both includeSpectrogram and includeWaveform are false. At least one type of data should be returned.")}summary(){return"microphone"}static async create(e={}){if(ee().get("IS_NODE"))throw new Error("microphone API is only supported in browser environment.");let t=new f7(e);return await t.start(),t}async start(){try{this.stream=await navigator.mediaDevices.getUserMedia({audio:this.audioTrackConstraints==null?!0:this.audioTrackConstraints,video:!1})}catch(n){throw new Error(`Error thrown while initializing video stream: ${n.message}`)}if(!this.stream)throw new Error("Could not obtain audio from microphone.");let e=window.AudioContext||window.webkitAudioContext;if(this.audioContext=new e,!this.sampleRateHz)this.sampleRateHz=this.audioContext.sampleRate;else if(this.audioContext.sampleRate!==this.sampleRateHz)throw new Error(`Mismatch in sampling rate: Expected: ${this.sampleRateHz}; Actual: ${this.audioContext.sampleRate}`);let t=this.audioContext.createMediaStreamSource(this.stream);this.analyser=this.audioContext.createAnalyser(),this.analyser.fftSize=this.fftSize*2,this.analyser.smoothingTimeConstant=this.smoothingTimeConstant,t.connect(this.analyser),this.freqData=new Float32Array(this.fftSize),this.timeData=new Float32Array(this.fftSize)}async next(){if(this.isClosed)return{value:null,done:!0};let e,t,n=await this.getAudioData();if(this.includeSpectrogram){let s=this.flattenQueue(n.freqDataQueue);e=this.getTensorFromAudioDataArray(s,[this.numFrames,this.columnTruncateLength,1])}if(this.includeWaveform){let s=this.flattenQueue(n.timeDataQueue);t=this.getTensorFromAudioDataArray(s,[this.numFrames*this.fftSize,1])}return{value:{spectrogram:e,waveform:t},done:!1}}async capture(){return(await this.next()).value}async getAudioData(){let e=[],t=[],n=0;return new Promise(s=>{let r=setInterval(()=>{this.includeSpectrogram&&(this.analyser.getFloatFrequencyData(this.freqData),this.freqData[0]===-1/0&&s({freqDataQueue:e,timeDataQueue:t}),e.push(this.freqData.slice(0,this.columnTruncateLength))),this.includeWaveform&&(this.analyser.getFloatTimeDomainData(this.timeData),t.push(this.timeData.slice())),++n===this.numFrames&&(clearInterval(r),s({freqDataQueue:e,timeDataQueue:t}))},this.fftSize/this.sampleRateHz*1e3)})}stop(){this.isClosed||(this.isClosed=!0,this.analyser.disconnect(),this.audioContext.close(),this.stream!=null&&this.stream.getTracks().length>0&&this.stream.getTracks()[0].stop())}toArray(){throw new Error("Can not convert infinite audio stream to array.")}getSampleRate(){return this.sampleRateHz}flattenQueue(e){let t=e[0].length,n=new Float32Array(e.length*t);return e.forEach((s,r)=>n.set(s,r*t)),n}getTensorFromAudioDataArray(e,t){let n=new Float32Array(I.sizeFromShape(t));return n.set(e,n.length-e.length),on(n,t)}},m7=class extends Yt{constructor(e,t){super();if(this.webcamVideoElement=e,this.webcamConfig=t,this.isClosed=!0,this.resize=!1,this.needToResize())if(this.resize=!0,this.cropSize=[this.webcamConfig.resizeHeight,this.webcamConfig.resizeWidth],this.cropBoxInd=Ot([0],"int32"),this.webcamConfig.centerCrop){let n=this.webcamConfig.resizeWidth*1/this.webcamVideoElement.width,s=this.webcamConfig.resizeHeight*1/this.webcamVideoElement.height,r=(1-n)/2,a=(1-s)/2,o=r+n,i=s+a;this.cropBox=_s([a,r,i,o],[1,4])}else this.cropBox=_s([0,0,1,1],[1,4])}summary(){return"webcam"}static async create(e,t={}){if(ee().get("IS_NODE"))throw new Error("tf.data.webcam is only supported in browser environment.");if(!e){if(e=document.createElement("video"),!t.resizeWidth||!t.resizeHeight)throw new Error("Please provide webcam video element, or resizeWidth and resizeHeight to create a hidden video element.");e.width=t.resizeWidth,e.height=t.resizeHeight}let n=new m7(e,t);return await n.start(),n}async start(){this.webcamConfig.facingMode&&I.assert(this.webcamConfig.facingMode==="user"||this.webcamConfig.facingMode==="environment",()=>`Invalid webcam facing mode: ${this.webcamConfig.facingMode}. Please provide 'user' or 'environment'`);try{this.stream=await navigator.mediaDevices.getUserMedia({video:{deviceId:this.webcamConfig.deviceId,facingMode:this.webcamConfig.facingMode?this.webcamConfig.facingMode:"user",width:this.webcamVideoElement.width,height:this.webcamVideoElement.height}})}catch(e){throw e.message=`Error thrown while initializing video stream: ${e.message}`,e}if(!this.stream)throw new Error("Could not obtain video from webcam.");try{this.webcamVideoElement.srcObject=this.stream}catch(e){console.log(e),this.webcamVideoElement.src=window.URL.createObjectURL(this.stream)}return this.webcamVideoElement.play(),this.isClosed=!1,new Promise(e=>{this.webcamVideoElement.onloadedmetadata=()=>{e()}})}async next(){if(this.isClosed)return{value:null,done:!0};let e;try{e=rs.fromPixels(this.webcamVideoElement)}catch(t){throw new Error(`Error thrown converting video to pixels: ${JSON.stringify(t)}`)}if(this.resize)try{return{value:this.cropAndResizeFrame(e),done:!1}}catch(t){throw new Error(`Error thrown cropping the video: ${t.message}`)}finally{e.dispose()}else return{value:e,done:!1}}needToResize(){return!!(this.webcamConfig.resizeWidth&&this.webcamConfig.resizeHeight&&(this.webcamVideoElement.width!==this.webcamConfig.resizeWidth||this.webcamVideoElement.height!==this.webcamConfig.resizeHeight))}cropAndResizeFrame(e){return H(()=>{let t=Ft(ce(e,"float32"),0),n;n=_e.cropAndResize(t,this.cropBox,this.cropBoxInd,this.cropSize,"bilinear");let s=n.shape;return U(n,s.slice(1))})}async capture(){return(await this.next()).value}stop(){this.stream.getTracks().forEach(t=>t.stop());try{this.webcamVideoElement.srcObject=null}catch(t){console.log(t),this.webcamVideoElement.src=null}this.isClosed=!0}toArray(){throw new Error("Can not convert infinite video stream to array.")}},A7=class{},g7=class extends Yt{split(e){return new iB(this,e)}},iB=class extends g7{constructor(e,t){super();this.upstream=e,this.impl=new lB(e,t)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},lB=class extends X2{constructor(e,t){super();this.upstream=e,this.separator=t,this.carryover=""}summary(){return`${this.upstream.summary()} -> Split('${this.separator}')`}async pump(){let e=await this.upstream.next();if(e.done)return this.carryover===""?!1:(this.outputQueue.push(this.carryover),this.carryover="",!0);let t=e.value.split(this.separator);t[0]=this.carryover+t[0];for(let n of t.slice(0,-1))this.outputQueue.push(n);return this.carryover=t[t.length-1],!0}},uB=class extends Yt{decodeUTF8(){return new cB(this)}},cB=class extends g7{constructor(e){super();this.upstream=e,this.impl=new dB(e)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},dB=class extends X2{constructor(e){super();if(this.upstream=e,ee().get("IS_BROWSER"))this.decoder=new TextDecoder("utf-8");else{let{StringDecoder:t}=OI();this.decoder=new t("utf8")}}summary(){return`${this.upstream.summary()} -> Utf8`}async pump(){let e=await this.upstream.next(),t;if(e.done)return!1;t=e.value;let n;return ee().get("IS_BROWSER")?n=this.decoder.decode(t,{stream:!0}):n=this.decoder.write(Buffer.from(t.buffer)),this.outputQueue.push(n),!0}},y7=class extends uB{constructor(e,t={}){super();this.file=e,this.options=t,I.assert(e instanceof Uint8Array||(ee().get("IS_BROWSER")?e instanceof File||e instanceof Blob:!1),()=>"FileChunkIterator only supports File, Blob and Uint8Array right now."),this.offset=t.offset||0,this.chunkSize=t.chunkSize||1024*1024}summary(){return`FileChunks ${this.file}`}async next(){return this.offset>=(this.file instanceof Uint8Array?this.file.byteLength:this.file.size)?{value:null,done:!0}:{value:await new Promise((t,n)=>{let s=this.offset+this.chunkSize;if(this.file instanceof Uint8Array)t(new Uint8Array(this.file.slice(this.offset,s)));else{let r=new FileReader;r.onload=o=>{let i=r.result;if(i instanceof ArrayBuffer&&(i=new Uint8Array(i)),!(i instanceof Uint8Array))return n(new TypeError("FileReader returned unknown type."));t(i)},r.onabort=o=>n(new Error("Aborted")),r.onerror=o=>n(new Error(o.type));let a=this.file.slice(this.offset,s);r.readAsArrayBuffer(a)}this.offset=s}),done:!1}}};async function hB(e,t={}){let n,s;typeof e=="string"?n=e:(n=e.url,s=pB(e));let r=await I.fetch(n,s);if(r.ok){let a=new Uint8Array(await r.arrayBuffer());return new y7(a,t)}else throw new Error(r.statusText)}var pB=e=>({method:e.method,headers:e.headers,body:e.body,mode:e.mode,credentials:e.credentials,cache:e.cache,redirect:e.redirect,referrer:e.referrer,integrity:e.integrity});function x7(e){return typeof e=="string"&&e.substr(0,7)==="file://"}var b7=class extends A7{constructor(e,t={}){super();this.input=e,this.options=t}async iterator(){if(x7(this.input)&&ee().get("IS_NODE")){let e=li("fs");this.input=e.readFileSync(this.input.substr(7))}return new y7(this.input,this.options)}},v7=class extends A7{constructor(e,t={}){super();this.url=e,this.fileOptions=t}async iterator(){return x7(this.url)?new b7(this.url,this.fileOptions).iterator():hB(this.url,this.fileOptions)}};function fB(e,t={}){return new p7(new v7(e),t)}function mB(e){let t=q2(e);return Gn(async()=>t)}function AB(e){return Gn(async()=>{let t=await e();return q2(()=>t.next())})}async function gB(e,t){return m7.create(e,t)}async function yB(e){return f7.create(e)}var xB="3.8.0";function ke(e,t){Array.isArray(e)||(e=[e]),e.forEach(n=>{n!=null&&I.assert(n.dtype!=="complex64",()=>`${t} does not support complex64 tensors in the CPU backend.`)})}var bB=Js.whereImpl,Kp=class extends ku{constructor(){super();this.blockSize=48,this.firstUse=!0,this.data=new Fd(this,Ar())}nextDataId(){return Kp.nextDataId++}write(e,t,n){this.firstUse&&(this.firstUse=!1,ee().get("IS_NODE")&&$.warn(`
============================
Hi there \u{1F44B}. Looks like you are running TensorFlow.js in Node.js. To speed things up dramatically, install our node backend, which binds to TensorFlow C++, by running npm i @tensorflow/tfjs-node, or npm i @tensorflow/tfjs-node-gpu if you have CUDA. Then call require('@tensorflow/tfjs-node'); (-gpu suffix for CUDA) at the start of your program. Visit https://github.com/tensorflow/tfjs-node for more details.
============================`));let s={id:this.nextDataId()};return this.data.set(s,{values:e,dtype:n,refCount:1}),s}makeTensorInfo(e,t,n){let s;if(t==="string"&&n!=null&&n.length>0&&I.isString(n[0])){let r=n.map(a=>I.encodeString(a));s=this.write(r,e,t)}else s=this.write(n,e,t);return{dataId:s,shape:e,dtype:t}}refCount(e){return this.data.has(e)?this.data.get(e).refCount:0}incRef(e){let t=this.data.get(e);t.refCount++}decRef(e){if(this.data.has(e)){let t=this.data.get(e);t.refCount--}}move(e,t,n,s,r){this.data.set(e,{values:t,dtype:s,refCount:r})}numDataIds(){return this.data.numDataIds()}async read(e){return this.readSync(e)}readSync(e){let{dtype:t,complexTensorInfos:n}=this.data.get(e);if(t==="complex64"){let s=this.readSync(n.real.dataId),r=this.readSync(n.imag.dataId);return $.mergeRealAndImagArrays(s,r)}return this.data.get(e).values}bufferSync(e){let t=this.readSync(e.dataId),n=t;if(e.dtype==="string")try{n=t.map(s=>I.decodeString(s))}catch(s){throw new Error("Failed to decode encoded string bytes into utf-8")}return Be(e.shape,e.dtype,n)}makeOutput(e,t,n){let s=this.write(e,t,n);return Ar().makeTensorFromDataId(s,t,n,this)}disposeData(e,t=!1){if(this.data.has(e)){if(this.data.get(e).refCount--,!t&&this.data.get(e).refCount>0)return!1;let{complexTensorInfos:n}=this.data.get(e);n!=null&&(this.disposeData(n.real.dataId,!0),this.disposeData(n.imag.dataId,!0)),this.data.delete(e)}return!0}disposeIntermediateTensorInfo(e){this.disposeData(e.dataId)}async time(e){let t=I.now();return e(),{kernelMs:I.now()-t}}memory(){return{unreliable:!0,reasons:["The reported memory is an upper bound. Due to automatic garbage collection, the true allocated memory may be less."]}}where(e){ke([e],"where");let t=this.readSync(e.dataId);return bB(e.shape,t)}dispose(){}floatPrecision(){return 32}epsilon(){return super.epsilon()}};Kp.nextDataId=0;var Z2={};Pe(Z2,{addImpl:()=>k7,bincountImpl:()=>J2,bincountReduceImpl:()=>I7,ceilImpl:()=>S7,concatImpl:()=>Q2,equalImpl:()=>C7,expImpl:()=>N7,expm1Impl:()=>R7,floorImpl:()=>_7,gatherNdImpl:()=>$7,gatherV2Impl:()=>F7,greaterEqualImpl:()=>O7,greaterImpl:()=>D7,lessEqualImpl:()=>M7,lessImpl:()=>P7,linSpaceImpl:()=>z7,logImpl:()=>L7,maxImpl:()=>B7,maximumImpl:()=>W7,minimumImpl:()=>V7,multiplyImpl:()=>e1,negImpl:()=>U7,notEqualImpl:()=>H7,prodImpl:()=>G7,rangeImpl:()=>n1,rsqrtImpl:()=>j7,simpleAbsImpl:()=>w7,sliceImpl:()=>Jp,sparseFillEmptyRowsImpl:()=>q7,sparseReshapeImpl:()=>X7,sparseSegmentReductionImpl:()=>s1,squaredDifferenceImpl:()=>K7,stridedSliceImpl:()=>Z7,stringNGramsImpl:()=>Y7,stringSplitImpl:()=>J7,stringToHashBucketFastImpl:()=>Q7,subImpl:()=>ew,tileImpl:()=>tw,topKImpl:()=>sw,transposeImpl:()=>t1,uniqueImpl:()=>rw});function w7(e){let t=new Float32Array(e.length);for(let n=0;n<e.length;++n)t[n]=Math.abs(e[n]);return t}var vB=e=>{let{x:t}=e.inputs,n=e.backend;ke(t,"abs");let s=new Float32Array(I.sizeFromShape(t.shape)),r=n.data.get(t.dataId).values;return s=w7(r),n.makeOutput(s,t.shape,"float32")},wB={kernelName:di,backendName:"cpu",kernelFunc:vB};function Mt(e){return(t,n,s,r,a)=>{let o=$.assertAndGetBroadcastShape(t,n),i=o.length,l=I.computeStrides(o),u=I.sizeFromShape(o),c=I.getTypedArrayFromDType(a,u),d=t.length,h=n.length,p=I.computeStrides(t),f=I.computeStrides(n),m=$.getBroadcastDims(t,o),A=$.getBroadcastDims(n,o);if(m.length+A.length===0)for(let g=0;g<c.length;++g)c[g]=e(s[g%s.length],r[g%r.length]);else for(let g=0;g<c.length;++g){let y=I.indexToLoc(g,i,l),x=y.slice(-d);m.forEach(w=>x[w]=0);let b=I.locToIndex(x,d,p),v=y.slice(-h);A.forEach(w=>v[w]=0);let k=I.locToIndex(v,h,f);c[g]=e(s[b],r[k])}return[c,o]}}function jn(e){let{inputs:t,backend:n}=e,{real:s,imag:r}=t,a=n.data.get(s.dataId).values,o=n.data.get(r.dataId).values,i=n.makeTensorInfo(s.shape,"complex64"),l=n.data.get(i.dataId);return l.complexTensorInfos={real:n.makeTensorInfo(s.shape,"float32",a),imag:n.makeTensorInfo(r.shape,"float32",o)},i}var kB={kernelName:Wd,backendName:"cpu",kernelFunc:jn};function Zp(e,t,n="float32"){if(n==="complex64"){let r=Zp(e,t,"float32"),a=Zp(e,t,"float32");return jn({inputs:{real:r,imag:a},backend:e})}let s=I.makeZerosTypedArray(I.sizeFromShape(t),n);return e.makeTensorInfo(t,n,s)}function rr(e){let{inputs:t,backend:n}=e,{x:s}=t;return n.incRef(s.dataId),{dataId:s.dataId,shape:s.shape,dtype:s.dtype}}var IB={kernelName:Oa,backendName:"cpu",kernelFunc:rr};function zo(e){let{inputs:t,backend:n}=e,{input:s}=t,r=n.data.get(s.dataId).complexTensorInfos.real,a=n.data.get(r.dataId).values;return n.makeTensorInfo(r.shape,r.dtype,a)}var SB={kernelName:lh,backendName:"cpu",kernelFunc:zo};function na(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{dtype:a}=s;if(a==="complex64"){if(r.dtype==="complex64")return rr({inputs:{x:r},backend:n});let o=Zp(n,r.shape,r.dtype),i=na({inputs:{x:r},backend:n,attrs:{dtype:"float32"}}),l=jn({inputs:{real:i,imag:o},backend:n});return n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(i),l}if(r.dtype==="complex64"){let o=zo({inputs:{input:r},backend:n}),i=na({inputs:{x:o},backend:n,attrs:{dtype:a}});return n.disposeIntermediateTensorInfo(o),i}if(!I.hasEncodingLoss(r.dtype,a)){let o=rr({inputs:{x:r},backend:n});return{dataId:o.dataId,shape:o.shape,dtype:a}}if(a==="int32"){let o=n.data.get(r.dataId).values,i=Int32Array.from(o);return n.makeTensorInfo(r.shape,"int32",i)}if(a==="bool"){let o=n.data.get(r.dataId).values,i=I.toTypedArray([0],r.dtype),[l,u]=Mt((c,d)=>c!==d?1:0)(r.shape,[],o,i,"bool");return n.makeTensorInfo(u,"bool",l)}throw new Error(`Error in Cast: failed to cast ${r.dtype} to ${a}`)}var CB={kernelName:va,backendName:"cpu",kernelFunc:na};function Jt(e,t,n,s){return n==null?({inputs:r,backend:a})=>{let{a:o,b:i}=r,l=a;ke([o,i],e);let u=l.data.get(o.dataId).values,c=l.data.get(i.dataId).values,d=o.dtype==="string"?$.fromUint8ToStringArray(u):u,h=o.dtype==="string"?$.fromUint8ToStringArray(c):c,p=s||o.dtype,[f,m]=t(o.shape,i.shape,d,h,p);return l.makeTensorInfo(m,p,f)}:({inputs:r,backend:a})=>{let{a:o,b:i}=r,l=a;if(o.dtype==="complex64"||i.dtype==="complex64"){let u=na({inputs:{x:o},backend:l,attrs:{dtype:"complex64"}}),c=l.data.get(u.dataId),d=c.complexTensorInfos.real,h=c.complexTensorInfos.imag,p=l.data.get(d.dataId).values,f=l.data.get(h.dataId).values,m=na({inputs:{x:i},backend:l,attrs:{dtype:"complex64"}}),A=l.data.get(m.dataId),g=A.complexTensorInfos.real,y=A.complexTensorInfos.imag,x=l.data.get(g.dataId).values,b=l.data.get(y.dataId).values,[v,k,w]=n(o.shape,i.shape,p,f,x,b),C=l.makeTensorInfo(w,"float32",v),E=l.makeTensorInfo(w,"float32",k),M=jn({inputs:{real:C,imag:E},backend:l});return l.disposeIntermediateTensorInfo(u),l.disposeIntermediateTensorInfo(m),l.disposeIntermediateTensorInfo(C),l.disposeIntermediateTensorInfo(E),M}else{let u=l.data.get(o.dataId).values,c=l.data.get(i.dataId).values,d=s||o.dtype,[h,p]=t(o.shape,i.shape,u,c,d);return l.makeTensorInfo(p,d,h)}}}function Y2(e){return(t,n,s,r,a,o)=>{let i=$.assertAndGetBroadcastShape(t,n),l=I.sizeFromShape(i),u=i.length,c=I.computeStrides(i),d=I.getTypedArrayFromDType("float32",l),h=I.getTypedArrayFromDType("float32",l),p=$.getBroadcastDims(t,i),f=$.getBroadcastDims(n,i),m=$.mergeRealAndImagArrays(s,r),A=$.mergeRealAndImagArrays(a,o),g=t.length,y=I.computeStrides(t),x=n.length,b=I.computeStrides(n);if(p.length+f.length===0)for(let v=0;v<d.length;v++){let k=v%m.length,w=v%A.length,C=e(m[k*2],m[k*2+1],A[w*2],A[w*2+1]);d[v]=C.real,h[v]=C.imag}else for(let v=0;v<d.length;v++){let k=I.indexToLoc(v,u,c),w=k.slice(-g);p.forEach(_=>w[_]=0);let C=I.locToIndex(w,g,y),E=k.slice(-x);f.forEach(_=>E[_]=0);let M=I.locToIndex(E,x,b),R=e(m[C*2],m[C*2+1],A[M*2],A[M*2+1]);d[v]=R.real,h[v]=R.imag}return[d,h,i]}}var k7=Mt((e,t)=>e+t),TB=Y2((e,t,n,s)=>({real:e+n,imag:t+s})),$c=Jt(Dr,k7,TB),NB={kernelName:Dr,backendName:"cpu",kernelFunc:$c};function J2(e,t,n,s,r){let a=I.sizeFromShape(s),o=I.makeZerosTypedArray(r,n);for(let i=0;i<e.length;i++){let l=e[i];if(l<0)throw new Error("Input x must be non-negative!");l>=r||(a>0?o[l]+=t[i]:o[l]+=1)}return o}function I7(e,t,n,s=!1){let r=e.shape[0],a=e.shape[1],o=Be([r,n],t.dtype);for(let i=0;i<r;i++)for(let l=0;l<a;l++){let u=e.get(i,l);if(u<0)throw new Error("Input x must be non-negative!");u>=n||(s?o.set(1,i,u):t.size>0?o.set(o.get(i,u)+t.get(i,l),i,u):o.set(o.get(i,u)+1,i,u))}return o}function Wl(e){return(t,n,s)=>{let r=I.getTypedArrayFromDType(n,t.length);for(let a=0;a<t.length;++a)r[a]=e(t[a],s);return r}}function rt(e,t,n){return({inputs:s,attrs:r,backend:a})=>{let{x:o}=s;if(ke(o,e),o.dtype==="string"||n==="string")throw new Error("unaryKernelFunc does not support string input/output");let i=a,l=i.data.get(o.dataId).values,u=I.sizeFromShape(o.shape),c=n||o.dtype,d=I.getArrayFromDType(c,u);for(let h=0;h<u;++h)d[h]=t(l[h],r);return i.makeTensorInfo(o.shape,c,d)}}function Vl(e,t,n){return({inputs:s,attrs:r,backend:a})=>{let{x:o}=s;if(ke(o,e),o.dtype==="string"||n==="string")throw new Error("unaryKernelFunc does not support string input/output");let i=a,l=i.data.get(o.dataId).values,u=n||o.dtype,c=t(l,u,r);return i.makeTensorInfo(o.shape,u,c)}}var S7=Wl(e=>Math.ceil(e)),EB=Vl(wa,S7),RB={kernelName:wa,backendName:"cpu",kernelFunc:EB};function Q2(e,t,n,s){let r=I.getArrayFromDType(n,I.sizeFromShape(t));if(s&&n!=="string"){let a=0;e.forEach(o=>{let i=I.sizeFromShape(o.shape);r.set(o.vals,a),a+=i})}else{let a=0;e.forEach(o=>{let i=n==="string"?$.fromUint8ToStringArray(o.vals):o.vals,l=0;for(let u=0;u<o.shape[0];++u){let c=u*t[1]+a;for(let d=0;d<o.shape[1];++d)r[c+d]=i[l++]}a+=o.shape[1]})}return r}var C7=Mt((e,t)=>e===t?1:0),T7=Jt(Ti,C7,null,"bool"),_B={kernelName:Ti,backendName:"cpu",kernelFunc:T7},N7=Wl(e=>Math.exp(e)),E7=Vl(Ra,N7),$B={kernelName:Ra,backendName:"cpu",kernelFunc:E7},R7=Wl(e=>Math.expm1(e)),FB=Vl(Ei,R7),DB={kernelName:Ei,backendName:"cpu",kernelFunc:FB},_7=Wl(e=>Math.floor(e)),OB=Vl(_a,_7),PB={kernelName:_a,backendName:"cpu",kernelFunc:OB};function $7(e,t,n,s,r,a,o,i,l){let u=Be([s,a],n);for(let c=0;c<s;c++){let d=[],h=0;for(let p=0;p<r;p++){let f=e[c*r+p];h+=f*o[p],d.push(f)}if(h<0||h>=l/a)throw new Error(`Invalid indices: ${d} does not index into ${i}`);for(let p=0;p<a;p++)u.values[c*a+p]=t.get(...t.indexToLoc(h*a+p))}return u}function F7(e,t,n){let s=Be(n,e.dtype);for(let r=0;r<s.size;++r){let o=s.indexToLoc(r).slice(),i=o[0],l=o[2],u=t.locToIndex([i,l]);o[2]=t.values[u];let c=e.locToIndex(o);s.values[r]=e.values[c]}return s}var D7=Mt((e,t)=>e>t?1:0),MB=Jt(Fi,D7,null,"bool"),zB={kernelName:Fi,backendName:"cpu",kernelFunc:MB},O7=Mt((e,t)=>e>=t?1:0),LB=Jt(Da,O7,null,"bool"),BB={kernelName:Da,backendName:"cpu",kernelFunc:LB},P7=Mt((e,t)=>e<t?1:0),WB=Jt(Mi,P7,null,"bool"),VB={kernelName:Mi,backendName:"cpu",kernelFunc:WB},M7=Mt((e,t)=>e<=t?1:0),UB=Jt(zi,M7,null,"bool"),HB={kernelName:zi,backendName:"cpu",kernelFunc:UB};function z7(e,t,n){let s=(t-e)/(n-1),r=I.makeZerosTypedArray(n,"float32");r[0]=e;for(let a=1;a<r.length;a++)r[a]=r[a-1]+s;return r}var L7=Wl(e=>Math.log(e)),GB=Vl(Ma,L7),jB={kernelName:Ma,backendName:"cpu",kernelFunc:GB};function B7(e,t,n,s){let r=I.getTypedArrayFromDType(s,I.sizeFromShape(n));for(let a=0;a<r.length;++a){let o=a*t,i=e[o];for(let l=0;l<t;++l){let u=e[o+l];(Number.isNaN(u)||u>i)&&(i=u)}r[a]=i}return r}var W7=Mt((e,t)=>Math.max(e,t)),qB=Jt(La,W7),XB={kernelName:La,backendName:"cpu",kernelFunc:qB},V7=Mt((e,t)=>Math.min(e,t)),KB=Jt(Ua,V7),ZB={kernelName:Ua,backendName:"cpu",kernelFunc:KB},e1=Mt((e,t)=>e*t),YB=Y2((e,t,n,s)=>({real:e*n-t*s,imag:e*s+t*n})),Yp=Jt(Ga,e1,YB),JB={kernelName:Ga,backendName:"cpu",kernelFunc:Yp};function U7(e,t,n){let s=I.createScalarValue(-1,n);return e1([],t,s,e,n)}function QB(e){let{inputs:t,backend:n}=e,{x:s}=t;ke(s,"neg");let r=n.data.get(s.dataId).values,[a,o]=U7(r,s.shape,s.dtype);return n.makeTensorInfo(o,s.dtype,a)}var eW={kernelName:Vi,backendName:"cpu",kernelFunc:QB},H7=Mt((e,t)=>e!==t?1:0),tW=Jt(Ui,H7,null,"bool"),nW={kernelName:Ui,backendName:"cpu",kernelFunc:tW};function t1(e,t,n,s,r){let a=t.length,o=I.sizeFromShape(t),i=I.computeStrides(t),l=I.computeStrides(r),u=I.getTypedArrayFromDType(n,I.sizeFromShape(r));for(let c=0;c<o;++c){let d=I.indexToLoc(c,a,i),h=new Array(d.length);for(let f=0;f<h.length;f++)h[f]=d[s[f]];let p=I.locToIndex(h,a,l);u[p]=e[c]}return u}function ps(e){let{inputs:t,attrs:n,backend:s}=e,{x:r}=t,{perm:a}=n;ke(r,"transpose");let o=r.shape.length,i=new Array(o);for(let d=0;d<i.length;d++)i[d]=r.shape[a[d]];let l=s.data.get(r.dataId).values,u=t1(l,r.shape,r.dtype,a,i);return{dataId:s.write(u,i,r.dtype),shape:i,dtype:r.dtype}}var sW={kernelName:ho,backendName:"cpu",kernelFunc:ps};function G7(e,t,n,s){let[r,a]=$.computeOutAndReduceShapes(e,s),o=bs(t,"int32"),i=I.makeZerosTypedArray(I.sizeFromShape(r),o),l=I.sizeFromShape(a);for(let u=0;u<i.length;++u){let c=u*l,d=1;for(let h=0;h<l;++h)d*=n[c+h];i[u]=d}return{outVals:i,outShape:r,outDtype:o}}function rW(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:o}=s;ke(r,"prod");let i=r.shape.length,l=I.parseAxisParam(a,r.shape),u=$.getAxesPermutation(l,i),c=l,d=r,h=[];u!=null&&(d=ps({inputs:{x:r},backend:n,attrs:{perm:u}}),h.push(d),c=$.getInnerMostAxes(c.length,i));let p=n.data.get(d.dataId).values,{outVals:f,outShape:m,outDtype:A}=G7(d.shape,d.dtype,p,c),g=m;return o&&(g=$.expandShapeToKeepDim(m,l)),h.forEach(y=>n.disposeIntermediateTensorInfo(y)),n.makeTensorInfo(g,A,f)}var aW={kernelName:Ki,backendName:"cpu",kernelFunc:rW};function n1(e,t,n,s){let r=e===t,a=e<t&&n<0,o=t<e&&n>1;if(r||a||o)return I.makeZerosTypedArray(0,s);let i=Math.abs(Math.ceil((t-e)/n)),l=I.makeZerosTypedArray(i,s);t<e&&n===1&&(n=-1),l[0]=e;for(let u=1;u<l.length;u++)l[u]=l[u-1]+n;return l}var j7=Wl(e=>1/Math.sqrt(e)),oW=Vl(to,j7),iW={kernelName:to,backendName:"cpu",kernelFunc:oW};function Jp(e,t,n,s,r){let a=An.isSliceContinous(s,t,n),o=I.sizeFromShape(n),i=I.computeStrides(s);if(a){let d=An.computeFlatOffset(t,i);return r==="string"?e.slice(d,d+o):e.subarray(d,d+o)}let l=r==="string"?$.fromUint8ToStringArray(e):e,u=Be(s,r,l),c=Be(n,r);for(let d=0;d<c.size;++d){let h=c.indexToLoc(d),p=h.map((f,m)=>f+t[m]);c.set(u.get(...p),...h)}return r==="string"?$.fromStringArrayToUint8(c.values):c.values}function Lo(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{begin:a,size:o}=s;ke(r,"slice");let[i,l]=An.parseSliceParams(r,a,o);An.assertParamsValid(r,i,l);let u=n.data.get(r.dataId).values,c=Jp(u,i,l,r.shape,r.dtype);return n.makeTensorInfo(l,r.dtype,c)}var lW={kernelName:tl,backendName:"cpu",kernelFunc:Lo};function q7(e,t,n,s,r,a,o){let i=t[0],l=a[0],u=new Array(l),c=new Array(i),d=t[1];if(l===0){if(i!==0)throw new Error(`Received SparseTensor with denseShape[0] = 0 but
         indices.shape[0] = ${i}`);let A=I.getArrayFromDType(n,0),g=I.getArrayFromDType(r,0);return[A,[0,d],g,u,c]}let h=!0,p=0,f=new Array(l).fill(0);for(let A=0;A<i;++A){let g=e[A*d];if(g<0)throw new Error(`indices(${A}, 0) is invalid: ${g} < 0`);if(g>=l)throw new Error(`indices(${A}, 0) is invalid: ${g} >= ${l}`);++f[g],h=h&&g>=p,p=g}let m=!0;for(let A=0;A<l;++A){let g=f[A]===0;u[A]=g,m=m&&!g,f[A]=Math.max(f[A],1),A>0&&(f[A]+=f[A-1])}if(m&&h){let A=e,g=s;for(let y=0;y<i;++y)c[y]=y;return[A,[i,d],g,u,c]}else{let A=f[l-1],g=I.getArrayFromDType(n,A*d),y=I.getArrayFromDType(r,A),x=new Array(l).fill(0);for(let b=0;b<i;++b){let v=e[b*d],k=x[v],w=(v===0?0:f[v-1])+k;x[v]++;for(let C=0;C<d;++C)g[w*d+C]=e[b*d+C];y[w]=s[b],c[b]=w}for(let b=0;b<l;++b)if(x[b]===0){let k=b===0?0:f[b-1];g[k*d+0]=b;for(let w=1;w<d;++w)g[k*d+w]=0;y[k]=o}return[g,[A,d],y,u,c]}}function X7(e,t,n,s,r){let a=I.sizeFromShape(s),o=t[0],i=r.length,l=[],u=1,c=-1;for(let A=0;A<i;++A){let g=r[A];if(g===-1){if(c!==-1)throw new Error(`only one output dimension may be -1, not both ${c} and ${A}`);c=A,l.push(1)}else{if(g<0)throw new Error(`size ${A} must be non-negative, not ${g}`);u*=g,l.push(g)}}if(c!==-1){if(u<=0)throw new Error("reshape cannot infer the missing input size for an empty tensor unless all specified input sizes are non-zero");let A=Math.trunc(a/u);if(u*A!==a)throw new Error(`Input to reshape is a SparseTensor with ${a}
          dense values, but the requested shape requires a multiple of ${u}. inputShape=${s} outputShape= ${l}`);l[c]=A}let d=I.sizeFromShape(l);if(d!==a)throw new Error(`Input to reshape is a tensor with ${a} dense values, but the requested shape has ${d}. inputShape=${s} outputShape=${l}`);let h=s.length,p=[];if(h>0){p[h-1]=1;for(let A=h-2;A>=0;--A)p[A]=p[A+1]*s[A+1]}let f=[];if(i>0){f[i-1]=1;for(let A=i-2;A>=0;--A)f[A]=f[A+1]*l[A+1]}let m=I.getArrayFromDType(n,o*i);for(let A=0;A<o;++A){let g=0;for(let y=0;y<h;++y)g+=e[A*h+y]*p[y];for(let y=0;y<i;++y)m[A*i+y]=Math.trunc(g/f[y]),g%=f[y]}return[m,[o,i],l]}function s1(e,t,n,s,r,a=!1,o=0){let i=s.length;if(i!==r.length)throw new Error("segmentIds and indices should have same size.");let l=[t[0],e.length/t[0]],u=l[1],d=i>0?r[i-1]+1:0;if(d<0)throw new Error("segment ids must be >= 0");let h=t.slice();h[0]=d;let p=h.reduce((x,b)=>x*b,1),f=I.getArrayFromDType(n,p);if(i===0)return d>0&&f.fill(o),[f,h];if(d<=0)throw new Error("segment ids must be >= 0");let m=0,A=1,g=0,y=r[m];for(;;){let x=0;if(A<i){if(x=r[A],y===x){++A;continue}if(y>=x)throw new Error("segment ids are not increasing")}if(y<0||y>=d)throw new Error(`Segment id ${y} out of range [0, ${d}), possibly because segmentIds input is not sorted.`);y>g&&f.fill(o,g*u,y*u);for(let b=m;b<A;++b){let v=s[b];if(v<0||v>=l[0])throw new Error(`Bad: indices[${b}] == ${s[b]} out of range [0, ${l[0]})`);for(let k=0;k<u;k++)f[y*u+k]+=e[v*u+k]}if(a)for(let b=0;b<u;b++)f[y*u+b]/=A-m;if(m=A,++A,g=y+1,y=x,A>i)break}return g<d&&f.fill(o,g*u,d*u),[f,h]}var K7=Mt((e,t)=>{let n=e-t;return n*n}),uW=Jt(io,K7),cW={kernelName:io,backendName:"cpu",kernelFunc:uW};function Z7(e,t,n,s){let r=Be(e,t.dtype);for(let a=0;a<r.size;a++){let o=r.indexToLoc(a),i=new Array(o.length);for(let l=0;l<i.length;l++)i[l]=o[l]*n[l]+s[l];r.set(t.get(...i),...o)}return r}var dW=class{constructor(e,t,n,s,r,a){this.separator=I.encodeString(e),this.nGramWidths=t,this.leftPad=I.encodeString(n),this.rightPad=I.encodeString(s),this.padWidth=r,this.preserveShort=a}getPadWidth(e){return Math.min(this.padWidth<0?e-1:this.padWidth,e-1)}getNumNGrams(e,t){let n=this.getPadWidth(t);return Math.max(0,e+2*n-t+1)}createNGrams(e,t,n,s,r,a){for(let o=0;o<r;++o){let i=this.getPadWidth(a),l=Math.max(0,i-o),u=Math.max(0,i-(r-(o+1))),c=a-(l+u),d=t+(l>0?0:o-i),h=0;h+=l*this.leftPad.length;for(let g=0;g<c;++g)h+=e[d+g].length;h+=u*this.rightPad.length,h+=(l+u+c-1)*this.separator.length,n[s+o]=new Uint8Array(h);let f=n[s+o],m=0,A=g=>g.forEach(y=>f[m++]=y);for(let g=0;g<l;++g)A(this.leftPad),A(this.separator);for(let g=0;g<c-1;++g)A(e[d+g]),A(this.separator);if(c>0){A(e[d+c-1]);for(let g=0;g<u;++g)A(this.separator),A(this.rightPad)}else{for(let g=0;g<u-1;++g)A(this.rightPad),A(this.separator);A(this.rightPad)}}}compute(e,t){let n=e.length,s=t.length;if(s>0){let i=t[0];if(i!==0)throw new Error(`First split value must be 0, got ${i}`);for(let l=1;l<s;++l){let u=t[l]>=i;if(u=u&&t[l]<=n,!u)throw new Error(`Invalid split value ${t[l]}, must be in [${i}, ${n}]`);i=t[l]}if(i!==n)throw new Error(`Last split value must be data size. Expected ${n}, got ${i}`)}let r=s-1,a=I.getArrayFromDType("int32",s);if(n===0||s===0){let i=new Array(n);for(let l=0;l<=r;++l)a[l]=0;return[i,a]}a[0]=0;for(let i=1;i<=r;++i){let l=t[i]-t[i-1],u=0;this.nGramWidths.forEach(c=>{u+=this.getNumNGrams(l,c)}),this.preserveShort&&l>0&&u===0&&(u=1),a[i]=a[i-1]+u}let o=new Array(a[r]);for(let i=0;i<r;++i){let l=t[i],u=a[i];if(this.nGramWidths.forEach(c=>{let d=t[i+1]-t[i],h=this.getNumNGrams(d,c);this.createNGrams(e,l,o,u,h,c),u+=h}),this.preserveShort&&u===a[i]){let c=t[i+1]-t[i];if(c===0)continue;let d=c+2*this.padWidth,h=1;this.createNGrams(e,l,o,u,h,d)}}return[o,a]}};function Y7(e,t,n,s,r,a,o,i){return new dW(n,s,r,a,o,i).compute(e,t)}function hW(e,t,n){if(!e.length)return[];if(t.length===0){let a=new Array(e.length);for(let o=0;o<e.length;++o)a[o]=e.subarray(o,o+1);return a}if(t.length===1){let a=t[0],o=[],i=e.indexOf(a);for(;i!==-1;){let l=e.subarray(0,i);(!n||l.length!==0)&&o.push(l),e=e.subarray(i+1),i=e.indexOf(a)}return(!n||e.length!==0)&&o.push(e),o}let s=[],r=0;for(let a=0;a<e.length+1;a++)if(a===e.length||t.indexOf(e[a])!==-1){let o=e.subarray(r,a);(!n||o.length!==0)&&s.push(o),r=a+1}return s}function J7(e,t,n){let s=e.length,r=[],a=0,o=0,i=new Array(s);for(let h=0;h<s;++h){let p=hW(e[h],t,n),f=p.length;i[h]=f,a+=f,o=Math.max(o,f),r.push(...p)}let l=I.getArrayFromDType("int32",a*2),u=new Array(a),c=[s,o],d=0;for(let h=0;h<s;++h)for(let p=0;p<i[h];++p)l[d*2]=h,l[d*2+1]=p,u[d]=r[d],++d;return[l,u,c]}function Q7(e,t){let n=I.getArrayFromDType("int32",e.length);for(let s=0;s<e.length;++s)n[s]=I.fingerPrint64(e[s]).modulo(t).getLowBitsUnsigned();return n}var ew=Mt((e,t)=>e-t),pW=Y2((e,t,n,s)=>({real:e-n,imag:t-s})),r1=Jt(lo,ew,pW),fW={kernelName:lo,backendName:"cpu",kernelFunc:r1};function tw(e,t){let n=new Array(e.rank);for(let r=0;r<n.length;r++)n[r]=e.shape[r]*t[r];let s=Be(n,e.dtype);for(let r=0;r<s.values.length;++r){let a=s.indexToLoc(r),o=new Array(e.rank);for(let l=0;l<o.length;l++)o[l]=a[l]%e.shape[l];let i=e.locToIndex(o);s.values[r]=e.values[i]}return s}var Fc=(e,t)=>{let n=t.value-e.value;return n===0?e.index-t.index:n};function nw(e,t,n=0,s=e.length-1){for(;s>n;){if(s-n>600){let i=s-n+1,l=t-n+1,u=Math.log(i),c=.5*Math.exp(2*u/3),d=.5*Math.sqrt(u*c*(i-c)/i)*Math.sign(l-i/2),h=Math.max(n,Math.floor(t-l*c/i+d)),p=Math.min(s,Math.floor(t+(i-l)*c/i+d));nw(e,t,h,p)}let r=e[t],a=n,o=s;for(I.swap(e,n,t),Fc(e[s],r)>0&&I.swap(e,n,s);a<o;){for(I.swap(e,a,o),a++,o--;Fc(e[a],r)<0;)a=a+1;for(;Fc(e[o],r)>0;)o=o-1}Fc(e[n],r)===0?I.swap(e,n,o):(o=o+1,I.swap(e,o,s)),o<=t&&(n=o+1),t<=o&&(s=o-1)}}function sw(e,t,n,s,r){let a=t[t.length-1],[o,i]=[e.length/a,a],l=I.getTypedArrayFromDType(n,o*s),u=I.getTypedArrayFromDType("int32",o*s);for(let d=0;d<o;d++){let h=d*i,p=e.subarray(h,h+i),f=new Array(p.length);p.forEach((y,x)=>f[x]={value:y,index:x}),s<f.length&&(nw(f,s),f=f.slice(0,s)),r&&f.sort(Fc);let m=d*s,A=l.subarray(m,m+s),g=u.subarray(m,m+s);for(let y=0;y<s;y++)A[y]=f[y].value,g[y]=f[y].index}let c=t.slice();return c[c.length-1]=s,[Be(c,n,l),Be(c,"int32",u)]}function rw(e,t,n,s){let r=I.parseAxisParam(t,n)[0],a=[1,n[0],1];for(let f=0;f<r;f++)a[0]*=n[f];a[1]=n[r];for(let f=r+1;f<n.length;f++)a[2]*=n[f];let o={},i=new Int32Array(n[r]),l=new Bt(a,s,e),u=[],c=a[0]===1&&a[2]===1;for(let f=0;f<n[r];f++){let m;if(c)m=e[f].toString();else{let A=[];for(let g=0;g<a[0];g++)for(let y=0;y<a[2];y++)A.push(l.get(g,f,y));m=A.join(",")}if(o[m]!==void 0)i[f]=o[m];else{let A=Object.keys(o).length;o[m]=A,i[f]=A,u.push(f)}}let d=a.slice();d[1]=Object.keys(o).length;let h=new Bt(d,s);u.forEach((f,m)=>{for(let A=0;A<a[0];A++)for(let g=0;g<a[2];g++)h.set(l.get(A,f,g),A,m,g)});let p=n.slice();return p[r]=d[1],{outputValues:h.values,outputShape:p,indices:i}}var mW="3.8.0";bl("cpu",()=>new Kp,1);var aw=rt(Si,e=>e>=0?e:Math.exp(e)-1),AW={kernelName:Si,backendName:"cpu",kernelFunc:aw};function ow(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{alpha:a}=s;ke([r],"leakyRelu");let o=I.sizeFromShape(r.shape),i=n.data.get(r.dataId).values,l=I.getTypedArrayFromDType("float32",o);for(let u=0;u<i.length;u++)l[u]=i[u]<0?a*i[u]:i[u];return n.makeTensorInfo(r.shape,"float32",l)}var gW={kernelName:Pa,backendName:"cpu",kernelFunc:ow},yW=Mt((e,t)=>e<0?t*e:e);function iw(e){let{inputs:t,backend:n}=e,{x:s,alpha:r}=t;ke([s,r],"prelu");let a=n.data.get(s.dataId).values,o=n.data.get(r.dataId).values,[i,l]=yW(s.shape,r.shape,a,o,s.dtype);return n.makeTensorInfo(l,s.dtype,i)}var xW={kernelName:Ka,backendName:"cpu",kernelFunc:iw},lw=rt(Za,e=>Math.max(0,e)),bW={kernelName:Za,backendName:"cpu",kernelFunc:lw},uw=rt(Ja,e=>Math.min(Math.max(0,e),6)),vW={kernelName:Ja,backendName:"cpu",kernelFunc:uw},cw=rt(so,e=>1/(1+Math.exp(-e))),wW={kernelName:so,backendName:"cpu",kernelFunc:cw};function a1(e,t,n,s,r){if(n==="linear")return rr({inputs:{x:t},backend:e});if(n==="relu")return lw({inputs:{x:t},backend:e});if(n==="elu")return aw({inputs:{x:t},backend:e});if(n==="relu6")return uw({inputs:{x:t},backend:e});if(n==="prelu")return iw({inputs:{x:t,alpha:s},backend:e});if(n==="leakyrelu")return ow({inputs:{x:t},backend:e,attrs:{alpha:r}});if(n==="sigmoid")return cw({inputs:{x:t},backend:e});throw new Error(`Activation ${n} has not been implemented for the CPU backend.`)}function gt(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{shape:a}=s,o=I.sizeFromShape(r.shape),i=I.inferFromImplicitShape(a,o),l=I.sizeFromShape(i);I.assert(o===l,()=>`The new shape (${i}) has ${l} elements and the old shape (${r.shape}) has ${o} elements. The new shape and old shape must have the same number of elements.`),n.incRef(r.dataId);let u=n.data.get(r.dataId);if(u.complexTensorInfos!=null){let c=u.complexTensorInfos.real,d=u.complexTensorInfos.imag;c.shape=i,d.shape=i}return{dataId:r.dataId,shape:i,dtype:r.dtype}}var kW={kernelName:Yi,backendName:"cpu",kernelFunc:gt};function dw(e){let{inputs:t,backend:n,attrs:s}=e,{a:r,b:a}=t,{transposeA:o,transposeB:i}=s;ke([r,a],"matMul");let l=r.shape.length,u=a.shape.length,c=o?r.shape[l-2]:r.shape[l-1],d=i?a.shape[u-1]:a.shape[u-2],h=o?r.shape[l-1]:r.shape[l-2],p=i?a.shape[u-2]:a.shape[u-1],f=r.shape.slice(0,-2),m=a.shape.slice(0,-2),A=I.sizeFromShape(f),g=I.sizeFromShape(m),y=A===g||A===1||g===1;I.assert(l>=2&&u>=2&&y,()=>`Error in matMul: the input batch dimensions must either be the same or at least one input batch dimension must be 1. Got input batch dimensions of (${f}) and (${m}).`);let b=(A>g?r.shape.slice(0,-2):a.shape.slice(0,-2)).concat([h,p]);I.assert(c===d,()=>`Error in matMul: inner shapes (${c}) and (${d}) of Tensors with shapes ${r.shape} and ${a.shape} and transposeA=${o} and transposeB=${i} must match.`);let v=o?[A,c,h]:[A,h,c],k=i?[g,p,d]:[g,d,p],w=gt({inputs:{x:r},backend:n,attrs:{shape:v}}),C=gt({inputs:{x:a},backend:n,attrs:{shape:k}}),E=o?w.shape[1]:w.shape[2],M=o?w.shape[2]:w.shape[1],R=i?C.shape[1]:C.shape[2],_=Math.max(A,g),N=n.data.get(w.dataId).values,O=n.data.get(C.dataId).values,W=I.computeStrides(w.shape),j=I.computeStrides(C.shape),[q,X,Q]=o?[W[0],1,W[1]]:[W[0],W[1],1],[ne,te,se]=i?[1,j[1],j[0]]:[j[1],1,j[0]],J=M*R,ie=Be([_,M,R],w.dtype),le=ie.values,he=n.blockSize;for(let ge=0;ge<_;ge++)for(let Ce=0;Ce<M;Ce+=he)for(let Te=0;Te<R;Te+=he)for(let $e=0;$e<E;$e+=he){let Me=Math.min(Ce+he,M),De=Math.min(Te+he,R),it=Math.min($e+he,E);for(let et=Ce;et<Me;et++)for(let tt=Te;tt<De;tt++){let Je=0;for(let at=$e;at<it;at++){let Ve=Math.min(ge,A-1)*q,In=Math.min(ge,g-1)*se,kt=N[Ve+et*X+at*Q],Mn=O[at*ne+tt*te+In];Je+=kt*Mn}le[ge*J+(et*R+tt)]+=Je}}return n.disposeIntermediateTensorInfo(w),n.disposeIntermediateTensorInfo(C),n.makeTensorInfo(b,ie.dtype,ie.values)}var IW={kernelName:ba,backendName:"cpu",kernelFunc:dw};function SW(e){let{inputs:t,backend:n,attrs:s}=e,{a:r,b:a,bias:o,preluActivationWeights:i}=t,{transposeA:l,transposeB:u,activation:c,leakyreluAlpha:d}=s,h,p,f,m=[];h=dw({inputs:{a:r,b:a},attrs:{transposeA:l,transposeB:u},backend:n}),o&&(p=$c({inputs:{a:h,b:o},backend:n}),m.push(h),h=p),c&&(f=a1(n,h,c,i,d),m.push(h),h=f);for(let g of m)n.disposeIntermediateTensorInfo(g);return h}var CW={kernelName:po,backendName:"cpu",kernelFunc:SW},TW=rt(hi,e=>Math.acos(e)),NW={kernelName:hi,backendName:"cpu",kernelFunc:TW},EW=rt(pi,e=>Math.acosh(e)),RW={kernelName:pi,backendName:"cpu",kernelFunc:EW};function _W(e){let{inputs:t,backend:n}=e,s=t;ke(t,"addN");let r=s.map(i=>n.data.get(i.dataId).values),a=Be(s[0].shape,s[0].dtype),o=a.values;for(let i=0;i<s.length;i++){let l=r[i];for(let u=0;u<o.length;u++)o[u]+=l[u]}return n.makeTensorInfo(a.shape,a.dtype,a.values)}var $W={kernelName:ga,backendName:"cpu",kernelFunc:_W};function FW(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:o}=s;ke(r,"all");let i=I.parseAxisParam(a,r.shape),l=i,u=$.getAxesPermutation(l,r.shape.length),c=r;u!=null&&(c=ps({inputs:{x:r},backend:n,attrs:{perm:u}}),l=$.getInnerMostAxes(l.length,r.shape.length)),$.assertAxesAreInnerMostDims("all",l,c.shape.length);let[d,h]=$.computeOutAndReduceShapes(c.shape,l),p=I.sizeFromShape(h),f=I.makeZerosTypedArray(I.sizeFromShape(d),c.dtype),m=n.data.get(c.dataId).values;for(let g=0;g<f.length;++g){let y=g*p,x=m[y];for(let b=0;b<p;++b){let v=m[y+b];x=x&&v}f[g]=x}u!=null&&n.disposeIntermediateTensorInfo(c);let A=n.makeTensorInfo(d,c.dtype,f);if(o){let g=$.expandShapeToKeepDim(d,i),y=gt({inputs:{x:A},backend:n,attrs:{shape:g}});return n.disposeIntermediateTensorInfo(A),y}return A}var DW={kernelName:fi,backendName:"cpu",kernelFunc:FW};function OW(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:o}=s;ke(r,"any");let i=I.parseAxisParam(a,r.shape),l=i,u=$.getAxesPermutation(l,r.shape.length),c=r;u!=null&&(c=ps({inputs:{x:r},backend:n,attrs:{perm:u}}),l=$.getInnerMostAxes(l.length,r.shape.length)),$.assertAxesAreInnerMostDims("any",l,c.shape.length);let[d,h]=$.computeOutAndReduceShapes(c.shape,l),p=I.sizeFromShape(h),f=I.makeZerosTypedArray(I.sizeFromShape(d),c.dtype),m=n.data.get(c.dataId).values;for(let g=0;g<f.length;++g){let y=g*p,x=m[y];for(let b=0;b<p;++b){let v=m[y+b];x=x||v}f[g]=x}u!=null&&n.disposeIntermediateTensorInfo(c);let A=n.makeTensorInfo(d,c.dtype,f);if(o){let g=$.expandShapeToKeepDim(d,i),y=gt({inputs:{x:A},backend:n,attrs:{shape:g}});return n.disposeIntermediateTensorInfo(A),y}return A}var PW={kernelName:mi,backendName:"cpu",kernelFunc:OW};function MW(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a}=s;ke(r,"argMax");let o=I.parseAxisParam(a,r.shape),i=$.getAxesPermutation(o,r.shape.length),l=r,u=[];i!=null&&(l=ps({inputs:{x:r},backend:n,attrs:{perm:i}}),u.push(l),o=$.getInnerMostAxes(o.length,l.shape.length)),o=[o[0]],$.assertAxesAreInnerMostDims("argMax",o,l.shape.length);let[c,d]=$.computeOutAndReduceShapes(l.shape,o),h=I.sizeFromShape(c),p=I.makeZerosTypedArray(h,"int32"),f=I.sizeFromShape(d),m=n.data.get(l.dataId).values;for(let A=0;A<p.length;++A){let g=A*f,y=m[g],x=0;for(let b=0;b<f;++b){let v=m[g+b];v>y&&(y=v,x=b)}p[A]=x}return u.forEach(A=>n.disposeIntermediateTensorInfo(A)),n.makeTensorInfo(c,"int32",p)}var zW={kernelName:ya,backendName:"cpu",kernelFunc:MW};function LW(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a}=s;ke(r,"argMin");let o=I.parseAxisParam(a,r.shape),i=$.getAxesPermutation(o,r.shape.length),l=r,u=[];i!=null&&(l=ps({inputs:{x:r},backend:n,attrs:{perm:i}}),u.push(l),o=$.getInnerMostAxes(o.length,l.shape.length)),o=[o[0]],$.assertAxesAreInnerMostDims("argMin",o,l.shape.length);let[c,d]=$.computeOutAndReduceShapes(l.shape,o),h=I.sizeFromShape(c),p=I.makeZerosTypedArray(h,"int32"),f=I.sizeFromShape(d),m=n.data.get(l.dataId).values;for(let A=0;A<p.length;++A){let g=A*f,y=m[g],x=0;for(let b=0;b<f;++b){let v=m[g+b];v<y&&(y=v,x=b)}p[A]=x}return u.forEach(A=>n.disposeIntermediateTensorInfo(A)),n.makeTensorInfo(c,"int32",p)}var BW={kernelName:Cu,backendName:"cpu",kernelFunc:LW},WW=rt(Ai,e=>Math.asin(e)),VW={kernelName:Ai,backendName:"cpu",kernelFunc:WW},UW=rt(gi,e=>Math.asinh(e)),HW={kernelName:gi,backendName:"cpu",kernelFunc:UW},GW=rt(yi,e=>Math.atan(e)),jW={kernelName:yi,backendName:"cpu",kernelFunc:GW},qW=Mt((e,t)=>Math.atan2(e,t)),XW=Jt(bi,qW),KW={kernelName:bi,backendName:"cpu",kernelFunc:XW},ZW=rt(xi,e=>Math.atanh(e)),YW={kernelName:xi,backendName:"cpu",kernelFunc:ZW};function o1(e,t,n,s,r,a){let o=r.strideHeight,i=r.strideWidth,l=r.dilationHeight,u=r.dilationWidth,c=r.effectiveFilterHeight,d=r.effectiveFilterWidth,h=r.padInfo.top,p=r.padInfo.left,f=a==="max"?Number.NEGATIVE_INFINITY:Number.POSITIVE_INFINITY,m=Be(r.outShape,n),A=m.values,g=r.outShape[1]*r.outShape[2]*r.outShape[3],y=r.outShape[2]*r.outShape[3],x=r.outShape[3];for(let b=0;b<r.batchSize;++b){let v=b*g,k=b*s[0];for(let w=0;w<r.inChannels;++w)for(let C=0;C<r.outHeight;++C){let E=C*o-h,M=Math.max(0,E),R=Math.min(r.inHeight,c+E),_=v+C*y;for(let N=0;N<r.outWidth;++N){let O=N*i-p,W=Math.max(0,O),j=Math.min(r.inWidth,d+O),q=f,X=0,Q=0;for(let te=M;te<R;te+=l){let se=k+te*s[1];for(let J=W;J<j;J+=u){let ie=se+J*s[2],le=e[ie+w];a==="max"&&le>q?q=le:a==="avg"&&(X+=le,Q++)}if(isNaN(q))break}let ne=_+N*x+w;A[ne]=a==="avg"?X/Q:q}}}return m}function hw(e,t,n,s,r=!1,a=!1){let o=Be(s.outShape,"int32"),i=s.strideHeight,l=s.strideWidth,u=s.dilationHeight,c=s.dilationWidth,d=s.effectiveFilterHeight,h=s.effectiveFilterWidth,p=s.padInfo.top,f=s.padInfo.left,m=Be(t,n,e);for(let A=0;A<s.batchSize;++A)for(let g=0;g<s.inChannels;++g)for(let y=0;y<s.outHeight;++y){let x=y*i-p,b=x;for(;b<0;)b+=u;let v=Math.min(s.inHeight,d+x);for(let k=0;k<s.outWidth;++k){let w=k*l-f,C=w;for(;C<0;)C+=c;let E=Math.min(s.inWidth,h+w),M=Number.NEGATIVE_INFINITY,R=-1;for(let _=b;_<v;_+=u){let N=_-x;for(let O=C;O<E;O+=c){let W=O-w,j=m.get(A,_,O,g);j>M&&(M=j,r?R=a?((A*s.inHeight+_)*s.inWidth+O)*s.inChannels+g:(_*s.inWidth+O)*s.inChannels+g:R=N*h+W)}}o.set(R,A,y,k,g)}}return o}function pw(e,t,n,s,r,a){let o=r.strideDepth,i=r.strideHeight,l=r.strideWidth,u=r.dilationDepth,c=r.dilationHeight,d=r.dilationWidth,h=r.effectiveFilterDepth,p=r.effectiveFilterHeight,f=r.effectiveFilterWidth,m=r.padInfo.front,A=r.padInfo.top,g=r.padInfo.left,y=a==="max"?Number.NEGATIVE_INFINITY:Number.POSITIVE_INFINITY,x=Be(r.outShape,n),b=x.values,v=r.outShape[1]*r.outShape[2]*r.outShape[3]*r.outShape[4],k=r.outShape[2]*r.outShape[3]*r.outShape[4],w=r.outShape[3]*r.outShape[4],C=r.outShape[4];for(let E=0;E<r.batchSize;++E){let M=E*v,R=E*s[0];for(let _=0;_<r.inChannels;++_)for(let N=0;N<r.outDepth;++N){let O=N*o-m,W=O;for(;W<0;)W+=u;let j=Math.min(r.inDepth,h+O),q=M+N*k;for(let X=0;X<r.outHeight;++X){let Q=X*i-A,ne=Q;for(;ne<0;)ne+=c;let te=Math.min(r.inHeight,p+Q),se=q+X*w;for(let J=0;J<r.outWidth;++J){let ie=J*l-g,le=ie;for(;le<0;)le+=d;let he=Math.min(r.inWidth,f+ie),ge=se+J*C,Ce=y,Te=0,$e=0;for(let De=W;De<j;De+=u){let it=R+De*s[1];for(let et=ne;et<te;et+=c){let tt=it+et*s[2];for(let Je=le;Je<he;Je+=d){let at=tt+Je*s[3],Ve=e[at+_];if(a==="max"&&Ve>Ce?Ce=Ve:a==="avg"&&(Te+=Ve,$e++),isNaN(Ce))break}if(isNaN(Ce))break}if(isNaN(Ce))break}let Me=ge+_;b[Me]=a==="avg"?Te/$e:Ce}}}}return x}function JW(e,t){let n=Be(t.outShape,"int32"),s=t.strideDepth,r=t.strideHeight,a=t.strideWidth,o=t.dilationDepth,i=t.dilationHeight,l=t.dilationWidth,u=t.effectiveFilterDepth,c=t.effectiveFilterHeight,d=t.effectiveFilterWidth,h=t.padInfo.front,p=t.padInfo.top,f=t.padInfo.left;for(let m=0;m<t.batchSize;++m)for(let A=0;A<t.inChannels;++A)for(let g=0;g<t.outDepth;++g){let y=g*s-h,x=y;for(;x<0;)x+=o;let b=Math.min(t.inDepth,u+y);for(let v=0;v<t.outHeight;++v){let k=v*r-p,w=k;for(;w<0;)w+=i;let C=Math.min(t.inHeight,c+k);for(let E=0;E<t.outWidth;++E){let M=E*a-f,R=M;for(;R<0;)R+=l;let _=Math.min(t.inWidth,d+M),N=Number.NEGATIVE_INFINITY,O=-1;for(let W=x;W<b;W+=o){let j=W-y;for(let q=w;q<C;q+=i){let X=q-k;for(let Q=R;Q<_;Q+=l){let ne=Q-M,te=e.get(m,W,q,Q,A);te>=N&&(N=te,O=j*c*d+X*c+ne)}}}n.set(O,m,g,v,E,A)}}}return n}function QW(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t;ke(r,"avgPool");let{filterSize:a,strides:o,pad:i,dimRoundingMode:l}=s,u=1;I.assert($.eitherStridesOrDilationsAreOne(o,u),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${o} and dilations '${u}'`);let c=$.computePool2DInfo(r.shape,a,o,u,i,l),d;if(c.filterWidth===1&&c.filterHeight===1&&I.arraysEqual(c.inShape,c.outShape))d=rr({inputs:{x:r},backend:n});else{let h=n.data.get(r.dataId).values,p=I.computeStrides(r.shape),f=o1(h,r.shape,r.dtype,p,c,"avg");d=n.makeTensorInfo(c.outShape,r.dtype,f.values)}return d}var eV={kernelName:xa,backendName:"cpu",kernelFunc:QW};function tV(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{filterSize:a,strides:o,pad:i,dimRoundingMode:l,dataFormat:u}=s;ke(r,"avgPool3d");let c=$.computePool3DInfo(r.shape,a,o,1,i,l,u),d=n.data.get(r.dataId).values,h=pw(d,r.shape,r.dtype,I.computeStrides(r.shape),c,"avg");return n.makeTensorInfo(h.shape,"float32",h.values)}var nV={kernelName:Tu,backendName:"cpu",kernelFunc:tV};function sV(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,input:a}=t,{filterSize:o,strides:i,pad:l,dimRoundingMode:u}=s;ke([r,a],"avgPool3DGrad");let c=$.computePool3DInfo(a.shape,o,i,1,l,u),d=c.strideDepth,h=c.strideHeight,p=c.strideWidth,f=c.filterDepth,m=c.filterHeight,A=c.filterWidth,g=c.dilationDepth,y=c.dilationHeight,x=c.dilationWidth,b=c.effectiveFilterDepth,v=c.effectiveFilterHeight,k=c.effectiveFilterWidth,w=b-1-c.padInfo.front,C=k-1-c.padInfo.left,E=v-1-c.padInfo.top,M=Be(a.shape,"float32"),R=1/(f*m*A),_=n.bufferSync(r);for(let N=0;N<c.batchSize;++N)for(let O=0;O<c.inChannels;++O)for(let W=0;W<c.inDepth;++W)for(let j=0;j<c.inHeight;++j)for(let q=0;q<c.inWidth;++q){let X=W-w,Q=j-E,ne=q-C,te=0;for(let se=0;se<b;se+=g){let J=(X+se)/d;if(!(J<0||J>=c.outDepth||Math.floor(J)!==J))for(let ie=0;ie<v;ie+=y){let le=(Q+ie)/h;if(!(le<0||le>=c.outHeight||Math.floor(le)!==le))for(let he=0;he<k;he+=x){let ge=(ne+he)/p;if(ge<0||ge>=c.outWidth||Math.floor(ge)!==ge)continue;te+=_.get(N,J,le,ge,O)}}}M.set(te*R,N,W,j,q,O)}return n.makeTensorInfo(M.shape,M.dtype,M.values)}var rV={kernelName:Ld,backendName:"cpu",kernelFunc:sV};function aV(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,input:a}=t,o=a;ke([r,a],"avgPoolGrad");let{filterSize:i,strides:l,pad:u}=s,c=$.computePool2DInfo(o.shape,i,l,1,u),d=c.strideHeight,h=c.strideWidth,p=c.filterHeight,f=c.filterWidth,m=c.dilationHeight,A=c.dilationWidth,g=c.effectiveFilterHeight,y=c.effectiveFilterWidth,x=y-1-c.padInfo.left,b=g-1-c.padInfo.top,v=Be(o.shape,"float32"),k=1/(p*f),w=n.data.get(r.dataId).values,C=Be(r.shape,"float32",w);for(let E=0;E<c.batchSize;++E)for(let M=0;M<c.inChannels;++M)for(let R=0;R<c.inHeight;++R)for(let _=0;_<c.inWidth;++_){let N=R-b,O=_-x,W=0;for(let j=0;j<g;j+=m){let q=(N+j)/d;if(!(q<0||q>=c.outHeight||Math.floor(q)!==q))for(let X=0;X<y;X+=A){let Q=(O+X)/h;if(Q<0||Q>=c.outWidth||Math.floor(Q)!==Q)continue;W+=C.get(E,q,Q,M)}}v.set(W*k,E,R,_,M)}return n.makeTensorInfo(v.shape,v.dtype,v.values)}var oV={kernelName:zd,backendName:"cpu",kernelFunc:aV};function iV(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,scale:a,offset:o,mean:i,variance:l}=t;I.assert(i.shape.length===l.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),I.assert(o==null||i.shape.length===o.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),I.assert(a==null||i.shape.length===a.shape.length,()=>"Batch normalization gradient requires mean and scale to have equal ranks."),ke([r,i,l,a,o],"batchNorm");let{varianceEpsilon:u}=s;u==null&&(u=.001);let c=n.data.get(r.dataId).values,d=n.data.get(i.dataId).values,h=n.data.get(l.dataId).values,p=a?n.data.get(a.dataId).values:new Float32Array([1]),f=o?n.data.get(o.dataId).values:new Float32Array([0]),m=new Float32Array(c.length),A=f.length,g=p.length,y=h.length,x=d.length,b=0,v=0,k=0,w=0;for(let C=0;C<c.length;++C)m[C]=f[b++]+(c[C]-d[v++])*p[k++]/Math.sqrt(h[w++]+u),b>=A&&(b=0),v>=x&&(v=0),k>=g&&(k=0),w>=y&&(w=0);return n.makeTensorInfo(r.shape,r.dtype,m)}var lV={kernelName:Fa,backendName:"cpu",kernelFunc:iV};function uV(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockShape:a,crops:o}=s;ke([r],"batchToSpaceND");let i=a.reduce((g,y)=>g*y),l=$.getReshaped(r.shape,a,i),u=$.getPermuted(l.length,a.length),c=$.getReshapedPermuted(r.shape,a,i),d=$.getSliceBeginCoords(o,a.length),h=$.getSliceSize(c,o,a.length),p=gt({inputs:{x:r},backend:n,attrs:{shape:l}}),f=ps({inputs:{x:p},backend:n,attrs:{perm:u}}),m=gt({inputs:{x:f},backend:n,attrs:{shape:c}}),A=Lo({inputs:{x:m},backend:n,attrs:{begin:d,size:h}});return n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(m),A}var cV={kernelName:vi,backendName:"cpu",kernelFunc:uV};function dV(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,weights:a}=t,{size:o}=s,i=n.data.get(r.dataId).values,l=n.data.get(a.dataId).values,u=J2(i,l,a.dtype,a.shape,o);return n.makeTensorInfo([o],a.dtype,u)}var hV={kernelName:Bd,backendName:"cpu",kernelFunc:dV},pV=rt(Or,(e,t)=>{let n=t;return e>n.clipValueMax?n.clipValueMax:e<n.clipValueMin?n.clipValueMin:e}),fV={kernelName:Or,backendName:"cpu",kernelFunc:pV},mV=e=>{let{x:t}=e.inputs,n=e.backend,s=new Float32Array(I.sizeFromShape(t.shape)),r=n.data.get(t.dataId),a=r.complexTensorInfos.real,o=r.complexTensorInfos.imag,i=n.data.get(a.dataId).values,l=n.data.get(o.dataId).values;for(let u=0;u<i.length;u++){let c=i[u],d=l[u];s[u]=Math.hypot(c,d)}return n.makeOutput(s,t.shape,"float32")},AV={kernelName:Nu,backendName:"cpu",kernelFunc:mV};function Ul(e){let{inputs:t,backend:n}=e,{input:s}=t,r=n.data.get(s.dataId).complexTensorInfos.imag,a=n.data.get(r.dataId).values;return n.makeTensorInfo(r.shape,r.dtype,a)}var gV={kernelName:th,backendName:"cpu",kernelFunc:Ul};function Hl(e){let{inputs:t,backend:n,attrs:s}=e,{axis:r}=s,a=I.parseAxisParam(r,t[0].shape)[0],o=$.computeOutShape(t.map(m=>m.shape),a);if(I.sizeFromShape(o)===0)return n.makeTensorInfo(o,t[0].dtype,[]);let i=t.filter(m=>I.sizeFromShape(m.shape)>0);if(i.length===1)return rr({inputs:{x:i[0]},backend:n});let l=i.map(m=>m.shape);if($.assertParamsConsistent(l,a),i[0].dtype==="complex64"){let m=i.map(b=>zo({inputs:{input:b},backend:n})),A=i.map(b=>Ul({inputs:{input:b},backend:n})),g=Hl({inputs:m,backend:n,attrs:{axis:a}}),y=Hl({inputs:A,backend:n,attrs:{axis:a}}),x=jn({inputs:{real:g,imag:y},backend:n});return m.forEach(b=>n.disposeIntermediateTensorInfo(b)),A.forEach(b=>n.disposeIntermediateTensorInfo(b)),n.disposeIntermediateTensorInfo(g),n.disposeIntermediateTensorInfo(y),x}let u=i.map(m=>{let A=I.sizeFromShape(m.shape.slice(a));return gt({inputs:{x:m},backend:n,attrs:{shape:[-1,A]}})}),c=u.map(m=>({vals:n.data.get(m.dataId).values,shape:m.shape}));o=$.computeOutShape(u.map(m=>m.shape),1);let d=u[0].shape[0]===1,h=Q2(c,o,t[0].dtype,d),p=$.computeOutShape(i.map(m=>m.shape),a),f=n.makeTensorInfo(p,t[0].dtype,h);return u.forEach(m=>n.disposeIntermediateTensorInfo(m)),f}var yV={kernelName:wi,backendName:"cpu",kernelFunc:Hl};function fw(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a}=t,{strides:o,pad:i,dataFormat:l,dilations:u,dimRoundingMode:c}=s;ke([r,a],"conv2d");let d=$.convertConv2DDataFormat(l),h=$.computeConv2DInfo(r.shape,a.shape,o,u,i,c,!1,d),p=h.filterHeight,f=h.filterWidth,m=h.dilationHeight,A=h.dilationWidth,g=h.padInfo.left,y=h.padInfo.top,x=h.dataFormat==="channelsLast",b=new Bt(h.outShape,r.dtype),v=I.computeStrides(r.shape),k=I.computeStrides(a.shape),w=v[0],C=x?v[1]:v[2],E=x?v[2]:1,M=x?1:v[1],R=b.strides[0],_=x?b.strides[1]:b.strides[2],N=x?b.strides[2]:1,O=x?1:b.strides[1],W=n.data.get(r.dataId).values,j=n.data.get(a.dataId).values,q=b.values;for(let X=0;X<h.batchSize;++X){let Q=X*w,ne=X*R;for(let te=0;te<h.outHeight;++te){let se=ne+te*_,J=te*h.strideHeight-y;for(let ie=0;ie<p;++ie){let le=J+ie*m;if(le<0||le>=h.inHeight)continue;let he=ie*k[0],ge=Q+le*C;for(let Ce=0;Ce<h.outWidth;++Ce){let Te=se+Ce*N,$e=Ce*h.strideWidth-g;for(let Me=0;Me<f;++Me){let De=$e+Me*A;if(De<0||De>=h.inWidth)continue;let it=he+Me*k[1],et=ge+De*E,tt=it;for(let Je=0;Je<h.inChannels;++Je){let at=W[et+Je*M];for(let Ve=0;Ve<h.outChannels;++Ve)q[Te+Ve*O]+=at*j[tt+Ve];tt+=h.outChannels}}}}}}return n.makeTensorInfo(b.shape,b.dtype,q)}var xV={kernelName:ka,backendName:"cpu",kernelFunc:fw};function bV(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,dy:a}=t,{strides:o,pad:i,dataFormat:l,dimRoundingMode:u,filterShape:c}=s;ke([r,a],"conv2dBackpropFilter");let d=$.convertConv2DDataFormat(l),h=$.computeConv2DInfo(r.shape,c,o,1,i,u,!1,d),{strideHeight:p,strideWidth:f,filterHeight:m,filterWidth:A}=h,g=h.dataFormat==="channelsLast",y=new Bt(h.filterShape,"float32"),x=h.padInfo.left,b=h.padInfo.top,v=n.data.get(r.dataId).values,k=n.data.get(a.dataId).values,w=new Bt(r.shape,r.dtype,v),C=new Bt(a.shape,a.dtype,k);for(let E=0;E<m;++E){let M=Math.max(0,Math.ceil((b-E)/p)),R=Math.min(h.outHeight,(h.inHeight+b-E)/p);for(let _=0;_<A;++_){let N=Math.max(0,Math.ceil((x-_)/f)),O=Math.min(h.outWidth,(h.inWidth+x-_)/f);for(let W=0;W<h.inChannels;++W)for(let j=0;j<h.outChannels;++j){let q=0;for(let X=0;X<h.batchSize;++X)for(let Q=M;Q<R;++Q){let ne=E+Q*p-b;for(let te=N;te<O;++te){let se=_+te*f-x;g?q+=w.get(X,ne,se,W)*C.get(X,Q,te,j):q+=w.get(X,W,ne,se)*C.get(X,j,Q,te)}}y.set(q,E,_,W,j)}}}return n.makeTensorInfo(y.shape,y.dtype,y.values)}var vV={kernelName:Vd,backendName:"cpu",kernelFunc:bV};function wV(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,filter:a}=t,{inputShape:o,strides:i,pad:l,dataFormat:u,dimRoundingMode:c}=s;ke([r,a],"conv2dBackpropInput");let d=I.computeStrides(a.shape),h=I.computeStrides(r.shape),p=$.convertConv2DDataFormat(u),f=$.computeConv2DInfo(o,a.shape,i,1,l,c,!1,p),m=new Bt(f.inShape,"float32"),A=m.values,g=n.data.get(r.dataId).values,y=n.data.get(a.dataId).values,[x,b,v]=d,{batchSize:k,filterHeight:w,filterWidth:C,inChannels:E,inHeight:M,inWidth:R,outChannels:_,outHeight:N,outWidth:O,strideHeight:W,strideWidth:j}=f;p=f.dataFormat;let q=w-1-f.padInfo.top,X=C-1-f.padInfo.left,Q=p==="channelsLast",ne=m.strides[0],te=Q?m.strides[1]:m.strides[2],se=Q?m.strides[2]:1,J=Q?1:m.strides[1],ie=h[0],le=Q?h[1]:h[2],he=Q?h[2]:1,ge=Q?1:h[1];for(let Ce=0;Ce<k;++Ce)for(let Te=0;Te<E;++Te)for(let $e=0;$e<M;++$e){let Me=$e-q,De=Math.max(0,Math.ceil(Me/W)),it=Math.min(N,(w+Me)/W);for(let et=0;et<R;++et){let tt=et-X,Je=Math.max(0,Math.ceil(tt/j)),at=Math.min(O,(C+tt)/j),Ve=0;for(let kt=De;kt<it;++kt){let Mn=kt*W-Me;for(let Qt=Je;Qt<at;++Qt){let gs=Qt*j-tt,cn=ie*Ce+le*kt+he*Qt,Yn=x*(w-1-Mn)+b*(C-1-gs)+v*Te;for(let Jn=0;Jn<_;++Jn){let en=g[cn+ge*Jn],Qn=y[Yn+Jn];Ve+=en*Qn}}}let In=ne*Ce+te*$e+se*et+J*Te;A[In]=Ve}}return n.makeTensorInfo(m.shape,m.dtype,m.values)}var kV={kernelName:Ia,backendName:"cpu",kernelFunc:wV};function IV(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a}=t,{strides:o,pad:i,dilations:l}=s;ke([r,a],"conv3d");let u=$.computeConv3DInfo(r.shape,a.shape,o,l,i),{filterDepth:c,filterHeight:d,filterWidth:h,dilationDepth:p,dilationHeight:f,dilationWidth:m,padInfo:A}=u,g=A.front,y=A.left,x=A.top,b=new Bt(u.outShape,r.dtype),v=n.data.get(r.dataId).values,k=n.data.get(a.dataId).values,w=b.values,C=I.computeStrides(r.shape),E=I.computeStrides(a.shape);for(let M=0;M<u.batchSize;++M){let R=M*C[0],_=M*b.strides[0];for(let N=0;N<u.outDepth;++N){let O=_+N*b.strides[1],W=N*u.strideDepth-g;for(let j=0;j<c;++j){let q=W+j*p;if(q<0||q>=u.inDepth)continue;let X=j*E[0],Q=R+q*C[1];for(let ne=0;ne<u.outHeight;++ne){let te=O+ne*b.strides[2],se=ne*u.strideHeight-x;for(let J=0;J<d;++J){let ie=se+J*f;if(ie<0||ie>=u.inHeight)continue;let le=X+J*E[1],he=Q+ie*C[2];for(let ge=0;ge<u.outWidth;++ge){let Ce=te+ge*u.outChannels,Te=ge*u.strideWidth-y;for(let $e=0;$e<h;++$e){let Me=Te+$e*m;if(Me<0||Me>=u.inWidth)continue;let De=le+$e*E[2],it=he+Me*u.inChannels,et=De;for(let tt=0;tt<u.inChannels;++tt){let Je=v[it+tt];for(let at=0;at<u.outChannels;++at)w[Ce+at]+=Je*k[et+at];et+=u.outChannels}}}}}}}}return n.makeTensorInfo(b.shape,b.dtype,b.values)}var SV={kernelName:Eu,backendName:"cpu",kernelFunc:IV};function CV(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,dy:a}=t,{strides:o,pad:i,filterShape:l}=s;ke([r,a],"conv3dBackpropFilterV2");let u=I.computeStrides(r.shape),c=I.computeStrides(a.shape),d=$.computeConv3DInfo(r.shape,l,o,1,i),h=d.strideDepth,p=d.strideHeight,f=d.strideWidth,m=d.filterDepth,A=d.filterHeight,g=d.filterWidth,y=new Bt(d.filterShape,"float32"),x=y.values,[b,v,k,w]=y.strides,C=n.data.get(a.dataId).values,[E,M,R,_]=c,N=n.data.get(r.dataId).values,[O,W,j,q]=u,X=d.padInfo.front,Q=d.padInfo.left,ne=d.padInfo.top;for(let te=0;te<m;++te){let se=Math.max(0,Math.ceil((X-te)/h)),J=Math.min(d.outDepth,(d.inDepth+X-te)/h),ie=te*b;for(let le=0;le<A;++le){let he=Math.max(0,Math.ceil((ne-le)/p)),ge=Math.min(d.outHeight,(d.inHeight+ne-le)/p),Ce=le*v+ie;for(let Te=0;Te<g;++Te){let $e=Math.max(0,Math.ceil((Q-Te)/f)),Me=Math.min(d.outWidth,(d.inWidth+Q-Te)/f),De=Te*k+Ce;for(let it=0;it<d.inChannels;++it){let et=it*w+De;for(let tt=0;tt<d.outChannels;++tt){let Je=0;for(let at=0;at<d.batchSize;++at){let Ve=at*O,In=at*E;for(let kt=se;kt<J;++kt){let Qt=(te+kt*h-X)*W+Ve,gs=kt*M+In;for(let cn=he;cn<ge;++cn){let Jn=(le+cn*p-ne)*j+Qt,en=cn*R+gs;for(let Qn=$e;Qn<Me;++Qn){let zn=(Te+Qn*f-Q)*q+Jn,Hs=Qn*_+en;Je+=N[zn+it]*C[Hs+tt]}}}}x[et+tt]=Je}}}}}return n.makeTensorInfo(y.shape,y.dtype,y.values)}var TV={kernelName:Ud,backendName:"cpu",kernelFunc:CV};function NV(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,filter:a}=t,{pad:o,strides:i,inputShape:l}=s;ke([r],"conv3dBackpropInputV2");let u=I.computeStrides(r.shape),c=I.computeStrides(a.shape),d=$.computeConv3DInfo(l,a.shape,i,1,o),h=new Bt(d.inShape,"float32"),p=h.values,[f,m,A,g]=h.strides,y=n.data.get(r.dataId).values,[x,b,v,k]=u,w=n.data.get(a.dataId).values,[C,E,M,R]=c,{batchSize:_,filterDepth:N,filterHeight:O,filterWidth:W,inChannels:j,inDepth:q,inHeight:X,inWidth:Q,outChannels:ne,outDepth:te,outHeight:se,outWidth:J,strideDepth:ie,strideHeight:le,strideWidth:he}=d,ge=N-1-d.padInfo.front,Ce=O-1-d.padInfo.top,Te=W-1-d.padInfo.left;for(let $e=0;$e<_;++$e)for(let Me=0;Me<j;++Me)for(let De=0;De<q;++De){let it=De-ge,et=Math.max(0,Math.ceil(it/ie)),tt=Math.min(te,(N+it)/ie);for(let Je=0;Je<X;++Je){let at=Je-Ce,Ve=Math.max(0,Math.ceil(at/le)),In=Math.min(se,(O+at)/le);for(let kt=0;kt<Q;++kt){let Mn=kt-Te,Qt=Math.max(0,Math.ceil(Mn/he)),gs=Math.min(J,(W+Mn)/he),cn=0;for(let Yn=et;Yn<tt;++Yn){let Jn=Yn*ie-it;for(let en=Ve;en<In;++en){let Qn=en*le-at;for(let es=Qt;es<gs;++es){let zn=es*he-Mn,Hs=x*$e+b*Yn+v*en+k*es,ur=C*(N-1-Jn)+E*(O-1-Qn)+M*(W-1-zn)+R*Me;for(let Cr=0;Cr<ne;++Cr){let Yo=y[Hs+Cr],Gs=w[ur+Cr];cn+=Yo*Gs}}}}p[f*$e+m*De+A*Je+g*kt+Me]=cn}}}return n.makeTensorInfo(h.shape,h.dtype,h.values)}var EV={kernelName:Hd,backendName:"cpu",kernelFunc:NV},RV=rt(Sa,e=>Math.cos(e)),_V={kernelName:Sa,backendName:"cpu",kernelFunc:RV},$V=rt(Ca,e=>Math.cosh(e)),FV={kernelName:Ca,backendName:"cpu",kernelFunc:$V};function DV(e){let{inputs:t,backend:n,attrs:s}=e,{image:r,boxes:a,boxInd:o}=t,{cropSize:i,method:l,extrapolationValue:u}=s,[c,d,h,p]=r.shape,f=a.shape[0],[m,A]=i,g=Be([f,m,A,p],"float32"),y=n.data.get(a.dataId).values,x=n.data.get(o.dataId).values,b=n.data.get(r.dataId).values,v=I.computeStrides(r.shape),k=I.computeStrides(g.shape);for(let w=0;w<f;w++){let C=w*4,E=y[C],M=y[C+1],R=y[C+2],_=y[C+3],N=x[w];if(N>=c)continue;let O=m>1?(R-E)*(d-1)/(m-1):0,W=A>1?(_-M)*(h-1)/(A-1):0;for(let j=0;j<m;j++){let q=m>1?E*(d-1)+j*O:.5*(E+R)*(d-1);if(q<0||q>d-1){for(let X=0;X<A;X++)for(let Q=0;Q<p;Q++){let ne=Q+X*k[2]+j*k[1]+w*k[0];g.values[ne]=u}continue}if(l==="bilinear"){let X=Math.floor(q),Q=Math.ceil(q),ne=q-X;for(let te=0;te<A;te++){let se=A>1?M*(h-1)+te*W:.5*(M+_)*(h-1);if(se<0||se>h-1){for(let he=0;he<p;he++){let ge=he+te*k[2]+j*k[1]+w*k[0];g.values[ge]=u}continue}let J=Math.floor(se),ie=Math.ceil(se),le=se-J;for(let he=0;he<p;he++){let ge=he+J*v[2]+X*v[1]+N*v[0],Ce=b[ge];ge=he+ie*v[2]+X*v[1]+N*v[0];let Te=b[ge];ge=he+J*v[2]+Q*v[1]+N*v[0];let $e=b[ge];ge=he+ie*v[2]+Q*v[1]+N*v[0];let Me=b[ge],De=Ce+(Te-Ce)*le,it=$e+(Me-$e)*le;ge=he+te*k[2]+j*k[1]+w*k[0],g.values[ge]=De+(it-De)*ne}}}else for(let X=0;X<A;++X){let Q=A>1?M*(h-1)+X*W:.5*(M+_)*(h-1);if(Q<0||Q>h-1){for(let se=0;se<p;se++){let J=se+X*k[2]+j*k[1]+w*k[0];g.values[J]=u}continue}let ne=Math.round(Q),te=Math.round(q);for(let se=0;se<p;se++){let J=se+ne*v[2]+te*v[1]+N*v[0],ie=se+X*k[2]+j*k[1]+w*k[0];g.values[ie]=b[J]}}}}return n.makeTensorInfo(g.shape,g.dtype,g.values)}var OV={kernelName:ki,backendName:"cpu",kernelFunc:DV};function PV(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,exclusive:o,reverse:i}=s;ke(r,"cumsum");let l=$.getAxesPermutation([a],r.shape.length),u=r;l!=null&&(u=ps({inputs:{x:r},backend:n,attrs:{perm:l}}));let c=$.getInnerMostAxes(1,r.shape.length)[0];if(c!==u.shape.length-1)throw new Error(`backend.cumsum in CPU expects an inner-most axis=${u.shape.length-1} but got axis=${c}`);let d=bs(u.dtype,"int32"),h=I.makeZerosTypedArray(I.sizeFromShape(u.shape),d),p=n.data.get(u.dataId).values,f=u.shape[u.shape.length-1],m=i?(g,y)=>g+f-y-1:(g,y)=>g+y;for(let g=0;g<p.length;g+=f)for(let y=0;y<f;y++){let x=m(g,y);if(y===0)h[x]=o?0:p[x];else{let b=m(g,y-1);h[x]=o?p[b]+h[b]:p[x]+h[b]}}let A=n.makeTensorInfo(u.shape,d,h);if(l!=null){let g=$.getUndoAxesPermutation(l),y=ps({inputs:{x:A},backend:n,attrs:{perm:g}});return n.disposeIntermediateTensorInfo(A),n.disposeIntermediateTensorInfo(u),y}return A}var MV={kernelName:Ta,backendName:"cpu",kernelFunc:PV};function zV(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,weights:a}=t,{size:o,binaryOutput:i}=s;if(r.shape.length===1){let l=n.data.get(r.dataId).values,u=n.data.get(a.dataId).values,c=J2(l,u,a.dtype,a.shape,o);return n.makeTensorInfo([o],a.dtype,c)}else if(r.shape.length===2){let l=n.bufferSync(r),u=n.bufferSync(a),c=I7(l,u,o,i);return n.makeTensorInfo(c.shape,a.dtype,c.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${r.shape.length}.`)}var LV={kernelName:Gd,backendName:"cpu",kernelFunc:zV};function BV(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockSize:a,dataFormat:o}=s;I.assert(o==="NHWC",()=>`Only NHWC dataFormat supported on CPU for depthToSpace. Got ${o}`),I.assert(a>1,()=>`blockSize should be > 1 for depthToSpace, but was: ${a}`);let i=r.shape[0],l=r.shape[1],u=r.shape[2],c=r.shape[3],d=l*a,h=u*a,p=c/(a*a),f=n.data.get(r.dataId).values,m=new Float32Array(i*d*h*p),A=0;for(let g=0;g<i;++g)for(let y=0;y<d;++y){let x=Math.floor(y/a),b=y%a;for(let v=0;v<h;++v){let k=Math.floor(v/a),w=v%a,C=(b*a+w)*p;for(let E=0;E<p;++E){let R=E+C+c*(k+u*(x+l*g));m[A++]=f[R]}}}return n.makeTensorInfo([i,d,h,p],r.dtype,m)}var WV={kernelName:Ii,backendName:"cpu",kernelFunc:BV};function mw(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a}=t,{strides:o,pad:i,dilations:l,dimRoundingMode:u}=s;ke([r,a],"depthwiseConv2DNative");let c=I.computeStrides(r.shape),d=I.computeStrides(a.shape),h=l;h==null&&(h=[1,1]),I.assert($.eitherStridesOrDilationsAreOne(o,h),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${o} and dilations '${h}'`);let p=$.computeConv2DInfo(r.shape,a.shape,o,h,i,u,!0),{filterHeight:f,filterWidth:m,dilationHeight:A,dilationWidth:g,padInfo:y}=p,x=y.left,b=y.top,v=p.outChannels/p.inChannels,k=new Bt(p.outShape,r.dtype),w=n.data.get(r.dataId).values,C=n.data.get(a.dataId).values,E=k.values;for(let M=0;M<p.batchSize;++M){let R=M*c[0],_=M*k.strides[0];for(let N=0;N<p.outHeight;++N){let O=_+N*k.strides[1],W=N*p.strideHeight-b;for(let j=0;j<f;++j){let q=W+j*A;if(q<0||q>=p.inHeight)continue;let X=j*d[0],Q=R+q*c[1];for(let ne=0;ne<p.outWidth;++ne){let te=O+ne*k.strides[2],se=ne*p.strideWidth-x;for(let J=0;J<m;++J){let ie=se+J*g;if(ie<0||ie>=p.inWidth)continue;let le=X+J*d[1],he=Q+ie*p.inChannels,ge=te,Ce=le;for(let Te=0;Te<p.inChannels;++Te){let $e=w[he+Te];for(let Me=0;Me<v;++Me)E[ge+Me]+=$e*C[Ce+Me];ge+=v,Ce+=v}}}}}}return n.makeTensorInfo(k.shape,k.dtype,k.values)}var VV={kernelName:Na,backendName:"cpu",kernelFunc:mw};function UV(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,dy:a}=t,{strides:o,dilations:i,pad:l,dimRoundingMode:u,filterShape:c}=s;ke([r,a],"depthwiseConv2dNativeBackpropFilter");let d=$.computeConv2DInfo(r.shape,c,o,i,l,u,!0),{strideHeight:h,strideWidth:p,filterHeight:f,filterWidth:m}=d,A=new Bt(d.filterShape,"float32"),g=d.padInfo.left,y=d.padInfo.top,x=d.outChannels/d.inChannels,b=n.data.get(r.dataId).values,v=new Bt(r.shape,r.dtype,b),k=n.data.get(a.dataId).values,w=new Bt(a.shape,a.dtype,k);for(let C=0;C<f;++C){let E=Math.max(0,Math.ceil((y-C)/h)),M=Math.min(d.outHeight,(d.inHeight+y-C)/h);for(let R=0;R<m;++R){let _=Math.max(0,Math.ceil((g-R)/p)),N=Math.min(d.outWidth,(d.inWidth+g-R)/p);for(let O=0;O<d.outChannels;++O){let W=Math.trunc(O/x),j=O%x,q=0;for(let X=0;X<d.batchSize;++X)for(let Q=E;Q<M;++Q){let ne=C+Q*h-y;for(let te=_;te<N;++te){let se=R+te*p-g;q+=v.get(X,ne,se,W)*w.get(X,Q,te,O)}}A.set(q,C,R,W,j)}}}return n.makeTensorInfo(A.shape,A.dtype,A.values)}var HV={kernelName:jd,backendName:"cpu",kernelFunc:UV};function GV(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,filter:a}=t,{strides:o,dilations:i,pad:l,dimRoundingMode:u,inputShape:c}=s;ke([r,a],"depthwiseConv2DNativeBackpropInput");let d=I.computeStrides(r.shape),h=I.computeStrides(a.shape),p=$.computeConv2DInfo(c,a.shape,o,i,l,u,!0),f=new Bt(p.inShape,"float32"),m=f.values,[A,g,y]=f.strides,x=n.data.get(r.dataId).values,[b,v,k]=d,w=n.data.get(a.dataId).values,[C,E,M]=h,{batchSize:R,filterHeight:_,filterWidth:N,inChannels:O,inHeight:W,inWidth:j,outChannels:q,outHeight:X,outWidth:Q,strideHeight:ne,strideWidth:te}=p,se=_-1-p.padInfo.top,J=N-1-p.padInfo.left,ie=q/O;for(let le=0;le<R;++le)for(let he=0;he<O;++he)for(let ge=0;ge<W;++ge){let Ce=ge-se,Te=Math.max(0,Math.ceil(Ce/ne)),$e=Math.min(X,(_+Ce)/ne);for(let Me=0;Me<j;++Me){let De=Me-J,it=Math.max(0,Math.ceil(De/te)),et=Math.min(Q,(N+De)/te),tt=0;for(let Je=Te;Je<$e;++Je){let at=Je*ne-Ce;for(let Ve=it;Ve<et;++Ve){let In=Ve*te-De,kt=b*le+v*Je+k*Ve,Mn=C*(_-1-at)+E*(N-1-In)+M*he;for(let Qt=0;Qt<ie;++Qt){let gs=he*ie+Qt,cn=x[kt+gs],Yn=w[Mn+Qt];tt+=cn*Yn}}}m[A*le+g*ge+y*Me+he]=tt}}return n.makeTensorInfo(f.shape,f.dtype,f.values)}var jV={kernelName:qd,backendName:"cpu",kernelFunc:GV};function qV(e){let{inputs:t,backend:n}=e,{x:s}=t,r=I.sizeFromShape(s.shape),a=n.data.get(s.dataId).values,o=Be([r,r],s.dtype),i=o.values;for(let u=0;u<a.length;u++)i[u*r+u]=a[u];let l=[...s.shape,...s.shape];return n.makeTensorInfo(l,o.dtype,o.values)}var XV={kernelName:Xd,backendName:"cpu",kernelFunc:qV},KV={kernelName:Ru,backendName:"cpu",kernelFunc:({inputs:e,backend:t,attrs:n})=>{let{x:s,filter:r}=e,{strides:a,pad:o,dilations:i}=n,l=t,u=l.data.get(s.dataId).values,c=s.shape.length,d=l.data.get(r.dataId).values,h=r.shape.length,{batchSize:p,inHeight:f,inWidth:m,inChannels:A,outHeight:g,outWidth:y,padInfo:x,strideHeight:b,strideWidth:v,filterHeight:k,filterWidth:w,dilationHeight:C,dilationWidth:E,outShape:M}=$.computeDilation2DInfo(s.shape,r.shape,a,o,"NHWC",i),R=I.sizeFromShape(M),_=M.length,N=I.getArrayFromDType(s.dtype,R);for(let W=0;W<p;++W)for(let j=0;j<g;++j){let q=j*b-x.top;for(let X=0;X<y;++X){let Q=X*v-x.left;for(let ne=0;ne<A;++ne){let te=Number.MIN_SAFE_INTEGER;for(let J=0;J<k;++J){let ie=q+J*C;if(ie>=0&&ie<f)for(let le=0;le<w;++le){let he=Q+le*E;if(he>=0&&he<m){let ge=I.locToIndex([W,ie,he,ne],c,I.computeStrides(s.shape)),Ce=I.locToIndex([J,le,ne],h,I.computeStrides(r.shape)),Te=u[ge]+d[Ce];Te>te&&(te=Te)}}}let se=I.locToIndex([W,j,X,ne],_,I.computeStrides(M));N[se]=te}}}return{dataId:l.write(I.toTypedArray(N,s.dtype),M,s.dtype),shape:M,dtype:s.dtype}}},ZV={kernelName:Zd,backendName:"cpu",kernelFunc:({inputs:e,backend:t,attrs:n})=>{let{x:s,filter:r,dy:a}=e,{strides:o,pad:i,dilations:l}=n,u=t,c=I.toNestedArray(s.shape,u.data.get(s.dataId).values),d=I.toNestedArray(r.shape,u.data.get(r.dataId).values),{batchSize:h,inHeight:p,inWidth:f,inChannels:m,outHeight:A,outWidth:g,padInfo:y,strideHeight:x,strideWidth:b,filterHeight:v,filterWidth:k,dilationHeight:w,dilationWidth:C,outShape:E}=$.computeDilation2DInfo(s.shape,r.shape,o,i,"NHWC",l);I.assert(a.rank===E.length,()=>`Error in ${Zd}, dy must have the same rank as output ${E.length}, but got ${a.rank}`);let M=I.toNestedArray(E,u.data.get(a.dataId).values),R=I.makeZerosNestedTypedArray(r.shape,r.dtype);for(let N=0;N<h;++N)for(let O=0;O<A;++O){let W=O*x-y.top;for(let j=0;j<g;++j){let q=j*b-y.left;for(let X=0;X<m;++X){let Q=Number.MIN_SAFE_INTEGER,ne=0,te=0;for(let se=0;se<v;++se){let J=W+se*w;if(J>=0&&J<p)for(let ie=0;ie<k;++ie){let le=q+ie*C;if(le>=0&&le<f){let he=c[N][J][le][X]+d[se][ie][X];he>Q&&(Q=he,ne=se,te=ie)}}}R[ne][te][X]+=M[N][O][j][X]}}}return{dataId:u.write(I.toTypedArray(R,s.dtype),r.shape,r.dtype),shape:r.shape,dtype:r.dtype}}},YV={kernelName:Kd,backendName:"cpu",kernelFunc:({inputs:e,backend:t,attrs:n})=>{let{x:s,filter:r,dy:a}=e,{strides:o,pad:i,dilations:l}=n,u=t,c=I.toNestedArray(s.shape,u.data.get(s.dataId).values),d=I.toNestedArray(r.shape,u.data.get(r.dataId).values),{batchSize:h,inHeight:p,inWidth:f,inChannels:m,outHeight:A,outWidth:g,padInfo:y,strideHeight:x,strideWidth:b,filterHeight:v,filterWidth:k,dilationHeight:w,dilationWidth:C,outShape:E}=$.computeDilation2DInfo(s.shape,r.shape,o,i,"NHWC",l);I.assert(a.rank===E.length,()=>`Error in ${Kd}, dy must have the same rank as output ${E.length}, but got ${a.rank}`);let M=I.toNestedArray(E,u.data.get(a.dataId).values),R=I.makeZerosNestedTypedArray(s.shape,s.dtype);for(let N=0;N<h;++N)for(let O=0;O<A;++O){let W=O*x-y.top;for(let j=0;j<g;++j){let q=j*b-y.left;for(let X=0;X<m;++X){let Q=Number.MIN_SAFE_INTEGER,ne=W<0?0:W,te=q<0?0:q;for(let se=0;se<v;++se){let J=W+se*w;if(J>=0&&J<p)for(let ie=0;ie<k;++ie){let le=q+ie*C;if(le>=0&&le<f){let he=c[N][J][le][X]+d[se][ie][X];he>Q&&(Q=he,ne=J,te=le)}}}R[N][ne][te][X]+=M[N][O][j][X]}}}return{dataId:u.write(I.toTypedArray(R,s.dtype),s.shape,s.dtype),shape:s.shape,dtype:s.dtype}}};function Dc(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:o}=s;ke(r,"sum");let i;r.dtype==="bool"?i=na({inputs:{x:r},backend:n,attrs:{dtype:"int32"}}):i=rr({inputs:{x:r},backend:n});let l=i.shape.length,u=I.parseAxisParam(a,i.shape),c=$.getAxesPermutation(u,l),d=u,h=i;c!=null&&(h=ps({inputs:{x:i},backend:n,attrs:{perm:c}}),d=$.getInnerMostAxes(d.length,l)),$.assertAxesAreInnerMostDims("sum",d,h.shape.length);let[p,f]=$.computeOutAndReduceShapes(h.shape,d),m=$.upcastType(h.dtype,"int32"),A=Zp(n,p,m),g=I.sizeFromShape(f),y=n.data.get(A.dataId).values,x=n.data.get(h.dataId).values;for(let b=0;b<y.length;++b){let v=b*g,k=0;for(let w=0;w<g;++w)k+=x[v+w];y[b]=k}if(o){let b=$.expandShapeToKeepDim(A.shape,u),v=A;A=gt({inputs:{x:A},backend:n,attrs:{shape:b}}),n.disposeIntermediateTensorInfo(v)}return n.disposeIntermediateTensorInfo(i),c!=null&&n.disposeIntermediateTensorInfo(h),A}var JV={kernelName:ao,backendName:"cpu",kernelFunc:Dc};function QV(e){let{inputs:t,backend:n,attrs:s}=e,{equation:r}=s,a=t,{allDims:o,summedDims:i,idDims:l}=$.decodeEinsumEquation(r,a.length);$.checkEinsumDimSizes(o.length,l,a);let{path:u,steps:c}=$.getEinsumComputePath(i,l),d=c.length,h=null,p=o.length,f=[];for(let m=0;m<d;++m){for(let A of c[m]){let{permutationIndices:g,expandDims:y}=$.getEinsumPermutation(p,l[A]),x;$.isIdentityPermutation(g)?x=a[A]:(x=ps({inputs:{x:a[A]},backend:n,attrs:{perm:g}}),f.push(x));let b=x.shape.slice();for(let v=0;v<y.length;++v)b.splice(y[v],0,1);I.arraysEqual(x.shape,b)||(x=gt({inputs:{x},backend:n,attrs:{shape:b}}),f.push(x)),h===null?h=x:(h=Yp({inputs:{a:x,b:h},backend:n}),f.push(h))}m<d-1&&(u[m]>=0&&(h=Dc({inputs:{x:h},backend:n,attrs:{axis:u[m]-(o.length-p),keepDims:!1}}),f.push(h)),p--)}for(let m of f)m!==h&&n.disposeIntermediateTensorInfo(m);return h}var eU={kernelName:Yd,backendName:"cpu",kernelFunc:QV};function tU(e){let{inputs:t,backend:n}=e,{dy:s,y:r}=t;ke([s,r],"eluGrad");let a=new Float32Array(I.sizeFromShape(r.shape)),o=n.data.get(r.dataId).values,i=n.data.get(s.dataId).values;for(let l=0;l<o.length;++l){let u=o[l];u>=1?a[l]=i[l]:a[l]=i[l]*(u+1)}return n.makeTensorInfo(r.shape,"float32",a)}var nU={kernelName:Jd,backendName:"cpu",kernelFunc:tU},sU=$.ERF_P,rU=$.ERF_A1,aU=$.ERF_A2,oU=$.ERF_A3,iU=$.ERF_A4,lU=$.ERF_A5,uU=rt(Ci,e=>{let t=Math.sign(e),n=Math.abs(e),s=1/(1+sU*n);return t*(1-((((lU*s+iU)*s+oU)*s+aU)*s+rU)*s*Math.exp(-n*n))}),cU={kernelName:Ci,backendName:"cpu",kernelFunc:uU};function Qp(e){let{inputs:t,backend:n,attrs:s}=e,{input:r}=t,{dim:a}=s,o=r.shape.length,i=r.shape.slice(),l=a;return a<0&&(I.assert(-(o+1)<=a,()=>`Axis must be in the interval [${-(o+1)}, ${o}]`),l=o+a+1),i.splice(l,0,1),gt({inputs:{x:r},backend:n,attrs:{shape:i}})}var dU={kernelName:Ni,backendName:"cpu",kernelFunc:Qp},hU=Mt((e,t)=>e/t),i1=Jt(Ea,hU),l1={kernelName:Ea,backendName:"cpu",kernelFunc:i1};function Aw(e,t,n){let s=e.shape,r=s[0],a=s[1],o=n.data.get(e.dataId),i=o.complexTensorInfos.real,l=o.complexTensorInfos.imag,u=[r,a],c=I.sizeFromShape(u),d=I.getTypedArrayFromDType("float32",c),h=I.getTypedArrayFromDType("float32",c);for(let A=0;A<r;A++){let g=Lo({inputs:{x:i},backend:n,attrs:{begin:[A,0],size:[1,a]}}),y=Lo({inputs:{x:l},backend:n,attrs:{begin:[A,0],size:[1,a]}}),x=jn({inputs:{real:g,imag:y},backend:n}),{real:b,imag:v}=pU(x,t,n),k=$.mergeRealAndImagArrays(b,v);for(let w=0;w<a;w++){let C=$.getComplexWithIndex(k,w);d[A*a+w]=C.real,h[A*a+w]=C.imag}n.disposeIntermediateTensorInfo(g),n.disposeIntermediateTensorInfo(y),n.disposeIntermediateTensorInfo(x)}let p=n.makeTensorInfo(u,"float32",d),f=n.makeTensorInfo(u,"float32",h),m=jn({inputs:{real:p,imag:f},backend:n});return n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(f),m}function pU(e,t,n){let s=I.sizeFromShape(e.shape),r=n.data.get(e.dataId),a=n.data.get(r.complexTensorInfos.real.dataId).values,o=n.data.get(r.complexTensorInfos.imag.dataId).values;if(fU(s)){let i=u1(a,o,s,t,n),l=[e.shape[0],e.shape[1]];if(t){let u=n.makeTensorInfo(l,"float32",i.real),c=n.makeTensorInfo(l,"float32",i.imag),d=n.makeTensorInfo([],"float32",I.createScalarValue(s,"float32")),h=rr({inputs:{x:d},backend:n}),p=l1.kernelFunc({inputs:{a:u,b:d},backend:n}),f=l1.kernelFunc({inputs:{a:c,b:h},backend:n}),m=n.data.get(p.dataId).values,A=n.data.get(f.dataId).values;return n.disposeIntermediateTensorInfo(u),n.disposeIntermediateTensorInfo(c),n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(f),{real:m,imag:A}}return i}else{let i=$.mergeRealAndImagArrays(a,o),l=mU(i,s,t);return $.splitRealAndImagArrays(l)}}function fU(e){return(e&e-1)==0}function u1(e,t,n,s,r){if(n===1)return{real:e,imag:t};let a=$.mergeRealAndImagArrays(e,t),o=n/2,i=$.complexWithEvenIndex(a),l=i.real,u=i.imag,c=[l.length],d=r.makeTensorInfo(c,"float32",l),h=r.makeTensorInfo(c,"float32",u),p=jn({inputs:{real:d,imag:h},backend:r}),f=$.complexWithOddIndex(a),m=f.real,A=f.imag,g=[m.length],y=r.makeTensorInfo(g,"float32",m),x=r.makeTensorInfo(g,"float32",A),b=jn({inputs:{real:y,imag:x},backend:r}),v=u1(l,u,o,s,r),k=v.real,w=v.imag,C=[k.length],E=r.makeTensorInfo(C,"float32",k),M=r.makeTensorInfo(C,"float32",w),R=jn({inputs:{real:E,imag:M},backend:r}),_=u1(m,A,o,s,r),N=_.real,O=_.imag,W=[N.length],j=r.makeTensorInfo(W,"float32",N),q=r.makeTensorInfo(W,"float32",O),X=jn({inputs:{real:j,imag:q},backend:r}),Q=$.exponents(n,s),ne=[Q.real.length],te=r.makeTensorInfo(ne,"float32",Q.real),se=r.makeTensorInfo(ne,"float32",Q.imag),J=jn({inputs:{real:te,imag:se},backend:r}),ie=Yp({inputs:{a:J,b:X},backend:r}),le=$c({inputs:{a:R,b:ie},backend:r}),he=r1({inputs:{a:R,b:ie},backend:r}),ge=zo({inputs:{input:le},backend:r}),Ce=zo({inputs:{input:he},backend:r}),Te=Ul({inputs:{input:le},backend:r}),$e=Ul({inputs:{input:he},backend:r}),Me=Hl({inputs:[ge,Ce],backend:r,attrs:{axis:0}}),De=Hl({inputs:[Te,$e],backend:r,attrs:{axis:0}}),it=r.data.get(Me.dataId).values,et=r.data.get(De.dataId).values;return r.disposeIntermediateTensorInfo(d),r.disposeIntermediateTensorInfo(h),r.disposeIntermediateTensorInfo(p),r.disposeIntermediateTensorInfo(y),r.disposeIntermediateTensorInfo(x),r.disposeIntermediateTensorInfo(b),r.disposeIntermediateTensorInfo(E),r.disposeIntermediateTensorInfo(M),r.disposeIntermediateTensorInfo(R),r.disposeIntermediateTensorInfo(j),r.disposeIntermediateTensorInfo(q),r.disposeIntermediateTensorInfo(X),r.disposeIntermediateTensorInfo(te),r.disposeIntermediateTensorInfo(se),r.disposeIntermediateTensorInfo(J),r.disposeIntermediateTensorInfo(ie),r.disposeIntermediateTensorInfo(le),r.disposeIntermediateTensorInfo(he),r.disposeIntermediateTensorInfo(ge),r.disposeIntermediateTensorInfo(Te),r.disposeIntermediateTensorInfo(Ce),r.disposeIntermediateTensorInfo($e),r.disposeIntermediateTensorInfo(Me),r.disposeIntermediateTensorInfo(De),{real:it,imag:et}}function mU(e,t,n){let s=new Float32Array(t*2);for(let r=0;r<t;r++){let a=0,o=0;for(let i=0;i<t;i++){let l=$.exponent(r*i,t,n),u=$.getComplexWithIndex(e,i);a+=u.real*l.real-u.imag*l.imag,o+=u.real*l.imag+u.imag*l.real}n&&(a/=t,o/=t),$.assignToTypedArray(s,a,o,r)}return s}function AU(e){let{inputs:t,backend:n}=e,{input:s}=t,r=I.sizeFromShape(s.shape),a=s.shape[s.shape.length-1],o=r/a,i=gt({inputs:{x:s},backend:n,attrs:{shape:[o,a]}}),l=Aw(i,!1,n),u=gt({inputs:{x:l},backend:n,attrs:{shape:s.shape}});return n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(l),u}var gU={kernelName:Qd,backendName:"cpu",kernelFunc:AU};function c1(e){let{backend:t,attrs:n}=e,{shape:s,value:r,dtype:a}=n,o=a||I.inferDtype(r),i=I.getArrayFromDType(o,I.sizeFromShape(s));return xU(i,r,o),t.makeTensorInfo(s,o,i)}var yU={kernelName:_u,backendName:"cpu",kernelFunc:c1};function xU(e,t,n){e.fill(t)}var bU={kernelName:Ri,backendName:"cpu",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{image:s}=e,r=n,a=I.getTypedArrayFromDType(s.dtype,I.sizeFromShape(s.shape)),[o,i,l,u]=s.shape,c=r.data.get(s.dataId).values;for(let h=0;h<o;h++){let p=h*l*i*u;for(let f=0;f<i;f++){let m=f*(l*u);for(let A=0;A<l;A++){let g=A*u;for(let y=0;y<u;y++){let x=Math.round(l-A-1),b=p+m+g+y,v=c[b];if(x>=0&&x<l){let k=x*u,w=p+m+k+y;v=c[w]}a[b]=v}}}}return{dataId:r.write(a,s.shape,s.dtype),shape:s.shape,dtype:s.dtype}}},vU=Mt((e,t)=>Math.floor(e/t)),wU=Jt($a,vU,null,"int32"),kU={kernelName:$a,backendName:"cpu",kernelFunc:wU};function IU(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a,bias:o,preluActivationWeights:i}=t,{strides:l,pad:u,dataFormat:c,dilations:d,dimRoundingMode:h,activation:p,leakyreluAlpha:f}=s,m=fw({inputs:{x:r,filter:a},backend:n,attrs:{strides:l,pad:u,dataFormat:c,dilations:d,dimRoundingMode:h}});if(o){let A=m;m=$c({inputs:{a:m,b:o},backend:n}),n.disposeIntermediateTensorInfo(A)}if(p){let A=m;m=a1(n,m,p,i,f),n.disposeIntermediateTensorInfo(A)}return m}var SU={kernelName:fo,backendName:"cpu",kernelFunc:IU};function CU(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a,bias:o,preluActivationWeights:i}=t,{strides:l,pad:u,dataFormat:c,dilations:d,dimRoundingMode:h,activation:p,leakyreluAlpha:f}=s,m=mw({inputs:{x:r,filter:a},backend:n,attrs:{strides:l,pad:u,dataFormat:c,dilations:d,dimRoundingMode:h}});if(o){let A=m;m=$c({inputs:{a:m,b:o},backend:n}),n.disposeIntermediateTensorInfo(A)}if(p){let A=m;m=a1(n,m,p,i,f),n.disposeIntermediateTensorInfo(A)}return m}var TU={kernelName:mo,backendName:"cpu",kernelFunc:CU};function NU(e){let{inputs:t,backend:n}=e,{params:s,indices:r}=t,a=I.sizeFromShape(s.shape),o=r.shape,i=o[o.length-1],[l,u,c,d]=$.prepareAndValidate(s,r);if(u===0)return n.makeTensorInfo(l,s.dtype,[]);let h=n.data.get(r.dataId).values,p=n.bufferSync(s),f=$7(h,p,s.dtype,u,i,c,d,s.shape,a);return n.makeTensorInfo(l,s.dtype,f.values)}var EU={kernelName:$i,backendName:"cpu",kernelFunc:NU};function RU(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,indices:a}=t,{axis:o,batchDims:i}=s;ke([r,a],"gatherV2");let l=i;i==null&&(l=0);let u=I.sizeFromShape(a.shape),c=I.parseAxisParam(o,r.shape)[0],d=$.segment_util.collectGatherOpShapeInfo(r,a,c,l),h=gt({inputs:{x:r},backend:n,attrs:{shape:[d.batchSize,d.outerSize,d.dimSize,d.sliceSize]}}),p=gt({inputs:{x:a},backend:n,attrs:{shape:[d.batchSize,u/d.batchSize]}}),f=[d.batchSize,d.outerSize,u/d.batchSize,d.sliceSize],m=n.bufferSync(p),A=n.bufferSync(h),g=F7(A,m,f);return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(p),n.makeTensorInfo(d.outputShape,g.dtype,g.values)}var _U={kernelName:_i,backendName:"cpu",kernelFunc:RU};function $U(e){let{inputs:t,backend:n}=e,{input:s}=t,r=I.sizeFromShape(s.shape),a=s.shape[s.shape.length-1],o=r/a,i=gt({inputs:{x:s},backend:n,attrs:{shape:[o,a]}}),l=Aw(i,!0,n),u=gt({inputs:{x:l},backend:n,attrs:{shape:s.shape}});return n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(l),u}var FU={kernelName:eh,backendName:"cpu",kernelFunc:$U},DU=rt(Di,e=>Number.isFinite(e)?1:0,"bool"),OU={kernelName:Di,backendName:"cpu",kernelFunc:DU},PU=rt(Oi,e=>Math.abs(e)===1/0?1:0,"bool"),MU={kernelName:Oi,backendName:"cpu",kernelFunc:PU},zU=rt(Pi,e=>Number.isNaN(e)?1:0,"bool"),LU={kernelName:Pi,backendName:"cpu",kernelFunc:zU};function BU(e){let{backend:t,attrs:n}=e,{start:s,stop:r,num:a}=n,o=z7(s,r,a);return t.makeTensorInfo([o.length],"float32",o)}var WU={kernelName:nh,backendName:"cpu",kernelFunc:BU},VU=rt(Li,e=>Math.log1p(e)),UU={kernelName:Li,backendName:"cpu",kernelFunc:VU},HU=Mt((e,t)=>e&&t),GU=Jt(Bi,HU,null,"bool"),jU={kernelName:Bi,backendName:"cpu",kernelFunc:GU},qU=rt($u,e=>e?0:1,"bool"),XU={kernelName:$u,backendName:"cpu",kernelFunc:qU},KU=Mt((e,t)=>e||t),ZU=Jt(Fu,KU,null,"bool"),YU={kernelName:Fu,backendName:"cpu",kernelFunc:ZU};function JU(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{depthRadius:a,bias:o,alpha:i,beta:l}=s;ke(r,"LRN");let u=r.shape[3],c=u-1,d=n.data.get(r.dataId).values,h=I.sizeFromShape(r.shape),p=new Float32Array(h);function f(m){let A=m%u,g=m-A+Math.max(0,A-a),y=m-A+Math.min(A+a,c),x=0;for(;g<=y;g++){let b=d[g];x+=b*b}return x}for(let m=0;m<h;m++){let A=f(m),g=d[m]*Math.pow(o+i*A,-l);p[m]=g}return n.makeTensorInfo(r.shape,r.dtype,p)}var QU={kernelName:Du,backendName:"cpu",kernelFunc:JU};function eH(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,y:a,dy:o}=t,{depthRadius:i,bias:l,alpha:u,beta:c}=s;ke(o,"LRNGrad");let d=I.sizeFromShape(o.shape),h=o.shape[3],p=n.data.get(o.dataId).values,f=n.data.get(r.dataId).values,m=n.data.get(a.dataId).values,A=new Float32Array(d),g=d;for(let y=0;y<g;y++){let x=y%h,b=y-x+Math.max(0,x-i),v=y-x+Math.min(h,x+i+1),k=0;for(let w=b;w<v;w++)k+=Math.pow(f[w],2);k=u*k+l;for(let w=b;w<v;w++){let C=-2*u*c*f[w]*m[y]/k;y===w&&(C+=Math.pow(k,-c)),C*=p[y],A[w]+=C}}return n.makeTensorInfo(o.shape,r.dtype,A)}var tH={kernelName:sh,backendName:"cpu",kernelFunc:eH};function gw(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{reductionIndices:a,keepDims:o}=s,i=n,l=r.shape,u=l.length,c=I.parseAxisParam(a,l),d=c,h=$.getAxesPermutation(d,u),p=i.data.get(r.dataId).values;if(h!=null){let b=new Array(u);for(let v=0;v<b.length;v++)b[v]=l[h[v]];p=t1(p,l,r.dtype,h,b),d=$.getInnerMostAxes(d.length,u),l=b}ke(r,"max"),$.assertAxesAreInnerMostDims("max",d,u);let[f,m]=$.computeOutAndReduceShapes(l,d),A=I.sizeFromShape(m),g=B7(p,A,f,r.dtype),y=i.write(g,f,r.dtype),x=f;return o&&(x=$.expandShapeToKeepDim(f,c)),{dataId:y,shape:x,dtype:r.dtype}}var nH={kernelName:za,backendName:"cpu",kernelFunc:gw};function sH(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t;ke(r,"maxPool");let{filterSize:a,strides:o,pad:i,dimRoundingMode:l}=s,u=1;I.assert($.eitherStridesOrDilationsAreOne(o,u),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${o} and dilations '${u}'`);let c=$.computePool2DInfo(r.shape,a,o,u,i,l),d;if(c.filterWidth===1&&c.filterHeight===1&&I.arraysEqual(c.inShape,c.outShape))d=rr({inputs:{x:r},backend:n});else{let h=n.data.get(r.dataId).values,p=I.computeStrides(r.shape),f=o1(h,r.shape,r.dtype,p,c,"max");d=n.makeTensorInfo(c.outShape,r.dtype,f.values)}return d}var rH={kernelName:Ba,backendName:"cpu",kernelFunc:sH};function aH(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{filterSize:a,strides:o,pad:i,dimRoundingMode:l,dataFormat:u}=s;ke(r,"maxPool3d");let c=$.computePool3DInfo(r.shape,a,o,1,i,l,u),d=n.data.get(r.dataId).values,h=pw(d,r.shape,r.dtype,I.computeStrides(r.shape),c,"max");return n.makeTensorInfo(h.shape,"float32",h.values)}var oH={kernelName:Ou,backendName:"cpu",kernelFunc:aH};function iH(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,input:a}=t,{filterSize:o,strides:i,pad:l,dimRoundingMode:u}=s;ke([r,a],"maxPool3DGrad");let c=$.computePool3DInfo(a.shape,o,i,1,l,u),d=n.bufferSync(a),h=JW(d,c),p=c.strideDepth,f=c.strideHeight,m=c.strideWidth,A=c.dilationDepth,g=c.dilationHeight,y=c.dilationWidth,x=c.effectiveFilterDepth,b=c.effectiveFilterHeight,v=c.effectiveFilterWidth,k=x-1-c.padInfo.front,w=v-1-c.padInfo.left,C=b-1-c.padInfo.top,E=Be(a.shape,"float32"),M=n.bufferSync(r);for(let R=0;R<c.batchSize;++R)for(let _=0;_<c.inChannels;++_)for(let N=0;N<c.inDepth;++N)for(let O=0;O<c.inHeight;++O)for(let W=0;W<c.inWidth;++W){let j=N-k,q=O-C,X=W-w,Q=0;for(let ne=0;ne<x;ne+=A){let te=(j+ne)/p;if(!(te<0||te>=c.outDepth||Math.floor(te)!==te))for(let se=0;se<b;se+=g){let J=(q+se)/f;if(!(J<0||J>=c.outHeight||Math.floor(J)!==J))for(let ie=0;ie<v;ie+=y){let le=(X+ie)/m;if(le<0||le>=c.outWidth||Math.floor(le)!==le)continue;let he=x*b*v-1-h.get(R,te,J,le,_),ge=ne*b*v+se*v+ie,Ce=he===ge?1:0;if(Ce===0)continue;Q+=M.get(R,te,J,le,_)*Ce}}}E.set(Q,R,N,O,W,_)}return n.makeTensorInfo(E.shape,E.dtype,E.values)}var lH={kernelName:ah,backendName:"cpu",kernelFunc:iH};function uH(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,input:a,output:o}=t,i=a;ke([a,o],"maxPoolGrad");let{filterSize:l,strides:u,pad:c,dimRoundingMode:d}=s,h=$.computePool2DInfo(i.shape,l,u,1,c,d),p=n.data.get(i.dataId).values,f=Be(h.outShape,i.dtype,hw(p,i.shape,i.dtype,h).values),m=h.strideHeight,A=h.strideWidth,g=h.dilationHeight,y=h.dilationWidth,x=h.effectiveFilterHeight,b=h.effectiveFilterWidth,v=b-1-h.padInfo.left,k=x-1-h.padInfo.top,w=Be(i.shape,"float32"),C=n.data.get(r.dataId).values,E=Be(r.shape,"float32",C);for(let M=0;M<h.batchSize;++M)for(let R=0;R<h.inChannels;++R)for(let _=0;_<h.inHeight;++_)for(let N=0;N<h.inWidth;++N){let O=_-k,W=N-v,j=0;for(let q=0;q<x;q+=g){let X=(O+q)/m;if(!(X<0||X>=h.outHeight||Math.floor(X)!==X))for(let Q=0;Q<b;Q+=y){let ne=(W+Q)/A;if(ne<0||ne>=h.outWidth||Math.floor(ne)!==ne)continue;let te=x*b-1-f.get(M,X,ne,R),se=q*b+Q,J=te===se?1:0;if(J===0)continue;j+=E.get(M,X,ne,R)*J}}w.set(j,M,_,N,R)}return n.makeTensorInfo(w.shape,w.dtype,w.values)}var cH={kernelName:rh,backendName:"cpu",kernelFunc:uH};function dH(e,t,n,s,r){let a=I.computeStrides(t),o=o1(e,t,n,a,r,"max"),i=hw(e,t,n,r,!0,s);return[o.values,i.values]}var hH={kernelName:oh,backendName:"cpu",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:s}=e,{filterSize:r,strides:a,pad:o,includeBatchInIndex:i}=t,l=n;ke(s,"MaxPoolWithArgmax");let u=l.data.get(s.dataId).values,c=$.computePool2DInfo(s.shape,r,a,[1,1],o),[d,h]=dH(u,s.shape,s.dtype,i,c),p=l.write(d,c.outShape,s.dtype),f=l.write(h,c.outShape,s.dtype);return[{dataId:p,shape:c.outShape,dtype:s.dtype},{dataId:f,shape:c.outShape,dtype:"int32"}]}};function pH(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:o}=s,i=I.parseAxisParam(a,r.shape),u=$.computeOutAndReduceShapes(r.shape,i)[1],c=I.sizeFromShape(u),d=[],h=n.makeTensorInfo([],"float32",new Float32Array([c]));d.push(h);let p=na({inputs:{x:r},backend:n,attrs:{dtype:"float32"}});d.push(p);let f=i1({inputs:{a:p,b:h},backend:n});d.push(f);let m=Dc({inputs:{x:f},backend:n,attrs:{axis:a,keepDims:o}});return d.forEach(A=>n.disposeIntermediateTensorInfo(A)),m}var fH={kernelName:Wa,backendName:"cpu",kernelFunc:pH};function mH(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:o}=s;ke(r,"min");let i=I.parseAxisParam(a,r.shape),l=i,u=$.getAxesPermutation(l,r.shape.length),c=r;u!=null&&(c=ps({inputs:{x:r},backend:n,attrs:{perm:u}}),l=$.getInnerMostAxes(l.length,r.shape.length)),$.assertAxesAreInnerMostDims("min",l,c.shape.length);let[d,h]=$.computeOutAndReduceShapes(c.shape,l),p=I.sizeFromShape(h),f=I.makeZerosTypedArray(I.sizeFromShape(d),c.dtype),m=n.data.get(c.dataId).values;for(let g=0;g<f.length;++g){let y=g*p,x=m[y];for(let b=0;b<p;++b){let v=m[y+b];(Number.isNaN(v)||v<x)&&(x=v)}f[g]=x}u!=null&&n.disposeIntermediateTensorInfo(c);let A=n.makeTensorInfo(d,c.dtype,f);if(o){let g=$.expandShapeToKeepDim(d,i),y=gt({inputs:{x:A},backend:n,attrs:{shape:g}});return n.disposeIntermediateTensorInfo(A),y}return A}var AH={kernelName:Va,backendName:"cpu",kernelFunc:mH};function gH(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{paddings:a,mode:o}=s;ke(r,"mirrorPad");let i=a.map((x,b)=>x[0]+r.shape[b]+x[1]),l=a.map(x=>x[0]),u=a.map((x,b)=>x[0]+r.shape[b]),c=o==="reflect"?0:1,d=n.data.get(r.dataId).values,h=r.shape.length,p=I.computeStrides(r.shape),f=I.sizeFromShape(i),m=i.length,A=I.computeStrides(i),g=I.getTypedArrayFromDType(r.dtype,f);for(let x=0;x<f;x++){let b=I.indexToLoc(x,m,A);for(let k=0;k<m;k++)b[k]<l[k]?b[k]=l[k]*2-b[k]-c:b[k]>=u[k]&&(b[k]=(u[k]-1)*2-b[k]+c);b=b.map((k,w)=>k-l[w]);let v=I.locToIndex(b,h,p);g[x]=d[v]}return{dataId:n.write(g,i,r.dtype),shape:i,dtype:r.dtype}}var yH={kernelName:Ha,backendName:"cpu",kernelFunc:gH},xH=Mt((e,t)=>{let n=e%t;return e<0&&t<0||e>=0&&t>=0?n:(n+t)%t}),bH=Jt(Wi,xH),vH={kernelName:Wi,backendName:"cpu",kernelFunc:bH},wH=fa(t5());function yw(e){let{inputs:t,backend:n,attrs:s}=e,{logits:r}=t,{dim:a}=s,o=r.shape.length,i=a;if(i===-1&&(i=o-1),i!==o-1)throw Error(`Softmax along a non-last dimension is not yet supported. Logits was rank ${o} and dim was ${i}`);let l=I.parseAxisParam([i],r.shape),u=gw({inputs:{x:r},backend:n,attrs:{reductionIndices:l,keepDims:!1}}),c=$.expandShapeToKeepDim(u.shape,l),d=gt({inputs:{x:u},backend:n,attrs:{shape:c}}),h=r1({inputs:{a:r,b:d},backend:n}),p=E7({inputs:{x:h},backend:n}),f=Dc({inputs:{x:p},backend:n,attrs:{axis:l,keepDims:!1}}),m=gt({inputs:{x:f},backend:n,attrs:{shape:c}}),A=i1({inputs:{a:p,b:m},backend:n});return n.disposeIntermediateTensorInfo(u),n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(m),A}var kH={kernelName:oo,backendName:"cpu",kernelFunc:yw};function IH(e){let{inputs:t,backend:n,attrs:s}=e,{logits:r}=t,{numSamples:a,seed:o,normalized:i}=s;ke(r,"multinomial");let l=i?r:yw({inputs:{logits:r},backend:n,attrs:{dim:-1}}),u=l.shape[0],c=l.shape[1],d=n.data.get(l.dataId).values,h=[u,a],p=I.makeZerosTypedArray(I.sizeFromShape(h),"int32");for(let f=0;f<u;++f){let m=f*c,A=new Float32Array(c-1);A[0]=d[m];for(let x=1;x<A.length;++x)A[x]=A[x-1]+d[m+x];let g=wH.alea(o.toString()),y=f*a;for(let x=0;x<a;++x){let b=g();p[y+x]=A.length;for(let v=0;v<A.length;v++)if(b<A[v]){p[y+x]=v;break}}}return i||n.disposeIntermediateTensorInfo(l),n.makeTensorInfo(h,"int32",p)}var SH={kernelName:ih,backendName:"cpu",kernelFunc:IH},CH=Js.nonMaxSuppressionV3Impl;function TH(e){let{inputs:t,backend:n,attrs:s}=e,{boxes:r,scores:a}=t,{maxOutputSize:o,iouThreshold:i,scoreThreshold:l}=s;ke(r,"NonMaxSuppression");let u=n.data.get(r.dataId).values,c=n.data.get(a.dataId).values,{selectedIndices:d}=CH(u,c,o,i,l);return n.makeTensorInfo([d.length],"int32",new Int32Array(d))}var NH={kernelName:Hi,backendName:"cpu",kernelFunc:TH},EH=Js.nonMaxSuppressionV4Impl;function RH(e){let{inputs:t,backend:n,attrs:s}=e,{boxes:r,scores:a}=t,{maxOutputSize:o,iouThreshold:i,scoreThreshold:l,padToMaxOutputSize:u}=s;ke(r,"NonMaxSuppressionPadded");let c=n.data.get(r.dataId).values,d=n.data.get(a.dataId).values,{selectedIndices:h,validOutputs:p}=EH(c,d,o,i,l,u);return[n.makeTensorInfo([h.length],"int32",new Int32Array(h)),n.makeTensorInfo([],"int32",new Int32Array([p]))]}var _H={kernelName:Gi,backendName:"cpu",kernelFunc:RH},$H=Js.nonMaxSuppressionV5Impl;function FH(e){let{inputs:t,backend:n,attrs:s}=e,{boxes:r,scores:a}=t,{maxOutputSize:o,iouThreshold:i,scoreThreshold:l,softNmsSigma:u}=s;ke(r,"NonMaxSuppressionWithScore");let c=n.data.get(r.dataId).values,d=n.data.get(a.dataId).values,h=o,p=i,f=l,m=u,{selectedIndices:A,selectedScores:g}=$H(c,d,h,p,f,m);return[n.makeTensorInfo([A.length],"int32",new Int32Array(A)),n.makeTensorInfo([g.length],"float32",new Float32Array(g))]}var DH={kernelName:ji,backendName:"cpu",kernelFunc:FH};function OH(e){let{inputs:t,backend:n,attrs:s}=e,{indices:r}=t,{depth:a,onValue:o,offValue:i}=s;ke(r,"oneHot");let l=I.sizeFromShape(r.shape),u=new Float32Array(l*a);u.fill(i);let c=n.data.get(r.dataId).values;for(let d=0;d<l;++d)c[d]>=0&&c[d]<a&&(u[d*a+c[d]]=o);return n.makeTensorInfo([...r.shape,a],"int32",u)}var PH={kernelName:ja,backendName:"cpu",kernelFunc:OH};function ef(e){let{inputs:t,backend:n}=e,{x:s}=t;if(s.dtype==="string")throw new Error("zerosLike is not supported for string tensors");if(s.dtype==="complex64"){let r=zo({inputs:{input:s},backend:n}),a=ef({inputs:{x:r},backend:n}),o=Ul({inputs:{input:s},backend:n}),i=ef({inputs:{x:o},backend:n}),l=jn({inputs:{real:a,imag:i},backend:n});return n.disposeIntermediateTensorInfo(r),n.disposeIntermediateTensorInfo(a),n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(i),l}else return c1({backend:n,attrs:{shape:s.shape,value:0,dtype:s.dtype}})}var MH={kernelName:dl,backendName:"cpu",kernelFunc:ef};function xw(e){let{inputs:t,backend:n}=e,{x:s}=t;if(s.dtype==="string")throw new Error("onesLike is not supported for string tensors");if(s.dtype==="complex64"){let r=zo({inputs:{input:s},backend:n}),a=xw({inputs:{x:r},backend:n}),o=Ul({inputs:{input:s},backend:n}),i=ef({inputs:{x:o},backend:n}),l=jn({inputs:{real:a,imag:i},backend:n});return n.disposeIntermediateTensorInfo(r),n.disposeIntermediateTensorInfo(a),n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(i),l}else return c1({backend:n,attrs:{shape:s.shape,value:1,dtype:s.dtype}})}var zH={kernelName:qi,backendName:"cpu",kernelFunc:xw};function bw(e){let{inputs:t,backend:n,attrs:s}=e,{axis:r}=s;if(t.length===1)return Qp({inputs:{input:t[0]},backend:n,attrs:{dim:r}});let a=t[0].shape,o=t[0].dtype;t.forEach(c=>{I.assertShapesMatch(a,c.shape,"All tensors passed to stack must have matching shapes"),I.assert(o===c.dtype,()=>"All tensors passed to stack must have matching dtypes")});let i=[],l=t.map(c=>{let d=Qp({inputs:{input:c},backend:n,attrs:{dim:r}});return i.push(d),d}),u=Hl({inputs:l,backend:n,attrs:{axis:r}});return i.forEach(c=>n.disposeIntermediateTensorInfo(c)),u}var LH={kernelName:Xi,backendName:"cpu",kernelFunc:bw};function BH(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{paddings:a,constantValue:o}=s;ke(r,"pad");let i=a.map((y,x)=>y[0]+r.shape[x]+y[1]),l=a.map(y=>y[0]),u=n.data.get(r.dataId).values,c=I.sizeFromShape(r.shape),d=r.shape.length,h=I.computeStrides(r.shape),p=I.sizeFromShape(i),f=i.length,m=I.computeStrides(i),A=I.getTypedArrayFromDType(r.dtype,p);o!==0&&A.fill(o);for(let y=0;y<c;y++){let b=I.indexToLoc(y,d,h).map((k,w)=>k+l[w]),v=I.locToIndex(b,f,m);A[v]=u[y]}return{dataId:n.write(A,i,r.dtype),shape:i,dtype:r.dtype}}var vw={kernelName:qa,backendName:"cpu",kernelFunc:BH},WH=Mt((e,t)=>Math.pow(e,t)),VH=Jt(Xa,WH),UH={kernelName:Xa,backendName:"cpu",kernelFunc:VH};function HH(e){let{backend:t,attrs:n}=e,{start:s,stop:r,dtype:a,step:o}=n,i=n1(s,r,o,a);return t.makeTensorInfo([i.length],a,i)}var GH={kernelName:Pu,backendName:"cpu",kernelFunc:HH},jH=rt(Zi,e=>1/e),qH={kernelName:Zi,backendName:"cpu",kernelFunc:jH};function XH(e){let{inputs:t,backend:n,attrs:s}=e,{images:r}=t,{alignCorners:a,halfPixelCenters:o,size:i}=s;ke(r,"resizeBilinear");let l=I.computeStrides(r.shape),[u,c]=i,[d,h,p,f]=r.shape,m=n.data.get(r.dataId).values,A=new Float32Array(I.sizeFromShape([d,u,c,f])),g=[a&&u>1?h-1:h,a&&c>1?p-1:p],y=[a&&u>1?u-1:u,a&&c>1?c-1:c],x=0,b=g[0]/y[0],v=g[1]/y[1];for(let k=0;k<d;k++)for(let w=0;w<u;w++){let C;o?C=b*(w+.5)-.5:C=b*w;let E=Math.max(0,Math.floor(C)),M=C-E,R=Math.min(h-1,Math.ceil(C)),_=k*l[0]+E*l[1],N=k*l[0]+R*l[1];for(let O=0;O<c;O++){let W;o?W=v*(O+.5)-.5:W=v*O;let j=Math.max(0,Math.floor(W)),q=W-j,X=Math.min(p-1,Math.ceil(W)),Q=_+j*l[2],ne=N+j*l[2],te=_+X*l[2],se=N+X*l[2];for(let J=0;J<f;J++){let ie=m[Q+J],le=m[ne+J],he=m[te+J],ge=m[se+J],Ce=ie+(he-ie)*q,Te=le+(ge-le)*q,$e=Ce+(Te-Ce)*M;A[x++]=$e}}}return n.makeTensorInfo([d,u,c,f],"float32",A)}var KH={kernelName:Ya,backendName:"cpu",kernelFunc:XH};function ZH(e){let{inputs:t,backend:n,attrs:s}=e,{images:r,dy:a}=t,{alignCorners:o}=s;ke([a,r],"resizeBilinearGrad");let i=I.computeStrides(r.shape),[l,u,c,d]=r.shape,[,h,p]=a.shape,f=new Float32Array(l*u*c*d),m=[o&&h>1?u-1:u,o&&p>1?c-1:c],A=[o&&h>1?h-1:h,o&&p>1?p-1:p],g=m[0]/A[0],y=m[1]/A[1],x=n.data.get(a.dataId).values,b=0;for(let v=0;v<l;v++){let k=v*i[0];for(let w=0;w<h;w++){let C=w*g,E=Math.floor(C),M=Math.min(Math.ceil(C),u-1),R=k+E*i[1],_=k+M*i[1],N=C-E,O=1-N;for(let W=0;W<p;W++){let j=W*y,q=Math.floor(j),X=Math.min(Math.ceil(j),c-1),Q=j-q,ne=1-Q,te=R+q*i[2],se=R+X*i[2],J=_+q*i[2],ie=_+X*i[2],le=O*ne,he=O*Q,ge=N*ne,Ce=N*Q;for(let Te=0;Te<d;Te++){let $e=x[b++];f[te+Te]+=$e*le,f[se+Te]+=$e*he,f[J+Te]+=$e*ge,f[ie+Te]+=$e*Ce}}}}return n.makeTensorInfo([l,c,u,d],"float32",f)}var YH={kernelName:ch,backendName:"cpu",kernelFunc:ZH};function JH(e){let{inputs:t,backend:n,attrs:s}=e,{images:r}=t,{alignCorners:a,halfPixelCenters:o,size:i}=s;ke(r,"resizeNearestNeighbor");let l=I.computeStrides(r.shape),[u,c]=i,[d,h,p,f]=r.shape,m=n.data.get(r.dataId).values,A=new Float32Array(d*u*c*f),g=[a&&u>1?h-1:h,a&&c>1?p-1:p],y=[a&&u>1?u-1:u,a&&c>1?c-1:c],x=g[0]/y[0],b=g[1]/y[1],v=0;for(let k=0;k<d;k++){let w=k*l[0];for(let C=0;C<u;C++){let E=o?x*(C+.5):x*C,M=Math.min(h-1,a?Math.round(E):Math.floor(E));o&&(M=Math.max(0,M));let R=w+M*l[1];for(let _=0;_<c;_++){let N=o?b*(_+.5):b*_,O=Math.min(p-1,a?Math.round(N):Math.floor(N));o&&(O=Math.max(0,O));let W=R+O*l[2];for(let j=0;j<f;j++){let q=m[W+j];A[v++]=q}}}}return n.makeTensorInfo([d,u,c,f],r.dtype,A)}var QH={kernelName:Mu,backendName:"cpu",kernelFunc:JH};function eG(e){let{inputs:t,backend:n,attrs:s}=e,{images:r,dy:a}=t,{alignCorners:o}=s;ke([a,r],"resizeNearestNeighborGrad");let i=I.computeStrides(r.shape),l=I.computeStrides(a.shape),[u,c,d,h]=r.shape,[,p,f]=a.shape,m=new Float32Array(u*c*d*h),A=n.data.get(a.dataId).values,g=[o&&p>1?c-1:c,o&&f>1?d-1:d],y=[o&&p>1?p-1:p,o&&f>1?f-1:f],x=g[0]/y[0],b=g[1]/y[1],v=1/x,k=1/b,w=Math.ceil(v)*2+2,C=Math.ceil(k)*2+2;for(let E=0;E<u;E++){let M=E*i[0];for(let R=0;R<c;R++){let _=M+R*i[1],N=Math.floor(R*v),O=Math.floor(N-w/2);for(let W=0;W<d;W++){let j=_+W*i[2],q=Math.floor(W*k),X=Math.floor(q-C/2);for(let Q=0;Q<h;Q++){let ne=0;for(let te=0;te<w;te++){let se=te+O;if(se<0||se>=p)continue;let J=M+se*l[1],ie=se*x,le=Math.min(c-1,o?Math.round(ie):Math.floor(ie));if(R===le)for(let he=0;he<C;he++){let ge=he+X;if(ge<0||ge>=f)continue;let Ce=J+ge*l[2],Te=ge*b,$e=Math.min(d-1,o?Math.round(Te):Math.floor(Te));W===$e&&(ne+=A[Ce+Q])}}m[j+Q]=ne}}}}return n.makeTensorInfo(r.shape,r.dtype,m)}var tG={kernelName:uh,backendName:"cpu",kernelFunc:eG};function nG(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{dims:a}=s;ke(r,"reverse");let o=r.shape.length,i=I.parseAxisParam(a,r.shape);if(o===0)return rr({inputs:{x:r},backend:n});let l=new Bt(r.shape,r.dtype),u=n.bufferSync(r);for(let c=0;c<l.size;c++){let d=l.indexToLoc(c),h=d.slice();i.forEach(p=>h[p]=r.shape[p]-1-h[p]),l.set(u.get(...h),...d)}return n.makeTensorInfo(l.shape,l.dtype,l.values)}var sG={kernelName:Qa,backendName:"cpu",kernelFunc:nG},rG={kernelName:hl,backendName:"cpu",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{image:s}=e,{radians:r,fillValue:a,center:o}=t,i=n,l=I.getTypedArrayFromDType(s.dtype,I.sizeFromShape(s.shape)),[u,c,d,h]=s.shape,[p,f]=$.getImageCenter(o,c,d),m=255,A=Math.sin(r),g=Math.cos(r),y=i.data.get(s.dataId).values;for(let b=0;b<u;b++){let v=b*d*c*h;for(let k=0;k<c;k++){let w=k*(d*h);for(let C=0;C<d;C++){let E=C*h;for(let M=0;M<h;M++){let R=[u,k,C,M],_=R[2],N=R[1],O=(_-p)*g-(N-f)*A,W=(_-p)*A+(N-f)*g;O=Math.round(O+p),W=Math.round(W+f);let j=a;if(typeof a!="number"&&(M===3?j=m:j=a[M]),O>=0&&O<d&&W>=0&&W<c){let X=W*(d*h),Q=O*h,ne=v+X+Q+M;j=y[ne]}let q=v+w+E+M;l[q]=j}}}}return{dataId:i.write(l,s.shape,s.dtype),shape:s.shape,dtype:s.dtype}}},aG=rt(eo,e=>{let t=Math.floor(e);return e-t<.5?Math.floor(e):e-t>.5?Math.ceil(e):t%2==0?t:t+1}),oG={kernelName:eo,backendName:"cpu",kernelFunc:aG};function ww(e,t,n,s,r,a,o,i,l,u){let c=[s/r,r],d=e.values,h=t.values;if(s===0)return Be(n,t.dtype);let p=Be(c,t.dtype);p.values.fill(l);for(let f=0;f<a;f++){let m=[],A=0;for(let g=0;g<o;g++){let y=d[f*o+g];m.push(y),A+=y*i[g]}if(A<0||A>=s/r)throw new Error(`Invalid indices: ${m} does not index into ${n}`);for(let g=0;g<r;g++)u?p.values[A*r+g]+=h[f*r+g]:p.values[A*r+g]=t.rank===0?h[0]:h[f*r+g]}return p}function iG(e){let{inputs:t,backend:n,attrs:s}=e,{indices:r,updates:a}=t,{shape:o}=s,{sliceRank:i,numUpdates:l,sliceSize:u,strides:c,outputSize:d}=$.calculateShapes(a,r,o),h=!0,p=n.bufferSync(r),f=n.bufferSync(a),m=ww(p,f,o,d,u,l,i,c,0,h);return n.makeTensorInfo(o,m.dtype,m.values)}var lG={kernelName:Ji,backendName:"cpu",kernelFunc:iG};function uG(e){let{inputs:t,backend:n}=e,{condition:s,t:r,e:a}=t;ke([s,r,a],"select");let o=s.shape.length,i=n.data.get(s.dataId).values,l=n.data.get(r.dataId).values,u=n.data.get(a.dataId).values,c=bs(r.dtype,a.dtype),d=I.makeZerosTypedArray(I.sizeFromShape(r.shape),c),h=0,p=o===0||o>1||r.shape.length===1?1:I.sizeFromShape(r.shape.slice(1));for(let f=0;f<i.length;f++)for(let m=0;m<p;m++)i[f]===1?d[h++]=l[f]:d[h++]=u[f];return n.makeTensorInfo(r.shape,c,d)}var cG={kernelName:Qi,backendName:"cpu",kernelFunc:uG},dG=$.SELU_SCALEALPHA,hG=$.SELU_SCALE,pG=rt(el,e=>e>=0?hG*e:dG*(Math.exp(e)-1)),fG={kernelName:el,backendName:"cpu",kernelFunc:pG},mG=rt(sl,e=>e<0?-1:e>0?1:0),AG={kernelName:sl,backendName:"cpu",kernelFunc:mG},gG=rt(no,e=>Math.sin(e)),yG={kernelName:no,backendName:"cpu",kernelFunc:gG},xG=rt(nl,e=>Math.sinh(e)),bG={kernelName:nl,backendName:"cpu",kernelFunc:xG},vG=11920928955078125e-23,kw=Math.log(vG)+2,wG=rt(rl,e=>{let t=e>-kw,n=e<kw,s=Math.exp(e),r;return n?r=s:t?r=e:r=Math.log(1+s),r}),kG={kernelName:rl,backendName:"cpu",kernelFunc:wG};function IG(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockShape:a,paddings:o}=s;ke([r],"spaceToBatchND");let i=I.sizeFromShape(a),l=[[0,0]];l.push(...o);for(let k=1+a.length;k<r.shape.length;++k)l.push([0,0]);let u=vw.kernelFunc({inputs:{x:r},backend:n,attrs:{paddings:l,constantValue:0}}),c=$.getReshaped(u.shape,a,i,!1),d=$.getPermuted(c.length,a.length,!1),h=$.getReshapedPermuted(u.shape,a,i,!1),m=gt({inputs:{x:u},backend:n,attrs:{shape:c}}),y=ps({inputs:{x:m},backend:n,attrs:{perm:d}}),v=gt({inputs:{x:y},backend:n,attrs:{shape:h}});return n.disposeIntermediateTensorInfo(u),n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(y),v}var SG={kernelName:al,backendName:"cpu",kernelFunc:IG};function CG(e){let{inputs:t,backend:n}=e,{indices:s,values:r,denseShape:a,defaultValue:o}=t;if(a.shape.length!==1)throw new Error(`Dense shape must be a vector, saw:
        ${a.shape}`);if(s.shape.length!==2)throw new Error(`Indices must be a matrix, saw:
        ${s.shape}`);if(r.shape.length!==1)throw new Error(`Values must be a vector, saw:
        ${r.shape}`);if(o.shape.length!==0)throw new Error(`Default value must be a scalar, saw:
        ${o.shape}`);let i=n.data.get(s.dataId).values,l=n.data.get(r.dataId).values,u=n.data.get(a.dataId).values,c=n.data.get(o.dataId).values[0],[d,h,p,f,m]=q7(i,s.shape,s.dtype,l,r.dtype,u,c);return[n.makeTensorInfo(h,s.dtype,d),n.makeTensorInfo([h[0]],r.dtype,p),n.makeTensorInfo([f.length],"bool",new Uint8Array(f.map(A=>Number(A)))),n.makeTensorInfo([m.length],s.dtype,new Int32Array(m))]}var TG={kernelName:dh,backendName:"cpu",kernelFunc:CG};function NG(e){let{inputs:t,backend:n}=e,{inputIndices:s,inputShape:r,newShape:a}=t;if(s.shape.length!==2)throw new Error(`Input indices should be a matrix but received shape
        ${s.shape}`);if(r.shape.length!==1)throw new Error(`Input shape should be a vector but received shape
        ${r.shape}`);if(a.shape.length!==1)throw new Error(`Target shape should be a vector but received shape ${a.shape}`);let o=Array.from(n.data.get(r.dataId).values),i=n.data.get(s.dataId).values,l=Array.from(n.data.get(a.dataId).values),[u,c,d]=X7(i,s.shape,s.dtype,o,l);return[n.makeTensorInfo(c,s.dtype,u),n.makeTensorInfo([d.length],a.dtype,new Int32Array(d))]}var EG={kernelName:hh,backendName:"cpu",kernelFunc:NG};function RG(e){let{inputs:t,backend:n}=e,{data:s,indices:r,segmentIds:a}=t;if(s.shape.length<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(r.shape.length!==1)throw new Error(`Indices should be a vector but received shape
          ${r.shape}`);if(a.shape.length!==1)throw new Error(`Segment ids should be a vector but received shape
          ${a.shape}`);let o=n.data.get(s.dataId).values,i=n.data.get(r.dataId).values,l=n.data.get(a.dataId).values,[u,c]=s1(o,s.shape,s.dtype,i,l,!0);return n.makeTensorInfo(c,s.dtype,u)}var _G={kernelName:ph,backendName:"cpu",kernelFunc:RG};function $G(e){let{inputs:t,backend:n}=e,{data:s,indices:r,segmentIds:a}=t;if(s.shape.length<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(r.shape.length!==1)throw new Error(`Indices should be a vector but received shape
         ${r.shape}`);if(a.shape.length!==1)throw new Error(`Segment ids should be a vector but received shape
         ${a.shape}`);let o=n.data.get(s.dataId).values,i=n.data.get(r.dataId).values,l=n.data.get(a.dataId).values,[u,c]=s1(o,s.shape,s.dtype,i,l);return n.makeTensorInfo(c,s.dtype,u)}var FG={kernelName:fh,backendName:"cpu",kernelFunc:$G};function DG(e){let{inputs:t,backend:n,attrs:s}=e,{sparseIndices:r,sparseValues:a,defaultValue:o}=t,{outputShape:i}=s,{sliceRank:l,numUpdates:u,sliceSize:c,strides:d,outputSize:h}=$.calculateShapes(a,r,i),p=!1,f=n.bufferSync(r),m=n.bufferSync(a),A=n.data.get(o.dataId).values[0],g=ww(f,m,i,h,c,u,l,d,A,p);return n.makeTensorInfo(i,g.dtype,g.values)}var OG={kernelName:mh,backendName:"cpu",kernelFunc:DG};function PG(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{numOrSizeSplits:a,axis:o}=s,i=I.parseAxisParam(o,r.shape)[0],l=$.prepareSplitSize(r,a,i),u=new Array(r.shape.length).fill(0),c=r.shape.slice();return l.map(d=>{let h=[...c];h[i]=d;let p=Lo({inputs:{x:r},backend:n,attrs:{begin:u,size:h}});return u[i]+=d,p})}var MG={kernelName:ol,backendName:"cpu",kernelFunc:PG},zG=rt(ro,e=>Math.sqrt(e)),LG={kernelName:ro,backendName:"cpu",kernelFunc:zG},BG={kernelName:zu,backendName:"cpu",kernelFunc:({inputs:e,backend:t})=>{let{x:n}=e,s=t;ke(n,"square");let r=s.data.get(n.dataId).values,a=new Float32Array(r.length);for(let i=0;i<r.length;++i){let l=r[i];a[i]=l*l}return{dataId:s.write(a,n.shape,n.dtype),shape:n.shape,dtype:n.dtype}}},WG=rt(Mr,(e,t)=>{let n=t;return isNaN(e)?NaN:e>0?1:n.alpha}),VG={kernelName:Mr,backendName:"cpu",kernelFunc:WG};function UG(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{begin:a,end:o,strides:i,beginMask:l,endMask:u,ellipsisMask:c,newAxisMask:d,shrinkAxisMask:h}=s;ke(r,"stridedSlice");let{nonStrided:p,$begin:f,$strides:m,size:A,newShape:g,outShape:y}=An.sliceInfo(r.shape,a,o,i,l,u,c,d,h),x=gt({inputs:{x:r},backend:n,attrs:{shape:g}}),b;if(p){let k=Lo({inputs:{x},backend:n,attrs:{begin:f,size:A}});b=gt({inputs:{x:k},backend:n,attrs:{shape:y}}),n.disposeIntermediateTensorInfo(k)}else if(y.some(k=>k===0))b=n.makeTensorInfo(y,r.dtype,[]);else{let k=n.bufferSync(x),w=Z7(y,k,m,f);b=n.makeTensorInfo(w.shape,w.dtype,w.values)}let v=gt({inputs:{x:b},backend:n,attrs:{shape:y}});return n.disposeIntermediateTensorInfo(x),n.disposeIntermediateTensorInfo(b),v}var HG={kernelName:il,backendName:"cpu",kernelFunc:UG};function GG(e){let{inputs:t,backend:n,attrs:s}=e,{separator:r,nGramWidths:a,leftPad:o,rightPad:i,padWidth:l,preserveShortSequences:u}=s,{data:c,dataSplits:d}=t,h=n.data.get(c.dataId).values,p=n.data.get(d.dataId).values,[f,m]=Y7(h,p,r,a,o,i,l,u);return[n.makeTensorInfo([f.length],"string",f),n.makeTensorInfo(d.shape,"int32",m)]}var jG={kernelName:Ah,backendName:"cpu",kernelFunc:GG};function qG(e){let{inputs:t,backend:n,attrs:s}=e,{skipEmpty:r}=s,{input:a,delimiter:o}=t;if(a.dtype!=="string")throw new Error("Input must be of datatype string");if(a.shape.length!==1)throw new Error(`Input must be a vector, got shape: ${a.shape}`);if(o.shape.length!==0)throw new Error(`Delimiter must be a scalar, got shape: ${o.shape}`);let i=n.data.get(a.dataId).values,l=n.data.get(o.dataId).values[0],[u,c,d]=J7(i,l,r),h=c.length;return[n.makeTensorInfo([h,2],"int32",u),n.makeTensorInfo([h],"string",c),n.makeTensorInfo([2],"int32",new Int32Array(d))]}var XG={kernelName:gh,backendName:"cpu",kernelFunc:qG};function KG(e){let{inputs:t,backend:n,attrs:s}=e,{numBuckets:r}=s,{input:a}=t;if(a.dtype!=="string")throw new Error("Input must be of datatype string");if(r<=0)throw new Error("Number of buckets must be at least 1");let o=n.data.get(a.dataId).values,i=Q7(o,r);return n.makeTensorInfo(a.shape,"int32",i)}var ZG={kernelName:yh,backendName:"cpu",kernelFunc:KG},YG=rt(uo,e=>Math.tan(e)),JG={kernelName:uo,backendName:"cpu",kernelFunc:YG},QG=rt(co,e=>Math.tanh(e)),ej={kernelName:co,backendName:"cpu",kernelFunc:QG};function tj(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{reps:a}=s;ke(r,"tile");let o=tw(n.bufferSync(r),a);return n.makeTensorInfo(o.shape,o.dtype,o.values)}var nj={kernelName:Pr,backendName:"cpu",kernelFunc:tj};function sj(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{k:a,sorted:o}=s;ke(r,"topk");let i=n.data.get(r.dataId).values,[l,u]=sw(i,r.shape,r.dtype,a,o);return[n.makeTensorInfo(l.shape,l.dtype,l.values),n.makeTensorInfo(u.shape,u.dtype,u.values)]}var rj={kernelName:ll,backendName:"cpu",kernelFunc:sj};function aj(e){let{inputs:t,attrs:n,backend:s}=e,{image:r,transforms:a}=t,{interpolation:o,fillMode:i,fillValue:l,outputShape:u}=n,[c,d,h,p]=r.shape,[f,m]=u!=null?u:[d,h],A=[c,f,m,p],g=I.computeStrides(r.shape),y=g[0],x=g[1],b=g[2],v=I.getTypedArrayFromDType(r.dtype,I.sizeFromShape(A));v.fill(l);let k=s.data.get(r.dataId).values,w=s.data.get(a.dataId).values;for(let E=0;E<c;++E){let M=a.shape[0]===1?w:w.subarray(E*8,E*8+8);for(let R=0;R<f;++R)for(let _=0;_<m;++_)for(let N=0;N<p;++N){let O,W=M[6]*_+M[7]*R+1;if(W===0)continue;let j=(M[0]*_+M[1]*R+M[2])/W,q=(M[3]*_+M[4]*R+M[5])/W,X=Iw(j,h,i),Q=Iw(q,d,i);switch(o){case"nearest":O=dj(k,d,h,y,x,b,E,Q,X,N,l);break;case"bilinear":O=hj(k,d,h,y,x,b,E,Q,X,N,l);break;default:throw new Error(`Error in Transform: Expect 'nearest' or 'bilinear', but got ${o}`)}let ne=E*y+R*x+_*b+N;v[ne]=O}return s.makeTensorInfo(A,r.dtype,v)}return{dataId:s.write(v,A,r.dtype),shape:r.shape,dtype:r.dtype}}var oj={kernelName:ul,backendName:"cpu",kernelFunc:aj};function Iw(e,t,n){switch(n){case"reflect":return ij(e,t);case"wrap":return lj(e,t);case"nearest":return cj(e,t);case"constant":default:return uj(e,t)}}function ij(e,t){let n=e;if(n<0)if(t<=1)n=0;else{let s=2*t;n<s&&(n=s*Math.trunc(-n/s)+n),n=n<-t?n+s:-n-1}else if(n>t-1)if(t<=1)n=0;else{let s=2*t;n-=s*Math.trunc(n/s),n>=t&&(n=s-n-1)}return I.clamp(0,n,t-1)}function lj(e,t){let n=e;if(n<0)if(t<=1)n=0;else{let s=t-1;n+=t*(Math.trunc(-n/s)+1)}else if(n>t-1)if(t<=1)n=0;else{let s=t-1;n-=t*Math.trunc(n/s)}return I.clamp(0,n,t-1)}function uj(e,t){return e}function cj(e,t){return I.clamp(0,e,t-1)}function Oc(e,t,n,s,r,a,o,i,l,u,c){let d=o*s+i*r+l*a+u;return 0<=i&&i<t&&0<=l&&l<n?e[d]:c}function dj(e,t,n,s,r,a,o,i,l,u,c){let d=Math.round(i),h=Math.round(l);return Oc(e,t,n,s,r,a,o,d,h,u,c)}function hj(e,t,n,s,r,a,o,i,l,u,c){let d=Math.floor(i),h=Math.floor(l),p=d+1,f=h+1,m=(f-l)*Oc(e,t,n,s,r,a,o,d,h,u,c)+(l-h)*Oc(e,t,n,s,r,a,o,d,f,u,c),A=(f-l)*Oc(e,t,n,s,r,a,o,p,h,u,c)+(l-h)*Oc(e,t,n,s,r,a,o,p,f,u,c);return(p-i)*m+(i-d)*A}function pj(e){let{inputs:t,attrs:n,backend:s}=e,{axis:r}=n,{x:a}=t;ke(a,"unique");let o=s.data.get(a.dataId).values,{outputValues:i,outputShape:l,indices:u}=rw(o,r,a.shape,a.dtype);return[s.makeTensorInfo(l,a.dtype,i),s.makeTensorInfo([u.length],"int32",u)]}var fj={kernelName:xh,backendName:"cpu",kernelFunc:pj};function mj(e){let{inputs:t,backend:n,attrs:s}=e,{value:r}=t,{axis:a}=s;a<0&&(a+=r.shape.length);let o=r.shape.length,i=r.shape[a],l=new Array(o-1),u=0;for(let p=0;p<o;p++)p!==a&&(l[u++]=r.shape[p]);let c=new Array(o).fill(0),d=r.shape.slice();d[a]=1;let h=new Array(i);for(let p=0;p<h.length;p++){c[a]=p;let f=Lo({inputs:{x:r},backend:n,attrs:{begin:c,size:d}});h[p]=gt({inputs:{x:f},backend:n,attrs:{shape:l}}),n.disposeIntermediateTensorInfo(f)}return h}var Aj={kernelName:cl,backendName:"cpu",kernelFunc:mj};function gj(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,segmentIds:a}=t,{numSegments:o}=s;ke(r,"unsortedSegmentSum");let i=r.shape.length,l=a.shape.length,u=[],c=[],d=i-l,h=a;for(let f=0;f<d;++f){let m=Qp({inputs:{input:h},backend:n,attrs:{dim:f+1}});h=m,c.push(m)}for(let f=0;f<o;++f){let m=I.createScalarValue(f,"int32"),A=n.makeTensorInfo([],"int32",m),g=T7({inputs:{a:A,b:h},backend:n}),y=na({inputs:{x:g},backend:n,attrs:{dtype:"float32"}}),x=Yp({inputs:{a:y,b:r},backend:n}),b=Dc({inputs:{x},backend:n,attrs:{axis:0,keepDims:!1}});u.push(b),c.push(A),c.push(g),c.push(y),c.push(x),c.push(b)}let p=bw({inputs:u,backend:n,attrs:{axis:0}});return c.forEach(f=>n.disposeIntermediateTensorInfo(f)),p}var yj={kernelName:Lu,backendName:"cpu",kernelFunc:gj},xj=[CW,wB,NW,RW,NB,$W,DW,PW,zW,BW,VW,HW,jW,KW,YW,eV,nV,rV,oV,IW,lV,cV,hV,CB,RB,fV,kB,AV,yV,vV,kV,xV,TV,EV,SV,_V,FV,OV,MV,LV,WV,VV,HV,jV,XV,KV,YV,ZV,l1,eU,AW,nU,_B,cU,$B,dU,DB,gU,yU,bU,PB,kU,SU,TU,EU,_U,zB,BB,IB,FU,gV,OU,MU,LU,gW,VB,HB,WU,jB,UU,jU,XU,YU,QU,tH,XB,rH,oH,lH,cH,hH,nH,fH,AH,ZB,yH,vH,SH,JB,eW,NH,_H,DH,nW,PH,zH,LH,vw,UH,xW,aW,GH,SB,qH,bW,vW,kW,KH,YH,QH,tG,sG,rG,oG,iW,lG,cG,fG,wW,AG,yG,bG,lW,kH,kG,SG,TG,EG,_G,FG,OG,MG,LG,BG,cW,VG,HG,jG,XG,ZG,fW,JV,JG,ej,nj,rj,sW,oj,fj,Aj,yj,MH];for(let e of xj)Ao(e);var Sw={};Pe(Sw,{assertNotComplex:()=>jl,bindCanvasToFramebuffer:()=>_j,bindColorTextureToFramebuffer:()=>sf,bindTextureToProgramUniformSampler:()=>Bw,bindTextureUnit:()=>Mw,bindVertexBufferToProgramAttribute:()=>p1,callAndCheck:()=>be,canBeRepresented:()=>Cw,createFragmentShader:()=>Ew,createFramebuffer:()=>Pw,createProgram:()=>Rw,createStaticIndexBuffer:()=>Fw,createStaticVertexBuffer:()=>$w,createTexture:()=>Dw,createVertexShader:()=>Nw,getBatchDim:()=>Wo,getExtensionOrThrow:()=>Lc,getFramebufferErrorMessage:()=>Ww,getMaxTexturesInShader:()=>Gw,getNumChannels:()=>Ej,getProgramUniformLocation:()=>Lw,getProgramUniformLocationOrThrow:()=>zw,getRowsCols:()=>Vo,getShapeAs3D:()=>rf,getTextureShapeFromLogicalShape:()=>Uw,getWebGLDisjointQueryTimerVersion:()=>jw,getWebGLErrorMessage:()=>Tw,getWebGLMaxTextureSize:()=>Hw,hasExtension:()=>ms,isCapableOfRenderingToFloatTexture:()=>qw,isDownloadFloatTextureEnabled:()=>Xw,isReshapeFree:()=>Wc,isWebGLFenceEnabled:()=>Kw,isWebGLVersionEnabled:()=>m1,linkProgram:()=>_w,resetMaxTextureSize:()=>$j,resetMaxTexturesInShader:()=>Fj,unbindColorTextureFromFramebuffer:()=>f1,unbindTextureUnit:()=>Rj,validateFramebuffer:()=>Bc,validateProgram:()=>nf,validateTextureSize:()=>Ow});var Bo={},d1={alpha:!1,antialias:!1,premultipliedAlpha:!1,preserveDrawingBuffer:!1,depth:!1,stencil:!1,failIfMajorPerformanceCaveat:!0};function tf(e,t){Bo[e]=t}function ar(e){if(!(e in Bo)){let n=vj(e);if(n!==null)Bo[e]=n;else return console.log("Could not get context for WebGL version",e),null}let t=Bo[e];return t.isContextLost()?(delete Bo[e],ar(e)):(t.disable(t.DEPTH_TEST),t.disable(t.STENCIL_TEST),t.disable(t.BLEND),t.disable(t.DITHER),t.disable(t.POLYGON_OFFSET_FILL),t.disable(t.SAMPLE_COVERAGE),t.enable(t.SCISSOR_TEST),t.enable(t.CULL_FACE),t.cullFace(t.BACK),Bo[e])}function bj(e){if(typeof OffscreenCanvas!="undefined"&&e===2)return new OffscreenCanvas(300,150);if(typeof document!="undefined")return document.createElement("canvas");throw new Error("Cannot create a canvas in this context")}function vj(e){if(e!==1&&e!==2)throw new Error("Cannot get WebGL rendering context, WebGL is disabled.");let t=bj(e);return t.addEventListener("webglcontextlost",n=>{n.preventDefault(),delete Bo[e]},!1),e===1?t.getContext("webgl",d1)||t.getContext("experimental-webgl",d1):t.getContext("webgl2",d1)}var Pc;(function(e){e[e.DENSE=0]="DENSE",e[e.SHARED_BATCH=1]="SHARED_BATCH"})(Pc||(Pc={}));var fs;(function(e){e[e.RENDER=0]="RENDER",e[e.UPLOAD=1]="UPLOAD",e[e.PIXELS=2]="PIXELS",e[e.DOWNLOAD=3]="DOWNLOAD"})(fs||(fs={}));var sn;(function(e){e[e.UNPACKED_FLOAT16=0]="UNPACKED_FLOAT16",e[e.UNPACKED_FLOAT32=1]="UNPACKED_FLOAT32",e[e.PACKED_4X1_UNSIGNED_BYTE=2]="PACKED_4X1_UNSIGNED_BYTE",e[e.PACKED_2X2_FLOAT32=3]="PACKED_2X2_FLOAT32",e[e.PACKED_2X2_FLOAT16=4]="PACKED_2X2_FLOAT16"})(sn||(sn={}));function Mc(e,t){return[t,e]}function wj(e,t){return e*t}function zc(e){let t=I.sizeFromShape(e),n=Math.ceil(t/4);return I.sizeToSquarishShape(n)}function Gl(e,t){return[Math.max(1,Math.ceil(t/2)),Math.max(1,Math.ceil(e/2))]}function kj(e,t){let[n,s]=Gl(e,t);return n*s*4}function h1(e,t){let n=e,s,r,a,o,i,l,u,c,d,h;return ee().getNumber("WEBGL_VERSION")===2?(s=n.R32F,r=n.R16F,a=n.RGBA16F,o=n.RGBA32F,i=n.RED,u=4,c=1,d=n.HALF_FLOAT,h=n.FLOAT):(s=e.RGBA,r=e.RGBA,a=e.RGBA,o=n.RGBA,i=e.RGBA,u=4,c=4,d=t!=null?t.HALF_FLOAT_OES:null,h=e.FLOAT),l=e.RGBA,{internalFormatFloat:s,internalFormatHalfFloat:r,internalFormatPackedHalfFloat:a,internalFormatPackedFloat:o,textureFormatFloat:i,downloadTextureFormat:l,downloadUnpackNumChannels:u,defaultNumChannels:c,textureTypeHalfFloat:d,textureTypeFloat:h}}function be(e,t){let n=t();return ee().getBool("DEBUG")&&Ij(e),n}function Ij(e){let t=e.getError();if(t!==e.NO_ERROR)throw new Error("WebGL Error: "+Tw(e,t))}var Sj=596e-10,Cj=65504;function Cw(e){return!!(ee().getBool("WEBGL_RENDER_FLOAT32_ENABLED")||e===0||Sj<Math.abs(e)&&Math.abs(e)<Cj)}function Tw(e,t){switch(t){case e.NO_ERROR:return"NO_ERROR";case e.INVALID_ENUM:return"INVALID_ENUM";case e.INVALID_VALUE:return"INVALID_VALUE";case e.INVALID_OPERATION:return"INVALID_OPERATION";case e.INVALID_FRAMEBUFFER_OPERATION:return"INVALID_FRAMEBUFFER_OPERATION";case e.OUT_OF_MEMORY:return"OUT_OF_MEMORY";case e.CONTEXT_LOST_WEBGL:return"CONTEXT_LOST_WEBGL";default:return`Unknown error code ${t}`}}function Lc(e,t){return Sr(e,()=>e.getExtension(t),'Extension "'+t+'" not supported on this browser.')}function Nw(e,t){let n=Sr(e,()=>e.createShader(e.VERTEX_SHADER),"Unable to create vertex WebGLShader.");if(be(e,()=>e.shaderSource(n,t)),be(e,()=>e.compileShader(n)),e.getShaderParameter(n,e.COMPILE_STATUS)===!1)throw console.log(e.getShaderInfoLog(n)),new Error("Failed to compile vertex shader.");return n}function Ew(e,t){let n=Sr(e,()=>e.createShader(e.FRAGMENT_SHADER),"Unable to create fragment WebGLShader.");if(be(e,()=>e.shaderSource(n,t)),be(e,()=>e.compileShader(n)),e.getShaderParameter(n,e.COMPILE_STATUS)===!1)throw Nj(t,e.getShaderInfoLog(n)),new Error("Failed to compile fragment shader.");return n}var Tj=/ERROR: [0-9]+:([0-9]+):/g;function Nj(e,t){let n=Tj.exec(t);if(n==null){console.log(`Couldn't parse line number in error: ${t}`),console.log(e);return}let s=+n[1],r=e.split(`
`),a=r.length.toString().length+2,o=r.map((d,h)=>I.rightPad((h+1).toString(),a)+d),i=0;for(let d=0;d<o.length;d++)i=Math.max(o[d].length,i);let l=o.slice(0,s-1),u=o.slice(s-1,s),c=o.slice(s);console.log(l.join(`
`)),console.log(t.split(`
`)[0]),console.log(`%c ${I.rightPad(u[0],i)}`,"border:1px solid red; background-color:#e3d2d2; color:#a61717"),console.log(c.join(`
`))}function Rw(e){return Sr(e,()=>e.createProgram(),"Unable to create WebGLProgram.")}function _w(e,t){if(be(e,()=>e.linkProgram(t)),e.getProgramParameter(t,e.LINK_STATUS)===!1)throw console.log(e.getProgramInfoLog(t)),new Error("Failed to link vertex and fragment shaders.")}function nf(e,t){if(be(e,()=>e.validateProgram(t)),e.getProgramParameter(t,e.VALIDATE_STATUS)===!1)throw console.log(e.getProgramInfoLog(t)),new Error("Shader program validation failed.")}function $w(e,t){let n=Sr(e,()=>e.createBuffer(),"Unable to create WebGLBuffer");return be(e,()=>e.bindBuffer(e.ARRAY_BUFFER,n)),be(e,()=>e.bufferData(e.ARRAY_BUFFER,t,e.STATIC_DRAW)),n}function Fw(e,t){let n=Sr(e,()=>e.createBuffer(),"Unable to create WebGLBuffer");return be(e,()=>e.bindBuffer(e.ELEMENT_ARRAY_BUFFER,n)),be(e,()=>e.bufferData(e.ELEMENT_ARRAY_BUFFER,t,e.STATIC_DRAW)),n}function Ej(){return ee().getNumber("WEBGL_VERSION")===2?1:4}function Dw(e){return Sr(e,()=>e.createTexture(),"Unable to create WebGLTexture.")}function Ow(e,t){let n=ee().getNumber("WEBGL_MAX_TEXTURE_SIZE");if(e<=0||t<=0){let s=`[${e}x${t}]`;throw new Error("Requested texture size "+s+" is invalid.")}if(e>n||t>n){let s=`[${e}x${t}]`,r=`[${n}x${n}]`;throw new Error("Requested texture size "+s+" greater than WebGL maximum on this browser / GPU "+r+".")}}function Pw(e){return Sr(e,()=>e.createFramebuffer(),"Unable to create WebGLFramebuffer.")}function p1(e,t,n,s,r,a,o){let i=e.getAttribLocation(t,n);return i===-1?!1:(be(e,()=>e.bindBuffer(e.ARRAY_BUFFER,s)),be(e,()=>e.vertexAttribPointer(i,r,e.FLOAT,!1,a,o)),be(e,()=>e.enableVertexAttribArray(i)),!0)}function Mw(e,t,n){Vw(e,n),be(e,()=>e.activeTexture(e.TEXTURE0+n)),be(e,()=>e.bindTexture(e.TEXTURE_2D,t))}function Rj(e,t){Vw(e,t),be(e,()=>e.activeTexture(e.TEXTURE0+t)),be(e,()=>e.bindTexture(e.TEXTURE_2D,null))}function zw(e,t,n){return Sr(e,()=>e.getUniformLocation(t,n),'uniform "'+n+'" not present in program.')}function Lw(e,t,n){return e.getUniformLocation(t,n)}function Bw(e,t,n,s){be(e,()=>Mw(e,t,s)),be(e,()=>e.uniform1i(n,s))}function _j(e){be(e,()=>e.bindFramebuffer(e.FRAMEBUFFER,null)),be(e,()=>e.viewport(0,0,e.canvas.width,e.canvas.height)),be(e,()=>e.scissor(0,0,e.canvas.width,e.canvas.height))}function sf(e,t,n){be(e,()=>e.bindFramebuffer(e.FRAMEBUFFER,n)),be(e,()=>e.framebufferTexture2D(e.FRAMEBUFFER,e.COLOR_ATTACHMENT0,e.TEXTURE_2D,t,0))}function f1(e,t){be(e,()=>e.bindFramebuffer(e.FRAMEBUFFER,t)),be(e,()=>e.framebufferTexture2D(e.FRAMEBUFFER,e.COLOR_ATTACHMENT0,e.TEXTURE_2D,null,0))}function Bc(e){let t=e.checkFramebufferStatus(e.FRAMEBUFFER);if(t!==e.FRAMEBUFFER_COMPLETE)throw new Error("Error binding framebuffer: "+Ww(e,t))}function Ww(e,t){switch(t){case e.FRAMEBUFFER_INCOMPLETE_ATTACHMENT:return"FRAMEBUFFER_INCOMPLETE_ATTACHMENT";case e.FRAMEBUFFER_INCOMPLETE_MISSING_ATTACHMENT:return"FRAMEBUFFER_INCOMPLETE_MISSING_ATTACHMENT";case e.FRAMEBUFFER_INCOMPLETE_DIMENSIONS:return"FRAMEBUFFER_INCOMPLETE_DIMENSIONS";case e.FRAMEBUFFER_UNSUPPORTED:return"FRAMEBUFFER_UNSUPPORTED";default:return`unknown error ${t}`}}function Sr(e,t,n){let s=be(e,()=>t());if(s==null)throw new Error(n);return s}function Vw(e,t){let n=e.MAX_COMBINED_TEXTURE_IMAGE_UNITS-1,s=t+e.TEXTURE0;if(s<e.TEXTURE0||s>n){let r=`[gl.TEXTURE0, gl.TEXTURE${n}]`;throw new Error(`textureUnit must be in ${r}.`)}}function Wo(e,t=2){return I.sizeFromShape(e.slice(0,e.length-t))}function Vo(e){if(e.length===0)throw Error("Cannot get rows and columns of an empty shape array.");return[e.length>1?e[e.length-2]:1,e[e.length-1]]}function rf(e){let t=[1,1,1];return e.length===0||e.length===1&&e[0]===1||(t=[Wo(e),...Vo(e)]),t}function Uw(e,t=!1){let n=ee().getNumber("WEBGL_MAX_TEXTURE_SIZE");t&&(n=n*2,e=e.map((r,a)=>a>=e.length-2?I.nearestLargerEven(e[a]):e[a]),e.length===1&&(e=[2,e[0]])),e.length!==2&&(e=I.squeezeShape(e).newShape);let s=I.sizeFromShape(e);if(e.length<=1&&s<=n)return[1,s];if(e.length===2&&e[0]<=n&&e[1]<=n)return e;if(e.length===3&&e[0]*e[1]<=n&&e[2]<=n)return[e[0]*e[1],e[2]];if(e.length===3&&e[0]<=n&&e[1]*e[2]<=n)return[e[0],e[1]*e[2]];if(e.length===4&&e[0]*e[1]*e[2]<=n&&e[3]<=n)return[e[0]*e[1]*e[2],e[3]];if(e.length===4&&e[0]<=n&&e[1]*e[2]*e[3]<=n)return[e[0],e[1]*e[2]*e[3]];if(t){let r=Wo(e),a=2,o=2;return e.length&&([a,o]=Vo(e)),s=r*(a/2)*(o/2),I.sizeToSquarishShape(s).map(i=>i*2)}return I.sizeToSquarishShape(s)}function af(e){return e%2==0}function Wc(e,t){if(e=e.slice(-2),t=t.slice(-2),I.arraysEqual(e,t)||!e.length||!t.length||e[0]===0||e[1]===0||t[0]===0||t[1]===0)return!0;if(e.length!==t.length){let n=e.slice(-1)[0],s=t.slice(-1)[0];if(n===s||af(n)&&af(s)&&(e[0]===1||t[0]===1))return!0}return e[1]===t[1]&&af(e[0])&&af(t[0])}var of,lf;function Hw(e){if(of==null){let t=ar(e);of=t.getParameter(t.MAX_TEXTURE_SIZE)}return of}function $j(){of=null}function Fj(){lf=null}function Gw(e){if(lf==null){let t=ar(e);lf=t.getParameter(t.MAX_TEXTURE_IMAGE_UNITS)}return Math.min(16,lf)}function jw(e){if(e===0)return 0;let t,n=ar(e);return ms(n,"EXT_disjoint_timer_query_webgl2")&&e===2?t=2:ms(n,"EXT_disjoint_timer_query")?t=1:t=0,t}function ms(e,t){return e.getExtension(t)!=null}function m1(e){try{if(ar(e)!=null)return!0}catch(t){return console.log("Error when getting WebGL context: ",t),!1}return!1}function qw(e){if(e===0)return!1;let t=ar(e);if(e===1){if(!ms(t,"OES_texture_float"))return!1}else if(!ms(t,"EXT_color_buffer_float"))return!1;return A1(t)}function Xw(e){if(e===0)return!1;let t=ar(e);if(e===1){if(!ms(t,"OES_texture_float")||!ms(t,"WEBGL_color_buffer_float"))return!1}else{if(ms(t,"EXT_color_buffer_float"))return A1(t);let s="EXT_color_buffer_half_float";if(ms(t,s)){let r=t.getExtension(s);return Dj(t,r)}return!1}return A1(t)}function A1(e){let t=h1(e),n=e.createTexture();e.bindTexture(e.TEXTURE_2D,n);let s=1,r=1;e.texImage2D(e.TEXTURE_2D,0,t.internalFormatFloat,s,r,0,t.textureFormatFloat,t.textureTypeFloat,null);let a=e.createFramebuffer();e.bindFramebuffer(e.FRAMEBUFFER,a),e.framebufferTexture2D(e.FRAMEBUFFER,e.COLOR_ATTACHMENT0,e.TEXTURE_2D,n,0);let o=e.checkFramebufferStatus(e.FRAMEBUFFER)===e.FRAMEBUFFER_COMPLETE;return e.bindTexture(e.TEXTURE_2D,null),e.bindFramebuffer(e.FRAMEBUFFER,null),e.deleteTexture(n),e.deleteFramebuffer(a),o}function Dj(e,t){let n=h1(e,t),s=e.createTexture();e.bindTexture(e.TEXTURE_2D,s);let r=1,a=1;e.texImage2D(e.TEXTURE_2D,0,n.internalFormatHalfFloat,r,a,0,n.textureFormatFloat,n.textureTypeHalfFloat,null);let o=e.createFramebuffer();e.bindFramebuffer(e.FRAMEBUFFER,o),e.framebufferTexture2D(e.FRAMEBUFFER,e.COLOR_ATTACHMENT0,e.TEXTURE_2D,s,0);let i=e.checkFramebufferStatus(e.FRAMEBUFFER)===e.FRAMEBUFFER_COMPLETE;return e.bindTexture(e.TEXTURE_2D,null),e.bindFramebuffer(e.FRAMEBUFFER,null),e.deleteTexture(s),e.deleteFramebuffer(o),i}function Kw(e){return e!==2?!1:ar(e).fenceSync!=null}function jl(e,t){Array.isArray(e)||(e=[e]),e.forEach(n=>{n!=null&&I.assert(n.dtype!=="complex64",()=>`${t} does not support complex64 tensors in the WebGL backend.`)})}var Ne=ee();Ne.registerFlag("HAS_WEBGL",()=>Ne.getNumber("WEBGL_VERSION")>0);Ne.registerFlag("WEBGL_VERSION",()=>m1(2)?2:m1(1)?1:0);Ne.registerFlag("WEBGL_CHECK_NUMERICAL_PROBLEMS",()=>!1);Ne.registerFlag("WEBGL_BUFFER_SUPPORTED",()=>Ne.get("WEBGL_VERSION")===2);Ne.registerFlag("WEBGL_CPU_FORWARD",()=>!0);Ne.registerFlag("WEBGL_FORCE_F16_TEXTURES",()=>!1);Ne.registerFlag("WEBGL_PACK",()=>Ne.getBool("HAS_WEBGL"));Ne.registerFlag("WEBGL_PACK_NORMALIZATION",()=>Ne.getBool("WEBGL_PACK"));Ne.registerFlag("WEBGL_PACK_CLIP",()=>Ne.getBool("WEBGL_PACK"));Ne.registerFlag("WEBGL_PACK_DEPTHWISECONV",()=>Ne.getBool("WEBGL_PACK"));Ne.registerFlag("WEBGL_PACK_BINARY_OPERATIONS",()=>Ne.getBool("WEBGL_PACK"));Ne.registerFlag("WEBGL_PACK_UNARY_OPERATIONS",()=>Ne.getBool("WEBGL_PACK"));Ne.registerFlag("WEBGL_PACK_ARRAY_OPERATIONS",()=>Ne.getBool("WEBGL_PACK"));Ne.registerFlag("WEBGL_PACK_IMAGE_OPERATIONS",()=>Ne.getBool("WEBGL_PACK"));Ne.registerFlag("WEBGL_PACK_REDUCE",()=>Ne.getBool("WEBGL_PACK"));Ne.registerFlag("WEBGL_LAZILY_UNPACK",()=>Ne.getBool("WEBGL_PACK"));Ne.registerFlag("WEBGL_CONV_IM2COL",()=>Ne.getBool("WEBGL_PACK"));Ne.registerFlag("WEBGL_MAX_TEXTURE_SIZE",()=>Hw(Ne.getNumber("WEBGL_VERSION")));Ne.registerFlag("WEBGL_MAX_TEXTURES_IN_SHADER",()=>Gw(Ne.getNumber("WEBGL_VERSION")));Ne.registerFlag("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION",()=>{let e=Ne.getNumber("WEBGL_VERSION");return e===0?0:jw(e)});Ne.registerFlag("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE",()=>Ne.getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")>0&&!Ku.isMobile());Ne.registerFlag("WEBGL_RENDER_FLOAT32_CAPABLE",()=>qw(Ne.getNumber("WEBGL_VERSION")));Ne.registerFlag("WEBGL_RENDER_FLOAT32_ENABLED",()=>Ne.getBool("WEBGL_FORCE_F16_TEXTURES")?!1:Ne.getBool("WEBGL_RENDER_FLOAT32_CAPABLE"));Ne.registerFlag("WEBGL_DOWNLOAD_FLOAT_ENABLED",()=>Xw(Ne.getNumber("WEBGL_VERSION")));Ne.registerFlag("WEBGL_FENCE_API_ENABLED",()=>Kw(Ne.getNumber("WEBGL_VERSION")));Ne.registerFlag("WEBGL_SIZE_UPLOAD_UNIFORM",()=>Ne.getBool("WEBGL_RENDER_FLOAT32_ENABLED")?4:0);Ne.registerFlag("WEBGL_DELETE_TEXTURE_THRESHOLD",()=>-1,e=>{if(e<0&&e!==-1)throw new Error(`WEBGL_DELETE_TEXTURE_THRESHOLD must be -1 (indicating never delete) or at least 0, but got ${e}.`)});Ne.registerFlag("WEBGL_FLUSH_THRESHOLD",()=>Ku.isMobile()&&Ne.getBool("IS_CHROME")?1:-1,e=>{if(e<0&&e!==-1)throw new Error(`WEBGL_FLUSH_THRESHOLD must be -1 (indicating never manual flush) or at least 0, but got ${e}.`)});Ne.registerFlag("CPU_HANDOFF_SIZE_THRESHOLD",()=>128);Ne.registerFlag("WEBGL_USE_SHAPES_UNIFORMS",()=>!1);Ne.registerFlag("TOPK_LAST_DIM_CPU_HANDOFF_SIZE_THRESHOLD",()=>1e5);Ne.registerFlag("TOPK_K_CPU_HANDOFF_THRESHOLD",()=>128);function bn(){let e,t,n,s,r,a,o,i,l,u;return ee().getNumber("WEBGL_VERSION")===2?(e="#version 300 es",t="in",n="out",s="in",r="texture",a="outputColor",o="out vec4 outputColor;",i=`
      bool isnan_custom(float val) {
        return (val > 0.0 || val < 0.0) ? false : val != 0.0;
      }

      bvec4 isnan_custom(vec4 val) {
        return bvec4(isnan_custom(val.x),
          isnan_custom(val.y), isnan_custom(val.z), isnan_custom(val.w));
      }

      #define isnan(value) isnan_custom(value)
    `,l="",u=`
      #define round(value) newRound(value)
      int newRound(float value) {
        return int(floor(value + 0.5));
      }

      ivec4 newRound(vec4 value) {
        return ivec4(floor(value + vec4(0.5)));
      }
    `):(e="",t="attribute",n="varying",s="varying",r="texture2D",a="gl_FragColor",o="",i=`
      #define isnan(value) isnan_custom(value)
      bool isnan_custom(float val) {
        return (val > 0. || val < 1. || val == 0.) ? false : true;
      }
      bvec4 isnan_custom(vec4 val) {
        return bvec4(isnan(val.x), isnan(val.y), isnan(val.z), isnan(val.w));
      }
    `,l=`
      uniform float INFINITY;

      bool isinf(float val) {
        return abs(val) == INFINITY;
      }
      bvec4 isinf(vec4 val) {
        return equal(abs(val), vec4(INFINITY));
      }
    `,u=`
      int round(float value) {
        return int(floor(value + 0.5));
      }

      ivec4 round(vec4 value) {
        return ivec4(floor(value + vec4(0.5)));
      }
    `),{version:e,attribute:t,varyingVs:n,varyingFs:s,texture2D:r,output:a,defineOutput:o,defineSpecialNaN:i,defineSpecialInf:l,defineRound:u}}function Uo(e,t,n="index"){let s=I.computeStrides(t);return s.map((r,a)=>{let o=`int ${e[a]} = ${n} / ${r}`,i=a===s.length-1?`int ${e[a+1]} = ${n} - ${e[a]} * ${r}`:`index -= ${e[a]} * ${r}`;return`${o}; ${i};`}).join("")}function Zw(e,t,n="index"){let s=I.computeStrides(t);return s.map((r,a)=>{let o=`int ${e[a]} = ${n} / outShapeStrides[${a}]`,i=a===s.length-1?`int ${e[a+1]} = ${n} - ${e[a]} * outShapeStrides[${a}]`:`index -= ${e[a]} * outShapeStrides[${a}]`;return`${o}; ${i};`}).join("")}function g1(e){let t=I.computeStrides(e).map(n=>n.toString());return`
  int getFlatIndex(ivec3 coords) {
    return coords.x * ${t[0]} + coords.y * ${t[1]} + coords.z;
  }
`}var Yw=`
  const float FLOAT_MAX = 1.70141184e38;
  const float FLOAT_MIN = 1.17549435e-38;

  lowp vec4 encode_float(highp float v) {
    if (isnan(v)) {
      return vec4(255, 255, 255, 255);
    }

    highp float av = abs(v);

    if(av < FLOAT_MIN) {
      return vec4(0.0, 0.0, 0.0, 0.0);
    } else if(v > FLOAT_MAX) {
      return vec4(0.0, 0.0, 128.0, 127.0) / 255.0;
    } else if(v < -FLOAT_MAX) {
      return vec4(0.0, 0.0,  128.0, 255.0) / 255.0;
    }

    highp vec4 c = vec4(0,0,0,0);

    highp float e = floor(log2(av));
    highp float m = exp2(fract(log2(av))) - 1.0;

    c[2] = floor(128.0 * m);
    m -= c[2] / 128.0;
    c[1] = floor(32768.0 * m);
    m -= c[1] / 32768.0;
    c[0] = floor(8388608.0 * m);

    highp float ebias = e + 127.0;
    c[3] = floor(ebias / 2.0);
    ebias -= c[3] * 2.0;
    c[2] += floor(ebias) * 128.0;

    c[3] += 128.0 * step(0.0, -v);

    return c / 255.0;
  }
`,Oj=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outPackingScheme=Pc.DENSE;let t=zc(e),n=bn();this.outputShape=e,this.userCode=`
      ivec3 outCoordsFromFlatIndex(int index) {
        ${Uo(["r","c","d"],e)}
        return ivec3(r, c, d);
      }

      void main() {
        ivec2 resTexRC = ivec2(resultUV.yx *
          vec2(${t[0]}, ${t[1]}));
        int index = 4 * (resTexRC.x * ${t[1]} + resTexRC.y);

        vec4 result = vec4(0.);

        for (int i=0; i<4; i++) {
          int flatIndex = index + i;
          ivec3 rc = outCoordsFromFlatIndex(flatIndex);
          result[i] = getA(rc.x, rc.y, rc.z);
        }

        ${n.output} = result;
      }
    `}},Pj=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outPackingScheme=Pc.DENSE;let t=zc(e),n=bn();this.outputShape=e,this.userCode=`
      ivec3 outCoordsFromFlatIndex(int index) {
        ${Uo(["r","c","d"],e)}
        return ivec3(r, c, d);
      }

      void main() {
        ivec2 resTexRC = ivec2(resultUV.yx *
          vec2(${t[0]}, ${t[1]}));
        int index = 4 * (resTexRC.x * ${t[1]} + resTexRC.y);

        vec4 result = vec4(0.);

        for (int i=0; i<4; i++) {
          int flatIndex = index + i;
          ivec3 rc = outCoordsFromFlatIndex(flatIndex);
          result[i] = getChannel(getA(rc.x, rc.y, rc.z), vec2(rc.y, rc.z));
        }

        ${n.output} = result;
      }
    `}},Mj=class{constructor(e){this.variableNames=["A"],this.outTexUsage=fs.DOWNLOAD;let t=bn();this.outputShape=e,this.userCode=`
      ${Yw}

      void main() {
        float x = getAAtOutCoords();
        ${t.output} = encode_float(x);
      }
    `}},zj=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outTexUsage=fs.DOWNLOAD;let t=bn();this.outputShape=e,this.userCode=`
      ${Yw}

      void main() {
        ivec3 coords = getOutputCoords();
        float x = getChannel(getAAtOutCoords(), vec2(coords.y, coords.z));
        ${t.output} = encode_float(x);
      }
    `}},Lj=class{constructor(e,t,n=!1){this.variableNames=["A"];let s=bn(),[r,a]=t;this.outputShape=e;let o="result";n&&(o="floor(result * 255. + 0.5)"),this.userCode=`
      ${g1(e)}

      void main() {
        ivec3 coords = getOutputCoords();

        int flatIndex = getFlatIndex(coords);
        int offset = imod(flatIndex, 4);

        flatIndex = idiv(flatIndex, 4, 1.);

        int r = flatIndex / ${a};
        int c = imod(flatIndex, ${a});
        vec2 uv = (vec2(c, r) + halfCR) / vec2(${a}.0, ${r}.0);
        vec4 values = ${s.texture2D}(A, uv);

        float result;

        if(offset == 0) {
          result = values[0];
        } else if(offset == 1) {
          result = values[1];
        } else if(offset == 2) {
          result = values[2];
        } else {
          result = values[3];
        }

        ${s.output} = vec4(${o}, 0., 0., 0.);
      }
    `}},Bj=class{constructor(e,t,n=!1){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;let s=bn(),[r,a]=t;this.outputShape=e;let o="",i="result";n&&(i="floor(result * 255. + 0.5)");for(let l=0;l<=1;l++)for(let u=0;u<=1;u++){let c=l*2+u;o+=`
          localCoords = coords;
          if(localCoords[2] + ${u} < ${e[2]}) {
            localCoords[2] += ${u};
            if(localCoords[1] + ${l} < ${e[1]}) {
              localCoords[1] += ${l};

              flatIndex = getFlatIndex(localCoords);
              offset = imod(flatIndex, 4);

              flatIndex = idiv(flatIndex, 4, 1.);

              r = flatIndex / ${a};
              c = imod(flatIndex, ${a});
              uv = (vec2(c, r) + halfCR) / vec2(${a}.0, ${r}.0);
              values = ${s.texture2D}(A, uv);

              if(offset == 0) {
                result[${c}] = values[0];
              } else if(offset == 1) {
                result[${c}] = values[1];
              } else if(offset == 2) {
                result[${c}] = values[2];
              } else {
                result[${c}] = values[3];
              }
            }
          }
        `}this.userCode=`
      ${g1(e)}

      void main() {
        ivec3 coords = getOutputCoords();

        vec4 result = vec4(0.);
        int flatIndex, r, c, offset;
        ivec3 localCoords;
        vec2 uv;
        vec4 values;

        ${o}

        ${s.output} = ${i};
      }
    `}},Jw={};Pe(Jw,{bindVertexProgramAttributeStreams:()=>i6,createBufferFromOutputTexture:()=>c6,createFloat16MatrixTexture:()=>s6,createFloat16PackedMatrixTexture:()=>o6,createFloat32MatrixTexture:()=>n6,createIndexBuffer:()=>t6,createPackedMatrixTexture:()=>a6,createUnsignedBytesMatrixTexture:()=>r6,createVertexBuffer:()=>e6,createVertexShader:()=>Qw,downloadByteEncodedFloatMatrixFromOutputTexture:()=>h6,downloadFloat32MatrixFromBuffer:()=>d6,downloadMatrixFromPackedOutputTexture:()=>f6,downloadPackedMatrixFromBuffer:()=>p6,getInternalFormatForFloat16MatrixTexture:()=>x1,getInternalFormatForFloat16PackedMatrixTexture:()=>w1,getInternalFormatForFloat32MatrixTexture:()=>y1,getInternalFormatForPackedMatrixTexture:()=>v1,getInternalFormatForUnsignedBytesMatrixTexture:()=>b1,uploadDenseMatrixToTexture:()=>l6,uploadPixelDataToTexture:()=>u6});function Qw(e){let t=bn(),n=`${t.version}
    precision highp float;
    ${t.attribute} vec3 clipSpacePos;
    ${t.attribute} vec2 uv;
    ${t.varyingVs} vec2 resultUV;

    void main() {
      gl_Position = vec4(clipSpacePos, 1);
      resultUV = uv;
    }`;return Nw(e,n)}function e6(e){let t=new Float32Array([-1,1,0,0,1,-1,-1,0,0,0,1,1,0,1,1,1,-1,0,1,0]);return $w(e,t)}function t6(e){let t=new Uint16Array([0,1,2,2,1,3]);return Fw(e,t)}function Vc(e,t,n,s,r,a){Ow(t,n);let o=Dw(e),i=e.TEXTURE_2D;return be(e,()=>e.bindTexture(i,o)),be(e,()=>e.texParameteri(i,e.TEXTURE_WRAP_S,e.CLAMP_TO_EDGE)),be(e,()=>e.texParameteri(i,e.TEXTURE_WRAP_T,e.CLAMP_TO_EDGE)),be(e,()=>e.texParameteri(i,e.TEXTURE_MIN_FILTER,e.NEAREST)),be(e,()=>e.texParameteri(i,e.TEXTURE_MAG_FILTER,e.NEAREST)),be(e,()=>e.texImage2D(i,0,s,t,n,0,r,a,null)),be(e,()=>e.bindTexture(e.TEXTURE_2D,null)),o}function y1(e){return e.internalFormatFloat}function n6(e,t,n,s){let[r,a]=Mc(t,n);return Vc(e,r,a,y1(s),s.textureFormatFloat,e.FLOAT)}function x1(e){return e.internalFormatHalfFloat}function s6(e,t,n,s){let[r,a]=Mc(t,n);return Vc(e,r,a,x1(s),s.textureFormatFloat,s.textureTypeHalfFloat)}function b1(e){return e.downloadTextureFormat}function r6(e,t,n,s){let[r,a]=Mc(t,n);return Vc(e,r,a,b1(s),e.RGBA,e.UNSIGNED_BYTE)}function v1(e){return e.internalFormatPackedFloat}function a6(e,t,n,s){let[r,a]=Gl(t,n);return Vc(e,r,a,v1(s),e.RGBA,e.FLOAT)}function w1(e){return e.internalFormatPackedHalfFloat}function o6(e,t,n,s){let[r,a]=Gl(t,n);return Vc(e,r,a,w1(s),e.RGBA,s.textureTypeHalfFloat)}function i6(e,t,n){let s=0,r=3*4,a=3*4+2*4;return be(e,()=>e.bindBuffer(e.ARRAY_BUFFER,n)),p1(e,t,"clipSpacePos",n,3,a,s)&&p1(e,t,"uv",n,2,a,r)}function l6(e,t,n,s,r,a){be(e,()=>e.bindTexture(e.TEXTURE_2D,t));let o,i,l;r instanceof Uint8Array?(o=new Uint8Array(n*s*4),i=e.UNSIGNED_BYTE,l=e.RGBA):(o=new Float32Array(n*s*4),i=e.FLOAT,l=a.internalFormatPackedFloat),o.set(r),be(e,()=>e.texImage2D(e.TEXTURE_2D,0,l,n,s,0,e.RGBA,i,o)),be(e,()=>e.bindTexture(e.TEXTURE_2D,null))}function u6(e,t,n){be(e,()=>e.bindTexture(e.TEXTURE_2D,t)),n.data instanceof Uint8Array?be(e,()=>e.texImage2D(e.TEXTURE_2D,0,e.RGBA,n.width,n.height,0,e.RGBA,e.UNSIGNED_BYTE,n.data)):be(e,()=>e.texImage2D(e.TEXTURE_2D,0,e.RGBA,e.RGBA,e.UNSIGNED_BYTE,n)),be(e,()=>e.bindTexture(e.TEXTURE_2D,null))}function c6(e,t,n,s){let r=e.createBuffer();be(e,()=>e.bindBuffer(e.PIXEL_PACK_BUFFER,r));let i=4*4*t*n;return be(e,()=>e.bufferData(e.PIXEL_PACK_BUFFER,i,e.STREAM_READ)),be(e,()=>e.readPixels(0,0,n,t,e.RGBA,e.FLOAT,0)),be(e,()=>e.bindBuffer(e.PIXEL_PACK_BUFFER,null)),r}function d6(e,t,n){let s=e,r=new Float32Array(n);return s.bindBuffer(s.PIXEL_PACK_BUFFER,t),s.getBufferSubData(s.PIXEL_PACK_BUFFER,0,r),s.bindBuffer(s.PIXEL_PACK_BUFFER,null),r}function h6(e,t,n,s){let[r,a]=Mc(t,n),o=4,i=new Uint8Array(wj(t*n,o));return be(e,()=>e.readPixels(0,0,r,a,s.downloadTextureFormat,e.UNSIGNED_BYTE,i)),new Float32Array(i.buffer)}function p6(e,t,n,s,r,a,o,i){let l=e,u=new Float32Array(kj(a,o));return l.bindBuffer(l.PIXEL_PACK_BUFFER,t),l.getBufferSubData(l.PIXEL_PACK_BUFFER,0,u),l.bindBuffer(l.PIXEL_PACK_BUFFER,null),u}function f6(e,t,n){let s=new Float32Array(t*n*4);return be(e,()=>e.readPixels(0,0,n,t,e.RGBA,e.FLOAT,s)),s}var uf=class{constructor(e){this.outputTexture=null,this.program=null,this.disposed=!1,this.vertexAttrsAreBound=!1,this.itemsToPoll=[];let t=ee().getNumber("WEBGL_VERSION");e!=null?(this.gl=e,tf(t,e)):this.gl=ar(t);let n="WEBGL_color_buffer_float",s="EXT_color_buffer_half_float";if(ee().getNumber("WEBGL_VERSION")===1){let r="OES_texture_float",a="OES_texture_half_float";if(this.textureFloatExtension=Lc(this.gl,r),ms(this.gl,a))this.textureHalfFloatExtension=Lc(this.gl,a);else if(ee().get("WEBGL_FORCE_F16_TEXTURES"))throw new Error("GL context does not support half float textures, yet the environment flag WEBGL_FORCE_F16_TEXTURES is set to true.");if(this.colorBufferFloatExtension=this.gl.getExtension(n),ms(this.gl,s))this.colorBufferHalfFloatExtension=Lc(this.gl,s);else if(ee().get("WEBGL_FORCE_F16_TEXTURES"))throw new Error("GL context does not support color renderable half floats, yet the environment flag WEBGL_FORCE_F16_TEXTURES is set to true.")}else if(n="EXT_color_buffer_float",ms(this.gl,n))this.colorBufferFloatExtension=this.gl.getExtension(n);else if(ms(this.gl,s))this.colorBufferHalfFloatExtension=this.gl.getExtension(s);else throw new Error("GL context does not support color renderable floats");this.vertexBuffer=e6(this.gl),this.indexBuffer=t6(this.gl),this.framebuffer=Pw(this.gl),this.textureConfig=h1(this.gl,this.textureHalfFloatExtension)}get debug(){return ee().getBool("DEBUG")}dispose(){if(this.disposed)return;this.program!=null&&console.warn("Disposing a GPGPUContext that still has a bound WebGLProgram. This is probably a resource leak, delete the program with GPGPUContext.deleteProgram before disposing."),this.outputTexture!=null&&console.warn("Disposing a GPGPUContext that still has a bound output matrix texture.  This is probably a resource leak, delete the output matrix texture with GPGPUContext.deleteMatrixTexture before disposing.");let e=this.gl;be(e,()=>e.finish()),be(e,()=>e.bindFramebuffer(e.FRAMEBUFFER,null)),be(e,()=>e.deleteFramebuffer(this.framebuffer)),be(e,()=>e.bindBuffer(e.ARRAY_BUFFER,null)),be(e,()=>e.bindBuffer(e.ELEMENT_ARRAY_BUFFER,null)),be(e,()=>e.deleteBuffer(this.indexBuffer)),this.disposed=!0}createFloat32MatrixTexture(e,t){return this.throwIfDisposed(),n6(this.gl,e,t,this.textureConfig)}createFloat16MatrixTexture(e,t){return this.throwIfDisposed(),s6(this.gl,e,t,this.textureConfig)}createUnsignedBytesMatrixTexture(e,t){return this.throwIfDisposed(),r6(this.gl,e,t,this.textureConfig)}uploadPixelDataToTexture(e,t){this.throwIfDisposed(),u6(this.gl,e,t)}uploadDenseMatrixToTexture(e,t,n,s){this.throwIfDisposed(),l6(this.gl,e,t,n,s,this.textureConfig)}createFloat16PackedMatrixTexture(e,t){return this.throwIfDisposed(),o6(this.gl,e,t,this.textureConfig)}createPackedMatrixTexture(e,t){return this.throwIfDisposed(),a6(this.gl,e,t,this.textureConfig)}deleteMatrixTexture(e){this.throwIfDisposed(),this.outputTexture===e&&(f1(this.gl,this.framebuffer),this.outputTexture=null),be(this.gl,()=>this.gl.deleteTexture(e))}downloadByteEncodedFloatMatrixFromOutputTexture(e,t,n){return this.downloadMatrixDriver(e,()=>h6(this.gl,t,n,this.textureConfig))}downloadPackedMatrixFromBuffer(e,t,n,s,r,a){return p6(this.gl,e,t,n,s,r,a,this.textureConfig)}downloadFloat32MatrixFromBuffer(e,t){return d6(this.gl,e,t)}createBufferFromTexture(e,t,n){this.bindTextureToFrameBuffer(e);let s=c6(this.gl,t,n,this.textureConfig);return this.unbindTextureToFrameBuffer(),s}createAndWaitForFence(){let e=this.createFence(this.gl);return this.pollFence(e)}createFence(e){let t,n;if(ee().getBool("WEBGL_FENCE_API_ENABLED")){let s=e,r=s.fenceSync(s.SYNC_GPU_COMMANDS_COMPLETE,0);e.flush(),n=()=>{let a=s.clientWaitSync(r,0,0);return a===s.ALREADY_SIGNALED||a===s.CONDITION_SATISFIED},t=r}else ee().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")>0?(t=this.beginQuery(),this.endQuery(),n=()=>this.isQueryAvailable(t,ee().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))):n=()=>!0;return{query:t,isFencePassed:n}}downloadMatrixFromPackedTexture(e,t,n){return this.downloadMatrixDriver(e,()=>f6(this.gl,t,n))}createProgram(e){this.throwIfDisposed();let t=this.gl,n=Ew(t,e);this.vertexShader==null&&(this.vertexShader=Qw(t));let s=Rw(t);return be(t,()=>t.attachShader(s,this.vertexShader)),be(t,()=>t.attachShader(s,n)),_w(t,s),this.debug&&nf(t,s),this.vertexAttrsAreBound||(this.setProgram(s),this.vertexAttrsAreBound=i6(t,this.program,this.vertexBuffer)),s}deleteProgram(e){this.throwIfDisposed(),e===this.program&&(this.program=null),e!=null&&be(this.gl,()=>this.gl.deleteProgram(e))}setProgram(e){this.throwIfDisposed(),this.program=e,this.program!=null&&this.debug&&nf(this.gl,this.program),be(this.gl,()=>this.gl.useProgram(e))}getUniformLocation(e,t,n=!0){return this.throwIfDisposed(),n?zw(this.gl,e,t):Lw(this.gl,e,t)}getAttributeLocation(e,t){return this.throwIfDisposed(),be(this.gl,()=>this.gl.getAttribLocation(e,t))}getUniformLocationNoThrow(e,t){return this.throwIfDisposed(),this.gl.getUniformLocation(e,t)}setInputMatrixTexture(e,t,n){this.throwIfDisposed(),this.throwIfNoProgram(),Bw(this.gl,e,t,n)}setOutputMatrixTexture(e,t,n){this.setOutputMatrixTextureDriver(e,n,t)}setOutputPackedMatrixTexture(e,t,n){this.throwIfDisposed();let[s,r]=Gl(t,n);this.setOutputMatrixTextureDriver(e,s,r)}setOutputMatrixWriteRegion(e,t,n,s){this.setOutputMatrixWriteRegionDriver(n,e,s,t)}setOutputPackedMatrixWriteRegion(e,t,n,s){throw new Error("setOutputPackedMatrixWriteRegion not implemented.")}debugValidate(){this.program!=null&&nf(this.gl,this.program),Bc(this.gl)}executeProgram(){this.throwIfDisposed(),this.throwIfNoProgram();let e=this.gl;this.debug&&this.debugValidate(),be(e,()=>e.drawElements(e.TRIANGLES,6,e.UNSIGNED_SHORT,0))}blockUntilAllProgramsCompleted(){this.throwIfDisposed(),be(this.gl,()=>this.gl.finish())}getQueryTimerExtension(){return this.disjointQueryTimerExtension==null&&(this.disjointQueryTimerExtension=Lc(this.gl,ee().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2?"EXT_disjoint_timer_query_webgl2":"EXT_disjoint_timer_query")),this.disjointQueryTimerExtension}getQueryTimerExtensionWebGL2(){return this.getQueryTimerExtension()}getQueryTimerExtensionWebGL1(){return this.getQueryTimerExtension()}beginQuery(){if(ee().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2){let n=this.gl,s=this.getQueryTimerExtensionWebGL2(),r=n.createQuery();return n.beginQuery(s.TIME_ELAPSED_EXT,r),r}let e=this.getQueryTimerExtensionWebGL1(),t=e.createQueryEXT();return e.beginQueryEXT(e.TIME_ELAPSED_EXT,t),t}endQuery(){if(ee().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2){let t=this.gl,n=this.getQueryTimerExtensionWebGL2();t.endQuery(n.TIME_ELAPSED_EXT);return}let e=this.getQueryTimerExtensionWebGL1();e.endQueryEXT(e.TIME_ELAPSED_EXT)}async waitForQueryAndGetTime(e){return await I.repeatedTry(()=>this.disposed||this.isQueryAvailable(e,ee().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))),this.getQueryTime(e,ee().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))}getQueryTime(e,t){if(t===0)return null;if(t===2){let n=this.gl;return n.getQueryParameter(e,n.QUERY_RESULT)/1e6}else{let n=this.getQueryTimerExtensionWebGL1();return n.getQueryObjectEXT(e,n.QUERY_RESULT_EXT)/1e6}}isQueryAvailable(e,t){if(t===0)return!0;if(t===2){let n=this.gl,s=this.getQueryTimerExtensionWebGL2(),r=n.getQueryParameter(e,n.QUERY_RESULT_AVAILABLE);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(s.GPU_DISJOINT_EXT)),r&&!this.disjoint}else{let n=this.getQueryTimerExtensionWebGL1(),s=n.getQueryObjectEXT(e,n.QUERY_RESULT_AVAILABLE_EXT);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(n.GPU_DISJOINT_EXT)),s&&!this.disjoint}}pollFence(e){return new Promise(t=>{this.addItemToPoll(()=>e.isFencePassed(),()=>t())})}pollItems(){let e=Wj(this.itemsToPoll.map(t=>t.isDoneFn));for(let t=0;t<=e;++t){let{resolveFn:n}=this.itemsToPoll[t];n()}this.itemsToPoll=this.itemsToPoll.slice(e+1)}addItemToPoll(e,t){this.itemsToPoll.push({isDoneFn:e,resolveFn:t}),!(this.itemsToPoll.length>1)&&I.repeatedTry(()=>(this.pollItems(),this.itemsToPoll.length===0))}bindTextureToFrameBuffer(e){this.throwIfDisposed(),sf(this.gl,e,this.framebuffer),this.debug&&Bc(this.gl)}unbindTextureToFrameBuffer(){this.outputTexture!=null?(sf(this.gl,this.outputTexture,this.framebuffer),this.debug&&Bc(this.gl)):f1(this.gl,this.framebuffer)}downloadMatrixDriver(e,t){this.bindTextureToFrameBuffer(e);let n=t();return this.unbindTextureToFrameBuffer(),n}setOutputMatrixTextureDriver(e,t,n){this.throwIfDisposed();let s=this.gl;sf(s,e,this.framebuffer),this.debug&&Bc(s),this.outputTexture=e,be(s,()=>s.viewport(0,0,t,n)),be(s,()=>s.scissor(0,0,t,n))}setOutputMatrixWriteRegionDriver(e,t,n,s){this.throwIfDisposed(),be(this.gl,()=>this.gl.scissor(e,t,n,s))}throwIfDisposed(){if(this.disposed)throw new Error("Attempted to use disposed GPGPUContext.")}throwIfNoProgram(){if(this.program==null)throw new Error("No GPU program is currently set.")}};function Wj(e){let t=0;for(;t<e.length&&e[t]();++t);return t-1}var{getBroadcastDims:m6}=$;function Vj(e,t,n){let s=[];if(e.forEach(p=>{let f=I.sizeFromShape(p.shapeInfo.logicalShape);if(p.shapeInfo.isUniform?s.push(`uniform float ${p.name}${f>1?`[${f}]`:""};`):(s.push(`uniform sampler2D ${p.name};`),s.push(`uniform int offset${p.name};`)),n.enableShapeUniforms){let{uniformShape:m}=k1(n.packedInputs,p.shapeInfo.logicalShape,p.shapeInfo.texShape);switch(m.length){case 1:s.push(`uniform int ${p.name}Shape;`);break;case 2:s.push(`uniform ivec2 ${p.name}Shape;`);break;case 3:s.push(`uniform ivec3 ${p.name}Shape;`);break;case 4:s.push(`uniform ivec4 ${p.name}Shape;`);break;default:break}s.push(`uniform ivec2 ${p.name}TexShape;`)}}),n.enableShapeUniforms){switch(t.logicalShape.length){case 1:s.push("uniform int outShape;");break;case 2:s.push("uniform ivec2 outShape;"),s.push("uniform int outShapeStrides;");break;case 3:s.push("uniform ivec3 outShape;"),s.push("uniform ivec2 outShapeStrides;");break;case 4:s.push("uniform ivec4 outShape;"),s.push("uniform ivec3 outShapeStrides;");break;default:break}s.push("uniform ivec2 outTexShape;")}n.customUniforms&&n.customUniforms.forEach(p=>{s.push(`uniform ${p.type} ${p.name}${p.arrayIndex?`[${p.arrayIndex}]`:""};`)});let r=s.join(`
`),a=e.map(p=>Uj(p,t,n.packedInputs,n.enableShapeUniforms)).join(`
`),o=t.texShape,i=bn(),l=jj(i),u,c,d=Kj(i);return t.isPacked?(u=Hj(t.logicalShape,o,n.enableShapeUniforms),c=Xj(i)):(u=Gj(t.logicalShape,o,n.enableShapeUniforms),c=qj(i)),n.packedInputs&&(d+=Qj),[d,l,c,r,u,a,n.userCode].join(`
`)}function ql(e,t=!1){let n=e.shapeInfo.logicalShape;switch(n.length){case 0:return dq(e,t);case 1:return pq(e,t);case 2:return mq(e,t);case 3:return gq(e,t);case 4:return xq(e,t);case 5:return bq(e);case 6:return vq(e);default:throw new Error(`${n.length}-D input sampling is not yet supported`)}}function A6(e,t){switch(e.shapeInfo.logicalShape.length){case 0:return cq(e);case 1:return hq(e,t);case 2:return fq(e,t);case 3:return Aq(e,t);default:return yq(e,t)}}function Uj(e,t,n=!1,s){let r="";n?r+=A6(e,s):r+=ql(e,s);let a=e.shapeInfo.logicalShape,o=t.logicalShape;return a.length<=o.length&&(n?r+=wq(e,t):r+=kq(e,t)),r}function Hj(e,t,n){switch(e.length){case 0:return g6();case 1:return eq(e,t,n);case 2:return lq(e,t,n);case 3:return nq(e,t,n);default:return rq(e,t,n)}}function Gj(e,t,n){switch(e.length){case 0:return g6();case 1:return tq(e,t,n);case 2:return uq(e,t,n);case 3:return sq(e,t,n);case 4:return aq(e,t,n);case 5:return oq(e,t);case 6:return iq(e,t);default:throw new Error(`${e.length}-D output sampling is not yet supported`)}}function jj(e){return`
    float sampleTexture(sampler2D textureSampler, vec2 uv) {
      return ${e.texture2D}(textureSampler, uv).r;
    }
  `}function qj(e){return`
    void setOutput(float val) {
      ${e.output} = vec4(val, 0, 0, 0);
    }
  `}function Xj(e){return`
    void setOutput(vec4 val) {
      ${e.output} = val;
    }
  `}function Kj(e){return`${e.version}
    precision highp float;
    precision highp int;
    precision highp sampler2D;
    ${e.varyingFs} vec2 resultUV;
    ${e.defineOutput}
    const vec2 halfCR = vec2(0.5, 0.5);

    struct ivec5
    {
      int x;
      int y;
      int z;
      int w;
      int u;
    };

    struct ivec6
    {
      int x;
      int y;
      int z;
      int w;
      int u;
      int v;
    };

    uniform float NAN;
    ${e.defineSpecialNaN}
    ${e.defineSpecialInf}
    ${e.defineRound}

    int imod(int x, int y) {
      return x - y * (x / y);
    }

    int idiv(int a, int b, float sign) {
      int res = a / b;
      int mod = imod(a, b);
      if (sign < 0. && mod != 0) {
        res -= 1;
      }
      return res;
    }

    //Based on the work of Dave Hoskins
    //https://www.shadertoy.com/view/4djSRW
    #define HASHSCALE1 443.8975
    float random(float seed){
      vec2 p = resultUV * seed;
      vec3 p3  = fract(vec3(p.xyx) * HASHSCALE1);
      p3 += dot(p3, p3.yzx + 19.19);
      return fract((p3.x + p3.y) * p3.z);
    }

    ${Zj}
    ${Yj}
    ${Jj}
  `}var Zj=`
vec2 uvFromFlat(int texNumR, int texNumC, int index) {
  int texR = index / texNumC;
  int texC = index - texR * texNumC;
  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);
}
vec2 packedUVfrom1D(int texNumR, int texNumC, int index) {
  int texelIndex = index / 2;
  int texR = texelIndex / texNumC;
  int texC = texelIndex - texR * texNumC;
  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);
}
`,Yj=`
vec2 packedUVfrom2D(int texelsInLogicalRow, int texNumR,
  int texNumC, int row, int col) {
  int texelIndex = (row / 2) * texelsInLogicalRow + (col / 2);
  int texR = texelIndex / texNumC;
  int texC = texelIndex - texR * texNumC;
  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);
}
`,Jj=`
vec2 packedUVfrom3D(int texNumR, int texNumC,
    int texelsInBatch, int texelsInLogicalRow, int b,
    int row, int col) {
  int index = b * texelsInBatch + (row / 2) * texelsInLogicalRow + (col / 2);
  int texR = index / texNumC;
  int texC = index - texR * texNumC;
  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);
}
`,Qj=`
  float getChannel(vec4 frag, vec2 innerDims) {
    vec2 modCoord = mod(innerDims, 2.);
    return modCoord.x == 0. ?
      (modCoord.y == 0. ? frag.r : frag.g) :
      (modCoord.y == 0. ? frag.b : frag.a);
  }
  float getChannel(vec4 frag, int dim) {
    float modCoord = mod(float(dim), 2.);
    return modCoord == 0. ? frag.r : frag.g;
  }
`;function g6(){return`
    int getOutputCoords() {
      return 0;
    }
  `}function eq(e,t,n){let s=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];return s[0]===1?n?`
      int getOutputCoords() {
        return 2 * int(resultUV.x * ceil(float(outTexShape[1]) / 2.0));
      }
    `:`
      int getOutputCoords() {
        return 2 * int(resultUV.x * ${s[1]}.0);
      }
    `:s[1]===1?n?`
      int getOutputCoords() {
        return 2 * int(resultUV.y * ceil(float(outTexShape[0]) / 2.0));
      }
    `:`
      int getOutputCoords() {
        return 2 * int(resultUV.y * ${s[0]}.0);
      }
    `:n?`
    int getOutputCoords() {
      ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
      ivec2 resTexRC = ivec2(resultUV.yx *
                             vec2(packedTexShape[0], packedTexShape[1]));
      return 2 * (resTexRC.x * packedTexShape[1] + resTexRC.y);
    }
  `:`
    int getOutputCoords() {
      ivec2 resTexRC = ivec2(resultUV.yx *
                             vec2(${s[0]}, ${s[1]}));
      return 2 * (resTexRC.x * ${s[1]} + resTexRC.y);
    }
  `}function tq(e,t,n){return t[0]===1?n?`
      int getOutputCoords() {
        return int(resultUV.x * float(outTexShape[1]));
      }
    `:`
      int getOutputCoords() {
        return int(resultUV.x * ${t[1]}.0);
      }
    `:t[1]===1?n?`
      int getOutputCoords() {
        return int(resultUV.y * float(outTexShape[0]));
      }
    `:`
      int getOutputCoords() {
        return int(resultUV.y * ${t[0]}.0);
      }
    `:n?`
    int getOutputCoords() {
      ivec2 resTexRC = ivec2(resultUV.yx *
                             vec2(outTexShape[0], outTexShape[1]));
      return resTexRC.x * outTexShape[1] + resTexRC.y;
    }
  `:`
    int getOutputCoords() {
      ivec2 resTexRC = ivec2(resultUV.yx *
                             vec2(${t[0]}, ${t[1]}));
      return resTexRC.x * ${t[1]} + resTexRC.y;
    }
  `}function nq(e,t,n){if(n)return`
    ivec3 getOutputCoords() {
      ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
      int texelsInLogicalRow = int(ceil(float(outShape[2]) / 2.0));
      int texelsInBatch = texelsInLogicalRow * int(ceil(float(outShape[1]) / 2.0));
      ivec2 resTexRC = ivec2(resultUV.yx *
                             vec2(packedTexShape[0], packedTexShape[1]));
      int index = resTexRC.x * packedTexShape[1] + resTexRC.y;

      int b = index / texelsInBatch;
      index -= b * texelsInBatch;

      int r = 2 * (index / texelsInLogicalRow);
      int c = imod(index, texelsInLogicalRow) * 2;

      return ivec3(b, r, c);
    }
  `;let s=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],r=Math.ceil(e[2]/2),a=r*Math.ceil(e[1]/2);return`
    ivec3 getOutputCoords() {
      ivec2 resTexRC = ivec2(resultUV.yx *
                             vec2(${s[0]}, ${s[1]}));
      int index = resTexRC.x * ${s[1]} + resTexRC.y;

      int b = index / ${a};
      index -= b * ${a};

      int r = 2 * (index / ${r});
      int c = imod(index, ${r}) * 2;

      return ivec3(b, r, c);
    }
  `}function sq(e,t,n){if(n)return`
  ivec3 getOutputCoords() {
    ivec2 resTexRC = ivec2(resultUV.yx *
                           vec2(outTexShape[0], outTexShape[1]));
    int index = resTexRC.x * outTexShape[1] + resTexRC.y;
    ${Zw(["r","c","d"],e)}
    return ivec3(r, c, d);
  }
`;let s=Uo(["r","c","d"],e);return`
    ivec3 getOutputCoords() {
      ivec2 resTexRC = ivec2(resultUV.yx *
                             vec2(${t[0]}, ${t[1]}));
      int index = resTexRC.x * ${t[1]} + resTexRC.y;
      ${s}
      return ivec3(r, c, d);
    }
  `}function rq(e,t,n){if(n)return`
    ivec4 getOutputCoords() {
      ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
      ivec2 resTexRC = ivec2(resultUV.yx *
                             vec2(packedTexShape[0], packedTexShape[1]));
      int index = resTexRC.x * packedTexShape[1] + resTexRC.y;

      int texelsInLogicalRow = int(ceil(float(outShape[3]) / 2.0));
      int texelsInBatch = texelsInLogicalRow * int(ceil(float(outShape[2]) / 2.0));
      int texelsInBatchN = texelsInBatch * outShape[1];

      int b2 = index / texelsInBatchN;
      index -= b2 * texelsInBatchN;

      int b = index / texelsInBatch;
      index -= b * texelsInBatch;

      int r = 2 * (index / texelsInLogicalRow);
      int c = imod(index, texelsInLogicalRow) * 2;

      return ivec4(b2, b, r, c);
    }
  `;let s=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],r=Math.ceil(e[e.length-1]/2),a=r*Math.ceil(e[e.length-2]/2),o=a,i="",l="b, r, c";for(let u=2;u<e.length-1;u++)o*=e[e.length-u-1],i=`
      int b${u} = index / ${o};
      index -= b${u} * ${o};
    `+i,l=`b${u}, `+l;return`
    ivec${e.length} getOutputCoords() {
      ivec2 resTexRC = ivec2(resultUV.yx *
                             vec2(${s[0]}, ${s[1]}));
      int index = resTexRC.x * ${s[1]} + resTexRC.y;

      ${i}

      int b = index / ${a};
      index -= b * ${a};

      int r = 2 * (index / ${r});
      int c = imod(index, ${r}) * 2;

      return ivec${e.length}(${l});
    }
  `}function aq(e,t,n){if(n)return`
    ivec4 getOutputCoords() {
      ivec2 resTexRC = ivec2(resultUV.yx *
        vec2(outTexShape[0], outTexShape[1]));
      int index = resTexRC.x * outTexShape[1] + resTexRC.y;
      ${Zw(["r","c","d","d2"],e)}
      return ivec4(r, c, d, d2);
    }
  `;let s=Uo(["r","c","d","d2"],e);return`
    ivec4 getOutputCoords() {
      ivec2 resTexRC = ivec2(resultUV.yx *
        vec2(${t[0]}, ${t[1]}));
      int index = resTexRC.x * ${t[1]} + resTexRC.y;
      ${s}
      return ivec4(r, c, d, d2);
    }
  `}function oq(e,t){let n=Uo(["r","c","d","d2","d3"],e);return`
    ivec5 getOutputCoords() {
      ivec2 resTexRC = ivec2(resultUV.yx * vec2(${t[0]},
                             ${t[1]}));

      int index = resTexRC.x * ${t[1]} + resTexRC.y;

      ${n}

      ivec5 outShape = ivec5(r, c, d, d2, d3);
      return outShape;
    }
  `}function iq(e,t){let n=Uo(["r","c","d","d2","d3","d4"],e);return`
    ivec6 getOutputCoords() {
      ivec2 resTexRC = ivec2(resultUV.yx *
        vec2(${t[0]}, ${t[1]}));
      int index = resTexRC.x * ${t[1]} + resTexRC.y;

      ${n}

      ivec6 result = ivec6(r, c, d, d2, d3, d4);
      return result;
    }
  `}function lq(e,t,n){let s=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];if(I.arraysEqual(e,t))return n?`
      ivec2 getOutputCoords() {
        ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
        return 2 * ivec2(resultUV.yx * vec2(packedTexShape[0], packedTexShape[1]));
      }
    `:`
      ivec2 getOutputCoords() {
        return 2 * ivec2(resultUV.yx * vec2(${s[0]}, ${s[1]}));
      }
    `;let r=Math.ceil(e[1]/2);return n?`
    ivec2 getOutputCoords() {
      ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
      int texelsInLogicalRow = int(ceil(float(outShape[1]) / 2.0));
      ivec2 resTexRC = ivec2(resultUV.yx *
                             vec2(packedTexShape[0], packedTexShape[1]));

      int index = resTexRC.x * packedTexShape[1] + resTexRC.y;
      int r = 2 * (index / texelsInLogicalRow);
      int c = imod(index, texelsInLogicalRow) * 2;

      return ivec2(r, c);
    }
  `:`
    ivec2 getOutputCoords() {
      ivec2 resTexRC = ivec2(resultUV.yx *
                             vec2(${s[0]}, ${s[1]}));

      int index = resTexRC.x * ${s[1]} + resTexRC.y;
      int r = 2 * (index / ${r});
      int c = imod(index, ${r}) * 2;

      return ivec2(r, c);
    }
  `}function uq(e,t,n){return I.arraysEqual(e,t)?n?`
      ivec2 getOutputCoords() {
        return ivec2(resultUV.yx * vec2(outTexShape[0], outTexShape[1]));
      }
    `:`
      ivec2 getOutputCoords() {
        return ivec2(resultUV.yx * vec2(${t[0]}, ${t[1]}));
      }
    `:e[1]===1?n?`
      ivec2 getOutputCoords() {
        ivec2 resTexRC = ivec2(resultUV.yx *
                               vec2(outTexShape[0], outTexShape[1]));
        int index = resTexRC.x * outTexShape[1] + resTexRC.y;
        return ivec2(index, 0);
      }
    `:`
      ivec2 getOutputCoords() {
        ivec2 resTexRC = ivec2(resultUV.yx *
                               vec2(${t[0]}, ${t[1]}));
        int index = resTexRC.x * ${t[1]} + resTexRC.y;
        return ivec2(index, 0);
      }
    `:e[0]===1?n?`
      ivec2 getOutputCoords() {
        ivec2 resTexRC = ivec2(resultUV.yx *
                               vec2(outTexShape[0], outTexShape[1]));
        int index = resTexRC.x * outTexShape[1] + resTexRC.y;
        return ivec2(0, index);
      }
    `:`
      ivec2 getOutputCoords() {
        ivec2 resTexRC = ivec2(resultUV.yx *
                               vec2(${t[0]}, ${t[1]}));
        int index = resTexRC.x * ${t[1]} + resTexRC.y;
        return ivec2(0, index);
      }
    `:n?`
    ivec2 getOutputCoords() {
      ivec2 resTexRC = ivec2(resultUV.yx *
                             vec2(outTexShape[0], outTexShape[1]));
      int index = resTexRC.x * outTexShape[1] + resTexRC.y;
      int r = index / outShape[1];
      int c = index - r * outShape[1];
      return ivec2(r, c);
    }
  `:`
    ivec2 getOutputCoords() {
      ivec2 resTexRC = ivec2(resultUV.yx *
                             vec2(${t[0]}, ${t[1]}));
      int index = resTexRC.x * ${t[1]} + resTexRC.y;
      int r = index / ${e[1]};
      int c = index - r * ${e[1]};
      return ivec2(r, c);
    }
  `}function Ho(e){return`offset${e}`}function cq(e){let t=e.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1),s=bn();return`
    vec4 ${n}() {
      return ${s.texture2D}(${t}, halfCR);
    }
  `}function dq(e,t){let n=e.name,s="get"+n.charAt(0).toUpperCase()+n.slice(1);if(e.shapeInfo.isUniform)return`float ${s}() {return ${n};}`;let[r,a]=e.shapeInfo.texShape;if(r===1&&a===1)return`
      float ${s}() {
        return sampleTexture(${n}, halfCR);
      }
    `;let o=Ho(n);if(t)return`
    float ${s}() {
      vec2 uv = uvFromFlat(${n}TexShape[0], ${n}TexShape[1], ${o});
      return sampleTexture(${n}, uv);
    }
  `;let[i,l]=e.shapeInfo.texShape;return`
    float ${s}() {
      vec2 uv = uvFromFlat(${i}, ${l}, ${o});
      return sampleTexture(${n}, uv);
    }
  `}function hq(e,t){let n=e.name,s="get"+n.charAt(0).toUpperCase()+n.slice(1),r=e.shapeInfo.texShape,a=bn();if(t)return`
    vec4 ${s}(int index) {
      ivec2 packedTexShape = ivec2(ceil(float(${n}TexShape[0]) / 2.0), ceil(float(${n}TexShape[1]) / 2.0));
      vec2 uv = packedUVfrom1D(
        packedTexShape[0], packedTexShape[1], index);
      return ${a.texture2D}(${n}, uv);
    }
  `;let o=[Math.ceil(r[0]/2),Math.ceil(r[1]/2)];return`
    vec4 ${s}(int index) {
      vec2 uv = packedUVfrom1D(
        ${o[0]}, ${o[1]}, index);
      return ${a.texture2D}(${n}, uv);
    }
  `}function pq(e,t){let n=e.name,s="get"+n.charAt(0).toUpperCase()+n.slice(1);if(e.shapeInfo.isUniform)return`
      float ${s}(int index) {
        ${Xl(e)}
      }
    `;let r=e.shapeInfo.texShape,a=r[0],o=r[1];if(o===1&&a===1)return`
      float ${s}(int index) {
        return sampleTexture(${n}, halfCR);
      }
    `;let i=Ho(n);return o===1?t?`
      float ${s}(int index) {
        vec2 uv = vec2(0.5, (float(index + ${i}) + 0.5) / float(${n}TexShape[0]));
        return sampleTexture(${n}, uv);
      }
    `:`
      float ${s}(int index) {
        vec2 uv = vec2(0.5, (float(index + ${i}) + 0.5) / ${a}.0);
        return sampleTexture(${n}, uv);
      }
    `:a===1?t?`
      float ${s}(int index) {
        vec2 uv = vec2((float(index + ${i}) + 0.5) / float(${n}TexShape[1]), 0.5);
        return sampleTexture(${n}, uv);
      }
    `:`
      float ${s}(int index) {
        vec2 uv = vec2((float(index + ${i}) + 0.5) / ${o}.0, 0.5);
        return sampleTexture(${n}, uv);
      }
    `:t?`
    float ${s}(int index) {
      vec2 uv = uvFromFlat(${n}TexShape[0], ${n}TexShape[1], index + ${i});
      return sampleTexture(${n}, uv);
    }
  `:`
    float ${s}(int index) {
      vec2 uv = uvFromFlat(${a}, ${o}, index + ${i});
      return sampleTexture(${n}, uv);
    }
  `}function fq(e,t){let n=e.shapeInfo.logicalShape,s=e.name,r="get"+s.charAt(0).toUpperCase()+s.slice(1),a=e.shapeInfo.texShape,o=a[0],i=a[1],l=bn();if(a!=null&&I.arraysEqual(n,a))return t?`
      vec4 ${r}(int row, int col) {
        vec2 uv = (vec2(col, row) + halfCR) / vec2(${s}TexShape[1], ${s}TexShape[0]);

        return ${l.texture2D}(${s}, uv);
      }
    `:`
      vec4 ${r}(int row, int col) {
        vec2 uv = (vec2(col, row) + halfCR) / vec2(${i}.0, ${o}.0);

        return ${l.texture2D}(${s}, uv);
      }
    `;if(t)return`
    vec4 ${r}(int row, int col) {
      ivec2 packedTexShape = ivec2(ceil(float(${s}TexShape[0]) / 2.0), ceil(float(${s}TexShape[1]) / 2.0));
      int valuesPerRow = int(ceil(float(${s}Shape[1]) / 2.0));
      vec2 uv = packedUVfrom2D(valuesPerRow, packedTexShape[0], packedTexShape[1], row, col);
      return ${l.texture2D}(${s}, uv);
    }
  `;let u=[Math.ceil(a[0]/2),Math.ceil(a[1]/2)],c=Math.ceil(n[1]/2);return`
    vec4 ${r}(int row, int col) {
      vec2 uv = packedUVfrom2D(${c}, ${u[0]}, ${u[1]}, row, col);
      return ${l.texture2D}(${s}, uv);
    }
  `}function mq(e,t){let n=e.shapeInfo.logicalShape,s=e.name,r="get"+s.charAt(0).toUpperCase()+s.slice(1),a=e.shapeInfo.texShape;if(a!=null&&I.arraysEqual(n,a)){if(t)return`
      float ${r}(int row, int col) {
        vec2 uv = (vec2(col, row) + halfCR) / vec2(${s}TexShape[1], ${s}TexShape[0]);
        return sampleTexture(${s}, uv);
      }
    `;let h=a[0],p=a[1];return`
    float ${r}(int row, int col) {
      vec2 uv = (vec2(col, row) + halfCR) / vec2(${p}.0, ${h}.0);
      return sampleTexture(${s}, uv);
    }
  `}let{newShape:o,keptDims:i}=I.squeezeShape(n),l=o;if(l.length<n.length){let h=Kl(e,l),p=["row","col"];return`
      ${ql(h,t)}
      float ${r}(int row, int col) {
        return ${r}(${Zl(p,i)});
      }
    `}if(e.shapeInfo.isUniform)return`
      float ${r}(int row, int col) {
        int index = round(dot(vec2(row, col), vec2(${n[1]}, 1)));
        ${Xl(e)}
      }
    `;let u=a[0],c=a[1],d=Ho(s);return c===1?t?`
      float ${r}(int row, int col) {
        float index = dot(vec3(row, col, ${d}), vec3(${s}Shape[1], 1, 1));
        vec2 uv = vec2(0.5, (index + 0.5) / float(${s}TexShape[0]));
        return sampleTexture(${s}, uv);
      }
    `:`
    float ${r}(int row, int col) {
      float index = dot(vec3(row, col, ${d}), vec3(${n[1]}, 1, 1));
      vec2 uv = vec2(0.5, (index + 0.5) / ${u}.0);
      return sampleTexture(${s}, uv);
    }
  `:u===1?t?`
      float ${r}(int row, int col) {
        float index = dot(vec3(row, col, ${d}), vec3(${s}Shape[1], 1, 1));
        vec2 uv = vec2((index + 0.5) / float(${s}TexShape[1]), 0.5);
        return sampleTexture(${s}, uv);
      }
    `:`
    float ${r}(int row, int col) {
      float index = dot(vec3(row, col, ${d}), vec3(${n[1]}, 1, 1));
      vec2 uv = vec2((index + 0.5) / ${c}.0, 0.5);
      return sampleTexture(${s}, uv);
    }
  `:t?`
      float ${r}(int row, int col) {
        // Explicitly use integer operations as dot() only works on floats.
        int index = row * ${s}Shape[1] + col + ${d};
        vec2 uv = uvFromFlat(${s}TexShape[0], ${s}TexShape[1], index);
        return sampleTexture(${s}, uv);
      }
    `:`
  float ${r}(int row, int col) {
    // Explicitly use integer operations as dot() only works on floats.
    int index = row * ${n[1]} + col + ${d};
    vec2 uv = uvFromFlat(${u}, ${c}, index);
    return sampleTexture(${s}, uv);
  }
`}function Aq(e,t){let n=e.shapeInfo.logicalShape,s=e.name,r="get"+s.charAt(0).toUpperCase()+s.slice(1),a=e.shapeInfo.texShape,o=[Math.ceil(a[0]/2),Math.ceil(a[1]/2)];if(n[0]===1){let h=n.slice(1),p=[1,2],f=Kl(e,h),m=["b","row","col"];return`
        ${A6(f,t)}
        vec4 ${r}(int b, int row, int col) {
          return ${r}(${Zl(m,p)});
        }
      `}let i=bn();if(t)return`
    vec4 ${r}(int b, int row, int col) {
      ivec2 packedTexShape = ivec2(ceil(float(${s}TexShape[0]) / 2.0), ceil(float(${s}TexShape[1]) / 2.0));
      int valuesPerRow = int(ceil(float(${s}Shape[2]) / 2.0));
      int texelsInBatch = valuesPerRow * int(ceil(float(${s}Shape[1]) / 2.0));
      vec2 uv = packedUVfrom3D(
        packedTexShape[0], packedTexShape[1], texelsInBatch, valuesPerRow, b, row, col);
      return ${i.texture2D}(${s}, uv);
    }
  `;let l=o[0],u=o[1],c=Math.ceil(n[2]/2),d=c*Math.ceil(n[1]/2);return`
    vec4 ${r}(int b, int row, int col) {
      vec2 uv = packedUVfrom3D(
        ${l}, ${u}, ${d}, ${c}, b, row, col);
      return ${i.texture2D}(${s}, uv);
    }
  `}function gq(e,t){let n=e.shapeInfo.logicalShape,s=e.name,r="get"+s.charAt(0).toUpperCase()+s.slice(1),a=n[1]*n[2],o=n[2],{newShape:i,keptDims:l}=I.squeezeShape(n),u=i;if(u.length<n.length){let m=Kl(e,u),A=["row","col","depth"];return`
        ${ql(m,t)}
        float ${r}(int row, int col, int depth) {
          return ${r}(${Zl(A,l)});
        }
      `}if(e.shapeInfo.isUniform)return`
      float ${r}(int row, int col, int depth) {
        int index = round(dot(vec3(row, col, depth),
                          vec3(${a}, ${o}, 1)));
        ${Xl(e)}
      }
    `;let c=e.shapeInfo.texShape,d=c[0],h=c[1],p=e.shapeInfo.flatOffset;if(h===a&&p==null)return t?`
      float ${r}(int row, int col, int depth) {
        int stride1 = ${s}Shape[2];
        float texR = float(row);
        float texC = dot(vec2(col, depth), vec2(stride1, 1));
        vec2 uv = (vec2(texC, texR) + halfCR) /
                   vec2(${s}TexShape[1], ${s}TexShape[0]);
        return sampleTexture(${s}, uv);
      }
    `:`
        float ${r}(int row, int col, int depth) {
          float texR = float(row);
          float texC = dot(vec2(col, depth), vec2(${o}, 1));
          vec2 uv = (vec2(texC, texR) + halfCR) /
                     vec2(${h}.0, ${d}.0);
          return sampleTexture(${s}, uv);
        }
      `;if(h===o&&p==null)return t?`
      float ${r}(int row, int col, int depth) {
        float texR = dot(vec2(row, col), vec2(${s}Shape[1], 1));
        float texC = float(depth);
        vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${s}TexShape[1], ${s}TexShape[0]);
        return sampleTexture(${s}, uv);
      }
    `:`
    float ${r}(int row, int col, int depth) {
      float texR = dot(vec2(row, col), vec2(${n[1]}, 1));
      float texC = float(depth);
      vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${h}.0, ${d}.0);
      return sampleTexture(${s}, uv);
    }
  `;let f=Ho(s);return t?`
    float ${r}(int row, int col, int depth) {
      // Explicitly use integer operations as dot() only works on floats.
      int stride0 = ${s}Shape[1] * ${s}Shape[2];
      int stride1 = ${s}Shape[2];
      int index = row * ${a} + col * ${o} + depth + ${f};
      vec2 uv = uvFromFlat(${s}TexShape[0], ${s}TexShape[1], index);
      return sampleTexture(${s}, uv);
    }
    `:`
      float ${r}(int row, int col, int depth) {
        // Explicitly use integer operations as dot() only works on floats.
        int index = row * ${a} + col * ${o} + depth + ${f};
        vec2 uv = uvFromFlat(${d}, ${h}, index);
        return sampleTexture(${s}, uv);
      }
  `}function yq(e,t){let n=e.name,s="get"+n.charAt(0).toUpperCase()+n.slice(1),r=bn();if(t)return`
    vec4 ${s}(int b2, int b, int row, int col) {
      int valuesPerRow = int(ceil(float(${n}Shape[3]) / 2.0));
      int texelsInBatch = valuesPerRow * int(ceil(float(${n}Shape[2]) / 2.0));
      int index = b * texelsInBatch + (row / 2) * valuesPerRow + (col / 2);
      texelsInBatch *= ${n}Shape[1];
      index = b2 * texelsInBatch + index;
      ivec2 packedTexShape = ivec2(ceil(float(${n}TexShape[0]) / 2.0), ceil(float(${n}TexShape[1]) / 2.0));
      int texR = index / packedTexShape[1];
      int texC = index - texR * packedTexShape[1];
      vec2 uv = (vec2(texC, texR) + halfCR) / vec2(packedTexShape[1], packedTexShape[0]); return ${r.texture2D}(${n}, uv);
    }
  `;let a=e.shapeInfo.logicalShape,o=a.length,i=e.shapeInfo.texShape,l=[Math.ceil(i[0]/2),Math.ceil(i[1]/2)],u=l[0],c=l[1],d=Math.ceil(a[o-1]/2),h=d*Math.ceil(a[o-2]/2),p="int b, int row, int col",f=`b * ${h} + (row / 2) * ${d} + (col / 2)`;for(let m=2;m<o-1;m++)p=`int b${m}, `+p,h*=a[o-m-1],f=`b${m} * ${h} + `+f;return`
    vec4 ${s}(${p}) {
      int index = ${f};
      int texR = index / ${c};
      int texC = index - texR * ${c};
      vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${c}, ${u});
      return ${r.texture2D}(${n}, uv);
    }
  `}function xq(e,t){let n=e.shapeInfo.logicalShape,s=e.name,r="get"+s.charAt(0).toUpperCase()+s.slice(1),a=n[3],o=n[2]*a,i=n[1]*o,{newShape:l,keptDims:u}=I.squeezeShape(n);if(l.length<n.length){let y=Kl(e,l),x=["row","col","depth","depth2"];return`
      ${ql(y,t)}
      float ${r}(int row, int col, int depth, int depth2) {
        return ${r}(${Zl(x,u)});
      }
    `}if(e.shapeInfo.isUniform)return`
      float ${r}(int row, int col, int depth, int depth2) {
        int index = round(dot(vec4(row, col, depth, depth2),
                          vec4(${i}, ${o}, ${a}, 1)));
        ${Xl(e)}
      }
    `;let c=e.shapeInfo.flatOffset,d=e.shapeInfo.texShape,h=d[0],p=d[1],f=`int stride2 = ${s}Shape[3];`,m=`int stride1 = ${s}Shape[2] * stride2;`,A=`int stride0 = ${s}Shape[1] * stride1;`;if(p===i&&c==null)return t?`
      float ${r}(int row, int col, int depth, int depth2) {
        ${f}
        ${m}
        float texR = float(row);
        float texC =
            dot(vec3(col, depth, depth2),
                vec3(stride1, stride2, 1));
        vec2 uv = (vec2(texC, texR) + halfCR) /
                   vec2(${s}TexShape[1], ${s}TexShape[0]);
        return sampleTexture(${s}, uv);
      }
    `:`
      float ${r}(int row, int col, int depth, int depth2) {
        float texR = float(row);
        float texC =
            dot(vec3(col, depth, depth2),
                vec3(${o}, ${a}, 1));
        vec2 uv = (vec2(texC, texR) + halfCR) /
                   vec2(${p}.0, ${h}.0);
        return sampleTexture(${s}, uv);
      }
    `;if(p===a&&c==null)return t?`
      float ${r}(int row, int col, int depth, int depth2) {
        float texR = dot(vec3(row, col, depth),
                         vec3(${s}Shape[1] * ${s}Shape[2], ${s}Shape[2], 1));
        float texC = float(depth2);
        vec2 uv = (vec2(texC, texR) + halfCR) /
                  vec2(${s}TexShape[1], ${s}TexShape[0]);
        return sampleTexture(${s}, uv);
      }
    `:`
      float ${r}(int row, int col, int depth, int depth2) {
        float texR = dot(vec3(row, col, depth),
                         vec3(${n[1]*n[2]}, ${n[2]}, 1));
        float texC = float(depth2);
        vec2 uv = (vec2(texC, texR) + halfCR) /
                  vec2(${p}.0, ${h}.0);
        return sampleTexture(${s}, uv);
      }
    `;let g=Ho(s);return t?`
    float ${r}(int row, int col, int depth, int depth2) {
      // Explicitly use integer operations as dot() only works on floats.
      ${f}
      ${m}
      ${A}
      int index = row * stride0 + col * stride1 +
          depth * stride2 + depth2;
      vec2 uv = uvFromFlat(${s}TexShape[0], ${s}TexShape[1], index + ${g});
      return sampleTexture(${s}, uv);
    }
  `:`
    float ${r}(int row, int col, int depth, int depth2) {
      // Explicitly use integer operations as dot() only works on floats.
      int index = row * ${i} + col * ${o} +
          depth * ${a} + depth2;
      vec2 uv = uvFromFlat(${h}, ${p}, index + ${g});
      return sampleTexture(${s}, uv);
    }
  `}function bq(e){let t=e.shapeInfo.logicalShape,n=e.name,s="get"+n.charAt(0).toUpperCase()+n.slice(1),r=t[4],a=t[3]*r,o=t[2]*a,i=t[1]*o,{newShape:l,keptDims:u}=I.squeezeShape(t);if(l.length<t.length){let m=Kl(e,l),A=["row","col","depth","depth2","depth3"];return`
      ${ql(m)}
      float ${s}(int row, int col, int depth, int depth2, int depth3) {
        return ${s}(${Zl(A,u)});
      }
    `}if(e.shapeInfo.isUniform)return`
      float ${s}(int row, int col, int depth, int depth2, int depth3) {
        float index = dot(
          vec4(row, col, depth, depth2),
          vec4(${i}, ${o}, ${a}, ${r})) +
          depth3;
        ${Xl(e)}
      }
    `;let c=e.shapeInfo.flatOffset,d=e.shapeInfo.texShape,h=d[0],p=d[1];if(p===i&&c==null)return`
      float ${s}(int row, int col, int depth, int depth2, int depth3) {
        int texR = row;
        float texC = dot(vec4(col, depth, depth2, depth3),
                         vec4(${o}, ${a}, ${r}, 1));
        vec2 uv = (vec2(texC, texR) + halfCR) /
                   vec2(${p}.0, ${h}.0);
        return sampleTexture(${n}, uv);
      }
    `;if(p===r&&c==null)return`
      float ${s}(int row, int col, int depth, int depth2, int depth3) {
        float texR = dot(
          vec4(row, col, depth, depth2),
          vec4(${t[1]*t[2]*t[3]},
               ${t[2]*t[3]}, ${t[3]}, 1));
        int texC = depth3;
        vec2 uv = (vec2(texC, texR) + halfCR) /
                  vec2(${p}.0, ${h}.0);
        return sampleTexture(${n}, uv);
      }
    `;let f=Ho(n);return`
    float ${s}(int row, int col, int depth, int depth2, int depth3) {
      // Explicitly use integer operations as dot() only works on floats.
      int index = row * ${i} + col * ${o} + depth * ${a} +
          depth2 * ${r} + depth3 + ${f};
      vec2 uv = uvFromFlat(${h}, ${p}, index);
      return sampleTexture(${n}, uv);
    }
  `}function vq(e){let t=e.shapeInfo.logicalShape,n=e.name,s="get"+n.charAt(0).toUpperCase()+n.slice(1),{newShape:r,keptDims:a}=I.squeezeShape(t);if(r.length<t.length){let A=Kl(e,r),g=["row","col","depth","depth2","depth3","depth4"];return`
      ${ql(A)}
      float ${s}(int row, int col, int depth,
                    int depth2, int depth3, int depth4) {
        return ${s}(${Zl(g,a)});
      }
    `}let o=t[5],i=t[4]*o,l=t[3]*i,u=t[2]*l,c=t[1]*u;if(e.shapeInfo.isUniform)return`
      float ${s}(int row, int col, int depth,
                  int depth2, int depth3, int depth4) {
        int index = round(dot(
          vec4(row, col, depth, depth2),
          vec4(${c}, ${u}, ${l}, ${i})) +
          dot(
            vec2(depth3, depth4),
            vec2(${o}, 1)));
        ${Xl(e)}
      }
    `;let d=e.shapeInfo.flatOffset,h=e.shapeInfo.texShape,p=h[0],f=h[1];if(f===c&&d==null)return`
      float ${s}(int row, int col, int depth,
                    int depth2, int depth3, int depth4) {
        int texR = row;
        float texC = dot(vec4(col, depth, depth2, depth3),
          vec4(${u}, ${l}, ${i}, ${o})) +
               float(depth4);
        vec2 uv = (vec2(texC, texR) + halfCR) /
                   vec2(${f}.0, ${p}.0);
        return sampleTexture(${n}, uv);
      }
    `;if(f===o&&d==null)return`
      float ${s}(int row, int col, int depth,
                    int depth2, int depth3, int depth4) {
        float texR = dot(vec4(row, col, depth, depth2),
          vec4(${t[1]*t[2]*t[3]*t[4]},
               ${t[2]*t[3]*t[4]},
               ${t[3]*t[4]},
               ${t[4]})) + float(depth3);
        int texC = depth4;
        vec2 uv = (vec2(texC, texR) + halfCR) /
                  vec2(${f}.0, ${p}.0);
        return sampleTexture(${n}, uv);
      }
    `;let m=Ho(n);return`
    float ${s}(int row, int col, int depth,
                  int depth2, int depth3, int depth4) {
      // Explicitly use integer operations as dot() only works on floats.
      int index = row * ${c} + col * ${u} + depth * ${l} +
          depth2 * ${i} + depth3 * ${o} + depth4 + ${m};
      vec2 uv = uvFromFlat(${p}, ${f}, index);
      return sampleTexture(${n}, uv);
    }
  `}function Xl(e){let t=e.name,n=I.sizeFromShape(e.shapeInfo.logicalShape);return n<2?`return ${t};`:`
    for (int i = 0; i < ${n}; i++) {
      if (i == index) {
        return ${t}[i];
      }
    }
  `}function wq(e,t){let n=e.name,s=n.charAt(0).toUpperCase()+n.slice(1),r="get"+s+"AtOutCoords",a=e.shapeInfo.logicalShape.length,o=t.logicalShape.length,i=m6(e.shapeInfo.logicalShape,t.logicalShape),l=ct(o),u=o-a,c,d=["x","y","z","w","u","v"];a===0?c="":o<2&&i.length>=1?c="coords = 0;":c=i.map(y=>`coords.${d[y+u]} = 0;`).join(`
`);let h="";o<2&&a>0?h="coords":h=e.shapeInfo.logicalShape.map((y,x)=>`coords.${d[x+u]}`).join(", ");let p="return outputValue;",m=I.sizeFromShape(e.shapeInfo.logicalShape)===1,g=I.sizeFromShape(t.logicalShape)===1;if(a===1&&!m&&!g)p=`
      return vec4(outputValue.xy, outputValue.xy);
    `;else if(m&&!g)o===1?p=`
        return vec4(outputValue.x, outputValue.x, 0., 0.);
      `:p=`
        return vec4(outputValue.x);
      `;else if(i.length){let y=a-2,x=a-1;i.indexOf(y)>-1&&i.indexOf(x)>-1?p="return vec4(outputValue.x);":i.indexOf(y)>-1?p="return vec4(outputValue.x, outputValue.y, outputValue.x, outputValue.y);":i.indexOf(x)>-1&&(p="return vec4(outputValue.xx, outputValue.zz);")}return`
    vec4 ${r}() {
      ${l} coords = getOutputCoords();
      ${c}
      vec4 outputValue = get${s}(${h});
      ${p}
    }
  `}function kq(e,t){let n=e.name,s=n.charAt(0).toUpperCase()+n.slice(1),r="get"+s+"AtOutCoords",a=t.texShape,o=e.shapeInfo.texShape,i=e.shapeInfo.logicalShape.length,l=t.logicalShape.length;if(!e.shapeInfo.isUniform&&i===l&&e.shapeInfo.flatOffset==null&&I.arraysEqual(o,a))return`
      float ${r}() {
        return sampleTexture(${n}, resultUV);
      }
    `;let u=ct(l),c=m6(e.shapeInfo.logicalShape,t.logicalShape),d=l-i,h,p=["x","y","z","w","u","v"];i===0?h="":l<2&&c.length>=1?h="coords = 0;":h=c.map(m=>`coords.${p[m+d]} = 0;`).join(`
`);let f="";return l<2&&i>0?f="coords":f=e.shapeInfo.logicalShape.map((m,A)=>`coords.${p[A+d]}`).join(", "),`
    float ${r}() {
      ${u} coords = getOutputCoords();
      ${h}
      return get${s}(${f});
    }
  `}function ct(e){if(e<=1)return"int";if(e===2)return"ivec2";if(e===3)return"ivec3";if(e===4)return"ivec4";if(e===5)return"ivec5";if(e===6)return"ivec6";throw Error(`GPU for rank ${e} is not yet supported`)}function k1(e,t,n){let{newShape:s}=I.squeezeShape(t),r=t.length,a=e&&r===3&&t[0]===1,o=a?t.slice(1):s,i=!e&&r>1&&!I.arraysEqual(t,n)&&s.length<r||a;return{useSqueezeShape:i,uniformShape:i?o:t}}function Kl(e,t){let n=JSON.parse(JSON.stringify(e));return n.shapeInfo.logicalShape=t,n}function Zl(e,t){return t.map(n=>e[n]).join(", ")}function Iq(e,t,n,s){let r=n.map((x,b)=>{let v={logicalShape:x.shape,texShape:x.isUniform?null:x.texData.texShape,isUniform:x.isUniform,isPacked:x.isUniform?!1:x.texData.isPacked,flatOffset:null};return x.texData!=null&&x.texData.slice!=null&&x.texData.slice.flatOffset>0&&(v.flatOffset=x.texData.slice.flatOffset),{name:t.variableNames[b],shapeInfo:v}}),a=r.map(x=>x.shapeInfo),o={logicalShape:s.shape,texShape:s.texData.texShape,isUniform:!1,isPacked:s.texData.isPacked,flatOffset:null},i=Vj(r,o,t),l=e.createProgram(i),u=null,c=e.getUniformLocation(l,"NAN",!1);ee().getNumber("WEBGL_VERSION")===1&&(u=e.getUniformLocation(l,"INFINITY",!1));let d=!1,h={},p={},f={};for(let x=0;x<t.variableNames.length;x++){let b=t.variableNames[x];h[b]=e.getUniformLocation(l,b,d),h[`offset${b}`]=e.getUniformLocation(l,`offset${b}`,d),t.enableShapeUniforms&&(p[`${b}Shape`]=e.getUniformLocation(l,`${b}Shape`,d),f[`${b}TexShape`]=e.getUniformLocation(l,`${b}TexShape`,d))}let m,A,g;t.enableShapeUniforms&&(m=e.getUniformLocation(l,"outShape",d),g=e.getUniformLocation(l,"outShapeStrides",d),A=e.getUniformLocation(l,"outTexShape",d));let y=[];return t.customUniforms&&t.customUniforms.forEach((x,b)=>{y[b]=e.getUniformLocation(l,x.name,d)}),{program:t,source:i,webGLProgram:l,uniformLocations:h,customUniformLocations:y,inShapeInfos:a,outShapeInfo:o,infLoc:u,nanLoc:c,inShapesLocations:p,inTexShapesLocations:f,outShapeLocation:m,outShapeStridesLocation:g,outTexShapeLocation:A}}function y6(e,t){if(e.length!==t.length)throw Error(`Binary was compiled with ${e.length} inputs, but was executed with ${t.length} inputs`);e.forEach((n,s)=>{let r=n.logicalShape,a=t[s],o=a.shape;if(!I.arraysEqual(r,o))throw Error(`Binary was compiled with different shapes than the current args. Shapes ${r} and ${o} must match`);if(n.isUniform&&a.isUniform)return;let i=n.texShape,l=a.isUniform?null:a.texData.texShape;if(!I.arraysEqual(i,l))throw Error(`Binary was compiled with different texture shapes than the current args. Shape ${i} and ${l} must match`)})}function Sq(e,t,n,s,r){t.program.enableShapeUniforms||(y6(t.inShapeInfos,n),y6([t.outShapeInfo],[s]));let a=s.texData.texture,o=s.texData.texShape;s.texData.isPacked?e.setOutputPackedMatrixTexture(a,o[0],o[1]):e.setOutputMatrixTexture(a,o[0],o[1]),e.setProgram(t.webGLProgram),ee().getNumber("WEBGL_VERSION")===1&&t.infLoc!==null&&e.gl.uniform1f(t.infLoc,1/0),t.nanLoc!==null&&e.gl.uniform1f(t.nanLoc,NaN),n.forEach((l,u)=>{let c=t.program.variableNames[u],d=t.uniformLocations[c],h=t.uniformLocations[`offset${c}`],p=t.inShapesLocations[`${c}Shape`],f=t.inTexShapesLocations[`${c}TexShape`];if(p){let{uniformShape:m}=k1(t.program.packedInputs,l.shape,l.texData.texShape);switch(m.length){case 1:e.gl.uniform1iv(p,new Int32Array(m));break;case 2:e.gl.uniform2iv(p,new Int32Array(m));break;case 3:e.gl.uniform3iv(p,new Int32Array(m));break;case 4:e.gl.uniform4iv(p,new Int32Array(m));break;default:break}}if(f&&e.gl.uniform2i(f,l.texData.texShape[0],l.texData.texShape[1]),d!=null){if(l.isUniform){if(I.sizeFromShape(l.shape)<2)e.gl.uniform1f(d,l.uniformValues[0]);else{let m=l.uniformValues;m instanceof Float32Array||(m=new Float32Array(m)),e.gl.uniform1fv(d,m)}return}l.texData.slice!=null&&h!=null&&e.gl.uniform1i(h,l.texData.slice.flatOffset),e.setInputMatrixTexture(l.texData.texture,d,u)}});let i=t.outShapeLocation;if(i)switch(s.shape.length){case 1:e.gl.uniform1iv(i,new Int32Array(s.shape));break;case 2:e.gl.uniform2iv(i,new Int32Array(s.shape));break;case 3:e.gl.uniform3iv(i,new Int32Array(s.shape));break;case 4:e.gl.uniform4iv(i,new Int32Array(s.shape));break;default:break}if(t.outShapeStridesLocation){let l=I.computeStrides(s.shape);switch(s.shape.length){case 2:e.gl.uniform1iv(t.outShapeStridesLocation,new Int32Array(l));break;case 3:e.gl.uniform2iv(t.outShapeStridesLocation,new Int32Array(l));break;case 4:e.gl.uniform3iv(t.outShapeStridesLocation,new Int32Array(l));break;default:break}}t.outTexShapeLocation&&e.gl.uniform2i(t.outTexShapeLocation,s.texData.texShape[0],s.texData.texShape[1]),t.program.customUniforms&&r&&t.program.customUniforms.forEach((l,u)=>{let c=t.customUniformLocations[u],d=r[u];if(l.type==="float")e.gl.uniform1fv(c,d);else if(l.type==="vec2")e.gl.uniform2fv(c,d);else if(l.type==="vec3")e.gl.uniform3fv(c,d);else if(l.type==="vec4")e.gl.uniform4fv(c,d);else if(l.type==="int")e.gl.uniform1iv(c,d);else if(l.type==="ivec2")e.gl.uniform2iv(c,d);else if(l.type==="ivec3")e.gl.uniform3iv(c,d);else if(l.type==="ivec4")e.gl.uniform4iv(c,d);else throw Error(`uniform type ${l.type} is not supported yet.`)}),e.executeProgram()}function Cq(e,t,n){let s="";t.concat(n).forEach(o=>{let i=o.texData!=null&&o.texData.slice!=null&&o.texData.slice.flatOffset>0;if(e.enableShapeUniforms&&!o.isUniform){let l=o.texData.texShape,{useSqueezeShape:u,uniformShape:c}=k1(e.packedInputs,o.shape,l),d="",h="",p="";if(c.length===1&&e.packedInputs){let b=[Math.ceil(l[0]/2),Math.ceil(l[1]/2)];d=`${b[0]>1}_${b[1]>1}`}else if(c.length===2&&!e.packedInputs)h=`${c[0]>1}_${c[1]>1}`;else if(c.length>2&&!e.packedInputs){let b=I.computeStrides(c);p=`${b[0]===l[1]}_${b[b.length-1]===l[1]}`}let f=o.shape.length,m=f===2&&I.arraysEqual(o.shape,l),A=I.sizeFromShape(o.shape)===1,g=$.getBroadcastDims(o.shape,n.shape),y=!e.packedInputs&&f===n.shape.length&&I.arraysEqual(l,n.texData.texShape),x=e.packedInputs||f>2?"":`${l[0]>1}_${l[1]>1}`;s+=`${f}_${y}_${u}_${c.length}_${A}_${g}_${m}_${d}_${h}_${p}_${x}_${i}`}else{let l=o.isUniform?"uniform":o.texData.texShape;s+=`${o.shape}_${l}_${i}`}});let r=e.userCode,a=e.constructor.name;return a+="_"+s+"_"+r+`${ee().getNumber("WEBGL_VERSION")}`,a}function cf(e){return ee().getBool("WEBGL_USE_SHAPES_UNIFORMS")&&e<=4}var{addImpl:Tq,bincountImpl:x6,bincountReduceImpl:Nq,ceilImpl:Eq,concatImpl:Rq,equalImpl:_q,expImpl:$q,expm1Impl:Fq,floorImpl:Dq,gatherNdImpl:Oq,gatherV2Impl:Pq,greaterImpl:Mq,greaterEqualImpl:zq,lessImpl:Lq,lessEqualImpl:Bq,linSpaceImpl:Wq,logImpl:Vq,maxImpl:Uq,maximumImpl:Hq,minimumImpl:Gq,multiplyImpl:jq,negImpl:qq,notEqualImpl:Xq,prodImpl:Kq,rangeImpl:Zq,rsqrtImpl:Yq,simpleAbsImpl:b6,sliceImpl:Jq,sparseFillEmptyRowsImpl:Qq,sparseReshapeImpl:eX,sparseSegmentReductionImpl:v6,stridedSliceImpl:tX,stringNGramsImpl:nX,stringSplitImpl:sX,stringToHashBucketFastImpl:rX,subImpl:aX,tileImpl:oX,topKImpl:iX,transposeImpl:I1,uniqueImpl:lX}=Z2;function w6(e,t){return["x","y","z","w","u","v"].slice(0,t).map(n=>`${e}.${n}`)}function vn(e,t){return t===1?[e]:w6(e,t)}function uX(e,t){if(e===1)return"rc";let n="";for(let s=0;s<e;s++)n+=t[s],s<e-1&&(n+=",");return n}var cX=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outputShape=e;let t=e.length;if(t===0)this.userCode=`
        void main() {
          setOutput(vec4(getA(), 0., 0., 0.));
        }
      `;else{let n=vn("rc",t),s=ct(t),r=hX(t,e,n),a=pX(t,e[e.length-1],e[e.length-2],n),o=fX(e,n);this.userCode=`
        void main() {
          ${s} rc = getOutputCoords();

          if(${r}) {
            setOutput(vec4(0));
          } else {
            ${a}

            setOutput(vec4(${o}));
          }
        }
      `}}};function dX(e,t){let n=[];for(let s=0;s<=1;s++)for(let r=0;r<=1;r++){let a=`${s===0?"r":"rp1"}, ${r===0?"c":"cp1"}`;for(let o=2;o<e;o++)a=`${t[t.length-1-o]},`+a;n.push(a)}return n}function hX(e,t,n){if(e===1)return`rc > ${t[0]}`;let s="";for(let r=e-2;r<e;r++)s+=`${n[r]} >= ${t[r]}`,r<e-1&&(s+="||");return s}function pX(e,t,n,s){if(e===1)return"";let r=s.slice(-2);return`
    int r = ${r[0]};
    int c = ${r[1]};
    int rp1 = r + 1;
    int cp1 = c + 1;

    bool cEdge = cp1 >= ${t};
    bool rEdge = rp1 >= ${n};
  `}function fX(e,t){let n=e.length,s=dX(n,t);return n===1?`getA(rc),
            rc + 1 >= ${e[0]} ? 0. : getA(rc + 1),
            0, 0`:`getA(${s[0]}),
          cEdge ? 0. : getA(${s[1]}),
          rEdge ? 0. : getA(${s[2]}),
          rEdge || cEdge ? 0. : getA(${s[3]})`}var k6=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e;let n="";for(let s=0;s<4;s++){let r="thisRC = rc;";s%2==1&&(r+="thisRC.z += 1;"),s>1&&(r+="thisRC.y += 1;"),n+=`
        ${r}
        ${s>0?"if(thisRC.y < rows && thisRC.z < cols){":""}
          int flatIndex = getFlatIndex(thisRC);

          ivec3 inputRC = inputCoordsFromReshapedOutCoords(flatIndex);
          vec2 inputRCInnerDims = vec2(float(inputRC.y),float(inputRC.z));

          result[${s}] =
            getChannel(getA(inputRC.x, inputRC.y, inputRC.z), inputRCInnerDims);
        ${s>0?"}":""}
      `}this.userCode=`
      ${mX(t)}
      ${g1(e)}

      void main() {
        ivec3 rc = getOutputCoords();

        vec4 result = vec4(0.);

        ivec3 thisRC;
        int rows = ${e[1]};
        int cols = ${e[2]};

        ${n}

        setOutput(result);
      }
    `}};function mX(e){return`
    ivec3 inputCoordsFromReshapedOutCoords(int index) {
      ${Uo(["r","c","d"],e)}
      return ivec3(r, c, d);
    }
  `}var AX=class{constructor(e){this.gpgpu=e,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0,this.freeTextures={},this.logEnabled=!1,this.usedTextures={}}acquireTexture(e,t,n){let s=S6(t,n),r=C6(e,s,n);r in this.freeTextures||(this.freeTextures[r]=[]),r in this.usedTextures||(this.usedTextures[r]=[]);let a=I6(e,s,this.gpgpu.gl,this.gpgpu.textureConfig,n);if(this.freeTextures[r].length>0){this.numFreeTextures--,this.numUsedTextures++,this._numBytesFree-=a,this.log();let i=this.freeTextures[r].shift();return this.usedTextures[r].push(i),i}let o;return s===sn.PACKED_2X2_FLOAT32?o=this.gpgpu.createPackedMatrixTexture(e[0],e[1]):s===sn.PACKED_2X2_FLOAT16?o=this.gpgpu.createFloat16PackedMatrixTexture(e[0],e[1]):s===sn.UNPACKED_FLOAT32?o=this.gpgpu.createFloat32MatrixTexture(e[0],e[1]):s===sn.UNPACKED_FLOAT16?o=this.gpgpu.createFloat16MatrixTexture(e[0],e[1]):s===sn.PACKED_4X1_UNSIGNED_BYTE&&(o=this.gpgpu.createUnsignedBytesMatrixTexture(e[0],e[1])),this.usedTextures[r].push(o),this.numUsedTextures++,this._numBytesAllocated+=a,this.log(),o}releaseTexture(e,t,n,s){if(this.freeTextures==null)return;let r=S6(n,s),a=C6(t,r,s);a in this.freeTextures||(this.freeTextures[a]=[]);let o=I6(t,r,this.gpgpu.gl,this.gpgpu.textureConfig,s),i=ee().get("WEBGL_DELETE_TEXTURE_THRESHOLD");i!==-1&&this._numBytesAllocated>i?(this.gpgpu.deleteMatrixTexture(e),this._numBytesAllocated-=o):(this.freeTextures[a].push(e),this.numFreeTextures++,this._numBytesFree+=o),this.numUsedTextures--;let l=this.usedTextures[a],u=l.indexOf(e);if(u<0)throw new Error("Cannot release a texture that was never provided by this texture manager");l.splice(u,1),this.log()}log(){if(!this.logEnabled)return;let e=this.numFreeTextures+this.numUsedTextures;console.log("Free/Used",`${this.numFreeTextures} / ${this.numUsedTextures}`,`(${e})`);let t=this._numBytesFree/this._numBytesAllocated;console.log(`Bytes allocated: ${this._numBytesAllocated}`),console.log(`Bytes unused: ${this._numBytesFree} (${Math.round(100*t)}%)`)}get numBytesAllocated(){return this._numBytesAllocated}get numBytesFree(){return this._numBytesFree}getNumUsedTextures(){return this.numUsedTextures}getNumFreeTextures(){return this.numFreeTextures}dispose(){if(this.freeTextures!=null){for(let e in this.freeTextures)this.freeTextures[e].forEach(t=>{this.gpgpu.deleteMatrixTexture(t)});for(let e in this.usedTextures)this.usedTextures[e].forEach(t=>{this.gpgpu.deleteMatrixTexture(t)});this.freeTextures=null,this.usedTextures=null,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0}}};function gX(e,t){let n=e;if(t===n.R32F)return 4;if(t===n.R16F)return 2;if(t===n.RGBA32F)return 16;if(t===e.RGBA)return 16;if(t===n.RGBA16F)return 8;throw new Error(`Unknown internal format ${t}`)}function I6(e,t,n,s,r){let a=yX(t,s),o;if(r){let[l,u]=Gl(e[0],e[1]);o=l*u}else{let[l,u]=Mc(e[0],e[1]);o=l*u}let i=gX(n,a);return o*i}function yX(e,t){switch(e){case sn.PACKED_2X2_FLOAT32:return v1(t);case sn.PACKED_2X2_FLOAT16:return w1(t);case sn.UNPACKED_FLOAT32:return y1(t);case sn.UNPACKED_FLOAT16:return x1(t);case sn.PACKED_4X1_UNSIGNED_BYTE:return b1(t);default:throw new Error(`Unknown physical texture type ${e}`)}}function xX(e){return ee().getBool("WEBGL_RENDER_FLOAT32_ENABLED")?e?sn.PACKED_2X2_FLOAT32:sn.UNPACKED_FLOAT32:e?sn.PACKED_2X2_FLOAT16:sn.UNPACKED_FLOAT16}function S6(e,t){if(e===fs.UPLOAD)return sn.PACKED_2X2_FLOAT32;if(e===fs.RENDER||e==null)return xX(t);if(e===fs.DOWNLOAD||e===fs.PIXELS)return sn.PACKED_4X1_UNSIGNED_BYTE;throw new Error(`Unknown logical texture type ${e}`)}function C6(e,t,n){return`${e[0]}_${e[1]}_${t}_${n}`}var sa=class{constructor(e,t){this.variableNames=["A"],this.outputShape=e,this.enableShapeUniforms=cf(this.outputShape.length),this.userCode=`
      float unaryOperation(float x) {
        ${t}
      }

      void main() {
        float x = getAAtOutCoords();
        float y = unaryOperation(x);

        setOutput(y);
      }
    `}},Bs="if (isnan(x)) return x;",bX="return x;",T6="return abs(x);",vX="return (x >= 0.0) ? x : (exp(x) - 1.0);",wX=Bs+`
  return (x < 0.0) ? 0.0 : x;
`,kX=Bs+`
  return (x < 0.0) ? 0.0 : min(6.0, x);
`,df="return x;",IX="return 1.0 / (1.0 + exp(-1.0 * x));",SX="return x;",CX=`
  vec4 result;

  result.r = (x.r >= 0.0) ? x.r : (exp(x.r) - 1.0);
  result.g = (x.g >= 0.0) ? x.g : (exp(x.g) - 1.0);
  result.b = (x.b >= 0.0) ? x.b : (exp(x.b) - 1.0);
  result.a = (x.a >= 0.0) ? x.a : (exp(x.a) - 1.0);

  return result;
`,TX=`
  vec4 result = x * vec4(greaterThanEqual(x, vec4(0.0)));
  bvec4 isNaN = isnan(x);

  result.r = isNaN.r ? x.r : result.r;
  result.g = isNaN.g ? x.g : result.g;
  result.b = isNaN.b ? x.b : result.b;
  result.a = isNaN.a ? x.a : result.a;

  return result;
`,NX=`
  vec4 result = min(x, vec4(6.)) * vec4(greaterThanEqual(x, vec4(0.0)));
  bvec4 isNaN = isnan(x);

  result.r = isNaN.r ? x.r : result.r;
  result.g = isNaN.g ? x.g : result.g;
  result.b = isNaN.b ? x.b : result.b;
  result.a = isNaN.a ? x.a : result.a;

  return result;
`,EX="return 1.0 / (1.0 + exp(-1.0 * x));",Yl=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.enableShapeUniforms=cf(this.outputShape.length),this.userCode=`
      vec4 unaryOperation(vec4 x) {
        ${t}
      }

      void main() {
        vec4 x = getAAtOutCoords();
        vec4 y = unaryOperation(x);

        setOutput(y);
      }
    `}},RX=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outputShape=e;let t=e.length,n=vn("rc",t),s=ct(t),r=uX(t,n),a=n.slice(-2),o=t<=1?"rc":`vec2(${a.join(",")})`;this.userCode=`
      void main() {
        ${s} rc = getOutputCoords();
        vec4 packedInput = getA(${r});

        setOutput(getChannel(packedInput, ${o}));
      }
    `}},_X=Js.whereImpl,$X=1e-7,FX=1e-4,hf={};function DX(e){return e in hf||(hf[e]={}),hf[e]}var OX=ee().getNumber("CPU_HANDOFF_SIZE_THRESHOLD"),PX=600;function MX(){return ee().global.screen==null?1024:ee().global.screen.height*ee().global.screen.width*window.devicePixelRatio*PX/1024/1024}var Jl=class extends ku{constructor(e){super();if(this.pendingRead=new WeakMap,this.pendingDisposal=new WeakSet,this.dataRefCount=new WeakMap,this.numBytesInGPU=0,this.uploadWaitMs=0,this.downloadWaitMs=0,this.lastGlFlushTime=0,this.warnedAboutMemory=!1,this.pendingDeletes=0,this.disposed=!1,!ee().getBool("HAS_WEBGL"))throw new Error("WebGL is not supported on this device");if(e==null){let t=ar(ee().getNumber("WEBGL_VERSION"));this.binaryCache=DX(ee().getNumber("WEBGL_VERSION")),this.gpgpu=new uf(t),this.canvas=t.canvas,this.gpgpuCreatedLocally=!0}else this.gpgpu=e,this.binaryCache={},this.gpgpuCreatedLocally=!1,this.canvas=e.gl.canvas;this.textureManager=new AX(this.gpgpu),this.numMBBeforeWarning=MX(),this.texData=new Fd(this,Ar())}nextDataId(){return Jl.nextDataId++}numDataIds(){return this.texData.numDataIds()-this.pendingDeletes}write(e,t,n){if((ee().getBool("WEBGL_CHECK_NUMERICAL_PROBLEMS")||ee().getBool("DEBUG"))&&this.checkNumericalProblems(e),n==="complex64"&&e!=null)throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");let s={id:this.nextDataId()};return this.texData.set(s,{shape:t,dtype:n,values:e,usage:fs.UPLOAD,refCount:1}),s}refCount(e){return this.texData.has(e)?this.texData.get(e).refCount:0}incRef(e){let t=this.texData.get(e);t.refCount++}decRef(e){if(this.texData.has(e)){let t=this.texData.get(e);t.refCount--}}move(e,t,n,s,r){if(ee().getBool("DEBUG")&&this.checkNumericalProblems(t),s==="complex64")throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");this.texData.set(e,{shape:n,dtype:s,values:t,usage:fs.UPLOAD,refCount:r})}disposeIntermediateTensorInfo(e){this.disposeData(e.dataId)}readSync(e){let t=this.texData.get(e),{values:n,dtype:s,complexTensorInfos:r,slice:a,shape:o,isPacked:i}=t;if(a!=null){let d;i?d=new Yl(o,df):d=new sa(o,df);let h=this.runWebGLProgram(d,[{dataId:e,shape:o,dtype:s}],s),p=this.readSync(h.dataId);return this.disposeIntermediateTensorInfo(h),p}if(n!=null)return this.convertAndCacheOnCPU(e);if(s==="string")return n;let l=this.activeTimers!=null,u;l&&(u=I.now());let c;if(s==="complex64"){let d=this.readSync(r.real.dataId),h=this.readSync(r.imag.dataId);c=$.mergeRealAndImagArrays(d,h)}else c=this.getValuesFromTexture(e);return l&&(this.downloadWaitMs+=I.now()-u),this.convertAndCacheOnCPU(e,c)}async read(e){if(this.pendingRead.has(e)){let p=this.pendingRead.get(e);return new Promise(f=>p.push(f))}let t=this.texData.get(e),{values:n,shape:s,slice:r,dtype:a,complexTensorInfos:o,isPacked:i}=t;if(r!=null){let p;i?p=new Yl(s,df):p=new sa(s,df);let f=this.runWebGLProgram(p,[{dataId:e,shape:s,dtype:a}],a),m=this.read(f.dataId);return this.disposeIntermediateTensorInfo(f),m}if(n!=null)return this.convertAndCacheOnCPU(e);if(!ee().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")&&ee().getNumber("WEBGL_VERSION")===2)throw new Error("tensor.data() with WEBGL_DOWNLOAD_FLOAT_ENABLED=false and WEBGL_VERSION=2 not yet supported.");let l=null,u;if(a!=="complex64"&&ee().get("WEBGL_BUFFER_SUPPORTED")){u=this.decode(e);let p=this.texData.get(u.dataId);l=this.gpgpu.createBufferFromTexture(p.texture,...zc(s))}this.pendingRead.set(e,[]),a!=="complex64"&&await this.gpgpu.createAndWaitForFence();let c;if(a==="complex64"){let p=await Promise.all([this.read(o.real.dataId),this.read(o.imag.dataId)]),f=p[0],m=p[1];c=$.mergeRealAndImagArrays(f,m)}else if(l==null)c=this.getValuesFromTexture(e);else{let p=I.sizeFromShape(s);c=this.gpgpu.downloadFloat32MatrixFromBuffer(l,p)}if(u!=null&&this.disposeIntermediateTensorInfo(u),l!=null){let p=this.gpgpu.gl;be(p,()=>p.deleteBuffer(l))}let d=this.convertAndCacheOnCPU(e,c),h=this.pendingRead.get(e);return this.pendingRead.delete(e),h.forEach(p=>p(d)),this.pendingDisposal.has(e)&&(this.pendingDisposal.delete(e),this.disposeData(e)&&Ar().removeDataId(e,this),this.pendingDeletes--),d}bufferSync(e){let t=this.readSync(e.dataId),n=t;if(e.dtype==="string")try{n=t.map(s=>I.decodeString(s))}catch(s){throw new Error("Failed to decode encoded string bytes into utf-8")}return Be(e.shape,e.dtype,n)}checkNumericalProblems(e){if(e!=null)for(let t=0;t<e.length;t++){let n=e[t];if(!Cw(n))throw ee().getBool("WEBGL_RENDER_FLOAT32_CAPABLE")?Error(`The value ${n} cannot be represented with your current settings. Consider enabling float32 rendering: 'tf.env().set('WEBGL_RENDER_FLOAT32_ENABLED', true);'`):Error(`The value ${n} cannot be represented on this device.`)}}getValuesFromTexture(e){let{shape:t,dtype:n,isPacked:s}=this.texData.get(e),r=I.sizeFromShape(t);if(ee().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")){let d=this.decode(e),h=this.texData.get(d.dataId),p=this.gpgpu.downloadMatrixFromPackedTexture(h.texture,...zc(t)).subarray(0,r);return this.disposeIntermediateTensorInfo(d),p}let a=ee().getBool("WEBGL_PACK")&&s===!0,o=a?rf(t):t,i=a?new zj(o):new Mj(o),l=this.runWebGLProgram(i,[{shape:o,dtype:n,dataId:e}],"float32"),u=this.texData.get(l.dataId),c=this.gpgpu.downloadByteEncodedFloatMatrixFromOutputTexture(u.texture,u.texShape[0],u.texShape[1]).subarray(0,r);return this.disposeIntermediateTensorInfo(l),c}timerAvailable(){return ee().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0}async time(e){let t=this.activeTimers,n=[],s=!1;this.programTimersStack==null?(this.programTimersStack=n,s=!0):this.activeTimers.push(n),this.activeTimers=n,e();let r=I.flatten(this.activeTimers.map(i=>i.query)).filter(i=>i!=null),a=I.flatten(this.activeTimers.map(i=>i.name)).filter(i=>i!=null);this.activeTimers=t,s&&(this.programTimersStack=null);let o={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null};if(ee().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0){let i=await Promise.all(r);o.kernelMs=I.sum(i),o.getExtraProfileInfo=()=>i.map((l,u)=>({name:a[u],ms:l})).map(l=>`${l.name}: ${l.ms}`).join(", ")}else o.kernelMs={error:"WebGL query timers are not supported in this environment."};return this.uploadWaitMs=0,this.downloadWaitMs=0,o}memory(){return{unreliable:!1,numBytesInGPU:this.numBytesInGPU,numBytesInGPUAllocated:this.textureManager.numBytesAllocated,numBytesInGPUFree:this.textureManager.numBytesFree}}startTimer(){return ee().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?this.gpgpu.beginQuery():{startMs:I.now(),endMs:null}}endTimer(e){return ee().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?(this.gpgpu.endQuery(),e):(e.endMs=I.now(),e)}async getQueryTime(e){if(ee().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0)return this.gpgpu.waitForQueryAndGetTime(e);let t=e;return t.endMs-t.startMs}disposeData(e,t=!1){if(this.pendingDisposal.has(e))return!1;if(!this.texData.has(e))return!0;if(t?this.texData.get(e).refCount=0:this.texData.get(e).refCount--,!t&&this.texData.get(e).refCount>0)return!1;if(this.pendingRead.has(e))return this.pendingDisposal.add(e),this.pendingDeletes++,!1;this.releaseGPUData(e);let{complexTensorInfos:n}=this.texData.get(e);return n!=null&&(this.disposeData(n.real.dataId,t),this.disposeData(n.imag.dataId,t)),this.texData.delete(e),!0}releaseGPUData(e){let{texture:t,dtype:n,texShape:s,usage:r,isPacked:a,slice:o}=this.texData.get(e),i=o&&o.origDataId||e,l=this.dataRefCount.get(i);l>1?this.dataRefCount.set(i,l-1):(this.dataRefCount.delete(i),t!=null&&(this.numBytesInGPU-=this.computeBytes(s,n),this.textureManager.releaseTexture(t,s,r,a)));let u=this.texData.get(e);u.texture=null,u.texShape=null,u.isPacked=!1,u.slice=null}getTexture(e){return this.uploadToGPU(e),this.texData.get(e).texture}getDataInfo(e){return this.texData.get(e)}shouldExecuteOnCPU(e,t=OX){return ee().getBool("WEBGL_CPU_FORWARD")&&e.every(n=>this.texData.get(n.dataId).texture==null&&I.sizeFromShape(n.shape)<t)}getGPGPUContext(){return this.gpgpu}where(e){$.warn("tf.where() in webgl locks the UI thread. Call tf.whereAsync() instead");let t=e.dataSync();return _X(e.shape,t)}packedUnaryOp(e,t,n){let s=new Yl(e.shape,t),r=this.compileAndRun(s,[e],n);return Ar().makeTensorFromDataId(r.dataId,r.shape,r.dtype)}abs(e){if(this.shouldExecuteOnCPU([e])&&e.dtype!=="complex64"){let s=b6(this.texData.get(e.dataId).values);return this.makeOutput(e.shape,e.dtype,s)}if(ee().getBool("WEBGL_PACK_UNARY_OPERATIONS"))return this.packedUnaryOp(e,T6,e.dtype);let t=new sa(e.shape,T6),n=this.compileAndRun(t,[e]);return Ar().makeTensorFromDataId(n.dataId,n.shape,n.dtype)}makeTensorInfo(e,t,n){let s;if(t==="string"&&n!=null&&n.length>0&&I.isString(n[0])){let r=n.map(a=>I.encodeString(a));s=this.write(r,e,t)}else s=this.write(n,e,t);return this.texData.get(s).usage=null,{dataId:s,shape:e,dtype:t}}makeOutput(e,t,n){let{dataId:s}=this.makeTensorInfo(e,t,n);return Ar().makeTensorFromDataId(s,e,t,this)}unpackTensor(e){let t=new RX(e.shape);return this.runWebGLProgram(t,[e],e.dtype)}packTensor(e){let t=new cX(e.shape),n=!0;return this.runWebGLProgram(t,[e],e.dtype,null,n)}packedReshape(e,t){let n=[Wo(e.shape),...Vo(e.shape)],s={dtype:e.dtype,shape:n,dataId:e.dataId},r=[Wo(t),...Vo(t)],a=new k6(r,n),o=!0,i=this.runWebGLProgram(a,[s],e.dtype,null,o);return{dataId:i.dataId,shape:t,dtype:i.dtype}}decode(e){let t=this.texData.get(e),{isPacked:n,shape:s,dtype:r}=t,a=rf(s),o;n?o=new Pj(a):o=new Oj(a);let i=!0,l=this.runWebGLProgram(o,[{shape:a,dtype:r,dataId:e}],r,null,i);return{dtype:r,shape:s,dataId:l.dataId}}runWebGLProgram(e,t,n,s,r=!1){let a=this.makeTensorInfo(e.outputShape,n),o=this.texData.get(a.dataId);if(e.packedOutput&&(o.isPacked=!0),e.outPackingScheme===Pc.DENSE){let m=zc(e.outputShape);o.texShape=m.map(A=>A*2)}if(e.outTexUsage!=null&&(o.usage=e.outTexUsage),I.sizeFromShape(a.shape)===0)return o.values=I.getTypedArrayFromDType(a.dtype,0),a;let i=[],l=t.map(m=>{if(m.dtype==="complex64")throw new Error("GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.");let A=this.texData.get(m.dataId);if(A.texture==null){if(!e.packedInputs&&I.sizeFromShape(m.shape)<=ee().getNumber("WEBGL_SIZE_UPLOAD_UNIFORM"))return{shape:m.shape,texData:null,isUniform:!0,uniformValues:A.values};e.packedInputs&&(A.isPacked=!0,A.shape=m.shape)}else if(!!A.isPacked!=!!e.packedInputs)m=A.isPacked?this.unpackTensor(m):this.packTensor(m),i.push(m),A=this.texData.get(m.dataId);else if(A.isPacked&&!Wc(A.shape,m.shape)){let g=m,y=m.shape;m.shape=A.shape,m=this.packedReshape(m,y),i.push(m),A=this.texData.get(m.dataId),g.shape=y}return this.uploadToGPU(m.dataId),{shape:m.shape,texData:A,isUniform:!1}});this.uploadToGPU(a.dataId);let u={shape:a.shape,texData:o,isUniform:!1},c=Cq(e,l,u),d=this.getAndSaveBinary(c,()=>Iq(this.gpgpu,e,l,u)),h=this.activeTimers!=null,p;h&&(p=this.startTimer()),Sq(this.gpgpu,d,l,u,s),i.forEach(m=>this.disposeIntermediateTensorInfo(m)),h&&(p=this.endTimer(p),this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(p)}));let f=ee().get("WEBGL_FLUSH_THRESHOLD");if(f>0){let m=I.now();m-this.lastGlFlushTime>f&&(this.gpgpu.gl.flush(),this.lastGlFlushTime=m)}if(!ee().getBool("WEBGL_LAZILY_UNPACK")&&o.isPacked&&r===!1){let m=this.unpackTensor(a);return this.disposeIntermediateTensorInfo(a),m}return a}compileAndRun(e,t,n,s,r=!1){return n=n||t[0].dtype,this.runWebGLProgram(e,t,n,s,r)}getAndSaveBinary(e,t){return e in this.binaryCache||(this.binaryCache[e]=t()),this.binaryCache[e]}getTextureManager(){return this.textureManager}dispose(){this.disposed||(ee().getBool("IS_TEST")||Object.keys(this.binaryCache).forEach(t=>{this.gpgpu.deleteProgram(this.binaryCache[t].webGLProgram),delete this.binaryCache[t]}),this.textureManager.dispose(),this.canvas!=null&&typeof HTMLCanvasElement!="undefined"&&this.canvas instanceof HTMLCanvasElement?this.canvas.remove():this.canvas=null,this.gpgpuCreatedLocally&&(this.gpgpu.program=null,this.gpgpu.dispose()),this.disposed=!0)}floatPrecision(){return this.floatPrecisionValue==null&&(this.floatPrecisionValue=H(()=>{if(!ee().get("WEBGL_RENDER_FLOAT32_ENABLED")){let e=ee().getBool("DEBUG");ee().set("DEBUG",!1);let t=this.abs(Ie(1e-8)).dataSync()[0];if(ee().set("DEBUG",e),t>0)return 32}return 16})),this.floatPrecisionValue}epsilon(){return this.floatPrecision()===32?$X:FX}uploadToGPU(e){let t=this.texData.get(e),{shape:n,dtype:s,values:r,texture:a,usage:o,isPacked:i}=t;if(a!=null)return;let l=this.activeTimers!=null,u;l&&(u=I.now());let c=t.texShape;if(c==null&&(c=Uw(n,i),t.texShape=c),r!=null){let d=rf(n),h,p=c[1],f=c[0],m=r instanceof Uint8Array;i?([p,f]=Gl(c[0],c[1]),h=new Bj(d,[f,p],m)):h=new Lj(d,[f,p],m);let A=this.makeTensorInfo([f,p],s);m?this.texData.get(A.dataId).usage=fs.PIXELS:this.texData.get(A.dataId).usage=fs.UPLOAD,this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(A.dataId),p,f,r);let g=!0,y=this.runWebGLProgram(h,[A],s,null,g),x=this.texData.get(y.dataId);t.texture=x.texture,t.texShape=x.texShape,t.isPacked=x.isPacked,t.usage=x.usage,this.disposeIntermediateTensorInfo(A),this.texData.delete(y.dataId),t.values=null,l&&(this.uploadWaitMs+=I.now()-u)}else{let d=this.acquireTexture(c,o,s,i);t.texture=d}}convertAndCacheOnCPU(e,t){let n=this.texData.get(e),{dtype:s}=n;return this.releaseGPUData(e),t!=null&&(n.values=zX(t,s)),n.values}acquireTexture(e,t,n,s){if(this.numBytesInGPU+=this.computeBytes(e,n),!this.warnedAboutMemory&&this.numBytesInGPU>this.numMBBeforeWarning*1024*1024){let r=(this.numBytesInGPU/1024/1024).toFixed(2);this.warnedAboutMemory=!0,console.warn(`High memory usage in GPU: ${r} MB, most likely due to a memory leak`)}return this.textureManager.acquireTexture(e,t,s)}computeBytes(e,t){return e[0]*e[1]*I.bytesPerElement(t)}};Jl.nextDataId=0;function zX(e,t){if(t==="float32"||t==="complex64")return e;if(t==="int32"||t==="bool"){let n=t==="int32"?new Int32Array(e.length):new Uint8Array(e.length);for(let s=0;s<n.length;++s)n[s]=Math.round(e[s]);return n}else throw new Error(`Unknown dtype ${t}`)}var LX="3.8.0";function N6(){ee().set("WEBGL_FORCE_F16_TEXTURES",!0)}Ku.isBrowser()&&bl("webgl",()=>new Jl,2);var BX={forceHalfFloat:N6},E6=`
  if (isnan(a)) return a;
  if (isnan(b)) return b;
`,Ql=class{constructor(e,t,n){this.variableNames=["A","B"],this.outputShape=$.assertAndGetBroadcastShape(t,n),this.enableShapeUniforms=cf(this.outputShape.length),this.userCode=`
      float binaryOperation(float a, float b) {
        ${e}
      }

      void main() {
        float a = getAAtOutCoords();
        float b = getBAtOutCoords();
        setOutput(binaryOperation(a, b));
      }
    `}},pf=`
  result.r = isNaN.r > 0. ? NAN : result.r;
  result.g = isNaN.g > 0. ? NAN : result.g;
  result.b = isNaN.b > 0. ? NAN : result.b;
  result.a = isNaN.a > 0. ? NAN : result.a;
`,Uc=class{constructor(e,t,n,s=!1){this.variableNames=["A","B"],this.supportsBroadcasting=!0,this.packedInputs=!0,this.packedOutput=!0,this.outputShape=$.assertAndGetBroadcastShape(t,n);let r=this.outputShape.length;this.enableShapeUniforms=cf(r);let a="";if(s)if(r===0||I.sizeFromShape(this.outputShape)===1)a=`
          result.y = 0.;
          result.z = 0.;
          result.w = 0.;
        `;else if(a=`
          ${ct(r)} coords = getOutputCoords();
        `,r===1)this.enableShapeUniforms?a+=`
            result.y = (coords + 1) >= outShape ? 0. : result.y;
            result.z = 0.;
            result.w = 0.;
          `:a+=`
            result.y = (coords + 1) >= ${this.outputShape[0]} ? 0. : result.y;
            result.z = 0.;
            result.w = 0.;
          `;else{let i=vn("coords",r);this.enableShapeUniforms?a+=`
            bool nextRowOutOfBounds =
              (${i[r-2]} + 1) >= outShape[${r} - 2];
            bool nextColOutOfBounds =
              (${i[r-1]} + 1) >= outShape[${r} - 1];
            result.y = nextColOutOfBounds ? 0. : result.y;
            result.z = nextRowOutOfBounds ? 0. : result.z;
            result.w = nextColOutOfBounds || nextRowOutOfBounds ? 0. : result.w;
          `:a+=`
            bool nextRowOutOfBounds =
              (${i[r-2]} + 1) >= ${this.outputShape[r-2]};
            bool nextColOutOfBounds =
              (${i[r-1]} + 1) >= ${this.outputShape[r-1]};
            result.y = nextColOutOfBounds ? 0. : result.y;
            result.z = nextRowOutOfBounds ? 0. : result.z;
            result.w = nextColOutOfBounds || nextRowOutOfBounds ? 0. : result.w;
          `}this.userCode=`
      vec4 binaryOperation(vec4 a, vec4 b) {
        ${e}
      }

      void main() {
        vec4 a = getAAtOutCoords();
        vec4 b = getBAtOutCoords();

        vec4 result = binaryOperation(a, b);
        ${a}

        setOutput(result);
      }
    `}};function qn(e){let{inputs:t,backend:n}=e,{x:s}=t;return n.incRef(s.dataId),{dataId:s.dataId,shape:s.shape,dtype:s.dtype}}var WX={kernelName:Oa,backendName:"webgl",kernelFunc:qn};function ra(e){let{inputs:t,backend:n}=e,{real:s,imag:r}=t,a=n.makeTensorInfo(s.shape,"complex64"),o=n.texData.get(a.dataId),i=qn({inputs:{x:s},backend:n}),l=qn({inputs:{x:r},backend:n});return o.complexTensorInfos={real:i,imag:l},a}var VX={kernelName:Wd,backendName:"webgl",kernelFunc:ra},R6="return (a < 0.) ? b * a : a;",_6=`
  vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));
  return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);
`;function UX(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{alpha:a}=s,o=n.makeTensorInfo([],"float32",I.createScalarValue(a,"float32")),i=ee().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Uc(_6,r.shape,o.shape):new Ql(R6,r.shape,o.shape),l=n.runWebGLProgram(i,[r,o],r.dtype);return n.disposeIntermediateTensorInfo(o),l}var HX={kernelName:Pa,backendName:"webgl",kernelFunc:UX},$6="return (a < 0.) ? b * a : a;",F6=`
  vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));
  return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);
`;function GX(e){let{inputs:t,backend:n}=e,{x:s,alpha:r}=t,a=ee().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Uc(F6,s.shape,r.shape):new Ql($6,s.shape,r.shape);return n.runWebGLProgram(a,[s,r],s.dtype)}var jX={kernelName:Ka,backendName:"webgl",kernelFunc:GX},D6="if (isnan(x)) return x;",qX=`
  if (isnan(a)) return a;
  if (isnan(b)) return b;
`,XX=`
  result.r = isNaN.r > 0. ? NAN : result.r;
  result.g = isNaN.g > 0. ? NAN : result.g;
  result.b = isNaN.b > 0. ? NAN : result.b;
  result.a = isNaN.a > 0. ? NAN : result.a;
`;function Ze({opSnippet:e,packedOpSnippet:t,cpuKernelImpl:n,dtype:s}){return({inputs:r,backend:a})=>{let{x:o}=r,i=a,l=s||o.dtype;if(i.shouldExecuteOnCPU([o])&&n!=null){let d=i.texData.get(o.dataId),h=n(d.values,l);return i.makeTensorInfo(o.shape,l,h)}let u=ee().getBool("WEBGL_PACK_UNARY_OPERATIONS")&&t!=null,c;return u?c=new Yl(o.shape,t):c=new sa(o.shape,e),i.runWebGLProgram(c,[o],l)}}function rn({opSnippet:e,packedOpSnippet:t,checkOutOfBounds:n=!1,supportsComplex:s=!1,cpuKernelImpl:r,dtype:a}){return({inputs:o,backend:i})=>{let{a:l,b:u}=o,c=i;if(s&&l.dtype==="complex64"){let f=c.texData.get(l.dataId),m=c.texData.get(u.dataId),[A,g]=[[f.complexTensorInfos.real,m.complexTensorInfos.real],[f.complexTensorInfos.imag,m.complexTensorInfos.imag]].map(x=>{let[b,v]=x,k={dataId:b.dataId,dtype:b.dtype,shape:l.shape},w={dataId:v.dataId,dtype:v.dtype,shape:u.shape},C=new Ql(e,l.shape,u.shape);return c.runWebGLProgram(C,[k,w],bs(b.dtype,v.dtype))}),y=ra({inputs:{real:A,imag:g},backend:c});return c.disposeIntermediateTensorInfo(A),c.disposeIntermediateTensorInfo(g),y}let d=a||bs(l.dtype,u.dtype);if((l.dtype==="string"||u.dtype==="string"||c.shouldExecuteOnCPU([l,u]))&&r!=null){let f=c.texData.get(l.dataId).values,m=c.texData.get(u.dataId).values,A=l.dtype==="string"?$.fromUint8ToStringArray(f):f,g=l.dtype==="string"?$.fromUint8ToStringArray(m):m,[y,x]=r(l.shape,u.shape,A,g,d),b=c.makeTensorInfo(x,d),v=c.texData.get(b.dataId);return v.values=y,b}let h=ee().getBool("WEBGL_PACK_BINARY_OPERATIONS")&&t!=null,p;return h?p=new Uc(t,l.shape,u.shape,n):p=new Ql(e,l.shape,u.shape),c.runWebGLProgram(p,[l,u],d)}}function ff(e,t=!1){if(e==="linear")return t?SX:bX;if(e==="relu")return t?TX:wX;if(e==="elu")return t?CX:vX;if(e==="relu6")return t?NX:kX;if(e==="prelu")return t?F6:$6;if(e==="leakyrelu")return t?_6:R6;if(e==="sigmoid")return t?EX:IX;throw new Error(`Activation ${e} has not been implemented for the WebGL backend.`)}var O6=class{constructor(e,t,n,s=!1,r=!1,a=!1,o=null,i=!1,l=!1){this.variableNames=["matrixA","matrixB"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=n;let u=s?e[1]:e[2],c=Math.ceil(u/2),d=s?"i * 2, rc.y":"rc.y, i * 2",h=r?"rc.z, i * 2":"i * 2, rc.z",p=s?["a.xxyy","a.zzww"]:["a.xxzz","a.yyww"],f=r?["b.xzxz","b.ywyw"]:["b.xyxy","b.zwzw"],m="",A="";o&&(i?m=`vec4 activation(vec4 a) {
          vec4 b = getPreluActivationWeightsAtOutCoords();
          ${o}
        }`:l?m=`vec4 activation(vec4 a) {
          vec4 b = getLeakyreluAlphaAtOutCoords();
          ${o}
        }`:m=`vec4 activation(vec4 x) {
          ${o}
        }`,A="result = activation(result);");let g=a?"result += getBiasAtOutCoords();":"";a&&this.variableNames.push("bias"),i&&this.variableNames.push("preluActivationWeights"),l&&this.variableNames.push("leakyreluAlpha");let y="rc.x",x="rc.x";e[0]<t[0]?y=`int(min(float(rc.x), ${e[0]-1}.))`:t[0]<e[0]&&(x=`int(min(float(rc.x), ${t[0]-1}.))`),this.userCode=`
      ${m}

      const float sharedDimension = ${c}.0;

      vec4 dot2x2ARowBCol(ivec3 rc) {
        vec4 result = vec4(0);
        for (int i = 0; i < ${c}; i++) {
          int batchA = ${y};
          int batchB = ${x};
          vec4 a = getMatrixA(batchA, ${d});
          vec4 b = getMatrixB(batchB, ${h});

          // These swizzled products need to be separately added.
          // See: https://github.com/tensorflow/tfjs/issues/1735
          result += (${p[0]} * ${f[0]});
          result += (${p[1]} * ${f[1]});
        }
        return result;
      }

      void main() {
        ivec3 rc = getOutputCoords();
        vec4 result = dot2x2ARowBCol(rc);

        ${g}

        ${A}

        setOutput(result);
      }
    `}},P6={REAL:"return areal * breal - aimag * bimag;",IMAG:"return areal * bimag + aimag * breal;"},M6=class{constructor(e,t,n){this.variableNames=["AReal","AImag","BReal","BImag"],this.outputShape=$.assertAndGetBroadcastShape(t,n),this.userCode=`
      float binaryOpComplex(
          float areal, float aimag, float breal, float bimag) {
        ${e}
      }

      void main() {
        float areal = getARealAtOutCoords();
        float aimag = getAImagAtOutCoords();
        float breal = getBRealAtOutCoords();
        float bimag = getBImagAtOutCoords();
        setOutput(binaryOpComplex(areal, aimag, breal, bimag));
      }
    `}},z6="return a * b;";function S1(e){let{inputs:t,backend:n}=e,{a:s,b:r}=t,a=$.upcastType(s.dtype,r.dtype);if(s.dtype==="complex64"){let i=n.texData.get(s.dataId),l=n.texData.get(r.dataId),u=new M6(P6.REAL,s.shape,r.shape),c=new M6(P6.IMAG,s.shape,r.shape),d=[{dataId:i.complexTensorInfos.real.dataId,dtype:i.complexTensorInfos.real.dtype,shape:s.shape},{dataId:i.complexTensorInfos.imag.dataId,dtype:i.complexTensorInfos.imag.dtype,shape:s.shape},{dataId:l.complexTensorInfos.real.dataId,dtype:l.complexTensorInfos.real.dtype,shape:r.shape},{dataId:l.complexTensorInfos.imag.dataId,dtype:l.complexTensorInfos.imag.dtype,shape:r.shape}],h=n.runWebGLProgram(u,d,"float32"),p=n.runWebGLProgram(c,d,"float32"),f=ra({inputs:{real:h,imag:p},backend:n});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(p),f}if(n.shouldExecuteOnCPU([s,r])){let i=n.texData.get(s.dataId),l=n.texData.get(r.dataId),[u,c]=jq(s.shape,r.shape,i.values,l.values,a),d=n.makeTensorInfo(c,a),h=n.texData.get(d.dataId);return h.values=u,d}let o;return ee().getBool("WEBGL_PACK_BINARY_OPERATIONS")?o=new Uc(z6,s.shape,r.shape):o=new Ql(z6,s.shape,r.shape),n.runWebGLProgram(o,[s,r],a)}var KX={kernelName:Ga,backendName:"webgl",kernelFunc:S1};function ZX(e,t,n){let s=[Wo(e.shape),...Vo(e.shape)],r={dtype:e.dtype,shape:s,dataId:e.dataId},a=[Wo(t),...Vo(t)],o=new k6(a,s),i=!0,l=n.runWebGLProgram(o,[r],e.dtype,null,i);return{dataId:l.dataId,shape:t,dtype:l.dtype}}function ye(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{shape:a}=s,o=n,i=I.sizeFromShape(r.shape),l=I.inferFromImplicitShape(a,i),u=I.sizeFromShape(l);I.assert(i===u,()=>`The new shape (${l}) has ${u} elements and the old shape (${r.shape}) has ${i} elements. The new shape and old shape must have the same number of elements.`);let c=o.texData.get(r.dataId);return c.isPacked&&!Wc(r.shape,l)&&!(c.texture!==null&&Wc(c.shape,l))?ZX(r,l,o):(o.incRef(r.dataId),{dataId:r.dataId,shape:l,dtype:r.dtype})}var YX={kernelName:Yi,backendName:"webgl",kernelFunc:ye},L6=class{constructor(e,t){this.variableNames=["x"];let{windowSize:n,batchSize:s,inSize:r,outSize:a}=e;this.outputShape=[s,a];let o=Math.floor(n/4)*4,i=n%4,l="sumValue += dot(values, ones);";if(t!=null){let c=1/t;l=`sumValue += dot(values * ${I.isInt(c)?c.toPrecision(2):c}, ones);`}let u="";r%n>0&&(u=`
        if (inIdx < 0 || inIdx >= ${r}) {
          return 0.0;
        }
      `),this.userCode=`
      const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);

      float getValue(int batch, int inIdx) {
        ${u}
        return getX(batch, inIdx);
      }

      void main() {
        ivec2 coords = getOutputCoords();
        int batch = coords[0];
        int outIdx = coords[1];
        int inOffset = outIdx * ${n};

        float sumValue = 0.0;

        for (int i = 0; i < ${o}; i += 4) {
          int inIdx = inOffset + i;
          vec4 values = vec4(
            getValue(batch, inIdx),
            getValue(batch, inIdx + 1),
            getValue(batch, inIdx + 2),
            getValue(batch, inIdx + 3)
          );

          ${l}
        }

        int inIdx = inOffset + ${o};
        if (${i===1}) {
          vec4 values = vec4(getValue(batch, inIdx), 0.0, 0.0, 0.0);

          ${l}
        } else if (${i===2}) {
          vec4 values = vec4(
            getValue(batch, inIdx),
            getValue(batch, inIdx + 1), 0.0, 0.0);

          ${l}
        } else if (${i===3}) {
          vec4 values = vec4(
            getValue(batch, inIdx),
            getValue(batch, inIdx + 1),
            getValue(batch, inIdx + 2), 0.0);

          ${l}
        }
        setOutput(sumValue);
      }
    `}},JX=class{constructor(e,t){this.variableNames=["x"];let{windowSize:n,batchSize:s,inSize:r,outSize:a}=e;this.outputShape=[s,a];let o="0.0",i="";t==="prod"?o="1.0":t==="min"?(o="1.0 / 1e-20",i="min"):t==="max"&&(o="-1.0 / 1e-20",i="max");let l=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="sum"?l="sumValue":t==="prod"?l="prodValue":t==="all"?l="allValue":t==="any"&&(l="anyValue");let u=Math.floor(n/4)*4,c=n%4,d=`
      if (${t==="sum"}) {
        sumValue += dot(values, ones);
      } else if (${t==="prod"}) {
        vec2 tmp = vec2(values[0], values[1]) * vec2(values[2], values[3]);
        prodValue *= tmp[0] * tmp[1];
      } else {
        minMaxValue = ${i}(values, minMaxValue);
        if (${t==="min"} || ${t==="max"}) {
          minMaxValue = ${i}(values, minMaxValue);
          bvec4 isNaN = isnan(values);
          if (isNaN.r || isNaN.g || isNaN.b || isNaN.a) {
            minMaxValue = vec4(NAN);
          }
        }
      }
    `,h="vec4";t==="all"?(o="1.0",d=`
        bool reducedAllValue = all(values);
        float floatedReducedAllValue = float(reducedAllValue);
        allValue = float(allValue >= 1.0 && floatedReducedAllValue >= 1.0);
      `,h="bvec4"):t==="any"&&(o="0.0",d=`
        bool reducedAnyValue = any(values);
        float floatedReducedAnyValue = float(reducedAnyValue);
        anyValue = float(anyValue >= 1.0 || floatedReducedAnyValue >= 1.0);
      `,h="bvec4");let p="";r%n>0&&(p=`
        if (inIdx < 0 || inIdx >= ${r}) {
          return initializationValue;
        }
      `),this.userCode=`
      const float initializationValue = ${o};
      const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);

      float getValue(int batch, int inIdx) {
        ${p}
        return getX(batch, inIdx);
      }

      void main() {
        ivec2 coords = getOutputCoords();
        int batch = coords[0];
        int outIdx = coords[1];
        int inOffset = outIdx * ${n};

        vec4 minMaxValue = vec4(${o});
        float prodValue = 1.0;
        float sumValue = 0.0;
        float allValue = 1.0;
        float anyValue = 0.0;

        for (int i = 0; i < ${u}; i += 4) {
          int inIdx = inOffset + i;
          ${h} values = ${h}(
            getValue(batch, inIdx),
            getValue(batch, inIdx + 1),
            getValue(batch, inIdx + 2),
            getValue(batch, inIdx + 3)
          );

          ${d}
        }

        int inIdx = inOffset + ${u};
        if (${c===1}) {
          ${h} values = ${h}(
            getValue(batch, inIdx),
            initializationValue,
            initializationValue,
            initializationValue
          );

          ${d}
        } else if (${c===2}) {
          ${h} values = ${h}(
            getValue(batch, inIdx),
            getValue(batch, inIdx + 1),
            initializationValue,
            initializationValue
          );

          ${d}
        } else if (${c===3}) {
          ${h} values = ${h}(
            getValue(batch, inIdx),
            getValue(batch, inIdx + 1),
            getValue(batch, inIdx + 2),
            initializationValue
          );

          ${d}
        }
        setOutput(${l});
      }
    `}};function QX(e){let t=[];for(;t.length===0||t[t.length-1].outSize!==1;){let n=t.length?t[t.length-1].outSize:e[1],s=$.computeOptimalWindowSize(n);t.push({inSize:n,windowSize:s,outSize:Math.ceil(n/s)})}return t}function Go(e,t,n,s){let r=QX(e.shape),a=e;for(let o=0;o<r.length;o++){let{inSize:i,windowSize:l,outSize:u}=r[o],c,d;n==="mean"?c=o===0?new L6({windowSize:l,inSize:i,batchSize:e.shape[0],outSize:u},i):new L6({windowSize:l,inSize:i,batchSize:e.shape[0],outSize:u}):c=new JX({windowSize:l,inSize:i,batchSize:e.shape[0],outSize:u},n),d=a,a=s.runWebGLProgram(c,[a],t),d.dataId!==e.dataId&&s.disposeIntermediateTensorInfo(d)}return a}var eK=class{constructor(e,t){this.variableNames=["A"];let n=new Array(e.length);for(let a=0;a<n.length;a++)n[a]=e[t[a]];this.outputShape=n,this.rank=n.length;let s=ct(this.rank),r=tK(t);this.userCode=`
    void main() {
      ${s} resRC = getOutputCoords();
      setOutput(getA(${r}));
    }
    `}};function tK(e){let t=e.length;if(t>6)throw Error(`Transpose for rank ${t} is not yet supported`);let n=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u","resRC.v"],s=new Array(t);for(let r=0;r<e.length;r++)s[e[r]]=n[r];return s.join()}var nK=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0;let n=new Array(e.length);for(let u=0;u<n.length;u++)n[u]=e[t[u]];if(this.outputShape=n,this.rank=n.length,this.rank>6)throw Error(`Packed transpose for rank ${this.rank} is not yet supported.`);let s=ct(this.rank),r=w6("rc",this.rank),a=new Array(this.rank);for(let u=0;u<t.length;u++)a[t[u]]=r[u];let o=`vec2(${a.slice(-2).join()})`,i=`++${r[this.rank-1]} < ${n[this.rank-1]}`,l=`getChannel(getA(${a.join()}), ${o})`;this.userCode=`
    void main() {
      ${s} rc = getOutputCoords();
      vec4 result = vec4(0.);
      result[0] = ${l};
      if(${i}) {
        result[1] = ${l};
      }
      --${r[this.rank-1]};
      if(++${r[this.rank-2]} < ${n[this.rank-2]}) {
        result[2] = ${l};
        if(${i}) {
          result[3] = ${l};
        }
      }
      setOutput(result);
    }
    `}};function mf(e,t,n){let s=ee().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new nK(e.shape,t):new eK(e.shape,t);return n.runWebGLProgram(s,[e],e.dtype)}function sK(e,t,n,s){let r=t,a=e.shape.length,o=I.parseAxisParam(r,e.shape),i=o,l=$.getAxesPermutation(i,a),u=l!=null,c=e;u&&(c=mf(e,l,s),i=$.getInnerMostAxes(i.length,a)),$.assertAxesAreInnerMostDims("sum",i,a);let[d,h]=$.computeOutAndReduceShapes(c.shape,i),p=d;n&&(p=$.expandShapeToKeepDim(d,o));let f=I.sizeFromShape(h),A=I.sizeFromShape(e.shape)/f,g=ye({inputs:{x:c},attrs:{shape:[A,f]},backend:s}),y=Ch(e.dtype),x=Go(g,y,"sum",s),b=ye({inputs:{x},attrs:{shape:p},backend:s});return s.disposeIntermediateTensorInfo(g),s.disposeIntermediateTensorInfo(x),u&&s.disposeIntermediateTensorInfo(c),b}function Af(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:o}=s;return sK(r,a,o,n)}var rK={kernelName:ao,backendName:"webgl",kernelFunc:Af};function wn(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{perm:a}=s,o=n,i=r.shape.length,l=new Array(i);for(let c=0;c<l.length;c++)l[c]=r.shape[a[c]];let u;if(o.shouldExecuteOnCPU([r])){let d=o.texData.get(r.dataId).values,h=I1(d,r.shape,r.dtype,a,l);u=o.makeTensorInfo(l,r.dtype);let p=o.texData.get(u.dataId);p.values=h}else u=mf(r,a,o);return u}var aK={kernelName:ho,backendName:"webgl",kernelFunc:wn},B6=1e3;function gf({a:e,b:t,transposeA:n,transposeB:s,backend:r,bias:a=null,preluActivationWeights:o=null,leakyreluAlpha:i=0,activation:l=null}){let u=e.shape.length,c=t.shape.length,d=n?e.shape[u-2]:e.shape[u-1],h=s?t.shape[c-1]:t.shape[c-2],p=n?e.shape[u-1]:e.shape[u-2],f=s?t.shape[c-2]:t.shape[c-1],m=e.shape.slice(0,-2),A=t.shape.slice(0,-2),g=I.sizeFromShape(m),y=I.sizeFromShape(A),x=g===y||g===1||y===1;I.assert(u>=2&&c>=2&&x,()=>`Error in matMul: the input batch dimensions must either be the same or at least one input batch dimension must be 1. Got input batch dimensions of (${m}) and (${A}).`);let v=(g>y?e.shape.slice(0,-2):t.shape.slice(0,-2)).concat([p,f]);I.assert(d===h,()=>`Error in matMul: inner shapes (${d}) and (${h}) of Tensors with shapes ${e.shape} and ${t.shape} and transposeA=${n} and transposeB=${s} must match.`);let k=n?[g,d,p]:[g,p,d],w=s?[y,f,h]:[y,h,f],C=ye({inputs:{x:e},backend:r,attrs:{shape:k}}),E=ye({inputs:{x:t},backend:r,attrs:{shape:w}}),M=[C,E],R=Math.max(g,y),_=n?C.shape[1]:C.shape[2],N=a!=null,O=o!=null,W=l==="leakyrelu",j=l!=null?ff(l,!0):null,q=N||O||W||j!=null,X;if((p===1||f===1)&&_>B6&&q===!1){let ne=C,te=E;n&&(ne=wn({inputs:{x:C},backend:r,attrs:{perm:[0,2,1]}}),M.push(ne)),s&&(te=wn({inputs:{x:E},backend:r,attrs:{perm:[0,2,1]}}),M.push(te));let se=f!==1,J=f===1,ie=ne;se&&(ie=ye({inputs:{x:ne},backend:r,attrs:{shape:[R,_,1]}}),M.push(ie));let le=f===1?2:1,he=te;J&&(he=ye({inputs:{x:te},backend:r,attrs:{shape:[R,1,_]}}),M.push(he));let ge=S1({inputs:{a:ie,b:he},backend:r});X=Af({inputs:{x:ge},backend:r,attrs:{axis:le,keepDims:!0}}),M.push(ge)}else{let ne=bs(e.dtype,t.dtype),te=new O6(k,w,[R,p,f],n,s,N,j,O,W),se=[C,E];if(a!=null&&se.push(a),O&&se.push(o),W){let J=r.makeTensorInfo([],"float32",I.createScalarValue(i,"float32"));se.push(J),M.push(J)}X=r.runWebGLProgram(te,se,ne)}let Q=ye({inputs:{x:X},backend:r,attrs:{shape:v}});M.push(X);for(let ne of M)r.disposeIntermediateTensorInfo(ne);return Q}function oK(e){let{inputs:t,backend:n,attrs:s}=e,{a:r,b:a,bias:o,preluActivationWeights:i}=t,{transposeA:l,transposeB:u,activation:c,leakyreluAlpha:d}=s;return gf({a:r,b:a,transposeA:l,transposeB:u,backend:n,bias:o,preluActivationWeights:i,leakyreluAlpha:d,activation:c})}var iK={kernelName:po,backendName:"webgl",kernelFunc:oK},W6="return abs(x);";function lK(e){let{inputs:t,backend:n}=e,{x:s}=t;if(n.shouldExecuteOnCPU([s])&&s.dtype!=="complex64"){let a=n.texData.get(s.dataId),o=b6(a.values);return n.makeTensorInfo(s.shape,s.dtype,o)}let r;return ee().getBool("WEBGL_PACK_UNARY_OPERATIONS")?r=new Yl(s.shape,W6):r=new sa(s.shape,W6),n.runWebGLProgram(r,[s],s.dtype)}var uK={kernelName:di,backendName:"webgl",kernelFunc:lK},cK=Bs+`
  if (abs(x) > 1.) {
    return NAN;
  }
  return acos(x);
`,dK=Ze({opSnippet:cK}),hK={kernelName:hi,backendName:"webgl",kernelFunc:dK},pK=Bs+`
  if (x < 1.0) return NAN;
return log(x + sqrt(x * x - 1.0));`,fK=Ze({opSnippet:pK}),mK={kernelName:pi,backendName:"webgl",kernelFunc:fK},V6="return a + b;",AK=rn({opSnippet:V6,packedOpSnippet:V6,supportsComplex:!0,cpuKernelImpl:Tq}),gK={kernelName:Dr,backendName:"webgl",kernelFunc:AK},yK=class{constructor(e,t){this.outputShape=[],this.outputShape=e,this.variableNames=t.map((r,a)=>`T${a}`);let n=[];this.variableNames.forEach(r=>{n.push(`float v${r} = get${r}AtOutCoords();`)});let s=this.variableNames.map(r=>`v${r}`).join(" + ");this.userCode=`
      void main() {
        ${n.join(`
        `)}

        float result = ${s};
        setOutput(result);
      }
    `}},xK=class{constructor(e,t){this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.variableNames=t.map((r,a)=>`T${a}`);let n=[];this.variableNames.forEach(r=>{n.push(`vec4 v${r} = get${r}AtOutCoords();`)});let s=this.variableNames.map(r=>`v${r}`).join(" + ");this.userCode=`
      void main() {
        ${n.join(`
        `)}

        vec4 result = ${s};
        setOutput(result);
      }
    `}};function yf(e){let{inputs:t,backend:n}=e,s=t;if(s.length===1)return qn({inputs:{x:s[0]},backend:n});if(s.length>ee().get("WEBGL_MAX_TEXTURES_IN_SHADER")){let l=Math.floor(s.length/2),u=yf({inputs:s.slice(0,l),backend:n}),c=yf({inputs:s.slice(l),backend:n});return yf({inputs:[u,c],backend:n})}let r=s.map(l=>l.dtype).reduce((l,u)=>bs(l,u)),a=s.map(l=>l.shape),i=ee().getBool("WEBGL_PACK")?new xK(s[0].shape,a):new yK(s[0].shape,a);return n.runWebGLProgram(i,s,r)}var bK={kernelName:ga,backendName:"webgl",kernelFunc:yf};function vK(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:o}=s,i=r.shape.length,l=I.parseAxisParam(a,r.shape),u=l,c=$.getAxesPermutation(u,i),d=r;c!=null&&(d=wn({inputs:{x:r},backend:n,attrs:{perm:c}}),u=$.getInnerMostAxes(u.length,i)),$.assertAxesAreInnerMostDims("all",u,i);let[h,p]=$.computeOutAndReduceShapes(d.shape,u),f=I.sizeFromShape(p),m=ye({inputs:{x:d},backend:n,attrs:{shape:[-1,f]}}),A=Go(m,m.dtype,"all",n),g;if(o){let y=$.expandShapeToKeepDim(h,l);g=ye({inputs:{x:A},backend:n,attrs:{shape:y}})}else g=ye({inputs:{x:A},backend:n,attrs:{shape:h}});return n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(A),c!=null&&n.disposeIntermediateTensorInfo(d),g}var wK={kernelName:fi,backendName:"webgl",kernelFunc:vK};function kK(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:o}=s,i=r.shape.length,l=I.parseAxisParam(a,r.shape),u=l,c=$.getAxesPermutation(u,i),d=r;c!=null&&(d=wn({inputs:{x:r},backend:n,attrs:{perm:c}}),u=$.getInnerMostAxes(u.length,i)),$.assertAxesAreInnerMostDims("any",u,i);let[h,p]=$.computeOutAndReduceShapes(d.shape,u),f=I.sizeFromShape(p),m=ye({inputs:{x:d},backend:n,attrs:{shape:[-1,f]}}),A=Go(m,m.dtype,"any",n),g;if(o){let y=$.expandShapeToKeepDim(h,l);g=ye({inputs:{x:A},backend:n,attrs:{shape:y}})}else g=ye({inputs:{x:A},backend:n,attrs:{shape:h}});return n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(A),c!=null&&n.disposeIntermediateTensorInfo(d),g}var IK={kernelName:mi,backendName:"webgl",kernelFunc:kK},SK=class{constructor(e,t,n){this.variableNames=["A"];let{windowSize:s,batchSize:r,outSize:a}=e;n||this.variableNames.push("bestIndicesA"),this.outputShape=[r,a];let o=t==="max"?">":"<",i=n?"inOffset + i;":"round(getBestIndicesA(batch, inOffset + i));";this.userCode=`
      void main() {
        ivec2 coords = getOutputCoords();
        int batch = coords[0];
        int outIdx = coords[1];
        int inOffset = outIdx * ${s};

        int bestIndex = inOffset;
        float bestValue = getA(batch, bestIndex);

        for (int i = 0; i < ${s}; i++) {
          int inIdx = ${i};
          float candidate = getA(batch, inIdx);
          if (candidate ${o} bestValue) {
            bestValue = candidate;
            bestIndex = inIdx;
          }
        }
        setOutput(float(bestIndex));
      }
    `}},CK=class{constructor(e,t,n,s){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,I.assert(e.length>2,()=>`Packed arg${n.charAt(0).toUpperCase()+n.slice(1)} supports only inputs with rank above 2.`);let r=e[e.length-1],a=Math.ceil(r/t);this.outputShape=e.slice(0,-1),a>1&&this.outputShape.push(a),s||this.variableNames.push("bestIndicesA");let o=this.outputShape,i=o.length,l=ct(i),u=vn("coords",i),c,d;if(a===1){d=i+1;let w=ct(d);c=`
        ${w} sourceLocR = ${w}(${u.join()}, 0);
        ++${u[i-1]};
        ${w} sourceLocG = ${w}(${u.join()}, 0);
        ++${u[i-2]};
        ${w} sourceLocA = ${w}(${u.join()}, 0);
        --${u[i-1]};
        ${w} sourceLocB = ${w}(${u.join()}, 0);
        --${u[i-2]};`}else d=i,c=`
        ${l} sourceLocR = coords;
        ++${u[i-1]};
        ${l} sourceLocG = coords;
        ++${u[i-2]};
        ${l} sourceLocA = coords;
        --${u[i-1]};
        ${l} sourceLocB = coords;
        --${u[i-2]};`;let h=["x","y","z","w","u","v"].slice(0,d),p="."+h[d-1],f=h.map(w=>"int "+w),m=vn("sourceLocR",d-1).concat("inIdx.r"),A=vn("sourceLocG",d-1).concat("inIdx.g"),g=vn("sourceLocB",d-1).concat("inIdx.b"),y=vn("sourceLocA",d-1).concat("inIdx.a"),x=n==="max"?"greaterThan":"lessThan",b=s?"":`
          inIdx = round(vec4(getBestIndicesAChannel(${m.join()}),
                             getBestIndicesAChannel(${A.join()}),
                             getBestIndicesAChannel(${g.join()}),
                             getBestIndicesAChannel(${y.join()})));`,v=`vec4(
            getAChannel(${m.join()}),
            hasNextCol ? getAChannel(${A.join()}) : 0.,
            hasNextRow ? getAChannel(${g.join()}) : 0.,
            hasNextRow && hasNextCol ? getAChannel(${y.join()}) : 0.)`,k=s?"":`
      float getBestIndicesAChannel(${f.join()}) {
        return getChannel(getBestIndicesA(${h.join()}),
                                          vec2(${h.slice(-2).join()}));
      }`;this.userCode=`
      float getAChannel(${f.join()}) {
        return getChannel(getA(${h.join()}),
                               vec2(${h.slice(-2).join()}));
      }
      ${k}
      void main() {
        ${l} coords = getOutputCoords();
        bool hasNextCol = ${u[i-1]} < ${o[i-1]-1};
        bool hasNextRow = ${u[i-2]} < ${o[i-2]-1};
        ${c}
        ivec4 srcIdx = ivec4(sourceLocR${p}, sourceLocG${p},
          sourceLocB${p}, sourceLocA${p}) * ${t};
        ivec4 inIdx = srcIdx;
        vec4 bestIndex = vec4(inIdx);
        vec4 bestValue = ${v};

        for (int i = 0; i < ${t}; i++) {
          inIdx = srcIdx;
          ${b}
          vec4 candidate = ${v};
          bvec4 nan = isnan(candidate);
          bvec4 replace = bvec4(
            vec4(${x}(candidate, bestValue)) * (vec4(1.0) - vec4(nan)));

          bestValue = vec4(replace.x  ? candidate.x : bestValue.x,
                           replace.y  ? candidate.y : bestValue.y,
                           replace.z  ? candidate.z : bestValue.z,
                           replace.w  ? candidate.w : bestValue.w);
          bestIndex = mix(bestIndex, vec4(inIdx), vec4(replace));
          srcIdx++;
        }
        setOutput(bestIndex);
      }
    `}};function U6(e,t,n,s=null){let r=t.shape[0],a=t.shape[1];s!=null&&(r=s.shape[0],a=s.shape[1]);let o=$.computeOptimalWindowSize(a),i={windowSize:o,inSize:a,batchSize:r,outSize:Math.ceil(a/o)},l=new SK(i,n,s==null),u=[t];s!=null&&u.push(s);let c=e.runWebGLProgram(l,u,"int32");if(c.shape[1]===1)return c;let d=U6(e,t,n,c);return e.disposeIntermediateTensorInfo(c),d}function H6(e,t,n,s=null){let r=s!=null?s.shape:t.shape,a=r[r.length-1],o=$.computeOptimalWindowSize(a),i=new CK(r,o,n,s==null),l=s==null?[t]:[t,s],u=e.runWebGLProgram(i,l,"int32");if(u.shape.length===t.shape.length){let c=H6(e,t,n,u);return e.disposeIntermediateTensorInfo(u),c}return u}function G6(e,t,n,s){let r=[n];if($.assertAxesAreInnerMostDims("arg"+s.charAt(0).toUpperCase()+s.slice(1),r,t.shape.length),!ee().getBool("WEBGL_PACK_REDUCE")||t.shape.length<=2){let a=[],[o,i]=$.computeOutAndReduceShapes(t.shape,r),l=I.sizeFromShape(i),u=ye({inputs:{x:t},backend:e,attrs:{shape:[-1,l]}});a.push(u);let c=U6(e,u,s);a.push(c);let d=ye({inputs:{x:c},backend:e,attrs:{shape:o}});return a.forEach(h=>e.disposeIntermediateTensorInfo(h)),d}return H6(e,t,s)}function TK(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a}=s,o=I.parseAxisParam(a,r.shape),i=$.getAxesPermutation(o,r.shape.length),l=r,u=[];i!=null&&(l=wn({inputs:{x:r},backend:n,attrs:{perm:i}}),u.push(l),o=$.getInnerMostAxes(o.length,l.shape.length)),$.assertAxesAreInnerMostDims("argMax",[o[0]],l.shape.length);let c=G6(n,l,o[0],"max");return u.forEach(d=>n.disposeIntermediateTensorInfo(d)),c}var NK={kernelName:ya,backendName:"webgl",kernelFunc:TK};function EK(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a}=s,o=I.parseAxisParam(a,r.shape),i=$.getAxesPermutation(o,r.shape.length),l=r,u=[];i!=null&&(l=wn({inputs:{x:r},backend:n,attrs:{perm:i}}),u.push(l),o=$.getInnerMostAxes(o.length,l.shape.length)),$.assertAxesAreInnerMostDims("argMin",[o[0]],l.shape.length);let c=G6(n,l,o[0],"min");return u.forEach(d=>n.disposeIntermediateTensorInfo(d)),c}var RK={kernelName:Cu,backendName:"webgl",kernelFunc:EK},_K=Bs+`
  if (abs(x) > 1.) {
    return NAN;
  }
  return asin(x);
`,$K=Ze({opSnippet:_K}),FK={kernelName:Ai,backendName:"webgl",kernelFunc:$K},DK=Bs+"return log(x + sqrt(x * x + 1.0));",OK=Ze({opSnippet:DK}),PK={kernelName:gi,backendName:"webgl",kernelFunc:OK},MK=Bs+`
  return atan(x);
`,zK=Ze({opSnippet:MK}),LK={kernelName:yi,backendName:"webgl",kernelFunc:zK},BK=qX+`
  return atan(a, b);
`,WK=`
  vec4 result = atan(a, b);
  vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
  `+XX+`
  return result;
`,VK=rn({opSnippet:BK,packedOpSnippet:WK}),UK={kernelName:bi,backendName:"webgl",kernelFunc:VK},HK=Bs+`
  if ((x < -1.0) || (x > 1.0)) return NAN;
return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,GK=Ze({opSnippet:HK}),jK={kernelName:xi,backendName:"webgl",kernelFunc:GK},Hc=class{constructor(e,t,n,s=!1,r=!1){if(this.variableNames=["x"],t==="avg"&&n)throw new Error("Cannot compute positions for average pool.");let a=e.filterWidth,o=e.strideHeight,i=e.strideWidth,l=e.dilationHeight,u=e.dilationWidth,c=e.effectiveFilterHeight,d=e.effectiveFilterWidth,h=e.padInfo.top,p=e.padInfo.left;this.outputShape=e.outShape;let f=t==="avg",m=`((batch  * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + d`,A=`(xR * ${e.inWidth} + xC) * ${e.inChannels} + d`,g="0.0";if(f||(g="-1.0 / 1e-20"),n){let w=">=";this.userCode=`
        const ivec2 strides = ivec2(${o}, ${i});
        const ivec2 pads = ivec2(${h}, ${p});

        void main() {
          ivec4 coords = getOutputCoords();
          int batch = coords[0];
          int d = coords[3];

          ivec2 xRCCorner = coords.yz * strides - pads;
          int xRCorner = xRCCorner.x;
          int xCCorner = xRCCorner.y;

          // max/min x(?, ?, d) to get y(yR, yC, d).
          // ? = to be determined
          float minMaxValue = 0.0;
          float minMaxValueFound = 0.0;
          int minMaxPosition = 0;
          float avgValue = 0.0;

          for (int wR = 0; wR < ${c};
              wR += ${l}) {
            int xR = xRCorner + wR;

            if (xR < 0 || xR >= ${e.inHeight}) {
              continue;
            }

            for (int wC = 0; wC < ${d};
                wC += ${u}) {
              int xC = xCCorner + wC;

              if (xC < 0 || xC >= ${e.inWidth}) {
                continue;
              }

              float value = getX(batch, xR, xC, d);

              // If a min / max value has already been found, use it. If not,
              // use the current value.
              float currMinMaxValue = mix(
                  value, minMaxValue, minMaxValueFound);
              if (value ${w} currMinMaxValue) {
                minMaxValue = value;
                minMaxValueFound = 1.0;
                minMaxPosition = ${s?r?m:A:`wR * ${d} + wC`};
              }
            }
          }
          setOutput(float(minMaxPosition));
        }
      `;return}let y="max",x=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="avg"&&(x="avgValue / count");let b=Math.floor(a/4)*4,v=a%4,k=`
      if (${f}) {
        avgValue += dot(values, ones);
      } else {
        minMaxValue = ${y}(values, minMaxValue);
      }
    `;this.userCode=`
      const ivec2 strides = ivec2(${o}, ${i});
      const ivec2 pads = ivec2(${h}, ${p});
      const float initializationValue = ${g};
      const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);

      float count = 0.0;

      float getValue(int batch, int xR, int xC, int d) {
        if (xC < 0 || xC >= ${e.inWidth}) {
          return initializationValue;
        }
        count += 1.0;
        return getX(batch, xR, xC, d);
      }

      void main() {
        ivec4 coords = getOutputCoords();
        int batch = coords[0];
        int d = coords[3];

        ivec2 xRCCorner = coords.yz * strides - pads;
        int xRCorner = xRCCorner.x;
        int xCCorner = xRCCorner.y;

        // max/min x(?, ?, d) to get y(yR, yC, d).
        // ? = to be determined
        vec4 minMaxValue = vec4(${g});
        float avgValue = 0.0;
        count = 0.0;

        for (int wR = 0; wR < ${c};
            wR += ${l}) {
          int xR = xRCorner + wR;

          if (xR < 0 || xR >= ${e.inHeight}) {
            continue;
          }

          for (int wC = 0; wC < ${b}; wC += 4) {
            int xC = xCCorner + wC * ${u};

            vec4 values = vec4(
              getValue(batch, xR, xC, d),
              getValue(batch, xR, xC + ${u}, d),
              getValue(batch, xR, xC + 2 * ${u}, d),
              getValue(batch, xR, xC + 3 * ${u}, d)
            );

            ${k}
          }

          int xC = xCCorner + ${b};
          if (${v===1}) {
            vec4 values = vec4(
              getValue(batch, xR, xC, d),
              initializationValue,
              initializationValue,
              initializationValue
            );

            ${k}
          } else if (${v===2}) {
            vec4 values = vec4(
              getValue(batch, xR, xC, d),
              getValue(batch, xR, xC + ${u}, d),
              initializationValue,
              initializationValue
            );

            ${k}
          } else if (${v===3}) {
            vec4 values = vec4(
              getValue(batch, xR, xC, d),
              getValue(batch, xR, xC + ${u}, d),
              getValue(batch, xR, xC + 2 * ${u}, d),
              initializationValue
            );

            ${k}
          }
        }
        setOutput(${x});
      }
    `}},C1=class{constructor(e,t,n,s=!1,r=!1){if(this.variableNames=["x"],t==="avg"&&n)throw new Error("Cannot compute positions for average pool.");let a=e.filterWidth,o=e.strideDepth,i=e.strideHeight,l=e.strideWidth,u=e.dilationDepth,c=e.dilationHeight,d=e.dilationWidth,h=e.effectiveFilterDepth,p=e.effectiveFilterHeight,f=e.effectiveFilterWidth,m=e.padInfo.front,A=e.padInfo.top,g=e.padInfo.left;this.outputShape=e.outShape;let y=t==="avg",x="0.0";if(y||(x="-1.0 / 1e-20"),n){let E=">=";this.userCode=`
        const ivec3 strides =
            ivec3(${o}, ${i}, ${l});
        const ivec3 pads = ivec3(${m}, ${A}, ${g});

        void main() {
          ivec5 coords = getOutputCoords();
          int batch = coords.x;
          int ch = coords.u;

          ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;
          int xDCorner = xCorner.x;
          int xRCorner = xCorner.y;
          int xCCorner = xCorner.z;

          // max/min x(?, ?, ?, ch) to get y(yD, yR, yC, ch).
          // ? = to be determined
          float minMaxValue = 0.0;
          float minMaxValueFound = 0.0;
          int minMaxPosition = 0;

          for (int wD = 0; wD < ${h};
              wD += ${u}) {
            int xD = xDCorner + wD;

            if (xD < 0 || xD >= ${e.inDepth}) {
              continue;
            }

            for (int wR = 0; wR < ${p};
                wR += ${c}) {
              int xR = xRCorner + wR;

              if (xR < 0 || xR >= ${e.inHeight}) {
                continue;
              }

              for (int wC = 0; wC < ${f};
                  wC += ${d}) {
                int xC = xCCorner + wC;

                if (xC < 0 || xC >= ${e.inWidth}) {
                  continue;
                }

                float value = getX(batch, xD, xR, xC, ch);

                // If a min / max value has already been found, use it. If not,
                // use the current value.
                float currMinMaxValue = mix(
                    value, minMaxValue, minMaxValueFound);
                if (value ${E} currMinMaxValue) {
                  minMaxValue = value;
                  minMaxValueFound = 1.0;
                  minMaxPosition = ${s?r?`(((batch * ${e.inDepth} + xD) * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + ch`:`((xD * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + ch`:`wD * ${p} * ${f} +
                      wR * ${f} + wC`};
                }
              }
            }
          }
          setOutput(float(minMaxPosition));
        }
      `;return}let b="max",v=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="avg"&&(v="avgValue / count");let k=Math.floor(a/4)*4,w=a%4,C=`
      if (${y}) {
        avgValue += dot(values, ones);
      } else {
        minMaxValue = ${b}(values, minMaxValue);
      }
    `;this.userCode=`
      const ivec3 strides =
        ivec3(${o}, ${i}, ${l});
      const ivec3 pads = ivec3(${m}, ${A}, ${g});
      const float initializationValue = ${x};
      const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);

      float count = 0.0;

      float getValue(int batch, int xD, int xR, int xC, int ch) {
        if (xC < 0 || xC >= ${e.inWidth}) {
          return initializationValue;
        }
        count += 1.0;
        return getX(batch, xD, xR, xC, ch);
      }

      void main() {
        ivec5 coords = getOutputCoords();
        int batch = coords.x;
        int ch = coords.u;

        ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;
        int xDCorner = xCorner.x;
        int xRCorner = xCorner.y;
        int xCCorner = xCorner.z;

        // max/min x(?, ?, ?, d) to get y(yD, yR, yC, ch).
        // ? = to be determined
        vec4 minMaxValue = vec4(${x});
        float avgValue = 0.0;
        count = 0.0;

        for (int wD = 0; wD < ${h};
            wD += ${u}) {
          int xD = xDCorner + wD;

          if (xD < 0 || xD >= ${e.inDepth}) {
            continue;
          }

          for (int wR = 0; wR < ${p};
            wR += ${c}) {
            int xR = xRCorner + wR;

            if (xR < 0 || xR >= ${e.inHeight}) {
              continue;
            }

            for (int wC = 0; wC < ${k}; wC += 4) {
              int xC = xCCorner + wC * ${d};

              vec4 values = vec4(
                getValue(batch, xD, xR, xC, ch),
                getValue(batch, xD, xR, xC + ${d}, ch),
                getValue(batch, xD, xR, xC + 2 * ${d}, ch),
                getValue(batch, xD, xR, xC + 3 * ${d}, ch)
              );

              ${C}
            }

            int xC = xCCorner + ${k};
            if (${w===1}) {
              vec4 values = vec4(
                getValue(batch, xD, xR, xC, ch),
                initializationValue,
                initializationValue,
                initializationValue
              );

              ${C}
            } else if (${w===2}) {
              vec4 values = vec4(
                getValue(batch, xD, xR, xC, ch),
                getValue(batch, xD, xR, xC + ${d}, ch),
                initializationValue,
                initializationValue
              );

              ${C}
            } else if (${w===3}) {
              vec4 values = vec4(
                getValue(batch, xD, xR, xC, ch),
                getValue(batch, xD, xR, xC + ${d}, ch),
                getValue(batch, xD, xR, xC + 2 * ${d}, ch),
                initializationValue
              );

              ${C}
            }
          }
          setOutput(${v});
        }
      }
    `}};function qK(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t;jl(r,"avgPool");let{filterSize:a,strides:o,pad:i,dimRoundingMode:l}=s,u=1;I.assert($.eitherStridesOrDilationsAreOne(o,u),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${o} and dilations '${u}'`);let c=$.computePool2DInfo(r.shape,a,o,u,i,l);if(c.filterWidth===1&&c.filterHeight===1&&I.arraysEqual(c.inShape,c.outShape))return qn({inputs:{x:r},backend:n});let d=new Hc(c,"avg",!1);return n.runWebGLProgram(d,[r],"float32")}var XK={kernelName:xa,backendName:"webgl",kernelFunc:qK};function KK(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{filterSize:a,strides:o,pad:i,dimRoundingMode:l,dataFormat:u}=s,c=[1,1,1],d=$.computePool3DInfo(r.shape,a,o,c,i,l,u),h=new C1(d,"avg",!1);return n.runWebGLProgram(h,[r],"float32")}var ZK={kernelName:Tu,backendName:"webgl",kernelFunc:KK},YK=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,s=e.strideHeight,r=e.strideWidth,a=e.dilationHeight,o=e.dilationWidth,i=e.effectiveFilterHeight,l=e.effectiveFilterWidth,u=i-1-e.padInfo.top,c=l-1-e.padInfo.left,d=1/(t*n);this.userCode=`
      const ivec2 pads = ivec2(${u}, ${c});
      const float avgMultiplier = float(${d});

      void main() {
        ivec4 coords = getOutputCoords();
        int b = coords[0];
        int d = coords[3];

        ivec2 dyRCCorner = coords.yz - pads;
        int dyRCorner = dyRCCorner.x;
        int dyCCorner = dyRCCorner.y;

        // Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).
        // ? = to be determined. : = across all values in that axis.
        float dotProd = 0.0;
        for (int wR = 0; wR < ${i};
            wR += ${a}) {
          float dyR = float(dyRCorner + wR) / ${s}.0;

          if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) {
            continue;
          }
          int idyR = int(dyR);

          for (int wC = 0; wC < ${l};
            wC+= ${o}) {
            float dyC = float(dyCCorner + wC) / ${r}.0;

            if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
                fract(dyC) > 0.0) {
              continue;
            }
            int idyC = int(dyC);

            float dyValue = getDy(b, idyR, idyC, d);

            dotProd += dyValue * avgMultiplier;
          }
        }
        setOutput(dotProd);
      }
    `}},JK=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterDepth,n=e.filterHeight,s=e.filterWidth,r=e.strideDepth,a=e.strideHeight,o=e.strideWidth,i=e.dilationDepth,l=e.dilationHeight,u=e.dilationWidth,c=e.effectiveFilterDepth,d=e.effectiveFilterHeight,h=e.effectiveFilterWidth,p=c-1-e.padInfo.front,f=d-1-e.padInfo.top,m=h-1-e.padInfo.left,A=1/(t*n*s);this.userCode=`
      const ivec3 pads = ivec3(${p}, ${f}, ${m});
      const float avgMultiplier = float(${A});

      void main() {
        ivec5 coords = getOutputCoords();
        int batch = coords.x;
        int ch = coords.u;

        ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;
        int dyDCorner = dyCorner.x;
        int dyRCorner = dyCorner.y;
        int dyCCorner = dyCorner.z;

        // Convolve dy(?, ?, ?, d) with pos mask(:, :, :, ch) to get
        // dx(xD, xR, xC, ch).
        // ? = to be determined. : = across all values in that axis.
        float dotProd = 0.0;

        for (int wD = 0; wD < ${c};
            wD += ${i}) {
          float dyD = float(dyDCorner + wD) / ${r}.0;

          if (dyD < 0.0 || dyD >= ${e.outDepth}.0 || fract(dyD) > 0.0) {
            continue;
          }
          int idyD = int(dyD);

          for (int wR = 0; wR < ${d};
              wR += ${l}) {
            float dyR = float(dyRCorner + wR) / ${a}.0;

            if (dyR < 0.0 || dyR >= ${e.outHeight}.0 ||
                fract(dyR) > 0.0) {
              continue;
            }
            int idyR = int(dyR);

            for (int wC = 0; wC < ${h};
                wC += ${u}) {
              float dyC = float(dyCCorner + wC) / ${o}.0;

              if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
                  fract(dyC) > 0.0) {
                continue;
              }
              int idyC = int(dyC);

              float dyValue = getDy(batch, idyD, idyR, idyC, ch);

              dotProd += dyValue * avgMultiplier;
            }
          }
        }
        setOutput(dotProd);
      }
    `}};function QK(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,input:a}=t,o=a,{filterSize:i,strides:l,pad:u,dimRoundingMode:c}=s,d=[1,1,1],h=$.computePool3DInfo(o.shape,i,l,d,u,c),p=new JK(h);return n.runWebGLProgram(p,[r],o.dtype)}var eZ={kernelName:Ld,backendName:"webgl",kernelFunc:QK};function tZ(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,input:a}=t,o=a;jl([r,a],"avgPoolGrad");let{filterSize:i,strides:l,pad:u}=s,c=$.computePool2DInfo(o.shape,i,l,1,u),d=new YK(c);return n.runWebGLProgram(d,[r],o.dtype)}var nZ={kernelName:zd,backendName:"webgl",kernelFunc:tZ};function sZ(e){let{inputs:t,backend:n,attrs:s}=e,{a:r,b:a}=t,{transposeA:o,transposeB:i}=s;return gf({a:r,b:a,transposeA:o,transposeB:i,backend:n})}var rZ={kernelName:ba,backendName:"webgl",kernelFunc:sZ},aZ=class{constructor(e,t,n,s,r,a){this.outputShape=[],this.variableNames=["x","mean","variance"],$.assertAndGetBroadcastShape(e,t),$.assertAndGetBroadcastShape(e,n);let o="0.0";s!=null&&($.assertAndGetBroadcastShape(e,s),this.variableNames.push("offset"),o="getOffsetAtOutCoords()");let i="1.0";r!=null&&($.assertAndGetBroadcastShape(e,r),this.variableNames.push("scale"),i="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=`
      void main() {
        float x = getXAtOutCoords();
        float mean = getMeanAtOutCoords();
        float variance = getVarianceAtOutCoords();
        float offset = ${o};
        float scale = ${i};
        float inv = scale * inversesqrt(variance + float(${a}));
        setOutput(dot(vec3(x, -mean, offset), vec3(inv, inv, 1)));
      }
    `}},oZ=class{constructor(e,t,n,s,r,a){this.packedInputs=!0,this.packedOutput=!0,this.variableNames=["x","mean","variance"],$.assertAndGetBroadcastShape(e,t),$.assertAndGetBroadcastShape(e,n);let o="vec4(0.0)";s!=null&&($.assertAndGetBroadcastShape(e,s),this.variableNames.push("offset"),o="getOffsetAtOutCoords()");let i="vec4(1.0)";r!=null&&($.assertAndGetBroadcastShape(e,r),this.variableNames.push("scale"),i="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=`
      void main() {
        vec4 offset = ${o};
        vec4 scale = ${i};

        vec4 x = getXAtOutCoords();
        vec4 mean = getMeanAtOutCoords();
        vec4 variance = getVarianceAtOutCoords();

        vec4 inv = scale * inversesqrt(variance + vec4(${a}));

        setOutput((x - mean) * inv + offset);
      }
    `}},iZ=({inputs:e,backend:t,attrs:n})=>{let{x:s,mean:r,variance:a,offset:o,scale:i}=e;I.assert(r.shape.length===a.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),I.assert(o==null||r.shape.length===o.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),I.assert(i==null||r.shape.length===i.shape.length,()=>"Batch normalization gradient requires mean and scale to have equal ranks.");let{varianceEpsilon:l}=n;l==null&&(l=.001);let u=[s,r,a],c=null;o!=null&&(c=o.shape,u.push(o));let d=null;i!=null&&(d=i.shape,u.push(i));let h=ee().getBool("WEBGL_PACK_NORMALIZATION")?new oZ(s.shape,r.shape,a.shape,c,d,l):new aZ(s.shape,r.shape,a.shape,c,d,l);return t.runWebGLProgram(h,u,u[0].dtype)},lZ={kernelName:Fa,backendName:"webgl",kernelFunc:iZ},uZ=class{constructor(e){this.variableNames=["source"],this.outputShape=e,this.rank=e.length;let t=ct(this.rank);this.customUniforms=[{name:"start",arrayIndex:this.rank,type:"int"}];let n=cZ(this.rank),s,r=e.map((a,o)=>`sourceLoc.${T1[o]} = start[${o}] + coords.${T1[o]};`);s=`
        ${t} sourceLoc;
        ${t} coords = getOutputCoords();
        ${r.join(`
`)}
      `,this.userCode=`
      void main() {
        ${s}
        setOutput(getSource(${n}));
      }
    `}},T1=["x","y","z","w","u","v"];function cZ(e){if(e===1)return"sourceLoc";if(e<=6)return T1.slice(0,e).map(t=>"sourceLoc."+t).join(",");throw Error(`Slicing for rank ${e} is not yet supported`)}var dZ=class{constructor(e){this.variableNames=["source"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.rank=e.length,this.customUniforms=[{name:"start",arrayIndex:this.rank,type:"int"}];let t=ct(this.rank),n=vn("coords",this.rank),s=vn("sourceLoc",this.rank),r=this.rank===1?"sourceLoc":`vec2(${s.slice(-2).join()})`,a=`getChannel(getSource(${s.join()}), ${r})`,o=`
      result.x = ${a};
      if (++${n[this.rank-1]} < ${e[this.rank-1]}) {
        ++${s[this.rank-1]};
        result.y = ${a};
        --${s[this.rank-1]};
      }
    `,i=this.rank===1?"":`
      --${n[this.rank-1]};
      if (++${n[this.rank-2]} < ${e[this.rank-2]}) {
        ++${s[this.rank-2]};
        result.z = ${a};
        if (++${n[this.rank-1]} < ${e[this.rank-1]}) {
          ++${s[this.rank-1]};
          result.w = ${a};
        }
      }
    `,l=this.rank<=4?`sourceLoc = coords +
            ${t}(${e.map((u,c)=>`start[${c}]`).join()});`:e.map((u,c)=>`${s[c]} = ${n[c]} + start[${c}];`).join(`
`);this.userCode=`
      void main() {
        ${t} coords = getOutputCoords();
        ${t} sourceLoc;
        ${l}
        vec4 result = vec4(0.);
        ${o}
        ${i}
        setOutput(result);
      }
    `}};function hZ(e,t,n,s){let r=s.texData.get(e.dataId),a=s.makeTensorInfo(n,e.dtype),o=s.texData.get(a.dataId);Object.assign(o,r),o.refCount=1,o.shape=n,o.dtype=e.dtype;let i=An.computeFlatOffset(t,I.computeStrides(e.shape));r.slice&&(i+=r.slice.flatOffset),o.slice={flatOffset:i,origDataId:r.slice&&r.slice.origDataId||e.dataId};let l=s.dataRefCount.get(o.slice.origDataId)||1;return s.dataRefCount.set(o.slice.origDataId,l+1),a}function eu(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{begin:a,size:o}=s,[i,l]=An.parseSliceParams(r,a,o);if(An.assertParamsValid(r,i,l),I.sizeFromShape(l)===0)return n.makeTensorInfo(l,r.dtype,[]);if(n.shouldExecuteOnCPU([r])||r.dtype==="string"){let d=n.texData.get(r.dataId),h=Jq(d.values,i,l,r.shape,r.dtype);return n.makeTensorInfo(l,r.dtype,h)}let{isPacked:u}=n.texData.get(r.dataId),c=An.isSliceContinous(r.shape,i,l);if(u||!c){let d=ee().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new dZ(l):new uZ(l),h=[i];return n.runWebGLProgram(d,[r],r.dtype,h)}return n.uploadToGPU(r.dataId),hZ(r,i,l,n)}var pZ={kernelName:tl,backendName:"webgl",kernelFunc:eu},fZ=e=>{let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockShape:a,crops:o}=s;I.assert(r.shape.length<=4,()=>"batchToSpaceND for rank > 4 with a WebGL backend not implemented yet");let i=a.reduce((y,x)=>y*x),l=$.getReshaped(r.shape,a,i),u=$.getPermuted(l.length,a.length),c=$.getReshapedPermuted(r.shape,a,i),d=$.getSliceBeginCoords(o,a.length),h=$.getSliceSize(c,o,a.length),p=[],f=ye({inputs:{x:r},backend:n,attrs:{shape:l}}),m=wn({inputs:{x:f},backend:n,attrs:{perm:u}}),A=ye({inputs:{x:m},backend:n,attrs:{shape:c}}),g=eu({inputs:{x:A},backend:n,attrs:{begin:d,size:h}});return p.push(f),p.push(m),p.push(A),p.forEach(y=>n.disposeIntermediateTensorInfo(y)),g},mZ={kernelName:vi,backendName:"webgl",kernelFunc:fZ};function AZ(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,weights:a}=t,{size:o}=s,i=n.readSync(r.dataId),l=n.readSync(a.dataId),u=x6(i,l,a.dtype,a.shape,o);return n.makeTensorInfo([o],a.dtype,u)}var gZ={kernelName:Bd,backendName:"webgl",kernelFunc:AZ},yZ="return float(a != b);",j6=rn({opSnippet:yZ,cpuKernelImpl:Xq,dtype:"bool"}),xZ={kernelName:Ui,backendName:"webgl",kernelFunc:j6};function Gc(e){let{inputs:t,backend:n}=e,{input:s}=t,r=n.texData.get(s.dataId);return qn({inputs:{x:r.complexTensorInfos.real},backend:n})}var bZ={kernelName:lh,backendName:"webgl",kernelFunc:Gc},vZ="return float(int(x));";function wZ(e,t){let n=new sa(e.shape,vZ),s=t.runWebGLProgram(n,[e],"int32");return{dataId:s.dataId,shape:s.shape,dtype:s.dtype}}function N1(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{dtype:a}=s;if(a==="complex64"){if(r.dtype==="complex64")return qn({inputs:{x:r},backend:n});let o=Dt(r.shape),i=N1({inputs:{x:r},backend:n,attrs:{dtype:"float32"}}),l=ra({inputs:{real:i,imag:o},backend:n});return o.dispose(),n.disposeIntermediateTensorInfo(i),l}if(r.dtype==="complex64"){let o=Gc({inputs:{input:r},backend:n}),i=N1({inputs:{x:o},backend:n,attrs:{dtype:a}});return n.disposeIntermediateTensorInfo(o),i}if(!I.hasEncodingLoss(r.dtype,a)){let o=qn({inputs:{x:r},backend:n});return{dataId:o.dataId,shape:o.shape,dtype:a}}if(a==="int32")return wZ(r,n);if(a==="bool"){let o=n.makeTensorInfo([],"bool",I.getTypedArrayFromDType("bool",1)),l=j6({inputs:{a:r,b:o},backend:n});return n.disposeIntermediateTensorInfo(o),l}throw new Error(`Error in Cast: failed to cast ${r.dtype} to ${a}`)}var kZ={kernelName:va,backendName:"webgl",kernelFunc:N1},q6="return ceil(x);",IZ=Ze({opSnippet:q6,packedOpSnippet:q6,cpuKernelImpl:Eq}),SZ={kernelName:wa,backendName:"webgl",kernelFunc:IZ},CZ=class{constructor(e){this.variableNames=["A"],this.customUniforms=[{name:"minVal",type:"float"},{name:"maxVal",type:"float"}],this.outputShape=e,this.userCode=`

      void main() {
        float value = getAAtOutCoords();
        if (isnan(value)) {
          setOutput(value);
          return;
        }

        setOutput(clamp(value, minVal, maxVal));
      }
    `}},TZ=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"minVal",type:"float"},{name:"maxVal",type:"float"}],this.outputShape=e,this.userCode=`
      void main() {
        vec4 value = getAAtOutCoords();

        if (any(isnan(value))) {
          setOutput(value);
          return;
        }

        setOutput(clamp(value, vec4(minVal), vec4(maxVal)));
      }
    `}};function NZ(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{clipValueMin:a,clipValueMax:o}=s,i;ee().getBool("WEBGL_PACK_CLIP")?i=new TZ(r.shape):i=new CZ(r.shape);let l=[[a],[o]];return n.runWebGLProgram(i,[r],r.dtype,l)}var EZ={kernelName:Or,backendName:"webgl",kernelFunc:NZ},RZ=class{constructor(e){this.variableNames=["real","imag"],this.outputShape=e,this.userCode=`
      void main() {
        float re = abs(getRealAtOutCoords());
        float im = abs(getImagAtOutCoords());
        float mx = max(re, im);

        // sadly the length function in glsl is not underflow-safe
        // (at least not on Intel GPUs). So the safe solution is
        // to ensure underflow-safety in all cases.
        setOutput(
          mx == 0.0 ? 0.0 : mx * length(vec2(1, min(re, im)/mx))
        );
      }
    `}};function X6(e,t){return{dataId:t.dataId,dtype:t.dtype,shape:e.shape}}function _Z(e){let{inputs:t,backend:n}=e,{x:s}=t,r=n.texData.get(s.dataId),a=new RZ(s.shape),o=[X6(s,r.complexTensorInfos.real),X6(s,r.complexTensorInfos.imag)];return n.runWebGLProgram(a,o,o[0].dtype)}var $Z={kernelName:Nu,backendName:"webgl",kernelFunc:_Z},FZ=class{constructor(e){this.outputShape=[],this.outputShape=$.computeOutShape(e,1),this.variableNames=e.map((a,o)=>`T${o}`);let t=new Array(e.length-1);t[0]=e[0][1];for(let a=1;a<t.length;a++)t[a]=t[a-1]+e[a][1];let n=[`if (yC < ${t[0]}) setOutput(getT0(yR, yC));`];for(let a=1;a<t.length;a++){let o=t[a-1];n.push(`else if (yC < ${t[a]}) setOutput(getT${a}(yR, yC-${o}));`)}let s=t.length,r=t[t.length-1];n.push(`else setOutput(getT${s}(yR, yC-${r}));`),this.userCode=`
      void main() {
        ivec2 coords = getOutputCoords();
        int yR = coords.x;
        int yC = coords.y;

        ${n.join(`
        `)}
      }
    `}},DZ=class{constructor(e,t){this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[],this.outputShape=$.computeOutShape(e,t);let n=this.outputShape,s=n.length,r=ct(s),a=vn("coords",s),o=["x","y","z","w","u","v"].slice(0,s);this.variableNames=e.map((f,m)=>`T${m}`);let i=new Array(e.length-1);i[0]=e[0][t];for(let f=1;f<i.length;f++)i[f]=i[f-1]+e[f][t];let l=o[t],u=o.slice(-2),c=o.join(),d=`if (${l} < ${i[0]}) {
        return getChannel(
            getT0(${c}), vec2(${u.join()}));
        }`;for(let f=1;f<i.length;f++){let m=i[f-1];d+=`
        if (${l} < ${i[f]}  && ${l} >= ${i[f-1]}) {
          return getChannel(
            getT${f}(${xf(o,l,m)}),
            vec2(${xf(u,l,m)}));
        }`}let h=i.length,p=i[i.length-1];d+=`
        return getChannel(
          getT${h}(${xf(o,l,p)}),
          vec2(${xf(u,l,p)}));`,this.userCode=`
      float getValue(${o.map(f=>"int "+f)}) {
        ${d}
      }

      void main() {
        ${r} coords = getOutputCoords();
        vec4 result = vec4(getValue(${a}), 0., 0., 0.);

        ${a[s-1]} = ${a[s-1]} + 1;
        if (${a[s-1]} < ${n[s-1]}) {
          result.g = getValue(${a});
        }

        ${a[s-2]} = ${a[s-2]} + 1;
        if (${a[s-2]} < ${n[s-2]}) {
          result.a = getValue(${a});
        }

        ${a[s-1]} = ${a[s-1]} - 1;
        if (${a[s-2]} < ${n[s-2]} &&
            ${a[s-1]} < ${n[s-1]}) {
          result.b = getValue(${a});
        }
        setOutput(result);
      }
    `}};function xf(e,t,n){let s=e.indexOf(t);return e.map((a,o)=>o===s?`${a} - ${n}`:a).join()}function bf(e){let{inputs:t,backend:n}=e,{input:s}=t,r=n.texData.get(s.dataId);return qn({inputs:{x:r.complexTensorInfos.imag},backend:n})}var OZ={kernelName:th,backendName:"webgl",kernelFunc:bf};function tu(e,t,n){let s=e[0].dtype;if(s==="complex64"){let c=e.map(m=>Gc({inputs:{input:m},backend:n})),d=e.map(m=>bf({inputs:{input:m},backend:n})),h=tu(c,t,n),p=tu(d,t,n),f=ra({inputs:{real:h,imag:p},backend:n});return c.forEach(m=>n.disposeIntermediateTensorInfo(m)),d.forEach(m=>n.disposeIntermediateTensorInfo(m)),n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(p),f}let r=n.shouldExecuteOnCPU(e);if(s==="string"&&(r=!0),r){let c=e.map(g=>{let y=I.sizeFromShape(g.shape.slice(t));return ye({inputs:{x:g},backend:n,attrs:{shape:[-1,y]}})}),d=c.map(g=>({vals:n.readSync(g.dataId),shape:g.shape})),h=$.computeOutShape(c.map(g=>g.shape),1),p=c[0].shape[0]===1,f=Rq(d,h,s,p),m=$.computeOutShape(e.map(g=>g.shape),t),A=n.makeTensorInfo(m,s,f);return c.forEach(g=>n.disposeIntermediateTensorInfo(g)),A}if(e.length>ee().getNumber("WEBGL_MAX_TEXTURES_IN_SHADER")){let c=Math.floor(e.length/2),d=tu(e.slice(0,c),t,n),h=tu(e.slice(c),t,n),p=tu([d,h],t,n);return n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(h),p}if(ee().getBool("WEBGL_PACK_ARRAY_OPERATIONS")&&e[0].shape.length>1){let c=new DZ(e.map(d=>d.shape),t);return n.runWebGLProgram(c,e,s)}let{tensors2D:a,outShape:o}=PZ(e,t,n),i=new FZ(a.map(c=>c.shape)),l=n.runWebGLProgram(i,a,s);a.forEach(c=>n.disposeIntermediateTensorInfo(c));let u=ye({inputs:{x:l},attrs:{shape:o},backend:n});return n.disposeIntermediateTensorInfo(l),u}function PZ(e,t,n){let s=$.computeOutShape(e.map(a=>a.shape),t);return{tensors2D:e.map(a=>ye({inputs:{x:a},attrs:{shape:[-1,I.sizeFromShape(a.shape.slice(t))]},backend:n})),outShape:s}}function K6(e){let{inputs:t,backend:n,attrs:s}=e,{axis:r}=s,a=I.parseAxisParam(r,t[0].shape)[0],o=$.computeOutShape(t.map(u=>u.shape),a);if(I.sizeFromShape(o)===0)return n.makeTensorInfo(o,t[0].dtype,[]);let i=t.filter(u=>I.sizeFromShape(u.shape)>0);if(i.length===1)return qn({inputs:{x:i[0]},backend:n});let l=i.map(u=>u.shape);return $.assertParamsConsistent(l,a),tu(i,a,n)}var MZ={kernelName:wi,backendName:"webgl",kernelFunc:K6},Z6=class{constructor(e,t=!1,n=null,s=!1,r=!1){this.variableNames=["x","W"],this.outputShape=e.outShape;let a=e.padInfo.top,o=e.padInfo.left,i=e.strideHeight,l=e.strideWidth,u=e.dilationHeight,c=e.dilationWidth,d=e.filterHeight,h=e.filterWidth,p=Math.floor(e.inChannels/4)*4,f=e.inChannels%4,m=e.dataFormat==="channelsLast",A=m?1:2,g=m?2:3,y=m?3:1,x="",b="";n&&(s?x=`float activation(float a) {
          float b = getPreluActivationWeightsAtOutCoords();
          ${n}
        }`:r?x=`float activation(float a) {
          float b = getLeakyreluAlphaAtOutCoords();
          ${n}
        }`:x=`
          float activation(float x) {
            ${n}
          }
        `,b="result = activation(result);");let v=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),s&&this.variableNames.push("preluActivationWeights"),r&&this.variableNames.push("leakyreluAlpha"),this.userCode=`
      ${x}

      const ivec2 strides = ivec2(${i}, ${l});
      const ivec2 pads = ivec2(${a}, ${o});

      void main() {
        ivec4 coords = getOutputCoords();
        int batch = coords[0];
        int d2 = coords[${y}];

        ivec2 xRCCorner =
            ivec2(coords[${A}], coords[${g}]) * strides - pads;
        int xRCorner = xRCCorner.x;
        int xCCorner = xRCCorner.y;

        // Convolve x(?, ?, d1) with w(:, :, d1, d2) to get y(yR, yC, d2).
        // ? = to be determined. : = across all values in that axis.
        float dotProd = 0.0;
        for (int wR = 0; wR < ${d}; wR++) {
          int xR = xRCorner + wR * ${u};

          if (xR < 0 || xR >= ${e.inHeight}) {
            continue;
          }

          for (int wC = 0; wC < ${h}; wC++) {
            int xC = xCCorner + wC * ${c};

            if (xC < 0 || xC >= ${e.inWidth}) {
              continue;
            }

            for (int d1 = 0; d1 < ${p}; d1 += 4) {
              vec4 wValues = vec4(
                getW(wR, wC, d1, d2),
                getW(wR, wC, d1 + 1, d2),
                getW(wR, wC, d1 + 2, d2),
                getW(wR, wC, d1 + 3, d2)
              );

              if (${m}) {
                vec4 xValues = vec4(
                  getX(batch, xR, xC, d1),
                  getX(batch, xR, xC, d1 + 1),
                  getX(batch, xR, xC, d1 + 2),
                  getX(batch, xR, xC, d1 + 3)
                );
                dotProd += dot(xValues, wValues);
              } else {
                vec4 xValues = vec4(
                  getX(batch, d1, xR, xC),
                  getX(batch, d1 + 1, xR, xC),
                  getX(batch, d1 + 2, xR, xC),
                  getX(batch, d1 + 3, xR, xC)
                );
                dotProd += dot(xValues, wValues);
              }
            }

            if (${f===1}) {

              if (${m}) {
                dotProd +=
                    getX(batch, xR, xC, ${p}) *
                    getW(wR, wC, ${p}, d2);
              } else {
                dotProd +=
                    getX(batch, ${p}, xR, xC) *
                    getW(wR, wC, ${p}, d2);
              }

            } else if (${f===2}) {
              vec2 wValues = vec2(
                getW(wR, wC, ${p}, d2),
                getW(wR, wC, ${p} + 1, d2)
              );

              if (${m}) {
                vec2 xValues = vec2(
                  getX(batch, xR, xC, ${p}),
                  getX(batch, xR, xC, ${p} + 1)
                );
                dotProd += dot(xValues, wValues);
              } else {
                vec2 xValues = vec2(
                  getX(batch, ${p}, xR, xC),
                  getX(batch, ${p} + 1, xR, xC)
                );
                dotProd += dot(xValues, wValues);
              }

            } else if (${f===3}) {
              vec3 wValues = vec3(
                getW(wR, wC, ${p}, d2),
                getW(wR, wC, ${p} + 1, d2),
                getW(wR, wC, ${p} + 2, d2)
              );

              if (${m}) {
                vec3 xValues = vec3(
                  getX(batch, xR, xC, ${p}),
                  getX(batch, xR, xC, ${p} + 1),
                  getX(batch, xR, xC, ${p} + 2)
                );
                dotProd += dot(xValues, wValues);
              } else {
                vec3 xValues = vec3(
                  getX(batch, ${p}, xR, xC),
                  getX(batch, ${p} + 1, xR, xC),
                  getX(batch, ${p} + 2, xR, xC)
                );
                dotProd += dot(xValues, wValues);
              }

            }
          }
        }

        float result = dotProd;
        ${v}
        ${b}
        setOutput(result);
      }
    `}},zZ=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let t=e.padInfo.front,n=e.padInfo.top,s=e.padInfo.left,r=e.strideDepth,a=e.strideHeight,o=e.strideWidth,i=e.dilationDepth,l=e.dilationHeight,u=e.dilationWidth,c=e.filterDepth,d=e.filterHeight,h=e.filterWidth,p=Math.floor(e.inChannels/4)*4,f=e.inChannels%4;this.userCode=`
      const ivec3 strides = ivec3(${r}, ${a}, ${o});
      const ivec3 pads = ivec3(${t}, ${n}, ${s});

      void main() {
        ivec5 coords = getOutputCoords();
        int batch = coords.x;
        int d2 = coords.u;

        ivec3 xFRCCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;
        int xFCorner = xFRCCorner.x;
        int xRCorner = xFRCCorner.y;
        int xCCorner = xFRCCorner.z;

        // Convolve x(?, ?, ?, d1) with w(:, :, :, d1, d2) to get
        // y(yF, yR, yC, d2). ? = to be determined. : = across all
        // values in that axis.
        float dotProd = 0.0;
        for (int wF = 0; wF < ${c}; wF++) {
          int xF = xFCorner + wF * ${i};

          if (xF < 0 || xF >= ${e.inDepth}) {
            continue;
          }

          for (int wR = 0; wR < ${d}; wR++) {
            int xR = xRCorner + wR * ${l};

            if (xR < 0 || xR >= ${e.inHeight}) {
              continue;
            }

            for (int wC = 0; wC < ${h}; wC++) {
              int xC = xCCorner + wC * ${u};

              if (xC < 0 || xC >= ${e.inWidth}) {
                continue;
              }

              for (int d1 = 0; d1 < ${p}; d1 += 4) {
                vec4 xValues = vec4(
                  getX(batch, xF, xR, xC, d1),
                  getX(batch, xF, xR, xC, d1 + 1),
                  getX(batch, xF, xR, xC, d1 + 2),
                  getX(batch, xF, xR, xC, d1 + 3)
                );
                vec4 wValues = vec4(
                  getW(wF, wR, wC, d1, d2),
                  getW(wF, wR, wC, d1 + 1, d2),
                  getW(wF, wR, wC, d1 + 2, d2),
                  getW(wF, wR, wC, d1 + 3, d2)
                );

                dotProd += dot(xValues, wValues);
              }

              if (${f===1}) {
                dotProd +=
                  getX(batch, xF, xR, xC, ${p}) *
                  getW(wF, wR, wC, ${p}, d2);
              } else if (${f===2}) {
                vec2 xValues = vec2(
                  getX(batch, xF, xR, xC, ${p}),
                  getX(batch, xF, xR, xC, ${p} + 1)
                );
                vec2 wValues = vec2(
                  getW(wF, wR, wC, ${p}, d2),
                  getW(wF, wR, wC, ${p} + 1, d2)
                );
                dotProd += dot(xValues, wValues);
              } else if (${f===3}) {
                vec3 xValues = vec3(
                  getX(batch, xF, xR, xC, ${p}),
                  getX(batch, xF, xR, xC, ${p} + 1),
                  getX(batch, xF, xR, xC, ${p} + 2)
                );
                vec3 wValues = vec3(
                  getW(wF, wR, wC, ${p}, d2),
                  getW(wF, wR, wC, ${p} + 1, d2),
                  getW(wF, wR, wC, ${p} + 2, d2)
                );
                dotProd += dot(xValues, wValues);
              }
            }
          }
        }
        setOutput(dotProd);
      }
    `}},LZ=class{constructor(e,t,n){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e;let{filterWidth:s,inChannels:r,strideWidth:a,strideHeight:o,padInfo:i,outWidth:l,dilationWidth:u,dilationHeight:c,dataFormat:d}=n,{left:h,top:p}=i,f=r*s,m=bn(),A=d==="channelsLast",g=A?0:1,y=A?1:2,x="";for(let b=0;b<=1;b++)for(let v=0;v<=1;v++)x+=`
          blockIndex = rc.y + ${v};
          pos = rc.x + ${b};

          if(blockIndex < ${e[1]} && pos < ${e[0]}) {
            offsetY = int(blockIndex / (${l})) * ${o} - ${p};
            d0 = offsetY + ${c} * (pos / ${f});

            if(d0 < ${t[g]} && d0 >= 0) {

              offsetX = int(mod(float(blockIndex), ${l}.) * ${a}. - ${h}.);
              d1 = offsetX + ${u} * (int(mod(float(pos), ${f}.) / ${r}.));

              if(d1 < ${t[y]} && d1 >= 0) {

                ch = int(mod(float(pos), ${r}.));

                if (${A}) {
                  innerDims = vec2(d1, ch);
                  result[${b*2+v}] = getChannel(
                    getA(d0, int(innerDims.x),
                    int(innerDims.y)), innerDims);
                } else {
                  innerDims = vec2(d0, d1);
                  result[${b*2+v}] = getChannel(
                    getA(ch, int(innerDims.x),
                    int(innerDims.y)), innerDims);
                }
              }
            }
          }
        `;this.userCode=`
      void main() {
        ivec2 rc = getOutputCoords();

        vec4 result = vec4(0);

        int blockIndex, pos, offsetY, d0, offsetX, d1, ch;
        vec2 innerDims;

        ${x}

        ${m.output} = result;
      }
    `}};function Y6({x:e,filter:t,convInfo:n,backend:s,bias:r=null,preluActivationWeights:a=null,leakyreluAlpha:o=0,activation:i=null}){let l=e.shape,u=s.texData.get(e.dataId),c=n.inChannels,d=l[0]*l[1]*l[2],h=n.outChannels,p=n.dataFormat==="channelsLast",f=!1,m=!1,A,g=[],y=(d===1||h===1)&&c>B6,x=l[2]%2!=0&&!!u.isPacked;if(y||!ee().getBool("WEBGL_LAZILY_UNPACK")||!ee().getBool("WEBGL_PACK_BINARY_OPERATIONS")||!x){let b=p?l[0]*l[1]*l[2]:l[0]*l[2]*l[3],v=ye({inputs:{x:e},backend:s,attrs:{shape:[1,b,n.inChannels]}}),k=ye({inputs:{x:t},backend:s,attrs:{shape:[1,n.inChannels,n.outChannels]}}),w=gf({a:v,b:k,transposeA:f,transposeB:m,backend:s,bias:r,activation:i,preluActivationWeights:a,leakyreluAlpha:o});A=ye({inputs:{x:w},backend:s,attrs:{shape:n.outShape}}),g.push(v),g.push(k),g.push(w)}else{let b=p?l[0]*l[1]*(l[2]+1):l[0]*l[2]*(l[3]+1),v={dataId:e.dataId,shape:[1,b,n.inChannels],dtype:e.dtype},k=u.shape;u.shape=u.shape.slice(),u.shape[u.shape.length-2]++,I.assert(Wc(u.shape,v.shape),()=>`packed reshape ${u.shape} to ${v.shape} isn't free`);let w=ye({inputs:{x:t},backend:s,attrs:{shape:[1,n.inChannels,n.outChannels]}});g.push(w);let C=gf({a:v,b:w,backend:s,transposeA:f,transposeB:m,bias:r,activation:i,preluActivationWeights:a,leakyreluAlpha:o}),E=s.texData.get(C.dataId);I.assert(E.isPacked,()=>"batchMatMul result is expected to be packed"),u.shape=k,E.shape=n.outShape,A=qn({inputs:{x:C},backend:s}),A.shape=n.outShape,g.push(C)}for(let b of g)s.disposeIntermediateTensorInfo(b);return A}function J6({x:e,filter:t,convInfo:n,backend:s,bias:r=null,preluActivationWeights:a=null,leakyreluAlpha:o=0,activation:i=null}){let{filterWidth:l,filterHeight:u,inChannels:c,outWidth:d,outHeight:h,dataFormat:p}=n,f=p==="channelsLast",m=l*u*c,A=h*d,g=[m,A],y=!0,x=!1,b=[],v=ye({inputs:{x:e},backend:s,attrs:{shape:e.shape.slice(1)}}),k=ye({inputs:{x:t},backend:s,attrs:{shape:[1,m,I.sizeFromShape(t.shape)/m]}});b.push(v),b.push(k);let w=new LZ(g,v.shape,n),C=s.runWebGLProgram(w,[v],"float32"),E=ye({inputs:{x:C},backend:s,attrs:{shape:[1,g[0],g[1]]}});b.push(C),b.push(E);let M=r!=null,R=a!=null,_=i==="leakyrelu",N=i?ff(i,!0):null,O=new O6(E.shape,k.shape,[1,A,n.outChannels],y,x,M,N,R,_),W=[E,k];if(r&&W.push(r),R&&W.push(a),_){let Q=s.makeTensorInfo([],"float32",I.createScalarValue(o,"float32"));W.push(Q),b.push(Q)}let j=s.runWebGLProgram(O,W,"float32"),q=f?[1,h,d,n.outChannels]:[1,n.outChannels,h,d],X=ye({inputs:{x:j},backend:s,attrs:{shape:q}});b.push(j);for(let Q of b)s.disposeIntermediateTensorInfo(Q);return X}function BZ(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a}=t,{strides:o,pad:i,dataFormat:l,dilations:u,dimRoundingMode:c}=s,d=$.convertConv2DDataFormat(l),h=$.computeConv2DInfo(r.shape,a.shape,o,u,i,c,!1,d),p;if(h.filterHeight===1&&h.filterWidth===1&&h.dilationHeight===1&&h.dilationWidth===1&&h.strideHeight===1&&h.strideWidth===1&&(h.padInfo.type==="SAME"||h.padInfo.type==="VALID"))p=Y6({x:r,filter:a,convInfo:h,backend:n});else if(ee().getBool("WEBGL_CONV_IM2COL")&&r.shape[0]===1)p=J6({x:r,filter:a,convInfo:h,backend:n});else{let m=new Z6(h);p=n.runWebGLProgram(m,[r,a],"float32")}let f=ye({inputs:{x:p},backend:n,attrs:{shape:h.outShape}});return n.disposeIntermediateTensorInfo(p),f}var WZ={kernelName:ka,backendName:"webgl",kernelFunc:BZ},VZ=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideHeight,n=e.strideWidth,s=e.padInfo.top,r=e.padInfo.left,a=e.dataFormat==="channelsLast";this.userCode=`
      void main() {
        ivec4 coords = getOutputCoords();
        int wR = coords.x;
        int wC = coords.y;
        int d1 = coords.z;
        int d2 = coords.w;

        // Convolve x(?, ?, d1) with dy(:, :, d2) to get dw(wR, wC, d1, d2).
        // ? = to be determined. : = across all values in that axis.
        float dotProd = 0.0;

        for (int b = 0; b < ${e.batchSize}; b++) {
          for (int yR = 0; yR < ${e.outHeight}; yR++) {
            int xR = wR + yR * ${t} - ${s};

            if (xR < 0 || xR >= ${e.inHeight}) {
              continue;
            }

            for (int yC = 0; yC < ${e.outWidth}; yC++) {
              int xC = wC + yC * ${n} - ${r};

              if (xC < 0 || xC >= ${e.inWidth}) {
                continue;
              }

              if (${a}) {
                float dyValue = getDy(b, yR, yC, d2);
                float xValue = getX(b, xR, xC, d1);
                dotProd += (xValue * dyValue);
              } else {
                float dyValue = getDy(b, d2, yR, yC);
                float xValue = getX(b, d1, xR, xC);
                dotProd += (xValue * dyValue);
              }

            }
          }
        }
        setOutput(dotProd);
      }
    `}},UZ=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,s=e.strideHeight,r=e.strideWidth,a=e.dataFormat==="channelsLast",o=t-1-e.padInfo.top,i=n-1-e.padInfo.left,l=a?1:2,u=a?2:3,c=a?3:1;this.userCode=`
      const ivec2 pads = ivec2(${o}, ${i});

      void main() {
        ivec4 coords = getOutputCoords();
        int batch = coords[0];
        int d1 = coords[${c}];

        ivec2 dyCorner = ivec2(coords[${l}], coords[${u}]) - pads;
        int dyRCorner = dyCorner.x;
        int dyCCorner = dyCorner.y;

        // Convolve dy(?, ?, d2) with w(:, :, d1, d2) to compute dx(xR, xC, d1).
        // ? = to be determined. : = across all values in that axis.
        float dotProd = 0.0;
        for (int wR = 0; wR < ${t}; wR++) {
          float dyR = float(dyRCorner + wR) / ${s}.0;

          if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) {
            continue;
          }
          int idyR = int(dyR);

          int wRPerm = ${t} - 1 - wR;

          for (int wC = 0; wC < ${n}; wC++) {
            float dyC = float(dyCCorner + wC) / ${r}.0;

            if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
                fract(dyC) > 0.0) {
              continue;
            }
            int idyC = int(dyC);

            int wCPerm = ${n} - 1 - wC;

            for (int d2 = 0; d2 < ${e.outChannels}; d2++) {

              if (${a}) {
                float xValue = getDy(batch, idyR, idyC, d2);
                float wValue = getW(wRPerm, wCPerm, d1, d2);
                dotProd += xValue * wValue;
              } else {
                float xValue = getDy(batch, d2, idyR, idyC);
                float wValue = getW(wRPerm, wCPerm, d1, d2);
                dotProd += xValue * wValue;
              }

            }
          }
        }
        setOutput(dotProd);
      }
    `}},HZ=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideDepth,n=e.strideHeight,s=e.strideWidth,r=e.padInfo.front,a=e.padInfo.top,o=e.padInfo.left;this.userCode=`
      void main() {
        ivec5 coords = getOutputCoords();
        int wF = coords.x;
        int wR = coords.y;
        int wC = coords.z;
        int d1 = coords.w;
        int d2 = coords.u;

        float dotProd = 0.0;

        for (int b = 0; b < ${e.batchSize}; b++) {
          for (int yF = 0; yF < ${e.outDepth}; yF++) {
            int xF = wF + yF * ${t} - ${r};

            if (xF < 0 || xF >= ${e.inDepth}) {
              continue;
            }

            for (int yR = 0; yR < ${e.outHeight}; yR++) {
              int xR = wR + yR * ${n} - ${a};

              if (xR < 0 || xR >= ${e.inHeight}) {
                continue;
              }

              for (int yC = 0; yC < ${e.outWidth}; yC++) {
                int xC = wC + yC * ${s} - ${o};

                if (xC < 0 || xC >= ${e.inWidth}) {
                  continue;
                }

                float dyValue = getDy(b, yF, yR, yC, d2);
                float xValue = getX(b, xF, xR, xC, d1);
                dotProd += (xValue * dyValue);
              }
            }
          }
        }
        setOutput(dotProd);
      }
    `}},GZ=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterDepth,n=e.filterHeight,s=e.filterWidth,r=e.strideDepth,a=e.strideHeight,o=e.strideWidth,i=t-1-e.padInfo.front,l=n-1-e.padInfo.top,u=s-1-e.padInfo.left;this.userCode=`
      const ivec3 pads = ivec3(${i}, ${l}, ${u});

      void main() {
        ivec5 coords = getOutputCoords();
        int batch = coords.x;
        int d1 = coords.u;


        ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;
        int dyFCorner = dyCorner.x;
        int dyRCorner = dyCorner.y;
        int dyCCorner = dyCorner.z;

        float dotProd = 0.0;
        for (int wF = 0; wF < ${t}; wF++) {
          float dyF = float(dyFCorner + wF) / ${r}.0;

          if (dyF < 0.0 || dyF >= ${e.outDepth}.0 || fract(dyF) > 0.0) {
            continue;
          }
          int idyF = int(dyF);

          int wFPerm = ${t} - 1 - wF;

          for (int wR = 0; wR < ${n}; wR++) {
            float dyR = float(dyRCorner + wR) / ${a}.0;

            if (dyR < 0.0 || dyR >= ${e.outHeight}.0 ||
              fract(dyR) > 0.0) {
              continue;
            }
            int idyR = int(dyR);

            int wRPerm = ${n} - 1 - wR;

            for (int wC = 0; wC < ${s}; wC++) {
              float dyC = float(dyCCorner + wC) / ${o}.0;

              if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
                  fract(dyC) > 0.0) {
                continue;
              }
              int idyC = int(dyC);

              int wCPerm = ${s} - 1 - wC;

              for (int d2 = 0; d2 < ${e.outChannels}; d2++) {
                float xValue = getDy(batch, idyF, idyR, idyC, d2);
                float wValue = getW(wFPerm, wRPerm, wCPerm, d1, d2);
                dotProd += xValue * wValue;
              }
            }
          }
        }
        setOutput(dotProd);
      }
    `}};function jZ(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,dy:a}=t,{strides:o,pad:i,dataFormat:l,dimRoundingMode:u,filterShape:c}=s,d=$.convertConv2DDataFormat(l),h=$.computeConv2DInfo(r.shape,c,o,1,i,u,!1,d),p=new VZ(h);return n.runWebGLProgram(p,[r,a],"float32")}var qZ={kernelName:Vd,backendName:"webgl",kernelFunc:jZ};function XZ(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,filter:a}=t,{inputShape:o,strides:i,pad:l,dataFormat:u,dimRoundingMode:c}=s,d=$.convertConv2DDataFormat(u),h=$.computeConv2DInfo(o,a.shape,i,1,l,c,!1,d),p=new UZ(h);return n.runWebGLProgram(p,[r,a],"float32")}var KZ={kernelName:Ia,backendName:"webgl",kernelFunc:XZ};function ZZ(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a}=t,{strides:o,pad:i,dilations:l}=s,u=$.computeConv3DInfo(r.shape,a.shape,o,l,i),c=new zZ(u);return n.runWebGLProgram(c,[r,a],"float32")}var YZ={kernelName:Eu,backendName:"webgl",kernelFunc:ZZ};function JZ(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,dy:a}=t,{strides:o,pad:i,filterShape:l}=s,u=$.computeConv3DInfo(r.shape,l,o,1,i),c=new HZ(u);return n.runWebGLProgram(c,[r,a],"float32")}var QZ={kernelName:Ud,backendName:"webgl",kernelFunc:JZ};function eY(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,filter:a}=t,{pad:o,strides:i,inputShape:l}=s,u=$.computeConv3DInfo(l,a.shape,i,1,o),c=new GZ(u);return n.runWebGLProgram(c,[r,a],"float32")}var tY={kernelName:Hd,backendName:"webgl",kernelFunc:eY},nY=D6+`
  return cos(x);
`,sY=Ze({opSnippet:nY}),rY={kernelName:Sa,backendName:"webgl",kernelFunc:sY},aY=`
  float e2x = exp(-x);
  return (e2x + 1.0 / e2x) / 2.0;
`,oY=Ze({opSnippet:aY}),iY={kernelName:Ca,backendName:"webgl",kernelFunc:oY},lY=class{constructor(e,t,n,s,r){this.variableNames=["Image","Boxes","BoxInd"],this.outputShape=[];let[a,o,i,l]=e,[u]=t,[c,d]=n;this.outputShape=[u,c,d,l];let h=s==="bilinear"?1:0,[p,f]=[`${o-1}.0`,`${i-1}.0`],[m,A,g]=c>1?[`${(o-1)/(c-1)}`,"(y2-y1) * height_ratio",`y1*${p} + float(y)*(height_scale)`]:["0.0","0.0",`0.5 * (y1+y2) * ${p}`],[y,x,b]=d>1?[`${(i-1)/(d-1)}`,"(x2-x1) * width_ratio",`x1*${f} + float(x)*(width_scale)`]:["0.0","0.0",`0.5 * (x1+x2) * ${f}`];this.userCode=`
      const float height_ratio = float(${m});
      const float width_ratio = float(${y});
      void main() {
        ivec4 coords = getOutputCoords();
        int b = coords[0];
        int y = coords[1];
        int x = coords[2];
        int d = coords[3];

        // get box vals
        float y1 = getBoxes(b,0);
        float x1 = getBoxes(b,1);
        float y2 = getBoxes(b,2);
        float x2 = getBoxes(b,3);

        // get image in batch index
        int bInd = round(getBoxInd(b));
        if(bInd < 0 || bInd >= ${a}) {
          return;
        }

        float height_scale = ${A};
        float width_scale = ${x};

        float in_y = ${g};
        if( in_y < 0.0 || in_y > ${p} ) {
          setOutput(float(${r}));
          return;
        }
        float in_x = ${b};
        if( in_x < 0.0 || in_x > ${f} ) {
          setOutput(float(${r}));
          return;
        }

        vec2 sourceFracIndexCR = vec2(in_x,in_y);
        if(${h} == 1) {
          // Compute the four integer indices.
          ivec2 sourceFloorCR = ivec2(sourceFracIndexCR);
          ivec2 sourceCeilCR = ivec2(ceil(sourceFracIndexCR));

          float topLeft = getImage(b, sourceFloorCR.y, sourceFloorCR.x, d);
          float bottomLeft = getImage(b, sourceCeilCR.y, sourceFloorCR.x, d);
          float topRight = getImage(b, sourceFloorCR.y, sourceCeilCR.x, d);
          float bottomRight = getImage(b, sourceCeilCR.y, sourceCeilCR.x, d);

          vec2 fracCR = sourceFracIndexCR - vec2(sourceFloorCR);

          float top = topLeft + (topRight - topLeft) * fracCR.x;
          float bottom = bottomLeft + (bottomRight - bottomLeft) * fracCR.x;
          float newValue = top + (bottom - top) * fracCR.y;
          setOutput(newValue);
        } else {
          // Compute the coordinators of nearest neighbor point.
          ivec2 sourceNearestCR = ivec2(floor(
            sourceFracIndexCR + vec2(0.5,0.5)));
          float newValue = getImage(b, sourceNearestCR.y, sourceNearestCR.x, d);
          setOutput(newValue);
        }
      }
    `}},uY=e=>{let{inputs:t,backend:n,attrs:s}=e,{image:r,boxes:a,boxInd:o}=t,{cropSize:i,method:l,extrapolationValue:u}=s,c=new lY(r.shape,a.shape,i,l,u);return n.runWebGLProgram(c,[r,a,o],"float32")},cY={kernelName:ki,backendName:"webgl",kernelFunc:uY},Q6=class{constructor(e,t,n){this.variableNames=["x"],this.customUniforms=[{name:"index",type:"float"}],this.outputShape=e;let s=e.length,r=t?"0.0":`getX(${e4(s,"coords")})`,a=e[e.length-1],o="",i="";t?(o=n?`end != ${a-1}`:"end != 0",i=n?"end + 1":"end - 1"):(o=n?`end + pow2 < ${a}`:"end >= pow2",i=n?"end + pow2":"end - pow2"),this.userCode=`
      void main() {
        ${ct(s)} coords = getOutputCoords();
        int end = ${t4(s,"coords")};
        float val = ${r};
        int pow2 = int(pow(2.0, index));
        if (${o}) {
          int idx = ${i};
          ${t4(s,"coords")} = idx;
          val += getX(${e4(s,"coords")});
        }
        setOutput(val);
      }
    `}};function e4(e,t){if(e===1)return`${t}`;if(e===2)return`${t}.x, ${t}.y`;if(e===3)return`${t}.x, ${t}.y, ${t}.z`;if(e===4)return`${t}.x, ${t}.y, ${t}.z, ${t}.w`;throw Error(`Cumulative sum for rank ${e} is not yet supported`)}function t4(e,t){if(e===1)return`${t}`;if(e===2)return`${t}.y`;if(e===3)return`${t}.z`;if(e===4)return`${t}.w`;throw Error(`Cumulative sum for rank ${e} is not yet supported`)}function dY(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,exclusive:o,reverse:i}=s,l=r.shape.length,u=$.getAxesPermutation([a],l),c=r;u!=null&&(c=wn({inputs:{x:r},backend:n,attrs:{perm:u}}));let d=$.getInnerMostAxes(1,l)[0];if(d!==l-1)throw new Error(`WebGL cumsum shader expects an inner-most axis=${r.shape.length-1} but got axis=${a}`);let h=c.shape[d],p=qn({inputs:{x:c},backend:n});for(let f=0;f<=Math.ceil(Math.log2(h))-1;f++){let m=new Q6(c.shape,!1,i),A=[[f]],g=p;p=n.runWebGLProgram(m,[p],p.dtype,A),n.disposeIntermediateTensorInfo(g)}if(o){let f=new Q6(c.shape,o,i),m=p;p=n.runWebGLProgram(f,[p],p.dtype),n.disposeIntermediateTensorInfo(m)}if(u!=null){let f=$.getUndoAxesPermutation(u),m=wn({inputs:{x:p},backend:n,attrs:{perm:f}});return n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(c),m}return p}var hY={kernelName:Ta,backendName:"webgl",kernelFunc:dY};function pY(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,weights:a}=t,{size:o,binaryOutput:i}=s;if(r.shape.length===1){let l=n.readSync(r.dataId),u=n.readSync(a.dataId),c=x6(l,u,a.dtype,a.shape,o);return n.makeTensorInfo([o],a.dtype,c)}else if(r.shape.length===2){let l=n.bufferSync(r),u=n.bufferSync(a),c=Nq(l,u,o,i);return n.makeTensorInfo(c.shape,a.dtype,c.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${r.shape.length}.`)}var fY={kernelName:Gd,backendName:"webgl",kernelFunc:pY},mY=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=[],this.outputShape=e,this.blockSize=t,this.dataFormat=n,this.userCode=`
    void main() {
      ivec4 coords = getOutputCoords();
      int b = coords[0];
      int h = ${this.getHeightCoordString()};
      int w = ${this.getWidthCoordString()};
      int d = ${this.getDepthCoordString()};

      int in_h = h / ${t};
      int offset_h = imod(h, ${t});
      int in_w = w / ${t};
      int offset_w = imod(w, ${t});
      int offset_d = (offset_h * ${t} + offset_w) *
        ${this.getOutputDepthSize()};
      int in_d = d + offset_d;

      float result = ${this.getInputSamplingString()};
      setOutput(result);
    }
  `}getHeightCoordString(){return this.dataFormat==="NHWC"?"coords[1]":"coords[2]"}getWidthCoordString(){return this.dataFormat==="NHWC"?"coords[2]":"coords[3]"}getDepthCoordString(){return this.dataFormat==="NHWC"?"coords[3]":"coords[1]"}getOutputDepthSize(){return this.dataFormat==="NHWC"?this.outputShape[3]:this.outputShape[1]}getInputSamplingString(){return this.dataFormat==="NHWC"?"getX(b, in_h, in_w, in_d)":"getX(b, in_d, in_h, in_w)"}};function AY(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockSize:a,dataFormat:o}=s;I.assert(a>1,()=>`blockSize should be > 1 for depthToSpace, but was: ${a}`);let i=r.shape[0],l=o==="NHWC"?r.shape[1]:r.shape[2],u=o==="NHWC"?r.shape[2]:r.shape[3],c=o==="NHWC"?r.shape[3]:r.shape[1],d=l*a,h=u*a,p=c/(a*a),f=o==="NHWC"?[i,d,h,p]:[i,p,d,h],m=new mY(f,a,o);return n.runWebGLProgram(m,[r],r.dtype)}var gY={kernelName:Ii,backendName:"webgl",kernelFunc:AY},n4=class{constructor(e,t=!1,n=null,s=!1,r=!1){this.variableNames=["x","W"],this.outputShape=e.outShape;let a=e.inHeight,o=e.inWidth,i=e.padInfo.top,l=e.padInfo.left,u=e.strideHeight,c=e.strideWidth,d=e.dilationHeight,h=e.dilationWidth,p=e.filterHeight,f=e.filterWidth,m=e.outChannels/e.inChannels,A="",g="";n&&(s?A=`float activation(float a) {
          float b = getPreluActivationWeightsAtOutCoords();
          ${n}
        }`:r?A=`float activation(float a) {
          float b = getLeakyreluAlphaAtOutCoords();
          ${n}
        }`:A=`
          float activation(float x) {
            ${n}
          }
        `,g="result = activation(result);");let y=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),s&&this.variableNames.push("preluActivationWeights"),r&&this.variableNames.push("leakyreluAlpha"),this.userCode=`
      ${A}

      const ivec2 strides = ivec2(${u}, ${c});
      const ivec2 pads = ivec2(${i}, ${l});

      void main() {
        ivec4 coords = getOutputCoords();
        int batch = coords.x;
        ivec2 xRCCorner = coords.yz * strides - pads;
        int d2 = coords.w;
        int d1 = d2 / ${m};
        int q = d2 - d1 * ${m};

        int xRCorner = xRCCorner.x;
        int xCCorner = xRCCorner.y;

        // Convolve x(?, ?, d1) with w(:, :, d1, q) to get y(yR, yC, d2).
        // ? = to be determined. : = across all values in that axis.
        float dotProd = 0.0;
        // TO DO(dsmilkov): Flatten the two for loops and vec4 the operations.
        for (int wR = 0; wR < ${p}; wR++) {
          int xR = xRCorner + wR * ${d};

          if (xR < 0 || xR >= ${a}) {
            continue;
          }

          for (int wC = 0; wC < ${f}; wC++) {
            int xC = xCCorner + wC * ${h};

            if (xC < 0 || xC >= ${o}) {
              continue;
            }

            float xVal = getX(batch, xR, xC, d1);
            float wVal = getW(wR, wC, d1, q);
            dotProd += xVal * wVal;
          }
        }

        float result = dotProd;
        ${y}
        ${g}
        setOutput(result);
      }
    `}},s4=class{constructor(e,t=!1,n=null,s=!1,r=!1){this.variableNames=["x","W"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e.outShape;let a=e.outChannels/e.inChannels,o=e.inHeight,i=e.inWidth,l=e.padInfo.top,u=e.padInfo.left,c=e.strideHeight,d=e.strideWidth,h=e.dilationHeight,p=e.dilationWidth,f=e.filterHeight,m=e.filterWidth,A=m,g=`
      int xR; int xC; int xCOffset;
      vec4 wTexel; vec4 previous; vec4 final;`;for(let v=0;v<m;v++)g+=`
          vec4 xTexelC${v*2};
          int xTexelC${v*2}Ready;
          vec4 xTexelC${v*2+1};
          int xTexelC${v*2+1}Ready;
          vec4 xC${v};`;for(let v=0;v<f;v++){for(let k=0;k<m;k++)g+=`
          xTexelC${k*2} = vec4(0.0);
          xTexelC${k*2}Ready = 0;
          xTexelC${k*2+1} = vec4(0.0);
          xTexelC${k*2+1}Ready = 0;
          xC${k} = vec4(0.0);`;g+=`
        xR = xRCorner + ${v*h};
        if (xR >=0 && xR < ${o}) {
      `;for(let k=0;k<(A+1)/2;k++){let w=k*2,C=w*p;if(g+=`
          xC = xCCorner + ${C};
          `,d===1){if(w<m&&(u%2==1?(g+=`
                xCOffset = xC + 1;
                if (xCOffset >= 0 && xCOffset < ${i} && xTexelC${w}Ready == 0) {
                  xTexelC${w} = getX(batch, xR, xCOffset, d1);

                  // Need to manually clear unused channels in case
                  // we're reading from recycled texture.
                  if (xCOffset + 1 >= ${i}) {
                    xTexelC${w}.zw = vec2(0.0);
                  }
                  xTexelC${w}Ready = 1;
                }
              `,p===1&&C>0?g+=`
                xC${w} = vec4(xTexelC${w-2}.zw, xTexelC${w}.xy);
                `:g+=`
                  xCOffset = xC + 1 - 2;

                  if (xCOffset >= 0 && xCOffset < ${i}) {
                    previous = getX(batch, xR, xCOffset, d1);

                    // Need to manually clear unused channels in case
                    // we're reading from recycled texture.
                    if (xCOffset + 1 >= ${i}) {
                      previous.zw = vec2(0.0);
                    }

                    xC${w} = vec4(previous.zw, xTexelC${w}.xy);
                  } else {
                    xC${w} = vec4(0.0, 0.0, xTexelC${w}.xy);
                  }
                  `):g+=`
                if (xC >= 0 && xC < ${i} && xTexelC${w}Ready == 0) {
                  xTexelC${w} = getX(batch, xR, xC, d1);
                  if (xC + 1 >= ${i}) {
                    xTexelC${w}.zw = vec2(0.0);
                  }
                  xTexelC${w}Ready = 1;
                }

                xC${w} = xTexelC${w};
                `,C+1<m)){let E=u%2==0?I.nearestLargerEven(p):p;p%2==0&&u%2==1||p%2!=0&&u%2!=1?(g+=`
                  xCOffset = xC + ${u%2} + ${E};

                  if (xCOffset >= 0 && xCOffset < ${i} && xTexelC${w+1}Ready == 0) {
                    xTexelC${w+1} = getX(batch, xR, xCOffset, d1);

                    // Need to manually clear unused channels in case
                    // we're reading from recycled texture.
                    if (xCOffset + 1 >= ${i}) {
                      xTexelC${w+1}.zw = vec2(0.0);
                    }
                    xTexelC${w+1}Ready = 1;
                  }
                  `,p>1&&(g+=`
                    xCOffset -= 2;
                    if (xCOffset >= 0 && xCOffset < ${i} && xTexelC${w}Ready == 0) {
                      xTexelC${w} = getX(batch, xR, xCOffset, d1);
                      xTexelC${w}Ready = 1;
                    }
                    `),g+=`
                  xC${w+1} = vec4(xTexelC${w}.zw, xTexelC${w+1}.xy);
                  `):E===1?g+=`
                    xC${w+1} = xTexelC${w};
                    `:g+=`
                    xCOffset = xC + ${E};

                    if (xCOffset >= 0 && xCOffset < ${i} && xTexelC${w+1}Ready == 0) {
                      xTexelC${w+1} = getX(batch, xR, xCOffset, d1);
                      if (xCOffset + 1 >= ${i}) {
                        xTexelC${w+1}.zw = vec2(0.0);
                      }
                      xTexelC${w+1}Ready = 1;
                    }

                    xC${w+1} = xTexelC${w+1};
                    `}}else C<m&&(u%2==1?(g+=`
                xCOffset = xC + 1 - ${d};
                if(xCOffset >= 0 && xCOffset < ${i} && xTexelC${w}Ready == 0) {
                  xTexelC${w} = getX(batch, xR, xCOffset, d1);
                  // Need to manually clear unused channels in case
                  // we're reading from recycled texture.
                  if (xCOffset + 1 >= ${i}) {
                    xTexelC${w}.zw = vec2(0.0);
                  }
                  xTexelC${w}Ready = 1;
                }

                if(xC + 1 >= 0 && xC + 1 < ${i} && xTexelC${w+1}Ready == 0) {
                  xTexelC${w+1} = getX(batch, xR, xC + 1, d1);
                  // Need to manually clear unused channels in case
                  // we're reading from recycled texture.
                  if (xC + 2 >= ${i}) {
                    xTexelC${w+1}.zw = vec2(0.0);
                  }
                  xTexelC${w+1}Ready = 1;
                }

                xC${w} = vec4(xTexelC${w}.zw, xTexelC${w+1}.zw);
              `,C+1<m&&(g+=`
                  final = vec4(0.0);
                  xCOffset = xC + 1 + ${d};
                  if(xCOffset >= 0 && xCOffset < ${i}) {
                    final = getX(batch, xR, xCOffset, d1);
                  }
                  xC${w+1} = vec4(xTexelC${w+1}.xy, final.xy);
                `)):(g+=`
                if(xC >= 0 && xC < ${i} && xTexelC${w}Ready == 0) {
                  xTexelC${w} = getX(batch, xR, xC, d1);
                  if (xC + 1 >= ${i}) {
                    xTexelC${w}.zw = vec2(0.0);
                  }
                  xTexelC${w}Ready = 1;
                }

                xCOffset = xC + ${d};
                if(xCOffset >= 0 && xCOffset < ${i} && xTexelC${w+1}Ready == 0) {
                  xTexelC${w+1} = getX(batch, xR, xCOffset, d1);
                  if (xCOffset + 1 >= ${i}) {
                    xTexelC${w+1}.zw = vec2(0.);
                  }
                  xTexelC${w+1}Ready = 1;
                }

                xC${w} = vec4(
                  xTexelC${w}.xy, xTexelC${w+1}.xy);
              `,C+1<m&&(g+=`
                  xC${w+1} = vec4(xTexelC${w}.zw, xTexelC${w+1}.zw);
                `)));w<m&&(g+=`
            wTexel = getW(${v}, ${C}, d1, q);
            dotProd += xC${w} * vec4(wTexel.xz, wTexel.xz);
          `,C+1<m&&(g+=`
              wTexel = getW(${v}, ${C+1}, d1, q);
              dotProd += xC${w+1} * vec4(wTexel.xz, wTexel.xz);
            `))}g+=`
        }
      `}let y="",x="";n&&(s?y=`vec4 activation(vec4 a) {
          vec4 b = getPreluActivationWeightsAtOutCoords();
          ${n}
        }`:r?y=`vec4 activation(vec4 a) {
          vec4 b = getLeakyreluAlphaAtOutCoords();
          ${n}
        }`:y=`vec4 activation(vec4 x) {
          ${n}
        }`,x="result = activation(result);");let b=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),s&&this.variableNames.push("preluActivationWeights"),r&&this.variableNames.push("leakyreluAlpha"),this.userCode=`
      ${y}

      const ivec2 strides = ivec2(${c}, ${d});
      const ivec2 pads = ivec2(${l}, ${u});

      void main() {

        ivec4 coords = getOutputCoords();
        int batch = coords.x;
        ivec2 xRCCorner = coords.yz * strides - pads;
        int d2 = coords.w;
        int d1 = d2 / ${a};
        int q = d2 - d1 * ${a};
        int xRCorner = xRCCorner.x;
        int xCCorner = xRCCorner.y;

        //intialize dotProd with a small epsilon seems to reduce GPU accuracy loss.
        vec4 dotProd = vec4(0.000000000000001);

        ${g}

        vec4 result = dotProd - vec4(0.000000000000001);
        ${b}
        ${x}
        setOutput(result);
      }
    `}};function yY(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a}=t,{strides:o,pad:i,dilations:l,dimRoundingMode:u}=s,c=l;c==null&&(c=[1,1]),I.assert($.eitherStridesOrDilationsAreOne(o,c),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${o} and dilations '${c}'`);let d=$.computeConv2DInfo(r.shape,a.shape,o,c,i,u,!0),h;return ee().getBool("WEBGL_PACK_DEPTHWISECONV")&&d.strideWidth<=2&&d.outChannels/d.inChannels==1?h=new s4(d):h=new n4(d),n.runWebGLProgram(h,[r,a],"float32")}var xY={kernelName:Na,backendName:"webgl",kernelFunc:yY},bY=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideHeight,n=e.strideWidth,s=e.padInfo.top,r=e.padInfo.left,a=e.outChannels/e.inChannels;this.userCode=`
      void main() {
        ivec4 coords = getOutputCoords();
        int wR = coords.x;
        int wC = coords.y;
        int d1 = coords.z;
        int dm = coords.w;
        int d2 = d1 * ${a} + dm;

        float dotProd = 0.0;

        // TO DO: Vec4 over the batch size
        for (int b = 0; b < ${e.batchSize}; b++) {
          for (int yR = 0; yR < ${e.outHeight}; yR++) {
            int xR = wR + yR * ${t} - ${s};

            if (xR < 0 || xR >= ${e.inHeight}) {
              continue;
            }

            for (int yC = 0; yC < ${e.outWidth}; yC++) {
              int xC = wC + yC * ${n} - ${r};

              if (xC < 0 || xC >= ${e.inWidth}) {
                continue;
              }

              float dyValue = getDy(b, yR, yC, d2);
              float xValue = getX(b, xR, xC, d1);
              dotProd += (xValue * dyValue);
            }
          }
        }
        setOutput(dotProd);
      }
    `}},vY=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,s=e.strideHeight,r=e.strideWidth,a=t-1-e.padInfo.top,o=n-1-e.padInfo.left,i=e.outChannels/e.inChannels;this.userCode=`
      const ivec2 pads = ivec2(${a}, ${o});

      void main() {
        ivec4 coords = getOutputCoords();
        int batch = coords[0];
        int d1 = coords[3];
        ivec2 dyCorner = coords.yz - pads;
        int dyRCorner = dyCorner.x;
        int dyCCorner = dyCorner.y;

        float dotProd = 0.0;

        for (int wR = 0; wR < ${t}; wR++) {
          float dyR = float(dyRCorner + wR) / ${s}.0;

          if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) {
            continue;
          }
          int idyR = int(dyR);

          int wRPerm = ${t} - 1 - wR;

          for (int wC = 0; wC < ${n}; wC++) {
            float dyC = float(dyCCorner + wC) / ${r}.0;

            if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
                fract(dyC) > 0.0) {
              continue;
            }
            int idyC = int(dyC);

            int wCPerm = ${n} - 1 - wC;

            // TO DO: Vec4 over the channelMul
            for (int dm = 0; dm < ${i}; dm++) {
              int d2 = d1 * ${i} + dm;
              float xValue = getDy(batch, idyR, idyC, d2);
              float wValue = getW(wRPerm, wCPerm, d1, dm);
              dotProd += xValue * wValue;
            }
          }
        }
        setOutput(dotProd);
      }
    `}};function wY(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,dy:a}=t,{strides:o,dilations:i,pad:l,dimRoundingMode:u,filterShape:c}=s,d=$.computeConv2DInfo(r.shape,c,o,i,l,u,!0),h=new bY(d);return n.runWebGLProgram(h,[r,a],"float32")}var kY={kernelName:jd,backendName:"webgl",kernelFunc:wY};function IY(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,filter:a}=t,{strides:o,dilations:i,pad:l,dimRoundingMode:u,inputShape:c}=s,d=$.computeConv2DInfo(c,a.shape,o,i,l,u,!0),h=new vY(d);return n.runWebGLProgram(h,[r,a],"float32")}var SY={kernelName:qd,backendName:"webgl",kernelFunc:IY},CY=class{constructor(e){this.variableNames=["X"],this.outputShape=[e,e],this.userCode=`
      void main() {
          ivec2 coords = getOutputCoords();
          float val = coords[0] == coords[1] ? getX(coords[0]) : 0.0;
          setOutput(val);
      }
    `}};function TY(e){let{inputs:t,backend:n}=e,{x:s}=t,r=[...s.shape,...s.shape],a=I.sizeFromShape(s.shape),o=ye({inputs:{x:s},backend:n,attrs:{shape:[a]}}),i=new CY(a),l=n.runWebGLProgram(i,[o],o.dtype),u=ye({inputs:{x:l},backend:n,attrs:{shape:r}});return n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(l),u}var NY={kernelName:Xd,backendName:"webgl",kernelFunc:TY},EY=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let{inHeight:t,inWidth:n,padInfo:s,strideHeight:r,strideWidth:a,filterHeight:o,filterWidth:i,dilationHeight:l,dilationWidth:u}=e,{top:c,left:d}=s;this.userCode=`
      const ivec2 strides = ivec2(${r}, ${a});
      const ivec2 pads = ivec2(${c}, ${d});
      const float neg_infinity = -3.4e38;

      void main() {
        ivec4 coords = getOutputCoords();
        int batch = coords.x;
        int d1 = coords.w;
        ivec2 outTopLeftCorner =
            coords.yz * strides - pads;
        int hBeg = outTopLeftCorner.x;
        int wBeg = outTopLeftCorner.y;

        float curVal = neg_infinity;
        for (int h = 0; h < ${o}; h++) {
          int hIn = hBeg + h * ${l};

          if (hIn >= 0 && hIn < ${t}) {
            for (int w = 0; w < ${i}; w++) {
              int wIn = wBeg + w * ${u};

              if (wIn >= 0 && wIn < ${n}) {
                float xVal = getX(batch, hIn, wIn, d1);
                float wVal = getW(h, w, d1);

                float val = xVal + wVal;
                if (val > curVal) {
                  curVal = val;
                }
              }
            }
          }
        }

        float result = curVal;
        setOutput(result);
      }
    `}};function RY(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a}=t,{strides:o,pad:i,dilations:l}=s,u=$.computeDilation2DInfo(r.shape,a.shape,o,i,"NHWC",l),c,d=new EY(u);c=n.runWebGLProgram(d,[r,a],"float32");let h=ye({inputs:{x:c},backend:n,attrs:{shape:u.outShape}});return n.disposeIntermediateTensorInfo(c),h}var _Y={kernelName:Ru,backendName:"webgl",kernelFunc:RY};function $Y(e){let{inputs:t,backend:n,attrs:s}=e,{equation:r}=s,a=t,{allDims:o,summedDims:i,idDims:l}=$.decodeEinsumEquation(r,a.length);$.checkEinsumDimSizes(o.length,l,a);let{path:u,steps:c}=$.getEinsumComputePath(i,l),d=c.length,h=null,p=o.length,f=[];for(let m=0;m<d;++m){for(let A of c[m]){let{permutationIndices:g,expandDims:y}=$.getEinsumPermutation(p,l[A]),x;$.isIdentityPermutation(g)?x=a[A]:(x=wn({inputs:{x:a[A]},backend:n,attrs:{perm:g}}),f.push(x));let b=x.shape.slice();for(let v=0;v<y.length;++v)b.splice(y[v],0,1);I.arraysEqual(x.shape,b)||(x=ye({inputs:{x},backend:n,attrs:{shape:b}}),f.push(x)),h===null?h=x:(h=S1({inputs:{a:x,b:h},backend:n}),f.push(h))}m<d-1&&(u[m]>=0&&(h=Af({inputs:{x:h},backend:n,attrs:{axis:u[m]-(o.length-p),keepDims:!1}}),f.push(h)),p--)}for(let m of f)m!==h&&n.disposeIntermediateTensorInfo(m);return h}var FY={kernelName:Yd,backendName:"webgl",kernelFunc:$Y},DY="return (x >= 0.0) ? x : (exp(x) - 1.0);",OY=`
  vec4 result;

  result.r = (x.r >= 0.0) ? x.r : (exp(x.r) - 1.0);
  result.g = (x.g >= 0.0) ? x.g : (exp(x.g) - 1.0);
  result.b = (x.b >= 0.0) ? x.b : (exp(x.b) - 1.0);
  result.a = (x.a >= 0.0) ? x.a : (exp(x.a) - 1.0);

  return result;
`,PY=Ze({opSnippet:DY,packedOpSnippet:OY}),MY={kernelName:Si,backendName:"webgl",kernelFunc:PY},zY="return (b >= 1.0) ? a : a * (b + 1.0);",LY=`
  vec4 bGTEZero = vec4(greaterThanEqual(b, vec4(0.)));
  return (bGTEZero * a) + ((vec4(1.0) - bGTEZero) * (a * (b + vec4(1.0))));
`,BY=e=>{let{inputs:t,backend:n}=e,{dy:s,y:r}=t,a=ee().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Uc(LY,s.shape,r.shape):new Ql(zY,s.shape,r.shape);return n.runWebGLProgram(a,[s,r],s.dtype)},WY={kernelName:Jd,backendName:"webgl",kernelFunc:BY},VY=`
  return vec4(equal(a, b));
`,UY="return float(a == b);",HY=rn({opSnippet:UY,packedOpSnippet:VY,dtype:"bool",cpuKernelImpl:_q}),GY={kernelName:Ti,backendName:"webgl",kernelFunc:HY},jY=`
  // Error function is calculated approximately with elementary function.
  // See "Handbook of Mathematical Functions with Formulas,
  // Graphs, and Mathematical Tables", Abramowitz and Stegun.
  float p = ${$.ERF_P};
  float a1 = ${$.ERF_A1};
  float a2 = ${$.ERF_A2};
  float a3 = ${$.ERF_A3};
  float a4 = ${$.ERF_A4};
  float a5 = ${$.ERF_A5};

  float sign = sign(x);
  x = abs(x);
  float t = 1.0 / (1.0 + p * x);
  return sign * (1.0 - (((((a5*t + a4)*t) + a3)*t + a2)*t + a1)*t*exp(-x*x));
`,qY=Ze({opSnippet:jY}),XY={kernelName:Ci,backendName:"webgl",kernelFunc:qY},r4="return exp(x);",a4=Ze({opSnippet:r4,packedOpSnippet:r4,cpuKernelImpl:$q}),KY={kernelName:Ra,backendName:"webgl",kernelFunc:a4};function E1(e){let{inputs:t,attrs:n,backend:s}=e,{dim:r}=n,{input:a}=t,o=a.shape.length,i=a.shape.slice(),l=r;return r<0&&(I.assert(-(o+1)<=r,()=>`Axis must be in the interval [${-(o+1)}, ${o}]`),l=o+r+1),i.splice(l,0,1),ye({inputs:{x:a},backend:s,attrs:{shape:i}})}var ZY={kernelName:Ni,backendName:"webgl",kernelFunc:E1},o4="return exp(x) - 1.0;",YY=Ze({opSnippet:o4,packedOpSnippet:o4,cpuKernelImpl:Fq}),JY={kernelName:Ei,backendName:"webgl",kernelFunc:YY},i4=class{constructor(e,t,n){this.variableNames=["real","imag"];let s=t[1];this.outputShape=t;let r=n?`2.0 * ${Math.PI}`:`-2.0 * ${Math.PI}`,a=n?`${s}.0`:"1.0",o;if(e==="real")o="return real * expR - imag * expI;";else if(e==="imag")o="return real * expI + imag * expR;";else throw new Error(`FFT component must be either "real" or "imag", got ${e}.`);this.userCode=`
      const float exponentMultiplier = ${r};

      float unaryOpComplex(float real, float expR, float imag, float expI) {
        ${o}
      }

      float mulMatDFT(int batch, int index) {
        float indexRatio = float(index) / float(${s});
        float exponentMultiplierTimesIndexRatio =
            exponentMultiplier * indexRatio;

        float result = 0.0;

        for (int i = 0; i < ${s}; i++) {
          // x = (-2|2 * PI / N) * index * i;
          float x = exponentMultiplierTimesIndexRatio * float(i);
          float expR = cos(x);
          float expI = sin(x);
          float real = getReal(batch, i);
          float imag = getImag(batch, i);

          result +=
              unaryOpComplex(real, expR, imag, expI) / ${a};
        }

        return result;
      }

      void main() {
        ivec2 coords = getOutputCoords();
        setOutput(mulMatDFT(coords[0], coords[1]));
      }
    `}};function l4(e,t,n){let s=n.texData.get(e.dataId),r=I.sizeFromShape(e.shape),a=e.shape[e.shape.length-1],o=r/a,i=ye({inputs:{x:e},backend:n,attrs:{shape:[o,a]}}),l=i.shape,u=new i4("real",l,t),c=new i4("imag",l,t),d=[{dataId:s.complexTensorInfos.real.dataId,dtype:s.complexTensorInfos.real.dtype,shape:l},{dataId:s.complexTensorInfos.imag.dataId,dtype:s.complexTensorInfos.imag.dtype,shape:l}],h=n.runWebGLProgram(u,d,"float32"),p=n.runWebGLProgram(c,d,"float32"),f=ra({inputs:{real:h,imag:p},backend:n});n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(p);let m=ye({inputs:{x:f},backend:n,attrs:{shape:e.shape}});return n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(f),m}function QY(e){let{inputs:t,backend:n}=e,{input:s}=t;return l4(s,!1,n)}var eJ={kernelName:Qd,backendName:"webgl",kernelFunc:QY},tJ=class{constructor(e,t){this.outputShape=[],this.customUniforms=[{name:"value",type:"float"}],this.variableNames=["x"],this.outputShape=e,this.userCode=`
      void main() {
        // Input can be obtained from uniform value.
        setOutput(value);
      }
    `}};function vf(e){let{backend:t,attrs:n}=e,{shape:s,value:r}=n,{dtype:a}=n;if(a=a||I.inferDtype(r),a==="string"){let o=I.getArrayFromDType(a,I.sizeFromShape(s));return o.fill(r),t.makeTensorInfo(s,a,o)}else{let o=new tJ(s,r),i=[[r]];return t.runWebGLProgram(o,[],a,i)}}var nJ={kernelName:_u,backendName:"webgl",kernelFunc:vf},sJ=class{constructor(e){this.variableNames=["Image"],this.outputShape=[];let t=e[2];this.outputShape=e,this.userCode=`
        void main() {
          ivec4 coords = getOutputCoords();
          int x = coords[2];

          int coordX = ${t} - x - 1;
          float outputValue;
          if(coordX >= 0 && coordX < ${t}) {
            outputValue = getImage(coords[0], coords[1], coordX, coords[3]);
          } else {
            outputValue = getImage(coords[0], coords[1], coords[2], coords[3]);
          }
          setOutput(outputValue);
        }
    `}},rJ={kernelName:Ri,backendName:"webgl",kernelFunc:({inputs:e,backend:t})=>{let{image:n}=e,s=t,r=new sJ(n.shape);return s.runWebGLProgram(r,[n],n.dtype)}},u4="return floor(x);",aJ=Ze({opSnippet:u4,packedOpSnippet:u4,cpuKernelImpl:Dq}),oJ={kernelName:_a,backendName:"webgl",kernelFunc:aJ},iJ=`
  float s = sign(a) * sign(b);
  int ia = round(a);
  int ib = round(b);
  if (ib != 0) {
    // Windows (D3D) wants guaranteed non-zero int division at compile-time.
    return float(idiv(ia, ib, s));
  } else {
    return NAN;
  }
`,lJ=`
  ivec4 ia = round(a);
  ivec4 ib = round(b);
  bvec4 cond = notEqual(ib, ivec4(0));
  ivec4 result = ivec4(0);
  vec4 s = sign(a) * sign(b);

  // Windows (D3D) wants guaranteed non-zero int division at compile-time.
  if (cond[0]) {
    result[0] = idiv(ia[0], ib[0], s[0]);
  }
  if (cond[1]) {
    result[1] = idiv(ia[1], ib[1], s[1]);
  }
  if (cond[2]) {
    result[2] = idiv(ia[2], ib[2], s[2]);
  }
  if (cond[3]) {
    result[3] = idiv(ia[3], ib[3], s[3]);
  }
  return vec4(result);
`,uJ=rn({opSnippet:iJ,packedOpSnippet:lJ,dtype:"int32"}),cJ={kernelName:$a,backendName:"webgl",kernelFunc:uJ},dJ=class{constructor(e){this.variableNames=["A"];let t=bn(),[n,s]=e;this.outputShape=e,this.userCode=`
      void main() {
        ivec3 coords = getOutputCoords();
        int texR = coords[0];
        int texC = coords[1];
        int depth = coords[2];
        vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${s}.0, ${n}.0);

        vec4 values = ${t.texture2D}(A, uv);
        float value;
        if (depth == 0) {
          value = values.r;
        } else if (depth == 1) {
          value = values.g;
        } else if (depth == 2) {
          value = values.b;
        } else if (depth == 3) {
          value = values.a;
        }

        setOutput(floor(value * 255.0 + 0.5));
      }
    `}},hJ=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;let t=bn(),[n,s]=e;this.outputShape=e,this.userCode=`
      void main() {
        ivec3 coords = getOutputCoords();
        int texR = coords[0];
        int texC = coords[1];
        int depth = coords[2];

        vec4 result = vec4(0.);

        for(int row=0; row<=1; row++) {
          for(int col=0; col<=1; col++) {
            texC = coords[1] + row;
            depth = coords[2] + col;

            vec2 uv = (vec2(texC, texR) + halfCR) /
                       vec2(${s}.0, ${n}.0);
            vec4 values = ${t.texture2D}(A, uv);
            float value;
            if (depth == 0) {
              value = values.r;
            } else if (depth == 1) {
              value = values.g;
            } else if (depth == 2) {
              value = values.b;
            } else if (depth == 3) {
              value = values.a;
            }

            result[row * 2 + col] = floor(value * 255.0 + 0.5);
          }
        }

        ${t.output} = result;
      }
    `}},pJ={kernelName:bh,backendName:"webgl",kernelFunc:fJ},nu;function fJ(e){let{inputs:t,backend:n,attrs:s}=e,{pixels:r}=t,{numChannels:a}=s,o=typeof HTMLVideoElement!="undefined"&&r instanceof HTMLVideoElement,i=typeof HTMLImageElement!="undefined"&&r instanceof HTMLImageElement,[l,u]=o?[r.videoWidth,r.videoHeight]:[r.width,r.height],c=[u,l],d=[u,l,a];(i||o)&&(nu==null&&(nu=document.createElement("canvas").getContext("2d")),nu.canvas.width=l,nu.canvas.height=u,nu.drawImage(r,0,0,l,u),r=nu.canvas);let h=n.makeTensorInfo(c,"int32");n.texData.get(h.dataId).usage=fs.PIXELS,n.gpgpu.uploadPixelDataToTexture(n.getTexture(h.dataId),r);let p=ee().getBool("WEBGL_PACK")?new hJ(d):new dJ(d),f=n.runWebGLProgram(p,[h],"int32");return n.disposeData(h.dataId),f}function mJ(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a,bias:o,preluActivationWeights:i}=t,{strides:l,pad:u,dataFormat:c,dilations:d,dimRoundingMode:h,activation:p,leakyreluAlpha:f}=s,m=$.convertConv2DDataFormat(c),A=$.computeConv2DInfo(r.shape,a.shape,l,d,u,h,!1,m),g,y=[];if(A.filterHeight===1&&A.filterWidth===1&&A.dilationHeight===1&&A.dilationWidth===1&&A.strideHeight===1&&A.strideWidth===1&&(A.padInfo.type==="SAME"||A.padInfo.type==="VALID"))g=Y6({x:r,filter:a,convInfo:A,backend:n,bias:o,activation:p,preluActivationWeights:i,leakyreluAlpha:f});else if(ee().getBool("WEBGL_CONV_IM2COL")&&r.shape[0]===1)g=J6({x:r,filter:a,convInfo:A,backend:n,bias:o,activation:p,preluActivationWeights:i,leakyreluAlpha:f});else{let b=o!=null,v=i!=null,k=p==="leakyrelu",w=p?ff(p,!1):null,C=new Z6(A,b,w,v,k),E=[r,a];if(o&&E.push(o),i&&E.push(i),k){let M=n.makeTensorInfo([],"float32",I.createScalarValue(f,"float32"));E.push(M),y.push(M)}g=n.runWebGLProgram(C,E,"float32")}let x=ye({inputs:{x:g},backend:n,attrs:{shape:A.outShape}});return y.push(g),y.forEach(b=>n.disposeIntermediateTensorInfo(b)),x}var AJ={kernelName:fo,backendName:"webgl",kernelFunc:mJ};function gJ(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a,bias:o,preluActivationWeights:i}=t,{strides:l,pad:u,dilations:c,dimRoundingMode:d,activation:h,leakyreluAlpha:p}=s,f=[],m=c;m==null&&(m=[1,1]),I.assert($.eitherStridesOrDilationsAreOne(l,m),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${l} and dilations '${m}'`);let A=$.computeConv2DInfo(r.shape,a.shape,l,m,u,d,!0),g=ee().getBool("WEBGL_PACK_DEPTHWISECONV")&&A.strideWidth<=2&&A.outChannels/A.inChannels==1,y=h?ff(h,g):null,x=[r,a],b=o!=null,v=i!=null,k=h==="leakyrelu";if(b&&x.push(o),v&&x.push(i),k){let E=n.makeTensorInfo([],"float32",I.createScalarValue(p,"float32"));x.push(E),f.push(E)}let w;g?w=new s4(A,b,y,v,k):w=new n4(A,b,y,v,k);let C=n.runWebGLProgram(w,x,"float32");return f.forEach(E=>n.disposeIntermediateTensorInfo(E)),C}var yJ={kernelName:mo,backendName:"webgl",kernelFunc:gJ},xJ=class{constructor(e,t,n){this.sliceDim=e,this.strides=t,this.variableNames=["x","indices"],this.outputShape=n;let s=ct(t.length),r=ct(n.length),a=this.sliceDim>1?"strides[j]":"strides";this.userCode=`
        ${s} strides = ${s}(${this.strides});
         void main() {
          ${r} coords = getOutputCoords();
          int flattenIndex = 0;
          for (int j = 0; j < ${this.sliceDim}; j++) {
            int index = round(getIndices(coords[0], j));
            flattenIndex += index * ${a};
          }
          setOutput(getX(flattenIndex, coords[1]));
        }
      `}};function bJ(e){let{inputs:t,backend:n}=e,{params:s,indices:r}=t,a=r.shape,o=a[a.length-1],i=I.sizeFromShape(s.shape),[l,u,c,d]=$.prepareAndValidate(s,r),h=ye({inputs:{x:r},backend:n,attrs:{shape:[u,o]}}),p=ye({inputs:{x:s},backend:n,attrs:{shape:[I.sizeFromShape(s.shape)/c,c]}});if(n.shouldExecuteOnCPU([s,r])||s.dtype==="string"){let g=n.readSync(r.dataId),y=n.bufferSync(s),x=Oq(g,y,s.dtype,u,o,c,d,s.shape,i);return n.makeTensorInfo(l,s.dtype,x.values)}let f=new xJ(o,d,[u,c]),m=n.runWebGLProgram(f,[p,h],p.dtype),A=ye({inputs:{x:m},backend:n,attrs:{shape:l}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(m),A}var vJ={kernelName:$i,backendName:"webgl",kernelFunc:bJ},wJ=class{constructor(e,t){this.variableNames=["A","indices"],this.outputShape=t,this.rank=t.length;let n=ct(this.rank),s=kJ(e,2);this.userCode=`
      void main() {
        ${n} resRC = getOutputCoords();
        setOutput(getA(${s}));
      }
    `}};function kJ(e,t){let n=["resRC.x","resRC.y","resRC.z","resRC.w"],s=[];for(let r=0;r<e.length;r++)r===2?s.push("int(getIndices(resRC.x, resRC.z))"):s.push(`${n[r]}`);return s.join()}function c4(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,indices:a}=t,{axis:o,batchDims:i}=s,l=I.parseAxisParam(o,r.shape)[0],u=$.segment_util.collectGatherOpShapeInfo(r,a,l,i),c=I.sizeFromShape(a.shape),d=[],h=ye({inputs:{x:r},backend:n,attrs:{shape:[u.batchSize,u.outerSize,u.dimSize,u.sliceSize]}}),p=ye({inputs:{x:a},backend:n,attrs:{shape:[u.batchSize,c/u.batchSize]}});d.push(h),d.push(p);let f=[u.batchSize,u.outerSize,c/u.batchSize,u.sliceSize];if(n.shouldExecuteOnCPU([r,a])||r.dtype==="string"){let y=n.bufferSync(p),x=n.bufferSync(h),b=Pq(x,y,f);return d.forEach(v=>n.disposeIntermediateTensorInfo(v)),n.makeTensorInfo(u.outputShape,b.dtype,b.values)}let m=new wJ(h.shape,f),A=n.runWebGLProgram(m,[h,p],h.dtype);d.push(A);let g=ye({inputs:{x:A},backend:n,attrs:{shape:u.outputShape}});return d.forEach(y=>n.disposeIntermediateTensorInfo(y)),g}var IJ={kernelName:_i,backendName:"webgl",kernelFunc:c4},SJ="return float(a > b);",CJ=`
  return vec4(greaterThan(a, b));
`,TJ=rn({opSnippet:SJ,packedOpSnippet:CJ,cpuKernelImpl:Mq,dtype:"bool"}),NJ={kernelName:Fi,backendName:"webgl",kernelFunc:TJ},EJ="return float(a >= b);",RJ=`
  return vec4(greaterThanEqual(a, b));
`,_J=rn({opSnippet:EJ,packedOpSnippet:RJ,dtype:"bool",cpuKernelImpl:zq}),$J={kernelName:Da,backendName:"webgl",kernelFunc:_J};function FJ(e){let{inputs:t,backend:n}=e,{input:s}=t;return l4(s,!0,n)}var DJ={kernelName:eh,backendName:"webgl",kernelFunc:FJ},OJ="return float(!isnan(x) && !isinf(x));",PJ=Ze({opSnippet:OJ,dtype:"bool"}),MJ={kernelName:Di,backendName:"webgl",kernelFunc:PJ},zJ="return float(isinf(x));",LJ=Ze({opSnippet:zJ,dtype:"bool"}),BJ={kernelName:Oi,backendName:"webgl",kernelFunc:LJ},WJ="return float(isnan(x));",VJ=Ze({opSnippet:WJ,dtype:"bool"}),UJ={kernelName:Pi,backendName:"webgl",kernelFunc:VJ},HJ="return float(a < b);",GJ=`
  return vec4(lessThan(a, b));
`,jJ=rn({opSnippet:HJ,packedOpSnippet:GJ,cpuKernelImpl:Lq,dtype:"bool"}),qJ={kernelName:Mi,backendName:"webgl",kernelFunc:jJ},XJ="return float(a <= b);",KJ=`
  return vec4(lessThanEqual(a, b));
`,ZJ=rn({opSnippet:XJ,packedOpSnippet:KJ,cpuKernelImpl:Bq,dtype:"bool"}),YJ={kernelName:zi,backendName:"webgl",kernelFunc:ZJ};function JJ(e){let{backend:t,attrs:n}=e,{start:s,stop:r,num:a}=n,o=Wq(s,r,a);return t.makeTensorInfo([o.length],"float32",o)}var QJ={kernelName:nh,backendName:"webgl",kernelFunc:JJ},eQ=`if (x < 0.0) return NAN;
  return log(x);`,tQ=`
  vec4 result = log(x);
  vec4 isNaN = vec4(lessThan(x, vec4(0.0)));
  result.r = isNaN.r == 1.0 ? NAN : result.r;
  result.g = isNaN.g == 1.0 ? NAN : result.g;
  result.b = isNaN.b == 1.0 ? NAN : result.b;
  result.a = isNaN.a == 1.0 ? NAN : result.a;

  return result;
`,nQ=Ze({opSnippet:eQ,packedOpSnippet:tQ,cpuKernelImpl:Vq}),sQ={kernelName:Ma,backendName:"webgl",kernelFunc:nQ},rQ="return log(1.0 + x);",aQ=Ze({opSnippet:rQ}),oQ={kernelName:Li,backendName:"webgl",kernelFunc:aQ},iQ="return float(a >= 1.0 && b >= 1.0);",lQ=`
  return vec4(
    vec4(greaterThanEqual(a, vec4(1.0))) *
    vec4(greaterThanEqual(b, vec4(1.0))));
`,uQ=rn({opSnippet:iQ,packedOpSnippet:lQ,dtype:"bool"}),cQ={kernelName:Bi,backendName:"webgl",kernelFunc:uQ},dQ="return float(!(x >= 1.0));",hQ=Ze({opSnippet:dQ}),pQ={kernelName:$u,backendName:"webgl",kernelFunc:hQ},fQ="return float(a >= 1.0 || b >= 1.0);",mQ=`
  return min(
    vec4(greaterThanEqual(a, vec4(1.0))) +
    vec4(greaterThanEqual(b, vec4(1.0))),
    vec4(1.0));
`,AQ=rn({opSnippet:fQ,packedOpSnippet:mQ,dtype:"bool"}),gQ={kernelName:Fu,backendName:"webgl",kernelFunc:AQ},yQ=class{constructor(e,t,n,s,r){this.variableNames=["x"],this.outputShape=[];let a=t,o=e[3]-1;this.outputShape=e;let i,l=`float(${n}) + float(${s}) * sum`;r===.5?i=`inversesqrt(${l})`:r===1?i=`1.0/(${l})`:i=`exp(log(${l}) * float(-${r}));`,this.userCode=`
      void main() {
        ivec4 coords = getOutputCoords();
        int b = coords[0];
        int r = coords[1];
        int c = coords[2];
        int d = coords[3];
        float x = getX(b, r, c, d);
        float sum = 0.0;
        for (int j = -${a}; j <= ${a}; j++) {
          int idx = d + j;
          if (idx >= 0 && idx <=  ${o}) {
            float z = getX(b, r, c, idx);
            sum += z * z;
          }
        }
        float val = x * ${i};
        setOutput(val);
      }
    `}},xQ=class{constructor(e,t,n,s,r){this.variableNames=["x"],this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0;let a=t,o=e[3]-1;this.outputShape=e;let i,l=`float(${n}) + float(${s}) * sum`;r===.5?i=`inversesqrt(${l})`:r===1?i=`1.0/(${l})`:i=`exp(log(${l}) * float(-${r}));`,this.userCode=`
      void main() {
        ivec4 coords = getOutputCoords();
        int b = coords.x;
        int r = coords.y;
        int c = coords.z;
        int d = coords.w;

        bool hasNextCol = d < ${this.outputShape[3]};
        bool hasNextRow = c < ${this.outputShape[2]};

        vec4 sum = vec4(0.);
        vec4 xFragAtOutputCoords = getX(b, r, c, d);

        vec4 xAtOutputCoords = vec4(
          getChannel(xFragAtOutputCoords, vec2(c, d)),
          hasNextCol ?
            getChannel(xFragAtOutputCoords, vec2(c, d + 1)) : 0.0,
          hasNextRow ?
            getChannel(xFragAtOutputCoords , vec2(c + 1, d)) : 0.0,
          (hasNextRow && hasNextCol) ?
            getChannel(xFragAtOutputCoords, vec2(c + 1, d + 1)) : 0.0
        );

        int firstChannel = d - ${a};
        vec2 cache = vec2(0.);
        if(firstChannel >= 0){
          vec4 firstChannelFrag = getX(b, r, c, firstChannel);
          cache.x = getChannel(firstChannelFrag, vec2(c, firstChannel));
            if(hasNextRow){
              cache.y = getChannel(firstChannelFrag, vec2(c + 1, firstChannel));
            }
        }

        ivec2 depth = ivec2(d, d + 1);
        for (int j = - ${a}; j <= ${a}; j++) {
          ivec2 idx = depth + j;
          bvec2 aboveLowerBound = greaterThanEqual(idx, ivec2(0));
          bvec2 belowUpperBound = lessThanEqual(idx, ivec2(${o}));

          bool depthInRange = aboveLowerBound.x && belowUpperBound.x;
          bool depthPlusOneInRange = aboveLowerBound.y && belowUpperBound.y;

          if(depthInRange || depthPlusOneInRange){
            vec4 z = vec4(0.);
            vec4 xFragAtCurrentDepth;
            z.xz = cache.xy;
            if(depthPlusOneInRange && hasNextCol){
              xFragAtCurrentDepth = idx.y != d ?
                getX(b, r, c, idx.y) : xFragAtOutputCoords;
              z.y = getChannel(xFragAtCurrentDepth, vec2(c, idx.y));
              if(hasNextRow){
                z.w = getChannel(xFragAtCurrentDepth, vec2(c + 1, idx.y));
              }
            }
            cache.xy = z.yw;
            sum += z * z;
          }
        }
        vec4 result = xAtOutputCoords * ${i};
        setOutput(result);
      }
    `}},bQ=e=>{let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{depthRadius:a,bias:o,alpha:i,beta:l}=s,u=ee().getBool("WEBGL_PACK_NORMALIZATION")?new xQ(r.shape,a,o,i,l):new yQ(r.shape,a,o,i,l);return n.runWebGLProgram(u,[r],r.dtype)},vQ={kernelName:Du,backendName:"webgl",kernelFunc:bQ},wQ=class{constructor(e,t,n,s,r){this.variableNames=["inputImage","outputImage","dy"],this.outputShape=[],this.outputShape=e,this.depth=e[3],this.depthRadius=t,this.bias=n,this.alpha=s,this.beta=r,this.userCode=`
      void main() {
        ivec4 coords = getOutputCoords();
        int b = coords[0];
        int r = coords[1];
        int c = coords[2];

        float result = 0.0;
        for (int d = 0; d < ${this.depth}; ++d) {
          int depthBegin = int(max(0.0, float(d - ${t})));
          int depthEnd = int(min(float(${this.depth}),
              float(d + ${t} + 1)));

          const int MIN_DEPTH_BEGIN = 0;
          const int MAX_DEPTH_END = ${this.depth};

          float norm = 0.0;
          for (int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k) {
            if (k < depthBegin){
              continue;
            }
            else if (k >= depthBegin && k < depthEnd) {
              norm += getInputImage(b, r, c, k) * getInputImage(b, r, c, k);
            }
            else {
              break;
            }
          }

          norm = float(${s}) * norm + float(${n});

          for(int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k){
            if (k < depthBegin){
              continue;
            }
            else if (k >= depthBegin && k < depthEnd){
              float dyi = -2.0 * float(${s})
                * float(${r})
                * getInputImage(b ,r ,c, k) * getOutputImage(b, r, c, d)
                / norm;
              if (k == d) {
                dyi += pow(norm, -1.0 * ${r});
              }
              if (k == coords[3]) {
                dyi *= getDy(b, r, c, d);
                result += dyi;
              }
            }
            else {
              break;
            }
          }
      }
      setOutput(result);
      }
    `}},kQ=e=>{let{inputs:t,backend:n,attrs:s}=e,{x:r,y:a,dy:o}=t,{depthRadius:i,bias:l,alpha:u,beta:c}=s,d=new wQ(r.shape,i,l,u,c);return n.runWebGLProgram(d,[r,a,o],r.dtype)},IQ={kernelName:sh,backendName:"webgl",kernelFunc:kQ};function SQ(e,t,n,s){let r=I.sizeFromShape(t),o=I.sizeFromShape(e.shape)/r,i=ye({inputs:{x:e},attrs:{shape:[o,r]},backend:s}),l=Go(i,e.dtype,"max",s),u=ye({inputs:{x:l},attrs:{shape:n},backend:s});return s.disposeIntermediateTensorInfo(i),s.disposeIntermediateTensorInfo(l),u}function d4(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{reductionIndices:a,keepDims:o}=s,i=r.shape.length,l=I.parseAxisParam(a,r.shape),u=l,c=$.getAxesPermutation(u,i),d=c!=null,h=n.shouldExecuteOnCPU([r]),p=r;if(d){if(h){let x=n.texData.get(p.dataId).values,b=new Array(i);for(let w=0;w<b.length;w++)b[w]=r.shape[c[w]];let v=I1(x,r.shape,r.dtype,c,b);p=n.makeTensorInfo(b,r.dtype);let k=n.texData.get(p.dataId);k.values=v}else p=mf(r,c,n);u=$.getInnerMostAxes(u.length,i)}$.assertAxesAreInnerMostDims("max",u,i);let[f,m]=$.computeOutAndReduceShapes(p.shape,u),A=f;o&&(A=$.expandShapeToKeepDim(f,l));let g;if(h){let x=n.texData.get(p.dataId).values,b=Uq(x,I.sizeFromShape(m),A,r.dtype);g=n.makeTensorInfo(A,r.dtype);let v=n.texData.get(g.dataId);v.values=b}else g=SQ(p,m,A,n);return d&&n.disposeIntermediateTensorInfo(p),g}var CQ={kernelName:za,backendName:"webgl",kernelFunc:d4},TQ=E6+`
  return max(a, b);
`,NQ=`
  vec4 result = vec4(max(a, b));
  vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
  `+pf+`
  return result;
`,EQ=rn({opSnippet:TQ,packedOpSnippet:NQ,cpuKernelImpl:Hq}),RQ={kernelName:La,backendName:"webgl",kernelFunc:EQ};function _Q(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t;jl(r,"maxPool");let{filterSize:a,strides:o,pad:i,dimRoundingMode:l}=s,u=1;I.assert($.eitherStridesOrDilationsAreOne(o,u),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${o} and dilations '${u}'`);let c=$.computePool2DInfo(r.shape,a,o,u,i,l);if(c.filterWidth===1&&c.filterHeight===1&&I.arraysEqual(c.inShape,c.outShape))return qn({inputs:{x:r},backend:n});let d=new Hc(c,"max",!1);return n.runWebGLProgram(d,[r],r.dtype)}var $Q={kernelName:Ba,backendName:"webgl",kernelFunc:_Q};function FQ(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{filterSize:a,strides:o,pad:i,dataFormat:l,dimRoundingMode:u}=s,c=[1,1,1],d=$.computePool3DInfo(r.shape,a,o,c,i,u,l),h=new C1(d,"max",!1);return n.runWebGLProgram(h,[r],r.dtype)}var DQ={kernelName:Ou,backendName:"webgl",kernelFunc:FQ},OQ=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideHeight,n=e.strideWidth,s=e.dilationHeight,r=e.effectiveFilterHeight,a=e.effectiveFilterWidth,o=r-1-e.padInfo.top,i=a-1-e.padInfo.left,l=r*a-1;this.userCode=`
      const ivec2 pads = ivec2(${o}, ${i});

      void main() {
        ivec4 coords = getOutputCoords();
        int b = coords[0];
        int d = coords[3];

        ivec2 dyRCCorner = coords.yz - pads;
        int dyRCorner = dyRCCorner.x;
        int dyCCorner = dyRCCorner.y;

        // Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).
        // ? = to be determined. : = across all values in that axis.
        float dotProd = 0.0;
        for (int wR = 0; wR < ${r};
          wR += ${s}) {
          float dyR = float(dyRCorner + wR) / ${t}.0;

          if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) {
            continue;
          }
          int idyR = int(dyR);

          for (int wC = 0; wC < ${a}; wC++) {
            float dyC = float(dyCCorner + wC) / ${n}.0;

            if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
                fract(dyC) > 0.0) {
              continue;
            }
            int idyC = int(dyC);

            float dyValue = getDy(b, idyR, idyC, d);
            int maxPosValue = ${l} - int(getMaxPos(b, idyR, idyC, d));

            // Get the current value, check it against the value from the
            // position matrix.
            int curPosValue = wR * ${a} + wC;
            float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);

            dotProd += dyValue * mask;
          }
        }
        setOutput(dotProd);
      }
    `}},PQ=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideDepth,n=e.strideHeight,s=e.strideWidth,r=e.dilationDepth,a=e.dilationHeight,o=e.dilationWidth,i=e.effectiveFilterDepth,l=e.effectiveFilterHeight,u=e.effectiveFilterWidth,c=i-1-e.padInfo.front,d=l-1-e.padInfo.top,h=u-1-e.padInfo.left,p=i*l*u-1;this.userCode=`
      const ivec3 pads = ivec3(${c}, ${d}, ${h});

      void main() {
        ivec5 coords = getOutputCoords();
        int batch = coords.x;
        int ch = coords.u;

        ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;
        int dyDCorner = dyCorner.x;
        int dyRCorner = dyCorner.y;
        int dyCCorner = dyCorner.z;

        // Convolve dy(?, ?, ?, ch) with pos mask(:, :, :, d) to get
        // dx(xD, xR, xC, ch).
        // ? = to be determined. : = across all values in that axis.
        float dotProd = 0.0;

        for (int wD = 0; wD < ${i};
           wD += ${r}) {
          float dyD = float(dyDCorner + wD) / ${t}.0;

          if (dyD < 0.0 || dyD >= ${e.outDepth}.0 || fract(dyD) > 0.0) {
            continue;
          }
          int idyD = int(dyD);

          for (int wR = 0; wR < ${l};
              wR += ${a}) {
            float dyR = float(dyRCorner + wR) / ${n}.0;

            if (dyR < 0.0 || dyR >= ${e.outHeight}.0 ||
                fract(dyR) > 0.0) {
              continue;
            }
            int idyR = int(dyR);

            for (int wC = 0; wC < ${u};
                wC += ${o}) {
              float dyC = float(dyCCorner + wC) / ${s}.0;

              if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
                  fract(dyC) > 0.0) {
                continue;
              }
              int idyC = int(dyC);

              float dyValue = getDy(batch, idyD, idyR, idyC, ch);
              int maxPosValue = ${p} -
                  int(getMaxPos(batch, idyD, idyR, idyC, ch));

              // Get the current value, check it against the value from the
              // position matrix.
              int curPosValue =
                  wD * ${l} * ${u} +
                  wR * ${u} + wC;
              float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);

              dotProd += dyValue * mask;
            }
          }
        }
        setOutput(dotProd);
      }
    `}};function MQ(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,input:a}=t,o=a,{filterSize:i,strides:l,pad:u,dimRoundingMode:c}=s,d=[1,1,1],h=$.computePool3DInfo(o.shape,i,l,d,u,c),p=new C1(h,"max",!0),f=n.runWebGLProgram(p,[o],o.dtype),m=new PQ(h),A=n.runWebGLProgram(m,[r,f],o.dtype);return n.disposeIntermediateTensorInfo(f),A}var zQ={kernelName:ah,backendName:"webgl",kernelFunc:MQ};function LQ(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,input:a,output:o}=t,i=a;jl([a,o],"maxPoolGrad");let{filterSize:l,strides:u,pad:c,dimRoundingMode:d}=s,h=$.computePool2DInfo(i.shape,l,u,1,c,d),p=!0,f=new Hc(h,"max",p),m=n.runWebGLProgram(f,[i],i.dtype),A=new OQ(h),g=n.runWebGLProgram(A,[r,m],i.dtype);return n.disposeIntermediateTensorInfo(m),g}var BQ={kernelName:rh,backendName:"webgl",kernelFunc:LQ};function WQ(e,t,n,s){let r=new Hc(n,"max",!1),a=s.runWebGLProgram(r,[e],"float32");r=new Hc(n,"max",!0,!0,t);let o=s.runWebGLProgram(r,[e],"float32");return[a,o]}var VQ={kernelName:oh,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:s}=e,{filterSize:r,strides:a,pad:o,includeBatchInIndex:i}=t,l=n;I.assert(s.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${s.shape.length}.`);let u=[1,1];I.assert($.eitherStridesOrDilationsAreOne(a,u),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${a} and dilations '${u}'`);let c=$.computePool2DInfo(s.shape,r,a,u,o),[d,h]=WQ(s,i,c,l);return[d,h]}};function UQ(e,t,n,s){let r=I.sizeFromShape(t),o=I.sizeFromShape(e.shape)/r,i=ye({inputs:{x:e},attrs:{shape:[o,r]},backend:s}),l=Go(i,"float32","mean",s),u=ye({inputs:{x:l},attrs:{shape:n},backend:s});return s.disposeIntermediateTensorInfo(i),s.disposeIntermediateTensorInfo(l),u}var HQ={kernelName:Wa,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:s}=e,{keepDims:r,axis:a}=t,o=n,i=s.shape.length,l=I.parseAxisParam(a,s.shape),u=l,c=$.getAxesPermutation(u,i),d=c!=null,h=o.shouldExecuteOnCPU([s]),p=[],f=s;if(d){if(h){let b=o.texData.get(f.dataId).values,v=new Array(i);for(let C=0;C<v.length;C++)v[C]=s.shape[c[C]];let k=I1(b,s.shape,s.dtype,c,v);f=o.makeTensorInfo(v,s.dtype);let w=o.texData.get(f.dataId);w.values=k}else f=mf(s,c,o);p.push(f),u=$.getInnerMostAxes(u.length,i)}$.assertAxesAreInnerMostDims("sum",u,i);let[m,A]=$.computeOutAndReduceShapes(f.shape,u),g=m;r&&(g=$.expandShapeToKeepDim(m,l));let y=UQ(f,A,g,o);for(let x of p)o.disposeIntermediateTensorInfo(x);return y}};function GQ(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:o}=s,i=r.shape.length,l=I.parseAxisParam(a,r.shape),u=l,c=$.getAxesPermutation(u,i),d=r;c!=null&&(d=wn({inputs:{x:r},backend:n,attrs:{perm:c}}),u=$.getInnerMostAxes(u.length,r.shape.length)),$.assertAxesAreInnerMostDims("min",u,i);let[h,p]=$.computeOutAndReduceShapes(d.shape,u),f=I.sizeFromShape(p),m=ye({inputs:{x:d},backend:n,attrs:{shape:[-1,f]}}),A=Go(m,m.dtype,"min",n),g;if(o){let y=$.expandShapeToKeepDim(h,l);g=ye({inputs:{x:A},backend:n,attrs:{shape:y}})}else g=ye({inputs:{x:A},backend:n,attrs:{shape:h}});return n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(A),c!=null&&n.disposeIntermediateTensorInfo(d),g}var jQ={kernelName:Va,backendName:"webgl",kernelFunc:GQ},qQ=E6+`
  return min(a, b);
`,XQ=`
  vec4 result = vec4(min(a, b));
  vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
  `+pf+`
  return result;
`,KQ=rn({opSnippet:qQ,packedOpSnippet:XQ,cpuKernelImpl:Gq}),ZQ={kernelName:Ua,backendName:"webgl",kernelFunc:KQ},YQ=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=t.map((u,c)=>u[0]+e[c]+u[1]);let s=e.length,r=ct(s),a=t.map(u=>u[0]).join(","),o=t.map((u,c)=>u[0]+e[c]).join(","),i=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,s),l=n==="reflect"?0:1;if(s===1){this.userCode=`
        int start = ${a};
        int end = ${o};

        void main() {
          int outC = getOutputCoords();
          if (outC < start) {
            outC = start * 2 - outC - ${l};
          } else if(outC >= end) {
            outC = (end - 1) * 2 - outC + ${l};
          }
          setOutput(getX(outC - start));
        }
      `;return}this.userCode=`
      ${r} start = ${r}(${a});
      ${r} end = ${r}(${o});

      void main() {
        ${r} outC = getOutputCoords();
        for (int i = 0; i < ${s}; i++) {
          if (outC[i] < start[i]) {
            outC[i] = start[i] * 2 - outC[i] - ${l};
          } else if(outC[i] >= end[i]) {
            outC[i] = (end[i] - 1) * 2 - outC[i] + ${l};
          }
        }
        ${r} coords = outC - start;
        setOutput(getX(${i}));
      }
    `}},JQ=class{constructor(e,t,n){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t.map((p,f)=>p[0]+e[f]+p[1]);let s=e.length,r=ct(s),a=t.map(p=>p[0]).join(","),o=t.map((p,f)=>p[0]+e[f]).join(","),i=vn("rc",s),l=vn("source",s),u=`${i[s-1]} < ${this.outputShape[s-1]}`,c=s===1?"source":`vec2(${l.slice(-2).join()})`,d=n==="reflect"?0:1,h="";if(s===1){let p=`
        ${r} source = rc;
        if (source < start) {
          source = start * 2 - source - ${d};
        } else if (source >= end) {
          source = (end - 1) * 2 - source + ${d};
        }
        source -= start;
      `;h=`
        ${r} rc = outputLoc;
        ${p}
        result[0] = getChannel(getX(${l.join()}), ${c});
        ${i[s-1]} += 1;
        if(${u}) {
          ${p}
          result[1] = getChannel(getX(${l.join()}), ${c});
        }
      `}else{let p=`
        ${r} source = rc;
        ${r} lt = ${r}(lessThan(source, start));
        ${r} gte = ${r}(greaterThanEqual(source, end));
        ${r} orig = 1 - (lt + gte);
        source = orig * source +
                lt * (start * 2 - source - ${d}) +
                gte * ((end - 1) * 2 - source + ${d});
        source -= start;
      `;h=`
        ${r} rc = outputLoc;
        ${p}
        result[0] = getChannel(getX(${l.join()}), ${c});
        ${i[s-1]} += 1;
        if(${u}) {
          ${p}
          result[1] = getChannel(getX(${l.join()}), ${c});
        }
        rc = outputLoc;
        ${i[s-2]} += 1;
        if(${i[s-2]} < ${this.outputShape[s-2]}) {
          ${p}
          result[2] = getChannel(getX(${l.join()}), ${c});
          ${i[s-1]} += 1;
          if(${u}) {
            ${p}
            result[3] = getChannel(getX(${l.join()}), ${c});
          }
        }
      `}this.userCode=`
      const ${r} start = ${r}(${a});
      const ${r} end = ${r}(${o});

      void main() {
        ${r} outputLoc = getOutputCoords();
        vec4 result = vec4(0.);
        ${h}
        setOutput(result);
      }
    `}},QQ=({inputs:e,backend:t,attrs:n})=>{let{x:s}=e,{paddings:r,mode:a}=n,o=ee().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new JQ(s.shape,r,a):new YQ(s.shape,r,a);return t.runWebGLProgram(o,[s],s.dtype)},eee={kernelName:Ha,backendName:"webgl",kernelFunc:QQ},tee=`if (b == 0.0) return NAN;
  return mod(a, b);`,nee=`
  vec4 result = mod(a, b);
  vec4 isNaN = vec4(equal(b, vec4(0.0)));
  `+pf+`
  return result;
`,see=rn({opSnippet:tee,packedOpSnippet:nee}),ree={kernelName:Wi,backendName:"webgl",kernelFunc:see},aee=class{constructor(e,t,n){this.variableNames=["probs"],this.customUniforms=[{name:"seed",type:"float"}],this.outputShape=[e,n],this.userCode=`
      void main() {
        ivec2 coords = getOutputCoords();
        int batch = coords[0];

        float r = random(seed);
        float cdf = 0.0;

        for (int i = 0; i < ${t-1}; i++) {
          cdf += getProbs(batch, i);

          if (r < cdf) {
            setOutput(float(i));
            return;
          }
        }

        // If no other event happened, last event happened.
        setOutput(float(${t-1}));
      }
    `}},oee=`
if (a == b) {
  return 1.0;
};
return a / b;`,iee=`
  // vec4 one = vec4(equal(a, b));
  // return one + (vec4(1.0) - one) * a / b;
  vec4 result = a / b;
  if(a.x == b.x) {
    result.x = 1.;
  }
  if(a.y == b.y) {
    result.y = 1.;
  }
  if(a.z == b.z) {
    result.z = 1.;
  }
  if(a.w == b.w) {
    result.w = 1.;
  }

  return result;
`,h4=rn({opSnippet:oee,packedOpSnippet:iee,checkOutOfBounds:!0}),lee={kernelName:Ea,backendName:"webgl",kernelFunc:h4},p4="return a - b;",f4=rn({opSnippet:p4,packedOpSnippet:p4,supportsComplex:!0,cpuKernelImpl:aX}),uee={kernelName:lo,backendName:"webgl",kernelFunc:f4};function m4(e){let{inputs:t,backend:n,attrs:s}=e,{logits:r}=t,{dim:a}=s,o=I.parseAxisParam([a],r.shape),i=d4({inputs:{x:r},backend:n,attrs:{reductionIndices:o,keepDims:!1}}),l=$.expandShapeToKeepDim(i.shape,o),u=ye({inputs:{x:i},backend:n,attrs:{shape:l}}),c=f4({inputs:{a:r,b:u},backend:n}),d=a4({inputs:{x:c},backend:n}),h=Af({inputs:{x:d},backend:n,attrs:{axis:o,keepDims:!1}}),p=ye({inputs:{x:h},backend:n,attrs:{shape:l}}),f=h4({inputs:{a:d,b:p},backend:n});return n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(u),n.disposeIntermediateTensorInfo(c),n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(p),f}var cee={kernelName:oo,backendName:"webgl",kernelFunc:m4};function dee(e){let{inputs:t,backend:n,attrs:s}=e,{logits:r}=t,{numSamples:a,seed:o,normalized:i}=s,l=i?r:m4({inputs:{logits:r},backend:n,attrs:{dim:r.shape.length-1}}),u=l.shape[0],c=l.shape[1],d=new aee(u,c,a),h=[[o]],p=n.runWebGLProgram(d,[l],"int32",h);return i||n.disposeIntermediateTensorInfo(l),p}var hee={kernelName:ih,backendName:"webgl",kernelFunc:dee},A4="return -x;";function pee(e){let{inputs:t,backend:n}=e,{x:s}=t;if(n.shouldExecuteOnCPU([s])){let a=n.texData.get(s.dataId),[o,i]=qq(a.values,s.shape,s.dtype);return n.makeTensorInfo(i,s.dtype,o)}let r;return ee().getBool("WEBGL_PACK_UNARY_OPERATIONS")?r=new Yl(s.shape,A4):r=new sa(s.shape,A4),n.runWebGLProgram(r,[s],s.dtype)}var fee={kernelName:Vi,backendName:"webgl",kernelFunc:pee},mee=Js.nonMaxSuppressionV3Impl;function Aee(e){$.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:s}=e,{boxes:r,scores:a}=t,{maxOutputSize:o,iouThreshold:i,scoreThreshold:l}=s,u=n.readSync(r.dataId),c=n.readSync(a.dataId),{selectedIndices:d}=mee(u,c,o,i,l);return n.makeTensorInfo([d.length],"int32",new Int32Array(d))}var gee={kernelName:Hi,backendName:"webgl",kernelFunc:Aee},yee=Js.nonMaxSuppressionV4Impl;function xee(e){$.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:s}=e,{boxes:r,scores:a}=t,{maxOutputSize:o,iouThreshold:i,scoreThreshold:l,padToMaxOutputSize:u}=s,c=n.readSync(r.dataId),d=n.readSync(a.dataId),{selectedIndices:h,validOutputs:p}=yee(c,d,o,i,l,u);return[n.makeTensorInfo([h.length],"int32",new Int32Array(h)),n.makeTensorInfo([],"int32",new Int32Array([p]))]}var bee={kernelName:Gi,backendName:"webgl",kernelFunc:xee},vee=Js.nonMaxSuppressionV5Impl;function wee(e){$.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:s}=e,{boxes:r,scores:a}=t,{maxOutputSize:o,iouThreshold:i,scoreThreshold:l,softNmsSigma:u}=s,c=n.readSync(r.dataId),d=n.readSync(a.dataId),h=o,p=i,f=l,m=u,{selectedIndices:A,selectedScores:g}=vee(c,d,h,p,f,m);return[n.makeTensorInfo([A.length],"int32",new Int32Array(A)),n.makeTensorInfo([g.length],"float32",new Float32Array(g))]}var kee={kernelName:ji,backendName:"webgl",kernelFunc:wee},Iee=class{constructor(e,t,n,s){this.variableNames=["indices"],this.outputShape=[e,t],this.userCode=`
      void main() {
        ivec2 coords = getOutputCoords();
        int index = round(getIndices(coords.x));
        setOutput(mix(float(${s}), float(${n}),
                      float(index == coords.y)));
      }
    `}},See=e=>{let{inputs:t,backend:n,attrs:s}=e,{indices:r}=t,{depth:a,onValue:o,offValue:i}=s,l=I.sizeFromShape(r.shape),u=new Iee(l,a,o,i),c=ye({inputs:{x:r},backend:n,attrs:{shape:[l]}}),d=n.runWebGLProgram(u,[c],r.dtype);n.disposeIntermediateTensorInfo(c);let h=[...r.shape,a],p=ye({inputs:{x:d},backend:n,attrs:{shape:h}});return n.disposeIntermediateTensorInfo(d),p},Cee={kernelName:ja,backendName:"webgl",kernelFunc:See};function wf(e){let{inputs:t,backend:n}=e,{x:s}=t;if(s.dtype==="complex64"){let r=Gc({inputs:{input:s},backend:n}),a=wf({inputs:{x:r},backend:n}),o=bf({inputs:{input:s},backend:n}),i=wf({inputs:{x:o},backend:n}),l=ra({inputs:{real:a,imag:i},backend:n});return n.disposeIntermediateTensorInfo(r),n.disposeIntermediateTensorInfo(a),n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(i),l}else return vf({attrs:{shape:s.shape,dtype:s.dtype,value:s.dtype==="string"?"":0},backend:n})}var Tee={kernelName:dl,backendName:"webgl",kernelFunc:wf};function g4(e){let{inputs:t,backend:n}=e,{x:s}=t;if(s.dtype==="string")throw new Error("onesLike is not supported under string dtype");if(s.dtype==="complex64"){let r=Gc({inputs:{input:s},backend:n}),a=g4({inputs:{x:r},backend:n}),o=bf({inputs:{input:s},backend:n}),i=wf({inputs:{x:o},backend:n}),l=ra({inputs:{real:a,imag:i},backend:n});return n.disposeIntermediateTensorInfo(r),n.disposeIntermediateTensorInfo(a),n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(i),l}else return vf({attrs:{shape:s.shape,dtype:s.dtype,value:1},backend:n})}var Nee={kernelName:qi,backendName:"webgl",kernelFunc:g4};function Eee(e){let{inputs:t,backend:n,attrs:s}=e,{axis:r}=s;if(t.length===1)return E1({inputs:{input:t[0]},backend:n,attrs:{dim:r}});let a=t[0].shape,o=t[0].dtype;t.forEach(c=>{I.assertShapesMatch(a,c.shape,"All tensors passed to stack must have matching shapes"),I.assert(o===c.dtype,()=>"All tensors passed to stack must have matching dtypes")});let i=[],l=t.map(c=>{let d=E1({inputs:{input:c},backend:n,attrs:{dim:r}});return i.push(d),d}),u=K6({inputs:l,backend:n,attrs:{axis:r}});return i.forEach(c=>n.disposeIntermediateTensorInfo(c)),u}var Ree={kernelName:Xi,backendName:"webgl",kernelFunc:Eee},_ee=class{constructor(e,t,n){this.variableNames=["x"],this.customUniforms=[{name:"value",type:"float"}],this.outputShape=t.map((l,u)=>l[0]+e[u]+l[1]);let s=e.length,r=ct(s),a=t.map(l=>l[0]).join(","),o=t.map((l,u)=>l[0]+e[u]).join(","),i=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,s);if(s===1){this.userCode=`
        int start = ${a};
        int end = ${o};

        void main() {
          int outC = getOutputCoords();
          if (outC < start || outC >= end) {
            setOutput(value);
          } else {
            setOutput(getX(outC - start));
          }
        }
      `;return}this.userCode=`
      ${r} start = ${r}(${a});
      ${r} end = ${r}(${o});

      void main() {
        ${r} outC = getOutputCoords();
        if (any(lessThan(outC, start)) || any(greaterThanEqual(outC, end))) {
          setOutput(value);
        } else {
          ${r} coords = outC - start;
          setOutput(getX(${i}));
        }
      }
    `}},$ee=class{constructor(e,t,n){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"value",type:"float"}],this.outputShape=t.map((f,m)=>f[0]+e[m]+f[1]);let s=e.length,r=ct(s),a=t.map(f=>f[0]).join(","),o=t.map((f,m)=>f[0]+e[m]).join(","),i=vn("rc",s),l=vn("source",s),u=`${i[s-1]} < ${this.outputShape[s-1]}`,c=s===1?"source":`vec2(${l.slice(-2).join()})`,d=[`${r} rc = outputLoc;`,`${i[s-1]} += 1;
       if(${u}) {
      `,s===1?"":`}
       rc = outputLoc;
       ${i[s-2]} += 1;
       if(${i[s-2]} < ${this.outputShape[s-2]}) {`,s===1?"":`  ${i[s-1]} += 1;
         if(${u}) {`],h=s===1?"rc < start || rc >= end":"any(lessThan(rc, start)) || any(greaterThanEqual(rc, end))",p="";for(let f=0,m=s===1?2:4;f<m;f++)p+=`
        ${d[f]}
        if (${h}) {
          result[${f}] = float(value);
        } else {
          ${r} source = rc - start;
          result[${f}] = getChannel(getX(${l.join()}), ${c});
        }
      `;p+=s===1?"} ":"}}",this.userCode=`
      const ${r} start = ${r}(${a});
      const ${r} end = ${r}(${o});

      void main() {
        ${r} outputLoc = getOutputCoords();
        vec4 result = vec4(0.);
        ${p}
        setOutput(result);
      }
    `}},y4=e=>{let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{paddings:a,constantValue:o}=s,i=ee().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new $ee(r.shape,a,o):new _ee(r.shape,a,o),l=[[o]];return n.runWebGLProgram(i,[r],r.dtype,l)},Fee={kernelName:qa,backendName:"webgl",kernelFunc:y4},Dee=`
  if(a < 0.0 && floor(b) < b){
    return NAN;
  }
  if (b == 0.0) {
    return 1.0;
  }
  return (round(mod(b, 2.0)) != 1) ?
      pow(abs(a), b) : sign(a) * pow(abs(a), b);
`,Oee=`
  // isModRound1 has 1 for components with round(mod(b, 2.0)) == 1, 0 otherwise.
  vec4 isModRound1 = vec4(equal(round(mod(b, 2.0)), ivec4(1)));
  vec4 multiplier = sign(a) * isModRound1 + (vec4(1.0) - isModRound1);
  vec4 result = multiplier * pow(abs(a), b);

  // Ensure that a^0 = 1, including 0^0 = 1 as this correspond to TF and JS
  bvec4 isExpZero = equal(b, vec4(0.0));
  result.r = isExpZero.r ? 1.0 : result.r;
  result.g = isExpZero.g ? 1.0 : result.g;
  result.b = isExpZero.b ? 1.0 : result.b;
  result.a = isExpZero.a ? 1.0 : result.a;

  vec4 isNaN = vec4(lessThan(a, vec4(0.0))) * vec4(lessThan(floor(b), b));
  `+pf+`
  return result;
`,Pee=rn({opSnippet:Dee,packedOpSnippet:Oee}),Mee={kernelName:Xa,backendName:"webgl",kernelFunc:Pee};function zee(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:o}=s,i=r.shape.length,l=[],u=I.parseAxisParam(a,r.shape),c=u,d=$.getAxesPermutation(c,i),h=r;d!=null&&(h=wn({inputs:{x:r},backend:n,attrs:{perm:d}}),c=$.getInnerMostAxes(c.length,i),l.push(h)),$.assertAxesAreInnerMostDims("prod",c,i);let p;if(n.shouldExecuteOnCPU([h])){let f=n.texData.get(h.dataId).values,{outVals:m,outShape:A,outDtype:g}=Kq(h.shape,h.dtype,f,c);p=n.makeTensorInfo(A,g,m)}else{let[f,m]=$.computeOutAndReduceShapes(h.shape,c),A=I.sizeFromShape(m),g=ye({inputs:{x:h},backend:n,attrs:{shape:[-1,A]}}),y=Ch(r.dtype),x=Go(g,y,"prod",n);p=ye({inputs:{x},backend:n,attrs:{shape:f}}),l.push(g),l.push(x)}if(o){l.push(p);let f=$.expandShapeToKeepDim(p.shape,u);p=ye({inputs:{x:p},backend:n,attrs:{shape:f}})}return l.forEach(f=>n.disposeIntermediateTensorInfo(f)),p}var Lee={kernelName:Ki,backendName:"webgl",kernelFunc:zee},x4=e=>{let{backend:t,attrs:n}=e,{start:s,stop:r,step:a,dtype:o}=n,i=Zq(s,r,a,o);return t.makeTensorInfo([i.length],o,i)},Bee={kernelName:Pu,backendName:"webgl",kernelFunc:x4},Wee="return 1.0 / x;",Vee=Ze({opSnippet:Wee}),Uee={kernelName:Zi,backendName:"webgl",kernelFunc:Vee},Hee=Bs+`
  return (x < 0.0) ? 0.0 : x;
`,Gee=`
  vec4 result = x * vec4(greaterThanEqual(x, vec4(0.0)));
  bvec4 isNaN = isnan(x);

  result.r = isNaN.r ? x.r : result.r;
  result.g = isNaN.g ? x.g : result.g;
  result.b = isNaN.b ? x.b : result.b;
  result.a = isNaN.a ? x.a : result.a;

  return result;
`,jee=Ze({opSnippet:Hee,packedOpSnippet:Gee}),qee={kernelName:Za,backendName:"webgl",kernelFunc:jee},Xee=Bs+`
  return (x < 0.0) ? 0.0 : min(6.0, x);
`,Kee=`
  vec4 result = min(x, vec4(6.)) * vec4(greaterThanEqual(x, vec4(0.0)));
  bvec4 isNaN = isnan(x);

  result.r = isNaN.r ? x.r : result.r;
  result.g = isNaN.g ? x.g : result.g;
  result.b = isNaN.b ? x.b : result.b;
  result.a = isNaN.a ? x.a : result.a;

  return result;
`,Zee=Ze({opSnippet:Xee,packedOpSnippet:Kee}),Yee={kernelName:Ja,backendName:"webgl",kernelFunc:Zee},Jee=class{constructor(e,t,n,s,r){this.variableNames=["A"],this.outputShape=[];let[a,o,i,l]=e;this.outputShape=[a,t,n,l];let u=[s&&t>1?o-1:o,s&&n>1?i-1:i],c=[s&&t>1?t-1:t,s&&n>1?n-1:n],d;r?d="(vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC - vec2(0.5)":d="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
      const vec2 effectiveInputOverOutputRatioRC = vec2(
          ${u[0]/c[0]},
          ${u[1]/c[1]});
      const vec2 inputShapeRC = vec2(${o}.0, ${i}.0);

      void main() {
        ivec4 coords = getOutputCoords();
        int b = coords[0];
        int d = coords[3];
        ivec2 yRC = coords.yz;

        // Fractional source index.
        vec2 sourceFracIndexRC = ${d};

        // Compute the four integer indices.
        ivec2 sourceFloorRC = ivec2(max(sourceFracIndexRC, vec2(0.0)));
        ivec2 sourceCeilRC = ivec2(
          min(inputShapeRC - 1.0, ceil(sourceFracIndexRC)));

        float topLeft = getA(b, sourceFloorRC.x, sourceFloorRC.y, d);
        float bottomLeft = getA(b, sourceCeilRC.x, sourceFloorRC.y, d);
        float topRight = getA(b, sourceFloorRC.x, sourceCeilRC.y, d);
        float bottomRight = getA(b, sourceCeilRC.x, sourceCeilRC.y, d);

        vec2 fracRC = sourceFracIndexRC - vec2(sourceFloorRC);

        float top = topLeft + (topRight - topLeft) * fracRC.y;
        float bottom = bottomLeft + (bottomRight - bottomLeft) * fracRC.y;
        float newValue = top + (bottom - top) * fracRC.x;

        setOutput(newValue);
      }
    `}},Qee=class{constructor(e,t,n,s,r){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[a,o,i,l]=e;this.outputShape=[a,t,n,l];let u=[s&&t>1?o-1:o,s&&n>1?i-1:i],c=[s&&t>1?t-1:t,s&&n>1?n-1:n],d;r?d="(vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC - vec3(0.5)":d="vec3(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
      const vec3 effectiveInputOverOutputRatioRC = vec3(
          ${u[0]/c[0]},
          ${u[1]/c[1]},
          ${u[1]/c[1]});
      const vec3 inputShapeRC = vec3(${o}.0, ${i}.0,
                                     ${i}.0);

      float getAValue(int b, int r, int c, int d) {
        return getChannel(getA(b, r, c, d), vec2(c, d));
      }

      void main() {
        ivec4 coords = getOutputCoords();
        int b = coords[0];
        int d = coords[3];
        // Calculate values for next column in yRC.z.
        ivec3 yRC = coords.yzz + ivec3(0, 0, 1);

        // Fractional source index.
        vec3 sourceFracIndexRC = ${d};

        // Compute the four integer indices.
        ivec3 sourceFloorRC = ivec3(max(sourceFracIndexRC, vec3(0.0)));
        ivec3 sourceCeilRC = ivec3(
          min(inputShapeRC - 1.0, ceil(sourceFracIndexRC)));

        // Should we calculate next column and row elements in 2x2 packed cell.
        bool hasNextCol = d < ${l-1};
        bool hasNextRow = coords.z < ${n-1};

        // In parallel, construct four corners for all four components in
        // packed 2x2 cell.
        vec4 topLeft = vec4(
          getAValue(b, sourceFloorRC.x, sourceFloorRC.y, d),
          hasNextCol ? getAValue(b, sourceFloorRC.x, sourceFloorRC.y, d + 1)
                     : 0.0,
          hasNextRow ? getAValue(b, sourceFloorRC.x, sourceFloorRC.z, d)
                     : 0.0,
          (hasNextRow && hasNextCol) ?
            getAValue(b, sourceFloorRC.x, sourceFloorRC.z, d + 1) : 0.0);

        vec4 bottomLeft = vec4(
          getAValue(b, sourceCeilRC.x, sourceFloorRC.y, d),
          hasNextCol ? getAValue(b, sourceCeilRC.x, sourceFloorRC.y, d + 1)
                     : 0.0,
          hasNextRow ? getAValue(b, sourceCeilRC.x, sourceFloorRC.z, d)
                     : 0.0,
          (hasNextRow && hasNextCol) ?
            getAValue(b, sourceCeilRC.x, sourceFloorRC.z, d + 1) : 0.0);

        vec4 topRight = vec4(
          getAValue(b, sourceFloorRC.x, sourceCeilRC.y, d),
          hasNextCol ? getAValue(b, sourceFloorRC.x, sourceCeilRC.y, d + 1)
                     : 0.0,
          hasNextRow ? getAValue(b, sourceFloorRC.x, sourceCeilRC.z, d)
                     : 0.0,
          (hasNextRow && hasNextCol) ?
            getAValue(b, sourceFloorRC.x, sourceCeilRC.z, d + 1) : 0.0);

        vec4 bottomRight = vec4(
          getAValue(b, sourceCeilRC.x, sourceCeilRC.y, d),
          hasNextCol ? getAValue(b, sourceCeilRC.x, sourceCeilRC.y, d + 1)
                     : 0.0,
          hasNextRow ? getAValue(b, sourceCeilRC.x, sourceCeilRC.z, d)
                     : 0.0,
          (hasNextRow && hasNextCol) ?
            getAValue(b, sourceCeilRC.x, sourceCeilRC.z, d + 1) : 0.0);

        vec3 fracRC = sourceFracIndexRC - vec3(sourceFloorRC);

        vec4 top = mix(topLeft, topRight, fracRC.yyzz);
        vec4 bottom = mix(bottomLeft, bottomRight, fracRC.yyzz);
        vec4 newValue = mix(top, bottom, fracRC.x);

        setOutput(newValue);
      }
    `}};function ete(e){let{inputs:t,backend:n,attrs:s}=e,{images:r}=t,{alignCorners:a,halfPixelCenters:o,size:i}=s,[l,u]=i,c=ee().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new Qee(r.shape,l,u,a,o):new Jee(r.shape,l,u,a,o);return n.runWebGLProgram(c,[r],"float32")}var tte={kernelName:Ya,backendName:"webgl",kernelFunc:ete},nte=class{constructor(e,t,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,s,r]=t,[,a,o]=e,i=[n&&a>1?s-1:s,n&&o>1?r-1:r],l=[n&&a>1?a-1:a,n&&o>1?o-1:o],u=i[0]/l[0],c=i[1]/l[1],d=1/u,h=1/c,p=Math.ceil(d)*2+2,f=Math.ceil(h)*2+2;this.userCode=`
      void main() {
        ivec4 coords = getOutputCoords();
        int b = coords[0];
        int d = coords[3];
        int r = coords[1];
        int c = coords[2];

        float accumulator = 0.0;

        const float heightScale = float(${u});
        const float widthScale = float(${c});

        const float invHeightScale = float(${d});
        const float invWidthScale = float(${h});

        const int winHeight = int(${p});
        const int winWidth = int(${f});

        // Compute bounds for where in dy we will look
        float startRLerp = floor(float(r) * invHeightScale);
        int startDyR = int(startRLerp - float(winHeight / 2));

        float startCLerp = floor(float(c) * invWidthScale);
        int startDyC = int(startCLerp - float(winWidth / 2));

        // Loop over dy
        for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) {
          int dyR = dyROffset + startDyR;

          // Guard against the window exceeding the bounds of dy
          if (dyR < 0 || dyR >= ${a}) {
            continue;
          }

          for (int dyCOffset = 0; dyCOffset < winWidth; dyCOffset++) {
            int dyC = dyCOffset + startDyC;

            // Guard against the window exceeding the bounds of dy
            if (dyC < 0 || dyC >= ${o}) {
              continue;
            }

            float dxR = float(dyR) * heightScale;
            int topDxRIndex = int(floor(dxR));
            int bottomDxRIndex = int(min(ceil(dxR), ${s-1}.0));
            float dxRLerp = dxR - float(topDxRIndex);
            float inverseDxRLerp = 1.0 - dxRLerp;

            float dxC = float(dyC) * widthScale;
            int leftDxCIndex = int(floor(dxC));
            int rightDxCIndex = int(min(ceil(dxC), ${r-1}.0));
            float dxCLerp = dxC - float(leftDxCIndex);
            float inverseDxCLerp = 1.0 - dxCLerp;

            if (r == topDxRIndex && c == leftDxCIndex) {
              // topLeft
              accumulator +=
                getDy(b, dyR, dyC, d) * inverseDxRLerp * inverseDxCLerp;
            }

            if (r == topDxRIndex && c == rightDxCIndex) {
              // topRight
              accumulator += getDy(b, dyR, dyC, d) * inverseDxRLerp * dxCLerp;
            }

            if (r == bottomDxRIndex && c == leftDxCIndex) {
              // bottomLeft
              accumulator += getDy(b, dyR, dyC, d) * dxRLerp * inverseDxCLerp;
            }

            if (r == bottomDxRIndex && c == rightDxCIndex) {
              // bottomRight
              accumulator += getDy(b, dyR, dyC, d) * dxRLerp * dxCLerp;
            }
          }
        }
        // End loop over dy

        setOutput(accumulator);
      }
    `}};function ste(e){let{inputs:t,backend:n,attrs:s}=e,{images:r,dy:a}=t,{alignCorners:o}=s,i=new nte(a.shape,r.shape,o);return n.runWebGLProgram(i,[a],a.dtype)}var rte={kernelName:ch,backendName:"webgl",kernelFunc:ste},ate=class{constructor(e,t,n,s,r){this.variableNames=["A"],this.outputShape=[];let[a,o,i,l]=e;this.outputShape=[a,t,n,l];let u=[s&&t>1?o-1:o,s&&n>1?i-1:i],c=[s&&t>1?t-1:t,s&&n>1?n-1:n],d=s?"0.5":"0.0",h;r?h="max((vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC, vec2(0.0))":h="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
      const vec2 effectiveInputOverOutputRatioRC = vec2(
          ${u[0]/c[0]},
          ${u[1]/c[1]});
      const vec2 inputShapeRC = vec2(${o}.0, ${i}.0);

      void main() {
        ivec4 coords = getOutputCoords();
        int b = coords[0];
        int d = coords[3];
        ivec2 yRC = coords.yz;

        // Fractional source index.
        vec2 sourceFracIndexRC = ${h};

        // Compute the coordinators of nearest neighbor point.
        ivec2 sourceNearestRC = ivec2(
          min(inputShapeRC - 1.0, floor(sourceFracIndexRC + ${d})));
        float newValue = getA(b, sourceNearestRC.x, sourceNearestRC.y, d);

        setOutput(newValue);
      }
    `}},ote=class{constructor(e,t,n,s,r){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[a,o,i,l]=e;this.outputShape=[a,t,n,l];let u=[s&&t>1?o-1:o,s&&n>1?i-1:i],c=[s&&t>1?t-1:t,s&&n>1?n-1:n],d=s?"0.5":"0.0",h;r?h="max((vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC, vec3(0.0))":h="vec3(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
      const vec3 effectiveInputOverOutputRatioRC = vec3(
          ${u[0]/c[0]},
          ${u[1]/c[1]},
          ${u[1]/c[1]});
      const vec3 inputShapeRC = vec3(${o}.0, ${i}.0,
                                     ${i}.0);

      float getAValue(int b, int r, int c, int d) {
        return getChannel(getA(b, r, c, d), vec2(c, d));
      }

      void main() {
        ivec4 coords = getOutputCoords();
        int b = coords[0];
        int d = coords[3];
        // Calculate values for next column in yRC.z.
        ivec3 yRC = coords.yzz + ivec3(0, 0, 1);

        // Fractional source index.
        vec3 sourceFracIndexRC = ${h};

        // Compute the coordinators of nearest neighbor point.
        ivec3 sourceNearestRC = ivec3(
          min(inputShapeRC - 1.0, floor(sourceFracIndexRC + ${d})));

        // Should we calculate next column and row elements in 2x2 packed cell.
        bool hasNextCol = d < ${l-1};
        bool hasNextRow = coords.z < ${n-1};

        vec4 newValue = vec4(
          getAValue(b, sourceNearestRC.x, sourceNearestRC.y, d),
          hasNextCol ? getAValue(b, sourceNearestRC.x, sourceNearestRC.y, d + 1)
                     : 0.0,
          hasNextRow ? getAValue(b, sourceNearestRC.x, sourceNearestRC.z, d)
                     : 0.0,
          (hasNextRow && hasNextCol) ?
            getAValue(b, sourceNearestRC.x, sourceNearestRC.z, d + 1) : 0.0);

        setOutput(newValue);
      }
    `}};function ite(e){let{inputs:t,backend:n,attrs:s}=e,{images:r}=t,{alignCorners:a,halfPixelCenters:o,size:i}=s,[l,u]=i,c=ee().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new ote(r.shape,l,u,a,o):new ate(r.shape,l,u,a,o);return n.runWebGLProgram(c,[r],r.dtype)}var lte={kernelName:Mu,backendName:"webgl",kernelFunc:ite},ute=class{constructor(e,t,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,s,r]=t,[,a,o]=e,i=[n&&a>1?s-1:s,n&&o>1?r-1:r],l=[n&&a>1?a-1:a,n&&o>1?o-1:o],u=i[0]/l[0],c=i[1]/l[1],d=1/u,h=1/c,p=Math.ceil(d)*2+2,f=Math.ceil(h)*2+2;this.userCode=`
      void main() {
        ivec4 coords = getOutputCoords();
        int b = coords[0];
        int d = coords[3];
        int r = coords[1];
        int c = coords[2];

        float accumulator = 0.0;

        const float heightScale = float(${u});
        const float widthScale = float(${c});

        const float invHeightScale = float(${d});
        const float invWidthScale = float(${h});

        const int winHeight = int(${p});
        const int winWidth = int(${f});

        // Compute bounds for where in dy we will look
        float startRLerp = floor(float(r) * invHeightScale);
        int startDyR = int(floor(startRLerp - float(winHeight / 2)));

        float startCLerp = floor(float(c) * invWidthScale);
        int startDyC = int(floor(startCLerp - float(winWidth / 2)));

        // Loop over dy
        for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) {
          int dyR = dyROffset + startDyR;

          // Guard against the window exceeding the bounds of dy
          if (dyR < 0 || dyR >= ${a}) {
            continue;
          }

          for (int dyCOffset = 0; dyCOffset < winWidth; dyCOffset++) {
            int dyC = dyCOffset + startDyC;

            // Guard against the window exceeding the bounds of dy
            if (dyC < 0 || dyC >= ${o}) {
              continue;
            }

            float sourceFracRow =
              float(${i[0]}) *
                (float(dyR) / float(${l[0]}));

            float sourceFracCol =
                float(${i[1]}) *
                  (float(dyC) / float(${l[1]}));

            int sourceNearestRow = int(min(
                float(int(${s}) - 1),
                ${n} ? float(round(sourceFracRow)) :
                                  float(floor(sourceFracRow))));

            int sourceNearestCol = int(min(
                float(int(${r}) - 1),
                ${n} ? float(round(sourceFracCol)) :
                                  float(floor(sourceFracCol))));

            if (r == sourceNearestRow && c == sourceNearestCol) {
              accumulator += getDy(b, dyR, dyC, d);
            }
          }
        }
        // End loop over dy

        setOutput(accumulator);
      }
    `}};function cte(e){let{inputs:t,backend:n,attrs:s}=e,{images:r,dy:a}=t,{alignCorners:o}=s,i=new ute(a.shape,r.shape,o);return n.runWebGLProgram(i,[a],a.dtype)}var dte={kernelName:uh,backendName:"webgl",kernelFunc:cte},hte=class{constructor(e,t){this.variableNames=["x"];let n=e.length;if(n>4)throw new Error(`WebGL backend: Reverse of rank-${n} tensor is not yet supported`);if(this.outputShape=e,n===1){this.userCode=`
        void main() {
          int coord = getOutputCoords();
          setOutput(getX(${e[0]} - coord - 1));
        }
      `;return}let s=o=>t.indexOf(o)!==-1&&e[o]!==1?`${e[o]} - coords[${o}] - 1`:`coords[${o}]`,r=e.map((o,i)=>s(i)).join(","),a=ct(n);this.userCode=`
      void main() {
        ${a} coords = getOutputCoords();
        setOutput(getX(${r}));
      }
    `}},pte=class{constructor(e,t){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0;let n=e.length;if(n>4)throw new Error(`WebGL backend: Reverse of rank-${n} tensor is not yet supported`);this.outputShape=e;let s=vn("rc",n),r=`${s[n-1]} + 1 < ${this.outputShape[n-1]}`,a=`${s[n-2]} + 1 < ${this.outputShape[n-2]}`,o=ct(n);n===1?this.userCode=`
        void main(){
          int rc = getOutputCoords();
          vec4 result = vec4(0.);
          result.r = getChannel(getX(${e[0]} - rc - 1),
            ${e[0]} - rc - 1);
          if(${r}){
              result.g = getChannel(getX(${e[0]} - (rc  + 1) - 1),
                ${e[0]} - (rc  + 1) - 1);
          }
          setOutput(result);
        }
      `:this.userCode=`
        void main() {
          ${o} rc = getOutputCoords();
          vec4 result = vec4(0.);
          result.r = ${i(s.slice())};
          if(${r}){
            result.g = ${l(s.slice())};
          }
          if(${a}) {
            result.b = ${u(s.slice())};
            if(${r}) {
              result.a = ${c(s.slice())};
            }
          }
          setOutput(result);
        }
    `;function i(p){return d(p)}function l(p){return p[n-1]="("+p[n-1]+" + 1)",d(p)}function u(p){return p[n-2]="("+p[n-2]+" + 1)",d(p)}function c(p){return p[n-1]="("+p[n-1]+" + 1)",p[n-2]="("+p[n-2]+" + 1)",d(p)}function d(p){let f=e.map((g,y)=>h(y,p)),m=f.join(","),A=f.slice(-2).join(",");return`getChannel(getX(${m}), vec2(${A}))`}function h(p,f){return t.indexOf(p)!==-1&&e[p]!==1?`${e[p]} - ${f[p]} - 1`:`${f[p]}`}}};function fte(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{dims:a}=s,o=r.shape.length,i=I.parseAxisParam(a,r.shape);if(o===0)return qn({inputs:{x:r},backend:n});let l=ee().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new pte(r.shape,i):new hte(r.shape,i);return n.runWebGLProgram(l,[r],r.dtype)}var mte={kernelName:Qa,backendName:"webgl",kernelFunc:fte},Ate=class{constructor(e,t){this.variableNames=["Image"],this.outputShape=[],this.customUniforms=[{name:"params",type:"vec4"}];let n=e[1],s=e[2];this.outputShape=e;let r="";typeof t=="number"?r=`float outputValue = ${t.toFixed(2)};`:r=`
        vec3 fill = vec3(${t.join(",")});
        float outputValue = fill[coords[3]];`,this.userCode=`
        void main() {
          ivec4 coords = getOutputCoords();
          int x = coords[2];
          int y = coords[1];
          float coordXFloat = (float(x) - params[0]) * params[3] -
            (float(y) - params[1]) * params[2];
          float coordYFloat = (float(x) - params[0]) * params[2] +
            (float(y) - params[1]) * params[3];
          int coordX = int(round(coordXFloat + params[0]));
          int coordY = int(round(coordYFloat + params[1]));
          ${r}
          if(coordX >= 0 && coordX < ${s} && coordY >= 0 && coordY < ${n}) {
            outputValue = getImage(coords[0], coordY, coordX, coords[3]);
          }
          setOutput(outputValue);
        }
    `}},gte={kernelName:hl,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{image:s}=e,{radians:r,fillValue:a,center:o}=t,i=n,l=new Ate(s.shape,a),[u,c]=$.getImageCenter(o,s.shape[1],s.shape[2]),d=[[u,c,Math.sin(r),Math.cos(r)]];return i.runWebGLProgram(l,[s],s.dtype,d)}},yte=`
  // OpenGL ES does not support round function.
  // The algorithm is based on banker's rounding.
  float base = floor(x);
  if ((x - base) < 0.5) {
    return floor(x);
  } else if ((x - base) > 0.5) {
    return ceil(x);
  } else {
    if (mod(base, 2.0) == 0.0) {
      return base;
    } else {
      return base + 1.0;
    }
  }
`,xte=Ze({opSnippet:yte}),bte={kernelName:eo,backendName:"webgl",kernelFunc:xte},vte="return inversesqrt(x);",wte=Ze({opSnippet:vte,cpuKernelImpl:Yq}),kte={kernelName:to,backendName:"webgl",kernelFunc:wte},b4=class{constructor(e,t,n,s,r,a,o=!0){this.variableNames=["updates","indices","defaultValue"],this.outputShape=a;let i=ct(r.length),l=ct(a.length),u="";n===1?u="i":n===2&&(u="i, j");let c=`getIndices(${u})`,d="";s===1?d="i":s===2&&(d="i, coords[1]");let h=`getUpdates(${d})`,p=t>1?"strides[j]":"strides";this.userCode=`
        ${i} strides = ${i}(${r});

        void main() {
          ${l} coords = getOutputCoords();
          float sum = 0.0;
          bool found = false;
          for (int i = 0; i < ${e}; i++) {
            int flattenedIndex = 0;
            for (int j = 0; j < ${t}; j++) {
              int index = round(${c});
              flattenedIndex += index * ${p};
            }
            if (flattenedIndex == coords[0]) {
              sum += ${h};
              found = true;
            }
          }
          setOutput(mix(getDefaultValue(), sum, float(found)));
        }
      `}};function Ite(e){let{inputs:t,backend:n,attrs:s}=e,{indices:r,updates:a}=t,{shape:o}=s,{sliceRank:i,numUpdates:l,sliceSize:u,strides:c,outputSize:d}=$.calculateShapes(a,r,o),h=[d/u,u];if(d===0)return n.makeTensorInfo(o,r.dtype);let p=ye({inputs:{x:r},backend:n,attrs:{shape:[l,i]}}),f=ye({inputs:{x:a},backend:n,attrs:{shape:[l,u]}}),m=n.makeTensorInfo([],"float32",new Float32Array([0])),A=new b4(l,i,p.shape.length,f.shape.length,c,h),g=n.runWebGLProgram(A,[f,p,m],f.dtype),y=ye({inputs:{x:g},backend:n,attrs:{shape:o}});return n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(g),n.disposeIntermediateTensorInfo(m),y}var Ste={kernelName:Ji,backendName:"webgl",kernelFunc:Ite},Cte=class{constructor(e,t,n){this.variableNames=["c","a","b"],this.outputShape=t;let s,r;if(n>4)throw Error(`Where for rank ${n} is not yet supported`);if(n===1)r="resRC",s="resRC";else{let o=["resRC.x","resRC.y","resRC.z","resRC.w"],i=[],l=[];for(let u=0;u<t.length;u++)l.push(`${o[u]}`),u<e&&i.push(`${o[u]}`);s=i.join(),r=l.join()}let a=ct(n);this.userCode=`
      void main() {
        ${a} resRC = getOutputCoords();
        float cVal = getC(${s});
        if (cVal >= 1.0) {
          setOutput(getA(${r}));
        } else {
          setOutput(getB(${r}));
        }
      }
    `}};function Tte(e){let{inputs:t,backend:n}=e,{condition:s,t:r,e:a}=t,o=new Cte(s.shape.length,r.shape,r.shape.length);return n.runWebGLProgram(o,[s,r,a],bs(r.dtype,a.dtype))}var Nte={kernelName:Qi,backendName:"webgl",kernelFunc:Tte},Ete=`
  // Stable and Attracting Fixed Point (0, 1) for Normalized Weights.
  // see: https://arxiv.org/abs/1706.02515
  float scaleAlpha = ${$.SELU_SCALEALPHA};
  float scale = ${$.SELU_SCALE};
  return (x >= 0.0) ? scale * x : scaleAlpha * (exp(x) - 1.0);
`,Rte=Ze({opSnippet:Ete}),_te={kernelName:el,backendName:"webgl",kernelFunc:Rte},$te="return 1.0 / (1.0 + exp(-1.0 * x));",Fte=Ze({opSnippet:$te}),Dte={kernelName:so,backendName:"webgl",kernelFunc:Fte},Ote=`
  if (isnan(x)) { return 0.0; }
  return sign(x);
`,Pte=Ze({opSnippet:Ote}),Mte={kernelName:sl,backendName:"webgl",kernelFunc:Pte},zte=D6+`
  return sin(x);
`,Lte=Ze({opSnippet:zte}),Bte={kernelName:no,backendName:"webgl",kernelFunc:Lte},Wte=`
  float e2x = exp(x);
  return (e2x - 1.0 / e2x) / 2.0;
`,Vte=Ze({opSnippet:Wte}),Ute={kernelName:nl,backendName:"webgl",kernelFunc:Vte},Hte=`
  float epsilon = 1.1920928955078125e-7;
  float threshold = log(epsilon) + 2.0;

  bool too_large = x > -threshold;
  bool too_small = x < threshold;

  float result;
  float exp_x = exp(x);

  if (too_large){
    result = x;
  }
  else if (too_small){
    result = exp_x;
  }
  else{
    result = log(exp_x + 1.0);
  }
  return result;
`,Gte=Ze({opSnippet:Hte}),jte={kernelName:rl,backendName:"webgl",kernelFunc:Gte},qte=e=>{let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockShape:a,paddings:o}=s;I.assert(r.shape.length<=4,()=>"spaceToBatchND for rank > 4 with a WebGL backend not implemented yet");let i=a.reduce((g,y)=>g*y),l=[[0,0]];l.push(...o);for(let g=1+a.length;g<r.shape.length;++g)l.push([0,0]);let u=[],c=y4({inputs:{x:r},backend:n,attrs:{paddings:l,constantValue:0}}),d=$.getReshaped(c.shape,a,i,!1),h=$.getPermuted(d.length,a.length,!1),p=$.getReshapedPermuted(c.shape,a,i,!1),f=ye({inputs:{x:c},backend:n,attrs:{shape:d}}),m=wn({inputs:{x:f},backend:n,attrs:{perm:h}}),A=ye({inputs:{x:m},backend:n,attrs:{shape:p}});return u.push(c),u.push(f),u.push(m),u.forEach(g=>n.disposeIntermediateTensorInfo(g)),A},Xte={kernelName:al,backendName:"webgl",kernelFunc:qte};function Kte(e){let{inputs:t,backend:n}=e,{indices:s,values:r,denseShape:a,defaultValue:o}=t;if(a.shape.length!==1)throw new Error(`Dense shape must be a vector, saw:
         ${a.shape}`);if(s.shape.length!==2)throw new Error(`Indices must be a matrix, saw:
         ${s.shape}`);if(r.shape.length!==1)throw new Error(`Values must be a vector, saw:
         ${r.shape}`);if(o.shape.length!==0)throw new Error(`Default value must be a scalar, saw:
        ${o.shape}`);let i=n.readSync(s.dataId),l=n.readSync(r.dataId),u=n.readSync(a.dataId),c=n.readSync(o.dataId)[0],[d,h,p,f,m]=Qq(i,s.shape,s.dtype,l,r.dtype,u,c);return[n.makeTensorInfo(h,s.dtype,d),n.makeTensorInfo([h[0]],r.dtype,p),n.makeTensorInfo([f.length],"bool",new Uint8Array(f.map(A=>Number(A)))),n.makeTensorInfo([m.length],s.dtype,new Int32Array(m))]}var Zte={kernelName:dh,backendName:"webgl",kernelFunc:Kte};function Yte(e){let{inputs:t,backend:n}=e,{inputIndices:s,inputShape:r,newShape:a}=t;if(s.shape.length!==2)throw new Error(`Input indices should be a matrix but received shape ${s.shape}`);if(r.shape.length!==1)throw new Error(`Input shape should be a vector but received shape ${r.shape}`);if(a.shape.length!==1)throw new Error(`Target shape should be a vector but received shape ${a.shape}`);let o=Array.from(n.readSync(r.dataId)),i=n.readSync(s.dataId),l=Array.from(n.readSync(a.dataId)),[u,c,d]=eX(i,s.shape,s.dtype,o,l);return[n.makeTensorInfo(c,s.dtype,u),n.makeTensorInfo([d.length],a.dtype,new Int32Array(d))]}var Jte={kernelName:hh,backendName:"webgl",kernelFunc:Yte};function Qte(e){let{inputs:t,backend:n}=e,{data:s,indices:r,segmentIds:a}=t;if(s.shape.length<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(r.shape.length!==1)throw new Error(`Indices should be a vector but received shape
              ${r.shape}`);if(a.shape.length!==1)throw new Error(`Segment ids should be a vector but received shape
              ${a.shape}`);let o=n.readSync(s.dataId),i=n.readSync(r.dataId),l=n.readSync(a.dataId),[u,c]=v6(o,s.shape,s.dtype,i,l,!0);return n.makeTensorInfo(c,s.dtype,u)}var ene={kernelName:ph,backendName:"webgl",kernelFunc:Qte};function tne(e){let{inputs:t,backend:n}=e,{data:s,indices:r,segmentIds:a}=t;if(s.shape.length<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(r.shape.length!==1)throw new Error(`Indices should be a vector but received shape
             ${r.shape}`);if(a.shape.length!==1)throw new Error(`Segment ids should be a vector but received shape
             ${a.shape}`);let o=n.readSync(s.dataId),i=n.readSync(r.dataId),l=n.readSync(a.dataId),[u,c]=v6(o,s.shape,s.dtype,i,l);return n.makeTensorInfo(c,s.dtype,u)}var nne={kernelName:fh,backendName:"webgl",kernelFunc:tne};function sne(e){let{inputs:t,backend:n,attrs:s}=e,{sparseIndices:r,sparseValues:a,defaultValue:o}=t,{outputShape:i}=s,{sliceRank:l,numUpdates:u,strides:c,outputSize:d}=$.calculateShapes(a,r,i),h=!1,p=new b4(u,l,r.shape.length,a.shape.length,c,[d,1],h),f=n.runWebGLProgram(p,[a,r,o],a.dtype),m=ye({inputs:{x:f},backend:n,attrs:{shape:i}});return n.disposeIntermediateTensorInfo(f),m}var rne={kernelName:mh,backendName:"webgl",kernelFunc:sne};function ane(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{numOrSizeSplits:a,axis:o}=s,i=I.parseAxisParam(o,r.shape)[0],l=$.prepareSplitSize(r,a,i),u=r.shape.length,c=new Array(u).fill(0),d=r.shape.slice();return l.map(h=>{let p=[...d];p[i]=h;let f=eu({inputs:{x:r},backend:n,attrs:{begin:c,size:p}});return c[i]+=h,f})}var one={kernelName:ol,backendName:"webgl",kernelFunc:ane},ine="return sqrt(x);",lne=Ze({opSnippet:ine}),une={kernelName:ro,backendName:"webgl",kernelFunc:lne},cne="return x * x;",dne=Ze({opSnippet:cne}),hne={kernelName:zu,backendName:"webgl",kernelFunc:dne},v4="return (a - b) * (a - b);",pne=rn({opSnippet:v4,packedOpSnippet:v4}),fne={kernelName:io,backendName:"webgl",kernelFunc:pne};function mne({inputs:e,attrs:t,backend:n}){let{x:s}=e,r=Bs+`
    return x > 0.0 ? 1.0 : float(${t.alpha});
  `,a=new sa(s.shape,r);return n.runWebGLProgram(a,[s],s.dtype)}var Ane={kernelName:Mr,backendName:"webgl",kernelFunc:mne},gne=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=n;let s=n.length,r=ct(n.length),a=ct(n.length),o="";if(s===1)o="coords * strides + begin";else{let i=0;o=n.map((l,u)=>(i++,n.length===1?`coords * strides[${u}] + begin[${u}]`:`coords[${i-1}] * strides[${u}] + begin[${u}]`)).join(",")}this.userCode=`
      ${r} begin = ${r}(${e});
      ${r} strides = ${r}(${t});

      void main() {
        ${a} coords = getOutputCoords();
        setOutput(getX(${o}));
      }
    `}};function yne(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{begin:a,end:o,strides:i,beginMask:l,endMask:u,ellipsisMask:c,newAxisMask:d,shrinkAxisMask:h}=s,{nonStrided:p,$begin:f,$strides:m,size:A,newShape:g,outShape:y}=An.sliceInfo(r.shape,a,o,i,l,u,c,d,h),x=ye({inputs:{x:r},backend:n,attrs:{shape:g}}),b;if(p){let k=eu({inputs:{x},backend:n,attrs:{begin:f,size:A}});b=ye({inputs:{x:k},backend:n,attrs:{shape:y}}),n.disposeIntermediateTensorInfo(k)}else if(y.some(k=>k===0))b=n.makeTensorInfo(y,r.dtype,[]);else if(n.shouldExecuteOnCPU([x])){let C=n.texData.get(x.dataId).values,E=Be(x.shape,x.dtype,C),M=tX(y,E,m,f);b=n.makeTensorInfo(y,x.dtype,M.values)}else{let w=new gne(f,m,y);b=n.runWebGLProgram(w,[x],x.dtype)}let v=ye({inputs:{x:b},backend:n,attrs:{shape:y}});return n.disposeIntermediateTensorInfo(x),n.disposeIntermediateTensorInfo(b),v}var xne={kernelName:il,backendName:"webgl",kernelFunc:yne};function bne(e){let{inputs:t,backend:n,attrs:s}=e,{separator:r,nGramWidths:a,leftPad:o,rightPad:i,padWidth:l,preserveShortSequences:u}=s,{data:c,dataSplits:d}=t,h=n.readSync(c.dataId),p=n.readSync(d.dataId),[f,m]=nX(h,p,r,a,o,i,l,u);return[n.makeTensorInfo([f.length],"string",f),n.makeTensorInfo(d.shape,"int32",m)]}var vne={kernelName:Ah,backendName:"webgl",kernelFunc:bne};function wne(e){let{inputs:t,backend:n,attrs:s}=e,{skipEmpty:r}=s,{input:a,delimiter:o}=t;if(a.dtype!=="string")throw new Error("Input must be of datatype string");if(a.shape.length!==1)throw new Error(`Input must be a vector, got shape: ${a.shape}`);if(o.shape.length!==0)throw new Error(`Delimiter must be a scalar, got shape: ${o.shape}`);let i=n.readSync(a.dataId),l=n.readSync(o.dataId)[0],[u,c,d]=sX(i,l,r),h=c.length;return[n.makeTensorInfo([h,2],"int32",u),n.makeTensorInfo([h],"string",c),n.makeTensorInfo([2],"int32",new Int32Array(d))]}var kne={kernelName:gh,backendName:"webgl",kernelFunc:wne};function Ine(e){let{inputs:t,backend:n,attrs:s}=e,{numBuckets:r}=s,{input:a}=t;if(a.dtype!=="string")throw new Error("Input must be of datatype string");if(r<=0)throw new Error("Number of buckets must be at least 1");let o=n.readSync(a.dataId),i=rX(o,r);return n.makeTensorInfo(a.shape,"int32",i)}var Sne={kernelName:yh,backendName:"webgl",kernelFunc:Ine},Cne="return tan(x);",Tne=Ze({opSnippet:Cne}),Nne={kernelName:uo,backendName:"webgl",kernelFunc:Tne},Ene=`
  float e2x = exp(-2.0 * abs(x));
  return sign(x) * (1.0 - e2x) / (1.0 + e2x);
`,Rne=Ze({opSnippet:Ene}),_ne={kernelName:co,backendName:"webgl",kernelFunc:Rne},$ne=class{constructor(e,t){this.variableNames=["A"];let n=new Array(e.length);for(let a=0;a<n.length;a++)n[a]=e[a]*t[a];this.outputShape=n,this.rank=n.length;let s=ct(this.rank),r=Fne(e);this.userCode=`
      void main() {
        ${s} resRC = getOutputCoords();
        setOutput(getA(${r}));
      }
    `}};function Fne(e){let t=e.length;if(t>5)throw Error(`Tile for rank ${t} is not yet supported`);if(t===1)return`imod(resRC, ${e[0]})`;let n=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u"],s=[];for(let r=0;r<e.length;r++)s.push(`imod(${n[r]}, ${e[r]})`);return s.join()}function w4(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{reps:a}=s;if(r.dtype==="string"||r.shape.length>5){let l=n.readSync(r.dataId),u=r.dtype==="string"?l.map(h=>I.decodeString(h)):l,c=Be(r.shape,r.dtype,u),d=oX(c,a);return n.makeTensorInfo(d.shape,d.dtype,d.values)}let o=new $ne(r.shape,a);return n.runWebGLProgram(o,[r],r.dtype)}var Dne={kernelName:Pr,backendName:"webgl",kernelFunc:w4},One=class{constructor(e){this.variableNames=["x","indices"],this.customUniforms=[{name:"n",type:"int"},{name:"firstPass",type:"int"},{name:"negativeInf",type:"float"},{name:"dir",type:"int"},{name:"inc",type:"int"}],this.outputShape=e,this.userCode=`
       void main() {
         ivec2 coords = getOutputCoords();
         int batch = coords[0];
         int elemIdx = coords[1];

         // We compare elements pair-wise within a group of size 2 * inc.
         // The comparing rule for each group alternates between ascending
         // and descending. Within each group, we compare each pair at
         // positions i and i+inc. To decide whether an element at position i
         // is x0 or x1, we mod it by 2 * inc, if the result is smaller than
         // inc, it is in the first half of the group, we denote it as x0,
         // otherwise we denote it as x1.
         // For example, as shown in the Bitonic top K paper referenced above,
         // Figure5(a) shows that element[1] is in the
         // second half of the group when group size is 2, but it is in the
         // first half of the group when group size is 4.

         bool isFirstInPair = imod(elemIdx, 2 * inc) < inc;
         int i = isFirstInPair ? elemIdx : elemIdx - inc;

         int i0 = firstPass == 1 ? i : int(getIndices(batch, i));
         int i1 = firstPass == 1 ? i + inc : int(getIndices(batch, i + inc));
         float x0 = i0 < n ? getX(batch, i0) : negativeInf;
         float x1 = i1 < n ? getX(batch, i1) : negativeInf;

         // Denotes which direction indices are in (ascending or descending).
         bool reverse = imod(elemIdx, 2 * dir) >= dir;
         bool isGreater = x0 > x1 || (x0 == x1 && i1 > i0);
         if (reverse == isGreater) { // Elements in opposite order of direction
           int iTemp = i0;
           i0 = i1;
           i1 = iTemp;
         }
         if (isFirstInPair) {
            setOutput(float(i0));
         } else {
            setOutput(float(i1));
         }
       }
     `}},Pne=class{constructor(e){this.variableNames=["x","indices"],this.customUniforms=[{name:"n",type:"int"},{name:"firstPass",type:"int"},{name:"k",type:"int"}],this.outputShape=e,this.userCode=`
    void main() {
         // Takes max of indices (0, k), (1, k + 1), (2, k + 2) ...
         ivec2 coords = getOutputCoords();
         int batch = coords[0];
         int elemIdx = coords[1];

         // The output size is half of the previous size.
         // If the previous sequence is | | | | _ _ _ _  | | | |  _ _ _ _ (k=4),
         // we only need to output the indices at positions |, the indices at
         // positions _ can be thrown away, see Figure5(b) After Phase 2
         // (Merge phase) in the Bitonic Top K paper referenced above.
         // For example, the paper shows we only need to output the orange bars.
         // The output sequence should look like this | | | | | | | |.
         // Because the sequence is halved, to map the output index back
         // to the previous sequence to find the corresponding value,
         // we need to double the index. When we double the index,
         // we basically interpolate a position, so 2i looks like
         // | _ | _ | _ | _ | _ | _ | _. We move the | to the first k position
         // of each 2k positions by - elemIdx % k. E.g. for output at
         // index 4,5,6,7, we want to get the corresponding element at
         // original index 8,9,10,11, for output at index 8,9,10,11,
         // we want to get the corresponding element at original index
         // 16,17,18,19, so on and so forth.

         int i = elemIdx < k ? elemIdx : (elemIdx * 2 - imod(elemIdx, k));
         int i0 = firstPass == 1 ? i : int(getIndices(batch, i));
         int i1 = firstPass == 1 ? i + k : int(getIndices(batch, i + k));

         float x0 = getX(batch, i0);
         float x1 = i1 < n ? getX(batch, i1) : x0;

         setOutput(x0 >= x1 ? float(i0) : float(i1));
       }
     `}};function jo(e,t){t!==null&&e.disposeIntermediateTensorInfo(t)}function k4(e){let t=1;for(;t<e;)t*=2;return t}function Mne(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{k:a,sorted:o}=s,i=ee().getNumber("TOPK_LAST_DIM_CPU_HANDOFF_SIZE_THRESHOLD"),l=ee().getNumber("TOPK_K_CPU_HANDOFF_THRESHOLD"),u=r.shape,c=u[u.length-1];if(n.shouldExecuteOnCPU([r])||c<i||a>l){let M=n.readSync(r.dataId),[R,_]=iX(M,u,r.dtype,a,o);return[n.makeTensorInfo(R.shape,R.dtype,R.values),n.makeTensorInfo(_.shape,_.dtype,_.values)]}if(a===0)return u[u.length-1]=0,[n.makeTensorInfo(u,r.dtype,[]),n.makeTensorInfo(u,"int32",[])];if(c===1)return[r,vf({attrs:{shape:u,dtype:"int32",value:0},backend:n})];let d=n.texData.get(r.dataId),h=d!==null&&d.isPacked,p=h?n.unpackTensor(r):r,m=I.sizeFromShape(u)/c,A=ye({inputs:{x:p},attrs:{shape:[m,c]},backend:n});h&&jo(n,p);let g=k4(a),y=k4(c),x=null,b=()=>x===null?[A,A]:[A,x],v=(M,R,_)=>{let N=b(),O=new One(_),j=[[c],[x===null?1:0],[Number.NEGATIVE_INFINITY],[M],[R]],q=x;x=n.runWebGLProgram(O,N,"int32",j),jo(n,q)};for(let M=1;M<g;M*=2){let R=M*2;for(let _=M;_>=1;_/=2)v(R,_,[m,y])}for(let M=y;M>g;M/=2){let R=b(),_=new Pne([m,M/2]),O=[[c],[x===null?1:0],[g]],W=x;x=n.runWebGLProgram(_,R,"int32",O),jo(n,W);let j=g/2,q=j*2;for(let X=j;X>=1;X/=2)v(q,X,x.shape)}let k=x;x=eu({inputs:{x},backend:n,attrs:{begin:0,size:[m,a]}}),jo(n,k);let w=c4({inputs:{x:A,indices:x},backend:n,attrs:{axis:1,batchDims:1}});jo(n,A);let C=u.slice(0,-1);C.push(a),k=x,x=ye({inputs:{x},attrs:{shape:C},backend:n}),jo(n,k);let E=w;return w=ye({inputs:{x:w},attrs:{shape:C},backend:n}),jo(n,E),[w,x]}var zne={kernelName:ll,backendName:"webgl",kernelFunc:Mne},Lne=class{constructor(e,t,n,s,r,a){this.variableNames=["Image","Transforms"],this.outputShape=a;let o=n==="nearest"?1:2,i;switch(s){case"constant":i=1;break;case"reflect":i=2;break;case"wrap":i=3;break;case"nearest":i=4;break;default:i=1;break}this.userCode=`
            float mapCoord(float outCoord, float len) {
              float inCoord = outCoord;
              if(${i} == 2) {
                if (inCoord < 0.0) {
                  if (len <= 1.0) {
                    inCoord = 0.0;
                  } else {
                    float sz2 = 2.0 * len;
                    if (inCoord < sz2) {
                      inCoord = sz2 * float(int(float(-inCoord / sz2))) +
                      inCoord;
                    }
                    inCoord = inCoord < -len ? inCoord + sz2 : -inCoord - 1.0;
                  }
                } else if (inCoord > len - 1.0) {
                  if (len <= 1.0) {
                    inCoord = 0.0;
                  } else {
                    float sz2 = 2.0 * len;
                    inCoord -= sz2 * float(int(float(inCoord / sz2)));
                    if (inCoord >= len) {
                      inCoord = sz2 - inCoord - 1.0;
                    }
                  }
                }
                return clamp(inCoord, 0.0, len - 1.0);
              } else if (${i} == 3) {
                if (inCoord < 0.0) {
                  if (len <= 1.0) {
                    inCoord = 0.0;
                  } else {
                    float sz = len - 1.0;
                    inCoord += len * (float(int(float(-inCoord / sz))) + 1.0);
                  }
                } else if (inCoord > len - 1.0) {
                  if (len <= 1.0) {
                    inCoord = 0.0;
                  } else {
                    float sz = len - 1.0;
                    inCoord -= len * float(int(float(inCoord / sz)));
                  }
                }
                return clamp(inCoord, 0.0, len - 1.0);
              } else if (${i} == 4) {
                return clamp(outCoord, 0.0, len - 1.0);
              } else {
                return outCoord;
              }
            }

            float readWithFillValue(int batch, int coordY, int coordX,
              int channel) {
              float outputValue;
              if (0 <= coordY && coordY < ${e} && 0 <= coordX && coordX < ${t}) {
                  outputValue = getImage(batch, coordY, coordX, channel);
              } else {
                outputValue = float(${r});
              }
              return outputValue;
            }

            void main() {
              ivec4 coords = getOutputCoords();
              float outputValue;
              int batch = coords[0];
              int x = coords[2];
              int y = coords[1];
              int channel = coords[3];
              float xf = float(x);
              float yf = float(y);
              float a1 = getTransforms(batch, 0);
              float a2 = getTransforms(batch, 1);
              float a3 = getTransforms(batch, 2);
              float b1 = getTransforms(batch, 3);
              float b2 = getTransforms(batch, 4);
              float b3 = getTransforms(batch, 5);
              float c1 = getTransforms(batch, 6);
              float c2 = getTransforms(batch, 7);
              float projection = c1 * xf + c2 * yf + 1.0;
              if (projection == 0.0) {
                outputValue = float(${r});
              } else {
                float inX = (a1 * xf + a2 * yf + a3) / projection;
                float inY = (b1 * xf + b2 * yf + b3) / projection;
                float mapX = mapCoord(inX, float(${t}));
                float mapY = mapCoord(inY, float(${e}));

                if (${o} == 1) {
                  int coordY = int(round(mapY));
                  int coordX = int(round(mapX));
                  outputValue = readWithFillValue(batch, coordY, coordX,
                    channel);
                } else {
                  float yFloor = floor(mapY);
                  float xFloor = floor(mapX);
                  float yCeil = yFloor + 1.0;
                  float xCeil = xFloor + 1.0;
                  float valueYFloor = (xCeil - mapX) *
                  readWithFillValue(batch, int(yFloor), int(xFloor), channel) +
                  (mapX - xFloor) *
                  readWithFillValue(batch, int(yFloor), int(xCeil), channel);
                  float valueYCeil = (xCeil - mapX) *
                  readWithFillValue(batch, int(yCeil), int(xFloor), channel) +
                  (mapX - xFloor) *
                  readWithFillValue(batch, int(yCeil), int(xCeil), channel);
                  outputValue = (yCeil - mapY) * valueYFloor +
                  (mapY - yFloor) * valueYCeil;
                }
              }
              setOutput(outputValue);
            }
        `}};function Bne(e){let{inputs:t,backend:n,attrs:s}=e,{image:r,transforms:a}=t,{interpolation:o,fillMode:i,fillValue:l,outputShape:u}=s,[c,d,h,p]=r.shape,[f,m]=u!=null?u:[d,h],A=[c,f,m,p],g=new Lne(d,h,o,i,l,A);return n.runWebGLProgram(g,[r,a],"float32")}var Wne={kernelName:ul,backendName:"webgl",kernelFunc:Bne};function Vne(e){let{inputs:t,attrs:n,backend:s}=e,{axis:r}=n,{x:a}=t;jl(a,"unique"),console.warn("WARNING: ","UI might be locked temporarily as data is being downloaded");let o=s.readSync(a.dataId),{outputValues:i,outputShape:l,indices:u}=lX(o,r,a.shape,a.dtype);return[s.makeTensorInfo(l,a.dtype,i),s.makeTensorInfo([u.length],"int32",u)]}var Une={kernelName:xh,backendName:"webgl",kernelFunc:Vne};function Hne(e){let{inputs:t,backend:n,attrs:s}=e,{value:r}=t,{axis:a}=s;a<0&&(a+=r.shape.length);let o=r,i=o.shape.length,l=r.shape[a],u=new Array(i-1),c=0;for(let m=0;m<i;m++)m!==a&&(u[c++]=o.shape[m]);let d=[],h=new Array(i).fill(0),p=o.shape.slice();p[a]=1;let f=new Array(l);for(let m=0;m<f.length;m++){h[a]=m;let A=eu({inputs:{x:o},backend:n,attrs:{begin:h,size:p}}),g=ye({inputs:{x:A},backend:n,attrs:{shape:u}});f[m]=g,d.push(A)}return d.forEach(m=>n.disposeIntermediateTensorInfo(m)),f}var Gne={kernelName:cl,backendName:"webgl",kernelFunc:Hne},jne=class{constructor(e,t){this.variableNames=["x","segmentIds"];let n=e.windowSize,s=e.batchSize,r=e.inSize,a=e.numSegments,o=a*Math.ceil(r/n);this.outputShape=[s,o];let i="0.0",l="sumValue",u=Math.floor(n/4)*4,c=n%4,d=`
        sumValue += dot(values, segFilter);
    `,h="";r%n>0&&(h=`
        if (inIdx < 0 || inIdx >= ${r}) {
          return initializationValue;
        }
      `);let p="";r%n>0&&(p=`
        if (inIdx < 0 || inIdx >= ${r}) {
          return -1.0;
        }
      `),this.userCode=`
      const float initializationValue = ${i};

      float getValue(int batch, int inIdx) {
        ${h}
        return getX(batch, inIdx);
      }

      float getSegmentIdAtIndex(int inIdx) {
        ${p}
        return getSegmentIds(inIdx);
      }

      void main() {
        ivec2 coords = getOutputCoords();
        int batch = coords[0];
        int outIdx = coords[1];
        int inOffset = int(floor(float(outIdx) / float(
          ${a})) * float(${n}));
        int currentSeg = int(mod(float(outIdx), float(${a})));

        float sumValue = 0.0;

        for (int i = 0; i < ${u}; i += 4) {
          int inIdx = inOffset + i;
          vec4 values = vec4(
            getValue(batch, inIdx),
            getValue(batch, inIdx + 1),
            getValue(batch, inIdx + 2),
            getValue(batch, inIdx + 3)
          );

          vec4 segFilter = vec4(
            int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,
            int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,
            int(getSegmentIdAtIndex(inIdx + 2)) == currentSeg ? 1 : 0,
            int(getSegmentIdAtIndex(inIdx + 3)) == currentSeg ? 1 : 0
          );

          ${d}
        }

        int inIdx = inOffset + ${u};
        if (${c===1}) {
          vec4 values = vec4(
            getValue(batch, inIdx),
            initializationValue,
            initializationValue,
            initializationValue
          );

          int inIdxSeg = int(getSegmentIdAtIndex(inIdx));

          vec4 segFilter = vec4(
            int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,
            0,
            0,
            0
          );

          ${d}
        } else if (${c===2}) {
          vec4 values = vec4(
            getValue(batch, inIdx),
            getValue(batch, inIdx + 1),
            initializationValue,
            initializationValue
          );

          vec4 segFilter = vec4(
            int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,
            int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,
              0,
              0
          );

          ${d}
        } else if (${c===3}) {
          vec4 values = vec4(
            getValue(batch, inIdx),
            getValue(batch, inIdx + 1),
            getValue(batch, inIdx + 2),
            initializationValue
          );

          vec4 segFilter = vec4(
            int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,
            int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,
            int(getSegmentIdAtIndex(inIdx + 2)) == currentSeg ? 1 : 0,
            0
          );

          ${d}
        }
        setOutput(${l});
      }
    `}};function qne(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,segmentIds:a}=t,{numSegments:o}=s,i=r.shape.length,l=[],u=0,c=$.getAxesPermutation([u],i),d=r;c!=null&&(d=wn({inputs:{x:r},backend:n,attrs:{perm:c}}),l.push(d),u=$.getInnerMostAxes(1,i)[0]);let h=$.segment_util.computeOutShape(d.shape,u,o),p=I.sizeFromShape([d.shape[u]]),f=ye({inputs:{x:d},backend:n,attrs:{shape:[-1,p]}});l.push(f);let m=Ch(r.dtype),A=(b,v,k,w,C)=>{let E=b.shape[0],M=b.shape[1],R=$.segment_util.segOpComputeOptimalWindowSize(M,C),_={windowSize:R,inSize:M,batchSize:E,numSegments:C},N=new jne(_,v),O=n.compileAndRun(N,[b,k],w);if(l.push(O),O.shape[1]===C)return O;let W=x4({backend:n,attrs:{start:0,stop:C,step:1,dtype:"float32"}}),j=w4({inputs:{x:W},backend:n,attrs:{reps:[M/R]}});return l.push(W),l.push(j),A(O,v,j,w,C)},g=A(f,"unsortedSegmentSum",a,m,o),y=ye({inputs:{x:g},backend:n,attrs:{shape:h}}),x=y;if(c!=null){l.push(y);let b=$.getUndoAxesPermutation(c);x=wn({inputs:{x},backend:n,attrs:{perm:b}})}return l.forEach(b=>n.disposeIntermediateTensorInfo(b)),x}var Xne={kernelName:Lu,backendName:"webgl",kernelFunc:qne},Kne=[vQ,IQ,iK,uK,hK,mK,gK,bK,wK,IK,NK,RK,FK,PK,UK,LK,jK,ZK,XK,eZ,nZ,rZ,lZ,mZ,gZ,kZ,SZ,EZ,$Z,VX,MZ,qZ,KZ,WZ,QZ,tY,YZ,rY,iY,cY,hY,fY,gY,kY,SY,xY,NY,_Y,FY,MY,WY,GY,XY,KY,ZY,JY,eJ,nJ,rJ,oJ,cJ,pJ,AJ,yJ,vJ,IJ,NJ,$J,WX,DJ,OZ,MJ,BJ,UJ,HX,qJ,YJ,QJ,oQ,sQ,cQ,pQ,gQ,CQ,DQ,$Q,zQ,BQ,VQ,RQ,HQ,jQ,ZQ,eee,ree,hee,KX,fee,gee,bee,kee,xZ,Cee,Nee,Ree,Fee,Mee,jX,Lee,Bee,bZ,lee,Uee,Yee,qee,YX,tte,rte,lte,dte,mte,gte,bte,kte,Ste,Nte,_te,Dte,Mte,Bte,Ute,pZ,cee,jte,Xte,Zte,Jte,ene,nne,rne,one,une,hne,fne,Ane,xne,vne,kne,Sne,uee,rK,Nne,_ne,Dne,zne,Wne,aK,Une,Gne,Xne,Tee];for(let e of Kne)Ao(e);var $n;(function(e){e[e.float32=0]="float32",e[e.int32=1]="int32",e[e.bool=2]="bool",e[e.string=3]="string",e[e.complex64=4]="complex64"})($n||($n={}));var jc;(function(e){e[e.linear=0]="linear",e[e.relu=1]="relu",e[e.relu6=2]="relu6",e[e.prelu=3]="prelu",e[e.leakyrelu=4]="leakyrelu",e[e.sigmoid=5]="sigmoid"})(jc||(jc={}));var I4;function Zne(e){I4=e.wasm.cwrap(po,null,["number","array","number","number","array","number","number","number","number","number","number","number","number"])}function Yne(e){let{inputs:t,backend:n,attrs:s}=e,{a:r,b:a,bias:o,preluActivationWeights:i}=t;if(r.dtype!=="float32"||a.dtype!=="float32")throw new Error("_FusedMatMul for non non-float32 tensors not yet supported.");let{transposeA:l,transposeB:u,activation:c,leakyreluAlpha:d}=s,h=n.dataIdMap.get(r.dataId).id,p=n.dataIdMap.get(a.dataId).id,f=0;if(o!=null){let C=n.dataIdMap.get(o.dataId);if(C.shape.length!==1)throw new Error(`_FusedMatMul only supports rank-1 bias but got rank ${C.shape.length}.`);f=C.id}let m=i==null?0:n.dataIdMap.get(i.dataId).id,A=jc[c];if(A==null)throw new Error(`${c} activation not yet supported for FusedConv2D in the wasm backend.`);let g=l?r.shape[2]:r.shape[1],y=u?a.shape[1]:a.shape[2],x=r.shape[0],b=n.makeOutput([x,g,y],r.dtype),v=n.dataIdMap.get(b.dataId).id,k=new Uint8Array(new Int32Array(r.shape).buffer),w=new Uint8Array(new Int32Array(a.shape).buffer);return I4(h,k,r.shape.length,p,w,a.shape.length,l,u,A,f,m,d||0,v),b}var Jne={kernelName:po,backendName:"wasm",setupFunc:Zne,kernelFunc:Yne};function un(e){let t;function n(r){t=r.wasm.cwrap(e,null,["number","number"])}function s(r){let{backend:a,inputs:{x:o}}=r,i=a.dataIdMap.get(o.dataId).id,l=a.makeOutput(o.shape,o.dtype),u=a.dataIdMap.get(l.dataId).id;return I.sizeFromShape(l.shape)===0||t(i,u),l}return{kernelName:e,backendName:"wasm",setupFunc:n,kernelFunc:s}}var Qne=un(di);function kn(e,t,n){let s;function r(o){s=o.wasm.cwrap(e,null,["number","array","number","number","array","number","number","number"])}function a(o){let{backend:i,inputs:l}=o,{a:u,b:c}=l,d=i.dataIdMap.get(u.dataId).id,h=i.dataIdMap.get(c.dataId).id,p=n!=null?n:u.dtype,f=$.assertAndGetBroadcastShape(u.shape,c.shape),m=i.makeOutput(f,p);if(I.sizeFromShape(f)===0)return m;let A=new Uint8Array(new Int32Array(u.shape).buffer),g=new Uint8Array(new Int32Array(c.shape).buffer),y=i.dataIdMap.get(m.dataId).id,x=()=>s(d,A,u.shape.length,h,g,c.shape.length,$n[u.dtype],y);if(t&&u.dtype==="float32")return x(),m;let b=$.getBroadcastDims(u.shape,f),v=$.getBroadcastDims(c.shape,f),k=b.every((C,E)=>C===E),w=v.every((C,E)=>C===E);if(k&&w)return x(),m;throw new Error(`Broadcasting along outer dims is not yet supported for ${u.dtype} ${e}.`)}return{kernelName:e,backendName:"wasm",setupFunc:r,kernelFunc:a}}var ese=!0,tse=kn(Dr,ese),S4;function nse(e){S4=e.wasm.cwrap(ga,null,["array","number","number","number"])}function sse(e){let{inputs:t,backend:n}=e,s=n.makeOutput(t[0].shape,t[0].dtype);if(I.sizeFromShape(s.shape)===0)return s;let r=t.map(i=>n.dataIdMap.get(i.dataId).id),a=new Uint8Array(new Int32Array(r).buffer),o=n.dataIdMap.get(s.dataId).id;return S4(a,r.length,$n[s.dtype],o),s}var rse={kernelName:ga,backendName:"wasm",setupFunc:nse,kernelFunc:sse};function kf(e){let{inputs:{x:t},backend:n}=e,s=n.makeOutput(t.shape,t.dtype),r=n.typedArrayFromHeap(t);return n.typedArrayFromHeap(s).set(r),s}var ase={kernelName:Oa,backendName:"wasm",kernelFunc:kf},C4;function ose(e){C4=e.wasm.cwrap(ho,null,["number","array","number","number","number","array","number"])}function su(e){let{inputs:t,backend:n,attrs:s}=e,[r,a]=lse(t.x.shape,s.perm),o=!0;for(let f=0;f<a.length;f++)a[f]!==f&&(o=!1);let i=ise(t.x.shape,s.perm),l={dataId:t.x.dataId,shape:r,dtype:t.x.dtype};if(o){let f=kf({inputs:t,backend:n});return f.shape=i,f}let u=n.makeOutput(i,l.dtype),c=n.dataIdMap.get(l.dataId).id,d=n.dataIdMap.get(u.dataId).id,h=new Uint8Array(new Int32Array(a).buffer),p=new Uint8Array(new Int32Array(l.shape).buffer);return C4(c,p,l.shape.length,$n[l.dtype],d,h,a.length),u}function ise(e,t){let n=new Array(e.length);for(let s=0;s<n.length;s++)n[s]=e[t[s]];return n}function lse(e,t){let n=[],s=[];for(let r=0;r<e.length;++r)e[r]!==1&&n.push(e[r]),e[t[r]]!==1&&s.push(t[r]);for(let r=0;r<s.length;++r){let a=-1;for(let o=0;o<s.length;++o)s[o]>=r&&(a===-1||s[a]>s[o])&&(a=o);s[a]=r}return[n,s]}var use={kernelName:ho,backendName:"wasm",kernelFunc:su,setupFunc:ose};function aa(e,t,n){let s=e.shape,r=e.shape.length,a=I.parseAxisParam(t,s),o=a,i=$.getAxesPermutation(o,r),l=null,u=!1;if(i!=null){let c=new Array(r);for(let p=0;p<c.length;p++)c[p]=s[i[p]];o=$.getInnerMostAxes(o.length,r),l=su({inputs:{x:e},attrs:{perm:i},backend:n});let d=n.dataIdMap.get(e.dataId).id;n.dataIdMap.get(l.dataId).id!==d&&(u=!0)}return{transposed:l,originalAxes:a,axes:o,inputWasTransposed:u}}var T4;function cse(e){T4=e.wasm.cwrap(fi,null,["number, number, number"])}function dse(e){let{backend:t,inputs:n,attrs:s}=e,{axis:r,keepDims:a}=s,{x:o}=n,l=t.dataIdMap.get(o.dataId).id,u=o,{transposed:c,axes:d,originalAxes:h,inputWasTransposed:p}=aa(o,r,t);if(p){let x=t.dataIdMap.get(c.dataId).id;u=c,l=x}let f=u.shape.length;$.assertAxesAreInnerMostDims("all",d,f);let[m,A]=$.computeOutAndReduceShapes(u.shape,d),g=I.sizeFromShape(A),y=t.makeOutput(m,o.dtype);if(I.sizeFromShape(u.shape)!==0){let x=t.dataIdMap.get(y.dataId).id;T4(l,g,x)}if(p&&t.disposeData(c.dataId),a){let x=$.expandShapeToKeepDim(y.shape,h);y.shape=x}return y}var hse={kernelName:fi,backendName:"wasm",setupFunc:cse,kernelFunc:dse},N4;function pse(e){N4=e.wasm.cwrap(mi,null,["number, number, number"])}function fse(e){let{backend:t,inputs:n,attrs:s}=e,{axis:r,keepDims:a}=s,{x:o}=n,l=t.dataIdMap.get(o.dataId).id,u=o,{transposed:c,axes:d,originalAxes:h,inputWasTransposed:p}=aa(o,r,t);if(p){let x=t.dataIdMap.get(c.dataId).id;u=c,l=x}let f=u.shape.length;$.assertAxesAreInnerMostDims("any",d,f);let[m,A]=$.computeOutAndReduceShapes(u.shape,d),g=I.sizeFromShape(A),y=t.makeOutput(m,o.dtype);if(I.sizeFromShape(u.shape)!==0){let x=t.dataIdMap.get(y.dataId).id;N4(l,g,x)}if(p&&t.disposeData(c.dataId),a){let x=$.expandShapeToKeepDim(y.shape,h);y.shape=x}return y}var mse={kernelName:mi,backendName:"wasm",setupFunc:pse,kernelFunc:fse},E4;function Ase(e){E4=e.wasm.cwrap(ya,null,["number","number","number","number","number"])}function gse(e){let{backend:t,inputs:n,attrs:s}=e,{axis:r}=s,{x:a}=n,o=t.dataIdMap.get(a.dataId).id,i=o,l=a,{transposed:u,axes:c,inputWasTransposed:d}=aa(a,r,t);if(d){let g=t.dataIdMap.get(u.dataId).id;g!==o&&(l=u,i=g)}let h=l.shape.slice(0,-1),p=t.makeOutput(h,"int32"),f=t.dataIdMap.get(p.dataId).id,m=I.sizeFromShape(p.shape),A=l.shape[c[0]];return E4(i,$n[l.dtype],m,A,f),d&&t.disposeData(u.dataId),p}var yse={kernelName:ya,backendName:"wasm",kernelFunc:gse,setupFunc:Ase},R4;function xse(e){R4=e.wasm.cwrap(xa,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function bse(e){let{inputs:t,attrs:n,backend:s}=e,r=t.x,a=s.dataIdMap.get(r.dataId).id,{filterSize:o,strides:i,pad:l,dimRoundingMode:u}=n,c=$.computePool2DInfo(r.shape,o,i,1,l,u),d=c.filterHeight,h=c.filterWidth,p=c.padInfo.top,f=c.padInfo.right,m=c.padInfo.bottom,A=c.padInfo.left,g=c.strideHeight,y=c.strideWidth,x=c.inChannels;if(c.dataFormat!=="channelsLast")throw new Error(`wasm backend does not support dataFormat:'${c.dataFormat}'. Please use 'channelsLast'.`);if(c.dilationWidth!==1||c.dilationHeight!==1)throw new Error(`was backend only supports average pooling with dilation = [1, 1], got [${c.dilationHeight}, ${c.dilationWidth}].`);let b=s.makeOutput(c.outShape,"float32"),v=s.dataIdMap.get(b.dataId).id;return R4(a,r.shape[0],r.shape[1],r.shape[2],d,h,p,f,m,A,g,y,x,v),b}var vse={kernelName:xa,backendName:"wasm",setupFunc:xse,kernelFunc:bse};function Fn(e){let{inputs:t,attrs:n}=e,{x:s}=t,{shape:r}=n,a=I.sizeFromShape(s.shape),o=I.inferFromImplicitShape(r,a);return I.assert(a===I.sizeFromShape(o),()=>`new shape: ${o}, old shape: ${s.shape}. New shape and old shape must have the same number of elements.`),e.backend.incRef(s.dataId),{dataId:s.dataId,shape:o,dtype:s.dtype}}var wse={kernelName:Yi,backendName:"wasm",kernelFunc:Fn},_4;function kse(e){_4=e.wasm.cwrap(ba,null,["number","array","number","number","array","number","number","number","number"])}function Ise(e){let{inputs:t,backend:n,attrs:s}=e,{a:r,b:a}=t,{transposeA:o,transposeB:i}=s;if(r.dtype!=="float32"||a.dtype!=="float32")throw new Error("BatchMatMul for non non-float32 tensors not yet supported.");let l=r.shape.length,u=a.shape.length,c=o?r.shape[l-2]:r.shape[l-1],d=i?a.shape[u-1]:a.shape[u-2],h=o?r.shape[l-1]:r.shape[l-2],p=i?a.shape[u-2]:a.shape[u-1],f=r.shape.slice(0,-2),m=a.shape.slice(0,-2),A=I.sizeFromShape(f),g=I.sizeFromShape(m),y=A===g||A===1||g===1;I.assert(l>=2&&u>=2&&y,()=>`Error in matMul: the input batch dimensions must either be the same or at least one input batch dimension must be 1. Got input batch dimensions of (${f}) and (${m}).`);let b=(A>g?r.shape.slice(0,-2):a.shape.slice(0,-2)).concat([h,p]);I.assert(c===d,()=>`Error in matMul: inner shapes (${c}) and (${d}) of Tensors with shapes ${r.shape} and ${a.shape} and transposeA=${o} and transposeB=${i} must match.`);let v=o?[A,c,h]:[A,h,c],k=i?[g,p,d]:[g,d,p],w=Fn({inputs:{x:r},backend:n,attrs:{shape:v}}),C=Fn({inputs:{x:a},backend:n,attrs:{shape:k}}),E=n.dataIdMap.get(w.dataId).id,M=n.dataIdMap.get(C.dataId).id,R=o?w.shape[2]:w.shape[1],_=i?C.shape[1]:C.shape[2],N=Math.max(A,g),O=n.makeOutput([N,R,_],w.dtype),W=n.dataIdMap.get(O.dataId).id,j=new Uint8Array(new Int32Array(w.shape).buffer),q=new Uint8Array(new Int32Array(C.shape).buffer);return _4(E,j,w.shape.length,M,q,C.shape.length,o,i,W),n.disposeData(w.dataId),n.disposeData(C.dataId),O.shape=b,O}var Sse={kernelName:ba,backendName:"wasm",setupFunc:kse,kernelFunc:Ise};function qc(e){let{inputs:{x:t},attrs:{begin:n,size:s},backend:r}=e,[a,o]=An.parseSliceParams(t,n,s),i=An.isSliceContinous(t.shape,a,o),l=r.readSync(t.dataId),u=r.makeOutput(o,t.dtype),c=I.computeStrides(t.shape),d=r.dataIdMap.get(u.dataId);if(i){let f=An.computeFlatOffset(a,c);return t.dtype==="string"?d.stringBytes=l.slice(f,f+I.sizeFromShape(o)):r.typedArrayFromHeap(u).set(l.subarray(f,f+I.sizeFromShape(o))),u}if(t.dtype==="string"){let f=Jp(l,a,o,t.shape,t.dtype);return d.stringBytes=f,u}let h=r.typedArrayFromHeap(u),p=t.shape.length;if(p===2)Cse(l,c[0],h,a,o);else if(p===3)Tse(l,c[0],c[1],h,a,o);else if(p===4)Nse(l,c[0],c[1],c[2],h,a,o);else{let f=Jp(l,a,o,t.shape,t.dtype);h.set(f)}return u}function Cse(e,t,n,s,r){let a=0,o=s[0],i=s[1],l=o+r[0];for(let u=o;u<l;u++){let c=u*t+i;n.set(e.subarray(c,c+r[1]),a),a+=r[1]}}function Tse(e,t,n,s,r,a){let o=0,i=r[0],l=r[1],u=r[2],c=i+a[0],d=l+a[1];for(let h=i;h<c;h++)for(let p=l;p<d;p++){let f=h*t+p*n+u;s.set(e.subarray(f,f+a[2]),o),o+=a[2]}}function Nse(e,t,n,s,r,a,o){let i=0,l=a[0],u=a[1],c=a[2],d=l+o[0],h=u+o[1],p=c+o[2],f=a[3];for(let m=l;m<d;m++)for(let A=u;A<h;A++)for(let g=c;g<p;g++){let y=m*t+A*n+g*s+f;r.set(e.subarray(y,y+o[3]),i),i+=o[3]}}var Ese={kernelName:tl,backendName:"wasm",kernelFunc:qc};function Rse(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockShape:a,crops:o}=s,i=a.reduce((g,y)=>g*y),l=$.getReshaped(r.shape,a,i),u=$.getPermuted(l.length,a.length),c=$.getReshapedPermuted(r.shape,a,i),d=$.getSliceBeginCoords(o,a.length),h=$.getSliceSize(c,o,a.length),p=Fn({inputs:{x:r},backend:n,attrs:{shape:l}}),f=su({inputs:{x:p},backend:n,attrs:{perm:u}}),m=Fn({inputs:{x:f},backend:n,attrs:{shape:c}}),A=qc({inputs:{x:m},backend:n,attrs:{begin:d,size:h}});return n.disposeData(p.dataId),n.disposeData(f.dataId),n.disposeData(p.dataId),A}var _se={kernelName:vi,backendName:"wasm",kernelFunc:Rse};function If(e){let{inputs:{x:t},attrs:{dtype:n},backend:s}=e,r=s.makeOutput(t.shape,n),a=s.typedArrayFromHeap(t);return s.typedArrayFromHeap(r).set(a),r}var $se={kernelName:va,backendName:"wasm",kernelFunc:If},Fse=un(wa),$4;function Dse(e){$4=e.wasm.cwrap(Or,null,["number","number","number","number"])}function Ose(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{clipValueMin:a,clipValueMax:o}=s,i=n.dataIdMap.get(r.dataId).id,l=n.makeOutput(r.shape,r.dtype),u=n.dataIdMap.get(l.dataId).id;return $4(i,a,o,u),l}var Pse={kernelName:Or,backendName:"wasm",setupFunc:Dse,kernelFunc:Ose};function F4(e){let{inputs:t,backend:n}=e,s=I.parseAxisParam(e.attrs.axis,t[0].shape)[0],r=$.computeOutShape(t.map(p=>p.shape),s),a=t.filter(p=>I.sizeFromShape(p.shape)>0);if(a.length===1)return kf({inputs:{x:a[0]},backend:n});let o=n.makeOutput(r,t[0].dtype);if(I.sizeFromShape(r)===0)return o;let i=a.map(p=>p.shape);if($.assertParamsConsistent(i,s),a[0].dtype==="string"){let p=a.map(x=>{let b=I.sizeFromShape(x.shape.slice(s));return Fn({inputs:{x},backend:n,attrs:{shape:[-1,b]}})}),f=p.map(x=>({vals:n.readSync(x.dataId),shape:x.shape}));r=$.computeOutShape(p.map(x=>x.shape),1);let m=p[0].shape[0]===1,A=Q2(f,r,t[0].dtype,m),g=$.computeOutShape(a.map(x=>x.shape),s);o.shape=g;let y=n.dataIdMap.get(o.dataId);return y.stringBytes=$.fromStringArrayToUint8(A),p.forEach(x=>n.disposeData(x.dataId)),o}let l=I.sizeFromShape(a[0].shape.slice(0,s)),u=0,c=a.map(p=>{let f=I.sizeFromShape(p.shape.slice(s));return u+=f,f}),d=a.map(p=>n.typedArrayFromHeap(p)),h=n.typedArrayFromHeap(o);for(let p=0;p<l;p++){let f=p*u;for(let m=0;m<d.length;m++){let A=c[m],g=p*A,y=d[m].subarray(g,g+A);h.set(y,f),f+=A}}return o}var Mse={kernelName:wi,backendName:"wasm",kernelFunc:F4},D4;function zse(e){D4=e.wasm.cwrap(ka,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Lse(e){let{inputs:t,attrs:n,backend:s}=e,{x:r,filter:a}=t,o=s.dataIdMap.get(r.dataId).id,i=s.dataIdMap.get(a.dataId).id,{strides:l,dilations:u,pad:c,dimRoundingMode:d,dataFormat:h}=n,p=$.convertConv2DDataFormat(h),f=$.computeConv2DInfo(r.shape,a.shape,l,u,c,d,!1,p),m=f.filterHeight,A=f.filterWidth,g=f.padInfo.top,y=f.padInfo.right,x=f.padInfo.bottom,b=f.padInfo.left,v=f.dilationHeight,k=f.dilationWidth,w=f.strideHeight,C=f.strideWidth,E=f.inChannels,M=f.outChannels,R=f.padInfo.type==="SAME"?1:0;if(f.dataFormat!=="channelsLast")throw new Error(`wasm backend Conv2D does not support dataFormat:'${f.dataFormat}'. Please use 'channelsLast'.`);let _=s.makeOutput(f.outShape,"float32"),N=s.dataIdMap.get(_.dataId).id;return D4(o,r.shape[0],r.shape[1],r.shape[2],i,m,A,g,y,x,b,R,v,k,w,C,E,M,N),_}var Bse={kernelName:ka,backendName:"wasm",setupFunc:zse,kernelFunc:Lse},O4;function Wse(e){O4=e.wasm.cwrap(Ia,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Vse(e){let{backend:t,inputs:n,attrs:s}=e,{dy:r,filter:a}=n,{strides:o,pad:i,dataFormat:l,dimRoundingMode:u,inputShape:c}=s,d=1,h=$.convertConv2DDataFormat(l),p=$.computeConv2DInfo(c,a.shape,o,d,i,u,!1,h),{batchSize:f,filterHeight:m,filterWidth:A,inChannels:g,inHeight:y,inWidth:x,outChannels:b,outHeight:v,outWidth:k,strideHeight:w,strideWidth:C}=p,E=m-1-p.padInfo.top,M=A-1-p.padInfo.left,R=p.dataFormat==="channelsLast",_=I.computeStrides(p.inShape),N=I.computeStrides(r.shape),[O,W,j]=I.computeStrides(a.shape),q=_[0],X=R?_[1]:_[2],Q=R?_[2]:1,ne=R?1:_[1],te=N[0],se=R?N[1]:N[2],J=R?N[2]:1,ie=R?1:N[1],le=t.makeOutput(p.inShape,"float32"),he=t.dataIdMap.get(le.dataId).id,ge=t.dataIdMap.get(r.dataId).id,Ce=t.dataIdMap.get(a.dataId).id;return O4(ge,Ce,f,m,A,y,x,g,v,k,b,w,C,E,M,O,W,j,q,X,Q,ne,te,se,J,ie,he),le}var Use={kernelName:Ia,backendName:"wasm",setupFunc:Wse,kernelFunc:Vse},Hse=un(Sa),Gse=un(Ca),R1;(function(e){e[e.bilinear=0]="bilinear",e[e.nearest=1]="nearest"})(R1||(R1={}));var P4;function jse(e){P4=e.wasm.cwrap(ki,null,["number","number","number","number","array","number","number","number","number","number"])}function qse(e){let{backend:t,inputs:n,attrs:s}=e,{method:r,extrapolationValue:a,cropSize:o}=s,{image:i,boxes:l,boxInd:u}=n,c=l.shape[0],[d,h]=o,p=[c,d,h,i.shape[3]],f=t.dataIdMap.get(i.dataId),m;i.dtype!=="float32"&&(m=If({backend:t,inputs:{x:i},attrs:{dtype:"float32"}}),f=t.dataIdMap.get(m.dataId));let A=f.id,g=t.dataIdMap.get(l.dataId).id,y=t.dataIdMap.get(u.dataId).id,x=t.makeOutput(p,"float32"),b=t.dataIdMap.get(x.dataId).id,v=new Uint8Array(new Int32Array(i.shape).buffer);return P4(A,g,y,c,v,d,h,R1[r],a,b),m!=null&&t.disposeData(m.dataId),x}var Xse={kernelName:ki,backendName:"wasm",setupFunc:jse,kernelFunc:qse},M4;function Kse(e){M4=e.wasm.cwrap(Ta,null,["number","number","number","number","number","number"])}function Zse(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,exclusive:o,reverse:i}=s,l=r.shape.length;I.assert(r.dtype==="float32"||r.dtype==="int32",()=>`cumsum does not support ${r.dtype} tensors in the WASM backend`);let u=$.getAxesPermutation([a],l),c=r;u!==null&&(c=su({inputs:{x:r},attrs:{perm:u},backend:n}));let d=$.getInnerMostAxes(1,l)[0];$.assertAxesAreInnerMostDims("cumsum",[d],l);let h=n.makeOutput(c.shape,c.dtype),p=c.shape[d],f=n.dataIdMap.get(c.dataId).id,m=n.dataIdMap.get(h.dataId).id;M4(f,o?1:0,i?1:0,p,m,$n[r.dtype]);let A=h;if(u!==null){let g=$.getUndoAxesPermutation(u);A=su({inputs:{x:h},attrs:{perm:g},backend:n}),n.disposeData(c.dataId),n.disposeData(h.dataId)}return A}var Yse={kernelName:Ta,backendName:"wasm",setupFunc:Kse,kernelFunc:Zse},z4;function Jse(e){z4=e.wasm.cwrap(Ii,null,["number","number","number","array","number","array","array","number","number"])}function Qse(e){let{backend:t,inputs:n,attrs:s}=e,{x:r}=n,{blockSize:a,dataFormat:o}=s;I.assert(a>1,()=>`blockSize should be > 1 for depthToSpace, but was: ${a}`);let i=r.shape[0],l=o==="NHWC"?r.shape[1]:r.shape[2],u=o==="NHWC"?r.shape[2]:r.shape[3],c=o==="NHWC"?r.shape[3]:r.shape[1],d=l*a,h=u*a,p=c/(a*a),f=o==="NHWC"?[i,d,h,p]:[i,p,d,h],m=t.makeOutput(f,"float32"),g=t.dataIdMap.get(r.dataId).id,y=new Uint8Array(new Int32Array(I.computeStrides(r.shape)).buffer),x=new Uint8Array(new Int32Array(f).buffer),b=new Uint8Array(new Int32Array(I.computeStrides(f)).buffer),v=t.dataIdMap.get(m.dataId).id;return z4(g,a,o==="NHWC"?1:0,y,r.shape.length-1,x,b,f.length,v),m}var ere={kernelName:Ii,backendName:"wasm",setupFunc:Jse,kernelFunc:Qse},L4;function tre(e){L4=e.wasm.cwrap(Na,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function nre(e){let{inputs:t,attrs:n,backend:s}=e,{x:r,filter:a}=t,o=s.dataIdMap.get(r.dataId).id,i=s.dataIdMap.get(a.dataId).id,{strides:l,dilations:u,pad:c,dimRoundingMode:d}=n,h=u==null?[1,1]:u,p=$.computeConv2DInfo(r.shape,a.shape,l,h,c,d,!0),f=p.filterHeight,m=p.filterWidth,A=p.padInfo.top,g=p.padInfo.right,y=p.padInfo.bottom,x=p.padInfo.left,b=p.dilationHeight,v=p.dilationWidth,k=p.strideHeight,w=p.strideWidth,C=p.inChannels,E=p.outChannels,M=p.padInfo.type==="SAME"?1:0;if(p.dataFormat!=="channelsLast")throw new Error(`wasm backend DepthwiseConv2dNative does not support dataFormat:'${p.dataFormat}'. Please use 'channelsLast'.`);let R=s.makeOutput(p.outShape,"float32"),_=s.dataIdMap.get(R.dataId).id;return L4(o,r.shape[0],r.shape[1],r.shape[2],i,f,m,A,g,y,x,M,b,v,k,w,C,E,_),R}var sre={kernelName:Na,backendName:"wasm",setupFunc:tre,kernelFunc:nre},rre=!1,are=kn(Ti,rre,"bool"),ore=un(Ra);function _1(e){let{inputs:t,attrs:n,backend:s}=e,{input:r}=t,{dim:a}=n,o=r.shape.length,i=r.shape.slice(),l=a;return a<0&&(I.assert(-(o+1)<=a,()=>`Axis must be in the interval [${-(o+1)}, ${o}]`),l=o+a+1),i.splice(l,0,1),Fn({inputs:{x:r},backend:s,attrs:{shape:i}})}var ire={kernelName:Ni,backendName:"wasm",kernelFunc:_1};function lre(e){let{attrs:{shape:t,value:n,dtype:s},backend:r}=e,a=r.makeOutput(t,s);return r.typedArrayFromHeap(a).fill(n),a}var ure={kernelName:_u,backendName:"wasm",kernelFunc:lre},B4;function cre(e){B4=e.wasm.cwrap(Ri,null,["number","number","number","number","number","number"])}function dre(e){let{inputs:t,backend:n}=e,{image:s}=t,r=n.makeOutput(s.shape,s.dtype),a=n.dataIdMap.get(s.dataId).id,o=n.dataIdMap.get(r.dataId).id,[i,l,u,c]=s.shape;return B4(a,i,l,u,c,o),r}var hre={kernelName:Ri,backendName:"wasm",kernelFunc:dre,setupFunc:cre},pre=un(_a),fre=!1,mre=kn($a,fre),W4;function Are(e){W4=e.wasm.cwrap(Fa,null,["number","number","number","number","number","number","number"])}function gre(e){let{backend:t,inputs:n,attrs:s}=e,{varianceEpsilon:r}=s,{x:a,mean:o,variance:i,offset:l,scale:u}=n,c=t.dataIdMap.get(a.dataId).id,d=t.dataIdMap.get(o.dataId).id,h=t.dataIdMap.get(i.dataId).id,p=l!=null?t.dataIdMap.get(l.dataId).id:0,f=u!=null?t.dataIdMap.get(u.dataId).id:0,m=t.makeOutput(a.shape,a.dtype);if(I.sizeFromShape(a.shape)===0)return m;let A=t.dataIdMap.get(m.dataId).id;return W4(c,d,h,p,f,r,A),m}var yre={kernelName:Fa,backendName:"wasm",setupFunc:Are,kernelFunc:gre},V4;function xre(e){V4=e.wasm.cwrap(fo,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function bre(e){let{inputs:t,attrs:n,backend:s}=e,{x:r,filter:a,bias:o,preluActivationWeights:i}=t,{strides:l,pad:u,dilations:c,dataFormat:d,dimRoundingMode:h,activation:p,leakyreluAlpha:f}=n,m=$.computeConv2DInfo(r.shape,a.shape,l,c,u,h),A=jc[p];if(A==null)throw new Error(`${p} activation not yet supported for FusedConv2D in the wasm backend.`);let g=s.dataIdMap.get(r.dataId).id,y=s.dataIdMap.get(a.dataId).id,x=m.outChannels,b=0;if(o!=null){let J=s.dataIdMap.get(o.dataId);if(J.shape.length!==1)throw new Error(`FusedConv2D only supports rank-1 bias but got rank ${J.shape.length}.`);if(J.shape[0]!==x)throw new Error(`FusedConv2D bias shape (${J.shape}) does not match the number of output channels (${x})`);b=J.id}let v=m.filterHeight,k=m.filterWidth,w=m.padInfo.top,C=m.padInfo.right,E=m.padInfo.bottom,M=m.padInfo.left,R=m.dilationHeight,_=m.dilationWidth,N=m.strideHeight,O=m.strideWidth,W=m.inChannels,j=m.padInfo.type==="SAME"?1:0,q=m.batchSize,X=m.inHeight,Q=m.inWidth;if(d!=="NHWC")throw new Error(`wasm backend FusedConv2D does not support dataFormat:'${d}'. Please use 'NHWC'.`);let ne=s.makeOutput(m.outShape,"float32"),te=s.dataIdMap.get(ne.dataId).id,se=i==null?0:s.dataIdMap.get(i.dataId).id;return V4(g,q,X,Q,y,v,k,b,w,C,E,M,j,R,_,N,O,W,x,A,se,f||0,te),ne}var vre={kernelName:fo,backendName:"wasm",setupFunc:xre,kernelFunc:bre},U4;function wre(e){U4=e.wasm.cwrap(mo,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function kre(e){let{inputs:t,attrs:n,backend:s}=e,{x:r,filter:a,bias:o,preluActivationWeights:i}=t,{strides:l,pad:u,dilations:c,dataFormat:d,dimRoundingMode:h,activation:p,leakyreluAlpha:f}=n,m=$.computeConv2DInfo(r.shape,a.shape,l,c,u,h,!0),A=jc[p];if(A==null)throw new Error(`${p} activation not yet supported for FusedDepthwiseConv2D in the wasm backend.`);let g=s.dataIdMap.get(r.dataId).id,y=s.dataIdMap.get(a.dataId).id,x=m.outChannels,b=0;if(o!=null){let J=s.dataIdMap.get(o.dataId);if(J.shape.length!==1)throw new Error(`FusedDepthwiseConv2D only supports rank-1 bias but got rank ${J.shape.length}.`);if(J.shape[0]!==x)throw new Error(`FusedDepthwiseConv2D bias shape (${J.shape}) does not match the number of output channels (${x})`);b=J.id}let v=m.filterHeight,k=m.filterWidth,w=m.padInfo.top,C=m.padInfo.right,E=m.padInfo.bottom,M=m.padInfo.left,R=m.dilationHeight,_=m.dilationWidth,N=m.strideHeight,O=m.strideWidth,W=m.inChannels,j=m.padInfo.type==="SAME"?1:0,q=m.batchSize,X=m.inHeight,Q=m.inWidth;if(d!=="NHWC")throw new Error(`wasm backend FusedDepthwiseConv2D does not support dataFormat:'${d}'. Please use 'NHWC'.`);let ne=s.makeOutput(m.outShape,"float32"),te=s.dataIdMap.get(ne.dataId).id,se=i==null?0:s.dataIdMap.get(i.dataId).id;return U4(g,q,X,Q,y,v,k,b,w,C,E,M,j,R,_,N,O,W,x,A,se,f||0,te),ne}var Ire={kernelName:mo,backendName:"wasm",setupFunc:wre,kernelFunc:kre},H4;function Sre(e){H4=e.wasm.cwrap($i,null,["number","number","number","number","number","number","array","number"])}function Cre(e){let{backend:t,inputs:n}=e,{params:s,indices:r}=n,[a,o,i,l]=Zm.prepareAndValidate(s,r),u=t.makeOutput(a,s.dtype);if(o===0)return u;let c=r.shape,d=c[c.length-1],p=t.dataIdMap.get(s.dataId).id,m=t.dataIdMap.get(r.dataId).id,A=new Uint8Array(new Int32Array(l).buffer),g=t.dataIdMap.get(u.dataId).id;return H4(p,$n[s.dtype],m,o,d,i,A,g),u}var Tre={kernelName:$i,backendName:"wasm",setupFunc:Sre,kernelFunc:Cre},G4;function Nre(e){G4=e.wasm.cwrap("Gather",null,["number","number","array","number","number","number","array","number"])}function Ere(e){let{backend:t,inputs:n,attrs:s}=e,{x:r,indices:a}=n,{axis:o,batchDims:i}=s,l=I.parseAxisParam(o,r.shape)[0],u=$.segment_util.collectGatherOpShapeInfo(r,a,l,i),c=Fn({inputs:{x:r},attrs:{shape:[u.batchSize,u.outerSize,u.dimSize,u.sliceSize]},backend:t}),d=I.sizeFromShape(a.shape),h=Fn({inputs:{x:a},attrs:{shape:[u.batchSize,d/u.batchSize]},backend:t}),p=[u.batchSize,u.outerSize,d/u.batchSize,u.sliceSize],f=t.makeOutput(p,r.dtype);if(I.sizeFromShape(r.shape)===0)return f;let m=c.shape.length-1,g=t.dataIdMap.get(c.dataId).id,x=t.dataIdMap.get(h.dataId).id,b=t.dataIdMap.get(f.dataId).id,v=new Uint8Array(new Int32Array(I.computeStrides(c.shape)).buffer),k=new Uint8Array(new Int32Array(I.computeStrides(p)).buffer);return G4(g,$n[r.dtype],v,m,x,u.batchSize,k,b),t.disposeData(c.dataId),t.disposeData(h.dataId),f.shape=u.outputShape,f}var Rre={kernelName:_i,backendName:"wasm",setupFunc:Nre,kernelFunc:Ere},_re=!1,$re=kn(Fi,_re,"bool"),Fre=!1,Dre=kn(Da,Fre,"bool"),j4;function Ore(e){j4=e.wasm.cwrap(Pa,null,["number","number","number"])}function Pre(e){let{inputs:{x:t},attrs:{alpha:n},backend:s}=e,r=s.dataIdMap.get(t.dataId).id,a=s.makeOutput(t.shape,t.dtype);if(I.sizeFromShape(t.shape)!==0){let o=s.dataIdMap.get(a.dataId).id;j4(r,n,o)}return a}var Mre={kernelName:Pa,backendName:"wasm",setupFunc:Ore,kernelFunc:Pre},zre=!1,Lre=kn(Mi,zre,"bool"),Bre=!1,Wre=kn(zi,Bre,"bool"),Vre=un(Ma),Ure=!1,Hre=kn(Bi,Ure,"bool"),q4;function Gre(e){q4=e.wasm.cwrap(za,null,["number, number, number"])}function jre(e){let{backend:t,inputs:n,attrs:s}=e,{reductionIndices:r,keepDims:a}=s,{x:o}=n,l=t.dataIdMap.get(o.dataId).id,u=o,{transposed:c,axes:d,originalAxes:h,inputWasTransposed:p}=aa(o,r,t);if(p){let x=t.dataIdMap.get(c.dataId).id;u=c,l=x}let f=u.shape.length;$.assertAxesAreInnerMostDims("max",d,f);let[m,A]=$.computeOutAndReduceShapes(u.shape,d),g=I.sizeFromShape(A),y=t.makeOutput(m,o.dtype);if(I.sizeFromShape(u.shape)!==0){let x=t.dataIdMap.get(y.dataId).id;q4(l,g,x)}if(p&&t.disposeData(c.dataId),a){let x=$.expandShapeToKeepDim(y.shape,h);y.shape=x}return y}var qre={kernelName:za,backendName:"wasm",setupFunc:Gre,kernelFunc:jre},Xre=!1,Kre=kn(La,Xre),X4;function Zre(e){X4=e.wasm.cwrap(Ba,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Yre(e){let{inputs:t,attrs:n,backend:s}=e,r=t.x,a=s.dataIdMap.get(r.dataId).id,{filterSize:o,strides:i,pad:l,dimRoundingMode:u}=n,c=$.computePool2DInfo(r.shape,o,i,1,l,u),d=c.filterHeight,h=c.filterWidth,p=c.padInfo.top,f=c.padInfo.right,m=c.padInfo.bottom,A=c.padInfo.left,g=c.dilationHeight,y=c.dilationWidth,x=c.strideHeight,b=c.strideWidth,v=c.inChannels,k=c.outChannels;if(c.dataFormat!=="channelsLast")throw new Error(`wasm backend does not support dataFormat:'${c.dataFormat}'. Please use 'channelsLast'.`);let w=s.makeOutput(c.outShape,"float32"),C=s.dataIdMap.get(w.dataId).id;return X4(a,r.shape[0],r.shape[1],r.shape[2],d,h,p,f,m,A,g,y,x,b,v,k,C),w}var Jre={kernelName:Ba,backendName:"wasm",setupFunc:Zre,kernelFunc:Yre},K4;function Qre(e){K4=e.wasm.cwrap(Wa,null,["number, number, number"])}function eae(e){let{backend:t,inputs:n,attrs:s}=e,{axis:r,keepDims:a}=s,{x:o}=n,i=t.dataIdMap.get(o.dataId).id,l=i,u=o,{transposed:c,axes:d,originalAxes:h,inputWasTransposed:p}=aa(o,r,t),f=d;if(p){let b=t.dataIdMap.get(c.dataId).id;b!==i&&(u=c,l=b,f=$.getInnerMostAxes(f.length,u.shape.length))}$.assertAxesAreInnerMostDims("mean",f,u.shape.length);let[m,A]=$.computeOutAndReduceShapes(u.shape,f),g=I.sizeFromShape(A),y=u;u.dtype!=="float32"&&(y=If({backend:t,inputs:{x:u},attrs:{dtype:"float32"}}),l=t.dataIdMap.get(y.dataId).id);let x=t.makeOutput(m,"float32");if(I.sizeFromShape(u.shape)!==0){let b=t.dataIdMap.get(x.dataId).id;K4(l,g,b)}if(p&&t.disposeData(c.dataId),a){let b=$.expandShapeToKeepDim(x.shape,h);x.shape=b}return u.dtype!=="float32"&&t.disposeData(y.dataId),x}var tae={kernelName:Wa,backendName:"wasm",setupFunc:Qre,kernelFunc:eae},Z4;function nae(e){Z4=e.wasm.cwrap(Va,null,["number, number, number"])}function sae(e){let{backend:t,inputs:n,attrs:s}=e,{axis:r,keepDims:a}=s,{x:o}=n,i=t.dataIdMap.get(o.dataId).id,l=i,u=o,{transposed:c,axes:d,originalAxes:h,inputWasTransposed:p}=aa(o,r,t);if(p){let x=t.dataIdMap.get(c.dataId).id;x!==i&&(u=c,l=x)}let f=u.shape.length;$.assertAxesAreInnerMostDims("min",d,f);let[m,A]=$.computeOutAndReduceShapes(u.shape,d),g=I.sizeFromShape(A),y=t.makeOutput(m,u.dtype);if(I.sizeFromShape(u.shape)!==0){let x=t.dataIdMap.get(y.dataId).id;Z4(l,g,x)}if(p&&t.disposeData(c.dataId),a){let x=$.expandShapeToKeepDim(y.shape,h);y.shape=x}return y}var rae={kernelName:Va,backendName:"wasm",setupFunc:nae,kernelFunc:sae},aae=!1,oae=kn(Ua,aae),$1;(function(e){e[e.reflect=0]="reflect",e[e.symmetric=1]="symmetric"})($1||($1={}));var Y4;function iae(e){Y4=e.wasm.cwrap(Ha,null,["number","array","number","number","array","array","number","number"])}function lae(e){let{inputs:{x:t},backend:n,attrs:{paddings:s,mode:r}}=e,a=s.map((f,m)=>f[0]+t.shape[m]+f[1]),o=n.dataIdMap.get(t.dataId).id,i=n.makeOutput(a,t.dtype),l=n.dataIdMap.get(i.dataId).id,u=new Uint8Array(new Int32Array(t.shape).buffer),c=s.map(f=>f[0]),d=s.map(f=>f[1]),h=new Uint8Array(new Int32Array(c).buffer),p=new Uint8Array(new Int32Array(d).buffer);return Y4(o,u,t.shape.length,$n[t.dtype],h,p,$1[r],l),i}var uae={kernelName:Ha,backendName:"wasm",kernelFunc:lae,setupFunc:iae},cae=!0,dae=kn(Ga,cae),hae=un(Vi);function F1(e,t){let n=new Int32Array(e.wasm.HEAPU8.buffer,t,4),s=n[0],r=n[1],a=n[2],o=n[3];return e.wasm._free(t),{pSelectedIndices:s,selectedSize:r,pSelectedScores:a,pValidOutputs:o}}var J4;function pae(e){J4=e.wasm.cwrap(Hi,"number",["number","number","number","number","number"])}function fae(e){let{backend:t,inputs:n,attrs:s}=e,{iouThreshold:r,maxOutputSize:a,scoreThreshold:o}=s,{boxes:i,scores:l}=n,u=t.dataIdMap.get(i.dataId).id,c=t.dataIdMap.get(l.dataId).id,d=J4(u,c,a,r,o),{pSelectedIndices:h,selectedSize:p,pSelectedScores:f,pValidOutputs:m}=F1(t,d);return t.wasm._free(f),t.wasm._free(m),t.makeOutput([p],"int32",h)}var mae={kernelName:Hi,backendName:"wasm",setupFunc:pae,kernelFunc:fae},Q4;function Aae(e){Q4=e.wasm.cwrap(Gi,"number",["number","number","number","number","number","bool"])}function gae(e){let{backend:t,inputs:n,attrs:s}=e,{iouThreshold:r,maxOutputSize:a,scoreThreshold:o,padToMaxOutputSize:i}=s,{boxes:l,scores:u}=n,c=t.dataIdMap.get(l.dataId).id,d=t.dataIdMap.get(u.dataId).id,h=Q4(c,d,a,r,o,i),{pSelectedIndices:p,selectedSize:f,pSelectedScores:m,pValidOutputs:A}=F1(t,h);t.wasm._free(m);let g=t.makeOutput([f],"int32",p),y=t.makeOutput([],"int32",A);return[g,y]}var yae={kernelName:Gi,backendName:"wasm",setupFunc:Aae,kernelFunc:gae},ek;function xae(e){ek=e.wasm.cwrap(ji,"number",["number","number","number","number","number","number"])}function bae(e){let{backend:t,inputs:n,attrs:s}=e,{iouThreshold:r,maxOutputSize:a,scoreThreshold:o,softNmsSigma:i}=s,{boxes:l,scores:u}=n,c=t.dataIdMap.get(l.dataId).id,d=t.dataIdMap.get(u.dataId).id,h=ek(c,d,a,r,o,i),{pSelectedIndices:p,selectedSize:f,pSelectedScores:m,pValidOutputs:A}=F1(t,h);t.wasm._free(A);let g=t.makeOutput([f],"int32",p),y=t.makeOutput([f],"float32",m);return[g,y]}var vae={kernelName:ji,backendName:"wasm",setupFunc:xae,kernelFunc:bae},wae=!1,kae=kn(Ui,wae,"bool"),tk;function Iae(e){tk=e.wasm.cwrap(ja,null,["number","number","number","number","number"])}function Sae(e){let{inputs:t,backend:n,attrs:s}=e,{indices:r}=t,{depth:a,onValue:o,offValue:i}=s,l=n.makeOutput([...r.shape,a],"int32"),u=n.dataIdMap.get(l.dataId).id,d=n.dataIdMap.get(r.dataId).id;return tk(d,a,o,i,u),l}var Cae={kernelName:ja,backendName:"wasm",setupFunc:Iae,kernelFunc:Sae};function Tae(e){let{inputs:{x:t},backend:n}=e,s=n.makeOutput(t.shape,t.dtype);return n.typedArrayFromHeap(s).fill(1),s}var Nae={kernelName:qi,backendName:"wasm",kernelFunc:Tae};function Eae(e){let{inputs:t,backend:n,attrs:s}=e,{axis:r}=s;if(t.length===1)return _1({inputs:{input:t[0]},backend:n,attrs:{dim:r}});let a=t[0].shape,o=t[0].dtype;t.forEach(c=>{I.assertShapesMatch(a,c.shape,"All tensors passed to stack must have matching shapes"),I.assert(o===c.dtype,()=>"All tensors passed to stack must have matching dtypes")});let i=[],l=t.map(c=>{let d=_1({inputs:{input:c},backend:n,attrs:{dim:r}});return i.push(d),d}),u=F4({inputs:l,backend:n,attrs:{axis:r}});return i.forEach(c=>n.disposeData(c.dataId)),u}var Rae={kernelName:Xi,backendName:"wasm",kernelFunc:Eae},nk;function _ae(e){nk=e.wasm.cwrap(qa,null,["number","array","number","number","array","array","number","number"])}function $ae(e){let{inputs:{x:t},backend:n,attrs:{paddings:s,constantValue:r}}=e,a=s.map((f,m)=>f[0]+t.shape[m]+f[1]),o=n.dataIdMap.get(t.dataId).id,i=n.makeOutput(a,t.dtype),l=n.dataIdMap.get(i.dataId).id,u=new Uint8Array(new Int32Array(t.shape).buffer),c=s.map(f=>f[0]),d=s.map(f=>f[1]),h=new Uint8Array(new Int32Array(c).buffer),p=new Uint8Array(new Int32Array(d).buffer);return nk(o,u,t.shape.length,$n[t.dtype],h,p,r,l),i}var sk={kernelName:qa,backendName:"wasm",kernelFunc:$ae,setupFunc:_ae},Fae=!1,Dae=kn(Xa,Fae),rk;function Oae(e){rk=e.wasm.cwrap(Ka,null,["number","number","number"])}function Pae(e){let{inputs:t,backend:n}=e,{x:s,alpha:r}=t,a=n.dataIdMap.get(s.dataId).id,o=n.dataIdMap.get(r.dataId).id,i=n.makeOutput(s.shape,"float32"),l=n.dataIdMap.get(i.dataId).id;return rk(a,o,l),i}var Mae={kernelName:Ka,backendName:"wasm",setupFunc:Oae,kernelFunc:Pae},ak;function zae(e){ak=e.wasm.cwrap(Ki,null,["number","number","number","number"])}function Lae(e){let{backend:t,inputs:n,attrs:s}=e,{axis:r,keepDims:a}=s,{x:o}=n,i=t.dataIdMap.get(o.dataId).id,l=i,u=o,{transposed:c,axes:d,originalAxes:h,inputWasTransposed:p}=aa(o,r,t),f=d;if(p){let x=t.dataIdMap.get(c.dataId).id;x!==i&&(u=c,l=x,f=$.getInnerMostAxes(f.length,u.shape.length))}$.assertAxesAreInnerMostDims("prod",f,u.shape.length);let[m,A]=$.computeOutAndReduceShapes(u.shape,f),g=I.sizeFromShape(A),y=t.makeOutput(m,u.dtype);if(I.sizeFromShape(u.shape)!==0){let x=t.dataIdMap.get(y.dataId).id;ak(l,g,$n[y.dtype],x)}if(p&&t.disposeData(c.dataId),a){let x=$.expandShapeToKeepDim(y.shape,h);y.shape=x}return y}var Bae={kernelName:Ki,backendName:"wasm",setupFunc:zae,kernelFunc:Lae},Wae=e=>{let{backend:t,attrs:n}=e,{start:s,stop:r,step:a,dtype:o}=n,i=n1(s,r,a,o),l=t.makeOutput([i.length],o);return t.typedArrayFromHeap(l).set(i),l},Vae={kernelName:Pu,backendName:"wasm",kernelFunc:Wae},Uae=!0,Hae=kn(Ea,Uae),Gae=un(Za),jae=un(Ja),ok;function qae(e){ok=e.wasm.cwrap(Ya,null,["number","number","number","number","number","number","number","number","number","number"])}function Xae(e){let{backend:t,inputs:n,attrs:s}=e,{images:r}=n,{alignCorners:a,halfPixelCenters:o,size:i}=s,[l,u]=i,[c,d,h,p]=r.shape,f=[c,l,u,p],m=t.dataIdMap.get(r.dataId),A;m.dtype!=="float32"&&(A=If({backend:t,inputs:{x:r},attrs:{dtype:"float32"}}),m=t.dataIdMap.get(A.dataId));let g=m.id,y=t.makeOutput(f,"float32");if(I.sizeFromShape(r.shape)===0)return y;let x=t.dataIdMap.get(y.dataId).id;return ok(g,c,d,h,p,l,u,a?1:0,o?1:0,x),A!=null&&t.disposeData(A.dataId),y}var Kae={kernelName:Ya,backendName:"wasm",setupFunc:qae,kernelFunc:Xae},ik;function Zae(e){ik=e.wasm.cwrap(Qa,null,["number","array","number","array","number","number"])}function Yae(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{dims:a}=s,o=I.parseAxisParam(a,r.shape);if(r.shape.length===0)return kf({inputs:{x:r},backend:n});let i=n.makeOutput(r.shape,r.dtype),l=n.dataIdMap.get(r.dataId).id,u=n.dataIdMap.get(i.dataId).id,c=new Uint8Array(new Int32Array(o).buffer),d=new Uint8Array(new Int32Array(r.shape).buffer);ik(l,c,o.length,d,r.shape.length,u);let h=Fn({inputs:{x:i},attrs:{shape:r.shape},backend:n});return n.disposeData(i.dataId),h}var Jae={kernelName:Qa,backendName:"wasm",kernelFunc:Yae,setupFunc:Zae},lk;function Qae(e){lk=e.wasm.cwrap(hl,null,["number","number","number","number","number","number","number","number","array","number","number"])}function eoe(e){let{inputs:t,backend:n,attrs:s}=e,{image:r}=t,{radians:a,fillValue:o,center:i}=s,l=n.makeOutput(r.shape,r.dtype),u=n.dataIdMap.get(r.dataId).id,c=n.dataIdMap.get(l.dataId).id,[d,h,p,f]=r.shape,[m,A]=$.getImageCenter(i,h,p),g=o===0,y=255,x=typeof o=="number"?[o,o,o,g?0:y]:[...o,y],b=new Uint8Array(new Int32Array(x).buffer);return lk(u,d,h,p,f,a,m,A,b,x.length,c),l}var toe={kernelName:hl,backendName:"wasm",kernelFunc:eoe,setupFunc:Qae},noe=un(eo),soe=un(to),uk;function roe(e){uk=e.wasm.cwrap(Ji,null,["number","number","number","number","number","number","array","number","number"])}function aoe(e){let{backend:t,inputs:n,attrs:s}=e,{indices:r,updates:a}=n,{shape:o}=s,i=t.makeOutput(o,a.dtype);if(I.sizeFromShape(o)===0)return i;let{sliceRank:l,numUpdates:u,sliceSize:c,strides:d,outputSize:h}=Ym.calculateShapes(a,r,o),f=t.dataIdMap.get(r.dataId).id,A=t.dataIdMap.get(a.dataId).id,g=new Uint8Array(new Int32Array(d).buffer),y=t.dataIdMap.get(i.dataId).id;return uk(f,A,$n[a.dtype],l,u,c,g,h,y),i}var ooe={kernelName:Ji,backendName:"wasm",setupFunc:roe,kernelFunc:aoe},ck;function ioe(e){ck=e.wasm.cwrap("SelectV2",null,["number","number","number","number","number"])}function loe(e){let{inputs:t,backend:n}=e,{condition:s,t:r,e:a}=t,o=n.dataIdMap.get(s.dataId).id,i=n.dataIdMap.get(r.dataId).id,l=n.dataIdMap.get(a.dataId).id,u=n.makeOutput(r.shape,r.dtype),c=n.dataIdMap.get(u.dataId).id,d=s.shape.length,h=r.shape.length,p=d===0||d>1||h===1?1:I.sizeFromShape(r.shape.slice(1));return ck(o,i,l,p,c),u}var uoe={kernelName:Qi,backendName:"wasm",kernelFunc:loe,setupFunc:ioe},dk;function coe(e){dk=e.wasm.cwrap(so,null,["number","number"])}function doe(e){let{backend:t,inputs:{x:n}}=e,s=t.dataIdMap.get(n.dataId).id,r=t.makeOutput(n.shape,n.dtype),a=t.dataIdMap.get(r.dataId).id;return I.sizeFromShape(r.shape)===0||dk(s,a),r}var hoe={kernelName:"Sigmoid",backendName:"wasm",setupFunc:coe,kernelFunc:doe},poe=un(no),hk;function foe(e){hk=e.wasm.cwrap(oo,null,["number","number","number","number"])}function moe(e){let{backend:t,inputs:{logits:n},attrs:{dim:s}}=e,r=t.dataIdMap.get(n.dataId).id,a=t.makeOutput(n.shape,n.dtype),o=t.dataIdMap.get(a.dataId).id,i=n.shape[s],l=I.sizeFromShape(n.shape)/i;return I.sizeFromShape(a.shape)===0||hk(r,o,i,l),a}var Aoe={kernelName:oo,backendName:"wasm",setupFunc:foe,kernelFunc:moe};function goe(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockShape:a,paddings:o}=s,i=I.sizeFromShape(a),l=[[0,0]];l.push(...o);for(let k=1+a.length;k<r.shape.length;++k)l.push([0,0]);let u=sk.kernelFunc({inputs:{x:r},backend:n,attrs:{paddings:l,constantValue:0}}),c=$.getReshaped(u.shape,a,i,!1),d=$.getPermuted(c.length,a.length,!1),h=$.getReshapedPermuted(u.shape,a,i,!1),m=Fn({inputs:{x:u},backend:n,attrs:{shape:c}}),y=su({inputs:{x:m},backend:n,attrs:{perm:d}}),v=Fn({inputs:{x:y},backend:n,attrs:{shape:h}});return n.disposeData(u.dataId),n.disposeData(m.dataId),n.disposeData(y.dataId),v}var yoe={kernelName:al,backendName:"wasm",kernelFunc:goe};function xoe(e){let{inputs:t,attrs:n,backend:s}=e,{x:r}=t,{numOrSizeSplits:a,axis:o}=n,i=I.parseAxisParam(o,r.shape)[0],l=$.prepareSplitSize(r,a,i),u=new Array(r.shape.length).fill(0),c=r.shape.slice();return l.map(d=>{let h=[...c];h[i]=d;let p=qc({inputs:{x:r},attrs:{begin:u,size:h},backend:s});return u[i]+=d,p})}var boe={kernelName:ol,backendName:"wasm",kernelFunc:xoe},voe=un(ro),woe=un(zu),koe=!0,Ioe=kn(io,koe),pk;function Soe(e){pk=e.wasm.cwrap(Mr,null,["number","number","number"])}function Coe(e){let{backend:t,inputs:n,attrs:s}=e,{alpha:r}=s,{x:a}=n,o=t.dataIdMap.get(a.dataId).id,i=t.makeOutput(a.shape,a.dtype),l=t.dataIdMap.get(i.dataId).id;return pk(o,r,l),i}var Toe={kernelName:Mr,backendName:"wasm",setupFunc:Soe,kernelFunc:Coe},fk;function Noe(e){fk=e.wasm.cwrap(il,null,["number","array","number","array","array","array","array","array","number","number"])}function Eoe(e){let{backend:t,inputs:n,attrs:s}=e,{x:r}=n,{begin:a,end:o,strides:i}=s;i==null&&(i=new Array(a.length));let{beginMask:l,endMask:u,ellipsisMask:c,newAxisMask:d,shrinkAxisMask:h}=s,p=$.slice_util.maskToAxes(c);if(p.length>1)throw new Error("Multiple ellipses in slice is not allowed.");if(c!==0&&d!==0)throw new Error("Using both ellipsisMask and newAxisMask is not yet supported.");if(c!==0&&h!==0)throw new Error("Using both ellipsisMask and shrinkAxisMask is not yet supported.");let f=r.shape.length-a.length,m=$.slice_util.maskToAxes(d),A=r.shape.slice();m.forEach(R=>{a[R]=0,o[R]=1,A.splice(R,0,1)});let g=Fn({inputs:{x:r},attrs:{shape:A},backend:t}),{begin:y,end:x,strides:b}=$.slice_util.getNormalizedAxes(g.shape,p,f,a,o,i,l,u,c);a=y,o=x,i=b;let v=$.slice_util.maskToAxes(h);v.forEach(R=>{o[R]=a[R]+1,i[R]=1});let k=$.slice_util.computeOutShape(a,o,i),w=k.filter((R,_)=>v.indexOf(_)===-1);if(i.every(R=>R===1)){let R=qc({inputs:{x:g},attrs:{begin:a,size:k},backend:t});t.disposeData(g.dataId);let _=Fn({inputs:{x:R},attrs:{shape:w},backend:t});return t.disposeData(R.dataId),_}let E=t.makeOutput(w,"float32");if(!w.some(R=>R===0)){let R=t.dataIdMap.get(g.dataId).id,_=new Uint8Array(new Int32Array(I.computeStrides(g.shape)).buffer),N=new Uint8Array(new Int32Array(a).buffer),O=new Uint8Array(new Int32Array(o).buffer),W=new Uint8Array(new Int32Array(i).buffer),j=new Uint8Array(new Int32Array(w).buffer),q=new Uint8Array(new Int32Array(I.computeStrides(w)).buffer),X=t.dataIdMap.get(E.dataId).id;fk(R,_,g.shape.length,N,O,W,j,q,w.length,X)}t.disposeData(g.dataId);let M=Fn({inputs:{x:E},attrs:{shape:w},backend:t});return t.disposeData(E.dataId),M}var Roe={kernelName:il,backendName:"wasm",setupFunc:Noe,kernelFunc:Eoe},_oe=!0,$oe=kn(lo,_oe),mk;function Foe(e){mk=e.wasm.cwrap(ao,null,["number, number, number"])}function Doe(e){let{backend:t,inputs:n,attrs:s}=e,{axis:r,keepDims:a}=s,{x:o}=n,i=t.dataIdMap.get(o.dataId).id,l=i,u=o,{transposed:c,axes:d,originalAxes:h,inputWasTransposed:p}=aa(o,r,t),f=d;if(p){let x=t.dataIdMap.get(c.dataId).id;x!==i&&(u=c,l=x,f=$.getInnerMostAxes(f.length,u.shape.length))}$.assertAxesAreInnerMostDims("sum",f,u.shape.length);let[m,A]=$.computeOutAndReduceShapes(u.shape,f),g=I.sizeFromShape(A),y=t.makeOutput(m,u.dtype);if(I.sizeFromShape(u.shape)!==0){let x=t.dataIdMap.get(y.dataId).id;mk(l,g,x)}if(p&&t.disposeData(c.dataId),a){let x=$.expandShapeToKeepDim(y.shape,h);y.shape=x}return y}var Ooe={kernelName:ao,backendName:"wasm",setupFunc:Foe,kernelFunc:Doe},Poe=un(uo),Moe=un(co),Ak;function zoe(e){Ak=e.wasm.cwrap(Pr,null,["number","array","number","array","number","number"])}function Loe(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,a=n.dataIdMap.get(r.dataId).id,{reps:o}=s,i=new Array(r.shape.length);for(let h=0;h<i.length;h++)i[h]=r.shape[h]*o[h];let l=new Uint8Array(new Int32Array(r.shape).buffer),u=new Uint8Array(new Int32Array(i).buffer),c=n.makeOutput(i,r.dtype),d=n.dataIdMap.get(c.dataId).id;return Ak(a,l,r.shape.length,u,i.length,$n[c.dtype],d),c}var Boe={kernelName:Pr,backendName:"wasm",setupFunc:zoe,kernelFunc:Loe},gk;function Woe(e){gk=e.wasm.cwrap(ll,null,["number","array","number","number","number","bool","number","number"])}var Voe=({inputs:e,backend:t,attrs:n})=>{let{x:s}=e,{k:r,sorted:a}=n,o=t.dataIdMap.get(s.dataId).id,i=new Uint8Array(new Int32Array(s.shape).buffer),l=s.shape.slice();l[l.length-1]=r;let u=t.makeOutput(l,s.dtype),c=t.dataIdMap.get(u.dataId).id,d=t.makeOutput(l,"int32"),h=t.dataIdMap.get(d.dataId).id;return gk(o,i,s.shape.length,$n[s.dtype],r,a,c,h),[u,d]},Uoe={kernelName:ll,backendName:"wasm",setupFunc:Woe,kernelFunc:Voe},yk;function Hoe(e){yk=e.wasm.cwrap(ul,null,["number","number","bool","number","number","number","number","number","number","array","number","number","number","number","number"])}function Goe(e){let{backend:t,inputs:n,attrs:s}=e,{image:r,transforms:a}=n,{interpolation:o,fillMode:i,fillValue:l,outputShape:u}=s,[c,d,h,p]=r.shape,[f,m]=u!=null?u:[d,h],A=[c,f,m,p],g=new Uint8Array(new Int32Array(I.computeStrides(r.shape)).buffer),y=t.makeOutput(A,r.dtype),x=t.dataIdMap.get(y.dataId).id,v=t.dataIdMap.get(r.dataId).id,w=t.dataIdMap.get(a.dataId).id,C=o==="nearest"?1:2,E;switch(i){case"constant":E=1;break;case"reflect":E=2;break;case"wrap":E=3;break;case"nearest":E=4;break;default:E=1;break}return yk(v,w,a.shape[0]>1,c,f,m,p,h,d,g,r.shape.length-1,C,E,l,x),y}var joe={kernelName:ul,backendName:"wasm",setupFunc:Hoe,kernelFunc:Goe};function qoe(e){let{inputs:t,backend:n,attrs:s}=e,{value:r}=t,{axis:a}=s;a<0&&(a+=r.shape.length);let o=r.shape[a],i=r.shape.length,l=new Array(i-1),u=0;for(let p=0;p<i;p++)p!==a&&(l[u++]=r.shape[p]);let c=new Array(o),d=new Array(i).fill(0),h=r.shape.slice();h[a]=1;for(let p=0;p<c.length;p++)d[a]=p,c[p]=qc({inputs:{x:r},attrs:{begin:d,size:h},backend:n});return c.map(({dataId:p,dtype:f})=>({dataId:p,dtype:f,shape:l}))}var Xoe={kernelName:cl,backendName:"wasm",kernelFunc:qoe};function Koe(e){let{inputs:{x:t},backend:n}=e,s=n.makeOutput(t.shape,t.dtype);return n.typedArrayFromHeap(s).fill(0),s}var Zoe={kernelName:dl,backendName:"wasm",kernelFunc:Koe},Yoe=[Qne,tse,rse,hse,mse,yse,vse,Sse,_se,$se,Fse,Pse,Mse,Bse,Use,Hse,Gse,Xse,Yse,ere,sre,are,ore,ire,ure,hre,pre,mre,Jne,yre,vre,Ire,Tre,Rre,$re,Dre,ase,Mre,Lre,Wre,Vre,Hre,qre,Kre,Jre,tae,rae,oae,uae,dae,hae,mae,yae,vae,kae,Cae,Nae,Rae,sk,Dae,Mae,Bae,Vae,Hae,Gae,jae,wse,Kae,Jae,toe,soe,noe,ooe,uoe,hoe,poe,Ese,Aoe,yoe,boe,voe,woe,Ioe,Toe,Roe,$oe,Ooe,Poe,Moe,Boe,Uoe,joe,use,Xoe,Zoe];for(let e of Yoe)Ao(e);var D1=ee();D1.registerFlag("WASM_HAS_SIMD_SUPPORT",async()=>WebAssembly.validate(new Uint8Array([0,97,115,109,1,0,0,0,1,4,1,96,0,0,3,2,1,0,10,9,1,7,0,65,0,253,15,26,11])));D1.registerFlag("WASM_HAS_MULTITHREAD_SUPPORT",async()=>{if(D1.get("IS_NODE"))return!1;try{return new MessageChannel().port1.postMessage(new SharedArrayBuffer(1)),WebAssembly.validate(new Uint8Array([0,97,115,109,1,0,0,0,1,4,1,96,0,0,3,2,1,0,5,4,1,3,1,1,10,11,1,9,0,65,0,254,16,2,0,26,11]))}catch(e){return!1}});var xk=fa(zI()),Joe='var Module={};function threadPrintErr(){var text=Array.prototype.slice.call(arguments).join(" ");console.error(text)}function threadAlert(){var text=Array.prototype.slice.call(arguments).join(" ");postMessage({cmd:"alert",text:text,threadId:Module["_pthread_self"]()})}var err=threadPrintErr;this.alert=threadAlert;Module["instantiateWasm"]=function(info,receiveInstance){var instance=new WebAssembly.Instance(Module["wasmModule"],info);Module["wasmModule"]=null;receiveInstance(instance);return instance.exports};function moduleLoaded(){}this.onmessage=function(e){try{if(e.data.cmd==="load"){Module["wasmModule"]=e.data.wasmModule;Module["wasmMemory"]=e.data.wasmMemory;Module["buffer"]=Module["wasmMemory"].buffer;Module["ENVIRONMENT_IS_PTHREAD"]=true;if(typeof e.data.urlOrBlob==="string"){importScripts(e.data.urlOrBlob)}else{var objectUrl=URL.createObjectURL(e.data.urlOrBlob);importScripts(objectUrl);URL.revokeObjectURL(objectUrl)}WasmBackendModuleThreadedSimd(Module).then(function(instance){Module=instance;moduleLoaded()})}else if(e.data.cmd==="objectTransfer"){Module["PThread"].receiveObjectTransfer(e.data)}else if(e.data.cmd==="run"){Module["__performance_now_clock_drift"]=performance.now()-e.data.time;Module["__emscripten_thread_init"](e.data.threadInfoStruct,0,0);var max=e.data.stackBase;var top=e.data.stackBase+e.data.stackSize;Module["establishStackSpace"](top,max);Module["_emscripten_tls_init"]();Module["PThread"].receiveObjectTransfer(e.data);Module["PThread"].setThreadStatus(Module["_pthread_self"](),1);try{var result=Module["invokeEntryPoint"](e.data.start_routine,e.data.arg);if(!Module["getNoExitRuntime"]())Module["PThread"].threadExit(result)}catch(ex){if(ex==="Canceled!"){Module["PThread"].threadCancel()}else if(ex!="unwind"){if(ex instanceof Module["ExitStatus"]){if(Module["getNoExitRuntime"]()){}else{Module["PThread"].threadExit(ex.status)}}else{Module["PThread"].threadExit(-2);throw ex}}}}else if(e.data.cmd==="cancel"){if(Module["_pthread_self"]()){Module["PThread"].threadCancel()}}else if(e.data.target==="setimmediate"){}else if(e.data.cmd==="processThreadQueue"){if(Module["_pthread_self"]()){Module["_emscripten_current_thread_process_queued_calls"]()}}else{err("worker.js received unknown command "+e.data.cmd);err(e.data)}}catch(ex){err("worker.js onmessage() captured an uncaught exception: "+ex);if(ex&&ex.stack)err(ex.stack);throw ex}};if(typeof process==="object"&&typeof process.versions==="object"&&typeof process.versions.node==="string"){self={location:{href:__filename}};var onmessage=this.onmessage;var nodeWorkerThreads=require("worker_threads");global.Worker=nodeWorkerThreads.Worker;var parentPort=nodeWorkerThreads.parentPort;parentPort.on("message",function(data){onmessage({data:data})});var nodeFS=require("fs");var nodeRead=function(filename){return nodeFS.readFileSync(filename,"utf8")};function globalEval(x){global.require=require;global.Module=Module;eval.call(null,x)}importScripts=function(f){globalEval(nodeRead(f))};postMessage=function(msg){parentPort.postMessage(msg)};if(typeof performance==="undefined"){performance={now:function(){return Date.now()}}}}',Qoe=fa(LI()),bk=class extends ku{constructor(e){super();this.wasm=e,this.dataIdNextNumber=1,this.wasm.tfjs.init(),this.dataIdMap=new Fd(this,Ar())}write(e,t,n){let s={id:this.dataIdNextNumber++};return this.move(s,e,t,n,1),s}numDataIds(){return this.dataIdMap.numDataIds()}async time(e){let t=I.now();return e(),{kernelMs:I.now()-t}}move(e,t,n,s,r){let a=this.dataIdNextNumber++;if(s==="string"){let u=t;this.dataIdMap.set(e,{id:a,stringBytes:u,shape:n,dtype:s,memoryOffset:null,refCount:r});return}let o=I.sizeFromShape(n),i=o*I.bytesPerElement(s),l=this.wasm._malloc(i);this.dataIdMap.set(e,{id:a,memoryOffset:l,shape:n,dtype:s,refCount:r}),this.wasm.tfjs.registerTensor(a,o,l),t!=null&&this.wasm.HEAPU8.set(new Uint8Array(t.buffer,t.byteOffset,i),l)}async read(e){return this.readSync(e)}readSync(e){let{memoryOffset:t,dtype:n,shape:s,stringBytes:r}=this.dataIdMap.get(e);if(n==="string")return r;let a=this.wasm.HEAPU8.slice(t,t+I.sizeFromShape(s)*I.bytesPerElement(n));return nie(a.buffer,n)}disposeData(e,t=!1){if(this.dataIdMap.has(e)){let n=this.dataIdMap.get(e);if(n.refCount--,!t&&n.refCount>0)return!1;this.wasm._free(n.memoryOffset),this.wasm.tfjs.disposeData(n.id),this.dataIdMap.delete(e)}return!0}refCount(e){return this.dataIdMap.has(e)?this.dataIdMap.get(e).refCount:0}incRef(e){let t=this.dataIdMap.get(e);t!=null&&t.refCount++}floatPrecision(){return 32}getMemoryOffset(e){return this.dataIdMap.get(e).memoryOffset}dispose(){this.wasm.tfjs.dispose(),"PThread"in this.wasm&&this.wasm.PThread.terminateAllThreads(),this.wasm=null}memory(){return{unreliable:!1}}makeOutput(e,t,n){let s;if(n==null)s=this.write(null,e,t);else{let r=this.dataIdNextNumber++;s={id:r},this.dataIdMap.set(s,{id:r,memoryOffset:n,shape:e,dtype:t,refCount:1});let a=I.sizeFromShape(e);this.wasm.tfjs.registerTensor(r,a,n)}return{dataId:s,shape:e,dtype:t}}typedArrayFromHeap({shape:e,dtype:t,dataId:n}){let s=this.wasm.HEAPU8.buffer,{memoryOffset:r}=this.dataIdMap.get(n),a=I.sizeFromShape(e);switch(t){case"float32":return new Float32Array(s,r,a);case"int32":return new Int32Array(s,r,a);case"bool":return new Uint8Array(s,r,a);default:throw new Error(`Unknown dtype ${t}`)}}};function eie(e){return(t,n)=>(I.fetch(e,{credentials:"same-origin"}).then(s=>{s.ok||t.env.a(`failed to load wasm binary file at '${e}'`),s.arrayBuffer().then(r=>{WebAssembly.instantiate(r,t).then(a=>{n(a.instance,a.module)})})}),{})}function vk(e,t,n){if(Sf!=null)return Sf;let s="tfjs-backend-wasm.wasm";return e&&t?s="tfjs-backend-wasm-threaded-simd.wasm":e&&(s="tfjs-backend-wasm-simd.wasm"),Kc!=null&&Kc[s]!=null?Kc[s]:n+s}async function tie(){let[e,t]=await Promise.all([ee().getAsync("WASM_HAS_SIMD_SUPPORT"),ee().getAsync("WASM_HAS_MULTITHREAD_SUPPORT")]);return new Promise((n,s)=>{let r={};r.locateFile=(i,l)=>{if(i.endsWith(".worker.js")){let u=Joe,c=new Blob([u],{type:"application/javascript"});return URL.createObjectURL(c)}return i.endsWith(".wasm")?vk(e,t,Xc!=null?Xc:l):l+i},O1&&(r.instantiateWasm=eie(vk(e,t,Xc!=null?Xc:"")));let a=!1;r.onAbort=()=>{if(a||Zc)return;Zc=!0,s({message:"Make sure the server can serve the `.wasm` file relative to the bundled js file. For more details see https://github.com/tensorflow/tfjs/blob/master/tfjs-backend-wasm/README.md#using-bundlers"})};let o;t&&e&&Sf==null?(r.mainScriptUrlOrBlob=new Blob(["var WasmBackendModuleThreadedSimd = "+xk.default.toString()],{type:"text/javascript"}),o=(0,xk.default)(r)):o=(0,Qoe.default)(r),o.then(i=>{a=!0,Zc=!1;let l=null;i.tfjs={init:i.cwrap("init",null,[]),registerTensor:i.cwrap("register_tensor",null,["number","number","number"]),disposeData:i.cwrap("dispose_data",l,["number"]),dispose:i.cwrap("dispose",l,[])},n({wasm:i})})})}function nie(e,t){switch(t){case"float32":return new Float32Array(e);case"int32":return new Int32Array(e);case"bool":return new Uint8Array(e);default:throw new Error(`Unknown dtype ${t}`)}}var sie=["tfjs-backend-wasm.wasm","tfjs-backend-wasm-simd.wasm","tfjs-backend-wasm-threaded-simd.wasm"],Sf=null,Xc=null,Kc={},Zc=!1,O1=!1;function rie(e,t=!1){if(sA("setWasmPath has been deprecated in favor of setWasmPaths and will be removed in a future release."),Zc)throw new Error("The WASM backend was already initialized. Make sure you call `setWasmPath()` before you call `tf.setBackend()` or `tf.ready()`");Sf=e,O1=t}function aie(e,t=!1){if(Zc)throw new Error("The WASM backend was already initialized. Make sure you call `setWasmPaths()` before you call `tf.setBackend()` or `tf.ready()`");if(typeof e=="string")Xc=e;else{Kc=e;let n=sie.filter(s=>Kc[s]==null);if(n.length>0)throw new Error(`There were no entries found for the following binaries: ${n.join(",")}. Please either call setWasmPaths with a map providing a path for each binary, or with a string indicating the directory where all the binaries can be found.`)}O1=t}var oie="3.8.0",iie=2;bl("wasm",async()=>{let{wasm:e}=await tie();return new bk(e)},iie);var lie={tfjs:BI,"tfjs-core":WI,"tfjs-data":VI,"tfjs-layers":UI,"tfjs-converter":HI,"tfjs-backend-cpu":GI,"tfjs-backend-webgl":jI,"tfjs-backend-wasm":qI};var Dn={name:"humangl",priority:99,canvas:null,gl:null,width:1024,height:1024,extensions:[],webGLattr:{alpha:!1,antialias:!1,premultipliedAlpha:!1,preserveDrawingBuffer:!1,depth:!1,stencil:!1,failIfMajorPerformanceCaveat:!1,desynchronized:!0}};function uie(){let e=Dn.gl;!e||(Dn.extensions=e.getSupportedExtensions())}function wk(){if(!rA(Dn.name)){try{Dn.canvas=typeof OffscreenCanvas!="undefined"?new OffscreenCanvas(Dn.width,Dn.height):document.createElement("canvas")}catch(e){ue("error: cannot create canvas:",e);return}try{Dn.gl=Dn.canvas.getContext("webgl2",Dn.webGLattr)}catch(e){ue("error: cannot get WebGL2 context:",e);return}try{tf(2,Dn.gl)}catch(e){ue("error: cannot set WebGL2 context:",e);return}try{let e=new uf(Dn.gl);bl(Dn.name,()=>new Jl(e),Dn.priority)}catch(e){ue("error: cannot register WebGL backend:",e);return}try{fl("webgl").forEach(t=>{let n={...t,backendName:Dn.name};Ao(n)})}catch(e){ue("error: cannot update WebGL backend registration:",e);return}try{ns.set("WEBGL_VERSION",2)}catch(e){ue("error: cannot set WebGL backend flags:",e);return}uie(),ue("backend registered:",Dn.name)}}function kk(e,t){let n=[e.startPoint[0]*t[0],e.startPoint[1]*t[1]],s=[e.endPoint[0]*t[0],e.endPoint[1]*t[1]];return{startPoint:n,endPoint:s}}function Jc(e){return[Math.abs(e.endPoint[0]-e.startPoint[0]),Math.abs(e.endPoint[1]-e.startPoint[1])]}function Qc(e){return[e.startPoint[0]+(e.endPoint[0]-e.startPoint[0])/2,e.startPoint[1]+(e.endPoint[1]-e.startPoint[1])/2]}function ed(e,t,n){let s=t.shape[1],r=t.shape[2],a=[[e.startPoint[1]/s,e.startPoint[0]/r,e.endPoint[1]/s,e.endPoint[0]/r]];return _e.cropAndResize(t,a,[0],n)}function Cf(e,t=1.5){let n=Qc(e),s=Jc(e),r=[t*s[0]/2,t*s[1]/2],a=[n[0]-r[0],n[1]-r[1]],o=[n[0]+r[0],n[1]+r[1]];return{startPoint:a,endPoint:o,landmarks:e.landmarks}}function Tf(e){let t=Qc(e),n=Jc(e),r=Math.max(...n)/2,a=[Math.round(t[0]-r),Math.round(t[1]-r)],o=[Math.round(t[0]+r),Math.round(t[1]+r)];return{startPoint:a,endPoint:o,landmarks:e.landmarks}}function P1(e){let t=e.map(a=>a[0]),n=e.map(a=>a[1]),s=[Math.min(...t),Math.min(...n)],r=[Math.max(...t),Math.max(...n)];return{startPoint:s,endPoint:r,landmarks:e}}var Ik=e=>({startPoint:Re(e,[0,0],[-1,2]),endPoint:Re(e,[0,2],[-1,2])});var Nf=[[1,0,0],[0,1,0],[0,0,1]];function cie(e){return e-2*Math.PI*Math.floor((e+Math.PI)/(2*Math.PI))}function Sk(e,t){let n=Math.PI/2-Math.atan2(-(t[1]-e[1]),t[0]-e[0]);return cie(n)}function Ck(e,t){return[[1,0,e],[0,1,t],[0,0,1]]}function oa(e,t){let n=0;for(let s=0;s<e.length;s++)n+=e[s]*t[s];return n}function die(e,t){let n=[];for(let s=0;s<e.length;s++)n.push(e[s][t]);return n}function Tk(e,t){let n=[],s=e.length;for(let r=0;r<s;r++){n.push([]);for(let a=0;a<s;a++)n[r].push(oa(e[r],die(t,a)))}return n}function M1(e,t){let n=Math.cos(e),s=Math.sin(e),r=[[n,-s,0],[s,n,0],[0,0,1]],a=Ck(t[0],t[1]),o=Tk(a,r),i=Ck(-t[0],-t[1]);return Tk(o,i)}function Nk(e){let t=[[e[0][0],e[1][0]],[e[0][1],e[1][1]]],n=[e[0][2],e[1][2]],s=[-oa(t[0],n),-oa(t[1],n)];return[t[0].concat(s[0]),t[1].concat(s[1]),[0,0,1]]}function Ek(e,t){return[oa(e,t[0]),oa(e,t[1])]}function Rk(e){let t={strides:[e/16,e/8],anchors:[2,6]},n=[];for(let s=0;s<t.strides.length;s++){let r=t.strides[s],a=Math.floor((e+r-1)/r),o=Math.floor((e+r-1)/r),i=t.anchors[s];for(let l=0;l<a;l++){let u=r*(l+.5);for(let c=0;c<o;c++){let d=r*(c+.5);for(let h=0;h<i;h++)n.push([d,u])}}}return n}var _k=6;function hie(e,t,n){let s=Re(e,[0,1],[-1,2]),r=ae(s,t),a=Re(e,[0,3],[-1,2]),o=de(a,n),i=de(r,n),l=de(o,2),u=Ae(i,l),c=ae(i,l),d=z(u,n),h=z(c,n);return Il([d,h],1)}var $k=class{constructor(t,n){this.model=t,this.anchorsData=Rk(t.inputs[0].shape[1]),this.anchors=_s(this.anchorsData),this.inputSize=t.inputs[0].shape[2],this.config=n}async getBoundingBoxes(t,n){if(!t||t.isDisposedInternal||t.shape.length!==4||t.shape[1]<1||t.shape[2]<1)return null;let[s,r,a]=H(()=>{let c=_e.resizeBilinear(t,[this.inputSize,this.inputSize]),d=Ae(de(c,127.5),.5),h=this.model.execute(d),p;if(Array.isArray(h)){let g=h.sort((v,k)=>v.size-k.size),y=ht([g[0],g[2]],2),x=ht([g[1],g[3]],2),b=ht([x,y],1);p=ot(b,0)}else p=ot(h);let f=hie(p,this.anchors,[this.inputSize,this.inputSize]),m=Re(p,[0,0],[-1,1]),A=ot(Bn(m));return[p,f,A]});this.config=pn(this.config,n);let o=await _e.nonMaxSuppressionAsync(r,a,this.config.face.detector.maxDetected,this.config.face.detector.iouThreshold,this.config.face.detector.minConfidence),i=await o.array();K(o);let l=[],u=await a.data();for(let c=0;c<i.length;c++){let d=u[i[c]];if(d>this.config.face.detector.minConfidence){let h=Re(r,[i[c],0],[1,-1]),p=Ik(h);K(h);let f=this.anchorsData[i[c]],m=H(()=>U(ot(Re(s,[i[c],_k-1],[1,-1])),[_k,-1]));l.push({box:p,landmarks:m,anchor:f,confidence:d})}}return K(s),K(r),K(a),{boxes:l,scaleFactor:[t.shape[2]/this.inputSize,t.shape[1]/this.inputSize]}}};async function Fk(e){let t=await pt(ft(e.modelBasePath,e.face.detector.modelPath),{fromTFHub:e.face.detector.modelPath.includes("tfhub.dev")}),n=new $k(t,e);return!t||!t.modelUrl?ue("load model failed:",e.face.detector.modelPath):e.debug&&ue("load model:",t.modelUrl),n}var or={silhouette:[10,338,297,332,284,251,389,356,454,323,361,288,397,365,379,378,400,377,152,148,176,149,150,136,172,58,132,93,234,127,162,21,54,103,67,109],lipsUpperOuter:[61,185,40,39,37,0,267,269,270,409,291],lipsLowerOuter:[146,91,181,84,17,314,405,321,375,291],lipsUpperInner:[78,191,80,81,82,13,312,311,310,415,308],lipsLowerInner:[78,95,88,178,87,14,317,402,318,324,308],rightEyeUpper0:[246,161,160,159,158,157,173],rightEyeLower0:[33,7,163,144,145,153,154,155,133],rightEyeUpper1:[247,30,29,27,28,56,190],rightEyeLower1:[130,25,110,24,23,22,26,112,243],rightEyeUpper2:[113,225,224,223,222,221,189],rightEyeLower2:[226,31,228,229,230,231,232,233,244],rightEyeLower3:[143,111,117,118,119,120,121,128,245],rightEyebrowUpper:[156,70,63,105,66,107,55,193],rightEyebrowLower:[35,124,46,53,52,65],rightEyeIris:[473,474,475,476,477],leftEyeUpper0:[466,388,387,386,385,384,398],leftEyeLower0:[263,249,390,373,374,380,381,382,362],leftEyeUpper1:[467,260,259,257,258,286,414],leftEyeLower1:[359,255,339,254,253,252,256,341,463],leftEyeUpper2:[342,445,444,443,442,441,413],leftEyeLower2:[446,261,448,449,450,451,452,453,464],leftEyeLower3:[372,340,346,347,348,349,350,357,465],leftEyebrowUpper:[383,300,293,334,296,336,285,417],leftEyebrowLower:[265,353,276,283,282,295],leftEyeIris:[468,469,470,471,472],midwayBetweenEyes:[168],noseTip:[1],noseBottom:[2],noseRightCorner:[98],noseLeftCorner:[327],rightCheek:[205],leftCheek:[425]},z1=[{key:"EyeUpper0",indices:[9,10,11,12,13,14,15]},{key:"EyeUpper1",indices:[25,26,27,28,29,30,31]},{key:"EyeUpper2",indices:[41,42,43,44,45,46,47]},{key:"EyeLower0",indices:[0,1,2,3,4,5,6,7,8]},{key:"EyeLower1",indices:[16,17,18,19,20,21,22,23,24]},{key:"EyeLower2",indices:[32,33,34,35,36,37,38,39,40]},{key:"EyeLower3",indices:[54,55,56,57,58,59,60,61,62]}],td=[[.499976992607117,.652534008026123],[.500025987625122,.547487020492554],[.499974012374878,.602371990680695],[.482113003730774,.471979022026062],[.500150978565216,.527155995368958],[.499909996986389,.498252987861633],[.499523013830185,.40106201171875],[.289712011814117,.380764007568359],[.499954998493195,.312398016452789],[.499987006187439,.269918978214264],[.500023007392883,.107050001621246],[.500023007392883,.666234016418457],[.5000159740448,.679224014282227],[.500023007392883,.692348003387451],[.499976992607117,.695277988910675],[.499976992607117,.70593398809433],[.499976992607117,.719385027885437],[.499976992607117,.737019002437592],[.499967992305756,.781370997428894],[.499816000461578,.562981009483337],[.473773002624512,.573909997940063],[.104906998574734,.254140973091125],[.365929991006851,.409575998783112],[.338757991790771,.41302502155304],[.311120003461838,.409460008144379],[.274657994508743,.389131009578705],[.393361985683441,.403706014156342],[.345234006643295,.344011008739471],[.370094001293182,.346076011657715],[.319321990013123,.347265005111694],[.297903001308441,.353591024875641],[.24779200553894,.410809993743896],[.396889001131058,.842755019664764],[.280097991228104,.375599980354309],[.106310002505779,.399955987930298],[.2099249958992,.391353011131287],[.355807989835739,.534406006336212],[.471751004457474,.65040397644043],[.474155008792877,.680191993713379],[.439785003662109,.657229006290436],[.414617002010345,.66654098033905],[.450374007225037,.680860996246338],[.428770989179611,.682690978050232],[.374971002340317,.727805018424988],[.486716985702515,.547628998756409],[.485300987958908,.527395009994507],[.257764995098114,.314490020275116],[.401223003864288,.455172002315521],[.429818987846375,.548614978790283],[.421351999044418,.533740997314453],[.276895999908447,.532056987285614],[.483370006084442,.499586999416351],[.33721199631691,.282882988452911],[.296391993761063,.293242990970612],[.169294998049736,.193813979625702],[.447580009698868,.302609980106354],[.392390012741089,.353887975215912],[.354490011930466,.696784019470215],[.067304998636246,.730105042457581],[.442739009857178,.572826027870178],[.457098007202148,.584792017936707],[.381974011659622,.694710969924927],[.392388999462128,.694203019142151],[.277076005935669,.271932005882263],[.422551989555359,.563233017921448],[.385919004678726,.281364023685455],[.383103013038635,.255840003490448],[.331431001424789,.119714021682739],[.229923993349075,.232002973556519],[.364500999450684,.189113974571228],[.229622006416321,.299540996551514],[.173287004232407,.278747975826263],[.472878992557526,.666198015213013],[.446828007698059,.668527007102966],[.422762006521225,.673889994621277],[.445307999849319,.580065965652466],[.388103008270264,.693961024284363],[.403039008378983,.706539988517761],[.403629004955292,.693953037261963],[.460041999816895,.557139039039612],[.431158006191254,.692366003990173],[.452181994915009,.692366003990173],[.475387006998062,.692366003990173],[.465828001499176,.779190003871918],[.472328990697861,.736225962638855],[.473087012767792,.717857003211975],[.473122000694275,.704625964164734],[.473033010959625,.695277988910675],[.427942007780075,.695277988910675],[.426479011774063,.703539967536926],[.423162013292313,.711845993995667],[.4183090031147,.720062971115112],[.390094995498657,.639572978019714],[.013953999616206,.560034036636353],[.499913990497589,.58014702796936],[.413199990987778,.69539999961853],[.409626007080078,.701822996139526],[.468080013990402,.601534962654114],[.422728985548019,.585985004901886],[.463079988956451,.593783974647522],[.37211999297142,.47341400384903],[.334562003612518,.496073007583618],[.411671012639999,.546965003013611],[.242175996303558,.14767599105835],[.290776997804642,.201445996761322],[.327338010072708,.256527006626129],[.399509996175766,.748921036720276],[.441727995872498,.261676013469696],[.429764986038208,.187834024429321],[.412198007106781,.108901023864746],[.288955003023148,.398952007293701],[.218936994671822,.435410976409912],[.41278201341629,.398970007896423],[.257135003805161,.355440020561218],[.427684992551804,.437960982322693],[.448339998722076,.536936044692993],[.178560003638268,.45755398273468],[.247308000922203,.457193970680237],[.286267012357712,.467674970626831],[.332827985286713,.460712015628815],[.368755996227264,.447206974029541],[.398963987827301,.432654976844788],[.476410001516342,.405806005001068],[.189241006970406,.523923993110657],[.228962004184723,.348950982093811],[.490725994110107,.562400996685028],[.404670000076294,.485132992267609],[.019469000399113,.401564002037048],[.426243007183075,.420431017875671],[.396993011236191,.548797011375427],[.266469985246658,.376977026462555],[.439121007919312,.51895797252655],[.032313998788595,.644356966018677],[.419054001569748,.387154996395111],[.462783008813858,.505746960639954],[.238978996872902,.779744982719421],[.198220998048782,.831938028335571],[.107550002634525,.540755033493042],[.183610007166862,.740257024765015],[.134409993886948,.333683013916016],[.385764002799988,.883153975009918],[.490967005491257,.579378008842468],[.382384985685349,.508572995662689],[.174399003386497,.397670984268188],[.318785011768341,.39623498916626],[.343364000320435,.400596976280212],[.396100014448166,.710216999053955],[.187885001301765,.588537991046906],[.430987000465393,.944064974784851],[.318993002176285,.898285031318665],[.266247987747192,.869701027870178],[.500023007392883,.190576016902924],[.499976992607117,.954452991485596],[.366169989109039,.398822009563446],[.393207013607025,.39553701877594],[.410373002290726,.391080021858215],[.194993004202843,.342101991176605],[.388664990663528,.362284004688263],[.365961998701096,.355970978736877],[.343364000320435,.355356991291046],[.318785011768341,.35834002494812],[.301414996385574,.363156020641327],[.058132998645306,.319076001644135],[.301414996385574,.387449026107788],[.499987989664078,.618434011936188],[.415838003158569,.624195992946625],[.445681989192963,.566076993942261],[.465844005346298,.620640993118286],[.49992299079895,.351523995399475],[.288718998432159,.819945991039276],[.335278987884521,.852819979190826],[.440512001514435,.902418971061707],[.128294005990028,.791940987110138],[.408771991729736,.373893976211548],[.455606997013092,.451801002025604],[.499877005815506,.908990025520325],[.375436991453171,.924192011356354],[.11421000212431,.615022003650665],[.448662012815475,.695277988910675],[.4480200111866,.704632043838501],[.447111994028091,.715808033943176],[.444831997156143,.730794012546539],[.430011987686157,.766808986663818],[.406787008047104,.685672998428345],[.400738000869751,.681069016456604],[.392399996519089,.677703022956848],[.367855995893478,.663918972015381],[.247923001646996,.601333022117615],[.452769994735718,.420849978923798],[.43639200925827,.359887003898621],[.416164010763168,.368713974952698],[.413385987281799,.692366003990173],[.228018000721931,.683571994304657],[.468268007040024,.352671027183533],[.411361992359161,.804327011108398],[.499989002943039,.469825029373169],[.479153990745544,.442654013633728],[.499974012374878,.439637005329132],[.432112008333206,.493588984012604],[.499886006116867,.866917014122009],[.49991300702095,.821729004383087],[.456548988819122,.819200992584229],[.344549000263214,.745438992977142],[.37890899181366,.574010014533997],[.374292999505997,.780184984207153],[.319687992334366,.570737957954407],[.357154995203018,.604269981384277],[.295284003019333,.621580958366394],[.447750002145767,.862477004528046],[.410986006259918,.508723020553589],[.31395098567009,.775308012962341],[.354128003120422,.812552988529205],[.324548006057739,.703992962837219],[.189096003770828,.646299958229065],[.279776990413666,.71465802192688],[.1338230073452,.682700991630554],[.336768001317978,.644733011722565],[.429883986711502,.466521978378296],[.455527991056442,.548622965812683],[.437114000320435,.558896005153656],[.467287987470627,.529924988746643],[.414712011814117,.335219979286194],[.37704598903656,.322777986526489],[.344107985496521,.320150971412659],[.312875986099243,.32233202457428],[.283526003360748,.333190023899078],[.241245999932289,.382785975933075],[.102986000478268,.468762993812561],[.267612010240555,.424560010433197],[.297879010438919,.433175981044769],[.333433985710144,.433878004550934],[.366427004337311,.426115989685059],[.396012008190155,.416696012020111],[.420121014118195,.41022801399231],[.007561000064015,.480777025222778],[.432949006557465,.569517970085144],[.458638995885849,.479089021682739],[.473466008901596,.545744001865387],[.476087987422943,.563830018043518],[.468472003936768,.555056989192963],[.433990985155106,.582361996173859],[.483518004417419,.562983989715576],[.482482999563217,.57784903049469],[.42645001411438,.389798998832703],[.438998997211456,.39649498462677],[.450067013502121,.400434017181396],[.289712011814117,.368252992630005],[.276670008897781,.363372981548309],[.517862021923065,.471948027610779],[.710287988185883,.380764007568359],[.526226997375488,.573909997940063],[.895093023777008,.254140973091125],[.634069979190826,.409575998783112],[.661242008209229,.41302502155304],[.688880026340485,.409460008144379],[.725341975688934,.389131009578705],[.606630027294159,.40370500087738],[.654766023159027,.344011008739471],[.629905998706818,.346076011657715],[.680678009986877,.347265005111694],[.702096998691559,.353591024875641],[.75221198797226,.410804986953735],[.602918028831482,.842862963676453],[.719901978969574,.375599980354309],[.893692970275879,.399959981441498],[.790081977844238,.391354024410248],[.643998026847839,.534487962722778],[.528249025344849,.65040397644043],[.525849997997284,.680191040039062],[.560214996337891,.657229006290436],[.585384011268616,.66654098033905],[.549625992774963,.680860996246338],[.57122802734375,.682691991329193],[.624852001667023,.72809898853302],[.513050019741058,.547281980514526],[.51509702205658,.527251958847046],[.742246985435486,.314507007598877],[.598631024360657,.454979002475739],[.570338010787964,.548575043678284],[.578631997108459,.533622980117798],[.723087012767792,.532054007053375],[.516445994377136,.499638974666595],[.662801027297974,.282917976379395],[.70362401008606,.293271005153656],[.830704987049103,.193813979625702],[.552385985851288,.302568018436432],[.607609987258911,.353887975215912],[.645429015159607,.696707010269165],[.932694971561432,.730105042457581],[.557260990142822,.572826027870178],[.542901992797852,.584792017936707],[.6180260181427,.694710969924927],[.607590973377228,.694203019142151],[.722943007946014,.271963000297546],[.577413976192474,.563166975975037],[.614082992076874,.281386971473694],[.616907000541687,.255886018276215],[.668509006500244,.119913995265961],[.770092010498047,.232020974159241],[.635536015033722,.189248979091644],[.77039098739624,.299556016921997],[.826722025871277,.278755009174347],[.527121007442474,.666198015213013],[.553171992301941,.668527007102966],[.577238023281097,.673889994621277],[.554691970348358,.580065965652466],[.611896991729736,.693961024284363],[.59696102142334,.706539988517761],[.596370995044708,.693953037261963],[.539958000183105,.557139039039612],[.568841993808746,.692366003990173],[.547818005084991,.692366003990173],[.52461302280426,.692366003990173],[.534089982509613,.779141008853912],[.527670979499817,.736225962638855],[.526912987232208,.717857003211975],[.526877999305725,.704625964164734],[.526966989040375,.695277988910675],[.572058022022247,.695277988910675],[.573521018028259,.703539967536926],[.57683801651001,.711845993995667],[.581691026687622,.720062971115112],[.609944999217987,.639909982681274],[.986046016216278,.560034036636353],[.5867999792099,.69539999961853],[.590372025966644,.701822996139526],[.531915009021759,.601536989212036],[.577268004417419,.585934996604919],[.536915004253387,.593786001205444],[.627542972564697,.473352015018463],[.665585994720459,.495950996875763],[.588353991508484,.546862006187439],[.757824003696442,.14767599105835],[.709249973297119,.201507985591888],[.672684013843536,.256581008434296],[.600408971309662,.74900496006012],[.55826598405838,.261672019958496],[.570303976535797,.187870979309082],[.588165998458862,.109044015407562],[.711045026779175,.398952007293701],[.781069993972778,.435405015945435],[.587247014045715,.398931980133057],[.742869973182678,.355445981025696],[.572156012058258,.437651991844177],[.55186802148819,.536570012569427],[.821442008018494,.457556009292603],[.752701997756958,.457181990146637],[.71375697851181,.467626988887787],[.66711300611496,.460672974586487],[.631101012229919,.447153985500336],[.6008620262146,.432473003864288],[.523481011390686,.405627012252808],[.810747981071472,.523926019668579],[.771045982837677,.348959028720856],[.509127020835876,.562718033790588],[.595292985439301,.485023975372314],[.980530977249146,.401564002037048],[.573499977588654,.420000016689301],[.602994978427887,.548687994480133],[.733529984951019,.376977026462555],[.560611009597778,.519016981124878],[.967685997486115,.644356966018677],[.580985009670258,.387160003185272],[.537728011608124,.505385041236877],[.760966002941132,.779752969741821],[.801778972148895,.831938028335571],[.892440974712372,.54076099395752],[.816350996494293,.740260004997253],[.865594983100891,.333687007427216],[.614073991775513,.883246004581451],[.508952975273132,.579437971115112],[.617941975593567,.508316040039062],[.825608015060425,.397674977779388],[.681214988231659,.39623498916626],[.656635999679565,.400596976280212],[.603900015354156,.710216999053955],[.81208598613739,.588539004325867],[.56801301240921,.944564998149872],[.681007981300354,.898285031318665],[.733752012252808,.869701027870178],[.633830010890961,.398822009563446],[.606792986392975,.39553701877594],[.589659988880157,.391062021255493],[.805015981197357,.342108011245728],[.611334979534149,.362284004688263],[.634037971496582,.355970978736877],[.656635999679565,.355356991291046],[.681214988231659,.35834002494812],[.698584973812103,.363156020641327],[.941866993904114,.319076001644135],[.698584973812103,.387449026107788],[.584177017211914,.624107003211975],[.554318010807037,.566076993942261],[.534153997898102,.62064003944397],[.711217999458313,.819975018501282],[.664629995822906,.852871000766754],[.559099972248077,.902631998062134],[.871706008911133,.791940987110138],[.591234028339386,.373893976211548],[.544341027736664,.451583981513977],[.624562978744507,.924192011356354],[.88577002286911,.615028977394104],[.551338016986847,.695277988910675],[.551980018615723,.704632043838501],[.552887976169586,.715808033943176],[.555167973041534,.730794012546539],[.569944024085999,.767035007476807],[.593203008174896,.685675978660583],[.599261999130249,.681069016456604],[.607599973678589,.677703022956848],[.631937980651855,.663500010967255],[.752032995223999,.601315021514893],[.547226011753082,.420395016670227],[.563543975353241,.359827995300293],[.583841025829315,.368713974952698],[.586614012718201,.692366003990173],[.771915018558502,.683578014373779],[.531597018241882,.352482974529266],[.588370978832245,.804440975189209],[.52079701423645,.442565023899078],[.567984998226166,.493479013442993],[.543282985687256,.819254994392395],[.655317008495331,.745514988899231],[.621008992195129,.574018001556396],[.625559985637665,.78031200170517],[.680198013782501,.570719003677368],[.64276397228241,.604337990283966],[.704662978649139,.621529996395111],[.552012026309967,.862591981887817],[.589071989059448,.508637011051178],[.685944974422455,.775357007980347],[.645735025405884,.812640011310577],[.675342977046967,.703978002071381],[.810858011245728,.646304965019226],[.72012197971344,.714666962623596],[.866151988506317,.682704985141754],[.663187026977539,.644596993923187],[.570082008838654,.466325998306274],[.544561982154846,.548375964164734],[.562758982181549,.558784961700439],[.531987011432648,.530140042304993],[.585271000862122,.335177004337311],[.622952997684479,.32277899980545],[.655896008014679,.320163011550903],[.687132000923157,.322345972061157],[.716481983661652,.333200991153717],[.758756995201111,.382786989212036],[.897013008594513,.468769013881683],[.732392013072968,.424547016620636],[.70211398601532,.433162987232208],[.66652500629425,.433866024017334],[.633504986763,.426087975502014],[.603875994682312,.416586995124817],[.579657971858978,.409945011138916],[.992439985275269,.480777025222778],[.567192018032074,.569419980049133],[.54136598110199,.478899002075195],[.526564002037048,.546118021011353],[.523913025856018,.563830018043518],[.531529009342194,.555056989192963],[.566035985946655,.582329034805298],[.51631098985672,.563053965568542],[.5174720287323,.577877044677734],[.573594987392426,.389806985855103],[.560697972774506,.395331978797913],[.549755990505219,.399751007556915],[.710287988185883,.368252992630005],[.723330020904541,.363372981548309]],qo=[127,34,139,11,0,37,232,231,120,72,37,39,128,121,47,232,121,128,104,69,67,175,171,148,157,154,155,118,50,101,73,39,40,9,151,108,48,115,131,194,204,211,74,40,185,80,42,183,40,92,186,230,229,118,202,212,214,83,18,17,76,61,146,160,29,30,56,157,173,106,204,194,135,214,192,203,165,98,21,71,68,51,45,4,144,24,23,77,146,91,205,50,187,201,200,18,91,106,182,90,91,181,85,84,17,206,203,36,148,171,140,92,40,39,193,189,244,159,158,28,247,246,161,236,3,196,54,68,104,193,168,8,117,228,31,189,193,55,98,97,99,126,47,100,166,79,218,155,154,26,209,49,131,135,136,150,47,126,217,223,52,53,45,51,134,211,170,140,67,69,108,43,106,91,230,119,120,226,130,247,63,53,52,238,20,242,46,70,156,78,62,96,46,53,63,143,34,227,173,155,133,123,117,111,44,125,19,236,134,51,216,206,205,154,153,22,39,37,167,200,201,208,36,142,100,57,212,202,20,60,99,28,158,157,35,226,113,160,159,27,204,202,210,113,225,46,43,202,204,62,76,77,137,123,116,41,38,72,203,129,142,64,98,240,49,102,64,41,73,74,212,216,207,42,74,184,169,170,211,170,149,176,105,66,69,122,6,168,123,147,187,96,77,90,65,55,107,89,90,180,101,100,120,63,105,104,93,137,227,15,86,85,129,102,49,14,87,86,55,8,9,100,47,121,145,23,22,88,89,179,6,122,196,88,95,96,138,172,136,215,58,172,115,48,219,42,80,81,195,3,51,43,146,61,171,175,199,81,82,38,53,46,225,144,163,110,246,33,7,52,65,66,229,228,117,34,127,234,107,108,69,109,108,151,48,64,235,62,78,191,129,209,126,111,35,143,163,161,246,117,123,50,222,65,52,19,125,141,221,55,65,3,195,197,25,7,33,220,237,44,70,71,139,122,193,245,247,130,33,71,21,162,153,158,159,170,169,150,188,174,196,216,186,92,144,160,161,2,97,167,141,125,241,164,167,37,72,38,12,145,159,160,38,82,13,63,68,71,226,35,111,158,153,154,101,50,205,206,92,165,209,198,217,165,167,97,220,115,218,133,112,243,239,238,241,214,135,169,190,173,133,171,208,32,125,44,237,86,87,178,85,86,179,84,85,180,83,84,181,201,83,182,137,93,132,76,62,183,61,76,184,57,61,185,212,57,186,214,207,187,34,143,156,79,239,237,123,137,177,44,1,4,201,194,32,64,102,129,213,215,138,59,166,219,242,99,97,2,94,141,75,59,235,24,110,228,25,130,226,23,24,229,22,23,230,26,22,231,112,26,232,189,190,243,221,56,190,28,56,221,27,28,222,29,27,223,30,29,224,247,30,225,238,79,20,166,59,75,60,75,240,147,177,215,20,79,166,187,147,213,112,233,244,233,128,245,128,114,188,114,217,174,131,115,220,217,198,236,198,131,134,177,132,58,143,35,124,110,163,7,228,110,25,356,389,368,11,302,267,452,350,349,302,303,269,357,343,277,452,453,357,333,332,297,175,152,377,384,398,382,347,348,330,303,304,270,9,336,337,278,279,360,418,262,431,304,408,409,310,415,407,270,409,410,450,348,347,422,430,434,313,314,17,306,307,375,387,388,260,286,414,398,335,406,418,364,367,416,423,358,327,251,284,298,281,5,4,373,374,253,307,320,321,425,427,411,421,313,18,321,405,406,320,404,405,315,16,17,426,425,266,377,400,369,322,391,269,417,465,464,386,257,258,466,260,388,456,399,419,284,332,333,417,285,8,346,340,261,413,441,285,327,460,328,355,371,329,392,439,438,382,341,256,429,420,360,364,394,379,277,343,437,443,444,283,275,440,363,431,262,369,297,338,337,273,375,321,450,451,349,446,342,467,293,334,282,458,461,462,276,353,383,308,324,325,276,300,293,372,345,447,382,398,362,352,345,340,274,1,19,456,248,281,436,427,425,381,256,252,269,391,393,200,199,428,266,330,329,287,273,422,250,462,328,258,286,384,265,353,342,387,259,257,424,431,430,342,353,276,273,335,424,292,325,307,366,447,345,271,303,302,423,266,371,294,455,460,279,278,294,271,272,304,432,434,427,272,407,408,394,430,431,395,369,400,334,333,299,351,417,168,352,280,411,325,319,320,295,296,336,319,403,404,330,348,349,293,298,333,323,454,447,15,16,315,358,429,279,14,15,316,285,336,9,329,349,350,374,380,252,318,402,403,6,197,419,318,319,325,367,364,365,435,367,397,344,438,439,272,271,311,195,5,281,273,287,291,396,428,199,311,271,268,283,444,445,373,254,339,263,466,249,282,334,296,449,347,346,264,447,454,336,296,299,338,10,151,278,439,455,292,407,415,358,371,355,340,345,372,390,249,466,346,347,280,442,443,282,19,94,370,441,442,295,248,419,197,263,255,359,440,275,274,300,383,368,351,412,465,263,467,466,301,368,389,380,374,386,395,378,379,412,351,419,436,426,322,373,390,388,2,164,393,370,462,461,164,0,267,302,11,12,374,373,387,268,12,13,293,300,301,446,261,340,385,384,381,330,266,425,426,423,391,429,355,437,391,327,326,440,457,438,341,382,362,459,457,461,434,430,394,414,463,362,396,369,262,354,461,457,316,403,402,315,404,403,314,405,404,313,406,405,421,418,406,366,401,361,306,408,407,291,409,408,287,410,409,432,436,410,434,416,411,264,368,383,309,438,457,352,376,401,274,275,4,421,428,262,294,327,358,433,416,367,289,455,439,462,370,326,2,326,370,305,460,455,254,449,448,255,261,446,253,450,449,252,451,450,256,452,451,341,453,452,413,464,463,441,413,414,258,442,441,257,443,442,259,444,443,260,445,444,467,342,445,459,458,250,289,392,290,290,328,460,376,433,435,250,290,392,411,416,433,341,463,464,453,464,465,357,465,412,343,412,399,360,363,440,437,399,456,420,456,363,401,435,288,372,383,353,339,255,249,448,261,255,133,243,190,133,155,112,33,246,247,33,130,25,398,384,286,362,398,414,362,463,341,263,359,467,263,249,255,466,467,260,75,60,166,238,239,79,162,127,139,72,11,37,121,232,120,73,72,39,114,128,47,233,232,128,103,104,67,152,175,148,173,157,155,119,118,101,74,73,40,107,9,108,49,48,131,32,194,211,184,74,185,191,80,183,185,40,186,119,230,118,210,202,214,84,83,17,77,76,146,161,160,30,190,56,173,182,106,194,138,135,192,129,203,98,54,21,68,5,51,4,145,144,23,90,77,91,207,205,187,83,201,18,181,91,182,180,90,181,16,85,17,205,206,36,176,148,140,165,92,39,245,193,244,27,159,28,30,247,161,174,236,196,103,54,104,55,193,8,111,117,31,221,189,55,240,98,99,142,126,100,219,166,218,112,155,26,198,209,131,169,135,150,114,47,217,224,223,53,220,45,134,32,211,140,109,67,108,146,43,91,231,230,120,113,226,247,105,63,52,241,238,242,124,46,156,95,78,96,70,46,63,116,143,227,116,123,111,1,44,19,3,236,51,207,216,205,26,154,22,165,39,167,199,200,208,101,36,100,43,57,202,242,20,99,56,28,157,124,35,113,29,160,27,211,204,210,124,113,46,106,43,204,96,62,77,227,137,116,73,41,72,36,203,142,235,64,240,48,49,64,42,41,74,214,212,207,183,42,184,210,169,211,140,170,176,104,105,69,193,122,168,50,123,187,89,96,90,66,65,107,179,89,180,119,101,120,68,63,104,234,93,227,16,15,85,209,129,49,15,14,86,107,55,9,120,100,121,153,145,22,178,88,179,197,6,196,89,88,96,135,138,136,138,215,172,218,115,219,41,42,81,5,195,51,57,43,61,208,171,199,41,81,38,224,53,225,24,144,110,105,52,66,118,229,117,227,34,234,66,107,69,10,109,151,219,48,235,183,62,191,142,129,126,116,111,143,7,163,246,118,117,50,223,222,52,94,19,141,222,221,65,196,3,197,45,220,44,156,70,139,188,122,245,139,71,162,145,153,159,149,170,150,122,188,196,206,216,92,163,144,161,164,2,167,242,141,241,0,164,37,11,72,12,144,145,160,12,38,13,70,63,71,31,226,111,157,158,154,36,101,205,203,206,165,126,209,217,98,165,97,237,220,218,237,239,241,210,214,169,140,171,32,241,125,237,179,86,178,180,85,179,181,84,180,182,83,181,194,201,182,177,137,132,184,76,183,185,61,184,186,57,185,216,212,186,192,214,187,139,34,156,218,79,237,147,123,177,45,44,4,208,201,32,98,64,129,192,213,138,235,59,219,141,242,97,97,2,141,240,75,235,229,24,228,31,25,226,230,23,229,231,22,230,232,26,231,233,112,232,244,189,243,189,221,190,222,28,221,223,27,222,224,29,223,225,30,224,113,247,225,99,60,240,213,147,215,60,20,166,192,187,213,243,112,244,244,233,245,245,128,188,188,114,174,134,131,220,174,217,236,236,198,134,215,177,58,156,143,124,25,110,7,31,228,25,264,356,368,0,11,267,451,452,349,267,302,269,350,357,277,350,452,357,299,333,297,396,175,377,381,384,382,280,347,330,269,303,270,151,9,337,344,278,360,424,418,431,270,304,409,272,310,407,322,270,410,449,450,347,432,422,434,18,313,17,291,306,375,259,387,260,424,335,418,434,364,416,391,423,327,301,251,298,275,281,4,254,373,253,375,307,321,280,425,411,200,421,18,335,321,406,321,320,405,314,315,17,423,426,266,396,377,369,270,322,269,413,417,464,385,386,258,248,456,419,298,284,333,168,417,8,448,346,261,417,413,285,326,327,328,277,355,329,309,392,438,381,382,256,279,429,360,365,364,379,355,277,437,282,443,283,281,275,363,395,431,369,299,297,337,335,273,321,348,450,349,359,446,467,283,293,282,250,458,462,300,276,383,292,308,325,283,276,293,264,372,447,346,352,340,354,274,19,363,456,281,426,436,425,380,381,252,267,269,393,421,200,428,371,266,329,432,287,422,290,250,328,385,258,384,446,265,342,386,387,257,422,424,430,445,342,276,422,273,424,306,292,307,352,366,345,268,271,302,358,423,371,327,294,460,331,279,294,303,271,304,436,432,427,304,272,408,395,394,431,378,395,400,296,334,299,6,351,168,376,352,411,307,325,320,285,295,336,320,319,404,329,330,349,334,293,333,366,323,447,316,15,315,331,358,279,317,14,316,8,285,9,277,329,350,253,374,252,319,318,403,351,6,419,324,318,325,397,367,365,288,435,397,278,344,439,310,272,311,248,195,281,375,273,291,175,396,199,312,311,268,276,283,445,390,373,339,295,282,296,448,449,346,356,264,454,337,336,299,337,338,151,294,278,455,308,292,415,429,358,355,265,340,372,388,390,466,352,346,280,295,442,282,354,19,370,285,441,295,195,248,197,457,440,274,301,300,368,417,351,465,251,301,389,385,380,386,394,395,379,399,412,419,410,436,322,387,373,388,326,2,393,354,370,461,393,164,267,268,302,12,386,374,387,312,268,13,298,293,301,265,446,340,380,385,381,280,330,425,322,426,391,420,429,437,393,391,326,344,440,438,458,459,461,364,434,394,428,396,262,274,354,457,317,316,402,316,315,403,315,314,404,314,313,405,313,421,406,323,366,361,292,306,407,306,291,408,291,287,409,287,432,410,427,434,411,372,264,383,459,309,457,366,352,401,1,274,4,418,421,262,331,294,358,435,433,367,392,289,439,328,462,326,94,2,370,289,305,455,339,254,448,359,255,446,254,253,449,253,252,450,252,256,451,256,341,452,414,413,463,286,441,414,286,258,441,258,257,442,257,259,443,259,260,444,260,467,445,309,459,250,305,289,290,305,290,460,401,376,435,309,250,392,376,411,433,453,341,464,357,453,465,343,357,412,437,343,399,344,360,440,420,437,456,360,420,363,361,401,288,265,372,353,390,339,249,339,448,255];var pie=[127,234,132,58,172,150,149,148,152,377,378,379,397,288,361,454,356,70,63,105,66,107,336,296,334,293,300,168,6,195,4,98,97,2,326,327,33,160,158,133,153,144,362,385,387,263,373,380,57,40,37,0,267,270,287,321,314,17,84,91,78,81,13,311,308,402,14,178],fie=[33,133,362,263,1,62,308,159,145,386,374,6,102,331,2,13,14,70,105,107,336,334,300,54,10,284,50,280,234,454,58,288,152],mie=[33,133,362,263,1,78,308],ile=pie.map(e=>td[e]),lle=fie.map(e=>td[e]),ule=mie.map(e=>td[e]);var L1=or.leftEyeLower0,B1=or.rightEyeLower0,ru={leftBounds:[L1[0],L1[L1.length-1]],rightBounds:[B1[0],B1[B1.length-1]]},Dk={count:468,mouth:13,symmetryLine:[13,or.midwayBetweenEyes[0]]},Aie={leftEye:0,rightEye:1,nose:2,mouth:3,leftEar:4,rightEar:5,symmetryLine:[3,2]},au={upperCenter:3,lowerCenter:4,index:71,numCoordinates:76};function Ef(e,t,n,s){for(let r=0;r<z1.length;r++){let{key:a,indices:o}=z1[r],i=or[`${n}${a}`];if(!s||s.includes(a))for(let l=0;l<o.length;l++){let u=o[l];e[i[l]]=[t[u][0],t[u][1],(t[u][2]+e[i[l]][2])/2]}}}var W1=class{constructor(t,n,s){var r,a;this.storedBoxes=[],this.boundingBoxDetector=t,this.meshDetector=n,this.irisModel=s,this.boxSize=((r=t==null?void 0:t.model)==null?void 0:r.inputs[0].shape[2])||0,this.meshSize=(n==null?void 0:n.inputs[0].shape[2])||((a=t==null?void 0:t.model)==null?void 0:a.inputs[0].shape[2]),this.irisSize=(s==null?void 0:s.inputs[0].shape[1])||0,this.irisEnlarge=2.3,this.skipped=0,this.detectedFaces=0}transformRawCoords(t,n,s,r){let a=Jc({startPoint:n.startPoint,endPoint:n.endPoint}),o=t.map(d=>[a[0]/this.meshSize*(d[0]-this.meshSize/2),a[1]/this.meshSize*(d[1]-this.meshSize/2),d[2]]),i=s!==0?M1(s,[0,0]):Nf,l=s!==0?o.map(d=>[...Ek(d,i),d[2]]):o,u=s!==0?Nk(r):Nf,c=[...Qc({startPoint:n.startPoint,endPoint:n.endPoint}),1];return l.map(d=>[Math.round(d[0]+oa(c,u[0])),Math.round(d[1]+oa(c,u[1])),Math.round(d[2])])}getLeftToRightEyeDepthDifference(t){let n=t[ru.leftBounds[0]][2],s=t[ru.rightBounds[0]][2];return n-s}getEyeBox(t,n,s,r,a=!1){let o=Tf(Cf(P1([t[s],t[r]]),this.irisEnlarge)),i=Jc(o),l=_e.cropAndResize(n,[[o.startPoint[1]/this.meshSize,o.startPoint[0]/this.meshSize,o.endPoint[1]/this.meshSize,o.endPoint[0]/this.meshSize]],[0],[this.irisSize,this.irisSize]);if(a&&ns.flags.IS_BROWSER){let u=_e.flipLeftRight(l);K(l),l=u}return{box:o,boxSize:i,crop:l}}getEyeCoords(t,n,s,r=!1){let a=[];for(let o=0;o<au.numCoordinates;o++){let i=t[o*3],l=t[o*3+1],u=t[o*3+2];a.push([(r?1-i/this.irisSize:i/this.irisSize)*s[0]+n.startPoint[0],l/this.irisSize*s[1]+n.startPoint[1],u])}return{rawCoords:a,iris:a.slice(au.index)}}getAdjustedIrisCoords(t,n,s){let r=t[or[`${s}EyeUpper0`][au.upperCenter]][2],a=t[or[`${s}EyeLower0`][au.lowerCenter]][2],o=(r+a)/2;return n.map((i,l)=>{let u=o;return l===2?u=r:l===4&&(u=a),[i[0],i[1],u]})}correctFaceRotation(t,n,s){let[r,a]=n.landmarks.length>=Dk.count?Dk.symmetryLine:Aie.symmetryLine,o=Sk(n.landmarks[r],n.landmarks[a]),i=Qc({startPoint:n.startPoint,endPoint:n.endPoint}),l=[i[0]/s.shape[2],i[1]/s.shape[1]],u=_e.rotateWithOffset(s,o,0,l),c=M1(-o,i),d=t.face.mesh.enabled?ed({startPoint:n.startPoint,endPoint:n.endPoint},u,[this.meshSize,this.meshSize]):ed({startPoint:n.startPoint,endPoint:n.endPoint},u,[this.boxSize,this.boxSize]),h=de(d,255);return K(d),K(u),[o,c,h]}async augmentIris(t,n){let{box:s,boxSize:r,crop:a}=this.getEyeBox(t,n,ru.leftBounds[0],ru.leftBounds[1],!0),{box:o,boxSize:i,crop:l}=this.getEyeBox(t,n,ru.rightBounds[0],ru.rightBounds[1]),u=ht([a,l]);K(a),K(l);let c=this.irisModel.predict(u);K(u);let d=await c.data();K(c);let h=d.slice(0,au.numCoordinates*3),{rawCoords:p,iris:f}=this.getEyeCoords(h,s,r,!0),m=d.slice(au.numCoordinates*3),{rawCoords:A,iris:g}=this.getEyeCoords(m,o,i),y=this.getLeftToRightEyeDepthDifference(t);Math.abs(y)<30?(Ef(t,p,"left",null),Ef(t,A,"right",null)):y<1?Ef(t,p,"left",["EyeUpper0","EyeLower0"]):Ef(t,A,"right",["EyeUpper0","EyeLower0"]);let x=this.getAdjustedIrisCoords(t,f,"left"),b=this.getAdjustedIrisCoords(t,g,"right");return t.concat(x).concat(b)}async predict(t,n){let s=!1,r;if((this.skipped===0||this.skipped>n.face.detector.skipFrames||!n.face.mesh.enabled||!n.skipFrame)&&(r=await this.boundingBoxDetector.getBoundingBoxes(t,n),this.skipped=0),n.skipFrame&&this.skipped++,!n.skipFrame||r&&r.boxes&&(!n.face.mesh.enabled||r.boxes.length!==this.detectedFaces&&this.detectedFaces!==n.face.detector.maxDetected)){this.storedBoxes=[],this.detectedFaces=0;for(let i of r.boxes){let l=await i.box.startPoint.data(),u=await i.box.endPoint.data(),c=await i.landmarks.array();this.storedBoxes.push({startPoint:l,endPoint:u,landmarks:c,confidence:i.confidence})}this.storedBoxes.length>0&&(s=!0)}if(s){if(!r||!r.boxes||r.boxes.length===0)return this.storedBoxes=[],this.detectedFaces=0,null;for(let i=0;i<this.storedBoxes.length;i++){let l=kk({startPoint:this.storedBoxes[i].startPoint,endPoint:this.storedBoxes[i].endPoint},r.scaleFactor),u=Cf(l),c=Tf(u),d=this.storedBoxes[i].landmarks,h=this.storedBoxes[i].confidence;this.storedBoxes[i]={...c,confidence:h,landmarks:d}}}r&&r.boxes&&r.boxes.forEach(i=>{K(i.box.startPoint),K(i.box.endPoint),K(i.landmarks)});let a=[],o=[];for(let i of this.storedBoxes){let l,u=0,c;if(n.face.detector.rotation&&n.face.mesh.enabled&&ns.flags.IS_BROWSER)[u,c,l]=this.correctFaceRotation(n,i,t);else{c=Nf;let d=t.clone(),h=n.face.mesh.enabled?ed({startPoint:i.startPoint,endPoint:i.endPoint},d,[this.meshSize,this.meshSize]):ed({startPoint:i.startPoint,endPoint:i.endPoint},d,[this.boxSize,this.boxSize]);l=de(h,255),K(h),K(d)}if(!n.face.mesh.enabled)a.push({mesh:[],box:i,faceConfidence:null,boxConfidence:i.confidence,confidence:i.confidence,image:l});else{let[d,h,p]=this.meshDetector.execute(l);K(d);let f=(await h.data())[0];K(h);let m=U(p,[-1,3]),A=await m.array();if(K(p),K(m),f<n.face.detector.minConfidence)i.confidence=f,K(l);else{n.face.iris.enabled&&(A=await this.augmentIris(A,l));let g=this.transformRawCoords(A,i,u,c);i={...Cf(P1(g),1.5),confidence:i.confidence},n.face.detector.rotation&&n.face.mesh.enabled&&n.face.description.enabled&&ns.flags.IS_BROWSER&&([u,c,l]=this.correctFaceRotation(n,i,t)),a.push({mesh:g,box:i,faceConfidence:f,boxConfidence:i.confidence,confidence:f,image:l}),i={...Tf(i),confidence:i.confidence,faceConfidence:f}}}o.push(i)}return n.face.mesh.enabled&&(this.storedBoxes=o.filter(i=>i.confidence>n.face.detector.minConfidence)),this.detectedFaces=a.length,a}};var Rt=[null,null,null],V1;async function Ok(e,t){let n=await V1.predict(e,t),s=[],r=0;for(let a of n||[]){if(!a||a.isDisposedInternal)continue;let o=a.mesh.map(c=>[c[0]/(e.shape[2]||0),c[1]/(e.shape[1]||0),c[2]/V1.meshSize]),i={};if(a.mesh&&a.mesh.length>0)for(let c of Object.keys(or))i[c]=or[c].map(d=>a.mesh[d]);let l=a.box?[Math.trunc(Math.max(0,a.box.startPoint[0])),Math.trunc(Math.max(0,a.box.startPoint[1])),Math.trunc(Math.min(e.shape[2]||0,a.box.endPoint[0])-Math.max(0,a.box.startPoint[0])),Math.trunc(Math.min(e.shape[1]||0,a.box.endPoint[1])-Math.max(0,a.box.startPoint[1]))]:[0,0,0,0],u=a.box?[a.box.startPoint[0]/(e.shape[2]||0),a.box.startPoint[1]/(e.shape[1]||0),(a.box.endPoint[0]-a.box.startPoint[0])/(e.shape[2]||0),(a.box.endPoint[1]-a.box.startPoint[1])/(e.shape[1]||0)]:[0,0,0,0];s.push({id:r++,score:Math.round(100*a.faceConfidence||100*a.boxConfidence||0)/100,boxScore:Math.round(100*a.boxConfidence)/100,faceScore:Math.round(100*a.faceConfidence)/100,box:l,boxRaw:u,mesh:a.mesh,meshRaw:o,annotations:i,tensor:a.image}),a.coords&&K(a.coords)}return s}async function U1(e){return!Rt[0]&&e.face.enabled||!Rt[1]&&e.face.mesh.enabled||!Rt[2]&&e.face.iris.enabled?(Rt=await Promise.all([!Rt[0]&&e.face.enabled?Fk(e):null,!Rt[1]&&e.face.mesh.enabled?pt(ft(e.modelBasePath,e.face.mesh.modelPath),{fromTFHub:e.face.mesh.modelPath.includes("tfhub.dev")}):null,!Rt[2]&&e.face.iris.enabled?pt(ft(e.modelBasePath,e.face.iris.modelPath),{fromTFHub:e.face.iris.modelPath.includes("tfhub.dev")}):null]),e.face.mesh.enabled&&(!Rt[1]||!Rt[1].modelUrl?ue("load model failed:",e.face.mesh.modelPath):e.debug&&ue("load model:",Rt[1].modelUrl)),e.face.iris.enabled&&(!Rt[2]||!Rt[2].modelUrl?ue("load model failed:",e.face.iris.modelPath):e.debug&&ue("load model:",Rt[2].modelUrl))):e.debug&&(Rt[0]&&ue("cached model:",Rt[0].model.modelUrl),Rt[1]&&ue("cached model:",Rt[1].modelUrl),Rt[2]&&ue("cached model:",Rt[2].modelUrl)),V1=new W1(Rt[0],Rt[1],Rt[2]),Rt}var Pk=qo,Mk=td;var Ws,Rf=[],zk=0,H1=Number.MAX_SAFE_INTEGER;async function G1(e){let t=ft(e.modelBasePath,e.face.description.modelPath);return Ws?e.debug&&ue("cached model:",t):(Ws=await pt(t),Ws?e.debug&&ue("load model:",t):ue("load model failed:",e.face.description.modelPath)),Ws}function j1(e,t,n=2){if(!e||!t||(e==null?void 0:e.length)===0||(t==null?void 0:t.length)===0||(e==null?void 0:e.length)!==(t==null?void 0:t.length))return 0;let s=5*e.map((a,o)=>Math.abs(e[o]-t[o])**n).reduce((a,o)=>a+o,0)**(1/n);return Math.max(0,100-s)/100}function Lk(e,t,n=0){let s={similarity:0,name:"",source:"",embedding:[]};if(!e||!t||!Array.isArray(e)||!Array.isArray(t))return s;for(let r of t)if(r.embedding&&r.name){let a=j1(e,r.embedding);a>n&&a>s.similarity&&(s={...r,similarity:a})}return s}function q1(e){return H(()=>{let n=e.image||e.tensor||e;if(!(n instanceof Ue))return null;let s=[[.05,.15,.85,.85]];if(!Ws.inputs[0].shape)return null;let r=n.shape.length===3?_e.cropAndResize(Ft(n,0),s,[0],[Ws.inputs[0].shape[2],Ws.inputs[0].shape[1]]):_e.cropAndResize(n,s,[0],[Ws.inputs[0].shape[2],Ws.inputs[0].shape[1]]);return z(r,255)})}async function X1(e,t,n,s){var r,a;return Ws?H1<t.face.description.skipFrames&&t.skipFrame&&zk===s&&((r=Rf[n])==null?void 0:r.age)&&((a=Rf[n])==null?void 0:a.age)>0?(H1++,Rf[n]):(H1=0,new Promise(async o=>{let i=q1(e),l,u={age:0,gender:"unknown",genderScore:0,descriptor:[]};if(t.face.description.enabled&&(l=await Ws.predict(i)),K(i),l){let c=await l.find(g=>g.shape[1]===1).data(),d=Math.trunc(200*Math.abs(c[0]-.5))/100;d>t.face.description.minConfidence&&(u.gender=c[0]<=.5?"female":"male",u.genderScore=Math.min(.99,d));let p=(await Xs(l.find(g=>g.shape[1]===100),1).data())[0],f=await l.find(g=>g.shape[1]===100).data();u.age=Math.round(f[p-1]>f[p+1]?10*p-100*f[p-1]:10*p+100*f[p+1])/10;let A=await l.find(g=>g.shape[1]===1024).data();u.descriptor=[...A],l.forEach(g=>K(g))}Rf[n]=u,zk=s,o(u)})):null}var gie=["angry","disgust","fear","happy","sad","surprise","neutral"],Vs,_f=[],Bk=0,K1=Number.MAX_SAFE_INTEGER,Z1=[.2989,.587,.114];async function Y1(e){return Vs?e.debug&&ue("cached model:",Vs.modelUrl):(Vs=await pt(ft(e.modelBasePath,e.face.emotion.modelPath)),!Vs||!Vs.modelUrl?ue("load model failed:",e.face.emotion.modelPath):e.debug&&ue("load model:",Vs.modelUrl)),Vs}async function J1(e,t,n,s){return Vs?K1<t.face.emotion.skipFrames&&t.skipFrame&&Bk===s&&_f[n]&&_f[n].length>0?(K1++,_f[n]):(K1=0,new Promise(async r=>{let a=_e.resizeBilinear(e,[Vs.inputs[0].shape[2],Vs.inputs[0].shape[1]],!1),[o,i,l]=nn(a,3,3);K(a);let u=z(o,Z1[0]),c=z(i,Z1[1]),d=z(l,Z1[2]);K(o),K(i),K(l);let h=$h([u,c,d]);K(u),K(c),K(d);let p=H(()=>z(Ae(h,.5),2));K(h);let f=[];if(t.face.emotion.enabled){let m=await Vs.predict(p),A=await m.data();K(m);for(let g=0;g<A.length;g++)A[g]>t.face.emotion.minConfidence&&f.push({score:Math.min(.99,Math.trunc(100*A[g])/100),emotion:gie[g]});f.sort((g,y)=>y.score-g.score)}K(p),_f[n]=f,Bk=s,r(f)})):null}var nd=["nose","leftEye","rightEye","leftEar","rightEar","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle"],Wk=nd.length,sd=nd.reduce((e,t,n)=>(e[t]=n,e),{}),yie=[["leftHip","leftShoulder"],["leftElbow","leftShoulder"],["leftElbow","leftWrist"],["leftHip","leftKnee"],["leftKnee","leftAnkle"],["rightHip","rightShoulder"],["rightElbow","rightShoulder"],["rightElbow","rightWrist"],["rightHip","rightKnee"],["rightKnee","rightAnkle"],["leftShoulder","rightShoulder"],["leftHip","rightHip"]],xie=yie.map(([e,t])=>[sd[e],sd[t]]),Vk=[["nose","leftEye"],["leftEye","leftEar"],["nose","rightEye"],["rightEye","rightEar"],["nose","leftShoulder"],["leftShoulder","leftElbow"],["leftElbow","leftWrist"],["leftShoulder","leftHip"],["leftHip","leftKnee"],["leftKnee","leftAnkle"],["nose","rightShoulder"],["rightShoulder","rightElbow"],["rightElbow","rightWrist"],["rightShoulder","rightHip"],["rightHip","rightKnee"],["rightKnee","rightAnkle"]];function Uk(e){let t=e.reduce(({maxX:n,maxY:s,minX:r,minY:a},{position:{x:o,y:i}})=>({maxX:Math.max(n,o),maxY:Math.max(s,i),minX:Math.min(r,o),minY:Math.min(a,i)}),{maxX:Number.NEGATIVE_INFINITY,maxY:Number.NEGATIVE_INFINITY,minX:Number.POSITIVE_INFINITY,minY:Number.POSITIVE_INFINITY});return[t.minX,t.minY,t.maxX-t.minX,t.maxY-t.minY]}function Hk(e,[t,n],[s,r]){let a=t/s,o=n/r,i=(u,c)=>({id:c,score:u.score,boxRaw:[u.box[0]/r,u.box[1]/s,u.box[2]/r,u.box[3]/s],box:[Math.trunc(u.box[0]*o),Math.trunc(u.box[1]*a),Math.trunc(u.box[2]*o),Math.trunc(u.box[3]*a)],keypoints:u.keypoints.map(({score:d,part:h,position:p})=>({score:d,part:h,position:[Math.trunc(p.x*o),Math.trunc(p.y*a)],positionRaw:[p.x/s,p.y/s]}))});return e.map((u,c)=>i(u,c))}var Q1=class{constructor(t,n){this.priorityQueue=new Array(t),this.numberOfElements=-1,this.getElementValue=n}enqueue(t){this.priorityQueue[++this.numberOfElements]=t,this.swim(this.numberOfElements)}dequeue(){let t=this.priorityQueue[0];return this.exchange(0,this.numberOfElements--),this.sink(0),this.priorityQueue[this.numberOfElements+1]=null,t}empty(){return this.numberOfElements===-1}size(){return this.numberOfElements+1}all(){return this.priorityQueue.slice(0,this.numberOfElements+1)}max(){return this.priorityQueue[0]}swim(t){for(;t>0&&this.less(Math.floor(t/2),t);)this.exchange(t,Math.floor(t/2)),t=Math.floor(t/2)}sink(t){for(;2*t<=this.numberOfElements;){let n=2*t;if(n<this.numberOfElements&&this.less(n,n+1)&&n++,!this.less(t,n))break;this.exchange(t,n),t=n}}getValueAt(t){return this.getElementValue(this.priorityQueue[t])}less(t,n){return this.getValueAt(t)<this.getValueAt(n)}exchange(t,n){let s=this.priorityQueue[t];this.priorityQueue[t]=this.priorityQueue[n],this.priorityQueue[n]=s}};function ey(e,t,n,s){return{y:s.get(e,t,n),x:s.get(e,t,n+Wk)}}function ty(e,t,n){let{heatmapY:s,heatmapX:r,id:a}=e,{y:o,x:i}=ey(s,r,a,n);return{x:e.heatmapX*t+i,y:e.heatmapY*t+o}}function ny(e,t,n){return e<t?t:e>n?n:e}function Gk(e,t,n,s){let r=n-e,a=s-t;return r*r+a*a}function sy(e,t){return{x:e.x+t.x,y:e.y+t.y}}var $f=1,ou=16,bie=50**2;function jk(e,t,n,s,r,a,o=2){let i=g=>({y:a.get(g.y,g.x,e),x:a.get(g.y,g.x,a.shape[2]/2+e)}),l=(g,y,x)=>({y:ny(Math.round(g.y/ou),0,y-1),x:ny(Math.round(g.x/ou),0,x-1)}),[u,c]=s.shape,d=l(t.position,u,c),h=i(d),f=sy(t.position,h);for(let g=0;g<o;g++){let y=l(f,u,c),x=ey(y.y,y.x,n,r);f=sy({x:y.x*ou,y:y.y*ou},{x:x.x,y:x.y})}let m=l(f,u,c),A=s.get(m.y,m.x,n);return{position:f,part:nd[n],score:A}}function vie(e,t,n,s,r){let a=Vk.map(([h,p])=>[sd[h],sd[p]]),o=a.map(([,h])=>h),i=a.map(([h])=>h),l=t.shape[2],u=o.length,c=new Array(l),d=ty(e.part,ou,n);c[e.part.id]={score:e.score,part:nd[e.part.id],position:d};for(let h=u-1;h>=0;--h){let p=o[h],f=i[h];c[p]&&!c[f]&&(c[f]=jk(h,c[p],f,t,n,r))}for(let h=0;h<u;++h){let p=i[h],f=o[h];c[p]&&!c[f]&&(c[f]=jk(h,c[p],f,t,n,s))}return c}function wie(e,t,n,s,r){let[a,o]=r.shape,i=!0,l=Math.max(n-$f,0),u=Math.min(n+$f+1,a);for(let c=l;c<u;++c){let d=Math.max(s-$f,0),h=Math.min(s+$f+1,o);for(let p=d;p<h;++p)if(r.get(c,p,e)>t){i=!1;break}if(!i)break}return i}function kie(e,t){let[n,s,r]=t.shape,a=new Q1(n*s*r,({score:o})=>o);for(let o=0;o<n;++o)for(let i=0;i<s;++i)for(let l=0;l<r;++l){let u=t.get(o,i,l);u<e||wie(l,u,o,i,t)&&a.enqueue({score:u,part:{heatmapY:o,heatmapX:i,id:l}})}return a}function qk(e,{x:t,y:n},s){return e.some(({keypoints:r})=>{var o;let a=(o=r[s])==null?void 0:o.position;return a?Gk(n,t,a.y,a.x)<=bie:!1})}function Iie(e,t){return t.reduce((s,{position:r,score:a},o)=>(qk(e,r,o)||(s+=a),s),0)/t.length}function Xk(e,t,n,s,r,a){let o=[],i=kie(a,t);for(;o.length<r&&!i.empty();){let l=i.dequeue(),u=ty(l.part,ou,e);if(qk(o,u,l.part.id))continue;let c=vie(l,t,e,n,s);c=c.filter(p=>p.score>a);let d=Iie(o,c),h=Uk(c);d>a&&o.push({keypoints:c,box:h,score:Math.round(100*d)/100})}return o}var Xn,Sie=["MobilenetV1/offset_2/BiasAdd","MobilenetV1/heatmap_2/BiasAdd","MobilenetV1/displacement_fwd_2/BiasAdd","MobilenetV1/displacement_bwd_2/BiasAdd"];async function ry(e,t){let n=H(()=>{if(!Xn.inputs[0].shape)return[];let o=_e.resizeBilinear(e,[Xn.inputs[0].shape[2],Xn.inputs[0].shape[1]]),i=Ae(de(ce(o,"float32"),127.5),1),u=Xn.execute(i,Sie).map(c=>ot(c,[0]));return u[1]=u[1].sigmoid(),u}),s=await Promise.all(n.map(o=>o.buffer()));for(let o of n)K(o);let r=await Xk(s[0],s[1],s[2],s[3],t.body.maxDetected,t.body.minConfidence);return Xn.inputs[0].shape?Hk(r,[e.shape[1],e.shape[2]],[Xn.inputs[0].shape[2],Xn.inputs[0].shape[1]]):[]}async function ay(e){return Xn?e.debug&&ue("cached model:",Xn.modelUrl):(Xn=await pt(ft(e.modelBasePath,e.body.modelPath)),!Xn||!Xn.modelUrl?ue("load model failed:",e.body.modelPath):e.debug&&ue("load model:",Xn.modelUrl)),Xn}function Ff(e){return[Math.abs(e.endPoint[0]-e.startPoint[0]),Math.abs(e.endPoint[1]-e.startPoint[1])]}function rd(e){return[e.startPoint[0]+(e.endPoint[0]-e.startPoint[0])/2,e.startPoint[1]+(e.endPoint[1]-e.startPoint[1])/2]}function Kk(e,t,n){let s=t.shape[1],r=t.shape[2],a=[[e.startPoint[1]/s,e.startPoint[0]/r,e.endPoint[1]/s,e.endPoint[0]/r]];return _e.cropAndResize(t,a,[0],n)}function Zk(e,t){let n=[e.startPoint[0]*t[0],e.startPoint[1]*t[1]],s=[e.endPoint[0]*t[0],e.endPoint[1]*t[1]],r=e.palmLandmarks.map(a=>[a[0]*t[0],a[1]*t[1]]);return{startPoint:n,endPoint:s,palmLandmarks:r,confidence:e.confidence}}function Df(e,t=1.5){let n=rd(e),s=Ff(e),r=[t*s[0]/2,t*s[1]/2],a=[n[0]-r[0],n[1]-r[1]],o=[n[0]+r[0],n[1]+r[1]];return{startPoint:a,endPoint:o,palmLandmarks:e.palmLandmarks}}function Of(e){let t=rd(e),n=Ff(e),r=Math.max(...n)/2,a=[t[0]-r,t[1]-r],o=[t[0]+r,t[1]+r];return{startPoint:a,endPoint:o,palmLandmarks:e.palmLandmarks}}var Yk=[{x:.015625,y:.015625},{x:.015625,y:.015625},{x:.046875,y:.015625},{x:.046875,y:.015625},{x:.078125,y:.015625},{x:.078125,y:.015625},{x:.109375,y:.015625},{x:.109375,y:.015625},{x:.140625,y:.015625},{x:.140625,y:.015625},{x:.171875,y:.015625},{x:.171875,y:.015625},{x:.203125,y:.015625},{x:.203125,y:.015625},{x:.234375,y:.015625},{x:.234375,y:.015625},{x:.265625,y:.015625},{x:.265625,y:.015625},{x:.296875,y:.015625},{x:.296875,y:.015625},{x:.328125,y:.015625},{x:.328125,y:.015625},{x:.359375,y:.015625},{x:.359375,y:.015625},{x:.390625,y:.015625},{x:.390625,y:.015625},{x:.421875,y:.015625},{x:.421875,y:.015625},{x:.453125,y:.015625},{x:.453125,y:.015625},{x:.484375,y:.015625},{x:.484375,y:.015625},{x:.515625,y:.015625},{x:.515625,y:.015625},{x:.546875,y:.015625},{x:.546875,y:.015625},{x:.578125,y:.015625},{x:.578125,y:.015625},{x:.609375,y:.015625},{x:.609375,y:.015625},{x:.640625,y:.015625},{x:.640625,y:.015625},{x:.671875,y:.015625},{x:.671875,y:.015625},{x:.703125,y:.015625},{x:.703125,y:.015625},{x:.734375,y:.015625},{x:.734375,y:.015625},{x:.765625,y:.015625},{x:.765625,y:.015625},{x:.796875,y:.015625},{x:.796875,y:.015625},{x:.828125,y:.015625},{x:.828125,y:.015625},{x:.859375,y:.015625},{x:.859375,y:.015625},{x:.890625,y:.015625},{x:.890625,y:.015625},{x:.921875,y:.015625},{x:.921875,y:.015625},{x:.953125,y:.015625},{x:.953125,y:.015625},{x:.984375,y:.015625},{x:.984375,y:.015625},{x:.015625,y:.046875},{x:.015625,y:.046875},{x:.046875,y:.046875},{x:.046875,y:.046875},{x:.078125,y:.046875},{x:.078125,y:.046875},{x:.109375,y:.046875},{x:.109375,y:.046875},{x:.140625,y:.046875},{x:.140625,y:.046875},{x:.171875,y:.046875},{x:.171875,y:.046875},{x:.203125,y:.046875},{x:.203125,y:.046875},{x:.234375,y:.046875},{x:.234375,y:.046875},{x:.265625,y:.046875},{x:.265625,y:.046875},{x:.296875,y:.046875},{x:.296875,y:.046875},{x:.328125,y:.046875},{x:.328125,y:.046875},{x:.359375,y:.046875},{x:.359375,y:.046875},{x:.390625,y:.046875},{x:.390625,y:.046875},{x:.421875,y:.046875},{x:.421875,y:.046875},{x:.453125,y:.046875},{x:.453125,y:.046875},{x:.484375,y:.046875},{x:.484375,y:.046875},{x:.515625,y:.046875},{x:.515625,y:.046875},{x:.546875,y:.046875},{x:.546875,y:.046875},{x:.578125,y:.046875},{x:.578125,y:.046875},{x:.609375,y:.046875},{x:.609375,y:.046875},{x:.640625,y:.046875},{x:.640625,y:.046875},{x:.671875,y:.046875},{x:.671875,y:.046875},{x:.703125,y:.046875},{x:.703125,y:.046875},{x:.734375,y:.046875},{x:.734375,y:.046875},{x:.765625,y:.046875},{x:.765625,y:.046875},{x:.796875,y:.046875},{x:.796875,y:.046875},{x:.828125,y:.046875},{x:.828125,y:.046875},{x:.859375,y:.046875},{x:.859375,y:.046875},{x:.890625,y:.046875},{x:.890625,y:.046875},{x:.921875,y:.046875},{x:.921875,y:.046875},{x:.953125,y:.046875},{x:.953125,y:.046875},{x:.984375,y:.046875},{x:.984375,y:.046875},{x:.015625,y:.078125},{x:.015625,y:.078125},{x:.046875,y:.078125},{x:.046875,y:.078125},{x:.078125,y:.078125},{x:.078125,y:.078125},{x:.109375,y:.078125},{x:.109375,y:.078125},{x:.140625,y:.078125},{x:.140625,y:.078125},{x:.171875,y:.078125},{x:.171875,y:.078125},{x:.203125,y:.078125},{x:.203125,y:.078125},{x:.234375,y:.078125},{x:.234375,y:.078125},{x:.265625,y:.078125},{x:.265625,y:.078125},{x:.296875,y:.078125},{x:.296875,y:.078125},{x:.328125,y:.078125},{x:.328125,y:.078125},{x:.359375,y:.078125},{x:.359375,y:.078125},{x:.390625,y:.078125},{x:.390625,y:.078125},{x:.421875,y:.078125},{x:.421875,y:.078125},{x:.453125,y:.078125},{x:.453125,y:.078125},{x:.484375,y:.078125},{x:.484375,y:.078125},{x:.515625,y:.078125},{x:.515625,y:.078125},{x:.546875,y:.078125},{x:.546875,y:.078125},{x:.578125,y:.078125},{x:.578125,y:.078125},{x:.609375,y:.078125},{x:.609375,y:.078125},{x:.640625,y:.078125},{x:.640625,y:.078125},{x:.671875,y:.078125},{x:.671875,y:.078125},{x:.703125,y:.078125},{x:.703125,y:.078125},{x:.734375,y:.078125},{x:.734375,y:.078125},{x:.765625,y:.078125},{x:.765625,y:.078125},{x:.796875,y:.078125},{x:.796875,y:.078125},{x:.828125,y:.078125},{x:.828125,y:.078125},{x:.859375,y:.078125},{x:.859375,y:.078125},{x:.890625,y:.078125},{x:.890625,y:.078125},{x:.921875,y:.078125},{x:.921875,y:.078125},{x:.953125,y:.078125},{x:.953125,y:.078125},{x:.984375,y:.078125},{x:.984375,y:.078125},{x:.015625,y:.109375},{x:.015625,y:.109375},{x:.046875,y:.109375},{x:.046875,y:.109375},{x:.078125,y:.109375},{x:.078125,y:.109375},{x:.109375,y:.109375},{x:.109375,y:.109375},{x:.140625,y:.109375},{x:.140625,y:.109375},{x:.171875,y:.109375},{x:.171875,y:.109375},{x:.203125,y:.109375},{x:.203125,y:.109375},{x:.234375,y:.109375},{x:.234375,y:.109375},{x:.265625,y:.109375},{x:.265625,y:.109375},{x:.296875,y:.109375},{x:.296875,y:.109375},{x:.328125,y:.109375},{x:.328125,y:.109375},{x:.359375,y:.109375},{x:.359375,y:.109375},{x:.390625,y:.109375},{x:.390625,y:.109375},{x:.421875,y:.109375},{x:.421875,y:.109375},{x:.453125,y:.109375},{x:.453125,y:.109375},{x:.484375,y:.109375},{x:.484375,y:.109375},{x:.515625,y:.109375},{x:.515625,y:.109375},{x:.546875,y:.109375},{x:.546875,y:.109375},{x:.578125,y:.109375},{x:.578125,y:.109375},{x:.609375,y:.109375},{x:.609375,y:.109375},{x:.640625,y:.109375},{x:.640625,y:.109375},{x:.671875,y:.109375},{x:.671875,y:.109375},{x:.703125,y:.109375},{x:.703125,y:.109375},{x:.734375,y:.109375},{x:.734375,y:.109375},{x:.765625,y:.109375},{x:.765625,y:.109375},{x:.796875,y:.109375},{x:.796875,y:.109375},{x:.828125,y:.109375},{x:.828125,y:.109375},{x:.859375,y:.109375},{x:.859375,y:.109375},{x:.890625,y:.109375},{x:.890625,y:.109375},{x:.921875,y:.109375},{x:.921875,y:.109375},{x:.953125,y:.109375},{x:.953125,y:.109375},{x:.984375,y:.109375},{x:.984375,y:.109375},{x:.015625,y:.140625},{x:.015625,y:.140625},{x:.046875,y:.140625},{x:.046875,y:.140625},{x:.078125,y:.140625},{x:.078125,y:.140625},{x:.109375,y:.140625},{x:.109375,y:.140625},{x:.140625,y:.140625},{x:.140625,y:.140625},{x:.171875,y:.140625},{x:.171875,y:.140625},{x:.203125,y:.140625},{x:.203125,y:.140625},{x:.234375,y:.140625},{x:.234375,y:.140625},{x:.265625,y:.140625},{x:.265625,y:.140625},{x:.296875,y:.140625},{x:.296875,y:.140625},{x:.328125,y:.140625},{x:.328125,y:.140625},{x:.359375,y:.140625},{x:.359375,y:.140625},{x:.390625,y:.140625},{x:.390625,y:.140625},{x:.421875,y:.140625},{x:.421875,y:.140625},{x:.453125,y:.140625},{x:.453125,y:.140625},{x:.484375,y:.140625},{x:.484375,y:.140625},{x:.515625,y:.140625},{x:.515625,y:.140625},{x:.546875,y:.140625},{x:.546875,y:.140625},{x:.578125,y:.140625},{x:.578125,y:.140625},{x:.609375,y:.140625},{x:.609375,y:.140625},{x:.640625,y:.140625},{x:.640625,y:.140625},{x:.671875,y:.140625},{x:.671875,y:.140625},{x:.703125,y:.140625},{x:.703125,y:.140625},{x:.734375,y:.140625},{x:.734375,y:.140625},{x:.765625,y:.140625},{x:.765625,y:.140625},{x:.796875,y:.140625},{x:.796875,y:.140625},{x:.828125,y:.140625},{x:.828125,y:.140625},{x:.859375,y:.140625},{x:.859375,y:.140625},{x:.890625,y:.140625},{x:.890625,y:.140625},{x:.921875,y:.140625},{x:.921875,y:.140625},{x:.953125,y:.140625},{x:.953125,y:.140625},{x:.984375,y:.140625},{x:.984375,y:.140625},{x:.015625,y:.171875},{x:.015625,y:.171875},{x:.046875,y:.171875},{x:.046875,y:.171875},{x:.078125,y:.171875},{x:.078125,y:.171875},{x:.109375,y:.171875},{x:.109375,y:.171875},{x:.140625,y:.171875},{x:.140625,y:.171875},{x:.171875,y:.171875},{x:.171875,y:.171875},{x:.203125,y:.171875},{x:.203125,y:.171875},{x:.234375,y:.171875},{x:.234375,y:.171875},{x:.265625,y:.171875},{x:.265625,y:.171875},{x:.296875,y:.171875},{x:.296875,y:.171875},{x:.328125,y:.171875},{x:.328125,y:.171875},{x:.359375,y:.171875},{x:.359375,y:.171875},{x:.390625,y:.171875},{x:.390625,y:.171875},{x:.421875,y:.171875},{x:.421875,y:.171875},{x:.453125,y:.171875},{x:.453125,y:.171875},{x:.484375,y:.171875},{x:.484375,y:.171875},{x:.515625,y:.171875},{x:.515625,y:.171875},{x:.546875,y:.171875},{x:.546875,y:.171875},{x:.578125,y:.171875},{x:.578125,y:.171875},{x:.609375,y:.171875},{x:.609375,y:.171875},{x:.640625,y:.171875},{x:.640625,y:.171875},{x:.671875,y:.171875},{x:.671875,y:.171875},{x:.703125,y:.171875},{x:.703125,y:.171875},{x:.734375,y:.171875},{x:.734375,y:.171875},{x:.765625,y:.171875},{x:.765625,y:.171875},{x:.796875,y:.171875},{x:.796875,y:.171875},{x:.828125,y:.171875},{x:.828125,y:.171875},{x:.859375,y:.171875},{x:.859375,y:.171875},{x:.890625,y:.171875},{x:.890625,y:.171875},{x:.921875,y:.171875},{x:.921875,y:.171875},{x:.953125,y:.171875},{x:.953125,y:.171875},{x:.984375,y:.171875},{x:.984375,y:.171875},{x:.015625,y:.203125},{x:.015625,y:.203125},{x:.046875,y:.203125},{x:.046875,y:.203125},{x:.078125,y:.203125},{x:.078125,y:.203125},{x:.109375,y:.203125},{x:.109375,y:.203125},{x:.140625,y:.203125},{x:.140625,y:.203125},{x:.171875,y:.203125},{x:.171875,y:.203125},{x:.203125,y:.203125},{x:.203125,y:.203125},{x:.234375,y:.203125},{x:.234375,y:.203125},{x:.265625,y:.203125},{x:.265625,y:.203125},{x:.296875,y:.203125},{x:.296875,y:.203125},{x:.328125,y:.203125},{x:.328125,y:.203125},{x:.359375,y:.203125},{x:.359375,y:.203125},{x:.390625,y:.203125},{x:.390625,y:.203125},{x:.421875,y:.203125},{x:.421875,y:.203125},{x:.453125,y:.203125},{x:.453125,y:.203125},{x:.484375,y:.203125},{x:.484375,y:.203125},{x:.515625,y:.203125},{x:.515625,y:.203125},{x:.546875,y:.203125},{x:.546875,y:.203125},{x:.578125,y:.203125},{x:.578125,y:.203125},{x:.609375,y:.203125},{x:.609375,y:.203125},{x:.640625,y:.203125},{x:.640625,y:.203125},{x:.671875,y:.203125},{x:.671875,y:.203125},{x:.703125,y:.203125},{x:.703125,y:.203125},{x:.734375,y:.203125},{x:.734375,y:.203125},{x:.765625,y:.203125},{x:.765625,y:.203125},{x:.796875,y:.203125},{x:.796875,y:.203125},{x:.828125,y:.203125},{x:.828125,y:.203125},{x:.859375,y:.203125},{x:.859375,y:.203125},{x:.890625,y:.203125},{x:.890625,y:.203125},{x:.921875,y:.203125},{x:.921875,y:.203125},{x:.953125,y:.203125},{x:.953125,y:.203125},{x:.984375,y:.203125},{x:.984375,y:.203125},{x:.015625,y:.234375},{x:.015625,y:.234375},{x:.046875,y:.234375},{x:.046875,y:.234375},{x:.078125,y:.234375},{x:.078125,y:.234375},{x:.109375,y:.234375},{x:.109375,y:.234375},{x:.140625,y:.234375},{x:.140625,y:.234375},{x:.171875,y:.234375},{x:.171875,y:.234375},{x:.203125,y:.234375},{x:.203125,y:.234375},{x:.234375,y:.234375},{x:.234375,y:.234375},{x:.265625,y:.234375},{x:.265625,y:.234375},{x:.296875,y:.234375},{x:.296875,y:.234375},{x:.328125,y:.234375},{x:.328125,y:.234375},{x:.359375,y:.234375},{x:.359375,y:.234375},{x:.390625,y:.234375},{x:.390625,y:.234375},{x:.421875,y:.234375},{x:.421875,y:.234375},{x:.453125,y:.234375},{x:.453125,y:.234375},{x:.484375,y:.234375},{x:.484375,y:.234375},{x:.515625,y:.234375},{x:.515625,y:.234375},{x:.546875,y:.234375},{x:.546875,y:.234375},{x:.578125,y:.234375},{x:.578125,y:.234375},{x:.609375,y:.234375},{x:.609375,y:.234375},{x:.640625,y:.234375},{x:.640625,y:.234375},{x:.671875,y:.234375},{x:.671875,y:.234375},{x:.703125,y:.234375},{x:.703125,y:.234375},{x:.734375,y:.234375},{x:.734375,y:.234375},{x:.765625,y:.234375},{x:.765625,y:.234375},{x:.796875,y:.234375},{x:.796875,y:.234375},{x:.828125,y:.234375},{x:.828125,y:.234375},{x:.859375,y:.234375},{x:.859375,y:.234375},{x:.890625,y:.234375},{x:.890625,y:.234375},{x:.921875,y:.234375},{x:.921875,y:.234375},{x:.953125,y:.234375},{x:.953125,y:.234375},{x:.984375,y:.234375},{x:.984375,y:.234375},{x:.015625,y:.265625},{x:.015625,y:.265625},{x:.046875,y:.265625},{x:.046875,y:.265625},{x:.078125,y:.265625},{x:.078125,y:.265625},{x:.109375,y:.265625},{x:.109375,y:.265625},{x:.140625,y:.265625},{x:.140625,y:.265625},{x:.171875,y:.265625},{x:.171875,y:.265625},{x:.203125,y:.265625},{x:.203125,y:.265625},{x:.234375,y:.265625},{x:.234375,y:.265625},{x:.265625,y:.265625},{x:.265625,y:.265625},{x:.296875,y:.265625},{x:.296875,y:.265625},{x:.328125,y:.265625},{x:.328125,y:.265625},{x:.359375,y:.265625},{x:.359375,y:.265625},{x:.390625,y:.265625},{x:.390625,y:.265625},{x:.421875,y:.265625},{x:.421875,y:.265625},{x:.453125,y:.265625},{x:.453125,y:.265625},{x:.484375,y:.265625},{x:.484375,y:.265625},{x:.515625,y:.265625},{x:.515625,y:.265625},{x:.546875,y:.265625},{x:.546875,y:.265625},{x:.578125,y:.265625},{x:.578125,y:.265625},{x:.609375,y:.265625},{x:.609375,y:.265625},{x:.640625,y:.265625},{x:.640625,y:.265625},{x:.671875,y:.265625},{x:.671875,y:.265625},{x:.703125,y:.265625},{x:.703125,y:.265625},{x:.734375,y:.265625},{x:.734375,y:.265625},{x:.765625,y:.265625},{x:.765625,y:.265625},{x:.796875,y:.265625},{x:.796875,y:.265625},{x:.828125,y:.265625},{x:.828125,y:.265625},{x:.859375,y:.265625},{x:.859375,y:.265625},{x:.890625,y:.265625},{x:.890625,y:.265625},{x:.921875,y:.265625},{x:.921875,y:.265625},{x:.953125,y:.265625},{x:.953125,y:.265625},{x:.984375,y:.265625},{x:.984375,y:.265625},{x:.015625,y:.296875},{x:.015625,y:.296875},{x:.046875,y:.296875},{x:.046875,y:.296875},{x:.078125,y:.296875},{x:.078125,y:.296875},{x:.109375,y:.296875},{x:.109375,y:.296875},{x:.140625,y:.296875},{x:.140625,y:.296875},{x:.171875,y:.296875},{x:.171875,y:.296875},{x:.203125,y:.296875},{x:.203125,y:.296875},{x:.234375,y:.296875},{x:.234375,y:.296875},{x:.265625,y:.296875},{x:.265625,y:.296875},{x:.296875,y:.296875},{x:.296875,y:.296875},{x:.328125,y:.296875},{x:.328125,y:.296875},{x:.359375,y:.296875},{x:.359375,y:.296875},{x:.390625,y:.296875},{x:.390625,y:.296875},{x:.421875,y:.296875},{x:.421875,y:.296875},{x:.453125,y:.296875},{x:.453125,y:.296875},{x:.484375,y:.296875},{x:.484375,y:.296875},{x:.515625,y:.296875},{x:.515625,y:.296875},{x:.546875,y:.296875},{x:.546875,y:.296875},{x:.578125,y:.296875},{x:.578125,y:.296875},{x:.609375,y:.296875},{x:.609375,y:.296875},{x:.640625,y:.296875},{x:.640625,y:.296875},{x:.671875,y:.296875},{x:.671875,y:.296875},{x:.703125,y:.296875},{x:.703125,y:.296875},{x:.734375,y:.296875},{x:.734375,y:.296875},{x:.765625,y:.296875},{x:.765625,y:.296875},{x:.796875,y:.296875},{x:.796875,y:.296875},{x:.828125,y:.296875},{x:.828125,y:.296875},{x:.859375,y:.296875},{x:.859375,y:.296875},{x:.890625,y:.296875},{x:.890625,y:.296875},{x:.921875,y:.296875},{x:.921875,y:.296875},{x:.953125,y:.296875},{x:.953125,y:.296875},{x:.984375,y:.296875},{x:.984375,y:.296875},{x:.015625,y:.328125},{x:.015625,y:.328125},{x:.046875,y:.328125},{x:.046875,y:.328125},{x:.078125,y:.328125},{x:.078125,y:.328125},{x:.109375,y:.328125},{x:.109375,y:.328125},{x:.140625,y:.328125},{x:.140625,y:.328125},{x:.171875,y:.328125},{x:.171875,y:.328125},{x:.203125,y:.328125},{x:.203125,y:.328125},{x:.234375,y:.328125},{x:.234375,y:.328125},{x:.265625,y:.328125},{x:.265625,y:.328125},{x:.296875,y:.328125},{x:.296875,y:.328125},{x:.328125,y:.328125},{x:.328125,y:.328125},{x:.359375,y:.328125},{x:.359375,y:.328125},{x:.390625,y:.328125},{x:.390625,y:.328125},{x:.421875,y:.328125},{x:.421875,y:.328125},{x:.453125,y:.328125},{x:.453125,y:.328125},{x:.484375,y:.328125},{x:.484375,y:.328125},{x:.515625,y:.328125},{x:.515625,y:.328125},{x:.546875,y:.328125},{x:.546875,y:.328125},{x:.578125,y:.328125},{x:.578125,y:.328125},{x:.609375,y:.328125},{x:.609375,y:.328125},{x:.640625,y:.328125},{x:.640625,y:.328125},{x:.671875,y:.328125},{x:.671875,y:.328125},{x:.703125,y:.328125},{x:.703125,y:.328125},{x:.734375,y:.328125},{x:.734375,y:.328125},{x:.765625,y:.328125},{x:.765625,y:.328125},{x:.796875,y:.328125},{x:.796875,y:.328125},{x:.828125,y:.328125},{x:.828125,y:.328125},{x:.859375,y:.328125},{x:.859375,y:.328125},{x:.890625,y:.328125},{x:.890625,y:.328125},{x:.921875,y:.328125},{x:.921875,y:.328125},{x:.953125,y:.328125},{x:.953125,y:.328125},{x:.984375,y:.328125},{x:.984375,y:.328125},{x:.015625,y:.359375},{x:.015625,y:.359375},{x:.046875,y:.359375},{x:.046875,y:.359375},{x:.078125,y:.359375},{x:.078125,y:.359375},{x:.109375,y:.359375},{x:.109375,y:.359375},{x:.140625,y:.359375},{x:.140625,y:.359375},{x:.171875,y:.359375},{x:.171875,y:.359375},{x:.203125,y:.359375},{x:.203125,y:.359375},{x:.234375,y:.359375},{x:.234375,y:.359375},{x:.265625,y:.359375},{x:.265625,y:.359375},{x:.296875,y:.359375},{x:.296875,y:.359375},{x:.328125,y:.359375},{x:.328125,y:.359375},{x:.359375,y:.359375},{x:.359375,y:.359375},{x:.390625,y:.359375},{x:.390625,y:.359375},{x:.421875,y:.359375},{x:.421875,y:.359375},{x:.453125,y:.359375},{x:.453125,y:.359375},{x:.484375,y:.359375},{x:.484375,y:.359375},{x:.515625,y:.359375},{x:.515625,y:.359375},{x:.546875,y:.359375},{x:.546875,y:.359375},{x:.578125,y:.359375},{x:.578125,y:.359375},{x:.609375,y:.359375},{x:.609375,y:.359375},{x:.640625,y:.359375},{x:.640625,y:.359375},{x:.671875,y:.359375},{x:.671875,y:.359375},{x:.703125,y:.359375},{x:.703125,y:.359375},{x:.734375,y:.359375},{x:.734375,y:.359375},{x:.765625,y:.359375},{x:.765625,y:.359375},{x:.796875,y:.359375},{x:.796875,y:.359375},{x:.828125,y:.359375},{x:.828125,y:.359375},{x:.859375,y:.359375},{x:.859375,y:.359375},{x:.890625,y:.359375},{x:.890625,y:.359375},{x:.921875,y:.359375},{x:.921875,y:.359375},{x:.953125,y:.359375},{x:.953125,y:.359375},{x:.984375,y:.359375},{x:.984375,y:.359375},{x:.015625,y:.390625},{x:.015625,y:.390625},{x:.046875,y:.390625},{x:.046875,y:.390625},{x:.078125,y:.390625},{x:.078125,y:.390625},{x:.109375,y:.390625},{x:.109375,y:.390625},{x:.140625,y:.390625},{x:.140625,y:.390625},{x:.171875,y:.390625},{x:.171875,y:.390625},{x:.203125,y:.390625},{x:.203125,y:.390625},{x:.234375,y:.390625},{x:.234375,y:.390625},{x:.265625,y:.390625},{x:.265625,y:.390625},{x:.296875,y:.390625},{x:.296875,y:.390625},{x:.328125,y:.390625},{x:.328125,y:.390625},{x:.359375,y:.390625},{x:.359375,y:.390625},{x:.390625,y:.390625},{x:.390625,y:.390625},{x:.421875,y:.390625},{x:.421875,y:.390625},{x:.453125,y:.390625},{x:.453125,y:.390625},{x:.484375,y:.390625},{x:.484375,y:.390625},{x:.515625,y:.390625},{x:.515625,y:.390625},{x:.546875,y:.390625},{x:.546875,y:.390625},{x:.578125,y:.390625},{x:.578125,y:.390625},{x:.609375,y:.390625},{x:.609375,y:.390625},{x:.640625,y:.390625},{x:.640625,y:.390625},{x:.671875,y:.390625},{x:.671875,y:.390625},{x:.703125,y:.390625},{x:.703125,y:.390625},{x:.734375,y:.390625},{x:.734375,y:.390625},{x:.765625,y:.390625},{x:.765625,y:.390625},{x:.796875,y:.390625},{x:.796875,y:.390625},{x:.828125,y:.390625},{x:.828125,y:.390625},{x:.859375,y:.390625},{x:.859375,y:.390625},{x:.890625,y:.390625},{x:.890625,y:.390625},{x:.921875,y:.390625},{x:.921875,y:.390625},{x:.953125,y:.390625},{x:.953125,y:.390625},{x:.984375,y:.390625},{x:.984375,y:.390625},{x:.015625,y:.421875},{x:.015625,y:.421875},{x:.046875,y:.421875},{x:.046875,y:.421875},{x:.078125,y:.421875},{x:.078125,y:.421875},{x:.109375,y:.421875},{x:.109375,y:.421875},{x:.140625,y:.421875},{x:.140625,y:.421875},{x:.171875,y:.421875},{x:.171875,y:.421875},{x:.203125,y:.421875},{x:.203125,y:.421875},{x:.234375,y:.421875},{x:.234375,y:.421875},{x:.265625,y:.421875},{x:.265625,y:.421875},{x:.296875,y:.421875},{x:.296875,y:.421875},{x:.328125,y:.421875},{x:.328125,y:.421875},{x:.359375,y:.421875},{x:.359375,y:.421875},{x:.390625,y:.421875},{x:.390625,y:.421875},{x:.421875,y:.421875},{x:.421875,y:.421875},{x:.453125,y:.421875},{x:.453125,y:.421875},{x:.484375,y:.421875},{x:.484375,y:.421875},{x:.515625,y:.421875},{x:.515625,y:.421875},{x:.546875,y:.421875},{x:.546875,y:.421875},{x:.578125,y:.421875},{x:.578125,y:.421875},{x:.609375,y:.421875},{x:.609375,y:.421875},{x:.640625,y:.421875},{x:.640625,y:.421875},{x:.671875,y:.421875},{x:.671875,y:.421875},{x:.703125,y:.421875},{x:.703125,y:.421875},{x:.734375,y:.421875},{x:.734375,y:.421875},{x:.765625,y:.421875},{x:.765625,y:.421875},{x:.796875,y:.421875},{x:.796875,y:.421875},{x:.828125,y:.421875},{x:.828125,y:.421875},{x:.859375,y:.421875},{x:.859375,y:.421875},{x:.890625,y:.421875},{x:.890625,y:.421875},{x:.921875,y:.421875},{x:.921875,y:.421875},{x:.953125,y:.421875},{x:.953125,y:.421875},{x:.984375,y:.421875},{x:.984375,y:.421875},{x:.015625,y:.453125},{x:.015625,y:.453125},{x:.046875,y:.453125},{x:.046875,y:.453125},{x:.078125,y:.453125},{x:.078125,y:.453125},{x:.109375,y:.453125},{x:.109375,y:.453125},{x:.140625,y:.453125},{x:.140625,y:.453125},{x:.171875,y:.453125},{x:.171875,y:.453125},{x:.203125,y:.453125},{x:.203125,y:.453125},{x:.234375,y:.453125},{x:.234375,y:.453125},{x:.265625,y:.453125},{x:.265625,y:.453125},{x:.296875,y:.453125},{x:.296875,y:.453125},{x:.328125,y:.453125},{x:.328125,y:.453125},{x:.359375,y:.453125},{x:.359375,y:.453125},{x:.390625,y:.453125},{x:.390625,y:.453125},{x:.421875,y:.453125},{x:.421875,y:.453125},{x:.453125,y:.453125},{x:.453125,y:.453125},{x:.484375,y:.453125},{x:.484375,y:.453125},{x:.515625,y:.453125},{x:.515625,y:.453125},{x:.546875,y:.453125},{x:.546875,y:.453125},{x:.578125,y:.453125},{x:.578125,y:.453125},{x:.609375,y:.453125},{x:.609375,y:.453125},{x:.640625,y:.453125},{x:.640625,y:.453125},{x:.671875,y:.453125},{x:.671875,y:.453125},{x:.703125,y:.453125},{x:.703125,y:.453125},{x:.734375,y:.453125},{x:.734375,y:.453125},{x:.765625,y:.453125},{x:.765625,y:.453125},{x:.796875,y:.453125},{x:.796875,y:.453125},{x:.828125,y:.453125},{x:.828125,y:.453125},{x:.859375,y:.453125},{x:.859375,y:.453125},{x:.890625,y:.453125},{x:.890625,y:.453125},{x:.921875,y:.453125},{x:.921875,y:.453125},{x:.953125,y:.453125},{x:.953125,y:.453125},{x:.984375,y:.453125},{x:.984375,y:.453125},{x:.015625,y:.484375},{x:.015625,y:.484375},{x:.046875,y:.484375},{x:.046875,y:.484375},{x:.078125,y:.484375},{x:.078125,y:.484375},{x:.109375,y:.484375},{x:.109375,y:.484375},{x:.140625,y:.484375},{x:.140625,y:.484375},{x:.171875,y:.484375},{x:.171875,y:.484375},{x:.203125,y:.484375},{x:.203125,y:.484375},{x:.234375,y:.484375},{x:.234375,y:.484375},{x:.265625,y:.484375},{x:.265625,y:.484375},{x:.296875,y:.484375},{x:.296875,y:.484375},{x:.328125,y:.484375},{x:.328125,y:.484375},{x:.359375,y:.484375},{x:.359375,y:.484375},{x:.390625,y:.484375},{x:.390625,y:.484375},{x:.421875,y:.484375},{x:.421875,y:.484375},{x:.453125,y:.484375},{x:.453125,y:.484375},{x:.484375,y:.484375},{x:.484375,y:.484375},{x:.515625,y:.484375},{x:.515625,y:.484375},{x:.546875,y:.484375},{x:.546875,y:.484375},{x:.578125,y:.484375},{x:.578125,y:.484375},{x:.609375,y:.484375},{x:.609375,y:.484375},{x:.640625,y:.484375},{x:.640625,y:.484375},{x:.671875,y:.484375},{x:.671875,y:.484375},{x:.703125,y:.484375},{x:.703125,y:.484375},{x:.734375,y:.484375},{x:.734375,y:.484375},{x:.765625,y:.484375},{x:.765625,y:.484375},{x:.796875,y:.484375},{x:.796875,y:.484375},{x:.828125,y:.484375},{x:.828125,y:.484375},{x:.859375,y:.484375},{x:.859375,y:.484375},{x:.890625,y:.484375},{x:.890625,y:.484375},{x:.921875,y:.484375},{x:.921875,y:.484375},{x:.953125,y:.484375},{x:.953125,y:.484375},{x:.984375,y:.484375},{x:.984375,y:.484375},{x:.015625,y:.515625},{x:.015625,y:.515625},{x:.046875,y:.515625},{x:.046875,y:.515625},{x:.078125,y:.515625},{x:.078125,y:.515625},{x:.109375,y:.515625},{x:.109375,y:.515625},{x:.140625,y:.515625},{x:.140625,y:.515625},{x:.171875,y:.515625},{x:.171875,y:.515625},{x:.203125,y:.515625},{x:.203125,y:.515625},{x:.234375,y:.515625},{x:.234375,y:.515625},{x:.265625,y:.515625},{x:.265625,y:.515625},{x:.296875,y:.515625},{x:.296875,y:.515625},{x:.328125,y:.515625},{x:.328125,y:.515625},{x:.359375,y:.515625},{x:.359375,y:.515625},{x:.390625,y:.515625},{x:.390625,y:.515625},{x:.421875,y:.515625},{x:.421875,y:.515625},{x:.453125,y:.515625},{x:.453125,y:.515625},{x:.484375,y:.515625},{x:.484375,y:.515625},{x:.515625,y:.515625},{x:.515625,y:.515625},{x:.546875,y:.515625},{x:.546875,y:.515625},{x:.578125,y:.515625},{x:.578125,y:.515625},{x:.609375,y:.515625},{x:.609375,y:.515625},{x:.640625,y:.515625},{x:.640625,y:.515625},{x:.671875,y:.515625},{x:.671875,y:.515625},{x:.703125,y:.515625},{x:.703125,y:.515625},{x:.734375,y:.515625},{x:.734375,y:.515625},{x:.765625,y:.515625},{x:.765625,y:.515625},{x:.796875,y:.515625},{x:.796875,y:.515625},{x:.828125,y:.515625},{x:.828125,y:.515625},{x:.859375,y:.515625},{x:.859375,y:.515625},{x:.890625,y:.515625},{x:.890625,y:.515625},{x:.921875,y:.515625},{x:.921875,y:.515625},{x:.953125,y:.515625},{x:.953125,y:.515625},{x:.984375,y:.515625},{x:.984375,y:.515625},{x:.015625,y:.546875},{x:.015625,y:.546875},{x:.046875,y:.546875},{x:.046875,y:.546875},{x:.078125,y:.546875},{x:.078125,y:.546875},{x:.109375,y:.546875},{x:.109375,y:.546875},{x:.140625,y:.546875},{x:.140625,y:.546875},{x:.171875,y:.546875},{x:.171875,y:.546875},{x:.203125,y:.546875},{x:.203125,y:.546875},{x:.234375,y:.546875},{x:.234375,y:.546875},{x:.265625,y:.546875},{x:.265625,y:.546875},{x:.296875,y:.546875},{x:.296875,y:.546875},{x:.328125,y:.546875},{x:.328125,y:.546875},{x:.359375,y:.546875},{x:.359375,y:.546875},{x:.390625,y:.546875},{x:.390625,y:.546875},{x:.421875,y:.546875},{x:.421875,y:.546875},{x:.453125,y:.546875},{x:.453125,y:.546875},{x:.484375,y:.546875},{x:.484375,y:.546875},{x:.515625,y:.546875},{x:.515625,y:.546875},{x:.546875,y:.546875},{x:.546875,y:.546875},{x:.578125,y:.546875},{x:.578125,y:.546875},{x:.609375,y:.546875},{x:.609375,y:.546875},{x:.640625,y:.546875},{x:.640625,y:.546875},{x:.671875,y:.546875},{x:.671875,y:.546875},{x:.703125,y:.546875},{x:.703125,y:.546875},{x:.734375,y:.546875},{x:.734375,y:.546875},{x:.765625,y:.546875},{x:.765625,y:.546875},{x:.796875,y:.546875},{x:.796875,y:.546875},{x:.828125,y:.546875},{x:.828125,y:.546875},{x:.859375,y:.546875},{x:.859375,y:.546875},{x:.890625,y:.546875},{x:.890625,y:.546875},{x:.921875,y:.546875},{x:.921875,y:.546875},{x:.953125,y:.546875},{x:.953125,y:.546875},{x:.984375,y:.546875},{x:.984375,y:.546875},{x:.015625,y:.578125},{x:.015625,y:.578125},{x:.046875,y:.578125},{x:.046875,y:.578125},{x:.078125,y:.578125},{x:.078125,y:.578125},{x:.109375,y:.578125},{x:.109375,y:.578125},{x:.140625,y:.578125},{x:.140625,y:.578125},{x:.171875,y:.578125},{x:.171875,y:.578125},{x:.203125,y:.578125},{x:.203125,y:.578125},{x:.234375,y:.578125},{x:.234375,y:.578125},{x:.265625,y:.578125},{x:.265625,y:.578125},{x:.296875,y:.578125},{x:.296875,y:.578125},{x:.328125,y:.578125},{x:.328125,y:.578125},{x:.359375,y:.578125},{x:.359375,y:.578125},{x:.390625,y:.578125},{x:.390625,y:.578125},{x:.421875,y:.578125},{x:.421875,y:.578125},{x:.453125,y:.578125},{x:.453125,y:.578125},{x:.484375,y:.578125},{x:.484375,y:.578125},{x:.515625,y:.578125},{x:.515625,y:.578125},{x:.546875,y:.578125},{x:.546875,y:.578125},{x:.578125,y:.578125},{x:.578125,y:.578125},{x:.609375,y:.578125},{x:.609375,y:.578125},{x:.640625,y:.578125},{x:.640625,y:.578125},{x:.671875,y:.578125},{x:.671875,y:.578125},{x:.703125,y:.578125},{x:.703125,y:.578125},{x:.734375,y:.578125},{x:.734375,y:.578125},{x:.765625,y:.578125},{x:.765625,y:.578125},{x:.796875,y:.578125},{x:.796875,y:.578125},{x:.828125,y:.578125},{x:.828125,y:.578125},{x:.859375,y:.578125},{x:.859375,y:.578125},{x:.890625,y:.578125},{x:.890625,y:.578125},{x:.921875,y:.578125},{x:.921875,y:.578125},{x:.953125,y:.578125},{x:.953125,y:.578125},{x:.984375,y:.578125},{x:.984375,y:.578125},{x:.015625,y:.609375},{x:.015625,y:.609375},{x:.046875,y:.609375},{x:.046875,y:.609375},{x:.078125,y:.609375},{x:.078125,y:.609375},{x:.109375,y:.609375},{x:.109375,y:.609375},{x:.140625,y:.609375},{x:.140625,y:.609375},{x:.171875,y:.609375},{x:.171875,y:.609375},{x:.203125,y:.609375},{x:.203125,y:.609375},{x:.234375,y:.609375},{x:.234375,y:.609375},{x:.265625,y:.609375},{x:.265625,y:.609375},{x:.296875,y:.609375},{x:.296875,y:.609375},{x:.328125,y:.609375},{x:.328125,y:.609375},{x:.359375,y:.609375},{x:.359375,y:.609375},{x:.390625,y:.609375},{x:.390625,y:.609375},{x:.421875,y:.609375},{x:.421875,y:.609375},{x:.453125,y:.609375},{x:.453125,y:.609375},{x:.484375,y:.609375},{x:.484375,y:.609375},{x:.515625,y:.609375},{x:.515625,y:.609375},{x:.546875,y:.609375},{x:.546875,y:.609375},{x:.578125,y:.609375},{x:.578125,y:.609375},{x:.609375,y:.609375},{x:.609375,y:.609375},{x:.640625,y:.609375},{x:.640625,y:.609375},{x:.671875,y:.609375},{x:.671875,y:.609375},{x:.703125,y:.609375},{x:.703125,y:.609375},{x:.734375,y:.609375},{x:.734375,y:.609375},{x:.765625,y:.609375},{x:.765625,y:.609375},{x:.796875,y:.609375},{x:.796875,y:.609375},{x:.828125,y:.609375},{x:.828125,y:.609375},{x:.859375,y:.609375},{x:.859375,y:.609375},{x:.890625,y:.609375},{x:.890625,y:.609375},{x:.921875,y:.609375},{x:.921875,y:.609375},{x:.953125,y:.609375},{x:.953125,y:.609375},{x:.984375,y:.609375},{x:.984375,y:.609375},{x:.015625,y:.640625},{x:.015625,y:.640625},{x:.046875,y:.640625},{x:.046875,y:.640625},{x:.078125,y:.640625},{x:.078125,y:.640625},{x:.109375,y:.640625},{x:.109375,y:.640625},{x:.140625,y:.640625},{x:.140625,y:.640625},{x:.171875,y:.640625},{x:.171875,y:.640625},{x:.203125,y:.640625},{x:.203125,y:.640625},{x:.234375,y:.640625},{x:.234375,y:.640625},{x:.265625,y:.640625},{x:.265625,y:.640625},{x:.296875,y:.640625},{x:.296875,y:.640625},{x:.328125,y:.640625},{x:.328125,y:.640625},{x:.359375,y:.640625},{x:.359375,y:.640625},{x:.390625,y:.640625},{x:.390625,y:.640625},{x:.421875,y:.640625},{x:.421875,y:.640625},{x:.453125,y:.640625},{x:.453125,y:.640625},{x:.484375,y:.640625},{x:.484375,y:.640625},{x:.515625,y:.640625},{x:.515625,y:.640625},{x:.546875,y:.640625},{x:.546875,y:.640625},{x:.578125,y:.640625},{x:.578125,y:.640625},{x:.609375,y:.640625},{x:.609375,y:.640625},{x:.640625,y:.640625},{x:.640625,y:.640625},{x:.671875,y:.640625},{x:.671875,y:.640625},{x:.703125,y:.640625},{x:.703125,y:.640625},{x:.734375,y:.640625},{x:.734375,y:.640625},{x:.765625,y:.640625},{x:.765625,y:.640625},{x:.796875,y:.640625},{x:.796875,y:.640625},{x:.828125,y:.640625},{x:.828125,y:.640625},{x:.859375,y:.640625},{x:.859375,y:.640625},{x:.890625,y:.640625},{x:.890625,y:.640625},{x:.921875,y:.640625},{x:.921875,y:.640625},{x:.953125,y:.640625},{x:.953125,y:.640625},{x:.984375,y:.640625},{x:.984375,y:.640625},{x:.015625,y:.671875},{x:.015625,y:.671875},{x:.046875,y:.671875},{x:.046875,y:.671875},{x:.078125,y:.671875},{x:.078125,y:.671875},{x:.109375,y:.671875},{x:.109375,y:.671875},{x:.140625,y:.671875},{x:.140625,y:.671875},{x:.171875,y:.671875},{x:.171875,y:.671875},{x:.203125,y:.671875},{x:.203125,y:.671875},{x:.234375,y:.671875},{x:.234375,y:.671875},{x:.265625,y:.671875},{x:.265625,y:.671875},{x:.296875,y:.671875},{x:.296875,y:.671875},{x:.328125,y:.671875},{x:.328125,y:.671875},{x:.359375,y:.671875},{x:.359375,y:.671875},{x:.390625,y:.671875},{x:.390625,y:.671875},{x:.421875,y:.671875},{x:.421875,y:.671875},{x:.453125,y:.671875},{x:.453125,y:.671875},{x:.484375,y:.671875},{x:.484375,y:.671875},{x:.515625,y:.671875},{x:.515625,y:.671875},{x:.546875,y:.671875},{x:.546875,y:.671875},{x:.578125,y:.671875},{x:.578125,y:.671875},{x:.609375,y:.671875},{x:.609375,y:.671875},{x:.640625,y:.671875},{x:.640625,y:.671875},{x:.671875,y:.671875},{x:.671875,y:.671875},{x:.703125,y:.671875},{x:.703125,y:.671875},{x:.734375,y:.671875},{x:.734375,y:.671875},{x:.765625,y:.671875},{x:.765625,y:.671875},{x:.796875,y:.671875},{x:.796875,y:.671875},{x:.828125,y:.671875},{x:.828125,y:.671875},{x:.859375,y:.671875},{x:.859375,y:.671875},{x:.890625,y:.671875},{x:.890625,y:.671875},{x:.921875,y:.671875},{x:.921875,y:.671875},{x:.953125,y:.671875},{x:.953125,y:.671875},{x:.984375,y:.671875},{x:.984375,y:.671875},{x:.015625,y:.703125},{x:.015625,y:.703125},{x:.046875,y:.703125},{x:.046875,y:.703125},{x:.078125,y:.703125},{x:.078125,y:.703125},{x:.109375,y:.703125},{x:.109375,y:.703125},{x:.140625,y:.703125},{x:.140625,y:.703125},{x:.171875,y:.703125},{x:.171875,y:.703125},{x:.203125,y:.703125},{x:.203125,y:.703125},{x:.234375,y:.703125},{x:.234375,y:.703125},{x:.265625,y:.703125},{x:.265625,y:.703125},{x:.296875,y:.703125},{x:.296875,y:.703125},{x:.328125,y:.703125},{x:.328125,y:.703125},{x:.359375,y:.703125},{x:.359375,y:.703125},{x:.390625,y:.703125},{x:.390625,y:.703125},{x:.421875,y:.703125},{x:.421875,y:.703125},{x:.453125,y:.703125},{x:.453125,y:.703125},{x:.484375,y:.703125},{x:.484375,y:.703125},{x:.515625,y:.703125},{x:.515625,y:.703125},{x:.546875,y:.703125},{x:.546875,y:.703125},{x:.578125,y:.703125},{x:.578125,y:.703125},{x:.609375,y:.703125},{x:.609375,y:.703125},{x:.640625,y:.703125},{x:.640625,y:.703125},{x:.671875,y:.703125},{x:.671875,y:.703125},{x:.703125,y:.703125},{x:.703125,y:.703125},{x:.734375,y:.703125},{x:.734375,y:.703125},{x:.765625,y:.703125},{x:.765625,y:.703125},{x:.796875,y:.703125},{x:.796875,y:.703125},{x:.828125,y:.703125},{x:.828125,y:.703125},{x:.859375,y:.703125},{x:.859375,y:.703125},{x:.890625,y:.703125},{x:.890625,y:.703125},{x:.921875,y:.703125},{x:.921875,y:.703125},{x:.953125,y:.703125},{x:.953125,y:.703125},{x:.984375,y:.703125},{x:.984375,y:.703125},{x:.015625,y:.734375},{x:.015625,y:.734375},{x:.046875,y:.734375},{x:.046875,y:.734375},{x:.078125,y:.734375},{x:.078125,y:.734375},{x:.109375,y:.734375},{x:.109375,y:.734375},{x:.140625,y:.734375},{x:.140625,y:.734375},{x:.171875,y:.734375},{x:.171875,y:.734375},{x:.203125,y:.734375},{x:.203125,y:.734375},{x:.234375,y:.734375},{x:.234375,y:.734375},{x:.265625,y:.734375},{x:.265625,y:.734375},{x:.296875,y:.734375},{x:.296875,y:.734375},{x:.328125,y:.734375},{x:.328125,y:.734375},{x:.359375,y:.734375},{x:.359375,y:.734375},{x:.390625,y:.734375},{x:.390625,y:.734375},{x:.421875,y:.734375},{x:.421875,y:.734375},{x:.453125,y:.734375},{x:.453125,y:.734375},{x:.484375,y:.734375},{x:.484375,y:.734375},{x:.515625,y:.734375},{x:.515625,y:.734375},{x:.546875,y:.734375},{x:.546875,y:.734375},{x:.578125,y:.734375},{x:.578125,y:.734375},{x:.609375,y:.734375},{x:.609375,y:.734375},{x:.640625,y:.734375},{x:.640625,y:.734375},{x:.671875,y:.734375},{x:.671875,y:.734375},{x:.703125,y:.734375},{x:.703125,y:.734375},{x:.734375,y:.734375},{x:.734375,y:.734375},{x:.765625,y:.734375},{x:.765625,y:.734375},{x:.796875,y:.734375},{x:.796875,y:.734375},{x:.828125,y:.734375},{x:.828125,y:.734375},{x:.859375,y:.734375},{x:.859375,y:.734375},{x:.890625,y:.734375},{x:.890625,y:.734375},{x:.921875,y:.734375},{x:.921875,y:.734375},{x:.953125,y:.734375},{x:.953125,y:.734375},{x:.984375,y:.734375},{x:.984375,y:.734375},{x:.015625,y:.765625},{x:.015625,y:.765625},{x:.046875,y:.765625},{x:.046875,y:.765625},{x:.078125,y:.765625},{x:.078125,y:.765625},{x:.109375,y:.765625},{x:.109375,y:.765625},{x:.140625,y:.765625},{x:.140625,y:.765625},{x:.171875,y:.765625},{x:.171875,y:.765625},{x:.203125,y:.765625},{x:.203125,y:.765625},{x:.234375,y:.765625},{x:.234375,y:.765625},{x:.265625,y:.765625},{x:.265625,y:.765625},{x:.296875,y:.765625},{x:.296875,y:.765625},{x:.328125,y:.765625},{x:.328125,y:.765625},{x:.359375,y:.765625},{x:.359375,y:.765625},{x:.390625,y:.765625},{x:.390625,y:.765625},{x:.421875,y:.765625},{x:.421875,y:.765625},{x:.453125,y:.765625},{x:.453125,y:.765625},{x:.484375,y:.765625},{x:.484375,y:.765625},{x:.515625,y:.765625},{x:.515625,y:.765625},{x:.546875,y:.765625},{x:.546875,y:.765625},{x:.578125,y:.765625},{x:.578125,y:.765625},{x:.609375,y:.765625},{x:.609375,y:.765625},{x:.640625,y:.765625},{x:.640625,y:.765625},{x:.671875,y:.765625},{x:.671875,y:.765625},{x:.703125,y:.765625},{x:.703125,y:.765625},{x:.734375,y:.765625},{x:.734375,y:.765625},{x:.765625,y:.765625},{x:.765625,y:.765625},{x:.796875,y:.765625},{x:.796875,y:.765625},{x:.828125,y:.765625},{x:.828125,y:.765625},{x:.859375,y:.765625},{x:.859375,y:.765625},{x:.890625,y:.765625},{x:.890625,y:.765625},{x:.921875,y:.765625},{x:.921875,y:.765625},{x:.953125,y:.765625},{x:.953125,y:.765625},{x:.984375,y:.765625},{x:.984375,y:.765625},{x:.015625,y:.796875},{x:.015625,y:.796875},{x:.046875,y:.796875},{x:.046875,y:.796875},{x:.078125,y:.796875},{x:.078125,y:.796875},{x:.109375,y:.796875},{x:.109375,y:.796875},{x:.140625,y:.796875},{x:.140625,y:.796875},{x:.171875,y:.796875},{x:.171875,y:.796875},{x:.203125,y:.796875},{x:.203125,y:.796875},{x:.234375,y:.796875},{x:.234375,y:.796875},{x:.265625,y:.796875},{x:.265625,y:.796875},{x:.296875,y:.796875},{x:.296875,y:.796875},{x:.328125,y:.796875},{x:.328125,y:.796875},{x:.359375,y:.796875},{x:.359375,y:.796875},{x:.390625,y:.796875},{x:.390625,y:.796875},{x:.421875,y:.796875},{x:.421875,y:.796875},{x:.453125,y:.796875},{x:.453125,y:.796875},{x:.484375,y:.796875},{x:.484375,y:.796875},{x:.515625,y:.796875},{x:.515625,y:.796875},{x:.546875,y:.796875},{x:.546875,y:.796875},{x:.578125,y:.796875},{x:.578125,y:.796875},{x:.609375,y:.796875},{x:.609375,y:.796875},{x:.640625,y:.796875},{x:.640625,y:.796875},{x:.671875,y:.796875},{x:.671875,y:.796875},{x:.703125,y:.796875},{x:.703125,y:.796875},{x:.734375,y:.796875},{x:.734375,y:.796875},{x:.765625,y:.796875},{x:.765625,y:.796875},{x:.796875,y:.796875},{x:.796875,y:.796875},{x:.828125,y:.796875},{x:.828125,y:.796875},{x:.859375,y:.796875},{x:.859375,y:.796875},{x:.890625,y:.796875},{x:.890625,y:.796875},{x:.921875,y:.796875},{x:.921875,y:.796875},{x:.953125,y:.796875},{x:.953125,y:.796875},{x:.984375,y:.796875},{x:.984375,y:.796875},{x:.015625,y:.828125},{x:.015625,y:.828125},{x:.046875,y:.828125},{x:.046875,y:.828125},{x:.078125,y:.828125},{x:.078125,y:.828125},{x:.109375,y:.828125},{x:.109375,y:.828125},{x:.140625,y:.828125},{x:.140625,y:.828125},{x:.171875,y:.828125},{x:.171875,y:.828125},{x:.203125,y:.828125},{x:.203125,y:.828125},{x:.234375,y:.828125},{x:.234375,y:.828125},{x:.265625,y:.828125},{x:.265625,y:.828125},{x:.296875,y:.828125},{x:.296875,y:.828125},{x:.328125,y:.828125},{x:.328125,y:.828125},{x:.359375,y:.828125},{x:.359375,y:.828125},{x:.390625,y:.828125},{x:.390625,y:.828125},{x:.421875,y:.828125},{x:.421875,y:.828125},{x:.453125,y:.828125},{x:.453125,y:.828125},{x:.484375,y:.828125},{x:.484375,y:.828125},{x:.515625,y:.828125},{x:.515625,y:.828125},{x:.546875,y:.828125},{x:.546875,y:.828125},{x:.578125,y:.828125},{x:.578125,y:.828125},{x:.609375,y:.828125},{x:.609375,y:.828125},{x:.640625,y:.828125},{x:.640625,y:.828125},{x:.671875,y:.828125},{x:.671875,y:.828125},{x:.703125,y:.828125},{x:.703125,y:.828125},{x:.734375,y:.828125},{x:.734375,y:.828125},{x:.765625,y:.828125},{x:.765625,y:.828125},{x:.796875,y:.828125},{x:.796875,y:.828125},{x:.828125,y:.828125},{x:.828125,y:.828125},{x:.859375,y:.828125},{x:.859375,y:.828125},{x:.890625,y:.828125},{x:.890625,y:.828125},{x:.921875,y:.828125},{x:.921875,y:.828125},{x:.953125,y:.828125},{x:.953125,y:.828125},{x:.984375,y:.828125},{x:.984375,y:.828125},{x:.015625,y:.859375},{x:.015625,y:.859375},{x:.046875,y:.859375},{x:.046875,y:.859375},{x:.078125,y:.859375},{x:.078125,y:.859375},{x:.109375,y:.859375},{x:.109375,y:.859375},{x:.140625,y:.859375},{x:.140625,y:.859375},{x:.171875,y:.859375},{x:.171875,y:.859375},{x:.203125,y:.859375},{x:.203125,y:.859375},{x:.234375,y:.859375},{x:.234375,y:.859375},{x:.265625,y:.859375},{x:.265625,y:.859375},{x:.296875,y:.859375},{x:.296875,y:.859375},{x:.328125,y:.859375},{x:.328125,y:.859375},{x:.359375,y:.859375},{x:.359375,y:.859375},{x:.390625,y:.859375},{x:.390625,y:.859375},{x:.421875,y:.859375},{x:.421875,y:.859375},{x:.453125,y:.859375},{x:.453125,y:.859375},{x:.484375,y:.859375},{x:.484375,y:.859375},{x:.515625,y:.859375},{x:.515625,y:.859375},{x:.546875,y:.859375},{x:.546875,y:.859375},{x:.578125,y:.859375},{x:.578125,y:.859375},{x:.609375,y:.859375},{x:.609375,y:.859375},{x:.640625,y:.859375},{x:.640625,y:.859375},{x:.671875,y:.859375},{x:.671875,y:.859375},{x:.703125,y:.859375},{x:.703125,y:.859375},{x:.734375,y:.859375},{x:.734375,y:.859375},{x:.765625,y:.859375},{x:.765625,y:.859375},{x:.796875,y:.859375},{x:.796875,y:.859375},{x:.828125,y:.859375},{x:.828125,y:.859375},{x:.859375,y:.859375},{x:.859375,y:.859375},{x:.890625,y:.859375},{x:.890625,y:.859375},{x:.921875,y:.859375},{x:.921875,y:.859375},{x:.953125,y:.859375},{x:.953125,y:.859375},{x:.984375,y:.859375},{x:.984375,y:.859375},{x:.015625,y:.890625},{x:.015625,y:.890625},{x:.046875,y:.890625},{x:.046875,y:.890625},{x:.078125,y:.890625},{x:.078125,y:.890625},{x:.109375,y:.890625},{x:.109375,y:.890625},{x:.140625,y:.890625},{x:.140625,y:.890625},{x:.171875,y:.890625},{x:.171875,y:.890625},{x:.203125,y:.890625},{x:.203125,y:.890625},{x:.234375,y:.890625},{x:.234375,y:.890625},{x:.265625,y:.890625},{x:.265625,y:.890625},{x:.296875,y:.890625},{x:.296875,y:.890625},{x:.328125,y:.890625},{x:.328125,y:.890625},{x:.359375,y:.890625},{x:.359375,y:.890625},{x:.390625,y:.890625},{x:.390625,y:.890625},{x:.421875,y:.890625},{x:.421875,y:.890625},{x:.453125,y:.890625},{x:.453125,y:.890625},{x:.484375,y:.890625},{x:.484375,y:.890625},{x:.515625,y:.890625},{x:.515625,y:.890625},{x:.546875,y:.890625},{x:.546875,y:.890625},{x:.578125,y:.890625},{x:.578125,y:.890625},{x:.609375,y:.890625},{x:.609375,y:.890625},{x:.640625,y:.890625},{x:.640625,y:.890625},{x:.671875,y:.890625},{x:.671875,y:.890625},{x:.703125,y:.890625},{x:.703125,y:.890625},{x:.734375,y:.890625},{x:.734375,y:.890625},{x:.765625,y:.890625},{x:.765625,y:.890625},{x:.796875,y:.890625},{x:.796875,y:.890625},{x:.828125,y:.890625},{x:.828125,y:.890625},{x:.859375,y:.890625},{x:.859375,y:.890625},{x:.890625,y:.890625},{x:.890625,y:.890625},{x:.921875,y:.890625},{x:.921875,y:.890625},{x:.953125,y:.890625},{x:.953125,y:.890625},{x:.984375,y:.890625},{x:.984375,y:.890625},{x:.015625,y:.921875},{x:.015625,y:.921875},{x:.046875,y:.921875},{x:.046875,y:.921875},{x:.078125,y:.921875},{x:.078125,y:.921875},{x:.109375,y:.921875},{x:.109375,y:.921875},{x:.140625,y:.921875},{x:.140625,y:.921875},{x:.171875,y:.921875},{x:.171875,y:.921875},{x:.203125,y:.921875},{x:.203125,y:.921875},{x:.234375,y:.921875},{x:.234375,y:.921875},{x:.265625,y:.921875},{x:.265625,y:.921875},{x:.296875,y:.921875},{x:.296875,y:.921875},{x:.328125,y:.921875},{x:.328125,y:.921875},{x:.359375,y:.921875},{x:.359375,y:.921875},{x:.390625,y:.921875},{x:.390625,y:.921875},{x:.421875,y:.921875},{x:.421875,y:.921875},{x:.453125,y:.921875},{x:.453125,y:.921875},{x:.484375,y:.921875},{x:.484375,y:.921875},{x:.515625,y:.921875},{x:.515625,y:.921875},{x:.546875,y:.921875},{x:.546875,y:.921875},{x:.578125,y:.921875},{x:.578125,y:.921875},{x:.609375,y:.921875},{x:.609375,y:.921875},{x:.640625,y:.921875},{x:.640625,y:.921875},{x:.671875,y:.921875},{x:.671875,y:.921875},{x:.703125,y:.921875},{x:.703125,y:.921875},{x:.734375,y:.921875},{x:.734375,y:.921875},{x:.765625,y:.921875},{x:.765625,y:.921875},{x:.796875,y:.921875},{x:.796875,y:.921875},{x:.828125,y:.921875},{x:.828125,y:.921875},{x:.859375,y:.921875},{x:.859375,y:.921875},{x:.890625,y:.921875},{x:.890625,y:.921875},{x:.921875,y:.921875},{x:.921875,y:.921875},{x:.953125,y:.921875},{x:.953125,y:.921875},{x:.984375,y:.921875},{x:.984375,y:.921875},{x:.015625,y:.953125},{x:.015625,y:.953125},{x:.046875,y:.953125},{x:.046875,y:.953125},{x:.078125,y:.953125},{x:.078125,y:.953125},{x:.109375,y:.953125},{x:.109375,y:.953125},{x:.140625,y:.953125},{x:.140625,y:.953125},{x:.171875,y:.953125},{x:.171875,y:.953125},{x:.203125,y:.953125},{x:.203125,y:.953125},{x:.234375,y:.953125},{x:.234375,y:.953125},{x:.265625,y:.953125},{x:.265625,y:.953125},{x:.296875,y:.953125},{x:.296875,y:.953125},{x:.328125,y:.953125},{x:.328125,y:.953125},{x:.359375,y:.953125},{x:.359375,y:.953125},{x:.390625,y:.953125},{x:.390625,y:.953125},{x:.421875,y:.953125},{x:.421875,y:.953125},{x:.453125,y:.953125},{x:.453125,y:.953125},{x:.484375,y:.953125},{x:.484375,y:.953125},{x:.515625,y:.953125},{x:.515625,y:.953125},{x:.546875,y:.953125},{x:.546875,y:.953125},{x:.578125,y:.953125},{x:.578125,y:.953125},{x:.609375,y:.953125},{x:.609375,y:.953125},{x:.640625,y:.953125},{x:.640625,y:.953125},{x:.671875,y:.953125},{x:.671875,y:.953125},{x:.703125,y:.953125},{x:.703125,y:.953125},{x:.734375,y:.953125},{x:.734375,y:.953125},{x:.765625,y:.953125},{x:.765625,y:.953125},{x:.796875,y:.953125},{x:.796875,y:.953125},{x:.828125,y:.953125},{x:.828125,y:.953125},{x:.859375,y:.953125},{x:.859375,y:.953125},{x:.890625,y:.953125},{x:.890625,y:.953125},{x:.921875,y:.953125},{x:.921875,y:.953125},{x:.953125,y:.953125},{x:.953125,y:.953125},{x:.984375,y:.953125},{x:.984375,y:.953125},{x:.015625,y:.984375},{x:.015625,y:.984375},{x:.046875,y:.984375},{x:.046875,y:.984375},{x:.078125,y:.984375},{x:.078125,y:.984375},{x:.109375,y:.984375},{x:.109375,y:.984375},{x:.140625,y:.984375},{x:.140625,y:.984375},{x:.171875,y:.984375},{x:.171875,y:.984375},{x:.203125,y:.984375},{x:.203125,y:.984375},{x:.234375,y:.984375},{x:.234375,y:.984375},{x:.265625,y:.984375},{x:.265625,y:.984375},{x:.296875,y:.984375},{x:.296875,y:.984375},{x:.328125,y:.984375},{x:.328125,y:.984375},{x:.359375,y:.984375},{x:.359375,y:.984375},{x:.390625,y:.984375},{x:.390625,y:.984375},{x:.421875,y:.984375},{x:.421875,y:.984375},{x:.453125,y:.984375},{x:.453125,y:.984375},{x:.484375,y:.984375},{x:.484375,y:.984375},{x:.515625,y:.984375},{x:.515625,y:.984375},{x:.546875,y:.984375},{x:.546875,y:.984375},{x:.578125,y:.984375},{x:.578125,y:.984375},{x:.609375,y:.984375},{x:.609375,y:.984375},{x:.640625,y:.984375},{x:.640625,y:.984375},{x:.671875,y:.984375},{x:.671875,y:.984375},{x:.703125,y:.984375},{x:.703125,y:.984375},{x:.734375,y:.984375},{x:.734375,y:.984375},{x:.765625,y:.984375},{x:.765625,y:.984375},{x:.796875,y:.984375},{x:.796875,y:.984375},{x:.828125,y:.984375},{x:.828125,y:.984375},{x:.859375,y:.984375},{x:.859375,y:.984375},{x:.890625,y:.984375},{x:.890625,y:.984375},{x:.921875,y:.984375},{x:.921875,y:.984375},{x:.953125,y:.984375},{x:.953125,y:.984375},{x:.984375,y:.984375},{x:.984375,y:.984375},{x:.03125,y:.03125},{x:.03125,y:.03125},{x:.09375,y:.03125},{x:.09375,y:.03125},{x:.15625,y:.03125},{x:.15625,y:.03125},{x:.21875,y:.03125},{x:.21875,y:.03125},{x:.28125,y:.03125},{x:.28125,y:.03125},{x:.34375,y:.03125},{x:.34375,y:.03125},{x:.40625,y:.03125},{x:.40625,y:.03125},{x:.46875,y:.03125},{x:.46875,y:.03125},{x:.53125,y:.03125},{x:.53125,y:.03125},{x:.59375,y:.03125},{x:.59375,y:.03125},{x:.65625,y:.03125},{x:.65625,y:.03125},{x:.71875,y:.03125},{x:.71875,y:.03125},{x:.78125,y:.03125},{x:.78125,y:.03125},{x:.84375,y:.03125},{x:.84375,y:.03125},{x:.90625,y:.03125},{x:.90625,y:.03125},{x:.96875,y:.03125},{x:.96875,y:.03125},{x:.03125,y:.09375},{x:.03125,y:.09375},{x:.09375,y:.09375},{x:.09375,y:.09375},{x:.15625,y:.09375},{x:.15625,y:.09375},{x:.21875,y:.09375},{x:.21875,y:.09375},{x:.28125,y:.09375},{x:.28125,y:.09375},{x:.34375,y:.09375},{x:.34375,y:.09375},{x:.40625,y:.09375},{x:.40625,y:.09375},{x:.46875,y:.09375},{x:.46875,y:.09375},{x:.53125,y:.09375},{x:.53125,y:.09375},{x:.59375,y:.09375},{x:.59375,y:.09375},{x:.65625,y:.09375},{x:.65625,y:.09375},{x:.71875,y:.09375},{x:.71875,y:.09375},{x:.78125,y:.09375},{x:.78125,y:.09375},{x:.84375,y:.09375},{x:.84375,y:.09375},{x:.90625,y:.09375},{x:.90625,y:.09375},{x:.96875,y:.09375},{x:.96875,y:.09375},{x:.03125,y:.15625},{x:.03125,y:.15625},{x:.09375,y:.15625},{x:.09375,y:.15625},{x:.15625,y:.15625},{x:.15625,y:.15625},{x:.21875,y:.15625},{x:.21875,y:.15625},{x:.28125,y:.15625},{x:.28125,y:.15625},{x:.34375,y:.15625},{x:.34375,y:.15625},{x:.40625,y:.15625},{x:.40625,y:.15625},{x:.46875,y:.15625},{x:.46875,y:.15625},{x:.53125,y:.15625},{x:.53125,y:.15625},{x:.59375,y:.15625},{x:.59375,y:.15625},{x:.65625,y:.15625},{x:.65625,y:.15625},{x:.71875,y:.15625},{x:.71875,y:.15625},{x:.78125,y:.15625},{x:.78125,y:.15625},{x:.84375,y:.15625},{x:.84375,y:.15625},{x:.90625,y:.15625},{x:.90625,y:.15625},{x:.96875,y:.15625},{x:.96875,y:.15625},{x:.03125,y:.21875},{x:.03125,y:.21875},{x:.09375,y:.21875},{x:.09375,y:.21875},{x:.15625,y:.21875},{x:.15625,y:.21875},{x:.21875,y:.21875},{x:.21875,y:.21875},{x:.28125,y:.21875},{x:.28125,y:.21875},{x:.34375,y:.21875},{x:.34375,y:.21875},{x:.40625,y:.21875},{x:.40625,y:.21875},{x:.46875,y:.21875},{x:.46875,y:.21875},{x:.53125,y:.21875},{x:.53125,y:.21875},{x:.59375,y:.21875},{x:.59375,y:.21875},{x:.65625,y:.21875},{x:.65625,y:.21875},{x:.71875,y:.21875},{x:.71875,y:.21875},{x:.78125,y:.21875},{x:.78125,y:.21875},{x:.84375,y:.21875},{x:.84375,y:.21875},{x:.90625,y:.21875},{x:.90625,y:.21875},{x:.96875,y:.21875},{x:.96875,y:.21875},{x:.03125,y:.28125},{x:.03125,y:.28125},{x:.09375,y:.28125},{x:.09375,y:.28125},{x:.15625,y:.28125},{x:.15625,y:.28125},{x:.21875,y:.28125},{x:.21875,y:.28125},{x:.28125,y:.28125},{x:.28125,y:.28125},{x:.34375,y:.28125},{x:.34375,y:.28125},{x:.40625,y:.28125},{x:.40625,y:.28125},{x:.46875,y:.28125},{x:.46875,y:.28125},{x:.53125,y:.28125},{x:.53125,y:.28125},{x:.59375,y:.28125},{x:.59375,y:.28125},{x:.65625,y:.28125},{x:.65625,y:.28125},{x:.71875,y:.28125},{x:.71875,y:.28125},{x:.78125,y:.28125},{x:.78125,y:.28125},{x:.84375,y:.28125},{x:.84375,y:.28125},{x:.90625,y:.28125},{x:.90625,y:.28125},{x:.96875,y:.28125},{x:.96875,y:.28125},{x:.03125,y:.34375},{x:.03125,y:.34375},{x:.09375,y:.34375},{x:.09375,y:.34375},{x:.15625,y:.34375},{x:.15625,y:.34375},{x:.21875,y:.34375},{x:.21875,y:.34375},{x:.28125,y:.34375},{x:.28125,y:.34375},{x:.34375,y:.34375},{x:.34375,y:.34375},{x:.40625,y:.34375},{x:.40625,y:.34375},{x:.46875,y:.34375},{x:.46875,y:.34375},{x:.53125,y:.34375},{x:.53125,y:.34375},{x:.59375,y:.34375},{x:.59375,y:.34375},{x:.65625,y:.34375},{x:.65625,y:.34375},{x:.71875,y:.34375},{x:.71875,y:.34375},{x:.78125,y:.34375},{x:.78125,y:.34375},{x:.84375,y:.34375},{x:.84375,y:.34375},{x:.90625,y:.34375},{x:.90625,y:.34375},{x:.96875,y:.34375},{x:.96875,y:.34375},{x:.03125,y:.40625},{x:.03125,y:.40625},{x:.09375,y:.40625},{x:.09375,y:.40625},{x:.15625,y:.40625},{x:.15625,y:.40625},{x:.21875,y:.40625},{x:.21875,y:.40625},{x:.28125,y:.40625},{x:.28125,y:.40625},{x:.34375,y:.40625},{x:.34375,y:.40625},{x:.40625,y:.40625},{x:.40625,y:.40625},{x:.46875,y:.40625},{x:.46875,y:.40625},{x:.53125,y:.40625},{x:.53125,y:.40625},{x:.59375,y:.40625},{x:.59375,y:.40625},{x:.65625,y:.40625},{x:.65625,y:.40625},{x:.71875,y:.40625},{x:.71875,y:.40625},{x:.78125,y:.40625},{x:.78125,y:.40625},{x:.84375,y:.40625},{x:.84375,y:.40625},{x:.90625,y:.40625},{x:.90625,y:.40625},{x:.96875,y:.40625},{x:.96875,y:.40625},{x:.03125,y:.46875},{x:.03125,y:.46875},{x:.09375,y:.46875},{x:.09375,y:.46875},{x:.15625,y:.46875},{x:.15625,y:.46875},{x:.21875,y:.46875},{x:.21875,y:.46875},{x:.28125,y:.46875},{x:.28125,y:.46875},{x:.34375,y:.46875},{x:.34375,y:.46875},{x:.40625,y:.46875},{x:.40625,y:.46875},{x:.46875,y:.46875},{x:.46875,y:.46875},{x:.53125,y:.46875},{x:.53125,y:.46875},{x:.59375,y:.46875},{x:.59375,y:.46875},{x:.65625,y:.46875},{x:.65625,y:.46875},{x:.71875,y:.46875},{x:.71875,y:.46875},{x:.78125,y:.46875},{x:.78125,y:.46875},{x:.84375,y:.46875},{x:.84375,y:.46875},{x:.90625,y:.46875},{x:.90625,y:.46875},{x:.96875,y:.46875},{x:.96875,y:.46875},{x:.03125,y:.53125},{x:.03125,y:.53125},{x:.09375,y:.53125},{x:.09375,y:.53125},{x:.15625,y:.53125},{x:.15625,y:.53125},{x:.21875,y:.53125},{x:.21875,y:.53125},{x:.28125,y:.53125},{x:.28125,y:.53125},{x:.34375,y:.53125},{x:.34375,y:.53125},{x:.40625,y:.53125},{x:.40625,y:.53125},{x:.46875,y:.53125},{x:.46875,y:.53125},{x:.53125,y:.53125},{x:.53125,y:.53125},{x:.59375,y:.53125},{x:.59375,y:.53125},{x:.65625,y:.53125},{x:.65625,y:.53125},{x:.71875,y:.53125},{x:.71875,y:.53125},{x:.78125,y:.53125},{x:.78125,y:.53125},{x:.84375,y:.53125},{x:.84375,y:.53125},{x:.90625,y:.53125},{x:.90625,y:.53125},{x:.96875,y:.53125},{x:.96875,y:.53125},{x:.03125,y:.59375},{x:.03125,y:.59375},{x:.09375,y:.59375},{x:.09375,y:.59375},{x:.15625,y:.59375},{x:.15625,y:.59375},{x:.21875,y:.59375},{x:.21875,y:.59375},{x:.28125,y:.59375},{x:.28125,y:.59375},{x:.34375,y:.59375},{x:.34375,y:.59375},{x:.40625,y:.59375},{x:.40625,y:.59375},{x:.46875,y:.59375},{x:.46875,y:.59375},{x:.53125,y:.59375},{x:.53125,y:.59375},{x:.59375,y:.59375},{x:.59375,y:.59375},{x:.65625,y:.59375},{x:.65625,y:.59375},{x:.71875,y:.59375},{x:.71875,y:.59375},{x:.78125,y:.59375},{x:.78125,y:.59375},{x:.84375,y:.59375},{x:.84375,y:.59375},{x:.90625,y:.59375},{x:.90625,y:.59375},{x:.96875,y:.59375},{x:.96875,y:.59375},{x:.03125,y:.65625},{x:.03125,y:.65625},{x:.09375,y:.65625},{x:.09375,y:.65625},{x:.15625,y:.65625},{x:.15625,y:.65625},{x:.21875,y:.65625},{x:.21875,y:.65625},{x:.28125,y:.65625},{x:.28125,y:.65625},{x:.34375,y:.65625},{x:.34375,y:.65625},{x:.40625,y:.65625},{x:.40625,y:.65625},{x:.46875,y:.65625},{x:.46875,y:.65625},{x:.53125,y:.65625},{x:.53125,y:.65625},{x:.59375,y:.65625},{x:.59375,y:.65625},{x:.65625,y:.65625},{x:.65625,y:.65625},{x:.71875,y:.65625},{x:.71875,y:.65625},{x:.78125,y:.65625},{x:.78125,y:.65625},{x:.84375,y:.65625},{x:.84375,y:.65625},{x:.90625,y:.65625},{x:.90625,y:.65625},{x:.96875,y:.65625},{x:.96875,y:.65625},{x:.03125,y:.71875},{x:.03125,y:.71875},{x:.09375,y:.71875},{x:.09375,y:.71875},{x:.15625,y:.71875},{x:.15625,y:.71875},{x:.21875,y:.71875},{x:.21875,y:.71875},{x:.28125,y:.71875},{x:.28125,y:.71875},{x:.34375,y:.71875},{x:.34375,y:.71875},{x:.40625,y:.71875},{x:.40625,y:.71875},{x:.46875,y:.71875},{x:.46875,y:.71875},{x:.53125,y:.71875},{x:.53125,y:.71875},{x:.59375,y:.71875},{x:.59375,y:.71875},{x:.65625,y:.71875},{x:.65625,y:.71875},{x:.71875,y:.71875},{x:.71875,y:.71875},{x:.78125,y:.71875},{x:.78125,y:.71875},{x:.84375,y:.71875},{x:.84375,y:.71875},{x:.90625,y:.71875},{x:.90625,y:.71875},{x:.96875,y:.71875},{x:.96875,y:.71875},{x:.03125,y:.78125},{x:.03125,y:.78125},{x:.09375,y:.78125},{x:.09375,y:.78125},{x:.15625,y:.78125},{x:.15625,y:.78125},{x:.21875,y:.78125},{x:.21875,y:.78125},{x:.28125,y:.78125},{x:.28125,y:.78125},{x:.34375,y:.78125},{x:.34375,y:.78125},{x:.40625,y:.78125},{x:.40625,y:.78125},{x:.46875,y:.78125},{x:.46875,y:.78125},{x:.53125,y:.78125},{x:.53125,y:.78125},{x:.59375,y:.78125},{x:.59375,y:.78125},{x:.65625,y:.78125},{x:.65625,y:.78125},{x:.71875,y:.78125},{x:.71875,y:.78125},{x:.78125,y:.78125},{x:.78125,y:.78125},{x:.84375,y:.78125},{x:.84375,y:.78125},{x:.90625,y:.78125},{x:.90625,y:.78125},{x:.96875,y:.78125},{x:.96875,y:.78125},{x:.03125,y:.84375},{x:.03125,y:.84375},{x:.09375,y:.84375},{x:.09375,y:.84375},{x:.15625,y:.84375},{x:.15625,y:.84375},{x:.21875,y:.84375},{x:.21875,y:.84375},{x:.28125,y:.84375},{x:.28125,y:.84375},{x:.34375,y:.84375},{x:.34375,y:.84375},{x:.40625,y:.84375},{x:.40625,y:.84375},{x:.46875,y:.84375},{x:.46875,y:.84375},{x:.53125,y:.84375},{x:.53125,y:.84375},{x:.59375,y:.84375},{x:.59375,y:.84375},{x:.65625,y:.84375},{x:.65625,y:.84375},{x:.71875,y:.84375},{x:.71875,y:.84375},{x:.78125,y:.84375},{x:.78125,y:.84375},{x:.84375,y:.84375},{x:.84375,y:.84375},{x:.90625,y:.84375},{x:.90625,y:.84375},{x:.96875,y:.84375},{x:.96875,y:.84375},{x:.03125,y:.90625},{x:.03125,y:.90625},{x:.09375,y:.90625},{x:.09375,y:.90625},{x:.15625,y:.90625},{x:.15625,y:.90625},{x:.21875,y:.90625},{x:.21875,y:.90625},{x:.28125,y:.90625},{x:.28125,y:.90625},{x:.34375,y:.90625},{x:.34375,y:.90625},{x:.40625,y:.90625},{x:.40625,y:.90625},{x:.46875,y:.90625},{x:.46875,y:.90625},{x:.53125,y:.90625},{x:.53125,y:.90625},{x:.59375,y:.90625},{x:.59375,y:.90625},{x:.65625,y:.90625},{x:.65625,y:.90625},{x:.71875,y:.90625},{x:.71875,y:.90625},{x:.78125,y:.90625},{x:.78125,y:.90625},{x:.84375,y:.90625},{x:.84375,y:.90625},{x:.90625,y:.90625},{x:.90625,y:.90625},{x:.96875,y:.90625},{x:.96875,y:.90625},{x:.03125,y:.96875},{x:.03125,y:.96875},{x:.09375,y:.96875},{x:.09375,y:.96875},{x:.15625,y:.96875},{x:.15625,y:.96875},{x:.21875,y:.96875},{x:.21875,y:.96875},{x:.28125,y:.96875},{x:.28125,y:.96875},{x:.34375,y:.96875},{x:.34375,y:.96875},{x:.40625,y:.96875},{x:.40625,y:.96875},{x:.46875,y:.96875},{x:.46875,y:.96875},{x:.53125,y:.96875},{x:.53125,y:.96875},{x:.59375,y:.96875},{x:.59375,y:.96875},{x:.65625,y:.96875},{x:.65625,y:.96875},{x:.71875,y:.96875},{x:.71875,y:.96875},{x:.78125,y:.96875},{x:.78125,y:.96875},{x:.84375,y:.96875},{x:.84375,y:.96875},{x:.90625,y:.96875},{x:.90625,y:.96875},{x:.96875,y:.96875},{x:.96875,y:.96875},{x:.0625,y:.0625},{x:.0625,y:.0625},{x:.0625,y:.0625},{x:.0625,y:.0625},{x:.0625,y:.0625},{x:.0625,y:.0625},{x:.1875,y:.0625},{x:.1875,y:.0625},{x:.1875,y:.0625},{x:.1875,y:.0625},{x:.1875,y:.0625},{x:.1875,y:.0625},{x:.3125,y:.0625},{x:.3125,y:.0625},{x:.3125,y:.0625},{x:.3125,y:.0625},{x:.3125,y:.0625},{x:.3125,y:.0625},{x:.4375,y:.0625},{x:.4375,y:.0625},{x:.4375,y:.0625},{x:.4375,y:.0625},{x:.4375,y:.0625},{x:.4375,y:.0625},{x:.5625,y:.0625},{x:.5625,y:.0625},{x:.5625,y:.0625},{x:.5625,y:.0625},{x:.5625,y:.0625},{x:.5625,y:.0625},{x:.6875,y:.0625},{x:.6875,y:.0625},{x:.6875,y:.0625},{x:.6875,y:.0625},{x:.6875,y:.0625},{x:.6875,y:.0625},{x:.8125,y:.0625},{x:.8125,y:.0625},{x:.8125,y:.0625},{x:.8125,y:.0625},{x:.8125,y:.0625},{x:.8125,y:.0625},{x:.9375,y:.0625},{x:.9375,y:.0625},{x:.9375,y:.0625},{x:.9375,y:.0625},{x:.9375,y:.0625},{x:.9375,y:.0625},{x:.0625,y:.1875},{x:.0625,y:.1875},{x:.0625,y:.1875},{x:.0625,y:.1875},{x:.0625,y:.1875},{x:.0625,y:.1875},{x:.1875,y:.1875},{x:.1875,y:.1875},{x:.1875,y:.1875},{x:.1875,y:.1875},{x:.1875,y:.1875},{x:.1875,y:.1875},{x:.3125,y:.1875},{x:.3125,y:.1875},{x:.3125,y:.1875},{x:.3125,y:.1875},{x:.3125,y:.1875},{x:.3125,y:.1875},{x:.4375,y:.1875},{x:.4375,y:.1875},{x:.4375,y:.1875},{x:.4375,y:.1875},{x:.4375,y:.1875},{x:.4375,y:.1875},{x:.5625,y:.1875},{x:.5625,y:.1875},{x:.5625,y:.1875},{x:.5625,y:.1875},{x:.5625,y:.1875},{x:.5625,y:.1875},{x:.6875,y:.1875},{x:.6875,y:.1875},{x:.6875,y:.1875},{x:.6875,y:.1875},{x:.6875,y:.1875},{x:.6875,y:.1875},{x:.8125,y:.1875},{x:.8125,y:.1875},{x:.8125,y:.1875},{x:.8125,y:.1875},{x:.8125,y:.1875},{x:.8125,y:.1875},{x:.9375,y:.1875},{x:.9375,y:.1875},{x:.9375,y:.1875},{x:.9375,y:.1875},{x:.9375,y:.1875},{x:.9375,y:.1875},{x:.0625,y:.3125},{x:.0625,y:.3125},{x:.0625,y:.3125},{x:.0625,y:.3125},{x:.0625,y:.3125},{x:.0625,y:.3125},{x:.1875,y:.3125},{x:.1875,y:.3125},{x:.1875,y:.3125},{x:.1875,y:.3125},{x:.1875,y:.3125},{x:.1875,y:.3125},{x:.3125,y:.3125},{x:.3125,y:.3125},{x:.3125,y:.3125},{x:.3125,y:.3125},{x:.3125,y:.3125},{x:.3125,y:.3125},{x:.4375,y:.3125},{x:.4375,y:.3125},{x:.4375,y:.3125},{x:.4375,y:.3125},{x:.4375,y:.3125},{x:.4375,y:.3125},{x:.5625,y:.3125},{x:.5625,y:.3125},{x:.5625,y:.3125},{x:.5625,y:.3125},{x:.5625,y:.3125},{x:.5625,y:.3125},{x:.6875,y:.3125},{x:.6875,y:.3125},{x:.6875,y:.3125},{x:.6875,y:.3125},{x:.6875,y:.3125},{x:.6875,y:.3125},{x:.8125,y:.3125},{x:.8125,y:.3125},{x:.8125,y:.3125},{x:.8125,y:.3125},{x:.8125,y:.3125},{x:.8125,y:.3125},{x:.9375,y:.3125},{x:.9375,y:.3125},{x:.9375,y:.3125},{x:.9375,y:.3125},{x:.9375,y:.3125},{x:.9375,y:.3125},{x:.0625,y:.4375},{x:.0625,y:.4375},{x:.0625,y:.4375},{x:.0625,y:.4375},{x:.0625,y:.4375},{x:.0625,y:.4375},{x:.1875,y:.4375},{x:.1875,y:.4375},{x:.1875,y:.4375},{x:.1875,y:.4375},{x:.1875,y:.4375},{x:.1875,y:.4375},{x:.3125,y:.4375},{x:.3125,y:.4375},{x:.3125,y:.4375},{x:.3125,y:.4375},{x:.3125,y:.4375},{x:.3125,y:.4375},{x:.4375,y:.4375},{x:.4375,y:.4375},{x:.4375,y:.4375},{x:.4375,y:.4375},{x:.4375,y:.4375},{x:.4375,y:.4375},{x:.5625,y:.4375},{x:.5625,y:.4375},{x:.5625,y:.4375},{x:.5625,y:.4375},{x:.5625,y:.4375},{x:.5625,y:.4375},{x:.6875,y:.4375},{x:.6875,y:.4375},{x:.6875,y:.4375},{x:.6875,y:.4375},{x:.6875,y:.4375},{x:.6875,y:.4375},{x:.8125,y:.4375},{x:.8125,y:.4375},{x:.8125,y:.4375},{x:.8125,y:.4375},{x:.8125,y:.4375},{x:.8125,y:.4375},{x:.9375,y:.4375},{x:.9375,y:.4375},{x:.9375,y:.4375},{x:.9375,y:.4375},{x:.9375,y:.4375},{x:.9375,y:.4375},{x:.0625,y:.5625},{x:.0625,y:.5625},{x:.0625,y:.5625},{x:.0625,y:.5625},{x:.0625,y:.5625},{x:.0625,y:.5625},{x:.1875,y:.5625},{x:.1875,y:.5625},{x:.1875,y:.5625},{x:.1875,y:.5625},{x:.1875,y:.5625},{x:.1875,y:.5625},{x:.3125,y:.5625},{x:.3125,y:.5625},{x:.3125,y:.5625},{x:.3125,y:.5625},{x:.3125,y:.5625},{x:.3125,y:.5625},{x:.4375,y:.5625},{x:.4375,y:.5625},{x:.4375,y:.5625},{x:.4375,y:.5625},{x:.4375,y:.5625},{x:.4375,y:.5625},{x:.5625,y:.5625},{x:.5625,y:.5625},{x:.5625,y:.5625},{x:.5625,y:.5625},{x:.5625,y:.5625},{x:.5625,y:.5625},{x:.6875,y:.5625},{x:.6875,y:.5625},{x:.6875,y:.5625},{x:.6875,y:.5625},{x:.6875,y:.5625},{x:.6875,y:.5625},{x:.8125,y:.5625},{x:.8125,y:.5625},{x:.8125,y:.5625},{x:.8125,y:.5625},{x:.8125,y:.5625},{x:.8125,y:.5625},{x:.9375,y:.5625},{x:.9375,y:.5625},{x:.9375,y:.5625},{x:.9375,y:.5625},{x:.9375,y:.5625},{x:.9375,y:.5625},{x:.0625,y:.6875},{x:.0625,y:.6875},{x:.0625,y:.6875},{x:.0625,y:.6875},{x:.0625,y:.6875},{x:.0625,y:.6875},{x:.1875,y:.6875},{x:.1875,y:.6875},{x:.1875,y:.6875},{x:.1875,y:.6875},{x:.1875,y:.6875},{x:.1875,y:.6875},{x:.3125,y:.6875},{x:.3125,y:.6875},{x:.3125,y:.6875},{x:.3125,y:.6875},{x:.3125,y:.6875},{x:.3125,y:.6875},{x:.4375,y:.6875},{x:.4375,y:.6875},{x:.4375,y:.6875},{x:.4375,y:.6875},{x:.4375,y:.6875},{x:.4375,y:.6875},{x:.5625,y:.6875},{x:.5625,y:.6875},{x:.5625,y:.6875},{x:.5625,y:.6875},{x:.5625,y:.6875},{x:.5625,y:.6875},{x:.6875,y:.6875},{x:.6875,y:.6875},{x:.6875,y:.6875},{x:.6875,y:.6875},{x:.6875,y:.6875},{x:.6875,y:.6875},{x:.8125,y:.6875},{x:.8125,y:.6875},{x:.8125,y:.6875},{x:.8125,y:.6875},{x:.8125,y:.6875},{x:.8125,y:.6875},{x:.9375,y:.6875},{x:.9375,y:.6875},{x:.9375,y:.6875},{x:.9375,y:.6875},{x:.9375,y:.6875},{x:.9375,y:.6875},{x:.0625,y:.8125},{x:.0625,y:.8125},{x:.0625,y:.8125},{x:.0625,y:.8125},{x:.0625,y:.8125},{x:.0625,y:.8125},{x:.1875,y:.8125},{x:.1875,y:.8125},{x:.1875,y:.8125},{x:.1875,y:.8125},{x:.1875,y:.8125},{x:.1875,y:.8125},{x:.3125,y:.8125},{x:.3125,y:.8125},{x:.3125,y:.8125},{x:.3125,y:.8125},{x:.3125,y:.8125},{x:.3125,y:.8125},{x:.4375,y:.8125},{x:.4375,y:.8125},{x:.4375,y:.8125},{x:.4375,y:.8125},{x:.4375,y:.8125},{x:.4375,y:.8125},{x:.5625,y:.8125},{x:.5625,y:.8125},{x:.5625,y:.8125},{x:.5625,y:.8125},{x:.5625,y:.8125},{x:.5625,y:.8125},{x:.6875,y:.8125},{x:.6875,y:.8125},{x:.6875,y:.8125},{x:.6875,y:.8125},{x:.6875,y:.8125},{x:.6875,y:.8125},{x:.8125,y:.8125},{x:.8125,y:.8125},{x:.8125,y:.8125},{x:.8125,y:.8125},{x:.8125,y:.8125},{x:.8125,y:.8125},{x:.9375,y:.8125},{x:.9375,y:.8125},{x:.9375,y:.8125},{x:.9375,y:.8125},{x:.9375,y:.8125},{x:.9375,y:.8125},{x:.0625,y:.9375},{x:.0625,y:.9375},{x:.0625,y:.9375},{x:.0625,y:.9375},{x:.0625,y:.9375},{x:.0625,y:.9375},{x:.1875,y:.9375},{x:.1875,y:.9375},{x:.1875,y:.9375},{x:.1875,y:.9375},{x:.1875,y:.9375},{x:.1875,y:.9375},{x:.3125,y:.9375},{x:.3125,y:.9375},{x:.3125,y:.9375},{x:.3125,y:.9375},{x:.3125,y:.9375},{x:.3125,y:.9375},{x:.4375,y:.9375},{x:.4375,y:.9375},{x:.4375,y:.9375},{x:.4375,y:.9375},{x:.4375,y:.9375},{x:.4375,y:.9375},{x:.5625,y:.9375},{x:.5625,y:.9375},{x:.5625,y:.9375},{x:.5625,y:.9375},{x:.5625,y:.9375},{x:.5625,y:.9375},{x:.6875,y:.9375},{x:.6875,y:.9375},{x:.6875,y:.9375},{x:.6875,y:.9375},{x:.6875,y:.9375},{x:.6875,y:.9375},{x:.8125,y:.9375},{x:.8125,y:.9375},{x:.8125,y:.9375},{x:.8125,y:.9375},{x:.8125,y:.9375},{x:.8125,y:.9375},{x:.9375,y:.9375},{x:.9375,y:.9375},{x:.9375,y:.9375},{x:.9375,y:.9375},{x:.9375,y:.9375},{x:.9375,y:.9375}];var oy=class{constructor(t){this.model=t,this.anchors=Yk.map(n=>[n.x,n.y]),this.anchorsTensor=_s(this.anchors),this.inputSize=this.model&&this.model.inputs&&this.model.inputs[0].shape?this.model.inputs[0].shape[2]:0,this.inputSizeTensor=Ot([this.inputSize,this.inputSize]),this.doubleInputSizeTensor=Ot([this.inputSize*2,this.inputSize*2])}normalizeBoxes(t){return H(()=>{let n=Re(t,[0,0],[-1,2]),s=Re(t,[0,2],[-1,2]),r=ae(de(n,this.inputSizeTensor),this.anchorsTensor),a=de(s,this.doubleInputSizeTensor),o=z(Ae(r,a),this.inputSizeTensor),i=z(ae(r,a),this.inputSizeTensor);return Il([o,i],1)})}normalizeLandmarks(t,n){return H(()=>{let s=ae(de(U(t,[-1,7,2]),this.inputSizeTensor),this.anchors[n]);return z(s,this.inputSizeTensor)})}async getBoxes(t,n){let s=this.model.predict(t),r=ot(s);K(s);let a=H(()=>ot(Bn(Re(r,[0,0],[-1,1])))),o=await a.data(),i=Re(r,[0,1],[-1,4]),l=this.normalizeBoxes(i);K(i);let u=await _e.nonMaxSuppressionAsync(l,o,n.hand.maxDetected,n.hand.iouThreshold,n.hand.minConfidence),c=await u.array();K(a),K(u);let d=[];for(let h of c)if(o[h]>=n.hand.minConfidence){let p=Re(l,[h,0],[1,-1]),f=Re(r,[h,5],[1,14]),m=H(()=>U(this.normalizeLandmarks(f,h),[-1,2]));K(f),d.push({box:p,palmLandmarks:m,confidence:o[h]})}return K(r),K(l),d}async estimateHandBounds(t,n){let s=t.shape[1],r=t.shape[2],a=H(()=>Ae(de(_e.resizeBilinear(t,[this.inputSize,this.inputSize]),127.5),1)),o=await this.getBoxes(a,n);K(a);let i=[];if(!o||o.length===0)return i;for(let l of o){let u=await l.box.data(),c=u.slice(0,2),d=u.slice(2,4),h=await l.palmLandmarks.array();K(l.box),K(l.palmLandmarks),i.push(Zk({startPoint:c,endPoint:d,palmLandmarks:h,confidence:l.confidence},[r/this.inputSize,s/this.inputSize]))}return i}};function Cie(e){return e-2*Math.PI*Math.floor((e+Math.PI)/(2*Math.PI))}function Jk(e,t){let n=Math.PI/2-Math.atan2(-(t[1]-e[1]),t[0]-e[0]);return Cie(n)}var Qk=(e,t)=>[[1,0,e],[0,1,t],[0,0,1]];function ia(e,t){let n=0;for(let s=0;s<e.length;s++)n+=e[s]*t[s];return n}function Tie(e,t){let n=[];for(let s=0;s<e.length;s++)n.push(e[s][t]);return n}function e8(e,t){let n=[],s=e.length;for(let r=0;r<s;r++){n.push([]);for(let a=0;a<s;a++)n[r].push(ia(e[r],Tie(t,a)))}return n}function iy(e,t){let n=Math.cos(e),s=Math.sin(e),r=[[n,-s,0],[s,n,0],[0,0,1]],a=Qk(t[0],t[1]),o=e8(a,r),i=Qk(-t[0],-t[1]);return e8(o,i)}function t8(e){let t=[[e[0][0],e[1][0]],[e[0][1],e[1][1]]],n=[e[0][2],e[1][2]],s=[-ia(t[0],n),-ia(t[1],n)];return[t[0].concat(s[0]),t[1].concat(s[1]),[0,0,1]]}function ly(e,t){return[ia(e,t[0]),ia(e,t[1])]}var Nie=5,n8=1.65,s8=[0,5,9,13,17,1,2],Eie=0,Rie=2,uy=class{constructor(t,n){var s;this.handDetector=t,this.handPoseModel=n,this.inputSize=(s=this.handPoseModel)==null?void 0:s.inputs[0].shape[2],this.storedBoxes=[],this.skipped=0,this.detectedHands=0}calculateLandmarksBoundingBox(t){let n=t.map(o=>o[0]),s=t.map(o=>o[1]),r=[Math.min(...n),Math.min(...s)],a=[Math.max(...n),Math.max(...s)];return{startPoint:r,endPoint:a}}getBoxForPalmLandmarks(t,n){let s=t.map(a=>ly([...a,1],n)),r=this.calculateLandmarksBoundingBox(s);return Df(Of(r),Nie)}getBoxForHandLandmarks(t){let n=this.calculateLandmarksBoundingBox(t),s=Df(Of(n),n8);s.palmLandmarks=[];for(let r=0;r<s8.length;r++)s.palmLandmarks.push(t[s8[r]].slice(0,2));return s}transformRawCoords(t,n,s,r){let a=Ff(n),o=[a[0]/this.inputSize,a[1]/this.inputSize,(a[0]+a[1])/this.inputSize/2],i=t.map(p=>[o[0]*(p[0]-this.inputSize/2),o[1]*(p[1]-this.inputSize/2),o[2]*p[2]]),l=iy(s,[0,0]),u=i.map(p=>[...ly(p,l),p[2]]),c=t8(r),d=[...rd(n),1],h=[ia(d,c[0]),ia(d,c[1])];return u.map(p=>[Math.trunc(p[0]+h[0]),Math.trunc(p[1]+h[1]),Math.trunc(p[2])])}async estimateHands(t,n){let s=!1,r;(this.skipped===0||this.skipped>n.hand.skipFrames||!n.hand.landmarks||!n.skipFrame)&&(r=await this.handDetector.estimateHandBounds(t,n),this.skipped=0),n.skipFrame&&this.skipped++,r&&r.length>0&&(r.length!==this.detectedHands&&this.detectedHands!==n.hand.maxDetected||!n.hand.landmarks)&&(this.detectedHands=0,this.storedBoxes=[...r],this.storedBoxes.length>0&&(s=!0));let a=[];for(let o=0;o<this.storedBoxes.length;o++){let i=this.storedBoxes[o];if(!!i)if(n.hand.landmarks){let l=n.hand.rotation?Jk(i.palmLandmarks[Eie],i.palmLandmarks[Rie]):0,u=rd(i),c=[u[0]/t.shape[2],u[1]/t.shape[1]],d=n.hand.rotation&&ns.flags.IS_BROWSER?_e.rotateWithOffset(t,l,0,c):t.clone(),h=iy(-l,u),p=s?this.getBoxForPalmLandmarks(i.palmLandmarks,h):i,f=Kk(p,d,[this.inputSize,this.inputSize]),m=de(f,255);K(f),K(d);let[A,g]=await this.handPoseModel.predict(m);K(m);let y=(await A.data())[0];if(K(A),y>=n.hand.minConfidence){let x=U(g,[-1,3]),b=await x.array();K(g),K(x);let v=this.transformRawCoords(b,p,l,h),k=this.getBoxForHandLandmarks(v);this.storedBoxes[o]={...k,confidence:y};let w={landmarks:v,confidence:y,box:{topLeft:k.startPoint,bottomRight:k.endPoint}};a.push(w)}else this.storedBoxes[o]=null;K(g)}else{let l=Df(Of(i),n8),u={confidence:i.confidence,box:{topLeft:l.startPoint,bottomRight:l.endPoint}};a.push(u)}}return this.storedBoxes=this.storedBoxes.filter(o=>o!==null),this.detectedHands=a.length,a}};var r8={thumb:[1,2,3,4],indexFinger:[5,6,7,8],middleFinger:[9,10,11,12],ringFinger:[13,14,15,16],pinky:[17,18,19,20],palmBase:[0]},la,ua,a8;async function cy(e,t){let n=await a8.estimateHands(e,t);if(!n)return[];let s=[];for(let r=0;r<n.length;r++){let a={};if(n[r].landmarks)for(let u of Object.keys(r8))a[u]=r8[u].map(c=>n[r].landmarks[c]);let o=n[r].landmarks,i=[Number.MAX_SAFE_INTEGER,Number.MAX_SAFE_INTEGER,0,0],l=[0,0,0,0];if(o&&o.length>0){for(let u of o)u[0]<i[0]&&(i[0]=u[0]),u[1]<i[1]&&(i[1]=u[1]),u[0]>i[2]&&(i[2]=u[0]),u[1]>i[3]&&(i[3]=u[1]);i[2]-=i[0],i[3]-=i[1],l=[i[0]/(e.shape[2]||0),i[1]/(e.shape[1]||0),i[2]/(e.shape[2]||0),i[3]/(e.shape[1]||0)]}else i=n[r].box?[Math.trunc(Math.max(0,n[r].box.topLeft[0])),Math.trunc(Math.max(0,n[r].box.topLeft[1])),Math.trunc(Math.min(e.shape[2]||0,n[r].box.bottomRight[0])-Math.max(0,n[r].box.topLeft[0])),Math.trunc(Math.min(e.shape[1]||0,n[r].box.bottomRight[1])-Math.max(0,n[r].box.topLeft[1]))]:[0,0,0,0],l=[n[r].box.topLeft[0]/(e.shape[2]||0),n[r].box.topLeft[1]/(e.shape[1]||0),(n[r].box.bottomRight[0]-n[r].box.topLeft[0])/(e.shape[2]||0),(n[r].box.bottomRight[1]-n[r].box.topLeft[1])/(e.shape[1]||0)];s.push({id:r,score:Math.round(100*n[r].confidence)/100,box:i,boxRaw:l,keypoints:o,annotations:a})}return s}async function dy(e){!la||!ua?([la,ua]=await Promise.all([e.hand.enabled?pt(ft(e.modelBasePath,e.hand.detector.modelPath),{fromTFHub:e.hand.detector.modelPath.includes("tfhub.dev")}):null,e.hand.landmarks?pt(ft(e.modelBasePath,e.hand.skeleton.modelPath),{fromTFHub:e.hand.skeleton.modelPath.includes("tfhub.dev")}):null]),e.hand.enabled&&(!la||!la.modelUrl?ue("load model failed:",e.hand.detector.modelPath):e.debug&&ue("load model:",la.modelUrl),!ua||!ua.modelUrl?ue("load model failed:",e.hand.skeleton.modelPath):e.debug&&ue("load model:",ua.modelUrl))):(e.debug&&ue("cached model:",la.modelUrl),e.debug&&ue("cached model:",ua.modelUrl));let t=new oy(la);return a8=new uy(t,ua),[la,ua]}var o8=["nose","leftEyeInside","leftEye","leftEyeOutside","rightEyeInside","rightEye","rightEyeOutside","leftEar","rightEar","leftMouth","rightMouth","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftPalm","rightPalm","leftIndex","rightIndex","leftPinky","rightPinky","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle","leftHeel","rightHeel","leftFoot","rightFoot","midHip","forehead","leftThumb","leftHand","rightThumb","rightHand"],i8=["nose","leftEyeInside","leftEye","leftEyeOutside","rightEyeInside","rightEye","rightEyeOutside","leftEar","rightEar","leftMouth","rightMouth","leftShoulder","rightShoulder","leftElbow","rightElbow","left:15","right:16","left:17","right:18","left:19","right:20","left:21","right:22","leftChest","rightChest","neck","forehead","left:27","right:28","left:29","right:30"];var On;async function Pf(e){return On?e.debug&&ue("cached model:",On.modelUrl):(On=await pt(ft(e.modelBasePath,e.body.modelPath)),On.width=parseInt(On.signature.inputs["input_1:0"].tensorShape.dim[2].size),On.height=parseInt(On.signature.inputs["input_1:0"].tensorShape.dim[1].size),!On||!On.modelUrl?ue("load model failed:",e.body.modelPath):e.debug&&ue("load model:",On.modelUrl)),On}async function hy(e,t){if(!On)return[];if(!t.body.enabled)return[];let n={width:e.shape[2]||0,height:e.shape[1]||0},s=_e.resizeBilinear(e,[On.width,On.height],!1),r=de(s,[255]);K(s);let a=await On.predict(r),o=a.find(A=>A.size===195||A.size===155),i=await(o==null?void 0:o.data())||[];a.forEach(A=>K(A)),K(r);let l=[],u=(i==null?void 0:i.length)===195?o8:i8,c=5;for(let A=0;A<i.length/c;A++)l.push({id:A,part:u[A],position:[Math.trunc(n.width*i[c*A+0]/255),Math.trunc(n.height*i[c*A+1]/255),Math.trunc(i[c*A+2])+0],positionRaw:[i[c*A+0]/255,i[c*A+1]/255,i[c*A+2]+0],score:(100-Math.trunc(100/(1+Math.exp(i[c*A+3]))))/100,presence:(100-Math.trunc(100/(1+Math.exp(i[c*A+4]))))/100});let d=l.map(A=>A.position[0]),h=l.map(A=>A.position[1]),p=[Math.min(...d),Math.min(...h),Math.max(...d)-Math.min(...d),Math.max(...h)-Math.min(...d)],f=[0,0,0,0],m=l.reduce((A,g)=>g.score>A?g.score:A,0);return[{id:0,score:m,box:p,boxRaw:f,keypoints:l}]}var Pn,ir=[],py=[0,0,0,0],fy=[0,0,0,0],Mf=0,my=Number.MAX_SAFE_INTEGER,_ie=["head","neck","rightShoulder","rightElbow","rightWrist","chest","leftShoulder","leftElbow","leftWrist","pelvis","rightHip","rightKnee","rightAnkle","leftHip","leftKnee","leftAnkle"];async function l8(e){return Pn?e.debug&&ue("cached model:",Pn.modelUrl):(Pn=await pt(ft(e.modelBasePath,e.body.modelPath)),!Pn||!Pn.modelUrl?ue("load model failed:",e.body.modelPath):e.debug&&ue("load model:",Pn.modelUrl)),Pn}function $ie(e,t){let[n,s]=e.shape;return H(()=>{let r=(i,l)=>Ae(i,z(de(i,Ie(l,"int32")),Ie(l,"int32"))),a=U(e,[s*n]),o=ls(a,0).dataSync()[0];if(o>t){let i=Xs(a,0),l=r(i,n).dataSync()[0],u=de(i,Ie(n,"int32")).dataSync()[0];return[l,u,o]}return[0,0,o]})}async function Ay(e,t){return my<t.body.skipFrames&&t.skipFrame&&Object.keys(ir).length>0?(my++,[{id:0,score:Mf,box:py,boxRaw:fy,keypoints:ir}]):(my=0,new Promise(async n=>{let s=H(()=>{if(!Pn.inputs[0].shape)return null;let u=_e.resizeBilinear(e,[Pn.inputs[0].shape[2],Pn.inputs[0].shape[1]],!1);return z(u,2).sub(1)}),r;if(t.body.enabled&&(r=await Pn.predict(s)),K(s),r){ir.length=0;let u=r.squeeze();K(r);let c=u.unstack(2);K(u);for(let d=0;d<c.length;d++){let[h,p,f]=$ie(c[d],t.body.minConfidence);Mf>t.body.minConfidence&&ir.push({score:Math.round(100*f)/100,part:_ie[d],positionRaw:[h/Pn.inputs[0].shape[2],p/Pn.inputs[0].shape[1]],position:[Math.round(e.shape[2]*h/Pn.inputs[0].shape[2]),Math.round(e.shape[1]*p/Pn.inputs[0].shape[1])]})}c.forEach(d=>K(d))}Mf=ir.reduce((u,c)=>c.score>u?c.score:u,0);let a=ir.map(u=>u.position[0]),o=ir.map(u=>u.position[1]);py=[Math.min(...a),Math.min(...o),Math.max(...a)-Math.min(...a),Math.max(...o)-Math.min(...o)];let i=ir.map(u=>u.positionRaw[0]),l=ir.map(u=>u.positionRaw[1]);fy=[Math.min(...i),Math.min(...l),Math.max(...i)-Math.min(...i),Math.max(...l)-Math.min(...l)],n([{id:0,score:Mf,box:py,boxRaw:fy,keypoints:ir}])}))}var Us,lr=[],gy=[0,0,0,0],yy=[0,0,0,0],iu=0,xy=Number.MAX_SAFE_INTEGER,Fie=["nose","leftEye","rightEye","leftEar","rightEar","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle"];async function by(e){return Us?e.debug&&ue("cached model:",Us.modelUrl):(Us=await pt(ft(e.modelBasePath,e.body.modelPath)),!Us||!Us.modelUrl?ue("load model failed:",e.body.modelPath):e.debug&&ue("load model:",Us.modelUrl)),Us}async function vy(e,t){return xy<t.body.skipFrames&&t.skipFrame&&Object.keys(lr).length>0?(xy++,[{id:0,score:iu,box:gy,boxRaw:yy,keypoints:lr}]):(xy=0,new Promise(async n=>{let s=H(()=>{if(!Us.inputs[0].shape)return null;let u=_e.resizeBilinear(e,[Us.inputs[0].shape[2],Us.inputs[0].shape[1]],!1);return ce(u,"int32")}),r;if(t.body.enabled&&(r=await Us.predict(s)),K(s),r){lr.length=0;let u=await r.array();K(r);let c=u[0][0];for(let d=0;d<c.length;d++)iu=c[d][2],iu>t.body.minConfidence&&lr.push({score:Math.round(100*iu)/100,part:Fie[d],positionRaw:[c[d][1],c[d][0]],position:[Math.round((e.shape[2]||0)*c[d][1]),Math.round((e.shape[1]||0)*c[d][0])]})}iu=lr.reduce((u,c)=>c.score>u?c.score:u,0);let a=lr.map(u=>u.position[0]),o=lr.map(u=>u.position[1]);gy=[Math.min(...a),Math.min(...o),Math.max(...a)-Math.min(...a),Math.max(...o)-Math.min(...o)];let i=lr.map(u=>u.positionRaw[0]),l=lr.map(u=>u.positionRaw[1]);yy=[Math.min(...i),Math.min(...l),Math.max(...i)-Math.min(...i),Math.max(...l)-Math.min(...l)],n([{id:0,score:iu,box:gy,boxRaw:yy,keypoints:lr}])}))}var lu=[{class:1,label:"person"},{class:2,label:"bicycle"},{class:3,label:"car"},{class:4,label:"motorcycle"},{class:5,label:"airplane"},{class:6,label:"bus"},{class:7,label:"train"},{class:8,label:"truck"},{class:9,label:"boat"},{class:10,label:"traffic light"},{class:11,label:"fire hydrant"},{class:12,label:"stop sign"},{class:13,label:"parking meter"},{class:14,label:"bench"},{class:15,label:"bird"},{class:16,label:"cat"},{class:17,label:"dog"},{class:18,label:"horse"},{class:19,label:"sheep"},{class:20,label:"cow"},{class:21,label:"elephant"},{class:22,label:"bear"},{class:23,label:"zebra"},{class:24,label:"giraffe"},{class:25,label:"backpack"},{class:26,label:"umbrella"},{class:27,label:"handbag"},{class:28,label:"tie"},{class:29,label:"suitcase"},{class:30,label:"frisbee"},{class:31,label:"skis"},{class:32,label:"snowboard"},{class:33,label:"sports ball"},{class:34,label:"kite"},{class:35,label:"baseball bat"},{class:36,label:"baseball glove"},{class:37,label:"skateboard"},{class:38,label:"surfboard"},{class:39,label:"tennis racket"},{class:40,label:"bottle"},{class:41,label:"wine glass"},{class:42,label:"cup"},{class:43,label:"fork"},{class:44,label:"knife"},{class:45,label:"spoon"},{class:46,label:"bowl"},{class:47,label:"banana"},{class:48,label:"apple"},{class:49,label:"sandwich"},{class:50,label:"orange"},{class:51,label:"broccoli"},{class:52,label:"carrot"},{class:53,label:"hot dog"},{class:54,label:"pizza"},{class:55,label:"donut"},{class:56,label:"cake"},{class:57,label:"chair"},{class:58,label:"couch"},{class:59,label:"potted plant"},{class:60,label:"bed"},{class:61,label:"dining table"},{class:62,label:"toilet"},{class:63,label:"tv"},{class:64,label:"laptop"},{class:65,label:"mouse"},{class:66,label:"remote"},{class:67,label:"keyboard"},{class:68,label:"cell phone"},{class:69,label:"microwave"},{class:70,label:"oven"},{class:71,label:"toaster"},{class:72,label:"sink"},{class:73,label:"refrigerator"},{class:74,label:"book"},{class:75,label:"clock"},{class:76,label:"vase"},{class:77,label:"scissors"},{class:78,label:"teddy bear"},{class:79,label:"hair drier"},{class:80,label:"toothbrush"}];var Kn,wy=[],ky=Number.MAX_SAFE_INTEGER,zf=2.5;async function Iy(e){if(Kn)e.debug&&ue("cached model:",Kn.modelUrl);else{Kn=await pt(ft(e.modelBasePath,e.object.modelPath));let t=Object.values(Kn.modelSignature.inputs);if(Kn.inputSize=Array.isArray(t)?parseInt(t[0].tensorShape.dim[2].size):null,!Kn.inputSize)throw new Error(`Human: Cannot determine model inputSize: ${e.object.modelPath}`);!Kn||!Kn.modelUrl?ue("load model failed:",e.object.modelPath):e.debug&&ue("load model:",Kn.modelUrl)}return Kn}async function Die(e,t,n,s){let r=0,a=[];for(let u of[1,2,4])H(async()=>{var A,g;let c=u*13,d=(A=e.find(y=>y.shape[1]===c**2&&y.shape[2]===lu.length))==null?void 0:A.squeeze(),h=(g=e.find(y=>y.shape[1]===c**2&&y.shape[2]<lu.length))==null?void 0:g.squeeze(),f=await h.reshape([-1,4,h.shape[1]/4]).argMax(2).array(),m=await d.array();for(let y=0;y<d.shape[0];y++)for(let x=0;x<d.shape[1];x++){let b=m[y][x];if(b>s.object.minConfidence&&x!==61){let v=(.5+Math.trunc(y%c))/c,k=(.5+Math.trunc(y/c))/c,w=f[y].map(W=>W*(c/u/t)),[C,E]=[v-zf/u*w[0],k-zf/u*w[1]],[M,R]=[v+zf/u*w[2]-C,k+zf/u*w[3]-E],_=[C,E,M,R];_=_.map(W=>Math.max(0,Math.min(W,1)));let N=[_[0]*n[0],_[1]*n[1],_[2]*n[0],_[3]*n[1]],O={id:r++,score:Math.round(100*b)/100,class:x+1,label:lu[x].label,box:N.map(W=>Math.trunc(W)),boxRaw:_};a.push(O)}}});e.forEach(u=>K(u));let o=a.map(u=>[u.boxRaw[1],u.boxRaw[0],u.boxRaw[3],u.boxRaw[2]]),i=a.map(u=>u.score),l=[];if(o&&o.length>0){let u=await _e.nonMaxSuppressionAsync(o,i,s.object.maxDetected,s.object.iouThreshold,s.object.minConfidence);l=await u.data(),K(u)}return a=a.filter((u,c)=>l.includes(c)).sort((u,c)=>c.score-u.score),a}async function Sy(e,t){return ky<t.object.skipFrames&&t.skipFrame&&wy.length>0?(ky++,wy):(ky=0,new Promise(async n=>{let s=[e.shape[2],e.shape[1]],r=_e.resizeBilinear(e,[Kn.inputSize,Kn.inputSize],!1),a=de(r,255),o=a.transpose([0,3,1,2]);K(a),K(r);let i;t.object.enabled&&(i=await Kn.predict(o)),K(o);let l=await Die(i,Kn.inputSize,s,t);wy=l,n(l)}))}var Zn,Cy=[],Ty=Number.MAX_SAFE_INTEGER;async function Ny(e){if(Zn)e.debug&&ue("cached model:",Zn.modelUrl);else{Zn=await pt(ft(e.modelBasePath,e.object.modelPath));let t=Object.values(Zn.modelSignature.inputs);if(Zn.inputSize=Array.isArray(t)?parseInt(t[0].tensorShape.dim[2].size):null,!Zn.inputSize)throw new Error(`Human: Cannot determine model inputSize: ${e.object.modelPath}`);!Zn||!Zn.modelUrl?ue("load model failed:",e.object.modelPath):e.debug&&ue("load model:",Zn.modelUrl)}return Zn}async function Oie(e,t,n,s){if(!e)return[];let r=[],a=await e.array(),o=ot(e);K(e);let i=nn(o,6,1);K(o);let l=Nn([i[1],i[0],i[3],i[2]],1),u=ot(l),c=ot(i[4]),d=ot(i[5]);i.forEach(m=>K(m));let h=await _e.nonMaxSuppressionAsync(u,c,s.object.maxDetected,s.object.iouThreshold,s.object.minConfidence);K(u),K(c),K(d);let p=await h.data();K(h);let f=0;for(let m of p){let A=Math.trunc(100*a[0][m][4])/100,g=a[0][m][5],y=lu[g].label,[x,b]=[a[0][m][0]/t,a[0][m][1]/t],v=[x,b,a[0][m][2]/t-x,a[0][m][3]/t-b],k=[Math.trunc(v[0]*n[0]),Math.trunc(v[1]*n[1]),Math.trunc(v[2]*n[0]),Math.trunc(v[3]*n[1])];r.push({id:f++,score:A,class:g,label:y,box:k,boxRaw:v})}return r}async function Ey(e,t){return Ty<t.object.skipFrames&&t.skipFrame&&Cy.length>0?(Ty++,Cy):(Ty=0,new Promise(async n=>{let s=[e.shape[2],e.shape[1]],r=_e.resizeBilinear(e,[Zn.inputSize,Zn.inputSize]),a=t.object.enabled?Zn.execute(r,["tower_0/detections"]):null;K(r);let o=await Oie(a,Zn.inputSize,s,t);Cy=o,n(o)}))}function Pie(e,t,n){let s=function(i,l,u){let c=new RegExp("\\b"+l+" \\w+ (\\w+)","ig");i.replace(c,(d,h)=>(u[h]=0,d))},r=function(i,l){let u=e.createShader(l);if(e.shaderSource(u,i),e.compileShader(u),!e.getShaderParameter(u,e.COMPILE_STATUS))throw new Error("Filter: GL compile failed",e.getShaderInfoLog(u));return u};this.uniform={},this.attribute={};let a=r(t,e.VERTEX_SHADER),o=r(n,e.FRAGMENT_SHADER);if(this.id=e.createProgram(),e.attachShader(this.id,a),e.attachShader(this.id,o),e.linkProgram(this.id),!e.getProgramParameter(this.id,e.LINK_STATUS))throw new Error("Filter: GL link failed",e.getProgramInfoLog(this.id));e.useProgram(this.id),s(t,"attribute",this.attribute);for(let i in this.attribute)this.attribute[i]=e.getAttribLocation(this.id,i);s(t,"uniform",this.uniform),s(n,"uniform",this.uniform);for(let i in this.uniform)this.uniform[i]=e.getUniformLocation(this.id,i)}function u8(e){e||(e={});let t=0,n=null,s=!1,r=-1,a=[null,null],o=[],i=-1,l=-1,u=null,c=null,d={},h=e.canvas||document.createElement("canvas"),p={},f={INTERMEDIATE:1},m=h.getContext("webgl");if(!m)throw new Error("Filter: getContext() failed");this.addFilter=function(v){let k=Array.prototype.slice.call(arguments,1),w=d[v];o.push({func:w,args:k})},this.reset=function(){o=[]};let A=function(v,k){if(!(v===i&&k===l)){if(h.width=v,i=v,h.height=k,l=k,!u){let w=new Float32Array([-1,-1,0,1,1,-1,1,1,-1,1,0,0,-1,1,0,0,1,-1,1,1,1,1,1,0]);u=m.createBuffer(),m.bindBuffer(m.ARRAY_BUFFER,u),m.bufferData(m.ARRAY_BUFFER,w,m.STATIC_DRAW),m.pixelStorei(m.UNPACK_PREMULTIPLY_ALPHA_WEBGL,!0)}m.viewport(0,0,i,l),a=[null,null]}},g=function(v,k){let w=m.createFramebuffer();m.bindFramebuffer(m.FRAMEBUFFER,w);let C=m.createRenderbuffer();m.bindRenderbuffer(m.RENDERBUFFER,C);let E=m.createTexture();return m.bindTexture(m.TEXTURE_2D,E),m.texImage2D(m.TEXTURE_2D,0,m.RGBA,v,k,0,m.RGBA,m.UNSIGNED_BYTE,null),m.texParameteri(m.TEXTURE_2D,m.TEXTURE_MAG_FILTER,m.LINEAR),m.texParameteri(m.TEXTURE_2D,m.TEXTURE_MIN_FILTER,m.LINEAR),m.texParameteri(m.TEXTURE_2D,m.TEXTURE_WRAP_S,m.CLAMP_TO_EDGE),m.texParameteri(m.TEXTURE_2D,m.TEXTURE_WRAP_T,m.CLAMP_TO_EDGE),m.framebufferTexture2D(m.FRAMEBUFFER,m.COLOR_ATTACHMENT0,m.TEXTURE_2D,E,0),m.bindTexture(m.TEXTURE_2D,null),m.bindFramebuffer(m.FRAMEBUFFER,null),{fbo:w,texture:E}},y=function(v){return a[v]=a[v]||g(i,l),a[v]},x=function(v=null){var E,M;let k=null,w=null,C=!1;t===0?k=n:k=(E=y(r))==null?void 0:E.texture,t++,s&&!(v&f.INTERMEDIATE)?(w=null,C=t%2==0):(r=(r+1)%2,w=(M=y(r))==null?void 0:M.fbo),m.bindTexture(m.TEXTURE_2D,k),m.bindFramebuffer(m.FRAMEBUFFER,w),m.uniform1f(c.uniform.flipY,C?-1:1),m.drawArrays(m.TRIANGLES,0,6)};this.apply=function(v){if(A(v.width,v.height),t=0,n||(n=m.createTexture()),m.bindTexture(m.TEXTURE_2D,n),m.texParameteri(m.TEXTURE_2D,m.TEXTURE_WRAP_S,m.CLAMP_TO_EDGE),m.texParameteri(m.TEXTURE_2D,m.TEXTURE_WRAP_T,m.CLAMP_TO_EDGE),m.texParameteri(m.TEXTURE_2D,m.TEXTURE_MIN_FILTER,m.NEAREST),m.texParameteri(m.TEXTURE_2D,m.TEXTURE_MAG_FILTER,m.NEAREST),m.texImage2D(m.TEXTURE_2D,0,m.RGBA,m.RGBA,m.UNSIGNED_BYTE,v),o.length===0)return x(),h;for(let k=0;k<o.length;k++){s=k===o.length-1;let w=o[k];w.func.apply(this,w.args||[])}return h};let b=function(v){if(p[v])return c=p[v],m.useProgram(c.id),c;let k={};k.VERTEX_IDENTITY=["precision highp float;","attribute vec2 pos;","attribute vec2 uv;","varying vec2 vUv;","uniform float flipY;","void main(void) {","vUv = uv;","gl_Position = vec4(pos.x, pos.y*flipY, 0.0, 1.);","}"].join(`
`),k.FRAGMENT_IDENTITY=["precision highp float;","varying vec2 vUv;","uniform sampler2D texture;","void main(void) {","gl_FragColor = texture2D(texture, vUv);","}"].join(`
`),c=new Pie(m,k.VERTEX_IDENTITY,v);let w=Float32Array.BYTES_PER_ELEMENT,C=4*w;return m.enableVertexAttribArray(c.attribute.pos),m.vertexAttribPointer(c.attribute.pos,2,m.FLOAT,!1,C,0*w),m.enableVertexAttribArray(c.attribute.uv),m.vertexAttribPointer(c.attribute.uv,2,m.FLOAT,!1,C,2*w),p[v]=c,c};d.colorMatrix=function(v){let k=new Float32Array(v);k[4]/=255,k[9]/=255,k[14]/=255,k[19]/=255;let w=k[18]===1&&k[3]===0&&k[8]===0&&k[13]===0&&k[15]===0&&k[16]===0&&k[17]===0&&k[19]===0?d.colorMatrix.SHADER.WITHOUT_ALPHA:d.colorMatrix.SHADER.WITH_ALPHA,C=b(w);m.uniform1fv(C.uniform.m,k),x()},d.colorMatrix.SHADER={},d.colorMatrix.SHADER.WITH_ALPHA=["precision highp float;","varying vec2 vUv;","uniform sampler2D texture;","uniform float m[20];","void main(void) {","vec4 c = texture2D(texture, vUv);","gl_FragColor.r = m[0] * c.r + m[1] * c.g + m[2] * c.b + m[3] * c.a + m[4];","gl_FragColor.g = m[5] * c.r + m[6] * c.g + m[7] * c.b + m[8] * c.a + m[9];","gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[13] * c.a + m[14];","gl_FragColor.a = m[15] * c.r + m[16] * c.g + m[17] * c.b + m[18] * c.a + m[19];","}"].join(`
`),d.colorMatrix.SHADER.WITHOUT_ALPHA=["precision highp float;","varying vec2 vUv;","uniform sampler2D texture;","uniform float m[20];","void main(void) {","vec4 c = texture2D(texture, vUv);","gl_FragColor.r = m[0] * c.r + m[1] * c.g + m[2] * c.b + m[4];","gl_FragColor.g = m[5] * c.r + m[6] * c.g + m[7] * c.b + m[9];","gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[14];","gl_FragColor.a = c.a;","}"].join(`
`),d.brightness=function(v){let k=(v||0)+1;d.colorMatrix([k,0,0,0,0,0,k,0,0,0,0,0,k,0,0,0,0,0,1,0])},d.saturation=function(v){let k=(v||0)*2/3+1,w=(k-1)*-.5;d.colorMatrix([k,w,w,0,0,w,k,w,0,0,w,w,k,0,0,0,0,0,1,0])},d.desaturate=function(){d.saturation(-1)},d.contrast=function(v){let k=(v||0)+1,w=-128*(k-1);d.colorMatrix([k,0,0,0,w,0,k,0,0,w,0,0,k,0,w,0,0,0,1,0])},d.negative=function(){d.contrast(-2)},d.hue=function(v){v=(v||0)/180*Math.PI;let k=Math.cos(v),w=Math.sin(v),C=.213,E=.715,M=.072;d.colorMatrix([C+k*(1-C)+w*-C,E+k*-E+w*-E,M+k*-M+w*(1-M),0,0,C+k*-C+w*.143,E+k*(1-E)+w*.14,M+k*-M+w*-.283,0,0,C+k*-C+w*-(1-C),E+k*-E+w*E,M+k*(1-M)+w*M,0,0,0,0,0,1,0])},d.desaturateLuminance=function(){d.colorMatrix([.2764723,.929708,.0938197,0,-37.1,.2764723,.929708,.0938197,0,-37.1,.2764723,.929708,.0938197,0,-37.1,0,0,0,1,0])},d.sepia=function(){d.colorMatrix([.393,.7689999,.18899999,0,0,.349,.6859999,.16799999,0,0,.272,.5339999,.13099999,0,0,0,0,0,1,0])},d.brownie=function(){d.colorMatrix([.5997023498159715,.34553243048391263,-.2708298674538042,0,47.43192855600873,-.037703249837783157,.8609577587992641,.15059552388459913,0,-36.96841498319127,.24113635128153335,-.07441037908422492,.44972182064877153,0,-7.562075277591283,0,0,0,1,0])},d.vintagePinhole=function(){d.colorMatrix([.6279345635605994,.3202183420819367,-.03965408211312453,0,9.651285835294123,.02578397704808868,.6441188644374771,.03259127616149294,0,7.462829176470591,.0466055556782719,-.0851232987247891,.5241648018700465,0,5.159190588235296,0,0,0,1,0])},d.kodachrome=function(){d.colorMatrix([1.1285582396593525,-.3967382283601348,-.03992559172921793,0,63.72958762196502,-.16404339962244616,1.0835251566291304,-.05498805115633132,0,24.732407896706203,-.16786010706155763,-.5603416277695248,1.6014850761964943,0,35.62982807460946,0,0,0,1,0])},d.technicolor=function(){d.colorMatrix([1.9125277891456083,-.8545344976951645,-.09155508482755585,0,11.793603434377337,-.3087833385928097,1.7658908555458428,-.10601743074722245,0,-70.35205161461398,-.231103377548616,-.7501899197440212,1.847597816108189,0,30.950940869491138,0,0,0,1,0])},d.polaroid=function(){d.colorMatrix([1.438,-.062,-.062,0,0,-.122,1.378,-.122,0,0,-.016,-.016,1.483,0,0,0,0,0,1,0])},d.shiftToBGR=function(){d.colorMatrix([0,0,1,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,1,0])},d.convolution=function(v){let k=new Float32Array(v),w=1/i,C=1/l,E=b(d.convolution.SHADER);m.uniform1fv(E.uniform.m,k),m.uniform2f(E.uniform.px,w,C),x()},d.convolution.SHADER=["precision highp float;","varying vec2 vUv;","uniform sampler2D texture;","uniform vec2 px;","uniform float m[9];","void main(void) {","vec4 c11 = texture2D(texture, vUv - px);","vec4 c12 = texture2D(texture, vec2(vUv.x, vUv.y - px.y));","vec4 c13 = texture2D(texture, vec2(vUv.x + px.x, vUv.y - px.y));","vec4 c21 = texture2D(texture, vec2(vUv.x - px.x, vUv.y) );","vec4 c22 = texture2D(texture, vUv);","vec4 c23 = texture2D(texture, vec2(vUv.x + px.x, vUv.y) );","vec4 c31 = texture2D(texture, vec2(vUv.x - px.x, vUv.y + px.y) );","vec4 c32 = texture2D(texture, vec2(vUv.x, vUv.y + px.y) );","vec4 c33 = texture2D(texture, vUv + px );","gl_FragColor = ","c11 * m[0] + c12 * m[1] + c22 * m[2] +","c21 * m[3] + c22 * m[4] + c23 * m[5] +","c31 * m[6] + c32 * m[7] + c33 * m[8];","gl_FragColor.a = c22.a;","}"].join(`
`),d.detectEdges=function(){d.convolution.call(this,[0,1,0,1,-4,1,0,1,0])},d.sobelX=function(){d.convolution.call(this,[-1,0,1,-2,0,2,-1,0,1])},d.sobelY=function(){d.convolution.call(this,[-1,-2,-1,0,0,0,1,2,1])},d.sharpen=function(v){let k=v||1;d.convolution.call(this,[0,-1*k,0,-1*k,1+4*k,-1*k,0,-1*k,0])},d.emboss=function(v){let k=v||1;d.convolution.call(this,[-2*k,-1*k,0,-1*k,1,1*k,0,1*k,2*k])},d.blur=function(v){let k=v/7/i,w=v/7/l,C=b(d.blur.SHADER);m.uniform2f(C.uniform.px,0,w),x(f.INTERMEDIATE),m.uniform2f(C.uniform.px,k,0),x()},d.blur.SHADER=["precision highp float;","varying vec2 vUv;","uniform sampler2D texture;","uniform vec2 px;","void main(void) {","gl_FragColor = vec4(0.0);","gl_FragColor += texture2D(texture, vUv + vec2(-7.0*px.x, -7.0*px.y))*0.0044299121055113265;","gl_FragColor += texture2D(texture, vUv + vec2(-6.0*px.x, -6.0*px.y))*0.00895781211794;","gl_FragColor += texture2D(texture, vUv + vec2(-5.0*px.x, -5.0*px.y))*0.0215963866053;","gl_FragColor += texture2D(texture, vUv + vec2(-4.0*px.x, -4.0*px.y))*0.0443683338718;","gl_FragColor += texture2D(texture, vUv + vec2(-3.0*px.x, -3.0*px.y))*0.0776744219933;","gl_FragColor += texture2D(texture, vUv + vec2(-2.0*px.x, -2.0*px.y))*0.115876621105;","gl_FragColor += texture2D(texture, vUv + vec2(-1.0*px.x, -1.0*px.y))*0.147308056121;","gl_FragColor += texture2D(texture, vUv                             )*0.159576912161;","gl_FragColor += texture2D(texture, vUv + vec2( 1.0*px.x,  1.0*px.y))*0.147308056121;","gl_FragColor += texture2D(texture, vUv + vec2( 2.0*px.x,  2.0*px.y))*0.115876621105;","gl_FragColor += texture2D(texture, vUv + vec2( 3.0*px.x,  3.0*px.y))*0.0776744219933;","gl_FragColor += texture2D(texture, vUv + vec2( 4.0*px.x,  4.0*px.y))*0.0443683338718;","gl_FragColor += texture2D(texture, vUv + vec2( 5.0*px.x,  5.0*px.y))*0.0215963866053;","gl_FragColor += texture2D(texture, vUv + vec2( 6.0*px.x,  6.0*px.y))*0.00895781211794;","gl_FragColor += texture2D(texture, vUv + vec2( 7.0*px.x,  7.0*px.y))*0.0044299121055113265;","}"].join(`
`),d.pixelate=function(v){let k=v/i,w=v/l,C=b(d.pixelate.SHADER);m.uniform2f(C.uniform.size,k,w),x()},d.pixelate.SHADER=["precision highp float;","varying vec2 vUv;","uniform vec2 size;","uniform sampler2D texture;","vec2 pixelate(vec2 coord, vec2 size) {","return floor( coord / size ) * size;","}","void main(void) {","gl_FragColor = vec4(0.0);","vec2 coord = pixelate(vUv, size);","gl_FragColor += texture2D(texture, coord);","}"].join(`
`)}var Lf=2048,Ee,wt,zt;function Xo(e,t){let n;if(!e)throw new Error("Human: Input is missing");if(!(e instanceof Ue)&&!(typeof Image!="undefined"&&e instanceof Image)&&!(typeof ImageData!="undefined"&&e instanceof ImageData)&&!(typeof ImageBitmap!="undefined"&&e instanceof ImageBitmap)&&!(typeof HTMLImageElement!="undefined"&&e instanceof HTMLImageElement)&&!(typeof HTMLMediaElement!="undefined"&&e instanceof HTMLMediaElement)&&!(typeof HTMLVideoElement!="undefined"&&e instanceof HTMLVideoElement)&&!(typeof HTMLCanvasElement!="undefined"&&e instanceof HTMLCanvasElement)&&!(typeof OffscreenCanvas!="undefined"&&e instanceof OffscreenCanvas))throw new Error("Human: Input type is not recognized");if(e instanceof Ue)if(e.shape&&e.shape.length===4&&e.shape[0]===1&&e.shape[3]===3)n=Ns(e);else throw new Error(`Human: Input tensor shape must be [1, height, width, 3] and instead was ${e.shape}`);else{let r=e.naturalWidth||e.videoWidth||e.width||e.shape&&e.shape[1]>0,a=e.naturalHeight||e.videoHeight||e.height||e.shape&&e.shape[2]>0;if(!r||!a)return{tensor:null,canvas:Ee};let o=r,i=a;if(o>Lf&&(o=Lf,i=o*a/r),i>Lf&&(i=Lf,o=i*r/a),t.filter.width>0?o=t.filter.width:t.filter.height>0&&(o=r*(t.filter.height/a)),t.filter.height>0?i=t.filter.height:t.filter.width>0&&(i=a*(t.filter.width/r)),!o||!i)throw new Error("Human: Input cannot determine dimension");(!Ee||(Ee==null?void 0:Ee.width)!==o||(Ee==null?void 0:Ee.height)!==i)&&(Ee=typeof OffscreenCanvas!="undefined"?new OffscreenCanvas(o,i):document.createElement("canvas"),(Ee==null?void 0:Ee.width)!==o&&(Ee.width=o),(Ee==null?void 0:Ee.height)!==i&&(Ee.height=i));let l=Ee.getContext("2d");if(e instanceof ImageData?l.putImageData(e,0,0):t.filter.flip&&typeof l.translate!="undefined"?(l.translate(r,0),l.scale(-1,1),l.drawImage(e,0,0,r,a,0,0,Ee==null?void 0:Ee.width,Ee==null?void 0:Ee.height),l.setTransform(1,0,0,1,0,0)):l.drawImage(e,0,0,r,a,0,0,Ee==null?void 0:Ee.width,Ee==null?void 0:Ee.height),t.filter.enabled){if((!zt||!wt||Ee.width!==wt.width||(Ee==null?void 0:Ee.height)!==(wt==null?void 0:wt.height))&&(wt=typeof OffscreenCanvas!="undefined"?new OffscreenCanvas(Ee==null?void 0:Ee.width,Ee==null?void 0:Ee.height):document.createElement("canvas"),(wt==null?void 0:wt.width)!==(Ee==null?void 0:Ee.width)&&(wt.width=Ee==null?void 0:Ee.width),(wt==null?void 0:wt.height)!==(Ee==null?void 0:Ee.height)&&(wt.height=Ee==null?void 0:Ee.height),zt=ns.flags.IS_BROWSER?new u8({canvas:wt}):null),!zt)return{tensor:null,canvas:Ee};zt.reset(),zt.addFilter("brightness",t.filter.brightness),t.filter.contrast!==0&&zt.addFilter("contrast",t.filter.contrast),t.filter.sharpness!==0&&zt.addFilter("sharpen",t.filter.sharpness),t.filter.blur!==0&&zt.addFilter("blur",t.filter.blur),t.filter.saturation!==0&&zt.addFilter("saturation",t.filter.saturation),t.filter.hue!==0&&zt.addFilter("hue",t.filter.hue),t.filter.negative&&zt.addFilter("negative"),t.filter.sepia&&zt.addFilter("sepia"),t.filter.vintage&&zt.addFilter("brownie"),t.filter.sepia&&zt.addFilter("sepia"),t.filter.kodachrome&&zt.addFilter("kodachrome"),t.filter.technicolor&&zt.addFilter("technicolor"),t.filter.polaroid&&zt.addFilter("polaroid"),t.filter.pixelate!==0&&zt.addFilter("pixelate",t.filter.pixelate),zt.apply(Ee)}else wt=Ee,zt&&(zt=null);if(!n){let u;if(wt.data){let c=[wt.height,wt.width,3];u=Eh(wt.data,c,"int32")}else if(wt instanceof ImageData)u=rs?rs.fromPixels(wt):null;else if(t.backend==="webgl"||t.backend==="humangl"){let c=typeof OffscreenCanvas!="undefined"?new OffscreenCanvas(o,i):document.createElement("canvas");c.width=o,c.height=i;let d=c.getContext("2d");d==null||d.drawImage(wt,0,0),u=rs?rs.fromPixels(c):null}else{let c=typeof OffscreenCanvas!="undefined"?new OffscreenCanvas(o,i):document.createElement("canvas");c.width=o,c.height=i;let d=c.getContext("2d");d==null||d.drawImage(wt,0,0);let h=d==null?void 0:d.getImageData(0,0,o,i);u=rs?rs.fromPixels(h):null}if(u){let c=ce(u,"float32");n=Ft(c,0),K(u),K(c)}}}let s=t.filter.return?wt:null;return{tensor:n,canvas:s}}var As,Ry=!1;async function Bf(e){return As?e.debug&&ue("cached model:",As.modelUrl):(As=await pt(ft(e.modelBasePath,e.segmentation.modelPath)),!As||!As.modelUrl?ue("load model failed:",e.segmentation.modelPath):e.debug&&ue("load model:",As.modelUrl)),As}async function _y(e){var f,m;let t=((f=e.tensor)==null?void 0:f.shape[1])||0,n=((m=e.tensor)==null?void 0:m.shape[2])||0;if(!e.tensor||!As||!As.inputs[0].shape)return null;let s=_e.resizeBilinear(e.tensor,[As.inputs[0].shape[1],As.inputs[0].shape[2]],!1),r=de(s,255),a=As.predict(r);K(s),K(r);let o=ot(a,0),i;if(o.shape[2]===2){let A=o.softmax(),[g,y]=ds(A,2),x=Ft(y,2),b=Ft(x,0);K(A),K(g),K(y);let v=_e.cropAndResize(b,[[0,0,.5,.5]],[0],[t,n]);i=ot(v,0),K(v),K(x),K(b)}else i=_e.resizeBilinear(o,[t,n]);if(typeof document=="undefined")return i.data();let l=typeof OffscreenCanvas!="undefined"?new OffscreenCanvas(t,n):document.createElement("canvas");l.width=t,l.height=n,rs&&await rs.toPixels(i,l),K(i),K(o),K(a);let u=typeof OffscreenCanvas!="undefined"?new OffscreenCanvas(t,n):document.createElement("canvas");u.width=t,u.height=n;let c=u.getContext("2d");c.filter="blur(8px",await c.drawImage(l,0,0);let d=c.getImageData(0,0,t,n).data,h=typeof OffscreenCanvas!="undefined"?new OffscreenCanvas(t,n):document.createElement("canvas");h.width=t,h.height=n;let p=h.getContext("2d");return e.canvas&&await p.drawImage(e.canvas,0,0),p.globalCompositeOperation="darken",p.filter="blur(8px)",await p.drawImage(l,0,0),p.globalCompositeOperation="source-over",p.filter="none",e.canvas=h,d}async function c8(e,t,n){var a;if(Ry)return null;Ry=!0,As||await Bf(n);let s=Xo(e,n),r=await _y(s);if(K(s.tensor),t&&r){let o=Xo(t,n),i=o.canvas;K(o.tensor);let l=s.canvas,u=(a=l.getContext("2d"))==null?void 0:a.getImageData(0,0,l.width,l.height).data,c=typeof OffscreenCanvas!="undefined"?new OffscreenCanvas(l.width,l.height):document.createElement("canvas");c.width=l.width,c.height=l.height;let d=c.getContext("2d");d.globalCompositeOperation="copy",d.drawImage(i,0,0,c.width,c.height);let h=d.getImageData(0,0,c.width,c.height);for(let p=0;p<c.width*c.height;p++)h.data[4*p+0]=(255-r[4*p+0])/255*h.data[4*p+0]+r[4*p+0]/255*u[4*p+0],h.data[4*p+1]=(255-r[4*p+1])/255*h.data[4*p+1]+r[4*p+1]/255*u[4*p+1],h.data[4*p+2]=(255-r[4*p+2])/255*h.data[4*p+2]+r[4*p+2]/255*u[4*p+2],h.data[4*p+3]=(255-r[4*p+3])/255*h.data[4*p+3]+r[4*p+3]/255*u[4*p+3];d.putImageData(h,0,0),s.canvas=c}return Ry=!1,s.canvas}async function d8(e){e.config.async?[e.models.face,e.models.emotion,e.models.handpose,e.models.posenet,e.models.blazepose,e.models.efficientpose,e.models.movenet,e.models.nanodet,e.models.centernet,e.models.faceres,e.models.segmentation]=await Promise.all([e.models.face||(e.config.face.enabled?U1(e.config):null),e.models.emotion||(e.config.face.enabled&&e.config.face.emotion.enabled?Y1(e.config):null),e.models.handpose||(e.config.hand.enabled?dy(e.config):null),e.models.posenet||(e.config.body.enabled&&e.config.body.modelPath.includes("posenet")?ay(e.config):null),e.models.blazepose||(e.config.body.enabled&&e.config.body.modelPath.includes("blazepose")?Pf(e.config):null),e.models.efficientpose||(e.config.body.enabled&&e.config.body.modelPath.includes("efficientpose")?l8(e.config):null),e.models.movenet||(e.config.body.enabled&&e.config.body.modelPath.includes("movenet")?by(e.config):null),e.models.nanodet||(e.config.object.enabled&&e.config.object.modelPath.includes("nanodet")?Iy(e.config):null),e.models.centernet||(e.config.object.enabled&&e.config.object.modelPath.includes("centernet")?Ny(e.config):null),e.models.faceres||(e.config.face.enabled&&e.config.face.description.enabled?G1(e.config):null),e.models.segmentation||(e.config.segmentation.enabled?Bf(e.config):null)]):(e.config.face.enabled&&!e.models.face&&(e.models.face=await U1(e.config)),e.config.face.enabled&&e.config.face.emotion.enabled&&!e.models.emotion&&(e.models.emotion=await Y1(e.config)),e.config.hand.enabled&&!e.models.handpose&&(e.models.handpose=await dy(e.config)),e.config.body.enabled&&!e.models.posenet&&e.config.body.modelPath.includes("posenet")&&(e.models.posenet=await ay(e.config)),e.config.body.enabled&&!e.models.blazepose&&e.config.body.modelPath.includes("blazepose")&&(e.models.blazepose=await Pf(e.config)),e.config.body.enabled&&!e.models.efficientpose&&e.config.body.modelPath.includes("efficientpose")&&(e.models.efficientpose=await Pf(e.config)),e.config.body.enabled&&!e.models.movenet&&e.config.body.modelPath.includes("movenet")&&(e.models.movenet=await by(e.config)),e.config.object.enabled&&!e.models.nanodet&&e.config.object.modelPath.includes("nanodet")&&(e.models.nanodet=await Iy(e.config)),e.config.object.enabled&&!e.models.centernet&&e.config.object.modelPath.includes("centernet")&&(e.models.centernet=await Ny(e.config)),e.config.face.enabled&&e.config.face.description.enabled&&!e.models.faceres&&(e.models.faceres=await G1(e.config)),e.config.segmentation.enabled&&!e.models.segmentation&&(e.models.segmentation=await Bf(e.config)))}var Mie=e=>{let t=(d,h)=>Math.atan2(d[1]-h[1],d[0]-h[0]);if(!e.annotations.rightEyeIris||!e.annotations.leftEyeIris)return{bearing:0,strength:0};let n=[0,-.1],s=1,r=e.mesh[33][2]>e.mesh[263][2],a=r?e.mesh[473]:e.mesh[468],o=r?[(e.mesh[133][0]+e.mesh[33][0])/2,(e.mesh[133][1]+e.mesh[33][1])/2]:[(e.mesh[263][0]+e.mesh[362][0])/2,(e.mesh[263][1]+e.mesh[362][1])/2],i=r?[e.mesh[133][0]-e.mesh[33][0],e.mesh[23][1]-e.mesh[27][1]]:[e.mesh[263][0]-e.mesh[362][0],e.mesh[253][1]-e.mesh[257][1]],l=[(o[0]-a[0])/i[0]-n[0],s*(a[1]-o[1])/i[1]-n[1]],u=Math.sqrt(l[0]**2+l[1]**2);return u=Math.min(u,e.boxRaw[2]/2,e.boxRaw[3]/2),{bearing:(t([0,0],l)+Math.PI/2)%Math.PI,strength:u}},zie=(e,t)=>{let n=A=>{let g=Math.sqrt(A[0]*A[0]+A[1]*A[1]+A[2]*A[2]);return A[0]/=g,A[1]/=g,A[2]/=g,A},s=(A,g)=>{let y=A[0]-g[0],x=A[1]-g[1],b=A[2]-g[2];return[y,x,b]},r=(A,g)=>{let y=A[1]*g[2]-A[2]*g[1],x=A[2]*g[0]-A[0]*g[2],b=A[0]*g[1]-A[1]*g[0];return[y,x,b]},a=A=>{let[g,y,x,b,v,k,w,C,E]=A,M,R,_;return b<1?b>-1?(_=Math.asin(b),R=Math.atan2(-w,g),M=Math.atan2(-k,v)):(_=-Math.PI/2,R=-Math.atan2(C,E),M=0):(_=Math.PI/2,R=Math.atan2(C,E),M=0),{pitch:2*-M,yaw:2*-R,roll:2*-_}},o=A=>{let g=(x,b,v,k)=>Math.atan2(k-b,v-x);return{pitch:g(A[10][1],A[10][2],A[152][1],A[152][2]),yaw:g(A[33][0],A[33][2],A[263][0],A[263][2]),roll:g(A[33][0],A[33][1],A[263][0],A[263][1])}},i=e.meshRaw;if(!i||i.length<300)return{angle:{pitch:0,yaw:0,roll:0},matrix:[1,0,0,0,1,0,0,0,1],gaze:{bearing:0,strength:0}};let l=Math.max(e.boxRaw[2]*t[0],e.boxRaw[3]*t[1])/1.5,u=[i[10],i[152],i[234],i[454]].map(A=>[A[0]*t[0]/l,A[1]*t[1]/l,A[2]]),c=n(s(u[1],u[0])),d=n(s(u[3],u[2])),h=n(r(d,c));d=r(c,h);let p=[d[0],d[1],d[2],c[0],c[1],c[2],h[0],h[1],h[2]],f=a(p),m=i.length===478?Mie(e):{bearing:0,strength:0};return{angle:f,matrix:p,gaze:m}},$y=async(e,t)=>{var d,h,p,f,m,A;let n,s,r,a,o,i,l,u=[];e.state="run:face",n=Ke();let c=await Ok(t,e.config);if(e.performance.face=Math.trunc(Ke()-n),!t.shape||t.shape.length!==4)return[];if(!c)return[];for(let g=0;g<c.length;g++){if(e.analyze("Get Face"),!c[g].tensor||c[g].tensor.isDisposedInternal){ue("Face object is disposed:",c[g].tensor);continue}let y=zie(c[g],[t.shape[2],t.shape[1]]);e.analyze("Start Emotion:"),e.config.async?o=e.config.face.emotion.enabled?J1(c[g].tensor||on([]),e.config,g,c.length):{}:(e.state="run:emotion",n=Ke(),o=e.config.face.emotion.enabled?await J1(c[g].tensor||on([]),e.config,g,c.length):{},e.performance.emotion=Math.trunc(Ke()-n)),e.analyze("End Emotion:"),e.analyze("Start Description:"),e.config.async?l=e.config.face.description.enabled?X1(c[g].tensor||on([]),e.config,g,c.length):[]:(e.state="run:description",n=Ke(),l=e.config.face.description.enabled?await X1(c[g].tensor||on([]),e.config,g,c.length):[],e.performance.embedding=Math.trunc(Ke()-n)),e.analyze("End Description:"),e.config.async&&([s,a,o,i,l,r]=await Promise.all([s,a,o,i,l,r])),e.analyze("Finish Face:"),!e.config.face.iris.enabled&&((h=(d=c[g])==null?void 0:d.annotations)==null?void 0:h.leftEyeIris)&&((f=(p=c[g])==null?void 0:p.annotations)==null?void 0:f.rightEyeIris)&&(delete c[g].annotations.leftEyeIris,delete c[g].annotations.rightEyeIris);let x=((m=c[g].annotations)==null?void 0:m.leftEyeIris)&&((A=c[g].annotations)==null?void 0:A.rightEyeIris)?Math.max(Math.abs(c[g].annotations.leftEyeIris[3][0]-c[g].annotations.leftEyeIris[1][0]),Math.abs(c[g].annotations.rightEyeIris[4][1]-c[g].annotations.rightEyeIris[2][1]))/t.shape[2]:0,b=e.config.face.detector.return?ot(c[g].tensor):null;K(c[g].tensor),c[g].tensor&&delete c[g].tensor,u.push({...c[g],id:g,age:l.age,gender:l.gender,genderScore:l.genderScore,embedding:l.descriptor,emotion:o,iris:x!==0?Math.trunc(500/x/11.7)/100:0,rotation:y,tensor:b}),e.analyze("End Face")}return e.analyze("End FaceMesh:"),e.config.async&&(e.performance.face&&delete e.performance.face,e.performance.age&&delete e.performance.age,e.performance.gender&&delete e.performance.gender,e.performance.emotion&&delete e.performance.emotion),u};var h8=e=>{if(!e)return[];let t=[];for(let n=0;n<e.length;n++){let s=e[n].keypoints.find(l=>l.part==="leftWrist"),r=e[n].keypoints.find(l=>l.part==="rightWrist"),a=e[n].keypoints.find(l=>l.part==="nose");a&&s&&r&&s.position.y<a.position.y&&r.position.y<a.position.y?t.push({body:n,gesture:"i give up"}):a&&s&&s.position.y<a.position.y?t.push({body:n,gesture:"raise left hand"}):a&&r&&r.position.y<a.position.y&&t.push({body:n,gesture:"raise right hand"});let o=e[n].keypoints.find(l=>l.part==="leftShoulder"),i=e[n].keypoints.find(l=>l.part==="rightShoulder");o&&i&&t.push({body:n,gesture:`leaning ${o.position.y>i.position.y?"left":"right"}`})}return t},p8=e=>{if(!e)return[];let t=[];for(let n=0;n<e.length;n++)if(e[n].mesh&&e[n].mesh.length>0){let s=e[n].mesh[33][2]-e[n].mesh[263][2];Math.abs(s)<10?t.push({face:n,gesture:"facing center"}):t.push({face:n,gesture:`facing ${s<0?"left":"right"}`}),Math.abs(e[n].mesh[374][1]-e[n].mesh[386][1])/Math.abs(e[n].mesh[443][1]-e[n].mesh[450][1])<.2&&t.push({face:n,gesture:"blink left eye"}),Math.abs(e[n].mesh[145][1]-e[n].mesh[159][1])/Math.abs(e[n].mesh[223][1]-e[n].mesh[230][1])<.2&&t.push({face:n,gesture:"blink right eye"});let o=Math.min(100,500*Math.abs(e[n].mesh[13][1]-e[n].mesh[14][1])/Math.abs(e[n].mesh[10][1]-e[n].mesh[152][1]));o>10&&t.push({face:n,gesture:`mouth ${Math.trunc(o)}% open`});let i=e[n].mesh[152][2];Math.abs(i)>10&&t.push({face:n,gesture:`head ${i<0?"up":"down"}`})}return t},f8=e=>{if(!e)return[];let t=[];for(let n=0;n<e.length;n++){if(!e[n].annotations||!e[n].annotations.leftEyeIris||!e[n].annotations.rightEyeIris)continue;let s=e[n].annotations.leftEyeIris[3][0]-e[n].annotations.leftEyeIris[1][0],r=e[n].annotations.leftEyeIris[4][1]-e[n].annotations.leftEyeIris[2][1],a=Math.abs(s*r),o=e[n].annotations.rightEyeIris[3][0]-e[n].annotations.rightEyeIris[1][0],i=e[n].annotations.rightEyeIris[4][1]-e[n].annotations.rightEyeIris[2][1],l=Math.abs(o*i),u=!1;Math.abs(a-l)/Math.max(a,l)<.25&&(u=!0,t.push({iris:n,gesture:"facing center"}));let d=Math.abs(e[n].mesh[33][0]-e[n].annotations.rightEyeIris[0][0])/e[n].box[2],h=Math.abs(e[n].mesh[263][0]-e[n].annotations.leftEyeIris[0][0])/e[n].box[2];(h>.06||d>.06)&&(u=!1),h>.06&&t.push({iris:n,gesture:"looking right"}),d>.06&&t.push({iris:n,gesture:"looking left"});let p=Math.abs(e[n].mesh[145][1]-e[n].annotations.rightEyeIris[0][1])/e[n].box[3],f=Math.abs(e[n].mesh[374][1]-e[n].annotations.leftEyeIris[0][1])/e[n].box[3];(f<.01||p<.01||f>.022||p>.022)&&(u=!1),(f<.01||p<.01)&&t.push({iris:n,gesture:"looking down"}),(f>.022||p>.022)&&t.push({iris:n,gesture:"looking up"}),u&&t.push({iris:n,gesture:"looking center"})}return t},m8=e=>{if(!e)return[];let t=[];for(let n=0;n<e.length;n++){let s=[];for(let[r,a]of Object.entries(e[n].annotations))r!=="palmBase"&&Array.isArray(a)&&s.push({name:r.toLowerCase(),position:a[0]});if(s&&s.length>0){let r=s.reduce((o,i)=>o.position[2]<i.position[2]?o:i);t.push({hand:n,gesture:`${r.name} forward`});let a=s.reduce((o,i)=>o.position[1]<i.position[1]?o:i);t.push({hand:n,gesture:`${a.name} up`})}}return t};var Oy={};Ky(Oy,{all:()=>Wie,body:()=>y8,canvas:()=>Bie,face:()=>g8,gesture:()=>A8,hand:()=>x8,object:()=>b8,options:()=>ca,person:()=>Lie});var ca={color:"rgba(173, 216, 230, 0.6)",labelColor:"rgba(173, 216, 230, 1)",shadowColor:"black",font:'small-caps 14px "Segoe UI"',lineHeight:18,lineWidth:4,pointSize:2,roundRect:8,drawPoints:!1,drawLabels:!0,drawBoxes:!0,drawPolygons:!0,drawGaze:!0,fillPolygons:!1,useDepth:!0,useCurves:!1,bufferedOutput:!0},Wf=e=>Math.round(e*180/Math.PI);function Fy(e,t,n,s=0,r){e.fillStyle=r.useDepth&&s?`rgba(${127.5+2*s}, ${127.5-2*s}, 255, 0.3)`:r.color,e.beginPath(),e.arc(t,n,r.pointSize,0,2*Math.PI),e.fill()}function ad(e,t,n,s,r,a){if(e.beginPath(),a.useCurves){let o=(t+t+s)/2,i=(n+n+r)/2;e.ellipse(o,i,s/2,r/2,0,0,2*Math.PI)}else e.lineWidth=a.lineWidth,e.moveTo(t+a.roundRect,n),e.lineTo(t+s-a.roundRect,n),e.quadraticCurveTo(t+s,n,t+s,n+a.roundRect),e.lineTo(t+s,n+r-a.roundRect),e.quadraticCurveTo(t+s,n+r,t+s-a.roundRect,n+r),e.lineTo(t+a.roundRect,n+r),e.quadraticCurveTo(t,n+r,t,n+r-a.roundRect),e.lineTo(t,n+a.roundRect),e.quadraticCurveTo(t,n,t+a.roundRect,n),e.closePath();e.stroke()}function Dy(e,t=[],n){if(!(t===void 0||t.length===0)){e.beginPath(),e.moveTo(t[0][0],t[0][1]);for(let s of t){let r=s[2]||0;e.strokeStyle=n.useDepth&&r?`rgba(${127.5+2*r}, ${127.5-2*r}, 255, 0.3)`:n.color,e.fillStyle=n.useDepth&&r?`rgba(${127.5+2*r}, ${127.5-2*r}, 255, 0.3)`:n.color,e.lineTo(s[0],Math.round(s[1]))}e.stroke(),n.fillPolygons&&(e.closePath(),e.fill())}}function od(e,t=[],n){if(!(t===void 0||t.length===0)){if(!n.useCurves||t.length<=2){Dy(e,t,n);return}e.moveTo(t[0][0],t[0][1]);for(let s=0;s<t.length-2;s++){let r=(t[s][0]+t[s+1][0])/2,a=(t[s][1]+t[s+1][1])/2;e.quadraticCurveTo(t[s][0],t[s][1],r,a)}e.quadraticCurveTo(t[t.length-2][0],t[t.length-2][1],t[t.length-1][0],t[t.length-1][1]),e.stroke(),n.fillPolygons&&(e.closePath(),e.fill())}}async function A8(e,t,n){let s=pn(ca,n);if(!t||!e||!(e instanceof HTMLCanvasElement))return;let r=e.getContext("2d");if(!r)return;r.font=s.font,r.fillStyle=s.color;let a=1;for(let o=0;o<t.length;o++){let i=[],l=[];if([i,l]=Object.entries(t[o]),l.length>1&&l[1].length>0){let u=i[1]>0?`#${i[1]}`:"",c=`${i[0]} ${u}: ${l[1]}`;s.shadowColor&&s.shadowColor!==""&&(r.fillStyle=s.shadowColor,r.fillText(c,8,2+a*s.lineHeight)),r.fillStyle=s.labelColor,r.fillText(c,6,0+a*s.lineHeight),a+=1}}}async function g8(e,t,n){var a,o,i,l;let s=pn(ca,n);if(!t||!e||!(e instanceof HTMLCanvasElement))return;let r=e.getContext("2d");if(!!r)for(let u of t){r.font=s.font,r.strokeStyle=s.color,r.fillStyle=s.color,s.drawBoxes&&ad(r,u.box[0],u.box[1],u.box[2],u.box[3],s);let c=[];if(c.push(`face: ${Math.trunc(100*u.score)}%`),u.genderScore&&c.push(`${u.gender||""} ${Math.trunc(100*u.genderScore)}%`),u.age&&c.push(`age: ${u.age||""}`),u.iris&&c.push(`distance: ${u.iris}`),u.emotion&&u.emotion.length>0){let d=u.emotion.map(h=>`${Math.trunc(100*h.score)}% ${h.emotion}`);d.length>3&&(d.length=3),c.push(d.join(" "))}u.rotation&&u.rotation.angle&&u.rotation.gaze&&(u.rotation.angle.roll&&c.push(`roll: ${Wf(u.rotation.angle.roll)}\xB0 yaw:${Wf(u.rotation.angle.yaw)}\xB0 pitch:${Wf(u.rotation.angle.pitch)}\xB0`),u.rotation.gaze.bearing&&c.push(`gaze: ${Wf(u.rotation.gaze.bearing)}\xB0`)),c.length===0&&c.push("face"),r.fillStyle=s.color;for(let d=c.length-1;d>=0;d--){let h=Math.max(u.box[0],0),p=d*s.lineHeight+u.box[1];s.shadowColor&&s.shadowColor!==""&&(r.fillStyle=s.shadowColor,r.fillText(c[d],h+5,p+16)),r.fillStyle=s.labelColor,r.fillText(c[d],h+4,p+15)}if(r.lineWidth=1,u.mesh&&u.mesh.length>0){if(s.drawPoints)for(let d of u.mesh)Fy(r,d[0],d[1],d[2],s);if(s.drawPolygons){r.lineWidth=1;for(let d=0;d<qo.length/3;d++){let h=[qo[d*3+0],qo[d*3+1],qo[d*3+2]].map(p=>u.mesh[p]);Dy(r,h,s)}if(u.annotations&&u.annotations.leftEyeIris){r.strokeStyle=s.useDepth?"rgba(255, 200, 255, 0.3)":s.color,r.beginPath();let d=Math.abs(u.annotations.leftEyeIris[3][0]-u.annotations.leftEyeIris[1][0])/2,h=Math.abs(u.annotations.leftEyeIris[4][1]-u.annotations.leftEyeIris[2][1])/2;r.ellipse(u.annotations.leftEyeIris[0][0],u.annotations.leftEyeIris[0][1],d,h,0,0,2*Math.PI),r.stroke(),s.fillPolygons&&(r.fillStyle=s.useDepth?"rgba(255, 255, 200, 0.3)":s.color,r.fill())}if(u.annotations&&u.annotations.rightEyeIris){r.strokeStyle=s.useDepth?"rgba(255, 200, 255, 0.3)":s.color,r.beginPath();let d=Math.abs(u.annotations.rightEyeIris[3][0]-u.annotations.rightEyeIris[1][0])/2,h=Math.abs(u.annotations.rightEyeIris[4][1]-u.annotations.rightEyeIris[2][1])/2;r.ellipse(u.annotations.rightEyeIris[0][0],u.annotations.rightEyeIris[0][1],d,h,0,0,2*Math.PI),r.stroke(),s.fillPolygons&&(r.fillStyle=s.useDepth?"rgba(255, 255, 200, 0.3)":s.color,r.fill())}if(s.drawGaze&&((o=(a=u.rotation)==null?void 0:a.gaze)==null?void 0:o.strength)&&((l=(i=u.rotation)==null?void 0:i.gaze)==null?void 0:l.bearing)&&u.annotations.leftEyeIris&&u.annotations.rightEyeIris&&u.annotations.leftEyeIris[0]&&u.annotations.rightEyeIris[0]){r.strokeStyle="pink",r.beginPath();let d=[u.annotations.leftEyeIris[0][0]+Math.sin(u.rotation.gaze.bearing)*u.rotation.gaze.strength*u.box[3],u.annotations.leftEyeIris[0][1]+Math.cos(u.rotation.gaze.bearing)*u.rotation.gaze.strength*u.box[2]];r.moveTo(u.annotations.leftEyeIris[0][0],u.annotations.leftEyeIris[0][1]),r.lineTo(d[0],d[1]);let h=[u.annotations.rightEyeIris[0][0]+Math.sin(u.rotation.gaze.bearing)*u.rotation.gaze.strength*u.box[3],u.annotations.rightEyeIris[0][1]+Math.cos(u.rotation.gaze.bearing)*u.rotation.gaze.strength*u.box[2]];r.moveTo(u.annotations.rightEyeIris[0][0],u.annotations.rightEyeIris[0][1]),r.lineTo(h[0],h[1]),r.stroke()}}}}}async function y8(e,t,n){var a;let s=pn(ca,n);if(!t||!e||!(e instanceof HTMLCanvasElement))return;let r=e.getContext("2d");if(!!r){r.lineJoin="round";for(let o=0;o<t.length;o++){if(r.strokeStyle=s.color,r.fillStyle=s.color,r.lineWidth=s.lineWidth,r.font=s.font,s.drawBoxes&&t[o].box&&((a=t[o].box)==null?void 0:a.length)===4&&(ad(r,t[o].box[0],t[o].box[1],t[o].box[2],t[o].box[3],s),s.drawLabels&&(s.shadowColor&&s.shadowColor!==""&&(r.fillStyle=s.shadowColor,r.fillText(`body ${100*t[o].score}%`,t[o].box[0]+3,1+t[o].box[1]+s.lineHeight,t[o].box[2])),r.fillStyle=s.labelColor,r.fillText(`body ${100*t[o].score}%`,t[o].box[0]+2,0+t[o].box[1]+s.lineHeight,t[o].box[2]))),s.drawPoints)for(let i=0;i<t[o].keypoints.length;i++)r.fillStyle=s.useDepth&&t[o].keypoints[i].position[2]?`rgba(${127.5+2*(t[o].keypoints[i].position[2]||0)}, ${127.5-2*(t[o].keypoints[i].position[2]||0)}, 255, 0.5)`:s.color,Fy(r,t[o].keypoints[i].position[0],t[o].keypoints[i].position[1],0,s);if(s.drawLabels&&(r.font=s.font,t[o].keypoints))for(let i of t[o].keypoints)r.fillStyle=s.useDepth&&i.position[2]?`rgba(${127.5+2*i.position[2]}, ${127.5-2*i.position[2]}, 255, 0.5)`:s.color,r.fillText(`${i.part} ${Math.trunc(100*i.score)}%`,i.position[0]+4,i.position[1]+4);if(s.drawPolygons&&t[o].keypoints){let i,l=[];l.length=0,i=t[o].keypoints.find(u=>u.part==="leftShoulder"),i&&l.push([i.position[0],i.position[1]]),i=t[o].keypoints.find(u=>u.part==="rightShoulder"),i&&l.push([i.position[0],i.position[1]]),od(r,l,s),l.length=0,i=t[o].keypoints.find(u=>u.part==="rightShoulder"),i&&l.push([i.position[0],i.position[1]]),i=t[o].keypoints.find(u=>u.part==="rightHip"),i&&l.push([i.position[0],i.position[1]]),i=t[o].keypoints.find(u=>u.part==="leftHip"),i&&l.push([i.position[0],i.position[1]]),i=t[o].keypoints.find(u=>u.part==="leftShoulder"),i&&l.push([i.position[0],i.position[1]]),l.length===4&&Dy(r,l,s),l.length=0,i=t[o].keypoints.find(u=>u.part==="leftHip"),i&&l.push([i.position[0],i.position[1]]),i=t[o].keypoints.find(u=>u.part==="leftKnee"),i&&l.push([i.position[0],i.position[1]]),i=t[o].keypoints.find(u=>u.part==="leftAnkle"),i&&l.push([i.position[0],i.position[1]]),i=t[o].keypoints.find(u=>u.part==="leftHeel"),i&&l.push([i.position[0],i.position[1]]),i=t[o].keypoints.find(u=>u.part==="leftFoot"),i&&l.push([i.position[0],i.position[1]]),od(r,l,s),l.length=0,i=t[o].keypoints.find(u=>u.part==="rightHip"),i&&l.push([i.position[0],i.position[1]]),i=t[o].keypoints.find(u=>u.part==="rightKnee"),i&&l.push([i.position[0],i.position[1]]),i=t[o].keypoints.find(u=>u.part==="rightAnkle"),i&&l.push([i.position[0],i.position[1]]),i=t[o].keypoints.find(u=>u.part==="rightHeel"),i&&l.push([i.position[0],i.position[1]]),i=t[o].keypoints.find(u=>u.part==="rightFoot"),i&&l.push([i.position[0],i.position[1]]),od(r,l,s),l.length=0,i=t[o].keypoints.find(u=>u.part==="leftShoulder"),i&&l.push([i.position[0],i.position[1]]),i=t[o].keypoints.find(u=>u.part==="leftElbow"),i&&l.push([i.position[0],i.position[1]]),i=t[o].keypoints.find(u=>u.part==="leftWrist"),i&&l.push([i.position[0],i.position[1]]),i=t[o].keypoints.find(u=>u.part==="leftPalm"),i&&l.push([i.position[0],i.position[1]]),od(r,l,s),l.length=0,i=t[o].keypoints.find(u=>u.part==="rightShoulder"),i&&l.push([i.position[0],i.position[1]]),i=t[o].keypoints.find(u=>u.part==="rightElbow"),i&&l.push([i.position[0],i.position[1]]),i=t[o].keypoints.find(u=>u.part==="rightWrist"),i&&l.push([i.position[0],i.position[1]]),i=t[o].keypoints.find(u=>u.part==="rightPalm"),i&&l.push([i.position[0],i.position[1]]),od(r,l,s)}}}}async function x8(e,t,n){let s=pn(ca,n);if(!t||!e||!(e instanceof HTMLCanvasElement))return;let r=e.getContext("2d");if(!!r){r.lineJoin="round",r.font=s.font;for(let a of t){if(s.drawBoxes&&(r.strokeStyle=s.color,r.fillStyle=s.color,ad(r,a.box[0],a.box[1],a.box[2],a.box[3],s),s.drawLabels&&(s.shadowColor&&s.shadowColor!==""&&(r.fillStyle=s.shadowColor,r.fillText("hand",a.box[0]+3,1+a.box[1]+s.lineHeight,a.box[2])),r.fillStyle=s.labelColor,r.fillText("hand",a.box[0]+2,0+a.box[1]+s.lineHeight,a.box[2])),r.stroke()),s.drawPoints&&a.keypoints&&a.keypoints.length>0)for(let o of a.keypoints)r.fillStyle=s.useDepth?`rgba(${127.5+2*o[2]}, ${127.5-2*o[2]}, 255, 0.5)`:s.color,Fy(r,o[0],o[1],0,s);if(s.drawLabels){let o=(i,l)=>{r.fillStyle=s.useDepth?`rgba(${127.5+2*i[i.length-1][2]}, ${127.5-2*i[i.length-1][2]}, 255, 0.5)`:s.color,r.fillText(l,i[i.length-1][0]+4,i[i.length-1][1]+4)};r.font=s.font,o(a.annotations.indexFinger,"index"),o(a.annotations.middleFinger,"middle"),o(a.annotations.ringFinger,"ring"),o(a.annotations.pinky,"pinky"),o(a.annotations.thumb,"thumb"),o(a.annotations.palmBase,"palm")}if(s.drawPolygons){let o=i=>{if(!!i)for(let l=0;l<i.length;l++)r.beginPath(),r.strokeStyle=s.useDepth?`rgba(${127.5+2*i[l][2]}, ${127.5-2*i[l][2]}, 255, 0.5)`:s.color,r.moveTo(i[l>0?l-1:0][0],i[l>0?l-1:0][1]),r.lineTo(i[l][0],i[l][1]),r.stroke()};r.lineWidth=s.lineWidth,o(a.annotations.indexFinger),o(a.annotations.middleFinger),o(a.annotations.ringFinger),o(a.annotations.pinky),o(a.annotations.thumb)}}}}async function b8(e,t,n){let s=pn(ca,n);if(!t||!e||!(e instanceof HTMLCanvasElement))return;let r=e.getContext("2d");if(!!r){r.lineJoin="round",r.font=s.font;for(let a of t)if(s.drawBoxes){if(r.strokeStyle=s.color,r.fillStyle=s.color,ad(r,a.box[0],a.box[1],a.box[2],a.box[3],s),s.drawLabels){let o=`${a.label} ${Math.round(100*a.score)}%`;s.shadowColor&&s.shadowColor!==""&&(r.fillStyle=s.shadowColor,r.fillText(o,a.box[0]+3,1+a.box[1]+s.lineHeight,a.box[2])),r.fillStyle=s.labelColor,r.fillText(o,a.box[0]+2,0+a.box[1]+s.lineHeight,a.box[2])}r.stroke()}}}async function Lie(e,t,n){let s=pn(ca,n);if(!t||!e||!(e instanceof HTMLCanvasElement))return;let r=e.getContext("2d");if(!!r){r.lineJoin="round",r.font=s.font;for(let a=0;a<t.length;a++)if(s.drawBoxes){if(r.strokeStyle=s.color,r.fillStyle=s.color,ad(r,t[a].box[0],t[a].box[1],t[a].box[2],t[a].box[3],s),s.drawLabels){let o=`person #${a}`;s.shadowColor&&s.shadowColor!==""&&(r.fillStyle=s.shadowColor,r.fillText(o,t[a].box[0]+3,1+t[a].box[1]+s.lineHeight,t[a].box[2])),r.fillStyle=s.labelColor,r.fillText(o,t[a].box[0]+2,0+t[a].box[1]+s.lineHeight,t[a].box[2])}r.stroke()}}}async function Bie(e,t){if(!e||!t||!(e instanceof HTMLCanvasElement)||!(t instanceof HTMLCanvasElement))return;let n=e.getContext("2d");n==null||n.drawImage(e,0,0)}async function Wie(e,t,n){let s=Ke(),r=pn(ca,n);if(!t||!e||!(e instanceof HTMLCanvasElement))return null;let a=Promise.all([g8(e,t.face,r),y8(e,t.body,r),x8(e,t.hand,r),b8(e,t.object,r),A8(e,t.gesture,r)]);return t.performance.draw=Math.trunc(Ke()-s),a}function v8(e,t,n,s,r){var i,l,u,c,d,h,p,f,m,A,g,y,x,b,v,k;let a=0,o=[];for(let w of e){let C={id:a++,face:w,body:null,hands:{left:null,right:null},gestures:[],box:[0,0,0,0]};for(let O of t)w.box[0]>O.box[0]&&w.box[0]<O.box[0]+O.box[2]&&w.box[1]+w.box[3]>O.box[1]&&w.box[1]+w.box[3]<O.box[1]+O.box[3]&&(C.body=O);if(C.body)for(let O of n)O.box[0]+O.box[2]>C.body.box[0]&&O.box[0]+O.box[2]<C.body.box[0]+C.body.box[2]&&O.box[1]+O.box[3]>C.body.box[1]&&O.box[1]+O.box[3]<C.body.box[1]+C.body.box[3]&&C.hands&&(C.hands.left=O),O.box[0]<C.body.box[0]+C.body.box[2]&&O.box[0]>C.body.box[0]&&O.box[1]+O.box[3]>C.body.box[1]&&O.box[1]+O.box[3]<C.body.box[1]+C.body.box[3]&&C.hands&&(C.hands.right=O);for(let O of s)O.face!==void 0&&O.face===w.id?(i=C.gestures)==null||i.push(O):O.iris!==void 0&&O.iris===w.id?(l=C.gestures)==null||l.push(O):O.body!==void 0&&O.body===((u=C.body)==null?void 0:u.id)?(c=C.gestures)==null||c.push(O):O.hand!==void 0&&O.hand===((h=(d=C.hands)==null?void 0:d.left)==null?void 0:h.id)?(p=C.gestures)==null||p.push(O):O.hand!==void 0&&O.hand===((m=(f=C.hands)==null?void 0:f.right)==null?void 0:m.id)&&((A=C.gestures)==null||A.push(O));let E=[],M=[],R=O=>{O&&O.length===4&&(E.push(O[0],O[0]+O[2]),M.push(O[1],O[1]+O[3]))};R((g=C.face)==null?void 0:g.box),R((y=C.body)==null?void 0:y.box),R((b=(x=C.hands)==null?void 0:x.left)==null?void 0:b.box),R((k=(v=C.hands)==null?void 0:v.right)==null?void 0:k.box);let _=Math.min(...E),N=Math.min(...M);C.box=[_,N,Math.max(...E)-_,Math.max(...M)-N],r&&r[1]&&r[2]&&(C.boxRaw=[C.box[0]/r[2],C.box[1]/r[1],C.box[2]/r[2],C.box[3]/r[1]]),o.push(C)}return o}var Fe={face:[],body:[],hand:[],gesture:[],object:[],persons:[],performance:{},timestamp:0};function w8(e){var s,r,a,o,i,l,u,c,d,h,p,f,m,A,g,y,x,b,v,k,w;if(!e)return{face:[],body:[],hand:[],gesture:[],object:[],persons:[],performance:{},timestamp:0};let t=Date.now()-e.timestamp,n=t<1e3?8-Math.log(t):1;if(Fe.canvas=e.canvas,!Fe.body||e.body.length!==Fe.body.length)Fe.body=JSON.parse(JSON.stringify(e.body));else for(let C=0;C<e.body.length;C++){let E=e.body[C].box.map((_,N)=>((n-1)*Fe.body[C].box[N]+_)/n),M=e.body[C].boxRaw.map((_,N)=>((n-1)*Fe.body[C].boxRaw[N]+_)/n),R=e.body[C].keypoints.map((_,N)=>({score:_.score,part:_.part,position:[Fe.body[C].keypoints[N]?((n-1)*Fe.body[C].keypoints[N].position[0]+_.position[0])/n:_.position[0],Fe.body[C].keypoints[N]?((n-1)*Fe.body[C].keypoints[N].position[1]+_.position[1])/n:_.position[1]],positionRaw:[Fe.body[C].keypoints[N]?((n-1)*Fe.body[C].keypoints[N].positionRaw[0]+_.positionRaw[0])/n:_.position[0],Fe.body[C].keypoints[N]?((n-1)*Fe.body[C].keypoints[N].positionRaw[1]+_.positionRaw[1])/n:_.position[1]]}));Fe.body[C]={...e.body[C],box:E,boxRaw:M,keypoints:R}}if(!Fe.hand||e.hand.length!==Fe.hand.length)Fe.hand=JSON.parse(JSON.stringify(e.hand));else for(let C=0;C<e.hand.length;C++){let E=e.hand[C].box.map((O,W)=>((n-1)*Fe.hand[C].box[W]+O)/n),M=e.hand[C].boxRaw.map((O,W)=>((n-1)*Fe.hand[C].boxRaw[W]+O)/n),R=e.hand[C].keypoints.map((O,W)=>O.map((j,q)=>((n-1)*Fe.hand[C].keypoints[W][q]+j)/n)),_=Object.keys(e.hand[C].annotations),N={};for(let O of _)N[O]=e.hand[C].annotations[O].map((W,j)=>W.map((q,X)=>((n-1)*Fe.hand[C].annotations[O][j][X]+q)/n));Fe.hand[C]={...e.hand[C],box:E,boxRaw:M,keypoints:R,annotations:N}}if(!Fe.face||e.face.length!==Fe.face.length)Fe.face=JSON.parse(JSON.stringify(e.face));else for(let C=0;C<e.face.length;C++){let E=e.face[C].box.map((_,N)=>((n-1)*Fe.face[C].box[N]+_)/n),M=e.face[C].boxRaw.map((_,N)=>((n-1)*Fe.face[C].boxRaw[N]+_)/n),R={matrix:[0,0,0,0,0,0,0,0,0],angle:{roll:0,yaw:0,pitch:0},gaze:{bearing:0,strength:0}};R.matrix=(s=e.face[C].rotation)==null?void 0:s.matrix,R.angle={roll:((n-1)*(((a=(r=Fe.face[C].rotation)==null?void 0:r.angle)==null?void 0:a.roll)||0)+(((i=(o=e.face[C].rotation)==null?void 0:o.angle)==null?void 0:i.roll)||0))/n,yaw:((n-1)*(((u=(l=Fe.face[C].rotation)==null?void 0:l.angle)==null?void 0:u.yaw)||0)+(((d=(c=e.face[C].rotation)==null?void 0:c.angle)==null?void 0:d.yaw)||0))/n,pitch:((n-1)*(((p=(h=Fe.face[C].rotation)==null?void 0:h.angle)==null?void 0:p.pitch)||0)+(((m=(f=e.face[C].rotation)==null?void 0:f.angle)==null?void 0:m.pitch)||0))/n},R.gaze={bearing:((n-1)*(((g=(A=Fe.face[C].rotation)==null?void 0:A.gaze)==null?void 0:g.bearing)||0)+(((x=(y=e.face[C].rotation)==null?void 0:y.gaze)==null?void 0:x.bearing)||0))/n,strength:((n-1)*(((v=(b=Fe.face[C].rotation)==null?void 0:b.gaze)==null?void 0:v.strength)||0)+(((w=(k=e.face[C].rotation)==null?void 0:k.gaze)==null?void 0:w.strength)||0))/n},Fe.face[C]={...e.face[C],rotation:R,box:E,boxRaw:M}}if(!Fe.object||e.object.length!==Fe.object.length)Fe.object=JSON.parse(JSON.stringify(e.object));else for(let C=0;C<e.object.length;C++){let E=e.object[C].box.map((R,_)=>((n-1)*Fe.object[C].box[_]+R)/n),M=e.object[C].boxRaw.map((R,_)=>((n-1)*Fe.object[C].boxRaw[_]+R)/n);Fe.object[C]={...e.object[C],box:E,boxRaw:M}}if(e.persons){let C=e.persons;if(!Fe.persons||C.length!==Fe.persons.length)Fe.persons=JSON.parse(JSON.stringify(C));else for(let E=0;E<C.length;E++)Fe.persons[E].box=C[E].box.map((M,R)=>((n-1)*Fe.persons[E].box[R]+M)/n)}return e.gesture&&(Fe.gesture=e.gesture),e.performance&&(Fe.performance=e.performance),Fe}var Vf=`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==`,Uf=`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`;var k8="2.1.3";var uu,id,ld,Ko,Zo,cu,Hf,ud,Gf,jf,qf,Xf,Uie=class{constructor(t){ts(this,uu,void 0);ts(this,id,void 0);ts(this,ld,void 0);ts(this,Ko,void 0);ts(this,Zo,void 0);ts(this,cu,void 0);this.analyze=(...t)=>{if(!hn(this,id))return;let n=this.tf.engine().state.numTensors,s=hn(this,uu);Ss(this,uu,n);let r=n-s;r!==0&&ue(...t,r)};ts(this,Hf,t=>{if(!hn(this,ld))return null;if(!t)return"input is not defined";if(this.tf.ENV.flags.IS_NODE&&!(t instanceof Ue))return"input must be a tensor";try{this.tf.getBackend()}catch(n){return"backend not loaded"}return null});ts(this,ud,async(t=!1)=>{var n;if(this.config.backend&&this.config.backend.length>0&&t||this.tf.getBackend()!==this.config.backend){let s=Ke();if(this.state="backend",this.config.backend&&this.config.backend.length>0){if(typeof window=="undefined"&&typeof WorkerGlobalScope!="undefined"&&this.config.debug&&ue("running inside web worker"),this.tf.ENV.flags.IS_BROWSER&&this.config.backend==="tensorflow"&&(ue("override: backend set to tensorflow while running in browser"),this.config.backend="humangl"),this.tf.ENV.flags.IS_NODE&&(this.config.backend==="webgl"||this.config.backend==="humangl")&&(ue("override: backend set to webgl while running in nodejs"),this.config.backend="tensorflow"),this.tf.ENV.flags.IS_BROWSER&&this.config.backend==="webgpu")if(typeof navigator=="undefined"||typeof navigator.gpu=="undefined")ue("override: backend set to webgpu but browser does not support webgpu"),this.config.backend="humangl";else{let a=await navigator.gpu.requestAdapter();this.config.debug&&ue("enumerated webgpu adapter:",a)}this.config.backend==="humangl"&&wk();let r=Object.keys(this.tf.engine().registryFactory);if(this.config.debug&&ue("available backends:",r),r.includes(this.config.backend)||(ue(`error: backend ${this.config.backend} not found in registry`),this.config.backend=this.tf.ENV.flags.IS_NODE?"tensorflow":"humangl",ue(`override: using backend ${this.config.backend} instead`)),this.config.debug&&ue("setting backend:",this.config.backend),this.config.backend==="wasm"){if(this.config.debug&&ue("wasm path:",this.config.wasmPath),typeof((n=this.tf)==null?void 0:n.setWasmPaths)!="undefined")this.tf.setWasmPaths(this.config.wasmPath);else throw new Error("Human: WASM backend is not loaded");let a=await this.tf.env().getAsync("WASM_HAS_SIMD_SUPPORT"),o=await this.tf.env().getAsync("WASM_HAS_MULTITHREAD_SUPPORT");this.config.debug&&ue(`wasm execution: ${a?"SIMD":"no SIMD"} ${o?"multithreaded":"singlethreaded"}`),this.config.debug&&!a&&ue("warning: wasm simd support is not enabled")}try{await this.tf.setBackend(this.config.backend)}catch(a){ue("error: cannot set backend:",this.config.backend,a)}}if(this.tf.getBackend()==="webgl"||this.tf.getBackend()==="humangl"){this.tf.ENV.set("CHECK_COMPUTATION_FOR_ERRORS",!1),this.tf.ENV.set("WEBGL_CPU_FORWARD",!0),this.tf.ENV.set("WEBGL_PACK_DEPTHWISECONV",!1),this.tf.ENV.set("WEBGL_USE_SHAPES_UNIFORMS",!0),typeof this.config.deallocate!="undefined"&&this.config.deallocate&&(ue("changing webgl: WEBGL_DELETE_TEXTURE_THRESHOLD:",!0),this.tf.ENV.set("WEBGL_DELETE_TEXTURE_THRESHOLD",0));let r=await this.tf.backend().getGPGPUContext().gl;this.config.debug&&ue(`gl version:${r.getParameter(r.VERSION)} renderer:${r.getParameter(r.RENDERER)}`)}this.tf.enableProdMode(),await this.tf.ready(),this.performance.backend=Math.trunc(Ke()-s)}});this.next=t=>w8(t||this.result);ts(this,Gf,async t=>{if(this.config.cacheSensitivity===0)return!1;let n=32;if(!t.shape[1]||!t.shape[2])return!1;let s=_e.resizeBilinear(t,[Math.trunc(t.shape[1]/n),Math.trunc(t.shape[2]/n)]),r=await s.data(),a=0;for(let l=0;l<r.length/3;l++)a+=r[3*l+2];s.dispose();let o=100*(Math.max(a,hn(this,Zo))/Math.min(a,hn(this,Zo))-1);Ss(this,Zo,a);let i=o<Math.max(this.config.cacheSensitivity,hn(this,cu));return Ss(this,cu,o>10*this.config.cacheSensitivity?0:o),i});ts(this,jf,async()=>{let t=(r,a="application/octet-stream")=>fetch(`data:${a};base64,${r}`).then(o=>o.blob()),n,s;switch(this.config.warmup){case"face":n=await t(Vf);break;case"full":n=await t(Uf);break;default:n=null}if(n){let r=await createImageBitmap(n);s=await this.detect(r,this.config),r.close()}return s});ts(this,qf,async()=>new Promise(t=>{let n,s=0;switch(this.config.warmup){case"face":s=256,n="data:image/jpeg;base64,"+Vf;break;case"full":case"body":s=1200,n="data:image/jpeg;base64,"+Uf;break;default:n=null}let r=new Image;r.onload=async()=>{let a=typeof OffscreenCanvas!="undefined"?new OffscreenCanvas(s,s):document.createElement("canvas");a.width=r.naturalWidth,a.height=r.naturalHeight;let o=a.getContext("2d");o==null||o.drawImage(r,0,0);let i=await this.detect(a,this.config);t(i)},n?r.src=n:t(null)}));ts(this,Xf,async()=>{let t=r=>Buffer.from(r,"base64"),n;if(this.config.warmup==="face"&&(n=t(Vf)),(this.config.warmup==="body"||this.config.warmup==="full")&&(n=t(Uf)),!n)return null;let s;if(typeof void 0!="undefined"){let r=(void 0).decodeJpeg(n),a=r.expandDims(0);this.tf.dispose(r),s=await this.detect(a,this.config),this.tf.dispose(a)}else this.config.debug&&ue("Warmup tfjs-node not loaded");return s});this.config=pn(Yy,t||{}),this.tf=Yc,this.draw=Oy,this.version=k8,this.state="idle",Ss(this,uu,0),Ss(this,id,!1),Ss(this,ld,!1),Ss(this,Ko,!0),Ss(this,cu,0),this.performance={backend:0,load:0,image:0,frames:0,cached:0,changed:0,total:0,draw:0},this.models={face:null,posenet:null,blazepose:null,efficientpose:null,movenet:null,handpose:null,age:null,gender:null,emotion:null,embedding:null,nanodet:null,centernet:null,faceres:null,segmentation:null},this.result={face:[],body:[],hand:[],gesture:[],object:[],performance:{},timestamp:0,persons:[]},this.image=n=>Xo(n,this.config),this.faceTriangulation=Pk,this.faceUVMap=Mk,this.sysinfo=Jy(),Ss(this,Zo,1)}similarity(t,n){return j1(t,n)}segmentation(t,n){return c8(t,n,this.config)}enhance(t){return q1(t)}match(t,n,s=0){return Lk(t,n,s)}async load(t){this.state="load";let n=Ke();t&&(this.config=pn(this.config,t)),hn(this,Ko)&&(this.config.debug&&ue(`version: ${this.version}`),this.config.debug&&ue(`tfjs version: ${this.tf.version_core}`),this.config.debug&&ue("platform:",this.sysinfo.platform),this.config.debug&&ue("agent:",this.sysinfo.agent),await hn(this,ud).call(this,!0),this.tf.ENV.flags.IS_BROWSER&&(this.config.debug&&ue("configuration:",this.config),this.config.debug&&ue("tf flags:",this.tf.ENV.flags))),await d8(this),hn(this,Ko)&&(this.config.debug&&ue("tf engine state:",this.tf.engine().state.numBytes,"bytes",this.tf.engine().state.numTensors,"tensors"),Ss(this,Ko,!1));let s=Math.trunc(Ke()-n);s>(this.performance.load||0)&&(this.performance.load=s)}async detect(t,n){return new Promise(async s=>{this.state="config";let r,a;this.config=pn(this.config,n),this.state="check";let o=hn(this,Hf).call(this,t);o&&(ue(o,t),s({error:o}));let i=Ke();await hn(this,ud).call(this),await this.load(),r=Ke();let l=Xo(t,this.config);if(this.performance.image=Math.trunc(Ke()-r),this.analyze("Get Image:"),this.config.segmentation.enabled&&l&&l.tensor&&(this.analyze("Start Segmentation:"),this.state="run:segmentation",r=Ke(),await _y(l),a=Math.trunc(Ke()-r),a>0&&(this.performance.segmentation=a),l.canvas&&(K(l.tensor),l=Xo(l.canvas,this.config)),this.analyze("End Segmentation:")),!l||!l.tensor){ue("could not convert input to tensor"),s({error:"could not convert input to tensor"});return}r=Ke(),this.config.skipFrame=await hn(this,Gf).call(this,l.tensor),this.performance.frames||(this.performance.frames=0),this.performance.cached||(this.performance.cached=0),this.performance.frames++,this.config.skipFrame&&this.performance.cached++,this.performance.changed=Math.trunc(Ke()-r),this.analyze("Check Changed:");let u=[],c=[],d=[],h=[];this.config.async?(u=this.config.face.enabled?$y(this,l.tensor):[],this.performance.face&&delete this.performance.face):(this.state="run:face",r=Ke(),u=this.config.face.enabled?await $y(this,l.tensor):[],a=Math.trunc(Ke()-r),a>0&&(this.performance.face=a)),this.analyze("Start Body:"),this.config.async?(this.config.body.modelPath.includes("posenet")?c=this.config.body.enabled?ry(l.tensor,this.config):[]:this.config.body.modelPath.includes("blazepose")?c=this.config.body.enabled?hy(l.tensor,this.config):[]:this.config.body.modelPath.includes("efficientpose")?c=this.config.body.enabled?Ay(l.tensor,this.config):[]:this.config.body.modelPath.includes("movenet")&&(c=this.config.body.enabled?vy(l.tensor,this.config):[]),this.performance.body&&delete this.performance.body):(this.state="run:body",r=Ke(),this.config.body.modelPath.includes("posenet")?c=this.config.body.enabled?await ry(l.tensor,this.config):[]:this.config.body.modelPath.includes("blazepose")?c=this.config.body.enabled?await hy(l.tensor,this.config):[]:this.config.body.modelPath.includes("efficientpose")?c=this.config.body.enabled?await Ay(l.tensor,this.config):[]:this.config.body.modelPath.includes("movenet")&&(c=this.config.body.enabled?await vy(l.tensor,this.config):[]),a=Math.trunc(Ke()-r),a>0&&(this.performance.body=a)),this.analyze("End Body:"),this.analyze("Start Hand:"),this.config.async?(d=this.config.hand.enabled?cy(l.tensor,this.config):[],this.performance.hand&&delete this.performance.hand):(this.state="run:hand",r=Ke(),d=this.config.hand.enabled?await cy(l.tensor,this.config):[],a=Math.trunc(Ke()-r),a>0&&(this.performance.hand=a)),this.analyze("End Hand:"),this.analyze("Start Object:"),this.config.async?(this.config.object.modelPath.includes("nanodet")?h=this.config.object.enabled?Sy(l.tensor,this.config):[]:this.config.object.modelPath.includes("centernet")&&(h=this.config.object.enabled?Ey(l.tensor,this.config):[]),this.performance.object&&delete this.performance.object):(this.state="run:object",r=Ke(),this.config.object.modelPath.includes("nanodet")?h=this.config.object.enabled?await Sy(l.tensor,this.config):[]:this.config.object.modelPath.includes("centernet")&&(h=this.config.object.enabled?await Ey(l.tensor,this.config):[]),a=Math.trunc(Ke()-r),a>0&&(this.performance.object=a)),this.analyze("End Object:"),this.config.async&&([u,c,d,h]=await Promise.all([u,c,d,h]));let p=[];this.config.gesture.enabled&&(r=Ke(),p=[...p8(u),...h8(c),...m8(d),...f8(u)],this.config.async?this.performance.gesture&&delete this.performance.gesture:this.performance.gesture=Math.trunc(Ke()-r)),this.performance.total=Math.trunc(Ke()-i),this.state="idle",this.result={face:u,body:c,hand:d,gesture:p,object:h,performance:this.performance,canvas:l.canvas,timestamp:Date.now(),get persons(){var f;return v8(u,c,d,p,(f=l==null?void 0:l.tensor)==null?void 0:f.shape)}},K(l.tensor),s(this.result)})}async warmup(t){let n=Ke();if(t&&(this.config=pn(this.config,t)),!this.config.warmup||this.config.warmup==="none")return{error:"null"};let s;typeof createImageBitmap=="function"?s=await hn(this,jf).call(this):typeof Image!="undefined"?s=await hn(this,qf).call(this):s=await hn(this,Xf).call(this);let r=Ke();return this.config.debug&&ue("Warmup",this.config.warmup,Math.round(r-n),"ms",s),s}};uu=new WeakMap,id=new WeakMap,ld=new WeakMap,Ko=new WeakMap,Zo=new WeakMap,cu=new WeakMap,Hf=new WeakMap,ud=new WeakMap,Gf=new WeakMap,jf=new WeakMap,qf=new WeakMap,Xf=new WeakMap;export{Uie as Human,Uie as default};
/**
 * @license
 * Copyright 2017 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */
/**
 * @license
 * Copyright 2018 Google LLC
 *
 * Use of this source code is governed by an MIT-style
 * license that can be found in the LICENSE file or at
 * https://opensource.org/licenses/MIT.
 * =============================================================================
 */
/**
 * @license
 * Copyright 2018 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 *
 * =============================================================================
 */
/**
 * @license
 * Copyright 2018 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */
/**
 * @license
 * Copyright 2019 Google LLC
 *
 * Use of this source code is governed by an MIT-style
 * license that can be found in the LICENSE file or at
 * https://opensource.org/licenses/MIT.
 * =============================================================================
 */
/**
 * @license
 * Copyright 2019 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 *
 * =============================================================================
 */
/**
 * @license
 * Copyright 2019 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */
/**
 * @license
 * Copyright 2020 Google Inc. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */
/**
 * @license
 * Copyright 2020 Google LLC
 *
 * Use of this source code is governed by an MIT-style
 * license that can be found in the LICENSE file or at
 * https://opensource.org/licenses/MIT.
 * =============================================================================
 */
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the License);
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an AS IS BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */
/**
 * @license
 * Copyright 2021 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */
/**
 * @license
 * Copyright 2021 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * https://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */
/** @license See the LICENSE file. */
//# sourceMappingURL=human.esm.js.map